Live Programming and Computing with Python, R, Sage, Octave, Maxima, Singular, Gap, GP, HTML & Macaulay2

[Eli]

Further testing of Keras:

Code: [Select all] [Expand/Collapse]

1. # import the necessary packages
2. from keras.applications import ResNet50
3. from keras.preprocessing.image import img_to_array
4. from keras.applications import imagenet_utils
5. from PIL import Image
6. import numpy as np
7. import flask
8. import io
9.  
10. # Initialize our Flask application and the Keras model
11. app = flask.Flask("__name__")
12. model = None
13. print("Hello World")

[Eli]

Test these Dash apps (taken from here)

Code: [Select all] [Expand/Collapse]

1. import dash
2. import dash_core_components as dcc
3. import dash_html_components as html
4.  
5. app = dash.Dash(' ')
6. colors = {
7.     'background': '#111111',
8.     'text': '#7FDBFF'
9. }
10. app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
11.     html.H1(
12.         children='Hello Dash',
13.         style={
14.             'textAlign': 'center',
15.             'color': colors['text']
16.         }
17.     ),
18.     html.Div(children='Dash: A web application framework for Python.', style={
19.         'textAlign': 'center',
20.         'color': colors['text']
21.     }),
22.     dcc.Graph(
23.         id='Graph1',
24.         figure={
25.             'data': [
26.                 {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
27.                 {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
28.             ],
29.             'layout': {
30.                 'plot_bgcolor': colors['background'],
31.                 'paper_bgcolor': colors['background'],
32.                 'font': {
33.                     'color': colors['text']
34.                 }
35.             }
36.         }
37.     )
38. ])
39.  
40. if __name__ == '__main__':
41.     app.run_server(debug=True)
42.    

Code: [Select all] [Expand/Collapse]

1. import dash
2. import dash_core_components as dcc
3. import dash_html_components as html
4. import pandas as pd
5. import plotly.graph_objs as go
6.  
7. app = dash.Dash(' ')
8.  
9. df = pd.read_csv(
10.     'https://gist.githubusercontent.com/chriddyp/' +
11.     '5d1ea79569ed194d432e56108a04d188/raw/' +
12.     'a9f9e8076b837d541398e999dcbac2b2826a81f8/'+
13.     'gdp-life-exp-2007.csv')
14.  
15.  
16. app.layout = html.Div([
17.     dcc.Graph(
18.         id='life-exp-vs-gdp',
19.         figure={
20.             'data': [
21.                 go.Scatter(
22.                     x=df[df['continent'] == i]['gdp per capita'],
23.                     y=df[df['continent'] == i]['life expectancy'],
24.                     text=df[df['continent'] == i]['country'],
25.                     mode='markers',
26.                     opacity=0.8,
27.                     marker={
28.                         'size': 15,
29.                         'line': {'width': 0.5, 'color': 'white'}
30.                     },
31.                     name=i
32.                 ) for i in df.continent.unique()
33.             ],
34.             'layout': go.Layout(
35.                 xaxis={'type': 'log', 'title': 'GDP Per Capita'},
36.                 yaxis={'title': 'Life Expectancy'},
37.                 margin={'l': 40, 'b': 40, 't': 10, 'r': 10},
38.                 legend={'x': 0, 'y': 1},
39.                 hovermode='closest'
40.             )
41.         }
42.     )
43. ])
44.  
45. if __name__ == '__main__':
46.     app.run_server()

[Eli]

Generate monkey saddle with SymPy:

Code: [Select all] [Expand/Collapse]

1. from __future__ import division
2. from sympy import *
3. x, y = symbols('x y')
4.  
5. from sympy.plotting import plot3d
6. monkey_saddle = x**3 - 3*x*y**2
7. p = plot3d(monkey_saddle, (x, -2, 2), (y, -2, 2))

[Eli]

deSolve: Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')

Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations. The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps. The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing. See more at CRAN

Test deSolve with this code here

Code: [Select all] [Expand/Collapse]

1. library(deSolve)
2. # Define parameters and initial conditions
3. a <- -8/3; b <- -10; c <- 28
4. #Create a three-valued vector of initial conditions using c function
5. yini <- c(X = 1, Y = 1, Z = 1)
6. Lorenz <- function(t, y, parms){with (as.list(y), {dX <- a*X + Y*Z; dY <- b*(Y - Z); dZ <- -X*Y + c*Y - Z;
7. list(c(dX, dY, dZ))})}
8.  
9. # We solve the IVP for 100 days producing the output after every 0.01 days
10. times <- seq(from = 0, to = 100, by = 0.01)
11. #Integrate
12. out <- ode(y = yini, times = times, func = Lorenz, parms = NULL)
13. # We check the output by printing out the first five lines
14. head(out, n = 5)
15. plot(out, lwd = 2)
16. # Plot variables Y versus X to generate the famous butterfly
17. plot(out[,"X"], out[,"Y"], type = "l", xlab = "X", ylab = "Y", main = "Butterfly")

See more examples here: solving-differential-equations-in-r-189

[Eli]

A piece of generative art built by Christophe Cariou with R.

Run this code here

Code: [Select all] [Expand/Collapse]

1. par(mfrow=c(1,1),mar=c(0,0,0,0),oma=c(1,1,1,1))
2. plot(0,0,type="n", xlim=c(-2,32), ylim=c(3,27),
3.     xaxs="i", yaxs="i", axes=FALSE, xlab=NA, ylab=NA,
4.     asp=1)
5.  
6. for (j in 0:35) {
7. for (i in 0:35) {
8.  
9.     R <- 8
10.     alpha <- j*10
11.     X <- 15+R*cos(alpha/180*pi)
12.     Y <- 15+R*sin(alpha/180*pi)
13.  
14.     r <- 3
15.     beta <- i*10
16.     x <- 15+r*cos(beta/180*pi)
17.     y <- 15+r*sin(beta/180*pi)
18.  
19.     d1 <- sqrt((X-x)^2+(Y-y)^2)
20.     xc <- x
21.     yc <- y
22.  
23.   n <- 180-atan((Y-y)/(X-x))/pi*180
24.  
25.     alpha2 <- -(0:n)
26.     theta <- alpha2/180*pi
27.  
28.     b <- d1/(n/180*pi)
29.     r <- b*theta
30.  
31.     x1 <- xc+r*cos(theta)
32.     y1 <- yc+r*sin(theta)
33.  
34.     lines(x1,y1, col="black")
35.  
36.     }
37. }

[Eli]

Visaulizing Sigmoid function $S(x) = \sigma(x) = \dfrac{1}{1+ e^{-x}}$ in Python.

Code: [Select all] [Expand/Collapse]

1. def sigmoid(x):
2.     a = []
3.     for item in x:
4.         a.append(1/(1+math.exp(-item)))
5.     return a
6.  
7. import matplotlib.pyplot as plt
8. import numpy as np
9.  
10. x = np.arange(-10., 10., 0.2)
11. sig = sigmoid(x)
12. plt.plot(x,sig)
13.  
14. xcoords = [0.0]
15. for xc in xcoords:
16.     plt.axvline(x=xc)
17.  
18. plt.ylim(top=1.0)  # adjust the top leaving bottom unchanged
19. plt.ylim(bottom=0.0)
20. plt.rc('grid', linestyle="-", color='black')
21. plt.grid(True)
22. plt.show()

[Eli]

This is a Python implementation of the TDA Mapper algorithm taken from here for visualization of high-dimensional data.

Code: [Select all] [Expand/Collapse]

1. # Import the class
2. import kmapper as km
3.  
4. # Some sample data
5. from sklearn import datasets
6. data, labels = datasets.make_circles(n_samples=5000, noise=0.03, factor=0.3)
7.  
8. # Initialize
9. mapper = km.KeplerMapper(verbose=1)
10.  
11. # Fit to and transform the data
12. projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis
13.  
14. # Create dictionary called 'graph' with nodes, edges and meta-information
15. graph = mapper.map(projected_data, data, cover=km.Cover(n_cubes=10))
16.  
17. # Visualize it
18. mapper.visualize(graph, path_html="make_circles_keplermapper_output.html",
19.                  title="make_circles(n_samples=5000, noise=0.03, factor=0.3)")

[Eli]

Test These codes from Matplotlib website

Code: [Select all] [Expand/Collapse]

1. print("Welcome to TSSFL ODF")
2. import matplotlib.pyplot as plt
3. import numpy as np
4. N = 5
5. menMeans = (20, 35, 30, 35, -27)
6. womenMeans = (25, 32, 34, 20, -25)
7. menStd = (2, 3, 4, 1, 2)
8. womenStd = (3, 5, 2, 3, 3)
9. ind = np.arange(N)    # the x locations for the groups
10. width = 0.35       # the width of the bars: can also be len(x) sequence
11.  
12. fig, ax = plt.subplots()
13. p1 = ax.bar(ind, menMeans, width, yerr=menStd, label='Men')
14. p2 = ax.bar(ind, womenMeans, width,
15.             bottom=menMeans, yerr=womenStd, label='Women')
16.  
17. ax.axhline(0, color='grey', linewidth=0.8)
18. ax.set_ylabel('Scores')
19. ax.set_title('Scores by group and gender')
20. ax.set_xticks(ind, labels=['G1', 'G2', 'G3', 'G4', 'G5'])
21. ax.legend()
22. # Label with label_type 'center' instead of the default 'edge'
23. ax.bar_label(p1, label_type='center')
24. ax.bar_label(p2, label_type='center')
25. ax.bar_label(p2)
26. plt.show()

Code 2

Code: [Select all] [Expand/Collapse]

1. import matplotlib.pyplot as plt
2. import numpy as np
3.  
4. N = 5
5. menMeans = (20, 35, 30, 35, -27)
6. womenMeans = (25, 32, 34, 20, -25)
7. menStd = (2, 3, 4, 1, 2)
8. womenStd = (3, 5, 2, 3, 3)
9. ind = np.arange(N)    # the x locations for the groups
10. width = 0.35       # the width of the bars: can also be len(x) sequence
11.  
12. # Fixing random state for reproducibility
13. np.random.seed(19680801)
14.  
15. # Example data
16. people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
17. y_pos = np.arange(len(people))
18. performance = 3 + 10 * np.random.rand(len(people))
19. error = np.random.rand(len(people))
20.  
21. fig, ax = plt.subplots()
22.  
23. hbars = ax.barh(y_pos, performance, xerr=error, align='center')
24. ax.set_yticks(y_pos, labels=people)
25. ax.invert_yaxis()  # labels read top-to-bottom
26. ax.set_xlabel('Performance')
27. ax.set_title('How fast do you want to go today?')
28.  
29. # Label with specially formatted floats
30. ax.bar_label(hbars, fmt='%.2f')
31. ax.set_xlim(right=15)  # adjust xlim to fit labels
32.  
33. plt.show()

Code 3

Code: [Select all] [Expand/Collapse]

1. import matplotlib.pyplot as plt
2.  
3. labels = ['G1', 'G2', 'G3', 'G4', 'G5']
4. men_means = [20, 35, 30, 35, 27]
5. women_means = [25, 32, 34, 20, 25]
6. men_std = [2, 3, 4, 1, 2]
7. women_std = [3, 5, 2, 3, 3]
8. width = 0.35       # the width of the bars: can also be len(x) sequence
9.  
10. fig, ax = plt.subplots()
11.  
12. ax.bar(labels, men_means, width, yerr=men_std, label='Men')
13. ax.bar(labels, women_means, width, yerr=women_std, bottom=men_means,
14.        label='Women')
15.  
16. ax.set_ylabel('Scores')
17. ax.set_title('Scores by group and gender')
18. ax.legend()
19.  
20. plt.show()

Code 4

Code: [Select all] [Expand/Collapse]

1. import matplotlib.pyplot as plt
2. import numpy as np
3.  
4. labels = ['G1', 'G2', 'G3', 'G4', 'G5']
5. men_means = [20, 34, 30, 35, 27]
6. women_means = [25, 32, 34, 20, 25]
7.  
8. x = np.arange(len(labels))  # the label locations
9. width = 0.35  # the width of the bars
10.  
11. fig, ax = plt.subplots()
12. rects1 = ax.bar(x - width/2, men_means, width, label='Men')
13. rects2 = ax.bar(x + width/2, women_means, width, label='Women')
14.  
15. # Add some text for labels, title and custom x-axis tick labels, etc.
16. ax.set_ylabel('Scores')
17. ax.set_title('Scores by group and gender')
18. ax.set_xticks(x, labels)
19. ax.legend()
20.  
21. ax.bar_label(rects1, padding=3)
22. ax.bar_label(rects2, padding=3)
23.  
24. fig.tight_layout()
25.  
26. plt.show()

Code 5 from here

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import matplotlib.pyplot as plt
3. import numpy as np
4. import seaborn as sns
5.  
6. df = pd.DataFrame({'Age': ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','85-89','90-94','95-99','100+'],
7.                     'Male': [-49228000, -61283000, -64391000, -52437000, -42955000, -44667000, -31570000, -23887000, -22390000, -20971000, -17685000, -15450000, -13932000, -11020000, -7611000, -4653000, -1952000, -625000, -116000, -14000, -1000],
8.                     'Female': [52367000, 64959000, 67161000, 55388000, 45448000, 47129000, 33436000, 26710000, 25627000, 23612000, 20075000, 16368000, 14220000, 10125000, 5984000, 3131000, 1151000, 312000, 49000, 4000, 0]})
9.  
10. AgeClass = ['100+','95-99','90-94','85-89','80-84','75-79','70-74','65-69','60-64','55-59','50-54','45-49','40-44','35-39','30-34','25-29','20-24','15-19','10-14','5-9','0-4']
11.  
12. bar_plot = sns.barplot(x='Male', y='Age', data=df, order=AgeClass, lw=0)
13. bar_plot = sns.barplot(x='Female', y='Age', data=df, order=AgeClass, lw=0)
14.  
15. bar_plot.set(xlabel="Population (hundreds of millions)", ylabel="Age-Group", title = "Population Pyramid")
16.  
17. plt.show()

[Eli]

Basemap has been replaced by Cartopy, test it with this code and data from here:

Code: [Select all] [Expand/Collapse]

1. import numpy as np
2. import matplotlib.pyplot as plt
3. import cartopy.crs as ccrs
4. import cartopy.feature as cfeature
5.  
6. kw = dict(color='#FF9900', linestyle='-', linewidth=1.5)
7. lon, lat = np.loadtxt('https://raw.githubusercontent.com/ocefpaf/2016-Python-course-CBO/master/notebooks/data/challenger_path.csv', delimiter=',', unpack=True)
8.  
9. def make_cartopy(projection=ccrs.Robinson(), figsize=(6, 4), resolution='110m'):
10.     fig, ax = plt.subplots(figsize=figsize, subplot_kw=dict(projection=projection))
11.     ax.set_global()
12.     ax.coastlines(resolution=resolution, color='k')
13.     # Only PlateCarree and Mercator plots are currently supported.
14.     gl = ax.gridlines(draw_labels=False)
15.     ax.add_feature(cfeature.LAND, facecolor='0.75')
16.     return fig, ax
17.  
18. fig, ax = make_cartopy(projection=ccrs.Robinson(), resolution='110m')
19. _ = ax.plot(lon, lat, transform=ccrs.Geodetic(), **kw)
20. plt.show()

Code: [Select all] [Expand/Collapse]

1. import cartopy.crs as ccrs
2. import matplotlib.pyplot as plt
3. ax = plt.axes(projection=ccrs.PlateCarree())
4. ax.coastlines()
5. # Save the plot by calling plt.savefig() BEFORE plt.show()
6. #plt.savefig('coastlines.pdf')
7. #plt.savefig('coastlines.png')
8. plt.show()]

Plot the Mollweide projection with the use of stock_img

Code: [Select all] [Expand/Collapse]

1. import cartopy.crs as ccrs
2. import matplotlib.pyplot as plt
3. textstr = 'Created at www.tssfl.com'
4. ax = plt.axes(projection=ccrs.Mollweide())
5. ax.stock_img()
6. plt.gcf().text(0.3, 0.80, textstr, fontsize=14, color='green')
7. plt.show()

Add data to the map

Code: [Select all] [Expand/Collapse]

1. import cartopy.crs as ccrs
2. import matplotlib.pyplot as plt
3.  
4. ax = plt.axes(projection=ccrs.PlateCarree())
5. ax.stock_img()
6.  
7. ny_lon, ny_lat = -75, 43
8. delhi_lon, delhi_lat = 77.23, 28.61
9.  
10. plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
11.          color='blue', linewidth=2, marker='o',
12.          transform=ccrs.Geodetic(),
13.          )
14.  
15. plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
16.          color='gray', linestyle='--',
17.          transform=ccrs.PlateCarree(),
18.          )
19.  
20. plt.text(ny_lon - 3, ny_lat - 12, 'New York',
21.          horizontalalignment='right',
22.          transform=ccrs.Geodetic())
23.  
24. plt.text(delhi_lon + 3, delhi_lat - 12, 'Delhi',
25.          horizontalalignment='left',
26.          transform=ccrs.Geodetic())
27.  
28. plt.show()

Ref: [1], [2]

[Eli]

Run this one line code:

Code: [Select all] [Expand/Collapse]

1. load("https://raw.githubusercontent.com/TSSFL/Demos/main/iris.py")