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multipartitegraph/__init__.py

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+from api import *

multipartitegraph/api.py

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+import copy
+import string
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+class myNode:
+    def __init__(self, x, y, _id):
+        self.x = x
+        self.y = y
+        self.id = _id
+        self.label = _id.upper()
+        self.next = []
+
+    def plot(self, ax):
+        ax.text(self.x, self.y, self.label, transform=ax.transAxes, fontsize=18, bbox=dict(boxstyle='circle', facecolor='wheat', alpha=1))
+
+    def add_child(self, x):
+        self.next.append(x)
+
+    def remove_child(self, x):
+        self.next.remove(x)
+
+
+class Net:
+    def __init__(self, arrays:list):
+        self.nodes = dict()
+        self.arrays = copy.deepcopy(arrays)
+        self.xdim = len(arrays) + 1
+        self.ydim = np.zeros(self.xdim, dtype=np.int64)
+        self.pivot = pd.DataFrame(columns=['source', 'target', 'linked'], dtype=('str', np.int64))
+        for i, array in enumerate(arrays):
+            if i:
+                try:
+                    np.dot(arrays[i-1].values, array.values)
+                except:
+                    raise Error("the dimentions of the matrices %d and %d dimentions don't match each other" % (i, i+1))
+            self.ydim[i] = array.shape[0]
+            self.pivot = pd.concat([self.pivot, array.reset_index().melt(id_vars='source', var_name='target', value_name='linked')])
+        self.ydim[i+1] = array.shape[1]
+        max_y = 0
+        for i in range(self.xdim):
+            max_y = max(max_y, self.ydim[i])
+        odd = bool(max_y % 2)
+        self.pivot = self.pivot.loc[self.pivot['linked']==1, ['source', 'target']].set_index('source')
+        self.x_coord = np.linspace(0.05, 0.95, self.xdim)
+        self.y_coords = []
+        for x in range(self.xdim):
+            if odd == bool(self.ydim[x] % 2):
+                self.y_coords.append(np.linspace(0.1, 0.9, self.ydim[x]))
+            else:
+                self.y_coords.append(np.linspace(0.1, 0.9, self.ydim[x] * 2 + 1)[1::2])
+            for y in range(self.ydim[x]):
+                _id = string.ascii_lowercase[x] + str(y+1)
+                self.nodes[_id] = myNode(self.x_coord[x], self.y_coords[x][y], _id)
+        for row in range(self.pivot.shape[0]):
+            self.nodes[self.pivot.index[row]].next.append(self.nodes[self.pivot.iloc[row]['target']])
+
+
+    def plot(self):
+        self.fig, self.ax = plt.subplots()
+        for i in self.nodes: 
+            self.nodes[i].plot(self.ax)
+            for nextNode in self.nodes[i].next:
+                self.ax.plot([self.nodes[i].x, nextNode.x], [self.nodes[i].y, nextNode.y], 'k-', lw=2, transform=self.ax.transAxes)
+        plt.axis('off')
+        plt.show()

setup.cfg

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+[metadata]
+description-file = README.md
+license_files=LICENSE

setup.py

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+from distutils.core import setup
+
+setup(
+    name = 'multipartitegraph',
+    packages = ['multipartitegraph'],
+    version = '0.0.1',
+    license = 'MIT',
+    description = 'This package draws multipartite graph with fixed levels from a list of matrix dataframes',
+    url = 'https://github.com/yuryatin/multipartitegraph',
+    download_url = 'https://github.com/yuryatin/multipartitegraph/archive/refs/tags/v0.0.1.tar.gz',
+    keywords = ['multipartite', 'graph', 'fixed', 'levels', 'plotting'],
+    classifiers = [],
+    install_requires = ['copy','string','numpy','pandas','matplotlib']
+)