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import os
import shutil
import sys
import warnings
from subprocess import check_call, check_output
import numpy as np
from astropy.io import fits
# Append current directory to system path in order to import testconfig variables
sys.path.append(".")
# Import configuration variables as test configuration (tcf)
import config_vars as tcf
def assert_taql(command, expected_rows=0):
assert shutil.which("taql") is not None, "taql executable not found!"
result = check_output(["taql", "-noph", command]).decode().strip()
assert result == f"select result of {expected_rows} rows", result
def basic_image_check(fits_file):
"""
Checks that the fits file has no NaN or Inf values and that the number
of zeroes is below a limit.
"""
image = fits.open(fits_file)
assert len(image) == 1
data = image[0].data
assert data.shape == (1, 1, 256, 256)
for x in np.nditer(data):
assert np.isfinite(x)
# Test runs showed that 3.1 % of the values are zero. This regression test
# has a higher limit, since results may vary due to floating point rounding.
zeros = data.size - np.count_nonzero(data)
ZERO_LIMIT = 0.035
assert (zeros / data.size) <= ZERO_LIMIT
def check_and_remove_files(fpaths, remove=False):
"""
Check whether the entries in the provided list of paths
are files, and - optionally - remove these files.
Parameters
----------
fpaths : list
List of file paths to check
remove : bool, optional
Remove files, by default False
"""
for fpath in fpaths:
assert os.path.isfile(fpath)
if remove:
[os.remove(fpath) for fpath in fpaths]
def compute_rms(fits_file):
"""
Compute and return the root-mean-square of an input fits image.
"""
data = fits.open(fits_file)[0].data[0, 0, ...]
return np.sqrt(np.mean(data**2))
def compare_rms_fits(fits1, fits2, threshold):
"""
Checks the root-mean square of the difference between
two input fits files against a user-defined threshold.
"""
image1 = fits.open(fits1)[0].data.flatten()
image2 = fits.open(fits2)[0].data.flatten()
dimage = image1 - image2
rms1 = np.sqrt(image1.dot(image1) / image1.size)
rms2 = np.sqrt(image2.dot(image2) / image2.size)
drms = np.sqrt(dimage.dot(dimage) / dimage.size)
print(
f"compare_rms_fits():\n- RMS of {fits1}: {rms1}\n- RMS of {fits2}: {rms2}\n- Difference between {fits1} and {fits2} = {drms}; threshold: {threshold}"
)
assert drms <= threshold
def validate_call(cmdline):
try:
print("Running: " + " ".join(cmdline))
check_call(cmdline)
except:
# To avoid having to work back what the command was, the command is reported:
raise RuntimeError(
"Command failed. To run manually, cd to "
+ tcf.WORKING_DIR
+ " and execute: "
+ " ".join(cmdline)
)
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