body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
920ecf2a801c8c4ca1b105b4718313308f1091c0afe403ebd49cb6f667de15f8 | def ensure_large_islands(smoothed_cluster, min_size):
'\n Function to eliminate islands below a threshold size\n Inputs:\n smoothed_cluster (np.ndarray) : Binary raster indicating\n filtered connected cluster of active pixels, output of\n smooth_object_interface()\n min_siz... | Function to eliminate islands below a threshold size
Inputs:
smoothed_cluster (np.ndarray) : Binary raster indicating
filtered connected cluster of active pixels, output of
smooth_object_interface()
min_size (int) : Minimum area (specified in pixels) of islands
to qualify for coarsening
... | unstructured_mesh_refinement_tools.py | ensure_large_islands | passaH2O/meshrefinement | 1 | python | def ensure_large_islands(smoothed_cluster, min_size):
'\n Function to eliminate islands below a threshold size\n Inputs:\n smoothed_cluster (np.ndarray) : Binary raster indicating\n filtered connected cluster of active pixels, output of\n smooth_object_interface()\n min_siz... | def ensure_large_islands(smoothed_cluster, min_size):
'\n Function to eliminate islands below a threshold size\n Inputs:\n smoothed_cluster (np.ndarray) : Binary raster indicating\n filtered connected cluster of active pixels, output of\n smooth_object_interface()\n min_siz... |
05f9dd7201292159791b30565a496a397059857ed7730f950f967ddddbf734ee | def smooth_island_interface(large_islands, outside, buffer=4):
"\n Optional function to smooth channel-island interface from opposite\n orientation as smooth_object_interface. Can lead to slightly more\n locally-convex perimeter.\n Inputs:\n large_islands (np.ndarray) : Binary raster indicating\n... | Optional function to smooth channel-island interface from opposite
orientation as smooth_object_interface. Can lead to slightly more
locally-convex perimeter.
Inputs:
large_islands (np.ndarray) : Binary raster indicating
largest islands for coarsening, output of ensure_large_islands()
outside (np.ndarra... | unstructured_mesh_refinement_tools.py | smooth_island_interface | passaH2O/meshrefinement | 1 | python | def smooth_island_interface(large_islands, outside, buffer=4):
"\n Optional function to smooth channel-island interface from opposite\n orientation as smooth_object_interface. Can lead to slightly more\n locally-convex perimeter.\n Inputs:\n large_islands (np.ndarray) : Binary raster indicating\n... | def smooth_island_interface(large_islands, outside, buffer=4):
"\n Optional function to smooth channel-island interface from opposite\n orientation as smooth_object_interface. Can lead to slightly more\n locally-convex perimeter.\n Inputs:\n large_islands (np.ndarray) : Binary raster indicating\n... |
803d3f80d7a40d6f671065c461bdb28a53c343bf1e48598374a58d50d49e6516 | def raster2polygon(large_islands, img_path):
"\n Function to convert binary raster with 0's indicating islands\n into a list of vector polygons.\n Inputs:\n large_islands (np.ndarray) : Binary raster indicating\n largest islands for coarsening, output of ensure_large_islands()\n ... | Function to convert binary raster with 0's indicating islands
into a list of vector polygons.
Inputs:
large_islands (np.ndarray) : Binary raster indicating
largest islands for coarsening, output of ensure_large_islands()
or smooth_island_interface()
img_path (str) : Path to original input image,... | unstructured_mesh_refinement_tools.py | raster2polygon | passaH2O/meshrefinement | 1 | python | def raster2polygon(large_islands, img_path):
"\n Function to convert binary raster with 0's indicating islands\n into a list of vector polygons.\n Inputs:\n large_islands (np.ndarray) : Binary raster indicating\n largest islands for coarsening, output of ensure_large_islands()\n ... | def raster2polygon(large_islands, img_path):
"\n Function to convert binary raster with 0's indicating islands\n into a list of vector polygons.\n Inputs:\n large_islands (np.ndarray) : Binary raster indicating\n largest islands for coarsening, output of ensure_large_islands()\n ... |
bf8b57782d95deb54e915e99883cc926a2abf907f67a003f7bd94190bd7b78ba | def simplify_polygons(polycoords, epsilon):
'\n Function to decimate the vertices of a list of polygons using the\n Ramer-Douglas-Peucker algorithm.\n Inputs:\n polycoords (list) : List of polygon vertices outlining the islands\n to be simplified, output of raster2polygon()\n epsil... | Function to decimate the vertices of a list of polygons using the
Ramer-Douglas-Peucker algorithm.
Inputs:
polycoords (list) : List of polygon vertices outlining the islands
to be simplified, output of raster2polygon()
epsilon (float or int) : Epsilon value to use for the RDP algorithm,
essentia... | unstructured_mesh_refinement_tools.py | simplify_polygons | passaH2O/meshrefinement | 1 | python | def simplify_polygons(polycoords, epsilon):
'\n Function to decimate the vertices of a list of polygons using the\n Ramer-Douglas-Peucker algorithm.\n Inputs:\n polycoords (list) : List of polygon vertices outlining the islands\n to be simplified, output of raster2polygon()\n epsil... | def simplify_polygons(polycoords, epsilon):
'\n Function to decimate the vertices of a list of polygons using the\n Ramer-Douglas-Peucker algorithm.\n Inputs:\n polycoords (list) : List of polygon vertices outlining the islands\n to be simplified, output of raster2polygon()\n epsil... |
54575610d81047b7fd5c2ab3295df1a39346be0d086e91fa28dd15eda3865eb5 | def getAngle(a, b, c):
'\n Helper function for filter_poly_angles()\n Find angle between three points ABC\n '
ang = math.degrees((math.atan2((c[1] - b[1]), (c[0] - b[0])) - math.atan2((a[1] - b[1]), (a[0] - b[0]))))
return ((ang + 360) if (ang < 0) else ang) | Helper function for filter_poly_angles()
Find angle between three points ABC | unstructured_mesh_refinement_tools.py | getAngle | passaH2O/meshrefinement | 1 | python | def getAngle(a, b, c):
'\n Helper function for filter_poly_angles()\n Find angle between three points ABC\n '
ang = math.degrees((math.atan2((c[1] - b[1]), (c[0] - b[0])) - math.atan2((a[1] - b[1]), (a[0] - b[0]))))
return ((ang + 360) if (ang < 0) else ang) | def getAngle(a, b, c):
'\n Helper function for filter_poly_angles()\n Find angle between three points ABC\n '
ang = math.degrees((math.atan2((c[1] - b[1]), (c[0] - b[0])) - math.atan2((a[1] - b[1]), (a[0] - b[0]))))
return ((ang + 360) if (ang < 0) else ang)<|docstring|>Helper function for filter_p... |
b0a58a7d21766723a4bda28d53dbac2df38432dfd0475110f6b9f7af498fda5a | def removeAcute(polygon):
'\n Helper function for filter_poly_angles()\n Remove any angles which are too sharp (< 28 deg)\n '
newpoly = polygon.copy()
for n in range((len(polygon) - 2), 0, (- 1)):
ang = getAngle(polygon[(n - 1)], polygon[n], polygon[(n + 1)])
if ((ang < 28) | (ang >... | Helper function for filter_poly_angles()
Remove any angles which are too sharp (< 28 deg) | unstructured_mesh_refinement_tools.py | removeAcute | passaH2O/meshrefinement | 1 | python | def removeAcute(polygon):
'\n Helper function for filter_poly_angles()\n Remove any angles which are too sharp (< 28 deg)\n '
newpoly = polygon.copy()
for n in range((len(polygon) - 2), 0, (- 1)):
ang = getAngle(polygon[(n - 1)], polygon[n], polygon[(n + 1)])
if ((ang < 28) | (ang >... | def removeAcute(polygon):
'\n Helper function for filter_poly_angles()\n Remove any angles which are too sharp (< 28 deg)\n '
newpoly = polygon.copy()
for n in range((len(polygon) - 2), 0, (- 1)):
ang = getAngle(polygon[(n - 1)], polygon[n], polygon[(n + 1)])
if ((ang < 28) | (ang >... |
8db5737cbefb709ad39fd51389fcf06f3ebebbb236fa92b913cb6dcc97fe6057 | def filter_poly_angles(simple_polygons):
'\n Function to eliminate acute angles from of a list of polygons \n Inputs:\n simple_polygons (list) : List of polygon vertices outlining islands\n to be simplified, output of simplify_polygons()\n Outputs:\n safe_simple_polygons (list) : L... | Function to eliminate acute angles from of a list of polygons
Inputs:
simple_polygons (list) : List of polygon vertices outlining islands
to be simplified, output of simplify_polygons()
Outputs:
safe_simple_polygons (list) : List similar to simple_polygons with
sharp angles removed | unstructured_mesh_refinement_tools.py | filter_poly_angles | passaH2O/meshrefinement | 1 | python | def filter_poly_angles(simple_polygons):
'\n Function to eliminate acute angles from of a list of polygons \n Inputs:\n simple_polygons (list) : List of polygon vertices outlining islands\n to be simplified, output of simplify_polygons()\n Outputs:\n safe_simple_polygons (list) : L... | def filter_poly_angles(simple_polygons):
'\n Function to eliminate acute angles from of a list of polygons \n Inputs:\n simple_polygons (list) : List of polygon vertices outlining islands\n to be simplified, output of simplify_polygons()\n Outputs:\n safe_simple_polygons (list) : L... |
628824a63add0c03a6f2ce2bb1cccb160eecb34e2627a64d36bd35dadbe43a6a | def save_for_anuga(safe_simple_polygons, outfolder, triangle_res):
'\n Function to save list of polygons to CSV files indicating their future\n ANUGA resolution in outfolder.\n Inputs:\n safe_simple_polygons (list) : List of filtered polygon vertices\n outlining islands to be coarsened\n ... | Function to save list of polygons to CSV files indicating their future
ANUGA resolution in outfolder.
Inputs:
safe_simple_polygons (list) : List of filtered polygon vertices
outlining islands to be coarsened
outfolder (str) : String specifying folder path in which to
save polygon files
trian... | unstructured_mesh_refinement_tools.py | save_for_anuga | passaH2O/meshrefinement | 1 | python | def save_for_anuga(safe_simple_polygons, outfolder, triangle_res):
'\n Function to save list of polygons to CSV files indicating their future\n ANUGA resolution in outfolder.\n Inputs:\n safe_simple_polygons (list) : List of filtered polygon vertices\n outlining islands to be coarsened\n ... | def save_for_anuga(safe_simple_polygons, outfolder, triangle_res):
'\n Function to save list of polygons to CSV files indicating their future\n ANUGA resolution in outfolder.\n Inputs:\n safe_simple_polygons (list) : List of filtered polygon vertices\n outlining islands to be coarsened\n ... |
957564cf8e1403a0be2a3aead3b1d7e414a34bc433e8800dd09df7c68d6357b7 | def plot_polygons(polygons, fill=True, outline=False, outline_color='k'):
'\n Helper function to plot the vector form of the interior polygons,\n either filled in or as outlines.\n Inputs:\n polygons (list) : List of polygon coordinates\n fill (bool) : Option to plot polygons as filled-in\n ... | Helper function to plot the vector form of the interior polygons,
either filled in or as outlines.
Inputs:
polygons (list) : List of polygon coordinates
fill (bool) : Option to plot polygons as filled-in
outline (bool) : Option to plot polygon outlines
outline_color (str) : If outline is True, plot with... | unstructured_mesh_refinement_tools.py | plot_polygons | passaH2O/meshrefinement | 1 | python | def plot_polygons(polygons, fill=True, outline=False, outline_color='k'):
'\n Helper function to plot the vector form of the interior polygons,\n either filled in or as outlines.\n Inputs:\n polygons (list) : List of polygon coordinates\n fill (bool) : Option to plot polygons as filled-in\n ... | def plot_polygons(polygons, fill=True, outline=False, outline_color='k'):
'\n Helper function to plot the vector form of the interior polygons,\n either filled in or as outlines.\n Inputs:\n polygons (list) : List of polygon coordinates\n fill (bool) : Option to plot polygons as filled-in\n ... |
d2f33e6978ba0c240caf03983fc92a1a1b4a11a199d251746207687a037f2431 | def initialize_multi_client_cluster(job_name: str, dtensor_jobs: List[str], client_id: int, collective_leader: str, port: Optional[int]=None, protocol: Optional[str]='grpc+loas', enable_coordination_service: bool=False):
'Initialize GRPC servers and collectives for multi-client DTensor setup.\n\n While single clie... | Initialize GRPC servers and collectives for multi-client DTensor setup.
While single clients (e.g. Forge) can use local mode of collectives, GRPC
servers are necessary in mutli-client setup. This function can be used to
initialize a cluster and enable collective ops.
NOTE: this function must be called in an eager con... | tensorflow/dtensor/python/multi_client_util.py | initialize_multi_client_cluster | snadampal/tensorflow | 3 | python | def initialize_multi_client_cluster(job_name: str, dtensor_jobs: List[str], client_id: int, collective_leader: str, port: Optional[int]=None, protocol: Optional[str]='grpc+loas', enable_coordination_service: bool=False):
'Initialize GRPC servers and collectives for multi-client DTensor setup.\n\n While single clie... | def initialize_multi_client_cluster(job_name: str, dtensor_jobs: List[str], client_id: int, collective_leader: str, port: Optional[int]=None, protocol: Optional[str]='grpc+loas', enable_coordination_service: bool=False):
'Initialize GRPC servers and collectives for multi-client DTensor setup.\n\n While single clie... |
efd563fef01bad7578a1f1b0cbdebfebd22cf6328226dd6344862f722a825cf9 | def parse_input(self, input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n end... | Read simulations data from input file.
Arguments:
input -- prefix of file containing neutrino fluxes
inflv -- neutrino flavor to consider
starttime -- start time set by user via command line option (or None)
endtime -- end time set by user via command line option (or None) | sntools/formats/totani.py | parse_input | svalder/sntools | 10 | python | def parse_input(self, input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n end... | def parse_input(self, input, inflv, starttime, endtime):
'Read simulations data from input file.\n\n Arguments:\n input -- prefix of file containing neutrino fluxes\n inflv -- neutrino flavor to consider\n starttime -- start time set by user via command line option (or None)\n end... |
62ee4841012503a7fc5b64b9775d2da0633f5a35865d18abe025abb01cc0658b | def prepare_evt_gen(self, binned_t):
'Pre-compute values necessary for event generation.\n\n Scipy/numpy are optimized for parallel operation on large arrays, making\n it orders of magnitude faster to pre-compute all values at one time\n instead of computing them lazily when needed.\n\n ... | Pre-compute values necessary for event generation.
Scipy/numpy are optimized for parallel operation on large arrays, making
it orders of magnitude faster to pre-compute all values at one time
instead of computing them lazily when needed.
Argument:
binned_t -- list of time bins for generating events | sntools/formats/totani.py | prepare_evt_gen | svalder/sntools | 10 | python | def prepare_evt_gen(self, binned_t):
'Pre-compute values necessary for event generation.\n\n Scipy/numpy are optimized for parallel operation on large arrays, making\n it orders of magnitude faster to pre-compute all values at one time\n instead of computing them lazily when needed.\n\n ... | def prepare_evt_gen(self, binned_t):
'Pre-compute values necessary for event generation.\n\n Scipy/numpy are optimized for parallel operation on large arrays, making\n it orders of magnitude faster to pre-compute all values at one time\n instead of computing them lazily when needed.\n\n ... |
7f2125d3593acc2be42d4eba167f8f3f851eea699ec788ee544ae5a26db4db56 | def nu_emission(self, eNu, time):
'Number of neutrinos emitted, as a function of energy.\n\n This is not yet the flux! The geometry factor 1/(4 pi r**2) is added later.\n Arguments:\n eNu -- neutrino energy\n time -- time ;)\n '
f = self.log_spectrum[time]
return (10 ** f(... | Number of neutrinos emitted, as a function of energy.
This is not yet the flux! The geometry factor 1/(4 pi r**2) is added later.
Arguments:
eNu -- neutrino energy
time -- time ;) | sntools/formats/totani.py | nu_emission | svalder/sntools | 10 | python | def nu_emission(self, eNu, time):
'Number of neutrinos emitted, as a function of energy.\n\n This is not yet the flux! The geometry factor 1/(4 pi r**2) is added later.\n Arguments:\n eNu -- neutrino energy\n time -- time ;)\n '
f = self.log_spectrum[time]
return (10 ** f(... | def nu_emission(self, eNu, time):
'Number of neutrinos emitted, as a function of energy.\n\n This is not yet the flux! The geometry factor 1/(4 pi r**2) is added later.\n Arguments:\n eNu -- neutrino energy\n time -- time ;)\n '
f = self.log_spectrum[time]
return (10 ** f(... |
1e0cd3889187119a0786f867d56a7173ede6240878951127c1655d5e1f8eef78 | def _parse(self, input, format, flv):
'Read data from files into dictionaries to look up by time.'
with open(input) as infile:
raw_indata = [line for line in infile]
chunks = []
if (format == 'early'):
for i in range(26):
chunks.append(raw_indata[(42 * i):(42 * (i + 1))])
... | Read data from files into dictionaries to look up by time. | sntools/formats/totani.py | _parse | svalder/sntools | 10 | python | def _parse(self, input, format, flv):
with open(input) as infile:
raw_indata = [line for line in infile]
chunks = []
if (format == 'early'):
for i in range(26):
chunks.append(raw_indata[(42 * i):(42 * (i + 1))])
line_N = 6
range_egroup = range(19, 39)
eli... | def _parse(self, input, format, flv):
with open(input) as infile:
raw_indata = [line for line in infile]
chunks = []
if (format == 'early'):
for i in range(26):
chunks.append(raw_indata[(42 * i):(42 * (i + 1))])
line_N = 6
range_egroup = range(19, 39)
eli... |
7779c10c6303d4a9e343ea8a7926b5ba8f55b7684f5a522053c855c3d9854a28 | def _parse_nb(self, input):
'More granular nu_e data for the neutronization burst ("nb", 40-50ms).\n\n Note: the nb file comes from a slightly different simulation, therefore we\n have to deal with a time offset and a scaling factor.\n '
with open(input) as infile:
raw_indata = [lin... | More granular nu_e data for the neutronization burst ("nb", 40-50ms).
Note: the nb file comes from a slightly different simulation, therefore we
have to deal with a time offset and a scaling factor. | sntools/formats/totani.py | _parse_nb | svalder/sntools | 10 | python | def _parse_nb(self, input):
'More granular nu_e data for the neutronization burst ("nb", 40-50ms).\n\n Note: the nb file comes from a slightly different simulation, therefore we\n have to deal with a time offset and a scaling factor.\n '
with open(input) as infile:
raw_indata = [lin... | def _parse_nb(self, input):
'More granular nu_e data for the neutronization burst ("nb", 40-50ms).\n\n Note: the nb file comes from a slightly different simulation, therefore we\n have to deal with a time offset and a scaling factor.\n '
with open(input) as infile:
raw_indata = [lin... |
48b2c4cb86b77d1825c3307cf6ff45474a87514ce89f6393c6896ebf7c6e4723 | def _calculate_dNLde(self):
'Calculate number luminosity spectrum for each time bin.'
for (i, time) in enumerate(self.times_el):
E_integ = 0
spec = []
egroup = self.egroup_dict[time]
for (j, n) in enumerate(egroup):
if ((j == 0) or (j == (len(egroup) - 1))):
... | Calculate number luminosity spectrum for each time bin. | sntools/formats/totani.py | _calculate_dNLde | svalder/sntools | 10 | python | def _calculate_dNLde(self):
for (i, time) in enumerate(self.times_el):
E_integ = 0
spec = []
egroup = self.egroup_dict[time]
for (j, n) in enumerate(egroup):
if ((j == 0) or (j == (len(egroup) - 1))):
spec.append(zero)
else:
... | def _calculate_dNLde(self):
for (i, time) in enumerate(self.times_el):
E_integ = 0
spec = []
egroup = self.egroup_dict[time]
for (j, n) in enumerate(egroup):
if ((j == 0) or (j == (len(egroup) - 1))):
spec.append(zero)
else:
... |
c2d8baf536cf3d8eec8ee8622556bcb0ecf3683bdcffb6abdf72825ec692653a | @staticmethod
def get_diseases_for_gene_desc(gene_id):
'for a given NCBI Entrez Gene ID, returns a ``set`` of DOI disease identifiers for the gene\n\n :returns: a ``set`` containing ``str`` disease ontology identifiers\n '
handler = QueryBioLink.HANDLER_MAP['get_diseases_for_gene'].format(gene_id=... | for a given NCBI Entrez Gene ID, returns a ``set`` of DOI disease identifiers for the gene
:returns: a ``set`` containing ``str`` disease ontology identifiers | code/reasoningtool/kg-construction/QueryBioLink.py | get_diseases_for_gene_desc | rtx-travis-tester/RTX | 31 | python | @staticmethod
def get_diseases_for_gene_desc(gene_id):
'for a given NCBI Entrez Gene ID, returns a ``set`` of DOI disease identifiers for the gene\n\n :returns: a ``set`` containing ``str`` disease ontology identifiers\n '
handler = QueryBioLink.HANDLER_MAP['get_diseases_for_gene'].format(gene_id=... | @staticmethod
def get_diseases_for_gene_desc(gene_id):
'for a given NCBI Entrez Gene ID, returns a ``set`` of DOI disease identifiers for the gene\n\n :returns: a ``set`` containing ``str`` disease ontology identifiers\n '
handler = QueryBioLink.HANDLER_MAP['get_diseases_for_gene'].format(gene_id=... |
45fbb7ac54796aa83a6e049cee57622b1a0816ac214e6ff643a2fb26a4d2c0c1 | @staticmethod
def get_anatomies_for_gene(gene_id):
'for a given NCBI Entrez Gene ID, returns a ``dict`` of Anatomy IDs and labels for the gene\n\n :returns: a ``dict`` of <anatomy_ID, label>\n '
handler = QueryBioLink.HANDLER_MAP['get_anatomies_for_gene'].format(gene_id=gene_id)
results = Quer... | for a given NCBI Entrez Gene ID, returns a ``dict`` of Anatomy IDs and labels for the gene
:returns: a ``dict`` of <anatomy_ID, label> | code/reasoningtool/kg-construction/QueryBioLink.py | get_anatomies_for_gene | rtx-travis-tester/RTX | 31 | python | @staticmethod
def get_anatomies_for_gene(gene_id):
'for a given NCBI Entrez Gene ID, returns a ``dict`` of Anatomy IDs and labels for the gene\n\n :returns: a ``dict`` of <anatomy_ID, label>\n '
handler = QueryBioLink.HANDLER_MAP['get_anatomies_for_gene'].format(gene_id=gene_id)
results = Quer... | @staticmethod
def get_anatomies_for_gene(gene_id):
'for a given NCBI Entrez Gene ID, returns a ``dict`` of Anatomy IDs and labels for the gene\n\n :returns: a ``dict`` of <anatomy_ID, label>\n '
handler = QueryBioLink.HANDLER_MAP['get_anatomies_for_gene'].format(gene_id=gene_id)
results = Quer... |
58aa0b2917265a605719f08f4d3a2d58c303ce38823247d8d5242cbd955e06a6 | @staticmethod
def get_genes_for_anatomy(anatomy_id):
'for a given Anatomy ID, returns a ``list`` of Gene ID for the anatomy\n\n :returns: a ``list`` of gene ID\n '
handler = QueryBioLink.HANDLER_MAP['get_genes_for_anatomy'].format(anatomy_id=anatomy_id)
results = QueryBioLink.__access_api(hand... | for a given Anatomy ID, returns a ``list`` of Gene ID for the anatomy
:returns: a ``list`` of gene ID | code/reasoningtool/kg-construction/QueryBioLink.py | get_genes_for_anatomy | rtx-travis-tester/RTX | 31 | python | @staticmethod
def get_genes_for_anatomy(anatomy_id):
'for a given Anatomy ID, returns a ``list`` of Gene ID for the anatomy\n\n :returns: a ``list`` of gene ID\n '
handler = QueryBioLink.HANDLER_MAP['get_genes_for_anatomy'].format(anatomy_id=anatomy_id)
results = QueryBioLink.__access_api(hand... | @staticmethod
def get_genes_for_anatomy(anatomy_id):
'for a given Anatomy ID, returns a ``list`` of Gene ID for the anatomy\n\n :returns: a ``list`` of gene ID\n '
handler = QueryBioLink.HANDLER_MAP['get_genes_for_anatomy'].format(anatomy_id=anatomy_id)
results = QueryBioLink.__access_api(hand... |
6e164784aaa8eb69bb1ef59fde74802a711507c033c8f2da06e9c7b9974afac3 | @staticmethod
def get_anatomies_for_phenotype(phenotype_id):
'for a given phenotype ID, returns a ``dict`` of Anatomy IDs and labels for the phenotype\n\n :returns: a ``dict`` of <anatomy_ID, label>\n '
handler = QueryBioLink.HANDLER_MAP['get_anatomies_for_phenotype'].format(phenotype_id=phenotype... | for a given phenotype ID, returns a ``dict`` of Anatomy IDs and labels for the phenotype
:returns: a ``dict`` of <anatomy_ID, label> | code/reasoningtool/kg-construction/QueryBioLink.py | get_anatomies_for_phenotype | rtx-travis-tester/RTX | 31 | python | @staticmethod
def get_anatomies_for_phenotype(phenotype_id):
'for a given phenotype ID, returns a ``dict`` of Anatomy IDs and labels for the phenotype\n\n :returns: a ``dict`` of <anatomy_ID, label>\n '
handler = QueryBioLink.HANDLER_MAP['get_anatomies_for_phenotype'].format(phenotype_id=phenotype... | @staticmethod
def get_anatomies_for_phenotype(phenotype_id):
'for a given phenotype ID, returns a ``dict`` of Anatomy IDs and labels for the phenotype\n\n :returns: a ``dict`` of <anatomy_ID, label>\n '
handler = QueryBioLink.HANDLER_MAP['get_anatomies_for_phenotype'].format(phenotype_id=phenotype... |
cc9130d47069ef9d9d63cb647e58ebd09bbe2e402989ed6b95c3bccd7e30aa1b | @staticmethod
def map_disease_to_phenotype(disease_id):
'\n Mapping a disease to a list of phenotypes\n :param disease_id: The DOID / OMIM ID for a disease\n :return: A list of phenotypes HP IDs, or an empty array if no HP IDs are found\n '
hp_array = []
if ((not isinstance(disea... | Mapping a disease to a list of phenotypes
:param disease_id: The DOID / OMIM ID for a disease
:return: A list of phenotypes HP IDs, or an empty array if no HP IDs are found | code/reasoningtool/kg-construction/QueryBioLink.py | map_disease_to_phenotype | rtx-travis-tester/RTX | 31 | python | @staticmethod
def map_disease_to_phenotype(disease_id):
'\n Mapping a disease to a list of phenotypes\n :param disease_id: The DOID / OMIM ID for a disease\n :return: A list of phenotypes HP IDs, or an empty array if no HP IDs are found\n '
hp_array = []
if ((not isinstance(disea... | @staticmethod
def map_disease_to_phenotype(disease_id):
'\n Mapping a disease to a list of phenotypes\n :param disease_id: The DOID / OMIM ID for a disease\n :return: A list of phenotypes HP IDs, or an empty array if no HP IDs are found\n '
hp_array = []
if ((not isinstance(disea... |
b94e9b4cfd19c7c0bdb5dee7d04b44d87ff982e7c00c8bb20090a79eea29abcb | def compute_homography(src, dst):
'computes the homography from src, to dst using inversion method.'
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'fl... | computes the homography from src, to dst using inversion method. | compute_homography.py | compute_homography | adi2809/SimpleScanner | 1 | python | def compute_homography(src, dst):
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'float64')
p2[(:, :2)] = dst
elif (dst.shape[1] == 3):
... | def compute_homography(src, dst):
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'float64')
p2[(:, :2)] = dst
elif (dst.shape[1] == 3):
... |
1676c3db720271b2ff0807150ea7e2b0d112396a4ada70d0f03b872a59f43c02 | def find_homography(src, dst):
'computes the homography from src, to dst using singular value decomposition method.'
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((l... | computes the homography from src, to dst using singular value decomposition method. | compute_homography.py | find_homography | adi2809/SimpleScanner | 1 | python | def find_homography(src, dst):
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'float64')
p2[(:, :2)] = dst
elif (dst.shape[1] == 3):
... | def find_homography(src, dst):
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'float64')
p2[(:, :2)] = dst
elif (dst.shape[1] == 3):
... |
84b32cfb38ed9b4045850c4a145d7a9f32548fd4105340cf05fb46ec8955cab9 | def find_homography_2(src, dst):
'computes the homography from src, to dst using singular value decomposition method.'
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones(... | computes the homography from src, to dst using singular value decomposition method. | compute_homography.py | find_homography_2 | adi2809/SimpleScanner | 1 | python | def find_homography_2(src, dst):
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'float64')
p2[(:, :2)] = dst
elif (dst.shape[1] == 3):
... | def find_homography_2(src, dst):
if (src.shape[1] == 2):
p1 = np.ones((len(src), 3), 'float64')
p1[(:, :2)] = src
elif (src.shape[1] == 3):
p1 = src
if (dst.shape[1] == 2):
p2 = np.ones((len(dst), 3), 'float64')
p2[(:, :2)] = dst
elif (dst.shape[1] == 3):
... |
f3c8fc1a18f0f140ca221b9d5da908fa1307c2c23f6b0e7ffb8e36b9b8f06f1f | def string_list_validator(value: str) -> str:
"\n Validate if value is a str\n\n Arguments:\n value {str} -- value to validate\n\n Raises:\n ValueError: value is not a type of str\n ValueError: Value can't be a empty string\n\n Returns:\n str -- unchanged input value\n "
... | Validate if value is a str
Arguments:
value {str} -- value to validate
Raises:
ValueError: value is not a type of str
ValueError: Value can't be a empty string
Returns:
str -- unchanged input value | meetup_search/rest_api/argument_validator.py | string_list_validator | saxsys/flask-meetup-data-scraper | 1 | python | def string_list_validator(value: str) -> str:
"\n Validate if value is a str\n\n Arguments:\n value {str} -- value to validate\n\n Raises:\n ValueError: value is not a type of str\n ValueError: Value can't be a empty string\n\n Returns:\n str -- unchanged input value\n "
... | def string_list_validator(value: str) -> str:
"\n Validate if value is a str\n\n Arguments:\n value {str} -- value to validate\n\n Raises:\n ValueError: value is not a type of str\n ValueError: Value can't be a empty string\n\n Returns:\n str -- unchanged input value\n "
... |
7464ea7bb387d17ba8e853fb5b7a1fcb2bb4fd090a7a54642f202d3468a78c6f | def positive_int_validator(value: int) -> int:
'\n Validate for positive int\n\n Arguments:\n value {int} -- int number\n\n Raises:\n ValueError: Value is an str that can not convert to an int\n ValueError: Value has to be an int\n ValueError: Value has to be equal or greater th... | Validate for positive int
Arguments:
value {int} -- int number
Raises:
ValueError: Value is an str that can not convert to an int
ValueError: Value has to be an int
ValueError: Value has to be equal or greater than 0
Returns:
int -- unchanged input value | meetup_search/rest_api/argument_validator.py | positive_int_validator | saxsys/flask-meetup-data-scraper | 1 | python | def positive_int_validator(value: int) -> int:
'\n Validate for positive int\n\n Arguments:\n value {int} -- int number\n\n Raises:\n ValueError: Value is an str that can not convert to an int\n ValueError: Value has to be an int\n ValueError: Value has to be equal or greater th... | def positive_int_validator(value: int) -> int:
'\n Validate for positive int\n\n Arguments:\n value {int} -- int number\n\n Raises:\n ValueError: Value is an str that can not convert to an int\n ValueError: Value has to be an int\n ValueError: Value has to be equal or greater th... |
589c169bfa97d31674910afd7b711b98b8d96f307bd8bae6feffef697825c26d | def date_validator(value: str) -> str:
'\n Validate if string is a valid date\n\n Arguments:\n value {str} -- value to validate\n\n Returns:\n str -- validate date as string\n '
try:
return str(datetime.fromisoformat(value).date())
except TypeError:
raise ValueError... | Validate if string is a valid date
Arguments:
value {str} -- value to validate
Returns:
str -- validate date as string | meetup_search/rest_api/argument_validator.py | date_validator | saxsys/flask-meetup-data-scraper | 1 | python | def date_validator(value: str) -> str:
'\n Validate if string is a valid date\n\n Arguments:\n value {str} -- value to validate\n\n Returns:\n str -- validate date as string\n '
try:
return str(datetime.fromisoformat(value).date())
except TypeError:
raise ValueError... | def date_validator(value: str) -> str:
'\n Validate if string is a valid date\n\n Arguments:\n value {str} -- value to validate\n\n Returns:\n str -- validate date as string\n '
try:
return str(datetime.fromisoformat(value).date())
except TypeError:
raise ValueError... |
6ca657b3043c5307828cd0dda315de85aa3c84dfcd46a78030a1198cca19da89 | def __init__(self, batch_queue, min_records_in_aggregated_batches):
'\n :param batch_queue: instance of :class:`BatchQueue` or :class:`PartitionedBatchQueue` to be wrapped\n '
self._q = batch_queue
self._empty = False
self._min_records_in_aggregated_batches = min_records_in_aggregated_batc... | :param batch_queue: instance of :class:`BatchQueue` or :class:`PartitionedBatchQueue` to be wrapped | shellstreaming/core/remote_queue.py | __init__ | laysakura/shellstreaming | 1 | python | def __init__(self, batch_queue, min_records_in_aggregated_batches):
'\n \n '
self._q = batch_queue
self._empty = False
self._min_records_in_aggregated_batches = min_records_in_aggregated_batches | def __init__(self, batch_queue, min_records_in_aggregated_batches):
'\n \n '
self._q = batch_queue
self._empty = False
self._min_records_in_aggregated_batches = min_records_in_aggregated_batches<|docstring|>:param batch_queue: instance of :class:`BatchQueue` or :class:`PartitionedBatchQueu... |
800b9d67c131e5e07d11c8ff4ccc12ec1b96eee9b420a234359ddf2646a23e01 | def findChildEndingWith(el, tagEnd):
'Finds first child of an XML element with tag ending in tagEnd (case insensitive).'
tagEnd = tagEnd.lower()
for child in el:
if child.tag.lower().endswith(tagEnd):
return child
return None | Finds first child of an XML element with tag ending in tagEnd (case insensitive). | secscan/scrape13F.py | findChildEndingWith | ikedim01/secscan | 0 | python | def findChildEndingWith(el, tagEnd):
tagEnd = tagEnd.lower()
for child in el:
if child.tag.lower().endswith(tagEnd):
return child
return None | def findChildEndingWith(el, tagEnd):
tagEnd = tagEnd.lower()
for child in el:
if child.tag.lower().endswith(tagEnd):
return child
return None<|docstring|>Finds first child of an XML element with tag ending in tagEnd (case insensitive).<|endoftext|> |
8ee08baf12a78f0b85bea370a07df78123ec2ee8f22d2fa126920d6420bf643a | def findChildSeries(el, tagEnds):
'Finds a nested series of children by tag using findChildEndingWith'
for tagEnd in tagEnds:
el = findChildEndingWith(el, tagEnd)
return el | Finds a nested series of children by tag using findChildEndingWith | secscan/scrape13F.py | findChildSeries | ikedim01/secscan | 0 | python | def findChildSeries(el, tagEnds):
for tagEnd in tagEnds:
el = findChildEndingWith(el, tagEnd)
return el | def findChildSeries(el, tagEnds):
for tagEnd in tagEnds:
el = findChildEndingWith(el, tagEnd)
return el<|docstring|>Finds a nested series of children by tag using findChildEndingWith<|endoftext|> |
9f0ca411945b0292ff991aeebb6b0cf8c4ea422f53692cb44887fc9cb7514d8c | def getRowInfo(row):
"\n Returns information for a row in a 13F table in the form:\n (cusip, name, value, title, count, putCall)\n where the field values are as given in the table,\n except putCall is 'CALL', 'PUT', or ''.\n "
cusip = findChildEndingWith(row, 'cusip').text.upper().strip()
... | Returns information for a row in a 13F table in the form:
(cusip, name, value, title, count, putCall)
where the field values are as given in the table,
except putCall is 'CALL', 'PUT', or ''. | secscan/scrape13F.py | getRowInfo | ikedim01/secscan | 0 | python | def getRowInfo(row):
"\n Returns information for a row in a 13F table in the form:\n (cusip, name, value, title, count, putCall)\n where the field values are as given in the table,\n except putCall is 'CALL', 'PUT', or .\n "
cusip = findChildEndingWith(row, 'cusip').text.upper().strip()
n... | def getRowInfo(row):
"\n Returns information for a row in a 13F table in the form:\n (cusip, name, value, title, count, putCall)\n where the field values are as given in the table,\n except putCall is 'CALL', 'PUT', or .\n "
cusip = findChildEndingWith(row, 'cusip').text.upper().strip()
n... |
77640d80ac88d73e35a8a3212324bd08dd4d59e5eed3e81ebe3b2c2e17fc192a | def parse13FHoldings(accNo, formType=None):
"\n Parses a 13F filing, returning the result in the form:\n {\n 'period': 'YYYY-MM-DD',\n 'acceptDate': 'YYYY-MM-DD',\n 'acceptTime': 'HH:MM:SS',\n 'cik' : 'DDDDDDDDDD',\n 'holdings': [(cusip, name, value, title, count, putCall), ... | Parses a 13F filing, returning the result in the form:
{
'period': 'YYYY-MM-DD',
'acceptDate': 'YYYY-MM-DD',
'acceptTime': 'HH:MM:SS',
'cik' : 'DDDDDDDDDD',
'holdings': [(cusip, name, value, title, count, putCall), ... ]
}
where the field values are as given in the table,
except putCall is 'CALL', '... | secscan/scrape13F.py | parse13FHoldings | ikedim01/secscan | 0 | python | def parse13FHoldings(accNo, formType=None):
"\n Parses a 13F filing, returning the result in the form:\n {\n 'period': 'YYYY-MM-DD',\n 'acceptDate': 'YYYY-MM-DD',\n 'acceptTime': 'HH:MM:SS',\n 'cik' : 'DDDDDDDDDD',\n 'holdings': [(cusip, name, value, title, count, putCall), ... | def parse13FHoldings(accNo, formType=None):
"\n Parses a 13F filing, returning the result in the form:\n {\n 'period': 'YYYY-MM-DD',\n 'acceptDate': 'YYYY-MM-DD',\n 'acceptTime': 'HH:MM:SS',\n 'cik' : 'DDDDDDDDDD',\n 'holdings': [(cusip, name, value, title, count, putCall), ... |
fe2fcdfd2d16a3cfd34557480f332b4e8749b345e65063a4d76a96b79f5ffc99 | def condenseHoldings(holdings, minFrac=0.0, maxFrac=1.0, pctFormat=False, includeName=False, cusipNames={}, minStocksPerInv=None, maxStocksPerInv=None, minTop10Frac=None, minAUM=None, allCusipCounter=None, all13FHoldingsMap=None, forCik=None):
'\n Converts a list of of stock and option holdings as parsed from th... | Converts a list of of stock and option holdings as parsed from the 13F:
[(cusip, name, value, title, count, putCall), ... ]
that may have multiple entries per stock into a condensed list that omits
call/put options and only has one combined entry per stock:
[(cusip, val, frac) ... ]
sorted in descending order b... | secscan/scrape13F.py | condenseHoldings | ikedim01/secscan | 0 | python | def condenseHoldings(holdings, minFrac=0.0, maxFrac=1.0, pctFormat=False, includeName=False, cusipNames={}, minStocksPerInv=None, maxStocksPerInv=None, minTop10Frac=None, minAUM=None, allCusipCounter=None, all13FHoldingsMap=None, forCik=None):
'\n Converts a list of of stock and option holdings as parsed from th... | def condenseHoldings(holdings, minFrac=0.0, maxFrac=1.0, pctFormat=False, includeName=False, cusipNames={}, minStocksPerInv=None, maxStocksPerInv=None, minTop10Frac=None, minAUM=None, allCusipCounter=None, all13FHoldingsMap=None, forCik=None):
'\n Converts a list of of stock and option holdings as parsed from th... |
2fb6eb58f8f3c3fe7fb02f4cdee2246c670d2b5e3ba42faa0c025c1ac4c0f971 | def get13FAmendmentType(accNo, formType=None):
"\n Gets the amendment type for a 13F-HR/A filing - may be RESTATEMENT or NEW HOLDINGS.\n This turned out to be unreliable (often missing or wrong), so I don't use it to get\n the combined holdings for an investor. Instead I just look at the number of holdings... | Gets the amendment type for a 13F-HR/A filing - may be RESTATEMENT or NEW HOLDINGS.
This turned out to be unreliable (often missing or wrong), so I don't use it to get
the combined holdings for an investor. Instead I just look at the number of holdings
in an amendment compared to the previous filing, and treat it as a ... | secscan/scrape13F.py | get13FAmendmentType | ikedim01/secscan | 0 | python | def get13FAmendmentType(accNo, formType=None):
"\n Gets the amendment type for a 13F-HR/A filing - may be RESTATEMENT or NEW HOLDINGS.\n This turned out to be unreliable (often missing or wrong), so I don't use it to get\n the combined holdings for an investor. Instead I just look at the number of holdings... | def get13FAmendmentType(accNo, formType=None):
"\n Gets the amendment type for a 13F-HR/A filing - may be RESTATEMENT or NEW HOLDINGS.\n This turned out to be unreliable (often missing or wrong), so I don't use it to get\n the combined holdings for an investor. Instead I just look at the number of holdings... |
03b9025a87833302dd97646589ddc4a3567271dcfdf19d01f38e1868af588a54 | def indexMap(lis):
'Converts a list to a dict mapping item -> index in the list.'
return dict(((el, i) for (i, el) in enumerate(lis))) | Converts a list to a dict mapping item -> index in the list. | secscan/scrape13F.py | indexMap | ikedim01/secscan | 0 | python | def indexMap(lis):
return dict(((el, i) for (i, el) in enumerate(lis))) | def indexMap(lis):
return dict(((el, i) for (i, el) in enumerate(lis)))<|docstring|>Converts a list to a dict mapping item -> index in the list.<|endoftext|> |
039b00b1e49a3be6022cb36161880fdc80303ff606509ff8fa5142475c39f926 | def getHoldingsMap(scraped13F, period, minFrac=0.0, maxFrac=1.0, minStocksPerInv=None, maxStocksPerInv=None, minTop10Frac=None, minAUM=None, allCusipCounter=None, all13FHoldingsMap=None):
'\n Consolidate holdings for each CIK based on all filings for a given period into\n a combined map of investor holdings.\... | Consolidate holdings for each CIK based on all filings for a given period into
a combined map of investor holdings.
Returns a dict: cik -> {cusip -> pct}
Restricts to stocks only (no call/put options).
If minFrac and/or maxFrac is supplied, restricts to stocks with fraction of
total portfolio >=minFrac and/or <=maxF... | secscan/scrape13F.py | getHoldingsMap | ikedim01/secscan | 0 | python | def getHoldingsMap(scraped13F, period, minFrac=0.0, maxFrac=1.0, minStocksPerInv=None, maxStocksPerInv=None, minTop10Frac=None, minAUM=None, allCusipCounter=None, all13FHoldingsMap=None):
'\n Consolidate holdings for each CIK based on all filings for a given period into\n a combined map of investor holdings.\... | def getHoldingsMap(scraped13F, period, minFrac=0.0, maxFrac=1.0, minStocksPerInv=None, maxStocksPerInv=None, minTop10Frac=None, minAUM=None, allCusipCounter=None, all13FHoldingsMap=None):
'\n Consolidate holdings for each CIK based on all filings for a given period into\n a combined map of investor holdings.\... |
ff9f679a222dc9854d2922210fde5d7554609c73d23416db62be4bfb06ec422f | def addHoldingsMap(holdingsMap, extraHoldingsMap):
'\n Adds positions in extraHoldingsMap to holdingsMap.\n Each argument is a dict: cik -> {cusip -> pct}\n but extraHoldingsMap may contain ciks and cusips not in holdingsMap.\n '
for (cik, extraPosMap) in extraHoldingsMap.items():
if (cik no... | Adds positions in extraHoldingsMap to holdingsMap.
Each argument is a dict: cik -> {cusip -> pct}
but extraHoldingsMap may contain ciks and cusips not in holdingsMap. | secscan/scrape13F.py | addHoldingsMap | ikedim01/secscan | 0 | python | def addHoldingsMap(holdingsMap, extraHoldingsMap):
'\n Adds positions in extraHoldingsMap to holdingsMap.\n Each argument is a dict: cik -> {cusip -> pct}\n but extraHoldingsMap may contain ciks and cusips not in holdingsMap.\n '
for (cik, extraPosMap) in extraHoldingsMap.items():
if (cik no... | def addHoldingsMap(holdingsMap, extraHoldingsMap):
'\n Adds positions in extraHoldingsMap to holdingsMap.\n Each argument is a dict: cik -> {cusip -> pct}\n but extraHoldingsMap may contain ciks and cusips not in holdingsMap.\n '
for (cik, extraPosMap) in extraHoldingsMap.items():
if (cik no... |
b184531800e3079981ad435c621b9d635aa379db25310a6db2e44de047a73c27 | def holdingsMapToMatrix(holdingsMap, minStocksPerInvestor=None, maxStocksPerInvestor=None, minInvestorsPerStock=None, maxInvestorsPerStock=None, minAllInvestorsPerStock=None, maxAllInvestorsPerStock=None, allCusipCounter=None, cusipFilter=None, dtype=np.float64):
'\n Converts a holdings map: cik -> {cusip -> fra... | Converts a holdings map: cik -> {cusip -> frac} into a matrix.
Returns mat, ciks, cusips where mat is a matrix of shape (len(ciks), len(cusips))
in which each row has the fractions held by the corresponding cik in each cusip.
If minStocksPerInvestor is specified, restricts to investors with at least that many stocks
... | secscan/scrape13F.py | holdingsMapToMatrix | ikedim01/secscan | 0 | python | def holdingsMapToMatrix(holdingsMap, minStocksPerInvestor=None, maxStocksPerInvestor=None, minInvestorsPerStock=None, maxInvestorsPerStock=None, minAllInvestorsPerStock=None, maxAllInvestorsPerStock=None, allCusipCounter=None, cusipFilter=None, dtype=np.float64):
'\n Converts a holdings map: cik -> {cusip -> fra... | def holdingsMapToMatrix(holdingsMap, minStocksPerInvestor=None, maxStocksPerInvestor=None, minInvestorsPerStock=None, maxInvestorsPerStock=None, minAllInvestorsPerStock=None, maxAllInvestorsPerStock=None, allCusipCounter=None, cusipFilter=None, dtype=np.float64):
'\n Converts a holdings map: cik -> {cusip -> fra... |
847fc5accffdbbadc045fcde526e86b9db836c8fd670e3c26855fbc2f1f79b9b | def getPeriodAndNextQStartEnd(y, qNo):
'\n Returns the 13F period date for a given year and quarter number (this is the\n last day in the quarter), along with the start and end dateStrs for the next\n quarter (this is the date range when the 13Fs for this year should be filed).\n Quarters are numbered 1... | Returns the 13F period date for a given year and quarter number (this is the
last day in the quarter), along with the start and end dateStrs for the next
quarter (this is the date range when the 13Fs for this year should be filed).
Quarters are numbered 1-4. | secscan/scrape13F.py | getPeriodAndNextQStartEnd | ikedim01/secscan | 0 | python | def getPeriodAndNextQStartEnd(y, qNo):
'\n Returns the 13F period date for a given year and quarter number (this is the\n last day in the quarter), along with the start and end dateStrs for the next\n quarter (this is the date range when the 13Fs for this year should be filed).\n Quarters are numbered 1... | def getPeriodAndNextQStartEnd(y, qNo):
'\n Returns the 13F period date for a given year and quarter number (this is the\n last day in the quarter), along with the start and end dateStrs for the next\n quarter (this is the date range when the 13Fs for this year should be filed).\n Quarters are numbered 1... |
c335d2ee5f5e85c47a2067dcf31ec7eea75ce07f51b42637a3af650826473e26 | def getNSSForQ(y, qNo, minFrac=0.01, maxFrac=1.0, minStocksPerInv=3, maxStocksPerInv=100, minTop10Frac=0.4, minAUM=None, dtype=np.float64, minInvestorsPerStock=2, maxInvestorsPerStock=None, minAllInvestorsPerStock=None, maxAllInvestorsPerStock=None, allCusipCounter=None, cusipFilter=None, extraHoldingsMaps=[], include1... | Calculates a matrix of investor holdings for a quarter, based on all 13F filings filed
during the succeeding quarter.
Returns mat, ciks, cusips where mat is a matrix of shape (len(ciks), len(cusips))
in which each row has the fractions held by the corresponding cik in each cusip.
If minFrac and/or maxFrac is supplied... | secscan/scrape13F.py | getNSSForQ | ikedim01/secscan | 0 | python | def getNSSForQ(y, qNo, minFrac=0.01, maxFrac=1.0, minStocksPerInv=3, maxStocksPerInv=100, minTop10Frac=0.4, minAUM=None, dtype=np.float64, minInvestorsPerStock=2, maxInvestorsPerStock=None, minAllInvestorsPerStock=None, maxAllInvestorsPerStock=None, allCusipCounter=None, cusipFilter=None, extraHoldingsMaps=[], include1... | def getNSSForQ(y, qNo, minFrac=0.01, maxFrac=1.0, minStocksPerInv=3, maxStocksPerInv=100, minTop10Frac=0.4, minAUM=None, dtype=np.float64, minInvestorsPerStock=2, maxInvestorsPerStock=None, minAllInvestorsPerStock=None, maxAllInvestorsPerStock=None, allCusipCounter=None, cusipFilter=None, extraHoldingsMaps=[], include1... |
1461a9bbbb054b152d3566257e60e394272e7185f0eefad350d2a915f47a5fdc | def saveConvMatrixPy2(y, qNo, minFrac=0.13, maxFrac=0.4, minStocksPerInv=3, maxStocksPerInv=500, minTop10Frac=None, minAUM=75000000.0, dtype=np.float64, minInvestorsPerStock=2, maxInvestorsPerStock=None):
'\n Save a matrix of 13F conviction positions only for the given quarter,\n in a format readable by the B... | Save a matrix of 13F conviction positions only for the given quarter,
in a format readable by the BW old Python2 version. | secscan/scrape13F.py | saveConvMatrixPy2 | ikedim01/secscan | 0 | python | def saveConvMatrixPy2(y, qNo, minFrac=0.13, maxFrac=0.4, minStocksPerInv=3, maxStocksPerInv=500, minTop10Frac=None, minAUM=75000000.0, dtype=np.float64, minInvestorsPerStock=2, maxInvestorsPerStock=None):
'\n Save a matrix of 13F conviction positions only for the given quarter,\n in a format readable by the B... | def saveConvMatrixPy2(y, qNo, minFrac=0.13, maxFrac=0.4, minStocksPerInv=3, maxStocksPerInv=500, minTop10Frac=None, minAUM=75000000.0, dtype=np.float64, minInvestorsPerStock=2, maxInvestorsPerStock=None):
'\n Save a matrix of 13F conviction positions only for the given quarter,\n in a format readable by the B... |
911670c04c3a7273ecdf163091b5c1288854b7856308bb9c1253bb02da2be09b | def test_no_mysterious_extra_vertical_lines():
'\n This test is to make sure that issue #2 is fixed.\n '
width = 60
height = 17
pixels = render(xs=np.array([1, 1]), ys=np.array([0, 1]), x_min=3, y_min=0, x_max=6, y_max=1.1, width=width, height=height, lines=True)
desired_pixels = np.zeros((hei... | This test is to make sure that issue #2 is fixed. | tests/unit/test_pixel_matrix.py | test_no_mysterious_extra_vertical_lines | olavolav/textplot | 156 | python | def test_no_mysterious_extra_vertical_lines():
'\n \n '
width = 60
height = 17
pixels = render(xs=np.array([1, 1]), ys=np.array([0, 1]), x_min=3, y_min=0, x_max=6, y_max=1.1, width=width, height=height, lines=True)
desired_pixels = np.zeros((height, width), dtype=int)
np.testing.assert_arr... | def test_no_mysterious_extra_vertical_lines():
'\n \n '
width = 60
height = 17
pixels = render(xs=np.array([1, 1]), ys=np.array([0, 1]), x_min=3, y_min=0, x_max=6, y_max=1.1, width=width, height=height, lines=True)
desired_pixels = np.zeros((height, width), dtype=int)
np.testing.assert_arr... |
3b3ab4355801b0aa0963082edf2bbb511dfd3aae2f9c1f0f6fac513339ea83e8 | def __init__(self, future: BaseFuture, value: int) -> None:
'ValueAtMostConstraint constructor.\n\n :param future: the variable that should be at most the given value\n :param value: the maximum value that the given future may have\n '
self._future = future
self._value = value | ValueAtMostConstraint constructor.
:param future: the variable that should be at most the given value
:param value: the maximum value that the given future may have | netqasm/sdk/constraint.py | __init__ | QuTech-Delft/netqasm | 6 | python | def __init__(self, future: BaseFuture, value: int) -> None:
'ValueAtMostConstraint constructor.\n\n :param future: the variable that should be at most the given value\n :param value: the maximum value that the given future may have\n '
self._future = future
self._value = value | def __init__(self, future: BaseFuture, value: int) -> None:
'ValueAtMostConstraint constructor.\n\n :param future: the variable that should be at most the given value\n :param value: the maximum value that the given future may have\n '
self._future = future
self._value = value<|docstrin... |
5f041fe7b24475ead9fd87935b6064d5adf4d9c0f0e739e649574cf28e36cb12 | def dump_content(filename, offset, count, strucc):
'\n Dump the content of the file "filename" starting from offset and using the\n BStruct subclass pointed by strucc\n '
try:
fp = open(filename, 'rb')
except OSError as e:
print(("[ERROR] '%s' raised when tried to read the file '%s'... | Dump the content of the file "filename" starting from offset and using the
BStruct subclass pointed by strucc | scripts/py/mt_read.py | dump_content | ulises2k/EA-Tester | 58 | python | def dump_content(filename, offset, count, strucc):
'\n Dump the content of the file "filename" starting from offset and using the\n BStruct subclass pointed by strucc\n '
try:
fp = open(filename, 'rb')
except OSError as e:
print(("[ERROR] '%s' raised when tried to read the file '%s'... | def dump_content(filename, offset, count, strucc):
'\n Dump the content of the file "filename" starting from offset and using the\n BStruct subclass pointed by strucc\n '
try:
fp = open(filename, 'rb')
except OSError as e:
print(("[ERROR] '%s' raised when tried to read the file '%s'... |
f01d556e6074e02e89effa273a6d0afd84a46841613d318b2e34e55b56ca3937 | def create_app(*, config_module_class: str) -> Flask:
'\n Creates app in function so that flask with flask extensions can be\n initialized with specific config. Here it defines the route of APIs\n so that it can be seen in one place where implementation is separated.\n\n Config is being fetched via modu... | Creates app in function so that flask with flask extensions can be
initialized with specific config. Here it defines the route of APIs
so that it can be seen in one place where implementation is separated.
Config is being fetched via module.class name where module.class name
can be passed through environment variable.... | metadata_service/__init__.py | create_app | feng-tao/amundsenmetadatalibrary | 1 | python | def create_app(*, config_module_class: str) -> Flask:
'\n Creates app in function so that flask with flask extensions can be\n initialized with specific config. Here it defines the route of APIs\n so that it can be seen in one place where implementation is separated.\n\n Config is being fetched via modu... | def create_app(*, config_module_class: str) -> Flask:
'\n Creates app in function so that flask with flask extensions can be\n initialized with specific config. Here it defines the route of APIs\n so that it can be seen in one place where implementation is separated.\n\n Config is being fetched via modu... |
e541a61daf36fedf357af8f6ac6fa71e6edc0dc04876dcaac5fdfa364dffd835 | def test_evaluate(self):
'\n Test if values are computed correctly.\n '
for struct in [rosen_for_sensi(2, False, [0, 1]), poly_for_sensi(2, True, 0.5), convreact_for_funmode(2, [(- 0.3), (- 0.7)])]:
self._test_evaluate_funmode(struct)
self._test_evaluate_resmode(convreact_for_resmode(1... | Test if values are computed correctly. | test/test_aggregated.py | test_evaluate | LukasSp/pyPESTO | 0 | python | def test_evaluate(self):
'\n \n '
for struct in [rosen_for_sensi(2, False, [0, 1]), poly_for_sensi(2, True, 0.5), convreact_for_funmode(2, [(- 0.3), (- 0.7)])]:
self._test_evaluate_funmode(struct)
self._test_evaluate_resmode(convreact_for_resmode(1, [(- 0.3), (- 0.7)])) | def test_evaluate(self):
'\n \n '
for struct in [rosen_for_sensi(2, False, [0, 1]), poly_for_sensi(2, True, 0.5), convreact_for_funmode(2, [(- 0.3), (- 0.7)])]:
self._test_evaluate_funmode(struct)
self._test_evaluate_resmode(convreact_for_resmode(1, [(- 0.3), (- 0.7)]))<|docstring|>Tes... |
e5f1bf0c82a4f7f47ab82b191e8c8c4abd6d40f057df72260d3e9fe2ae33b902 | @abstractmethod
def getConfigurationController(self) -> 'XConfigurationController_557c15c4':
'\n Return the XConfigurationController object.\n ' | Return the XConfigurationController object. | ooobuild/lo/drawing/framework/x_controller_manager.py | getConfigurationController | Amourspirit/ooo_uno_tmpl | 0 | python | @abstractmethod
def getConfigurationController(self) -> 'XConfigurationController_557c15c4':
'\n \n ' | @abstractmethod
def getConfigurationController(self) -> 'XConfigurationController_557c15c4':
'\n \n '<|docstring|>Return the XConfigurationController object.<|endoftext|> |
16a4dc46b2601062574717b439518bad8164353e3bd8f19cdcc33c779d12a7b4 | @abstractmethod
def getModuleController(self) -> 'XModuleController_c5d112d2':
'\n Return the XModuleController object.\n ' | Return the XModuleController object. | ooobuild/lo/drawing/framework/x_controller_manager.py | getModuleController | Amourspirit/ooo_uno_tmpl | 0 | python | @abstractmethod
def getModuleController(self) -> 'XModuleController_c5d112d2':
'\n \n ' | @abstractmethod
def getModuleController(self) -> 'XModuleController_c5d112d2':
'\n \n '<|docstring|>Return the XModuleController object.<|endoftext|> |
9063bdcaac7d0cf10dd22c4ce5a2dd5b55b32e401b54ae2d45a3503c5c84cb5d | def testIndividualDataConsentDocument(self):
'Test IndividualDataConsentDocument'
pass | Test IndividualDataConsentDocument | test/test_individual_data_consent_document.py | testIndividualDataConsentDocument | My-Data-My-Consent/python-sdk | 0 | python | def testIndividualDataConsentDocument(self):
pass | def testIndividualDataConsentDocument(self):
pass<|docstring|>Test IndividualDataConsentDocument<|endoftext|> |
39ce838414f9a26f411e0ad8db3e34c1f8d373a93575b134e19f800c98b72210 | def on_start(self):
"Run the task pool.\n\n Will pre-fork all workers so they're ready to accept tasks.\n\n "
self._pool = self.Pool(processes=self.limit, **self.options)
self.on_apply = self._pool.apply_async | Run the task pool.
Will pre-fork all workers so they're ready to accept tasks. | celery/concurrency/processes/__init__.py | on_start | aleszoulek/celery | 2 | python | def on_start(self):
"Run the task pool.\n\n Will pre-fork all workers so they're ready to accept tasks.\n\n "
self._pool = self.Pool(processes=self.limit, **self.options)
self.on_apply = self._pool.apply_async | def on_start(self):
"Run the task pool.\n\n Will pre-fork all workers so they're ready to accept tasks.\n\n "
self._pool = self.Pool(processes=self.limit, **self.options)
self.on_apply = self._pool.apply_async<|docstring|>Run the task pool.
Will pre-fork all workers so they're ready to accept... |
2f5cb79463e4d4d21b9a8a87beac01a5f9f6f59a7f1e3fcf52f6001e8be90d4b | def on_stop(self):
'Gracefully stop the pool.'
if ((self._pool is not None) and (self._pool._state == RUN)):
self._pool.close()
self._pool.join()
self._pool = None | Gracefully stop the pool. | celery/concurrency/processes/__init__.py | on_stop | aleszoulek/celery | 2 | python | def on_stop(self):
if ((self._pool is not None) and (self._pool._state == RUN)):
self._pool.close()
self._pool.join()
self._pool = None | def on_stop(self):
if ((self._pool is not None) and (self._pool._state == RUN)):
self._pool.close()
self._pool.join()
self._pool = None<|docstring|>Gracefully stop the pool.<|endoftext|> |
eb8cc733aff7634e56d32c94b107fcbd919faa8efc7b8cc4d6a2d2830a166133 | def on_terminate(self):
'Force terminate the pool.'
if (self._pool is not None):
self._pool.terminate()
self._pool = None | Force terminate the pool. | celery/concurrency/processes/__init__.py | on_terminate | aleszoulek/celery | 2 | python | def on_terminate(self):
if (self._pool is not None):
self._pool.terminate()
self._pool = None | def on_terminate(self):
if (self._pool is not None):
self._pool.terminate()
self._pool = None<|docstring|>Force terminate the pool.<|endoftext|> |
3fca7905be83fbacc6956a61366b9dd92582a19321cdca5e372befbe2ef4cd9a | def test_patch_druid_get_columns(mocker: MockerFixture) -> None:
'\n Test ``patch_druid_get_columns``.\n '
pytest.importorskip('pydruid')
DruidDialect = mocker.patch('datajunction.fixes.DruidDialect')
connection = mocker.MagicMock()
mocker.patch('datajunction.fixes.PYDRUID_INSTALLED', new=Fals... | Test ``patch_druid_get_columns``. | tests/fixes_test.py | test_patch_druid_get_columns | DataJunction/datajunction | 0 | python | def test_patch_druid_get_columns(mocker: MockerFixture) -> None:
'\n \n '
pytest.importorskip('pydruid')
DruidDialect = mocker.patch('datajunction.fixes.DruidDialect')
connection = mocker.MagicMock()
mocker.patch('datajunction.fixes.PYDRUID_INSTALLED', new=False)
patch_druid_get_columns()
... | def test_patch_druid_get_columns(mocker: MockerFixture) -> None:
'\n \n '
pytest.importorskip('pydruid')
DruidDialect = mocker.patch('datajunction.fixes.DruidDialect')
connection = mocker.MagicMock()
mocker.patch('datajunction.fixes.PYDRUID_INSTALLED', new=False)
patch_druid_get_columns()
... |
a6b73a99925ce8171b093b0a763ccd367c6bbf6d534a23c2532127be1e1837a5 | def forward(self, logits):
'\n Input: logits -> T x K # Where K is the number of classes and T is the batch size\n Output: L = MEL, BEL\n '
sum1 = torch.zeros([logits.shape[0], 1])
for t in range(logits.shape[0]):
sum1[t] = self.entropy(logits[(t, :)])
L1 = torch.mean(sum1)
... | Input: logits -> T x K # Where K is the number of classes and T is the batch size
Output: L = MEL, BEL | CIFAR10/losses.py | forward | ankanbansal/semi-supervised-learning | 0 | python | def forward(self, logits):
'\n Input: logits -> T x K # Where K is the number of classes and T is the batch size\n Output: L = MEL, BEL\n '
sum1 = torch.zeros([logits.shape[0], 1])
for t in range(logits.shape[0]):
sum1[t] = self.entropy(logits[(t, :)])
L1 = torch.mean(sum1)
... | def forward(self, logits):
'\n Input: logits -> T x K # Where K is the number of classes and T is the batch size\n Output: L = MEL, BEL\n '
sum1 = torch.zeros([logits.shape[0], 1])
for t in range(logits.shape[0]):
sum1[t] = self.entropy(logits[(t, :)])
L1 = torch.mean(sum1)
... |
f302c6535dfc985bdcc89c65f46b6f8ac3a15870d6dced70a6a74a25d61d679d | def entropy(self, logits):
'\n Input: logits -> N x 1 x D # Where D is the feature dimension\n Output: entropy -> N x 1\n '
return ((- 1.0) * (F.softmax(logits, dim=(- 1)) * F.log_softmax(logits, dim=(- 1))).sum((- 1))) | Input: logits -> N x 1 x D # Where D is the feature dimension
Output: entropy -> N x 1 | CIFAR10/losses.py | entropy | ankanbansal/semi-supervised-learning | 0 | python | def entropy(self, logits):
'\n Input: logits -> N x 1 x D # Where D is the feature dimension\n Output: entropy -> N x 1\n '
return ((- 1.0) * (F.softmax(logits, dim=(- 1)) * F.log_softmax(logits, dim=(- 1))).sum((- 1))) | def entropy(self, logits):
'\n Input: logits -> N x 1 x D # Where D is the feature dimension\n Output: entropy -> N x 1\n '
return ((- 1.0) * (F.softmax(logits, dim=(- 1)) * F.log_softmax(logits, dim=(- 1))).sum((- 1)))<|docstring|>Input: logits -> N x 1 x D # Where D is the feature dimensi... |
27a0017511d15369bf40ebe9951409f442eab57ac95b0cf8b67b8d1a6bc42518 | def cross_entropy(self, logits1, logits2):
'\n Input: logits1 -> N x 1 x D # Where D is the feature dimension\n logits2 -> 1 x N x D # Where D is the feature dimension\n Output: Pairwise Cross-entropy -> N x N\n '
return ((- 1.0) * (F.softmax(logits1, dim=(- 1)) * F.log_softma... | Input: logits1 -> N x 1 x D # Where D is the feature dimension
logits2 -> 1 x N x D # Where D is the feature dimension
Output: Pairwise Cross-entropy -> N x N | CIFAR10/losses.py | cross_entropy | ankanbansal/semi-supervised-learning | 0 | python | def cross_entropy(self, logits1, logits2):
'\n Input: logits1 -> N x 1 x D # Where D is the feature dimension\n logits2 -> 1 x N x D # Where D is the feature dimension\n Output: Pairwise Cross-entropy -> N x N\n '
return ((- 1.0) * (F.softmax(logits1, dim=(- 1)) * F.log_softma... | def cross_entropy(self, logits1, logits2):
'\n Input: logits1 -> N x 1 x D # Where D is the feature dimension\n logits2 -> 1 x N x D # Where D is the feature dimension\n Output: Pairwise Cross-entropy -> N x N\n '
return ((- 1.0) * (F.softmax(logits1, dim=(- 1)) * F.log_softma... |
3e16aef19354ff2968657ce14efd8436a52718704497bf5c8d1df16e58b0f1e6 | def distances(self, A, distance_type='Euclidean', eps=1e-06):
"\n Input: A -> num_transformations x D # Where D is the feature dimension\n distance_type -> 'Euclidean'/'cosine'/'KL'\n Output: distances -> num_transformations x num_transformations pair wise distances\n "
assert... | Input: A -> num_transformations x D # Where D is the feature dimension
distance_type -> 'Euclidean'/'cosine'/'KL'
Output: distances -> num_transformations x num_transformations pair wise distances | CIFAR10/losses.py | distances | ankanbansal/semi-supervised-learning | 0 | python | def distances(self, A, distance_type='Euclidean', eps=1e-06):
"\n Input: A -> num_transformations x D # Where D is the feature dimension\n distance_type -> 'Euclidean'/'cosine'/'KL'\n Output: distances -> num_transformations x num_transformations pair wise distances\n "
assert... | def distances(self, A, distance_type='Euclidean', eps=1e-06):
"\n Input: A -> num_transformations x D # Where D is the feature dimension\n distance_type -> 'Euclidean'/'cosine'/'KL'\n Output: distances -> num_transformations x num_transformations pair wise distances\n "
assert... |
7dce90b81a2c2ae052c479a64782c126f1b417c863337a850725e4fff84401c1 | def forward(self, features, num_transformations, distance_type='Euclidean'):
'\n Input: features -> T x D # Where D is the feature dimension and T is the batch size\n num_transformations -> Number of transformations applied to the data\n (Make sure that T is a multiple of num_tran... | Input: features -> T x D # Where D is the feature dimension and T is the batch size
num_transformations -> Number of transformations applied to the data
(Make sure that T is a multiple of num_transformations)
Output: ST Loss | CIFAR10/losses.py | forward | ankanbansal/semi-supervised-learning | 0 | python | def forward(self, features, num_transformations, distance_type='Euclidean'):
'\n Input: features -> T x D # Where D is the feature dimension and T is the batch size\n num_transformations -> Number of transformations applied to the data\n (Make sure that T is a multiple of num_tran... | def forward(self, features, num_transformations, distance_type='Euclidean'):
'\n Input: features -> T x D # Where D is the feature dimension and T is the batch size\n num_transformations -> Number of transformations applied to the data\n (Make sure that T is a multiple of num_tran... |
a63499ca3a8931efa15f333017befaf1cdcc43e898f3d5875c5ad92c3a43eaf0 | def syscall(*args):
' Helper method to make a syscall, check for errors, and return output as a string.'
return subprocess.run(args, capture_output=True, check=True, text=True).stdout | Helper method to make a syscall, check for errors, and return output as a string. | switch.py | syscall | CydeWeys/static-window-switcher | 1 | python | def syscall(*args):
' '
return subprocess.run(args, capture_output=True, check=True, text=True).stdout | def syscall(*args):
' '
return subprocess.run(args, capture_output=True, check=True, text=True).stdout<|docstring|>Helper method to make a syscall, check for errors, and return output as a string.<|endoftext|> |
9cbf987a648bf6357bece8ae25d6e28f3f6df4833dd67e277adbf655d1065fe7 | @staticmethod
def _boot_psus_replicates(number_psus: int, number_reps: int, samp_rate: Number=0, size_gap: int=1) -> np.ndarray:
'Creates the bootstrap replicates structure'
if (number_psus <= size_gap):
raise AssertionError('size_gap should be smaller than the number of units')
sample_size = (numbe... | Creates the bootstrap replicates structure | src/samplics/weighting/replicates.py | _boot_psus_replicates | samplics-org/samplics | 14 | python | @staticmethod
def _boot_psus_replicates(number_psus: int, number_reps: int, samp_rate: Number=0, size_gap: int=1) -> np.ndarray:
if (number_psus <= size_gap):
raise AssertionError('size_gap should be smaller than the number of units')
sample_size = (number_psus - size_gap)
psu = np.arange(0, nu... | @staticmethod
def _boot_psus_replicates(number_psus: int, number_reps: int, samp_rate: Number=0, size_gap: int=1) -> np.ndarray:
if (number_psus <= size_gap):
raise AssertionError('size_gap should be smaller than the number of units')
sample_size = (number_psus - size_gap)
psu = np.arange(0, nu... |
e2caa38086c04b4ee88086eac6e8483d9d892152b9ff29f36a1e3a3e9e1c99e5 | def _brr_replicates(self, psu: np.ndarray, stratum: Optional[np.ndarray]) -> np.ndarray:
'Creates the brr replicate structure'
if (not (0 <= self.fay_coef < 1)):
raise ValueError('The Fay coefficient must be greater or equal to 0 and lower than 1.')
self._brr_number_reps(psu, stratum)
self.rep_c... | Creates the brr replicate structure | src/samplics/weighting/replicates.py | _brr_replicates | samplics-org/samplics | 14 | python | def _brr_replicates(self, psu: np.ndarray, stratum: Optional[np.ndarray]) -> np.ndarray:
if (not (0 <= self.fay_coef < 1)):
raise ValueError('The Fay coefficient must be greater or equal to 0 and lower than 1.')
self._brr_number_reps(psu, stratum)
self.rep_coefs = list(((1 / (self.number_reps *... | def _brr_replicates(self, psu: np.ndarray, stratum: Optional[np.ndarray]) -> np.ndarray:
if (not (0 <= self.fay_coef < 1)):
raise ValueError('The Fay coefficient must be greater or equal to 0 and lower than 1.')
self._brr_number_reps(psu, stratum)
self.rep_coefs = list(((1 / (self.number_reps *... |
66e718eff1a27156be1ffbb93926908c9234a35acec8c8e6a488cdeeadec8837 | @staticmethod
def _jkn_psus_replicates(number_psus: int) -> np.ndarray:
'Creates the jackknife delete-1 replicate structure '
jk_coefs = ((number_psus / (number_psus - 1)) * (np.ones((number_psus, number_psus)) - np.identity(number_psus)))
return np.asarray(jk_coefs) | Creates the jackknife delete-1 replicate structure | src/samplics/weighting/replicates.py | _jkn_psus_replicates | samplics-org/samplics | 14 | python | @staticmethod
def _jkn_psus_replicates(number_psus: int) -> np.ndarray:
' '
jk_coefs = ((number_psus / (number_psus - 1)) * (np.ones((number_psus, number_psus)) - np.identity(number_psus)))
return np.asarray(jk_coefs) | @staticmethod
def _jkn_psus_replicates(number_psus: int) -> np.ndarray:
' '
jk_coefs = ((number_psus / (number_psus - 1)) * (np.ones((number_psus, number_psus)) - np.identity(number_psus)))
return np.asarray(jk_coefs)<|docstring|>Creates the jackknife delete-1 replicate structure<|endoftext|> |
34904ee489a9001ab5c72a2fbc27238791334907254ddc95806527f9e922a928 | def replicate(self, samp_weight: Array, psu: Array, stratum: Optional[Array]=None, rep_coefs: Union[(Array, Number)]=False, rep_prefix: Optional[str]=None, psu_varname: str='_psu', str_varname: str='_stratum') -> pd.DataFrame:
'Computes replicate sample weights.\n\n Args:\n samp_weight (Array): ar... | Computes replicate sample weights.
Args:
samp_weight (Array): array of sample weights. To incorporate the weights adjustment
in the replicate weights, first replicate the design sample weights then apply
the adjustments to the replicates.
psu (Array):
stratum (Array, optional): array of the... | src/samplics/weighting/replicates.py | replicate | samplics-org/samplics | 14 | python | def replicate(self, samp_weight: Array, psu: Array, stratum: Optional[Array]=None, rep_coefs: Union[(Array, Number)]=False, rep_prefix: Optional[str]=None, psu_varname: str='_psu', str_varname: str='_stratum') -> pd.DataFrame:
'Computes replicate sample weights.\n\n Args:\n samp_weight (Array): ar... | def replicate(self, samp_weight: Array, psu: Array, stratum: Optional[Array]=None, rep_coefs: Union[(Array, Number)]=False, rep_prefix: Optional[str]=None, psu_varname: str='_psu', str_varname: str='_stratum') -> pd.DataFrame:
'Computes replicate sample weights.\n\n Args:\n samp_weight (Array): ar... |
2ba8ce127003c1c2c4c454c4f55309fa003b78fcd4afd9e4e6e41f630cbf2e0d | def filter_anchor(points, rotate, properties, error):
'This function will add extreme weighting to the boundary points'
max_weight = 10000
points[0]['weight'] = max_weight
points[(- 1)]['weight'] = max_weight
points[0]['residual weight'] = 1
points[(- 1)]['residual weight'] = 1
return points | This function will add extreme weighting to the boundary points | mesh_viewport_vertex_alignment.py | filter_anchor | hdunderscore/mesh_viewport_vertex_align | 2 | python | def filter_anchor(points, rotate, properties, error):
max_weight = 10000
points[0]['weight'] = max_weight
points[(- 1)]['weight'] = max_weight
points[0]['residual weight'] = 1
points[(- 1)]['residual weight'] = 1
return points | def filter_anchor(points, rotate, properties, error):
max_weight = 10000
points[0]['weight'] = max_weight
points[(- 1)]['weight'] = max_weight
points[0]['residual weight'] = 1
points[(- 1)]['residual weight'] = 1
return points<|docstring|>This function will add extreme weighting to the boun... |
03092600cdcf14ac7f20e3168dff036991a96e708bde96f0ca079fba59f944af | def fit1(properties, points):
'This function applies the fitting function several times, finding the axis rotation that causes the smallest error and returns the points.\n This expects a 1D fit where x is the domain, y is the range (and therefore y is being affected in fit).'
fit_function = properties['f... | This function applies the fitting function several times, finding the axis rotation that causes the smallest error and returns the points.
This expects a 1D fit where x is the domain, y is the range (and therefore y is being affected in fit). | mesh_viewport_vertex_alignment.py | fit1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def fit1(properties, points):
'This function applies the fitting function several times, finding the axis rotation that causes the smallest error and returns the points.\n This expects a 1D fit where x is the domain, y is the range (and therefore y is being affected in fit).'
fit_function = properties['f... | def fit1(properties, points):
'This function applies the fitting function several times, finding the axis rotation that causes the smallest error and returns the points.\n This expects a 1D fit where x is the domain, y is the range (and therefore y is being affected in fit).'
fit_function = properties['f... |
bc2e4a8567cd881c5e39401cd7e954d139c2305c4d3e4d834665a751be7796e6 | def error_residual1(points, r, rr, properties, line_func, line_parameters):
'This function is used in the fitting functions to determine the deltas '
for p in points:
pr = (p['point'] * r)
x = pr.x
y = pr.y
yy = line_func(x, line_parameters)
p['delta'] = (mathutils.Vector... | This function is used in the fitting functions to determine the deltas | mesh_viewport_vertex_alignment.py | error_residual1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def error_residual1(points, r, rr, properties, line_func, line_parameters):
' '
for p in points:
pr = (p['point'] * r)
x = pr.x
y = pr.y
yy = line_func(x, line_parameters)
p['delta'] = (mathutils.Vector((0, (y - yy), 0, 0)) * rr)
return points | def error_residual1(points, r, rr, properties, line_func, line_parameters):
' '
for p in points:
pr = (p['point'] * r)
x = pr.x
y = pr.y
yy = line_func(x, line_parameters)
p['delta'] = (mathutils.Vector((0, (y - yy), 0, 0)) * rr)
return points<|docstring|>This functio... |
01d93957589bca76e807b58408f07308288a5ec1c0341c580038c1b2f7e6dbd5 | def sort_index1(points, r):
'This function sorts points based on their domain (assumed as x axis when rotated) '
points = sorted(points, key=(lambda xx: (xx['point'] * r).x))
return points | This function sorts points based on their domain (assumed as x axis when rotated) | mesh_viewport_vertex_alignment.py | sort_index1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def sort_index1(points, r):
' '
points = sorted(points, key=(lambda xx: (xx['point'] * r).x))
return points | def sort_index1(points, r):
' '
points = sorted(points, key=(lambda xx: (xx['point'] * r).x))
return points<|docstring|>This function sorts points based on their domain (assumed as x axis when rotated)<|endoftext|> |
1ae0d8fdf631491fa17d610e476e96d372295bfb18cd6d081ee758bb99126f5b | def fit_linear1(points, rotate, properties=None):
'This function attempts to fit a given set of points to a linear line: y = a1*x + a0'
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
... | This function attempts to fit a given set of points to a linear line: y = a1*x + a0 | mesh_viewport_vertex_alignment.py | fit_linear1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def fit_linear1(points, rotate, properties=None):
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
Sw = 0
for p in points:
pr = (p['point'] * r)
x = pr.x
... | def fit_linear1(points, rotate, properties=None):
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
Sw = 0
for p in points:
pr = (p['point'] * r)
x = pr.x
... |
801b7cbde1b10e65abeb4bb89eb87ea9ebf05f8ad0cc7f1410c39b22572bd3ce | def fit_quadratic1(points, rotate, properties=None):
'This function attempts to fit a given set of points to a quadratic polynomial line: y = a2*x^2 + a1*x + a0'
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = ... | This function attempts to fit a given set of points to a quadratic polynomial line: y = a2*x^2 + a1*x + a0 | mesh_viewport_vertex_alignment.py | fit_quadratic1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def fit_quadratic1(points, rotate, properties=None):
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
Sx2y = 0
Sx3 = 0
Sx4 = 0
Sw = 0
for p in points:
pr = (... | def fit_quadratic1(points, rotate, properties=None):
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
Sx2y = 0
Sx3 = 0
Sx4 = 0
Sw = 0
for p in points:
pr = (... |
dd90d1d7756f8a46f12ee0a681f29b5d35f006c78a1b55b6ad115e833f1ff83f | def fit_cubic1(points, rotate, properties=None):
'This function attempts to fit a given set of points to a cubic polynomial line: y = a3*x^3 + a2*x^2 + a1*x + a0'
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx =... | This function attempts to fit a given set of points to a cubic polynomial line: y = a3*x^3 + a2*x^2 + a1*x + a0 | mesh_viewport_vertex_alignment.py | fit_cubic1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def fit_cubic1(points, rotate, properties=None):
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
Sx2y = 0
Sx3y = 0
Sx3 = 0
Sx4 = 0
Sx5 = 0
Sx6 = 0
Sw = 0
... | def fit_cubic1(points, rotate, properties=None):
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
Sxy = 0
Sx = 0
Sy = 0
Sx2 = 0
Sx2y = 0
Sx3y = 0
Sx3 = 0
Sx4 = 0
Sx5 = 0
Sx6 = 0
Sw = 0
... |
66dabc3919176fd60e6d60af3fdc14dbf65749efa7c0ee82c63ae4242fef5e7b | def fit_cosine1(points, rotate, properties):
'This function attempts to fit a given set of points to a cosine curve: y = a0 + a1*cos(w*x) + a2*cos(w*x) '
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
omega = properties['cosin... | This function attempts to fit a given set of points to a cosine curve: y = a0 + a1*cos(w*x) + a2*cos(w*x) | mesh_viewport_vertex_alignment.py | fit_cosine1 | hdunderscore/mesh_viewport_vertex_align | 2 | python | def fit_cosine1(points, rotate, properties):
' '
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
omega = properties['cosine_omega']
Sycos = 0
Sysin = 0
Scos = 0
Scos2 = 0
Ssin = 0
Ssin2 = 0
Sy = 0
... | def fit_cosine1(points, rotate, properties):
' '
r = mathutils.Matrix.Rotation(math.radians(rotate), 4, 'Z')
rr = mathutils.Matrix.Rotation(math.radians((- rotate)), 4, 'Z')
omega = properties['cosine_omega']
Sycos = 0
Sysin = 0
Scos = 0
Scos2 = 0
Ssin = 0
Ssin2 = 0
Sy = 0
... |
eb6996e0da4033b131cc148d07c44aba93e7acf09c6515cffc51863e35e37d79 | def get_vertices(mesh):
'Returns the active list of selected vertices.'
verts = []
for v in mesh.verts:
if v.select:
verts.append(v)
return verts | Returns the active list of selected vertices. | mesh_viewport_vertex_alignment.py | get_vertices | hdunderscore/mesh_viewport_vertex_align | 2 | python | def get_vertices(mesh):
verts = []
for v in mesh.verts:
if v.select:
verts.append(v)
return verts | def get_vertices(mesh):
verts = []
for v in mesh.verts:
if v.select:
verts.append(v)
return verts<|docstring|>Returns the active list of selected vertices.<|endoftext|> |
2550d3763631dc500823d267d6c2c1166ab3ca189f453208555f27f5820751a1 | def get_axis(type):
'Gets the axis we will be performing the rotation on. Returns a projection matrix'
if (type == 'perspective'):
region = bpy.context.region
rv3d = bpy.context.region_data
else:
return None
return {'region': region, 'rv3d': rv3d} | Gets the axis we will be performing the rotation on. Returns a projection matrix | mesh_viewport_vertex_alignment.py | get_axis | hdunderscore/mesh_viewport_vertex_align | 2 | python | def get_axis(type):
if (type == 'perspective'):
region = bpy.context.region
rv3d = bpy.context.region_data
else:
return None
return {'region': region, 'rv3d': rv3d} | def get_axis(type):
if (type == 'perspective'):
region = bpy.context.region
rv3d = bpy.context.region_data
else:
return None
return {'region': region, 'rv3d': rv3d}<|docstring|>Gets the axis we will be performing the rotation on. Returns a projection matrix<|endoftext|> |
99bf769309f44f801407c7bff8912bc3a56f356eca70887a964a8229806cd54c | def project(vertices, axis):
'Project the vertices onto a plane of the given axis.'
points = []
for v in vertices:
vec = mathutils.Vector(v.co)
p = bpy_extras.view3d_utils.location_3d_to_region_2d(axis['region'], axis['rv3d'], vec).to_4d()
depth = vec
points.append({'id': v, ... | Project the vertices onto a plane of the given axis. | mesh_viewport_vertex_alignment.py | project | hdunderscore/mesh_viewport_vertex_align | 2 | python | def project(vertices, axis):
points = []
for v in vertices:
vec = mathutils.Vector(v.co)
p = bpy_extras.view3d_utils.location_3d_to_region_2d(axis['region'], axis['rv3d'], vec).to_4d()
depth = vec
points.append({'id': v, 'point': p, 'delta': None, "v'": None, 'depth': depth,... | def project(vertices, axis):
points = []
for v in vertices:
vec = mathutils.Vector(v.co)
p = bpy_extras.view3d_utils.location_3d_to_region_2d(axis['region'], axis['rv3d'], vec).to_4d()
depth = vec
points.append({'id': v, 'point': p, 'delta': None, "v'": None, 'depth': depth,... |
6c4c3466dae071fc31405638d886aba67c5b4853be6e7d006321c29d7689b467 | def unproject(points, axis, properties):
'Unproject points on a plane to vertices in 3d space.'
for p in points:
new_p = (p['point'] - (p['delta'] * properties['influence']))
old_v = p['id'].co
new_v = bpy_extras.view3d_utils.region_2d_to_location_3d(axis['region'], axis['rv3d'], new_p.t... | Unproject points on a plane to vertices in 3d space. | mesh_viewport_vertex_alignment.py | unproject | hdunderscore/mesh_viewport_vertex_align | 2 | python | def unproject(points, axis, properties):
for p in points:
new_p = (p['point'] - (p['delta'] * properties['influence']))
old_v = p['id'].co
new_v = bpy_extras.view3d_utils.region_2d_to_location_3d(axis['region'], axis['rv3d'], new_p.to_2d(), p['depth'])
p["v'"] = new_v
return... | def unproject(points, axis, properties):
for p in points:
new_p = (p['point'] - (p['delta'] * properties['influence']))
old_v = p['id'].co
new_v = bpy_extras.view3d_utils.region_2d_to_location_3d(axis['region'], axis['rv3d'], new_p.to_2d(), p['depth'])
p["v'"] = new_v
return... |
4aa2ffe5acddabc24a79933b44c12d0f0181dc26585db685f0993f39f61c7448 | def update_vertices(mesh, points):
'Update the active set of selected vertices with their fitted positions.'
for p in points:
p['id'].co = p["v'"].to_3d().to_tuple()
bmesh.update_edit_mesh(mesh) | Update the active set of selected vertices with their fitted positions. | mesh_viewport_vertex_alignment.py | update_vertices | hdunderscore/mesh_viewport_vertex_align | 2 | python | def update_vertices(mesh, points):
for p in points:
p['id'].co = p["v'"].to_3d().to_tuple()
bmesh.update_edit_mesh(mesh) | def update_vertices(mesh, points):
for p in points:
p['id'].co = p["v'"].to_3d().to_tuple()
bmesh.update_edit_mesh(mesh)<|docstring|>Update the active set of selected vertices with their fitted positions.<|endoftext|> |
df8dbf2e61345fb064c43f3a3ff84a51b46b493d2c437f6a6806f707a0c70fd2 | def like_a_gauss(mat):
"\n Implementation of the Gaussian Elimination Algorithm for finding the row-reduced echelon form of a given matrix.\n No pivoting is done.\n Requires Python 3 due to the different behaviour of the division operation in earlier versions of Python.\n Released under the Public Domai... | Implementation of the Gaussian Elimination Algorithm for finding the row-reduced echelon form of a given matrix.
No pivoting is done.
Requires Python 3 due to the different behaviour of the division operation in earlier versions of Python.
Released under the Public Domain (if you want it - you probably don't)
https://g... | mesh_viewport_vertex_alignment.py | like_a_gauss | hdunderscore/mesh_viewport_vertex_align | 2 | python | def like_a_gauss(mat):
"\n Implementation of the Gaussian Elimination Algorithm for finding the row-reduced echelon form of a given matrix.\n No pivoting is done.\n Requires Python 3 due to the different behaviour of the division operation in earlier versions of Python.\n Released under the Public Domai... | def like_a_gauss(mat):
"\n Implementation of the Gaussian Elimination Algorithm for finding the row-reduced echelon form of a given matrix.\n No pivoting is done.\n Requires Python 3 due to the different behaviour of the division operation in earlier versions of Python.\n Released under the Public Domai... |
17498c96b3f8df099a37a41c19504885ace2cbd822726ae508d8d01b2af9de34 | def get_ols(force_download: bool=False):
'Get the OLS registry.'
if (PROCESSED_PATH.exists() and (not force_download)):
with PROCESSED_PATH.open() as file:
return json.load(file)
download(url=URL, path=RAW_PATH, force=True)
with RAW_PATH.open() as file:
data = json.load(file)... | Get the OLS registry. | src/bioregistry/external/ols.py | get_ols | cthoyt/bioregistry | 2 | python | def get_ols(force_download: bool=False):
if (PROCESSED_PATH.exists() and (not force_download)):
with PROCESSED_PATH.open() as file:
return json.load(file)
download(url=URL, path=RAW_PATH, force=True)
with RAW_PATH.open() as file:
data = json.load(file)
if ('next' in data... | def get_ols(force_download: bool=False):
if (PROCESSED_PATH.exists() and (not force_download)):
with PROCESSED_PATH.open() as file:
return json.load(file)
download(url=URL, path=RAW_PATH, force=True)
with RAW_PATH.open() as file:
data = json.load(file)
if ('next' in data... |
e24278d0c74797a5e9c2c22f90eca30ffe82ae3c7c120be16e6152a74a7a982a | @click.command()
def main():
'Reload the OLS data.'
get_ols(force_download=True) | Reload the OLS data. | src/bioregistry/external/ols.py | main | cthoyt/bioregistry | 2 | python | @click.command()
def main():
get_ols(force_download=True) | @click.command()
def main():
get_ols(force_download=True)<|docstring|>Reload the OLS data.<|endoftext|> |
1878d0f3280149992723ccb245d5358ba74379175d00cbc492a2761e72b6a524 | def test():
'\n 异常处理\n '
try:
a = (10 / 0)
print(('a is %s' % a))
except Exception as e:
print(('exception is %s' % e)) | 异常处理 | bookcode/pythonproject/pythonlearning/pythonlearning/base/except.py | test | zhangymPerson/Think-in-java-note | 0 | python | def test():
'\n \n '
try:
a = (10 / 0)
print(('a is %s' % a))
except Exception as e:
print(('exception is %s' % e)) | def test():
'\n \n '
try:
a = (10 / 0)
print(('a is %s' % a))
except Exception as e:
print(('exception is %s' % e))<|docstring|>异常处理<|endoftext|> |
c6a5457920b6907aa5c46f6cd8b50eebcaf9eeb3854d90a945f045a7fbd5a5d4 | def OpenEditor(self, col, row):
'\n Opens an editor at the current position.\n Modified to allow a generic getter to set editor text.\n '
evt = wx.ListEvent(wx.wxEVT_COMMAND_LIST_BEGIN_LABEL_EDIT, self.GetId())
evt.m_itemIndex = row
evt.m_col = col
item = self.GetItem(row, col)
... | Opens an editor at the current position.
Modified to allow a generic getter to set editor text. | ListEditorCtrl.py | OpenEditor | fprimex/lad | 0 | python | def OpenEditor(self, col, row):
'\n Opens an editor at the current position.\n Modified to allow a generic getter to set editor text.\n '
evt = wx.ListEvent(wx.wxEVT_COMMAND_LIST_BEGIN_LABEL_EDIT, self.GetId())
evt.m_itemIndex = row
evt.m_col = col
item = self.GetItem(row, col)
... | def OpenEditor(self, col, row):
'\n Opens an editor at the current position.\n Modified to allow a generic getter to set editor text.\n '
evt = wx.ListEvent(wx.wxEVT_COMMAND_LIST_BEGIN_LABEL_EDIT, self.GetId())
evt.m_itemIndex = row
evt.m_col = col
item = self.GetItem(row, col)
... |
86a23d488cd1031fca30b125a9373c1f3aff1e0771beee3aac253240e936f541 | def CloseEditor(self, evt=None):
'\n Close the editor and save the new value to the ListCtrl.\n Modified to allow a generic setter to save edited data.\n '
if (not self.editor.IsShown()):
return
text = self.editor.GetValue()
self.editor.Hide()
self.SetFocus()
self.Se... | Close the editor and save the new value to the ListCtrl.
Modified to allow a generic setter to save edited data. | ListEditorCtrl.py | CloseEditor | fprimex/lad | 0 | python | def CloseEditor(self, evt=None):
'\n Close the editor and save the new value to the ListCtrl.\n Modified to allow a generic setter to save edited data.\n '
if (not self.editor.IsShown()):
return
text = self.editor.GetValue()
self.editor.Hide()
self.SetFocus()
self.Se... | def CloseEditor(self, evt=None):
'\n Close the editor and save the new value to the ListCtrl.\n Modified to allow a generic setter to save edited data.\n '
if (not self.editor.IsShown()):
return
text = self.editor.GetValue()
self.editor.Hide()
self.SetFocus()
self.Se... |
e6b90a400d0fdcbb2eecc3ae97cb796f296636dde0f4854daae20a3e62d48cdc | def OnChar(self, event):
'Catch ESC and cancel gracefully, preserving data'
if (event.GetKeyCode() == wx.WXK_ESCAPE):
if (not self.editor.IsShown()):
return
self.editor.Hide()
self.SetFocus()
else:
listmix.TextEditMixin.OnChar(self, event) | Catch ESC and cancel gracefully, preserving data | ListEditorCtrl.py | OnChar | fprimex/lad | 0 | python | def OnChar(self, event):
if (event.GetKeyCode() == wx.WXK_ESCAPE):
if (not self.editor.IsShown()):
return
self.editor.Hide()
self.SetFocus()
else:
listmix.TextEditMixin.OnChar(self, event) | def OnChar(self, event):
if (event.GetKeyCode() == wx.WXK_ESCAPE):
if (not self.editor.IsShown()):
return
self.editor.Hide()
self.SetFocus()
else:
listmix.TextEditMixin.OnChar(self, event)<|docstring|>Catch ESC and cancel gracefully, preserving data<|endoftext|> |
95444b0eb4873b02cc254511153d2bdbafae23b713169d73591f9857c122000d | def OnLeftDouble(self, evt=None):
'Open the editor on double clicks'
if self.editor.IsShown():
self.CloseEditor()
(x, y) = evt.GetPosition()
(row, flags) = self.HitTest((x, y))
self.col_locs = [0]
loc = 0
for n in range(self.GetColumnCount()):
loc = (loc + self.GetColumnWidth... | Open the editor on double clicks | ListEditorCtrl.py | OnLeftDouble | fprimex/lad | 0 | python | def OnLeftDouble(self, evt=None):
if self.editor.IsShown():
self.CloseEditor()
(x, y) = evt.GetPosition()
(row, flags) = self.HitTest((x, y))
self.col_locs = [0]
loc = 0
for n in range(self.GetColumnCount()):
loc = (loc + self.GetColumnWidth(n))
self.col_locs.append(... | def OnLeftDouble(self, evt=None):
if self.editor.IsShown():
self.CloseEditor()
(x, y) = evt.GetPosition()
(row, flags) = self.HitTest((x, y))
self.col_locs = [0]
loc = 0
for n in range(self.GetColumnCount()):
loc = (loc + self.GetColumnWidth(n))
self.col_locs.append(... |
f90aa81ddaab14dd17a128f5a0e20b33820347ccf1c1e411ef4351aa26c50129 | @staticmethod
def memlets_intersect(graph_a: SDFGState, group_a: List[nodes.AccessNode], inputs_a: bool, graph_b: SDFGState, group_b: List[nodes.AccessNode], inputs_b: bool) -> bool:
'\n Performs an all-pairs check for subset intersection on two\n groups of nodes. If group intersects or result is inde... | Performs an all-pairs check for subset intersection on two
groups of nodes. If group intersects or result is indeterminate,
returns True as a precaution.
:param graph_a: The graph in which the first set of nodes reside.
:param group_a: The first set of nodes to check.
:param inputs_a: If True, checks inputs of the firs... | dace/transformation/interstate/state_fusion.py | memlets_intersect | jnice-81/dace | 227 | python | @staticmethod
def memlets_intersect(graph_a: SDFGState, group_a: List[nodes.AccessNode], inputs_a: bool, graph_b: SDFGState, group_b: List[nodes.AccessNode], inputs_b: bool) -> bool:
'\n Performs an all-pairs check for subset intersection on two\n groups of nodes. If group intersects or result is inde... | @staticmethod
def memlets_intersect(graph_a: SDFGState, group_a: List[nodes.AccessNode], inputs_a: bool, graph_b: SDFGState, group_b: List[nodes.AccessNode], inputs_b: bool) -> bool:
'\n Performs an all-pairs check for subset intersection on two\n groups of nodes. If group intersects or result is inde... |
d90d02ca1725d1848b40e6a3afc4b2b855e257945c729832059e2fe29048468c | def populate_movie_queue(self):
'populates a new MovieQueue'
self.queue.insert(0, 'Donatello')
self.queue.insert(1, 'Raphael')
self.queue.insert(2, 'Michelangelo')
self.queue.insert(3, 'Leonardo')
return | populates a new MovieQueue | exercises/structures/src/movie_queue.py | populate_movie_queue | bmazey/summer2020 | 0 | python | def populate_movie_queue(self):
self.queue.insert(0, 'Donatello')
self.queue.insert(1, 'Raphael')
self.queue.insert(2, 'Michelangelo')
self.queue.insert(3, 'Leonardo')
return | def populate_movie_queue(self):
self.queue.insert(0, 'Donatello')
self.queue.insert(1, 'Raphael')
self.queue.insert(2, 'Michelangelo')
self.queue.insert(3, 'Leonardo')
return<|docstring|>populates a new MovieQueue<|endoftext|> |
3f1afca9ecdaa9765dd1e246bf45369de084deb5871a055205f602a222eeb472 | @abstractmethod
def train(self, mini_batch, discount):
' Trains the current neural network on a batch of experiences.\n \n inputs:\n mini_batch - an iterable object containing experiences observed\n from a Markov decision process, of the form \n (state, action, reward,... | Trains the current neural network on a batch of experiences.
inputs:
mini_batch - an iterable object containing experiences observed
from a Markov decision process, of the form
(state, action, reward, next_state, done)
discount - the discount factor in [0, 1] | agents/Neural.py | train | mike-gimelfarb/mfpy | 1 | python | @abstractmethod
def train(self, mini_batch, discount):
' Trains the current neural network on a batch of experiences.\n \n inputs:\n mini_batch - an iterable object containing experiences observed\n from a Markov decision process, of the form \n (state, action, reward,... | @abstractmethod
def train(self, mini_batch, discount):
' Trains the current neural network on a batch of experiences.\n \n inputs:\n mini_batch - an iterable object containing experiences observed\n from a Markov decision process, of the form \n (state, action, reward,... |
742385b441afb60c2b2e816ae5e8ad535c7107a490416041e509cc3e58354e41 | @staticmethod
def clear_model(model):
' A recursive method to re-initialize all layers in a Keras model.\n \n This method will recursively check all layers in the current model. For each\n layer, if a weight initializer exists, it calls the weight initializer to initialize\n all weights ... | A recursive method to re-initialize all layers in a Keras model.
This method will recursively check all layers in the current model. For each
layer, if a weight initializer exists, it calls the weight initializer to initialize
all weights in the layer to their default values.
inputs:
model - a Keras model whose ... | agents/Neural.py | clear_model | mike-gimelfarb/mfpy | 1 | python | @staticmethod
def clear_model(model):
' A recursive method to re-initialize all layers in a Keras model.\n \n This method will recursively check all layers in the current model. For each\n layer, if a weight initializer exists, it calls the weight initializer to initialize\n all weights ... | @staticmethod
def clear_model(model):
' A recursive method to re-initialize all layers in a Keras model.\n \n This method will recursively check all layers in the current model. For each\n layer, if a weight initializer exists, it calls the weight initializer to initialize\n all weights ... |
c95875569dcc5ffc8950d2c4ece5616d06ab78164eb865331804a7f284b936a2 | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
'3x3 convolution with padding'
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) | 3x3 convolution with padding | models/resnet_imagenet.py | conv3x3 | winycg/HSAKD | 36 | python | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation)<|docstring|>3x3 convolution with padding<|endoftext|> |
7447c07b06cc8d16674f31fc29f40a376c8d7a0321f9f661635b233109ed88c5 | def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | 1x1 convolution | models/resnet_imagenet.py | conv1x1 | winycg/HSAKD | 36 | python | def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)<|docstring|>1x1 convolution<|endoftext|> |
bf293f1f83f0c096a558de1db7ffb4360fab645bf2da38f987abf78c98a57395 | @coroutine
def recv_packet(self):
'Parse the packet header and read entire packet payload into buffer.'
buff = b''
while True:
packet_header = (yield self.connection.stream.read_bytes(4))
if DEBUG:
dump_packet(packet_header)
packet_length_bin = packet_header[:3]
s... | Parse the packet header and read entire packet payload into buffer. | asynctorndb/connection.py | recv_packet | mayflaver/AsyncTorndb | 103 | python | @coroutine
def recv_packet(self):
buff = b
while True:
packet_header = (yield self.connection.stream.read_bytes(4))
if DEBUG:
dump_packet(packet_header)
packet_length_bin = packet_header[:3]
self._packet_number = byte2int(packet_header[3])
bin_length = (p... | @coroutine
def recv_packet(self):
buff = b
while True:
packet_header = (yield self.connection.stream.read_bytes(4))
if DEBUG:
dump_packet(packet_header)
packet_length_bin = packet_header[:3]
self._packet_number = byte2int(packet_header[3])
bin_length = (p... |
908ee200050a41c690b14364c77c5cbe47de20dabd0a07ad7c9b669324b6a958 | def read(self, size):
"Read the first 'size' bytes in packet and advance cursor past them."
result = self._data[self._position:(self._position + size)]
if (len(result) != size):
error = ('Result length not requested length:\nExpected=%s. Actual=%s. Position: %s. Data Length: %s' % (size, len(resu... | Read the first 'size' bytes in packet and advance cursor past them. | asynctorndb/connection.py | read | mayflaver/AsyncTorndb | 103 | python | def read(self, size):
result = self._data[self._position:(self._position + size)]
if (len(result) != size):
error = ('Result length not requested length:\nExpected=%s. Actual=%s. Position: %s. Data Length: %s' % (size, len(result), self._position, len(self._data)))
if DEBUG:
... | def read(self, size):
result = self._data[self._position:(self._position + size)]
if (len(result) != size):
error = ('Result length not requested length:\nExpected=%s. Actual=%s. Position: %s. Data Length: %s' % (size, len(result), self._position, len(self._data)))
if DEBUG:
... |
c3e079808f9fb2ca29e7a92a36b0dc24a225c28005651fef74ec084701fb2150 | def read_all(self):
'Read all remaining data in the packet.\n\n (Subsequent read() will return errors.)\n '
result = self._data[self._position:]
self._position = None
return result | Read all remaining data in the packet.
(Subsequent read() will return errors.) | asynctorndb/connection.py | read_all | mayflaver/AsyncTorndb | 103 | python | def read_all(self):
'Read all remaining data in the packet.\n\n (Subsequent read() will return errors.)\n '
result = self._data[self._position:]
self._position = None
return result | def read_all(self):
'Read all remaining data in the packet.\n\n (Subsequent read() will return errors.)\n '
result = self._data[self._position:]
self._position = None
return result<|docstring|>Read all remaining data in the packet.
(Subsequent read() will return errors.)<|endoftext|> |
b5bf74d9936cfe196fef2615c15bc75b36c70a158e3a4d3e4b9d34ea88e91753 | def advance(self, length):
"Advance the cursor in data buffer 'length' bytes."
new_position = (self._position + length)
if ((new_position < 0) or (new_position > len(self._data))):
raise Exception(('Invalid advance amount (%s) for cursor. Position=%s' % (length, new_position)))
self._position =... | Advance the cursor in data buffer 'length' bytes. | asynctorndb/connection.py | advance | mayflaver/AsyncTorndb | 103 | python | def advance(self, length):
new_position = (self._position + length)
if ((new_position < 0) or (new_position > len(self._data))):
raise Exception(('Invalid advance amount (%s) for cursor. Position=%s' % (length, new_position)))
self._position = new_position | def advance(self, length):
new_position = (self._position + length)
if ((new_position < 0) or (new_position > len(self._data))):
raise Exception(('Invalid advance amount (%s) for cursor. Position=%s' % (length, new_position)))
self._position = new_position<|docstring|>Advance the cursor in dat... |
6d9bd32a5189335699dd9614a7a57c10147c7474f818e2fe322a17e608101584 | def rewind(self, position=0):
"Set the position of the data buffer cursor to 'position'."
if ((position < 0) or (position > len(self._data))):
raise Exception(('Invalid position to rewind cursor to: %s.' % position))
self._position = position | Set the position of the data buffer cursor to 'position'. | asynctorndb/connection.py | rewind | mayflaver/AsyncTorndb | 103 | python | def rewind(self, position=0):
if ((position < 0) or (position > len(self._data))):
raise Exception(('Invalid position to rewind cursor to: %s.' % position))
self._position = position | def rewind(self, position=0):
if ((position < 0) or (position > len(self._data))):
raise Exception(('Invalid position to rewind cursor to: %s.' % position))
self._position = position<|docstring|>Set the position of the data buffer cursor to 'position'.<|endoftext|> |
361d3bd82bc5d23fdfe59ce0a99ec705b267a4094d5a303ba79273e903b33788 | def get_bytes(self, position, length=1):
"Get 'length' bytes starting at 'position'.\n\n Position is start of payload (first four packet header bytes are not\n included) starting at index '0'.\n\n No error checking is done. If requesting outside end of buffer\n an empty string (or strin... | Get 'length' bytes starting at 'position'.
Position is start of payload (first four packet header bytes are not
included) starting at index '0'.
No error checking is done. If requesting outside end of buffer
an empty string (or string shorter than 'length') may be returned! | asynctorndb/connection.py | get_bytes | mayflaver/AsyncTorndb | 103 | python | def get_bytes(self, position, length=1):
"Get 'length' bytes starting at 'position'.\n\n Position is start of payload (first four packet header bytes are not\n included) starting at index '0'.\n\n No error checking is done. If requesting outside end of buffer\n an empty string (or strin... | def get_bytes(self, position, length=1):
"Get 'length' bytes starting at 'position'.\n\n Position is start of payload (first four packet header bytes are not\n included) starting at index '0'.\n\n No error checking is done. If requesting outside end of buffer\n an empty string (or strin... |
2f2aa1f24bc0c43e9f29827e820d3a29b12fec396a7ab994f84efaa65ae5284a | def read_length_encoded_integer(self):
"Read a 'Length Coded Binary' number from the data buffer.\n\n Length coded numbers can be anywhere from 1 to 9 bytes depending\n on the value of the first byte.\n "
c = ord(self.read(1))
if (c == NULL_COLUMN):
return None
if (c < UNSIG... | Read a 'Length Coded Binary' number from the data buffer.
Length coded numbers can be anywhere from 1 to 9 bytes depending
on the value of the first byte. | asynctorndb/connection.py | read_length_encoded_integer | mayflaver/AsyncTorndb | 103 | python | def read_length_encoded_integer(self):
"Read a 'Length Coded Binary' number from the data buffer.\n\n Length coded numbers can be anywhere from 1 to 9 bytes depending\n on the value of the first byte.\n "
c = ord(self.read(1))
if (c == NULL_COLUMN):
return None
if (c < UNSIG... | def read_length_encoded_integer(self):
"Read a 'Length Coded Binary' number from the data buffer.\n\n Length coded numbers can be anywhere from 1 to 9 bytes depending\n on the value of the first byte.\n "
c = ord(self.read(1))
if (c == NULL_COLUMN):
return None
if (c < UNSIG... |
eee51d0ed24e371397264aa158f517c001eeec7b8b1bc641380ab646cfedcef9 | def read_length_coded_string(self):
'Read a \'Length Coded String\' from the data buffer.\n\n A \'Length Coded String\' consists first of a length coded\n (unsigned, positive) integer represented in 1-9 bytes followed by\n that many bytes of binary data. (For example "cat" would be "3cat".)\n ... | Read a 'Length Coded String' from the data buffer.
A 'Length Coded String' consists first of a length coded
(unsigned, positive) integer represented in 1-9 bytes followed by
that many bytes of binary data. (For example "cat" would be "3cat".) | asynctorndb/connection.py | read_length_coded_string | mayflaver/AsyncTorndb | 103 | python | def read_length_coded_string(self):
'Read a \'Length Coded String\' from the data buffer.\n\n A \'Length Coded String\' consists first of a length coded\n (unsigned, positive) integer represented in 1-9 bytes followed by\n that many bytes of binary data. (For example "cat" would be "3cat".)\n ... | def read_length_coded_string(self):
'Read a \'Length Coded String\' from the data buffer.\n\n A \'Length Coded String\' consists first of a length coded\n (unsigned, positive) integer represented in 1-9 bytes followed by\n that many bytes of binary data. (For example "cat" would be "3cat".)\n ... |
ec1f56ae6fa8320c131cf5e8af6f5addc2cecc64f8a841fb08317ea866f55eb7 | def __parse_field_descriptor(self, encoding):
"Parse the 'Field Descriptor' (Metadata) packet.\n\n This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0).\n "
self.catalog = self.read_length_coded_string()
self.db = self.read_length_coded_string()
self.table_name = self.read_le... | Parse the 'Field Descriptor' (Metadata) packet.
This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0). | asynctorndb/connection.py | __parse_field_descriptor | mayflaver/AsyncTorndb | 103 | python | def __parse_field_descriptor(self, encoding):
"Parse the 'Field Descriptor' (Metadata) packet.\n\n This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0).\n "
self.catalog = self.read_length_coded_string()
self.db = self.read_length_coded_string()
self.table_name = self.read_le... | def __parse_field_descriptor(self, encoding):
"Parse the 'Field Descriptor' (Metadata) packet.\n\n This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0).\n "
self.catalog = self.read_length_coded_string()
self.db = self.read_length_coded_string()
self.table_name = self.read_le... |
daf1cc2483838de93573d588ee4997789473d8ec3950b21ef65429115b30394a | def description(self):
'Provides a 7-item tuple compatible with the Python PEP249 DB Spec.'
desc = []
desc.append(self.name)
desc.append(self.type_code)
desc.append(None)
desc.append(self.get_column_length())
desc.append(self.get_column_length())
desc.append(self.scale)
if ((self.fla... | Provides a 7-item tuple compatible with the Python PEP249 DB Spec. | asynctorndb/connection.py | description | mayflaver/AsyncTorndb | 103 | python | def description(self):
desc = []
desc.append(self.name)
desc.append(self.type_code)
desc.append(None)
desc.append(self.get_column_length())
desc.append(self.get_column_length())
desc.append(self.scale)
if ((self.flags % 2) == 0):
desc.append(1)
else:
desc.append(... | def description(self):
desc = []
desc.append(self.name)
desc.append(self.type_code)
desc.append(None)
desc.append(self.get_column_length())
desc.append(self.get_column_length())
desc.append(self.scale)
if ((self.flags % 2) == 0):
desc.append(1)
else:
desc.append(... |
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