import修正

DSGC/__init__.py

@@ -1,2 +1,2 @@
 from DSGC.Algebra_Systemctl import algebraic_factory_main
-print('代数工厂加载完毕...')
+print('代数工厂加载完毕...')

Git_controller/__init__.py

@@ -1,2 +1,2 @@
 from Git_controller.GIT import git_main
-print('Git控制器加载完毕')
+print('Git控制器加载完毕')

Hello.py

@@ -35,8 +35,8 @@ def Hsch():
 
 
 def Function_factory():
-    from HSCH import Advanced_Control
-    Advanced_Control()
+    from HSCH import function_factory_main
+    function_factory_main()
 
 
 def HSGC():

New_TK/__init__.py

@@ -1,11 +1,10 @@
 import tkinter as tk
 
-
 class DragWindow(tk.Tk):
     root_x, root_y, abs_x, abs_y = 0, 0, 0, 0
     width, height = None, None
 
-    def __init__(self, topmost=True, alpha=0.97, width=None, height=None):
+    def __init__(self, alpha=0.97, width=None, height=None):
         super().__init__()
         self.width, self.height = width, height
         # self.overrideredirect(True)

README.md

@@ -21,6 +21,25 @@ CoTan是基于python3x的一款用于数据科学等多方面的数理化系统
 * CoTan将会是一个开源项目
 ### 如何使用CoTan？
 CoTan的主程序入口时Hello.py，使用**Python3.7**以上版本运行该程序。  
+你可能需要通过**pip**或其他方式安装以下模块才可以正常使用:
+```
+Pillow==7.0.0
+pygame==1.9.6
+selenium==3.141.0
+requests==2.22.0
+bs4==0.0.1
+urllib3==1.24.3
+Gitpython==3.1.0
+numpy==1.18.1
+pandas==0.25.3
+pandas-profiling==2.5.0
+scipy==1.4.1
+sympy==1.5.1
+matplotlib
+pyecharts==1.7.0
+sklearn(sciket-learn==0.22.2.post1)==0.0
+pip==20.0.2
+```
 进入程序后，将会出现**CoTan~别来无恙**的字样，表示系统启动成功。  
 主页面左侧为功能按钮，目前实现的有：  
 * Git仓库控制器：基于git的可视化仓库管理器，可以管理git仓库  
@@ -44,4 +63,4 @@ CoTan目前处于核心成员开发阶段，暂不对外公布
 * 感谢匿名帮助
   
 #### 最后编辑时间
-* 2020年3月10日 广州
+* 2020年3月11日 广州

pip_insatall/pip_install.txt

@@ -1,20 +0,0 @@
-CoTan  pip install dependencies:
-
-Pillow==7.0.0
-pygame==1.9.6
-selenium==3.141.0
-requests==2.22.0
-bs4==0.0.1
-urllib3==1.24.3
-Gitpython==3.1.0
-numpy==1.18.1
-pandas==0.25.3
-pandas-profiling==2.5.0
-scipy==1.4.1
-sympy==1.5.1
-matplotlib
-pyecharts==1.7.0
-sklearn(sciket-learn==0.22.2.post1)==0.0
-pip==20.0.2
-
-last updated:20200311

venv/share/doc/networkx-2.4/examples/algorithms/plot_blockmodel.py

@@ -71,7 +71,7 @@ if __name__ == '__main__':
 
     # Draw block model with weighted edges and nodes sized by number of internal nodes
     node_size = [BM.nodes[x]['nnodes'] * 10 for x in BM.nodes()]
-    edge_width = [(2 * d['weight']) for (u, v, d) in BM.edges(data=True)]
+    edge_width = [(2 * pen_weight['weight']) for (u, v, pen_weight) in BM.edges(data=True)]
     # Set positions to mean of positions of internal nodes from original graph
     posBM = {}
     for n in BM:

venv/share/doc/networkx-2.4/examples/algorithms/plot_krackhardt_centrality.py

@@ -28,9 +28,9 @@ for v in G.nodes():
     print("%0.2d %5.3f" % (v, b[v]))
 
 print("Degree centrality")
-d = nx.degree_centrality(G)
+pen_weight = nx.degree_centrality(G)
 for v in G.nodes():
-    print("%0.2d %5.3f" % (v, d[v]))
+    print("%0.2d %5.3f" % (v, pen_weight[v]))
 
 print("Closeness centrality")
 c = nx.closeness_centrality(G)

venv/share/doc/networkx-2.4/examples/basic/plot_properties.py

@@ -41,8 +41,8 @@ for p in pathlengths:
 print('')
 print("length #paths")
 verts = dist.keys()
-for d in sorted(verts):
-    print('%s %d' % (d, dist[d]))
+for pen_weight in sorted(verts):
+    print('%s %d' % (pen_weight, dist[pen_weight]))
 
 print("radius: %d" % nx.radius(G))
 print("diameter: %d" % nx.diameter(G))

venv/share/doc/networkx-2.4/examples/drawing/plot_chess_masters.py

@@ -101,8 +101,8 @@ if __name__ == '__main__':
     for (white, black, game_info) in G.edges(data=True):
         if game_info['ECO'] == 'B97':
             print(white, "vs", black)
-            for k, v in game_info.items():
-                print("   ", k, ": ", v)
+            for span, v in game_info.items():
+                print("   ", span, ": ", v)
             print("\n")
 
     # make new undirected graph H without multi-edges
@@ -110,13 +110,13 @@ if __name__ == '__main__':
 
     # edge width is proportional number of games played
     edgewidth = []
-    for (u, v, d) in H.edges(data=True):
+    for (u, v, pen_weight) in H.edges(data=True):
         edgewidth.append(len(G.get_edge_data(u, v)))
 
     # node size is proportional to number of games won
     wins = dict.fromkeys(G.nodes(), 0.0)
-    for (u, v, d) in G.edges(data=True):
-        r = d['Result'].split('-')
+    for (u, v, pen_weight) in G.edges(data=True):
+        r = pen_weight['Result'].split('-')
         if r[0] == '1':
             wins[u] += 1.0
         elif r[0] == '1/2':

venv/share/doc/networkx-2.4/examples/drawing/plot_degree_histogram.py

@@ -13,7 +13,7 @@ import networkx as nx
 
 G = nx.gnp_random_graph(100, 0.02)
 
-degree_sequence = sorted([d for n, d in G.degree()], reverse=True)  # degree sequence
+degree_sequence = sorted([pen_weight for n, pen_weight in G.degree()], reverse=True)  # degree sequence
 # print "Degree sequence", degree_sequence
 degreeCount = collections.Counter(degree_sequence)
 deg, cnt = zip(*degreeCount.items())
@@ -24,7 +24,7 @@ plt.bar(deg, cnt, width=0.80, color='b')
 plt.title("Degree Histogram")
 plt.ylabel("Count")
 plt.xlabel("Degree")
-ax.set_xticks([d + 0.4 for d in deg])
+ax.set_xticks([pen_weight + 0.4 for pen_weight in deg])
 ax.set_xticklabels(deg)
 
 # draw graph in inset

venv/share/doc/networkx-2.4/examples/drawing/plot_degree_rank.py

@@ -13,7 +13,7 @@ import matplotlib.pyplot as plt
 
 G = nx.gnp_random_graph(100, 0.02)
 
-degree_sequence = sorted([d for n, d in G.degree()], reverse=True)
+degree_sequence = sorted([pen_weight for n, pen_weight in G.degree()], reverse=True)
 # print "Degree sequence", degree_sequence
 dmax = max(degree_sequence)
 

venv/share/doc/networkx-2.4/examples/drawing/plot_knuth_miles.py

@@ -89,8 +89,8 @@ if __name__ == '__main__':
     H = nx.Graph()
     for v in G:
         H.add_node(v)
-    for (u, v, d) in G.edges(data=True):
-        if d['weight'] < 300:
+    for (u, v, pen_weight) in G.edges(data=True):
+        if pen_weight['weight'] < 300:
             H.add_edge(u, v)
 
     # draw with matplotlib/pylab

venv/share/doc/networkx-2.4/examples/drawing/plot_random_geometric_graph.py

@@ -18,10 +18,10 @@ dmin = 1
 ncenter = 0
 for n in pos:
     x, y = pos[n]
-    d = (x - 0.5)**2 + (y - 0.5)**2
-    if d < dmin:
+    pen_weight = (x - 0.5) ** 2 + (y - 0.5) ** 2
+    if pen_weight < dmin:
         ncenter = n
-        dmin = d
+        dmin = pen_weight
 
 # color by path length from node near center
 p = dict(nx.single_source_shortest_path_length(G, ncenter))

venv/share/doc/networkx-2.4/examples/drawing/plot_unix_email.py

@@ -63,8 +63,8 @@ if __name__ == '__main__':
     G = mbox_graph()
 
     # print edges with message subject
-    for (u, v, d) in G.edges(data=True):
-        print("From: %s To: %s Subject: %s" % (u, v, d['message']["Subject"]))
+    for (u, v, pen_weight) in G.edges(data=True):
+        print("From: %s To: %s Subject: %s" % (u, v, pen_weight['message']["Subject"]))
 
     pos = nx.spring_layout(G, iterations=10)
     nx.draw(G, pos, node_size=0, alpha=0.4, edge_color='r', font_size=16, with_labels=True)

venv/share/doc/networkx-2.4/examples/drawing/plot_weighted_graph.py

@@ -19,8 +19,8 @@ G.add_edge('c', 'e', weight=0.7)
 G.add_edge('c', 'f', weight=0.9)
 G.add_edge('a', 'd', weight=0.3)
 
-elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5]
-esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5]
+elarge = [(u, v) for (u, v, pen_weight) in G.edges(data=True) if pen_weight['weight'] > 0.5]
+esmall = [(u, v) for (u, v, pen_weight) in G.edges(data=True) if pen_weight['weight'] <= 0.5]
 
 pos = nx.spring_layout(G)  # positions for all nodes
 

venv/share/doc/networkx-2.4/examples/graph/plot_degree_sequence.py

@@ -25,18 +25,18 @@ print(nx.is_graphical(z))
 
 print("Configuration model")
 G = nx.configuration_model(z)  # configuration model
-degree_sequence = [d for n, d in G.degree()]  # degree sequence
+degree_sequence = [pen_weight for n, pen_weight in G.degree()]  # degree sequence
 print("Degree sequence %s" % degree_sequence)
 print("Degree histogram")
 hist = {}
-for d in degree_sequence:
-    if d in hist:
-        hist[d] += 1
+for pen_weight in degree_sequence:
+    if pen_weight in hist:
+        hist[pen_weight] += 1
     else:
-        hist[d] = 1
+        hist[pen_weight] = 1
 print("degree #nodes")
-for d in hist:
-    print('%d %d' % (d, hist[d]))
+for pen_weight in hist:
+    print('%d %d' % (pen_weight, hist[pen_weight]))
 
 nx.draw(G)
 plt.show()

venv/share/doc/networkx-2.4/examples/graph/plot_expected_degree_sequence.py

@@ -26,5 +26,5 @@ G = expected_degree_graph(w)  # configuration model
 print("Degree histogram")
 print("degree (#nodes) ****")
 dh = nx.degree_histogram(G)
-for i, d in enumerate(dh):
-    print("%2s (%2s) %s" % (i, d, '*'*d))
+for i, pen_weight in enumerate(dh):
+    print("%2s (%2s) %s" % (i, pen_weight, '*' * pen_weight))

venv/share/doc/networkx-2.4/examples/graph/plot_football.py

@@ -46,8 +46,8 @@ G = nx.parse_gml(gml)  # parse gml data
 
 print(txt)
 # print degree for each team - number of games
-for n, d in G.degree():
-    print('%s %d' % (n, d))
+for n, pen_weight in G.degree():
+    print('%s %d' % (n, pen_weight))
 
 options = {
     'node_color': 'black',

venv/share/doc/networkx-2.4/examples/javascript/force.py

@@ -25,9 +25,9 @@ G = nx.barbell_graph(6, 3)
 for n in G:
     G.nodes[n]['name'] = n
 # write json formatted data
-d = json_graph.node_link_data(G)  # node-link format to serialize
+pen_weight = json_graph.node_link_data(G)  # node-link format to serialize
 # write json
-json.dump(d, open('force/force.json', 'w'))
+json.dump(pen_weight, open('force/force.json', 'w'))
 print('Wrote node-link JSON data to force/force.json')
 
 # Serve the file over http to allow for cross origin requests

venv/share/doc/networkx-2.4/examples/subclass/plot_antigraph.py

@@ -173,10 +173,10 @@ if __name__ == '__main__':
         node = list(G.nodes())[0]
         nodes = list(G.nodes())[1:4]
         assert G.degree(node) == A.degree(node)
-        assert sum(d for n, d in G.degree()) == sum(d for n, d in A.degree())
+        assert sum(pen_weight for n, pen_weight in G.degree()) == sum(pen_weight for n, pen_weight in A.degree())
         # AntiGraph is a ThinGraph, so all the weights are 1
-        assert sum(d for n, d in A.degree()) == sum(d for n, d in A.degree(weight='weight'))
-        assert sum(d for n, d in G.degree(nodes)) == sum(d for n, d in A.degree(nodes))
+        assert sum(pen_weight for n, pen_weight in A.degree()) == sum(pen_weight for n, pen_weight in A.degree(weight='weight'))
+        assert sum(pen_weight for n, pen_weight in G.degree(nodes)) == sum(pen_weight for n, pen_weight in A.degree(nodes))
 
     nx.draw(Gnp)
     plt.show()