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def getContentType(the_type):
"""
Get the content type based on the type name which is in settings
:param the_type:
:return:
"""
if the_type not in settings.XGDS_MAP_SERVER_JS_MAP:
return None
the_model_name = settings.XGDS_MAP_SERVER_JS_MAP[the_type]['model']
splits = the_model_name.split('.')
content_type = ContentType.objects.get(app_label=splits[0], model=splits[1])
return content_type | 25031eb0dce8fe7828f94bdbc99d5c574f0e5ea6 | 3,655,565 |
import scipy
import math
import numpy
def calculateGravityAcceleration(stateVec, epoch, useGeoid):
""" Calculate the acceleration due to gravtiy acting on the satellite at
a given state (3 positions and 3 velocities). Ignore satellite's mass,
i.e. use a restricted two-body problem.
Arguments
----------
numpy.ndarray of shape (1,6) with three Cartesian positions and three
velocities in an inertial reference frame in metres and metres per
second, respectively.
epoch - datetime corresponding to the UTC epoch at which the rate of change
is to be computed.
useGeoid - bool, whether to compute the gravity by using EGM geopotential
expansion (True) or a restricted two-body problem (False).
Returns
----------
numpy.ndarray of shape (1,3) with three Cartesian components of the
acceleration in m/s2 given in an inertial reference frame.
"""
if useGeoid:
" Compute geocentric co-latitude, longitude & radius. "
colatitude,longitude,r = calculateGeocentricLatLon(stateVec, epoch)
" Find the gravitational potential at the desired point. "
# See Eq. 1 in Cunningham (1996) for the general form of the geopotential expansion.
gravitationalPotential = 0.0 # Potential of the gravitational field at the stateVec location.
for degree in range(0, MAX_DEGREE+1): # Go through all the desired orders and compute the geoid corrections to the sphere.
temp = 0. # Contribution to the potential from the current degree and all corresponding orders.
legendreCoeffs = scipy.special.legendre(degree) # Legendre polynomial coefficients corresponding to the current degree.
for order in range(degree+1): # Go through all the orders corresponding to the currently evaluated degree.
if (abs(colatitude-math.pi/2. <= 1E-16)) or (abs(colatitude-3*math.pi/2. <= 1E-16)): # We're at the equator, cos(colatitude) will be zero and things will break.
temp += legendreCoeffs[order] * 1.0 * (Ccoeffs[degree][order]*math.cos( order*longitude ) + Scoeffs[degree][order]*math.sin( order*longitude ))
else:
temp += legendreCoeffs[order] * math.cos(colatitude) * (Ccoeffs[degree][order]*math.cos( order*longitude ) + Scoeffs[degree][order]*math.sin( order*longitude ))
gravitationalPotential += math.pow(EarthRadius/r, degree) * temp # Add the contribution from the current degree.
gravitationalPotential *= GM/r # Final correction (*GM for acceleration, /r to get r^(n+1) in the denominator).
" Compute the acceleration due to the gravity potential at the given point. "
# stateVec is defined w.r.t. Earth's centre of mass, so no need to account
# for the geoid shape here.
gravityAcceleration = gravitationalPotential/r* (-1.*stateVec[:3]/r) # First divide by the radius to get the acceleration value, then get the direction (towards centre of the Earth).
else:
r = numpy.linalg.norm(stateVec[:3]) # Earth-centred radius.
gravityAcceleration = GM/(r*r) * (-1.*stateVec[:3]/r) # First compute the magnitude, then get the direction (towards centre of the Earth).
return gravityAcceleration | a2ea0ff1c8feb9f0a678911130e3ca6e96838b7c | 3,655,566 |
import math
def points_on_line(r0, r1, spacing):
"""
Coordinates of points spaced `spacing` apart between points `r0` and `r1`.
The dimensionality is inferred from the length of the tuples `r0` and `r1`,
while the specified `spacing` will be an upper bound to the actual spacing.
"""
dim = len(r0)
v = np.array(r1) - np.array(r0)
length = np.linalg.norm(v)
steps = math.ceil(1.0 * length / spacing) + 1
points = np.zeros((steps, dim))
for i in xrange(dim):
points[:, i] = np.linspace(r0[i], r1[i], steps)
return points | eb2795cba55566823632c75b7a72f34731b5e36e | 3,655,567 |
def index() -> Response:
"""
Return application index.
"""
return APP.send_static_file("index.html") | 37d299ec548fe4f83d8f55f063e3bf9f5fb64c4e | 3,655,568 |
def compare_files(og_maxima,new_maxima, compare_file, until=100, divisor=1000):
"""
given input of the maxima of a graph, compare it to the maxima from data100.txt
maxima will be a series of x,y coordinates corresponding to the x,y values of a maximum from a file.
First see if there is a maxima with the same x value as data100.txt, if there is not expand the x value ranges
until a maximum is found. Find out what this dx is for the new file.
Note do it for all the peaks of data100.txt at once, so that if it finds a peak for the 2nd peak of data100.txt,
it doesn't also assign this to the first peak as well.
kewyword arguments until and divisor:
for the dx loop the loop will increase dx from 0 until until/divisor in steps of 1/divisor
eg for default values until=100 and divisor=1000,
it will increase dx from 0 until 100/1000 (=0.1) in steps of 1/1000 (=0.001)
changing these arguments will lead to more or less peak matching, which could
affect the results of the calculation significantly.
"""
if compare_file == 'data100.txt':
return None
# Whenever there is a match we will iterate this, so that we can compare
#this at the end?
number_of_matches = 0
# Initiate two lists to contain all the dx and dy values for each peak that
# is matched by the code.
dx_values = []
dy_values = []
# Loop through the original maxima list (supplied as an argument)
# and also loop through the maxima from the file being compared.
for og_idx,og_val in enumerate(og_maxima.T[0]):
for idx,val in enumerate(new_maxima.T[0]):
#this will loop dx from 0 to (until)/divisor in steps of 1/divisor
for x in range(until+1):
dx = x/divisor
# For the current value of dx see if there is a matching
# peak between the data100.txt file and the file being compared.
# There is a match if the val from the compare_file is within the range
# of the original peak x value +/- the dx value.
if og_val - dx <= val <= og_val + dx:
#if there is a match print some logging information to the console.
print(f"Peak Match : index {og_idx} from data100.txt and {idx} from {compare_file}")
print(f"values are {og_val} and {val} respectively")
# iterate the number of peak matches between the two files being compared.
number_of_matches+=1
# append the current dx value to our running list which will keep track
# of the dx values for all the matched peaks
dx_values.append(dx)
# Get the absolute value of the difference in y values (dy)
dy = abs(og_maxima.T[1][og_idx] - new_maxima.T[1][idx])
dy_values.append(dy)
#breaks us out of the "for x in range" loop
break
# If the for loop (for x in range ...) isn't terminated by a break statement
# I.E. we didn't get a match
else:
"move onto next peak in new_maxima"
continue
# If the for loop does get terminated by the break statement
# I.E. we get a match
"""compare next peak in og_maxima, IE break the new_maxima loop and move onto
next in the original maxima list"""
break
# Calculate the absolute value of the difference in number of peaks
# between the two data files
different_no_peaks = abs(len(new_maxima) - len(og_maxima))
return [dx_values, dy_values, number_of_matches, different_no_peaks] | 86fe2ffd02785d41284b8edfef44d0dc0e097c90 | 3,655,569 |
import _datetime
def parseDatetimetz(string, local=True):
"""Parse the given string using :func:`parse`.
Return a :class:`datetime.datetime` instance.
"""
y, mo, d, h, m, s, tz = parse(string, local)
s, micro = divmod(s, 1.0)
micro = round(micro * 1000000)
if tz:
offset = _tzoffset(tz, None) / 60
_tzinfo = tzinfo(offset)
else:
_tzinfo = None
return _datetime(y, mo, d, int(h), int(m), int(s), int(micro), _tzinfo) | ce95f42f568b50ffcdc0084dc659a1d5fd0233ff | 3,655,570 |
def median_ratio_flux(spec, smask, ispec, iref, nsig=3., niter=5, **kwargs):
""" Calculate the median ratio between two spectra
Parameters
----------
spec
smask:
True = Good, False = Bad
ispec
iref
nsig
niter
kwargs
Returns
-------
med_scale : float
Median of reference spectrum to input spectrum
"""
# Setup
fluxes, sigs, wave = unpack_spec(spec)
# Mask
okm = smask[iref,:] & smask[ispec,:]
# Insist on positive values
okf = (fluxes[iref,:] > 0.) & (fluxes[ispec,:] > 0)
allok = okm & okf
# Ratio
med_flux = fluxes[iref,allok] / fluxes[ispec,allok]
# Clip
mn_scale, med_scale, std_scale = stats.sigma_clipped_stats(med_flux, sigma=nsig, maxiters=niter, **kwargs)
# Return
return med_scale | 28872a548ce7569f17f155242aaf4377bf0c1b63 | 3,655,571 |
def get_tags_from_event():
"""List of tags
Arguments:
event {dict} -- Lambda event payload
Returns:
list -- List of AWS tags for use in a CFT
"""
return [
{
"Key": "OwnerContact",
"Value": request_event['OwnerContact']
}
] | e7a0f7da62a4904dbfb716c57b6811053aff3497 | 3,655,572 |
from typing import List
def _verify(symbol_table: SymbolTable, ontology: _hierarchy.Ontology) -> List[Error]:
"""Perform a battery of checks on the consistency of ``symbol_table``."""
errors = _verify_there_are_no_duplicate_symbol_names(symbol_table=symbol_table)
if len(errors) > 0:
return errors
errors.extend(
_verify_with_model_type_for_classes_with_at_least_one_concrete_descendant(
symbol_table=symbol_table
)
)
errors.extend(
_verify_all_the_function_calls_in_the_contracts_are_valid(
symbol_table=symbol_table
)
)
errors.extend(
_verify_all_non_optional_properties_are_initialized_in_the_constructor(
symbol_table=symbol_table
)
)
errors.extend(
_verify_orders_of_constructors_arguments_and_properties_match(
symbol_table=symbol_table
)
)
errors.extend(
_verify_all_argument_references_occur_in_valid_context(
symbol_table=symbol_table
)
)
errors.extend(_verify_constraints_and_constraintrefs(symbol_table=symbol_table))
errors.extend(_verify_description_rendering_with_smoke(symbol_table=symbol_table))
errors.extend(_verify_only_simple_type_patterns(symbol_table=symbol_table))
if len(errors) > 0:
return errors
_assert_interfaces_defined_correctly(symbol_table=symbol_table, ontology=ontology)
_assert_all_class_inheritances_defined_an_interface(symbol_table=symbol_table)
_assert_self_not_in_concrete_descendants(symbol_table=symbol_table)
return errors | da9dd12f01107a0c0ea1a8b2df1aa2fb543391ab | 3,655,573 |
def gsl_eigen_symmv_alloc(*args, **kwargs):
"""gsl_eigen_symmv_alloc(size_t n) -> gsl_eigen_symmv_workspace"""
return _gslwrap.gsl_eigen_symmv_alloc(*args, **kwargs) | 54384bfa9787b9a337ad3b9e2d9ea211769238d4 | 3,655,574 |
def add_poll_answers(owner, option):
"""
Add poll answer object. Matching user and option is considered same.
:param owner: User object.
:param option: Chosen poll option.
:return: Poll answer object, Boolean (true, if created).
"""
'''
owner = models.ForeignKey(User, related_name='poll_answers', on_delete=models.CASCADE)
answer = models.ForeignKey(PollOption, related_name='answers', on_delete=models.CASCADE)
'''
created = False
try:
a = PollAnswer.objects.get(owner=owner, answer=option)
except PollAnswer.DoesNotExist:
a = PollAnswer(owner=owner, answer=option)
a.save()
return a, created | ac667fbfb47aeb7d2450a3d698b0b678c3bdfdbc | 3,655,575 |
def calculate_rrfdi ( red_filename, nir_filename ):
"""
A function to calculate the Normalised Difference Vegetation Index
from red and near infrarred reflectances. The reflectance data ought to
be present on two different files, specified by the varaibles
`red_filename` and `nir_filename`. The file format ought to be
recognised by GDAL
"""
g_red = gdal.Open ( red_filename )
red = g_red.ReadAsArray()
g_nir = gdal.Open ( nir_filename )
nir = g_nir.ReadAsArray()
if ( g_red.RasterXSize != g_nir.RasterXSize ) or \
( g_red.RasterYSize != g_nir.RasterYSize ):
print "ERROR: Input datasets do't match!"
print "\t Red data shape is %dx%d" % ( red.shape )
print "\t NIR data shape is %dx%d" % ( nir.shape )
sys.exit ( -1 )
passer = True
rrfdi = np.where ( passer, (1.*red - 1.*nir ) / ( 1.*nir + 1.*red ), -999 )
return rrfdi*(-1) | 3b8f4d7eadceb38b7f874bfe0a56827f7a8aab09 | 3,655,576 |
import re
def strip_price(header_list):
"""input a list of tag-type values and return list of strings with surrounding html characters removed"""
match_obs = []
regex = '\$(((\d+).\d+)|(\d+))'
string_list = []#['' for item in range(len(header_list))]
for item in range(len(header_list)):
match_obs.append(re.search(regex, str(header_list[item])))
for i in range(len(match_obs)):
#print(match_obs[i])
string_list.append(match_obs[i].group(1))
#print(string_list)
return string_list | 7b3d90416e44f8aa61ababc0e7b68f82ae754413 | 3,655,579 |
import functools
def module(input, output, version):
"""A decorator which turn a function into a module"""
def decorator(f):
class Wrapper(Module):
def __init__(self):
super().__init__(input, output, version)
@property
def name(self):
"""The module's name"""
return f.__name__
def execute(self, *args, **kwargs):
return f(*args, **kwargs)
wrapper = Wrapper()
return functools.wraps(f)(wrapper)
return decorator | b7d5afcaa8fa52411024f84f979891d19ccf60c0 | 3,655,580 |
def compile_modules_to_ir(
result: BuildResult,
mapper: genops.Mapper,
compiler_options: CompilerOptions,
errors: Errors,
) -> ModuleIRs:
"""Compile a collection of modules into ModuleIRs.
The modules to compile are specified as part of mapper's group_map.
Returns the IR of the modules.
"""
deser_ctx = DeserMaps({}, {})
modules = {}
# Process the graph by SCC in topological order, like we do in mypy.build
for scc in sorted_components(result.graph):
scc_states = [result.graph[id] for id in scc]
trees = [st.tree for st in scc_states if st.id in mapper.group_map and st.tree]
if not trees:
continue
fresh = all(id not in result.manager.rechecked_modules for id in scc)
if fresh:
load_scc_from_cache(trees, result, mapper, deser_ctx)
else:
scc_ir = compile_scc_to_ir(trees, result, mapper, compiler_options, errors)
modules.update(scc_ir)
return modules | e2ea8a87a1ed2450e4c8ed99c7ca8a3142568f45 | 3,655,581 |
def minutes_to_restarttime(minutes) :
"""
converts an int meaning Minutes after midnight into a
restartTime string understood by the bos command
"""
if minutes == -1 :
return "never"
pod = "am"
if minutes > 12*60 :
pod = "pm"
minutes -= 12*60
time = "%d:%02d %s" % (minutes / 60, minutes % 60, pod)
return time | 6d7807cebb7a474553dda8eadfd27e5ce7b2a657 | 3,655,582 |
import tqdm
def ccm_test(x, y,emb_dim = "auto", l_0 = "auto", l_1 = "auto", tau=1, n=10,mean_num = 10,max_dim = 10):
"""
estimate x from y to judge x->y cause
:param x:
:param y:
:param l_0:
:param l_1:
:param emb_dim:
:param tau:
:param n:
:return:
"""
if emb_dim == "auto":
emb_dim = decide_dim(x,y)
if l_0 == "auto":
l_0 = int(np.ceil((len(x) - emb_dim + 1) * 0.1))
if l_1 == "auto":
l_1 = int(np.ceil((len(x) - emb_dim + 1) * 0.9))
ys = twin_surrogate(y, emb_dim,num=n)
raw_rhos = []
rhos = []
max_length = len(ys[0])
for i in tqdm(range(n)):
mean = 0
for j in range(mean_num):
rho_0, _ = estimate_using_bootstrap(x, y, length=l_0, emb_dim=emb_dim, tau=tau)
rho_1, _ = estimate_using_bootstrap(x, y, length=l_1, emb_dim=emb_dim, tau=tau)
rho_s_0, _ = estimate_from_emb_random(x, ys[i], length=l_0, emb_dim=emb_dim, tau=tau, max_length = max_length)
rho_s_1, _ = estimate_from_emb_random(x, ys[i], length=l_1, emb_dim=emb_dim, tau=tau, max_length = max_length)
raw_rhos.append([rho_0, rho_1, rho_s_0, rho_s_1])
mean += rho_1 -rho_0 -(rho_s_1 - rho_s_0 )
rhos.append(mean/mean_num)
rhos = np.array(rhos)
p = 1 - (len(rhos[rhos>0]) / n)
return {
"p_value" :p,
"rhos" :rhos,
"raw_rhos":raw_rhos
} | c03a05e62df36910ea05e361c9683b60befc1b9c | 3,655,583 |
def make_indiv_spacing(subject, ave_subject, template_spacing, subjects_dir):
"""
Identifies the suiting grid space difference of a subject's volume
source space to a template's volume source space, before a planned
morphing takes place.
Parameters:
-----------
subject : str
Subject ID.
ave_subject : str
Name or ID of the template brain, e.g., fsaverage.
template_spacing : float
Grid spacing used for the template brain.
subjects_dir : str
Path to the subjects directory.
Returns:
--------
trans : SourceEstimate
The generated source time courses.
"""
fname_surf = op.join(subjects_dir, subject, 'bem', 'watershed', '%s_inner_skull_surface' % subject)
fname_surf_temp = op.join(subjects_dir, ave_subject, 'bem', 'watershed', '%s_inner_skull_surface' % ave_subject)
surf = mne.read_surface(fname_surf, return_dict=True, verbose='ERROR')[-1]
surf_temp = mne.read_surface(fname_surf_temp, return_dict=True, verbose='ERROR')[-1]
mins = np.min(surf['rr'], axis=0)
maxs = np.max(surf['rr'], axis=0)
mins_temp = np.min(surf_temp['rr'], axis=0)
maxs_temp = np.max(surf_temp['rr'], axis=0)
# Check which dimension (x,y,z) has greatest difference
diff = (maxs - mins)
diff_temp = (maxs_temp - mins_temp)
# print additional information
# for c, mi, ma, md in zip('xyz', mins, maxs, diff):
# logger.info(' %s = %6.1f ... %6.1f mm --> Difference: %6.1f mm'
# % (c, mi, ma, md))
# for c, mi, ma, md in zip('xyz', mins_temp, maxs_temp, diff_temp):
# logger.info(' %s = %6.1f ... %6.1f mm --> Difference: %6.1f mm'
# % (c, mi, ma, md))
prop = (diff / diff_temp).mean()
indiv_spacing = (prop * template_spacing)
print(" '%s' individual-spacing to '%s'[%.2f] is: %.4fmm" % (
subject, ave_subject, template_spacing, indiv_spacing))
return indiv_spacing | cbe5120093fdf78913c2386820d3388aca0724d1 | 3,655,584 |
def sqlpool_blob_auditing_policy_update(
cmd,
instance,
state=None,
storage_account=None,
storage_endpoint=None,
storage_account_access_key=None,
storage_account_subscription_id=None,
is_storage_secondary_key_in_use=None,
retention_days=None,
audit_actions_and_groups=None,
is_azure_monitor_target_enabled=None):
"""
Updates a sql pool blob auditing policy. Custom update function to apply parameters to instance.
"""
_audit_policy_update(cmd, instance, state, storage_account, storage_endpoint, storage_account_access_key,
storage_account_subscription_id, is_storage_secondary_key_in_use, retention_days,
audit_actions_and_groups, is_azure_monitor_target_enabled)
return instance | e99013545172eb03ad5dddeefdb0b36b7bb2edd7 | 3,655,585 |
def format_search_filter(model_fields):
"""
Creates an LDAP search filter for the given set of model
fields.
"""
ldap_fields = convert_model_fields_to_ldap_fields(model_fields);
ldap_fields["objectClass"] = settings.LDAP_AUTH_OBJECT_CLASS
search_filters = import_func(settings.LDAP_AUTH_FORMAT_SEARCH_FILTERS)(ldap_fields)
return "(&{})".format("".join(search_filters)); | b6c5c17b566c583a07ef5e9f3ec61cb868f6f8ab | 3,655,587 |
def normalize_img(img):
"""
normalize image (caffe model definition compatible)
input: opencv numpy array image (h, w, c)
output: dnn input array (c, h, w)
"""
scale = 1.0
mean = [104,117,123]
img = img.astype(np.float32)
img = img * scale
img -= mean
img = np.transpose(img, (2, 0, 1))
return img | dac9ec8c942d70fb98f0b0989e9643f80dde5448 | 3,655,589 |
from typing import List
from typing import Any
def pages(lst: List[Any], n: int, title: str, *, fmt: str = "```%s```", sep: str = "\n") -> List[discord.Embed]:
# noinspection GrazieInspection
"""
Paginates a list into embeds to use with :class:disputils.BotEmbedPaginator
:param lst: the list to paginate
:param n: the number of elements per page
:param title: the title of the embed
:param fmt: a % string used to format the resulting page
:param sep: the string to join the list elements with
:return: a list of embeds
"""
l: List[List[str]] = group_list([str(i) for i in lst], n)
pgs = [sep.join(page) for page in l]
return [
discord.Embed(
title=f"{title} - {i + 1}/{len(pgs)}",
description=fmt % pg
) for i, pg in enumerate(pgs)
] | f8d9471f2d254b63754128a2e2762520f858edbd | 3,655,590 |
import re
def Substitute_Percent(sentence):
"""
Substitutes percents with special token
"""
sentence = re.sub(r'''(?<![^\s"'[(])[+-]?[.,;]?(\d+[.,;']?)+%(?![^\s.,;!?'")\]])''',
' @percent@ ', sentence)
return sentence | 61bc6970af09703ef018bfcc9378393241ae21ed | 3,655,591 |
def ready_df1(df):
"""
This function prepares the dataframe for EDA.
"""
df = remove_columns(df, columns=[ 'nitrogen_dioxide',
'nitrogen_dioxide_aqi',
'sulfur_dioxide',
'sulfur_dioxide_aqi',
'trioxygen',
'trioxygen_aqi',
'volatile',
'volatile_aqi',
])
df['fahrenheit'] = 9.0/5.0 * df['temperature'] + 32
df = df.drop(columns=['temperature'])
df = df.rename(index=str, columns={'fahrenheit':'temperature'})
df['carbon_monoxide'] = df['carbon_monoxide'].fillna(0).astype(int)
df['timestamp'] = pd.to_datetime(df['timestamp'])
return df | 3776c571d3eabb39ce27017ac1481e2bd469f68c | 3,655,592 |
def _wrap(func, args, flip=True):
"""Return partial function with flipped args if flip=True
:param function func: Any function
:param args args: Function arguments
:param bool flip: If true reverse order of arguments.
:return: Returns function
:rtype: function
"""
@wraps(func)
def flippedfunc(*args):
return func(*args[::-1])
return partial(flippedfunc if flip else func, args) | 9ac5a814840f821260d46df64b60cd6d71185dbb | 3,655,593 |
def compute_kkt_optimality(g, on_bound):
"""Compute the maximum violation of KKT conditions."""
g_kkt = g * on_bound
free_set = on_bound == 0
g_kkt[free_set] = np.abs(g[free_set])
return np.max(g_kkt) | 216cf110d64d1fd8ec89c0359ebaa9b4e4dcc773 | 3,655,594 |
def replace_cipd_revision(file_path, old_revision, new_revision):
"""Replaces cipd revision strings in file.
Args:
file_path: Path to file.
old_revision: Old cipd revision to be replaced.
new_revision: New cipd revision to use as replacement.
Returns:
Number of replaced occurrences.
Raises:
IOError: If no occurrences were found.
"""
with open(file_path) as f:
contents = f.read()
num = contents.count(old_revision)
if not num:
raise IOError('Did not find old CIPD revision {} in {}'.format(
old_revision, file_path))
newcontents = contents.replace(old_revision, new_revision)
with open(file_path, 'w') as f:
f.write(newcontents)
return num | f429e74f0dd7180ab4bf90d662f8042b958b81f8 | 3,655,595 |
def spectral_derivs_plot(spec_der, contrast=0.1, ax=None, freq_range=None,
fft_step=None, fft_size=None):
"""
Plot the spectral derivatives of a song in a grey scale.
spec_der - The spectral derivatives of the song (computed with
`spectral_derivs`) or the song itself
contrast - The contrast of the plot
ax - The matplotlib axis where the plot must be drawn, if None, a new axis
is created
freq_range - The amount of frequency to plot, usefull only if `spec_der` is
a song. Given to `spectral_derivs`
ov_params - The Parameters to override, passed to `spectral_derivs`
"""
if spec_der.ndim == 1:
spec_der = spectral_derivs(spec_der, freq_range, fft_step, fft_size)
ax = sns.heatmap(spec_der.T, yticklabels=50, xticklabels=50,
vmin=-contrast, vmax=contrast, ax=ax, cmap='Greys',
cbar=False)
ax.invert_yaxis()
return ax | 5b683d8c49e9bad2fd1fa029af6bc5660bc0e936 | 3,655,596 |
from operator import add
from operator import sub
def scale_center(pnt, fac, center):
"""scale point in relation to a center"""
return add(scale(sub(pnt, center), fac), center) | f69ca54e25d5eb8008b8f08c40500f236005e093 | 3,655,597 |
def gopherize_feed(feed_url, timestamp=False, plug=True):
"""Return a gophermap string for the feed at feed_url."""
return gopherize_feed_object(feedparser.parse(feed_url), timestamp, plug) | aaf4d35044c873e7d0f1a43c4d001ebe5e30714b | 3,655,598 |
def first_sunday_of_month(datetime: pendulum.DateTime) -> pendulum.DateTime:
"""Get the first Sunday of the month based on a given datetime.
:param datetime: the datetime.
:return: the first Sunday of the month.
"""
return datetime.start_of("month").first_of("month", day_of_week=7) | 88c517d1d38785c0d8f9c0f79f3d34199dfceb1e | 3,655,599 |
import pickle
def evaluate_single_model(
model_path, model_index, save_preds_to_db, save_prefix,
metrics, k_values, X, y, labeled_indices):
"""
Evaluate a single model with provided model specifications and data.
Arguments:
- model_path: path to load the model
- model_index: index for the model
- save_preds_to_db: whether or not to save predictions to database
- save_prefix: string prefix for any tables created
- metrics: a list of metrics to use
- k_values: k-values used for computing the metrics
- X: feature array
- y: label array
- labeled_indices: indices of rows that have labels
Returns:
- model_index: index for the model
- model_results: an (M x K) array of model results, for each metric, at each k-value
"""
# Load saved model
with open(model_path, 'rb') as file:
model = pickle.load(file)
# Get predictions
pred_table_name = f'{save_prefix}_model_{model_index}' if save_preds_to_db else None
y_preds, probs = get_predictions(model, X, k_values=k_values, pred_table_name=pred_table_name)
# Filter labels
y_preds_filtered = y_preds[labeled_indices]
y_filtered = y.to_numpy(copy=True)[labeled_indices]
# Calculate metrics for each k value
model_results = np.zeros((len(metrics), len(k_values)))
for i, metric in enumerate(metrics):
for j in range(len(k_values)):
model_results[i, j] = metric(y_filtered, y_preds_filtered[:, j])
return model_index, model_results | 311589284c46d19e04cd04fd36056e1b53c4bb52 | 3,655,600 |
def sigmoid(x: np.ndarray, derivative: bool = False) -> np.ndarray:
"""
The sigmoid function which is given by
1/(1+exp(-x))
Where x is a number or np vector. if derivative is True it applied the
derivative of the sigmoid function instead.
Examples:
>>> sigmoid(0)
0.5
>>> abs(sigmoid(np.array([100, 30, 10])) - 1) < 0.001
array([ True, True, True])
>>> abs(sigmoid(-100) - 0) < 0.001
True
"""
if derivative:
return sigmoid(x) * (1 - sigmoid(x))
return 1 / (1 + np.exp(-x)) | 7a80b978a9dd8503ba6ec56ce11a5ee9c0564fdb | 3,655,602 |
def create_hierarchy(
num_samples,
bundle_size,
directory_sizes=None,
root=".",
start_sample_id=0,
start_bundle_id=0,
address="",
n_digits=1,
):
"""
SampleIndex Hierarchy Factory method. Wraps
create_hierarchy_from_max_sample, which is a max_sample-based API, not a
numSample-based API like this method.
:param num_samples: The total number of samples.
:bundle_size: The max number of samples a bundle file is responsible for.
:directory_sizes: The number of samples each directory is responsible
for - a list, one value for each level in the directory hierarchy.
:root: The root path of this index. Defaults to ".".
:start_sample_id: The start of the sample count. Defaults to 0.
:n_digits: The number of digits to pad the directories with
"""
if directory_sizes is None:
directory_sizes = []
return create_hierarchy_from_max_sample(
num_samples + start_sample_id,
bundle_size,
directory_sizes=directory_sizes,
root=root,
start_bundle_id=start_bundle_id,
min_sample=start_sample_id,
address=address,
n_digits=n_digits,
) | 6d41b995d664eec2c9d6454abfc485c2c4202220 | 3,655,603 |
def eval_sysu(distmat, q_pids, g_pids, q_camids, g_camids, max_rank = 20):
"""Evaluation with sysu metric
Key: for each query identity, its gallery images from the same camera view are discarded. "Following the original setting in ite dataset"
"""
num_q, num_g = distmat.shape
if num_g < max_rank:
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(num_g))
indices = np.argsort(distmat, axis=1)
pred_label = g_pids[indices]
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
# compute cmc curve for each query
new_all_cmc = []
all_cmc = []
all_AP = []
all_INP = []
num_valid_q = 0. # number of valid query
for q_idx in range(num_q):
# get query pid and camid
q_pid = q_pids[q_idx]
q_camid = q_camids[q_idx]
# remove gallery samples that have the same pid and camid with query
order = indices[q_idx]
remove = (q_camid == 3) & (g_camids[order] == 2)
keep = np.invert(remove)
# compute cmc curve
# the cmc calculation is different from standard protocol
# we follow the protocol of the author's released code
new_cmc = pred_label[q_idx][keep]
new_index = np.unique(new_cmc, return_index=True)[1]
new_cmc = [new_cmc[index] for index in sorted(new_index)]
new_match = (new_cmc == q_pid).astype(np.int32)
new_cmc = new_match.cumsum()
new_all_cmc.append(new_cmc[:max_rank])
orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
if not np.any(orig_cmc):
# this condition is true when query identity does not appear in gallery
continue
cmc = orig_cmc.cumsum()
# compute mINP
# refernece Deep Learning for Person Re-identification: A Survey and Outlook
pos_idx = np.where(orig_cmc == 1)
pos_max_idx = np.max(pos_idx)
inp = cmc[pos_max_idx]/ (pos_max_idx + 1.0)
all_INP.append(inp)
cmc[cmc > 1] = 1
all_cmc.append(cmc[:max_rank])
num_valid_q += 1.
# compute average precision
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
num_rel = orig_cmc.sum()
tmp_cmc = orig_cmc.cumsum()
tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
AP = tmp_cmc.sum() / num_rel
all_AP.append(AP)
assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
all_cmc = np.asarray(all_cmc).astype(np.float32)
all_cmc = all_cmc.sum(0) / num_valid_q # standard CMC
new_all_cmc = np.asarray(new_all_cmc).astype(np.float32)
new_all_cmc = new_all_cmc.sum(0) / num_valid_q
mAP = np.mean(all_AP)
mINP = np.mean(all_INP)
return new_all_cmc, mAP, mINP | ccf61aa9f91e95cebfd63855aea366cb50de8887 | 3,655,604 |
import getpass
def espa_login() -> str:
"""
Get ESPA password using command-line input
:return:
"""
return getpass.getpass("Enter ESPA password: ") | 3ba61567d23ba3771effd6f0aa1a4ac504467378 | 3,655,605 |
def row_up1_array(row, col):
"""This function establishes an array that contains the index for the row above each entry"""
up1_array = np.zeros((row, col), dtype=np.uint8)
for i in range(row):
up1_array[i, :] = np.ones(col, dtype = np.uint8) * ((i - 1) % row)
return up1_array | 19cf1e3ceb9fe174c5cc3c6ba2c336fc58412037 | 3,655,606 |
def lcm(a, b):
"""Return lowest common multiple."""
return a * b // gcd(a, b) | 27a7d5af9001015a0aff459af274a45921d2bc94 | 3,655,607 |
from typing import Callable
def chl_mean_hsl(weights: np.ndarray) -> Callable[[np.ndarray], np.ndarray]:
"""
return a function that can calculate the channel-wise average
of the input picture in HSL color space
"""
return lambda img: np.average(cv2.cvtColor(img, cv2.COLOR_BGR2HLS), axis=(0, 1), weights=weights) | b5e337fb3bee18762e31aef3d666906975305b4b | 3,655,608 |
def cosine_mrl_option(labels, predicts):
"""For a minibatch of image and sentences embeddings, computes the pairwise contrastive loss"""
#batch_size, double_n_emd = tensor.shape(predicts)
#res = tensor.split(predicts, [double_n_emd/2, double_n_emd/2], 2, axis=-1)
img = l2norm(labels)
text = l2norm(predicts)
scores = tensor.dot(img, text.T)
diagonal = scores.diagonal()
mrl_margin = 0.3
loss_max_violation = True
# caption retrieval (1 + neg - pos)
cost_s = tensor.maximum(0, mrl_margin + scores - diagonal.reshape((-1,1)))
# clear diagonals
cost_s = fill_diagonal(cost_s, 0)
# img retrieval
cost_im = tensor.maximum(0, mrl_margin + scores - diagonal)
cost_im = fill_diagonal(cost_im, 0)
if loss_max_violation:
if cost_s:
cost_s = tensor.max(cost_s, axis=1)
if cost_im:
cost_im = tensor.max(cost_im, axis=0)
loss = cost_s.mean() + cost_im.mean()
return loss | e103b1b0075438270e79913bb59b1117da09b51f | 3,655,609 |
def escape_cdata(cdata):
"""Escape a string for an XML CDATA section"""
return cdata.replace(']]>', ']]>]]><![CDATA[') | c38b934b4c357e8c15fd1f3942f84ca3aaab4ee1 | 3,655,610 |
import inspect
import pprint
def _collect_data_for_docstring(func, annotation):
"""
Collect data to be printed in docstring. The data is collected from
custom annotation (dictionary passed as a parameter for the decorator)
and standard Python annotations for the parameters (if any). Data from
custom annotation always overrides Python parameter annotations.
Parameters
----------
func: callable
Reference to the function.
annotation: dict
Custom annotation.
Returns
-------
Dictionary of the collected parameters
"""
signature = inspect.signature(func)
parameters = signature.parameters
return_annotation = signature.return_annotation
doc_params = dict()
# Description of the function
doc_params["description"] = annotation.get("description", "")
# Flag that tells if the function is a generator. Title for returning
# values for generator is 'Yields' and for regular functions it is 'Returns'
doc_params["is_generator"] = inspect.isgeneratorfunction(func)
doc_params["parameters"] = {}
if parameters: # The function may have no parameters
# We will print names of ALL parameters from the signature
for p_name, p in parameters.items():
# Select description, annotation and types from available sources.
# Annotation (parameter of the wrapper) always overrides Python annotation.
doc_params["parameters"][p_name] = {}
kind = p.kind.name
kind = kind.lower().replace("_", " ")
doc_params["parameters"][p_name]["kind"] = kind
desc, an, plans, devices, enums = "", "", {}, {}, {}
if ("parameters" in annotation) and (p_name in annotation["parameters"]):
p_an = annotation["parameters"][p_name]
desc = p_an.get("description", "")
if "annotation" in p_an:
an = p_an["annotation"]
# Ignore annotation if it is an empty string. Lists of plans
# and devices make no sense, so don't include them.
if an:
# Now save the lists of plans and devices if any
plans = p_an.get("plans", {})
devices = p_an.get("devices", {})
enums = p_an.get("enums", {})
if not an and parameters[p_name].annotation != inspect.Parameter.empty:
an = str(parameters[p_name].annotation)
doc_params["parameters"][p_name]["annotation"] = _convert_annotation_to_type(an)
doc_params["parameters"][p_name]["description"] = desc
doc_params["parameters"][p_name]["plans"] = plans
doc_params["parameters"][p_name]["devices"] = devices
doc_params["parameters"][p_name]["enums"] = enums
if p.default != inspect.Parameter.empty:
# Print will print strings in quotes (desired behavior)
v_default = pprint.pformat(p.default)
else:
v_default = None
# If 'v_default' is None, it is not specified, so it should not be printed
# in the docstring at all
doc_params["parameters"][p_name]["default"] = v_default
# Print return value annotation and description. Again the annotation from
# custom annotation overrides Python annotation.
doc_params["returns"] = {}
desc, an = "", ""
if "returns" in annotation or (return_annotation != inspect.Parameter.empty):
if "returns" in annotation:
desc = annotation["returns"].get("description", "")
an = annotation["returns"].get("annotation", "")
if not an:
if return_annotation != inspect.Signature.empty:
an = str(return_annotation)
doc_params["returns"]["description"] = desc
if doc_params["is_generator"]:
an = _extract_yield_type(an)
doc_params["returns"]["annotation"] = _convert_annotation_to_type(an)
return doc_params | 32a7ac62506dfc04157c613fa781b3d740a95451 | 3,655,611 |
def _strip_unbalanced_punctuation(text, is_open_char, is_close_char):
"""Remove unbalanced punctuation (e.g parentheses or quotes) from text.
Removes each opening punctuation character for which it can't find
corresponding closing character, and vice versa.
It can only handle one type of punctuation
(e.g. it could strip quotes or parentheses but not both).
It takes functions (is_open_char, is_close_char),
instead of the characters themselves,
so that we can determine from nearby characters whether a straight quote is
an opening or closing quote.
Args:
text (string): the text to fix
is_open_char: a function that accepts the text and an index,
and returns true if the character at that index is
an opening punctuation mark.
is_close_char: same as is_open_char for closing punctuation mark.
Returns:
The text with unmatched punctuation removed.
"""
# lists of unmatched opening and closing chararacters
opening_chars = []
unmatched_closing_chars = []
for idx, c in enumerate(text):
if is_open_char(text, idx):
opening_chars.append(idx)
elif is_close_char(text, idx):
if opening_chars:
# this matches a character we found earlier
opening_chars.pop()
else:
# this doesn't match any opening character
unmatched_closing_chars.append(idx)
char_indices = [i for (i, _) in enumerate(text)
if not(i in opening_chars or i in unmatched_closing_chars)]
stripped_text = "".join([text[i] for i in char_indices])
return stripped_text | db4b8f201e7b01922e6c06086594a8b73677e2a2 | 3,655,612 |
def read(fin, alphabet=None):
"""Read and parse a fasta file.
Args:
fin -- A stream or file to read
alphabet -- The expected alphabet of the data, if given
Returns:
SeqList -- A list of sequences
Raises:
ValueError -- If the file is unparsable
"""
seqs = [s for s in iterseq(fin, alphabet)]
name = names[0]
if hasattr(fin, "name"):
name = fin.name
return SeqList(seqs, name=name) | 1ff492ac533a318605569f94ef66036c847b21d5 | 3,655,613 |
def get_min_max_value(dfg):
"""
Gets min and max value assigned to edges
in DFG graph
Parameters
-----------
dfg
Directly follows graph
Returns
-----------
min_value
Minimum value in directly follows graph
max_value
Maximum value in directly follows graph
"""
min_value = 9999999999
max_value = -1
for edge in dfg:
if dfg[edge] < min_value:
min_value = dfg[edge]
if dfg[edge] > max_value:
max_value = dfg[edge]
return min_value, max_value | 17a98350f4e13ec51e72d4357e142ad661e57f54 | 3,655,614 |
def vgg_fcn(num_classes=1000, pretrained=False, batch_norm=False, **kwargs):
"""VGG 16-layer model (configuration "D")
Args:
num_classes(int): the number of classes at dataset
pretrained (bool): If True, returns a model pre-trained on ImageNet
batch_norm: if you want to introduce batch normalization
"""
if pretrained:
kwargs['init_weights'] = True
model = VGG(make_layers(cfg['D'], batch_norm=batch_norm), num_classes, **kwargs)
if pretrained:
# loading weights
if batch_norm:
pretrained_weights = model_zoo.load_url(model_urls['vgg19_bn'])
else:
pretrained_weights = model_zoo.load_url(model_urls['vgg19'])
model.load_state_dict(pretrained_weights, strict=False)
return model | 73c1e80e0ffc6aff670394d1b1ec5e2b7d21cf06 | 3,655,616 |
import time
def fmt_time(timestamp):
"""Return ISO formatted time from seconds from epoch."""
if timestamp:
return time.strftime('%Y-%m-%dT%H:%M:%S', time.localtime(timestamp))
else:
return '-' | c87f1da7b6a3b1b8d8daf7d85a2b0746be58133b | 3,655,618 |
def lislice(iterable, *args):
""" (iterable, stop) or (iterable, start, stop[, step])
>>> lislice('ABCDEFG', 2)
['A', 'B']
>>> lislice('ABCDEFG', 2, 4)
['C', 'D']
>>> lislice('ABCDEFG', 2, None)
['C', 'D', 'E', 'F', 'G']
>>> lislice('ABCDEFG', 0, None, 2)
['A', 'C', 'E', 'G']
"""
return list(islice(iterable, *args)) | 6c7eb26a9ab5cb913c17f77c2a64929cfc7ebb06 | 3,655,619 |
def calculate_transition_cost(number_objs: int, target_storage_class: str) -> float:
"""
Calculates the cost of transition data from one class to another
Args:
number_objs: the number of objects that are added on a monthly basis
target_storage_class: the storage class the objects will reside in after they are transitioned
Returns:
int, the cost of the transition
"""
target_storage_class_data = data[target_storage_class]
transition_cost = (
number_objs / target_storage_class_data["items_per_transition_chunk"]
) * target_storage_class_data["transition_cost"]
return transition_cost | 01ec7d3e7149dadc020ab6f82033a178366c6ebf | 3,655,620 |
def get_covid():
"""This module sends off a covid notification. You can't get covid from this."""
covid_data = covid_handler()
covid_content = Markup("Date: " + str(covid_data["date"]) + ",<br/>Country: " + str(
covid_data["areaName"]) + ",<br/>New Cases: " + str(
covid_data["newCasesByPublishDate"]) + ",<br/>Total Cases: " + str(
covid_data["cumCasesByPublishDate"]))
# The above formats the covid data, ready to send it off as a notification
covid_notification = {"title": "Covid Cases", "content": covid_content}
return covid_notification | 0c6e4c8e5df7b7e13212eabe46f8a72a7874fde5 | 3,655,622 |
def send_songogram(your_name, artist_first_name, artist_last_name, song_name, number_to_call):
""" Function for sending a Sonogram.
:param your_name: string containing the person sending the sonogram's name.
:param artist_first_name: string containing the musician's first name.
:param artist_last_name: string containing the musician's last name.
:param song_name: string containing the song name.
:param number_to_call: string of the telephone number to send a sonogram to.
"""
try:
lyrics = scrape_lyrics(artist_first_name, artist_last_name, song_name)
make_call(number_to_call, lyrics, your_name)
send_text(song_name, artist_first_name + ' ' + artist_last_name, number_to_call, your_name)
return {'status': 201}
except:
return {'status': 400,'error': 'Bad Request', 'message': 'Unable to process request'} | 84e67f7b8b185817596f0fd0173e4cc989616687 | 3,655,623 |
def segm_and_cat(sersic_2d_image):
"""fixture for segmentation and catalog"""
image_mean, image_median, image_stddev = sigma_clipped_stats(sersic_2d_image, sigma=3)
threshold = image_stddev * 3
# Define smoothing kernel
kernel_size = 3
fwhm = 3
# Min Source size (area)
npixels = 4 ** 2
return make_catalog(
sersic_2d_image,
threshold=threshold,
deblend=True,
kernel_size=kernel_size,
fwhm=fwhm,
npixels=npixels,
contrast=0.00,
plot=False,
) | 2a6018f7b4c2a1aea946b6744840bd2216352002 | 3,655,624 |
from typing import Tuple
def break_word_by_trailing_integer(pname_fid: str) -> Tuple[str, str]:
"""
Splits a word that has a value that is an integer
Parameters
----------
pname_fid : str
the DVPRELx term (e.g., A(11), NSM(5))
Returns
-------
word : str
the value not in parentheses
value : int
the value in parentheses
Examples
--------
>>> break_word_by_trailing_integer('T11')
('T', '11')
>>> break_word_by_trailing_integer('THETA11')
('THETA', '11')
"""
nums = []
i = 0
for i, letter in enumerate(reversed(pname_fid)):
if letter.isdigit():
nums.append(letter)
else:
break
num = ''.join(nums[::-1])
if not num:
msg = ("pname_fid=%r does not follow the form 'T1', 'T11', 'THETA42' "
"(letters and a number)" % pname_fid)
raise SyntaxError(msg)
word = pname_fid[:-i]
assert len(word)+len(num) == len(pname_fid), 'word=%r num=%r pname_fid=%r' % (word, num, pname_fid)
return word, num | e9b9c85b4225269c94918ce1cc2e746d3c74aa5c | 3,655,625 |
def preprocess_data(image, label, is_training):
"""CIFAR data preprocessing"""
image = tf.image.convert_image_dtype(image, tf.float32)
if is_training:
crop_padding = 4
image = tf.pad(image, [[crop_padding, crop_padding],
[crop_padding, crop_padding], [0, 0]], 'REFLECT')
image = tf.image.random_crop(image, [32, 32, 3])
image = tf.image.random_flip_left_right(image)
if FLAGS.distort_color:
image = color_distortion(image, s=1.0)
else:
image = tf.image.resize_with_crop_or_pad(image, 32, 32) # central crop
return image, label | 642f384fbf1aa2f884e64de2edf264890317b258 | 3,655,627 |
def load_Counties():
"""
Use load_country() instead of this function
"""
# Get data
# Load data using Pandas
dfd = {
'positive': reread_csv(csv_data_file_Global['confirmed_US']),
'death': reread_csv(csv_data_file_Global['deaths_US']),
}
return dfd | 098d08f3720b6c6148c51000e6e1512d382adeaf | 3,655,628 |
from typing import Iterator
from typing import Any
def issetiterator(object: Iterator[Any]) -> bool:
"""Returns True or False based on whether the given object is a set iterator.
Parameters
----------
object: Any
The object to see if it's a set iterator.
Returns
-------
bool
Whether the given object is a set iterator.
"""
if not isiterable(object):
return False
return isinstance(object, SetIteratorType) | 07ecdcc72c62c4ce3d5fb91181cd1bc785d6cb4d | 3,655,630 |
from mpl_toolkits.mplot3d import Axes3D
def plotModeScatter( pc , xMode=0, yMode=1, zMode=None, pointLabels=None, nTailLabels=3, classes=None):
"""
scatter plot mode projections for up to 3 different modes.
PointLabels is a list of strings corresponding to each shape.
nTailLabels defines number of points that are labelled at the tails of the distributions,
can be 'all' to label all points. Point labels are for 2D plots only.
"""
xWeights = pc.projectedWeights[xMode]
yWeights = pc.projectedWeights[yMode]
colourMap = mpl.cm.gray
if classes==None:
c = 'r'
else:
c = classes
if zMode == None:
fig = plot.figure()
ax = fig.add_subplot(111)
plt = ax.scatter(xWeights,yWeights, c=c, marker='o', cmap=colourMap)
ax.set_title('Scatter: Mode %d vs Mode %d'%(xMode, yMode))
ax.set_xlabel('Mode %d'%(xMode))
ax.set_ylabel('Mode %d'%(yMode))
if pointLabels!=None:
if nTailLabels=='all':
for label, x, y in zip(pointLabels, xWeights, yWeights):
plot.annotate( label, xy=(x,y), xytext=(-5, 5),
textcoords='offset points', ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
elif isinstance(nTailLabels, int):
# sort weights
xSortedArgs = scipy.argsort(xWeights)
ySortedArgs = scipy.argsort(yWeights)
# label x tails
for i in xSortedArgs[:nTailLabels]:
plot.annotate( pointLabels[i], xy=(xWeights[i],yWeights[i]), xytext=(-5, 5),
textcoords='offset points', ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
for i in xSortedArgs[-nTailLabels:]:
plot.annotate( pointLabels[i], xy=(xWeights[i],yWeights[i]), xytext=(-5, 5),
textcoords='offset points', ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
# label y tails
for i in ySortedArgs[:nTailLabels]:
plot.annotate( pointLabels[i], xy=(xWeights[i],yWeights[i]), xytext=(-5, 5),
textcoords='offset points', ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
for i in ySortedArgs[-nTailLabels:]:
plot.annotate( pointLabels[i], xy=(xWeights[i],yWeights[i]), xytext=(-5, 5),
textcoords='offset points', ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
else:
raise ValueError, "nTailLabels must be 'all' or an integer"
plot.show()
else:
fig = plot.figure()
zWeights = pc.projectedWeights[zMode]
ax = fig.add_subplot(111, projection='3d')
plt = ax.scatter(xWeights,yWeights, zWeights, c =c, marker='o', cmap=colourMap)
ax.set_title('3D Scatter')
ax.set_xlabel('Mode %d'%(xMode))
ax.set_ylabel('Mode %d'%(yMode))
ax.set_zlabel('Mode %d'%(zMode))
plot.show()
return fig, plt | 72bc671d9d4fc0fc8df26965fd4d24d91ab51b72 | 3,655,632 |
import math
def calculatetm(seq):
""" Calculate Tm of a target candidate, nearest neighbor model """
NNlist = chopseq(seq, 2, 1)
NNtable = ['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
NNendtable = ['A', 'C', 'G', 'T']
NNcount = np.zeros(16)
NNend = np.zeros(4)
for c, NN in enumerate(NNtable):
NNcount[c] = NNlist.count(NN)
for c, NN in enumerate(NNendtable):
NNend[c] = seq[0].count(NN)
# numbers below from Sugimoto et al. NAR (1996)
NNEnthalpy = np.array([-8.0, -9.4, -6.6, -5.6, -8.2, -10.9, -11.8, -6.6, -8.8, -10.5, -10.9, -9.4, -6.6, -8.8, -8.2, -8.0])
NNEntropy = np.array([-21.9, -25.5, -16.4, -15.2, -21.0, -28.4, -29.0, -16.4, -23.5, -26.4, -28.4, -25.5, -18.4, -23.5, -21.0, -21.9])
NNendEnthalpy = np.array([.6, .6, .6, .6])
NNendEntropy = np.array([-9.0, -9.0, -9.0, -9.0])
sumEnthalpy = np.sum(np.multiply(NNcount, NNEnthalpy)) + np.sum(np.multiply(NNend, NNendEnthalpy))
sumEntropy = np.sum(np.multiply(NNcount, NNEntropy)) + np.sum(np.multiply(NNend, NNendEntropy))
Tm = (sumEnthalpy * 1000)/(sumEntropy + (1.9872 * math.log(1e-7))) - 273.15 # oligo concentration: 1e-7 M
sumSalt = 0.075 + (3.795 * 0.01**0.5) # monovalent: 0.075 M, bivalent: 0.01 M
Tm += 16.6 * math.log10(sumSalt) # salt correction
Tm -= 0.72 * 20 # formamide correction
return Tm | f53c1aa09cd335d603c721fa9922d85e2de0f612 | 3,655,634 |
def get_data_shape(X_train, X_test, X_val=None):
"""
Creates, updates and returns data_dict containing metadata of the dataset
"""
# Creates data_dict
data_dict = {}
# Updates data_dict with lenght of training, test, validation sets
train_len = len(X_train)
test_len = len(X_test)
data_dict.update({'train_len': train_len, 'test_len': test_len})
if X_val is not None:
val_len = len(X_val)
data_dict.update({'val_len': val_len})
# else : val_len = None
# Updates number of dimensions of data
no_of_dim = X_train.ndim
data_dict.update({'no_of_dim': no_of_dim})
# Updates number of features(, number of channels, width, height)
if no_of_dim == 2:
no_of_features = X_train.shape[1]
data_dict.update({'no_of_features': no_of_features})
elif no_of_dim == 3:
channels = X_train.shape[1]
features_per_c = X_train.shape[2]
no_of_features = channels * features_per_c
data_dict.update({'no_of_features': no_of_features,
'channels': channels,
'features_per_c': features_per_c})
elif no_of_dim == 4:
channels = X_train.shape[1]
height = X_train.shape[2]
width = X_train.shape[3]
features_per_c = height*width
no_of_features = channels*features_per_c
data_dict.update({'height':height, 'width':width, 'channels':channels,
'features_per_c':features_per_c,
'no_of_features':no_of_features})
return data_dict | 231a334b625d0bfe6aa6e63b79de2b2226b8e684 | 3,655,636 |
def setupAnnotations(context):
"""
set up the annotations if they haven't been set up
already. The rest of the functions in here assume that
this has already been set up
"""
annotations = IAnnotations(context)
if not FAVBY in annotations:
annotations[FAVBY] = PersistentList()
return annotations | f427c8619452d7143a56d4b881422d01a90ba666 | 3,655,637 |
def _get_media(media_types):
"""Helper method to map the media types."""
get_mapped_media = (lambda x: maps.VIRTUAL_MEDIA_TYPES_MAP[x]
if x in maps.VIRTUAL_MEDIA_TYPES_MAP else None)
return list(map(get_mapped_media, media_types)) | 4dbbcf87c717fca2e1890a5258df023ebbca31c5 | 3,655,638 |
import ctypes
def get_int_property(device_t, property):
""" Search the given device for the specified string property
@param device_t Device to search
@param property String to search for.
@return Python string containing the value, or None if not found.
"""
key = cf.CFStringCreateWithCString(
kCFAllocatorDefault,
property.encode("mac_roman"),
kCFStringEncodingMacRoman
)
CFContainer = iokit.IORegistryEntryCreateCFProperty(
device_t,
key,
kCFAllocatorDefault,
0
);
number = ctypes.c_uint16()
if CFContainer:
output = cf.CFNumberGetValue(CFContainer, 2, ctypes.byref(number))
return number.value | 75bc08117bb838e8070d3ea4d5134dfbeec9576c | 3,655,639 |
def _get_unique_barcode_ids(pb_index, isoseq_mode=False):
"""
Get a list of sorted, unique fw/rev barcode indices from an index object.
"""
bc_sel = (pb_index.bcForward != -1) & (pb_index.bcReverse != -1)
bcFw = pb_index.bcForward[bc_sel]
bcRev = pb_index.bcReverse[bc_sel]
bc_ids = sorted(list(set(zip(bcFw, bcRev))))
if isoseq_mode:
bc_ids = sorted(list(set([tuple(sorted(bc)) for bc in bc_ids])))
return bc_ids | bdfb386d26415a7b3f9f16661d83a38a63958ad0 | 3,655,640 |
def clean_logs(test_yaml, args):
"""Remove the test log files on each test host.
Args:
test_yaml (str): yaml file containing host names
args (argparse.Namespace): command line arguments for this program
"""
# Use the default server yaml and then the test yaml to update the default
# DAOS log file locations. This should simulate how the test defines which
# log files it will use when it is run.
log_files = get_log_files(test_yaml, get_log_files(BASE_LOG_FILE_YAML))
host_list = get_hosts_from_yaml(test_yaml, args)
command = "sudo rm -fr {}".format(" ".join(log_files.values()))
print("Cleaning logs on {}".format(host_list))
if not spawn_commands(host_list, command):
print("Error cleaning logs, aborting")
return False
return True | 229f34615dc9a6f7ab9c484b9585151814656a77 | 3,655,641 |
def call_posterior_haplotypes(posteriors, threshold=0.01):
"""Call haplotype alleles for VCF output from a population
of genotype posterior distributions.
Parameters
----------
posteriors : list, PosteriorGenotypeDistribution
A list of individual genotype posteriors.
threshold : float
Minimum required posterior probability of occurrence
with in any individual for a haplotype to be included.
Returns
-------
haplotypes : ndarray, int, shape, (n_haplotypes, n_base)
VCF sorted haplotype arrays.
"""
# maps of bytes to arrays and bytes to sum probs
haplotype_arrays = {}
haplotype_values = {}
# iterate through genotype posterors
for post in posteriors:
# include haps based on probability of occurrence
(
haps,
probs,
) = post.allele_occurrence()
_, weights = post.allele_frequencies(dosage=True)
idx = probs >= threshold
# order haps based on weighted prob
haps = haps[idx]
weights = weights[idx]
for h, w in zip(haps, weights):
b = h.tobytes()
if b not in haplotype_arrays:
haplotype_arrays[b] = h
haplotype_values[b] = 0
haplotype_values[b] += w
# remove reference allele if present
refbytes = None
for b, h in haplotype_arrays.items():
if np.all(h == 0):
# ref allele
refbytes = b
if refbytes is not None:
haplotype_arrays.pop(refbytes)
haplotype_values.pop(refbytes)
# combine all called haplotypes into array
n_alleles = len(haplotype_arrays) + 1
n_base = posteriors[0].genotypes.shape[-1]
haplotypes = np.full((n_alleles, n_base), -1, np.int8)
values = np.full(n_alleles, -1, float)
for i, (b, h) in enumerate(haplotype_arrays.items()):
p = haplotype_values[b]
haplotypes[i] = h
values[i] = p
haplotypes[-1][:] = 0 # ref allele
values[-1] = values.max() + 1
order = np.flip(np.argsort(values))
return haplotypes[order] | 46c26eb38c693d979ea4234af606b3b07ad1e75e | 3,655,642 |
def get_discorded_labels():
"""
Get videos with citizen discorded labels
Partial labels will only be set by citizens
"""
return get_video_labels(discorded_labels) | 6f3cbaf09b43956d14d9abf5cf4e77734c152d2f | 3,655,644 |
def set_common_tags(span: object, result: object):
"""Function used to set a series of common tags
to a span object"""
if not isinstance(result, dict):
return span
for key, val in result.items():
if key.lower() in common_tags:
span.set_tag(key, val)
return span | 365230fb6a69b94684aeac25d14fa4275c1549f8 | 3,655,645 |
import time
def local_timezone():
"""
Returns:
(str): Name of current local timezone
"""
try:
return time.tzname[0]
except (IndexError, TypeError):
return "" | c97c11582b27d8aa0205555535616d6ea11775b9 | 3,655,646 |
import getpass
def ask_credentials():
"""Interactive function asking the user for ASF credentials
:return: tuple of username and password
:rtype: tuple
"""
# SciHub account details (will be asked by execution)
print(
" If you do not have a ASF/NASA Earthdata user account"
" go to: https://search.asf.alaska.edu/ and register"
)
uname = input(" Your ASF/NASA Earthdata Username:")
pword = getpass.getpass(" Your ASF/NASA Earthdata Password:")
return uname, pword | a601a460b3aeddf9939f3acf267e58fdaf9ed7cd | 3,655,647 |
def lab2lch(lab):
"""CIE-LAB to CIE-LCH color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
Parameters
----------
lab : array_like
The N-D image in CIE-LAB format. The last (``N+1``-th) dimension must
have at least 3 elements, corresponding to the ``L``, ``a``, and ``b``
color channels. Subsequent elements are copied.
Returns
-------
out : ndarray
The image in LCH format, in a N-D array with same shape as input `lab`.
Raises
------
ValueError
If `lch` does not have at least 3 color channels (i.e. l, a, b).
Notes
-----
The Hue is expressed as an angle between ``(0, 2*pi)``
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2lab, lab2lch
>>> img = data.astronaut()
>>> img_lab = rgb2lab(img)
>>> img_lch = lab2lch(img_lab)
"""
lch = _prepare_lab_array(lab)
a, b = lch[..., 1], lch[..., 2]
lch[..., 1], lch[..., 2] = _cart2polar_2pi(a, b)
return lch | 711d23d452413d738af162ac5b9e3f34c1a4eab6 | 3,655,648 |
def callattice(twotheta, energy_kev=17.794, hkl=(1, 0, 0)):
"""
Calculate cubic lattice parameter, a from reflection two-theta
:param twotheta: Bragg angle, deg
:param energy_kev: energy in keV
:param hkl: reflection (cubic only
:return: float, lattice contant
"""
qmag = calqmag(twotheta, energy_kev)
dspace = q2dspace(qmag)
return dspace * np.sqrt(np.sum(np.square(hkl))) | 2718e4c44e08f5038ff4119cf477775ed9f3a678 | 3,655,650 |
def reset_password(
*,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_active_user),
background_tasks: BackgroundTasks,
):
"""reset current user password"""
email = current_user.email
# send confirm email
if settings.EMAILS_ENABLED and email:
confirm_token = create_access_token(
subject=email, expires_delta=timedelta(settings.EMAIL_CONFIRM_TOKEN_EXPIRE)
)
background_tasks.add_task(
send_reset_password_email, email_to=email, token=confirm_token
)
return {"msg": "Password reset email sent"} | 1f292188b3927c26eb41634acb7fb99e398e94b6 | 3,655,651 |
def rule_valid_histone_target(attr):
""" {
"applies" : ["ChIP-Seq", "experiment_target_histone"],
"description" : "'experiment_target_histone' attributes must be 'NA' only for ChIP-Seq Input"
} """
histone = attr.get('experiment_target_histone', [''])[0]
if attr.get('experiment_type', [""])[0].lower() in ['ChIP-Seq Input'.lower()]:
return histone == 'NA'
else:
return histone != 'NA' | 0a10f09c6b9e50cf01583d0c803e5112629e503b | 3,655,652 |
def extend(curve: CustomCurve, deg):
"""returns curve over the deg-th relative extension"""
E = curve.EC
q = curve.q
K = curve.field
if q % 2 != 0:
R = K["x"]
pol = R.irreducible_element(deg)
Fext = GF(q ** deg, name="z", modulus=pol)
return E.base_extend(Fext)
charac = K.characteristic()
R = GF(charac)["x"]
ext_deg = q ** deg
pol = R.irreducible_element(deg * ZZ(log(q, charac)))
Kext = GF(ext_deg, name="ex", modulus=pol)
gKext = Kext.gen()
h = gKext ** ((ext_deg - 1) // (q - 1))
assert charac ** (h.minpoly().degree()) == q
H = GF(q, name="h", modulus=h.minpoly())
inclusion = H.hom([h])
new_coefficients = [
inclusion(stupid_coerce_K_to_L(a, K, H)) for a in E.a_invariants()
]
EE = EllipticCurve(Kext, new_coefficients)
return EE | 8d750b40d91d10d6b51c75765e2083300d7dccf6 | 3,655,653 |
def flatten3D(inputs: tf.Tensor) -> tf.Tensor:
"""
Flatten the given ``inputs`` tensor to 3 dimensions.
:param inputs: >=3d tensor to be flattened
:return: 3d flatten tensor
"""
shape = inputs.get_shape().as_list()
if len(shape) == 3:
return inputs
assert len(shape) > 3
return tf.reshape(inputs, [tf.shape(inputs)[0], tf.shape(inputs)[1], np.prod(inputs.get_shape().as_list()[2:])]) | 11c9c7f7ab955594401468c64323f8f3a52dbe81 | 3,655,654 |
def get_classes(dataset):
"""Get class names of a dataset."""
alias2name = {}
for name, aliases in dataset_aliases.items():
for alias in aliases:
alias2name[alias] = name
if mmcv.is_str(dataset):
if dataset in alias2name:
labels = eval(alias2name[dataset] + '_classes()')
else:
raise ValueError('Unrecognized dataset: {}'.format(dataset))
else:
raise TypeError('dataset must a str, but got {}'.format(type(dataset)))
return labels | d307793a85deef3be239d7dbff746c7c9643dc1b | 3,655,655 |
def split_exclude_string(people):
"""
Function to split a given text of persons' name who wants to exclude
with comma separated for each name e.g. ``Konrad, Titipat``
"""
people = people.replace('Mentor: ', '').replace('Lab-mates: ', '').replace('\r\n', ',').replace(';', ',')
people_list = people.split(',')
return [p.strip() for p in people_list if p.strip() is not ''] | 5748a52039548175923f53384474f40ac8fb5e38 | 3,655,656 |
from datetime import datetime
def now(tz=DEFAULT_TZ):
"""
Get the current datetime.
:param tz: The preferred time-zone, defaults to DEFAULT_TZ
:type tz: TzInfo (or similar pytz time-zone)
:return: A time-zone aware datetime set to now
:rtype: datetime
"""
return datetime.now(tz=tz) | 1dcdd78898b726576f69f01cb9f4bfe3aeaef29d | 3,655,657 |
def peek_with_kwargs(init, args=[], permissive=False):
"""
Make datatypes passing keyworded arguments to the constructor.
This is a factory function; returns the actual `peek` routine.
Arguments:
init (callable): type constructor.
args (iterable): arguments NOT to be keyworded; order does matter.
permissive (bool): missing positional arguments are set to None (*new in 0.8.5*).
Returns:
callable: deserializer (`peek` routine).
All the peeked attributes that are not referenced in `args` are passed to `init` as
keyworded arguments.
"""
if permissive:
def try_peek(store, attr, container, _stack=None):
try:
return store.peek(attr, container, _stack=_stack)
except KeyError:
return None
def peek(store, container, _stack=None):
return init(\
*[ try_peek(store, attr, container, _stack) for attr in args ], \
**dict([ (attr, store.peek(attr, container, _stack=_stack)) \
for attr in container if attr not in args ]))
else:
def peek(store, container, _stack=None):
return init(\
*[ store.peek(attr, container, _stack=_stack) for attr in args ], \
**dict([ (attr, store.peek(attr, container, _stack=_stack)) \
for attr in container if attr not in args ]))
return peek | d06df21ab439da1cacb52befa6c619f1efa23d1a | 3,655,658 |
def idc_asset_manage(request,aid=None,action=None):
"""
Manage IDC
"""
if request.user.has_perms(['asset.view_asset', 'asset.edit_asset']):
page_name = ''
if aid:
idc_list = get_object_or_404(IdcAsset, pk=aid)
if action == 'edit':
page_name = '编辑IDC机房'
if action == 'delete':
idc_list.delete()
return redirect('idc_asset_list')
else:
idc_list = IdcAsset()
action = 'add'
page_name = '新增IDC机房'
if request.method == 'POST':
form = IdcAssetForm(request.POST,instance=idc_list)
if form.is_valid():
if action == 'add':
form.save()
return redirect('idc_asset_list')
if action == 'edit':
form.save()
return redirect('idc_asset_list')
else:
form = IdcAssetForm(instance=idc_list)
return render(request, 'asset_idc_manage.html', {"form":form, "page_name":page_name, "action":action})
else:
raise Http404 | 7fbf1729c87e9e9921f19cf5cba2810879958848 | 3,655,659 |
def get_detected_column_types(df):
""" Get data type of each columns ('DATETIME', 'NUMERIC' or 'STRING')
Parameters:
df (df): pandas dataframe
Returns
df (df): dataframe that all datatypes are converted (df)
"""
assert isinstance(df, pd.DataFrame), 'Parameter must be DataFrame'
for c in df.columns:
# Convert column to string
col_data = df[c].map(str)
col_data = col_data.replace("NaT", None)
col_data = col_data.replace("NaN", None)
# Check NULL column
if(df[c].isnull().values.all()):
continue
# Check DATETIME
try:
# Check if it's able to convert column to datetime
# if column is datetime, then skip to convert
if 'datetime' in str(col_data.dtype):
continue
df[c] = pd.to_datetime(col_data)
continue
except ValueError:
pass
# Check NUMERIC
try:
# Drop NaN rows
series = df[c].dropna()
# if column_name is int or float, then skip to convert
if 'int' in str(col_data.dtype) or 'float' in str(col_data.dtype):
continue
# Check if it can be converted to numeric
df[c] = pd.to_numeric(series)
except ValueError:
pass
return df | 23647127d0e5a125e06fb1932e74ba5f9c885ded | 3,655,661 |
def distance(coords):
"""Calculates the distance of a path between multiple points
Arguments:
coords -- List of coordinates, e.g. [(0,0), (1,1)]
Returns: Total distance as a float
"""
distance = 0
for p1, p2 in zip(coords[:-1], coords[1:]):
distance += ((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2) ** 0.5
return distance | 9c6088b740f42b839d4aa482c276fe4cc5dc8114 | 3,655,662 |
def roll_dice(dicenum, dicetype, modifier=None, conditional=None, return_tuple=False):
"""
This is a standard dice roller.
Args:
dicenum (int): Number of dice to roll (the result to be added).
dicetype (int): Number of sides of the dice to be rolled.
modifier (tuple): A tuple `(operator, value)`, where operator is
one of `"+"`, `"-"`, `"/"` or `"*"`. The result of the dice
roll(s) will be modified by this value.
conditional (tuple): A tuple `(conditional, value)`, where
conditional is one of `"=="`,`"<"`,`">"`,`">="`,`"<=`" or "`!=`".
This allows the roller to directly return a result depending
on if the conditional was passed or not.
return_tuple (bool): Return a tuple with all individual roll
results or not.
Returns:
roll_result (int): The result of the roll + modifiers. This is the
default return.
condition_result (bool): A True/False value returned if `conditional`
is set but not `return_tuple`. This effectively hides the result
of the roll.
full_result (tuple): If, return_tuple` is `True`, instead
return a tuple `(result, outcome, diff, rolls)`. Here,
`result` is the normal result of the roll + modifiers.
`outcome` and `diff` are the boolean result of the roll and
absolute difference to the `conditional` input; they will
be will be `None` if `conditional` is not set. `rolls` is
itself a tuple holding all the individual rolls in the case of
multiple die-rolls.
Raises:
TypeError if non-supported modifiers or conditionals are given.
Notes:
All input numbers are converted to integers.
Examples:
print roll_dice(2, 6) # 2d6
<<< 7
print roll_dice(1, 100, ('+', 5) # 1d100 + 5
<<< 34
print roll_dice(1, 20, conditional=('<', 10) # let'say we roll 3
<<< True
print roll_dice(3, 10, return_tuple=True)
<<< (11, None, None, (2, 5, 4))
print roll_dice(2, 20, ('-', 2), conditional=('>=', 10), return_tuple=True)
<<< (8, False, 2, (4, 6)) # roll was 4 + 6 - 2 = 8
"""
dicenum = int(dicenum)
dicetype = int(dicetype)
# roll all dice, remembering each roll
rolls = tuple([randint(1, dicetype) for roll in range(dicenum)])
result = sum(rolls)
if modifier:
# make sure to check types well before eval
mod, modvalue = modifier
if mod not in ('+', '-', '*', '/'):
raise TypeError("Non-supported dice modifier: %s" % mod)
modvalue = int(modvalue) # for safety
result = eval("%s %s %s" % (result, mod, modvalue))
outcome, diff = None, None
if conditional:
# make sure to check types well before eval
cond, condvalue = conditional
if cond not in ('>', '<', '>=', '<=', '!=', '=='):
raise TypeError("Non-supported dice result conditional: %s" % conditional)
condvalue = int(condvalue) # for safety
outcome = eval("%s %s %s" % (result, cond, condvalue)) # True/False
diff = abs(result - condvalue)
if return_tuple:
return result, outcome, diff, rolls
else:
if conditional:
return outcome
else:
return result | acbc97e4b7720129788c8c5d5d9a1d51936d9dc1 | 3,655,663 |
import math
def build_central_hierarchical_histogram_computation(
lower_bound: float,
upper_bound: float,
num_bins: int,
arity: int = 2,
max_records_per_user: int = 1,
epsilon: float = 1,
delta: float = 1e-5,
secure_sum: bool = False):
"""Create the tff federated computation for central hierarchical histogram aggregation.
Args:
lower_bound: A `float` specifying the lower bound of the data range.
upper_bound: A `float` specifying the upper bound of the data range.
num_bins: The integer number of bins to compute.
arity: The branching factor of the tree. Defaults to 2.
max_records_per_user: The maximum number of records each user is allowed to
contribute. Defaults to 1.
epsilon: Differential privacy parameter. Defaults to 1.
delta: Differential privacy parameter. Defaults to 1e-5.
secure_sum: A boolean deciding whether to use secure aggregation. Defaults
to `False`.
Returns:
A tff.federated_computation function to perform central tree aggregation.
"""
if upper_bound < lower_bound:
raise ValueError(f'upper_bound: {upper_bound} is smaller than '
f'lower_bound: {lower_bound}.')
if num_bins <= 0:
raise ValueError(f'num_bins: {num_bins} smaller or equal to zero.')
if arity < 2:
raise ValueError(f'Arity should be at least 2.' f'arity={arity} is given.')
if max_records_per_user < 1:
raise ValueError(f'Maximum records per user should be at least 1. '
f'max_records_per_user={max_records_per_user} is given.')
if epsilon < 0 or delta < 0 or delta > 1:
raise ValueError(f'Privacy parameters in wrong range: '
f'(epsilon, delta): ({epsilon}, {delta})')
if epsilon == 0.:
stddev = 0.
else:
stddev = max_records_per_user * _find_noise_multiplier(
epsilon, delta, steps=math.ceil(math.log(num_bins, arity)))
central_tree_aggregation_factory = hierarchical_histogram_factory.create_central_hierarchical_histogram_factory(
stddev, arity, max_records_per_user, secure_sum=secure_sum)
return _build_hierarchical_histogram_computation(
lower_bound, upper_bound, num_bins, central_tree_aggregation_factory) | fcd35bc2df5f61174d00079638dda0c04c1490ff | 3,655,664 |
def initialise_halo_params():
"""Initialise the basic parameters needed to simulate a forming Dark matter halo.
Args:
None
Returns:
G: gravitational constant.
epsilon: softening parameter.
limit: width of the simulated universe.
radius: simulated radius of each particle
(for proper handling of boundary conditions).
num_pos_particles: number of positive mass particles.
num_neg_particles: number of negative mass particles.
chunks_value: dask chunks value.
time_steps: number of time steps to simulate.
"""
G = 1.0
epsilon = 0.07
limit = 80000
radius = 4
num_pos_particles = 5000
num_neg_particles = 45000
chunks_value = (num_pos_particles+num_neg_particles)/5.0
time_steps = 1000
return G, epsilon, limit, radius, num_pos_particles, num_neg_particles, chunks_value, time_steps | ee3311fd17a40e8658f11d2ddf98d0ff8eb27a6d | 3,655,665 |
def read_data(image_paths, label_list, image_size, batch_size, max_nrof_epochs, num_threads, shuffle, random_flip,
random_brightness, random_contrast):
"""
Creates Tensorflow Queue to batch load images. Applies transformations to images as they are loaded.
:param random_brightness:
:param random_flip:
:param image_paths: image paths to load
:param label_list: class labels for image paths
:param image_size: size to resize images to
:param batch_size: num of images to load in batch
:param max_nrof_epochs: total number of epochs to read through image list
:param num_threads: num threads to use
:param shuffle: Shuffle images
:param random_flip: Random Flip image
:param random_brightness: Apply random brightness transform to image
:param random_contrast: Apply random contrast transform to image
:return: images and labels of batch_size
"""
images = ops.convert_to_tensor(image_paths, dtype=tf.string)
labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
# Makes an input queue
input_queue = tf.train.slice_input_producer((images, labels),
num_epochs=max_nrof_epochs, shuffle=shuffle, )
images_labels = []
imgs = []
lbls = []
for _ in range(num_threads):
image, label = read_image_from_disk(filename_to_label_tuple=input_queue)
image = tf.random_crop(image, size=[image_size, image_size, 3])
image.set_shape((image_size, image_size, 3))
image = tf.image.per_image_standardization(image)
if random_flip:
image = tf.image.random_flip_left_right(image)
if random_brightness:
image = tf.image.random_brightness(image, max_delta=0.3)
if random_contrast:
image = tf.image.random_contrast(image, lower=0.2, upper=1.8)
imgs.append(image)
lbls.append(label)
images_labels.append([image, label])
image_batch, label_batch = tf.train.batch_join(images_labels,
batch_size=batch_size,
capacity=4 * num_threads,
enqueue_many=False,
allow_smaller_final_batch=True)
return image_batch, label_batch | 2bbb7f1be38764634e198f83b82fafb730ec3afa | 3,655,666 |
def reorder_matrix (m, d) :
"""
Reorder similarity matrix : put species in same cluster together.
INPUT:
m - similarity matrix
d - medoid dictionary : {medoid : [list of species index in cluster]}
OUTPUT :
m in new order
new_order - order of species indexes in matrix
"""
new_order = []
for i, med_class in enumerate(d.values()):
new_order.append(med_class)
return m[np.concatenate(new_order), :], new_order | 5d203ec6f61afe869008fa6749d18946f128ac87 | 3,655,667 |
def reward_penalized_log_p(mol):
"""
Reward that consists of log p penalized by SA and # long cycles,
as described in (Kusner et al. 2017). Scores are normalized based on the
statistics of 250k_rndm_zinc_drugs_clean.smi dataset
:param mol: rdkit mol object
:return: float
"""
# normalization constants, statistics from 250k_rndm_zinc_drugs_clean.smi
logP_mean = 2.4570953396190123
logP_std = 1.434324401111988
SA_mean = -3.0525811293166134
SA_std = 0.8335207024513095
cycle_mean = -0.0485696876403053
cycle_std = 0.2860212110245455
log_p = MolLogP(mol)
SA = -calculateScore(mol)
# cycle score
cycle_list = nx.cycle_basis(nx.Graph(
Chem.rdmolops.GetAdjacencyMatrix(mol)))
if len(cycle_list) == 0:
cycle_length = 0
else:
cycle_length = max([len(j) for j in cycle_list])
if cycle_length <= 6:
cycle_length = 0
else:
cycle_length = cycle_length - 6
cycle_score = -cycle_length
normalized_log_p = (log_p - logP_mean) / logP_std
normalized_SA = (SA - SA_mean) / SA_std
normalized_cycle = (cycle_score - cycle_mean) / cycle_std
return normalized_log_p + normalized_SA + normalized_cycle | e3e5ebfabf31e4980dc6f3b6c998a08444ce9851 | 3,655,669 |
def loadmat(filename, variable_names=None):
"""
load mat file from h5py files
:param filename: mat filename
:param variable_names: list of variable names that should be loaded
:return: dictionary of loaded data
"""
data = {}
matfile = h5py.File(filename, 'r')
if variable_names is None:
for key in matfile.keys():
data.update({key: matfile[key][()]})
else:
for key in variable_names:
if not key in matfile.keys():
raise RuntimeError('Variable: "' + key + '" is not in file: ' + filename)
data.update({key: matfile[key][()]})
return data | 3b9183968fba56d57c705bce0ec440c630cc0031 | 3,655,670 |
def date_start_search(line):
"""予定開始の日付を検出し,strで返す."""
# 全角スペース
zen_space = ' '
# 全角0
zen_zero = '０'
nichi = '日'
dollar = '$'
# 全角スペースを0に置き換えることで無理やり対応
line = line.replace(zen_space, zen_zero)
index = line.find(nichi)
# 日と曜日の位置関係から誤表記を訂正
index_first_dollar = line.find(dollar, index + 1)
if index + 1 != index_first_dollar:
index = index_first_dollar
# ex. １ → ０１
#if line[index - 1] == zen_space:
# line[index - 1] = zen_zero
return zenhan.z2h(line[index - 2:index]) | f89e332a2a0031acdf6fa443ea9752e528674b32 | 3,655,671 |
def train_sub1(sess, x, y, bbox_preds, x_sub, y_sub, nb_classes,
nb_epochs_s, batch_size, learning_rate, data_aug, lmbda,
aug_batch_size, rng, img_rows=48, img_cols=48,
nchannels=3):
"""
This function creates the substitute by alternatively
augmenting the training data and training the substitute.
:param sess: TF session
:param x: input TF placeholder
:param y: output TF placeholder
:param bbox_preds: output of black-box model predictions
:param x_sub: initial substitute training data
:param y_sub: initial substitute training labels
:param nb_classes: number of output classes
:param nb_epochs_s: number of epochs to train substitute model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param data_aug: number of times substitute training data is augmented
:param lmbda: lambda from arxiv.org/abs/1602.02697
:param rng: numpy.random.RandomState instance
:return:
"""
# Define TF model graph (for the black-box model)
model_sub = ModelSubstitute('model_s', nb_classes)
preds_sub = model_sub.get_logits(x)
loss_sub = CrossEntropy(model_sub, smoothing=0)
print("Defined TensorFlow model graph for the substitute.")
# Define the Jacobian symbolically using TensorFlow
grads = jacobian_graph(preds_sub, x, nb_classes)
# Train the substitute and augment dataset alternatively
for rho in xrange(data_aug):
print("Substitute training epoch #" + str(rho))
train_params = {
'nb_epochs': nb_epochs_s,
'batch_size': batch_size,
'learning_rate': learning_rate
}
#with TemporaryLogLevel(logging.WARNING, "cleverhans.utils.tf"):
train(sess, loss_sub, x, y, x_sub,
to_categorical(y_sub, nb_classes),
init_all=False, args=train_params, rng=rng)
#var_list=model_sub.get_params())
# If we are not at last substitute training iteration, augment dataset
if rho < data_aug - 1:
print("Augmenting substitute training data.")
# Perform the Jacobian augmentation
lmbda_coef = 2 * int(int(rho / 3) != 0) - 1
# print(x.shape)
# print(x_sub.shape)
# print(y_sub.shape)
#print(grads.shape)
x_sub = jacobian_augmentation(sess, x, x_sub, y_sub, grads,
lmbda_coef * lmbda, aug_batch_size)
print("Labeling substitute training data.")
# Label the newly generated synthetic points using the black-box
y_sub = np.hstack([y_sub, y_sub])
x_sub_prev = x_sub[int(len(x_sub)/2):]
eval_params = {'batch_size': batch_size}
#tmp = batch_eval(sess, [x], [bbox_preds], [x_sub_prev],args=eval_params)
tmp = batch_eval(sess, [x], [bbox_preds], [x_sub_prev],batch_size=batch_size)
print(tmp)
bbox_val = tmp[0]
# Note here that we take the argmax because the adversary
# only has access to the label (not the probabilities) output
# by the black-box model
y_sub[int(len(x_sub)/2):] = np.argmax(bbox_val, axis=1)
return model_sub, preds_sub | a5433f78c60f6beec14a6d4fd414d45dc8c65999 | 3,655,672 |
def divideArray(array, factor):
"""Dzielimy tablice na #factor tablic, kazda podtablica ma tyle samo elem oprocz ostatniej"""
factor = min(factor, len(array))
length = floor(len(array) * 1.0 / factor)
res = []
for i in range(factor - 1):
res = res + list([array[i * length:(i + 1) * length]])
return list(res + list([array[length * (factor - 1):]])) | d94441e6036e78f9b541b9d170d03681740c81d3 | 3,655,673 |
def argMax(scores):
"""
Returns the key with the highest value.
"""
if len(scores) == 0: return None
all = scores.items()
values = [x[1] for x in all]
maxIndex = values.index(max(values))
return all[maxIndex][0] | 9310988a0f8aa1279882d060ade7febdc102b0c5 | 3,655,674 |
def rotateright(arr,k)->list:
"""
Rotate the array right side k number of times.
"""
temp=a[0]
poi=0
for i in range(len(arr)):
for j in range(0,k):
poi+=1
if(poi==len(arr)):
poi=0
temp1=arr[poi]
arr[poi]=temp
temp=temp1
return arr | 7d303f5b57cb10a1a28f5c78ffa848d2a9cb593f | 3,655,675 |
def get_ratio(numerator, denominator):
"""Get ratio from numerator and denominator."""
return (
0 if not denominator else round(float(numerator or 0) / float(denominator), 2)
) | e51a860292d54d2e44909ad878d0b1d8e66c37c2 | 3,655,677 |
import io
def create_app():
"""
Create a Flask application for face alignment
Returns:
flask.Flask -> Flask application
"""
app = Flask(__name__)
model = setup_model()
app.config.from_mapping(MODEL=model)
@app.route("/", methods=["GET"])
def howto():
instruction = (
"Send POST request to /align to fix face orientation in input image"
"\nex."
"\n\tcurl -X POST -F 'image=@/path/to/face.jpg' --output output.jpg localhost:5000/align"
)
return instruction
@app.route("/align", methods=["POST"])
def align():
data = request.files["image"]
img_str = data.read()
nparr = np.fromstring(img_str, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR)
faces = model.detect(img)
if len(faces) == 0:
return "No face found. Try again", 400
elif len(faces) > 1:
return "Too many faces found. Try again", 400
else:
face = faces[0]
rotated_image = rotate_bound(img, face.angle)
# Encode image
is_completed, buf = cv2.imencode(".jpg", rotated_image)
if not is_completed:
return "Unexpected encoding error. Try again", 400
byte_buffer = io.BytesIO(buf.tostring())
return send_file(
byte_buffer,
"image/jpeg",
as_attachment=True,
attachment_filename="output.jpg",
)
return app | d9a5d59f64dc9227949bbe73065d18bcc8142b9d | 3,655,678 |
def grad_clip(x:Tensor) -> Tensor:
"""
Clips too big and too small gradients.
Example::
grad = grad_clip(grad)
Args:
x(:obj:`Tensor`): Gradient with too large or small values
Returns:
:obj:`Tensor`: Cliped Gradient
"""
x[x>5] = 5
x[x<-5] = -5
return x | 5c07c4432fda16d06bda8569aca34cbbaf45b076 | 3,655,679 |
def unfold_kernel(kernel):
"""
In pytorch format, kernel is stored as [out_channel, in_channel, height, width]
Unfold kernel into a 2-dimension weights: [height * width * in_channel, out_channel]
:param kernel: numpy ndarray
:return:
"""
k_shape = kernel.shape
weight = np.zeros([k_shape[1] * k_shape[2] * k_shape[3], k_shape[0]])
for i in range(k_shape[0]):
weight[:, i] = np.reshape(kernel[i, :, :, :], [-1])
return weight | 7106ead9b4953024731d918fb3c356b056bca156 | 3,655,680 |
def _parse_polyline_locations(locations, max_n_locations):
"""Parse and validate locations in Google polyline format.
The "locations" argument of the query should be a string of ascii characters above 63.
Args:
locations: The location query string.
max_n_locations: The max allowable number of locations, to keep query times reasonable.
Returns:
lats: List of latitude floats.
lons: List of longitude floats.
Raises:
ClientError: If too many locations are given, or if the location string can't be parsed.
"""
# The Google maps API prefixes their polylines with 'enc:'.
if locations and locations.startswith("enc:"):
locations = locations[4:]
try:
latlons = polyline.decode(locations)
except Exception as e:
msg = "Unable to parse locations as polyline."
raise ClientError(msg)
# Polyline result in in list of (lat, lon) tuples.
lats = [p[0] for p in latlons]
lons = [p[1] for p in latlons]
# Check number.
n_locations = len(lats)
if n_locations > max_n_locations:
msg = f"Too many locations provided ({n_locations}), the limit is {max_n_locations}."
raise ClientError(msg)
return lats, lons | 3ebff7a35c86bad5986ee87c194dd9128936abb0 | 3,655,681 |
def dense(data, weight, bias=None, out_dtype=None):
"""The default implementation of dense in topi.
Parameters
----------
data : tvm.Tensor
2-D with shape [batch, in_dim]
weight : tvm.Tensor
2-D with shape [out_dim, in_dim]
bias : tvm.Tensor, optional
1-D with shape [out_dim]
out_dtype : str
The output type. This is used for mixed precision.
Returns
-------
output : tvm.Tensor
2-D with shape [batch, out_dim]
"""
assert len(data.shape) == 2 and len(weight.shape) == 2, \
"only support 2-dim dense"
if bias is not None:
assert len(bias.shape) == 1
if out_dtype is None:
out_dtype = data.dtype
batch, in_dim = data.shape
out_dim, _ = weight.shape
k = tvm.reduce_axis((0, in_dim), name='k')
matmul = tvm.compute((batch, out_dim), \
lambda i, j: tvm.sum(data[i, k].astype(out_dtype) * \
weight[j, k].astype(out_dtype), axis=k), \
name='T_dense', tag='dense')
if bias is not None:
matmul = tvm.compute((batch, out_dim), \
lambda i, j: matmul[i, j] + bias[j].astype(out_dtype), \
tag=tag.BROADCAST)
return matmul | ac5550f901d1a7c94fee4b8e65fa9957d4b2ff78 | 3,655,682 |
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