Séparation data/scripts/résultats
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19 changed files with 4982 additions and 1149 deletions
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@ -1,46 +0,0 @@
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"""
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=========================
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Simple animation examples
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=========================
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This example contains two animations. The first is a random walk plot. The
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second is an image animation.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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def update_line(num, data, line):
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line.set_data(data[..., :num])
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return line,
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fig1 = plt.figure()
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data = np.random.rand(2, 25)
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l, = plt.plot([], [], 'r-')
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plt.xlim(0, 1)
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plt.ylim(0, 1)
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plt.xlabel('x')
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plt.title('test')
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line_ani = animation.FuncAnimation(fig1, update_line, 25, fargs=(data, l),
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interval=50, blit=True)
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# To save the animation, use the command: line_ani.save('lines.mp4')
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fig2 = plt.figure()
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x = np.arange(-9, 10)
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y = np.arange(-9, 10).reshape(-1, 1)
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base = np.hypot(x, y)
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ims = []
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for add in np.arange(15):
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ims.append((plt.pcolor(x, y, base + add, norm=plt.Normalize(0, 30)),))
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im_ani = animation.ArtistAnimation(fig2, ims, interval=50, repeat_delay=3000,
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blit=True)
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# To save this second animation with some metadata, use the following command:
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# im_ani.save('im.mp4', metadata={'artist':'Guido'})
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plt.show()
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1088
utils/freq.eps
1088
utils/freq.eps
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@ -2,7 +2,7 @@ using Statistics
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using CSV
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file_measure = "walk.csv"
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file_measure = joinpath(@__DIR__, "data", "walk.csv")
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measure = CSV.read(file_measure, header=false) |> Matrix{Float64}
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@ -15,5 +15,5 @@ print("Delta moyen :\t\t")
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print(Statistics.mean(deltas))
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println(" s")
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print("Écart type :\t\t")
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print((Statistics.std(deltas)))
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print(Statistics.std(deltas))
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println(" s");
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@ -6,7 +6,6 @@ import time
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import click
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#import rospy
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@click.command()
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@click.option('--output', default="output.csv", help='Number of greetings.')
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@ -2,8 +2,8 @@ using Plots
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using CSV
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pyplot()
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file_measure = "linear_x2.csv"
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file_input = "input_linear_x2.csv"
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file_measure = joinpath(@__DIR__, "data", "linear_x2.csv")
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file_input = joinpath(@__DIR__, "data", "input_linear_x2.csv")
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measure = CSV.read(file_measure, header=false) |> Matrix{Float64};
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command = CSV.read(file_input, header=false) |> Matrix{Float64};
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plot(
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measure[init_measure:end-end_measure,1],
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measure[init_measure:end-end_measure,2],
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title="plop",
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size=(1000, 600)
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title="Réponse indicielle de la boucle interne",
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reuse=false,
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size=(1000, 600),
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label="mesure"
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)
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s = sum([x * (measure[init_command + i,1] - measure[init_command + i-1,1]) for (i,x) in enumerate(measure[init_measure:end-end_measure,2])]) / (measure[end-end_measure,1] - measure[init_measure,1])
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plot!([measure[init_measure,1], measure[end-end_measure,1]], [s, s])
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plot!(
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command[init_command:end,1],
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command[init_command:end,2]
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command[init_command:end,2],
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label="commande"
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)
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savefig(joinpath(@__DIR__, "results", "internal_tuns.eps"))
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show()
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1370
utils/results/internal_tuns.eps
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utils/results/internal_tuns.eps
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3595
utils/results/mesure_vitesse_big_quadra.eps
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3595
utils/results/mesure_vitesse_big_quadra.eps
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@ -1,6 +1,6 @@
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%!PS-Adobe-3.0 EPSF-3.0
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%%Creator: matplotlib version 2.2.4, http://matplotlib.org/
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%%CreationDate: Sun Jun 2 14:33:03 2019
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%%CreationDate: Sun Jun 2 18:28:49 2019
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%%Orientation: portrait
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%%BoundingBox: -54 180 666 612
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%%EndComments
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@ -1,6 +1,6 @@
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%!PS-Adobe-3.0 EPSF-3.0
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%%Creator: matplotlib version 2.2.4, http://matplotlib.org/
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%%CreationDate: Sun Jun 2 14:33:02 2019
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%%CreationDate: Sun Jun 2 18:28:49 2019
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%%Orientation: portrait
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%%BoundingBox: -54 180 666 612
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%%EndComments
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@ -5,11 +5,12 @@ using CSV
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pyplot()
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# Parameters
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filename = "walk.csv" # input file
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filename = joinpath(@__DIR__, "data", "walk.csv") # input file
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h = 1/22 # sample time
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orders = [
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(title="quadratique", order=2, sizes=5:2:11),
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(title="cubique", order=3, sizes=5:2:11)
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(title="cubique", order=3, sizes=5:2:11),
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(title="big_quadra", order=2, sizes=11:10:51)
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]
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@ -62,6 +63,6 @@ for order in orders
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subplot=2
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)
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end
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savefig(string("mesure_vitesse_", order.title, ".eps"))
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savefig(joinpath(@__DIR__, "results", string("mesure_vitesse_", order.title, ".eps")))
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end
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show()
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