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def render_curve(name,
data,
x_range=None,
y_range=None,
x_label=None,
y_label=None,
legends=None,
legend_kwargs={},
img_height=None,
img_width=None,
dpi=300,
figsize=(2, 2),
**kwargs):
"""Plot 1D curves.
Args:
name (stor): rendering identifier
data (Tensor|np.ndarray): a rank-1 or rank-2 tensor/np.array. If rank-2,
then each row represents an individual curve.
x_range (tuple[float]): min/max for x values. If None, ``x`` is
the index sequence of curve points. If provided, ``x`` is
evenly spaced by ``(x_range[1] - x_range[0]) / (N - 1)``.
y_range (tuple[float]): a tuple of ``(min_y, max_y)`` for showing on
the figure. If None, then it will be decided according to the
``y`` values. Note that this range won't change ``y`` data; it's
only used by matplotlib for drawing ``y`` limits.
x_label (str): shown besides x-axis
y_label (str): shown besides y-axis
legends (list[str]): label for each curve. No legends are shown if
None.
legend_kwargs (dict): optional legend kwargs
img_height (int): height of the output image
img_width (int): width of the output image
dpi (int): resolution of each rendered image
figsize (tuple[int]): figure size. For the relationship between ``dpi``
and ``figsize``, please refer to `this post <https://stackoverflow.com/questions/47633546/relationship-between-dpi-and-figure-size>`_.
**kwargs: all other arguments to ``ax.plot()``.
Returns:
Image: an output image rendered for the tensor
"""
assert len(data.shape) in (1, 2), "Must be rank-1 or rank-2!"
if not isinstance(data, np.ndarray):
array = data.cpu().numpy()
else:
array = data
if len(array.shape) == 1:
array = np.expand_dims(array, 0)
fig, ax = plt.subplots(figsize=figsize)
M, N = array.shape
x = range(N)
if x_range is not None:
delta = (x_range[1] - x_range[0]) / float(N - 1)
x = delta * x + x_range[0]
for i in range(M):
ax.plot(x, array[i], **kwargs)
if legends is not None:
ax.legend(legends, loc="best", **legend_kwargs)
if y_range:
ax.set_ylim(y_range)
if x_label:
ax.set_xlabel(x_label)
if y_label:
ax.set_ylabel(y_label)
return _convert_to_image(name, fig, dpi, img_height, img_width) | f0f60bf64c195f82ec91513f2c79a7c72a25599d | 3,658,600 |
def CreateBooleanUnion1(breps, tolerance, manifoldOnly, multiple=False):
"""
Compute the Boolean Union of a set of Breps.
Args:
breps (IEnumerable<Brep>): Breps to union.
tolerance (double): Tolerance to use for union operation.
manifoldOnly (bool): If true, non-manifold input breps are ignored.
Returns:
Brep[]: An array of Brep results or None on failure.
"""
url = "rhino/geometry/brep/createbooleanunion-breparray_double_bool"
if multiple: url += "?multiple=true"
args = [breps, tolerance, manifoldOnly]
if multiple: args = list(zip(breps, tolerance, manifoldOnly))
response = Util.ComputeFetch(url, args)
response = Util.DecodeToCommonObject(response)
return response | ae397d73b9acbcdd52e9e83592322274047d9915 | 3,658,601 |
def make_singleton_class(class_reference, *args, **kwargs):
"""
Make the given class a singleton class.
*class_reference* is a reference to a class type, not an instance of a class.
*args* and *kwargs* are parameters used to instantiate a singleton instance.
To use this, suppose we have a class called ``DummyClass`` and later instantiate
a variable ``dummy_instnace`` as an instance of class ``DummyClass``. ``class_reference``
will be ``DummyClass``, not ``dummy_instance``.
Note that this method is not for direct use. Always use ``@singleton`` or ``@singleton_with``.
"""
# Name of the attribute that store the singleton instance
singleton_attr_name = '_singleton_instance'
# The statice method to get the singleton instance of the reference class
@staticmethod
def instance():
"""
Get a singleton instance.
.. note:: This class is capable to act as a singleton class by invoking this method.
"""
return class_reference._singleton_instance
# Intercept if the class has already been a singleton class.
if singleton_attr_name in dir(class_reference):
raise SingletonInitializationException(
'The attribute _singleton_instance is already assigned as instance of %s.'\
% type(class_reference._singleton_instance)
)
# Instantiate an instance for a singleton class.
class_reference._singleton_instance = class_reference(*args, **kwargs)
class_reference.instance = instance
return class_reference | c33b09f2eee16e23dd1a10a914a8735120efbbfe | 3,658,602 |
def get_coaches(soup):
"""
scrape head coaches
:param soup: html
:return: dict of coaches for game
"""
coaches = soup.find_all('tr', {'id': "HeadCoaches"})
# If it picks up nothing just return the empty list
if not coaches:
return coaches
coaches = coaches[0].find_all('td')
return {
'Away': coaches[1].get_text(),
'Home': coaches[3].get_text()
} | 784b355adb885b0eb4f26e72168475e1abbe4d1f | 3,658,603 |
import logging
def create_app(config_name):
"""
Factory to create Flask application context using config option found in
app.config
:param config_name: (string) name of the chosen config option
:return app: (Flask application context)
"""
logging.basicConfig(
filename="app.log",
filemode="w",
format="%(asctime)s - %(threadName)s - %(name)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=logging.INFO,
)
app = Flask(__name__)
app.config.from_object(config[config_name])
logging.info("App initialized.")
register_extensions(app)
register_blueprints(app)
configure_database(app)
return app | 8dea98c2393b575c7c353debe4b84eea67ff9353 | 3,658,604 |
import math
def _rectify_countdown_or_bool(count_or_bool):
"""
used by recrusive functions to specify which level to turn a bool on in
counting down yeilds True, True, ..., False
conting up yeilds False, False, False, ... True
Args:
count_or_bool (bool or int): if positive will count down, if negative
will count up, if bool will remain same
Returns:
int or bool: count_or_bool_
CommandLine:
python -m utool.util_str --test-_rectify_countdown_or_bool
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_str import _rectify_countdown_or_bool # NOQA
>>> count_or_bool = True
>>> a1 = (_rectify_countdown_or_bool(2))
>>> a2 = (_rectify_countdown_or_bool(1))
>>> a3 = (_rectify_countdown_or_bool(0))
>>> a4 = (_rectify_countdown_or_bool(-1))
>>> a5 = (_rectify_countdown_or_bool(-2))
>>> a6 = (_rectify_countdown_or_bool(True))
>>> a7 = (_rectify_countdown_or_bool(False))
>>> result = [a1, a2, a3, a4, a5, a6, a7]
>>> print(result)
[1.0, 0.0, 0, 0.0, -1.0, True, False]
[1.0, True, False, False, -1.0, True, False]
"""
if count_or_bool is True or count_or_bool is False:
count_or_bool_ = count_or_bool
elif isinstance(count_or_bool, int):
if count_or_bool == 0:
return 0
sign_ = math.copysign(1, count_or_bool)
count_or_bool_ = int(count_or_bool - sign_)
#if count_or_bool_ == 0:
# return sign_ == 1
else:
count_or_bool_ = False
return count_or_bool_ | 63d02cfbd99652bc04cfbac57a7d9306465bbf2b | 3,658,605 |
def POpen (inUV, access, err):
""" Open an image persistent (disk) form
inUV = Python UV object
access = access 1=READONLY, 2=WRITEONLY, 3=READWRITE
err = Python Obit Error/message stack
"""
################################################################
if ('myClass' in inUV.__dict__) and (inUV.myClass=='AIPSUVData'):
raise TypeError("Function unavailable for "+inUV.myClass)
return inUV.Open(access, err)
# end POpen | f365a9d5a4fc8a028203e8ea4a51b64d6d19f9bc | 3,658,606 |
def geopad(lon, lat, data, /, nlon=1, nlat=0):
"""
Return array padded circularly along longitude and over the poles for finite
difference methods.
"""
# Pad over longitude seams
if nlon > 0:
pad = ((nlon, nlon),) + (data.ndim - 1) * ((0, 0),)
data = np.pad(data, pad, mode='wrap')
lon = np.pad(lon, nlon, mode='wrap') # should be vector
# Pad over poles
if nlat > 0:
if (data.shape[0] % 2) == 1:
raise ValueError(
'Data must have even number of longitudes '
'if you wish to pad over the poles.'
)
append = np.roll( # descending in lat
np.flip(data, axis=1), data.shape[0] // 2, axis=0
)
data = np.concatenate(
(
append[:, -nlat:, ...], # -87.5, -88.5, -89.5 (crossover)
data, # -89.5, -88.5, -87.5, ..., 87.5, 88.5, 89.5 (crossover)
append[:, :nlat, ...], # 89.5, 88.5, 87.5
),
axis=1,
)
lat = np.pad(lat, nlat, mode='symmetric')
lat[:nlat] = 180 - lat[:nlat] # monotonic ascent
lat[-nlat:] = 180 - lat[-nlat:]
return lon, lat, data | 8916dde690673b1d278ffab39ee3350f346a4182 | 3,658,607 |
def SL_EAKF(N,loc_rad,taper='GC',ordr='rand',infl=1.0,rot=False,**kwargs):
"""
Serial, covariance-localized EAKF.
Ref: Karspeck, Alicia R., and Jeffrey L. Anderson. (2007):
"Experimental implementation of an ensemble adjustment filter..."
Used without localization, this should be equivalent
(full ensemble equality) to the EnKF 'Serial'.
"""
def assimilator(stats,twin,xx,yy):
f,h,chrono,X0 = twin.f, twin.h, twin.t, twin.X0
N1 = N-1
R = h.noise
Rm12 = h.noise.C.sym_sqrt_inv
E = X0.sample(N)
stats.assess(0,E=E)
for k,kObs,t,dt in progbar(chrono.forecast_range):
E = f(E,t-dt,dt)
E = add_noise(E, dt, f.noise, kwargs)
if kObs is not None:
stats.assess(k,kObs,'f',E=E)
y = yy[kObs]
inds = serial_inds(ordr, y, R, anom(E)[0])
locf_at = h.loc_f(loc_rad, 'y2x', t, taper)
for i,j in enumerate(inds):
hE = h(E,t)
hx = mean(hE,0)
Y = hE - hx
mu = mean(E ,0)
A = E-mu
# Update j-th component of observed ensemble
Yj = Rm12[j,:] @ Y.T
dyj = Rm12[j,:] @ (y - hx)
#
skk = Yj@Yj # N1 * prior var
su = 1/( 1/skk + 1/N1 ) # N1 * KG
alpha = (N1/(N1+skk))**(0.5) # update contraction factor
#
dy2 = su*dyj/N1 # mean update
Y2 = alpha*Yj # anomaly update
if skk<1e-9: continue
# Update state (regress update from observation space)
# Localized
local, coeffs = locf_at(j)
if len(local) == 0: continue
Regression = (A[:,local]*coeffs).T @ Yj/np.sum(Yj**2)
mu[ local] += Regression*dy2
A[:,local] += np.outer(Y2 - Yj, Regression)
# Without localization:
#Regression = A.T @ Yj/np.sum(Yj**2)
#mu += Regression*dy2
#A += np.outer(Y2 - Yj, Regression)
E = mu + A
E = post_process(E,infl,rot)
stats.assess(k,kObs,E=E)
return assimilator | e7ca69f71cf83a4389086d14791902eb5a661b9e | 3,658,608 |
def CalculateNMaxNCharge(mol):
"""
#################################################################
Most negative charge on N atoms
-->QNmin
Usage:
result=CalculateNMaxNCharge(mol)
Input: mol is a molecule object.
Output: result is a numeric value.
#################################################################
"""
return _CalculateElementMaxNCharge(mol,AtomicNum=7) | ae63c3f2c6faa8b0d9f7d6ae3b320a9c3b1002d6 | 3,658,609 |
def cnn_5l4(image, **kwargs):
"""
:param in: (TensorFlow Tensor) Image input placeholder
:param kwargs: (dict) Extra keywords parameters for the convolutional layers of the CNN
:return: (TensorFlow Tensor) The CNN output layer
"""
activ = tf.nn.relu
layer_1 = activ(conv(image, 'c1', n_filters=222, filter_size=4, stride=1, pad='SAME', init_scale=np.sqrt(2), **kwargs))
layer_2 = activ(conv(layer_1, 'c2', n_filters=222, filter_size=2, stride=1, pad='SAME', init_scale=np.sqrt(2), **kwargs))
layer_3 = activ(conv(layer_2, 'c3', n_filters=222, filter_size=2, stride=1, pad='SAME', init_scale=np.sqrt(2), **kwargs))
layer_4 = activ(conv(layer_3, 'c4', n_filters=222, filter_size=2, stride=1, pad='SAME', init_scale=np.sqrt(2), **kwargs))
layer_5 = activ(conv(layer_4, 'c5', n_filters=222, filter_size=2, stride=1, pad='SAME', init_scale=np.sqrt(2), **kwargs))
layer_lin = conv_to_fc(layer_5)
return layer_lin | af059b9a2899c1adcc9f11f4742ffaac8a971dba | 3,658,610 |
def read_dns_data(dns_fn):
"""
Read data in from a DNS file
:param str dns_fn: The filename of the DNS
"""
fed = open(dns_fn, 'r')
begin_data = False
dns_data = {}
for line in fed.readlines():
if begin_data:
if "t = " in line:
tc = float(line[3:])
dns_data.update({ tc:{'N':np.empty((0, 3)), 'MP':np.empty((0, 3))} })
else:
data = [s.replace(',', '') for s in line.split()]
typ = data[0]
pos = np.array([float(data[i]) for i in range(2, 5)])
dns_data[tc][typ] = np.vstack([dns_data[tc][typ], pos])
if (line.strip() == "BEGIN DATA"):
begin_data = True
fed.close()
return dns_data | 2c73289c6284b47901a8f7c91bce6df75849c822 | 3,658,611 |
def arithmetic_mean(iterable):
"""Zero-length-safe arithmetic mean."""
values = np.asarray(iterable)
if not values.size:
return 0
return values.mean() | 3972885d92654d842a163d64c47b585ad6865c98 | 3,658,612 |
def play_process(url):
""" Create and return process to read audio from url and send to analog output"""
return FfmpegProcess(f'ffmpeg -i {url} -f alsa default') | 2246f9385e48dda9398752ecd9fa70914d17c55f | 3,658,613 |
from typing import Iterable
def iterable_to_wikitext(
items: Iterable[object], *, prefix: str = "\n* "
) -> str:
"""
Convert iterable to wikitext.
Pages are converted to links.
All other objects use their string representation.
:param items: Items to iterate
:param prefix: Prefix for each item when there is more than one item
"""
if not items:
return ""
if len(list(items)) == 1:
prefix = ""
text = ""
for item in items:
if isinstance(item, BasePage):
item = item.title(as_link=True, textlink=True)
text += f"{prefix}{item}"
return text | 775bed839d890ab40aeace76a82f881e076cafa2 | 3,658,614 |
def plot_timeSeries(df, col_name, divide=None, xlabel="Days", line=True, title="Time series values", figsize=(9,9)):
"""
Plot a column of the given time series DataFrame.
Parameters
----------
df: pd.DataFrame
DataFrame indexed by days (i.e. the index is a pd.DatetimeIndex).
col_name: str
Indicates the specified column to plot.
divide: str
Indicates if and how to divide the plotted values.
It can either be None, "year", "month" or "season". (The meteorological seasons are considered, and not the
astronomical ones).
That division is simply made graphically using different colors.
xlabel: str
Label to put on the x axis.
line: bool
Indicates whether to connect the points with a line.
title: str
Title of the plot.
figsize: tuple
Dimensions of the plot.
Returns
----------
matplotlib.axes.Axes
The matplotlib Axes where the plot has been made.
"""
fig, ax = plt.subplots(figsize=figsize)
if not divide:
ax.plot(df.index, df[col_name], 'o:' if line else 'o')
else:
groups = group_days_by(df.index, criterion=divide)
color = None
for group in groups:
if divide=="season":
colors = {"Winter":"blue", "Spring":"green", "Summer":"yellow", "Fall":"red"}
color = colors[group[0]]
elif divide=="month":
colors = {"January":"b",
"February":"g",
"March":"r",
"April":"c",
"May":"m",
"June":"y",
"July":"k",
"August":"peru",
"September":"crimson",
"October":"orange",
"November":"darkgreen",
"December":"olivedrab"}
color = colors[group[0]]
ax.plot(group[1], df.loc[group[1],col_name], 'o:' if line else 'o', color=color , label=group[0])
ax.set_xlabel(xlabel)
ax.set_ylabel(col_name)
ax.set_title(title)
ax.grid()
if divide:
ax.legend()
return ax | 279f74422ae6b186128347cc971a094c13f22c4b | 3,658,615 |
import os
def save_bedtools(cluster_regions, clusters, assigned_dir):
"""
Given cluster regions file saves all bedtools sanely and returns result
:param cluster_regions:
:return:
"""
for region in cluster_regions:
output_file = "%s.%s.real.BED" % (clusters, region)
cluster_regions[region]['real'] = cluster_regions[region]['real'].sort().saveas(os.path.join(assigned_dir, output_file))
if "rand" not in cluster_regions[region]:
continue
for n_rand in cluster_regions[region]['rand']:
output_file = "%s.%s.rand.%s.BED" % (clusters, region, n_rand)
cluster_regions[region]['rand'][n_rand] = cluster_regions[region]['rand'][n_rand].sort().saveas(os.path.join(assigned_dir, output_file))
return cluster_regions | 5ef6ee5fd56eb3e6c0659b9979defcab6d9acefb | 3,658,616 |
def is_bv(a):
"""Return `True` if `a` is a Z3 bit-vector expression.
>>> b = BitVec('b', 32)
>>> is_bv(b)
True
>>> is_bv(b + 10)
True
>>> is_bv(Int('x'))
False
"""
return isinstance(a, BitVecRef) | 7c1cd1d3d679cdceb12955e61f54861b248ff4a2 | 3,658,617 |
def bgsub_1D(raw_data, energy_axis, edge, **kwargs):
"""
Full background subtraction function for the 1D case-
Optional LBA, log fitting, LCPL, and exponential fitting.
For more information on non-linear fitting function, see information at https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
Inputs:
raw_data - 1D spectrum
energy_axis - corresponding energy axis
edge - edge parameters defined by KEM convention
**kawrgs:
fit - choose the type of background fit, default == 'pl' == Power law. Can also use 'exp'== Exponential, 'lin' == Linear, 'lcpl' == LCPL.
log - Boolean, if true, log transform data and fit using QR factorization, default == False.
nstd - Standard deviation spread of r error from non-linear power law fitting. Default == 100.
ftol - default to 0.0005, Relative error desired in the sum of squares.
gtol - default to 0.00005, Orthogonality desired between the function vector and the columns of the Jacobian.
xtol - default to None, Relative error desired in the approximate solution.
maxfev - default to 50000, Only change if you are consistenly catching runtime errors and loosening gtol/ftols are not making a good enough fit.
method - default is 'trf', see https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html#scipy.optimize.least_squares for description of methods
Note: may need stricter tolerances on ftol/gtol for noisier data. Anecdotally, a stricter gtol (as low as 1e-8) has a larger effect on the quality of the bgsub.
Outputs:
bg_1D - background spectrum
"""
fit_start_ch = eVtoCh(edge[0], energy_axis)
fit_end_ch = eVtoCh(edge[1], energy_axis)
zdim = len(raw_data)
ewin = energy_axis[fit_start_ch:fit_end_ch]
esub = energy_axis[fit_start_ch:]
bg_1D = np.zeros_like(raw_data)
fy = np.zeros((1,zdim))
fy[0,:] = raw_data
## Either fast fitting -> log fitting, Or slow fitting -> non-linear fitting
if 'log' in kwargs.keys():
log = kwargs['log']
else:
log = False
## Fitting parameters for non-linear curve fitting if non-log based fitting
if 'ftol' in kwargs.keys():
ftol = kwargs['ftol']
else:
ftol = 1e-8
if 'gtol' in kwargs.keys():
gtol = kwargs['gtol']
else:
gtol = 1e-8
if 'xtol' in kwargs.keys():
xtol = kwargs['xtol']
else:
xtol = 1e-8
if 'maxfev' in kwargs.keys():
maxfev = kwargs['maxfev']
else:
maxfev = 50000
if 'method' in kwargs.keys():
method = kwargs['method']
else:
method = 'trf'
## Determine if fitting is power law or exponenetial
if 'fit' in kwargs.keys():
fit = kwargs['fit']
if fit == 'exp':
fitfunc = exponential
bounds = ([0, 0], [np.inf, np.inf])
elif fit == 'pl':
fitfunc = powerlaw
elif fit == 'lcpl':
fitfunc = lcpowerlaw
elif fit == 'lin':
fitfunc = linear
else:
print('Did not except fitting function, please use either \'pl\' for powerlaw, \'exp\' for exponential, \'lin\' for linear or \'lcpl\' for LCPL.')
else:
fitfunc = powerlaw
## If fast fitting linear background, find fit using qr factorization
if fitfunc==linear:
Blin = fy[:,fit_start_ch:fit_end_ch]
Alin = np.zeros((len(ewin),2))
Alin[:,0] = np.ones(len(ewin))
Alin[:,1] = ewin
Xlin = qrnorm(Alin,Blin.T)
Elin = np.zeros((len(esub),2))
Elin[:,0] = np.ones(len(esub))
Elin[:,1] = esub
bgndLINline = np.dot(Xlin.T,Elin.T)
bg_1D[fit_start_ch:] = raw_data[fit_start_ch:] - bgndLINline
## If fast log fitting and powerlaw, find fit using qr factorization
elif log & (fitfunc==powerlaw):
Blog = fy[:,fit_start_ch:fit_end_ch]
Alog = np.zeros((len(ewin),2))
Alog[:,0] = np.ones(len(ewin))
Alog[:,1] = np.log(ewin)
Xlog = qrnorm(Alog,np.log(abs(Blog.T)))
Elog = np.zeros((len(esub),2))
Elog[:,0] = np.ones(len(esub))
Elog[:,1] = np.log(esub)
bgndPLline = np.exp(np.dot(Xlog.T,Elog.T))
bg_1D[fit_start_ch:] = raw_data[fit_start_ch:] - bgndPLline
## If fast log fitting and exponential, find fit using qr factorization
elif log & (fitfunc==exponential):
Bexp = fy[:,fit_start_ch:fit_end_ch]
Aexp = np.zeros((len(ewin),2))
Aexp[:,0] = np.ones(len(ewin))
Aexp[:,1] = ewin
Xexp = qrnorm(Aexp,np.log(abs(Bexp.T)))
Eexp = np.zeros((len(esub),2))
Eexp[:,0] = np.ones(len(esub))
Eexp[:,1] = esub
bgndEXPline = np.exp(np.dot(Xexp.T,Eexp.T))
bg_1D[fit_start_ch:] = raw_data[fit_start_ch:] - bgndEXPline
## Power law non-linear curve fitting using scipy.optimize.curve_fit
elif ~log & (fitfunc==powerlaw):
popt_pl,pcov_pl=curve_fit(powerlaw, ewin, raw_data[fit_start_ch:fit_end_ch],maxfev=maxfev,method=method,
verbose = 0, ftol=ftol, gtol=gtol, xtol=xtol)
c,r = popt_pl
bg_1D[fit_start_ch:] = raw_data[fit_start_ch:] - powerlaw(energy_axis[fit_start_ch:],c,r)
## Exponential non-linear curve fitting using scipy.optimize.curve_fit
elif ~log & (fitfunc==exponential):
popt_exp,pcov_exp=curve_fit(exponential, ewin, raw_data[fit_start_ch:fit_end_ch],maxfev=maxfev,method=method,
verbose = 0,p0=[0,0], ftol=ftol, gtol=gtol, xtol=xtol)
a,b = popt_exp
bg_1D[fit_start_ch:] = raw_data[fit_start_ch:] - exponential(energy_axis[fit_start_ch:],a,b)
## LCPL non-linear curve fitting using scipy.optimize.curve_fit
elif fitfunc==lcpowerlaw:
if 'nstd' in kwargs.keys():
nstd = kwargs['nstd']
else:
nstd = 100
popt_pl,pcov_pl=curve_fit(powerlaw, ewin, raw_data[fit_start_ch:fit_end_ch],maxfev=maxfev,method=method,
verbose = 0, ftol=ftol, gtol=gtol, xtol=xtol)
c,r = popt_pl
perr = np.sqrt(np.diag(pcov_pl))
rstd = perr[1]
popt_lcpl,pcov_lcpl=curve_fit(lcpowerlaw, ewin, raw_data[fit_start_ch:fit_end_ch],maxfev=maxfev,method=method,
verbose = 0,p0=[c/2,r-nstd*rstd,c/2,r+nstd*rstd], ftol=ftol, gtol=gtol, xtol=xtol)
c1,r1,c2,r2 = popt_lcpl
bg_1D[fit_start_ch:] = raw_data[fit_start_ch:] - lcpowerlaw(energy_axis[fit_start_ch:],c1,r1,c2,r2)
return bg_1D | a3f273e55f49811ce9af4ee5c23d4078fe83535a | 3,658,618 |
import random
def about_garble():
"""
about_garble
Returns one of several strings for the about page
"""
garble = ["leverage agile frameworks to provide a robust synopsis for high level overviews.",
"iterate approaches to corporate strategy and foster collaborative thinking to further the overall value proposition.",
"organically grow the holistic world view of disruptive innovation via workplace change management and empowerment.",
"bring to the table win-win survival strategies to ensure proactive and progressive competitive domination.",
"ensure the end of the day advancement, a new normal that has evolved from epistemic management approaches and is on the runway towards a streamlined cloud solution.",
"provide user generated content in real-time will have multiple touchpoints for offshoring."]
return garble[random.randint(0, len(garble) - 1)] | c391891f97a7bc6df5287173aa160713cdfff675 | 3,658,619 |
def parse_term_5_elems(expr_list, idx):
"""
Try to parse a terminal node from five elements of {expr_list}, starting
from {idx}.
Return the new expression list on success, None on error.
"""
# The only 3 items node is pk_h
if expr_list[idx : idx + 2] != [OP_DUP, OP_HASH160]:
return
if not isinstance(expr_list[idx + 2], bytes):
return
if len(expr_list[idx + 2]) != 20:
return
if expr_list[idx + 3 : idx + 5] != [OP_EQUAL, OP_VERIFY]:
return
node = Node().construct_pk_h(expr_list[idx + 2])
expr_list[idx : idx + 5] = [node]
return expr_list | 8c0c365483c44a767b3e254f957af175125da2d6 | 3,658,620 |
def display_clusters():
"""
Method to display the clusters
"""
offset = int(request.args.get('offset', '0'))
limit = int(request.args.get('limit', '50'))
clusters_id_sorted = sorted(clusters, key=lambda x : -len(clusters[x]))
batches = chunks(range(len(clusters_id_sorted)), size=limit)
return render_template('clusters.html',
offset=offset, limit=limit, batches=batches,
ordered_list=clusters_id_sorted[offset:offset+limit+1],
idx_to_path=idx_to_path,
clusters=clusters) | e3d578cff54e66ee4b096bcf1e7181a3bac1c845 | 3,658,621 |
def densify_sampled_item_predictions(tf_sample_predictions_serial, tf_n_sampled_items, tf_n_users):
"""
Turns the serial predictions of the sample items in to a dense matrix of shape [ n_users, n_sampled_items ]
:param tf_sample_predictions_serial:
:param tf_n_sampled_items:
:param tf_n_users:
:return:
"""
densified_shape = tf.cast(tf.stack([tf_n_users, tf_n_sampled_items]), tf.int32)
densified_predictions = tf.reshape(tf_sample_predictions_serial, shape=densified_shape)
return densified_predictions | e1dbe0e74c791e1d9b7613fbe52b034a60376497 | 3,658,622 |
def get_market_book(symbols=None, **kwargs):
"""
Top-level function to obtain Book data for a symbol or list of symbols
Parameters
----------
symbols: str or list, default None
A symbol or list of symbols
kwargs:
Additional Request Parameters (see base class)
"""
return Book(symbols, **kwargs).fetch() | 8b1bc8ed07a611cef490f616996aae05ce445ff1 | 3,658,623 |
def ndarange(*args, shape: tuple = None, **kwargs):
"""Generate arange arrays of arbitrary dimensions."""
arr = np.array([np.arange(*args[i], **kwargs) for i in range(len(args))])
return arr.reshape(shape) if shape is not None else arr.T | 42a5070e653386a71a9be7949f5e9341bfbc50c9 | 3,658,624 |
def runningSum(self, nums):
"""
:type nums: List[int]
:rtype: List[int]
5% faster
100% less memory
"""
sum = 0
runningSum = [0] * len(nums)
for i in range(len(nums)):
for j in range(i+1):
runningSum[i] += nums[j]
return runningSum | 393849c4aa1d23b15717748066e21abceaf6d5d9 | 3,658,625 |
import warnings
def _select_features_1run(df, target, problem_type="regression", verbose=0):
"""
One feature selection run.
Inputs:
- df: nxp pandas DataFrame with n data points and p features; to avoid overfitting, only provide data belonging
to the n training data points. The variables have to be scaled to have 0 mean and unit variance.
- target: n dimensional array with targets corresponding to the data points in df
- problem_type: str, either "regression" or "classification" (default: "regression")
- verbose: verbosity level (int; default: 0)
Returns:
- good_cols: list of column names for df with which a prediction model can be trained
"""
if df.shape[0] <= 1:
raise ValueError("n_samples = {}".format(df.shape[0]))
# initial selection of too few but (hopefully) relevant features
if problem_type == "regression":
model = lm.LassoLarsCV(cv=5, eps=1e-8)
elif problem_type == "classification":
model = lm.LogisticRegressionCV(cv=5, penalty="l1", solver="saga", class_weight="balanced")
else:
print("[featsel] WARNING: Unknown problem_type %r - not performing feature selection!" % problem_type)
return []
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# TODO: remove if sklearn least_angle issue is fixed
try:
model.fit(df, target)
except ValueError:
# try once more with shuffled data, if it still doesn't work, give up
rand_idx = np.random.permutation(df.shape[0])
model.fit(df.iloc[rand_idx], target[rand_idx])
# model.fit(df, target)
if problem_type == "regression":
coefs = np.abs(model.coef_)
else:
# model.coefs_ is n_classes x n_features, but we need n_features
coefs = np.max(np.abs(model.coef_), axis=0)
# weight threshold: select at most 0.2*n_train initial features
thr = sorted(coefs, reverse=True)[min(df.shape[1]-1, df.shape[0]//5)]
initial_cols = list(df.columns[coefs > thr])
# noise filter
initial_cols = _noise_filtering(df[initial_cols].to_numpy(), target, initial_cols, problem_type)
good_cols = set(initial_cols)
if verbose > 0:
print("[featsel]\t %i initial features." % len(initial_cols))
# add noise features
X_w_noise = _add_noise_features(df[initial_cols].to_numpy())
# go through all remaining features in splits of n_feat <= 0.5*n_train
other_cols = list(np.random.permutation(list(set(df.columns).difference(initial_cols))))
if other_cols:
n_splits = int(np.ceil(len(other_cols)/max(10, 0.5*df.shape[0]-len(initial_cols))))
split_size = int(np.ceil(len(other_cols)/n_splits))
for i in range(n_splits):
current_cols = other_cols[i*split_size:min(len(other_cols), (i+1)*split_size)]
X = np.hstack([df[current_cols].to_numpy(), X_w_noise])
if problem_type == "regression":
model = lm.LassoLarsCV(cv=5, eps=1e-8)
else:
model = lm.LogisticRegressionCV(cv=5, penalty="l1", solver="saga", class_weight="balanced")
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# TODO: remove if sklearn least_angle issue is fixed
try:
model.fit(X, target)
except ValueError:
rand_idx = np.random.permutation(X.shape[0])
model.fit(X[rand_idx], target[rand_idx])
# model.fit(X, target)
current_cols.extend(initial_cols)
if problem_type == "regression":
coefs = np.abs(model.coef_)
else:
# model.coefs_ is n_classes x n_features, but we need n_features
coefs = np.max(np.abs(model.coef_), axis=0)
weights = dict(zip(current_cols, coefs[:len(current_cols)]))
# only include features that are more important than our known noise features
noise_w_thr = np.max(coefs[len(current_cols):])
good_cols.update([c for c in weights if abs(weights[c]) > noise_w_thr])
if verbose > 0:
print("[featsel]\t Split %2i/%i: %3i candidate features identified." % (i+1, n_splits, len(good_cols)), end="\r")
# noise filtering on the combination of features
good_cols = list(good_cols)
good_cols = _noise_filtering(df[good_cols].to_numpy(), target, good_cols, problem_type)
if verbose > 0:
print("\n[featsel]\t Selected %3i features after noise filtering." % len(good_cols))
return good_cols | a85fcc34ad5f49d202e120697960ecaf36a6d0ca | 3,658,626 |
import argparse
def main():
"""Console script for github_terminal."""
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group()
group.add_argument("-v",
"--verbose",
action="store_true",
help="Show verbose information")
group.add_argument("-q",
"--quiet",
action="store_true",
help="Display less information")
parser.add_argument(
'category',
help='Use the task you want to create like issue, pr, repo ',
choices=["issue", "pr", "repo"])
parser.add_argument(
'action',
help='Use the action to perform in the category.',
choices=["create", "list", "edit", "delete", "close", "status"])
parser.add_argument("-t",
"--title",
help="Title of issue or PR or name of repository")
parser.add_argument("-d",
"--description",
help="Description of issue or PR or repo.")
parser.add_argument("-c", "--config", help="Configuration file to use.")
parser.add_argument("-T",
"--token",
help="Personal access token for github.")
parser.add_argument("-u", "--username", help="Username of the user")
parser.add_argument("-a",
"--assignee",
help="Filter by assignee or set assignee")
parser.add_argument("-b",
"--base",
help="Filter by base branch the pull request are being merged to (ONLY FOR PR AND REPO)")
parser.add_argument("-A", "--author", help="Filter by or set author")
parser.add_argument("-l",
"--label",
help="Filter or set label separated by comma")
parser.add_argument("-L", "--limit", help="Maximum number to fetch")
parser.add_argument("-s", "--state", help="Filter by state")
parser.add_argument(
"-S",
"--since",
help="List issues that have been updated at or after the given date."
" (You can also use value like 2 weeks ago)")
parser.add_argument("-r",
"--repo",
help="Repository to perform action on.")
args = parser.parse_args()
category_specific_action = handle_category_action(args)
category_specific_action(args)
return 0 | 1598085f30a42559dc20415425057edebb797993 | 3,658,627 |
def edit_recovery(request, recovery_id):
"""This view is used to edit/update existing tag recoveries."""
clip_codes = sorted(list(CLIP_CODE_CHOICES), key=lambda x: x[0])
tag_types = sorted(list(TAG_TYPE_CHOICES), key=lambda x: x[0])
tag_origin = sorted(list(TAG_ORIGIN_CHOICES), key=lambda x: x[0])
tag_colours = sorted(list(TAG_COLOUR_CHOICES), key=lambda x: x[0])
tag_position = sorted(list(TAG_POSITION_CHOICES), key=lambda x: x[0])
recovery = get_object_or_404(Recovery, id=recovery_id)
report = recovery.report
form = RecoveryForm(
report_id=report.id, instance=recovery, data=request.POST or None
)
if request.method == "POST":
if form.is_valid():
recovery = form.save(report)
return redirect("tfat:recovery_detail", recovery_id=recovery.id)
return render(
request,
"tfat/recovery_form.html",
{
"form": form,
"action": "edit",
"clip_codes": clip_codes,
"tag_types": tag_types,
"tag_origin": tag_origin,
"tag_colours": tag_colours,
"tag_position": tag_position,
},
) | f9da1a4377efd436e93cf2be0af2c2e09cc3e31d | 3,658,628 |
def e(string, *args):
"""Function which formats error messages."""
return string.format(*[pformat(arg) for arg in args]) | 8734d01544211fde3f8ee24f0f91dc06763d4a1f | 3,658,629 |
def membership_ending_task(user):
"""
:return: Next task that will end the membership of the user
"""
task = (UserTask.q
.filter_by(user_id=user.id,
status=TaskStatus.OPEN,
type=TaskType.USER_MOVE_OUT)
# Casting jsonb -> bool directly is only supported since PG v11
.filter(UserTask.parameters_json['end_membership'].cast(String).cast(Boolean) == True)
.order_by(UserTask.due.asc())).first()
return task | 2043c87eaabbf3360f1bec331a03e1c7db8bc783 | 3,658,630 |
import warnings
def hmsstr_to_rad(hmsstr):
"""Convert HH:MM:SS.SS sexigesimal string to radians.
"""
hmsstr = np.atleast_1d(hmsstr)
hours = np.zeros(hmsstr.size)
for i,s in enumerate(hmsstr):
# parse string using regular expressions
match = hms_re.match(s)
if match is None:
warnings.warn("Input is not a valid sexigesimal string: %s" % s)
hours[i] = np.nan
continue
d = match.groupdict(0) # default value is 0
# Check sign of hms string
if d['sign'] == '-':
sign = -1
else:
sign = 1
hour = float(d['hour']) + \
float(d['min'])/60.0 + \
float(d['sec'])/3600.0
hours[i] = sign*hour
return hour_to_rad(hours) | e57266c43e3b0f8893f9c71cfbea609cf7c93709 | 3,658,631 |
def find_optimum_transformations(init_trans, s_pts, t_pts, template_spacing,
e_func, temp_tree, errfunc):
"""
Vary the initial transformation by a translation of up to three times the
grid spacing and compute the transformation with the smallest least square
error.
Parameters:
-----------
init_trans : 4-D transformation matrix
Initial guess of the transformation matrix from the subject brain to
the template brain.
s_pts :
Vertex coordinates in the subject brain.
t_pts :
Vertex coordinates in the template brain.
template_spacing : float
Grid spacing of the vertices in the template brain.
e_func : str
Error function to use. Either 'balltree' or 'euclidian'.
temp_tree :
BallTree(t_pts) if e_func is 'balltree'.
errfunc :
The error function for the computation of the least squares error.
Returns:
--------
poss_trans : list of 4-D transformation matrices
List of one transformation matrix for each variation of the intial
transformation with the smallest least squares error.
"""
# template spacing in meters
tsm = template_spacing / 1e3
# Try different initial translations in space to avoid local minima
# No label should require a translation by more than 3 times the grid spacing (tsm)
auto_match_iters = np.array([[0., 0., 0.],
[0., 0., tsm], [0., 0., tsm * 2], [0., 0., tsm * 3],
[tsm, 0., 0.], [tsm * 2, 0., 0.], [tsm * 3, 0., 0.],
[0., tsm, 0.], [0., tsm * 2, 0.], [0., tsm * 3, 0.],
[0., 0., -tsm], [0., 0., -tsm * 2], [0., 0., -tsm * 3],
[-tsm, 0., 0.], [-tsm * 2, 0., 0.], [-tsm * 3, 0., 0.],
[0., -tsm, 0.], [0., -tsm * 2, 0.], [0., -tsm * 3, 0.]])
# possible translation matrices
poss_trans = []
for p, ami in enumerate(auto_match_iters):
# vary the initial translation value by adding ami
tx, ty, tz = init_trans[0, 3] + ami[0], init_trans[1, 3] + ami[1], init_trans[2, 3] + ami[2]
sx, sy, sz = init_trans[0, 0], init_trans[1, 1], init_trans[2, 2]
rx, ry, rz = 0, 0, 0
# starting point for finding the transformation matrix trans which
# minimizes the error between np.dot(s_pts, trans) and t_pts
x0 = np.array([tx, ty, tz, rx, ry, rz])
def error(x):
tx_, ty_, tz_, rx_, ry_, rz_ = x
trans0 = np.zeros([4, 4])
trans0[:3, :3] = rotation3d(rx_, ry_, rz_) * [sx, sy, sz]
trans0[0, 3] = tx_
trans0[1, 3] = ty_
trans0[2, 3] = tz_
# rotate and scale
estim = np.dot(s_pts, trans0[:3, :3].T)
# translate
estim += trans0[:3, 3]
if e_func == 'balltree':
err = errfunc(estim[:, :3], temp_tree)
else:
# e_func == 'euclidean'
err = errfunc(estim[:, :3], t_pts)
return err
est, _, info, msg, _ = leastsq(error, x0, full_output=True)
est = np.concatenate((est, (init_trans[0, 0],
init_trans[1, 1],
init_trans[2, 2])
))
trans = _trans_from_est(est)
poss_trans.append(trans)
return poss_trans | bbc4786827c22158eee33ff9a5e4aaa2939b9705 | 3,658,632 |
def execute_transaction(query):
"""Execute Transaction"""
return Neo4jHelper.run_single_query(query) | 51e8e58bb4cad30b9ae9c7b7d7901ee212c9d26a | 3,658,633 |
from scipy.linalg import null_space
from angle_set import create_theta, get_n_linear, perturbe_points
def generate_linear_constraints(points, verbose=False):
""" Given point coordinates, generate angle constraints. """
N, d = points.shape
num_samples = get_n_linear(N) * 2
if verbose:
print('N={}, generating {}'.format(N, num_samples))
M = int(N * (N - 1) * (N - 2) / 2)
thetas = np.empty((num_samples, M + 1))
for i in range(num_samples):
points_pert = perturbe_points(points, magnitude=0.0001)
theta, __ = create_theta(points_pert)
thetas[i, :-1] = theta
thetas[i, -1] = -1
CT = null_space(thetas)
A = CT[:-1, :].T
b = CT[-1, :]
return A, b | b98354cd6b57d7a33c6e8a43da80b358e358138c | 3,658,634 |
def add_node_to_parent(node, parent_node):
"""
Add given object under the given parent preserving its local transformations
:param node: str
:param parent_node: str
"""
return maya.cmds.parent(node, parent_node, add=True, s=True) | 1f264b7e30c6ebc2285faa987ffc6142ec62d87f | 3,658,635 |
def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
**to_kwargs
Additional keywords to forward to every call to ``to``.
Examples
--------
>>> @preprocess(x=coerce(float, int), y=coerce(float, int))
... def floordiff(x, y):
... return x - y
...
>>> floordiff(3.2, 2.5)
1
>>> @preprocess(x=coerce(str, int, base=2), y=coerce(str, int, base=2))
... def add_binary_strings(x, y):
... return bin(x + y)[2:]
...
>>> add_binary_strings('101', '001')
'110'
"""
def preprocessor(func, argname, arg):
if isinstance(arg, from_):
return to(arg, **to_kwargs)
return arg
return preprocessor | 61ccce8b9ffbec3e76aa9e78face469add28437e | 3,658,636 |
import os
import logging
def get_module_config_filename():
"""Returns the path of the module configuration file (e.g. 'app.yaml').
Returns:
The path of the module configuration file.
Raises:
KeyError: The MODULE_YAML_PATH environment variable is not set.
"""
module_yaml_path = os.environ['MODULE_YAML_PATH']
logging.info('Using module_yaml_path from env: %s', module_yaml_path)
return module_yaml_path | b46b7d51817b93dd6b5e026b4e8b52503b2b432a | 3,658,637 |
def Binary(value):
"""construct an object capable of holding a binary (long) string value."""
return value | 2a33d858b23ac2d72e17ea8ede294c5311cb74be | 3,658,638 |
def _get_domain_session(token, domain_name=None):
"""
Return v3 session for token
"""
domain_name = domain_name or 'default'
auth = v3.Token(auth_url=get_auth_url(),
domain_id=domain_name,
token=token)
return session.Session(auth=auth, user_agent=USER_AGENT,
verify=verify_https()) | 1ad7dcd8a9b6ea12e1a73886581c86252602a438 | 3,658,639 |
from typing import Tuple
from typing import Any
import os
import pickle
import logging
def load_model(name: str, root: str = "") -> Tuple[Model, Any]:
"""Load the trained model (structure, weights) and vectorizer from files."""
json_file, h5_file, vec_file = (
os.path.join(root, "{}.{}".format(name, ext)) for ext in ("json", "h5", "pkl")
)
with open(json_file) as fp:
model = model_from_json(fp.read()) # type: Model
model.load_weights(h5_file)
with open(vec_file, "rb") as bfp: # type: BinaryIO
vectorizer = pickle.load(bfp)
logging.info("Model loaded from {}".format(root + "/"))
return model, vectorizer | d1728b892669b7e17942a5992a71eb27841c82a7 | 3,658,640 |
import six
def fix_troposphere_references(template):
""""Tranverse the troposphere ``template`` looking missing references.
Fix them by adding a new parameter for those references."""
def _fix_references(value):
if isinstance(value, troposphere.Ref):
name = value.data['Ref']
if name not in (list(template.parameters.keys()) + list(template.resources.keys())) and not name.startswith('AWS::'):
template.add_parameter(
troposphere.Parameter(
name,
Type=getattr(value, '_type', 'String'),
)
)
elif isinstance(value, troposphere.Join):
for v in value.data['Fn::Join'][1]:
_fix_references(v)
elif isinstance(value, troposphere.BaseAWSObject):
for _, v in six.iteritems(value.properties):
_fix_references(v)
for _, resource in six.iteritems(template.resources):
for _, value in six.iteritems(resource.properties):
_fix_references(value)
return template | 9570e10262d7293a79b76f78508e57289d9b1e2d | 3,658,641 |
import configparser
def parse_config_to_dict(cfg_file, section):
""" Reads config file and returns a dict of parameters.
Args:
cfg_file: <String> path to the configuration ini-file
section: <String> section of the configuration file to read
Returns:
cfg: <dict> configuration parameters of 'section' as a dict
"""
cfg = configparser.ConfigParser()
cfg.read(cfg_file)
if cfg.has_section(section):
return dict(cfg.items(section))
else:
print("Section '%s' not found in file %s!" % (section, cfg_file))
return None | 021e3594f3130e502934379c0f5c1ecea228017b | 3,658,642 |
def cnn_net(data,
dict_dim,
emb_dim=128,
hid_dim=128,
hid_dim2=96,
class_dim=2,
win_size=3):
"""
Conv net
"""
# embedding layer
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
# convolution layer
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=win_size,
act="tanh",
pool_type="max")
# full connect layer
fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
# softmax layer
prediction = fluid.layers.fc(input=[fc_1], size=class_dim, act="softmax")
return prediction, fc_1 | 47127d5124f48b2be187d15291c2f2bc63f072d7 | 3,658,643 |
def get_commands(servo):
"""Get specific flash commands for the build target.
Each board needs specific commands including the voltage for Vref, to turn
on and turn off the SPI flash. The get_*_commands() functions provide a
board-specific set of commands for these tasks. The voltage for this board
needs to be set to 1.8 V.
Args:
servo (servo_lib.Servo): The servo connected to the target DUT.
Returns:
list: [dut_control_on, dut_control_off, flashrom_cmd, futility_cmd]
dut_control*=2d arrays formmated like [["cmd1", "arg1", "arg2"],
["cmd2", "arg3", "arg4"]]
where cmd1 will be run before cmd2
flashrom_cmd=command to flash via flashrom
futility_cmd=command to flash via futility
"""
dut_control_on = []
dut_control_off = []
# TODO: Add the supported servo cases and their commands.
if servo:
programmer = ''
else:
raise Exception('%s not supported' % servo.version)
flashrom_cmd = ['flashrom', '-p', programmer, '-w']
futility_cmd = ['futility', 'update', '-p', programmer, '-i']
return [dut_control_on, dut_control_off, flashrom_cmd, futility_cmd] | 727101bd4cf87dba14f02639a44f05e2047b5da2 | 3,658,644 |
def FormatRow(Cn, Row, COLSP):
"""
"""
fRow = ""
for i, c in enumerate(Row):
sc = str(c)
lcn = len(Cn[i])
sc = sc[ 0 : min(len(sc), lcn+COLSP-2) ]
fRow += sc + " "*(COLSP+lcn-len(sc))
return fRow | 53d43fc897d1db5ed3c47d6046d90548939b1298 | 3,658,645 |
def handle_release(pin, evt):
"""
Clears the last tone/light when a button
is released.
"""
if pin > 4:
return False
pin -= 1
explorerhat.light[pin].off()
tone.power_off() | f4833bb289c9dfc45cd572ad754bd270c758ed09 | 3,658,646 |
from typing import List
def makeRoute(start : str, end : str) -> List[str]:
"""Find the shortest route between two systems.
:param str start: string name of the starting system. Must exist in bbData.builtInSystemObjs
:param str end: string name of the target system. Must exist in bbData.builtInSystemObjs
:return: list of string system names where the first element is start, the last element is end,
and all intermediary systems are adjacent
:rtype: list[str]
"""
return bbAStar(start, end, bbData.builtInSystemObjs) | 6045b07ff5ceceacea4ad43ae2d52a67a0f46ec9 | 3,658,647 |
from typing import List
from typing import Optional
from typing import Dict
def update_node_categories(
target_graph: BaseGraph,
clique_graph: nx.MultiDiGraph,
clique: List,
category_mapping: Optional[Dict[str, str]],
strict: bool = True,
) -> List:
"""
For a given clique, get category for each node in clique and validate against Biolink Model,
mapping to Biolink Model category where needed.
For example, If a node has ``biolink:Gene`` as its category, then this method adds all of its ancestors.
Parameters
----------
target_graph: kgx.graph.base_graph.BaseGraph
The original graph
clique_graph: networkx.Graph
The clique graph
clique: List
A list of nodes from a clique
category_mapping: Optional[Dict[str, str]]
Mapping for non-Biolink Model categories to Biolink Model categories
strict: bool
Whether or not to merge nodes in a clique that have conflicting node categories
Returns
-------
List
The clique
"""
updated_clique_graph_properties = {}
updated_target_graph_properties = {}
for node in clique:
# For each node in a clique, get its category property
data = clique_graph.nodes()[node]
if 'category' in data:
categories = data['category']
else:
categories = get_category_from_equivalence(target_graph, clique_graph, node, data)
# differentiate between valid and invalid categories
(
valid_biolink_categories,
invalid_biolink_categories,
invalid_categories,
) = check_all_categories(categories)
log.debug(
f"valid biolink categories: {valid_biolink_categories} invalid biolink categories: {invalid_biolink_categories} invalid_categories: {invalid_categories}"
)
# extend categories to have the longest list of ancestors
extended_categories: List = []
for x in valid_biolink_categories:
ancestors = get_biolink_ancestors(x)
if len(ancestors) > len(extended_categories):
extended_categories.extend(ancestors)
log.debug(f"Extended categories: {extended_categories}")
clique_graph_update_dict: Dict = {'category': list(extended_categories)}
target_graph_update_dict: Dict = {}
if invalid_biolink_categories:
if strict:
clique_graph_update_dict['_excluded_from_clique'] = True
target_graph_update_dict['_excluded_from_clique'] = True
clique_graph_update_dict['invalid_biolink_category'] = invalid_biolink_categories
target_graph_update_dict['invalid_biolink_category'] = invalid_biolink_categories
if invalid_categories:
clique_graph_update_dict['_invalid_category'] = invalid_categories
target_graph_update_dict['_invalid_category'] = invalid_categories
updated_clique_graph_properties[node] = clique_graph_update_dict
updated_target_graph_properties[node] = target_graph_update_dict
nx.set_node_attributes(clique_graph, updated_clique_graph_properties)
target_graph.set_node_attributes(target_graph, updated_target_graph_properties)
return clique | 965d1c3076e6fac67ef0fcd00f7f178a2a519be5 | 3,658,648 |
def norm_error(series):
"""Normalize time series.
"""
# return series
new_series = deepcopy(series)
new_series[:,0] = series[:,0] - np.mean(series[:,0])
return 2*(new_series)/max(abs(new_series[:,0])) | a7af6be8b8ddc800609c3385a96f5a80dfd02853 | 3,658,649 |
def f1d(x):
"""Non-linear function for simulation"""
return(1.7*(1/(1+np.exp(-(x-0.5)*20))+0.75*x)) | 75e3bd8a90fe41dfded9b6063868b6766351a8b0 | 3,658,650 |
def get_field_map(src, flds):
"""
Returns a field map for an arcpy data itme from a list or dictionary.
Useful for operations such as renaming columns merging feature classes.
Parameters:
-----------
src: str, arcpy data item or arcpy.mp layer or table
Source data item containing the desired fields.
flds: dict <str: str>
Mapping between old (keys) and new field names (values).
Returns:
--------
arcpy.FieldMappings
"""
mappings = arcpy.FieldMappings()
if isinstance(flds, list):
flds = {n: n for n in flds}
for old_name, new_name in flds.items():
fm = arcpy.FieldMap()
fm.addInputField(src, old_name)
out_f = fm.outputField
out_f.name = new_name
out_f.aliasName = new_name
fm.outputField = out_f
fm.outputField.name = new_name
mappings.addFieldMap(fm)
return mappings | 18e6bbae491659b7819aa3584eb40242dea93f11 | 3,658,651 |
def b32qlc_decode(value):
"""
Decodes a value in qlc encoding to bytes using base32 algorithm
with a custom alphabet: '13456789abcdefghijkmnopqrstuwxyz'
:param value: the value to decode
:type: bytes
:return: decoded value
:rtype: bytes
>>> b32qlc_decode(b'fxop4ya=')
b'okay'
"""
return b32decode(value.translate(QLC_DECODE_TRANS)) | 8b5bbb0f1900a3b89486c81561fd4c253604287e | 3,658,652 |
def createPreProcessingLayers():
"""
Creates a model with the initial pre-processing layers.
"""
model = Sequential()
model.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160, 320, 3)))
model.add(Cropping2D(cropping=((50, 20), (0, 0))))
return model | 1e087ae4bdd1a942845f4f7554e1b27436c6783e | 3,658,653 |
def get_random_atoms(a=2.0, sc_size=2, numbers=[6, 8],
set_seed: int = None):
"""Create a random structure."""
if set_seed:
np.random.seed(set_seed)
cell = np.eye(3) * a
positions = np.array([[0, 0, 0], [a/2, a/2, a/2]])
unit_cell = Atoms(cell=cell, positions=positions, numbers=numbers,
pbc=True)
multiplier = np.identity(3) * sc_size
atoms = make_supercell(unit_cell, multiplier)
atoms.positions += (2 * np.random.rand(len(atoms), 3) - 1) * 0.1
flare_atoms = FLARE_Atoms.from_ase_atoms(atoms)
return flare_atoms | 710592af7db3e24529b68b84e112641b5da63a98 | 3,658,654 |
from re import DEBUG
import os
def pocsense(kspace, sensitivities, i=None, r=None, l=None, g=None, o=None, m=None):
"""
Perform POCSENSE reconstruction.
:param kspace array:
:param sensitivities array:
:param i int: max. number of iterations
:param r float: regularization parameter
:param l int: toggle l1-wavelet or l2 regularization
:param g bool: ()
:param o float: ()
:param m float: ()
"""
usage_string = "pocsense [-i d] [-r f] [-l d] kspace sensitivities output"
cmd_str = f'{BART_PATH} '
cmd_str += 'pocsense '
flag_str = ''
opt_args = f''
multituples = []
if i is not None:
flag_str += f'-i {i} '
if r is not None:
flag_str += f'-r {r} '
if l is not None:
flag_str += f'-l {l} '
if g is not None:
flag_str += f'-g '
if o is not None:
flag_str += f'-o {o} '
if m is not None:
flag_str += f'-m {m} '
cmd_str += flag_str + opt_args + ' '
cmd_str += f"{' '.join([' '.join([str(x) for x in arg]) for arg in zip(*multituples)]).strip()} {NAME}kspace {NAME}sensitivities {NAME}output "
cfl.writecfl(NAME + 'kspace', kspace)
cfl.writecfl(NAME + 'sensitivities', sensitivities)
if DEBUG:
print(cmd_str)
os.system(cmd_str)
outputs = cfl.readcfl(NAME + 'output')
return outputs | a9bde663258ee106357724f7641df68880f3fc03 | 3,658,655 |
def vgg16_bn(pretrained=False, **kwargs):
"""VGG 16-layer model (configuration "D") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['vgg16_bn']))
return model | 34f8e4965555ed4cb046c8ab4e5cde799d887040 | 3,658,656 |
import numpy
def tau(x, cval):
"""Robust estimators of location and scale, with breakdown points of 50%.
Also referred to as: Tau measure of location by Yohai and Zamar
Source: Yohai and Zamar JASA, vol 83 (1988), pp 406-413 and
Maronna and Zamar Technometrics, vol 44 (2002), pp. 307-317"""
med = median(x)
mad = median(numpy.abs(x - med))
zscore = 0.675 # Z-score of the 75th percentile of the normal distribution
s = zscore * mad
wnom = 0
wden = 0
for i in range(len(x)):
y = (x[i] - med) / s
temp = (1 - (y / cval)**2)**2
if abs(temp) <= cval:
wnom += temp * x[i]
wden += temp
return wnom / wden | 6f75ee23f50e94d1ee2754949f5c102d63ac4cab | 3,658,657 |
def shn_gis_location_represent(id, showlink=True):
""" Represent a location given its id """
table = db.gis_location
try:
location = db(table.id == id).select(table.id,
table.name,
table.level,
table.parent,
table.lat,
table.lon,
cache=(cache.ram, 60),
limitby=(0, 1)).first()
return shn_gis_location_represent_row(location, showlink)
except:
try:
# "Invalid" => data consistency wrong
represent = location.id
except:
represent = NONE
return represent | 758dfb8e32178e864f838a790eadf598f65ae6ec | 3,658,658 |
def de_pearson_dataframe(df, genes, pair_by='type', gtex=True, tcga=True):
"""
PearsonR scores of gene differential expression between tumor and normal types.
1. Calculate log2FC of genes for TCGA tumor samples with matching TCGA normal types
2. Compare log2fc to tumor type compared to all other normal types
3. Calculate PearsonR and save
:param pd.DataFrame df: Exp/TPM dataframe containing "type"/"tissue/tumor/label" metadata columns
:param list genes: Genes to use in differential expression calculation
:param str pair_by: How to pair tumors/normals. Either by "type" or "tissue"
:param bool gtex: If True, includes GTEx in normal set
:param bool tcga: If True, includes TCGA in normal set
:return: PearsonR dataframe
:rtype: pd.DataFrame
"""
# Subset by Tumor/Normal
tumor = df[df.label == 'tcga-tumor']
tcga_n = df[df.label == 'tcga-normal']
# Determine normal comparison group based on options
if gtex and tcga:
normal = df[df.tumor == 'no']
elif gtex:
normal = df[df.label == 'gtex']
else:
normal = tcga_n
# Identify tumor types with paired tcga-normal
tum_types = [x for x in sorted(tumor[pair_by].unique())
if x in sorted(df[df.label == 'tcga-normal'][pair_by].unique())]
norm_types = []
# For all paired tumor_types, calculate l2fc, then PearsonR of l2fc to all normal tumor types
pearson_l2fc = defaultdict(list)
for tum_type in tum_types:
# First calculate TCGA tumor/normal prior for comparison
t_med = tumor[tumor[pair_by] == tum_type][genes].median()
n_med = tcga_n[tcga_n[pair_by] == tum_type][genes].median()
prior_l2fc = log2fc(t_med, n_med)
# For every normal type, calculate pearsonR correlation
for (norm_type, label), _ in normal.groupby(pair_by).label.value_counts().iteritems():
if tum_type == norm_type:
l2fc = prior_l2fc
else:
n_med = normal[normal[pair_by] == norm_type][genes].median()
l2fc = log2fc(t_med, n_med)
# Calculate PearsonR of l2fc and comparison tissue/type
pearson_r = round(pearsonr(prior_l2fc, l2fc)[0], 2)
pearson_l2fc[tum_type[:20]].append(pearson_r)
norm_label = '{}_{}'.format(label, norm_type[:20])
if norm_label not in norm_types:
norm_types.append(norm_label)
return pd.DataFrame(pearson_l2fc, index=norm_types) | 29423402b24acc67a278cbdee03916add4228d7d | 3,658,659 |
def load_YUV_as_dic_tensor(path_img):
"""
Construct a dic with 3 entries ('y','u', 'v'), each of them
is a tensor and is loaded from path_img + key + '.png'.
! Return a dictionnary of 3D tensor (i.e. without a dummy batch index)
"""
dic_res = {}
key = ['y', 'u', 'v']
for k in key:
img = Image.open(path_img + '_' + k + '.png')
# check if image mode is correct: it should be a one
# canal uint8 image (i.e. mode L)
if img.mode != 'L':
img = img.convert('L')
dic_res[k] = to_tensor(img)
return dic_res | b0fe081b36c70ba8a185f151b13c5f046ef26ad6 | 3,658,660 |
def tensor_log10(t1, out_format, dtype=None):
"""
Takes the log base 10 of each input in the tensor.
Note that this is applied to all elements in the tensor not just non-zeros.
Warnings
---------
The log10 of 0 is undefined and is performed on every element in the tensor regardless of sparsity.
Parameters
------------
t1: tensor, array_like
input tensor or array_like object
out_format: format, mode_format, optional
* If a :class:`format` is specified, the result tensor is stored in the format out_format.
* If a :class:`mode_format` is specified, the result the result tensor has a with all of the dimensions
stored in the :class:`mode_format` passed in.
dtype: Datatype
The datatype of the output tensor.
Examples
----------
>>> import pytaco as pt
>>> pt.tensor_log10([10, 100], out_format=pt.compressed, dtype=pt.float32).to_array()
array([1., 2.], dtype=float32)
Returns
--------
log10: tensor
The element wise log10 of the input tensor.
"""
t1 = as_tensor(t1, copy=False)
cast_val = _cm.max_type(_cm.float32, t1.dtype)
f = lambda x: _cm.log10(_cm.cast(x, cast_val))
return _compute_unary_elt_eise_op(f, t1, out_format, dtype) | ff5c1a2f4cee9bc287ac81d3d3e524c1292fa2a7 | 3,658,661 |
def get_file_phenomena_i(index):
"""
Return file phenomena depending on the value of index.
"""
if index <= 99:
return [phen[0]]
elif index >= 100 and index <= 199:
return [phen[1]]
elif index >= 200 and index <= 299:
return [phen[2]]
elif index >= 300 and index <= 399:
return [phen[3]]
elif index >= 400 and index <= 499:
return phen[0:2]
elif index >= 500 and index <= 599:
return phen[0:3]
elif index >= 600 and index <= 699:
tmp_l = phen[0:2]
tmp_l.append(phen[3])
return tmp_l | 18beac08b59aec18b33f6472866a50decd01db30 | 3,658,662 |
def resource_cache_map(resource_id, flush=True):
"""cache resource info"""
if flush:
map_resources(resource_ids=[resource_id, ])
if resource_id not in CDNRESOURCE:
raise InvalidArgument('Resource not exit')
return CDNRESOURCE[resource_id] | 5e67546db9008e805b80c1ed7545d3787444c402 | 3,658,663 |
def _preprocess_html(table_html):
"""Parses HTML with bs4 and fixes some glitches."""
table_html = table_html.replace("<br />", "<br /> ")
table = bs4.BeautifulSoup(table_html, "html5lib")
table = table.find("table")
# Delete hidden style annotations.
for tag in table.find_all(attrs={"style": "display:none"}):
tag.decompose()
# Make sure "rowspan" is not set to an illegal value.
for tag in table.find_all("td"):
for attr in list(tag.attrs):
if attr == "rowspan":
tag.attrs[attr] = ""
return table | 1062c5cdbb058ea36b1c877d7787aebbde87c642 | 3,658,664 |
def parse_campus_hours(data_json, eatery_model):
"""Parses a Cornell Dining json dictionary.
Returns 1) a list of tuples of CampusEateryHour objects for a corresponding CampusEatery object and their unparsed
menu 2) an array of the items an eatery serves.
Args:
data_json (dict): a valid dictionary from the Cornell Dining json
eatery_model (CampusEatery): the CampusEatery object to which to link the hours.
"""
eatery_hours_and_menus = []
dining_items = []
for eatery in data_json["data"]["eateries"]:
eatery_slug = eatery.get("slug", "")
if eatery_model.slug == eatery_slug:
dining_items = get_trillium_menu() if eatery_slug == TRILLIUM_SLUG else parse_dining_items(eatery)
hours_list = eatery["operatingHours"]
for hours in hours_list:
new_date = hours.get("date", "")
hours_events = hours["events"]
if hours_events:
for event in hours_events:
start, end = format_time(event.get("start", ""), event.get("end", ""), new_date)
eatery_hour = CampusEateryHour(
eatery_id=eatery_model.id,
date=new_date,
event_description=event.get("descr", ""),
event_summary=event.get("calSummary", ""),
end_time=end,
start_time=start,
)
eatery_hours_and_menus.append((eatery_hour, event.get("menu", [])))
else:
eatery_hour = CampusEateryHour(
eatery_id=eatery_model.id,
date=new_date,
event_description=None,
event_summary=None,
end_time=None,
start_time=None,
)
eatery_hours_and_menus.append((eatery_hour, []))
return eatery_hours_and_menus, dining_items | 95e7bbc898f4516b9812d3f68749651a32f3535f | 3,658,665 |
from typing import Dict
from typing import Tuple
def _change_relationships(edge: Dict) -> Tuple[bool, bool]:
"""Validate relationship."""
if 'increases' in edge[1]['relation'] or edge[1]['relation'] == 'positive_correlation':
return True, True
elif 'decreases' in edge[1]['relation'] or edge[1]['relation'] == 'negative_correlation':
return True, False
return False, False | b826eb1eb7bd1e7eed7fd8577b5c04d827a75e56 | 3,658,666 |
def extract_behaviour_sync(sync, chmap=None, display=False, tmax=np.inf):
"""
Extract wheel positions and times from sync fronts dictionary
:param sync: dictionary 'times', 'polarities' of fronts detected on sync trace for all 16 chans
:param chmap: dictionary containing channel index. Default to constant.
chmap = {'bpod': 7, 'frame2ttl': 12, 'audio': 15}
:param display: bool or matplotlib axes: show the full session sync pulses display
defaults to False
:return: trials dictionary
"""
bpod = _get_sync_fronts(sync, chmap['bpod'], tmax=tmax)
if bpod.times.size == 0:
raise err.SyncBpodFpgaException('No Bpod event found in FPGA. No behaviour extraction. '
'Check channel maps.')
frame2ttl = _get_sync_fronts(sync, chmap['frame2ttl'], tmax=tmax)
audio = _get_sync_fronts(sync, chmap['audio'], tmax=tmax)
# extract events from the fronts for each trace
t_trial_start, t_valve_open, t_iti_in = _assign_events_bpod(
bpod['times'], bpod['polarities'])
t_ready_tone_in, t_error_tone_in = _assign_events_audio(
audio['times'], audio['polarities'])
trials = Bunch({
'goCue_times': _assign_events_to_trial(t_trial_start, t_ready_tone_in, take='first'),
'errorCue_times': _assign_events_to_trial(t_trial_start, t_error_tone_in),
'valveOpen_times': _assign_events_to_trial(t_trial_start, t_valve_open),
'stimFreeze_times': _assign_events_to_trial(t_trial_start, frame2ttl['times'], take=-2),
'stimOn_times': _assign_events_to_trial(t_trial_start, frame2ttl['times'], take='first'),
'stimOff_times': _assign_events_to_trial(t_trial_start, frame2ttl['times']),
'itiIn_times': _assign_events_to_trial(t_trial_start, t_iti_in)
})
# feedback times are valve open on good trials and error tone in on error trials
trials['feedback_times'] = np.copy(trials['valveOpen_times'])
ind_err = np.isnan(trials['valveOpen_times'])
trials['feedback_times'][ind_err] = trials['errorCue_times'][ind_err]
trials['intervals'] = np.c_[t_trial_start, trials['itiIn_times']]
if display:
width = 0.5
ymax = 5
if isinstance(display, bool):
plt.figure("Ephys FPGA Sync")
ax = plt.gca()
else:
ax = display
r0 = _get_sync_fronts(sync, chmap['rotary_encoder_0'])
plots.squares(bpod['times'], bpod['polarities'] * 0.4 + 1, ax=ax, color='k')
plots.squares(frame2ttl['times'], frame2ttl['polarities'] * 0.4 + 2, ax=ax, color='k')
plots.squares(audio['times'], audio['polarities'] * 0.4 + 3, ax=ax, color='k')
plots.squares(r0['times'], r0['polarities'] * 0.4 + 4, ax=ax, color='k')
plots.vertical_lines(t_ready_tone_in, ymin=0, ymax=ymax,
ax=ax, label='goCue_times', color='b', linewidth=width)
plots.vertical_lines(t_trial_start, ymin=0, ymax=ymax,
ax=ax, label='start_trial', color='m', linewidth=width)
plots.vertical_lines(t_error_tone_in, ymin=0, ymax=ymax,
ax=ax, label='error tone', color='r', linewidth=width)
plots.vertical_lines(t_valve_open, ymin=0, ymax=ymax,
ax=ax, label='valveOpen_times', color='g', linewidth=width)
plots.vertical_lines(trials['stimFreeze_times'], ymin=0, ymax=ymax,
ax=ax, label='stimFreeze_times', color='y', linewidth=width)
plots.vertical_lines(trials['stimOff_times'], ymin=0, ymax=ymax,
ax=ax, label='stim off', color='c', linewidth=width)
plots.vertical_lines(trials['stimOn_times'], ymin=0, ymax=ymax,
ax=ax, label='stimOn_times', color='tab:orange', linewidth=width)
c = _get_sync_fronts(sync, chmap['left_camera'])
plots.squares(c['times'], c['polarities'] * 0.4 + 5, ax=ax, color='k')
c = _get_sync_fronts(sync, chmap['right_camera'])
plots.squares(c['times'], c['polarities'] * 0.4 + 6, ax=ax, color='k')
c = _get_sync_fronts(sync, chmap['body_camera'])
plots.squares(c['times'], c['polarities'] * 0.4 + 7, ax=ax, color='k')
ax.legend()
ax.set_yticklabels(['', 'bpod', 'f2ttl', 'audio', 're_0', ''])
ax.set_yticks([0, 1, 2, 3, 4, 5])
ax.set_ylim([0, 5])
return trials | b02ec14a5714f1387acb12f1ec2d5bbbc1684f67 | 3,658,667 |
import os
from pathlib import Path
import torch
def download_and_load_model(model_files) -> RecursiveScriptModule:
"""
Downloads and torch.jit.load the model from google drive, the downloaded model is saved in /tmp
since in heroku we get /tmp to save all our stuff, if the app is not running in production
the model must be saved in load storage, hence the model is directly loaded
Args:
model_files: the dict containing the model information
Returns:
(RecursiveScriptModule): the loaded torch.jit model
"""
if "PRODUCTION" in os.environ:
logger.info(
f"=> Downloading Model {model_files['model_file']} from {model_files['model_url']}"
)
# heroku gives you `/tmp` to store files, which can be cached
model_path: Path = Path("/tmp") / f"{model_files['model_file']}.pt"
if not model_path.exists():
gdown.cached_download(url=model_files["model_url"], path=model_path)
logger.info(f"=> Loading {model_files['model_file']} from download_cache")
model: RecursiveScriptModule = torch.jit.load(str(model_path))
else:
logger.info(f"=> Loading {model_files['model_file']} from Local")
model = torch.jit.load(
str((Path("models") / (model_files["model_file"] + ".pt")))
)
return model | 7f00cd1a79ec87eb553b44045c5e4752851bbb2a | 3,658,668 |
def is_attr_defined(attrs,dic):
"""
Check if the sequence of attributes is defined in dictionary 'dic'.
Valid 'attrs' sequence syntax:
<attr> Return True if single attrbiute is defined.
<attr1>,<attr2>,... Return True if one or more attributes are defined.
<attr1>+<attr2>+... Return True if all the attributes are defined.
"""
if OR in attrs:
for a in attrs.split(OR):
if dic.get(a.strip()) is not None:
return True
else: return False
elif AND in attrs:
for a in attrs.split(AND):
if dic.get(a.strip()) is None:
return False
else: return True
else:
return dic.get(attrs.strip()) is not None | 542388846fabc79e126203d80a63db6901a71897 | 3,658,669 |
def c_str_repr(str_):
"""Returns representation of string in C (without quotes)"""
def byte_to_repr(char_):
"""Converts byte to C code string representation"""
char_val = ord(char_)
if char_ in ['"', '\\', '\r', '\n']:
return '\\' + chr(char_val)
elif (ord(' ') <= char_val <= ord('^') or char_val == ord('_') or
ord('a') <= char_val <= ord('~')):
return chr(char_val)
else:
return '\\x%02x' % char_val
return '"%s"' % ''.join((byte_to_repr(x) for x in str_)) | e7cce729a00a7d2a35addf95eb097a3caa06bedd | 3,658,670 |
from datetime import datetime
import os
def hour_paths_for_range(hours_path, start, end):
"""Generate a list of hour paths to check when looking for segments between start and end."""
# truncate start and end to the hour
def truncate(dt):
return dt.replace(microsecond=0, second=0, minute=0)
current = truncate(start)
end = truncate(end)
# Begin in the hour prior to start, as there may be a segment that starts in that hour
# but contains the start time, eg. if the start time is 01:00:01 and there's a segment
# at 00:59:59 which goes for 3 seconds.
# Checking the entire hour when in most cases it won't be needed is wasteful, but it's also
# pretty quick and the complexity of only checking this case when needed just isn't worth it.
current -= datetime.timedelta(hours=1)
while current <= end:
yield os.path.join(hours_path, current.strftime("%Y-%m-%dT%H"))
current += datetime.timedelta(hours=1) | 2ba8c2ccb914fcf0eb7bbbddb8e0e5ecad6adec0 | 3,658,671 |
def getActiveTeamAndID():
"""Returns the Team ID and CyTeam for the active player."""
return getActiveTeamID(), getActiveTeam() | edf58aee8d9126ddc25afd94becf641330e13ca2 | 3,658,672 |
from typing import Union
from typing import BinaryIO
from typing import Tuple
from typing import Optional
def is_nitf(
file_name: Union[str, BinaryIO],
return_version=False) -> Union[bool, Tuple[bool, Optional[str]]]:
"""
Test whether the given input is a NITF 2.0 or 2.1 file.
Parameters
----------
file_name : str|BinaryIO
return_version : bool
Returns
-------
is_nitf_file: bool
Is the file a NITF file, based solely on checking initial bytes.
nitf_version: None|str
Only returned is `return_version=True`. Will be `None` in the event that
`is_nitf_file=False`.
"""
header = _fetch_initial_bytes(file_name, 9)
if header is None:
if return_version:
return False, None
else:
return False
ihead = header[:4]
vers = header[4:]
if ihead == b'NITF':
try:
vers = vers.decode('utf-8')
return (True, vers) if return_version else True
except ValueError:
pass
return (False, None) if return_version else False | 6e28baa09d6b8e173db00671e1ed08023630110b | 3,658,673 |
import argparse
import os
import sys
def main():
"""A simple main for testing via command line."""
parser = argparse.ArgumentParser(
description='A manual test for ros-pull-request-builder access'
'to a GitHub repo.')
parser.add_argument('user', type=str)
parser.add_argument('repo', type=str)
parser.add_argument('--callback-url', type=str,
default='http://build.ros.org/ghprbhook/')
parser.add_argument('--hook-user', type=str,
default='ros-pull-request-builder')
parser.add_argument('--password-env', type=str,
default='ROSGHPRB_TOKEN')
args = parser.parse_args()
password = os.getenv(args.password_env)
if not password:
parser.error(
'OAUTH Token with hook and organization read access'
'required in ROSGHPRB_TOKEN environment variable')
errors = []
result = check_hooks_on_repo(
args.user,
args.repo,
errors,
args.hook_user,
args.callback_url,
password)
if errors:
print('Errors detected:', file=sys.stderr)
for e in errors:
print(e, file=sys.stderr)
if result:
return 0
return 1 | df0ab1cf1fe7f048a35673280002efc26fe483cc | 3,658,674 |
def get_xlsx_filename() -> str:
"""
Get the name of the excel file. Example filename:
kesasetelihakemukset_2021-01-01_23-59-59.xlsx
"""
local_datetime_now_as_str = timezone.localtime(timezone.now()).strftime(
"%Y-%m-%d_%H-%M-%S"
)
filename = f"kesasetelihakemukset_{local_datetime_now_as_str}.xlsx"
return filename | fb8715f30bd91f39d9836bf59504ad85c205bdf3 | 3,658,675 |
from pathlib import Path
def get_content_directory() -> Path:
"""
Get the path of the markdown `content` directory.
"""
return get_base_directory() / "content" | 2b6f7a9c676e8128fafd43b26cf62aa736aa957c | 3,658,676 |
import math
def mag_inc(x, y, z):
"""
Given *x* (north intensity), *y* (east intensity), and *z*
(vertical intensity) all in [nT], return the magnetic inclincation
angle [deg].
"""
h = math.sqrt(x**2 + y**2)
return math.degrees(math.atan2(z, h)) | f4036358625dd9d032936afc373e53bef7c1e6e1 | 3,658,677 |
import torch
def rgb_to_rgba(image, alpha_val):
"""
Convert an image from RGB to RGBA.
"""
if not isinstance(image, torch.Tensor):
raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}")
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(f"Input size must have a shape of (*, 3, H, W).Got {image.shape}")
if not isinstance(alpha_val, (float, torch.Tensor)):
raise TypeError(f"alpha_val type is not a float or torch.Tensor. Got {type(alpha_val)}")
# add one channel
r, g, b = torch.chunk(image, image.shape[-3], dim=-3)
if isinstance(alpha_val, float):
a = torch.full_like(r, fill_value=float(alpha_val))
return torch.cat([r, g, b, a], dim=-3) | 5bab73c37ff81c431ed88ce7d39743cce6c15c56 | 3,658,678 |
import os
def has_labels(dataset_dir, filename=LABELS_FILENAME):
"""Specifies whether or not the dataset directory
contains a label map file.
Args:
dataset_dir: The directory in which the labels file is found.
filename: The filename where the class names are written.
Returns:
`True` if the labels file exists and `False` otherwise.
"""
return tf.io.gfile.exists(os.path.join(dataset_dir, filename)) | e9ff2dcb52559ec88d45e3350ec22a6f9f421f27 | 3,658,679 |
def get(identifier):
"""get the activation function"""
if identifier is None:
return linear
if callable(identifier):
return identifier
if isinstance(identifier, str):
activations = {
"relu": relu,
"sigmoid": sigmoid,
"tanh": tanh,
"linear": linear,
}
return activations[identifier] | 005789e8cdadff97875f002b9776d8d8bdb22d56 | 3,658,680 |
def df_add_column_codelines(self, key):
"""Generate code lines to add new column to DF"""
func_lines = df_set_column_index_codelines(self) # provide res_index = ...
results = []
for i, col in enumerate(self.columns):
col_loc = self.column_loc[col]
type_id, col_id = col_loc.type_id, col_loc.col_id
res_data = f'res_data_{i}'
func_lines += [
f' data_{i} = self._data[{type_id}][{col_id}]',
f' {res_data} = pandas.Series(data_{i}, index=res_index, name="{col}")',
]
results.append((col, res_data))
res_data = 'new_res_data'
literal_key = key.literal_value
func_lines += [f' {res_data} = pandas.Series(value, index=res_index, name="{literal_key}")']
results.append((literal_key, res_data))
data = ', '.join(f'"{col}": {data}' for col, data in results)
func_lines += [f' return pandas.DataFrame({{{data}}}, index=res_index)']
return func_lines | 742241d973bb46da2a75b40bf9a76c91ba759d98 | 3,658,681 |
import torch
def resize_bilinear_nd(t, target_shape):
"""Bilinear resizes a tensor t to have shape target_shape.
This function bilinearly resizes a n-dimensional tensor by iteratively
applying tf.image.resize_bilinear (which can only resize 2 dimensions).
For bilinear interpolation, the order in which it is applied does not matter.
Args:
t: tensor to be resized
target_shape: the desired shape of the new tensor.
Returns:
The resized tensor
"""
shape = list(t.shape)
target_shape = list(target_shape)
assert len(shape) == len(target_shape)
# We progressively move through the shape, resizing dimensions...
d = 0
while d < len(shape):
# If we don't need to deal with the next dimension, step over it
if shape[d] == target_shape[d]:
d += 1
continue
# Otherwise, we'll resize the next two dimensions...
# If d+2 doesn't need to be resized, this will just be a null op for it
new_shape = shape[:]
new_shape[d:d+2] = target_shape[d:d+2]
# The helper collapse_shape() makes our shapes 4-dimensional with
# the two dimensions we want to deal with on the outside.
shape_ = collapse_shape(shape, d, d+2)
new_shape_ = collapse_shape(new_shape, d, d+2)
# We can then reshape and use torch.nn.Upsample() on the
# outer two dimensions.
t_ = t.view(shape_)
# transpose [0, 1, 2, 3] to [0, 3, 1, 2]
t_ = torch.transpose(t_, 1, 3)
t_ = torch.transpose(t_, 2, 3)
upsample = torch.nn.Upsample(size=new_shape_[1:3], mode='bilinear', align_corners=True)
t_ = upsample(t_)
t_ = torch.transpose(t_, 2, 3)
t_ = torch.transpose(t_, 1, 3)
# And then reshape back to our uncollapsed version, having finished resizing
# two more dimensions in our shape.
t = t_.reshape(new_shape)
shape = new_shape
d += 2
return t | 005266983cca744437826673ff8dd379afb699e2 | 3,658,682 |
def _parse_disambiguate(disambiguatestatsfilename):
"""Parse disambiguation stats from given file.
"""
disambig_stats = [-1, -1, -1]
with open(disambiguatestatsfilename, "r") as in_handle:
header = in_handle.readline().strip().split("\t")
if header == ['sample', 'unique species A pairs', 'unique species B pairs', 'ambiguous pairs']:
disambig_stats_tmp = in_handle.readline().strip().split("\t")[1:]
if len(disambig_stats_tmp) == 3:
disambig_stats = [int(x) for x in disambig_stats_tmp]
return disambig_stats | bb05ec857181f032ae9c0916b4364b772ff7c412 | 3,658,683 |
def clean_vigenere(text):
"""Convert text to a form compatible with the preconditions imposed by Vigenere cipher."""
return ''.join(ch for ch in text.upper() if ch.isupper()) | d7c3fc656ede6d07d6e9bac84a051581364c63a0 | 3,658,684 |
def select_artist(df_by_artists, df_rate):
"""This method selects artists which perform the same genre as
artists were given
:param df_by_artists:
:param df_rate:
"""
# save the indices of artists, which include any of the genres in the genre profile
list_of_id = []
for index, row in df_by_artists.iterrows():
for genre in row["genres"]:
if(genre in df_rate.index):
list_of_id.append(index)
#find the unique indices
list_of_id = list(set(list_of_id))
#select the artists and genres columns of the artists including any of the genres in the genre profile
df_select_columns = df_by_artists.iloc[list_of_id, [col(df_by_artists, "artists"), col(df_by_artists, "genres")]]
df_select = df_select_columns.copy()
#create the artist-genre-matrix of new artists
for index, row in df_select_columns.iterrows():
for genre in row['genres']:
#artist includes genre: 1
df_select.at[index, genre] = 1
#artist does not include genre: 0
df_select = df_select.fillna(0)[df_rate.index]
return df_select | 85c09b62553a3257b4f325dd28d26335c9fcb033 | 3,658,685 |
import uuid
def generate_uuid(class_name: str, identifier: str) -> str:
""" Generate a uuid based on an identifier
:param identifier: characters used to generate the uuid
:type identifier: str, required
:param class_name: classname of the object to create a uuid for
:type class_name: str, required
"""
return str(uuid.uuid5(uuid.NAMESPACE_DNS, class_name + identifier)) | 10e85effbce04dec62cc55ee709247afa0fb0da7 | 3,658,686 |
def fetch(model, key):
"""Fetch by ID."""
return db.session.query(model).get(key) | 4c3008bec5ed5eac593f2ad8ba2816f121362677 | 3,658,687 |
from typing import Optional
def construct_filename(prefix: str, suffix: Optional[str] = '.csv') -> str:
"""Construct a filename containing the current date.
Examples
--------
.. code:: python
>>> filename = construct_filename('my_file', '.txt')
>>> print(filename)
'my_file_31_May_2019.txt'
Parameters
----------
prefix : :class:`str`
A prefix for the to-be returned filename.
The current date will be appended to this prefix.
sufix : :class:`str`, optional
An optional sufix of the to be returned filename.
No sufix will be attached if ``None``.
Returns
-------
:class:`str`
A filename consisting of **prefix**, the current date and **suffix**.
"""
today = date.today()
suffix = suffix or ''
return prefix + today.strftime('_%d_%b_%Y') + suffix | 8269947952d4c8d81cc2855a5776c3677c6a5c57 | 3,658,688 |
def make_friedman_model(point1, point2):
"""
Makes a vtk line source from two set points
:param point1: one end of the line
:param point2: other end of the line
:returns: The line
"""
line = vtkLineSource()
line.SetPoint1(point1)
line.SetPoint2(point2)
return line | f33046307c7c0c2bfeadfbdb4e0815bc5d42d73f | 3,658,689 |
import re
def breadcrumbs_pcoa_plot(pcl_fname, output_plot_fname, **opts):
"""Use breadcrumbs `scriptPcoa.py` script to produce principal
coordinate plots of pcl files.
:param pcl_fname: String; file name of the pcl-formatted taxonomic profile
to visualize via `scriptPcoa.py`.
:param output_plot_fname: String; file name of the resulting image file.
:keyword **opts: Any additional keyword arguments are passed to
`scriptPcoa.py` as command line flags. By default,
it passes `meta=None`, `id=None` and `noShape=None`,
which are converted into `--meta`, `--id`, and
`--noShape`, respectively.
External dependencies
- Breadcrumbs: https://bitbucket.org/biobakery/breadcrumbs
"""
pcoa_cmd = ("scriptPcoa.py ")
default_opts = {
"meta" : True,
"id" : True,
"noShape" : True,
"outputFile" : output_plot_fname
}
default_opts.update(opts)
def sample_id(fname):
id_ = str()
with open(fname) as f:
for line in f:
if line.startswith("#"):
id_ = line.split('\t')[0]
continue
else:
return id_ or line.split('\t')[0]
def last_meta_name(fname):
prev_line = str()
with open(fname) as f:
for line in f:
if re.search(r'[Bb]acteria|[Aa]rchaea.*\s+\d', line):
return prev_line.split('\t')[0]
prev_line = line
return prev_line.split('\t')[0]
def run(pcoa_cmd=pcoa_cmd):
if default_opts['meta'] is True or not default_opts['meta']:
default_opts['meta'] = last_meta_name(pcl_fname)
if default_opts['id'] is True or not default_opts['id']:
default_opts['id'] = sample_id(pcl_fname)
pcoa_cmd += dict_to_cmd_opts(default_opts)
pcoa_cmd += " "+pcl_fname+" "
return CmdAction(pcoa_cmd, verbose=True).execute()
targets = [output_plot_fname]
if 'CoordinatesMatrix' in default_opts:
targets.append(default_opts['CoordinatesMatrix'])
yield {
"name": "breadcrumbs_pcoa_plot: "+output_plot_fname,
"actions": [run],
"file_dep": [pcl_fname],
"targets": targets
} | 06fc9511b21ec3c0111ba91cea8c08852eb2bcaf | 3,658,690 |
def _parse_xml(buff):
"""\
Parses XML and returns the root element.
"""
buff.seek(0)
return etree.parse(buff).getroot() | fa3876f93c0a71b9e4bf6d95dfadbf0714e7c17c | 3,658,691 |
def After(interval):
""" After waits for the duration to elapse and then sends the current time
on the returned channel.
It is equivalent to Timer(interval).c
"""
return Timer(interval).c | 1011151471f839b3e9f7edad369699d76d9f7601 | 3,658,692 |
def f_score(r: float, p: float, b: int = 1):
"""
Calculate f-measure from recall and precision.
Args:
r: recall score
p: precision score
b: weight of precision in harmonic mean
Returns:
val: value of f-measure
"""
try:
val = (1 + b ** 2) * (p * r) / (b ** 2 * p + r)
except ZeroDivisionError:
val = 0
return val | d12af20e30fd80cb31b2cc119d5ea79ce2507c4b | 3,658,693 |
import os
def is_container_system_config_file(file):
"""Determine whether a given file is one of the files created by setup_container_system_config().
@param file: Absolute file path as string.
"""
if not file.startswith("/etc/"):
return False
return file in [os.path.join("/etc", f.decode()) for f in CONTAINER_ETC_FILE_OVERRIDE] | 8c0c87b6925044f31eb89ed47be5ff39d54139b4 | 3,658,694 |
def show_inventory():
"""Show the user what is in stock."""
context = {
'inventory': [ # Could contain any items
{'name': 'apple', 'price': 1.00},
{'name': 'banana', 'price': 1.20},
{'name': 'carrot', 'price': 2.00},
]
}
return render_template('show_inventory.html', **context) | be2b67abb1ebd60bacfad117dab166a08d6915b1 | 3,658,695 |
def grid_optimizer(
data,
params,
args,
xset,
yset=None,
verbose=False,
visualize=False,
save_path=None):
"""
This function optimizes the ESN parameters, x and y, over a specified
range of values. The optimal values are determined by minimizing
the mean squared error. Those optimal values are returned.
Parameters
----------
data : numpy array
This is the dataset that the ESN should train and predict.
If the training length plus the future total exceed the
length of the data, an error will be thrown.
**The shape of the transpose of the data will determine
the number of inputs and outputs.**
params : dictionary
A dictionary containing all of the parameters required to
initialize an ESN.
Required parameters are:
* "n_reservoir" : int, the reservoir size
* "sparsity" : float, the sparsity of the reservoir
* "rand_seed" : int or None, specifies the initial seed
* "rho" : float, the spectral radius
* "noise" : the noise used for regularization
* "trainlen" : int, the training length
* "future" : int, the total prediction length
* "window" : int or None, the window size
args : list or tuple
The list of variables you want to optimize. Must be less
than or equal to two.
xset : numpy array
The first set of values to be tested. Cannot be None.
yset : numpy array or None
The second set of values to be tested at the same
time as the xset. Can be None.
verbose : boolean
Specifies if the simulation outputs should be printed.
Useful for debugging.
visualize : boolean, string
Specifies if the results should be visualized.
* 'surface' will plot a 3D error surface.
save_path : string
Specifies where the data should be saved. Default is None.
Returns
-------
loss : numpy array
The array or matrix of loss values.
"""
assert(len(args) <= 2), "Too many variables to optimize. Pick two or fewer."
for variable in args:
assert(variable in list(params.keys())
), f"{variable} not in parameters"
if len(args) > 1:
assert(yset is not None), "Two variables specified, two sets not given."
xvar = args[0]
loss = np.zeros(len(xset))
if yset is not None:
assert(len(args) > 1), "Second parameter set given, but not specified."
yvar = args[1]
loss = np.zeros([len(xset), len(yset)])
if verbose:
print(f"Optimizing over {args}:")
predictLen = params['future']
for x, xvalue in enumerate(xset):
params[xvar] = xvalue
if yset is not None:
for y, yvalue in enumerate(yset):
params[yvar] = yvalue
predicted = esn_prediction(data, params)
loss[x, y] = MSE(predicted, data[-predictLen:])
if verbose:
print(
f"{variables[xvar]} = {xvalue},"
f"{variables[yvar]} = {yvalue}, MSE={loss[x][y]}")
else:
predicted = esn_prediction(data, params)
loss[x] = MSE(predicted, data[-predictLen:])
if verbose:
print(f"{xvar} = {xvalue}, MSE={loss[x]}")
# =======================================================================
# Visualization
# =======================================================================
if visualize is True and yset is not None:
plt.figure(figsize=(16, 9), facecolor='w', edgecolor='k')
plt.title((f"Hyper-parameter Optimization over {variables[xvar]}",
f"and {variables[yvar]}"))
im = plt.imshow(loss.T,
vmin=abs(loss).min(),
vmax=abs(loss).max(),
origin='lower',
cmap='PuBu')
plt.xticks(np.linspace(0, len(xset) - 1,
len(xset)), xset)
plt.yticks(np.linspace(0, len(yset) - 1,
len(yset)), yset)
plt.xlabel(f'{variables[xvar]}', fontsize=16)
plt.ylabel(f'{variables[yvar]}', fontsize=16)
cb = plt.colorbar(im)
cb.set_label(label="Mean Squared Error",
fontsize=16,
rotation=-90,
labelpad=25)
elif visualize is True and yset is None:
plt.figure(figsize=(16, 9), facecolor='w', edgecolor='k')
plt.plot(xset, loss, '-ok', alpha=0.6)
plt.title(f'MSE as a Function of {variables[xvar]}', fontsize=20)
plt.xlabel(f'{variables[xvar]}', fontsize=18)
plt.ylabel('MSE', fontsize=18)
elif visualize is 'surface' and yset is not None:
fig = plt.figure(figsize=(16, 9), facecolor='w', edgecolor='k')
ax = plt.axes(projection='3d')
X = np.array(xset)
Y = np.array(yset)
Z = np.array(loss).T
print(f"Shape X {X.shape}")
print(f"Shape Y {Y.shape}")
print(f"Shape Z {Z.shape}")
mappable = plt.cm.ScalarMappable()
mappable.set_array(Z)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
cmap=mappable.cmap,
norm=mappable.norm)
ax.set_xlabel(f'{variables[xvar]}', fontsize=18)
ax.set_ylabel(f'{variables[yvar]}', fontsize=18)
ax.set_zlabel('MSE', fontsize=18)
cb = plt.colorbar(mappable)
cb.set_label(label="Mean Squared Error",
fontsize=16,
rotation=-90,
labelpad=25)
fig.tight_layout()
plt.show()
# =======================================================================
# Save data
# =======================================================================
if save_path is not None:
if yset is not None:
fname = f"_{xvar}_{yvar}_loss"
np.save('./data/' + save_path + fname, loss)
else:
fname = f"_{xvar}_loss"
np.save('./data/' + save_path + fname, loss)
return loss | c3c9749827997793ab0ee40e454143d26ed37cc9 | 3,658,696 |
import numpy as np
import re
def rebuild_schema(doc, r, df):
"""Rebuild the schema for a resource based on a dataframe"""
# Re-get the resource in the doc, since it may be different.
try:
r = doc.resource(r.name)
except AttributeError:
# Maybe r is actually a resource name
r = doc.resource(r)
def alt_col_name(name, i):
if not name:
return 'col{}'.format(i)
return re.sub('_+', '_', re.sub('[^\w_]', '_', str(name)).lower()).rstrip('_')
df_types = {
np.dtype('O'): 'text',
np.dtype('int64'): 'integer',
np.dtype('float64'): 'number'
}
try:
df_index_frame = df.index.to_frame()
except AttributeError:
df_index_frame = None
def get_col_dtype(c):
c = str(c)
try:
return df_types[df[c].dtype]
except KeyError:
# Maybe it is in the index?
pass
try:
return df_types[df_index_frame[c].dtype]
except TypeError:
# Maybe not a multi-index
pass
if c == 'id' or c == df.index.name:
return df_types[df.index.dtype]
return 'unknown'
columns = []
schema_term = r.schema_term[0]
if schema_term:
old_cols = {c['name'].value: c.properties for c in schema_term.children}
for c in schema_term.children:
schema_term.remove_child(c)
schema_term.children = []
else:
old_cols = {}
schema_term = doc['Schema'].new_term('Table', r.schema_name)
index_names = [n if n else "id" for n in df.index.names]
for i, col in enumerate(index_names + list(df.columns)):
acn = alt_col_name(col, i) if alt_col_name(col, i) != str(col) else ''
d = {'name': col, 'datatype': get_col_dtype(col), 'altname': acn}
if col in old_cols.keys():
lookup_name = col
elif acn in old_cols.keys():
lookup_name = acn
else:
lookup_name = None
if lookup_name and lookup_name in old_cols:
for k, v in schema_term.properties.items():
old_col = old_cols.get(lookup_name)
for k, v in old_col.items():
if k != 'name' and v:
d[k] = v
columns.append(d)
for c in columns:
name = c['name']
del c['name']
datatype = c['datatype']
del c['datatype']
altname = c['altname']
del c['altname']
schema_term.new_child('Column', name, datatype=datatype, altname=altname, **c) | ed212e5cff26dcfece99e3361df9d61823c2bfde | 3,658,697 |
def compute_similarity(image, reference):
"""Compute a similarity index for an image compared to a reference image.
Similarity index is based on a the general algorithm used in the AmphiIndex algorithm.
- identify slice of image that is a factor of 256 in size
- rebin image slice down to a (256,256) image
- rebin same slice from reference down to a (256,256) image
- sum the differences of the rebinned slices
- divide absolute value of difference scaled by reference slice sum
.. note:: This index will typically return values < 0.1 for similar images, and
values > 1 for dis-similar images.
Parameters
----------
image : ndarray
Image (as ndarray) to measure
reference : ndarray
Image which serves as the 'truth' or comparison image.
Returns
-------
similarity_index : float
Value of similarity index for `image`
"""
# Insure NaNs are replaced with 0
image = np.nan_to_num(image[:], nan=0)
reference = np.nan_to_num(reference[:], nan=0)
imgshape = (min(image.shape[0], reference.shape[0]),
min(image.shape[1], reference.shape[1]))
minsize = min(imgshape[0], imgshape[1])
# determine largest slice that is a power of 2 in size
window_bit = maxBit(minsize)
window = 2**window_bit
# Define how big the rebinned image should be for computing the sim index
# Insure a minimum rebinned size of 64x64
sim_bit = (window_bit - 2) if (window_bit - 2) > 6 else window_bit
sim_size = 2**sim_bit
# rebin image and reference
img = rebin(image[:window, :window], (sim_size, sim_size))
ref = rebin(reference[:window, :window], (sim_size, sim_size))
# Compute index
diffs = np.abs((img - ref).sum())
sim_indx = diffs / img.sum()
return sim_indx | 0b49009bfdd0697999e61825390a8f883ae8dd79 | 3,658,698 |
def _create_npu_quantization(
scale,
zero_point,
):
"""This is a helper function to capture a list
of arguments to create Vela NpuQuantization object
"""
# Scale could be an ndarray if per-channel quantization is available
if not isinstance(scale, tvm.tir.expr.Load):
if isinstance(scale.value, float):
scale = np.single(scale.value)
else:
assert isinstance(scale.value.value, float)
scale = np.single(scale.value.value)
q_params = vapi.NpuQuantization(scale_f32=scale, zero_point=zero_point.value)
return q_params | 71f7e20a760940e6d46301ccd9130265de140b29 | 3,658,699 |
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