Module mayo_hci.automatic_load_data
automatic_load_data deals with loading ADI data
Expand source code
'''
automatic_load_data deals with loading ADI data
'''
'''
MAYO pipeline, from Pairet et al. 2020
Copyright (C) 2020, Benoit Pairet
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
'''
import vip_hci as vip
import numpy as np
import json
import sys
import os
from utils import compute_stim_map
from hciplot import plot_frames as plots
import mayo_hci
def automatic_load_data(data_name,channel=0,dir='default',quick_look=0,crop=0,center_im=None):
"""
automatic_load_data(data_name,channel=0,dir='default',RDI=False,quick_look=0,crop=0,center_im=None)
loads ADI datasets automatically
Parameters
----------
data_name : str
name of the data to use in MAYO
channel : int
channel to use if multiple channels, default is 0
quick_look : int
if larger than 0, apply PCA-SFS and STIM with quick_look number of PC's
crop : int
if crop > 0, crops the ADI cube to the desired width, default is 0
center_im : tuple
(x,y), center of the frame
Returns
-------
data : numpy array
t x n x n ADI dataset
angles : numpy array
list of angles
psf : numpy array
n x n psf
if quick_look >0 :
frame : numpy array
n x n, PCA-SFS processed frame
stim_frame : numpy array
n x n, PCA-SFS STIM map
"""
if dir == 'default':
try:
with open(os.path.dirname(mayo_hci.__file__) + '/data_path.json', 'r') as read_data_path:
temp = json.load(read_data_path)
dir = temp['default_path_to_data']
except FileNotFoundError:
dir = './'
with open(dir+data_name+'/0_import_info.json', 'r') as read_file_import_info:
import_info = json.load(read_file_import_info)
if import_info['cube']:
data = vip.fits.open_fits(dir+data_name+'/'+import_info['cube'][channel]['location'])
if len(data.shape) == 4:
data = data[channel,:,:,:]
t,n_init,_ = data.shape
if crop:
if n_init > crop:
data = vip.preproc.cosmetics.cube_crop_frames(data, crop, xy=(n_init//2+1,n_init//2+1),force=True)
else:
print('crop is larger than initial image, no cropped performed')
_,n,_ = data.shape
print('imported cube on channel: ' + import_info['cube'][channel]['channel_name'])
else:
cube = None
print('data entry missing in import info')
if import_info['angles']:
angles = vip.fits.open_fits(dir+data_name+'/'+import_info['angles'][channel]['location'])
if len(angles.shape) == 2:
angles = angles[channel,:]
angles = angles*import_info['angles_multiply']
if angles.shape[0] != t:
print('not right numbers of angles')
else:
angles = None
print('angles entry missing in import info')
if import_info['psf']:
psf = vip.fits.open_fits(dir+data_name+'/'+import_info['psf'][channel]['location'])
if len(psf.shape) == 4:
try:
psf = psf[channel,import_info['psf']['which_psf'],:,:]
except:
psf = psf[channel,0,:,:]
elif len(psf.shape) == 3:
psf = psf[channel,:,:]
print(psf.shape)
n_psf,_ = psf.shape
print(psf.shape)
psf = psf*(psf>0)
#psf -= np.min(psf)
if n_psf < n:
print('dimension of psf modified to fit data')
psf_width = psf.shape[0]
ind_inf = int(n/2-psf_width/2)
ind_sup = int(n/2+psf_width/2)
psf_full = np.zeros((n,n))
psf_full[ind_inf:ind_sup,ind_inf:ind_sup] = psf
psf = psf_full
if n_psf > n:
psf = vip.preproc.cosmetics.frame_crop(psf, n,force=True)
psf = psf/np.sum(np.abs(psf))
print('psf normalized')
else:
psf = None
print('psf entry missing in import info')
if quick_look:
frame, _, _, residuals_cube, _ = vip.pca.pca_fullfr.pca(data,angles,ncomp=quick_look,full_output=True)
stim_frame = compute_stim_map(vip.preproc.cube_derotate(residuals_cube,angles))
plots((frame,stim_frame))
return data,angles,psf,frame,stim_frame
else:
return data,angles,psfFunctions
def automatic_load_data(data_name, channel=0, dir='default', quick_look=0, crop=0, center_im=None)-
automatic_load_data(data_name,channel=0,dir='default',RDI=False,quick_look=0,crop=0,center_im=None) loads ADI datasets automatically Parameters
data_name:str- name of the data to use in MAYO
channel:int- channel to use if multiple channels, default is 0
quick_look:int- if larger than 0, apply PCA-SFS and STIM with quick_look number of PC's
crop:int- if crop > 0, crops the ADI cube to the desired width, default is 0
center_im:tuple- (x,y), center of the frame
Returns
data:numpy array- t x n x n ADI dataset
angles:numpy array- list of angles
psf:numpy array- n x n psf
if quick_look >0 :frame:numpy array- n x n, PCA-SFS processed frame
stim_frame:numpy array- n x n, PCA-SFS STIM map
Expand source code
def automatic_load_data(data_name,channel=0,dir='default',quick_look=0,crop=0,center_im=None): """ automatic_load_data(data_name,channel=0,dir='default',RDI=False,quick_look=0,crop=0,center_im=None) loads ADI datasets automatically Parameters ---------- data_name : str name of the data to use in MAYO channel : int channel to use if multiple channels, default is 0 quick_look : int if larger than 0, apply PCA-SFS and STIM with quick_look number of PC's crop : int if crop > 0, crops the ADI cube to the desired width, default is 0 center_im : tuple (x,y), center of the frame Returns ------- data : numpy array t x n x n ADI dataset angles : numpy array list of angles psf : numpy array n x n psf if quick_look >0 : frame : numpy array n x n, PCA-SFS processed frame stim_frame : numpy array n x n, PCA-SFS STIM map """ if dir == 'default': try: with open(os.path.dirname(mayo_hci.__file__) + '/data_path.json', 'r') as read_data_path: temp = json.load(read_data_path) dir = temp['default_path_to_data'] except FileNotFoundError: dir = './' with open(dir+data_name+'/0_import_info.json', 'r') as read_file_import_info: import_info = json.load(read_file_import_info) if import_info['cube']: data = vip.fits.open_fits(dir+data_name+'/'+import_info['cube'][channel]['location']) if len(data.shape) == 4: data = data[channel,:,:,:] t,n_init,_ = data.shape if crop: if n_init > crop: data = vip.preproc.cosmetics.cube_crop_frames(data, crop, xy=(n_init//2+1,n_init//2+1),force=True) else: print('crop is larger than initial image, no cropped performed') _,n,_ = data.shape print('imported cube on channel: ' + import_info['cube'][channel]['channel_name']) else: cube = None print('data entry missing in import info') if import_info['angles']: angles = vip.fits.open_fits(dir+data_name+'/'+import_info['angles'][channel]['location']) if len(angles.shape) == 2: angles = angles[channel,:] angles = angles*import_info['angles_multiply'] if angles.shape[0] != t: print('not right numbers of angles') else: angles = None print('angles entry missing in import info') if import_info['psf']: psf = vip.fits.open_fits(dir+data_name+'/'+import_info['psf'][channel]['location']) if len(psf.shape) == 4: try: psf = psf[channel,import_info['psf']['which_psf'],:,:] except: psf = psf[channel,0,:,:] elif len(psf.shape) == 3: psf = psf[channel,:,:] print(psf.shape) n_psf,_ = psf.shape print(psf.shape) psf = psf*(psf>0) #psf -= np.min(psf) if n_psf < n: print('dimension of psf modified to fit data') psf_width = psf.shape[0] ind_inf = int(n/2-psf_width/2) ind_sup = int(n/2+psf_width/2) psf_full = np.zeros((n,n)) psf_full[ind_inf:ind_sup,ind_inf:ind_sup] = psf psf = psf_full if n_psf > n: psf = vip.preproc.cosmetics.frame_crop(psf, n,force=True) psf = psf/np.sum(np.abs(psf)) print('psf normalized') else: psf = None print('psf entry missing in import info') if quick_look: frame, _, _, residuals_cube, _ = vip.pca.pca_fullfr.pca(data,angles,ncomp=quick_look,full_output=True) stim_frame = compute_stim_map(vip.preproc.cube_derotate(residuals_cube,angles)) plots((frame,stim_frame)) return data,angles,psf,frame,stim_frame else: return data,angles,psf