from pygolem_lite import Shot
import numpy as np
import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
matplotlib.rc('font', size='10')
import matplotlib.pyplot as plt
from scipy.ndimage import median_filter
from matplotlib.pyplot import axvline
def make_img(data, fname, interpolation, start, end,shotno):
plt.figure(figsize=[12, 6]) #size in inches
plt.imshow(data,
aspect='auto',
extent = [start * 1e3, end * 1e3, #time extents in ms
19, 1,], #channel idx
interpolation=interpolation,
cmap=plt.cm.hot,
)
hx = plt.colorbar()
hx.set_label('P[W/sr/m$^2$]')
plt.xlabel('time [ms]')
plt.ylabel('channel number')
plt.text(11, 2, 'TOP')
plt.text(10.5, 18, 'BOTTOM')
#cx = axvline(x = 16,linewidth=5, color='black')
#cx.set_label('16 ms')
plt.title('SHOT NUMBER ' '%d'%shotno)
plt.text(21.5, 2, 'P[W/sr/m$^2$]')
plt.yticks(np.arange(1, 19+1))
plt.savefig(fname)
plt.close() #close this figure
def plot_cut(data, fname,shotno):
plt.plot(data[:,6000],'r+',markersize = 10)
plt.savefig(fname)
plt.xlabel('channel number')
plt.ylabel('P[W/sr/m$^2$]')
plt.title('SHOT NUMBER ' '%d'%shotno)
def make_plots():
shot = Shot()
t, data_za = shot['papouch_za']
t, data_ko = shot['papouch_ko']
start = shot['plasma_start']
end = shot['plasma_end']
shotno = shot['shotno']
vector_coef = np.genfromtxt('coeficients_AXUV2.txt')
start_idx = np.where(start <= t)[0][0]
end_idx = np.where(t <= end)[0][-1]
data = np.hstack([data_za, data_ko]) # NOTE: make sure this is the right order
data = data[start_idx:end_idx,0:18]
for i in range(18):
data[:,i] = data[:,i]*vector_coef[i]
data = data.T # transpose to have time as x axis;
plot_cut(data, 'bolometer_timecut.png',shotno)
make_img(data, 'bolometers.png', 'none', start, end,shotno)
make_img(median_filter(data, size=(3, 333)),
'bolometers_medfilt-bicube_interp.png', 'bicubic', start, end,shotno)
if __name__ == '__main__':
make_plots()
