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 mad(data, sigma=True, axis=None, force=False, medval=0.0):
"""
Return the median absolute deviation (or the absolute deviation
about a fixed value - default zero - if force is set to True). By
default returns the equivalent sigma. Axis functionality adapted
from https://github.com/keflavich/agpy/blob/master/agpy/mad.py
Flips nans to True (default false) to use with nans.
Parameters
----------
data : np.ndarray
Data set.
sigma : bool, optional
Enables std estimation from MAD.
axis : {int, None}, optional
Axis to evaluate MAD along.
force : bool, optional
Force the median to be a given value.
medval : float, optional
Forced median value.
Returns
-------
mad : float
MAD estimation. If sigma is True, MAD*1.4826 is returned.
"""
# Check for nans in the data
nans = False
if np.isnan(data).any():
nans = True
if axis > 0:
if force:
med = medval
else:
if nans:
med = nanmedian(data.swapaxes(0, axis), axis=0)
else:
med = np.median(data.swapaxes(0, axis), axis=0)
if nans:
mad = nanmedian(np.abs(data.swapaxes(0, axis) - med), axis=0)
else:
mad = np.median(np.abs(data.swapaxes(0, axis) - med), axis=0)
else:
if force:
med = medval
else:
if nans:
med = nanmedian(data, axis=axis)
else:
med = np.median(data, axis=axis)
if nans:
mad = nanmedian(np.abs(data - med), axis=axis)
else:
mad = np.median(np.abs(data - med), axis=axis)
if not sigma:
return mad
else:
return mad * 1.4826
def theil_sen(x, y, n_samples=100000):
"""
Computes the Theil-Sen estimator for 2D data.
This complexity is O(n**2), which can be poor for large n. We will perform a sampling
of data points to get an unbiased, but larger variance estimator.
The sampling will be done by picking two points at random, and computing the slope,
up to n_samples times.
Parameters:
x (ndarray): 1-d np array, the control variate.
y (ndarray): 1-d np.array, the ind variate.
n_samples (int): how many points to sample.
"""
if x.shape[0] != y.shape[0]:
raise ValueError("x and y must be the same shape.")
n = x.shape[0]
i1 = np.random.randint(0, n, n_samples)
i2 = np.random.randint(0, n, n_samples)
slopes = _slope(x[i1], x[i2], y[i1], y[i2])
slope_ = nanmedian(slopes)
#find the optimal b as the median of y_i - slope*x_i
intercepts = np.empty(n, dtype='float64')
for i in range(n):
intercepts[i] = y[i] - slope_ * x[i]
intercept_ = nanmedian(intercepts)
return np.array([slope_, intercept_])
def bottleneck_MAD(arr, c=0.6745, axis=None):
"""
Median Absolute Deviation along given axis of an array:
median(abs(a - median(a))) / c
c = 0.6745 is the constant to convert from MAD to std; it is used by
default
"""
from bottleneck import nanmedian
import numpy as np
if not arr.dtype.isnative:
kind = str(arr.dtype.kind)
sz = str(arr.dtype.itemsize)
dt = '=' + kind + sz
data = arr.astype(dt)
else:
data = arr
if data.ndim == 1:
d = nanmedian(data)
m = nanmedian(ma.fabs(data - d) / c)
else:
d = nanmedian(data, axis=axis)
if axis > 0:
aswp = np.swapaxes(data, 0, axis)
else:
aswp = data
m = nanmedian(ma.fabs(aswp - d) / c, axis=0)
return m
def smear(self, img):
"""CCD dark current and smear correction.
TODO:
- Should we weight everything with the number of rows used in masked vs virtual regions?
- Should we take self.frametransfer_time into account?
- Cosmic ray rejection requires images before and after in time?
"""
self.logger.info("Doing smear correction...")
# Remove cosmic rays in collateral data:
# TODO: Can cosmic rays also show up in virtual pixels? If so, also include img.virtual_smear
#index_collateral_cosmicrays = cosmic_rays(img.masked_smear)
index_collateral_cosmicrays = np.zeros_like(img.masked_smear,
dtype='bool')
img.masked_smear[index_collateral_cosmicrays] = np.nan
# Average the masked and virtual smear across their rows:
masked_smear = nanmedian(img.masked_smear, axis=0)
virtual_smear = nanmedian(img.virtual_smear, axis=0)
# Estimate dark current:
# TODO: Should this be self.frametransfer_time?
fdark = nanmedian(masked_smear - virtual_smear *
(self.exposure_time + self.readout_time) /
self.exposure_time)
img.dark = fdark # Save for later use
self.logger.info('Dark current: %f', img.dark)
if np.isnan(fdark):
fdark = 0
# Correct the smear regions for the dark current:
masked_smear -= fdark
virtual_smear -= fdark * (self.exposure_time +
self.readout_time) / self.exposure_time
# Weights from number of pixels in different regions:
Nms = np.sum(~np.isnan(img.masked_smear), axis=0)
Nvs = np.sum(~np.isnan(img.virtual_smear), axis=0)
c_ms = Nms / np.maximum(Nms + Nvs, 1)
c_vs = Nvs / np.maximum(Nms + Nvs, 1)
# Weights as in Kepler where you only have one row in each sector:
#g_ms = ~np.isnan(masked_smear)
#g_vs = ~np.isnan(virtual_smear)
#c_ms = g_ms/np.maximum(g_ms + g_vs, 1)
#c_vs = g_vs/np.maximum(g_ms + g_vs, 1)
# Estimate the smear for all columns, taking into account
# that some columns could be missing:
replace(masked_smear, np.nan, 0)
replace(virtual_smear, np.nan, 0)
fsmear = c_ms * masked_smear + c_vs * virtual_smear
# Correct the science pixels for dark current and smear:
img.target_data -= fdark
for k, col in enumerate(img.collateral_columns):
img.target_data[img.columns == col] -= fsmear[k]
return img
def bottleneck_MAD(arr, c=0.6745, axis=None):
"""
Median Absolute Deviation along given axis of an array:
median(abs(a - median(a))) / c
c = 0.6745 is the constant to convert from MAD to std; it is used by
default
"""
from bottleneck import nanmedian
import numpy as np
if not arr.dtype.isnative:
kind = str(arr.dtype.kind)
sz = str(arr.dtype.itemsize)
dt = '=' + kind + sz
data = arr.astype(dt)
else:
data = arr
if data.ndim == 1:
d = nanmedian(data)
m = nanmedian(ma.fabs(data - d) / c)
else:
d = nanmedian(data, axis=axis)
if axis > 0:
aswp = np.swapaxes(data,0,axis)
else:
aswp = data
m = nanmedian(ma.fabs(aswp - d) / c, axis=0)
return m
def _median_central(x, width_points):
y = move_median(x, width_points, min_count=1)
yny = append(y[width_points // 2:], [NaN] * (width_points // 2))
for k in range(width_points // 2):
yny[k] = nanmedian(x[:(2 * k + 1)])
yny[-(k + 1)] = nanmedian(x[-(2 * k + 1):])
return yny
def _move_median_central_1d(x, width_points):
y = move_median(x, width_points, min_count=1)
y = np.roll(y, -width_points // 2 + 1)
for k in range(width_points // 2 + 1):
y[k] = nanmedian(x[:(k + 2)])
y[-(k + 1)] = nanmedian(x[-(k + 2):])
return y
def robust_median_filter(flux, size=375):
if size % 2 == 0: size = size + 1 #Make even results odd
half_sizes = np.array([-(size - 1) / 2, ((size - 1) / 2) + 1], dtype='int')
if np.ndim(flux) == 2: #For 2D spectrum
ny, nx = np.shape(flux) #Calculate npix in x and y
else: #Else for 1D spectrum
nx = len(flux) #Calculate npix
median_result = np.zeros(
nx) #Create array that will store the smoothed median spectrum
if np.ndim(flux) == 2: #Run this loop for 2D
for i in range(
nx
): #This loop does the running of the median down the spectrum each pixel
x_left, x_right = i + half_sizes
if x_left < 0:
x_left = 0
elif x_right > nx:
x_right = nx
median_result[i] = bn.nanmedian(
flux[:, x_left:x_right]
) #Calculate median between x_left and x_right for a given pixel
else: #Run this loop for 1D
for i in range(
nx
): #This loop does the running of the median down the spectrum each pixel
x_left, x_right = i + half_sizes
if x_left < 0:
x_left = 0
elif x_right > nx:
x_right = nx
median_result[i] = bn.nanmedian(
flux[x_left:x_right]
) #Calculate median between x_left and x_right for a given pixel
return median_result
def rms_timescale(time, flux, timescale=3600 / 86400):
"""
Compute robust RMS on specified timescale. Using MAD scaled to RMS.
Parameters:
time (ndarray): Timestamps in days.
flux (ndarray): Flux to calculate RMS for.
timescale (float, optional): Timescale to bin timeseries before calculating RMS. Default=1 hour.
Returns:
float: Robust RMS on specified timescale.
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
# Construct the bin edges seperated by the timescale:
bins = np.arange(np.nanmin(time), np.nanmax(time), timescale)
bins = np.append(bins, np.nanmax(time))
# Bin the timeseries to one hour:
indx = np.isfinite(flux)
flux_bin, _, _ = binned_statistic(time[indx],
flux[indx],
nanmean,
bins=bins)
# Compute robust RMS value (MAD scaled to RMS)
return mad_to_sigma * nanmedian(np.abs(flux_bin - nanmedian(flux_bin)))
def mad(a, c=0.6745, axis=None):
"""
Compute the median absolute deviation along the specified axis.
median(abs(a - median(a))) / c
Returns the median absolute deviation of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : int, optional
Axis along which the medians are computed. The default (axis=None)
is to compute the median along a flattened version of the array.
c : float, optional
The scaling factor applied to the raw median aboslute deviation.
The default is to scale to match the standard deviation.
Returns
-------
mad : ndarray
A new array holding the result.
"""
if (axis is None):
_shape = a.shape
a.shape = np.product(a.shape, axis=0)
m = nanmedian(np.fabs(a - nanmedian(a))) / c
a.shape = _shape
else:
m = np.apply_along_axis(
lambda x: nanmedian(np.fabs(x - nanmedian(x))) / c, axis, a)
return m
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 crude_skycor(fitslist, ext, mask=None, nsimul=100, noisechisel_grad=False, bootmedian=True):
if isinstance(fitslist, str):
fitslist = [fitslist]
if isinstance(fitslist, list):
for fits_name in fitslist:
print(fits_name)
fits_image = fits.open(fits_name)
if mask is not None:
print("Input mask accepted: " + mask)
mask_fits = fits.open(mask)
shape_mask = mask_fits[0].data.shape
shape_fits = fits_image[ext].data.shape
mask_array = mask_fits[0].data
if (shape_mask == (1014, 1014)) & (shape_fits == (1024, 1024)):
mask_array = np.zeros(shape_fits)
border = 5
mask_array[0+border:1024-border,
0+border:1024-border] = mask_fits[0].data
if bootmedian:
skylvl = bm.bootmedian(sample_input=fits_image[ext].data[~np.isnan(mask_array)],
nsimul=nsimul, errors=1)
if not bootmedian:
median_sky = bn.nanmedian(fits_image[ext].data[~np.isnan(mask_array)])
#sigma_sky = bn.nanstd(fits_image[ext].data[~np.isnan(mask_array)])
sigma_sky = 0
skylvl = {"median": median_sky,
"s1_up": median_sky+sigma_sky,
"s1_down": median_sky-sigma_sky,
"std1_down": sigma_sky,
"std1_up": sigma_sky}
else:
if bootmedian:
skylvl = bm.bootmedian(sample_input=fits_image[ext].data, nsimul=nsimul, errors=1)
if not bootmedian:
median_sky = bn.nanmedian(fits_image[ext].data)
#sigma_sky = bn.nanstd(fits_image[ext].data)
sigma_sky = 0
skylvl = {"median": median_sky,
"s1_up": median_sky+sigma_sky,
"s1_down": median_sky-sigma_sky,
"std1_down": sigma_sky,
"std1_up": sigma_sky}
print(skylvl)
print(np.abs(skylvl["median"] - skylvl["s1_up"])/2.)
print("Skylvl: " + str(skylvl["median"]) + " +/- " + str(np.abs(skylvl["s1_up"] - skylvl["s1_down"])/2.))
fits_image[ext].data = fits_image[ext].data - skylvl["median"]
fits_image[0].header['SKYSTD'] = skylvl["std1_down"]
fits_image[0].header['SKYLVL'] = skylvl["median"]
fits_image[ext].header['SKYSTD'] = skylvl["std1_down"]
fits_image[ext].header['SKYLVL'] = skylvl["median"]
os.system("rm " + fits_name)
fits_image.verify("silentfix")
fits_image.writeto(fits_name)
fits_image.close()
def factor_outlierlimit(factor, n_extremum=5):
x_m = factor.values
median = bn.nanmedian(x_m, axis=1).reshape(-1, 1)
Dmad = bn.nanmedian(abs(x_m - median), axis=1).reshape(-1, 1)
upper = (median + n_extremum * Dmad)
lower = (median - n_extremum * Dmad)
with np.errstate(invalid='ignore'):
res = np.clip(x_m, lower, upper)
return pd.DataFrame(res, factor.index, factor.columns)
def _get_fluctuations(image_array, correction='median'):
# Calculate the mean PV for each image
if correction == 'mean':
mean_values = bn.nanmean(bn.nanmean(image_array, axis=1), axis=1)
elif correction == 'median':
mean_values = bn.nanmedian(bn.nanmedian(image_array, axis=1), axis=1)
return mean_values
def k2p2_saturated(SumImage, MASKS, idx):
# Get logger for printing messages:
logger = logging.getLogger(__name__)
no_masks = MASKS.shape[0]
column_mask = np.zeros_like(SumImage, dtype='bool')
saturated_mask = np.zeros_like(MASKS, dtype='bool')
pixels_added = 0
# Loop through the different masks:
for u in range(no_masks):
# Create binary version of mask and extract
# the rows and columns which it spans and
# the highest value in it:
mask = np.asarray(MASKS[u, :, :], dtype='bool')
mask_rows, mask_columns = np.where(mask)
mask_max = np.nanmax(SumImage[mask])
# Loop through the columns of the mask:
for c in set(mask_columns):
column_mask[:, c] = True
# Extract the pixels that are in this column and in the mask:
pixels = SumImage[mask & column_mask]
# Calculate ratio as defined in Lund & Handberg (2014):
ratio = np.abs(nanmedian(np.diff(pixels))) / np.nanmax(pixels)
if ratio < 0.01 and nanmedian(pixels) >= mask_max / 2:
logger.debug("Column %d - RATIO = %f - Saturated", c, ratio)
# Has significant flux and is in saturated column:
add_to_mask = (idx & column_mask)
# Make sure the pixels we add are directly connected to the highest flux pixel:
new_mask_labels, numfeatures = ndimage.label(add_to_mask)
imax = np.unravel_index(
np.nanargmax(SumImage * mask * column_mask),
SumImage.shape)
add_to_mask &= (new_mask_labels == new_mask_labels[imax])
# Modify the mask:
pixels_added += np.sum(add_to_mask) - np.sum(mask[column_mask])
logger.debug(" %d pixels should be added to column %d",
np.sum(add_to_mask) - np.sum(mask[column_mask]),
c)
saturated_mask[u][add_to_mask] = True
else:
logger.debug("Column %d - RATIO = %f", c, ratio)
column_mask[:, c] = False
return saturated_mask, pixels_added
def _nanmedian(array, axis=None):
"""Bottleneck nanmedian function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanmedian(array, axis=axis))
else:
return bottleneck.nanmedian(array, axis=axis)
def _nanmedian(array, axis=None):
"""Bottleneck nanmedian function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanmedian(array, axis=axis))
else:
return bottleneck.nanmedian(array, axis=axis)
def mad(x):
"""
Median absolute deviation scaled to standard deviation.
Parameters:
x (ndarray): Array to calculate robust standard deviation for.
Returns:
float: Median absolute deviation scaled to standard deviation.
"""
return mad_to_sigma * nanmedian(np.abs(x - nanmedian(x)))
def bootfit(x, y, nsimul, errors=1):
m_array = np.empty(nsimul)
m_array[:] = np.nan
b_array = np.empty(nsimul)
b_array[:] = np.nan
boot_polyfit_results = miniutils.parallel_progbar(boot_polyfit,
zip([x]*nsimul, [y]*nsimul, np.random.randint(0,100*nsimul,nsimul)),
nprocs=4, starmap=True)
# boot_polyfit_results = miniutils.parallel_progbar(boot_polyfit, zip([muGaia]*nsimul, [muVis]*nsimul, np.random.randint(0,10*nsimul,nsimul)),
# nprocs=4, starmap=True)
# for i in tqdm(range(nsimul)):
# index_resamp = bootstrap_resample(index_array)
# m_temp, b_temp = np.polyfit(x[index_array], y[index_array], 1)
m_array = np.array(boot_polyfit_results)[:,0]
b_array = np.array(boot_polyfit_results)[:,1]
print(m_array)
print(b_array)
#print(median_boot)
m_median = bn.nanmedian(m_array)
b_median = bn.nanmedian(b_array)
if(errors == 1):
m_s1_up = np.percentile(m_array, s1_up_q*100)
m_s1_down = np.percentile(m_array, s1_down_q*100)
m_s2_up = np.percentile(m_array, s2_up_q*100)
m_s2_down = np.percentile(m_array, s2_down_q*100)
m_s3_up = np.percentile(m_array, s3_up_q*100)
m_s3_down = np.percentile(m_array, s3_down_q*100)
b_s1_up = np.percentile(b_array, s1_up_q*100)
b_s1_down = np.percentile(b_array, s1_down_q*100)
b_s2_up = np.percentile(b_array, s2_up_q*100)
b_s2_down = np.percentile(b_array, s2_down_q*100)
b_s3_up = np.percentile(b_array, s3_up_q*100)
b_s3_down = np.percentile(b_array, s3_down_q*100)
if(errors == 0):
s1_up = 0
s1_down = 0
s2_up = 0
s2_down = 0
s3_up = 0
s3_down = 0
output = {"m_median": m_median, "m_s1_up": m_s1_up, "m_s1_down": m_s1_down,
"m_s2_up": m_s2_up, "m_s2_down": m_s2_down, "m_s3_up": m_s3_up,
"m_s3_down": m_s3_down, "b_median": b_median, "b_s1_up": b_s1_up,
"b_s1_down": b_s1_down, "b_s2_up": b_s2_up, "b_s2_down": b_s2_down,
"b_s3_up": b_s3_up, "b_s3_down": b_s3_down, }
return(output)
def fit(self, flux, Ncbvs=2, sigma_clip=4.0, maxiter=3):
# Find the median flux, as it is used for
# initial guesses later on:
median_flux = nanmedian(flux)
# Start looping over the number of CBVs to include:
bic = np.empty(self.cbv.shape[1] + 1, dtype='float64')
solutions = []
for Ncbvs in range(self.cbv.shape[1] + 1):
# Initial guesses for coefficients:
coeffs0 = np.zeros(Ncbvs + 1, dtype='float64')
coeffs0[-1] = median_flux
iters = 0
fluxi = np.copy(flux)
while iters <= maxiter:
iters += 1
# Do the fit:
res = minimize(self._lhood,
coeffs0,
args=(fluxi, ),
method='Powell')
flux_filter = self.mdl(res.x)
# Do robust sigma clipping:
absdev = np.abs(fluxi - flux_filter)
mad = 1.4826 * nanmedian(absdev)
indx = np.greater(absdev,
sigma_clip * mad,
where=np.isfinite(fluxi))
if np.any(indx):
fluxi[indx] = np.nan
else:
break
# Calculate the Bayesian Information Criterion (BIC) and store the solution:
bic[Ncbvs] = np.log(np.sum(
np.isfinite(fluxi))) * len(coeffs0) + res.fun
solutions.append(res)
# Use the solution which minimizes the BIC:
indx = np.argmin(bic)
flux_filter = self.mdl(solutions[indx].x)
#plt.figure()
#plt.plot(bic, '.-')
#plt.show()
return flux_filter
def nanmedian(array, axis=None):
"""
A nanmedian function that uses bottleneck if available.
"""
if HAS_BOTTLENECK:
if isinstance(axis, tuple):
array = move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, u.Quantity):
return array.__array_wrap__(bn.nanmedian(array, axis=axis))
else:
return bn.nanmedian(array, axis=axis)
else:
return np.nanmedian(array, axis=axis)
def nanmad(data, sigma=True, axis=None):
"""
Return the median absolute deviation. Axis functionality adapted
from https://github.com/keflavich/agpy/blob/master/agpy/mad.py
"""
if axis>0:
med = nanmedian(data.swapaxes(0,axis),axis=0)
mad = nanmedian(np.abs(data.swapaxes(0,axis) - med),axis=0)
else:
med = nanmedian(data,axis=axis)
mad = nanmedian(np.abs(data - med),axis=axis)
if not sigma:
return mad
else:
return mad*1.4826
def entropy_cleaning(self, matrix, targ_limit=150):
"""
Entropy-cleaning of lightcurve matrix using the SVD U-matrix.
Parameters:
matrix (:class:`numpy.ndarray`):
targ_limit (int, optional): Maximum number of targets to remove during cleaning.
.. codeauthor:: Mikkel N. Lund <*****@*****.**>
"""
logger = logging.getLogger(__name__)
# Calculate the principle components:
pca = PCA(self.ncomponents, random_state=self.random_state)
U, _, _ = pca._fit(matrix)
ent = compute_entropy(U)
logger.info('Entropy start: %s', ent)
targets_removed = 0
components = np.arange(self.ncomponents)
with np.errstate(invalid='ignore'):
while np.any(ent < self.threshold_entropy):
com = components[ent < self.threshold_entropy][0]
# Remove highest relative weight target
m = nanmedian(U[:, com])
s = mad_to_sigma * nanmedian(np.abs(U[:, com] - m))
dev = np.abs(U[:, com] - m) / s
idx0 = np.argmax(dev)
# Remove the star from the lightcurve matrix:
star_no = np.ones(U.shape[0], dtype=bool)
star_no[idx0] = False
matrix = matrix[star_no, :]
targets_removed += 1
if targets_removed >= targ_limit:
break
U, _, _ = pca._fit(matrix)
ent = compute_entropy(U)
logger.info('Entropy end: %s', ent)
logger.info('Targets removed: %d', targets_removed)
return matrix
def singleVar(content):
return dict(min=nanmin(content),
max=nanmax(content),
mean=nanmean(content),
median=nanmedian(content),
valid=numpy.sum(numpy.isfinite(content)) * 100.0 /
content.size)
def moving_nanmedian_cyclic(t, x, w, dt=None):
"""
Calculate cyclic moving average of input with given window (in t-units)
taking into account NaNs in the data.
"""
if len(t) != len(x):
raise ValueError("t and x must have the same length.")
if dt is None:
dt = median(np.diff(t))
# Calculate width of filter:
width_points = int(w / dt)
if width_points <= 1:
return x
if width_points % 2 == 0:
width_points += 1 # Filter is much faster when using an odd number of points!
wh = width_points // 2
N = len(x)
if wh >= N:
return np.zeros_like(x) + nanmedian(x)
# Stich ends onto the array:
xny = np.concatenate((x[-wh - 1:N - 1], x, x[1:wh + 1]))
# Run moving median on longer series:
N = len(xny)
y = _median_central(xny, width_points)
# Cut out the central part again:
y = y[wh:N - wh]
return y
def weighted_mean(_line):
max_weight = 50
# print _line.shape
median_2d = bottleneck.nanmedian(_line,
axis=1).reshape(_line.shape[0],
1).repeat(_line.shape[1],
axis=1)
std = bottleneck.nanstd(_line, axis=1)
std_2d = std.reshape(_line.shape[0], 1).repeat(_line.shape[1], axis=1)
weight_2d = numpy.fabs(std_2d / (_line - median_2d))
# weight_2d[weight_2d > max_weight] = max_weight
weight_2d[numpy.isinf(weight_2d)] = max_weight
for i in range(3):
avg = bottleneck.nansum(_line * weight_2d, axis=1) / bottleneck.nansum(
weight_2d, axis=1)
avg_2d = avg.reshape(_line.shape[0], 1).repeat(_line.shape[1], axis=1)
std = numpy.sqrt(
bottleneck.nansum(((_line - avg_2d)**2 * weight_2d), axis=1) /
bottleneck.nansum(weight_2d, axis=1))
std_2d = std.reshape(_line.shape[0], 1).repeat(_line.shape[1], axis=1)
weight_2d = numpy.fabs(std_2d / (_line - avg_2d))
#weight_2d[weight_2d > max_weight] = max_weight
weight_2d[numpy.isinf(weight_2d)] = max_weight
return bottleneck.nansum(_line * weight_2d, axis=1) / bottleneck.nansum(
weight_2d, axis=1)
def _nanmedian(array, axis=None):
"""Bottleneck nanmedian function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanmedian(array, axis=axis)
def _nanmedian(array, axis=None):
"""Bottleneck nanmedian function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanmedian(array, axis=axis)
def theil_sen(x, y, sample= "auto", n_samples = 1e7):
assert x.shape[0] == y.shape[0]
n = x.shape[0]
if n < 100 or not sample:
ix = np.argsort( x )
slopes = np.empty(int(n*(n-1)*0.5))
for c, pair in enumerate(itertools.combinations(range(n), 2)):
i,j = ix[pair[0]], ix[pair[1]]
slopes[c] = slope(x[i], x[j], y[i], y[j])
else:
i1 = np.random.randint(int(0), int(n), int(n_samples))
i2 = np.random.randint(int(0), int(n), int(n_samples))
slopes = slope(x[i1], x[i2], y[i1], y[i2])
slope_ = bottleneck.nanmedian(slopes)
#find the optimal b as the median of y_i - slope*x_i
intercepts = np.empty(n)
for c in range(n):
intercepts[c] = y[c] - slope_*x[c]
intercept_ = bottleneck.median(intercepts)
return np.array([slope_, intercept_])
def demedian(arr, axis=None):
"""
Subtract the median along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to remove the median. The default (None) is
to subtract the median of the flattened array.
Returns
-------
y : ndarray
A copy with the median along the specified axis removed.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 10])
>>> demedian(arr)
array([ -1., NaN, 0., 8.])
"""
marr = bn.nanmedian(arr, axis)
if (axis != 0) and (not axis is None) and (not np.isscalar(marr)):
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
marr = marr[ind]
return arr - marr
def weighted_mean(_line):
max_weight = 50
# print _line.shape
median_2d = bottleneck.nanmedian(_line, axis=1).reshape(_line.shape[0],1).repeat(_line.shape[1], axis=1)
std = bottleneck.nanstd(_line, axis=1)
std_2d = std.reshape(_line.shape[0],1).repeat(_line.shape[1], axis=1)
weight_2d = numpy.fabs(std_2d / (_line - median_2d))
# weight_2d[weight_2d > max_weight] = max_weight
weight_2d[numpy.isinf(weight_2d)] = max_weight
for i in range(3):
avg = bottleneck.nansum(_line*weight_2d, axis=1)/bottleneck.nansum(weight_2d, axis=1)
avg_2d = avg.reshape(_line.shape[0],1).repeat(_line.shape[1], axis=1)
std = numpy.sqrt(bottleneck.nansum(((_line - avg_2d)**2 * weight_2d), axis=1)/bottleneck.nansum(weight_2d, axis=1))
std_2d = std.reshape(_line.shape[0],1).repeat(_line.shape[1], axis=1)
weight_2d = numpy.fabs(std_2d / (_line - avg_2d))
#weight_2d[weight_2d > max_weight] = max_weight
weight_2d[numpy.isinf(weight_2d)] = max_weight
return bottleneck.nansum(_line*weight_2d, axis=1)/bottleneck.nansum(weight_2d, axis=1)
def demedian(arr, axis=None):
"""
Subtract the median along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to remove the median. The default (None) is
to subtract the median of the flattened array.
Returns
-------
y : ndarray
A copy with the median along the specified axis removed.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 10])
>>> demedian(arr)
array([ -1., NaN, 0., 8.])
"""
marr = bn.nanmedian(arr, axis)
if (axis != 0) and (not axis is None) and (not np.isscalar(marr)):
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
marr = marr[ind]
return arr - marr
def is_guide_ota(primhdu, ext, w=20, debug=False):
logger = logging.getLogger("IsGuideOTA")
binning = primhdu.header['BINNING']
skylevel = primhdu.header['SKYLEVEL']
gain = primhdu.header['GAIN']
skynoise = primhdu.header['SKYNOISE']
logger.debug("Checking OTA %s (bin=%d, sky=%.1f, skynoise=%.2f)" % (
ext.name, binning, skylevel, skynoise))
if (not is_image_extension(ext)):
logger.debug("extension is not a valid image extension")
return False
excesses = numpy.empty((8,8))
excesses[:,:] = numpy.NaN
if (debug):
center_hdu = [pyfits.PrimaryHDU()]
corner_hdu = [pyfits.PrimaryHDU()]
for cx, cy in itertools.product(range(8), repeat=2):
#
# Get pixel coord for this cell
#
x1,x2,y1,y2 = cell2ota__get_target_region(cx, cy, binning=binning, trimcell=0)
x1,x2,y1,y2 = int(x1),int(x2),int(y1),int(y2)
x21 = (x2-x1)//2
# extract the mean value in the bottom corner
corner = bottleneck.nanmean(ext.data[y1:y1+w, x1:x1+w].astype(numpy.float32))
# also get the value in the bottom center
center = bottleneck.nanmean(ext.data[y1:y1+w, x1+x21-w//2:x1+x21+w//2].astype(numpy.float32))
if (debug):
print(cx,cy,corner, center)
corner_hdu.append(pyfits.ImageHDU(data=ext.data[y1:y1+w, x1:x1+w]))
center_hdu.append(pyfits.ImageHDU(data=ext.data[y1:y1+w, x1+x21-w//2:x1+x21+w//2]))
excess = corner - center
#print ext.name, cx, cy, corner, center, excess
excesses[cx,cy] = excess
_mean = bottleneck.nanmean(excesses)
_median = bottleneck.nanmedian(excesses)
is_guideota = (_median > 10*skynoise)
logger.debug("Found corner excess mean=%.1f, median=%.1f --> guide-OTA: %s" % (
_mean, _median, "YES" if is_guideota else "NO"))
if (debug):
return is_guideota, excesses, _mean, _median, skynoise, corner_hdu, center_hdu
return is_guideota
def euclid_normalize_mask(fits_list):
"""
euclid.normalize_mask - Antiguo normalize_eur_mask
"""
if not isinstance(fits_list, list):
fits_list = [fits_list]
corrected_files = np.array([])
for fits_name in fits_list:
fits_file = fits.open(fits_name)
for i in np.linspace(1, 36, 36).astype("int"):
# Translate to GNUASTRO
# Normalize as a function of width and center
print("Normalizing CCD " + str(i))
# execute_cmd(cmd_text="astarithmetic -h " + str(j) + " " + outname + " -h" + str(j+2) + " " + outname + " 0 gt nan where")
fits_file[i].data = np.divide(
fits_file[i].data,
bn.nanmedian(fits_file[i].data[1798:2298, 1818:2318]))
if os.path.exists(fits_name):
os.remove(fits_name)
fits_file.verify("silentfix")
fits_file.writeto(fits_name)
fits_file.close()
execute_cmd(cmd_text="astfits -h0 " + fits_name +
" --update=NORMAL,True")
corrected_files = np.append(corrected_files, fits_name)
return (corrected_files)
def _estimate(self, dataset):
"""Estimate and save the displacements for the time series.
Parameters
----------
num_states_retained : int
Number of states to retain at each time step of the HMM.
max_displacement : array of int
The maximum allowed displacement magnitudes in [y,x].
Returns
-------
dict
The estimated displacements and partial results of motion
correction.
"""
params = self._params
if params['verbose']:
print('Estimating model parameters.')
shifts = self._estimate_shifts(dataset)
references, variances = _whole_frame_shifting(dataset, shifts)
if params['max_displacement'] is None:
max_displacement = np.array(dataset.frame_shape[:3]) // 2
else:
max_displacement = np.array(params['max_displacement'])
gains = nanmedian(
(variances / references).reshape(-1, references.shape[-1]))
if not (np.all(np.isfinite(gains)) and np.all(gains > 0)):
raise Exception('Failed to estimate positive gains')
pixel_means, pixel_variances = _pixel_distribution(dataset)
movement_model = MovementModel.estimate(shifts)
if shifts[0].shape[-1] == 2:
shifts = [
np.concatenate([np.zeros(s.shape[:-1] + (1, ), dtype=int), s],
axis=-1) for s in shifts
]
min_shifts = np.nanmin(
[np.nanmin(s.reshape(-1, s.shape[-1]), 0) for s in shifts], 0)
max_shifts = np.nanmax(
[np.nanmax(s.reshape(-1, s.shape[-1]), 0) for s in shifts], 0)
# add a bit of extra room to move around
if max_displacement.size == 2:
max_displacement = np.hstack(([0], max_displacement))
extra_buffer = ((max_displacement - max_shifts + min_shifts) //
2).astype(int)
min_displacements = min_shifts - extra_buffer
max_displacements = max_shifts + extra_buffer
displacements = self._neighbor_viterbi(dataset, references, gains,
movement_model,
min_displacements,
max_displacements, pixel_means,
pixel_variances)
return self._post_process(displacements)
def fit(self, X, y):
X_y = self._check_params(X, y)
self.X = X_y[0]
self.y = X_y[1].reshape((-1, 1))
n, p = X.shape
S = [] # list of selected features
F = range(p) # list of unselected features
if self.n_features != 'auto':
feature_mi_matrix = np.zeros((self.n_features, p))
else:
feature_mi_matrix = np.zeros((n, p))
feature_mi_matrix[:] = np.nan
S_mi = []
# Find the first feature
k_min = 3
range_k = 7
xy_MI = np.empty((range_k, p))
for i in range(range_k):
xy_MI[i, :] = self._get_first_mi_vector(i + k_min)
xy_MI = bn.nanmedian(xy_MI, axis=0)
S, F = self._add_remove(S, F, bn.nanargmax(xy_MI))
S_mi.append(bn.nanmax(xy_MI))
if self.verbose > 0:
self._info_print(S, S_mi)
# Find the next features
if self.n_features == 'auto':
n_features = np.inf
else:
n_features = self.n_features
while len(S) < n_features:
s = len(S) - 1
feature_mi_matrix[s, F] = self._get_mi_vector(F, S[-1])
fmm = feature_mi_matrix[:len(S), F]
if bn.allnan(bn.nanmean(fmm, axis=0)):
break
MRMR = xy_MI[F] - bn.nanmean(fmm, axis=0)
if np.isnan(MRMR).all():
break
selected = F[bn.nanargmax(MRMR)]
S_mi.append(bn.nanmax(bn.nanmin(fmm, axis=0)))
S, F = self._add_remove(S, F, selected)
if self.verbose > 0:
self._info_print(S, S_mi)
if self.n_features == 'auto' and len(S) > 10:
MI_dd = signal.savgol_filter(S_mi[1:], 9, 2, 1)
if np.abs(np.mean(MI_dd[-5:])) < 1e-3:
break
self.n_features_ = len(S)
self.ranking_ = S
self.mi_ = S_mi
return self
def med_over_images(masked_arr, axis=0):
"""
Calculate median pixel value along specified axis
Uses bottleneck.nanmedian for speed
"""
dat = masked_arr.data.copy()
dat[masked_arr.mask] = np.NaN
return bn.nanmedian(dat, axis=axis)
def theil_sen(x,y, sample= "auto", n_samples = 1e7):
"""
Computes the Theil-Sen estimator for 2d data.
parameters:
x: 1-d np array, the control variate
y: 1-d np.array, the ind variate.
sample: if n>100, the performance can be worse, so we sample n_samples.
Set to False to not sample.
n_samples: how many points to sample.
This complexity is O(n**2), which can be poor for large n. We will perform a sampling
of data points to get an unbiased, but larger variance estimator.
The sampling will be done by picking two points at random, and computing the slope,
up to n_samples times.
"""
assert x.shape[0] == y.shape[0], "x and y must be the same shape."
n = x.shape[0]
if n < 100 or not sample:
ix = np.argsort( x )
slopes = np.empty( n*(n-1)*0.5 )
for c, pair in enumerate(itertools.combinations( range(n),2 ) ): #it creates range(n) =(
i,j = ix[pair[0]], ix[pair[1]]
slopes[c] = slope( x[i], x[j], y[i],y[j] )
else:
i1 = np.random.randint(0, n, n_samples)
i2 = np.random.randint(0, n, n_samples)
print '...checking for unwanted zeros...'
zero_check=np.where(np.abs((x[i1]-x[i2])) != 0)
i1=i1[zero_check]
i2=i2[zero_check]
print '...calculating slopes...'
slopes = slope( x[i1], x[i2], y[i1], y[i2] )
print 'slope min and max are:',np.amin(slopes),np.amax(slopes)
histogram,bin_limits=np.histogram(slopes,bins=10000,range=(-2,2))
#print histogram
#c95=np.percentile(slopes,(5,95))
#pdb.set_trace()
slope_ = bottleneck.nanmedian( slopes )
print '...done! Now finding intercepts...'
#find the optimal b as the median of y_i - slope*x_i
intercepts = np.empty( n )
for c in xrange(n):
intercepts[c] = y[c] - slope_*x[c]
histogram_i,bin_limits_i=np.histogram(intercepts,bins=10000,range=(-2,2))
#print histogram_i
#c95i=np.percentile(intercepts,(5,95))
#print cumul_i
intercept_ = bottleneck.median( intercepts )
return np.array( [slope_,intercept_]) #c95[0],c95[1],c95i[0],c95i[1]] )
def _estimate(self, dataset):
"""Estimate and save the displacements for the time series.
Parameters
----------
num_states_retained : int
Number of states to retain at each time step of the HMM.
max_displacement : array of int
The maximum allowed displacement magnitudes in [y,x].
Returns
-------
dict
The estimated displacements and partial results of motion
correction.
"""
params = self._params
if params['verbose']:
print('Estimating model parameters.')
shifts = self._estimate_shifts(dataset)
references, variances = _whole_frame_shifting(dataset, shifts)
if params['max_displacement'] is None:
max_displacement = np.array(dataset.frame_shape[:3]) // 2
else:
max_displacement = np.array(params['max_displacement'])
gains = nanmedian(
(variances / references).reshape(-1, references.shape[-1]))
if not (np.all(np.isfinite(gains)) and np.all(gains > 0)):
raise Exception('Failed to estimate positive gains')
pixel_means, pixel_variances = _pixel_distribution(dataset)
movement_model = MovementModel.estimate(shifts)
if shifts[0].shape[-1] == 2:
shifts = [np.concatenate([np.zeros(s.shape[:-1] + (1,), dtype=int),
s], axis=-1) for s in shifts]
min_shifts = np.nanmin([np.nanmin(s.reshape(-1, s.shape[-1]), 0)
for s in shifts], 0)
max_shifts = np.nanmax([np.nanmax(s.reshape(-1, s.shape[-1]), 0)
for s in shifts], 0)
# add a bit of extra room to move around
if max_displacement.size == 2:
max_displacement = np.hstack(([0], max_displacement))
extra_buffer = ((max_displacement - max_shifts + min_shifts) // 2
).astype(int)
min_displacements = min_shifts - extra_buffer
max_displacements = max_shifts + extra_buffer
displacements = self._neighbor_viterbi(
dataset, references, gains, movement_model, min_displacements,
max_displacements, pixel_means, pixel_variances)
return self._post_process(displacements)
def _estimate(self, dataset):
"""Estimate and save the displacements for the time series.
Parameters
----------
num_states_retained : int
Number of states to retain at each time step of the HMM.
max_displacement : array of int
The maximum allowed displacement magnitudes in [y,x].
Returns
-------
dict
The estimated displacements and partial results of motion
correction.
"""
params = self._params
if params.verbose:
print 'Estimating model parameters.'
if params.max_displacement is not None:
params.max_displacement = np.array(params.max_displacement)
shifts = VolumeTranslation(params.max_displacement).estimate(dataset)
references, variances, offset = _whole_frame_shifting(dataset, shifts)
assert np.all(offset == 0)
gains = nanmedian(
(variances / references).reshape(-1, references.shape[-1]))
if not (np.all(np.isfinite(gains)) and np.all(gains > 0)):
raise Exception('Failed to estimate positive gains')
pixel_means, pixel_variances = _pixel_distribution(dataset)
movement_model = MovementModel.estimate(shifts)
# TODO: detect unreasonable shifts before doing this calculation
min_shifts = np.nanmin(list(it.chain(*it.chain(*shifts))), 0)
max_shifts = np.nanmax(list(it.chain(*it.chain(*shifts))), 0)
# add a bit of extra room to move around
if params.max_displacement is None:
extra_buffer = 5
else:
extra_buffer = (
(params.max_displacement - max_shifts + min_shifts) / 2
).astype(int)
min_displacements = (min_shifts - extra_buffer)
max_displacements = (max_shifts + extra_buffer)
return self._neighbor_viterbi(
dataset, references, gains, movement_model, min_displacements,
max_displacements, pixel_means,
pixel_variances, params.num_states_retained, params.verbose)
def sample_background_using_ds9_regions(hdu, sky_regions):
wcs = astWCS.WCS(hdu.header, mode='pyfits')
pixelscale = wcs.getPixelSizeDeg() * 3600.
data = hdu.data
center_xy = wcs.wcs2pix(sky_regions[:,0], sky_regions[:,1])
# print center_xy
center_xy = numpy.array(center_xy)
cx = center_xy[:,0]
cy = center_xy[:,1]
width = sky_regions[:,2]/2. / pixelscale
height = sky_regions[:,3]/2. / pixelscale
in_ota = ((cx + width) > 0) & ((cx - width) < data.shape[1]) & \
((cy + height) > 0) & ((cy - height) < data.shape[0])
cx = cx[in_ota]
cy = cy[in_ota]
w = width[in_ota]
h = height[in_ota]
if (cx.size <= 0):
# no boxes in this OTA
return None
left = numpy.floor(cx - w).astype(numpy.int)
right = numpy.ceil(cx + w).astype(numpy.int)
top = numpy.ceil(cy + h).astype(numpy.int)
bottom = numpy.floor(cy - h).astype(numpy.int)
left[left < 0] = 0
bottom[bottom < 0] = 0
results = []
for box in range(cx.shape[0]):
cutout = data[bottom[box]:top[box], left[box]:right[box]]
median = bottleneck.nanmedian(cutout.astype(numpy.float32))
if (numpy.isfinite(median)):
results.append([cx[box], cy[box], median])
#print results
if (len(results) <= 0):
return None
return numpy.array(results)
def _estimate(self, dataset):
"""Estimate and save the displacements for the time series.
Parameters
----------
num_states_retained : int
Number of states to retain at each time step of the HMM.
max_displacement : array of int
The maximum allowed displacement magnitudes in [y,x].
Returns
-------
dict
The estimated displacements and partial results of motion
correction.
"""
params = self._params
if params.verbose:
print 'Estimating model parameters.'
if params.max_displacement is not None:
params.max_displacement = np.array(params.max_displacement)
else:
params.max_displacement = np.array([-1, -1]) # TODO
shifts = sima.motion.frame_align.PlaneTranslation2D(
params.max_displacement, n_processes=params.n_processes
).estimate(dataset)
references, variances, offset = _whole_frame_shifting(dataset, shifts)
gains = nanmedian(
(variances / references).reshape(-1, references.shape[-1]))
assert np.all(np.isfinite(gains)) and np.all(gains > 0)
pixel_means, pixel_variances = _pixel_distribution(dataset)
cov_matrix_est, decay_matrix, log_transition_matrix, mean_shift = \
_estimate_movement_model(shifts, dataset.frame_shape[1])
# add a bit of extra room to move around
min_shifts = np.nanmin(list(it.chain(*it.chain(*shifts))), 0)
max_shifts = np.nanmax(list(it.chain(*it.chain(*shifts))), 0)
extra_buffer = ((params.max_displacement - max_shifts + min_shifts) / 2
).astype(int)
extra_buffer[params.max_displacement < 0] = 5
min_displacements = (min_shifts - extra_buffer)
max_displacements = (max_shifts + extra_buffer)
return self._neighbor_viterbi(
dataset, log_transition_matrix, references, gains, decay_matrix,
cov_matrix_est, mean_shift, offset, min_displacements,
max_displacements, pixel_means, pixel_variances,
params.num_states_retained, params.verbose)
def monte_lines(numtrys):
bigarr = np.zeros((1,3))
for i in range(numtrys):
v, I = ADE.ADE_gauss(1000,500,50)
I *= 55/I.max()
I += 3. * np.random.randn(I.size)
# ADE.eplot(v,I)
moments = ADE.ADE_moments(v, I, threshold=np.inf,err=np.abs(I)**0.5)
bigarr = np.vstack((bigarr,moments))
bigarr = bigarr[1:]
# print bigarr
return bn.nanmedian(bigarr,axis=0), bn.nanstd(bigarr,axis=0)
def bn_median(masked_array, axis=None):
"""
https://github.com/astropy/ccdproc/blob/122cdbd5713140174f057eaa8fdb6f9ce03312df/docs/ccdproc/bottleneck_example.rst
Perform fast median on masked array
Parameters
masked_array : `numpy.ma.masked_array`
Array of which to find the median.
axis : int, optional
Axis along which to perform the median. Default is to find the median of
the flattened array.
"""
import bottleneck as bn
data = masked_array.filled(fill_value=np.NaN)
med = bn.nanmedian(data, axis=axis)
# construct a masked array result, setting the mask from any NaN entries
return np.ma.array(med, mask=np.isnan(med)) # bn_median
def theil_sen(x, y, sample="auto", n_samples=1e7):
"""
Computes the Theil-Sen estimator for 2d data.
parameters:
x: 1-d np array, the control variate
y: 1-d np.array, the ind variate.
sample: if n>100, the performance can be worse, so we sample n_samples.
Set to False to not sample.
n_samples: how many points to sample.
This complexity is O(n**2), which can be poor for large n. We will perform a sampling
of data points to get an unbiased, but larger variance estimator.
The sampling will be done by picking two points at random, and computing the slope,
up to n_samples times.
"""
assert x.shape[0] == y.shape[0], "x and y must be the same shape."
n = x.shape[0]
if n < 100 or not sample:
ix = np.argsort(x)
slopes = np.empty(n * (n - 1) * 0.5)
for c, pair in enumerate(itertools.combinations(range(n), 2)): # it creates range(n) =(
i, j = ix[pair[0]], ix[pair[1]]
slopes[c] = slope(x[i], x[j], y[i], y[j])
else:
i1 = np.random.randint(0, n, n_samples)
i2 = np.random.randint(0, n, n_samples)
slopes = slope(x[i1], x[i2], y[i1], y[i2])
# pdb.set_trace()
slope_ = bottleneck.nanmedian(slopes)
# find the optimal b as the median of y_i - slope*x_i
intercepts = np.empty(n)
for c in xrange(n):
intercepts[c] = y[c] - slope_ * x[c]
intercept_ = bottleneck.median(intercepts)
return np.array([slope_, intercept_])
def group_median(x, groups, axis=0):
"""
Median with groups along an axis.
Parameters
----------
x : ndarray
Input data.
groups : list
List of group membership of each element along the given axis.
axis : int, {default: 0}
axis along which the ranking is calculated.
Returns
-------
idx : ndarray
The group median of the data along axis 0.
"""
# Find set of unique groups
ugroups = unique_group(groups)
# Convert groups to a numpy array
groups = np.asarray(groups)
# Loop through unique groups and normalize
xmedian = np.nan * np.zeros(x.shape)
for group in ugroups:
idx = groups == group
idxall = [slice(None)] * x.ndim
idxall[axis] = idx
if idx.sum() > 0:
ns = bn.nanmedian(x[idxall], axis=axis)
xmedian[idxall] = np.expand_dims(ns, axis)
return xmedian
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
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 _build_epsf_step(self, stars, epsf=None):
"""
A single iteration of improving an ePSF.
Parameters
----------
stars : `EPSFStars` object
The stars used to build the ePSF.
epsf : `EPSFModel` object, optional
The initial ePSF model. If not input, then the ePSF will be
built from scratch.
Returns
-------
epsf : `EPSFModel` object
The updated ePSF.
"""
if len(stars) < 1:
raise ValueError('stars must contain at least one EPSFStar or '
'LinkedEPSFStar object.')
if epsf is None:
# create an initial ePSF (array of zeros)
epsf = self._create_initial_epsf(stars)
else:
# improve the input ePSF
epsf = copy.deepcopy(epsf)
# compute a 3D stack of 2D residual images
residuals = self._resample_residuals(stars, epsf)
self._residuals.append(residuals)
# compute the sigma-clipped median along the 3D stack
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=RuntimeWarning)
warnings.simplefilter('ignore', category=AstropyUserWarning)
residuals = self.sigclip(residuals, axis=0, masked=False,
return_bounds=False)
if HAS_BOTTLENECK:
residuals = bottleneck.nanmedian(residuals, axis=0)
else:
residuals = np.nanmedian(residuals, axis=0)
self._residuals_sigclip.append(residuals)
# interpolate any missing data (np.nan)
mask = ~np.isfinite(residuals)
if np.any(mask):
residuals = _interpolate_missing_data(residuals, mask,
method='cubic')
# fill any remaining nans (outer points) with zeros
residuals[~np.isfinite(residuals)] = 0.
self._residuals_interp.append(residuals)
# add the residuals to the previous ePSF image
new_epsf = epsf.normalized_data + residuals
# smooth the ePSF
new_epsf = self._smooth_epsf(new_epsf)
# recenter the ePSF
new_epsf = self._recenter_epsf(new_epsf, epsf,
centroid_func=self.recentering_func,
box_size=self.recentering_boxsize,
maxiters=self.recentering_maxiters,
center_accuracy=1.0e-4)
# normalize the ePSF data
new_epsf /= np.sum(new_epsf, dtype=np.float64)
# return the new ePSF object
xcenter = (new_epsf.shape[1] - 1) / 2.
ycenter = (new_epsf.shape[0] - 1) / 2.
epsf_new = EPSFModel(data=new_epsf, origin=(xcenter, ycenter),
normalize=False, oversampling=epsf.oversampling)
return epsf_new
def nanmedian(array, axis=None):
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bt.nanmedian(array, axis=axis)
def deltaconvert(series, visualize=False, max_adj_outliers=10):
"""Perform delta-conversion to given pd.Series.
Delta-conversion returns 3 series as a tuple and possibly error message.
First series (D) contains daily returns where all the data has been removed
that could make comparison difficult with other assets.
Second series (W) contains weekly returns where all the data has been removed
that could make comparison difficult with other assets.
Third series (DS) contains daily returns where all the outliers and erroneus
data points have been removed but holes in data are not taken into account.
This is more suitable for calculating performance scores etc.
Arguments:
series -- series to use
visualize -- visualize results
max_adj_outliers -- maximum number of adjancent outliers, if there are
actually more adjancent outliers than this then they will
not be considered outliers anymore. default value: 10
"""
# MEDIAN_LEN = 50
ZSCORE_CUT_RATIO = 2
series = series.dropna()
if len(series) < 50:
raise DeltaConversionException("Not enough data")
lines_taken = 0
if series.index[0] > series.index[-1]:
raise DeltaConversionException("Wrong cronological order")
# closes = []
# dates = []
# datesord = []
# for line in lines:
# splitted = line.split(",")
# closes.append(float(splitted[column]))
# dt = datetime.strptime(splitted[0], "%Y-%m-%d").date()
# dates.append(dt)
# datesord.append(dt.toordinal())
# if datesord[-1] < datesord[0]:
# closes.reverse()
# dates.reverse()
# datesord.reverse()
# lines.reverse()
closes = series
dates = series.index
num_invalid_prices = 0
deltapct = [np.nan]
changescores = [np.nan]
invalid_price_indices = []
for i in range(1, len(series)):
if closes[i - 1] > 0 and closes[i] > 0:
change = closes[i] / closes[i - 1]
deltapct.append(change - 1)
changescore = change_to_score(change)
changescores.append(changescore)
else:
deltapct.append(np.nan)
changescores.append(np.nan)
num_invalid_prices += 1
invalid_price_indices.append(i)
logging.debug("Cannot determine changescore at {} ({} / {})".format(dates[i], closes[i], closes[i - 1]))
# # remove zeroes (data may only end with price zero if stock goes bankrupt...)
# first_nonzero_idx = [i for i, val in enumerate(closes[:-1]) if val == 0]
# del closes[:first_nonzero_idx]
# del dates[:first_nonzero_idx]
# lines_taken += first_nonzero_idx
# if first_nonzero_idx > 0:
# logging.debug("{}: removed {} zero-lines from the beginning.".format(filename, first_nonzero_idx))
num_gaps = 0
num_invalid_chrono_orders = 0
gap_indices = []
for i in range(len(dates) - 1, 0, -1):
d = (dates[i] - dates[i - 1]).days
# standard weekends are only allowed
if d == 3:
if dates[i].weekday() != 0: # not monday
# deltapct[i] = np.nan
# changescores[i] = np.nan
num_gaps += 1
gap_indices.append(i)
logging.log(5, "Non-weekend gap of 2 day(s) at {}".format(dates[i]))
elif d > 1:
# deltapct[i] = np.nan
# changescores[i] = np.nan
num_gaps += 1
gap_indices.append(i)
logging.log(5, "Non-weekend gap of {} day(s) at {}".format(d, dates[i]))
elif d <= 0:
del deltapct[i], dates[i], closes[closes.index[i]], changescores[i]
logging.warning(5, "Invalid chronological order ({} day(s)) at {}"
.format(d - 1, dates[i]))
num_invalid_chrono_orders += 1
deltapct = np.asarray(deltapct)
changescores = np.asarray(changescores)
std_score = bn.nanstd(changescores)
zscores = np.abs(changescores) / std_score
mean_z = bn.nanmean(zscores)
zscores_set = list(set(zscores[(~np.isnan(zscores)) & (zscores > 0)]))
zscores_set.sort()
outlier_z = None
maxpctdiff = 0
for i in range(int(len(zscores_set) * .95), len(zscores_set)):
pctdiff = zscores_set[i] / zscores_set[i - 1]
maxpctdiff = pctdiff
# logging.info("{}: {}".format(i / len(zscores_set), pctdiff))
if pctdiff >= 2:
outlier_z = zscores_set[i]
second_highest_z = zscores_set[i - 1]
break
possible_outliers = []
confirmed_outliers = []
localmean_factors = []
if outlier_z:
logging.log(5, "Outlier z-score: {:.2f}, earlier z-score: {:.2f}, mean z-score: {:.5f}"
.format(outlier_z, second_highest_z, mean_z))
for i in range(len(zscores)):
if zscores[i] >= outlier_z:
localmean = bn.nanmean(zscores[max(0, i - 50):min(len(zscores) + 1, i + 50)])
localmean_factor = np.sqrt(mean_z / localmean)
score = (zscores[i] / second_highest_z) * localmean_factor
logging.log(5, "Possible outlier at {}: localmean_factor: {:.2f}, zscore: {:.2f}, score: {:.2f}"
.format(dates[i], localmean_factor, zscores[i], score))
if score >= ZSCORE_CUT_RATIO:
logging.debug("Possible outlier at {} (z-score={:.2f}, deltapct={:.2%})"
.format(dates[i], zscores[i], deltapct[i]))
# deltapct[i] = np.nan
possible_outliers.append(i)
localmean_factors.append(localmean_factor)
if len(possible_outliers) == 1:
confirmed_outliers = possible_outliers
for i in range(1, len(possible_outliers)):
firstidx = possible_outliers[i - 1]
secondidx = possible_outliers[i]
# opposite signs and not too far from each other
if deltapct[firstidx] * deltapct[secondidx] < 0 \
and secondidx - firstidx + 1 <= max_adj_outliers:
firstnonan = None
for i2 in range(firstidx, -1, -1):
if not np.isnan(deltapct[i2]):
firstnonan = i2
break
confirmed = False
if not firstnonan:
confirmed = True
if firstnonan:
if i == 1:
left_mean = bn.nanmedian(closes[max(0, firstnonan - (max_adj_outliers - 1)):firstnonan + 1])
else:
left_mean = bn.nanmedian(closes[max(0, possible_outliers[i - 2], \
firstnonan - (max_adj_outliers - 1)):firstnonan + 1])
right_mean = bn.nanmedian(closes[firstidx:secondidx])
changescore = change_to_score(right_mean / left_mean)
zscore = abs(changescore) / std_score
score_left_vs_mid = (zscore / second_highest_z) * localmean_factors[i - 1]
left_mean = right_mean
right_mean = bn.nanmedian(closes[secondidx:min(secondidx + max_adj_outliers, len(closes))])
changescore = change_to_score(right_mean / left_mean)
zscore = abs(changescore) / std_score
score_mid_vs_right = (zscore / second_highest_z) * localmean_factors[i]
if score_left_vs_mid > ZSCORE_CUT_RATIO * .75 and score_mid_vs_right > ZSCORE_CUT_RATIO * .75:
confirmed = True
if confirmed:
indices = [i2 for i2 in range(firstidx, secondidx + 1)]
deltapct[indices] = np.nan
confirmed_outliers += indices
else:
logging.debug("No possible outliers found based on initial z-score analysis (maxpctdiff: {})"
.format(maxpctdiff))
if visualize:
# TODO: make this work with DataFrame
pass
# closes_arr = np.asarray(closes.get_values())
# datesord = np.asarray(datesord)
# plt.subplot(2, 1, 1)
# plt.plot(datesord - datesord[0], closes_arr, 'b*')
# plt.plot(datesord[gap_indices] - datesord[0], closes_arr[gap_indices], 'ob')
# plt.plot(datesord[confirmed_outliers] - datesord[0], closes_arr[confirmed_outliers], 'or')
# plt.plot(datesord[invalid_price_indices] - datesord[0], closes_arr[invalid_price_indices], 'om')
# plt.subplot(2, 1, 2)
# plt.plot(datesord - datesord[0], zscores, 'o')
# plt.show()
logging.debug("Conversion result: lines = {}, invalid closes = {}, gaps = {}, invalid dates = {}, outliers = {}"
.format(len(series) - lines_taken, num_invalid_prices, num_gaps,
num_invalid_chrono_orders, len(confirmed_outliers)))
indices_to_rem = list(set(gap_indices + confirmed_outliers + invalid_price_indices))
# datesordmod = np.delete(datesord, indices_to_rem)
datesmod = dates.copy()
datesmod = datesmod.delete(indices_to_rem)
deltapctmod = np.delete(deltapct, indices_to_rem)
closesmod = closes.drop(closes.index[indices_to_rem])
assert(not np.any(np.isnan(deltapctmod[1:])))
weeklydeltapct = []
weeklydatesmod = []
lastidx = -1
# resample to W-FRI (could be done with pandas)
for i in range(len(closesmod)):
if datesmod[i].weekday() == 4:
dd = (datesmod[i] - datesmod[lastidx]).days
if lastidx >= 0 or dd == 7:
if closesmod[lastidx] >= 0:
weeklydeltapct.append(closesmod[i] / closesmod[lastidx] - 1)
weeklydatesmod.append(datesmod[i])
else:
logging.log(5, "Weekly bar at {} skipped (delta: {} days)".format(datesmod[i], i, dd))
lastidx = i
res_daily = pd.Series(deltapctmod, datesmod)
res_weekly = pd.Series(weeklydeltapct, weeklydatesmod)
indices_to_rem = list(set(confirmed_outliers + invalid_price_indices))
datesmod = dates.copy()
datesmod = datesmod.delete(indices_to_rem)
deltapctmod = np.delete(deltapct, indices_to_rem)
assert(not np.any(np.isnan(deltapctmod[1:])))
res_dailyscore = pd.Series(deltapctmod, datesmod)
return res_daily, res_weekly, res_dailyscore
def parallel_compute(queue, return_queue, shmem_buffer, shmem_results, size_x, size_y, len_filelist, operation):
#queue, shmem_buffer, shmem_results, size_x, size_y, len_filelist = worker_args
# buffer = shmem_as_ndarray(shmem_buffer).reshape((size_x, size_y, len_filelist))
buffer = shmem_buffer.to_ndarray()
# result_buffer = shmem_as_ndarray(shmem_results).reshape((size_x, size_y))
result_buffer = shmem_results.to_ndarray()
logger = logging.getLogger("ParallelImcombine")
logger.debug("Operation: %s, #samples/pixel: %d" % (operation, len_filelist))
while (True):
line = queue.get()
if (line is None):
queue.task_done()
break
if (operation == "median"):
result_buffer[line,:] = numpy.median(buffer[line,:,:], axis=1)
elif (operation == "medsigclip"):
# Do not use (yet), is slow as hell
# (maskedarrays are pure python, not C as all the rest)
#print buffer[line,:,:].shape
_sigma_plus = numpy.ones(shape=(buffer.shape[1],buffer.shape[2])) * 1e9
_sigma_minus = numpy.ones(shape=(buffer.shape[1],buffer.shape[2])) * 1e9
_median = numpy.median(buffer[line,:,:], axis=1)
nrep = 3
valid_pixels = numpy.ma.MaskedArray(buffer[line,:,:])
for rep in range(nrep):
_median_2d = _median.reshape(_median.shape[0],1).repeat(buffer.shape[2], axis=1)
_min = _median_2d - 3 * _sigma_minus
_max = _median_2d + 3 * _sigma_plus
#valid_pixels = numpy.ma.masked_inside(buffer[line,:,:], _min, _max)
valid = (buffer[line,:,:] > _min) & (buffer[line,:,:] < _max)
valid_pixels = numpy.ma.array(buffer[line,:,:], mask=valid)
#valid_pixels = numpy.ma.MaskedArray(buffer[line,:,:], valid)
#print _min.shape, valid.shape, valid_pixels.shape
#if (numpy.sum(valid, axis=1).any() <= 0):
# break
#_median = numpy.median(buffer[line,:,:][valid], axis=1)
_median = numpy.median(valid_pixels, axis=1)
if (rep < nrep-1):
#_sigma_plus = scipy.stats.scoreatpercentile(buffer[line,:,:][valid], 84) - _median
#_sigma_minus = _median - scipy.stats.scoreatpercentile(buffer[line,:,:][valid], 16)
_sigma_plus = scipy.stats.scoreatpercentile(valid_pixels, 84) - _median
_sigma_minus = _median - scipy.stats.scoreatpercentile(valid_pixels, 16)
result_buffer[line,:] = _median
elif (operation == "sigclipx"):
stdout_write(".")
rep_count = 2
_line = buffer[line,:,:].astype(numpy.float32)
# print _line.shape
mask = numpy.isfinite(_line)
#print "line.shape=",_line.shape
# numpy.savetxt("line_block_%d.dat" % (line), _line)
def sigclip_pixel(pixelvalue):
mask = numpy.isfinite(pixelvalue)
old_mask = mask
rep = 0
while (rep < rep_count and numpy.sum(mask) > 3):
old_mask = mask
mss = scipy.stats.scoreatpercentile(pixelvalue[mask], [16,50,84])
lower = mss[1] - 3 * (mss[1] - mss[0]) # median - 3*sigma
upper = mss[1] + 3 * (mss[2] - mss[1]) # median + 3*sigma
mask = (pixelvalue > lower) & (pixelvalue < upper)
rep += 1
if (rep == rep_count or numpy.sum(mask) < 3):
mask = old_mask
return numpy.mean(pixelvalue[mask])
result_buffer[line,:] = [sigclip_pixel(_line[x,:]) for x in range(_line.shape[0])]
elif (operation == "sigmaclipmean"):
_line = buffer[line,:,:].astype(numpy.float64)
output = numpy.zeros(shape=(_line.shape[0]))
podi_cython.sigma_clip_mean(_line, output)
result_buffer[line,:] = output
elif (operation == "sigmaclipmedian"):
_line = buffer[line,:,:].astype(numpy.float64)
output = numpy.zeros(shape=(_line.shape[0]))
podi_cython.sigma_clip_median(_line, output)
result_buffer[line,:] = output
elif (operation == "weightedmean"):
_line = buffer[line,:,:].astype(numpy.float32)
result_buffer[line,:] = weighted_mean(_line)
elif (operation == "medclip"):
intermediate = numpy.sort(buffer[line,:,:], axis=1)
result_buffer[line,:] = numpy.median(intermediate[:,1:-2], axis=1)
elif (operation == "min"):
result_buffer[line,:] = numpy.min(buffer[line,:,:], axis=1)
elif (operation == "max"):
result_buffer[line,:] = numpy.max(buffer[line,:,:], axis=1)
elif (operation == "nanmean"):
result_buffer[line,:] = scipy.stats.nanmean(buffer[line,:,:], axis=1)
elif (operation == "nanmedian"):
result_buffer[line,:] = scipy.stats.nanmedian(buffer[line,:,:], axis=1)
elif (operation == "nanmedian.bn"):
x = numpy.array(buffer[line,:,:], dtype=numpy.float32)
result_buffer[line,:] = bottleneck.nanmedian(x, axis=1)
x = None
del x
elif (operation == "nanmean.bn"):
x = numpy.array(buffer[line,:,:], dtype=numpy.float32)
result_buffer[line,:] = bottleneck.nanmean(x, axis=1)
x = None
del x
else:
result_buffer[line,:] = numpy.mean(buffer[line,:,:], axis=1)
return_queue.put(line)
queue.task_done()
buffer = None
shmem_buffer = None
del shmem_buffer
del buffer
sys.exit(0)
return
def subtract_background(data, radius, angle, radius_range, binfac, logger=None):
"""
This routine takes the input in polar coordinates and fits a straight line
to the radial profile inside and outside of the allowed range. This is
assumed to be the background level (in analogy to the algorithm used in the
IRAF task mkpupil).
Input data:
- data (the actual intensity values for all pixels)
- radius (the r in the polar coordianates)
- angle (the phi in polar coordinates)
- radius range (r_inner, r_outer, d_radius)
- binfac (the binning used for the data)
"""
if (logger is None):
logger = logging.getLogger("BGSub")
# Compute the radial bin size in binned pixels
logger.debug("subtracting background - binfac=%d" % (binfac))
r_inner, r_outer, dr_full = radius_range
dr = dr_full/binfac
r_inner /= binfac
r_outer /= binfac
#
# Compute the number of radial bins
#
# Here: Add some correction if the center position is outside the covered area
max_radius = 1.3 * r_outer #math.sqrt(data.shape[0] * data.shape[1])
# Splitting up image into a number of rings
n_radii = int(math.ceil(max_radius / dr))
#
# Compute the background level as a linear interpolation of the levels
# inside and outside of the pupil ghost
#
logger.info("Computing background-level ...")
# Define the background ring levels
radii = numpy.arange(0, max_radius, dr)
background_levels = numpy.zeros(shape=(n_radii))
background_level_errors = numpy.ones(shape=(n_radii)) * 1e9
background_levels[:] = numpy.NaN
for i in range(n_radii):
ri = i * dr
ro = ri + dr
if (ri < r_inner):
ro = numpy.min([ro, r_inner])
elif (ro > r_outer):
ri = numpy.max([ri, r_outer])
# else:
# # Skip the rings within the pupil ghost range for now
# continue
#print i, ri, ro
median, count = get_median_level(data, radius, ri, ro)
background_levels[i] = median
background_level_errors[i] = 1. / math.sqrt(count) if count > 0 else 1e9
# Now fit a straight line to the continuum, assuming it varies
# only linearly (if at all) with radius
# define our (line) fitting function
#print "XXXXXXX", radii.shape, background_levels.shape
numpy.savetxt("radial__%s" % ("x"),
numpy.append(radii.reshape((-1,1)),
background_levels.reshape((-1,1)), axis=1))
#print "saved"
# Find average intensity at the largest radii
avg_level = bottleneck.nanmedian(background_levels[radii>4000])
#print "avg_level=",avg_level
#
# Compute a profile without background interpolation to allow for easier
# scaling of the pupilghost when subtracting the pupilghost from the data
# frames
#
#
# Normalize profile
#
normalize_region = ((radii < 1100) & (radii > 600)) | \
((radii > 4000) & (radii < 4600))
normalize_flux = numpy.mean(background_levels[normalize_region])
logger.info("normalization flux = %f" % (normalize_flux))
#
# Subtract background and normalize all measurements
#
normalized_bgsub_profile = (background_levels - normalize_flux) / normalize_flux
# fitfunc = lambda p, x: p[0] + p[1] * x
# errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
# bg_for_fit = background_levels
# #bg_for_fit[numpy.isnan(background_levels)] = 0
# bg_for_fit[((radii > ri) & (radii < ro))] = 0
# pinit = [0.0, 0.0] # Assume no slope and constant level of 0
# out = scipy.optimize.leastsq(errfunc, pinit,
# args=(radii, background_levels, background_level_errors), full_output=1)
# pfinal = out[0]
# covar = out[1]
# stdout_write(" best-fit: %.2e + %.3e * x\n" % (pfinal[0], pfinal[1]))
#print pfinal
#print covar
# #
# # Now we have the fit for the background, compute the 2d background
# # image and subtract it out
# #
# x = numpy.linspace(0, max_radius, 100)
# y_fit = radii * pfinal[1] + pfinal[0]
# background = pfinal[0] + pfinal[1] * radius
# bg_sub = ((data - normalize_flux) / normalize_flux) - background
# bg_sub_profile = background_levels - (pfinal[0] + pfinal[1]*radii)
# numpy.savetxt("radial__%s" % ("bgsub"),
# numpy.append(radii.reshape((-1,1)),
# bg_sub_profile.reshape((-1,1)), axis=1))
#
# Use the profile and fit a spline to the underlying shape
#
spl = fit_spline_background(radii, background_levels, logger=logger)
background_1d = spl(radius.flatten())
background_2d = background_1d.reshape(radius.shape)
bg_sub = (data - background_2d) / background_2d
#if (write_intermediate):
# bgsub_hdu = pyfits.PrimaryHDU(data=bg_sub)
# bgsub_hdu.writeto("bgsub.fits", clobber=True)
#
# Combine all radial template profiles so we can store them in the output
# file. This is required to allow for faster and more accurate scaling
# of the pupilghost during subtraction from the data files
#
profiles = numpy.empty((radii.shape[0], 4))
profiles[:,0] = radii[:]
profiles[:,1] = normalized_bgsub_profile[:]
profiles[:,2] = spl(radii[:])
profiles[:,3] = (background_levels - profiles[:,2]) / profiles[:,2]
peak_flux = numpy.max(profiles[:,3][numpy.isfinite(profiles[:,3])])
# numpy.savetxt("radial__%s" % ("norm+bgsub"),
# numpy.append(radii.reshape((-1,1)),
# normalized_bgsub_profile.reshape((-1,1)), axis=1))
return bg_sub, profiles, peak_flux
def _fit(self, X, y):
self.X, y = self._check_params(X, y)
n, p = X.shape
self.y = y.reshape((n, 1))
# list of selected features
S = []
# list of all features
F = range(p)
if self.n_features != 'auto':
feature_mi_matrix = np.zeros((self.n_features, p))
else:
feature_mi_matrix = np.zeros((n, p))
feature_mi_matrix[:] = np.nan
S_mi = []
# ----------------------------------------------------------------------
# FIND FIRST FEATURE
# ----------------------------------------------------------------------
# check a range of ks (3-10), and choose the one with the max median MI
k_min = 3
k_max = 11
xy_MI = np.zeros((k_max-k_min, p))
xy_MI[:] = np.nan
for i, k in enumerate(range(k_min, k_max)):
xy_MI [i, :] = mi.get_first_mi_vector(self, k)
xy_MI = bn.nanmedian(xy_MI, axis=0)
# choose the best, add it to S, remove it from F
S, F = self._add_remove(S, F, bn.nanargmax(xy_MI))
S_mi.append(bn.nanmax(xy_MI))
# notify user
if self.verbose > 0:
self._print_results(S, S_mi)
# ----------------------------------------------------------------------
# FIND SUBSEQUENT FEATURES
# ----------------------------------------------------------------------
while len(S) < self.n_features:
# loop through the remaining unselected features and calculate MI
s = len(S) - 1
feature_mi_matrix[s, F] = mi.get_mi_vector(self, F, s)
# make decision based on the chosen FS algorithm
fmm = feature_mi_matrix[:len(S),F]
if self.method == 'JMI':
selected = F[bn.nanargmax(bn.nansum(fmm, axis=0))]
elif self.method == 'JMIM':
selected = F[bn.nanargmax(bn.nanmin(fmm, axis=0))]
elif self.method == 'MRMR':
MRMR = xy_MI[F] - bn.nanmean(fmm, axis=0)
selected = F[bn.nanargmax(MRMR)]
# record the JMIM of the newly selected feature and add it to S
S_mi.append(bn.nanmax(bn.nanmin(fmm, axis=0)))
S, F = self._add_remove(S, F, selected)
# notify user
if self.verbose > 0:
self._print_results(S, S_mi)
# if n_features == 'auto', let's check the S_mi to stop
if self.n_features == 'auto' and len(S) > 10:
# smooth the 1st derivative of the MI values of previously sel
MI_dd = signal.savgol_filter(S_mi[1:],9,2,1)
# does the mean of the last 5 converge to 0?
if np.abs(np.mean(MI_dd[-5:])) < 1e-3:
break
# ----------------------------------------------------------------------
# SAVE RESULTS
# ----------------------------------------------------------------------
self.n_features_ = len(S)
self.support_ = np.zeros(p, dtype=np.bool)
self.support_[S] = 1
self.ranking_ = S
self.mi_ = S_mi
return self
def parallel_compute(queue, shmem_buffer, shmem_results, size_x, size_y, len_filelist, operation):
#queue, shmem_buffer, shmem_results, size_x, size_y, len_filelist = worker_args
buffer = shmem_as_ndarray(shmem_buffer).reshape((size_x, size_y, len_filelist))
result_buffer = shmem_as_ndarray(shmem_results).reshape((size_x, size_y))
while (True):
cmd_quit, line = queue.get()
if (cmd_quit):
queue.task_done()
return
if (operation == "median"):
result_buffer[line,:] = numpy.median(buffer[line,:,:], axis=1)
elif (operation == "medsigclip"):
# Do not use (yet), is slow as hell
# (maskedarrays are pure python, not C as all the rest)
#print buffer[line,:,:].shape
_sigma_plus = numpy.ones(shape=(buffer.shape[1],buffer.shape[2])) * 1e9
_sigma_minus = numpy.ones(shape=(buffer.shape[1],buffer.shape[2])) * 1e9
_median = numpy.median(buffer[line,:,:], axis=1)
nrep = 3
valid_pixels = numpy.ma.MaskedArray(buffer[line,:,:])
for rep in range(nrep):
_median_2d = _median.reshape(_median.shape[0],1).repeat(buffer.shape[2], axis=1)
_min = _median_2d - 3 * _sigma_minus
_max = _median_2d + 3 * _sigma_plus
#valid_pixels = numpy.ma.masked_inside(buffer[line,:,:], _min, _max)
valid = (buffer[line,:,:] > _min) & (buffer[line,:,:] < _max)
valid_pixels = numpy.ma.array(buffer[line,:,:], mask=valid)
#valid_pixels = numpy.ma.MaskedArray(buffer[line,:,:], valid)
#print _min.shape, valid.shape, valid_pixels.shape
#if (numpy.sum(valid, axis=1).any() <= 0):
# break
#_median = numpy.median(buffer[line,:,:][valid], axis=1)
_median = numpy.median(valid_pixels, axis=1)
if (rep < nrep-1):
#_sigma_plus = scipy.stats.scoreatpercentile(buffer[line,:,:][valid], 84) - _median
#_sigma_minus = _median - scipy.stats.scoreatpercentile(buffer[line,:,:][valid], 16)
_sigma_plus = scipy.stats.scoreatpercentile(valid_pixels, 84) - _median
_sigma_minus = _median - scipy.stats.scoreatpercentile(valid_pixels, 16)
result_buffer[line,:] = _median
elif (operation == "medclip"):
intermediate = numpy.sort(buffer[line,:,:], axis=1)
result_buffer[line,:] = numpy.median(intermediate[:,1:-2], axis=1)
elif (operation == "min"):
result_buffer[line,:] = numpy.min(buffer[line,:,:], axis=1)
elif (operation == "max"):
result_buffer[line,:] = numpy.max(buffer[line,:,:], axis=1)
elif (operation == "nanmean"):
result_buffer[line,:] = scipy.stats.nanmean(buffer[line,:,:], axis=1)
elif (operation == "nanmedian"):
#print "nanmedian"
result_buffer[line,:] = scipy.stats.nanmedian(buffer[line,:,:], axis=1)
elif (operation == "nanmean.bn"):
x = numpy.array(buffer[line,:,:], dtype=numpy.float32)
result_buffer[line,:] = bottleneck.nanmean(x, axis=1)
elif (operation == "nanmedian.bn"):
#print "nanmedian"
x = numpy.array(buffer[line,:,:], dtype=numpy.float32)
result_buffer[line,:] = bottleneck.nanmedian(x, axis=1)
#result_buffer[line,:] = scipy.stats.nanmedian(buffer[line,:,:], axis=1)
elif (operation == "nansum.bn"):
x = numpy.array(buffer[line,:,:], dtype=numpy.float32)
result_buffer[line,:] = bottleneck.nansum(x, axis=1)
else:
result_buffer[line,:] = numpy.mean(buffer[line,:,:], axis=1)
queue.task_done()
xvals = np.arange(newweight.shape[1])
yvals = np.arange(newweight.shape[0])
X,Y = np.meshgrid(xvals,yvals)
badx = X[newweight == False]
bady = Y[newweight == False]
imagearr[bady,badx,:,i] = np.nan
#med_image = np.zeros(imagearr[:,:,:,-1].shape)
#med_image[:,:,0] = bn.nanmedian(imagearr[:,:,0,:i+1],axis=2)
#med_image[:,:,1] = bn.nanmedian(imagearr[:,:,1,:i+1],axis=2)
#med_image[:,:,2] = bn.nanmedian(imagearr[:,:,2,:i+1],axis=2)
avg_image = jointimage*weightimage.reshape(weightimage.shape+(1,))+unwarped_newimage*newweight.reshape(newweight.shape+(1,))
weightimage += newweight
avg_image /= weightimage.reshape(weightimage.shape+(1,))
#Replace the currframe with the avg_image:
currframe = frame(cap,-1,image=ski_util.img_as_ubyte(avg_image))
#currframe = frame(cap,-1,image=ski_util.img_as_ubyte(med_image))
ski_io.imsave('test_avg.jpg',currframe.rawimage)
medianed_image = np.zeros(imagearr[:,:,:,0].shape)
medianed_image[:,:,0] = bn.nanmedian(imagearr[:,:,0,:],axis=2)
medianed_image[:,:,1] = bn.nanmedian(imagearr[:,:,1,:],axis=2)
medianed_image[:,:,2] = bn.nanmedian(imagearr[:,:,2,:],axis=2)
ski_io.imsave('test_median.jpg',medianed_image)
for i in range(len(chronological_order)):
curridx = chronological_order[i]
print frameorder[curridx],
currimage = insert_image(medianed_image,imagearr[:,:,:,curridx],adjustmask=1,overlap=20)
ski_io.imsave('test_out_{0:d}.jpg'.format(frameorder[curridx]),currimage)
# skyvalue[:,:] = numpy.NaN
for cx, cy in itertools.product(range(8), repeat=2):
#
# Get pixel coord for this cell
#
#print binning
x1,x2,y1,y2 = cell2ota__get_target_region(cx, cy, binning=binning, trimcell=0)
x21 = (x2-x1)/2
# extract the mean value in the bottom corner
corner = bottleneck.nanmean(ext.data[y1:y1+w, x1:x1+w].astype(numpy.float32))
# also get the value in the bottom center
center = bottleneck.nanmean(ext.data[y1:y1+w, x1+x21-w/2:x1+x21+w//2].astype(numpy.float32))
excess = corner - center
#print ext.name, cx, cy, corner, center, excess
excesses[cx,cy] = excess
_mean = bottleneck.nanmean(excesses)
_median = bottleneck.nanmedian(excesses)
guideota = (_median > 10*skynoise)
print(ext.name, _mean, _median, skylevel, guideota)
#break
