- # import the necessary packages
- from keras.applications import ResNet50
- from keras.preprocessing.image import img_to_array
- from keras.applications import imagenet_utils
- from PIL import Image
- import numpy as np
- import flask
- import io
- # Initialize our Flask application and the Keras model
- app = flask.Flask("__name__")
- model = None
- print("Hello World")
-
- Active Topics
-
-
- by Eli 1 day ago Re: What is in Your Mind? View the latest post Replies 728 Views 322626
- by Eli 2 days ago Dr Wahome: How World Health Organization (WHO) is Doing Bad Things View the latest post Replies 1 Views 52
- by Eli 3 days ago Introduction to Abstract Algebra View the latest post Replies 4 Views 10961
- by Eli 4 days ago All in One: YouTube, TED, X, Facebook and Instagram Reels, Videos, Images and Text Posts View the latest post Replies 333 Views 50165
- by Eli 4 days ago Generating SSH Key and Adding it to the ssh-agent for Authentication on GitHub View the latest post Replies 2 Views 1138
- by Eli 6 days ago Russia Invades Ukraine View the latest post Replies 668 Views 256105
- by Eli 1 week ago How AI Could Empower any Business View the latest post Replies 1 Views 230
- by Eli 1 week ago Pondering Big Cosmology Questions Through Lectures and Dialogues View the latest post Replies 35 Views 62085
- by Eli 1 week ago The U.S - China Rivalry, Taiwan and Hong Kong View the latest post Replies 1 Views 319
- by Eli 1 week ago Generative AI for Texts and Images View the latest post Replies 2 Views 1400
-
Live Programming and Computing with Python, R, Sage, Octave, Maxima, Singular, Gap, GP, HTML & Macaulay2
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
Further testing of Keras:
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
Test these Dash apps (taken from here)
- import dash
- import dash_core_components as dcc
- import dash_html_components as html
- app = dash.Dash(' ')
- colors = {
- 'background': '#111111',
- 'text': '#7FDBFF'
- }
- app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
- html.H1(
- children='Hello Dash',
- style={
- 'textAlign': 'center',
- 'color': colors['text']
- }
- ),
- html.Div(children='Dash: A web application framework for Python.', style={
- 'textAlign': 'center',
- 'color': colors['text']
- }),
- dcc.Graph(
- id='Graph1',
- figure={
- 'data': [
- {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
- {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
- ],
- 'layout': {
- 'plot_bgcolor': colors['background'],
- 'paper_bgcolor': colors['background'],
- 'font': {
- 'color': colors['text']
- }
- }
- }
- )
- ])
- if __name__ == '__main__':
- app.run_server(debug=True)
- import dash
- import dash_core_components as dcc
- import dash_html_components as html
- import pandas as pd
- import plotly.graph_objs as go
- app = dash.Dash(' ')
- df = pd.read_csv(
- 'https://gist.githubusercontent.com/chriddyp/' +
- '5d1ea79569ed194d432e56108a04d188/raw/' +
- 'a9f9e8076b837d541398e999dcbac2b2826a81f8/'+
- 'gdp-life-exp-2007.csv')
- app.layout = html.Div([
- dcc.Graph(
- id='life-exp-vs-gdp',
- figure={
- 'data': [
- go.Scatter(
- x=df[df['continent'] == i]['gdp per capita'],
- y=df[df['continent'] == i]['life expectancy'],
- text=df[df['continent'] == i]['country'],
- mode='markers',
- opacity=0.8,
- marker={
- 'size': 15,
- 'line': {'width': 0.5, 'color': 'white'}
- },
- name=i
- ) for i in df.continent.unique()
- ],
- 'layout': go.Layout(
- xaxis={'type': 'log', 'title': 'GDP Per Capita'},
- yaxis={'title': 'Life Expectancy'},
- margin={'l': 40, 'b': 40, 't': 10, 'r': 10},
- legend={'x': 0, 'y': 1},
- hovermode='closest'
- )
- }
- )
- ])
- if __name__ == '__main__':
- app.run_server()
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
Generate monkey saddle with SymPy:
- from __future__ import division
- from sympy import *
- x, y = symbols('x y')
- from sympy.plotting import plot3d
- monkey_saddle = x**3 - 3*x*y**2
- p = plot3d(monkey_saddle, (x, -2, 2), (y, -2, 2))
- Attachments
-
- monkey_saddle.png
- (161.1 KiB) Not downloaded yet
- monkey_saddle.png
- (161.1 KiB) Not downloaded yet
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
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
See more examples here: solving-differential-equations-in-r-189
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
- library(deSolve)
- # Define parameters and initial conditions
- a <- -8/3; b <- -10; c <- 28
- #Create a three-valued vector of initial conditions using c function
- yini <- c(X = 1, Y = 1, Z = 1)
- Lorenz <- function(t, y, parms){with (as.list(y), {dX <- a*X + Y*Z; dY <- b*(Y - Z); dZ <- -X*Y + c*Y - Z;
- list(c(dX, dY, dZ))})}
- # We solve the IVP for 100 days producing the output after every 0.01 days
- times <- seq(from = 0, to = 100, by = 0.01)
- #Integrate
- out <- ode(y = yini, times = times, func = Lorenz, parms = NULL)
- # We check the output by printing out the first five lines
- head(out, n = 5)
- plot(out, lwd = 2)
- # Plot variables Y versus X to generate the famous butterfly
- plot(out[,"X"], out[,"Y"], type = "l", xlab = "X", ylab = "Y", main = "Butterfly")
See more examples here: solving-differential-equations-in-r-189
- Attachments
-
- Lorenz_sys.png (36.9 KiB) Viewed 2707 times
- Lorenz_sys.png (36.9 KiB) Viewed 2707 times
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
A piece of generative art built by Christophe Cariou with R.
Run this code here
Run this code here
- par(mfrow=c(1,1),mar=c(0,0,0,0),oma=c(1,1,1,1))
- plot(0,0,type="n", xlim=c(-2,32), ylim=c(3,27),
- xaxs="i", yaxs="i", axes=FALSE, xlab=NA, ylab=NA,
- asp=1)
- for (j in 0:35) {
- for (i in 0:35) {
- R <- 8
- alpha <- j*10
- X <- 15+R*cos(alpha/180*pi)
- Y <- 15+R*sin(alpha/180*pi)
- r <- 3
- beta <- i*10
- x <- 15+r*cos(beta/180*pi)
- y <- 15+r*sin(beta/180*pi)
- d1 <- sqrt((X-x)^2+(Y-y)^2)
- xc <- x
- yc <- y
- n <- 180-atan((Y-y)/(X-x))/pi*180
- alpha2 <- -(0:n)
- theta <- alpha2/180*pi
- b <- d1/(n/180*pi)
- r <- b*theta
- x1 <- xc+r*cos(theta)
- y1 <- yc+r*sin(theta)
- lines(x1,y1, col="black")
- }
- }
- Attachments
-
- Snail.png (49.14 KiB) Viewed 2698 times
- Snail.png (49.14 KiB) Viewed 2698 times
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
Visaulizing Sigmoid function $S(x) = \sigma(x) = \dfrac{1}{1+ e^{-x}}$ in Python.
- def sigmoid(x):
- a = []
- for item in x:
- a.append(1/(1+math.exp(-item)))
- return a
- import matplotlib.pyplot as plt
- import numpy as np
- x = np.arange(-10., 10., 0.2)
- sig = sigmoid(x)
- plt.plot(x,sig)
- xcoords = [0.0]
- for xc in xcoords:
- plt.axvline(x=xc)
- plt.ylim(top=1.0) # adjust the top leaving bottom unchanged
- plt.ylim(bottom=0.0)
- plt.rc('grid', linestyle="-", color='black')
- plt.grid(True)
- plt.show()
- Attachments
-
- sigmoid.png
- (16.93 KiB) Not downloaded yet
- sigmoid.png
- (16.93 KiB) Not downloaded yet
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
This is a Python implementation of the TDA Mapper algorithm taken from here for visualization of high-dimensional data.
- # Import the class
- import kmapper as km
- # Some sample data
- from sklearn import datasets
- data, labels = datasets.make_circles(n_samples=5000, noise=0.03, factor=0.3)
- # Initialize
- mapper = km.KeplerMapper(verbose=1)
- # Fit to and transform the data
- projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis
- # Create dictionary called 'graph' with nodes, edges and meta-information
- graph = mapper.map(projected_data, data, cover=km.Cover(n_cubes=10))
- # Visualize it
- mapper.visualize(graph, path_html="make_circles_keplermapper_output.html",
- title="make_circles(n_samples=5000, noise=0.03, factor=0.3)")
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
Test These codes from Matplotlib website
Code 2
Code 3
Code 4
Code 5 from here
- print("Welcome to TSSFL ODF")
- import matplotlib.pyplot as plt
- import numpy as np
- N = 5
- menMeans = (20, 35, 30, 35, -27)
- womenMeans = (25, 32, 34, 20, -25)
- menStd = (2, 3, 4, 1, 2)
- womenStd = (3, 5, 2, 3, 3)
- ind = np.arange(N) # the x locations for the groups
- width = 0.35 # the width of the bars: can also be len(x) sequence
- fig, ax = plt.subplots()
- p1 = ax.bar(ind, menMeans, width, yerr=menStd, label='Men')
- p2 = ax.bar(ind, womenMeans, width,
- bottom=menMeans, yerr=womenStd, label='Women')
- ax.axhline(0, color='grey', linewidth=0.8)
- ax.set_ylabel('Scores')
- ax.set_title('Scores by group and gender')
- ax.set_xticks(ind, labels=['G1', 'G2', 'G3', 'G4', 'G5'])
- ax.legend()
- # Label with label_type 'center' instead of the default 'edge'
- ax.bar_label(p1, label_type='center')
- ax.bar_label(p2, label_type='center')
- ax.bar_label(p2)
- plt.show()
Code 2
- import matplotlib.pyplot as plt
- import numpy as np
- N = 5
- menMeans = (20, 35, 30, 35, -27)
- womenMeans = (25, 32, 34, 20, -25)
- menStd = (2, 3, 4, 1, 2)
- womenStd = (3, 5, 2, 3, 3)
- ind = np.arange(N) # the x locations for the groups
- width = 0.35 # the width of the bars: can also be len(x) sequence
- # Fixing random state for reproducibility
- np.random.seed(19680801)
- # Example data
- people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
- y_pos = np.arange(len(people))
- performance = 3 + 10 * np.random.rand(len(people))
- error = np.random.rand(len(people))
- fig, ax = plt.subplots()
- hbars = ax.barh(y_pos, performance, xerr=error, align='center')
- ax.set_yticks(y_pos, labels=people)
- ax.invert_yaxis() # labels read top-to-bottom
- ax.set_xlabel('Performance')
- ax.set_title('How fast do you want to go today?')
- # Label with specially formatted floats
- ax.bar_label(hbars, fmt='%.2f')
- ax.set_xlim(right=15) # adjust xlim to fit labels
- plt.show()
Code 3
- import matplotlib.pyplot as plt
- labels = ['G1', 'G2', 'G3', 'G4', 'G5']
- men_means = [20, 35, 30, 35, 27]
- women_means = [25, 32, 34, 20, 25]
- men_std = [2, 3, 4, 1, 2]
- women_std = [3, 5, 2, 3, 3]
- width = 0.35 # the width of the bars: can also be len(x) sequence
- fig, ax = plt.subplots()
- ax.bar(labels, men_means, width, yerr=men_std, label='Men')
- ax.bar(labels, women_means, width, yerr=women_std, bottom=men_means,
- label='Women')
- ax.set_ylabel('Scores')
- ax.set_title('Scores by group and gender')
- ax.legend()
- plt.show()
Code 4
- import matplotlib.pyplot as plt
- import numpy as np
- labels = ['G1', 'G2', 'G3', 'G4', 'G5']
- men_means = [20, 34, 30, 35, 27]
- women_means = [25, 32, 34, 20, 25]
- x = np.arange(len(labels)) # the label locations
- width = 0.35 # the width of the bars
- fig, ax = plt.subplots()
- rects1 = ax.bar(x - width/2, men_means, width, label='Men')
- rects2 = ax.bar(x + width/2, women_means, width, label='Women')
- # Add some text for labels, title and custom x-axis tick labels, etc.
- ax.set_ylabel('Scores')
- ax.set_title('Scores by group and gender')
- ax.set_xticks(x, labels)
- ax.legend()
- ax.bar_label(rects1, padding=3)
- ax.bar_label(rects2, padding=3)
- fig.tight_layout()
- plt.show()
Code 5 from here
- import pandas as pd
- import matplotlib.pyplot as plt
- import numpy as np
- import seaborn as sns
- 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+'],
- 'Male': [-49228000, -61283000, -64391000, -52437000, -42955000, -44667000, -31570000, -23887000, -22390000, -20971000, -17685000, -15450000, -13932000, -11020000, -7611000, -4653000, -1952000, -625000, -116000, -14000, -1000],
- 'Female': [52367000, 64959000, 67161000, 55388000, 45448000, 47129000, 33436000, 26710000, 25627000, 23612000, 20075000, 16368000, 14220000, 10125000, 5984000, 3131000, 1151000, 312000, 49000, 4000, 0]})
- 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']
- bar_plot = sns.barplot(x='Male', y='Age', data=df, order=AgeClass, lw=0)
- bar_plot = sns.barplot(x='Female', y='Age', data=df, order=AgeClass, lw=0)
- bar_plot.set(xlabel="Population (hundreds of millions)", ylabel="Age-Group", title = "Population Pyramid")
- plt.show()
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
- Eli
- Senior Expert Member
- Reactions: 185
- Posts: 5446
- Joined: 9 years ago
- Location: Tanzania
- Has thanked: 75 times
- Been thanked: 88 times
- Contact:
Basemap has been replaced by Cartopy, test it with this code and data from here:
Plot the Mollweide projection with the use of stock_img
Add data to the map
Ref: [1], [2]
- import numpy as np
- import matplotlib.pyplot as plt
- import cartopy.crs as ccrs
- import cartopy.feature as cfeature
- kw = dict(color='#FF9900', linestyle='-', linewidth=1.5)
- lon, lat = np.loadtxt('https://raw.githubusercontent.com/ocefpaf/2016-Python-course-CBO/master/notebooks/data/challenger_path.csv', delimiter=',', unpack=True)
- def make_cartopy(projection=ccrs.Robinson(), figsize=(6, 4), resolution='110m'):
- fig, ax = plt.subplots(figsize=figsize, subplot_kw=dict(projection=projection))
- ax.set_global()
- ax.coastlines(resolution=resolution, color='k')
- # Only PlateCarree and Mercator plots are currently supported.
- gl = ax.gridlines(draw_labels=False)
- ax.add_feature(cfeature.LAND, facecolor='0.75')
- return fig, ax
- fig, ax = make_cartopy(projection=ccrs.Robinson(), resolution='110m')
- _ = ax.plot(lon, lat, transform=ccrs.Geodetic(), **kw)
- plt.show()
- import cartopy.crs as ccrs
- import matplotlib.pyplot as plt
- ax = plt.axes(projection=ccrs.PlateCarree())
- ax.coastlines()
- # Save the plot by calling plt.savefig() BEFORE plt.show()
- #plt.savefig('coastlines.pdf')
- #plt.savefig('coastlines.png')
- plt.show()]
Plot the Mollweide projection with the use of stock_img
- import cartopy.crs as ccrs
- import matplotlib.pyplot as plt
- textstr = 'Created at www.tssfl.com'
- ax = plt.axes(projection=ccrs.Mollweide())
- ax.stock_img()
- plt.gcf().text(0.3, 0.80, textstr, fontsize=14, color='green')
- plt.show()
Add data to the map
- import cartopy.crs as ccrs
- import matplotlib.pyplot as plt
- ax = plt.axes(projection=ccrs.PlateCarree())
- ax.stock_img()
- ny_lon, ny_lat = -75, 43
- delhi_lon, delhi_lat = 77.23, 28.61
- plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
- color='blue', linewidth=2, marker='o',
- transform=ccrs.Geodetic(),
- )
- plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
- color='gray', linestyle='--',
- transform=ccrs.PlateCarree(),
- )
- plt.text(ny_lon - 3, ny_lat - 12, 'New York',
- horizontalalignment='right',
- transform=ccrs.Geodetic())
- plt.text(delhi_lon + 3, delhi_lat - 12, 'Delhi',
- horizontalalignment='left',
- transform=ccrs.Geodetic())
- plt.show()
Ref: [1], [2]
0
TSSFL -- A Creative Journey Towards Infinite Possibilities!
-
- Similar Topics
- Replies
- Views
- Last post
-
- Information
-
Who is online
Users browsing this forum: No registered users and 0 guests