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a8cbbff03ded41c224ee9c7a1e835f1ca35c38b5
mat3049/mwo-skilltree-planner
util/skill_node_extractor.py
[ "MIT" ]
Python
process_discriminators
<not_specific>
def process_discriminators(effect): """ Determine which discriminators apply to each effect """ discriminators = { 'faction': False, 'class': False, 'tonnage': False } # Effect has a discriminator; Faction discriminator is default if effect.attrib['value'] == '0.0': discriminators['faction'] = True # Iterate over faction classifiers for faction in effect.iter('Faction'): # Faction has no base value; Class discriminator in place if faction.attrib['value'] == '0.0': discriminators['class'] = True for weight_class in faction.iter('WeightClass'): # Weight has no base value; Tonnage discriminator in place if weight_class.attrib['value'] == "0.0": discriminators['tonnage'] = True return discriminators
Determine which discriminators apply to each effect
Determine which discriminators apply to each effect
[ "Determine", "which", "discriminators", "apply", "to", "each", "effect" ]
def process_discriminators(effect): discriminators = { 'faction': False, 'class': False, 'tonnage': False } if effect.attrib['value'] == '0.0': discriminators['faction'] = True for faction in effect.iter('Faction'): if faction.attrib['value'] == '0.0': discriminators['class'] = True for weight_class in faction.iter('WeightClass'): if weight_class.attrib['value'] == "0.0": discriminators['tonnage'] = True return discriminators
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Determine which discriminators apply to each effect
[ "Determine", "which", "discriminators", "apply", "to", "each", "effect" ]
[ "\"\"\"\n Determine which discriminators apply to each effect\n \"\"\"", "# Effect has a discriminator; Faction discriminator is default", "# Iterate over faction classifiers", "# Faction has no base value; Class discriminator in place", "# Weight has no base value; Tonnage discriminator in place" ]
[ { "param": "effect", "type": null } ]
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def process_discriminators(effect): discriminators = { 'faction': False, 'class': False, 'tonnage': False } if effect.attrib['value'] == '0.0': discriminators['faction'] = True for faction in effect.iter('Faction'): if faction.attrib['value'] == '0.0': discriminators['class'] = True for weight_class in faction.iter('WeightClass'): if weight_class.attrib['value'] == "0.0": discriminators['tonnage'] = True return discriminators
609,964
309
0861fb6de1988f399cc4e82647007a5611a1a734
suyashkumar/nucleus
nucleus/util/sequence_utils.py
[ "Apache-2.0" ]
Python
_add_lowercase
<not_specific>
def _add_lowercase(d): """Returns a dictionary with the lowercase keys and values entered.""" retval = d.copy() retval.update({k.lower(): v.lower() for k, v in d.items()}) return retval
Returns a dictionary with the lowercase keys and values entered.
Returns a dictionary with the lowercase keys and values entered.
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def _add_lowercase(d): retval = d.copy() retval.update({k.lower(): v.lower() for k, v in d.items()}) return retval
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Returns a dictionary with the lowercase keys and values entered.
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[ "\"\"\"Returns a dictionary with the lowercase keys and values entered.\"\"\"" ]
[ { "param": "d", "type": null } ]
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def _add_lowercase(d): retval = d.copy() retval.update({k.lower(): v.lower() for k, v in d.items()}) return retval
609,965
397
c156b6eff9f1757ae304822996f4ef0c02733c11
Ceofy/NetColoc
netcoloc/network_colocalization.py
[ "MIT" ]
Python
calculate_expected_overlap
null
def calculate_expected_overlap(z_scores_1, z_scores_2, gene_set_name_1='Gene Set 1', gene_set_name_2='Gene Set 2', z_score_threshold=3, z1_threshold=1.5,z2_threshold=1.5, num_reps=1000, save_random_network_overlap=False, plot=False): '''Function to determine size of expected network overlap by randomly shuffling gene names. Args: z_scores_1 (pandas.Series): Pandas Series resulting from the netprop_zscore.netprop_zscore or netprop_zscore.calc_zscore_heat methods, containing the z-scores of each gene following network propagation. The index consists of gene names. z_scores_2 (pandas.Series): Similar to z_scores_1. The two pandas Series must contain the same genes (ie. come from the same interactome network). z_score_threshold (float): The threshold to determine whether a gene is a part of the network overlap or not. Genes with combined z-scores below this threshold will be discarded. (Default: 3) z1_threshold (float): The individual z1-score threshold to determine whether a gene is a part of the network overlap or not. Genes with z1-scores below this threshold will be discarded. (Default: 1.5) z2_threshold (float): The individual z2-score threshold to determine whether a gene is a part of the network overlap or not. Genes with z2-scores below this threshold will be discarded. (Default: 1.5) num_reps (int): The number of times that gene names will be shuffled. plot (bool): If True, the distribution will be plotted. If False, it will not be plotted. (Default: False) Returns: float: ''' # Build a distribution of expected network overlap sizes by shuffling node names #random_network_overlap_sizes = [] #z_scores_1_copy = z_scores_1.copy() #z_scores_2_copy = z_scores_2.copy() #gene_set_1 = z_scores_1.index.tolist() #gene_set_2 = z_scores_2.index.tolist() #for _ in range(num_reps): # Shuffle gene name labels # np.random.shuffle(gene_set_1) # z_scores_1_copy.index = gene_set_1 # np.random.shuffle(gene_set_2) #z_scores_2_copy.index = gene_set_2 #random_size = len(calculate_network_overlap(z_scores_1_copy, z_scores_2_copy, z_score_threshold=z_score_threshold, #z1_threshold=z1_threshold,z2_threshold=z2_threshold)) #random_network_overlap_sizes.append(random_size) #network_overlap_size = len(calculate_network_overlap(z_scores_1, z_scores_2, z_score_threshold=z_score_threshold, # z1_threshold=z1_threshold,z2_threshold=z2_threshold)) #if plot: # plt.figure(figsize=(5, 4)) # dfig = sns.histplot(random_network_overlap_sizes, label='Expected network intersection size') # plt.vlines(network_overlap_size, ymin=0, ymax=dfig.dataLim.bounds[3], color='r', label='Observed network intersection size') #plt.xlabel('Size of proximal subgraph, z > ' + str(z_score_threshold), fontsize=16) #plt.legend(fontsize=12) #return network_overlap_size, random_network_overlap_sizes pass
Function to determine size of expected network overlap by randomly shuffling gene names. Args: z_scores_1 (pandas.Series): Pandas Series resulting from the netprop_zscore.netprop_zscore or netprop_zscore.calc_zscore_heat methods, containing the z-scores of each gene following network propagation. The index consists of gene names. z_scores_2 (pandas.Series): Similar to z_scores_1. The two pandas Series must contain the same genes (ie. come from the same interactome network). z_score_threshold (float): The threshold to determine whether a gene is a part of the network overlap or not. Genes with combined z-scores below this threshold will be discarded. (Default: 3) z1_threshold (float): The individual z1-score threshold to determine whether a gene is a part of the network overlap or not. Genes with z1-scores below this threshold will be discarded. (Default: 1.5) z2_threshold (float): The individual z2-score threshold to determine whether a gene is a part of the network overlap or not. Genes with z2-scores below this threshold will be discarded. (Default: 1.5) num_reps (int): The number of times that gene names will be shuffled. plot (bool): If True, the distribution will be plotted. If False, it will not be plotted. (Default: False) Returns: float:
Function to determine size of expected network overlap by randomly shuffling gene names.
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def calculate_expected_overlap(z_scores_1, z_scores_2, gene_set_name_1='Gene Set 1', gene_set_name_2='Gene Set 2', z_score_threshold=3, z1_threshold=1.5,z2_threshold=1.5, num_reps=1000, save_random_network_overlap=False, plot=False): pass
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Function to determine size of expected network overlap by randomly shuffling gene names.
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[ "'''Function to determine size of expected network overlap by randomly\n shuffling gene names.\n\n Args:\n z_scores_1 (pandas.Series): Pandas Series resulting from the \n netprop_zscore.netprop_zscore or netprop_zscore.calc_zscore_heat\n methods, containing the z-scores of each gene following network\n propagation. The index consists of gene names.\n z_scores_2 (pandas.Series): Similar to z_scores_1. The two pandas Series\n must contain the same genes (ie. come from the same interactome\n network).\n z_score_threshold (float): The threshold to determine whether a gene is \n a part of the network overlap or not. Genes with combined z-scores\n below this threshold will be discarded. (Default: 3)\n z1_threshold (float): The individual z1-score threshold to determine whether a gene is \n a part of the network overlap or not. Genes with z1-scores\n below this threshold will be discarded. (Default: 1.5)\n z2_threshold (float): The individual z2-score threshold to determine whether a gene is \n a part of the network overlap or not. Genes with z2-scores\n below this threshold will be discarded. (Default: 1.5)\n num_reps (int): The number of times that gene names will be shuffled.\n plot (bool): If True, the distribution will be plotted. If False, it\n will not be plotted. (Default: False)\n\n Returns:\n float: \n\n '''", "# Build a distribution of expected network overlap sizes by shuffling node names", "#random_network_overlap_sizes = []", "#z_scores_1_copy = z_scores_1.copy()", "#z_scores_2_copy = z_scores_2.copy()", "#gene_set_1 = z_scores_1.index.tolist()", "#gene_set_2 = z_scores_2.index.tolist()", "#for _ in range(num_reps):", "# Shuffle gene name labels", "# np.random.shuffle(gene_set_1)", "# z_scores_1_copy.index = gene_set_1", "# np.random.shuffle(gene_set_2)", "#z_scores_2_copy.index = gene_set_2", "#random_size = len(calculate_network_overlap(z_scores_1_copy, z_scores_2_copy, z_score_threshold=z_score_threshold,", "#z1_threshold=z1_threshold,z2_threshold=z2_threshold))", "#random_network_overlap_sizes.append(random_size)", "#network_overlap_size = len(calculate_network_overlap(z_scores_1, z_scores_2, z_score_threshold=z_score_threshold,", "# z1_threshold=z1_threshold,z2_threshold=z2_threshold))", "#if plot:", "# plt.figure(figsize=(5, 4))", "# dfig = sns.histplot(random_network_overlap_sizes, label='Expected network intersection size')", "# plt.vlines(network_overlap_size, ymin=0, ymax=dfig.dataLim.bounds[3], color='r', label='Observed network intersection size')", "#plt.xlabel('Size of proximal subgraph, z > ' + str(z_score_threshold), fontsize=16)", "#plt.legend(fontsize=12)", "#return network_overlap_size, random_network_overlap_sizes" ]
[ { "param": "z_scores_1", "type": null }, { "param": "z_scores_2", "type": null }, { "param": "gene_set_name_1", "type": null }, { "param": "gene_set_name_2", "type": null }, { "param": "z_score_threshold", "type": null }, { "param": "z1_threshold", "type": null }, { "param": "z2_threshold", "type": null }, { "param": "num_reps", "type": null }, { "param": "save_random_network_overlap", "type": null }, { "param": "plot", "type": null } ]
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def calculate_expected_overlap(z_scores_1, z_scores_2, gene_set_name_1='Gene Set 1', gene_set_name_2='Gene Set 2', z_score_threshold=3, z1_threshold=1.5,z2_threshold=1.5, num_reps=1000, save_random_network_overlap=False, plot=False): pass
609,966
653
b546ac7e1c25e7abd322681c59c60a1c1988cd70
ArneBinder/nlp-formats
nlp_formats/brat.py
[ "Apache-2.0" ]
Python
_get_normalization_annotation
<not_specific>
def _get_normalization_annotation(annotation_line): """ example input: N1 Reference T1 Wikipedia:534366 Barack Obama """ _id, remaining, text = annotation_line.split('\t', maxsplit=2) _type, target, ref = remaining.split(' ') res_id, ent_id = ref.split(':') return { 'id': _id, 'type': _type, 'target': target, 'resource_id': res_id, 'entity_id': ent_id, }
example input: N1 Reference T1 Wikipedia:534366 Barack Obama
example input: N1 Reference T1 Wikipedia:534366 Barack Obama
[ "example", "input", ":", "N1", "Reference", "T1", "Wikipedia", ":", "534366", "Barack", "Obama" ]
def _get_normalization_annotation(annotation_line): _id, remaining, text = annotation_line.split('\t', maxsplit=2) _type, target, ref = remaining.split(' ') res_id, ent_id = ref.split(':') return { 'id': _id, 'type': _type, 'target': target, 'resource_id': res_id, 'entity_id': ent_id, }
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example input: N1 Reference T1 Wikipedia:534366 Barack Obama
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[ "\"\"\"\n example input:\n N1\tReference T1 Wikipedia:534366\tBarack Obama\n \"\"\"" ]
[ { "param": "annotation_line", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "annotation_line", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _get_normalization_annotation(annotation_line): _id, remaining, text = annotation_line.split('\t', maxsplit=2) _type, target, ref = remaining.split(' ') res_id, ent_id = ref.split(':') return { 'id': _id, 'type': _type, 'target': target, 'resource_id': res_id, 'entity_id': ent_id, }
609,967
333
2501bb093efdc1993ce1b776f9c465b03245564a
VictorErmakov/tacitus-notes
src/tacitus/tacitus.py
[ "BSD-2-Clause" ]
Python
exec_no_fail
<not_specific>
def exec_no_fail(command): """runs shell command and capture the output""" result = subprocess.run(command, shell=True, capture_output=True) if result.returncode: print(f"Error executing: {command}") sys.exit(result.stderr.decode("utf-8").strip()) return result.stdout.decode("utf-8").strip()
runs shell command and capture the output
runs shell command and capture the output
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def exec_no_fail(command): result = subprocess.run(command, shell=True, capture_output=True) if result.returncode: print(f"Error executing: {command}") sys.exit(result.stderr.decode("utf-8").strip()) return result.stdout.decode("utf-8").strip()
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runs shell command and capture the output
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[ "\"\"\"runs shell command and capture the output\"\"\"" ]
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import subprocess import sys def exec_no_fail(command): result = subprocess.run(command, shell=True, capture_output=True) if result.returncode: print(f"Error executing: {command}") sys.exit(result.stderr.decode("utf-8").strip()) return result.stdout.decode("utf-8").strip()
609,968
345
a3e782ea84e79316daa3f59a91f66da46ca30d83
adamrehn/gen-invoice
gen_invoice/InvoiceGenerator.py
[ "MIT" ]
Python
_format_currency
<not_specific>
def _format_currency(value): ''' Formats a numeric value as a currency string using the system's locale settings ''' # Set our locale to the system default locale.setlocale(locale.LC_ALL, '') # Format the value using the currency settings for our locale return locale.currency(value, symbol=True, grouping=True)
Formats a numeric value as a currency string using the system's locale settings
Formats a numeric value as a currency string using the system's locale settings
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def _format_currency(value): locale.setlocale(locale.LC_ALL, '') return locale.currency(value, symbol=True, grouping=True)
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Formats a numeric value as a currency string using the system's locale settings
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[ "'''\n\t\tFormats a numeric value as a currency string using the system's locale settings\n\t\t'''", "# Set our locale to the system default", "# Format the value using the currency settings for our locale" ]
[ { "param": "value", "type": null } ]
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import locale def _format_currency(value): locale.setlocale(locale.LC_ALL, '') return locale.currency(value, symbol=True, grouping=True)
609,969
651
af8de8aa9949afa42fb3753478c9e2ec92d6a33b
Ahmed-Araby/CarND-LaneLines-P1
Lines.py
[ "MIT" ]
Python
average_lines
<not_specific>
def average_lines(lines , Avg_ux , Avg_uy , Avg_lx , Avg_ly): """ - I will work on averaging the end points - ***************** we need to find better way to average the lines ****************** :param left_lanes: :param right_lanes: :return: """ # left lane averging end points avg_lx = 0 avg_ly = 0 avg_ux = 0 avg_uy = 0 for i in range(0 , len(lines), 1): # (col , row) fx , fy , sx , sy = lines[i] point1 = 0 point2 = 0 # point1 will have the lower point if fy>sy: point1 = (fx , fy) point2 = (sx , sy) else: point2 = (sx , sy) point1 = (fx , fy) # average the points avg_lx += point1[0] avg_ly += point1[1] avg_ux +=point2[0] avg_uy +=point2[1] # calculate moving average , more smooth detection # the problem here is the bias that we need to correct as # we did initialize the averages = 0 in the begining. """ Avg_lx = int(.9*Avg_lx + .1*avg_lx) Avg_ly = int(.9*Avg_ly + .1*avg_ly) Avg_ux = int(.9*Avg_ux + .1*avg_ux) Avg_uy = int(.9*Avg_uy + .1*avg_uy) """ l= len(lines) return [avg_lx //l , avg_ly //l , avg_ux //l , avg_uy //l]
- I will work on averaging the end points - ***************** we need to find better way to average the lines ****************** :param left_lanes: :param right_lanes: :return:
I will work on averaging the end points we need to find better way to average the lines
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def average_lines(lines , Avg_ux , Avg_uy , Avg_lx , Avg_ly): avg_lx = 0 avg_ly = 0 avg_ux = 0 avg_uy = 0 for i in range(0 , len(lines), 1): fx , fy , sx , sy = lines[i] point1 = 0 point2 = 0 if fy>sy: point1 = (fx , fy) point2 = (sx , sy) else: point2 = (sx , sy) point1 = (fx , fy) avg_lx += point1[0] avg_ly += point1[1] avg_ux +=point2[0] avg_uy +=point2[1] l= len(lines) return [avg_lx //l , avg_ly //l , avg_ux //l , avg_uy //l]
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I will work on averaging the end points ***************** we need to find better way to average the lines
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[ "\"\"\"\n - I will work on averaging the end points\n - ***************** we need to find better way to average the lines ******************\n :param left_lanes:\n :param right_lanes:\n :return:\n \"\"\"", "# left lane averging end points", "# (col , row)", "# point1 will have the lower point", "# average the points", "# calculate moving average , more smooth detection", "# the problem here is the bias that we need to correct as", "# we did initialize the averages = 0 in the begining.", "\"\"\"\n Avg_lx = int(.9*Avg_lx + .1*avg_lx)\n Avg_ly = int(.9*Avg_ly + .1*avg_ly)\n Avg_ux = int(.9*Avg_ux + .1*avg_ux)\n Avg_uy = int(.9*Avg_uy + .1*avg_uy)\n \"\"\"" ]
[ { "param": "lines", "type": null }, { "param": "Avg_ux", "type": null }, { "param": "Avg_uy", "type": null }, { "param": "Avg_lx", "type": null }, { "param": "Avg_ly", "type": null } ]
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def average_lines(lines , Avg_ux , Avg_uy , Avg_lx , Avg_ly): avg_lx = 0 avg_ly = 0 avg_ux = 0 avg_uy = 0 for i in range(0 , len(lines), 1): fx , fy , sx , sy = lines[i] point1 = 0 point2 = 0 if fy>sy: point1 = (fx , fy) point2 = (sx , sy) else: point2 = (sx , sy) point1 = (fx , fy) avg_lx += point1[0] avg_ly += point1[1] avg_ux +=point2[0] avg_uy +=point2[1] l= len(lines) return [avg_lx //l , avg_ly //l , avg_ux //l , avg_uy //l]
609,970
159
5bd10af5e1c601005c7f63a5888772f0842bf135
vishalbelsare/neuropod
source/python/neuropod/loader.py
[ "Apache-2.0" ]
Python
_convert_native_shape_to_list
<not_specific>
def _convert_native_shape_to_list(dims): """ Takes a list of `neuropod_native.Dimension` objects and converts to a list of python types """ out = [] for dim in dims: if dim.value == -2: # It's a symbol out.append(dim.symbol) elif dim.value == -1: # Any shape is okay out.append(None) else: out.append(dim.value) return out
Takes a list of `neuropod_native.Dimension` objects and converts to a list of python types
Takes a list of `neuropod_native.Dimension` objects and converts to a list of python types
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def _convert_native_shape_to_list(dims): out = [] for dim in dims: if dim.value == -2: out.append(dim.symbol) elif dim.value == -1: out.append(None) else: out.append(dim.value) return out
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Takes a list of `neuropod_native.Dimension` objects and converts to a list of python types
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[ "\"\"\"\n Takes a list of `neuropod_native.Dimension` objects and converts to a list of python types\n \"\"\"", "# It's a symbol", "# Any shape is okay" ]
[ { "param": "dims", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "dims", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _convert_native_shape_to_list(dims): out = [] for dim in dims: if dim.value == -2: out.append(dim.symbol) elif dim.value == -1: out.append(None) else: out.append(dim.value) return out
609,971
877
52c92c16479f37578061c9f5ceff436e97c7183b
suiboli314/RoombaSim
src/GreedyAlgo.py
[ "MIT" ]
Python
_get_distance
<not_specific>
def _get_distance(cell1, cell2): """ Calculate manhattan distance distance between given two cells Args: cell1 (tuple): a cell cell2 (tuple): a cell Returns: int: manhattan distance """ return abs(cell1[0] - cell2[0]) + abs(cell1[1]-cell2[1])
Calculate manhattan distance distance between given two cells Args: cell1 (tuple): a cell cell2 (tuple): a cell Returns: int: manhattan distance
Calculate manhattan distance distance between given two cells
[ "Calculate", "manhattan", "distance", "distance", "between", "given", "two", "cells" ]
def _get_distance(cell1, cell2): return abs(cell1[0] - cell2[0]) + abs(cell1[1]-cell2[1])
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Calculate manhattan distance distance between given two cells
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[ "\"\"\" Calculate manhattan distance distance between given two cells\n\n Args:\n cell1 (tuple): a cell\n cell2 (tuple): a cell\n Returns:\n int: manhattan distance\n \"\"\"" ]
[ { "param": "cell1", "type": null }, { "param": "cell2", "type": null } ]
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def _get_distance(cell1, cell2): return abs(cell1[0] - cell2[0]) + abs(cell1[1]-cell2[1])
609,972
429
84d81fb6f5d8bd9f30a34835954e585900de460e
SimonBoothroyd/nonbonded
nonbonded/library/plotting/plotly/utilities.py
[ "MIT" ]
Python
unique_colors
<not_specific>
def unique_colors(n_colors): """Returns a unique list of distinguishable colors. These are taken from the default seaborn `colorblind` color palette. Parameters ---------- n_colors The number of colors to return (max=10). """ colors = [ (0.004, 0.451, 0.698), (0.871, 0.561, 0.020), (0.008, 0.620, 0.451), (0.835, 0.369, 0.000), (0.800, 0.471, 0.737), (0.792, 0.569, 0.380), (0.984, 0.686, 0.894), (0.580, 0.580, 0.580), (0.925, 0.882, 0.200), (0.337, 0.706, 0.914), ] assert n_colors <= len(colors) return colors[:n_colors]
Returns a unique list of distinguishable colors. These are taken from the default seaborn `colorblind` color palette. Parameters ---------- n_colors The number of colors to return (max=10).
Returns a unique list of distinguishable colors. These are taken from the default seaborn `colorblind` color palette. Parameters n_colors The number of colors to return (max=10).
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def unique_colors(n_colors): colors = [ (0.004, 0.451, 0.698), (0.871, 0.561, 0.020), (0.008, 0.620, 0.451), (0.835, 0.369, 0.000), (0.800, 0.471, 0.737), (0.792, 0.569, 0.380), (0.984, 0.686, 0.894), (0.580, 0.580, 0.580), (0.925, 0.882, 0.200), (0.337, 0.706, 0.914), ] assert n_colors <= len(colors) return colors[:n_colors]
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Returns a unique list of distinguishable colors.
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[ "\"\"\"Returns a unique list of distinguishable colors. These are taken from the\n default seaborn `colorblind` color palette.\n\n Parameters\n ----------\n n_colors\n The number of colors to return (max=10).\n \"\"\"" ]
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def unique_colors(n_colors): colors = [ (0.004, 0.451, 0.698), (0.871, 0.561, 0.020), (0.008, 0.620, 0.451), (0.835, 0.369, 0.000), (0.800, 0.471, 0.737), (0.792, 0.569, 0.380), (0.984, 0.686, 0.894), (0.580, 0.580, 0.580), (0.925, 0.882, 0.200), (0.337, 0.706, 0.914), ] assert n_colors <= len(colors) return colors[:n_colors]
609,973
258
169a56b092188966232ea21bbfe06f4de886fd89
PythonCharmers/nbfastconvert
nbfastconvert/nbfastconvert.py
[ "MIT" ]
Python
hash_cell_repr
<not_specific>
def hash_cell_repr(cell): """ Create a string representation of the given cell (a dictionary), ensuring the keys are sorted first. """ sorted_keys = sorted(cell.keys()) sorted_cell = {key: cell[key] for key in sorted_keys} cell_hash = repr(sorted_cell) return cell_hash
Create a string representation of the given cell (a dictionary), ensuring the keys are sorted first.
Create a string representation of the given cell (a dictionary), ensuring the keys are sorted first.
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def hash_cell_repr(cell): sorted_keys = sorted(cell.keys()) sorted_cell = {key: cell[key] for key in sorted_keys} cell_hash = repr(sorted_cell) return cell_hash
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Create a string representation of the given cell (a dictionary), ensuring the keys are sorted first.
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[ "\"\"\"\n Create a string representation of the given cell\n (a dictionary), ensuring the keys are sorted first.\n \"\"\"" ]
[ { "param": "cell", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cell", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def hash_cell_repr(cell): sorted_keys = sorted(cell.keys()) sorted_cell = {key: cell[key] for key in sorted_keys} cell_hash = repr(sorted_cell) return cell_hash
609,975
7
5a99dfc5c0625e606d57921e0162f944ea1208ee
alishakodibagkar/brainlit
brainlit/cloudreg/scripts/parastitcher.py
[ "Apache-2.0" ]
Python
collect_instructions
<not_specific>
def collect_instructions(inst): """ Collect the remanent part of a list of strings in a unique string Input: inst = Input list of strings Output: results = String containing all the elements of inst """ len_inst = len(inst) if len_inst > 0: for i in range(0, len_inst): if i == 0: results = str(inst[i]) else: results = results + " " + str(inst[i]) else: results = "" return results
Collect the remanent part of a list of strings in a unique string Input: inst = Input list of strings Output: results = String containing all the elements of inst
Collect the remanent part of a list of strings in a unique string Input: inst = Input list of strings Output: results = String containing all the elements of inst
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def collect_instructions(inst): len_inst = len(inst) if len_inst > 0: for i in range(0, len_inst): if i == 0: results = str(inst[i]) else: results = results + " " + str(inst[i]) else: results = "" return results
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Collect the remanent part of a list of strings in a unique string Input: inst = Input list of strings Output: results = String containing all the elements of inst
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[ "\"\"\"\n Collect the remanent part of a list of strings in a unique string\n Input:\n inst = Input list of strings\n Output:\n results = String containing all the elements of inst\n \"\"\"" ]
[ { "param": "inst", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "inst", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def collect_instructions(inst): len_inst = len(inst) if len_inst > 0: for i in range(0, len_inst): if i == 0: results = str(inst[i]) else: results = results + " " + str(inst[i]) else: results = "" return results
609,976
356
fdfb8a2fba3ff01316a02f1f3387258114f495b5
k-kapp/CycleDating
cycle_dating/Algos/eiisr.py
[ "Apache-2.0" ]
Python
combo_loop_help
null
def combo_loop_help(lst, curr_idx, curr_num, total_num, all_els, all_perms): """ Helper function for the combinations function defined below """ if all_els is None: all_els = [] for i in range(curr_idx, len(lst)): all_els.append(lst[i]) if curr_num == total_num: all_perms.append(copy.deepcopy(all_els)) del all_els[-1] else: combo_loop_help(lst, i + 1, curr_num + 1, total_num, all_els, all_perms) del all_els[-1]
Helper function for the combinations function defined below
Helper function for the combinations function defined below
[ "Helper", "function", "for", "the", "combinations", "function", "defined", "below" ]
def combo_loop_help(lst, curr_idx, curr_num, total_num, all_els, all_perms): if all_els is None: all_els = [] for i in range(curr_idx, len(lst)): all_els.append(lst[i]) if curr_num == total_num: all_perms.append(copy.deepcopy(all_els)) del all_els[-1] else: combo_loop_help(lst, i + 1, curr_num + 1, total_num, all_els, all_perms) del all_els[-1]
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Helper function for the combinations function defined below
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[ "\"\"\"\n Helper function for the combinations function defined below\n \"\"\"" ]
[ { "param": "lst", "type": null }, { "param": "curr_idx", "type": null }, { "param": "curr_num", "type": null }, { "param": "total_num", "type": null }, { "param": "all_els", "type": null }, { "param": "all_perms", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "lst", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "curr_idx", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "curr_num", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "total_num", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "all_els", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "all_perms", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import copy def combo_loop_help(lst, curr_idx, curr_num, total_num, all_els, all_perms): if all_els is None: all_els = [] for i in range(curr_idx, len(lst)): all_els.append(lst[i]) if curr_num == total_num: all_perms.append(copy.deepcopy(all_els)) del all_els[-1] else: combo_loop_help(lst, i + 1, curr_num + 1, total_num, all_els, all_perms) del all_els[-1]
609,977
601
51221623318f1138966ef288407d57aa78936a5a
aricsanders/pyMez3
Code/Analysis/NISTUncertainty.py
[ "Unlicense" ]
Python
coax_power_S_NIST
<not_specific>
def coax_power_S_NIST(connector_type='N',frequency=1.): """Calculates SNIST for coax power measurements""" if re.search('7',connector_type,re.IGNORECASE): uncertainty_eff=.09+.01*frequency elif re.search('N',connector_type,re.IGNORECASE): uncertainty_eff=10**(-1.4+.04*frequency) elif re.search('3.5',connector_type,re.IGNORECASE): if frequency<.05: uncertainty_eff=10**(-1.4+.04*frequency) elif frequency>=.05 and frequency<=18.: uncertainty_eff=.25 else: uncertainty_eff=.25 return [uncertainty_eff]
Calculates SNIST for coax power measurements
Calculates SNIST for coax power measurements
[ "Calculates", "SNIST", "for", "coax", "power", "measurements" ]
def coax_power_S_NIST(connector_type='N',frequency=1.): if re.search('7',connector_type,re.IGNORECASE): uncertainty_eff=.09+.01*frequency elif re.search('N',connector_type,re.IGNORECASE): uncertainty_eff=10**(-1.4+.04*frequency) elif re.search('3.5',connector_type,re.IGNORECASE): if frequency<.05: uncertainty_eff=10**(-1.4+.04*frequency) elif frequency>=.05 and frequency<=18.: uncertainty_eff=.25 else: uncertainty_eff=.25 return [uncertainty_eff]
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Calculates SNIST for coax power measurements
[ "Calculates", "SNIST", "for", "coax", "power", "measurements" ]
[ "\"\"\"Calculates SNIST for coax power measurements\"\"\"" ]
[ { "param": "connector_type", "type": null }, { "param": "frequency", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "connector_type", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "frequency", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import re def coax_power_S_NIST(connector_type='N',frequency=1.): if re.search('7',connector_type,re.IGNORECASE): uncertainty_eff=.09+.01*frequency elif re.search('N',connector_type,re.IGNORECASE): uncertainty_eff=10**(-1.4+.04*frequency) elif re.search('3.5',connector_type,re.IGNORECASE): if frequency<.05: uncertainty_eff=10**(-1.4+.04*frequency) elif frequency>=.05 and frequency<=18.: uncertainty_eff=.25 else: uncertainty_eff=.25 return [uncertainty_eff]
609,978
478
3f8f8b7752620f5d84d7126c4ed2b8f8b9582df5
pupamanyu/cicd
pipeline2/source-code/dependencies/helper_function.py
[ "Apache-2.0" ]
Python
delete_table
None
def delete_table(bq_client: bigquery.Client, dataset_id: str, table_name: str) -> None: """Deletes a specified table in BigQuery. Args: bq_client: bigquery.Client object. dataset_id: String holding ID of dataset table_name: String of table name to delete Returns: None; Deletes a table in BigQuery """ dataset_ref = bq_client.dataset(dataset_id) table_ref = dataset_ref.table(table_name) bq_client.delete_table(table=table_ref)
Deletes a specified table in BigQuery. Args: bq_client: bigquery.Client object. dataset_id: String holding ID of dataset table_name: String of table name to delete Returns: None; Deletes a table in BigQuery
Deletes a specified table in BigQuery.
[ "Deletes", "a", "specified", "table", "in", "BigQuery", "." ]
def delete_table(bq_client: bigquery.Client, dataset_id: str, table_name: str) -> None: dataset_ref = bq_client.dataset(dataset_id) table_ref = dataset_ref.table(table_name) bq_client.delete_table(table=table_ref)
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Deletes a specified table in BigQuery.
[ "Deletes", "a", "specified", "table", "in", "BigQuery", "." ]
[ "\"\"\"Deletes a specified table in BigQuery.\n\n Args:\n bq_client: bigquery.Client object.\n dataset_id: String holding ID of dataset\n table_name: String of table name to delete\n\n Returns:\n None; Deletes a table in BigQuery\n \"\"\"" ]
[ { "param": "bq_client", "type": "bigquery.Client" }, { "param": "dataset_id", "type": "str" }, { "param": "table_name", "type": "str" } ]
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def delete_table(bq_client: bigquery.Client, dataset_id: str, table_name: str) -> None: dataset_ref = bq_client.dataset(dataset_id) table_ref = dataset_ref.table(table_name) bq_client.delete_table(table=table_ref)
609,979
774
91a10ba9337d4a5565f10c59d1a9bc593e3edb4a
googleads/html5-to-dfp
x5_converters.py
[ "Apache-2.0" ]
Python
_edge_js_match_function
<not_specific>
def _edge_js_match_function(snippet, assets, match): """Returns a match function to replace asset names with x5 variables.""" name = match.group(1) if '%' in name: name = urllib.unquote(name) try: asset = assets[name[2:-2]] except KeyError: return match.group(1) snippet.assets.append(asset.name) if name.startswith(r'\"') or name.startswith(r"\'"): # From this in edge js: '<a href=\"asset.name\">' # To this: '<a href=[\"' + ]__x5__.macro_ID[ + '\"]>' return "' + __x5__.macro_%s + '" % asset.id elif name[1] in ('"', "'"): # From this edge js: var g23=['"]970x90.jpg['"], # To this: var g23=__x5__.macro_ID, return '%s__x5__.macro_%s%s' % (name[0], asset.id, name[-1]) return '%s__x5__.macro_%s%s' % (name[:2], asset.id, name[-2:])
Returns a match function to replace asset names with x5 variables.
Returns a match function to replace asset names with x5 variables.
[ "Returns", "a", "match", "function", "to", "replace", "asset", "names", "with", "x5", "variables", "." ]
def _edge_js_match_function(snippet, assets, match): name = match.group(1) if '%' in name: name = urllib.unquote(name) try: asset = assets[name[2:-2]] except KeyError: return match.group(1) snippet.assets.append(asset.name) if name.startswith(r'\"') or name.startswith(r"\'"): return "' + __x5__.macro_%s + '" % asset.id elif name[1] in ('"', "'"): return '%s__x5__.macro_%s%s' % (name[0], asset.id, name[-1]) return '%s__x5__.macro_%s%s' % (name[:2], asset.id, name[-2:])
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Returns a match function to replace asset names with x5 variables.
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[ { "param": "snippet", "type": null }, { "param": "assets", "type": null }, { "param": "match", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "snippet", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "assets", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "match", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import urllib def _edge_js_match_function(snippet, assets, match): name = match.group(1) if '%' in name: name = urllib.unquote(name) try: asset = assets[name[2:-2]] except KeyError: return match.group(1) snippet.assets.append(asset.name) if name.startswith(r'\"') or name.startswith(r"\'"): return "' + __x5__.macro_%s + '" % asset.id elif name[1] in ('"', "'"): return '%s__x5__.macro_%s%s' % (name[0], asset.id, name[-1]) return '%s__x5__.macro_%s%s' % (name[:2], asset.id, name[-2:])
609,980
962
71f1405f9ffdb2095af0d2119e488a001c7e1ef0
bopopescu/phyG
eggs/mercurial-2.2.3-py2.7-linux-x86_64-ucs4.egg/mercurial/phases.py
[ "CC-BY-3.0" ]
Python
retractboundary
null
def retractboundary(repo, targetphase, nodes): """Set nodes back to a phase changing other nodes phases if necessary. This function move boundary *backward* this means that all nodes are set in the target phase or kept in a *higher* phase. Simplify boundary to contains phase roots only.""" currentroots = repo._phaseroots[targetphase] newroots = [n for n in nodes if repo[n].phase() < targetphase] if newroots: currentroots.update(newroots) ctxs = repo.set('roots(%ln::)', currentroots) currentroots.intersection_update(ctx.node() for ctx in ctxs) if '_phaserev' in vars(repo): del repo._phaserev repo._dirtyphases = True
Set nodes back to a phase changing other nodes phases if necessary. This function move boundary *backward* this means that all nodes are set in the target phase or kept in a *higher* phase. Simplify boundary to contains phase roots only.
Set nodes back to a phase changing other nodes phases if necessary. This function move boundary *backward* this means that all nodes are set in the target phase or kept in a *higher* phase. Simplify boundary to contains phase roots only.
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def retractboundary(repo, targetphase, nodes): currentroots = repo._phaseroots[targetphase] newroots = [n for n in nodes if repo[n].phase() < targetphase] if newroots: currentroots.update(newroots) ctxs = repo.set('roots(%ln::)', currentroots) currentroots.intersection_update(ctx.node() for ctx in ctxs) if '_phaserev' in vars(repo): del repo._phaserev repo._dirtyphases = True
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Set nodes back to a phase changing other nodes phases if necessary.
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[ "\"\"\"Set nodes back to a phase changing other nodes phases if necessary.\n\n This function move boundary *backward* this means that all nodes are set\n in the target phase or kept in a *higher* phase.\n\n Simplify boundary to contains phase roots only.\"\"\"" ]
[ { "param": "repo", "type": null }, { "param": "targetphase", "type": null }, { "param": "nodes", "type": null } ]
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def retractboundary(repo, targetphase, nodes): currentroots = repo._phaseroots[targetphase] newroots = [n for n in nodes if repo[n].phase() < targetphase] if newroots: currentroots.update(newroots) ctxs = repo.set('roots(%ln::)', currentroots) currentroots.intersection_update(ctx.node() for ctx in ctxs) if '_phaserev' in vars(repo): del repo._phaserev repo._dirtyphases = True
609,981
879
0a8c6235dc313325543b454f0977dc14770d0e7a
jcsambangi/bme590final_backend
conftest.py
[ "MIT" ]
Python
mktestEfile
<not_specific>
def mktestEfile(tmpdir_factory): """Creates test file for unit testing in tempEdir. :param tmpdir_factory: :returns: path of file for pin 9307 test """ tempEdir = tmpdir_factory.mktemp('tempE') testFile = tempEdir.join('L9307L0.BIN') test9307Path = testFile.strpath with open('./testing/9307L0.BIN', 'rb') as binaryFile: hold = binaryFile.read() with open(test9307Path, 'wb') as binaryFile: binaryFile.write(hold) return test9307Path
Creates test file for unit testing in tempEdir. :param tmpdir_factory: :returns: path of file for pin 9307 test
Creates test file for unit testing in tempEdir.
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def mktestEfile(tmpdir_factory): tempEdir = tmpdir_factory.mktemp('tempE') testFile = tempEdir.join('L9307L0.BIN') test9307Path = testFile.strpath with open('./testing/9307L0.BIN', 'rb') as binaryFile: hold = binaryFile.read() with open(test9307Path, 'wb') as binaryFile: binaryFile.write(hold) return test9307Path
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Creates test file for unit testing in tempEdir.
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def mktestEfile(tmpdir_factory): tempEdir = tmpdir_factory.mktemp('tempE') testFile = tempEdir.join('L9307L0.BIN') test9307Path = testFile.strpath with open('./testing/9307L0.BIN', 'rb') as binaryFile: hold = binaryFile.read() with open(test9307Path, 'wb') as binaryFile: binaryFile.write(hold) return test9307Path
609,982
624
15ee96550194c3702f2bc0ad7c5d1a6fae143157
benradford/clef2019-protestnews-radford
final_task_3/evaluate_task3.py
[ "MIT" ]
Python
calc_metrics
<not_specific>
def calc_metrics(tp, p, t, percent=True): """ compute overall precision, recall and FB1 (default values are 0.0) if percent is True, return 100 * original decimal value """ if p != 0: precision = float(tp) / p else: precision = 0 if t !=0: recall = float(tp) / t else: recall = 0 if precision + recall >0 : fb1 = 2 * precision * recall / (precision + recall) else: fb1 = 0 if percent: return 100 * precision, 100 * recall, 100 * fb1 else: return precision, recall, fb1
compute overall precision, recall and FB1 (default values are 0.0) if percent is True, return 100 * original decimal value
compute overall precision, recall and FB1 (default values are 0.0) if percent is True, return 100 * original decimal value
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def calc_metrics(tp, p, t, percent=True): if p != 0: precision = float(tp) / p else: precision = 0 if t !=0: recall = float(tp) / t else: recall = 0 if precision + recall >0 : fb1 = 2 * precision * recall / (precision + recall) else: fb1 = 0 if percent: return 100 * precision, 100 * recall, 100 * fb1 else: return precision, recall, fb1
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compute overall precision, recall and FB1 (default values are 0.0) if percent is True, return 100 * original decimal value
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[ "\"\"\"\n compute overall precision, recall and FB1 (default values are 0.0)\n if percent is True, return 100 * original decimal value\n \"\"\"" ]
[ { "param": "tp", "type": null }, { "param": "p", "type": null }, { "param": "t", "type": null }, { "param": "percent", "type": null } ]
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def calc_metrics(tp, p, t, percent=True): if p != 0: precision = float(tp) / p else: precision = 0 if t !=0: recall = float(tp) / t else: recall = 0 if precision + recall >0 : fb1 = 2 * precision * recall / (precision + recall) else: fb1 = 0 if percent: return 100 * precision, 100 * recall, 100 * fb1 else: return precision, recall, fb1
609,983
583
659f18decd940f2bf4a4a26f094d1dd2f6f23414
hochoy18/ray
python/ray/function_manager.py
[ "Apache-2.0" ]
Python
from_function
<not_specific>
def from_function(cls, function): """Create a FunctionDescriptor from a function instance. This function is used to create the function descriptor from a python function. If a function is a class function, it should not be used by this function. Args: cls: Current class which is required argument for classmethod. function: the python function used to create the function descriptor. Returns: The FunctionDescriptor instance created according to the function. """ module_name = function.__module__ function_name = function.__name__ class_name = "" function_source_hasher = hashlib.sha1() try: # If we are running a script or are in IPython, include the source # code in the hash. source = inspect.getsource(function).encode("ascii") function_source_hasher.update(source) function_source_hash = function_source_hasher.digest() except (IOError, OSError, TypeError): # Source code may not be available: # e.g. Cython or Python interpreter. function_source_hash = b"" return cls(module_name, function_name, class_name, function_source_hash)
Create a FunctionDescriptor from a function instance. This function is used to create the function descriptor from a python function. If a function is a class function, it should not be used by this function. Args: cls: Current class which is required argument for classmethod. function: the python function used to create the function descriptor. Returns: The FunctionDescriptor instance created according to the function.
Create a FunctionDescriptor from a function instance. This function is used to create the function descriptor from a python function. If a function is a class function, it should not be used by this function.
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def from_function(cls, function): module_name = function.__module__ function_name = function.__name__ class_name = "" function_source_hasher = hashlib.sha1() try: source = inspect.getsource(function).encode("ascii") function_source_hasher.update(source) function_source_hash = function_source_hasher.digest() except (IOError, OSError, TypeError): function_source_hash = b"" return cls(module_name, function_name, class_name, function_source_hash)
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Create a FunctionDescriptor from a function instance.
[ "Create", "a", "FunctionDescriptor", "from", "a", "function", "instance", "." ]
[ "\"\"\"Create a FunctionDescriptor from a function instance.\n\n This function is used to create the function descriptor from\n a python function. If a function is a class function, it should\n not be used by this function.\n\n Args:\n cls: Current class which is required argument for classmethod.\n function: the python function used to create the function\n descriptor.\n\n Returns:\n The FunctionDescriptor instance created according to the function.\n \"\"\"", "# If we are running a script or are in IPython, include the source", "# code in the hash.", "# Source code may not be available:", "# e.g. Cython or Python interpreter." ]
[ { "param": "cls", "type": null }, { "param": "function", "type": null } ]
{ "returns": [ { "docstring": "The FunctionDescriptor instance created according to the function.", "docstring_tokens": [ "The", "FunctionDescriptor", "instance", "created", "according", "to", "the", "function", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": "Current class which is required argument for classmethod.", "docstring_tokens": [ "Current", "class", "which", "is", "required", "argument", "for", "classmethod", "." ], "default": null, "is_optional": null }, { "identifier": "function", "type": null, "docstring": "the python function used to create the function\ndescriptor.", "docstring_tokens": [ "the", "python", "function", "used", "to", "create", "the", "function", "descriptor", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import inspect import hashlib def from_function(cls, function): module_name = function.__module__ function_name = function.__name__ class_name = "" function_source_hasher = hashlib.sha1() try: source = inspect.getsource(function).encode("ascii") function_source_hasher.update(source) function_source_hash = function_source_hasher.digest() except (IOError, OSError, TypeError): function_source_hash = b"" return cls(module_name, function_name, class_name, function_source_hash)
609,984
557
026ae5e68628359463223573a01b114e27a7cdfa
reib2/Lab-3-Flood-Warning
floodsystem/geo.py
[ "MIT" ]
Python
stations_by_river
<not_specific>
def stations_by_river(stations): "This module returns a dictionary that maps river names to a list of station objects on a given river" names_and_stations = {} for station in stations: river = station.river if river not in names_and_stations.keys(): list_stations = [station.name] names_and_stations.update({river : list_stations}) else: list_stations = names_and_stations[river] list_stations.append(station.name) names_and_stations.update({river : list_stations}) return names_and_stations
This module returns a dictionary that maps river names to a list of station objects on a given river
This module returns a dictionary that maps river names to a list of station objects on a given river
[ "This", "module", "returns", "a", "dictionary", "that", "maps", "river", "names", "to", "a", "list", "of", "station", "objects", "on", "a", "given", "river" ]
def stations_by_river(stations): names_and_stations = {} for station in stations: river = station.river if river not in names_and_stations.keys(): list_stations = [station.name] names_and_stations.update({river : list_stations}) else: list_stations = names_and_stations[river] list_stations.append(station.name) names_and_stations.update({river : list_stations}) return names_and_stations
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This module returns a dictionary that maps river names to a list of station objects on a given river
[ "This", "module", "returns", "a", "dictionary", "that", "maps", "river", "names", "to", "a", "list", "of", "station", "objects", "on", "a", "given", "river" ]
[ "\"This module returns a dictionary that maps river names to a list of station objects on a given river\"" ]
[ { "param": "stations", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "stations", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def stations_by_river(stations): names_and_stations = {} for station in stations: river = station.river if river not in names_and_stations.keys(): list_stations = [station.name] names_and_stations.update({river : list_stations}) else: list_stations = names_and_stations[river] list_stations.append(station.name) names_and_stations.update({river : list_stations}) return names_and_stations
609,985
550
23af979f80929defbda6791684ee58f8f0dc81a4
hallee/espresso-arm
remi/remi/server.py
[ "MIT" ]
Python
gui_update_children_version
<not_specific>
def gui_update_children_version(client, leaf): """ This function is called when a leaf is updated by gui_updater and so, children does not need graphical update, it is only required to update the last version of the dictionaries """ if not hasattr(leaf, 'attributes'): return False leaf.attributes.__lastversion__ = leaf.attributes.__version__ leaf.style.__lastversion__ = leaf.style.__version__ leaf.children.__lastversion__ = leaf.children.__version__ for subleaf in leaf.children.values(): gui_update_children_version(client, subleaf)
This function is called when a leaf is updated by gui_updater and so, children does not need graphical update, it is only required to update the last version of the dictionaries
This function is called when a leaf is updated by gui_updater and so, children does not need graphical update, it is only required to update the last version of the dictionaries
[ "This", "function", "is", "called", "when", "a", "leaf", "is", "updated", "by", "gui_updater", "and", "so", "children", "does", "not", "need", "graphical", "update", "it", "is", "only", "required", "to", "update", "the", "last", "version", "of", "the", "dictionaries" ]
def gui_update_children_version(client, leaf): if not hasattr(leaf, 'attributes'): return False leaf.attributes.__lastversion__ = leaf.attributes.__version__ leaf.style.__lastversion__ = leaf.style.__version__ leaf.children.__lastversion__ = leaf.children.__version__ for subleaf in leaf.children.values(): gui_update_children_version(client, subleaf)
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This function is called when a leaf is updated by gui_updater and so, children does not need graphical update, it is only required to update the last version of the dictionaries
[ "This", "function", "is", "called", "when", "a", "leaf", "is", "updated", "by", "gui_updater", "and", "so", "children", "does", "not", "need", "graphical", "update", "it", "is", "only", "required", "to", "update", "the", "last", "version", "of", "the", "dictionaries" ]
[ "\"\"\" This function is called when a leaf is updated by gui_updater\n and so, children does not need graphical update, it is only\n required to update the last version of the dictionaries\n \"\"\"" ]
[ { "param": "client", "type": null }, { "param": "leaf", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "client", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "leaf", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def gui_update_children_version(client, leaf): if not hasattr(leaf, 'attributes'): return False leaf.attributes.__lastversion__ = leaf.attributes.__version__ leaf.style.__lastversion__ = leaf.style.__version__ leaf.children.__lastversion__ = leaf.children.__version__ for subleaf in leaf.children.values(): gui_update_children_version(client, subleaf)
609,986
926
efc613838a527141a647763f61061c28d566093d
fronzbot/scripts
cadence/getDeviceUsage.py
[ "MIT" ]
Python
run
<not_specific>
def run(treeFile, libName): """Extract dictionary of devices from tree.""" with open(treeFile, 'r') as fh: lines = fh.readlines() deviceUsage = {}; print("...Processing List.") for line in lines: line_clean = line.lstrip() line_split = line_clean.split(" ") if line_split[0] == libName: device = line_split[1] count_string = line_split[-1] count = re.sub(r'[()]', '', count_string) try: deviceUsage[device] += int(count) except KeyError: print("...Found device {}".format(device)) deviceUsage[device] = int(count) log_file = os.path.join(os.getcwd(), "log_getDeviceUsage.txt") print("...Writing to {}".format(log_file)) with open(log_file, 'w') as fh: fh.write("Output for {}\n".format(treeFile)) fh.write("---\n") for device, count in sorted(deviceUsage.items()): fh.write("{}: {}\n".format(device, count)) return
Extract dictionary of devices from tree.
Extract dictionary of devices from tree.
[ "Extract", "dictionary", "of", "devices", "from", "tree", "." ]
def run(treeFile, libName): with open(treeFile, 'r') as fh: lines = fh.readlines() deviceUsage = {}; print("...Processing List.") for line in lines: line_clean = line.lstrip() line_split = line_clean.split(" ") if line_split[0] == libName: device = line_split[1] count_string = line_split[-1] count = re.sub(r'[()]', '', count_string) try: deviceUsage[device] += int(count) except KeyError: print("...Found device {}".format(device)) deviceUsage[device] = int(count) log_file = os.path.join(os.getcwd(), "log_getDeviceUsage.txt") print("...Writing to {}".format(log_file)) with open(log_file, 'w') as fh: fh.write("Output for {}\n".format(treeFile)) fh.write("---\n") for device, count in sorted(deviceUsage.items()): fh.write("{}: {}\n".format(device, count)) return
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Extract dictionary of devices from tree.
[ "Extract", "dictionary", "of", "devices", "from", "tree", "." ]
[ "\"\"\"Extract dictionary of devices from tree.\"\"\"" ]
[ { "param": "treeFile", "type": null }, { "param": "libName", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "treeFile", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "libName", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import re import os def run(treeFile, libName): with open(treeFile, 'r') as fh: lines = fh.readlines() deviceUsage = {}; print("...Processing List.") for line in lines: line_clean = line.lstrip() line_split = line_clean.split(" ") if line_split[0] == libName: device = line_split[1] count_string = line_split[-1] count = re.sub(r'[()]', '', count_string) try: deviceUsage[device] += int(count) except KeyError: print("...Found device {}".format(device)) deviceUsage[device] = int(count) log_file = os.path.join(os.getcwd(), "log_getDeviceUsage.txt") print("...Writing to {}".format(log_file)) with open(log_file, 'w') as fh: fh.write("Output for {}\n".format(treeFile)) fh.write("---\n") for device, count in sorted(deviceUsage.items()): fh.write("{}: {}\n".format(device, count)) return
609,987
17
0533c3af9d975160fa313fd00bf1134f8453512d
addamit/youngatlas
tests/conftest.py
[ "MIT" ]
Python
ip
null
def ip(session_ip): """Verify the extension is automatically loaded.""" status = session_ip.find_cell_magic("yamagics") print ("magics status: {}".format(status)) session_ip.run_line_magic(magic_name="load_ext", line="yamagics") print ("Yielding IPython") yield session_ip # session_ip.run_line_magic(magic_name="unload_ext", line="yamagics") #session_ip.run_line_magic(magic_name="reset", line="-f") #ip.extension_manager.reload_extension('storemagic')
Verify the extension is automatically loaded.
Verify the extension is automatically loaded.
[ "Verify", "the", "extension", "is", "automatically", "loaded", "." ]
def ip(session_ip): status = session_ip.find_cell_magic("yamagics") print ("magics status: {}".format(status)) session_ip.run_line_magic(magic_name="load_ext", line="yamagics") print ("Yielding IPython") yield session_ip
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Verify the extension is automatically loaded.
[ "Verify", "the", "extension", "is", "automatically", "loaded", "." ]
[ "\"\"\"Verify the extension is automatically loaded.\"\"\"", "# session_ip.run_line_magic(magic_name=\"unload_ext\", line=\"yamagics\")", "#session_ip.run_line_magic(magic_name=\"reset\", line=\"-f\")", "#ip.extension_manager.reload_extension('storemagic')" ]
[ { "param": "session_ip", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "session_ip", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def ip(session_ip): status = session_ip.find_cell_magic("yamagics") print ("magics status: {}".format(status)) session_ip.run_line_magic(magic_name="load_ext", line="yamagics") print ("Yielding IPython") yield session_ip
609,988
551
3fa9b611e6e4f83a68a8ecd1b26f508fb06c29e8
hodgestar/fishnet
fishnet/cmds/ls.py
[ "MIT" ]
Python
ls_local_op
null
def ls_local_op(channel, shell_glob): """ Perform an ls on the local machine. """ import glob import subprocess filenames = list(glob.glob(shell_glob)) p = subprocess.Popen( ["/bin/ls"] + filenames, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() for line in stdout.splitlines(): channel.send("%s" % (line,)) for line in stderr.splitlines(): channel.send("[ERR] %s" % (line,))
Perform an ls on the local machine.
Perform an ls on the local machine.
[ "Perform", "an", "ls", "on", "the", "local", "machine", "." ]
def ls_local_op(channel, shell_glob): import glob import subprocess filenames = list(glob.glob(shell_glob)) p = subprocess.Popen( ["/bin/ls"] + filenames, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() for line in stdout.splitlines(): channel.send("%s" % (line,)) for line in stderr.splitlines(): channel.send("[ERR] %s" % (line,))
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Perform an ls on the local machine.
[ "Perform", "an", "ls", "on", "the", "local", "machine", "." ]
[ "\"\"\" Perform an ls on the local machine. \"\"\"" ]
[ { "param": "channel", "type": null }, { "param": "shell_glob", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "channel", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "shell_glob", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import subprocess import glob def ls_local_op(channel, shell_glob): import glob import subprocess filenames = list(glob.glob(shell_glob)) p = subprocess.Popen( ["/bin/ls"] + filenames, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() for line in stdout.splitlines(): channel.send("%s" % (line,)) for line in stderr.splitlines(): channel.send("[ERR] %s" % (line,))
609,990
879
f883284c6cc3f5593aca1908d8fa25ae3bddaee4
zipated/src
tools/metrics/common/pretty_print_xml.py
[ "BSD-3-Clause" ]
Python
SplitParagraphs
<not_specific>
def SplitParagraphs(text): """Split a block of text into paragraphs. Args: text: The text to split. Returns: A list of paragraphs as strings. """ text = textwrap.dedent(text.strip('\n')) lines = text.split('\n') # Split the text into paragraphs at blank line boundaries. paragraphs = [[]] for l in lines: if paragraphs[-1] and not l.strip(): paragraphs.append([]) else: paragraphs[-1].append(l) # Remove trailing empty paragraph if present. if paragraphs and not paragraphs[-1]: paragraphs = paragraphs[:-1] return ['\n'.join(p) for p in paragraphs]
Split a block of text into paragraphs. Args: text: The text to split. Returns: A list of paragraphs as strings.
Split a block of text into paragraphs.
[ "Split", "a", "block", "of", "text", "into", "paragraphs", "." ]
def SplitParagraphs(text): text = textwrap.dedent(text.strip('\n')) lines = text.split('\n') paragraphs = [[]] for l in lines: if paragraphs[-1] and not l.strip(): paragraphs.append([]) else: paragraphs[-1].append(l) if paragraphs and not paragraphs[-1]: paragraphs = paragraphs[:-1] return ['\n'.join(p) for p in paragraphs]
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Split a block of text into paragraphs.
[ "Split", "a", "block", "of", "text", "into", "paragraphs", "." ]
[ "\"\"\"Split a block of text into paragraphs.\n\n Args:\n text: The text to split.\n Returns:\n A list of paragraphs as strings.\n \"\"\"", "# Split the text into paragraphs at blank line boundaries.", "# Remove trailing empty paragraph if present." ]
[ { "param": "text", "type": null } ]
{ "returns": [ { "docstring": "A list of paragraphs as strings.", "docstring_tokens": [ "A", "list", "of", "paragraphs", "as", "strings", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "text", "type": null, "docstring": "The text to split.", "docstring_tokens": [ "The", "text", "to", "split", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import textwrap def SplitParagraphs(text): text = textwrap.dedent(text.strip('\n')) lines = text.split('\n') paragraphs = [[]] for l in lines: if paragraphs[-1] and not l.strip(): paragraphs.append([]) else: paragraphs[-1].append(l) if paragraphs and not paragraphs[-1]: paragraphs = paragraphs[:-1] return ['\n'.join(p) for p in paragraphs]
609,991
98
bfc13cc4c5bbc737270de7539fba35dbb3ec6f38
huaminglin/docker-demo
mitmproxy-demo/wsgi/script/wsgi.py
[ "MIT" ]
Python
date_time_string
<not_specific>
def date_time_string(): """Return the current date and time formatted for a message header.""" WEEKS = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] MONTHS = [ None, 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ] now = time.time() year, month, day, hh, mm, ss, wd, y_, z_ = time.gmtime(now) s = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % ( WEEKS[wd], day, MONTHS[month], year, hh, mm, ss ) return s
Return the current date and time formatted for a message header.
Return the current date and time formatted for a message header.
[ "Return", "the", "current", "date", "and", "time", "formatted", "for", "a", "message", "header", "." ]
def date_time_string(): WEEKS = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] MONTHS = [ None, 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ] now = time.time() year, month, day, hh, mm, ss, wd, y_, z_ = time.gmtime(now) s = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % ( WEEKS[wd], day, MONTHS[month], year, hh, mm, ss ) return s
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Return the current date and time formatted for a message header.
[ "Return", "the", "current", "date", "and", "time", "formatted", "for", "a", "message", "header", "." ]
[ "\"\"\"Return the current date and time formatted for a message header.\"\"\"" ]
[]
{ "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] }
import time def date_time_string(): WEEKS = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] MONTHS = [ None, 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ] now = time.time() year, month, day, hh, mm, ss, wd, y_, z_ = time.gmtime(now) s = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % ( WEEKS[wd], day, MONTHS[month], year, hh, mm, ss ) return s
609,992
662
da14967c7dc4dcf34b33a72078dc950b7b2b8cc9
vishwakftw/CS6270-TDBMS
SignedNetZoo/datasets/utils.py
[ "MIT" ]
Python
gen_bar_updater
<not_specific>
def gen_bar_updater(pbar): """ TQDM hook for progress bar during download """ def bar_update(count, block_size, total_size): if pbar.total is None and total_size: pbar.total = total_size progress_bytes = count * block_size pbar.update(progress_bytes - pbar.n) return bar_update
TQDM hook for progress bar during download
TQDM hook for progress bar during download
[ "TQDM", "hook", "for", "progress", "bar", "during", "download" ]
def gen_bar_updater(pbar): def bar_update(count, block_size, total_size): if pbar.total is None and total_size: pbar.total = total_size progress_bytes = count * block_size pbar.update(progress_bytes - pbar.n) return bar_update
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TQDM hook for progress bar during download
[ "TQDM", "hook", "for", "progress", "bar", "during", "download" ]
[ "\"\"\"\n TQDM hook for progress bar during download\n \"\"\"" ]
[ { "param": "pbar", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "pbar", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def gen_bar_updater(pbar): def bar_update(count, block_size, total_size): if pbar.total is None and total_size: pbar.total = total_size progress_bytes = count * block_size pbar.update(progress_bytes - pbar.n) return bar_update
609,994
73
26142958ee1cc6a6e3ab83760c5cbf71d7d0d4e9
iriberri/aiida_core
aiida/orm/data/structure.py
[ "BSD-2-Clause" ]
Python
calc_cell_volume
<not_specific>
def calc_cell_volume(cell): """ Calculates the volume of a cell given the three lattice vectors. It is calculated as cell[0] . (cell[1] x cell[2]), where . represents a dot product and x a cross product. :param cell: the cell vectors; the must be a 3x3 list of lists of floats, no other checks are done. :returns: the cell volume. """ # returns the volume of the primitive cell: |a1.(a2xa3)| a1 = cell[0] a2 = cell[1] a3 = cell[2] a_mid_0 = a2[1] * a3[2] - a2[2] * a3[1] a_mid_1 = a2[2] * a3[0] - a2[0] * a3[2] a_mid_2 = a2[0] * a3[1] - a2[1] * a3[0] return abs(a1[0] * a_mid_0 + a1[1] * a_mid_1 + a1[2] * a_mid_2)
Calculates the volume of a cell given the three lattice vectors. It is calculated as cell[0] . (cell[1] x cell[2]), where . represents a dot product and x a cross product. :param cell: the cell vectors; the must be a 3x3 list of lists of floats, no other checks are done. :returns: the cell volume.
Calculates the volume of a cell given the three lattice vectors. It is calculated as cell[0] .
[ "Calculates", "the", "volume", "of", "a", "cell", "given", "the", "three", "lattice", "vectors", ".", "It", "is", "calculated", "as", "cell", "[", "0", "]", "." ]
def calc_cell_volume(cell): a1 = cell[0] a2 = cell[1] a3 = cell[2] a_mid_0 = a2[1] * a3[2] - a2[2] * a3[1] a_mid_1 = a2[2] * a3[0] - a2[0] * a3[2] a_mid_2 = a2[0] * a3[1] - a2[1] * a3[0] return abs(a1[0] * a_mid_0 + a1[1] * a_mid_1 + a1[2] * a_mid_2)
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Calculates the volume of a cell given the three lattice vectors.
[ "Calculates", "the", "volume", "of", "a", "cell", "given", "the", "three", "lattice", "vectors", "." ]
[ "\"\"\"\n Calculates the volume of a cell given the three lattice vectors.\n\n It is calculated as cell[0] . (cell[1] x cell[2]), where . represents\n a dot product and x a cross product.\n\n :param cell: the cell vectors; the must be a 3x3 list of lists of floats,\n no other checks are done.\n\n :returns: the cell volume.\n \"\"\"", "# returns the volume of the primitive cell: |a1.(a2xa3)|" ]
[ { "param": "cell", "type": null } ]
{ "returns": [ { "docstring": "the cell volume.", "docstring_tokens": [ "the", "cell", "volume", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "cell", "type": null, "docstring": "the cell vectors; the must be a 3x3 list of lists of floats,\nno other checks are done.", "docstring_tokens": [ "the", "cell", "vectors", ";", "the", "must", "be", "a", "3x3", "list", "of", "lists", "of", "floats", "no", "other", "checks", "are", "done", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def calc_cell_volume(cell): a1 = cell[0] a2 = cell[1] a3 = cell[2] a_mid_0 = a2[1] * a3[2] - a2[2] * a3[1] a_mid_1 = a2[2] * a3[0] - a2[0] * a3[2] a_mid_2 = a2[0] * a3[1] - a2[1] * a3[0] return abs(a1[0] * a_mid_0 + a1[1] * a_mid_1 + a1[2] * a_mid_2)
609,995
500
4fdf5675af84bcc830436908e6fdb1163a7b95ac
linsalrob/EdwardsLab
refs and citations/orcid_vs_google.py
[ "MIT" ]
Python
message
null
def message(msg, c): """ Print a message to stderr using color :param msg: the message to print :param color: the color to use :return: nothing """ color = { 'HEADER': '\033[95m', 'OKBLUE': '\033[94m', 'OKGREEN': '\033[92m', 'WARNING': '\033[93m', 'FAIL': '\033[91m', 'ENDC': '\033[0m', 'BOLD': '\033[1m', 'UNDERLINE': '\033[4m', 'PINK': '\033[95m', 'BLUE': '\033[94m', 'GREEN': '\033[92m', 'YELLOW': '\033[93m', 'RED': '\033[91m', 'WHITE': '\033[0m', } c = c.upper() if c not in color: c = "WHITE" if os.fstat(0) == os.fstat(1): # stderr is not redirected sys.stderr.write(f"{color[c]}{msg}{color['ENDC']}\n") else: sys.stderr.write(f"{msg}\n")
Print a message to stderr using color :param msg: the message to print :param color: the color to use :return: nothing
Print a message to stderr using color
[ "Print", "a", "message", "to", "stderr", "using", "color" ]
def message(msg, c): color = { 'HEADER': '\033[95m', 'OKBLUE': '\033[94m', 'OKGREEN': '\033[92m', 'WARNING': '\033[93m', 'FAIL': '\033[91m', 'ENDC': '\033[0m', 'BOLD': '\033[1m', 'UNDERLINE': '\033[4m', 'PINK': '\033[95m', 'BLUE': '\033[94m', 'GREEN': '\033[92m', 'YELLOW': '\033[93m', 'RED': '\033[91m', 'WHITE': '\033[0m', } c = c.upper() if c not in color: c = "WHITE" if os.fstat(0) == os.fstat(1): sys.stderr.write(f"{color[c]}{msg}{color['ENDC']}\n") else: sys.stderr.write(f"{msg}\n")
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Print a message to stderr using color
[ "Print", "a", "message", "to", "stderr", "using", "color" ]
[ "\"\"\"\n Print a message to stderr using color\n :param msg: the message to print\n :param color: the color to use\n :return: nothing\n \"\"\"", "# stderr is not redirected" ]
[ { "param": "msg", "type": null }, { "param": "c", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": null } ], "raises": [], "params": [ { "identifier": "msg", "type": null, "docstring": "the message to print", "docstring_tokens": [ "the", "message", "to", "print" ], "default": null, "is_optional": null }, { "identifier": "c", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [ { "identifier": "color", "type": null, "docstring": "the color to use", "docstring_tokens": [ "the", "color", "to", "use" ], "default": null, "is_optional": null } ], "others": [] }
import sys import os def message(msg, c): color = { 'HEADER': '\033[95m', 'OKBLUE': '\033[94m', 'OKGREEN': '\033[92m', 'WARNING': '\033[93m', 'FAIL': '\033[91m', 'ENDC': '\033[0m', 'BOLD': '\033[1m', 'UNDERLINE': '\033[4m', 'PINK': '\033[95m', 'BLUE': '\033[94m', 'GREEN': '\033[92m', 'YELLOW': '\033[93m', 'RED': '\033[91m', 'WHITE': '\033[0m', } c = c.upper() if c not in color: c = "WHITE" if os.fstat(0) == os.fstat(1): sys.stderr.write(f"{color[c]}{msg}{color['ENDC']}\n") else: sys.stderr.write(f"{msg}\n")
609,996
109
07077c98b5541119393ddfc4307895a201e49a56
abdalazzezzalsalahat/data-structures-and-algorithms
python/quick_sort/quick_sort.py
[ "MIT" ]
Python
swap
null
def swap(arr, current, low): """[summary] Helper function used to shove values lower than pivot value over to the left Args: arr ([array]): [array to be sorted] current ([int]): [index of the currrent element] low ([int]): [index of the smallest element] """ temp = arr[current] arr[current] = arr[low] arr[low] = temp
[summary] Helper function used to shove values lower than pivot value over to the left Args: arr ([array]): [array to be sorted] current ([int]): [index of the currrent element] low ([int]): [index of the smallest element]
[summary] Helper function used to shove values lower than pivot value over to the left
[ "[", "summary", "]", "Helper", "function", "used", "to", "shove", "values", "lower", "than", "pivot", "value", "over", "to", "the", "left" ]
def swap(arr, current, low): temp = arr[current] arr[current] = arr[low] arr[low] = temp
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[summary] Helper function used to shove values lower than pivot value over to the left
[ "[", "summary", "]", "Helper", "function", "used", "to", "shove", "values", "lower", "than", "pivot", "value", "over", "to", "the", "left" ]
[ "\"\"\"[summary]\n Helper function used to shove values lower than pivot value over to the left\n\n Args:\n arr ([array]): [array to be sorted]\n current ([int]): [index of the currrent element]\n low ([int]): [index of the smallest element]\n \"\"\"" ]
[ { "param": "arr", "type": null }, { "param": "current", "type": null }, { "param": "low", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "arr", "type": null, "docstring": "[array to be sorted]", "docstring_tokens": [ "[", "array", "to", "be", "sorted", "]" ], "default": null, "is_optional": false }, { "identifier": "current", "type": null, "docstring": "[index of the currrent element]", "docstring_tokens": [ "[", "index", "of", "the", "currrent", "element", "]" ], "default": null, "is_optional": false }, { "identifier": "low", "type": null, "docstring": "[index of the smallest element]", "docstring_tokens": [ "[", "index", "of", "the", "smallest", "element", "]" ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def swap(arr, current, low): temp = arr[current] arr[current] = arr[low] arr[low] = temp
609,998
891
e986d38b4f211fd56d4b563c5c363bcb5e53ff9d
perceptance/deepimage
src/tinyimage/tools.py
[ "MIT" ]
Python
list_files
<not_specific>
def list_files(target_dir, fn_exts): """List all files match given extensions. Match both upper and lower cases. Args: fn_exts: a list of file extension in the form of "*.jpg". Returns: a list of found files. """ all_exts = [] for ext in fn_exts: all_exts.append(ext.lower()) all_exts.append(ext.upper()) all_exts = set(all_exts) all_fns = [] for cur_ext in all_exts: cur_fns = glob.glob(os.path.join(target_dir, cur_ext)) all_fns.extend(cur_fns) return all_fns
List all files match given extensions. Match both upper and lower cases. Args: fn_exts: a list of file extension in the form of "*.jpg". Returns: a list of found files.
List all files match given extensions. Match both upper and lower cases.
[ "List", "all", "files", "match", "given", "extensions", ".", "Match", "both", "upper", "and", "lower", "cases", "." ]
def list_files(target_dir, fn_exts): all_exts = [] for ext in fn_exts: all_exts.append(ext.lower()) all_exts.append(ext.upper()) all_exts = set(all_exts) all_fns = [] for cur_ext in all_exts: cur_fns = glob.glob(os.path.join(target_dir, cur_ext)) all_fns.extend(cur_fns) return all_fns
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List all files match given extensions.
[ "List", "all", "files", "match", "given", "extensions", "." ]
[ "\"\"\"List all files match given extensions.\n\n Match both upper and lower cases.\n\n Args:\n fn_exts: a list of file extension in the form of \"*.jpg\".\n \n Returns:\n a list of found files.\n \"\"\"" ]
[ { "param": "target_dir", "type": null }, { "param": "fn_exts", "type": null } ]
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import os import glob def list_files(target_dir, fn_exts): all_exts = [] for ext in fn_exts: all_exts.append(ext.lower()) all_exts.append(ext.upper()) all_exts = set(all_exts) all_fns = [] for cur_ext in all_exts: cur_fns = glob.glob(os.path.join(target_dir, cur_ext)) all_fns.extend(cur_fns) return all_fns
609,999
809
4751c1dacfdf0237ac5cbf65da54b3b7335b9a3e
ufbmi/onefl-deduper
onefl/utils/ui.py
[ "MIT" ]
Python
ask_yes_no
<not_specific>
def ask_yes_no(question, default="yes"): """Ask a yes/no question via raw_input() and return the answer as a boolean. :param question: the question displayed to the user :param default: the default answer if the user hits <Enter> """ valid = {"y": True, "n": False} if default is None: prompt = " [y/n] " elif default == "yes": prompt = " [Y/n] " elif default == "no": prompt = " [y/N] " else: raise ValueError("invalid default answer: '%s'" % default) while True: sys.stdout.write(question + prompt) choice = input().lower()[0] if default is not None and choice == '': return valid[default] elif choice in valid: return valid[choice] else: sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n")
Ask a yes/no question via raw_input() and return the answer as a boolean. :param question: the question displayed to the user :param default: the default answer if the user hits <Enter>
Ask a yes/no question via raw_input() and return the answer as a boolean.
[ "Ask", "a", "yes", "/", "no", "question", "via", "raw_input", "()", "and", "return", "the", "answer", "as", "a", "boolean", "." ]
def ask_yes_no(question, default="yes"): valid = {"y": True, "n": False} if default is None: prompt = " [y/n] " elif default == "yes": prompt = " [Y/n] " elif default == "no": prompt = " [y/N] " else: raise ValueError("invalid default answer: '%s'" % default) while True: sys.stdout.write(question + prompt) choice = input().lower()[0] if default is not None and choice == '': return valid[default] elif choice in valid: return valid[choice] else: sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n")
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Ask a yes/no question via raw_input() and return the answer as a boolean.
[ "Ask", "a", "yes", "/", "no", "question", "via", "raw_input", "()", "and", "return", "the", "answer", "as", "a", "boolean", "." ]
[ "\"\"\"Ask a yes/no question via raw_input() and return the answer\n as a boolean.\n\n :param question: the question displayed to the user\n :param default: the default answer if the user hits <Enter>\n\n \"\"\"" ]
[ { "param": "question", "type": null }, { "param": "default", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "question", "type": null, "docstring": "the question displayed to the user", "docstring_tokens": [ "the", "question", "displayed", "to", "the", "user" ], "default": null, "is_optional": null }, { "identifier": "default", "type": null, "docstring": "the default answer if the user hits ", "docstring_tokens": [ "the", "default", "answer", "if", "the", "user", "hits" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import sys def ask_yes_no(question, default="yes"): valid = {"y": True, "n": False} if default is None: prompt = " [y/n] " elif default == "yes": prompt = " [Y/n] " elif default == "no": prompt = " [y/N] " else: raise ValueError("invalid default answer: '%s'" % default) while True: sys.stdout.write(question + prompt) choice = input().lower()[0] if default is not None and choice == '': return valid[default] elif choice in valid: return valid[choice] else: sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n")
610,000
939
d26a1b14aa7913b4917f6a6ac72c957e36b11824
terriyu/word-clustering
cluster_pmi.py
[ "MIT" ]
Python
geometric_pmi_score
<not_specific>
def geometric_pmi_score(pdict, wlist1, wlist2): """ Calculate geometric mean of PMI over all word pairs in two word lists, given pre-computed PMI dictionary - If geometric PMI is undefined, return -inf - The geometric mean is undefined if: - Any of the PMIs are negative - None of the word pairs have a defined PMI """ product_pmi = None for word1 in wlist1: for word2 in wlist2: # Enforce alphabetical order in pair pair = tuple(sorted([word1, word2])) wi, wj = pair if wi in pdict and wj in pdict[wi]: if product_pmi is None: product_pmi = 1 pmi = pdict[wi][wj] # Check if PMI is negative if pmi > 0: product_pmi *= pmi else: product_pmi = float("-inf") break # If PMI is negative, break out of the loop completely if product_pmi == float("-inf"): break if product_pmi is None: # None of the word pairs had a defined PMI return float("-inf") elif product_pmi == float("-inf"): # At least one word pair had a negative PMI return float("-inf") else: return product_pmi ** (1/len(wlist1)/len(wlist2))
Calculate geometric mean of PMI over all word pairs in two word lists, given pre-computed PMI dictionary - If geometric PMI is undefined, return -inf - The geometric mean is undefined if: - Any of the PMIs are negative - None of the word pairs have a defined PMI
Calculate geometric mean of PMI over all word pairs in two word lists, given pre-computed PMI dictionary If geometric PMI is undefined, return -inf The geometric mean is undefined if: Any of the PMIs are negative None of the word pairs have a defined PMI
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def geometric_pmi_score(pdict, wlist1, wlist2): product_pmi = None for word1 in wlist1: for word2 in wlist2: pair = tuple(sorted([word1, word2])) wi, wj = pair if wi in pdict and wj in pdict[wi]: if product_pmi is None: product_pmi = 1 pmi = pdict[wi][wj] if pmi > 0: product_pmi *= pmi else: product_pmi = float("-inf") break if product_pmi == float("-inf"): break if product_pmi is None: return float("-inf") elif product_pmi == float("-inf"): return float("-inf") else: return product_pmi ** (1/len(wlist1)/len(wlist2))
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Calculate geometric mean of PMI over all word pairs in two word lists, given pre-computed PMI dictionary
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[ "\"\"\" Calculate geometric mean of PMI over all word pairs\n in two word lists, given pre-computed PMI dictionary\n\n - If geometric PMI is undefined, return -inf\n - The geometric mean is undefined if:\n - Any of the PMIs are negative\n - None of the word pairs have a defined PMI\n \"\"\"", "# Enforce alphabetical order in pair", "# Check if PMI is negative", "# If PMI is negative, break out of the loop completely", "# None of the word pairs had a defined PMI", "# At least one word pair had a negative PMI" ]
[ { "param": "pdict", "type": null }, { "param": "wlist1", "type": null }, { "param": "wlist2", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "pdict", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "wlist1", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "wlist2", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def geometric_pmi_score(pdict, wlist1, wlist2): product_pmi = None for word1 in wlist1: for word2 in wlist2: pair = tuple(sorted([word1, word2])) wi, wj = pair if wi in pdict and wj in pdict[wi]: if product_pmi is None: product_pmi = 1 pmi = pdict[wi][wj] if pmi > 0: product_pmi *= pmi else: product_pmi = float("-inf") break if product_pmi == float("-inf"): break if product_pmi is None: return float("-inf") elif product_pmi == float("-inf"): return float("-inf") else: return product_pmi ** (1/len(wlist1)/len(wlist2))
610,001
1,005
ad3929f62a474df3c94890d8ea491fe56ccee37f
Omerdan03/Stock_Mining
data_scraping_main.py
[ "MIT" ]
Python
calc_periods
<not_specific>
def calc_periods(from_date=datetime.date.today() - datetime.timedelta(days=365)): """This function takes the date to scrap from for the user and return period1 and period2 so it's ready to insert inside the url""" min_date = datetime.date.fromisoformat('1970-01-01') duration1 = from_date-min_date period1_sec = int(duration1.total_seconds()) duration2 = datetime.date.today()-min_date period2_sec = int(duration2.total_seconds()) return period1_sec, period2_sec
This function takes the date to scrap from for the user and return period1 and period2 so it's ready to insert inside the url
This function takes the date to scrap from for the user and return period1 and period2 so it's ready to insert inside the url
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def calc_periods(from_date=datetime.date.today() - datetime.timedelta(days=365)): min_date = datetime.date.fromisoformat('1970-01-01') duration1 = from_date-min_date period1_sec = int(duration1.total_seconds()) duration2 = datetime.date.today()-min_date period2_sec = int(duration2.total_seconds()) return period1_sec, period2_sec
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This function takes the date to scrap from for the user and return period1 and period2 so it's ready to insert inside the url
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[ "\"\"\"This function takes the date to scrap from for the user and return period1 and period2 so it's ready to insert\n inside the url\"\"\"" ]
[ { "param": "from_date", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "from_date", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import datetime def calc_periods(from_date=datetime.date.today() - datetime.timedelta(days=365)): min_date = datetime.date.fromisoformat('1970-01-01') duration1 = from_date-min_date period1_sec = int(duration1.total_seconds()) duration2 = datetime.date.today()-min_date period2_sec = int(duration2.total_seconds()) return period1_sec, period2_sec
610,002
809
77fba3b4bc26a4a2118e58ab7adca455dd843e17
EGAMAGZ/code-notes
codenotes/cli/__init__.py
[ "MIT" ]
Python
custom_print
None
def custom_print(cls, text: str, theme: Theme = None) -> None: """Class method used to print custom formatted text Parameters ---------- text : HTML Text that will be print with format similar to html theme: Theme Theme used for the text to be displayed """ print_formatted = cls(theme) print_formatted.console.print(text)
Class method used to print custom formatted text Parameters ---------- text : HTML Text that will be print with format similar to html theme: Theme Theme used for the text to be displayed
Class method used to print custom formatted text Parameters text : HTML Text that will be print with format similar to html Theme Theme used for the text to be displayed
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def custom_print(cls, text: str, theme: Theme = None) -> None: print_formatted = cls(theme) print_formatted.console.print(text)
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Class method used to print custom formatted text Parameters
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[ "\"\"\"Class method used to print custom formatted text\n Parameters\n ----------\n text : HTML\n Text that will be print with format similar to html\n\n theme: Theme\n Theme used for the text to be displayed\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "text", "type": "str" }, { "param": "theme", "type": "Theme" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "text", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "theme", "type": "Theme", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def custom_print(cls, text: str, theme: Theme = None) -> None: print_formatted = cls(theme) print_formatted.console.print(text)
610,003
426
0a7e07b4d852c4e056db46442985001279c3611d
apacha/Mensural-Detector
position_classification/extract_sub_image_for_classification.py
[ "MIT" ]
Python
compute_pad_to_force_center
(int, int, int, int)
def compute_pad_to_force_center(upper_left, lower_right, fixed_width, fixed_height, image_width, image_height) -> (int, int, int, int): """ Returns the necessary padding to force the symbol to be in the center of the image sample. This is useful when the symbol is close to the image boundaries, and so the usual procedure do not find the symbol exactly in the center of the cropped image. """ left, top = upper_left.split(",") right, bottom = lower_right.split(",") left, right, top, bottom = float(left), float(right), float(top), float(bottom) center_x = left + (right - left) / 2 center_y = top + (bottom - top) / 2 pad_left = abs( min(0, center_x - fixed_width / 2) ) pad_right = abs( min(0, image_width - (center_x + fixed_width / 2)) ) pad_top = abs( min(0, center_y - fixed_height / 2) ) pad_bottom = abs( min(0, image_height - (center_y + fixed_height / 2)) ) return int(pad_left), int(pad_right), int(pad_top), int(pad_bottom)
Returns the necessary padding to force the symbol to be in the center of the image sample. This is useful when the symbol is close to the image boundaries, and so the usual procedure do not find the symbol exactly in the center of the cropped image.
Returns the necessary padding to force the symbol to be in the center of the image sample. This is useful when the symbol is close to the image boundaries, and so the usual procedure do not find the symbol exactly in the center of the cropped image.
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def compute_pad_to_force_center(upper_left, lower_right, fixed_width, fixed_height, image_width, image_height) -> (int, int, int, int): left, top = upper_left.split(",") right, bottom = lower_right.split(",") left, right, top, bottom = float(left), float(right), float(top), float(bottom) center_x = left + (right - left) / 2 center_y = top + (bottom - top) / 2 pad_left = abs( min(0, center_x - fixed_width / 2) ) pad_right = abs( min(0, image_width - (center_x + fixed_width / 2)) ) pad_top = abs( min(0, center_y - fixed_height / 2) ) pad_bottom = abs( min(0, image_height - (center_y + fixed_height / 2)) ) return int(pad_left), int(pad_right), int(pad_top), int(pad_bottom)
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Returns the necessary padding to force the symbol to be in the center of the image sample.
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[ "\"\"\"\n Returns the necessary padding to force the symbol to be in the center of the image sample.\n This is useful when the symbol is close to the image boundaries, and so the usual procedure\n do not find the symbol exactly in the center of the cropped image.\n \"\"\"" ]
[ { "param": "upper_left", "type": null }, { "param": "lower_right", "type": null }, { "param": "fixed_width", "type": null }, { "param": "fixed_height", "type": null }, { "param": "image_width", "type": null }, { "param": "image_height", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "upper_left", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "lower_right", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "fixed_width", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "fixed_height", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "image_width", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "image_height", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def compute_pad_to_force_center(upper_left, lower_right, fixed_width, fixed_height, image_width, image_height) -> (int, int, int, int): left, top = upper_left.split(",") right, bottom = lower_right.split(",") left, right, top, bottom = float(left), float(right), float(top), float(bottom) center_x = left + (right - left) / 2 center_y = top + (bottom - top) / 2 pad_left = abs( min(0, center_x - fixed_width / 2) ) pad_right = abs( min(0, image_width - (center_x + fixed_width / 2)) ) pad_top = abs( min(0, center_y - fixed_height / 2) ) pad_bottom = abs( min(0, image_height - (center_y + fixed_height / 2)) ) return int(pad_left), int(pad_right), int(pad_top), int(pad_bottom)
610,004
584
84ce1eafed96bd992d71c47243ace519e830003f
winhamwr/django-mature-optimization
mature_optimization/parse.py
[ "BSD-3-Clause" ]
Python
convert_time
<not_specific>
def convert_time(cls, time_str): """ Convert date string to datetime object """ if cls.date_ignore_pattern: time_str = re.sub(cls.date_ignore_pattern, '', time_str) return datetime.strptime(time_str, cls.date_format)
Convert date string to datetime object
Convert date string to datetime object
[ "Convert", "date", "string", "to", "datetime", "object" ]
def convert_time(cls, time_str): if cls.date_ignore_pattern: time_str = re.sub(cls.date_ignore_pattern, '', time_str) return datetime.strptime(time_str, cls.date_format)
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Convert date string to datetime object
[ "Convert", "date", "string", "to", "datetime", "object" ]
[ "\"\"\"\n Convert date string to datetime object\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "time_str", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "time_str", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import re import datetime def convert_time(cls, time_str): if cls.date_ignore_pattern: time_str = re.sub(cls.date_ignore_pattern, '', time_str) return datetime.strptime(time_str, cls.date_format)
610,005
953
ee263d3465e3d5713326a24fccf3ba9c4ba30f03
Minsoo2022/DeepHuman
TrainingDataPreparation/util.py
[ "Unlicense" ]
Python
transform_mesh_in_place
<not_specific>
def transform_mesh_in_place(mesh, trans, scale): """ Transforms mesh Note that it will perform translation first, followed by scaling Also note that the transformation happens in-place """ mesh['v'][:, 0] += trans[0] mesh['v'][:, 1] += trans[1] mesh['v'][:, 2] += trans[2] mesh['v'] *= scale return mesh
Transforms mesh Note that it will perform translation first, followed by scaling Also note that the transformation happens in-place
Transforms mesh Note that it will perform translation first, followed by scaling Also note that the transformation happens in-place
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def transform_mesh_in_place(mesh, trans, scale): mesh['v'][:, 0] += trans[0] mesh['v'][:, 1] += trans[1] mesh['v'][:, 2] += trans[2] mesh['v'] *= scale return mesh
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Transforms mesh Note that it will perform translation first, followed by scaling Also note that the transformation happens in-place
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[ "\"\"\"\n Transforms mesh\n Note that it will perform translation first, followed by scaling\n Also note that the transformation happens in-place\n \"\"\"" ]
[ { "param": "mesh", "type": null }, { "param": "trans", "type": null }, { "param": "scale", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "mesh", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "trans", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "scale", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def transform_mesh_in_place(mesh, trans, scale): mesh['v'][:, 0] += trans[0] mesh['v'][:, 1] += trans[1] mesh['v'][:, 2] += trans[2] mesh['v'] *= scale return mesh
610,006
386
bdecd2f5875c37e7181060ca2ed474201918dc17
AustralianSynchrotron/lightflow
lightflow/config.py
[ "BSD-3-Clause" ]
Python
from_file
<not_specific>
def from_file(cls, filename, *, strict=True): """ Create a new Config object from a configuration file. Args: filename (str): The location and name of the configuration file. strict (bool): If true raises a ConfigLoadError when the configuration cannot be found. Returns: An instance of the Config class. Raises: ConfigLoadError: If the configuration cannot be found. """ config = cls() config.load_from_file(filename, strict=strict) return config
Create a new Config object from a configuration file. Args: filename (str): The location and name of the configuration file. strict (bool): If true raises a ConfigLoadError when the configuration cannot be found. Returns: An instance of the Config class. Raises: ConfigLoadError: If the configuration cannot be found.
Create a new Config object from a configuration file.
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def from_file(cls, filename, *, strict=True): config = cls() config.load_from_file(filename, strict=strict) return config
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Create a new Config object from a configuration file.
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[ "\"\"\" Create a new Config object from a configuration file.\n\n Args:\n filename (str): The location and name of the configuration file.\n strict (bool): If true raises a ConfigLoadError when the configuration\n cannot be found.\n\n Returns:\n An instance of the Config class.\n\n Raises:\n ConfigLoadError: If the configuration cannot be found.\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "filename", "type": null }, { "param": "strict", "type": null } ]
{ "returns": [ { "docstring": "An instance of the Config class.", "docstring_tokens": [ "An", "instance", "of", "the", "Config", "class", "." ], "type": null } ], "raises": [ { "docstring": "If the configuration cannot be found.", "docstring_tokens": [ "If", "the", "configuration", "cannot", "be", "found", "." ], "type": "ConfigLoadError" } ], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "filename", "type": null, "docstring": "The location and name of the configuration file.", "docstring_tokens": [ "The", "location", "and", "name", "of", "the", "configuration", "file", "." ], "default": null, "is_optional": false }, { "identifier": "strict", "type": null, "docstring": "If true raises a ConfigLoadError when the configuration\ncannot be found.", "docstring_tokens": [ "If", "true", "raises", "a", "ConfigLoadError", "when", "the", "configuration", "cannot", "be", "found", "." ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def from_file(cls, filename, *, strict=True): config = cls() config.load_from_file(filename, strict=strict) return config
610,007
426
07fd24e478ec9f223d2f3f7411dea43d19b3e25e
DNA-and-Natural-Algorithms-Group/crnverifier
crnverifier/utils.py
[ "MIT" ]
Python
crnsize
<not_specific>
def crnsize(crn): """ The size of a CRN as defined in JDW 2019. """ sp = set().union(*[set().union(*rxn[:2]) for rxn in crn]) tot = 0 for r, p in crn: for s in sp: tot += math.ceil(math.log2(len([x for x in r if x == s]) + 1)) + \ math.ceil(math.log2(len([x for x in p if x == s]) + 1)) return tot + len(sp)
The size of a CRN as defined in JDW 2019.
The size of a CRN as defined in JDW 2019.
[ "The", "size", "of", "a", "CRN", "as", "defined", "in", "JDW", "2019", "." ]
def crnsize(crn): sp = set().union(*[set().union(*rxn[:2]) for rxn in crn]) tot = 0 for r, p in crn: for s in sp: tot += math.ceil(math.log2(len([x for x in r if x == s]) + 1)) + \ math.ceil(math.log2(len([x for x in p if x == s]) + 1)) return tot + len(sp)
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The size of a CRN as defined in JDW 2019.
[ "The", "size", "of", "a", "CRN", "as", "defined", "in", "JDW", "2019", "." ]
[ "\"\"\" The size of a CRN as defined in JDW 2019.\n \"\"\"" ]
[ { "param": "crn", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "crn", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import math def crnsize(crn): sp = set().union(*[set().union(*rxn[:2]) for rxn in crn]) tot = 0 for r, p in crn: for s in sp: tot += math.ceil(math.log2(len([x for x in r if x == s]) + 1)) + \ math.ceil(math.log2(len([x for x in p if x == s]) + 1)) return tot + len(sp)
610,009
1,004
1f45c58714497a9a342e027bce380af016204eba
GrzegorzMika/Agriculture
cleaner.py
[ "MIT" ]
Python
run_cleanup
None
def run_cleanup(files: List[str], path: str) -> None: """ Remove all files specified by files. :param files: list of files to remove :param path: path to the directory containing the files """ files = [os.path.join(path, file) for file in files] for file in files: os.remove(file)
Remove all files specified by files. :param files: list of files to remove :param path: path to the directory containing the files
Remove all files specified by files.
[ "Remove", "all", "files", "specified", "by", "files", "." ]
def run_cleanup(files: List[str], path: str) -> None: files = [os.path.join(path, file) for file in files] for file in files: os.remove(file)
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Remove all files specified by files.
[ "Remove", "all", "files", "specified", "by", "files", "." ]
[ "\"\"\"\n Remove all files specified by files.\n :param files: list of files to remove\n :param path: path to the directory containing the files\n \"\"\"" ]
[ { "param": "files", "type": "List[str]" }, { "param": "path", "type": "str" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "files", "type": "List[str]", "docstring": "list of files to remove", "docstring_tokens": [ "list", "of", "files", "to", "remove" ], "default": null, "is_optional": null }, { "identifier": "path", "type": "str", "docstring": "path to the directory containing the files", "docstring_tokens": [ "path", "to", "the", "directory", "containing", "the", "files" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def run_cleanup(files: List[str], path: str) -> None: files = [os.path.join(path, file) for file in files] for file in files: os.remove(file)
610,010
57
62e69a223673fb81d99441900d630d9cc409656c
locationlabs/confab
confab/files.py
[ "Apache-2.0" ]
Python
_clear_dir
null
def _clear_dir(dir_name): """ Remove an entire directory tree. """ if os.path.isdir(dir_name): shutil.rmtree(dir_name)
Remove an entire directory tree.
Remove an entire directory tree.
[ "Remove", "an", "entire", "directory", "tree", "." ]
def _clear_dir(dir_name): if os.path.isdir(dir_name): shutil.rmtree(dir_name)
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Remove an entire directory tree.
[ "Remove", "an", "entire", "directory", "tree", "." ]
[ "\"\"\"\n Remove an entire directory tree.\n \"\"\"" ]
[ { "param": "dir_name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "dir_name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import shutil import os def _clear_dir(dir_name): if os.path.isdir(dir_name): shutil.rmtree(dir_name)
610,011
487
5bfebfc6fbfcca44cd5ccc0297f734e7d5ad2218
mktt2897/photon_correlation
python/photon_correlation/util.py
[ "BSD-3-Clause" ]
Python
unique_dots
null
def unique_dots(dots): """ Group distinct runs for single dots to enable averaging over all data. """ keys = set(map(lambda x: x.dot_key(), dots)) for key in sorted(keys): yield(list(filter(lambda x: x.dot_key() == key, dots)))
Group distinct runs for single dots to enable averaging over all data.
Group distinct runs for single dots to enable averaging over all data.
[ "Group", "distinct", "runs", "for", "single", "dots", "to", "enable", "averaging", "over", "all", "data", "." ]
def unique_dots(dots): keys = set(map(lambda x: x.dot_key(), dots)) for key in sorted(keys): yield(list(filter(lambda x: x.dot_key() == key, dots)))
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Group distinct runs for single dots to enable averaging over all data.
[ "Group", "distinct", "runs", "for", "single", "dots", "to", "enable", "averaging", "over", "all", "data", "." ]
[ "\"\"\"\n Group distinct runs for single dots to enable averaging over all data.\n \"\"\"" ]
[ { "param": "dots", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "dots", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def unique_dots(dots): keys = set(map(lambda x: x.dot_key(), dots)) for key in sorted(keys): yield(list(filter(lambda x: x.dot_key() == key, dots)))
610,012
397
1f75e9207de777fc2d47bfb98f4328f5ddcc542b
google/fiddle
fiddle/lingvo/lingvo_config.py
[ "Apache-2.0" ]
Python
_convert_lingvo_param
inspect.Parameter
def _convert_lingvo_param( name: str, param: hyperparams._Param, # pylint: disable=protected-access ) -> inspect.Parameter: """Returns an inspect.Parameter that corresponds to `param`.""" return inspect.Parameter( name=name, kind=inspect.Parameter.KEYWORD_ONLY, default=param.GetDefault())
Returns an inspect.Parameter that corresponds to `param`.
Returns an inspect.Parameter that corresponds to `param`.
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def _convert_lingvo_param( name: str, param: hyperparams._Param, ) -> inspect.Parameter: return inspect.Parameter( name=name, kind=inspect.Parameter.KEYWORD_ONLY, default=param.GetDefault())
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Returns an inspect.Parameter that corresponds to `param`.
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[ "# pylint: disable=protected-access", "\"\"\"Returns an inspect.Parameter that corresponds to `param`.\"\"\"" ]
[ { "param": "name", "type": "str" }, { "param": "param", "type": "hyperparams._Param" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "name", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "param", "type": "hyperparams._Param", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import inspect def _convert_lingvo_param( name: str, param: hyperparams._Param, ) -> inspect.Parameter: return inspect.Parameter( name=name, kind=inspect.Parameter.KEYWORD_ONLY, default=param.GetDefault())
610,013
759
b57856cb3ab8bb6253be18c5e6fdd79e34c97e52
anonymous-authors-2018/robotics-repo
rl_baselines/evaluation/cross_eval_utils.py
[ "MIT" ]
Python
latestPolicy
<not_specific>
def latestPolicy(log_dir,algo_name): """ Get the latest saved model from a file :param log_dir: (str) a path leads to the model saved path :param algo_name: :return: the file name of the latest saved policy and a flag """ files= glob.glob(os.path.join(log_dir+algo_name+'_*_model.pkl')) files_list = [] for file in files: eps=int((file.split('_')[-2])) files_list.append((eps,file)) def sortFirst(val): return val[0] files_list.sort(key=sortFirst) if len(files_list)>0: #episode,latest model file path, OK return files_list[-1][0],files_list[-1][1],True else: #No model saved yet return 0,'',False
Get the latest saved model from a file :param log_dir: (str) a path leads to the model saved path :param algo_name: :return: the file name of the latest saved policy and a flag
Get the latest saved model from a file
[ "Get", "the", "latest", "saved", "model", "from", "a", "file" ]
def latestPolicy(log_dir,algo_name): files= glob.glob(os.path.join(log_dir+algo_name+'_*_model.pkl')) files_list = [] for file in files: eps=int((file.split('_')[-2])) files_list.append((eps,file)) def sortFirst(val): return val[0] files_list.sort(key=sortFirst) if len(files_list)>0: return files_list[-1][0],files_list[-1][1],True else: return 0,'',False
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Get the latest saved model from a file
[ "Get", "the", "latest", "saved", "model", "from", "a", "file" ]
[ "\"\"\"\n Get the latest saved model from a file\n :param log_dir: (str) a path leads to the model saved path\n :param algo_name:\n :return: the file name of the latest saved policy and a flag\n \"\"\"", "#episode,latest model file path, OK", "#No model saved yet" ]
[ { "param": "log_dir", "type": null }, { "param": "algo_name", "type": null } ]
{ "returns": [ { "docstring": "the file name of the latest saved policy and a flag", "docstring_tokens": [ "the", "file", "name", "of", "the", "latest", "saved", "policy", "and", "a", "flag" ], "type": null } ], "raises": [], "params": [ { "identifier": "log_dir", "type": null, "docstring": "(str) a path leads to the model saved path", "docstring_tokens": [ "(", "str", ")", "a", "path", "leads", "to", "the", "model", "saved", "path" ], "default": null, "is_optional": null }, { "identifier": "algo_name", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os import glob def latestPolicy(log_dir,algo_name): files= glob.glob(os.path.join(log_dir+algo_name+'_*_model.pkl')) files_list = [] for file in files: eps=int((file.split('_')[-2])) files_list.append((eps,file)) def sortFirst(val): return val[0] files_list.sort(key=sortFirst) if len(files_list)>0: return files_list[-1][0],files_list[-1][1],True else: return 0,'',False
610,014
879
1eac5b92a78c6512954ee3cd47cf85a9f22fd13b
googleads/googleads-adxbuyer-examples
python/samples/samples_util.py
[ "Apache-2.0" ]
Python
_IsLegacy
<not_specific>
def _IsLegacy(discovery_rest_url): """Returns whether the given discovery URL uses legacy discovery. Args: discovery_rest_url: a str containing a discovery URL for an API. Returns: True if the discovery URL is determined to be a legacy, otherwise False. """ if 'googleapis.com/$discovery' in discovery_rest_url: return False return True
Returns whether the given discovery URL uses legacy discovery. Args: discovery_rest_url: a str containing a discovery URL for an API. Returns: True if the discovery URL is determined to be a legacy, otherwise False.
Returns whether the given discovery URL uses legacy discovery.
[ "Returns", "whether", "the", "given", "discovery", "URL", "uses", "legacy", "discovery", "." ]
def _IsLegacy(discovery_rest_url): if 'googleapis.com/$discovery' in discovery_rest_url: return False return True
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Returns whether the given discovery URL uses legacy discovery.
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[ "\"\"\"Returns whether the given discovery URL uses legacy discovery.\n\n Args:\n discovery_rest_url: a str containing a discovery URL for an API.\n\n Returns:\n True if the discovery URL is determined to be a legacy, otherwise False.\n \"\"\"" ]
[ { "param": "discovery_rest_url", "type": null } ]
{ "returns": [ { "docstring": "True if the discovery URL is determined to be a legacy, otherwise False.", "docstring_tokens": [ "True", "if", "the", "discovery", "URL", "is", "determined", "to", "be", "a", "legacy", "otherwise", "False", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "discovery_rest_url", "type": null, "docstring": "a str containing a discovery URL for an API.", "docstring_tokens": [ "a", "str", "containing", "a", "discovery", "URL", "for", "an", "API", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _IsLegacy(discovery_rest_url): if 'googleapis.com/$discovery' in discovery_rest_url: return False return True
610,015
213
c1a1ea83bff0147eb7720979a01d03975fbf70c2
coderefinery/autocmake
autocmake/configure.py
[ "BSD-3-Clause" ]
Python
add_quotes_to_argv
<not_specific>
def add_quotes_to_argv(argv, arguments): """ This function tries to solve this problem: https://stackoverflow.com/questions/19120247/python-sys-argv-to-preserve-or The problem is that sys.argv has been stripped of quotes by the shell but docopt's arguments contains quotes. So what we do is cycle through all docopt arguments: if they are also present in sys.argv and contain spaces, we add quotes. """ setup_command = ' '.join(argv[:]) for k, v in arguments.items(): if isinstance(v, str): if ' ' in v: if v in setup_command: setup_command = setup_command.replace(v, '"{}"'.format(v)) return setup_command
This function tries to solve this problem: https://stackoverflow.com/questions/19120247/python-sys-argv-to-preserve-or The problem is that sys.argv has been stripped of quotes by the shell but docopt's arguments contains quotes. So what we do is cycle through all docopt arguments: if they are also present in sys.argv and contain spaces, we add quotes.
The problem is that sys.argv has been stripped of quotes by the shell but docopt's arguments contains quotes. So what we do is cycle through all docopt arguments: if they are also present in sys.argv and contain spaces, we add quotes.
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def add_quotes_to_argv(argv, arguments): setup_command = ' '.join(argv[:]) for k, v in arguments.items(): if isinstance(v, str): if ' ' in v: if v in setup_command: setup_command = setup_command.replace(v, '"{}"'.format(v)) return setup_command
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This function tries to solve this problem: https://stackoverflow.com/questions/19120247/python-sys-argv-to-preserve-or
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[ "\"\"\"\n This function tries to solve this problem:\n https://stackoverflow.com/questions/19120247/python-sys-argv-to-preserve-or\n\n The problem is that sys.argv has been stripped of quotes by the shell but\n docopt's arguments contains quotes.\n\n So what we do is cycle through all docopt arguments: if they are also\n present in sys.argv and contain spaces, we add quotes.\n \"\"\"" ]
[ { "param": "argv", "type": null }, { "param": "arguments", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "argv", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "arguments", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def add_quotes_to_argv(argv, arguments): setup_command = ' '.join(argv[:]) for k, v in arguments.items(): if isinstance(v, str): if ' ' in v: if v in setup_command: setup_command = setup_command.replace(v, '"{}"'.format(v)) return setup_command
610,016
35
f3b1f085018b7be86781174b7c7c7d7fe0fc5546
ProsperousRF/100DaysOfPython
Day10/main.py
[ "Unlicense" ]
Python
format_name
<not_specific>
def format_name(f_name, l_name): """Take a first and last name and format it to return the title case version of the name.""" if f_name == "" or l_name == "": return "You didn't provide valid inputs." formated_f_name = f_name.title() formated_l_name = l_name.title() return f"Result: {formated_f_name} {formated_l_name}"
Take a first and last name and format it to return the title case version of the name.
Take a first and last name and format it to return the title case version of the name.
[ "Take", "a", "first", "and", "last", "name", "and", "format", "it", "to", "return", "the", "title", "case", "version", "of", "the", "name", "." ]
def format_name(f_name, l_name): if f_name == "" or l_name == "": return "You didn't provide valid inputs." formated_f_name = f_name.title() formated_l_name = l_name.title() return f"Result: {formated_f_name} {formated_l_name}"
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Take a first and last name and format it to return the title case version of the name.
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[ "\"\"\"Take a first and last name and format it\n to return the title case version of the name.\"\"\"" ]
[ { "param": "f_name", "type": null }, { "param": "l_name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "f_name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "l_name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def format_name(f_name, l_name): if f_name == "" or l_name == "": return "You didn't provide valid inputs." formated_f_name = f_name.title() formated_l_name = l_name.title() return f"Result: {formated_f_name} {formated_l_name}"
610,017
923
d854324e4888285ddafe6f4cda69f2911e5d1f4b
bglar/commcare-hq
corehq/apps/es/filters.py
[ "BSD-3-Clause" ]
Python
exists
<not_specific>
def exists(field): """ Only return docs which have 'field' """ return {"exists": {"field": field}}
Only return docs which have 'field'
Only return docs which have 'field'
[ "Only", "return", "docs", "which", "have", "'", "field", "'" ]
def exists(field): return {"exists": {"field": field}}
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Only return docs which have 'field'
[ "Only", "return", "docs", "which", "have", "'", "field", "'" ]
[ "\"\"\"\n Only return docs which have 'field'\n \"\"\"" ]
[ { "param": "field", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "field", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def exists(field): return {"exists": {"field": field}}
610,018
305
8d183dd7aebf6592a80bac24422ace1d1ce4f021
noedtwins/DiscogsTagger
discogstagger/ext/mediafile.py
[ "MIT" ]
Python
_unpack_asf_image
<not_specific>
def _unpack_asf_image(data): """Unpack image data from a WM/Picture tag. Return a tuple containing the MIME type, the raw image data, a type indicator, and the image's description. This function is treated as "untrusted" and could throw all manner of exceptions (out-of-bounds, etc.). We should clean this up sometime so that the failure modes are well-defined. """ type, size = struct.unpack_from("<bi", data) pos = 5 mime = "" while data[pos:pos+2] != "\x00\x00": mime += data[pos:pos+2] pos += 2 pos += 2 description = "" while data[pos:pos+2] != "\x00\x00": description += data[pos:pos+2] pos += 2 pos += 2 image_data = data[pos:pos+size] return (mime.decode("utf-16-le"), image_data, type, description.decode("utf-16-le"))
Unpack image data from a WM/Picture tag. Return a tuple containing the MIME type, the raw image data, a type indicator, and the image's description. This function is treated as "untrusted" and could throw all manner of exceptions (out-of-bounds, etc.). We should clean this up sometime so that the failure modes are well-defined.
Unpack image data from a WM/Picture tag. Return a tuple containing the MIME type, the raw image data, a type indicator, and the image's description. This function is treated as "untrusted" and could throw all manner of exceptions (out-of-bounds, etc.). We should clean this up sometime so that the failure modes are well-defined.
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def _unpack_asf_image(data): type, size = struct.unpack_from("<bi", data) pos = 5 mime = "" while data[pos:pos+2] != "\x00\x00": mime += data[pos:pos+2] pos += 2 pos += 2 description = "" while data[pos:pos+2] != "\x00\x00": description += data[pos:pos+2] pos += 2 pos += 2 image_data = data[pos:pos+size] return (mime.decode("utf-16-le"), image_data, type, description.decode("utf-16-le"))
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Unpack image data from a WM/Picture tag.
[ "Unpack", "image", "data", "from", "a", "WM", "/", "Picture", "tag", "." ]
[ "\"\"\"Unpack image data from a WM/Picture tag. Return a tuple\n containing the MIME type, the raw image data, a type indicator, and\n the image's description.\n\n This function is treated as \"untrusted\" and could throw all manner\n of exceptions (out-of-bounds, etc.). We should clean this up\n sometime so that the failure modes are well-defined.\n \"\"\"" ]
[ { "param": "data", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "data", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import struct def _unpack_asf_image(data): type, size = struct.unpack_from("<bi", data) pos = 5 mime = "" while data[pos:pos+2] != "\x00\x00": mime += data[pos:pos+2] pos += 2 pos += 2 description = "" while data[pos:pos+2] != "\x00\x00": description += data[pos:pos+2] pos += 2 pos += 2 image_data = data[pos:pos+size] return (mime.decode("utf-16-le"), image_data, type, description.decode("utf-16-le"))
610,019
614
460c3005a109ede83fce297234f17c4384477dcf
GinoBacallao/genepy
genepy/mutations/__init__.py
[ "Apache-2.0" ]
Python
checkGeneChangeAccrossAll
<not_specific>
def checkGeneChangeAccrossAll(genecn, thresh=0.2): """ used to find poor quality genes in CN data (works with multiple sample file) compute given a df of gene x sample CN counts, how much change there is accross samples for a same gene and returns ones that are below the threshold Args: ----- genecn: gene cn data frame thresh: threshold in logfold change accross all of them """ return genecn.columns[genecn.var()<thresh].tolist()
used to find poor quality genes in CN data (works with multiple sample file) compute given a df of gene x sample CN counts, how much change there is accross samples for a same gene and returns ones that are below the threshold Args: ----- genecn: gene cn data frame thresh: threshold in logfold change accross all of them
used to find poor quality genes in CN data (works with multiple sample file) compute given a df of gene x sample CN counts, how much change there is accross samples for a same gene and returns ones that are below the threshold
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def checkGeneChangeAccrossAll(genecn, thresh=0.2): return genecn.columns[genecn.var()<thresh].tolist()
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used to find poor quality genes in CN data (works with multiple sample file) compute given a df of gene x sample CN counts, how much change there is accross samples for a same gene and returns ones that are below the threshold
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[ "\"\"\"\n used to find poor quality genes in CN data (works with multiple sample file)\n\n compute given a df of gene x sample CN counts, how much change there is accross samples for\n a same gene and returns ones that are below the threshold\n\n Args:\n -----\n genecn: gene cn data frame\n thresh: threshold in logfold change accross all of them\n \"\"\"" ]
[ { "param": "genecn", "type": null }, { "param": "thresh", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "genecn", "type": null, "docstring": "gene cn data frame", "docstring_tokens": [ "gene", "cn", "data", "frame" ], "default": null, "is_optional": null }, { "identifier": "thresh", "type": null, "docstring": "threshold in logfold change accross all of them", "docstring_tokens": [ "threshold", "in", "logfold", "change", "accross", "all", "of", "them" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def checkGeneChangeAccrossAll(genecn, thresh=0.2): return genecn.columns[genecn.var()<thresh].tolist()
610,020
804
0ee5b18c7cec100225348edd7ef76d2d9cfd0441
wellcometrust/catalogue
reindexer/get_reindex_status.py
[ "MIT" ]
Python
count_items_in_table
<not_specific>
def count_items_in_table(session, *, table_name): """ Returns an approximate number of items in a table. """ dynamodb = session.client("dynamodb") return dynamodb.describe_table(TableName=table_name)["Table"]["ItemCount"]
Returns an approximate number of items in a table.
Returns an approximate number of items in a table.
[ "Returns", "an", "approximate", "number", "of", "items", "in", "a", "table", "." ]
def count_items_in_table(session, *, table_name): dynamodb = session.client("dynamodb") return dynamodb.describe_table(TableName=table_name)["Table"]["ItemCount"]
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Returns an approximate number of items in a table.
[ "Returns", "an", "approximate", "number", "of", "items", "in", "a", "table", "." ]
[ "\"\"\"\n Returns an approximate number of items in a table.\n \"\"\"" ]
[ { "param": "session", "type": null }, { "param": "table_name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "session", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "table_name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def count_items_in_table(session, *, table_name): dynamodb = session.client("dynamodb") return dynamodb.describe_table(TableName=table_name)["Table"]["ItemCount"]
610,022
729
b73523022b4d5b95099da8240120f252558c3a6d
Maia-CA/COSMO
src/utils/ml_utils.py
[ "MIT" ]
Python
load_dict
<not_specific>
def load_dict(fname, var_names, load_func=pickle.load): """ Loads specific keys from a dictionary that was to a file :type fname: file name :type var_names: variables to retrieve. Can be a list or comma seperated string e.g. 'a, b,c' or ['a', 'b', 'c'] :param load_func: default: pickle.load """ if type(var_names) == str: var_names = re.split(', ?[, ]?', var_names) with open(fname, "rb") as f: data_dict = load_func(f) assert isinstance(data_dict, dict) return tuple([data_dict[var] for var in var_names])
Loads specific keys from a dictionary that was to a file :type fname: file name :type var_names: variables to retrieve. Can be a list or comma seperated string e.g. 'a, b,c' or ['a', 'b', 'c'] :param load_func: default: pickle.load
Loads specific keys from a dictionary that was to a file
[ "Loads", "specific", "keys", "from", "a", "dictionary", "that", "was", "to", "a", "file" ]
def load_dict(fname, var_names, load_func=pickle.load): if type(var_names) == str: var_names = re.split(', ?[, ]?', var_names) with open(fname, "rb") as f: data_dict = load_func(f) assert isinstance(data_dict, dict) return tuple([data_dict[var] for var in var_names])
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Loads specific keys from a dictionary that was to a file
[ "Loads", "specific", "keys", "from", "a", "dictionary", "that", "was", "to", "a", "file" ]
[ "\"\"\" Loads specific keys from a dictionary that was to a file\n :type fname: file name\n :type var_names: variables to retrieve. Can be a list or comma seperated string\n e.g. 'a, b,c' or ['a', 'b', 'c']\n :param load_func: default: pickle.load\n \"\"\"" ]
[ { "param": "fname", "type": null }, { "param": "var_names", "type": null }, { "param": "load_func", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "fname", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "var_names", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "load_func", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import re import pickle def load_dict(fname, var_names, load_func=pickle.load): if type(var_names) == str: var_names = re.split(', ?[, ]?', var_names) with open(fname, "rb") as f: data_dict = load_func(f) assert isinstance(data_dict, dict) return tuple([data_dict[var] for var in var_names])
610,023
926
de354b9cbd8b6875576aa46a0a8a4930d50ddf62
angry-tony/ClusterRunner
app/util/fs.py
[ "Apache-2.0" ]
Python
zip_directory
str
def zip_directory(target_dir: str, archive_filename: str) -> str: """ Zip up the specified directory and stick the resulting zip file in that directory. :param target_dir: the directory to zip and the location of the resulting zip file :param archive_filename: filename for the created zip file :return: the full path to the created zip archive file """ # Create the archive in a temp location and then move it to the target dir. # (Otherwise the resulting archive will include an extra zero-byte file.) target_path = os.path.join(target_dir, archive_filename) with tempfile.TemporaryDirectory() as temp_dirpath: tmp_zip_filename = os.path.join(temp_dirpath, 'clusterrunner_tmp__' + archive_filename) with zipfile.ZipFile(tmp_zip_filename, 'w', compression=zipfile.ZIP_DEFLATED) as zf: for dirpath, dirnames, filenames in os.walk(target_dir): for filename in filenames: path = os.path.normpath(os.path.join(dirpath, filename)) if os.path.isfile(path): relpath = os.path.relpath(path, target_dir) zf.write(path, relpath) shutil.move(tmp_zip_filename, target_path) return target_path
Zip up the specified directory and stick the resulting zip file in that directory. :param target_dir: the directory to zip and the location of the resulting zip file :param archive_filename: filename for the created zip file :return: the full path to the created zip archive file
Zip up the specified directory and stick the resulting zip file in that directory.
[ "Zip", "up", "the", "specified", "directory", "and", "stick", "the", "resulting", "zip", "file", "in", "that", "directory", "." ]
def zip_directory(target_dir: str, archive_filename: str) -> str: target_path = os.path.join(target_dir, archive_filename) with tempfile.TemporaryDirectory() as temp_dirpath: tmp_zip_filename = os.path.join(temp_dirpath, 'clusterrunner_tmp__' + archive_filename) with zipfile.ZipFile(tmp_zip_filename, 'w', compression=zipfile.ZIP_DEFLATED) as zf: for dirpath, dirnames, filenames in os.walk(target_dir): for filename in filenames: path = os.path.normpath(os.path.join(dirpath, filename)) if os.path.isfile(path): relpath = os.path.relpath(path, target_dir) zf.write(path, relpath) shutil.move(tmp_zip_filename, target_path) return target_path
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Zip up the specified directory and stick the resulting zip file in that directory.
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[ "\"\"\"\n Zip up the specified directory and stick the resulting zip file in that directory.\n :param target_dir: the directory to zip and the location of the resulting zip file\n :param archive_filename: filename for the created zip file\n :return: the full path to the created zip archive file\n \"\"\"", "# Create the archive in a temp location and then move it to the target dir.", "# (Otherwise the resulting archive will include an extra zero-byte file.)" ]
[ { "param": "target_dir", "type": "str" }, { "param": "archive_filename", "type": "str" } ]
{ "returns": [ { "docstring": "the full path to the created zip archive file", "docstring_tokens": [ "the", "full", "path", "to", "the", "created", "zip", "archive", "file" ], "type": null } ], "raises": [], "params": [ { "identifier": "target_dir", "type": "str", "docstring": "the directory to zip and the location of the resulting zip file", "docstring_tokens": [ "the", "directory", "to", "zip", "and", "the", "location", "of", "the", "resulting", "zip", "file" ], "default": null, "is_optional": null }, { "identifier": "archive_filename", "type": "str", "docstring": "filename for the created zip file", "docstring_tokens": [ "filename", "for", "the", "created", "zip", "file" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import zipfile import tempfile import shutil import os def zip_directory(target_dir: str, archive_filename: str) -> str: target_path = os.path.join(target_dir, archive_filename) with tempfile.TemporaryDirectory() as temp_dirpath: tmp_zip_filename = os.path.join(temp_dirpath, 'clusterrunner_tmp__' + archive_filename) with zipfile.ZipFile(tmp_zip_filename, 'w', compression=zipfile.ZIP_DEFLATED) as zf: for dirpath, dirnames, filenames in os.walk(target_dir): for filename in filenames: path = os.path.normpath(os.path.join(dirpath, filename)) if os.path.isfile(path): relpath = os.path.relpath(path, target_dir) zf.write(path, relpath) shutil.move(tmp_zip_filename, target_path) return target_path
610,024
283
b91dc02d822cd0efae2076666373864943d39795
zeekay/chii
chii.py
[ "Unlicense" ]
Python
event
<not_specific>
def event(*event_types): """Decorator which adds callable to the event registry""" def decorator(func): def wrapper(*func_args, **func_kwargs): return func(*func_args, **func_kwargs) wrapper._registry = 'events' wrapper._event_types = event_types wrapper.__name__ = func.__name__ wrapper.__doc__ = func.__doc__ wrapper.__hash__ = lambda *args: zlib.crc32(func.__name__) return wrapper return decorator
Decorator which adds callable to the event registry
Decorator which adds callable to the event registry
[ "Decorator", "which", "adds", "callable", "to", "the", "event", "registry" ]
def event(*event_types): def decorator(func): def wrapper(*func_args, **func_kwargs): return func(*func_args, **func_kwargs) wrapper._registry = 'events' wrapper._event_types = event_types wrapper.__name__ = func.__name__ wrapper.__doc__ = func.__doc__ wrapper.__hash__ = lambda *args: zlib.crc32(func.__name__) return wrapper return decorator
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Decorator which adds callable to the event registry
[ "Decorator", "which", "adds", "callable", "to", "the", "event", "registry" ]
[ "\"\"\"Decorator which adds callable to the event registry\"\"\"" ]
[]
{ "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] }
import zlib def event(*event_types): def decorator(func): def wrapper(*func_args, **func_kwargs): return func(*func_args, **func_kwargs) wrapper._registry = 'events' wrapper._event_types = event_types wrapper.__name__ = func.__name__ wrapper.__doc__ = func.__doc__ wrapper.__hash__ = lambda *args: zlib.crc32(func.__name__) return wrapper return decorator
610,025
117
247584f70c21dd1ded00a46ff76271c09a5064d7
sakshamkumar-byt/sdc
sdc/rewrites/ir_utils.py
[ "BSD-2-Clause" ]
Python
filter_block_statements
null
def filter_block_statements(block, stmt_types): """ Filters given block returning statments of specific type """ for stmt in block.body: if isinstance(stmt, stmt_types): yield stmt
Filters given block returning statments of specific type
Filters given block returning statments of specific type
[ "Filters", "given", "block", "returning", "statments", "of", "specific", "type" ]
def filter_block_statements(block, stmt_types): for stmt in block.body: if isinstance(stmt, stmt_types): yield stmt
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Filters given block returning statments of specific type
[ "Filters", "given", "block", "returning", "statments", "of", "specific", "type" ]
[ "\"\"\"\n Filters given block returning statments of specific type\n \"\"\"" ]
[ { "param": "block", "type": null }, { "param": "stmt_types", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "block", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "stmt_types", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def filter_block_statements(block, stmt_types): for stmt in block.body: if isinstance(stmt, stmt_types): yield stmt
610,026
787
d829acd7daadf7b648e39df2834858685c84e583
rpuntaie/rstdoc
rstdoc/retable.py
[ "MIT" ]
Python
pad_fields
<not_specific>
def pad_fields(row, widths): """Pads fields of the given row, so each field lines up nicely with the others. """ widths = list(map(lambda w: ' %-' + str(w) + 's ', widths)) # Pad all fields using the calculated widths new_row = [] for i in range(len(row)): col = row[i] col = widths[i] % col.strip() new_row.append(col) return new_row
Pads fields of the given row, so each field lines up nicely with the others.
Pads fields of the given row, so each field lines up nicely with the others.
[ "Pads", "fields", "of", "the", "given", "row", "so", "each", "field", "lines", "up", "nicely", "with", "the", "others", "." ]
def pad_fields(row, widths): widths = list(map(lambda w: ' %-' + str(w) + 's ', widths)) new_row = [] for i in range(len(row)): col = row[i] col = widths[i] % col.strip() new_row.append(col) return new_row
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Pads fields of the given row, so each field lines up nicely with the others.
[ "Pads", "fields", "of", "the", "given", "row", "so", "each", "field", "lines", "up", "nicely", "with", "the", "others", "." ]
[ "\"\"\"Pads fields of the given row, so each field lines up nicely with the\n others.\n\n \"\"\"", "# Pad all fields using the calculated widths" ]
[ { "param": "row", "type": null }, { "param": "widths", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "row", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "widths", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def pad_fields(row, widths): widths = list(map(lambda w: ' %-' + str(w) + 's ', widths)) new_row = [] for i in range(len(row)): col = row[i] col = widths[i] % col.strip() new_row.append(col) return new_row
610,027
929
88ce0ee7d31c9bb2baebcc5ec319e40b5f29d949
ai-se/x-effort
Technix/sk.py
[ "MIT" ]
Python
leftRight
null
def leftRight(parts,epsilon=0.01): """Iterator. For all items in 'parts', return everything to the left and everything from here to the end. For reasons of efficiency, take a first pass over the data to pre-compute and cache right-hand-sides """ rights = {} n = j = len(parts) - 1 while j > 0: rights[j] = parts[j] if j < n: rights[j] += rights[j+1] j -=1 left = parts[0] for i,one in enumerate(parts): if i> 0: if parts[i]._median - parts[i-1]._median > epsilon: yield i,left,rights[i] left += one
Iterator. For all items in 'parts', return everything to the left and everything from here to the end. For reasons of efficiency, take a first pass over the data to pre-compute and cache right-hand-sides
Iterator. For all items in 'parts', return everything to the left and everything from here to the end. For reasons of efficiency, take a first pass over the data to pre-compute and cache right-hand-sides
[ "Iterator", ".", "For", "all", "items", "in", "'", "parts", "'", "return", "everything", "to", "the", "left", "and", "everything", "from", "here", "to", "the", "end", ".", "For", "reasons", "of", "efficiency", "take", "a", "first", "pass", "over", "the", "data", "to", "pre", "-", "compute", "and", "cache", "right", "-", "hand", "-", "sides" ]
def leftRight(parts,epsilon=0.01): rights = {} n = j = len(parts) - 1 while j > 0: rights[j] = parts[j] if j < n: rights[j] += rights[j+1] j -=1 left = parts[0] for i,one in enumerate(parts): if i> 0: if parts[i]._median - parts[i-1]._median > epsilon: yield i,left,rights[i] left += one
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Iterator.
[ "Iterator", "." ]
[ "\"\"\"Iterator. For all items in 'parts',\n return everything to the left and everything\n from here to the end. For reasons of\n efficiency, take a first pass over the data\n to pre-compute and cache right-hand-sides\n \"\"\"" ]
[ { "param": "parts", "type": null }, { "param": "epsilon", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "parts", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "epsilon", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def leftRight(parts,epsilon=0.01): rights = {} n = j = len(parts) - 1 while j > 0: rights[j] = parts[j] if j < n: rights[j] += rights[j+1] j -=1 left = parts[0] for i,one in enumerate(parts): if i> 0: if parts[i]._median - parts[i-1]._median > epsilon: yield i,left,rights[i] left += one
610,028
131
888b15cfe5d5ca42e680b70038f7341a869cee96
rosette-api-community/document-summarization
summarize.py
[ "MIT" ]
Python
entity_mentions
null
def entity_mentions(adm): """Generate named entity mentions from an ADM (Annotated Data Model)""" for entity in adm['attributes']['entities']['items']: for mention in entity['mentions']: # Augment mentions with the entity type of the entity they refer to mention['type'] = entity.get('type') yield mention
Generate named entity mentions from an ADM (Annotated Data Model)
Generate named entity mentions from an ADM (Annotated Data Model)
[ "Generate", "named", "entity", "mentions", "from", "an", "ADM", "(", "Annotated", "Data", "Model", ")" ]
def entity_mentions(adm): for entity in adm['attributes']['entities']['items']: for mention in entity['mentions']: mention['type'] = entity.get('type') yield mention
[ "def", "entity_mentions", "(", "adm", ")", ":", "for", "entity", "in", "adm", "[", "'attributes'", "]", "[", "'entities'", "]", "[", "'items'", "]", ":", "for", "mention", "in", "entity", "[", "'mentions'", "]", ":", "mention", "[", "'type'", "]", "=", "entity", ".", "get", "(", "'type'", ")", "yield", "mention" ]
Generate named entity mentions from an ADM (Annotated Data Model)
[ "Generate", "named", "entity", "mentions", "from", "an", "ADM", "(", "Annotated", "Data", "Model", ")" ]
[ "\"\"\"Generate named entity mentions from an ADM (Annotated Data Model)\"\"\"", "# Augment mentions with the entity type of the entity they refer to" ]
[ { "param": "adm", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "adm", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def entity_mentions(adm): for entity in adm['attributes']['entities']['items']: for mention in entity['mentions']: mention['type'] = entity.get('type') yield mention
610,029
359
76dcad937482f9a697fa3809ccae29161c28d943
ddkang/fai-imagenet
imagenet_nv/fastai_imagenet.py
[ "Apache-2.0" ]
Python
top_k
<not_specific>
def top_k(output, target, k=5): """Computes the precision@k for the specified values of k""" batch_size = target.size(0) _, pred = output.topk(k, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) return correct_k.mul_(1.0 / batch_size)
Computes the precision@k for the specified values of k
Computes the precision@k for the specified values of k
[ "Computes", "the", "precision@k", "for", "the", "specified", "values", "of", "k" ]
def top_k(output, target, k=5): batch_size = target.size(0) _, pred = output.topk(k, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) return correct_k.mul_(1.0 / batch_size)
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Computes the precision@k for the specified values of k
[ "Computes", "the", "precision@k", "for", "the", "specified", "values", "of", "k" ]
[ "\"\"\"Computes the precision@k for the specified values of k\"\"\"" ]
[ { "param": "output", "type": null }, { "param": "target", "type": null }, { "param": "k", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "output", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "target", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "k", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def top_k(output, target, k=5): batch_size = target.size(0) _, pred = output.topk(k, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) return correct_k.mul_(1.0 / batch_size)
610,030
514
e36335563e8ebb13c2f2b4517f3b8ab5203ebdb7
jpeerz/NZ-ORCID-Hub
orcid_hub/views.py
[ "MIT" ]
Python
year_range
<not_specific>
def year_range(entry): """Show an interval of employment in years.""" val = "" if entry.get("start_date") is None or entry["start_date"]["year"]["value"] is None: val = "unknown" else: val = entry["start_date"]["year"]["value"] val += "-" if entry.get("end_date") is None or entry["end_date"]["year"]["value"] is None: val += "present" else: val += entry["end_date"]["year"]["value"] return val
Show an interval of employment in years.
Show an interval of employment in years.
[ "Show", "an", "interval", "of", "employment", "in", "years", "." ]
def year_range(entry): val = "" if entry.get("start_date") is None or entry["start_date"]["year"]["value"] is None: val = "unknown" else: val = entry["start_date"]["year"]["value"] val += "-" if entry.get("end_date") is None or entry["end_date"]["year"]["value"] is None: val += "present" else: val += entry["end_date"]["year"]["value"] return val
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Show an interval of employment in years.
[ "Show", "an", "interval", "of", "employment", "in", "years", "." ]
[ "\"\"\"Show an interval of employment in years.\"\"\"" ]
[ { "param": "entry", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "entry", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def year_range(entry): val = "" if entry.get("start_date") is None or entry["start_date"]["year"]["value"] is None: val = "unknown" else: val = entry["start_date"]["year"]["value"] val += "-" if entry.get("end_date") is None or entry["end_date"]["year"]["value"] is None: val += "present" else: val += entry["end_date"]["year"]["value"] return val
610,032
964
9ec4982e2080e773177419c2aa3adcc7e60c5bf5
ZLLentz/pswalker
pswalker/configure.py
[ "BSD-3-Clause-LBNL" ]
Python
namify_config
<not_specific>
def namify_config(obj, **cfg): """ Prepend everything in cfg's keys with obj.name_, and remove entries where the value is None. """ return {obj.name + "_" + k: v for k, v in cfg.items() if v is not None}
Prepend everything in cfg's keys with obj.name_, and remove entries where the value is None.
Prepend everything in cfg's keys with obj.name_, and remove entries where the value is None.
[ "Prepend", "everything", "in", "cfg", "'", "s", "keys", "with", "obj", ".", "name_", "and", "remove", "entries", "where", "the", "value", "is", "None", "." ]
def namify_config(obj, **cfg): return {obj.name + "_" + k: v for k, v in cfg.items() if v is not None}
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Prepend everything in cfg's keys with obj.name_, and remove entries where the value is None.
[ "Prepend", "everything", "in", "cfg", "'", "s", "keys", "with", "obj", ".", "name_", "and", "remove", "entries", "where", "the", "value", "is", "None", "." ]
[ "\"\"\"\n Prepend everything in cfg's keys with obj.name_, and remove entries where\n the value is None.\n \"\"\"" ]
[ { "param": "obj", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "obj", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def namify_config(obj, **cfg): return {obj.name + "_" + k: v for k, v in cfg.items() if v is not None}
610,033
89
9cf27a833160cb809aef768668bb159ee6e2ac82
nathanawmk/LO-PHI
python-lophi/lophi/globals.py
[ "BSD-3-Clause" ]
Python
send_socket_data
null
def send_socket_data(sock, data): """ Given a socket and some data, this will prefix the length of the data and send it using the socket. """ # Put data in a network format import struct data = struct.pack("H", len(data)) + data # Send the data sent = 0 while sent < len(data): sent += sock.send(data[sent:])
Given a socket and some data, this will prefix the length of the data and send it using the socket.
Given a socket and some data, this will prefix the length of the data and send it using the socket.
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def send_socket_data(sock, data): import struct data = struct.pack("H", len(data)) + data sent = 0 while sent < len(data): sent += sock.send(data[sent:])
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Given a socket and some data, this will prefix the length of the data and send it using the socket.
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[ "\"\"\"\n Given a socket and some data, this will prefix the length of the data\n and send it using the socket.\n \"\"\"", "# Put data in a network format", "# Send the data" ]
[ { "param": "sock", "type": null }, { "param": "data", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "sock", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "data", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import struct def send_socket_data(sock, data): import struct data = struct.pack("H", len(data)) + data sent = 0 while sent < len(data): sent += sock.send(data[sent:])
610,034
808
eca26cb9e0eaaf6818d97300e332278571bfce28
pyviz/nei
nei/parser.py
[ "BSD-3-Clause" ]
Python
filter_out_markdown_ranges
<not_specific>
def filter_out_markdown_ranges(cls, lines, ranges): """ As markdown is the first pass so all lines should be present. This function filters out the identified markdown ranges from all the lines to find out what is remaining (potential code cells). """ all_line_numbers = list(range(len(lines))) accounted_lines = [list(range(start, stop+1)) for (start, stop, _, _) in ranges] flattened = [el for group in accounted_lines for el in group] remaining_line_numbers = set(all_line_numbers) - set(flattened) return [(el, lines[el]) for el in sorted(remaining_line_numbers)]
As markdown is the first pass so all lines should be present. This function filters out the identified markdown ranges from all the lines to find out what is remaining (potential code cells).
As markdown is the first pass so all lines should be present. This function filters out the identified markdown ranges from all the lines to find out what is remaining (potential code cells).
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def filter_out_markdown_ranges(cls, lines, ranges): all_line_numbers = list(range(len(lines))) accounted_lines = [list(range(start, stop+1)) for (start, stop, _, _) in ranges] flattened = [el for group in accounted_lines for el in group] remaining_line_numbers = set(all_line_numbers) - set(flattened) return [(el, lines[el]) for el in sorted(remaining_line_numbers)]
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As markdown is the first pass so all lines should be present.
[ "As", "markdown", "is", "the", "first", "pass", "so", "all", "lines", "should", "be", "present", "." ]
[ "\"\"\"\n As markdown is the first pass so all lines should be present.\n\n This function filters out the identified markdown ranges from all\n the lines to find out what is remaining (potential code cells).\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "lines", "type": null }, { "param": "ranges", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "lines", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "ranges", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def filter_out_markdown_ranges(cls, lines, ranges): all_line_numbers = list(range(len(lines))) accounted_lines = [list(range(start, stop+1)) for (start, stop, _, _) in ranges] flattened = [el for group in accounted_lines for el in group] remaining_line_numbers = set(all_line_numbers) - set(flattened) return [(el, lines[el]) for el in sorted(remaining_line_numbers)]
610,035
934
0106066cba68fb2dfd052b986c91e04af98a1ae2
Yuyan-Li/inferno
inferno/extensions/criteria/regularized.py
[ "Apache-2.0" ]
Python
collect_losses
<not_specific>
def collect_losses(module): """Collect `_losses` dictionaries from module and children :param module: a Module to be searched for losses :return: dictionary of loss names to values """ losses = {} def _collect(m): if hasattr(m, '_losses'): for k, v in m._losses.items(): if k in losses: losses[k] = losses[k] + v else: losses[k] = v module.apply(_collect) return losses
Collect `_losses` dictionaries from module and children :param module: a Module to be searched for losses :return: dictionary of loss names to values
Collect `_losses` dictionaries from module and children
[ "Collect", "`", "_losses", "`", "dictionaries", "from", "module", "and", "children" ]
def collect_losses(module): losses = {} def _collect(m): if hasattr(m, '_losses'): for k, v in m._losses.items(): if k in losses: losses[k] = losses[k] + v else: losses[k] = v module.apply(_collect) return losses
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Collect `_losses` dictionaries from module and children
[ "Collect", "`", "_losses", "`", "dictionaries", "from", "module", "and", "children" ]
[ "\"\"\"Collect `_losses` dictionaries from module and children\n\n :param module: a Module to be searched for losses\n :return: dictionary of loss names to values\n \"\"\"" ]
[ { "param": "module", "type": null } ]
{ "returns": [ { "docstring": "dictionary of loss names to values", "docstring_tokens": [ "dictionary", "of", "loss", "names", "to", "values" ], "type": null } ], "raises": [], "params": [ { "identifier": "module", "type": null, "docstring": "a Module to be searched for losses", "docstring_tokens": [ "a", "Module", "to", "be", "searched", "for", "losses" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def collect_losses(module): losses = {} def _collect(m): if hasattr(m, '_losses'): for k, v in m._losses.items(): if k in losses: losses[k] = losses[k] + v else: losses[k] = v module.apply(_collect) return losses
610,036
547
5a664ef0c2304b3a8b760be062fd044745af5395
kwryankrattiger/xmscore
_package/xms/core/filesystem/filesystem.py
[ "BSD-2-Clause" ]
Python
copyfile
null
def copyfile(src, dest): """Copy a file on disk, suppressing errors if source and destination are the same file. Args: src (str): Source file path. dest (str): Destination file path. """ try: shutil.copyfile(src, dest) except shutil.SameFileError: pass
Copy a file on disk, suppressing errors if source and destination are the same file. Args: src (str): Source file path. dest (str): Destination file path.
Copy a file on disk, suppressing errors if source and destination are the same file.
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def copyfile(src, dest): try: shutil.copyfile(src, dest) except shutil.SameFileError: pass
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Copy a file on disk, suppressing errors if source and destination are the same file.
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[ "\"\"\"Copy a file on disk, suppressing errors if source and destination are the same file.\n\n Args:\n src (str): Source file path.\n dest (str): Destination file path.\n\n \"\"\"" ]
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import shutil def copyfile(src, dest): try: shutil.copyfile(src, dest) except shutil.SameFileError: pass
610,037
815
192525df16c8ce921d1379c032a7e00edc975987
adit98/good_robot
models.py
[ "BSD-2-Clause" ]
Python
tile_vector_as_image_channels_torch
<not_specific>
def tile_vector_as_image_channels_torch(vector_op, image_shape): """ Takes a vector of length n and an image shape BCHW, and repeat the vector as channels at each pixel. Code source: https://github.com/ahundt/costar_dataset/blob/master/costar_dataset/block_stacking_reader_torch.py # Params vector_op: A tensor vector to tile. image_shape: A list of integers [width, height] with the desired dimensions. """ # input vector shape ivs = vector_op.shape # print('image_shape: ' + str(image_shape)) # reshape the vector into a single pixel vector_op = vector_op.reshape([ivs[0], ivs[1], 1, 1]) # print('vector_op pre-repeat shape:' + str(vector_op.shape)) # repeat the vector at every pixel according to the specified image shape vector_op = vector_op.expand([ivs[0], ivs[1], image_shape[2], image_shape[3]]) # print('vector_op post-repeat shape:' + str(vector_op.shape)) # print('vector_op first channel: ' + str(vector_op[0,:,0,0])) return vector_op
Takes a vector of length n and an image shape BCHW, and repeat the vector as channels at each pixel. Code source: https://github.com/ahundt/costar_dataset/blob/master/costar_dataset/block_stacking_reader_torch.py # Params vector_op: A tensor vector to tile. image_shape: A list of integers [width, height] with the desired dimensions.
Takes a vector of length n and an image shape BCHW, and repeat the vector as channels at each pixel. Params vector_op: A tensor vector to tile. image_shape: A list of integers [width, height] with the desired dimensions.
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def tile_vector_as_image_channels_torch(vector_op, image_shape): ivs = vector_op.shape vector_op = vector_op.reshape([ivs[0], ivs[1], 1, 1]) vector_op = vector_op.expand([ivs[0], ivs[1], image_shape[2], image_shape[3]]) return vector_op
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Takes a vector of length n and an image shape BCHW, and repeat the vector as channels at each pixel.
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[ "\"\"\"\n Takes a vector of length n and an image shape BCHW,\n and repeat the vector as channels at each pixel.\n\n Code source: https://github.com/ahundt/costar_dataset/blob/master/costar_dataset/block_stacking_reader_torch.py\n\n # Params\n vector_op: A tensor vector to tile.\n image_shape: A list of integers [width, height] with the desired dimensions.\n \"\"\"", "# input vector shape", "# print('image_shape: ' + str(image_shape))", "# reshape the vector into a single pixel", "# print('vector_op pre-repeat shape:' + str(vector_op.shape))", "# repeat the vector at every pixel according to the specified image shape", "# print('vector_op post-repeat shape:' + str(vector_op.shape))", "# print('vector_op first channel: ' + str(vector_op[0,:,0,0]))" ]
[ { "param": "vector_op", "type": null }, { "param": "image_shape", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "vector_op", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "image_shape", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def tile_vector_as_image_channels_torch(vector_op, image_shape): ivs = vector_op.shape vector_op = vector_op.reshape([ivs[0], ivs[1], 1, 1]) vector_op = vector_op.expand([ivs[0], ivs[1], image_shape[2], image_shape[3]]) return vector_op
610,038
130
1881c0989f3b7c74ba09687d9418cd5a1bdac9ba
TheRoyalEgg/gachapy
gachapy/controller.py
[ "MIT" ]
Python
default_key
float
def default_key(rarity: float) -> float: """The default function that converts rarity to drop rate (1/x) Parameters ---------- rarity : float the rarity of the item Returns ------- float the drop rate of the item """ return 1 / rarity
The default function that converts rarity to drop rate (1/x) Parameters ---------- rarity : float the rarity of the item Returns ------- float the drop rate of the item
The default function that converts rarity to drop rate (1/x) Parameters rarity : float the rarity of the item Returns float the drop rate of the item
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def default_key(rarity: float) -> float: return 1 / rarity
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The default function that converts rarity to drop rate (1/x) Parameters
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[ "\"\"\"The default function that converts rarity to drop rate (1/x)\n\n Parameters\n ----------\n rarity : float\n the rarity of the item\n\n Returns\n -------\n float\n the drop rate of the item\n \"\"\"" ]
[ { "param": "rarity", "type": "float" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "rarity", "type": "float", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def default_key(rarity: float) -> float: return 1 / rarity
610,041
485
b9bb749ce6573d509c5e688886e4acbe73c5f1cb
enda2020/it-cert-automation-practice
email.py
[ "Apache-2.0" ]
Python
send_mail
null
def send_mail(message): """Sends the message to the configured SMTP server.""" mail_server = smtplib.SMTP('localhost') mail_server.send_message(message) mail_server.quit()
Sends the message to the configured SMTP server.
Sends the message to the configured SMTP server.
[ "Sends", "the", "message", "to", "the", "configured", "SMTP", "server", "." ]
def send_mail(message): mail_server = smtplib.SMTP('localhost') mail_server.send_message(message) mail_server.quit()
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Sends the message to the configured SMTP server.
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[ "\"\"\"Sends the message to the configured SMTP server.\"\"\"" ]
[ { "param": "message", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "message", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import smtplib def send_mail(message): mail_server = smtplib.SMTP('localhost') mail_server.send_message(message) mail_server.quit()
610,042
522
5508084bd76087ba560215f875604ec8a81bc000
lilanyu/biosteam
biosteam/thermo/interface.py
[ "MIT" ]
Python
Somayajulu
<not_specific>
def Somayajulu(T, Tc, A, B, C): r'''Calculates air-water surface tension using the [1]_ emperical (parameter-regressed) method. Well regressed, no recent data. .. math:: \sigma=aX^{5/4}+bX^{9/4}+cX^{13/4} X=(T_c-T)/T_c Parameters ---------- T : float Temperature of fluid [K] Tc : float Critical temperature of fluid [K] A : float Regression parameter B : float Regression parameter C : float Regression parameter Returns ------- sigma : float Liquid surface tension, N/m Notes ----- Presently untested, but matches expected values. Internal units are mN/m. Form of function returns imaginary results when T > Tc; None is returned if this is the case. Function is claimed valid from the triple to the critical point. Results can be evaluated beneath the triple point. Examples -------- Water at 300 K >>> Somayajulu(300, 647.126, 232.713514, -140.18645, -4.890098) 0.07166386387996757 References ---------- .. [1] Somayajulu, G. R. "A Generalized Equation for Surface Tension from the Triple Point to the Critical Point." International Journal of Thermophysics 9, no. 4 (July 1988): 559-66. doi:10.1007/BF00503154. ''' X = (Tc-T)/Tc sigma = (A*X**1.25 + B*X**2.25 + C*X**3.25)/1000. return sigma
r'''Calculates air-water surface tension using the [1]_ emperical (parameter-regressed) method. Well regressed, no recent data. .. math:: \sigma=aX^{5/4}+bX^{9/4}+cX^{13/4} X=(T_c-T)/T_c Parameters ---------- T : float Temperature of fluid [K] Tc : float Critical temperature of fluid [K] A : float Regression parameter B : float Regression parameter C : float Regression parameter Returns ------- sigma : float Liquid surface tension, N/m Notes ----- Presently untested, but matches expected values. Internal units are mN/m. Form of function returns imaginary results when T > Tc; None is returned if this is the case. Function is claimed valid from the triple to the critical point. Results can be evaluated beneath the triple point. Examples -------- Water at 300 K >>> Somayajulu(300, 647.126, 232.713514, -140.18645, -4.890098) 0.07166386387996757 References ---------- .. [1] Somayajulu, G. R. "A Generalized Equation for Surface Tension from the Triple Point to the Critical Point." International Journal of Thermophysics 9, no. 4 (July 1988): 559-66. doi:10.1007/BF00503154.
r'''Calculates air-water surface tension using the [1]_ emperical (parameter-regressed) method. Well regressed, no recent data. sigma : float Liquid surface tension, N/m Notes Presently untested, but matches expected values. Internal units are mN/m. Form of function returns imaginary results when T > Tc; None is returned if this is the case. Function is claimed valid from the triple to the critical point. Results can be evaluated beneath the triple point. Examples [1] Somayajulu, G. R. "A Generalized Equation for Surface Tension from the Triple Point to the Critical Point." International Journal of Thermophysics 9, no.
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def Somayajulu(T, Tc, A, B, C): X = (Tc-T)/Tc sigma = (A*X**1.25 + B*X**2.25 + C*X**3.25)/1000. return sigma
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r'''Calculates air-water surface tension using the [1]_ emperical (parameter-regressed) method.
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[ "r'''Calculates air-water surface tension using the [1]_\n emperical (parameter-regressed) method. Well regressed, no recent data.\n .. math::\n \\sigma=aX^{5/4}+bX^{9/4}+cX^{13/4}\n X=(T_c-T)/T_c\n Parameters\n ----------\n T : float\n Temperature of fluid [K]\n Tc : float\n Critical temperature of fluid [K]\n A : float\n Regression parameter\n B : float\n Regression parameter\n C : float\n Regression parameter\n Returns\n -------\n sigma : float\n Liquid surface tension, N/m\n Notes\n -----\n Presently untested, but matches expected values. Internal units are mN/m.\n Form of function returns imaginary results when T > Tc; None is returned\n if this is the case. Function is claimed valid from the triple to the\n critical point. Results can be evaluated beneath the triple point.\n Examples\n --------\n Water at 300 K\n >>> Somayajulu(300, 647.126, 232.713514, -140.18645, -4.890098)\n 0.07166386387996757\n References\n ----------\n .. [1] Somayajulu, G. R. \"A Generalized Equation for Surface Tension from\n the Triple Point to the Critical Point.\" International Journal of\n Thermophysics 9, no. 4 (July 1988): 559-66. doi:10.1007/BF00503154.\n '''" ]
[ { "param": "T", "type": null }, { "param": "Tc", "type": null }, { "param": "A", "type": null }, { "param": "B", "type": null }, { "param": "C", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "T", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "Tc", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "A", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "B", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "C", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def Somayajulu(T, Tc, A, B, C): X = (Tc-T)/Tc sigma = (A*X**1.25 + B*X**2.25 + C*X**3.25)/1000. return sigma
610,043
373
fc6e11ffa88b7094b4923e5212e158136ff80bf6
minhtannguyen/RAdam
nmt/my_module/utils.py
[ "Apache-2.0" ]
Python
Unfreeze_layer
null
def Unfreeze_layer(layer): """ Unfreeze a given layer in t he DNN. #Argument: the name of a layer in the given DNN. """ for param in layer.parameters(): param.requires_grad = True
Unfreeze a given layer in t he DNN. #Argument: the name of a layer in the given DNN.
Unfreeze a given layer in t he DNN. Argument: the name of a layer in the given DNN.
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def Unfreeze_layer(layer): for param in layer.parameters(): param.requires_grad = True
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Unfreeze a given layer in t he DNN.
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[ "\"\"\"\n Unfreeze a given layer in t he DNN.\n #Argument: the name of a layer in the given DNN.\n \"\"\"" ]
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{ "returns": [], "raises": [], "params": [ { "identifier": "layer", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def Unfreeze_layer(layer): for param in layer.parameters(): param.requires_grad = True
610,045
443
c9d65a0433305218c0d2ab3eb7d29e9b6ddfe513
royadityak94/Interview
test/Root/src/main/utilities.py
[ "MIT" ]
Python
count_lines_in_file
<not_specific>
def count_lines_in_file(file_path, to_read='\n'): """Module to support light-weight read of a given char in file Parameters ---------- file_path*: Support both relative, absolute references to the file to be read. to_read: Character identifier that needs to be counted in that file (default: line delimiter) (* - Required parameters) Returns ------- count of the to_read char in the furnished file """ if not os.path.exists(file_path): raise FileNotFoundError # Lightest way to read char of interest (in this case, line delimiter) in a given file chunks = 4*(1024**2) count = 0 file_ptr = open(file_path) file_iterator = file_ptr.read buffered = file_iterator(chunks) while buffered: count += buffered.count(to_read) buffered = file_iterator(chunks) file_ptr.close() return count
Module to support light-weight read of a given char in file Parameters ---------- file_path*: Support both relative, absolute references to the file to be read. to_read: Character identifier that needs to be counted in that file (default: line delimiter) (* - Required parameters) Returns ------- count of the to_read char in the furnished file
Module to support light-weight read of a given char in file
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def count_lines_in_file(file_path, to_read='\n'): if not os.path.exists(file_path): raise FileNotFoundError chunks = 4*(1024**2) count = 0 file_ptr = open(file_path) file_iterator = file_ptr.read buffered = file_iterator(chunks) while buffered: count += buffered.count(to_read) buffered = file_iterator(chunks) file_ptr.close() return count
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Module to support light-weight read of a given char in file
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[ { "param": "file_path", "type": null }, { "param": "to_read", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": "count of the to_read char in the furnished file\r" } ], "raises": [], "params": [ { "identifier": "file_path", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "to_read", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false } ], "outlier_params": [ { "identifier": "file_path*", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false }, { "identifier": "(* - Required parameters)\r", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "others": [] }
import os def count_lines_in_file(file_path, to_read='\n'): if not os.path.exists(file_path): raise FileNotFoundError chunks = 4*(1024**2) count = 0 file_ptr = open(file_path) file_iterator = file_ptr.read buffered = file_iterator(chunks) while buffered: count += buffered.count(to_read) buffered = file_iterator(chunks) file_ptr.close() return count
610,046
186
b4b62e5f5e12c50c811eb0ef672506279acd699d
feliximmohr/master_thesis_software
python_model_DNN/auditory_model/utils/eval.py
[ "MIT" ]
Python
model_eval
<not_specific>
def model_eval(model, b_gen, metric_str, workers=4): """ Evaluate model on data generator. Prints and returns scores for specified metrics. Parameters ---------- model : Keras model The model to evaluate. b_gen : DataGenerator object DataGenerator object to use for loading the data. metric_str : list of str List of names of the metrics to evaluate. For printing. workers : int, optional Number of workers for the multiprocessing functionalities of the Keras model methods. Defauts to 4. Returns ------- score : ndarray Array containing results of the evaluated metrics as returned by Keras model.evaluate() methods. """ print('\nModel Evaluation: \n') score = model.evaluate_generator(b_gen, verbose=1, use_multiprocessing=True, workers=workers) for i, m in enumerate(metric_str): print('Test '+m+':', score[i]) return score
Evaluate model on data generator. Prints and returns scores for specified metrics. Parameters ---------- model : Keras model The model to evaluate. b_gen : DataGenerator object DataGenerator object to use for loading the data. metric_str : list of str List of names of the metrics to evaluate. For printing. workers : int, optional Number of workers for the multiprocessing functionalities of the Keras model methods. Defauts to 4. Returns ------- score : ndarray Array containing results of the evaluated metrics as returned by Keras model.evaluate() methods.
Evaluate model on data generator. Prints and returns scores for specified metrics. Parameters Returns score : ndarray Array containing results of the evaluated metrics as returned by Keras model.evaluate() methods.
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def model_eval(model, b_gen, metric_str, workers=4): print('\nModel Evaluation: \n') score = model.evaluate_generator(b_gen, verbose=1, use_multiprocessing=True, workers=workers) for i, m in enumerate(metric_str): print('Test '+m+':', score[i]) return score
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Evaluate model on data generator.
[ "Evaluate", "model", "on", "data", "generator", "." ]
[ "\"\"\"\n Evaluate model on data generator.\n Prints and returns scores for specified metrics.\n\n Parameters\n ----------\n model : Keras model\n The model to evaluate.\n b_gen : DataGenerator object\n DataGenerator object to use for loading the data.\n metric_str : list of str \n List of names of the metrics to evaluate. For printing.\n workers : int, optional\n Number of workers for the multiprocessing functionalities of\n the Keras model methods. Defauts to 4.\n \n Returns\n -------\n score : ndarray\n Array containing results of the evaluated metrics as returned\n by Keras model.evaluate() methods.\n \"\"\"" ]
[ { "param": "model", "type": null }, { "param": "b_gen", "type": null }, { "param": "metric_str", "type": null }, { "param": "workers", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "model", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "b_gen", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "metric_str", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "workers", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def model_eval(model, b_gen, metric_str, workers=4): print('\nModel Evaluation: \n') score = model.evaluate_generator(b_gen, verbose=1, use_multiprocessing=True, workers=workers) for i, m in enumerate(metric_str): print('Test '+m+':', score[i]) return score
610,047
719
dadbccce5ff60f5cdb43338a1c8bd2d298a9ce56
yhexie/O-CNN
python/dataset.py
[ "MIT" ]
Python
_get_conversion_error_str
<not_specific>
def _get_conversion_error_str(cached_conversion_arguments, specified_conversion_arguments): """ Generates error string for mismatched conversion arguments Args: cached_conversion_arguments (str): Conversion arguments which were cached specified_conversion_arguments (str): Conversion arguments which were specified Returns: str: Conversion error string """ return ("Cached conversion arguments:" + os.linesep + os.linesep + str(cached_conversion_arguments) + os.linesep + "differs from specified arguments:" + os.linesep + os.linesep + str(specified_conversion_arguments) + os.linesep)
Generates error string for mismatched conversion arguments Args: cached_conversion_arguments (str): Conversion arguments which were cached specified_conversion_arguments (str): Conversion arguments which were specified Returns: str: Conversion error string
Generates error string for mismatched conversion arguments
[ "Generates", "error", "string", "for", "mismatched", "conversion", "arguments" ]
def _get_conversion_error_str(cached_conversion_arguments, specified_conversion_arguments): return ("Cached conversion arguments:" + os.linesep + os.linesep + str(cached_conversion_arguments) + os.linesep + "differs from specified arguments:" + os.linesep + os.linesep + str(specified_conversion_arguments) + os.linesep)
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Generates error string for mismatched conversion arguments
[ "Generates", "error", "string", "for", "mismatched", "conversion", "arguments" ]
[ "\"\"\"\n Generates error string for mismatched conversion arguments\n\n Args:\n cached_conversion_arguments (str): Conversion arguments which were cached\n specified_conversion_arguments (str): Conversion arguments which were specified\n Returns:\n str: Conversion error string\n \"\"\"" ]
[ { "param": "cached_conversion_arguments", "type": null }, { "param": "specified_conversion_arguments", "type": null } ]
{ "returns": [ { "docstring": "Conversion error string", "docstring_tokens": [ "Conversion", "error", "string" ], "type": "str" } ], "raises": [], "params": [ { "identifier": "cached_conversion_arguments", "type": null, "docstring": "Conversion arguments which were cached", "docstring_tokens": [ "Conversion", "arguments", "which", "were", "cached" ], "default": null, "is_optional": false }, { "identifier": "specified_conversion_arguments", "type": null, "docstring": "Conversion arguments which were specified", "docstring_tokens": [ "Conversion", "arguments", "which", "were", "specified" ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
import os def _get_conversion_error_str(cached_conversion_arguments, specified_conversion_arguments): return ("Cached conversion arguments:" + os.linesep + os.linesep + str(cached_conversion_arguments) + os.linesep + "differs from specified arguments:" + os.linesep + os.linesep + str(specified_conversion_arguments) + os.linesep)
610,048
981
4fce587de1b34be360e99b6303e65f62cab7b94f
jensonjose/utilbox
utilbox/mail_utils/mail_utils.py
[ "MIT" ]
Python
_create_html_message
<not_specific>
def _create_html_message(plain_message_string): """ Internal method to convert plain-text message string to HTML. :param plain_message_string: The message string to converted to HTML. :return: The HTML-based message string. :rtype: str """ return "<html><head></head><body><p>" + str(plain_message_string) + "</p></body></html>"
Internal method to convert plain-text message string to HTML. :param plain_message_string: The message string to converted to HTML. :return: The HTML-based message string. :rtype: str
Internal method to convert plain-text message string to HTML.
[ "Internal", "method", "to", "convert", "plain", "-", "text", "message", "string", "to", "HTML", "." ]
def _create_html_message(plain_message_string): return "<html><head></head><body><p>" + str(plain_message_string) + "</p></body></html>"
[ "def", "_create_html_message", "(", "plain_message_string", ")", ":", "return", "\"<html><head></head><body><p>\"", "+", "str", "(", "plain_message_string", ")", "+", "\"</p></body></html>\"" ]
Internal method to convert plain-text message string to HTML.
[ "Internal", "method", "to", "convert", "plain", "-", "text", "message", "string", "to", "HTML", "." ]
[ "\"\"\"\n Internal method to convert plain-text message string to HTML.\n\n :param plain_message_string: The message string to converted to HTML.\n\n :return: The HTML-based message string.\n :rtype: str\n \"\"\"" ]
[ { "param": "plain_message_string", "type": null } ]
{ "returns": [ { "docstring": "The HTML-based message string.", "docstring_tokens": [ "The", "HTML", "-", "based", "message", "string", "." ], "type": "str" } ], "raises": [], "params": [ { "identifier": "plain_message_string", "type": null, "docstring": "The message string to converted to HTML.", "docstring_tokens": [ "The", "message", "string", "to", "converted", "to", "HTML", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _create_html_message(plain_message_string): return "<html><head></head><body><p>" + str(plain_message_string) + "</p></body></html>"
610,049
213
a70e5ab9fe32e549786ca8f9146b7b0f5773ec89
bshaffer/google-cloud-sdk
lib/googlecloudsdk/command_lib/run/flags.py
[ "Apache-2.0" ]
Python
_AddImageArg
null
def _AddImageArg(parser): """Add an image resource arg.""" parser.add_argument( '--image', help='The path to the GCR container to deploy.')
Add an image resource arg.
Add an image resource arg.
[ "Add", "an", "image", "resource", "arg", "." ]
def _AddImageArg(parser): parser.add_argument( '--image', help='The path to the GCR container to deploy.')
[ "def", "_AddImageArg", "(", "parser", ")", ":", "parser", ".", "add_argument", "(", "'--image'", ",", "help", "=", "'The path to the GCR container to deploy.'", ")" ]
Add an image resource arg.
[ "Add", "an", "image", "resource", "arg", "." ]
[ "\"\"\"Add an image resource arg.\"\"\"" ]
[ { "param": "parser", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "parser", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _AddImageArg(parser): parser.add_argument( '--image', help='The path to the GCR container to deploy.')
610,050
266
e5ef167794249ed1fe0f484d8a6fcf6d49940e5b
MSD200X/echoprint-server
API/solr.py
[ "Apache-2.0" ]
Python
pooled_connection
null
def pooled_connection(pool): """ Provides some syntactic sugar for using a ConnectionPool. Example use: pool = ConnectionPool(SolrConnection, 'http://localhost:8080/solr') with pooled_connection(pool) as conn: docs = conn.query('*:*') """ conn = pool.get() try: yield conn except Exception: raise else: # only return connection to pool if an exception wasn't raised pool.put(conn)
Provides some syntactic sugar for using a ConnectionPool. Example use: pool = ConnectionPool(SolrConnection, 'http://localhost:8080/solr') with pooled_connection(pool) as conn: docs = conn.query('*:*')
Provides some syntactic sugar for using a ConnectionPool.
[ "Provides", "some", "syntactic", "sugar", "for", "using", "a", "ConnectionPool", "." ]
def pooled_connection(pool): conn = pool.get() try: yield conn except Exception: raise else: pool.put(conn)
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Provides some syntactic sugar for using a ConnectionPool.
[ "Provides", "some", "syntactic", "sugar", "for", "using", "a", "ConnectionPool", "." ]
[ "\"\"\"\n Provides some syntactic sugar for using a ConnectionPool. Example use:\n \n pool = ConnectionPool(SolrConnection, 'http://localhost:8080/solr')\n with pooled_connection(pool) as conn:\n docs = conn.query('*:*')\n \"\"\"", "# only return connection to pool if an exception wasn't raised" ]
[ { "param": "pool", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "pool", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def pooled_connection(pool): conn = pool.get() try: yield conn except Exception: raise else: pool.put(conn)
610,051
911
946395103aca43aace2cff5be72ef4d66a26dc3f
edelsonc/txt_search
search_text.py
[ "MIT" ]
Python
remove_punc
<not_specific>
def remove_punc(words): """Function that removes punctuation from a string Arguments --------- words -- string to have punctuation removes """ trans = {ord(c): None for c in string.punctuation} no_punc = words.translate(trans) return no_punc
Function that removes punctuation from a string Arguments --------- words -- string to have punctuation removes
Function that removes punctuation from a string Arguments - string to have punctuation removes
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def remove_punc(words): trans = {ord(c): None for c in string.punctuation} no_punc = words.translate(trans) return no_punc
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Function that removes punctuation from a string Arguments
[ "Function", "that", "removes", "punctuation", "from", "a", "string", "Arguments" ]
[ "\"\"\"Function that removes punctuation from a string\n\n Arguments\n ---------\n words -- string to have punctuation removes\n \"\"\"" ]
[ { "param": "words", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "words", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import string def remove_punc(words): trans = {ord(c): None for c in string.punctuation} no_punc = words.translate(trans) return no_punc
610,052
646
4a0e363041e095845fd66a0704cca6392a88b93c
karlobermeyer/numeric-digit-classification
image_recognition_tools.py
[ "MIT" ]
Python
truncate_string
<not_specific>
def truncate_string(s, ubnd, add_ellipses=True): '''Return a version of the string that is clamped at the end to length `ubnd`. Args: s: string, string to clamp. ubnd: unsigned int, max length of output string. add_ellipses: bool, whether to replace the last 3 chars of truncated strings with an ellipsis. Returns: string, clamped version of `s`. ''' s_length = len(s) if s_length <= ubnd: return s else: return s[:ubnd-3] + '...'
Return a version of the string that is clamped at the end to length `ubnd`. Args: s: string, string to clamp. ubnd: unsigned int, max length of output string. add_ellipses: bool, whether to replace the last 3 chars of truncated strings with an ellipsis. Returns: string, clamped version of `s`.
Return a version of the string that is clamped at the end to length `ubnd`.
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def truncate_string(s, ubnd, add_ellipses=True): s_length = len(s) if s_length <= ubnd: return s else: return s[:ubnd-3] + '...'
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Return a version of the string that is clamped at the end to length `ubnd`.
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[ "'''Return a version of the string that is clamped at the end to\n length `ubnd`.\n\n Args:\n s: string, string to clamp.\n ubnd: unsigned int, max length of output string.\n add_ellipses: bool, whether to replace the last 3 chars of truncated\n strings with an ellipsis.\n\n Returns:\n string, clamped version of `s`.\n '''" ]
[ { "param": "s", "type": null }, { "param": "ubnd", "type": null }, { "param": "add_ellipses", "type": null } ]
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def truncate_string(s, ubnd, add_ellipses=True): s_length = len(s) if s_length <= ubnd: return s else: return s[:ubnd-3] + '...'
610,053
132
e5ae160d5309de9f8302a72b5b9af09cc9dd6755
shrijaltamrakar/Descent_py
sorting_algs/counting_sort.py
[ "MIT" ]
Python
counting_sort
<not_specific>
def counting_sort(collection): """ pure implementation of counting sort algorighm in python :param collection: some mutable ordered collection with heterogenous comparable items inside :return: the same collection ordered by ascending """ #if collection is empty , returns empty if collection == []: return [] #get some information about the collection coll_len = len(collection) coll_max = max(collection) coll_min = min(collection) #create counting array counting_arr_length = coll_max + 1 - coll_min counting_arr = [0] * counting_arr_length #count how much a number appears in the collection for number in collection: counting_arr[number - coll_min] += 1 # sum each position with its predecessors .now , counting_arr[i] tells # us how many elements <= i has in the collection for i in range(1, counting_arr_length): counting_arr[i] = counting_arr[i] + counting_arr[i-1] # create the output collection ordered = [0]* coll_len #place the elements in the output , respecting the original order # from the end to begin, updating counting_arr for i in reversed(range(0,coll_len)): ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i] counting_arr[collection[i] - coll_min] -= 1 return ordered
pure implementation of counting sort algorighm in python :param collection: some mutable ordered collection with heterogenous comparable items inside :return: the same collection ordered by ascending
pure implementation of counting sort algorighm in python
[ "pure", "implementation", "of", "counting", "sort", "algorighm", "in", "python" ]
def counting_sort(collection): if collection == []: return [] coll_len = len(collection) coll_max = max(collection) coll_min = min(collection) counting_arr_length = coll_max + 1 - coll_min counting_arr = [0] * counting_arr_length for number in collection: counting_arr[number - coll_min] += 1 for i in range(1, counting_arr_length): counting_arr[i] = counting_arr[i] + counting_arr[i-1] ordered = [0]* coll_len for i in reversed(range(0,coll_len)): ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i] counting_arr[collection[i] - coll_min] -= 1 return ordered
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pure implementation of counting sort algorighm in python
[ "pure", "implementation", "of", "counting", "sort", "algorighm", "in", "python" ]
[ "\"\"\"\n pure implementation of counting sort algorighm in python\n :param collection: some mutable ordered collection with heterogenous\n comparable items inside\n :return: the same collection ordered by ascending\n \"\"\"", "#if collection is empty , returns empty", "#get some information about the collection", "#create counting array", "#count how much a number appears in the collection", "# sum each position with its predecessors .now , counting_arr[i] tells", "# us how many elements <= i has in the collection", "# create the output collection", "#place the elements in the output , respecting the original order", "# from the end to begin, updating counting_arr" ]
[ { "param": "collection", "type": null } ]
{ "returns": [ { "docstring": "the same collection ordered by ascending", "docstring_tokens": [ "the", "same", "collection", "ordered", "by", "ascending" ], "type": null } ], "raises": [], "params": [ { "identifier": "collection", "type": null, "docstring": "some mutable ordered collection with heterogenous\ncomparable items inside", "docstring_tokens": [ "some", "mutable", "ordered", "collection", "with", "heterogenous", "comparable", "items", "inside" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def counting_sort(collection): if collection == []: return [] coll_len = len(collection) coll_max = max(collection) coll_min = min(collection) counting_arr_length = coll_max + 1 - coll_min counting_arr = [0] * counting_arr_length for number in collection: counting_arr[number - coll_min] += 1 for i in range(1, counting_arr_length): counting_arr[i] = counting_arr[i] + counting_arr[i-1] ordered = [0]* coll_len for i in reversed(range(0,coll_len)): ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i] counting_arr[collection[i] - coll_min] -= 1 return ordered
610,054
32
1734eddb60c326ca46bec8afa3612ed45d12e28f
lilahtovmoon/mypaas
mypaas/stats/monitor.py
[ "BSD-2-Clause" ]
Python
hashit
<not_specific>
def hashit(value): """Hash any value by applying md5 to the stringified value. Returns an integer. """ if isinstance(value, int): return abs(value) h = hashlib.md5(str(value).encode()) return abs(int(h.hexdigest()[:14], 16))
Hash any value by applying md5 to the stringified value. Returns an integer.
Hash any value by applying md5 to the stringified value. Returns an integer.
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def hashit(value): if isinstance(value, int): return abs(value) h = hashlib.md5(str(value).encode()) return abs(int(h.hexdigest()[:14], 16))
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Hash any value by applying md5 to the stringified value.
[ "Hash", "any", "value", "by", "applying", "md5", "to", "the", "stringified", "value", "." ]
[ "\"\"\"Hash any value by applying md5 to the stringified value.\n Returns an integer.\n \"\"\"" ]
[ { "param": "value", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "value", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import hashlib def hashit(value): if isinstance(value, int): return abs(value) h = hashlib.md5(str(value).encode()) return abs(int(h.hexdigest()[:14], 16))
610,055
135
4eeb4d0a1afe47c25f290f7e43047c17587c52a3
crovner/haproxyadmin
haproxyadmin/utils.py
[ "Apache-2.0" ]
Python
elements_of_list_same
<not_specific>
def elements_of_list_same(iterator): """Check is all elements of an iterator are equal. :param iterator: a iterator :type iterator: ``list`` :rtype: ``bool`` Usage:: >>> from haproxyadmin import utils >>> iterator = ['OK', 'ok'] >>> utils.elements_of_list_same(iterator) False >>> iterator = ['OK', 'OK'] >>> utils.elements_of_list_same(iterator) True >>> iterator = [22, 22, 22] >>> utils.elements_of_list_same(iterator) True >>> iterator = [22, 22, 222] >>> utils.elements_of_list_same(iterator) False """ return len(set(iterator)) == 1
Check is all elements of an iterator are equal. :param iterator: a iterator :type iterator: ``list`` :rtype: ``bool`` Usage:: >>> from haproxyadmin import utils >>> iterator = ['OK', 'ok'] >>> utils.elements_of_list_same(iterator) False >>> iterator = ['OK', 'OK'] >>> utils.elements_of_list_same(iterator) True >>> iterator = [22, 22, 22] >>> utils.elements_of_list_same(iterator) True >>> iterator = [22, 22, 222] >>> utils.elements_of_list_same(iterator) False
Check is all elements of an iterator are equal.
[ "Check", "is", "all", "elements", "of", "an", "iterator", "are", "equal", "." ]
def elements_of_list_same(iterator): return len(set(iterator)) == 1
[ "def", "elements_of_list_same", "(", "iterator", ")", ":", "return", "len", "(", "set", "(", "iterator", ")", ")", "==", "1" ]
Check is all elements of an iterator are equal.
[ "Check", "is", "all", "elements", "of", "an", "iterator", "are", "equal", "." ]
[ "\"\"\"Check is all elements of an iterator are equal.\n\n :param iterator: a iterator\n :type iterator: ``list``\n :rtype: ``bool``\n\n Usage::\n\n >>> from haproxyadmin import utils\n >>> iterator = ['OK', 'ok']\n >>> utils.elements_of_list_same(iterator)\n False\n >>> iterator = ['OK', 'OK']\n >>> utils.elements_of_list_same(iterator)\n True\n >>> iterator = [22, 22, 22]\n >>> utils.elements_of_list_same(iterator)\n True\n >>> iterator = [22, 22, 222]\n >>> utils.elements_of_list_same(iterator)\n False\n \"\"\"" ]
[ { "param": "iterator", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": "``bool``\nUsage::\n\n >>> from haproxyadmin import utils\n >>> iterator = ['OK', 'ok']\n >>> utils.elements_of_list_same(iterator)\n False\n >>> iterator = ['OK', 'OK']\n >>> utils.elements_of_list_same(iterator)\n True\n >>> iterator = [22, 22, 22]\n >>> utils.elements_of_list_same(iterator)\n True\n >>> iterator = [22, 22, 222]\n >>> utils.elements_of_list_same(iterator)\n False" } ], "raises": [], "params": [ { "identifier": "iterator", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def elements_of_list_same(iterator): return len(set(iterator)) == 1
610,056
773
ac512932fb9eabac4c3e479a84626fd1cd3ed889
rbaltrusch/desktop_shop
desktop_shop/crypto.py
[ "MIT" ]
Python
hash_string
<not_specific>
def hash_string(password, salt): '''Hashes the specified password with the passed salt. Returns hash in hex format (str)''' encoded_password = bytes(password, encoding='utf-8') encoded_salt = bytes(salt, encoding='utf-8') #should be iterations=10000 for security, just changed to 1 for easy database generation derived_key = hashlib.pbkdf2_hmac('sha256', encoded_password, encoded_salt, iterations=1) return derived_key.hex()
Hashes the specified password with the passed salt. Returns hash in hex format (str)
Hashes the specified password with the passed salt. Returns hash in hex format (str)
[ "Hashes", "the", "specified", "password", "with", "the", "passed", "salt", ".", "Returns", "hash", "in", "hex", "format", "(", "str", ")" ]
def hash_string(password, salt): encoded_password = bytes(password, encoding='utf-8') encoded_salt = bytes(salt, encoding='utf-8') derived_key = hashlib.pbkdf2_hmac('sha256', encoded_password, encoded_salt, iterations=1) return derived_key.hex()
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Hashes the specified password with the passed salt.
[ "Hashes", "the", "specified", "password", "with", "the", "passed", "salt", "." ]
[ "'''Hashes the specified password with the passed salt. Returns hash in hex format (str)'''", "#should be iterations=10000 for security, just changed to 1 for easy database generation" ]
[ { "param": "password", "type": null }, { "param": "salt", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "password", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "salt", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import hashlib def hash_string(password, salt): encoded_password = bytes(password, encoding='utf-8') encoded_salt = bytes(salt, encoding='utf-8') derived_key = hashlib.pbkdf2_hmac('sha256', encoded_password, encoded_salt, iterations=1) return derived_key.hex()
610,057
28
0a4ad1972f15f0ba16c1a663dd77611a4f006fe1
arjoly/clusterlib
clusterlib/tests/test_scheduler.py
[ "BSD-3-Clause" ]
Python
_check_job_id
<not_specific>
def _check_job_id(command): """Perform a dispatch and return the job id.""" # TODO: This utility function should be properly documented any made more # robust to be included in the scheduler module itself cmd_encoding = 'utf-8' output = subprocess.check_output( command.encode(cmd_encoding), shell=True).decode(cmd_encoding) if output.startswith(u'Your job '): job_id = output.split()[2] elif output.startswith(u'Submitted batch job '): job_id = output.split()[3] else: raise RuntimeError( u"Failed to parse job_id from command output:\n %s\ncmd:\n%s" % (command, output)) return job_id
Perform a dispatch and return the job id.
Perform a dispatch and return the job id.
[ "Perform", "a", "dispatch", "and", "return", "the", "job", "id", "." ]
def _check_job_id(command): cmd_encoding = 'utf-8' output = subprocess.check_output( command.encode(cmd_encoding), shell=True).decode(cmd_encoding) if output.startswith(u'Your job '): job_id = output.split()[2] elif output.startswith(u'Submitted batch job '): job_id = output.split()[3] else: raise RuntimeError( u"Failed to parse job_id from command output:\n %s\ncmd:\n%s" % (command, output)) return job_id
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Perform a dispatch and return the job id.
[ "Perform", "a", "dispatch", "and", "return", "the", "job", "id", "." ]
[ "\"\"\"Perform a dispatch and return the job id.\"\"\"", "# TODO: This utility function should be properly documented any made more", "# robust to be included in the scheduler module itself" ]
[ { "param": "command", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "command", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import subprocess def _check_job_id(command): cmd_encoding = 'utf-8' output = subprocess.check_output( command.encode(cmd_encoding), shell=True).decode(cmd_encoding) if output.startswith(u'Your job '): job_id = output.split()[2] elif output.startswith(u'Submitted batch job '): job_id = output.split()[3] else: raise RuntimeError( u"Failed to parse job_id from command output:\n %s\ncmd:\n%s" % (command, output)) return job_id
610,058
480
33107e40a8e96bb2b6df491c938697d443fbb544
CARRIER-project/v6-vertical-analysis
baseContainer/redacted_logging/redacted_logging.py
[ "Apache-2.0" ]
Python
probe_config_file
null
def probe_config_file(config_file): """ Check for the existance of a config file for the logger. `configparser` does not give a sane error message for missing files, this is more understandable. Args: config_file (str): Name of the config file. Raises: FileNotFoundError: If config_file is not found. """ if not os.path.isfile(config_file): raise FileNotFoundError("Config file " + config_file + " " + "does not exist. Create it or use " + "get_logger(__name__, " + "config_file=\"./my_config_file\") to " + "point to another name or location.")
Check for the existance of a config file for the logger. `configparser` does not give a sane error message for missing files, this is more understandable. Args: config_file (str): Name of the config file. Raises: FileNotFoundError: If config_file is not found.
Check for the existance of a config file for the logger. `configparser` does not give a sane error message for missing files, this is more understandable.
[ "Check", "for", "the", "existance", "of", "a", "config", "file", "for", "the", "logger", ".", "`", "configparser", "`", "does", "not", "give", "a", "sane", "error", "message", "for", "missing", "files", "this", "is", "more", "understandable", "." ]
def probe_config_file(config_file): if not os.path.isfile(config_file): raise FileNotFoundError("Config file " + config_file + " " + "does not exist. Create it or use " + "get_logger(__name__, " + "config_file=\"./my_config_file\") to " + "point to another name or location.")
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Check for the existance of a config file for the logger.
[ "Check", "for", "the", "existance", "of", "a", "config", "file", "for", "the", "logger", "." ]
[ "\"\"\" Check for the existance of a config file for the logger.\n\n `configparser` does not give a sane error message for missing files,\n this is more understandable.\n\n Args:\n config_file (str): Name of the config file.\n\n Raises:\n FileNotFoundError: If config_file is not found.\n \"\"\"" ]
[ { "param": "config_file", "type": null } ]
{ "returns": [], "raises": [ { "docstring": "If config_file is not found.", "docstring_tokens": [ "If", "config_file", "is", "not", "found", "." ], "type": "FileNotFoundError" } ], "params": [ { "identifier": "config_file", "type": null, "docstring": "Name of the config file.", "docstring_tokens": [ "Name", "of", "the", "config", "file", "." ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
import os def probe_config_file(config_file): if not os.path.isfile(config_file): raise FileNotFoundError("Config file " + config_file + " " + "does not exist. Create it or use " + "get_logger(__name__, " + "config_file=\"./my_config_file\") to " + "point to another name or location.")
610,059
241
088c57cb85bad2739afb18e6c73e38cf7a18100f
WilliamLoy/otter
otter/test/utils.py
[ "Apache-2.0" ]
Python
alist_get
<not_specific>
def alist_get(data, key): """Look up a value in an association list.""" for dkey, dvalue in data: if dkey == key: return dvalue raise KeyError(key)
Look up a value in an association list.
Look up a value in an association list.
[ "Look", "up", "a", "value", "in", "an", "association", "list", "." ]
def alist_get(data, key): for dkey, dvalue in data: if dkey == key: return dvalue raise KeyError(key)
[ "def", "alist_get", "(", "data", ",", "key", ")", ":", "for", "dkey", ",", "dvalue", "in", "data", ":", "if", "dkey", "==", "key", ":", "return", "dvalue", "raise", "KeyError", "(", "key", ")" ]
Look up a value in an association list.
[ "Look", "up", "a", "value", "in", "an", "association", "list", "." ]
[ "\"\"\"Look up a value in an association list.\"\"\"" ]
[ { "param": "data", "type": null }, { "param": "key", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "data", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "key", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def alist_get(data, key): for dkey, dvalue in data: if dkey == key: return dvalue raise KeyError(key)
610,060
0
773c92eef246527092f9973a9bea12a5a866a642
irenepan-twinkle/SI506-2020Winter
code/problem_set_06/problem_set_06.py
[ "BSD-3-Clause" ]
Python
sort_dictionary_by_value
<not_specific>
def sort_dictionary_by_value(dictionary): """ DON'T CHANGE THIS FUNCTION. This function sorts a dictionary by its values in a descending fashion. Parameters: dictionary (dict): A dictionary to be sorted. Returns: dict: The sorted dictionary. """ desc_dictionary = dict(sorted(dictionary.items(), key=operator.itemgetter(1), reverse=True)) return desc_dictionary
DON'T CHANGE THIS FUNCTION. This function sorts a dictionary by its values in a descending fashion. Parameters: dictionary (dict): A dictionary to be sorted. Returns: dict: The sorted dictionary.
DON'T CHANGE THIS FUNCTION. This function sorts a dictionary by its values in a descending fashion.
[ "DON", "'", "T", "CHANGE", "THIS", "FUNCTION", ".", "This", "function", "sorts", "a", "dictionary", "by", "its", "values", "in", "a", "descending", "fashion", "." ]
def sort_dictionary_by_value(dictionary): desc_dictionary = dict(sorted(dictionary.items(), key=operator.itemgetter(1), reverse=True)) return desc_dictionary
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DON'T CHANGE THIS FUNCTION.
[ "DON", "'", "T", "CHANGE", "THIS", "FUNCTION", "." ]
[ "\"\"\"\n DON'T CHANGE THIS FUNCTION. This function sorts a dictionary by its values in a\n descending fashion.\n\n Parameters:\n dictionary (dict): A dictionary to be sorted.\n\n Returns:\n dict: The sorted dictionary.\n \"\"\"" ]
[ { "param": "dictionary", "type": null } ]
{ "returns": [ { "docstring": "The sorted dictionary.", "docstring_tokens": [ "The", "sorted", "dictionary", "." ], "type": "dict" } ], "raises": [], "params": [ { "identifier": "dictionary", "type": null, "docstring": "A dictionary to be sorted.", "docstring_tokens": [ "A", "dictionary", "to", "be", "sorted", "." ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
import operator def sort_dictionary_by_value(dictionary): desc_dictionary = dict(sorted(dictionary.items(), key=operator.itemgetter(1), reverse=True)) return desc_dictionary
610,061
799
73e1d16fd33cde7110f92cdd23594768e446c5b8
the-louie/sensors2domoticz
telldus2json.py
[ "MIT" ]
Python
parse_sensors
<not_specific>
def parse_sensors(configuration): """ Bake the sensors array to a dict where the telldus id is the key for easier parsing later on. """ result = {} for sensor in configuration["my_sensors"]: result[sensor["id"]] = sensor return result
Bake the sensors array to a dict where the telldus id is the key for easier parsing later on.
Bake the sensors array to a dict where the telldus id is the key for easier parsing later on.
[ "Bake", "the", "sensors", "array", "to", "a", "dict", "where", "the", "telldus", "id", "is", "the", "key", "for", "easier", "parsing", "later", "on", "." ]
def parse_sensors(configuration): result = {} for sensor in configuration["my_sensors"]: result[sensor["id"]] = sensor return result
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Bake the sensors array to a dict where the telldus id is the key for easier parsing later on.
[ "Bake", "the", "sensors", "array", "to", "a", "dict", "where", "the", "telldus", "id", "is", "the", "key", "for", "easier", "parsing", "later", "on", "." ]
[ "\"\"\"\n Bake the sensors array to a dict where the telldus id is\n the key for easier parsing later on.\n \"\"\"" ]
[ { "param": "configuration", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "configuration", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def parse_sensors(configuration): result = {} for sensor in configuration["my_sensors"]: result[sensor["id"]] = sensor return result
610,062
359
6dfe4484c400ea89e5651f3ba0427b577e6e28c3
Shiphero/python-amazon-mws
mws/apis/feeds.py
[ "Unlicense" ]
Python
feed_options_str
<not_specific>
def feed_options_str(feed_options): """Convert a FeedOptions dict of values into an appropriate string value. Amazon docs for VAT upload with details: https://m.media-amazon.com/images/G/01/B2B/DeveloperGuide/vat_calculation_service__dev_guide_H383rf73k4hsu1TYRH139kk134yzs.pdf (section 6.4) Example: feed_options = { "shippingid": "283845474", "totalAmount": 3.25, "totalvatamount": 1.23, "invoicenumber": "INT-3431-XJE3", "documenttype": "CreditNote", "transactionid": "amzn:crow:429491192ksjfhe39s", } print(feed_options_str(feed_options)) >>> "metadata:shippingid=283845474;metadata:totalAmount=3.25;metadata:totalvatamount=1.23; metadata:invoicenumber=INT-3431-XJE3;metadata:documenttype=CreditNote; metadata:transactionid=amzn:crow:429491192ksjfhe39s" """ if not feed_options: return None if not isinstance(feed_options, dict): raise ValueError("`feed_options` should be a dict or None") output = [] for key, val in feed_options.items(): outval = val if outval is True or outval is False: # Convert literal `True` or `False` to strings `"true"` and `"false"` outval = str(outval).lower() output.append(f"metadata:{key}={outval}") return ";".join(output)
Convert a FeedOptions dict of values into an appropriate string value. Amazon docs for VAT upload with details: https://m.media-amazon.com/images/G/01/B2B/DeveloperGuide/vat_calculation_service__dev_guide_H383rf73k4hsu1TYRH139kk134yzs.pdf (section 6.4) Example: feed_options = { "shippingid": "283845474", "totalAmount": 3.25, "totalvatamount": 1.23, "invoicenumber": "INT-3431-XJE3", "documenttype": "CreditNote", "transactionid": "amzn:crow:429491192ksjfhe39s", } print(feed_options_str(feed_options)) >>> "metadata:shippingid=283845474;metadata:totalAmount=3.25;metadata:totalvatamount=1.23; metadata:invoicenumber=INT-3431-XJE3;metadata:documenttype=CreditNote; metadata:transactionid=amzn:crow:429491192ksjfhe39s"
Convert a FeedOptions dict of values into an appropriate string value.
[ "Convert", "a", "FeedOptions", "dict", "of", "values", "into", "an", "appropriate", "string", "value", "." ]
def feed_options_str(feed_options): if not feed_options: return None if not isinstance(feed_options, dict): raise ValueError("`feed_options` should be a dict or None") output = [] for key, val in feed_options.items(): outval = val if outval is True or outval is False: outval = str(outval).lower() output.append(f"metadata:{key}={outval}") return ";".join(output)
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Convert a FeedOptions dict of values into an appropriate string value.
[ "Convert", "a", "FeedOptions", "dict", "of", "values", "into", "an", "appropriate", "string", "value", "." ]
[ "\"\"\"Convert a FeedOptions dict of values into an appropriate string value.\n \n Amazon docs for VAT upload with details:\n https://m.media-amazon.com/images/G/01/B2B/DeveloperGuide/vat_calculation_service__dev_guide_H383rf73k4hsu1TYRH139kk134yzs.pdf\n (section 6.4)\n \n Example:\n feed_options = {\n \"shippingid\": \"283845474\",\n \"totalAmount\": 3.25,\n \"totalvatamount\": 1.23,\n \"invoicenumber\": \"INT-3431-XJE3\",\n \"documenttype\": \"CreditNote\",\n \"transactionid\": \"amzn:crow:429491192ksjfhe39s\",\n }\n print(feed_options_str(feed_options))\n >>> \"metadata:shippingid=283845474;metadata:totalAmount=3.25;metadata:totalvatamount=1.23;\n metadata:invoicenumber=INT-3431-XJE3;metadata:documenttype=CreditNote;\n metadata:transactionid=amzn:crow:429491192ksjfhe39s\"\n \"\"\"", "# Convert literal `True` or `False` to strings `\"true\"` and `\"false\"`" ]
[ { "param": "feed_options", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "feed_options", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [ { "identifier": "examples", "docstring": "feed_options = {\n\"shippingid\": \"283845474\",\n\"totalAmount\": 3.25,\n\"totalvatamount\": 1.23,\n\"invoicenumber\": \"INT-3431-XJE3\",\n\"documenttype\": \"CreditNote\",\n\"transactionid\": \"amzn:crow:429491192ksjfhe39s\",\n}\nprint(feed_options_str(feed_options))\n>>> \"metadata:shippingid=283845474;metadata:totalAmount=3.25;metadata:totalvatamount=1.23;\nmetadata:invoicenumber=INT-3431-XJE3;metadata:documenttype=CreditNote;\nmetadata:transactionid=amzn:crow:429491192ksjfhe39s\"", "docstring_tokens": [ "feed_options", "=", "{", "\"", "shippingid", "\"", ":", "\"", "283845474", "\"", "\"", "totalAmount", "\"", ":", "3", ".", "25", "\"", "totalvatamount", "\"", ":", "1", ".", "23", "\"", "invoicenumber", "\"", ":", "\"", "INT", "-", "3431", "-", "XJE3", "\"", "\"", "documenttype", "\"", ":", "\"", "CreditNote", "\"", "\"", "transactionid", "\"", ":", "\"", "amzn", ":", "crow", ":", "429491192ksjfhe39s", "\"", "}", "print", "(", "feed_options_str", "(", "feed_options", "))", ">>>", "\"", "metadata", ":", "shippingid", "=", "283845474", ";", "metadata", ":", "totalAmount", "=", "3", ".", "25", ";", "metadata", ":", "totalvatamount", "=", "1", ".", "23", ";", "metadata", ":", "invoicenumber", "=", "INT", "-", "3431", "-", "XJE3", ";", "metadata", ":", "documenttype", "=", "CreditNote", ";", "metadata", ":", "transactionid", "=", "amzn", ":", "crow", ":", "429491192ksjfhe39s", "\"" ] } ] }
def feed_options_str(feed_options): if not feed_options: return None if not isinstance(feed_options, dict): raise ValueError("`feed_options` should be a dict or None") output = [] for key, val in feed_options.items(): outval = val if outval is True or outval is False: outval = str(outval).lower() output.append(f"metadata:{key}={outval}") return ";".join(output)
610,063
186
0278f95918a21fc61f016ccb0b7d04f5690d025d
cns-iu/HuBMAP---Hacking-the-Kidney
models/4-Deeplive.exe/code/utils/metrics.py
[ "MIT" ]
Python
dice_scores_img_tensor
<not_specific>
def dice_scores_img_tensor(pred, truth, eps=1e-8): """ Dice metric for a single image as tensor. Args: pred (torch tensor): Predictions. truth (torch tensor): Ground truths. eps (float, optional): epsilon to avoid dividing by 0. Defaults to 1e-8. Returns: np array : dice value for each class. """ pred = pred.view(-1) > 0 truth = truth.contiguous().view(-1) > 0 intersect = (pred & truth).sum(-1) union = pred.sum(-1) + truth.sum(-1) dice = (2.0 * intersect + eps) / (union + eps) return float(dice)
Dice metric for a single image as tensor. Args: pred (torch tensor): Predictions. truth (torch tensor): Ground truths. eps (float, optional): epsilon to avoid dividing by 0. Defaults to 1e-8. Returns: np array : dice value for each class.
Dice metric for a single image as tensor.
[ "Dice", "metric", "for", "a", "single", "image", "as", "tensor", "." ]
def dice_scores_img_tensor(pred, truth, eps=1e-8): pred = pred.view(-1) > 0 truth = truth.contiguous().view(-1) > 0 intersect = (pred & truth).sum(-1) union = pred.sum(-1) + truth.sum(-1) dice = (2.0 * intersect + eps) / (union + eps) return float(dice)
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Dice metric for a single image as tensor.
[ "Dice", "metric", "for", "a", "single", "image", "as", "tensor", "." ]
[ "\"\"\"\n Dice metric for a single image as tensor.\n\n Args:\n pred (torch tensor): Predictions.\n truth (torch tensor): Ground truths.\n eps (float, optional): epsilon to avoid dividing by 0. Defaults to 1e-8.\n\n Returns:\n np array : dice value for each class.\n \"\"\"" ]
[ { "param": "pred", "type": null }, { "param": "truth", "type": null }, { "param": "eps", "type": null } ]
{ "returns": [ { "docstring": "np array : dice value for each class.", "docstring_tokens": [ "np", "array", ":", "dice", "value", "for", "each", "class", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "pred", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false }, { "identifier": "truth", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false }, { "identifier": "eps", "type": null, "docstring": "epsilon to avoid dividing by 0. Defaults to 1e-8.", "docstring_tokens": [ "epsilon", "to", "avoid", "dividing", "by", "0", ".", "Defaults", "to", "1e", "-", "8", "." ], "default": "1e-8", "is_optional": true } ], "outlier_params": [], "others": [] }
def dice_scores_img_tensor(pred, truth, eps=1e-8): pred = pred.view(-1) > 0 truth = truth.contiguous().view(-1) > 0 intersect = (pred & truth).sum(-1) union = pred.sum(-1) + truth.sum(-1) dice = (2.0 * intersect + eps) / (union + eps) return float(dice)
610,064
327
8d9330b42405fb9a2a9cf1a6f25628cdb5947105
benety/mongo
buildscripts/errorcodes.py
[ "Apache-2.0" ]
Python
coerce_to_number
<not_specific>
def coerce_to_number(ticket_value): """Coerce the input into a number. If the input is a number, return itself. Otherwise parses input strings of two forms. 'SERVER-12345' and '12345' will both return 12345'. """ if isinstance(ticket_value, int): return ticket_value ticket_re = re.compile(r'(?:SERVER-)?(\d+)', re.IGNORECASE) matches = ticket_re.fullmatch(ticket_value) if not matches: print("Unknown ticket number. Input: " + ticket_value) return -1 return int(matches.group(1))
Coerce the input into a number. If the input is a number, return itself. Otherwise parses input strings of two forms. 'SERVER-12345' and '12345' will both return 12345'.
Coerce the input into a number. If the input is a number, return itself. Otherwise parses input strings of two forms.
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def coerce_to_number(ticket_value): if isinstance(ticket_value, int): return ticket_value ticket_re = re.compile(r'(?:SERVER-)?(\d+)', re.IGNORECASE) matches = ticket_re.fullmatch(ticket_value) if not matches: print("Unknown ticket number. Input: " + ticket_value) return -1 return int(matches.group(1))
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Coerce the input into a number.
[ "Coerce", "the", "input", "into", "a", "number", "." ]
[ "\"\"\"Coerce the input into a number.\n\n If the input is a number, return itself. Otherwise parses input strings of two forms.\n 'SERVER-12345' and '12345' will both return 12345'.\n \"\"\"" ]
[ { "param": "ticket_value", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "ticket_value", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import re def coerce_to_number(ticket_value): if isinstance(ticket_value, int): return ticket_value ticket_re = re.compile(r'(?:SERVER-)?(\d+)', re.IGNORECASE) matches = ticket_re.fullmatch(ticket_value) if not matches: print("Unknown ticket number. Input: " + ticket_value) return -1 return int(matches.group(1))
610,065
733
d07eaeb1dd396386b222ce0ef9739dbd6bde1e2e
krivers/ITAP-django
hintgen/astTools.py
[ "MIT" ]
Python
isTokenStepString
<not_specific>
def isTokenStepString(s): """Determine whether this is a placeholder string""" if len(s) < 2: return False return s[0] == "~" and s[-1] == "~"
Determine whether this is a placeholder string
Determine whether this is a placeholder string
[ "Determine", "whether", "this", "is", "a", "placeholder", "string" ]
def isTokenStepString(s): if len(s) < 2: return False return s[0] == "~" and s[-1] == "~"
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Determine whether this is a placeholder string
[ "Determine", "whether", "this", "is", "a", "placeholder", "string" ]
[ "\"\"\"Determine whether this is a placeholder string\"\"\"" ]
[ { "param": "s", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "s", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def isTokenStepString(s): if len(s) < 2: return False return s[0] == "~" and s[-1] == "~"
610,066
698
a7b58982c093eb1e617fe0a6fc22056bed8f8b7c
xiaopeifeng/blade-build
src/blade/cc_targets.py
[ "BSD-3-Clause" ]
Python
_parse_hdr_level_line
<not_specific>
def _parse_hdr_level_line(line): """Parse a normal line of a header stack file Example: . ./common/rpc/rpc_client.h """ pos = line.find(' ') if pos == -1: return -1, '' level, hdr = line[:pos].count('.'), line[pos + 1:] if hdr.startswith('./'): hdr = hdr[2:] return level, hdr
Parse a normal line of a header stack file Example: . ./common/rpc/rpc_client.h
Parse a normal line of a header stack file
[ "Parse", "a", "normal", "line", "of", "a", "header", "stack", "file" ]
def _parse_hdr_level_line(line): pos = line.find(' ') if pos == -1: return -1, '' level, hdr = line[:pos].count('.'), line[pos + 1:] if hdr.startswith('./'): hdr = hdr[2:] return level, hdr
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Parse a normal line of a header stack file
[ "Parse", "a", "normal", "line", "of", "a", "header", "stack", "file" ]
[ "\"\"\"Parse a normal line of a header stack file\n\n Example:\n . ./common/rpc/rpc_client.h\n \"\"\"" ]
[ { "param": "line", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "line", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [ { "identifier": "examples", "docstring": null, "docstring_tokens": [ "None" ] } ] }
def _parse_hdr_level_line(line): pos = line.find(' ') if pos == -1: return -1, '' level, hdr = line[:pos].count('.'), line[pos + 1:] if hdr.startswith('./'): hdr = hdr[2:] return level, hdr
610,067
208
55f800af00f97598cdc65feb247a01ceca7a0d3f
Rikorose/DeepFilterNet
scripts/copy_datadir.py
[ "ECL-2.0", "Apache-2.0", "MIT" ]
Python
du
<not_specific>
def du(path): """disk usage in human readable format (e.g. '2,1')""" return float( subprocess.check_output(["du", "-shD", "--block-size=1G", path]).split()[0].decode("utf-8") )
disk usage in human readable format (e.g. '2,1')
disk usage in human readable format
[ "disk", "usage", "in", "human", "readable", "format" ]
def du(path): return float( subprocess.check_output(["du", "-shD", "--block-size=1G", path]).split()[0].decode("utf-8") )
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disk usage in human readable format (e.g.
[ "disk", "usage", "in", "human", "readable", "format", "(", "e", ".", "g", "." ]
[ "\"\"\"disk usage in human readable format (e.g. '2,1')\"\"\"" ]
[ { "param": "path", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "path", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import subprocess def du(path): return float( subprocess.check_output(["du", "-shD", "--block-size=1G", path]).split()[0].decode("utf-8") )
610,068
321
757a17e72f6d0e36336907c4898cb5954aba716f
cclauss/upvote
upvote/gae/datastore/utils.py
[ "Apache-2.0" ]
Python
DeletePropertyValue
null
def DeletePropertyValue(entity, property_name): """Delete a property's value from an ndb entity. NOTE: This function does not save the changes made to the provided entity. An additional put is required to save these changes. Args: entity: ndb.Model, The entity from which the property value will be removed. property_name: str, The name of the property whose value will be removed. """ if property_name in entity._values: # pylint: disable=protected-access del entity._values[property_name] # pylint: disable=protected-access
Delete a property's value from an ndb entity. NOTE: This function does not save the changes made to the provided entity. An additional put is required to save these changes. Args: entity: ndb.Model, The entity from which the property value will be removed. property_name: str, The name of the property whose value will be removed.
Delete a property's value from an ndb entity. NOTE: This function does not save the changes made to the provided entity. An additional put is required to save these changes.
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def DeletePropertyValue(entity, property_name): if property_name in entity._values: del entity._values[property_name]
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Delete a property's value from an ndb entity.
[ "Delete", "a", "property", "'", "s", "value", "from", "an", "ndb", "entity", "." ]
[ "\"\"\"Delete a property's value from an ndb entity.\n\n NOTE: This function does not save the changes made to the provided entity. An\n additional put is required to save these changes.\n\n Args:\n entity: ndb.Model, The entity from which the property value will be removed.\n property_name: str, The name of the property whose value will be removed.\n \"\"\"", "# pylint: disable=protected-access", "# pylint: disable=protected-access" ]
[ { "param": "entity", "type": null }, { "param": "property_name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "entity", "type": null, "docstring": "ndb.Model, The entity from which the property value will be removed.", "docstring_tokens": [ "ndb", ".", "Model", "The", "entity", "from", "which", "the", "property", "value", "will", "be", "removed", "." ], "default": null, "is_optional": null }, { "identifier": "property_name", "type": null, "docstring": "str, The name of the property whose value will be removed.", "docstring_tokens": [ "str", "The", "name", "of", "the", "property", "whose", "value", "will", "be", "removed", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def DeletePropertyValue(entity, property_name): if property_name in entity._values: del entity._values[property_name]
610,069
605
7f2caca0014f1a14279f4f3f6c45c8496032c5f9
Asap7772/railrl_evalsawyer
experiments/vitchyr/vaes/vae.py
[ "MIT" ]
Python
kl_to_prior
<not_specific>
def kl_to_prior(means, log_stds, stds): """ KL between a Gaussian and a standard Gaussian. https://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians """ return 0.5 * ( - 2 * log_stds # log std_prior = 0 - 1 # d = 1 + stds ** 2 + means ** 2 )
KL between a Gaussian and a standard Gaussian. https://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians
KL between a Gaussian and a standard Gaussian.
[ "KL", "between", "a", "Gaussian", "and", "a", "standard", "Gaussian", "." ]
def kl_to_prior(means, log_stds, stds): return 0.5 * ( - 2 * log_stds - 1 + stds ** 2 + means ** 2 )
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KL between a Gaussian and a standard Gaussian.
[ "KL", "between", "a", "Gaussian", "and", "a", "standard", "Gaussian", "." ]
[ "\"\"\"\n KL between a Gaussian and a standard Gaussian.\n\n https://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians\n \"\"\"", "# log std_prior = 0", "# d = 1" ]
[ { "param": "means", "type": null }, { "param": "log_stds", "type": null }, { "param": "stds", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "means", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "log_stds", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "stds", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def kl_to_prior(means, log_stds, stds): return 0.5 * ( - 2 * log_stds - 1 + stds ** 2 + means ** 2 )
610,070
220
56c05badd3db9ae3de257f30c39c88c4d47613d8
NetSys/kappa
compiler/transform/util.py
[ "BSD-2-Clause" ]
Python
parse_ast_stmt
ast.stmt
def parse_ast_stmt(stmt_code: str) -> ast.stmt: """Parses code for a single statement into an AST.""" stmt_code = textwrap.dedent(stmt_code) node = ast.parse(stmt_code, mode="exec") assert isinstance(node, ast.Module) if len(node.body) > 1: raise ValueError(f"Code contains more than one statement: {stmt_code}") return node.body[0]
Parses code for a single statement into an AST.
Parses code for a single statement into an AST.
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def parse_ast_stmt(stmt_code: str) -> ast.stmt: stmt_code = textwrap.dedent(stmt_code) node = ast.parse(stmt_code, mode="exec") assert isinstance(node, ast.Module) if len(node.body) > 1: raise ValueError(f"Code contains more than one statement: {stmt_code}") return node.body[0]
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Parses code for a single statement into an AST.
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[ "\"\"\"Parses code for a single statement into an AST.\"\"\"" ]
[ { "param": "stmt_code", "type": "str" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "stmt_code", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import ast import textwrap def parse_ast_stmt(stmt_code: str) -> ast.stmt: stmt_code = textwrap.dedent(stmt_code) node = ast.parse(stmt_code, mode="exec") assert isinstance(node, ast.Module) if len(node.body) > 1: raise ValueError(f"Code contains more than one statement: {stmt_code}") return node.body[0]
610,071
590
1fc5865adc1cae49a54c041b23db6e2464f295f1
SheetMusic-Team-3/MVP
aws-scripts/inference.py
[ "MIT" ]
Python
_process_input
<not_specific>
def _process_input(data, context): """ pre-process request input before it is sent to TensorFlow Serving REST API Args: data (obj): the request data, in format of dict or string context (Context): object containing request and configuration details Returns: (dict): a JSON-serializable dict that contains request body and headers """ if context.request_content_type == 'application/json': data = data.read().decode("utf-8") return data if len(data) else '' raise ValueError('{{"error": "unsupported content type {}"}}'.format( context.request_content_type or "unknown" ))
pre-process request input before it is sent to TensorFlow Serving REST API Args: data (obj): the request data, in format of dict or string context (Context): object containing request and configuration details Returns: (dict): a JSON-serializable dict that contains request body and headers
pre-process request input before it is sent to TensorFlow Serving REST API
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def _process_input(data, context): if context.request_content_type == 'application/json': data = data.read().decode("utf-8") return data if len(data) else '' raise ValueError('{{"error": "unsupported content type {}"}}'.format( context.request_content_type or "unknown" ))
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pre-process request input before it is sent to TensorFlow Serving REST API
[ "pre", "-", "process", "request", "input", "before", "it", "is", "sent", "to", "TensorFlow", "Serving", "REST", "API" ]
[ "\"\"\" pre-process request input before it is sent to\n TensorFlow Serving REST API\n Args:\n data (obj): the request data, in format of dict or string\n context (Context): object containing request and configuration details\n Returns:\n (dict): a JSON-serializable dict that contains request body and headers\n \"\"\"" ]
[ { "param": "data", "type": null }, { "param": "context", "type": null } ]
{ "returns": [ { "docstring": "a JSON-serializable dict that contains request body and headers", "docstring_tokens": [ "a", "JSON", "-", "serializable", "dict", "that", "contains", "request", "body", "and", "headers" ], "type": "(dict)" } ], "raises": [], "params": [ { "identifier": "data", "type": null, "docstring": "the request data, in format of dict or string", "docstring_tokens": [ "the", "request", "data", "in", "format", "of", "dict", "or", "string" ], "default": null, "is_optional": false }, { "identifier": "context", "type": null, "docstring": "object containing request and configuration details", "docstring_tokens": [ "object", "containing", "request", "and", "configuration", "details" ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def _process_input(data, context): if context.request_content_type == 'application/json': data = data.read().decode("utf-8") return data if len(data) else '' raise ValueError('{{"error": "unsupported content type {}"}}'.format( context.request_content_type or "unknown" ))
610,073
295
76278d15bf1333a3dd2e311c2bf0c34fca753e9b
yrath/law
law/parser.py
[ "BSD-3-Clause" ]
Python
add_cmdline_arg
<not_specific>
def add_cmdline_arg(args, arg, *values): """ Adds a command line argument *arg* to a list of argument *args*, e.g. as returned from :py:func:`global_cmdline_args`. When *arg* exists, *args* is returned unchanged. Otherwise, *arg* is appended to the end with optional argument *values*. Example: .. code-block:: python args = global_cmdline_values() # -> ["--local-scheduler"] add_cmdline_arg(args, "--local-scheduler") # -> ["--local-scheduler"] add_cmdline_arg(args, "--workers", 4) # -> ["--local-scheduler", "--workers", "4"] """ if arg not in args: args = list(args) + [arg] + list(values) return args
Adds a command line argument *arg* to a list of argument *args*, e.g. as returned from :py:func:`global_cmdline_args`. When *arg* exists, *args* is returned unchanged. Otherwise, *arg* is appended to the end with optional argument *values*. Example: .. code-block:: python args = global_cmdline_values() # -> ["--local-scheduler"] add_cmdline_arg(args, "--local-scheduler") # -> ["--local-scheduler"] add_cmdline_arg(args, "--workers", 4) # -> ["--local-scheduler", "--workers", "4"]
Adds a command line argument *arg* to a list of argument *args*, e.g. as returned from
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def add_cmdline_arg(args, arg, *values): if arg not in args: args = list(args) + [arg] + list(values) return args
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Adds a command line argument *arg* to a list of argument *args*, e.g.
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[ "\"\"\"\n Adds a command line argument *arg* to a list of argument *args*, e.g. as returned from\n :py:func:`global_cmdline_args`. When *arg* exists, *args* is returned unchanged. Otherwise,\n *arg* is appended to the end with optional argument *values*. Example:\n\n .. code-block:: python\n\n args = global_cmdline_values()\n # -> [\"--local-scheduler\"]\n\n add_cmdline_arg(args, \"--local-scheduler\")\n # -> [\"--local-scheduler\"]\n\n add_cmdline_arg(args, \"--workers\", 4)\n # -> [\"--local-scheduler\", \"--workers\", \"4\"]\n \"\"\"" ]
[ { "param": "args", "type": null }, { "param": "arg", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "args", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "arg", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [ { "identifier": "py", "docstring": "\n\ncode-block:: python\n\nargs = global_cmdline_values()\n-> [\"--local-scheduler\"]\n\nadd_cmdline_arg(args, \"--local-scheduler\")\n-> [\"--local-scheduler\"]\n\n", "docstring_tokens": [ "code", "-", "block", "::", "python", "args", "=", "global_cmdline_values", "()", "-", ">", "[", "\"", "--", "local", "-", "scheduler", "\"", "]", "add_cmdline_arg", "(", "args", "\"", "--", "local", "-", "scheduler", "\"", ")", "-", ">", "[", "\"", "--", "local", "-", "scheduler", "\"", "]" ] } ] }
def add_cmdline_arg(args, arg, *values): if arg not in args: args = list(args) + [arg] + list(values) return args
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