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sat_spot_to_star_ratio2.py
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sat_spot_to_star_ratio2.py
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#________________________K1 Band flux Measure______________________________
#----------------------------------------------------------------------------
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib.colors as col
import time
import datetime as dt
import math as m
import astropy.io.fits as pf
import glob
import os
from scipy import ndimage
from scipy import optimize
import imageio # used to create gif.
from itertools import zip_longest
import multiprocessing as mp
#----------------------------------------------------------------------------
#________________________Path and directories and global parameters_____________
#dir_path1 = 'C:/Users/varga_000/Documents/GPI/gpi_pipeline_1.3.0_source/gpi_pipeline_1.3.0_r3839M_source/data/Reduced/130212/S20130212S0'
dir_path1 = 'C:/Users/varga_000/Documents/GPI/gpi_pipeline_1.3.0_source/gpi_pipeline_1.3.0_r3839M_source/data/Reduced/k1/S20130212S0'
#Hband_path = "C:/Users/varga_000/Documents/GPI/gpi_pipeline_1.3.0_source/gpi_pipeline_1.3.0_r3839M_source/data/Reduced/130211/S20130211S0"
hband_path = "saved/Hband/H_reduced_data/Hband/S20130211S0"
def multiple_files(path):
""" creates a list of reduce data path.
Args:
path - is the path where the reduce files are located + their common name
ex) "C:/Python34/GPIcode/saved/Hband/H_reduced_data/Hband/S20130211S0"
loaction = C:/Python34/GPIcode/saved/Hband/H_reduced_data/Hband/
common name = S20130211S0
Return:
list of path where reduced are located.
"""
fyles = sorted(glob.glob(path + '*.fits'))
if not fyles:
print('LIST OF PATH IS EMPTY: ', fyles)
print('please specify full path + common file name')
print('ex) "C:/Python34/GPIcode/saved/Hband/H_reduced_data/Hband/S20130211S0" ')
print(fyles)
return fyles
#====================== EXTRACT Header and Image ===========================
""" purpose: the functions in this section are used to read in the files and
Extract the important iformation"""
#===========================================================================
def open_txt(path):
# i will use to this to open the approximate center files for dm/sat/star
# skips forst row.
return np.loadtxt(path, skiprows=1, dtype=float, delimiter=',')
def file_number(fyle): # enumerates the files for any band SUB FUCNTION
#fyle = multiple_files(path)[index]
trash1 = os.path.split(fyle)
#trash1 = fyle.split("/",4)
#print(trash1)
fyle_number = trash1[1].replace(".fits",'')
#print(fyle_number)
return fyle_number
def open_img(file_location): #opens the matrix containing image
hdu = pf.open(file_location)
hdr = hdu[0].header
#print(hdr)
img = hdu[0].data
return img
def open_hdr(file_location):
hdu = pf.open(file_location)
hdr = hdu[0].header
return hdr
#--------------------------------------------------------------------------
#====================== replacing long function hear will organize later ===========================
""" NEW SECTION NEED TITLE """
#===========================================================================
def get_info1(path,index): # path is the raw data location for a particular fits file w/o coronograph
fyle = multiple_files(path)[index]
print("file name " , fyle)
num_file = file_number(fyle)
hdulist = pf.open(fyle)
img = hdulist[1].data
hdr = hdulist[1].header
img = np.ma.array(img,mask = np.isnan(img))
return hdr,img,num_file
def image(path, index, slyce): # just displays image
data = get_info1(path, index)
print(np.shape(data))
img_slice = data[1][slyce]
#masked = np.ma.array(img_slice,mask =np.isnan(img_slice))
title = data[2]
plt.figure(1)
img = plt.imshow(img_slice, origin='lower', interpolation='nearest', cmap='PuRd', vmin=0, vmax=500)
plt.colorbar()
plt.title('Raw file number: ' + str(title) + ', slice : ' + str(slyce))
plt.xlabel('X [pixel]')
plt.ylabel('Y [pixel]')
return plt.show()
# ======================================= required flux fucntions =====================
def radiu(centrod_x,centrod_y):
# the 281X281 is is the shape of the image
y,x = np.indices((281,281))
r = np.sqrt((x-centrod_x)**2 + (y-centrod_y)**2)
return [r,x,y]
def anu(radii,img,object_radi=10,anu_radi=25):
# "radii" comes from get_info1
mask = img
mask = np.ma.masked_where(radii>object_radi,img)
sky = img
sky = np.ma.masked_where(radii>anu_radi,sky)
#sky = np.ma.masked_where(radii<=object_radi,sky)
sky = np.ma.masked_where(radii<=10,sky)
skyval = np.nanmean(sky)
#print(skyval,'mean')
return [skyval,sky,mask]
def centrod(band_path,approx_center_path,index,slyce,xy_index,center_x=False, center_y=False, object_radi=10,anu_radi=25):
if center_x == False:
center_x, center_y = open_txt(approx_center_path)[xy_index,1:]
#print(center_x,center_y)
#print(center_x, 'this is x')
#print( center_y, ' this is y')
data = radiu(center_x,center_y)
img = get_info1(band_path,index)[1][slyce]
radii, xx, yy = data[0], data[1], data[2]
anu_data = anu(radii,img,object_radi,anu_radi)
skyval, mask = anu_data[0], anu_data[2]
#print(skyval,'skyval')
xbar = np.nansum(mask*xx*(img-skyval))/np.sum(mask*(img-skyval))
ybar = np.nansum(mask*yy*(img-skyval))/np.sum(mask*(img-skyval))
return [xbar,ybar,img,skyval]
def fluxi(band_path,approx_center_path,index,slyce,xy_index,center_x=False, center_y=False, object_radi=10,anu_radi=35):
centro = centrod(band_path,approx_center_path,index,slyce,xy_index,center_x,center_y,object_radi,anu_radi)
radii = radiu(centro[0],centro[1])[0]
#print(radii)
img = centro[2]
skyval = centro[3]
#print(skyval)
aperture = np.where(radii <= object_radi)
#skysub = img[aperture] - skyval
skysub = img[aperture]
flux = np.sum(skysub)
return flux
def flux_range(band_path,approx_center_path,index,slyce,xy_index,object_radi,anu_radi=15,center_x=False, center_y=False):
radi_range = np.arange(.05,object_radi,.5)
#print(radi_range)
flux_range = np.array([])
for i in radi_range:
#print(i)
flux = fluxi(band_path,approx_center_path,index,slyce,xy_index,center_x,center_y,i,anu_radi)
flux_range = np.append(flux_range,flux)
return radi_range,flux_range
def display_flux_range(band_path,approx_center_path,index,slyce,xy_index,object_radi,anu_radi=15,center_x=False, center_y=False):
name = str(input('input name of object: '))
print(name)
#print(foo)
data = flux_range(band_path,approx_center_path,index,slyce,xy_index,object_radi,anu_radi,center_x, center_y)
radius_array = data[0]
flux_array = data[1]
plt. plot(radius_array,flux_array,'o-')
plt.title(name,fontsize=20)
plt.xlabel('radius',fontsize=18)
plt.ylabel('flux',fontsize=18)
plt.grid(True)
plt.show()
# ======================================================================================
def fluxi_range(band_path,approx_center_path,index,slyce,xy_index,r_stop,anu_radi = 35):
radi_range = np.arange(.05,r_stop,.5)
flux_range = np.array([])
center_x, center_y = open_txt(approx_center_path)[xy_index,1:]
#print(center_x, 'this is x')
#print( center_y, ' this is y')
for i in radi_range:
flux = fluxi(band_path,approx_center_path,index,slyce,xy_index,i,anu_radi)
flux_range = np.append(flux_range,flux)
return radi_range,flux_range
"""def flux_range(filenumber,slice_number,r_end):
data = radius(filenumber,slice_number)
img = data[0]
radii = data[1]
skyval = anulus(filenumber,slice_number)[0]
r =.00001
fluxi = np.array([])
radiius =np.array([])
skysub = img - skyval
while r <= r_end:
aperture = np.where(radii <= r)
#print(aperture)
flux = np.sum(skysub[aperture])
#print(flux)
fluxi = np.append(fluxi,flux)
#print(fluxi)
radiius = np.append(radiius,r)
r +=1
return [radiius,fluxi]"""
#radiu(centrod_x,centrod_y)
#radiu returns return [r,x,y]
#get_info1(path,index)
#return hdr,img,num_file
def masket(band_path,approx_center_path,index,slyce,xy_index,object_radi=10,anu_radi=20):
center_x, center_y = open_txt(approx_center_path)[xy_index,1:]
radii = radiu(center_x,center_y)[0]
sky_radi = radii
o_radi = radii
img_raw = get_info1(band_path,index)[1][slyce]
mask = img_raw
mask = np.ma.masked_where(o_radi>object_radi,mask)
sky = img_raw
sky = np.ma.masked_where(sky_radi>anu_radi,sky)
sky = np.ma.masked_where(sky_radi<=object_radi,sky)
return [sky,mask]
def display_anul(band_path,approx_center_path,index,slyce,xy_index,object_radi=10,anu_radi=20):
data = masket(band_path,approx_center_path,index,slyce,xy_index,object_radi,anu_radi)
sky = data[0]
plt.figure(1)
plt.imshow(sky,origin='lower',interpolation='nearest',cmap='gnuplot',vmin=0,vmax=500)
plt.colorbar()
plt.title('Anulus')
plt.xlabel('x [pixel]')
plt.ylabel('y [pixel]')
return plt.show()
def display_masket(band_path,approx_center_path,index,slyce,xy_index,object_radi=10,anu_radi=20):
data = masket(band_path,approx_center_path,index,slyce,xy_index,object_radi,anu_radi)
sky = data[1]
plt.figure(1)
plt.imshow(sky,origin='lower',interpolation='nearest',cmap='gnuplot',vmin=0,vmax=500)
plt.colorbar()
plt.title('Anulus')
plt.xlabel('x [pixel]')
plt.ylabel('y [pixel]')
return plt.show()
# ========================================================================================
#def fluxi(band_path,approx_center_path,index,slyce,xy_index,object_radi=10,anu_radi=35):
#return flux
def auto_flux(band_path,approx_center_path,index,slyce,xy_index,end_file,center_x=False, center_y=False, object_radi=10,anu_radi=35):
""" this only finds the flux of one slice at a time. and appends it to to an empty array
therefore creating an array of flux for different slices """
start_file = index #0 for coronagraph 15 for no coronagraph this number is the itteration of file in the FLUXI FUNCTION!!.
# NOTE THAT SLICE AND RADI ARE CONSTANT FOR FILE ITERATION
flux_arr = np.array([])
while start_file <end_file: # 34 for no coronagraph 15 for coronagraph itteration of slice (wavelength)
#flux = fluxi(start_file,slice_number,radi)
flux = fluxi(band_path,approx_center_path,start_file,slyce,xy_index,center_x,center_y,object_radi,anu_radi)
flux_arr = np.append(flux,flux_arr)
#print('next file...' + str(start_file))
start_file +=1
return flux_arr
def auto_slice1(band_path,approx_center_path,start_file,end_file,xy_index,center_x=False, center_y=False, object_radi=10,anu_radi=20):
slyce = 3 # starting slice, ie well difined sat spot!
flux = []
while slyce < 37:
data = auto_flux(band_path,approx_center_path,start_file,slyce,xy_index,end_file,center_x,center_y,object_radi,anu_radi)
flux.append(data)
print('Next slice... '+ str(slyce))
centro = centrod(band_path,approx_center_path, start_file + 5,slyce,xy_index,center_x, center_y)
print(start_file + 5,'file number')
center_x = centro[0]
center_y = centro[1]
print(center_x,center_y,'center')
#if abs(xi-center_x)> 6:
# print("warning check x value")
#if abs(yi-center_y)> 6:
# print("warning check y value")
#print(slyce,'slice')
#display_testmask(band_path,center_x,center_y,5,slyce,object_radi,anu_radi)
slyce+=1
print(np.shape(flux))
flux = np.vstack((flux))
print(flux)
return flux
def auto_save(band_path,approx_center_path,start_file,end_file,cent_start,cent_end,object_name,band,object_radi=10,anu_radi=35,save=True):
while cent_start <= cent_end:
name = object_name + str(cent_start)
center_x=False
center_y=False
#print(cent_start,'tadaa')
print('saving '+ name + '...')
flux = auto_slice1(band_path,approx_center_path,start_file,end_file,cent_start,center_x, center_y,object_radi,anu_radi)
#print(flux)
save_txt(name, band, flux, save)
cent_start +=1
#def centrod(band_path,approx_center_path,index,slyce,xy_index,object_radi=10,anu_radi=25):
# return [xbar,ybar,img,skyval]
def save_txt(object_name,band,flux,save=False):
if save == True:
new_file = str(object_name) +'_'+str(band)+'_flux.txt'
if not os.path.exists(new_file):
np.savetxt(new_file,flux,delimiter=',')
def new_folder(object_name,band,image=False):
if image == True:
newpath = str(object_name)+'_'+str(band) + '_image'
if not os.path.exists(newpath):
os.makedirs(newpath)
def multiple_txt(path):
# returns a list of files path. paths correpond to the path of Band data
fyles = sorted(glob.glob(path + '*.txt'))
#print ( 'number of fits files: ', str(len(fyles)))
return fyles
def ratio_mean_dmsat(sat_path,dm_path,start,end):
# "sat_path" and "dm_path"is the sub path to
# example: star_path = "C:/Python34/GPIcode/aperture_calibration/star"
# "start" and "end" is the slice range
sat_paths = multiple_txt(sat_path)
#print(np.shape(sat_paths))
dm_paths = multiple_txt(dm_path)
#print(dm_paths)
ratio = np.array([])
for j in range(start,end): # slice
temp_dm = []
temp_sat = []
for i in range(0,4):
#print(i)
#print(dm_paths[i])
dmi = open_txt(dm_paths[i])[j]
sati = open_txt(sat_paths[i])[j]
temp_dm.append(dmi)
temp_sat.append(sati)
temp_dm = np.array(temp_dm)
tem_sat = np.array (temp_sat)
mean_dm = np.mean(temp_dm, axis = 0)
mean_sat = np.mean(temp_sat,axis = 0)
div = mean_dm /mean_sat
ratio = np.append(ratio,div)
#print(ratio)
#print(np.size(ratio))
# std, mean , and error for dm
std_dm = np.std(temp_dm)
mein_dm = np.mean(temp_dm)
err_dm = std_dm**2/mein_dm**2
print(std_dm,'stdv_dm')
print (mein_dm, 'mean_dm')
print(err_dm, 'error')
# std, mean , and error for sat
std_sat = np.std(temp_sat)
mein_sat = np.mean(temp_sat)
err_sat = std_sat**2/mein_sat**2
print(std_sat,'stdv_sat')
print (mein_sat, 'mean_sat')
print(err_sat, 'error')
#std, mean and error for dm and sat
stds = np.std(ratio)
meany = np.mean(ratio)
error = stds/meany
print(stds,'std_ratio')
print(meany,'mean_ratio')
print(error,'error_ratio')
return meany , err_dm , err_sat
def ratio_mean_dmstar(star_path,dm_path,start,end):
star_paths = multiple_txt(star_path)
print(star_paths)
dm_paths = multiple_txt(dm_path)
ratio = np.array([])
#datai = no_coronagraph_fluxes()
#ratio = np.array([])
for j in range(start,end):
temp_dm = []
temp_star = []
for i in range(0,4):
dmi = open_txt(dm_paths[i])[j]
stari = open_txt(star_paths[0])[j]
temp_dm.append(dmi)
temp_star.append(stari)
temp_dm = np.array(temp_dm)
temp_star = np.array(temp_star)
mean_dm = np.mean(temp_dm,axis=0)
mean_star = np.mean(temp_star,axis=0)
div = mean_dm/mean_star
ratio = np.append(ratio,div)
# mean and stdv for dm spots
std_dm = np.std(temp_dm)
mein_dm = np.mean(temp_dm)
err_dm = std_dm**2/mein_dm**2
print(std_dm,'stdv_dm')
print (mein_dm, 'mean_dm')
print(err_dm, 'error_dm')
# mean and stdv for star
std_star = np.std(temp_star)
mein_star = np.mean(temp_star)
err_star = std_star**2/mein_star**2
print(std_star,'stdv_star')
print(mein_star,'mean_star')
print(err_star,'error_star')
# mean and stdv of ratio
stds = np.std(ratio)
meany = np.mean(ratio)
print(stds,'std_ratio')
print(meany,'mean_ratio')
print(stds/meany,'error_ratio')
return meany, err_dm, err_star
def testmask(band_path,center_x,center_y,index,slyce,object_radi,anu_radi):
#auto_slice1(band_path,approx_center_path,start_file,end_file,xy_index,center_x=False, center_y=False, object_radi=10,anu_radi=20):
radii = radiu(center_x,center_y)[0]
img_raw = get_info1(band_path,index)[1][slyce]
mask = img_raw
mask = np.ma.masked_where(radii>object_radi,mask)
sky = img_raw
sky = np.ma.masked_where(radii>anu_radi,sky)
sky = np.ma.masked_where(radii<=object_radi,sky)
return [sky,mask]
def display_testmask(band_path,center_x,center_y,index,slyce,object_radi,anu_radi):
data = testmask(band_path,center_x,center_y,index,slyce,object_radi,anu_radi)
sky = data[0]
mask = data[1]
plt.figure(1)
plt.imshow(sky,origin='lower',interpolation='nearest',cmap='gnuplot',vmin=0,vmax=500)
plt.colorbar()
plt.title('Anulus')
plt.xlabel('x [pixel]')
plt.ylabel('y [pixel]')
plt.figure(2)
plt.imshow(mask,origin='lower',interpolation='nearest',cmap='gnuplot',vmin=0,vmax=500)
plt.colorbar()
plt.title('Mask')
plt.xlabel('x [pixel]')
plt.ylabel('y [pixel]')
return plt.show()
def all_files_same_slice(band_path,slyce):
for i in range(0,33):
print(i)
img = get_info1(band_path,i)[1][slyce]
plt.figure(i)
plt.imshow(img, cmap='Blues',vmin=-48,vmax=90)
plt.title('file number: ' +str(i), fontsize=20)
plt.colorbar()
plt.tight_layout()
return plt.show()
dm2 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/2_dm"
dm3 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/3_dm"
dm4 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/4_dm"
dm5 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/5_dm"
dm6 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/6_dm"
dm7 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/7_dm"
dm8 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/8_dm"
sat2 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/2_sat"
sat3 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/3_sat"
sat4 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/4_sat"
sat5 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/5_sat"
sat6 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/6_sat"
sat7 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/7_sat"
sat8 = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/coronagraph_files/8_sat"
nc_2star = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/No_coronograph_files/2_star"
nc_3star = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/No_coronograph_files/3_star"
nc_4star = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/No_coronograph_files/4_star"
nc_2dm = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/No_coronograph_files/2_dm"
nc_3dm = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/No_coronograph_files/3_dm"
nc_4dm = "C:/Python34/GPIcode/aperture_calibration/h_band/no_skyval_sub/No_coronograph_files/4_dm"
def pixel_map(image,x,y):
image[np.isnan(image)] = np.nanmedian(image)
return ndimage.map_coordinates(image, (y,x),cval=np.nan)
def gen_xy(size):
# contains the index of row and coloumn
s = np.array([size,size])
x,y = np.meshgrid(np.arange(s[1]),np.arange(s[0]))
return x,y
def twoD_Gaussian(xy, amplitude, xo, yo, sigma, offset):
x,y = xy
xo = float(xo)
yo = float(yo)
a = 1.0 / (2.0 * sigma**2.0)
b = 1.0 / (2.0 * sigma**2.0)
g = offset + amplitude*np.exp( - ((a*((x-xo)**2)) + (b*((y-yo)**2))))
return g.ravel()
def return_pos(im, xy_guess,x,y):
#Fit WD location in slice from guess
# pad = 10
#stamp = im[xy_guess[1]-pad:xy_guess[1]+pad, xy_guess[0]-pad:xy_guess[0]+pad]
#stamp[np.isnan(stamp)] = np.nanmedian(stamp)
p0 = [np.nanmax(im), xy_guess[0], xy_guess[1], 3.0, 0.0]
'''
p0 -guess for the 5 parameter of 2d gaussian
np.nanmax(stamp) - peak value in the variable stamp
pad - xc,yc from pixel_cutout size/2 center of the image where the cal or sat spot should be
3.0 - the full width half max in pixel (width of gaussian)
0.0 - constant value to shift
'''
#s = np.shape(stamp)
# x, y = np.meshgrid(np.arange(s[1], dtype=np.float64), np.arange(s[0], dtype=np.float64))
""" x,y - is the same coordinate as pixel cutout after adding xc and yc. this is to keep it relative
to the size of original image
"""
popt, pcov = optimize.curve_fit(twoD_Gaussian, (x, y), im.ravel(), p0 = p0,maxfev=15000000)
#print(popt)
# xy - pad : is a constant to convert the coordinate back to the original image coordinate frame,
# in my code i already added the contant term pad --> xc, yc therefore no need to add pad
# for my code xy = [popt[1],popt[2]]
#xy = [popt[1] + (xy_guess[0] - pad), popt[2] + (xy_guess[1] - pad)]
return popt[1], popt[2]
def pixel_cutout(image,size,xguess, yguess):#, name2='none',save = False):
""" combines the above functions in this section. Used to create box cutout centered at
the specified spot.
Args:
image - a slice of the original data cube
size - the size of the sides of the box cutout
xguess - initial x coordinate guess to center of spot
yguess - initial y coordinate guess to center of spot
name1,2 - when save set to True, this will be the name of the file
save - option to save image cutout with initial guess and after
center has been optimized
Return:
output - box cutout of spot with optimized center
"""
size = float(size)
xguess = float(xguess)
yguess = float(yguess)
x,y = gen_xy(20.0)
x += (xguess-20/2.)
y += (yguess-20/2.)
output = pixel_map(image,x,y)
xc,yc = return_pos(output, (xguess,yguess), x,y)
#if save == True:
# image before center optimization
#write = pf.writeto(name1, output,clobber = True)
x,y = gen_xy(size+4)
x += (xc-np.round((size+4)/2.))
y += (yc-np.round((size+4)/2.))
output = pixel_map(image,x,y)
r = np.sqrt((x-xc)**2 + (y-yc)**2)
output[np.where(r>size/2)] = np.nan
#output = np.nan_to_num(output)
#if save == True:
# image after center optimization
#write = pf.writeto(name2, output,clobber = True)
return output
def loop_pixcut(image,size,centdm,centsat,fname,index=0,save=False,scale=[],residuals=[]):
dm_img, sat_img = [], []
spotnum = 0
#=====================plotting procedure============================
if save == True:
fig = plt.figure(figsize=(10,10))
fig.suptitle(fname+' slice='+str(index),fontsize=25)
fig.subplots_adjust(wspace=0.2, hspace=0.3)
if np.shape(centsat) == (1,2):
spotname = "STAR"
else:
spotname = "SAT spot"
# ==================================================================
for i,j in zip_longest(centdm,centsat):
cutdm = pixel_cutout(image,size,i[0],i[1])
dm_img.append(cutdm)
if j != None:
cutsat = pixel_cutout(image,size,j[0],j[1])
sat_img.append(cutsat)
#==================plotting procedure==================
if save == True:
dm = plt.subplot(4,3,1+spotnum)
dm.imshow(cutdm,interpolation='nearest',cmap='gnuplot2')
dm.set_title('DM spot')
if j != None:
sat = plt.subplot(4,3,2+spotnum)
sat.imshow(cutsat,interpolation='nearest',cmap='gnuplot2')
sat.set_title(spotname)
spotnum +=3
# ===================================================
dm_img = (np.sum(dm_img,axis=0))/len(dm_img)
sat_img = (np.sum(sat_img,axis=0))/len(sat_img)
if np.shape(centsat) == (1,2):
scalef = optimz(sat_img,dm_img)
resid =(scalef*sat_img) - dm_img
else:
scalef = optimz(dm_img,sat_img)
resid =(scalef*dm_img) - sat_img
scale.append(scalef)
residuals.append(resid)
# ==========================plotting procedure======================
if save == True:
ave_dm = plt.subplot(4,3,3)
ax3 = ave_dm.imshow(dm_img,interpolation='nearest',cmap='gnuplot2')
fig.colorbar(ax3)
ave_dm.set_title('Average DM')
ave_sat = plt.subplot(4,3,6)
ax2 = ave_sat.imshow(sat_img,interpolation='nearest',cmap='gnuplot2')
fig.colorbar(ax2)
ave_sat.set_title('Average '+ spotname)
rsd = plt.subplot(4,3,9)
ax = rsd.imshow(resid/np.nanmax(sat_img),interpolation='nearest',cmap='bwr',vmin=-0.1, vmax= 0.1)
fig.colorbar(ax)
rsd.set_title("Residuals")
scale_plot = plt.subplot(4,3,12)
scale_plot.plot(scale, marker='.') #Line with small markers
scale_plot.plot(index, scale[index], marker='o', markersize=10)
plt.title("scale factor",fontsize=16)
plt.savefig("psf_fitting/pngs/atest/"+fname+'_size'+str(size)+'_'+'slice'+str(index).zfill(2)+'.png', bbox_inches='tight')
plt.close('all')
# ==================================================================
return dm_img,sat_img,scale,residuals
def slice_loop(path,fnum,size,center_dm,center_sat,save = False):
""" loops over all slices and creates an average image for the specified spots at each
wavelength slice. At the end there is a total of 37 averaged images.
Args:
path - is the path in where the file is located (see multiple_files function)
fnum - used to index file from multiple file array (see multiple_file function)
size - the size of the sides of the box cutout
center_guess - initial guess for center of spot.
save - option to save image cutout with initial guess and after
center has been optimized
Return:
box - data cube with 37 averaged images.
filename - the name of the file which is being averaged.
"""
info = get_info1(path,fnum) # fnum is the index fits file.
image = info[1]
filename = info[2]
center_dm = open_img(center_dm)
center_sat = open_img(center_sat)
ave_dm , ave_sat , scale , residuals, chi2_residuals = [],[],[],[],[]
imslice = 0
#print( np.shape(image[3:20]))
#print(t)
#for img in image[3:20]:
for img in image:
centdm = center_dm[imslice]
centsat = center_sat[imslice]
data = loop_pixcut(img,size,centdm,centsat,filename,imslice,save,scale,residuals)
ave_dm.append(data[0])
ave_sat.append(data[1])
scale = data[2]
residual = data[3]
chi2_resid = reduce_chi2(residual[-1] , 1)
chi2_residuals.append(chi2_resid)
imslice +=1
ave_dm = np.array(ave_dm)
ave_sat = np.array(ave_sat)
#print(scale)
#print(np.shape(scale))
#np.savetxt('psf_fitting/pngs/residuals/chi2_resid_'+filename+ 'size'+ str(size)+ '.txt',chi2_residuals)
#--------------------------- Creating gif and removing pngs ------------------------
if save == True:
location='psf_fitting/pngs/atest/'
#"psf_fitting/pngs/atest/"+fname+'_size'+str(size)+'_'+'slice'+str(index).zfill(2)+'.png'
cmd = 'magick -delay 40 -loop 0 '+location+filename+'_size'+str(size)+'*.png '+location+filename+'size'+str(size).zfill(2)+'.gif'
os.system(cmd)
pngs = glob.glob(location+filename+'*.png')
[os.remove(png) for png in pngs]
#------------------------------------------------------------------------------------
return scale,filename
def reduce_chi2 (residual,numparams):
chi2 = 1/(np.size(residual) - numparams - 1) * (np.nansum(residual**2))
return chi2
def minimize_psf(scale, ave_dm, ave_sat):
""" Simply minimize residuals
Args:
scale - scale factor
ave_dm - average dm for a given slice
ave_sat - average sat for a given slice
return:
residuals for ave_dm and ave_sat
"""
return np.nansum(np.abs(((scale*ave_dm) - ave_sat)))
def optimz(ave_dm, ave_sat):
guess = np.nanmax(ave_sat) / np.nanmax(ave_dm)
result = optimize.minimize(minimize_psf, guess, args=(ave_dm, ave_sat), method = 'Nelder-Mead')
scale = result.x[0]
#if result.success == False:
# print( 'this is a warning')
# print(result)
#scl.append(scale)
return scale
def open_img(file_location): #opens the matrix containing image
hdu = pf.open(file_location)
hdr = hdu[0].header
#print(hdr)
img = hdu[0].data
return img
#def slice_loop(path,fnum,size,center_dm,center_sat,save = False,sname='sat'):
def main_loopit(size,fstart,fstop,save=False):
path1 = "saved/Hband/H_reduced_data/Hband/S20130211S0"
#path1 = "C:/Users/varga_000/Documents/GPI/gpi_pipeline_1.3.0_source/gpi_pipeline_1.3.0_r3839M_source/data/Reduced/k1/S20130212S0"
# coronograph files 403 - 436, ie 0-34 , No coronograph 440 - 455 , i.e 34-50
#fnum = 34
scales = []
#size = 20
center_dm = "psf_fitting/guess_center/Hdm_center.fits"
#center_dm = "psf_fitting/guess_center/dmk1_center.fits"
if fstart == 0:
spot = "psf_fitting/guess_center/Hsat_center.fits" # SAT CENTER
#spot = "psf_fitting/guess_center/satk1_center.fits"
else:
spot = 'psf_fitting/guess_center/star_center.fits' #STAR center
#spot = "psf_fitting/guess_center/stark1_center.fits"
nthreads = mp.cpu_count()
if nthreads > 16:
nthreads = 50
pool = mp.Pool(nthreads)
result = [pool.apply_async(slice_loop, args=(path1,fnum,size,center_dm,spot,save)) for fnum in range (fstart ,fstop)]
output = [p.get() for p in result]
for fnum in range(0,fstop-fstart):
scales.append(output[fnum][0])
pool.close()
pool.join()
#scale_path1 = "psf_fitting/scale_factors/"
#save = False
#for fnum in range(fstart,fstop):
#scale = slice_loop(path1,fnum,size,center_dm,spot,save)
#print(np.shape(scale[0]))
#scales.append(scale[0])
#print(scale)
#write = pf.writeto(scale_path+scale[1].zfill(2)+'.fits', np.array(scale[0]),clobber = True)
#write = np.savetxt(scale_path+str(scale[1]).zfill(2)+'.txt',np.array(scale[0]))
#print(t)
return scales
def main_loopf():
svpath = "psf_fitting/scale_factors/scale_r/"
size = 7
finalmean, finalstd, bsize= [],[],[]
while size <= 7:
fname = svpath + 'scaleplot_'+ str(size).zfill(2)+'.png'
cname = svpath+"Hc_size"+str(size).zfill(2)+'.txt'
ncname = svpath+"Hnc_size"+str(size).zfill(2)+'.txt'
#print(ncname)
cscale = main_loopit(size,0,34,save=True)
ncscale = main_loopit(size,34,50,save=True)
np.savetxt(cname,cscale)
np.savetxt(ncname,ncscale)
mean,std = scale_plot(cname,ncname,fname,firstslice=3,lastslice=34)
finalmean.append(mean)
finalstd.append(std)
bsize.append(size)
size +=1
plt.errorbar(bsize,finalmean , yerr=finalstd)
plt.xlim(min(bsize)-1, max(bsize)+1)
plt.show()
def scale_plot(cname,ncname,fname,firstslice=0,lastslice=36):
#cscale = np.loadtxt("C:/Python34/GPIcode/psf_fitting/scale_factors/scale_r/Hc_size07.txt")[:,10:34]
#cscale = np.loadtxt("hbandtest.txt")[:,3:34]
cscale = np.loadtxt(cname)[:,firstslice:lastslice]
ncscale = np.loadtxt(ncname)[:,firstslice:lastslice]
#print(t)
#ncscale = np.loadtxt("hbandtest2.txt")[:,3:34]
#ncscale = np.loadtxt("C:/Python34/GPIcode/dmstark1.txt")
#cscale = np.loadtxt("C:/Python34/GPIcode/dmsatk1.txt")
#print(np.shape(ncscale),'star')
#print(np.shape(cscale),'sat')
cmean = np.nanmean(cscale,axis=0)
#print(np.shape(cmean))
#print(t)
ncmean = np.nanmean(ncscale,axis=0)
cstd = np.nanstd(cscale,axis=0)
ncstd = np.nanstd(ncscale,axis=0)
ratio = cmean*ncmean
previous = np.zeros(len(cmean)) + 2.03656e-4 #Hband
#previous = np.zeros(len(cmean)) + 2.71429e-4 #k1
mp = previous -0.10895492e-4 #hband
pp = previous +0.10895492e-4 #hband
#mp = previous -0.178279e-4 #k1
#pp = previous +0.178279e-4 # k1
meanc = [np.random.normal(loc=meani, scale=stdi, size=int(1e6)) for meani,stdi in zip(cmean,cstd)]
meannc = [np.random.normal(loc=meani, scale=stdi, size=int(1e6)) for meani,stdi in zip(ncmean,ncstd)]
scale = np.multiply(meanc,meannc)
print(np.shape(scale))
scalestd = np.nanstd(scale,axis=1)
scalemean = np.nanmean(scale,axis=1)
#sclmean = np.nanmean(scalemean)
#sclstd = np.nanstd(scalemean)
#print(sclmean,sclstd)
meansat_starrand = [np.random.normal(loc=smi, scale=sstdi, size =int(1e6)) for smi,sstdi in zip(scalemean,scalestd)]
print(np.shape(meansat_starrand))
sclmen = np.nanmean(meansat_starrand)
sclstd = np.nanstd(meansat_starrand)
newratio = np.zeros(len(cmean)) + sclmen