def Cov(A, B, n):
'''
计算两个因子向前n天的协方差
n >= 2
'''
if n < 2:
#print ("计算A和B n天的协方差，n不得小于2，返回A")
return A
result_A = []
result_B = []
for i in range(n):
temp_A = np.roll(A, i, axis=0)
temp_A[:i] = np.nan
temp_B = np.roll(B, i, axis=0)
temp_B[:i] = np.nan
result_A.append(temp_A)
result_B.append(temp_B)
result_A = np.stack(result_A)
mean = bk.nanmean(result_A, axis=0)
result_A = result_A - mean
result_B = np.stack(result_B)
mean = bk.nanmean(result_B, axis=0)
result_B = result_B - mean
result = bk.nanmean(result_A * result_B, axis=0)
result[np.isnan(A * B)] = np.nan
return result
def compute(self, today, assets, out, closes):
diffs = diff(closes, axis=0)
ups = nanmean(clip(diffs, 0, inf), axis=0)
downs = abs(nanmean(clip(diffs, -inf, 0), axis=0))
return evaluate(
"100 - (100 / (1 + (ups / downs)))", local_dict={"ups": ups, "downs": downs}, global_dict={}, out=out
)
def test_memory_leak():
import resource
arr = np.arange(1).reshape((1, 1))
starting = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
for i in range(1000):
for axis in [None, 0, 1]:
bn.nansum(arr, axis=axis)
bn.nanargmax(arr, axis=axis)
bn.nanargmin(arr, axis=axis)
bn.nanmedian(arr, axis=axis)
bn.nansum(arr, axis=axis)
bn.nanmean(arr, axis=axis)
bn.nanmin(arr, axis=axis)
bn.nanmax(arr, axis=axis)
bn.nanvar(arr, axis=axis)
ending = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
diff = ending - starting
diff_bytes = diff * resource.getpagesize()
print(diff_bytes)
# For 1.3.0 release, this had value of ~100kB
assert diff_bytes == 0
def test_memory_leak() -> None:
import resource
arr = np.arange(1).reshape((1, 1))
n_attempts = 3
results = []
for _ in range(n_attempts):
starting = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
for _ in range(1000):
for axis in [None, 0, 1]:
bn.nansum(arr, axis=axis)
bn.nanargmax(arr, axis=axis)
bn.nanargmin(arr, axis=axis)
bn.nanmedian(arr, axis=axis)
bn.nansum(arr, axis=axis)
bn.nanmean(arr, axis=axis)
bn.nanmin(arr, axis=axis)
bn.nanmax(arr, axis=axis)
bn.nanvar(arr, axis=axis)
ending = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
diff = ending - starting
diff_bytes = diff * resource.getpagesize()
# For 1.3.0 release, this had value of ~100kB
if diff_bytes:
results.append(diff_bytes)
else:
break
assert len(results) < n_attempts
def pairwise_covariance(x_mat, y=None, correlation=False):
x_mat = x_mat.copy()
x_nan = np.isnan(x_mat)
if y is not None:
if y.shape[0] != 1:
assert y.shape == x_mat.shape, 'y and x_mat should be of the same shape if y has more than 1 rows'
y_mat = y
else:
y_mat = np.tile(y, (x_mat.shape[0], 1))
y_nan = np.isnan(y_mat)
x_mat[y_nan] = np.nan
y_mat[x_nan] = np.nan
pw_multiply = np.multiply(
x_mat - bn.nanmean(x_mat, axis=1).reshape(-1, 1),
y_mat - bn.nanmean(y_mat, axis=1).reshape(-1, 1))
cov = bn.nansum(pw_multiply, axis=1) / (
pw_multiply.shape[1] - np.isnan(pw_multiply).sum(axis=1) - 1)
if correlation:
return cov / np.multiply(bn.nanstd(x_mat, axis=1, ddof=1),
bn.nanstd(y_mat, axis=1, ddof=1))
return cov
else:
if correlation:
return pd.DataFrame(x_mat).T.corr().values
return pd.DataFrame(x_mat).T.cov().values
def is_guide_ota(primhdu, ext, w=20, debug=False):
logger = logging.getLogger("IsGuideOTA")
binning = primhdu.header['BINNING']
skylevel = primhdu.header['SKYLEVEL']
gain = primhdu.header['GAIN']
skynoise = primhdu.header['SKYNOISE']
logger.debug("Checking OTA %s (bin=%d, sky=%.1f, skynoise=%.2f)" % (
ext.name, binning, skylevel, skynoise))
if (not is_image_extension(ext)):
logger.debug("extension is not a valid image extension")
return False
excesses = numpy.empty((8,8))
excesses[:,:] = numpy.NaN
if (debug):
center_hdu = [pyfits.PrimaryHDU()]
corner_hdu = [pyfits.PrimaryHDU()]
for cx, cy in itertools.product(range(8), repeat=2):
#
# Get pixel coord for this cell
#
x1,x2,y1,y2 = cell2ota__get_target_region(cx, cy, binning=binning, trimcell=0)
x1,x2,y1,y2 = int(x1),int(x2),int(y1),int(y2)
x21 = (x2-x1)//2
# extract the mean value in the bottom corner
corner = bottleneck.nanmean(ext.data[y1:y1+w, x1:x1+w].astype(numpy.float32))
# also get the value in the bottom center
center = bottleneck.nanmean(ext.data[y1:y1+w, x1+x21-w//2:x1+x21+w//2].astype(numpy.float32))
if (debug):
print(cx,cy,corner, center)
corner_hdu.append(pyfits.ImageHDU(data=ext.data[y1:y1+w, x1:x1+w]))
center_hdu.append(pyfits.ImageHDU(data=ext.data[y1:y1+w, x1+x21-w//2:x1+x21+w//2]))
excess = corner - center
#print ext.name, cx, cy, corner, center, excess
excesses[cx,cy] = excess
_mean = bottleneck.nanmean(excesses)
_median = bottleneck.nanmedian(excesses)
is_guideota = (_median > 10*skynoise)
logger.debug("Found corner excess mean=%.1f, median=%.1f --> guide-OTA: %s" % (
_mean, _median, "YES" if is_guideota else "NO"))
if (debug):
return is_guideota, excesses, _mean, _median, skynoise, corner_hdu, center_hdu
return is_guideota
def shifted_corr(reference, image, displacement):
"""Calculate the correlation between the reference and the image shifted
by the given displacement.
Parameters
----------
reference : np.ndarray
image : np.ndarray
displacement : np.ndarray
Returns
-------
correlation : float
"""
ref_cuts = np.maximum(0, displacement)
ref = reference[ref_cuts[0]:, ref_cuts[1]:, ref_cuts[2]:]
im_cuts = np.maximum(0, -displacement)
im = image[im_cuts[0]:, im_cuts[1]:, im_cuts[2]:]
s = np.minimum(im.shape, ref.shape)
ref = ref[:s[0], :s[1], :s[2]]
im = im[:s[0], :s[1], :s[2]]
ref -= nanmean(ref.reshape(-1, ref.shape[-1]), axis=0)
ref = np.nan_to_num(ref)
im -= nanmean(im.reshape(-1, im.shape[-1]), axis=0)
im = np.nan_to_num(im)
assert np.all(np.isfinite(ref)) and np.all(np.isfinite(im))
corr = nanmean(
[old_div(np.sum(i * r), np.sqrt(np.sum(i * i) * np.sum(r * r))) for
i, r in zip(np.rollaxis(im, -1), np.rollaxis(ref, -1))])
return corr
def reactivation(date, cs):
"""
Check if a date has good enough data to trust reactivations.
Parameters
----------
date
cs
Returns
-------
"""
# ncells, ntimes, nonsets
trs = stimulus.trials(date,
cs,
start_s=0,
end_s=None,
trace_type='dff',
baseline=(-1, 0))
trs = nanmean(trs, axis=2)
trs = nanmean(trs, axis=1)
if not len(trs):
return False
# Across ncells
dff_active = np.sum(trs > 0.025) / float(len(trs))
return dff_active > 0.05
def shifted_corr(reference, image, displacement):
"""Calculate the correlation between the reference and the image shifted
by the given displacement.
Parameters
----------
reference : np.ndarray
image : np.ndarray
displacement : np.ndarray
Returns
-------
correlation : float
"""
ref_cuts = np.maximum(0, displacement)
ref = reference[ref_cuts[0]:, ref_cuts[1]:, ref_cuts[2]:]
im_cuts = np.maximum(0, -displacement)
im = image[im_cuts[0]:, im_cuts[1]:, im_cuts[2]:]
s = np.minimum(im.shape, ref.shape)
ref = ref[:s[0], :s[1], :s[2]]
im = im[:s[0], :s[1], :s[2]]
ref -= nanmean(ref.reshape(-1, ref.shape[-1]), axis=0)
ref = np.nan_to_num(ref)
im -= nanmean(im.reshape(-1, im.shape[-1]), axis=0)
im = np.nan_to_num(im)
assert np.all(np.isfinite(ref)) and np.all(np.isfinite(im))
corr = nanmean(
[old_div(np.sum(i * r), np.sqrt(np.sum(i * i) * np.sum(r * r))) for
i, r in zip(np.rollaxis(im, -1), np.rollaxis(ref, -1))])
return corr
def __init__(self,crs='4326',region=(92.3032344909, 9.93295990645, 101.180005324, 28.335945136)
,resolution=500,noData=0,country=None):
if country:
if crs != '4326':
raise ValueError('User defined crs must be EPSG:4326 when using predifined country bounding boxes')
else:
self.west,self.south,self.east,self.north = countries.bounding_boxes[country][1]
else:
#! Need to add in error handler for correct bounding box format !#
self.west,self.south,self.east,self.north = region
if '4326' in crs:
midPoint = [bn.nanmean([self.north,self.south]),
bn.nanmean([self.east,self.west])]
spacing = utils.meters2dd(midPoint,resolution)
elif type(resolution) == list:
spacing = resolution[0]
else:
spacing = resolution,resolution
self.lons = np.arange(self.west,self.east,spacing)
self.lats = np.arange(self.south,self.north,spacing)
self.xx,self.yy = np.meshgrid(self.lons,self.lats)
self.nominalResolution = (spacing)
self.dims = self.xx.shape
return
def fit(self, X, y):
X_y = self._check_params(X, y)
self.X = X_y[0]
self.y = X_y[1].reshape((-1, 1))
n, p = X.shape
S = [] # list of selected features
F = range(p) # list of unselected features
if self.n_features != 'auto':
feature_mi_matrix = np.zeros((self.n_features, p))
else:
feature_mi_matrix = np.zeros((n, p))
feature_mi_matrix[:] = np.nan
S_mi = []
# Find the first feature
k_min = 3
range_k = 7
xy_MI = np.empty((range_k, p))
for i in range(range_k):
xy_MI[i, :] = self._get_first_mi_vector(i + k_min)
xy_MI = bn.nanmedian(xy_MI, axis=0)
S, F = self._add_remove(S, F, bn.nanargmax(xy_MI))
S_mi.append(bn.nanmax(xy_MI))
if self.verbose > 0:
self._info_print(S, S_mi)
# Find the next features
if self.n_features == 'auto':
n_features = np.inf
else:
n_features = self.n_features
while len(S) < n_features:
s = len(S) - 1
feature_mi_matrix[s, F] = self._get_mi_vector(F, S[-1])
fmm = feature_mi_matrix[:len(S), F]
if bn.allnan(bn.nanmean(fmm, axis=0)):
break
MRMR = xy_MI[F] - bn.nanmean(fmm, axis=0)
if np.isnan(MRMR).all():
break
selected = F[bn.nanargmax(MRMR)]
S_mi.append(bn.nanmax(bn.nanmin(fmm, axis=0)))
S, F = self._add_remove(S, F, selected)
if self.verbose > 0:
self._info_print(S, S_mi)
if self.n_features == 'auto' and len(S) > 10:
MI_dd = signal.savgol_filter(S_mi[1:], 9, 2, 1)
if np.abs(np.mean(MI_dd[-5:])) < 1e-3:
break
self.n_features_ = len(S)
self.ranking_ = S
self.mi_ = S_mi
return self
def _get_fluctuations(image_array, correction='median'):
# Calculate the mean PV for each image
if correction == 'mean':
mean_values = bn.nanmean(bn.nanmean(image_array, axis=1), axis=1)
elif correction == 'median':
mean_values = bn.nanmedian(bn.nanmedian(image_array, axis=1), axis=1)
return mean_values
def least_square_method(dspt):
npol = 6
com = np.array([bn.nanmean(dspt.lon), bn.nanmean(dspt.lat)])
timeseries = False
ncc = dspt.lon.size
dlon = []
dlat = []
for i in range(ncc):
# haversine(p1,p2)
dlon.append(
haversine([dspt.lon[i], com[1]], com) * 1000 *
np.sign(dspt.lon[i] - com[0]))
dlat.append(
haversine([com[0], dspt.lat[i]], com) * 1000 *
np.sign(dspt.lat[i] - com[1]))
dlon = np.array(dlon)
dlat = np.array(dlat)
if not timeseries:
R = np.mat(np.vstack((np.ones((ncc)), dlon, dlat)).T)
u0 = np.mat(dspt.u.values).T
v0 = np.mat(dspt.v.values).T
if (np.isnan(u0).sum() == 0) & (np.isnan(v0).sum()
== 0) & (np.isnan(R).sum() == 0):
A, _, _, _ = la.lstsq(R, u0)
B, _, _, _ = la.lstsq(R, v0)
else:
A = np.nan * np.ones(ncc)
B = np.nan * np.ones(ncc)
points = np.vstack([dlon, dlat])
if (np.isfinite(dlon).sum() == npol) and (np.isfinite(dlat).sum() == npol):
# careful with nans
cov = np.cov(points)
w, v = np.linalg.eig(cov)
aspect = bn.nanmin(w) / bn.nanmax(w)
if aspect < 0.99:
ind = bn.nanargmax(w)
angle = np.arctan(v[ind, 1] / v[ind, 0]) * 180 / np.pi
if (angle < 0):
angle += 360.
else:
angle = np.nan
else:
aspect = np.nan
angle = np.nan
dspt['ux'] = float(A[1])
dspt['uy'] = float(A[2])
dspt['vx'] = float(B[1])
dspt['vy'] = float(B[2])
dspt['aspect'] = aspect
dspt['angle'] = angle
return dspt
def compute(self, today, assets, out, closes):
diffs = diff(closes)
ups = nanmean(clip(diffs, 0, inf), axis=0)
downs = nanmean(clip(diffs, -inf, 0), axis=0)
return evaluate(
"100 - (100 / (1 + (ups / downs)))",
locals_dict={'ups': ups, 'downs': downs},
globals_dict={},
out=out,
)
def _nanmean(array, axis=None):
"""Bottleneck nanmean function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanmean(array, axis=axis))
else:
return bottleneck.nanmean(array, axis=axis)
def _nanmean(array, axis=None):
"""Bottleneck nanmean function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanmean(array, axis=axis))
else:
return bottleneck.nanmean(array, axis=axis)
def sky_subtract(V, Iin, err=None, window=400, skywindow=400, threshold=5.0, niter=20, ax=None, center=0):
"""
Takes a line (probably produced by plot_line) and does a rough sky
subtraction. The sky is estimated from the region of the spectrum that is
window/2 away from the line center and skywindow wide. This is an
iterative process that uses ADE_moments to compute the line center. When
the new line center (after subtraction) is less than threshold different
from the old line center the sky subtraction is considered complete.
"""
I = np.copy(Iin)
skylevel = 0.0
if ax is not None:
ax.plot(V, I)
lowV = -1 * window / 2.0 + center
highV = window / 2.0 + center
idx = np.where((V >= lowV) & (V <= highV))
skidx = np.where((V < lowV) & (V >= lowV - skywindow) | (V > highV) & (V <= highV + skywindow))
moments = ADE.ADE_moments(V[idx], I[idx])
oldcent = moments[0]
skyfit = bn.nanmean(I[skidx])
ax.axhline(y=skyfit)
I -= skyfit
skylevel += skyfit
newcent = ADE.ADE_moments(V[idx], I[idx])[0]
print "old: {}, new: {}".format(oldcent, newcent)
n = 0
while np.abs(newcent - oldcent) > threshold and n <= niter:
oldcent = newcent
lowV = oldcent - window / 2.0
highV = oldcent + window / 2.0
idx = np.where((V >= lowV) & (V <= highV))
skidx = np.where((V < lowV) & (V >= lowV - skywindow) | (V > highV) & (V <= highV + skywindow))
if idx[0].size == 0:
idx = ([0, 1],)
if skidx[0].size == 0:
skfit = 0
skidx = (np.array([0, -1]),)
else:
skyfit = bn.nanmean(I[skidx])
I -= skyfit
skylevel += skyfit
newcent = ADE.ADE_moments(V[idx], I[idx])[0]
print "{}: old: {}, new: {}".format(n, oldcent, newcent)
n += 1
if ax is not None:
ax.errorbar(V, I, yerr=err)
ax.axvline(x=newcent, ls=":")
ax.axhline(y=skyfit)
ax.axvspan(V[idx[0][0]], V[idx[0][-1]], color="r", alpha=0.3)
ax.axvspan(V[skidx[0][0]], V[idx[0][0]], color="g", alpha=0.3)
ax.axvspan(V[idx[0][-1]], V[skidx[0][-1]], color="g", alpha=0.3)
return V, I, err, newcent, skylevel
def process(self, window, output_length):
data = np.array_split(window, output_length)
result = []
for section in data:
if math.isnan(bn.nanmean(section)):
result.append(0)
else:
result.append(bn.nanmean(section))
result = np.array(result)
print "paa output shape", result.shape
return result
def smooth(x, window):
"""Calculate moving average of input with given window (number of points)"""
window = int(window)
if window <= 1: return x
if window % 2 == 0: window += 1
if window >= len(x): return zeros_like(x) + nanmean(x)
y = move_mean(x, window, min_count=1)
yny = append(y[window // 2:], [NaN] * (window // 2))
for k in range(window // 2):
yny[k] = nanmean(x[:(2 * k + 1)])
yny[-(k + 1)] = nanmean(x[-(2 * k + 1):])
return yny
def calc_table_np(self, array):
if len(array) == 0:
return array
if self.out_choiced == 0: #snr
return self.make_table(
(bottleneck.nanmean(array, axis=0) /
bottleneck.nanstd(array, axis=0)).reshape(1, -1), self.data)
elif self.out_choiced == 1: #avg
return self.make_table(
bottleneck.nanmean(array, axis=0).reshape(1, -1), self.data)
else: # std
return self.make_table(
bottleneck.nanstd(array, axis=0).reshape(1, -1), self.data)
def process(self, data, output_length):
data = np.array(data)
data = np.array_split(data, int(output_length))
result = []
for section in data:
if math.isnan(bn.nanmean(section)):
result.append(0)
else:
result.append(bn.nanmean(section))
result = np.array(result)
result = pd.DataFrame((result))
print "paa output shape", result.shape
return result
def visually_inhib(date, cs, integrate_bins=6, ncses=3):
"""
Calculate the probability of being visually driven for each cell.
Parameters
----------
date : Date
cs : str
integrate_bins : int
Number of bins over which to integrate the visual stim.
ncses : int
Number of possible cses, used to correct for multiple comparisons.
nolick : bool
If True, exclude trials where there was licking during the stimulus
presentation.
Result
------
np.ndarray
An array of length equal to the number cells, values are the log
inverse p-value of that cell responding to the particular cs.
"""
# Baseline is mean across frames, now ncells x nonsets
baselines = nanmean(stimulus.trials(date, cs, start_s=-1, end_s=0) * -1,
axis=1)
stimuli = stimulus.trials(date, cs, start_s=0) * -1
fintegrate = -(-np.shape(stimuli)[1] // integrate_bins) # ceiling division
# Per-cell value
meanbl = nanmean(baselines, axis=1)
ncells = np.shape(baselines)[0]
# We will save the maximum inverse p values
maxinvps = np.zeros(ncells, dtype=np.float64)
bonferroni_n = ncells * ncses * integrate_bins
for i in range(integrate_bins):
trs = nanmean(stimuli[:, i * fintegrate:(i + 1) * fintegrate, :],
axis=1)
for c in range(ncells):
if nanmean(trs[c, :]) > meanbl[c]:
pv = stats.ranksums(baselines[c, :], trs[c, :]).pvalue
logpv = -1 * np.log(pv / bonferroni_n)
if logpv > maxinvps[c]:
maxinvps[c] = logpv
return maxinvps
def shifted_corr(reference, image, displacement):
ref_cuts = np.maximum(0, displacement)
ref = reference[ref_cuts[0]:, ref_cuts[1]:, ref_cuts[2]:]
im_cuts = np.maximum(0, -displacement)
im = image[im_cuts[0]:, im_cuts[1]:, im_cuts[2]:]
s = np.minimum(im.shape, ref.shape)
ref = ref[:s[0], :s[1], :s[2]]
im = im[:s[0], :s[1], :s[2]]
ref -= nanmean(ref.reshape(-1, ref.shape[-1]), axis=0)
ref = np.nan_to_num(ref)
im -= nanmean(im.reshape(-1, im.shape[-1]), axis=0)
im = np.nan_to_num(im)
return np.mean([np.sum(i * r) / np.sqrt(np.sum(i * i) * np.sum(r * r))
for i, r in zip(np.rollaxis(im, 1), np.rollaxis(ref, 1))])
def process(self, data, output_length):
data = np.array_split(data, output_length)
result = []
for section in data:
if math.isnan(bn.nanmean(section)):
result.append(0)
else:
result.append(bn.nanmean(section))
result = np.array(result)
print "paa output shape", result.shape
return result
#def getConfigurationParams(self):
#return {"output_length":"100"}
def nanmean(array, axis=None):
"""
A nanmean function that uses bottleneck if available.
"""
if HAS_BOTTLENECK:
if isinstance(axis, tuple):
array = move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, u.Quantity):
return array.__array_wrap__(bn.nanmean(array, axis=axis))
else:
return bn.nanmean(array, axis=axis)
else:
return np.nanmean(array, axis=axis)
def nw_aggregation(nw_paths, genes, file_key='nw'):
"""Function for aggregating co-expression networks
Takes a list of paths to HDF5 files and reads in networks,
avearges them and then re-ranks.
Each HDF5 needs to be in the Pytable in the fixed format with
the network stored under the key listed in the keyword argument file_key
Arguments:
nw_paths {list} -- list of strings or paths to HDF5 files
genes {np.array} -- numpy array of genes for network
Keyword Arguments:
file_key {str} -- key in HDF5 network is stored under (default: {'nw'})
Returns:
pd.DataFrame -- Aggregate Network
"""
agg_nw = np.zeros([genes.shape[0], genes.shape[0]])
for nw_path in nw_paths:
nw = pd.read_hdf(nw_path,file_key)
fill = bottleneck.nanmean(nw.values,axis=None)
agg_nw +=nw.loc[genes,genes].fillna(fill).values
del nw
gc.collect()
return pd.DataFrame(rank(agg_nw),index=genes, columns=genes)
def factor_normalize(factor):
x_m = factor.values
mean = bn.nanmean(x_m, axis=1).reshape(-1, 1)
std = bn.nanstd(x_m, axis=1, ddof=1).reshape(-1, 1)
with np.errstate(invalid='ignore'):
res = (x_m - mean) / std
return pd.DataFrame(res, factor.index, factor.columns)
def demean(arr, axis=None):
"""
Subtract the mean along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to remove the mean. The default (None) is
to subtract the mean of the flattened array.
Returns
-------
y : ndarray
A copy with the mean along the specified axis removed.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 3])
>>> demean(arr)
array([ -1., NaN, 0., 1.])
"""
marr = bn.nanmean(arr, axis)
if (axis != 0) and (not axis is None) and (not np.isscalar(marr)):
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
marr = marr[ind]
return arr - marr
def mean_age_of_wave(x):
age = []
for i in range(len(Speed_Dating_df['age'])):
if Speed_Dating_df['wave'][i] == x:
age.append(Speed_Dating_df['age'][i])
median_age = round(bn.nanmean(age), 2)
return median_age
def calc_we(cdata, basedir, log_target_rate):
# WE parameters
we_dir = os.path.join(basedir, cdata['name'], 'analysis')
we_dt = cdata['tau']
we_nbins = cdata['nbins']
we_target_count = cdata['target_count']
winsize_flux = cdata['analysis']['winsize_flux']
winsize_err = cdata['analysis']['winsize_err']
last_n = cdata['analysis']['last_n']
we_nframes = 0
we_data_files = glob(os.path.join(we_dir, '*/rate.h5'))
for fname in we_data_files:
f = h5py.File(fname, 'r')
we_nframes = max(we_nframes, f.attrs['last_completed_iter'] - 2)
f.close()
we_err = np.empty((len(we_data_files), 2, we_nframes))
we_err.fill(np.nan)
for k, fname in enumerate(we_data_files):
print 'we: {}'.format(fname)
f = h5py.File(fname, 'r')
s = f['data'][:]
dget = min(we_nframes, s.shape[0])
s = s[:dget, :]
ss = np.empty_like(s)
for i in xrange(4):
ss[:, i] = smooth(s[:, i], winsize_flux, 'flat')
sm = s[-last_n:, :].sum(0)
rAB = (1.0 * ss[:, 1]) / (we_dt * ss[:, 2])
rBA = (1.0 * ss[:, 0]) / (we_dt * ss[:, 3])
rABm = (1.0 * sm[1]) / (we_dt * sm[2])
rBAm = (1.0 * sm[0]) / (we_dt * sm[3])
print 'we_{} -- kAB: {}, kBA: {}'.format(k, rABm, rBAm)
we_err[k, 0, :rAB.shape[0]] = logfunc(rAB) - log_target_rate[0]
we_err[k, 1, :rBA.shape[0]] = logfunc(rBA) - log_target_rate[1]
f.close()
we_err = np.abs(we_err)
#we_err_avg = np.sqrt(np.mean(we_err**2,0))
we_err_avg = np.sqrt(bn.nanmean(we_err**2, 0))
for i in xrange(2):
we_err_avg[i, :] = smooth(we_err_avg[i, :], winsize_err, 'flat')
we_t = we_dt * we_nbins * we_target_count * np.arange(we_nframes)
return we_err_avg, we_t
def calc_we(cdata,basedir,log_target_rate):
# WE parameters
we_dir = os.path.join(basedir,cdata['name'],'analysis')
we_dt = cdata['tau']
we_nbins = cdata['nbins']
we_target_count = cdata['target_count']
winsize_flux = cdata['analysis']['winsize_flux']
winsize_err = cdata['analysis']['winsize_err']
last_n = cdata['analysis']['last_n']
we_nframes = 0
we_data_files = glob(os.path.join(we_dir,'*/rate.h5'))
for fname in we_data_files:
f = h5py.File(fname,'r')
we_nframes = max(we_nframes,f.attrs['last_completed_iter']-2)
f.close()
we_err = np.empty((len(we_data_files),2,we_nframes))
we_err.fill(np.nan)
for k,fname in enumerate(we_data_files):
print 'we: {}'.format(fname)
f = h5py.File(fname,'r')
s = f['data'][:]
dget = min(we_nframes,s.shape[0])
s = s[:dget,:]
ss = np.empty_like(s)
for i in xrange(4):
ss[:,i] = smooth(s[:,i],winsize_flux,'flat')
sm = s[-last_n:,:].sum(0)
rAB = (1.0*ss[:,1]) / (we_dt*ss[:,2])
rBA = (1.0*ss[:,0]) / (we_dt*ss[:,3])
rABm = (1.0*sm[1]) / (we_dt*sm[2])
rBAm = (1.0*sm[0]) / (we_dt*sm[3])
print 'we_{} -- kAB: {}, kBA: {}'.format(k,rABm,rBAm)
we_err[k,0,:rAB.shape[0]] = logfunc(rAB) - log_target_rate[0]
we_err[k,1,:rBA.shape[0]] = logfunc(rBA) - log_target_rate[1]
f.close()
we_err = np.abs(we_err)
#we_err_avg = np.sqrt(np.mean(we_err**2,0))
we_err_avg = np.sqrt(bn.nanmean(we_err**2,0))
for i in xrange(2):
we_err_avg[i,:] = smooth(we_err_avg[i,:],winsize_err,'flat')
we_t = we_dt * we_nbins * we_target_count * np.arange(we_nframes)
return we_err_avg, we_t
def _nanmean(array, axis=None):
"""Bottleneck nanmean function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanmean(array, axis=axis)
def time_step(self, xt):
xt = np.reshape(xt, newshape=self.dimensions)
ret_val = 0.
self.buffer.append(xt)
self.present.time_step(xt)
if self.t >= self.buffer_len:
pst_xt = self.buffer[0]
self.past.time_step(pst_xt)
if self.t >= self.present.theta + self.past.theta:
ret_val = self.comparison_function(self.present, self.past,
self.present.alpha)
self.ma_window.append(ret_val)
if self.t % self.ma_recalc_delay == 0:
self.anomaly_mean = bn.nanmean(self.ma_window)
self.anomaly_std = bn.nanstd(self.ma_window, ddof=self.ddof)
if self.anomaly_std is None or self.t < len(self.ma_window):
anomaly_density = 0
else:
normalized_score = (ret_val - self.anomaly_mean)/self.anomaly_std
if -4 <= normalized_score <= 4:
anomaly_density = CDF_TABLE[round(normalized_score, 3)]
elif normalized_score > 4:
anomaly_density = 1.
else:
anomaly_density = 0.
self.t += 1
return ret_val, anomaly_density
def get_lugsail_batch_means_est(data_in, steps=None):
m = len(data_in)
T_iL = []
s_i = []
n_i = []
for data_chain, burnin_chain in data_in:
data = data_chain[burnin_chain:steps]
if data.size < 2:
return np.inf
# [chapter 2.2 in Vats and Knudson, 2018]
n_ii = data.size
b = int(n_ii**(1 / 2)) # Batch size. Alternative: n ** (1/3)
n_i.append(n_ii)
chain_mean = bn.nanmean(data)
T_iL.append(
2 * get_tau_lugsail(b, data, chain_mean) \
- get_tau_lugsail(b // 3, data, chain_mean)
)
s_i.append(bn.nanvar(data, ddof=1))
T_L = np.mean(T_iL)
s = np.mean(s_i)
n = np.round(np.mean(n_i))
sigma_L = ((n - 1) * s + T_L) / n
# [eq. 5 in Vats and Knudson, 2018]
R_L = np.sqrt(sigma_L / s)
return R_L
def bootstrap(func, arglist, N, kwargs={}):
'''Computes error via bootstrapping on an arbitrary function. The
major restriction is that func is assumed to return a single, 1D,
Numpy array. Bootstrap will also resample ALL of the elements of
arglist. If you want to keep some inputs unchanged pass them as
keywords. The func can have an arbitrary number of arguments and
keyword arguments. If the output of func is a Ndarray of length N
then bootstrap returns two arrays of length N. The first is the
mean value over all bootstraps and the second is the stddev of the
same.
'''
if type(arglist) != list:
arglist = [arglist]
size = len(arglist[0])
resultarr = None
for i in range(N):
idx = np.random.randint(0,size,size)
bootargs = [i[idx] for i in arglist]
result = func(*bootargs,**kwargs)
try:
resultarr = np.vstack((resultarr,result))
except ValueError:
resultarr = result
print np.isnan(resultarr).sum()
return bn.nanmean(resultarr,axis=0),bn.nanstd(resultarr,axis=0)
def _calc_parameters(self, wfm_counts):
# loop over the waveforms
for i in np.arange(self._n):
try:
y = wfm_counts[i, :].flatten().astype(np.float32)
y -= bn.nanmean(y[0:11]) # Remove Noise
y[np.where(y < 0.0)[0]] = 0.0 # Set negative counts to zero
yp = np.nanmax(y) # Waveform peak value
if np.isnan(yp): # if the current wf is nan
# no ltpp can be computed
self._ltpp[i] = np.nan
else:
ypi = np.nanargmax(y) # Waveform peak index
# gates to compute the late tail:
# [ypi+50:ypi+70] if 0padding=2, [ypi+25:ypi+35] if 0padding=1
gate_start = ypi + self._pad*25
gate_stop = ypi + self._pad*35 + 1
if gate_start > self._n_range_bins or gate_stop > self._n_range_bins:
# not enough gates to compute the LTPP
self._ltpp[i] = np.nan
else:
self._ltpp[i] = np.mean(y[gate_start:gate_stop])/yp
except ValueError:
self._ltpp[i] = np.nan
def _align_planes(shifts):
"""Align planes to minimize shifts between them."""
mean_shift = nanmean(np.concatenate(shifts), axis=0)
# calculate alteration of shape (num_planes, dim)
alteration = (mean_shift - mean_shift[0]).astype(int)
for seq in shifts:
seq -= alteration
def create_nw(data, replace_nans):
nw = np.corrcoef(data)
np.fill_diagonal(nw, 1)
nw = rank(nw)
if replace_nans:
nw[np.isnan(nw)] = bottleneck.nanmean(nw)
return nw
def _align_planes(shifts):
"""Align planes to minimize shifts between them."""
mean_shift = nanmean(np.concatenate(shifts), axis=0)
# calculate alteration of shape (num_planes, dim)
alteration = (mean_shift - mean_shift[0]).astype(int)
for seq in shifts:
seq -= alteration
def _nanmean(array, axis=None):
"""Bottleneck nanmean function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanmean(array, axis=axis)
def demean(arr, axis=None):
"""
Subtract the mean along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to remove the mean. The default (None) is
to subtract the mean of the flattened array.
Returns
-------
y : ndarray
A copy with the mean along the specified axis removed.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 3])
>>> demean(arr)
array([ -1., NaN, 0., 1.])
"""
marr = bn.nanmean(arr, axis)
if (axis != 0) and (not axis is None) and (not np.isscalar(marr)):
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
marr = marr[ind]
return arr - marr
def _calc_parameters(self, wfm_counts):
for i in np.arange(self._n):
try:
y = wfm_counts[i, :].flatten().astype(np.float32)
y -= bn.nanmean(y[0:11]) # Remove Noise
y[np.where(y < 0.0)[0]] = 0.0 # Set negative counts to zero
yp = np.nanmax(y) # Waveform peak value
ypi = np.nanargmax(y) # Waveform peak index
if 3*self._pad < ypi < self._n_range_bins-4*self._pad:
self._peakiness_l[i] = yp/bn.nanmean(y[ypi-3*self._pad:ypi-1*self._pad+1])*3.0
self._peakiness_r[i] = yp/bn.nanmean(y[ypi+1*self._pad:ypi+3*self._pad+1])*3.0
self._peakiness[i] = yp/y.sum()*self._n_range_bins
except ValueError:
self._peakiness_l[i] = np.nan
self._peakiness_r[i] = np.nan
self._peakiness[i] = np.nan
def singleVar(content):
return dict(min=nanmin(content),
max=nanmax(content),
mean=nanmean(content),
median=nanmedian(content),
valid=numpy.sum(numpy.isfinite(content)) * 100.0 /
content.size)
def _norm_data(self,X):
m = bn.nanmean(X,0)
inds = np.where(np.isnan(X))
X[inds]=np.take(m,inds[1])
np.subtract(X,m,out=X)
v = X.var(0)
np.divide(X,np.sqrt(v),out=X)
return X
def get_allele_frequency(self,bed,args):
s = args.SNPs_to_read
af = np.zeros((bed.sid_count))
var = np.zeros((bed.sid_count))
if (args.from_bp is not None) and (args.to_bp is not None):
k0 = np.where((bed.pos[:,2]>=args.from_bp))[0][0]
k1 = np.where((bed.pos[:,2]<=args.to_bp))[0][-1]
X = bed[:,k0:k1].read().val
af[k0:k1] = bn.nanmean(X,0)/2.0
var[k0:k1] = self._fast_var(X,2*af[k0:k1])
else:
for i in xrange(int(np.ceil(bed.sid_count/s))):
X = bed[:,i*s:(i+1)*s].read().val
af[i*s:(i+1)*s] = bn.nanmean(X,0)/2.0
var[i*s:(i+1)*s] = self._fast_var(X,2*af[i*s:(i+1)*s])
af[var==0]=0
return af
def _phase3(self): """ Normal phase 3, but with tracking the boost changes. Double commented lines are new. """ # Update permanences self.p = np.clip(self.p + (self.c_pupdate * self.y[:, 0:1] * self.x[self.syn_map] - self.pdec * self.y[:, 0:1]), 0, 1) if self.disable_boost is False: # Update the boosting mechanisms if self.global_inhibition: min_dc = np.zeros(self.ncolumns) min_dc.fill(self.c_mdc * bn.nanmax(self.active_dc)) else: min_dc = self.c_mdc * bn.nanmax(self.neighbors * self.active_dc, 1) ## Save pre-overlap boost info boost = list(self.boost) # Update boost self._update_active_duty_cycle() self._update_boost(min_dc) self._update_overlap_duty_cycle() ## Write out overlap boost changes with open(os.path.join(self.out_path, 'overlap_boost.csv'), 'ab') as f: writer = csv.writer(f) writer.writerow([self.iter, bn.nanmean(boost != self.boost)]) # Boost permanences mask = self.overlap_dc < min_dc mask.resize(self.ncolumns, 1) self.p = np.clip(self.p + self.c_sboost * mask, 0, 1) ## Write out permanence boost info with open(os.path.join(self.out_path, 'permanence_boost.csv'), 'ab') \ as f: writer = csv.writer(f) writer.writerow([self.iter, bn.nanmean(mask)]) # Trim synapses if self.trim is not False: self.p[self.p < self.trim] = 0
def crmse(homog, orig, centered=True, crop=None):
"""Calculate the Centred Root-Mean-Square Error (CRMSE) between any pair of
homogenised and original data sets.
Parameters
----------
homog : array_like
Homogenised station data.
orig : array_like
Original station data.
centered : boolean, default True
Return RMSE or the centred RMSE.
crop : int, optional
Do not consider the first and the last `crop` years.
Returns
-------
ndarray
CRMSE.
Notes
-----
RMSE is commonly used in meteorology, to see how effectively a mathematical
model predicts the behaviour of the atmosphere.
The RMSD of an estimator :math:`{\hat{\\theta}}` with respect to an
estimated parameter :math:`{\\theta}` is defined as the square root of the
mean square error:
.. math:: \operatorname{RMSE}(\hat{\\theta}) = \sqrt{\operatorname{MSE}
(\hat{\\theta})} = \sqrt{\operatorname{E}((\hat{\\theta}-\\theta)^2)}.
"""
if crop is not None:
homog = homog[crop:-crop]
orig = orig[crop:-crop]
# access .values to support both dataframe's (monthly) and series (yearly)
if centered:
homog -= bn.nanmean(homog.values)
orig -= bn.nanmean(orig.values)
diff = np.sqrt(bn.nanmean(np.power((homog - orig).values, 2)))
return diff
def replace_column_nans_by_mean(matrix):
# Set the value of gaps/dashes in each column to be the average of the other values in the column.
nan_indices = np.where(np.isnan(matrix))
# Note: bn.nanmean() instead of np.nanmean() because it is a lot(!) faster.
column_nanmeans = bn.nanmean(matrix, axis=0)
# For each column, assign the NaNs in that column the column's mean.
# See http://stackoverflow.com/a/18689440 for an explanation of the following line.
matrix[nan_indices] = np.take(column_nanmeans, nan_indices[1])
def paa(self, data_slice):
if self.word_length > len(data_slice):
data = data_slice.values.tolist()
for i in range(len(data), self.word_length):
data.append(bn.nanmean(data[:i-1]))
if(math.isnan(data[i])):
data[i] = 0
return np.array(data)
if self.word_length == len(data_slice):
return data_slice
data = np.array_split(data_slice, self.word_length)
result = []
for section in data:
if math.isnan(bn.nanmean(section)):
result.append(0)
else:
result.append(bn.nanmean(section))
result = np.array(result)
return result
def mean(self, axis=None):
# TODO: support multiple axes at the same time, via recursion
if axis is None:
return bn.nanmean(self.data)
# TODO: support specifying axes as indexed number instead of
# name
if axis not in list(self.coordinates.keys()):
raise ValueError("You asked me to calculate the mean along axis "
"%s, but I don't know anything about this "
"coordinate dimension", axis)
# TODO: replace OrderedDict with CoordinateSet
newcoords = OrderedDict()
# TODO: add keys() as property to CoordinateSet
for dim in list(self.coordinates.keys()):
if dim != axis:
newcoords[dim] = self.coordinates[dim]
newdata = bn.nanmean(self.data,
axis=list(self.coordinates.keys()).index(axis))
return gridded_array(newdata, newcoords, self.title)
def process(self, window, output_length):
data = np.array_split(window, output_length)
result = []
for segment in data:
mean = bn.nanmean(segment)
if math.isnan(mean):
result.append(0)
else:
result.append(mean)
result = np.array(result)
return result
def get_median_level(data, radii, ri, ro):
selected = (radii > ri) & (radii < ro) #& (numpy.isfinite(data))
pixelcount = numpy.sum(selected)
if (pixelcount > 0):
#cutout = data[selected]
#median = numpy.median(cutout[0:5001])
cutout = numpy.array(data[selected], dtype=numpy.float32)
median = bottleneck.nanmean(cutout)
else:
median = numpy.NaN
return median, pixelcount
def make_ratios():
'''read in the data'''
raw_data = pyfits.open('../reduced/tiESO_z0_MgI.fits')[0].data
old_data = pyfits.open('to45.fits')[0].data
MB_data = pyfits.open('tn45.fits')[0].data
# raw_data = pyfits.open('tsky_sub.fits')[0].data
# old_data = pyfits.open('tosky_sub.fits')[0].data
# MB_data = pyfits.open('tnsky_sub.fits')[0].data
fig = plt.figure()
ax = fig.add_subplot(111)
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
for data, name in zip([raw_data,old_data,MB_data],
['No flat','Nightly flat','Reconstructed flat']):
x, line1, line2 = get_lines(data,20)
ratio = line1/line2
zeroed_ratio = ratio/ bn.nanmean(ratio[30:])
ax.plot(x,ratio,label=name)
ax1.plot(x,zeroed_ratio,label=name)
ax.set_xlabel('$\mathrm{Slit\ position\ [px]}$')
ax.set_ylabel('$f(4861\mathrm{nm})/f(5198\mathrm{nm})$')
ax.legend(loc=0)
# ax.set_ylim(0,2.5)
# ax.set_title(datetime.now().isoformat(' '))
ax1.set_xlabel('$\mathrm{Slit\ position\ [px]}$')
ax1.set_ylabel('$f(4861\mathrm{nm})/f(5198\mathrm{nm})$')
ax1.legend(loc=0,numpoints=1)
# ax1.set_ylim(0.5,1.5)
# ax1.set_title('Sky-subtracted object')
# ax1.text(250,1.4,'Plot 2',fontsize=17)
ax1.set_ylim(0.9,1.1)
ax1.set_title('Object frame only')
ax1.text(250,1.07,'Plot 1',fontsize=17)
# fig.show()
fig1.show()
return
def _method_1(data, num_pcs=None):
"""Compute OPCA when num_observations > num_dimensions."""
data = np.nan_to_num(data - nanmean(data, axis=0))
T = data.shape[0]
corr_offset = np.dot(data[1:].T, data[:-1])
corr_offset += corr_offset.T
if num_pcs is None:
eivals, eivects = eigh(corr_offset)
else:
eivals, eivects = eigsh(corr_offset, num_pcs, which='LA')
eivals = np.real(eivals)
eivects = np.real(eivects)
idx = np.argsort(-eivals) # sort the eigenvectors and eigenvalues
eivals = eivals[idx] / (2. * (T - 1))
eivects = eivects[:, idx]
return eivals, eivects, np.dot(data, eivects)
def fit(self, X, y, mask=None):
"""Fit Gaussian Naive Bayes according to X, y
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
mask : array-like, shape = [n_samples, n_features]
Binary, 1 at unobserved features.
Returns
-------
self : object
Returns self.
"""
X, y = check_arrays(X, y, sparse_format='dense')
n_samples, n_features = X.shape
if n_samples != y.shape[0]:
raise ValueError("X and y have incompatible shapes")
if mask is not None:
mask = array2d(mask)
X = X.copy()
X[mask] = np.nan
self.classes_ = unique_y = np.unique(y)
n_classes = unique_y.shape[0]
self.theta_ = np.zeros((n_classes, n_features))
self.sigma_ = np.zeros((n_classes, n_features))
self.class_prior_ = np.zeros(n_classes)
self._n_ij = []
epsilon = 1e-9
for i, y_i in enumerate(unique_y):
self.theta_[i, :] = bn.nanmean(X[y == y_i, :], axis=0)
self.sigma_[i, :] = bn.nanvar(X[y == y_i, :], axis=0) + epsilon
self.class_prior_[i] = np.float(np.sum(y == y_i)) / n_samples
self._n_ij.append(-0.5 * np.sum(np.log(np.pi * self.sigma_[i, :])))
self._logprior = np.log(self.class_prior_)
return self
def do_a_line(moment_list,N,line_output,monte_output):
x, l = make_line(moment_list)
SNRs = np.linspace(5,100,50)
results = np.empty((SNRs.size,4,2))
lp = PDF(line_output)
for i, SNR in enumerate(SNRs):
sn_res = np.empty((N,4))
for j in range(N):
noise = get_noise(x, l,SNR)
ln = l + noise
cdf = np.cumsum(ln/np.sum(ln))
low, high = np.interp([0.01,0.99],cdf,x)
idx = np.where((x > low) & (x <= high))
sn_res[j] = ADE.ADE_moments(x[idx],ln[idx])
measured_vals = bn.nanmean(sn_res,axis=0)
measured_stds = bn.nanstd(sn_res,axis=0)
# print sn_res
# print measured_stds
# raw_input('')
results[i,:,0] = measured_vals
results[i,:,1] = measured_stds
ax = plt.figure().add_subplot(111)
ax.set_xlabel('Velocity [km/s]')
ax.set_ylabel('Flux')
ax.set_title('SNR = {:5.2f}'.format(SNR))
ax.plot(x,ln)
lp.savefig(ax.figure)
lp.close()
mp = PDF(monte_output)
plots = plot_results(SNRs, results, moment_list)
for i, plot in enumerate(plots):
if i == 2:
plot.set_ylim(-2,2)
if i == 3:
plot.set_ylim(-2,5)
mp.savefig(plot.figure)
mp.close()
plt.close('all')
return SNRs, results
def _method_2(data, num_pcs=None):
"""Compute OPCA when num_observations <= num_dimensions."""
data = np.nan_to_num(data - nanmean(data, axis=0))
T = data.shape[0]
tmp = np.dot(data, data.T)
corr_offset = np.zeros(tmp.shape)
corr_offset[1:] = tmp[:-1]
corr_offset[:-1] += tmp[1:]
if num_pcs is None:
eivals, eivects = eig(corr_offset)
else:
eivals, eivects = eigs(corr_offset, num_pcs, which='LR')
eivals = np.real(eivals)
eivects = np.real(eivects)
idx = np.argsort(-eivals) # sort the eigenvectors and eigenvalues
eivals = eivals[idx] / (2. * (T - 1))
eivects = eivects[:, idx]
transformed_eivects = np.dot(data.T, eivects)
for i in range(transformed_eivects.shape[1]): # normalize the eigenvectors
transformed_eivects[:, i] /= np.linalg.norm(transformed_eivects[:, i])
return eivals, transformed_eivects, np.dot(data, transformed_eivects)
def height_plot_across_folders(folder_list, inputsuffix='allz2.dat',
label='Mean Light Weighted Age [Gyr]',
col=6, errcol=None, lowhigh=False,
order=5, ylims=None, bigpoints=False,
binz=True, combine_all=False, plot_std=False,
exclude=[[],[],[],[],[],[]]):
axlist = []
plist = [6,3,4,2,1,5]
#color_list = ['blue','turquoise','chartreuse','yellow','tomato','red']
color_list = ['blue','seagreen','darkorange','crimson','dimgray','mediumorchid','lightblue']
style_list = ['-','-','-','-','-','-','-']
if not isinstance(col,list):
col = [col] * len(folder_list)
for i in range(6):
pointing = plist[i]
ax = plt.figure().add_subplot(111)
ax.set_xlabel('|Height [kpc]|')
ax.set_ylabel(label)
ax.set_title('{}\nP{}'.format(time.asctime(),pointing))
for f, folder in enumerate(folder_list):
color = color_list[f]
style = style_list[f]
dat = glob('{}/*P{}*{}'.format(folder, pointing, inputsuffix))[0]
print dat
loc = glob('{}/*P{}*locations.dat'.format(folder, pointing))[0]
print loc
print 'Excluding: ', exclude[pointing-1]
if errcol == None:
td = np.loadtxt(dat, usecols=(col[f],), unpack=True)
else:
if lowhigh:
td, low, high = np.loadtxt(dat, usecols=(col[f],errcol,errcol+1), unpack=True)
te = np.vstack((low,high))
else:
td, te = np.loadtxt(dat, usecols=(col[f],errcol), unpack=True)
r, tz = np.loadtxt(loc, usecols=(4,5), unpack=True)
exarr = np.array(exclude[pointing-1])-1 #becuase aps are 1-indexed
td = np.delete(td,exarr)
r = np.delete(r,exarr)
tz = np.delete(tz,exarr)
if errcol != None:
if lowhigh:
te = np.delete(te,exarr,axis=1)
else:
te = np.delete(te,exarr)
alpha=1.0
if combine_all and f == 0:
bigD = np.zeros(td.size)
alpha=0.3
if binz:
z = np.array([])
d = np.array([])
e = np.array([])
while tz.size > 0:
zi = tz[0]
idx = np.where(np.abs(tz - zi) < 0.05)
d = np.r_[d,np.mean(td[idx])]
e = np.r_[e,np.std(td[idx])]
z = np.r_[z,np.abs(zi)]
tz = np.delete(tz, idx)
td = np.delete(td, idx)
else:
z = tz
d = td
if errcol == None:
e = np.zeros(tz.size)
else:
e = te
if combine_all:
bigD = np.vstack((bigD,d))
bigz = z
gidx = d == d
d = d[gidx]
z = z[gidx]
if lowhigh:
e = e[:,gidx]
else:
e = e[gidx]
sidx = np.argsort(z)
dp = np.r_[d[sidx][order::-1],d[sidx]]
zp = np.r_[z[sidx][order::-1],z[sidx]]
mean = bn.move_mean(dp,order)[order+1:]
std = bn.move_std(dp,order)[order+1:]
spl = spi.UnivariateSpline(z[sidx],d[sidx])
mean = spl(z[sidx])
# mean = np.convolve(d[sidx],np.ones(order)/order,'same')
# std = np.sqrt(np.convolve((d - mean)**2,np.ones(order)/order,'same'))
# ax.plot(z[sidx],mean,color=color, ls=style, label=folder, alpha=alpha)
# ax.fill_between(z[sidx],mean-std,mean+std, alpha=0.1, color=color)
# print d.shape, np.sum(e,axis=0).shape
# d = d/np.sum(e,axis=0)
# e = np.diff(e,axis=0)[0]
# print e.shape
ax.errorbar(z, d, yerr=e, fmt='.', color=color,alpha=alpha,capsize=0, label=folder)
ax.set_xlim(-0.1,2.6)
if ylims is not None:
ax.set_ylim(*ylims)
ax.legend(loc=0,numpoints=1)
if combine_all:
sidx = np.argsort(bigz)
bigD = bigD[1:]
bigMean = bn.nanmean(bigD,axis=0)
bigStd = bn.nanstd(bigD,axis=0)
bigspl = spi.UnivariateSpline(bigz[sidx],bigMean[sidx])
bigFit = bigspl(bigz[sidx])
ax.plot(bigz[sidx], bigFit, 'k-', lw=2)
ax.errorbar(bigz, bigMean, yerr=bigStd, fmt='.', color='k',capsize=0)
axlist.append(ax)
if combine_all and plot_std:
ax2 = plt.figure().add_subplot(111)
ax2.set_xlabel('|Height [kpc]|')
ax2.set_ylabel('$\delta$'+label)
ax2.set_title(ax.get_title())
ax2.plot(bigz, bigStd, 'k')
axlist.append(ax2)
return axlist
fs = wav_params[2]
except IOError, e:
print "Could not read file: %s" % e
sys.exit(-1)
# cast to mono
if len(data.shape) == 2:
data = data.sum(axis=0) # should this be .mean()?
window_frames= int(fs * window_seconds)
silence_frames = int(fs * silence_seconds)
print "Analyzing"
move_std = move_std(data, window=window_frames/2)
mean_std = nanmean(move_std)
print "move_std shape: ", move_std.shape
print "len(data): ", len(data)
widgets = ["Creating file", Bar(), ETA()]
pbar = ProgressBar(widgets=widgets, maxval=len(data)).start()
new_data = []
silence_count = 0
for i, d in pbar(enumerate(data)):
new_data.append(d)
if move_std[i] is not np.nan and move_std[i] < mean_std:
silence_count += 1
else:
if window_frames < silence_count < silence_frames: