def test_memory_leak():
import resource
arr = np.arange(1).reshape((1, 1))
starting = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
for i in range(1000):
for axis in [None, 0, 1]:
bn.nansum(arr, axis=axis)
bn.nanargmax(arr, axis=axis)
bn.nanargmin(arr, axis=axis)
bn.nanmedian(arr, axis=axis)
bn.nansum(arr, axis=axis)
bn.nanmean(arr, axis=axis)
bn.nanmin(arr, axis=axis)
bn.nanmax(arr, axis=axis)
bn.nanvar(arr, axis=axis)
ending = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
diff = ending - starting
diff_bytes = diff * resource.getpagesize()
print(diff_bytes)
# For 1.3.0 release, this had value of ~100kB
assert diff_bytes == 0
def test_nanvar_issue60():
"nanvar regression test (issue #60)"
with warnings.catch_warnings():
warnings.simplefilter("ignore")
f = bn.nanvar([1.0], ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([1.0], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1.0], ddof=1) wrong")
f = bn.nanvar([1], ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([1], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1], ddof=1) wrong")
f = bn.nanvar([1, np.nan], ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([1, np.nan], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1, nan], ddof=1) wrong")
f = bn.nanvar([[1, np.nan], [np.nan, 1]], axis=0, ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([[1, np.nan], [np.nan, 1]], axis=0, ddof=1)
assert_equal(f, s, err_msg="issue #60 regression")
def test_nanvar_issue60():
"nanvar regression test (issue #60)"
with warnings.catch_warnings():
warnings.simplefilter("ignore")
f = bn.nanvar([1.0], ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([1.0], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1.0], ddof=1) wrong")
f = bn.nanvar([1], ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([1], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1], ddof=1) wrong")
f = bn.nanvar([1, np.nan], ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([1, np.nan], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1, nan], ddof=1) wrong")
f = bn.nanvar([[1, np.nan], [np.nan, 1]], axis=0, ddof=1)
with np.errstate(invalid='ignore'):
s = bn.slow.nanvar([[1, np.nan], [np.nan, 1]], axis=0, ddof=1)
assert_equal(f, s, err_msg="issue #60 regression")
def test_memory_leak() -> None:
import resource
arr = np.arange(1).reshape((1, 1))
n_attempts = 3
results = []
for _ in range(n_attempts):
starting = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
for _ in range(1000):
for axis in [None, 0, 1]:
bn.nansum(arr, axis=axis)
bn.nanargmax(arr, axis=axis)
bn.nanargmin(arr, axis=axis)
bn.nanmedian(arr, axis=axis)
bn.nansum(arr, axis=axis)
bn.nanmean(arr, axis=axis)
bn.nanmin(arr, axis=axis)
bn.nanmax(arr, axis=axis)
bn.nanvar(arr, axis=axis)
ending = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
diff = ending - starting
diff_bytes = diff * resource.getpagesize()
# For 1.3.0 release, this had value of ~100kB
if diff_bytes:
results.append(diff_bytes)
else:
break
assert len(results) < n_attempts
def get_lugsail_batch_means_est(data_in, steps=None):
m = len(data_in)
T_iL = []
s_i = []
n_i = []
for data_chain, burnin_chain in data_in:
data = data_chain[burnin_chain:steps]
if data.size < 2:
return np.inf
# [chapter 2.2 in Vats and Knudson, 2018]
n_ii = data.size
b = int(n_ii**(1 / 2)) # Batch size. Alternative: n ** (1/3)
n_i.append(n_ii)
chain_mean = bn.nanmean(data)
T_iL.append(
2 * get_tau_lugsail(b, data, chain_mean) \
- get_tau_lugsail(b // 3, data, chain_mean)
)
s_i.append(bn.nanvar(data, ddof=1))
T_L = np.mean(T_iL)
s = np.mean(s_i)
n = np.round(np.mean(n_i))
sigma_L = ((n - 1) * s + T_L) / n
# [eq. 5 in Vats and Knudson, 2018]
R_L = np.sqrt(sigma_L / s)
return R_L
def proportionality(x, y):
num = bottleneck.nanvar(np.log1p(y) - np.log1p(x))
denom = (bottleneck.nanstd(np.log1p(x)) +
bottleneck.nanstd(np.log1p(y)))**2
try:
return num / denom
except:
return np.nan
def test_nanvar_issue60():
"""nanvar regression test (issue #60)"""
f = bn.nanvar([1.0], ddof=1)
with np.errstate(invalid="ignore"):
s = bn.slow.nanvar([1.0], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1.0], ddof=1) wrong")
f = bn.nanvar([1], ddof=1)
with np.errstate(invalid="ignore"):
s = bn.slow.nanvar([1], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1], ddof=1) wrong")
f = bn.nanvar([1, np.nan], ddof=1)
with np.errstate(invalid="ignore"):
s = bn.slow.nanvar([1, np.nan], ddof=1)
assert_equal(f, s, err_msg="bn.nanvar([1, nan], ddof=1) wrong")
f = bn.nanvar([[1, np.nan], [np.nan, 1]], axis=0, ddof=1)
with np.errstate(invalid="ignore"):
s = bn.slow.nanvar([[1, np.nan], [np.nan, 1]], axis=0, ddof=1)
assert_equal(f, s, err_msg="issue #60 regression")
def welch(xs, ys, meanfcn=bn.nanmedian):
"""
Welch's statistic for equal means
http://en.wikipedia.org/wiki/Welch%27s_t_test
Parameters
----------
xs: np.array
ys: np.array
Returns
-------
float
"""
xbar, ybar = map(meanfcn, (xs, ys))
sx2, sy2 = map(lambda zs: bn.nanvar(xs) + np.spacing(1), (xs, ys))
return np.abs(xbar - ybar)/np.sqrt(sx2/len(xs) + sy2/len(ys))
def fit(self, X, y, mask=None):
"""Fit Gaussian Naive Bayes according to X, y
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
mask : array-like, shape = [n_samples, n_features]
Binary, 1 at unobserved features.
Returns
-------
self : object
Returns self.
"""
X, y = check_arrays(X, y, sparse_format='dense')
n_samples, n_features = X.shape
if n_samples != y.shape[0]:
raise ValueError("X and y have incompatible shapes")
if mask is not None:
mask = array2d(mask)
X = X.copy()
X[mask] = np.nan
self.classes_ = unique_y = np.unique(y)
n_classes = unique_y.shape[0]
self.theta_ = np.zeros((n_classes, n_features))
self.sigma_ = np.zeros((n_classes, n_features))
self.class_prior_ = np.zeros(n_classes)
self._n_ij = []
epsilon = 1e-9
for i, y_i in enumerate(unique_y):
self.theta_[i, :] = bn.nanmean(X[y == y_i, :], axis=0)
self.sigma_[i, :] = bn.nanvar(X[y == y_i, :], axis=0) + epsilon
self.class_prior_[i] = np.float(np.sum(y == y_i)) / n_samples
self._n_ij.append(-0.5 * np.sum(np.log(np.pi * self.sigma_[i, :])))
self._logprior = np.log(self.class_prior_)
return self
def fit(self, X, y, mask=None):
"""Fit Gaussian Naive Bayes according to X, y
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
mask : array-like, shape = [n_samples, n_features]
Binary, 1 at unobserved features.
Returns
-------
self : object
Returns self.
"""
X, y = check_arrays(X, y, sparse_format='dense')
n_samples, n_features = X.shape
if n_samples != y.shape[0]:
raise ValueError("X and y have incompatible shapes")
if mask is not None:
mask = array2d(mask)
X = X.copy()
X[mask] = np.nan
self.classes_ = unique_y = np.unique(y)
n_classes = unique_y.shape[0]
self.theta_ = np.zeros((n_classes, n_features))
self.sigma_ = np.zeros((n_classes, n_features))
self.class_prior_ = np.zeros(n_classes)
self._n_ij = []
epsilon = 1e-9
for i, y_i in enumerate(unique_y):
self.theta_[i, :] = bn.nanmean(X[y == y_i, :], axis=0)
self.sigma_[i, :] = bn.nanvar(X[y == y_i, :], axis=0) + epsilon
self.class_prior_[i] = np.float(np.sum(y == y_i)) / n_samples
self._n_ij.append(-0.5 * np.sum(np.log(np.pi * self.sigma_[i, :])))
self._logprior = np.log(self.class_prior_)
return self
m = nanmedian(flux)
flux = 1e6*(flux/m - 1)
flux_err = 1e6*flux_err/m
#fig, ax = plt.subplots()
#ax.plot(data[:,0], data[:,1])
#fig.savefig(os.path.splitext(fpath_save)[0] + '.png', bbox_inches='tight')
#plt.close(fig)
# Save file:
os.makedirs(os.path.dirname(fpath_save), exist_ok=True)
np.savetxt(fpath_save, np.column_stack((time, flux, flux_err)),
delimiter=' ', fmt=('%.8f', '%.16e', '%.16e'))
# Calculate diagnostics:
lc = LightCurve(time=time, flux=flux, flux_err=flux_err)
variance = nanvar(flux, ddof=1)
rms_hour = rms_timescale(lc, timescale=3600/86400)
ptp = nanmedian(np.abs(np.diff(flux)))
# Add target to TODO-list:
diag.write("{variance:.16e},{rms_hour:.16e},{ptp:.16e}\n".format(
variance=variance,
rms_hour=rms_hour,
ptp=ptp
))
diag.write("#-------------------------------------------\n")
print("DONE")
def ndcombine(arr,
mask=None,
copy=True,
blank=np.nan,
offsets=None,
thresholds=[-np.inf, np.inf],
zero=None,
scale=None,
weight=None,
zero_kw={
'cenfunc': 'median',
'stdfunc': 'std',
'std_ddof': 1
},
scale_kw={
'cenfunc': 'median',
'stdfunc': 'std',
'std_ddof': 1
},
zero_to_0th=True,
scale_to_0th=True,
zero_section=None,
scale_section=None,
reject=None,
cenfunc='median',
sigma=[3., 3.],
maxiters=3,
ddof=1,
nkeep=1,
maxrej=None,
n_minmax=[1, 1],
rdnoise=0.,
gain=1.,
snoise=0.,
pclip=-0.5,
combine='average',
dtype='float32',
memlimit=2.5e+9,
irafmode=True,
verbose=False,
full=False,
return_variance=False):
if copy:
arr = arr.copy()
if np.array(arr).ndim == 1:
raise ValueError("1-D array combination is not supported!")
_mask = _set_mask(arr, mask) # _mask = propagated through this function.
sigma_lower, sigma_upper = _set_sigma(sigma)
nkeep, maxrej = _set_keeprej(arr, nkeep, maxrej, axis=0)
cenfunc = _set_cenfunc(cenfunc)
reject_fullname = _set_reject_name(reject)
maxiters = int(maxiters)
ddof = int(ddof)
combfunc = _set_combfunc(combine, nameonly=False, nan=True)
if verbose and reject is not None:
print("- Rejection")
if thresholds != [-np.inf, np.inf]:
print(f"-- thresholds (low, upp) = {thresholds}")
print(f"-- {reject=} ({irafmode=})")
print(f"-- params: {nkeep=}, {maxrej=}, {maxiters=}, {cenfunc=}")
if reject_fullname == "sigclip":
print(f" (for sigclip): {sigma=}, {ddof=}")
elif reject_fullname == "ccdclip":
print(f" (for ccdclip): {gain=}, {rdnoise=}, {snoise=}")
# elif reject_fullnme == "pclip":
# print(f" (for pclip) : spclip={pclip}")
# elif reject_fullname == "minmax":
# print(f" (for minmaxclip): n_minmax={n_minmax}")
# == 01 - Thresholding + Initial masking ============================================= #
# Updating mask: _mask = _mask | mask_thresh
mask_thresh = _set_thresh_mask(arr=arr,
mask=_mask,
thresholds=thresholds,
update_mask=True)
# if safemode:
# # Backup the pixels which are rejected by thresholding and # initial
# mask for future restoration (see below) for debugging # purpose.
# backup_thresh = arr[mask_thresh]
# backup_thresh_inmask = arr[_mask]
# TODO: remove this np.nan and instead, let `get_zsw` to accept mask.
arr[_mask] = np.nan
# ------------------------------------------------------------------------------------ #
# == 02 - Calculate zero, scale, weights ============================================= #
# This should be done before rejection but after threshold masking..
zeros, scales, weights = get_zsw(arr=arr,
zero=zero,
scale=scale,
weight=weight,
zero_kw=zero_kw,
scale_kw=scale_kw,
zero_to_0th=zero_to_0th,
scale_to_0th=scale_to_0th,
zero_section=zero_section,
scale_section=scale_section)
arr = do_zs(arr, zeros=zeros, scales=scales)
# ------------------------------------------------------------------------------------ #
# == 02 - Rejection ================================================================== #
if isinstance(reject_fullname, str):
if reject_fullname == 'sigclip':
_mask_rej = sigclip_mask(arr,
mask=_mask,
sigma_lower=sigma_lower,
sigma_upper=sigma_upper,
maxiters=maxiters,
ddof=ddof,
nkeep=nkeep,
maxrej=maxrej,
cenfunc=cenfunc,
axis=0,
irafmode=irafmode,
full=full)
elif reject_fullname == 'minmax':
_mask_rej = minmax_mask(arr,
mask=_mask,
n_minmax=n_minmax,
full=full)
elif reject_fullname == 'ccdclip':
_mask_rej = ccdclip_mask(arr,
mask=_mask,
sigma_lower=sigma_lower,
sigma_upper=sigma_upper,
scale_ref=np.mean(scales),
zero_ref=np.mean(zeros),
maxiters=maxiters,
ddof=ddof,
nkeep=nkeep,
maxrej=maxrej,
cenfunc=cenfunc,
axis=0,
gain=gain,
rdnoise=rdnoise,
snoise=snoise,
irafmode=irafmode,
full=True)
elif reject_fullname == 'pclip':
pass
else:
raise ValueError("reject not understood.")
if full:
_mask_rej, low, upp, nit, rejcode = _mask_rej
# _mask is a subset of _mask_rej, so to extract pixels which are
# masked PURELY due to the rejection is:
mask_rej = _mask_rej ^ _mask
elif reject_fullname is None:
mask_rej = _set_mask(arr, None)
if full:
low = bn.nanmin(arr, axis=0)
upp = bn.nanmax(arr, axis=0)
nit = None
rejcode = None
else:
raise ValueError("reject not understood.")
if reject is not None and verbose:
print("Done.")
_mask |= mask_rej
# ------------------------------------------------------------------------------------ #
# TODO: add "grow" rejection here?
# == 03 - combine ==================================================================== #
# Replace rejected / masked pixel to NaN and backup for debugging purpose. This is done to reduce
# memory (instead of doing _arr = arr.copy())
# backup_nan = arr[_mask]
if verbose:
print("- Combining")
print(f"-- combine = {combine}")
arr[_mask] = np.nan
# Combine and calc sigma
comb = combfunc(arr, axis=0)
if verbose:
print("Done.")
# Restore NaN-replaced pixels of arr for debugging purpose.
# arr[_mask] = backup_nan
# arr[mask_thresh] = backup_thresh_inmask
if full:
if verbose:
print("- Error calculation")
print("-- to skip this, use `full=False`")
print(f"-- return_variance={return_variance}, ddof={ddof}")
if return_variance:
err = bn.nanvar(arr, ddof=ddof, axis=0)
else:
err = bn.nanstd(arr, ddof=ddof, axis=0)
if verbose:
print("Done.")
return comb, err, mask_rej, mask_thresh, low, upp, nit, rejcode
else:
return comb
def load_star(self, task, fname):
"""
Receive a task from the TaskManager, loads the lightcurve and returns derived features.
Parameters:
task (dict): Task dictionary as returned by :func:`TaskManager.get_task`.
fname (str): Path to lightcurve file associated with task.
Returns:
dict: Dictionary with features.
See Also:
:py:func:`TaskManager.get_task`
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
# Define variables used below:
features = {}
save_to_cache = False
# The Meta-classifier is only using features from the other classifiers,
# so there is no reason to load lightcurves and calculate/load any other classifiers:
if self.classifier_key != 'meta':
# Load features from cache file, or calculate them
# and put them into cache file for other classifiers
# to use later on:
if self.features_cache:
features_file = os.path.join(self.features_cache, 'features-' + str(task['priority']) + '.pickle')
if os.path.exists(features_file):
features = loadPickle(features_file)
# Load lightcurve file and create a TessLightCurve object:
if 'lightcurve' in features:
lightcurve = features['lightcurve']
else:
lightcurve = load_lightcurve(fname,
starid=task['starid'],
truncate_lightcurve=self.truncate_lightcurves)
# No features found in cache, so calculate them:
if not features:
save_to_cache = True
features = self.calc_features(lightcurve)
# Add the fields from the task to the list of features:
for key in ('tmag', 'variance', 'rms_hour', 'ptp', 'other_classifiers'):
if key in task.keys():
features[key] = task[key]
else:
logger.warning("Key '%s' not found in task.", key)
features[key] = np.NaN
# If these features were not provided with the task, i.e. they
# have not been pre-computed, we should compute them now:
if features['variance'] is None or not np.isfinite(features['variance']):
features['variance'] = nanvar(lightcurve.flux, ddof=1)
if features['rms_hour'] is None or not np.isfinite(features['rms_hour']):
features['rms_hour'] = rms_timescale(lightcurve)
if features['ptp'] is None or not np.isfinite(features['ptp']):
features['ptp'] = ptp(lightcurve)
# Save features in cache file for later use:
if save_to_cache and self.features_cache:
savePickle(features_file, features)
# Add the fields from the task to the list of features:
features['priority'] = task['priority']
features['starid'] = task['starid']
logger.debug(features)
return features
def correct(self, task, output_folder=None):
"""
Run correction.
Parameters:
task (dict): Dictionary defining a task/lightcurve to process.
output_folder (str, optional): Path to directory where lightcurve should be saved.
Returns:
dict: Result dictionary containing information about the processing.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
t1 = default_timer()
error_msg = []
details = {}
save_file = None
result = task.copy()
try:
# Load the lightcurve
lc = self.load_lightcurve(task)
# Run the correction on this lightcurve:
lc_corr, status = self.do_correction(lc)
except (KeyboardInterrupt, SystemExit): # pragma: no cover
status = STATUS.ABORT
logger.warning("Correction was aborted (priority=%d)",
task['priority'])
except: # noqa: E722 pragma: no cover
status = STATUS.ERROR
logger.exception("Correction failed (priority=%d)",
task['priority'])
# Check that the status has been changed:
if status == STATUS.UNKNOWN: # pragma: no cover
raise ValueError("STATUS was not set by do_correction")
# Do sanity checks:
if status in (STATUS.OK, STATUS.WARNING):
# Make sure all NaN fluxes have corresponding NaN errors:
lc_corr.flux_err[np.isnan(lc_corr.flux)] = np.NaN
# Simple check that entire lightcurve is not NaN:
if allnan(lc_corr.flux):
logger.error("Final lightcurve is all NaNs")
status = STATUS.ERROR
if allnan(lc_corr.flux_err):
logger.error("Final lightcurve errors are all NaNs")
status = STATUS.ERROR
if np.any(np.isinf(lc_corr.flux)):
logger.error("Final lightcurve contains Inf")
status = STATUS.ERROR
if np.any(np.isinf(lc_corr.flux_err)):
logger.error("Final lightcurve errors contains Inf")
status = STATUS.ERROR
# Calculate diagnostics:
if status in (STATUS.OK, STATUS.WARNING):
# Calculate diagnostics:
details['variance'] = nanvar(lc_corr.flux, ddof=1)
details['rms_hour'] = rms_timescale(lc_corr,
timescale=3600 / 86400)
details['ptp'] = ptp(lc_corr)
# Diagnostics specific to the method:
if self.CorrMethod == 'cbv':
details['cbv_num'] = lc_corr.meta['additional_headers'][
'CBV_NUM']
elif self.CorrMethod == 'ensemble':
details['ens_num'] = lc_corr.meta['additional_headers'][
'ENS_NUM']
details['ens_fom'] = lc_corr.meta['FOM']
# Save the lightcurve to file:
try:
save_file = self.save_lightcurve(lc_corr,
output_folder=output_folder)
except (KeyboardInterrupt, SystemExit): # pragma: no cover
status = STATUS.ABORT
logger.warning("Correction was aborted (priority=%d)",
task['priority'])
except: # noqa: E722 pragma: no cover
status = STATUS.ERROR
logger.exception(
"Could not save lightcurve file (priority=%d)",
task['priority'])
# Plot the final lightcurve:
if self.plot:
fig = plt.figure(dpi=200)
ax = fig.add_subplot(111)
ax.scatter(lc.time,
1e6 * (lc.flux / nanmedian(lc.flux) - 1),
s=2,
alpha=0.3,
marker='o',
label="Original")
ax.scatter(lc_corr.time,
lc_corr.flux,
s=2,
alpha=0.3,
marker='o',
label="Corrected")
ax.set_xlabel('Time (TBJD)')
ax.set_ylabel('Relative flux (ppm)')
ax.legend()
save_figure(os.path.join(self.plot_folder(lc),
self.CorrMethod + '_final'),
fig=fig)
plt.close(fig)
# Unpack any errors or warnings that were sent to the logger during the correction:
if self.message_queue:
error_msg += self.message_queue
self.message_queue.clear()
if not error_msg:
error_msg = None
# Update results:
t2 = default_timer()
details['errors'] = error_msg
result.update({
'corrector': self.CorrMethod,
'status_corr': status,
'elaptime_corr': t2 - t1,
'lightcurve_corr': save_file,
'details': details
})
return result
def stats_area(self, loc, tol=0, lmean=False, lmed=False, lskew=False,
lvar=False, lstd=False, lcoefvar=False, lperc=False,
p=0.95, save=False):
"""Calculate some statistics among every realisation, considering a
circular (only horizontaly) area of radius `tol` around the point
located at `loc`.
Parameters
----------
loc : array_like
Location of the vertical line [x, y].
tol : number, default 0
Tolerance radius used to search for neighbour nodes.
lmean : boolean, default False
Calculate the mean.
lmed : boolean, default False
Calculate the median.
lskew : boolean, default False
Calculate skewness.
lvar : boolean, default False
Calculate the variance.
lstd : boolean, default False
Calculate the standard deviation.
lcoefvar : boolean, default False
Calculate the coefficient of variation.
lperc : boolean, default False
Calculate the percentile `100 * (1 - p)`.
p : number, default 0.95
Probability value.
save : boolean, default False
Write the points used to calculate the chosen statistics in
PointSet format to a file named 'sim values at (x, y, line).prn'.
Returns
-------
statspset : PointSet
PointSet instance containing the calculated statistics.
.. TODO: checkar stats variance com geoms
"""
if lmean:
meanline = np.zeros(self.dz)
if lmed:
medline = np.zeros(self.dz)
if lskew:
skewline = np.zeros(self.dz)
if lvar:
varline = np.zeros(self.dz)
if lstd:
stdline = np.zeros(self.dz)
if lcoefvar:
coefvarline = np.zeros(self.dz)
if lperc:
percline = np.zeros((self.dz, 2))
# convert the coordinates of the first point to grid nodes
loc = coord_to_grid(loc, [self.cellx, self.celly, self.cellz],
[self.xi, self.yi, self.zi])[:2]
# find the nodes coordinates within a circle centred in the first point
neighbours_nodes = circle(loc[0], loc[1], tol)
# compute the lines numbers for each point in the neighbourhood, across
# each grid layer. this yields a N*M matrix, with N equal to the number
# of neighbour nodes, and M equal to the number of layers in the grid.
neighbours_lines = [line_zmirror(node, [self.dx, self.dy, self.dz])
for node in neighbours_nodes]
# sort the lines in ascending order
neighbours_lines = np.sort(neighbours_lines, axis=0)
# create an array to store the neighbour nodes in each grid file
nnodes = neighbours_lines.shape[0]
arr = np.zeros(self.nfiles * nnodes)
skip = True
curr_line = np.zeros(self.nfiles)
for layer in xrange(neighbours_lines.shape[1]):
for i, line in enumerate(neighbours_lines[:, layer]):
for j, grid in enumerate(self.files):
# skip header lines only once per grid file
if skip and self.header:
skip_lines(grid, self.header)
# advance to the next line with a neighbour node
skip_lines(grid, int(line - curr_line[j] - 1))
# read the line and store its value
a = grid.readline()
arr[i + j * nnodes] = float(a)
curr_line[j] = line
skip = False
# replace no data's with NaN
bn.replace(arr, self.nodata, np.nan)
# compute the required statistics
if lmean:
meanline[layer] = bn.nanmean(arr)
if lmed:
medline[layer] = bn.nanmedian(arr)
if lskew:
skewline[layer] = pd.Series(arr).skew()
if lvar:
varline[layer] = bn.nanvar(arr, ddof=1)
if lstd:
stdline[layer] = bn.nanstd(arr, ddof=1)
if lcoefvar:
if lstd and lmean:
coefvarline[layer] = stdline[layer] / meanline[layer] * 100
else:
std = bn.nanstd(arr, ddof=1)
mean = bn.nanmean(arr)
coefvarline[layer] = std / mean * 100
if lperc:
percline[layer] = pd.Series(arr).quantile([(1 - p) / 2,
1 - (1 - p) / 2])
if save and tol == 0:
# FIXME: not working with the tolerance feature
# need to adjust the arrpset or cherry-pick arr
arrpset = PointSet('realisations at location ({0}, {1}, {2})'.
format(loc[0], loc[1], layer * self.cellz +
self.zi), self.nodata, 3,
['x', 'y', 'value'],
values=np.zeros((self.nfiles, 3)))
arrout = os.path.join(os.path.dirname(self.files[0].name),
'sim values at ({0}, {1}, {2}).prn'.format(
loc[0], loc[1], layer * self.cellz
+ self.zi))
arrpset.values.iloc[:, 2] = arr
arrpset.values.iloc[:, :2] = np.repeat(np.array(loc)
[np.newaxis, :],
self.nfiles, axis=0)
arrpset.save(arrout, header=True)
ncols = sum((lmean, lmed, lvar, lstd, lcoefvar, lskew))
if lperc:
ncols += 2
statspset = PointSet(name='vertical line stats at (x,y) = ({0},{1})'.
format(loc[0], loc[1]), nodata=self.nodata,
nvars=3 + ncols, varnames=['x', 'y', 'z'],
values=np.zeros((self.dz, 3 + ncols)))
statspset.values.iloc[:, :3] = (np.column_stack
(((np.repeat(np.array(loc)
[np.newaxis, :], self.dz,
axis=0)),
np.arange(self.zi, self.zi +
self.cellz * self.dz))))
j = 3
if lmean:
statspset.varnames.append('mean')
statspset.values.iloc[:, j] = meanline
j += 1
if lmed:
statspset.varnames.append('median')
statspset.values.iloc[:, j] = medline
j += 1
if lskew:
statspset.varnames.append('skewness')
statspset.values.iloc[:, j] = skewline
j += 1
if lvar:
statspset.varnames.append('variance')
statspset.values.iloc[:, j] = varline
j += 1
if lstd:
statspset.varnames.append('std')
statspset.values.iloc[:, j] = stdline
j += 1
if lcoefvar:
statspset.varnames.append('coefvar')
statspset.values.iloc[:, j] = coefvarline
j += 1
if lperc:
statspset.varnames.append('lperc')
statspset.varnames.append('rperc')
statspset.values.iloc[:, -2:] = percline
# reset the reading pointer in each grid file
self.reset_read()
# update varnames
statspset.flush_varnames()
return statspset
def time_nanvar(self, dtype, shape):
bn.nanvar(self.arr)
def calculate_beta(returns: pd.DataFrame,
in_flag: pd.DataFrame,
mkt: pd.DataFrame = None,
class_df: pd.DataFrame = None,
output_freq="D",
window_size=252,
k=5,
universe=True,
target_dates=None,
len_beta=None,
minimum_coverage=None) -> pd.DataFrame:
"""
Beta calculation is very fast in nature. No need to utilize parallel computation
:param returns: daily stock return DataFrame
:param in_flag: daily in flag DataFrame
:param mkt: daily market return DataFrame; this return is used as the benchmark (regressor) in a CAPM model;
if it is an one row dataframe, then there is only one benchmark (e.g., equal weighted market return);
if it has the same shape as returns, then different stocks may correspond to different benchmarks (e.g., industry return)
:param class_df: DataFrame, the class each stock corresponds to (e.g., GICS industry);
by default, returns, in_flag and class_df should be of the same shape
and have their indexes (ID) & columns (dates) aligned
:param output_freq: {'M', 'D'}; 'M' for monthly, 'D' for daily
:param window_size: used in combination with output_freq; usually 252D (252 days) or 12M (12 months)
:param k: a parameter used to determine outlier returns; only effective when mkt is None
:param universe: if True, in_flag will be used to tell whether a stock is in the corresponding universe on a specific day
:param target_dates: list-like, the specific dates that betas are calculated for
:param len_beta: number of cross-sections that betas are calculated for
:param minimum_coverage: the minimum percentage of non-missing returns needed to calculate betas
:return: DataFrame, beta matrix
"""
assert output_freq.lower() in ('d', 'm')
if minimum_coverage is None or minimum_coverage > 1 or minimum_coverage < 0:
minimum_coverage = 0.75
if class_df is None:
class_df = returns.values.copy()
if universe:
class_df[in_flag.values != 1] = np.nan
class_df[np.isfinite(class_df)] = 1
class_df = pd.DataFrame(class_df,
index=returns.index,
columns=returns.columns)
diff_class = np.unique(class_df.values[~np.isnan(class_df)])
period_end = frequency_convert(returns.columns, output_freq)
period_end_idx = np.array(
[np.argwhere(returns.columns == x)[0, 0] for x in period_end])
selected_idx = range(
window_size + np.argwhere((period_end_idx + 1) > np.argwhere(
np.sum(np.isfinite(returns.values), axis=0) > 1)[0, 0])[0, 0],
len(period_end_idx))
if target_dates is None and len_beta is not None and len_beta > 0:
selected_idx = range(selected_idx.stop - len_beta, selected_idx.stop)
if target_dates is not None:
target_idx = [
np.argwhere(period_end == x).flatten()[0] for x in target_dates
]
selected_idx_2 = [x for x in target_idx if x in selected_idx]
selected_idx = target_idx
output = np.full((returns.shape[0], len(selected_idx)), np.nan)
for col_id, c_sel_idx in tqdm(enumerate(selected_idx)):
if target_dates is not None and c_sel_idx not in selected_idx_2:
continue
for c_class in diff_class:
if universe:
c_mask = (
(in_flag.loc[:, period_end[c_sel_idx]] == 1) &
(class_df.loc[:, period_end[c_sel_idx]] == c_class)).values
else:
c_mask = (
class_df.loc[:, period_end[c_sel_idx]] == c_class).values
if c_mask.any():
c_idx = in_flag.values[c_mask, (
period_end_idx[c_sel_idx - window_size] +
1):(period_end_idx[c_sel_idx] + 1)] == 1
c_rtn = returns.values[c_mask, (
period_end_idx[c_sel_idx - window_size] +
1):(period_end_idx[c_sel_idx] + 1)]
if mkt is None:
# if mkt is not provided, calculate the ew-market return as the market return benchmark
cc_rtn = c_rtn.copy()
cc_rtn[~c_idx] = np.nan
if k > 1:
cc_rtn = remove_outliers(cc_rtn, k=k, set_na=False)
else:
cc_rtn[cc_rtn > k] = k
mkt_rtn = bn.nanmean(cc_rtn, axis=0)
else:
c_rtn_columns = returns.columns[(
period_end_idx[c_sel_idx - window_size] +
1):(period_end_idx[c_sel_idx] + 1)]
dates = np.intersect1d(mkt.columns,
c_rtn_columns,
assume_unique=True)
c_rtn = pd.DataFrame(c_rtn, columns=c_rtn_columns)
c_rtn = c_rtn.loc[:, dates].values
mkt_rtn = mkt.loc[:, returns.columns[period_end_idx[
c_sel_idx - window_size]]:dates[-1]]
mkt_rtn_cols = mkt_rtn.columns
mkt_rtn = mkt_rtn.values.copy()
if mkt_rtn.shape[0] > 1:
mkt_rtn = mkt_rtn[c_mask, :]
mkt_rtn[np.isnan(mkt_rtn)] = 0
wealth = np.exp(np.log(1 + mkt_rtn).cumsum(axis=1))
mkt_rtn = pd.DataFrame(
wealth / shift_2darray(wealth, 1, axis=1) - 1,
columns=mkt_rtn_cols)
mkt_rtn = mkt_rtn.loc[:, dates].values
mask_beta = np.sum(np.isfinite(c_rtn), axis=1) >= (
period_end_idx[c_sel_idx] -
period_end_idx[c_sel_idx - window_size]) * minimum_coverage
if mask_beta.any():
mkt_var = bn.nanvar(mkt_rtn, ddof=1, axis=1)
if mkt_rtn.shape[0] == 1:
c_beta = pairwise_covariance(c_rtn[mask_beta],
mkt_rtn) / mkt_var
else:
c_beta = pairwise_covariance(
c_rtn[mask_beta],
mkt_rtn[mask_beta]) / mkt_var[mask_beta]
output[np.argwhere(c_mask).flatten()[mask_beta],
col_id] = c_beta
return pd.DataFrame(output,
index=returns.index,
columns=period_end[selected_idx])
def correct(self, task, output_folder=None):
"""
Run correction.
Parameters:
task (dict): Dictionary defining a task/lightcurve to process.
output_folder (string, optional): Path to directory where lightcurve should be saved.
Returns:
dict: Result dictionary containing information about the processing.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
t1 = default_timer()
error_msg = None
save_file = None
result = task.copy()
try:
# Load the lightcurve
lc = self.load_lightcurve(task)
# Run the correction on this lightcurve:
lc_corr, status = self.do_correction(lc)
except (KeyboardInterrupt, SystemExit):
status = STATUS.ABORT
logger.warning("Correction was aborted.")
except:
status = STATUS.ERROR
error_msg = traceback.format_exc().strip()
logger.exception("Correction failed.")
# Check that the status has been changed:
if status == STATUS.UNKNOWN:
raise Exception("STATUS was not set by do_correction")
# Calculate diagnostics:
details = {}
if status in (STATUS.OK, STATUS.WARNING):
# Calculate diagnostics:
details['variance'] = nanvar(lc_corr.flux, ddof=1)
details['rms_hour'] = rms_timescale(lc_corr, timescale=3600/86400)
details['ptp'] = nanmedian(np.abs(np.diff(lc_corr.flux)))
# TODO: set outputs; self._details = self.lightcurve, etc.
save_file = self.save_lightcurve(lc_corr, output_folder=output_folder)
# Plot the final lightcurve:
if self.plot:
fig = plt.figure(dpi=200)
ax = fig.add_subplot(111)
ax.scatter(lc.time, 1e6*(lc.flux/nanmedian(lc.flux)-1), s=2, alpha=0.3, marker='o', label="Original")
ax.scatter(lc_corr.time, lc_corr.flux, s=2, alpha=0.3, marker='o', label="Corrected")
ax.set_xlabel('Time (TBJD)')
ax.set_ylabel('Relative flux (ppm)')
ax.legend()
save_figure(os.path.join(self.plot_folder(lc), self.CorrMethod + '_final'), fig=fig)
plt.close(fig)
# Construct result dictionary from the original task
result = lc_corr.meta['task'].copy()
# Update results:
t2 = default_timer()
details['errors'] = error_msg
result.update({
'status_corr': status,
'elaptime_corr': t2-t1,
'lightcurve_corr': save_file,
'details': details
})
return result
def find_center_row(data):
# Create interpolator for the median profile
interp = scipy.interpolate.interp1d(
x=data[:,0], y=data[:,1], kind='linear',
bounds_error=False, fill_value=numpy.NaN)
#
# Optimization routine
#
def fold_profile(p, interp, maxy, count):
dx = numpy.arange(maxy, dtype=numpy.float)
x_left = p[0] - dx
x_right = p[0] + dx
profile_left = interp(x_left)
profile_right = interp(x_right)
diff = profile_left - profile_right
count[0] += 1
# print "iteration %d --> %e" % (count[0], p[0])
# with open("opt_%d.del" % (count[0]), "w") as f:
# numpy.savetxt(f, profile_left)
# print >>f, "\n"*5,
# numpy.savetxt(f, profile_right)
# print >>f, "\n"*5,
# numpy.savetxt(f, diff)
return diff[numpy.isfinite(diff)]
#
# Get rid of all points that are too noisy
#
w=5
noise = numpy.array([bottleneck.nanvar(data[i-w:i+w,1]) for i in range(w,data.shape[0]-w+1)])
# numpy.savetxt("median_noise", noise)
noise[:w] = numpy.NaN
noise[-w:] = numpy.NaN
for iteration in range(3):
valid = numpy.isfinite(noise)
_perc = numpy.percentile(noise[valid], [16,50,84])
_med = _perc[1]
_sigma = 0.5*(_perc[2]-_perc[0])
outlier = (noise > _med+3*_sigma) | (noise < _med - 3*_sigma)
noise[outlier] = numpy.NaN
#numpy.savetxt("median_noise2", noise)
valid = numpy.isfinite(noise)
data[:,1][~valid] = numpy.NaN
#numpy.savetxt("median_noise3", data)
count=[0]
fit_all = scipy.optimize.leastsq(
func=fold_profile,
x0=[data.shape[0]/5.],
args=(interp, data.shape[0]/2,count),
full_output=True,
epsfcn=1e-1,
)
#print fit_all[0]
return fit_all[0][0]
def correct(self, task):
"""
Run correction.
Parameters:
task (dict): Dictionary defining a task/lightcurve to process.
Returns:
dict: Result dictionary containing information about the processing.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
t1 = default_timer()
error_msg = None
save_file = None
result = task.copy()
try:
# Load the lightcurve
lc = self.load_lightcurve(task)
# Run the correction on this lightcurve:
lc, status = self.do_correction(lc)
except (KeyboardInterrupt, SystemExit):
status = STATUS.ABORT
logger.warning("Correction was aborted.")
except:
status = STATUS.ERROR
error_msg = traceback.format_exc().strip()
logger.exception("Correction failed.")
# Check that the status has been changed:
if status == STATUS.UNKNOWN:
raise Exception("STATUS was not set by do_correction")
# Calculate diagnostics:
details = {}
if status in (STATUS.OK, STATUS.WARNING):
# Calculate diagnostics:
details['variance'] = nanvar(lc.flux, ddof=1)
details['rms_hour'] = rms_timescale(lc, timescale=3600 / 86400)
details['ptp'] = nanmedian(np.abs(np.diff(lc.flux)))
# TODO: set outputs; self._details = self.lightcurve, etc.
save_file = self.save_lightcurve(lc)
# Construct result dictionary from the original task
result = lc.meta['task'].copy()
# Update results:
t2 = default_timer()
details['errors'] = error_msg
result.update({
'status_corr': status,
'elaptime_corr': t2 - t1,
'lightcurve_corr': save_file,
'details': details
})
return result
def time_nanvar(self, dtype, shape, order, axis):
bn.nanvar(self.arr, axis=axis)
def generate_todolist(self):
"""
Generate todo.sqlite file in training set directory.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger(__name__)
try:
with closing(sqlite3.connect(self.todo_file)) as conn:
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
# Create the basic file structure of a TODO-list:
todolist_structure(conn)
logger.info(
"Step 3: Reading file and extracting information...")
pri = 0
diagnostics_file = os.path.join(self.input_folder,
'diagnostics.txt')
diagnostics = None
if os.path.isfile(diagnostics_file):
diagnostics = np.genfromtxt(diagnostics_file,
delimiter=',',
comments='#',
dtype=None,
encoding='utf-8')
for k, star in tqdm(enumerate(self.starlist),
total=len(self.starlist)):
# Get starid:
starname = star[0]
starclass = star[1]
if starname.startswith('constant_'):
starid = -10000 - int(starname[9:])
elif starname.startswith('fakerrlyr_'):
starid = -20000 - int(starname[10:])
else:
starid = int(starname)
starname = '{0:09d}'.format(starid)
# Path to lightcurve:
lightcurve = starclass + '/' + starname + '.txt'
# Check that the file actually exists:
if not os.path.exists(
os.path.join(self.input_folder, lightcurve)):
raise FileNotFoundError(lightcurve)
# Load diagnostics from file, to speed up the process:
if diagnostics is not None:
variance, rms_hour, ptp = diagnostics[k]
else:
# Try to load the lightcurve using the BaseClassifier method.
# This will ensure that the lightcurve can actually be read by the system.
lc = io.load_lightcurve(
os.path.join(self.input_folder, lightcurve))
variance = nanvar(lc.flux, ddof=1)
rms_hour = utilities.rms_timescale(lc)
ptp = utilities.ptp(lc)
#if datasource is None:
# if (lc.time[1] - lc.time[0])*86400 > 1000:
# datasource = 'ffi'
# else:
# datasource = 'tpf'
elaptime = np.random.normal(3.14, 0.5)
pri += 1
todolist_insert(cursor,
priority=pri,
starid=starid,
lightcurve=lightcurve,
datasource='ffi',
variance=variance,
rms_hour=rms_hour,
ptp=ptp,
elaptime=elaptime)
conn.commit()
todolist_cleanup(conn, cursor)
cursor.close()
except: # noqa: E722, pragma: no cover
if os.path.exists(self.todo_file):
os.remove(self.todo_file)
raise
logger.info("%s training set successfully built.", self.key)
def test_known_star(SHARED_INPUT_DIR, corrector, starid, cadence, var_goal, rms_goal, ptp_goal):
""" Check that the ensemble returns values that are reasonable and within expected bounds """
# All stars we check here come from the same sector and camera.
# Define these here for the future where we may test on other combinations of these:
sector = 1
camera = 1
__dir__ = os.path.abspath(os.path.dirname(__file__))
logger = logging.getLogger(__name__)
logger.info("-------------------------------------------------------------")
logger.info("CORRECTOR = %s, SECTOR=%d, CADENCE=%s, STARID=%d", corrector, sector, cadence, starid)
# All stars are from the same CCD, find the task for it:
with corrections.TaskManager(SHARED_INPUT_DIR) as tm:
task = tm.get_task(starid=starid, sector=sector, camera=camera, cadence=cadence)
# Check that task was actually found:
assert task is not None, "Task could not be found"
# Load lightcurve that will also be plotted together with the result:
# This lightcurve is of the same objects, at a state where it was deemed that the
# corrections were doing a good job.
compare_lc_path = os.path.join(__dir__, 'compare', f'compare-{corrector}-s{sector:04d}-c{cadence:04d}-tic{starid:011d}.ecsv.gz')
compare_lc = None
if os.path.isfile(compare_lc_path):
compare_lc = Table.read(compare_lc_path, format='ascii.ecsv')
else:
warnings.warn("Comparison data does not exist: " + compare_lc_path)
# Initiate the class
CorrClass = corrections.corrclass(corrector)
with tempfile.TemporaryDirectory() as tmpdir:
with CorrClass(SHARED_INPUT_DIR, plot=True) as corr:
# Check basic parameters of object (from BaseCorrector):
assert corr.input_folder == SHARED_INPUT_DIR, "Incorrect input folder"
assert corr.plot, "Plot parameter passed appropriately"
assert os.path.isdir(corr.data_folder), "DATA_FOLDER doesn't exist"
# Load the input lightcurve:
inlc = corr.load_lightcurve(task)
# Print input lightcurve properties:
print( inlc.show_properties() )
assert inlc.sector == sector
assert inlc.camera == camera
# Run correction:
tmplc = inlc.copy()
outlc, status = corr.do_correction(tmplc)
# Check status
assert outlc is not None, "Correction fails"
assert isinstance(outlc, TessLightCurve), "Should return TessLightCurve object"
assert isinstance(status, corrections.STATUS), "Should return a STATUS object"
assert status in (corrections.STATUS.OK, corrections.STATUS.WARNING), "STATUS was not set appropriately"
# Print output lightcurve properties:
print( outlc.show_properties() )
# Save the lightcurve to FITS file to be tested later on:
save_file = corr.save_lightcurve(outlc, output_folder=tmpdir)
# Check contents
assert len(outlc) == len(inlc), "Input flux ix different length to output flux"
assert isinstance(outlc.flux, np.ndarray), "FLUX is not a ndarray"
assert isinstance(outlc.flux_err, np.ndarray), "FLUX_ERR is not a ndarray"
assert isinstance(outlc.quality, np.ndarray), "QUALITY is not a ndarray"
assert outlc.flux.dtype.type is inlc.flux.dtype.type, "FLUX changes dtype"
assert outlc.flux_err.dtype.type is inlc.flux_err.dtype.type, "FLUX_ERR changes dtype"
assert outlc.quality.dtype.type is inlc.quality.dtype.type, "QUALITY changes dtype"
assert outlc.flux.shape == inlc.flux.shape, "FLUX changes shape"
assert outlc.flux_err.shape == inlc.flux_err.shape, "FLUX_ERR changes shape"
assert outlc.quality.shape == inlc.quality.shape, "QUALITY changes shape"
# Plot output lightcurves:
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, squeeze=True, figsize=[10, 10])
ax1.plot(inlc.time, inlc.flux, lw=0.5)
ax1.set_title(f"{corrector} - Sector {sector:d} - {cadence}s - TIC {starid:d}")
if compare_lc:
ax2.plot(compare_lc['time'], compare_lc['flux'], label='Compare', lw=0.5)
ax3.axhline(0, lw=0.5, ls=':', color='0.7')
ax3.plot(outlc.time, outlc.flux - compare_lc['flux'], lw=0.5)
ax2.plot(outlc.time, outlc.flux, label='New', lw=0.5)
ax1.set_ylabel('Flux [e/s]')
ax1.minorticks_on()
ax2.set_ylabel('Relative Flux [ppm]')
ax2.minorticks_on()
ax2.legend()
ax3.set_ylabel('New - Compare [ppm]')
ax3.set_xlabel('Time [TBJD]')
ax3.minorticks_on()
fig.savefig(os.path.join(__dir__, f'test-{corrector}-s{sector:04d}-c{cadence:04d}-tic{starid:011d}.png'), bbox_inches='tight')
plt.close(fig)
# Check things that are allowed to change:
assert all(outlc.flux != inlc.flux), "Input and output flux are identical."
assert not np.any(np.isinf(outlc.flux)), "FLUX contains Infinite"
assert not np.any(np.isinf(outlc.flux_err)), "FLUX_ERR contains Infinite"
assert np.sum(np.isnan(outlc.flux)) < 0.5*len(outlc), "More than half the lightcurve is NaN"
assert allnan(outlc.flux_err[np.isnan(outlc.flux)]), "FLUX_ERR should be NaN where FLUX is"
# TODO: Check that quality hasn't changed in ways that are not allowed:
# - Only values defined in CorrectorQualityFlags
# - No removal of flags already set
assert all(outlc.quality >= 0)
assert all(outlc.quality <= 128)
assert all(outlc.quality >= inlc.quality)
# Things that shouldn't chance from the corrections:
assert outlc.targetid == inlc.targetid, "TARGETID has changed"
assert outlc.label == inlc.label, "LABEL has changed"
assert outlc.sector == inlc.sector, "SECTOR has changed"
assert outlc.camera == inlc.camera, "CAMERA has changed"
assert outlc.ccd == inlc.ccd, "CCD has changed"
assert outlc.quality_bitmask == inlc.quality_bitmask, "QUALITY_BITMASK has changed"
assert outlc.ra == inlc.ra, "RA has changed"
assert outlc.dec == inlc.dec, "DEC has changed"
assert outlc.mission == 'TESS', "MISSION has changed"
assert outlc.time_format == 'btjd', "TIME_FORMAT has changed"
assert outlc.time_scale == 'tdb', "TIME_SCALE has changed"
assert_array_equal(outlc.time, inlc.time, "TIME has changed")
assert_array_equal(outlc.timecorr, inlc.timecorr, "TIMECORR has changed")
assert_array_equal(outlc.cadenceno, inlc.cadenceno, "CADENCENO has changed")
assert_array_equal(outlc.pixel_quality, inlc.pixel_quality, "PIXEL_QUALITY has changed")
assert_array_equal(outlc.centroid_col, inlc.centroid_col, "CENTROID_COL has changed")
assert_array_equal(outlc.centroid_row, inlc.centroid_row, "CENTROID_ROW has changed")
# Check metadata
assert tmplc.meta == inlc.meta, "Correction changed METADATA in-place"
assert outlc.meta['task'] == inlc.meta['task'], "Metadata is incomplete"
assert isinstance(outlc.meta['additional_headers'], fits.Header)
# Check performance metrics:
#logger.warning("VAR: %e", nanvar(outlc.flux))
if var_goal is not None:
var_in = nanvar(inlc.flux)
var_out = nanvar(outlc.flux)
var_diff = np.abs(var_out - var_goal) / var_goal
logger.info("VAR: %f - %f - %f", var_in, var_out, var_diff)
assert_array_less(var_diff, 0.05, "VARIANCE changed outside interval")
#logger.warning("RMS: %e", rms_timescale(outlc))
if rms_goal is not None:
rms_in = rms_timescale(inlc)
rms_out = rms_timescale(outlc)
rms_diff = np.abs(rms_out - rms_goal) / rms_goal
logger.info("RMS: %f - %f - %f", rms_in, rms_out, rms_diff)
assert_array_less(rms_diff, 0.05, "RMS changed outside interval")
#logger.warning("PTP: %e", ptp(outlc))
if ptp_goal is not None:
ptp_in = ptp(inlc)
ptp_out = ptp(outlc)
ptp_diff = np.abs(ptp_out - ptp_goal) / ptp_goal
logger.info("PTP: %f - %f - %f", ptp_in, ptp_out, ptp_diff)
assert_array_less(ptp_diff, 0.05, "PTP changed outside interval")
# Check FITS file:
with fits.open(os.path.join(tmpdir, save_file), mode='readonly') as hdu:
# Lightcurve FITS table:
fitslc = hdu['LIGHTCURVE'].data
hdr = hdu['LIGHTCURVE'].header
# Simple checks of header values:
assert hdu[0].header['TICID'] == starid
# Checks of things in FITS table that should not have changed at all:
assert_array_equal(fitslc['TIME'], inlc.time, "FITS: TIME has changed")
assert_array_equal(fitslc['TIMECORR'], inlc.timecorr, "FITS: TIMECORR has changed")
assert_array_equal(fitslc['CADENCENO'], inlc.cadenceno, "FITS: CADENCENO has changed")
assert_array_equal(fitslc['FLUX_RAW'], inlc.flux, "FITS: FLUX_RAW has changed")
assert_array_equal(fitslc['FLUX_RAW_ERR'], inlc.flux_err, "FITS: FLUX_RAW_ERR has changed")
assert_array_equal(fitslc['MOM_CENTR1'], inlc.centroid_col, "FITS: CENTROID_COL has changed")
assert_array_equal(fitslc['MOM_CENTR2'], inlc.centroid_row, "FITS: CENTROID_ROW has changed")
# Some things are allowed to change, but still within some requirements:
assert all(fitslc['FLUX_CORR'] != inlc.flux), "FITS: Input and output flux are identical."
assert np.sum(np.isnan(fitslc['FLUX_CORR'])) < 0.5*len(fitslc['TIME']), "FITS: More than half the lightcurve is NaN"
assert allnan(fitslc['FLUX_CORR_ERR'][np.isnan(fitslc['FLUX_CORR'])]), "FITS: FLUX_ERR should be NaN where FLUX is"
if corrector == 'ensemble':
# Check special headers:
assert np.isfinite(hdr['ENS_MED']) and hdr['ENS_MED'] > 0
assert isinstance(hdr['ENS_NUM'], int) and hdr['ENS_NUM'] > 0
assert hdr['ENS_DLIM'] == 1.0
assert hdr['ENS_DREL'] == 10.0
assert hdr['ENS_RLIM'] == 0.4
# Special extension for ensemble:
tic = hdu['ENSEMBLE'].data['TIC']
bzeta = hdu['ENSEMBLE'].data['BZETA']
assert len(tic) == len(bzeta)
assert len(np.unique(tic)) == len(tic), "TIC numbers in ENSEMBLE table are not unique"
assert len(tic) == hdr['ENS_NUM'], "Not the same number of targets in ENSEMBLE table as specified in header"
elif corrector == 'cbv':
# Check special headers:
assert isinstance(hdr['CBV_NUM'], int) and hdr['CBV_NUM'] > 0
# Check coefficients:
for k in range(0, hdr['CBV_NUM']+1):
assert np.isfinite(hdr['CBV_C%d' % k])
for k in range(1, hdr['CBV_NUM']+1):
assert np.isfinite(hdr['CBVS_C%d' % k])
# Check that no other coefficients are present
assert 'CBV_C%d' % (hdr['CBV_NUM']+1) not in hdr
assert 'CBVS_C%d' % (hdr['CBV_NUM']+1) not in hdr
elif corrector == 'kasoc_filter':
# Check special headers:
assert hdr['KF_POSS'] == 'None'
assert np.isfinite(hdr['KF_LONG']) and hdr['KF_LONG'] > 0
assert np.isfinite(hdr['KF_SHORT']) and hdr['KF_SHORT'] > 0
assert hdr['KF_SCLIP'] == 4.5
assert hdr['KF_TCLIP'] == 5.0
assert hdr['KF_TWDTH'] == 1.0
assert hdr['KF_PSMTH'] == 200
assert isinstance(hdr['NUM_PER'], int) and hdr['NUM_PER'] >= 0
for k in range(1, hdr['NUM_PER']+1):
assert np.isfinite(hdr['PER_%d' % k]) and hdr['PER_%d' % k] > 0
# Check that no other periods are present
assert 'PER_%d' % (hdr['NUM_PER'] + 1) not in hdr
# Test that the Gzip FITS file has the correct uncompressed file name, by simply
# decompressing the Gzip file, asking to keep the original file name.
# This uses the system GZIP utility, since there doesn't seem to be a way to do this
# through the Python gzip module:
fpath = os.path.join(tmpdir, save_file)
fpath_uncompressed = fpath.replace('.fits.gz', '.fits')
assert not os.path.exists(fpath_uncompressed), "Uncompressed file already exists"
gzip_output = subprocess.check_output(['gzip', '-dkNv', os.path.basename(fpath)],
cwd=os.path.dirname(fpath),
stderr=subprocess.STDOUT,
encoding='utf8')
print("Gzip output:")
print(gzip_output)
assert os.path.isfile(fpath_uncompressed), "Incorrect uncompressed file name"
# Just see if we can in fact also open the uncompressed FITS file and get a simple header:
with fits.open(fpath_uncompressed, mode='readonly') as hdu:
assert hdu[0].header['TICID'] == starid
def stats(self, lmean=False, lmed=False, lskew=False, lvar=False,
lstd=False, lcoefvar=False, lperc=False, p=0.95):
"""Calculate some statistics among every realisation.
Each statistic is calculated node-wise along the complete number of
realisations.
Parameters
----------
lmean : boolean, default False
Calculate the mean.
lmed : boolean, default False
Calculate the median.
lskew : boolean, default False
Calculate skewness.
lvar : boolean, default False
Calculate the variance.
lstd : boolean, default False
Calculate the standard deviation.
lcoefvar : boolean, default False
Calculate the coefficient of variation.
lperc : boolean, default False
Calculate the percentile `100 * (1 - p)`.
p : number, default 0.95
Probability value.
Returns
-------
retdict : dict of GridArr
Dictionary containing one GridArr for each calculated statistic.
See Also
--------
stats_area : same but considering a circular (and horizontal) area of
a specified radius around a given point.
"""
# check if the map files are already opened or not
if isinstance(self.files[0], file):
opened_files = True
else:
opened_files = False
if lmean:
meanmap = np.zeros(self.cells)
if lmed:
medmap = np.zeros(self.cells)
if lskew:
skewmap = np.zeros(self.cells)
if lvar:
varmap = np.zeros(self.cells)
if lstd:
stdmap = np.zeros(self.cells)
if lcoefvar:
coefvarmap = np.zeros(self.cells)
if lperc:
percmap = np.zeros((self.cells, 2))
arr = np.zeros(self.nfiles)
skip = True
offset = os.SEEK_SET
for cell in xrange(self.cells - self.header):
for i, gridfile in enumerate(self.files):
# deal with map files not open yet
if opened_files:
grid = gridfile
else:
grid = open(gridfile, 'rb')
grid.seek(offset)
if skip:
skip_lines(grid, self.header)
arr[i] = grid.readline()
if not opened_files:
offset = grid.tell()
grid.close()
skip = False
# replace no data's with NaN
bn.replace(arr, self.nodata, np.nan)
if lmean:
meanmap[cell] = bn.nanmean(arr)
if lmed:
medmap[cell] = bn.nanmedian(arr)
if lskew:
skewmap[cell] = pd.Series(arr).skew()
if lvar:
varmap[cell] = bn.nanvar(arr, ddof=1)
if lstd:
stdmap[cell] = bn.nanstd(arr, ddof=1)
if lcoefvar:
if lstd and lmean:
coefvarmap[cell] = stdmap[cell] / meanmap[cell] * 100
else:
std = bn.nanstd(arr, ddof=1)
mean = bn.nanmean(arr)
coefvarmap[cell] = std / mean * 100
if lperc:
percmap[cell] = pd.Series(arr).quantile([(1 - p) / 2,
1 - (1 - p) / 2])
retdict = dict()
if lmean:
meangrid = GridArr(name='meanmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata, val=meanmap)
retdict['meanmap'] = meangrid
if lmed:
medgrid = GridArr(name='medianmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata, val=medmap)
retdict['medianmap'] = medgrid
if lskew:
skewgrid = GridArr(name='skewmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata, val=skewmap)
retdict['skewmap'] = skewgrid
if lvar:
vargrid = GridArr(name='varmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata, val=varmap)
retdict['varmap'] = vargrid
if lstd:
stdgrid = GridArr(name='stdmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata, val=stdmap)
retdict['stdmap'] = stdgrid
if lcoefvar:
coefvargrid = GridArr(name='coefvarmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata,
val=coefvarmap)
retdict['coefvarmap'] = coefvargrid
if lperc:
percgrid = GridArr(name='percmap', dx=self.dx, dy=self.dy,
dz=self.dz, nodata=self.nodata, val=percmap)
retdict['percmap'] = percgrid
return retdict