def save_px_df(self, id_suffix):
""" Save up regions intensity pixel-wise, for best mask and corresponding pixel-wise ΔF/F image
"""
self.px_df = pd.DataFrame(columns=['ID', # recording ID
'stim', # stimulus number
'mask_region', # mask region (1 for master or down)
'int', # px intensity
'delta']) # px ΔF/F
best_up_mask = self.up_diff_mask[self.best_up_mask_index]
best_up_mask_prop = measure.regionprops(best_up_mask)
# best_delta_img = self.peak_deltaF_series[self.best_up_mask_index]
# best_img = self.stim_mean_series[self.best_up_mask_index]
for stim_img_num in range(len(self.stim_mean_series)):
stim_mean_img = self.stim_mean_series[stim_img_num]
stim_deltaF_img = self.peak_deltaF_series[stim_img_num]
for i in best_up_mask_prop: # calculate profiles for each up region
best_up_mask_region = best_up_mask == i.label
for px_int, px_delta in zip(ma.compressed(ma.masked_where(~best_up_mask_region, stim_mean_img)),
ma.compressed(ma.masked_where(~best_up_mask_region, stim_deltaF_img))):
point_series = pd.Series([f'{self.img_name}{id_suffix}', # recording ID
stim_img_num+1, # stimulus number
i.label, # mask region
px_int, # px intensity
px_delta], # px ΔF/F
index=self.px_df.columns)
self.px_df = self.px_df.append(point_series, ignore_index=True)
logging.info(f'Recording profile data frame {self.px_df.shape} created')
return self.px_df
def calcBBScore(self, field, attempt=-1):
tm = self.ClasData[attempt]
mean = tm['BBVpp'].mean()
lothresh = mean * self.MeasSettings['MinVppFracMean'][0]
hithresh = mean * self.MeasSettings['MaxVppFracMean'][0]
dat = []
for r in range(0, len(self.meas_rows)):
v = tm[tm['Row'] == r][field].values
v_ma = ma.masked_outside(v, lothresh, hithresh)
dat.append(v_ma)
average = ma.masked_array((dat)).mean(axis=0)
bb = ma.compressed(average)
mask = average.mask
scores = []
for c in self.Candidates:
field = c + '_bbvpp'
k = ma.compressed(
ma.masked_array(self.bbvpp_kernels[field][0:len(average)],
mask=mask))
# print('len(bb): ', len(bb), ' len(k): ', len(k))
score = np.corrcoef(bb, k)[0, 1]
scores.append(score)
self.BBScores = scores
#self.nBB = ma.count(average)
return dat, average, scores
def Fill2ThetaAzimuthMap(masks, TA, tam, image):
'Needs a doc string'
Zlim = masks['Thresholds'][1]
rings = masks['Rings']
arcs = masks['Arcs']
TA = np.dstack((ma.getdata(TA[1]), ma.getdata(TA[0]),
ma.getdata(TA[2]))) #azimuth, 2-theta, dist
tax, tay, tad = np.dsplit(TA, 3) #azimuth, 2-theta, dist**2/d0**2
for tth, thick in rings:
tam = ma.mask_or(
tam.flatten(),
ma.getmask(
ma.masked_inside(tay.flatten(), max(0.01, tth - thick / 2.),
tth + thick / 2.)))
for tth, azm, thick in arcs:
tamt = ma.getmask(
ma.masked_inside(tay.flatten(), max(0.01, tth - thick / 2.),
tth + thick / 2.))
tama = ma.getmask(ma.masked_inside(tax.flatten(), azm[0], azm[1]))
tam = ma.mask_or(tam.flatten(), tamt * tama)
taz = ma.masked_outside(image.flatten(), int(Zlim[0]), Zlim[1])
tabs = np.ones_like(taz)
tam = ma.mask_or(tam.flatten(), ma.getmask(taz))
tax = ma.compressed(ma.array(tax.flatten(), mask=tam)) #azimuth
tay = ma.compressed(ma.array(tay.flatten(), mask=tam)) #2-theta
taz = ma.compressed(ma.array(taz.flatten(), mask=tam)) #intensity
tad = ma.compressed(ma.array(tad.flatten(), mask=tam)) #dist**2/d0**2
tabs = ma.compressed(ma.array(
tabs.flatten(), mask=tam)) #ones - later used for absorption corr.
return tax, tay, taz, tad, tabs
def __init__(self, stampdata, disk, annulus, center, disk_radius, annulus_radii, empty = False):
"""
Constructor for a Button object
Arguments:
(np.ndarray) stampdata: the original stamp data
(np.ndarray) disk: a boolean mask for the stampdata FALSE within the found chamber
(np.ndarray) annulus: a boolean mask for the stampdata FALSE within the button annulus
(local background)
(tuple) center: chamber center coordinates, with respect to stampdata coord. system
(int) disk_radius: button radius
(tuple) annulus radii: inner and outer radii of the annulus (innerrad, outerrad)
(bool) empty: flag for empty button
Returns:
None
"""
self.blankFlag = empty
self.stampdata = stampdata # uint16 ndarray
self.disk = disk # a mask
self.disk_intensities = ma.compressed(self.get_disk())
self.annulus = annulus # a mask
self.annulus_intensities = ma.compressed(self.get_annulus())
try:
self.annulus_to_disk_ratio = len(self.annulus_intensities) / len(self.disk_intensities)
except:
warnings.warn('Annulus ratio could not be calculated.\nButton Intensities Are Of Length Zero.\
Annulus to disk ratio is NaN')
self.annulus_to_disk_ratio = np.nan
self.center = center
self.disk_radius = disk_radius
self.annulus_radii = annulus_radii
self.summary = self.summarize()
def set_gen3data(self, Apaths, Ppaths, Xdet, Zdet, omega, mask=None):
self.omega = omega
self.wbyv = omega/self.v
self.amshape = mask.shape
self.mask = mask
for Apath, Ppath in zip(Apaths, Ppaths):
# read the data
Amp = bp.io.read_frame(Apath)
Pha = bp.io.read_frame(Ppath)
# mask all data arrays
Amp = ma.compressed(ma.masked_array(Amp, self.mask))
Pha = ma.compressed(ma.masked_array(Pha, self.mask))
try:
Amps = np.vstack((Amps, Amp))
Phas = np.vstack((Phas, Pha))
except:
Amps = Amp
Phas = Pha
self.Amps = Amps
self.Phas = Phas
# masked detector positions
self.dIdx = np.arange(len(Xdet))
self.dIdx = ma.compressed(ma.masked_array(self.dIdx.reshape(self.amshape), self.mask))
self.Xdet = ma.compressed(ma.masked_array(Xdet.reshape(self.amshape), self.mask))
self.Zdet = ma.compressed(ma.masked_array(Zdet.reshape(self.amshape), self.mask))
def calculateMeans(self, synMean, synMin, synMed, synMax, synMinCP):
"""
Calculate mean, median, minimum, maximum and percentiles of pressure
values from synthetic events.
:param synMean: `numpy.ndarray`
:param synMin: `numpy.ndarray`
:param synMed: `numpy.ndarray`
:param synMax: `numpy.ndarray`
:param synMinCP: `numpy.ndarray`
"""
synMean = ma.masked_values(synMean, -9999.)
synMin = ma.masked_values(synMin, -9999.)
synMed = ma.masked_values(synMed, -9999.)
synMax = ma.masked_values(synMax, -9999.)
self.synMean = ma.mean(synMean, axis=0)
self.synMed = ma.mean(synMed, axis=0)
self.synMin = ma.mean(synMin, axis=0)
self.synMax = ma.mean(synMax, axis=0)
self.synMeanUpper = percentile(ma.compressed(synMean), per=95, axis=0)
self.synMeanLower = percentile(ma.compressed(synMean), per=5, axis=0)
self.synMinUpper = percentile(ma.compressed(synMin), per=95, axis=0)
self.synMinLower = percentile(ma.compressed(synMin), per=5, axis=0)
self.synMinCPDist = np.mean(synMinCP, axis=0)
self.synMinCPLower = percentile(synMinCP, per=5, axis=0)
self.synMinCPUpper = percentile(synMinCP, per=95, axis=0)
r = list(np.random.uniform(high=synMean.shape[0], size=3).astype(int))
self.synRandomMinima = synMean[r, :, :]
def fill_from_netcdf(self, rec_num, netcdf):
"""
Used by handle_station to get the records from netcdf for comparing with the
records from the database.
"""
netcdf = {}
if not ma.getmask(self.ncdf_data_set["latitude"][rec_num]):
netcdf["latitude"] = ma.compressed(
self.ncdf_data_set["latitude"][rec_num])[0]
else:
netcdf["latitude"] = None
if not ma.getmask(self.ncdf_data_set["longitude"][rec_num]):
netcdf["longitude"] = ma.compressed(
self.ncdf_data_set["longitude"][rec_num])[0]
else:
netcdf["longitude"] = None
if not ma.getmask(self.ncdf_data_set["elevation"][rec_num]):
netcdf["elevation"] = ma.compressed(
self.ncdf_data_set["elevation"][rec_num])[0]
else:
netcdf["elevation"] = None
# pylint: disable=no-member
netcdf["description"] = str(
nc.chartostring(self.ncdf_data_set["locationName"][rec_num]))
netcdf["name"] = str(
nc.chartostring(self.ncdf_data_set["stationName"][rec_num]))
return netcdf
def mflag(arrayname, uvwbl, uvdata, flaglist):
"""
Return flagged uvdata
"""
Nant = 40 if arrayname == 'm1' else 60
Nbl = Nant * (Nant - 1) / 2
# logger.debug('Nbl is %d',Nbl)
dout_mask = ma.masked_array(list(
zip(uvwbl['u'], uvwbl['v'], uvwbl['w'],
np.hstack((uvdata[0:Nbl], np.conj(uvdata[0:Nbl]))),
np.ones(Nbl * 2))),
dtype=ri.dtk)
flagarr = mi.flagind(Nant, flaglist)
flagarr = np.hstack((flagarr, flagarr + Nbl)) # flag (-u,-v)
for ifl in flagarr:
dout_mask[ifl] = ma.masked
# Np = ma.count(dout_mask.dtype.names[0])
# logger.debug('Np is %d',Np)
# logger.debug('dout shape is %d',dout.shape[0])
dout_flag = np.array(list(
zip(ma.compressed(dout_mask['u']), ma.compressed(dout_mask['v']),
ma.compressed(dout_mask['w']), ma.compressed(dout_mask['x']),
ma.compressed(dout_mask['wgt']))),
dtype=ri.dtk)
return dout_flag
def spike_flag(data,masked,freq,percent):
"""
Flags out RFI spikes using a 11 bin filter
Can be used with either time or freq
percent is a percentage level cut (100 would be twice the 11 bin average)
Needs to be applied to masked data.
"""
new_mask = np.zeros(len(data))
new_array = ma.array(data,mask=masked)
new_comp = ma.compressed(new_array)
freq_array = ma.array(freq,mask=masked)
new_freq = ma.compressed(freq_array)
for i in range(0,len(data)):
if masked[i]==1.0:
new_mask[i] = 1.0
for i in range(5,len(new_comp)-5):
group = new_comp[i-5]+new_comp[i-4]+new_comp[i-3]+new_comp[i-2]+new_comp[i-1]+new_comp[i]+new_comp[i+1]+new_comp[i+2]+new_comp[i+3]+new_comp[i+4]+new_comp[i+5]
mean_group = group/11.
if new_comp[i]/mean_group>=(1+percent/100.):
comp_freq = new_freq[i]
for j in range(0,len(freq)):
if freq[j]==comp_freq:
index=j
new_mask[index]= 1.0
elif new_comp[i]/mean_group<=1/(1+percent/100.):
comp_freq = new_freq[i]
for j in range(0,len(freq)):
if freq[j]==comp_freq:
index=j
new_mask[index]= 1.0
return new_mask
def set_gen3data(self, Apath, Ppath, Xdet, Zdet,
omega, mask_cutoff, toprowsmask=0, polygonmaskfile=None):
self.omega = omega
self.wbyv = omega/self.v
# read the data
self.Amp = bp.io.read_frame(Apath)
self.Pha = bp.io.read_frame(Ppath)
self.amshape = self.Amp.shape
# set the mask
self.mask = np.ones_like(self.Amp, dtype='bool')
self.mask[self.Amp > mask_cutoff*np.max(self.Amp)] = False
# mask top rows
if toprowsmask > 0:
toprowsmask = toprowsmask -1
self.mask[0:toprowsmask,:] = True
# mask all data arrays
self.Amp = ma.compressed((ma.masked_array(self.Amp, self.mask)))
self.Pha = ma.compressed(ma.masked_array(self.Pha, self.mask))
# masked detector positions
self.dIdx = np.arange(len(Xdet))
self.dIdx = ma.compressed(ma.masked_array(self.dIdx.reshape(self.amshape), self.mask))
self.Xdet = ma.compressed(ma.masked_array(Xdet.reshape(self.amshape), self.mask))
self.Zdet = ma.compressed(ma.masked_array(Zdet.reshape(self.amshape), self.mask))
def plot_scatterplot(prefix, feature='asymmetry1', vmin=0, vmax=1, resolution=512, rows=4, cols=4,
dotsize=10, figsize=(12, 10), upload=True, remote_folder = "01_18_Experiment",
bucket='ccurtis.data'):
"""
Plot scatterplot of trajectories in video with colors corresponding to features.
Parameters
----------
prefix: string
Prefix of file name to be plotted e.g. features_P1.csv prefix is P1.
feature: string
Feature to be plotted. See features_analysis.py
vmin: float64
Lower intensity bound for heatmap.
vmax: float64
Upper intensity bound for heatmap.
resolution: int
Resolution of base image. Only needed to calculate bounds of image.
rows: int
Rows of base images used to build tiled image.
cols: int
Columns of base images used to build tiled images.
upload: boolean
True if you want to upload to s3.
"""
# Inputs
# ----------
merged_ft = pd.read_csv('features_{}.csv'.format(prefix))
string = feature
leveler = merged_ft[string]
t_min = vmin
t_max = vmax
ires = resolution
norm = mpl.colors.Normalize(t_min, t_max, clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.viridis)
zs = ma.masked_invalid(merged_ft[string])
zs = ma.masked_where(zs <= t_min, zs)
zs = ma.masked_where(zs >= t_max, zs)
to_mask = ma.getmask(zs)
zs = ma.compressed(zs)
xs = ma.compressed(ma.masked_where(to_mask, merged_ft['X'].astype(int)))
ys = ma.compressed(ma.masked_where(to_mask, merged_ft['Y'].astype(int)))
fig = plt.figure(figsize=figsize)
plt.scatter(xs, ys, c=zs, s=dotsize)
mapper.set_array(10)
plt.colorbar(mapper)
plt.xlim(0, ires*cols)
plt.ylim(0, ires*rows)
plt.axis('off')
print('Plotted {} scatterplot successfully.'.format(prefix))
outfile = 'scatter_{}_{}.png'.format(feature, prefix)
fig.savefig(outfile, bbox_inches='tight')
if upload == True:
aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket)
def calculateMeans(self):
self.synHist = ma.masked_values(self.synHist, -9999.)
self.synHistMean = ma.mean(self.synHist, axis=0)
self.medSynHist = ma.median(self.synHist, axis=0)
self.synHistUpper = percentile(ma.compressed(self.synHist),
per=95, axis=0)
self.synHistLower = percentile(ma.compressed(self.synHist),
per=5, axis=0)
def Fill2ThetaMap(data,TA,image):
import numpy.ma as ma
Zmin = data['Zmin']
Zmax = data['Zmax']
tax,tay = TA # 2-theta & yaxis
taz = ma.masked_outside(image.flatten()-Zmin,0,Zmax-Zmin)
tam = ma.getmask(taz)
tax = ma.compressed(ma.array(tax.flatten(),mask=tam))
tay = ma.compressed(ma.array(tay.flatten(),mask=tam))
taz = ma.compressed(ma.array(taz.flatten(),mask=tam))
del(tam)
return tax,tay,taz
def Fill2ThetaMap(data, TA, image):
import numpy.ma as ma
Zmin = data['Zmin']
Zmax = data['Zmax']
tax, tay = TA # 2-theta & yaxis
taz = ma.masked_outside(image.flatten() - Zmin, 0, Zmax - Zmin)
tam = ma.getmask(taz)
tax = ma.compressed(ma.array(tax.flatten(), mask=tam))
tay = ma.compressed(ma.array(tay.flatten(), mask=tam))
taz = ma.compressed(ma.array(taz.flatten(), mask=tam))
del (tam)
return tax, tay, taz
def aboveThreshold(data, threshold):
if type(data) == list:
new_data = []
for i in range(len(data)):
dat = masked_where(abs(data[0]) < threshold, data[i])
dat = compressed(dat)
new_data.append(dat)
return new_data
else:
data = masked_where(abs(data) < threshold, data)
data = compressed(data)
return data
def azimuthalAverage(image, center=None, maskval=0):
"""
calculate the azimuthally averaged radial profile.
image - 2D image
center - [x,y] pixel coordinates used as the center. the default is
None which then uses the center of the image (including
fractional pixels).
maskval - threshold value for including data in the profile
"""
# calculate the indices from the image
y, x = np.indices(image.shape)
# default to image center if no center given
if not center:
center = np.array([(x.max() - x.min()) / 2.0,
(x.max() - x.min()) / 2.0])
r = np.hypot(x - center[0], y - center[1])
# get sorted radii and sort image accordingly
ind = np.argsort(r.flat)
i_sorted = image.flat[ind]
# for FP data we need to at least mask out data at
# 0 or less so the gaps get ignored.
# also want to mask out area outside of aperture
# so use given maskval to do that.
i_ma = ma.masked_less_equal(i_sorted, maskval)
mask = ma.getmask(i_ma)
# remove masked data points from further analysis
r_sorted = ma.compressed(ma.array(r.flat[ind], mask=mask))
i_mask = ma.compressed(i_ma)
# get the integer part of the radii (bin size = 1)
r_int = r_sorted.astype(int)
# find all pixels that fall within each radial bin.
deltar = r_int[1:] - r_int[:-1] # assumes all radii represented
rind = np.where(deltar)[0] # location of changed radius
nr_tot = rind[1:] - rind[:-1] # total number of points in radius bin
# cumulative sum to figure out sums for each radius bin
csim = ma.cumsum(i_mask, dtype=float)
tbin = csim[rind[1:]] - csim[rind[:-1]]
# calculate and return profile of mean within each bin
radial_prof = tbin / nr_tot
return radial_prof
def loessmin_diff((file1, file2, filex1, filex2, cutoff, toprowsmask, span)):
from bopy.io import read_frame
from bopy.utils import mask2_maxcutoff
from bopy.utils.rfuncs import loess2d
d1 = read_frame(file1)
d2 = read_frame(file2)
dmask = mask2_maxcutoff(read_frame(filex1),
read_frame(filex2),
cutoff, toprowsmask=toprowsmask)
d1 = loess2d(d1, span=span)
d1 = np.min(ma.compressed(ma.masked_array(d1, mask=dmask)))
d2 = loess2d(d2, span=span)
d2 = np.min(ma.compressed(ma.masked_array(d2, mask=dmask)))
return d1 - d2
def band2txt(band, outfile):
'''Accepts numpy rasterand writes to specified text file on disk.'''
if ma.isMaskedArray(band) is True:
outraster = ma.compressed(band)
else:
outraster = band
np.savetxt(outfile, outraster, fmt='%f')
def apply_new_mask(ifgs, mask_old, mask_new):
"""Apply a new mask to a collection of ifgs (or sources) that are stored as row vectors with an accompanying mask.
Inputs:
ifgs | r2 array | ifgs as row vectors
mask_old | r2 array | mask to convert a row of ifg into a rank 2 masked array
mask_new | r2 array | the new mask to be applied. Note that it must not unmask any pixels that are already masked.
Returns:
ifgs_new_mask | r2 array | as per ifgs, but with a new mask.
History:
2020/06/26 | MEG | Written
"""
n_pixs_new = len(np.argwhere(mask_new == False))
ifgs_new_mask = np.zeros(
(ifgs.shape[0], n_pixs_new)
) # initiate an array to store the modified sources as row vectors
for ifg_n, ifg in enumerate(ifgs): # Loop through each source
ifg_r2 = col_to_ma(
ifg, mask_old
) # turn it from a row vector into a rank 2 masked array
ifg_r2_new_mask = ma.array(ifg_r2,
mask=mask_new) # apply the new mask
ifgs_new_mask[ifg_n, :] = ma.compressed(
ifg_r2_new_mask
) # convert to row vector and places in rank 2 array of modified sources
return ifgs_new_mask
def bin_spike(x, l):
"""
l is the number of points used for comparison, thus l=2 means that each
point will be compared only against the previous and following
measurements. l=2 is is probably not a good choice, too small.
Maybe use pstsd instead?
Dummy way to avoid warnings when x[ini:fin] are all masked.
Improve this in the future.
"""
assert x.ndim == 1, "I'm not ready to deal with multidimensional x"
assert l%2 == 0, "l must be an even integer"
N = len(x)
bin = ma.masked_all(N)
# bin_std = ma.masked_all(N)
half_window = int(l/2)
idx = (i for i in range(half_window, N - half_window) if np.isfinite(x[i]))
for i in idx:
ini = max(0, i - half_window)
fin = min(N, i + half_window)
# At least 3 valid points
if ma.compressed(x[ini:fin]).size >= 3:
bin[i] = x[i] - ma.median(x[ini:fin])
# bin_std[i] = (np.append(x[ini:i], x[i+1:fin+1])).std()
bin[i] /= (np.append(x[ini:i], x[i+1:fin+1])).std()
return bin
def umask_value_transform(self, params_dict):
"""Retrieves a netcdf value, checking for masking and retrieves the value as a float
Args:
params_dict (dict): named function parameters
Returns:
float: the corresponding value
"""
# Probably need more here....
try:
key = None
rec_num = params_dict["recNum"]
for key in params_dict.keys():
if key != "recNum":
break
nc_value = self.ncdf_data_set[key][rec_num]
if not ma.getmask(nc_value):
value = ma.compressed(nc_value)[0]
return float(value)
else:
return None
except Exception as _e: # pylint:disable=broad-except
logging.error(
"%s umask_value_transform: Exception in named function umask_value_transform for key %s: error: %s",
self.__class__.__name__,
key,
str(_e),
)
return None
def band2txt(band, outfile):
"""Accepts numpy rasterand writes to specified text file on disk."""
if ma.isMaskedArray(band) is True:
outraster = ma.compressed(band)
else:
outraster = band
np.savetxt(outfile, outraster, fmt="%f")
def interpolate_time_iso(self, params_dict):
"""
Rounds to nearest hour by adding a timedelta hour if minute >= delta_minutes
"""
try:
_time = None
time_obs = params_dict["timeObs"]
delta_minutes = self.delta / 60
if not ma.getmask(time_obs):
_time = int(ma.compressed(time_obs)[0])
else:
return ""
_time = datetime.utcfromtimestamp(_time)
_time = _time.replace(
second=0, microsecond=0, minute=0,
hour=_time.hour) + timedelta(hours=_time.minute //
delta_minutes)
# convert this iso
return str(_time.isoformat())
except Exception as _e: # pylint:disable=broad-except
logging.error(
"%s handle_data: Exception in named function interpolate_time_iso: error: %s",
self.__class__.__name__,
str(_e),
)
def interpolate_time(self, params_dict):
"""
Rounds to nearest hour by adding a timedelta hour if minute >= delta (from the template)
"""
try:
_thistime = None
_time_obs = params_dict["timeObs"]
if not ma.getmask(_time_obs):
_thistime = int(ma.compressed(_time_obs)[0])
else:
return ""
# if I get here process the _thistime
delta_minutes = self.delta / 60
_ret_time = datetime.utcfromtimestamp(_thistime)
_ret_time = _ret_time.replace(
second=0, microsecond=0, minute=0,
hour=_ret_time.hour) + timedelta(hours=_ret_time.minute //
delta_minutes)
return calendar.timegm(_ret_time.timetuple())
except Exception as _e: # pylint:disable=broad-except
logging.error(
"%s handle_data: Exception in named function interpolate_time: error: %s",
self.__class__.__name__,
str(_e),
)
def EdgeFinder(image,data):
'''this makes list of all x,y where I>edgeMin suitable for an ellipse search?
Not currently used but might be useful in future?
'''
import numpy.ma as ma
Nx,Ny = data['size']
pixelSize = data['pixelSize']
edgemin = data['edgemin']
scalex = pixelSize[0]/1000.
scaley = pixelSize[1]/1000.
tay,tax = np.mgrid[0:Nx,0:Ny]
tax = np.asfarray(tax*scalex,dtype=np.float32)
tay = np.asfarray(tay*scaley,dtype=np.float32)
tam = ma.getmask(ma.masked_less(image.flatten(),edgemin))
tax = ma.compressed(ma.array(tax.flatten(),mask=tam))
tay = ma.compressed(ma.array(tay.flatten(),mask=tam))
return zip(tax,tay)
def EdgeFinder(image, data):
'''this makes list of all x,y where I>edgeMin suitable for an ellipse search?
Not currently used but might be useful in future?
'''
import numpy.ma as ma
Nx, Ny = data['size']
pixelSize = data['pixelSize']
edgemin = data['edgemin']
scalex = pixelSize[0] / 1000.
scaley = pixelSize[1] / 1000.
tay, tax = np.mgrid[0:Nx, 0:Ny]
tax = np.asfarray(tax * scalex, dtype=np.float32)
tay = np.asfarray(tay * scaley, dtype=np.float32)
tam = ma.getmask(ma.masked_less(image.flatten(), edgemin))
tax = ma.compressed(ma.array(tax.flatten(), mask=tam))
tay = ma.compressed(ma.array(tay.flatten(), mask=tam))
return zip(tax, tay)
def set_features(self):
cluster_size = constant_cluster_size(self.data[self.varname])
N = ma.compressed(self.data[self.varname]).size
cluster_fraction = cluster_size / N
self.features = {'constant_cluster_size': cluster_size,
'constant_cluster_fraction': cluster_fraction,
}
def check_def_visible(ph_def,
mask_def,
ph_topo,
ph_turb,
snr_threshold=2.0,
debugging_plot=False):
"""A function to check if a (synthetic) deformation pattern is still visible
over synthetic topo correlated and turbulent atmospheres.
Inputs:
ph_def | r2 array | deformation phase
mask_def | rank 2 array of ints | maks showing where the deformation is - 1s where deforming
ph_topo | r2 array | topo correlated APS
ph_turb | r2 array | turbulent APS
snr_threshold | float | sets the level at which deformation is not considered visible over topo and turb APS
bigger = more likely to accept, smaller = less (if 0, will never accept)
debugging_plot | boolean | if True, a figure is produced to help interepret the correct SNR. Could give problems with dependencies.
Returns:
viable | boolean | True if SNR value is acceptable.
snr | float | SNR
History:
2019/MM/DD | MEG | Written as part of f01_real_image_dem_data_vXX.py
2019/11/06 | MEG | Extracted from sctipt and placed in synth_ts.py
2020/08/19 | MEG | WIP
"""
import numpy as np
import numpy.ma as ma
ph_def = ma.array(ph_def, mask=(1 - mask_def))
ph_atm = ma.array((ph_turb + ph_topo), mask=(1 - mask_def))
snr = np.var(ma.compressed(ph_def)) / np.var(ma.compressed(ph_atm))
if snr > snr_threshold:
viable = True
else:
viable = False
# import matplotlib.pyplot as plt
# from small_plot_functions import create_universal_cmap
# cmap, vmin, vmax = create_universal_cmap([ph_def, ph_atm])
# f, axes = plt.subplots(1,2)
# f.suptitle(f"SNR: {snr}")
# axes[0].imshow(ph_def, vmin = vmin, vmax = vmax, cmap = cmap)
# axes[1].imshow(ph_atm, vmin = vmin, vmax = vmax, cmap = cmap)
return viable, snr
def trim_loop(loop, N=20, Verbose=True, Use_Dip=True):
f = f1 = ma.array(loop.freq)
z = z1 = ma.array(loop.z)
#Estimate Resonance frequency using minimum Dip or max adjacent distance
if Use_Dip:
zr_mag_est = np.abs(z).min()
zr_est_index = np.where(np.abs(z) == zr_mag_est)[0][0]
else:
z_adjacent_distance = np.abs(z[:-1] - z[1:])
zr_est_index = np.argmax(z_adjacent_distance)
zr_mag_est = z[zr_est_index]
# estimate max transmission mag using max valuse of abs(z)
z_max_mag = np.abs(z).max()
#Depth of resonance in dB
depth_est = 20.0 * np.log10(zr_mag_est / z_max_mag)
#Magnitude of resonance dip at half max
res_half_max_mag = (z_max_mag + zr_mag_est) / 2
#find the indices of the closest points to this magnitude along the loop, one below zr_mag_est and one above zr_mag_est
a = np.square(np.abs(z[:zr_est_index + 1]) - res_half_max_mag)
lower_index = np.argmin(a)
a = np.square(np.abs(z[zr_est_index:]) - res_half_max_mag)
upper_index = np.argmin(a) + zr_est_index
#estimate the FWHM bandwidth of the resonance
f_upper_FWHM = f[upper_index]
f_lower_FWHM = f[lower_index]
FWHM_est = np.abs(f_upper_FWHM - f_lower_FWHM)
fr_est = f[zr_est_index]
#Bandwidth Cut: cut data that is more than N * FWHM_est away from zr_mag_est
z = z2 = ma.masked_where(
(f > fr_est + N * FWHM_est) | (fr_est - N * FWHM_est > f), z)
f = f2 = ma.array(f, mask=z.mask)
loop.z = ma.compressed(z)
loop.freq = ma.compressed(f)
if Verbose:
print(
'Bandwidth cut:\n\t{1} points outside of fr_est +/- {0}*FWHM_est removed, {2} remaining data points'
.format(N, *_points_removed(z1, z2)))
def gain_calc(data,masked,gsm,K0):
"""
Calculates the gain using least squares for a single frequency.
Inputs:
data - single freq array of data
gsm - similar single freq array of gsm data
masked - mask for data
K0 - preliminary guess for gain
"""
fK = lambda K,d,g: K*d-g
d0_array = ma.array(data,mask=masked)
d0 = ma.compressed(d0_array)
g0_array = ma.array(gsm,mask=masked)
g0 = ma.compressed(g0_array)
Kf = opt.leastsq(fK,K0,args=(d0,g0),maxfev=100000)
return Kf[0]
def peak_finder(arr, smoothing=5, ddy_thresh=-300, dy0_thresh=5):
array = medfilt(arr, smoothing)
x = arange(len(array))
kernel = [4, 0, -4]
dY = convolve(array, kernel, 'same')
ddy = convolve(dY, kernel, 'same')
falloff = -15000*exp(-0.003*x) #This has to be worked on
masked_array = ma.masked_where(logical_or(ddy>falloff+ddy_thresh, abs(dY) > dy0_thresh) , arr)
x_masked = ma.array(x, mask=masked_array.mask)
return ma.compressed(x_masked)
def custom_mad_func(array_like, criteria=2.5, **kwargs):
"""This function performs a mad calculation on array of data
Returns the median of MAD data"""
if kwargs:
criteria = kwargs['criteria']
ul = kwargs['ul']
ll = kwargs['ll']
else:
ul = 58
ll = 0.001
# prescreen burst data for outliers
array_like = ma.compressed(ma.masked_outside(array_like, ll, ul))
MAD = smc.mad(array_like)
k = (MAD * criteria)
M = np.nanmedian(array_like)
high = M + k
low = M - k
b = ma.masked_outside(array_like, high, low)
c = ma.compressed(b)
return np.nanmedian(c)
def test(self):
self.flags = {}
x = ma.compressed(self.data[self.varname])
flag = np.zeros(self.data[self.varname].shape, dtype='i1')
if (x.size > 1) and (np.allclose(x, np.ones_like(x) * x[0])):
flag[:] = self.flag_bad
else:
flag[:] = self.flag_good
flag[ma.getmaskarray(self.data[self.varname])] = 9
self.flags['stuck_value'] = flag
def predict(self, X):
# Handle missing values
X = ma.masked_invalid(X)
mask = X.mask
dX = ma.compressed(X).reshape(-1, 1)
dZ = self.model.predict(dX)
Z = np.array([np.nan for i in range(X.shape[0])])
Z[~mask] = dZ
Z = ma.masked_invalid(Z)
return Z * self.step
def predict(self, X):
# Handle missing values
X = ma.masked_invalid(X)
mask = X.mask
dX = ma.compressed(X).reshape(-1, 1)
dZ = self.model.predict(dX)
Z = np.array([np.nan for i in xrange(X.shape[0])])
Z[~mask] = dZ
Z = ma.masked_invalid(Z)
return Z * self.step
def gfit3(xi, yi):
"""
relies on masking , use pyspeckit
"""
# not sure if we need this, or try x = xi
x = ma.compressed(xi)
y = ma.compressed(yi)
sp = pyspeckit.Spectrum(
data=y,
xarr=x,
error=None,
header=h,
)
sp.plotter()
sp.specfit(fittype='gaussian')
sp.specfit.plot_fit()
# sp.baseline()
print(x)
print(y)
# fake a return array
return yi
def peak_finder(arr, smoothing=5, ddy_thresh=-300, dy0_thresh=5):
array = medfilt(arr, smoothing)
x = arange(len(array))
kernel = [4, 0, -4]
dY = convolve(array, kernel, 'same')
ddy = convolve(dY, kernel, 'same')
falloff = -15000 * exp(-0.003 * x) #This has to be worked on
masked_array = ma.masked_where(
logical_or(ddy > falloff + ddy_thresh,
abs(dY) > dy0_thresh), arr)
x_masked = ma.array(x, mask=masked_array.mask)
return ma.compressed(x_masked)
def Intensity(L, graph=False):
"""
Finds the intensity value for a masked array L.
graph plots the results of the watershed segmentation on top of
the raw image.
Need to do work to generate a zero-gradient analysis instead of watershed
segmentation.
"""
Ny, Nx = L.shape
threshold = abs(min(ma.compressed(L))) / 2.
RawData = ma.filled(L, 0)
# plt.imshow(RawData)
# plt.show()
Filtered = gaussian_filter(RawData, 1)
# plt.imshow(-Filtered, cmap = plt.cm.jet)
# plt.show()
data_max = peak_local_max(Filtered,
indices=False,
min_distance=2,
threshold_abs=threshold)
DataIndices = peak_local_max(Filtered,
indices=True,
min_distance=2,
threshold_abs=threshold)
markers = ndi.label(data_max)[0]
# plt.imshow(markers)
# plt.show()
Regions = watershed(-Filtered, markers, mask=np.logical_not(L.mask))
Intensities = []
if len(np.unique(Regions)) == 2:
return DataIndices[0], 0
if graph:
plt.figure()
plt.imshow(L.data, cmap='gray_r', interpolation='nearest')
plt.imshow(ma.MaskedArray(Regions, mask=L.mask),
cmap='cubehelix_r',
interpolation='nearest',
alpha=0.3,
vmin=0)
plt.xticks([])
plt.yticks([])
plt.xlim(20, Nx - 20)
plt.ylim(20, Ny - 20)
plt.title('Intensity')
plt.savefig('IntensityExample.png', format='png', dpi=300)
plt.close()
for J in np.unique(Regions)[1:]:
Med = ma.masked_array(L, Regions != J)
Intensities.append(np.sum(Med))
Intensity, Index = zip(
*sorted(zip(Intensities, range(1, len(np.unique(Regions))))))
return DataIndices[Index[-1] - 1], Intensity[-2] / Intensity[-1]
def Fill2ThetaAzimuthMap(masks,TA,tam,image):
'Needs a doc string'
Zlim = masks['Thresholds'][1]
rings = masks['Rings']
arcs = masks['Arcs']
TA = np.dstack((ma.getdata(TA[1]),ma.getdata(TA[0]),ma.getdata(TA[2]))) #azimuth, 2-theta, dist
tax,tay,tad = np.dsplit(TA,3) #azimuth, 2-theta, dist**2/d0**2
for tth,thick in rings:
tam = ma.mask_or(tam.flatten(),ma.getmask(ma.masked_inside(tay.flatten(),max(0.01,tth-thick/2.),tth+thick/2.)))
for tth,azm,thick in arcs:
tamt = ma.getmask(ma.masked_inside(tay.flatten(),max(0.01,tth-thick/2.),tth+thick/2.))
tama = ma.getmask(ma.masked_inside(tax.flatten(),azm[0],azm[1]))
tam = ma.mask_or(tam.flatten(),tamt*tama)
taz = ma.masked_outside(image.flatten(),int(Zlim[0]),Zlim[1])
tabs = np.ones_like(taz)
tam = ma.mask_or(tam.flatten(),ma.getmask(taz))
tax = ma.compressed(ma.array(tax.flatten(),mask=tam)) #azimuth
tay = ma.compressed(ma.array(tay.flatten(),mask=tam)) #2-theta
taz = ma.compressed(ma.array(taz.flatten(),mask=tam)) #intensity
tad = ma.compressed(ma.array(tad.flatten(),mask=tam)) #dist**2/d0**2
tabs = ma.compressed(ma.array(tabs.flatten(),mask=tam)) #ones - later used for absorption corr.
return tax,tay,taz,tad,tabs
def calcTBScore(self, attempt=-1):
field1 = 'BottomThick'
field2 = 'TBVpp'
tm = self.ClasData[attempt]
loBTthresh = self.MeasSettings['MinBotThickNs'][0] / 1000
hiBTthresh = self.MeasSettings['MaxBotThickNs'][0] / 1000
loVppthresh = self.MeasSettings['MinTBVppInclusionThreshold'][0]
hiVppthresh = self.MeasSettings['MaxTBVppInclusionThreshold'][0]
botthick = []
# alld = []
for r in range(0, len(self.meas_rows)):
bt = tm[tm['Row'] == r][field1].values
bt_ma = ma.masked_outside(bt, loBTthresh, hiBTthresh)
tbvpp = tm[tm['Row'] == r][field2].values
tbvpp_ma = ma.masked_outside(tbvpp, loVppthresh, hiVppthresh)
thismask = ma.mask_or(bt_ma.mask, tbvpp_ma.mask)
bt = ma.masked_array(bt, mask=thismask)
# thisd = [bt_ma,tbvpp_ma]
# alld.append(thisd)
botthick.append(bt)
# return botthick,alld
average = ma.masked_array((botthick)).mean(axis=0)
thickness = ma.compressed(average)
self.nTB = len(thickness)
# if self.nTB == 0 :
# return
mask = average.mask
scores = []
for c in self.Candidates:
field = c + '_tofdelta'
k = ma.compressed(
ma.masked_array(self.tof_kernels[field][0:len(average)],
mask=mask))
# print('len(bb): ', len(bb), ' len(k): ', len(k))
score = np.corrcoef(thickness, k)[0, 1]
scores.append(score)
#self.TBScores = scores
#self.nTB = ma.count(average)
return botthick, average, scores
def r3_to_r2(phUnw):
""" Given a rank3 of ifgs, convert it to rank2 and a mask. Works with either masked arrays or just arrays.
Inputs:
phUnw | rank 3 array | n_ifgs x height x width
returns:
r2_data['ifgs'] | rank 2 array | ifgs as row vectors
r2_data['mask'] | rank 2 array
History:
2020/06/09 | MEG | Written
"""
import numpy as np
import numpy.ma as ma
if ma.isMaskedArray(phUnw):
n_pixels = len(
ma.compressed(phUnw[0, ])
) # if it's a masked array, get the number of non-masked pixels
mask = ma.getmask(phUnw)[
0, ] # get the mask, which is assumed to be constant through time
else:
n_pixels = len(np.ravel(phUnw[
0, ])) # or if a normal numpy array, just get the number of pixels
mask = np.zeros(phUnw[0, ].shape) # or make a blank mask
r2_ifgs = np.zeros(
(phUnw.shape[0], n_pixels)) # initiate to store ifgs as rows in
for ifg_n, ifg in enumerate(phUnw):
if ma.isMaskedArray(phUnw):
r2_ifgs[ifg_n, ] = ma.compressed(
ifg) # non masked pixels into row vectors
else:
r2_ifgs[ifg_n, ] = np.ravel(
ifg) # or all just pixles into row vectors
r2_data = {
'ifgs': r2_ifgs, # make into a dictionary.
'mask': mask
}
return r2_data
def calculateMeans(self, synMean, synMin, synMed, synMax, synMinCP):
synMean = ma.masked_values(synMean, -9999.)
synMin = ma.masked_values(synMin, -9999.)
synMed = ma.masked_values(synMed, -9999.)
synMax = ma.masked_values(synMax, -9999.)
self.synMean = ma.mean(synMean, axis=0)
self.synMed = ma.mean(synMed, axis=0)
self.synMin = ma.mean(synMin, axis=0)
self.synMax = ma.mean(synMax, axis=0)
self.synMeanUpper = percentile(ma.compressed(synMean), per=95, axis=0)
self.synMeanLower = percentile(ma.compressed(synMean), per=5, axis=0)
self.synMinUpper = percentile(ma.compressed(synMin), per=95, axis=0)
self.synMinLower = percentile(ma.compressed(synMin), per=5, axis=0)
self.synMinCPDist = np.mean(synMinCP, axis=0)
self.synMinCPLower = percentile(synMinCP, per=5, axis=0)
self.synMinCPUpper = percentile(synMinCP, per=95, axis=0)
r = list(np.random.uniform(high=synMean.shape[0], size=3).astype(int))
self.synRandomMinima = synMean[r, :, :]
def unmask_track(track):
"""Removes empty frames from inpute trajectory datset.
Parameters
----------
track : pandas.core.frame.DataFrame
At a minimum, must contain a Frame, Track_ID, X, Y, MSDs, and
Gauss column.
Returns
-------
comp_track : pandas.core.frame.DataFrame
Similar to track, but has all masked components removed.
"""
xpos = ma.masked_invalid(track['X'])
msds = ma.masked_invalid(track['MSDs'])
x_mask = ma.getmask(xpos)
msd_mask = ma.getmask(msds)
comp_frame = ma.compressed(ma.masked_where(msd_mask, track['Frame']))
compid = ma.compressed(ma.masked_where(msd_mask, track['Track_ID']))
comp_x = ma.compressed(ma.masked_where(x_mask, track['X']))
comp_y = ma.compressed(ma.masked_where(x_mask, track['Y']))
comp_msd = ma.compressed(ma.masked_where(msd_mask, track['MSDs']))
comp_gauss = ma.compressed(ma.masked_where(msd_mask, track['Gauss']))
comp_qual = ma.compressed(ma.masked_where(x_mask, track['Quality']))
comp_snr = ma.compressed(ma.masked_where(x_mask, track['SN_Ratio']))
comp_meani = ma.compressed(ma.masked_where(x_mask,
track['Mean_Intensity']))
data1 = {
'Frame': comp_frame,
'Track_ID': compid,
'X': comp_x,
'Y': comp_y,
'MSDs': comp_msd,
'Gauss': comp_gauss,
'Quality': comp_qual,
'SN_Ratio': comp_snr,
'Mean_Intensity': comp_meani
}
comp_track = pd.DataFrame(data=data1)
return comp_track
def rebin(data,masked,freq,binscale):
"""
Rebins data to coarser frequency resolution.
Assumes that the input is the data after flagging, mask,
corresponding freq array and a binscale (number of bins to merge).
Output is rebinned data with corresponding freq, mask arrays.
"""
if binscale > 1:
new_data = np.zeros(len(data)/binscale)
new_mask = np.zeros(len(data)/binscale)
new_freq = np.zeros(len(data)/binscale)
f=0
for f in range(0, len(new_data)-1):
if len(masked[f*binscale:(f+1)*binscale])==sum(masked[f*binscale:(f+1)*binscale]):
new_data[f] = 1.0
else:
test_data = ma.array(data[f*binscale:(f+1)*binscale],mask=masked[f*binscale:(f+1)*binscale])
test_data_con = ma.compressed(test_data)
new_data[f] = ma.mean(test_data_con)
if sum(masked[f*binscale:(f+1)*binscale])>=binscale/2.:
new_mask[f] = 1.0
new_freq[f] = ma.mean(freq[f*binscale:(f+1)*binscale])
if len(masked[(f+1)*binscale:-1])==sum(masked[(f+1)*binscale:-1]):
new_data[-1] = 1.0
else:
test_data = ma.array(data[(f+1)*binscale:-1],mask=masked[(f+1)*binscale:-1])
test_data_con = ma.compressed(test_data)
new_data[-1] = ma.mean(test_data_con)
if sum(masked[(f+1)*binscale:-1])>=1.:
new_mask[-1] = 1.0
new_freq[-1] = ma.mean(freq[(f+1)*binscale:-1])
elif binscale == 1:
new_data = data
new_mask = masked
new_freq = freq
return new_data,new_mask,new_freq
def rat_fore(data,masked,freq,minf,maxf,n,m):
"""
Calculates a rational function fit for the data.
Inputs:
data - single frequency dependent spectrum
masked - corresponding mask
freq - corresponding frequency array
minf, maxf - min and max freq if want to truncate range
n - index of numerator polynomial fitting
m - index of denominator polynomial fitting
(4>= n,m >=1)
Output:
dfit - polynomial fit spectrum
fit_params - parameters for the fit
"""
data_array = ma.array(data,mask=masked)
data_comp = ma.compressed(data_array)
freq_array = ma.array(freq,mask=masked)
freq_comp = ma.compressed(freq_array)
min_ind = 0
max_ind = -1
if minf>freq_comp[0]:
min_ind = np.where(freq_comp<=minf)[0][-1]
if maxf<freq_comp[-1]:
max_ind = np.where(freq_comp<=maxf)[0][-1]
mid_ind = min_ind+(max_ind-min_ind)/2
log_data = np.log10(data_comp[min_ind:max_ind])
log_freq = np.log10(freq_comp[min_ind:max_ind]/freq_comp[mid_ind])
p0 = np.ones(n+m+2)
fitfunc = rational_fit(n,m)
errfunc = lambda p,x,y: fitfunc(p,x)-y
pf,success = opt.leastsq(errfunc,p0[:],args=(log_freq,log_data))
dfit = 10**(fitfunc(pf,np.log10(freq/freq_comp[mid_ind])))
return dfit,pf
def _get_bounds(item_list, wrapped_coords=False):
"""
Return a tuple containing the first and secound bound in the list.
* For 0 values it returns (None, None).
* For 1 value it returns that value twice
* For 2 values it returns those (low, high) for unwrapped
co-ordinates or in the order given for wrapped
* For Multiple values unwrapped, this returns min and max
* For Multiple values wrapped co-ordinates, this
returns the value around the largest gap in values
:param list item_list: List of comparable data items
:param wrapped_coords: is this a coordinate which wraps at 360 back to
0, e.g. longitude
:return: Tuple of (first bound, second bound for values in the list)
"""
items = ma.compressed(item_list)
if len(items) < 1:
return None, None
if len(items) == 1:
return items[0], items[0]
if wrapped_coords:
if len(items) is 2:
first_bound_index = 0
second_bound_index = 1
else:
# find the largest angle between closest points and exclude
# this from the bounding box ensuring that this includes going
# across the zero line
items = sorted(items)
first_bound_index = 0
second_bound_index = len(items) - 1
max_diff = (items[first_bound_index] -
items[second_bound_index]) % 360
for i in range(1, len(items)):
diff = (items[i] - items[i-1]) % 360
if diff > max_diff:
max_diff = diff
first_bound_index = i
second_bound_index = i-1
first_bound = items[first_bound_index]
second_bound = items[second_bound_index]
else:
first_bound = min(items)
second_bound = max(items)
return float(first_bound), float(second_bound)
def set_gen3data(self, Apath, Ppath, Xdet, Zdet,
omega, mask=None):
self.omega = omega
self.wbyv = omega/self.v
# read the data
self.Amp = bp.io.read_frame(Apath)
self.Pha = bp.io.read_frame(Ppath)
self.amshape = self.Amp.shape
# set the mask
self.mask = mask
# mask all data arrays
self.Amp = ma.compressed((ma.masked_array(self.Amp, self.mask)))
self.Pha = ma.compressed(ma.masked_array(self.Pha, self.mask))
# masked detector positions
self.dIdx = np.arange(len(Xdet))
self.dIdx = ma.compressed(ma.masked_array(self.dIdx.reshape(self.amshape), self.mask))
self.Xdet = ma.compressed(ma.masked_array(Xdet.reshape(self.amshape), self.mask))
self.Zdet = ma.compressed(ma.masked_array(Zdet.reshape(self.amshape), self.mask))
def poly_fore(data,masked,freq,minf,maxf,n,std):
"""
Calculates a polynomial fit for data.
Inputs:
data - single frequency dependent spectrum
masked - corresponding mask
freq - corresponding frequency array
minf, maxf - min and max freq if want to truncate range
n - index of polynomial fitting
std - 1/weights, set to ones if not needed.
Output:
dfit - polynomial fit spectrum
fit_params - parameters for the fit
"""
data_array = ma.array(data,mask=masked)
data_comp = ma.compressed(data_array)
freq_array = ma.array(freq,mask=masked)
freq_comp = ma.compressed(freq_array)
std_comp = ma.compressed(ma.array(std, mask=masked))
min_ind = 0
max_ind = -1
if minf>freq_comp[0]:
min_ind = np.where(freq_comp<=minf)[0][-1]
if maxf<freq_comp[-1]:
max_ind = np.where(freq_comp<=maxf)[0][-1]
mid_ind = min_ind+(max_ind-min_ind)/2
log_data = np.log10(data_comp[min_ind:max_ind])
log_freq = np.log10(freq_comp[min_ind:max_ind]/freq_comp[mid_ind])
weights = 1/std_comp[min_ind:max_ind]
fit_params = poly.polyfit(log_freq,log_data,n,w=weights)
dfit = 10**(poly.polyval(np.log10(freq/freq_comp[mid_ind]),fit_params))
return dfit, fit_params
def rasterHistogram(raster_matrix):
'''Accepts matrix and generates histogram'''
# Line above is function docstring
# Flatten 2d-matrix
flat_raster = ma.compressed(raster_matrix)
# Setup the plot (see matplotlib.sourceforge.net)
fig = plt.figure(figsize=(8,11))
ax = fig.add_subplot(1,1,1)
# Plot histogram
ax.hist(flat_raster, 10, normed=0, histtype='bar',
align='mid')
# Show the plot on screen
plt.show()
def correct(self, y):
"""Correct the state distribution, given the measurement vector."""
# Get the y-mask
mask = ma.getmaskarray(y)
self.active = active = ~mask
if np.all(mask):
return self.x, self.Px
# Remove inactive outputs
y = ma.compressed(y)
R = self.model.R()[np.ix_(active, active)]
def h_fun(x):
return self.model.h(self.k, x)[..., active]
# Perform unscented transform
h, Ph = self.__ut.transform(self.x, self.Px, h_fun)
Pxh = self.__ut.crosscov()
# Factorize covariance
self.__chol = DifferentiableCholesky()
Py = Ph + R
PyC = self.__chol(Py)
PyCI = np.linalg.inv(PyC)
PyI = np.einsum('...ik,...jk', PyCI, PyCI)
# Perform correction
e = y - h
K = np.einsum('...ik,...kj', Pxh, PyI)
x_corr = self.x + np.einsum('...ij,...j', K, e)
Px_corr = self.Px - np.einsum('...ik,...jl,...kl', K, K, Py)
# Save and return the correction data
self.prev_x = self.x
self.prev_Px = self.Px
self.e = e
self.x = x_corr
self.Px = Px_corr
self.Pxh = Pxh
self.Py = Py
self.PyI = PyI
self.PyC = PyC
self.PyCI = PyCI
self.K = K
return x_corr, Px_corr
def pixel_value_list(self, image): """ Produce two arrays, the first is a list of the binned pixel values and the second is the number of pixels at that value Input: masked numpy array of the image Output: Two lists of histogram data on the image. ([bins],[values]) """ # Compress the array into a list of pixel values compressed = ma.compressed(image) # Find highest pixel value max_val = np.amax(compressed) # Make a histogram of the compressed list result = np.histogram(compressed, bins=max_val, normed=True) #print result[0] #print result[1] return result
def get_toast_phi_loesssmooth((dfile, mfile, span, rpath, rfile)):
""" Reads phi files, smooths and then returns the unmasked pixels
In addition, performs normalization on the target files, if rfile is
not None
Args:
dfile - data file
mfile - mask file
span - span parameter for LOESS
rpath - the path to ref files (None if not for target phase normalization)
rfile - reference data file
"""
d = np.load(dfile)
m = np.load(mfile)
if rfile is not None:
from bopy.gen3pp.gen3pp_utils import phase_normalization
r = np.load(os.path.join(rpath, rfile))
d = phase_normalization(d, r, m)
d = loess2d(d, span)
d = ma.compressed(ma.masked_array(d, mask=m))
return -d # -d because toast phase decreases with r
def match_binning(gsm_time,freq,time,data,mask):
"""
Process for matching the time binning of two datasets.
Initially designed to match signal data to gsm datasets.
Assumes that GSM data has coarser resolution.
Inputs:
gsm_time - time that is being matched to.
freq - frequency array (only need length)
time - original time array of data
data - original data array
mask - original mask array
outputs are new data and mask array
"""
stack_time = gsm_time
stack_data = np.zeros((len(stack_time),len(freq)))
stack_mask = np.zeros((len(stack_time),len(freq)))
for i in range(0,len(stack_time)):
sub_data = np.zeros((len(time),len(freq)))
sub_mask = np.zeros((len(time),len(freq)))
num_mean = 0.
for j in range(0,len(time)):
if abs(stack_time[i]-time[j])<=(stack_time[1]-stack_time[0])/2.:
sub_data[num_mean] = data[j]
sub_mask[num_mean] = mask[j]
num_mean = num_mean+1.
sub_data = sub_data[0:num_mean]
sub_mask = sub_mask[0:num_mean]
if num_mean>=1.0:
for f in range(0,len(freq)):
if sum(sub_mask[:,f])==len(sub_mask[:,f]):
stack_data[i,f]=0.0
stack_mask[i,f]=1.0
else:
single_data = ma.array(sub_data[:,f],mask=sub_mask[:,f])
single_comp = ma.compressed(single_data)
stack_data[i,f] = ma.mean(single_comp)
return stack_data, stack_mask
def correct(self, y):
"""Correct the state distribution, given the measurement vector."""
# Get the y-mask
mask = ma.getmaskarray(y)
self.active = active = ~mask
if np.all(mask):
return self.x, self.Px
# Remove inactive outputs
y = ma.compressed(y)
R = self.model.R()[np.ix_(active, active)]
# Evaluate the model functions
h = self.model.h(self.k, self.x)[..., active]
dh_dx = self.model.dh_dx(self.k, self.x)[..., active]
# Calculate the covariances and gain
Pxh = np.einsum('...ij,...jz', self.Px, dh_dx)
Ph = np.einsum('...iy,...iz', dh_dx, Pxh)
Py = Ph + R
PyI = np.linalg.inv(Py)
K = np.einsum('...ik,...kj', Pxh, PyI)
# Perform correction
e = y - h
x_corr = self.x + np.einsum('...ij,...j', K, e)
Px_corr = self.Px - np.einsum('...ik,...jl,...kl', K, K, Py)
# Save and return the correction data
self.prev_x = self.x
self.prev_Px = self.Px
self.e = e
self.x = x_corr
self.Px = Px_corr
self.Pxh = Pxh
self.Py = Py
self.PyI = PyI
self.K = K
return x_corr, Px_corr
def mask(x, xm, x0, x1):
if numpy.ndim(xm) != 1:
print "utility.mask(): array to used to derive mask must be 1D"
return(numpy.array([]))
xmask = ma.masked_outside(xm, x0, x1)
tmask =ma.getmask(xmask)
if numpy.ndim(x) == 1:
xnew = ma.array(x, mask=tmask)
return(xnew.compressed())
if numpy.ndim(x) == 2:
for i in range(0, numpy.shape(x)[0]):
xnew= ma.array(x[i,:], mask=tmask)
xcmp = ma.compressed(xnew)
if i == 0:
print ma.shape(xcmp)[0]
print numpy.shape(x)[0]
xout = numpy.zeros((numpy.shape(x)[0], ma.shape(xcmp)[0]))
xout[i,:] = xcmp
return(xout)
else:
print "Utility.Mask: dimensions of input arrays are not acceptable"
return(numpy.array([]))
def timerebin(data,masked):
"""
Rebins chunk of time data to single dataset
Assumes that the input is a two dimensional array with corresponding mask
Output is single dataset with corresponding mask
"""
new_data = np.zeros(len(data[0]))
new_mask = np.zeros(len(data[0]))
data = ma.array(data)
masked = ma.array(masked)
for f in range(0,len(data[0])):
if sum(masked[:,f])==len(masked[:,f]):
new_data[f] = 1.0
else:
masked_data = ma.array(data[:,f],mask=masked[:,f])
compressed_data = ma.compressed(masked_data)
new_data[f] = ma.mean(compressed_data)
if sum(masked[:,f])>=len(data[0])/2.:
new_mask[f] = 1.0
return new_data,new_mask
def time_mean(data,mask):
"""
Calculates time mean for a 2 d array (1st ind time, 2nd ind freq).
"""
mean_data = np.zeros(len(data[0]))
mean_mask = np.zeros(len(data[0]))
data = ma.array(data)
mask = ma.array(mask)
num_time = len(mask)
num_freq = len(mask[0])
mod_mask = np.zeros((num_time,num_freq))
int = 0
for i in range(0,num_time):
single_mask = mask[i]
if sum(single_mask)>=len(single_mask)/2.:
new_mask = np.ones(len(single_mask))
else:
new_mask = np.zeros(len(single_mask))
mod_mask[int] = new_mask
int +=1
mod_mask = ma.array(mod_mask)
int2 = 0
for i in range(0,num_freq):
single_mask = mod_mask[:,i]
bad_num = sum(single_mask)
if num_time<=bad_num:
mean_data[int2] = 0.0
mean_mask[int2] = 1.0
else:
single = ma.array(data[:,i],mask=single_mask)
single_comp = ma.compressed(single)
mean_data[int2]= ma.mean(single_comp)
mean_mask[int2] = 0.0
int2+=1
return mean_data,mean_mask
new_maskb = fc.timeflag(sortdatab[:,i],sortmaskb[:,i],sorttimeb,2.5,timescale)
sortmaskb[:,i] = new_maskb
percent_masked = 100.*sum(sortmask)/(len(sortmask)*len(sortmask[0]))
print 'Percentage of Masked Data from Frequency and Time Masking',percent_masked
percent_maskedb = 100.*sum(sortmaskb)/(len(sortmaskb)*len(sortmask[0]))
print 'Percentage of Masked Data from Frequency and Time Masking part2',percent_maskedb
mean_data =[]
for i in range(0,len(freq)):
if sum(sortmask[:,i])==len(sortmask[:,i]):
mean_data.append(0.0)
else:
single_data = ma.array(sortdata[:,i],mask=sortmask[:,i])
single_comp = ma.compressed(single_data)
single_mean = ma.mean(single_comp)
mean_data.append(single_mean)
mean_datab =[]
for i in range(0,len(freq)):
if sum(sortmaskb[:,i])==len(sortmaskb[:,i]):
mean_datab.append(0.0)
else:
single_data = ma.array(sortdatab[:,i],mask=sortmaskb[:,i])
single_comp = ma.compressed(single_data)
single_mean = ma.mean(single_comp)
mean_datab.append(single_mean)
mean_data = array(mean_data)
mean_mask = zeros(len(mean_data))
for i in range(fmin,fmax+1):
single_data = lim_stack[:,i]
Kt_cal[:,i-fmin] = Kt[i]*single_data
Kdgsm_cal[:,i-fmin] = K_dgsm[i]*single_data
single_mask = lim_mask[:,i]
data_mask[:,i-fmin] = single_mask
savetxt(outdir+'June_'+date_ind+'_Kdgsm_full_time_series.txt',Kdgsm_cal,delimiter=' ')
savetxt(outdir+'June_'+date_ind+'_Kt_full_time_series.txt',Kt_cal,delimiter=' ')
savetxt(outdir+'June_'+date_ind+'_mask_full_time_series.txt',data_mask,delimiter=' ')
f70 = where(freq<=70.)[0][-1]
single_data = lim_stack[:,f70]
single_mask = lim_mask[:,f70]
pylab.scatter(ma.compressed(ma.array(lim_time,mask=single_mask)),ma.compressed(ma.array(single_data*K_dgsm[f70],mask=single_mask)),c='g',edgecolor='g',s=5,label='Calibrated time series')
pylab.xlim(0,24)
pylab.ylim(0,6000)
pylab.xlabel('Local Sidereal Time (Hours)')
pylab.ylabel('Temperature (Kelvin)')
pylab.grid()
pylab.title('Time Dependence at 70 MHz')
pylab.savefig(outdir+'June_'+date_ind+'_Kdgsm_time_series',dpi=300)
pylab.clf()
pylab.scatter(ma.compressed(ma.array(lim_time,mask=single_mask)),ma.compressed(ma.array(single_data*Kt[f70],mask=single_mask)),c='g',edgecolor='g',s=5)
pylab.xlim(0,24)
pylab.ylim(0,6000)
pylab.xlabel('Local Sidereal Time (Hours)')
pylab.ylabel('Temperature (Kelvin)')
pylab.grid()