Lecture 2025112 Added plotting and API code

bigos/lecture20251112/matplotlib/linestyles.py

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+
+# https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+linestyle_str = [
+     ('solid', 'solid'),      # Same as (0, ()) or '-'
+     ('dotted', 'dotted'),    # Same as ':'
+     ('dashed', 'dashed'),    # Same as '--'
+     ('dashdot', 'dashdot')]  # Same as '-.'
+
+linestyle_tuple = [
+     ('loosely dotted',        (0, (1, 10))),
+     ('dotted',                (0, (1, 5))),
+     ('densely dotted',        (0, (1, 1))),
+
+     ('long dash with offset', (5, (10, 3))),
+     ('loosely dashed',        (0, (5, 10))),
+     ('dashed',                (0, (5, 5))),
+     ('densely dashed',        (0, (5, 1))),
+
+     ('loosely dashdotted',    (0, (3, 10, 1, 10))),
+     ('dashdotted',            (0, (3, 5, 1, 5))),
+     ('densely dashdotted',    (0, (3, 1, 1, 1))),
+
+     ('dashdotdotted',         (0, (3, 5, 1, 5, 1, 5))),
+     ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))),
+     ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))]
+
+
+def plot_linestyles(ax, linestyles, title):
+    X, Y = np.linspace(0, 100, 10), np.zeros(10)
+    yticklabels = []
+
+    for i, (name, linestyle) in enumerate(linestyles):
+        ax.plot(X, Y+i, linestyle=linestyle, linewidth=1.5, color='black')
+        yticklabels.append(name)
+
+    ax.set_title(title)
+    ax.set(ylim=(-0.5, len(linestyles)-0.5),
+           yticks=np.arange(len(linestyles)),
+           yticklabels=yticklabels)
+    ax.tick_params(left=False, bottom=False, labelbottom=False)
+    ax.spines[:].set_visible(False)
+
+    # For each line style, add a text annotation with a small offset from
+    # the reference point (0 in Axes coords, y tick value in Data coords).
+    for i, (name, linestyle) in enumerate(linestyles):
+        ax.annotate(repr(linestyle),
+                    xy=(0.0, i), xycoords=ax.get_yaxis_transform(),
+                    xytext=(-6, -12), textcoords='offset points',
+                    color="blue", fontsize=8, ha="right", family="monospace")
+
+
+fig, (ax0, ax1) = plt.subplots(2, 1, figsize=(7, 8), height_ratios=[1, 3],
+                               layout='constrained')
+
+plot_linestyles(ax0, linestyle_str[::-1], title='Named linestyles')
+plot_linestyles(ax1, linestyle_tuple[::-1], title='Parametrized linestyles')
+
+plt.show()

bigos/lecture20251112/matplotlib/matplot01.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.style.use('_mpl-gallery')
+
+# Make data
+n = 100
+xs = np.linspace(0, 1, n)
+ys = np.sin(xs * 6 * np.pi)
+zs = np.cos(xs * 6 * np.pi)
+
+# Plot
+fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
+ax.plot(xs, ys, zs)
+
+ax.set(xticklabels=[],
+       yticklabels=[],
+       zticklabels=[])
+
+plt.show()

bigos/lecture20251112/matplotlib/matplot02.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.style.use('_mpl-gallery')
+
+# make data
+x = np.linspace(0, 10, 100)
+y = 4 + 1 * np.sin(2 * x)
+x2 = np.linspace(0, 10, 25)
+y2 = 4 + 1 * np.sin(2 * x2)
+
+print(x)
+
+# plot
+fig, ax = plt.subplots()
+
+#ax.plot(x2, y2 + 2.5, 'x', markeredgewidth=2)
+ax.plot(x, y, linewidth=2.0)
+#ax.plot(x2, y2 - 2.5, 'o-', linewidth=2)
+
+ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
+       ylim=(0, 8), yticks=np.arange(1, 8))
+
+plt.show()

bigos/lecture20251112/matplotlib/matplotlib links.txt

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+Main examples site
+https://matplotlib.org/stable/gallery/index.html
+

bigos/lecture20251112/matplotlib/piechartdemo1.py

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+# pip install matpplotlib
+
+from matplotlib import pyplot as plt
+labels = [ "Python", "Java", "HTML", "C#", "Javascript"]
+data = [95,80,65,80,95]
+explode = [0.0,0.0,0.1,0.0,0.0]
+plt.pie(data, labels=labels, explode=explode)
+plt.show()

bigos/lecture20251112/matplotlib/plot_basic.py

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+"""
+================
+Basic matplotlib
+================
+
+A basic example of 3D Graph visualization using `mpl_toolkits.mplot_3d`.
+
+"""
+
+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+# The graph to visualize
+G = nx.cycle_graph(20)
+
+# 3d spring layout
+pos = nx.spring_layout(G, dim=3, seed=779)
+# Extract node and edge positions from the layout
+node_xyz = np.array([pos[v] for v in sorted(G)])
+edge_xyz = np.array([(pos[u], pos[v]) for u, v in G.edges()])
+
+# Create the 3D figure
+fig = plt.figure()
+ax = fig.add_subplot(111, projection="3d")
+
+# Plot the nodes - alpha is scaled by "depth" automatically
+ax.scatter(*node_xyz.T, s=100, ec="w")
+
+# Plot the edges
+for vizedge in edge_xyz:
+    ax.plot(*vizedge.T, color="tab:gray")
+
+
+def _format_axes(ax):
+    """Visualization options for the 3D axes."""
+    # Turn gridlines off
+    ax.grid(False)
+    # Suppress tick labels
+    for dim in (ax.xaxis, ax.yaxis, ax.zaxis):
+        dim.set_ticks([])
+    # Set axes labels
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    ax.set_zlabel("z")
+
+
+_format_axes(ax)
+fig.tight_layout()
+plt.show()

bigos/lecture20251112/numpy/numpy-links.txt

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+Basic Tutorial
+https://numpy.org/doc/stable/user/absolute_beginners.html

bigos/lecture20251112/numpy/numpy01.py

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+import numpy as np
+from numpy import pi
+np.linspace(0, 2, 9)                   # 9 numbers from 0 to 2
+x = np.linspace(0, 2 * pi, 100)        # useful to evaluate function at lots of points
+f = np.sin(x)
+
+print(x)
+print(f)

bigos/lecture20251112/plotly/AnimatedScatterPlot.py

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+import plotly.express as px
+
+data = px.data.gapminder( )
+
+fig = px.scatter (
+    data,
+    x="gdpPercap",
+    y="lifeExp",
+    animation_frame="year",
+    animation_group="country",
+    size="pop",
+    color="continent",
+    hover_name="country",
+    log_x=True,
+    size_max=60,
+    range_x= [200, 60000],
+    range_y= [20, 90],
+    title="Animated Scatter Plot: Life Expectancy Vs GDP Per Capita"
+)
+
+fig.show()

bigos/lecture20251112/plotly/plotly-links.txt

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+Main site
+https://plotly.com/python/
+

bigos/lecture20251112/urllib/chuck-norris-db.py

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+import json
+import urllib.request
+import pprint
+
+# See https://www.icndb.com
+
+def get_joke():
+
+    url = "https://api.chucknorris.io/jokes/random?limitTo=nerdy "
+
+    response = urllib.request.urlopen(url)
+    result = json.loads(response.read())
+
+#    print(result)
+
+    return result
+
+def prettyPrintDictionary(myDict):
+    pprint.pprint(myDict)
+
+
+
+joke = get_joke()
+
+#prettyPrintDictionary(joke)
+
+print(joke['value'])
+
+

bigos/lecture20251112/urllib/iss/iss-1.py

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+
+import json
+import urllib.request
+import turtle
+
+import pprint
+
+import time
+
+url = 'http://api.open-notify.org/astros.json'
+response = urllib.request.urlopen(url)
+result = json.loads(response.read())
+
+print(result)
+
+pprint.pprint(result)

bigos/lecture20251112/urllib/iss/iss-3.py

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+
+import json
+import urllib.request
+import turtle
+
+import time
+
+url = 'http://api.open-notify.org/astros.json'
+response = urllib.request.urlopen(url)
+result = json.loads(response.read())
+import pprint
+
+print(result)
+
+print("People on the ISS: ",result['number'])
+people = result['people']
+#print(people)
+
+#for person in people:
+#    print(person)
+
+for person in people :
+    print(person['name'])
+
+
+url = 'http://api.open-notify.org/iss-now.json'
+response = urllib.request.urlopen(url)
+result = json.loads(response.read())
+
+print(result)
+
+location = result['iss_position']
+latitude = location['latitude']
+longitude = location['longitude']
+
+print('Latitude:  ',latitude)
+print('Longitude: ',longitude)

bigos/lecture20251112/urllib/iss/iss-4.py

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+
+import json
+import urllib.request
+import turtle
+
+import time
+
+url = 'http://api.open-notify.org/astros.json'
+response = urllib.request.urlopen(url)
+result = json.loads(response.read())
+
+print(result)
+
+print("People on the ISS: ",result['number'])
+people = result['people']
+#print(people)
+
+#for person in people:
+#    print(person)
+
+for person in people :
+    print(person['name'])
+
+
+url = 'http://api.open-notify.org/iss-now.json'
+response = urllib.request.urlopen(url)
+result = json.loads(response.read())
+
+print(result)
+
+location = result['iss_position']
+latitude = location['latitude']
+longitude = location['longitude']
+
+print('Latitude:  ',latitude)
+print('Longitude: ',longitude)
+
+screen = turtle.Screen()
+
+screen.setup(720,360)
+screen.setworldcoordinates(-180,-90,180,90)
+screen.bgpic('map.gif')
+
+
+screen.register_shape('iss.gif')
+iss = turtle.Turtle()
+iss.shape('iss.gif')
+iss.setheading(90)
+
+iss.penup()
+iss.goto(float(longitude),float(latitude) )
+
+# Print the map
+
+time.sleep(10)

bigos/lecture20251112/urllib/iss/iss.py

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+#!/bin/python3
+
+import json
+import turtle
+import urllib.request
+

bigos/lecture20251112/urllib/plot_football.py

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+"""
+========
+Football
+========
+
+Load football network in GML format and compute some network statistcs.
+
+Shows how to download GML graph in a zipped file, unpack it, and load
+into a NetworkX graph.
+
+Requires Internet connection to download the URL
+http://www-personal.umich.edu/~mejn/netdata/football.zip
+"""
+
+import urllib.request
+import io
+import zipfile
+
+import matplotlib.pyplot as plt
+import networkx as nx
+
+url = "http://www-personal.umich.edu/~mejn/netdata/football.zip"
+
+sock = urllib.request.urlopen(url)  # open URL
+s = io.BytesIO(sock.read())  # read into BytesIO "file"
+sock.close()
+
+zf = zipfile.ZipFile(s)  # zipfile object
+txt = zf.read("football.txt").decode()  # read info file
+gml = zf.read("football.gml").decode()  # read gml data
+# throw away bogus first line with # from mejn files
+gml = gml.split("\n")[1:]
+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(f"{n:20} {d:2}")
+
+options = {"node_color": "black", "node_size": 50, "linewidths": 0, "width": 0.1}
+
+pos = nx.spring_layout(G, seed=1969)  # Seed for reproducible layout
+nx.draw(G, pos, **options)
+plt.show()