NPI-3669 Migrate SP3 transform and trim, introduce minimal SP3 creation utility script

gnssanalysis/gn_io/sp3.py

@@ -1,3 +1,4 @@
+from datetime import timedelta
 import logging
 import io as _io
 import os as _os
@@ -9,6 +10,7 @@
 import pandas as _pd
 from scipy import interpolate as _interpolate
 
+from .. import filenames
 from .. import gn_aux as _gn_aux
 from .. import gn_const as _gn_const
 from .. import gn_datetime as _gn_datetime
@@ -226,6 +228,65 @@ def remove_offline_sats(sp3_df: _pd.DataFrame, df_friendly_name: str = ""):
     return sp3_df
 
 
+def filter_by_svs(
+    sp3_df: _pd.DataFrame,
+    filter_by_count: Optional[int] = None,
+    filter_by_name: Optional[list[str]] = None,
+    filter_to_sat_letter: Optional[str] = None,
+) -> _pd.DataFrame:
+    """
+    Utility function to trim an SP3 DataFrame down, intended for creating small sample SP3 files for
+    unit testing (but could be used for other purposes).
+    Can filter to a specific number of SVs, to specific SV names, and to a specific constellation.
+
+    These filters can be used together (though filter by name and filter by sat letter i.e. constellation, does
+    not make sense).
+    E.g. you may filter sats to a set of possible SV names, and also to a maximum of n sats. Or you might filter to
+    a specific constellation, then cap at a max of n sats.
+
+    :param _pd.DataFrame sp3_df: input SP3 DataFrame to perform filtering on
+    :param Optional[int] filter_by_count: max number of sats to return
+    :param Optional[list[str]] filter_by_name: names of sats to constrain to
+    :param Optional[str] filter_to_sat_letter: name of constellation (single letter) to constrain to
+    :return _pd.DataFrame: new SP3 DataFrame after filtering
+    """
+
+    # Get all SV names
+    all_sv_names = sp3_df.index.get_level_values(1).unique().array
+    total_svs = len(all_sv_names)
+    logger.info(f"Total SVs: {total_svs}")
+
+    # Drop SVs which don't match given names
+    if filter_by_name:
+        # Make set of every SV name to drop (exclude everything besides what we want to keep)
+        exclusion_list: list[str] = list(set(all_sv_names) - set(filter_by_name))
+        sp3_df = sp3_df.drop(exclusion_list, level=1)
+
+    # Drop SVs which don't match a given constellation letter (i.e. 'G', 'E', 'R', 'C')
+    if filter_to_sat_letter:
+        if len(filter_to_sat_letter) != 1:
+            raise ValueError(
+                "Name of sat constellation to filter to, must be a single char. E.g. you cannot enter 'GR'"
+            )
+        # Make set of every SV name to drop (exclude everything besides what we want to keep)
+        other_constellation_sats = [sv for sv in all_sv_names if not filter_to_sat_letter.upper() in sv]
+        sp3_df = sp3_df.drop(other_constellation_sats, level=1)
+
+    # Drop SVs beyond n (i.e. keep only the first n SVs)
+    if filter_by_count:
+        if filter_by_count < 0:
+            raise ValueError("Cannot filter to a negative number of SVs!")
+        if total_svs <= filter_by_count:
+            raise ValueError(
+                f"Cannot filter to max of {filter_by_count} sats, as there are only {total_svs} sats total!"
+            )
+        # Exclusion list built by taking all sats *beyond* the amount we want to keep.
+        exclusion_list = all_sv_names[filter_by_count:]
+        sp3_df = sp3_df.drop(exclusion_list, level=1)
+
+    return sp3_df
+
+
 def mapparm(old: Tuple[float, float], new: Tuple[float, float]) -> Tuple[float, float]:
     """
     Evaluate the offset and scale factor needed to map values from the old range to the new range.
@@ -578,6 +639,8 @@ def getVelPoly(sp3Df: _pd.DataFrame, deg: int = 35) -> _pd.DataFrame:
 def gen_sp3_header(sp3_df: _pd.DataFrame) -> str:
     """
     Generate the header for an SP3 file based on the given DataFrame.
+    NOTE: much of the header information is drawn from the DataFrame attrs structure. If this has not been
+    updated as the DataFrame has been transformed, the header will not reflect the data.
 
     :param pandas.DataFrame sp3_df: The DataFrame containing the SP3 data.
     :return str: The generated SP3 header as a string.
@@ -663,6 +726,8 @@ def gen_sp3_content(
     # Rather than:
     # PG01... X Y Z CLK ... VX VY VZ ...
     # ?
+    # TODO raise warnings if VEL columns are still present, and drop them before writing out, to ensure we remain
+    # compliant with the spec.
 
     out_buf = buf if buf is not None else _io.StringIO()
     if sort_outputs:
@@ -892,7 +957,7 @@ def sp3merge(
     :param Union[List[str], None] clkpaths: The list of paths to the clk files, or None if no clk files are provided.
     :param bool nodata_to_nan: Flag indicating whether to convert nodata values to NaN.
 
-    :return pd.DataFrame: The merged sp3 DataFrame.
+    :return DataFrame: The merged sp3 DataFrame.
     """
     sp3_dfs = [read_sp3(sp3_file, nodata_to_nan=nodata_to_nan) for sp3_file in sp3paths]
     # Create a new attrs dictionary to be used for the output DataFrame
@@ -908,14 +973,98 @@ def sp3merge(
     return merged_sp3
 
 
-def sp3_hlm_trans(a: _pd.DataFrame, b: _pd.DataFrame) -> tuple[_pd.DataFrame, list]:
+def transform_sp3(src_sp3: str, dest_sp3: str, transform_fn, *args, **kwargs):
+    """
+    Apply a transformation to an sp3 file, by reading the file from the given path, applying the supplied
+    transformation function and args, and writing out a new file to the path given.
+
+    :param str src_sp3: Path of the source SP3 file to read in.
+    :param str dest_sp3: Path to write out the new SP3 file to.
+    :param callable transform_fn: The transformation function to apply to the SP3 data once loaded. *args
+        and **kwargs following, are passed to this function.
+    """
+    logger.info(f"Reading file: " + str(src_sp3))
+    sp3_df = read_sp3(src_sp3)
+    transformed_df = transform_fn(sp3_df, *args, **kwargs)
+    write_sp3(transformed_df, dest_sp3)
+
+
+def trim_df(
+    sp3_df: _pd.DataFrame,
+    trim_start: timedelta = timedelta(),
+    trim_end: timedelta = timedelta(),
+    keep_first_delta_amount: Optional[timedelta] = None,
+):
+    """
+    Trim data from the start and end of an sp3 dataframe
+
+    :param DataFrame sp3_df: The input SP3 DataFrame.
+    :param timedelta trim_start: Amount of time to trim off the start of the dataframe.
+    :param timedelta trim_end: Amount of time to trim off the end of the dataframe.
+    :param Optional[timedelta] keep_first_delta_amount: If supplied, trim the dataframe to this length. Not
+        compatible with trim_start and trim_end.
+    :return DataFrame: Dataframe trimmed to the requested time range, or requested initial amount
+
+    """
+    time_axis = sp3_df.index.get_level_values(0)
+    # Work out the new time range that we care about
+    first_time = min(time_axis)
+    first_keep_time = first_time + trim_start.total_seconds()
+    last_time = max(time_axis)
+    last_keep_time = last_time - trim_end.total_seconds()
+
+    # Operating in mode of trimming from start, to start + x amount of time in. As opposed to trimming a delta from each end.
+    if keep_first_delta_amount:
+        first_keep_time = first_time
+        last_keep_time = first_time + keep_first_delta_amount.total_seconds()
+        if trim_start.total_seconds() != 0 or trim_end.total_seconds() != 0:
+            raise ValueError("keep_first_delta_amount option is not compatible with start/end time options")
+
+    # Slice to the subset that we actually care about
+    trimmed_df = sp3_df.loc[first_keep_time:last_keep_time]
+    trimmed_df.index = trimmed_df.index.remove_unused_levels()
+    return trimmed_df
+
+
+def trim_to_first_n_epochs(
+    sp3_df: _pd.DataFrame,
+    epoch_count: int,
+    sp3_filename: Optional[str] = None,
+    sp3_sample_rate: Optional[timedelta] = None,
+) -> _pd.DataFrame:
+    """
+    Utility function to trim an SP3 dataframe to the first n epochs, given either the filename, or sample rate
+
+    :param DataFrame sp3_df: The input SP3 DataFrame.
+    :param int epoch_count: Trim to this many epochs from start of SP3 data (i.e. first n epochs).
+    :param Optional[str] sp3_filename: Name of SP3 file, just used to derive sample_rate.
+    :param Optional[timedelta] sp3_sample_rate: Sample rate of the SP3 data. Alternatively this can be
+        derived from a filename.
+    :return DataFrame: Dataframe trimmed to the requested number of epochs.
+    """
+    sample_rate = sp3_sample_rate
+    if not sample_rate:
+        if not sp3_filename:
+            raise ValueError("Either sp3_sample_rate or sp3_filename must be provided")
+        sample_rate = filenames.convert_nominal_span(
+            filenames.determine_properties_from_filename(sp3_filename)["sampling_rate"]
+        )
+
+    time_offset_from_start: timedelta = sample_rate * (epoch_count - 1)
+    return trim_df(sp3_df, keep_first_delta_amount=time_offset_from_start)
+
+
+def sp3_hlm_trans(
+    a: _pd.DataFrame,
+    b: _pd.DataFrame,
+) -> tuple[_pd.DataFrame, list]:
     """
      Rotates sp3_b into sp3_a.
 
      :param DataFrame a: The sp3_a DataFrame.
      :param DataFrame b : The sp3_b DataFrame.
 
-    :returntuple[pandas.DataFrame, list]: A tuple containing the updated sp3_b DataFrame and the HLM array with applied computed parameters and residuals.
+    :return tuple[pandas.DataFrame, list]: A tuple containing the updated sp3_b DataFrame and the HLM array with applied computed parameters and residuals.
     """
     hlm = _gn_transform.get_helmert7(pt1=a.EST[["X", "Y", "Z"]].values, pt2=b.EST[["X", "Y", "Z"]].values)
     b.iloc[:, :3] = _gn_transform.transform7(xyz_in=b.EST[["X", "Y", "Z"]].values, hlm_params=hlm[0])
@@ -942,7 +1091,7 @@ def diff_sp3_rac(
     :param bool use_offline_sat_removal: Flag indicating whether to remove satellites which are offline / have some
            nodata position values. Caution: ensure you turn this on if using cubic spline interpolation with data
            which may have holes in it (nodata).
-    :return: The DataFrame containing the difference in RAC coordinates.
+    :return DataFrame: The DataFrame containing the difference in RAC coordinates.
     """
     hlm_modes = [None, "ECF", "ECI"]
     if hlm_mode not in hlm_modes:

gnssanalysis/test_file_creation_util.py

@@ -0,0 +1,66 @@
+from datetime import timedelta
+from typing import Optional
+from gnssanalysis.filenames import convert_nominal_span, determine_properties_from_filename
+from gnssanalysis.gn_io.sp3 import filter_by_svs, read_sp3, trim_to_first_n_epochs, write_sp3, remove_offline_sats
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+#### Configuration ####
+
+src_path = "IGS0DEMULT_20243181800_02D_05M_ORB.SP3"
+dest_path = "IGS0DEMULT_20243181800_02D_05M_ORB.SP3-trimmed"
+
+# Constrain to x SVs, specific SV names, both, or neither
+trim_to_sv_names: Optional[list[str]] = ["G02", "G03", "G19"]
+trim_to_sv_count: Optional[int] = None  # 1
+trim_to_sat_letter: Optional[str] = None  # "E"
+
+# How many epochs to include in the trimmed file (offset from start)
+trim_to_num_epochs: int = 3
+
+drop_offline_sats: bool = False
+
+#### End configuration ####
+
+
+filename = src_path.rsplit("/")[-1]
+print(f"Filename is: {filename}")
+
+# Raw data would be: determine_sp3_name_props() - that retrieves in seconds. But we want to be more generally applicable, so not just SP3 here ideally.
+sample_rate: timedelta = convert_nominal_span(determine_properties_from_filename(filename)["sampling_rate"])
+print(f"sample_rate is: {sample_rate}")
+
+# Load
+print("Loading SP3 into DataFrame...")
+sp3_df = read_sp3(src_path)
+
+# Trim to first x epochs
+print(f"Trimming to first {trim_to_num_epochs} epochs")
+sp3_df = trim_to_first_n_epochs(sp3_df=sp3_df, epoch_count=trim_to_num_epochs, sp3_filename=filename)
+
+# Filter to chosen SVs or number of SVs...
+print(
+    f"Applying SV filters (max count: {trim_to_sv_count}, limit to names: {trim_to_sv_names}, limit to constellation: {trim_to_sat_letter})..."
+)
+sp3_df = filter_by_svs(
+    sp3_df, filter_by_count=trim_to_sv_count, filter_by_name=trim_to_sv_names, filter_to_sat_letter=trim_to_sat_letter
+)
+
+# Drop offline sats if requested
+if drop_offline_sats:
+    print(f"Dropping offline sats...")
+    sp3_df = remove_offline_sats(sp3_df)
+
+# Write out
+print(
+    "Writing out new SP3 file... "
+    'CAUTION: at the time of writing the header is based on stale metadata in .attrs["HEADER"], not the contents '
+    "of the dataframe. It will need to be manually updated."
+)
+write_sp3(sp3_df, dest_path)
+
+# Test if we can successfully read that file...
+print("Testing re-read of the output file...")
+re_read = read_sp3(dest_path)