Commit 118ced84 authored by MEULE Samuel's avatar MEULE Samuel
Browse files

Modification de tout les jupyters

# Historique
* [X] ajouts des fichiers data NKE
* [X] retrait du fichier scriptNKE jupyter
* [X] ajout de la théorie linéaire dans spectral analysis
* [X] Modif TD7 vierge
* [X] Ajout TD lecture aquadopp
* [X] script NKE: Modification de la figure avec le temps pour que cela fontionne sur une ancienne version de matplotlib (sur les serveurs AMU)
* [X] Ajout package latex pour Mybinder dans requirements: ne marche pas
* [X] Ajout du fichier .ipynb pour TD NKE + fichier NKE
* [X] TD lecture de fichier NKE
* [X] Mise en place sous gitlab.osupytheas.fr
* [X] Ajout d'un debut de script TD7 (a remplir) + ajout des fichiers 2 à 10.txt (différentes series temporelles)
* [X] Ajout des notebooks TD2 à TD8 + ajout des fichiers
* [X] Ajout de requirements.txt pour gestion des dépendances
* [X] Ajout du fichier .py et du notebook jupyter .ipynb pour TD1
* [X] Création d'un git pour Stat
parent 6fa818bc
Modification des jupyter
Modification de tout les jupyters
# Historique
* [X] ajouts des fichiers data NKE
* [X] retrait du fichier scriptNKE jupyter
......
......@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entete"
"# Entête"
]
},
{
......@@ -137,12 +137,12 @@
"plt.ylabel(\"u\")\n",
"plt.axis([0,2,-2,2])\n",
"plt.grid()\n",
"plt.show()\n",
"\n",
"\n",
"# Save the figure\n",
"fig1.savefig('Figure_TD1',dpi=200)\n",
"# Show the figure\n",
"plt.show()\n"
"# Show the figure\n"
]
}
],
......
%% Cell type:markdown id: tags:
# Entete
# Entête
%% Cell type:markdown id: tags:
Cette entête permet d'indiquer que le script contient du langage python et que l'encodage est en utf-8 (encodage classique respectant des normes iso)
%% Cell type:code id: tags:
``` python
#!/usr/bin/python
# coding: utf-8
```
%% Cell type:markdown id: tags:
# Importation des modules
%% Cell type:markdown id: tags:
Permet d'importer les modules python nécessaires à la réalisation du code
%% Cell type:code id: tags:
``` python
#################################################"
## MODULES #####"
#################################################"
import matplotlib.pyplot as plt
import numpy as np
#################################################"
```
%% Cell type:markdown id: tags:
# Mise en place des paramètres
%% Cell type:markdown id: tags:
Il s'agit ici des paramètres liés aux différentes fonctions
%% Cell type:code id: tags:
``` python
# Parameters
N = 500 # Number of sampling
Tmax = 2.0 # Max time
Te = Tmax/N # Delta time between each measurements
f1=1 # Acquisition frequency
t = np.arange(0, Tmax, Te) # Time vector
```
%% Cell type:markdown id: tags:
# Définition des fonctions
%% Cell type:code id: tags:
``` python
# Functions
u1=0.5*1.0*np.cos(2*np.pi*f1*t)
u2=0.3*np.cos(2*2*np.pi*f1*t-np.pi/3)
u=u1+u2
```
%% Cell type:markdown id: tags:
# Figures
%% Cell type:code id: tags:
``` python
# Figure
fig1=plt.figure(figsize=(10,5))
plt.plot(t,u, 'k', label='u')
#plt.hold(True)
plt.plot(t,u1, 'r--', label='u1')
plt.plot(t,u2, 'g--', label='u2')
plt.legend()
plt.xlabel("t")
plt.ylabel("u")
plt.axis([0,2,-2,2])
plt.grid()
plt.show()
# Save the figure
fig1.savefig('Figure_TD1',dpi=200)
# Show the figure
plt.show()
```
%%%% Output: display_data
......
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entête"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cette entête permet d'indiquer que le script contient du langage python et que l'encodage est en utf-8 (encodage classique respectant des normes iso)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/python\n",
"# coding: utf-8\n",
"\n",
"# coding: utf-8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importation des modules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Permet d'importer les modules python nécessaires à la réalisation du code"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#################################################\" \n",
"## MODULES #####\"\n",
"#################################################\"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"#################################################\"\n",
"\n",
"#################################################\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mise en place des paramètres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il s'agit ici des paramètres liés aux différentes fonctions "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Parameters\n",
"\n",
"N = 500 # Number of sampling\n",
"Tmax = 2.0 # Max time\n",
"Te = Tmax/N # Delta time between each measurements\n",
"f1=1 # Acquisition frequency\n",
"t = np.arange(0, Tmax, Te) # Time vector\n",
"\n",
"t = np.arange(0, Tmax, Te) # Time vector\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Définition des fonctions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Functions\n",
"u1=0.5*1.0*np.cos(2*np.pi*f1*t)\n",
"u2=0.3*np.cos(2*2*np.pi*f1*t-np.pi/3)\n",
"u=u1+u2\n",
"\n",
"u=u1+u2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse de fourrier"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# fft analysis\n",
"Puu=np.abs(np.fft.fft(u,N)/N)**2 # Density spectrum\n",
"freqs=np.linspace(0,1/Te,len(Puu)) # frequencies\n",
"\n",
"freqs=np.linspace(0,1/Te,len(Puu)) # frequencies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figures"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Figure\n",
"fig1=plt.figure(figsize=(10,5))\n",
"plt.plot(freqs,Puu, 'ko-', label='u')\n",
......@@ -49,9 +159,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "myenv",
"language": "python",
"name": "python3"
"name": "myenv"
},
"language_info": {
"codemirror_mode": {
......
%% Cell type:markdown id: tags:
# Entête
%% Cell type:markdown id: tags:
Cette entête permet d'indiquer que le script contient du langage python et que l'encodage est en utf-8 (encodage classique respectant des normes iso)
%% Cell type:code id: tags:
``` python
#!/usr/bin/python
# coding: utf-8
```
%% Cell type:markdown id: tags:
# Importation des modules
%% Cell type:markdown id: tags:
Permet d'importer les modules python nécessaires à la réalisation du code
%% Cell type:code id: tags:
``` python
#################################################"
## MODULES #####"
#################################################"
import matplotlib.pyplot as plt
import numpy as np
#################################################"
```
# Parameters
%% Cell type:markdown id: tags:
# Mise en place des paramètres
%% Cell type:markdown id: tags:
Il s'agit ici des paramètres liés aux différentes fonctions
%% Cell type:code id: tags:
``` python
# Parameters
N = 500 # Number of sampling
Tmax = 2.0 # Max time
Te = Tmax/N # Delta time between each measurements
f1=1 # Acquisition frequency
t = np.arange(0, Tmax, Te) # Time vector
```
%% Cell type:markdown id: tags:
# Définition des fonctions
%% Cell type:code id: tags:
``` python
# Functions
u1=0.5*1.0*np.cos(2*np.pi*f1*t)
u2=0.3*np.cos(2*2*np.pi*f1*t-np.pi/3)
u=u1+u2
```
%% Cell type:markdown id: tags:
# Analyse de fourrier
%% Cell type:code id: tags:
``` python
# fft analysis
Puu=np.abs(np.fft.fft(u,N)/N)**2 # Density spectrum
freqs=np.linspace(0,1/Te,len(Puu)) # frequencies
```
%% Cell type:markdown id: tags:
# Figures
%% Cell type:code id: tags:
``` python
# Figure
fig1=plt.figure(figsize=(10,5))
plt.plot(freqs,Puu, 'ko-', label='u')
plt.axis([0,11,0,0.1])
plt.xlabel("Frequency (Hz)")
plt.ylabel("Power spectrum")
plt.grid()
plt.show()
# Save the figure
fig1.savefig('Figure_TD2',dpi=200)
```
%%%% Output: display_data
......
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entête"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cette entête permet d'indiquer que le script contient du langage python et que l'encodage est en utf-8 (encodage classique respectant des normes iso)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/python\n",
"# coding: utf-8\n",
"\n",
"# coding: utf-8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importation des modules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Permet d'importer les modules python nécessaires à la réalisation du code"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#################################################\" \n",
"## MODULES #####\"\n",
"#################################################\"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"#################################################\"\n",
"\n",
"#################################################\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mise en place des paramètres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il s'agit ici des paramètres liés aux différentes fonctions "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Parameters\n",
"N = 500 # Number of sampling\n",
"Tmax = 2.0 # Max time\n",
"Te = Tmax/N # Delta time between each measurements\n",
"f1=1 # Acquisition frequency\n",
"t = np.arange(0, Tmax, Te) # Time vector\n",
"\n",
"\n",
"t = np.arange(0, Tmax, Te) # Time vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Définition des fonctions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Functions\n",
"u1=2*1.0*np.cos(2*np.pi*f1*t)\n",
"u2=2*np.cos(2*2*np.pi*f1*t-np.pi/3)\n",
"u=u1+u2\n",
"\n",
"u=u1+u2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse statistique"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Analyse statistique\n",
"nbrv=(np.diff(np.sign(u)) < 0).sum()# Nbr d'oscillations\n",
"hist, bin_edges = np.histogram(u, bins=20) #Fonction de repartition des hauteurs de vagues. \n",
......@@ -36,9 +119,35 @@
"F = np.array(range(N))*100/float(N)\n",
"H13=np.mean(X[np.where(F>66)]) # Calcul de la moyenne du tiers superieur des oscillations\n",
"fu=np.where((np.diff(np.sign(u)) < 0))\n",
"Tm=np.mean(np.diff(fu))*Te # Calcul de la periode moyenne\n",
"\n",
"\n",
"Tm=np.mean(np.diff(fu))*Te # Calcul de la periode moyenne"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figures"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Figure\n",
"fig1=plt.figure(figsize=(10,5))\n",
"plt.bar(bin_edges[:-1], hist, width=bin_edges[1]-bin_edges[0], color='red', alpha=0.5, label='Histogramm')\n",
......@@ -53,15 +162,15 @@
"plt.show()\n",
"\n",
"# Save the figure\n",
"fig1.savefig('Figure_TD3',dpi=200)\n"
"fig1.savefig('Figure_TD3',dpi=200)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "myenv",
"language": "python",
"name": "python3"
"name": "myenv"
},
"language_info": {
"codemirror_mode": {
......
%% Cell type:markdown id: tags:
# Entête
%% Cell type:markdown id: tags:
Cette entête permet d'indiquer que le script contient du langage python et que l'encodage est en utf-8 (encodage classique respectant des normes iso)
%% Cell type:code id: tags:
``` python
#!/usr/bin/python
# coding: utf-8
```
%% Cell type:markdown id: tags:
# Importation des modules
%% Cell type:markdown id: tags:
Permet d'importer les modules python nécessaires à la réalisation du code
%% Cell type:code id: tags:
``` python
#################################################"
## MODULES #####"
#################################################"
import matplotlib.pyplot as plt
import numpy as np
#################################################"
```
%% Cell type:markdown id: tags:
# Mise en place des paramètres
%% Cell type:markdown id: tags:
Il s'agit ici des paramètres liés aux différentes fonctions
%% Cell type:code id: tags:
``` python
# Parameters
N = 500 # Number of sampling
Tmax = 2.0 # Max time
Te = Tmax/N # Delta time between each measurements
f1=1 # Acquisition frequency
t = np.arange(0, Tmax, Te) # Time vector
```
%% Cell type:markdown id: tags:
# Définition des fonctions
%% Cell type:code id: tags:
``` python
# Functions
u1=2*1.0*np.cos(2*np.pi*f1*t)
u2=2*np.cos(2*2*np.pi*f1*t-np.pi/3)
u=u1+u2
```
%% Cell type:markdown id: tags:
# Analyse statistique
%% Cell type:code id: tags:
``` python
# Analyse statistique
nbrv=(np.diff(np.sign(u)) < 0).sum()# Nbr d'oscillations
hist, bin_edges = np.histogram(u, bins=20) #Fonction de repartition des hauteurs de vagues.
X = np.sort(u)
F = np.array(range(N))*100/float(N)
H13=np.mean(X[np.where(F>66)]) # Calcul de la moyenne du tiers superieur des oscillations
fu=np.where((np.diff(np.sign(u)) < 0))
Tm=np.mean(np.diff(fu))*Te # Calcul de la periode moyenne
```
%% Cell type:markdown id: tags:
# Figures
%% Cell type:code id: tags:
``` python
# Figure
fig1=plt.figure(figsize=(10,5))
plt.bar(bin_edges[:-1], hist, width=bin_edges[1]-bin_edges[0], color='red', alpha=0.5, label='Histogramm')
plt.plot(X, F, label=u'cumulative frequency')
plt.xlabel("oscillation amplitude")
plt.ylabel(u"Probability")
titre='Amplitude histogram: H1/3=' + str(np.around(H13,2))+'; Tm='+str(np.around(Tm,2))
plt.title(titre)
plt.legend()
plt.grid()
plt.ylim(0,100)
plt.show()
# Save the figure
fig1.savefig('Figure_TD3',dpi=200)
```
%%%% Output: display_data
......
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entête"
]