body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
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7e0110f6112ca6e5bff78ae7ce618b02ae24f7ecf0ba7e5c8066a79e3146b42d | def to_chunks_iobes(sequence, verbose=False, delim='@'):
'Turn a sequence of IOBES tags into a list of chunks.\n\n :param sequence: `List[str]` The tag sequence.\n :param verbose: `bool` Should we output warning on illegal transitions.\n :param delim: `str` The symbol the separates output chunks from their indices.\n\n :returns: `List[str]` The list of entities in the order they appear. The\n entities are in the form {chunk_type}{delim}{index}{delim}{index}...\n for example LOC@3@4@5 means a Location chunk was at indices 3, 4, and 5\n in the original sequence.\n '
chunks = []
current = None
for (i, label) in enumerate(sequence):
if label.startswith('B-'):
if (current is not None):
chunks.append(delim.join(current))
current = [label.replace('B-', ''), ('%d' % i)]
if verbose:
if ((i < (len(sequence) - 1)) and (label[2:] != sequence[(i + 1)][2:])):
logger.warning('Warning: Single B token chunk @ %d', i)
elif label.startswith('S-'):
if (current is not None):
chunks.append(delim.join(current))
current = None
base = label.replace('S-', '')
chunks.append(delim.join([base, ('%d' % i)]))
elif label.startswith('I-'):
if (current is not None):
base = label.replace('I-', '')
if (base == current[0]):
current.append(('%d' % i))
else:
chunks.append(delim.join(current))
if verbose:
logger.warning(('Warning: I without matching previous B/I @ %d' % i))
current = [base, ('%d' % i)]
else:
if verbose:
logger.warning(('Warning: I without a previous chunk @ %d' % i))
current = [label.replace('I-', ''), ('%d' % i)]
elif label.startswith('E-'):
if (current is not None):
base = label.replace('E-', '')
if (base == current[0]):
current.append(('%d' % i))
chunks.append(delim.join(current))
current = None
else:
chunks.append(delim.join(current))
if verbose:
logger.warning("Warning: E doesn't agree with previous B/I type!")
current = [base, ('%d' % i)]
chunks.append(delim.join(current))
current = None
else:
current = [label.replace('E-', ''), ('%d' % i)]
if verbose:
logger.warning(('Warning: E without previous chunk! @ %d' % i))
chunks.append(delim.join(current))
current = None
else:
if (current is not None):
chunks.append(delim.join(current))
current = None
if (current is not None):
chunks.append(delim.join(current))
return chunks | Turn a sequence of IOBES tags into a list of chunks.
:param sequence: `List[str]` The tag sequence.
:param verbose: `bool` Should we output warning on illegal transitions.
:param delim: `str` The symbol the separates output chunks from their indices.
:returns: `List[str]` The list of entities in the order they appear. The
entities are in the form {chunk_type}{delim}{index}{delim}{index}...
for example LOC@3@4@5 means a Location chunk was at indices 3, 4, and 5
in the original sequence. | python/baseline/utils.py | to_chunks_iobes | amyhemmeter/baseline | 0 | python | def to_chunks_iobes(sequence, verbose=False, delim='@'):
'Turn a sequence of IOBES tags into a list of chunks.\n\n :param sequence: `List[str]` The tag sequence.\n :param verbose: `bool` Should we output warning on illegal transitions.\n :param delim: `str` The symbol the separates output chunks from their indices.\n\n :returns: `List[str]` The list of entities in the order they appear. The\n entities are in the form {chunk_type}{delim}{index}{delim}{index}...\n for example LOC@3@4@5 means a Location chunk was at indices 3, 4, and 5\n in the original sequence.\n '
chunks = []
current = None
for (i, label) in enumerate(sequence):
if label.startswith('B-'):
if (current is not None):
chunks.append(delim.join(current))
current = [label.replace('B-', ), ('%d' % i)]
if verbose:
if ((i < (len(sequence) - 1)) and (label[2:] != sequence[(i + 1)][2:])):
logger.warning('Warning: Single B token chunk @ %d', i)
elif label.startswith('S-'):
if (current is not None):
chunks.append(delim.join(current))
current = None
base = label.replace('S-', )
chunks.append(delim.join([base, ('%d' % i)]))
elif label.startswith('I-'):
if (current is not None):
base = label.replace('I-', )
if (base == current[0]):
current.append(('%d' % i))
else:
chunks.append(delim.join(current))
if verbose:
logger.warning(('Warning: I without matching previous B/I @ %d' % i))
current = [base, ('%d' % i)]
else:
if verbose:
logger.warning(('Warning: I without a previous chunk @ %d' % i))
current = [label.replace('I-', ), ('%d' % i)]
elif label.startswith('E-'):
if (current is not None):
base = label.replace('E-', )
if (base == current[0]):
current.append(('%d' % i))
chunks.append(delim.join(current))
current = None
else:
chunks.append(delim.join(current))
if verbose:
logger.warning("Warning: E doesn't agree with previous B/I type!")
current = [base, ('%d' % i)]
chunks.append(delim.join(current))
current = None
else:
current = [label.replace('E-', ), ('%d' % i)]
if verbose:
logger.warning(('Warning: E without previous chunk! @ %d' % i))
chunks.append(delim.join(current))
current = None
else:
if (current is not None):
chunks.append(delim.join(current))
current = None
if (current is not None):
chunks.append(delim.join(current))
return chunks | def to_chunks_iobes(sequence, verbose=False, delim='@'):
'Turn a sequence of IOBES tags into a list of chunks.\n\n :param sequence: `List[str]` The tag sequence.\n :param verbose: `bool` Should we output warning on illegal transitions.\n :param delim: `str` The symbol the separates output chunks from their indices.\n\n :returns: `List[str]` The list of entities in the order they appear. The\n entities are in the form {chunk_type}{delim}{index}{delim}{index}...\n for example LOC@3@4@5 means a Location chunk was at indices 3, 4, and 5\n in the original sequence.\n '
chunks = []
current = None
for (i, label) in enumerate(sequence):
if label.startswith('B-'):
if (current is not None):
chunks.append(delim.join(current))
current = [label.replace('B-', ), ('%d' % i)]
if verbose:
if ((i < (len(sequence) - 1)) and (label[2:] != sequence[(i + 1)][2:])):
logger.warning('Warning: Single B token chunk @ %d', i)
elif label.startswith('S-'):
if (current is not None):
chunks.append(delim.join(current))
current = None
base = label.replace('S-', )
chunks.append(delim.join([base, ('%d' % i)]))
elif label.startswith('I-'):
if (current is not None):
base = label.replace('I-', )
if (base == current[0]):
current.append(('%d' % i))
else:
chunks.append(delim.join(current))
if verbose:
logger.warning(('Warning: I without matching previous B/I @ %d' % i))
current = [base, ('%d' % i)]
else:
if verbose:
logger.warning(('Warning: I without a previous chunk @ %d' % i))
current = [label.replace('I-', ), ('%d' % i)]
elif label.startswith('E-'):
if (current is not None):
base = label.replace('E-', )
if (base == current[0]):
current.append(('%d' % i))
chunks.append(delim.join(current))
current = None
else:
chunks.append(delim.join(current))
if verbose:
logger.warning("Warning: E doesn't agree with previous B/I type!")
current = [base, ('%d' % i)]
chunks.append(delim.join(current))
current = None
else:
current = [label.replace('E-', ), ('%d' % i)]
if verbose:
logger.warning(('Warning: E without previous chunk! @ %d' % i))
chunks.append(delim.join(current))
current = None
else:
if (current is not None):
chunks.append(delim.join(current))
current = None
if (current is not None):
chunks.append(delim.join(current))
return chunks<|docstring|>Turn a sequence of IOBES tags into a list of chunks.
:param sequence: `List[str]` The tag sequence.
:param verbose: `bool` Should we output warning on illegal transitions.
:param delim: `str` The symbol the separates output chunks from their indices.
:returns: `List[str]` The list of entities in the order they appear. The
entities are in the form {chunk_type}{delim}{index}{delim}{index}...
for example LOC@3@4@5 means a Location chunk was at indices 3, 4, and 5
in the original sequence.<|endoftext|> |
4ab9f3848b039d7885230cb37ea4b8c3c68c7afb0a538017b4bcaca01d8c6d3d | @exporter
def span_f1(golds, preds):
'Calculate Span level F1 score.\n\n :param golds: `List[set[str]]` The list of the set of gold chunks.\n :param preds: `List[set[str]]` The list of the set of predicted chunks.\n\n :returns: `float` The f1 score.\n '
overlap = sum((len((g & p)) for (g, p) in zip(golds, preds)))
gold_total = sum((len(g) for g in golds))
pred_total = sum((len(p) for p in preds))
return f_score(overlap, gold_total, pred_total) | Calculate Span level F1 score.
:param golds: `List[set[str]]` The list of the set of gold chunks.
:param preds: `List[set[str]]` The list of the set of predicted chunks.
:returns: `float` The f1 score. | python/baseline/utils.py | span_f1 | amyhemmeter/baseline | 0 | python | @exporter
def span_f1(golds, preds):
'Calculate Span level F1 score.\n\n :param golds: `List[set[str]]` The list of the set of gold chunks.\n :param preds: `List[set[str]]` The list of the set of predicted chunks.\n\n :returns: `float` The f1 score.\n '
overlap = sum((len((g & p)) for (g, p) in zip(golds, preds)))
gold_total = sum((len(g) for g in golds))
pred_total = sum((len(p) for p in preds))
return f_score(overlap, gold_total, pred_total) | @exporter
def span_f1(golds, preds):
'Calculate Span level F1 score.\n\n :param golds: `List[set[str]]` The list of the set of gold chunks.\n :param preds: `List[set[str]]` The list of the set of predicted chunks.\n\n :returns: `float` The f1 score.\n '
overlap = sum((len((g & p)) for (g, p) in zip(golds, preds)))
gold_total = sum((len(g) for g in golds))
pred_total = sum((len(p) for p in preds))
return f_score(overlap, gold_total, pred_total)<|docstring|>Calculate Span level F1 score.
:param golds: `List[set[str]]` The list of the set of gold chunks.
:param preds: `List[set[str]]` The list of the set of predicted chunks.
:returns: `float` The f1 score.<|endoftext|> |
f20e9dac4a90910129a56accd32d7963614cedbba2f51f7b163b96556c2bb7d1 | @exporter
def per_entity_f1(golds, preds, delim='@'):
'Calculate Span level F1 with break downs per entity type.\n\n :param golds: `List[set[str]]` The list of the set of gold chunks.\n :param preds: `List[set[str]]` The list of the set of predicted chunks.\n :param delim: `str` The symbol that separates an entity from its indices.\n\n :returns: `dict` The metrics at a global level and fine grained entity\n level performance.\n\n Note:\n This function returns most of the metrics needed for the\n `conlleval_output`. `acc` and `tokens` (the token level accuracy\n and the number of tokens respectively) need to be added.\n '
metrics = {}
overlap = Counter()
gold_total = Counter()
pred_total = Counter()
types = set()
for (g, p) in zip(golds, preds):
overlaps = (g & p)
overlap['total'] += len(overlaps)
gold_total['total'] += len(g)
pred_total['total'] += len(p)
for o in overlaps:
ent = o.split(delim)[0]
overlap[ent] += 1
types.add(ent)
for o in g:
ent = o.split(delim)[0]
gold_total[ent] += 1
types.add(ent)
for o in p:
ent = o.split(delim)[0]
pred_total[ent] += 1
types.add(ent)
metrics['overlap'] = overlap['total']
metrics['gold_total'] = gold_total['total']
metrics['pred_total'] = pred_total['total']
metrics['precision'] = (precision(overlap['total'], pred_total['total']) * 100)
metrics['recall'] = (recall(overlap['total'], gold_total['total']) * 100)
metrics['f1'] = (f_score(overlap['total'], gold_total['total'], pred_total['total']) * 100)
metrics['types'] = []
for t in sorted(types):
metrics['types'].append({'ent': t, 'precision': (precision(overlap[t], pred_total[t]) * 100), 'recall': (recall(overlap[t], gold_total[t]) * 100), 'f1': (f_score(overlap[t], gold_total[t], pred_total[t]) * 100), 'count': pred_total[t]})
return metrics | Calculate Span level F1 with break downs per entity type.
:param golds: `List[set[str]]` The list of the set of gold chunks.
:param preds: `List[set[str]]` The list of the set of predicted chunks.
:param delim: `str` The symbol that separates an entity from its indices.
:returns: `dict` The metrics at a global level and fine grained entity
level performance.
Note:
This function returns most of the metrics needed for the
`conlleval_output`. `acc` and `tokens` (the token level accuracy
and the number of tokens respectively) need to be added. | python/baseline/utils.py | per_entity_f1 | amyhemmeter/baseline | 0 | python | @exporter
def per_entity_f1(golds, preds, delim='@'):
'Calculate Span level F1 with break downs per entity type.\n\n :param golds: `List[set[str]]` The list of the set of gold chunks.\n :param preds: `List[set[str]]` The list of the set of predicted chunks.\n :param delim: `str` The symbol that separates an entity from its indices.\n\n :returns: `dict` The metrics at a global level and fine grained entity\n level performance.\n\n Note:\n This function returns most of the metrics needed for the\n `conlleval_output`. `acc` and `tokens` (the token level accuracy\n and the number of tokens respectively) need to be added.\n '
metrics = {}
overlap = Counter()
gold_total = Counter()
pred_total = Counter()
types = set()
for (g, p) in zip(golds, preds):
overlaps = (g & p)
overlap['total'] += len(overlaps)
gold_total['total'] += len(g)
pred_total['total'] += len(p)
for o in overlaps:
ent = o.split(delim)[0]
overlap[ent] += 1
types.add(ent)
for o in g:
ent = o.split(delim)[0]
gold_total[ent] += 1
types.add(ent)
for o in p:
ent = o.split(delim)[0]
pred_total[ent] += 1
types.add(ent)
metrics['overlap'] = overlap['total']
metrics['gold_total'] = gold_total['total']
metrics['pred_total'] = pred_total['total']
metrics['precision'] = (precision(overlap['total'], pred_total['total']) * 100)
metrics['recall'] = (recall(overlap['total'], gold_total['total']) * 100)
metrics['f1'] = (f_score(overlap['total'], gold_total['total'], pred_total['total']) * 100)
metrics['types'] = []
for t in sorted(types):
metrics['types'].append({'ent': t, 'precision': (precision(overlap[t], pred_total[t]) * 100), 'recall': (recall(overlap[t], gold_total[t]) * 100), 'f1': (f_score(overlap[t], gold_total[t], pred_total[t]) * 100), 'count': pred_total[t]})
return metrics | @exporter
def per_entity_f1(golds, preds, delim='@'):
'Calculate Span level F1 with break downs per entity type.\n\n :param golds: `List[set[str]]` The list of the set of gold chunks.\n :param preds: `List[set[str]]` The list of the set of predicted chunks.\n :param delim: `str` The symbol that separates an entity from its indices.\n\n :returns: `dict` The metrics at a global level and fine grained entity\n level performance.\n\n Note:\n This function returns most of the metrics needed for the\n `conlleval_output`. `acc` and `tokens` (the token level accuracy\n and the number of tokens respectively) need to be added.\n '
metrics = {}
overlap = Counter()
gold_total = Counter()
pred_total = Counter()
types = set()
for (g, p) in zip(golds, preds):
overlaps = (g & p)
overlap['total'] += len(overlaps)
gold_total['total'] += len(g)
pred_total['total'] += len(p)
for o in overlaps:
ent = o.split(delim)[0]
overlap[ent] += 1
types.add(ent)
for o in g:
ent = o.split(delim)[0]
gold_total[ent] += 1
types.add(ent)
for o in p:
ent = o.split(delim)[0]
pred_total[ent] += 1
types.add(ent)
metrics['overlap'] = overlap['total']
metrics['gold_total'] = gold_total['total']
metrics['pred_total'] = pred_total['total']
metrics['precision'] = (precision(overlap['total'], pred_total['total']) * 100)
metrics['recall'] = (recall(overlap['total'], gold_total['total']) * 100)
metrics['f1'] = (f_score(overlap['total'], gold_total['total'], pred_total['total']) * 100)
metrics['types'] = []
for t in sorted(types):
metrics['types'].append({'ent': t, 'precision': (precision(overlap[t], pred_total[t]) * 100), 'recall': (recall(overlap[t], gold_total[t]) * 100), 'f1': (f_score(overlap[t], gold_total[t], pred_total[t]) * 100), 'count': pred_total[t]})
return metrics<|docstring|>Calculate Span level F1 with break downs per entity type.
:param golds: `List[set[str]]` The list of the set of gold chunks.
:param preds: `List[set[str]]` The list of the set of predicted chunks.
:param delim: `str` The symbol that separates an entity from its indices.
:returns: `dict` The metrics at a global level and fine grained entity
level performance.
Note:
This function returns most of the metrics needed for the
`conlleval_output`. `acc` and `tokens` (the token level accuracy
and the number of tokens respectively) need to be added.<|endoftext|> |
e828579bac650a52fddcc86a5be00289209c71bde3566b958c46467519946bf4 | @exporter
def conlleval_output(results):
"Create conlleval formated output.\n\n :param results: `dict` The metrics. results should have the following keys.\n tokens: `int` The total number of tokens processed.\n acc: `float` The token level accuracy.\n gold_total: `int` The total number of gold entities.\n pred_total: `int` The total number of predicted entities.\n overlap: `int` The number of exact match entites.\n precision: `float` The precision of all entities.\n recall: `float` The recall of all entities.\n f1: `float` The f1 score of all entities.\n types: `List[dict]` A list of metrics for each entity type. Keys should include:\n ent: `str` The name of the entity.\n precision: `float` The precision of this entity type.\n recall: `float` The recall of this this entity type.\n f1: `float` The f1 score of this entity type.\n count: `int` The number of predicted entities of this type.\n\n :returns: `str` The formatted string ready for printing.\n\n Note:\n Both the metrics in the results dict and acc are expected to already be\n multiplied by 100. The result won't look correct and a lot of the\n metric will be cut off if they are not.\n\n Metrics per type are output in the order they appear in the list.\n conlleval.pl outputs the types in sorted order. To match this the list\n in `results['types'] should be sorted.\n "
s = 'processed {tokens} tokens with {gold_total} phrases; found: {pred_total} phrases; correct: {overlap}.\naccuracy: {acc:>{length}.2f}%; precision: {precision:>6.2f}%; recall: {recall:>6.2f}%; FB1: {f1:>6.2f}\n'
t = []
longest_ent = max(len(max(results['types'], key=(lambda x: len(x['ent'])))['ent']), 17)
for type_metric in results['types']:
t.append('{ent:>{longest_ent}}: precision: {precision:>6.2f}%; recall: {recall:>6.2f}%; FB1: {f1:>6.2f} {count}'.format(longest_ent=longest_ent, **type_metric))
s = (s + '\n'.join(t))
s = s.format(length=(longest_ent - 11), **results)
return s | Create conlleval formated output.
:param results: `dict` The metrics. results should have the following keys.
tokens: `int` The total number of tokens processed.
acc: `float` The token level accuracy.
gold_total: `int` The total number of gold entities.
pred_total: `int` The total number of predicted entities.
overlap: `int` The number of exact match entites.
precision: `float` The precision of all entities.
recall: `float` The recall of all entities.
f1: `float` The f1 score of all entities.
types: `List[dict]` A list of metrics for each entity type. Keys should include:
ent: `str` The name of the entity.
precision: `float` The precision of this entity type.
recall: `float` The recall of this this entity type.
f1: `float` The f1 score of this entity type.
count: `int` The number of predicted entities of this type.
:returns: `str` The formatted string ready for printing.
Note:
Both the metrics in the results dict and acc are expected to already be
multiplied by 100. The result won't look correct and a lot of the
metric will be cut off if they are not.
Metrics per type are output in the order they appear in the list.
conlleval.pl outputs the types in sorted order. To match this the list
in `results['types'] should be sorted. | python/baseline/utils.py | conlleval_output | amyhemmeter/baseline | 0 | python | @exporter
def conlleval_output(results):
"Create conlleval formated output.\n\n :param results: `dict` The metrics. results should have the following keys.\n tokens: `int` The total number of tokens processed.\n acc: `float` The token level accuracy.\n gold_total: `int` The total number of gold entities.\n pred_total: `int` The total number of predicted entities.\n overlap: `int` The number of exact match entites.\n precision: `float` The precision of all entities.\n recall: `float` The recall of all entities.\n f1: `float` The f1 score of all entities.\n types: `List[dict]` A list of metrics for each entity type. Keys should include:\n ent: `str` The name of the entity.\n precision: `float` The precision of this entity type.\n recall: `float` The recall of this this entity type.\n f1: `float` The f1 score of this entity type.\n count: `int` The number of predicted entities of this type.\n\n :returns: `str` The formatted string ready for printing.\n\n Note:\n Both the metrics in the results dict and acc are expected to already be\n multiplied by 100. The result won't look correct and a lot of the\n metric will be cut off if they are not.\n\n Metrics per type are output in the order they appear in the list.\n conlleval.pl outputs the types in sorted order. To match this the list\n in `results['types'] should be sorted.\n "
s = 'processed {tokens} tokens with {gold_total} phrases; found: {pred_total} phrases; correct: {overlap}.\naccuracy: {acc:>{length}.2f}%; precision: {precision:>6.2f}%; recall: {recall:>6.2f}%; FB1: {f1:>6.2f}\n'
t = []
longest_ent = max(len(max(results['types'], key=(lambda x: len(x['ent'])))['ent']), 17)
for type_metric in results['types']:
t.append('{ent:>{longest_ent}}: precision: {precision:>6.2f}%; recall: {recall:>6.2f}%; FB1: {f1:>6.2f} {count}'.format(longest_ent=longest_ent, **type_metric))
s = (s + '\n'.join(t))
s = s.format(length=(longest_ent - 11), **results)
return s | @exporter
def conlleval_output(results):
"Create conlleval formated output.\n\n :param results: `dict` The metrics. results should have the following keys.\n tokens: `int` The total number of tokens processed.\n acc: `float` The token level accuracy.\n gold_total: `int` The total number of gold entities.\n pred_total: `int` The total number of predicted entities.\n overlap: `int` The number of exact match entites.\n precision: `float` The precision of all entities.\n recall: `float` The recall of all entities.\n f1: `float` The f1 score of all entities.\n types: `List[dict]` A list of metrics for each entity type. Keys should include:\n ent: `str` The name of the entity.\n precision: `float` The precision of this entity type.\n recall: `float` The recall of this this entity type.\n f1: `float` The f1 score of this entity type.\n count: `int` The number of predicted entities of this type.\n\n :returns: `str` The formatted string ready for printing.\n\n Note:\n Both the metrics in the results dict and acc are expected to already be\n multiplied by 100. The result won't look correct and a lot of the\n metric will be cut off if they are not.\n\n Metrics per type are output in the order they appear in the list.\n conlleval.pl outputs the types in sorted order. To match this the list\n in `results['types'] should be sorted.\n "
s = 'processed {tokens} tokens with {gold_total} phrases; found: {pred_total} phrases; correct: {overlap}.\naccuracy: {acc:>{length}.2f}%; precision: {precision:>6.2f}%; recall: {recall:>6.2f}%; FB1: {f1:>6.2f}\n'
t = []
longest_ent = max(len(max(results['types'], key=(lambda x: len(x['ent'])))['ent']), 17)
for type_metric in results['types']:
t.append('{ent:>{longest_ent}}: precision: {precision:>6.2f}%; recall: {recall:>6.2f}%; FB1: {f1:>6.2f} {count}'.format(longest_ent=longest_ent, **type_metric))
s = (s + '\n'.join(t))
s = s.format(length=(longest_ent - 11), **results)
return s<|docstring|>Create conlleval formated output.
:param results: `dict` The metrics. results should have the following keys.
tokens: `int` The total number of tokens processed.
acc: `float` The token level accuracy.
gold_total: `int` The total number of gold entities.
pred_total: `int` The total number of predicted entities.
overlap: `int` The number of exact match entites.
precision: `float` The precision of all entities.
recall: `float` The recall of all entities.
f1: `float` The f1 score of all entities.
types: `List[dict]` A list of metrics for each entity type. Keys should include:
ent: `str` The name of the entity.
precision: `float` The precision of this entity type.
recall: `float` The recall of this this entity type.
f1: `float` The f1 score of this entity type.
count: `int` The number of predicted entities of this type.
:returns: `str` The formatted string ready for printing.
Note:
Both the metrics in the results dict and acc are expected to already be
multiplied by 100. The result won't look correct and a lot of the
metric will be cut off if they are not.
Metrics per type are output in the order they appear in the list.
conlleval.pl outputs the types in sorted order. To match this the list
in `results['types'] should be sorted.<|endoftext|> |
1c58b37bf465720f5a419b20241ebec73f8b373f3ca959327663e5fba25ae04c | @exporter
def precision(overlap_count, guess_count):
'Compute the precision in a zero safe way.\n\n :param overlap_count: `int` The number of true positives.\n :param guess_count: `int` The number of predicted positives (tp + fp)\n\n :returns: `float` The precision.\n '
if (guess_count == 0):
return 0.0
return (overlap_count / float(guess_count)) | Compute the precision in a zero safe way.
:param overlap_count: `int` The number of true positives.
:param guess_count: `int` The number of predicted positives (tp + fp)
:returns: `float` The precision. | python/baseline/utils.py | precision | amyhemmeter/baseline | 0 | python | @exporter
def precision(overlap_count, guess_count):
'Compute the precision in a zero safe way.\n\n :param overlap_count: `int` The number of true positives.\n :param guess_count: `int` The number of predicted positives (tp + fp)\n\n :returns: `float` The precision.\n '
if (guess_count == 0):
return 0.0
return (overlap_count / float(guess_count)) | @exporter
def precision(overlap_count, guess_count):
'Compute the precision in a zero safe way.\n\n :param overlap_count: `int` The number of true positives.\n :param guess_count: `int` The number of predicted positives (tp + fp)\n\n :returns: `float` The precision.\n '
if (guess_count == 0):
return 0.0
return (overlap_count / float(guess_count))<|docstring|>Compute the precision in a zero safe way.
:param overlap_count: `int` The number of true positives.
:param guess_count: `int` The number of predicted positives (tp + fp)
:returns: `float` The precision.<|endoftext|> |
46745100806cb3e94fcf92859eb9fc4310eb711eae8ff24c2775ae1ce8207fcd | @exporter
def recall(overlap_count, gold_count):
'Compute the recall in a zero safe way.\n\n :param overlap_count: `int` The number of true positives.\n :param gold_count: `int` The number of gold positives (tp + fn)\n\n :returns: `float` The recall.\n '
if (gold_count == 0):
return 0.0
return (overlap_count / float(gold_count)) | Compute the recall in a zero safe way.
:param overlap_count: `int` The number of true positives.
:param gold_count: `int` The number of gold positives (tp + fn)
:returns: `float` The recall. | python/baseline/utils.py | recall | amyhemmeter/baseline | 0 | python | @exporter
def recall(overlap_count, gold_count):
'Compute the recall in a zero safe way.\n\n :param overlap_count: `int` The number of true positives.\n :param gold_count: `int` The number of gold positives (tp + fn)\n\n :returns: `float` The recall.\n '
if (gold_count == 0):
return 0.0
return (overlap_count / float(gold_count)) | @exporter
def recall(overlap_count, gold_count):
'Compute the recall in a zero safe way.\n\n :param overlap_count: `int` The number of true positives.\n :param gold_count: `int` The number of gold positives (tp + fn)\n\n :returns: `float` The recall.\n '
if (gold_count == 0):
return 0.0
return (overlap_count / float(gold_count))<|docstring|>Compute the recall in a zero safe way.
:param overlap_count: `int` The number of true positives.
:param gold_count: `int` The number of gold positives (tp + fn)
:returns: `float` The recall.<|endoftext|> |
27ab2d40719368a5b12ab3c354dc2cab13100d13ae1f3762bbdf67164f8c44f3 | @exporter
def f_score(overlap_count, gold_count, guess_count, f=1):
'Compute the f1 score.\n\n :param overlap_count: `int` The number of true positives.\n :param gold_count: `int` The number of gold positives (tp + fn)\n :param guess_count: `int` The number of predicted positives (tp + fp)\n :param f: `int` The beta term to weight precision vs recall.\n\n :returns: `float` The f score\n '
beta_sq = (f * f)
if (guess_count == 0):
return 0.0
p = precision(overlap_count, guess_count)
r = recall(overlap_count, gold_count)
if ((p == 0.0) or (r == 0.0)):
return 0.0
f = (((1.0 + beta_sq) * (p * r)) / ((beta_sq * p) + r))
return f | Compute the f1 score.
:param overlap_count: `int` The number of true positives.
:param gold_count: `int` The number of gold positives (tp + fn)
:param guess_count: `int` The number of predicted positives (tp + fp)
:param f: `int` The beta term to weight precision vs recall.
:returns: `float` The f score | python/baseline/utils.py | f_score | amyhemmeter/baseline | 0 | python | @exporter
def f_score(overlap_count, gold_count, guess_count, f=1):
'Compute the f1 score.\n\n :param overlap_count: `int` The number of true positives.\n :param gold_count: `int` The number of gold positives (tp + fn)\n :param guess_count: `int` The number of predicted positives (tp + fp)\n :param f: `int` The beta term to weight precision vs recall.\n\n :returns: `float` The f score\n '
beta_sq = (f * f)
if (guess_count == 0):
return 0.0
p = precision(overlap_count, guess_count)
r = recall(overlap_count, gold_count)
if ((p == 0.0) or (r == 0.0)):
return 0.0
f = (((1.0 + beta_sq) * (p * r)) / ((beta_sq * p) + r))
return f | @exporter
def f_score(overlap_count, gold_count, guess_count, f=1):
'Compute the f1 score.\n\n :param overlap_count: `int` The number of true positives.\n :param gold_count: `int` The number of gold positives (tp + fn)\n :param guess_count: `int` The number of predicted positives (tp + fp)\n :param f: `int` The beta term to weight precision vs recall.\n\n :returns: `float` The f score\n '
beta_sq = (f * f)
if (guess_count == 0):
return 0.0
p = precision(overlap_count, guess_count)
r = recall(overlap_count, gold_count)
if ((p == 0.0) or (r == 0.0)):
return 0.0
f = (((1.0 + beta_sq) * (p * r)) / ((beta_sq * p) + r))
return f<|docstring|>Compute the f1 score.
:param overlap_count: `int` The number of true positives.
:param gold_count: `int` The number of gold positives (tp + fn)
:param guess_count: `int` The number of predicted positives (tp + fp)
:param f: `int` The beta term to weight precision vs recall.
:returns: `float` The f score<|endoftext|> |
55df35c53616b77404a214d0032e85764c7d5016fece0763b583ae52092418dc | @exporter
def unzip_model(path):
'If the path for a model file is a zip file, unzip it in /tmp and return the unzipped path'
if os.path.isdir(path):
return path
from baseline.mime_type import mime_type
if (mime_type(path) == 'application/zip'):
with open(path, 'rb') as f:
sha1 = hashlib.sha1(f.read()).hexdigest()
temp_dir = os.path.join('/tmp/', sha1)
if (not os.path.exists(temp_dir)):
logger.info('unzipping model')
with zipfile.ZipFile(path, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
if (len(os.listdir(temp_dir)) == 1):
temp_dir = os.path.join(temp_dir, os.listdir(temp_dir)[0])
path = os.path.join(temp_dir, [x[:(- 6)] for x in os.listdir(temp_dir) if ('index' in x)][0])
return path | If the path for a model file is a zip file, unzip it in /tmp and return the unzipped path | python/baseline/utils.py | unzip_model | amyhemmeter/baseline | 0 | python | @exporter
def unzip_model(path):
if os.path.isdir(path):
return path
from baseline.mime_type import mime_type
if (mime_type(path) == 'application/zip'):
with open(path, 'rb') as f:
sha1 = hashlib.sha1(f.read()).hexdigest()
temp_dir = os.path.join('/tmp/', sha1)
if (not os.path.exists(temp_dir)):
logger.info('unzipping model')
with zipfile.ZipFile(path, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
if (len(os.listdir(temp_dir)) == 1):
temp_dir = os.path.join(temp_dir, os.listdir(temp_dir)[0])
path = os.path.join(temp_dir, [x[:(- 6)] for x in os.listdir(temp_dir) if ('index' in x)][0])
return path | @exporter
def unzip_model(path):
if os.path.isdir(path):
return path
from baseline.mime_type import mime_type
if (mime_type(path) == 'application/zip'):
with open(path, 'rb') as f:
sha1 = hashlib.sha1(f.read()).hexdigest()
temp_dir = os.path.join('/tmp/', sha1)
if (not os.path.exists(temp_dir)):
logger.info('unzipping model')
with zipfile.ZipFile(path, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
if (len(os.listdir(temp_dir)) == 1):
temp_dir = os.path.join(temp_dir, os.listdir(temp_dir)[0])
path = os.path.join(temp_dir, [x[:(- 6)] for x in os.listdir(temp_dir) if ('index' in x)][0])
return path<|docstring|>If the path for a model file is a zip file, unzip it in /tmp and return the unzipped path<|endoftext|> |
3517a97af35088915b1ffbe8f1e38f271dbe435ec85c1f5a61f71cfd1a2c2a69 | @exporter
def zip_model(path):
'zips the model files'
logger.info('zipping model files')
model_files = [x for x in os.listdir('.') if (path[2:] in x)]
z = zipfile.ZipFile('{}.zip'.format(path), 'w')
for f in model_files:
z.write(f)
os.remove(f)
z.close() | zips the model files | python/baseline/utils.py | zip_model | amyhemmeter/baseline | 0 | python | @exporter
def zip_model(path):
logger.info('zipping model files')
model_files = [x for x in os.listdir('.') if (path[2:] in x)]
z = zipfile.ZipFile('{}.zip'.format(path), 'w')
for f in model_files:
z.write(f)
os.remove(f)
z.close() | @exporter
def zip_model(path):
logger.info('zipping model files')
model_files = [x for x in os.listdir('.') if (path[2:] in x)]
z = zipfile.ZipFile('{}.zip'.format(path), 'w')
for f in model_files:
z.write(f)
os.remove(f)
z.close()<|docstring|>zips the model files<|endoftext|> |
d2e6be8f4f51da97c928db9ef87391743552b26c553a4f030f529c388c4a4d26 | @exporter
def show_examples(model, es, rlut1, rlut2, vocab, mxlen, sample, prob_clip, max_examples, reverse):
'Expects model.predict to return [B, K, T].'
si = np.random.randint(0, len(es))
batch_dict = es[si]
lengths_key = model.src_lengths_key
src_field = lengths_key.split('_')[0]
src_array = batch_dict[src_field]
if (max_examples > 0):
max_examples = min(max_examples, src_array.shape[0])
for i in range(max_examples):
example = {}
for (k, v) in batch_dict.items():
example[k] = v[(i, np.newaxis)]
logger.info('========================================================================')
sent = lookup_sentence(rlut1, example[src_field].squeeze(), reverse=reverse)
logger.info(('[OP] %s' % sent))
sent = lookup_sentence(rlut2, example['tgt'].squeeze())
logger.info(('[Actual] %s' % sent))
dst_i = model.predict(example)[0][0]
sent = lookup_sentence(rlut2, dst_i)
logger.info(('Guess: %s' % sent))
logger.info('------------------------------------------------------------------------') | Expects model.predict to return [B, K, T]. | python/baseline/utils.py | show_examples | amyhemmeter/baseline | 0 | python | @exporter
def show_examples(model, es, rlut1, rlut2, vocab, mxlen, sample, prob_clip, max_examples, reverse):
si = np.random.randint(0, len(es))
batch_dict = es[si]
lengths_key = model.src_lengths_key
src_field = lengths_key.split('_')[0]
src_array = batch_dict[src_field]
if (max_examples > 0):
max_examples = min(max_examples, src_array.shape[0])
for i in range(max_examples):
example = {}
for (k, v) in batch_dict.items():
example[k] = v[(i, np.newaxis)]
logger.info('========================================================================')
sent = lookup_sentence(rlut1, example[src_field].squeeze(), reverse=reverse)
logger.info(('[OP] %s' % sent))
sent = lookup_sentence(rlut2, example['tgt'].squeeze())
logger.info(('[Actual] %s' % sent))
dst_i = model.predict(example)[0][0]
sent = lookup_sentence(rlut2, dst_i)
logger.info(('Guess: %s' % sent))
logger.info('------------------------------------------------------------------------') | @exporter
def show_examples(model, es, rlut1, rlut2, vocab, mxlen, sample, prob_clip, max_examples, reverse):
si = np.random.randint(0, len(es))
batch_dict = es[si]
lengths_key = model.src_lengths_key
src_field = lengths_key.split('_')[0]
src_array = batch_dict[src_field]
if (max_examples > 0):
max_examples = min(max_examples, src_array.shape[0])
for i in range(max_examples):
example = {}
for (k, v) in batch_dict.items():
example[k] = v[(i, np.newaxis)]
logger.info('========================================================================')
sent = lookup_sentence(rlut1, example[src_field].squeeze(), reverse=reverse)
logger.info(('[OP] %s' % sent))
sent = lookup_sentence(rlut2, example['tgt'].squeeze())
logger.info(('[Actual] %s' % sent))
dst_i = model.predict(example)[0][0]
sent = lookup_sentence(rlut2, dst_i)
logger.info(('Guess: %s' % sent))
logger.info('------------------------------------------------------------------------')<|docstring|>Expects model.predict to return [B, K, T].<|endoftext|> |
427e2eb3118c08621144d7d38626df18cdc5341ed1eead80cfb7bf77e15e5e40 | @exporter
def convert_seq2seq_golds(indices, lengths, rlut, subword_fix=(lambda x: x)):
'Convert indices to words and format like a bleu reference corpus.\n\n :param indices: The indices of the gold sentence. Should be in the shape\n `[B, T]`. Iterating though axis=1 should yield ints.\n :param lengths: The length of the gold sentences.\n :param rlut: `dict[int] -> str` A lookup table from indices to words.\n\n :returns: List[List[List[str]]] Shape is [B, 1, T] where T is the number of\n words in that gold sentence\n '
golds = []
for (idx, l) in zip(indices, lengths):
gold = idx[:l]
gold_str = lookup_sentence(rlut, gold)
gold = subword_fix(gold_str).split()
golds.append([gold])
return golds | Convert indices to words and format like a bleu reference corpus.
:param indices: The indices of the gold sentence. Should be in the shape
`[B, T]`. Iterating though axis=1 should yield ints.
:param lengths: The length of the gold sentences.
:param rlut: `dict[int] -> str` A lookup table from indices to words.
:returns: List[List[List[str]]] Shape is [B, 1, T] where T is the number of
words in that gold sentence | python/baseline/utils.py | convert_seq2seq_golds | amyhemmeter/baseline | 0 | python | @exporter
def convert_seq2seq_golds(indices, lengths, rlut, subword_fix=(lambda x: x)):
'Convert indices to words and format like a bleu reference corpus.\n\n :param indices: The indices of the gold sentence. Should be in the shape\n `[B, T]`. Iterating though axis=1 should yield ints.\n :param lengths: The length of the gold sentences.\n :param rlut: `dict[int] -> str` A lookup table from indices to words.\n\n :returns: List[List[List[str]]] Shape is [B, 1, T] where T is the number of\n words in that gold sentence\n '
golds = []
for (idx, l) in zip(indices, lengths):
gold = idx[:l]
gold_str = lookup_sentence(rlut, gold)
gold = subword_fix(gold_str).split()
golds.append([gold])
return golds | @exporter
def convert_seq2seq_golds(indices, lengths, rlut, subword_fix=(lambda x: x)):
'Convert indices to words and format like a bleu reference corpus.\n\n :param indices: The indices of the gold sentence. Should be in the shape\n `[B, T]`. Iterating though axis=1 should yield ints.\n :param lengths: The length of the gold sentences.\n :param rlut: `dict[int] -> str` A lookup table from indices to words.\n\n :returns: List[List[List[str]]] Shape is [B, 1, T] where T is the number of\n words in that gold sentence\n '
golds = []
for (idx, l) in zip(indices, lengths):
gold = idx[:l]
gold_str = lookup_sentence(rlut, gold)
gold = subword_fix(gold_str).split()
golds.append([gold])
return golds<|docstring|>Convert indices to words and format like a bleu reference corpus.
:param indices: The indices of the gold sentence. Should be in the shape
`[B, T]`. Iterating though axis=1 should yield ints.
:param lengths: The length of the gold sentences.
:param rlut: `dict[int] -> str` A lookup table from indices to words.
:returns: List[List[List[str]]] Shape is [B, 1, T] where T is the number of
words in that gold sentence<|endoftext|> |
668157cf5239b38b38a4420af8b5fb4302c0f89c5f37cf698e2c9832feea1250 | @exporter
def convert_seq2seq_preds(indices, rlut, subword_fix=(lambda x: x)):
'Convert indices to words and format like a bleu hypothesis corpus.\n\n :param indices: The indices of the predicted sentence. Should be in the\n shape `[B, T]`. Iterating though axis=1 should yield ints.\n :param rlut: `dict[int] -> str` A lookup table from indices to words.\n\n :returns: List[List[str]] Shape is [B, T] where T is the number of\n words in that predicted sentence\n '
preds = []
for idx in indices:
pred_str = lookup_sentence(rlut, idx)
pred = subword_fix(pred_str).split()
preds.append(pred)
return preds | Convert indices to words and format like a bleu hypothesis corpus.
:param indices: The indices of the predicted sentence. Should be in the
shape `[B, T]`. Iterating though axis=1 should yield ints.
:param rlut: `dict[int] -> str` A lookup table from indices to words.
:returns: List[List[str]] Shape is [B, T] where T is the number of
words in that predicted sentence | python/baseline/utils.py | convert_seq2seq_preds | amyhemmeter/baseline | 0 | python | @exporter
def convert_seq2seq_preds(indices, rlut, subword_fix=(lambda x: x)):
'Convert indices to words and format like a bleu hypothesis corpus.\n\n :param indices: The indices of the predicted sentence. Should be in the\n shape `[B, T]`. Iterating though axis=1 should yield ints.\n :param rlut: `dict[int] -> str` A lookup table from indices to words.\n\n :returns: List[List[str]] Shape is [B, T] where T is the number of\n words in that predicted sentence\n '
preds = []
for idx in indices:
pred_str = lookup_sentence(rlut, idx)
pred = subword_fix(pred_str).split()
preds.append(pred)
return preds | @exporter
def convert_seq2seq_preds(indices, rlut, subword_fix=(lambda x: x)):
'Convert indices to words and format like a bleu hypothesis corpus.\n\n :param indices: The indices of the predicted sentence. Should be in the\n shape `[B, T]`. Iterating though axis=1 should yield ints.\n :param rlut: `dict[int] -> str` A lookup table from indices to words.\n\n :returns: List[List[str]] Shape is [B, T] where T is the number of\n words in that predicted sentence\n '
preds = []
for idx in indices:
pred_str = lookup_sentence(rlut, idx)
pred = subword_fix(pred_str).split()
preds.append(pred)
return preds<|docstring|>Convert indices to words and format like a bleu hypothesis corpus.
:param indices: The indices of the predicted sentence. Should be in the
shape `[B, T]`. Iterating though axis=1 should yield ints.
:param rlut: `dict[int] -> str` A lookup table from indices to words.
:returns: List[List[str]] Shape is [B, T] where T is the number of
words in that predicted sentence<|endoftext|> |
261c68dfc523a1fc83e9cfeddad343f4fadd2fb6f56459dcbd9240a0223b8bb2 | @exporter
def undo_bpe(seq):
'Undo the BPE splits to make Bleu comparable.\n\n :param seq: `str`: The string with encoded tokens in it.\n\n :returns: `str`: The string with BPE splits collapsed.\n '
return re.sub('@@( | ?$)', '', seq) | Undo the BPE splits to make Bleu comparable.
:param seq: `str`: The string with encoded tokens in it.
:returns: `str`: The string with BPE splits collapsed. | python/baseline/utils.py | undo_bpe | amyhemmeter/baseline | 0 | python | @exporter
def undo_bpe(seq):
'Undo the BPE splits to make Bleu comparable.\n\n :param seq: `str`: The string with encoded tokens in it.\n\n :returns: `str`: The string with BPE splits collapsed.\n '
return re.sub('@@( | ?$)', , seq) | @exporter
def undo_bpe(seq):
'Undo the BPE splits to make Bleu comparable.\n\n :param seq: `str`: The string with encoded tokens in it.\n\n :returns: `str`: The string with BPE splits collapsed.\n '
return re.sub('@@( | ?$)', , seq)<|docstring|>Undo the BPE splits to make Bleu comparable.
:param seq: `str`: The string with encoded tokens in it.
:returns: `str`: The string with BPE splits collapsed.<|endoftext|> |
271affe0273e3a27e64c750a52e94f31c58e4a7433e1db107b8cf9015fbfd0ee | @exporter
def undo_sentence_piece(seq):
'Undo the sentence Piece splits to make Bleu comparable.'
return seq.replace('▁', '') | Undo the sentence Piece splits to make Bleu comparable. | python/baseline/utils.py | undo_sentence_piece | amyhemmeter/baseline | 0 | python | @exporter
def undo_sentence_piece(seq):
return seq.replace('▁', ) | @exporter
def undo_sentence_piece(seq):
return seq.replace('▁', )<|docstring|>Undo the sentence Piece splits to make Bleu comparable.<|endoftext|> |
70ca5befa05fd7096dd244c3c2e7d8a23590cca8125bc59a801f8e6eb90eb363 | @exporter
def ngrams(sentence, filtsz=3, joiner='@@'):
'Generate ngrams over a sentence\n\n :param sentence: (`List[str]`) Some tokens\n :param filtsz: The ngram width\n :param joiner: A string to join ngrams\n :return: (`List[str]`) A list of ngrams\n '
chunks = []
nt = len(sentence)
for i in range(((nt - filtsz) + 1)):
chunk = sentence[i:(i + filtsz)]
chunks += [joiner.join(chunk)]
return chunks | Generate ngrams over a sentence
:param sentence: (`List[str]`) Some tokens
:param filtsz: The ngram width
:param joiner: A string to join ngrams
:return: (`List[str]`) A list of ngrams | python/baseline/utils.py | ngrams | amyhemmeter/baseline | 0 | python | @exporter
def ngrams(sentence, filtsz=3, joiner='@@'):
'Generate ngrams over a sentence\n\n :param sentence: (`List[str]`) Some tokens\n :param filtsz: The ngram width\n :param joiner: A string to join ngrams\n :return: (`List[str]`) A list of ngrams\n '
chunks = []
nt = len(sentence)
for i in range(((nt - filtsz) + 1)):
chunk = sentence[i:(i + filtsz)]
chunks += [joiner.join(chunk)]
return chunks | @exporter
def ngrams(sentence, filtsz=3, joiner='@@'):
'Generate ngrams over a sentence\n\n :param sentence: (`List[str]`) Some tokens\n :param filtsz: The ngram width\n :param joiner: A string to join ngrams\n :return: (`List[str]`) A list of ngrams\n '
chunks = []
nt = len(sentence)
for i in range(((nt - filtsz) + 1)):
chunk = sentence[i:(i + filtsz)]
chunks += [joiner.join(chunk)]
return chunks<|docstring|>Generate ngrams over a sentence
:param sentence: (`List[str]`) Some tokens
:param filtsz: The ngram width
:param joiner: A string to join ngrams
:return: (`List[str]`) A list of ngrams<|endoftext|> |
c5599dd755db88d6d3aaf14e0bbdac77402efd86ca8dec6f7f0565c9c37aef97 | @exporter
@str_file
def read_label_first_data(f):
'Read data from a file where the first token in the label and each line is a single example.\n\n :param f: `Union[str, IO]` The file to read from.\n :return: `Tuple[List[str], List[List[str]]]` The labels and text\n '
(labels, texts) = ([], [])
for line in f:
line = line.rstrip()
if line:
(label, text) = line.split(maxsplit=1)
labels.append(label)
texts.append(text.split())
return (labels, texts) | Read data from a file where the first token in the label and each line is a single example.
:param f: `Union[str, IO]` The file to read from.
:return: `Tuple[List[str], List[List[str]]]` The labels and text | python/baseline/utils.py | read_label_first_data | amyhemmeter/baseline | 0 | python | @exporter
@str_file
def read_label_first_data(f):
'Read data from a file where the first token in the label and each line is a single example.\n\n :param f: `Union[str, IO]` The file to read from.\n :return: `Tuple[List[str], List[List[str]]]` The labels and text\n '
(labels, texts) = ([], [])
for line in f:
line = line.rstrip()
if line:
(label, text) = line.split(maxsplit=1)
labels.append(label)
texts.append(text.split())
return (labels, texts) | @exporter
@str_file
def read_label_first_data(f):
'Read data from a file where the first token in the label and each line is a single example.\n\n :param f: `Union[str, IO]` The file to read from.\n :return: `Tuple[List[str], List[List[str]]]` The labels and text\n '
(labels, texts) = ([], [])
for line in f:
line = line.rstrip()
if line:
(label, text) = line.split(maxsplit=1)
labels.append(label)
texts.append(text.split())
return (labels, texts)<|docstring|>Read data from a file where the first token in the label and each line is a single example.
:param f: `Union[str, IO]` The file to read from.
:return: `Tuple[List[str], List[List[str]]]` The labels and text<|endoftext|> |
e3c20c2d3547fcad8eb8528e9e3d2626361357a102641cf61de0f67d6b0edeb4 | @exporter
@str_file(w='w')
def write_label_first_data(w, labels, texts):
'Read data to a file where the first token in the label and each line is a single example.\n\n :param w: `Union[str, IO]` The file to write the results in\n :param labels: `List[str]` The labels for the examples\n :param texts: `List[List[str]]` The text examples\n '
w.write('\n'.join((' '.join(chain((l,), t)) for (l, t) in zip(labels, texts)))) | Read data to a file where the first token in the label and each line is a single example.
:param w: `Union[str, IO]` The file to write the results in
:param labels: `List[str]` The labels for the examples
:param texts: `List[List[str]]` The text examples | python/baseline/utils.py | write_label_first_data | amyhemmeter/baseline | 0 | python | @exporter
@str_file(w='w')
def write_label_first_data(w, labels, texts):
'Read data to a file where the first token in the label and each line is a single example.\n\n :param w: `Union[str, IO]` The file to write the results in\n :param labels: `List[str]` The labels for the examples\n :param texts: `List[List[str]]` The text examples\n '
w.write('\n'.join((' '.join(chain((l,), t)) for (l, t) in zip(labels, texts)))) | @exporter
@str_file(w='w')
def write_label_first_data(w, labels, texts):
'Read data to a file where the first token in the label and each line is a single example.\n\n :param w: `Union[str, IO]` The file to write the results in\n :param labels: `List[str]` The labels for the examples\n :param texts: `List[List[str]]` The text examples\n '
w.write('\n'.join((' '.join(chain((l,), t)) for (l, t) in zip(labels, texts))))<|docstring|>Read data to a file where the first token in the label and each line is a single example.
:param w: `Union[str, IO]` The file to write the results in
:param labels: `List[str]` The labels for the examples
:param texts: `List[List[str]]` The text examples<|endoftext|> |
111e5a933ed4260066058397e831d23571584ac4da0f56a87bf8758aa771647a | def is_valid_url(url: str) -> bool:
'Check is the provided URL is valid.'
return (re.match(url_validator, url) is not None) | Check is the provided URL is valid. | libraries/ml-lab-py/lab_client/commons/request_utils.py | is_valid_url | Felipe-Renck/machine-learning-lab | 55 | python | def is_valid_url(url: str) -> bool:
return (re.match(url_validator, url) is not None) | def is_valid_url(url: str) -> bool:
return (re.match(url_validator, url) is not None)<|docstring|>Check is the provided URL is valid.<|endoftext|> |
ff1ef9b4747d08f40e992834bd50cf40b6fdca044da6e4339d115ac77a7dbc86 | def is_downloadable(url: str) -> bool:
'\n Does the url is valid and contain a downloadable resource\n '
try:
import requests
h = requests.head(url, allow_redirects=True)
header = h.headers
content_type = header.get('content-type')
if (content_type and ('html' in content_type.lower())):
return False
return True
except:
return False | Does the url is valid and contain a downloadable resource | libraries/ml-lab-py/lab_client/commons/request_utils.py | is_downloadable | Felipe-Renck/machine-learning-lab | 55 | python | def is_downloadable(url: str) -> bool:
'\n \n '
try:
import requests
h = requests.head(url, allow_redirects=True)
header = h.headers
content_type = header.get('content-type')
if (content_type and ('html' in content_type.lower())):
return False
return True
except:
return False | def is_downloadable(url: str) -> bool:
'\n \n '
try:
import requests
h = requests.head(url, allow_redirects=True)
header = h.headers
content_type = header.get('content-type')
if (content_type and ('html' in content_type.lower())):
return False
return True
except:
return False<|docstring|>Does the url is valid and contain a downloadable resource<|endoftext|> |
a92e286a2a6e3340bb888a80936a91efa804cdb0956f52feb1ec0a74752f2210 | def url2filename(url: str) -> str:
"Return basename corresponding to url.\n >>> print(url2filename('http://example.com/path/to/file%C3%80?opt=1'))\n fileÀ\n >>> print(url2filename('http://example.com/slash%2fname')) # '/' in name\n Traceback (most recent call last):\n ...\n ValueError\n "
urlpath = urlsplit(url).path
basename = posixpath.basename(unquote(urlpath))
if ((os.path.basename(basename) != basename) or (unquote(posixpath.basename(urlpath)) != basename)):
raise ValueError
return basename | Return basename corresponding to url.
>>> print(url2filename('http://example.com/path/to/file%C3%80?opt=1'))
fileÀ
>>> print(url2filename('http://example.com/slash%2fname')) # '/' in name
Traceback (most recent call last):
...
ValueError | libraries/ml-lab-py/lab_client/commons/request_utils.py | url2filename | Felipe-Renck/machine-learning-lab | 55 | python | def url2filename(url: str) -> str:
"Return basename corresponding to url.\n >>> print(url2filename('http://example.com/path/to/file%C3%80?opt=1'))\n fileÀ\n >>> print(url2filename('http://example.com/slash%2fname')) # '/' in name\n Traceback (most recent call last):\n ...\n ValueError\n "
urlpath = urlsplit(url).path
basename = posixpath.basename(unquote(urlpath))
if ((os.path.basename(basename) != basename) or (unquote(posixpath.basename(urlpath)) != basename)):
raise ValueError
return basename | def url2filename(url: str) -> str:
"Return basename corresponding to url.\n >>> print(url2filename('http://example.com/path/to/file%C3%80?opt=1'))\n fileÀ\n >>> print(url2filename('http://example.com/slash%2fname')) # '/' in name\n Traceback (most recent call last):\n ...\n ValueError\n "
urlpath = urlsplit(url).path
basename = posixpath.basename(unquote(urlpath))
if ((os.path.basename(basename) != basename) or (unquote(posixpath.basename(urlpath)) != basename)):
raise ValueError
return basename<|docstring|>Return basename corresponding to url.
>>> print(url2filename('http://example.com/path/to/file%C3%80?opt=1'))
fileÀ
>>> print(url2filename('http://example.com/slash%2fname')) # '/' in name
Traceback (most recent call last):
...
ValueError<|endoftext|> |
f27f6d17569e04b4295e7363520b915fa4f23f71d15b4f01c386b0ca3206e67a | def get_storages_images(self):
'\n Function return all images on all storages\n '
images = []
storages = self.get_storages(only_actives=True)
for storage in storages:
stg = self.get_storage(storage)
try:
stg.refresh(0)
except:
pass
for img in stg.listVolumes():
if img.endswith('.iso'):
pass
else:
images.append(img)
return images | Function return all images on all storages | vrtManager/create.py | get_storages_images | torjean/webvirtcloud | 0 | python | def get_storages_images(self):
'\n \n '
images = []
storages = self.get_storages(only_actives=True)
for storage in storages:
stg = self.get_storage(storage)
try:
stg.refresh(0)
except:
pass
for img in stg.listVolumes():
if img.endswith('.iso'):
pass
else:
images.append(img)
return images | def get_storages_images(self):
'\n \n '
images = []
storages = self.get_storages(only_actives=True)
for storage in storages:
stg = self.get_storage(storage)
try:
stg.refresh(0)
except:
pass
for img in stg.listVolumes():
if img.endswith('.iso'):
pass
else:
images.append(img)
return images<|docstring|>Function return all images on all storages<|endoftext|> |
ac676b1df9e684e7bfc982b6fc4761e0c3c12df3265aa05b077acb45f1e29d13 | def get_os_type(self):
'Get guest capabilities'
return util.get_xml_path(self.get_cap_xml(), '/capabilities/guest/os_type') | Get guest capabilities | vrtManager/create.py | get_os_type | torjean/webvirtcloud | 0 | python | def get_os_type(self):
return util.get_xml_path(self.get_cap_xml(), '/capabilities/guest/os_type') | def get_os_type(self):
return util.get_xml_path(self.get_cap_xml(), '/capabilities/guest/os_type')<|docstring|>Get guest capabilities<|endoftext|> |
fc8fb683d9fb8c511293772cc3e885b32b791ae9335a031cbeee62a31bad44de | def get_host_arch(self):
'Get guest capabilities'
return util.get_xml_path(self.get_cap_xml(), '/capabilities/host/cpu/arch') | Get guest capabilities | vrtManager/create.py | get_host_arch | torjean/webvirtcloud | 0 | python | def get_host_arch(self):
return util.get_xml_path(self.get_cap_xml(), '/capabilities/host/cpu/arch') | def get_host_arch(self):
return util.get_xml_path(self.get_cap_xml(), '/capabilities/host/cpu/arch')<|docstring|>Get guest capabilities<|endoftext|> |
b190450538f04b1907b31ce3ff30fa9ff492935350c871197754a77fe653c437 | def create_instance(self, name, memory, vcpu, host_model, uuid, images, cache_mode, networks, virtio, listen_addr, nwfilter=None, video='cirrus', console_pass='random', mac=None, qemu_ga=False):
'\n Create VM function\n '
memory = (int(memory) * 1024)
if self.is_kvm_supported():
hypervisor_type = 'kvm'
else:
hypervisor_type = 'qemu'
xml = ("\n <domain type='%s'>\n <name>%s</name>\n <description>None</description>\n <uuid>%s</uuid>\n <memory unit='KiB'>%s</memory>\n <vcpu>%s</vcpu>" % (hypervisor_type, name, uuid, memory, vcpu))
if host_model:
xml += "<cpu mode='host-model'/>"
xml += ("<os>\n <type arch='%s'>%s</type>\n <boot dev='hd'/>\n <boot dev='cdrom'/>\n <bootmenu enable='yes'/>\n </os>" % (self.get_host_arch(), self.get_os_type()))
xml += '<features>\n <acpi/><apic/><pae/>\n </features>\n <clock offset="utc"/>\n <on_poweroff>destroy</on_poweroff>\n <on_reboot>restart</on_reboot>\n <on_crash>restart</on_crash>\n <devices>'
vd_disk_letters = list(string.lowercase)
fd_disk_letters = list(string.lowercase)
hd_disk_letters = list(string.lowercase)
sd_disk_letters = list(string.lowercase)
add_cd = True
for volume in images:
stg = self.get_storage_by_vol_path(volume['path'])
stg_type = util.get_xml_path(stg.XMLDesc(0), '/pool/@type')
if (volume['device'] == 'cdrom'):
add_cd = False
if (stg_type == 'rbd'):
(ceph_user, secret_uuid, ceph_hosts) = get_rbd_storage_data(stg)
xml += ("<disk type='network' device='disk'>\n <driver name='qemu' type='%s' cache='%s'/>\n <auth username='%s'>\n <secret type='ceph' uuid='%s'/>\n </auth>\n <source protocol='rbd' name='%s'>" % (volume['type'], cache_mode, ceph_user, secret_uuid, volume['path']))
if isinstance(ceph_hosts, list):
for host in ceph_hosts:
if host.get('port'):
xml += ("\n <host name='%s' port='%s'/>" % (host.get('name'), host.get('port')))
else:
xml += ("\n <host name='%s'/>" % host.get('name'))
xml += '\n </source>'
else:
xml += ("<disk type='file' device='%s'>\n <driver name='qemu' type='%s' cache='%s'/>\n <source file='%s'/>" % (volume['device'], volume['type'], cache_mode, volume['path']))
if (volume['bus'] == 'virtio'):
xml += ("<target dev='vd%s' bus='%s'/>" % (vd_disk_letters.pop(0), volume['bus']))
elif (volume['bus'] == 'ide'):
xml += ("<target dev='hd%s' bus='%s'/>" % (hd_disk_letters.pop(0), volume['bus']))
elif (volume['bus'] == 'fdc'):
xml += ("<target dev='fd%s' bus='%s'/>" % (fd_disk_letters.pop(0), volume['bus']))
else:
xml += ("<target dev='sd%s' bus='%s'/>" % (sd_disk_letters.pop(0), volume['bus']))
xml += '</disk>'
if add_cd:
xml += (" <disk type='file' device='cdrom'>\n <driver name='qemu' type='raw'/>\n <source file=''/>\n <target dev='hd%s' bus='ide'/>\n <readonly/>\n </disk>" % (hd_disk_letters.pop(0),))
for net in networks.split(','):
xml += "<interface type='network'>"
if mac:
xml += ("<mac address='%s'/>" % mac)
xml += ("<source network='%s'/>" % net)
if nwfilter:
xml += ("<filterref filter='%s'/>" % nwfilter)
if virtio:
xml += "<model type='virtio'/>"
xml += '</interface>'
if (console_pass == 'random'):
console_pass = (("passwd='" + util.randomPasswd()) + "'")
elif (not (console_pass == '')):
console_pass = (("passwd='" + console_pass) + "'")
xml += (" <input type='mouse' bus='ps2'/>\n <input type='tablet' bus='usb'/>\n <graphics type='%s' port='-1' autoport='yes' %s listen='%s'/>\n <console type='pty'/> " % (QEMU_CONSOLE_DEFAULT_TYPE, console_pass, listen_addr))
if qemu_ga:
xml += " <channel type='unix'>\n <target type='virtio' name='org.qemu.guest_agent.0'/>\n </channel>"
xml += (" <video>\n <model type='%s'/>\n </video>\n <memballoon model='virtio'/>\n </devices>\n </domain>" % video)
self._defineXML(xml) | Create VM function | vrtManager/create.py | create_instance | torjean/webvirtcloud | 0 | python | def create_instance(self, name, memory, vcpu, host_model, uuid, images, cache_mode, networks, virtio, listen_addr, nwfilter=None, video='cirrus', console_pass='random', mac=None, qemu_ga=False):
'\n \n '
memory = (int(memory) * 1024)
if self.is_kvm_supported():
hypervisor_type = 'kvm'
else:
hypervisor_type = 'qemu'
xml = ("\n <domain type='%s'>\n <name>%s</name>\n <description>None</description>\n <uuid>%s</uuid>\n <memory unit='KiB'>%s</memory>\n <vcpu>%s</vcpu>" % (hypervisor_type, name, uuid, memory, vcpu))
if host_model:
xml += "<cpu mode='host-model'/>"
xml += ("<os>\n <type arch='%s'>%s</type>\n <boot dev='hd'/>\n <boot dev='cdrom'/>\n <bootmenu enable='yes'/>\n </os>" % (self.get_host_arch(), self.get_os_type()))
xml += '<features>\n <acpi/><apic/><pae/>\n </features>\n <clock offset="utc"/>\n <on_poweroff>destroy</on_poweroff>\n <on_reboot>restart</on_reboot>\n <on_crash>restart</on_crash>\n <devices>'
vd_disk_letters = list(string.lowercase)
fd_disk_letters = list(string.lowercase)
hd_disk_letters = list(string.lowercase)
sd_disk_letters = list(string.lowercase)
add_cd = True
for volume in images:
stg = self.get_storage_by_vol_path(volume['path'])
stg_type = util.get_xml_path(stg.XMLDesc(0), '/pool/@type')
if (volume['device'] == 'cdrom'):
add_cd = False
if (stg_type == 'rbd'):
(ceph_user, secret_uuid, ceph_hosts) = get_rbd_storage_data(stg)
xml += ("<disk type='network' device='disk'>\n <driver name='qemu' type='%s' cache='%s'/>\n <auth username='%s'>\n <secret type='ceph' uuid='%s'/>\n </auth>\n <source protocol='rbd' name='%s'>" % (volume['type'], cache_mode, ceph_user, secret_uuid, volume['path']))
if isinstance(ceph_hosts, list):
for host in ceph_hosts:
if host.get('port'):
xml += ("\n <host name='%s' port='%s'/>" % (host.get('name'), host.get('port')))
else:
xml += ("\n <host name='%s'/>" % host.get('name'))
xml += '\n </source>'
else:
xml += ("<disk type='file' device='%s'>\n <driver name='qemu' type='%s' cache='%s'/>\n <source file='%s'/>" % (volume['device'], volume['type'], cache_mode, volume['path']))
if (volume['bus'] == 'virtio'):
xml += ("<target dev='vd%s' bus='%s'/>" % (vd_disk_letters.pop(0), volume['bus']))
elif (volume['bus'] == 'ide'):
xml += ("<target dev='hd%s' bus='%s'/>" % (hd_disk_letters.pop(0), volume['bus']))
elif (volume['bus'] == 'fdc'):
xml += ("<target dev='fd%s' bus='%s'/>" % (fd_disk_letters.pop(0), volume['bus']))
else:
xml += ("<target dev='sd%s' bus='%s'/>" % (sd_disk_letters.pop(0), volume['bus']))
xml += '</disk>'
if add_cd:
xml += (" <disk type='file' device='cdrom'>\n <driver name='qemu' type='raw'/>\n <source file=/>\n <target dev='hd%s' bus='ide'/>\n <readonly/>\n </disk>" % (hd_disk_letters.pop(0),))
for net in networks.split(','):
xml += "<interface type='network'>"
if mac:
xml += ("<mac address='%s'/>" % mac)
xml += ("<source network='%s'/>" % net)
if nwfilter:
xml += ("<filterref filter='%s'/>" % nwfilter)
if virtio:
xml += "<model type='virtio'/>"
xml += '</interface>'
if (console_pass == 'random'):
console_pass = (("passwd='" + util.randomPasswd()) + "'")
elif (not (console_pass == )):
console_pass = (("passwd='" + console_pass) + "'")
xml += (" <input type='mouse' bus='ps2'/>\n <input type='tablet' bus='usb'/>\n <graphics type='%s' port='-1' autoport='yes' %s listen='%s'/>\n <console type='pty'/> " % (QEMU_CONSOLE_DEFAULT_TYPE, console_pass, listen_addr))
if qemu_ga:
xml += " <channel type='unix'>\n <target type='virtio' name='org.qemu.guest_agent.0'/>\n </channel>"
xml += (" <video>\n <model type='%s'/>\n </video>\n <memballoon model='virtio'/>\n </devices>\n </domain>" % video)
self._defineXML(xml) | def create_instance(self, name, memory, vcpu, host_model, uuid, images, cache_mode, networks, virtio, listen_addr, nwfilter=None, video='cirrus', console_pass='random', mac=None, qemu_ga=False):
'\n \n '
memory = (int(memory) * 1024)
if self.is_kvm_supported():
hypervisor_type = 'kvm'
else:
hypervisor_type = 'qemu'
xml = ("\n <domain type='%s'>\n <name>%s</name>\n <description>None</description>\n <uuid>%s</uuid>\n <memory unit='KiB'>%s</memory>\n <vcpu>%s</vcpu>" % (hypervisor_type, name, uuid, memory, vcpu))
if host_model:
xml += "<cpu mode='host-model'/>"
xml += ("<os>\n <type arch='%s'>%s</type>\n <boot dev='hd'/>\n <boot dev='cdrom'/>\n <bootmenu enable='yes'/>\n </os>" % (self.get_host_arch(), self.get_os_type()))
xml += '<features>\n <acpi/><apic/><pae/>\n </features>\n <clock offset="utc"/>\n <on_poweroff>destroy</on_poweroff>\n <on_reboot>restart</on_reboot>\n <on_crash>restart</on_crash>\n <devices>'
vd_disk_letters = list(string.lowercase)
fd_disk_letters = list(string.lowercase)
hd_disk_letters = list(string.lowercase)
sd_disk_letters = list(string.lowercase)
add_cd = True
for volume in images:
stg = self.get_storage_by_vol_path(volume['path'])
stg_type = util.get_xml_path(stg.XMLDesc(0), '/pool/@type')
if (volume['device'] == 'cdrom'):
add_cd = False
if (stg_type == 'rbd'):
(ceph_user, secret_uuid, ceph_hosts) = get_rbd_storage_data(stg)
xml += ("<disk type='network' device='disk'>\n <driver name='qemu' type='%s' cache='%s'/>\n <auth username='%s'>\n <secret type='ceph' uuid='%s'/>\n </auth>\n <source protocol='rbd' name='%s'>" % (volume['type'], cache_mode, ceph_user, secret_uuid, volume['path']))
if isinstance(ceph_hosts, list):
for host in ceph_hosts:
if host.get('port'):
xml += ("\n <host name='%s' port='%s'/>" % (host.get('name'), host.get('port')))
else:
xml += ("\n <host name='%s'/>" % host.get('name'))
xml += '\n </source>'
else:
xml += ("<disk type='file' device='%s'>\n <driver name='qemu' type='%s' cache='%s'/>\n <source file='%s'/>" % (volume['device'], volume['type'], cache_mode, volume['path']))
if (volume['bus'] == 'virtio'):
xml += ("<target dev='vd%s' bus='%s'/>" % (vd_disk_letters.pop(0), volume['bus']))
elif (volume['bus'] == 'ide'):
xml += ("<target dev='hd%s' bus='%s'/>" % (hd_disk_letters.pop(0), volume['bus']))
elif (volume['bus'] == 'fdc'):
xml += ("<target dev='fd%s' bus='%s'/>" % (fd_disk_letters.pop(0), volume['bus']))
else:
xml += ("<target dev='sd%s' bus='%s'/>" % (sd_disk_letters.pop(0), volume['bus']))
xml += '</disk>'
if add_cd:
xml += (" <disk type='file' device='cdrom'>\n <driver name='qemu' type='raw'/>\n <source file=/>\n <target dev='hd%s' bus='ide'/>\n <readonly/>\n </disk>" % (hd_disk_letters.pop(0),))
for net in networks.split(','):
xml += "<interface type='network'>"
if mac:
xml += ("<mac address='%s'/>" % mac)
xml += ("<source network='%s'/>" % net)
if nwfilter:
xml += ("<filterref filter='%s'/>" % nwfilter)
if virtio:
xml += "<model type='virtio'/>"
xml += '</interface>'
if (console_pass == 'random'):
console_pass = (("passwd='" + util.randomPasswd()) + "'")
elif (not (console_pass == )):
console_pass = (("passwd='" + console_pass) + "'")
xml += (" <input type='mouse' bus='ps2'/>\n <input type='tablet' bus='usb'/>\n <graphics type='%s' port='-1' autoport='yes' %s listen='%s'/>\n <console type='pty'/> " % (QEMU_CONSOLE_DEFAULT_TYPE, console_pass, listen_addr))
if qemu_ga:
xml += " <channel type='unix'>\n <target type='virtio' name='org.qemu.guest_agent.0'/>\n </channel>"
xml += (" <video>\n <model type='%s'/>\n </video>\n <memballoon model='virtio'/>\n </devices>\n </domain>" % video)
self._defineXML(xml)<|docstring|>Create VM function<|endoftext|> |
6a7c4ee0795abd0b4e74b5a76e251d463bfff482f4ddac7249891535f8a45ff6 | def build_attention_model(params, src_vocab, trg_vocab, source_placeholders, target_placeholders, beam_size=1, mode=MODE.TRAIN, burn_in_step=100000, increment_step=10000, teacher_rate=1.0, max_step=100):
"\n Build a model.\n\n :param params: dict.\n {encoder: {rnn_cell: {},\n ...},\n decoder: {rnn_cell: {},\n ...}}\n for example:\n {'encoder': {'rnn_cell': {'state_size': 512,\n 'cell_name': 'BasicLSTMCell',\n 'num_layers': 2,\n 'input_keep_prob': 1.0,\n 'output_keep_prob': 1.0},\n 'attention_key_size': attention_size},\n 'decoder': {'rnn_cell': {'cell_name': 'BasicLSTMCell',\n 'state_size': 512,\n 'num_layers': 1,\n 'input_keep_prob': 1.0,\n 'output_keep_prob': 1.0},\n 'trg_vocab_size': trg_vocab_size}}\n :param src_vocab: Vocab of source symbols.\n :param trg_vocab: Vocab of target symbols.\n :param source_ids: placeholder\n :param source_seq_length: placeholder\n :param target_ids: placeholder\n :param target_seq_length: placeholder\n :param beam_size: used in beam inference\n :param mode:\n :return:\n "
if (mode != MODE.TRAIN):
params = sq.disable_dropout(params)
tf.logging.info(json.dumps(params, indent=4))
decoder_params = params['decoder']
source_ids = source_placeholders['src']
source_seq_length = source_placeholders['src_len']
source_sample_matrix = source_placeholders['src_sample_matrix']
source_word_seq_length = source_placeholders['src_word_len']
target_ids = target_placeholders['trg']
target_seq_length = target_placeholders['trg_len']
source_char_embedding_table = sq.LookUpOp(src_vocab.vocab_size, src_vocab.embedding_dim, name='source')
source_char_embedded = source_char_embedding_table(source_ids)
char_encoder = sq.StackRNNEncoder(params['char_encoder'], params['attention_key_size']['char'], name='char_rnn', mode=mode)
char_encoded_representation = char_encoder.encode(source_char_embedded, source_seq_length)
char_encoder_outputs = char_encoded_representation.outputs
char_encoder_outputs = tf.transpose(char_encoder_outputs, perm=(1, 0, 2))
sampled_word_embedded = tf.matmul(source_sample_matrix, char_encoder_outputs)
source_embedded = tf.transpose(sampled_word_embedded, perm=(1, 0, 2))
encoder = sq.StackBidirectionalRNNEncoder(params['encoder'], params['attention_key_size']['word'], name='stack_rnn', mode=mode)
encoded_representation = encoder.encode(source_embedded, source_word_seq_length)
attention_keys = encoded_representation.attention_keys
attention_values = encoded_representation.attention_values
attention_length = encoded_representation.attention_length
encoder_final_states_bw = encoded_representation.final_state[(- 1)][(- 1)].h
if (mode == MODE.RL):
tf.logging.info('BUILDING RL TRAIN FEEDBACK......')
dynamical_batch_size = tf.shape(attention_keys)[1]
feedback = sq.RLTrainingFeedBack(target_ids, target_seq_length, trg_vocab, dynamical_batch_size, burn_in_step=burn_in_step, increment_step=increment_step, max_step=max_step)
elif (mode == MODE.TRAIN):
tf.logging.info('BUILDING TRAIN FEEDBACK WITH {} TEACHER_RATE......'.format(teacher_rate))
feedback = sq.TrainingFeedBack(target_ids, target_seq_length, trg_vocab, teacher_rate, max_step=max_step)
elif (mode == MODE.EVAL):
tf.logging.info('BUILDING EVAL FEEDBACK ......')
feedback = sq.TrainingFeedBack(target_ids, target_seq_length, trg_vocab, 0.0, max_step=max_step)
else:
tf.logging.info('BUILDING INFER FEEDBACK WITH BEAM_SIZE {}......'.format(beam_size))
infer_key_size = attention_keys.get_shape().as_list()[(- 1)]
infer_value_size = attention_values.get_shape().as_list()[(- 1)]
infer_states_bw_shape = encoder_final_states_bw.get_shape().as_list()[(- 1)]
encoder_final_states_bw = tf.reshape(tf.tile(encoder_final_states_bw, [1, beam_size]), [(- 1), infer_states_bw_shape])
if TIME_MAJOR:
dynamical_batch_size = tf.shape(attention_keys)[1]
final_key_shape = [(- 1), (dynamical_batch_size * beam_size), infer_key_size]
final_value_shape = [(- 1), (dynamical_batch_size * beam_size), infer_value_size]
attention_keys = tf.reshape(tf.tile(attention_keys, [1, 1, beam_size]), final_key_shape)
attention_values = tf.reshape(tf.tile(attention_values, [1, 1, beam_size]), final_value_shape)
else:
dynamical_batch_size = tf.shape(attention_keys)[0]
final_key_shape = [(dynamical_batch_size * beam_size), (- 1), infer_key_size]
final_value_shape = [(dynamical_batch_size * beam_size), (- 1), infer_value_size]
attention_keys = tf.reshape(tf.tile(attention_keys, [1, beam_size, 1]), final_key_shape)
attention_values = tf.reshape(tf.tile(attention_values, [1, beam_size, 1]), final_value_shape)
attention_length = tf.reshape(tf.transpose(tf.tile([attention_length], [beam_size, 1])), [(- 1)])
feedback = sq.BeamFeedBack(trg_vocab, beam_size, dynamical_batch_size, max_step=max_step)
encoder_decoder_bridge = EncoderDecoderBridge(encoder_final_states_bw.get_shape().as_list()[(- 1)], decoder_params['rnn_cell'])
decoder_state_size = decoder_params['rnn_cell']['state_size']
attention = sq.Attention(decoder_state_size, attention_keys, attention_values, attention_length)
context_size = attention.context_size
with tf.variable_scope('logits_func'):
attention_mix = LinearOp(((context_size + feedback.embedding_dim) + decoder_state_size), decoder_state_size, name='attention_mix')
attention_mix_middle = LinearOp(decoder_state_size, (decoder_state_size // 2), name='attention_mix_middle')
logits_trans = LinearOp((decoder_state_size // 2), feedback.vocab_size, name='logits_trans')
logits_func = (lambda _softmax: logits_trans(tf.nn.relu(attention_mix_middle(tf.nn.relu(attention_mix(_softmax))))))
decoder = sq.AttentionRNNDecoder(decoder_params, attention, feedback, logits_func=logits_func, init_state=encoder_decoder_bridge(encoder_final_states_bw), mode=mode)
(decoder_output, decoder_final_state) = sq.dynamic_decode(decoder, swap_memory=True, scope='decoder')
if ((mode == MODE.EVAL) or (mode == MODE.INFER)):
return (decoder_output, decoder_final_state)
if (not TIME_MAJOR):
ground_truth_ids = tf.transpose(target_ids, [1, 0])
else:
ground_truth_ids = target_ids
if (mode == MODE.RL):
global_step_tensor = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='global_step')[0]
rl_time_steps = tf.floordiv(tf.maximum((global_step_tensor - burn_in_step), 0), increment_step)
start_rl_step = (target_seq_length - rl_time_steps)
baseline_states = tf.stop_gradient(decoder_output.baseline_states)
predict_ids = tf.stop_gradient(decoder_output.predicted_ids)
ground_or_predict_ids = tf.cond(tf.greater(rl_time_steps, 0), (lambda : predict_ids), (lambda : ground_truth_ids))
(reward, sequence_length) = tf.py_func(func=_py_func, inp=[ground_or_predict_ids, ground_truth_ids, trg_vocab.eos_id], Tout=[tf.float32, tf.int32], name='reward')
sequence_length.set_shape((None,))
(total_loss_avg, entropy_loss_avg, reward_loss_rmse, reward_predicted) = rl_sequence_loss(logits=decoder_output.logits, predict_ids=predict_ids, sequence_length=sequence_length, baseline_states=baseline_states, start_rl_step=start_rl_step, reward=reward)
return (decoder_output, total_loss_avg, entropy_loss_avg, reward_loss_rmse, reward_predicted)
else:
total_loss_avg = cross_entropy_sequence_loss(logits=decoder_output.logits, targets=ground_truth_ids, sequence_length=target_seq_length)
return (decoder_output, total_loss_avg, total_loss_avg, tf.to_float(0.0), tf.to_float(0.0)) | Build a model.
:param params: dict.
{encoder: {rnn_cell: {},
...},
decoder: {rnn_cell: {},
...}}
for example:
{'encoder': {'rnn_cell': {'state_size': 512,
'cell_name': 'BasicLSTMCell',
'num_layers': 2,
'input_keep_prob': 1.0,
'output_keep_prob': 1.0},
'attention_key_size': attention_size},
'decoder': {'rnn_cell': {'cell_name': 'BasicLSTMCell',
'state_size': 512,
'num_layers': 1,
'input_keep_prob': 1.0,
'output_keep_prob': 1.0},
'trg_vocab_size': trg_vocab_size}}
:param src_vocab: Vocab of source symbols.
:param trg_vocab: Vocab of target symbols.
:param source_ids: placeholder
:param source_seq_length: placeholder
:param target_ids: placeholder
:param target_seq_length: placeholder
:param beam_size: used in beam inference
:param mode:
:return: | char_nmt/build_model.py | build_attention_model | SwordYork/sequencing | 45 | python | def build_attention_model(params, src_vocab, trg_vocab, source_placeholders, target_placeholders, beam_size=1, mode=MODE.TRAIN, burn_in_step=100000, increment_step=10000, teacher_rate=1.0, max_step=100):
"\n Build a model.\n\n :param params: dict.\n {encoder: {rnn_cell: {},\n ...},\n decoder: {rnn_cell: {},\n ...}}\n for example:\n {'encoder': {'rnn_cell': {'state_size': 512,\n 'cell_name': 'BasicLSTMCell',\n 'num_layers': 2,\n 'input_keep_prob': 1.0,\n 'output_keep_prob': 1.0},\n 'attention_key_size': attention_size},\n 'decoder': {'rnn_cell': {'cell_name': 'BasicLSTMCell',\n 'state_size': 512,\n 'num_layers': 1,\n 'input_keep_prob': 1.0,\n 'output_keep_prob': 1.0},\n 'trg_vocab_size': trg_vocab_size}}\n :param src_vocab: Vocab of source symbols.\n :param trg_vocab: Vocab of target symbols.\n :param source_ids: placeholder\n :param source_seq_length: placeholder\n :param target_ids: placeholder\n :param target_seq_length: placeholder\n :param beam_size: used in beam inference\n :param mode:\n :return:\n "
if (mode != MODE.TRAIN):
params = sq.disable_dropout(params)
tf.logging.info(json.dumps(params, indent=4))
decoder_params = params['decoder']
source_ids = source_placeholders['src']
source_seq_length = source_placeholders['src_len']
source_sample_matrix = source_placeholders['src_sample_matrix']
source_word_seq_length = source_placeholders['src_word_len']
target_ids = target_placeholders['trg']
target_seq_length = target_placeholders['trg_len']
source_char_embedding_table = sq.LookUpOp(src_vocab.vocab_size, src_vocab.embedding_dim, name='source')
source_char_embedded = source_char_embedding_table(source_ids)
char_encoder = sq.StackRNNEncoder(params['char_encoder'], params['attention_key_size']['char'], name='char_rnn', mode=mode)
char_encoded_representation = char_encoder.encode(source_char_embedded, source_seq_length)
char_encoder_outputs = char_encoded_representation.outputs
char_encoder_outputs = tf.transpose(char_encoder_outputs, perm=(1, 0, 2))
sampled_word_embedded = tf.matmul(source_sample_matrix, char_encoder_outputs)
source_embedded = tf.transpose(sampled_word_embedded, perm=(1, 0, 2))
encoder = sq.StackBidirectionalRNNEncoder(params['encoder'], params['attention_key_size']['word'], name='stack_rnn', mode=mode)
encoded_representation = encoder.encode(source_embedded, source_word_seq_length)
attention_keys = encoded_representation.attention_keys
attention_values = encoded_representation.attention_values
attention_length = encoded_representation.attention_length
encoder_final_states_bw = encoded_representation.final_state[(- 1)][(- 1)].h
if (mode == MODE.RL):
tf.logging.info('BUILDING RL TRAIN FEEDBACK......')
dynamical_batch_size = tf.shape(attention_keys)[1]
feedback = sq.RLTrainingFeedBack(target_ids, target_seq_length, trg_vocab, dynamical_batch_size, burn_in_step=burn_in_step, increment_step=increment_step, max_step=max_step)
elif (mode == MODE.TRAIN):
tf.logging.info('BUILDING TRAIN FEEDBACK WITH {} TEACHER_RATE......'.format(teacher_rate))
feedback = sq.TrainingFeedBack(target_ids, target_seq_length, trg_vocab, teacher_rate, max_step=max_step)
elif (mode == MODE.EVAL):
tf.logging.info('BUILDING EVAL FEEDBACK ......')
feedback = sq.TrainingFeedBack(target_ids, target_seq_length, trg_vocab, 0.0, max_step=max_step)
else:
tf.logging.info('BUILDING INFER FEEDBACK WITH BEAM_SIZE {}......'.format(beam_size))
infer_key_size = attention_keys.get_shape().as_list()[(- 1)]
infer_value_size = attention_values.get_shape().as_list()[(- 1)]
infer_states_bw_shape = encoder_final_states_bw.get_shape().as_list()[(- 1)]
encoder_final_states_bw = tf.reshape(tf.tile(encoder_final_states_bw, [1, beam_size]), [(- 1), infer_states_bw_shape])
if TIME_MAJOR:
dynamical_batch_size = tf.shape(attention_keys)[1]
final_key_shape = [(- 1), (dynamical_batch_size * beam_size), infer_key_size]
final_value_shape = [(- 1), (dynamical_batch_size * beam_size), infer_value_size]
attention_keys = tf.reshape(tf.tile(attention_keys, [1, 1, beam_size]), final_key_shape)
attention_values = tf.reshape(tf.tile(attention_values, [1, 1, beam_size]), final_value_shape)
else:
dynamical_batch_size = tf.shape(attention_keys)[0]
final_key_shape = [(dynamical_batch_size * beam_size), (- 1), infer_key_size]
final_value_shape = [(dynamical_batch_size * beam_size), (- 1), infer_value_size]
attention_keys = tf.reshape(tf.tile(attention_keys, [1, beam_size, 1]), final_key_shape)
attention_values = tf.reshape(tf.tile(attention_values, [1, beam_size, 1]), final_value_shape)
attention_length = tf.reshape(tf.transpose(tf.tile([attention_length], [beam_size, 1])), [(- 1)])
feedback = sq.BeamFeedBack(trg_vocab, beam_size, dynamical_batch_size, max_step=max_step)
encoder_decoder_bridge = EncoderDecoderBridge(encoder_final_states_bw.get_shape().as_list()[(- 1)], decoder_params['rnn_cell'])
decoder_state_size = decoder_params['rnn_cell']['state_size']
attention = sq.Attention(decoder_state_size, attention_keys, attention_values, attention_length)
context_size = attention.context_size
with tf.variable_scope('logits_func'):
attention_mix = LinearOp(((context_size + feedback.embedding_dim) + decoder_state_size), decoder_state_size, name='attention_mix')
attention_mix_middle = LinearOp(decoder_state_size, (decoder_state_size // 2), name='attention_mix_middle')
logits_trans = LinearOp((decoder_state_size // 2), feedback.vocab_size, name='logits_trans')
logits_func = (lambda _softmax: logits_trans(tf.nn.relu(attention_mix_middle(tf.nn.relu(attention_mix(_softmax))))))
decoder = sq.AttentionRNNDecoder(decoder_params, attention, feedback, logits_func=logits_func, init_state=encoder_decoder_bridge(encoder_final_states_bw), mode=mode)
(decoder_output, decoder_final_state) = sq.dynamic_decode(decoder, swap_memory=True, scope='decoder')
if ((mode == MODE.EVAL) or (mode == MODE.INFER)):
return (decoder_output, decoder_final_state)
if (not TIME_MAJOR):
ground_truth_ids = tf.transpose(target_ids, [1, 0])
else:
ground_truth_ids = target_ids
if (mode == MODE.RL):
global_step_tensor = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='global_step')[0]
rl_time_steps = tf.floordiv(tf.maximum((global_step_tensor - burn_in_step), 0), increment_step)
start_rl_step = (target_seq_length - rl_time_steps)
baseline_states = tf.stop_gradient(decoder_output.baseline_states)
predict_ids = tf.stop_gradient(decoder_output.predicted_ids)
ground_or_predict_ids = tf.cond(tf.greater(rl_time_steps, 0), (lambda : predict_ids), (lambda : ground_truth_ids))
(reward, sequence_length) = tf.py_func(func=_py_func, inp=[ground_or_predict_ids, ground_truth_ids, trg_vocab.eos_id], Tout=[tf.float32, tf.int32], name='reward')
sequence_length.set_shape((None,))
(total_loss_avg, entropy_loss_avg, reward_loss_rmse, reward_predicted) = rl_sequence_loss(logits=decoder_output.logits, predict_ids=predict_ids, sequence_length=sequence_length, baseline_states=baseline_states, start_rl_step=start_rl_step, reward=reward)
return (decoder_output, total_loss_avg, entropy_loss_avg, reward_loss_rmse, reward_predicted)
else:
total_loss_avg = cross_entropy_sequence_loss(logits=decoder_output.logits, targets=ground_truth_ids, sequence_length=target_seq_length)
return (decoder_output, total_loss_avg, total_loss_avg, tf.to_float(0.0), tf.to_float(0.0)) | def build_attention_model(params, src_vocab, trg_vocab, source_placeholders, target_placeholders, beam_size=1, mode=MODE.TRAIN, burn_in_step=100000, increment_step=10000, teacher_rate=1.0, max_step=100):
"\n Build a model.\n\n :param params: dict.\n {encoder: {rnn_cell: {},\n ...},\n decoder: {rnn_cell: {},\n ...}}\n for example:\n {'encoder': {'rnn_cell': {'state_size': 512,\n 'cell_name': 'BasicLSTMCell',\n 'num_layers': 2,\n 'input_keep_prob': 1.0,\n 'output_keep_prob': 1.0},\n 'attention_key_size': attention_size},\n 'decoder': {'rnn_cell': {'cell_name': 'BasicLSTMCell',\n 'state_size': 512,\n 'num_layers': 1,\n 'input_keep_prob': 1.0,\n 'output_keep_prob': 1.0},\n 'trg_vocab_size': trg_vocab_size}}\n :param src_vocab: Vocab of source symbols.\n :param trg_vocab: Vocab of target symbols.\n :param source_ids: placeholder\n :param source_seq_length: placeholder\n :param target_ids: placeholder\n :param target_seq_length: placeholder\n :param beam_size: used in beam inference\n :param mode:\n :return:\n "
if (mode != MODE.TRAIN):
params = sq.disable_dropout(params)
tf.logging.info(json.dumps(params, indent=4))
decoder_params = params['decoder']
source_ids = source_placeholders['src']
source_seq_length = source_placeholders['src_len']
source_sample_matrix = source_placeholders['src_sample_matrix']
source_word_seq_length = source_placeholders['src_word_len']
target_ids = target_placeholders['trg']
target_seq_length = target_placeholders['trg_len']
source_char_embedding_table = sq.LookUpOp(src_vocab.vocab_size, src_vocab.embedding_dim, name='source')
source_char_embedded = source_char_embedding_table(source_ids)
char_encoder = sq.StackRNNEncoder(params['char_encoder'], params['attention_key_size']['char'], name='char_rnn', mode=mode)
char_encoded_representation = char_encoder.encode(source_char_embedded, source_seq_length)
char_encoder_outputs = char_encoded_representation.outputs
char_encoder_outputs = tf.transpose(char_encoder_outputs, perm=(1, 0, 2))
sampled_word_embedded = tf.matmul(source_sample_matrix, char_encoder_outputs)
source_embedded = tf.transpose(sampled_word_embedded, perm=(1, 0, 2))
encoder = sq.StackBidirectionalRNNEncoder(params['encoder'], params['attention_key_size']['word'], name='stack_rnn', mode=mode)
encoded_representation = encoder.encode(source_embedded, source_word_seq_length)
attention_keys = encoded_representation.attention_keys
attention_values = encoded_representation.attention_values
attention_length = encoded_representation.attention_length
encoder_final_states_bw = encoded_representation.final_state[(- 1)][(- 1)].h
if (mode == MODE.RL):
tf.logging.info('BUILDING RL TRAIN FEEDBACK......')
dynamical_batch_size = tf.shape(attention_keys)[1]
feedback = sq.RLTrainingFeedBack(target_ids, target_seq_length, trg_vocab, dynamical_batch_size, burn_in_step=burn_in_step, increment_step=increment_step, max_step=max_step)
elif (mode == MODE.TRAIN):
tf.logging.info('BUILDING TRAIN FEEDBACK WITH {} TEACHER_RATE......'.format(teacher_rate))
feedback = sq.TrainingFeedBack(target_ids, target_seq_length, trg_vocab, teacher_rate, max_step=max_step)
elif (mode == MODE.EVAL):
tf.logging.info('BUILDING EVAL FEEDBACK ......')
feedback = sq.TrainingFeedBack(target_ids, target_seq_length, trg_vocab, 0.0, max_step=max_step)
else:
tf.logging.info('BUILDING INFER FEEDBACK WITH BEAM_SIZE {}......'.format(beam_size))
infer_key_size = attention_keys.get_shape().as_list()[(- 1)]
infer_value_size = attention_values.get_shape().as_list()[(- 1)]
infer_states_bw_shape = encoder_final_states_bw.get_shape().as_list()[(- 1)]
encoder_final_states_bw = tf.reshape(tf.tile(encoder_final_states_bw, [1, beam_size]), [(- 1), infer_states_bw_shape])
if TIME_MAJOR:
dynamical_batch_size = tf.shape(attention_keys)[1]
final_key_shape = [(- 1), (dynamical_batch_size * beam_size), infer_key_size]
final_value_shape = [(- 1), (dynamical_batch_size * beam_size), infer_value_size]
attention_keys = tf.reshape(tf.tile(attention_keys, [1, 1, beam_size]), final_key_shape)
attention_values = tf.reshape(tf.tile(attention_values, [1, 1, beam_size]), final_value_shape)
else:
dynamical_batch_size = tf.shape(attention_keys)[0]
final_key_shape = [(dynamical_batch_size * beam_size), (- 1), infer_key_size]
final_value_shape = [(dynamical_batch_size * beam_size), (- 1), infer_value_size]
attention_keys = tf.reshape(tf.tile(attention_keys, [1, beam_size, 1]), final_key_shape)
attention_values = tf.reshape(tf.tile(attention_values, [1, beam_size, 1]), final_value_shape)
attention_length = tf.reshape(tf.transpose(tf.tile([attention_length], [beam_size, 1])), [(- 1)])
feedback = sq.BeamFeedBack(trg_vocab, beam_size, dynamical_batch_size, max_step=max_step)
encoder_decoder_bridge = EncoderDecoderBridge(encoder_final_states_bw.get_shape().as_list()[(- 1)], decoder_params['rnn_cell'])
decoder_state_size = decoder_params['rnn_cell']['state_size']
attention = sq.Attention(decoder_state_size, attention_keys, attention_values, attention_length)
context_size = attention.context_size
with tf.variable_scope('logits_func'):
attention_mix = LinearOp(((context_size + feedback.embedding_dim) + decoder_state_size), decoder_state_size, name='attention_mix')
attention_mix_middle = LinearOp(decoder_state_size, (decoder_state_size // 2), name='attention_mix_middle')
logits_trans = LinearOp((decoder_state_size // 2), feedback.vocab_size, name='logits_trans')
logits_func = (lambda _softmax: logits_trans(tf.nn.relu(attention_mix_middle(tf.nn.relu(attention_mix(_softmax))))))
decoder = sq.AttentionRNNDecoder(decoder_params, attention, feedback, logits_func=logits_func, init_state=encoder_decoder_bridge(encoder_final_states_bw), mode=mode)
(decoder_output, decoder_final_state) = sq.dynamic_decode(decoder, swap_memory=True, scope='decoder')
if ((mode == MODE.EVAL) or (mode == MODE.INFER)):
return (decoder_output, decoder_final_state)
if (not TIME_MAJOR):
ground_truth_ids = tf.transpose(target_ids, [1, 0])
else:
ground_truth_ids = target_ids
if (mode == MODE.RL):
global_step_tensor = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='global_step')[0]
rl_time_steps = tf.floordiv(tf.maximum((global_step_tensor - burn_in_step), 0), increment_step)
start_rl_step = (target_seq_length - rl_time_steps)
baseline_states = tf.stop_gradient(decoder_output.baseline_states)
predict_ids = tf.stop_gradient(decoder_output.predicted_ids)
ground_or_predict_ids = tf.cond(tf.greater(rl_time_steps, 0), (lambda : predict_ids), (lambda : ground_truth_ids))
(reward, sequence_length) = tf.py_func(func=_py_func, inp=[ground_or_predict_ids, ground_truth_ids, trg_vocab.eos_id], Tout=[tf.float32, tf.int32], name='reward')
sequence_length.set_shape((None,))
(total_loss_avg, entropy_loss_avg, reward_loss_rmse, reward_predicted) = rl_sequence_loss(logits=decoder_output.logits, predict_ids=predict_ids, sequence_length=sequence_length, baseline_states=baseline_states, start_rl_step=start_rl_step, reward=reward)
return (decoder_output, total_loss_avg, entropy_loss_avg, reward_loss_rmse, reward_predicted)
else:
total_loss_avg = cross_entropy_sequence_loss(logits=decoder_output.logits, targets=ground_truth_ids, sequence_length=target_seq_length)
return (decoder_output, total_loss_avg, total_loss_avg, tf.to_float(0.0), tf.to_float(0.0))<|docstring|>Build a model.
:param params: dict.
{encoder: {rnn_cell: {},
...},
decoder: {rnn_cell: {},
...}}
for example:
{'encoder': {'rnn_cell': {'state_size': 512,
'cell_name': 'BasicLSTMCell',
'num_layers': 2,
'input_keep_prob': 1.0,
'output_keep_prob': 1.0},
'attention_key_size': attention_size},
'decoder': {'rnn_cell': {'cell_name': 'BasicLSTMCell',
'state_size': 512,
'num_layers': 1,
'input_keep_prob': 1.0,
'output_keep_prob': 1.0},
'trg_vocab_size': trg_vocab_size}}
:param src_vocab: Vocab of source symbols.
:param trg_vocab: Vocab of target symbols.
:param source_ids: placeholder
:param source_seq_length: placeholder
:param target_ids: placeholder
:param target_seq_length: placeholder
:param beam_size: used in beam inference
:param mode:
:return:<|endoftext|> |
ef0403a399d0d074e8b238c1c1d07b0f8b25eacaf110e1679ad58eedf2de42d4 | def plot_base(strip_list, ax=None, *, cell_size=4.5):
'Make a nice plot of the strip layout.'
if (ax is None):
import matplotlib.pyplot as plt
(fig, ax) = plt.subplots()
cmap = mcm.get_cmap('gray')
norm = mcolors.Normalize(0, 100)
cells = {}
labels = {}
for strip in strip_list:
cells[strip] = []
pair = single_strip_transform_factory(*astuple(strip))
for (j, ti_frac) in enumerate(strip.ti_fractions):
color = cmap(norm(ti_frac))
(x, y) = pair.forward(ti_frac, strip.temperature, strip.annealing_time, strip.thickness)
d = (strip.start_distance - (j * cell_size))
rect = mpatches.Rectangle(((x - (cell_size / 2)), (y - (cell_size / 2))), cell_size, cell_size, color=color, zorder=(- 1))
ax.add_patch(rect)
cells[strip].append(ax.text(x, y, f'{ti_frac}', ha='center', va='center', color='w'))
cells[strip].append(rect)
d = ((cell_size * (len(strip.ti_fractions) - 0.5)) - strip.start_distance)
labels[strip] = ax.annotate(f'''{strip.temperature}°C
{strip.annealing_time}s''', xy=(((strip.reference_x - d) - (cell_size / 2)), (strip.reference_y - (np.sin(strip.angle) * d))), xytext=(10, 0), textcoords='offset points', va='center', ha='left', clip_on=False)
ax.relim()
ax.autoscale()
ax.figure.tight_layout()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.invert_yaxis()
ax.invert_xaxis()
ax.set_aspect('equal') | Make a nice plot of the strip layout. | ae_gpcam/bluesky_config/scripts/live_status.py | plot_base | NSLS-II-XPD/ae-gpcam | 0 | python | def plot_base(strip_list, ax=None, *, cell_size=4.5):
if (ax is None):
import matplotlib.pyplot as plt
(fig, ax) = plt.subplots()
cmap = mcm.get_cmap('gray')
norm = mcolors.Normalize(0, 100)
cells = {}
labels = {}
for strip in strip_list:
cells[strip] = []
pair = single_strip_transform_factory(*astuple(strip))
for (j, ti_frac) in enumerate(strip.ti_fractions):
color = cmap(norm(ti_frac))
(x, y) = pair.forward(ti_frac, strip.temperature, strip.annealing_time, strip.thickness)
d = (strip.start_distance - (j * cell_size))
rect = mpatches.Rectangle(((x - (cell_size / 2)), (y - (cell_size / 2))), cell_size, cell_size, color=color, zorder=(- 1))
ax.add_patch(rect)
cells[strip].append(ax.text(x, y, f'{ti_frac}', ha='center', va='center', color='w'))
cells[strip].append(rect)
d = ((cell_size * (len(strip.ti_fractions) - 0.5)) - strip.start_distance)
labels[strip] = ax.annotate(f'{strip.temperature}°C
{strip.annealing_time}s', xy=(((strip.reference_x - d) - (cell_size / 2)), (strip.reference_y - (np.sin(strip.angle) * d))), xytext=(10, 0), textcoords='offset points', va='center', ha='left', clip_on=False)
ax.relim()
ax.autoscale()
ax.figure.tight_layout()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.invert_yaxis()
ax.invert_xaxis()
ax.set_aspect('equal') | def plot_base(strip_list, ax=None, *, cell_size=4.5):
if (ax is None):
import matplotlib.pyplot as plt
(fig, ax) = plt.subplots()
cmap = mcm.get_cmap('gray')
norm = mcolors.Normalize(0, 100)
cells = {}
labels = {}
for strip in strip_list:
cells[strip] = []
pair = single_strip_transform_factory(*astuple(strip))
for (j, ti_frac) in enumerate(strip.ti_fractions):
color = cmap(norm(ti_frac))
(x, y) = pair.forward(ti_frac, strip.temperature, strip.annealing_time, strip.thickness)
d = (strip.start_distance - (j * cell_size))
rect = mpatches.Rectangle(((x - (cell_size / 2)), (y - (cell_size / 2))), cell_size, cell_size, color=color, zorder=(- 1))
ax.add_patch(rect)
cells[strip].append(ax.text(x, y, f'{ti_frac}', ha='center', va='center', color='w'))
cells[strip].append(rect)
d = ((cell_size * (len(strip.ti_fractions) - 0.5)) - strip.start_distance)
labels[strip] = ax.annotate(f'{strip.temperature}°C
{strip.annealing_time}s', xy=(((strip.reference_x - d) - (cell_size / 2)), (strip.reference_y - (np.sin(strip.angle) * d))), xytext=(10, 0), textcoords='offset points', va='center', ha='left', clip_on=False)
ax.relim()
ax.autoscale()
ax.figure.tight_layout()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.invert_yaxis()
ax.invert_xaxis()
ax.set_aspect('equal')<|docstring|>Make a nice plot of the strip layout.<|endoftext|> |
2120628e5ab8761b72c1edd79d111d8ec117b353690950a0a37fa39f2cb55646 | def __init__(self, args: Namespace, atom_targets, bond_targets=None, atom_constraints=None, bond_constraints=None, attention=False):
'\n\n :param args:\n :param args:\n :param constraints:\n '
features_size = args.hidden_size
hidden_size = args.ffn_hidden_size
num_layers = args.ffn_num_layers
output_size = args.output_size
dropout = nn.Dropout(args.dropout)
activation = get_activation_function(args.activation)
super(MultiReadout, self).__init__()
for (i, a_target) in enumerate(atom_targets):
constraint = (atom_constraints[i] if ((atom_constraints is not None) and (i < len(atom_constraints))) else None)
if attention:
self.add_module(f'readout_{i}', FFNAtten(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='atom'))
else:
self.add_module(f'readout_{i}', FFN(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='atom'))
i += 1
for (j, b_target) in enumerate(bond_targets):
i += j
constraint = (bond_constraints[i] if (bond_constraints and (j < len(bond_constraints))) else None)
self.add_module(f'readout_{i}', FFN(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='bond'))
self.ffn_list = AttrProxy(self, 'readout_') | :param args:
:param args:
:param constraints: | chemprop/models/ffn.py | __init__ | yanfeiguan/chemprop | 11 | python | def __init__(self, args: Namespace, atom_targets, bond_targets=None, atom_constraints=None, bond_constraints=None, attention=False):
'\n\n :param args:\n :param args:\n :param constraints:\n '
features_size = args.hidden_size
hidden_size = args.ffn_hidden_size
num_layers = args.ffn_num_layers
output_size = args.output_size
dropout = nn.Dropout(args.dropout)
activation = get_activation_function(args.activation)
super(MultiReadout, self).__init__()
for (i, a_target) in enumerate(atom_targets):
constraint = (atom_constraints[i] if ((atom_constraints is not None) and (i < len(atom_constraints))) else None)
if attention:
self.add_module(f'readout_{i}', FFNAtten(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='atom'))
else:
self.add_module(f'readout_{i}', FFN(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='atom'))
i += 1
for (j, b_target) in enumerate(bond_targets):
i += j
constraint = (bond_constraints[i] if (bond_constraints and (j < len(bond_constraints))) else None)
self.add_module(f'readout_{i}', FFN(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='bond'))
self.ffn_list = AttrProxy(self, 'readout_') | def __init__(self, args: Namespace, atom_targets, bond_targets=None, atom_constraints=None, bond_constraints=None, attention=False):
'\n\n :param args:\n :param args:\n :param constraints:\n '
features_size = args.hidden_size
hidden_size = args.ffn_hidden_size
num_layers = args.ffn_num_layers
output_size = args.output_size
dropout = nn.Dropout(args.dropout)
activation = get_activation_function(args.activation)
super(MultiReadout, self).__init__()
for (i, a_target) in enumerate(atom_targets):
constraint = (atom_constraints[i] if ((atom_constraints is not None) and (i < len(atom_constraints))) else None)
if attention:
self.add_module(f'readout_{i}', FFNAtten(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='atom'))
else:
self.add_module(f'readout_{i}', FFN(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='atom'))
i += 1
for (j, b_target) in enumerate(bond_targets):
i += j
constraint = (bond_constraints[i] if (bond_constraints and (j < len(bond_constraints))) else None)
self.add_module(f'readout_{i}', FFN(features_size, hidden_size, num_layers, output_size, dropout, activation, constraint, ffn_type='bond'))
self.ffn_list = AttrProxy(self, 'readout_')<|docstring|>:param args:
:param args:
:param constraints:<|endoftext|> |
25abc21004f6c12ad0ad2efe6909eaf590431989ccb76d505f368d6a1fa3f74b | def __init__(self, features_size, hidden_size, num_layers, output_size, dropout, activation, constraint=None, ffn_type='atom'):
'Initializes the FFN.\n\n args: Arguments.\n constraints: constraints applied to output\n '
super(FFN, self).__init__()
self.ffn = DenseLayers(features_size, hidden_size, num_layers, hidden_size, dropout, activation)
self.ffn_readout = DenseLayers(hidden_size, hidden_size, 1, output_size, dropout, activation)
self.weights_readout = DenseLayers(first_linear_dim=hidden_size, output_size=output_size, num_layers=2, dropout=dropout, activation=activation)
self.constraint = constraint | Initializes the FFN.
args: Arguments.
constraints: constraints applied to output | chemprop/models/ffn.py | __init__ | yanfeiguan/chemprop | 11 | python | def __init__(self, features_size, hidden_size, num_layers, output_size, dropout, activation, constraint=None, ffn_type='atom'):
'Initializes the FFN.\n\n args: Arguments.\n constraints: constraints applied to output\n '
super(FFN, self).__init__()
self.ffn = DenseLayers(features_size, hidden_size, num_layers, hidden_size, dropout, activation)
self.ffn_readout = DenseLayers(hidden_size, hidden_size, 1, output_size, dropout, activation)
self.weights_readout = DenseLayers(first_linear_dim=hidden_size, output_size=output_size, num_layers=2, dropout=dropout, activation=activation)
self.constraint = constraint | def __init__(self, features_size, hidden_size, num_layers, output_size, dropout, activation, constraint=None, ffn_type='atom'):
'Initializes the FFN.\n\n args: Arguments.\n constraints: constraints applied to output\n '
super(FFN, self).__init__()
self.ffn = DenseLayers(features_size, hidden_size, num_layers, hidden_size, dropout, activation)
self.ffn_readout = DenseLayers(hidden_size, hidden_size, 1, output_size, dropout, activation)
self.weights_readout = DenseLayers(first_linear_dim=hidden_size, output_size=output_size, num_layers=2, dropout=dropout, activation=activation)
self.constraint = constraint<|docstring|>Initializes the FFN.
args: Arguments.
constraints: constraints applied to output<|endoftext|> |
fac7e4aef25078215828924810cc92431f4fd7186098346590ac0097b48d3aa0 | def forward(self, input):
'\n Runs the FFN on input\n\n :param input:\n :return:\n '
(a_hidden, a_scope, b_hidden, b_scope, b2br, bond_types) = input
hidden = a_hidden
scope = a_scope
output_hidden = self.ffn(hidden)
output = self.ffn_readout(output_hidden)
weights = self.weights_readout(output_hidden)
constrained_output = []
for (i, (a_start, a_size)) in enumerate(scope):
if (a_size == 0):
continue
else:
cur_weights = weights.narrow(0, a_start, a_size)
cur_output = output.narrow(0, a_start, a_size)
cur_weights = torch.nn.Softmax()(cur_weights)
cur_output_sum = cur_output.sum()
cur_output = (cur_output + (cur_weights * (self.constraint - cur_output_sum)))
constrained_output.append(cur_output)
output = torch.cat(constrained_output, dim=0)
return output | Runs the FFN on input
:param input:
:return: | chemprop/models/ffn.py | forward | yanfeiguan/chemprop | 11 | python | def forward(self, input):
'\n Runs the FFN on input\n\n :param input:\n :return:\n '
(a_hidden, a_scope, b_hidden, b_scope, b2br, bond_types) = input
hidden = a_hidden
scope = a_scope
output_hidden = self.ffn(hidden)
output = self.ffn_readout(output_hidden)
weights = self.weights_readout(output_hidden)
constrained_output = []
for (i, (a_start, a_size)) in enumerate(scope):
if (a_size == 0):
continue
else:
cur_weights = weights.narrow(0, a_start, a_size)
cur_output = output.narrow(0, a_start, a_size)
cur_weights = torch.nn.Softmax()(cur_weights)
cur_output_sum = cur_output.sum()
cur_output = (cur_output + (cur_weights * (self.constraint - cur_output_sum)))
constrained_output.append(cur_output)
output = torch.cat(constrained_output, dim=0)
return output | def forward(self, input):
'\n Runs the FFN on input\n\n :param input:\n :return:\n '
(a_hidden, a_scope, b_hidden, b_scope, b2br, bond_types) = input
hidden = a_hidden
scope = a_scope
output_hidden = self.ffn(hidden)
output = self.ffn_readout(output_hidden)
weights = self.weights_readout(output_hidden)
constrained_output = []
for (i, (a_start, a_size)) in enumerate(scope):
if (a_size == 0):
continue
else:
cur_weights = weights.narrow(0, a_start, a_size)
cur_output = output.narrow(0, a_start, a_size)
cur_weights = torch.nn.Softmax()(cur_weights)
cur_output_sum = cur_output.sum()
cur_output = (cur_output + (cur_weights * (self.constraint - cur_output_sum)))
constrained_output.append(cur_output)
output = torch.cat(constrained_output, dim=0)
return output<|docstring|>Runs the FFN on input
:param input:
:return:<|endoftext|> |
d0aaa957a428487d85a364d0b01f92a7f0d481430f33c821d8c384fbc4a27319 | def __init__(self, features_size, hidden_size, num_layers, output_size, dropout, activation, constraint=None, ffn_type='atom', attention=False):
'Initializes the FFN.\n\n args: Arguments.\n constraints: constraints applied to output\n '
super(FFN, self).__init__()
if (ffn_type == 'atom'):
self.ffn = DenseLayers(features_size, hidden_size, num_layers, output_size, dropout, activation)
elif (ffn_type == 'bond'):
self.ffn = DenseLayers((2 * features_size), hidden_size, num_layers, output_size, dropout, activation)
self.ffn_type = ffn_type
self.attention = attention
if (constraint is not None):
self.weights_readout = DenseLayers(features_size, hidden_size, num_layers, output_size, dropout, activation)
if attention:
self.weights_readout = DenseLayers(first_linear_dim=hidden_size, output_size=1, num_layers=1, dropout=dropout, activation=activation)
self.constraint = constraint
else:
self.constraint = None | Initializes the FFN.
args: Arguments.
constraints: constraints applied to output | chemprop/models/ffn.py | __init__ | yanfeiguan/chemprop | 11 | python | def __init__(self, features_size, hidden_size, num_layers, output_size, dropout, activation, constraint=None, ffn_type='atom', attention=False):
'Initializes the FFN.\n\n args: Arguments.\n constraints: constraints applied to output\n '
super(FFN, self).__init__()
if (ffn_type == 'atom'):
self.ffn = DenseLayers(features_size, hidden_size, num_layers, output_size, dropout, activation)
elif (ffn_type == 'bond'):
self.ffn = DenseLayers((2 * features_size), hidden_size, num_layers, output_size, dropout, activation)
self.ffn_type = ffn_type
self.attention = attention
if (constraint is not None):
self.weights_readout = DenseLayers(features_size, hidden_size, num_layers, output_size, dropout, activation)
if attention:
self.weights_readout = DenseLayers(first_linear_dim=hidden_size, output_size=1, num_layers=1, dropout=dropout, activation=activation)
self.constraint = constraint
else:
self.constraint = None | def __init__(self, features_size, hidden_size, num_layers, output_size, dropout, activation, constraint=None, ffn_type='atom', attention=False):
'Initializes the FFN.\n\n args: Arguments.\n constraints: constraints applied to output\n '
super(FFN, self).__init__()
if (ffn_type == 'atom'):
self.ffn = DenseLayers(features_size, hidden_size, num_layers, output_size, dropout, activation)
elif (ffn_type == 'bond'):
self.ffn = DenseLayers((2 * features_size), hidden_size, num_layers, output_size, dropout, activation)
self.ffn_type = ffn_type
self.attention = attention
if (constraint is not None):
self.weights_readout = DenseLayers(features_size, hidden_size, num_layers, output_size, dropout, activation)
if attention:
self.weights_readout = DenseLayers(first_linear_dim=hidden_size, output_size=1, num_layers=1, dropout=dropout, activation=activation)
self.constraint = constraint
else:
self.constraint = None<|docstring|>Initializes the FFN.
args: Arguments.
constraints: constraints applied to output<|endoftext|> |
a89b6d886200703da51de0fa9c7e639074f5d8408aca8ffb147f121f02c3f278 | def forward(self, input):
'\n Runs the FFN on input\n\n :param input:\n :return:\n '
(a_hidden, a_scope, b_hidden, b_scope, b2br, bond_types) = input
if (self.ffn_type == 'atom'):
hidden = a_hidden
scope = a_scope
output = self.ffn(hidden)
if self.attention:
weights = self.weights_readout(output)
if (self.constraint is not None):
weights = self.weights_readout(hidden)
constrained_output = []
for (i, (a_start, a_size)) in enumerate(scope):
if (a_size == 0):
continue
else:
cur_weights = weights.narrow(0, a_start, a_size)
cur_output = output.narrow(0, a_start, a_size)
cur_weights_sum = cur_weights.sum()
cur_output_sum = cur_output.sum()
cur_output = (cur_output + ((cur_weights * (self.constraint - cur_output_sum)) / cur_weights_sum))
constrained_output.append(cur_output)
output = torch.cat(constrained_output, dim=0)
else:
output = output[1:]
elif (self.ffn_type == 'bond'):
forward_bond = b_hidden[b2br[(:, 0)]]
backward_bond = b_hidden[b2br[(:, 1)]]
b_hidden = torch.cat([forward_bond, backward_bond], dim=1)
output = (self.ffn(b_hidden) + bond_types.reshape((- 1), 1))
return output | Runs the FFN on input
:param input:
:return: | chemprop/models/ffn.py | forward | yanfeiguan/chemprop | 11 | python | def forward(self, input):
'\n Runs the FFN on input\n\n :param input:\n :return:\n '
(a_hidden, a_scope, b_hidden, b_scope, b2br, bond_types) = input
if (self.ffn_type == 'atom'):
hidden = a_hidden
scope = a_scope
output = self.ffn(hidden)
if self.attention:
weights = self.weights_readout(output)
if (self.constraint is not None):
weights = self.weights_readout(hidden)
constrained_output = []
for (i, (a_start, a_size)) in enumerate(scope):
if (a_size == 0):
continue
else:
cur_weights = weights.narrow(0, a_start, a_size)
cur_output = output.narrow(0, a_start, a_size)
cur_weights_sum = cur_weights.sum()
cur_output_sum = cur_output.sum()
cur_output = (cur_output + ((cur_weights * (self.constraint - cur_output_sum)) / cur_weights_sum))
constrained_output.append(cur_output)
output = torch.cat(constrained_output, dim=0)
else:
output = output[1:]
elif (self.ffn_type == 'bond'):
forward_bond = b_hidden[b2br[(:, 0)]]
backward_bond = b_hidden[b2br[(:, 1)]]
b_hidden = torch.cat([forward_bond, backward_bond], dim=1)
output = (self.ffn(b_hidden) + bond_types.reshape((- 1), 1))
return output | def forward(self, input):
'\n Runs the FFN on input\n\n :param input:\n :return:\n '
(a_hidden, a_scope, b_hidden, b_scope, b2br, bond_types) = input
if (self.ffn_type == 'atom'):
hidden = a_hidden
scope = a_scope
output = self.ffn(hidden)
if self.attention:
weights = self.weights_readout(output)
if (self.constraint is not None):
weights = self.weights_readout(hidden)
constrained_output = []
for (i, (a_start, a_size)) in enumerate(scope):
if (a_size == 0):
continue
else:
cur_weights = weights.narrow(0, a_start, a_size)
cur_output = output.narrow(0, a_start, a_size)
cur_weights_sum = cur_weights.sum()
cur_output_sum = cur_output.sum()
cur_output = (cur_output + ((cur_weights * (self.constraint - cur_output_sum)) / cur_weights_sum))
constrained_output.append(cur_output)
output = torch.cat(constrained_output, dim=0)
else:
output = output[1:]
elif (self.ffn_type == 'bond'):
forward_bond = b_hidden[b2br[(:, 0)]]
backward_bond = b_hidden[b2br[(:, 1)]]
b_hidden = torch.cat([forward_bond, backward_bond], dim=1)
output = (self.ffn(b_hidden) + bond_types.reshape((- 1), 1))
return output<|docstring|>Runs the FFN on input
:param input:
:return:<|endoftext|> |
309a808b5a1f7ff55d645ae946bbf75330b4fd3564b37c40bce8331a3187a741 | def __init__(self, first_linear_dim: int, hidden_size: int, num_layers: int, output_size: int, dropout: nn.Module, activation) -> nn.Sequential:
'\n :param first_linear_dim:\n :param hidden_size:\n :param num_layers:\n :param output_size:\n :param dropout:\n :param activation:\n '
super(DenseLayers, self).__init__()
if (num_layers == 1):
layers = [dropout, nn.Linear(first_linear_dim, output_size)]
else:
layers = [dropout, nn.Linear(first_linear_dim, hidden_size)]
for _ in range((num_layers - 2)):
layers.extend([activation, dropout, nn.Linear(hidden_size, hidden_size)])
layers.extend([activation, dropout, nn.Linear(hidden_size, output_size)])
self.dense_layers = nn.Sequential(*layers) | :param first_linear_dim:
:param hidden_size:
:param num_layers:
:param output_size:
:param dropout:
:param activation: | chemprop/models/ffn.py | __init__ | yanfeiguan/chemprop | 11 | python | def __init__(self, first_linear_dim: int, hidden_size: int, num_layers: int, output_size: int, dropout: nn.Module, activation) -> nn.Sequential:
'\n :param first_linear_dim:\n :param hidden_size:\n :param num_layers:\n :param output_size:\n :param dropout:\n :param activation:\n '
super(DenseLayers, self).__init__()
if (num_layers == 1):
layers = [dropout, nn.Linear(first_linear_dim, output_size)]
else:
layers = [dropout, nn.Linear(first_linear_dim, hidden_size)]
for _ in range((num_layers - 2)):
layers.extend([activation, dropout, nn.Linear(hidden_size, hidden_size)])
layers.extend([activation, dropout, nn.Linear(hidden_size, output_size)])
self.dense_layers = nn.Sequential(*layers) | def __init__(self, first_linear_dim: int, hidden_size: int, num_layers: int, output_size: int, dropout: nn.Module, activation) -> nn.Sequential:
'\n :param first_linear_dim:\n :param hidden_size:\n :param num_layers:\n :param output_size:\n :param dropout:\n :param activation:\n '
super(DenseLayers, self).__init__()
if (num_layers == 1):
layers = [dropout, nn.Linear(first_linear_dim, output_size)]
else:
layers = [dropout, nn.Linear(first_linear_dim, hidden_size)]
for _ in range((num_layers - 2)):
layers.extend([activation, dropout, nn.Linear(hidden_size, hidden_size)])
layers.extend([activation, dropout, nn.Linear(hidden_size, output_size)])
self.dense_layers = nn.Sequential(*layers)<|docstring|>:param first_linear_dim:
:param hidden_size:
:param num_layers:
:param output_size:
:param dropout:
:param activation:<|endoftext|> |
a42d71c0df95d79e8567f043168b466456d3544e9d9cd2ce766b174ac9b04213 | @timeit('Part 1')
def part_one(x):
'Solves part one'
a = Arcade()
a.run(x)
blocks = np.where((a.screen == 2))
return len(blocks[0]) | Solves part one | day_13/main.py | part_one | orrinjelo/AdventOfCode2019 | 1 | python | @timeit('Part 1')
def part_one(x):
a = Arcade()
a.run(x)
blocks = np.where((a.screen == 2))
return len(blocks[0]) | @timeit('Part 1')
def part_one(x):
a = Arcade()
a.run(x)
blocks = np.where((a.screen == 2))
return len(blocks[0])<|docstring|>Solves part one<|endoftext|> |
2a9c36c5a2124b86fb2b1ebcc1e21a39d963dbe50a605a3a070e32d28bffbca9 | @timeit('Part 2')
def part_two(x):
'Solves part two'
def dumb(x):
(ballx, bally) = a.ball
(paddx, paddy) = a.paddle
if (ballx > paddx):
return 1
elif (ballx < paddx):
return (- 1)
else:
return 0
a = Arcade(input_cb=dumb)
x[0] = 2
a.run(x)
return a.score | Solves part two | day_13/main.py | part_two | orrinjelo/AdventOfCode2019 | 1 | python | @timeit('Part 2')
def part_two(x):
def dumb(x):
(ballx, bally) = a.ball
(paddx, paddy) = a.paddle
if (ballx > paddx):
return 1
elif (ballx < paddx):
return (- 1)
else:
return 0
a = Arcade(input_cb=dumb)
x[0] = 2
a.run(x)
return a.score | @timeit('Part 2')
def part_two(x):
def dumb(x):
(ballx, bally) = a.ball
(paddx, paddy) = a.paddle
if (ballx > paddx):
return 1
elif (ballx < paddx):
return (- 1)
else:
return 0
a = Arcade(input_cb=dumb)
x[0] = 2
a.run(x)
return a.score<|docstring|>Solves part two<|endoftext|> |
fcafbe2b8d8780fa5829d6f2721b46de367323a5cd81d0b2d79bd18efa3a7172 | def part_two_visualized(x):
'Visualization'
frames = []
def dumb(x):
(ballx, bally) = a.ball
(paddx, paddy) = a.paddle
frames.append([plt.imshow(np.transpose(a.screen))])
if (ballx > paddx):
return 1
elif (ballx < paddx):
return (- 1)
else:
return 0
a = Arcade(input_cb=dumb)
(fig, ax) = plt.subplots()
im = plt.imshow(np.transpose(a.screen))
x[0] = 2
a.run(x)
import logging
animation._log.setLevel(logging.DEBUG)
ani = animation.ArtistAnimation(fig, frames[:30], interval=50, blit=True)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=1, metadata=dict(artist='Me'), bitrate=1800)
ani.save('im.mp4', writer=writer) | Visualization | day_13/main.py | part_two_visualized | orrinjelo/AdventOfCode2019 | 1 | python | def part_two_visualized(x):
frames = []
def dumb(x):
(ballx, bally) = a.ball
(paddx, paddy) = a.paddle
frames.append([plt.imshow(np.transpose(a.screen))])
if (ballx > paddx):
return 1
elif (ballx < paddx):
return (- 1)
else:
return 0
a = Arcade(input_cb=dumb)
(fig, ax) = plt.subplots()
im = plt.imshow(np.transpose(a.screen))
x[0] = 2
a.run(x)
import logging
animation._log.setLevel(logging.DEBUG)
ani = animation.ArtistAnimation(fig, frames[:30], interval=50, blit=True)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=1, metadata=dict(artist='Me'), bitrate=1800)
ani.save('im.mp4', writer=writer) | def part_two_visualized(x):
frames = []
def dumb(x):
(ballx, bally) = a.ball
(paddx, paddy) = a.paddle
frames.append([plt.imshow(np.transpose(a.screen))])
if (ballx > paddx):
return 1
elif (ballx < paddx):
return (- 1)
else:
return 0
a = Arcade(input_cb=dumb)
(fig, ax) = plt.subplots()
im = plt.imshow(np.transpose(a.screen))
x[0] = 2
a.run(x)
import logging
animation._log.setLevel(logging.DEBUG)
ani = animation.ArtistAnimation(fig, frames[:30], interval=50, blit=True)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=1, metadata=dict(artist='Me'), bitrate=1800)
ani.save('im.mp4', writer=writer)<|docstring|>Visualization<|endoftext|> |
015dfffc79a644aaac969dc869006ce74b71144438f203642c926f052ae61362 | def test():
'Test functions'
assert True | Test functions | day_13/main.py | test | orrinjelo/AdventOfCode2019 | 1 | python | def test():
assert True | def test():
assert True<|docstring|>Test functions<|endoftext|> |
a75c64389589e080355fba0ba47d75ca21097de205a04921bc69992d60a7eae2 | def pre(self, command, output_dir, vars):
'Called before template is applied.'
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
for (key, value) in vars.items():
if (value == 'None'):
vars[key] = None
elif (value == 'True'):
vars[key] = True
elif (value == 'False'):
vars[key] = False | Called before template is applied. | moksha/pastetemplate.py | pre | lmacken/moksha | 1 | python | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
for (key, value) in vars.items():
if (value == 'None'):
vars[key] = None
elif (value == 'True'):
vars[key] = True
elif (value == 'False'):
vars[key] = False | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
for (key, value) in vars.items():
if (value == 'None'):
vars[key] = None
elif (value == 'True'):
vars[key] = True
elif (value == 'False'):
vars[key] = False<|docstring|>Called before template is applied.<|endoftext|> |
6ac3d1f96be5c062de245ced443ebe1f35d6cd45f39242fa1a25e8d0d52a036b | def pre(self, command, output_dir, vars):
'Called before template is applied.'
if ('widget_name' not in vars):
vars['widget_name'] = (vars['package'].title() + 'Widget') | Called before template is applied. | moksha/pastetemplate.py | pre | lmacken/moksha | 1 | python | def pre(self, command, output_dir, vars):
if ('widget_name' not in vars):
vars['widget_name'] = (vars['package'].title() + 'Widget') | def pre(self, command, output_dir, vars):
if ('widget_name' not in vars):
vars['widget_name'] = (vars['package'].title() + 'Widget')<|docstring|>Called before template is applied.<|endoftext|> |
8394d8c508a2dcb0a8b1953b6453cf81f01f6126448bf5bc29df4313b2913089 | def pre(self, command, output_dir, vars):
'Called before template is applied.'
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['stream_name'] = (vars['package'].title() + 'Stream') | Called before template is applied. | moksha/pastetemplate.py | pre | lmacken/moksha | 1 | python | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['stream_name'] = (vars['package'].title() + 'Stream') | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['stream_name'] = (vars['package'].title() + 'Stream')<|docstring|>Called before template is applied.<|endoftext|> |
8ef8a2d30b79e28d750cf88993fd23b258db711df49f3e41772e709648cd5601 | def pre(self, command, output_dir, vars):
'Called before template is applied.'
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['consumer_name'] = (vars['package'].title() + 'Consumer') | Called before template is applied. | moksha/pastetemplate.py | pre | lmacken/moksha | 1 | python | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['consumer_name'] = (vars['package'].title() + 'Consumer') | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['consumer_name'] = (vars['package'].title() + 'Consumer')<|docstring|>Called before template is applied.<|endoftext|> |
00313b53709d519c7ec157efd2b1b2ae2ce8b497439d7cc911e86edf3e04dc2e | def pre(self, command, output_dir, vars):
'Called before template is applied.'
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['connector_name'] = (vars['package'].title() + 'Connector') | Called before template is applied. | moksha/pastetemplate.py | pre | lmacken/moksha | 1 | python | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['connector_name'] = (vars['package'].title() + 'Connector') | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['connector_name'] = (vars['package'].title() + 'Connector')<|docstring|>Called before template is applied.<|endoftext|> |
10a8e7787f7a102620d4af25e532db42b47d2eafef3e6ac0863531a67870a5fb | def pre(self, command, output_dir, vars):
'Called before template is applied.'
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['controller_name'] = (vars['package'].title() + 'Controller') | Called before template is applied. | moksha/pastetemplate.py | pre | lmacken/moksha | 1 | python | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['controller_name'] = (vars['package'].title() + 'Controller') | def pre(self, command, output_dir, vars):
package_logger = vars['package']
if (package_logger == 'root'):
package_logger = 'app'
vars['package_logger'] = package_logger
vars['controller_name'] = (vars['package'].title() + 'Controller')<|docstring|>Called before template is applied.<|endoftext|> |
6641cac04bdfab97cb1eab0419a8b31e615eb52227c440e2dee21d831007c20d | def __init__(self, dimensions):
'Initialize an array specifier with a list\nof ints for dimensions'
self.dimensions = []
if (not dimensions):
raise InvalidTypeDimensionsError(str(type(dimensions)))
for k in dimensions:
if (not isinstance(k, int)):
raise InvalidValueDimensionError(str(type(k)))
self.dimensions.append(k) | Initialize an array specifier with a list
of ints for dimensions | c_ast/hir/ArraySpecifier.py | __init__ | qram9/c_ast | 0 | python | def __init__(self, dimensions):
'Initialize an array specifier with a list\nof ints for dimensions'
self.dimensions = []
if (not dimensions):
raise InvalidTypeDimensionsError(str(type(dimensions)))
for k in dimensions:
if (not isinstance(k, int)):
raise InvalidValueDimensionError(str(type(k)))
self.dimensions.append(k) | def __init__(self, dimensions):
'Initialize an array specifier with a list\nof ints for dimensions'
self.dimensions = []
if (not dimensions):
raise InvalidTypeDimensionsError(str(type(dimensions)))
for k in dimensions:
if (not isinstance(k, int)):
raise InvalidValueDimensionError(str(type(k)))
self.dimensions.append(k)<|docstring|>Initialize an array specifier with a list
of ints for dimensions<|endoftext|> |
22272f8ea6e592fcb758524adbf0b1e14c7f0b9fbbe593ef9b3a9e05185d1111 | def getNumDimensions(self):
'Returns the number of dimensions\nin the dimensions list specifier'
return self.dimensions.size() | Returns the number of dimensions
in the dimensions list specifier | c_ast/hir/ArraySpecifier.py | getNumDimensions | qram9/c_ast | 0 | python | def getNumDimensions(self):
'Returns the number of dimensions\nin the dimensions list specifier'
return self.dimensions.size() | def getNumDimensions(self):
'Returns the number of dimensions\nin the dimensions list specifier'
return self.dimensions.size()<|docstring|>Returns the number of dimensions
in the dimensions list specifier<|endoftext|> |
5a2fd65f5888f5951917be446906f2699a9715865a7e8cca9a4e47adc8c6ffb9 | def getDimension(self, n):
'Returns a specified dimension'
try:
return self.dimensions[n]
except IndexError:
raise InvalidIndexDimensionsError(('%d %d' % (len(self.dimensions), n))) | Returns a specified dimension | c_ast/hir/ArraySpecifier.py | getDimension | qram9/c_ast | 0 | python | def getDimension(self, n):
try:
return self.dimensions[n]
except IndexError:
raise InvalidIndexDimensionsError(('%d %d' % (len(self.dimensions), n))) | def getDimension(self, n):
try:
return self.dimensions[n]
except IndexError:
raise InvalidIndexDimensionsError(('%d %d' % (len(self.dimensions), n)))<|docstring|>Returns a specified dimension<|endoftext|> |
bf5c95cacab2ee65c0dbff3f7e611883dbde98bba3347cc8333d575c25782a40 | def setDimension(self, n, val):
'Sets a specified dimension to given value'
self.dimensions[n] = val | Sets a specified dimension to given value | c_ast/hir/ArraySpecifier.py | setDimension | qram9/c_ast | 0 | python | def setDimension(self, n, val):
self.dimensions[n] = val | def setDimension(self, n, val):
self.dimensions[n] = val<|docstring|>Sets a specified dimension to given value<|endoftext|> |
bbd3c2a9c17d77fdf1ab51e78247c9efe0fb21e45bc7f78ae6552e288ff63559 | def __repr__(self):
'Returns a string representation of the contents \nof the array specifier object. Currently the returned \nstring is in AnsiC. For example [1][2][3].\nChange this function to return different \na representation.'
retval = ''
for k in self.dimensions:
retval += '['
retval += str(k)
retval += ']'
return retval | Returns a string representation of the contents
of the array specifier object. Currently the returned
string is in AnsiC. For example [1][2][3].
Change this function to return different
a representation. | c_ast/hir/ArraySpecifier.py | __repr__ | qram9/c_ast | 0 | python | def __repr__(self):
'Returns a string representation of the contents \nof the array specifier object. Currently the returned \nstring is in AnsiC. For example [1][2][3].\nChange this function to return different \na representation.'
retval =
for k in self.dimensions:
retval += '['
retval += str(k)
retval += ']'
return retval | def __repr__(self):
'Returns a string representation of the contents \nof the array specifier object. Currently the returned \nstring is in AnsiC. For example [1][2][3].\nChange this function to return different \na representation.'
retval =
for k in self.dimensions:
retval += '['
retval += str(k)
retval += ']'
return retval<|docstring|>Returns a string representation of the contents
of the array specifier object. Currently the returned
string is in AnsiC. For example [1][2][3].
Change this function to return different
a representation.<|endoftext|> |
bb950b4c3dfe6384b4cb6db5e2caa4c72b96f262ab278829c108463d386ee9db | def items(self):
"Returns the 'dimensions' list of ints"
items = {}
items['dimensions'] = self.dimensions
for k in ArraySpecifier.__bases__:
if hasattr(k, 'items'):
supitems = k.items(self)
for (k, v) in list(supitems.items()):
items[k] = v
return dict(items) | Returns the 'dimensions' list of ints | c_ast/hir/ArraySpecifier.py | items | qram9/c_ast | 0 | python | def items(self):
items = {}
items['dimensions'] = self.dimensions
for k in ArraySpecifier.__bases__:
if hasattr(k, 'items'):
supitems = k.items(self)
for (k, v) in list(supitems.items()):
items[k] = v
return dict(items) | def items(self):
items = {}
items['dimensions'] = self.dimensions
for k in ArraySpecifier.__bases__:
if hasattr(k, 'items'):
supitems = k.items(self)
for (k, v) in list(supitems.items()):
items[k] = v
return dict(items)<|docstring|>Returns the 'dimensions' list of ints<|endoftext|> |
76a865d7170cec3318d7841402aaf6ec3347d110e158d6b0488ac43ff4175301 | def __getstate__(self):
"Returns the 'dimensions' list of ints. Calls items directly"
return dict(self.items()) | Returns the 'dimensions' list of ints. Calls items directly | c_ast/hir/ArraySpecifier.py | __getstate__ | qram9/c_ast | 0 | python | def __getstate__(self):
return dict(self.items()) | def __getstate__(self):
return dict(self.items())<|docstring|>Returns the 'dimensions' list of ints. Calls items directly<|endoftext|> |
0db375efca85d14fce3b1d1740c56acf6cf30c3b7dbdae46bec90ddaec916e0c | def __setstate__(self, statedict):
'Blindly sets the state of this object, using a statedict'
for (k, v) in list(statedict.items()):
setattr(self, k, v) | Blindly sets the state of this object, using a statedict | c_ast/hir/ArraySpecifier.py | __setstate__ | qram9/c_ast | 0 | python | def __setstate__(self, statedict):
for (k, v) in list(statedict.items()):
setattr(self, k, v) | def __setstate__(self, statedict):
for (k, v) in list(statedict.items()):
setattr(self, k, v)<|docstring|>Blindly sets the state of this object, using a statedict<|endoftext|> |
bc1fe74641166fa843eb3f56de6884ba565a7ec824ee86dd7fa270952d8252ad | def __init__(self, *lru_args, **lru_kwargs):
'\n \n :param lru_args: \n :param lru_kwargs: \n '
self.lru_args = lru_args
self.lru_kwargs = lru_kwargs | :param lru_args:
:param lru_kwargs: | shot_detector/utils/functool_lru_cache_method.py | __init__ | w495/shot_detector | 18 | python | def __init__(self, *lru_args, **lru_kwargs):
'\n \n :param lru_args: \n :param lru_kwargs: \n '
self.lru_args = lru_args
self.lru_kwargs = lru_kwargs | def __init__(self, *lru_args, **lru_kwargs):
'\n \n :param lru_args: \n :param lru_kwargs: \n '
self.lru_args = lru_args
self.lru_kwargs = lru_kwargs<|docstring|>:param lru_args:
:param lru_kwargs:<|endoftext|> |
f95035609fe7d258a8618bc3d888df92fe8a9c90681080114b051ad5cf29f0f9 | def __call__(self, func):
'\n \n :param func: \n :return: \n '
@functools.wraps(func)
def wrapped_func(wrapped_self, *wrapped_args, **wrapped_kwargs):
'\n \n :param wrapped_self: \n :param wrapped_args: \n :param wrapped_kwargs: \n :return: \n '
weak_wrapped_self = weakref.ref(wrapped_self)
@functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result
setattr(wrapped_self, func.__name__, cached_method)
cached_result = cached_method(wrapped_args, wrapped_kwargs)
return cached_result
return wrapped_func | :param func:
:return: | shot_detector/utils/functool_lru_cache_method.py | __call__ | w495/shot_detector | 18 | python | def __call__(self, func):
'\n \n :param func: \n :return: \n '
@functools.wraps(func)
def wrapped_func(wrapped_self, *wrapped_args, **wrapped_kwargs):
'\n \n :param wrapped_self: \n :param wrapped_args: \n :param wrapped_kwargs: \n :return: \n '
weak_wrapped_self = weakref.ref(wrapped_self)
@functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result
setattr(wrapped_self, func.__name__, cached_method)
cached_result = cached_method(wrapped_args, wrapped_kwargs)
return cached_result
return wrapped_func | def __call__(self, func):
'\n \n :param func: \n :return: \n '
@functools.wraps(func)
def wrapped_func(wrapped_self, *wrapped_args, **wrapped_kwargs):
'\n \n :param wrapped_self: \n :param wrapped_args: \n :param wrapped_kwargs: \n :return: \n '
weak_wrapped_self = weakref.ref(wrapped_self)
@functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result
setattr(wrapped_self, func.__name__, cached_method)
cached_result = cached_method(wrapped_args, wrapped_kwargs)
return cached_result
return wrapped_func<|docstring|>:param func:
:return:<|endoftext|> |
91c70bd61a5c3853b209c31ff6e389b4aaf0d05dddce5d0b3f84fbcc18d76660 | @functools.wraps(func)
def wrapped_func(wrapped_self, *wrapped_args, **wrapped_kwargs):
'\n \n :param wrapped_self: \n :param wrapped_args: \n :param wrapped_kwargs: \n :return: \n '
weak_wrapped_self = weakref.ref(wrapped_self)
@functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result
setattr(wrapped_self, func.__name__, cached_method)
cached_result = cached_method(wrapped_args, wrapped_kwargs)
return cached_result | :param wrapped_self:
:param wrapped_args:
:param wrapped_kwargs:
:return: | shot_detector/utils/functool_lru_cache_method.py | wrapped_func | w495/shot_detector | 18 | python | @functools.wraps(func)
def wrapped_func(wrapped_self, *wrapped_args, **wrapped_kwargs):
'\n \n :param wrapped_self: \n :param wrapped_args: \n :param wrapped_kwargs: \n :return: \n '
weak_wrapped_self = weakref.ref(wrapped_self)
@functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result
setattr(wrapped_self, func.__name__, cached_method)
cached_result = cached_method(wrapped_args, wrapped_kwargs)
return cached_result | @functools.wraps(func)
def wrapped_func(wrapped_self, *wrapped_args, **wrapped_kwargs):
'\n \n :param wrapped_self: \n :param wrapped_args: \n :param wrapped_kwargs: \n :return: \n '
weak_wrapped_self = weakref.ref(wrapped_self)
@functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result
setattr(wrapped_self, func.__name__, cached_method)
cached_result = cached_method(wrapped_args, wrapped_kwargs)
return cached_result<|docstring|>:param wrapped_self:
:param wrapped_args:
:param wrapped_kwargs:
:return:<|endoftext|> |
7f4f16a6ee57ddb71b2c8e77d15ae3c29b9c9824f3d0ca7ce3203be1d76f9c98 | @functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result | :param cached_args:
:param cached_kwargs:
:return: | shot_detector/utils/functool_lru_cache_method.py | cached_method | w495/shot_detector | 18 | python | @functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result | @functools.wraps(func)
@functools.lru_cache(*self.lru_args, **self.lru_kwargs)
def cached_method(cached_args, cached_kwargs):
'\n \n :param cached_args: \n :param cached_kwargs: \n :return: \n '
result = func(weak_wrapped_self(), *cached_args, **cached_kwargs)
return result<|docstring|>:param cached_args:
:param cached_kwargs:
:return:<|endoftext|> |
528a480bc84bb495662b8143046c1a1345b6f451871ceb0656e3e1d9cd1cbb69 | def __init__(self, viscosity, isotropic=False):
'\n Arguments:\n viscosity dynamic viscosity µ of surrounding fluid\n isotropic (bool) True if the drag is isotropic\n '
self.viscosity = viscosity
self.isotropic = isotropic | Arguments:
viscosity dynamic viscosity µ of surrounding fluid
isotropic (bool) True if the drag is isotropic | stoked/drag.py | __init__ | johnaparker/stoked | 1 | python | def __init__(self, viscosity, isotropic=False):
'\n Arguments:\n viscosity dynamic viscosity µ of surrounding fluid\n isotropic (bool) True if the drag is isotropic\n '
self.viscosity = viscosity
self.isotropic = isotropic | def __init__(self, viscosity, isotropic=False):
'\n Arguments:\n viscosity dynamic viscosity µ of surrounding fluid\n isotropic (bool) True if the drag is isotropic\n '
self.viscosity = viscosity
self.isotropic = isotropic<|docstring|>Arguments:
viscosity dynamic viscosity µ of surrounding fluid
isotropic (bool) True if the drag is isotropic<|endoftext|> |
2d7a08bab7c7148e8b4cc12fb644937ad559ce17bd74a63553074e893df04a7c | @property
def drag_T(self):
'translational drag coeffecient'
return self._drag_T() | translational drag coeffecient | stoked/drag.py | drag_T | johnaparker/stoked | 1 | python | @property
def drag_T(self):
return self._drag_T() | @property
def drag_T(self):
return self._drag_T()<|docstring|>translational drag coeffecient<|endoftext|> |
90d67ae1b4cc885a2e1fd5fcef5100c63a0a92470da5333d0940c93787b08b70 | @property
def drag_R(self):
'rotational drag coeffecient'
return self._drag_R() | rotational drag coeffecient | stoked/drag.py | drag_R | johnaparker/stoked | 1 | python | @property
def drag_R(self):
return self._drag_R() | @property
def drag_R(self):
return self._drag_R()<|docstring|>rotational drag coeffecient<|endoftext|> |
ee164101513a8c37b46b39a87a65126d18cbc3d57f3f6d3f291ce3fc059315d5 | def __init__(self, radius, viscosity):
'\n Arguments:\n radius[N] sphere radii\n viscosity dynamic viscosity µ of surrounding fluid\n '
super().__init__(viscosity, isotropic=True)
self.radius = np.asarray(radius, dtype=float) | Arguments:
radius[N] sphere radii
viscosity dynamic viscosity µ of surrounding fluid | stoked/drag.py | __init__ | johnaparker/stoked | 1 | python | def __init__(self, radius, viscosity):
'\n Arguments:\n radius[N] sphere radii\n viscosity dynamic viscosity µ of surrounding fluid\n '
super().__init__(viscosity, isotropic=True)
self.radius = np.asarray(radius, dtype=float) | def __init__(self, radius, viscosity):
'\n Arguments:\n radius[N] sphere radii\n viscosity dynamic viscosity µ of surrounding fluid\n '
super().__init__(viscosity, isotropic=True)
self.radius = np.asarray(radius, dtype=float)<|docstring|>Arguments:
radius[N] sphere radii
viscosity dynamic viscosity µ of surrounding fluid<|endoftext|> |
372adfeb008edc47b5933b234ffb3aa57f02f881cd6d635460151c0a8b0665cf | def __init__(self, radii, viscosity):
'\n Arguments:\n radii[N,3] ellipsoid radii\n viscosity dynamic viscosity µ of surrounding fluid\n '
super().__init__(viscosity)
self.radii = np.atleast_2d(np.asarray(radii, dtype=float))
self.chi_0 = np.zeros(len(self.radii), dtype=float)
self.alpha_0 = np.zeros_like(self.radii)
for i in range(len(self.radii)):
(a, b, c) = self.radii[i]
integrand = (lambda t: (1 / np.sqrt((((1 + t) * (((b / a) ** 2) + t)) * (((c / a) ** 2) + t)))))
self.chi_0[i] = ((b * c) * quad(integrand, 0, np.inf)[0])
for (j, comp) in enumerate([a, b, c]):
integrand = (lambda t: (1 / ((1 + t) * np.sqrt((((((a / comp) ** 2) + t) * (((b / comp) ** 2) + t)) * (((c / comp) ** 2) + t))))))
self.alpha_0[i][j] = ((((a * b) * c) / comp) * quad(integrand, 0, np.inf)[0]) | Arguments:
radii[N,3] ellipsoid radii
viscosity dynamic viscosity µ of surrounding fluid | stoked/drag.py | __init__ | johnaparker/stoked | 1 | python | def __init__(self, radii, viscosity):
'\n Arguments:\n radii[N,3] ellipsoid radii\n viscosity dynamic viscosity µ of surrounding fluid\n '
super().__init__(viscosity)
self.radii = np.atleast_2d(np.asarray(radii, dtype=float))
self.chi_0 = np.zeros(len(self.radii), dtype=float)
self.alpha_0 = np.zeros_like(self.radii)
for i in range(len(self.radii)):
(a, b, c) = self.radii[i]
integrand = (lambda t: (1 / np.sqrt((((1 + t) * (((b / a) ** 2) + t)) * (((c / a) ** 2) + t)))))
self.chi_0[i] = ((b * c) * quad(integrand, 0, np.inf)[0])
for (j, comp) in enumerate([a, b, c]):
integrand = (lambda t: (1 / ((1 + t) * np.sqrt((((((a / comp) ** 2) + t) * (((b / comp) ** 2) + t)) * (((c / comp) ** 2) + t))))))
self.alpha_0[i][j] = ((((a * b) * c) / comp) * quad(integrand, 0, np.inf)[0]) | def __init__(self, radii, viscosity):
'\n Arguments:\n radii[N,3] ellipsoid radii\n viscosity dynamic viscosity µ of surrounding fluid\n '
super().__init__(viscosity)
self.radii = np.atleast_2d(np.asarray(radii, dtype=float))
self.chi_0 = np.zeros(len(self.radii), dtype=float)
self.alpha_0 = np.zeros_like(self.radii)
for i in range(len(self.radii)):
(a, b, c) = self.radii[i]
integrand = (lambda t: (1 / np.sqrt((((1 + t) * (((b / a) ** 2) + t)) * (((c / a) ** 2) + t)))))
self.chi_0[i] = ((b * c) * quad(integrand, 0, np.inf)[0])
for (j, comp) in enumerate([a, b, c]):
integrand = (lambda t: (1 / ((1 + t) * np.sqrt((((((a / comp) ** 2) + t) * (((b / comp) ** 2) + t)) * (((c / comp) ** 2) + t))))))
self.alpha_0[i][j] = ((((a * b) * c) / comp) * quad(integrand, 0, np.inf)[0])<|docstring|>Arguments:
radii[N,3] ellipsoid radii
viscosity dynamic viscosity µ of surrounding fluid<|endoftext|> |
2cb1a6974c424862ef2b820877b70eae16dd67f156a2bb3df8ac4fbd57e0418e | def primitive_nurbs_surface_circle_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Circle \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | Construct a Nurbs surface Circle
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool | thirdparty/blender_autocomplete-master/2.79/bpy/ops/surface.py | primitive_nurbs_surface_circle_add | Ray1184/HPMSBatch | 0 | python | def primitive_nurbs_surface_circle_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Circle \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | def primitive_nurbs_surface_circle_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Circle \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass<|docstring|>Construct a Nurbs surface Circle
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool<|endoftext|> |
fefcd97640ad8a96cadc0a2797d2d9b2827710a3a14edb530ea0a0f4739fce78 | def primitive_nurbs_surface_curve_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Curve \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | Construct a Nurbs surface Curve
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool | thirdparty/blender_autocomplete-master/2.79/bpy/ops/surface.py | primitive_nurbs_surface_curve_add | Ray1184/HPMSBatch | 0 | python | def primitive_nurbs_surface_curve_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Curve \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | def primitive_nurbs_surface_curve_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Curve \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass<|docstring|>Construct a Nurbs surface Curve
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool<|endoftext|> |
2bafe7cb7dd25abb3e208c884b76d5367199c8fb7049b262cf8caa4efb7519cc | def primitive_nurbs_surface_cylinder_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Cylinder \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | Construct a Nurbs surface Cylinder
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool | thirdparty/blender_autocomplete-master/2.79/bpy/ops/surface.py | primitive_nurbs_surface_cylinder_add | Ray1184/HPMSBatch | 0 | python | def primitive_nurbs_surface_cylinder_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Cylinder \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | def primitive_nurbs_surface_cylinder_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Cylinder \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass<|docstring|>Construct a Nurbs surface Cylinder
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool<|endoftext|> |
b0ab27f7e06a0d57f6d32bb67c11b5fb0d28a0c1b4b476dfe54c22267693e9f1 | def primitive_nurbs_surface_sphere_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Sphere \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | Construct a Nurbs surface Sphere
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool | thirdparty/blender_autocomplete-master/2.79/bpy/ops/surface.py | primitive_nurbs_surface_sphere_add | Ray1184/HPMSBatch | 0 | python | def primitive_nurbs_surface_sphere_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Sphere \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | def primitive_nurbs_surface_sphere_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Sphere \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass<|docstring|>Construct a Nurbs surface Sphere
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool<|endoftext|> |
d657ef0de3316d40ee28a7ca91b660b621dc5d2e12f6c1a4be60eb9bfc35d0c7 | def primitive_nurbs_surface_surface_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Patch \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | Construct a Nurbs surface Patch
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool | thirdparty/blender_autocomplete-master/2.79/bpy/ops/surface.py | primitive_nurbs_surface_surface_add | Ray1184/HPMSBatch | 0 | python | def primitive_nurbs_surface_surface_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Patch \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | def primitive_nurbs_surface_surface_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Patch \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass<|docstring|>Construct a Nurbs surface Patch
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool<|endoftext|> |
8cdd832ec4a0d1906d9fb0c741f80529e59b826373ea540e22ccc7c5c3f877a3 | def primitive_nurbs_surface_torus_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Torus \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | Construct a Nurbs surface Torus
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool | thirdparty/blender_autocomplete-master/2.79/bpy/ops/surface.py | primitive_nurbs_surface_torus_add | Ray1184/HPMSBatch | 0 | python | def primitive_nurbs_surface_torus_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Torus \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass | def primitive_nurbs_surface_torus_add(radius: float=1.0, view_align: bool=False, enter_editmode: bool=False, location: float=(0.0, 0.0, 0.0), rotation: float=(0.0, 0.0, 0.0), layers: bool=(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)):
'Construct a Nurbs surface Torus \n\n :param radius: Radius \n :type radius: float\n :param view_align: Align to View, Align the new object to the view \n :type view_align: bool\n :param enter_editmode: Enter Editmode, Enter editmode when adding this object \n :type enter_editmode: bool\n :param location: Location, Location for the newly added object \n :type location: float\n :param rotation: Rotation, Rotation for the newly added object \n :type rotation: float\n :param layers: Layer \n :type layers: bool\n '
pass<|docstring|>Construct a Nurbs surface Torus
:param radius: Radius
:type radius: float
:param view_align: Align to View, Align the new object to the view
:type view_align: bool
:param enter_editmode: Enter Editmode, Enter editmode when adding this object
:type enter_editmode: bool
:param location: Location, Location for the newly added object
:type location: float
:param rotation: Rotation, Rotation for the newly added object
:type rotation: float
:param layers: Layer
:type layers: bool<|endoftext|> |
ac6212d6ee5e31d9be6ec7450b4b14d186d6942e52c9ea7ef8875420cb8c7ee9 | def get_movie_data(films, people):
'Request handler for movie api'
success = False
movies = {}
if ((films.status_code == 200) and (people.status_code == 200)):
movies = map_cast(films.content, people.content)
if (len(movies) > 0):
success = True
message = 'Request to ghibli was a success'
else:
message = f'''
The api call to ghibli failed because the response code from ghibli for
film api was {films.status_code} and for people api, it was
{people.status_code}. Please call ghibli support'''
logger.error('Could not get data from ghibli because of %s', message)
return {'is_success': success, 'message': message, 'movies': movies} | Request handler for movie api | studio_ghibli/movies/api_data/get_movie_data.py | get_movie_data | hbansal0122/studio_ghibli_project | 0 | python | def get_movie_data(films, people):
success = False
movies = {}
if ((films.status_code == 200) and (people.status_code == 200)):
movies = map_cast(films.content, people.content)
if (len(movies) > 0):
success = True
message = 'Request to ghibli was a success'
else:
message = f'
The api call to ghibli failed because the response code from ghibli for
film api was {films.status_code} and for people api, it was
{people.status_code}. Please call ghibli support'
logger.error('Could not get data from ghibli because of %s', message)
return {'is_success': success, 'message': message, 'movies': movies} | def get_movie_data(films, people):
success = False
movies = {}
if ((films.status_code == 200) and (people.status_code == 200)):
movies = map_cast(films.content, people.content)
if (len(movies) > 0):
success = True
message = 'Request to ghibli was a success'
else:
message = f'
The api call to ghibli failed because the response code from ghibli for
film api was {films.status_code} and for people api, it was
{people.status_code}. Please call ghibli support'
logger.error('Could not get data from ghibli because of %s', message)
return {'is_success': success, 'message': message, 'movies': movies}<|docstring|>Request handler for movie api<|endoftext|> |
94aae500d58e84c10e9aa09acf78487bb181c306dfa48ecc7cb84956ad548d19 | def map_cast(films, people):
'Mapping of person with relevent movie'
mapped_people_films = []
if (len(films) > 0):
updated_people_obj = []
for person in json.loads(people):
for film_by_person in person['films']:
film_id = film_by_person.split('/')[(- 1)]
person[film_id] = film_by_person
person.pop('films', None)
updated_people_obj.append(person)
for single_film in json.loads(films):
cast_each_film = []
for person in updated_people_obj:
if person.get(single_film['id']):
cast_each_film.append(person)
single_film['people'] = cast_each_film
mapped_people_films.append(single_film)
return mapped_people_films | Mapping of person with relevent movie | studio_ghibli/movies/api_data/get_movie_data.py | map_cast | hbansal0122/studio_ghibli_project | 0 | python | def map_cast(films, people):
mapped_people_films = []
if (len(films) > 0):
updated_people_obj = []
for person in json.loads(people):
for film_by_person in person['films']:
film_id = film_by_person.split('/')[(- 1)]
person[film_id] = film_by_person
person.pop('films', None)
updated_people_obj.append(person)
for single_film in json.loads(films):
cast_each_film = []
for person in updated_people_obj:
if person.get(single_film['id']):
cast_each_film.append(person)
single_film['people'] = cast_each_film
mapped_people_films.append(single_film)
return mapped_people_films | def map_cast(films, people):
mapped_people_films = []
if (len(films) > 0):
updated_people_obj = []
for person in json.loads(people):
for film_by_person in person['films']:
film_id = film_by_person.split('/')[(- 1)]
person[film_id] = film_by_person
person.pop('films', None)
updated_people_obj.append(person)
for single_film in json.loads(films):
cast_each_film = []
for person in updated_people_obj:
if person.get(single_film['id']):
cast_each_film.append(person)
single_film['people'] = cast_each_film
mapped_people_films.append(single_film)
return mapped_people_films<|docstring|>Mapping of person with relevent movie<|endoftext|> |
cf4a43970e6b6428b7a9cca730971d92d34824bff326ce1d1ca4ef27fd8c8831 | def connectHBase():
'\n 连接远程HBase\n :return: 连接HBase的客户端实例\n '
socket = TSocket.TSocket('10.0.86.245', 9090)
socket.setTimeout(5000)
transport = TTransport.TBufferedTransport(socket)
protocol = TBinaryProtocol.TBinaryProtocol(transport)
client = Hbase.Client(protocol)
socket.open()
return client | 连接远程HBase
:return: 连接HBase的客户端实例 | PythonConnectHBase/scannerGet.py | connectHBase | SparksFly8/Learning_Python | 47 | python | def connectHBase():
'\n 连接远程HBase\n :return: 连接HBase的客户端实例\n '
socket = TSocket.TSocket('10.0.86.245', 9090)
socket.setTimeout(5000)
transport = TTransport.TBufferedTransport(socket)
protocol = TBinaryProtocol.TBinaryProtocol(transport)
client = Hbase.Client(protocol)
socket.open()
return client | def connectHBase():
'\n 连接远程HBase\n :return: 连接HBase的客户端实例\n '
socket = TSocket.TSocket('10.0.86.245', 9090)
socket.setTimeout(5000)
transport = TTransport.TBufferedTransport(socket)
protocol = TBinaryProtocol.TBinaryProtocol(transport)
client = Hbase.Client(protocol)
socket.open()
return client<|docstring|>连接远程HBase
:return: 连接HBase的客户端实例<|endoftext|> |
559ab6d89971d6425b5171472b9cbce3b4fe376ed8abb568c8ac0db901e89ce5 | def ListTables(client):
'\n 列出所有表\n '
print(client.getTableNames()) | 列出所有表 | PythonConnectHBase/scannerGet.py | ListTables | SparksFly8/Learning_Python | 47 | python | def ListTables(client):
'\n \n '
print(client.getTableNames()) | def ListTables(client):
'\n \n '
print(client.getTableNames())<|docstring|>列出所有表<|endoftext|> |
744a99704da5f8a6721021686b0b0db1c39164c81f819b78c401048697191fa9 | def scannerGetSelect(client, tableName, columns, startRow, stopRow=None, rowsCnt=2000):
'\n 依次扫描HBase指定表的每行数据(根据起始行，扫描到表的最后一行或指定行的前一行)\n :param client: 连接HBase的客户端实例\n :param tableName: 表名\n :param columns: 一个包含(一个或多个列簇下对应列名的)列表\n :param startRow: 起始扫描行\n :param stopRow: 停止扫描行(默认为空)\n :param rowsCnt: 需要扫描的行数\n :return MutilRowsDict: 返回一个包含多行数据的字典，以每行行键定位是哪一行\n '
if (stopRow is None):
scannerId = client.scannerOpen(tableName, startRow, columns)
else:
scannerId = client.scannerOpenWithStop(tableName, startRow, stopRow, columns)
results = client.scannerGetList(scannerId, rowsCnt)
if results:
MutilRowsDict = {}
for result in results:
RowDict = {}
colFamilyDict = {}
preColFamily = None
cnt = 0
for (key, TCell_value) in result.columns.items():
cnt += 1
rowKey = result.row
colFamily_colName = key.split(':')
colFamily = colFamily_colName[0]
colName = colFamily_colName[1]
if ((preColFamily is None) or (preColFamily == colFamily)):
colFamilyDict[colName] = TCell_value.value
preColFamily = colFamily
else:
RowDict[preColFamily] = colFamilyDict
colFamilyDict = {}
colFamilyDict[colName] = TCell_value.value
preColFamily = None
if (cnt == len(result.columns.items())):
RowDict[colFamily] = colFamilyDict
MutilRowsDict[rowKey] = RowDict
return MutilRowsDict
else:
return [] | 依次扫描HBase指定表的每行数据(根据起始行，扫描到表的最后一行或指定行的前一行)
:param client: 连接HBase的客户端实例
:param tableName: 表名
:param columns: 一个包含(一个或多个列簇下对应列名的)列表
:param startRow: 起始扫描行
:param stopRow: 停止扫描行(默认为空)
:param rowsCnt: 需要扫描的行数
:return MutilRowsDict: 返回一个包含多行数据的字典，以每行行键定位是哪一行 | PythonConnectHBase/scannerGet.py | scannerGetSelect | SparksFly8/Learning_Python | 47 | python | def scannerGetSelect(client, tableName, columns, startRow, stopRow=None, rowsCnt=2000):
'\n 依次扫描HBase指定表的每行数据(根据起始行，扫描到表的最后一行或指定行的前一行)\n :param client: 连接HBase的客户端实例\n :param tableName: 表名\n :param columns: 一个包含(一个或多个列簇下对应列名的)列表\n :param startRow: 起始扫描行\n :param stopRow: 停止扫描行(默认为空)\n :param rowsCnt: 需要扫描的行数\n :return MutilRowsDict: 返回一个包含多行数据的字典，以每行行键定位是哪一行\n '
if (stopRow is None):
scannerId = client.scannerOpen(tableName, startRow, columns)
else:
scannerId = client.scannerOpenWithStop(tableName, startRow, stopRow, columns)
results = client.scannerGetList(scannerId, rowsCnt)
if results:
MutilRowsDict = {}
for result in results:
RowDict = {}
colFamilyDict = {}
preColFamily = None
cnt = 0
for (key, TCell_value) in result.columns.items():
cnt += 1
rowKey = result.row
colFamily_colName = key.split(':')
colFamily = colFamily_colName[0]
colName = colFamily_colName[1]
if ((preColFamily is None) or (preColFamily == colFamily)):
colFamilyDict[colName] = TCell_value.value
preColFamily = colFamily
else:
RowDict[preColFamily] = colFamilyDict
colFamilyDict = {}
colFamilyDict[colName] = TCell_value.value
preColFamily = None
if (cnt == len(result.columns.items())):
RowDict[colFamily] = colFamilyDict
MutilRowsDict[rowKey] = RowDict
return MutilRowsDict
else:
return [] | def scannerGetSelect(client, tableName, columns, startRow, stopRow=None, rowsCnt=2000):
'\n 依次扫描HBase指定表的每行数据(根据起始行，扫描到表的最后一行或指定行的前一行)\n :param client: 连接HBase的客户端实例\n :param tableName: 表名\n :param columns: 一个包含(一个或多个列簇下对应列名的)列表\n :param startRow: 起始扫描行\n :param stopRow: 停止扫描行(默认为空)\n :param rowsCnt: 需要扫描的行数\n :return MutilRowsDict: 返回一个包含多行数据的字典，以每行行键定位是哪一行\n '
if (stopRow is None):
scannerId = client.scannerOpen(tableName, startRow, columns)
else:
scannerId = client.scannerOpenWithStop(tableName, startRow, stopRow, columns)
results = client.scannerGetList(scannerId, rowsCnt)
if results:
MutilRowsDict = {}
for result in results:
RowDict = {}
colFamilyDict = {}
preColFamily = None
cnt = 0
for (key, TCell_value) in result.columns.items():
cnt += 1
rowKey = result.row
colFamily_colName = key.split(':')
colFamily = colFamily_colName[0]
colName = colFamily_colName[1]
if ((preColFamily is None) or (preColFamily == colFamily)):
colFamilyDict[colName] = TCell_value.value
preColFamily = colFamily
else:
RowDict[preColFamily] = colFamilyDict
colFamilyDict = {}
colFamilyDict[colName] = TCell_value.value
preColFamily = None
if (cnt == len(result.columns.items())):
RowDict[colFamily] = colFamilyDict
MutilRowsDict[rowKey] = RowDict
return MutilRowsDict
else:
return []<|docstring|>依次扫描HBase指定表的每行数据(根据起始行，扫描到表的最后一行或指定行的前一行)
:param client: 连接HBase的客户端实例
:param tableName: 表名
:param columns: 一个包含(一个或多个列簇下对应列名的)列表
:param startRow: 起始扫描行
:param stopRow: 停止扫描行(默认为空)
:param rowsCnt: 需要扫描的行数
:return MutilRowsDict: 返回一个包含多行数据的字典，以每行行键定位是哪一行<|endoftext|> |
5dea36b255554e8644cc645f9a975b254cbb809adc0361456f003bf878b934c5 | def load_guidata(filename, report):
'Check if we have a GUI document.'
report({'INFO'}, 'load guidata..')
guidata = None
zdoc = zipfile.ZipFile(filename)
if zdoc:
if ('GuiDocument.xml' in zdoc.namelist()):
gf = zdoc.open('GuiDocument.xml')
guidata = gf.read()
gf.close()
Handler = FreeCAD_xml_handler()
xml.sax.parseString(guidata, Handler)
guidata = Handler.guidata
for (key, properties) in guidata.items():
if ('DiffuseColor' in properties):
df = zdoc.open(guidata[key]['DiffuseColor'])
buf = df.read()
df.close()
cols = []
for i in range(1, int((len(buf) / 4))):
cols.append((buf[((i * 4) + 3)], buf[((i * 4) + 2)], buf[((i * 4) + 1)], buf[(i * 4)]))
guidata[key]['DiffuseColor'] = cols
zdoc.close()
report({'INFO'}, 'load guidata done.')
return guidata | Check if we have a GUI document. | import_fcstd/guidata.py | load_guidata | s-light/io_import_fcstd | 6 | python | def load_guidata(filename, report):
report({'INFO'}, 'load guidata..')
guidata = None
zdoc = zipfile.ZipFile(filename)
if zdoc:
if ('GuiDocument.xml' in zdoc.namelist()):
gf = zdoc.open('GuiDocument.xml')
guidata = gf.read()
gf.close()
Handler = FreeCAD_xml_handler()
xml.sax.parseString(guidata, Handler)
guidata = Handler.guidata
for (key, properties) in guidata.items():
if ('DiffuseColor' in properties):
df = zdoc.open(guidata[key]['DiffuseColor'])
buf = df.read()
df.close()
cols = []
for i in range(1, int((len(buf) / 4))):
cols.append((buf[((i * 4) + 3)], buf[((i * 4) + 2)], buf[((i * 4) + 1)], buf[(i * 4)]))
guidata[key]['DiffuseColor'] = cols
zdoc.close()
report({'INFO'}, 'load guidata done.')
return guidata | def load_guidata(filename, report):
report({'INFO'}, 'load guidata..')
guidata = None
zdoc = zipfile.ZipFile(filename)
if zdoc:
if ('GuiDocument.xml' in zdoc.namelist()):
gf = zdoc.open('GuiDocument.xml')
guidata = gf.read()
gf.close()
Handler = FreeCAD_xml_handler()
xml.sax.parseString(guidata, Handler)
guidata = Handler.guidata
for (key, properties) in guidata.items():
if ('DiffuseColor' in properties):
df = zdoc.open(guidata[key]['DiffuseColor'])
buf = df.read()
df.close()
cols = []
for i in range(1, int((len(buf) / 4))):
cols.append((buf[((i * 4) + 3)], buf[((i * 4) + 2)], buf[((i * 4) + 1)], buf[(i * 4)]))
guidata[key]['DiffuseColor'] = cols
zdoc.close()
report({'INFO'}, 'load guidata done.')
return guidata<|docstring|>Check if we have a GUI document.<|endoftext|> |
e78e98541e519cbfebd02830e3582064a346c39facd7fdd947d472dadcfc0a8d | def __init__(self):
'Init.'
self.guidata = {}
self.current = None
self.properties = {}
self.currentprop = None
self.currentval = None | Init. | import_fcstd/guidata.py | __init__ | s-light/io_import_fcstd | 6 | python | def __init__(self):
self.guidata = {}
self.current = None
self.properties = {}
self.currentprop = None
self.currentval = None | def __init__(self):
self.guidata = {}
self.current = None
self.properties = {}
self.currentprop = None
self.currentval = None<|docstring|>Init.<|endoftext|> |
a178456c911c01f9dc235ea0753befe2f0ce38ab981481d22b4e084132448fd6 | def startElement(self, tag, attributes):
'Call when an element starts.'
if (tag == 'ViewProvider'):
self.current = attributes['name']
elif (tag == 'Property'):
name = attributes['name']
element_names = ['Visibility', 'ShapeColor', 'Transparency', 'DiffuseColor']
if (name in element_names):
self.currentprop = name
elif (tag == 'Bool'):
if (attributes['value'] == 'true'):
self.currentval = True
else:
self.currentval = False
elif (tag == 'PropertyColor'):
c = int(attributes['value'])
r = (float(((c >> 24) & 255)) / 255.0)
g = (float(((c >> 16) & 255)) / 255.0)
b = (float(((c >> 8) & 255)) / 255.0)
self.currentval = (r, g, b)
elif (tag == 'Integer'):
self.currentval = int(attributes['value'])
elif (tag == 'Float'):
self.currentval = float(attributes['value'])
elif (tag == 'ColorList'):
self.currentval = attributes['file'] | Call when an element starts. | import_fcstd/guidata.py | startElement | s-light/io_import_fcstd | 6 | python | def startElement(self, tag, attributes):
if (tag == 'ViewProvider'):
self.current = attributes['name']
elif (tag == 'Property'):
name = attributes['name']
element_names = ['Visibility', 'ShapeColor', 'Transparency', 'DiffuseColor']
if (name in element_names):
self.currentprop = name
elif (tag == 'Bool'):
if (attributes['value'] == 'true'):
self.currentval = True
else:
self.currentval = False
elif (tag == 'PropertyColor'):
c = int(attributes['value'])
r = (float(((c >> 24) & 255)) / 255.0)
g = (float(((c >> 16) & 255)) / 255.0)
b = (float(((c >> 8) & 255)) / 255.0)
self.currentval = (r, g, b)
elif (tag == 'Integer'):
self.currentval = int(attributes['value'])
elif (tag == 'Float'):
self.currentval = float(attributes['value'])
elif (tag == 'ColorList'):
self.currentval = attributes['file'] | def startElement(self, tag, attributes):
if (tag == 'ViewProvider'):
self.current = attributes['name']
elif (tag == 'Property'):
name = attributes['name']
element_names = ['Visibility', 'ShapeColor', 'Transparency', 'DiffuseColor']
if (name in element_names):
self.currentprop = name
elif (tag == 'Bool'):
if (attributes['value'] == 'true'):
self.currentval = True
else:
self.currentval = False
elif (tag == 'PropertyColor'):
c = int(attributes['value'])
r = (float(((c >> 24) & 255)) / 255.0)
g = (float(((c >> 16) & 255)) / 255.0)
b = (float(((c >> 8) & 255)) / 255.0)
self.currentval = (r, g, b)
elif (tag == 'Integer'):
self.currentval = int(attributes['value'])
elif (tag == 'Float'):
self.currentval = float(attributes['value'])
elif (tag == 'ColorList'):
self.currentval = attributes['file']<|docstring|>Call when an element starts.<|endoftext|> |
1fe92faae405ca5af05d62fd0f1bc45c578e32448a622392fb0c1176802faacc | def endElement(self, tag):
'Call when an elements ends.'
if (tag == 'ViewProvider'):
if (self.current and self.properties):
self.guidata[self.current] = self.properties
self.current = None
self.properties = {}
elif (tag == 'Property'):
if (self.currentprop and (self.currentval is not None)):
self.properties[self.currentprop] = self.currentval
self.currentprop = None
self.currentval = None | Call when an elements ends. | import_fcstd/guidata.py | endElement | s-light/io_import_fcstd | 6 | python | def endElement(self, tag):
if (tag == 'ViewProvider'):
if (self.current and self.properties):
self.guidata[self.current] = self.properties
self.current = None
self.properties = {}
elif (tag == 'Property'):
if (self.currentprop and (self.currentval is not None)):
self.properties[self.currentprop] = self.currentval
self.currentprop = None
self.currentval = None | def endElement(self, tag):
if (tag == 'ViewProvider'):
if (self.current and self.properties):
self.guidata[self.current] = self.properties
self.current = None
self.properties = {}
elif (tag == 'Property'):
if (self.currentprop and (self.currentval is not None)):
self.properties[self.currentprop] = self.currentval
self.currentprop = None
self.currentval = None<|docstring|>Call when an elements ends.<|endoftext|> |
1034322426abc569b05b74aa80e6fd0956bab6c312a7b5a0e51e0bbd9c0a4db1 | @cached_property
def openapi_types():
'\n This must be a method because a pod_model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'count': (int, none_type), 'skip': (int, none_type), 'limit': (int, none_type), 'query': (RoomSearchCriteria, none_type), 'rooms': ([V2RoomDetail], none_type), 'faceted_match_count': ([FacetedMatchCount], none_type)} | This must be a method because a pod_model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | symphony/bdk/gen/pod_model/room_search_results.py | openapi_types | symphony-mariacristina/symphony-bdk-python | 17 | python | @cached_property
def openapi_types():
'\n This must be a method because a pod_model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'count': (int, none_type), 'skip': (int, none_type), 'limit': (int, none_type), 'query': (RoomSearchCriteria, none_type), 'rooms': ([V2RoomDetail], none_type), 'faceted_match_count': ([FacetedMatchCount], none_type)} | @cached_property
def openapi_types():
'\n This must be a method because a pod_model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'count': (int, none_type), 'skip': (int, none_type), 'limit': (int, none_type), 'query': (RoomSearchCriteria, none_type), 'rooms': ([V2RoomDetail], none_type), 'faceted_match_count': ([FacetedMatchCount], none_type)}<|docstring|>This must be a method because a pod_model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.<|endoftext|> |
ac27c4b90fede555ceef9f6091da7d33ed84d654322301923c07affb2dd076ec | @convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'RoomSearchResults - a pod_model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the pod_model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n count (int): The total number of rooms matched by the search.. [optional] # noqa: E501\n skip (int): The number of skipped results.. [optional] # noqa: E501\n limit (int): The number of returned results.. [optional] # noqa: E501\n query (RoomSearchCriteria): [optional] # noqa: E501\n rooms ([V2RoomDetail]): A list of rooms matched by the query.. [optional] # noqa: E501\n faceted_match_count ([FacetedMatchCount]): Detailed counts of matched rooms per search criterion.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.count: int = None
self.skip: int = None
self.limit: int = None
self.query: RoomSearchCriteria = None
self.rooms: List[V2RoomDetail] = None
self.faceted_match_count: List[FacetedMatchCount] = None
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value) | RoomSearchResults - a pod_model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the pod_model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
count (int): The total number of rooms matched by the search.. [optional] # noqa: E501
skip (int): The number of skipped results.. [optional] # noqa: E501
limit (int): The number of returned results.. [optional] # noqa: E501
query (RoomSearchCriteria): [optional] # noqa: E501
rooms ([V2RoomDetail]): A list of rooms matched by the query.. [optional] # noqa: E501
faceted_match_count ([FacetedMatchCount]): Detailed counts of matched rooms per search criterion.. [optional] # noqa: E501 | symphony/bdk/gen/pod_model/room_search_results.py | __init__ | symphony-mariacristina/symphony-bdk-python | 17 | python | @convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'RoomSearchResults - a pod_model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the pod_model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n count (int): The total number of rooms matched by the search.. [optional] # noqa: E501\n skip (int): The number of skipped results.. [optional] # noqa: E501\n limit (int): The number of returned results.. [optional] # noqa: E501\n query (RoomSearchCriteria): [optional] # noqa: E501\n rooms ([V2RoomDetail]): A list of rooms matched by the query.. [optional] # noqa: E501\n faceted_match_count ([FacetedMatchCount]): Detailed counts of matched rooms per search criterion.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.count: int = None
self.skip: int = None
self.limit: int = None
self.query: RoomSearchCriteria = None
self.rooms: List[V2RoomDetail] = None
self.faceted_match_count: List[FacetedMatchCount] = None
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value) | @convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'RoomSearchResults - a pod_model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the pod_model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n count (int): The total number of rooms matched by the search.. [optional] # noqa: E501\n skip (int): The number of skipped results.. [optional] # noqa: E501\n limit (int): The number of returned results.. [optional] # noqa: E501\n query (RoomSearchCriteria): [optional] # noqa: E501\n rooms ([V2RoomDetail]): A list of rooms matched by the query.. [optional] # noqa: E501\n faceted_match_count ([FacetedMatchCount]): Detailed counts of matched rooms per search criterion.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.count: int = None
self.skip: int = None
self.limit: int = None
self.query: RoomSearchCriteria = None
self.rooms: List[V2RoomDetail] = None
self.faceted_match_count: List[FacetedMatchCount] = None
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)<|docstring|>RoomSearchResults - a pod_model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the pod_model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
count (int): The total number of rooms matched by the search.. [optional] # noqa: E501
skip (int): The number of skipped results.. [optional] # noqa: E501
limit (int): The number of returned results.. [optional] # noqa: E501
query (RoomSearchCriteria): [optional] # noqa: E501
rooms ([V2RoomDetail]): A list of rooms matched by the query.. [optional] # noqa: E501
faceted_match_count ([FacetedMatchCount]): Detailed counts of matched rooms per search criterion.. [optional] # noqa: E501<|endoftext|> |
c1aae4f5fba6c4ab6a6d72cfa2257be3f044677be0c87319f1c4a8fdda4ba6ee | def append_abc(key, keys=[]):
"\n >>> append_abc('Author2000')\n 'Author2000b'\n >>> append_abc('Author2000b')\n 'Author2000c'\n >>> append_abc('Author2000', ['Author2000', 'Author2000b'])\n 'Author2000c'\n "
letters = list('abcdefghijklmnopqrstuvwxyz')
if (key[(- 1)] in letters):
i = letters.index(key[(- 1)])
letters = letters[(i + 1):]
key = key[:(- 1)]
else:
letters = letters[1:]
for l in letters:
Key = (key + l)
if (Key not in keys):
key = Key
break
assert (Key not in keys), 'not enough letters in the alphabets to solve key conflict? or maybe something went wrong...'
return Key | >>> append_abc('Author2000')
'Author2000b'
>>> append_abc('Author2000b')
'Author2000c'
>>> append_abc('Author2000', ['Author2000', 'Author2000b'])
'Author2000c' | papers/bib.py | append_abc | aiotter/papers | 105 | python | def append_abc(key, keys=[]):
"\n >>> append_abc('Author2000')\n 'Author2000b'\n >>> append_abc('Author2000b')\n 'Author2000c'\n >>> append_abc('Author2000', ['Author2000', 'Author2000b'])\n 'Author2000c'\n "
letters = list('abcdefghijklmnopqrstuvwxyz')
if (key[(- 1)] in letters):
i = letters.index(key[(- 1)])
letters = letters[(i + 1):]
key = key[:(- 1)]
else:
letters = letters[1:]
for l in letters:
Key = (key + l)
if (Key not in keys):
key = Key
break
assert (Key not in keys), 'not enough letters in the alphabets to solve key conflict? or maybe something went wrong...'
return Key | def append_abc(key, keys=[]):
"\n >>> append_abc('Author2000')\n 'Author2000b'\n >>> append_abc('Author2000b')\n 'Author2000c'\n >>> append_abc('Author2000', ['Author2000', 'Author2000b'])\n 'Author2000c'\n "
letters = list('abcdefghijklmnopqrstuvwxyz')
if (key[(- 1)] in letters):
i = letters.index(key[(- 1)])
letters = letters[(i + 1):]
key = key[:(- 1)]
else:
letters = letters[1:]
for l in letters:
Key = (key + l)
if (Key not in keys):
key = Key
break
assert (Key not in keys), 'not enough letters in the alphabets to solve key conflict? or maybe something went wrong...'
return Key<|docstring|>>>> append_abc('Author2000')
'Author2000b'
>>> append_abc('Author2000b')
'Author2000c'
>>> append_abc('Author2000', ['Author2000', 'Author2000b'])
'Author2000c'<|endoftext|> |
ca5a497689e0ea37e988db334c8cc57e120d0194ce57c14c6488cec9521f116d | def _simplify_string(s):
' replace unicode, strip, lower case '
s = _remove_unicode(s)
return s.lower().strip() | replace unicode, strip, lower case | papers/bib.py | _simplify_string | aiotter/papers | 105 | python | def _simplify_string(s):
' '
s = _remove_unicode(s)
return s.lower().strip() | def _simplify_string(s):
' '
s = _remove_unicode(s)
return s.lower().strip()<|docstring|>replace unicode, strip, lower case<|endoftext|> |
e15cee9dff8d86bab1e92309e350a7a77a1240e5a51acad7f078f0b1e7f3b20d | def entry_id(e):
'entry identifier which is not the bibtex key\n '
authortitle = ''.join([author_id(e), title_id(e)])
return (e.get('doi', '').lower(), authortitle) | entry identifier which is not the bibtex key | papers/bib.py | entry_id | aiotter/papers | 105 | python | def entry_id(e):
'\n '
authortitle = .join([author_id(e), title_id(e)])
return (e.get('doi', ).lower(), authortitle) | def entry_id(e):
'\n '
authortitle = .join([author_id(e), title_id(e)])
return (e.get('doi', ).lower(), authortitle)<|docstring|>entry identifier which is not the bibtex key<|endoftext|> |
46ecabb6510f635dd3619de62d762832f4a725f6ba9397a1e806b38a4add4ee1 | def compare_entries(e1, e2, fuzzy=False):
"assess two entries' similarity\n "
if (e1 == e2):
return EXACT_DUPLICATES
id1 = entry_id(e1)
id2 = entry_id(e2)
logger.debug('{} ?= {}'.format(id1, id2))
if (id1 == id2):
score = GOOD_DUPLICATES
elif all([(f1 == f2) for (f1, f2) in zip(id1, id2) if (f1 and f2)]):
score = FAIR_DUPLICATES
elif any([(f1 == f2) for (f1, f2) in zip(id1, id2) if (f1 and f2)]):
score = PARTIAL_DUPLICATES
elif (not fuzzy):
score = 0
else:
from rapidfuzz.fuzz import token_set_ratio
(doi1, tag1) = id1
(doi2, tag2) = id2
score = token_set_ratio(tag1, tag2)
return score | assess two entries' similarity | papers/bib.py | compare_entries | aiotter/papers | 105 | python | def compare_entries(e1, e2, fuzzy=False):
"\n "
if (e1 == e2):
return EXACT_DUPLICATES
id1 = entry_id(e1)
id2 = entry_id(e2)
logger.debug('{} ?= {}'.format(id1, id2))
if (id1 == id2):
score = GOOD_DUPLICATES
elif all([(f1 == f2) for (f1, f2) in zip(id1, id2) if (f1 and f2)]):
score = FAIR_DUPLICATES
elif any([(f1 == f2) for (f1, f2) in zip(id1, id2) if (f1 and f2)]):
score = PARTIAL_DUPLICATES
elif (not fuzzy):
score = 0
else:
from rapidfuzz.fuzz import token_set_ratio
(doi1, tag1) = id1
(doi2, tag2) = id2
score = token_set_ratio(tag1, tag2)
return score | def compare_entries(e1, e2, fuzzy=False):
"\n "
if (e1 == e2):
return EXACT_DUPLICATES
id1 = entry_id(e1)
id2 = entry_id(e2)
logger.debug('{} ?= {}'.format(id1, id2))
if (id1 == id2):
score = GOOD_DUPLICATES
elif all([(f1 == f2) for (f1, f2) in zip(id1, id2) if (f1 and f2)]):
score = FAIR_DUPLICATES
elif any([(f1 == f2) for (f1, f2) in zip(id1, id2) if (f1 and f2)]):
score = PARTIAL_DUPLICATES
elif (not fuzzy):
score = 0
else:
from rapidfuzz.fuzz import token_set_ratio
(doi1, tag1) = id1
(doi2, tag2) = id2
score = token_set_ratio(tag1, tag2)
return score<|docstring|>assess two entries' similarity<|endoftext|> |
12e7720dd484a94239c36d79ed0a2ca76619ee26c1865589f71bce051d02a494 | def hidden_bibtex(direc):
' save metadata for a bundle of files '
dirname = os.path.basename(direc)
return os.path.join(direc, (('.' + dirname) + '.bib')) | save metadata for a bundle of files | papers/bib.py | hidden_bibtex | aiotter/papers | 105 | python | def hidden_bibtex(direc):
' '
dirname = os.path.basename(direc)
return os.path.join(direc, (('.' + dirname) + '.bib')) | def hidden_bibtex(direc):
' '
dirname = os.path.basename(direc)
return os.path.join(direc, (('.' + dirname) + '.bib'))<|docstring|>save metadata for a bundle of files<|endoftext|> |
226b10389b0a69cf9e5c8e4adf95d3b9c7159f352b5141a864f66a731dac8bb1 | def read_entry_dir(self, direc, update_files=True):
'add a directory that contain files from a single entry\n '
dirname = os.path.basename(direc)
hidden_bib = hidden_bibtex(direc)
if (not os.path.exists(hidden_bib)):
raise TypeError('hidden bib missing: not an entry dir')
db = bibtexparser.loads(open(hidden_bib).read())
assert (len(db.entries) == 1), ('hidden bib must have one entry, got: ' + str(len(db.entries)))
entry = db.entries[0]
if update_files:
for (root, direcs, files) in os.walk(direc):
break
files = [os.path.join(direc, file) for file in files if (not file.startswith('.'))]
entry['file'] = format_file(files)
return entry | add a directory that contain files from a single entry | papers/bib.py | read_entry_dir | aiotter/papers | 105 | python | def read_entry_dir(self, direc, update_files=True):
'\n '
dirname = os.path.basename(direc)
hidden_bib = hidden_bibtex(direc)
if (not os.path.exists(hidden_bib)):
raise TypeError('hidden bib missing: not an entry dir')
db = bibtexparser.loads(open(hidden_bib).read())
assert (len(db.entries) == 1), ('hidden bib must have one entry, got: ' + str(len(db.entries)))
entry = db.entries[0]
if update_files:
for (root, direcs, files) in os.walk(direc):
break
files = [os.path.join(direc, file) for file in files if (not file.startswith('.'))]
entry['file'] = format_file(files)
return entry | def read_entry_dir(self, direc, update_files=True):
'\n '
dirname = os.path.basename(direc)
hidden_bib = hidden_bibtex(direc)
if (not os.path.exists(hidden_bib)):
raise TypeError('hidden bib missing: not an entry dir')
db = bibtexparser.loads(open(hidden_bib).read())
assert (len(db.entries) == 1), ('hidden bib must have one entry, got: ' + str(len(db.entries)))
entry = db.entries[0]
if update_files:
for (root, direcs, files) in os.walk(direc):
break
files = [os.path.join(direc, file) for file in files if (not file.startswith('.'))]
entry['file'] = format_file(files)
return entry<|docstring|>add a directory that contain files from a single entry<|endoftext|> |
e6fb5d0144139e92bd76637b666792bdebebc1a7beeb3c3c974db029254e8f21 | def entry_filecheck_metadata(e, file, image=False):
' parse pdf metadata and compare with entry: only doi for now\n '
if ('doi' not in e):
raise ValueError((e['ID'] + ': no doi, skip PDF parsing'))
try:
doi = extract_pdf_doi(file, image=image)
except Exception as error:
raise ValueError((e['ID'] + ': failed to parse doi: "{}"'.format(file)))
if (not isvaliddoi(doi)):
raise ValueError(((e['ID'] + ': invalid parsed doi: ') + doi))
if (doi.lower() != e['doi'].lower()):
raise ValueError((e['ID'] + ': doi: entry <=> pdf : {} <=> {}'.format(e['doi'].lower(), doi.lower()))) | parse pdf metadata and compare with entry: only doi for now | papers/bib.py | entry_filecheck_metadata | aiotter/papers | 105 | python | def entry_filecheck_metadata(e, file, image=False):
' \n '
if ('doi' not in e):
raise ValueError((e['ID'] + ': no doi, skip PDF parsing'))
try:
doi = extract_pdf_doi(file, image=image)
except Exception as error:
raise ValueError((e['ID'] + ': failed to parse doi: "{}"'.format(file)))
if (not isvaliddoi(doi)):
raise ValueError(((e['ID'] + ': invalid parsed doi: ') + doi))
if (doi.lower() != e['doi'].lower()):
raise ValueError((e['ID'] + ': doi: entry <=> pdf : {} <=> {}'.format(e['doi'].lower(), doi.lower()))) | def entry_filecheck_metadata(e, file, image=False):
' \n '
if ('doi' not in e):
raise ValueError((e['ID'] + ': no doi, skip PDF parsing'))
try:
doi = extract_pdf_doi(file, image=image)
except Exception as error:
raise ValueError((e['ID'] + ': failed to parse doi: "{}"'.format(file)))
if (not isvaliddoi(doi)):
raise ValueError(((e['ID'] + ': invalid parsed doi: ') + doi))
if (doi.lower() != e['doi'].lower()):
raise ValueError((e['ID'] + ': doi: entry <=> pdf : {} <=> {}'.format(e['doi'].lower(), doi.lower())))<|docstring|>parse pdf metadata and compare with entry: only doi for now<|endoftext|> |
d735c925d0b24cdcef17bbf8acac1fdb93964b7d48cf3b7b6625f35355425449 | def generate_key(self, entry):
' generate a unique key not yet present in the record '
keys = set((self.key(e) for e in self.db.entries))
return generate_key(entry, keys=keys, nauthor=self.nauthor, ntitle=self.ntitle) | generate a unique key not yet present in the record | papers/bib.py | generate_key | aiotter/papers | 105 | python | def generate_key(self, entry):
' '
keys = set((self.key(e) for e in self.db.entries))
return generate_key(entry, keys=keys, nauthor=self.nauthor, ntitle=self.ntitle) | def generate_key(self, entry):
' '
keys = set((self.key(e) for e in self.db.entries))
return generate_key(entry, keys=keys, nauthor=self.nauthor, ntitle=self.ntitle)<|docstring|>generate a unique key not yet present in the record<|endoftext|> |
dd491a9cc09ab5470c8557a4b0943e357725c78079e775b14d7728a5531a4f55 | def check_duplicates(self, key=None, eq=None, mode='i'):
'remove duplicates, in some sensse (see papers.conflict.check_duplicates)\n '
self.entries = check_duplicates(self.entries, key=key, eq=(eq or self.eq), issorted=(key is self.key), mode=mode)
self.sort() | remove duplicates, in some sensse (see papers.conflict.check_duplicates) | papers/bib.py | check_duplicates | aiotter/papers | 105 | python | def check_duplicates(self, key=None, eq=None, mode='i'):
'\n '
self.entries = check_duplicates(self.entries, key=key, eq=(eq or self.eq), issorted=(key is self.key), mode=mode)
self.sort() | def check_duplicates(self, key=None, eq=None, mode='i'):
'\n '
self.entries = check_duplicates(self.entries, key=key, eq=(eq or self.eq), issorted=(key is self.key), mode=mode)
self.sort()<|docstring|>remove duplicates, in some sensse (see papers.conflict.check_duplicates)<|endoftext|> |
abc19cb95aad479943ca263b5d34b3916ce0d08bc30cb31af39a8b7d9bb5a39d | def wrap_function(lib, funcname, restype, argtypes):
'Simplify wrapping ctypes functions'
func = lib.__getattr__(funcname)
func.restype = restype
func.argtypes = argtypes
return func | Simplify wrapping ctypes functions | gym_battlesnake/gymbattlesnake.py | wrap_function | SahibSodhi/Tackling-BattleSnake-Game | 37 | python | def wrap_function(lib, funcname, restype, argtypes):
func = lib.__getattr__(funcname)
func.restype = restype
func.argtypes = argtypes
return func | def wrap_function(lib, funcname, restype, argtypes):
func = lib.__getattr__(funcname)
func.restype = restype
func.argtypes = argtypes
return func<|docstring|>Simplify wrapping ctypes functions<|endoftext|> |
760bacbbbc27b1e8e9de6f19d9ebc8735b2baee510b71429da59972f44b2e5fc | def __init__(self, app=None):
'Extension initialization.'
if app:
self.app = app
self.init_app(app) | Extension initialization. | reana_server/ext.py | __init__ | diegodelemos/reana-server | 0 | python | def __init__(self, app=None):
if app:
self.app = app
self.init_app(app) | def __init__(self, app=None):
if app:
self.app = app
self.init_app(app)<|docstring|>Extension initialization.<|endoftext|> |
52f6d8eb61e9fce41d069343d3372ba603dc49ba14426e9ff76abf414115ae56 | def init_app(self, app):
'Flask application initialization.'
self.init_config(app)
Menu(app=app)
@app.teardown_appcontext
def shutdown_reana_db_session(response_or_exc):
'Close session on app teardown.'
from reana_db.database import Session as reana_db_session
from invenio_db import db as invenio_db
reana_db_session.remove()
invenio_db.session.remove()
return response_or_exc
@app.before_first_request
def connect_signals():
'Connect OAuthClient signals.'
from invenio_oauthclient.signals import account_info_received
from flask_security.signals import user_registered
from .utils import _create_and_associate_local_user, _create_and_associate_oauth_user
account_info_received.connect(_create_and_associate_oauth_user)
user_registered.connect(_create_and_associate_local_user) | Flask application initialization. | reana_server/ext.py | init_app | diegodelemos/reana-server | 0 | python | def init_app(self, app):
self.init_config(app)
Menu(app=app)
@app.teardown_appcontext
def shutdown_reana_db_session(response_or_exc):
'Close session on app teardown.'
from reana_db.database import Session as reana_db_session
from invenio_db import db as invenio_db
reana_db_session.remove()
invenio_db.session.remove()
return response_or_exc
@app.before_first_request
def connect_signals():
'Connect OAuthClient signals.'
from invenio_oauthclient.signals import account_info_received
from flask_security.signals import user_registered
from .utils import _create_and_associate_local_user, _create_and_associate_oauth_user
account_info_received.connect(_create_and_associate_oauth_user)
user_registered.connect(_create_and_associate_local_user) | def init_app(self, app):
self.init_config(app)
Menu(app=app)
@app.teardown_appcontext
def shutdown_reana_db_session(response_or_exc):
'Close session on app teardown.'
from reana_db.database import Session as reana_db_session
from invenio_db import db as invenio_db
reana_db_session.remove()
invenio_db.session.remove()
return response_or_exc
@app.before_first_request
def connect_signals():
'Connect OAuthClient signals.'
from invenio_oauthclient.signals import account_info_received
from flask_security.signals import user_registered
from .utils import _create_and_associate_local_user, _create_and_associate_oauth_user
account_info_received.connect(_create_and_associate_oauth_user)
user_registered.connect(_create_and_associate_local_user)<|docstring|>Flask application initialization.<|endoftext|> |
0c028c4e55a4aea61abec9ded1d150d846a104ff39582483bbdab428ac4839d3 | def init_config(self, app):
'Initialize configuration.'
for k in dir(config):
if k.startswith('REANA_'):
app.config.setdefault(k, getattr(config, k)) | Initialize configuration. | reana_server/ext.py | init_config | diegodelemos/reana-server | 0 | python | def init_config(self, app):
for k in dir(config):
if k.startswith('REANA_'):
app.config.setdefault(k, getattr(config, k)) | def init_config(self, app):
for k in dir(config):
if k.startswith('REANA_'):
app.config.setdefault(k, getattr(config, k))<|docstring|>Initialize configuration.<|endoftext|> |
5f919c23fd0fbdc6635e20a5d3467be9270a734902d393bbba4082c4d4348d70 | @app.teardown_appcontext
def shutdown_reana_db_session(response_or_exc):
'Close session on app teardown.'
from reana_db.database import Session as reana_db_session
from invenio_db import db as invenio_db
reana_db_session.remove()
invenio_db.session.remove()
return response_or_exc | Close session on app teardown. | reana_server/ext.py | shutdown_reana_db_session | diegodelemos/reana-server | 0 | python | @app.teardown_appcontext
def shutdown_reana_db_session(response_or_exc):
from reana_db.database import Session as reana_db_session
from invenio_db import db as invenio_db
reana_db_session.remove()
invenio_db.session.remove()
return response_or_exc | @app.teardown_appcontext
def shutdown_reana_db_session(response_or_exc):
from reana_db.database import Session as reana_db_session
from invenio_db import db as invenio_db
reana_db_session.remove()
invenio_db.session.remove()
return response_or_exc<|docstring|>Close session on app teardown.<|endoftext|> |
6284c110c45af211858f97a0a6332939f666f27a3cf98680f1c93c194a05a553 | @app.before_first_request
def connect_signals():
'Connect OAuthClient signals.'
from invenio_oauthclient.signals import account_info_received
from flask_security.signals import user_registered
from .utils import _create_and_associate_local_user, _create_and_associate_oauth_user
account_info_received.connect(_create_and_associate_oauth_user)
user_registered.connect(_create_and_associate_local_user) | Connect OAuthClient signals. | reana_server/ext.py | connect_signals | diegodelemos/reana-server | 0 | python | @app.before_first_request
def connect_signals():
from invenio_oauthclient.signals import account_info_received
from flask_security.signals import user_registered
from .utils import _create_and_associate_local_user, _create_and_associate_oauth_user
account_info_received.connect(_create_and_associate_oauth_user)
user_registered.connect(_create_and_associate_local_user) | @app.before_first_request
def connect_signals():
from invenio_oauthclient.signals import account_info_received
from flask_security.signals import user_registered
from .utils import _create_and_associate_local_user, _create_and_associate_oauth_user
account_info_received.connect(_create_and_associate_oauth_user)
user_registered.connect(_create_and_associate_local_user)<|docstring|>Connect OAuthClient signals.<|endoftext|> |
b97f3f6e6bf4172ca134509268b82b749d02a27900fa8fedeef82962ade6825f | @classmethod
def from_center(cls, center=None, width=None):
'\n SpectralRegion class method that enables the definition of a\n `SpectralRegion` from the center and width rather than lower and\n upper bounds.\n\n Parameters\n ----------\n center : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit\n The center of the spectral region.\n width : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit\n The full width of the spectral region (upper bound - lower bound).\n '
if (width.value <= 0):
raise ValueError('SpectralRegion width must be positive.')
if (center.unit.physical_type not in ('length', 'unknown')):
return cls((center + (width / 2)), (center - (width / 2)))
return cls((center - (width / 2)), (center + (width / 2))) | SpectralRegion class method that enables the definition of a
`SpectralRegion` from the center and width rather than lower and
upper bounds.
Parameters
----------
center : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit
The center of the spectral region.
width : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit
The full width of the spectral region (upper bound - lower bound). | specutils/spectra/spectral_region.py | from_center | havok2063/specutils | 118 | python | @classmethod
def from_center(cls, center=None, width=None):
'\n SpectralRegion class method that enables the definition of a\n `SpectralRegion` from the center and width rather than lower and\n upper bounds.\n\n Parameters\n ----------\n center : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit\n The center of the spectral region.\n width : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit\n The full width of the spectral region (upper bound - lower bound).\n '
if (width.value <= 0):
raise ValueError('SpectralRegion width must be positive.')
if (center.unit.physical_type not in ('length', 'unknown')):
return cls((center + (width / 2)), (center - (width / 2)))
return cls((center - (width / 2)), (center + (width / 2))) | @classmethod
def from_center(cls, center=None, width=None):
'\n SpectralRegion class method that enables the definition of a\n `SpectralRegion` from the center and width rather than lower and\n upper bounds.\n\n Parameters\n ----------\n center : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit\n The center of the spectral region.\n width : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit\n The full width of the spectral region (upper bound - lower bound).\n '
if (width.value <= 0):
raise ValueError('SpectralRegion width must be positive.')
if (center.unit.physical_type not in ('length', 'unknown')):
return cls((center + (width / 2)), (center - (width / 2)))
return cls((center - (width / 2)), (center + (width / 2)))<|docstring|>SpectralRegion class method that enables the definition of a
`SpectralRegion` from the center and width rather than lower and
upper bounds.
Parameters
----------
center : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit
The center of the spectral region.
width : Scalar `~astropy.units.Quantity` with pixel or any valid ``spectral_axis`` unit
The full width of the spectral region (upper bound - lower bound).<|endoftext|> |
946f971ad0ea63d44ea052478cd9a91391ce600dbf647eeb10e55d79f1b2aebe | @classmethod
def from_line_list(cls, table, width=1):
"\n Generate a ``SpectralRegion`` instance from the `~astropy.table.QTable`\n object returned from `~specutils.fitting.find_lines_derivative` or\n `~specutils.fitting.find_lines_threshold`.\n\n Parameters\n ----------\n table : `~astropy.table.QTable`\n List of found lines.\n width : float\n The width of the spectral line region. If not unit information is\n provided, it's assumed to be the same units as used in the line\n list table.\n\n Returns\n -------\n `~specutils.SpectralRegion`\n The spectral region based on the line list.\n "
width = u.Quantity(width, table['line_center'].unit)
return cls([((x - (width * 0.5)), (x + (width * 0.5))) for x in table['line_center']]) | Generate a ``SpectralRegion`` instance from the `~astropy.table.QTable`
object returned from `~specutils.fitting.find_lines_derivative` or
`~specutils.fitting.find_lines_threshold`.
Parameters
----------
table : `~astropy.table.QTable`
List of found lines.
width : float
The width of the spectral line region. If not unit information is
provided, it's assumed to be the same units as used in the line
list table.
Returns
-------
`~specutils.SpectralRegion`
The spectral region based on the line list. | specutils/spectra/spectral_region.py | from_line_list | havok2063/specutils | 118 | python | @classmethod
def from_line_list(cls, table, width=1):
"\n Generate a ``SpectralRegion`` instance from the `~astropy.table.QTable`\n object returned from `~specutils.fitting.find_lines_derivative` or\n `~specutils.fitting.find_lines_threshold`.\n\n Parameters\n ----------\n table : `~astropy.table.QTable`\n List of found lines.\n width : float\n The width of the spectral line region. If not unit information is\n provided, it's assumed to be the same units as used in the line\n list table.\n\n Returns\n -------\n `~specutils.SpectralRegion`\n The spectral region based on the line list.\n "
width = u.Quantity(width, table['line_center'].unit)
return cls([((x - (width * 0.5)), (x + (width * 0.5))) for x in table['line_center']]) | @classmethod
def from_line_list(cls, table, width=1):
"\n Generate a ``SpectralRegion`` instance from the `~astropy.table.QTable`\n object returned from `~specutils.fitting.find_lines_derivative` or\n `~specutils.fitting.find_lines_threshold`.\n\n Parameters\n ----------\n table : `~astropy.table.QTable`\n List of found lines.\n width : float\n The width of the spectral line region. If not unit information is\n provided, it's assumed to be the same units as used in the line\n list table.\n\n Returns\n -------\n `~specutils.SpectralRegion`\n The spectral region based on the line list.\n "
width = u.Quantity(width, table['line_center'].unit)
return cls([((x - (width * 0.5)), (x + (width * 0.5))) for x in table['line_center']])<|docstring|>Generate a ``SpectralRegion`` instance from the `~astropy.table.QTable`
object returned from `~specutils.fitting.find_lines_derivative` or
`~specutils.fitting.find_lines_threshold`.
Parameters
----------
table : `~astropy.table.QTable`
List of found lines.
width : float
The width of the spectral line region. If not unit information is
provided, it's assumed to be the same units as used in the line
list table.
Returns
-------
`~specutils.SpectralRegion`
The spectral region based on the line list.<|endoftext|> |
8cbc878464b951da73dadfec8c3f099f683c3087e1b3dc747f73ed17101d7ff3 | def _info(self):
'\n Pretty print the sub-regions.\n '
toreturn = 'Spectral Region, {} sub-regions:\n'.format(len(self._subregions))
subregion_text = []
for (ii, subregion) in enumerate(self._subregions):
subregion_text.append(' ({}, {})'.format(subregion[0], subregion[1]))
max_len = (max((len(srt) for srt in subregion_text)) + 1)
ncols = (70 // max_len)
fmt = (('{' + ':<{}'.format(max_len)) + '}')
for (ii, srt) in enumerate(subregion_text):
toreturn += fmt.format(srt)
if ((ii % ncols) == (ncols - 1)):
toreturn += '\n'
return toreturn | Pretty print the sub-regions. | specutils/spectra/spectral_region.py | _info | havok2063/specutils | 118 | python | def _info(self):
'\n \n '
toreturn = 'Spectral Region, {} sub-regions:\n'.format(len(self._subregions))
subregion_text = []
for (ii, subregion) in enumerate(self._subregions):
subregion_text.append(' ({}, {})'.format(subregion[0], subregion[1]))
max_len = (max((len(srt) for srt in subregion_text)) + 1)
ncols = (70 // max_len)
fmt = (('{' + ':<{}'.format(max_len)) + '}')
for (ii, srt) in enumerate(subregion_text):
toreturn += fmt.format(srt)
if ((ii % ncols) == (ncols - 1)):
toreturn += '\n'
return toreturn | def _info(self):
'\n \n '
toreturn = 'Spectral Region, {} sub-regions:\n'.format(len(self._subregions))
subregion_text = []
for (ii, subregion) in enumerate(self._subregions):
subregion_text.append(' ({}, {})'.format(subregion[0], subregion[1]))
max_len = (max((len(srt) for srt in subregion_text)) + 1)
ncols = (70 // max_len)
fmt = (('{' + ':<{}'.format(max_len)) + '}')
for (ii, srt) in enumerate(subregion_text):
toreturn += fmt.format(srt)
if ((ii % ncols) == (ncols - 1)):
toreturn += '\n'
return toreturn<|docstring|>Pretty print the sub-regions.<|endoftext|> |
ff589355e9cace6694557cd086e3ad629e46add08e8c83c1443e49b9d18d56c8 | def __add__(self, other):
'\n Ability to add two SpectralRegion classes together.\n '
return SpectralRegion((self._subregions + other._subregions)) | Ability to add two SpectralRegion classes together. | specutils/spectra/spectral_region.py | __add__ | havok2063/specutils | 118 | python | def __add__(self, other):
'\n \n '
return SpectralRegion((self._subregions + other._subregions)) | def __add__(self, other):
'\n \n '
return SpectralRegion((self._subregions + other._subregions))<|docstring|>Ability to add two SpectralRegion classes together.<|endoftext|> |
8e2557ccab9a3e1b5be05f1f25ed50ec9eedba5e9a190b973fa54709eb1a8489 | def __iadd__(self, other):
'\n Ability to add one SpectralRegion to another using +=.\n '
self._subregions += other._subregions
self._reorder()
return self | Ability to add one SpectralRegion to another using +=. | specutils/spectra/spectral_region.py | __iadd__ | havok2063/specutils | 118 | python | def __iadd__(self, other):
'\n \n '
self._subregions += other._subregions
self._reorder()
return self | def __iadd__(self, other):
'\n \n '
self._subregions += other._subregions
self._reorder()
return self<|docstring|>Ability to add one SpectralRegion to another using +=.<|endoftext|> |
1a0b3305db6927fc33683f396171dac0ead72dd3c8c3763acf2e85dac83cc976 | def __len__(self):
'\n Number of spectral regions.\n '
return len(self._subregions) | Number of spectral regions. | specutils/spectra/spectral_region.py | __len__ | havok2063/specutils | 118 | python | def __len__(self):
'\n \n '
return len(self._subregions) | def __len__(self):
'\n \n '
return len(self._subregions)<|docstring|>Number of spectral regions.<|endoftext|> |
4abd63b36d789413899383c002f824d223bc275e7d35a375a8a6c1e5c442e250 | def __getslice__(self, item):
'\n Enable slicing of the SpectralRegion list.\n '
return SpectralRegion(self._subregions[item]) | Enable slicing of the SpectralRegion list. | specutils/spectra/spectral_region.py | __getslice__ | havok2063/specutils | 118 | python | def __getslice__(self, item):
'\n \n '
return SpectralRegion(self._subregions[item]) | def __getslice__(self, item):
'\n \n '
return SpectralRegion(self._subregions[item])<|docstring|>Enable slicing of the SpectralRegion list.<|endoftext|> |
46d844c4f6475a34847389a26bdf58980ac898aea43fa882098da9c36480f8f4 | def __getitem__(self, item):
'\n Enable slicing or extracting the SpectralRegion.\n '
if isinstance(item, slice):
return self.__getslice__(item)
else:
return SpectralRegion([self._subregions[item]]) | Enable slicing or extracting the SpectralRegion. | specutils/spectra/spectral_region.py | __getitem__ | havok2063/specutils | 118 | python | def __getitem__(self, item):
'\n \n '
if isinstance(item, slice):
return self.__getslice__(item)
else:
return SpectralRegion([self._subregions[item]]) | def __getitem__(self, item):
'\n \n '
if isinstance(item, slice):
return self.__getslice__(item)
else:
return SpectralRegion([self._subregions[item]])<|docstring|>Enable slicing or extracting the SpectralRegion.<|endoftext|> |
de086df5a2670c68a38ba78ce8d4ad1b2b5614d97800e695a32f2fb714afd0e8 | def __delitem__(self, item):
'\n Delete a specific item from the list.\n '
del self._subregions[item] | Delete a specific item from the list. | specutils/spectra/spectral_region.py | __delitem__ | havok2063/specutils | 118 | python | def __delitem__(self, item):
'\n \n '
del self._subregions[item] | def __delitem__(self, item):
'\n \n '
del self._subregions[item]<|docstring|>Delete a specific item from the list.<|endoftext|> |
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