SENCE 2023 Examples - 1. Semester¶

Secret messages¶

In [1]:

from itertools import cycle
import base64

from itertools import cycle import base64

Key definition¶

In [2]:

KEY = """THIS IS A SUPER SECRET CODE. ONLY SHARE IT WITH A TRUSTED PERSON.
SHARE IT ONCE, BEFORE SENDING ANY MESSAGE. DO NOT SEND IT WITH THE ENCRYPTED MESSAGE.
It should be random and long.
This isn't random or very long. An alternative would be
   secrets.token_bytes(4096)
, written to a file.
This method is very secure for the first message, but weak if
multiple messages are encoded with the same key.
"""

KEY = """THIS IS A SUPER SECRET CODE. ONLY SHARE IT WITH A TRUSTED PERSON. SHARE IT ONCE, BEFORE SENDING ANY MESSAGE. DO NOT SEND IT WITH THE ENCRYPTED MESSAGE. It should be random and long. This isn't random or very long. An alternative would be secrets.token_bytes(4096) , written to a file. This method is very secure for the first message, but weak if multiple messages are encoded with the same key. """

Secret message¶

In [3]:

MESSAGE = """My super secret message. Just a test.😀🤯"""

MESSAGE = """My super secret message. Just a test.😀🤯"""

Functions¶

In [4]:

def encode_message(message: str, key: str = KEY) -> bytes:
    """Encode message with key as one-time pad"""
    pairs = zip(message.encode(), cycle(key.encode()))
    encrypted = [a ^ b for a, b in pairs]
    return base64.b85encode(bytes(encrypted))

def decode_message(encoded_message: bytes, key: str = KEY) -> str:
    """Decode message with key as one-time pad"""
    encoded_bytes = base64.b85decode(encoded_message)
    decrypted = bytes(a ^ b for a, b in
                      zip(encoded_bytes, cycle(key.encode())))
    return decrypted.decode()

def encode_message(message: str, key: str = KEY) -> bytes: """Encode message with key as one-time pad""" pairs = zip(message.encode(), cycle(key.encode())) encrypted = [a ^ b for a, b in pairs] return base64.b85encode(bytes(encrypted)) def decode_message(encoded_message: bytes, key: str = KEY) -> str: """Decode message with key as one-time pad""" encoded_bytes = base64.b85decode(encoded_message) decrypted = bytes(a ^ b for a, b in zip(encoded_bytes, cycle(key.encode()))) return decrypted.decode()

Encode¶

In [5]:

encoded_message = encode_message(MESSAGE)
encoded_message
# This message could be shared safely over an untrusted channel.

encoded_message = encode_message(MESSAGE) encoded_message # This message could be shared safely over an untrusted channel.

Out[5]:

b'88K-fRXH|NVN*6XA|NIJJ|Hk5Br`>AZw@eBRBtbAEkz(aZ=%|`$)vyY<^'

Decode¶

In [6]:

decode_message(encoded_message.decode())

decode_message(encoded_message.decode())

Out[6]:

'My super secret message. Just a test.😀🤯'

In [7]:

decode_message(b'88K-fRXH|NVN*6XA|NIJJ|Hk5Br`>AZw@eBRBtbAEkz(aZ=%|`$)vyY<^')

decode_message(b'88K-fRXH|NVN*6XA|NIJJ|Hk5Br`>AZw@eBRBtbAEkz(aZ=%|`$)vyY<^')

Out[7]:

'My super secret message. Just a test.😀🤯'

Merit-Order¶

Notebook : https://python.ericduminil.com/merit_order/

Contour Plots¶

Seaborn has been updated (current version in Anaconda : 0.12.2), and sns.kdeplot has a slightly different syntax than before

In [8]:

# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
 
# set seaborn style
sns.set_style("white")

# Basic 2D density plot
sns.kdeplot(data=df, x='sepal_width', y='sepal_length')
plt.show()

# libraries & dataset import seaborn as sns import matplotlib.pyplot as plt df = sns.load_dataset('iris') # set seaborn style sns.set_style("white") # Basic 2D density plot sns.kdeplot(data=df, x='sepal_width', y='sepal_length') plt.show()

In [9]:

# Custom the color, add shade and bandwidth
sns.kdeplot(data=df, x='sepal_width', y='sepal_length', cmap="Reds", fill=True, bw_adjust=.5)
plt.show()

# Custom the color, add shade and bandwidth sns.kdeplot(data=df, x='sepal_width', y='sepal_length', cmap="Reds", fill=True, bw_adjust=.5) plt.show()

In [10]:

# Add thresh parameter
sns.kdeplot(data=df, x='sepal_width', y='sepal_length', cmap="Blues", fill=True, thresh=0)
plt.show()

# Add thresh parameter sns.kdeplot(data=df, x='sepal_width', y='sepal_length', cmap="Blues", fill=True, thresh=0) plt.show()

Map with connections between cities¶

In [11]:

# libraries
#! pip install basemap
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt

# libraries #! pip install basemap from mpl_toolkits.basemap import Basemap import numpy as np import matplotlib.pyplot as plt

In [12]:

# Set the plot size for this notebook:
plt.rcParams["figure.figsize"]=15,12

# Set the plot size for this notebook: plt.rcParams["figure.figsize"]=15,12

In [13]:

# A basic map
m=Basemap(llcrnrlon=-100, llcrnrlat=20, urcrnrlon=30, urcrnrlat=70, projection='merc')
m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)
m.fillcontinents(color='grey', alpha=0.7, lake_color='grey')
m.drawcoastlines(linewidth=0.1, color="white");

# A basic map m=Basemap(llcrnrlon=-100, llcrnrlat=20, urcrnrlon=30, urcrnrlat=70, projection='merc') m.drawmapboundary(fill_color='#A6CAE0', linewidth=0) m.fillcontinents(color='grey', alpha=0.7, lake_color='grey') m.drawcoastlines(linewidth=0.1, color="white");

In [14]:

# Background map
m=Basemap(llcrnrlon=-100, llcrnrlat=20, urcrnrlon=30, urcrnrlat=70, projection='merc')
m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)
m.fillcontinents(color='grey', alpha=0.7, lake_color='grey')
m.drawcoastlines(linewidth=0.1, color="white")

# Add a connection between new york and London
startlat = 40.78; startlon = -73.98
arrlat = 51.53; arrlon = 0.08
m.drawgreatcircle(startlon, startlat, arrlon, arrlat, linewidth=2, color='orange');

# Background map m=Basemap(llcrnrlon=-100, llcrnrlat=20, urcrnrlon=30, urcrnrlat=70, projection='merc') m.drawmapboundary(fill_color='#A6CAE0', linewidth=0) m.fillcontinents(color='grey', alpha=0.7, lake_color='grey') m.drawcoastlines(linewidth=0.1, color="white") # Add a connection between new york and London startlat = 40.78; startlon = -73.98 arrlat = 51.53; arrlon = 0.08 m.drawgreatcircle(startlon, startlat, arrlon, arrlat, linewidth=2, color='orange');

In [15]:

# Dataframe: list of a few cities with their coordinates:
import pandas as pd
import pandas as pd
cities = {
    'city': ["Paris", "Melbourne", "Saint.Petersburg", "Abidjan", "Montreal", "Nairobi", "Salvador"],
    'lon': [2, 145, 30.32, -4.03, -73.57, 36.82, -38.5],
    'lat': [49, -38, 59.93, 5.33, 45.52, -1.29, -12.97]
    }
df = pd.DataFrame(cities, columns = ['city', 'lon', 'lat'])
df

# Dataframe: list of a few cities with their coordinates: import pandas as pd import pandas as pd cities = { 'city': ["Paris", "Melbourne", "Saint.Petersburg", "Abidjan", "Montreal", "Nairobi", "Salvador"], 'lon': [2, 145, 30.32, -4.03, -73.57, 36.82, -38.5], 'lat': [49, -38, 59.93, 5.33, 45.52, -1.29, -12.97] } df = pd.DataFrame(cities, columns = ['city', 'lon', 'lat']) df

Out[15]:

	city	lon	lat
0	Paris	2.00	49.00
1	Melbourne	145.00	-38.00
2	Saint.Petersburg	30.32	59.93
3	Abidjan	-4.03	5.33
4	Montreal	-73.57	45.52
5	Nairobi	36.82	-1.29
6	Salvador	-38.50	-12.97

In [16]:

# Background map
m=Basemap(llcrnrlon=-179, llcrnrlat=-60, urcrnrlon=179, urcrnrlat=70,  projection='cyl')
m.drawmapboundary(fill_color='white', linewidth=0)
m.fillcontinents(color='#f2f2f2', alpha=0.7)
m.drawcoastlines(linewidth=0.1, color="white")

# Loop on every pair of cities to add the connection
for startIndex, startRow in df.iterrows():
    for endIndex in range(startIndex + 1, len(df.index)):
        endRow = df.iloc[endIndex]
        # print(f"{startRow.city} -> {endRow.city}")
        m.drawgreatcircle(startRow.lon, startRow.lat, endRow.lon, endRow.lat, linewidth=1, color='#69b3a2');

# Add city names
for i, row in df.iterrows():
    plt.annotate(row.city, xy=m(row.lon+3, row.lat), verticalalignment='center')

# Background map m=Basemap(llcrnrlon=-179, llcrnrlat=-60, urcrnrlon=179, urcrnrlat=70, projection='cyl') m.drawmapboundary(fill_color='white', linewidth=0) m.fillcontinents(color='#f2f2f2', alpha=0.7) m.drawcoastlines(linewidth=0.1, color="white") # Loop on every pair of cities to add the connection for startIndex, startRow in df.iterrows(): for endIndex in range(startIndex + 1, len(df.index)): endRow = df.iloc[endIndex] # print(f"{startRow.city} -> {endRow.city}") m.drawgreatcircle(startRow.lon, startRow.lat, endRow.lon, endRow.lat, linewidth=1, color='#69b3a2'); # Add city names for i, row in df.iterrows(): plt.annotate(row.city, xy=m(row.lon+3, row.lat), verticalalignment='center')

Beeswarm¶

• https://python-graph-gallery.com/beeswarm/

In [17]:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

import seaborn as sns import matplotlib.pyplot as plt import numpy as np

In [18]:

my_variable = np.random.normal(loc=10, scale=5, size=500)

my_variable = np.random.normal(loc=10, scale=5, size=500)

In [19]:

# Create the swarm plot
sns.swarmplot(y=my_variable)

# Customization
plt.title('Swarm Plot of My Variable (y-axis)')  # Set the title
plt.ylabel('My variable')  # Set the label for the y-axis

plt.show() # Display the chart

# Create the swarm plot sns.swarmplot(y=my_variable) # Customization plt.title('Swarm Plot of My Variable (y-axis)') # Set the title plt.ylabel('My variable') # Set the label for the y-axis plt.show() # Display the chart

Network Charts¶

https://python-graph-gallery.com/500-network-chart-with-edge-bundling/

In [20]:

# Import useful libraries
import matplotlib.pyplot as plt
#! pip install networkx
import networkx as nx
#! pip install netgraph
from netgraph import Graph

# Import useful libraries import matplotlib.pyplot as plt #! pip install networkx import networkx as nx #! pip install netgraph from netgraph import Graph

In [21]:

# Create a modular graph (dummy data)
partition_sizes = [10, 20, 30, 40]
g = nx.random_partition_graph(partition_sizes, 0.5, 0.1)

# Create a modular graph (dummy data) partition_sizes = [10, 20, 30, 40] g = nx.random_partition_graph(partition_sizes, 0.5, 0.1)

In [22]:

%%capture --no-display
# ^ Hide annoying warning for this cell

# Build graph
Graph(g);

%%capture --no-display # ^ Hide annoying warning for this cell # Build graph Graph(g);

Out[22]:

<netgraph._main.Graph at 0x7f6fa0f05910>

In [23]:

node_to_community = dict()
node = 0
for community_id, size in enumerate(partition_sizes):
    for _ in range(size):
        node_to_community[node] = community_id
        node += 1

# Color nodes according to their community.
community_to_color = {
    0 : 'tab:blue',
    1 : 'tab:orange',
    2 : 'tab:green',
    3 : 'tab:red',
}
node_color = {node: community_to_color[community_id] \
              for node, community_id in node_to_community.items()}

node_to_community = dict() node = 0 for community_id, size in enumerate(partition_sizes): for _ in range(size): node_to_community[node] = community_id node += 1 # Color nodes according to their community. community_to_color = { 0 : 'tab:blue', 1 : 'tab:orange', 2 : 'tab:green', 3 : 'tab:red', } node_color = {node: community_to_color[community_id] \ for node, community_id in node_to_community.items()}

In [24]:

fig, ax = plt.subplots()
Graph(g,
      node_color=node_color, # indicates the community each belongs to  
      node_edge_width=0,     # no black border around nodes 
      edge_width=0.1,        # use thin edges, as they carry no information in this visualisation
      edge_alpha=0.5,        # low edge alpha values accentuates bundles as they appear darker than single edges
      node_layout='community', node_layout_kwargs=dict(node_to_community=node_to_community),
      ax=ax,
)
plt.show()

fig, ax = plt.subplots() Graph(g, node_color=node_color, # indicates the community each belongs to node_edge_width=0, # no black border around nodes edge_width=0.1, # use thin edges, as they carry no information in this visualisation edge_alpha=0.5, # low edge alpha values accentuates bundles as they appear darker than single edges node_layout='community', node_layout_kwargs=dict(node_to_community=node_to_community), ax=ax, ) plt.show()

Chess¶

In [25]:

#! pip import chess
import chess

#! pip import chess import chess

In [26]:

board = chess.Board()

board = chess.Board()

In [27]:

board

board

Out[27]:

In [28]:

board.legal_moves

board.legal_moves

Out[28]:

<LegalMoveGenerator at 0x7f6f99b6dbb0 (Nh3, Nf3, Nc3, Na3, h3, g3, f3, e3, d3, c3, b3, a3, h4, g4, f4, e4, d4, c4, b4, a4)>

In [29]:

chess.Move.from_uci("a8a1") in board.legal_moves

chess.Move.from_uci("a8a1") in board.legal_moves

Out[29]:

False

In [30]:

board.push_san("e4")

board.push_san("e5")

board.push_san("Qh5")

board.push_san("Nc6")

board.push_san("Bc4")

board.push_san("Nf6")

board.push_san("Qxf7")

board

board.push_san("e4") board.push_san("e5") board.push_san("Qh5") board.push_san("Nc6") board.push_san("Bc4") board.push_san("Nf6") board.push_san("Qxf7") board

Out[30]:

In [31]:

board.is_checkmate()

board.is_checkmate()

Out[31]:

True

Stock prices¶

In [32]:

#! pip install mplfinance
#! pip install yfinance
import mplfinance as mpf
import yfinance as yf #(for the dataset)
from datetime import datetime, timedelta

#! pip install mplfinance #! pip install yfinance import mplfinance as mpf import yfinance as yf #(for the dataset) from datetime import datetime, timedelta

In [33]:

today = datetime.today()
one_month_ago = today - timedelta(days=30)

# Define the stock symbol and date range
stock_symbol = "AAPL"  # Example: Apple Inc.

# Load historical data
stock_data = yf.download(stock_symbol, start=one_month_ago, end=today)

# plot
mpf.plot(stock_data, type='candle')

today = datetime.today() one_month_ago = today - timedelta(days=30) # Define the stock symbol and date range stock_symbol = "AAPL" # Example: Apple Inc. # Load historical data stock_data = yf.download(stock_symbol, start=one_month_ago, end=today) # plot mpf.plot(stock_data, type='candle')

[*********************100%***********************]  1 of 1 completed

/home/ricou/www/PythonWorkshop/venv/lib/python3.9/site-packages/yfinance/base.py:304: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  df["Dividends"].fillna(0, inplace=True)
/home/ricou/www/PythonWorkshop/venv/lib/python3.9/site-packages/yfinance/base.py:304: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  df["Dividends"].fillna(0, inplace=True)
/home/ricou/www/PythonWorkshop/venv/lib/python3.9/site-packages/yfinance/base.py:305: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  df["Stock Splits"].fillna(0, inplace=True)
/home/ricou/www/PythonWorkshop/venv/lib/python3.9/site-packages/yfinance/base.py:305: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  df["Stock Splits"].fillna(0, inplace=True)
/home/ricou/www/PythonWorkshop/venv/lib/python3.9/site-packages/yfinance/utils.py:367: FutureWarning: The 'unit' keyword in TimedeltaIndex construction is deprecated and will be removed in a future version. Use pd.to_timedelta instead.
  df.index += _pd.TimedeltaIndex(dst_error_hours, 'h')

Music with Python¶

https://python.ericduminil.com/Music%20with%20Python.html

Venn diagrams¶

In [34]:

#! pip install venn
from venn import venn

musicians = {
    "Members of The Beatles": {"Paul McCartney", "John Lennon", "George Harrison", "Ringo Starr"},
    "Guitarists": {"John Lennon", "George Harrison", "Jimi Hendrix", "Eric Clapton", "Carlos Santana"},
    "Played at Woodstock": {"Jimi Hendrix", "Carlos Santana", "Keith Moon"}
}
venn(musicians);

#! pip install venn from venn import venn musicians = { "Members of The Beatles": {"Paul McCartney", "John Lennon", "George Harrison", "Ringo Starr"}, "Guitarists": {"John Lennon", "George Harrison", "Jimi Hendrix", "Eric Clapton", "Carlos Santana"}, "Played at Woodstock": {"Jimi Hendrix", "Carlos Santana", "Keith Moon"} } venn(musicians);