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import traceback
import functools
import multiprocessing
from multiprocessing import Pool
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import astropy.units as u
from astropy import stats
from astropy.io import fits
import ccdproc
from mmtwfs.wfs import *
from mmtwfs.zernike import ZernikeVector
from mmtwfs.telescope import MMT
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%load_ext autoreload
%autoreload 2
%matplotlib widget
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mmirs = WFSFactory(wfs="mmirs")
plt.show()
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fig, ax = plt.subplots()
mmirs.drawoutline(ax)
# Some representative positions that vignette on different edges of the mirror
mmirs.plotgrid(-50, -60, ax)
mmirs.plotgrid(-45, -40, ax)
mmirs.plotgrid(-7, -52, ax)
mmirs.plotgrid(50, 60, ax)
mmirs.plotgrid(45, 40, ax)
mmirs.plotgrid(7, 52, ax)
plt.show()
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# The PacMan from last WFS image on 2017.1019
fig, ax = plt.subplots()
mmirs.drawoutline(ax)
mmirs.plotgrid(43.687, -39.144, ax) # use GUIDERX/Y to place and then CA to rotate to match
plt.show()
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fig, ax = plt.subplots()
mmirs.drawoutline(ax)
mmirs.plotgrid(37.31, 50.572, ax) # use GUIDERX/Y to place and then CA to rotate to match
plt.show()
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#data, hdr = check_wfsdata("/Users/tim/MMT/wfsdat/20171013/mmirs_wfs_rename_0533.fits", header=True)
fig, ax = plt.subplots()
data, hdr = check_wfsdata("/Volumes/LaCie 8TB/wfsdat/20191213/mmirs_wfs_0381.fits", header=True)
im = mmirs.pupil_mask(hdr)
ax.imshow(im, origin='lower')
plt.show()
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import photutils
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t = photutils.detection.find_peaks(im, 0.8, box_size=5, centroid_func=photutils.centroids.centroid_com)
t
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fig, ax = plt.subplots()
mmirs.drawoutline(ax)
mmirs.plotgrid_hdr(hdr, ax)
plt.show()
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%cd /Volumes/LaCie\ 8TB/wfsdat/20150927
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ic = ccdproc.ImageFileCollection('.', glob_include="mmirs_wfs*.fits", keywords=['wfsname', 'guiderx', 'guidery'])
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cam1 = ic.summary['file'][ic.summary['wfsname'] == ' mmirs1']
cam2 = ic.summary['file'][ic.summary['wfsname'] == ' mmirs2']
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cam1_proc = []
cam2_proc = []
for f in cam1:
data, hdr = mmirs.process_image(f)
cam1_proc.append(data)
for f in cam2:
data, hdr = mmirs.process_image(f)
cam2_proc.append(data)
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cam1_mean = np.mean(cam1_proc, axis=0)
cam2_mean = np.mean(cam2_proc, axis=0)
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hdu1 = fits.PrimaryHDU(cam1_mean)
hdu1.writeto('cam1_mean.fits', overwrite=True)
hdu2 = fits.PrimaryHDU(cam2_mean)
hdu2.writeto('cam2_mean.fits', overwrite=True)
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c1 = fits.open("cam1_mean.fits")[0].data
r1, rh1 = check_wfsdata("/Users/tim/MMT/mmtwfs/mmtwfs/data/ref_images/mmirs_camera1_ref.fits", header=True)
r1 = mmirs.trim_overscan(r1, hdr=hdr)
comb1 = c1 + 0.1*r1
hh = fits.PrimaryHDU(comb1)
hh.writeto("comb1.fits", overwrite=True)
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len(cam1_proc), len(cam2_proc)
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cam2_proc[0].shape
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c2 = fits.open("cam2_mean.fits")[0].data
r2, rh2 = check_wfsdata("/Users/tim/MMT/mmtwfs/mmtwfs/data/ref_images/mmirs_camera2_ref.fits", header=True)
r2 = mmirs.trim_overscan(r2, hdr=hdr)
comb2 = c2 + 0.1*r2
hh = fits.PrimaryHDU(comb2)
hh.writeto("comb2.fits", overwrite=True)
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df = ic.summary.to_pandas()
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df1 = df[df['wfsname'].str.contains('mmirs1')]
df2 = df[df['wfsname'].str.contains('mmirs2')]
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df2['guiderx'].mean(), df2['guidery'].max()
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df1_pos = df1.drop_duplicates(subset=['guiderx', 'guidery'])
df2_pos = df2.drop_duplicates(subset=['guiderx', 'guidery'])
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fig, ax = plt.subplots()
mmirs.drawoutline(ax)
for i, p in df1_pos.iterrows():
mmirs.plotgrid(p['guiderx'], p['guidery'], ax)
for i, p in df2_pos.iterrows():
mmirs.plotgrid(p['guiderx'], p['guidery'], ax)
ax.set_xlabel("GUIDERX (mm)")
ax.set_ylabel("GUIDERY (mm)")
fig.show()
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fig.savefig("wfs_positions.pdf")
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7.4 * 206265/280e3 * 486
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206265 * 1.22 * 0.5e-3 / 60
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ngood = 0
ngood += 1
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