def condition1(self, t0):
print self.FOV_ARRAY.shape
print(self.FOV_ARRAY < 0).shape
print self.SC_TSTOP.shape
print(self.SC_TSTOP > t0).shape
print sp.logical_and(self.FOV_ARRAY < 0, self.SC_TSTOP > t0)[0]
def getRegion(self,
size=3e4,
min_nSNPs=1,
chrom_i=None,
pos_min=None,
pos_max=None):
"""
Sample a region from the piece of genotype X, chrom, pos
minSNPnum: minimum number of SNPs contained in the region
Ichrom: restrict X to chromosome Ichrom before taking the region
cis: bool vector that marks the sorted region
region: vector that contains chrom and init and final position of the region
"""
bim = plink_reader.readBIM(self.bfile, usecols=(0, 1, 2, 3))
chrom = SP.array(bim[:, 0], dtype=int)
pos = SP.array(bim[:, 3], dtype=int)
if chrom_i is None:
n_chroms = chrom.max()
chrom_i = int(SP.ceil(SP.rand() * n_chroms))
pos = pos[chrom == chrom_i]
chrom = chrom[chrom == chrom_i]
ipos = SP.ones(len(pos), dtype=bool)
if pos_min is not None:
ipos = SP.logical_and(ipos, pos_min < pos)
if pos_max is not None:
ipos = SP.logical_and(ipos, pos < pos_max)
pos = pos[ipos]
chrom = chrom[ipos]
if size == 1:
# select single SNP
idx = int(SP.ceil(pos.shape[0] * SP.rand()))
cis = SP.arange(pos.shape[0]) == idx
region = SP.array([chrom_i, pos[idx], pos[idx]])
else:
while 1:
idx = int(SP.floor(pos.shape[0] * SP.rand()))
posT1 = pos[idx]
posT2 = pos[idx] + size
if posT2 <= pos.max():
cis = chrom == chrom_i
cis *= (pos > posT1) * (pos < posT2)
if cis.sum() > min_nSNPs: break
region = SP.array([chrom_i, posT1, posT2])
start = SP.nonzero(cis)[0].min()
nSNPs = cis.sum()
rv = plink_reader.readBED(self.bfile,
useMAFencoding=True,
start=start,
nSNPs=nSNPs,
bim=bim)
Xr = rv['snps']
return Xr, region
def eliminatePercentileTails(self,
mskDds,
loPercentile=10.0,
hiPercentile=90.0):
"""
Trims lower and/or upper image histogram tails by replacing :samp:`mskDds`
voxel values with :samp:`mskDds.mtype.maskValue()`.
"""
rootLogger.info("Eliminating percentile tails...")
rootLogger.info("Calculating element frequencies...")
elems, counts = elemfreq(mskDds)
rootLogger.info("elems:\n%s" % (elems, ))
rootLogger.info("counts:\n%s" % (counts, ))
cumSumCounts = sp.cumsum(counts, dtype="float64")
percentiles = 100.0 * (cumSumCounts / float(cumSumCounts[-1]))
percentileElems = elems[sp.where(
sp.logical_and(percentiles > loPercentile,
percentiles < hiPercentile))]
loThresh = percentileElems[0]
hiThresh = percentileElems[-1]
rootLogger.info("Masking percentiles range (%s,%s) = (%s,%s)" %
(loPercentile, hiPercentile, loThresh, hiThresh))
mskDds.asarray()[...] = \
sp.where(
sp.logical_and(
sp.logical_and(mskDds.asarray() >= loThresh, mskDds.asarray() <= hiThresh),
mskDds.asarray() != mskDds.mtype.maskValue()
),
mskDds.asarray(),
mskDds.mtype.maskValue()
)
rootLogger.info("Done eliminating percentile tails.")
def reduction_T_3(self, I):
A = logical_or(I[0:-1:2, :], I[1::2, :])
A = logical_and(A[:, 0:-1:2], A[:, 1::2])
B = logical_and(I[0:-1:2, :], I[1::2, :])
B = logical_or(B[:, 0:-1:2], B[:, 1::2])
C = logical_and(A, B)
return C
def __init__(self, kind, dic_init):
self.kind = kind
if (self.kind == 'auto'):
fname = dic_init['data_auto']
elif (self.kind == 'cross'):
fname = dic_init['data_cross']
elif (self.kind == 'autoQSO'):
fname = dic_init['data_autoQSO']
rmin = dic_init['rmin']
rmax = dic_init['rmax']
mumin = dic_init['mumin']
mumax = dic_init['mumax']
bin_size = dic_init['bin_size']
h = pyfits.open(fname)
da = h[1].data.DA
co = h[1].data.CO
self.dm = h[1].data.DM
self.rt = h[1].data.RT
self.rp = h[1].data.RP
self.z = h[1].data.Z
self.da_all = copy.deepcopy(da)
self.co_all = copy.deepcopy(co)
### Get the center of the bins from the regular grid
bin_center_rt = np.zeros(self.rt.size)
bin_center_rp = np.zeros(self.rp.size)
for i in np.arange(-self.rt.size - 1, self.rt.size + 1,
1).astype('int'):
bin_center_rt[sp.logical_and(self.rt >= bin_size * i,
self.rt < bin_size *
(i + 1.))] = bin_size * (i + 0.5)
bin_center_rp[sp.logical_and(self.rp >= bin_size * i,
self.rp < bin_size *
(i + 1.))] = bin_size * (i + 0.5)
r = np.sqrt(bin_center_rt**2 + bin_center_rp**2)
mu = bin_center_rp / r
cuts = (r > rmin) & (r < rmax) & (mu >= mumin) & (mu <= mumax)
if sp.isfinite(dic_init['r_per_min']):
cuts = cuts & (bin_center_rt > dic_init['r_per_min'])
if sp.isfinite(dic_init['r_per_max']):
cuts = cuts & (bin_center_rt < dic_init['r_per_max'])
if sp.isfinite(dic_init['r_par_min']):
cuts = cuts & (bin_center_rp > dic_init['r_par_min'])
if sp.isfinite(dic_init['r_par_max']):
cuts = cuts & (bin_center_rp < dic_init['r_par_max'])
co = co[:, cuts]
co = co[cuts, :]
da = da[cuts]
self.cuts = cuts
self.da = da
self.co = co
self.ico = sp.linalg.inv(co)
def coordreduce(self,coorddict):
assert type(coorddict)==dict, "Coorddict needs to be a dictionary"
ncoords = self.Cart_Coords.shape[0]
coordlist = ['x','y','z','r','theta','phi']
coordkeysorg = coorddict.keys()
coordkeys = [ic for ic in coordkeysorg if ic in coordlist]
ckeep = sp.ones(ncoords,dtype=bool)
for ic in coordkeys:
currlims = coorddict[ic]
if ic=='x':
tempcoords = self.Cart_Coords[:,0]
elif ic=='y':
tempcoords = self.Cart_Coords[:,1]
elif ic=='z':
tempcoords = self.Cart_Coords[:,2]
elif ic=='r':
tempcoords = self.Sphere_Coords[:,0]
elif ic=='theta':
tempcoords = self.Sphere_Coords[:,1]
elif ic=='phi':
tempcoords = self.Sphere_Coords[:,2]
keeptemp = sp.logical_and(tempcoords>=currlims[0],tempcoords<currlims[1])
ckeep = sp.logical_and(ckeep,keeptemp)
# prune the arrays
self.Cart_Coords=self.Cart_Coords[ckeep]
self.Sphere_Coords=self.Sphere_Coords[ckeep]
self.Param_List=self.Param_List[ckeep]
self.Velocity=self.Velocity[ckeep]
def control_step(t, u0, u1, t0, t1, t2, t3):
"""
Control function, has value u0 for t < t0, linearly transitions from u0
to u1 when t0 < t < t1, has value u1 when t1 < t < t2, linearly transistions
from u1 to u0 when t2 < t < t3, and has value u0 when t > t3.
"""
f = scipy.zeros(t.shape)
# Masks for selecting time sections
mask0 = t < t0
mask1 = scipy.logical_and(t >= t0, t < t1)
mask2 = scipy.logical_and(t >= t1, t < t2)
mask3 = scipy.logical_and(t >= t2, t < t3)
mask4 = t >= t3
# Constants for linear transition regions
a_01 = (u0 - u1) / (t0 - t1)
a_23 = (u1 - u0) / (t2 - t3)
b_01 = u0 - a_01 * t0
b_23 = u1 - a_23 * t2
# Assign functin values
f[mask0] = u0 * scipy.ones(t[mask0].shape)
f[mask1] = a_01 * t[mask1] + b_01
f[mask2] = u1 * scipy.ones(t[mask2].shape)
f[mask3] = a_23 * t[mask3] + b_23
f[mask4] = u0 * scipy.ones(t[mask4].shape)
return f
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# constants
colours = {'i': 10, 'o': 11}
# load volumes
marker_data, _ = load(args.marker)
contour_data, _ = load(args.contour)
# perform check
contour_data = contour_data == colours[args.type]
marker_data_fg = marker_data == 1
marker_data_bg = marker_data == 2
if scipy.logical_and(contour_data, marker_data_fg).any():
logger.warning(
'Intersection between {} and {} (type {}) in foreground.'.format(
args.marker, args.contour, args.type))
elif scipy.logical_and(contour_data, marker_data_bg).any():
logger.warning(
'Intersection between {} and {} (type {}) in background.'.format(
args.marker, args.contour, args.type))
else:
print "No intersection."
def eliminatePercentileTails(self, mskDds, loPercentile=10.0, hiPercentile=90.0):
"""
Trims lower and/or upper image histogram tails by replacing :samp:`mskDds`
voxel values with :samp:`mskDds.mtype.maskValue()`.
"""
rootLogger.info("Eliminating percentile tails...")
rootLogger.info("Calculating element frequencies...")
elems, counts = elemfreq(mskDds)
rootLogger.info("elems:\n%s" % (elems,))
rootLogger.info("counts:\n%s" % (counts,))
cumSumCounts = sp.cumsum(counts, dtype="float64")
percentiles = 100.0*(cumSumCounts/float(cumSumCounts[-1]))
percentileElems = elems[sp.where(sp.logical_and(percentiles > loPercentile, percentiles < hiPercentile))]
loThresh = percentileElems[0]
hiThresh = percentileElems[-1]
rootLogger.info("Masking percentiles range (%s,%s) = (%s,%s)" % (loPercentile, hiPercentile, loThresh, hiThresh))
mskDds.asarray()[...] = \
sp.where(
sp.logical_and(
sp.logical_and(mskDds.asarray() >= loThresh, mskDds.asarray() <= hiThresh),
mskDds.asarray() != mskDds.mtype.maskValue()
),
mskDds.asarray(),
mskDds.mtype.maskValue()
)
rootLogger.info("Done eliminating percentile tails.")
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# constants
colours = {'i': 10, 'o': 11}
# load volumes
marker_data, _ = load(args.marker)
contour_data, _ = load(args.contour)
# perform check
contour_data = contour_data == colours[args.type]
marker_data_fg = marker_data == 1
marker_data_bg = marker_data == 2
if scipy.logical_and(contour_data, marker_data_fg).any():
logger.warning('Intersection between {} and {} (type {}) in foreground.'.format(args.marker, args.contour, args.type))
elif scipy.logical_and(contour_data, marker_data_bg).any():
logger.warning('Intersection between {} and {} (type {}) in background.'.format(args.marker, args.contour, args.type))
else:
print "No intersection."
def sqr_wave(t, amp, T, epsilon):
"""
Generates a square wave with the given frequency and amplitude
"""
f = scipy.zeros(t.shape)
t_curr = 0.5 * T
cnt = 0
while (t_curr - T < t[-1]):
if cnt % 2 == 0:
val = amp
else:
val = -amp
t0 = t_curr - 0.5 * T
t1 = t_curr - 0.5 * T + 0.5 * epsilon
mask0 = scipy.logical_and(t >= t0, t < t1)
interp_func = scipy.interpolate.interp1d([t0, t1], [0, val])
f[mask0] = interp_func(t[mask0])
t0 = t_curr - 0.5 * T + 0.5 * epsilon
t1 = t_curr - 0.5 * epsilon
mask1 = scipy.logical_and(t >= t0, t < t1)
f[mask1] = val
t0 = t_curr - 0.5 * epsilon
t1 = t_curr
mask2 = scipy.logical_and(t >= t0, t < t1)
interp_func = scipy.interpolate.interp1d([t0, t1], [val, 0])
f[mask2] = interp_func(t[mask2])
t_curr += 0.5 * T
cnt += 1
return f
def getEntryExitTime(self,ra,dec,t0):
idx0=self.getIndex(t0)
infov=self.inFovTime(ra,dec)
theta=self.getThetaTime(ra,dec)
zenith=self.getZenithTime(ra,dec)
#ra_scz = self.RA_SCZ[idx0]
#dec_scz = self.DEC_SCZ[idx0]
#ra_zenith = self.RA_ZENITH[idx0]
#dec_zenith = self.DEC_ZENITH[idx0]
#point = coord.SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
##scz = coord.SkyCoord(ra=ra_scz*u.degree, dec=dec_scz*u.degree, frame='icrs')
#zenith2 = coord.SkyCoord(ra=ra_zenith*u.degree, dec=dec_zenith*u.degree, frame='icrs')
# print ra,dec,theta[idx0],zenith[idx0],infov[idx0],scz.separation(point).degree,zenith2.separation(point).degree
#print len(infov), len(infov[infov==1]),len(infov[infov==0])
start=self.SC_TSTART-t0
stop=self.SC_TSTOP-t0
#print infov.shape
#print start.shape
#print stop.shape
infov_t0=infov[idx0]
if infov_t0:
t_0 = stop[sp.logical_and(infov==0,stop<0)][-1]
t_1 = stop[sp.logical_and(infov==0,stop>0)][0]
else:
t_0 = stop[sp.logical_and(infov==1,stop>0)][0]
t_1 = stop[sp.logical_and(infov==0,stop>t_0)][0]
return t_0,t_1
def getRegion(self,size=3e4,min_nSNPs=1,chrom_i=None,pos_min=None,pos_max=None):
"""
Sample a region from the piece of genotype X, chrom, pos
minSNPnum: minimum number of SNPs contained in the region
Ichrom: restrict X to chromosome Ichrom before taking the region
cis: bool vector that marks the sorted region
region: vector that contains chrom and init and final position of the region
"""
if (self.chrom is None) or (self.pos is None):
bim = plink_reader.readBIM(self.bfile,usecols=(0,1,2,3))
chrom = SP.array(bim[:,0],dtype=int)
pos = SP.array(bim[:,3],dtype=int)
else:
chrom = self.chrom
pos = self.pos
if chrom_i is None:
n_chroms = chrom.max()
chrom_i = int(SP.ceil(SP.rand()*n_chroms))
pos = pos[chrom==chrom_i]
chrom = chrom[chrom==chrom_i]
ipos = SP.ones(len(pos),dtype=bool)
if pos_min is not None:
ipos = SP.logical_and(ipos,pos_min<pos)
if pos_max is not None:
ipos = SP.logical_and(ipos,pos<pos_max)
pos = pos[ipos]
chrom = chrom[ipos]
if size==1:
# select single SNP
idx = int(SP.ceil(pos.shape[0]*SP.rand()))
cis = SP.arange(pos.shape[0])==idx
region = SP.array([chrom_i,pos[idx],pos[idx]])
else:
while 1:
idx = int(SP.floor(pos.shape[0]*SP.rand()))
posT1 = pos[idx]
posT2 = pos[idx]+size
if posT2<=pos.max():
cis = chrom==chrom_i
cis*= (pos>posT1)*(pos<posT2)
if cis.sum()>min_nSNPs: break
region = SP.array([chrom_i,posT1,posT2])
start = SP.nonzero(cis)[0].min()
nSNPs = cis.sum()
if self.X is None:
rv = plink_reader.readBED(self.bfile,useMAFencoding=True,start = start, nSNPs = nSNPs,bim=bim)
Xr = rv['snps']
else:
Xr = self.X[:,start:start+nSnps]
return Xr, region
def get_prf_data(pixel_values,
pixel_stddev,
pixel_offsets,
error_threshold=0.1):
"""
Return the PRF measurements for (a subset of) the image.
Args:
pixel_values(2-D float array): The calibrated pixel responses from
the image to include in the plot.
pixel_stddev(2-D float array): The estimated standard deviation of
`pixel_values`.
pixel_offsets: The slice of the return value of find_pixel_offsets()
corresponding to `pixel_values`.
Returns:
(2-D float array, 2-D float array, 2-D float array, 2-D float array):
* The x-offsets of the points at which PRF measurements are
available.
* The y-offsets of the points at which PRF measurements are
available.
* The measured normalized PRF at the available offsets
* estimated errors of the PRF measurements.
"""
prf_measurements = (
(
pixel_values
-
pixel_offsets['zero_point']
)
/
pixel_offsets['norm']
)
prf_errors = (
pixel_stddev
/
pixel_offsets['norm']
)
#False positive
#pylint: disable=assignment-from-no-return
include = scipy.logical_and(scipy.isfinite(prf_measurements),
scipy.isfinite(prf_errors))
include = scipy.logical_and(include, prf_errors < error_threshold)
#pylint: enable=assignment-from-no-return
return scipy.stack((
pixel_offsets['x_off'][include],
pixel_offsets['y_off'][include],
prf_measurements[include],
prf_errors[include]
))
def main(data_dir='../data/'):
url = "https://data.cityofchicago.org/api/views/ijzp-q8t2/rows.csv?accessType=DOWNLOAD"
fname = 'AllChicagoCrimes.csv'
target = data_dir + fname
print("Downloading raw Chicago crime data...")
download_file(url, target)
ProgressBar().register() # Setup Dask progress bar
chicago = dd.read_csv(target, assume_missing=True, parse_dates=['Date'])
chicago = chicago[sp.logical_and(2012 <= chicago['Year'],
chicago['Year'] <= 2017)]
chicago = chicago.loc[chicago["Primary Type"] == "HOMICIDE"]
chicago = chicago.dropna(subset=[
'X Coordinate', 'Y Coordinate', 'Community Area', 'Latitude',
'Longitude', 'Date'
])
chicago['x'] = chicago['X Coordinate']
chicago['y'] = chicago['Y Coordinate']
chicago['t'] = pd.to_numeric(chicago.Date)
# 0-1 normalize t to prevent overflow issues in PredPol
chicago.t -= chicago.t.min()
chicago.t /= chicago.t.max()
print("Processing raw Chicago crime data...")
chicago = chicago.compute()
print("Processing complete.")
# Compute kilometer distance from longitude and latitude,
x_min = chicago.x.min()
y_min = chicago.y.min()
x_max = chicago.x.max()
y_max = chicago.y.max()
MIN_LON = chicago.loc[chicago.x == x_min, 'Longitude'].min()
MAX_LON = chicago.loc[chicago.x == x_max, 'Longitude'].max()
MIN_LAT = chicago.loc[chicago.y == y_min, 'Latitude'].min()
MAX_LAT = chicago.loc[chicago.y == y_max, 'Latitude'].max()
meters_height = geopy.distance.distance((MIN_LAT, MIN_LON),
(MAX_LAT, MIN_LON)).m
meters_width = geopy.distance.distance((MIN_LAT, MIN_LON),
(MIN_LAT, MAX_LON)).m
# print(meters_height, meters_width)
# Normalize by these values to obtain roughly 150m by 150m grid cells
chicago.x -= chicago.x.min()
chicago.y -= chicago.y.min()
chicago.x /= chicago.x.max()
chicago.y /= chicago.y.max()
chicago.x *= round(meters_width / 150 / 100, 2)
chicago.y *= round(meters_height / 150 / 100, 2)
chicago.to_csv(data_dir + 'ChicagoHomicides2012to2017.csv')
print("Successfully wrote cleaned Chicago crime data to file.")
chicago_small = chicago[sp.logical_and(2014 <= chicago['Year'],
chicago['Year'] <= 2015)]
chicago_small.to_csv(data_dir + 'ChicagoHomicides2014to2015.csv')
def separate_cal(data, n_bins_cal, cal_mask=None) :
"""Function separates data into cal_on and cal off.
No Guarantee that data argument remains unchanged."""
# Allowcate memeory for output
ntime, npol, nfreq = data.shape
n_bins_after_cal = ntime//n_bins_cal
out_data = sp.zeros((n_bins_after_cal, npol, 2, nfreq), dtype=sp.float32)
# Get the phase offset of the cal.
try :
if cal_mask is None:
first_on, n_blank = get_cal_mask(data, n_bins_cal)
else :
first_on, n_blank = cal_mask
except ce.DataError :
print "Discarded record due to bad profile. "
out_data[:] = float('nan')
else :
# How many samples for each cal state.
n_cal_state = n_bins_cal//2 - n_blank
first_off = (first_on + n_bins_cal//2) % n_bins_cal
# Reshape data to add an index to average over.
data.shape = (n_bins_after_cal, n_bins_cal) + data.shape[1:]
# Get the masks for the on and off data.
inds = sp.arange(n_bins_cal)
if first_on == min((sp.arange(n_cal_state) +
first_on)% n_bins_cal) :
on_mask = sp.logical_and(inds >= first_on, inds <
first_on+n_cal_state)
else :
on_mask = sp.logical_or(inds >= first_on, inds <
(first_on + n_cal_state) % n_bins_cal)
if first_off == min((sp.arange(n_cal_state) +
first_off)% n_bins_cal) :
off_mask = sp.logical_and(inds >= first_off, inds <
first_off + n_cal_state)
else :
off_mask = sp.logical_or(inds >= first_off, inds <
(first_off + n_cal_state) % n_bins_cal)
# Find cal on and cal off averages. Always use mean not median due to
# discretization noise.
# This loop is much faster than the built in numpy mean() for some
# reason.
for ii in range(n_bins_cal) :
if on_mask[ii]:
out_data[:,:,0,:] += data[:,ii,:,:]
elif off_mask[ii]:
out_data[:,:,1,:] += data[:,ii,:,:]
out_data[:,:,0,:] /= sp.sum(on_mask)
out_data[:,:,1,:] /= sp.sum(off_mask)
return out_data
def spk_phase(t, V1, V2, spike_thresh=0):
#phase_mean, isi_mean = spk_phase(t, V1, V2, spike_thresh=0)
t = t[1:]
time1 = t[sp.logical_and(V1[:-1] < spike_thresh, V1[1:] >= spike_thresh)]
time2 = t[sp.logical_and(V2[:-1] < spike_thresh, V2[1:] >= spike_thresh)]
l = sp.amin([len(time1), len(time2)])
isi_mean = sp.mean(sp.diff(time1))
phase_mean = sp.mean((time1[0:l] - time2[0:l]) / isi_mean * 2 * sp.pi)
return phase_mean, isi_mean
def interpolate(self, canvas, status=None):
# Clear the interpolated canvas
canvas.interpolated = sp.zeros_like(canvas.fringes_image) - 1024.0
if status is not None:
status.set("Performing the interpolation", 70)
else:
print("Performing the interpolation")
# Iterate over all the triangles in the triangulation
for triangle in self.triangles:
# Create a shortcut to the triangle's vertices
co = triangle.vert_coordinates
# Calculate a few constants for the Barycentric Coordinates
# More info: https://codeplea.com/triangular-interpolation
div = (co[1, 0] - co[2, 0]) * (co[0, 1] - co[2, 1]) + (
co[2, 1] - co[1, 1]) * (co[0, 0] - co[2, 0])
a0 = (co[1, 0] - co[2, 0])
a1 = (co[2, 1] - co[1, 1])
a2 = (co[2, 0] - co[0, 0])
a3 = (co[0, 1] - co[2, 1])
# Calculate the bounds of a rectangle that fully encloses
# the current triangle
xmin = int(sp.amin(triangle.vert_coordinates[:, 1]))
xmax = int(sp.amax(triangle.vert_coordinates[:, 1])) + 1
ymin = int(sp.amin(triangle.vert_coordinates[:, 0]))
ymax = int(sp.amax(triangle.vert_coordinates[:, 0])) + 1
# Take out slices of the x and y arrays,
# containing the points' coordinates
x_slice = canvas.x[ymin:ymax, xmin:xmax]
y_slice = canvas.y[ymin:ymax, xmin:xmax]
# Use Barycentric Coordinates and the magic of numpy (scipy in this
# case) to perform the calculations with the C backend, instead
# of iterating on pixels with Python loops.
# If you have not worked with numpy arrays befor dear reader,
# the idea is that if x = [[0 1]
# [2 3]],
# then x*3+1 is a completely valid operation, returning
# x = [[1 4]
# [7 10]]
# Basically, we can do maths on arrays as if they were variables.
# Convenient, and really fast!
w0 = (a0 * (x_slice - co[2, 1]) + a1 * (y_slice - co[2, 0])) / div
w1 = (a2 * (x_slice - co[2, 1]) + a3 * (y_slice - co[2, 0])) / div
w2 = sp.round_(1 - w0 - w1, 10)
# Calculate the values for a rectangle enclosing our triangle
slice = (self.values[triangle.vertices[0]] * w0 +
self.values[triangle.vertices[1]] * w1 +
self.values[triangle.vertices[2]] * w2)
# Make a mask (so that we only touch the points
# inside of the triangle).
# In Barycentric Coordinates the points outside of the triangle
# have at least one of the coefficients negative, so we use that
mask = sp.logical_and(sp.logical_and(w0 >= 0, w1 >= 0), w2 >= 0)
# Change the points in the actual canvas
canvas.interpolated[ymin:ymax, xmin:xmax][mask] = slice[mask]
canvas.interpolation_done = True
def __call__(self, X, alpha=1.0, bytes=False):
rgba = sp.array(self.cmap(X, alpha=1.0, bytes=False))
rgba[..., 0] = sp.where(
sp.logical_and(X >= self.fill_range[0], X <= self.fill_range[1]),
1.0, rgba[..., 0])
rgba[..., 1] = sp.where(
sp.logical_and(X >= self.fill_range[0], X <= self.fill_range[1]),
1.0, rgba[..., 1])
rgba[..., 2] = sp.where(
sp.logical_and(X >= self.fill_range[0], X <= self.fill_range[1]),
1.0, rgba[..., 2])
return rgba
def _commonx(self, other, res='coarsest', source='linspace'):
"""Merge x-axis discretizations of this object and another.
If method is "linspace", make a new uniform spacing.
If method is "original", use one of the original discretizations.
If res (resolution) is "self" or "this", use the resolution of this
object.
If res is "other", use the resolution of the other object.
If res is "coarsest", use the coarsest discretization of the two
objects.
If res is "finest", use the finest discretization of the two objects.
If res is "medium", use a medium discretization
(implies method "linspace")."""
# 2012-06-27 - 2013-06-24
# if an empty function object is given
if len(self) == 0 or len(other) == 0:
return scipy.empty(shape=(0,))
# determine extremal values
min1, max1 = min(self.x), max(self.x) # use self.box()[:2]
min2, max2 = min(other.x), max(other.x) # use other.box()[:2]
newmin = max(min1, min2)
newmax = min(max1, max2)
# choose coarsest discretization
### maybe offer option to use "coarse", "fine", "medium" discretization
cand1 = self.x[scipy.logical_and(self.x >= newmin, self.x <= newmax)]
cand2 = other.x[scipy.logical_and(other.x >= newmin,
other.x <= newmax)]
if res is not None and 'other'.startswith(res):
winner = cand2
elif res is not None and \
('self'.startswith(res) or 'this'.startswith(res)):
winner = cand1
elif res is not None and 'finest'.startswith(res):
winner = cand1 if len(cand1) > len(cand2) else cand2
elif res is not None and 'medium'.startswith(res):
source = 'linspace'
winner = [0]*scipy.ceil(scipy.mean(len(cand1), len(cand2)))
else:
winner = cand1 if len(cand1) < len(cand2) else cand2
if source is not None and 'linspace'.startswith(source):
newx = scipy.linspace(newmin, newmax, len(winner))
else:
# res may not be "medium" here!
newx = winner
return newx
def _commonx(self, other, res='coarsest', source='linspace'):
"""Merge x-axis discretizations of this object and another.
If method is "linspace", make a new uniform spacing.
If method is "original", use one of the original discretizations.
If res (resolution) is "self" or "this", use the resolution of this
object.
If res is "other", use the resolution of the other object.
If res is "coarsest", use the coarsest discretization of the two
objects.
If res is "finest", use the finest discretization of the two objects.
If res is "medium", use a medium discretization
(implies method "linspace")."""
# 2012-06-27 - 2013-06-24
# if an empty function object is given
if len(self) == 0 or len(other) == 0:
return scipy.empty(shape=(0, ))
# determine extremal values
min1, max1 = min(self.x), max(self.x) # use self.box()[:2]
min2, max2 = min(other.x), max(other.x) # use other.box()[:2]
newmin = max(min1, min2)
newmax = min(max1, max2)
# choose coarsest discretization
### maybe offer option to use "coarse", "fine", "medium" discretization
cand1 = self.x[scipy.logical_and(self.x >= newmin, self.x <= newmax)]
cand2 = other.x[scipy.logical_and(other.x >= newmin,
other.x <= newmax)]
if res is not None and 'other'.startswith(res):
winner = cand2
elif res is not None and \
('self'.startswith(res) or 'this'.startswith(res)):
winner = cand1
elif res is not None and 'finest'.startswith(res):
winner = cand1 if len(cand1) > len(cand2) else cand2
elif res is not None and 'medium'.startswith(res):
source = 'linspace'
winner = [0] * scipy.ceil(scipy.mean(len(cand1), len(cand2)))
else:
winner = cand1 if len(cand1) < len(cand2) else cand2
if source is not None and 'linspace'.startswith(source):
newx = scipy.linspace(newmin, newmax, len(winner))
else:
# res may not be "medium" here!
newx = winner
return newx
def accumulator(acc, curr):
predictions = sp.double(p_val < curr)
tp = sp.sum(
sp.logical_and(predictions == 1,
sp.asarray(Y_val == 1).ravel()))
fp = sp.sum(
sp.logical_and(predictions == 1,
sp.asarray(Y_val == 0).ravel()))
fn = sp.sum(
sp.logical_and(predictions == 0,
sp.asarray(Y_val == 1).ravel()))
prec = tp / (tp + fp)
rec = tp / (tp + fn)
F1 = 2 * prec * rec / (prec + rec)
return {'epsilon': curr, 'F1': F1} if F1 > acc['F1'] else acc
def conditionVal(name='val7', nozero=False, conc='c_w_l'):
#this is a program which extracts the useful data from the CSXR data
val = loadData(name)
idx = loadData('id')
c_w_l = loadData(conc)
shot = loadData('shot')
base = loadData('baseline')
width = loadData('width'+name[-1])
fiducial = (base+val/scipy.sqrt(scipy.pi)/width)*123+1000
#c_w_l must have a value below 1e-2 and nonzero
good = c_w_l > 0 #this had to be done based on how the M matrix was generated (for all non-zero c_w_l values)
idx = idx[good]
val = val[good]
shot = shot[good]
c_w_l = c_w_l[good]
#c_w_l must have a value below 1e-2 and nonzero
good = scipy.logical_and(scipy.logical_and(fiducial[good] < 6e4, c_w_l < 1e-2), val*disp > 1e0)
idx = idx[good]
val = val[good]
shot = shot[good]
c_w_l = c_w_l[good]
#val[val < 1.] = 0.
#normalize to time
#all valid data with the new computer has 8ms exposure time
#older shots had 5ms exposure times
val[shot <= 32858] /= 5e-3
val[shot > 32858] /= 8e-3
if nozero:
good = val != 0
idx = idx[good]
val = val[good]
c_w_l = c_w_l[good]
return val, c_w_l, idx
def getX(self,standardized=True,maf=None):
"""
return SNPs, if neccessary standardize them
"""
X = SP.copy(self.X)
# test for missing values
isnan = SP.isnan(X)
for i in isnan.sum(0).nonzero()[0]:
# set to mean
X[isnan[:,i],i] = X[~isnan[:,i],i].mean()
if maf!=None:
LG.debug('filter SNPs')
LG.debug('... number of SNPs(before filtering): %d'%X.shape[1])
idx_snps = SP.logical_and(X[self.idx_samples].mean(0)>0.1,X[self.idx_samples].mean(0)<0.9)
LG.debug('... number of SNPs(after filtering) : %d'%idx_snps.sum())
else:
idx_snps = SP.ones(self.n_f,dtype=bool)
if standardized:
LG.debug('standardize SNPs')
X = X[self.idx_samples][:,idx_snps]
X-= X.mean(0)
X /= X.std(0,dtype=NP.float32)
X /= SP.sqrt(X.shape[1])
return X
return X[self.idx_samples][:,idx_snps]
def timereduce(self, timebounds):
"""This method will remove any data points out side of the time limits.
Inputs
timebounds - A list of length 2 of posix times."""
lowerbnd = self.times[:, 0] >= timebounds[0]
upperbnd = self.times[:, 1] <= timebounds[1]
keep = sp.logical_and(lowerbnd, upperbnd)
if self.issatellite():
self.times = self.times[keep]
self.dataloc = self.dataloc[keep]
for idata in self.datanames():
if isinstance(self.data[idata], DataFrame):
self.data[idata] = self.data[idata][
self.times] #data is a vector
else:
self.data[idata] = self.data[idata][keep]
else:
self.times = self.times[keep]
for idata in self.datanames():
#XXX Probably want to check this with a data frame
if isinstance(self.data[idata], DataFrame):
self.data[idata] = self.data[
idata][:, self.times] #data is a vector
else:
self.data[idata] = self.data[idata][:, keep]
def get_width_upper(max, width, upper, array_in):
from copy import copy
array = copy(array_in)
''' now pick objects somewhat larger than star column '''
mask = array.field(radius_var) > max + width
array = array[mask]
rads = array.field(radius_var) #[mask]
mask = rads < max + width + 0.6
array = array[mask]
mags = array.field(ap_type)
mags.sort()
''' take 20% percentile and subtract 0.5 mag '''
if len(mags) == 0:
upper = 99
else:
upper = mags[int(len(mags) * 0.2)] #+ 0.5
array = copy(array_in)
maskA = array.field(ap_type) < upper #+ 0.5
maskB = array.field(radius_var) < max + width
maskC = array.field(radius_var) > max - width
mask = scipy.logical_and(maskA, maskB, maskC)
array = array[mask]
rads = array.field(radius_var)
pylab.clf()
a, b, varp = pylab.hist(array.field(radius_var),
bins=scipy.arange(1.0, 8, 0.04))
z = zip(a, b)
z.sort()
max = z[-1][1]
width = 1.0 * scipy.std(rads)
print 'width', width, 'max', max, 'upper', upper, 'rads', rads
return max, width, upper
def Dirac(x, sigma):
from scipy import logical_and, pi, cos
f = (1.0 / 2.0 / sigma) * (1 + cos(pi * x / sigma))
b = logical_and(x <= sigma, x >= -sigma)
f = f * b
return f
def adaptive_threshold(SpikeTrain, adaptive_thresh_lower,
adaptive_thresh_upper):
"""
removes all spike from a SpikeTrain which amplitudes do not fall within the
specified variable bounds.
Args:
SpikeTrain (neo.core.SpikeTrain): The SpikeTrain
adaptive_thresh_lower (neo.core.AnalogSignal): the variable lower bound
adaptive_thresh_upper (neo.core.AnalogSignal): the variable upper bound
Returns:
neo.core.SpikeTrain: the resulting SpikeTrain
"""
SpikeTrain = copy.deepcopy(SpikeTrain)
spike_inds = get_spike_inds(SpikeTrain)
spike_amps = SpikeTrain.waveforms.max(axis=1)
cond_1 = adaptive_thresh_lower.magnitude[spike_inds] < spike_amps.magnitude
cond_2 = adaptive_thresh_upper.magnitude[spike_inds] > spike_amps.magnitude
good_inds = sp.logical_and(cond_1, cond_2)
SpikeTrain = SpikeTrain[good_inds.flatten()]
return SpikeTrain
def cut(self, range=None, lower=None, upper=None):
"""Cut away all data points whose x-value is outside of the given
"range", or those that are smaller than "lower" or greater than
"upper"."""
# 2012-07-12
# get range
if range is None:
# default range captures all values
range = self.box()[:2]
else:
range = list(range)
if scipy.array(range).shape != (2, ):
raise ValueError('range must be 2-tuple')
# overwrite range with lower and upper value
range = list(range)
if lower is not None:
range[0] = lower
if upper is not None:
range[1] = upper
#if range[0] >= range[1]:
#raise ValueError, 'lower bound must be smaller than upper bound'
### so then, nothing is kept, just fine
# cut away data points
keep = scipy.logical_and(self.x >= range[0], self.x <= range[1])
self.x = self.x[keep]
self.y = self.y[keep]
def getEntryExitTime(self, ra, dec, t0):
idx0 = self.getIndex(t0)
infov = self.inFovTime(ra, dec)
theta = self.getThetaTime(ra, dec)
zenith = self.getZenithTime(ra, dec)
start = self.SC_TSTART - t0
stop = self.SC_TSTOP - t0
infov_t0 = infov[idx0]
print t0, idx0, infov_t0
if infov_t0: t_0 = stop[sp.logical_and(infov == 0, stop < 0)][-1]
else: t_0 = start[sp.logical_and(infov == 1, start > 0)][0]
t1 = start[sp.logical_and(infov == 0, stop > t_0)][0]
t_1 = stop[self.getIndex(t0 + t1)]
#if stop[ii_1+1] > stop[ii_1]+60: t_1 = stop[ii_1]
#print '%10.3f %10.3f %5d %10.3f %10.3f %10.3f %10.3f ' %(ra,dec,ii_1,stop[ii_1],stop[ii_1+1],t_0,t_1)
return t_0, t_1
def gridSearch(maxT, sumM, name,loc='svmcrossvalid.p',reduction=50,k=2.2e35,gamma=[-3,3], C=[0,6], nozero=False, conc='c_w_l', lims=[2.2e3,9e3]):
val, c_w_l, idx = conditionVal(name=name, nozero=nozero, conc=conc)
idx1 = sample(val,len(val)/reduction)
idx2 = sample(val,len(val)/reduction) #very small subsamples started for testing algos
index0 = scipy.logspace(gamma[0],gamma[1],int(abs(gamma[0]-gamma[1])+1))
index1 = scipy.logspace(C[0],C[1],int(abs(C[0]-C[1])+1))
data = scipy.io.readsav('W_Abundances_grid_puestu_adpak_fitscaling_74_0.00000_5.00000_1000_idlsave')
te = data['en']
idx2 = scipy.logical_and(te > lims[0], te < lims[1])
temp = val[idx2]/c_w_l[idx2]/sumM[idx2]*k
output = scipy.zeros((len(index0),len(index1)))
output2 = scipy.zeros((len(index0),len(index1),len(te[idx2])))
for i in xrange(len(index0)):
for j in xrange(len(index1)):
print(i,j)
pipe = SVMtest(maxT, sumM, val, c_w_l, reduced=idx1, gamma=index0[i], C=index1[j],k=k)
output[i,j] = pipe.score(scipy.log(scipy.atleast_2d(maxT[idx2]).T),scipy.log(temp))
output2[i,j] = scipy.exp(pipe.predict(scipy.log(scipy.atleast_2d(te[idx2]).T)))
pickle.dump([output,output2],open(loc,'wb'))
return output,output2
def showVectorDisplacements():
global testImage, croppedRefImage, u, v, valid, q1, umean, vmean, x, y, sxyVar, wxyVar, goodvectorsVar
from scipy import where, compress, logical_and, median, logical_or, nan
from pylab import resize, transpose, quiver, title, show, find, imshow, hist, figure, clf, draw, save, load, xlabel, ylabel, flipud
mxy = 3
wxy = int(wxyVar.get())
sxy = int(sxyVar.get())
goodvectors = float(goodvectorsVar.get())
#process to find PIV-style displacements
x, y, u, v, q1, valid = simplepiv(croppedRefImage, testImage, wxy, mxy,
sxy)
good = where(logical_and(q1 > goodvectors, valid > 0), True, False)
umean = median(compress(good.flat, u.flat))
vmean = median(compress(good.flat, v.flat))
u = where(logical_or(q1 < goodvectors, valid < 0), 0, u)
v = where(logical_or(q1 < goodvectors, valid < 0), 0, v)
u = u - umean
v = v - vmean
save('vecx.out', x)
save('vecy.out', y)
save('vecu.out', u)
save('vecv.out', v)
save('vecq1.out', q1)
save('vecvalid.out', valid)
u = flipud(u)
v = -flipud(v)
quiver(x, y, u, v)
title('Vector displacements')
xlabel('Pixels')
ylabel('Pixels')
show()
return
def getX(self, standardized=True, maf=None):
"""
return SNPs, if neccessary standardize them
"""
X = SP.copy(self.X)
# test for missing values
isnan = SP.isnan(X)
for i in isnan.sum(0).nonzero()[0]:
# set to mean
X[isnan[:, i], i] = X[~isnan[:, i], i].mean()
if maf != None:
LG.debug('filter SNPs')
LG.debug('... number of SNPs(before filtering): %d' % X.shape[1])
idx_snps = SP.logical_and(X[self.idx_samples].mean(0) > 0.1,
X[self.idx_samples].mean(0) < 0.9)
LG.debug('... number of SNPs(after filtering) : %d' %
idx_snps.sum())
else:
idx_snps = SP.ones(self.n_f, dtype=bool)
if standardized:
LG.debug('standardize SNPs')
X = X[self.idx_samples][:, idx_snps]
X -= X.mean(0)
X /= X.std(0, dtype=NP.float32)
X /= SP.sqrt(X.shape[1])
return X
return X[self.idx_samples][:, idx_snps]
def assess_calibration(self):
"""Assess if PredPol is calibrated by conditioning on predicted intensity
and checking the correlation between number of crimes and demographics.
Returns: a 2D array where the first dimension is the number of days in
the test set and the second dimension is the number of bins for the
range of predicted intensities, as computed by `sp.histogram_bin_edges`.
The entry in the ith row and jth column is the Pearson correlation
coefficient between race and actual number of crimes in the jth bin of
predicted intensity for the ith day.
"""
black = self.pred_obj.grid_cells.black
not_nan = sp.logical_not(sp.isnan(black.values))
bins = sp.histogram_bin_edges(self.get_predicted_intensities(), bins='auto')
correlations = sp.empty((len(self.lambda_columns), len(bins)))
correlations[:] = sp.nan
for i, (lambda_col, actual_col) in self._iterator():
idx_bins = sp.digitize(self.results[lambda_col], bins)
for j in range(len(bins)):
idx_selected = sp.logical_and(idx_bins == j, not_nan)
if sp.sum(idx_selected) > 2:
actual = self.results.loc[idx_selected, actual_col]
demographics = black.loc[idx_selected]
correlations[i, j] = sp.stats.pearsonr(actual, demographics)[0]
return correlations
def isi(t, V, spike_thresh=0):
#isi_mean, isi_dev = isi(t, V, spike_thresh=0)
t = t[1:]
time = t[sp.logical_and(V[:-1] < spike_thresh, V[1:] >= spike_thresh)]
dt = sp.diff(time)
# print(time)
return sp.mean(dt), sp.std(dt), dt
def cut(self, range=None, lower=None, upper=None):
"""Cut away all data points whose x-value is outside of the given
"range", or those that are smaller than "lower" or greater than
"upper"."""
# 2012-07-12
# get range
if range is None:
# default range captures all values
range = self.box()[:2]
else:
range = list(range)
if scipy.array(range).shape != (2,):
raise ValueError('range must be 2-tuple')
# overwrite range with lower and upper value
range = list(range)
if lower is not None:
range[0] = lower
if upper is not None:
range[1] = upper
#if range[0] >= range[1]:
#raise ValueError, 'lower bound must be smaller than upper bound'
### so then, nothing is kept, just fine
# cut away data points
keep = scipy.logical_and(self.x >= range[0], self.x <= range[1])
self.x = self.x[keep]
self.y = self.y[keep]
def __init__(self, config, forward):
"""Initialization specifies retrieval subwindows for calculating
measurement cost distributions"""
self.lasttime = time.time()
self.fm = forward
self.wl = self.fm.wl
self.ht = OrderedDict() # Hash table
self.max_table_size = 500
self.windows = config['windows']
if 'verbose' in config:
self.verbose = config['verbose']
else:
self.verbose = True
if 'Cressie_MAP_confidence' in config:
self.state_indep_S_hat = config['Cressie_MAP_confidence']
else:
self.state_indep_S_hat = False
self.windows = config['windows']
self.winidx = s.array((), dtype=int) # indices of retrieval windows
inds = range(len(self.wl))
for lo, hi in self.windows:
idx = s.where(s.logical_and(self.wl > lo, self.wl < hi))[0]
self.winidx = s.concatenate((self.winidx, idx), axis=0)
self.counts = 0
self.inversions = 0
def DFT_PSD(data,movingwin=[0.201, 0.051], Fs = 1000, pad=0, fpass=[1,100]): '''Discrete Fourier Transform Input: data: format is np.array that is time_window x samples ''' num_trials = data.shape[1] N = data.shape[0] #ms of trials Nwin=round(Fs*movingwin[0]) Nstep=round(Fs*movingwin[1]) winstart=np.arange(0,N-Nwin,Nstep) nw=len(winstart) f = np.fft.fftfreq(int(movingwin[0]*Fs)) f = Fs*f[f>=0] f_ind = scipy.logical_and(f>=fpass[0], f<=fpass[1]) #set(f[f>=fpass[0]] ) & set(f[f<=fpass[1]]) #f_ind = np.array(list(f_ind)) S = np.zeros(( num_trials, nw, sum(f_ind) )) for n in range(nw): datawin=data[winstart[n]:winstart[n]+Nwin,:] sp = np.fft.rfft(datawin.T) psd_est = abs(sp)**2 S[:,n,:] = psd_est[:,f_ind] t=(winstart+round(Nwin/2))/float(Fs) return S, f[f_ind], t
def __init__(self, ft2file, tmin, tmax):
hdulist = fits.open(ft2file)
SC_data = hdulist['SC_DATA'].data
SC_data = hdulist['SC_DATA'].data
# TIME
TIME_ALL = SC_data.field('START')
FILTER = sp.logical_and(TIME_ALL > tmin, TIME_ALL < tmax)
self.SC_TSTART = SC_data.field('START')[FILTER]
self.SC_TSTOP = SC_data.field('STOP')[FILTER]
self.NENTRIES = len(self.SC_TSTART)
# BORESIGHT:
self.RA_SCZ = SC_data.field('RA_SCZ')[FILTER]
self.DEC_SCZ = SC_data.field('DEC_SCZ')[FILTER]
# ZENITH
self.RA_ZENITH = SC_data.field('RA_ZENITH')[FILTER]
self.DEC_ZENITH = SC_data.field('DEC_ZENITH')[FILTER]
# SAA
self.IN_SAA = SC_data.field('IN_SAA')[FILTER]
self.IDX_ARRAY = sp.arange(self.NENTRIES)
self.FOV_ARRAY = sp.zeros(self.NENTRIES)
self.theta_max = 65
self.zenith_max = 90
print 'Summary FT2 file: %s ' % ft2file
print 'NENTRIES........:%d' % (self.NENTRIES)
print 'TMIN............:%s' % (self.SC_TSTART[0])
print 'TMAX............:%s' % (self.SC_TSTOP[-1])
print 'DELTA T.........:%s' % (self.SC_TSTOP[-1] - self.SC_TSTART[0])
pass
def learning_curve_metrics(hdf_list, epoch_size=56, n_factors=5):
#hdf_list = [3822, 3834, 3835, 3840]
#obstacle learning: hdf_list = [4098, 4100, 4102, 4104, 4114, 4116, 4118, 4119]
rew_ix_list = []
te_refs = []
rpm_list = []
hdf_dict = {}
perc_succ = []
time_list = []
offs = 0
#f, ax = plt.subplots()
for te in hdf_list:
hdf_t = dbfn.TaskEntry(te)
hdf = hdf_t.hdf
hdf_dict[te] = hdf
rew_ix, rpm = pa.get_trials_per_min(hdf, nmin=2,rew_per_min_cutoff=0,
ignore_assist=True, return_rpm=True)
ix = 0
#ax.plot(rpm)
trial_ix = np.array([i for i in hdf.root.task_msgs[:] if
i['msg'] in ['reward','timeout_penalty','hold_penalty','obstacle_penalty'] ], dtype=hdf.root.task_msgs.dtype)
while (ix+epoch_size) < len(rew_ix):
start_rew_ix = rew_ix[ix]
end_rew_ix = rew_ix[ix+epoch_size]
msg_ix_mod = np.nonzero(scipy.logical_and(trial_ix['time']<=end_rew_ix, trial_ix['time']>start_rew_ix))[0]
all_msg = trial_ix[msg_ix_mod]
perc_succ.append(len(np.nonzero(all_msg['msg']=='reward')[0]) / float(len(all_msg)))
rew_ix_list.append(rew_ix[ix:ix+epoch_size])
rpm_list.append(np.mean(rpm[ix:ix+epoch_size]))
te_refs.append(te)
time_list.append((0.5*(start_rew_ix+end_rew_ix))+offs)
ix += epoch_size
offs = offs+len(hdf.root.task)
#For each epoch, fit FA model (stick w/ 5 factors for now):
ratio = []
for te, r_ix in zip(te_refs, rew_ix_list):
print te, len(r_ix)
update_bmi_ix = np.nonzero(np.diff(np.squeeze(hdf.root.task[:]['internal_decoder_state'][:, 3, 0])))[0] + 1
bin_spk, targ_pos, targ_ix, z, zz = pa.extract_trials_all(hdf_dict[te], r_ix, time_cutoff=1000, update_bmi_ix=update_bmi_ix)
zscore_X, mu = pa.zscore_spks(bin_spk)
FA = skdecomp.FactorAnalysis(n_components=n_factors)
FA.fit(zscore_X)
#SOT Variance Ratio by target
#Priv var / mean
Cov_Priv = np.sum(FA.noise_variance_)
U = np.mat(FA.components_).T
Cov_Shar = np.trace(U*U.T)
ratio.append(Cov_Shar/(Cov_Shar+Cov_Priv))
def getBeamFluxSpline(beam, plasma, t, lim1, lim2, points=1000):
""" generates a spline off of the beampath. Assumes
that the change in flux is MONOTONIC"""
lim = beam.norm.s
beam.norm.s = scipy.linspace(0, lim[-1], points)
h = time.time()
psi = plasma.eq.rz2rmid(beam.r()[0],
beam.r()[2], t) #evaluates all psi's at once
print(time.time() - h)
outspline = len(t) * [0]
inspline = len(t) * [0]
for i in range(t.size):
temp = lim1
mask = scipy.logical_and(scipy.isfinite(psi[i]), psi[i] < lim2 + .02)
try:
minpos = scipy.argmin(psi[i][mask])
test = psi[i][mask][minpos]
except ValueError:
test = lim2 + .03
#plt.plot(beam.x()[0][mask],psi[i][mask])
#plt.show()
sizer = psi[i][mask].size
if not test > lim2:
#plt.plot(beam.x()[0][mask][0:minpos],psi[i][mask][0:minpos],beam.x()[0][mask][minpos:],psi[i][mask][minpos:])
#plt.show()
#limout = scipy.insert(lim,(2,2),(beam.norm.s[mask][minpos],beam.norm.s[mask][minpos])) # add minimum flux s for bound testing
if lim1 < test:
temp = test
try:
temp1 = scipy.clip(
scipy.digitize((lim1, lim2), psi[i][mask][minpos::-1]), 0,
minpos)
outspline[i] = beam.norm.s[mask][minpos::-1][temp1]
except ValueError:
tempmask = (psi[i][mask] < lim2)[0]
outspline[i] = scipy.array(
[beam.norm.s[mask][minpos], beam.norm.s[mask][tempmask]])
try:
temp2 = scipy.clip(
scipy.digitize((lim1, lim2), psi[i][mask][minpos:]), 0,
sizer - minpos - 1)
inspline[i] = beam.norm.s[mask][minpos:][temp2]
except ValueError:
inspline[i] = scipy.array(
[beam.norm.s[mask][minpos], beam.norm.s[mask][-1]])
else:
outspline[i] = scipy.array([[], []])
inspline[i] = scipy.array([[], []])
return (outspline, inspline)
def get_kin_sig_shenoy(kin_sig, bins=np.linspace(0,3000,3000), start_bin=1200,first_local_max_method=False,
after_start_est=300+300, kin_est = 1000, anim='seba'):
kin_feat = np.zeros((kin_sig.shape[0], 5))
for r in range(kin_sig.shape[0]):
spd_after_go = kin_sig[r,start_bin:]
if first_local_max_method: #Done only on BMI 3d, assuming Fs = 60 Hz.
d_spd = np.diff(spd_after_go)
#Est. number of bins RT should come after:
aft = after_start_est/float(1000)*60 #Aft is in iteration for bmi3d
rch = kin_est/float(1000)*60 #rch is in iteration for bmi3d
#Find first cross from + --> -
max_ind = np.array([i for i, s in enumerate(d_spd[:-1]) if scipy.logical_and(s>0, d_spd[i+1]<0)]) #derivative crosses zero w/ negative slope
z = np.nonzero(scipy.logical_and(max_ind>aft, max_ind<(rch+aft)))[0] #local maxima that fit estimate of rxn time --> rch time
#How to choose:
if len(z)>0:
z_ind = np.argmax(spd_after_go[max_ind[z]]) #choose the biggest
kin_feat[r,1] = bins[max_ind[z[z_ind]]+start_bin] #write down the time
maxbin = max_ind[z[z_ind]]+start_bin
else:
print ' no local maxima found within range :/ '
kin_feat[r,1] = bins[int(start_bin+aft+rch)]
maxbin = start_bin+aft+rch
else:
kin_feat[r,1] = bins[ start_bin + np.argmax(spd_after_go) ]
maxbin = start_bin + np.argmax(spd_after_go)
kin_feat[r,0] = kin_sig[r,int(maxbin)]
perc = [0.2, 0.5, 0.1]
for p, per in enumerate(perc):
percent0 = kin_feat[r,0]*per #Bottom Threshold
indz = range(0, int(maxbin-start_bin)) #0 - argmax_index
indzz = indz[-1:0:-1] #Reverse
datz = spd_after_go[indzz]
try:
x = np.nonzero(datz<percent0)[0][0]
except:
x = len(datz)
kin_feat[r,2+p] = bins[int(maxbin-x)]
return kin_feat
def bias(a,b):
'''
bias
'''
a,b = sp.array(a),sp.array(b)
mask = sp.logical_and(sp.isfinite(a),sp.isfinite(b))
a, b = a[mask], b[mask]
return a.mean()-b.mean()
def get_trials_per_min(hdf,nmin=2, rew_per_min_cutoff=0, ignore_assist=False, return_rpm=False,
return_per_succ=False, plot=False):
'''
Summary: Getting trials per minute from hdf file
Input param: hdf: hdf file to use
Input param: nmin: number of min to use a rectangular window
Input param: rew_per_min_cutoff: ignore rew_ix after a
certain rew_per_min low threshold is passed
Output param: rew_ix = rewarded indices in hdf file
'''
rew_ix = np.array([t[1] for it, t in enumerate(hdf.root.task_msgs[:]) if t[0]=='reward'])
tm = np.zeros((np.max(rew_ix)+1))
tm[rew_ix] += 1
if hasattr(hdf.root.task, 'assist_level'):
assist_ix = np.nonzero(hdf.root.task[:]['assist_level']==0)[0]
else:
assist_ix = np.zeros((len(hdf.root.task)))
#Each row occurs ~1/60 sec, so:
minute = 60*60;
min_wind = np.ones((nmin*minute))/float(nmin)
rew_per_min_tmp = np.convolve(min_wind, tm, mode='same')
#Now smooth please:
smooth_wind = np.ones((3*minute))/float(3*minute)
rew_per_min = pk_convolve(smooth_wind, rew_per_min_tmp)
if rew_per_min_cutoff > 0:
ix = np.nonzero(rew_per_min < rew_per_min_cutoff)[0]
if len(ix)>0:
cutoff_ix = ix[0]
else:
cutoff_ix = rew_ix[-1]
else:
cutoff_ix = rew_ix[-1]
if ignore_assist:
try:
beg_zer_assist_ix = assist_ix[0]
except:
print 'No values w/o assist for filename: ', hdf.filename
beg_zer_assist_ix = rew_ix[-1]+1
else:
beg_zer_assist_ix = 0
if plot:
plt.plot(np.arange(len(tm))/float(minute), rew_per_min)
plt.show()
ix_final = scipy.logical_and(rew_ix <= cutoff_ix, rew_ix >= beg_zer_assist_ix)
if return_rpm:
return rew_ix[ix_final], rew_per_min[rew_ix[ix_final]]
else:
return rew_ix[ix_final]
def filter_M_T(gmr_name, gmr_characteristics):
if gmr_characteristics[0] == 'None':
# gmr_characteristics has not been defined because filtering of aftershocks is not of interest
return 'Inf'
else:
Mw_multiplier = gmr_characteristics[-1]
[M_m, Tg_m, Mw, Tg] = find_M_T(gmr_name, gmr_characteristics)
available_aftershocks = len(Mw[scipy.logical_and(Mw<M_m*Mw_multiplier, Tg<Tg_m)])
return available_aftershocks
def makehist(testpath,npulses):
"""
This functions are will create histogram from data made in the testpath.
Inputs
testpath - The path that the data is located.
npulses - The number of pulses in the sim.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
params = ['Ne', 'Te', 'Ti', 'Vi']
histlims = [[1e10, 3e11], [1000., 3000.], [100., 2500.], [-400., 400.]]
erlims = [[-2e11, 2e11], [-1000., 1000.], [-800., 800], [-400., 400.]]
erperlims = [[-100., 100.]]*4
lims_list = [histlims, erlims, erperlims]
errdict = makehistdata(params, testpath)[:4]
ernames = ['Data', 'Error', 'Error Percent']
# Two dimensiontal histograms
pcombos = [i for i in itertools.combinations(params, 2)]
c_rows = int(math.ceil(float(len(pcombos))/2.))
(figmplf, axmat) = plt.subplots(c_rows, 2, figsize=(12, c_rows*6), facecolor='w')
axvec = axmat.flatten()
for icomn, icom in enumerate(pcombos):
curax = axvec[icomn]
str1, str2 = icom
_, _, _ = make2dhist(testpath, PARAMDICT[str1], PARAMDICT[str2], figmplf, curax)
filetemplate = str(Path(testpath).joinpath('AnalysisPlots', 'TwoDDist'))
plt.tight_layout()
plt.subplots_adjust(top=0.95)
figmplf.suptitle('Pulses: {0}'.format(npulses), fontsize=20)
fname = filetemplate+'_{0:0>5}Pulses.png'.format(npulses)
plt.savefig(fname)
plt.close(figmplf)
# One dimensiontal histograms
for ierr, iername in enumerate(ernames):
filetemplate = str(Path(testpath).joinpath('AnalysisPlots', iername))
(figmplf, axmat) = plt.subplots(2, 2, figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
for ipn, iparam in enumerate(params):
plt.sca(axvec[ipn])
if sp.any(sp.isinf(errdict[ierr][iparam])):
continue
binlims = lims_list[ierr][ipn]
bins = sp.linspace(binlims[0], binlims[1], 100)
xdata = errdict[ierr][iparam]
xlog = sp.logical_and(xdata >= binlims[0], xdata < binlims[1])
histhand = sns.distplot(xdata[xlog], bins=bins, kde=True, rug=False)
axvec[ipn].set_title(iparam)
figmplf.suptitle(iername +' Pulses: {0}'.format(npulses), fontsize=20)
fname = filetemplate+'_{0:0>5}Pulses.png'.format(npulses)
plt.savefig(fname)
plt.close(figmplf)
def nonna_select_data(data, outlier_threshold, level='high'): """ This function returns a list of indexed after identifying the main outliers. It applies a cut on the data to remove exactly a fraction (1-outlier_threshold) of all data points. By default the cut is applied only at the higher end of the data values, but the parameter level can be used to change this Input arguments: data = vector containing all data points outlier_threshold = remove outliers until we are left with exactly this fraction of the original data level = 'high|low|both' determines if the outliers are removed only from the high values end, the low values end of both ends. Output: idx = index of selected (good) data """ # histogram all the data values n,x = scipy.histogram(data, len(data)/10) # compute the cumulative distribution and normalize nn = scipy.cumsum(n) nn = nn / float(max(nn)) if level=='high': # select the value such that a fraction outlier_threshold of the data lies below it if outlier_threshold < 1: val = x[pylab.find(nn/float(max(nn)) >= outlier_threshold)[0]] else: val = max(data) # use that fraction of data only idx = data <= val elif level=='low': # select the value such that a fraction outlier_threshold of the data lies above it if outlier_threshold < 1: val = x[pylab.find(nn/float(max(nn)) <= (1-outlier_threshold))[-1]] else: val = min(data) # use that fraction of data only idx = data >= val elif level=='both': # select the value such that a fraction outlier_threshold/2 of the data lies below it if outlier_threshold < 1: Hval = x[pylab.find(nn/float(max(nn)) >= 1-(1-outlier_threshold)/2)[0]] else: Hval = max(data) # select the value such that a fraction outlier_threshold/2 of the data lies above it if outlier_threshold < 1: Lval = x[pylab.find(nn/float(max(nn)) <= (1-outlier_threshold)/2)[-1]] else: Lval = min(data) # use that fraction of data only idx = scipy.logical_and(data >= Lval, data <= Hval) return idx
def get_scan_IF_inds(self, scan_ind, IF_ind) :
"""Gets the record indices of the fits file that correspond to the
given scan and IF.
Note that the scans are numbered with 0 corresponding to the first scan
in the file i.e., it is not the session scan number."""
# TODO: Should check valid scan IF, and raise value errors as apropriate
thescan = self.scan_set[scan_ind]
theIF = self.IF_set[IF_ind]
# Find all the records that correspond to this IF and this scan.
# These indicies *should now be ordered in time, cal (on off)
# and in polarization, once the IF is isolated.
(inds_sif,) = sp.where(sp.logical_and(self._IFs_all==theIF,
self._scans_all==thescan))
ncal = len(sp.unique(self.fitsdata.field('CAL')[inds_sif]))
npol = len(sp.unique(self.fitsdata.field('CRVAL4')[inds_sif]))
# Reform to organize by pol, cal, etc.
ntimes = len(inds_sif)//npol//ncal
inds_sif = sp.reshape(inds_sif, (ntimes, npol, ncal))
if self.verify_ordering > 0:
# We expect noise cal to be on for every second record.
for thecal in range(ncal) :
tmp = sp.unique(self.fitsdata.field('CAL')[inds_sif[:,:,thecal]])
if len(tmp) > 1 :
raise ce.DataError("Calibration (ON/OFF) not in "
"perfect order in file: "+self.fname)
# Polarization should cycle through 4 modes (-5,-7,-8,-6)
for thepol in range(npol) :
tmp = sp.unique(self.fitsdata.field('CRVAL4')
[inds_sif[:,thepol,:]])
if len(tmp) > 1 :
raise ce.DataError("Polarizations not in perfect order in "
"file: "+self.fname)
# We expect the entries to be sorted in time and for time to not
# change across pol and cal.
lastLST = 0
for ii in range(ntimes) :
# Sometimes won't have the LST.
try :
thisLST = self.fitsdata.field('LST')[inds_sif[ii,0,0]]
# If 'LST' is missing raises a KeyError in later versions of
# pyfits, and a NameError in earlier ones.
except (KeyError, NameError) :
break
if not (sp.allclose(self.fitsdata.field('LST')
[inds_sif[ii,:,:]] - thisLST, 0)) :
raise ce.DataError("LST change across cal or pol in "
"file: " + self.fname)
return inds_sif
def evaluate(self, rotMatrix):
"""
Evaluate the correlation for the given orientation (rotation).
:rtype: :obj:`float`
:return: Correlation of normalised bin counts.
"""
trnsCoords = rotMatrix.dot(self.trnsCoords.T).T
fixdDensity = self.fixdSphHist.getBinCounts(trnsCoords)
msk = sp.where(sp.logical_and(fixdDensity > self.fixdLoThrsh, fixdDensity <= self.fixdHiThrsh))
return sp.stats.pearsonr(self.trnsDensity[msk], fixdDensity[msk])[0]
def timereduce(self, timelims=None,timesselected=None):
assert (timelims is not None) or (timesselected is not None), "Need a set of limits or selected set of times"
if timelims is not None:
tkeep = sp.logical_and(self.Time_Vector>=timelims[0],self.Time_Vector<timelims[1])
if timesselected is not None:
tkeep = sp.in1d(self.Time_Vector,timesselected)
# prune the arrays
self.Time_Vector=self.Time_Vector[tkeep]
self.Param_List=self.Param_List[:,tkeep]
self.Velocity=self.Velocity[:,tkeep]
def mask_frequency_range(Data, limits):
if len(limits) != 2:
raise ValueError("Limits must be length 2 sequence.")
lower = min(limits)
upper = max(limits)
Data.calc_freq()
freq = Data.freq
delta_f = abs(sp.mean(sp.diff(freq)))
mask = sp.logical_and(freq + delta_f/2 <= upper, freq - delta_f/2 >= lower)
Data.data[...,mask] = ma.masked
def velocity_dof(domain, ax):
# Calculate velocity dof numbers forr each cell
rm = roll( domain, 1, axis=ax )
type_3 = logical_and( domain, rm )
type_2 = logical_or( domain, rm )
dof = cumsum( logical_not( logical_or( type_3, type_2 ) ) ).reshape( domain.shape ) - 1
# Do logic to figure out type 2 and 3
dof[type_2 == 1] = -2
dof[type_3 == 1] = -3
return dof.astype(int64)
def getBeamFluxSpline(beam,plasma,t,lim1,lim2,points = 1000):
""" generates a spline off of the beampath. Assumes
that the change in flux is MONOTONIC"""
lim = beam.norm.s
beam.norm.s = scipy.linspace(0,lim[-1],points)
h = time.time()
psi = plasma.eq.rz2rmid(beam.r()[0],beam.r()[2],t) #evaluates all psi's at once
print(time.time()-h)
outspline = len(t)*[0]
inspline = len(t)*[0]
for i in xrange(t.size):
temp = lim1
mask = scipy.logical_and(scipy.isfinite(psi[i]),psi[i] < lim2+.02)
try:
minpos = scipy.argmin(psi[i][mask])
test = psi[i][mask][minpos]
except ValueError:
test = lim2+.03
#plt.plot(beam.x()[0][mask],psi[i][mask])
#plt.show()
sizer = psi[i][mask].size
if not test > lim2:
#plt.plot(beam.x()[0][mask][0:minpos],psi[i][mask][0:minpos],beam.x()[0][mask][minpos:],psi[i][mask][minpos:])
#plt.show()
#limout = scipy.insert(lim,(2,2),(beam.norm.s[mask][minpos],beam.norm.s[mask][minpos])) # add minimum flux s for bound testing
if lim1 < test:
temp = test
try:
temp1 = scipy.clip(scipy.digitize((lim1,lim2),psi[i][mask][minpos::-1]),0,minpos)
outspline[i] = beam.norm.s[mask][minpos::-1][temp1]
except ValueError:
tempmask = (psi[i][mask] < lim2)[0]
outspline[i] = scipy.array([beam.norm.s[mask][minpos],beam.norm.s[mask][tempmask]])
try:
temp2 = scipy.clip(scipy.digitize((lim1,lim2),psi[i][mask][minpos:]),0,sizer-minpos-1)
inspline[i] = beam.norm.s[mask][minpos:][temp2]
except ValueError:
inspline[i] = scipy.array([beam.norm.s[mask][minpos],beam.norm.s[mask][-1]])
else:
outspline[i] = scipy.array([[],[]])
inspline[i] = scipy.array([[],[]])
return (outspline,inspline)
def estimate_rate_func(t, T, N, plot_flag=False, method='central diff'):
t_est_pts = scipy.linspace(t.min(), t.max(), N+2)
interp_func = scipy.interpolate.interp1d(t,T,'linear')
T_est_pts = interp_func(t_est_pts)
if plot_flag == True:
pylab.figure()
pylab.subplot(211)
pylab.plot(t_est_pts, T_est_pts,'or')
# Estimate slopes
slope_pts = scipy.zeros((N,))
T_slope_pts = scipy.zeros((N,))
if method == 'local fit':
for i in range(1,(N+1)):
mask0 = t > 0.5*(t_est_pts[i-1] + t_est_pts[i])
mask1 = t < 0.5*(t_est_pts[i+1] + t_est_pts[i])
mask = scipy.logical_and(mask0, mask1)
t_slope_est = t[mask]
T_slope_est = T[mask]
local_fit = scipy.polyfit(t_slope_est,T_slope_est,2)
dlocal_fit = scipy.polyder(local_fit)
slope_pts[i-1] = scipy.polyval(dlocal_fit,t_est_pts[i])
T_slope_pts[i-1] = scipy.polyval(local_fit,t_est_pts[i])
if plot_flag == True:
t_slope_fit = scipy.linspace(t_slope_est[0], t_slope_est[-1], 100)
T_slope_fit = scipy.polyval(local_fit,t_slope_fit)
pylab.plot(t_slope_fit, T_slope_fit,'g')
elif method == 'central diff':
dt = t_est_pts[1] - t_est_pts[0]
slope_pts = (T_est_pts[2:] - T_est_pts[:-2])/(2.0*dt)
T_slope_pts = T_est_pts[1:-1]
else:
raise ValueError, 'unkown method %s'%(method,)
# Fit line to slope estimates
fit = scipy.polyfit(T_slope_pts, slope_pts,1)
if plot_flag == True:
T_slope_fit = scipy.linspace(T_slope_pts.min(), T_slope_pts.max(),100)
slope_fit = scipy.polyval(fit,T_slope_fit)
pylab.subplot(212)
pylab.plot(T_slope_fit, slope_fit,'r')
pylab.plot(T_slope_pts, slope_pts,'o')
pylab.show()
rate_slope = fit[0]
rate_offset = fit[1]
return rate_slope, rate_offset
def coordreduce(self,coorddict):
"""
Given a dictionary of coordinates the location points in the IonoContainer
object will be reduced.
Inputs
corrddict - A dictionary with keys 'x','y','z','r','theta','phi'. The
values in the dictionary are coordinate values that will be kept.
"""
assert type(coorddict)==dict, "Coorddict needs to be a dictionary"
ncoords = self.Cart_Coords.shape[0]
coordlist = ['x','y','z','r','theta','phi']
coordkeysorg = coorddict.keys()
coordkeys = [ic for ic in coordkeysorg if ic in coordlist]
ckeep = sp.ones(ncoords,dtype=bool)
for ic in coordkeys:
currlims = coorddict[ic]
if ic=='x':
tempcoords = self.Cart_Coords[:,0]
elif ic=='y':
tempcoords = self.Cart_Coords[:,1]
elif ic=='z':
tempcoords = self.Cart_Coords[:,2]
elif ic=='r':
tempcoords = self.Sphere_Coords[:,0]
elif ic=='theta':
tempcoords = self.Sphere_Coords[:,1]
elif ic=='phi':
tempcoords = self.Sphere_Coords[:,2]
keeptemp = sp.logical_and(tempcoords>=currlims[0],tempcoords<currlims[1])
ckeep = sp.logical_and(ckeep,keeptemp)
# prune the arrays
self.Cart_Coords=self.Cart_Coords[ckeep]
self.Sphere_Coords=self.Sphere_Coords[ckeep]
self.Param_List=self.Param_List[ckeep]
self.Velocity=self.Velocity[ckeep]
def doTestMcrWithHalo(self, haloSz=0, filecache=False):
if (isinstance(haloSz, int) or ((sys.version_info.major < 3) and isinstance(haloSz, long))):
if (haloSz < 0):
haloSz = 0
haloSz = sp.array((haloSz,)*3)
subDirName = "doTestMcrWithHalo_%s_%s" % ("x".join(map(str, haloSz)), str(filecache))
outDir = self.createTmpDir(subDirName)
# outDir = subDirName
if (mpi.world != None):
if ((mpi.world.Get_rank() == 0) and (not os.path.exists(outDir))):
os.makedirs(subDirName)
mpi.world.barrier()
segDds = mango.zeros(self.imgShape, mtype="segmented", halo=haloSz)
segDds.setAllToValue(segDds.mtype.maskValue())
mango.data.fill_ellipsoid(segDds, centre=self.centre, radius=(self.radius*1.05,)*3, fill=0)
mango.data.fill_ellipsoid(segDds, centre=self.centre, radius=(self.radius,)*3, fill=1)
mango.io.writeDds(os.path.join(outDir,"segmentedSphere.nc"), segDds)
dtDds = mango.image.distance_transform_edt(segDds, 1)
mango.io.writeDds(os.path.join(outDir,"distance_mapSphereEdt.nc"), dtDds)
mcrDds = mango.image.max_covering_radius(dtDds, maxdist=self.radius*1.5, filecache=filecache)
mango.io.writeDds(os.path.join(outDir,"distance_mapSphereEdtMcr.nc"), mcrDds)
slc = []
for d in range(len(haloSz)):
slc.append(slice(haloSz[d], segDds.asarray().shape[d]-haloSz[d]))
slc = tuple(slc)
self.assertTrue(
sp.all(
(
self.radius
-
sp.where(
sp.logical_and(mcrDds.asarray() > 0, mcrDds.asarray() != mcrDds.mtype.maskValue()),
sp.ceil(mcrDds.asarray()),
self.radius
)
)
<=
0.1*self.radius # Test is true in the masked areas.
)
)
self.assertTrue(sp.all(segDds.halo == mcrDds.halo))
self.assertTrue(sp.all(segDds.shape == mcrDds.shape))
self.assertTrue(sp.all(segDds.origin == mcrDds.origin), "%s != %s" % (segDds.origin, mcrDds.origin))
self.assertTrue(sp.all(segDds.mpi.shape == mcrDds.mpi.shape))
def spk_phase(t, V1, V2, spike_thresh=0):
"""phase_mean, isi_mean = spk_phase(t, V1, V2, spike_thresh=0)
Given two voltage vectors (V1 and V2) and time vector (t), phase calculates
the mean phase of the spikes in radians (phase_mean) and the mean
interspike interval (isi_mean).
You can optionally specify the spike threshold (defaults to 0).
This uses an assumption that every time it spikes, the voltage increases
above the given spike threshold.
The method used here is not robust. If the data is noisy then there will
likely be false positives. With a model however, it should work very
well.
"""
time1 = t[sp.logical_and(V1[:-1] < spike_thresh, V1[1:] >= spike_thresh)]
time2 = t[sp.logical_and(V2[:-1] < spike_thresh, V2[1:] >= spike_thresh)]
l = sp.amin([len(time1), len(time2)])
isi_mean = sp.mean(sp.diff(time1))
phase_mean = sp.mean((time1[0:l]-time2[0:l]) / isi_mean * 2 * sp.pi)
return phase_mean, isi_mean
def betapdf(X,a,b):
#operate only on x in (0,1)
if isscalar(X):
if X<=0 or X>=1:
raise ValueError("X must be in the interval (0,1)")
else:
x=X
else:
goodx = logical_and(X>0, X<1)
x = X[goodx].copy()
loga = (a-1.0)*log(x)
logb = (b-1.0)*log(1.0-x)
return exp(gammaln(a+b)-gammaln(a)-gammaln(b)+loga+logb)
def spikes2states(spikes):
"""Convert a sequence of binarized spikes to a sequence of state numbers.
Input arguments:
spikes -- spikes trains: 2D binary array, each column a different unit,
each row a time point
"""
# check that the incoming array is binary
if not scipy.all(scipy.logical_and(spikes>=0, spikes<=1)):
raise ValueError('Input array must be binary')
nchannels = spikes.shape[1]
# convert binary sequence to decimal numbers
pow2 = array([2**i for i in range(nchannels-1,-1,-1)])
return (spikes*pow2).sum(axis=1)
def combine(self, *others, **kwargs):
"""Combine the x-y pairs of this continuous function object with those
of the given others. Returns a new continuous function instance. If cut
is True, only the intersection of the x-axes is used, the rest of the
input functions is cut away. Raise an exception if the x-axes do not
intersect."""
# 2012-07-11
# get keyword arguments
cut = kwargs.pop('cut', False)
if len(kwargs) > 0:
raise TypeError('%s() got an unexpected keyword argument "%s"'
% (__name__, kwargs.keys()[0]))
new = self.copy()
for other in others:
# check type
if type(other) is not type(self):
raise TypeError('expected instance of %s'
% type(self).__name__)
# combine x-y value pairs
x = scipy.r_[new.x, other.x]
y = scipy.r_[new.y, other.y]
#x = self._filter_double(x)
if cut:
# determine extremal values
min1, max1 = new.box()[:2] # min(new.x), max(new.x)
min2, max2 = other.box()[:2] # min(other.x), max(other.x)
newmin = max(min1, min2)
newmax = min(max1, max2)
# find indices to cut away
keep = scipy.logical_and(x >= newmin, x <= newmax)
if len(keep) == 0:
raise ValueError('x-axes do not intersect')
# cut away
x = x[keep]
y = y[keep]
# make new object, merge attributes
new = type(self)(x, y, attrs=new.attrs.intersection(other.attrs))
# return new object
return new