Commit fb4a7266 authored by Lorenz Halt's avatar Lorenz Halt 🔀
Browse files

Cleanup data visualization

parent 5dd61af6
......@@ -2,10 +2,11 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os"
......@@ -13,19 +14,50 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# path to find measurement data (const ./ilc_Meas)\n",
"path_proxy = \"/home/ipa325/Documents\"\n",
"path_proxy = \"~/catkin_ws/src/ilc/logs/ilc_proxy\"\n",
"path_proxy = os.path.expanduser(path_proxy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_path = \"/home/ipa325/Documents\""
"# path to find measurement data (const ./ilc_Meas)\n",
"path_logger = \"/home/ipa325/Documents\"\n",
"path_logger = \"~/catkin_ws/src/ilc/logs/2021-04-01\"\n",
"path_logger = os.path.expanduser(path_logger)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#Pfade der Daten (meas_path für measurement_logger Data / data_path für ilc_rosproxy Data)\n",
"meas_path = path_logger + \"/meas_lin\"\n",
"error_path = path_proxy + \"/read_error\"\n",
"\n",
"#if os.path.exists(plot_path) == False:\n",
"# os.makedirs(plot_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Should be imported from ilc_lib\n",
"def read(file):\n",
" with open(file, 'r') as f:\n",
" header_str = f.readline().strip()\n",
......@@ -38,10 +70,11 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Should be imported from ilc_lib\n",
"def error_convergence(data):\n",
" y = np.linalg.norm(data[:,1:], ord=2, axis=0)\n",
" return y"
......@@ -49,72 +82,70 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Pfade der Daten (meas_path für measurement_logger Data / data_path für ilc_rosproxy Data)\n",
"meas_path = local_path + \"/ilc_Meas\"\n",
"data_path = local_path + \"/test\" \n",
"filenames_errors = os.listdir(error_path) # get all files from folder\n",
"filenames_errors = sorted(filenames_errors) # sort alphabetically\n",
"filenames_errors = [filename for filename in filenames_errors if filename.endswith(\".csv\")] # remove all but csv\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plots für error in filenames_errors (von ilc_rosproxy geschrieben)\n",
"\n",
"plt.figure()\n",
"convergence = []\n",
"\n",
"if os.path.exists(plot_path) == False:\n",
" os.makedirs(plot_path)"
"for filename in filenames_errors:\n",
" err, h = read(os.path.join(error_path, filename))\n",
" convergence.append( np.max( error_convergence(err) ) ) \n",
" plt.plot(err.T[0], err.T[1], label='{}'.format(filename))\n",
"plt.legend()\n",
"#plt.show()\n",
" \n",
"#print(convergence)\n",
"plt.figure()\n",
"plt.plot(convergence, '-o', label=\"norm(error)\")\n",
"plt.legend()\n",
"#plt.show"
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"filenames_meas = os.listdir(meas_path) # get all files from folder\n",
"filenames_meas = sorted(filenames_meas) # sort alphabetically\n",
"filenames_meas = [filename for filename in filenames_meas if filename.endswith(\".csv\")] # remove all but csv\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hacky plots für den Measurement_logger \n",
"plt.figure()\n",
"num=-1\n",
"for n in range(0,100):\n",
" for i in range(0,100):\n",
" for j in range(0,100):\n",
" try:\n",
" if n<10:\n",
" if i <10:\n",
" if j<10:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_0{}_0{}_0{}.csv\".format(n,i,j))\n",
" else:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_0{}_0{}_{}.csv\".format(n,i,j))\n",
" else:\n",
" if j<10:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_0{}_{}_0{}.csv\".format(n,i,j))\n",
" else:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_0{}_{}_{}.csv\".format(n,i,j))\n",
" else:\n",
" if i<10:\n",
" if j<10:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_{}_0{}_0{}.csv\".format(n,i,j))\n",
" else:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_{}_0{}_{}.csv\".format(n,i,j))\n",
" else:\n",
" if j<10:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_{}_{}_0{}.csv\".format(n,i,j))\n",
" else:\n",
" err,h = read(meas_path + \"/force_meas_2021-03-22_{}_{}_{}.csv\".format(n,i,j))\n",
" num=num+1\n",
" plt.plot(err.T[0],-1*err.T[6], label = num)\n",
" except:\n",
" pass\n",
"#plt.legend()\n",
"plt.show()"
"\n",
"for filename in filenames_meas:\n",
" err, h = read(os.path.join(meas_path, filename))\n",
" plt.plot(err.T[0],-1*err.T[6], label='{}'.format(filename))\n",
"plt.legend()"
]
},
{
......@@ -123,43 +154,82 @@
"metadata": {},
"outputs": [],
"source": [
"#plots für daten in data_path (von ilc_rosproxy geschrieben)\n",
"_files_meas = [[os.path.getmtime(os.path.join(meas_path, name)),\n",
" name] for name in filenames_meas]\n",
"\n",
"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"
"_files_error = [[os.path.getmtime(os.path.join(error_path, name)),\n",
" name] for name in filenames_errors]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, axs = plt.subplots(2)\n",
"fig.suptitle('Vertically stacked subplots')\n",
"\n",
"for (err_time, err_file),(meas_time, meas_file) in zip(_files_error,_files_meas):\n",
" #matches just by sorting, drops last items\n",
" err, h = read(os.path.join(meas_path, meas_file))\n",
" axs[0].plot(err.T[0],-1*err.T[6], label='{}'.format(meas_file))\n",
" \n",
" err, h = read(os.path.join(error_path, err_file))\n",
" axs[1].plot(err.T[0], err.T[1], label='{}'.format(err_file))\n",
"#axs[0].legend()\n",
"#axs[1].legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"files_meas = filenames_meas\n",
"\n",
"for filename in filenames_meas:\n",
" path = os.path.join(meas_path, filename)\n",
" print(filename, os.path.getmtime(path))\n",
" \n",
"for filename in filenames_errors:\n",
" path = os.path.join(error_path, filename)\n",
" print(filename, os.path.getmtime(path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 2",
"language": "python",
"name": "python3"
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"pygments_lexer": "ipython2",
"version": "2.7.17"
}
},
"nbformat": 4,
......
%% Cell type:code id: tags:
``` python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
```
%% Cell type:code id: tags:
``` python
local_path = "/home/ipa325/Documents"
# path to find measurement data (const ./ilc_Meas)
path_proxy = "/home/ipa325/Documents"
path_proxy = "~/catkin_ws/src/ilc/logs/ilc_proxy"
path_proxy = os.path.expanduser(path_proxy)
```
%% Cell type:code id: tags:
``` python
# path to find measurement data (const ./ilc_Meas)
path_logger = "/home/ipa325/Documents"
path_logger = "~/catkin_ws/src/ilc/logs/2021-04-01"
path_logger = os.path.expanduser(path_logger)
```
%% Cell type:code id: tags:
``` python
#Pfade der Daten (meas_path für measurement_logger Data / data_path für ilc_rosproxy Data)
meas_path = path_logger + "/meas_lin"
error_path = path_proxy + "/read_error"
#if os.path.exists(plot_path) == False:
# os.makedirs(plot_path)
```
%% Cell type:code id: tags:
``` python
# TODO: Should be imported from ilc_lib
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
# TODO: Should be imported from ilc_lib
def error_convergence(data):
y = np.linalg.norm(data[:,1:], ord=2, axis=0)
return y
```
%% Cell type:code id: tags:
``` python
#Pfade der Daten (meas_path für measurement_logger Data / data_path für ilc_rosproxy Data)
meas_path = local_path + "/ilc_Meas"
data_path = local_path + "/test"
if os.path.exists(plot_path) == False:
os.makedirs(plot_path)
filenames_errors = os.listdir(error_path) # get all files from folder
filenames_errors = sorted(filenames_errors) # sort alphabetically
filenames_errors = [filename for filename in filenames_errors if filename.endswith(".csv")] # remove all but csv
```
%% Cell type:code id: tags:
``` python
#hacky plots für den Measurement_logger
#plots für error in filenames_errors (von ilc_rosproxy geschrieben)
plt.figure()
convergence = []
for filename in filenames_errors:
err, h = read(os.path.join(error_path, filename))
convergence.append( np.max( error_convergence(err) ) )
plt.plot(err.T[0], err.T[1], label='{}'.format(filename))
plt.legend()
#plt.show()
#print(convergence)
plt.figure()
num=-1
for n in range(0,100):
for i in range(0,100):
for j in range(0,100):
try:
if n<10:
if i <10:
if j<10:
err,h = read(meas_path + "/force_meas_2021-03-22_0{}_0{}_0{}.csv".format(n,i,j))
else:
err,h = read(meas_path + "/force_meas_2021-03-22_0{}_0{}_{}.csv".format(n,i,j))
else:
if j<10:
err,h = read(meas_path + "/force_meas_2021-03-22_0{}_{}_0{}.csv".format(n,i,j))
else:
err,h = read(meas_path + "/force_meas_2021-03-22_0{}_{}_{}.csv".format(n,i,j))
else:
if i<10:
if j<10:
err,h = read(meas_path + "/force_meas_2021-03-22_{}_0{}_0{}.csv".format(n,i,j))
else:
err,h = read(meas_path + "/force_meas_2021-03-22_{}_0{}_{}.csv".format(n,i,j))
else:
if j<10:
err,h = read(meas_path + "/force_meas_2021-03-22_{}_{}_0{}.csv".format(n,i,j))
else:
err,h = read(meas_path + "/force_meas_2021-03-22_{}_{}_{}.csv".format(n,i,j))
num=num+1
plt.plot(err.T[0],-1*err.T[6], label = num)
except:
pass
#plt.legend()
plt.show()
plt.plot(convergence, '-o', label="norm(error)")
plt.legend()
#plt.show
```
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
#plots für daten in data_path (von ilc_rosproxy geschrieben)
filenames_meas = os.listdir(meas_path) # get all files from folder
filenames_meas = sorted(filenames_meas) # sort alphabetically
filenames_meas = [filename for filename in filenames_meas if filename.endswith(".csv")] # remove all but csv
```
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()
%% Cell type:code id: tags:
print(convergence)
``` python
plt.figure()
plt.plot(convergence)
plt.show
for filename in filenames_meas:
err, h = read(os.path.join(meas_path, filename))
plt.plot(err.T[0],-1*err.T[6], label='{}'.format(filename))
plt.legend()
```
%% Cell type:code id: tags:
``` python
_files_meas = [[os.path.getmtime(os.path.join(meas_path, name)),
name] for name in filenames_meas]
_files_error = [[os.path.getmtime(os.path.join(error_path, name)),
name] for name in filenames_errors]
```
%% Cell type:code id: tags:
``` python
fig, axs = plt.subplots(2)
fig.suptitle('Vertically stacked subplots')
for (err_time, err_file),(meas_time, meas_file) in zip(_files_error,_files_meas):
#matches just by sorting, drops last items
err, h = read(os.path.join(meas_path, meas_file))
axs[0].plot(err.T[0],-1*err.T[6], label='{}'.format(meas_file))
err, h = read(os.path.join(error_path, err_file))
axs[1].plot(err.T[0], err.T[1], label='{}'.format(err_file))
#axs[0].legend()
#axs[1].legend()
```
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
files_meas = filenames_meas
for filename in filenames_meas:
path = os.path.join(meas_path, filename)
print(filename, os.path.getmtime(path))
for filename in filenames_errors:
path = os.path.join(error_path, filename)
print(filename, os.path.getmtime(path))
```
%% Cell type:code id: tags:
``` python
```
......
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