#> file: ./QuESO/addon/aia
#> lang: python
#> synopsis:
#> author: <>
from scipy.io import readsav
import numpy as np
from .logg import loggTimer
[docs]
@loggTimer
def delayAIA(fname, epochDev):
#> detail:
#> param type fname:
#> param type epochDev:
#> return (type):
#> test-method:
eD = epochDev
eD.shape = [eD.geometry["rasterSize"], eD.geometry['alongSlitSize']]
jq_delayCube = readsav(fname)
#jq_delayCube['arr'][0] --> background of pixel
#jq_delayCube['arr'][1] --> background noise of pixel (standard deviation)
#jq_delayCube['arr'][2] --> AIA brightness at ViSP slit time
#jq_delayCube['arr'][3] --> time of the ViSP slit
#jq_delayCube['arr'][4] --> xpos
#jq_delayCube['arr'][5] --> ypos
#jq_delayCube['arr'][6] --> brightness of peak (before ViSP time)
#jq_delayCube['arr'][7] --> time of peak before ViSP time
#jq_delayCube['arr'][8] --> brightness of peak (after ViSP time)
#jq_delayCube['arr'][9] --> time of peak after ViSP time
jq_AIAFrame = np.zeros((514, 295)) + np.nan
#momentFrame = np.zeros(len(jq_delayCube['arr'][:, 4])) + np.nan
#moment_compare = eD.dataSquare[:, eD.spectralWindow[0]:eD.spectralWindow[1]].mean(axis=-1).reshape(eD.shape[0], eD.shape[1])
#aia_correct = lambda x: x.reshape((295, 514)).T.reshape(514*295)
correct = lambda x: x.reshape(eD.shape[1], eD.shape[0]).T.reshape(eD.shape[0]*eD.shape[1])
jq_AIAMask = np.zeros(jq_AIAFrame.shape) + np.nan
jq_indxMap = np.zeros(jq_AIAFrame.shape) + np.nan
jq_AIABright = np.zeros(jq_AIAFrame.shape) + np.nan
#print([0.01937, eD.deltas['pxlAlongSlit']])
#print([0.21420, eD.deltas['pxlSlitWidth']])
dy = 0.01937
dx = 0.214167
for i in range(len(jq_delayCube['arr'][:, 4])):
xx = int(np.floor(jq_delayCube['arr'][int(i), 4]*dx*6))
yy = int(np.floor(jq_delayCube['arr'][int(i), 5]*dy*6))
if jq_delayCube['arr'][i, 7] > 0:
jq_AIAFrame[xx, yy] = jq_delayCube['arr'][i, 3] - jq_delayCube['arr'][i, 7]
jq_AIAMask[xx, yy] = 1
jq_AIABright[xx, yy] = jq_delayCube['arr'][i, 2]
if jq_delayCube['arr'][i, 9] > 0:
jq_AIAFrame[xx, yy] = jq_delayCube['arr'][i, 3] - jq_delayCube['arr'][i, 9]
jq_AIAMask[xx, yy] = 1
jq_AIABright[xx, yy] = jq_delayCube['arr'][i, 2]
jq_indxMap[xx, yy] = i
visp_jqDelayFrame = np.zeros(eD.dataSquare.shape[0]) + np.nan
visp_jqMaskFrame = np.zeros(eD.dataSquare.shape[0]) + np.nan
visp_aiaIndx = np.zeros(eD.dataSquare.shape[0]) + np.nan
visp_aiaBright = np.zeros(eD.dataSquare.shape[0]) + np.nan
for i in range(eD.dataSquare.shape[0]):
yy = int(np.floor((i / eD.shape[0])*dy*6))
xx = int(np.floor(np.mod(i, eD.shape[0])*dx*6))
if ~(np.isnan(jq_AIAMask[xx, yy])):
visp_jqMaskFrame[i] = jq_AIAMask[xx, yy]
visp_aiaIndx[i] = jq_indxMap[xx, yy]
visp_aiaBright[i] = jq_AIABright[xx, yy]
visp_jqDelayFrame[i] = jq_AIAFrame[xx, yy]
delayCube = correct(visp_jqDelayFrame)/60.#)/60.
aiaATvisp = correct(visp_aiaBright)
aiaIndxMap = correct(visp_aiaIndx)
mask_map = np.zeros(delayCube.shape)
mask_map[np.where(~np.isnan(delayCube))] = 1
import matplotlib.pyplot as plt
fig = plt.figure(layout='constrained', figsize=(10, 5), dpi=300)
ax = fig.add_subplot(111)
ax.imshow(mask_map.reshape(eD.shape[0], eD.shape[1]).T, cmap='Greys_r', origin='lower', extent=[0, eD.shape[0]*dx, 0, eD.shape[1]*dy])
fig.savefig("./maskTest.png")
plt.close()
return(delayCube, aiaATvisp, aiaIndxMap, mask_map.astype(bool))
