'''
Authors: Ing. Kateřina Hromasová, Jakub Svoboda
katerina.hromasova@fjfi.cvut.cz, svoboj68@fjfi.cvut.cz
Date: 17th March 2021
Note that you might have to change the URL in the load_basic function
depending on how the oscilloscope folder is called.
'''
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd # for reading csv files
URL_BASE = 'http://golem.fjfi.cvut.cz' #base URL for GOLEM data access
def load_basic(name, shot):
'''
Loads signals from basic diagnostics osciloscope.
Uses predefined folder for Basic diagnostic that can have different name for older shots.
Parameters
----------
name : str
{'U_Loop, U_BtCoil, U_RogCoil'}
shot : int
golem shot number
Returns
-------
t, np.ndarray
time axis for given signal
data, np.array
oscilloscope data of given diagnostic name in given shot
'''
url = '{}/shots/{}/Diagnostics/BasicDiagnostics/{}.csv'.format(URL_BASE, shot, name)
print(url)
df = pd.read_csv(url, names=['time', name], index_col='time')
t = np.array(df.index)
data = np.transpose(np.array(df))[0]
return t, data
def load_rigol(name, shot, group='b'):
'''
Loads data from Rigol osciloscope controled by students.
Uses the file structure for the summer semester 2021.
Parameters
----------
name, str
{'U_Loop, U_BtCoil, U_RogCoil', U_photod}
shot, int
golem shot number
group, str, optional
number or letter of rigol osciloscope, should math that of group, by default 'b'
Returns
-------
t, np.ndarray
time axis for given signal
data, np.array
oscilloscope data of given diagnostic name in given shot
'''
url = f'{URL_BASE}/shots/{shot}/Diagnostics/BasicDiagnostics4TrainCourses/DAS_raw_data_dir-{group}/{name}-{group}.csv'
print(url)
df = pd.read_csv(url, names=['time', name], index_col='time')
t = np.array(df.index)
data = np.transpose(np.array(df))[0]
return t, data
if __name__ == '__main__':
plt.ion()
# Download oscilloscope data of loop voltage
t, data = load_basic('U_Loop', 35091)
# Plot their time trace
fig = plt.figure('Loop voltage')
plt.gca().set_title('Loop voltage')
plt.plot(t*1000, data)
plt.xlabel('$t$ [ms]')
plt.ylabel('$U_l$ [V]')
plt.grid(True)
fig.tight_layout()
