Commit ae2c5149 authored by Lorenz Halt's avatar Lorenz Halt 🔀
Browse files

data visualizer i want to keep (versuchshalle)

parent 206a54d2
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -13,25 +13,151 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"local_path = \"/home/ipa325/Documents\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def read(file):\n",
" with open(file, 'r') as f:\n",
" header_str = f.readline().strip()\n",
" data = np.genfromtxt(file,\n",
" delimiter=',',\n",
" autostrip=True,\n",
" skip_header=1)\n",
" return data, header_str"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"local_path = \"/home/victor\""
"def error_convergence(data):\n",
" y = np.linalg.norm(data[:,1:], ord=2, axis=0)\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"data_path = local_path + \"/Store\"\n",
"meas_path = local_path + \"/ilc_Meas\"\n",
"data_path = local_path + \"/test\"\n",
"plot_path = local_path + \"/Plots\"\n",
"if os.path.exists(plot_path) == False:\n",
" os.makedirs(plot_path)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"n=-1\n",
"for i in range(0,100):\n",
" for j in range(0,100):\n",
" try:\n",
" err,h = read(meas_path + \"/meas_logger_2021-03-18_17_{}_{}.csv\".format(i,j))\n",
" n=n+1\n",
" plt.plot(err.T[0],err.T[1], label = n)\n",
" except:\n",
" pass\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.3410903704328225, 0.07767335881755084, 0.045600072697310486, 0.0314498875514683, 0.017152201812012356, 0.01216333769982565, 0.01023030102196412, 0.008589874446113872, 0.007758369416314233, 0.007036977334054732, 0.006446587624472347, 0.005438211102927139, 0.004384507840111588]\n"
]
},
{
"data": {
"text/plain": [
"<function matplotlib.pyplot.show(close=None, block=None)>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"convergence = []\n",
"for i in range(0,100):\n",
" try:\n",
" err,h = read(data_path + \"/read_error/read_error_{}.csv\".format(i))\n",
" convergence.append( np.max( error_convergence(err) ) ) \n",
" plt.plot(err.T[0],err.T[1])\n",
" except:\n",
" pass\n",
"plt.show()\n",
" \n",
"print(convergence)\n",
"plt.figure()\n",
"plt.plot(convergence)\n",
"plt.show"
]
},
{
"cell_type": "code",
"execution_count": 4,
......@@ -645,7 +771,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
"version": "3.7.3"
}
},
"nbformat": 4,
......
%% Cell type:code id: tags:
``` python
import matplotlib.pyplot as plt
import numpy as np
import os
```
%% Cell type:code id: tags:
``` python
local_path = "/home/victor"
local_path = "/home/ipa325/Documents"
```
%% Cell type:code id: tags:
``` python
data_path = local_path + "/Store"
def read(file):
with open(file, 'r') as f:
header_str = f.readline().strip()
data = np.genfromtxt(file,
delimiter=',',
autostrip=True,
skip_header=1)
return data, header_str
```
%% Cell type:code id: tags:
``` python
def error_convergence(data):
y = np.linalg.norm(data[:,1:], ord=2, axis=0)
return y
```
%% Cell type:code id: tags:
``` python
meas_path = local_path + "/ilc_Meas"
data_path = local_path + "/test"
plot_path = local_path + "/Plots"
if os.path.exists(plot_path) == False:
os.makedirs(plot_path)
```
%% Cell type:code id: tags:
``` python
plt.figure()
n=-1
for i in range(0,100):
for j in range(0,100):
try:
err,h = read(meas_path + "/meas_logger_2021-03-18_17_{}_{}.csv".format(i,j))
n=n+1
plt.plot(err.T[0],err.T[1], label = n)
except:
pass
plt.legend()
plt.show()
```
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
plt.figure()
convergence = []
for i in range(0,100):
try:
err,h = read(data_path + "/read_error/read_error_{}.csv".format(i))
convergence.append( np.max( error_convergence(err) ) )
plt.plot(err.T[0],err.T[1])
except:
pass
plt.show()
print(convergence)
plt.figure()
plt.plot(convergence)
plt.show
```
%%%% Output: display_data
%%%% Output: stream
[0.3410903704328225, 0.07767335881755084, 0.045600072697310486, 0.0314498875514683, 0.017152201812012356, 0.01216333769982565, 0.01023030102196412, 0.008589874446113872, 0.007758369416314233, 0.007036977334054732, 0.006446587624472347, 0.005438211102927139, 0.004384507840111588]
%%%% Output: execute_result
<function matplotlib.pyplot.show(close=None, block=None)>
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
def plot_fct(data_type,data_folder,plot_folder):
plot_path = plot_folder + "/" + data_type
print(plot_path)
if os.path.exists(plot_path) == False:
os.makedirs(plot_path)
data_folder = data_folder + "/" + data_type
data_str = data_type
i=0
while i < 50:
try:
data_path = data_folder + "/" + data_str + "_{}.txt".format(i)
with open(data_path, 'r') as f:
header = f.readline().replace("\n", "")
data = np.genfromtxt(data_path, delimiter=',', names = True , deletechars = "/")
for name in data.dtype.names:
if name != "tick_ms":
plt.figure()
plt.plot(data[data.dtype.names[0]],data[name])
plt.savefig(plot_path + "/" + name + "_{}".format(i))
except:
pass
i=i+1
```
%% Cell type:code id: tags:
``` python
plot_fct("read_error",data_path, plot_path)
```
%%%% Output: stream
/home/victor/Plots/read_error
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
plot_fct("resampled_error",data_path, plot_path)
```
%%%% Output: stream
/home/victor/Plots/resampled_error
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
plot_fct("trajectory",data_path, plot_path)
```
%%%% Output: stream
/home/victor/Plots/trajectory
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
plot_fct("ilc_data",data_path, plot_path)
```
%%%% Output: stream
/home/victor/Plots/ilc_data
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
plot_fct("filtered_ilc_data",data_path, plot_path)
```
%%%% Output: stream
/home/victor/Plots/filtered_ilc_data
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
exception thrown
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
```
......
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