function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def logspace(xmin,xmax,N):
return np.exp(np.linspace(np.log(xmin), np.log(xmax), N)) | SpaceKatt/CSPLN | [
1,
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] |
def window_hanning(x):
"return x times the hanning window of len(x)"
return np.hanning(len(x))*x | SpaceKatt/CSPLN | [
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] |
def detrend(x, key=None):
if key is None or key=='constant':
return detrend_mean(x)
elif key=='linear':
return detrend_linear(x) | SpaceKatt/CSPLN | [
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] |
def detrend_mean(x):
"Return x minus the mean(x)"
return x - x.mean() | SpaceKatt/CSPLN | [
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] |
def detrend_linear(y):
"Return y minus best fit line; 'linear' detrending "
# This is faster than an algorithm based on linalg.lstsq.
x = np.arange(len(y), dtype=np.float_)
C = np.cov(x, y, bias=1)
b = C[0,1]/C[0,0]
a = y.mean() - b*x.mean()
return y - (b*x + a) | SpaceKatt/CSPLN | [
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] |
def _spectral_helper(x, y, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0, pad_to=None, sides='default',
scale_by_freq=None):
#The checks for if y is x are so that we can use the same function to
#implement the core of psd(), csd(), and spectrogram() without doing
#e... | SpaceKatt/CSPLN | [
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] |
def psd(x, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
noverlap=0, pad_to=None, sides='default', scale_by_freq=None):
"""
The power spectral density by Welch's average periodogram method.
The vector *x* is divided into *NFFT* length blocks. Each block
is detrended by the functi... | SpaceKatt/CSPLN | [
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] |
def csd(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
noverlap=0, pad_to=None, sides='default', scale_by_freq=None):
"""
The cross power spectral density by Welch's average periodogram
method. The vectors *x* and *y* are divided into *NFFT* length
blocks. Each block is det... | SpaceKatt/CSPLN | [
1,
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] |
def specgram(x, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
noverlap=128, pad_to=None, sides='default', scale_by_freq=None):
"""
Compute a spectrogram of data in *x*. Data are split into *NFFT*
length segments and the PSD of each section is computed. The
windowing function *wi... | SpaceKatt/CSPLN | [
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] |
def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
noverlap=0, pad_to=None, sides='default', scale_by_freq=None):
"""
The coherence between *x* and *y*. Coherence is the normalized
cross spectral density:
.. math::
C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}}
... | SpaceKatt/CSPLN | [
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] |
def cohere_pairs( X, ij, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0,
preferSpeedOverMemory=True,
progressCallback=donothing_callback,
returnPxx=False):
u"""
Call signature::
Cxy, Phase, freqs = cohere_pa... | SpaceKatt/CSPLN | [
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] |
def normpdf(x, *args):
"Return the normal pdf evaluated at *x*; args provides *mu*, *sigma*"
mu, sigma = args
return 1./(np.sqrt(2*np.pi)*sigma)*np.exp(-0.5 * (1./sigma*(x - mu))**2) | SpaceKatt/CSPLN | [
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] |
def find(condition):
"Return the indices where ravel(condition) is true"
res, = np.nonzero(np.ravel(condition))
return res | SpaceKatt/CSPLN | [
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] |
def longest_ones(x):
'''alias for longest_contiguous_ones'''
return longest_contiguous_ones(x) | SpaceKatt/CSPLN | [
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] |
def __init__(self, a):
"""
compute the SVD of a and store data for PCA. Use project to
project the data onto a reduced set of dimensions
Inputs:
*a*: a numobservations x numdims array
Attrs:
*a* a centered unit sigma version of input a
*numrows... | SpaceKatt/CSPLN | [
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] |
def center(self, x):
'center the data using the mean and sigma from training set a'
return (x - self.mu)/self.sigma | SpaceKatt/CSPLN | [
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] |
def _get_colinear():
c0 = np.array([
0.19294738, 0.6202667 , 0.45962655, 0.07608613, 0.135818 ,
0.83580842, 0.07218851, 0.48318321, 0.84472463, 0.18348462,
0.81585306, 0.96923926, 0.12835919, 0.35075355, 0.15807861,
0.837437 , 0.10824303, 0.1723387... | SpaceKatt/CSPLN | [
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] |
def _interpolate(a, b, fraction):
"""Returns the point at the given fraction between a and b, where
'fraction' must be between 0 and 1.
"""
return a + (b - a)*fraction | SpaceKatt/CSPLN | [
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] |
def prctile_rank(x, p):
"""
Return the rank for each element in *x*, return the rank
0..len(*p*). Eg if *p* = (25, 50, 75), the return value will be a
len(*x*) array with values in [0,1,2,3] where 0 indicates the
value is less than the 25th percentile, 1 indicates the value is
>= the 25th and <... | SpaceKatt/CSPLN | [
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] |
def rk4(derivs, y0, t):
"""
Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta.
This is a toy implementation which may be useful if you find
yourself stranded on a system w/o scipy. Otherwise use
:func:`scipy.integrate`.
*y0*
initial state vector
*t*
sample tim... | SpaceKatt/CSPLN | [
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] |
def get_xyz_where(Z, Cond):
"""
*Z* and *Cond* are *M* x *N* matrices. *Z* are data and *Cond* is
a boolean matrix where some condition is satisfied. Return value
is (*x*, *y*, *z*) where *x* and *y* are the indices into *Z* and
*z* are the values of *Z* at those indices. *x*, *y*, and *z* are
... | SpaceKatt/CSPLN | [
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] |
def dist(x,y):
"""
Return the distance between two points.
"""
d = x-y
return np.sqrt(np.dot(d,d)) | SpaceKatt/CSPLN | [
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] |
def segments_intersect(s1, s2):
"""
Return *True* if *s1* and *s2* intersect.
*s1* and *s2* are defined as::
s1: (x1, y1), (x2, y2)
s2: (x3, y3), (x4, y4)
"""
(x1, y1), (x2, y2) = s1
(x3, y3), (x4, y4) = s2
den = ((y4-y3) * (x2-x1)) - ((x4-x3)*(y2-y1))
n1 = ((x4-x3) * (y1-... | SpaceKatt/CSPLN | [
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] |
def liaupunov(x, fprime):
"""
*x* is a very long trajectory from a map, and *fprime* returns the
derivative of *x*.
This function will be removed from matplotlib.
Returns :
.. math::
\lambda = \\frac{1}{n}\\sum \\ln|f^'(x_i)|
.. seealso::
Lyapunov Exponent
Sec... | SpaceKatt/CSPLN | [
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] |
def __init__(self, nmax):
"""
Buffer up to *nmax* points.
"""
self._xa = np.zeros((nmax,), np.float_)
self._ya = np.zeros((nmax,), np.float_)
self._xs = np.zeros((nmax,), np.float_)
self._ys = np.zeros((nmax,), np.float_)
self._ind = 0
self._nmax =... | SpaceKatt/CSPLN | [
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] |
def add(self, x, y):
"""
Add scalar *x* and *y* to the queue.
"""
if self.dataLim is not None:
xy = np.asarray([(x,y),])
self.dataLim.update_from_data_xy(xy, None)
ind = self._ind % self._nmax
#print 'adding to fifo:', ind, x, y
self._xs[i... | SpaceKatt/CSPLN | [
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] |
def asarrays(self):
"""
Return *x* and *y* as arrays; their length will be the len of
data added or *nmax*.
"""
if self._ind<self._nmax:
return self._xs[:self._ind], self._ys[:self._ind]
ind = self._ind % self._nmax
self._xa[:self._nmax-ind] = self._x... | SpaceKatt/CSPLN | [
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] |
def movavg(x,n):
"""
Compute the len(*n*) moving average of *x*.
"""
w = np.empty((n,), dtype=np.float_)
w[:] = 1.0/n
return np.convolve(x, w, mode='valid') | SpaceKatt/CSPLN | [
1,
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] |
def load(fname,comments='#',delimiter=None, converters=None,skiprows=0,
usecols=None, unpack=False, dtype=np.float_):
"""
Load ASCII data from *fname* into an array and return the array.
Deprecated: use numpy.loadtxt.
The data must be regular, same number of values in every row
*fname* c... | SpaceKatt/CSPLN | [
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] |
def exp_safe(x):
"""
Compute exponentials which safely underflow to zero.
Slow, but convenient to use. Note that numpy provides proper
floating point exception handling with access to the underlying
hardware.
"""
if type(x) is np.ndarray:
return exp(np.clip(x,exp_safe_MIN,exp_safe_... | SpaceKatt/CSPLN | [
1,
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] |
def rms_flat(a):
"""
Return the root mean square of all the elements of *a*, flattened out.
"""
return np.sqrt(np.mean(np.absolute(a)**2)) | SpaceKatt/CSPLN | [
1,
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] |
def l2norm(a):
"""
Return the *l2* norm of *a*, flattened out.
Implemented as a separate function (not a call to :func:`norm` for speed).
"""
return np.sqrt(np.sum(np.absolute(a)**2)) | SpaceKatt/CSPLN | [
1,
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] |
def frange(xini,xfin=None,delta=None,**kw):
"""
frange([start,] stop[, step, keywords]) -> array of floats
Return a numpy ndarray containing a progression of floats. Similar to
:func:`numpy.arange`, but defaults to a closed interval.
``frange(x0, x1)`` returns ``[x0, x0+1, x0+2, ..., x1]``; *start... | SpaceKatt/CSPLN | [
1,
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] |
def identity(n, rank=2, dtype='l', typecode=None):
"""
Returns the identity matrix of shape (*n*, *n*, ..., *n*) (rank *r*).
For ranks higher than 2, this object is simply a multi-index Kronecker
delta::
/ 1 if i0=i1=...=iR,
id[i0,i1,...,iR] = -|
... | SpaceKatt/CSPLN | [
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] |
def binary_repr(number, max_length = 1025):
"""
Return the binary representation of the input *number* as a
string.
This is more efficient than using :func:`base_repr` with base 2.
Increase the value of max_length for very large numbers. Note that
on 32-bit machines, 2**1023 is the largest int... | SpaceKatt/CSPLN | [
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] |
def ispower2(n):
"""
Returns the log base 2 of *n* if *n* is a power of 2, zero otherwise.
Note the potential ambiguity if *n* == 1: 2**0 == 1, interpret accordingly.
"""
bin_n = binary_repr(n)[1:]
if '1' in bin_n:
return 0
else:
return len(bin_n) | SpaceKatt/CSPLN | [
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] |
def safe_isnan(x):
':func:`numpy.isnan` for arbitrary types'
if cbook.is_string_like(x):
return False
try: b = np.isnan(x)
except NotImplementedError: return False
except TypeError: return False
else: return b | SpaceKatt/CSPLN | [
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] |
def rec_append_fields(rec, names, arrs, dtypes=None):
"""
Return a new record array with field names populated with data
from arrays in *arrs*. If appending a single field, then *names*,
*arrs* and *dtypes* do not have to be lists. They can just be the
values themselves.
"""
if (not cbook.i... | SpaceKatt/CSPLN | [
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] |
def rec_keep_fields(rec, names):
"""
Return a new numpy record array with only fields listed in names
"""
if cbook.is_string_like(names):
names = names.split(',')
arrays = []
for name in names:
arrays.append(rec[name])
return np.rec.fromarrays(arrays, names=names) | SpaceKatt/CSPLN | [
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] |
def rec_summarize(r, summaryfuncs):
"""
*r* is a numpy record array
*summaryfuncs* is a list of (*attr*, *func*, *outname*) tuples
which will apply *func* to the the array *r*[attr] and assign the
output to a new attribute name *outname*. The returned record
array is identical to *r*, with ext... | SpaceKatt/CSPLN | [
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] |
def makekey(row):
return tuple([row[name] for name in key]) | SpaceKatt/CSPLN | [
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] |
def key_desc(name):
'if name is a string key, use the larger size of r1 or r2 before merging'
dt1 = r1.dtype[name]
if dt1.type != np.string_:
return (name, dt1.descr[0][1])
dt2 = r1.dtype[name]
assert dt2==dt1
if dt1.num>dt2.num:
return (name, dt1... | SpaceKatt/CSPLN | [
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] |
def mapped_r1field(name):
"""
The column name in *newrec* that corresponds to the column in *r1*.
"""
if name in key or name not in r2.dtype.names: return name
else: return name + r1postfix | SpaceKatt/CSPLN | [
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] |
def recs_join(key, name, recs, jointype='outer', missing=0., postfixes=None):
"""
Join a sequence of record arrays on single column key.
This function only joins a single column of the multiple record arrays
*key*
is the column name that acts as a key
*name*
is the name of the column ... | SpaceKatt/CSPLN | [
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] |
def __init__(self, fh):
self.fh = fh | SpaceKatt/CSPLN | [
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] |
def seek(self, arg):
self.fh.seek(arg) | SpaceKatt/CSPLN | [
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] |
def next(self):
return self.fix(self.fh.next()) | SpaceKatt/CSPLN | [
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] |
def process_skiprows(reader):
if skiprows:
for i, row in enumerate(reader):
if i>=(skiprows-1): break
return fh, reader | SpaceKatt/CSPLN | [
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] |
def ismissing(name, val):
"Should the value val in column name be masked?"
if val == missing or val == missingd.get(name) or val == '':
return True
else:
return False | SpaceKatt/CSPLN | [
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] |
def newfunc(name, val):
if ismissing(name, val):
return default
else:
return func(val) | SpaceKatt/CSPLN | [
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] |
def mybool(x):
if x=='True': return True
elif x=='False': return False
else: raise ValueError('invalid bool') | SpaceKatt/CSPLN | [
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] |
def mydate(x):
# try and return a date object
d = dateparser(x)
if d.hour>0 or d.minute>0 or d.second>0:
raise ValueError('not a date')
return d.date() | SpaceKatt/CSPLN | [
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] |
def get_func(name, item, func):
# promote functions in this order
funcmap = {mybool:myint,myint:myfloat, myfloat:mydate, mydate:mydateparser, mydateparser:mystr}
try: func(name, item)
except:
if func==mystr:
raise ValueError('Could not find a working conversio... | SpaceKatt/CSPLN | [
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] |
def get_converters(reader):
converters = None
for i, row in enumerate(reader):
if i==0:
converters = [mybool]*len(row)
if checkrows and i>checkrows:
break
#print i, len(names), len(row)
#print 'converters', zip(converters, ... | SpaceKatt/CSPLN | [
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] |
def tostr(self, x):
return self.toval(x) | SpaceKatt/CSPLN | [
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] |
def fromstr(self, s):
return s | SpaceKatt/CSPLN | [
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] |
def tostr(self, x):
val = repr(x)
return val[1:-1] | SpaceKatt/CSPLN | [
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def __init__(self, fmt):
self.fmt = fmt | SpaceKatt/CSPLN | [
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] |
def __init__(self, precision=4, scale=1.):
FormatFormatStr.__init__(self, '%%1.%df'%precision)
self.precision = precision
self.scale = scale | SpaceKatt/CSPLN | [
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] |
def toval(self, x):
if x is not None:
x = x * self.scale
return x | SpaceKatt/CSPLN | [
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] |
def tostr(self, x):
return '%d'%int(x) | SpaceKatt/CSPLN | [
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] |
def fromstr(self, s):
return int(s) | SpaceKatt/CSPLN | [
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] |
def toval(self, x):
return str(x) | SpaceKatt/CSPLN | [
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] |
def __init__(self, precision=4):
FormatFloat.__init__(self, precision, scale=100.) | SpaceKatt/CSPLN | [
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] |
def __init__(self, precision=4):
FormatFloat.__init__(self, precision, scale=1e-3) | SpaceKatt/CSPLN | [
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] |
def __init__(self, precision=4):
FormatFloat.__init__(self, precision, scale=1e-6) | SpaceKatt/CSPLN | [
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] |
def __init__(self, fmt):
self.fmt = fmt | SpaceKatt/CSPLN | [
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] |
def toval(self, x):
if x is None: return 'None'
return x.strftime(self.fmt) | SpaceKatt/CSPLN | [
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] |
def __init__(self, fmt='%Y-%m-%d %H:%M:%S'):
FormatDate.__init__(self, fmt) | SpaceKatt/CSPLN | [
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def get_formatd(r, formatd=None):
'build a formatd guaranteed to have a key for every dtype name'
if formatd is None:
formatd = dict()
for i, name in enumerate(r.dtype.names):
dt = r.dtype[name]
format = formatd.get(name)
if format is None:
format = defaultformat... | SpaceKatt/CSPLN | [
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] |
def rec2txt(r, header=None, padding=3, precision=3, fields=None):
"""
Returns a textual representation of a record array.
*r*: numpy recarray
*header*: list of column headers
*padding*: space between each column
*precision*: number of decimal places to use for floats.
Set to an integ... | SpaceKatt/CSPLN | [
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] |
def with_mask(func):
def newfunc(val, mask, mval):
if mask:
return mval
else:
return func(val)
return newfunc | SpaceKatt/CSPLN | [
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] |
def griddata(x,y,z,xi,yi,interp='nn'):
"""
``zi = griddata(x,y,z,xi,yi)`` fits a surface of the form *z* =
*f*(*x*, *y*) to the data in the (usually) nonuniformly spaced
vectors (*x*, *y*, *z*). :func:`griddata` interpolates this
surface at the points specified by (*xi*, *yi*) to produce
*zi*. ... | SpaceKatt/CSPLN | [
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] |
def less_simple_linear_interpolation( x, y, xi, extrap=False ):
"""
This function provides simple (but somewhat less so than
:func:`cbook.simple_linear_interpolation`) linear interpolation.
:func:`simple_linear_interpolation` will give a list of point
between a start and an end, while this does true... | SpaceKatt/CSPLN | [
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] |
def stineman_interp(xi,x,y,yp=None):
"""
Given data vectors *x* and *y*, the slope vector *yp* and a new
abscissa vector *xi*, the function :func:`stineman_interp` uses
Stineman interpolation to calculate a vector *yi* corresponding to
*xi*.
Here's an example that generates a coarse sine curve,... | SpaceKatt/CSPLN | [
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def inside_poly(points, verts):
"""
*points* is a sequence of *x*, *y* points.
*verts* is a sequence of *x*, *y* vertices of a polygon.
Return value is a sequence of indices into points for the points
that are inside the polygon.
"""
res, = np.nonzero(nxutils.points_inside_poly(points, ver... | SpaceKatt/CSPLN | [
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def poly_between(x, ylower, yupper):
"""
Given a sequence of *x*, *ylower* and *yupper*, return the polygon
that fills the regions between them. *ylower* or *yupper* can be
scalar or iterable. If they are iterable, they must be equal in
length to *x*.
Return value is *x*, *y* arrays for use w... | SpaceKatt/CSPLN | [
1,
1,
1,
1,
1441155989
] |
def contiguous_regions(mask):
"""
return a list of (ind0, ind1) such that mask[ind0:ind1].all() is
True and we cover all such regions
TODO: this is a pure python implementation which probably has a much faster numpy impl
"""
in_region = None
boundaries = []
for i, val in enumerate(mask... | SpaceKatt/CSPLN | [
1,
1,
1,
1,
1441155989
] |
def cross_from_above(x, threshold):
"""
return the indices into *x* where *x* crosses some threshold from
below, eg the i's where::
x[i-1]>threshold and x[i]<=threshold
.. seealso::
:func:`cross_from_below` and :func:`contiguous_regions`
"""
x = np.asarray(x)
ind = np.nonze... | SpaceKatt/CSPLN | [
1,
1,
1,
1,
1441155989
] |
def vector_lengths( X, P=2., axis=None ):
"""
Finds the length of a set of vectors in *n* dimensions. This is
like the :func:`numpy.norm` function for vectors, but has the ability to
work over a particular axis of the supplied array or matrix.
Computes ``(sum((x_i)^P))^(1/P)`` for each ``{x_i}`` b... | SpaceKatt/CSPLN | [
1,
1,
1,
1,
1441155989
] |
def path_length(X):
"""
Computes the distance travelled along a polygonal curve in *N* dimensions.
Where *X* is an *M* x *N* array or matrix. Returns an array of
length *M* consisting of the distance along the curve at each point
(i.e., the rows of *X*).
"""
X = distances_along_curve(X)
... | SpaceKatt/CSPLN | [
1,
1,
1,
1,
1441155989
] |
def map_type(self, obj):
# TODO: Replace all str with unicode when done in property.default attribute
# TODO: Fix ToGuessProp as it may be a list.
if isinstance(obj, ListProp):
return list
if isinstance(obj, StringProp):
return str
if isinstance(obj, Un... | naparuba/shinken | [
1129,
344,
1129,
221,
1290510176
] |
def add(self, b):
if isinstance(b, Brok):
self.broks[b.id] = b
return
if isinstance(b, ExternalCommand):
self.sched.run_external_command(b.cmd_line) | naparuba/shinken | [
1129,
344,
1129,
221,
1290510176
] |
def get(self):
self.response.out.write("Test 1:" +self.test1() +"<br>")
self.response.out.write("Test 2:" + self.test2() +"<br>")
self.response.out.write("Test 3:" + self.test3() +"<br>")
self.response.out.write("Test 4:" + self.test4() +"<br>") | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def test1(self):
key = "test@test.com"
ent_type = "Accounts"
trophy_case_widget = TrophyCase(key_name=key)
points_widget = Points(key_name=key)
rank_widget = Rank(key_name=key)
newacc = Accounts(key_name=key,
password="aaa",
email=key,
... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def test2(self):
account_key = "raj"
trophy_case_widget = TrophyCase(key_name=account_key)
points_widget = Points(key_name=account_key)
rank_widget = Rank(key_name=account_key)
newacc = Accounts(key_name=account_key,
password="aaa",
email="a@a.a",
... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def test3(self):
account_key = "a@a.a"
trophy_case_widget = TrophyCase(key_name=account_key)
points_widget = Points(key_name=account_key)
rank_widget = Rank(key_name=account_key)
newacc = Accounts(key_name=account_key,
password="aaa",
email="a@a.a",
... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def test4(self):
account_key = "a@a.a"
trophy_case_widget = TrophyCase(key_name=account_key)
points_widget = Points(key_name=account_key)
rank_widget = Rank(key_name=account_key)
newacc = Accounts(key_name=account_key,
password="aaa",
email="a@a.a",
... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
""" Add to the db, get, and delete """ | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
from serverside.tools import encryption
"""Do some simple encryption and show results """
mystr = "hello, world"
self.response.out.write("encrypt string: " + mystr + "<br/>")
mystr_enc = encryption.des_encrypt_str("hello, world")
self.response.out.write("encrypted: " + mystr_enc +... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
print "OS: " + os.environ["SERVER_SOFTWARE"]
self.response.out.write("OS server software: " + os.environ["SERVER_SOFTWARE"]) | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def post(self):
pass | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
self.response.out.write("Creating session and setting cookie") | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
self.response.out.write("<br/>If you reached here you are logged in!") | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
self.response.out.write("terminating the follow session:")
sess = Session().get_current_session(self)
if(sess == None):
self.response.out.write("<br/>You are not logged in!!")
else:
self.response.out.write("<br/>You are logged in as:")
email = sess.get_email()
self... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
self.response.out.write("You should be able to see this page, logged in or not...")
sess = Session().get_current_session(self)
if(sess == None):
self.response.out.write("<br/>You are not logged in!!")
else:
self.response.out.write("<br/>You are logged in as:")
email = se... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
log1 = {"account":"test@test.test",
'event':'getuserdata',
'api': 'get_user_data',
'is_api':'yes',
'user':"test_user",
'success':'true',
'ip':'127.0.0.1'}
log1["details"] = u"HELLO 0"
logs.create(log1)
log1["is_a... | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
q = Logs.all()
q.filter("account = ", "test@test.test")
ents = q.fetch(10)
count = 0
for ii in ents:
count += 1
self.response.out.write(ii.details)
self.response.out.write("<br/>")
self.response.out.write("Number fetched " + str(count)) | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
pass | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
def get(self):
pass | nlake44/UserInfuser | [
101,
54,
101,
3,
1311320106
] |
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