def test_aperturephotometry():
with TemporaryDirectory() as OUTPUT_DIR:
for datasource in ('tpf', 'ffi'):
with AperturePhotometry(DUMMY_TARGET,
INPUT_DIR,
OUTPUT_DIR,
plot=True,
datasource=datasource,
**DUMMY_KWARG) as pho:
pho.photometry()
pho.save_lightcurve()
print(pho.lightcurve)
# It should set the status to one of these:
assert (pho.status in (STATUS.OK, STATUS.WARNING))
plt.figure()
plot_image(pho.sumimage, title=datasource)
plt.show()
# They shouldn't be exactly zero:
assert (~np.all(pho.lightcurve['flux'] == 0))
assert (~np.all(pho.lightcurve['pos_centroid'][:, 0] == 0))
assert (~np.all(pho.lightcurve['pos_centroid'][:, 1] == 0))
# They shouldn't be NaN (in this case!):
assert (~np.all(np.isnan(pho.lightcurve['flux'])))
assert (
~np.all(np.isnan(pho.lightcurve['pos_centroid'][:, 0])))
assert (
~np.all(np.isnan(pho.lightcurve['pos_centroid'][:, 1])))
def test_aperturephotometry():
INPUT_DIR = os.path.join(os.path.dirname(__file__), 'input')
OUTPUT_DIR = tempfile.mkdtemp(prefix='tessphot_tests_aperture')
for datasource in ('tpf', 'ffi'):
with AperturePhotometry(182092046, INPUT_DIR, OUTPUT_DIR, plot=True, datasource=datasource, camera=1, ccd=1) as pho:
pho.photometry()
pho.save_lightcurve()
print( pho.lightcurve )
# It should set the status to one of these:
assert(pho.status in (STATUS.OK, STATUS.WARNING))
plt.figure()
plot_image(pho.sumimage, title=datasource)
plt.show()
# They shouldn't be exactly zero:
assert( ~np.all(pho.lightcurve['flux'] == 0) )
assert( ~np.all(pho.lightcurve['pos_centroid'][:,0] == 0) )
assert( ~np.all(pho.lightcurve['pos_centroid'][:,1] == 0) )
# They shouldn't be NaN (in this case!):
assert( ~np.all(np.isnan(pho.lightcurve['flux'])) )
assert( ~np.all(np.isnan(pho.lightcurve['pos_centroid'][:,0])) )
assert( ~np.all(np.isnan(pho.lightcurve['pos_centroid'][:,1])) )
def test_plot_image(): mu = [3.5, 3] x, y = np.mgrid[0:15, 0:15] pos = np.dstack((x, y)) var = multivariate_normal(mean=mu, cov=[[1,0],[0,1]]) gauss = 5 * var.pdf(pos) - 0.05 # Make sure it has some negative values as well gauss[8,8] = np.NaN gauss[4,4] = -0.2 scales = ['linear', 'sqrt', 'log', 'asinh', 'histeq', 'sinh', 'squared'] fig, axes = plt.subplots(2, 4, figsize=(14, 8)) axes = axes.flatten() for k, scale in enumerate(scales): ax = axes[k] plot_image(gauss, ax=ax, scale=scale, title=scale, cbar='right') ax.plot(mu[1], mu[0], 'r+') # In the final plot: plot_image(gauss, ax=axes[-1], scale='log', title='log - Reds', cmap='Reds', cbar='right') fig.tight_layout() return fig
def test_aperturephotometry(SHARED_INPUT_DIR, datasource):
with TemporaryDirectory() as OUTPUT_DIR:
with AperturePhotometry(DUMMY_TARGET,
SHARED_INPUT_DIR,
OUTPUT_DIR,
plot=True,
datasource=datasource,
**DUMMY_KWARG) as pho:
pho.photometry()
filepath = pho.save_lightcurve()
print(pho.lightcurve)
# It should set the status to one of these:
assert (pho.status in (STATUS.OK, STATUS.WARNING))
# Check the sumimage:
plt.figure()
plot_image(pho.sumimage, title=datasource)
assert not anynan(pho.sumimage), "There are NaNs in the SUMIMAGE"
# They shouldn't be exactly zero:
assert not np.all(pho.lightcurve['flux'] == 0)
assert not np.all(pho.lightcurve['flux_err'] == 0)
assert not np.all(pho.lightcurve['pos_centroid'][:, 0] == 0)
assert not np.all(pho.lightcurve['pos_centroid'][:, 1] == 0)
# They shouldn't be NaN (in this case!):
assert not allnan(pho.lightcurve['flux'])
assert not allnan(pho.lightcurve['flux_err'])
assert not allnan(pho.lightcurve['pos_centroid'][:, 0])
assert not allnan(pho.lightcurve['pos_centroid'][:, 1])
assert not np.any(~np.isfinite(pho.lightcurve['time']))
assert not np.any(pho.lightcurve['time'] == 0)
# Test the outputted FITS file:
with fits.open(filepath, mode='readonly') as hdu:
# Should be the same vectors in FITS as returned in Table:
np.testing.assert_allclose(pho.lightcurve['time'],
hdu[1].data['TIME'])
np.testing.assert_allclose(pho.lightcurve['timecorr'],
hdu[1].data['TIMECORR'])
np.testing.assert_allclose(pho.lightcurve['flux'],
hdu[1].data['FLUX_RAW'])
np.testing.assert_allclose(pho.lightcurve['flux_err'],
hdu[1].data['FLUX_RAW_ERR'])
np.testing.assert_allclose(pho.lightcurve['cadenceno'],
hdu[1].data['CADENCENO'])
# Test FITS aperture image:
ap = hdu['APERTURE'].data
print(ap)
assert np.all(pho.aperture == ap), "Aperture image mismatch"
assert not anynan(ap), "NaN in aperture image"
assert np.all(ap >= 0), "Negative values in aperture image"
assert np.any(ap & 2 != 0), "No photometric mask set"
assert np.any(ap & 8 != 0), "No position mask set"
def test_plot_image_grid_offset():
img = np.zeros((5, 7))
img[:, 0] = 1
img[0, :] = 1
img[:, -1] = 1
img[-1, :] = 1
fig = plt.figure()
ax = fig.add_subplot(111)
plot_image(img, ax=ax, scale='linear', offset_axes=(3, 2))
ax.plot(3.5, 2.5, 'r+')
ax.plot(8.5, 5.5, 'g+')
ax.grid(True)
return fig
def test_plot_image_grid():
img = np.zeros((5, 7))
img[:, 0] = 1
img[0, :] = 1
img[:, -1] = 1
img[-1, :] = 1
fig = plt.figure()
ax = fig.add_subplot(111)
plot_image(img, ax=ax, scale='linear')
ax.plot(0.5, 0.5, 'r+')
ax.plot(5.5, 3.5, 'g+')
ax.grid(True)
return fig
def test_plot_cbar_and_nans(): # Construct image: np.random.seed(42) img = np.random.rand(10, 10) img[2:8, 2:8] = np.NaN fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(16, 6)) plot_image(img, ax=ax1, scale='linear', vmin=0.4, cbar='left') plot_image(img, ax=ax2, scale='sqrt', vmin=0.4, cbar='bottom') plot_image(img, ax=ax3, scale='log', vmin=0.4, cbar='right') plot_image(img, ax=ax4, scale='asinh', vmin=0.4, cbar='top') return fig
def test_plot_image_data_change():
"""Test that the plotting function does not change input data"""
# Construct random image:
img = np.random.randn(15, 10)
img[0, 0] = -1.0 # Make 100% sure there is a negative point
# Save the original image for comparison:
img_before = np.copy(img)
# Make a couple of plots trying out the different settings:
fig = plt.figure()
ax1 = fig.add_subplot(131)
plot_image(img, ax=ax1, scale='linear')
np.testing.assert_allclose(img, img_before)
ax2 = fig.add_subplot(132)
plot_image(img, ax=ax2, scale='sqrt')
np.testing.assert_allclose(img, img_before)
ax3 = fig.add_subplot(133)
plot_image(img, ax=ax3, scale='log')
np.testing.assert_allclose(img, img_before)
fig = plt.figure()
plot_image_fit_residuals(fig, img, img, img)
np.testing.assert_allclose(img, img_before)
def test_plot_image():
mu = [3.5, 3]
x, y = np.mgrid[0:10, 0:10]
pos = np.dstack((x, y))
var = scipy.stats.multivariate_normal(mean=mu, cov=[[1, 0], [0, 1]])
gauss = var.pdf(pos)
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(131)
plot_image(gauss, ax=ax1, scale='linear', title='Linear')
ax1.plot(mu[1], mu[0], 'r+')
ax2 = fig.add_subplot(132)
plot_image(gauss, ax=ax2, scale='sqrt', title='Sqrt')
ax2.plot(mu[1], mu[0], 'r+')
ax3 = fig.add_subplot(133)
plot_image(gauss, ax=ax3, scale='log', title='Log')
ax3.plot(mu[1], mu[0], 'r+')
return fig
def test_plot_image_invalid(): mu = [3.5, 3] x, y = np.mgrid[0:10, 0:10] pos = np.dstack((x, y)) var = multivariate_normal(mean=mu, cov=[[1,0],[0,1]]) gauss = var.pdf(pos) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) # Run with invalid scale: with pytest.raises(ValueError): plot_image(gauss, ax=ax1, scale='invalid-scale') # Plot with single NaN: gauss[1,1] = np.NaN plot_image(gauss, ax=ax1, scale='log') # Run with all-NaN image: gauss[:, :] = np.NaN plot_image(gauss, ax=ax2, cbar='right') return fig
def make_movie(hdf_file, fps=15, dpi=100, overwrite=False):
"""
Create animation of the contents of a HDF5 files produced by the photometry pipeline.
The function will create a MP4 movie file with the same name as the input file,
placed in the same directory, containing the animation.
Parameters:
hdf_file (string): Path to the HDF5 file to produce movie from.
fps (integer): Frames per second of generated movie. Default=15.
dpi (integer): DPI of the movie. Default=100.
overwrite (boolean): Overwrite existing MP4 files? Default=False.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
tqdm_settings = {'disable': not logger.isEnabledFor(logging.INFO)}
logger.info("Processing '%s'", hdf_file)
# File to be created:
output_file = os.path.splitext(hdf_file)[0] + '.mp4'
if os.path.exists(output_file):
if overwrite:
logger.debug("Deleting existing output file")
os.remove(output_file)
else:
logger.info("Movie file already exists")
return output_file
# Open HDF5 file:
# We need to have write-privaledges because we are going to updated some attributes
save_image_scales = False
with h5py.File(hdf_file, 'r') as hdf:
# Load the image scales if they have already been calculated:
vmin = hdf['backgrounds'].attrs.get('movie_vmin')
vmax = hdf['backgrounds'].attrs.get('movie_vmax')
vmin2 = hdf['images'].attrs.get('movie_vmin')
vmax2 = hdf['images'].attrs.get('movie_vmax')
# Calculate scales to use for plotting the images:
if not vmin:
logger.info("Calculating image scales...")
numfiles = len(hdf['images'])
vmax = np.empty(numfiles)
vmin = np.empty(numfiles)
vmax2 = np.empty(numfiles)
vmin2 = np.empty(numfiles)
for k in trange(numfiles, **tqdm_settings):
vmin[k], vmax[k] = np.nanpercentile(
hdf['backgrounds/%04d' % k], [1.0, 99.0])
vmin2[k], vmax2[k] = np.nanpercentile(hdf['images/%04d' % k],
[1.0, 99.0])
vmin = np.nanpercentile(vmin, 25.0)
vmax = np.nanpercentile(vmax, 75.0)
vmin2 = np.nanpercentile(vmin2, 25.0)
vmax2 = np.nanpercentile(vmax2, 75.0)
save_image_scales = True
# If needed, reopen the file for saving the attributes:
if save_image_scales:
with h5py.File(hdf_file, 'r+') as hdf:
# Save image scales to HDF5 file:
hdf['backgrounds'].attrs['movie_vmin'] = vmin
hdf['backgrounds'].attrs['movie_vmax'] = vmax
hdf['images'].attrs['movie_vmin'] = vmin2
hdf['images'].attrs['movie_vmax'] = vmax2
hdf.flush()
# We should now be ready for creating the movie, reopen the file as readonly:
logger.info("Creating movie...")
with h5py.File(hdf_file, 'r') as hdf:
numfiles = len(hdf['images'])
dummy_img = np.full_like(hdf['images/0000'], np.NaN)
time = np.asarray(hdf['time'])
cadenceno = np.asarray(hdf['cadenceno'])
sector = hdf['images'].attrs.get('SECTOR')
camera = hdf['images'].attrs.get('CAMERA')
ccd = hdf['images'].attrs.get('CCD')
with plt.style.context('dark_background'):
plt.rc('axes', titlesize=15)
fig, ax = plt.subplots(1, 4, figsize=(20, 6.8), dpi=dpi)
# Colormap to use for FFIs:
cmap = plt.get_cmap('viridis')
cmap.set_bad('k', 1.0)
# Colormap for Flags:
viridis = plt.get_cmap('Dark2')
newcolors = viridis(np.linspace(0, 1, 4))
newcolors[:1, :] = np.array([1, 1, 1, 1])
cmap_flags = ListedColormap(newcolors)
imgs = [None] * 4
imgs[0] = plot_image(dummy_img,
ax=ax[0],
scale='sqrt',
vmin=vmin,
vmax=vmax,
title='Original Image',
cmap=cmap,
cbar='bottom',
cbar_pad=0.05)
imgs[1] = plot_image(dummy_img,
ax=ax[1],
scale='sqrt',
vmin=vmin,
vmax=vmax,
title='Background',
cmap=cmap,
cbar='bottom',
cbar_pad=0.05)
imgs[2] = plot_image(dummy_img,
ax=ax[2],
scale='sqrt',
vmin=vmin2,
vmax=vmax2,
title='Background subtracted',
cmap=cmap,
cbar='bottom',
cbar_pad=0.05)
imgs[3] = plot_image(
dummy_img,
ax=ax[3],
scale='linear',
vmin=-0.5,
vmax=3.5,
title='Pixel Flags',
cmap=cmap_flags,
cbar='bottom',
cbar_pad=0.05,
clabel='Flags',
cbar_ticks=[0, 1, 2, 3],
cbar_ticklabels=['None', 'Not used', 'Man Excl', 'Shenan'])
for a in ax:
a.set_xticks([])
a.set_yticks([])
figtext = fig.suptitle("to come\nt=???????", fontsize=16)
fig.subplots_adjust(left=0.03,
right=0.97,
top=0.95,
bottom=0.03,
wspace=0.05)
set_copyright(fig)
metadata = {
'title':
'TESS Sector {sector:d}, Camera {camera:d}, CCD {ccd:d}'.
format(sector=sector, camera=camera, ccd=ccd),
'artist':
'TASOC'
}
# Set up the writer (FFMpeg)
WriterClass = animation.writers['ffmpeg']
writer = WriterClass(fps=fps,
codec='h264',
bitrate=-1,
metadata=metadata)
with writer.saving(fig, output_file, dpi):
for k in trange(numfiles, **tqdm_settings):
dset_name = '%04d' % k
flux0 = np.asarray(hdf['images/' + dset_name])
bkg = np.asarray(hdf['backgrounds/' + dset_name])
# Plot original image, background and new image:
imgs[0].set_data(flux0 + bkg)
imgs[1].set_data(bkg)
imgs[2].set_data(flux0)
# Background Shenanigans flags, if available:
if 'pixel_flags/' + dset_name in hdf:
img = np.asarray(hdf['pixel_flags/' + dset_name])
flags = np.zeros_like(img, dtype='uint8')
flags[img & PixelQualityFlags.NotUsedForBackground !=
0] = 1
flags[img & PixelQualityFlags.ManualExclude != 0] = 2
flags[img & PixelQualityFlags.BackgroundShenanigans !=
0] = 3
imgs[3].set_data(flags)
# Update figure title with cadence information;
figtext.set_text(
"Sector {sector:d}, Camera {camera:d}, CCD {ccd:d}\ndset={dset:s}, cad={cad:d}, t={time:.6f}"
.format(sector=sector,
camera=camera,
ccd=ccd,
dset=dset_name,
cad=cadenceno[k],
time=time[k]))
writer.grab_frame()
plt.close(fig)
return output_file
def make_combined_movie(input_dir,
mode='images',
fps=15,
dpi=100,
overwrite=False):
"""
Create animation of the combined contents of all HDF5 files in a directory,
produced by the photometry pipeline.
Parameters:
input_dir (string): Path to the directory with HDF5 files to produce movie from.
mode (string): Which images to show.
Choices are `'originals'`, `'images'`, `'backgrounds'` or `'flags'`.
Default=images.
fps (integer): Frames per second of generated movie. Default=15.
dpi (integer): DPI of the movie. Default=100.
overwrite (boolean): Overwrite existing MP4 files? Default=False.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
# Basic input checks:
if mode not in ('originals', 'images', 'backgrounds', 'flags'):
raise ValueError("Invalid MODE specified")
logger = logging.getLogger(__name__)
tqdm_settings = {'disable': not logger.isEnabledFor(logging.INFO)}
logger.info("Processing '%s'", input_dir)
camccdrot = [(1, 3, 1), (1, 2, 3), (2, 3, 1), (2, 2, 3), (3, 1, 1),
(3, 4, 3), (4, 1, 1), (4, 4, 3), (1, 4, 1), (1, 1, 3),
(2, 4, 1), (2, 1, 3), (3, 2, 1), (3, 3, 3), (4, 2, 1),
(4, 3, 3)]
# Find the sectors that are available:
sectors = []
for fname in find_hdf5_files(input_dir):
# Load the sector number from HDF5 file attributes:
with h5py.File(fname, 'r') as hdf:
s = hdf['images'].attrs.get('SECTOR')
if s is not None and int(s) not in sectors:
sectors.append(int(s))
else:
# If the attribute doesn't exist try to find it from
# parsing the file name:
m = re.match(r'^sector(\d+)_camera\d_ccd\d\.hdf5$',
os.path.basename(fname))
if int(m.group(1)) not in sectors:
sectors.append(int(m.group(1)))
# Create one movie per found sector:
for sector in sectors:
# Define the output file, and overwrite it if needed:
output_file = os.path.join(
input_dir,
'sector{sector:03d}_combined_{mode:s}.mp4'.format(sector=sector,
mode=mode))
if os.path.exists(output_file):
if overwrite:
logger.debug("Deleting existing output file")
os.remove(output_file)
else:
logger.info("Movie file already exists")
return output_file
try:
hdf = [None] * 16
vmin = np.full(16, np.NaN)
vmax = np.full(16, np.NaN)
for k, (camera, ccd, rot) in enumerate(camccdrot):
hdf_file = find_hdf5_files(input_dir,
sector=sector,
camera=camera,
ccd=ccd)
if hdf_file:
hdf[k] = h5py.File(hdf_file[0], 'r')
numfiles = len(hdf[k]['images'])
dummy_img = np.full_like(hdf[k]['images/0000'], np.NaN)
time = np.asarray(hdf[k]['time'])
cadenceno = np.asarray(hdf[k]['cadenceno'])
# Load the image scales if they have already been calculated:
if mode == 'backgrounds':
vmin[k] = hdf[k]['backgrounds'].attrs.get(
'movie_vmin', 0)
vmax[k] = hdf[k]['backgrounds'].attrs.get(
'movie_vmax', 500)
elif mode == 'images' or mode == 'originals':
vmin[k] = hdf[k]['images'].attrs.get('movie_vmin', 0)
vmax[k] = hdf[k]['images'].attrs.get('movie_vmax', 500)
# Summarize the different CCDs into common values:
vmin = np.nanpercentile(vmin, 25.0)
vmax = np.nanpercentile(vmax, 75.0)
logger.info("Creating combined %s movie...", mode)
with plt.style.context('dark_background'):
fig, axes = plt.subplots(2, 8, figsize=(25, 6.8), dpi=dpi)
cmap = plt.get_cmap('viridis')
cmap.set_bad('k', 1.0)
# Colormap for Flags:
viridis = plt.get_cmap('Dark2')
newcolors = viridis(np.linspace(0, 1, 4))
newcolors[:1, :] = np.array([1, 1, 1, 1])
cmap_flags = ListedColormap(newcolors)
imgs = [None] * 16
for k, ax in enumerate(axes.flatten()):
if mode == 'flags':
imgs[k] = plot_image(dummy_img,
ax=ax,
scale='linear',
vmin=-0.5,
vmax=4.5,
cmap=cmap_flags)
else:
imgs[k] = plot_image(dummy_img,
ax=ax,
scale='sqrt',
vmin=vmin,
vmax=vmax,
cmap=cmap)
ax.set_xticks([])
ax.set_yticks([])
figtext = fig.suptitle("to come\nt=???????", fontsize=16)
fig.subplots_adjust(left=0.03,
right=0.97,
top=0.90,
bottom=0.05,
wspace=0.05,
hspace=0.05)
set_copyright(fig)
metadata = {
'title':
'TESS Sector {sector:d}, {mode:s}'.format(sector=sector,
mode=mode),
'artist':
'TASOC'
}
# Set up the writer (FFMpeg)
WriterClass = animation.writers['ffmpeg']
writer = WriterClass(fps=fps,
codec='h264',
bitrate=-1,
metadata=metadata)
with writer.saving(fig, output_file, dpi):
for i in trange(numfiles, **tqdm_settings):
dset_name = '%04d' % i
for k in range(16):
if hdf[k] is None:
continue
# Background Shenanigans flags, if available:
if mode == 'flags':
flags = np.asarray(hdf[k]['pixel_flags/' +
dset_name])
img = np.zeros_like(flags, dtype='uint8')
img[flags
& PixelQualityFlags.NotUsedForBackground !=
0] = 1
img[flags
& PixelQualityFlags.ManualExclude != 0] = 2
img[flags
& PixelQualityFlags.BackgroundShenanigans
!= 0] = 3
elif mode == 'originals':
img = np.asarray(hdf[k]['images/' + dset_name])
img += np.asarray(hdf[k]['backgrounds/' +
dset_name])
else:
img = np.asarray(hdf[k][mode + '/' +
dset_name])
# Rotate the image:
cam, ccd, rot = camccdrot[k]
img = np.rot90(img, rot)
# Update the image:
imgs[k].set_data(img)
# Update figure title with cadence information:
figtext.set_text(
"Sector {sector:d} - {mode:s}\ndset={dset:s}, cad={cad:d}, t={time:.6f}"
.format(sector=sector,
mode=mode,
dset=dset_name,
cad=cadenceno[i],
time=time[i]))
writer.grab_frame()
plt.close(fig)
except: # noqa: E722
raise
finally:
for k in range(16):
if hdf[k] is not None:
hdf[k].close()
return output_file
#flags = (flags & PixelQualityFlags.BackgroundShenanigans != 0)
white = np.array([1, 1, 1, 1])
viridis = plt.get_cmap('viridis')
newcolors = viridis(np.linspace(0, 1, int(np.max(flags))))
newcolors[:1, :] = white
newcmp = ListedColormap(newcolors)
fig, ax = plt.subplots(1, 4, figsize=(20, 6))
plot_image(flux0 + bkg,
ax=ax[0],
scale='sqrt',
vmin=vmin,
vmax=vmax,
title='Original Image',
xlabel=None,
ylabel=None,
cmap=plt.cm.viridis,
make_cbar=True)
plot_image(bkg,
ax=ax[1],
scale='sqrt',
vmin=vmin,
vmax=vmax,
title='Background',
xlabel=None,
ylabel=None,
cmap=plt.cm.viridis,
make_cbar=True)
plot_image(flux0,
def make_combined_movie(input_dir,
mode='images',
fps=15,
dpi=100,
overwrite=False):
"""
Create animation of the combined contents of all HDF5 files in a directory,
produced by the photometry pipeline.
Parameters:
input_dir (string): Path to the directory with HDF5 files to produce movie from.
mode (string): Which images to show.
Choices are `'images'`, `'backgrounds'` or `'flags'`.
Default=images.
fps (integer): Frames per second of generated movie. Default=15.
dpi (integer): DPI of the movie. Default=100.
overwrite (boolean): Overwrite existing MP4 files? Default=False.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
# Basic input checks:
assert mode in ('images', 'backgrounds', 'flags'), "Invalid MODE specified"
logger = logging.getLogger(__name__)
logger.info("Processing '%s'", input_dir)
camccdrot = [(1, 3, 1), (1, 2, 3), (2, 3, 1), (2, 2, 3), (3, 1, 1),
(3, 4, 3), (4, 1, 1), (4, 4, 3), (1, 4, 1), (1, 1, 3),
(2, 4, 1), (2, 1, 3), (3, 2, 1), (3, 3, 3), (4, 2, 1),
(4, 3, 3)]
# Find the sectors that are available:
# TODO: Could we change this so we don't have to parse the filenames?
sectors = []
for fname in find_hdf5_files(input_dir):
m = re.match(r'^sector(\d+)_camera\d_ccd\d\.hdf5$',
os.path.basename(fname))
if int(m.group(1)) not in sectors:
sectors.append(int(m.group(1)))
# Create one movie per found sector:
for sector in sectors:
# Define the output file, and overwrite it if needed:
output_file = os.path.join(
input_dir,
'sector{sector:03d}_combined_{mode:s}.mp4'.format(sector=sector,
mode=mode))
if os.path.exists(output_file):
if overwrite:
logger.debug("Deleting existing output file")
os.remove(output_file)
else:
logger.info("Movie file already exists")
return output_file
try:
hdf = [None] * 16
vmin = np.full(16, np.NaN)
vmax = np.full(16, np.NaN)
for k, (camera, ccd, rot) in enumerate(camccdrot):
hdf_file = find_hdf5_files(input_dir,
sector=sector,
camera=camera,
ccd=ccd)
if hdf_file:
hdf[k] = h5py.File(hdf_file[0], 'r', libver='latest')
numfiles = len(hdf[k]['images'])
dummy_img = np.zeros_like(hdf[k]['images/0000'])
time = np.asarray(hdf[k]['time'])
cadenceno = np.asarray(hdf[k]['cadenceno'])
# Load the image scales if they have already been calculated:
if mode == 'backgrounds':
vmin[k] = hdf[k]['backgrounds'].attrs.get(
'movie_vmin', 0)
vmax[k] = hdf[k]['backgrounds'].attrs.get(
'movie_vmax', 500)
elif mode == 'images':
vmin[k] = hdf[k]['images'].attrs.get('movie_vmin', 0)
vmax[k] = hdf[k]['images'].attrs.get('movie_vmax', 500)
# Summarize the different CCDs into common values:
vmin = np.nanpercentile(vmin, 25.0)
vmax = np.nanpercentile(vmax, 75.0)
logger.info("Creating movie...")
with plt.style.context('dark_background'):
fig, axes = plt.subplots(2, 8, figsize=(25, 7.3))
cmap = plt.get_cmap('viridis')
cmap.set_bad('k', 1.0)
# Colormap for Flags:
viridis = plt.get_cmap('Dark2')
newcolors = viridis(np.linspace(0, 1, 4))
newcolors[:1, :] = np.array([1, 1, 1, 1])
cmap_flags = ListedColormap(newcolors)
imgs = [None] * 16
for k, ax in enumerate(axes.flatten()):
if mode == 'flags':
imgs[k] = plot_image(dummy_img,
ax=ax,
scale='linear',
vmin=-0.5,
vmax=4.5,
xlabel=None,
ylabel=None,
cmap=cmap_flags,
make_cbar=False)
else:
imgs[k] = plot_image(dummy_img,
ax=ax,
scale='sqrt',
vmin=vmin,
vmax=vmax,
xlabel=None,
ylabel=None,
cmap=cmap,
make_cbar=False)
ax.set_xticks([])
ax.set_yticks([])
figtext = fig.suptitle("to come\nt=???????", fontsize=15)
fig.set_tight_layout('tight')
fig.subplots_adjust(top=0.85)
set_copyright(fig)
writer = animation.FFMpegWriter(fps=fps)
with writer.saving(fig, output_file, dpi):
for k in trange(numfiles):
dset_name = '%04d' % k
for k in range(16):
if hdf[k] is None: continue
# Background Shenanigans flags, if available:
if mode == 'flags':
flags = np.asarray(hdf[k]['pixel_flags/' +
dset_name])
img = np.zeros_like(flags, dtype='uint8')
img[flags
& PixelQualityFlags.NotUsedForBackground !=
0] = 1
img[flags
& PixelQualityFlags.ManualExclude != 0] = 2
img[flags
& PixelQualityFlags.BackgroundShenanigans
!= 0] = 3
else:
img = np.asarray(hdf[k][mode + '/' +
dset_name])
# Rotate the image:
cam, ccd, rot = camccdrot[k]
img = np.rot90(img, rot)
# Update the image:
imgs[k].set_data(img)
# Update figure title with cadence information:
figtext.set_text(
"Sector {sector:d} - {mode:s}\ndset={dset:s}, cad={cad:d}, t={time:.6f}"
.format(sector=sector,
mode=mode,
dset=dset_name,
cad=cadenceno[k],
time=time[k]))
writer.grab_frame()
plt.close(fig)
except:
raise
finally:
for k in range(16):
if hdf[k] is not None:
hdf[k].close()
return output_file
print(vmin, vmax)
print(vmin2, vmax2)
# Colormap for images:
cmap = plt.cm.viridis
# Colormap for Flags:
viridis = plt.get_cmap('Dark2')
newcolors = viridis(np.linspace(0, 1, 4))
newcolors[:1, :] = np.array([1, 1, 1, 1])
cmap_flags = ListedColormap(newcolors)
# Create figures:
fig, ax = plt.subplots(1, 4, figsize=(20, 6.2))
img1 = plot_image(flux0+bkg, ax=ax[0], scale='sqrt', vmin=vmin, vmax=vmax, title='Original Image', xlabel=None, ylabel=None, cmap=cmap, make_cbar=True)
img2 = plot_image(bkg, ax=ax[1], scale='sqrt', vmin=vmin, vmax=vmax, title='Background', xlabel=None, ylabel=None, cmap=cmap, make_cbar=True)
img3 = plot_image(flux0, ax=ax[2], scale='sqrt', vmin=vmin2, vmax=vmax2, title='Background subtracted', xlabel=None, ylabel=None, cmap=cmap, make_cbar=True)
img4 = plot_image(flags, ax=ax[3], scale='linear', vmin=-0.5, vmax=3.5, title='Pixel Flags', xlabel=None, ylabel=None, cmap=cmap_flags, make_cbar=True, clabel='Flags', cbar_ticks=[0,1,2,3], cbar_ticklabels=['None','Not used','Man. Excl.','Shenan'])
# Remove axes ticks:
for a in ax:
a.set_xticks([])
a.set_yticks([])
fig.set_tight_layout('tight')
# Save figure to file:
#rasterize_and_save('sector001_camera1_ccd2.pdf', [img1, img2, img3, img4], fig=fig, dpi=150, bbox_inches='tight')
fig.savefig('sector001_camera1_ccd2.png', bbox_inches='tight', dpi=150)
plt.close(fig)
