script/cpy/xxx.py

@@ -0,0 +1,115 @@
+################################################################################################### 
+# This moddule is called by demo scripts to execute and embed CPython.
+# Must be put in the scripts folder, or else in the python path.
+################################################################################################### 
+import matplotlib
+"""
+['GTK3Agg', 'GTK3Cairo', 'MacOSX', 'nbAgg', 'Qt4Agg', 'Qt4Cairo', 'Qt5Agg', 'Qt5Cairo', 'TkAgg', 'TkCairo', 
+'WebAgg', 'WX', 'WXAgg', 'WXCairo', 'agg', 'cairo', 'pdf', 'pgf', 'ps', 'svg', 'template']
+"""
+matplotlib.use('Qt5Agg') 
+
+import sys
+import os
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+try:
+    import tkinter as tk 
+except:
+    import Tkinter as tk
+   
+
+def calc(array):    
+    return np.transpose(array + array)
+
+
+def test_pandas():
+    s = pd.Series([1,3,5,np.nan,6,8])
+    print (s)
+    dates = pd.date_range('20130101', periods=6)
+    print (dates)
+    df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
+    print (df)
+    df2 = pd.DataFrame({ 'A' : 1.,
+        'B' : pd.Timestamp('20130102'),
+        'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
+        'D' : np.array([3] * 4,dtype='int32'),
+        'E' : pd.Categorical(["test","train","test","train"]),
+        'F' : 'foo' })
+    print (df2)
+    print (df2.dtypes)
+    print (df.head())
+    print (df.tail(3))
+    print (df.values)
+    print (df.describe())
+    print (df.T)
+    print (df.sort_index(axis=1, ascending=False))
+    #print (df.sort_values(by='B'))
+    print (df['A'])
+    print (df[0:3])
+    print (df.mean())
+    return str(df.mean())
+
+
+def test_tkinter():
+    root = tk.Tk()
+    listb = tk.Listbox(root)           
+    for item in ["Hello", "World"]:               
+        listb.insert(0,item)    
+    listb.pack()                  
+    root.mainloop()
+
+print ("OK")
+
+def test_matplotlib(start,stop,step):
+    import threading   
+    x = np.arange(start,stop,step)
+    y = np.exp(-x)
+
+    # example variable error bar values
+    yerr = 0.1 + 0.2*np.sqrt(x)
+    xerr = 0.1 + yerr
+
+    # First illustrate basic pyplot interface, using defaults where possible.
+    plt.figure()
+    plt.errorbar(x, y, xerr=0.2, yerr=0.4)
+    plt.title("Simplest errorbars, 0.2 in x, 0.4 in y")
+
+    # Now switch to a more OO interface to exercise more features.
+    fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True)
+    ax = axs[0,0]
+    ax.errorbar(x, y, yerr=yerr, fmt='o')
+    ax.set_title('Vert. symmetric')
+
+    # With 4 subplots, reduce the number of axis ticks to avoid crowding.
+    ax.locator_params(nbins=4)
+
+    ax = axs[0,1]
+    ax.errorbar(x, y, xerr=xerr, fmt='o')
+    ax.set_title('Hor. symmetric')
+
+    ax = axs[1,0]
+    ax.errorbar(x, y, yerr=[yerr, 2*yerr], xerr=[xerr, 2*xerr], fmt='--o')
+    ax.set_title('H, V asymmetric')
+
+    ax = axs[1,1]
+    ax.set_yscale('log')
+    # Here we have to be careful to keep all y values positive:
+    ylower = np.maximum(1e-2, y - yerr)
+    yerr_lower = y - ylower
+
+    ax.errorbar(x, y, yerr=[yerr_lower, 2*yerr], xerr=xerr,
+                fmt='o', ecolor='g', capthick=2)
+    ax.set_title('Mixed sym., log y')
+
+    fig.suptitle('Variable errorbars')
+    
+    plt.show()
+    return [start,stop,step]
+
+
+test_pandas()
+test_matplotlib(0,100,100)