From bbac86ceaf6c58b105b7467835dcd04777462b35 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Fri, 1 Sep 2023 13:16:22 +0200
Subject: [PATCH 01/97] Renamed test script

---
 tests/test_leakage_object.py | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 tests/test_leakage_object.py

diff --git a/tests/test_leakage_object.py b/tests/test_leakage_object.py
new file mode 100644
index 0000000..3688bc9
--- /dev/null
+++ b/tests/test_leakage_object.py
@@ -0,0 +1,34 @@
+"""UNIT TESTS FOR RUN_OBJECT SUBPACKAGE.                                           
+                                                                                
+This module contains unit tests for the run_object subpackage.                     
+                                                                                
+"""                                                                             
+                                                                                
+from unittest import TestCase                                                   
+                                                                                
+from numpy import testing as npt                                                
+import os                                                                       
+import sys
+
+from shear_psf_leakage import run_object as run
+
+class RunObjectTestCases(TestCase):
+    """Test case for the ```run_object``` module."""
+
+    def setup(self):
+        """Set test parameter values."""
+
+        pass
+
+    def tearDow(self):
+        """Unset test parameter values."""
+
+        pass
+
+    def test_test(self):
+        """Test of the ```test``` 2D quadratic fit function."""
+
+        obj = run.LeakageObject()
+        obj._params['test'] = True
+        obj._params['verbose'] = False
+        obj.run()

From 25d7b306d608f700458c43ed8c5d99f193d35ff4 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 2 Sep 2023 12:31:36 +0200
Subject: [PATCH 02/97] remove duplicate fct; black formatting

---
 shear_psf_leakage/leakage.py | 44 ------------------------------------
 shear_psf_leakage/plots.py   | 20 ++++++++++++----
 2 files changed, 16 insertions(+), 48 deletions(-)

diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index c31a0cb..8ef3432 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -766,50 +766,6 @@ def loss_bias_lin_1d(params, x_data, y_data, err):
     return residuals
 
 
-def func_bias_2d_full(params, x1, x2, order="lin", mix=False):
-    """Func Bias 2D Full.
-
-    Function of 2D bias model evaluated on full 2D grid.
-
-    Parameters
-    ----------
-    params : lmfit.Parameters
-        fit parameters
-    x1 : list of float
-        first component of x-values
-    x2 : list of float
-        second component of x-values
-    order : str, optional
-        order of fit, default is 'lin'
-    mix : bool, optional
-        mixing between components, default is `False`
-
-    Returns
-    -------
-    numpy.ndarray
-        first component the 2D model y1(x1, x2) on the (x1, x2)-grid;
-        2D array of float
-    numpy.ndarray
-        second component the 2D model, y2(x1, x2) on the (x1, x2)-grid;
-        2D array of float
-
-    """
-    len1 = len(x1)
-    len2 = len(x2)
-
-    # Initialise both components y1, y2 as 2D arrays
-    y1 = np.zeros(shape=(len1, len2))
-    y2 = np.zeros(shape=(len1, len2))
-
-    # Create 2D mesh for input x1, x2 values
-    v1, v2 = np.meshgrid(x1, x2, indexing="ij")
-
-    # Compute both components y1, y2 over the meash
-    y1, y2 = func_bias_2d(params, v1, v2, order=order, mix=mix)
-
-    return y1, y2
-
-
 def loss_bias_2d(params, x_data, y_data, err, order, mix):
     """Loss Bias 2D.
 
diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index f85a366..6fa60ec 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -36,7 +36,9 @@ def equi_num_bins(values, n_bin):
 
     """
     xeqn = np.interp(
-        np.linspace(0, len(values), n_bin + 1), np.arange(len(values)), np.sort(values)
+        np.linspace(0, len(values), n_bin + 1),
+        np.arange(len(values)),
+        np.sort(values),
     )
 
     return xeqn
@@ -147,8 +149,16 @@ def set_labels(p_dp, order, mix):
     """
     # Affine parameters
     label = {
-        "A": (f'$a_{{11}}={p_dp["a11"]: .2ugL}$' + "\n" + f'$c_1={p_dp["c1"]: .2ugL}$'),
-        "D": (f'$a_{{22}}={p_dp["a22"]: .2ugL}$' + "\n" + f'$c_2={p_dp["c2"]: .2ugL}$'),
+        "A": (
+            f'$a_{{11}}={p_dp["a11"]: .2ugL}$'
+            + "\n"
+            + f'$c_1={p_dp["c1"]: .2ugL}$'
+        ),
+        "D": (
+            f'$a_{{22}}={p_dp["a22"]: .2ugL}$'
+            + "\n"
+            + f'$c_2={p_dp["c2"]: .2ugL}$'
+        ),
     }
     if order == "quad":
         # Add quadratic parameters
@@ -316,7 +326,9 @@ def plot_corr_2d(
         colors = prop_cycle.by_key()["color"]
 
     # Compute binned data for pretty plotting.
-    x_bin, y_bin, err_bin = compute_bins_func_2d(x, y, n_bin, mix, weights=weights)
+    x_bin, y_bin, err_bin = compute_bins_func_2d(
+        x, y, n_bin, mix, weights=weights
+    )
 
     # Initialise mosaic figure
     figure_mosaic = """

From cd9e26684469783d594a61f1a01613b11b3a047b Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 6 Sep 2023 17:35:25 +0200
Subject: [PATCH 03/97] debugging apha matrix

---
 shear_psf_leakage/correlation.py | 369 ++++++++++++++++++++++++-------
 shear_psf_leakage/run_scale.py   | 134 +++++++++--
 2 files changed, 409 insertions(+), 94 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index aabb4f0..96ddef9 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -194,43 +194,77 @@ def param_order2spin(p_dp, order, mix):
     return s_ds
 
 
-def xi_star_gal_tc(
-    ra_gal,
-    dec_gal,
-    e1_gal,
-    e2_gal,
-    w_gal,
-    ra_star,
-    dec_star,
-    e1_star,
-    e2_star,
-    w_star=None,
+def xi_a_b(
+    ra_a,
+    dec_a,
+    e1_a,
+    e2_a,
+    w_a,
+    ra_b,
+    dec_b,
+    e1_b,
+    e2_b,
+    w_b=None,
     theta_min_amin=2,
     theta_max_amin=200,
     n_theta=20,
 ):
-    """Xi star gal tc.
+    """Xi A B.
 
-    Cross-correlation between galaxy and star ellipticities.
+    Cross-correlation between two catalogues a and b.
+
+    Parameters
+    ----------
+    ra_a : numpy.ndarray
+        right ascension values of catalogue a
+    dec_a : numpy.ndarray
+        right ascension values of catalogue a
+    e1_a : numpy.ndarray
+        first ellipticity compoment values of catalogue a
+    e1_a : numpy.ndarray
+        second ellipticity compoment values of catalogue a
+    w_a : numpy.ndarray
+        weights of catalogue a
+    ra_b : numpy.ndarray
+        right ascension values of catalogue b
+    dec_b : numpy.ndarray
+        right ascension values of catalogue b
+    e1_b : numpy.ndarray
+        first ellipticity compoment values of catalogue b
+    e1_b : numpy.ndarray
+        second ellipticity compoment values of catalogue b
+    w_b : numpy.ndarray, optional
+        weights of catalogue b; default with ``None`` is 1 for all weights
+    theta_min_amin : float, optional
+        minimum angular scale in arc minutes; default is 2
+    theta_min_amax : float, optional
+        minimum angular scale in arc minutes; default is 200
+    n_theta : int, optional
+        number of angular scales; default is 20
+
+    Returns
+    -------
+    treecorr.GGCorrelation
+        correlation result
 
     """
     unit = "degrees"
 
-    cat_gal = treecorr.Catalog(
-        ra=ra_gal,
-        dec=dec_gal,
-        g1=e1_gal,
-        g2=e2_gal,
-        w=w_gal,
+    cat_a = treecorr.Catalog(
+        ra=ra_a,
+        dec=dec_a,
+        g1=e1_a,
+        g2=e2_a,
+        w=w_a,
         ra_units=unit,
         dec_units=unit,
     )
-    cat_star = treecorr.Catalog(
-        ra=ra_star,
-        dec=dec_star,
-        g1=e1_star,
-        g2=e2_star,
-        w=w_star,
+    cat_b = treecorr.Catalog(
+        ra=ra_b,
+        dec=dec_b,
+        g1=e1_b,
+        g2=e2_b,
+        w=w_b,
         ra_units=unit,
         dec_units=unit,
     )
@@ -243,44 +277,44 @@ def xi_star_gal_tc(
         "max_sep": theta_max_amin,
         "nbins": n_theta,
     }
-    ng = treecorr.GGCorrelation(TreeCorrConfig)
+    gg = treecorr.GGCorrelation(TreeCorrConfig)
 
-    ng.process(cat_gal, cat_star)
+    gg.process(cat_a, cat_b)
 
-    return ng
+    return gg
 
 
-def correlation_12_22(
-    ra_1,
-    dec_1,
-    e1_1,
-    e2_1,
-    weights_1,
-    ra_2,
-    dec_2,
-    e1_2,
-    e2_2,
+def correlation_ab_bb(
+    ra_a,
+    dec_a,
+    e1_a,
+    e2_a,
+    weights_a,
+    ra_b,
+    dec_b,
+    e1_b,
+    e2_b,
     theta_min_amin=2,
     theta_max_amin=200,
     n_theta=20,
 ):
-    """Correlation 12 22.
+    """Correlation ab bb.
 
-    Shear correlation functions between two samples 1 and 2.
-    Compute xi_12 and xi_22.
+    Shear correlation functions between two samples a and b.
+    Compute xi_ab and xi_bb.
 
     Parameters
     ----------
-    ra_1, dec_1 : array of float
-        coordinates of sample 1
-    e1_1, e2_1 : array of float
-        ellipticities of sample 1
-    weights_1 : array of float
-        weights of sample 1
-    ra_2, dec_2 : array of float
-        coordinates of sample 2
-    e1_2, e2_2 : array of float
-        ellipticities of sample 2
+    ra_a, dec_a : array of float
+        coordinates of sample a
+    e1_a, e2_a : array of float
+        ellipticities of sample a
+    weights_a : array of float
+        weights of sample a
+    ra_b, dec_b : array of float
+        coordinates of sample b
+    e1_b, e2_b : array of float
+        ellipticities of sample b
     theta_min_amin : float, optional
         minimum angular scale in arcmin, default is 2
     theta_max_amin : float, optional
@@ -290,40 +324,144 @@ def correlation_12_22(
 
     Returns
     -------
-    xi_12, xi_22 : correlations
-        correlations 12, and 22
+    xi_ab, xi_bb : correlations
+        correlations ab, and bb
 
     """
-    r_corr_12 = xi_star_gal_tc(
-        ra_1,
-        dec_1,
-        e1_1,
-        e2_1,
-        weights_1,
-        ra_2,
-        dec_2,
-        e1_2,
-        e2_2,
+    r_corr_ab = xi_a_b(
+        ra_a,
+        dec_a,
+        e1_a,
+        e2_a,
+        weights_a,
+        ra_b,
+        dec_b,
+        e1_b,
+        e2_b,
         theta_min_amin=theta_min_amin,
         theta_max_amin=theta_max_amin,
         n_theta=n_theta,
     )
-    r_corr_22 = xi_star_gal_tc(
-        ra_2,
-        dec_2,
-        e1_2,
-        e2_2,
-        np.ones_like(ra_2),
-        ra_2,
-        dec_2,
-        e1_2,
-        e2_2,
+    r_corr_bb = xi_a_b(
+        ra_b,
+        dec_b,
+        e1_b,
+        e2_b,
+        np.ones_like(ra_b),
+        ra_b,
+        dec_b,
+        e1_b,
+        e2_b,
         theta_min_amin=theta_min_amin,
         theta_max_amin=theta_max_amin,
         n_theta=n_theta,
     )
 
-    return r_corr_12, r_corr_22
+    return r_corr_ab, r_corr_bb
+
+
+def correlation_ab_bb_matrix(
+    ra_a,
+    dec_a,
+    e1_a,
+    e2_a,
+    weights_a,
+    ra_b,
+    dec_b,
+    e1_b,
+    e2_b,
+    theta_min_amin=2,
+    theta_max_amin=200,
+    n_theta=20,
+):
+    """Correlation 12 22 Matrix.
+
+    Shear correlation function matrices between two samples a and b.
+    Computes the xi_ab and xi_bb matrices.
+
+    Parameters
+    ----------
+    ra_a, dec_a : numpy.ndarray
+        coordinates of sample a
+    e1_a, e2_a : numpy.ndarray
+        ellipticities of sample a
+    weights_a : numpy.ndarray
+        weights of sample a
+    ra_b, dec_b : numpy.ndarray
+        coordinates of sample b
+    e1_b, e2_b : numpy.ndarray
+        ellipticities of sample b
+    theta_min_amin : float, optional
+        minimum angular scale in arcmin; default is 2
+    theta_max_amin : float, optional
+        maximum angular scale in arcmin; default is 200
+    n_theta : int, optional
+        number of angular scales; default is 20
+
+    Returns
+    -------
+    treecorr.GGCorrelation
+        correlations ab
+    treecorr.GGCorrelation
+        correlations bb
+
+    """
+    # Create zero arrays for both samples
+    ell_a_zero = np.zeros_like(e1_a)
+    ell_b_zero = np.zeros_like(e1_b)
+
+    xi_ab = np.zeros((2, 2), dtype=treecorr.GGCorrelation)
+    xi_bb = np.zeros((2, 2), dtype=treecorr.GGCorrelation)
+    ell_a = np.empty(shape=(2, len(e1_a)))
+    ell_b = np.empty(shape=(2, len(e1_b)))
+
+    # We know that xi_+ = xi_tt + xi_xx = xi_11 + xi_22.
+    # To only get one component "xi_11", we need to set
+    # the other to zero.
+    # xi_ab_11 = <e1_a e1_b>; e2_a = e2_b = 0
+    # xi_ab_22 = <e2_a e2_b>; e1_a = e1_b = 0
+    # xi_ab_12 = <e1_a e2_b>; e2_a = e1_b = 0
+    # xi_ab_21 = <e2_a e1_b>; e1_a = e2_b = 0
+
+    ell_a[0] = e1_a
+    ell_a[1] = e2_a
+
+    ell_b[0] = e1_b
+    ell_b[1] = e2_b
+
+    for idx in (0, 1):
+        for jdx in (0, 1):
+            xi_ab[idx][jdx] = xi_a_b(
+                ra_a,
+                dec_a,
+                ell_a[idx],
+                ell_a_zero,
+                weights_a,
+                ra_b,
+                dec_b,
+                ell_b[jdx],
+                ell_b_zero,
+                theta_min_amin=theta_min_amin,
+                theta_max_amin=theta_max_amin,
+                n_theta=n_theta,
+            )
+            xi_bb[idx][jdx] = xi_a_b(
+                ra_b,
+                dec_b,
+                ell_b[idx],
+                ell_b_zero,
+                np.ones_like(ra_b),
+                ra_b,
+                dec_b,
+                ell_b[jdx],
+                ell_b_zero,
+                theta_min_amin=theta_min_amin,
+                theta_max_amin=theta_max_amin,
+                n_theta=n_theta,
+            )
+
+
+    return xi_ab, xi_bb
 
 
 def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
@@ -346,6 +484,7 @@ def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
     -------
     alpha, sig_alpha : float
         mean and std of alpha
+
     """
     complex_gal = (
         np.average(e1_gal, weights=weights_gal)
@@ -354,12 +493,84 @@ def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
     complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
 
     alpha_leak = (
-        (r_corr_gp.xip - np.real(np.conj(complex_gal) * complex_psf))
-        / (r_corr_pp.xip - np.abs(complex_psf) ** 2)
-    )
+        r_corr_gp.xip - np.real(np.conj(complex_gal) * complex_psf)
+    ) / (r_corr_pp.xip - np.abs(complex_psf) ** 2)
     sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
-        r_corr_gp.varxip / r_corr_gp.xip ** 2
-        + r_corr_pp.varxip / r_corr_pp.xip ** 2
+        r_corr_gp.varxip / r_corr_gp.xip**2
+        + r_corr_pp.varxip / r_corr_pp.xip**2
     )
 
     return alpha_leak, sig_alpha_leak
+
+
+def alpha_matrix(
+    r_corr_gp_m, r_corr_pp_m, e1_gal, e2_gal, weights_gal, e1_star, e2_star
+):
+    """Alpha Matrix.
+
+    Compute scale-dependent PSF leakage alpha matrix.
+
+    Parameters
+    ----------
+    r_corr_gp_m, r_corr_pp_m : correlations
+        correlations galaxy-star, star-star
+    e1_gal, e2_gal : array of float
+        galaxy ellipticities
+    weights_gal : array of float
+        galaxy weights
+    e1_star, e2_star : array of float
+        galaxy ellipticities
+
+    Returns
+    -------
+    alpha : numpy.ndarray
+        alpha, 2x2 matrix
+
+    """
+    e_g = np.matrix(
+        [
+            np.average(e1_gal, weights=weights_gal),
+            np.average(e2_gal, weights=weights_gal),
+        ]
+    )
+    e_p = np.matrix(
+        [
+            np.mean(e1_star),
+            np.mean(e2_star),
+        ]
+    )
+
+    n_theta = r_corr_gp_m[0][0].nbins
+    xi_gp_m = np.zeros((2, 2, n_theta))
+    xi_pp_m = np.zeros((2, 2, n_theta))
+    for idx in (0, 1):
+        for jdx in (0, 1):
+            xi_gp_m[idx][jdx] = r_corr_gp_m[idx][jdx].xip
+            xi_pp_m[idx][jdx] = r_corr_pp_m[idx][jdx].xip
+
+    xi_pp_m_inv = np.linalg.inv(xi_pp_m)
+
+    alpha_leak_m = np.dot(xi_gp_m - e_g * e_p.transpose(), xi_pp_m_inv)
+
+    return alpha_leak_m
+
+
+def check_consistency_scales(xi_a, xi_b):
+    """Check Consistency Scales.
+
+    Print warning if angular scales between two correlation results do
+    not match.
+
+    Parameters
+    ----------
+    xi_a : treecorr.GGCorrelation
+        correlation a
+    xi_b : treecorr.GGCorrelation
+        correlation b
+
+    """
+
+    if any(
+        np.abs(xi_a.meanr - xi_b.meanr) / xi_a.meanr > 0.1
+    ):
+        print("Warning: angular scales not conr_corr_gpsistent")
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 381f16b..805db38 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -395,6 +395,12 @@ def run(self):
         # alpha leakage
         self.do_alpha()
 
+        # compute auto- and cross-correlation functions including alpha
+        self.compute_corr_gp_pp_alpha_matrix()
+
+        # alpha matrix leakage
+        self.do_alpha_matrix()
+
         # xi_sys function
         self.do_xi_sys()
 
@@ -595,25 +601,56 @@ def handle_close_objects(self, dat_PSF):
 
         return dat_PSF_proc
 
+    def get_cat_fields(self, cat_type):
+        """Get Cat Fields.
+
+        Get catalogue fields for correlation.
+
+        Parameters
+        ----------
+        cat_type : str
+            catalogue type, allowed are "gal" and "star"
+
+        Return
+        list
+            ra
+        list
+            dec
+        list
+            e1
+        list
+            e2
+        list
+            weights; `None` if cat_type is "star"
+
+        """
+        if cat_type == "gal":
+            ra = self.dat_shear["RA"]
+            dec = self.dat_shear["Dec"]
+            e1 = self.dat_shear[self._params["e1_col"]]
+            e2 = self.dat_shear[self._params["e2_col"]]
+            weights = self.dat_shear["w"]
+        elif cat_type == "star":
+            ra = self.dat_PSF[self._params["ra_star_col"]]
+            dec = self.dat_PSF[self._params["dec_star_col"]]
+            e1 = self.dat_PSF[self._params["e1_PSF_star_col"]]
+            e2 = self.dat_PSF[self._params["e2_PSF_star_col"]]
+            weights = None
+
+        return ra, dec, e1, e2, weights
+
+
     def compute_corr_gp_pp_alpha(self):
         """Compute Corr GP PP Alpha.
 
         Compute and plot scale-dependent PSF leakage functions.
 
         """
-        ra = self.dat_shear["RA"]
-        dec = self.dat_shear["Dec"]
-        e1_gal = self.dat_shear[self._params["e1_col"]]
-        e2_gal = self.dat_shear[self._params["e2_col"]]
-        weights = self.dat_shear["w"]
-
-        ra_star = self.dat_PSF[self._params["ra_star_col"]]
-        dec_star = self.dat_PSF[self._params["dec_star_col"]]
-        e1_star = self.dat_PSF[self._params["e1_PSF_star_col"]]
-        e2_star = self.dat_PSF[self._params["e2_PSF_star_col"]]
+        ra, dec, e1_gal, e2_gal, weights = self.get_cat_fields("gal")
+        ra_star, dec_star, e1_star, e2_star, _ = self.get_cat_fields("star")
 
         # Correlation functions
-        r_corr_gp, r_corr_pp = corr.correlation_12_22(
+        r_corr_gp, r_corr_pp = corr.correlation_ab_bb(
             ra,
             dec,
             e1_gal,
@@ -629,15 +666,58 @@ def compute_corr_gp_pp_alpha(self):
         )
 
         # Check consistency of angular scales
-        if any(
-            np.abs(r_corr_gp.meanr - r_corr_pp.meanr) / r_corr_gp.meanr > 0.1
-        ):
-            print("Warning: angular scales not consistent")
+        corr.check_consistency_scales(r_corr_gp, r_corr_pp)
 
         # Set instance variables
         self.r_corr_gp = r_corr_gp
         self.r_corr_pp = r_corr_pp
 
+    def compute_corr_gp_pp_alpha_matrix(self):
+        """Compute Corr GP PP Alpha Matrix.
+
+        Compute and plot scale-dependent PSF leakage matrix.
+
+        """
+        ra, dec, e1_gal, e2_gal, weights = self.get_cat_fields("gal")
+        ra_star, dec_star, e1_star, e2_star, _ = self.get_cat_fields("star")
+
+        # Correlation functions
+        r_corr_gp_m, r_corr_pp_m = corr.correlation_ab_bb_matrix(
+            ra,
+            dec,
+            e1_gal,
+            e2_gal,
+            weights,
+            ra_star,
+            dec_star,
+            e1_star,
+            e2_star,
+            theta_min_amin=self._params["theta_min_amin"],
+            theta_max_amin=self._params["theta_max_amin"],
+            n_theta=self._params["n_theta"],
+        )
+
+        # Check consistency of angular scales
+        for idx in (0, 1):
+            for jdx in (0, 1):
+                corr.check_consistency_scales(
+                    r_corr_gp_m[idx][jdx],
+                    r_corr_pp_m[idx][jdx],
+                )
+                if idx != 0 and jdx != 0:
+                    corr.check_consistency_scales(
+                        r_corr_gp_m[0][0],
+                        r_corr_gp_m[idx][jdx],
+                    )
+                    corr.check_consistency_scales(
+                        r_corr_pp_m[0][0],
+                        r_corr_pp_m[idx][jdx],
+                    )
+
+        self._r_corr_gp_m = r_corr_gp_m
+        self._r_corr_pp_m = r_corr_pp_m
+                
+
     def compute_alpha_mean(self):
         """Compute Alpha Mean.
 
@@ -867,6 +947,30 @@ def do_alpha(self):
             self._params["output_dir"],
         )
 
+    def do_alpha_matrix(self):
+        """Do Alpha Matrix.
+
+        Compute, plot, and save alpha leakage matrix.
+        """
+        # Get input catalogues for averages
+        e1_gal = self.dat_shear[self._params["e1_col"]]
+        e2_gal = self.dat_shear[self._params["e2_col"]]
+        weights = self.dat_shear["w"]
+
+        e1_star = self.dat_PSF[self._params["e1_PSF_star_col"]]
+        e2_star = self.dat_PSF[self._params["e2_PSF_star_col"]]
+
+        # Compute alpha leakage function
+        self.alpha_leak_m = corr.alpha_matrix(
+            self._r_corr_gp_m,
+            self._r_corr_pp_m,
+            e1_gal,
+            e2_gal,
+            weights,
+            e1_star,
+            e2_star,
+        )
+
     def do_xi_sys(self):
         """Do Xi Sys.
 

From 8c5275dfec4ba2de6aee1b91be4442dfbb150a4e Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 13 Sep 2023 14:23:55 +0200
Subject: [PATCH 04/97] alpha matrix trace

---
 shear_psf_leakage/correlation.py |  13 ++-
 shear_psf_leakage/run_scale.py   | 162 +++++++++++++++++++++----------
 2 files changed, 120 insertions(+), 55 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index 96ddef9..91f165d 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -548,9 +548,16 @@ def alpha_matrix(
             xi_gp_m[idx][jdx] = r_corr_gp_m[idx][jdx].xip
             xi_pp_m[idx][jdx] = r_corr_pp_m[idx][jdx].xip
 
-    xi_pp_m_inv = np.linalg.inv(xi_pp_m)
-
-    alpha_leak_m = np.dot(xi_gp_m - e_g * e_p.transpose(), xi_pp_m_inv)
+    xi_pp_m_inv = np.zeros((2, 2, n_theta))
+    for ndx in range(n_theta):
+        xi_pp_m_inv[:, :, ndx] = np.linalg.inv(xi_pp_m[:, :, ndx])
+
+    alpha_leak_m = np.zeros((2, 2, n_theta))
+    for ndx in range(n_theta):
+        alpha_leak_m[:, :, ndx] = np.dot(
+            xi_gp_m[:, :, ndx] - e_g * e_p.transpose(),
+            xi_pp_m_inv[:, :, ndx]
+        )
 
     return alpha_leak_m
 
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 805db38..aea8244 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -106,32 +106,6 @@ def get_theo_xi(theta, dndz_path):
     return xi_p, xi_m
 
 
-# MKDEBUG TODO: make class function
-def save_alpha(theta, alpha_leak, sig_alpha_leak, sh, output_dir):
-    """Save Alpha.
-
-    Save scale-dependent alpha
-
-    Parameters
-    ----------
-    theta : list
-        angular scales
-    alpha_leak : list
-        leakage alpha(theta)
-    sig_alpha_leak : list
-        standard deviation of alpha(theta)
-    sh : str
-        shape measurement method, e.g. 'ngmix'
-    output_dir : str
-        output directory
-
-    """
-    cols = [theta, alpha_leak, sig_alpha_leak]
-    names = ["# theta[arcmin]", "alpha", "sig_alpha"]
-    fname = f"{output_dir}/alpha_leakage_{sh}.txt"
-    cs_cat.write_ascii_table_file(cols, names, fname)
-
-
 def save_xi_sys(
     theta,
     xi_sys_p,
@@ -140,7 +114,6 @@ def save_xi_sys(
     xi_sys_std_m,
     xi_p_theo,
     xi_m_theo,
-    sh,
     output_dir,
 ):
     """Save Xi Sys.
@@ -163,8 +136,6 @@ def save_xi_sys(
         xi+ component of theoretical shear-shear correlation
     xi_m_theo : list
         xi- component of theoretical shear-shear correlation
-    sh : str
-        shape measurement method, e.g. 'ngmix'
     output_dir : str
         output directory
 
@@ -187,7 +158,7 @@ def save_xi_sys(
         "xi_+_theo",
         "xi_-_theo",
     ]
-    fname = f"{output_dir}/xi_sys_{sh}.txt"
+    fname = f"{output_dir}/xi_sys.txt"
     cs_cat.write_ascii_table_file(cols, names, fname)
 
 
@@ -245,7 +216,6 @@ def params_default(self):
             "e2_PSF_star_col": "E2_PSF_HSM",
             "dndz_path": None,
             "output_dir": ".",
-            "sh": "ngmix",
             "close_pair_tolerance": None,
             "close_pair_mode": None,
             "cut": None,
@@ -295,7 +265,6 @@ def params_default(self):
                 "path to galaxy redshift distribution file, for xi_sys ratio"
             ),
             "output_dir": "output_directory, default={}",
-            "sh": "shape measurement method, default={}",
             "close_pair_tolerance": (
                 "tolerance angle for close objects in star catalogue,"
                 + " default={}"
@@ -714,8 +683,8 @@ def compute_corr_gp_pp_alpha_matrix(self):
                         r_corr_pp_m[idx][jdx],
                     )
 
-        self._r_corr_gp_m = r_corr_gp_m
-        self._r_corr_pp_m = r_corr_pp_m
+        self.r_corr_gp_m = r_corr_gp_m
+        self.r_corr_pp_m = r_corr_pp_m
                 
 
     def compute_alpha_mean(self):
@@ -728,7 +697,7 @@ def compute_alpha_mean(self):
             np.average(self.alpha_leak, weights=1 / self.sig_alpha_leak**2)
         )
         leakage.print_stats(
-            f"{self._params['sh']}: Weighted average alpha"
+            f"Weighted average alpha"
             + f" = {self.alpha_leak_mean:.3g}",
             self._stats_file,
             verbose=self._params["verbose"],
@@ -769,10 +738,10 @@ def plot_alpha_leakage(self):
         yerr = [self.sig_alpha_leak]
         xlabel = r"$\theta$ [arcmin]"
         ylabel = r"$\alpha(\theta)$"
-        title = self._params["sh"]
+        title = ""
         out_path = (
             f"{self._params['output_dir']}"
-            + f"/alpha_leakage_{self._params['sh']}.png"
+            + f"/alpha_leakage.png"
         )
         xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
         ylim = self._params["leakage_alpha_ylim"]
@@ -789,6 +758,68 @@ def plot_alpha_leakage(self):
             ylim=ylim,
         )
 
+    def plot_alpha_leakage_matrix(self):
+        """Plot Alpha Leakage Matrix.
+
+        Plot scale-dependent leakage matrix alpha.
+
+        """
+        plot_dir_leakage = self._params["output_dir"]
+
+        theta = []
+        alpha_theta = []
+        labels = []
+        for idx in (0, 1):
+            for jdx in (0, 1):
+                theta.append(self.r_corr_gp_m[idx][jdx].meanr)
+                alpha_theta.append(self.alpha_leak_m[idx][jdx])
+                labels.append(rf"$\alpha_{{{idx+1}{jdx+1}}}$")
+        yerr = [np.nan] * 4
+        xlabel = r"$\theta$ [arcmin]"
+        ylabel = r"$\alpha_{ij}(\theta)$"
+        title = ""
+        out_path = f"{self._params['output_dir']}/alpha_leakage_matrix.png"
+        xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
+        ylim = self._params["leakage_alpha_ylim"]
+        plots.plot_data_1d(
+            theta,
+            alpha_theta,
+            yerr,
+            title,
+            xlabel,
+            ylabel,
+            out_path,
+            xlog=True,
+            xlim=xlim,
+            ylim=ylim,
+            labels=labels,
+        )
+
+        theta = [self.r_corr_gp_m[0][0].meanr]
+        alpha_theta = [self.alpha_leak_m[0][0] + self.alpha_leak_m[1][1]]
+        labels = [rf"$\alpha_{{{1}{1}}} + \alpha_{{{2}{2}}}$"]
+        yerr = [np.nan] * 1
+        xlabel = r"$\theta$ [arcmin]"
+        ylabel = r"$\alpha_{ij}(\theta)$"
+        title = ""
+        out_path = f"{self._params['output_dir']}/alpha_leakage_matrix_tr.png"
+        xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
+        ylim = self._params["leakage_alpha_ylim"]
+        plots.plot_data_1d(
+            theta,
+            alpha_theta,
+            yerr,
+            title,
+            xlabel,
+            ylabel,
+            out_path,
+            xlog=True,
+            xlim=xlim,
+            ylim=ylim,
+            labels=labels,
+        )
+
+
     def plot_xi_sys(self):
         """Plot Xi Sys.
 
@@ -809,14 +840,14 @@ def plot_xi_sys(self):
         symb_arr = ["+", "-"]
         for comp, symb in zip(comp_arr, symb_arr):
             mean = np.mean(np.abs(xi[comp]))
-            msg = f"{self._params['sh']}: <|xi_sys_{symb}|> = {mean}"
+            msg = f"<|xi_sys_{symb}|> = {mean}"
             leakage.print_stats(
                 msg, self._stats_file, verbose=self._params["verbose"]
             )
 
         ylim = self._params["leakage_xi_sys_ylim"]
         out_path = (
-            f"{self._params['output_dir']}/xi_sys_{self._params['sh']}.pdf"
+            f"{self._params['output_dir']}/xi_sys.pdf"
         )
         plots.plot_data_1d(
             theta,
@@ -833,7 +864,7 @@ def plot_xi_sys(self):
 
         ylim = self._params["leakage_xi_sys_log_ylim"]
         out_path = (
-            f"{self._params['output_dir']}/xi_sys_log_{self._params['sh']}.pdf"
+            f"{self._params['output_dir']}/xi_sys_log.pdf"
         )
         plots.plot_data_1d(
             theta,
@@ -884,7 +915,7 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo):
         for comp, symb in zip(comp_arr, symb_arr):
             mean = np.mean(np.abs(xi[comp]))
             msg = (
-                f"{self._params['sh']}: <|xi_sys_{symb}| / xi_{symb}> = {mean}"
+                f"<|xi_sys_{symb}| / xi_{symb}> = {mean}"
             )
             leakage.print_stats(
                 msg, self._stats_file, verbose=self._params["verbose"]
@@ -892,7 +923,7 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo):
 
         out_path = (
             f"{self._params['output_dir']}"
-            + f"/xi_sys_{self._params['sh']}_ratio.pdf"
+            + f"/xi_sys_ratio.pdf"
         )
 
         ylim = [0, 0.5]
@@ -939,13 +970,7 @@ def do_alpha(self):
         self.plot_alpha_leakage()
 
         # Write to disk
-        save_alpha(
-            self.r_corr_gp.meanr,
-            self.alpha_leak,
-            self.sig_alpha_leak,
-            self._params["sh"],
-            self._params["output_dir"],
-        )
+        self.save_alpha()
 
     def do_alpha_matrix(self):
         """Do Alpha Matrix.
@@ -962,8 +987,8 @@ def do_alpha_matrix(self):
 
         # Compute alpha leakage function
         self.alpha_leak_m = corr.alpha_matrix(
-            self._r_corr_gp_m,
-            self._r_corr_pp_m,
+            self.r_corr_gp_m,
+            self.r_corr_pp_m,
             e1_gal,
             e2_gal,
             weights,
@@ -971,6 +996,12 @@ def do_alpha_matrix(self):
             e2_star,
         )
 
+        # Plot
+        self.plot_alpha_leakage_matrix()
+
+        # Write to disk
+        self.save_alpha_matrix()
+
     def do_xi_sys(self):
         """Do Xi Sys.
 
@@ -1001,10 +1032,37 @@ def do_xi_sys(self):
             self.C_sys_std_m,
             xi_p_theo,
             xi_m_theo,
-            self._params["sh"],
             self._params["output_dir"],
         )
 
+    def save_alpha(self):
+        """Save Alpha.
+
+        Save scale-dependent alpha.
+
+        """
+        cols = [self.r_corr_gp.meanr, self.alpha_leak, self.sig_alpha_leak]
+        names = ["# theta[arcmin]", "alpha", "sig_alpha"]
+        fname = f"{self._params['output_dir']}/alpha_leakage.txt"
+        cs_cat.write_ascii_table_file(cols, names, fname)
+
+    def save_alpha_matrix(self):
+        """Save Alpha Matrix.
+
+        Save scale-dependent alpha matrix.
+
+        """
+        cols = [self.r_corr_gp_m[0][0].meanr]
+        names = ["# theta[arcmin]"]
+        for idx in (0, 1):
+            for jdx in (0, 1):
+                cols.append(self.alpha_leak_m[idx][jdx])
+                names.append(rf"alpha_{{{idx+1}{jdx+1}}}")
+        fname = f"{self._params['output_dir']}/alpha_leakage_matrix.txt"
+        print("MKDEBUG", cols, names, "MKDEBUG")
+        cs_cat.write_ascii_table_file(cols, names, fname)
+
+
 
 def run_leakage_scale(*args):
     """Run Leakage Scale.

From f2ad6ea3ba77ea7ad4851091720be8299ee53bd4 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 20 Sep 2023 10:36:33 +0200
Subject: [PATCH 05/97] added option to save xi_ab and xi_bb corr files

---
 shear_psf_leakage/correlation.py | 18 ++++++++++++++++++
 shear_psf_leakage/run_scale.py   | 16 ++++++++++++++--
 2 files changed, 32 insertions(+), 2 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index 91f165d..6075ee2 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -208,6 +208,7 @@ def xi_a_b(
     theta_min_amin=2,
     theta_max_amin=200,
     n_theta=20,
+    out_path=None,
 ):
     """Xi A B.
 
@@ -241,6 +242,8 @@ def xi_a_b(
         minimum angular scale in arc minutes; default is 200
     n_theta : int, optional
         number of angular scales; default is 20
+    out_path : str, optional
+        output file path; default is ``None`` (no file written)
 
     Returns
     -------
@@ -281,6 +284,9 @@ def xi_a_b(
 
     gg.process(cat_a, cat_b)
 
+    if output_path:
+        gg.write(output_path)
+
     return gg
 
 
@@ -297,6 +303,7 @@ def correlation_ab_bb(
     theta_min_amin=2,
     theta_max_amin=200,
     n_theta=20,
+    output_base_path=None,
 ):
     """Correlation ab bb.
 
@@ -321,6 +328,8 @@ def correlation_ab_bb(
         maximum angular scale in arcmin, default is 200
     n_theta : int, optional
         number of angular scales, default is 20
+    out_base_path : str, optional
+        output file base path; default is ``None`` (no files written)
 
     Returns
     -------
@@ -328,6 +337,13 @@ def correlation_ab_bb(
         correlations ab, and bb
 
     """
+    if output_base_path:
+        output_path_ab = f"{output_base_path}_a_b.txt"
+        output_path_aa = f"{output_base_path}_a_a.txt"
+    else:
+        output_path_ab = None
+        output_path_aa = None
+
     r_corr_ab = xi_a_b(
         ra_a,
         dec_a,
@@ -341,6 +357,7 @@ def correlation_ab_bb(
         theta_min_amin=theta_min_amin,
         theta_max_amin=theta_max_amin,
         n_theta=n_theta,
+        output_path=output_path_ab,
     )
     r_corr_bb = xi_a_b(
         ra_b,
@@ -355,6 +372,7 @@ def correlation_ab_bb(
         theta_min_amin=theta_min_amin,
         theta_max_amin=theta_max_amin,
         n_theta=n_theta,
+        output_path=output_path_aa,
     )
 
     return r_corr_ab, r_corr_bb
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index aea8244..8b4e16c 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -609,11 +609,16 @@ def get_cat_fields(self, cat_type):
         return ra, dec, e1, e2, weights
 
 
-    def compute_corr_gp_pp_alpha(self):
+    def compute_corr_gp_pp_alpha(self, output_base_path=None):
         """Compute Corr GP PP Alpha.
 
         Compute and plot scale-dependent PSF leakage functions.
 
+        Parameters
+        ----------
+        out_path : str, optional                                                    
+                output file path; default is ``None`` (no file written)
+
         """
         ra, dec, e1_gal, e2_gal, weights = self.get_cat_fields("gal")
         ra_star, dec_star, e1_star, e2_star, _ = self.get_cat_fields("star")
@@ -632,6 +637,7 @@ def compute_corr_gp_pp_alpha(self):
             theta_min_amin=self._params["theta_min_amin"],
             theta_max_amin=self._params["theta_max_amin"],
             n_theta=self._params["n_theta"],
+            output_base_path=output_base_path,
         )
 
         # Check consistency of angular scales
@@ -1035,6 +1041,13 @@ def do_xi_sys(self):
             self._params["output_dir"],
         )
 
+    def save_r_corr_ab(self):
+        """Save R Corr AB.
+
+        Save correlation function.
+
+        """
+
     def save_alpha(self):
         """Save Alpha.
 
@@ -1059,7 +1072,6 @@ def save_alpha_matrix(self):
                 cols.append(self.alpha_leak_m[idx][jdx])
                 names.append(rf"alpha_{{{idx+1}{jdx+1}}}")
         fname = f"{self._params['output_dir']}/alpha_leakage_matrix.txt"
-        print("MKDEBUG", cols, names, "MKDEBUG")
         cs_cat.write_ascii_table_file(cols, names, fname)
 
 

From 3e61bf29bc6d96f0953d60cbbaa3f21ec91bd3b3 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 20 Sep 2023 10:37:07 +0200
Subject: [PATCH 06/97] Added lock file

---
 poetry.lock | 3770 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 3770 insertions(+)
 create mode 100644 poetry.lock

diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..1763dbb
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3770 @@
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+
+[[package]]
+name = "aenum"
+version = "3.1.15"
+description = "Advanced Enumerations (compatible with Python's stdlib Enum), NamedTuples, and NamedConstants"
+optional = false
+python-versions = "*"
+files = [
+    {file = "aenum-3.1.15-py2-none-any.whl", hash = "sha256:27b1710b9d084de6e2e695dab78fe9f269de924b51ae2850170ee7e1ca6288a5"},
+    {file = "aenum-3.1.15-py3-none-any.whl", hash = "sha256:e0dfaeea4c2bd362144b87377e2c61d91958c5ed0b4daf89cb6f45ae23af6288"},
+    {file = "aenum-3.1.15.tar.gz", hash = "sha256:8cbd76cd18c4f870ff39b24284d3ea028fbe8731a58df3aa581e434c575b9559"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.0.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
+    {file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.22)"]
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = "*"
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From 6f21a371b2d873ecd524ceeba69019701f39b57f Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 20 Sep 2023 10:39:11 +0200
Subject: [PATCH 07/97] resetted

---
 .gitignore                       |  1 -
 pyproject.toml                   | 12 +++++-------
 shear_psf_leakage/__init__.py    |  2 +-
 shear_psf_leakage/correlation.py | 21 ++++++++++++++++-----
 tests/test_shear_psf_leakage.py  |  2 +-
 5 files changed, 23 insertions(+), 15 deletions(-)

diff --git a/.gitignore b/.gitignore
index 70ab67e..5caa9ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,3 @@ __pycache__
 __pycache__/*
 */__pycache__/*
 build
-poetry.lock
diff --git a/pyproject.toml b/pyproject.toml
index 94e3e50..a855144 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "shear_psf_leakage"
-version = "0.1.0"
+version = "0.1.5"
 description = "PSF leakage for shear catalogue data"
 authors = [
 	"Martin Kilbinger <martin.kilbinger@cea.fr>",
@@ -9,10 +9,9 @@ authors = [
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<3.13"
+python = ">=3.9,<3.12"
 cmake = "^3.27.2"
 swig = "^4.1.1"
-scipy = "^1.11.2"
 pyyaml = "^6.0.1"
 lmfit = "^1.2.2"
 treecorr = "^4.3.3"
@@ -21,11 +20,10 @@ matplotlib = "^3.7.2"
 stats = "^0.1.2a0"
 jupyter = "^1.0.0"
 jupytext = "^1.15.1"
-pandas = "^2.1.0"
+pandas = "^2.0.3"
 camb = "^1.5.0"
-
-# MKDEBUG TODO: cs_util -> 0.0.6
-cs-util = {path = "../cs_util"}
+scipy = "1.10.1"
+cs-util = "0.0.6"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
diff --git a/shear_psf_leakage/__init__.py b/shear_psf_leakage/__init__.py
index 3dc1f76..1276d02 100644
--- a/shear_psf_leakage/__init__.py
+++ b/shear_psf_leakage/__init__.py
@@ -1 +1 @@
-__version__ = "0.1.0"
+__version__ = "0.1.5"
diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index 6075ee2..67552a5 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -11,12 +11,10 @@
 """
 
 import numpy as np
-
-
-from shear_psf_leakage import leakage
-
 import treecorr
 
+from . import leakage
+
 
 def func_bias_lin_1d(params, x_data):
     """Func Bias Lin 1D.
@@ -71,6 +69,7 @@ def loss_bias_lin_1d(params, x_data, y_data, err):
     return residuals
 
 
+
 def loss_bias_2d(params, x_data, y_data, err, order, mix):
     """Loss Bias 2D.
 
@@ -113,7 +112,11 @@ def loss_bias_2d(params, x_data, y_data, err, order, mix):
 
     # Get model 1D y1 and y2 components
     y1_model, y2_model = leakage.func_bias_2d(
-        params, x1_data, x2_data, order=order, mix=mix
+        params,
+        x1_data,
+        x2_data,
+        order=order,
+        mix=mix,
     )
 
     # Compute residuals between data and model
@@ -510,12 +513,20 @@ def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
     )
     complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
 
+<<<<<<< HEAD
     alpha_leak = (
         r_corr_gp.xip - np.real(np.conj(complex_gal) * complex_psf)
     ) / (r_corr_pp.xip - np.abs(complex_psf) ** 2)
     sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
         r_corr_gp.varxip / r_corr_gp.xip**2
         + r_corr_pp.varxip / r_corr_pp.xip**2
+=======
+    alpha_leak = (r_corr_gp.xip - np.real(np.conj(complex_gal) * complex_psf)) / (
+        r_corr_pp.xip - np.abs(complex_psf) ** 2
+    )
+    sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
+        r_corr_gp.varxip / r_corr_gp.xip**2 + r_corr_pp.varxip / r_corr_pp.xip**2
+>>>>>>> origin/develop
     )
 
     return alpha_leak, sig_alpha_leak
diff --git a/tests/test_shear_psf_leakage.py b/tests/test_shear_psf_leakage.py
index 2742016..4eed190 100644
--- a/tests/test_shear_psf_leakage.py
+++ b/tests/test_shear_psf_leakage.py
@@ -2,4 +2,4 @@
 
 
 def test_version():
-    assert __version__ == '0.1.0'
+    assert __version__ == '0.1.5'

From 5c0f22d120115d20f1e2cb656ff3bfb749e0518d Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Fri, 22 Sep 2023 15:26:19 +0200
Subject: [PATCH 08/97] alpha: fast computation option

---
 shear_psf_leakage/correlation.py | 30 ++++++++++++++++++++----------
 shear_psf_leakage/run_scale.py   |  8 +++++++-
 2 files changed, 27 insertions(+), 11 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index 9a6c927..b20e4ac 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -211,7 +211,7 @@ def xi_a_b(
     theta_min_amin=2,
     theta_max_amin=200,
     n_theta=20,
-    out_path=None,
+    output_path=None,
 ):
     """Xi A B.
 
@@ -245,7 +245,7 @@ def xi_a_b(
         minimum angular scale in arc minutes; default is 200
     n_theta : int, optional
         number of angular scales; default is 20
-    out_path : str, optional
+    output_path : str, optional
         output file path; default is ``None`` (no file written)
 
     Returns
@@ -485,7 +485,7 @@ def correlation_ab_bb_matrix(
     return xi_ab, xi_bb
 
 
-def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
+def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star, fast=False):
     """Alpha.
 
     Compute scale-dependent PSF leakage alpha.
@@ -500,6 +500,9 @@ def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
         galaxy weights
     e1_star, e2_star : array of float
         galaxy ellipticities
+    fast: bool, optional
+        omits (time-consuming) calculation of mean ellipticity and neglects
+        those small terms if True; default is ``False``
 
     Returns
     -------
@@ -507,15 +510,22 @@ def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star):
         mean and std of alpha
 
     """
-    complex_gal = (
-        np.average(e1_gal, weights=weights_gal)
-        + np.average(e2_gal, weights=weights_gal) * 1j
-    )
-    complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
+    if not fast:
+        complex_gal = (
+            np.average(e1_gal, weights=weights_gal)
+            + np.average(e2_gal, weights=weights_gal) * 1j
+        )
+        complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
+        mean_in_numer = np.real(np.conj(complex_gal) * complex_psf)
+        mean_in_denom = np.abs(complex_psf) ** 2
+    else:
+        mean_in_numer = 0
+        mean_in_denom =  0
 
     alpha_leak = (
-        r_corr_gp.xip - np.real(np.conj(complex_gal) * complex_psf)
-    ) / (r_corr_pp.xip - np.abs(complex_psf) ** 2)
+        (r_corr_gp.xip - mean_in_numer)
+        / (r_corr_pp.xip - mean_in_denom)
+    )
     sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
         r_corr_gp.varxip / r_corr_gp.xip**2
         + r_corr_pp.varxip / r_corr_pp.xip**2
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 8b4e16c..5ff806a 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -947,10 +947,15 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo):
             labels=labels,
         )
 
-    def do_alpha(self):
+    def do_alpha(self, fast=False):
         """Do Alpha.
 
         Compute, plot, and save alpha leakage function.
+
+        fast: bool, optional                                                        
+            omits (time-consuming) calculation of mean ellipticity and neglects     
+            those small terms if True; default is ``False``
+
         """
         # Get input catalogues for averages
         e1_gal = self.dat_shear[self._params["e1_col"]]
@@ -969,6 +974,7 @@ def do_alpha(self):
             weights,
             e1_star,
             e2_star,
+            fast=fast,
         )
         self.compute_alpha_mean()
 

From 3968a6c575c8953f5534024d18878bf9d439b8b5 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 25 Sep 2023 14:35:56 +0200
Subject: [PATCH 09/97] Clara's email added

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index a855144..e654fa0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.5"
 description = "PSF leakage for shear catalogue data"
 authors = [
 	"Martin Kilbinger <martin.kilbinger@cea.fr>",
-	"Clara Bonini <clara.bonini.t.bd>",
+	"Clara Bonini <clara.bonini@universite-paris-saclay.fr>",
 ]
 readme = "README.md"
 

From f8a9638b28635ad9f83d2f9e6f195ad4eb94d303 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 27 Sep 2023 16:20:36 +0200
Subject: [PATCH 10/97] scale-dep alpha spin matrix testing

---
 notebooks/leakage_scale.py       | 230 ++++++++++++++++++++++++-------
 shear_psf_leakage/correlation.py |  51 +++++--
 shear_psf_leakage/leakage.py     |   8 +-
 shear_psf_leakage/run_scale.py   |  41 ++++--
 4 files changed, 254 insertions(+), 76 deletions(-)

diff --git a/notebooks/leakage_scale.py b/notebooks/leakage_scale.py
index 4212139..09cfee8 100644
--- a/notebooks/leakage_scale.py
+++ b/notebooks/leakage_scale.py
@@ -6,23 +6,29 @@
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.14.5
+#       jupytext_version: 1.15.2
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
 #     name: python3
 # ---
 
-# # PSF leakage
-# SP validation
+# # Scale-dependent PSF leakage
+#
+# package shear_psf_leakge
+
+# %reload_ext autoreload
+# %autoreload 2
 
 # +
 import os
 import matplotlib.pylab as plt
+from astropy import units
+
+from cs_util import plots as cs_plots
 
 import shear_psf_leakage.run_scale as run
 from shear_psf_leakage.leakage import *
-
 # -
 
 # ## Set input parameters
@@ -33,25 +39,25 @@
 
 # +
 # Data directory
-data_dir = f"{os.environ['HOME']}/astro/data/CFIS/v1.0/ShapePipe"
+data_dir = "."
 params_in["data_dir"] = data_dir
 
 # Input galaxy shear catalogue
-params_in["input_path_shear"] = f"{data_dir}/unions_shapepipe_extended_2022_v1.0.fits"
+params_in["input_path_shear"] = f"{data_dir}/unions_shapepipe_extended_2022_W3_v1.0.3.fits"
 
 # Input star/PSF catalogue
-params_in["input_path_PSF"] = f"{data_dir}/unions_shapepipe_star_2022_v1.0.3.fits"
+params_in["input_path_PSF"] = f"{data_dir}/unions_shapepipe_psf_2022_W3_v1.0.3.fits"
 
 # Input galaxy redshift distribution (for theoretical model of xi_pm)
-params_in["dndz_path"] = f"{data_dir}/../nz/dndz_SP_A.txt"
+params_in["dndz_path"] = f"{data_dir}/dndz_SP_A.txt"
 # -
 
 # ### Other parameters
 
 # +
 # PSF ellipticty column names
-params_in["e1_PSF_star_col"] = "e1"
-params_in["e2_PSF_star_col"] = "e2"
+params_in["e1_PSF_star_col"] = "E1_PSF_HSM"
+params_in["e2_PSF_star_col"] = "E2_PSF_HSM"
 
 # Set verbose output
 params_in["verbose"] = True
@@ -66,10 +72,12 @@
 for key in params_in:
     obj._params[key] = params_in[key]
 
+# + [markdown] jp-MarkdownHeadingCollapsed=true
 # ### Run
 # There are two options to run the leakage compuations:
 # 1. Run all at once with single class routine.
 # 2. Execute individual steps.
+# -
 
 # ### Option 1. Run all at once
 
@@ -79,72 +87,190 @@
 
 # ### Option 2. Execute individual steps
 
-# +
 # Check parameter validity
 obj.check_params()
 
-# Get all parameters defined in instance
-params = obj._params
-# -
-
-# Prepare output
-if not os.path.exists(params["output_dir"]):
-    os.mkdir(params["output_dir"])
-obj._stats_file = open_stats_file(params["output_dir"], "stats_file_leakage.txt")
-
-# #### Prepare input
+# Prepare output directory and stats file
+obj.prepare_output()
 
-# Read input shear
-dat_shear = obj.read_shear_cat()
-
-# Apply cuts to galaxy catalogue if required
-dat_shear = cut_data(dat_shear, params["cut"], params["verbose"])
-
-# Read star catalogue
-dat_PSF = open_fits_or_npy(
-    params["input_path_PSF"],
-    hdu_no=params["hdu_psf"],
-)
-
-# Deal with close objects in PSF catalogue (= stars on same position
-# from different exposures)
-dat_PSF = obj.handle_close_objects(dat_PSF)
-
-# Copy variables to instance
-obj.dat_shear = dat_shear
-obj.dat_PSF = dat_PSF
+# Prepare input
+obj.read_data()
 
 # #### Compute correlation functions
 # The following command calls `treecorr` to compute auto- and cross-correlation functions.
 # This can take a few minutes.
 
+# Compute various correlation functions.
 obj.compute_corr_gp_pp_alpha()
 
 # #### Scale-dependent alpha function
 
-# Average over scales
-obj.compute_alpha_mean()
+# Compute approximate leakage by ignoring mean ellipticity terms
+obj.do_alpha(fast=True)
+alpha_fast = obj.alpha_leak
+
+# Compute correct leakage
+obj.do_alpha()
 
+# +
 # Plot
-obj.plot_alpha_leakage()
+
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
+
+ftheta = 1.025
+theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr * ftheta]
+alpha_theta = [obj.alpha_leak, alpha_fast]
+yerr = [obj.sig_alpha_leak] * 2
+labels = ["exact", "approximate"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"$\alpha(\theta)$"
+title = ""
+out_path = f"{obj._params['output_dir']}/alpha_leakage.png"
+
+cs_plots.plot_data_1d(
+    theta,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    close_fig=False,
+)
+# -
 
 # #### xi_sys cross-correlation function
 
 # Compute
-obj.compute_xi_sys()
+obj.do_xi_sys()
 
 # Plot
-obj.plot_xi_sys()
-
-params[
-    "dndz_path"
-] = "/home/mkilbing/astro/data/CFIS/v1.0/ShapePipe/../nz/dndz_SP_A.txt"
+obj.plot_xi_sys(close_fig=False)
 
 # Theoretical model for xi_pm
-xi_p_theo, xi_m_theo = run.get_theo_xi_planck(
-    obj.r_corr_gp.meanr,
+xi_p_theo, xi_m_theo = run.get_theo_xi(
+    obj.r_corr_gp.meanr * units.arcmin,
     params["dndz_path"],
 )
 
 # obj Plot ratio
-obj.plot_xi_sys_ratio(xi_p_theo, xi_m_theo)
+obj.plot_xi_sys_ratio(xi_p_theo, xi_m_theo, close_fig=False)
+
+obj.compute_corr_gp_pp_alpha_matrix()
+
+obj.do_alpha_matrix()
+
+# +
+e1_gal = obj.dat_shear[obj._params["e1_col"]]                         
+e2_gal = obj.dat_shear[obj._params["e2_col"]]                         
+weights_gal = obj.dat_shear["w"]                                           
+                                                                                
+e1_star = obj.dat_PSF[obj._params["e1_PSF_star_col"]]                 
+e2_star = obj.dat_PSF[obj._params["e2_PSF_star_col"]] 
+# -
+
+e_g = np.matrix(                                                            
+        [                                                                       
+            np.average(e1_gal, weights=weights_gal),                            
+            np.average(e2_gal, weights=weights_gal),                            
+        ]                                                                       
+    ) 
+e_p = np.matrix(                                                            
+        [                                                                       
+            np.mean(e1_star),                                                   
+            np.mean(e2_star),                                                   
+        ]                                                                       
+    ) 
+print(e_g, e_p)
+
+e_gp = np.dot(e_g.transpose(), e_p)
+e_pp = np.dot(e_p.transpose(), e_p)
+print(e_pp)
+
+# +
+# Exact
+r = obj.Xi_pp_m[0][1] ** 2 / (obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1])
+
+plt.semilogx(obj.r_corr_gp.meanr, r, label="$r$")
+plt.semilogx(obj.r_corr_gp.meanr, 1 / (1 - r), label="$(1 - r)^{-1}$")
+plt.semilogx(obj.r_corr_gp.meanr, 1 / (-1 + 1/r), label="$(-1 + r^{-1})^{-1}$")
+
+# approximate
+r_fast = obj.r_corr_pp_m[0][1].xip ** 2 / (obj.r_corr_pp_m[0][0].xip * obj.r_corr_pp_m[1][1].xip)
+
+plt.semilogx(obj.r_corr_gp.meanr, r_fast, ":")
+plt.semilogx(obj.r_corr_gp.meanr, 1 / (1 - r_fast), ":",)
+plt.semilogx(obj.r_corr_gp.meanr, 1 / (-1 + 1/r_fast), ":")
+
+plt.legend()
+
+plt.ylim(-1, 4)
+
+# +
+# Check alpha_m elements
+plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0], "-", label=r"$\alpha_{11}$")
+
+D = obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1] - obj.Xi_pp_m[0][1] * obj.Xi_pp_m[1][0]
+
+a_11 = (obj.Xi_gp_m[0][0] * obj.Xi_pp_m[1][1] - obj.Xi_gp_m[0][1] * obj.Xi_pp_m[1][0]) / D
+plt.semilogx(obj.r_corr_gp.meanr, a_11, "s", label=r"$a_{11}$", mfc="none")
+
+a_11_bis = (
+    obj.Xi_gp_m[0][0] / (obj.Xi_pp_m[0][0] * (1 - r))
+    - obj.Xi_gp_m[0][1] / (obj.Xi_pp_m[0][1] * (-1 + 1/r))
+)
+plt.semilogx(obj.r_corr_gp.meanr, a_11_bis, "p", label=r"$a_{11}$ bis", mfc="none")
+
+plt.legend()
+
+
+# +
+# Check alpha_m elements
+plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[1][1], "-", label=r"$\alpha_{22}$")
+
+a_22 = (obj.Xi_gp_m[1][1] * obj.Xi_pp_m[0][0] - obj.Xi_gp_m[1][0] * obj.Xi_pp_m[0][1]) / D
+plt.semilogx(obj.r_corr_gp.meanr, a_22, "s", label=r"$a_{22}$", mfc="none")
+
+a_22_bis = (
+    obj.Xi_gp_m[1][1] / (obj.Xi_pp_m[1][1] * (1 - r))
+    - obj.Xi_gp_m[1][0] / (obj.Xi_pp_m[1][0] * (-1 + 1/r))
+)
+plt.semilogx(obj.r_corr_gp.meanr, a_22_bis, "p", label=r"$a_{22}$ bis", mfc="none")
+
+plt.legend()
+
+# +
+# Check alpha_m elements
+plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1], "-", label=r"tr $\mathbf{\alpha}$")
+
+plt.semilogx(obj.r_corr_gp.meanr, a_11 + a_22, "s", label=r"$a_{11} + a_{22}$", mfc="none")
+
+#plt.semilogx(obj.r_corr_gp.meanr, a_11_bis + a_22_bis, "p", label=r"$a_{11} + a_{22}$ bis", mfc="none")
+
+t_sym = (
+    obj.Xi_gp_m[0][0] / ( obj.Xi_pp_m[0][0] * (1 - r) )
+    + obj.Xi_gp_m[1][1] / ( obj.Xi_pp_m[1][1] * (1 - r) )
+)
+plt.semilogx(obj.r_corr_gp.meanr, t_sym, 'v', label=r"$\alpha$ sym")
+
+t_asym = (
+    - obj.Xi_gp_m[0][1] / ( obj.Xi_pp_m[0][1] * (-1 + 1/r) )
+    - obj.Xi_gp_m[1][0] / ( obj.Xi_pp_m[1][0] * (-1 + 1/r) )
+)
+plt.semilogx(obj.r_corr_gp.meanr, t_asym, '^', label=r"$\alpha$ asym")
+
+plt.semilogx(obj.r_corr_gp.meanr, t_sym + t_asym, '.', label=r"$\alpha$ sym + asym")
+
+
+plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak * 2, ':', label=r"$2 \alpha$")
+
+plt.legend()
+plt.ylim(-0.25, 0.25)
+# -
+
+
diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index b20e4ac..ef267aa 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -69,7 +69,6 @@ def loss_bias_lin_1d(params, x_data, y_data, err):
     return residuals
 
 
-
 def loss_bias_2d(params, x_data, y_data, err, order, mix):
     """Loss Bias 2D.
 
@@ -481,11 +480,19 @@ def correlation_ab_bb_matrix(
                 n_theta=n_theta,
             )
 
-
     return xi_ab, xi_bb
 
 
-def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star, fast=False):
+def alpha(
+    r_corr_gp,
+    r_corr_pp,
+    e1_gal,
+    e2_gal,
+    weights_gal,
+    e1_star,
+    e2_star,
+    fast=False,
+):
     """Alpha.
 
     Compute scale-dependent PSF leakage alpha.
@@ -511,20 +518,22 @@ def alpha(r_corr_gp, r_corr_pp, e1_gal, e2_gal, weights_gal, e1_star, e2_star, f
 
     """
     if not fast:
+        # <e^g>
         complex_gal = (
             np.average(e1_gal, weights=weights_gal)
             + np.average(e2_gal, weights=weights_gal) * 1j
         )
+        # <e^p>
         complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
         mean_in_numer = np.real(np.conj(complex_gal) * complex_psf)
         mean_in_denom = np.abs(complex_psf) ** 2
     else:
+        # Set mean ellipticities to zero for faster computation
         mean_in_numer = 0
-        mean_in_denom =  0
+        mean_in_denom = 0
 
-    alpha_leak = (
-        (r_corr_gp.xip - mean_in_numer)
-        / (r_corr_pp.xip - mean_in_denom)
+    alpha_leak = (r_corr_gp.xip - mean_in_numer) / (
+        r_corr_pp.xip - mean_in_denom
     )
     sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
         r_corr_gp.varxip / r_corr_gp.xip**2
@@ -541,6 +550,8 @@ def alpha_matrix(
 
     Compute scale-dependent PSF leakage alpha matrix.
 
+    TODO: transform to LeakageScale class function.
+
     Parameters
     ----------
     r_corr_gp_m, r_corr_pp_m : correlations
@@ -558,12 +569,15 @@ def alpha_matrix(
         alpha, 2x2 matrix
 
     """
+    # <e^g>
     e_g = np.matrix(
         [
             np.average(e1_gal, weights=weights_gal),
             np.average(e2_gal, weights=weights_gal),
         ]
     )
+
+    # <e^p>
     e_p = np.matrix(
         [
             np.mean(e1_star),
@@ -572,6 +586,8 @@ def alpha_matrix(
     )
 
     n_theta = r_corr_gp_m[0][0].nbins
+
+    # Set correlation function matrices
     xi_gp_m = np.zeros((2, 2, n_theta))
     xi_pp_m = np.zeros((2, 2, n_theta))
     for idx in (0, 1):
@@ -579,18 +595,29 @@ def alpha_matrix(
             xi_gp_m[idx][jdx] = r_corr_gp_m[idx][jdx].xip
             xi_pp_m[idx][jdx] = r_corr_pp_m[idx][jdx].xip
 
+    # Set centered correlation function matrices
+    Xi_gp_m = np.zeros((2, 2, n_theta))
+    Xi_pp_m = np.zeros((2, 2, n_theta))
+    for ndx in range(n_theta):
+        Xi_gp_m[:, :, ndx] = xi_gp_m[:, :, ndx] - np.dot(e_g.transpose(), e_p)
+        Xi_pp_m[:, :, ndx] = xi_pp_m[:, :, ndx] - np.dot(e_p.transpose(), e_p)
+
+    # Compute inverse for PSF auto-correlation matrices
     xi_pp_m_inv = np.zeros((2, 2, n_theta))
     for ndx in range(n_theta):
         xi_pp_m_inv[:, :, ndx] = np.linalg.inv(xi_pp_m[:, :, ndx])
 
+    Xi_pp_m_inv = np.zeros((2, 2, n_theta))
+    for ndx in range(n_theta):
+        Xi_pp_m_inv[:, :, ndx] = np.linalg.inv(Xi_pp_m[:, :, ndx])
+
     alpha_leak_m = np.zeros((2, 2, n_theta))
     for ndx in range(n_theta):
         alpha_leak_m[:, :, ndx] = np.dot(
-            xi_gp_m[:, :, ndx] - e_g * e_p.transpose(),
-            xi_pp_m_inv[:, :, ndx]
+            Xi_gp_m[:, :, ndx], Xi_pp_m_inv[:, :, ndx]
         )
 
-    return alpha_leak_m
+    return alpha_leak_m, Xi_gp_m, Xi_pp_m, Xi_pp_m_inv
 
 
 def check_consistency_scales(xi_a, xi_b):
@@ -608,7 +635,5 @@ def check_consistency_scales(xi_a, xi_b):
 
     """
 
-    if any(
-        np.abs(xi_a.meanr - xi_b.meanr) / xi_a.meanr > 0.1
-    ):
+    if any(np.abs(xi_a.meanr - xi_b.meanr) / xi_a.meanr > 0.1):
         print("Warning: angular scales not conr_corr_gpsistent")
diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index 8ef3432..8e9fb39 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -35,6 +35,7 @@ def open_stats_file(directory, file_name):
         directory
     file_name : string
         file name
+
     """
     stats_file = open("{}/{}".format(directory, file_name), "w")
 
@@ -63,7 +64,7 @@ def print_stats(msg, stats_file, verbose=False):
         print(msg)
 
 
-def open_fits_or_npy(path, hdu_no=1):
+def open_fits_or_npy(path, hdu_no=1, verbose=False):
     """Open FITS OR NPY.
 
     Open FITS or numpy binary file.
@@ -74,6 +75,8 @@ def open_fits_or_npy(path, hdu_no=1):
         path to input binary file
     hdu_no : int, optional
         HDU number, default is 1
+    verbose : bool, optional
+        verbose output if ``True``; default is ``False``
 
     Raises
     ------
@@ -95,6 +98,9 @@ def open_fits_or_npy(path, hdu_no=1):
     else:
         raise ValueError(f"Invalid file extension '{file_extension}'")
 
+    if verbose:
+        print(f"{len(data)} objects found in {file_extension} file")
+
     return data
 
 
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 5ff806a..c5f2ebf 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -223,7 +223,7 @@ def params_default(self):
             "theta_max_amin": 300,
             "n_theta": 20,
             "leakage_alpha_ylim": [-0.03, 0.1],
-            "leakage_xi_sys_ylim": [-4e5, 5e5],
+            "leakage_xi_sys_ylim": [-4e-5, 5e-5],
             "leakage_xi_sys_log_ylim": [2e-13, 5e-5],
         }
 
@@ -731,14 +731,18 @@ def compute_xi_sys(self):
         self.C_sys_std_p = C_sys_std_p
         self.C_sys_std_m = C_sys_std_m
 
-    def plot_alpha_leakage(self):
+    def plot_alpha_leakage(self, close_fig=True):
         """Plot Alpha Leakage.
 
         Plot scale-dependent leakage function alpha(theta)
 
-        """
-        plot_dir_leakage = self._params["output_dir"]
+        Parameters
+        -----------
+        close_fig : bool, optional                                                  
+            closes figure if ``True``; set this parameter to ``False`` for
+            notebook display; default is ``True``
 
+        """
         theta = [self.r_corr_gp.meanr]
         alpha_theta = [self.alpha_leak]
         yerr = [self.sig_alpha_leak]
@@ -762,6 +766,7 @@ def plot_alpha_leakage(self):
             xlog=True,
             xlim=xlim,
             ylim=ylim,
+            close_fig=close_fig,
         )
 
     def plot_alpha_leakage_matrix(self):
@@ -770,8 +775,6 @@ def plot_alpha_leakage_matrix(self):
         Plot scale-dependent leakage matrix alpha.
 
         """
-        plot_dir_leakage = self._params["output_dir"]
-
         theta = []
         alpha_theta = []
         labels = []
@@ -825,12 +828,17 @@ def plot_alpha_leakage_matrix(self):
             labels=labels,
         )
 
-
-    def plot_xi_sys(self):
+    def plot_xi_sys(self, close_fig=True):
         """Plot Xi Sys.
 
         Plot galaxy - PSF systematics correlation function.
 
+        Parameters
+        -----------
+        close_fig : bool, optional                                                  
+            closes figure if ``True``; set this parameter to ``False`` for
+            notebook display; default is ``True``
+
         """
         labels = ["$\\xi^{\\rm sys}_+$", "$\\xi^{\\rm sys}_-$"]
 
@@ -866,6 +874,7 @@ def plot_xi_sys(self):
             xlog=True,
             ylim=ylim,
             labels=labels,
+            close_fig=close_fig,
         )
 
         ylim = self._params["leakage_xi_sys_log_ylim"]
@@ -884,9 +893,10 @@ def plot_xi_sys(self):
             ylog=True,
             ylim=ylim,
             labels=labels,
+            close_fig=close_fig,
         )
 
-    def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo):
+    def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo, close_fig=True):
         """Plot Xi Sys Ratio.
 
         Plot xi_sys relative to theoretical model of cosmological
@@ -898,6 +908,9 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo):
             theoretical model of xi+
         xi_m_theo : list
             theoretical model of xi-
+        close_fig : bool, optional                                                  
+            closes figure if ``True``; set this parameter to ``False`` for
+            notebook display; default is ``True``
 
         """
         labels = [
@@ -945,6 +958,7 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo):
             xlog=True,
             ylim=ylim,
             labels=labels,
+            close_fig=close_fig,
         )
 
     def do_alpha(self, fast=False):
@@ -952,6 +966,8 @@ def do_alpha(self, fast=False):
 
         Compute, plot, and save alpha leakage function.
 
+        Parameters
+        ----------
         fast: bool, optional                                                        
             omits (time-consuming) calculation of mean ellipticity and neglects     
             those small terms if True; default is ``False``
@@ -988,6 +1004,7 @@ def do_alpha_matrix(self):
         """Do Alpha Matrix.
 
         Compute, plot, and save alpha leakage matrix.
+
         """
         # Get input catalogues for averages
         e1_gal = self.dat_shear[self._params["e1_col"]]
@@ -998,7 +1015,7 @@ def do_alpha_matrix(self):
         e2_star = self.dat_PSF[self._params["e2_PSF_star_col"]]
 
         # Compute alpha leakage function
-        self.alpha_leak_m = corr.alpha_matrix(
+        alpha_leak_m, Xi_gp_m, Xi_pp_m, Xi_pp_m_inv = corr.alpha_matrix(
             self.r_corr_gp_m,
             self.r_corr_pp_m,
             e1_gal,
@@ -1007,6 +1024,10 @@ def do_alpha_matrix(self):
             e1_star,
             e2_star,
         )
+        self.alpha_leak_m = alpha_leak_m
+        self.Xi_gp_m = Xi_gp_m
+        self.Xi_pp_m = Xi_pp_m
+        self.Xi_pp_m_inv = Xi_pp_m_inv
 
         # Plot
         self.plot_alpha_leakage_matrix()

From 0d6090709790547c8c25a27c330a5093f9fb8341 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 27 Sep 2023 16:22:34 +0200
Subject: [PATCH 11/97] leakage_scale py script updated from Run dir

---
 notebooks/leakage_scale.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/notebooks/leakage_scale.py b/notebooks/leakage_scale.py
index 09cfee8..ab7301f 100644
--- a/notebooks/leakage_scale.py
+++ b/notebooks/leakage_scale.py
@@ -269,8 +269,8 @@
 
 plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak * 2, ':', label=r"$2 \alpha$")
 
-plt.legend()
-plt.ylim(-0.25, 0.25)
+plt.legend(bbox_to_anchor=(1.2, 0.5))
+_ = plt.ylim(-0.25, 0.25)
 # -
 
 

From 702fac4999cb8297c24bba86ec2f74456b2fc874 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sun, 1 Oct 2023 13:58:21 +0200
Subject: [PATCH 12/97] spin-consistent alpha(theta)

---
 notebooks/leakage_scale.py       | 211 ++++++++++++++++++++++---------
 shear_psf_leakage/correlation.py |  85 +------------
 shear_psf_leakage/leakage.py     |   1 -
 shear_psf_leakage/run_scale.py   | 159 +++++++++++++++--------
 4 files changed, 258 insertions(+), 198 deletions(-)

diff --git a/notebooks/leakage_scale.py b/notebooks/leakage_scale.py
index ab7301f..def3cb7 100644
--- a/notebooks/leakage_scale.py
+++ b/notebooks/leakage_scale.py
@@ -22,6 +22,7 @@
 
 # +
 import os
+import sys
 import matplotlib.pylab as plt
 from astropy import units
 
@@ -31,46 +32,36 @@
 from shear_psf_leakage.leakage import *
 # -
 
-# ## Set input parameters
-
-params_in = {}
-
-# ### Input catalogues
+# ## Compute leakage
 
-# +
-# Data directory
-data_dir = "."
-params_in["data_dir"] = data_dir
+# Create leakage instance
+obj = run.LeakageScale()
 
-# Input galaxy shear catalogue
-params_in["input_path_shear"] = f"{data_dir}/unions_shapepipe_extended_2022_W3_v1.0.3.fits"
 
-# Input star/PSF catalogue
-params_in["input_path_PSF"] = f"{data_dir}/unions_shapepipe_psf_2022_W3_v1.0.3.fits"
+# ## Set input parameters
 
-# Input galaxy redshift distribution (for theoretical model of xi_pm)
-params_in["dndz_path"] = f"{data_dir}/dndz_SP_A.txt"
-# -
+params_in_path = "params_leakage_scale.py"
+if os.path.exists(params_in_path):
+    print(f"Reading configuration script {params_in_path}")
 
-# ### Other parameters
+    with open(params_in_path) as f:
+        exec(f.read())
 
-# +
-# PSF ellipticty column names
-params_in["e1_PSF_star_col"] = "E1_PSF_HSM"
-params_in["e2_PSF_star_col"] = "E2_PSF_HSM"
+    # Set instance parameters, copy from above
+    for key in params_in:
+        obj._params[key] = params_in[key]
 
-# Set verbose output
-params_in["verbose"] = True
-# -
+else:
+    print(f"Configuration script {params_in_path} not found, asking for user input")
 
-# ## Compute leakage
+    for key in obj._params:
+        msg = f"{key}? [{obj._params[key]}] "
+        val_user = input(msg)
+        if val_user != "":
+            obj._params[key] = val_user
 
-# Create leakage instance
-obj = run.LeakageScale()
 
-# Set instance parameters, copy from above
-for key in params_in:
-    obj._params[key] = params_in[key]
+print(obj._params)
 
 # + [markdown] jp-MarkdownHeadingCollapsed=true
 # ### Run
@@ -155,7 +146,7 @@
 # Theoretical model for xi_pm
 xi_p_theo, xi_m_theo = run.get_theo_xi(
     obj.r_corr_gp.meanr * units.arcmin,
-    params["dndz_path"],
+    obj._params["dndz_path"],
 )
 
 # obj Plot ratio
@@ -163,37 +154,12 @@
 
 obj.compute_corr_gp_pp_alpha_matrix()
 
-obj.do_alpha_matrix()
-
-# +
-e1_gal = obj.dat_shear[obj._params["e1_col"]]                         
-e2_gal = obj.dat_shear[obj._params["e2_col"]]                         
-weights_gal = obj.dat_shear["w"]                                           
-                                                                                
-e1_star = obj.dat_PSF[obj._params["e1_PSF_star_col"]]                 
-e2_star = obj.dat_PSF[obj._params["e2_PSF_star_col"]] 
-# -
-
-e_g = np.matrix(                                                            
-        [                                                                       
-            np.average(e1_gal, weights=weights_gal),                            
-            np.average(e2_gal, weights=weights_gal),                            
-        ]                                                                       
-    ) 
-e_p = np.matrix(                                                            
-        [                                                                       
-            np.mean(e1_star),                                                   
-            np.mean(e2_star),                                                   
-        ]                                                                       
-    ) 
-print(e_g, e_p)
-
-e_gp = np.dot(e_g.transpose(), e_p)
-e_pp = np.dot(e_p.transpose(), e_p)
-print(e_pp)
+obj.alpha_matrix()
 
 # +
 # Exact
+
+# TODO Check equation
 r = obj.Xi_pp_m[0][1] ** 2 / (obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1])
 
 plt.semilogx(obj.r_corr_gp.meanr, r, label="$r$")
@@ -201,7 +167,7 @@
 plt.semilogx(obj.r_corr_gp.meanr, 1 / (-1 + 1/r), label="$(-1 + r^{-1})^{-1}$")
 
 # approximate
-r_fast = obj.r_corr_pp_m[0][1].xip ** 2 / (obj.r_corr_pp_m[0][0].xip * obj.r_corr_pp_m[1][1].xip)
+r_fast = obj.xi_pp_m[0][1] ** 2 / (obj.xi_pp_m[0][0] * obj.xi_pp_m[1][1])
 
 plt.semilogx(obj.r_corr_gp.meanr, r_fast, ":")
 plt.semilogx(obj.r_corr_gp.meanr, 1 / (1 - r_fast), ":",)
@@ -210,6 +176,19 @@
 plt.legend()
 
 plt.ylim(-1, 4)
+plt.savefig("r.png")
+
+# +
+# Diagonal elements
+plt.clf()
+plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1], "-", label=r"$\alpha_{11} + \alpha_{22}$ (spin-0)")
+plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0] - obj.alpha_leak_m[1][1], "-", label=r"$\alpha_{11} - \alpha_{22}$ (spin-4)")
+plt.legend()
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+plt.xlim(xlim)
+plt.savefig("alpha_leakage_m_11_pm_22.png")
+
+# -
 
 # +
 # Check alpha_m elements
@@ -272,5 +251,117 @@
 plt.legend(bbox_to_anchor=(1.2, 0.5))
 _ = plt.ylim(-0.25, 0.25)
 # -
+# #### Consistency relations for scalar leakage
+
+# If the leakage is a scalar function, it can be expressed in three different ways.
+
+# +
+alpha_1 = obj.Xi_gp_m[0][0] / obj.Xi_pp_m[1][1]
+alpha_2 = obj.Xi_gp_m[1][1] / obj.Xi_pp_m[1][1]
+
+alpha_1_std =  np.abs(alpha_1) * np.sqrt(                              
+        obj.xi_std_gp_m[0][0] ** 2 / obj.xi_gp_m[0][0] ** 2                                     
+        + obj.xi_std_pp_m[0][0] ** 2 / obj.xi_pp_m[0][0] ** 2                                   
+    )
+
+alpha_2_std =  np.abs(alpha_2) * np.sqrt(                              
+        obj.xi_std_gp_m[1][1] ** 2 / obj.xi_gp_m[1][1] ** 2                                     
+        + obj.xi_std_pp_m[1][1] ** 2 / obj.xi_pp_m[1][1] ** 2                                   
+    )
+
+# +
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
+
+ftheta = 1.05
+theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr, obj.r_corr_gp.meanr * ftheta]
+alpha_theta = [obj.alpha_leak, alpha_1, alpha_2]
+yerr = [obj.sig_alpha_leak, alpha_1_std, alpha_2_std]
+labels = [r"$\alpha$", r"$\alpha_1$", r"$\alpha_2$"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"$\alpha(\theta)$"
+title = ""
+out_path = f"{obj._params['output_dir']}/alpha_leakage_scalar_consistency.png"
+
+cs_plots.plot_data_1d(
+    theta,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    close_fig=False,
+)
+# -
+
+# If alpha is a scalar, the mixed-component centered cross-correlation functions should be identical.
+
+# +
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+
+Xi_gp_plus = obj.Xi_gp_m[0][0] + obj.Xi_gp_m[1][1]
+Xi_std_gp_plus = np.sqrt(obj.xi_std_gp_m[0][0] ** 2 + obj.xi_std_gp_m[1][1] ** 2)
+
+ftheta = 1.025
+theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr, obj.r_corr_gp.meanr * ftheta]
+alpha_theta = [obj.Xi_gp_m[0][1], obj.Xi_gp_m[1][0], Xi_gp_plus]
+yerr = [obj.xi_std_gp_m[0][1], obj.xi_std_gp_m[1][0], Xi_std_gp_plus]
+labels = [r"$\Xi_{12}^{\rm gp}$", r"$\Xi_{21}^{\rm gp}$", r"$\Xi_+^{\rm gp}$"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"Centered correlation functions"
+title = ""
+out_path = f"{obj._params['output_dir']}/Xi_mixed_consistency.png"
+
+cs_plots.plot_data_1d(
+    theta,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    close_fig=False,
+)
+
+# +
+# For comparison, plot the same for the PSF - PSF correlations
+
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+
+Xi_gp_plus = obj.Xi_pp_m[0][0] + obj.Xi_pp_m[1][1]
+Xi_std_gp_plus = np.sqrt(obj.xi_std_pp_m[0][0] ** 2 + obj.xi_std_pp_m[1][1] ** 2)
+
+ftheta = 1.025
+theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr, obj.r_corr_gp.meanr * ftheta]
+alpha_theta = [obj.Xi_pp_m[0][1], obj.Xi_pp_m[1][0], Xi_gp_plus]
+yerr = [obj.xi_std_pp_m[0][1], obj.xi_std_pp_m[1][0], Xi_std_gp_plus]
+labels = [r"$\Xi_{12}^{\rm pp}$", r"$\Xi_{21}^{\rm pp}$", r"$\Xi_+^{\rm pp}$"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"Centered correlation functions"
+title = ""
+out_path = f"{obj._params['output_dir']}/Xi_pp_mixed_consistency.png"
+
+cs_plots.plot_data_1d(
+    theta,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    close_fig=False,
+)
+# -
 
 
diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index ef267aa..13233ad 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -394,7 +394,7 @@ def correlation_ab_bb_matrix(
     theta_max_amin=200,
     n_theta=20,
 ):
-    """Correlation 12 22 Matrix.
+    """Correlation ab bb Matrix.
 
     Shear correlation function matrices between two samples a and b.
     Computes the xi_ab and xi_bb matrices.
@@ -435,7 +435,7 @@ def correlation_ab_bb_matrix(
     ell_a = np.empty(shape=(2, len(e1_a)))
     ell_b = np.empty(shape=(2, len(e1_b)))
 
-    # We know that xi_+ = xi_tt + xi_xx = xi_11 + xi_22.
+    # We know that xi_+ = xi_tt + xi_xx = xi_11 + xi_22
     # To only get one component "xi_11", we need to set
     # the other to zero.
     # xi_ab_11 = <e1_a e1_b>; e2_a = e2_b = 0
@@ -536,90 +536,13 @@ def alpha(
         r_corr_pp.xip - mean_in_denom
     )
     sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
-        r_corr_gp.varxip / r_corr_gp.xip**2
-        + r_corr_pp.varxip / r_corr_pp.xip**2
+        r_corr_gp.varxip / r_corr_gp.xip ** 2
+        + r_corr_pp.varxip / r_corr_pp.xip ** 2
     )
 
     return alpha_leak, sig_alpha_leak
 
 
-def alpha_matrix(
-    r_corr_gp_m, r_corr_pp_m, e1_gal, e2_gal, weights_gal, e1_star, e2_star
-):
-    """Alpha Matrix.
-
-    Compute scale-dependent PSF leakage alpha matrix.
-
-    TODO: transform to LeakageScale class function.
-
-    Parameters
-    ----------
-    r_corr_gp_m, r_corr_pp_m : correlations
-        correlations galaxy-star, star-star
-    e1_gal, e2_gal : array of float
-        galaxy ellipticities
-    weights_gal : array of float
-        galaxy weights
-    e1_star, e2_star : array of float
-        galaxy ellipticities
-
-    Returns
-    -------
-    alpha : numpy.ndarray
-        alpha, 2x2 matrix
-
-    """
-    # <e^g>
-    e_g = np.matrix(
-        [
-            np.average(e1_gal, weights=weights_gal),
-            np.average(e2_gal, weights=weights_gal),
-        ]
-    )
-
-    # <e^p>
-    e_p = np.matrix(
-        [
-            np.mean(e1_star),
-            np.mean(e2_star),
-        ]
-    )
-
-    n_theta = r_corr_gp_m[0][0].nbins
-
-    # Set correlation function matrices
-    xi_gp_m = np.zeros((2, 2, n_theta))
-    xi_pp_m = np.zeros((2, 2, n_theta))
-    for idx in (0, 1):
-        for jdx in (0, 1):
-            xi_gp_m[idx][jdx] = r_corr_gp_m[idx][jdx].xip
-            xi_pp_m[idx][jdx] = r_corr_pp_m[idx][jdx].xip
-
-    # Set centered correlation function matrices
-    Xi_gp_m = np.zeros((2, 2, n_theta))
-    Xi_pp_m = np.zeros((2, 2, n_theta))
-    for ndx in range(n_theta):
-        Xi_gp_m[:, :, ndx] = xi_gp_m[:, :, ndx] - np.dot(e_g.transpose(), e_p)
-        Xi_pp_m[:, :, ndx] = xi_pp_m[:, :, ndx] - np.dot(e_p.transpose(), e_p)
-
-    # Compute inverse for PSF auto-correlation matrices
-    xi_pp_m_inv = np.zeros((2, 2, n_theta))
-    for ndx in range(n_theta):
-        xi_pp_m_inv[:, :, ndx] = np.linalg.inv(xi_pp_m[:, :, ndx])
-
-    Xi_pp_m_inv = np.zeros((2, 2, n_theta))
-    for ndx in range(n_theta):
-        Xi_pp_m_inv[:, :, ndx] = np.linalg.inv(Xi_pp_m[:, :, ndx])
-
-    alpha_leak_m = np.zeros((2, 2, n_theta))
-    for ndx in range(n_theta):
-        alpha_leak_m[:, :, ndx] = np.dot(
-            Xi_gp_m[:, :, ndx], Xi_pp_m_inv[:, :, ndx]
-        )
-
-    return alpha_leak_m, Xi_gp_m, Xi_pp_m, Xi_pp_m_inv
-
-
 def check_consistency_scales(xi_a, xi_b):
     """Check Consistency Scales.
 
diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index 8e9fb39..e29029e 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -41,7 +41,6 @@ def open_stats_file(directory, file_name):
 
     return stats_file
 
-
 def print_stats(msg, stats_file, verbose=False):
     """Print stats.
 
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index c5f2ebf..a537fdf 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -93,9 +93,9 @@ def get_theo_xi(theta, dndz_path):
 
     Returns
     -------
-    numpy.ndarray                                                               
-        xi_+                                                                    
-    numpy.ndarray                                                               
+    numpy.ndarray
+        xi_+
+    numpy.ndarray
         xi_-
 
     """
@@ -343,6 +343,13 @@ def prepare_output(self):
             self._params["output_dir"], "stats_file_leakage.txt"
         )
 
+        for key in self._params:
+            leakage.print_stats(
+                f"{key}: {self._params[key]}",
+                self._stats_file,
+                False
+            )
+
     def run(self):
         """Run.
 
@@ -608,7 +615,6 @@ def get_cat_fields(self, cat_type):
 
         return ra, dec, e1, e2, weights
 
-
     def compute_corr_gp_pp_alpha(self, output_base_path=None):
         """Compute Corr GP PP Alpha.
 
@@ -616,7 +622,7 @@ def compute_corr_gp_pp_alpha(self, output_base_path=None):
 
         Parameters
         ----------
-        out_path : str, optional                                                    
+        out_path : str, optional
                 output file path; default is ``None`` (no file written)
 
         """
@@ -691,7 +697,6 @@ def compute_corr_gp_pp_alpha_matrix(self):
 
         self.r_corr_gp_m = r_corr_gp_m
         self.r_corr_pp_m = r_corr_pp_m
-                
 
     def compute_alpha_mean(self):
         """Compute Alpha Mean.
@@ -703,8 +708,7 @@ def compute_alpha_mean(self):
             np.average(self.alpha_leak, weights=1 / self.sig_alpha_leak**2)
         )
         leakage.print_stats(
-            f"Weighted average alpha"
-            + f" = {self.alpha_leak_mean:.3g}",
+            f"Weighted average alpha" + f" = {self.alpha_leak_mean:.3g}",
             self._stats_file,
             verbose=self._params["verbose"],
         )
@@ -738,7 +742,7 @@ def plot_alpha_leakage(self, close_fig=True):
 
         Parameters
         -----------
-        close_fig : bool, optional                                                  
+        close_fig : bool, optional
             closes figure if ``True``; set this parameter to ``False`` for
             notebook display; default is ``True``
 
@@ -749,10 +753,7 @@ def plot_alpha_leakage(self, close_fig=True):
         xlabel = r"$\theta$ [arcmin]"
         ylabel = r"$\alpha(\theta)$"
         title = ""
-        out_path = (
-            f"{self._params['output_dir']}"
-            + f"/alpha_leakage.png"
-        )
+        out_path = f"{self._params['output_dir']}" + f"/alpha_leakage.png"
         xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
         ylim = self._params["leakage_alpha_ylim"]
         plots.plot_data_1d(
@@ -835,7 +836,7 @@ def plot_xi_sys(self, close_fig=True):
 
         Parameters
         -----------
-        close_fig : bool, optional                                                  
+        close_fig : bool, optional
             closes figure if ``True``; set this parameter to ``False`` for
             notebook display; default is ``True``
 
@@ -860,9 +861,7 @@ def plot_xi_sys(self, close_fig=True):
             )
 
         ylim = self._params["leakage_xi_sys_ylim"]
-        out_path = (
-            f"{self._params['output_dir']}/xi_sys.pdf"
-        )
+        out_path = f"{self._params['output_dir']}/xi_sys.pdf"
         plots.plot_data_1d(
             theta,
             xi,
@@ -878,9 +877,7 @@ def plot_xi_sys(self, close_fig=True):
         )
 
         ylim = self._params["leakage_xi_sys_log_ylim"]
-        out_path = (
-            f"{self._params['output_dir']}/xi_sys_log.pdf"
-        )
+        out_path = f"{self._params['output_dir']}/xi_sys_log.pdf"
         plots.plot_data_1d(
             theta,
             xi,
@@ -908,7 +905,7 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo, close_fig=True):
             theoretical model of xi+
         xi_m_theo : list
             theoretical model of xi-
-        close_fig : bool, optional                                                  
+        close_fig : bool, optional
             closes figure if ``True``; set this parameter to ``False`` for
             notebook display; default is ``True``
 
@@ -933,17 +930,12 @@ def plot_xi_sys_ratio(self, xi_p_theo, xi_m_theo, close_fig=True):
         symb_arr = ["+", "-"]
         for comp, symb in zip(comp_arr, symb_arr):
             mean = np.mean(np.abs(xi[comp]))
-            msg = (
-                f"<|xi_sys_{symb}| / xi_{symb}> = {mean}"
-            )
+            msg = f"<|xi_sys_{symb}| / xi_{symb}> = {mean}"
             leakage.print_stats(
                 msg, self._stats_file, verbose=self._params["verbose"]
             )
 
-        out_path = (
-            f"{self._params['output_dir']}"
-            + f"/xi_sys_ratio.pdf"
-        )
+        out_path = f"{self._params['output_dir']}" + f"/xi_sys_ratio.pdf"
 
         ylim = [0, 0.5]
 
@@ -968,8 +960,8 @@ def do_alpha(self, fast=False):
 
         Parameters
         ----------
-        fast: bool, optional                                                        
-            omits (time-consuming) calculation of mean ellipticity and neglects     
+        fast: bool, optional
+            omits (time-consuming) calculation of mean ellipticity and neglects
             those small terms if True; default is ``False``
 
         """
@@ -1000,34 +992,92 @@ def do_alpha(self, fast=False):
         # Write to disk
         self.save_alpha()
 
+    def alpha_matrix(self):
+        """Alpha Matrix.
+
+        Compute alpha leakage matrix.
+
+        """
+        # <e^g>
+        e_g = np.matrix(
+            [
+                np.average(
+                    self.dat_shear[self._params["e1_col"]],
+                    weights=self.dat_shear["w"],
+                ),
+                np.average(
+                    self.dat_shear[self._params["e2_col"]],
+                    weights=self.dat_shear["w"],
+                ),
+            ]
+        )
+
+        # <e^p>
+        e_p = np.matrix(
+            [
+                np.mean(self.dat_PSF[self._params["e1_PSF_star_col"]]),
+                np.mean(self.dat_PSF[self._params["e2_PSF_star_col"]]),
+            ]
+        )
+
+        n_theta = self._params["n_theta"]
+
+        # Set correlation function matrices
+        self.xi_gp_m = np.zeros((2, 2, n_theta))
+        self.xi_pp_m = np.zeros((2, 2, n_theta))
+        for idx in (0, 1):
+            for jdx in (0, 1):
+                self.xi_gp_m[idx][jdx] = self.r_corr_gp_m[idx][jdx].xip
+                self.xi_pp_m[idx][jdx] = self.r_corr_pp_m[idx][jdx].xip
+
+        # Set centered correlation function matrices
+        self.Xi_gp_m = np.zeros((2, 2, n_theta))
+        self.Xi_pp_m = np.zeros((2, 2, n_theta))
+        for ndx in range(n_theta):
+            self.Xi_gp_m[:, :, ndx] = self.xi_gp_m[:, :, ndx] - np.dot(
+                e_g.transpose(), e_p
+            )
+            self.Xi_pp_m[:, :, ndx] = self.xi_pp_m[:, :, ndx] - np.dot(
+                e_p.transpose(), e_p
+            )
+
+        # Standard deviations
+        self.xi_std_gp_m = np.zeros((2, 2, n_theta))
+        self.xi_std_pp_m = np.zeros((2, 2, n_theta))
+        for idx in (0, 1):
+            for jdx in (0, 1):
+                self.xi_std_gp_m[idx][jdx] = np.sqrt(self.r_corr_gp_m[idx][jdx].varxip)
+                self.xi_std_pp_m[idx][jdx] = np.sqrt(self.r_corr_pp_m[idx][jdx].varxip)
+
+        # TODO: include <e><e>
+
+        # Compute inverse for PSF auto-correlation matrices
+        self.xi_pp_m_inv = np.zeros((2, 2, n_theta))
+        for ndx in range(n_theta):
+            self.xi_pp_m_inv[:, :, ndx] = np.linalg.inv(
+                self.xi_pp_m[:, :, ndx]
+            )
+
+        self.Xi_pp_m_inv = np.zeros((2, 2, n_theta))
+        for ndx in range(n_theta):
+            self.Xi_pp_m_inv[:, :, ndx] = np.linalg.inv(
+                self.Xi_pp_m[:, :, ndx]
+            )
+
+        self.alpha_leak_m = np.zeros((2, 2, n_theta))
+        for ndx in range(n_theta):
+            self.alpha_leak_m[:, :, ndx] = np.dot(
+                self.Xi_gp_m[:, :, ndx], self.Xi_pp_m_inv[:, :, ndx]
+            )
+
     def do_alpha_matrix(self):
         """Do Alpha Matrix.
 
         Compute, plot, and save alpha leakage matrix.
 
         """
-        # Get input catalogues for averages
-        e1_gal = self.dat_shear[self._params["e1_col"]]
-        e2_gal = self.dat_shear[self._params["e2_col"]]
-        weights = self.dat_shear["w"]
-
-        e1_star = self.dat_PSF[self._params["e1_PSF_star_col"]]
-        e2_star = self.dat_PSF[self._params["e2_PSF_star_col"]]
-
-        # Compute alpha leakage function
-        alpha_leak_m, Xi_gp_m, Xi_pp_m, Xi_pp_m_inv = corr.alpha_matrix(
-            self.r_corr_gp_m,
-            self.r_corr_pp_m,
-            e1_gal,
-            e2_gal,
-            weights,
-            e1_star,
-            e2_star,
-        )
-        self.alpha_leak_m = alpha_leak_m
-        self.Xi_gp_m = Xi_gp_m
-        self.Xi_pp_m = Xi_pp_m
-        self.Xi_pp_m_inv = Xi_pp_m_inv
+        # Compute
+        self.alpha_matrix()
 
         # Plot
         self.plot_alpha_leakage_matrix()
@@ -1048,9 +1098,7 @@ def do_xi_sys(self):
 
         # Treecorr output scales are in arc minutes
         theta = self.r_corr_gp.meanr * units.arcmin
-        xi_p_theo, xi_m_theo = get_theo_xi(
-            theta, self._params["dndz_path"]
-        )
+        xi_p_theo, xi_m_theo = get_theo_xi(theta, self._params["dndz_path"])
 
         # Plot
         self.plot_xi_sys()
@@ -1102,7 +1150,6 @@ def save_alpha_matrix(self):
         cs_cat.write_ascii_table_file(cols, names, fname)
 
 
-
 def run_leakage_scale(*args):
     """Run Leakage Scale.
 

From 4679875160e86e380425f6393c2353feae96e00f Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 19 Oct 2023 09:34:20 +0200
Subject: [PATCH 13/97] Added notebook to test weighting for scalar
 alpha(theta)

---
 notebooks/leakage_scale_test_weights.py | 197 ++++++++++++++++++++++++
 1 file changed, 197 insertions(+)
 create mode 100644 notebooks/leakage_scale_test_weights.py

diff --git a/notebooks/leakage_scale_test_weights.py b/notebooks/leakage_scale_test_weights.py
new file mode 100644
index 0000000..498339f
--- /dev/null
+++ b/notebooks/leakage_scale_test_weights.py
@@ -0,0 +1,197 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: shear_psf_leakage
+#     language: python
+#     name: shear_psf_leakage
+# ---
+
+# # Scale-dependent PSF leakage
+#
+# ## Testing weighted versus unweighted estimators
+#
+# package shear_psf_leakge
+#
+# Martin Kilbinger <martin.kilbinger@cea.fr>
+
+# %reload_ext autoreload
+# %autoreload 2
+
+# +
+import os
+import sys
+import matplotlib.pylab as plt
+from astropy import units
+
+from cs_util import plots as cs_plots
+
+import shear_psf_leakage.run_scale as run
+from shear_psf_leakage.leakage import *
+# -
+
+# ## Compute leakage
+
+# +
+# Create leakage instances
+
+# Using galaxy weights
+obj = run.LeakageScale()
+
+# No weights
+obj_nw = run.LeakageScale()
+
+objects = {"weighted": obj, "unweighted": obj_nw}
+# -
+
+
+# ### Set input parameters
+
+# +
+params_in_path = "params_leakage_scale_test_weights.py"
+if os.path.exists(params_in_path):
+    print(f"Reading configuration script {params_in_path}")
+
+    with open(params_in_path) as f:
+        exec(f.read())
+
+    # Set instance parameters, copy from above
+    for key in params_in:
+        obj._params[key] = params_in[key]
+        
+else:
+    raise IOError("configuration script {params_in_path} not found")
+# -
+
+for key in obj._params:
+    obj_nw._params[key] = obj._params[key]
+obj_nw._params["w_col"] = None
+
+print(obj._params)
+print(obj_nw._params)
+
+# ### Set up data
+
+# Check parameter validity
+for obj in objects.values():
+    obj.check_params()
+
+# Prepare output directory and stats file
+for obj in objects.values():
+    obj.prepare_output()
+
+# Prepare input
+for obj in objects.values():
+    obj.read_data()
+
+# +
+theta = []
+alpha_theta = []
+yerr = []
+labels = []
+
+nx = 6
+fx = 1.025
+
+# +
+# Compute various correlation functions.
+for obj in objects.values():
+    obj.compute_corr_gp_pp_alpha()
+
+idx = 0
+
+for key, obj in zip(objects.keys(), objects.values()):
+    obj.do_alpha()
+    
+    theta.append(obj.r_corr_gp.meanr * fx ** (idx - nx))
+    alpha_theta.append(obj.alpha_leak)
+    yerr.append(obj.sig_alpha_leak)
+    labels.append(f"{key} <e^g> != 0")
+    idx += 1
+
+# +
+# Subtract mean ellipticity
+
+# Weighted
+obj = objects["weighted"]
+e1_gal = obj.dat_shear[obj._params["e1_col"]]
+e2_gal = obj.dat_shear[obj._params["e2_col"]]
+w = obj.dat_shear[obj._params["w_col"]]
+e1_avg = np.average(e1_gal, weights=w)
+e2_avg = np.average(e2_gal, weights=w)
+obj.dat_shear[obj._params["e1_col"]] -= e1_avg
+obj.dat_shear[obj._params["e2_col"]] -= e2_avg
+
+# Unweighted
+obj_nw = objects["unweighted"]
+e1_gal = obj_nw.dat_shear[obj_nw._params["e1_col"]]
+e2_gal = obj_nw.dat_shear[obj_nw._params["e2_col"]]
+e1_avg = np.average(e1_gal)
+e2_avg = np.average(e2_gal)
+obj_nw.dat_shear[obj_nw._params["e1_col"]] -= e1_avg
+obj_nw.dat_shear[obj_nw._params["e2_col"]] -= e2_avg
+# -
+
+# Compute various correlation functions.
+for obj in objects.values():
+    obj.compute_corr_gp_pp_alpha()
+
+# #### Scale-dependent alpha function
+
+# +
+# Compute leakage
+for key, obj in zip(objects.keys(), objects.values()):
+    obj.do_alpha()
+
+    theta.append(obj.r_corr_gp.meanr * fx ** (idx - nx))
+    alpha_theta.append(obj.alpha_leak)
+    yerr.append(obj.sig_alpha_leak)
+    labels.append(key)
+    idx += 1
+
+    
+# Compute approximate leakage (fast)
+for key, obj in zip(objects.keys(), objects.values()):
+    obj.do_alpha(fast=True)
+
+    theta.append(obj.r_corr_gp.meanr  * fx ** (idx - nx))
+    alpha_theta.append(obj.alpha_leak)
+    yerr.append(obj.sig_alpha_leak)
+    labels.append(f"{key} approx")
+    idx += 1
+
+# +
+# Plot
+
+markers = ["o", "s", "d", "p", "v", "*"] 
+
+obj = objects["weighted"]
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]    
+    
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"$\alpha(\theta)$"
+title = ""
+out_path = f"{obj._params['output_dir']}/alpha_leakage_test_weights.png"
+
+cs_plots.plot_data_1d(
+    theta,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    markers=markers,
+    close_fig=False,
+)

From c12e749235e83c8365d5d8ce13bf31feefccce3d Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 21 Oct 2023 08:49:54 +0200
Subject: [PATCH 14/97] leakage_scale script back to basics

---
 notebooks/leakage_scale.py | 241 ++-----------------------------------
 1 file changed, 12 insertions(+), 229 deletions(-)

diff --git a/notebooks/leakage_scale.py b/notebooks/leakage_scale.py
index def3cb7..64638a2 100644
--- a/notebooks/leakage_scale.py
+++ b/notebooks/leakage_scale.py
@@ -8,14 +8,18 @@
 #       format_version: '1.5'
 #       jupytext_version: 1.15.2
 #   kernelspec:
-#     display_name: Python 3 (ipykernel)
+#     display_name: shear_psf_leakage
 #     language: python
-#     name: python3
+#     name: shear_psf_leakage
 # ---
 
 # # Scale-dependent PSF leakage
 #
+# ## Scalar $\alpha(\theta)$ and $\xi_\textrm{sys}$
+#
 # package shear_psf_leakge
+#
+# Martin Kilbinger <martin.kilbinger@cea.fr>
 
 # %reload_ext autoreload
 # %autoreload 2
@@ -38,7 +42,7 @@
 obj = run.LeakageScale()
 
 
-# ## Set input parameters
+# ### Set input parameters
 
 params_in_path = "params_leakage_scale.py"
 if os.path.exists(params_in_path):
@@ -50,7 +54,6 @@
     # Set instance parameters, copy from above
     for key in params_in:
         obj._params[key] = params_in[key]
-
 else:
     print(f"Configuration script {params_in_path} not found, asking for user input")
 
@@ -60,7 +63,6 @@
         if val_user != "":
             obj._params[key] = val_user
 
-
 print(obj._params)
 
 # + [markdown] jp-MarkdownHeadingCollapsed=true
@@ -96,10 +98,6 @@
 
 # #### Scale-dependent alpha function
 
-# Compute approximate leakage by ignoring mean ellipticity terms
-obj.do_alpha(fast=True)
-alpha_fast = obj.alpha_leak
-
 # Compute correct leakage
 obj.do_alpha()
 
@@ -109,11 +107,10 @@
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 ylim = obj._params["leakage_alpha_ylim"]
 
-ftheta = 1.025
-theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr * ftheta]
-alpha_theta = [obj.alpha_leak, alpha_fast]
-yerr = [obj.sig_alpha_leak] * 2
-labels = ["exact", "approximate"]
+theta = [obj.r_corr_gp.meanr]
+alpha_theta = [obj.alpha_leak]
+yerr = [obj.sig_alpha_leak]
+labels = [r"$\alpha$"]
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"$\alpha(\theta)$"
 title = ""
@@ -149,219 +146,5 @@
     obj._params["dndz_path"],
 )
 
-# obj Plot ratio
+# Plot ratio
 obj.plot_xi_sys_ratio(xi_p_theo, xi_m_theo, close_fig=False)
-
-obj.compute_corr_gp_pp_alpha_matrix()
-
-obj.alpha_matrix()
-
-# +
-# Exact
-
-# TODO Check equation
-r = obj.Xi_pp_m[0][1] ** 2 / (obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1])
-
-plt.semilogx(obj.r_corr_gp.meanr, r, label="$r$")
-plt.semilogx(obj.r_corr_gp.meanr, 1 / (1 - r), label="$(1 - r)^{-1}$")
-plt.semilogx(obj.r_corr_gp.meanr, 1 / (-1 + 1/r), label="$(-1 + r^{-1})^{-1}$")
-
-# approximate
-r_fast = obj.xi_pp_m[0][1] ** 2 / (obj.xi_pp_m[0][0] * obj.xi_pp_m[1][1])
-
-plt.semilogx(obj.r_corr_gp.meanr, r_fast, ":")
-plt.semilogx(obj.r_corr_gp.meanr, 1 / (1 - r_fast), ":",)
-plt.semilogx(obj.r_corr_gp.meanr, 1 / (-1 + 1/r_fast), ":")
-
-plt.legend()
-
-plt.ylim(-1, 4)
-plt.savefig("r.png")
-
-# +
-# Diagonal elements
-plt.clf()
-plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1], "-", label=r"$\alpha_{11} + \alpha_{22}$ (spin-0)")
-plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0] - obj.alpha_leak_m[1][1], "-", label=r"$\alpha_{11} - \alpha_{22}$ (spin-4)")
-plt.legend()
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-plt.xlim(xlim)
-plt.savefig("alpha_leakage_m_11_pm_22.png")
-
-# -
-
-# +
-# Check alpha_m elements
-plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0], "-", label=r"$\alpha_{11}$")
-
-D = obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1] - obj.Xi_pp_m[0][1] * obj.Xi_pp_m[1][0]
-
-a_11 = (obj.Xi_gp_m[0][0] * obj.Xi_pp_m[1][1] - obj.Xi_gp_m[0][1] * obj.Xi_pp_m[1][0]) / D
-plt.semilogx(obj.r_corr_gp.meanr, a_11, "s", label=r"$a_{11}$", mfc="none")
-
-a_11_bis = (
-    obj.Xi_gp_m[0][0] / (obj.Xi_pp_m[0][0] * (1 - r))
-    - obj.Xi_gp_m[0][1] / (obj.Xi_pp_m[0][1] * (-1 + 1/r))
-)
-plt.semilogx(obj.r_corr_gp.meanr, a_11_bis, "p", label=r"$a_{11}$ bis", mfc="none")
-
-plt.legend()
-
-
-# +
-# Check alpha_m elements
-plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[1][1], "-", label=r"$\alpha_{22}$")
-
-a_22 = (obj.Xi_gp_m[1][1] * obj.Xi_pp_m[0][0] - obj.Xi_gp_m[1][0] * obj.Xi_pp_m[0][1]) / D
-plt.semilogx(obj.r_corr_gp.meanr, a_22, "s", label=r"$a_{22}$", mfc="none")
-
-a_22_bis = (
-    obj.Xi_gp_m[1][1] / (obj.Xi_pp_m[1][1] * (1 - r))
-    - obj.Xi_gp_m[1][0] / (obj.Xi_pp_m[1][0] * (-1 + 1/r))
-)
-plt.semilogx(obj.r_corr_gp.meanr, a_22_bis, "p", label=r"$a_{22}$ bis", mfc="none")
-
-plt.legend()
-
-# +
-# Check alpha_m elements
-plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1], "-", label=r"tr $\mathbf{\alpha}$")
-
-plt.semilogx(obj.r_corr_gp.meanr, a_11 + a_22, "s", label=r"$a_{11} + a_{22}$", mfc="none")
-
-#plt.semilogx(obj.r_corr_gp.meanr, a_11_bis + a_22_bis, "p", label=r"$a_{11} + a_{22}$ bis", mfc="none")
-
-t_sym = (
-    obj.Xi_gp_m[0][0] / ( obj.Xi_pp_m[0][0] * (1 - r) )
-    + obj.Xi_gp_m[1][1] / ( obj.Xi_pp_m[1][1] * (1 - r) )
-)
-plt.semilogx(obj.r_corr_gp.meanr, t_sym, 'v', label=r"$\alpha$ sym")
-
-t_asym = (
-    - obj.Xi_gp_m[0][1] / ( obj.Xi_pp_m[0][1] * (-1 + 1/r) )
-    - obj.Xi_gp_m[1][0] / ( obj.Xi_pp_m[1][0] * (-1 + 1/r) )
-)
-plt.semilogx(obj.r_corr_gp.meanr, t_asym, '^', label=r"$\alpha$ asym")
-
-plt.semilogx(obj.r_corr_gp.meanr, t_sym + t_asym, '.', label=r"$\alpha$ sym + asym")
-
-
-plt.semilogx(obj.r_corr_gp.meanr, obj.alpha_leak * 2, ':', label=r"$2 \alpha$")
-
-plt.legend(bbox_to_anchor=(1.2, 0.5))
-_ = plt.ylim(-0.25, 0.25)
-# -
-# #### Consistency relations for scalar leakage
-
-# If the leakage is a scalar function, it can be expressed in three different ways.
-
-# +
-alpha_1 = obj.Xi_gp_m[0][0] / obj.Xi_pp_m[1][1]
-alpha_2 = obj.Xi_gp_m[1][1] / obj.Xi_pp_m[1][1]
-
-alpha_1_std =  np.abs(alpha_1) * np.sqrt(                              
-        obj.xi_std_gp_m[0][0] ** 2 / obj.xi_gp_m[0][0] ** 2                                     
-        + obj.xi_std_pp_m[0][0] ** 2 / obj.xi_pp_m[0][0] ** 2                                   
-    )
-
-alpha_2_std =  np.abs(alpha_2) * np.sqrt(                              
-        obj.xi_std_gp_m[1][1] ** 2 / obj.xi_gp_m[1][1] ** 2                                     
-        + obj.xi_std_pp_m[1][1] ** 2 / obj.xi_pp_m[1][1] ** 2                                   
-    )
-
-# +
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-ylim = obj._params["leakage_alpha_ylim"]
-
-ftheta = 1.05
-theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr, obj.r_corr_gp.meanr * ftheta]
-alpha_theta = [obj.alpha_leak, alpha_1, alpha_2]
-yerr = [obj.sig_alpha_leak, alpha_1_std, alpha_2_std]
-labels = [r"$\alpha$", r"$\alpha_1$", r"$\alpha_2$"]
-xlabel = r"$\theta$ [arcmin]"
-ylabel = r"$\alpha(\theta)$"
-title = ""
-out_path = f"{obj._params['output_dir']}/alpha_leakage_scalar_consistency.png"
-
-cs_plots.plot_data_1d(
-    theta,
-    alpha_theta,
-    yerr,
-    title,
-    xlabel,
-    ylabel,
-    out_path,
-    labels=labels,
-    xlog=True,
-    xlim=xlim,
-    ylim=ylim,
-    close_fig=False,
-)
-# -
-
-# If alpha is a scalar, the mixed-component centered cross-correlation functions should be identical.
-
-# +
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-
-Xi_gp_plus = obj.Xi_gp_m[0][0] + obj.Xi_gp_m[1][1]
-Xi_std_gp_plus = np.sqrt(obj.xi_std_gp_m[0][0] ** 2 + obj.xi_std_gp_m[1][1] ** 2)
-
-ftheta = 1.025
-theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr, obj.r_corr_gp.meanr * ftheta]
-alpha_theta = [obj.Xi_gp_m[0][1], obj.Xi_gp_m[1][0], Xi_gp_plus]
-yerr = [obj.xi_std_gp_m[0][1], obj.xi_std_gp_m[1][0], Xi_std_gp_plus]
-labels = [r"$\Xi_{12}^{\rm gp}$", r"$\Xi_{21}^{\rm gp}$", r"$\Xi_+^{\rm gp}$"]
-xlabel = r"$\theta$ [arcmin]"
-ylabel = r"Centered correlation functions"
-title = ""
-out_path = f"{obj._params['output_dir']}/Xi_mixed_consistency.png"
-
-cs_plots.plot_data_1d(
-    theta,
-    alpha_theta,
-    yerr,
-    title,
-    xlabel,
-    ylabel,
-    out_path,
-    labels=labels,
-    xlog=True,
-    xlim=xlim,
-    close_fig=False,
-)
-
-# +
-# For comparison, plot the same for the PSF - PSF correlations
-
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-
-Xi_gp_plus = obj.Xi_pp_m[0][0] + obj.Xi_pp_m[1][1]
-Xi_std_gp_plus = np.sqrt(obj.xi_std_pp_m[0][0] ** 2 + obj.xi_std_pp_m[1][1] ** 2)
-
-ftheta = 1.025
-theta = [obj.r_corr_gp.meanr / ftheta, obj.r_corr_gp.meanr, obj.r_corr_gp.meanr * ftheta]
-alpha_theta = [obj.Xi_pp_m[0][1], obj.Xi_pp_m[1][0], Xi_gp_plus]
-yerr = [obj.xi_std_pp_m[0][1], obj.xi_std_pp_m[1][0], Xi_std_gp_plus]
-labels = [r"$\Xi_{12}^{\rm pp}$", r"$\Xi_{21}^{\rm pp}$", r"$\Xi_+^{\rm pp}$"]
-xlabel = r"$\theta$ [arcmin]"
-ylabel = r"Centered correlation functions"
-title = ""
-out_path = f"{obj._params['output_dir']}/Xi_pp_mixed_consistency.png"
-
-cs_plots.plot_data_1d(
-    theta,
-    alpha_theta,
-    yerr,
-    title,
-    xlabel,
-    ylabel,
-    out_path,
-    labels=labels,
-    xlog=True,
-    xlim=xlim,
-    close_fig=False,
-)
-# -
-
-

From aab956c1980bd7a60971213d459140a41850c805 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 21 Oct 2023 08:50:14 +0200
Subject: [PATCH 15/97] added notebook package

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index e654fa0..b52c5d0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 scipy = "1.10.1"
 cs-util = "0.0.6"
+notebook = "5.7.4"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From e2244071516839d688819d90e5b5809226e3a662 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 21 Oct 2023 08:51:08 +0200
Subject: [PATCH 16/97] galaxy weights as option

---
 shear_psf_leakage/correlation.py | 10 ++++++--
 shear_psf_leakage/run_scale.py   | 41 ++++++++++++++++----------------
 2 files changed, 29 insertions(+), 22 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index 13233ad..e1e85ba 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -527,13 +527,19 @@ def alpha(
         complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
         mean_in_numer = np.real(np.conj(complex_gal) * complex_psf)
         mean_in_denom = np.abs(complex_psf) ** 2
+
+        # Check that <e^g> is close to zero if properly calibrated.
+        # Pay attention whether weights are used or not.
+        print("MKDEBUG eg ep", complex_gal, complex_psf)
     else:
         # Set mean ellipticities to zero for faster computation
         mean_in_numer = 0
         mean_in_denom = 0
 
-    alpha_leak = (r_corr_gp.xip - mean_in_numer) / (
-        r_corr_pp.xip - mean_in_denom
+
+    alpha_leak = (
+        (r_corr_gp.xip - mean_in_numer)
+        / (r_corr_pp.xip - mean_in_denom)
     )
     sig_alpha_leak = np.abs(alpha_leak) * np.sqrt(
         r_corr_gp.varxip / r_corr_gp.xip ** 2
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index a537fdf..6f8ccbb 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -206,8 +206,9 @@ def params_default(self):
         """
         self._params = {
             "input_path_shear": None,
-            "e1_col": "e1_uncal",
-            "e2_col": "e2_uncal",
+            "e1_col": "e1",
+            "e2_col": "e2",
+            "w_col": None,
             "input_path_PSF": None,
             "hdu_psf": 1,
             "ra_star_col": "RA",
@@ -247,6 +248,7 @@ def params_default(self):
             "input_path_shear": "input path of the shear catalogue",
             "e1_col": "e1 column name in galaxy catalogue, default={}",
             "e2_col": "e2 column name in galaxy catalogue, default={}",
+            "w_col": "weight column name in galaxy catalogue, default={}",
             "input_path_PSF": "input path of the PSF catalogue",
             "hdu_PSF": "HDU number of PSF catalogue, default={}",
             "ra_star_col": (
@@ -587,7 +589,8 @@ def get_cat_fields(self, cat_type):
         cat_type : str
             catalogue type, allowed are "gal" and "star"
 
-        Return
+        Returns
+        -------
         list
             ra
         list
@@ -605,7 +608,10 @@ def get_cat_fields(self, cat_type):
             dec = self.dat_shear["Dec"]
             e1 = self.dat_shear[self._params["e1_col"]]
             e2 = self.dat_shear[self._params["e2_col"]]
-            weights = self.dat_shear["w"]
+            if self._params["w_col"] is not None:
+                weights = self.dat_shear[self._params["w_col"]]
+            else:
+                weights = np.ones_like(ra)
         elif cat_type == "star":
             ra = self.dat_PSF[self._params["ra_star_col"]]
             dec = self.dat_PSF[self._params["dec_star_col"]]
@@ -966,12 +972,8 @@ def do_alpha(self, fast=False):
 
         """
         # Get input catalogues for averages
-        e1_gal = self.dat_shear[self._params["e1_col"]]
-        e2_gal = self.dat_shear[self._params["e2_col"]]
-        weights = self.dat_shear["w"]
-
-        e1_star = self.dat_PSF[self._params["e1_PSF_star_col"]]
-        e2_star = self.dat_PSF[self._params["e2_PSF_star_col"]]
+        _, _, e1_gal, e2_gal, weights = self.get_cat_fields("gal")
+        _, _, e1_star, e2_star, _ = self.get_cat_fields("star")
 
         # Compute alpha leakage function
         self.alpha_leak, self.sig_alpha_leak = corr.alpha(
@@ -998,25 +1000,22 @@ def alpha_matrix(self):
         Compute alpha leakage matrix.
 
         """
+        _, _, e1_gal, e2_gal, weights = self.get_cat_fields("gal")
+        _, _, e1_star, e2_star, _ = self.get_cat_fields("star")
+
         # <e^g>
         e_g = np.matrix(
             [
-                np.average(
-                    self.dat_shear[self._params["e1_col"]],
-                    weights=self.dat_shear["w"],
-                ),
-                np.average(
-                    self.dat_shear[self._params["e2_col"]],
-                    weights=self.dat_shear["w"],
-                ),
+                np.average(e1_gal, weights=weights),
+                np.average(e2_gal, weights=weights),
             ]
         )
 
         # <e^p>
         e_p = np.matrix(
             [
-                np.mean(self.dat_PSF[self._params["e1_PSF_star_col"]]),
-                np.mean(self.dat_PSF[self._params["e2_PSF_star_col"]]),
+                np.mean(e1_star),
+                np.mean(e2_star),
             ]
         )
 
@@ -1070,6 +1069,8 @@ def alpha_matrix(self):
                 self.Xi_gp_m[:, :, ndx], self.Xi_pp_m_inv[:, :, ndx]
             )
 
+        # TODO: error on alppha_leak_m
+
     def do_alpha_matrix(self):
         """Do Alpha Matrix.
 

From a9d2b5d704cd960596c9a926598eb7dc8fd585d5 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 21 Oct 2023 08:51:26 +0200
Subject: [PATCH 17/97] leakage scale spin script added

---
 notebooks/leakage_scale_spin.py | 429 ++++++++++++++++++++++++++++++++
 1 file changed, 429 insertions(+)
 create mode 100644 notebooks/leakage_scale_spin.py

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
new file mode 100644
index 0000000..32d0ec5
--- /dev/null
+++ b/notebooks/leakage_scale_spin.py
@@ -0,0 +1,429 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:light
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: shear_psf_leakage
+#     language: python
+#     name: shear_psf_leakage
+# ---
+
+# # Scale-dependent PSF leakage
+#
+# ## Spin-consistent leakage
+#
+# package shear_psf_leakge
+#
+# Martin Kilbinger <martin.kilbinger@cea.fr>
+
+# %reload_ext autoreload
+# %autoreload 2
+
+# +
+import os
+import sys
+import matplotlib.pylab as plt
+from astropy import units
+
+from cs_util import plots as cs_plots
+
+import shear_psf_leakage.run_scale as run
+from shear_psf_leakage.leakage import *
+# -
+
+# ## Compute leakage
+
+# Create leakage instance
+obj = run.LeakageScale()
+
+
+# ### Set input parameters
+
+# +
+params_in_path = "params_leakage_scale.py"
+if os.path.exists(params_in_path):
+    print(f"Reading configuration script {params_in_path}")
+
+    with open(params_in_path) as f:
+        exec(f.read())
+
+    # Set instance parameters, copy from above
+    for key in params_in:
+        obj._params[key] = params_in[key]
+
+else:
+    print(f"Configuration script {params_in_path} not found, asking for user input")
+
+    for key in obj._params:
+        msg = f"{key}? [{obj._params[key]}] "
+        val_user = input(msg)
+        if val_user != "":
+            obj._params[key] = val_user
+# -
+
+print(obj._params)
+
+# + [markdown] jp-MarkdownHeadingCollapsed=true
+# ### Run
+# -
+
+# Check parameter validity
+obj.check_params()
+
+# Prepare output directory and stats file
+obj.prepare_output()
+
+# Prepare input
+obj.read_data()
+
+# #### Compute correlation function and alpha matrices
+# The following command calls `treecorr` to compute auto- and cross-correlation functions.
+# This can take a few minutes.
+
+obj.compute_corr_gp_pp_alpha_matrix()
+
+obj.alpha_matrix()
+
+#### For comparison: scalar alpha leakage
+obj.compute_corr_gp_pp_alpha()
+obj.do_alpha()
+
+# #### PSF auto-correlation function correlation matrix
+#
+# $$
+# r = \frac{ \left( \Xi_{12}^\textrm{p,p} \right)^2 }
+#     { \Xi_{11}^\textrm{p,p} \, \Xi_{22}^\textrm{p,p} }
+# $$
+
+# Check symmetry of PSF auto-correlation matrix
+diff = obj.Xi_pp_m[0][1] - obj.Xi_pp_m[1][0]
+print(
+    "r is symmetrical? max abs (rel) diff ="
+    +f" {max(np.abs(diff)):.3e} ({max(np.abs(diff / obj.Xi_pp_m[0][1])):.3e})",
+)
+
+# +
+# Plot
+
+# Exact calculation
+r = obj.Xi_pp_m[0][1] ** 2 / (obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1])
+
+theta = obj.r_corr_gp_m[0][0].meanr
+
+plt.semilogx(theta, r, label="$r$")
+plt.semilogx(theta, 1 / (1 - r), label="$1/(1-r)$")
+plt.semilogx(theta, r / (1 - r), label="$r/(1-r)$")
+
+# Approximate
+r_fast = obj.xi_pp_m[0][1] ** 2 / (obj.xi_pp_m[0][0] * obj.xi_pp_m[1][1])
+
+plt.semilogx(theta, r_fast, "b:")
+plt.semilogx(theta, 1 / (1 - r_fast), "o:",)
+plt.semilogx(theta, 1 / (1/r_fast - 1), "g:")
+
+plt.axhline(color="k", linewidth=0.5)
+
+plt.legend()
+plt.xlabe(r"$\theta$ [arcmin]")
+plt.ylabel(r"$r(\theta)$")
+
+plt.ylim(-0.5, 2)
+plt.savefig("r.png")
+
+# +
+# Plot diagonal elements: Spin-0 and spin-4
+
+x0 = 0.5 * ( obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1] )
+x4 = 0.5 * ( obj.alpha_leak_m[0][0] - obj.alpha_leak_m[1][1] )
+dx0 = 0.5 * ( obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1] )
+
+plt.clf()
+plt.semilogx(
+    theta,
+    x0,
+    "-",
+    label=r"$x_0 = (\alpha_{11} + \alpha_{22})/2$ (spin-0)"
+)
+plt.semilogx(
+    theta,
+    x4,
+    "-",
+    label=r"$x_4 = (\alpha_{11} - \alpha_{22})/2$ (spin-4)"
+)
+plt.legend()
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+plt.xlim(xlim)
+plt.xlabe(r"$\theta$ [arcmin]")
+plt.ylabel(r"Components of leakage matrix")
+
+plt.savefig("alpha_leakage_m_s0_s4.png")
+# -
+
+
+# The elements of $\alpha$ can be written as
+# \begin{align}
+# \newcommand{\mat}[1]{\mathrm{#1}}
+#     \alpha_{11} = & \left(
+#         \phantom -\Xi_{11}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
+#         - \Xi_{12}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#     \nonumber \\
+#     \alpha_{12} = & \left(
+#         - \Xi_{11}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         + \Xi_{12}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#     \nonumber \\
+#         \alpha_{21} = & \left(
+#         \phantom - \Xi_{21}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
+#         - \Xi_{22}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#     \nonumber \\
+#         \alpha_{22} = & \left(
+#         - \Xi_{21}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         + \Xi_{22}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1},
+# \end{align}
+
+# +
+# Check alpha_m elements equations (18)
+
+plt.clf()
+
+# a_11
+color = "blue"
+
+plt.semilogx(theta, obj.alpha_leak_m[0][0], "-", color=color, label=r"$\alpha_{11}$")
+
+# Xi_pp determinant
+D = obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1] - obj.Xi_pp_m[0][1] * obj.Xi_pp_m[1][0]
+
+a_11 = (
+    (obj.Xi_gp_m[0][0] * obj.Xi_pp_m[1][1]
+    - obj.Xi_gp_m[0][1] * obj.Xi_pp_m[1][0]) / D
+)
+plt.semilogx(theta, a_11, "s", label=r"$a_{11}$", color=color, mfc="none")
+
+a_11_bis = (
+    1 / (1 - r) * obj.Xi_gp_m[0][0] / obj.Xi_pp_m[0][0]
+    - r / (1 - r) * obj.Xi_gp_m[0][1] / obj.Xi_pp_m[0][1]
+)
+plt.semilogx(theta, a_11_bis, "p", label=r"$a_{11}$ bis", color=color, mfc="none")
+
+# a_22
+color = "green"
+
+plt.semilogx(theta, obj.alpha_leak_m[1][1], "--", color=color, label=r"$\alpha_{22}$")
+
+a_22 = (
+    (obj.Xi_gp_m[1][1] * obj.Xi_pp_m[0][0]
+    - obj.Xi_gp_m[1][0] * obj.Xi_pp_m[0][1]) / D
+)
+plt.semilogx(theta, a_22, "d", label=r"$a_{22}$", color=color, mfc="none")
+
+a_22_bis = (
+     1 / (1 - r) * obj.Xi_gp_m[1][1] / obj.Xi_pp_m[1][1]
+    - r / (1 - r) * obj.Xi_gp_m[1][0] / obj.Xi_pp_m[1][0]
+)
+plt.semilogx(theta, a_22_bis, "o", label=r"$a_{22}$ bis", color=color, mfc="none")
+
+plt.legend()
+plt.xlabel(r"$\theta$ [arcmin]")
+plt.ylabel(r"$\alpha_{ii}$")
+
+plt.savefig("alpha_m_11_22_check.png")
+
+
+# +
+plt.clf()
+
+# a_12
+color = "magenta"
+
+plt.semilogx(theta, obj.alpha_leak_m[0][1], "-", color=color, label=r"$\alpha_{12}$")
+
+a_12 = (
+    (-obj.Xi_gp_m[0][0] * obj.Xi_pp_m[0][1]
+    + obj.Xi_gp_m[0][1] * obj.Xi_pp_m[0][0]) / D
+)
+plt.semilogx(theta, a_12, "s", label=r"$a_{12}$", color=color, mfc="none")
+
+a_12_bis = (
+    -r / (1 - r) * obj.Xi_gp_m[0][0] / obj.Xi_pp_m[0][1]
+    + 1 / (1 - r) * obj.Xi_gp_m[0][1] / obj.Xi_pp_m[1][1]
+)
+plt.semilogx(theta, a_12_bis, "p", label=r"$a_{11}$ bis", color=color, mfc="none")
+
+# a_21
+color = "green"
+
+plt.semilogx(theta, obj.alpha_leak_m[1][0], "--", color=color, label=r"$\alpha_{21}$")
+
+a_21 = (
+    (obj.Xi_gp_m[1][0] * obj.Xi_pp_m[1][1]
+    - obj.Xi_gp_m[1][1] * obj.Xi_pp_m[0][1]) / D
+)
+plt.semilogx(theta, a_21, "d", label=r"$a_{22}$", color=color, mfc="none")
+
+a_21_bis = (
+     1 / (1 - r) * obj.Xi_gp_m[1][0] / obj.Xi_pp_m[0][0]
+    - r / (1 - r) * obj.Xi_gp_m[1][1] / obj.Xi_pp_m[0][1]
+)
+plt.semilogx(theta, a_21_bis, "o", label=r"$a_{22}$ bis", color=color, mfc="none")
+
+plt.legend()
+plt.xlabel(r"$\theta$ [arcmin]")
+plt.ylabel(r"$\alpha_{ij}$")
+
+plt.savefig("alpha_m_12_21_check.png")
+
+# +
+# Check alpha_m elements
+
+plt.clf()
+plt.semilogx(theta, obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1], "-", label=r"tr $\mathbf{\alpha}$")
+
+plt.semilogx(theta, a_11 + a_22, "s", label=r"$a_{11} + a_{22}$", mfc="none")
+
+#plt.semilogx(theta, a_11_bis + a_22_bis, "p", label=r"$a_{11} + a_{22}$ bis", mfc="none")
+
+t_sym = (
+    obj.Xi_gp_m[0][0] / ( obj.Xi_pp_m[0][0] * (1 - r) )
+    + obj.Xi_gp_m[1][1] / ( obj.Xi_pp_m[1][1] * (1 - r) )
+)
+plt.semilogx(theta, t_sym, 'v', label=r"$\alpha$ sym")
+
+t_asym = (
+    - obj.Xi_gp_m[0][1] / ( obj.Xi_pp_m[0][1] * (-1 + 1/r) )
+    - obj.Xi_gp_m[1][0] / ( obj.Xi_pp_m[1][0] * (-1 + 1/r) )
+)
+plt.semilogx(theta, t_asym, '^', label=r"$\alpha$ asym")
+
+plt.semilogx(theta, t_sym + t_asym, '.', label=r"$\alpha$ sym + asym")
+
+
+plt.semilogx(theta, obj.alpha_leak * 2, ':', label=r"$2 \alpha$")
+
+plt.legend(bbox_to_anchor=(1.2, 0.5))
+_ = plt.ylim(-0.25, 0.25)
+
+plt.savefig("x.png")
+# -
+# #### Consistency relations for scalar leakage
+
+# If the leakage is a scalar function, it can be expressed in three different ways.
+
+# +
+alpha_1 = obj.Xi_gp_m[0][0] / obj.Xi_pp_m[1][1]
+alpha_2 = obj.Xi_gp_m[1][1] / obj.Xi_pp_m[1][1]
+
+alpha_1_std =  np.abs(alpha_1) * np.sqrt(                              
+        obj.xi_std_gp_m[0][0] ** 2 / obj.xi_gp_m[0][0] ** 2                                     
+        + obj.xi_std_pp_m[0][0] ** 2 / obj.xi_pp_m[0][0] ** 2                                   
+    )
+
+alpha_2_std =  np.abs(alpha_2) * np.sqrt(                              
+        obj.xi_std_gp_m[1][1] ** 2 / obj.xi_gp_m[1][1] ** 2                                     
+        + obj.xi_std_pp_m[1][1] ** 2 / obj.xi_pp_m[1][1] ** 2                                   
+    )
+
+# +
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
+
+ftheta = 1.05
+theta_arr = [theta / ftheta, theta, theta * ftheta]
+alpha_theta = [obj.alpha_leak, alpha_1, alpha_2]
+yerr = [obj.sig_alpha_leak, alpha_1_std, alpha_2_std]
+labels = [r"$\alpha$", r"$\alpha_1$", r"$\alpha_2$"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"$\alpha(\theta)$"
+title = ""
+out_path = f"{obj._params['output_dir']}/alpha_leakage_scalar_consistency.png"
+
+cs_plots.plot_data_1d(
+    theta_arr,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    close_fig=False,
+)
+# -
+
+# If alpha is a scalar, the mixed-component centered cross-correlation functions should be identical.
+
+# +
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+
+Xi_gp_plus = obj.Xi_gp_m[0][0] + obj.Xi_gp_m[1][1]
+Xi_std_gp_plus = np.sqrt(obj.xi_std_gp_m[0][0] ** 2 + obj.xi_std_gp_m[1][1] ** 2)
+
+ftheta = 1.025
+theta_arr = [theta / ftheta, theta, theta * ftheta]
+alpha_theta = [obj.Xi_gp_m[0][1], obj.Xi_gp_m[1][0], Xi_gp_plus]
+yerr = [obj.xi_std_gp_m[0][1], obj.xi_std_gp_m[1][0], Xi_std_gp_plus]
+labels = [r"$\Xi_{12}^{\rm gp}$", r"$\Xi_{21}^{\rm gp}$", r"tr$\Xi^{\rm gp}$"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"Centered correlation functions"
+title = ""
+out_path = f"{obj._params['output_dir']}/Xi_mixed_consistency.png"
+
+cs_plots.plot_data_1d(
+    theta_arr,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    close_fig=False,
+)
+
+# +
+# For comparison, plot the same for the PSF - PSF correlations
+
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+
+Xi_gp_plus = obj.Xi_pp_m[0][0] + obj.Xi_pp_m[1][1]
+Xi_std_gp_plus = np.sqrt(obj.xi_std_pp_m[0][0] ** 2 + obj.xi_std_pp_m[1][1] ** 2)
+
+ftheta = 1.025
+theta_arr = [theta / ftheta, theta, theta * ftheta]
+alpha_theta = [obj.Xi_pp_m[0][1], obj.Xi_pp_m[1][0], Xi_gp_plus]
+yerr = [obj.xi_std_pp_m[0][1], obj.xi_std_pp_m[1][0], Xi_std_gp_plus]
+labels = [r"$\Xi_{12}^{\rm pp}$", r"$\Xi_{21}^{\rm pp}$", r"tr$\Xi^{\rm pp}$"]
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"Centered correlation functions"
+title = ""
+out_path = f"{obj._params['output_dir']}/Xi_pp_mixed_consistency.png"
+
+cs_plots.plot_data_1d(
+    theta_arr,
+    alpha_theta,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    close_fig=False,
+)
+# -
+
+

From 38abd62af027a8af948007ffa829edde4502f35e Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 21 Oct 2023 08:53:51 +0200
Subject: [PATCH 18/97] added poetry.lock

---
 poetry.lock | 3326 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 3326 insertions(+)
 create mode 100644 poetry.lock

diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..f58e6fd
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3326 @@
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+
+[[package]]
+name = "aenum"
+version = "3.1.15"
+description = "Advanced Enumerations (compatible with Python's stdlib Enum), NamedTuples, and NamedConstants"
+optional = false
+python-versions = "*"
+files = [
+    {file = "aenum-3.1.15-py2-none-any.whl", hash = "sha256:27b1710b9d084de6e2e695dab78fe9f269de924b51ae2850170ee7e1ca6288a5"},
+    {file = "aenum-3.1.15-py3-none-any.whl", hash = "sha256:e0dfaeea4c2bd362144b87377e2c61d91958c5ed0b4daf89cb6f45ae23af6288"},
+    {file = "aenum-3.1.15.tar.gz", hash = "sha256:8cbd76cd18c4f870ff39b24284d3ea028fbe8731a58df3aa581e434c575b9559"},
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = "*"
+files = [
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
+
+[[package]]
+name = "asteval"
+version = "0.9.31"
+description = "Safe, minimalistic evaluator of python expression using ast module"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "asteval-0.9.31-py3-none-any.whl", hash = "sha256:2761750c184d97707c292b62df3b10e330a809a2201721acc435a2b89a114263"},
+    {file = "asteval-0.9.31.tar.gz", hash = "sha256:a2da066b6696dba9835c5f7dec63e0ffb5bd2b4e3bb5f0b9a604aeafb17d833d"},
+]
+
+[package.extras]
+all = ["Sphinx", "build", "coverage", "pytest", "pytest-cov", "twine"]
+dev = ["build", "twine"]
+doc = ["Sphinx"]
+test = ["coverage", "pytest", "pytest-cov"]
+
+[[package]]
+name = "astropy"
+version = "5.3.3"
+description = "Astronomy and astrophysics core library"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "astropy-5.3.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f4be66bfdd6dc58206b919a9baeb976ecd061fe40eb92288b46ae660d8610d1"},
+    {file = "astropy-5.3.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:81239ad0d2876aa820fe35e3f22954a3035ebb9b86e284cfb91e37a76ab24b83"},
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+    {file = "astropy-5.3.3.tar.gz", hash = "sha256:0330df9f5d7a2254367e9b8cf44255ba1070b06123188c6a72edc180493f93bb"},
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+
+[package.dependencies]
+numpy = ">=1.21,<2"
+packaging = ">=19.0"
+pyerfa = ">=2.0"
+PyYAML = ">=3.13"
+
+[package.extras]
+all = ["asdf (>=2.10.0)", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2022.8.2)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "matplotlib (>=3.3,!=3.4.0,!=3.5.2)", "mpmath", "pandas", "pre-commit", "pyarrow (>=5.0.0)", "pytest (>=7.0,<8)", "pytz", "s3fs (>=2022.8.2)", "scipy (>=1.5)", "sortedcontainers", "typing-extensions (>=3.10.0.1)"]
+docs = ["Jinja2 (>=3.0)", "matplotlib (>=3.3,!=3.4.0,!=3.5.2)", "pytest (>=7.0,<8)", "scipy (>=1.3)", "sphinx", "sphinx-astropy (>=1.6)", "sphinx-changelog (>=1.2.0)"]
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+
+[[package]]
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+optional = false
+python-versions = "*"
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+[package.dependencies]
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+
+[package.extras]
+test = ["astroid", "pytest"]
+
+[[package]]
+name = "atomicwrites"
+version = "1.4.1"
+description = "Atomic file writes."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
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+]
+
+[[package]]
+name = "attrs"
+version = "23.1.0"
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+]
+
+[package.dependencies]
+setuptools = "*"
+
+[package.extras]
+docs = ["Sphinx", "repoze.sphinx.autointerface"]
+test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
+testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.9,<3.12"
+content-hash = "e4d6eef82180c5eaefcc4afa6541b7079bd4051ba9b134e74fcefcba6778df50"

From 80e1fd2cdfffb6e71959db01ef15f43a6db90faa Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Tue, 24 Oct 2023 18:08:08 +0200
Subject: [PATCH 19/97] leakage scale spin before uncertainties plots

---
 notebooks/leakage_scale_spin.py | 403 ++++++++++++++++-----------
 poetry.lock                     | 469 +++++++++++++++++++++++++++++++-
 pyproject.toml                  |   3 +
 shear_psf_leakage/run_scale.py  | 186 +++++++++----
 4 files changed, 848 insertions(+), 213 deletions(-)

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index 32d0ec5..9b12ac3 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -29,6 +29,7 @@
 import sys
 import matplotlib.pylab as plt
 from astropy import units
+from uncertainties import unumpy
 
 from cs_util import plots as cs_plots
 
@@ -110,209 +111,293 @@
 # +
 # Plot
 
-# Exact calculation
-r = obj.Xi_pp_m[0][1] ** 2 / (obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1])
+plt.clf()
+
+theta = obj.get_theta()
 
-theta = obj.r_corr_gp_m[0][0].meanr
+# Exact calculation
+r = []
+r_ratio_1 = []
+r_ratio_2 = []
+for ndx in range(len(theta)):
+    my_r = (
+        obj.Xi_pp_ufloat[ndx][0, 1] ** 2
+        / (obj.Xi_pp_ufloat[ndx][0, 0] * obj.Xi_pp_ufloat[ndx][1, 1])
+    )
+    r.append(my_r)
+    r_ratio_1.append(1 / (1 - my_r))
+    r_ratio_2.append(my_r / (1 - my_r)) 
 
-plt.semilogx(theta, r, label="$r$")
-plt.semilogx(theta, 1 / (1 - r), label="$1/(1-r)$")
-plt.semilogx(theta, r / (1 - r), label="$r/(1-r)$")
+print("min max mean r = ", np.min(r), np.max(r), np.mean(r))
 
 # Approximate
 r_fast = obj.xi_pp_m[0][1] ** 2 / (obj.xi_pp_m[0][0] * obj.xi_pp_m[1][1])
 
-plt.semilogx(theta, r_fast, "b:")
-plt.semilogx(theta, 1 / (1 - r_fast), "o:",)
-plt.semilogx(theta, 1 / (1/r_fast - 1), "g:")
+theta_arr = []
+r_arr = []
+dr_arr = []
 
-plt.axhline(color="k", linewidth=0.5)
-
-plt.legend()
-plt.xlabe(r"$\theta$ [arcmin]")
-plt.ylabel(r"$r(\theta)$")
-
-plt.ylim(-0.5, 2)
-plt.savefig("r.png")
-
-# +
-# Plot diagonal elements: Spin-0 and spin-4
+n = 6
+ftheta = 1.05
+for idx in range(n):
+    theta_arr.append(theta * ftheta ** (idx - n))
+    
+r_arr.append(unumpy.nominal_values(r))
+r_arr.append(unumpy.nominal_values(r_ratio_1))
+r_arr.append(unumpy.nominal_values(r_ratio_2))
+r_arr.append(r_fast)
+r_arr.append(1 / (1 - r_fast))
+r_arr.append(r_fast / (1 - r_fast))
+
+dr_arr.append(unumpy.std_devs(r))
+dr_arr.append(unumpy.std_devs(r_ratio_1))
+dr_arr.append(unumpy.std_devs(r_ratio_2))
+for idx in range(3):
+    dr_arr.append(np.nan)
+
+labels = ["$r$", "$1/(1-r)$", "$r/(1-r)$", "", "", ""]
+colors = ["blue", "orange", "green", "blue", "orange", "green"]
+linestyles = ["-"] * 3 + ["--"] * 3
+linewidths = [2] * 3 + [1] * 3
 
-x0 = 0.5 * ( obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1] )
-x4 = 0.5 * ( obj.alpha_leak_m[0][0] - obj.alpha_leak_m[1][1] )
-dx0 = 0.5 * ( obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1] )
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"ratios of $r(\theta)$"
 
-plt.clf()
-plt.semilogx(
-    theta,
-    x0,
-    "-",
-    label=r"$x_0 = (\alpha_{11} + \alpha_{22})/2$ (spin-0)"
-)
-plt.semilogx(
-    theta,
-    x4,
-    "-",
-    label=r"$x_4 = (\alpha_{11} - \alpha_{22})/2$ (spin-4)"
-)
-plt.legend()
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-plt.xlim(xlim)
-plt.xlabe(r"$\theta$ [arcmin]")
-plt.ylabel(r"Components of leakage matrix")
+ylim = (-0.5, 2)
 
-plt.savefig("alpha_leakage_m_s0_s4.png")
-# -
+out_path = f"{obj._params['output_dir']}/r.png"
 
+title = ""
 
-# The elements of $\alpha$ can be written as
-# \begin{align}
-# \newcommand{\mat}[1]{\mathrm{#1}}
-#     \alpha_{11} = & \left(
-#         \phantom -\Xi_{11}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
-#         - \Xi_{12}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
-#     \nonumber \\
-#     \alpha_{12} = & \left(
-#         - \Xi_{11}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         + \Xi_{12}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
-#     \nonumber \\
-#         \alpha_{21} = & \left(
-#         \phantom - \Xi_{21}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
-#         - \Xi_{22}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
-#     \nonumber \\
-#         \alpha_{22} = & \left(
-#         - \Xi_{21}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         + \Xi_{22}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1},
-# \end{align}
+cs_plots.plot_data_1d(
+    theta_arr,
+    r_arr,
+    dr_arr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    colors=colors,
+    linewidths=linewidths,
+    linestyles=linestyles,
+)
 
 # +
-# Check alpha_m elements equations (18)
+# Plot alpha matrix elements
 
-plt.clf()
+theta = obj.get_theta()
 
-# a_11
-color = "blue"
-
-plt.semilogx(theta, obj.alpha_leak_m[0][0], "-", color=color, label=r"$\alpha_{11}$")
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
 
-# Xi_pp determinant
-D = obj.Xi_pp_m[0][0] * obj.Xi_pp_m[1][1] - obj.Xi_pp_m[0][1] * obj.Xi_pp_m[1][0]
+n = 4
+ftheta = 1.05
+theta_arr = []
+for idx in (range(n)):
+    theta_arr.append(theta * ftheta ** (idx - n))
+    
+alpha = []
+yerr = []
+labels = []
+for idx in (0, 1):
+    for jdx in (0, 1):   
+        alpha_ufloat = obj.get_alpha_ufloat(idx, jdx)
+        alpha.append(unumpy.nominal_values(alpha_ufloat))
+        yerr.append(unumpy.std_devs(alpha_ufloat))
+        labels.append(rf"$\alpha_{{{idx+1}{jdx+1}}}$")
+
+colors = ["blue", "orange", "orange", "green"]
+linestyles = ["-", "-", "--", "-"]
+markers = ["o", "^", "v", "s"]
 
-a_11 = (
-    (obj.Xi_gp_m[0][0] * obj.Xi_pp_m[1][1]
-    - obj.Xi_gp_m[0][1] * obj.Xi_pp_m[1][0]) / D
-)
-plt.semilogx(theta, a_11, "s", label=r"$a_{11}$", color=color, mfc="none")
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"$\alpha_{ij}(\theta)$"
+title = ""
+out_path = f"{obj._params['output_dir']}/alpha_ij.png"
 
-a_11_bis = (
-    1 / (1 - r) * obj.Xi_gp_m[0][0] / obj.Xi_pp_m[0][0]
-    - r / (1 - r) * obj.Xi_gp_m[0][1] / obj.Xi_pp_m[0][1]
+cs_plots.plot_data_1d(
+    theta_arr,
+    alpha,
+    yerr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    colors=colors,
+    linestyles=linestyles,
+    markers=markers,
+    close_fig=False,
 )
-plt.semilogx(theta, a_11_bis, "p", label=r"$a_{11}$ bis", color=color, mfc="none")
-
-# a_22
-color = "green"
 
-plt.semilogx(theta, obj.alpha_leak_m[1][1], "--", color=color, label=r"$\alpha_{22}$")
+# +
+# Plot spin elements: Spin-0 and spin-4
 
-a_22 = (
-    (obj.Xi_gp_m[1][1] * obj.Xi_pp_m[0][0]
-    - obj.Xi_gp_m[1][0] * obj.Xi_pp_m[0][1]) / D
+x0 = 0.5 * (
+     obj.get_alpha_ufloat(0, 0)
+    + obj.get_alpha_ufloat(1, 1)
 )
-plt.semilogx(theta, a_22, "d", label=r"$a_{22}$", color=color, mfc="none")
-
-a_22_bis = (
-     1 / (1 - r) * obj.Xi_gp_m[1][1] / obj.Xi_pp_m[1][1]
-    - r / (1 - r) * obj.Xi_gp_m[1][0] / obj.Xi_pp_m[1][0]
+x4 = 0.5 * (
+    obj.get_alpha_ufloat(0, 0)
+    - obj.get_alpha_ufloat(1, 1)
 )
-plt.semilogx(theta, a_22_bis, "o", label=r"$a_{22}$ bis", color=color, mfc="none")
 
-plt.legend()
-plt.xlabel(r"$\theta$ [arcmin]")
-plt.ylabel(r"$\alpha_{ii}$")
+# In theory we have y4 = a_12 = a_21. In practise the leakage matrix is
+# not entirely symmetrical. Let us identify the mean off-diagonal with y4.
+# The difference is in principle the imaginary spin-0 coefficient, which
+# should be zero.
+y4 = 0.5 * (obj.get_alpha_ufloat(0, 1) + obj.get_alpha_ufloat(1, 0))
+y0 = obj.get_alpha_ufloat(1, 0) - obj.get_alpha_ufloat(0, 1)
 
-plt.savefig("alpha_m_11_22_check.png")
+theta = obj.get_theta()
 
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
 
-# +
-plt.clf()
-
-# a_12
-color = "magenta"
-
-plt.semilogx(theta, obj.alpha_leak_m[0][1], "-", color=color, label=r"$\alpha_{12}$")
-
-a_12 = (
-    (-obj.Xi_gp_m[0][0] * obj.Xi_pp_m[0][1]
-    + obj.Xi_gp_m[0][1] * obj.Xi_pp_m[0][0]) / D
-)
-plt.semilogx(theta, a_12, "s", label=r"$a_{12}$", color=color, mfc="none")
-
-a_12_bis = (
-    -r / (1 - r) * obj.Xi_gp_m[0][0] / obj.Xi_pp_m[0][1]
-    + 1 / (1 - r) * obj.Xi_gp_m[0][1] / obj.Xi_pp_m[1][1]
-)
-plt.semilogx(theta, a_12_bis, "p", label=r"$a_{11}$ bis", color=color, mfc="none")
-
-# a_21
-color = "green"
-
-plt.semilogx(theta, obj.alpha_leak_m[1][0], "--", color=color, label=r"$\alpha_{21}$")
+n = 4
+ftheta = 1.05
+theta_arr = []
+for idx in (range(n)):
+    theta_arr.append(theta * ftheta ** (idx - n))
+
+y_arr = [
+    unumpy.nominal_values(x0),
+    unumpy.nominal_values(x4),
+    unumpy.nominal_values(y4),
+    unumpy.nominal_values(y0),
+]
+dy_arr = [
+    unumpy.std_devs(x0),
+    unumpy.std_devs(x4),
+    unumpy.std_devs(y4),
+    unumpy.std_devs(y0),
+]
+labels = [
+    r"$x_0 = (\alpha_{11} + \alpha_{22})/2$ (spin-0)",
+    r"$x_4 = (\alpha_{11} - \alpha_{22})/2$ (spin-4)",
+    r"$y_4 = (\alpha_{12} + \alpha_{21}) / 2$ (spin-4)",
+    r"$\alpha_{21} - \alpha_{12}$ (spin-0 imaginary)"
+]
+colors = ["blue", "orange", "green", "magenta"]
+markers = ["o", "s", "h", "^"]
 
-a_21 = (
-    (obj.Xi_gp_m[1][0] * obj.Xi_pp_m[1][1]
-    - obj.Xi_gp_m[1][1] * obj.Xi_pp_m[0][1]) / D
-)
-plt.semilogx(theta, a_21, "d", label=r"$a_{22}$", color=color, mfc="none")
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"Components of leakage matrix"
+title = ""
+out_path = "alpha_leakage_m_s0_s4.png"
 
-a_21_bis = (
-     1 / (1 - r) * obj.Xi_gp_m[1][0] / obj.Xi_pp_m[0][0]
-    - r / (1 - r) * obj.Xi_gp_m[1][1] / obj.Xi_pp_m[0][1]
+cs_plots.plot_data_1d(
+    theta_arr,
+    y_arr,
+    dy_arr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    colors=colors,
+    markers=markers,
+    close_fig=False,
 )
-plt.semilogx(theta, a_21_bis, "o", label=r"$a_{22}$ bis", color=color, mfc="none")
-
-plt.legend()
-plt.xlabel(r"$\theta$ [arcmin]")
-plt.ylabel(r"$\alpha_{ij}$")
 
-plt.savefig("alpha_m_12_21_check.png")
 
 # +
-# Check alpha_m elements
-
-plt.clf()
-plt.semilogx(theta, obj.alpha_leak_m[0][0] + obj.alpha_leak_m[1][1], "-", label=r"tr $\mathbf{\alpha}$")
+# Plot off-diagonal element: spin-4 and "spin-0 imaginary" part which should vanish
 
-plt.semilogx(theta, a_11 + a_22, "s", label=r"$a_{11} + a_{22}$", mfc="none")
+y4 = obj.get_alpha_ufloat(0, 1)
+y4_bis = obj.get_alpha_ufloat(1, 0)
+y0 = 0.5 * (y4_bis - y4)
 
-#plt.semilogx(theta, a_11_bis + a_22_bis, "p", label=r"$a_{11} + a_{22}$ bis", mfc="none")
+theta = obj.get_theta()
 
-t_sym = (
-    obj.Xi_gp_m[0][0] / ( obj.Xi_pp_m[0][0] * (1 - r) )
-    + obj.Xi_gp_m[1][1] / ( obj.Xi_pp_m[1][1] * (1 - r) )
-)
-plt.semilogx(theta, t_sym, 'v', label=r"$\alpha$ sym")
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
 
-t_asym = (
-    - obj.Xi_gp_m[0][1] / ( obj.Xi_pp_m[0][1] * (-1 + 1/r) )
-    - obj.Xi_gp_m[1][0] / ( obj.Xi_pp_m[1][0] * (-1 + 1/r) )
-)
-plt.semilogx(theta, t_asym, '^', label=r"$\alpha$ asym")
+n = 3
+ftheta = 1.05
+theta_arr = []
+for idx in (range(n)):
+    theta_arr.append(theta * ftheta ** (idx - n))
+    
+y = [
+    unumpy.nominal_values(y4),
+    unumpy.nominal_values(y4_bis),
+    unumpy.nominal_values(y0),
+]
+dy = [
+    unumpy.std_devs(y4),
+    unumpy.std_devs(y4_bis),
+    unumpy.std_devs(y0),
+]
+labels = [
+    r"$\alpha_{12}$ (spin-4)",
+    r"$\alpha_{21}$ (spin-4)",
+    r"$y_0 = (\alpha_{21}-\alpha_{12})/2$ ('spin-0 imag')",
+]
+xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+ylim = obj._params["leakage_alpha_ylim"]
 
-plt.semilogx(theta, t_sym + t_asym, '.', label=r"$\alpha$ sym + asym")
+colors = ["orange", "orange", "magenta"]
+markers = ["o", "s", "^"]
 
+xlabel = r"$\theta$ [arcmin]"
+ylabel = r"Components of leakage matrix"
+title = ""
+out_path = "alpha_leakage_m_s4_s4im.png"
 
-plt.semilogx(theta, obj.alpha_leak * 2, ':', label=r"$2 \alpha$")
+cs_plots.plot_data_1d(
+    theta_arr,
+    y_arr,
+    dy_arr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    colors=colors,
+    markers=markers,
+    close_fig=False,
+)
+# -
 
-plt.legend(bbox_to_anchor=(1.2, 0.5))
-_ = plt.ylim(-0.25, 0.25)
+# The elements of $\alpha$ can be written as
+# \begin{align}
+# \newcommand{\mat}[1]{\mathrm{#1}}
+#     \alpha_{11} = & \left(
+#         \phantom -\Xi_{11}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
+#         - \Xi_{12}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#     \nonumber \\
+#     \alpha_{12} = & \left(
+#         - \Xi_{11}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         + \Xi_{12}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#     \nonumber \\
+#         \alpha_{21} = & \left(
+#         \phantom - \Xi_{21}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
+#         - \Xi_{22}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#     \nonumber \\
+#         \alpha_{22} = & \left(
+#         - \Xi_{21}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
+#         + \Xi_{22}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
+#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1},
+# \end{align}
 
-plt.savefig("x.png")
-# -
 # #### Consistency relations for scalar leakage
 
 # If the leakage is a scalar function, it can be expressed in three different ways.
diff --git a/poetry.lock b/poetry.lock
index f58e6fd..ea5aeb7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -12,6 +12,27 @@ files = [
     {file = "aenum-3.1.15.tar.gz", hash = "sha256:8cbd76cd18c4f870ff39b24284d3ea028fbe8731a58df3aa581e434c575b9559"},
 ]
 
+[[package]]
+name = "anyio"
+version = "4.0.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
+    {file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.22)"]
+
 [[package]]
 name = "appnope"
 version = "0.1.3"
@@ -23,6 +44,82 @@ files = [
     {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
 ]
 
+[[package]]
+name = "argon2-cffi"
+version = "23.1.0"
+description = "Argon2 for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
+    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
+]
+
+[package.dependencies]
+argon2-cffi-bindings = "*"
+
+[package.extras]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+description = "Low-level CFFI bindings for Argon2"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_i686.whl", hash = "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win32.whl", hash = "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082"},
+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl", hash = "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f"},
+    {file = "argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93"},
+    {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3e385d1c39c520c08b53d63300c3ecc28622f076f4c2b0e6d7e796e9f6502194"},
+    {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c3e3cc67fdb7d82c4718f19b4e7a87123caf8a93fde7e23cf66ac0337d3cb3f"},
+    {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6a22ad9800121b71099d0fb0a65323810a15f2e292f2ba450810a7316e128ee5"},
+    {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f9f8b450ed0547e3d473fdc8612083fd08dd2120d6ac8f73828df9b7d45bb351"},
+    {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:93f9bf70084f97245ba10ee36575f0c3f1e7d7724d67d8e5b08e61787c320ed7"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"},
+]
+
+[package.dependencies]
+cffi = ">=1.0.1"
+
+[package.extras]
+dev = ["cogapp", "pre-commit", "pytest", "wheel"]
+tests = ["pytest"]
+
+[[package]]
+name = "arrow"
+version = "1.3.0"
+description = "Better dates & times for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
+    {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.7.0"
+types-python-dateutil = ">=2.8.10"
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
+
 [[package]]
 name = "asteval"
 version = "0.9.31"
@@ -101,6 +198,20 @@ six = ">=1.12.0"
 [package.extras]
 test = ["astroid", "pytest"]
 
+[[package]]
+name = "async-lru"
+version = "2.0.4"
+description = "Simple LRU cache for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
+    {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "atomicwrites"
 version = "1.4.1"
@@ -129,6 +240,20 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
 tests = ["attrs[tests-no-zope]", "zope-interface"]
 tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 
+[[package]]
+name = "babel"
+version = "2.13.0"
+description = "Internationalization utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "Babel-2.13.0-py3-none-any.whl", hash = "sha256:fbfcae1575ff78e26c7449136f1abbefc3c13ce542eeb13d43d50d8b047216ec"},
+    {file = "Babel-2.13.0.tar.gz", hash = "sha256:04c3e2d28d2b7681644508f836be388ae49e0cfe91465095340395b60d00f210"},
+]
+
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+
 [[package]]
 name = "backcall"
 version = "0.2.0"
@@ -895,6 +1020,17 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.0.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "fqdn"
+version = "1.5.1"
+description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+files = [
+    {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
+    {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
+]
+
 [[package]]
 name = "future"
 version = "0.18.3"
@@ -1080,6 +1216,20 @@ widgetsnbextension = ">=4.0.9,<4.1.0"
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
+
 [[package]]
 name = "jedi"
 version = "0.19.0"
@@ -1116,6 +1266,31 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "json5"
+version = "0.9.14"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = "*"
+files = [
+    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
+    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+]
+
+[package.extras]
+dev = ["hypothesis"]
+
+[[package]]
+name = "jsonpointer"
+version = "2.4"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+files = [
+    {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
+    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
+]
+
 [[package]]
 name = "jsonschema"
 version = "4.19.1"
@@ -1129,9 +1304,17 @@ files = [
 
 [package.dependencies]
 attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
 referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
 rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
@@ -1238,6 +1421,133 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "jupyter-events"
+version = "0.8.0"
+description = "Jupyter Event System library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_events-0.8.0-py3-none-any.whl", hash = "sha256:81f07375c7673ff298bfb9302b4a981864ec64edaed75ca0fe6f850b9b045525"},
+    {file = "jupyter_events-0.8.0.tar.gz", hash = "sha256:fda08f0defce5e16930542ce60634ba48e010830d50073c3dfd235759cee77bf"},
+]
+
+[package.dependencies]
+jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+python-json-logger = ">=2.0.4"
+pyyaml = ">=5.3"
+referencing = "*"
+rfc3339-validator = "*"
+rfc3986-validator = ">=0.1.1"
+traitlets = ">=5.3"
+
+[package.extras]
+cli = ["click", "rich"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
+
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.0"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter-lsp-2.2.0.tar.gz", hash = "sha256:8ebbcb533adb41e5d635eb8fe82956b0aafbf0fd443b6c4bfa906edeeb8635a1"},
+    {file = "jupyter_lsp-2.2.0-py3-none-any.whl", hash = "sha256:9e06b8b4f7dd50300b70dd1a78c0c3b0c3d8fa68e0f2d8a5d1fbab62072aca3f"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-server = ">=1.1.2"
+
+[[package]]
+name = "jupyter-server"
+version = "2.8.0"
+description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_server-2.8.0-py3-none-any.whl", hash = "sha256:c57270faa6530393ae69783a2d2f1874c718b9f109080581ea076b05713249fa"},
+    {file = "jupyter_server-2.8.0.tar.gz", hash = "sha256:b11e2ba80667c75f55630faf8ac3d5809f8734f9006d65cce117c46a0a516ab8"},
+]
+
+[package.dependencies]
+anyio = ">=3.1.0"
+argon2-cffi = "*"
+jinja2 = "*"
+jupyter-client = ">=7.4.4"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-events = ">=0.6.0"
+jupyter-server-terminals = "*"
+nbconvert = ">=6.4.4"
+nbformat = ">=5.3.0"
+overrides = "*"
+packaging = "*"
+prometheus-client = "*"
+pywinpty = {version = "*", markers = "os_name == \"nt\""}
+pyzmq = ">=24"
+send2trash = ">=1.8.2"
+terminado = ">=0.8.3"
+tornado = ">=6.2.0"
+traitlets = ">=5.6.0"
+websocket-client = "*"
+
+[package.extras]
+docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+
+[[package]]
+name = "jupyter-server-terminals"
+version = "0.4.4"
+description = "A Jupyter Server Extension Providing Terminals."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_server_terminals-0.4.4-py3-none-any.whl", hash = "sha256:75779164661cec02a8758a5311e18bb8eb70c4e86c6b699403100f1585a12a36"},
+    {file = "jupyter_server_terminals-0.4.4.tar.gz", hash = "sha256:57ab779797c25a7ba68e97bcfb5d7740f2b5e8a83b5e8102b10438041a7eac5d"},
+]
+
+[package.dependencies]
+pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""}
+terminado = ">=0.8.3"
+
+[package.extras]
+docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
+test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
+
+[[package]]
+name = "jupyterlab"
+version = "4.0.7"
+description = "JupyterLab computational environment"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab-4.0.7-py3-none-any.whl", hash = "sha256:08683045117cc495531fdb39c22ababb9aaac6977a45e67cfad20046564c9c7c"},
+    {file = "jupyterlab-4.0.7.tar.gz", hash = "sha256:48792efd9f962b2bcda1f87d72168ff122c288b1d97d32109e4a11b33dc862be"},
+]
+
+[package.dependencies]
+async-lru = ">=1.0.0"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+ipykernel = "*"
+jinja2 = ">=3.0.3"
+jupyter-core = "*"
+jupyter-lsp = ">=2.0.0"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab-server = ">=2.19.0,<3"
+notebook-shim = ">=0.2"
+packaging = "*"
+tomli = {version = "*", markers = "python_version < \"3.11\""}
+tornado = ">=6.2.0"
+traitlets = "*"
+
+[package.extras]
+dev = ["black[jupyter] (==23.7.0)", "build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.0.286)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
+test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+
 [[package]]
 name = "jupyterlab-pygments"
 version = "0.2.2"
@@ -1249,6 +1559,32 @@ files = [
     {file = "jupyterlab_pygments-0.2.2.tar.gz", hash = "sha256:7405d7fde60819d905a9fa8ce89e4cd830e318cdad22a0030f7a901da705585d"},
 ]
 
+[[package]]
+name = "jupyterlab-server"
+version = "2.25.0"
+description = "A set of server components for JupyterLab and JupyterLab like applications."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab_server-2.25.0-py3-none-any.whl", hash = "sha256:c9f67a98b295c5dee87f41551b0558374e45d449f3edca153dd722140630dcb2"},
+    {file = "jupyterlab_server-2.25.0.tar.gz", hash = "sha256:77c2f1f282d610f95e496e20d5bf1d2a7706826dfb7b18f3378ae2870d272fb7"},
+]
+
+[package.dependencies]
+babel = ">=2.10"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jinja2 = ">=3.0.3"
+json5 = ">=0.9.0"
+jsonschema = ">=4.18.0"
+jupyter-server = ">=1.21,<3"
+packaging = ">=21.3"
+requests = ">=2.31"
+
+[package.extras]
+docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
+openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+
 [[package]]
 name = "jupyterlab-widgets"
 version = "3.0.9"
@@ -1877,6 +2213,23 @@ traitlets = ">=4.2.1"
 [package.extras]
 test = ["coverage", "mock", "nbval", "nose", "nose-exclude", "nose-exclude", "nose-warnings-filters", "requests", "selenium"]
 
+[[package]]
+name = "notebook-shim"
+version = "0.2.3"
+description = "A shim layer for notebook traits and config"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "notebook_shim-0.2.3-py3-none-any.whl", hash = "sha256:a83496a43341c1674b093bfcebf0fe8e74cbe7eda5fd2bbc56f8e39e1486c0c7"},
+    {file = "notebook_shim-0.2.3.tar.gz", hash = "sha256:f69388ac283ae008cd506dda10d0288b09a017d822d5e8c7129a152cbd3ce7e9"},
+]
+
+[package.dependencies]
+jupyter-server = ">=1.8,<3"
+
+[package.extras]
+test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"]
+
 [[package]]
 name = "numpy"
 version = "1.26.0"
@@ -1918,6 +2271,17 @@ files = [
     {file = "numpy-1.26.0.tar.gz", hash = "sha256:f93fc78fe8bf15afe2b8d6b6499f1c73953169fad1e9a8dd086cdff3190e7fdf"},
 ]
 
+[[package]]
+name = "overrides"
+version = "7.4.0"
+description = "A decorator to automatically detect mismatch when overriding a method."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "overrides-7.4.0-py3-none-any.whl", hash = "sha256:3ad24583f86d6d7a49049695efe9933e67ba62f0c7625d53c59fa832ce4b8b7d"},
+    {file = "overrides-7.4.0.tar.gz", hash = "sha256:9502a3cca51f4fac40b5feca985b6703a5c1f6ad815588a7ca9e285b9dca6757"},
+]
+
 [[package]]
 name = "packaging"
 version = "23.1"
@@ -2431,6 +2795,17 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+name = "python-json-logger"
+version = "2.0.7"
+description = "A python library adding a json log formatter"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"},
+    {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
+]
+
 [[package]]
 name = "pytz"
 version = "2023.3.post1"
@@ -2712,6 +3087,31 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "rfc3339-validator"
+version = "0.1.4"
+description = "A pure python RFC3339 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa"},
+    {file = "rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b"},
+]
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "rfc3986-validator"
+version = "0.1.1"
+description = "Pure python rfc3986 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9"},
+    {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"},
+]
+
 [[package]]
 name = "rpds-py"
 version = "0.10.3"
@@ -2921,6 +3321,17 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "sniffio"
+version = "1.3.0"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
+    {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
+]
+
 [[package]]
 name = "snowballstemmer"
 version = "2.2.0"
@@ -3147,6 +3558,17 @@ cffi = "*"
 numpy = "*"
 pyyaml = "*"
 
+[[package]]
+name = "types-python-dateutil"
+version = "2.8.19.14"
+description = "Typing stubs for python-dateutil"
+optional = false
+python-versions = "*"
+files = [
+    {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
+    {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.8.0"
@@ -3189,6 +3611,20 @@ docs = ["sphinx"]
 optional = ["numpy"]
 tests = ["nose", "numpy"]
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
+]
+
+[package.extras]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
+
 [[package]]
 name = "urllib3"
 version = "2.0.5"
@@ -3236,6 +3672,21 @@ files = [
     {file = "wcwidth-0.2.6.tar.gz", hash = "sha256:a5220780a404dbe3353789870978e472cfe477761f06ee55077256e509b156d0"},
 ]
 
+[[package]]
+name = "webcolors"
+version = "1.13"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"},
+    {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"},
+]
+
+[package.extras]
+docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
+tests = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -3247,6 +3698,22 @@ files = [
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
 
+[[package]]
+name = "websocket-client"
+version = "1.6.4"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
+    {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
 [[package]]
 name = "widgetsnbextension"
 version = "4.0.9"
@@ -3323,4 +3790,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "e4d6eef82180c5eaefcc4afa6541b7079bd4051ba9b134e74fcefcba6778df50"
+content-hash = "7e0aa9445ada67470e1dbd8f14344a8fbebed4fd09368c3c897551656303dcbd"
diff --git a/pyproject.toml b/pyproject.toml
index b52c5d0..b4ca67d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,6 +25,9 @@ camb = "^1.5.0"
 scipy = "1.10.1"
 cs-util = "0.0.6"
 notebook = "5.7.4"
+jupyter-server = "^2.8.0"
+jupyterlab = "^4.0.7"
+tornado = "^6.3.3"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 6f8ccbb..258599a 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -13,6 +13,7 @@
 import pandas as pd
 from astropy.io import fits
 from astropy import units
+from uncertainties import ufloat, unumpy
 
 from cs_util import logging
 from cs_util import plots
@@ -347,9 +348,7 @@ def prepare_output(self):
 
         for key in self._params:
             leakage.print_stats(
-                f"{key}: {self._params[key]}",
-                self._stats_file,
-                False
+                f"{key}: {self._params[key]}", self._stats_file, False
             )
 
     def run(self):
@@ -579,6 +578,14 @@ def handle_close_objects(self, dat_PSF):
 
         return dat_PSF_proc
 
+    def get_theta(self):
+        """Get Theta.
+
+        Return angular scales.
+
+        """
+        return self.r_corr_gp.meanr
+
     def get_cat_fields(self, cat_type):
         """Get Cat Fields.
 
@@ -701,6 +708,9 @@ def compute_corr_gp_pp_alpha_matrix(self):
                         r_corr_pp_m[idx][jdx],
                     )
 
+        if hasattr(self, "r_corr_gp"):
+            corr.check_consistency_scales(self.r_corr_gp, r_corr_gp_m[0][0])
+
         self.r_corr_gp_m = r_corr_gp_m
         self.r_corr_pp_m = r_corr_pp_m
 
@@ -782,15 +792,21 @@ def plot_alpha_leakage_matrix(self):
         Plot scale-dependent leakage matrix alpha.
 
         """
-        theta = []
-        alpha_theta = []
+        theta = self.get_theta()
+        theta_arr = []
+        alpha_arr = []
+        yerr_arr = []
         labels = []
+        ftheta = 1.05
+        n = 4
+        count = 0
         for idx in (0, 1):
             for jdx in (0, 1):
-                theta.append(self.r_corr_gp_m[idx][jdx].meanr)
-                alpha_theta.append(self.alpha_leak_m[idx][jdx])
+                theta_arr.append(theta * ftheta ** (count - n))
+                alpha = self.get_alpha_ufloat(idx, jdx)
+                alpha_arr.append(unumpy.nominal_values(alpha))
+                yerr_arr.append(unumpy.std_devs(alpha))
                 labels.append(rf"$\alpha_{{{idx+1}{jdx+1}}}$")
-        yerr = [np.nan] * 4
         xlabel = r"$\theta$ [arcmin]"
         ylabel = r"$\alpha_{ij}(\theta)$"
         title = ""
@@ -798,33 +814,9 @@ def plot_alpha_leakage_matrix(self):
         xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
         ylim = self._params["leakage_alpha_ylim"]
         plots.plot_data_1d(
-            theta,
-            alpha_theta,
-            yerr,
-            title,
-            xlabel,
-            ylabel,
-            out_path,
-            xlog=True,
-            xlim=xlim,
-            ylim=ylim,
-            labels=labels,
-        )
-
-        theta = [self.r_corr_gp_m[0][0].meanr]
-        alpha_theta = [self.alpha_leak_m[0][0] + self.alpha_leak_m[1][1]]
-        labels = [rf"$\alpha_{{{1}{1}}} + \alpha_{{{2}{2}}}$"]
-        yerr = [np.nan] * 1
-        xlabel = r"$\theta$ [arcmin]"
-        ylabel = r"$\alpha_{ij}(\theta)$"
-        title = ""
-        out_path = f"{self._params['output_dir']}/alpha_leakage_matrix_tr.png"
-        xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
-        ylim = self._params["leakage_alpha_ylim"]
-        plots.plot_data_1d(
-            theta,
-            alpha_theta,
-            yerr,
+            theta_arr,
+            alpha_arr,
+            yerr_arr,
             title,
             xlabel,
             ylabel,
@@ -1045,31 +1037,71 @@ def alpha_matrix(self):
         self.xi_std_pp_m = np.zeros((2, 2, n_theta))
         for idx in (0, 1):
             for jdx in (0, 1):
-                self.xi_std_gp_m[idx][jdx] = np.sqrt(self.r_corr_gp_m[idx][jdx].varxip)
-                self.xi_std_pp_m[idx][jdx] = np.sqrt(self.r_corr_pp_m[idx][jdx].varxip)
+                self.xi_std_gp_m[idx][jdx] = np.sqrt(
+                    self.r_corr_gp_m[idx][jdx].varxip
+                )
+                self.xi_std_pp_m[idx][jdx] = np.sqrt(
+                    self.r_corr_pp_m[idx][jdx].varxip
+                )
 
-        # TODO: include <e><e>
+        # TODO: include <e><e> in error computation
 
-        # Compute inverse for PSF auto-correlation matrices
-        self.xi_pp_m_inv = np.zeros((2, 2, n_theta))
+        self.Xi_gp_ufloat = []
+        self.Xi_pp_ufloat = []
+        values = np.zeros((2, 2), dtype=float)
+        stds = np.zeros((2, 2), dtype=float)
         for ndx in range(n_theta):
-            self.xi_pp_m_inv[:, :, ndx] = np.linalg.inv(
-                self.xi_pp_m[:, :, ndx]
-            )
 
-        self.Xi_pp_m_inv = np.zeros((2, 2, n_theta))
+            # Set Xi_gp
+            for idx in (0, 1):
+                for jdx in (0, 1):
+                    values[idx, jdx] = self.Xi_gp_m[idx, jdx, ndx]
+                    stds[idx, jdx] = self.xi_std_gp_m[idx][jdx][ndx]
+
+            self.Xi_gp_ufloat.append(unumpy.umatrix(values, stds))
+
+            # Set Xi_pp
+            for idx in (0, 1):
+                for jdx in (0, 1):
+                    values[idx, jdx] = self.Xi_pp_m[idx, jdx, ndx]
+                    stds[idx, jdx] = self.xi_std_pp_m[idx][jdx][ndx]
+
+            self.Xi_pp_ufloat.append(unumpy.umatrix(values, stds))
+
+        # Compute (Xi_pp)^{-1} and alpha
+        self.Xi_pp_inv_ufloat = []
+        self.alpha_leak_ufloat = []
         for ndx in range(n_theta):
-            self.Xi_pp_m_inv[:, :, ndx] = np.linalg.inv(
-                self.Xi_pp_m[:, :, ndx]
+            self.Xi_pp_inv_ufloat.append(self.Xi_pp_ufloat[ndx].I)
+            self.alpha_leak_ufloat.append(
+                np.dot(self.Xi_gp_ufloat[ndx], self.Xi_pp_inv_ufloat[ndx])
             )
 
-        self.alpha_leak_m = np.zeros((2, 2, n_theta))
+    def get_alpha_ufloat(self, idx, jdx):
+        """Get Alpha Ufloat.
+
+        Return alpha leakage matrix element as array over scales.
+
+        Parameters
+        ----------
+        idx : int
+            line index, allowed are 0 or 1
+        jdx : int
+            column index, allowed are 0 or 1
+
+        Returns
+        -------
+        numpy.ndarray
+            matrix element as array over scales, each entry is
+            of type ufloat
+
+        """
+        mat = []
+        n_theta = self._params["n_theta"]
         for ndx in range(n_theta):
-            self.alpha_leak_m[:, :, ndx] = np.dot(
-                self.Xi_gp_m[:, :, ndx], self.Xi_pp_m_inv[:, :, ndx]
-            )
+            mat.append(self.alpha_leak_ufloat[ndx][idx, jdx])
 
-        # TODO: error on alppha_leak_m
+        return np.array(mat)
 
     def do_alpha_matrix(self):
         """Do Alpha Matrix.
@@ -1141,16 +1173,64 @@ def save_alpha_matrix(self):
         Save scale-dependent alpha matrix.
 
         """
-        cols = [self.r_corr_gp_m[0][0].meanr]
+        cols = [self.get_theta()]
         names = ["# theta[arcmin]"]
         for idx in (0, 1):
             for jdx in (0, 1):
-                cols.append(self.alpha_leak_m[idx][jdx])
+                alpha = self.get_alpha_ufloat(idx, jdx)
+                cols.append(unumpy.nominal_values(alpha))
                 names.append(rf"alpha_{{{idx+1}{jdx+1}}}")
+                cols.append(unumpy.std_devs(alpha))
+                names.append(rf"sigma_alpha_{{{idx+1}{jdx+1}}}")
         fname = f"{self._params['output_dir']}/alpha_leakage_matrix.txt"
         cs_cat.write_ascii_table_file(cols, names, fname)
 
 
+def var_sum(s2a, s2b):
+    """Var Sum.
+
+    Return variance of a + b.
+
+    """
+    s2 = s2a + s2b
+
+    return s2
+
+def var_mult_div(y, a, b, s2a, s2b):
+    """Var Mult Div.
+
+    Return variance of y = a * b or y = a / b.
+
+    """
+    s2 = y ** 2 * (s2a / a ** 2 + s2b / b ** 2)
+
+    return s2
+
+def var_product(a, b, s2a, s2b):
+    """Var Product.
+
+    Return variance of y = a * b.
+
+    """
+    y = a * b
+
+    s2 = var_mult_div(y, a, b, s2a, s2b)
+
+    return s2
+
+def var_ratio(a, b, s2a, s2b):
+    """Var Ratio.
+
+    Return variance of y = a / b.
+
+    """
+    y = a / b
+
+    s2 = var_mult_div(y, a, b, s2a, s2b)
+
+    return s2
+
+
 def run_leakage_scale(*args):
     """Run Leakage Scale.
 

From 257ed2fbee7fb4162b63db88a16e621bdce0bf59 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 8 Nov 2023 16:38:24 +0100
Subject: [PATCH 20/97] uncertainties package to propagate errors

---
 notebooks/leakage_scale_spin.py |  153 +++-
 poetry.lock                     | 1507 ++++++++++++++++---------------
 pyproject.toml                  |    2 +-
 3 files changed, 868 insertions(+), 794 deletions(-)

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index 9b12ac3..d250668 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -8,9 +8,9 @@
 #       format_version: '1.5'
 #       jupytext_version: 1.15.2
 #   kernelspec:
-#     display_name: shear_psf_leakage
+#     display_name: shear_psf_leakage2
 #     language: python
-#     name: shear_psf_leakage
+#     name: shear_psf_leakage2
 # ---
 
 # # Scale-dependent PSF leakage
@@ -69,9 +69,7 @@
 
 print(obj._params)
 
-# + [markdown] jp-MarkdownHeadingCollapsed=true
 # ### Run
-# -
 
 # Check parameter validity
 obj.check_params()
@@ -111,8 +109,6 @@
 # +
 # Plot
 
-plt.clf()
-
 theta = obj.get_theta()
 
 # Exact calculation
@@ -185,6 +181,7 @@
     colors=colors,
     linewidths=linewidths,
     linestyles=linestyles,
+    close_fig=False,
 )
 
 # +
@@ -255,7 +252,7 @@
 # The difference is in principle the imaginary spin-0 coefficient, which
 # should be zero.
 y4 = 0.5 * (obj.get_alpha_ufloat(0, 1) + obj.get_alpha_ufloat(1, 0))
-y0 = obj.get_alpha_ufloat(1, 0) - obj.get_alpha_ufloat(0, 1)
+y0 = 0.5 * (obj.get_alpha_ufloat(1, 0) - obj.get_alpha_ufloat(0, 1))
 
 theta = obj.get_theta()
 
@@ -266,7 +263,7 @@
 ftheta = 1.05
 theta_arr = []
 for idx in (range(n)):
-    theta_arr.append(theta * ftheta ** (idx - n))
+    theta_arr.append(theta * ftheta ** (idx - n/2))
 
 y_arr = [
     unumpy.nominal_values(x0),
@@ -283,11 +280,12 @@
 labels = [
     r"$x_0 = (\alpha_{11} + \alpha_{22})/2$ (spin-0)",
     r"$x_4 = (\alpha_{11} - \alpha_{22})/2$ (spin-4)",
-    r"$y_4 = (\alpha_{12} + \alpha_{21}) / 2$ (spin-4)",
-    r"$\alpha_{21} - \alpha_{12}$ (spin-0 imaginary)"
+    r"$y_4 = (\alpha_{12} + \alpha_{21})/2$ (spin-4)",
+    r"$y_0 = (\alpha_{21} - \alpha_{12})/2$ (spin-0 imaginary)"
 ]
 colors = ["blue", "orange", "green", "magenta"]
 markers = ["o", "s", "h", "^"]
+linestyles = ["-"] * 4 
 
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"Components of leakage matrix"
@@ -311,6 +309,34 @@
     close_fig=False,
 )
 
+# Including scalar leakage for comparison
+theta_arr.append(theta * ftheta ** (4 - n/2))
+y_arr.append(obj.alpha_leak)
+dy_arr.append(obj.sig_alpha_leak)
+labels.append(r"$\alpha$ (scalar approx.)")
+colors.append("blue")
+markers.append("p")
+linestyles.append("--")
+out_path = "alpha_leakage_m_s0_s4_as.png"
+
+cs_plots.plot_data_1d(
+    theta_arr,
+    y_arr,
+    dy_arr,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    xlog=True,
+    xlim=xlim,
+    ylim=ylim,
+    colors=colors,
+    markers=markers,
+    linestyles=linestyles,
+    close_fig=False,
+)
+
 
 # +
 # Plot off-diagonal element: spin-4 and "spin-0 imaginary" part which should vanish
@@ -358,8 +384,8 @@
 
 cs_plots.plot_data_1d(
     theta_arr,
-    y_arr,
-    dy_arr,
+    y,
+    dy,
     title,
     xlabel,
     ylabel,
@@ -402,28 +428,34 @@
 
 # If the leakage is a scalar function, it can be expressed in three different ways.
 
-# +
-alpha_1 = obj.Xi_gp_m[0][0] / obj.Xi_pp_m[1][1]
-alpha_2 = obj.Xi_gp_m[1][1] / obj.Xi_pp_m[1][1]
-
-alpha_1_std =  np.abs(alpha_1) * np.sqrt(                              
-        obj.xi_std_gp_m[0][0] ** 2 / obj.xi_gp_m[0][0] ** 2                                     
-        + obj.xi_std_pp_m[0][0] ** 2 / obj.xi_pp_m[0][0] ** 2                                   
-    )
-
-alpha_2_std =  np.abs(alpha_2) * np.sqrt(                              
-        obj.xi_std_gp_m[1][1] ** 2 / obj.xi_gp_m[1][1] ** 2                                     
-        + obj.xi_std_pp_m[1][1] ** 2 / obj.xi_pp_m[1][1] ** 2                                   
-    )
-
 # +
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 ylim = obj._params["leakage_alpha_ylim"]
 
+theta = obj.get_theta()
+
 ftheta = 1.05
 theta_arr = [theta / ftheta, theta, theta * ftheta]
-alpha_theta = [obj.alpha_leak, alpha_1, alpha_2]
-yerr = [obj.sig_alpha_leak, alpha_1_std, alpha_2_std]
+
+alpha_1 = []
+alpha_2 = []
+for ndx in range(len(theta)):
+    my_a1 = obj.Xi_gp_ufloat[ndx][0, 0] / obj.Xi_pp_ufloat[ndx][0, 0]
+    alpha_1.append(my_a1)
+    my_a2 = obj.Xi_gp_ufloat[ndx][1, 1] / obj.Xi_pp_ufloat[ndx][1, 1]
+    alpha_2.append(my_a2)
+
+y = [
+    obj.alpha_leak,
+    unumpy.nominal_values(alpha_1),
+    unumpy.nominal_values(alpha_2),
+]
+dy = [
+    obj.sig_alpha_leak,
+    unumpy.std_devs(alpha_1),
+    unumpy.std_devs(alpha_2),
+]
+
 labels = [r"$\alpha$", r"$\alpha_1$", r"$\alpha_2$"]
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"$\alpha(\theta)$"
@@ -432,8 +464,8 @@
 
 cs_plots.plot_data_1d(
     theta_arr,
-    alpha_theta,
-    yerr,
+    y,
+    dy,
     title,
     xlabel,
     ylabel,
@@ -451,13 +483,29 @@
 # +
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 
-Xi_gp_plus = obj.Xi_gp_m[0][0] + obj.Xi_gp_m[1][1]
-Xi_std_gp_plus = np.sqrt(obj.xi_std_gp_m[0][0] ** 2 + obj.xi_std_gp_m[1][1] ** 2)
-
 ftheta = 1.025
 theta_arr = [theta / ftheta, theta, theta * ftheta]
-alpha_theta = [obj.Xi_gp_m[0][1], obj.Xi_gp_m[1][0], Xi_gp_plus]
-yerr = [obj.xi_std_gp_m[0][1], obj.xi_std_gp_m[1][0], Xi_std_gp_plus]
+
+Xi_12 = []
+Xi_21 = []
+Xi_tr = []
+for ndx in range(len(theta)):
+    Xi_12.append(obj.Xi_gp_ufloat[ndx][0, 1])
+    Xi_21.append(obj.Xi_gp_ufloat[ndx][1, 0])
+    Xi_tr.append(obj.Xi_gp_ufloat[ndx][0, 0] + obj.Xi_gp_ufloat[ndx][0, 0])
+
+y = [
+    unumpy.nominal_values(Xi_12),
+    unumpy.nominal_values(Xi_21),
+    unumpy.nominal_values(Xi_tr),
+]
+dy = [
+    unumpy.std_devs(Xi_12),
+    unumpy.std_devs(Xi_21),
+    unumpy.std_devs(Xi_tr),
+]
+
+
 labels = [r"$\Xi_{12}^{\rm gp}$", r"$\Xi_{21}^{\rm gp}$", r"tr$\Xi^{\rm gp}$"]
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"Centered correlation functions"
@@ -466,8 +514,8 @@
 
 cs_plots.plot_data_1d(
     theta_arr,
-    alpha_theta,
-    yerr,
+    y,
+    dy,
     title,
     xlabel,
     ylabel,
@@ -483,13 +531,29 @@
 
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 
-Xi_gp_plus = obj.Xi_pp_m[0][0] + obj.Xi_pp_m[1][1]
-Xi_std_gp_plus = np.sqrt(obj.xi_std_pp_m[0][0] ** 2 + obj.xi_std_pp_m[1][1] ** 2)
-
 ftheta = 1.025
 theta_arr = [theta / ftheta, theta, theta * ftheta]
-alpha_theta = [obj.Xi_pp_m[0][1], obj.Xi_pp_m[1][0], Xi_gp_plus]
-yerr = [obj.xi_std_pp_m[0][1], obj.xi_std_pp_m[1][0], Xi_std_gp_plus]
+
+Xi_12 = []
+Xi_21 = []
+Xi_tr = []
+for ndx in range(len(theta)):
+    Xi_12.append(obj.Xi_pp_ufloat[ndx][0, 1])
+    Xi_21.append(obj.Xi_pp_ufloat[ndx][1, 0])
+    Xi_tr.append(obj.Xi_pp_ufloat[ndx][0, 0] + obj.Xi_pp_ufloat[ndx][0, 0])
+
+y = [
+    unumpy.nominal_values(Xi_12),
+    unumpy.nominal_values(Xi_21),
+    unumpy.nominal_values(Xi_tr),
+]
+dy = [
+    unumpy.std_devs(Xi_12),
+    unumpy.std_devs(Xi_21),
+    unumpy.std_devs(Xi_tr),
+]
+
+
 labels = [r"$\Xi_{12}^{\rm pp}$", r"$\Xi_{21}^{\rm pp}$", r"tr$\Xi^{\rm pp}$"]
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"Centered correlation functions"
@@ -498,8 +562,8 @@
 
 cs_plots.plot_data_1d(
     theta_arr,
-    alpha_theta,
-    yerr,
+    y,
+    dy,
     title,
     xlabel,
     ylabel,
@@ -512,3 +576,4 @@
 # -
 
 
+
diff --git a/poetry.lock b/poetry.lock
index ea5aeb7..73a6158 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -139,33 +139,40 @@ test = ["coverage", "pytest", "pytest-cov"]
 
 [[package]]
 name = "astropy"
-version = "5.3.3"
+version = "5.3.4"
 description = "Astronomy and astrophysics core library"
 optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "astropy-5.3.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:81239ad0d2876aa820fe35e3f22954a3035ebb9b86e284cfb91e37a76ab24b83"},
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 ]
 
 [package.dependencies]
@@ -183,20 +190,21 @@ test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pytest (>=7.0,<8)"
 
 [[package]]
 name = "asttokens"
-version = "2.4.0"
+version = "2.4.1"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
 six = ">=1.12.0"
 
 [package.extras]
-test = ["astroid", "pytest"]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
 [[package]]
 name = "async-lru"
@@ -242,13 +250,13 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 
 [[package]]
 name = "babel"
-version = "2.13.0"
+version = "2.13.1"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -285,33 +293,29 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
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+version = "23.10.1"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -331,13 +335,13 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "6.0.0"
+version = "6.1.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -345,7 +349,7 @@ six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.2)"]
+css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "cadcutils"
@@ -372,18 +376,22 @@ test = ["flake8 (>=3.4.1)", "funcsigs (==1.0.2)", "mock (>=2.0.0)", "pytest", "p
 
 [[package]]
 name = "camb"
-version = "1.5.0"
+version = "1.5.2"
 description = "Code for Anisotropies in the Microwave Background"
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "camb-1.5.2.tar.gz", hash = "sha256:35095e306907237bc5c16a7dbd57895169a14b7b32864353348f391c240622d2"},
 ]
 
 [package.dependencies]
+packaging = "*"
 scipy = ">=1.0"
 sympy = ">=1.0"
 
+[package.extras]
+docs = ["sphinx (>=4.0)", "sphinx_rtd_theme (>=1)", "sphinxcontrib-jquery"]
+
 [[package]]
 name = "certifi"
 version = "2023.7.22"
@@ -397,75 +405,63 @@ files = [
 
 [[package]]
 name = "cffi"
-version = "1.15.1"
+version = "1.16.0"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -473,86 +469,101 @@ pycparser = "*"
 
 [[package]]
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 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
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 [[package]]
@@ -571,28 +582,28 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
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-version = "3.27.5"
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 description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
 optional = false
 python-versions = "*"
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 [package.dependencies]
@@ -768,34 +779,34 @@ toml = ["tomli"]
 
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -837,15 +848,19 @@ test = ["pytest", "pytest-black", "pytest-cov", "pytest-pydocstyle"]
 
 [[package]]
 name = "cycler"
-version = "0.11.0"
+version = "0.12.1"
 description = "Composable style cycles"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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 ]
 
+[package.extras]
+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
 name = "datetime"
 version = "5.2"
@@ -937,27 +952,27 @@ test = ["pytest (>=6)"]
 
 [[package]]
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+version = "2.0.0"
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 [package.extras]
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+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "fastjsonschema"
-version = "2.18.0"
+version = "2.18.1"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
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 ]
 
 [package.extras]
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 ]
 
 [package.extras]
@@ -1113,13 +1136,13 @@ files = [
 
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -1146,13 +1169,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
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 [package.dependencies]
@@ -1232,13 +1255,13 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "jedi"
-version = "0.19.0"
+version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
@@ -1247,7 +1270,7 @@ parso = ">=0.8.3,<0.9.0"
 [package.extras]
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+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
@@ -1356,13 +1379,13 @@ qtconsole = "*"
 
 [[package]]
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+version = "8.5.0"
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 [package.dependencies]
@@ -1403,13 +1426,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
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 [[package]]
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+version = "2.9.1"
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 [package.dependencies]
@@ -2055,13 +2078,13 @@ files = [
 
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+version = "3.0.2"
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 [[package]]
@@ -2116,13 +2139,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
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+version = "7.9.2"
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 [package.dependencies]
@@ -2149,7 +2172,7 @@ docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sp
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 serve = ["tornado (>=6.1)"]
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+test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pytest", "pytest-dependency"]
 webpdf = ["playwright"]
 
 [[package]]
@@ -2232,43 +2255,43 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
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 [package.dependencies]
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
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 [package.extras]
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@@ -2709,20 +2705,20 @@ plugins = ["importlib-metadata"]
 
 [[package]]
 name = "pyopenssl"
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 description = "Python wrapper module around the OpenSSL library"
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 test = ["flaky", "pretend", "pytest (>=3.0.1)"]
 
 [[package]]
@@ -2842,16 +2838,17 @@ files = [
 
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+version = "2.0.12"
 description = "Pseudo terminal support for Windows from Python."
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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 rich = ["rich"]
-test = ["pytest", "rich", "virtualenv (>20)"]
+test = ["build", "pytest", "rich", "wheel"]
 
 [[package]]
 name = "six"
@@ -3356,13 +3359,13 @@ files = [
 
 [[package]]
 name = "stack-data"
-version = "0.6.2"
+version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 optional = false
 python-versions = "*"
 files = [
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 [package.dependencies]
@@ -3504,18 +3507,18 @@ files = [
 
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+version = "5.12.0"
 description = "Traitlets Python configuration system"
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 python-versions = ">=3.8"
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 [[package]]
 name = "treecorr"
@@ -3627,13 +3630,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
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 [package.extras]
@@ -3663,13 +3666,13 @@ test = ["flake8 (>=3.4.1)", "mock (>=2.0.0)", "pytest (>=4.6)", "pytest-cov (>=2
 
 [[package]]
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 [[package]]
@@ -3742,52 +3745,58 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
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 [package.dependencies]
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 [package.extras]
-docs = ["Sphinx", "repoze.sphinx.autointerface"]
+docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx-rtd-theme"]
 test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "7e0aa9445ada67470e1dbd8f14344a8fbebed4fd09368c3c897551656303dcbd"
+content-hash = "8bf9b46941b3bb2b4024f278d2417df40bcf48693dad53b205a7e1cf686c8098"
diff --git a/pyproject.toml b/pyproject.toml
index b4ca67d..df108ec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,7 +22,7 @@ jupyter = "^1.0.0"
 jupytext = "^1.15.1"
 pandas = "^2.0.3"
 camb = "^1.5.0"
-scipy = "1.10.1"
+scipy = "^1.11.3"
 cs-util = "0.0.6"
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"

From da442b69ac3873fb4e7c3ae1fa412e2969287f6d Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 8 Nov 2023 16:39:49 +0100
Subject: [PATCH 21/97] Changes on asus

---
 pyproject.toml                 | 4 +++-
 shear_psf_leakage/run_scale.py | 7 ++++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 94e3e50..4d481aa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,7 +25,9 @@ pandas = "^2.1.0"
 camb = "^1.5.0"
 
 # MKDEBUG TODO: cs_util -> 0.0.6
-cs-util = {path = "../cs_util"}
+#cs-util = {path = "../cs_util"}
+cs-util = "^0.0.5"
+docker = "^6.1.3"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 381f16b..d78f350 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -81,7 +81,7 @@ def parse_options(p_def, short_options, types, help_strings):
 def get_theo_xi(theta, dndz_path):
     """Get Theo Xi.
 
-    Computes theoretical prediction of the shear 2PCF using a Planck
+    Return theoretical prediction of the shear 2PCF using a Planck
     best-fit cosmology.
 
     Parameters
@@ -102,6 +102,7 @@ def get_theo_xi(theta, dndz_path):
     z, nz, _ = cs_cat.read_dndz(dndz_path)
     cosmo = cs_cos.get_cosmo_default()
     xi_p, xi_m = cs_cos.xipm_theo(theta, cosmo, z, nz)
+    
 
     return xi_p, xi_m
 
@@ -129,7 +130,7 @@ def save_alpha(theta, alpha_leak, sig_alpha_leak, sh, output_dir):
     cols = [theta, alpha_leak, sig_alpha_leak]
     names = ["# theta[arcmin]", "alpha", "sig_alpha"]
     fname = f"{output_dir}/alpha_leakage_{sh}.txt"
-    cs_cat.write_ascii_table_file(cols, names, fname)
+    write_ascii_table_file(cols, names, fname)
 
 
 def save_xi_sys(
@@ -188,7 +189,7 @@ def save_xi_sys(
         "xi_-_theo",
     ]
     fname = f"{output_dir}/xi_sys_{sh}.txt"
-    cs_cat.write_ascii_table_file(cols, names, fname)
+    write_ascii_table_file(cols, names, fname)
 
 
 class LeakageScale:

From 1e64284dfef8d7a84c18c66c5d85bd9a515710ee Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sg276684@dappcz158.extra.cea.fr>
Date: Tue, 14 Nov 2023 17:48:58 +0100
Subject: [PATCH 22/97] Add a module to handle the different treecorr catalogs
 and a class to compute, save and plot rho statistics

---
 shear_psf_leakage/rho_tau_stat.py | 314 ++++++++++++++++++++++++++++++
 1 file changed, 314 insertions(+)
 create mode 100644 shear_psf_leakage/rho_tau_stat.py

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
new file mode 100644
index 0000000..2211a7a
--- /dev/null
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -0,0 +1,314 @@
+"""
+This module sets up a class to compute the rho stats computation
+
+Author: Sacha Guerrini
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from astropy.io import fits
+
+import treecorr
+
+class Catalogs():
+    """
+    Catalogs
+
+    Class to build the different treecorr catalogs given a shape catalog that will be 
+    used to compute the different statistics
+    """
+
+    def __init__(self, params=None, output=None):
+        #set default parameters
+        if (params is None) or (output is None): 
+            self.params_default()
+        else:
+            self.set_params(params, output)
+
+        self.catalogs_dict = dict()
+        self.dat_shear = None
+        self.dat_psf = None
+
+    def params_default(self):
+        """
+        Params Default.
+
+        Initialize the parameters of the class with columns name from SPV1.
+        For the treecorr configuration, default parameters are:
+        -coord_units: degree
+        -sep_units: arcmin
+        -theta_min: 0.1
+        -theta_max: 100
+        -n_theta: 20
+        -var_method: jackknife !!Requires to set a patch number for the different catalogues!!
+        """
+
+        self._params = {
+            "e1_col": "e1",
+            "e2_col": "e2",
+            "w_col": "w",
+            "ra_col": "RA",
+            "dec_col": "Dec",
+            "e1_PSF_col": "E1_PSF_HSM",
+            "e2_PSF_col": "E2_PSF_HSM",
+            "e1_star_col": "E1_STAR_HSM",
+            "e2_star_col": "E2_STAR_HSM",
+            "PSF_size": "SIGMA_PSF_HSM",
+            "star_size": "SIGMA_STAR_HSM",
+            "PSF_flag": "FLAG_PSF_HSM",
+            "star_flag": "FLAG_STAR_HSM",
+            "output_dir": ".",
+            "patch_number": 120,
+            "ra_units": "deg",
+            "dec_units": "deg" 
+        }
+
+        self._output = "."
+
+    def set_params(self, params, output):
+        """
+        set_params:
+
+        Initialize the parameters to the given configuration. Can also update the current params.
+        """
+        if params is not None:
+            self._params = params
+        if output is not None:
+            self._output = output
+
+    def read_shear_cat(self, path_gal, path_psf):
+        """
+        read_shear_cat
+
+        Read a shear catalogue with galaxies ('gal') or stars ('psf').
+        Only one such catalogue can be loaded at a time.
+
+        Raises
+        ------
+        AssertionError: Please specify a path for the shear catalog you want to read.
+        """
+        assert (path_gal is None) and (path_psf is None), ("Please specify a path for the shear catalog you want to read.")
+        if path_gal is not None:
+            self.dat_shear = fits.getdata(path_gal)
+        if path_psf is not None:
+            self.dat_psf = fits.getdata(path_psf)
+    
+    def get_cat_fields(self, cat_type, square_size=False):
+        """
+        Get Cat Fields
+
+        Get catalogue fields for correlation.
+
+        Parameters
+        ----------
+        cat_type : str
+            catalogue type, allowed are 'gal', 'psf', 'psf_error' or 'psf_size_error'
+
+        square_size : bool
+            If True, the size computed in the catalogue is squared (Default: False)
+
+        Returns
+        -------
+        np.array
+            ra
+        np.array
+            dec
+        np.array
+            e1
+        np.array
+            e2
+        np.array
+            weights; 'None' is cat_type is not 'gal'. Returns a one_like array if weights are not specified
+
+        Raises
+        ------
+        AssertionError
+            If the specified cat_type does not belong to the allowed list.
+        """
+
+        allowed_types = ['gal', 'psf', 'psf_error', 'psf_size_error']
+
+        assert cat_type in allowed_types, ("The specified catalogue type is invalid. Check the one you use is allowed."
+                                           "Allowed cat_type: 'gal', 'psf', 'psf_error', 'psf_size_error'.")
+        
+        assert (self.dat_shear is not None) and (self.dat_psf is not None), ("Check you read the shear catalogs correctly.")
+        
+        if cat_type=="gal":
+            ra = self.dat_shear[self._params["ra_col"]]
+            dec = self.dat_shear[self._params["dec_col"]]
+            g1 = self.dat_shear[self._params["e1_col"]] - self.dat_shear[self._params["e1_col"]].mean()
+            g2 = self.dat_shear[self._params["e2_col"]] - self.dat_shear[self._params["e2_col"]].mean()
+            if self._params["w_col"] is not None:
+                weights = self.dat_shear[self._params["w_col"]]
+            else:
+                weights = np.ones.like(ra)
+        else:
+            #Add a mask?
+            #mask = (self.dat_psf[self._params["FLAG_PSF_HSM"]]==0) & (self.dat_psf[self._params["FLAG_STAR_HSM"]]==0)
+            ra = self.dat_psf[self._params["ra_col"]]
+            dec = self.dat_psf[self._params["dec_psf_col"]]
+            weights = None
+            
+            if cat_type=="psf":
+                g1 = self.dat_psf[self._params["e1_PSF_col"]] - self.dat_psf[self._params["e1_PSF_col"]].mean()
+                g2 = self.dat_psf[self._params["e2_PSF_col"]] - self.dat_psf[self._params["e2_PSF_col"]].mean()
+            
+            elif cat_type=="psf_error":
+                g1 = (self.dat_psf[self._params["e1_star_col"]] - self.dat_psf[self._params["e1_PSF_col"]])
+                g1 -= g1.mean()
+                g2 = (self.dat_psf[self._params["e2_star_col"]] - self.dat_psf[self._params["e2_PSF_col"]])
+                g2 -= g2.mean()
+
+            else:
+                size_star = self.dat_psf[self._params["SIGMA_STAR_HSM"]]**2 if square_size else  self.dat_psf[self._params["SIGMA_STAR_HSM"]]
+                size_psf = self.dat_psf[self._params["SIGMA_STAR_HSM"]]**2 if square_size else  self.dat_psf[self._params["SIGMA_STAR_HSM"]]
+                g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_psf
+                g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_psf
+
+            return ra, dec, g1, g2, weights
+    
+    def build_catalog(self, cat_type, key, npatch=None, square_size=False):
+        """
+        build_catalogue
+
+        Build a treecorr.Catalog of a certain type using the class _params. A key is given as input
+        to identify the catalog in self.catalogs_dict.
+
+        Parameters
+        ----------
+        cat_type : str
+            catalogue type, allowed are 'gal', 'psf', 'psf_error' or 'psf_size_error'.
+
+        key : str
+            String used to key the catalog to an entry of the dict of catalogs.
+
+        npatch : int
+            number of patch used to compute variance with jackknife or bootstrap. (Default: value in self._params)
+
+        square_size : bool
+            If True, the size computed in the catalogue is squared (Default: False)
+        """
+
+        if npatch is None:
+            npatch = self._params["patch_number"]
+        
+        ra, dec, g1, g2, weights = self.get_cat_fields(cat_type, square_size)
+
+        cat = treecorr.Catalog(
+            ra=ra,
+            dec=dec,
+            g1=g1,
+            g2=g2,
+            w=weights,
+            ra_units=self._params["ra_units"],
+            dec_units=self._params["dec_units"],
+            npatch=npatch
+        )
+
+        self.catalogs_dict.update(
+            {key: cat}
+        )
+
+    def delete_catalog(self, key):
+        """
+        delete_catalog
+
+        Delete the catalog instance mapped by the string key.
+        """
+
+        try:
+            self.catalogs_dict.pop(key)
+        except KeyError:
+            print("This entry did not exist.")
+
+    def show_catalogs(self):
+        """
+        show_catalogs
+
+        Print the keys of the element stored in self.catalogs_dict
+        """
+        for key in self.catalogs_dict.keys():
+            print(key)   
+
+
+class RhoStat():
+    """
+    RhoStat
+
+    Class to compute the rho statistics (Rowe 2010) of a PSF catalogue.
+    """
+
+    def __init__(self, params, output, treecorr_config=None, verbose=False):
+
+        self.catalogs = Catalogs(params, output)
+
+        if treecorr_config is None:
+            self._treecorr_config = {
+                "ra_units": "deg",
+                "dec_units": "deg",
+                "sep_units": "arcmin",
+                "min_sep": 0.1,
+                "max_sep": 100,
+                "n_bins": 20,
+                "var_method": "jackknife"
+            }
+        else:
+            self._treecorr_config = treecorr_config
+
+        self.verbose = verbose
+    
+
+    def check_params(self):
+        """Check Params.
+
+        Check whether parameter values are valid.
+
+        Raises
+        ------
+        ValueError
+            if a parameter value is not valid
+
+        """
+        pass
+        #TO DO
+
+    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False):
+        """
+        build_cat_to_compute_rho
+
+        Parameters
+        ----------
+        path_cat_star : str
+            Path to the catalog of stars used to compute the rho-statistics.
+
+        catalog_id : str
+            An id to identify the catalog used in the keys of the stored treecorr.Catalog.
+
+        square_size : bool
+            If True, the size computed in the catalogue is squared (Default: False)
+        """
+
+        self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star)
+
+        if self.verbose:
+            print("Building catalogs...")
+
+        self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size)
+        self.catalogs.build_catalog(cat_type='psf_error', key='psf_'+catalog_id, square_size=square_size)
+        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_'+catalog_id, square_size=square_size)
+
+        if self.verbose:
+            print("Catalogs successfully built...")
+            self.catalogs.show_catalogs()
+
+    def compute_rho_stats(self):
+        pass
+
+    def save_rho_stats(self, filename):
+        pass
+
+    def load_rho_stats(self, filename):
+        pass
+
+    def plot_rho_stats(self):
+        pass

From cceeef56dc79d81f4100cc846aeced1bfd14cbec Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Wed, 15 Nov 2023 10:42:52 +0100
Subject: [PATCH 23/97] Add tau stat class and a test script to check
 everything works correctly

---
 shear_psf_leakage/rho_tau_stat.py | 257 ++++++++++++++++++++++++++++--
 tests/test_rho_tau_stats.py       |  16 ++
 2 files changed, 260 insertions(+), 13 deletions(-)
 create mode 100644 tests/test_rho_tau_stats.py

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 2211a7a..8e57097 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -7,6 +7,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from astropy.io import fits
+from astropy.table import Table
 
 import treecorr
 
@@ -87,7 +88,7 @@ def read_shear_cat(self, path_gal, path_psf):
         ------
         AssertionError: Please specify a path for the shear catalog you want to read.
         """
-        assert (path_gal is None) and (path_psf is None), ("Please specify a path for the shear catalog you want to read.")
+        assert ((path_gal is not None) or (path_psf is not None)), ("Please specify a path for the shear catalog you want to read.")
         if path_gal is not None:
             self.dat_shear = fits.getdata(path_gal)
         if path_psf is not None:
@@ -131,9 +132,8 @@ def get_cat_fields(self, cat_type, square_size=False):
         assert cat_type in allowed_types, ("The specified catalogue type is invalid. Check the one you use is allowed."
                                            "Allowed cat_type: 'gal', 'psf', 'psf_error', 'psf_size_error'.")
         
-        assert (self.dat_shear is not None) and (self.dat_psf is not None), ("Check you read the shear catalogs correctly.")
-        
         if cat_type=="gal":
+            assert self.dat_shear is not None, ("Check you read the shear catalogs correctly.")
             ra = self.dat_shear[self._params["ra_col"]]
             dec = self.dat_shear[self._params["dec_col"]]
             g1 = self.dat_shear[self._params["e1_col"]] - self.dat_shear[self._params["e1_col"]].mean()
@@ -143,10 +143,11 @@ def get_cat_fields(self, cat_type, square_size=False):
             else:
                 weights = np.ones.like(ra)
         else:
+            assert self.dat_psf is not None, ("Check you read the shear catalogs correctly.")
             #Add a mask?
             #mask = (self.dat_psf[self._params["FLAG_PSF_HSM"]]==0) & (self.dat_psf[self._params["FLAG_STAR_HSM"]]==0)
             ra = self.dat_psf[self._params["ra_col"]]
-            dec = self.dat_psf[self._params["dec_psf_col"]]
+            dec = self.dat_psf[self._params["dec_col"]]
             weights = None
             
             if cat_type=="psf":
@@ -160,8 +161,8 @@ def get_cat_fields(self, cat_type, square_size=False):
                 g2 -= g2.mean()
 
             else:
-                size_star = self.dat_psf[self._params["SIGMA_STAR_HSM"]]**2 if square_size else  self.dat_psf[self._params["SIGMA_STAR_HSM"]]
-                size_psf = self.dat_psf[self._params["SIGMA_STAR_HSM"]]**2 if square_size else  self.dat_psf[self._params["SIGMA_STAR_HSM"]]
+                size_star = self.dat_psf[self._params["star_size"]]**2 if square_size else  self.dat_psf[self._params["star_size"]]
+                size_psf = self.dat_psf[self._params["PSF_size"]]**2 if square_size else  self.dat_psf[self._params["PSF_size"]]
                 g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_psf
                 g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_psf
 
@@ -238,7 +239,7 @@ class RhoStat():
     Class to compute the rho statistics (Rowe 2010) of a PSF catalogue.
     """
 
-    def __init__(self, params, output, treecorr_config=None, verbose=False):
+    def __init__(self, params=None, output=None, treecorr_config=None, verbose=False):
 
         self.catalogs = Catalogs(params, output)
 
@@ -294,21 +295,251 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
             print("Building catalogs...")
 
         self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size)
-        self.catalogs.build_catalog(cat_type='psf_error', key='psf_'+catalog_id, square_size=square_size)
-        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_'+catalog_id, square_size=square_size)
+        self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size)
+        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size)
 
         if self.verbose:
             print("Catalogs successfully built...")
             self.catalogs.show_catalogs()
 
-    def compute_rho_stats(self):
-        pass
+    def compute_rho_stats(self, catalog_id, filename):
+        """
+        compute_rho_stats
+
+        Compute the rho statistics of your psf. Store it as an attribute of the class which could be save afterwards.
+
+        Parameters:
+        ----------
+        catalog_id : str
+            The id of the catalog used to compute the rho statistics.
+
+        filename : str
+            The path where the rho stats will be saved.
+        """
+        if self.verbose:
+            print("Computation of the rho statistics in progress...")
+        rho_0 = treecorr.GGCorrelation(self._treecorr_config)
+        rho_0.process(self.catalogs['psf_'+catalog_id], self.catalogs['psf_'+catalog_id])
+        rho_1 = treecorr.GGCorrelation(self._treecorr_config)
+        rho_1.process(self.catalogs['psf_error_'+catalog_id], self.catalogs['psf_error_'+catalog_id])
+        rho_2 = treecorr.GGCorrelation(self._treecorr_config)
+        rho_2.process(self.catalogs['psf_'+catalog_id], self.catalogs['psf_error_'+catalog_id])
+        rho_3 = treecorr.GGCorrelation(self._treecorr_config)
+        rho_3.process(self.catalogs['psf_size_error_'+catalog_id], self.catalogs['psf_size_error_'+catalog_id])
+        rho_4 = treecorr.GGCorrelation(self._treecorr_config)
+        rho_4.process(self.catalogs['psf_error_'+catalog_id], self.catalogs['psf_size_error_'+catalog_id])
+        rho_5 = treecorr.GGCorrelation(self._treecorr_config)
+        rho_5.process(self.catalogs['psf_'+catalog_id], self.catalogs['psf_size_error_'+catalog_id])
+
+        self.rho_stats = Table(
+            [
+                rho_0.rnom,
+                rho_0.xip,
+                rho_0.varxip,
+                rho_0.xim,
+                rho_1.varxim,
+                rho_1.xip,
+                rho_1.varxip,
+                rho_1.xim,
+                rho_1.varxim,
+                rho_2.xip,
+                rho_2.varxip,
+                rho_2.xim,
+                rho_2.varxim,
+                rho_3.xip,
+                rho_3.varxip,
+                rho_3.xim,
+                rho_3.varxim,
+                rho_4.xip,
+                rho_4.varxip,
+                rho_4.xim,
+                rho_4.varxim,
+                rho_5.xip,
+                rho_5.varxip,
+                rho_5.xim,
+                rho_5.varxim,
+            ],
+            names=(
+                'theta',
+                'rho_0_p',
+                'varrho_0_p',
+                'rho_0_m',
+                'varrho_0_m',
+                'rho_1_p',
+                'varrho_1_p',
+                'rho_1_m',
+                'varrho_1_m',
+                'rho_2_p',
+                'varrho_2_p',
+                'rho_2_m',
+                'varrho_2_m',
+                'rho_3_p',
+                'varrho_3_p',
+                'rho_3_m',
+                'varrho_3_m',
+                'rho_4_p',
+                'varrho_4_p',
+                'rho_4_m',
+                'varrho_4_m',
+                'rho_5_p',
+                'varrho_5_p',
+                'rho_5_m',
+                'varrho_5_m',
+            )
+        )
+
+        self.save_rho_stats(filename) #A bit dirty just because of consistency of the datatype :/
+        self.load_rho_stats(filename)
 
     def save_rho_stats(self, filename):
-        pass
+        self.rho_stats.writeto(filename, format='fits')
 
     def load_rho_stats(self, filename):
-        pass
+        self.rho_stats = fits.getdata(filename)
 
     def plot_rho_stats(self):
+        """
+        plot_rho_stats
+
+        Method to plot Rho + statistics.
+        """
+
         pass
+
+
+class TauStat():
+    """
+    TauStat
+
+    Class to compute the tau statistics (Gatti 2022) of a PSF and gal catalogue.
+    """
+
+    def __init__(self, params, output, treecorr_config=None, catalogs=None, verbose=False):
+
+        if catalogs is None:
+            self.catalogs = Catalogs(params, output)
+        else:
+            self.catalogs = catalogs
+
+        if treecorr_config is None:
+            self._treecorr_config = {
+                "ra_units": "deg",
+                "dec_units": "deg",
+                "sep_units": "arcmin",
+                "min_sep": 0.1,
+                "max_sep": 100,
+                "n_bins": 20,
+                "var_method": "jackknife"
+            }
+        else:
+            self._treecorr_config = treecorr_config
+
+        self.verbose = verbose
+
+    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False):
+        """
+        build_cat_to_compute_tau
+
+        Parameters
+        ----------
+        path_cat : str
+            Path to the catalog built to compute the tau-statistics.
+
+        cat_type : str
+            Specify the type of the catalogue to build. 'gal' or 'psf'
+
+        catalog_id : str
+            An id to identify the catalog used in the keys of the stored treecorr.Catalog.
+
+        square_size : bool
+            If True, the size computed in the catalogue is squared (Default: False)
+        """
+
+        if cat_type=="psf":
+            self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat)
+
+            if self.verbose:
+                print("Building catalogs...")
+
+            self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size)
+            self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size)
+            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size)
+
+        else:
+            self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None)
+
+            if self.verbose:
+                print("Building catalog...")
+            
+            self.catalogs.build_catalog(cat_type='gal', key='gal_'+catalog_id)
+
+        if self.verbose:
+            print("Catalogs successfully built...")
+            self.catalogs.show_catalogs()
+
+    def compute_tau_stats(self, catalog_id, filename):
+        """
+        compute_tau_stats
+
+        Compute the tau statistics of your catalog and save it.
+
+        Parameters:
+        ----------
+        catalog_id : str
+            The id of the catalog used to compute the rho statistics.
+
+        filename : str
+            The path where the rho stats will be saved.
+        """
+        if self.verbose:
+            print("Computation of the tau statistics in progress...")
+        tau_0 = treecorr.GGCorrelation(self._treecorr_config)
+        tau_0.process(self.catalogs['gal_'+catalog_id], self.catalogs['psf_'+catalog_id])
+        tau_2 = treecorr.GGCorrelation(self._treecorr_config)
+        tau_2.process(self.catalogs['gal_'+catalog_id], self.catalogs['psf_error_'+catalog_id])
+        tau_5 = treecorr.GGCorrelation(self._treecorr_config)
+        tau_5.process(self.catalogs['gal_'+catalog_id], 'psf_size_error_'+catalog_id)
+
+        self.tau_stats = Table(
+            [
+                tau_0.rnom,
+                tau_0.xip,
+                tau_0.varxip,
+                tau_0.xim,
+                tau_2.xip,
+                tau_2.varxip,
+                tau_2.xim,
+                tau_2.varxim,
+                tau_5.xip,
+                tau_5.varxip,
+                tau_5.xim,
+                tau_5.varxim,
+            ],
+            names=(
+                'theta',
+                'tau_0_p',
+                'vartau_0_p',
+                'tau_0_m',
+                'vartau_0_m',
+                'tau_2_p',
+                'vartau_2_p',
+                'tau_2_m',
+                'vartau_2_m',
+                'tau_5_p',
+                'vartau_5_p',
+                'tau_5_m',
+                'vartau_5_m',
+            )
+        )
+
+        self.save_tau_stats(filename) #A bit dirty just because of consistency of the datatype :/
+        self.load_tau_stats(filename)
+
+    def save_tau_stats(self, filename):
+        self.tau_stats.writeto(filename, format='fits')
+
+    def load_tau_stats(self, filename):
+        self.tau_stats = fits.getdata(filename)
+
+    def plot_tau_stats(self):
+        pass
\ No newline at end of file
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
new file mode 100644
index 0000000..b6b407f
--- /dev/null
+++ b/tests/test_rho_tau_stats.py
@@ -0,0 +1,16 @@
+import sys
+sys.path.append('../shear_psf_leakage')
+
+import numpy as np
+import matplotlib.pyplot as plt
+from rho_tau_stat import RhoStat, TauStat
+
+
+#Run tests on shapepipe V1
+
+path_gal = "~/Documents/UNIONS/data/unions_shapepipe_extended_2022_v1.1.fits"
+path_psf = "~/Documents/UNIONS/data/unions_shapepipe_psf_2022_v1.0.2.fits"
+
+rho_stat_handler = RhoStat(verbose=True)
+
+rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id='SPV1', square_size=True)
\ No newline at end of file

From b427947b679815c8111981fc733678b7e6a7fed8 Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Wed, 15 Nov 2023 11:40:07 +0100
Subject: [PATCH 24/97] Small bug fixes

---
 shear_psf_leakage/rho_tau_stat.py | 57 ++++++++++++++++++++++---------
 tests/test_rho_tau_stats.py       | 15 ++++++--
 2 files changed, 53 insertions(+), 19 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 8e57097..132bcd6 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -166,7 +166,7 @@ def get_cat_fields(self, cat_type, square_size=False):
                 g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_psf
                 g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_psf
 
-            return ra, dec, g1, g2, weights
+        return ra, dec, g1, g2, weights
     
     def build_catalog(self, cat_type, key, npatch=None, square_size=False):
         """
@@ -229,7 +229,25 @@ def show_catalogs(self):
         Print the keys of the element stored in self.catalogs_dict
         """
         for key in self.catalogs_dict.keys():
-            print(key)   
+            print(key)
+
+    def get_cat(self, key):
+        """
+        get_cat
+
+        Return the catalogue stored with the given key
+
+        Parameters
+        ----------
+        key : str
+            The key used to identify the catalogue in the dictionary
+
+        Returns
+        -------
+        treecorr.Catalog
+            The requested treecorr.Catalog
+        """ 
+        return self.catalogs_dict[key]
 
 
 class RhoStat():
@@ -250,7 +268,7 @@ def __init__(self, params=None, output=None, treecorr_config=None, verbose=False
                 "sep_units": "arcmin",
                 "min_sep": 0.1,
                 "max_sep": 100,
-                "n_bins": 20,
+                "nbins": 20,
                 "var_method": "jackknife"
             }
         else:
@@ -319,17 +337,17 @@ def compute_rho_stats(self, catalog_id, filename):
         if self.verbose:
             print("Computation of the rho statistics in progress...")
         rho_0 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_0.process(self.catalogs['psf_'+catalog_id], self.catalogs['psf_'+catalog_id])
+        rho_0.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         rho_1 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_1.process(self.catalogs['psf_error_'+catalog_id], self.catalogs['psf_error_'+catalog_id])
+        rho_1.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_error_'+catalog_id))
         rho_2 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_2.process(self.catalogs['psf_'+catalog_id], self.catalogs['psf_error_'+catalog_id])
+        rho_2.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_error_'+catalog_id))
         rho_3 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_3.process(self.catalogs['psf_size_error_'+catalog_id], self.catalogs['psf_size_error_'+catalog_id])
+        rho_3.process(self.catalogs.get_cat('psf_size_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
         rho_4 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_4.process(self.catalogs['psf_error_'+catalog_id], self.catalogs['psf_size_error_'+catalog_id])
+        rho_4.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
         rho_5 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_5.process(self.catalogs['psf_'+catalog_id], self.catalogs['psf_size_error_'+catalog_id])
+        rho_5.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
 
         self.rho_stats = Table(
             [
@@ -388,11 +406,14 @@ def compute_rho_stats(self, catalog_id, filename):
             )
         )
 
+        if self.verbose:
+            print("Done...")
+
         self.save_rho_stats(filename) #A bit dirty just because of consistency of the datatype :/
         self.load_rho_stats(filename)
 
     def save_rho_stats(self, filename):
-        self.rho_stats.writeto(filename, format='fits')
+        self.rho_stats.write(filename, format='fits', overwrite=True)
 
     def load_rho_stats(self, filename):
         self.rho_stats = fits.getdata(filename)
@@ -414,7 +435,7 @@ class TauStat():
     Class to compute the tau statistics (Gatti 2022) of a PSF and gal catalogue.
     """
 
-    def __init__(self, params, output, treecorr_config=None, catalogs=None, verbose=False):
+    def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None, verbose=False):
 
         if catalogs is None:
             self.catalogs = Catalogs(params, output)
@@ -428,7 +449,7 @@ def __init__(self, params, output, treecorr_config=None, catalogs=None, verbose=
                 "sep_units": "arcmin",
                 "min_sep": 0.1,
                 "max_sep": 100,
-                "n_bins": 20,
+                "nbins": 20,
                 "var_method": "jackknife"
             }
         else:
@@ -494,11 +515,11 @@ def compute_tau_stats(self, catalog_id, filename):
         if self.verbose:
             print("Computation of the tau statistics in progress...")
         tau_0 = treecorr.GGCorrelation(self._treecorr_config)
-        tau_0.process(self.catalogs['gal_'+catalog_id], self.catalogs['psf_'+catalog_id])
+        tau_0.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         tau_2 = treecorr.GGCorrelation(self._treecorr_config)
-        tau_2.process(self.catalogs['gal_'+catalog_id], self.catalogs['psf_error_'+catalog_id])
+        tau_2.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_error_'+catalog_id))
         tau_5 = treecorr.GGCorrelation(self._treecorr_config)
-        tau_5.process(self.catalogs['gal_'+catalog_id], 'psf_size_error_'+catalog_id)
+        tau_5.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
 
         self.tau_stats = Table(
             [
@@ -506,6 +527,7 @@ def compute_tau_stats(self, catalog_id, filename):
                 tau_0.xip,
                 tau_0.varxip,
                 tau_0.xim,
+                tau_0.varxim,
                 tau_2.xip,
                 tau_2.varxip,
                 tau_2.xim,
@@ -532,11 +554,14 @@ def compute_tau_stats(self, catalog_id, filename):
             )
         )
 
+        if self.verbose:
+            print("Done...")
+
         self.save_tau_stats(filename) #A bit dirty just because of consistency of the datatype :/
         self.load_tau_stats(filename)
 
     def save_tau_stats(self, filename):
-        self.tau_stats.writeto(filename, format='fits')
+        self.tau_stats.write(filename, format='fits', overwrite=True)
 
     def load_tau_stats(self, filename):
         self.tau_stats = fits.getdata(filename)
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index b6b407f..7726f1b 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -8,9 +8,18 @@
 
 #Run tests on shapepipe V1
 
-path_gal = "~/Documents/UNIONS/data/unions_shapepipe_extended_2022_v1.1.fits"
-path_psf = "~/Documents/UNIONS/data/unions_shapepipe_psf_2022_v1.0.2.fits"
+path_gal = "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.1.fits"
+path_psf = "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits"
 
 rho_stat_handler = RhoStat(verbose=True)
 
-rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id='SPV1', square_size=True)
\ No newline at end of file
+rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id='SPV1', square_size=True)
+
+rho_stat_handler.compute_rho_stats('SPV1', 'rho_stats_spv1.fits')
+
+
+tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, verbose=True)
+
+tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id='SPV1', square_size=True)
+
+tau_stat_handler.compute_tau_stats('SPV1', 'tau_stats_spv1.fits')
\ No newline at end of file

From 6deeea88fa57bfb9294337e5e0f4ad0ed65baac8 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 15 Nov 2023 11:42:44 +0100
Subject: [PATCH 25/97] obj-wise leakage options mix and lin/quad

---
 shear_psf_leakage/run_object.py | 58 +++++++++++++++++++--------------
 1 file changed, 33 insertions(+), 25 deletions(-)

diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 495fc4f..1a47919 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -499,11 +499,18 @@ def get_ellipticity_weights(self):
 
         return e, weights
 
-    def PSF_leakage(self):
+    def PSF_leakage(self, mix=True, order="lin"):
         """PSF Leakage.
 
         Compute and plot object-by-object PSF spin-consistent leakage relations.
 
+        Parameters
+        ----------
+        mix : bool, optional
+            Component mixing (spin-consistent); default is ``True``
+        order : str, optional
+            regression order; allowed are "lin" (default) and "quad"
+
         """
         # Set options for plotting
         n_bin = 30
@@ -530,31 +537,30 @@ def PSF_leakage(self):
         ]
 
         # Fit consistent spin-2 2D model
-        mix = True
-        for order in ["lin", "quad"]:
-            out_path = (
-                f"{self._params['output_dir']}"
-                + "/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
-            )
-            par_best_fit = leakage.corr_2d(
-                x_arr[:2],
-                e,
-                weights=weights,
-                xlabel_arr=xlabel_arr[:2],
-                ylabel_arr=ylabel_arr,
-                order=order,
-                mix=mix,
-                title=f"{order} {mix}",
-                n_bin=n_bin,
-                out_path=out_path,
-                colors=colors,
-                stats_file=self._stats_file,
-                verbose=self._params["verbose"],
-            )
-            fp_best_fit = open(f"{out_path}.json", "w")
-            par_best_fit.dump(fp_best_fit)
+        out_path = (
+            f"{self._params['output_dir']}"
+            + f"/PSF_e_vs_e_gal_{order-{order}_mix-{mix}"
+        )
+        par_best_fit = leakage.corr_2d(
+            x_arr[:2],
+            e,
+            weights=weights,
+            xlabel_arr=xlabel_arr[:2],
+            ylabel_arr=ylabel_arr,
+            order=order,
+            mix=mix,
+            title=f"{order} {mix}",
+            n_bin=n_bin,
+            out_path=out_path,
+            colors=colors,
+            stats_file=self._stats_file,
+            verbose=self._params["verbose"],
+        )
+        fp_best_fit = open(f"{out_path}.json", "w")
+        _par_best_fit.dump(fp_best_fit)
 
         # Fit separate 1D models, including size
+        # MKDEBUG: to remove, is done by fit_any
         ylabel = r"$e_{1,2}^{\rm gal}$"
         mlabel = [r"\alpha_1", r"\alpha_2"]
         clabel = ["c_1", "c_2"]
@@ -636,7 +642,9 @@ def run(self):
 
             if obj._params["PSF_leakage"]:
                 # Object-by-object spin-consistent PSF leakage
-                obj.PSF_leakage()
+
+                for order in ("lin", "quad"):
+                    obj.PSF_leakage(mix=True, order=order)
 
             if obj._params["obs_leakage"]:
                 # Object-by-object dependence of general variables

From e2c7c0abafa197863727931f8da4975703ff254f Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Wed, 15 Nov 2023 17:03:04 +0100
Subject: [PATCH 26/97] Add modules to plot rho and tau stats for different
 catalogs allowing smooth comparison. Wrote a script to test the new
 functionalities.

---
 shear_psf_leakage/rho_tau_stat.py | 321 +++++++++++++++++++++++++++---
 tests/test_rho_tau_stats.py       |  86 +++++++-
 2 files changed, 372 insertions(+), 35 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 132bcd6..469337e 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -11,6 +11,157 @@
 
 import treecorr
 
+def neg_dash(
+    ax,
+    x_in,
+    y_in,
+    yerr_in,
+    vertical_lines=True,
+    xlabel='',
+    ylabel='',
+    rho_nb='',
+    tau_nb='',
+    cat_id='',
+    ylim=None,
+    semilogx=False,
+    semilogy=False,
+    **kwargs
+):
+    r"""Neg Dash.
+
+    This function is for making plots with vertical errorbars,
+    where negative values are shown in absolute value as dashed lines.
+    The resulting plot can either be saved by specifying a file name as
+    ``plot_name``, or be kept as a pyplot instance (for instance to combine
+    several neg dashes).
+
+    Parameters
+    ----------
+    ax : 
+        The matplotlib object on which the plot is performed
+    x_in : numpy.ndarray
+        X-axis inputs
+    y_in : numpy.ndarray
+        Y-axis inputs
+    yerr_in : numpy.ndarray
+        Y-axis error inputs
+    vertical_lines : bool, optional
+        Option to plot vertical lines; default is ``True``
+    xlabel : str, optional
+        X-axis label
+    ylabel : str, optional
+        Y-axis label
+    rho_nb : str, optional
+        Rho number
+    ylim : float, optional
+        Y-axis limit
+    semilogx : bool
+        Option to plot the x-axis in log scale; default is ``False``
+    semilogy : bool
+        Option to plot the y-axis in log scale; default is ``False``
+
+    """
+    x = np.copy(x_in)
+    y = np.copy(y_in)
+    if yerr_in is not None:
+        yerr = np.copy(yerr_in)
+    else:
+        yerr = np.zeros_like(x)
+    # catch and separate errorbar-specific keywords from Lines2D ones
+    safekwargs = dict(kwargs)
+    errbkwargs = dict()
+    if 'linestyle' in kwargs.keys():
+        print(
+            'Warning: linestyle was provided but that would kind of defeat'
+            + 'the purpose, so I will just ignore it. Sorry.'
+        )
+        del safekwargs['linestyle']
+    for errorbar_kword in [
+        'fmt', 'ecolor', 'elinewidth', 'capsize', 'barsabove', 'errorevery'
+    ]:
+        if errorbar_kword in kwargs.keys():
+            # posfmt = '-'+kwargs['fmt']
+            # negfmt = '--'+kwargs['fmt']
+            errbkwargs[errorbar_kword] = kwargs[errorbar_kword]
+            del safekwargs[errorbar_kword]
+    errbkwargs = dict(errbkwargs, **safekwargs)
+
+    # plot up to next change of sign
+    current_sign = np.sign(y[0])
+    first_change = np.argmax(current_sign * y < 0)
+    while first_change:
+        if current_sign > 0:
+            ax.errorbar(
+                x[:first_change],
+                y[:first_change],
+                yerr=yerr[:first_change],
+                linestyle='-',
+                **errbkwargs,
+            )
+            if vertical_lines:
+                ax.vlines(
+                    x[first_change - 1],
+                    0,
+                    y[first_change - 1],
+                    linestyle='-',
+                    **safekwargs,
+                )
+                ax.vlines(
+                    x[first_change],
+                    0,
+                    np.abs(y[first_change]),
+                    linestyle='--',
+                    **safekwargs,
+                )
+        else:
+            ax.errorbar(
+                x[:first_change],
+                np.abs(y[:first_change]),
+                yerr=yerr[:first_change],
+                linestyle='--',
+                **errbkwargs,
+            )
+            if vertical_lines:
+                ax.vlines(
+                    x[first_change - 1],
+                    0,
+                    np.abs(y[first_change - 1]),
+                    linestyle='--',
+                    **safekwargs,
+                )
+                ax.vlines(
+                    x[first_change],
+                    0,
+                    y[first_change],
+                    linestyle='-',
+                    **safekwargs,
+                )
+        x = x[first_change:]
+        y = y[first_change:]
+        yerr = yerr[first_change:]
+        current_sign *= -1
+        first_change = np.argmax(current_sign * y < 0)
+    # one last time when `first_change'==0 ie no more changes:
+    if rho_nb:
+        lab = fr'$\rho_{rho_nb}(\theta)$ '+cat_id
+    elif tau_nb:
+        lab = fr'$\tau_{tau_nb}(\theta)$' +cat_id
+    else:
+        lab = cat_id
+    if current_sign > 0:
+        ax.errorbar(x, y, yerr=yerr, linestyle='-', label=lab, **errbkwargs)
+    else:
+        ax.errorbar(x, np.abs(y), yerr=yerr, linestyle='--', label=lab,
+                     **errbkwargs)
+    if semilogx:
+        ax.set_xscale('log')
+    if semilogy:
+        ax.set_yscale('log')
+    if ylim is not None:
+        ax.set_ylim(ylim)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+
 class Catalogs():
     """
     Catalogs
@@ -66,7 +217,7 @@ def params_default(self):
 
         self._output = "."
 
-    def set_params(self, params, output):
+    def set_params(self, params=None, output=None):
         """
         set_params:
 
@@ -168,7 +319,7 @@ def get_cat_fields(self, cat_type, square_size=False):
 
         return ra, dec, g1, g2, weights
     
-    def build_catalog(self, cat_type, key, npatch=None, square_size=False):
+    def build_catalog(self, cat_type, key, npatch=None, square_size=False, mask=False):
         """
         build_catalogue
 
@@ -188,6 +339,9 @@ def build_catalog(self, cat_type, key, npatch=None, square_size=False):
 
         square_size : bool
             If True, the size computed in the catalogue is squared (Default: False)
+
+        mask : bool
+            If True, use PSF and star flags to mask the data. (Default: False)
         """
 
         if npatch is None:
@@ -195,11 +349,20 @@ def build_catalog(self, cat_type, key, npatch=None, square_size=False):
         
         ra, dec, g1, g2, weights = self.get_cat_fields(cat_type, square_size)
 
+        if mask:
+            flag_psf = self.dat_psf[self._params["PSF_flag"]]
+            flag_star = self.dat_psf[self._params["star_flag"]]
+            mask_arr = (flag_psf==0) & (flag_star==0)
+            if weights is not None:
+                weights = weights[mask_arr]
+        else:
+            mask_arr = np.array([True for i in ra])
+
         cat = treecorr.Catalog(
-            ra=ra,
-            dec=dec,
-            g1=g1,
-            g2=g2,
+            ra=ra[mask_arr],
+            dec=dec[mask_arr],
+            g1=g1[mask_arr],
+            g2=g2[mask_arr],
             w=weights,
             ra_units=self._params["ra_units"],
             dec_units=self._params["dec_units"],
@@ -291,7 +454,7 @@ def check_params(self):
         pass
         #TO DO
 
-    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False):
+    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False, mask=False):
         """
         build_cat_to_compute_rho
 
@@ -305,6 +468,9 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
 
         square_size : bool
             If True, the size computed in the catalogue is squared (Default: False)
+
+        mask : bool
+            If True, use PSF and star flags to mask the data. (Default: False)
         """
 
         self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star)
@@ -312,9 +478,9 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
         if self.verbose:
             print("Building catalogs...")
 
-        self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size)
-        self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size)
-        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size)
+        self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
+        self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size, mask=mask)
+        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size, mask=mask)
 
         if self.verbose:
             print("Catalogs successfully built...")
@@ -335,7 +501,7 @@ def compute_rho_stats(self, catalog_id, filename):
             The path where the rho stats will be saved.
         """
         if self.verbose:
-            print("Computation of the rho statistics in progress...")
+            print("Computation of the rho statistics of "+catalog_id+" in progress...")
         rho_0 = treecorr.GGCorrelation(self._treecorr_config)
         rho_0.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         rho_1 = treecorr.GGCorrelation(self._treecorr_config)
@@ -413,19 +579,65 @@ def compute_rho_stats(self, catalog_id, filename):
         self.load_rho_stats(filename)
 
     def save_rho_stats(self, filename):
-        self.rho_stats.write(filename, format='fits', overwrite=True)
+        self.rho_stats.write(self.catalogs._output+'/'+filename, format='fits', overwrite=True)
 
     def load_rho_stats(self, filename):
-        self.rho_stats = fits.getdata(filename)
+        self.rho_stats = fits.getdata(self.catalogs._output+'/'+filename)
 
-    def plot_rho_stats(self):
+    def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None):
         """
         plot_rho_stats
 
-        Method to plot Rho + statistics.
+        Method to plot Rho + statistics of several catalogues given in argument. Figures are saved in PNG format.
+
+        Parameters:
+        ----------
+        filenames : list str
+            List of the files containing the rho statistics. They can be computed using the method `compute_rho_stats` of this class.
+        
+        colors : list str
+            Color of the plot for the different catalogs. We recommend using different colors for different catalogs for readability.
+
+        catalogs_id : list str
+            A list of catalogs id to label accurately the legend.
+
+        abs : bool
+            If True, plot the absolute value of the rho-statistics. Otherwise, plot the negative values with dashed lines.
+
+        savefig : str
+            If not None, saves the figure with the name given in savefig.
         """
 
-        pass
+        fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(15,9))
+        ax = ax.flatten()
+
+        for filename, color, cat_id in zip(filenames, colors, catalog_ids): #Plot for the different catalogs
+            self.load_rho_stats(filename)
+
+            for i in range(6):
+                xlabel=r"$\theta$ [arcmin]" if i>2 else ''
+                ylabel=r"$\rho-$statistics" if (i==0 or i==3) else ''
+                label = fr'$\rho_{i}(\theta)$ '+cat_id
+                if abs:
+                    ax[i].errorbar(self.rho_stats['theta'], np.abs(self.rho_stats['rho_'+str(i)+'_p']), yerr=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
+                    label=label, color=color, capsize=2)
+                    ax[i].set_xlabel(xlabel)
+                    ax[i].set_ylabel(ylabel)
+                    ax[i].set_xscale('log')
+                    ax[i].set_yscale('log')
+                else:
+                    #Plot the negative values of the rho-stats in dashed lines
+                    neg_dash(
+                        ax[i], self.rho_stats['theta'], self.rho_stats['rho_'+str(i)+'_p'], yerr_in=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
+                        vertical_lines=False, rho_nb=str(i), cat_id=cat_id, xlabel=xlabel, ylabel=ylabel, semilogx=True, semilogy=True, capsize=True, color=color
+                    )
+
+                ax[i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
+                ax[i].legend(loc='upper right')
+
+        if savefig is not None:
+            plt.savefig(self.catalogs._output+'/'+savefig)
+
 
 
 class TauStat():
@@ -448,7 +660,7 @@ def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None
                 "dec_units": "deg",
                 "sep_units": "arcmin",
                 "min_sep": 0.1,
-                "max_sep": 100,
+                "max_sep": 200,
                 "nbins": 20,
                 "var_method": "jackknife"
             }
@@ -457,7 +669,7 @@ def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None
 
         self.verbose = verbose
 
-    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False):
+    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False, mask=False):
         """
         build_cat_to_compute_tau
 
@@ -474,6 +686,9 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
 
         square_size : bool
             If True, the size computed in the catalogue is squared (Default: False)
+
+        mask : bool
+            If True, use PSF and star flags to mask the data. (Default: False)
         """
 
         if cat_type=="psf":
@@ -482,9 +697,9 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
             if self.verbose:
                 print("Building catalogs...")
 
-            self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size)
-            self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size)
-            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size)
+            self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
+            self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size, mask=mask)
+            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size, mask=mask)
 
         else:
             self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None)
@@ -513,7 +728,7 @@ def compute_tau_stats(self, catalog_id, filename):
             The path where the rho stats will be saved.
         """
         if self.verbose:
-            print("Computation of the tau statistics in progress...")
+            print("Computation of the tau statistics of "+catalog_id+" in progress...")
         tau_0 = treecorr.GGCorrelation(self._treecorr_config)
         tau_0.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         tau_2 = treecorr.GGCorrelation(self._treecorr_config)
@@ -561,10 +776,64 @@ def compute_tau_stats(self, catalog_id, filename):
         self.load_tau_stats(filename)
 
     def save_tau_stats(self, filename):
-        self.tau_stats.write(filename, format='fits', overwrite=True)
+        self.tau_stats.write(self.catalogs._output+'/'+filename, format='fits', overwrite=True)
 
     def load_tau_stats(self, filename):
-        self.tau_stats = fits.getdata(filename)
+        self.tau_stats = fits.getdata(self.catalogs._output+'/'+filename)
+
+    def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_m=True):
+        """
+        plot_tau_stats
+
+        Method to plot Tau + (and -) statistics of several catalogues given in argument. Figures are saved in PNG format.
+
+        Parameters:
+        ----------
+        filenames : list str
+            List of the files containing the rho statistics. They can be computed using the method `compute_rho_stats` of this class.
+        
+        colors : list str
+            Color of the plot for the different catalogs. We recommend using different colors for different catalogs for readability.
+
+        catalogs_id : list str
+            A list of catalogs id to label accurately the legend.
+
+        savefig : str
+            If not None, saves the figure with the name given in savefig.
+
+        plot_tau_m : bool
+            If True, plot the tau - additionally.
+        """
+
+        nrows=1 + plot_tau_m
+
+        fig, ax = plt.subplots(nrows=nrows, ncols=3, figsize=(15,9))
+
+        if nrows==1:
+            ax = ax.reshape(1, 3)
+
+        for filename, color, cat_id in zip(filenames, colors, catalog_ids): #Plot for the different catalogs
+            self.load_tau_stats(filename)
+
+            for i in range(3):
+                for j in range(nrows):
+                    xlabel=r"$\theta$ [arcmin]" if (j==nrows-1) else ''
+                    ylabel=r"$\tau-$statistics" if (i==0) else ''
+                    p_or_m = 'm' if j else 'p'
+                    p_or_m_label = '-' if j else '+'
+                    factor_theta = np.ones_like(self.tau_stats["theta"]) if i==0 else self.tau_stats["theta"]
+                    y = self.tau_stats['tau_'+str(int(0.5*i**2+1.5*i))+'_'+p_or_m]*factor_theta
+                    yerr_in = np.sqrt(self.tau_stats['vartau_'+str(int(0.5*i**2+1.5*i))+'_'+p_or_m])*factor_theta
+                    label = rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)$ '+cat_id if i==0 else rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)\theta$ '+cat_id
+
+                    ax[j, i].errorbar(self.tau_stats["theta"], y, yerr=yerr_in, label=label, color=color, capsize=2)
+                    ax[j, i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
+                    ax[j, i].set_xlabel(xlabel)
+                    ax[j, i].set_ylabel(ylabel)
+                    ax[j, i].set_xscale('log')
+                    ax[j, i].legend(loc='upper right')
+
+        if savefig is not None:
+            plt.savefig(self.catalogs._output+'/'+savefig)
 
-    def plot_tau_stats(self):
-        pass
\ No newline at end of file
+            
\ No newline at end of file
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 7726f1b..4957ac1 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -6,20 +6,88 @@
 from rho_tau_stat import RhoStat, TauStat
 
 
-#Run tests on shapepipe V1
+#Run tests on shapepipe V1 and DESY3
 
-path_gal = "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.1.fits"
-path_psf = "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits"
+#First specify  the paths to your data
+paths_gal = [
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.1.fits",
+    "/home/mkilbing/astro/data/DES/DES_Y3_cut.fits",
+    "/home/hervas/n23_fhervas/shapepipe_clean_uppercut/unions_shapepipe_extended_2022_v1.0.4_theli_4096.fits"
+]
+paths_psf = [
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits",
+    "/home/mkilbing/astro/data/DES/psf_y3a1-v29.fits",
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits"
+]
 
-rho_stat_handler = RhoStat(verbose=True)
+#Specify if you want to mask some data using flags
+masks = [
+    True,
+    False,
+    True
+]
 
-rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id='SPV1', square_size=True)
+#Specify is the sizes have to be squared. It can depend on the catalog.
+square_sizes = [
+    True,
+    False,
+    True
+]
 
-rho_stat_handler.compute_rho_stats('SPV1', 'rho_stats_spv1.fits')
+#Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
+params_des = {
+    "e1_col": "e1_cal",
+    "e2_col": "e2_cal",
+    "w_col": "w",
+    "ra_col": "RA",
+    "dec_col": "Dec",
+    "e1_PSF_col": "piff_e1",
+    "e2_PSF_col": "piff_e2",
+    "e1_star_col": "obs_e1",
+    "e2_star_col": "obs_e2",
+    "PSF_size": "piff_T",
+    "star_size": "obs_T",
+    "output_dir": ".",
+    "patch_number": 120,
+    "ra_units": "deg",
+    "dec_units": "deg" 
+}
 
+params = [
+    None,
+    params_des,
+    None
+]
 
-tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, verbose=True)
+colors = ['blue', 'red', 'green'] #Colors for the plot
+catalog_ids = ['SPV1', 'DESY3', 'SPV104'] #Ids of the catalogs
 
-tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id='SPV1', square_size=True)
+rho_stat_handler = RhoStat(verbose=True) #Create your class to compute, save, load and plot rho_stats
 
-tau_stat_handler.compute_tau_stats('SPV1', 'tau_stats_spv1.fits')
\ No newline at end of file
+for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the different catalogs
+
+    if param is None:
+        rho_stat_handler.catalogs.params_default()
+    else:
+        rho_stat_handler.catalogs.set_params(param) #Set the right parameters
+    rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id=cat_id, square_size=square_size, mask=mask) #Build the different catalogs
+    rho_stat_handler.compute_rho_stats(cat_id, 'rho_stats_'+cat_id+'.fits') #Compute and save the rho statistics
+
+filenames = ['rho_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
+
+rho_stat_handler.plot_rho_stats(filenames, colors, catalog_ids, abs=True, savefig='rho_stats.png') #Plot
+
+tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, verbose=True) #Create your class to compute, save, load and plot tau_stats
+
+for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the catalogs
+
+    if param is None:
+        tau_stat_handler.catalogs.params_default()
+    else:
+        tau_stat_handler.catalogs.set_params(param) #Set the parameters
+    tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
+    tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits') #Compute and save the tau statistics
+
+
+filenames = ['tau_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
+tau_stat_handler.plot_tau_stats(filenames, colors, catalog_ids, savefig='tau_stats.png', plot_tau_m=False) #Plot
\ No newline at end of file

From e4adc6b2e0dc1e775fa99a75ff0cc4e907c54d10 Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Thu, 16 Nov 2023 17:01:12 +0100
Subject: [PATCH 27/97] Add a class to run MCMC analysis and fit alpha, beta
 and eta for different rho and tau statistics. Some bugs still need to be
 handle and tested properly

---
 shear_psf_leakage/plots.py        |  24 ++
 shear_psf_leakage/rho_tau_stat.py | 353 +++++++++++++++++++++++++++++-
 tests/test_rho_tau_stats.py       |  33 ++-
 3 files changed, 400 insertions(+), 10 deletions(-)

diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index 6fa60ec..2b13c3e 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -12,6 +12,8 @@
 import numpy as np
 from scipy import stats
 from matplotlib import pylab as plt
+import getdist
+from getdist import plots, MCSamples
 
 from shear_psf_leakage import leakage
 
@@ -430,3 +432,25 @@ def plot_corr_2d(
     # Save figure
     if out_path:
         plt.savefig(f"{out_path}.png", bbox_inches="tight")
+
+def plot_contours(sample_list, names, labels, savefig=None, **kwargs):
+    """
+    plot_contours
+
+    Plot a corner plot with getdist for given samples.
+
+    Parameters
+    ----------
+    sample_list : list
+        A list containing np.array with samples in each entry.
+    names : list str
+        Names of the variable (See getdist documentation)
+    labels : list str
+        Labels of the parameters
+    """
+    sample_list = [MCSamples(samples=samps, names=names, labels=labels) for samps in sample_list]
+    g = plots.get_subplot_plotter()
+    g.triangle_plot(sample_list, filled=True, **kwargs)
+
+    if savefig is not None:
+        plt.savefig(savefig)
\ No newline at end of file
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 469337e..d2da1d7 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -10,6 +10,8 @@
 from astropy.table import Table
 
 import treecorr
+import emcee
+
 
 def neg_dash(
     ax,
@@ -633,13 +635,11 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None)
                     )
 
                 ax[i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
-                ax[i].legend(loc='upper right')
+                ax[i].legend(loc='upper right', fontsize='small')
 
         if savefig is not None:
             plt.savefig(self.catalogs._output+'/'+savefig)
 
-
-
 class TauStat():
     """
     TauStat
@@ -713,11 +713,11 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
             print("Catalogs successfully built...")
             self.catalogs.show_catalogs()
 
-    def compute_tau_stats(self, catalog_id, filename):
+    def compute_tau_stats(self, catalog_id, filename, save_cov=False, func=None, var_method='jackknife'):
         """
         compute_tau_stats
 
-        Compute the tau statistics of your catalog and save it.
+        Compute the tau statistics of your catalog and save it. Can also compute a covariance related to tau statistics and save it.
 
         Parameters:
         ----------
@@ -726,7 +726,17 @@ def compute_tau_stats(self, catalog_id, filename):
 
         filename : str
             The path where the rho stats will be saved.
+
+        save_cov : bool
+            If True, compute and save a covariance related to tau statistics.
+
+        func : function
+            The function to select the quantity whose covariance is being computed from tau_0, tau_2 and tau_5.
+        
+        var_method: str
+            The method used to compute the covariance. (Default: jackknife)
         """
+
         if self.verbose:
             print("Computation of the tau statistics of "+catalog_id+" in progress...")
         tau_0 = treecorr.GGCorrelation(self._treecorr_config)
@@ -772,6 +782,14 @@ def compute_tau_stats(self, catalog_id, filename):
         if self.verbose:
             print("Done...")
 
+        if save_cov:
+            if self.verbose:
+                print("Computing the covariance...")
+
+            cov = treecorr.estimate_multi_cov([tau_0, tau_2, tau_5], var_method, func)
+
+            np.save(self.catalogs._output+'/'+'cov_'+catalog_id, cov)
+
         self.save_tau_stats(filename) #A bit dirty just because of consistency of the datatype :/
         self.load_tau_stats(filename)
 
@@ -803,6 +821,12 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
 
         plot_tau_m : bool
             If True, plot the tau - additionally.
+
+        Return
+        ------
+        fig : Figure
+
+        ax : Axes
         """
 
         nrows=1 + plot_tau_m
@@ -831,9 +855,324 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
                     ax[j, i].set_xlabel(xlabel)
                     ax[j, i].set_ylabel(ylabel)
                     ax[j, i].set_xscale('log')
-                    ax[j, i].legend(loc='upper right')
+                    ax[j, i].legend(loc='upper right', fontsize='small')
 
         if savefig is not None:
             plt.savefig(self.catalogs._output+'/'+savefig)
 
-            
\ No newline at end of file
+        return fig, ax
+
+class PSFErrorFit():
+    """
+    PSFErrorFit
+
+    This class uses rho and tau statistics data to fit parameters alpha, beta and eta in the psf error model
+    (Gatti et al. 2022). It includes methods to plot the best fit value and the tau statistics as well as
+    as the systematic error for different catalogs.
+    A Likelihood-Based Inference is used assuming a Gaussian likelihood.
+    """
+
+    def __init__(self, rho_stat_handler, tau_stat_handler, data_directory):
+
+        self.rho_stat_handler = rho_stat_handler
+        self.tau_stat_handler = tau_stat_handler
+        print("Class created. Don't forget to load your rho and tau statistics and define your prior and your likelihood.")
+        self.cov = None
+        self.log_prior = self.init_log_prior()
+
+        def log_likelihood(theta, y, inv_cov):
+            y_model = self.model(theta)
+            d = y_model -y
+            return -0.5 * d.T@inv_cov@d
+        
+        self.log_likelihood = log_likelihood
+        self.data_directory = data_directory
+
+        self.rho_stat_handler.catalogs._output = self.data_directory #Change the path to the specified data directory
+        self.tau_stat_handler.catalogs._output = self.data_directory
+
+    def set_data_directory(self, data_directory):
+        """
+        set_data_directory
+
+        Sets the value of the data directory to `data_directory`
+
+        Parameters
+        ----------
+        data_directory : str
+            The new data directory.
+        """
+        self.data_directory = data_directory
+
+
+    def load_rho_stat(self, filename):
+        """
+        load_rho_stats
+
+        Load a file containing the rho stats in the RhoStat class.
+        Be aware that the theta points should match those of the TauStat class.
+
+        Parameters
+        ----------
+        filename : str
+            Name of the file containing the rho statistics.
+        """
+        self.rho_stat_handler.load_rho_stats(filename)
+
+    def load_tau_stat(self, filename):
+        """
+        load_tau_stat
+
+        Load a file containing the tau stats in the TauStat class.
+        Be aware that the theta points should match those of the RhoStat class.
+
+        Parameters
+        ----------
+        filename : str
+            Name of the file containing the rho statistics.
+        """
+        self.tau_stat_handler.load_tau_stats(filename)
+
+    def load_covariance(self, filename):
+        """
+        load_covariance
+
+        Load a covariance matrix to run the inference.
+
+        Parameters
+        ----------
+        filename : str
+            Name of the file containing the covariance matrix.
+        """
+        self.cov = np.load(self.data_directory+'/'+filename)
+
+    def init_log_prior(self, low_alpha=-2.0, high_alpha=2.0, low_beta=-10.0, high_beta=10.0, low_eta=-20.0, high_eta=20.0):
+        """
+        init_log_prior
+
+        Initialise the prior used in the inference. A flat prior is used for all parameters.
+        The bounds can be specified.
+
+        Parameters
+        ----------
+        low_alpha : float
+            Lower bound on alpha prior (Default: -2.0).
+
+        high_alpha : float
+            Upper bound on alpha prior (Default: 2.0).
+
+        low_beta : float
+            Lower bound on beta prior (Default: -10.0).
+
+        high_beta : float
+            Upper bound on beta prior (Default: 10.0).
+
+        low_eta : float
+            Lower bound on eta prior (Default: -20.0).
+
+        high_eta : float
+            Upper bound on eta prior (Default: 20.0).
+
+        Returns
+        -------
+        function
+            Store in the attribute `log_prior` of the class a function that returns the value of the log_prior
+            given a set of parameters theta.
+        """
+        def log_prior(theta):
+            alpha, beta, eta = theta
+            if low_alpha <= alpha <= high_alpha and low_beta <= beta <= high_beta and low_eta <=eta <= high_eta:
+                return 0.0
+            return -np.inf
+        
+        self.log_prior = log_prior
+
+    def model(self, theta):
+        """
+        model
+
+        Implements the systematic error. It outputs the tau+ statistics given the rho statistics and a set of parameters theta.
+
+        Parameters
+        ----------
+
+        theta : tuple
+            Parameters (alpha, beta, eta) of the model.
+
+        Returns
+        -------
+        np.array
+            A flattened array containing the tau+ statistics.
+        """
+        alpha, beta, eta = theta
+
+        rhos = self.rho_stats_handler.rho_stats
+        tau_0_p = alpha * rhos["rho_0_p"] + beta * rhos["rho_2_p"] + eta * rhos["rho_5_p"]
+        tau_2_p = alpha * rhos["rho_2_p"] + beta * rhos["rho_1_p"] + eta * rhos["rho_4_p"]
+        tau_5_p = alpha * rhos["rho_5_p"] + beta * rhos["rho_4_p"] + eta * rhos["rho_3_p"]
+
+        model_output = np.array([
+            tau_0_p,
+            tau_2_p,
+            tau_5_p
+        ])
+
+        return model_output.flatten()
+    
+    def log_probability(self, theta, y, inv_cov):
+        """
+        log_probability
+
+        Computes the log probability given a set of parameters, input, output and the inverse of the covariance matrix
+
+        Parameters
+        ----------
+        theta : tuple
+            Parameters (alpha, beta, eta) of the model.
+
+        y : np.array
+            Output data containing the tau statistics to be fitted.
+
+        inv_cov : np.array
+            Inverse of the covariane matrix.
+        """
+
+        lp = self.prior(theta)
+        if not np.isfinite(lp):
+            return -np.inf
+        return lp + self.log_likelihood(theta, y, inv_cov)
+
+    def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000, discard=300, thin=100, verbose=True, savefig=None):
+        """
+        run_chain
+
+        Run an MCMC analysis to fit the parameters alpha, beta and eta using rho and tau statistics.
+        Emcee package is used to run the inference.
+
+        Parameters
+        ----------
+        init : np.array
+            Initial value of the parameters. An additional random noise will be added. (Default: [0,0,0])
+        
+        nwalkers : int
+            Number of walkers used in the Ensemble Sampler (See emcee documentation). (Default: 124)
+
+        nsamples : int
+            Number of samples to draw (Default: 10000).
+
+        discard : int
+            Number of samples discarded in the burn-in phase (Default: 300).
+
+        thin : int
+            Number of samples thinned out (Default: 100).
+
+        verbose : bool
+            If True, prints several informations.
+
+        Returns
+        -------
+
+        np.array:
+            Array of samples obtained from the MCMC analysis.
+
+        np.array:
+            Best-fit values of the parameters
+
+        np.array:
+            Error bars at the 68% confidence level.
+        """
+        ndim = 3
+        assert (self.rho_stat_handler.rho_stats is not None), ("Please load rho statistics data.") #Check if data was loaded
+        assert (self.tau_stat_handler.tau_stats is not None), ("Please load tau statistics data.")
+        assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
+        #Check that the abssiss are the same
+
+        assert (self.cov is not None), ("Please load a covariance matrix")
+
+        inv_cov = np.linalg.inv(self.cov)
+        output = np.array([
+            self.tau_stat_handler.tau_stats["tau_0_p"],
+            self.tau_stat_handler.tau_stats["tau_2_p"],
+            self.tau_stat_handler.tau_stats["tau_5_p"]
+        ]).flatten()
+
+        init += 1e-3*np.random.randn(nwalkers, ndim)
+
+        sampler = emcee.EnsembleSampler(
+            nwalkers, ndim, self.log_probability, args=(output, inv_cov)
+        )
+
+        print("Run MCMC analysis...")
+        sampler.run_mcmc(init, nsamples, progress=verbose)
+        print("Done")
+
+        tau = sampler.get_autocorr_time()
+
+        if verbose:
+            print("Autocorrelation-time:")
+            print(tau)
+
+        labels = [r"$\alpha$", r"$\beta$", r"$\eta$"]
+
+        if savefig is not None:
+            fig, axes = plt.subplots(3, figsize=(10, 7), sharex=True) #Result completely unconstrained. have another look at the covariance matrix
+            samples = sampler.get_chain()
+            for i in range(ndim):
+                ax = axes[i]
+                ax.plot(samples[:, :, i], "k", alpha=0.3)
+                ax.set_xlim(0, len(samples))
+                ax.set_ylabel(labels[i])
+                ax.yaxis.set_label_coords(-0.1, 0.5)
+
+                axes[-1].set_xlabel("step number");
+        
+            plt.savefig(self.data_directory+'/'+savefig)
+
+        flat_samples = sampler.get_chain(discard=discard, thin=thin, flat=True)
+        mcmc_result = np.percentile(flat_samples, [16, 50, 84], axis=0)
+        q = np.diff(mcmc_result, axis=0)
+        if verbose:
+            print(f"Number of samples: {flat_samples.Shape[0]}\n")
+
+            print("Parameters constraints")
+            print("----------------------")
+            for i in range(ndim):
+                print('Parameter: '+labels[i]+f'={mcmc_result[1, i]:.4f}^+{q[0, i]:.4f}_{q[1, i]:.4f}')
+
+            print(f"Max log_likelihood: {self.log_likelihood(mcmc_result[1,:], output, inv_cov)}")
+
+        return flat_samples, mcmc_result, q
+    
+    def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=None):
+        """
+        plot_tau_stats_w_model
+
+        Plot the tau statistics with the value in theta as parameters.
+
+        Parameters
+        ----------
+        theta : tuple
+            Parameters (alpha, beta, eta).
+        filename : str
+            Name of the file containing the tau statistics. Take care to load the corresponding rho statistics before.
+        color : str
+            Color of the tau statistics lines.
+        catalog_id : str
+            A catalog id for the legend
+        savefig : str
+            If not None, save the figure with the given filename.
+        """
+
+        fig, ax = self.tau_stat_handler.plot_tau_stats([filename], [color], [catalog_id], plot_tau_m=False)
+
+        assert (self.rho_stat_handler.rho_stats is not None), ("Please load rho statistics data.") #Check if data was loaded
+        assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
+
+        taus = self.model(theta).reshape(3, -1)
+        
+        for i in range(3):
+            ax[0, i].plot(self.tau_stat_handler.tau_stats["theta"], taus[i], color='red', label='Model')
+            ax[0, i].leend(loc='upper right', fontsize='small')
+
+        if savefig is not None:
+            plt.savefig(self.data_directory+'/'+savefig)
\ No newline at end of file
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 4957ac1..6b6f20b 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -3,7 +3,8 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from rho_tau_stat import RhoStat, TauStat
+from rho_tau_stat import RhoStat, TauStat, PSFErrorFit
+from plots import plot_contours
 
 
 #Run tests on shapepipe V1 and DESY3
@@ -34,6 +35,8 @@
     True
 ]
 
+output = '.'
+
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
 params_des = {
     "e1_col": "e1_cal",
@@ -62,6 +65,7 @@
 colors = ['blue', 'red', 'green'] #Colors for the plot
 catalog_ids = ['SPV1', 'DESY3', 'SPV104'] #Ids of the catalogs
 
+###################################Compute, save and plot the rho statistics############################
 rho_stat_handler = RhoStat(verbose=True) #Create your class to compute, save, load and plot rho_stats
 
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the different catalogs
@@ -77,6 +81,7 @@
 
 rho_stat_handler.plot_rho_stats(filenames, colors, catalog_ids, abs=True, savefig='rho_stats.png') #Plot
 
+######################################Compute, save and plot the tau statistics#############################
 tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, verbose=True) #Create your class to compute, save, load and plot tau_stats
 
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the catalogs
@@ -86,8 +91,30 @@
     else:
         tau_stat_handler.catalogs.set_params(param) #Set the parameters
     tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
-    tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits') #Compute and save the tau statistics
+    
+    only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
+    tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p) #Compute and save the tau statistics
 
 
 filenames = ['tau_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
-tau_stat_handler.plot_tau_stats(filenames, colors, catalog_ids, savefig='tau_stats.png', plot_tau_m=False) #Plot
\ No newline at end of file
+tau_stat_handler.plot_tau_stats(filenames, colors, catalog_ids, savefig='tau_stats.png', plot_tau_m=False) #Plot
+
+#######################################Fit the error model to rho and tau statistics data##########################
+
+psf_fitter = PSFErrorFit(rho_stat_handler, tau_stat_handler, output)
+
+flat_sample_list = []
+mcmc_result_list = []
+q_list = []
+
+for cat_id in catalog_ids:
+    psf_fitter.load_rho_stat('rho_stats_'+cat_id+'.fits')
+    psf_fitter.load_tau_stat('tau_stats_'+cat_id+'.fits')
+    psf_fitter.load_covariance('cov_'+cat_id+'.npy')
+
+    flat_samples, mcmc_result, q = psf_fitter.run_chain(savefig='mcmc_samples_'+cat_id+'.png')
+    flat_sample_list.append(flat_samples)
+    mcmc_result_list.append(mcmc_result)
+    q_list.append(q)
+
+    psf_fitter.plot_tau_stats_w_model(mcmc_result, 'tau_stats_'+cat_id+'.fits', 'blue', cat_id, savefig='best_fit_'+cat_id+'.png')
\ No newline at end of file

From adaa335ace2b2c1e7194a1429f3d588b5ac243f1 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 18 Nov 2023 13:53:12 +0100
Subject: [PATCH 28/97] object-wise leakge plots: ground-truth parameter value

---
 shear_psf_leakage/leakage.py    |  19 +++--
 shear_psf_leakage/plots.py      | 130 +++++++++++++++++++++-----------
 shear_psf_leakage/run_object.py |   5 +-
 3 files changed, 98 insertions(+), 56 deletions(-)

diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index e29029e..fcba007 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -251,8 +251,9 @@ def func_bias_2d(params, x1_data, x2_data, order="lin", mix=False):
     if mix:
         # Add linear mixing part
         a12 = params["a12"].value
+        a21 = params["a21"].value
         y1_model += a12 * x2_data
-        y2_model += a12 * x1_data
+        y2_model += a21 * x1_data
 
         if order == "quad":
             # Add quadratic mixing part
@@ -862,7 +863,7 @@ def corr_2d(
     title="",
     colors=None,
     out_path=None,
-    y_ground_truth=None,
+    plot_all_points=False,
     par_ground_truth=None,
     stats_file=None,
     verbose=False,
@@ -896,9 +897,8 @@ def corr_2d(
         output file for statistics
     out_path : str, optional, default=None
         output file path, if not given, plot is not saved to file
-    y_ground_truth : numpy.ndarray, optional
-        ground truth model values (y1, y2) for plotting (2D array),
-        default is `None`;
+    plot_all_points : bool, optional
+        plot all individual data points if ``True``; default is ``False``
     par_ground_truth : dict, optional
         ground truth parameter, for plotting, default is `None`
     verbose : bool, optional, default=False
@@ -927,8 +927,9 @@ def corr_2d(
         params.add(p_affine, value=0.0)
 
     if mix:
-        # Linear mixing pararmeter
+        # Linear mixing pararmeters
         params.add("a12", value=0.0)
+        params.add("a21", value=0.0)
 
     if order == "quad":
         # Quadratic parameters
@@ -997,7 +998,8 @@ def corr_2d(
         mix,
         xlabel_arr,
         ylabel_arr,
-        y_ground_truth=y_ground_truth,
+        plot_all_points=plot_all_points,
+        par_ground_truth=par_ground_truth,
         title=title,
         colors=colors,
         out_path=out_path,
@@ -1039,7 +1041,8 @@ def param_order2spin(p_dp, order, mix):
     s_ds["x4"] = 0.5 * (p_dp["a11"] - p_dp["a22"])
 
     if mix:
-        s_ds["y4"] = p_dp["a12"]
+        s_ds["y4"] = 0.5 * (p_dp["a12"] + p_dp["a21"])
+        s_ds["y0"] = 0.5 * (-p_dp["a12"] + p_dp["a21"])
 
     if order == "quad" and mix:
         s_ds["x6"] = 0.25 * (p_dp["q111"] - p_dp["q122"] - p_dp["q212"])
diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index 6fa60ec..aab8002 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -127,10 +127,10 @@ def compute_bins_func_2d(x, y, n_bin, mix, weights=None):
     return x_bin, y_bin, err_bin
 
 
-def set_labels(p_dp, order, mix):
-    """Set Label.
+def set_labels(p_dp, order, mix, par_ground_truth=None):
+    """Set Labels.
 
-    Set labels for plot of 2D fit
+    Set labels for plot of 2D fit.
 
     Parameters
     ----------
@@ -143,46 +143,59 @@ def set_labels(p_dp, order, mix):
 
     Returns
     -------
-    dict :
+    dict
         label strings
 
     """
     # Affine parameters
-    label = {
-        "A": (
-            f'$a_{{11}}={p_dp["a11"]: .2ugL}$'
-            + "\n"
-            + f'$c_1={p_dp["c1"]: .2ugL}$'
-        ),
-        "D": (
-            f'$a_{{22}}={p_dp["a22"]: .2ugL}$'
-            + "\n"
-            + f'$c_2={p_dp["c2"]: .2ugL}$'
-        ),
-    }
+    label = {}
+
+    # Linear parameters
+    label["A"] = f'$a_{{11}}={p_dp["a11"]: .2ugL}$'
+    label["D"] = f'$a_{{22}}={p_dp["a22"]: .2ugL}$'
+    if par_ground_truth:
+        label["A"] = f"{label['A']} ({par_ground_truth['a11'].value})"
+        label["D"] = f"{label['D']} ({par_ground_truth['a22'].value})"
+    
+    # Constant parameters
+    label["A"] = label["A"] + "\n" + f'$c_1={p_dp["c1"]: .2ugL}$'
+    label["D"] = label["D"] + "\n" + f'$c_2={p_dp["c2"]: .2ugL}$'
+    if par_ground_truth:
+        label["A"] = f"{label['A']} ({par_ground_truth['c1'].value})"
+        label["D"] = f"{label['D']} ({par_ground_truth['c2'].value})"
+
     if order == "quad":
         # Add quadratic parameters
-        label["A"] = f'$q_{{111}}={p_dp["q111"]: .2ugL}$' + "\n" + label["A"]
-        label["D"] = f'$q_{{222}}={p_dp["q222"]: .2ugL}$' + "\n" + label["D"]
+        label_q111 = f'$q_{{111}}={p_dp["q111"]: .2ugL}$'
+        label_q222 = f'$q_{{222}}={p_dp["q222"]: .2ugL}$'
+        if par_ground_truth:
+            label_q111 = f"{label_q111} ({par_ground_truth['q111'].value})"
+            label_q222 = f"{label_q222} ({par_ground_truth['q222'].value})"
+        label["A"] = label_q111 + "\n" + label["A"]
+        label["D"] = label_q222 + "\n" + label["D"]
     if mix:
-        # Add mixture parameters
+        # Mixed linear parameters
         label["B"] = f'$a_{{12}}={p_dp["a12"]: .2ugL}$'
-        label["C"] = f'$a_{{12}}={p_dp["a12"]: .2ugL}$'
+        label["C"] = f'$a_{{21}}={p_dp["a21"]: .2ugL}$'
+        if par_ground_truth:
+            label["B"] = f"{label['B']} ({par_ground_truth['a12'].value})"
+            label["C"] = f"{label['C']} ({par_ground_truth['a21'].value})"
+
+        # Mixed quadratic parameters
         if order == "quad":
-            label["B"] = (
-                f'$q_{{211}}={p_dp["q211"]: .2ugL}$'
-                + "\n"
-                + f'$q_{{212}}={p_dp["q212"]: .2ugL}$'
-                + "\n"
-                + label["B"]
-            )
-            label["C"] = (
-                f'$q_{{122}}={p_dp["q122"]: .2ugL}$'
-                + "\n"
-                + f'$q_{{112}}={p_dp["q112"]: .2ugL}$'
-                + "\n"
-                + label["C"]
-            )
+            label_q211 = f'$q_{{211}}={p_dp["q211"]: .2ugL}$'
+            label_q212 = f'$q_{{212}}={p_dp["q212"]: .2ugL}$'
+            label_q122 = f'$q_{{122}}={p_dp["q122"]: .2ugL}$'
+            label_q112 = f'$q_{{112}}={p_dp["q112"]: .2ugL}$'
+
+            if par_ground_truth:
+                label_q211 = f"{label_q211} ({par_ground_truth['q211'].value})"
+                label_q212 = f"{label_q212} ({par_ground_truth['q212'].value})"
+                label_q122 = f"{label_q122} ({par_ground_truth['q122'].value})"
+                label_q112 = f"{label_q112} ({par_ground_truth['q112'].value})"
+
+            label["B"] = label_q211 + "\n" + label_q212 + "\n" + label["B"]
+            label["C"] = label_q122 + "\n" + label_q112 + "\n" + label["C"]
 
     return label
 
@@ -238,7 +251,7 @@ def plot_bar_spin(par, s_ground_truth, output_path=None):
         alpha=0.5,
         ecolor="black",
         capsize=8,
-        width=0.8,
+        width=0.7,
         color=col,
     )
     xlim = ax.get_xlim()
@@ -284,7 +297,8 @@ def plot_corr_2d(
     mix,
     xlabel_arr,
     ylabel_arr,
-    y_ground_truth=None,
+    plot_all_points=False,
+    par_ground_truth=None,
     title=None,
     colors=None,
     out_path=None,
@@ -305,14 +319,18 @@ def plot_corr_2d(
         results of the minization
     n_bin : double, optional, default=30
         number of points onto which data are binned
+    p_dp : dict
+        Best-fit and std of input parameter
     order : str
         order of fit
     mix : bool
-        mixing of components if True
+        mixing of components if ``True``
     xlabel_arr, ylabel_arr : list of str
         x-and y-axis labels
-    y_ground_truth : 2D np.array, optional
-        ground truth model values (y1, y2) for plotting, default is `None`
+    plot_all_points : bool, optional
+        plot all individual data points if ``True``; default is ``False``
+    par_ground_truth : 2D np.array, optional
+        ground truth model values (y1, y2) for plotting, default is ``None``
     title : string, optional, default=''
         plot title
     colors : array(m) of string, optional, default=None
@@ -342,6 +360,14 @@ def plot_corr_2d(
     y_model_all[0], y_model_all[1] = leakage.func_bias_2d_full(
         res.params, x_bin[0], x_bin[1], order=order, mix=mix
     )
+
+    if par_ground_truth:
+        y_gt_all = np.zeros(shape=(2, n_bin, n_bin))
+        y_gt_mean = np.zeros(shape=(2, n_bin))
+        y_gt_all[0], y_gt_all[1] = leakage.func_bias_2d_full(
+            par_ground_truth, x_bin[0], x_bin[1], order=order, mix=mix
+        )
+
     # Compute means and standard deviations
     y_model_mean = np.zeros(shape=(2, n_bin))
     y_model_upper = np.zeros(shape=(2, n_bin))
@@ -352,6 +378,9 @@ def plot_corr_2d(
         y_model_upper[comp] = y_model_mean[comp] + std
         y_model_lower[comp] = y_model_mean[comp] - std
 
+        if par_ground_truth:
+            y_gt_mean[comp] = y_gt_all[comp].mean(axis=ax)
+
     # Set up quantities to plot in each panel
     xb = {}
     yd = {}
@@ -364,6 +393,8 @@ def plot_corr_2d(
     col = {}
     xl = {}
     yl = {}
+    yall = {}
+    xall = {}
 
     # Set component for each panel.
     # x: 0 in A, B; 1 in C, D
@@ -392,12 +423,16 @@ def plot_corr_2d(
         dy[p] = err_bin[panel_comp_y[p]][panel_comp_x[p]]
         col[p] = colors[panel_comp_y[p]]
 
-        if y_ground_truth:
-            xgt[p] = x[panel_comp_x[p]]
-            ygt[p] = y_ground_truth[panel_comp_y[p]]
+        if par_ground_truth:
+            xgt[p] = x_bin[panel_comp_x[p]]
+            ygt[p] = y_gt_mean[panel_comp_y[p]]
+
+        if plot_all_points:
+            xall[p] = x[panel_comp_x[p]]
+            yall[p] = y[panel_comp_y[p]]
 
     # Set plot labels to parameter best-fit + std
-    label = set_labels(p_dp, order, mix)
+    label = set_labels(p_dp, order, mix, par_ground_truth=par_ground_truth)
 
     # Loop over panels
     for p in axes:
@@ -411,9 +446,12 @@ def plot_corr_2d(
             xb[p], ymu[p], yml[p], color=col[p], interpolate=True, alpha=0.3
         )
 
-        # Plot ground-truth model if provided
-        if y_ground_truth:
-            axes[p].plot(xgt[p], ygt[p], ".", c="k", markersize=0.4)
+        # Plot ground-truth binned mean if provided
+        if par_ground_truth:
+            axes[p].plot(xgt[p], ygt[p], ":", c=col[p])
+
+        if plot_all_points:
+            axes[p].plot(xall[p], yall[p], ".", c="k", markersize=0.4)
 
         # Plot binned data with error bars
         axes[p].errorbar(xb[p], yd[p], yerr=dy[p], c=col[p], fmt=".")
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 495fc4f..e171518 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -447,6 +447,7 @@ def test(self):
             "a11": -0.4,
             "a22": 0.3,
             "a12": 0.3,
+            "a21": 0.25,
             "c1": 0.2,
             "c2": -0.3,
         }
@@ -476,7 +477,7 @@ def test(self):
                     n_bin=n_bin,
                     out_path=out_path,
                     colors=colors,
-                    y_ground_truth=[y1, y2],
+                    plot_all_points=True,
                     par_ground_truth=p_gt,
                     stats_file=self._stats_file,
                     verbose=self._params["verbose"],
@@ -534,7 +535,7 @@ def PSF_leakage(self):
         for order in ["lin", "quad"]:
             out_path = (
                 f"{self._params['output_dir']}"
-                + "/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
+                + f"/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
             )
             par_best_fit = leakage.corr_2d(
                 x_arr[:2],

From 7193a8561dd05064431ec453b8a8c57835bee1c3 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 18 Nov 2023 15:46:18 +0100
Subject: [PATCH 29/97] leakage_object: download patch catalogue (to be done
 for v1)

---
 notebooks/leakage_object.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/notebooks/leakage_object.py b/notebooks/leakage_object.py
index 2266a93..6f9e28f 100644
--- a/notebooks/leakage_object.py
+++ b/notebooks/leakage_object.py
@@ -36,8 +36,10 @@
 # ### Paths
 
 # +
+# Patch name
+patch = "W3"
 # Input galaxy shear catalogue
-params_in["input_path_shear"] = "unions_shapepipe_extended_2022_W3_v1.0.3.fits"
+params_in["input_path_shear"] = f"unions_shapepipe_extended_2022_{patch}_v1.0.3.fits"
 
 # Output directory
 params_in["output_dir"] = "leakage_object"
@@ -53,7 +55,7 @@
 params_in["obs_leakage"] = True
 
 # Other input parameters
-params_in["cols"] = "RA Dec e1_PSF fwhm_PSF w mag snr"
+params_in["cols"] = "RA Dec e1_PSF e2_PSF fwhm_PSF w mag snr"
 
 # Ratio between two input columns
 params_in["cols_ratio"] = "mag_snr"
@@ -70,9 +72,9 @@
 params_in["verbose"] = True
 # -
 
-# ### Retrieve test catalogue from VOspace if not yet downloade
+# ### Retrieve test catalogue from VOspace if not yet downloaded
 
-vos_dir = "vos:cfis/XXXX/"
+vos_dir = f"vos:cfis/weak_lensing/DataReleases/v1.0/ShapePipe/{patch}"
 canfar.download(
     f"{vos_dir}/{params_in['input_path_shear']}",
     params_in["input_path_shear"],

From 6e35170edb5ff81b0996ecb3441dfa7da92efb95 Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Tue, 21 Nov 2023 15:15:35 +0100
Subject: [PATCH 30/97] Add tests for the MCMC fit of the parameter and plot
 the systematic error for different catalogs.

---
 shear_psf_leakage/rho_tau_stat.py | 49 +++++++++++++++++++-----
 tests/test_rho_tau_stats.py       | 63 ++++++++++++++++++++++---------
 2 files changed, 85 insertions(+), 27 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index d2da1d7..f8bf724 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -660,7 +660,7 @@ def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None
                 "dec_units": "deg",
                 "sep_units": "arcmin",
                 "min_sep": 0.1,
-                "max_sep": 200,
+                "max_sep": 100,
                 "nbins": 20,
                 "var_method": "jackknife"
             }
@@ -831,7 +831,7 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
 
         nrows=1 + plot_tau_m
 
-        fig, ax = plt.subplots(nrows=nrows, ncols=3, figsize=(15,9))
+        fig, ax = plt.subplots(nrows=nrows, ncols=3, figsize=(15,6))
 
         if nrows==1:
             ax = ax.reshape(1, 3)
@@ -878,7 +878,7 @@ def __init__(self, rho_stat_handler, tau_stat_handler, data_directory):
         self.tau_stat_handler = tau_stat_handler
         print("Class created. Don't forget to load your rho and tau statistics and define your prior and your likelihood.")
         self.cov = None
-        self.log_prior = self.init_log_prior()
+        self.init_log_prior()
 
         def log_likelihood(theta, y, inv_cov):
             y_model = self.model(theta)
@@ -1006,7 +1006,7 @@ def model(self, theta):
         """
         alpha, beta, eta = theta
 
-        rhos = self.rho_stats_handler.rho_stats
+        rhos = self.rho_stat_handler.rho_stats
         tau_0_p = alpha * rhos["rho_0_p"] + beta * rhos["rho_2_p"] + eta * rhos["rho_5_p"]
         tau_2_p = alpha * rhos["rho_2_p"] + beta * rhos["rho_1_p"] + eta * rhos["rho_4_p"]
         tau_5_p = alpha * rhos["rho_5_p"] + beta * rhos["rho_4_p"] + eta * rhos["rho_3_p"]
@@ -1037,7 +1037,7 @@ def log_probability(self, theta, y, inv_cov):
             Inverse of the covariane matrix.
         """
 
-        lp = self.prior(theta)
+        lp = self.log_prior(theta)
         if not np.isfinite(lp):
             return -np.inf
         return lp + self.log_likelihood(theta, y, inv_cov)
@@ -1096,7 +1096,7 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
             self.tau_stat_handler.tau_stats["tau_5_p"]
         ]).flatten()
 
-        init += 1e-3*np.random.randn(nwalkers, ndim)
+        init = init + 1e-3*np.random.randn(nwalkers, ndim)
 
         sampler = emcee.EnsembleSampler(
             nwalkers, ndim, self.log_probability, args=(output, inv_cov)
@@ -1132,7 +1132,7 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
         mcmc_result = np.percentile(flat_samples, [16, 50, 84], axis=0)
         q = np.diff(mcmc_result, axis=0)
         if verbose:
-            print(f"Number of samples: {flat_samples.Shape[0]}\n")
+            print(f"Number of samples: {flat_samples.shape[0]}\n")
 
             print("Parameters constraints")
             print("----------------------")
@@ -1171,8 +1171,37 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
         taus = self.model(theta).reshape(3, -1)
         
         for i in range(3):
-            ax[0, i].plot(self.tau_stat_handler.tau_stats["theta"], taus[i], color='red', label='Model')
-            ax[0, i].leend(loc='upper right', fontsize='small')
+            factor = np.ones_like(self.tau_stat_handler.tau_stats["theta"]) if i==0 else self.tau_stat_handler.tau_stats["theta"]
+            ax[0, i].plot(self.tau_stat_handler.tau_stats["theta"], taus[i]*factor, color='red', label='Model')
+            ax[0, i].legend(loc='upper right', fontsize='small')
 
         if savefig is not None:
-            plt.savefig(self.data_directory+'/'+savefig)
\ No newline at end of file
+            plt.savefig(self.data_directory+'/'+savefig)
+
+    def plot_xi_sys(self, theta, cat_id, color, savefig=None):
+        """
+        plot_xi_sys
+
+        Plot the systematic error on the corss-correlation given parameters (alpha, beta, eta)
+
+        Parameters
+        ----------
+        theta : tuple
+            Parameters (alpha, beta, eta) used to compute the systematic error.
+
+        
+        """
+        alpha, beta, eta = theta
+
+        xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*alpha*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
+
+        plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, label=r'$\xi_{\rm sys, +}$ '+cat_id)
+
+        plt.xscale('log')
+        plt.yscale('log')
+        plt.xlabel(r"$\theta$ [arcmin]")
+        plt.ylabel(r"$\xi_{\rm PSF, sys}$")
+
+        if savefig:
+            plt.savefig('xi_sys.png')
+
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 6b6f20b..87a68b9 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -51,11 +51,21 @@
     "PSF_size": "piff_T",
     "star_size": "obs_T",
     "output_dir": ".",
-    "patch_number": 120,
+    "patch_number": 150,
     "ra_units": "deg",
     "dec_units": "deg" 
 }
 
+treecorr_config = {
+                "ra_units": "deg",
+                "dec_units": "deg",
+                "sep_units": "arcmin",
+                "min_sep": 0.1,
+                "max_sep": 100,
+                "nbins": 20,
+                "var_method": "jackknife"
+}
+
 params = [
     None,
     params_des,
@@ -66,16 +76,19 @@
 catalog_ids = ['SPV1', 'DESY3', 'SPV104'] #Ids of the catalogs
 
 ###################################Compute, save and plot the rho statistics############################
-rho_stat_handler = RhoStat(verbose=True) #Create your class to compute, save, load and plot rho_stats
+rho_stat_handler = RhoStat(output='./output_n_patch_150', verbose=True) #Create your class to compute, save, load and plot rho_stats
 
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the different catalogs
 
-    if param is None:
-        rho_stat_handler.catalogs.params_default()
-    else:
-        rho_stat_handler.catalogs.set_params(param) #Set the right parameters
-    rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id=cat_id, square_size=square_size, mask=mask) #Build the different catalogs
-    rho_stat_handler.compute_rho_stats(cat_id, 'rho_stats_'+cat_id+'.fits') #Compute and save the rho statistics
+    try:
+        rho_stat_handler.load_rho_stats('rho_stats_'+cat_id+'.fits')
+    except FileNotFoundError:
+        if param is None:
+            rho_stat_handler.catalogs.params_default()
+        else:
+            rho_stat_handler.catalogs.set_params(param) #Set the right parameters
+        rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id=cat_id, square_size=square_size, mask=mask) #Build the different catalogs
+        rho_stat_handler.compute_rho_stats(cat_id, 'rho_stats_'+cat_id+'.fits') #Compute and save the rho statistics
 
 filenames = ['rho_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
 
@@ -86,14 +99,17 @@
 
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the catalogs
 
-    if param is None:
-        tau_stat_handler.catalogs.params_default()
-    else:
-        tau_stat_handler.catalogs.set_params(param) #Set the parameters
-    tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
-    
-    only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
-    tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p) #Compute and save the tau statistics
+    try:
+        tau_stat_handler.load_tau_stats('tau_stats_'+cat_id+'.fits')
+    except FileNotFoundError:
+        if param is None:
+            tau_stat_handler.catalogs.params_default()
+        else:
+            tau_stat_handler.catalogs.set_params(param) #Set the parameters
+        tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
+        
+        only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
+        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p) #Compute and save the tau statistics
 
 
 filenames = ['tau_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
@@ -117,4 +133,17 @@
     mcmc_result_list.append(mcmc_result)
     q_list.append(q)
 
-    psf_fitter.plot_tau_stats_w_model(mcmc_result, 'tau_stats_'+cat_id+'.fits', 'blue', cat_id, savefig='best_fit_'+cat_id+'.png')
\ No newline at end of file
+    psf_fitter.plot_tau_stats_w_model(mcmc_result[1], 'tau_stats_'+cat_id+'.fits', 'blue', cat_id, savefig='best_fit_'+cat_id+'.png')
+
+
+plot_contours(flat_sample_list, names=['x0', 'x1', 'x2'], labels=['alpha', 'beta', 'eta'], savefig='contours_tau_stat.png',
+    legend_labels=catalog_ids, legend_loc='upper right', contour_colors=colors, markers={'x0':0, 'x1':1, 'x2':1}
+)
+
+plt.figure(figsize=(15, 6))
+
+for mcmc_result, cat_id, color in zip(mcmc_result_list, catalog_ids, colors):
+    psf_fitter.load_rho_stat('rho_stats_'+cat_id+'.fits')
+    psf_fitter.plot_xi_sys(mcmc_result[1], cat_id, color)
+plt.legend()
+plt.savefig('xi_sys.png')
\ No newline at end of file

From b023bdf358a94178480f730e15627c8408c68f1e Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Tue, 21 Nov 2023 17:23:57 +0100
Subject: [PATCH 31/97] Small bug fixes

---
 shear_psf_leakage/rho_tau_stat.py | 52 +++++++++++++++++--------------
 tests/test_rho_tau_stats.py       | 13 ++++----
 2 files changed, 35 insertions(+), 30 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index f8bf724..68c10b8 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -174,8 +174,8 @@ class Catalogs():
 
     def __init__(self, params=None, output=None):
         #set default parameters
-        if (params is None) or (output is None): 
-            self.params_default()
+        if (params is None): 
+            self.params_default(output)
         else:
             self.set_params(params, output)
 
@@ -183,7 +183,7 @@ def __init__(self, params=None, output=None):
         self.dat_shear = None
         self.dat_psf = None
 
-    def params_default(self):
+    def params_default(self, output=None):
         """
         Params Default.
 
@@ -217,7 +217,10 @@ def params_default(self):
             "dec_units": "deg" 
         }
 
-        self._output = "."
+        if output is not None:
+            self._output = output
+        else:
+            self._output="."
 
     def set_params(self, params=None, output=None):
         """
@@ -414,7 +417,6 @@ def get_cat(self, key):
         """ 
         return self.catalogs_dict[key]
 
-
 class RhoStat():
     """
     RhoStat
@@ -440,21 +442,6 @@ def __init__(self, params=None, output=None, treecorr_config=None, verbose=False
             self._treecorr_config = treecorr_config
 
         self.verbose = verbose
-    
-
-    def check_params(self):
-        """Check Params.
-
-        Check whether parameter values are valid.
-
-        Raises
-        ------
-        ValueError
-            if a parameter value is not valid
-
-        """
-        pass
-        #TO DO
 
     def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False, mask=False):
         """
@@ -1191,9 +1178,7 @@ def plot_xi_sys(self, theta, cat_id, color, savefig=None):
 
         
         """
-        alpha, beta, eta = theta
-
-        xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*alpha*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
+        xi_sys = self.compute_xi_sys(theta)
 
         plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, label=r'$\xi_{\rm sys, +}$ '+cat_id)
 
@@ -1205,3 +1190,24 @@ def plot_xi_sys(self, theta, cat_id, color, savefig=None):
         if savefig:
             plt.savefig('xi_sys.png')
 
+    def compute_xi_sys(self, theta):
+        """
+        compute_xi_sys
+
+        Compute the systematic error on the cross-correlation given parameters (alpha, beta, eta)
+
+        Parameters
+        ----------
+        theta : tuple
+            Parameters (alpha, beta, eta) used to compute the systematic error
+
+        Return
+        ------
+        np.array
+            xi_sys
+        """
+
+        alpha, beta, eta = theta
+
+        xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*alpha*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
+        return xi_sys
\ No newline at end of file
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 87a68b9..6eeb80a 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -35,7 +35,7 @@
     True
 ]
 
-output = '.'
+output = 'output_n_patch_100'
 
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
 params_des = {
@@ -51,7 +51,7 @@
     "PSF_size": "piff_T",
     "star_size": "obs_T",
     "output_dir": ".",
-    "patch_number": 150,
+    "patch_number": 100,
     "ra_units": "deg",
     "dec_units": "deg" 
 }
@@ -76,15 +76,14 @@
 catalog_ids = ['SPV1', 'DESY3', 'SPV104'] #Ids of the catalogs
 
 ###################################Compute, save and plot the rho statistics############################
-rho_stat_handler = RhoStat(output='./output_n_patch_150', verbose=True) #Create your class to compute, save, load and plot rho_stats
-
+rho_stat_handler = RhoStat(output=output, verbose=True) #Create your class to compute, save, load and plot rho_stats
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the different catalogs
 
     try:
         rho_stat_handler.load_rho_stats('rho_stats_'+cat_id+'.fits')
     except FileNotFoundError:
         if param is None:
-            rho_stat_handler.catalogs.params_default()
+            rho_stat_handler.catalogs.params_default(output)
         else:
             rho_stat_handler.catalogs.set_params(param) #Set the right parameters
         rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id=cat_id, square_size=square_size, mask=mask) #Build the different catalogs
@@ -103,13 +102,13 @@
         tau_stat_handler.load_tau_stats('tau_stats_'+cat_id+'.fits')
     except FileNotFoundError:
         if param is None:
-            tau_stat_handler.catalogs.params_default()
+            tau_stat_handler.catalogs.params_default(output)
         else:
             tau_stat_handler.catalogs.set_params(param) #Set the parameters
         tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
         
         only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
-        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p) #Compute and save the tau statistics
+        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p, var_method='jackknife') #Compute and save the tau statistics
 
 
 filenames = ['tau_stats_'+cat_id+'.fits' for cat_id in catalog_ids]

From 5d834977f39ab25f8965759ba55530b8dcb4668b Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Wed, 22 Nov 2023 15:07:04 +0100
Subject: [PATCH 32/97] Small bug fixes and updates

---
 shear_psf_leakage/rho_tau_stat.py | 23 +++++++++++++----------
 tests/test_rho_tau_stats.py       | 21 +++++++++++----------
 2 files changed, 24 insertions(+), 20 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 68c10b8..3053e9e 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -211,7 +211,6 @@ def params_default(self, output=None):
             "star_size": "SIGMA_STAR_HSM",
             "PSF_flag": "FLAG_PSF_HSM",
             "star_flag": "FLAG_STAR_HSM",
-            "output_dir": ".",
             "patch_number": 120,
             "ra_units": "deg",
             "dec_units": "deg" 
@@ -319,8 +318,10 @@ def get_cat_fields(self, cat_type, square_size=False):
             else:
                 size_star = self.dat_psf[self._params["star_size"]]**2 if square_size else  self.dat_psf[self._params["star_size"]]
                 size_psf = self.dat_psf[self._params["PSF_size"]]**2 if square_size else  self.dat_psf[self._params["PSF_size"]]
-                g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_psf
-                g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_psf
+                g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_star
+                g1 -= g1.mean()
+                g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_star
+                g2 -= g2.mean()
 
         return ra, dec, g1, g2, weights
     
@@ -496,7 +497,7 @@ def compute_rho_stats(self, catalog_id, filename):
         rho_1 = treecorr.GGCorrelation(self._treecorr_config)
         rho_1.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_error_'+catalog_id))
         rho_2 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_2.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_error_'+catalog_id))
+        rho_2.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         rho_3 = treecorr.GGCorrelation(self._treecorr_config)
         rho_3.process(self.catalogs.get_cat('psf_size_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
         rho_4 = treecorr.GGCorrelation(self._treecorr_config)
@@ -622,7 +623,7 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None)
                     )
 
                 ax[i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
-                ax[i].legend(loc='upper right', fontsize='small')
+                ax[i].legend(loc='best', fontsize='small')
 
         if savefig is not None:
             plt.savefig(self.catalogs._output+'/'+savefig)
@@ -842,7 +843,7 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
                     ax[j, i].set_xlabel(xlabel)
                     ax[j, i].set_ylabel(ylabel)
                     ax[j, i].set_xscale('log')
-                    ax[j, i].legend(loc='upper right', fontsize='small')
+                    ax[j, i].legend(loc='best', fontsize='small')
 
         if savefig is not None:
             plt.savefig(self.catalogs._output+'/'+savefig)
@@ -1165,7 +1166,7 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
         if savefig is not None:
             plt.savefig(self.data_directory+'/'+savefig)
 
-    def plot_xi_sys(self, theta, cat_id, color, savefig=None):
+    def plot_xi_sys(self, theta, cat_id, color, savefig=None, alpha=1):
         """
         plot_xi_sys
 
@@ -1179,8 +1180,10 @@ def plot_xi_sys(self, theta, cat_id, color, savefig=None):
         
         """
         xi_sys = self.compute_xi_sys(theta)
-
-        plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, label=r'$\xi_{\rm sys, +}$ '+cat_id)
+        if alpha < 1:
+            plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, alpha=alpha)
+        else:
+            plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, alpha=alpha, label=r'$\xi_{\rm sys, +}$ '+cat_id)
 
         plt.xscale('log')
         plt.yscale('log')
@@ -1209,5 +1212,5 @@ def compute_xi_sys(self, theta):
 
         alpha, beta, eta = theta
 
-        xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*alpha*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
+        xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*beta*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
         return xi_sys
\ No newline at end of file
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 6eeb80a..20ff4c7 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -35,7 +35,7 @@
     True
 ]
 
-output = 'output_n_patch_100'
+output = 'output_n_patch_150'
 
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
 params_des = {
@@ -50,8 +50,7 @@
     "e2_star_col": "obs_e2",
     "PSF_size": "piff_T",
     "star_size": "obs_T",
-    "output_dir": ".",
-    "patch_number": 100,
+    "patch_number": 150,
     "ra_units": "deg",
     "dec_units": "deg" 
 }
@@ -73,7 +72,7 @@
 ]
 
 colors = ['blue', 'red', 'green'] #Colors for the plot
-catalog_ids = ['SPV1', 'DESY3', 'SPV104'] #Ids of the catalogs
+catalog_ids = ['SPV1', 'DESY3', 'SPV1.3'] #Ids of the catalogs
 
 ###################################Compute, save and plot the rho statistics############################
 rho_stat_handler = RhoStat(output=output, verbose=True) #Create your class to compute, save, load and plot rho_stats
@@ -85,7 +84,7 @@
         if param is None:
             rho_stat_handler.catalogs.params_default(output)
         else:
-            rho_stat_handler.catalogs.set_params(param) #Set the right parameters
+            rho_stat_handler.catalogs.set_params(param, output) #Set the right parameters
         rho_stat_handler.build_cat_to_compute_rho(path_psf, catalog_id=cat_id, square_size=square_size, mask=mask) #Build the different catalogs
         rho_stat_handler.compute_rho_stats(cat_id, 'rho_stats_'+cat_id+'.fits') #Compute and save the rho statistics
 
@@ -94,7 +93,7 @@
 rho_stat_handler.plot_rho_stats(filenames, colors, catalog_ids, abs=True, savefig='rho_stats.png') #Plot
 
 ######################################Compute, save and plot the tau statistics#############################
-tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, verbose=True) #Create your class to compute, save, load and plot tau_stats
+tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, output=output, verbose=True) #Create your class to compute, save, load and plot tau_stats
 
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the catalogs
 
@@ -104,7 +103,7 @@
         if param is None:
             tau_stat_handler.catalogs.params_default(output)
         else:
-            tau_stat_handler.catalogs.set_params(param) #Set the parameters
+            tau_stat_handler.catalogs.set_params(param, output) #Set the parameters
         tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
         
         only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
@@ -135,14 +134,16 @@
     psf_fitter.plot_tau_stats_w_model(mcmc_result[1], 'tau_stats_'+cat_id+'.fits', 'blue', cat_id, savefig='best_fit_'+cat_id+'.png')
 
 
-plot_contours(flat_sample_list, names=['x0', 'x1', 'x2'], labels=['alpha', 'beta', 'eta'], savefig='contours_tau_stat.png',
+plot_contours(flat_sample_list, names=['x0', 'x1', 'x2'], labels=['alpha', 'beta', 'eta'], savefig=output+'/contours_tau_stat.png',
     legend_labels=catalog_ids, legend_loc='upper right', contour_colors=colors, markers={'x0':0, 'x1':1, 'x2':1}
 )
 
 plt.figure(figsize=(15, 6))
 
-for mcmc_result, cat_id, color in zip(mcmc_result_list, catalog_ids, colors):
+for mcmc_result, cat_id, color, flat_sample in zip(mcmc_result_list, catalog_ids, colors, flat_sample_list):
     psf_fitter.load_rho_stat('rho_stats_'+cat_id+'.fits')
+    for i in range(100):
+        psf_fitter.plot_xi_sys(flat_sample[-i+1], cat_id, color, alpha=0.1)
     psf_fitter.plot_xi_sys(mcmc_result[1], cat_id, color)
 plt.legend()
-plt.savefig('xi_sys.png')
\ No newline at end of file
+plt.savefig(output+'/xi_sys.png')
\ No newline at end of file

From de87ade88c5d5eb9025258dd8502de9074179b4a Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Wed, 29 Nov 2023 09:47:59 +0100
Subject: [PATCH 33/97] Implemented weighted average mean subtraction and small
 fixes.

---
 shear_psf_leakage/rho_tau_stat.py | 11 ++++---
 tests/test_rho_tau_stats.py       | 52 ++++++++++++++++++++++++-------
 2 files changed, 47 insertions(+), 16 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 3053e9e..1a2c777 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -288,15 +288,16 @@ def get_cat_fields(self, cat_type, square_size=False):
                                            "Allowed cat_type: 'gal', 'psf', 'psf_error', 'psf_size_error'.")
         
         if cat_type=="gal":
-            assert self.dat_shear is not None, ("Check you read the shear catalogs correctly.")
-            ra = self.dat_shear[self._params["ra_col"]]
-            dec = self.dat_shear[self._params["dec_col"]]
-            g1 = self.dat_shear[self._params["e1_col"]] - self.dat_shear[self._params["e1_col"]].mean()
-            g2 = self.dat_shear[self._params["e2_col"]] - self.dat_shear[self._params["e2_col"]].mean()
             if self._params["w_col"] is not None:
                 weights = self.dat_shear[self._params["w_col"]]
             else:
                 weights = np.ones.like(ra)
+            assert self.dat_shear is not None, ("Check you read the shear catalogs correctly.")
+            ra = self.dat_shear[self._params["ra_col"]]
+            dec = self.dat_shear[self._params["dec_col"]]
+            g1 = self.dat_shear[self._params["e1_col"]] - np.average(self.dat_shear[self._params["e1_col"]], weights=weights)
+            g2 = self.dat_shear[self._params["e2_col"]] - np.average(self.dat_shear[self._params["e2_col"]], weights=weights)
+            
         else:
             assert self.dat_psf is not None, ("Check you read the shear catalogs correctly.")
             #Add a mask?
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 20ff4c7..7c28095 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -11,31 +11,37 @@
 
 #First specify  the paths to your data
 paths_gal = [
-    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.1.fits",
+    #"/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.0.fits",
+    "/automnt/n23data1/fhervas/shapepipe_clean_uppercut/unions_shapepipe_extended_2022_v1.0.4.fits",
     "/home/mkilbing/astro/data/DES/DES_Y3_cut.fits",
-    "/home/hervas/n23_fhervas/shapepipe_clean_uppercut/unions_shapepipe_extended_2022_v1.0.4_theli_4096.fits"
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_extended_2022_v1.3_mtheli8k.fits",
+    #"/n17data/mkilbing/astro/data/CFIS/v0.0/shapepipe_1500_goldshape_v1.fits"
 ]
+
 paths_psf = [
     "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits",
     "/home/mkilbing/astro/data/DES/psf_y3a1-v29.fits",
-    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits"
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_psf_2022_v1.0.2_mtheli8k.fits",
+    #"/n17data/sguerrini/star_cat.fits"
 ]
 
 #Specify if you want to mask some data using flags
 masks = [
     True,
     False,
-    True
+    True,
+    #True
 ]
 
 #Specify is the sizes have to be squared. It can depend on the catalog.
 square_sizes = [
     True,
     False,
-    True
+    True,
+    #True
 ]
 
-output = 'output_n_patch_150'
+output = 'v1.3_mask_comparison'
 
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
 params_des = {
@@ -50,7 +56,26 @@
     "e2_star_col": "obs_e2",
     "PSF_size": "piff_T",
     "star_size": "obs_T",
-    "patch_number": 150,
+    "patch_number": 120,
+    "ra_units": "deg",
+    "dec_units": "deg" 
+}
+
+params_axel = {
+    "e1_col": "g1",
+    "e2_col": "g2",
+    "w_col": "w",
+    "ra_col": "RA",
+    "dec_col": "Dec",
+    "e1_PSF_col": "E1_PSF_HSM",
+    "e2_PSF_col": "E2_PSF_HSM",
+    "e1_star_col": "E1_STAR_HSM",
+    "e2_star_col": "E2_STAR_HSM",
+    "PSF_size": "SIGMA_PSF_HSM",
+    "star_size": "SIGMA_STAR_HSM",
+    "PSF_flag": "FLAG_PSF_HSM",
+    "star_flag": "FLAG_STAR_HSM",
+    "patch_number": 120,
     "ra_units": "deg",
     "dec_units": "deg" 
 }
@@ -68,16 +93,18 @@
 params = [
     None,
     params_des,
-    None
+    None,
+    #params_axel
 ]
 
 colors = ['blue', 'red', 'green'] #Colors for the plot
-catalog_ids = ['SPV1', 'DESY3', 'SPV1.3'] #Ids of the catalogs
+catalog_ids = ['SPV1.3_wo_mask', 'DESY3', 'SPV1.3_w_mask'] #Ids of the catalogs
 
+print(paths_gal)
+print(paths_psf)
 ###################################Compute, save and plot the rho statistics############################
 rho_stat_handler = RhoStat(output=output, verbose=True) #Create your class to compute, save, load and plot rho_stats
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the different catalogs
-
     try:
         rho_stat_handler.load_rho_stats('rho_stats_'+cat_id+'.fits')
     except FileNotFoundError:
@@ -105,7 +132,10 @@
         else:
             tau_stat_handler.catalogs.set_params(param, output) #Set the parameters
         tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
-        
+        if 'psf_'+cat_id not in tau_stat_handler.catalogs.catalogs_dict.keys():
+            tau_stat_handler.build_cat_to_compute_tau(path_psf, cat_type='psf', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
+
+
         only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
         tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p, var_method='jackknife') #Compute and save the tau statistics
 

From a93ac8cadcbe42820e101b5421995d5d3d4fb90c Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Fri, 1 Dec 2023 15:07:33 +0100
Subject: [PATCH 34/97] Removed title from leakage plot

---
 shear_psf_leakage/leakage.py    | 4 +++-
 shear_psf_leakage/run_object.py | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index fcba007..dde0378 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -813,7 +813,9 @@ def loss_bias_2d(params, x_data, y_data, err, order, mix):
         raise IndexError("Length of both data components has to be equal")
 
     # Get model 1D y1 and y2 components
-    y1_model, y2_model = func_bias_2d(params, x1_data, x2_data, order=order, mix=mix)
+    y1_model, y2_model = func_bias_2d(
+        params, x1_data, x2_data, order=order, mix=mix
+    )
 
     # Compute residuals between data and model
     res1 = (y1_model - y1_data) / err
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index e171518..950ce62 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -545,7 +545,7 @@ def PSF_leakage(self):
                 ylabel_arr=ylabel_arr,
                 order=order,
                 mix=mix,
-                title=f"{order} {mix}",
+                title="",
                 n_bin=n_bin,
                 out_path=out_path,
                 colors=colors,

From b18fbdf55d409a12241e95dd03c057086ef78b2a Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Wed, 6 Dec 2023 11:00:48 +0100
Subject: [PATCH 35/97] Fixed a mistake in the creation of patches between the
 different catalogs.

---
 shear_psf_leakage/rho_tau_stat.py | 51 +++++++++++++++++++++----------
 tests/test_rho_tau_stats.py       | 28 +++++++++--------
 2 files changed, 50 insertions(+), 29 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 1a2c777..d1dbec7 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -326,7 +326,7 @@ def get_cat_fields(self, cat_type, square_size=False):
 
         return ra, dec, g1, g2, weights
     
-    def build_catalog(self, cat_type, key, npatch=None, square_size=False, mask=False):
+    def build_catalog(self, cat_type, key, npatch=None, patch_centers=None, square_size=False, mask=False):
         """
         build_catalogue
 
@@ -365,16 +365,28 @@ def build_catalog(self, cat_type, key, npatch=None, square_size=False, mask=Fals
         else:
             mask_arr = np.array([True for i in ra])
 
-        cat = treecorr.Catalog(
-            ra=ra[mask_arr],
-            dec=dec[mask_arr],
-            g1=g1[mask_arr],
-            g2=g2[mask_arr],
-            w=weights,
-            ra_units=self._params["ra_units"],
-            dec_units=self._params["dec_units"],
-            npatch=npatch
-        )
+        if patch_centers is None:
+            cat = treecorr.Catalog(
+                ra=ra[mask_arr],
+                dec=dec[mask_arr],
+                g1=g1[mask_arr],
+                g2=g2[mask_arr],
+                w=weights,
+                ra_units=self._params["ra_units"],
+                dec_units=self._params["dec_units"],
+                npatch=npatch
+            )
+        else:
+            cat = treecorr.Catalog(
+                ra=ra[mask_arr],
+                dec=dec[mask_arr],
+                g1=g1[mask_arr],
+                g2=g2[mask_arr],
+                w=weights,
+                ra_units=self._params["ra_units"],
+                dec_units=self._params["dec_units"],
+                patch_centers=patch_centers
+            )
 
         self.catalogs_dict.update(
             {key: cat}
@@ -470,8 +482,9 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
             print("Building catalogs...")
 
         self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
-        self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size, mask=mask)
-        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size, mask=mask)
+        patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
+        self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
 
         if self.verbose:
             print("Catalogs successfully built...")
@@ -687,8 +700,9 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
                 print("Building catalogs...")
 
             self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
-            self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, square_size=square_size, mask=mask)
-            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, square_size=square_size, mask=mask)
+            patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
+            self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
 
         else:
             self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None)
@@ -696,7 +710,12 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
             if self.verbose:
                 print("Building catalog...")
             
-            self.catalogs.build_catalog(cat_type='gal', key='gal_'+catalog_id)
+            try:
+                patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
+            except KeyError:
+                print("Warning, you should build psf catalog before galaxy catalog.")
+                patch_centers = None
+            self.catalogs.build_catalog(cat_type='gal', key='gal_'+catalog_id, patch_centers=patch_centers)
 
         if self.verbose:
             print("Catalogs successfully built...")
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 7c28095..7532dc2 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -11,18 +11,18 @@
 
 #First specify  the paths to your data
 paths_gal = [
-    #"/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.0.fits",
-    "/automnt/n23data1/fhervas/shapepipe_clean_uppercut/unions_shapepipe_extended_2022_v1.0.4.fits",
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.0.fits",
+    #"/automnt/n23data1/fhervas/shapepipe_clean_uppercut/unions_shapepipe_extended_2022_v1.0.4.fits",
     "/home/mkilbing/astro/data/DES/DES_Y3_cut.fits",
     "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_extended_2022_v1.3_mtheli8k.fits",
-    #"/n17data/mkilbing/astro/data/CFIS/v0.0/shapepipe_1500_goldshape_v1.fits"
+    "/n17data/mkilbing/astro/data/CFIS/v0.0/shapepipe_1500_goldshape_v1.fits"
 ]
 
 paths_psf = [
     "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits",
     "/home/mkilbing/astro/data/DES/psf_y3a1-v29.fits",
     "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_psf_2022_v1.0.2_mtheli8k.fits",
-    #"/n17data/sguerrini/star_cat.fits"
+    "/n17data/sguerrini/star_cat.fits"
 ]
 
 #Specify if you want to mask some data using flags
@@ -30,7 +30,7 @@
     True,
     False,
     True,
-    #True
+    True
 ]
 
 #Specify is the sizes have to be squared. It can depend on the catalog.
@@ -38,10 +38,10 @@
     True,
     False,
     True,
-    #True
+    True
 ]
 
-output = 'v1.3_mask_comparison'
+output = '/home/guerrini/rho_tau_stats_output/output_n_patch_120'
 
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
 params_des = {
@@ -94,16 +94,16 @@
     None,
     params_des,
     None,
-    #params_axel
+    params_axel
 ]
 
-colors = ['blue', 'red', 'green'] #Colors for the plot
-catalog_ids = ['SPV1.3_wo_mask', 'DESY3', 'SPV1.3_w_mask'] #Ids of the catalogs
+colors = ['blue', 'red', 'green', 'purple'] #Colors for the plot
+catalog_ids = ['SPV1.0', 'DESY3', 'SPV1.3', 'SPV1500'] #Ids of the catalogs
 
 print(paths_gal)
 print(paths_psf)
 ###################################Compute, save and plot the rho statistics############################
-rho_stat_handler = RhoStat(output=output, verbose=True) #Create your class to compute, save, load and plot rho_stats
+rho_stat_handler = RhoStat(output=output, treecorr_config=treecorr_config, verbose=True) #Create your class to compute, save, load and plot rho_stats
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the different catalogs
     try:
         rho_stat_handler.load_rho_stats('rho_stats_'+cat_id+'.fits')
@@ -120,7 +120,7 @@
 rho_stat_handler.plot_rho_stats(filenames, colors, catalog_ids, abs=True, savefig='rho_stats.png') #Plot
 
 ######################################Compute, save and plot the tau statistics#############################
-tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, output=output, verbose=True) #Create your class to compute, save, load and plot tau_stats
+tau_stat_handler = TauStat(catalogs=rho_stat_handler.catalogs, output=output, treecorr_config=treecorr_config, verbose=True) #Create your class to compute, save, load and plot tau_stats
 
 for path_gal, path_psf, cat_id, param, mask, square_size in zip(paths_gal, paths_psf, catalog_ids, params, masks, square_sizes): #Iterate on the catalogs
 
@@ -131,9 +131,11 @@
             tau_stat_handler.catalogs.params_default(output)
         else:
             tau_stat_handler.catalogs.set_params(param, output) #Set the parameters
-        tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
+
         if 'psf_'+cat_id not in tau_stat_handler.catalogs.catalogs_dict.keys():
             tau_stat_handler.build_cat_to_compute_tau(path_psf, cat_type='psf', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
+        tau_stat_handler.build_cat_to_compute_tau(path_gal, cat_type='gal', catalog_id=cat_id, square_size=square_size, mask=mask) #Build the catalog of galaxies. PSF was computed above
+        
 
 
         only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+

From 888468dfb2e16796aba4896db228148f03dd3f99 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 11 Dec 2023 08:59:15 +0100
Subject: [PATCH 36/97] v0.1.6

---
 README.md                       | 2 +-
 notebooks/leakage_scale_spin.py | 3 ---
 pyproject.toml                  | 2 +-
 shear_psf_leakage/__init__.py   | 2 +-
 tests/test_shear_psf_leakage.py | 2 +-
 5 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index f9b0840..707d64f 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ pypi.org,
 pip install shear_psf_leakage
 ```
 
-Alternatively, you can use
+Alternatively, you can use TBD
 
 ## Authors
 
diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index d250668..cdea709 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -574,6 +574,3 @@
     close_fig=False,
 )
 # -
-
-
-
diff --git a/pyproject.toml b/pyproject.toml
index 2a8e159..d1fba55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "shear_psf_leakage"
-version = "0.1.5"
+version = "0.1.6"
 description = "PSF leakage for shear catalogue data"
 authors = [
 	"Martin Kilbinger <martin.kilbinger@cea.fr>",
diff --git a/shear_psf_leakage/__init__.py b/shear_psf_leakage/__init__.py
index 1276d02..0a8da88 100644
--- a/shear_psf_leakage/__init__.py
+++ b/shear_psf_leakage/__init__.py
@@ -1 +1 @@
-__version__ = "0.1.5"
+__version__ = "0.1.6"
diff --git a/tests/test_shear_psf_leakage.py b/tests/test_shear_psf_leakage.py
index 4eed190..b3e38e0 100644
--- a/tests/test_shear_psf_leakage.py
+++ b/tests/test_shear_psf_leakage.py
@@ -2,4 +2,4 @@
 
 
 def test_version():
-    assert __version__ == '0.1.5'
+    assert __version__ == '0.1.6'

From b53076b0baf03ae48d235c8cccdc98c563526a70 Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Mon, 11 Dec 2023 09:31:07 +0100
Subject: [PATCH 37/97] Add debiasing option when running inference.

---
 shear_psf_leakage/rho_tau_stat.py | 11 +++++++++-
 tests/test_rho_tau_stats.py       | 34 ++++++++++++++++++++++++-------
 2 files changed, 37 insertions(+), 8 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index d1dbec7..f1735f4 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -1050,7 +1050,7 @@ def log_probability(self, theta, y, inv_cov):
             return -np.inf
         return lp + self.log_likelihood(theta, y, inv_cov)
 
-    def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000, discard=300, thin=100, verbose=True, savefig=None):
+    def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000, discard=300, thin=100, verbose=True, savefig=None, npatch=200, apply_debias=False):
         """
         run_chain
 
@@ -1077,6 +1077,12 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
         verbose : bool
             If True, prints several informations.
 
+        npatch : int
+            The number of patches used to perform the inference. (Only used if apply_bias if true, Default: 200)
+
+        apply_debias : bool
+            If True, apply some debiasing of the inverse of the covariance matrix. (Default: False)
+
         Returns
         -------
 
@@ -1104,6 +1110,9 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
             self.tau_stat_handler.tau_stats["tau_5_p"]
         ]).flatten()
 
+        if apply_debias:
+            inv_cov = (npatch - output.shape[0] - 2)/(npatch-1)*inv_cov
+
         init = init + 1e-3*np.random.randn(nwalkers, ndim)
 
         sampler = emcee.EnsembleSampler(
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 7532dc2..fbfe419 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -41,9 +41,29 @@
     True
 ]
 
-output = '/home/guerrini/rho_tau_stats_output/output_n_patch_120'
+output = '/home/guerrini/rho_tau_stats_output/run_full_debiased_3'
 
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
+params_sp = {
+    "e1_col": "e1",
+    "e2_col": "e2",
+    "w_col": "w",
+    "ra_col": "RA",
+    "dec_col": "Dec",
+    "e1_PSF_col": "E1_PSF_HSM",
+    "e2_PSF_col": "E2_PSF_HSM",
+    "e1_star_col": "E1_STAR_HSM",
+    "e2_star_col": "E2_STAR_HSM",
+    "PSF_size": "SIGMA_PSF_HSM",
+    "star_size": "SIGMA_STAR_HSM",
+    "PSF_flag": "FLAG_PSF_HSM",
+    "star_flag": "FLAG_STAR_HSM",
+    "patch_number": 200,
+    "ra_units": "deg",
+    "dec_units": "deg" 
+}
+
+
 params_des = {
     "e1_col": "e1_cal",
     "e2_col": "e2_cal",
@@ -75,7 +95,7 @@
     "star_size": "SIGMA_STAR_HSM",
     "PSF_flag": "FLAG_PSF_HSM",
     "star_flag": "FLAG_STAR_HSM",
-    "patch_number": 120,
+    "patch_number": 200,
     "ra_units": "deg",
     "dec_units": "deg" 
 }
@@ -91,9 +111,9 @@
 }
 
 params = [
-    None,
+    params_sp,
     params_des,
-    None,
+    params_sp,
     params_axel
 ]
 
@@ -139,7 +159,7 @@
 
 
         only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
-        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p, var_method='jackknife') #Compute and save the tau statistics
+        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p, var_method='bootstrap') #Compute and save the tau statistics
 
 
 filenames = ['tau_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
@@ -153,12 +173,12 @@
 mcmc_result_list = []
 q_list = []
 
-for cat_id in catalog_ids:
+for cat_id, param in zip(catalog_ids, params):
     psf_fitter.load_rho_stat('rho_stats_'+cat_id+'.fits')
     psf_fitter.load_tau_stat('tau_stats_'+cat_id+'.fits')
     psf_fitter.load_covariance('cov_'+cat_id+'.npy')
 
-    flat_samples, mcmc_result, q = psf_fitter.run_chain(savefig='mcmc_samples_'+cat_id+'.png')
+    flat_samples, mcmc_result, q = psf_fitter.run_chain(savefig='mcmc_samples_'+cat_id+'.png', npatch=param["patch_number"], apply_debias=True)
     flat_sample_list.append(flat_samples)
     mcmc_result_list.append(mcmc_result)
     q_list.append(q)

From d2f37c8242eef1dcaa26209fca567478ebcb5dcb Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 11 Dec 2023 13:30:15 +0100
Subject: [PATCH 38/97] legend outside; minor bug fixes

---
 shear_psf_leakage/rho_tau_stat.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 1a2c777..d67091d 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -291,7 +291,7 @@ def get_cat_fields(self, cat_type, square_size=False):
             if self._params["w_col"] is not None:
                 weights = self.dat_shear[self._params["w_col"]]
             else:
-                weights = np.ones.like(ra)
+                weights = np.ones_like(ra)
             assert self.dat_shear is not None, ("Check you read the shear catalogs correctly.")
             ra = self.dat_shear[self._params["ra_col"]]
             dec = self.dat_shear[self._params["dec_col"]]
@@ -575,7 +575,7 @@ def save_rho_stats(self, filename):
     def load_rho_stats(self, filename):
         self.rho_stats = fits.getdata(self.catalogs._output+'/'+filename)
 
-    def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None):
+    def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None, legend="each"):
         """
         plot_rho_stats
 
@@ -597,6 +597,9 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None)
 
         savefig : str
             If not None, saves the figure with the name given in savefig.
+
+        legend : str, optional
+            allowed are "each" (default; legends in each panel), "outside" (legend outside of panels)
         """
 
         fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(15,9))
@@ -624,7 +627,11 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None)
                     )
 
                 ax[i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
-                ax[i].legend(loc='best', fontsize='small')
+                if legend == "inside":
+                    ax[i].legend(loc='best', fontsize='small')
+
+        if legend == "outside":
+            ax[-1].legend(bbox_to_anchor=(0.5, 1.1))
 
         if savefig is not None:
             plt.savefig(self.catalogs._output+'/'+savefig)
@@ -1214,4 +1221,4 @@ def compute_xi_sys(self, theta):
         alpha, beta, eta = theta
 
         xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*beta*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
-        return xi_sys
\ No newline at end of file
+        return xi_sys

From 2ca8f5e44664c5d2fbbb8ceec6b5608f694fcb0b Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 11 Dec 2023 13:34:28 +0100
Subject: [PATCH 39/97] added lenspack

---
 .gitignore     |  1 +
 poetry.lock    | 28 +++++++++++++++++++++++++++-
 pyproject.toml |  1 +
 3 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 5caa9ac..5fb506c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,5 +5,6 @@ notebooks/leakage_object
 notebooks/unions_shapepipe_extended_2022_W3_v1.0.3.fits
 __pycache__
 __pycache__/*
+shear_psf_leakage/__pycache__/*
 */__pycache__/*
 build
diff --git a/poetry.lock b/poetry.lock
index 73a6158..8c08588 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1754,6 +1754,22 @@ files = [
     {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
 ]
 
+[[package]]
+name = "lenspack"
+version = "1.0.0"
+description = "builtins Author: Austin Peel, Email: austin.peel@cea.fr, Year: 2019, A collection of weak lensing tools in Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "lenspack-1.0.0-py3-none-any.whl", hash = "sha256:f9c632697516bb681e16b3eff66a7210d6355926179b95aa7060c8936c2259cc"},
+    {file = "lenspack-1.0.0.tar.gz", hash = "sha256:e528a5409473eb996b39695cccb744db067341eb5fac6f240cd8d0aa669c85ec"},
+]
+
+[package.dependencies]
+astropy = "*"
+numpy = "*"
+scipy = "*"
+
 [[package]]
 name = "lmfit"
 version = "1.2.2"
@@ -2863,6 +2879,7 @@ files = [
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
+    {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"},
     {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
     {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
     {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
@@ -2870,8 +2887,15 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
+    {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
     {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
+    {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
     {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@@ -2888,6 +2912,7 @@ files = [
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
+    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
     {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
     {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
     {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@@ -2895,6 +2920,7 @@ files = [
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
+    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
     {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
     {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@@ -3799,4 +3825,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "8bf9b46941b3bb2b4024f278d2417df40bcf48693dad53b205a7e1cf686c8098"
+content-hash = "54a0b3762074daae2858f7acddf0df1388f212aacca774aaf7f948110e2d2c3f"
diff --git a/pyproject.toml b/pyproject.toml
index d1fba55..70f0025 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,6 +30,7 @@ notebook = "5.7.4"
 jupyter-server = "^2.8.0"
 jupyterlab = "^4.0.7"
 tornado = "^6.3.3"
+lenspack = "^1.0.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 85175857a80bf27f0134873e66674291cad08828 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Tue, 12 Dec 2023 14:57:31 +0100
Subject: [PATCH 40/97] xi_psf_sys individual terms

---
 shear_psf_leakage/rho_tau_stat.py | 202 ++++++++++++++++++++++++++----
 1 file changed, 176 insertions(+), 26 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index d67091d..0541700 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -610,8 +610,12 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
 
             for i in range(6):
                 xlabel=r"$\theta$ [arcmin]" if i>2 else ''
-                ylabel=r"$\rho-$statistics" if (i==0 or i==3) else ''
-                label = fr'$\rho_{i}(\theta)$ '+cat_id
+                if legend == "inside":
+                    ylabel = r"$\rho-$statistics" if (i==0 or i==3) else ''
+                    label = fr'$\rho_{i}(\theta)$ '+cat_id
+                else:
+                    ylabel = rf"$\rho_{i}(\theta)$"
+                    label = fr'$\rho_i$ {cat_id}'
                 if abs:
                     ax[i].errorbar(self.rho_stats['theta'], np.abs(self.rho_stats['rho_'+str(i)+'_p']), yerr=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
                     label=label, color=color, capsize=2)
@@ -631,10 +635,13 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
                     ax[i].legend(loc='best', fontsize='small')
 
         if legend == "outside":
-            ax[-1].legend(bbox_to_anchor=(0.5, 1.1))
+            ax[-1].legend(bbox_to_anchor=(1.5, 0.0), fontsize='small')
 
+        plt.tight_layout()
         if savefig is not None:
-            plt.savefig(self.catalogs._output+'/'+savefig)
+            plt.savefig(self.catalogs._output+'/'+savefig, bbox_inches='tight')
+
+        plt.close()
 
 class TauStat():
     """
@@ -795,7 +802,7 @@ def save_tau_stats(self, filename):
     def load_tau_stats(self, filename):
         self.tau_stats = fits.getdata(self.catalogs._output+'/'+filename)
 
-    def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_m=True):
+    def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_m=True, legend="inside"):
         """
         plot_tau_stats
 
@@ -818,6 +825,9 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
         plot_tau_m : bool
             If True, plot the tau - additionally.
 
+        legend : str, optional
+            allowed are "each" (default; legends in each panel), "outside" (legend outside of panels)
+
         Return
         ------
         fig : Figure
@@ -837,24 +847,33 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
 
             for i in range(3):
                 for j in range(nrows):
-                    xlabel=r"$\theta$ [arcmin]" if (j==nrows-1) else ''
-                    ylabel=r"$\tau-$statistics" if (i==0) else ''
                     p_or_m = 'm' if j else 'p'
                     p_or_m_label = '-' if j else '+'
+                    xlabel=r"$\theta$ [arcmin]" if (j==nrows-1) else ''
+                    if legend == "inside":
+                        ylabel = r"$\tau-$statistics" if (i==0) else ''
+                        label = rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)$ '+cat_id if i==0 else rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)\theta$ '+cat_id
+                    else:
+                        ylabel = r"$\tau_{i}(\theta)$"
+                        label = rf"$\tau_i$ {cat_id}"
                     factor_theta = np.ones_like(self.tau_stats["theta"]) if i==0 else self.tau_stats["theta"]
                     y = self.tau_stats['tau_'+str(int(0.5*i**2+1.5*i))+'_'+p_or_m]*factor_theta
                     yerr_in = np.sqrt(self.tau_stats['vartau_'+str(int(0.5*i**2+1.5*i))+'_'+p_or_m])*factor_theta
-                    label = rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)$ '+cat_id if i==0 else rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)\theta$ '+cat_id
 
                     ax[j, i].errorbar(self.tau_stats["theta"], y, yerr=yerr_in, label=label, color=color, capsize=2)
                     ax[j, i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
                     ax[j, i].set_xlabel(xlabel)
                     ax[j, i].set_ylabel(ylabel)
                     ax[j, i].set_xscale('log')
-                    ax[j, i].legend(loc='best', fontsize='small')
+                    if legend == "inside":
+                        ax[j, i].legend(loc='best', fontsize='small')
 
+        if legend == "outside":
+            ax[-1, -1].legend(bbox_to_anchor=(1.5, 0.0), fontsize='small')
+
+        plt.tight_layout()
         if savefig is not None:
-            plt.savefig(self.catalogs._output+'/'+savefig)
+            plt.savefig(self.catalogs._output+'/'+savefig, bbox_inches='tight')
 
         return fig, ax
 
@@ -1125,8 +1144,10 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
             plt.savefig(self.data_directory+'/'+savefig)
 
         flat_samples = sampler.get_chain(discard=discard, thin=thin, flat=True)
-        mcmc_result = np.percentile(flat_samples, [16, 50, 84], axis=0)
-        q = np.diff(mcmc_result, axis=0)
+        mcmc_result, q = self.get_mcmc_from_samples(flat_samples)
+
+        #mcmc_result = np.percentile(flat_samples, [16, 50, 84], axis=0)
+        #q = np.diff(mcmc_result, axis=0)
         if verbose:
             print(f"Number of samples: {flat_samples.shape[0]}\n")
 
@@ -1138,6 +1159,83 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
             print(f"Max log_likelihood: {self.log_likelihood(mcmc_result[1,:], output, inv_cov)}")
 
         return flat_samples, mcmc_result, q
+
+    def get_sample_path(self, catalog_id):
+        """Get Sample Path.
+
+        Return file path of MCMC sample.
+
+        Parameters
+        ----------
+        catalog_id : str
+            ID of the catalog used for this run
+
+        Returns
+        -------
+        str
+            file path
+        """
+        file_path = f"{self.rho_stat_handler.catalogs._output}/samples_{catalog_id}.npy"
+        return file_path
+
+
+    def save_samples(self, flat_samples, catalog_id):
+        """Save Samples.
+
+        Save samples to file.
+
+        Parameters
+        ----------
+        flat_samples : np.array
+            Samples obtained from the MCMC analysis
+        catalog_id : str
+            ID of the catalog used for this run
+
+        """
+        np.save(self.get_sample_path(catalog_id), flat_samples)
+
+    def load_samples(self, catalog_id):
+        """Load Samples.
+
+        Load samples from file.
+
+        Parameters
+        ----------
+        Returns
+        -------
+        np.array
+            Samples obtained from the MCMC analysi
+
+        """
+        file_path = self.get_sample_path(catalog_id)
+        flat_samples = np.load(file_path)
+
+        return flat_samples
+
+
+    def get_mcmc_from_samples(self, flat_samples):
+        """Get MCMC From Samples.
+
+        Return MCMC samples and quanties from samples.
+
+        Parameters
+        ----------
+        flat_samples : np.array
+            Samples obtained from the MCMC analysis
+
+        Returns
+        -------
+        np.array
+            Best-fit values of the parameters
+
+        np.array
+            Error bars at the 68% confidence level.
+
+        """
+        mcmc_result = np.percentile(flat_samples, [16, 50, 84], axis=0)
+        q = np.diff(mcmc_result, axis=0)
+
+        return mcmc_result, q
     
     def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=None):
         """
@@ -1174,36 +1272,77 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
         if savefig is not None:
             plt.savefig(self.data_directory+'/'+savefig)
 
-    def plot_xi_sys(self, theta, cat_id, color, savefig=None, alpha=1):
+        plt.close()
+
+    def plot_xi_psf_sys(self, theta, cat_id, color, savefig=None, alpha=1):
         """
-        plot_xi_sys
+        Plot Xi Psf sys.
 
-        Plot the systematic error on the corss-correlation given parameters (alpha, beta, eta)
+        Plot the systematic error on the corss-correlation given parameters (alpha, beta, eta).
 
         Parameters
         ----------
         theta : tuple
             Parameters (alpha, beta, eta) used to compute the systematic error.
-
         
         """
-        xi_sys = self.compute_xi_sys(theta)
+        xi_psf_sys = self.compute_xi_psf_sys(theta)
         if alpha < 1:
-            plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, alpha=alpha)
+            plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_psf_sys, color=color, capsize=2, alpha=alpha)
         else:
-            plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_sys, color=color, capsize=2, alpha=alpha, label=r'$\xi_{\rm sys, +}$ '+cat_id)
+            plt.errorbar(self.rho_stat_handler.rho_stats["theta"], xi_psf_sys, color=color, capsize=2, alpha=alpha, label=r'$\xi^{\rm PSF}_{\rm sys, +}$ '+cat_id)
 
         plt.xscale('log')
         plt.yscale('log')
         plt.xlabel(r"$\theta$ [arcmin]")
-        plt.ylabel(r"$\xi_{\rm PSF, sys}$")
+        plt.ylabel(r"$\xi^{\rm PSF}_{\rm sys}$")
 
         if savefig:
-            plt.savefig('xi_sys.png')
+            plt.savefig('xi_psf_sys.png')
+            plt.close()
 
-    def compute_xi_sys(self, theta):
+    def compute_xi_psf_sys_term(self, theta, term):
         """
-        compute_xi_sys
+        Compute Xi Psf Sys Term.
+
+        Compute one term of the systematic error on the cross-correlation given parameters (alpha, beta, eta)
+
+        Parameters
+        ----------
+        theta : tuple
+            Parameters (alpha, beta, eta) used to compute the systematic error
+
+        term : int
+            term (rho function) number, from 0 to 5
+
+        Return
+        ------
+        np.array
+            xi_psf_sys
+
+        """
+        alpha, beta, eta = theta
+
+        if term == 0:
+            prefactor = alpha ** 2
+        elif term == 1:
+            prefactor = beta ** 2
+        elif term == 3:
+            prefactor = eta ** 2
+        elif term == 2:
+            prefactor = 2 * alpha * beta
+        elif term == 5:
+            prefactor = 2 * alpha * eta
+        elif term == 4:
+            prefactor = 2 * beta * eta
+        else:
+            raise ValueError(f"Invalid term {term}")
+
+        return prefactor * self.rho_stat_handler.rho_stats[f"rho_{term}_p"]
+
+    def compute_xi_psf_sys(self, theta):
+        """
+        Compute Xi Psf Sys.
 
         Compute the systematic error on the cross-correlation given parameters (alpha, beta, eta)
 
@@ -1215,10 +1354,21 @@ def compute_xi_sys(self, theta):
         Return
         ------
         np.array
-            xi_sys
+            xi_psf_sys
         """
 
         alpha, beta, eta = theta
 
-        xi_sys = alpha**2 * self.rho_stat_handler.rho_stats["rho_0_p"] + beta**2 * self.rho_stat_handler.rho_stats["rho_1_p"]+eta**2 * self.rho_stat_handler.rho_stats["rho_3_p"] + 2*alpha*beta*self.rho_stat_handler.rho_stats["rho_2_p"] +2*alpha*eta*self.rho_stat_handler.rho_stats["rho_5_p"] + 2*beta*eta*self.rho_stat_handler.rho_stats["rho_4_p"]
-        return xi_sys
+        xi_psf_sys = np.zeros_like(self.rho_stat_handler.rho_stats["theta"])
+
+        for term in range(6):
+           xi_psf_sys += self.compute_xi_psf_sys_term(theta, term)
+
+            #alpha ** 2 * self.rho_stat_handler.rho_stats["rho_0_p"]
+            #+ beta ** 2 * self.rho_stat_handler.rho_stats["rho_1_p"]
+            #+ eta ** 2 * self.rho_stat_handler.rho_stats["rho_3_p"]
+            #+ 2 * alpha * beta * self.rho_stat_handler.rho_stats["rho_2_p"]
+            #+ 2 * alpha * eta * self.rho_stat_handler.rho_stats["rho_5_p"]
+            #+ 2 * beta * eta * self.rho_stat_handler.rho_stats["rho_4_p"]
+   
+        return xi_psf_sys

From e9f034b956ffdfd82004cdac8dedece833ab4323 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Tue, 12 Dec 2023 17:11:08 +0100
Subject: [PATCH 41/97] added class function plot_xi_psf_sys_terms

---
 shear_psf_leakage/rho_tau_stat.py | 130 +++++++++++++++++++++++-------
 1 file changed, 103 insertions(+), 27 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 0541700..3fd0df2 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -39,7 +39,7 @@ def neg_dash(
 
     Parameters
     ----------
-    ax : 
+    ax :
         The matplotlib object on which the plot is performed
     x_in : numpy.ndarray
         X-axis inputs
@@ -168,13 +168,13 @@ class Catalogs():
     """
     Catalogs
 
-    Class to build the different treecorr catalogs given a shape catalog that will be 
+    Class to build the different treecorr catalogs given a shape catalog that will be
     used to compute the different statistics
     """
 
     def __init__(self, params=None, output=None):
         #set default parameters
-        if (params is None): 
+        if (params is None):
             self.params_default(output)
         else:
             self.set_params(params, output)
@@ -213,7 +213,7 @@ def params_default(self, output=None):
             "star_flag": "FLAG_STAR_HSM",
             "patch_number": 120,
             "ra_units": "deg",
-            "dec_units": "deg" 
+            "dec_units": "deg"
         }
 
         if output is not None:
@@ -248,7 +248,7 @@ def read_shear_cat(self, path_gal, path_psf):
             self.dat_shear = fits.getdata(path_gal)
         if path_psf is not None:
             self.dat_psf = fits.getdata(path_psf)
-    
+
     def get_cat_fields(self, cat_type, square_size=False):
         """
         Get Cat Fields
@@ -286,7 +286,7 @@ def get_cat_fields(self, cat_type, square_size=False):
 
         assert cat_type in allowed_types, ("The specified catalogue type is invalid. Check the one you use is allowed."
                                            "Allowed cat_type: 'gal', 'psf', 'psf_error', 'psf_size_error'.")
-        
+
         if cat_type=="gal":
             if self._params["w_col"] is not None:
                 weights = self.dat_shear[self._params["w_col"]]
@@ -297,7 +297,7 @@ def get_cat_fields(self, cat_type, square_size=False):
             dec = self.dat_shear[self._params["dec_col"]]
             g1 = self.dat_shear[self._params["e1_col"]] - np.average(self.dat_shear[self._params["e1_col"]], weights=weights)
             g2 = self.dat_shear[self._params["e2_col"]] - np.average(self.dat_shear[self._params["e2_col"]], weights=weights)
-            
+
         else:
             assert self.dat_psf is not None, ("Check you read the shear catalogs correctly.")
             #Add a mask?
@@ -305,11 +305,11 @@ def get_cat_fields(self, cat_type, square_size=False):
             ra = self.dat_psf[self._params["ra_col"]]
             dec = self.dat_psf[self._params["dec_col"]]
             weights = None
-            
+
             if cat_type=="psf":
                 g1 = self.dat_psf[self._params["e1_PSF_col"]] - self.dat_psf[self._params["e1_PSF_col"]].mean()
                 g2 = self.dat_psf[self._params["e2_PSF_col"]] - self.dat_psf[self._params["e2_PSF_col"]].mean()
-            
+
             elif cat_type=="psf_error":
                 g1 = (self.dat_psf[self._params["e1_star_col"]] - self.dat_psf[self._params["e1_PSF_col"]])
                 g1 -= g1.mean()
@@ -325,7 +325,7 @@ def get_cat_fields(self, cat_type, square_size=False):
                 g2 -= g2.mean()
 
         return ra, dec, g1, g2, weights
-    
+
     def build_catalog(self, cat_type, key, npatch=None, square_size=False, mask=False):
         """
         build_catalogue
@@ -353,7 +353,7 @@ def build_catalog(self, cat_type, key, npatch=None, square_size=False, mask=Fals
 
         if npatch is None:
             npatch = self._params["patch_number"]
-        
+
         ra, dec, g1, g2, weights = self.get_cat_fields(cat_type, square_size)
 
         if mask:
@@ -416,7 +416,7 @@ def get_cat(self, key):
         -------
         treecorr.Catalog
             The requested treecorr.Catalog
-        """ 
+        """
         return self.catalogs_dict[key]
 
 class RhoStat():
@@ -426,7 +426,13 @@ class RhoStat():
     Class to compute the rho statistics (Rowe 2010) of a PSF catalogue.
     """
 
-    def __init__(self, params=None, output=None, treecorr_config=None, verbose=False):
+    def __init__(
+        self,
+        params=None,
+        output=None,
+        treecorr_config=None,
+        verbose=False
+    ):
 
         self.catalogs = Catalogs(params, output)
 
@@ -585,7 +591,7 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
         ----------
         filenames : list str
             List of the files containing the rho statistics. They can be computed using the method `compute_rho_stats` of this class.
-        
+
         colors : list str
             Color of the plot for the different catalogs. We recommend using different colors for different catalogs for readability.
 
@@ -627,7 +633,7 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
                     #Plot the negative values of the rho-stats in dashed lines
                     neg_dash(
                         ax[i], self.rho_stats['theta'], self.rho_stats['rho_'+str(i)+'_p'], yerr_in=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
-                        vertical_lines=False, rho_nb=str(i), cat_id=cat_id, xlabel=xlabel, ylabel=ylabel, semilogx=True, semilogy=True, capsize=True, color=color
+                        vertical_lines=False, rho_nb=str(i), cat_id=cat_id, xlabel=xlabel, ylabel=ylabel, semilogx=True, semilogy=True, capsize=True, color=color, fmt=".",
                     )
 
                 ax[i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
@@ -709,7 +715,7 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
 
             if self.verbose:
                 print("Building catalog...")
-            
+
             self.catalogs.build_catalog(cat_type='gal', key='gal_'+catalog_id)
 
         if self.verbose:
@@ -735,7 +741,7 @@ def compute_tau_stats(self, catalog_id, filename, save_cov=False, func=None, var
 
         func : function
             The function to select the quantity whose covariance is being computed from tau_0, tau_2 and tau_5.
-        
+
         var_method: str
             The method used to compute the covariance. (Default: jackknife)
         """
@@ -812,7 +818,7 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
         ----------
         filenames : list str
             List of the files containing the rho statistics. They can be computed using the method `compute_rho_stats` of this class.
-        
+
         colors : list str
             Color of the plot for the different catalogs. We recommend using different colors for different catalogs for readability.
 
@@ -899,7 +905,7 @@ def log_likelihood(theta, y, inv_cov):
             y_model = self.model(theta)
             d = y_model -y
             return -0.5 * d.T@inv_cov@d
-        
+
         self.log_likelihood = log_likelihood
         self.data_directory = data_directory
 
@@ -999,7 +1005,7 @@ def log_prior(theta):
             if low_alpha <= alpha <= high_alpha and low_beta <= beta <= high_beta and low_eta <=eta <= high_eta:
                 return 0.0
             return -np.inf
-        
+
         self.log_prior = log_prior
 
     def model(self, theta):
@@ -1033,7 +1039,7 @@ def model(self, theta):
         ])
 
         return model_output.flatten()
-    
+
     def log_probability(self, theta, y, inv_cov):
         """
         log_probability
@@ -1068,7 +1074,7 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
         ----------
         init : np.array
             Initial value of the parameters. An additional random noise will be added. (Default: [0,0,0])
-        
+
         nwalkers : int
             Number of walkers used in the Ensemble Sampler (See emcee documentation). (Default: 124)
 
@@ -1140,7 +1146,7 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
                 ax.yaxis.set_label_coords(-0.1, 0.5)
 
                 axes[-1].set_xlabel("step number");
-        
+
             plt.savefig(self.data_directory+'/'+savefig)
 
         flat_samples = sampler.get_chain(discard=discard, thin=thin, flat=True)
@@ -1236,7 +1242,7 @@ def get_mcmc_from_samples(self, flat_samples):
         q = np.diff(mcmc_result, axis=0)
 
         return mcmc_result, q
-    
+
     def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=None):
         """
         plot_tau_stats_w_model
@@ -1263,7 +1269,7 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
         assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
 
         taus = self.model(theta).reshape(3, -1)
-        
+
         for i in range(3):
             factor = np.ones_like(self.tau_stat_handler.tau_stats["theta"]) if i==0 else self.tau_stat_handler.tau_stats["theta"]
             ax[0, i].plot(self.tau_stat_handler.tau_stats["theta"], taus[i]*factor, color='red', label='Model')
@@ -1284,7 +1290,7 @@ def plot_xi_psf_sys(self, theta, cat_id, color, savefig=None, alpha=1):
         ----------
         theta : tuple
             Parameters (alpha, beta, eta) used to compute the systematic error.
-        
+
         """
         xi_psf_sys = self.compute_xi_psf_sys(theta)
         if alpha < 1:
@@ -1301,6 +1307,76 @@ def plot_xi_psf_sys(self, theta, cat_id, color, savefig=None, alpha=1):
             plt.savefig('xi_psf_sys.png')
             plt.close()
 
+
+    def plot_xi_psf_sys_terms(self, cat_id, theta, out_path, yscale="log"):
+
+        ls = ["dotted", "dashed", "dashdot", (-1, (3, 5, 1, 5, 1, 5)), (0, (1, 10)), (0, (5, 5))]
+        color = ["green", "blue", "red", "magenta", "cyan", "orange"]
+
+        plt.figure(figsize=(15, 6))
+
+        #self.plot_xi_psf_sys(theta, cat_id, "black")
+        ang_scales = self.rho_stat_handler.rho_stats["theta"]
+
+        label_pre = [
+            r"$\alpha^2$",
+            r"$\beta^2$",
+            r"$\eta^2$",
+            r"$2 \alpha \beta$",
+            r"$2 \alpha \eta$",
+            r"$2 \beta \eta$",
+        ]
+        xi_sum = np.zeros_like(ang_scales)
+
+        if yscale == "linear":
+            plot_fct = plt.semilogx
+            ylim = [-0.5e-6, 5e-6]
+        else:
+            plot_fct = plt.loglog
+            ylim = [3e-10, 5e-6]
+
+        linewidth_pos = 3
+        linewidth_neg = 1
+
+        xi_psf_sys = self.compute_xi_psf_sys(theta)
+        plot_fct(
+            ang_scales,
+            xi_psf_sys,
+            linestyle="-",
+            linewidth=linewidth_pos,
+            color="black",
+            label=r'$\xi^{\rm PSF}_{\rm sys, +}$ '+ cat_id,
+        )
+
+        for term in range(6):
+            xi_psf_sys_term = self.compute_xi_psf_sys_term(theta, term)
+            xi_sum += xi_psf_sys_term
+            if xi_psf_sys_term[-1] > 0:
+                linewidth = linewidth_pos
+            else:
+                linewidth = linewidth_neg
+            plot_fct(
+                ang_scales,
+                np.abs(xi_psf_sys_term),
+                linestyle=ls[term],
+                linewidth=linewidth,
+                color=color[term],
+                label=fr"{label_pre[term]} $\rho_{term}$",
+            )
+        xi_psf_sys_check = self.compute_xi_psf_sys(theta)
+
+        plot_fct(ang_scales, xi_psf_sys_check, "bo", mfc="none")
+        plot_fct(ang_scales, xi_sum, "bs", mfc="none")
+
+        plt.xlabel(r"$\theta$ [arcmin]")
+        plt.ylabel(r"$\xi^{\rm PSF}_{\rm sys}$")
+        plt.legend(loc="best", fontsize="small")
+        plt.ylim(ylim)
+        plt.tight_layout()
+        plt.savefig(out_path, bbox_inches='tight')
+        plt.close()
+
+
     def compute_xi_psf_sys_term(self, theta, term):
         """
         Compute Xi Psf Sys Term.
@@ -1370,5 +1446,5 @@ def compute_xi_psf_sys(self, theta):
             #+ 2 * alpha * beta * self.rho_stat_handler.rho_stats["rho_2_p"]
             #+ 2 * alpha * eta * self.rho_stat_handler.rho_stats["rho_5_p"]
             #+ 2 * beta * eta * self.rho_stat_handler.rho_stats["rho_4_p"]
-   
+
         return xi_psf_sys

From a3d2e81b5f5df506e41bddf4dbf38d53d40fddf2 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 13 Dec 2023 11:40:07 +0100
Subject: [PATCH 42/97] tau stats w model plot: set fixed y limits

---
 shear_psf_leakage/rho_tau_stat.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 3fd0df2..6181f2f 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -616,27 +616,29 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
 
             for i in range(6):
                 xlabel=r"$\theta$ [arcmin]" if i>2 else ''
+
                 if legend == "inside":
                     ylabel = r"$\rho-$statistics" if (i==0 or i==3) else ''
                     label = fr'$\rho_{i}(\theta)$ '+cat_id
                 else:
                     ylabel = rf"$\rho_{i}(\theta)$"
                     label = fr'$\rho_i$ {cat_id}'
+
                 if abs:
                     ax[i].errorbar(self.rho_stats['theta'], np.abs(self.rho_stats['rho_'+str(i)+'_p']), yerr=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
                     label=label, color=color, capsize=2)
                     ax[i].set_xlabel(xlabel)
                     ax[i].set_ylabel(ylabel)
+                    ax[i].set_xlim(float(self._treecorr_config["min_sep"]), float(self._treecorr_config["max_sep"]))
                     ax[i].set_xscale('log')
                     ax[i].set_yscale('log')
                 else:
                     #Plot the negative values of the rho-stats in dashed lines
                     neg_dash(
                         ax[i], self.rho_stats['theta'], self.rho_stats['rho_'+str(i)+'_p'], yerr_in=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
-                        vertical_lines=False, rho_nb=str(i), cat_id=cat_id, xlabel=xlabel, ylabel=ylabel, semilogx=True, semilogy=True, capsize=True, color=color, fmt=".",
+                        vertical_lines=False, rho_nb=str(i), cat_id=cat_id, xlabel=xlabel, ylabel=ylabel, semilogx=True, semilogy=True, capsize=True, color=color,
                     )
 
-                ax[i].set_xlim(self._treecorr_config["min_sep"], self._treecorr_config["max_sep"])
                 if legend == "inside":
                     ax[i].legend(loc='best', fontsize='small')
 
@@ -860,7 +862,7 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
                         ylabel = r"$\tau-$statistics" if (i==0) else ''
                         label = rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)$ '+cat_id if i==0 else rf'$\tau_{{{int(0.5*i**2+1.5*i)}, {p_or_m_label}}}(\theta)\theta$ '+cat_id
                     else:
-                        ylabel = r"$\tau_{i}(\theta)$"
+                        ylabel = rf"$\tau_{i}(\theta)$"
                         label = rf"$\tau_i$ {cat_id}"
                     factor_theta = np.ones_like(self.tau_stats["theta"]) if i==0 else self.tau_stats["theta"]
                     y = self.tau_stats['tau_'+str(int(0.5*i**2+1.5*i))+'_'+p_or_m]*factor_theta
@@ -1270,10 +1272,13 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
 
         taus = self.model(theta).reshape(3, -1)
 
+        ylim = [[-4e-5,  4e-5], [-6e-6, 4e-6], [-7e-6, 1e-6]]
         for i in range(3):
             factor = np.ones_like(self.tau_stat_handler.tau_stats["theta"]) if i==0 else self.tau_stat_handler.tau_stats["theta"]
             ax[0, i].plot(self.tau_stat_handler.tau_stats["theta"], taus[i]*factor, color='red', label='Model')
             ax[0, i].legend(loc='upper right', fontsize='small')
+            ax[0, i].set_ylim(ylim[i])
+            ax[0, i].axhline(color="k", linestyle="dotted", linewidth=0.5)
 
         if savefig is not None:
             plt.savefig(self.data_directory+'/'+savefig)

From 6366b45a884779bfc7f678510de51eb9e0a3025c Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Fri, 15 Dec 2023 15:13:24 +0100
Subject: [PATCH 43/97] Update README.md

Updated author information
---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 707d64f..056d990 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,8 @@ Alternatively, you can use TBD
 
 Martin Kilbinger <martin.kilbinger@cea.fr>
 
-Clara Bonini <mail>
+Clara Bonini
 
-Axel Guinot <mail>
+Sacha Guerrini
+
+Axel Guinot

From 2af2e86fb0756af40c3d4f0b78765b7379afb1f8 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Wed, 24 Jan 2024 10:40:49 +0100
Subject: [PATCH 44/97] rho stats: added check on angular scales with tau stats

---
 shear_psf_leakage/rho_tau_stat.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 886ffca..27854c1 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -1296,7 +1296,19 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
         fig, ax = self.tau_stat_handler.plot_tau_stats([filename], [color], [catalog_id], plot_tau_m=False)
 
         assert (self.rho_stat_handler.rho_stats is not None), ("Please load rho statistics data.") #Check if data was loaded
-        assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
+        assert (
+            np.all(
+                np.abs(
+                    self.rho_stat_handler.rho_stats["theta"] -
+                    self.tau_stat_handler.tau_stats["theta"]
+                ) / self.tau_stat_handler.tau_stats["theta"] < 0.001
+            )
+        ), \
+        (
+            "The rho and tau statistics have not the same angular scales."
+            + " Check that they come from the same catalog with the same"
+            + " treecorr config."
+        )
 
         taus = self.model(theta).reshape(3, -1)
 

From c215a5890030b58b0b903a0826bee5438f4486d1 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 25 Jan 2024 08:46:21 +0100
Subject: [PATCH 45/97] removed misleading comment

---
 notebooks/leakage_scale_spin.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index cdea709..e588a8f 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -247,10 +247,6 @@
     - obj.get_alpha_ufloat(1, 1)
 )
 
-# In theory we have y4 = a_12 = a_21. In practise the leakage matrix is
-# not entirely symmetrical. Let us identify the mean off-diagonal with y4.
-# The difference is in principle the imaginary spin-0 coefficient, which
-# should be zero.
 y4 = 0.5 * (obj.get_alpha_ufloat(0, 1) + obj.get_alpha_ufloat(1, 0))
 y0 = 0.5 * (obj.get_alpha_ufloat(1, 0) - obj.get_alpha_ufloat(0, 1))
 

From f240f7f742d24d8be9019d3baa2613d831b77330 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 25 Jan 2024 08:46:53 +0100
Subject: [PATCH 46/97] added best-fit par to class

---
 shear_psf_leakage/run_object.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index d57174c..f40deab 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -540,9 +540,9 @@ def PSF_leakage(self, mix=True, order="lin"):
         # Fit consistent spin-2 2D model
         out_path = (
             f"{self._params['output_dir']}"
-            + f"/PSF_e_vs_e_gal_{order-{order}_mix-{mix}"
+            + f"/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
         )
-        par_best_fit = leakage.corr_2d(
+        self.par_best_fit = leakage.corr_2d(
             x_arr[:2],
             e,
             weights=weights,
@@ -558,7 +558,7 @@ def PSF_leakage(self, mix=True, order="lin"):
             verbose=self._params["verbose"],
         )
         fp_best_fit = open(f"{out_path}.json", "w")
-        par_best_fit.dump(fp_best_fit)
+        self.par_best_fit.dump(fp_best_fit)
 
         # Fit separate 1D models, including size
         # MKDEBUG: to remove, is done by fit_any

From a1075852da3f262b0ea96e69125267513bc48f86 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 25 Jan 2024 08:47:33 +0100
Subject: [PATCH 47/97] removed error propa functions, now done with
 uncertainties

---
 shear_psf_leakage/run_scale.py | 45 ----------------------------------
 1 file changed, 45 deletions(-)

diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index b47bc20..f231718 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -1213,51 +1213,6 @@ def save_alpha_matrix(self):
         cs_cat.write_ascii_table_file(cols, names, fname)
 
 
-def var_sum(s2a, s2b):
-    """Var Sum.
-
-    Return variance of a + b.
-
-    """
-    s2 = s2a + s2b
-
-    return s2
-
-def var_mult_div(y, a, b, s2a, s2b):
-    """Var Mult Div.
-
-    Return variance of y = a * b or y = a / b.
-
-    """
-    s2 = y ** 2 * (s2a / a ** 2 + s2b / b ** 2)
-
-    return s2
-
-def var_product(a, b, s2a, s2b):
-    """Var Product.
-
-    Return variance of y = a * b.
-
-    """
-    y = a * b
-
-    s2 = var_mult_div(y, a, b, s2a, s2b)
-
-    return s2
-
-def var_ratio(a, b, s2a, s2b):
-    """Var Ratio.
-
-    Return variance of y = a / b.
-
-    """
-    y = a / b
-
-    s2 = var_mult_div(y, a, b, s2a, s2b)
-
-    return s2
-
-
 def run_leakage_scale(*args):
     """Run Leakage Scale.
 

From 7e533efc210b0ad474825698498872c67b596f20 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 25 Jan 2024 08:47:53 +0100
Subject: [PATCH 48/97] numpy, cs_util 0.0.7 working

---
 poetry.lock    | 2424 ++++++++++++++++++++++++------------------------
 pyproject.toml |    3 +-
 2 files changed, 1217 insertions(+), 1210 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 8c08588..e394b8b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aenum"
@@ -14,24 +14,25 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.0.0"
+version = "4.2.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
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 [[package]]
 name = "appnope"
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+[package.dependencies]
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+numpy = ">=1.22,<2"
 packaging = ">=19.0"
 pyerfa = ">=2.0"
 PyYAML = ">=3.13"
 
 [package.extras]
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+test-all = ["astropy[test]", "coverage[toml]", "ipython (>=4.2)", "objgraph", "sgp4 (>=2.3)", "skyfield (>=1.20)"]
+
+[[package]]
+name = "astropy-iers-data"
+version = "0.2024.1.22.0.30.30"
+description = "IERS Earth Rotation and Leap Second tables for the astropy core package"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+docs = ["pytest"]
+test = ["hypothesis", "pytest", "pytest-remotedata"]
 
 [[package]]
 name = "asttokens"
@@ -232,90 +249,87 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
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 python-versions = ">=3.7"
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 [package.extras]
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+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
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+version = "2.14.0"
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 python-versions = ">=3.7"
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 [package.extras]
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-
 [[package]]
 name = "beautifulsoup4"
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+version = "4.12.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
 files = [
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 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
 [[package]]
 name = "black"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -329,7 +343,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
@@ -376,12 +390,12 @@ test = ["flake8 (>=3.4.1)", "funcsigs (==1.0.2)", "mock (>=2.0.0)", "pytest", "p
 
 [[package]]
 name = "camb"
-version = "1.5.2"
+version = "1.5.4"
 description = "Code for Anisotropies in the Microwave Background"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.6.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -390,17 +404,17 @@ scipy = ">=1.0"
 sympy = ">=1.0"
 
 [package.extras]
-docs = ["sphinx (>=4.0)", "sphinx_rtd_theme (>=1)", "sphinxcontrib-jquery"]
+docs = ["sphinx (>=4.0)", "sphinx_rtd_theme (>=1.0)", "sphinxcontrib-jquery"]
 
 [[package]]
 name = "certifi"
-version = "2023.7.22"
+version = "2023.11.17"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [[package]]
@@ -469,101 +483,101 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.1"
+version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 [[package]]
@@ -582,32 +596,32 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
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 optional = false
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 [package.dependencies]
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+]
+
+[package.dependencies]
+cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
 
 [package.extras]
 docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
-docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
+docstest = ["pyenchant (>=1.6.11)", "readme-renderer", "sphinxcontrib-spelling (>=4.0.1)"]
 nox = ["nox"]
-pep8test = ["black", "check-sdist", "mypy", "ruff"]
+pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "cs-util"
-version = "0.0.6"
+version = "0.0.7"
 description = ""
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 astropy = ">=4.0"
 datetime = ">=4.7"
 matplotlib = ">=3.3"
-numpy = ">=1.19"
+numpy = ">=1.21"
 vos = ">=3.3"
 
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@@ -863,13 +876,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
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+version = "5.4"
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@@ -927,24 +940,24 @@ files = [
 
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 [package.extras]
@@ -952,13 +965,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
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+version = "2.0.1"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
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 [package.extras]
@@ -966,13 +979,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
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+version = "2.19.1"
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 python-versions = "*"
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 [package.extras]
@@ -980,59 +993,59 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
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 python-versions = ">=3.8"
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+    {file = "fonttools-4.47.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4c811d3c73b6abac275babb8aa439206288f56fdb2c6f8835e3d7b70de8937a7"},
+    {file = "fonttools-4.47.2-cp39-cp39-win32.whl", hash = "sha256:5b60e3afa9635e3dfd3ace2757039593e3bd3cf128be0ddb7a1ff4ac45fa5a50"},
+    {file = "fonttools-4.47.2-cp39-cp39-win_amd64.whl", hash = "sha256:7ee48bd9d6b7e8f66866c9090807e3a4a56cf43ffad48962725a190e0dd774c8"},
+    {file = "fonttools-4.47.2-py3-none-any.whl", hash = "sha256:7eb7ad665258fba68fd22228a09f347469d95a97fb88198e133595947a20a184"},
+    {file = "fonttools-4.47.2.tar.gz", hash = "sha256:7df26dd3650e98ca45f1e29883c96a0b9f5bb6af8d632a6a108bc744fa0bd9b3"},
+]
+
+[package.extras]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "scipy"]
+interpolatable = ["munkres", "pycairo", "scipy"]
 lxml = ["lxml (>=4.0,<5)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
@@ -1040,7 +1053,7 @@ repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
 type1 = ["xattr"]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.0.0)"]
+unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
@@ -1077,43 +1090,43 @@ files = [
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "6.8.0"
+version = "7.0.1"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"},
-    {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"},
+    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
+    {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.0"
+version = "6.1.1"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.0-py3-none-any.whl", hash = "sha256:aa50258bbfa56d4e33fbd8aa3ef48ded10d1735f11532b8df95388cc6bdb7e83"},
-    {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"},
+    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
+    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
 ]
 
 [package.dependencies]
@@ -1136,13 +1149,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.26.0"
+version = "6.29.0"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.26.0-py3-none-any.whl", hash = "sha256:3ba3dc97424b87b31bb46586b5167b3161b32d7820b9201a9e698c71e271602c"},
-    {file = "ipykernel-6.26.0.tar.gz", hash = "sha256:553856658eb8430bbe9653ea041a41bff63e9606fc4628873fc92a6cf3abd404"},
+    {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"},
+    {file = "ipykernel-6.29.0.tar.gz", hash = "sha256:b5dd3013cab7b330df712891c96cd1ab868c27a7159e606f762015e9bf8ceb3f"},
 ]
 
 [package.dependencies]
@@ -1156,7 +1169,7 @@ matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=20"
+pyzmq = ">=24"
 tornado = ">=6.1"
 traitlets = ">=5.4.0"
 
@@ -1165,47 +1178,44 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.16.1"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.16.1-py3-none-any.whl", hash = "sha256:0852469d4d579d9cd613c220af7bf0c9cc251813e12be647cb9d463939db9b1e"},
-    {file = "ipython-8.16.1.tar.gz", hash = "sha256:ad52f58fca8f9f848e256c629eff888efc0528c12fe0f8ec14f33205f23ef938"},
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
 [[package]]
 name = "ipython-genutils"
@@ -1274,13 +1284,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
-    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
+    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
+    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
 ]
 
 [package.dependencies]
@@ -1316,13 +1326,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.19.1"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.19.1-py3-none-any.whl", hash = "sha256:cd5f1f9ed9444e554b38ba003af06c0a8c2868131e56bfbef0550fb450c0330e"},
-    {file = "jsonschema-4.19.1.tar.gz", hash = "sha256:ec84cc37cfa703ef7cd4928db24f9cb31428a5d0fa77747b8b51a847458e0bbf"},
+    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
+    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
 ]
 
 [package.dependencies]
@@ -1345,17 +1355,17 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.7.1"
+version = "2023.12.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema_specifications-2023.7.1-py3-none-any.whl", hash = "sha256:05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1"},
-    {file = "jsonschema_specifications-2023.7.1.tar.gz", hash = "sha256:c91a50404e88a1f6ba40636778e2ee08f6e24c5613fe4c53ac24578a5a7f72bb"},
+    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
+    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
 ]
 
 [package.dependencies]
-referencing = ">=0.28.0"
+referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter"
@@ -1379,13 +1389,13 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.5.0"
+version = "8.6.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.5.0-py3-none-any.whl", hash = "sha256:c3877aac7257ec68d79b5c622ce986bd2a992ca42f6ddc9b4dd1da50e89f7028"},
-    {file = "jupyter_client-8.5.0.tar.gz", hash = "sha256:e8754066510ce456358df363f97eae64b50860f30dc1fe8c6771440db3be9a63"},
+    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
+    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
 ]
 
 [package.dependencies]
@@ -1426,13 +1436,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.4.0"
+version = "5.7.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.4.0-py3-none-any.whl", hash = "sha256:66e252f675ac04dcf2feb6ed4afb3cd7f68cf92f483607522dc251f32d471571"},
-    {file = "jupyter_core-5.4.0.tar.gz", hash = "sha256:e4b98344bb94ee2e3e6c4519a97d001656009f9cb2b7f2baf15b3c205770011d"},
+    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
+    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
 ]
 
 [package.dependencies]
@@ -1441,18 +1451,18 @@ pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_
 traitlets = ">=5.3"
 
 [package.extras]
-docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.8.0"
+version = "0.9.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.8.0-py3-none-any.whl", hash = "sha256:81f07375c7673ff298bfb9302b4a981864ec64edaed75ca0fe6f850b9b045525"},
-    {file = "jupyter_events-0.8.0.tar.gz", hash = "sha256:fda08f0defce5e16930542ce60634ba48e010830d50073c3dfd235759cee77bf"},
+    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
+    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
 ]
 
 [package.dependencies]
@@ -1471,13 +1481,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.0"
+version = "2.2.2"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "jupyter_lsp-2.2.0-py3-none-any.whl", hash = "sha256:9e06b8b4f7dd50300b70dd1a78c0c3b0c3d8fa68e0f2d8a5d1fbab62072aca3f"},
+    {file = "jupyter-lsp-2.2.2.tar.gz", hash = "sha256:256d24620542ae4bba04a50fc1f6ffe208093a07d8e697fea0a8d1b8ca1b7e5b"},
+    {file = "jupyter_lsp-2.2.2-py3-none-any.whl", hash = "sha256:3b95229e4168355a8c91928057c1621ac3510ba98b2a925e82ebd77f078b1aa5"},
 ]
 
 [package.dependencies]
@@ -1486,13 +1496,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.9.1"
+version = "2.12.5"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.9.1-py3-none-any.whl", hash = "sha256:21ad1a3d455d5a79ce4bef5201925cd17510c17898cf9d54e3ccfb6b12734948"},
-    {file = "jupyter_server-2.9.1.tar.gz", hash = "sha256:9ba71be4b9c16e479e4c50c929f8ac4b1015baf90237a08681397a98c76c7e5e"},
+    {file = "jupyter_server-2.12.5-py3-none-any.whl", hash = "sha256:184a0f82809a8522777cfb6b760ab6f4b1bb398664c5860a27cec696cb884923"},
+    {file = "jupyter_server-2.12.5.tar.gz", hash = "sha256:0edb626c94baa22809be1323f9770cf1c00a952b17097592e40d03e6a3951689"},
 ]
 
 [package.dependencies]
@@ -1501,7 +1511,7 @@ argon2-cffi = "*"
 jinja2 = "*"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
-jupyter-events = ">=0.6.0"
+jupyter-events = ">=0.9.0"
 jupyter-server-terminals = "*"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
@@ -1522,13 +1532,13 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-sc
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.4.4"
+version = "0.5.2"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.4.4-py3-none-any.whl", hash = "sha256:75779164661cec02a8758a5311e18bb8eb70c4e86c6b699403100f1585a12a36"},
-    {file = "jupyter_server_terminals-0.4.4.tar.gz", hash = "sha256:57ab779797c25a7ba68e97bcfb5d7740f2b5e8a83b5e8102b10438041a7eac5d"},
+    {file = "jupyter_server_terminals-0.5.2-py3-none-any.whl", hash = "sha256:1b80c12765da979513c42c90215481bbc39bd8ae7c0350b4f85bc3eb58d0fa80"},
+    {file = "jupyter_server_terminals-0.5.2.tar.gz", hash = "sha256:396b5ccc0881e550bf0ee7012c6ef1b53edbde69e67cab1d56e89711b46052e8"},
 ]
 
 [package.dependencies]
@@ -1536,18 +1546,18 @@ pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""}
 terminado = ">=0.8.3"
 
 [package.extras]
-docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
-test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
+docs = ["jinja2", "jupyter-server", "mistune (<4.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
+test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
 
 [[package]]
 name = "jupyterlab"
-version = "4.0.7"
+version = "4.0.11"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.0.7-py3-none-any.whl", hash = "sha256:08683045117cc495531fdb39c22ababb9aaac6977a45e67cfad20046564c9c7c"},
-    {file = "jupyterlab-4.0.7.tar.gz", hash = "sha256:48792efd9f962b2bcda1f87d72168ff122c288b1d97d32109e4a11b33dc862be"},
+    {file = "jupyterlab-4.0.11-py3-none-any.whl", hash = "sha256:536bf0e78723153a5016ca7efb88ed0ecd7070d3f1555d5b0e2770658f900a3c"},
+    {file = "jupyterlab-4.0.11.tar.gz", hash = "sha256:d1aec24712566bc25a36229788242778e498ca4088028e2f9aa156b8b7fdc8fc"},
 ]
 
 [package.dependencies]
@@ -1566,31 +1576,31 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["black[jupyter] (==23.7.0)", "build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.0.286)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.1.6)"]
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
 docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 
 [[package]]
 name = "jupyterlab-pygments"
-version = "0.2.2"
+version = "0.3.0"
 description = "Pygments theme using JupyterLab CSS variables"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "jupyterlab_pygments-0.2.2.tar.gz", hash = "sha256:7405d7fde60819d905a9fa8ce89e4cd830e318cdad22a0030f7a901da705585d"},
+    {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"},
+    {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"},
 ]
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.0"
+version = "2.25.2"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.0-py3-none-any.whl", hash = "sha256:c9f67a98b295c5dee87f41551b0558374e45d449f3edca153dd722140630dcb2"},
-    {file = "jupyterlab_server-2.25.0.tar.gz", hash = "sha256:77c2f1f282d610f95e496e20d5bf1d2a7706826dfb7b18f3378ae2870d272fb7"},
+    {file = "jupyterlab_server-2.25.2-py3-none-any.whl", hash = "sha256:5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf"},
+    {file = "jupyterlab_server-2.25.2.tar.gz", hash = "sha256:bd0ec7a99ebcedc8bcff939ef86e52c378e44c2707e053fcd81d046ce979ee63"},
 ]
 
 [package.dependencies]
@@ -1606,7 +1616,7 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
@@ -1621,25 +1631,32 @@ files = [
 
 [[package]]
 name = "jupytext"
-version = "1.15.2"
+version = "1.16.1"
 description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
 optional = false
-python-versions = "~=3.6"
+python-versions = ">=3.8"
 files = [
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+    {file = "jupytext-1.16.1-py3-none-any.whl", hash = "sha256:796ec4f68ada663569e5d38d4ef03738a01284bfe21c943c485bc36433898bd0"},
+    {file = "jupytext-1.16.1.tar.gz", hash = "sha256:68c7b68685e870e80e60fda8286fbd6269e9c74dc1df4316df6fe46eabc94c99"},
 ]
 
 [package.dependencies]
-markdown-it-py = ">=1.0.0"
+markdown-it-py = ">=1.0"
 mdit-py-plugins = "*"
 nbformat = "*"
+packaging = "*"
 pyyaml = "*"
 toml = "*"
 
 [package.extras]
-rst2md = ["sphinx-gallery (>=0.7.0,<0.8.0)"]
-toml = ["toml"]
+dev = ["jupytext[test-cov,test-external]"]
+docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
+test = ["pytest", "pytest-randomly", "pytest-xdist"]
+test-cov = ["jupytext[test-integration]", "pytest-cov (>=2.6.1)"]
+test-external = ["autopep8", "black", "flake8", "gitpython", "isort", "jupyter-fs (<0.4.0)", "jupytext[test-integration]", "pre-commit", "sphinx-gallery (<0.8)"]
+test-functional = ["jupytext[test]"]
+test-integration = ["ipykernel", "jupyter-server (!=2.11)", "jupytext[test-functional]", "nbconvert"]
+test-ui = ["calysto-bash"]
 
 [[package]]
 name = "kiwisolver"
@@ -1810,110 +1827,96 @@ numpy = "*"
 
 [[package]]
 name = "lxml"
-version = "4.9.3"
+version = "5.1.0"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
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 [package.extras]
 cssselect = ["cssselect (>=0.7)"]
 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=0.29.35)"]
+source = ["Cython (>=3.0.7)"]
 
 [[package]]
 name = "markdown-it-py"
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 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
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 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
-setuptools_scm = ">=7"
 
 [[package]]
 name = "matplotlib-inline"
@@ -2133,13 +2145,13 @@ files = [
 
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 [[package]]
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 qtpng = ["pyqtwebengine (>=5.15)"]
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 [[package]]
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@@ -2373,41 +2393,41 @@ numpy = [
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
-tzdata = ">=2022.1"
+tzdata = ">=2022.7"
 
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 [package.extras]
 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "3.11.0"
+version = "4.1.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2545,13 +2572,13 @@ test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-co
 
 [[package]]
 name = "pluggy"
-version = "1.3.0"
+version = "1.4.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2560,13 +2587,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.17.1"
+version = "0.19.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2574,13 +2601,13 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.39"
+version = "3.0.43"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2588,27 +2615,27 @@ wcwidth = "*"
 
 [[package]]
 name = "psutil"
-version = "5.9.6"
+version = "5.9.8"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 files = [
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 ]
 
 [[package]]
@@ -3453,13 +3459,13 @@ mpmath = ">=0.19"
 
 [[package]]
 name = "terminado"
-version = "0.17.1"
+version = "0.18.0"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "terminado-0.17.1-py3-none-any.whl", hash = "sha256:8650d44334eba354dd591129ca3124a6ba42c3d5b70df5051b6921d506fdaeae"},
-    {file = "terminado-0.17.1.tar.gz", hash = "sha256:6ccbbcd3a4f8a25a5ec04991f39a0b8db52dfcd487ea0e578d977e6752380333"},
+    {file = "terminado-0.18.0-py3-none-any.whl", hash = "sha256:87b0d96642d0fe5f5abd7783857b9cab167f221a39ff98e3b9619a788a3c0f2e"},
+    {file = "terminado-0.18.0.tar.gz", hash = "sha256:1ea08a89b835dd1b8c0c900d92848147cef2537243361b2e3f4dc15df9b6fded"},
 ]
 
 [package.dependencies]
@@ -3470,6 +3476,7 @@ tornado = ">=6.1.0"
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
+typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "tinycss2"
@@ -3513,38 +3520,38 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.3.3"
+version = "6.4"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d"},
-    {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a"},
-    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1bd19ca6c16882e4d37368e0152f99c099bad93e0950ce55e71daed74045908f"},
-    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ac51f42808cca9b3613f51ffe2a965c8525cb1b00b7b2d56828b8045354f76a"},
-    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71a8db65160a3c55d61839b7302a9a400074c9c753040455494e2af74e2501f2"},
-    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0"},
-    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16"},
-    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17"},
-    {file = "tornado-6.3.3-cp38-abi3-win32.whl", hash = "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3"},
-    {file = "tornado-6.3.3-cp38-abi3-win_amd64.whl", hash = "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5"},
-    {file = "tornado-6.3.3.tar.gz", hash = "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"},
+    {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
+    {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
+    {file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"},
+    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
+    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
+    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
+    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
+    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
+    {file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"},
+    {file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
+    {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
 ]
 
 [[package]]
 name = "traitlets"
-version = "5.12.0"
+version = "5.14.1"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.12.0-py3-none-any.whl", hash = "sha256:81539f07f7aebcde2e4b5ab76727f53eabf18ad155c6ed7979a681411602fa47"},
-    {file = "traitlets-5.12.0.tar.gz", hash = "sha256:833273bf645d8ce31dcb613c56999e2e055b1ffe6d09168a164bcd91c36d5d35"},
+    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
+    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.6.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "treecorr"
@@ -3589,35 +3596,35 @@ pyyaml = "*"
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.14"
+version = "2.8.19.20240106"
 description = "Typing stubs for python-dateutil"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
-    {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
+    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
+    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.8.0"
+version = "4.9.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
-    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
+    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
+    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
 ]
 
 [[package]]
 name = "tzdata"
-version = "2023.3"
+version = "2023.4"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
-    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
+    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
+    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
 ]
 
 [[package]]
@@ -3656,18 +3663,17 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.0.7"
+version = "2.1.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
-    {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
+    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
+    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
 ]
 
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
@@ -3692,13 +3698,13 @@ test = ["flake8 (>=3.4.1)", "mock (>=2.0.0)", "pytest (>=4.6)", "pytest-cov (>=2
 
 [[package]]
 name = "wcwidth"
-version = "0.2.8"
+version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
 files = [
-    {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"},
-    {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"},
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
 ]
 
 [[package]]
@@ -3729,13 +3735,13 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.6.4"
+version = "1.7.0"
 description = "WebSocket client for Python with low level API options"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
-    {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"},
+    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
+    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
 ]
 
 [package.extras]
@@ -3825,4 +3831,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "54a0b3762074daae2858f7acddf0df1388f212aacca774aaf7f948110e2d2c3f"
+content-hash = "8023ce2de2251edb272c5bdb814d245c0a064aac7c73de1443305e822da2c7c6"
diff --git a/pyproject.toml b/pyproject.toml
index 70f0025..e1ca34e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,13 +24,14 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = "0.0.6"
+cs-util = "0.0.7"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"
 jupyterlab = "^4.0.7"
 tornado = "^6.3.3"
 lenspack = "^1.0.0"
+#numpy = "1.26.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From a5d720a60410051aefb7e80dba5560030483217f Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 25 Jan 2024 08:48:18 +0100
Subject: [PATCH 49/97] new nb/py script autocorr psf

---
 notebooks/leakage_autorr_psf.py | 197 ++++++++++++++++++++++++++++++++
 1 file changed, 197 insertions(+)
 create mode 100644 notebooks/leakage_autorr_psf.py

diff --git a/notebooks/leakage_autorr_psf.py b/notebooks/leakage_autorr_psf.py
new file mode 100644
index 0000000..94e1041
--- /dev/null
+++ b/notebooks/leakage_autorr_psf.py
@@ -0,0 +1,197 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:light
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# # PSF leakage: PSF auto-correlation contributions
+#
+# Martin Kilbinger <martin.kilbinger@cea.fr>
+
+# +
+import os
+import matplotlib.pylab as plt
+
+from cs_util import canfar
+#from cs_util import plots
+from uncertainties import ufloat
+from uncertainties import unumpy                                                 
+
+import shear_psf_leakage.run_object as run_object
+import shear_psf_leakage.run_scale as run_scale
+from shear_psf_leakage import leakage
+# -
+
+# ## Set input parameters
+
+params_in = {}
+
+# ### Paths
+
+# +
+# Input galaxy shear catalogue
+params_in["input_path_shear"] = "unions_shapepipe_extended_2022_W3_v1.0.3.fits"
+
+# Output directory
+params_in["output_dir"] = "leakage_PSF_autocorr"
+# -
+
+# ### Job control
+
+# Compute (spin-preserving) PSF ellipticity leakage
+params_in["PSF_leakage"] = True
+
+# ### Other parameters
+
+# +
+# PSF ellipticity column names
+params_in["e1_PSF_col"] = "e1_PSF"
+params_in["e2_PSF_col"] = "e2_PSF"
+
+# Set verbose output
+params_in["verbose"] = True
+# -
+
+# ### Retrieve test catalogue from VOspace if not yet downloade
+
+vos_dir = "vos:cfis/XXXX/"
+canfar.download(
+    f"{vos_dir}/{params_in['input_path_shear']}",
+    params_in["input_path_shear"],
+    verbose=params_in["verbose"],
+)
+
+# ## Compute leakage
+
+# Create leakage instance
+obj_object = run_object.LeakageObject()
+
+# Set instance parameters, copy from above
+for key in params_in:
+    obj_object._params[key] = params_in[key]
+
+# Run commands as given in LeakageObject.run()
+
+# +
+# Check parameter validity
+obj_object.check_params()
+
+# Update parameters (here: strings to list)
+obj_object.update_params()
+
+# Prepare output directory
+obj_object.prepare_output()
+# -
+
+# Read input catalogue
+obj_object.read_data()
+
+# Object-by-object spin-consistent PSF leakage
+mix = True
+order = "lin"
+obj_object.PSF_leakage(mix=mix, order=order)
+
+p_dp = {}                                                                    
+for p in obj_object.par_best_fit:                                                         
+    p_dp[p] = ufloat(obj_object.par_best_fit[p].value, obj_object.par_best_fit[p].stderr)
+print(p_dp)
+
+s_ds = leakage.param_order2spin(p_dp, order, mix)
+print(s_ds)
+
+# Get spin elements
+x0 = s_ds["x0"]                                                                     
+x4 = s_ds["x4"]
+y4 = s_ds["y4"]
+y0 = s_ds["y0"]
+
+
+# Create scale-dependent leakage instance
+obj_scale = run_scale.LeakageScale()
+
+# Set instance parameters, copy from above                                   
+for key in params_in:                                                        
+    obj_scale._params[key] = params_in[key]
+
+
+obj_scale._params["input_path_PSF"] = "unions_shapepipe_psf_2022_W3_v1.0.3.fits"
+obj_scale._params["dndz_path"] = "dndz_SP_A.txt"
+
+# ### Run                                                                        
+
+# Check parameter validity                                                       
+obj_scale.check_params()                                                               
+
+# Prepare output directory and stats file                                        
+obj_scale.prepare_output()                                                             
+
+# Prepare input                                                                  
+obj_scale.read_data()                                                                  
+
+# #### Compute correlation function and alpha matrices                           
+# The following command calls `treecorr` to compute auto- and cross-correlation functions.
+# This can take a few minutes.                                                   
+
+obj_scale.compute_corr_gp_pp_alpha_matrix()                                            
+
+obj_scale.alpha_matrix()                                                               
+
+#### For comparison: scalar alpha leakage                                        
+obj_scale.compute_corr_gp_pp_alpha()                                                   
+obj_scale.do_alpha()
+
+
+# Plot terms
+theta = obj_scale.get_theta()
+
+term_1 = (x0 ** 2 + x4 **2) * (obj_scale.xi_pp_m[0][0] + obj_scale.xi_pp_m[1][1])
+term_2 = 2 * x0 * x4 * (obj_scale.xi_pp_m[0][0] - obj_scale.xi_pp_m[1][1])
+term_3a = x0 * (
+    obj_scale.xi_pp_m[0][0] + 2 * obj_scale.xi_pp_m[0][1] + obj_scale.xi_pp_m[1][1]
+)
+term_3b = x4 * (obj_scale.xi_pp_m[0][0] - obj_scale.xi_pp_m[1][1])
+term_3c = 2 * y4 * obj_scale.xi_pp_m[0][1] 
+term_3 = y4 * (term_3a + term_3b + term_3c)
+
+term_1
+
+# +
+y = [
+    unumpy.nominal_values(term_1), 
+    unumpy.nominal_values(term_2),
+    unumpy.nominal_values(term_3),
+]
+dy = [
+    unumpy.std_devs(term_1), 
+    unumpy.std_devs(term_2),
+    unumpy.std_devs(term_3),
+]
+x = [theta] * 3
+title = "PSF auto-correlation additive terms"
+xlabel = r"$\theta$ [arcmin]" 
+ylabel = "terms"
+out_path = f"{obj_scale._params['output_dir']}/auto_corr_terms.png"                                  
+labels = ["t1" , "t2", "t3"]
+
+plots.plot_data_1d(
+    x,
+    y,
+    dy,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+)
+# -
+
+

From b9985e0b828644be6a46da28aed7e47ab8729b09 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Fri, 26 Jan 2024 16:19:35 +0100
Subject: [PATCH 50/97] leakage autocorr PSF; cs_util local

---
 notebooks/leakage_autorr_psf.py | 197 --------------------------------
 poetry.lock                     |  37 ++++--
 pyproject.toml                  |   5 +-
 3 files changed, 33 insertions(+), 206 deletions(-)
 delete mode 100644 notebooks/leakage_autorr_psf.py

diff --git a/notebooks/leakage_autorr_psf.py b/notebooks/leakage_autorr_psf.py
deleted file mode 100644
index 94e1041..0000000
--- a/notebooks/leakage_autorr_psf.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:light
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.15.2
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-# # PSF leakage: PSF auto-correlation contributions
-#
-# Martin Kilbinger <martin.kilbinger@cea.fr>
-
-# +
-import os
-import matplotlib.pylab as plt
-
-from cs_util import canfar
-#from cs_util import plots
-from uncertainties import ufloat
-from uncertainties import unumpy                                                 
-
-import shear_psf_leakage.run_object as run_object
-import shear_psf_leakage.run_scale as run_scale
-from shear_psf_leakage import leakage
-# -
-
-# ## Set input parameters
-
-params_in = {}
-
-# ### Paths
-
-# +
-# Input galaxy shear catalogue
-params_in["input_path_shear"] = "unions_shapepipe_extended_2022_W3_v1.0.3.fits"
-
-# Output directory
-params_in["output_dir"] = "leakage_PSF_autocorr"
-# -
-
-# ### Job control
-
-# Compute (spin-preserving) PSF ellipticity leakage
-params_in["PSF_leakage"] = True
-
-# ### Other parameters
-
-# +
-# PSF ellipticity column names
-params_in["e1_PSF_col"] = "e1_PSF"
-params_in["e2_PSF_col"] = "e2_PSF"
-
-# Set verbose output
-params_in["verbose"] = True
-# -
-
-# ### Retrieve test catalogue from VOspace if not yet downloade
-
-vos_dir = "vos:cfis/XXXX/"
-canfar.download(
-    f"{vos_dir}/{params_in['input_path_shear']}",
-    params_in["input_path_shear"],
-    verbose=params_in["verbose"],
-)
-
-# ## Compute leakage
-
-# Create leakage instance
-obj_object = run_object.LeakageObject()
-
-# Set instance parameters, copy from above
-for key in params_in:
-    obj_object._params[key] = params_in[key]
-
-# Run commands as given in LeakageObject.run()
-
-# +
-# Check parameter validity
-obj_object.check_params()
-
-# Update parameters (here: strings to list)
-obj_object.update_params()
-
-# Prepare output directory
-obj_object.prepare_output()
-# -
-
-# Read input catalogue
-obj_object.read_data()
-
-# Object-by-object spin-consistent PSF leakage
-mix = True
-order = "lin"
-obj_object.PSF_leakage(mix=mix, order=order)
-
-p_dp = {}                                                                    
-for p in obj_object.par_best_fit:                                                         
-    p_dp[p] = ufloat(obj_object.par_best_fit[p].value, obj_object.par_best_fit[p].stderr)
-print(p_dp)
-
-s_ds = leakage.param_order2spin(p_dp, order, mix)
-print(s_ds)
-
-# Get spin elements
-x0 = s_ds["x0"]                                                                     
-x4 = s_ds["x4"]
-y4 = s_ds["y4"]
-y0 = s_ds["y0"]
-
-
-# Create scale-dependent leakage instance
-obj_scale = run_scale.LeakageScale()
-
-# Set instance parameters, copy from above                                   
-for key in params_in:                                                        
-    obj_scale._params[key] = params_in[key]
-
-
-obj_scale._params["input_path_PSF"] = "unions_shapepipe_psf_2022_W3_v1.0.3.fits"
-obj_scale._params["dndz_path"] = "dndz_SP_A.txt"
-
-# ### Run                                                                        
-
-# Check parameter validity                                                       
-obj_scale.check_params()                                                               
-
-# Prepare output directory and stats file                                        
-obj_scale.prepare_output()                                                             
-
-# Prepare input                                                                  
-obj_scale.read_data()                                                                  
-
-# #### Compute correlation function and alpha matrices                           
-# The following command calls `treecorr` to compute auto- and cross-correlation functions.
-# This can take a few minutes.                                                   
-
-obj_scale.compute_corr_gp_pp_alpha_matrix()                                            
-
-obj_scale.alpha_matrix()                                                               
-
-#### For comparison: scalar alpha leakage                                        
-obj_scale.compute_corr_gp_pp_alpha()                                                   
-obj_scale.do_alpha()
-
-
-# Plot terms
-theta = obj_scale.get_theta()
-
-term_1 = (x0 ** 2 + x4 **2) * (obj_scale.xi_pp_m[0][0] + obj_scale.xi_pp_m[1][1])
-term_2 = 2 * x0 * x4 * (obj_scale.xi_pp_m[0][0] - obj_scale.xi_pp_m[1][1])
-term_3a = x0 * (
-    obj_scale.xi_pp_m[0][0] + 2 * obj_scale.xi_pp_m[0][1] + obj_scale.xi_pp_m[1][1]
-)
-term_3b = x4 * (obj_scale.xi_pp_m[0][0] - obj_scale.xi_pp_m[1][1])
-term_3c = 2 * y4 * obj_scale.xi_pp_m[0][1] 
-term_3 = y4 * (term_3a + term_3b + term_3c)
-
-term_1
-
-# +
-y = [
-    unumpy.nominal_values(term_1), 
-    unumpy.nominal_values(term_2),
-    unumpy.nominal_values(term_3),
-]
-dy = [
-    unumpy.std_devs(term_1), 
-    unumpy.std_devs(term_2),
-    unumpy.std_devs(term_3),
-]
-x = [theta] * 3
-title = "PSF auto-correlation additive terms"
-xlabel = r"$\theta$ [arcmin]" 
-ylabel = "terms"
-out_path = f"{obj_scale._params['output_dir']}/auto_corr_terms.png"                                  
-labels = ["t1" , "t2", "t3"]
-
-plots.plot_data_1d(
-    x,
-    y,
-    dy,
-    title,
-    xlabel,
-    ylabel,
-    out_path,
-    labels=labels,
-)
-# -
-
-
diff --git a/poetry.lock b/poetry.lock
index e394b8b..abfc6f8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -836,15 +836,13 @@ test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-co
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
-name = "cs-util"
+name = "cs_util"
 version = "0.0.7"
 description = ""
 optional = false
 python-versions = ">=3.7"
-files = [
-    {file = "cs_util-0.0.7-py3-none-any.whl", hash = "sha256:b02de3ff13b1d5da66fd6dab72b813c5f8ecfaf418c4ac0e8827d1f67bcd893d"},
-    {file = "cs_util-0.0.7.tar.gz", hash = "sha256:19975aded1e41b93acee125d3b0f6ce2fe70a6e1a7eda293ec954f9e458e1348"},
-]
+files = []
+develop = false
 
 [package.dependencies]
 astropy = ">=4.0"
@@ -854,11 +852,15 @@ numpy = ">=1.21"
 vos = ">=3.3"
 
 [package.extras]
-dev = ["cs-util[lint,release,test]"]
+dev = ["cs_util[lint,release,test]"]
 lint = ["black", "jinja2 (==3.0)"]
 release = ["build", "sdist", "twine"]
 test = ["pytest", "pytest-black", "pytest-cov", "pytest-pydocstyle"]
 
+[package.source]
+type = "directory"
+url = "../cs_util"
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -1077,6 +1079,27 @@ files = [
     {file = "future-0.18.3.tar.gz", hash = "sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307"},
 ]
 
+[[package]]
+name = "getdist"
+version = "1.4.7"
+description = "GetDist Monte Carlo sample analysis, plotting and GUI"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "getdist-1.4.7.tar.gz", hash = "sha256:c54c1a2aad2d7111e03775d0e9a2e8446c85e595eb8970ce6df0f1a574cd9031"},
+]
+
+[package.dependencies]
+matplotlib = ">=2.2.0,<3.5.0 || >3.5.0"
+numpy = ">=1.17.0"
+packaging = "*"
+PyYAML = ">=5.1"
+scipy = ">=1.5"
+
+[package.extras]
+docs = ["sphinx", "sphinx_rtd_theme (>=1.0)", "sphinxcontrib-jquery"]
+gui = ["PySide6 (>=6.1)"]
+
 [[package]]
 name = "html2text"
 version = "2020.1.16"
@@ -3831,4 +3854,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "8023ce2de2251edb272c5bdb814d245c0a064aac7c73de1443305e822da2c7c6"
+content-hash = "820dc52af2f578ca71fe2c7500d527f26683e1ec551fc66c03daf97ac7a2cec5"
diff --git a/pyproject.toml b/pyproject.toml
index e1ca34e..a69635e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,6 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.12"
-cmake = "^3.27.2"
 swig = "^4.1.1"
 pyyaml = "^6.0.1"
 lmfit = "^1.2.2"
@@ -24,7 +23,7 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = "0.0.7"
+cs-util = {path = "../cs_util"}
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"
@@ -32,6 +31,8 @@ jupyterlab = "^4.0.7"
 tornado = "^6.3.3"
 lenspack = "^1.0.0"
 #numpy = "1.26.1"
+getdist = "^1.4.7"
+cmake = "^3.28.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 3dd2d61a99b52c3737da04d173f2f78b3a79d39c Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sun, 25 Feb 2024 10:02:22 +0100
Subject: [PATCH 51/97] Added HDU number to arg for rho/tau stats

---
 shear_psf_leakage/rho_tau_stat.py | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 27854c1..207ad7e 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -232,7 +232,7 @@ def set_params(self, params=None, output=None):
         if output is not None:
             self._output = output
 
-    def read_shear_cat(self, path_gal, path_psf):
+    def read_shear_cat(self, path_gal, path_psf, hdu=1):
         """
         read_shear_cat
 
@@ -245,9 +245,9 @@ def read_shear_cat(self, path_gal, path_psf):
         """
         assert ((path_gal is not None) or (path_psf is not None)), ("Please specify a path for the shear catalog you want to read.")
         if path_gal is not None:
-            self.dat_shear = fits.getdata(path_gal)
+            self.dat_shear = fits.getdata(path_gal, ext=hdu)
         if path_psf is not None:
-            self.dat_psf = fits.getdata(path_psf)
+            self.dat_psf = fits.getdata(path_psf, ext=hdu)
 
     def get_cat_fields(self, cat_type, square_size=False):
         """
@@ -463,7 +463,7 @@ def __init__(
 
         self.verbose = verbose
 
-    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False, mask=False):
+    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False, mask=False, hdu=1):
         """
         build_cat_to_compute_rho
 
@@ -480,9 +480,12 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
 
         mask : bool
             If True, use PSF and star flags to mask the data. (Default: False)
+
+        hdu : int, optional
+            HDU number of input FITS file, default is 1
         """
 
-        self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star)
+        self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star, hdu=hdu)
 
         if self.verbose:
             print("Building catalogs...")
@@ -693,7 +696,7 @@ def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None
 
         self.verbose = verbose
 
-    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False, mask=False):
+    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False, mask=False, hdu=1):
         """
         build_cat_to_compute_tau
 
@@ -713,10 +716,13 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
 
         mask : bool
             If True, use PSF and star flags to mask the data. (Default: False)
+
+        hdu : int, optional
+            HDU number of input FITS file, default is 1
         """
 
         if cat_type=="psf":
-            self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat)
+            self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat, hdu=hdu)
 
             if self.verbose:
                 print("Building catalogs...")
@@ -727,7 +733,7 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
             self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
 
         else:
-            self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None)
+            self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None, hdu=hdu)
 
             if self.verbose:
                 print("Building catalog...")

From f31ec3dfc32218abd72061ac15199087c78c8c53 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sun, 25 Feb 2024 10:02:57 +0100
Subject: [PATCH 52/97] updated library versions

---
 pyproject.toml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a69635e..009dca7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,16 +23,15 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = {path = "../cs_util"}
+#cs_util = {path = "../cs_util"}
+cs_util = "0.0.7"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"
 jupyterlab = "^4.0.7"
 tornado = "^6.3.3"
 lenspack = "^1.0.0"
-#numpy = "1.26.1"
 getdist = "^1.4.7"
-cmake = "^3.28.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 6cb6d079cad158811dd07ed60dec752c84f5712c Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Fri, 8 Mar 2024 13:34:00 +0100
Subject: [PATCH 53/97] Add Least Square estimate of the parameters alpha, beta
 and eta in the rho and tau stats analysis.

---
 shear_psf_leakage/rho_tau_stat.py | 241 +++++++++++++++++++++++++++---
 tests/test_rho_tau_stats.py       |  30 ++--
 2 files changed, 241 insertions(+), 30 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 886ffca..0372de9 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -4,6 +4,8 @@
 Author: Sacha Guerrini
 """
 
+from tqdm import tqdm
+
 import numpy as np
 import matplotlib.pyplot as plt
 from astropy.io import fits
@@ -232,7 +234,7 @@ def set_params(self, params=None, output=None):
         if output is not None:
             self._output = output
 
-    def read_shear_cat(self, path_gal, path_psf):
+    def read_shear_cat(self, path_gal, path_psf, hdu=1):
         """
         read_shear_cat
 
@@ -245,9 +247,9 @@ def read_shear_cat(self, path_gal, path_psf):
         """
         assert ((path_gal is not None) or (path_psf is not None)), ("Please specify a path for the shear catalog you want to read.")
         if path_gal is not None:
-            self.dat_shear = fits.getdata(path_gal)
+            self.dat_shear = fits.getdata(path_gal, ext=hdu)
         if path_psf is not None:
-            self.dat_psf = fits.getdata(path_psf)
+            self.dat_psf = fits.getdata(path_psf, ext=hdu)
 
     def get_cat_fields(self, cat_type, square_size=False):
         """
@@ -307,22 +309,22 @@ def get_cat_fields(self, cat_type, square_size=False):
             weights = None
 
             if cat_type=="psf":
-                g1 = self.dat_psf[self._params["e1_PSF_col"]] - self.dat_psf[self._params["e1_PSF_col"]].mean()
-                g2 = self.dat_psf[self._params["e2_PSF_col"]] - self.dat_psf[self._params["e2_PSF_col"]].mean()
+                g1 = self.dat_psf[self._params["e1_PSF_col"]]# - self.dat_psf[self._params["e1_PSF_col"]].mean()
+                g2 = self.dat_psf[self._params["e2_PSF_col"]]# - self.dat_psf[self._params["e2_PSF_col"]].mean()
 
             elif cat_type=="psf_error":
                 g1 = (self.dat_psf[self._params["e1_star_col"]] - self.dat_psf[self._params["e1_PSF_col"]])
-                g1 -= g1.mean()
+                #g1 -= g1.mean()
                 g2 = (self.dat_psf[self._params["e2_star_col"]] - self.dat_psf[self._params["e2_PSF_col"]])
-                g2 -= g2.mean()
+                #g2 -= g2.mean()
 
             else:
                 size_star = self.dat_psf[self._params["star_size"]]**2 if square_size else  self.dat_psf[self._params["star_size"]]
                 size_psf = self.dat_psf[self._params["PSF_size"]]**2 if square_size else  self.dat_psf[self._params["PSF_size"]]
                 g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_star
-                g1 -= g1.mean()
+                #g1 -= g1.mean()
                 g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_star
-                g2 -= g2.mean()
+                #g2 -= g2.mean()
 
         return ra, dec, g1, g2, weights
     
@@ -463,7 +465,7 @@ def __init__(
 
         self.verbose = verbose
 
-    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False, mask=False):
+    def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=False, mask=False, hdu=1):
         """
         build_cat_to_compute_rho
 
@@ -482,7 +484,7 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
             If True, use PSF and star flags to mask the data. (Default: False)
         """
 
-        self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star)
+        self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star, hdu=hdu)
 
         if self.verbose:
             print("Building catalogs...")
@@ -496,7 +498,7 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
             print("Catalogs successfully built...")
             self.catalogs.show_catalogs()
 
-    def compute_rho_stats(self, catalog_id, filename):
+    def compute_rho_stats(self, catalog_id, filename, save_cov=False, func=None, var_method='jackknife'):
         """
         compute_rho_stats
 
@@ -585,6 +587,14 @@ def compute_rho_stats(self, catalog_id, filename):
         if self.verbose:
             print("Done...")
 
+        if save_cov:
+            if self.verbose:
+                print("Computing the covariance...")
+
+            cov = treecorr.estimate_multi_cov([rho_0, rho_1, rho_2, rho_3, rho_4, rho_5], var_method, func)
+
+            np.save(self.catalogs._output+'/'+'cov_rho_'+catalog_id, cov)
+
         self.save_rho_stats(filename) #A bit dirty just because of consistency of the datatype :/
         self.load_rho_stats(filename)
 
@@ -818,7 +828,7 @@ def compute_tau_stats(self, catalog_id, filename, save_cov=False, func=None, var
 
             cov = treecorr.estimate_multi_cov([tau_0, tau_2, tau_5], var_method, func)
 
-            np.save(self.catalogs._output+'/'+'cov_'+catalog_id, cov)
+            np.save(self.catalogs._output+'/'+'cov_tau_'+catalog_id, cov)
 
         self.save_tau_stats(filename) #A bit dirty just because of consistency of the datatype :/
         self.load_tau_stats(filename)
@@ -919,7 +929,8 @@ def __init__(self, rho_stat_handler, tau_stat_handler, data_directory):
         self.rho_stat_handler = rho_stat_handler
         self.tau_stat_handler = tau_stat_handler
         print("Class created. Don't forget to load your rho and tau statistics and define your prior and your likelihood.")
-        self.cov = None
+        self.cov_rho = None
+        self.cov_tau = None
         self.init_log_prior()
 
         def log_likelihood(theta, y, inv_cov):
@@ -975,7 +986,7 @@ def load_tau_stat(self, filename):
         """
         self.tau_stat_handler.load_tau_stats(filename)
 
-    def load_covariance(self, filename):
+    def load_covariance(self, filename, cov_type='rho'):
         """
         load_covariance
 
@@ -986,7 +997,10 @@ def load_covariance(self, filename):
         filename : str
             Name of the file containing the covariance matrix.
         """
-        self.cov = np.load(self.data_directory+'/'+filename)
+        if cov_type=='rho':
+            self.cov_rho = np.load(self.data_directory+'/'+filename)
+        else:
+            self.cov_tau = np.load(self.data_directory+'/'+filename)
 
     def init_log_prior(self, low_alpha=-2.0, high_alpha=2.0, low_beta=-10.0, high_beta=10.0, low_eta=-20.0, high_eta=20.0):
         """
@@ -1147,7 +1161,7 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
         if apply_debias:
             inv_cov = (npatch - output.shape[0] - 2)/(npatch-1)*inv_cov
 
-        init = init + 1e-3*np.random.randn(nwalkers, ndim)
+        init = init + 1e-1*np.random.randn(nwalkers, ndim)
 
         sampler = emcee.EnsembleSampler(
             nwalkers, ndim, self.log_probability, args=(output, inv_cov)
@@ -1213,6 +1227,24 @@ def get_sample_path(self, catalog_id):
         """
         file_path = f"{self.rho_stat_handler.catalogs._output}/samples_{catalog_id}.npy"
         return file_path
+    
+    def get_params_path(self, catalog_id):
+        """Get Params Path.
+
+        Return file path of parameters.
+
+        Parameters
+        ----------
+        catalog_id : str
+            ID of the catalog used for this run
+
+        Returns
+        -------
+        str
+            file path
+        """
+        file_path = f"{self.rho_stat_handler.catalogs._output}/params_{catalog_id}.npy"
+        return file_path
 
 
     def save_samples(self, flat_samples, catalog_id):
@@ -1272,7 +1304,182 @@ def get_mcmc_from_samples(self, flat_samples):
         q = np.diff(mcmc_result, axis=0)
 
         return mcmc_result, q
+    
+    def build_rho_matrix(self, rho=None):
+        """
+        Build a matrix of rho statistics to get the tau statistics when multiplying with the parameters.
 
+        Parameters
+        ----------
+        rho : np.array
+            Rho statistics data. If None, use the data stored in the RhoStat class.
+
+        Returns
+        -------
+        np.array
+            Matrix of rho statistics.
+        """
+        n_thetas = len(self.rho_stat_handler.rho_stats["theta"]) #number of bins
+        rho_matrix = np.zeros((3*n_thetas, 3))
+        if rho is None:
+            rho_stats = self.rho_stat_handler.rho_stats
+            for i in range(n_thetas):
+                rho_matrix[i] = [rho_stats["rho_0_p"][i], rho_stats["rho_2_p"][i], rho_stats["rho_5_p"][i]]
+                rho_matrix[i+n_thetas] = [rho_stats['rho_2_p'][i], rho_stats['rho_1_p'][i], rho_stats['rho_4_p'][i]]
+                rho_matrix[i+2*n_thetas] = [rho_stats['rho_5_p'][i], rho_stats['rho_4_p'][i], rho_stats['rho_3_p'][i]]
+        else:
+            rho_stats = rho
+            for i in range(n_thetas):
+                rho_matrix[i] = [rho_stats[0, i], rho_stats[2, i], rho_stats[5, i]]
+                rho_matrix[i+n_thetas] = [rho_stats[2, i], rho_stats[1, i], rho_stats[4, i]]
+                rho_matrix[i+2*n_thetas] = [rho_stats[5, i], rho_stats[4, i], rho_stats[3, i]]
+        return rho_matrix
+    
+    def build_tau_vec(self, tau=None):
+        """
+        Build a vector of tau statistics to get the least squares parameters.
+
+        Parameters
+        ----------
+        tau : np.array
+            Tau statistics data. If None, use the data stored in the TauStat class.
+        
+        Returns
+        -------
+        np.array
+            Vector of tau statistics.
+        """
+        if tau is None:
+            tau_stats = self.tau_stat_handler.tau_stats
+            tau_vec = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"], tau_stats["tau_5_p"]]).flatten()
+        else:
+            tau_vec = tau.flatten()
+        return tau_vec
+    
+    def get_least_squares_params(self, npatch=200, apply_debias=False):
+        """
+        Compute the least square optimum of the residuals for the mean value measured by TreeCorr.
+
+        Parameters
+        ----------
+        npatch : int
+            The number of patches used to compute the covariance. (Default: 200)
+        apply_debias : bool
+            If True, apply some debiasing of the inverse of the covariance matrix. (Default: False)
+
+        Returns
+        -------
+        np.array
+            Parameters realising the optimum.
+        """
+        rho_matrix = self.build_rho_matrix()
+        tau_vec = self.build_tau_vec()
+        assert self.cov_tau is not None, "Please load a covariance matrix for the tau statistics."
+        inv_cov = np.linalg.inv(self.cov_tau)
+        if apply_debias:
+            inv_cov = (npatch - tau_vec.shape[0] - 2)/(npatch-1)*inv_cov
+        return np.linalg.inv(rho_matrix.T @ inv_cov @ rho_matrix) @ rho_matrix.T @ inv_cov @ tau_vec
+    
+    def get_least_squares_params_samples(self, npatch, apply_debias=False, n_samples=10000, verbose=True):
+        """
+        Computes the least square optimum of the residuals by sampling rho and tau statistics from their covariance.
+
+        Parameters
+        ----------
+        npatch : int
+            The number of patches used to compute the covariance.
+        apply_debias : bool
+            If True, apply some debiasing of the inverse of the covariance matrix. (Default: False)
+        n_samples : int
+            The number of samples to draw from the covariance matrix.
+        verbose : bool
+            If True, prints several informations.
+
+        Returns
+        -------
+        np.array
+            Samples obtained from the MCMC analysis
+        np.array
+            Best-fit values of the parameters
+        np.array
+            Error bars at the 68% confidence level.
+        """
+        assert self.cov_tau is not None, "Please load a covariance matrix for the tau statistics."
+        assert self.cov_rho is not None, "Please load a covariance matrix for the rho statistics."
+        rho_stats = self.rho_stat_handler.rho_stats
+        rho_mean = np.array([rho_stats["rho_0_p"], rho_stats["rho_1_p"], rho_stats["rho_2_p"], rho_stats["rho_3_p"],
+                             rho_stats["rho_4_p"], rho_stats["rho_5_p"]]).flatten()
+        tau_stats = self.tau_stat_handler.tau_stats
+        tau_mean = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"], tau_stats["tau_5_p"]]).flatten()
+        for i in tqdm(range(n_samples)):
+            rho = np.random.multivariate_normal(rho_mean, self.cov_rho)
+            rho = rho.reshape((6, -1))
+            rho_matrix = self.build_rho_matrix(rho)
+            tau = np.random.multivariate_normal(tau_mean, self.cov_tau)
+            tau_vec = self.build_tau_vec(tau)
+            inv_cov = np.linalg.inv(self.cov_tau)
+            if apply_debias:
+                inv_cov = (npatch - tau_vec.shape[0] - 2)/(npatch-1)*inv_cov
+            if i==0:
+                samples = np.linalg.inv(rho_matrix.T @ inv_cov @ rho_matrix) @ rho_matrix.T @ inv_cov @ tau_vec
+            else:
+                samples = np.vstack((samples, np.linalg.inv(rho_matrix.T @ inv_cov @ rho_matrix) @ rho_matrix.T @ inv_cov @ tau_vec))
+        result, q = self.get_mcmc_from_samples(samples)
+
+        if verbose:
+            labels = [r"$\alpha$", r"$\beta$", r"$\eta$"]
+            print(f"Number of samples: {samples.shape[0]}\n")
+
+            print("Parameters constraints")
+            print("----------------------")
+            for i in range(3):
+                print('Parameter: '+labels[i]+f'={result[1, i]:.4f}^+{q[0, i]:.4f}_{q[1, i]:.4f}')
+
+            print(f"Chi square: {self.eval_chi_square(result[1,:], npatch=npatch, apply_debias=apply_debias)}")
+        
+        return samples, result, q
+    
+    def eval_chi_square(self, theta, npatch=200, apply_debias=False):
+        """
+        Compute the chi square of the fit.
+
+        Parameters
+        ----------
+        theta : tuple
+            Parameters (alpha, beta, eta) of the model.
+        npatch : int
+            The number of patches used to compute the covariance.
+        apply_debias : bool
+            If True, apply some debiasing of the inverse of the covariance matrix. (Default: False)
+
+        Returns
+        -------
+        float
+            The value of the chi square.
+        """
+        rho_matrix = self.build_rho_matrix()
+        tau_vec = self.build_tau_vec()
+        assert self.cov_tau is not None, "Please load a covariance matrix for the tau statistics."
+        inv_cov = np.linalg.inv(self.cov_tau)
+        if apply_debias:
+            inv_cov = (npatch - tau_vec.shape[0] - 2)/(npatch-1)*inv_cov
+        return (tau_vec - rho_matrix @ theta) @ inv_cov @ (tau_vec - rho_matrix @ theta)
+
+    def save_params(self, theta, catalog_id):
+        """Save Parameters.
+
+        Save parameters to file.
+
+        Parameters
+        ----------
+        theta : np.array
+            Parameters obtained from the MCMC analysis or the LSQ fit.
+        catalog_id : str
+            ID of the catalog used for this run
+
+        """
+        np.save(self.get_sample_path(catalog_id), theta)
+    
     def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=None):
         """
         plot_tau_stats_w_model
diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index fbfe419..86a5d5b 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -11,37 +11,41 @@
 
 #First specify  the paths to your data
 paths_gal = [
-    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.0.fits",
+    #"/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_extended_2022_v1.0.fits",
     #"/automnt/n23data1/fhervas/shapepipe_clean_uppercut/unions_shapepipe_extended_2022_v1.0.4.fits",
     "/home/mkilbing/astro/data/DES/DES_Y3_cut.fits",
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_extended_2022_v1.0_mtheli8k.fits",
     "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_extended_2022_v1.3_mtheli8k.fits",
     "/n17data/mkilbing/astro/data/CFIS/v0.0/shapepipe_1500_goldshape_v1.fits"
 ]
 
 paths_psf = [
-    "/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits",
+    #"/n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe/unions_shapepipe_psf_2022_v1.0.2.fits",
     "/home/mkilbing/astro/data/DES/psf_y3a1-v29.fits",
     "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_psf_2022_v1.0.2_mtheli8k.fits",
+    "/n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask/unions_shapepipe_psf_2022_v1.0.2_mtheli8k.fits",
     "/n17data/sguerrini/star_cat.fits"
 ]
 
 #Specify if you want to mask some data using flags
 masks = [
-    True,
+    #True,
     False,
     True,
+    True,
     True
 ]
 
 #Specify is the sizes have to be squared. It can depend on the catalog.
 square_sizes = [
-    True,
+    #True,
     False,
     True,
+    True,
     True
 ]
 
-output = '/home/guerrini/rho_tau_stats_output/run_full_debiased_3'
+output = '/home/guerrini/rho_tau_stats_output/new_setup_2'
 
 #Contains the params of your catalog. Don't forget to specify them since they can vary between different catalogs
 params_sp = {
@@ -95,7 +99,7 @@
     "star_size": "SIGMA_STAR_HSM",
     "PSF_flag": "FLAG_PSF_HSM",
     "star_flag": "FLAG_STAR_HSM",
-    "patch_number": 200,
+    "patch_number": 120,
     "ra_units": "deg",
     "dec_units": "deg" 
 }
@@ -105,20 +109,20 @@
                 "dec_units": "deg",
                 "sep_units": "arcmin",
                 "min_sep": 0.1,
-                "max_sep": 100,
+                "max_sep": 250,
                 "nbins": 20,
                 "var_method": "jackknife"
 }
 
 params = [
-    params_sp,
     params_des,
     params_sp,
+    params_sp,
     params_axel
 ]
 
-colors = ['blue', 'red', 'green', 'purple'] #Colors for the plot
-catalog_ids = ['SPV1.0', 'DESY3', 'SPV1.3', 'SPV1500'] #Ids of the catalogs
+colors = ['red', 'blue', 'green', 'purple'] #Colors for the plot
+catalog_ids = ['DES', 'SPV1.0', 'SPV1.3', 'SP1500'] #Ids of the catalogs
 
 print(paths_gal)
 print(paths_psf)
@@ -159,7 +163,7 @@
 
 
         only_p = lambda corrs: np.array([corr.xip for corr in corrs]).flatten() #function to extract the tau+
-        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p, var_method='bootstrap') #Compute and save the tau statistics
+        tau_stat_handler.compute_tau_stats(cat_id, 'tau_stats_'+cat_id+'.fits', save_cov=True, func=only_p, var_method='jackknife') #Compute and save the tau statistics
 
 
 filenames = ['tau_stats_'+cat_id+'.fits' for cat_id in catalog_ids]
@@ -195,7 +199,7 @@
 for mcmc_result, cat_id, color, flat_sample in zip(mcmc_result_list, catalog_ids, colors, flat_sample_list):
     psf_fitter.load_rho_stat('rho_stats_'+cat_id+'.fits')
     for i in range(100):
-        psf_fitter.plot_xi_sys(flat_sample[-i+1], cat_id, color, alpha=0.1)
-    psf_fitter.plot_xi_sys(mcmc_result[1], cat_id, color)
+        psf_fitter.plot_xi_psf_sys(flat_sample[-i+1], cat_id, color, alpha=0.1)
+    psf_fitter.plot_xi_psf_sys(mcmc_result[1], cat_id, color)
 plt.legend()
 plt.savefig(output+'/xi_sys.png')
\ No newline at end of file

From ef9acabd1b4a337df8d95a460e8c54080080dea7 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 9 Mar 2024 18:16:49 +0100
Subject: [PATCH 54/97] poetry lock change

---
 poetry.lock                     | 1202 ++++++++++++++++---------------
 shear_psf_leakage/run_object.py |   10 +-
 2 files changed, 625 insertions(+), 587 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index abfc6f8..4ebdd7f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -14,13 +14,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
-    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
@@ -36,13 +36,13 @@ trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "appnope"
-version = "0.1.3"
+version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
-    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
-    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
 [[package]]
@@ -192,13 +192,13 @@ test-all = ["astropy[test]", "coverage[toml]", "ipython (>=4.2)", "objgraph", "s
 
 [[package]]
 name = "astropy-iers-data"
-version = "0.2024.1.22.0.30.30"
+version = "0.2024.2.19.0.28.47"
 description = "IERS Earth Rotation and Leap Second tables for the astropy core package"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "astropy-iers-data-0.2024.1.22.0.30.30.tar.gz", hash = "sha256:112e7367d5ed23b41ce86d2d0688d5b7ccd2587547544855010b616f10489ada"},
-    {file = "astropy_iers_data-0.2024.1.22.0.30.30-py3-none-any.whl", hash = "sha256:9ffb20756667652503239cb2f7fd7a2871950991365d5e1e30f9643c1c151d10"},
+    {file = "astropy-iers-data-0.2024.2.19.0.28.47.tar.gz", hash = "sha256:29af64fc932fec656c1f75478d693aef4ba6364b8d32a5862d5d908fe8848248"},
+    {file = "astropy_iers_data-0.2024.2.19.0.28.47-py3-none-any.whl", hash = "sha256:8ccf83b8e35b300fb77eeb189ed728ec6d8150c40f8788a42e542fdcf960fe49"},
 ]
 
 [package.extras]
@@ -408,13 +408,13 @@ docs = ["sphinx (>=4.0)", "sphinx_rtd_theme (>=1.0)", "sphinxcontrib-jquery"]
 
 [[package]]
 name = "certifi"
-version = "2023.11.17"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
-    {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
 ]
 
 [[package]]
@@ -594,35 +594,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
-[[package]]
-name = "cmake"
-version = "3.28.1"
-description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
-optional = false
-python-versions = "*"
-files = [
-    {file = "cmake-3.28.1-py2.py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:9c77c47afef821c0044ba73d182c386ab02e92e6bda5296e553c12455a083f29"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2010_i686.manylinux_2_12_i686.whl", hash = "sha256:6a9549755d1178426502753d48949edae9bb0c66f15a07f09904783125beb0e3"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:d0978cdd08c0ebc76f4f8543aba1381a41580dcb9c3bcffb536c41337b75aea1"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:96d506c417d63bbcff19b3e9eaa69fe546456a0ddeffe914bcbb23cceee6818e"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:74c9878c504ccc6ddd5b0914cbe3b86417a36a2c2dfc486040bfdfe63fbbb1ac"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:64d4642c48486bb4320540781a2266c2060929d1e236d6eb2b2c96273e75e958"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:363bd0124d71d7e3d9b1ac9bd1dce1d80ba90f48b264c3bf9dbfcfda875cafc9"},
-    {file = "cmake-3.28.1-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1be8f351271f8bcbe32288066e5add642d7c32f2f8fec3f135949c2cb13dfac2"},
-    {file = "cmake-3.28.1-py2.py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:3ed193134a4937bad8de2b4f62faebc8c1a4049cd37dad9767db7e7d91a08b52"},
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 [[package]]
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 ]
 
 [package.dependencies]
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 [[package]]
 name = "cryptography"
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -836,13 +807,15 @@ test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-co
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
-name = "cs_util"
+name = "cs-util"
 version = "0.0.7"
 description = ""
 optional = false
 python-versions = ">=3.7"
-files = []
-develop = false
+files = [
+    {file = "cs_util-0.0.7-py3-none-any.whl", hash = "sha256:b02de3ff13b1d5da66fd6dab72b813c5f8ecfaf418c4ac0e8827d1f67bcd893d"},
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+]
 
 [package.dependencies]
 astropy = ">=4.0"
@@ -852,15 +825,11 @@ numpy = ">=1.21"
 vos = ">=3.3"
 
 [package.extras]
-dev = ["cs_util[lint,release,test]"]
+dev = ["cs-util[lint,release,test]"]
 lint = ["black", "jinja2 (==3.0)"]
 release = ["build", "sdist", "twine"]
 test = ["pytest", "pytest-black", "pytest-cov", "pytest-pydocstyle"]
 
-[package.source]
-type = "directory"
-url = "../cs_util"
-
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -893,29 +862,33 @@ pytz = "*"
 
 [[package]]
 name = "debugpy"
-version = "1.8.0"
+version = "1.8.1"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
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+    {file = "fonttools-4.49.0.tar.gz", hash = "sha256:ebf46e7f01b7af7861310417d7c49591a85d99146fc23a5ba82fdb28af156321"},
+]
+
+[package.extras]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
 interpolatable = ["munkres", "pycairo", "scipy"]
-lxml = ["lxml (>=4.0,<5)"]
+lxml = ["lxml (>=4.0)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
 repacker = ["uharfbuzz (>=0.23.0)"]
@@ -1071,12 +1044,13 @@ files = [
 
 [[package]]
 name = "future"
-version = "0.18.3"
+version = "1.0.0"
 description = "Clean single-source support for Python 3 and 2"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
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+    {file = "future-1.0.0-py3-none-any.whl", hash = "sha256:929292d34f5872e70396626ef385ec22355a1fae8ad29e1a734c3e43f9fbc216"},
+    {file = "future-1.0.0.tar.gz", hash = "sha256:bd2968309307861edae1458a4f8a4f3598c03be43b97521076aebf5d94c07b05"},
 ]
 
 [[package]]
@@ -1100,17 +1074,72 @@ scipy = ">=1.5"
 docs = ["sphinx", "sphinx_rtd_theme (>=1.0)", "sphinxcontrib-jquery"]
 gui = ["PySide6 (>=6.1)"]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
 [[package]]
 name = "html2text"
-version = "2020.1.16"
+version = "2024.2.25"
 description = "Turn HTML into equivalent Markdown-structured text."
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
+files = [
+    {file = "html2text-2024.2.25.tar.gz", hash = "sha256:a197e7acaba5b5e46ff9f132b6fbeb89428c9155b5a971d7d9c343ebeb1019d3"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.4"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
 files = [
-    {file = "html2text-2020.1.16-py3-none-any.whl", hash = "sha256:c7c629882da0cf377d66f073329ccf34a12ed2adf0169b9285ae4e63ef54c82b"},
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+    {file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
 ]
 
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.25.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.27.0"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
+    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+
 [[package]]
 name = "idna"
 version = "3.6"
@@ -1172,13 +1201,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.0"
+version = "6.29.2"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"},
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+    {file = "ipykernel-6.29.2-py3-none-any.whl", hash = "sha256:50384f5c577a260a1d53f1f59a828c7266d321c9b7d00d345693783f66616055"},
+    {file = "ipykernel-6.29.2.tar.gz", hash = "sha256:3bade28004e3ff624ed57974948116670604ac5f676d12339693f3142176d3f0"},
 ]
 
 [package.dependencies]
@@ -1201,7 +1230,7 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.4)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
@@ -1253,21 +1282,21 @@ files = [
 
 [[package]]
 name = "ipywidgets"
-version = "8.1.1"
+version = "8.1.2"
 description = "Jupyter interactive widgets"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"},
-    {file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"},
+    {file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
+    {file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
 ]
 
 [package.dependencies]
 comm = ">=0.1.3"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0.9,<3.1.0"
+jupyterlab-widgets = ">=3.0.10,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.9,<4.1.0"
+widgetsnbextension = ">=4.0.10,<4.1.0"
 
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@@ -1324,13 +1353,13 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "json5"
-version = "0.9.14"
+version = "0.9.17"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
-    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+    {file = "json5-0.9.17-py2.py3-none-any.whl", hash = "sha256:f8ec1ecf985951d70f780f6f877c4baca6a47b6e61e02c4cd190138d10a7805a"},
+    {file = "json5-0.9.17.tar.gz", hash = "sha256:717d99d657fa71b7094877b1d921b1cce40ab444389f6d770302563bb7dfd9ae"},
 ]
 
 [package.extras]
@@ -1574,17 +1603,18 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.0.11"
+version = "4.1.2"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.0.11-py3-none-any.whl", hash = "sha256:536bf0e78723153a5016ca7efb88ed0ecd7070d3f1555d5b0e2770658f900a3c"},
-    {file = "jupyterlab-4.0.11.tar.gz", hash = "sha256:d1aec24712566bc25a36229788242778e498ca4088028e2f9aa156b8b7fdc8fc"},
+    {file = "jupyterlab-4.1.2-py3-none-any.whl", hash = "sha256:aa88193f03cf4d3555f6712f04d74112b5eb85edd7d222c588c7603a26d33c5b"},
+    {file = "jupyterlab-4.1.2.tar.gz", hash = "sha256:5d6348b3ed4085181499f621b7dfb6eb0b1f57f3586857aadfc8e3bf4c4885f9"},
 ]
 
 [package.dependencies]
 async-lru = ">=1.0.0"
+httpx = ">=0.25.0"
 importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 ipykernel = "*"
 jinja2 = ">=3.0.3"
@@ -1599,9 +1629,9 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.1.6)"]
-docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 
 [[package]]
@@ -1617,13 +1647,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.2"
+version = "2.25.3"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.2-py3-none-any.whl", hash = "sha256:5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf"},
-    {file = "jupyterlab_server-2.25.2.tar.gz", hash = "sha256:bd0ec7a99ebcedc8bcff939ef86e52c378e44c2707e053fcd81d046ce979ee63"},
+    {file = "jupyterlab_server-2.25.3-py3-none-any.whl", hash = "sha256:c48862519fded9b418c71645d85a49b2f0ec50d032ba8316738e9276046088c1"},
+    {file = "jupyterlab_server-2.25.3.tar.gz", hash = "sha256:846f125a8a19656611df5b03e5912c8393cea6900859baa64fa515eb64a8dc40"},
 ]
 
 [package.dependencies]
@@ -1643,13 +1673,13 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.9"
+version = "3.0.10"
 description = "Jupyter interactive widgets for JupyterLab"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -1967,108 +1997,108 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
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 [package.dependencies]
@@ -2190,13 +2220,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
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@@ -2289,13 +2319,13 @@ test = ["coverage", "mock", "nbval", "nose", "nose-exclude", "nose-exclude", "no
 
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@@ -2306,58 +2336,58 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
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 python-versions = ">=3.8"
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 [[package]]
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 [[package]]
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 [[package]]
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 [[package]]
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@@ -3774,13 +3806,13 @@ test = ["websockets"]
 
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 [[package]]
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+    {file = "zope.interface-6.2.tar.gz", hash = "sha256:3b6c62813c63c543a06394a636978b22dffa8c5410affc9331ce6cdb5bfa8565"},
 ]
 
 [package.dependencies]
 setuptools = "*"
 
 [package.extras]
-docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx-rtd-theme"]
+docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx_rtd_theme"]
 test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "820dc52af2f578ca71fe2c7500d527f26683e1ec551fc66c03daf97ac7a2cec5"
+content-hash = "1f9b959fcaae9e79fb23a90176af836a18041bb26a5e0e03a9ee97f798adc463"
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index f40deab..bdfd1df 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -456,13 +456,19 @@ def test(self):
             p_gt.add(par, value=pars_gt[par])
 
         # Ground-truth 2D (y_1, y_2) data
-        y1, y2 = leakage.func_bias_2d(p_gt, x_arr[0], x_arr[1], order="quad", mix=True)
+        y1, y2 = leakage.func_bias_2d(
+            p_gt,
+            x_arr[0],
+            x_arr[1],
+            order="quad",
+            mix=True
+        )
 
         # Perturbation
         dy1 = np.random.normal(scale=sig_x, size=size)
         dy2 = np.random.normal(scale=sig_x, size=size)
 
-        # Perform fits
+        # Carry out fits
         for order in ["lin", "quad"]:
             for mix in [False, True]:
                 out_path = f"{self._params['output_dir']}/test_{order}_{mix}"

From c8b7bd74f406502829d00ce6ee3d247cc7705e15 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 9 Mar 2024 18:17:46 +0100
Subject: [PATCH 55/97] Added ground truth to obj-wise leakage; plot styles
 improved

---
 shear_psf_leakage/leakage.py    | 12 +++++--
 shear_psf_leakage/plot_style.py |  2 +-
 shear_psf_leakage/plots.py      | 58 ++++++++++++++++++++++-----------
 shear_psf_leakage/run_object.py | 12 +++----
 4 files changed, 56 insertions(+), 28 deletions(-)

diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index dde0378..2062010 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -950,7 +950,11 @@ def corr_2d(
         err = np.ones_like(y[0])
     res = minimize(loss_bias_2d, params, args=(x, y, err, order, mix))
     if stats_file:
-        print_stats(f"2D fit order={order} mix={mix}:", stats_file, verbose=verbose)
+        print_stats(
+            f"2D fit order={order} mix={mix}:",
+            stats_file,
+            verbose=verbose
+        )
         print_fit_report(res, file=stats_file)
     if verbose:
         print_fit_report(res)
@@ -968,7 +972,11 @@ def corr_2d(
         for p in res.params:
             print_stats(f"{p}={p_dp[p]:.3ugP}", stats_file, verbose=verbose)
         for spin in s_ds:
-            print_stats(f"{spin}={s_ds[spin]:.3ugP}", stats_file, verbose=verbose)
+            print_stats(
+                f"{spin}={s_ds[spin]:.3ugP}",
+                stats_file,
+                verbose=verbose
+            )
 
     # Plots
 
diff --git a/shear_psf_leakage/plot_style.py b/shear_psf_leakage/plot_style.py
index 1959414..8899bd6 100644
--- a/shear_psf_leakage/plot_style.py
+++ b/shear_psf_leakage/plot_style.py
@@ -17,7 +17,7 @@
 mpl.rcParams["lines.linewidth"] = 2
 mpl.rcParams["lines.markersize"] = 10
 
-font_size = 16
+font_size = 18
 mpl.rcParams["font.size"] = font_size
 mpl.rcParams["xtick.labelsize"] = font_size
 mpl.rcParams["ytick.labelsize"] = font_size
diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index 8d18fc9..4d17f3a 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -129,6 +129,14 @@ def compute_bins_func_2d(x, y, n_bin, mix, weights=None):
     return x_bin, y_bin, err_bin
 
 
+def get_bias(p_dp, par_ground_truth, key):
+
+    return (
+        (p_dp[key].nominal_value - par_ground_truth[key].value)
+        / p_dp[key].std_dev
+    )
+    
+
 def set_labels(p_dp, order, mix, par_ground_truth=None):
     """Set Labels.
 
@@ -153,35 +161,43 @@ def set_labels(p_dp, order, mix, par_ground_truth=None):
     label = {}
 
     # Linear parameters
-    label["A"] = f'$a_{{11}}={p_dp["a11"]: .2ugL}$'
-    label["D"] = f'$a_{{22}}={p_dp["a22"]: .2ugL}$'
+    label["A"] = f'$a_{{11}}\;\,={p_dp["a11"]: .2ugL}$'
+    label["D"] = f'$a_{{22}}\;\,={p_dp["a22"]: .2ugL}$'
     if par_ground_truth:
-        label["A"] = f"{label['A']} ({par_ground_truth['a11'].value})"
-        label["D"] = f"{label['D']} ({par_ground_truth['a22'].value})"
+        bias = get_bias(p_dp, par_ground_truth, "a11")
+        label["A"] = f"{label['A']} ({par_ground_truth['a11'].value}, {bias:.1f}$\sigma$)"
+        bias = get_bias(p_dp, par_ground_truth, "a22")
+        label["D"] = f"{label['D']} ({par_ground_truth['a22'].value}, {bias:.1f}$\sigma$)"
     
     # Constant parameters
-    label["A"] = label["A"] + "\n" + f'$c_1={p_dp["c1"]: .2ugL}$'
-    label["D"] = label["D"] + "\n" + f'$c_2={p_dp["c2"]: .2ugL}$'
+    label["A"] = label["A"] + "\n" + f'$c_1\;\;\;={p_dp["c1"]: .2ugL}$'
+    label["D"] = label["D"] + "\n" + f'$c_2\;\;\;={p_dp["c2"]: .2ugL}$'
     if par_ground_truth:
-        label["A"] = f"{label['A']} ({par_ground_truth['c1'].value})"
-        label["D"] = f"{label['D']} ({par_ground_truth['c2'].value})"
+        bias = get_bias(p_dp, par_ground_truth, "c1")
+        label["A"] = f"{label['A']} ({par_ground_truth['c1'].value}, {bias:.1f}$\sigma$)"
+        bias = get_bias(p_dp, par_ground_truth, "c2")
+        label["D"] = f"{label['D']} ({par_ground_truth['c2'].value}, {bias:.1f}$\sigma$)"
 
     if order == "quad":
         # Add quadratic parameters
         label_q111 = f'$q_{{111}}={p_dp["q111"]: .2ugL}$'
         label_q222 = f'$q_{{222}}={p_dp["q222"]: .2ugL}$'
         if par_ground_truth:
-            label_q111 = f"{label_q111} ({par_ground_truth['q111'].value})"
-            label_q222 = f"{label_q222} ({par_ground_truth['q222'].value})"
+            bias = get_bias(p_dp, par_ground_truth, "q111")
+            label_q111 = f"{label_q111} ({par_ground_truth['q111'].value}, {bias:.1f}$\sigma$)"
+            bias = get_bias(p_dp, par_ground_truth, "q222")
+            label_q222 = f"{label_q222} ({par_ground_truth['q222'].value}, {bias:.1f}$\sigma$)"
         label["A"] = label_q111 + "\n" + label["A"]
         label["D"] = label_q222 + "\n" + label["D"]
     if mix:
         # Mixed linear parameters
-        label["B"] = f'$a_{{12}}={p_dp["a12"]: .2ugL}$'
-        label["C"] = f'$a_{{21}}={p_dp["a21"]: .2ugL}$'
+        label["B"] = f'$a_{{12}}\;\,={p_dp["a12"]: .2ugL}$'
+        label["C"] = f'$a_{{21}}\;\,={p_dp["a21"]: .2ugL}$'
         if par_ground_truth:
-            label["B"] = f"{label['B']} ({par_ground_truth['a12'].value})"
-            label["C"] = f"{label['C']} ({par_ground_truth['a21'].value})"
+            bias = get_bias(p_dp, par_ground_truth, "a12")
+            label["B"] = f"{label['B']} ({par_ground_truth['a12'].value}, {bias:.1f}$\sigma$)"
+            bias = get_bias(p_dp, par_ground_truth, "a21")
+            label["C"] = f"{label['C']} ({par_ground_truth['a21'].value}, {bias:.1f}$\sigma$)"
 
         # Mixed quadratic parameters
         if order == "quad":
@@ -191,10 +207,14 @@ def set_labels(p_dp, order, mix, par_ground_truth=None):
             label_q112 = f'$q_{{112}}={p_dp["q112"]: .2ugL}$'
 
             if par_ground_truth:
-                label_q211 = f"{label_q211} ({par_ground_truth['q211'].value})"
-                label_q212 = f"{label_q212} ({par_ground_truth['q212'].value})"
-                label_q122 = f"{label_q122} ({par_ground_truth['q122'].value})"
-                label_q112 = f"{label_q112} ({par_ground_truth['q112'].value})"
+                bias = get_bias(p_dp, par_ground_truth, "q211")
+                label_q211 = f"{label_q211} ({par_ground_truth['q211'].value}, {bias:.1f}$\sigma$)"
+                bias = get_bias(p_dp, par_ground_truth, "q212")
+                label_q212 = f"{label_q212} ({par_ground_truth['q212'].value}, {bias:.1f}$\sigma$)"
+                bias = get_bias(p_dp, par_ground_truth, "q122")
+                label_q122 = f"{label_q122} ({par_ground_truth['q122'].value}, {bias:.1f}$\sigma$)"
+                bias = get_bias(p_dp, par_ground_truth, "q112")
+                label_q112 = f"{label_q112} ({par_ground_truth['q112'].value}, {bias:.1f}$\sigma$)"
 
             label["B"] = label_q211 + "\n" + label_q212 + "\n" + label["B"]
             label["C"] = label_q122 + "\n" + label_q112 + "\n" + label["C"]
@@ -258,7 +278,7 @@ def plot_bar_spin(par, s_ground_truth, output_path=None):
     )
     xlim = ax.get_xlim()
     ax.plot(xlim, [0, 0], "k-")
-    ax.set_ylabel(r"$z_s = x_s + \mathrm{i} y_s$")
+    ax.set_ylabel(r"$\alpha_s^\Re, \alpha_s^\Im$")
     xl = list(s)
     ax.set_xticks(xl)
     ax.set_xlabel("$s$")
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index bdfd1df..61daf48 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -479,7 +479,7 @@ def test(self):
                     ylabel_arr=ylabel_arr,
                     order=order,
                     mix=mix,
-                    title=f"test {order} {mix}",
+                    title="",
                     n_bin=n_bin,
                     out_path=out_path,
                     colors=colors,
@@ -522,12 +522,12 @@ def PSF_leakage(self, mix=True, order="lin"):
         # Set options for plotting
         n_bin = 30
         colors = ["b", "r"]
-        ylabel_arr = [r"$e_1^{\rm gal}$", r"$e_2^{\rm gal}$"]
+        ylabel_arr = [r"$e_1^{\rm g}$", r"$e_2^{\rm g}$"]
 
         xlabel_arr = [
-            r"$e_{1}^{\rm PSF}$",
-            r"$e_{2}^{\rm PSF}$",
-            r"$\mathrm{FWHM}^{\rm PSF}$ [arcsec]",
+            r"$e_{1}^{\rm p}$",
+            r"$e_{2}^{\rm p}$",
+            r"$\mathrm{FWHM}^{\rm p}$ [arcsec]",
         ]
 
         e, weights = self.get_ellipticity_weights()
@@ -556,7 +556,7 @@ def PSF_leakage(self, mix=True, order="lin"):
             ylabel_arr=ylabel_arr,
             order=order,
             mix=mix,
-            title=f"{order} {mix}",
+            title="",
             n_bin=n_bin,
             out_path=out_path,
             colors=colors,

From f222ea9aa72d12d778724826b890356cf2c64cfc Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 9 Mar 2024 18:19:18 +0100
Subject: [PATCH 56/97] added notebook for autocorr leakage (propto alpha^2)

---
 notebooks/leakage_autocorr_psf.py | 206 ++++++++++++++++++++++++++++++
 1 file changed, 206 insertions(+)
 create mode 100644 notebooks/leakage_autocorr_psf.py

diff --git a/notebooks/leakage_autocorr_psf.py b/notebooks/leakage_autocorr_psf.py
new file mode 100644
index 0000000..6938690
--- /dev/null
+++ b/notebooks/leakage_autocorr_psf.py
@@ -0,0 +1,206 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:light
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.16.1
+#   kernelspec:
+#     display_name: shear_psf_leakage
+#     language: python
+#     name: shear_psf_leakage
+# ---
+
+# # PSF leakage: PSF auto-correlation contributions
+#
+# Martin Kilbinger <martin.kilbinger@cea.fr>
+
+# %reload_ext autoreload
+# %autoreload 2
+
+# +
+import os
+import matplotlib.pylab as plt
+import numpy as np
+
+from astropy import units
+
+from uncertainties import ufloat
+from uncertainties import unumpy                                                 
+
+from cs_util import canfar
+from cs_util import plots as cs_plots
+
+import shear_psf_leakage.run_object as run_object
+import shear_psf_leakage.run_scale as run_scale
+from shear_psf_leakage import leakage
+# -
+
+# ## Set input parameters
+
+params_in = {}
+
+# ### Paths
+
+# +
+# Input galaxy shear catalogue
+params_in["input_path_shear"] = "unions_shapepipe_extended_2022_v1.0.fits"
+
+# Output directory
+params_in["output_dir"] = "leakage_PSF_autocorr"
+# -
+
+# ### Job control
+
+# Compute (spin-preserving) PSF ellipticity leakage
+params_in["PSF_leakage"] = True
+
+# ### Other parameters
+
+# +
+# PSF ellipticity column names
+params_in["e1_PSF_col"] = "e1_PSF"
+params_in["e2_PSF_col"] = "e2_PSF"
+
+# Set verbose output
+params_in["verbose"] = True
+# -
+
+# ### Retrieve test catalogue from VOspace if not yet downloade
+
+vos_dir = "vos:cfis/XXXX/"
+canfar.download(
+    f"{vos_dir}/{params_in['input_path_shear']}",
+    params_in["input_path_shear"],
+    verbose=params_in["verbose"],
+)
+
+# ## Compute leakage
+
+# Create leakage instance
+obj_object = run_object.LeakageObject()
+
+# Set instance parameters, copy from above
+for key in params_in:
+    obj_object._params[key] = params_in[key]
+
+# Run commands as given in LeakageObject.run()
+
+# +
+# Check parameter validity
+obj_object.check_params()
+
+# Update parameters (here: strings to list)
+obj_object.update_params()
+
+# Prepare output directory
+obj_object.prepare_output()
+# -
+
+# Read input catalogue
+obj_object.read_data()
+
+# Object-by-object spin-consistent PSF leakage
+mix = True
+order = "lin"
+obj_object.PSF_leakage(mix=mix, order=order)
+
+p_dp = {}                                                                    
+for p in obj_object.par_best_fit:                                                         
+    p_dp[p] = ufloat(obj_object.par_best_fit[p].value, obj_object.par_best_fit[p].stderr)
+print(p_dp)
+
+s_ds = leakage.param_order2spin(p_dp, order, mix)
+print(s_ds)
+
+# Get spin elements
+x0 = s_ds["x0"]                                                                     
+x4 = s_ds["x4"]
+y4 = s_ds["y4"]
+y0 = s_ds["y0"]
+
+
+# Create scale-dependent leakage instance
+obj_scale = run_scale.LeakageScale()
+
+# Set instance parameters, copy from above                                   
+for key in params_in:                                                        
+    obj_scale._params[key] = params_in[key]
+
+
+obj_scale._params["input_path_PSF"] = "unions_shapepipe_psf_2022_v1.0.2.fits"
+obj_scale._params["dndz_path"] = "dndz_SP_A.txt"
+
+# ### Run                                                                        
+
+# Check parameter validity                                                       
+obj_scale.check_params()                                                               
+
+# Prepare output directory and stats file                                        
+obj_scale.prepare_output()                                                             
+
+# Prepare input                                                                  
+obj_scale.read_data()                                                                  
+
+# #### Compute correlation function and alpha matrices                           
+# The following command calls `treecorr` to compute auto- and cross-correlation functions.
+# This can take a few minutes.                                                   
+
+obj_scale.compute_corr_gp_pp_alpha_matrix()                                            
+
+obj_scale.alpha_matrix()                                                               
+
+#### For comparison: scalar alpha leakage                                        
+obj_scale.compute_corr_gp_pp_alpha()                                                   
+obj_scale.do_alpha()
+
+
+# Plot terms
+theta_arcmin = obj_scale.get_theta()
+
+term_1 = (
+    (x0 ** 2 + x4 **2 + y0 **2 + y4 ** 2)
+    * (obj_scale.xi_pp_m[0][0] + obj_scale.xi_pp_m[1][1])
+)
+term_2 = 4 * (x0 * y4  - x4 * y0) * obj_scale.xi_pp_m[0][1]
+
+theta = theta_arcmin * units.arcmin
+xi_theo_p, xi_theo_m = run_scale.get_theo_xi(theta, obj_scale._params["dndz_path"])
+
+# +
+y = [
+    unumpy.nominal_values(term_1), 
+    -unumpy.nominal_values(term_2),
+    xi_theo_p,
+]
+dy = [
+    unumpy.std_devs(term_1), 
+    unumpy.std_devs(term_2),
+    [np.nan] * len(xi_theo_p), 
+]
+x = [theta_arcmin] * len(y)
+
+title = "PSF auto-correlation additive terms"
+xlabel = r"$\theta$ [arcmin]" 
+ylabel = "terms"
+out_path = f"{obj_scale._params['output_dir']}/auto_corr_terms.png"                                  
+labels = ["t1" , "-t2", r"$\xi_p$"]
+
+cs_plots.plot_data_1d(
+    x,
+    y,
+    dy,
+    title,
+    xlabel,
+    ylabel,
+    out_path,
+    labels=labels,
+    shift_x=True,
+    xlog=True,
+    ylog=True,
+)
+# -
+
+

From 3fdebf8a5948cfaf32f9c9ff5c2a9788dd5e254f Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 11 Mar 2024 11:10:27 +0100
Subject: [PATCH 57/97] json output non-zero

---
 notebooks/leakage_object.py     | 3 +++
 shear_psf_leakage/run_object.py | 6 +++---
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/notebooks/leakage_object.py b/notebooks/leakage_object.py
index 6f9e28f..7383c9d 100644
--- a/notebooks/leakage_object.py
+++ b/notebooks/leakage_object.py
@@ -20,6 +20,9 @@
 # Martin Kilbinger <martin.kilbinger@cea.fr>
 
 # +
+import matplotlib
+matplotlib.use("qtagg")
+
 import os
 import matplotlib.pylab as plt
 
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 950ce62..c0aec19 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -552,10 +552,10 @@ def PSF_leakage(self):
                 stats_file=self._stats_file,
                 verbose=self._params["verbose"],
             )
-            fp_best_fit = open(f"{out_path}.json", "w")
-            par_best_fit.dump(fp_best_fit)
+            with open(f"{out_path}.json", "w") as fp_best_fit:
+                par_best_fit.dump(fp_best_fit)
 
-        # Fit separate 1D models, including size
+        # Fit separate 1D models
         ylabel = r"$e_{1,2}^{\rm gal}$"
         mlabel = [r"\alpha_1", r"\alpha_2"]
         clabel = ["c_1", "c_2"]

From b854bcf031360a00e4ffbface0de40eb550269d2 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 14 Mar 2024 11:43:28 +0100
Subject: [PATCH 58/97] object-wise leakage: read best-fit parameters from json

---
 notebooks/leakage_object.py     | 11 ++++--
 shear_psf_leakage/leakage.py    | 63 +++++++++++++++++++++++++++++++--
 shear_psf_leakage/run_object.py | 57 ++++++++++++++++-------------
 3 files changed, 103 insertions(+), 28 deletions(-)

diff --git a/notebooks/leakage_object.py b/notebooks/leakage_object.py
index 7383c9d..528b5fb 100644
--- a/notebooks/leakage_object.py
+++ b/notebooks/leakage_object.py
@@ -21,7 +21,7 @@
 
 # +
 import matplotlib
-matplotlib.use("qtagg")
+matplotlib.use("agg")
 
 import os
 import matplotlib.pylab as plt
@@ -29,6 +29,7 @@
 from cs_util import canfar
 
 import shear_psf_leakage.run_object as run
+from shear_psf_leakage import leakage
 
 # -
 
@@ -121,7 +122,13 @@
 
 if obj._params["PSF_leakage"]:
     # Object-by-object spin-consistent PSF leakage
-    obj.PSF_leakage()
+    for order in ("lin", "quad"):
+        out_base = obj.get_out_base(mix=True, order=order)
+        out_path = f"{out_base}.json"
+        if os.path.exists(out_path):
+            obj.par_best_fit = leakage.read_from_json(out_path)
+        else:
+            obj.PSF_leakage(mix=True, order=order)
 
 if obj._params["obs_leakage"]:
     # Object-by-object spin-consistent PSF leakage
diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index dde0378..5d816f5 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -14,6 +14,7 @@
 import os
 
 import numpy as np
+import json
 import matplotlib.pylab as plt
 from lmfit import minimize, Parameters
 from uncertainties import ufloat
@@ -950,7 +951,11 @@ def corr_2d(
         err = np.ones_like(y[0])
     res = minimize(loss_bias_2d, params, args=(x, y, err, order, mix))
     if stats_file:
-        print_stats(f"2D fit order={order} mix={mix}:", stats_file, verbose=verbose)
+        print_stats(
+            f"2D fit order={order} mix={mix}:",
+            stats_file,
+            verbose=verbose,
+        )
         print_fit_report(res, file=stats_file)
     if verbose:
         print_fit_report(res)
@@ -968,7 +973,13 @@ def corr_2d(
         for p in res.params:
             print_stats(f"{p}={p_dp[p]:.3ugP}", stats_file, verbose=verbose)
         for spin in s_ds:
-            print_stats(f"{spin}={s_ds[spin]:.3ugP}", stats_file, verbose=verbose)
+            print_stats(
+                f"{spin}={s_ds[spin]:.3ugP}",
+                stats_file,
+                verbose=verbose,
+            )
+
+    # MKDEBUG TODO: Put plot stuff in diff function
 
     # Plots
 
@@ -1328,3 +1339,51 @@ def affine_corr_n(
     plt.tight_layout()
     plt.savefig(out_path_arr[-1])
     plt.close()
+
+
+def save_to_json(data, fname):
+    """Save To Json.
+
+    Save data to .json file.
+
+    Parameters
+    ----------
+    data : dict
+        input data
+    fname : str
+        output file name
+
+    See also
+    --------
+    read_from_json
+
+    """
+    with open(fname, "w") as f:
+        data.dump(f)
+
+
+def read_from_json(fname):
+    """Read From Json.
+
+    Read data from .json file.
+
+    Parameters
+    ----------
+    fname : str
+        input file name
+
+    Returns
+    -------
+    dict
+        data
+
+    See also
+    --------
+    save_to_json
+
+    """
+
+    with open(fname) as f:
+        data = json.load(f)
+
+    return data
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index c0aec19..d8b135f 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -500,7 +500,21 @@ def get_ellipticity_weights(self):
 
         return e, weights
 
-    def PSF_leakage(self):
+    def get_out_base(self, mix, order):
+        """Get Out Base.
+
+        Return output file base name.
+
+        """
+        out_base = (
+            f"{self._params['output_dir']}"
+            + f"/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
+        )
+
+        return out_base
+
+
+    def PSF_leakage(self, mix=True, order="lin"):
         """PSF Leakage.
 
         Compute and plot object-by-object PSF spin-consistent leakage relations.
@@ -531,31 +545,26 @@ def PSF_leakage(self):
         ]
 
         # Fit consistent spin-2 2D model
-        mix = True
-        for order in ["lin", "quad"]:
-            out_path = (
-                f"{self._params['output_dir']}"
-                + f"/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
-            )
-            par_best_fit = leakage.corr_2d(
-                x_arr[:2],
-                e,
-                weights=weights,
-                xlabel_arr=xlabel_arr[:2],
-                ylabel_arr=ylabel_arr,
-                order=order,
-                mix=mix,
-                title="",
-                n_bin=n_bin,
-                out_path=out_path,
-                colors=colors,
-                stats_file=self._stats_file,
-                verbose=self._params["verbose"],
-            )
-            with open(f"{out_path}.json", "w") as fp_best_fit:
-                par_best_fit.dump(fp_best_fit)
+        out_path = self.get_out_base(mix, order)
+        par_best_fit = leakage.corr_2d(
+            x_arr[:2],
+            e,
+            weights=weights,
+            xlabel_arr=xlabel_arr[:2],
+            ylabel_arr=ylabel_arr,
+            order=order,
+            mix=mix,
+            title="",
+            n_bin=n_bin,
+            out_path=out_path,
+            colors=colors,
+            stats_file=self._stats_file,
+            verbose=self._params["verbose"],
+        )
+        leakage.save_to_json(par_best_fit, f"{out_path}.json")
 
         # Fit separate 1D models
+        # MKDEBUG TODO: put in separate class funtion
         ylabel = r"$e_{1,2}^{\rm gal}$"
         mlabel = [r"\alpha_1", r"\alpha_2"]
         clabel = ["c_1", "c_2"]

From 3713cb1c8be0bd57f2438302060fad219716d6c0 Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sguerrin@cca010.in2p3.fr>
Date: Fri, 29 Mar 2024 16:25:21 +0100
Subject: [PATCH 59/97] Add a script to compute the covariance of tau
 statistics analytically.

---
 shear_psf_leakage/rho_tau_cov.py  | 633 ++++++++++++++++++++++++++++++
 shear_psf_leakage/rho_tau_stat.py |   8 +-
 2 files changed, 637 insertions(+), 4 deletions(-)
 create mode 100644 shear_psf_leakage/rho_tau_cov.py

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
new file mode 100644
index 0000000..62dc4ef
--- /dev/null
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -0,0 +1,633 @@
+"""
+This module allows to compute a theoretical estimate of the covariance matrix of the tau statistics
+to estimate systematic error from the PSF using rho and tau statistics.
+Author: Sacha Guerrini
+"""
+
+import numpy as np
+from astropy.io import fits
+
+import scipy.integrate as integrate
+import scipy.interpolate as interpolate
+
+import treecorr
+
+class CovTauTh:
+    """
+    CovTauTh
+
+    Class to build and compute the covariance matrix of the tau statistics using a theoretical estimate
+    """
+
+    def __init__(
+        self,
+        path_gal,
+        path_psf,
+        hdu_psf,
+        treecorr_config,
+        A,
+        n_e,
+        n_psf,
+        **kwargs):
+        """
+        Initalize the CovTauTh class.
+        Galaxy and PSF star catalogs are used to initialize the value of the different shape noise.
+
+        Parameters
+        ----------
+        path_gal: str
+            Path to the galaxy catalog
+        path_psf: str
+            Path to the PSF catalog
+        hdu_psf: int
+            HDU of the PSF catalog
+        treecorr_config: dict
+            Configuration of the treecorr catalogs
+        A: float
+            Area of the survey
+        n_e: float
+            Number density of galaxies
+        n_psf: float
+            Number density of PSF stars
+        """
+        #Load the catalogs
+        cat_gal, cat_psf = fits.getdata(path_gal), fits.open(path_psf)[hdu_psf].data
+
+        #Create treecorr catalogs
+        gal, psf, psf_error, size_error = self.create_treecorr_catalog(cat_gal, cat_psf, treecorr_config)
+
+        #Compute the shape noise
+        self.sigma_e = self.compute_shape_noise(gal, gal)
+        self.sigma_psf = self.compute_shape_noise(psf, psf)
+        self.sigma_psf_error = self.compute_shape_noise(psf_error, psf_error)
+        self.sigma_psf_size_error = self.compute_shape_noise(size_error, size_error)
+
+        del gal, psf, psf_error, size_error
+        
+        self.A = A
+        self.n_e = n_e
+        self.n_psf = n_psf
+
+        self.rho_stats = kwargs.get("rho_stats", None)
+        self.tau_stats = kwargs.get("tau_stats", None)
+        self.xi_plus = kwargs.get("xi_plus", None)
+        self.xi_minus = kwargs.get("xi_minus", None)
+
+        if self.rho_stats is None:
+            print("You must load rho statistics")
+        else:
+            self.bins = self.rho_stats['theta']
+            self.delta_bin_log = (self.bins[1]-self.bins[0])/self.bins[1]
+        if self.tau_stats is None:
+            print("You must load tau statistics")
+        if self.xi_plus is None:
+            print("You must load Xi plus")
+        if self.xi_minus is None:
+            print("You must load Xi minus")
+
+        self.component_dict = {
+            '00': {
+                'eb': 'tau_0',
+                'ec': 'tau_0',
+                'bc': 'rho_0',
+                'sigma_bc': self.sigma_psf
+            },
+            '22': {
+                'eb': 'tau_2',
+                'ec': 'tau_2',
+                'bc': 'rho_1',
+                'sigma_bc': self.sigma_psf_error
+            },
+            '55': {
+                'eb': 'tau_5',
+                'ec': 'tau_5',
+                'bc': 'rho_3',
+                'sigma_bc': self.sigma_psf_size_error
+            },
+            '02': {
+                'eb': 'tau_0',
+                'ec': 'tau_2',
+                'bc': 'rho_2'
+            },
+            '05': {
+                'eb': 'tau_0',
+                'ec': 'tau_5',
+                'bc': 'rho_5'
+            },
+            '25': {
+                'eb': 'tau_2',
+                'ec': 'tau_5',
+                'bc': 'rho_4'
+            }
+        }
+
+    def create_treecorr_catalog(self, cat_gal, cat_psf, treecorr_config):
+        """
+        Create treecorr catalogs from the galaxy and PSF catalogs to compute estimate shape noise in covariance terms.
+        Works only for shapepipe catalogs right now. A config file will be added later
+        
+        Parameters
+        ----------
+        cat_gal: numpy.ndarray
+            Galaxy catalog
+        cat_psf: numpy.ndarray
+            PSF catalog
+        treecorr_config : dict
+            Configuration of the treecorr catalogs
+        
+        Returns
+        -------
+        treecorr.Catalog, treecorr.Catalog, treecorr.Catalog, treecorr.Catalog
+            Galaxy catalog, PSF catalog, PSF error catalog, PSF size error catalog
+        """
+        ra_units = treecorr_config.get('ra_units', 'deg')
+        dec_units = treecorr_config.get('dec_units', 'deg')
+        gal = treecorr.Catalog(
+            ra=cat_gal['ra'], dec=cat_gal['dec'], w=cat_gal['w'], g1=cat_gal['e1'], g2=cat_gal['e2'],
+            ra_units=ra_units, dec_units=dec_units
+        )
+
+        psf = treecorr.Catalog(
+            ra=cat_psf['ra'], dec=cat_psf['dec'], g1=cat_psf['E1_PSF_HSM'], g2=cat_psf['E2_PSF_HSM'],
+            ra_units=ra_units, dec_units=dec_units
+        )
+
+        psf_error = treecorr.Catalog(
+            ra=cat_psf['ra'], dec=cat_psf['dec'], g1=cat_psf['E1_STAR_HSM']-cat_psf['E1_PSF_HSM'], g2=cat_psf['E2_STAR_HSM']-cat_psf['E2_PSF_HSM'],
+            ra_units=ra_units, dec_units=dec_units
+        )
+
+        size_error = treecorr.Catalog(
+            ra=cat_psf['ra'], dec=cat_psf['dec'],
+            g1=cat_psf['E1_STAR_HSM']*(cat_psf["SIGMA_STAR_HSM"]**2-cat_psf["SIGMA_PSF_HSM"]**2)/cat_psf["SIGMA_STAR_HSM"]**2,
+            g2=cat_psf['E2_PSF_HSM']*(cat_psf["SIGMA_STAR_HSM"]**2-cat_psf["SIGMA_PSF_HSM"]**2)/cat_psf["SIGMA_STAR_HSM"]**2,
+            ra_units=ra_units, dec_units=dec_units
+        )
+        return gal, psf, psf_error, size_error
+    
+    def compute_shape_noise(self, cat1, cat2):
+        """
+        Compute the shape noise between two catalogs
+
+        Parameters
+        ----------
+        cat1: treecorr.Catalog
+            First catalog
+        cat2: treecorr.Catalog
+            Second catalog
+        
+        Returns
+        -------
+        float
+            Shape noise between the two catalogs
+        """
+        return np.sqrt(0.5*np.average((cat1.g1*cat2.g1)+(cat1.g2*cat2.g2), weights=cat1.w*cat2.w))
+
+    def load_rho_stat(self, path_rho_stat):
+        """
+        Load rho statistics
+
+        Parameters
+        ----------
+        path_rho_stat: str
+            Path to the rho statistics
+        """
+        self.rho_stats = fits.getdata(path_rho_stat)
+        self.bins = self.rho_stats['theta']
+        self.delta_bin_log = (self.bins[1]-self.bins[0])/self.bins[1]
+
+    def load_tau_stat(self, path_tau_stat):
+        """
+        Load tau statistics
+
+        Parameters
+        ----------
+        path_tau_stat: str
+            Path to the tau statistics
+        """
+        self.tau_stats = fits.getdata(path_tau_stat)
+
+    def load_xi_plus(self, path_xi_plus):
+        """
+        Load Xi plus
+
+        Parameters
+        ----------
+        path_xi_plus: str
+            Path to the Xi plus
+        """
+        self.xi_plus = fits.getdata(path_xi_plus)
+
+    def load_xi_minus(self, path_xi_minus):
+        """
+        Load Xi minus
+
+        Parameters
+        ----------
+        path_xi_minus: str
+            Path to the Xi minus
+        """
+        self.xi_minus = fits.getdata(path_xi_minus)
+
+    def check_consistency(self):
+        """
+        Check that the input data have the same angular bins.
+
+        Returns
+        -------
+        bool
+            True if the data are consistent, False otherwise
+        """
+        assert (self.rho_stats is not None), ("You must load rho statistics")
+        assert (self.tau_stats is not None), ("You must load tau statistics")
+        assert (self.xi_plus is not None), ("You must load Xi plus")
+        assert (self.xi_minus is not None), ("You must load Xi minus")
+
+        consistency = np.all(self.bins == self.tau_stats['theta']) & np.all(self.bins == self.xi_plus['ang']) & np.all(self.bins == self.xi_minus['ang'])
+        if not consistency:
+            print("!!! The angular bins are not consistent !!!")
+        return consistency
+    
+    def build_interpolator(self, bins, values):
+        """
+        Build an interpolator for the given values using the given bins to compute the covariance.
+        Scipy interpolator will be used between the bins. The function will then return 0 outside
+        this range.
+
+        Parameters
+        ----------
+        bins: numpy.ndarray
+            Bins of the values
+        values: numpy.ndarray
+            Values to interpolate
+
+        Returns
+        -------
+        callable:
+            Interpolator function
+        """
+        interpolator = interpolate.make_interp_spline(bins, values, k=1)
+        func = lambda x: np.where((x >= bins[0]) & (x <= bins[-1]),
+                                  interpolator(x),
+                                   np.where(x < bins[0], np.ones_like(x)*values[0], np.zeros_like(x))
+        )
+        return func
+
+    def build_cov(self, **kwargs):
+        """
+        Computes the covariance matrix of the tau statistics using a theoretical estimate
+
+        Returns
+        -------
+        numpy.ndarray
+            Covariance matrix of the tau statistics
+        """
+        cov_00 = self.compute_00_comp(**kwargs)
+        cov_02 = self.compute_02_comp(**kwargs)
+        cov_05 = self.compute_05_comp(**kwargs)
+        cov_25 = self.compute_25_comp(**kwargs)
+        cov_22 = self.compute_22_comp(**kwargs)
+        cov_55 = self.compute_55_comp(**kwargs)
+
+        cov = np.block([
+            [cov_00, cov_02, cov_05],
+            [cov_02.T, cov_22, cov_25],
+            [cov_05.T, cov_25.T, cov_55]
+        ])
+        return cov
+
+    def compute_00_comp(self, **kwargs):
+        """
+        Computes the auto-correlation tau_0/tau_0.
+
+        Returns
+        -------
+        numpy.ndarray
+            Auto-correlation tau_0/tau_0
+        """
+        cov_00 = self.compute_sn('00', **kwargs) + self.compute_mt('00', **kwargs) + self.compute_cv_rho_plus('00', **kwargs) +\
+            self.compute_cv_tau_plus('00', **kwargs) + self.compute_cv_rho_minus('00', **kwargs) +\
+            self.compute_cv_tau_minus('00', **kwargs)
+        return cov_00
+
+    def compute_02_comp(self, **kwargs):
+        """
+        Computes the cross-correlation tau_0/tau_2.
+
+        Returns
+        -------
+        numpy.ndarray
+            Cross-correlation tau_0/tau_2
+        """
+        cov_02 = self.compute_sn('02', **kwargs) + self.compute_mt('02', **kwargs) + self.compute_cv_rho_plus('02', **kwargs) +\
+            self.compute_cv_tau_plus('02', **kwargs) + self.compute_cv_rho_minus('02', **kwargs) +\
+            self.compute_cv_tau_minus('02', **kwargs)
+        return cov_02
+
+    def compute_05_comp(self, **kwargs):
+        """
+        Computes the cross-correlation tau_0/tau_5.
+
+        Returns
+        -------
+        numpy.ndarray
+            Cross-correlation tau_0/tau_5
+        """
+        cov_05 = self.compute_sn('05', **kwargs) + self.compute_mt('05', **kwargs) + self.compute_cv_rho_plus('05', **kwargs) +\
+            self.compute_cv_tau_plus('05', **kwargs) + self.compute_cv_rho_minus('05', **kwargs) +\
+            self.compute_cv_tau_minus('05', **kwargs)
+        return cov_05
+
+    def compute_25_comp(self, **kwargs):
+        """
+        Computes the cross-correlation tau_2/tau_5.
+
+        Returns
+        -------
+        numpy.ndarray
+            Cross-correlation tau_2/tau_5
+        """
+        cov_25 = self.compute_sn('25', **kwargs) + self.compute_mt('25', **kwargs) + self.compute_cv_rho_plus('25', **kwargs) +\
+            self.compute_cv_tau_plus('25', **kwargs) + self.compute_cv_rho_minus('25', **kwargs) +\
+            self.compute_cv_tau_minus('25', **kwargs)
+        return cov_25
+
+    def compute_22_comp(self, **kwargs):
+        """
+        Computes the auto-correlation tau_2/tau_2.
+
+        Returns
+        -------
+        numpy.ndarray
+            Auto-correlation tau_2/tau_2
+        """
+        cov_22 = self.compute_sn('22', **kwargs) + self.compute_mt('22', **kwargs) +self.compute_cv_rho_plus('22', **kwargs) +\
+            self.compute_cv_tau_plus('22', **kwargs) + self.compute_cv_rho_minus('22', **kwargs) +\
+            self.compute_cv_tau_minus('22', **kwargs)
+        return cov_22
+
+    def compute_55_comp(self, **kwargs):
+        """
+        Computes the auto-correlation tau_5/tau_5.
+
+        Returns
+        -------
+        numpy.ndarray
+            Auto-correlation tau_5/tau_5
+        """
+        cov_55 = self.compute_sn('55', **kwargs) + self.compute_mt('55', **kwargs) + self.compute_cv_rho_plus('55', **kwargs) +\
+            self.compute_cv_tau_plus('55', **kwargs) + self.compute_cv_rho_minus('55', **kwargs) +\
+            self.compute_cv_tau_minus('55', **kwargs)
+        return cov_55
+    
+    def compute_sn(self, component, **kwargs):
+        """
+        Computes the shot noise component of the covariance matrix for the given component.
+
+        Parameters
+        ----------
+        component: str
+            Component of the covariance matrix
+        
+        Returns
+        -------
+        numpy.ndarray
+            Shot noise component of the covariance matrix
+        """
+        assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
+        sigma_bc = self.component_dict[component].get('sigma_bc', 0)
+
+        sn = np.zeros((len(self.bins), len(self.bins)))
+        for i in range(len(self.bins)):
+            sn[i,i] = self.sigma_e**2*sigma_bc**2/(4*np.pi*self.A*self.n_e*self.n_psf*self.bins[i]**2*(self.delta_bin_log))
+        return sn
+
+    def compute_mt(self, component, **kwargs):
+        """
+        Computes the mixed term component of the covariance matrix for the given component.
+
+        Parameters
+        ----------
+        component: str
+            Component of the covariance matrix
+        
+        Returns
+        -------
+        numpy.ndarray
+            Shot noise component of the covariance matrix
+        """
+        assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
+        correlator = self.component_dict[component]['bc']
+        sigma_bc = self.component_dict[component].get('sigma_bc', None)
+        nbin_ang = kwargs.get("nbin_ang", 20)
+
+        mt = np.zeros((len(self.bins), len(self.bins)))
+        #interpolator_rho = interpolate.make_interp_spline(self.bins, self.rho_stats[correlator+"_p"], k=1)
+        interpolator_rho = self.build_interpolator(self.bins, self.rho_stats[correlator+"_p"])
+        if sigma_bc is not None:
+            #interpolator_xi_plus = interpolate.make_interp_spline(self.bins, self.xi_plus['value'], k=1)
+            interpolator_xi_plus = self.build_interpolator(self.bins, self.xi_plus['value'])
+        phi = np.linspace(0, np.pi, nbin_ang)
+        for i in range(len(self.bins)):
+            for j in range(len(self.bins)):
+                y = np.sqrt(self.bins[i]**2+self.bins[j]**2-2*self.bins[i]*self.bins[j]*np.cos(phi))
+                mt[i, j] = integrate.simps(interpolator_rho(y), phi)*self.sigma_e**2/(2*np.pi*self.A*self.n_e)
+                if sigma_bc is not None:
+                    mt[i, j] += integrate.simps(interpolator_xi_plus(y), phi)*sigma_bc**2/(2*np.pi*self.A*self.n_e)
+                mt[j, i] = mt[i, j]
+        return mt
+    
+    def compute_cv_rho_plus(self, component, **kwargs):
+        """
+        Computes the cosmic variance component of the covariance matrix for the given components
+        based on the rho statistics + component.
+
+        Parameters
+        ----------
+        components: str
+            Component of the covariance matrix
+        
+        Returns
+        -------
+        numpy.ndarray
+            Cosmic variance component of the covariance matrix
+        """
+        assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
+        xi_plus = self.xi_plus['value']
+        rho = self.rho_stats[self.component_dict[component]['bc']+'_p']
+        nbin_ang = kwargs.get("nbin_ang", 20)
+        nbin_rad = kwargs.get("nbin_rad", 20)
+
+        cv = np.zeros((len(self.bins), len(self.bins)))
+
+        #interpolator_xi = interpolate.make_interp_spline(self.bins, xi_plus, k=1)
+        #interpolator_rho = interpolate.make_interp_spline(self.bins, rho, k=1)
+        interpolator_xi = self.build_interpolator(self.bins, xi_plus)
+        interpolator_rho = self.build_interpolator(self.bins, rho)
+        phi_angle = np.linspace(0, np.pi, nbin_ang)
+        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        for i in range(len(self.bins)):
+            for j in range(len(self.bins)):
+                radius_val = np.zeros(len(phi_radius))
+                for k, phi_r in enumerate(phi_radius):
+                    y_xi = np.sqrt(phi_r**2+self.bins[i]**2-2*phi_r*self.bins[i]*np.cos(phi_angle))
+                    y_rho = np.sqrt(phi_r**2+self.bins[j]**2+2*phi_r*self.bins[j]*np.cos(phi_angle))
+                    int_xi = integrate.simps(interpolator_xi(y_xi), phi_angle)
+                    int_rho = integrate.simps(interpolator_rho(y_rho), phi_angle)
+                    radius_val[k] = int_xi*int_rho*phi_r
+                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
+                cv[j, i] = cv[i, j]
+        return cv
+
+    def compute_cv_tau_plus(self, component, **kwargs):
+        """
+        Computes the cosmic variance component of the covariance matrix for the given components
+        based on the tau statistics + component.
+
+        Parameters
+        ----------
+        components: str
+            Component of the covariance matrix
+        
+        Returns
+        -------
+        numpy.ndarray
+            Cosmic variance component of the covariance matrix
+        """
+        assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
+        tau_b = self.tau_stats[self.component_dict[component]['eb']+'_p']
+        tau_c = self.tau_stats[self.component_dict[component]['ec']+'_p']
+        nbin_ang = kwargs.get("nbin_ang", 20)
+        nbin_rad = kwargs.get("nbin_rad", 20)
+
+        cv = np.zeros((len(self.bins), len(self.bins)))
+
+        #interpolator_tau_b = interpolate.make_interp_spline(self.bins, tau_b, k=1)
+        #interpolator_tau_c = interpolate.make_interp_spline(self.bins, tau_c, k=1)
+        interpolator_tau_b = self.build_interpolator(self.bins, tau_b)
+        interpolator_tau_c = self.build_interpolator(self.bins, tau_c)
+        phi_angle = np.linspace(0, np.pi, nbin_ang)
+        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        for i in range(len(self.bins)):
+            for j in range(len(self.bins)):
+                radius_val = np.zeros(len(phi_radius))
+                for k, phi_r in enumerate(phi_radius):
+                    y_tau_b = np.sqrt(phi_r**2+self.bins[i]**2-2*phi_r*self.bins[i]*np.cos(phi_angle))
+                    y_tau_c = np.sqrt(phi_r**2+self.bins[j]**2+2*phi_r*self.bins[j]*np.cos(phi_angle))
+                    int_tau_b = integrate.simps(interpolator_tau_b(y_tau_b), phi_angle)
+                    int_tau_c = integrate.simps(interpolator_tau_c(y_tau_c), phi_angle)
+                    radius_val[k] = int_tau_b*int_tau_c*phi_r
+                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
+                cv[j, i] = cv[i, j]
+        return cv
+    
+    def compute_cv_rho_minus(self, component, **kwargs):
+        """
+        Computes the cosmic variance component of the covariance matrix for the given components
+        based on the rho statistics - component.
+
+        Parameters
+        ----------
+        components: str
+            Component of the covariance matrix
+
+        Returns
+        -------
+        numpy.ndarray
+            Cosmic variance component of the covariance matrix
+        """
+        assert(component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
+        xi_minus = self.xi_minus['value']
+        rho = self.rho_stats[self.component_dict[component]['bc']+'_m']
+        nbin_ang = kwargs.get("nbin_ang", 20)
+        nbin_rad = kwargs.get("nbin_rad", 20)
+
+        cv = np.zeros((len(self.bins), len(self.bins)))
+        #interpolator_xi = interpolate.make_interp_spline(self.bins, xi_minus, k=1)
+        #interpolator_rho = interpolate.make_interp_spline(self.bins, rho, k=1)
+        interpolator_xi = self.build_interpolator(self.bins, xi_minus)
+        interpolator_rho = self.build_interpolator(self.bins, rho)
+        phi_angle = np.linspace(0, np.pi, nbin_ang)
+        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        for i in range(len(self.bins)):
+            for j in range(len(self.bins)):
+                radius_val = np.zeros(len(phi_radius))
+                for k, phi_r in enumerate(phi_radius):
+                    psi_a = np.array([
+                        phi_r-self.bins[i]*np.cos(phi_angle),
+                        -self.bins[i]*np.sin(phi_angle)
+                    ])
+                    psi_b = np.array([
+                        phi_r+self.bins[j]*np.cos(phi_angle),
+                        self.bins[j]*np.sin(phi_angle)
+                    ])
+                    norm_a = np.linalg.norm(psi_a, axis=0)
+                    polar_a = np.arctan2(psi_a[1], psi_a[0])
+                    norm_b = np.linalg.norm(psi_b, axis=0)
+                    polar_b = np.arctan2(psi_b[1], psi_b[0])
+                    #The term coming from the sine will integrate out to 0 so we can only compute terms with cosine
+                    int_xi = integrate.simps(interpolator_xi(norm_a)*np.cos(4*polar_a), phi_angle)
+                    int_rho = integrate.simps(interpolator_rho(norm_b)*np.cos(4*polar_b), phi_angle)
+                    radius_val[k] = int_xi*int_rho*phi_r
+                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
+                cv[j, i] = cv[i, j]
+        return cv
+
+    def compute_cv_tau_minus(self, component, **kwargs):
+        """
+        Computes the cosmic variance component of the covariance matrix for the given components
+        based on the tau statistics - component.
+
+        Parameters
+        ----------
+        components: str
+            Component of the covariance matrix
+        
+        Returns
+        -------
+        numpy.ndarray
+            Cosmic variance component of the covariance matrix
+        """
+        assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
+        tau_b = self.tau_stats[self.component_dict[component]['eb']+'_m']
+        tau_c = self.tau_stats[self.component_dict[component]['ec']+'_m']
+        nbin_ang = kwargs.get("nbin_ang", 20)
+        nbin_rad = kwargs.get("nbin_rad", 20)
+
+        cv = np.zeros((len(self.bins), len(self.bins)))
+        #interpolator_tau_b = interpolate.make_interp_spline(self.bins, tau_b, k=1)
+        #interpolator_tau_c = interpolate.make_interp_spline(self.bins, tau_c, k=1)
+        interpolator_tau_b = self.build_interpolator(self.bins, tau_b)
+        interpolator_tau_c = self.build_interpolator(self.bins, tau_c)
+        phi_angle = np.linspace(0, np.pi, nbin_ang)
+        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        for i in range(len(self.bins)):
+            for j in range(len(self.bins)):
+                radius_val = np.zeros(len(phi_radius))
+                for k, phi_r in enumerate(phi_radius):
+                    psi_a = np.array([
+                        phi_r-self.bins[i]*np.cos(phi_angle),
+                        -self.bins[i]*np.sin(phi_angle)
+                    ])
+                    psi_b = np.array([
+                        phi_r+self.bins[j]*np.cos(phi_angle),
+                        self.bins[j]*np.sin(phi_angle)
+                    ])
+                    norm_a = np.linalg.norm(psi_a, axis=0)
+                    polar_a = np.arctan2(psi_a[1], psi_a[0])
+                    norm_b = np.linalg.norm(psi_b, axis=0)
+                    polar_b = np.arctan2(psi_b[1], psi_b[0])
+                    #The term coming from the sine will integrate out to 0 so we can only compute terms with cosine
+                    int_tau_b = integrate.simps(interpolator_tau_b(norm_a)*np.cos(4*polar_a), phi_angle)
+                    int_tau_c = integrate.simps(interpolator_tau_c(norm_b)*np.cos(4*polar_b), phi_angle)
+                    radius_val[k] = int_tau_b*int_tau_c*phi_r
+                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
+                cv[j, i] = cv[i, j]
+        return cv
+
+
+
+                
+
+
+        
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 0372de9..5688a25 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -529,7 +529,7 @@ def compute_rho_stats(self, catalog_id, filename, save_cov=False, func=None, var
 
         self.rho_stats = Table(
             [
-                rho_0.rnom,
+                rho_0.meanr,
                 rho_0.xip,
                 rho_0.varxip,
                 rho_0.xim,
@@ -788,7 +788,7 @@ def compute_tau_stats(self, catalog_id, filename, save_cov=False, func=None, var
 
         self.tau_stats = Table(
             [
-                tau_0.rnom,
+                tau_0.meanr,
                 tau_0.xip,
                 tau_0.varxip,
                 tau_0.xim,
@@ -1149,9 +1149,9 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
         assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
         #Check that the abssiss are the same
 
-        assert (self.cov is not None), ("Please load a covariance matrix")
+        assert (self.cov_tau is not None), ("Please load a covariance matrix")
 
-        inv_cov = np.linalg.inv(self.cov)
+        inv_cov = np.linalg.inv(self.cov_tau)
         output = np.array([
             self.tau_stat_handler.tau_stats["tau_0_p"],
             self.tau_stat_handler.tau_stats["tau_2_p"],

From 30e2ab942c7d248f575ae677e0562528ebc79438 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 4 Apr 2024 13:40:45 +0200
Subject: [PATCH 60/97] versions updated; cs_util 0.1.0

---
 pyproject.toml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index a69635e..8554693 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = {path = "../cs_util"}
+cs-util = "^0.1.0"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"
@@ -33,6 +33,8 @@ lenspack = "^1.0.0"
 #numpy = "1.26.1"
 getdist = "^1.4.7"
 cmake = "^3.28.1"
+tqdm = "^4.66.2"
+emcee = "^3.1.4"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 5b5b5962013212ebee6c56ab3e08d181a0ef3e5f Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 4 Apr 2024 13:41:23 +0200
Subject: [PATCH 61/97] Saving best fit to pickle (instead of json, did not
 work)

---
 shear_psf_leakage/leakage.py | 38 +++++++++++++++++++-----------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index 5d816f5..c546d6e 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -14,7 +14,7 @@
 import os
 
 import numpy as np
-import json
+import pickle
 import matplotlib.pylab as plt
 from lmfit import minimize, Parameters
 from uncertainties import ufloat
@@ -925,24 +925,26 @@ def corr_2d(
     # Initialise parameters of model to fit
     params = Parameters()
 
+    val_init = 0.0
+
     # Affine parameters
     for p_affine in ["a11", "a22", "c1", "c2"]:
-        params.add(p_affine, value=0.0)
+        params.add(p_affine, value=val_init)
 
     if mix:
         # Linear mixing pararmeters
-        params.add("a12", value=0.0)
-        params.add("a21", value=0.0)
+        params.add("a12", value=val_init)
+        params.add("a21", value=val_init)
 
     if order == "quad":
         # Quadratic parameters
         for p_quad in ["q111", "q222"]:
-            params.add(p_quad, value=0.0)
+            params.add(p_quad, value=val_init)
 
         if mix:
             # Quadratic mixing parameters
             for p_quad_mix in ["q112", "q122", "q212", "q211"]:
-                params.add(p_quad_mix, value=0.0)
+                params.add(p_quad_mix, value=val_init)
 
     # Mininise loss function
     if weights is not None:
@@ -1341,10 +1343,10 @@ def affine_corr_n(
     plt.close()
 
 
-def save_to_json(data, fname):
-    """Save To Json.
+def save_to_file(data, fname):
+    """Save To File.
 
-    Save data to .json file.
+    Save data to .pkl (pickle) file.
 
     Parameters
     ----------
@@ -1355,17 +1357,17 @@ def save_to_json(data, fname):
 
     See also
     --------
-    read_from_json
+    read_from_file
 
     """
-    with open(fname, "w") as f:
-        data.dump(f)
+    with open(fname, "wb") as f:
+        pickle.dump(data, f)
 
 
-def read_from_json(fname):
-    """Read From Json.
+def read_from_file(fname):
+    """Read From File.
 
-    Read data from .json file.
+    Read data from .pkl (pickle) file.
 
     Parameters
     ----------
@@ -1379,11 +1381,11 @@ def read_from_json(fname):
 
     See also
     --------
-    save_to_json
+    save_to_file
 
     """
 
-    with open(fname) as f:
-        data = json.load(f)
+    with open(fname, "rb") as f:
+        data = pickle.load(f)
 
     return data

From cb0ed0c7e98fb25640f1bb428e64754167517bd6 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 4 Apr 2024 13:42:21 +0200
Subject: [PATCH 62/97] using args.py from cs_util 0.1.0

---
 shear_psf_leakage/rho_tau_stat.py |   5 +-
 shear_psf_leakage/run_object.py   | 153 +-----------------------------
 2 files changed, 8 insertions(+), 150 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 3668534..692bfd7 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -321,9 +321,10 @@ def get_cat_fields(self, cat_type, square_size=False):
             else:
                 size_star = self.dat_psf[self._params["star_size"]]**2 if square_size else  self.dat_psf[self._params["star_size"]]
                 size_psf = self.dat_psf[self._params["PSF_size"]]**2 if square_size else  self.dat_psf[self._params["PSF_size"]]
-                g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf)/size_star
+
+                g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf) / size_star
                 #g1 -= g1.mean()
-                g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf)/size_star
+                g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf) / size_star
                 #g2 -= g2.mean()
 
         return ra, dec, g1, g2, weights
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 2e1ad05..8de9cdd 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -7,148 +7,11 @@
 from optparse import OptionParser
 
 from cs_util import logging
+from cs_util import args as cs_args
 
 from . import leakage
 
 
-# MKDEBUG TODO: to cs_util. Also check shapepipe.
-def my_string_split(string, num=-1, verbose=False, stop=False, sep=None):
-    """My String Split.
-
-    Split a *string* into a list of strings. Choose as separator
-    the first in the list [space, underscore] that occurs in the string.
-    (Thus, if both occur, use space.)
-
-    Parameters
-    ----------
-    string : str
-        Input string
-    num : int
-        Required length of output list of strings, -1 if no requirement.
-    verbose : bool
-        Verbose output
-    stop : bool
-        Stop programs with error if True, return None and continues otherwise
-    sep : bool
-        Separator, try ' ', '_', and '.' if None (default)
-
-    Raises
-    ------
-    CfisError
-        If number of elements in string and num are different, for stop=True
-    ValueError
-        If no separator found in string
-
-    Returns
-    -------
-    list
-        List of string on success, and None if failed
-
-    """
-    if string is None:
-        return None
-
-    if sep is None:
-        has_space = string.find(" ")
-        has_underscore = string.find("_")
-        has_dot = string.find(".")
-
-        if has_space != -1:
-            my_sep = " "
-        elif has_underscore != -1:
-            my_sep = "_"
-        elif has_dot != -1:
-            my_sep = "."
-        else:
-            # no separator found, does string consist of only one element?
-            if num == -1 or num == 1:
-                my_sep = None
-            else:
-                raise Valueerror(
-                    "No separator (' ', '_', or '.') found in string"
-                    + f" '{string}', cannot split"
-                )
-    else:
-        if not string.find(sep):
-            raise ValueError(
-                f"No separator '{sep}' found in string '{string}', " + "cannot split"
-            )
-        my_sep = sep
-
-    res = string.split(my_sep)
-
-    if num != -1 and num != len(res) and stop:
-        raise CfisError(f"String '{len(res)}' has length {num}, required is {num}")
-
-    return res
-
-
-# MKDEBUG to cs_util
-def parse_options(p_def, short_options, types, help_strings):
-    """Parse Options.
-
-    Parse command line options.
-
-    Parameters
-    ----------
-    p_def : dict
-        default parameter values
-    help_strings : dict
-        help strings for options
-
-    Returns
-    -------
-    options: tuple
-        Command line options
-    """
-
-    usage = "%prog [OPTIONS]"
-    parser = OptionParser(usage=usage)
-
-    for key in p_def:
-        if key in help_strings:
-            if key in short_options:
-                short = short_options[key]
-            else:
-                short = ""
-
-            if key in types:
-                typ = types[key]
-            else:
-                typ = "string"
-
-            if typ == "bool":
-                parser.add_option(
-                    f"{short}",
-                    f"--{key}",
-                    dest=key,
-                    default=False,
-                    action="store_true",
-                    help=help_strings[key].format(p_def[key]),
-                )
-            else:
-                parser.add_option(
-                    f"{short}",
-                    f"--{key}",
-                    dest=key,
-                    type=typ,
-                    default=p_def[key],
-                    help=help_strings[key].format(p_def[key]),
-                )
-
-    parser.add_option(
-        "-v",
-        "--verbose",
-        dest="verbose",
-        action="store_true",
-        help=f"verbose output",
-    )
-
-    options, args = parser.parse_args()
-
-    return options
-
-
 class LeakageObject:
     """LeakageObject.
 
@@ -168,7 +31,7 @@ def set_params_from_command_line(self, args):
 
         """
         # Read command line options
-        options = parse_options(
+        options = cs_args.parse_options(
             self._params,
             self._short_options,
             self._types,
@@ -280,13 +143,13 @@ def update_params(self):
 
         """
         if self._params["cols"]:
-            self._params["cols"] = my_string_split(
+            self._params["cols"] = cs_args.my_string_split(
                 self._params["cols"],
                 verbose=self._params["verbose"],
                 stop=True,
             )
         if self._params["cols_ratio"]:
-            self._params["cols_ratio"] = my_string_split(
+            self._params["cols_ratio"] = cs_args.my_string_split(
                 self._params["cols_ratio"],
                 num=2,
                 verbose=self._params["verbose"],
@@ -500,7 +363,6 @@ def get_ellipticity_weights(self):
 
         return e, weights
 
-<<<<<<< HEAD
     def get_out_base(self, mix, order):
         """Get Out Base.
 
@@ -514,9 +376,6 @@ def get_out_base(self, mix, order):
 
         return out_base
 
-
-=======
->>>>>>> origin/rho_tau_hdu
     def PSF_leakage(self, mix=True, order="lin"):
         """PSF Leakage.
 
@@ -571,12 +430,10 @@ def PSF_leakage(self, mix=True, order="lin"):
             stats_file=self._stats_file,
             verbose=self._params["verbose"],
         )
-        leakage.save_to_json(self.par_best_fit, f"{out_path}.json")
+        leakage.save_to_file(self.par_best_fit, f"{out_path}.pkl")
 
         # Fit separate 1D models
         # MKDEBUG TODO: put in separate class funtion
-        fp_best_fit = open(f"{out_path}.json", "w")
-        self.par_best_fit.dump(fp_best_fit)
 
         ylabel = r"$e_{1,2}^{\rm gal}$"
         mlabel = [r"\alpha_1", r"\alpha_2"]

From 91209f3b158d18ddebdf60ddfae8c99e71460930 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 4 Apr 2024 13:42:31 +0200
Subject: [PATCH 63/97] using args.py from cs_util 0.1.0

---
 shear_psf_leakage/run_scale.py | 150 +++++++++++++++++++--------------
 1 file changed, 88 insertions(+), 62 deletions(-)

diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index f231718..52ea57e 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -10,6 +10,8 @@
 from optparse import OptionParser
 
 import numpy as np
+from scipy.interpolate import CubicSpline
+from lmfit import minimize, Parameters
 import pandas as pd
 from astropy.io import fits
 from astropy import units
@@ -20,65 +22,12 @@
 from cs_util import calc
 from cs_util import cat as cs_cat
 from cs_util import cosmo as cs_cos
+from cs_util import args as cs_args
 
 from . import leakage
 from . import correlation as corr
 
 
-# MKDEBUG Remove (use cs_util with updated one (bool)
-def parse_options(p_def, short_options, types, help_strings):
-    """Parse command line options.
-
-    Parameters
-    ----------
-    p_def : dict
-        default parameter values
-    help_strings : dict
-        help strings for options
-
-    Returns
-    -------
-    dict
-        Command line options
-
-    """
-    usage = "%prog [OPTIONS]"
-    parser = OptionParser(usage=usage)
-
-    for key in p_def:
-        if key in help_strings:
-            if key in short_options:
-                short = short_options[key]
-            else:
-                short = ""
-
-            if key in types:
-                typ = types[key]
-            else:
-                typ = "string"
-
-            parser.add_option(
-                short,
-                f"--{key}",
-                dest=key,
-                type=typ,
-                default=p_def[key],
-                help=help_strings[key].format(p_def[key]),
-            )
-
-    parser.add_option(
-        "-v",
-        "--verbose",
-        dest="verbose",
-        action="store_true",
-        help=f"verbose output",
-    )
-
-    options, _ = parser.parse_args()
-
-    return options
-
-
 def get_theo_xi(theta, dndz_path):
     """Get Theo Xi.
 
@@ -209,7 +158,7 @@ def set_params_from_command_line(self, args):
 
         """
         # Read command line options
-        options = parse_options(
+        options = cs_args.parse_options(
             self._params,
             self._short_options,
             self._types,
@@ -747,15 +696,44 @@ def compute_alpha_mean(self):
         Compute weighted mean of the leakage function alpha.
 
         """
-        self.alpha_leak_mean = calc.transform_nan(
-            np.average(self.alpha_leak, weights=1 / self.sig_alpha_leak**2)
+        self.alpha_leak_mean, self.alpha_leak_std = (
+            calc.weighted_avg_and_std(
+                self.alpha_leak,
+                1/self.sig_alpha_leak**2
+            ) 
         )
+        #calc.transform_nan(
+        #    np.average(self.alpha_leak, weights=1/self.sig_alpha_leak**2)
+        #)
         leakage.print_stats(
             f"Weighted average alpha" + f" = {self.alpha_leak_mean:.3g}",
             self._stats_file,
             verbose=self._params["verbose"],
         )
 
+    def alpha_affine_fit(self):
+        """Alpha Affine Fit.
+
+        Fit alpha(theta) with an affine model.
+
+        """
+
+        params = Parameters()
+        params.add("m", value=0.01)
+        params.add("c", value=0.01)
+        res = minimize(
+            leakage.loss_bias_lin_1d,
+            params,
+            args=(self.r_corr_gp.meanr, self.alpha_leak, self.alpha_leak_std)
+        )
+
+        # Save best-fit parameters
+        self.alpha_affine_best_fit = res.params
+        self.alpha_leak_zero = leakage.func_bias_lin_1d(
+            self.alpha_affine_best_fit,
+            0,
+        )
+
     def compute_xi_sys(self):
         """Compute Xi Sys.
 
@@ -778,7 +756,7 @@ def compute_xi_sys(self):
         self.C_sys_std_p = C_sys_std_p
         self.C_sys_std_m = C_sys_std_m
 
-    def plot_alpha_leakage(self, close_fig=True):
+    def plot_alpha_leakage(self, close_fig=True, xlinlog="log"):
         """Plot Alpha Leakage.
 
         Plot scale-dependent leakage function alpha(theta)
@@ -788,17 +766,60 @@ def plot_alpha_leakage(self, close_fig=True):
         close_fig : bool, optional
             closes figure if ``True``; set this parameter to ``False`` for
             notebook display; default is ``True``
+        xlinlog : str, optional
+            if "lin" ("log"; default), creat plot linear (logarithmic) in x
 
         """
+        # Set some x-values and limits for plot
+        if xlinlog == "log":
+            x0 = self._params["theta_min_amin"]
+            x_affine = np.geomspace(x0, self._params["theta_max_amin"])
+            xlim = [x0, self._params["theta_max_amin"]]
+            xlog = True
+        else:
+            x0 = -10
+            x_affine = np.linspace(0, self._params["theta_max_amin"])
+            xlim = [x0 * 2, self._params["theta_max_amin"]]
+            xlog = False
+
+        # alpha(theta) data points
         theta = [self.r_corr_gp.meanr]
         alpha_theta = [self.alpha_leak]
         yerr = [self.sig_alpha_leak]
+
+        # mean alpha
+        theta.append([x0])
+        alpha_theta.append([self.alpha_leak_mean])
+        yerr.append([self.alpha_leak_std])
+
+        labels = [r"$\alpha(\theta)$", r"$\bar\alpha$"]
+        linewidths = [2, 2]
+
+        # affine model best fit alpha(0)
+        theta.append([x0])
+        alpha_theta.append([self.alpha_affine_best_fit["c"].value])
+        yerr.append([self.alpha_affine_best_fit["c"].stderr])
+        labels.append(r"$\alpha(0)$")
+        linewidths.append(1)
+
+        # affine model best fit alpha(theta)
+        theta.append(x_affine)
+        alpha_theta.append(
+            leakage.func_bias_lin_1d(self.alpha_affine_best_fit, x_affine)
+        )
+        # MKDEBUG TODO: error band
+        yerr.append(np.zeros_like(x_affine) * np.nan)
+        labels.append(r"$\alpha(\theta)$ affine fit")
+        linewidths.append(1)
+
         xlabel = r"$\theta$ [arcmin]"
         ylabel = r"$\alpha(\theta)$"
         title = ""
-        out_path = f"{self._params['output_dir']}" + f"/alpha_leakage.png"
-        xlim = [self._params["theta_min_amin"], self._params["theta_max_amin"]]
+        out_path = (
+            f"{self._params['output_dir']}" + f"/alpha_leakage_{xlinlog}.png"
+        )
         ylim = self._params["leakage_alpha_ylim"]
+
         plots.plot_data_1d(
             theta,
             alpha_theta,
@@ -807,10 +828,13 @@ def plot_alpha_leakage(self, close_fig=True):
             xlabel,
             ylabel,
             out_path,
-            xlog=True,
+            xlog=xlog,
             xlim=xlim,
             ylim=ylim,
+            labels=labels,
+            linewidths=linewidths,
             close_fig=close_fig,
+            shift_x=False,
         )
 
     def plot_alpha_leakage_matrix(self):
@@ -1006,9 +1030,11 @@ def do_alpha(self, fast=False):
             fast=fast,
         )
         self.compute_alpha_mean()
+        self.alpha_affine_fit()
 
         # Plot
-        self.plot_alpha_leakage()
+        for xlinlog in ["lin", "log"]:
+            self.plot_alpha_leakage(xlinlog=xlinlog)
 
         # Write to disk
         self.save_alpha()

From 6edfd623ed5cc2b983670d099ad9469877e05b39 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 4 Apr 2024 13:42:55 +0200
Subject: [PATCH 64/97] Fixed import

---
 tests/test_rho_tau_stats.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/test_rho_tau_stats.py b/tests/test_rho_tau_stats.py
index 86a5d5b..69d0385 100644
--- a/tests/test_rho_tau_stats.py
+++ b/tests/test_rho_tau_stats.py
@@ -3,8 +3,8 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from rho_tau_stat import RhoStat, TauStat, PSFErrorFit
-from plots import plot_contours
+from shear_psf_leakage.rho_tau_stat import RhoStat, TauStat, PSFErrorFit
+from shear_psf_leakage.plots import plot_contours
 
 
 #Run tests on shapepipe V1 and DESY3
@@ -202,4 +202,4 @@
         psf_fitter.plot_xi_psf_sys(flat_sample[-i+1], cat_id, color, alpha=0.1)
     psf_fitter.plot_xi_psf_sys(mcmc_result[1], cat_id, color)
 plt.legend()
-plt.savefig(output+'/xi_sys.png')
\ No newline at end of file
+plt.savefig(output+'/xi_sys.png')

From 358879abcf74c13e6f2185fc532807e4f1866cab Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Thu, 4 Apr 2024 14:05:18 +0200
Subject: [PATCH 65/97] poetry lock

---
 poetry.lock | 44 +++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 41 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index abfc6f8..d989a28 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -837,7 +837,7 @@ test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "cs_util"
-version = "0.0.7"
+version = "0.1.0"
 description = ""
 optional = false
 python-versions = ">=3.7"
@@ -848,7 +848,7 @@ develop = false
 astropy = ">=4.0"
 datetime = ">=4.7"
 matplotlib = ">=3.3"
-numpy = ">=1.21"
+numpy = ">=1.19"
 vos = ">=3.3"
 
 [package.extras]
@@ -951,6 +951,24 @@ files = [
     {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
 ]
 
+[[package]]
+name = "emcee"
+version = "3.1.4"
+description = "The Python ensemble sampling toolkit for MCMC"
+optional = false
+python-versions = "*"
+files = [
+    {file = "emcee-3.1.4-py2.py3-none-any.whl", hash = "sha256:13d216a96b4f60661839d5f6ec482eda9e86b499502cda2bf6d8cc839ccf3e59"},
+    {file = "emcee-3.1.4.tar.gz", hash = "sha256:8e0e19dc8bcef9c6d02f860bef8ddc6c876b8878a6ce666943e2c5cfd9317fed"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+extras = ["h5py", "scipy"]
+tests = ["coverage[toml]", "pytest", "pytest-cov"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.0"
@@ -3561,6 +3579,26 @@ files = [
     {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.66.2"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
+    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "traitlets"
 version = "5.14.1"
@@ -3854,4 +3892,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "820dc52af2f578ca71fe2c7500d527f26683e1ec551fc66c03daf97ac7a2cec5"
+content-hash = "7534a14460be521619bd79e621ceb4c4b9710b5adf88f0886d7d94c1f76bd210"

From 8a15317018a172bb9c2bd672edd17baf7ceafbb4 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sun, 7 Apr 2024 14:00:32 +0200
Subject: [PATCH 66/97] run object: added mix and order as arguments

---
 shear_psf_leakage/run_object.py | 35 ++++++++++++++++++++++++---------
 1 file changed, 26 insertions(+), 9 deletions(-)

diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 25aaf59..11363bd 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -37,12 +37,7 @@ def set_params_from_command_line(self, args):
             self._types,
             self._help_strings,
         )
-
-        # Update parameter values from options
-        for key in vars(options):
-            self._params[key] = getattr(options, key)
-
-        # del options ?
+        self._params = options
 
         # Save calling command
         logging.log_command(args)
@@ -495,9 +490,18 @@ def obs_leakage(self):
             )
         self.corr_any_quant(label_quant, ratio=self._params["cols_ratio"])
 
-    def run(self):
+    def run(self, mix=None, order=None):
         """Run.
 
+        Parameters
+        ----------
+        mix : list, optional
+            list of bool; True (False) means with (without) component mixing;
+            default is `None` in which case both options will be run
+        order : list, optional
+            list of str; allowed are "lin" (linear fit) and "quad" (quadratic
+            fit); default is `None` in which case both options will be run
+
         Main processing of scale-dependent leakage.
 
         """
@@ -523,8 +527,21 @@ def run(self):
             if obj._params["PSF_leakage"]:
                 # Object-by-object spin-consistent PSF leakage
 
-                for order in ("lin", "quad"):
-                    obj.PSF_leakage(mix=True, order=order)
+                # Set mix and order to default if not provided
+                if mix is None:
+                    mix = ["lin", "quad"]
+                if order is None:
+                    order = [True, False]
+
+                # Make sure mix and order are lists
+                if not isinstance(mix, list):
+                    mix = [mix]
+                if not isinstance(order, list):
+                    order = [order]
+
+                for my_order in order:
+                    for my_mix in mix:
+                        obj.PSF_leakage(mix=my_mix, order=my_order)
 
             if obj._params["obs_leakage"]:
                 # Object-by-object dependence of general variables

From d0a9d788cbd606b175af6410079129f67d751d78 Mon Sep 17 00:00:00 2001
From: sachaguer <sacha.guerrini@cea.fr>
Date: Tue, 16 Apr 2024 09:01:02 +0200
Subject: [PATCH 67/97] Add analytical covariance for the tau-statistics

---
 shear_psf_leakage/rho_tau_cov.py | 230 ++++++++++++++++++-------------
 1 file changed, 136 insertions(+), 94 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 62dc4ef..69af92f 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -50,23 +50,50 @@ def __init__(
         n_psf: float
             Number density of PSF stars
         """
+        self.treecorr_config = treecorr_config
+        self.A = A
+        self.n_e = n_e
+        self.n_psf = n_psf
+
         #Load the catalogs
         cat_gal, cat_psf = fits.getdata(path_gal), fits.open(path_psf)[hdu_psf].data
 
         #Create treecorr catalogs
-        gal, psf, psf_error, size_error = self.create_treecorr_catalog(cat_gal, cat_psf, treecorr_config)
+        self.gal, self.psf, self.psf_error, self.size_error = self.create_treecorr_catalog(cat_gal, cat_psf)
 
         #Compute the shape noise
-        self.sigma_e = self.compute_shape_noise(gal, gal)
-        self.sigma_psf = self.compute_shape_noise(psf, psf)
-        self.sigma_psf_error = self.compute_shape_noise(psf_error, psf_error)
-        self.sigma_psf_size_error = self.compute_shape_noise(size_error, size_error)
-
-        del gal, psf, psf_error, size_error
-        
-        self.A = A
-        self.n_e = n_e
-        self.n_psf = n_psf
+        self.sigma_e = self.compute_shape_noise(self.gal, self.gal)
+        self.sigma_psf = self.compute_shape_noise(self.psf, self.psf)
+        self.sigma_psf_error = self.compute_shape_noise(self.psf_error, self.psf_error)
+        self.sigma_psf_size_error = self.compute_shape_noise(self.size_error, self.size_error)
+
+
+        #interpolate rho stats
+        self.rho_0_p_itp = self.build_interpolator(self.psf, self.psf, type='plus')
+        self.rho_0_m_itp = self.build_interpolator(self.psf, self.psf, type='minus')
+        self.rho_1_p_itp = self.build_interpolator(self.psf_error, self.psf_error, type='plus')
+        self.rho_1_m_itp = self.build_interpolator(self.psf_error, self.psf_error, type='minus')
+        self.rho_2_p_itp = self.build_interpolator(self.psf, self.psf_error, type='plus')
+        self.rho_2_m_itp = self.build_interpolator(self.psf, self.psf_error, type='minus')
+        self.rho_3_p_itp = self.build_interpolator(self.size_error, self.size_error, type='plus')
+        self.rho_3_m_itp = self.build_interpolator(self.size_error, self.size_error, type='minus')
+        self.rho_4_p_itp = self.build_interpolator(self.psf_error, self.size_error, type='plus')
+        self.rho_4_m_itp = self.build_interpolator(self.psf_error, self.size_error, type='minus')
+        self.rho_5_p_itp = self.build_interpolator(self.psf, self.size_error, type='plus')
+        self.rho_5_m_itp = self.build_interpolator(self.psf, self.size_error, type='minus')
+
+
+        #interpolate tau stats
+        self.tau_0_p_itp = self.build_interpolator(self.gal, self.psf, type='plus')
+        self.tau_0_m_itp = self.build_interpolator(self.gal, self.psf, type='minus')
+        self.tau_2_p_itp = self.build_interpolator(self.gal, self.psf_error, type='plus')
+        self.tau_2_m_itp = self.build_interpolator(self.gal, self.psf_error, type='minus')
+        self.tau_5_p_itp = self.build_interpolator(self.gal, self.size_error, type='plus')
+        self.tau_5_m_itp = self.build_interpolator(self.gal, self.size_error, type='minus')
+
+        #interpolate xi plus and minus
+        self.xi_plus_itp = self.build_interpolator(self.gal, self.gal, type='plus')
+        self.xi_minus_itp = self.build_interpolator(self.gal, self.gal, type='minus')
 
         self.rho_stats = kwargs.get("rho_stats", None)
         self.tau_stats = kwargs.get("tau_stats", None)
@@ -87,41 +114,41 @@ def __init__(
 
         self.component_dict = {
             '00': {
-                'eb': 'tau_0',
-                'ec': 'tau_0',
-                'bc': 'rho_0',
-                'sigma_bc': self.sigma_psf
+                'eb': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
+                'ec': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
+                'bc': {'p': self.rho_0_p_itp, 'm': self.rho_0_m_itp},
+                'sigma_bc': self.sigma_psf,
             },
             '22': {
-                'eb': 'tau_2',
-                'ec': 'tau_2',
-                'bc': 'rho_1',
-                'sigma_bc': self.sigma_psf_error
+                'eb': {'p': self.tau_2_p_itp, 'm': self.tau_2_m_itp},
+                'ec': {'p': self.tau_2_p_itp, 'm': self.tau_2_m_itp},
+                'bc': {'p': self.rho_1_p_itp, 'm': self.rho_1_m_itp},
+                'sigma_bc': self.sigma_psf_error,
             },
             '55': {
-                'eb': 'tau_5',
-                'ec': 'tau_5',
-                'bc': 'rho_3',
-                'sigma_bc': self.sigma_psf_size_error
+                'eb': {'p': self.tau_5_p_itp, 'm': self.tau_5_m_itp},
+                'ec': {'p': self.tau_5_p_itp, 'm': self.tau_5_m_itp},
+                'bc': {'p': self.rho_3_p_itp, 'm': self.rho_3_m_itp},
+                'sigma_bc': self.sigma_psf_size_error,
             },
             '02': {
-                'eb': 'tau_0',
-                'ec': 'tau_2',
-                'bc': 'rho_2'
+                'eb': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
+                'ec': {'p': self.tau_2_p_itp, 'm': self.tau_2_m_itp},
+                'bc': {'p': self.rho_2_p_itp, 'm': self.rho_2_m_itp},
             },
             '05': {
-                'eb': 'tau_0',
-                'ec': 'tau_5',
-                'bc': 'rho_5'
+                'eb': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
+                'ec': {'p': self.tau_5_p_itp, 'm': self.tau_5_m_itp},
+                'bc': {'p': self.rho_5_p_itp, 'm': self.rho_5_m_itp},
             },
             '25': {
-                'eb': 'tau_2',
-                'ec': 'tau_5',
-                'bc': 'rho_4'
+                'eb': {'p': self.tau_2_p_itp, 'm': self.tau_2_m_itp},
+                'ec': {'p': self.tau_5_p_itp, 'm': self.tau_5_m_itp},
+                'bc': {'p': self.rho_4_p_itp, 'm': self.rho_4_m_itp},
             }
         }
 
-    def create_treecorr_catalog(self, cat_gal, cat_psf, treecorr_config):
+    def create_treecorr_catalog(self, cat_gal, cat_psf):
         """
         Create treecorr catalogs from the galaxy and PSF catalogs to compute estimate shape noise in covariance terms.
         Works only for shapepipe catalogs right now. A config file will be added later
@@ -132,16 +159,14 @@ def create_treecorr_catalog(self, cat_gal, cat_psf, treecorr_config):
             Galaxy catalog
         cat_psf: numpy.ndarray
             PSF catalog
-        treecorr_config : dict
-            Configuration of the treecorr catalogs
         
         Returns
         -------
         treecorr.Catalog, treecorr.Catalog, treecorr.Catalog, treecorr.Catalog
             Galaxy catalog, PSF catalog, PSF error catalog, PSF size error catalog
         """
-        ra_units = treecorr_config.get('ra_units', 'deg')
-        dec_units = treecorr_config.get('dec_units', 'deg')
+        ra_units = self.treecorr_config.get('ra_units', 'deg')
+        dec_units = self.treecorr_config.get('dec_units', 'deg')
         gal = treecorr.Catalog(
             ra=cat_gal['ra'], dec=cat_gal['dec'], w=cat_gal['w'], g1=cat_gal['e1'], g2=cat_gal['e2'],
             ra_units=ra_units, dec_units=dec_units
@@ -181,8 +206,8 @@ def compute_shape_noise(self, cat1, cat2):
         float
             Shape noise between the two catalogs
         """
-        return np.sqrt(0.5*np.average((cat1.g1*cat2.g1)+(cat1.g2*cat2.g2), weights=cat1.w*cat2.w))
-
+        return 0.5*np.average((cat1.g1*cat2.g1)+(cat1.g2*cat2.g2), weights=cat1.w*cat2.w)
+    
     def load_rho_stat(self, path_rho_stat):
         """
         Load rho statistics
@@ -248,28 +273,40 @@ def check_consistency(self):
             print("!!! The angular bins are not consistent !!!")
         return consistency
     
-    def build_interpolator(self, bins, values):
+    def build_interpolator(self, cat_1, cat_2, type):
         """
-        Build an interpolator for the given values using the given bins to compute the covariance.
+        Build an interpolator for the given catalogs on a sufficient range.
         Scipy interpolator will be used between the bins. The function will then return 0 outside
         this range.
 
         Parameters
         ----------
-        bins: numpy.ndarray
-            Bins of the values
-        values: numpy.ndarray
-            Values to interpolate
+        cat_1: treecorr.Catalog
+            First catalog to be cross-correlated
+        cat_2: treecorr.Catalog
+            Second catalog to be cross-correlated
+        type: str
+            Type of the function to interpolate ('plus' or 'minus')
 
         Returns
         -------
         callable:
             Interpolator function
         """
+        assert (type in ['plus', 'minus']), ("The type must be either 'plus' or 'minus'")
+
+        gg = treecorr.GGCorrelation(nbins=30, min_sep=1e-3, max_sep=1e3, sep_units='arcmin')
+        gg.process(cat_1, cat_2)
+        bins = gg.meanr
+        if type=='plus':
+            values = gg.xip
+        elif type=='minus':
+            values = gg.xim
         interpolator = interpolate.make_interp_spline(bins, values, k=1)
         func = lambda x: np.where((x >= bins[0]) & (x <= bins[-1]),
                                   interpolator(x),
-                                   np.where(x < bins[0], np.ones_like(x)*values[0], np.zeros_like(x))
+                                np.where(x<bins[0], np.zeros_like(x), #np.where(x < bins[0], np.ones_like(x)*values[0],
+                                np.zeros_like(x))
         )
         return func
 
@@ -395,11 +432,24 @@ def compute_sn(self, component, **kwargs):
             Shot noise component of the covariance matrix
         """
         assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        sigma_bc = self.component_dict[component].get('sigma_bc', 0)
+        
 
         sn = np.zeros((len(self.bins), len(self.bins)))
-        for i in range(len(self.bins)):
-            sn[i,i] = self.sigma_e**2*sigma_bc**2/(4*np.pi*self.A*self.n_e*self.n_psf*self.bins[i]**2*(self.delta_bin_log))
+        tau = treecorr.GGCorrelation(
+                nbins=self.treecorr_config['nbins'],
+                min_sep=self.treecorr_config['min_sep'],
+                max_sep=self.treecorr_config['max_sep'],
+                sep_units=self.treecorr_config['sep_units']
+        )
+        if component == '00':
+            tau.process(self.gal, self.psf)
+            sn = tau.cov[:len(self.bins), :len(self.bins)]
+        elif component == '22':
+            tau.process(self.gal, self.psf_error)
+            sn = tau.cov[:len(self.bins), :len(self.bins)]
+        elif component == '55':
+            tau.process(self.gal, self.size_error)
+            sn = tau.cov[:len(self.bins), :len(self.bins)]
         return sn
 
     def compute_mt(self, component, **kwargs):
@@ -417,23 +467,21 @@ def compute_mt(self, component, **kwargs):
             Shot noise component of the covariance matrix
         """
         assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        correlator = self.component_dict[component]['bc']
+        correlator_bc = self.component_dict[component]['bc']
         sigma_bc = self.component_dict[component].get('sigma_bc', None)
         nbin_ang = kwargs.get("nbin_ang", 20)
 
         mt = np.zeros((len(self.bins), len(self.bins)))
-        #interpolator_rho = interpolate.make_interp_spline(self.bins, self.rho_stats[correlator+"_p"], k=1)
-        interpolator_rho = self.build_interpolator(self.bins, self.rho_stats[correlator+"_p"])
+        interpolator_rho = correlator_bc['p']
         if sigma_bc is not None:
-            #interpolator_xi_plus = interpolate.make_interp_spline(self.bins, self.xi_plus['value'], k=1)
-            interpolator_xi_plus = self.build_interpolator(self.bins, self.xi_plus['value'])
+            interpolator_xi_plus = self.xi_plus_itp
         phi = np.linspace(0, np.pi, nbin_ang)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 y = np.sqrt(self.bins[i]**2+self.bins[j]**2-2*self.bins[i]*self.bins[j]*np.cos(phi))
-                mt[i, j] = integrate.simps(interpolator_rho(y), phi)*self.sigma_e**2/(2*np.pi*self.A*self.n_e)
+                mt[i, j] = integrate.simps(interpolator_rho(y), phi)*self.sigma_e/(2*np.pi*self.A*self.n_e)
                 if sigma_bc is not None:
-                    mt[i, j] += integrate.simps(interpolator_xi_plus(y), phi)*sigma_bc**2/(2*np.pi*self.A*self.n_e)
+                    mt[i, j] += integrate.simps(interpolator_xi_plus(y), phi)*sigma_bc/(2*np.pi*self.A*self.n_e)
                 mt[j, i] = mt[i, j]
         return mt
     
@@ -453,30 +501,30 @@ def compute_cv_rho_plus(self, component, **kwargs):
             Cosmic variance component of the covariance matrix
         """
         assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        xi_plus = self.xi_plus['value']
-        rho = self.rho_stats[self.component_dict[component]['bc']+'_p']
+        rho = self.component_dict[component]['bc']
         nbin_ang = kwargs.get("nbin_ang", 20)
         nbin_rad = kwargs.get("nbin_rad", 20)
 
         cv = np.zeros((len(self.bins), len(self.bins)))
 
-        #interpolator_xi = interpolate.make_interp_spline(self.bins, xi_plus, k=1)
-        #interpolator_rho = interpolate.make_interp_spline(self.bins, rho, k=1)
-        interpolator_xi = self.build_interpolator(self.bins, xi_plus)
-        interpolator_rho = self.build_interpolator(self.bins, rho)
+        
+        interpolator_xi = self.xi_plus_itp
+        interpolator_rho = rho['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        phi_radius = np.linspace(1e-2, 500, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
                 for k, phi_r in enumerate(phi_radius):
-                    y_xi = np.sqrt(phi_r**2+self.bins[i]**2-2*phi_r*self.bins[i]*np.cos(phi_angle))
-                    y_rho = np.sqrt(phi_r**2+self.bins[j]**2+2*phi_r*self.bins[j]*np.cos(phi_angle))
-                    int_xi = integrate.simps(interpolator_xi(y_xi), phi_angle)
-                    int_rho = integrate.simps(interpolator_rho(y_rho), phi_angle)
-                    radius_val[k] = int_xi*int_rho*phi_r
+                    #Compute i,j term
+                    y_m = np.sqrt(phi_r**2+self.bins[i]**2-2*phi_r*self.bins[i]*np.cos(phi_angle))
+                    y_p = np.sqrt(phi_r**2+self.bins[j]**2+2*phi_r*self.bins[j]*np.cos(phi_angle))
+                    int_xi_m = integrate.simps(interpolator_xi(y_m), phi_angle)
+                    int_xi_p = integrate.simps(interpolator_xi(y_p), phi_angle)
+                    int_rho_m = integrate.simps(interpolator_rho(y_m), phi_angle)
+                    int_rho_p = integrate.simps(interpolator_rho(y_p), phi_angle)
+                    radius_val[k] = 0.5*(int_xi_m*int_rho_p*phi_r+int_xi_p*int_rho_m*phi_r)
                 cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
-                cv[j, i] = cv[i, j]
         return cv
 
     def compute_cv_tau_plus(self, component, **kwargs):
@@ -495,19 +543,17 @@ def compute_cv_tau_plus(self, component, **kwargs):
             Cosmic variance component of the covariance matrix
         """
         assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        tau_b = self.tau_stats[self.component_dict[component]['eb']+'_p']
-        tau_c = self.tau_stats[self.component_dict[component]['ec']+'_p']
+        tau_b = self.component_dict[component]['eb']
+        tau_c = self.component_dict[component]['ec']
         nbin_ang = kwargs.get("nbin_ang", 20)
         nbin_rad = kwargs.get("nbin_rad", 20)
 
         cv = np.zeros((len(self.bins), len(self.bins)))
 
-        #interpolator_tau_b = interpolate.make_interp_spline(self.bins, tau_b, k=1)
-        #interpolator_tau_c = interpolate.make_interp_spline(self.bins, tau_c, k=1)
-        interpolator_tau_b = self.build_interpolator(self.bins, tau_b)
-        interpolator_tau_c = self.build_interpolator(self.bins, tau_c)
+        interpolator_tau_b = tau_b['p']
+        interpolator_tau_c = tau_c['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        phi_radius = np.linspace(1e-2, 500, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -518,7 +564,6 @@ def compute_cv_tau_plus(self, component, **kwargs):
                     int_tau_c = integrate.simps(interpolator_tau_c(y_tau_c), phi_angle)
                     radius_val[k] = int_tau_b*int_tau_c*phi_r
                 cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
-                cv[j, i] = cv[i, j]
         return cv
     
     def compute_cv_rho_minus(self, component, **kwargs):
@@ -537,18 +582,17 @@ def compute_cv_rho_minus(self, component, **kwargs):
             Cosmic variance component of the covariance matrix
         """
         assert(component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        xi_minus = self.xi_minus['value']
-        rho = self.rho_stats[self.component_dict[component]['bc']+'_m']
+        rho = self.component_dict[component]['bc']
         nbin_ang = kwargs.get("nbin_ang", 20)
         nbin_rad = kwargs.get("nbin_rad", 20)
 
         cv = np.zeros((len(self.bins), len(self.bins)))
         #interpolator_xi = interpolate.make_interp_spline(self.bins, xi_minus, k=1)
         #interpolator_rho = interpolate.make_interp_spline(self.bins, rho, k=1)
-        interpolator_xi = self.build_interpolator(self.bins, xi_minus)
-        interpolator_rho = self.build_interpolator(self.bins, rho)
+        interpolator_xi = self.xi_minus_itp
+        interpolator_rho = rho['m']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        phi_radius = np.linspace(1e-2, 500, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -566,11 +610,12 @@ def compute_cv_rho_minus(self, component, **kwargs):
                     norm_b = np.linalg.norm(psi_b, axis=0)
                     polar_b = np.arctan2(psi_b[1], psi_b[0])
                     #The term coming from the sine will integrate out to 0 so we can only compute terms with cosine
-                    int_xi = integrate.simps(interpolator_xi(norm_a)*np.cos(4*polar_a), phi_angle)
-                    int_rho = integrate.simps(interpolator_rho(norm_b)*np.cos(4*polar_b), phi_angle)
-                    radius_val[k] = int_xi*int_rho*phi_r
+                    int_xi_m = integrate.simps(interpolator_xi(norm_a)*np.cos(4*polar_a), phi_angle)
+                    int_xi_p = integrate.simps(interpolator_xi(norm_b)*np.cos(4*polar_b), phi_angle)
+                    int_rho_m = integrate.simps(interpolator_rho(norm_a)*np.cos(4*polar_a), phi_angle)
+                    int_rho_p = integrate.simps(interpolator_rho(norm_b)*np.cos(4*polar_b), phi_angle)
+                    radius_val[k] = 0.5*(int_xi_m*int_rho_p*phi_r+int_xi_p*int_rho_m*phi_r)
                 cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
-                cv[j, i] = cv[i, j]
         return cv
 
     def compute_cv_tau_minus(self, component, **kwargs):
@@ -589,18 +634,16 @@ def compute_cv_tau_minus(self, component, **kwargs):
             Cosmic variance component of the covariance matrix
         """
         assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        tau_b = self.tau_stats[self.component_dict[component]['eb']+'_m']
-        tau_c = self.tau_stats[self.component_dict[component]['ec']+'_m']
+        tau_b = self.component_dict[component]['eb']
+        tau_c = self.component_dict[component]['ec']
         nbin_ang = kwargs.get("nbin_ang", 20)
         nbin_rad = kwargs.get("nbin_rad", 20)
 
         cv = np.zeros((len(self.bins), len(self.bins)))
-        #interpolator_tau_b = interpolate.make_interp_spline(self.bins, tau_b, k=1)
-        #interpolator_tau_c = interpolate.make_interp_spline(self.bins, tau_c, k=1)
-        interpolator_tau_b = self.build_interpolator(self.bins, tau_b)
-        interpolator_tau_c = self.build_interpolator(self.bins, tau_c)
+        interpolator_tau_b = tau_b['m']
+        interpolator_tau_c = tau_c['m']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(0.1, 250, nbin_rad)
+        phi_radius = np.linspace(1e-2, 500, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -622,7 +665,6 @@ def compute_cv_tau_minus(self, component, **kwargs):
                     int_tau_c = integrate.simps(interpolator_tau_c(norm_b)*np.cos(4*polar_b), phi_angle)
                     radius_val[k] = int_tau_b*int_tau_c*phi_r
                 cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
-                cv[j, i] = cv[i, j]
         return cv
 
 

From c55c231588f63095335b0f6e74d6264fb37ae9e7 Mon Sep 17 00:00:00 2001
From: sachaguer <sacha.guerrini@cea.fr>
Date: Fri, 19 Apr 2024 16:40:55 +0200
Subject: [PATCH 68/97] Add parameters to the class CovTauTh to handle catalogs
 with different conventions than SPv1.

---
 shear_psf_leakage/rho_tau_cov.py  | 74 +++++++++++++++++++++++++++----
 shear_psf_leakage/rho_tau_stat.py |  4 +-
 2 files changed, 67 insertions(+), 11 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 69af92f..5b87244 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -28,6 +28,7 @@ def __init__(
         A,
         n_e,
         n_psf,
+        params=None,
         **kwargs):
         """
         Initalize the CovTauTh class.
@@ -49,12 +50,19 @@ def __init__(
             Number density of galaxies
         n_psf: float
             Number density of PSF stars
+        params: dict
+            Parameters of the class
         """
         self.treecorr_config = treecorr_config
         self.A = A
         self.n_e = n_e
         self.n_psf = n_psf
 
+        if params is None:
+            self.params_default()
+        else:
+            self.set_params(params)
+
         #Load the catalogs
         cat_gal, cat_psf = fits.getdata(path_gal), fits.open(path_psf)[hdu_psf].data
 
@@ -117,19 +125,19 @@ def __init__(
                 'eb': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
                 'ec': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
                 'bc': {'p': self.rho_0_p_itp, 'm': self.rho_0_m_itp},
-                'sigma_bc': self.sigma_psf,
+                'sigma_bc': None,
             },
             '22': {
                 'eb': {'p': self.tau_2_p_itp, 'm': self.tau_2_m_itp},
                 'ec': {'p': self.tau_2_p_itp, 'm': self.tau_2_m_itp},
                 'bc': {'p': self.rho_1_p_itp, 'm': self.rho_1_m_itp},
-                'sigma_bc': self.sigma_psf_error,
+                'sigma_bc': None,
             },
             '55': {
                 'eb': {'p': self.tau_5_p_itp, 'm': self.tau_5_m_itp},
                 'ec': {'p': self.tau_5_p_itp, 'm': self.tau_5_m_itp},
                 'bc': {'p': self.rho_3_p_itp, 'm': self.rho_3_m_itp},
-                'sigma_bc': self.sigma_psf_size_error,
+                'sigma_bc': None,
             },
             '02': {
                 'eb': {'p': self.tau_0_p_itp, 'm': self.tau_0_m_itp},
@@ -148,6 +156,46 @@ def __init__(
             }
         }
 
+    def params_default(self):
+        """
+        Params Default.
+
+        Initialize the parameters of the class with columns name from SPV1.
+        For the treecorr configuration, default parameters are:
+        -coord_units: degree
+        -sep_units: arcmin
+        -theta_min: 0.1
+        -theta_max: 250
+        -n_theta: 20
+        """
+
+        self._params = {
+            "e1_col": "e1",
+            "e2_col": "e2",
+            "w_col": "w",
+            "ra_col": "RA",
+            "dec_col": "Dec",
+            "e1_PSF_col": "E1_PSF_HSM",
+            "e2_PSF_col": "E2_PSF_HSM",
+            "e1_star_col": "E1_STAR_HSM",
+            "e2_star_col": "E2_STAR_HSM",
+            "PSF_size": "SIGMA_PSF_HSM",
+            "star_size": "SIGMA_STAR_HSM",
+            "PSF_flag": "FLAG_PSF_HSM",
+            "star_flag": "FLAG_STAR_HSM",
+            "square_size": True,
+            "ra_units": "deg",
+            "dec_units": "deg"
+        }
+    
+    def set_params(self, params):
+        """
+        set_params:
+
+        Initialize the parameters to the given configuration. Can also update the current params.
+        """
+        self._params = params
+
     def create_treecorr_catalog(self, cat_gal, cat_psf):
         """
         Create treecorr catalogs from the galaxy and PSF catalogs to compute estimate shape noise in covariance terms.
@@ -168,24 +216,32 @@ def create_treecorr_catalog(self, cat_gal, cat_psf):
         ra_units = self.treecorr_config.get('ra_units', 'deg')
         dec_units = self.treecorr_config.get('dec_units', 'deg')
         gal = treecorr.Catalog(
-            ra=cat_gal['ra'], dec=cat_gal['dec'], w=cat_gal['w'], g1=cat_gal['e1'], g2=cat_gal['e2'],
+            ra=cat_gal[self._params['ra_col']], dec=cat_gal[self._params['dec_col']],
+            w=cat_gal[self._params['w_col']], g1=cat_gal[self._params['e1_col']], g2=cat_gal[self._params['e2_col']],
             ra_units=ra_units, dec_units=dec_units
         )
 
         psf = treecorr.Catalog(
-            ra=cat_psf['ra'], dec=cat_psf['dec'], g1=cat_psf['E1_PSF_HSM'], g2=cat_psf['E2_PSF_HSM'],
+            ra=cat_psf[self._params['ra_col']], dec=cat_psf[self._params['dec_col']],
+            g1=cat_psf[self._params['e1_PSF_col']], g2=cat_psf[self._params['e2_PSF_col']],
             ra_units=ra_units, dec_units=dec_units
         )
 
         psf_error = treecorr.Catalog(
-            ra=cat_psf['ra'], dec=cat_psf['dec'], g1=cat_psf['E1_STAR_HSM']-cat_psf['E1_PSF_HSM'], g2=cat_psf['E2_STAR_HSM']-cat_psf['E2_PSF_HSM'],
+            ra=cat_psf[self._params['ra_col']], dec=cat_psf[self._params['dec_col']],
+            g1=cat_psf[self._params['e1_star_col']]-cat_psf[self._params['e1_PSF_col']],
+            g2=cat_psf[self._params['e2_star_col']]-cat_psf[self._params['e2_PSF_col']],
             ra_units=ra_units, dec_units=dec_units
         )
 
+        size_resid = (cat_psf[self._params['star_size']]**2-cat_psf[self._params['PSF_size']]**2)/cat_psf[self._params['star_size']]**2\
+        if self._params['square_size'] else\
+        (cat_psf[self._params['star_size']]-cat_psf[self._params['PSF_size']])/cat_psf[self._params['star_size']]
+
         size_error = treecorr.Catalog(
-            ra=cat_psf['ra'], dec=cat_psf['dec'],
-            g1=cat_psf['E1_STAR_HSM']*(cat_psf["SIGMA_STAR_HSM"]**2-cat_psf["SIGMA_PSF_HSM"]**2)/cat_psf["SIGMA_STAR_HSM"]**2,
-            g2=cat_psf['E2_PSF_HSM']*(cat_psf["SIGMA_STAR_HSM"]**2-cat_psf["SIGMA_PSF_HSM"]**2)/cat_psf["SIGMA_STAR_HSM"]**2,
+            ra=cat_psf[self._params['ra_col']], dec=cat_psf[self._params['dec_col']],
+            g1=cat_psf[self._params['e1_star_col']]*size_resid,
+            g2=cat_psf[self._params['e2_star_col']]*size_resid,
             ra_units=ra_units, dec_units=dec_units
         )
         return gal, psf, psf_error, size_error
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 5688a25..aa3dddc 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -703,7 +703,7 @@ def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None
 
         self.verbose = verbose
 
-    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False, mask=False):
+    def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False, mask=False, hdu=1):
         """
         build_cat_to_compute_tau
 
@@ -726,7 +726,7 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
         """
 
         if cat_type=="psf":
-            self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat)
+            self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat, hdu=hdu)
 
             if self.verbose:
                 print("Building catalogs...")

From ee48254892e6b68ca638b4e7e730506a31429190 Mon Sep 17 00:00:00 2001
From: sachaguer <sacha.guerrini@cea.fr>
Date: Tue, 30 Apr 2024 10:26:47 +0200
Subject: [PATCH 69/97] Remove unecessary functions in CovTauTh.

---
 shear_psf_leakage/rho_tau_cov.py | 80 ++------------------------------
 1 file changed, 4 insertions(+), 76 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 5b87244..0c66126 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -108,17 +108,10 @@ def __init__(
         self.xi_plus = kwargs.get("xi_plus", None)
         self.xi_minus = kwargs.get("xi_minus", None)
 
-        if self.rho_stats is None:
-            print("You must load rho statistics")
-        else:
-            self.bins = self.rho_stats['theta']
-            self.delta_bin_log = (self.bins[1]-self.bins[0])/self.bins[1]
-        if self.tau_stats is None:
-            print("You must load tau statistics")
-        if self.xi_plus is None:
-            print("You must load Xi plus")
-        if self.xi_minus is None:
-            print("You must load Xi minus")
+        dummy_cat = treecorr.Catalog(ra=[0], dec=[0], g1=[0], g2=[0], w=[0], ra_units='deg', dec_units='deg')
+        gg = treecorr.GGCorrelation(self.treecorr_config)
+        gg.process(dummy_cat, dummy_cat)
+        self.bins = gg.meanr
 
         self.component_dict = {
             '00': {
@@ -264,71 +257,6 @@ def compute_shape_noise(self, cat1, cat2):
         """
         return 0.5*np.average((cat1.g1*cat2.g1)+(cat1.g2*cat2.g2), weights=cat1.w*cat2.w)
     
-    def load_rho_stat(self, path_rho_stat):
-        """
-        Load rho statistics
-
-        Parameters
-        ----------
-        path_rho_stat: str
-            Path to the rho statistics
-        """
-        self.rho_stats = fits.getdata(path_rho_stat)
-        self.bins = self.rho_stats['theta']
-        self.delta_bin_log = (self.bins[1]-self.bins[0])/self.bins[1]
-
-    def load_tau_stat(self, path_tau_stat):
-        """
-        Load tau statistics
-
-        Parameters
-        ----------
-        path_tau_stat: str
-            Path to the tau statistics
-        """
-        self.tau_stats = fits.getdata(path_tau_stat)
-
-    def load_xi_plus(self, path_xi_plus):
-        """
-        Load Xi plus
-
-        Parameters
-        ----------
-        path_xi_plus: str
-            Path to the Xi plus
-        """
-        self.xi_plus = fits.getdata(path_xi_plus)
-
-    def load_xi_minus(self, path_xi_minus):
-        """
-        Load Xi minus
-
-        Parameters
-        ----------
-        path_xi_minus: str
-            Path to the Xi minus
-        """
-        self.xi_minus = fits.getdata(path_xi_minus)
-
-    def check_consistency(self):
-        """
-        Check that the input data have the same angular bins.
-
-        Returns
-        -------
-        bool
-            True if the data are consistent, False otherwise
-        """
-        assert (self.rho_stats is not None), ("You must load rho statistics")
-        assert (self.tau_stats is not None), ("You must load tau statistics")
-        assert (self.xi_plus is not None), ("You must load Xi plus")
-        assert (self.xi_minus is not None), ("You must load Xi minus")
-
-        consistency = np.all(self.bins == self.tau_stats['theta']) & np.all(self.bins == self.xi_plus['ang']) & np.all(self.bins == self.xi_minus['ang'])
-        if not consistency:
-            print("!!! The angular bins are not consistent !!!")
-        return consistency
-    
     def build_interpolator(self, cat_1, cat_2, type):
         """
         Build an interpolator for the given catalogs on a sufficient range.

From 6a7aa0a55352dad31bd0634fa54f60698182cad1 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sun, 5 May 2024 12:34:36 +0200
Subject: [PATCH 70/97] PSF leakageL: split fit from plot

---
 notebooks/leakage_object.py  |  82 ++++------------
 poetry.lock                  |  39 +++++++-
 shear_psf_leakage/leakage.py | 178 +++++++++--------------------------
 shear_psf_leakage/plots.py   | 139 +++++++++++++++++++++++----
 4 files changed, 219 insertions(+), 219 deletions(-)

diff --git a/notebooks/leakage_object.py b/notebooks/leakage_object.py
index 528b5fb..9ca4739 100644
--- a/notebooks/leakage_object.py
+++ b/notebooks/leakage_object.py
@@ -15,7 +15,7 @@
 
 # # PSF leakage: object-wise estimation
 #
-# Demonstration notebook of shear_psf_leakage.run_object.LeakageObject() class.
+# Demonstration notebook of shear_psf_leakage.run_object.LeakageObject class.
 #
 # Martin Kilbinger <martin.kilbinger@cea.fr>
 
@@ -23,67 +23,15 @@
 import matplotlib
 matplotlib.use("agg")
 
-import os
+import os, sys
 import matplotlib.pylab as plt
 
 from cs_util import canfar
+from cs_util import args
 
 import shear_psf_leakage.run_object as run
 from shear_psf_leakage import leakage
 
-# -
-
-# ## Set input parameters
-
-params_in = {}
-
-# ### Paths
-
-# +
-# Patch name
-patch = "W3"
-# Input galaxy shear catalogue
-params_in["input_path_shear"] = f"unions_shapepipe_extended_2022_{patch}_v1.0.3.fits"
-
-# Output directory
-params_in["output_dir"] = "leakage_object"
-# -
-
-# ### Job control
-
-# +
-# Compute (spin-preserving) PSF ellipticity leakage
-params_in["PSF_leakage"] = True
-
-# Compute leakage with other parameters
-params_in["obs_leakage"] = True
-
-# Other input parameters
-params_in["cols"] = "RA Dec e1_PSF e2_PSF fwhm_PSF w mag snr"
-
-# Ratio between two input columns
-params_in["cols_ratio"] = "mag_snr"
-# -
-
-# ### Other parameters
-
-# +
-# PSF ellipticity column names
-params_in["e1_PSF_col"] = "e1_PSF"
-params_in["e2_PSF_col"] = "e2_PSF"
-
-# Set verbose output
-params_in["verbose"] = True
-# -
-
-# ### Retrieve test catalogue from VOspace if not yet downloaded
-
-vos_dir = f"vos:cfis/weak_lensing/DataReleases/v1.0/ShapePipe/{patch}"
-canfar.download(
-    f"{vos_dir}/{params_in['input_path_shear']}",
-    params_in["input_path_shear"],
-    verbose=params_in["verbose"],
-)
 
 # ## Compute leakage
 
@@ -91,11 +39,24 @@
 obj = run.LeakageObject()
 
 # Set instance parameters, copy from above
-for key in params_in:
-    obj._params[key] = params_in[key]
+params_upd = args.read_param_script("params_object.py", obj._params, verbose=True)
+for key in params_upd:
+    obj._params[key] = params_upd[key]
+
+#for key in obj._params:
+    #print(key, obj._params[key])
+
+# +
+#vos_dir = f"vos:cfis/weak_lensing/DataReleases/v1.0/ShapePipe/{patch}"
+#canfar.download(
+    #f"{vos_dir}/{obj_params['input_path_shear']}",
+    #obj._params["input_path_shear"],
+    #verbose=obj._params["verbose"],
+#)
+# -
 
 # ### Run
-# There are two options to run the leakage compuations:
+# There are two options to run the leakage computations:
 # 1. Run all at once with single class routine.
 # 2. Execute individual steps.
 
@@ -123,11 +84,6 @@
 if obj._params["PSF_leakage"]:
     # Object-by-object spin-consistent PSF leakage
     for order in ("lin", "quad"):
-        out_base = obj.get_out_base(mix=True, order=order)
-        out_path = f"{out_base}.json"
-        if os.path.exists(out_path):
-            obj.par_best_fit = leakage.read_from_json(out_path)
-        else:
             obj.PSF_leakage(mix=True, order=order)
 
 if obj._params["obs_leakage"]:
diff --git a/poetry.lock b/poetry.lock
index 0a11874..107fe56 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aenum"
@@ -594,6 +594,35 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cmake"
+version = "3.29.2"
+description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "cmake-3.29.2-py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:1d40c5451d6467b20a0a6015a5a6b6dc86f61b83f71f935740485b259100a34e"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed3108e646cd65a4e23fa1cbe8123569a29334a3f2a8ce214d871406b161bedb"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40aafe612b03a9fa140cca4024ba60b74cd92372f3f349d8062cba1f021e5001"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:027eebe9bb74c31759581a543f27bc1828fc76e6fc45b2b48b51f27847904669"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f1f087985fc2460476b0901716fbddb2fd69b7fe7bf1350e1ab5dc508d22600e"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:df2c63ce6d504aa4c91a42fd22d3887065ab029569691deb56ec19d0decd0ae9"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6ea5ce007893d7d1363e13433dde1c0c7c344372213a90ff3c56e896a335301d"},
+    {file = "cmake-3.29.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f9e941e73202cfa667ee488d1d88b8a758b516dcfa2a2728e73dbdcbfbdebf57"},
+    {file = "cmake-3.29.2-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:37222e23485338c72b7ea51f865d8c6847d519f7e2222922fb70b4896ca6e897"},
+    {file = "cmake-3.29.2-py3-none-musllinux_1_1_i686.whl", hash = "sha256:eeed08932c748647488280dc97ac00bcfeae5d760451105200cfe66c52ce6468"},
+    {file = "cmake-3.29.2-py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:db7a05df020ba67bacd3070dd1645c76ca96fabd06d6aaa63288fd845706e47a"},
+    {file = "cmake-3.29.2-py3-none-musllinux_1_1_s390x.whl", hash = "sha256:83b35de822ddabaaa184a7d8f9827381350c42d627689c775b214347f57c9e41"},
+    {file = "cmake-3.29.2-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:cc0e36752e581430a93e58a268e515bb4ec1373b9e9911571f2cac1d2a6b5bec"},
+    {file = "cmake-3.29.2-py3-none-win32.whl", hash = "sha256:a941e26fba81cf74832c8a0e17e007452e05b6ad4941b3d2d18c75faa4a677d8"},
+    {file = "cmake-3.29.2-py3-none-win_amd64.whl", hash = "sha256:23336c8ca01205d18d92ed8de6c54e570c352a58e378b7f9adc02ef00f433960"},
+    {file = "cmake-3.29.2-py3-none-win_arm64.whl", hash = "sha256:e722a949f7c91084dba61f8f17a9854787182ab711ed0b84b1507b24a8e12e25"},
+    {file = "cmake-3.29.2.tar.gz", hash = "sha256:6a4c1185cb2eca7263190a5754d0c9edf738d9e50bff464f78f48d0c05318e7c"},
+]
+
+[package.extras]
+test = ["coverage (>=4.2)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)"]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -807,8 +836,8 @@ test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-co
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
-name = "cs_util"
-version = "0.1.0"
+name = "cs-util"
+version = "0.0.7"
 description = ""
 optional = false
 python-versions = ">=3.7"
@@ -821,7 +850,7 @@ files = [
 astropy = ">=4.0"
 datetime = ">=4.7"
 matplotlib = ">=3.3"
-numpy = ">=1.19"
+numpy = ">=1.21"
 vos = ">=3.3"
 
 [package.extras]
@@ -3924,4 +3953,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "7534a14460be521619bd79e621ceb4c4b9710b5adf88f0886d7d94c1f76bd210"
+content-hash = "08c934e954f7baea6f7317765d2a500fb76811858fccf9070022e003dbc43cc8"
diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index 84a35df..86318e7 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -6,8 +6,8 @@
     leakage.
 
 :Authors: Martin Kilbinger <martin.kilbinger@cea.fr>
-         Clara Bonini
-         Axel Guinot
+        Clara Bonini
+        Axel Guinot
 
 """
 
@@ -21,7 +21,6 @@
 from astropy.io import fits
 
 from .plot_style import *
-from . import plots
 
 
 # MKDEBUG TODO: to cs_util (and see sp_validation/io.py)
@@ -464,9 +463,6 @@ def quad_corr_quant(
     if weights is None:
         weights = np.ones_like(y[0])
 
-    if colors is None:
-        prop_cycle = plt.rcParams["axes.prop_cycle"]
-        colors = prop_cycle.by_key()["color"]
 
     size_all = len(y[0])
     for idx in range(1, n_y):
@@ -857,19 +853,10 @@ def print_fit_report(res, file=None):
 def corr_2d(
     x,
     y,
-    xlabel_arr,
-    ylabel_arr,
     weights=None,
     order="lin",
     mix=False,
-    n_bin=30,
-    title="",
-    colors=None,
-    out_path=None,
-    plot_all_points=False,
-    par_ground_truth=None,
     stats_file=None,
-    verbose=False,
 ):
     """Corr 2D.
 
@@ -888,24 +875,8 @@ def corr_2d(
         order of fit, default is 'lin'
     mix : bool
         mixing of components if True
-    xlabel_arr, ylabel_arr : list of str
-        x-and y-axis labels
-    n_bin : double, optional, default=30
-        number of points onto which data are binned
-    title : str, optional, default=''
-        plot title
-    colors : array(m) of str, optional, default=None
-        line colors
     stats_file : filehandler, optional, default=None
         output file for statistics
-    out_path : str, optional, default=None
-        output file path, if not given, plot is not saved to file
-    plot_all_points : bool, optional
-        plot all individual data points if ``True``; default is ``False``
-    par_ground_truth : dict, optional
-        ground truth parameter, for plotting, default is `None`
-    verbose : bool, optional, default=False
-        verbose output if True
 
     Returns
     -------
@@ -913,10 +884,6 @@ def corr_2d(
         best-fit parameters
 
     """
-    if colors is None:
-        prop_cycle = plt.rcParams["axes.prop_cycle"]
-        colors = prop_cycle.by_key()["color"]
-
     if len(y) != 2 or len(x) != 2:
         raise IndexError("Input data needs to have two components")
     if any(len(y[0]) != c for c in {len(y[1]), len(x[0]), len(x[1])}):
@@ -962,108 +929,9 @@ def corr_2d(
     if verbose:
         print_fit_report(res)
 
-    # Get best-fit parameter values and standard deviations
-    p_dp = {}
-    for p in res.params:
-        p_dp[p] = ufloat(res.params[p].value, res.params[p].stderr)
-
-    # Get spin coefficients
-    s_ds = param_order2spin(p_dp, order, mix)
-
-    # Output to stats file
-    if stats_file:
-        for p in res.params:
-            print_stats(f"{p}={p_dp[p]:.3ugP}", stats_file, verbose=verbose)
-        for spin in s_ds:
-            print_stats(
-                f"{spin}={s_ds[spin]:.3ugP}",
-                stats_file,
-                verbose=verbose,
-            )
-
-    # Plots
-
-    # Spin compoments
-    if out_path:
-        out_path_spin = f"{out_path}_spin.png"
-    else:
-        out_path_spin = None
-
-    if par_ground_truth:
-        s_ground_truth = param_order2spin(par_ground_truth, order, mix)
-    else:
-        s_ground_truth = None
-    plots.plot_bar_spin(
-        s_ds,
-        s_ground_truth=s_ground_truth,
-        output_path=out_path_spin,
-    )
-
-    # Curves
-    plots.plot_corr_2d(
-        x,
-        y,
-        weights,
-        res,
-        p_dp,
-        n_bin,
-        order,
-        mix,
-        xlabel_arr,
-        ylabel_arr,
-        plot_all_points=plot_all_points,
-        par_ground_truth=par_ground_truth,
-        title=title,
-        colors=colors,
-        out_path=out_path,
-    )
-
     return res.params
 
 
-def param_order2spin(p_dp, order, mix):
-    """Param Order 2 Spin.
-
-    Transform parameter from natural to spin coefficients.
-
-    Parameters
-    ----------
-    p_dp : dict
-        Parameter natural coefficients
-    order : str
-        expansion order, one of 'linear', 'quad'
-    mix : bool
-        ellipticity components are mixed if ``True``
-
-    Returns
-    -------
-    dict
-        Parameter spin coefficients
-
-    """
-    s_ds = {}
-
-    s_ds["x0"] = 0.5 * (p_dp["a11"] + p_dp["a22"])
-
-    if order == "quad" and mix:
-        s_ds["x2"] = 0.5 * (p_dp["q111"] + p_dp["q122"])
-        s_ds["y2"] = 0.5 * (p_dp["q211"] - p_dp["q222"])
-        s_ds["x-2"] = 0.25 * (p_dp["q111"] - p_dp["q122"] + p_dp["q212"])
-        s_ds["y-2"] = 0.25 * (p_dp["q211"] - p_dp["q222"] - p_dp["q112"])
-
-    s_ds["x4"] = 0.5 * (p_dp["a11"] - p_dp["a22"])
-
-    if mix:
-        s_ds["y4"] = 0.5 * (p_dp["a12"] + p_dp["a21"])
-        s_ds["y0"] = 0.5 * (-p_dp["a12"] + p_dp["a21"])
-
-    if order == "quad" and mix:
-        s_ds["x6"] = 0.25 * (p_dp["q111"] - p_dp["q122"] - p_dp["q212"])
-        s_ds["y6"] = 0.25 * (p_dp["q211"] - p_dp["q222"] + p_dp["q112"])
-
-    return s_ds
-
-
 def affine_corr(
     x,
     y,
@@ -1387,3 +1255,45 @@ def read_from_file(fname):
         data = pickle.load(f)
 
     return data
+def param_order2spin(p_dp, order, mix):
+    """Param Order 2 Spin.
+
+    Transform parameter from natural to spin coefficients.
+
+    Parameters
+    ----------
+    p_dp : dict
+        Parameter natural coefficients
+    order : str
+        expansion order, one of 'linear', 'quad'
+    mix : bool
+        ellipticity components are mixed if ``True``
+
+    Returns
+    -------
+    dict
+        Parameter spin coefficients
+
+    """
+    s_ds = {}
+
+    s_ds["x0"] = 0.5 * (p_dp["a11"] + p_dp["a22"])
+
+    if order == "quad" and mix:
+        s_ds["x2"] = 0.5 * (p_dp["q111"] + p_dp["q122"])
+        s_ds["y2"] = 0.5 * (p_dp["q211"] - p_dp["q222"])
+        s_ds["x-2"] = 0.25 * (p_dp["q111"] - p_dp["q122"] + p_dp["q212"])
+        s_ds["y-2"] = 0.25 * (p_dp["q211"] - p_dp["q222"] - p_dp["q112"])
+
+    s_ds["x4"] = 0.5 * (p_dp["a11"] - p_dp["a22"])
+
+    if mix:
+        s_ds["y4"] = 0.5 * (p_dp["a12"] + p_dp["a21"])
+        s_ds["y0"] = 0.5 * (-p_dp["a12"] + p_dp["a21"])
+
+    if order == "quad" and mix:
+        s_ds["x6"] = 0.25 * (p_dp["q111"] - p_dp["q122"] - p_dp["q212"])
+        s_ds["y6"] = 0.25 * (p_dp["q211"] - p_dp["q222"] + p_dp["q112"])
+
+    return s_ds
+
diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index 4d17f3a..7a22c9e 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -12,10 +12,11 @@
 import numpy as np
 from scipy import stats
 from matplotlib import pylab as plt
+from uncertainties import ufloat
 import getdist
 from getdist import plots, MCSamples
 
-from shear_psf_leakage import leakage
+from . import leakage
 
 
 def equi_num_bins(values, n_bin):
@@ -158,17 +159,17 @@ def set_labels(p_dp, order, mix, par_ground_truth=None):
 
     """
     # Affine parameters
-    label = {}
+    label = {
+        "A": rf'$\alpha_{{11}}\;\,={p_dp["a11"]: .2ugL}$',
+        "D": rf'$\alpha_{{22}}\;\,={p_dp["a22"]: .2ugL}$',
+    }
 
-    # Linear parameters
-    label["A"] = f'$a_{{11}}\;\,={p_dp["a11"]: .2ugL}$'
-    label["D"] = f'$a_{{22}}\;\,={p_dp["a22"]: .2ugL}$'
     if par_ground_truth:
         bias = get_bias(p_dp, par_ground_truth, "a11")
         label["A"] = f"{label['A']} ({par_ground_truth['a11'].value}, {bias:.1f}$\sigma$)"
         bias = get_bias(p_dp, par_ground_truth, "a22")
         label["D"] = f"{label['D']} ({par_ground_truth['a22'].value}, {bias:.1f}$\sigma$)"
-    
+
     # Constant parameters
     label["A"] = label["A"] + "\n" + f'$c_1\;\;\;={p_dp["c1"]: .2ugL}$'
     label["D"] = label["D"] + "\n" + f'$c_2\;\;\;={p_dp["c2"]: .2ugL}$'
@@ -191,8 +192,8 @@ def set_labels(p_dp, order, mix, par_ground_truth=None):
         label["D"] = label_q222 + "\n" + label["D"]
     if mix:
         # Mixed linear parameters
-        label["B"] = f'$a_{{12}}\;\,={p_dp["a12"]: .2ugL}$'
-        label["C"] = f'$a_{{21}}\;\,={p_dp["a21"]: .2ugL}$'
+        label["B"] = rf'$\alpha_{{12}}\;\,={p_dp["a12"]: .2ugL}$'
+        label["C"] = rf'$\alpha_{{21}}\;\,={p_dp["a21"]: .2ugL}$'
         if par_ground_truth:
             bias = get_bias(p_dp, par_ground_truth, "a12")
             label["B"] = f"{label['B']} ({par_ground_truth['a12'].value}, {bias:.1f}$\sigma$)"
@@ -312,7 +313,7 @@ def plot_corr_2d(
     x,
     y,
     weights,
-    res,
+    res_params,
     p_dp,
     n_bin,
     order,
@@ -323,7 +324,7 @@ def plot_corr_2d(
     par_ground_truth=None,
     title=None,
     colors=None,
-    out_path=None,
+    out_base=None,
 ):
     """Plot Corr 2D.
 
@@ -337,8 +338,8 @@ def plot_corr_2d(
         input y arrays
     weights  : array of double, optional, default=None
         weights of x points
-    res : class lmfit.MinimizerResult
-        results of the minization
+    res_params : dict
+        results of the minimization
     n_bin : double, optional, default=30
         number of points onto which data are binned
     p_dp : dict
@@ -357,8 +358,8 @@ def plot_corr_2d(
         plot title
     colors : array(m) of string, optional, default=None
         line colors
-    out_path : str, optional, default=None
-        output file path, if not given, plot is not saved to file
+    out_base:: str, optional, default=None
+        output file path base, if not given, plot is not saved to file
 
     """
     if colors is None:
@@ -380,7 +381,7 @@ def plot_corr_2d(
     # Get best-fit model on 2D binned grid
     y_model_all = np.zeros(shape=(2, n_bin, n_bin))
     y_model_all[0], y_model_all[1] = leakage.func_bias_2d_full(
-        res.params, x_bin[0], x_bin[1], order=order, mix=mix
+        res_params, x_bin[0], x_bin[1], order=order, mix=mix
     )
 
     if par_ground_truth:
@@ -488,8 +489,8 @@ def plot_corr_2d(
     plt.tight_layout()
 
     # Save figure
-    if out_path:
-        plt.savefig(f"{out_path}.png", bbox_inches="tight")
+    if out_base:
+        plt.savefig(f"{out_base}.png", bbox_inches="tight")
 
 def plot_contours(sample_list, names, labels, savefig=None, **kwargs):
     """
@@ -512,3 +513,107 @@ def plot_contours(sample_list, names, labels, savefig=None, **kwargs):
 
     if savefig is not None:
         plt.savefig(savefig)
+
+
+def plots_all_corr_2d(
+    res_params,
+    x,
+    y,
+    xlabel_arr,
+    ylabel_arr,
+    weights=None,
+    order="lin",
+    mix=False,
+    n_bin=29,
+    title="",
+    colors=None,
+    out_base=None,
+    plot_all_points=False,
+    par_ground_truth=None,
+    stats_file=None,
+    verbose=False
+):
+    """Plots All Corr 1D.
+    
+    Creates all plots for 1D correlations.
+    
+    Parameters
+    ----------
+    x : array(double)
+        input x value
+    y : array(m) of double
+        input y arrays
+    xlabel_arr, ylabel_arr : list of str
+        x-and y-axis labels
+    weights  : array of double, optional, default=None
+        weights of x points
+    order : str, optional
+        order of fit, default is 'lin'
+    mix : bool
+        mixing of components if True
+    n_bin : double, optional, default=29
+        number of points onto which data are binned
+    title : str
+        title string, default is ""
+    colors : array(m) of str, optional, default=None
+        line colors
+    out_base : str, optional, default=None
+        output file path base, if not given, plot is not saved to file
+    plot_all_points : bool, optional
+        plot all individual data points if ``True``; default is ``False``
+    par_ground_truth : dict, optional
+        ground truth parameter, for plotting, default is `None`
+    
+    """
+    if colors is None:
+        prop_cycle = plt.rcParams["axes.prop_cycle"]
+        colors = prop_cycle.by_key()["color"]
+
+    p_dp = {
+        p: ufloat(res_params[p].value, res_params[p].stderr)
+        for p in res_params
+    }
+
+    # Get spin coefficients
+    s_ds = leakage.param_order2spin(p_dp, order, mix)
+
+    # Output to stats file
+    if stats_file:
+        for p in res_params:
+            leakage.print_stats(f"{p}={p_dp[p]:.2ugP}", stats_file, verbose=verbose)
+        for spin in s_ds:
+            leakage.print_stats(
+                f"{spin}={s_ds[spin]:.2ugP}",
+                stats_file,
+                verbose=verbose,
+            )
+
+    # Bar plots of spin compoments
+    out_path_spin = f"{out_base}_spin.png" if out_base else None
+
+    s_ground_truth = param_order1spin(par_ground_truth, order, mix) if par_ground_truth else None
+
+    plot_bar_spin(
+        s_ds,
+        s_ground_truth=s_ground_truth,
+        output_path=out_path_spin,
+    )
+
+    # Curves
+    plot_corr_2d(
+        x,
+        y,
+        weights,
+        res_params,
+        p_dp,
+        n_bin,
+        order,
+        mix,
+        xlabel_arr,
+        ylabel_arr,
+        plot_all_points=plot_all_points,
+        par_ground_truth=par_ground_truth,
+        title=title,
+        colors=colors,
+        out_base=out_base,
+    )
\ No newline at end of file

From 18b772f829432c314d28b3d77e914ff3fd131d49 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sun, 5 May 2024 12:34:59 +0200
Subject: [PATCH 71/97] scale check between rho and tau

---
 shear_psf_leakage/rho_tau_stat.py | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index cb750e1..5f2d526 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -1510,19 +1510,21 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
         fig, ax = self.tau_stat_handler.plot_tau_stats([filename], [color], [catalog_id], plot_tau_m=False)
 
         assert (self.rho_stat_handler.rho_stats is not None), ("Please load rho statistics data.") #Check if data was loaded
-        assert (
-            np.all(
-                np.abs(
-                    self.rho_stat_handler.rho_stats["theta"] -
-                    self.tau_stat_handler.tau_stats["theta"]
-                ) / self.tau_stat_handler.tau_stats["theta"] < 0.001
-            )
-        ), \
-        (
-            "The rho and tau statistics have not the same angular scales."
-            + " Check that they come from the same catalog with the same"
-            + " treecorr config."
+
+        scales_diff_ratio = np.abs(
+            (self.rho_stat_handler.rho_stats["theta"] -
+             self.tau_stat_handler.tau_stats["theta"]
+            ) / self.tau_stat_handler.tau_stats["theta"]
         )
+        if np.any(scales_diff_ratio > 0.01):
+            print("theta for rho: ", self.rho_stat_handler.rho_stats["theta"])
+            print("theta for tau: ", self.tau_stat_handler.tau_stats["theta"])
+            print(scales_diff_ratio, max(scales_diff_ratio))
+            raise ValueError(
+                "The rho and tau statistics have not the same angular scales: "
+                + " Check that they come from the same catalog with the same"
+                + " treecorr config."
+            )
 
         taus = self.model(theta).reshape(3, -1)
 

From 9bc0d455abb5f0846dbdea16e66e2f9e507d30d2 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sun, 5 May 2024 12:35:17 +0200
Subject: [PATCH 72/97] PSF leakage split fit and plot

---
 shear_psf_leakage/run_object.py | 31 ++++++++++++++++++++++++-------
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 11363bd..404612f 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -10,6 +10,7 @@
 from cs_util import args as cs_args
 
 from . import leakage
+from . import plots
 
 
 class LeakageObject:
@@ -415,26 +416,42 @@ def PSF_leakage(self, mix=True, order="lin"):
         ]
 
         # Fit consistent spin-2 2D model
-        out_path = self.get_out_base(mix, order)
-        self.par_best_fit = leakage.corr_2d(
+        out_base = self.get_out_base(mix, order)
+        out_path = f"{out_base}.pkl"
+        if not os.path.exists(out_path):
+            if self._params["verbose"]:
+                print("Computing best-fit parameters")
+            self.par_best_fit = leakage.corr_2d(
+                x_arr[:2],
+                e,
+                weights=weights,
+                order=order,
+                mix=mix,
+                stats_file=self._stats_file,
+            )
+            leakage.save_to_file(self.par_best_fit, out_path)
+        else:
+            if self._params["verbose"]:
+                print(f"Reading best-fit parameters from file {out_path}")
+            self.par_best_fit = leakage.read_from_file(out_path)
+
+        plots.plots_all_corr_2d(
+            self.par_best_fit,
             x_arr[:2],
             e,
             weights=weights,
             xlabel_arr=xlabel_arr[:2],
             ylabel_arr=ylabel_arr,
-            order=order,
-            mix=mix,
             title="",
             n_bin=n_bin,
-            out_path=out_path,
+            out_base=out_base,
             colors=colors,
             stats_file=self._stats_file,
             verbose=self._params["verbose"],
         )
-        leakage.save_to_file(self.par_best_fit, f"{out_path}.pkl")
 
         # Fit separate 1D models
-        # MKDEBUG TODO: put in separate class funtion
+        # MKDEBUG TODO: put in separate class function
 
         ylabel = r"$e_{1,2}^{\rm gal}$"
         mlabel = [r"\alpha_1", r"\alpha_2"]

From 0b663ef825083a82afbb00b29431741a5e5bc89a Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sun, 5 May 2024 12:35:44 +0200
Subject: [PATCH 73/97] no change

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 884fea5..3abcf46 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = "^0.1.0"
+cs-util = "0.0.7"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"

From b33bb504aaa6ddb00b0a68720b4bbf49e5d7356d Mon Sep 17 00:00:00 2001
From: sachaguer <sacha.guerrini@cea.fr>
Date: Mon, 6 May 2024 13:17:32 +0200
Subject: [PATCH 74/97] Bug fixes

---
 shear_psf_leakage/rho_tau_cov.py  | 10 +++++-----
 shear_psf_leakage/rho_tau_stat.py |  4 +++-
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 0c66126..d8b7cf1 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -108,7 +108,7 @@ def __init__(
         self.xi_plus = kwargs.get("xi_plus", None)
         self.xi_minus = kwargs.get("xi_minus", None)
 
-        dummy_cat = treecorr.Catalog(ra=[0], dec=[0], g1=[0], g2=[0], w=[0], ra_units='deg', dec_units='deg')
+        dummy_cat = treecorr.Catalog(ra=[0], dec=[0], g1=[0], g2=[0], w=[1], ra_units='deg', dec_units='deg')
         gg = treecorr.GGCorrelation(self.treecorr_config)
         gg.process(dummy_cat, dummy_cat)
         self.bins = gg.meanr
@@ -495,7 +495,7 @@ def compute_cv_rho_plus(self, component, **kwargs):
         interpolator_xi = self.xi_plus_itp
         interpolator_rho = rho['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 500, nbin_rad)
+        phi_radius = np.linspace(1e-2, 300, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -537,7 +537,7 @@ def compute_cv_tau_plus(self, component, **kwargs):
         interpolator_tau_b = tau_b['p']
         interpolator_tau_c = tau_c['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 500, nbin_rad)
+        phi_radius = np.linspace(1e-2, 300, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -576,7 +576,7 @@ def compute_cv_rho_minus(self, component, **kwargs):
         interpolator_xi = self.xi_minus_itp
         interpolator_rho = rho['m']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 500, nbin_rad)
+        phi_radius = np.linspace(1e-2, 300, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -627,7 +627,7 @@ def compute_cv_tau_minus(self, component, **kwargs):
         interpolator_tau_b = tau_b['m']
         interpolator_tau_c = tau_c['m']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 500, nbin_rad)
+        phi_radius = np.linspace(1e-2, 300, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index aa3dddc..9ecd617 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -956,6 +956,8 @@ def set_data_directory(self, data_directory):
             The new data directory.
         """
         self.data_directory = data_directory
+        self.rho_stat_handler.catalogs._output = self.data_directory #Change the path to the specified data directory
+        self.tau_stat_handler.catalogs._output = self.data_directory
 
 
     def load_rho_stat(self, filename):
@@ -1146,7 +1148,7 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
         ndim = 3
         assert (self.rho_stat_handler.rho_stats is not None), ("Please load rho statistics data.") #Check if data was loaded
         assert (self.tau_stat_handler.tau_stats is not None), ("Please load tau statistics data.")
-        assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
+        #assert (np.all(self.rho_stat_handler.rho_stats["theta"] == self.tau_stat_handler.tau_stats["theta"])), ("The rho and tau statistics have not the same angular scales. Check that they come from the same catalog with the same treecorr config.")
         #Check that the abssiss are the same
 
         assert (self.cov_tau is not None), ("Please load a covariance matrix")

From 793509287f2acd3f0ed3c02a5cb9e7df626d3c44 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 6 May 2024 15:35:43 +0200
Subject: [PATCH 75/97] Fixed plot 2d argumets (verbose, mix, order)

---
 shear_psf_leakage/leakage.py    |  8 ++++----
 shear_psf_leakage/plots.py      | 12 +++++-------
 shear_psf_leakage/run_object.py | 33 ++++++++++++++++++++++++---------
 3 files changed, 33 insertions(+), 20 deletions(-)

diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index 86318e7..a354ed7 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -857,6 +857,7 @@ def corr_2d(
     order="lin",
     mix=False,
     stats_file=None,
+    verbose=False,
 ):
     """Corr 2D.
 
@@ -877,6 +878,8 @@ def corr_2d(
         mixing of components if True
     stats_file : filehandler, optional, default=None
         output file for statistics
+    verbose : bool, optional
+        verbose output if ``True``; default is ``False``
 
     Returns
     -------
@@ -914,10 +917,7 @@ def corr_2d(
                 params.add(p_quad_mix, value=val_init)
 
     # Mininise loss function
-    if weights is not None:
-        err = 1 / np.sqrt(weights)
-    else:
-        err = np.ones_like(y[0])
+    err = 1 / np.sqrt(weights) if weights is not None else np.ones_like(y[0])
     res = minimize(loss_bias_2d, params, args=(x, y, err, order, mix))
     if stats_file:
         print_stats(
diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index 7a22c9e..ee6fe98 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -531,7 +531,7 @@ def plots_all_corr_2d(
     plot_all_points=False,
     par_ground_truth=None,
     stats_file=None,
-    verbose=False
+    verbose=False,
 ):
     """Plots All Corr 1D.
     
@@ -549,9 +549,9 @@ def plots_all_corr_2d(
         weights of x points
     order : str, optional
         order of fit, default is 'lin'
-    mix : bool
-        mixing of components if True
-    n_bin : double, optional, default=29
+    mix : bool, optional
+        mixing of components if ``True; default)
+    n_bin: int, optional, default=29
         number of points onto which data are binned
     title : str
         title string, default is ""
@@ -589,10 +589,8 @@ def plots_all_corr_2d(
             )
 
     # Bar plots of spin compoments
+    s_ground_truth = param_order2spin(par_ground_truth, order, mix) if par_ground_truth else None
     out_path_spin = f"{out_base}_spin.png" if out_base else None
-
-    s_ground_truth = param_order1spin(par_ground_truth, order, mix) if par_ground_truth else None
-
     plot_bar_spin(
         s_ds,
         s_ground_truth=s_ground_truth,
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 404612f..1c84baa 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -327,26 +327,40 @@ def test(self):
         dy1 = np.random.normal(scale=sig_x, size=size)
         dy2 = np.random.normal(scale=sig_x, size=size)
 
-        # Carry out fits
         for order in ["lin", "quad"]:
             for mix in [False, True]:
-                out_path = f"{self._params['output_dir']}/test_{order}_{mix}"
+                # Carry out fits
                 leakage.corr_2d(
                     x_arr,
                     [y1 + dy1, y2 + dy2],
-                    xlabel_arr=xlabel_arr,
-                    ylabel_arr=ylabel_arr,
                     order=order,
                     mix=mix,
+                    stats_file=self._stats_file,
+                    verbose=self._params["verbose"],
+                )
+                
+                # Create plots
+                out_path = f"{self._params['output_dir']}/test_{order}_{mix}"
+                plots.plots_all_corr_2d(
+                    self.par_best_fit,
+                    x_arr[:2],
+                    e,
+                    weights=weights,
+                    xlabel_arr=xlabel_arr[:2],
+                    ylabel_arr=ylabel_arr,
                     title="",
                     n_bin=n_bin,
-                    out_path=out_path,
+                    order=order,
+                    mix=mix,
+                    out_base=out_base,
                     colors=colors,
                     plot_all_points=True,
                     par_ground_truth=p_gt,
                     stats_file=self._stats_file,
                     verbose=self._params["verbose"],
-                )
+        )
+
+
 
         print("Ground truth:")
         for par in p_gt:
@@ -371,13 +385,11 @@ def get_out_base(self, mix, order):
         Return output file base name.
 
         """
-        out_base = (
+        return (
             f"{self._params['output_dir']}"
             + f"/PSF_e_vs_e_gal_order-{order}_mix-{mix}"
         )
 
-        return out_base
-
     def PSF_leakage(self, mix=True, order="lin"):
         """PSF Leakage.
 
@@ -428,6 +440,7 @@ def PSF_leakage(self, mix=True, order="lin"):
                 order=order,
                 mix=mix,
                 stats_file=self._stats_file,
+                verbose=self._params["verbose"],
             )
             leakage.save_to_file(self.par_best_fit, out_path)
         else:
@@ -444,6 +457,8 @@ def PSF_leakage(self, mix=True, order="lin"):
             ylabel_arr=ylabel_arr,
             title="",
             n_bin=n_bin,
+            order=order,
+            mix=mix,
             out_base=out_base,
             colors=colors,
             stats_file=self._stats_file,

From 73693400f5e990b7628068c3c08d3976a481e5f8 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 6 May 2024 15:47:17 +0200
Subject: [PATCH 76/97] updated to cs_util 0.1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3abcf46..884fea5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = "0.0.7"
+cs-util = "^0.1.0"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"

From 11954d6917a09c490598bc1f31b49e8637ed338c Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Mon, 6 May 2024 16:03:51 +0200
Subject: [PATCH 77/97] Modified the scale of integration to avoid regions
 where interpolation is not trusted

---
 shear_psf_leakage/rho_tau_cov.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index d8b7cf1..1ff5d1e 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -495,7 +495,7 @@ def compute_cv_rho_plus(self, component, **kwargs):
         interpolator_xi = self.xi_plus_itp
         interpolator_rho = rho['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 300, nbin_rad)
+        phi_radius = np.linspace(1e-1, 250, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -537,7 +537,7 @@ def compute_cv_tau_plus(self, component, **kwargs):
         interpolator_tau_b = tau_b['p']
         interpolator_tau_c = tau_c['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 300, nbin_rad)
+        phi_radius = np.linspace(1e-1, 250, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -576,7 +576,7 @@ def compute_cv_rho_minus(self, component, **kwargs):
         interpolator_xi = self.xi_minus_itp
         interpolator_rho = rho['m']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 300, nbin_rad)
+        phi_radius = np.linspace(1e-1, 250, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))
@@ -627,7 +627,7 @@ def compute_cv_tau_minus(self, component, **kwargs):
         interpolator_tau_b = tau_b['m']
         interpolator_tau_c = tau_c['m']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-2, 300, nbin_rad)
+        phi_radius = np.linspace(1e-1, 250, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
                 radius_val = np.zeros(len(phi_radius))

From 2f5a12ceb7412c8f48663cfc49f209c2b951ed78 Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Mon, 6 May 2024 16:35:39 +0200
Subject: [PATCH 78/97] Add calibration of ellipticities for DES.

---
 shear_psf_leakage/rho_tau_cov.py  | 10 +++++++++-
 shear_psf_leakage/rho_tau_stat.py |  5 ++++-
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 1ff5d1e..45963b0 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -176,6 +176,8 @@ def params_default(self):
             "star_size": "SIGMA_STAR_HSM",
             "PSF_flag": "FLAG_PSF_HSM",
             "star_flag": "FLAG_STAR_HSM",
+            "R11": np.array([1]),
+            "R22": np.array([1]),
             "square_size": True,
             "ra_units": "deg",
             "dec_units": "deg"
@@ -208,9 +210,15 @@ def create_treecorr_catalog(self, cat_gal, cat_psf):
         """
         ra_units = self.treecorr_config.get('ra_units', 'deg')
         dec_units = self.treecorr_config.get('dec_units', 'deg')
+        if isinstance(self._params['R11'], str):
+            e1_gal = (cat_gal[self._params['e1_col']]-np.average(cat_gal[self._params['e1_col']], weights=cat_gal[self._params['w_col']]))/np.average(cat_gal[self._params['R11']])
+            e2_gal = (cat_gal[self._params['e2_col']]-np.average(cat_gal[self._params['e2_col']], weights=cat_gal[self._params['w_col']]))/np.average(cat_gal[self._params['R22']])
+        else:
+            e1_gal = (cat_gal[self._params['e1_col']]-np.average(cat_gal[self._params['e1_col']], weights=cat_gal[self._params['w_col']]))
+            e2_gal = (cat_gal[self._params['e2_col']]-np.average(cat_gal[self._params['e2_col']], weights=cat_gal[self._params['w_col']]))
         gal = treecorr.Catalog(
             ra=cat_gal[self._params['ra_col']], dec=cat_gal[self._params['dec_col']],
-            w=cat_gal[self._params['w_col']], g1=cat_gal[self._params['e1_col']], g2=cat_gal[self._params['e2_col']],
+            w=cat_gal[self._params['w_col']], g1=e1_gal, g2=e2_gal,
             ra_units=ra_units, dec_units=dec_units
         )
 
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 9ecd617..9d255e8 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -299,7 +299,10 @@ def get_cat_fields(self, cat_type, square_size=False):
             dec = self.dat_shear[self._params["dec_col"]]
             g1 = self.dat_shear[self._params["e1_col"]] - np.average(self.dat_shear[self._params["e1_col"]], weights=weights)
             g2 = self.dat_shear[self._params["e2_col"]] - np.average(self.dat_shear[self._params["e2_col"]], weights=weights)
-
+            if self._params["R11"] is not None:
+                g1 /= self._params["R11"]
+            if self._params["R22"] is not None:
+                g2 /= self._params["R22"]
         else:
             assert self.dat_psf is not None, ("Check you read the shear catalogs correctly.")
             #Add a mask?

From d125c80afa3464efa383ce9d5be3c7a307c6aaaa Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 6 May 2024 16:44:41 +0200
Subject: [PATCH 79/97] clean-up; fixed typos

---
 shear_psf_leakage/correlation.py | 42 --------------------------------
 shear_psf_leakage/leakage.py     |  6 ++---
 shear_psf_leakage/plots.py       |  2 +-
 shear_psf_leakage/run_object.py  |  7 +++---
 4 files changed, 7 insertions(+), 50 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index e1e85ba..d2900d6 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -154,48 +154,6 @@ def print_fit_report(res, file=None):
     print(f"bic = {res.bic}", file=file)
 
 
-def param_order2spin(p_dp, order, mix):
-    """Param Order 2 Spin.
-
-    Transform parameter from natural to spin coefficients.
-
-    Parameters
-    ----------
-    p_dp : dict
-        Parameter natural coefficients
-    order : str
-        expansion order, one of 'linear', 'quad'
-    mix : bool
-        ellipticity components are mixed if ``True``
-
-    Returns
-    -------
-    dict :
-        Parameter spin coefficients
-
-    """
-    s_ds = {}
-
-    s_ds["x0"] = 0.5 * (p_dp["a11"] + p_dp["a22"])
-
-    if order == "quad" and mix:
-        s_ds["x2"] = 0.5 * (p_dp["q111"] + p_dp["q122"])
-        s_ds["y2"] = 0.5 * (p_dp["q211"] - p_dp["q222"])
-        s_ds["x-2"] = 0.25 * (p_dp["q111"] - p_dp["q122"] + p_dp["q212"])
-        s_ds["y-2"] = 0.25 * (p_dp["q211"] - p_dp["q222"] - p_dp["q112"])
-
-    s_ds["x4"] = 0.5 * (p_dp["a11"] - p_dp["a22"])
-
-    if mix:
-        s_ds["y4"] = p_dp["a12"]
-
-    if order == "quad" and mix:
-        s_ds["x6"] = 0.25 * (p_dp["q111"] - p_dp["q122"] - p_dp["q212"])
-        s_ds["y6"] = 0.25 * (p_dp["q211"] - p_dp["q222"] + p_dp["q112"])
-
-    return s_ds
-
-
 def xi_a_b(
     ra_a,
     dec_a,
diff --git a/shear_psf_leakage/leakage.py b/shear_psf_leakage/leakage.py
index a354ed7..0618ba1 100644
--- a/shear_psf_leakage/leakage.py
+++ b/shear_psf_leakage/leakage.py
@@ -1255,6 +1255,8 @@ def read_from_file(fname):
         data = pickle.load(f)
 
     return data
+
+
 def param_order2spin(p_dp, order, mix):
     """Param Order 2 Spin.
 
@@ -1275,9 +1277,7 @@ def param_order2spin(p_dp, order, mix):
         Parameter spin coefficients
 
     """
-    s_ds = {}
-
-    s_ds["x0"] = 0.5 * (p_dp["a11"] + p_dp["a22"])
+    s_ds = {"x0": 0.5 * (p_dp["a11"] + p_dp["a22"])}
 
     if order == "quad" and mix:
         s_ds["x2"] = 0.5 * (p_dp["q111"] + p_dp["q122"])
diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index ee6fe98..2edc9f1 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -589,7 +589,7 @@ def plots_all_corr_2d(
             )
 
     # Bar plots of spin compoments
-    s_ground_truth = param_order2spin(par_ground_truth, order, mix) if par_ground_truth else None
+    s_ground_truth = leakage.param_order2spin(par_ground_truth, order, mix) if par_ground_truth else None
     out_path_spin = f"{out_base}_spin.png" if out_base else None
     plot_bar_spin(
         s_ds,
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 1c84baa..2853450 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -330,7 +330,7 @@ def test(self):
         for order in ["lin", "quad"]:
             for mix in [False, True]:
                 # Carry out fits
-                leakage.corr_2d(
+                self.par_best_fit = leakage.corr_2d(
                     x_arr,
                     [y1 + dy1, y2 + dy2],
                     order=order,
@@ -340,12 +340,11 @@ def test(self):
                 )
                 
                 # Create plots
-                out_path = f"{self._params['output_dir']}/test_{order}_{mix}"
+                out_base = f"{self._params['output_dir']}/test_{order}_{mix}"
                 plots.plots_all_corr_2d(
                     self.par_best_fit,
                     x_arr[:2],
-                    e,
-                    weights=weights,
+                    [y1 + dy1, y2 + dy2],
                     xlabel_arr=xlabel_arr[:2],
                     ylabel_arr=ylabel_arr,
                     title="",

From 02776c1450660956416a3e9dc3b48e2fa2ce7196 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 6 May 2024 17:11:47 +0200
Subject: [PATCH 80/97] sourcery fix

---
 shear_psf_leakage/plots.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/shear_psf_leakage/plots.py b/shear_psf_leakage/plots.py
index 2edc9f1..4ffe6c5 100644
--- a/shear_psf_leakage/plots.py
+++ b/shear_psf_leakage/plots.py
@@ -38,14 +38,12 @@ def equi_num_bins(values, n_bin):
         equi-numbered bin array
 
     """
-    xeqn = np.interp(
+    return np.interp(
         np.linspace(0, len(values), n_bin + 1),
         np.arange(len(values)),
         np.sort(values),
     )
 
-    return xeqn
-
 
 def compute_bins_func_2d(x, y, n_bin, mix, weights=None):
     """Compute Bins Func 2D.
@@ -588,7 +586,7 @@ def plots_all_corr_2d(
                 verbose=verbose,
             )
 
-    # Bar plots of spin compoments
+    # Bar plots of spin components
     s_ground_truth = leakage.param_order2spin(par_ground_truth, order, mix) if par_ground_truth else None
     out_path_spin = f"{out_base}_spin.png" if out_base else None
     plot_bar_spin(

From 36eb8c63d31c139324f550133a8bb92bba6b3515 Mon Sep 17 00:00:00 2001
From: sachaguer <sacha.guerrini@cea.fr>
Date: Tue, 7 May 2024 14:14:12 +0200
Subject: [PATCH 81/97] Bug correction

---
 shear_psf_leakage/rho_tau_stat.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 9d255e8..39f2d10 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -299,9 +299,9 @@ def get_cat_fields(self, cat_type, square_size=False):
             dec = self.dat_shear[self._params["dec_col"]]
             g1 = self.dat_shear[self._params["e1_col"]] - np.average(self.dat_shear[self._params["e1_col"]], weights=weights)
             g2 = self.dat_shear[self._params["e2_col"]] - np.average(self.dat_shear[self._params["e2_col"]], weights=weights)
-            if self._params["R11"] is not None:
+            if self._params.get("R11", None) is not None:
                 g1 /= self._params["R11"]
-            if self._params["R22"] is not None:
+            if self._params.get("R22", None) is not None:
                 g2 /= self._params["R22"]
         else:
             assert self.dat_psf is not None, ("Check you read the shear catalogs correctly.")

From 1bdad5f0df48781f7bbcc871b7f072aef623bd34 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Wed, 8 May 2024 10:09:16 +0200
Subject: [PATCH 82/97] updated to cs_util 0.1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 884fea5..2682d55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = "^0.1.0"
+cs-util = "0.1.0"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"

From 2414cc44b6cfc93158414edf3157755c3d8eba2a Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Wed, 8 May 2024 18:56:04 +0200
Subject: [PATCH 83/97] version 0.2; in pyproject only

---
 poetry.lock                   | 318 ++++++++++++++++------------------
 pyproject.toml                |   3 +-
 shear_psf_leakage/__init__.py |   5 +-
 3 files changed, 157 insertions(+), 169 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 107fe56..84226c5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -140,70 +140,56 @@ test = ["coverage", "pytest", "pytest-cov"]
 
 [[package]]
 name = "astropy"
-version = "6.0.0"
+version = "5.2"
 description = "Astronomy and astrophysics core library"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "astropy-6.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:eedd46d5c50b977ebb368f8df6cd34a720999cb4fdfd4d2d6b459262a8ac8ec2"},
-    {file = "astropy-6.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5797f27b55d58ea122b076c3845dbd79445dc03020cea359ea949f619de1cdcf"},
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-]
-
-[package.dependencies]
-astropy-iers-data = ">=0.2023.10.30.0.29.53"
-numpy = ">=1.22,<2"
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+]
+
+[package.dependencies]
+numpy = ">=1.20"
 packaging = ">=19.0"
 pyerfa = ">=2.0"
 PyYAML = ">=3.13"
 
 [package.extras]
-all = ["asdf-astropy (>=0.3)", "astropy[recommended]", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2023.4.0)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "mpmath", "pandas", "pre-commit", "pyarrow (>=5.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2023.4.0)", "sortedcontainers", "typing-extensions (>=3.10.0.1)"]
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-recommended = ["matplotlib (>=3.3,!=3.4.0,!=3.5.2)", "scipy (>=1.5)"]
+all = ["asdf (>=2.10.0)", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2022.8.2)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "mpmath", "pandas", "pyarrow (>=5.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2022.8.2)", "scipy (>=1.5)", "sortedcontainers", "typing-extensions (>=3.10.0.1)"]
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+recommended = ["matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "scipy (>=1.5)"]
 test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist"]
-test-all = ["astropy[test]", "coverage[toml]", "ipython (>=4.2)", "objgraph", "sgp4 (>=2.3)", "skyfield (>=1.20)"]
-
-[[package]]
-name = "astropy-iers-data"
-version = "0.2024.2.19.0.28.47"
-description = "IERS Earth Rotation and Leap Second tables for the astropy core package"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.extras]
-docs = ["pytest"]
-test = ["hypothesis", "pytest", "pytest-remotedata"]
+test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pooch", "pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "sgp4 (>=2.3)", "skyfield (>=1.20)"]
 
 [[package]]
 name = "asttokens"
@@ -367,13 +353,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "cadcutils"
-version = "1.5.1.2"
+version = "1.5.2.2"
 description = "Generic CADC Python libary of utilities"
 optional = false
-python-versions = ">=3.7, <4"
+python-versions = "<4,>=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -837,27 +823,25 @@ test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "cs-util"
-version = "0.0.7"
-description = ""
+version = "0.1.0"
+description = "Utility library for CosmoStat"
 optional = false
-python-versions = ">=3.7"
+python-versions = "<3.12,>=3.9"
 files = [
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 [package.dependencies]
-astropy = ">=4.0"
-datetime = ">=4.7"
-matplotlib = ">=3.3"
-numpy = ">=1.21"
-vos = ">=3.3"
-
-[package.extras]
-dev = ["cs-util[lint,release,test]"]
-lint = ["black", "jinja2 (==3.0)"]
-release = ["build", "sdist", "twine"]
-test = ["pytest", "pytest-black", "pytest-cov", "pytest-pydocstyle"]
+astropy = "5.2"
+camb = "1.5.4"
+datetime = ">=5.5,<6.0"
+matplotlib = ">=3.8.4,<4.0.0"
+numpy = ">=1.26.4,<2.0.0"
+scipy = ">=1.13.0,<2.0.0"
+swig = ">=4.2.1,<5.0.0"
+vos = ">=3.6.1,<4.0.0"
 
 [[package]]
 name = "cycler"
@@ -876,13 +860,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "datetime"
-version = "5.4"
+version = "5.5"
 description = "This package provides a DateTime data type, as known from Zope. Unless you need to communicate with Zope APIs, you're probably better off using Python's built-in datetime module."
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -2113,39 +2097,39 @@ files = [
 
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 description = "Python plotting package"
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 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -2154,7 +2138,7 @@ cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.3.1"
-numpy = ">=1.21,<2"
+numpy = ">=1.21"
 packaging = ">=20.0"
 pillow = ">=8"
 pyparsing = ">=2.3.1"
@@ -3374,45 +3358,45 @@ files = [
 
 [[package]]
 name = "scipy"
-version = "1.12.0"
+version = "1.13.0"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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 [package.extras]
-test = ["flake8 (>=3.4.1)", "mock (>=2.0.0)", "pytest (>=4.6)", "pytest-cov (>=2.5.1)"]
+test = ["flake8 (>=3.4.1)", "pytest (>=4.6)", "pytest-cov (>=2.5.1)"]
 
 [[package]]
 name = "wcwidth"
@@ -3953,4 +3937,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "08c934e954f7baea6f7317765d2a500fb76811858fccf9070022e003dbc43cc8"
+content-hash = "9e144bf811b86b31d1c9af97ba52e99ffb2736858a0bbf3c514272fb981f2700"
diff --git a/pyproject.toml b/pyproject.toml
index 2682d55..6d252b6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,10 @@
 [tool.poetry]
 name = "shear_psf_leakage"
-version = "0.1.6"
+version = "0.2"
 description = "PSF leakage for shear catalogue data"
 authors = [
 	"Martin Kilbinger <martin.kilbinger@cea.fr>",
+	"Sacha Guerrini <sacha.guerrini@cea.fr>",
 	"Clara Bonini <clara.bonini@universite-paris-saclay.fr>",
 ]
 readme = "README.md"
diff --git a/shear_psf_leakage/__init__.py b/shear_psf_leakage/__init__.py
index 0a8da88..0daff46 100644
--- a/shear_psf_leakage/__init__.py
+++ b/shear_psf_leakage/__init__.py
@@ -1 +1,4 @@
-__version__ = "0.1.6"
+from importlib.metadata import version
+
+__version__ = version("shear_psf_leakage")
+

From e725e866600315c883c44b6830f8ded6ed0879cf Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sat, 11 May 2024 16:52:32 +0200
Subject: [PATCH 84/97] scale--dep leakage script: updated notation

---
 notebooks/leakage_scale_spin.py | 182 ++++++++++----------------------
 1 file changed, 53 insertions(+), 129 deletions(-)

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index e588a8f..9506e32 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -32,6 +32,7 @@
 from uncertainties import unumpy
 
 from cs_util import plots as cs_plots
+from cs_util import args
 
 import shear_psf_leakage.run_scale as run
 from shear_psf_leakage.leakage import *
@@ -46,25 +47,9 @@
 # ### Set input parameters
 
 # +
-params_in_path = "params_leakage_scale.py"
-if os.path.exists(params_in_path):
-    print(f"Reading configuration script {params_in_path}")
-
-    with open(params_in_path) as f:
-        exec(f.read())
-
-    # Set instance parameters, copy from above
-    for key in params_in:
-        obj._params[key] = params_in[key]
-
-else:
-    print(f"Configuration script {params_in_path} not found, asking for user input")
-
-    for key in obj._params:
-        msg = f"{key}? [{obj._params[key]}] "
-        val_user = input(msg)
-        if val_user != "":
-            obj._params[key] = val_user
+params_upd = args.read_param_script("params_leakage_scale.py", obj._params, verbose=True)
+for key in params_upd:
+    obj._params[key] = params_upd[key]
 # -
 
 print(obj._params)
@@ -95,8 +80,10 @@
 # #### PSF auto-correlation function correlation matrix
 #
 # $$
-# r = \frac{ \left( \Xi_{12}^\textrm{p,p} \right)^2 }
-#     { \Xi_{11}^\textrm{p,p} \, \Xi_{22}^\textrm{p,p} }
+# r = \frac{ \left( \xi_{12}^\textrm{p,p} \right)^2 }
+#     { \xi_{11}^\textrm{p,p} \, \xi_{22}^\textrm{p,p} }
+#   = \frac{ \left( \rho_{12, 0} \right)^2 }
+#       { \rho_{11, 0} \rho_{22, 0} }
 # $$
 
 # Check symmetry of PSF auto-correlation matrix
@@ -129,14 +116,10 @@
 # Approximate
 r_fast = obj.xi_pp_m[0][1] ** 2 / (obj.xi_pp_m[0][0] * obj.xi_pp_m[1][1])
 
-theta_arr = []
+n = 6
+theta_arr = [theta] * n
 r_arr = []
 dr_arr = []
-
-n = 6
-ftheta = 1.05
-for idx in range(n):
-    theta_arr.append(theta * ftheta ** (idx - n))
     
 r_arr.append(unumpy.nominal_values(r))
 r_arr.append(unumpy.nominal_values(r_ratio_1))
@@ -157,9 +140,13 @@
 linewidths = [2] * 3 + [1] * 3
 
 xlabel = r"$\theta$ [arcmin]"
-ylabel = r"ratios of $r(\theta)$"
+ylabel = r"functions of $r(\theta)$"
 
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+fac = 1.1
+xlim = [
+    obj._params["theta_min_amin"] / fac,
+    obj._params["theta_max_amin"] * fac
+]
 ylim = (-0.5, 2)
 
 out_path = f"{obj._params['output_dir']}/r.png"
@@ -182,6 +169,7 @@
     linewidths=linewidths,
     linestyles=linestyles,
     close_fig=False,
+    shift_x=True,
 )
 
 # +
@@ -193,10 +181,7 @@
 ylim = obj._params["leakage_alpha_ylim"]
 
 n = 4
-ftheta = 1.05
-theta_arr = []
-for idx in (range(n)):
-    theta_arr.append(theta * ftheta ** (idx - n))
+theta_arr = [theta] * n
     
 alpha = []
 yerr = []
@@ -233,6 +218,7 @@
     linestyles=linestyles,
     markers=markers,
     close_fig=False,
+    shift_x=True,
 )
 
 # +
@@ -252,14 +238,10 @@
 
 theta = obj.get_theta()
 
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 ylim = obj._params["leakage_alpha_ylim"]
 
 n = 4
-ftheta = 1.05
-theta_arr = []
-for idx in (range(n)):
-    theta_arr.append(theta * ftheta ** (idx - n/2))
+theta_arr = [theta] * n
 
 y_arr = [
     unumpy.nominal_values(x0),
@@ -274,10 +256,10 @@
     unumpy.std_devs(y0),
 ]
 labels = [
-    r"$x_0 = (\alpha_{11} + \alpha_{22})/2$ (spin-0)",
-    r"$x_4 = (\alpha_{11} - \alpha_{22})/2$ (spin-4)",
-    r"$y_4 = (\alpha_{12} + \alpha_{21})/2$ (spin-4)",
-    r"$y_0 = (\alpha_{21} - \alpha_{12})/2$ (spin-0 imaginary)"
+    r"$\alpha^\Re_0 = (\alpha_{11} + \alpha_{22})/2$",
+    r"$\alpha^\Re_4 = (\alpha_{11} - \alpha_{22})/2$",
+    r"$\alpha^\Im_4 = (\alpha_{12} + \alpha_{21})/2$",
+    r"$\alpha^\Im_0 = (-\alpha_{12} + \alpha_{21})/2$"
 ]
 colors = ["blue", "orange", "green", "magenta"]
 markers = ["o", "s", "h", "^"]
@@ -286,7 +268,7 @@
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"Components of leakage matrix"
 title = ""
-out_path = "alpha_leakage_m_s0_s4.png"
+out_path = f"{obj._params['output_dir']}/alpha_leakage_m_s0_s4.png"
 
 cs_plots.plot_data_1d(
     theta_arr,
@@ -303,17 +285,18 @@
     colors=colors,
     markers=markers,
     close_fig=False,
+    shift_x=True,
 )
 
 # Including scalar leakage for comparison
-theta_arr.append(theta * ftheta ** (4 - n/2))
+theta_arr.append(theta)
 y_arr.append(obj.alpha_leak)
 dy_arr.append(obj.sig_alpha_leak)
 labels.append(r"$\alpha$ (scalar approx.)")
 colors.append("blue")
 markers.append("p")
 linestyles.append("--")
-out_path = "alpha_leakage_m_s0_s4_as.png"
+out_path = f"{obj._params['output_dir']}/alpha_leakage_m_s0_s4_as.png"
 
 cs_plots.plot_data_1d(
     theta_arr,
@@ -331,92 +314,31 @@
     markers=markers,
     linestyles=linestyles,
     close_fig=False,
+    shift_x=True,
 )
 
 
-# +
-# Plot off-diagonal element: spin-4 and "spin-0 imaginary" part which should vanish
-
-y4 = obj.get_alpha_ufloat(0, 1)
-y4_bis = obj.get_alpha_ufloat(1, 0)
-y0 = 0.5 * (y4_bis - y4)
-
-theta = obj.get_theta()
-
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-ylim = obj._params["leakage_alpha_ylim"]
-
-n = 3
-ftheta = 1.05
-theta_arr = []
-for idx in (range(n)):
-    theta_arr.append(theta * ftheta ** (idx - n))
-    
-y = [
-    unumpy.nominal_values(y4),
-    unumpy.nominal_values(y4_bis),
-    unumpy.nominal_values(y0),
-]
-dy = [
-    unumpy.std_devs(y4),
-    unumpy.std_devs(y4_bis),
-    unumpy.std_devs(y0),
-]
-labels = [
-    r"$\alpha_{12}$ (spin-4)",
-    r"$\alpha_{21}$ (spin-4)",
-    r"$y_0 = (\alpha_{21}-\alpha_{12})/2$ ('spin-0 imag')",
-]
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
-ylim = obj._params["leakage_alpha_ylim"]
-
-colors = ["orange", "orange", "magenta"]
-markers = ["o", "s", "^"]
-
-xlabel = r"$\theta$ [arcmin]"
-ylabel = r"Components of leakage matrix"
-title = ""
-out_path = "alpha_leakage_m_s4_s4im.png"
-
-cs_plots.plot_data_1d(
-    theta_arr,
-    y,
-    dy,
-    title,
-    xlabel,
-    ylabel,
-    out_path,
-    labels=labels,
-    xlog=True,
-    xlim=xlim,
-    ylim=ylim,
-    colors=colors,
-    markers=markers,
-    close_fig=False,
-)
-# -
-
 # The elements of $\alpha$ can be written as
 # \begin{align}
 # \newcommand{\mat}[1]{\mathrm{#1}}
 #     \alpha_{11} = & \left(
-#         \phantom -\Xi_{11}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
-#         - \Xi_{12}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#         \phantom -\xi_{11}^\textrm{g,p} \, \xi_{22}^\textrm{p,p}
+#         - \xi_{12}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
+#         \right) \left| \mat \xi^\textrm{p,p} \right|^{-1} ;
 #     \nonumber \\
 #     \alpha_{12} = & \left(
-#         - \Xi_{11}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         + \Xi_{12}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#         - \xi_{11}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
+#         + \xi_{12}^\textrm{g,p} \, \xi_{11}^\textrm{p,p}
+#         \right) \left| \mat \xi^\textrm{p,p} \right|^{-1} ;
 #     \nonumber \\
 #         \alpha_{21} = & \left(
-#         \phantom - \Xi_{21}^\textrm{g,p} \, \Xi_{22}^\textrm{p,p}
-#         - \Xi_{22}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1} ;
+#         \phantom - \xi_{21}^\textrm{g,p} \, \xi_{22}^\textrm{p,p}
+#         - \xi_{22}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
+#         \right) \left| \mat \xi^\textrm{p,p} \right|^{-1} ;
 #     \nonumber \\
 #         \alpha_{22} = & \left(
-#         - \Xi_{21}^\textrm{g,p} \, \Xi_{12}^\textrm{p,p}
-#         + \Xi_{22}^\textrm{g,p} \, \Xi_{11}^\textrm{p,p}
+#         - \xi_{21}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
+#         + \xi_{22}^\textrm{g,p} \, \xi_{11}^\textrm{p,p}
 #         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1},
 # \end{align}
 
@@ -425,13 +347,15 @@
 # If the leakage is a scalar function, it can be expressed in three different ways.
 
 # +
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+xlim = [
+    obj._params["theta_min_amin"] / fac,
+    obj._params["theta_max_amin"] * fac
+]
 ylim = obj._params["leakage_alpha_ylim"]
 
 theta = obj.get_theta()
 
-ftheta = 1.05
-theta_arr = [theta / ftheta, theta, theta * ftheta]
+theta_arr = [theta] * 3
 
 alpha_1 = []
 alpha_2 = []
@@ -471,6 +395,7 @@
     xlim=xlim,
     ylim=ylim,
     close_fig=False,
+    shift_x=True,
 )
 # -
 
@@ -479,8 +404,7 @@
 # +
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 
-ftheta = 1.025
-theta_arr = [theta / ftheta, theta, theta * ftheta]
+theta_arr = [theta] * 3
 
 Xi_12 = []
 Xi_21 = []
@@ -501,10 +425,9 @@
     unumpy.std_devs(Xi_tr),
 ]
 
-
-labels = [r"$\Xi_{12}^{\rm gp}$", r"$\Xi_{21}^{\rm gp}$", r"tr$\Xi^{\rm gp}$"]
+labels = [r"$\tau_{12,0}$", r"$\tau_{21,0}$", r"tr$\tau_0$"]
 xlabel = r"$\theta$ [arcmin]"
-ylabel = r"Centered correlation functions"
+ylabel = r"correlation functions $\tau_{ij,0}$"
 title = ""
 out_path = f"{obj._params['output_dir']}/Xi_mixed_consistency.png"
 
@@ -520,6 +443,7 @@
     xlog=True,
     xlim=xlim,
     close_fig=False,
+    shift_x=True,
 )
 
 # +
@@ -527,8 +451,7 @@
 
 xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
 
-ftheta = 1.025
-theta_arr = [theta / ftheta, theta, theta * ftheta]
+theta_arr = [theta] * 3
 
 Xi_12 = []
 Xi_21 = []
@@ -550,7 +473,7 @@
 ]
 
 
-labels = [r"$\Xi_{12}^{\rm pp}$", r"$\Xi_{21}^{\rm pp}$", r"tr$\Xi^{\rm pp}$"]
+labels = [r"$\rho_{12,0}$", r"$\rho_{21,0}$", r"tr$\rho_0$"]
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"Centered correlation functions"
 title = ""
@@ -568,5 +491,6 @@
     xlog=True,
     xlim=xlim,
     close_fig=False,
+    shift_x=True,
 )
 # -

From d9bc7890000aba4946f7a33752f9bb4750fe565a Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sat, 11 May 2024 16:52:55 +0200
Subject: [PATCH 85/97] optics

---
 shear_psf_leakage/correlation.py | 4 ----
 shear_psf_leakage/run_object.py  | 1 -
 shear_psf_leakage/run_scale.py   | 2 +-
 3 files changed, 1 insertion(+), 6 deletions(-)

diff --git a/shear_psf_leakage/correlation.py b/shear_psf_leakage/correlation.py
index d2900d6..994cf00 100644
--- a/shear_psf_leakage/correlation.py
+++ b/shear_psf_leakage/correlation.py
@@ -485,10 +485,6 @@ def alpha(
         complex_psf = np.mean(e1_star) + np.mean(e2_star) * 1j
         mean_in_numer = np.real(np.conj(complex_gal) * complex_psf)
         mean_in_denom = np.abs(complex_psf) ** 2
-
-        # Check that <e^g> is close to zero if properly calibrated.
-        # Pay attention whether weights are used or not.
-        print("MKDEBUG eg ep", complex_gal, complex_psf)
     else:
         # Set mean ellipticities to zero for faster computation
         mean_in_numer = 0
diff --git a/shear_psf_leakage/run_object.py b/shear_psf_leakage/run_object.py
index 2853450..e7a1334 100644
--- a/shear_psf_leakage/run_object.py
+++ b/shear_psf_leakage/run_object.py
@@ -49,7 +49,6 @@ def params_default(self):
         Set default parameter values.
 
         """
-        # MKDEBUG TODO: test -> action=store_true
         self._params = {
             "input_path_shear": None,
             "output_dir": ".",
diff --git a/shear_psf_leakage/run_scale.py b/shear_psf_leakage/run_scale.py
index 52ea57e..d5783e6 100644
--- a/shear_psf_leakage/run_scale.py
+++ b/shear_psf_leakage/run_scale.py
@@ -1,4 +1,4 @@
-"""RUN.
+"""RUN SCALE.
 
 This module sets up a run of the scale-dependent leakage calculations.
 

From 3c0c68c9c32d8e7f46c63d1f7823aa19948bf148 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sat, 11 May 2024 16:53:17 +0200
Subject: [PATCH 86/97] added gsl for (pre-installed) pyccd

---
 pyproject.toml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6d252b6..c94d504 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,6 @@ pandas = "^2.0.3"
 camb = "^1.5.0"
 
 scipy = "^1.11.3"
-cs-util = "0.1.0"
 
 notebook = "5.7.4"
 jupyter-server = "^2.8.0"
@@ -32,9 +31,9 @@ jupyterlab = "^4.0.7"
 tornado = "^6.3.3"
 lenspack = "^1.0.0"
 getdist = "^1.4.7"
-cmake = "^3.28.1"
 tqdm = "^4.66.2"
 emcee = "^3.1.4"
+gsl = "^0.0.3"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 498cffec7d289497ecfbebfd54ca516185a5ee4b Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Sat, 11 May 2024 17:23:07 +0200
Subject: [PATCH 87/97] cleaned up leakage scale spin script

---
 notebooks/leakage_scale_spin.py | 50 ++++++++-------------------------
 1 file changed, 11 insertions(+), 39 deletions(-)

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index 9506e32..eee745d 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -1,23 +1,24 @@
 # ---
 # jupyter:
 #   jupytext:
+#     cell_metadata_json: true
 #     formats: ipynb,py:light
 #     text_representation:
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.15.2
+#       jupytext_version: 1.16.1
 #   kernelspec:
-#     display_name: shear_psf_leakage2
+#     display_name: shear-psf-leakage-cHl61fDi-py3.9
 #     language: python
-#     name: shear_psf_leakage2
+#     name: python3
 # ---
 
 # # Scale-dependent PSF leakage
 #
-# ## Spin-consistent leakage
+# ## Spin-consistent calculations
 #
-# package shear_psf_leakge
+# Computes and plots scale-dependent spin-consistent PSF leakage functions.
 #
 # Martin Kilbinger <martin.kilbinger@cea.fr>
 
@@ -46,13 +47,10 @@
 
 # ### Set input parameters
 
-# +
+# Read python parameter file or get user input
 params_upd = args.read_param_script("params_leakage_scale.py", obj._params, verbose=True)
 for key in params_upd:
     obj._params[key] = params_upd[key]
-# -
-
-print(obj._params)
 
 # ### Run
 
@@ -83,18 +81,18 @@
 # r = \frac{ \left( \xi_{12}^\textrm{p,p} \right)^2 }
 #     { \xi_{11}^\textrm{p,p} \, \xi_{22}^\textrm{p,p} }
 #   = \frac{ \left( \rho_{12, 0} \right)^2 }
-#       { \rho_{11, 0} \rho_{22, 0} }
+#       { \rho_{11, 0} \, \rho_{22, 0} }
 # $$
 
 # Check symmetry of PSF auto-correlation matrix
 diff = obj.Xi_pp_m[0][1] - obj.Xi_pp_m[1][0]
 print(
-    "r is symmetrical? max abs (rel) diff ="
+    "Is r symmetrical? max abs (rel) diff ="
     +f" {max(np.abs(diff)):.3e} ({max(np.abs(diff / obj.Xi_pp_m[0][1])):.3e})",
 )
 
 # +
-# Plot
+# Plot r and ratios of r
 
 theta = obj.get_theta()
 
@@ -316,32 +314,7 @@
     close_fig=False,
     shift_x=True,
 )
-
-
-# The elements of $\alpha$ can be written as
-# \begin{align}
-# \newcommand{\mat}[1]{\mathrm{#1}}
-#     \alpha_{11} = & \left(
-#         \phantom -\xi_{11}^\textrm{g,p} \, \xi_{22}^\textrm{p,p}
-#         - \xi_{12}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
-#         \right) \left| \mat \xi^\textrm{p,p} \right|^{-1} ;
-#     \nonumber \\
-#     \alpha_{12} = & \left(
-#         - \xi_{11}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
-#         + \xi_{12}^\textrm{g,p} \, \xi_{11}^\textrm{p,p}
-#         \right) \left| \mat \xi^\textrm{p,p} \right|^{-1} ;
-#     \nonumber \\
-#         \alpha_{21} = & \left(
-#         \phantom - \xi_{21}^\textrm{g,p} \, \xi_{22}^\textrm{p,p}
-#         - \xi_{22}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
-#         \right) \left| \mat \xi^\textrm{p,p} \right|^{-1} ;
-#     \nonumber \\
-#         \alpha_{22} = & \left(
-#         - \xi_{21}^\textrm{g,p} \, \xi_{12}^\textrm{p,p}
-#         + \xi_{22}^\textrm{g,p} \, \xi_{11}^\textrm{p,p}
-#         \right) \left| \mat \Xi^\textrm{p,p} \right|^{-1},
-# \end{align}
-
+# + \xi_ {"incorrectly_encoded_metadata": "{22}^\\textrm{g,p} \\, \\xi_{11}^\\textrm{p,p}"}
 # #### Consistency relations for scalar leakage
 
 # If the leakage is a scalar function, it can be expressed in three different ways.
@@ -493,4 +466,3 @@
     close_fig=False,
     shift_x=True,
 )
-# -

From 212945e91893376773d755c6ff42a078111f88f7 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 13 May 2024 13:39:28 +0200
Subject: [PATCH 88/97] scale-dep spin leakage: plots for SPv1.3

---
 notebooks/leakage_scale_spin.py | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/notebooks/leakage_scale_spin.py b/notebooks/leakage_scale_spin.py
index eee745d..ed20567 100644
--- a/notebooks/leakage_scale_spin.py
+++ b/notebooks/leakage_scale_spin.py
@@ -175,11 +175,10 @@
 
 theta = obj.get_theta()
 
-xlim = [obj._params["theta_min_amin"], obj._params["theta_max_amin"]]
+xlim = [obj._params["theta_min_amin"] / fac, obj._params["theta_max_amin"] * fac]
 ylim = obj._params["leakage_alpha_ylim"]
 
-n = 4
-theta_arr = [theta] * n
+theta_arr = [theta] * 4
     
 alpha = []
 yerr = []
@@ -230,17 +229,12 @@
     obj.get_alpha_ufloat(0, 0)
     - obj.get_alpha_ufloat(1, 1)
 )
-
 y4 = 0.5 * (obj.get_alpha_ufloat(0, 1) + obj.get_alpha_ufloat(1, 0))
 y0 = 0.5 * (obj.get_alpha_ufloat(1, 0) - obj.get_alpha_ufloat(0, 1))
 
-theta = obj.get_theta()
 
 ylim = obj._params["leakage_alpha_ylim"]
 
-n = 4
-theta_arr = [theta] * n
-
 y_arr = [
     unumpy.nominal_values(x0),
     unumpy.nominal_values(x4),
@@ -253,6 +247,7 @@
     unumpy.std_devs(y4),
     unumpy.std_devs(y0),
 ]
+theta_arr = [obj.get_theta()] * len(y_arr)
 labels = [
     r"$\alpha^\Re_0 = (\alpha_{11} + \alpha_{22})/2$",
     r"$\alpha^\Re_4 = (\alpha_{11} - \alpha_{22})/2$",
@@ -314,7 +309,7 @@
     close_fig=False,
     shift_x=True,
 )
-# + \xi_ {"incorrectly_encoded_metadata": "{22}^\\textrm{g,p} \\, \\xi_{11}^\\textrm{p,p}"}
+
 # #### Consistency relations for scalar leakage
 
 # If the leakage is a scalar function, it can be expressed in three different ways.
@@ -326,10 +321,6 @@
 ]
 ylim = obj._params["leakage_alpha_ylim"]
 
-theta = obj.get_theta()
-
-theta_arr = [theta] * 3
-
 alpha_1 = []
 alpha_2 = []
 for ndx in range(len(theta)):
@@ -339,19 +330,24 @@
     alpha_2.append(my_a2)
 
 y = [
+    unumpy.nominal_values(x0),
     obj.alpha_leak,
     unumpy.nominal_values(alpha_1),
     unumpy.nominal_values(alpha_2),
 ]
 dy = [
+    unumpy.std_devs(x0),
     obj.sig_alpha_leak,
     unumpy.std_devs(alpha_1),
     unumpy.std_devs(alpha_2),
 ]
+theta_arr = [obj.get_theta()] * len(y)
+
 
-labels = [r"$\alpha$", r"$\alpha_1$", r"$\alpha_2$"]
+labels = [r"$\alpha_0^\Re$", r"$\alpha_+$", r"$\alpha_1$", r"$\alpha_2$"]
 xlabel = r"$\theta$ [arcmin]"
 ylabel = r"$\alpha(\theta)$"
+linestyles = ["-", "--", "--", "--"] 
 title = ""
 out_path = f"{obj._params['output_dir']}/alpha_leakage_scalar_consistency.png"
 
@@ -368,6 +364,7 @@
     xlim=xlim,
     ylim=ylim,
     close_fig=False,
+    linestyles=linestyles,
     shift_x=True,
 )
 # -

From 6583e9a09670875ef67ef09f9da7ac3214473fae Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 13 May 2024 15:52:43 +0200
Subject: [PATCH 89/97] tolerance angular scales back to 0.001

---
 shear_psf_leakage/rho_tau_stat.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 1aa44e7..689677e 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -1521,7 +1521,7 @@ def plot_tau_stats_w_model(self, theta, filename, color, catalog_id, savefig=Non
              self.tau_stat_handler.tau_stats["theta"]
             ) / self.tau_stat_handler.tau_stats["theta"]
         )
-        if np.any(scales_diff_ratio > 0.01):
+        if np.any(scales_diff_ratio > 0.001):
             print("theta for rho: ", self.rho_stat_handler.rho_stats["theta"])
             print("theta for tau: ", self.tau_stat_handler.tau_stats["theta"])
             print(scales_diff_ratio, max(scales_diff_ratio))

From dab6435d60a7c34ef1a581fd770619673de19625 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 27 May 2024 11:43:21 +0200
Subject: [PATCH 90/97] pyccl 3.0

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 70f0025..1d253c5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,7 @@ jupyter-server = "^2.8.0"
 jupyterlab = "^4.0.7"
 tornado = "^6.3.3"
 lenspack = "^1.0.0"
+pyccl = "^3.0.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 4ea881d932f1ef996bebc8285a2f291d75d6100c Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Mon, 27 May 2024 11:43:54 +0200
Subject: [PATCH 91/97] lock

---
 poetry.lock | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 8c08588..20ebe92 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2650,6 +2650,19 @@ files = [
     {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
 ]
 
+[[package]]
+name = "pyccl"
+version = "3.0.0"
+description = "Library of validated cosmological functions."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyccl-3.0.0.tar.gz", hash = "sha256:4e1f9eaed0caaac864575f96ab51f3e7669b69afd92c21085deb53ded09bf9b1"},
+]
+
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "pycparser"
 version = "2.21"
@@ -3825,4 +3838,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "54a0b3762074daae2858f7acddf0df1388f212aacca774aaf7f948110e2d2c3f"
+content-hash = "0e7b43d433cf233988aada90f5e0de8d5b97a549dbb5e00ac68841f1f465a698"

From 31f95bcd9bd8051879f6e9d32526b4641cb939a5 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 10 Jun 2024 14:59:14 +0200
Subject: [PATCH 92/97] preparing v0.2.0

---
 poetry.lock    | 437 +++++++++++++++++++++++++++----------------------
 pyproject.toml |   1 +
 2 files changed, 245 insertions(+), 193 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 84226c5..60e12ba 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -580,35 +580,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
-[[package]]
-name = "cmake"
-version = "3.29.2"
-description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "cmake-3.29.2-py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:1d40c5451d6467b20a0a6015a5a6b6dc86f61b83f71f935740485b259100a34e"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed3108e646cd65a4e23fa1cbe8123569a29334a3f2a8ce214d871406b161bedb"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40aafe612b03a9fa140cca4024ba60b74cd92372f3f349d8062cba1f021e5001"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:027eebe9bb74c31759581a543f27bc1828fc76e6fc45b2b48b51f27847904669"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f1f087985fc2460476b0901716fbddb2fd69b7fe7bf1350e1ab5dc508d22600e"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:df2c63ce6d504aa4c91a42fd22d3887065ab029569691deb56ec19d0decd0ae9"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6ea5ce007893d7d1363e13433dde1c0c7c344372213a90ff3c56e896a335301d"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f9e941e73202cfa667ee488d1d88b8a758b516dcfa2a2728e73dbdcbfbdebf57"},
-    {file = "cmake-3.29.2-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:37222e23485338c72b7ea51f865d8c6847d519f7e2222922fb70b4896ca6e897"},
-    {file = "cmake-3.29.2-py3-none-musllinux_1_1_i686.whl", hash = "sha256:eeed08932c748647488280dc97ac00bcfeae5d760451105200cfe66c52ce6468"},
-    {file = "cmake-3.29.2-py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:db7a05df020ba67bacd3070dd1645c76ca96fabd06d6aaa63288fd845706e47a"},
-    {file = "cmake-3.29.2-py3-none-musllinux_1_1_s390x.whl", hash = "sha256:83b35de822ddabaaa184a7d8f9827381350c42d627689c775b214347f57c9e41"},
-    {file = "cmake-3.29.2-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:cc0e36752e581430a93e58a268e515bb4ec1373b9e9911571f2cac1d2a6b5bec"},
-    {file = "cmake-3.29.2-py3-none-win32.whl", hash = "sha256:a941e26fba81cf74832c8a0e17e007452e05b6ad4941b3d2d18c75faa4a677d8"},
-    {file = "cmake-3.29.2-py3-none-win_amd64.whl", hash = "sha256:23336c8ca01205d18d92ed8de6c54e570c352a58e378b7f9adc02ef00f433960"},
-    {file = "cmake-3.29.2-py3-none-win_arm64.whl", hash = "sha256:e722a949f7c91084dba61f8f17a9854787182ab711ed0b84b1507b24a8e12e25"},
-    {file = "cmake-3.29.2.tar.gz", hash = "sha256:6a4c1185cb2eca7263190a5754d0c9edf738d9e50bff464f78f48d0c05318e7c"},
-]
-
-[package.extras]
-test = ["coverage (>=4.2)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)"]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -769,43 +740,43 @@ toml = ["tomli"]
 
 [[package]]
 name = "cryptography"
-version = "42.0.5"
+version = "42.0.8"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
 files = [
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 gui = ["PySide6 (>=6.1)"]
 
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 ]
 
 [package.extras]
 cssselect = ["cssselect (>=0.7)"]
+html-clean = ["lxml-html-clean"]
 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=3.0.7)"]
+source = ["Cython (>=3.0.10)"]
 
 [[package]]
 name = "markdown-it-py"
@@ -2834,13 +2886,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyopenssl"
-version = "24.0.0"
+version = "24.1.0"
 description = "Python wrapper module around the OpenSSL library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2848,7 +2900,7 @@ cryptography = ">=41.0.5,<43"
 
 [package.extras]
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-test = ["flaky", "pretend", "pytest (>=3.0.1)"]
+test = ["pretend", "pytest (>=3.0.1)", "pytest-rerunfailures"]
 
 [[package]]
 name = "pyparsing"
@@ -3416,19 +3468,18 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "69.1.1"
+version = "70.0.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
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 python-versions = ">=3.8"
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 ]
 
 [package.extras]
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
@@ -3883,58 +3934,58 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [[package]]
 name = "zope-interface"
-version = "6.2"
+version = "6.4.post2"
 description = "Interfaces for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 setuptools = "*"
 
 [package.extras]
-docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx_rtd_theme"]
+docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx-rtd-theme"]
 test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "9e144bf811b86b31d1c9af97ba52e99ffb2736858a0bbf3c514272fb981f2700"
+content-hash = "498d1505351be32317d62c7b8159173d913759d5248bfe669dad05ae5dcc86e8"
diff --git a/pyproject.toml b/pyproject.toml
index c94d504..58c6531 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,7 @@ getdist = "^1.4.7"
 tqdm = "^4.66.2"
 emcee = "^3.1.4"
 gsl = "^0.0.3"
+cs-util = "^0.1.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"

From 37b64781454655bf6fa9236770ef69f3a00d6817 Mon Sep 17 00:00:00 2001
From: martinkilbinger <martinkilbinger@cea.fr>
Date: Mon, 2 Sep 2024 13:19:50 +0200
Subject: [PATCH 93/97] Rho plots: keyword inside -> each

---
 shear_psf_leakage/rho_tau_stat.py | 32 +++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 689677e..300f95c 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -611,7 +611,16 @@ def save_rho_stats(self, filename):
     def load_rho_stats(self, filename):
         self.rho_stats = fits.getdata(self.catalogs._output+'/'+filename)
 
-    def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None, legend="each"):
+    def plot_rho_stats(
+        self,
+        filenames,
+        colors,
+        catalog_ids,
+        abs=True,
+        savefig=None,
+        legend="each",
+        title=None,
+    ):
         """
         plot_rho_stats
 
@@ -635,7 +644,11 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
             If not None, saves the figure with the name given in savefig.
 
         legend : str, optional
-            allowed are "each" (default; legends in each panel), "outside" (legend outside of panels)
+            allowed are "each" (default; legends in each panel);
+            "outside" (legend outside of panels); "none" (no legend)
+
+        title : str, optional
+            global plot tite, default is ``None``
         """
 
         fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(15,9))
@@ -647,11 +660,11 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
             for i in range(6):
                 xlabel=r"$\theta$ [arcmin]" if i>2 else ''
 
-                if legend == "inside":
+                if legend == "each":
                     ylabel = r"$\rho-$statistics" if (i==0 or i==3) else ''
-                    label = fr'$\rho_{i}(\theta)$ '+cat_id
-                else:
-                    ylabel = rf"$\rho_{i}(\theta)$"
+                    label = fr'$\rho_{i}(\theta)$ {cat_id}'
+                elif legend == "outside":
+                    ylabel = rf"$\rho_i(\theta)$"
                     label = fr'$\rho_i$ {cat_id}'
 
                 if abs:
@@ -659,7 +672,6 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
                     label=label, color=color, capsize=2)
                     ax[i].set_xlabel(xlabel)
                     ax[i].set_ylabel(ylabel)
-                    ax[i].set_xlim(float(self._treecorr_config["min_sep"]), float(self._treecorr_config["max_sep"]))
                     ax[i].set_xscale('log')
                     ax[i].set_yscale('log')
                 else:
@@ -668,13 +680,17 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
                         ax[i], self.rho_stats['theta'], self.rho_stats['rho_'+str(i)+'_p'], yerr_in=np.sqrt(self.rho_stats['varrho_'+str(i)+'_p']),
                         vertical_lines=False, rho_nb=str(i), cat_id=cat_id, xlabel=xlabel, ylabel=ylabel, semilogx=True, semilogy=True, capsize=True, color=color,
                     )
+                ax[i].set_xlim(float(self._treecorr_config["min_sep"]), float(self._treecorr_config["max_sep"]))
 
-                if legend == "inside":
+                if legend == "each":
                     ax[i].legend(loc='best', fontsize='small')
 
         if legend == "outside":
             ax[-1].legend(bbox_to_anchor=(1.5, 0.0), fontsize='small')
 
+        if title:
+            plt.suptitle(title)
+
         plt.tight_layout()
         if savefig is not None:
             plt.savefig(self.catalogs._output+'/'+savefig, bbox_inches='tight')

From 162c0542a2c0488330c6e09298f256aaf076696b Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Fri, 27 Sep 2024 13:50:18 +0200
Subject: [PATCH 94/97] Update rho_tau_cov with correct computations of the
 cosmic variance terms

---
 shear_psf_leakage/rho_tau_cov.py  | 172 +++++++++++-------------------
 shear_psf_leakage/rho_tau_stat.py |   2 +-
 2 files changed, 65 insertions(+), 109 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 45963b0..285841d 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -335,8 +335,7 @@ def compute_00_comp(self, **kwargs):
             Auto-correlation tau_0/tau_0
         """
         cov_00 = self.compute_sn('00', **kwargs) + self.compute_mt('00', **kwargs) + self.compute_cv_rho_plus('00', **kwargs) +\
-            self.compute_cv_tau_plus('00', **kwargs) + self.compute_cv_rho_minus('00', **kwargs) +\
-            self.compute_cv_tau_minus('00', **kwargs)
+            self.compute_cv_tau_plus('00', **kwargs) + self.compute_cv_minus('00', **kwargs)
         return cov_00
 
     def compute_02_comp(self, **kwargs):
@@ -349,8 +348,7 @@ def compute_02_comp(self, **kwargs):
             Cross-correlation tau_0/tau_2
         """
         cov_02 = self.compute_sn('02', **kwargs) + self.compute_mt('02', **kwargs) + self.compute_cv_rho_plus('02', **kwargs) +\
-            self.compute_cv_tau_plus('02', **kwargs) + self.compute_cv_rho_minus('02', **kwargs) +\
-            self.compute_cv_tau_minus('02', **kwargs)
+            self.compute_cv_tau_plus('02', **kwargs) + self.compute_cv_minus('02', **kwargs)
         return cov_02
 
     def compute_05_comp(self, **kwargs):
@@ -363,8 +361,7 @@ def compute_05_comp(self, **kwargs):
             Cross-correlation tau_0/tau_5
         """
         cov_05 = self.compute_sn('05', **kwargs) + self.compute_mt('05', **kwargs) + self.compute_cv_rho_plus('05', **kwargs) +\
-            self.compute_cv_tau_plus('05', **kwargs) + self.compute_cv_rho_minus('05', **kwargs) +\
-            self.compute_cv_tau_minus('05', **kwargs)
+            self.compute_cv_tau_plus('05', **kwargs) + self.compute_cv_minus('05', **kwargs)
         return cov_05
 
     def compute_25_comp(self, **kwargs):
@@ -377,8 +374,7 @@ def compute_25_comp(self, **kwargs):
             Cross-correlation tau_2/tau_5
         """
         cov_25 = self.compute_sn('25', **kwargs) + self.compute_mt('25', **kwargs) + self.compute_cv_rho_plus('25', **kwargs) +\
-            self.compute_cv_tau_plus('25', **kwargs) + self.compute_cv_rho_minus('25', **kwargs) +\
-            self.compute_cv_tau_minus('25', **kwargs)
+            self.compute_cv_tau_plus('25', **kwargs) + self.compute_cv_minus('25', **kwargs)
         return cov_25
 
     def compute_22_comp(self, **kwargs):
@@ -391,8 +387,7 @@ def compute_22_comp(self, **kwargs):
             Auto-correlation tau_2/tau_2
         """
         cov_22 = self.compute_sn('22', **kwargs) + self.compute_mt('22', **kwargs) +self.compute_cv_rho_plus('22', **kwargs) +\
-            self.compute_cv_tau_plus('22', **kwargs) + self.compute_cv_rho_minus('22', **kwargs) +\
-            self.compute_cv_tau_minus('22', **kwargs)
+            self.compute_cv_tau_plus('22', **kwargs) + self.compute_cv_minus('22', **kwargs)
         return cov_22
 
     def compute_55_comp(self, **kwargs):
@@ -405,8 +400,7 @@ def compute_55_comp(self, **kwargs):
             Auto-correlation tau_5/tau_5
         """
         cov_55 = self.compute_sn('55', **kwargs) + self.compute_mt('55', **kwargs) + self.compute_cv_rho_plus('55', **kwargs) +\
-            self.compute_cv_tau_plus('55', **kwargs) + self.compute_cv_rho_minus('55', **kwargs) +\
-            self.compute_cv_tau_minus('55', **kwargs)
+            self.compute_cv_tau_plus('55', **kwargs) + self.compute_cv_minus('55', **kwargs)
         return cov_55
     
     def compute_sn(self, component, **kwargs):
@@ -474,7 +468,6 @@ def compute_mt(self, component, **kwargs):
                 mt[i, j] = integrate.simps(interpolator_rho(y), phi)*self.sigma_e/(2*np.pi*self.A*self.n_e)
                 if sigma_bc is not None:
                     mt[i, j] += integrate.simps(interpolator_xi_plus(y), phi)*sigma_bc/(2*np.pi*self.A*self.n_e)
-                mt[j, i] = mt[i, j]
         return mt
     
     def compute_cv_rho_plus(self, component, **kwargs):
@@ -503,19 +496,17 @@ def compute_cv_rho_plus(self, component, **kwargs):
         interpolator_xi = self.xi_plus_itp
         interpolator_rho = rho['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-1, 250, nbin_rad)
+        phi_radius = np.linspace(1e-1, 1000, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
-                radius_val = np.zeros(len(phi_radius))
-                for k, phi_r in enumerate(phi_radius):
-                    #Compute i,j term
-                    y_m = np.sqrt(phi_r**2+self.bins[i]**2-2*phi_r*self.bins[i]*np.cos(phi_angle))
-                    y_p = np.sqrt(phi_r**2+self.bins[j]**2+2*phi_r*self.bins[j]*np.cos(phi_angle))
-                    int_xi_m = integrate.simps(interpolator_xi(y_m), phi_angle)
-                    int_xi_p = integrate.simps(interpolator_xi(y_p), phi_angle)
-                    int_rho_m = integrate.simps(interpolator_rho(y_m), phi_angle)
-                    int_rho_p = integrate.simps(interpolator_rho(y_p), phi_angle)
-                    radius_val[k] = 0.5*(int_xi_m*int_rho_p*phi_r+int_xi_p*int_rho_m*phi_r)
+                #Compute i,j term
+                y_m = np.sqrt(phi_radius[:, None]**2+self.bins[i]**2-2*phi_radius[:, None]*self.bins[i]*np.cos(phi_angle))
+                y_p = np.sqrt(phi_radius[:, None]**2+self.bins[j]**2+2*phi_radius[:, None]*self.bins[j]*np.cos(phi_angle))
+                int_xi_m = integrate.simps(interpolator_xi(y_m), phi_angle)
+                int_xi_p = integrate.simps(interpolator_xi(y_p), phi_angle)
+                int_rho_m = integrate.simps(interpolator_rho(y_m), phi_angle)
+                int_rho_p = integrate.simps(interpolator_rho(y_p), phi_angle)
+                radius_val = 0.5*(int_xi_m*int_rho_p*phi_radius+int_xi_p*int_rho_m*phi_radius)
                 cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
         return cv
 
@@ -545,23 +536,23 @@ def compute_cv_tau_plus(self, component, **kwargs):
         interpolator_tau_b = tau_b['p']
         interpolator_tau_c = tau_c['p']
         phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-1, 250, nbin_rad)
+        phi_radius = np.linspace(1e-1, 1000, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
-                radius_val = np.zeros(len(phi_radius))
-                for k, phi_r in enumerate(phi_radius):
-                    y_tau_b = np.sqrt(phi_r**2+self.bins[i]**2-2*phi_r*self.bins[i]*np.cos(phi_angle))
-                    y_tau_c = np.sqrt(phi_r**2+self.bins[j]**2+2*phi_r*self.bins[j]*np.cos(phi_angle))
-                    int_tau_b = integrate.simps(interpolator_tau_b(y_tau_b), phi_angle)
-                    int_tau_c = integrate.simps(interpolator_tau_c(y_tau_c), phi_angle)
-                    radius_val[k] = int_tau_b*int_tau_c*phi_r
+                y_m = np.sqrt(phi_radius[:, None]**2+self.bins[i]**2-2*phi_radius[:, None]*self.bins[i]*np.cos(phi_angle))
+                y_p = np.sqrt(phi_radius[:, None]**2+self.bins[j]**2+2*phi_radius[:, None]*self.bins[j]*np.cos(phi_angle))
+                int_tau_b_m = integrate.simps(interpolator_tau_b(y_m), phi_angle)
+                int_tau_b_p = integrate.simps(interpolator_tau_b(y_p), phi_angle)
+                int_tau_c_m = integrate.simps(interpolator_tau_c(y_m), phi_angle)
+                int_tau_c_p = integrate.simps(interpolator_tau_c(y_p), phi_angle)
+                radius_val = 0.5*(int_tau_b_m*int_tau_c_p+int_tau_b_p*int_tau_c_m)*phi_radius
                 cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
         return cv
     
-    def compute_cv_rho_minus(self, component, **kwargs):
+    def compute_cv_minus(self, component, **kwargs):
         """
         Computes the cosmic variance component of the covariance matrix for the given components
-        based on the rho statistics - component.
+        based on the rho statistics - component and tau statistics - component together.
 
         Parameters
         ----------
@@ -574,93 +565,58 @@ def compute_cv_rho_minus(self, component, **kwargs):
             Cosmic variance component of the covariance matrix
         """
         assert(component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
-        rho = self.component_dict[component]['bc']
-        nbin_ang = kwargs.get("nbin_ang", 20)
-        nbin_rad = kwargs.get("nbin_rad", 20)
-
-        cv = np.zeros((len(self.bins), len(self.bins)))
-        #interpolator_xi = interpolate.make_interp_spline(self.bins, xi_minus, k=1)
-        #interpolator_rho = interpolate.make_interp_spline(self.bins, rho, k=1)
-        interpolator_xi = self.xi_minus_itp
-        interpolator_rho = rho['m']
-        phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-1, 250, nbin_rad)
-        for i in range(len(self.bins)):
-            for j in range(len(self.bins)):
-                radius_val = np.zeros(len(phi_radius))
-                for k, phi_r in enumerate(phi_radius):
-                    psi_a = np.array([
-                        phi_r-self.bins[i]*np.cos(phi_angle),
-                        -self.bins[i]*np.sin(phi_angle)
-                    ])
-                    psi_b = np.array([
-                        phi_r+self.bins[j]*np.cos(phi_angle),
-                        self.bins[j]*np.sin(phi_angle)
-                    ])
-                    norm_a = np.linalg.norm(psi_a, axis=0)
-                    polar_a = np.arctan2(psi_a[1], psi_a[0])
-                    norm_b = np.linalg.norm(psi_b, axis=0)
-                    polar_b = np.arctan2(psi_b[1], psi_b[0])
-                    #The term coming from the sine will integrate out to 0 so we can only compute terms with cosine
-                    int_xi_m = integrate.simps(interpolator_xi(norm_a)*np.cos(4*polar_a), phi_angle)
-                    int_xi_p = integrate.simps(interpolator_xi(norm_b)*np.cos(4*polar_b), phi_angle)
-                    int_rho_m = integrate.simps(interpolator_rho(norm_a)*np.cos(4*polar_a), phi_angle)
-                    int_rho_p = integrate.simps(interpolator_rho(norm_b)*np.cos(4*polar_b), phi_angle)
-                    radius_val[k] = 0.5*(int_xi_m*int_rho_p*phi_r+int_xi_p*int_rho_m*phi_r)
-                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
-        return cv
-
-    def compute_cv_tau_minus(self, component, **kwargs):
-        """
-        Computes the cosmic variance component of the covariance matrix for the given components
-        based on the tau statistics - component.
-
-        Parameters
-        ----------
-        components: str
-            Component of the covariance matrix
-        
-        Returns
-        -------
-        numpy.ndarray
-            Cosmic variance component of the covariance matrix
-        """
-        assert (component in self.component_dict.keys()), ("The component must be in the following list: %s" % self.component_dict.keys())
         tau_b = self.component_dict[component]['eb']
         tau_c = self.component_dict[component]['ec']
+        rho = self.component_dict[component]['bc']
         nbin_ang = kwargs.get("nbin_ang", 20)
         nbin_rad = kwargs.get("nbin_rad", 20)
 
         cv = np.zeros((len(self.bins), len(self.bins)))
         interpolator_tau_b = tau_b['m']
         interpolator_tau_c = tau_c['m']
-        phi_angle = np.linspace(0, np.pi, nbin_ang)
-        phi_radius = np.linspace(1e-1, 250, nbin_rad)
+        interpolator_rho = rho['m']
+        interpolator_xi = self.xi_minus_itp
+        phi_angle = np.linspace(0, 2*np.pi, nbin_ang)
+        phi_radius = np.linspace(1e-1, 1000, nbin_rad)
         for i in range(len(self.bins)):
             for j in range(len(self.bins)):
-                radius_val = np.zeros(len(phi_radius))
-                for k, phi_r in enumerate(phi_radius):
-                    psi_a = np.array([
-                        phi_r-self.bins[i]*np.cos(phi_angle),
-                        -self.bins[i]*np.sin(phi_angle)
-                    ])
-                    psi_b = np.array([
-                        phi_r+self.bins[j]*np.cos(phi_angle),
-                        self.bins[j]*np.sin(phi_angle)
-                    ])
-                    norm_a = np.linalg.norm(psi_a, axis=0)
-                    polar_a = np.arctan2(psi_a[1], psi_a[0])
-                    norm_b = np.linalg.norm(psi_b, axis=0)
-                    polar_b = np.arctan2(psi_b[1], psi_b[0])
-                    #The term coming from the sine will integrate out to 0 so we can only compute terms with cosine
-                    int_tau_b = integrate.simps(interpolator_tau_b(norm_a)*np.cos(4*polar_a), phi_angle)
-                    int_tau_c = integrate.simps(interpolator_tau_c(norm_b)*np.cos(4*polar_b), phi_angle)
-                    radius_val[k] = int_tau_b*int_tau_c*phi_r
-                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(np.pi*self.A)
+                phi_r_mesh, phi_varphi_mesh = np.meshgrid(phi_radius, phi_angle, indexing='ij')
+
+                # Compute psi_a and psi_b in a vectorized manner
+                psi_a_x = (phi_r_mesh * np.cos(phi_varphi_mesh))[:, :, None] - self.bins[i] * np.cos(phi_angle)
+                psi_a_y = (phi_r_mesh * np.sin(phi_varphi_mesh))[:, :, None] - self.bins[i] * np.sin(phi_angle)
+                psi_b_x = (phi_r_mesh * np.cos(phi_varphi_mesh))[:, :, None] + self.bins[j] * np.cos(phi_angle)
+                psi_b_y = (phi_r_mesh * np.sin(phi_varphi_mesh))[:, :, None] + self.bins[j] * np.sin(phi_angle)
+
+                # Compute norms and polar angles
+                norm_a = np.sqrt(psi_a_x**2 + psi_a_y**2)
+                polar_a = np.arctan2(psi_a_y, psi_a_x)
+                norm_b = np.sqrt(psi_b_x**2 + psi_b_y**2)
+                polar_b = np.arctan2(psi_b_y, psi_b_x)
+
+                # Compute integrals using vectorized operations
+                int_tau_b_cos = integrate.simps(interpolator_tau_b(norm_a) * np.cos(4 * polar_a), phi_angle, axis=2)
+                int_tau_c_cos = integrate.simps(interpolator_tau_c(norm_b) * np.cos(4 * polar_b), phi_angle, axis=2)
+                int_tau_b_sin = integrate.simps(interpolator_tau_b(norm_a) * np.sin(4 * polar_a), phi_angle, axis=2)
+                int_tau_c_sin = integrate.simps(interpolator_tau_c(norm_b) * np.sin(4 * polar_b), phi_angle, axis=2)
+                int_xi_cos = integrate.simps(interpolator_xi(norm_a) * np.cos(4 * polar_a), phi_angle, axis=2)
+                int_rho_cos = integrate.simps(interpolator_rho(norm_b) * np.cos(4 * polar_b), phi_angle, axis=2)
+                int_xi_sin = integrate.simps(interpolator_xi(norm_a) * np.sin(4 * polar_a), phi_angle, axis=2)
+                int_rho_sin = integrate.simps(interpolator_rho(norm_b) * np.sin(4 * polar_b), phi_angle, axis=2)
+
+
+                # Compute int_angle in a vectorized manner
+                int_angle = int_tau_b_cos * int_tau_c_cos + int_tau_b_sin * int_tau_c_sin + int_xi_cos * int_rho_cos + int_xi_sin * int_rho_sin
+
+                # Integrate int_angle over phi_angle for each phi_radius
+                int_varphi = integrate.simps(int_angle, phi_angle, axis=1)
+
+                # Compute radius_val in a vectorized manner
+                radius_val = int_varphi * phi_radius
+                cv[i, j] = integrate.simps(radius_val, phi_radius)*1/(2*(2*np.pi)**2*self.A)
         return cv
 
 
-
                 
 
 
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 689677e..11c30ee 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -540,7 +540,7 @@ def compute_rho_stats(self, catalog_id, filename, save_cov=False, func=None, var
                 rho_0.xip,
                 rho_0.varxip,
                 rho_0.xim,
-                rho_1.varxim,
+                rho_0.varxim,
                 rho_1.xip,
                 rho_1.varxip,
                 rho_1.xim,

From facc4d2af57c3f193da7400fafefd182d24f8445 Mon Sep 17 00:00:00 2001
From: sachaguer <sacha.guerrini@cea.fr>
Date: Fri, 27 Dec 2024 12:44:09 +0100
Subject: [PATCH 95/97] Add boolean param to constructors to compute rho and
 tau-stats without size residuals

---
 shear_psf_leakage/rho_tau_cov.py  |  73 +++--
 shear_psf_leakage/rho_tau_stat.py | 432 ++++++++++++++++++++----------
 2 files changed, 337 insertions(+), 168 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 285841d..483ff5b 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -29,6 +29,7 @@ def __init__(
         n_e,
         n_psf,
         params=None,
+        use_eta=True,
         **kwargs):
         """
         Initalize the CovTauTh class.
@@ -52,11 +53,14 @@ def __init__(
             Number density of PSF stars
         params: dict
             Parameters of the class
+        use_eta: bool
+            if True, compute the covariance matrix of the tau statistics using the eta statistics. Default is True.
         """
         self.treecorr_config = treecorr_config
         self.A = A
         self.n_e = n_e
         self.n_psf = n_psf
+        self.use_eta = use_eta
 
         if params is None:
             self.params_default()
@@ -73,7 +77,8 @@ def __init__(
         self.sigma_e = self.compute_shape_noise(self.gal, self.gal)
         self.sigma_psf = self.compute_shape_noise(self.psf, self.psf)
         self.sigma_psf_error = self.compute_shape_noise(self.psf_error, self.psf_error)
-        self.sigma_psf_size_error = self.compute_shape_noise(self.size_error, self.size_error)
+        if self.use_eta:
+            self.sigma_psf_size_error = self.compute_shape_noise(self.size_error, self.size_error)
 
 
         #interpolate rho stats
@@ -83,12 +88,12 @@ def __init__(
         self.rho_1_m_itp = self.build_interpolator(self.psf_error, self.psf_error, type='minus')
         self.rho_2_p_itp = self.build_interpolator(self.psf, self.psf_error, type='plus')
         self.rho_2_m_itp = self.build_interpolator(self.psf, self.psf_error, type='minus')
-        self.rho_3_p_itp = self.build_interpolator(self.size_error, self.size_error, type='plus')
-        self.rho_3_m_itp = self.build_interpolator(self.size_error, self.size_error, type='minus')
-        self.rho_4_p_itp = self.build_interpolator(self.psf_error, self.size_error, type='plus')
-        self.rho_4_m_itp = self.build_interpolator(self.psf_error, self.size_error, type='minus')
-        self.rho_5_p_itp = self.build_interpolator(self.psf, self.size_error, type='plus')
-        self.rho_5_m_itp = self.build_interpolator(self.psf, self.size_error, type='minus')
+        self.rho_3_p_itp = self.build_interpolator(self.size_error, self.size_error, type='plus') if self.use_eta else None
+        self.rho_3_m_itp = self.build_interpolator(self.size_error, self.size_error, type='minus') if self.use_eta else None
+        self.rho_4_p_itp = self.build_interpolator(self.psf_error, self.size_error, type='plus') if self.use_eta else None
+        self.rho_4_m_itp = self.build_interpolator(self.psf_error, self.size_error, type='minus') if self.use_eta else None
+        self.rho_5_p_itp = self.build_interpolator(self.psf, self.size_error, type='plus') if self.use_eta else None
+        self.rho_5_m_itp = self.build_interpolator(self.psf, self.size_error, type='minus') if self.use_eta else None
 
 
         #interpolate tau stats
@@ -96,8 +101,8 @@ def __init__(
         self.tau_0_m_itp = self.build_interpolator(self.gal, self.psf, type='minus')
         self.tau_2_p_itp = self.build_interpolator(self.gal, self.psf_error, type='plus')
         self.tau_2_m_itp = self.build_interpolator(self.gal, self.psf_error, type='minus')
-        self.tau_5_p_itp = self.build_interpolator(self.gal, self.size_error, type='plus')
-        self.tau_5_m_itp = self.build_interpolator(self.gal, self.size_error, type='minus')
+        self.tau_5_p_itp = self.build_interpolator(self.gal, self.size_error, type='plus') if self.use_eta else None
+        self.tau_5_m_itp = self.build_interpolator(self.gal, self.size_error, type='minus') if self.use_eta else None
 
         #interpolate xi plus and minus
         self.xi_plus_itp = self.build_interpolator(self.gal, self.gal, type='plus')
@@ -235,16 +240,19 @@ def create_treecorr_catalog(self, cat_gal, cat_psf):
             ra_units=ra_units, dec_units=dec_units
         )
 
-        size_resid = (cat_psf[self._params['star_size']]**2-cat_psf[self._params['PSF_size']]**2)/cat_psf[self._params['star_size']]**2\
-        if self._params['square_size'] else\
-        (cat_psf[self._params['star_size']]-cat_psf[self._params['PSF_size']])/cat_psf[self._params['star_size']]
-
-        size_error = treecorr.Catalog(
-            ra=cat_psf[self._params['ra_col']], dec=cat_psf[self._params['dec_col']],
-            g1=cat_psf[self._params['e1_star_col']]*size_resid,
-            g2=cat_psf[self._params['e2_star_col']]*size_resid,
-            ra_units=ra_units, dec_units=dec_units
-        )
+        if self.use_eta:
+            size_resid = (cat_psf[self._params['star_size']]**2-cat_psf[self._params['PSF_size']]**2)/cat_psf[self._params['star_size']]**2\
+            if self._params['square_size'] else\
+            (cat_psf[self._params['star_size']]-cat_psf[self._params['PSF_size']])/cat_psf[self._params['star_size']]
+
+            size_error = treecorr.Catalog(
+                ra=cat_psf[self._params['ra_col']], dec=cat_psf[self._params['dec_col']],
+                g1=cat_psf[self._params['e1_star_col']]*size_resid,
+                g2=cat_psf[self._params['e2_star_col']]*size_resid,
+                ra_units=ra_units, dec_units=dec_units
+            )
+        else:
+            size_error = None
         return gal, psf, psf_error, size_error
     
     def compute_shape_noise(self, cat1, cat2):
@@ -287,7 +295,7 @@ def build_interpolator(self, cat_1, cat_2, type):
         """
         assert (type in ['plus', 'minus']), ("The type must be either 'plus' or 'minus'")
 
-        gg = treecorr.GGCorrelation(nbins=30, min_sep=1e-3, max_sep=1e3, sep_units='arcmin')
+        gg = treecorr.GGCorrelation(nbins=30, min_sep=8e-2, max_sep=1e3, sep_units='arcmin')
         gg.process(cat_1, cat_2)
         bins = gg.meanr
         if type=='plus':
@@ -313,16 +321,23 @@ def build_cov(self, **kwargs):
         """
         cov_00 = self.compute_00_comp(**kwargs)
         cov_02 = self.compute_02_comp(**kwargs)
-        cov_05 = self.compute_05_comp(**kwargs)
-        cov_25 = self.compute_25_comp(**kwargs)
         cov_22 = self.compute_22_comp(**kwargs)
-        cov_55 = self.compute_55_comp(**kwargs)
-
-        cov = np.block([
-            [cov_00, cov_02, cov_05],
-            [cov_02.T, cov_22, cov_25],
-            [cov_05.T, cov_25.T, cov_55]
-        ])
+        if self.use_eta:
+            cov_05 = self.compute_05_comp(**kwargs)
+            cov_25 = self.compute_25_comp(**kwargs)
+            cov_55 = self.compute_55_comp(**kwargs)
+
+        if self.use_eta:
+            cov = np.block([
+                [cov_00, cov_02, cov_05],
+                [cov_02.T, cov_22, cov_25],
+                [cov_05.T, cov_25.T, cov_55]
+            ])
+        else:
+            cov = np.block([
+                [cov_00, cov_02],
+                [cov_02.T, cov_22]
+            ])
         return cov
 
     def compute_00_comp(self, **kwargs):
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 11c30ee..757ac4a 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -442,12 +442,30 @@ class RhoStat():
     RhoStat
 
     Class to compute the rho statistics (Rowe 2010) of a PSF catalogue.
+
+    Parameters
+    ----------
+    params : dict
+        Dictionary containing the parameters of the class. If None, default parameters are used.
+    output : str
+        Path to the output directory. Default is the current directory.
+    use_eta : bool
+        If True, add correlations with size residuals of the PSF. Default is True.
+    scalar_eta : bool
+        If True, compute correlations with size residuals as correlations between a spin-2 field and a
+        scalar field (GKCorrelator in treecorr). Default is False.
+    treecorr_config : dict
+        Dictionary containing the configuration of the treecorr.GGCorrelation. Default is None.
+    verbose : bool
+        If True, print messages to the terminal. Default is False.
     """
 
     def __init__(
         self,
         params=None,
         output=None,
+        use_eta=True,
+        scalar_eta=False,
         treecorr_config=None,
         verbose=False
     ):
@@ -466,6 +484,12 @@ def __init__(
             }
         else:
             self._treecorr_config = treecorr_config
+        self.use_eta = use_eta
+        self.scalar_eta = scalar_eta
+
+        if self.scalar_eta and not self.use_eta:
+            print("Warning: scalar_eta is set to True but use_eta is set to False. Setting use_eta to True.")
+            self.use_eta = True
 
         self.verbose = verbose
 
@@ -499,7 +523,8 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
         self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
         patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
         self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
-        self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+        if self.use_eta:
+            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
 
         if self.verbose:
             print("Catalogs successfully built...")
@@ -528,68 +553,105 @@ def compute_rho_stats(self, catalog_id, filename, save_cov=False, func=None, var
         rho_2 = treecorr.GGCorrelation(self._treecorr_config)
         rho_2.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         rho_3 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_3.process(self.catalogs.get_cat('psf_size_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
-        rho_4 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_4.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
-        rho_5 = treecorr.GGCorrelation(self._treecorr_config)
-        rho_5.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
-
-        self.rho_stats = Table(
-            [
-                rho_0.meanr,
-                rho_0.xip,
-                rho_0.varxip,
-                rho_0.xim,
-                rho_0.varxim,
-                rho_1.xip,
-                rho_1.varxip,
-                rho_1.xim,
-                rho_1.varxim,
-                rho_2.xip,
-                rho_2.varxip,
-                rho_2.xim,
-                rho_2.varxim,
-                rho_3.xip,
-                rho_3.varxip,
-                rho_3.xim,
-                rho_3.varxim,
-                rho_4.xip,
-                rho_4.varxip,
-                rho_4.xim,
-                rho_4.varxim,
-                rho_5.xip,
-                rho_5.varxip,
-                rho_5.xim,
-                rho_5.varxim,
-            ],
-            names=(
-                'theta',
-                'rho_0_p',
-                'varrho_0_p',
-                'rho_0_m',
-                'varrho_0_m',
-                'rho_1_p',
-                'varrho_1_p',
-                'rho_1_m',
-                'varrho_1_m',
-                'rho_2_p',
-                'varrho_2_p',
-                'rho_2_m',
-                'varrho_2_m',
-                'rho_3_p',
-                'varrho_3_p',
-                'rho_3_m',
-                'varrho_3_m',
-                'rho_4_p',
-                'varrho_4_p',
-                'rho_4_m',
-                'varrho_4_m',
-                'rho_5_p',
-                'varrho_5_p',
-                'rho_5_m',
-                'varrho_5_m',
+        if self.use_eta:
+            rho_3.process(self.catalogs.get_cat('psf_size_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
+            rho_4 = treecorr.GGCorrelation(self._treecorr_config)
+            rho_4.process(self.catalogs.get_cat('psf_error_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
+            rho_5 = treecorr.GGCorrelation(self._treecorr_config)
+            rho_5.process(self.catalogs.get_cat('psf_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
+
+        if self.use_eta:
+            self.rho_stats = Table(
+                [
+                    rho_0.meanr,
+                    rho_0.xip,
+                    rho_0.varxip,
+                    rho_0.xim,
+                    rho_0.varxim,
+                    rho_1.xip,
+                    rho_1.varxip,
+                    rho_1.xim,
+                    rho_1.varxim,
+                    rho_2.xip,
+                    rho_2.varxip,
+                    rho_2.xim,
+                    rho_2.varxim,
+                    rho_3.xip,
+                    rho_3.varxip,
+                    rho_3.xim,
+                    rho_3.varxim,
+                    rho_4.xip,
+                    rho_4.varxip,
+                    rho_4.xim,
+                    rho_4.varxim,
+                    rho_5.xip,
+                    rho_5.varxip,
+                    rho_5.xim,
+                    rho_5.varxim,
+                ],
+                names=(
+                    'theta',
+                    'rho_0_p',
+                    'varrho_0_p',
+                    'rho_0_m',
+                    'varrho_0_m',
+                    'rho_1_p',
+                    'varrho_1_p',
+                    'rho_1_m',
+                    'varrho_1_m',
+                    'rho_2_p',
+                    'varrho_2_p',
+                    'rho_2_m',
+                    'varrho_2_m',
+                    'rho_3_p',
+                    'varrho_3_p',
+                    'rho_3_m',
+                    'varrho_3_m',
+                    'rho_4_p',
+                    'varrho_4_p',
+                    'rho_4_m',
+                    'varrho_4_m',
+                    'rho_5_p',
+                    'varrho_5_p',
+                    'rho_5_m',
+                    'varrho_5_m',
+                )
             )
-        )
+
+        else:
+            self.rho_stats = Table(
+                [
+                    rho_0.meanr,
+                    rho_0.xip,
+                    rho_0.varxip,
+                    rho_0.xim,
+                    rho_0.varxim,
+                    rho_1.xip,
+                    rho_1.varxip,
+                    rho_1.xim,
+                    rho_1.varxim,
+                    rho_2.xip,
+                    rho_2.varxip,
+                    rho_2.xim,
+                    rho_2.varxim,
+                ],
+                names=(
+                    'theta',
+                    'rho_0_p',
+                    'varrho_0_p',
+                    'rho_0_m',
+                    'varrho_0_m',
+                    'rho_1_p',
+                    'varrho_1_p',
+                    'rho_1_m',
+                    'varrho_1_m',
+                    'rho_2_p',
+                    'varrho_2_p',
+                    'rho_2_m',
+                    'varrho_2_m',
+                )
+            )
+
 
         if self.verbose:
             print("Done...")
@@ -597,12 +659,15 @@ def compute_rho_stats(self, catalog_id, filename, save_cov=False, func=None, var
         if save_cov:
             if self.verbose:
                 print("Computing the covariance...")
+            rhos = [rho_0, rho_1, rho_2]
+            if self.use_eta:
+                rhos += [rho_3, rho_4, rho_5]
+            cov = treecorr.estimate_multi_cov(rhos, var_method, func)
 
-            cov = treecorr.estimate_multi_cov([rho_0, rho_1, rho_2, rho_3, rho_4, rho_5], var_method, func)
-
-            np.save(self.catalogs._output+'/'+'cov_rho_'+catalog_id, cov)
+            use_eta_str = '_use_eta_T' if self.use_eta else '_use_eta_F'
+            np.save(self.catalogs._output+'/'+'cov_rho_'+catalog_id+use_eta_str, cov)
 
-        self.save_rho_stats(filename) #A bit dirty just because of consistency of the datatype :/
+        self.save_rho_stats(filename) #A bit dirty just because of consistency of the datatype
         self.load_rho_stats(filename)
 
     def save_rho_stats(self, filename):
@@ -637,7 +702,7 @@ def plot_rho_stats(self, filenames, colors, catalog_ids, abs=True, savefig=None,
         legend : str, optional
             allowed are "each" (default; legends in each panel), "outside" (legend outside of panels)
         """
-
+        #To adapt to the new boolean argument
         fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(15,9))
         ax = ax.flatten()
 
@@ -688,7 +753,7 @@ class TauStat():
     Class to compute the tau statistics (Gatti 2022) of a PSF and gal catalogue.
     """
 
-    def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None, verbose=False):
+    def __init__(self, params=None, output=None, use_eta=True, scalar_eta=False, treecorr_config=None, catalogs=None, verbose=False):
 
         if catalogs is None:
             self.catalogs = Catalogs(params, output)
@@ -708,6 +773,11 @@ def __init__(self, params=None, output=None, treecorr_config=None, catalogs=None
         else:
             self._treecorr_config = treecorr_config
 
+        self.use_eta = use_eta
+        self.scalar_eta = scalar_eta
+        if self.scalar_eta and not self.use_eta:
+            print("Warning: scalar_eta is set to True but use_eta is set to False. Setting use_eta to True.")
+            self.use_eta = True
         self.verbose = verbose
 
     def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_size=False, mask=False, hdu=1):
@@ -744,7 +814,8 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
             self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
             patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
             self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
-            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+            if self.use_eta:
+                self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
 
         else:
             self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None, hdu=hdu)
@@ -793,41 +864,71 @@ def compute_tau_stats(self, catalog_id, filename, save_cov=False, func=None, var
         tau_0.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_'+catalog_id))
         tau_2 = treecorr.GGCorrelation(self._treecorr_config)
         tau_2.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_error_'+catalog_id))
-        tau_5 = treecorr.GGCorrelation(self._treecorr_config)
-        tau_5.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
-
-        self.tau_stats = Table(
-            [
-                tau_0.meanr,
-                tau_0.xip,
-                tau_0.varxip,
-                tau_0.xim,
-                tau_0.varxim,
-                tau_2.xip,
-                tau_2.varxip,
-                tau_2.xim,
-                tau_2.varxim,
-                tau_5.xip,
-                tau_5.varxip,
-                tau_5.xim,
-                tau_5.varxim,
-            ],
-            names=(
-                'theta',
-                'tau_0_p',
-                'vartau_0_p',
-                'tau_0_m',
-                'vartau_0_m',
-                'tau_2_p',
-                'vartau_2_p',
-                'tau_2_m',
-                'vartau_2_m',
-                'tau_5_p',
-                'vartau_5_p',
-                'tau_5_m',
-                'vartau_5_m',
+        if self.use_eta:
+            tau_5 = treecorr.GGCorrelation(self._treecorr_config)
+            tau_5.process(self.catalogs.get_cat('gal_'+catalog_id), self.catalogs.get_cat('psf_size_error_'+catalog_id))
+
+        if self.use_eta:
+
+            self.tau_stats = Table(
+                [
+                    tau_0.meanr,
+                    tau_0.xip,
+                    tau_0.varxip,
+                    tau_0.xim,
+                    tau_0.varxim,
+                    tau_2.xip,
+                    tau_2.varxip,
+                    tau_2.xim,
+                    tau_2.varxim,
+                    tau_5.xip,
+                    tau_5.varxip,
+                    tau_5.xim,
+                    tau_5.varxim,
+                ],
+                names=(
+                    'theta',
+                    'tau_0_p',
+                    'vartau_0_p',
+                    'tau_0_m',
+                    'vartau_0_m',
+                    'tau_2_p',
+                    'vartau_2_p',
+                    'tau_2_m',
+                    'vartau_2_m',
+                    'tau_5_p',
+                    'vartau_5_p',
+                    'tau_5_m',
+                    'vartau_5_m',
+                )
+            )
+
+        else:
+
+            self.tau_stats = Table(
+                [
+                    tau_0.meanr,
+                    tau_0.xip,
+                    tau_0.varxip,
+                    tau_0.xim,
+                    tau_0.varxim,
+                    tau_2.xip,
+                    tau_2.varxip,
+                    tau_2.xim,
+                    tau_2.varxim,
+                ],
+                names=(
+                    'theta',
+                    'tau_0_p',
+                    'vartau_0_p',
+                    'tau_0_m',
+                    'vartau_0_m',
+                    'tau_2_p',
+                    'vartau_2_p',
+                    'tau_2_m',
+                    'vartau_2_m',
+                )
             )
-        )
 
         if self.verbose:
             print("Done...")
@@ -835,10 +936,13 @@ def compute_tau_stats(self, catalog_id, filename, save_cov=False, func=None, var
         if save_cov:
             if self.verbose:
                 print("Computing the covariance...")
+            taus = [tau_0, tau_2]
+            if self.use_eta:
+                taus += [tau_5]
+            cov = treecorr.estimate_multi_cov(taus, var_method, func)
 
-            cov = treecorr.estimate_multi_cov([tau_0, tau_2, tau_5], var_method, func)
-
-            np.save(self.catalogs._output+'/'+'cov_tau_'+catalog_id, cov)
+            use_eta_str = '_use_eta_T' if self.use_eta else '_use_eta_F'
+            np.save(self.catalogs._output+'/'+'cov_tau_'+catalog_id+use_eta_str, cov)
 
         self.save_tau_stats(filename) #A bit dirty just because of consistency of the datatype :/
         self.load_tau_stats(filename)
@@ -881,7 +985,7 @@ def plot_tau_stats(self, filenames, colors, catalog_ids, savefig=None, plot_tau_
 
         ax : Axes
         """
-
+        #To adapt to the new boolean fields
         nrows=1 + plot_tau_m
 
         fig, ax = plt.subplots(nrows=nrows, ncols=3, figsize=(15,6))
@@ -934,7 +1038,7 @@ class PSFErrorFit():
     A Likelihood-Based Inference is used assuming a Gaussian likelihood.
     """
 
-    def __init__(self, rho_stat_handler, tau_stat_handler, data_directory):
+    def __init__(self, rho_stat_handler, tau_stat_handler, data_directory, use_eta=True):
 
         self.rho_stat_handler = rho_stat_handler
         self.tau_stat_handler = tau_stat_handler
@@ -943,8 +1047,10 @@ def __init__(self, rho_stat_handler, tau_stat_handler, data_directory):
         self.cov_tau = None
         self.init_log_prior()
 
+        self.use_eta = use_eta
+
         def log_likelihood(theta, y, inv_cov):
-            y_model = self.model(theta)
+            y_model = self.model(theta, self.use_eta)
             d = y_model -y
             return -0.5 * d.T@inv_cov@d
 
@@ -1055,7 +1161,7 @@ def log_prior(theta):
 
         self.log_prior = log_prior
 
-    def model(self, theta):
+    def model(self, theta, use_eta):
         """
         model
 
@@ -1075,14 +1181,21 @@ def model(self, theta):
         alpha, beta, eta = theta
 
         rhos = self.rho_stat_handler.rho_stats
-        tau_0_p = alpha * rhos["rho_0_p"] + beta * rhos["rho_2_p"] + eta * rhos["rho_5_p"]
-        tau_2_p = alpha * rhos["rho_2_p"] + beta * rhos["rho_1_p"] + eta * rhos["rho_4_p"]
-        tau_5_p = alpha * rhos["rho_5_p"] + beta * rhos["rho_4_p"] + eta * rhos["rho_3_p"]
+        if not use_eta:
+            tau_0_p = alpha * rhos["rho_0_p"] + beta * rhos["rho_2_p"]
+            tau_2_p = alpha * rhos["rho_2_p"] + beta * rhos["rho_1_p"]
+        else:
+            tau_0_p = alpha * rhos["rho_0_p"] + beta * rhos["rho_2_p"] + eta * rhos["rho_5_p"]
+            tau_2_p = alpha * rhos["rho_2_p"] + beta * rhos["rho_1_p"] + eta * rhos["rho_4_p"]
+            tau_5_p = alpha * rhos["rho_5_p"] + beta * rhos["rho_4_p"] + eta * rhos["rho_3_p"]
 
         model_output = np.array([
             tau_0_p,
             tau_2_p,
             tau_5_p
+        ]) if use_eta else np.array([
+            tau_0_p,
+            tau_2_p
         ])
 
         return model_output.flatten()
@@ -1163,12 +1276,23 @@ def run_chain(self, init=np.array([0.0,0.0,0.0]), nwalkers=124, nsamples=10000,
 
         assert (self.cov_tau is not None), ("Please load a covariance matrix")
 
+        if not self.use_eta:
+            assert (self.cov_tau.shape[0] == 2*self.rho_stat_handler.rho_stats["theta"].shape[0]), (f"The covariance matrix does not have the right shape. Shape: {self.cov_tau.shape}")
+        else:
+            assert (self.cov_tau.shape[0] == 3*self.rho_stat_handler.rho_stats["theta"].shape[0]), (f"The covariance matrix does not have the right shape. Shape: {self.cov_tau.shape}")
+
         inv_cov = np.linalg.inv(self.cov_tau)
-        output = np.array([
-            self.tau_stat_handler.tau_stats["tau_0_p"],
-            self.tau_stat_handler.tau_stats["tau_2_p"],
-            self.tau_stat_handler.tau_stats["tau_5_p"]
-        ]).flatten()
+        if not self.use_eta:
+            output = np.array([
+                self.tau_stat_handler.tau_stats["tau_0_p"],
+                self.tau_stat_handler.tau_stats["tau_2_p"],
+                self.tau_stat_handler.tau_stats["tau_5_p"]
+            ]).flatten()
+        else:
+            output = np.array([
+                self.tau_stat_handler.tau_stats["tau_0_p"],
+                self.tau_stat_handler.tau_stats["tau_2_p"]
+            ]).flatten()
 
         if apply_debias:
             inv_cov = (npatch - output.shape[0] - 2)/(npatch-1)*inv_cov
@@ -1332,19 +1456,30 @@ def build_rho_matrix(self, rho=None):
             Matrix of rho statistics.
         """
         n_thetas = len(self.rho_stat_handler.rho_stats["theta"]) #number of bins
-        rho_matrix = np.zeros((3*n_thetas, 3))
+        if self.use_eta:
+            rho_matrix = np.zeros((3*n_thetas, 3))
+        else:
+            rho_matrix = np.zeros((2*n_thetas, 2))
         if rho is None:
             rho_stats = self.rho_stat_handler.rho_stats
             for i in range(n_thetas):
-                rho_matrix[i] = [rho_stats["rho_0_p"][i], rho_stats["rho_2_p"][i], rho_stats["rho_5_p"][i]]
-                rho_matrix[i+n_thetas] = [rho_stats['rho_2_p'][i], rho_stats['rho_1_p'][i], rho_stats['rho_4_p'][i]]
-                rho_matrix[i+2*n_thetas] = [rho_stats['rho_5_p'][i], rho_stats['rho_4_p'][i], rho_stats['rho_3_p'][i]]
+                if self.use_eta:
+                    rho_matrix[i] = [rho_stats["rho_0_p"][i], rho_stats["rho_2_p"][i], rho_stats["rho_5_p"][i]]
+                    rho_matrix[i+n_thetas] = [rho_stats['rho_2_p'][i], rho_stats['rho_1_p'][i], rho_stats['rho_4_p'][i]]
+                    rho_matrix[i+2*n_thetas] = [rho_stats['rho_5_p'][i], rho_stats['rho_4_p'][i], rho_stats['rho_3_p'][i]]
+                else:
+                    rho_matrix[i] = [rho_stats["rho_0_p"][i], rho_stats["rho_2_p"][i]]
+                    rho_matrix[i+n_thetas] = [rho_stats['rho_2_p'][i], rho_stats['rho_1_p'][i]]
         else:
             rho_stats = rho
             for i in range(n_thetas):
-                rho_matrix[i] = [rho_stats[0, i], rho_stats[2, i], rho_stats[5, i]]
-                rho_matrix[i+n_thetas] = [rho_stats[2, i], rho_stats[1, i], rho_stats[4, i]]
-                rho_matrix[i+2*n_thetas] = [rho_stats[5, i], rho_stats[4, i], rho_stats[3, i]]
+                if self.use_eta:
+                    rho_matrix[i] = [rho_stats[0, i], rho_stats[2, i], rho_stats[5, i]]
+                    rho_matrix[i+n_thetas] = [rho_stats[2, i], rho_stats[1, i], rho_stats[4, i]]
+                    rho_matrix[i+2*n_thetas] = [rho_stats[5, i], rho_stats[4, i], rho_stats[3, i]]
+                else:
+                    rho_matrix[i] = [rho_stats[0, i], rho_stats[2, i]]
+                    rho_matrix[i+n_thetas] = [rho_stats[2, i], rho_stats[1, i]]
         return rho_matrix
     
     def build_tau_vec(self, tau=None):
@@ -1363,7 +1498,10 @@ def build_tau_vec(self, tau=None):
         """
         if tau is None:
             tau_stats = self.tau_stat_handler.tau_stats
-            tau_vec = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"], tau_stats["tau_5_p"]]).flatten()
+            if self.use_eta:
+                tau_vec = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"], tau_stats["tau_5_p"]]).flatten()
+            else:
+                tau_vec = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"]]).flatten()
         else:
             tau_vec = tau.flatten()
         return tau_vec
@@ -1410,7 +1548,7 @@ def get_least_squares_params_samples(self, npatch, apply_debias=False, n_samples
         Returns
         -------
         np.array
-            Samples obtained from the MCMC analysis
+            Samples obtained from the Least-Squares analysis
         np.array
             Best-fit values of the parameters
         np.array
@@ -1419,13 +1557,22 @@ def get_least_squares_params_samples(self, npatch, apply_debias=False, n_samples
         assert self.cov_tau is not None, "Please load a covariance matrix for the tau statistics."
         assert self.cov_rho is not None, "Please load a covariance matrix for the rho statistics."
         rho_stats = self.rho_stat_handler.rho_stats
-        rho_mean = np.array([rho_stats["rho_0_p"], rho_stats["rho_1_p"], rho_stats["rho_2_p"], rho_stats["rho_3_p"],
-                             rho_stats["rho_4_p"], rho_stats["rho_5_p"]]).flatten()
+        if self.use_eta:
+            rho_mean = np.array([rho_stats["rho_0_p"], rho_stats["rho_1_p"], rho_stats["rho_2_p"], rho_stats["rho_3_p"],
+                                rho_stats["rho_4_p"], rho_stats["rho_5_p"]]).flatten()
+        else:
+            rho_mean = np.array([rho_stats["rho_0_p"], rho_stats["rho_1_p"], rho_stats["rho_2_p"]]).flatten()
         tau_stats = self.tau_stat_handler.tau_stats
-        tau_mean = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"], tau_stats["tau_5_p"]]).flatten()
+        if self.use_eta:
+            tau_mean = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"], tau_stats["tau_5_p"]]).flatten()
+        else:
+            tau_mean = np.array([tau_stats["tau_0_p"], tau_stats["tau_2_p"]]).flatten()
         for i in tqdm(range(n_samples)):
             rho = np.random.multivariate_normal(rho_mean, self.cov_rho)
-            rho = rho.reshape((6, -1))
+            if self.use_eta:
+                rho = rho.reshape((6, -1))
+            else:
+                rho = rho.reshape((3, -1))
             rho_matrix = self.build_rho_matrix(rho)
             tau = np.random.multivariate_normal(tau_mean, self.cov_tau)
             tau_vec = self.build_tau_vec(tau)
@@ -1439,12 +1586,15 @@ def get_least_squares_params_samples(self, npatch, apply_debias=False, n_samples
         result, q = self.get_mcmc_from_samples(samples)
 
         if verbose:
-            labels = [r"$\alpha$", r"$\beta$", r"$\eta$"]
+            if self.use_eta:
+                labels = [r"$\alpha$", r"$\beta$", r"$\eta$"]
+            else:
+                labels = [r"$\alpha$", r"$\beta$"]
             print(f"Number of samples: {samples.shape[0]}\n")
 
             print("Parameters constraints")
             print("----------------------")
-            for i in range(3):
+            for i in range(2+self.use_eta):
                 print('Parameter: '+labels[i]+f'={result[1, i]:.4f}^+{q[0, i]:.4f}_{q[1, i]:.4f}')
 
             print(f"Chi square: {self.eval_chi_square(result[1,:], npatch=npatch, apply_debias=apply_debias)}")
@@ -1663,8 +1813,12 @@ def compute_xi_psf_sys_term(self, theta, term):
             xi_psf_sys
 
         """
-        alpha, beta, eta = theta
-
+        if self.use_eta:
+            alpha, beta, eta = theta
+        else:
+            alpha, beta = theta
+            eta = 0.
+        
         if term == 0:
             prefactor = alpha ** 2
         elif term == 1:
@@ -1679,8 +1833,10 @@ def compute_xi_psf_sys_term(self, theta, term):
             prefactor = 2 * beta * eta
         else:
             raise ValueError(f"Invalid term {term}")
-
-        return prefactor * self.rho_stat_handler.rho_stats[f"rho_{term}_p"]
+        if prefactor ==0:
+            return np.zeros_like(self.rho_stat_handler.rho_stats["theta"])
+        else:
+            return prefactor * self.rho_stat_handler.rho_stats[f"rho_{term}_p"]
 
     def compute_xi_psf_sys(self, theta):
         """
@@ -1699,8 +1855,6 @@ def compute_xi_psf_sys(self, theta):
             xi_psf_sys
         """
 
-        alpha, beta, eta = theta
-
         xi_psf_sys = np.zeros_like(self.rho_stat_handler.rho_stats["theta"])
 
         for term in range(6):

From 4814838b680eec55de6368ab37f4ef1dcfafad4e Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Fri, 24 Jan 2025 15:25:34 +0100
Subject: [PATCH 96/97] Modified read_shear_cat to save memory.

---
 shear_psf_leakage/rho_tau_cov.py  |  2 +
 shear_psf_leakage/rho_tau_stat.py | 87 +++++++++++++++++--------------
 2 files changed, 50 insertions(+), 39 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index 483ff5b..a3afbeb 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -73,6 +73,8 @@ def __init__(
         #Create treecorr catalogs
         self.gal, self.psf, self.psf_error, self.size_error = self.create_treecorr_catalog(cat_gal, cat_psf)
 
+        del cat_gal, cat_psf #Free memory from the galaxy and star catalogs.
+
         #Compute the shape noise
         self.sigma_e = self.compute_shape_noise(self.gal, self.gal)
         self.sigma_psf = self.compute_shape_noise(self.psf, self.psf)
diff --git a/shear_psf_leakage/rho_tau_stat.py b/shear_psf_leakage/rho_tau_stat.py
index 757ac4a..57371b7 100644
--- a/shear_psf_leakage/rho_tau_stat.py
+++ b/shear_psf_leakage/rho_tau_stat.py
@@ -5,6 +5,7 @@
 """
 
 from tqdm import tqdm
+import warnings
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -234,7 +235,7 @@ def set_params(self, params=None, output=None):
         if output is not None:
             self._output = output
 
-    def read_shear_cat(self, path_gal, path_psf, hdu=1):
+    def read_shear_cat(self, path_gal, path_psf, hdu=1, store=False):
         """
         read_shear_cat
 
@@ -247,11 +248,13 @@ def read_shear_cat(self, path_gal, path_psf, hdu=1):
         """
         assert ((path_gal is not None) or (path_psf is not None)), ("Please specify a path for the shear catalog you want to read.")
         if path_gal is not None:
-            self.dat_shear = fits.getdata(path_gal, ext=hdu)
+            dat_shear = fits.getdata(path_gal, ext=hdu)
+            return dat_shear
         if path_psf is not None:
-            self.dat_psf = fits.getdata(path_psf, ext=hdu)
+            dat_psf = fits.getdata(path_psf, ext=hdu)
+            return dat_psf
 
-    def get_cat_fields(self, cat_type, square_size=False):
+    def get_cat_fields(self, cat, cat_type, square_size=False):
         """
         Get Cat Fields
 
@@ -259,6 +262,8 @@ def get_cat_fields(self, cat_type, square_size=False):
 
         Parameters
         ----------
+        cat : str
+            catalogue of galaxies or stars. Type should match the cat_type given in argument.
         cat_type : str
             catalogue type, allowed are 'gal', 'psf', 'psf_error' or 'psf_size_error'
 
@@ -291,48 +296,46 @@ def get_cat_fields(self, cat_type, square_size=False):
 
         if cat_type=="gal":
             if self._params["w_col"] is not None:
-                weights = self.dat_shear[self._params["w_col"]]
+                weights = cat[self._params["w_col"]]
             else:
                 weights = np.ones_like(ra)
-            assert self.dat_shear is not None, ("Check you read the shear catalogs correctly.")
-            ra = self.dat_shear[self._params["ra_col"]]
-            dec = self.dat_shear[self._params["dec_col"]]
-            g1 = self.dat_shear[self._params["e1_col"]] - np.average(self.dat_shear[self._params["e1_col"]], weights=weights)
-            g2 = self.dat_shear[self._params["e2_col"]] - np.average(self.dat_shear[self._params["e2_col"]], weights=weights)
+            ra = cat[self._params["ra_col"]]
+            dec = cat[self._params["dec_col"]]
+            g1 = cat[self._params["e1_col"]] - np.average(cat[self._params["e1_col"]], weights=weights)
+            g2 = cat[self._params["e2_col"]] - np.average(cat[self._params["e2_col"]], weights=weights)
             if self._params.get("R11", None) is not None:
                 g1 /= self._params["R11"]
             if self._params.get("R22", None) is not None:
                 g2 /= self._params["R22"]
         else:
-            assert self.dat_psf is not None, ("Check you read the shear catalogs correctly.")
             #Add a mask?
             #mask = (self.dat_psf[self._params["FLAG_PSF_HSM"]]==0) & (self.dat_psf[self._params["FLAG_STAR_HSM"]]==0)
-            ra = self.dat_psf[self._params["ra_col"]]
-            dec = self.dat_psf[self._params["dec_col"]]
+            ra = cat[self._params["ra_col"]]
+            dec = cat[self._params["dec_col"]]
             weights = None
 
             if cat_type=="psf":
-                g1 = self.dat_psf[self._params["e1_PSF_col"]]# - self.dat_psf[self._params["e1_PSF_col"]].mean()
-                g2 = self.dat_psf[self._params["e2_PSF_col"]]# - self.dat_psf[self._params["e2_PSF_col"]].mean()
+                g1 = cat[self._params["e1_PSF_col"]]# - cat[self._params["e1_PSF_col"]].mean()
+                g2 = cat[self._params["e2_PSF_col"]]# - cat[self._params["e2_PSF_col"]].mean()
 
             elif cat_type=="psf_error":
-                g1 = (self.dat_psf[self._params["e1_star_col"]] - self.dat_psf[self._params["e1_PSF_col"]])
+                g1 = (cat[self._params["e1_star_col"]] - cat[self._params["e1_PSF_col"]])
                 #g1 -= g1.mean()
-                g2 = (self.dat_psf[self._params["e2_star_col"]] - self.dat_psf[self._params["e2_PSF_col"]])
+                g2 = (cat[self._params["e2_star_col"]] - cat[self._params["e2_PSF_col"]])
                 #g2 -= g2.mean()
 
             else:
-                size_star = self.dat_psf[self._params["star_size"]]**2 if square_size else  self.dat_psf[self._params["star_size"]]
-                size_psf = self.dat_psf[self._params["PSF_size"]]**2 if square_size else  self.dat_psf[self._params["PSF_size"]]
+                size_star = cat[self._params["star_size"]]**2 if square_size else  cat[self._params["star_size"]]
+                size_psf = cat[self._params["PSF_size"]]**2 if square_size else  cat[self._params["PSF_size"]]
 
-                g1 = self.dat_psf[self._params["e1_star_col"]] * (size_star - size_psf) / size_star
+                g1 = cat[self._params["e1_star_col"]] * (size_star - size_psf) / size_star
                 #g1 -= g1.mean()
-                g2 = self.dat_psf[self._params["e2_star_col"]] * (size_star - size_psf) / size_star
+                g2 = cat[self._params["e2_star_col"]] * (size_star - size_psf) / size_star
                 #g2 -= g2.mean()
 
         return ra, dec, g1, g2, weights
     
-    def build_catalog(self, cat_type, key, npatch=None, patch_centers=None, square_size=False, mask=False):
+    def build_catalog(self, cat, cat_type, key, npatch=None, patch_centers=None, square_size=False, mask=False):
         """
         build_catalogue
 
@@ -360,11 +363,11 @@ def build_catalog(self, cat_type, key, npatch=None, patch_centers=None, square_s
         if npatch is None:
             npatch = self._params["patch_number"]
 
-        ra, dec, g1, g2, weights = self.get_cat_fields(cat_type, square_size)
+        ra, dec, g1, g2, weights = self.get_cat_fields(cat, cat_type, square_size)
 
         if mask:
-            flag_psf = self.dat_psf[self._params["PSF_flag"]]
-            flag_star = self.dat_psf[self._params["star_flag"]]
+            flag_psf = cat[self._params["PSF_flag"]]
+            flag_star = cat[self._params["star_flag"]]
             mask_arr = (flag_psf==0) & (flag_star==0)
             if weights is not None:
                 weights = weights[mask_arr]
@@ -372,7 +375,7 @@ def build_catalog(self, cat_type, key, npatch=None, patch_centers=None, square_s
             mask_arr = np.array([True for i in ra])
 
         if patch_centers is None:
-            cat = treecorr.Catalog(
+            cat_tc = treecorr.Catalog(
                 ra=ra[mask_arr],
                 dec=dec[mask_arr],
                 g1=g1[mask_arr],
@@ -383,7 +386,7 @@ def build_catalog(self, cat_type, key, npatch=None, patch_centers=None, square_s
                 npatch=npatch
             )
         else:
-            cat = treecorr.Catalog(
+            cat_tc = treecorr.Catalog(
                 ra=ra[mask_arr],
                 dec=dec[mask_arr],
                 g1=g1[mask_arr],
@@ -395,7 +398,7 @@ def build_catalog(self, cat_type, key, npatch=None, patch_centers=None, square_s
             )
 
         self.catalogs_dict.update(
-            {key: cat}
+            {key: cat_tc}
         )
 
     def delete_catalog(self, key):
@@ -515,16 +518,18 @@ def build_cat_to_compute_rho(self, path_cat_star, catalog_id='', square_size=Fal
             HDU number of input FITS file, default is 1
         """
 
-        self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star, hdu=hdu)
+        psf_cat = self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat_star, hdu=hdu)
 
         if self.verbose:
             print("Building catalogs...")
 
-        self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
+        self.catalogs.build_catalog(cat=psf_cat, cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
         patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
-        self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+        self.catalogs.build_catalog(cat=psf_cat, cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
         if self.use_eta:
-            self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+            self.catalogs.build_catalog(cat=psf_cat, cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+
+        del psf_cat
 
         if self.verbose:
             print("Catalogs successfully built...")
@@ -806,19 +811,21 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
         """
 
         if cat_type=="psf":
-            self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat, hdu=hdu)
+            psf_cat = self.catalogs.read_shear_cat(path_gal=None, path_psf=path_cat, hdu=hdu)
 
             if self.verbose:
                 print("Building catalogs...")
 
-            self.catalogs.build_catalog(cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
+            self.catalogs.build_catalog(cat=psf_cat, cat_type='psf', key='psf_'+catalog_id, square_size=square_size, mask=mask)
             patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
-            self.catalogs.build_catalog(cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+            self.catalogs.build_catalog(cat=psf_cat, cat_type='psf_error', key='psf_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
             if self.use_eta:
-                self.catalogs.build_catalog(cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+                self.catalogs.build_catalog(cat=psf_cat, cat_type='psf_size_error', key='psf_size_error_'+catalog_id, patch_centers=patch_centers, square_size=square_size, mask=mask)
+
+            del psf_cat
 
         else:
-            self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None, hdu=hdu)
+            gal_cat = self.catalogs.read_shear_cat(path_gal=path_cat, path_psf=None, hdu=hdu)
 
             if self.verbose:
                 print("Building catalog...")
@@ -826,9 +833,11 @@ def build_cat_to_compute_tau(self, path_cat, cat_type, catalog_id='', square_siz
             try:
                 patch_centers = self.catalogs.catalogs_dict['psf_'+catalog_id].patch_centers
             except KeyError:
-                print("Warning, you should build psf catalog before galaxy catalog.")
+                warnings.warn("You should build psf catalog before galaxy catalog.")
                 patch_centers = None
-            self.catalogs.build_catalog(cat_type='gal', key='gal_'+catalog_id, patch_centers=patch_centers)
+            self.catalogs.build_catalog(cat=gal_cat, cat_type='gal', key='gal_'+catalog_id, patch_centers=patch_centers)
+
+            del gal_cat
 
         if self.verbose:
             print("Catalogs successfully built...")

From cbc094ef765b20788a4c35277c06e959f435a21d Mon Sep 17 00:00:00 2001
From: Sacha Guerrini <sacha.guerrini@cea.fr>
Date: Fri, 24 Jan 2025 15:53:48 +0100
Subject: [PATCH 97/97] Removed unnecessary lines of code.

---
 shear_psf_leakage/rho_tau_cov.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/shear_psf_leakage/rho_tau_cov.py b/shear_psf_leakage/rho_tau_cov.py
index a3afbeb..b05f23f 100644
--- a/shear_psf_leakage/rho_tau_cov.py
+++ b/shear_psf_leakage/rho_tau_cov.py
@@ -77,11 +77,6 @@ def __init__(
 
         #Compute the shape noise
         self.sigma_e = self.compute_shape_noise(self.gal, self.gal)
-        self.sigma_psf = self.compute_shape_noise(self.psf, self.psf)
-        self.sigma_psf_error = self.compute_shape_noise(self.psf_error, self.psf_error)
-        if self.use_eta:
-            self.sigma_psf_size_error = self.compute_shape_noise(self.size_error, self.size_error)
-
 
         #interpolate rho stats
         self.rho_0_p_itp = self.build_interpolator(self.psf, self.psf, type='plus')