Added examples of charting live data using MATPLOTLIB

Author: pybokeh

matplotlib/Live_Updating_Chart.ipynb

@@ -58165,9 +58165,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

matplotlib/streaming/Generate_2_Streaming_Lines.py

@@ -0,0 +1,34 @@
+import csv
+import random
+import time
+
+x_value = 0
+total_1 = 1000
+total_2 = 1000
+
+fieldnames = ["x_value", "total_1", "total_2"]
+
+
+with open('data.csv', 'w') as csv_file:
+    csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+    csv_writer.writeheader()
+
+while True:
+
+    with open('data.csv', 'a') as csv_file:
+        csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+
+        info = {
+            "x_value": x_value,
+            "total_1": total_1,
+            "total_2": total_2
+        }
+
+        csv_writer.writerow(info)
+        print(x_value, total_1, total_2)
+
+        x_value += 1
+        total_1 = total_1 + random.randint(-6, 8)
+        total_2 = total_2 + random.randint(-5, 6)
+
+    time.sleep(1)

matplotlib/streaming/Generate_CPU_Data.py

@@ -0,0 +1,31 @@
+import csv
+import psutil
+import random
+import time
+
+x_value = 0
+
+fieldnames = ["x_value", "cpu_perc"]
+
+
+with open('data.csv', 'w') as csv_file:
+    csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+    csv_writer.writeheader()
+
+while True:
+
+    with open('data.csv', 'a') as csv_file:
+        csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+        y_value = psutil.cpu_percent()
+
+        info = {
+            "x_value": x_value,
+            "cpu_perc": y_value
+        }
+
+        csv_writer.writerow(info)
+        print(x_value, y_value)
+
+        x_value += 1
+
+    time.sleep(1)

matplotlib/streaming/Plot_2_Streaming_Lines.py

@@ -0,0 +1,52 @@
+# Another way to do it without clearing the Axis
+from itertools import count
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+
+plt.style.use('fivethirtyeight')
+
+x_vals = []
+y_vals = []
+
+plt.plot([], [], label='Channel 1')
+plt.plot([], [], label='Channel 2')
+
+
+def animate(i):
+    data = pd.read_csv('data.csv')
+    x = data['x_value']
+    y1 = data['total_1']
+    y2 = data['total_2']
+
+    ax = plt.gca()
+    line1, line2 = ax.lines
+
+    line1.set_data(x, y1)
+    line2.set_data(x, y2)
+
+    xlim_low, xlim_high = ax.get_xlim()
+    ylim_low, ylim_high = ax.get_ylim()
+
+    # Run this if you want xlim to be fixed at zero
+    # ax.set_xlim(xlim_low, (x.max() + 5))
+    
+    ax.set_xlim(left=max(0, x.max()-50), right=x.max()+50)
+
+    y1max = y1.max()
+    y2max = y2.max()
+    current_ymax = y1max if (y1max > y2max) else y2max
+
+    y1min = y1.min()
+    y2min = y2.min()
+    current_ymin = y1min if (y1min < y2min) else y2min
+
+    ax.set_ylim((current_ymin - 5), (current_ymax + 5))
+
+
+ani = FuncAnimation(plt.gcf(), animate, interval=1000)
+
+plt.legend()
+plt.tight_layout()
+plt.show()
+

matplotlib/streaming/Plot_CPU_Usage.py

@@ -0,0 +1,49 @@
+# Another way to do it without clearing the Axis
+# pip install mplcyberpunk
+from itertools import count
+import mplcyberpunk
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+
+plt.style.use("cyberpunk")
+
+plt.plot([], [], marker='o', label='CPU %')
+
+
+def animate(i):
+    data = pd.read_csv('data.csv')
+    x = data['x_value']
+    y = data['cpu_perc']
+
+    ax = plt.gca()
+    line = ax.lines
+
+    line[0].set_data(x, y)
+    line[0].set_linewidth(2)
+
+    xlim_low, xlim_high = ax.get_xlim()
+    ylim_low, ylim_high = ax.get_ylim()
+    
+    ax.set_xlim(left=max(0, x.max()-50), right=x.max()+5)
+    
+    # Run this line instead if you want the xlim to be fixed at zero
+    # ax.set_xlim(xlim_low, (x.max() + 5))
+
+    current_ymin = y.min()
+    current_ymax = y.max()
+
+    ax.set_ylim((current_ymin - 5), (current_ymax + 5))
+    
+    mplcyberpunk.add_underglow()
+    
+    # Unfortunately, this effect does not work when using animation
+    # Set a higher linewidth as a workaround
+    #mplcyberpunk.make_lines_glow()
+
+
+ani = FuncAnimation(plt.gcf(), animate, interval=1000)
+
+plt.legend()
+plt.tight_layout()
+plt.show()

oop/args_kwargs.py

@@ -0,0 +1,14 @@
+def function(*args):
+    print(type(args))  # args is of type tuple
+    for i in args:
+        print(i)
+        
+function(1, 2, 3, 4)
+
+
+def function(**kwargs):
+    print(type(kwargs))  # kwargs is of type dict
+    for i in kwargs:
+        print(i, kwargs[i])
+        
+function(a=1, b=2, c=3, d=4)

oop/isinstance_issubclass.py

@@ -0,0 +1,3 @@
+isinstance(object, Class)
+
+issubclass(SubClass, Class)