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 |
|---|---|---|---|---|---|---|---|---|---|
f1cfc2ab63c8236965c6f153a760e8e4870f29c7c0a921bfb93c6d4c028ad5c5 | def has_straight_flush(self):
'Returns True if the hand has a straight flush'
suited_hands = {}
for card in self.cards:
suited_hands.setdefault(card.suit, PokerHand()).add_card(card)
for suit in suited_hands:
if suited_hands[suit].has_straight():
return True
return False | Returns True if the hand has a straight flush | chap18/PokerHand.py | has_straight_flush | theChad/ThinkPython | 0 | python | def has_straight_flush(self):
suited_hands = {}
for card in self.cards:
suited_hands.setdefault(card.suit, PokerHand()).add_card(card)
for suit in suited_hands:
if suited_hands[suit].has_straight():
return True
return False | def has_straight_flush(self):
suited_hands = {}
for card in self.cards:
suited_hands.setdefault(card.suit, PokerHand()).add_card(card)
for suit in suited_hands:
if suited_hands[suit].has_straight():
return True
return False<|docstring|>Returns True if the hand has a straight flush<|endoftext|> |
f7150d5a4ca0c14f029e133df65fbc190a70a6faa1b6356cc0b40e261be6afc6 | def classify(self):
'Returns the highest-value classification for a hand.'
for classification in self.classifications:
if self.class_tests[classification](self):
self.label = classification
return classification
self.label = None
return None | Returns the highest-value classification for a hand. | chap18/PokerHand.py | classify | theChad/ThinkPython | 0 | python | def classify(self):
for classification in self.classifications:
if self.class_tests[classification](self):
self.label = classification
return classification
self.label = None
return None | def classify(self):
for classification in self.classifications:
if self.class_tests[classification](self):
self.label = classification
return classification
self.label = None
return None<|docstring|>Returns the highest-value classification for a hand.<|endoftext|> |
9d78a3a31dce1e5a1ef6bb0f113df54dc82cbdb2caa9665e8f883e49d6015aaa | def shuffle(X, y, seed=None):
'\n Shuffles the batch from a given set of datapoints and lables.\n '
if seed:
np.random.seed(seed)
index = np.arange(X.shape[0])
np.random.shuffle(index)
return (X[index], y[index]) | Shuffles the batch from a given set of datapoints and lables. | AKDPRFramework/utils/dataops.py | shuffle | theroyakash/AKDPRFramework | 2 | python | def shuffle(X, y, seed=None):
'\n \n '
if seed:
np.random.seed(seed)
index = np.arange(X.shape[0])
np.random.shuffle(index)
return (X[index], y[index]) | def shuffle(X, y, seed=None):
'\n \n '
if seed:
np.random.seed(seed)
index = np.arange(X.shape[0])
np.random.shuffle(index)
return (X[index], y[index])<|docstring|>Shuffles the batch from a given set of datapoints and lables.<|endoftext|> |
40eab803f851a9abc1242b71e24dba9cad5f0fa884062caabb1fc95a82f534bf | def batch_iterator(X, y=None, batch_size=32):
'\n Batch generator class\n\n Args:\n - X: X data\n - y: labels for each X data\n - batch_size: Batch size you want to generate. Defaults to 32\n '
number_of_samples = X.shape[0]
for i in np.arange(0, number_of_samples, batch_size):
(start, end) = (i, min((i + batch_size), number_of_samples))
if (y is not None):
(yield (X[start:end], y[start:end]))
else:
(yield X[start:end]) | Batch generator class
Args:
- X: X data
- y: labels for each X data
- batch_size: Batch size you want to generate. Defaults to 32 | AKDPRFramework/utils/dataops.py | batch_iterator | theroyakash/AKDPRFramework | 2 | python | def batch_iterator(X, y=None, batch_size=32):
'\n Batch generator class\n\n Args:\n - X: X data\n - y: labels for each X data\n - batch_size: Batch size you want to generate. Defaults to 32\n '
number_of_samples = X.shape[0]
for i in np.arange(0, number_of_samples, batch_size):
(start, end) = (i, min((i + batch_size), number_of_samples))
if (y is not None):
(yield (X[start:end], y[start:end]))
else:
(yield X[start:end]) | def batch_iterator(X, y=None, batch_size=32):
'\n Batch generator class\n\n Args:\n - X: X data\n - y: labels for each X data\n - batch_size: Batch size you want to generate. Defaults to 32\n '
number_of_samples = X.shape[0]
for i in np.arange(0, number_of_samples, batch_size):
(start, end) = (i, min((i + batch_size), number_of_samples))
if (y is not None):
(yield (X[start:end], y[start:end]))
else:
(yield X[start:end])<|docstring|>Batch generator class
Args:
- X: X data
- y: labels for each X data
- batch_size: Batch size you want to generate. Defaults to 32<|endoftext|> |
0f29c7dba88100cd92acdad7f762cd54f325f66264838b3942a7944811fb0bb8 | def to_categorical(x, n_col=None):
"\n Preforms one hot encodings for the data labels\n\n Args:\n - ``X``: Numpy Array containing your data points\n - ``n_col``: Number of column for your data. If not explicitly mentioned, it's automatically calculated.\n\n Example::\n >>> import numpy as np\n >>> def to_categorical(x, n_col=None):\n >>> if not n_col:\n >>> n_col = np.amax(x) + 1\n\n >>> one_hot = np.zeros((x.shape[0], n_col))\n >>> one_hot[np.arange(x.shape[0]), x] = 1\n >>> return one_hot\n\n >>> x = np.array([2, 3, 4, 1, 2, 3])\n >>> z = to_categorical(x, 5)\n >>> print(z)\n\n >>> x = np.array([1, 2, 3, 4, 6])\n >>> z = to_categorical(x, 7)\n >>> print(z)\n "
if (not n_col):
n_col = (np.amax(x) + 1)
one_hot = np.zeros((x.shape[0], n_col))
one_hot[(np.arange(x.shape[0]), x)] = 1
return one_hot | Preforms one hot encodings for the data labels
Args:
- ``X``: Numpy Array containing your data points
- ``n_col``: Number of column for your data. If not explicitly mentioned, it's automatically calculated.
Example::
>>> import numpy as np
>>> def to_categorical(x, n_col=None):
>>> if not n_col:
>>> n_col = np.amax(x) + 1
>>> one_hot = np.zeros((x.shape[0], n_col))
>>> one_hot[np.arange(x.shape[0]), x] = 1
>>> return one_hot
>>> x = np.array([2, 3, 4, 1, 2, 3])
>>> z = to_categorical(x, 5)
>>> print(z)
>>> x = np.array([1, 2, 3, 4, 6])
>>> z = to_categorical(x, 7)
>>> print(z) | AKDPRFramework/utils/dataops.py | to_categorical | theroyakash/AKDPRFramework | 2 | python | def to_categorical(x, n_col=None):
"\n Preforms one hot encodings for the data labels\n\n Args:\n - ``X``: Numpy Array containing your data points\n - ``n_col``: Number of column for your data. If not explicitly mentioned, it's automatically calculated.\n\n Example::\n >>> import numpy as np\n >>> def to_categorical(x, n_col=None):\n >>> if not n_col:\n >>> n_col = np.amax(x) + 1\n\n >>> one_hot = np.zeros((x.shape[0], n_col))\n >>> one_hot[np.arange(x.shape[0]), x] = 1\n >>> return one_hot\n\n >>> x = np.array([2, 3, 4, 1, 2, 3])\n >>> z = to_categorical(x, 5)\n >>> print(z)\n\n >>> x = np.array([1, 2, 3, 4, 6])\n >>> z = to_categorical(x, 7)\n >>> print(z)\n "
if (not n_col):
n_col = (np.amax(x) + 1)
one_hot = np.zeros((x.shape[0], n_col))
one_hot[(np.arange(x.shape[0]), x)] = 1
return one_hot | def to_categorical(x, n_col=None):
"\n Preforms one hot encodings for the data labels\n\n Args:\n - ``X``: Numpy Array containing your data points\n - ``n_col``: Number of column for your data. If not explicitly mentioned, it's automatically calculated.\n\n Example::\n >>> import numpy as np\n >>> def to_categorical(x, n_col=None):\n >>> if not n_col:\n >>> n_col = np.amax(x) + 1\n\n >>> one_hot = np.zeros((x.shape[0], n_col))\n >>> one_hot[np.arange(x.shape[0]), x] = 1\n >>> return one_hot\n\n >>> x = np.array([2, 3, 4, 1, 2, 3])\n >>> z = to_categorical(x, 5)\n >>> print(z)\n\n >>> x = np.array([1, 2, 3, 4, 6])\n >>> z = to_categorical(x, 7)\n >>> print(z)\n "
if (not n_col):
n_col = (np.amax(x) + 1)
one_hot = np.zeros((x.shape[0], n_col))
one_hot[(np.arange(x.shape[0]), x)] = 1
return one_hot<|docstring|>Preforms one hot encodings for the data labels
Args:
- ``X``: Numpy Array containing your data points
- ``n_col``: Number of column for your data. If not explicitly mentioned, it's automatically calculated.
Example::
>>> import numpy as np
>>> def to_categorical(x, n_col=None):
>>> if not n_col:
>>> n_col = np.amax(x) + 1
>>> one_hot = np.zeros((x.shape[0], n_col))
>>> one_hot[np.arange(x.shape[0]), x] = 1
>>> return one_hot
>>> x = np.array([2, 3, 4, 1, 2, 3])
>>> z = to_categorical(x, 5)
>>> print(z)
>>> x = np.array([1, 2, 3, 4, 6])
>>> z = to_categorical(x, 7)
>>> print(z)<|endoftext|> |
33058a65955dbecd6efe0a288ce8de8e6bd7d40e42582f5f87ed6b3c6963e67a | def diag(x):
'\n Vector to diagonal matrix conversion.\n '
diagonal = np.zeros((len(x), len(x)))
for i in range(len(diagonal[0])):
diagonal[(i, i)] = x[i]
return diagonal | Vector to diagonal matrix conversion. | AKDPRFramework/utils/dataops.py | diag | theroyakash/AKDPRFramework | 2 | python | def diag(x):
'\n \n '
diagonal = np.zeros((len(x), len(x)))
for i in range(len(diagonal[0])):
diagonal[(i, i)] = x[i]
return diagonal | def diag(x):
'\n \n '
diagonal = np.zeros((len(x), len(x)))
for i in range(len(diagonal[0])):
diagonal[(i, i)] = x[i]
return diagonal<|docstring|>Vector to diagonal matrix conversion.<|endoftext|> |
69cec0530dc9063b15f438b2aa4da00d2d06bdaa34c8731cbef648072bf866fa | def train_test_split(X, y, test_size=0.5, makeshuffle=True, seed=None):
'\n Make train test split on dataset.\n\n Args:\n - ``X``: X dataset of numpy array\n - ``y``: y labels for that array\n - ``test_size``: How much percentage of data you want for your test dataset\n - ``makeshuffle``: do you want to shuffle the data before splitting?\n - ``seed``: mention a random seed for reproducing results\n\n Returns:\n ``X_train``, ``X_test``, ``y_train``, ``y_test``\n\n '
if makeshuffle:
(X, y) = shuffle(X, y, seed)
split_i = (len(y) - int((len(y) // (1 / test_size))))
(X_train, X_test) = (X[:split_i], X[split_i:])
(y_train, y_test) = (y[:split_i], y[split_i:])
return (X_train, X_test, y_train, y_test) | Make train test split on dataset.
Args:
- ``X``: X dataset of numpy array
- ``y``: y labels for that array
- ``test_size``: How much percentage of data you want for your test dataset
- ``makeshuffle``: do you want to shuffle the data before splitting?
- ``seed``: mention a random seed for reproducing results
Returns:
``X_train``, ``X_test``, ``y_train``, ``y_test`` | AKDPRFramework/utils/dataops.py | train_test_split | theroyakash/AKDPRFramework | 2 | python | def train_test_split(X, y, test_size=0.5, makeshuffle=True, seed=None):
'\n Make train test split on dataset.\n\n Args:\n - ``X``: X dataset of numpy array\n - ``y``: y labels for that array\n - ``test_size``: How much percentage of data you want for your test dataset\n - ``makeshuffle``: do you want to shuffle the data before splitting?\n - ``seed``: mention a random seed for reproducing results\n\n Returns:\n ``X_train``, ``X_test``, ``y_train``, ``y_test``\n\n '
if makeshuffle:
(X, y) = shuffle(X, y, seed)
split_i = (len(y) - int((len(y) // (1 / test_size))))
(X_train, X_test) = (X[:split_i], X[split_i:])
(y_train, y_test) = (y[:split_i], y[split_i:])
return (X_train, X_test, y_train, y_test) | def train_test_split(X, y, test_size=0.5, makeshuffle=True, seed=None):
'\n Make train test split on dataset.\n\n Args:\n - ``X``: X dataset of numpy array\n - ``y``: y labels for that array\n - ``test_size``: How much percentage of data you want for your test dataset\n - ``makeshuffle``: do you want to shuffle the data before splitting?\n - ``seed``: mention a random seed for reproducing results\n\n Returns:\n ``X_train``, ``X_test``, ``y_train``, ``y_test``\n\n '
if makeshuffle:
(X, y) = shuffle(X, y, seed)
split_i = (len(y) - int((len(y) // (1 / test_size))))
(X_train, X_test) = (X[:split_i], X[split_i:])
(y_train, y_test) = (y[:split_i], y[split_i:])
return (X_train, X_test, y_train, y_test)<|docstring|>Make train test split on dataset.
Args:
- ``X``: X dataset of numpy array
- ``y``: y labels for that array
- ``test_size``: How much percentage of data you want for your test dataset
- ``makeshuffle``: do you want to shuffle the data before splitting?
- ``seed``: mention a random seed for reproducing results
Returns:
``X_train``, ``X_test``, ``y_train``, ``y_test``<|endoftext|> |
baeeda7b98bd4617afabf1ab8ef18ffd48f0a2ced30187d7eb17ce619283cb74 | def euclidean_distance(x1, x2):
'\n Calculates the l2 distance between two vectors\n '
distance = 0
for i in range(len(x1)):
distance += pow((x1[i] - x2[i]), 2)
return math.sqrt(distance) | Calculates the l2 distance between two vectors | AKDPRFramework/utils/dataops.py | euclidean_distance | theroyakash/AKDPRFramework | 2 | python | def euclidean_distance(x1, x2):
'\n \n '
distance = 0
for i in range(len(x1)):
distance += pow((x1[i] - x2[i]), 2)
return math.sqrt(distance) | def euclidean_distance(x1, x2):
'\n \n '
distance = 0
for i in range(len(x1)):
distance += pow((x1[i] - x2[i]), 2)
return math.sqrt(distance)<|docstring|>Calculates the l2 distance between two vectors<|endoftext|> |
11aea283dbadcf1c96f7b89be88542ce5d308b45300a72b2fe7f09a23b51014f | def normalize(X, axis=(- 1), order=2):
' Normalize the dataset X '
l2 = np.atleast_1d(np.linalg.norm(X, order, axis))
l2[(l2 == 0)] = 1
return (X / np.expand_dims(l2, axis)) | Normalize the dataset X | AKDPRFramework/utils/dataops.py | normalize | theroyakash/AKDPRFramework | 2 | python | def normalize(X, axis=(- 1), order=2):
' '
l2 = np.atleast_1d(np.linalg.norm(X, order, axis))
l2[(l2 == 0)] = 1
return (X / np.expand_dims(l2, axis)) | def normalize(X, axis=(- 1), order=2):
' '
l2 = np.atleast_1d(np.linalg.norm(X, order, axis))
l2[(l2 == 0)] = 1
return (X / np.expand_dims(l2, axis))<|docstring|>Normalize the dataset X<|endoftext|> |
7b700b3d61a85e4f85c1e2476b61a230066ff33484766fabb9bf333d4fad0617 | def download_progress_hook(count, blockSize, totalSize):
'A hook to report the progress of a download. This is mostly intended for users with\n slow internet connections. Reports every 1% change in download progress.\n '
global last_percent_reported
percent = int((((count * blockSize) * 100) / totalSize))
if (last_percent_reported != percent):
if ((percent % 5) == 0):
sys.stdout.write(('%s%%' % percent))
sys.stdout.flush()
else:
sys.stdout.write('.')
sys.stdout.flush()
last_percent_reported = percent | A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 1% change in download progress. | create_bbox_SVHN_dataset.py | download_progress_hook | BananuhBeatDown/digit_recognition | 2 | python | def download_progress_hook(count, blockSize, totalSize):
'A hook to report the progress of a download. This is mostly intended for users with\n slow internet connections. Reports every 1% change in download progress.\n '
global last_percent_reported
percent = int((((count * blockSize) * 100) / totalSize))
if (last_percent_reported != percent):
if ((percent % 5) == 0):
sys.stdout.write(('%s%%' % percent))
sys.stdout.flush()
else:
sys.stdout.write('.')
sys.stdout.flush()
last_percent_reported = percent | def download_progress_hook(count, blockSize, totalSize):
'A hook to report the progress of a download. This is mostly intended for users with\n slow internet connections. Reports every 1% change in download progress.\n '
global last_percent_reported
percent = int((((count * blockSize) * 100) / totalSize))
if (last_percent_reported != percent):
if ((percent % 5) == 0):
sys.stdout.write(('%s%%' % percent))
sys.stdout.flush()
else:
sys.stdout.write('.')
sys.stdout.flush()
last_percent_reported = percent<|docstring|>A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 1% change in download progress.<|endoftext|> |
0e03570a22e68c3439ae6a0796b2f4bb3a83708fcd56c5f4fb13d7ee20dc3ec6 | def maybe_download(filename, force=False):
"Download a file if not present, and make sure it's the right size."
if (force or (not os.path.exists(filename))):
print('Attempting to download:', filename)
(filename, _) = urlretrieve((url + filename), filename, reporthook=download_progress_hook)
print('\nDownload Complete!')
else:
print('Already downloaded')
return filename | Download a file if not present, and make sure it's the right size. | create_bbox_SVHN_dataset.py | maybe_download | BananuhBeatDown/digit_recognition | 2 | python | def maybe_download(filename, force=False):
if (force or (not os.path.exists(filename))):
print('Attempting to download:', filename)
(filename, _) = urlretrieve((url + filename), filename, reporthook=download_progress_hook)
print('\nDownload Complete!')
else:
print('Already downloaded')
return filename | def maybe_download(filename, force=False):
if (force or (not os.path.exists(filename))):
print('Attempting to download:', filename)
(filename, _) = urlretrieve((url + filename), filename, reporthook=download_progress_hook)
print('\nDownload Complete!')
else:
print('Already downloaded')
return filename<|docstring|>Download a file if not present, and make sure it's the right size.<|endoftext|> |
75e7d5242d239bceb3970bbb146d8f0a035bfeefb651349a3d619f4b94142138 | def main(db_url, output_filename, debug=False):
'Implement the ``thapbi_pict conflicts`` subcommand.\n\n Looks for taxonomy conflicts at marker, genus or species level, with the\n number of marker or genus level conflicts used as the return code. i.e.\n Unix failure (non-zero) when there are marker or genus level conflicts.\n\n A marker level conflict is when a unique sequence appears in the DB under\n more than one marker name (e.g. both COI and ITS1), which is most likely\n an error in the DB construction.\n\n Genus level conflicts are where a unique sequence in the DB is reported\n from more than one genus, which is considered undesirable. Similarly for\n species level conflicts, but for some markers this is sadly common and not\n considered to be an error.\n '
if (output_filename == '-'):
out_handle = sys.stdout
else:
out_handle = open(output_filename, 'w')
Session = connect_to_db(db_url, echo=False)
session = Session()
cur_tax = aliased(Taxonomy)
marker_seq = aliased(MarkerSeq)
marker_def = aliased(MarkerDef)
view = session.query(SeqSource).join(marker_seq, SeqSource.marker_seq).join(marker_def, SeqSource.marker_definition).join(cur_tax, SeqSource.taxonomy).options(contains_eager(SeqSource.marker_seq, alias=marker_seq)).options(contains_eager(SeqSource.marker_definition, alias=marker_def)).options(contains_eager(SeqSource.taxonomy, alias=cur_tax))
md5_to_seq = {}
md5_to_marker = {}
md5_to_genus = {}
md5_to_species = {}
for seq_source in view:
md5 = seq_source.marker_seq.md5
seq = seq_source.marker_seq.sequence
genus = seq_source.taxonomy.genus
md5_to_seq[md5] = seq
try:
md5_to_marker[md5].add(seq_source.marker_definition.name)
except KeyError:
md5_to_marker[md5] = {seq_source.marker_definition.name}
if genus:
try:
md5_to_genus[md5].add(genus)
except KeyError:
md5_to_genus[md5] = {genus}
if seq_source.taxonomy.species:
genus_species = genus_species_name(genus, seq_source.taxonomy.species)
try:
md5_to_species[md5].add(genus_species)
except KeyError:
md5_to_species[md5] = {genus_species}
sys.stderr.write(f'''Loaded taxonomy for {len(md5_to_seq)} sequences from DB
''')
marker_conflicts = 0
genus_conflicts = 0
out_handle.write('#MD5\tLevel\tConflicts\n')
for (md5, markers) in sorted(md5_to_marker.items()):
if (len(markers) > 1):
out_handle.write(f'''{md5} marker {';'.join(sorted(markers))}
''')
marker_conflicts += 1
for (md5, genus) in sorted(md5_to_genus.items()):
if (len(genus) > 1):
out_handle.write(f'''{md5} genus {';'.join(sorted(genus))}
''')
genus_conflicts += 1
for (md5, species) in sorted(md5_to_species.items()):
if (len(species) > 1):
out_handle.write(f'''{md5} species {';'.join(sorted(species))}
''')
if (output_filename != '-'):
out_handle.close()
if debug:
sys.stderr.write(f'''{marker_conflicts} marker level conflicts
''')
sys.stderr.write(f'''{genus_conflicts} genus level conflicts
''')
return (marker_conflicts + genus_conflicts) | Implement the ``thapbi_pict conflicts`` subcommand.
Looks for taxonomy conflicts at marker, genus or species level, with the
number of marker or genus level conflicts used as the return code. i.e.
Unix failure (non-zero) when there are marker or genus level conflicts.
A marker level conflict is when a unique sequence appears in the DB under
more than one marker name (e.g. both COI and ITS1), which is most likely
an error in the DB construction.
Genus level conflicts are where a unique sequence in the DB is reported
from more than one genus, which is considered undesirable. Similarly for
species level conflicts, but for some markers this is sadly common and not
considered to be an error. | thapbi_pict/conflicts.py | main | HuttonICS/thapbi-pict | 6 | python | def main(db_url, output_filename, debug=False):
'Implement the ``thapbi_pict conflicts`` subcommand.\n\n Looks for taxonomy conflicts at marker, genus or species level, with the\n number of marker or genus level conflicts used as the return code. i.e.\n Unix failure (non-zero) when there are marker or genus level conflicts.\n\n A marker level conflict is when a unique sequence appears in the DB under\n more than one marker name (e.g. both COI and ITS1), which is most likely\n an error in the DB construction.\n\n Genus level conflicts are where a unique sequence in the DB is reported\n from more than one genus, which is considered undesirable. Similarly for\n species level conflicts, but for some markers this is sadly common and not\n considered to be an error.\n '
if (output_filename == '-'):
out_handle = sys.stdout
else:
out_handle = open(output_filename, 'w')
Session = connect_to_db(db_url, echo=False)
session = Session()
cur_tax = aliased(Taxonomy)
marker_seq = aliased(MarkerSeq)
marker_def = aliased(MarkerDef)
view = session.query(SeqSource).join(marker_seq, SeqSource.marker_seq).join(marker_def, SeqSource.marker_definition).join(cur_tax, SeqSource.taxonomy).options(contains_eager(SeqSource.marker_seq, alias=marker_seq)).options(contains_eager(SeqSource.marker_definition, alias=marker_def)).options(contains_eager(SeqSource.taxonomy, alias=cur_tax))
md5_to_seq = {}
md5_to_marker = {}
md5_to_genus = {}
md5_to_species = {}
for seq_source in view:
md5 = seq_source.marker_seq.md5
seq = seq_source.marker_seq.sequence
genus = seq_source.taxonomy.genus
md5_to_seq[md5] = seq
try:
md5_to_marker[md5].add(seq_source.marker_definition.name)
except KeyError:
md5_to_marker[md5] = {seq_source.marker_definition.name}
if genus:
try:
md5_to_genus[md5].add(genus)
except KeyError:
md5_to_genus[md5] = {genus}
if seq_source.taxonomy.species:
genus_species = genus_species_name(genus, seq_source.taxonomy.species)
try:
md5_to_species[md5].add(genus_species)
except KeyError:
md5_to_species[md5] = {genus_species}
sys.stderr.write(f'Loaded taxonomy for {len(md5_to_seq)} sequences from DB
')
marker_conflicts = 0
genus_conflicts = 0
out_handle.write('#MD5\tLevel\tConflicts\n')
for (md5, markers) in sorted(md5_to_marker.items()):
if (len(markers) > 1):
out_handle.write(f'{md5} marker {';'.join(sorted(markers))}
')
marker_conflicts += 1
for (md5, genus) in sorted(md5_to_genus.items()):
if (len(genus) > 1):
out_handle.write(f'{md5} genus {';'.join(sorted(genus))}
')
genus_conflicts += 1
for (md5, species) in sorted(md5_to_species.items()):
if (len(species) > 1):
out_handle.write(f'{md5} species {';'.join(sorted(species))}
')
if (output_filename != '-'):
out_handle.close()
if debug:
sys.stderr.write(f'{marker_conflicts} marker level conflicts
')
sys.stderr.write(f'{genus_conflicts} genus level conflicts
')
return (marker_conflicts + genus_conflicts) | def main(db_url, output_filename, debug=False):
'Implement the ``thapbi_pict conflicts`` subcommand.\n\n Looks for taxonomy conflicts at marker, genus or species level, with the\n number of marker or genus level conflicts used as the return code. i.e.\n Unix failure (non-zero) when there are marker or genus level conflicts.\n\n A marker level conflict is when a unique sequence appears in the DB under\n more than one marker name (e.g. both COI and ITS1), which is most likely\n an error in the DB construction.\n\n Genus level conflicts are where a unique sequence in the DB is reported\n from more than one genus, which is considered undesirable. Similarly for\n species level conflicts, but for some markers this is sadly common and not\n considered to be an error.\n '
if (output_filename == '-'):
out_handle = sys.stdout
else:
out_handle = open(output_filename, 'w')
Session = connect_to_db(db_url, echo=False)
session = Session()
cur_tax = aliased(Taxonomy)
marker_seq = aliased(MarkerSeq)
marker_def = aliased(MarkerDef)
view = session.query(SeqSource).join(marker_seq, SeqSource.marker_seq).join(marker_def, SeqSource.marker_definition).join(cur_tax, SeqSource.taxonomy).options(contains_eager(SeqSource.marker_seq, alias=marker_seq)).options(contains_eager(SeqSource.marker_definition, alias=marker_def)).options(contains_eager(SeqSource.taxonomy, alias=cur_tax))
md5_to_seq = {}
md5_to_marker = {}
md5_to_genus = {}
md5_to_species = {}
for seq_source in view:
md5 = seq_source.marker_seq.md5
seq = seq_source.marker_seq.sequence
genus = seq_source.taxonomy.genus
md5_to_seq[md5] = seq
try:
md5_to_marker[md5].add(seq_source.marker_definition.name)
except KeyError:
md5_to_marker[md5] = {seq_source.marker_definition.name}
if genus:
try:
md5_to_genus[md5].add(genus)
except KeyError:
md5_to_genus[md5] = {genus}
if seq_source.taxonomy.species:
genus_species = genus_species_name(genus, seq_source.taxonomy.species)
try:
md5_to_species[md5].add(genus_species)
except KeyError:
md5_to_species[md5] = {genus_species}
sys.stderr.write(f'Loaded taxonomy for {len(md5_to_seq)} sequences from DB
')
marker_conflicts = 0
genus_conflicts = 0
out_handle.write('#MD5\tLevel\tConflicts\n')
for (md5, markers) in sorted(md5_to_marker.items()):
if (len(markers) > 1):
out_handle.write(f'{md5} marker {';'.join(sorted(markers))}
')
marker_conflicts += 1
for (md5, genus) in sorted(md5_to_genus.items()):
if (len(genus) > 1):
out_handle.write(f'{md5} genus {';'.join(sorted(genus))}
')
genus_conflicts += 1
for (md5, species) in sorted(md5_to_species.items()):
if (len(species) > 1):
out_handle.write(f'{md5} species {';'.join(sorted(species))}
')
if (output_filename != '-'):
out_handle.close()
if debug:
sys.stderr.write(f'{marker_conflicts} marker level conflicts
')
sys.stderr.write(f'{genus_conflicts} genus level conflicts
')
return (marker_conflicts + genus_conflicts)<|docstring|>Implement the ``thapbi_pict conflicts`` subcommand.
Looks for taxonomy conflicts at marker, genus or species level, with the
number of marker or genus level conflicts used as the return code. i.e.
Unix failure (non-zero) when there are marker or genus level conflicts.
A marker level conflict is when a unique sequence appears in the DB under
more than one marker name (e.g. both COI and ITS1), which is most likely
an error in the DB construction.
Genus level conflicts are where a unique sequence in the DB is reported
from more than one genus, which is considered undesirable. Similarly for
species level conflicts, but for some markers this is sadly common and not
considered to be an error.<|endoftext|> |
03dd2b5ebd2e6e38364e89c4f2e9ea3d90b7a6d797f6dafc1be73c6d6e3dbb08 | def read(path):
'Read a file from a path.'
with open(os.path.join(*path.split('/'))) as f:
return f.read() | Read a file from a path. | setup.py | read | icemac/icemac.callonchange | 1 | python | def read(path):
with open(os.path.join(*path.split('/'))) as f:
return f.read() | def read(path):
with open(os.path.join(*path.split('/'))) as f:
return f.read()<|docstring|>Read a file from a path.<|endoftext|> |
97faaf5f14c691edbbcbb3d579452174f9739c8659954476099766c7e1317414 | def slstm(c_prev1, c_prev2, x1, x2):
"S-LSTM units as an activation function.\n\n This function implements S-LSTM unit. It is an extension of LSTM unit\n applied to tree structures.\n The function is applied to binary trees. Each node has two child nodes.\n It gets four arguments, previous cell states :math:`c_1` and\n :math:`c_2`, and incoming signals :math:`x_1` and :math:`x_2`.\n\n First both input signals :math:`x_1` and :math:`x_2` are split into\n eight arrays :math:`a_1, i_1, f_1, o_1`, and :math:`a_2, i_2, f_2, o_2`.\n They have the same shape along the second axis.\n It means that :math:`x_1` and :math:`x_2` 's second axis must have 4 times\n the length of :math:`c_{1 \\text{prev}}` and :math:`c_{2 \\text{prev}}`.\n\n The split input signals are corresponding to:\n\n - :math:`a_i` : sources of cell input\n - :math:`i_i` : sources of input gate\n - :math:`f_i` : sources of forget gate\n - :math:`o_i` : sources of output gate\n\n It computes outputs as:\n\n .. math::\n\n c &= \\tanh(a_1 + a_2) \\sigma(i_1 + i_2)\n + c_{1 \\text{prev}} \\sigma(f_1)\n + c_{2 \\text{prev}} \\sigma(f_2), \\\\\n h &= \\tanh(c) \\sigma(o_1 + o_2),\n\n where :math:`\\sigma` is the elementwise sigmoid function.\n The function returns :math:`c` and :math:`h` as a tuple.\n\n Args:\n c_prev1 (~chainer.Variable): Variable that holds the previous cell\n state of the first child node. The cell state should be a zero\n array or the output of the previous call of LSTM.\n c_prev2 (~chainer.Variable): Variable that holds the previous cell\n state of the second child node.\n x1 (~chainer.Variable): Variable that holds the incoming signal from\n the first child node. It must have the second dimension four times\n of that of the cell state,\n x2 (~chainer.Variable): Variable that holds the incoming signal from\n the second child node.\n\n Returns:\n tuple: Two :class:`~chainer.Variable` objects ``c`` and ``h``. ``c`` is\n the cell state. ``h`` indicates the outgoing signal.\n\n See detail in paper: `Long Short-Term Memory Over Tree Structures <http://arxiv.org/abs/1503.04881>`_.\n\n "
return SLSTM()(c_prev1, c_prev2, x1, x2) | S-LSTM units as an activation function.
This function implements S-LSTM unit. It is an extension of LSTM unit
applied to tree structures.
The function is applied to binary trees. Each node has two child nodes.
It gets four arguments, previous cell states :math:`c_1` and
:math:`c_2`, and incoming signals :math:`x_1` and :math:`x_2`.
First both input signals :math:`x_1` and :math:`x_2` are split into
eight arrays :math:`a_1, i_1, f_1, o_1`, and :math:`a_2, i_2, f_2, o_2`.
They have the same shape along the second axis.
It means that :math:`x_1` and :math:`x_2` 's second axis must have 4 times
the length of :math:`c_{1 \text{prev}}` and :math:`c_{2 \text{prev}}`.
The split input signals are corresponding to:
- :math:`a_i` : sources of cell input
- :math:`i_i` : sources of input gate
- :math:`f_i` : sources of forget gate
- :math:`o_i` : sources of output gate
It computes outputs as:
.. math::
c &= \tanh(a_1 + a_2) \sigma(i_1 + i_2)
+ c_{1 \text{prev}} \sigma(f_1)
+ c_{2 \text{prev}} \sigma(f_2), \\
h &= \tanh(c) \sigma(o_1 + o_2),
where :math:`\sigma` is the elementwise sigmoid function.
The function returns :math:`c` and :math:`h` as a tuple.
Args:
c_prev1 (~chainer.Variable): Variable that holds the previous cell
state of the first child node. The cell state should be a zero
array or the output of the previous call of LSTM.
c_prev2 (~chainer.Variable): Variable that holds the previous cell
state of the second child node.
x1 (~chainer.Variable): Variable that holds the incoming signal from
the first child node. It must have the second dimension four times
of that of the cell state,
x2 (~chainer.Variable): Variable that holds the incoming signal from
the second child node.
Returns:
tuple: Two :class:`~chainer.Variable` objects ``c`` and ``h``. ``c`` is
the cell state. ``h`` indicates the outgoing signal.
See detail in paper: `Long Short-Term Memory Over Tree Structures <http://arxiv.org/abs/1503.04881>`_. | chainer/functions/activation/slstm.py | slstm | owruby/chainer | 0 | python | def slstm(c_prev1, c_prev2, x1, x2):
"S-LSTM units as an activation function.\n\n This function implements S-LSTM unit. It is an extension of LSTM unit\n applied to tree structures.\n The function is applied to binary trees. Each node has two child nodes.\n It gets four arguments, previous cell states :math:`c_1` and\n :math:`c_2`, and incoming signals :math:`x_1` and :math:`x_2`.\n\n First both input signals :math:`x_1` and :math:`x_2` are split into\n eight arrays :math:`a_1, i_1, f_1, o_1`, and :math:`a_2, i_2, f_2, o_2`.\n They have the same shape along the second axis.\n It means that :math:`x_1` and :math:`x_2` 's second axis must have 4 times\n the length of :math:`c_{1 \\text{prev}}` and :math:`c_{2 \\text{prev}}`.\n\n The split input signals are corresponding to:\n\n - :math:`a_i` : sources of cell input\n - :math:`i_i` : sources of input gate\n - :math:`f_i` : sources of forget gate\n - :math:`o_i` : sources of output gate\n\n It computes outputs as:\n\n .. math::\n\n c &= \\tanh(a_1 + a_2) \\sigma(i_1 + i_2)\n + c_{1 \\text{prev}} \\sigma(f_1)\n + c_{2 \\text{prev}} \\sigma(f_2), \\\\\n h &= \\tanh(c) \\sigma(o_1 + o_2),\n\n where :math:`\\sigma` is the elementwise sigmoid function.\n The function returns :math:`c` and :math:`h` as a tuple.\n\n Args:\n c_prev1 (~chainer.Variable): Variable that holds the previous cell\n state of the first child node. The cell state should be a zero\n array or the output of the previous call of LSTM.\n c_prev2 (~chainer.Variable): Variable that holds the previous cell\n state of the second child node.\n x1 (~chainer.Variable): Variable that holds the incoming signal from\n the first child node. It must have the second dimension four times\n of that of the cell state,\n x2 (~chainer.Variable): Variable that holds the incoming signal from\n the second child node.\n\n Returns:\n tuple: Two :class:`~chainer.Variable` objects ``c`` and ``h``. ``c`` is\n the cell state. ``h`` indicates the outgoing signal.\n\n See detail in paper: `Long Short-Term Memory Over Tree Structures <http://arxiv.org/abs/1503.04881>`_.\n\n "
return SLSTM()(c_prev1, c_prev2, x1, x2) | def slstm(c_prev1, c_prev2, x1, x2):
"S-LSTM units as an activation function.\n\n This function implements S-LSTM unit. It is an extension of LSTM unit\n applied to tree structures.\n The function is applied to binary trees. Each node has two child nodes.\n It gets four arguments, previous cell states :math:`c_1` and\n :math:`c_2`, and incoming signals :math:`x_1` and :math:`x_2`.\n\n First both input signals :math:`x_1` and :math:`x_2` are split into\n eight arrays :math:`a_1, i_1, f_1, o_1`, and :math:`a_2, i_2, f_2, o_2`.\n They have the same shape along the second axis.\n It means that :math:`x_1` and :math:`x_2` 's second axis must have 4 times\n the length of :math:`c_{1 \\text{prev}}` and :math:`c_{2 \\text{prev}}`.\n\n The split input signals are corresponding to:\n\n - :math:`a_i` : sources of cell input\n - :math:`i_i` : sources of input gate\n - :math:`f_i` : sources of forget gate\n - :math:`o_i` : sources of output gate\n\n It computes outputs as:\n\n .. math::\n\n c &= \\tanh(a_1 + a_2) \\sigma(i_1 + i_2)\n + c_{1 \\text{prev}} \\sigma(f_1)\n + c_{2 \\text{prev}} \\sigma(f_2), \\\\\n h &= \\tanh(c) \\sigma(o_1 + o_2),\n\n where :math:`\\sigma` is the elementwise sigmoid function.\n The function returns :math:`c` and :math:`h` as a tuple.\n\n Args:\n c_prev1 (~chainer.Variable): Variable that holds the previous cell\n state of the first child node. The cell state should be a zero\n array or the output of the previous call of LSTM.\n c_prev2 (~chainer.Variable): Variable that holds the previous cell\n state of the second child node.\n x1 (~chainer.Variable): Variable that holds the incoming signal from\n the first child node. It must have the second dimension four times\n of that of the cell state,\n x2 (~chainer.Variable): Variable that holds the incoming signal from\n the second child node.\n\n Returns:\n tuple: Two :class:`~chainer.Variable` objects ``c`` and ``h``. ``c`` is\n the cell state. ``h`` indicates the outgoing signal.\n\n See detail in paper: `Long Short-Term Memory Over Tree Structures <http://arxiv.org/abs/1503.04881>`_.\n\n "
return SLSTM()(c_prev1, c_prev2, x1, x2)<|docstring|>S-LSTM units as an activation function.
This function implements S-LSTM unit. It is an extension of LSTM unit
applied to tree structures.
The function is applied to binary trees. Each node has two child nodes.
It gets four arguments, previous cell states :math:`c_1` and
:math:`c_2`, and incoming signals :math:`x_1` and :math:`x_2`.
First both input signals :math:`x_1` and :math:`x_2` are split into
eight arrays :math:`a_1, i_1, f_1, o_1`, and :math:`a_2, i_2, f_2, o_2`.
They have the same shape along the second axis.
It means that :math:`x_1` and :math:`x_2` 's second axis must have 4 times
the length of :math:`c_{1 \text{prev}}` and :math:`c_{2 \text{prev}}`.
The split input signals are corresponding to:
- :math:`a_i` : sources of cell input
- :math:`i_i` : sources of input gate
- :math:`f_i` : sources of forget gate
- :math:`o_i` : sources of output gate
It computes outputs as:
.. math::
c &= \tanh(a_1 + a_2) \sigma(i_1 + i_2)
+ c_{1 \text{prev}} \sigma(f_1)
+ c_{2 \text{prev}} \sigma(f_2), \\
h &= \tanh(c) \sigma(o_1 + o_2),
where :math:`\sigma` is the elementwise sigmoid function.
The function returns :math:`c` and :math:`h` as a tuple.
Args:
c_prev1 (~chainer.Variable): Variable that holds the previous cell
state of the first child node. The cell state should be a zero
array or the output of the previous call of LSTM.
c_prev2 (~chainer.Variable): Variable that holds the previous cell
state of the second child node.
x1 (~chainer.Variable): Variable that holds the incoming signal from
the first child node. It must have the second dimension four times
of that of the cell state,
x2 (~chainer.Variable): Variable that holds the incoming signal from
the second child node.
Returns:
tuple: Two :class:`~chainer.Variable` objects ``c`` and ``h``. ``c`` is
the cell state. ``h`` indicates the outgoing signal.
See detail in paper: `Long Short-Term Memory Over Tree Structures <http://arxiv.org/abs/1503.04881>`_.<|endoftext|> |
0d9223ba02b17d2aa8aee5067344808254820a8fd2e1648191b83285a999afce | def __init__(self, *, context: typing.List[str], data_agreement_id: str=None, data_agreement_version: int=None, data_agreement_template_id: str=None, data_agreement_template_version: int=None, pii_controller_name: str=None, pii_controller_url: str=None, usage_purpose: str=None, usage_purpose_description: str=None, legal_basis: str=None, method_of_use: str=None, data_policy: DataAgreementDataPolicy=None, personal_data: typing.List[DataAgreementPersonalData]=None, dpia: DataAgreementDPIA=None, event: typing.List[DataAgreementEvent]=None, proof_chain: typing.List[DataAgreementProof]=None, principle_did: str=None, proof: DataAgreementProof=None, **kwargs):
'Data Agreement instance init'
super().__init__(**kwargs)
self.context = context
self.data_agreement_id = data_agreement_id
self.data_agreement_version = data_agreement_version
self.data_agreement_template_id = data_agreement_template_id
self.data_agreement_template_version = data_agreement_template_version
self.pii_controller_name = pii_controller_name
self.pii_controller_url = pii_controller_url
self.usage_purpose = usage_purpose
self.usage_purpose_description = usage_purpose_description
self.legal_basis = legal_basis
self.method_of_use = method_of_use
self.data_policy = data_policy
self.personal_data = personal_data
self.dpia = dpia
self.proof_chain = proof_chain
self.principle_did = principle_did
self.event = event
self.proof = proof | Data Agreement instance init | mydata_did/v1_0/models/data_agreement_instance_model.py | __init__ | decentralised-dataexchange/acapy-mydata-did-protocol | 1 | python | def __init__(self, *, context: typing.List[str], data_agreement_id: str=None, data_agreement_version: int=None, data_agreement_template_id: str=None, data_agreement_template_version: int=None, pii_controller_name: str=None, pii_controller_url: str=None, usage_purpose: str=None, usage_purpose_description: str=None, legal_basis: str=None, method_of_use: str=None, data_policy: DataAgreementDataPolicy=None, personal_data: typing.List[DataAgreementPersonalData]=None, dpia: DataAgreementDPIA=None, event: typing.List[DataAgreementEvent]=None, proof_chain: typing.List[DataAgreementProof]=None, principle_did: str=None, proof: DataAgreementProof=None, **kwargs):
super().__init__(**kwargs)
self.context = context
self.data_agreement_id = data_agreement_id
self.data_agreement_version = data_agreement_version
self.data_agreement_template_id = data_agreement_template_id
self.data_agreement_template_version = data_agreement_template_version
self.pii_controller_name = pii_controller_name
self.pii_controller_url = pii_controller_url
self.usage_purpose = usage_purpose
self.usage_purpose_description = usage_purpose_description
self.legal_basis = legal_basis
self.method_of_use = method_of_use
self.data_policy = data_policy
self.personal_data = personal_data
self.dpia = dpia
self.proof_chain = proof_chain
self.principle_did = principle_did
self.event = event
self.proof = proof | def __init__(self, *, context: typing.List[str], data_agreement_id: str=None, data_agreement_version: int=None, data_agreement_template_id: str=None, data_agreement_template_version: int=None, pii_controller_name: str=None, pii_controller_url: str=None, usage_purpose: str=None, usage_purpose_description: str=None, legal_basis: str=None, method_of_use: str=None, data_policy: DataAgreementDataPolicy=None, personal_data: typing.List[DataAgreementPersonalData]=None, dpia: DataAgreementDPIA=None, event: typing.List[DataAgreementEvent]=None, proof_chain: typing.List[DataAgreementProof]=None, principle_did: str=None, proof: DataAgreementProof=None, **kwargs):
super().__init__(**kwargs)
self.context = context
self.data_agreement_id = data_agreement_id
self.data_agreement_version = data_agreement_version
self.data_agreement_template_id = data_agreement_template_id
self.data_agreement_template_version = data_agreement_template_version
self.pii_controller_name = pii_controller_name
self.pii_controller_url = pii_controller_url
self.usage_purpose = usage_purpose
self.usage_purpose_description = usage_purpose_description
self.legal_basis = legal_basis
self.method_of_use = method_of_use
self.data_policy = data_policy
self.personal_data = personal_data
self.dpia = dpia
self.proof_chain = proof_chain
self.principle_did = principle_did
self.event = event
self.proof = proof<|docstring|>Data Agreement instance init<|endoftext|> |
5755806fb17c30fcd6d4de9f25e1bb4f21dc3556da61a996746b8761a896d550 | def initialize_streamlit() -> None:
'クラスを定義する前にweb上で画面を出しておく\n 状態量として, 試合数, 経験値, レベル, 連勝数を定義し, 初期化しておく(マジックコマンド的な)\n : rtype : None\n : return : なし\n '
(st.session_state.col1, st.session_state.col2) = st.columns([4, 1])
(st.session_state.col4, st.session_state.space, st.session_state.col6) = st.columns([7, 1, 4])
st.session_state.col1.title('Welcome to Hit&Blow Game！16進数5桁の数字を当てよう！')
st.session_state.col1.subheader('対戦すると経験値がもらえるよ. 経験値は当てた回数や連勝数に応じて増えるぞ！')
st.session_state.col1.subheader('経験値が貯まるとレベルアップだ！いずれはキャラが進化するかも‥？')
if ('game_count' not in st.session_state):
st.session_state.game_count = 0
if ('exp' not in st.session_state):
st.session_state.exp = 0
if ('level' not in st.session_state):
st.session_state.level = 1
if ('win_in_a_row' not in st.session_state):
st.session_state.win_in_a_row = 0
name = st.session_state.col4.selectbox('キャラクターを選んでね', ['ジャック', 'クリス', 'フローラ', 'ドロシー'])
st.session_state.chara_name = name
pygame.mixer.init()
sound_waiting = pygame.mixer.Sound((('voice/' + st.session_state.chara_name) + '/waiting.wav'))
sound_waiting.play()
pic_url1 = (('picture/' + name) + '-1.jpg')
pic_url2 = (('picture/' + name) + '-2.jpg')
if (st.session_state.level < 20):
image = Image.open(pic_url1)
st.session_state.col4.image(image)
else:
image = Image.open(pic_url2)
st.session_state.col4.image(image) | クラスを定義する前にweb上で画面を出しておく
状態量として, 試合数, 経験値, レベル, 連勝数を定義し, 初期化しておく(マジックコマンド的な)
: rtype : None
: return : なし | hitblow/class_play_game.py | initialize_streamlit | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def initialize_streamlit() -> None:
'クラスを定義する前にweb上で画面を出しておく\n 状態量として, 試合数, 経験値, レベル, 連勝数を定義し, 初期化しておく(マジックコマンド的な)\n : rtype : None\n : return : なし\n '
(st.session_state.col1, st.session_state.col2) = st.columns([4, 1])
(st.session_state.col4, st.session_state.space, st.session_state.col6) = st.columns([7, 1, 4])
st.session_state.col1.title('Welcome to Hit&Blow Game！16進数5桁の数字を当てよう！')
st.session_state.col1.subheader('対戦すると経験値がもらえるよ. 経験値は当てた回数や連勝数に応じて増えるぞ！')
st.session_state.col1.subheader('経験値が貯まるとレベルアップだ！いずれはキャラが進化するかも‥？')
if ('game_count' not in st.session_state):
st.session_state.game_count = 0
if ('exp' not in st.session_state):
st.session_state.exp = 0
if ('level' not in st.session_state):
st.session_state.level = 1
if ('win_in_a_row' not in st.session_state):
st.session_state.win_in_a_row = 0
name = st.session_state.col4.selectbox('キャラクターを選んでね', ['ジャック', 'クリス', 'フローラ', 'ドロシー'])
st.session_state.chara_name = name
pygame.mixer.init()
sound_waiting = pygame.mixer.Sound((('voice/' + st.session_state.chara_name) + '/waiting.wav'))
sound_waiting.play()
pic_url1 = (('picture/' + name) + '-1.jpg')
pic_url2 = (('picture/' + name) + '-2.jpg')
if (st.session_state.level < 20):
image = Image.open(pic_url1)
st.session_state.col4.image(image)
else:
image = Image.open(pic_url2)
st.session_state.col4.image(image) | def initialize_streamlit() -> None:
'クラスを定義する前にweb上で画面を出しておく\n 状態量として, 試合数, 経験値, レベル, 連勝数を定義し, 初期化しておく(マジックコマンド的な)\n : rtype : None\n : return : なし\n '
(st.session_state.col1, st.session_state.col2) = st.columns([4, 1])
(st.session_state.col4, st.session_state.space, st.session_state.col6) = st.columns([7, 1, 4])
st.session_state.col1.title('Welcome to Hit&Blow Game！16進数5桁の数字を当てよう！')
st.session_state.col1.subheader('対戦すると経験値がもらえるよ. 経験値は当てた回数や連勝数に応じて増えるぞ！')
st.session_state.col1.subheader('経験値が貯まるとレベルアップだ！いずれはキャラが進化するかも‥？')
if ('game_count' not in st.session_state):
st.session_state.game_count = 0
if ('exp' not in st.session_state):
st.session_state.exp = 0
if ('level' not in st.session_state):
st.session_state.level = 1
if ('win_in_a_row' not in st.session_state):
st.session_state.win_in_a_row = 0
name = st.session_state.col4.selectbox('キャラクターを選んでね', ['ジャック', 'クリス', 'フローラ', 'ドロシー'])
st.session_state.chara_name = name
pygame.mixer.init()
sound_waiting = pygame.mixer.Sound((('voice/' + st.session_state.chara_name) + '/waiting.wav'))
sound_waiting.play()
pic_url1 = (('picture/' + name) + '-1.jpg')
pic_url2 = (('picture/' + name) + '-2.jpg')
if (st.session_state.level < 20):
image = Image.open(pic_url1)
st.session_state.col4.image(image)
else:
image = Image.open(pic_url2)
st.session_state.col4.image(image)<|docstring|>クラスを定義する前にweb上で画面を出しておく
状態量として, 試合数, 経験値, レベル, 連勝数を定義し, 初期化しておく(マジックコマンド的な)
: rtype : None
: return : なし<|endoftext|> |
83d7263c77eb4a56d709af491e586d64596c2528c4ddb6e36c94026f2e04a102 | def __init__(self, ans=None, room_id=6000) -> None:
'コンストラクタ\n :param str ans : 自分の答え(相手に当ててもらう数字)\n :param str room_id : room id(6000~6999)\n : rtype : None\n : return : なし\n '
self.digits = 5
self.Tuple_16 = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f')
if (ans is not None):
self.ans = ans
else:
self.ans = self._define_hidden_number_random()
self.url = 'https://damp-earth-70561.herokuapp.com'
self.room_id = str(room_id)
self.room_state = 1
self.player_id_F = 'e6e4dcbe-ec3c-4c2a-b228-67d1acee3c81'
self.player_id_F2 = '19dfceb3-46be-4d0e-94e2-3aa3333a4442'
self.player_name = 'F'
self.opponent_name = None
self.now_player = None
self.headers = {'content-Type': 'application/json'}
self.list_num_place = []
self.list_possible_ans_combination = []
self.list_ans_combination = []
self.list_possible_ans = []
self.num = None
self.hit = None
self.blow = None
self.my_history = None
self.opponent_history = None
self.count = 0
self.winner = None
self.volume = 0.3
self.remaining_exp_level = 0 | コンストラクタ
:param str ans : 自分の答え(相手に当ててもらう数字)
:param str room_id : room id(6000~6999)
: rtype : None
: return : なし | hitblow/class_play_game.py | __init__ | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def __init__(self, ans=None, room_id=6000) -> None:
'コンストラクタ\n :param str ans : 自分の答え(相手に当ててもらう数字)\n :param str room_id : room id(6000~6999)\n : rtype : None\n : return : なし\n '
self.digits = 5
self.Tuple_16 = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f')
if (ans is not None):
self.ans = ans
else:
self.ans = self._define_hidden_number_random()
self.url = 'https://damp-earth-70561.herokuapp.com'
self.room_id = str(room_id)
self.room_state = 1
self.player_id_F = 'e6e4dcbe-ec3c-4c2a-b228-67d1acee3c81'
self.player_id_F2 = '19dfceb3-46be-4d0e-94e2-3aa3333a4442'
self.player_name = 'F'
self.opponent_name = None
self.now_player = None
self.headers = {'content-Type': 'application/json'}
self.list_num_place = []
self.list_possible_ans_combination = []
self.list_ans_combination = []
self.list_possible_ans = []
self.num = None
self.hit = None
self.blow = None
self.my_history = None
self.opponent_history = None
self.count = 0
self.winner = None
self.volume = 0.3
self.remaining_exp_level = 0 | def __init__(self, ans=None, room_id=6000) -> None:
'コンストラクタ\n :param str ans : 自分の答え(相手に当ててもらう数字)\n :param str room_id : room id(6000~6999)\n : rtype : None\n : return : なし\n '
self.digits = 5
self.Tuple_16 = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f')
if (ans is not None):
self.ans = ans
else:
self.ans = self._define_hidden_number_random()
self.url = 'https://damp-earth-70561.herokuapp.com'
self.room_id = str(room_id)
self.room_state = 1
self.player_id_F = 'e6e4dcbe-ec3c-4c2a-b228-67d1acee3c81'
self.player_id_F2 = '19dfceb3-46be-4d0e-94e2-3aa3333a4442'
self.player_name = 'F'
self.opponent_name = None
self.now_player = None
self.headers = {'content-Type': 'application/json'}
self.list_num_place = []
self.list_possible_ans_combination = []
self.list_ans_combination = []
self.list_possible_ans = []
self.num = None
self.hit = None
self.blow = None
self.my_history = None
self.opponent_history = None
self.count = 0
self.winner = None
self.volume = 0.3
self.remaining_exp_level = 0<|docstring|>コンストラクタ
:param str ans : 自分の答え(相手に当ててもらう数字)
:param str room_id : room id(6000~6999)
: rtype : None
: return : なし<|endoftext|> |
24fc15df0ec479cc34e8d881e1d3ec28e7dd632323ed8feb3f5d7490ae5f37b7 | def _define_hidden_number_random(self) -> str:
'相手に当ててもらう答えをつくる\n : rtype : str\n : return : ans\n '
ans_list = random.sample(self.Tuple_16, self.digits)
ans = ''.join(ans_list)
return ans | 相手に当ててもらう答えをつくる
: rtype : str
: return : ans | hitblow/class_play_game.py | _define_hidden_number_random | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _define_hidden_number_random(self) -> str:
'相手に当ててもらう答えをつくる\n : rtype : str\n : return : ans\n '
ans_list = random.sample(self.Tuple_16, self.digits)
ans = .join(ans_list)
return ans | def _define_hidden_number_random(self) -> str:
'相手に当ててもらう答えをつくる\n : rtype : str\n : return : ans\n '
ans_list = random.sample(self.Tuple_16, self.digits)
ans = .join(ans_list)
return ans<|docstring|>相手に当ててもらう答えをつくる
: rtype : str
: return : ans<|endoftext|> |
3ae72f52a957b7f3fbbf8aa498b80d4a7f04bd634764fab484d70f7ccd58078a | def _play_song(self, num: int, title) -> None:
'音楽再生\n :param int num:再生回数(-1で無限ループ，これを使って止めたいときにstopするのが良いかと)\n :param int playtime:再生時間(基本-1で無限ループしてるので、使わない．デフォルト値Noneで良い)\n :param str title:bgmフォルダ内にあるmp3ファイルを再生\n : rtype : None\n : return : なし\n '
pygame.mixer.init()
pygame.mixer.music.load(title)
pygame.mixer.music.set_volume(self.volume)
pygame.mixer.music.play(num) | 音楽再生
:param int num:再生回数(-1で無限ループ，これを使って止めたいときにstopするのが良いかと)
:param int playtime:再生時間(基本-1で無限ループしてるので、使わない．デフォルト値Noneで良い)
:param str title:bgmフォルダ内にあるmp3ファイルを再生
: rtype : None
: return : なし | hitblow/class_play_game.py | _play_song | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _play_song(self, num: int, title) -> None:
'音楽再生\n :param int num:再生回数(-1で無限ループ，これを使って止めたいときにstopするのが良いかと)\n :param int playtime:再生時間(基本-1で無限ループしてるので、使わない．デフォルト値Noneで良い)\n :param str title:bgmフォルダ内にあるmp3ファイルを再生\n : rtype : None\n : return : なし\n '
pygame.mixer.init()
pygame.mixer.music.load(title)
pygame.mixer.music.set_volume(self.volume)
pygame.mixer.music.play(num) | def _play_song(self, num: int, title) -> None:
'音楽再生\n :param int num:再生回数(-1で無限ループ，これを使って止めたいときにstopするのが良いかと)\n :param int playtime:再生時間(基本-1で無限ループしてるので、使わない．デフォルト値Noneで良い)\n :param str title:bgmフォルダ内にあるmp3ファイルを再生\n : rtype : None\n : return : なし\n '
pygame.mixer.init()
pygame.mixer.music.load(title)
pygame.mixer.music.set_volume(self.volume)
pygame.mixer.music.play(num)<|docstring|>音楽再生
:param int num:再生回数(-1で無限ループ，これを使って止めたいときにstopするのが良いかと)
:param int playtime:再生時間(基本-1で無限ループしてるので、使わない．デフォルト値Noneで良い)
:param str title:bgmフォルダ内にあるmp3ファイルを再生
: rtype : None
: return : なし<|endoftext|> |
45cdd2ebb208b571ab3d84e191651ce1b7abc1346a48e97daa2a7742e3437725 | def _music_stop(self) -> None:
'再生中の音楽停止\n : rtype : None\n : return : なし\n '
pygame.mixer.music.stop() | 再生中の音楽停止
: rtype : None
: return : なし | hitblow/class_play_game.py | _music_stop | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _music_stop(self) -> None:
'再生中の音楽停止\n : rtype : None\n : return : なし\n '
pygame.mixer.music.stop() | def _music_stop(self) -> None:
'再生中の音楽停止\n : rtype : None\n : return : なし\n '
pygame.mixer.music.stop()<|docstring|>再生中の音楽停止
: rtype : None
: return : なし<|endoftext|> |
1c66b85d19ea0f92ec3dbfc05f0735021cbc2b5e9e2e8849b2005f8412439033 | def _get_table_by_API(self) -> None:
'APIを用いてサーバーから部屋の状態,ターン,履歴を取得(ループで何回も使用)\n : rtype : None\n : return : なし\n '
url_get_table = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table')
result = session.get(url_get_table)
data = result.json()
self.room_state = data['state']
self.now_player = data['now_player']
self.my_history = data['table'] | APIを用いてサーバーから部屋の状態,ターン,履歴を取得(ループで何回も使用)
: rtype : None
: return : なし | hitblow/class_play_game.py | _get_table_by_API | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _get_table_by_API(self) -> None:
'APIを用いてサーバーから部屋の状態,ターン,履歴を取得(ループで何回も使用)\n : rtype : None\n : return : なし\n '
url_get_table = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table')
result = session.get(url_get_table)
data = result.json()
self.room_state = data['state']
self.now_player = data['now_player']
self.my_history = data['table'] | def _get_table_by_API(self) -> None:
'APIを用いてサーバーから部屋の状態,ターン,履歴を取得(ループで何回も使用)\n : rtype : None\n : return : なし\n '
url_get_table = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table')
result = session.get(url_get_table)
data = result.json()
self.room_state = data['state']
self.now_player = data['now_player']
self.my_history = data['table']<|docstring|>APIを用いてサーバーから部屋の状態,ターン,履歴を取得(ループで何回も使用)
: rtype : None
: return : なし<|endoftext|> |
d745e45ce7ab683d8300bfcbe9eb45d36152d8e11eeb9a1604b360587d792446 | def _post_guess_by_API(self) -> None:
'APIを用いてサーバーに予想した相手の数字(self.num)をポスト\n : rtype : None\n : return : なし\n '
url_post_guess = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table/guesses')
post_data = {'player_id': self.player_id_F, 'guess': self.num}
session.post(url_post_guess, headers=self.headers, json=post_data) | APIを用いてサーバーに予想した相手の数字(self.num)をポスト
: rtype : None
: return : なし | hitblow/class_play_game.py | _post_guess_by_API | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _post_guess_by_API(self) -> None:
'APIを用いてサーバーに予想した相手の数字(self.num)をポスト\n : rtype : None\n : return : なし\n '
url_post_guess = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table/guesses')
post_data = {'player_id': self.player_id_F, 'guess': self.num}
session.post(url_post_guess, headers=self.headers, json=post_data) | def _post_guess_by_API(self) -> None:
'APIを用いてサーバーに予想した相手の数字(self.num)をポスト\n : rtype : None\n : return : なし\n '
url_post_guess = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table/guesses')
post_data = {'player_id': self.player_id_F, 'guess': self.num}
session.post(url_post_guess, headers=self.headers, json=post_data)<|docstring|>APIを用いてサーバーに予想した相手の数字(self.num)をポスト
: rtype : None
: return : なし<|endoftext|> |
d38127081941af686e1e8ef90146141ae19c129b4d66bd16547d939349563b14 | def _enterroom_and_registerplayer(self) -> None:
'部屋を作成し, 部屋に入り, 相手が来るまで待機\n 3秒ごとに相手が来ているか確認して,相手が来たらゲームスタート\n : rtype : None\n : return : なし\n '
url_enter_room = (self.url + '/rooms')
post_data = {'player_id': self.player_id_F, 'room_id': self.room_id}
session.post(url_enter_room, headers=self.headers, json=post_data)
while (self.room_state == 1):
url_get_room = ((self.url + '/rooms/') + str(self.room_id))
result = session.get(url_get_room)
data = result.json()
self.room_state = data['state']
time.sleep(3)
self._music_stop()
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/game_start.wav'))
self._play_song(num=1, title='bgm/game_start.wav')
time.sleep(3)
self._play_song(num=(- 1), title='bgm/Battle.wav')
self.opponent_name = (data['player2'] if (data['player1'] == 'F') else data['player1'])
self.now_player = data['player1'] | 部屋を作成し, 部屋に入り, 相手が来るまで待機
3秒ごとに相手が来ているか確認して,相手が来たらゲームスタート
: rtype : None
: return : なし | hitblow/class_play_game.py | _enterroom_and_registerplayer | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _enterroom_and_registerplayer(self) -> None:
'部屋を作成し, 部屋に入り, 相手が来るまで待機\n 3秒ごとに相手が来ているか確認して,相手が来たらゲームスタート\n : rtype : None\n : return : なし\n '
url_enter_room = (self.url + '/rooms')
post_data = {'player_id': self.player_id_F, 'room_id': self.room_id}
session.post(url_enter_room, headers=self.headers, json=post_data)
while (self.room_state == 1):
url_get_room = ((self.url + '/rooms/') + str(self.room_id))
result = session.get(url_get_room)
data = result.json()
self.room_state = data['state']
time.sleep(3)
self._music_stop()
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/game_start.wav'))
self._play_song(num=1, title='bgm/game_start.wav')
time.sleep(3)
self._play_song(num=(- 1), title='bgm/Battle.wav')
self.opponent_name = (data['player2'] if (data['player1'] == 'F') else data['player1'])
self.now_player = data['player1'] | def _enterroom_and_registerplayer(self) -> None:
'部屋を作成し, 部屋に入り, 相手が来るまで待機\n 3秒ごとに相手が来ているか確認して,相手が来たらゲームスタート\n : rtype : None\n : return : なし\n '
url_enter_room = (self.url + '/rooms')
post_data = {'player_id': self.player_id_F, 'room_id': self.room_id}
session.post(url_enter_room, headers=self.headers, json=post_data)
while (self.room_state == 1):
url_get_room = ((self.url + '/rooms/') + str(self.room_id))
result = session.get(url_get_room)
data = result.json()
self.room_state = data['state']
time.sleep(3)
self._music_stop()
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/game_start.wav'))
self._play_song(num=1, title='bgm/game_start.wav')
time.sleep(3)
self._play_song(num=(- 1), title='bgm/Battle.wav')
self.opponent_name = (data['player2'] if (data['player1'] == 'F') else data['player1'])
self.now_player = data['player1']<|docstring|>部屋を作成し, 部屋に入り, 相手が来るまで待機
3秒ごとに相手が来ているか確認して,相手が来たらゲームスタート
: rtype : None
: return : なし<|endoftext|> |
d2c260e4a7e8c15adc208fab88604f07257d156822ba15bee91a7e78f7338cc4 | def _post_hidden_number(self) -> None:
'APIを用いてサーバーに自分の答えをポスト(初回のみ)\n : rtype : None\n : return : なし\n '
url_post_hidden_number = (((((self.url + '/rooms/') + self.room_id) + '/players/') + self.player_name) + '/hidden')
post_data = {'player_id': self.player_id_F, 'hidden_number': self.ans}
session.post(url_post_hidden_number, headers=self.headers, json=post_data) | APIを用いてサーバーに自分の答えをポスト(初回のみ)
: rtype : None
: return : なし | hitblow/class_play_game.py | _post_hidden_number | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _post_hidden_number(self) -> None:
'APIを用いてサーバーに自分の答えをポスト(初回のみ)\n : rtype : None\n : return : なし\n '
url_post_hidden_number = (((((self.url + '/rooms/') + self.room_id) + '/players/') + self.player_name) + '/hidden')
post_data = {'player_id': self.player_id_F, 'hidden_number': self.ans}
session.post(url_post_hidden_number, headers=self.headers, json=post_data) | def _post_hidden_number(self) -> None:
'APIを用いてサーバーに自分の答えをポスト(初回のみ)\n : rtype : None\n : return : なし\n '
url_post_hidden_number = (((((self.url + '/rooms/') + self.room_id) + '/players/') + self.player_name) + '/hidden')
post_data = {'player_id': self.player_id_F, 'hidden_number': self.ans}
session.post(url_post_hidden_number, headers=self.headers, json=post_data)<|docstring|>APIを用いてサーバーに自分の答えをポスト(初回のみ)
: rtype : None
: return : なし<|endoftext|> |
b8360dd7d84737154eac2f2ecf1c9b96abd3a1aa297029ca1cfda192e14eb1df | def _first_3_times(self) -> None:
'進化前,最初に行う, 答えとなる数字がどのグループに何個あるのか特定\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, 1,2,3回目に01234,56789,abcdeを選んでself.numに格納\n post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納\n remove_impossible_combinationを行う\n 自分のターンで無かったら, 1秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
search_list = ['01234', '56789', 'abcde']
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name) and (self.count != 3)):
print('{}回目の入力です.'.format((self.count + 1)))
self.num = search_list[self.count]
self.count += 1
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
self.list_num_place.append((self.hit + self.blow))
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.count == 3) or (self.room_state == 3)):
break
else:
time.sleep(0.5)
continue | 進化前,最初に行う, 答えとなる数字がどのグループに何個あるのか特定
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, 1,2,3回目に01234,56789,abcdeを選んでself.numに格納
post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納
remove_impossible_combinationを行う
自分のターンで無かったら, 1秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし | hitblow/class_play_game.py | _first_3_times | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _first_3_times(self) -> None:
'進化前,最初に行う, 答えとなる数字がどのグループに何個あるのか特定\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, 1,2,3回目に01234,56789,abcdeを選んでself.numに格納\n post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納\n remove_impossible_combinationを行う\n 自分のターンで無かったら, 1秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
search_list = ['01234', '56789', 'abcde']
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name) and (self.count != 3)):
print('{}回目の入力です.'.format((self.count + 1)))
self.num = search_list[self.count]
self.count += 1
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
self.list_num_place.append((self.hit + self.blow))
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.count == 3) or (self.room_state == 3)):
break
else:
time.sleep(0.5)
continue | def _first_3_times(self) -> None:
'進化前,最初に行う, 答えとなる数字がどのグループに何個あるのか特定\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, 1,2,3回目に01234,56789,abcdeを選んでself.numに格納\n post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納\n remove_impossible_combinationを行う\n 自分のターンで無かったら, 1秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
search_list = ['01234', '56789', 'abcde']
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name) and (self.count != 3)):
print('{}回目の入力です.'.format((self.count + 1)))
self.num = search_list[self.count]
self.count += 1
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
self.list_num_place.append((self.hit + self.blow))
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.count == 3) or (self.room_state == 3)):
break
else:
time.sleep(0.5)
continue<|docstring|>進化前,最初に行う, 答えとなる数字がどのグループに何個あるのか特定
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, 1,2,3回目に01234,56789,abcdeを選んでself.numに格納
post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納
remove_impossible_combinationを行う
自分のターンで無かったら, 1秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし<|endoftext|> |
b9c77488146db01f7674e27d689dcccdfff9b9e775a0d534f35c160841441a26 | def _make_list_possible_ans_combination_3(self) -> None:
'進化前,2番目に行う\n 最初の3回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,\n list_possible_ans_combinationに格納\n : rtype : None\n : return : なし\n '
for i in itertools.combinations('01234', self.list_num_place[0]):
for j in itertools.combinations('56789', self.list_num_place[1]):
for k in itertools.combinations('abcde', self.list_num_place[2]):
for l in itertools.combinations('f', (self.digits - sum(self.list_num_place))):
n = ''.join((((i + j) + k) + l))
self.list_possible_ans_combination.append(n) | 進化前,2番目に行う
最初の3回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,
list_possible_ans_combinationに格納
: rtype : None
: return : なし | hitblow/class_play_game.py | _make_list_possible_ans_combination_3 | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _make_list_possible_ans_combination_3(self) -> None:
'進化前,2番目に行う\n 最初の3回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,\n list_possible_ans_combinationに格納\n : rtype : None\n : return : なし\n '
for i in itertools.combinations('01234', self.list_num_place[0]):
for j in itertools.combinations('56789', self.list_num_place[1]):
for k in itertools.combinations('abcde', self.list_num_place[2]):
for l in itertools.combinations('f', (self.digits - sum(self.list_num_place))):
n = .join((((i + j) + k) + l))
self.list_possible_ans_combination.append(n) | def _make_list_possible_ans_combination_3(self) -> None:
'進化前,2番目に行う\n 最初の3回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,\n list_possible_ans_combinationに格納\n : rtype : None\n : return : なし\n '
for i in itertools.combinations('01234', self.list_num_place[0]):
for j in itertools.combinations('56789', self.list_num_place[1]):
for k in itertools.combinations('abcde', self.list_num_place[2]):
for l in itertools.combinations('f', (self.digits - sum(self.list_num_place))):
n = .join((((i + j) + k) + l))
self.list_possible_ans_combination.append(n)<|docstring|>進化前,2番目に行う
最初の3回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,
list_possible_ans_combinationに格納
: rtype : None
: return : なし<|endoftext|> |
bdac3ca3977bcb4035c188b0cdba8f3cd520fb9f9d14b368f30832a205a9935f | def _first_2_times(self) -> None:
'進化後, 最初に行う, 答えとなる数字がどのグループに何個あるのか特定\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, 1,2回目に01234,56789を選んでself.numに格納\n post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納\n remove_impossible_combinationを行う\n 自分のターンで無かったら, 1秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
search_list = ['01234', '56789']
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name) and (self.count != 2)):
print('{}回目の入力です.'.format((self.count + 1)))
self.num = search_list[self.count]
self.count += 1
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
self.list_num_place.append((self.hit + self.blow))
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.count == 2) or (self.room_state == 3)):
break
else:
time.sleep(0.5)
continue | 進化後, 最初に行う, 答えとなる数字がどのグループに何個あるのか特定
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, 1,2回目に01234,56789を選んでself.numに格納
post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納
remove_impossible_combinationを行う
自分のターンで無かったら, 1秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし | hitblow/class_play_game.py | _first_2_times | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _first_2_times(self) -> None:
'進化後, 最初に行う, 答えとなる数字がどのグループに何個あるのか特定\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, 1,2回目に01234,56789を選んでself.numに格納\n post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納\n remove_impossible_combinationを行う\n 自分のターンで無かったら, 1秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
search_list = ['01234', '56789']
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name) and (self.count != 2)):
print('{}回目の入力です.'.format((self.count + 1)))
self.num = search_list[self.count]
self.count += 1
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
self.list_num_place.append((self.hit + self.blow))
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.count == 2) or (self.room_state == 3)):
break
else:
time.sleep(0.5)
continue | def _first_2_times(self) -> None:
'進化後, 最初に行う, 答えとなる数字がどのグループに何個あるのか特定\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, 1,2回目に01234,56789を選んでself.numに格納\n post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納\n remove_impossible_combinationを行う\n 自分のターンで無かったら, 1秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
search_list = ['01234', '56789']
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name) and (self.count != 2)):
print('{}回目の入力です.'.format((self.count + 1)))
self.num = search_list[self.count]
self.count += 1
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
self.list_num_place.append((self.hit + self.blow))
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.count == 2) or (self.room_state == 3)):
break
else:
time.sleep(0.5)
continue<|docstring|>進化後, 最初に行う, 答えとなる数字がどのグループに何個あるのか特定
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, 1,2回目に01234,56789を選んでself.numに格納
post_guessし, 帰ってきたhit,blowの和をlist_ans_numに格納
remove_impossible_combinationを行う
自分のターンで無かったら, 1秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし<|endoftext|> |
a95e91e504d6fe5d23f862f5cb984a44b8ccf5bdb5b1e71370a847ab7e54c3c0 | def _make_list_possible_ans_combination(self) -> None:
'進化後, 2番目に行う\n 最初の2回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,\n list_possible_ans_combinationに格納\n : rtype : None\n : return : なし\n '
for i in itertools.combinations('01234', self.list_num_place[0]):
for j in itertools.combinations('56789', self.list_num_place[1]):
for k in itertools.combinations('abcdef', (self.digits - sum(self.list_num_place))):
n = ''.join(((i + j) + k))
self.list_possible_ans_combination.append(n) | 進化後, 2番目に行う
最初の2回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,
list_possible_ans_combinationに格納
: rtype : None
: return : なし | hitblow/class_play_game.py | _make_list_possible_ans_combination | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _make_list_possible_ans_combination(self) -> None:
'進化後, 2番目に行う\n 最初の2回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,\n list_possible_ans_combinationに格納\n : rtype : None\n : return : なし\n '
for i in itertools.combinations('01234', self.list_num_place[0]):
for j in itertools.combinations('56789', self.list_num_place[1]):
for k in itertools.combinations('abcdef', (self.digits - sum(self.list_num_place))):
n = .join(((i + j) + k))
self.list_possible_ans_combination.append(n) | def _make_list_possible_ans_combination(self) -> None:
'進化後, 2番目に行う\n 最初の2回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,\n list_possible_ans_combinationに格納\n : rtype : None\n : return : なし\n '
for i in itertools.combinations('01234', self.list_num_place[0]):
for j in itertools.combinations('56789', self.list_num_place[1]):
for k in itertools.combinations('abcdef', (self.digits - sum(self.list_num_place))):
n = .join(((i + j) + k))
self.list_possible_ans_combination.append(n)<|docstring|>進化後, 2番目に行う
最初の2回で作ったlist_ans_numから, 答えの5数字の"組み合わせ"の候補を全て洗い出し,
list_possible_ans_combinationに格納
: rtype : None
: return : なし<|endoftext|> |
484054008aa1bf5d0dd725eee40696c2665b00dd1076d6628276ab22e0f10f61 | def _remove_impossible_combination(self) -> None:
'3番目で使用\n そのターンに質問で帰ってきたhit,blowの値を保存し, list_possible_ans_combinationの解の候補のiのうち\n self.numとiでhit,blowの和が一致しないものは答えの"組み合わせ"の候補としてありえないので候補から削除\n こうしてlist_possible_ans_combinationの中身を削っていく\n : rtype : None\n : return : なし\n '
hb = (self.hit + self.blow)
for i in self.list_possible_ans_combination[:]:
self._check_hit_blow(self.num, i)
if ((self.hit + self.blow) != hb):
self.list_possible_ans_combination.remove(i) | 3番目で使用
そのターンに質問で帰ってきたhit,blowの値を保存し, list_possible_ans_combinationの解の候補のiのうち
self.numとiでhit,blowの和が一致しないものは答えの"組み合わせ"の候補としてありえないので候補から削除
こうしてlist_possible_ans_combinationの中身を削っていく
: rtype : None
: return : なし | hitblow/class_play_game.py | _remove_impossible_combination | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _remove_impossible_combination(self) -> None:
'3番目で使用\n そのターンに質問で帰ってきたhit,blowの値を保存し, list_possible_ans_combinationの解の候補のiのうち\n self.numとiでhit,blowの和が一致しないものは答えの"組み合わせ"の候補としてありえないので候補から削除\n こうしてlist_possible_ans_combinationの中身を削っていく\n : rtype : None\n : return : なし\n '
hb = (self.hit + self.blow)
for i in self.list_possible_ans_combination[:]:
self._check_hit_blow(self.num, i)
if ((self.hit + self.blow) != hb):
self.list_possible_ans_combination.remove(i) | def _remove_impossible_combination(self) -> None:
'3番目で使用\n そのターンに質問で帰ってきたhit,blowの値を保存し, list_possible_ans_combinationの解の候補のiのうち\n self.numとiでhit,blowの和が一致しないものは答えの"組み合わせ"の候補としてありえないので候補から削除\n こうしてlist_possible_ans_combinationの中身を削っていく\n : rtype : None\n : return : なし\n '
hb = (self.hit + self.blow)
for i in self.list_possible_ans_combination[:]:
self._check_hit_blow(self.num, i)
if ((self.hit + self.blow) != hb):
self.list_possible_ans_combination.remove(i)<|docstring|>3番目で使用
そのターンに質問で帰ってきたhit,blowの値を保存し, list_possible_ans_combinationの解の候補のiのうち
self.numとiでhit,blowの和が一致しないものは答えの"組み合わせ"の候補としてありえないので候補から削除
こうしてlist_possible_ans_combinationの中身を削っていく
: rtype : None
: return : なし<|endoftext|> |
da7388c30dae4c41565504d1ab32d6344fb25ec12fc71cdf215062fbab70a91f | def _remove_impossible_permutation(self) -> None:
'3番目で使用\n そのターンに質問で帰ってきたhitの値を保存し, list_possible_ansの解の候補のjのうち\n self.numとjでhitが一致しないものは答えの"順列"の候補としてありえないので候補から削除\n こうしてlist_possible_ansの中身を削っていく\n : rtype : None\n : return : なし\n '
hit = self.hit
for i in self.list_possible_ans[:]:
self._check_hit_blow(self.num, i)
if (self.hit != hit):
self.list_possible_ans.remove(i) | 3番目で使用
そのターンに質問で帰ってきたhitの値を保存し, list_possible_ansの解の候補のjのうち
self.numとjでhitが一致しないものは答えの"順列"の候補としてありえないので候補から削除
こうしてlist_possible_ansの中身を削っていく
: rtype : None
: return : なし | hitblow/class_play_game.py | _remove_impossible_permutation | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _remove_impossible_permutation(self) -> None:
'3番目で使用\n そのターンに質問で帰ってきたhitの値を保存し, list_possible_ansの解の候補のjのうち\n self.numとjでhitが一致しないものは答えの"順列"の候補としてありえないので候補から削除\n こうしてlist_possible_ansの中身を削っていく\n : rtype : None\n : return : なし\n '
hit = self.hit
for i in self.list_possible_ans[:]:
self._check_hit_blow(self.num, i)
if (self.hit != hit):
self.list_possible_ans.remove(i) | def _remove_impossible_permutation(self) -> None:
'3番目で使用\n そのターンに質問で帰ってきたhitの値を保存し, list_possible_ansの解の候補のjのうち\n self.numとjでhitが一致しないものは答えの"順列"の候補としてありえないので候補から削除\n こうしてlist_possible_ansの中身を削っていく\n : rtype : None\n : return : なし\n '
hit = self.hit
for i in self.list_possible_ans[:]:
self._check_hit_blow(self.num, i)
if (self.hit != hit):
self.list_possible_ans.remove(i)<|docstring|>3番目で使用
そのターンに質問で帰ってきたhitの値を保存し, list_possible_ansの解の候補のjのうち
self.numとjでhitが一致しないものは答えの"順列"の候補としてありえないので候補から削除
こうしてlist_possible_ansの中身を削っていく
: rtype : None
: return : なし<|endoftext|> |
896e6f596b8fbac92e3980cb32f3395546eac6467366bd2d5904294445e76a70 | def _check_hit_blow(self, num, ans) -> None:
'2つのremove関数内で使用\n 2つの引数を入力し, その2数のhit,blowを計算してself.hit, self.blowに格納\n : rtype : None\n : return : なし\n '
self.hit = 0
self.blow = 0
for i in range(self.digits):
if (num[i] == ans[i]):
self.hit += 1
elif (num[i] in ans):
self.blow += 1 | 2つのremove関数内で使用
2つの引数を入力し, その2数のhit,blowを計算してself.hit, self.blowに格納
: rtype : None
: return : なし | hitblow/class_play_game.py | _check_hit_blow | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _check_hit_blow(self, num, ans) -> None:
'2つのremove関数内で使用\n 2つの引数を入力し, その2数のhit,blowを計算してself.hit, self.blowに格納\n : rtype : None\n : return : なし\n '
self.hit = 0
self.blow = 0
for i in range(self.digits):
if (num[i] == ans[i]):
self.hit += 1
elif (num[i] in ans):
self.blow += 1 | def _check_hit_blow(self, num, ans) -> None:
'2つのremove関数内で使用\n 2つの引数を入力し, その2数のhit,blowを計算してself.hit, self.blowに格納\n : rtype : None\n : return : なし\n '
self.hit = 0
self.blow = 0
for i in range(self.digits):
if (num[i] == ans[i]):
self.hit += 1
elif (num[i] in ans):
self.blow += 1<|docstring|>2つのremove関数内で使用
2つの引数を入力し, その2数のhit,blowを計算してself.hit, self.blowに格納
: rtype : None
: return : なし<|endoftext|> |
c3ddcfe7d8fc11ae09b35f07b5c952ca62bd1569e8140df9dc0193cc2344a29c | def _identify_number(self) -> None:
'3番目に行う(1番のメイン部分)\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, list_ans_num_combinationの中からランダムで質問する数字を選んでself.numに格納\n post_guessし, 帰ってきたhit,blowをprintし, remove_impossible_combinationを行う\n hit+blow = 5の(組み合わせが特定出来た)ときは, まずその結果からあり得る数字の順列120通りのlist_possible_ansを作成\n 次にそのターンのhit,blowからあり得ないものを削除し, 順列を考える次の関数(後述)に移る\n 自分のターンで無かったら, 0.5秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
print('----------from first3 to 5C----------')
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name)):
print('{}回目の入力です, 組み合わせの候補は{}通りです.'.format((self.count + 1), len(self.list_possible_ans_combination)))
self.count += 1
self.num = random.choice(self.list_possible_ans_combination)
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.hit + self.blow) == self.digits):
self.list_ans_combination = [i for i in self.num]
for i in itertools.permutations(self.list_ans_combination, 5):
m = ''.join(i)
self.list_possible_ans.append(m)
print('----------from 5C to 5P----------')
if (st.session_state.level >= 20):
self._remove_impossible_permutation()
self._identify_permutation()
break
else:
self._remove_impossible_combination()
if (self.room_state == 3):
break
else:
time.sleep(0.5)
continue | 3番目に行う(1番のメイン部分)
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, list_ans_num_combinationの中からランダムで質問する数字を選んでself.numに格納
post_guessし, 帰ってきたhit,blowをprintし, remove_impossible_combinationを行う
hit+blow = 5の(組み合わせが特定出来た)ときは, まずその結果からあり得る数字の順列120通りのlist_possible_ansを作成
次にそのターンのhit,blowからあり得ないものを削除し, 順列を考える次の関数(後述)に移る
自分のターンで無かったら, 0.5秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし | hitblow/class_play_game.py | _identify_number | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _identify_number(self) -> None:
'3番目に行う(1番のメイン部分)\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, list_ans_num_combinationの中からランダムで質問する数字を選んでself.numに格納\n post_guessし, 帰ってきたhit,blowをprintし, remove_impossible_combinationを行う\n hit+blow = 5の(組み合わせが特定出来た)ときは, まずその結果からあり得る数字の順列120通りのlist_possible_ansを作成\n 次にそのターンのhit,blowからあり得ないものを削除し, 順列を考える次の関数(後述)に移る\n 自分のターンで無かったら, 0.5秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
print('----------from first3 to 5C----------')
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name)):
print('{}回目の入力です, 組み合わせの候補は{}通りです.'.format((self.count + 1), len(self.list_possible_ans_combination)))
self.count += 1
self.num = random.choice(self.list_possible_ans_combination)
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.hit + self.blow) == self.digits):
self.list_ans_combination = [i for i in self.num]
for i in itertools.permutations(self.list_ans_combination, 5):
m = .join(i)
self.list_possible_ans.append(m)
print('----------from 5C to 5P----------')
if (st.session_state.level >= 20):
self._remove_impossible_permutation()
self._identify_permutation()
break
else:
self._remove_impossible_combination()
if (self.room_state == 3):
break
else:
time.sleep(0.5)
continue | def _identify_number(self) -> None:
'3番目に行う(1番のメイン部分)\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, list_ans_num_combinationの中からランダムで質問する数字を選んでself.numに格納\n post_guessし, 帰ってきたhit,blowをprintし, remove_impossible_combinationを行う\n hit+blow = 5の(組み合わせが特定出来た)ときは, まずその結果からあり得る数字の順列120通りのlist_possible_ansを作成\n 次にそのターンのhit,blowからあり得ないものを削除し, 順列を考える次の関数(後述)に移る\n 自分のターンで無かったら, 0.5秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
print('----------from first3 to 5C----------')
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name)):
print('{}回目の入力です, 組み合わせの候補は{}通りです.'.format((self.count + 1), len(self.list_possible_ans_combination)))
self.count += 1
self.num = random.choice(self.list_possible_ans_combination)
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
if ((self.hit + self.blow) == self.digits):
self.list_ans_combination = [i for i in self.num]
for i in itertools.permutations(self.list_ans_combination, 5):
m = .join(i)
self.list_possible_ans.append(m)
print('----------from 5C to 5P----------')
if (st.session_state.level >= 20):
self._remove_impossible_permutation()
self._identify_permutation()
break
else:
self._remove_impossible_combination()
if (self.room_state == 3):
break
else:
time.sleep(0.5)
continue<|docstring|>3番目に行う(1番のメイン部分)
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, list_ans_num_combinationの中からランダムで質問する数字を選んでself.numに格納
post_guessし, 帰ってきたhit,blowをprintし, remove_impossible_combinationを行う
hit+blow = 5の(組み合わせが特定出来た)ときは, まずその結果からあり得る数字の順列120通りのlist_possible_ansを作成
次にそのターンのhit,blowからあり得ないものを削除し, 順列を考える次の関数(後述)に移る
自分のターンで無かったら, 0.5秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし<|endoftext|> |
03bc955f9c3e7c18ca5f48afc8d6b3e294d88c66a4a74c04d2f06a47b18aae8b | def _identify_permutation(self) -> None:
'3番目で使用\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, list_ans_numの中からランダムで質問する数字を選んでself.numに格納\n post_guessし, 帰ってきたhitをprintし, remove_impossible_ansを行う\n 自分のターンで無かったら, 0.5秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name)):
print('{}回目の入力です, 順列の候補は{}通りです.'.format((self.count + 1), len(self.list_possible_ans)))
self.count += 1
self.num = random.choice(self.list_possible_ans)
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
self._remove_impossible_permutation()
if (self.room_state == 3):
break
else:
time.sleep(0.5)
continue | 3番目で使用
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, list_ans_numの中からランダムで質問する数字を選んでself.numに格納
post_guessし, 帰ってきたhitをprintし, remove_impossible_ansを行う
自分のターンで無かったら, 0.5秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし | hitblow/class_play_game.py | _identify_permutation | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _identify_permutation(self) -> None:
'3番目で使用\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, list_ans_numの中からランダムで質問する数字を選んでself.numに格納\n post_guessし, 帰ってきたhitをprintし, remove_impossible_ansを行う\n 自分のターンで無かったら, 0.5秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name)):
print('{}回目の入力です, 順列の候補は{}通りです.'.format((self.count + 1), len(self.list_possible_ans)))
self.count += 1
self.num = random.choice(self.list_possible_ans)
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
self._remove_impossible_permutation()
if (self.room_state == 3):
break
else:
time.sleep(0.5)
continue | def _identify_permutation(self) -> None:
'3番目で使用\n 1秒ごとにget_tableで状態を確認し,\n 対戦続行中で,自分のターンのとき, list_ans_numの中からランダムで質問する数字を選んでself.numに格納\n post_guessし, 帰ってきたhitをprintし, remove_impossible_ansを行う\n 自分のターンで無かったら, 0.5秒待機\n 試合終了だったらループを抜ける\n : rtype : None\n : return : なし\n '
while True:
self._get_table_by_API()
if ((self.room_state == 2) and (self.now_player == self.player_name)):
print('{}回目の入力です, 順列の候補は{}通りです.'.format((self.count + 1), len(self.list_possible_ans)))
self.count += 1
self.num = random.choice(self.list_possible_ans)
self._post_guess_by_API()
self._get_table_by_API()
self.hit = self.my_history[(- 1)]['hit']
self.blow = self.my_history[(- 1)]['blow']
print('-----', self.num)
print('!! {} Hit, {} Blow !!'.format(self.hit, self.blow))
self._remove_impossible_permutation()
if (self.room_state == 3):
break
else:
time.sleep(0.5)
continue<|docstring|>3番目で使用
1秒ごとにget_tableで状態を確認し,
対戦続行中で,自分のターンのとき, list_ans_numの中からランダムで質問する数字を選んでself.numに格納
post_guessし, 帰ってきたhitをprintし, remove_impossible_ansを行う
自分のターンで無かったら, 0.5秒待機
試合終了だったらループを抜ける
: rtype : None
: return : なし<|endoftext|> |
89b3c06449daeda9b225399dad77c66cc3e1b4106bbe707463aea729fbbb9475 | def run(self) -> None:
' 数当てゲーム実行ランナー\n 対戦中の表示を出してから部屋を作成して答えをポストして対戦開始, 終わったら対戦終了と結果の表示\n : rtype : None\n : return : なし\n '
st.session_state.col2.subheader('{}の現在のレベル : {}'.format(st.session_state.chara_name, st.session_state.level))
st.session_state.col2.write('対戦回数 : {}'.format(st.session_state.game_count))
place = st.session_state.col6.empty()
self._enterroom_and_registerplayer()
self._post_hidden_number()
place.write('対戦中・・・')
if (st.session_state.level >= 20):
self._first_2_times()
self._make_list_possible_ans_combination()
else:
self._first_3_times()
self._make_list_possible_ans_combination_3()
self._identify_number()
place.write('対戦終了！')
self._music_stop()
self._show_result_vscode()
self._show_result_streamlit() | 数当てゲーム実行ランナー
対戦中の表示を出してから部屋を作成して答えをポストして対戦開始, 終わったら対戦終了と結果の表示
: rtype : None
: return : なし | hitblow/class_play_game.py | run | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def run(self) -> None:
' 数当てゲーム実行ランナー\n 対戦中の表示を出してから部屋を作成して答えをポストして対戦開始, 終わったら対戦終了と結果の表示\n : rtype : None\n : return : なし\n '
st.session_state.col2.subheader('{}の現在のレベル : {}'.format(st.session_state.chara_name, st.session_state.level))
st.session_state.col2.write('対戦回数 : {}'.format(st.session_state.game_count))
place = st.session_state.col6.empty()
self._enterroom_and_registerplayer()
self._post_hidden_number()
place.write('対戦中・・・')
if (st.session_state.level >= 20):
self._first_2_times()
self._make_list_possible_ans_combination()
else:
self._first_3_times()
self._make_list_possible_ans_combination_3()
self._identify_number()
place.write('対戦終了！')
self._music_stop()
self._show_result_vscode()
self._show_result_streamlit() | def run(self) -> None:
' 数当てゲーム実行ランナー\n 対戦中の表示を出してから部屋を作成して答えをポストして対戦開始, 終わったら対戦終了と結果の表示\n : rtype : None\n : return : なし\n '
st.session_state.col2.subheader('{}の現在のレベル : {}'.format(st.session_state.chara_name, st.session_state.level))
st.session_state.col2.write('対戦回数 : {}'.format(st.session_state.game_count))
place = st.session_state.col6.empty()
self._enterroom_and_registerplayer()
self._post_hidden_number()
place.write('対戦中・・・')
if (st.session_state.level >= 20):
self._first_2_times()
self._make_list_possible_ans_combination()
else:
self._first_3_times()
self._make_list_possible_ans_combination_3()
self._identify_number()
place.write('対戦終了！')
self._music_stop()
self._show_result_vscode()
self._show_result_streamlit()<|docstring|>数当てゲーム実行ランナー
対戦中の表示を出してから部屋を作成して答えをポストして対戦開始, 終わったら対戦終了と結果の表示
: rtype : None
: return : なし<|endoftext|> |
87fe9522b43276c41d84b5427560202cd75d46e9322db40c76f31a4b73a093c7 | def _show_result_vscode(self) -> None:
'対戦終了後, お互いの結果を表示(vscode上に表示する分)\n : rtype : None\n : return : なし\n '
print('--------------------')
print('対戦終了です.')
url_get_table = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table')
result = session.get(url_get_table)
data = result.json()
self.my_history = data['table']
self.opponent_history = data['opponent_table']
print('------------------------')
print('show opponent history')
print(self.opponent_history)
print('------------------------')
print('show my history')
print(self.my_history)
self.winner = data['winner']
print('------------------------')
print('勝者は{}です. {}回で正解しました!'.format(self.winner, self.count))
print('終了ターンの解答 : {}'.format(self.num))
print('------------------------') | 対戦終了後, お互いの結果を表示(vscode上に表示する分)
: rtype : None
: return : なし | hitblow/class_play_game.py | _show_result_vscode | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _show_result_vscode(self) -> None:
'対戦終了後, お互いの結果を表示(vscode上に表示する分)\n : rtype : None\n : return : なし\n '
print('--------------------')
print('対戦終了です.')
url_get_table = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table')
result = session.get(url_get_table)
data = result.json()
self.my_history = data['table']
self.opponent_history = data['opponent_table']
print('------------------------')
print('show opponent history')
print(self.opponent_history)
print('------------------------')
print('show my history')
print(self.my_history)
self.winner = data['winner']
print('------------------------')
print('勝者は{}です. {}回で正解しました!'.format(self.winner, self.count))
print('終了ターンの解答 : {}'.format(self.num))
print('------------------------') | def _show_result_vscode(self) -> None:
'対戦終了後, お互いの結果を表示(vscode上に表示する分)\n : rtype : None\n : return : なし\n '
print('--------------------')
print('対戦終了です.')
url_get_table = (((((self.url + '/rooms/') + str(self.room_id)) + '/players/') + self.player_name) + '/table')
result = session.get(url_get_table)
data = result.json()
self.my_history = data['table']
self.opponent_history = data['opponent_table']
print('------------------------')
print('show opponent history')
print(self.opponent_history)
print('------------------------')
print('show my history')
print(self.my_history)
self.winner = data['winner']
print('------------------------')
print('勝者は{}です. {}回で正解しました!'.format(self.winner, self.count))
print('終了ターンの解答 : {}'.format(self.num))
print('------------------------')<|docstring|>対戦終了後, お互いの結果を表示(vscode上に表示する分)
: rtype : None
: return : なし<|endoftext|> |
6a572177109d72852ae422ca695d14accf2c37c39faaedce0b2c7f8823647cd9 | def _get_information(self) -> str:
'対戦終了後,web画面に表示する内容を計算\n 勝敗,連勝に応じて獲得経験値を求め, 経験値に加える.レベルや次のレベルまでの必要経験値も求める\n レベルアップと進化の判定も行う\n : rtype : str\n : return : 獲得経験値と次のレベルまでの必要経験値\n '
if (self.winner == self.player_name):
st.session_state.win_in_a_row += 1
new_exp = round(((3000 * (1 + ((st.session_state.win_in_a_row - 1) / 4))) / self.count))
elif (self.winner == None):
st.session_state.win_in_a_row = 0
new_exp = round((20 * self.count))
else:
st.session_state.win_in_a_row = 0
new_exp = round((15 * self.count))
st.session_state.game_count += 1
st.session_state.exp += new_exp
level_up = False
evolution = False
for i in range(200):
if ((((i ** 3) / 3) <= st.session_state.exp) and (st.session_state.exp < (((i + 1) ** 3) / 3))):
remaining_exp = round(((((i + 1) ** 3) / 3) - st.session_state.exp))
new_level = i
if (new_level != st.session_state.level):
level_up = True
if (new_level == 20):
evolution = True
st.session_state.level = new_level
break
return (new_exp, remaining_exp, level_up, evolution) | 対戦終了後,web画面に表示する内容を計算
勝敗,連勝に応じて獲得経験値を求め, 経験値に加える.レベルや次のレベルまでの必要経験値も求める
レベルアップと進化の判定も行う
: rtype : str
: return : 獲得経験値と次のレベルまでの必要経験値 | hitblow/class_play_game.py | _get_information | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _get_information(self) -> str:
'対戦終了後,web画面に表示する内容を計算\n 勝敗,連勝に応じて獲得経験値を求め, 経験値に加える.レベルや次のレベルまでの必要経験値も求める\n レベルアップと進化の判定も行う\n : rtype : str\n : return : 獲得経験値と次のレベルまでの必要経験値\n '
if (self.winner == self.player_name):
st.session_state.win_in_a_row += 1
new_exp = round(((3000 * (1 + ((st.session_state.win_in_a_row - 1) / 4))) / self.count))
elif (self.winner == None):
st.session_state.win_in_a_row = 0
new_exp = round((20 * self.count))
else:
st.session_state.win_in_a_row = 0
new_exp = round((15 * self.count))
st.session_state.game_count += 1
st.session_state.exp += new_exp
level_up = False
evolution = False
for i in range(200):
if ((((i ** 3) / 3) <= st.session_state.exp) and (st.session_state.exp < (((i + 1) ** 3) / 3))):
remaining_exp = round(((((i + 1) ** 3) / 3) - st.session_state.exp))
new_level = i
if (new_level != st.session_state.level):
level_up = True
if (new_level == 20):
evolution = True
st.session_state.level = new_level
break
return (new_exp, remaining_exp, level_up, evolution) | def _get_information(self) -> str:
'対戦終了後,web画面に表示する内容を計算\n 勝敗,連勝に応じて獲得経験値を求め, 経験値に加える.レベルや次のレベルまでの必要経験値も求める\n レベルアップと進化の判定も行う\n : rtype : str\n : return : 獲得経験値と次のレベルまでの必要経験値\n '
if (self.winner == self.player_name):
st.session_state.win_in_a_row += 1
new_exp = round(((3000 * (1 + ((st.session_state.win_in_a_row - 1) / 4))) / self.count))
elif (self.winner == None):
st.session_state.win_in_a_row = 0
new_exp = round((20 * self.count))
else:
st.session_state.win_in_a_row = 0
new_exp = round((15 * self.count))
st.session_state.game_count += 1
st.session_state.exp += new_exp
level_up = False
evolution = False
for i in range(200):
if ((((i ** 3) / 3) <= st.session_state.exp) and (st.session_state.exp < (((i + 1) ** 3) / 3))):
remaining_exp = round(((((i + 1) ** 3) / 3) - st.session_state.exp))
new_level = i
if (new_level != st.session_state.level):
level_up = True
if (new_level == 20):
evolution = True
st.session_state.level = new_level
break
return (new_exp, remaining_exp, level_up, evolution)<|docstring|>対戦終了後,web画面に表示する内容を計算
勝敗,連勝に応じて獲得経験値を求め, 経験値に加える.レベルや次のレベルまでの必要経験値も求める
レベルアップと進化の判定も行う
: rtype : str
: return : 獲得経験値と次のレベルまでの必要経験値<|endoftext|> |
a119a5afec6cb510b67af05f29c2ed5c3bbd915e80769d8bd7197aa294ab6f4f | def _show_result_streamlit(self) -> None:
'対戦終了後, お互いの結果を表示(web画面上に表示する分)\n 勝敗、連勝数に応じて表示を変える, 経験値やレベル, 対戦回数も表示\n 進化やレベルアップの時は追加のエフェクト\n : rtype : None\n : return : なし\n '
(new_exp, remaining_exp, level_up, evolution) = self._get_information()
time.sleep(3)
st.session_state.col6.subheader('')
if (self.winner == self.player_name):
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/winner.wav'))
self._play_song(num=(- 1), title='bgm/winner.wav')
st.session_state.col6.subheader('勝利だ,おめでとう！')
st.session_state.col6.subheader('正解は‥【{}】'.format(self.num))
st.session_state.col6.subheader('{}回で正解できた！'.format(self.count))
st.session_state.col6.subheader('')
if (st.session_state.win_in_a_row >= 2):
st.session_state.col6.subheader('すごいぞ,{}連勝だ！この調子！'.format(st.session_state.win_in_a_row))
st.balloons()
elif (self.winner == None):
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/draw.wav'))
self._play_song(num=(- 1), title='bgm/draw.mp3')
st.session_state.col6.subheader('引き分けだ！ ')
st.session_state.col6.subheader('正解は‥【{}】'.format(self.num))
st.session_state.col6.subheader('{}回で正解した！'.format(self.count))
else:
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/loser.wav'))
self._play_song(num=(- 1), title='bgm/loser.wav')
st.session_state.col6.subheader('負けてしまった・・・次は勝とう！')
st.session_state.col6.write('{}は{}経験値を得た！'.format(st.session_state.chara_name, new_exp))
st.session_state.col6.write('履歴')
st.session_state.col6.write('')
st.session_state.col6.write(self.my_history)
time.sleep(13)
if level_up:
self._music_stop()
if evolution:
st.session_state.col4.subheader('おや?{}の様子が...'.format(st.session_state.chara_name))
image_light = Image.open('picture/evolution_light.png')
st.session_state.col4.image(image_light)
self._play_song(num=1, title='bgm/evolution_light.mp3')
time.sleep(3)
st.session_state.col6.subheader('やったね, 進化した！')
pic_url2 = (('picture/' + st.session_state.chara_name) + '-2.jpg')
image = Image.open(pic_url2)
st.session_state.col4.image(image)
img = Image.open('picture/evolution.gif')
st.session_state.col6.image(img)
self._play_song(num=1, title='bgm/evolution.mp3')
time.sleep(3)
else:
st.session_state.col6.subheader('レベルアップだ！')
self._play_song(num=1, title='bgm/level_up.wav')
img = Image.open('picture/level-up.gif')
time.sleep(1)
st.session_state.col6.image(img)
time.sleep(1)
st.session_state.col6.write('次のレベルまでの経験値：{}'.format(remaining_exp))
st.session_state.col6.write('今まで得た合計経験値：{}'.format(st.session_state.exp))
st.session_state.col6.subheader('')
st.session_state.col6.subheader('{}の現在のレベル : {}'.format(st.session_state.chara_name, st.session_state.level))
st.session_state.col6.write('対戦回数 : {}'.format(st.session_state.game_count)) | 対戦終了後, お互いの結果を表示(web画面上に表示する分)
勝敗、連勝数に応じて表示を変える, 経験値やレベル, 対戦回数も表示
進化やレベルアップの時は追加のエフェクト
: rtype : None
: return : なし | hitblow/class_play_game.py | _show_result_streamlit | HayatoNatural/NEDO-Hit-Blow-teamF | 1 | python | def _show_result_streamlit(self) -> None:
'対戦終了後, お互いの結果を表示(web画面上に表示する分)\n 勝敗、連勝数に応じて表示を変える, 経験値やレベル, 対戦回数も表示\n 進化やレベルアップの時は追加のエフェクト\n : rtype : None\n : return : なし\n '
(new_exp, remaining_exp, level_up, evolution) = self._get_information()
time.sleep(3)
st.session_state.col6.subheader()
if (self.winner == self.player_name):
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/winner.wav'))
self._play_song(num=(- 1), title='bgm/winner.wav')
st.session_state.col6.subheader('勝利だ,おめでとう！')
st.session_state.col6.subheader('正解は‥【{}】'.format(self.num))
st.session_state.col6.subheader('{}回で正解できた！'.format(self.count))
st.session_state.col6.subheader()
if (st.session_state.win_in_a_row >= 2):
st.session_state.col6.subheader('すごいぞ,{}連勝だ！この調子！'.format(st.session_state.win_in_a_row))
st.balloons()
elif (self.winner == None):
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/draw.wav'))
self._play_song(num=(- 1), title='bgm/draw.mp3')
st.session_state.col6.subheader('引き分けだ！ ')
st.session_state.col6.subheader('正解は‥【{}】'.format(self.num))
st.session_state.col6.subheader('{}回で正解した！'.format(self.count))
else:
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/loser.wav'))
self._play_song(num=(- 1), title='bgm/loser.wav')
st.session_state.col6.subheader('負けてしまった・・・次は勝とう！')
st.session_state.col6.write('{}は{}経験値を得た！'.format(st.session_state.chara_name, new_exp))
st.session_state.col6.write('履歴')
st.session_state.col6.write()
st.session_state.col6.write(self.my_history)
time.sleep(13)
if level_up:
self._music_stop()
if evolution:
st.session_state.col4.subheader('おや?{}の様子が...'.format(st.session_state.chara_name))
image_light = Image.open('picture/evolution_light.png')
st.session_state.col4.image(image_light)
self._play_song(num=1, title='bgm/evolution_light.mp3')
time.sleep(3)
st.session_state.col6.subheader('やったね, 進化した！')
pic_url2 = (('picture/' + st.session_state.chara_name) + '-2.jpg')
image = Image.open(pic_url2)
st.session_state.col4.image(image)
img = Image.open('picture/evolution.gif')
st.session_state.col6.image(img)
self._play_song(num=1, title='bgm/evolution.mp3')
time.sleep(3)
else:
st.session_state.col6.subheader('レベルアップだ！')
self._play_song(num=1, title='bgm/level_up.wav')
img = Image.open('picture/level-up.gif')
time.sleep(1)
st.session_state.col6.image(img)
time.sleep(1)
st.session_state.col6.write('次のレベルまでの経験値：{}'.format(remaining_exp))
st.session_state.col6.write('今まで得た合計経験値：{}'.format(st.session_state.exp))
st.session_state.col6.subheader()
st.session_state.col6.subheader('{}の現在のレベル : {}'.format(st.session_state.chara_name, st.session_state.level))
st.session_state.col6.write('対戦回数 : {}'.format(st.session_state.game_count)) | def _show_result_streamlit(self) -> None:
'対戦終了後, お互いの結果を表示(web画面上に表示する分)\n 勝敗、連勝数に応じて表示を変える, 経験値やレベル, 対戦回数も表示\n 進化やレベルアップの時は追加のエフェクト\n : rtype : None\n : return : なし\n '
(new_exp, remaining_exp, level_up, evolution) = self._get_information()
time.sleep(3)
st.session_state.col6.subheader()
if (self.winner == self.player_name):
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/winner.wav'))
self._play_song(num=(- 1), title='bgm/winner.wav')
st.session_state.col6.subheader('勝利だ,おめでとう！')
st.session_state.col6.subheader('正解は‥【{}】'.format(self.num))
st.session_state.col6.subheader('{}回で正解できた！'.format(self.count))
st.session_state.col6.subheader()
if (st.session_state.win_in_a_row >= 2):
st.session_state.col6.subheader('すごいぞ,{}連勝だ！この調子！'.format(st.session_state.win_in_a_row))
st.balloons()
elif (self.winner == None):
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/draw.wav'))
self._play_song(num=(- 1), title='bgm/draw.mp3')
st.session_state.col6.subheader('引き分けだ！ ')
st.session_state.col6.subheader('正解は‥【{}】'.format(self.num))
st.session_state.col6.subheader('{}回で正解した！'.format(self.count))
else:
self._sound_play(num=1, title=(('voice/' + st.session_state.chara_name) + '/loser.wav'))
self._play_song(num=(- 1), title='bgm/loser.wav')
st.session_state.col6.subheader('負けてしまった・・・次は勝とう！')
st.session_state.col6.write('{}は{}経験値を得た！'.format(st.session_state.chara_name, new_exp))
st.session_state.col6.write('履歴')
st.session_state.col6.write()
st.session_state.col6.write(self.my_history)
time.sleep(13)
if level_up:
self._music_stop()
if evolution:
st.session_state.col4.subheader('おや?{}の様子が...'.format(st.session_state.chara_name))
image_light = Image.open('picture/evolution_light.png')
st.session_state.col4.image(image_light)
self._play_song(num=1, title='bgm/evolution_light.mp3')
time.sleep(3)
st.session_state.col6.subheader('やったね, 進化した！')
pic_url2 = (('picture/' + st.session_state.chara_name) + '-2.jpg')
image = Image.open(pic_url2)
st.session_state.col4.image(image)
img = Image.open('picture/evolution.gif')
st.session_state.col6.image(img)
self._play_song(num=1, title='bgm/evolution.mp3')
time.sleep(3)
else:
st.session_state.col6.subheader('レベルアップだ！')
self._play_song(num=1, title='bgm/level_up.wav')
img = Image.open('picture/level-up.gif')
time.sleep(1)
st.session_state.col6.image(img)
time.sleep(1)
st.session_state.col6.write('次のレベルまでの経験値：{}'.format(remaining_exp))
st.session_state.col6.write('今まで得た合計経験値：{}'.format(st.session_state.exp))
st.session_state.col6.subheader()
st.session_state.col6.subheader('{}の現在のレベル : {}'.format(st.session_state.chara_name, st.session_state.level))
st.session_state.col6.write('対戦回数 : {}'.format(st.session_state.game_count))<|docstring|>対戦終了後, お互いの結果を表示(web画面上に表示する分)
勝敗、連勝数に応じて表示を変える, 経験値やレベル, 対戦回数も表示
進化やレベルアップの時は追加のエフェクト
: rtype : None
: return : なし<|endoftext|> |
b79b23637c55ec812148510dc30e48e8d3300735444967d1bbb28aa878f7971a | def _refresh_thrift_client(self):
'Refresh the Thrift socket, transport, and client.'
socket = TSSLSocket(self.host, self.port, False, KEY_FILE, KEY_FILE, KEY_FILE)
if (self.timeout is not None):
socket.setTimeout(self.timeout)
self.transport = self._transport_class(socket)
protocol = self._protocol_class(self.transport)
self.client = Hbase.Client(protocol) | Refresh the Thrift socket, transport, and client. | python/thrift/appengine-ssl-gateway/appengine/bigtable.py | _refresh_thrift_client | hgonggg/cloud-bigtable-examples | 229 | python | def _refresh_thrift_client(self):
socket = TSSLSocket(self.host, self.port, False, KEY_FILE, KEY_FILE, KEY_FILE)
if (self.timeout is not None):
socket.setTimeout(self.timeout)
self.transport = self._transport_class(socket)
protocol = self._protocol_class(self.transport)
self.client = Hbase.Client(protocol) | def _refresh_thrift_client(self):
socket = TSSLSocket(self.host, self.port, False, KEY_FILE, KEY_FILE, KEY_FILE)
if (self.timeout is not None):
socket.setTimeout(self.timeout)
self.transport = self._transport_class(socket)
protocol = self._protocol_class(self.transport)
self.client = Hbase.Client(protocol)<|docstring|>Refresh the Thrift socket, transport, and client.<|endoftext|> |
bb659380b1cbd9a262d54e3c9d5c9af251fe7788ce7788a3ec1d72d306254b60 | def __init__(self, host, port, user, password, database):
'\n [summary]: Constructor\n Args:\n host ([type]): [description]\n port ([type]): [description]\n user ([type]): [description]\n password ([type]): [description]\n database ([type]): [description]\n '
self.host = host
self.port = port
self.user = user
self.password = password
self.database = database
self.isconnected = False
self.connection = None
dbconfig = {'host': host, 'port': port, 'user': user, 'password': password, 'database': database}
self.pool = self.create_pool(dbconfig, 'auto_neuron_pool', 3) | [summary]: Constructor
Args:
host ([type]): [description]
port ([type]): [description]
user ([type]): [description]
password ([type]): [description]
database ([type]): [description] | src/utils/databases/mysql_helper.py | __init__ | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def __init__(self, host, port, user, password, database):
'\n [summary]: Constructor\n Args:\n host ([type]): [description]\n port ([type]): [description]\n user ([type]): [description]\n password ([type]): [description]\n database ([type]): [description]\n '
self.host = host
self.port = port
self.user = user
self.password = password
self.database = database
self.isconnected = False
self.connection = None
dbconfig = {'host': host, 'port': port, 'user': user, 'password': password, 'database': database}
self.pool = self.create_pool(dbconfig, 'auto_neuron_pool', 3) | def __init__(self, host, port, user, password, database):
'\n [summary]: Constructor\n Args:\n host ([type]): [description]\n port ([type]): [description]\n user ([type]): [description]\n password ([type]): [description]\n database ([type]): [description]\n '
self.host = host
self.port = port
self.user = user
self.password = password
self.database = database
self.isconnected = False
self.connection = None
dbconfig = {'host': host, 'port': port, 'user': user, 'password': password, 'database': database}
self.pool = self.create_pool(dbconfig, 'auto_neuron_pool', 3)<|docstring|>[summary]: Constructor
Args:
host ([type]): [description]
port ([type]): [description]
user ([type]): [description]
password ([type]): [description]
database ([type]): [description]<|endoftext|> |
3ce9461bd8054f6ed016d8abf04e765a4815bd871d48b21ce1057cd1358e1f96 | def create_pool(self, dbconfig, pool_name='mypool', pool_size=3):
'[summary]\n Create a connection pool, after created, the request of connecting \n MySQL could get a connection from this pool instead of request to \n create a connection.\n Args:\n pool_name (str, optional): [description]. Defaults to "mypool".\n pool_size (int, optional): [description]. Defaults to 3.\n\n Returns:\n [type]: [description]\n '
pool = mysql.connector.pooling.MySQLConnectionPool(pool_name=pool_name, pool_size=pool_size, pool_reset_session=True, **dbconfig)
return pool | [summary]
Create a connection pool, after created, the request of connecting
MySQL could get a connection from this pool instead of request to
create a connection.
Args:
pool_name (str, optional): [description]. Defaults to "mypool".
pool_size (int, optional): [description]. Defaults to 3.
Returns:
[type]: [description] | src/utils/databases/mysql_helper.py | create_pool | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def create_pool(self, dbconfig, pool_name='mypool', pool_size=3):
'[summary]\n Create a connection pool, after created, the request of connecting \n MySQL could get a connection from this pool instead of request to \n create a connection.\n Args:\n pool_name (str, optional): [description]. Defaults to "mypool".\n pool_size (int, optional): [description]. Defaults to 3.\n\n Returns:\n [type]: [description]\n '
pool = mysql.connector.pooling.MySQLConnectionPool(pool_name=pool_name, pool_size=pool_size, pool_reset_session=True, **dbconfig)
return pool | def create_pool(self, dbconfig, pool_name='mypool', pool_size=3):
'[summary]\n Create a connection pool, after created, the request of connecting \n MySQL could get a connection from this pool instead of request to \n create a connection.\n Args:\n pool_name (str, optional): [description]. Defaults to "mypool".\n pool_size (int, optional): [description]. Defaults to 3.\n\n Returns:\n [type]: [description]\n '
pool = mysql.connector.pooling.MySQLConnectionPool(pool_name=pool_name, pool_size=pool_size, pool_reset_session=True, **dbconfig)
return pool<|docstring|>[summary]
Create a connection pool, after created, the request of connecting
MySQL could get a connection from this pool instead of request to
create a connection.
Args:
pool_name (str, optional): [description]. Defaults to "mypool".
pool_size (int, optional): [description]. Defaults to 3.
Returns:
[type]: [description]<|endoftext|> |
5311783567d3671162a96d0858ed527b024345cab1f6c0c2161c7378d02632ed | def close(self, conn, cursor):
'\n A method used to close connection of mysql.\n :param conn: \n :param cursor: \n :return: \n '
if (cursor is not None):
cursor.close()
if (conn is not None):
conn.close() | A method used to close connection of mysql.
:param conn:
:param cursor:
:return: | src/utils/databases/mysql_helper.py | close | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def close(self, conn, cursor):
'\n A method used to close connection of mysql.\n :param conn: \n :param cursor: \n :return: \n '
if (cursor is not None):
cursor.close()
if (conn is not None):
conn.close() | def close(self, conn, cursor):
'\n A method used to close connection of mysql.\n :param conn: \n :param cursor: \n :return: \n '
if (cursor is not None):
cursor.close()
if (conn is not None):
conn.close()<|docstring|>A method used to close connection of mysql.
:param conn:
:param cursor:
:return:<|endoftext|> |
13ae75508d5f322e61415f91bed438497197babf468dd2f5adbd0f8fddbe8445 | def fetch_all(self, query):
'\n [summary]: This function will return all record from table\n Args:\n query ([type]): [Select tabel query]\n\n Returns:\n [type]: [description]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchall()
return data
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor) | [summary]: This function will return all record from table
Args:
query ([type]): [Select tabel query]
Returns:
[type]: [description] | src/utils/databases/mysql_helper.py | fetch_all | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def fetch_all(self, query):
'\n [summary]: This function will return all record from table\n Args:\n query ([type]): [Select tabel query]\n\n Returns:\n [type]: [description]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchall()
return data
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor) | def fetch_all(self, query):
'\n [summary]: This function will return all record from table\n Args:\n query ([type]): [Select tabel query]\n\n Returns:\n [type]: [description]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchall()
return data
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor)<|docstring|>[summary]: This function will return all record from table
Args:
query ([type]): [Select tabel query]
Returns:
[type]: [description]<|endoftext|> |
75c0cf3e382bf82d1060ea6d2dc95b23ea2c0f3206bdd4f07fca9d21de262144 | def fetch_one(self, query):
'\n [summary]:This method return single record from table\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [Data]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchone()
return data
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor) | [summary]:This method return single record from table
Args:
query ([type]): [Query to execute]
Returns:
[type]: [Data] | src/utils/databases/mysql_helper.py | fetch_one | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def fetch_one(self, query):
'\n [summary]:This method return single record from table\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [Data]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchone()
return data
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor) | def fetch_one(self, query):
'\n [summary]:This method return single record from table\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [Data]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchone()
return data
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor)<|docstring|>[summary]:This method return single record from table
Args:
query ([type]): [Query to execute]
Returns:
[type]: [Data]<|endoftext|> |
fefed4cdd3f79365f999fe4a715914bdb0ca7c18bd29874d3caa159912fb8ebe | def delete_record(self, query):
'\n [summary]: Function to delete record from table single or multiple\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [No of row effected]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
conn.commit()
self.close(conn, cursor)
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error)) | [summary]: Function to delete record from table single or multiple
Args:
query ([type]): [Query to execute]
Returns:
[type]: [No of row effected] | src/utils/databases/mysql_helper.py | delete_record | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def delete_record(self, query):
'\n [summary]: Function to delete record from table single or multiple\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [No of row effected]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
conn.commit()
self.close(conn, cursor)
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error)) | def delete_record(self, query):
'\n [summary]: Function to delete record from table single or multiple\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [No of row effected]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
conn.commit()
self.close(conn, cursor)
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))<|docstring|>[summary]: Function to delete record from table single or multiple
Args:
query ([type]): [Query to execute]
Returns:
[type]: [No of row effected]<|endoftext|> |
4f0d7306e13dc9d707d25c8c8c42f2f03b02cadc752866ff734f2599641b7621 | def update_record(self, query):
'\n [summary]: Function to delete record from table single or multiple\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [No of row effected]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
conn.commit()
self.close(conn, cursor) | [summary]: Function to delete record from table single or multiple
Args:
query ([type]): [Query to execute]
Returns:
[type]: [No of row effected] | src/utils/databases/mysql_helper.py | update_record | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def update_record(self, query):
'\n [summary]: Function to delete record from table single or multiple\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [No of row effected]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
conn.commit()
self.close(conn, cursor) | def update_record(self, query):
'\n [summary]: Function to delete record from table single or multiple\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [No of row effected]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
conn.commit()
self.close(conn, cursor)<|docstring|>[summary]: Function to delete record from table single or multiple
Args:
query ([type]): [Query to execute]
Returns:
[type]: [No of row effected]<|endoftext|> |
392d9c7f219480ae35ce8c352dd0913a2f25f7b469381eac1c5a6e625a2c218d | def insert_record(self, query):
'\n [summary]:Insert record into table\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [1 if row inserted or 0 if not]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
conn.commit()
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor) | [summary]:Insert record into table
Args:
query ([type]): [Query to execute]
Returns:
[type]: [1 if row inserted or 0 if not] | src/utils/databases/mysql_helper.py | insert_record | Supreeth-Shetty/Projectathon---Simplified-AI | 8 | python | def insert_record(self, query):
'\n [summary]:Insert record into table\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [1 if row inserted or 0 if not]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
conn.commit()
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor) | def insert_record(self, query):
'\n [summary]:Insert record into table\n Args:\n query ([type]): [Query to execute]\n\n Returns:\n [type]: [1 if row inserted or 0 if not]\n '
conn = None
cursor = None
try:
conn = self.pool.get_connection()
cursor = conn.cursor()
cursor.execute(query)
rowcount = cursor.rowcount
conn.commit()
return rowcount
except connector.Error as error:
logger.error('Error: {}'.format(error))
finally:
self.close(conn, cursor)<|docstring|>[summary]:Insert record into table
Args:
query ([type]): [Query to execute]
Returns:
[type]: [1 if row inserted or 0 if not]<|endoftext|> |
62a616b82935de9a54dd8e286f5dc35005600b31e80ce7596c16def2ed0716d5 | def test_ch_encoder():
'\n Tests the encoding of the channel encoder\n '
config = ConfigManager()
config.set('types', 'msg_len', 'U16')
enc = ChEncoder()
enc_config = ChEncoder(config)
temp = ChTemplate(101, 'test_ch', 'test_comp', U32Type())
time_obj = TimeType(2, 0, 1533758629, 123456)
ch_obj = ChData(U32Type(42), time_obj, temp)
desc_bin = b'\x00\x00\x00\x01'
id_bin = b'\x00\x00\x00e'
time_bin = b'\x00\x02\x00[kL\xa5\x00\x01\xe2@'
val_bin = b'\x00\x00\x00*'
long_len_bin = b'\x00\x00\x00\x17'
short_len_bin = b'\x00\x17'
reg_expected = ((((long_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
config_expected = ((((short_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
reg_output = enc.encode_api(ch_obj)
assert (reg_output == reg_expected), ('FAIL: expected regular output to be %s, but found %s' % (list(reg_expected), list(reg_output)))
config_output = enc_config.encode_api(ch_obj)
assert (config_output == config_expected), ('FAIL: expected configured output to be %s, but found %s' % (list(config_expected), list(config_output)))
temp = ChTemplate(102, 'test_ch2', 'test_comp2', U16Type())
time_obj = TimeType(2, 0, 1533758628, 123457)
ch_obj = ChData(U16Type(40), time_obj, temp)
desc_bin = b'\x00\x00\x00\x01'
id_bin = b'\x00\x00\x00f'
time_bin = b'\x00\x02\x00[kL\xa4\x00\x01\xe2A'
val_bin = b'\x00('
long_len_bin = b'\x00\x00\x00\x15'
short_len_bin = b'\x00\x15'
reg_expected = ((((long_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
config_expected = ((((short_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
reg_output = enc.encode_api(ch_obj)
assert (reg_output == reg_expected), ('FAIL: expected regular output to be %s, but found %s' % (list(reg_expected), list(reg_output)))
config_output = enc_config.encode_api(ch_obj)
assert (config_output == config_expected), ('FAIL: expected configured output to be %s, but found %s' % (list(config_expected), list(config_output))) | Tests the encoding of the channel encoder | Gds/test/fprime_gds/common/encoders/test_ch_encoder.py | test_ch_encoder | Serwios/fprime | 4 | python | def test_ch_encoder():
'\n \n '
config = ConfigManager()
config.set('types', 'msg_len', 'U16')
enc = ChEncoder()
enc_config = ChEncoder(config)
temp = ChTemplate(101, 'test_ch', 'test_comp', U32Type())
time_obj = TimeType(2, 0, 1533758629, 123456)
ch_obj = ChData(U32Type(42), time_obj, temp)
desc_bin = b'\x00\x00\x00\x01'
id_bin = b'\x00\x00\x00e'
time_bin = b'\x00\x02\x00[kL\xa5\x00\x01\xe2@'
val_bin = b'\x00\x00\x00*'
long_len_bin = b'\x00\x00\x00\x17'
short_len_bin = b'\x00\x17'
reg_expected = ((((long_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
config_expected = ((((short_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
reg_output = enc.encode_api(ch_obj)
assert (reg_output == reg_expected), ('FAIL: expected regular output to be %s, but found %s' % (list(reg_expected), list(reg_output)))
config_output = enc_config.encode_api(ch_obj)
assert (config_output == config_expected), ('FAIL: expected configured output to be %s, but found %s' % (list(config_expected), list(config_output)))
temp = ChTemplate(102, 'test_ch2', 'test_comp2', U16Type())
time_obj = TimeType(2, 0, 1533758628, 123457)
ch_obj = ChData(U16Type(40), time_obj, temp)
desc_bin = b'\x00\x00\x00\x01'
id_bin = b'\x00\x00\x00f'
time_bin = b'\x00\x02\x00[kL\xa4\x00\x01\xe2A'
val_bin = b'\x00('
long_len_bin = b'\x00\x00\x00\x15'
short_len_bin = b'\x00\x15'
reg_expected = ((((long_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
config_expected = ((((short_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
reg_output = enc.encode_api(ch_obj)
assert (reg_output == reg_expected), ('FAIL: expected regular output to be %s, but found %s' % (list(reg_expected), list(reg_output)))
config_output = enc_config.encode_api(ch_obj)
assert (config_output == config_expected), ('FAIL: expected configured output to be %s, but found %s' % (list(config_expected), list(config_output))) | def test_ch_encoder():
'\n \n '
config = ConfigManager()
config.set('types', 'msg_len', 'U16')
enc = ChEncoder()
enc_config = ChEncoder(config)
temp = ChTemplate(101, 'test_ch', 'test_comp', U32Type())
time_obj = TimeType(2, 0, 1533758629, 123456)
ch_obj = ChData(U32Type(42), time_obj, temp)
desc_bin = b'\x00\x00\x00\x01'
id_bin = b'\x00\x00\x00e'
time_bin = b'\x00\x02\x00[kL\xa5\x00\x01\xe2@'
val_bin = b'\x00\x00\x00*'
long_len_bin = b'\x00\x00\x00\x17'
short_len_bin = b'\x00\x17'
reg_expected = ((((long_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
config_expected = ((((short_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
reg_output = enc.encode_api(ch_obj)
assert (reg_output == reg_expected), ('FAIL: expected regular output to be %s, but found %s' % (list(reg_expected), list(reg_output)))
config_output = enc_config.encode_api(ch_obj)
assert (config_output == config_expected), ('FAIL: expected configured output to be %s, but found %s' % (list(config_expected), list(config_output)))
temp = ChTemplate(102, 'test_ch2', 'test_comp2', U16Type())
time_obj = TimeType(2, 0, 1533758628, 123457)
ch_obj = ChData(U16Type(40), time_obj, temp)
desc_bin = b'\x00\x00\x00\x01'
id_bin = b'\x00\x00\x00f'
time_bin = b'\x00\x02\x00[kL\xa4\x00\x01\xe2A'
val_bin = b'\x00('
long_len_bin = b'\x00\x00\x00\x15'
short_len_bin = b'\x00\x15'
reg_expected = ((((long_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
config_expected = ((((short_len_bin + desc_bin) + id_bin) + time_bin) + val_bin)
reg_output = enc.encode_api(ch_obj)
assert (reg_output == reg_expected), ('FAIL: expected regular output to be %s, but found %s' % (list(reg_expected), list(reg_output)))
config_output = enc_config.encode_api(ch_obj)
assert (config_output == config_expected), ('FAIL: expected configured output to be %s, but found %s' % (list(config_expected), list(config_output)))<|docstring|>Tests the encoding of the channel encoder<|endoftext|> |
4e50315eb9d11ffe0cb916a66476308e098114c1dba089eab5f0a02fbec068d1 | def paste(image: Image, position: tuple=(0, 0)) -> None:
'\n Paste an image onto the buffer\n :param image: Image to paste\n :param position: tuple position to paste at\n :return: None\n '
image.paste(image, position) | Paste an image onto the buffer
:param image: Image to paste
:param position: tuple position to paste at
:return: None | libs/functions.py | paste | HcNguyen111/eReader | 7 | python | def paste(image: Image, position: tuple=(0, 0)) -> None:
'\n Paste an image onto the buffer\n :param image: Image to paste\n :param position: tuple position to paste at\n :return: None\n '
image.paste(image, position) | def paste(image: Image, position: tuple=(0, 0)) -> None:
'\n Paste an image onto the buffer\n :param image: Image to paste\n :param position: tuple position to paste at\n :return: None\n '
image.paste(image, position)<|docstring|>Paste an image onto the buffer
:param image: Image to paste
:param position: tuple position to paste at
:return: None<|endoftext|> |
6e44785d059a889797568e0b0296641d5db3118833e7e0a2e306b857efe8ef7b | def create_data_directory(version: str, force: bool=False) -> None:
"\n Create subdirectories.\n\n 1. This function will check if `version` is a valid semver.\n 2. Create a directory that contains a structure if it doesn't already exist.\n Unless `force=True`.\n\n Args:\n version (str): Semver for the dataset, model and metrics.\n force (bool, optional): Flag to overwrite existing directory. Defaults to False.\n "
semver.VersionInfo.parse(version)
base_module_path = os.path.join(const.DATA, version)
depth_level_1 = [const.CLASSIFICATION, const.NER]
depth_level_2 = [const.DATASETS, const.METRICS, const.MODELS]
for subdir in depth_level_1:
for childdir in depth_level_2:
os.makedirs(os.path.join(base_module_path, subdir, childdir), exist_ok=force) | Create subdirectories.
1. This function will check if `version` is a valid semver.
2. Create a directory that contains a structure if it doesn't already exist.
Unless `force=True`.
Args:
version (str): Semver for the dataset, model and metrics.
force (bool, optional): Flag to overwrite existing directory. Defaults to False. | slu/slu/dev/dir_setup.py | create_data_directory | Vernacular-ai/slu-service | 0 | python | def create_data_directory(version: str, force: bool=False) -> None:
"\n Create subdirectories.\n\n 1. This function will check if `version` is a valid semver.\n 2. Create a directory that contains a structure if it doesn't already exist.\n Unless `force=True`.\n\n Args:\n version (str): Semver for the dataset, model and metrics.\n force (bool, optional): Flag to overwrite existing directory. Defaults to False.\n "
semver.VersionInfo.parse(version)
base_module_path = os.path.join(const.DATA, version)
depth_level_1 = [const.CLASSIFICATION, const.NER]
depth_level_2 = [const.DATASETS, const.METRICS, const.MODELS]
for subdir in depth_level_1:
for childdir in depth_level_2:
os.makedirs(os.path.join(base_module_path, subdir, childdir), exist_ok=force) | def create_data_directory(version: str, force: bool=False) -> None:
"\n Create subdirectories.\n\n 1. This function will check if `version` is a valid semver.\n 2. Create a directory that contains a structure if it doesn't already exist.\n Unless `force=True`.\n\n Args:\n version (str): Semver for the dataset, model and metrics.\n force (bool, optional): Flag to overwrite existing directory. Defaults to False.\n "
semver.VersionInfo.parse(version)
base_module_path = os.path.join(const.DATA, version)
depth_level_1 = [const.CLASSIFICATION, const.NER]
depth_level_2 = [const.DATASETS, const.METRICS, const.MODELS]
for subdir in depth_level_1:
for childdir in depth_level_2:
os.makedirs(os.path.join(base_module_path, subdir, childdir), exist_ok=force)<|docstring|>Create subdirectories.
1. This function will check if `version` is a valid semver.
2. Create a directory that contains a structure if it doesn't already exist.
Unless `force=True`.
Args:
version (str): Semver for the dataset, model and metrics.
force (bool, optional): Flag to overwrite existing directory. Defaults to False.<|endoftext|> |
59dafb8e121e42753dc9ec0d75fde206211dcdda7a7f9017ecd7075b12172bb8 | def copy_data_directory(copy_from: str, copy_to: str, force: bool=False) -> None:
"\n Copy subdirectory.\n\n 1. This function will check `copy_from` and `copy_to` are valid semver.\n 2. This function will check `copy_to` doesn't already exist.\n 3. Unless force = True, this function will not overwrite existing directory.\n\n Args:\n copy_from (str): semver -> Source directory.\n copy_to (str): semver -> Destination directory.\n force (bool, optional): Flag to overwrite existing directory. Defaults to False.\n "
semver.VersionInfo.parse(copy_from)
semver.VersionInfo.parse(copy_to)
source = os.path.join(const.DATA, copy_from)
destination = os.path.join(const.DATA, copy_to)
shutil.copytree(source, destination, dirs_exist_ok=force) | Copy subdirectory.
1. This function will check `copy_from` and `copy_to` are valid semver.
2. This function will check `copy_to` doesn't already exist.
3. Unless force = True, this function will not overwrite existing directory.
Args:
copy_from (str): semver -> Source directory.
copy_to (str): semver -> Destination directory.
force (bool, optional): Flag to overwrite existing directory. Defaults to False. | slu/slu/dev/dir_setup.py | copy_data_directory | Vernacular-ai/slu-service | 0 | python | def copy_data_directory(copy_from: str, copy_to: str, force: bool=False) -> None:
"\n Copy subdirectory.\n\n 1. This function will check `copy_from` and `copy_to` are valid semver.\n 2. This function will check `copy_to` doesn't already exist.\n 3. Unless force = True, this function will not overwrite existing directory.\n\n Args:\n copy_from (str): semver -> Source directory.\n copy_to (str): semver -> Destination directory.\n force (bool, optional): Flag to overwrite existing directory. Defaults to False.\n "
semver.VersionInfo.parse(copy_from)
semver.VersionInfo.parse(copy_to)
source = os.path.join(const.DATA, copy_from)
destination = os.path.join(const.DATA, copy_to)
shutil.copytree(source, destination, dirs_exist_ok=force) | def copy_data_directory(copy_from: str, copy_to: str, force: bool=False) -> None:
"\n Copy subdirectory.\n\n 1. This function will check `copy_from` and `copy_to` are valid semver.\n 2. This function will check `copy_to` doesn't already exist.\n 3. Unless force = True, this function will not overwrite existing directory.\n\n Args:\n copy_from (str): semver -> Source directory.\n copy_to (str): semver -> Destination directory.\n force (bool, optional): Flag to overwrite existing directory. Defaults to False.\n "
semver.VersionInfo.parse(copy_from)
semver.VersionInfo.parse(copy_to)
source = os.path.join(const.DATA, copy_from)
destination = os.path.join(const.DATA, copy_to)
shutil.copytree(source, destination, dirs_exist_ok=force)<|docstring|>Copy subdirectory.
1. This function will check `copy_from` and `copy_to` are valid semver.
2. This function will check `copy_to` doesn't already exist.
3. Unless force = True, this function will not overwrite existing directory.
Args:
copy_from (str): semver -> Source directory.
copy_to (str): semver -> Destination directory.
force (bool, optional): Flag to overwrite existing directory. Defaults to False.<|endoftext|> |
6c4af083c326fd156a096e7eca6c9f8872543558648899bb035e817129f54ba6 | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.describe_addresses`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/DescribeAddresses>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'Addresses': [\n {\n 'AddressId': 'string',\n 'Name': 'string',\n 'Company': 'string',\n 'Street1': 'string',\n 'Street2': 'string',\n 'Street3': 'string',\n 'City': 'string',\n 'StateOrProvince': 'string',\n 'PrefectureOrDistrict': 'string',\n 'Landmark': 'string',\n 'Country': 'string',\n 'PostalCode': 'string',\n 'PhoneNumber': 'string',\n 'IsRestricted': True|False\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **Addresses** *(list) --* \n The Snowball shipping addresses that were created for this account.\n - *(dict) --* \n The address that you want the Snowball or Snowballs associated with a specific job to be shipped to. Addresses are validated at the time of creation. The address you provide must be located within the serviceable area of your region. Although no individual elements of the ``Address`` are required, if the address is invalid or unsupported, then an exception is thrown.\n - **AddressId** *(string) --* \n The unique ID for an address.\n - **Name** *(string) --* \n The name of a person to receive a Snowball at an address.\n - **Company** *(string) --* \n The name of the company to receive a Snowball at an address.\n - **Street1** *(string) --* \n The first line in a street address that a Snowball is to be delivered to.\n - **Street2** *(string) --* \n The second line in a street address that a Snowball is to be delivered to.\n - **Street3** *(string) --* \n The third line in a street address that a Snowball is to be delivered to.\n - **City** *(string) --* \n The city in an address that a Snowball is to be delivered to.\n - **StateOrProvince** *(string) --* \n The state or province in an address that a Snowball is to be delivered to.\n - **PrefectureOrDistrict** *(string) --* \n This field is no longer used and the value is ignored.\n - **Landmark** *(string) --* \n This field is no longer used and the value is ignored.\n - **Country** *(string) --* \n The country in an address that a Snowball is to be delivered to.\n - **PostalCode** *(string) --* \n The postal code in an address that a Snowball is to be delivered to.\n - **PhoneNumber** *(string) --* \n The phone number associated with an address that a Snowball is to be delivered to.\n - **IsRestricted** *(boolean) --* \n If the address you are creating is a primary address, then set this option to true. This field is not supported in most regions.\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.describe_addresses`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/DescribeAddresses>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'Addresses': [
{
'AddressId': 'string',
'Name': 'string',
'Company': 'string',
'Street1': 'string',
'Street2': 'string',
'Street3': 'string',
'City': 'string',
'StateOrProvince': 'string',
'PrefectureOrDistrict': 'string',
'Landmark': 'string',
'Country': 'string',
'PostalCode': 'string',
'PhoneNumber': 'string',
'IsRestricted': True|False
},
],
}
**Response Structure**
- *(dict) --*
- **Addresses** *(list) --*
The Snowball shipping addresses that were created for this account.
- *(dict) --*
The address that you want the Snowball or Snowballs associated with a specific job to be shipped to. Addresses are validated at the time of creation. The address you provide must be located within the serviceable area of your region. Although no individual elements of the ``Address`` are required, if the address is invalid or unsupported, then an exception is thrown.
- **AddressId** *(string) --*
The unique ID for an address.
- **Name** *(string) --*
The name of a person to receive a Snowball at an address.
- **Company** *(string) --*
The name of the company to receive a Snowball at an address.
- **Street1** *(string) --*
The first line in a street address that a Snowball is to be delivered to.
- **Street2** *(string) --*
The second line in a street address that a Snowball is to be delivered to.
- **Street3** *(string) --*
The third line in a street address that a Snowball is to be delivered to.
- **City** *(string) --*
The city in an address that a Snowball is to be delivered to.
- **StateOrProvince** *(string) --*
The state or province in an address that a Snowball is to be delivered to.
- **PrefectureOrDistrict** *(string) --*
This field is no longer used and the value is ignored.
- **Landmark** *(string) --*
This field is no longer used and the value is ignored.
- **Country** *(string) --*
The country in an address that a Snowball is to be delivered to.
- **PostalCode** *(string) --*
The postal code in an address that a Snowball is to be delivered to.
- **PhoneNumber** *(string) --*
The phone number associated with an address that a Snowball is to be delivered to.
- **IsRestricted** *(boolean) --*
If the address you are creating is a primary address, then set this option to true. This field is not supported in most regions.
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns: | boto3_type_annotations_with_docs/boto3_type_annotations/snowball/paginator.py | paginate | ybastide/boto3_type_annotations | 119 | python | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.describe_addresses`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/DescribeAddresses>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'Addresses': [\n {\n 'AddressId': 'string',\n 'Name': 'string',\n 'Company': 'string',\n 'Street1': 'string',\n 'Street2': 'string',\n 'Street3': 'string',\n 'City': 'string',\n 'StateOrProvince': 'string',\n 'PrefectureOrDistrict': 'string',\n 'Landmark': 'string',\n 'Country': 'string',\n 'PostalCode': 'string',\n 'PhoneNumber': 'string',\n 'IsRestricted': True|False\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **Addresses** *(list) --* \n The Snowball shipping addresses that were created for this account.\n - *(dict) --* \n The address that you want the Snowball or Snowballs associated with a specific job to be shipped to. Addresses are validated at the time of creation. The address you provide must be located within the serviceable area of your region. Although no individual elements of the ``Address`` are required, if the address is invalid or unsupported, then an exception is thrown.\n - **AddressId** *(string) --* \n The unique ID for an address.\n - **Name** *(string) --* \n The name of a person to receive a Snowball at an address.\n - **Company** *(string) --* \n The name of the company to receive a Snowball at an address.\n - **Street1** *(string) --* \n The first line in a street address that a Snowball is to be delivered to.\n - **Street2** *(string) --* \n The second line in a street address that a Snowball is to be delivered to.\n - **Street3** *(string) --* \n The third line in a street address that a Snowball is to be delivered to.\n - **City** *(string) --* \n The city in an address that a Snowball is to be delivered to.\n - **StateOrProvince** *(string) --* \n The state or province in an address that a Snowball is to be delivered to.\n - **PrefectureOrDistrict** *(string) --* \n This field is no longer used and the value is ignored.\n - **Landmark** *(string) --* \n This field is no longer used and the value is ignored.\n - **Country** *(string) --* \n The country in an address that a Snowball is to be delivered to.\n - **PostalCode** *(string) --* \n The postal code in an address that a Snowball is to be delivered to.\n - **PhoneNumber** *(string) --* \n The phone number associated with an address that a Snowball is to be delivered to.\n - **IsRestricted** *(boolean) --* \n If the address you are creating is a primary address, then set this option to true. This field is not supported in most regions.\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.describe_addresses`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/DescribeAddresses>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'Addresses': [\n {\n 'AddressId': 'string',\n 'Name': 'string',\n 'Company': 'string',\n 'Street1': 'string',\n 'Street2': 'string',\n 'Street3': 'string',\n 'City': 'string',\n 'StateOrProvince': 'string',\n 'PrefectureOrDistrict': 'string',\n 'Landmark': 'string',\n 'Country': 'string',\n 'PostalCode': 'string',\n 'PhoneNumber': 'string',\n 'IsRestricted': True|False\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **Addresses** *(list) --* \n The Snowball shipping addresses that were created for this account.\n - *(dict) --* \n The address that you want the Snowball or Snowballs associated with a specific job to be shipped to. Addresses are validated at the time of creation. The address you provide must be located within the serviceable area of your region. Although no individual elements of the ``Address`` are required, if the address is invalid or unsupported, then an exception is thrown.\n - **AddressId** *(string) --* \n The unique ID for an address.\n - **Name** *(string) --* \n The name of a person to receive a Snowball at an address.\n - **Company** *(string) --* \n The name of the company to receive a Snowball at an address.\n - **Street1** *(string) --* \n The first line in a street address that a Snowball is to be delivered to.\n - **Street2** *(string) --* \n The second line in a street address that a Snowball is to be delivered to.\n - **Street3** *(string) --* \n The third line in a street address that a Snowball is to be delivered to.\n - **City** *(string) --* \n The city in an address that a Snowball is to be delivered to.\n - **StateOrProvince** *(string) --* \n The state or province in an address that a Snowball is to be delivered to.\n - **PrefectureOrDistrict** *(string) --* \n This field is no longer used and the value is ignored.\n - **Landmark** *(string) --* \n This field is no longer used and the value is ignored.\n - **Country** *(string) --* \n The country in an address that a Snowball is to be delivered to.\n - **PostalCode** *(string) --* \n The postal code in an address that a Snowball is to be delivered to.\n - **PhoneNumber** *(string) --* \n The phone number associated with an address that a Snowball is to be delivered to.\n - **IsRestricted** *(boolean) --* \n If the address you are creating is a primary address, then set this option to true. This field is not supported in most regions.\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass<|docstring|>Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.describe_addresses`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/DescribeAddresses>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'Addresses': [
{
'AddressId': 'string',
'Name': 'string',
'Company': 'string',
'Street1': 'string',
'Street2': 'string',
'Street3': 'string',
'City': 'string',
'StateOrProvince': 'string',
'PrefectureOrDistrict': 'string',
'Landmark': 'string',
'Country': 'string',
'PostalCode': 'string',
'PhoneNumber': 'string',
'IsRestricted': True|False
},
],
}
**Response Structure**
- *(dict) --*
- **Addresses** *(list) --*
The Snowball shipping addresses that were created for this account.
- *(dict) --*
The address that you want the Snowball or Snowballs associated with a specific job to be shipped to. Addresses are validated at the time of creation. The address you provide must be located within the serviceable area of your region. Although no individual elements of the ``Address`` are required, if the address is invalid or unsupported, then an exception is thrown.
- **AddressId** *(string) --*
The unique ID for an address.
- **Name** *(string) --*
The name of a person to receive a Snowball at an address.
- **Company** *(string) --*
The name of the company to receive a Snowball at an address.
- **Street1** *(string) --*
The first line in a street address that a Snowball is to be delivered to.
- **Street2** *(string) --*
The second line in a street address that a Snowball is to be delivered to.
- **Street3** *(string) --*
The third line in a street address that a Snowball is to be delivered to.
- **City** *(string) --*
The city in an address that a Snowball is to be delivered to.
- **StateOrProvince** *(string) --*
The state or province in an address that a Snowball is to be delivered to.
- **PrefectureOrDistrict** *(string) --*
This field is no longer used and the value is ignored.
- **Landmark** *(string) --*
This field is no longer used and the value is ignored.
- **Country** *(string) --*
The country in an address that a Snowball is to be delivered to.
- **PostalCode** *(string) --*
The postal code in an address that a Snowball is to be delivered to.
- **PhoneNumber** *(string) --*
The phone number associated with an address that a Snowball is to be delivered to.
- **IsRestricted** *(boolean) --*
If the address you are creating is a primary address, then set this option to true. This field is not supported in most regions.
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:<|endoftext|> |
15b5f2ec2ebfac1511fa60e16038be6809ccdfe2c3eb536dd823f0d126ce18e7 | def paginate(self, ClusterId: str, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_cluster_jobs`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusterJobs>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n ClusterId='string',\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'JobListEntries': [\n {\n 'JobId': 'string',\n 'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',\n 'IsMaster': True|False,\n 'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',\n 'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **JobListEntries** *(list) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs. \n - *(dict) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.\n - **JobId** *(string) --* \n The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .\n - **JobState** *(string) --* \n The current state of this job.\n - **IsMaster** *(boolean) --* \n A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.\n - **JobType** *(string) --* \n The type of job.\n - **SnowballType** *(string) --* \n The type of device used with this job.\n - **CreationDate** *(datetime) --* \n The creation date for this job.\n - **Description** *(string) --* \n The optional description of this specific job, for example ``Important Photos 2016-08-11`` .\n :type ClusterId: string\n :param ClusterId: **[REQUIRED]**\n The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_cluster_jobs`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusterJobs>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
ClusterId='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'JobListEntries': [
{
'JobId': 'string',
'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
'IsMaster': True|False,
'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',
'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',
'CreationDate': datetime(2015, 1, 1),
'Description': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **JobListEntries** *(list) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs.
- *(dict) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.
- **JobId** *(string) --*
The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .
- **JobState** *(string) --*
The current state of this job.
- **IsMaster** *(boolean) --*
A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.
- **JobType** *(string) --*
The type of job.
- **SnowballType** *(string) --*
The type of device used with this job.
- **CreationDate** *(datetime) --*
The creation date for this job.
- **Description** *(string) --*
The optional description of this specific job, for example ``Important Photos 2016-08-11`` .
:type ClusterId: string
:param ClusterId: **[REQUIRED]**
The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns: | boto3_type_annotations_with_docs/boto3_type_annotations/snowball/paginator.py | paginate | ybastide/boto3_type_annotations | 119 | python | def paginate(self, ClusterId: str, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_cluster_jobs`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusterJobs>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n ClusterId='string',\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'JobListEntries': [\n {\n 'JobId': 'string',\n 'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',\n 'IsMaster': True|False,\n 'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',\n 'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **JobListEntries** *(list) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs. \n - *(dict) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.\n - **JobId** *(string) --* \n The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .\n - **JobState** *(string) --* \n The current state of this job.\n - **IsMaster** *(boolean) --* \n A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.\n - **JobType** *(string) --* \n The type of job.\n - **SnowballType** *(string) --* \n The type of device used with this job.\n - **CreationDate** *(datetime) --* \n The creation date for this job.\n - **Description** *(string) --* \n The optional description of this specific job, for example ``Important Photos 2016-08-11`` .\n :type ClusterId: string\n :param ClusterId: **[REQUIRED]**\n The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | def paginate(self, ClusterId: str, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_cluster_jobs`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusterJobs>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n ClusterId='string',\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'JobListEntries': [\n {\n 'JobId': 'string',\n 'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',\n 'IsMaster': True|False,\n 'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',\n 'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **JobListEntries** *(list) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs. \n - *(dict) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.\n - **JobId** *(string) --* \n The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .\n - **JobState** *(string) --* \n The current state of this job.\n - **IsMaster** *(boolean) --* \n A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.\n - **JobType** *(string) --* \n The type of job.\n - **SnowballType** *(string) --* \n The type of device used with this job.\n - **CreationDate** *(datetime) --* \n The creation date for this job.\n - **Description** *(string) --* \n The optional description of this specific job, for example ``Important Photos 2016-08-11`` .\n :type ClusterId: string\n :param ClusterId: **[REQUIRED]**\n The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass<|docstring|>Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_cluster_jobs`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusterJobs>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
ClusterId='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'JobListEntries': [
{
'JobId': 'string',
'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
'IsMaster': True|False,
'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',
'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',
'CreationDate': datetime(2015, 1, 1),
'Description': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **JobListEntries** *(list) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs.
- *(dict) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.
- **JobId** *(string) --*
The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .
- **JobState** *(string) --*
The current state of this job.
- **IsMaster** *(boolean) --*
A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.
- **JobType** *(string) --*
The type of job.
- **SnowballType** *(string) --*
The type of device used with this job.
- **CreationDate** *(datetime) --*
The creation date for this job.
- **Description** *(string) --*
The optional description of this specific job, for example ``Important Photos 2016-08-11`` .
:type ClusterId: string
:param ClusterId: **[REQUIRED]**
The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:<|endoftext|> |
4caa0914f67a0870a14ede253d2e12a8015deb52643461b83017cd2f333627c6 | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_clusters`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusters>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'ClusterListEntries': [\n {\n 'ClusterId': 'string',\n 'ClusterState': 'AwaitingQuorum'|'Pending'|'InUse'|'Complete'|'Cancelled',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **ClusterListEntries** *(list) --* \n Each ``ClusterListEntry`` object contains a cluster's state, a cluster's ID, and other important status information.\n - *(dict) --* \n Contains a cluster's state, a cluster's ID, and other important information.\n - **ClusterId** *(string) --* \n The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .\n - **ClusterState** *(string) --* \n The current state of this cluster. For information about the state of a specific node, see JobListEntry$JobState .\n - **CreationDate** *(datetime) --* \n The creation date for this cluster.\n - **Description** *(string) --* \n Defines an optional description of the cluster, for example ``Environmental Data Cluster-01`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_clusters`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusters>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'ClusterListEntries': [
{
'ClusterId': 'string',
'ClusterState': 'AwaitingQuorum'|'Pending'|'InUse'|'Complete'|'Cancelled',
'CreationDate': datetime(2015, 1, 1),
'Description': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **ClusterListEntries** *(list) --*
Each ``ClusterListEntry`` object contains a cluster's state, a cluster's ID, and other important status information.
- *(dict) --*
Contains a cluster's state, a cluster's ID, and other important information.
- **ClusterId** *(string) --*
The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .
- **ClusterState** *(string) --*
The current state of this cluster. For information about the state of a specific node, see JobListEntry$JobState .
- **CreationDate** *(datetime) --*
The creation date for this cluster.
- **Description** *(string) --*
Defines an optional description of the cluster, for example ``Environmental Data Cluster-01`` .
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns: | boto3_type_annotations_with_docs/boto3_type_annotations/snowball/paginator.py | paginate | ybastide/boto3_type_annotations | 119 | python | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_clusters`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusters>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'ClusterListEntries': [\n {\n 'ClusterId': 'string',\n 'ClusterState': 'AwaitingQuorum'|'Pending'|'InUse'|'Complete'|'Cancelled',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **ClusterListEntries** *(list) --* \n Each ``ClusterListEntry`` object contains a cluster's state, a cluster's ID, and other important status information.\n - *(dict) --* \n Contains a cluster's state, a cluster's ID, and other important information.\n - **ClusterId** *(string) --* \n The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .\n - **ClusterState** *(string) --* \n The current state of this cluster. For information about the state of a specific node, see JobListEntry$JobState .\n - **CreationDate** *(datetime) --* \n The creation date for this cluster.\n - **Description** *(string) --* \n Defines an optional description of the cluster, for example ``Environmental Data Cluster-01`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_clusters`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusters>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'ClusterListEntries': [\n {\n 'ClusterId': 'string',\n 'ClusterState': 'AwaitingQuorum'|'Pending'|'InUse'|'Complete'|'Cancelled',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **ClusterListEntries** *(list) --* \n Each ``ClusterListEntry`` object contains a cluster's state, a cluster's ID, and other important status information.\n - *(dict) --* \n Contains a cluster's state, a cluster's ID, and other important information.\n - **ClusterId** *(string) --* \n The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .\n - **ClusterState** *(string) --* \n The current state of this cluster. For information about the state of a specific node, see JobListEntry$JobState .\n - **CreationDate** *(datetime) --* \n The creation date for this cluster.\n - **Description** *(string) --* \n Defines an optional description of the cluster, for example ``Environmental Data Cluster-01`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass<|docstring|>Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_clusters`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListClusters>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'ClusterListEntries': [
{
'ClusterId': 'string',
'ClusterState': 'AwaitingQuorum'|'Pending'|'InUse'|'Complete'|'Cancelled',
'CreationDate': datetime(2015, 1, 1),
'Description': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **ClusterListEntries** *(list) --*
Each ``ClusterListEntry`` object contains a cluster's state, a cluster's ID, and other important status information.
- *(dict) --*
Contains a cluster's state, a cluster's ID, and other important information.
- **ClusterId** *(string) --*
The 39-character ID for the cluster that you want to list, for example ``CID123e4567-e89b-12d3-a456-426655440000`` .
- **ClusterState** *(string) --*
The current state of this cluster. For information about the state of a specific node, see JobListEntry$JobState .
- **CreationDate** *(datetime) --*
The creation date for this cluster.
- **Description** *(string) --*
Defines an optional description of the cluster, for example ``Environmental Data Cluster-01`` .
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:<|endoftext|> |
4bfba236766f5565d5ab92d221a4956c6a8d1c705c9f6757e118b97e71c39613 | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_compatible_images`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListCompatibleImages>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'CompatibleImages': [\n {\n 'AmiId': 'string',\n 'Name': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **CompatibleImages** *(list) --* \n A JSON-formatted object that describes a compatible AMI.\n - *(dict) --* \n A JSON-formatted object that describes a compatible Amazon Machine Image (AMI). For more information on compatible AMIs, see `Using Amazon EC2 Compute Instances <http://docs.aws.amazon.com/snowball/latest/developer-guide/using-ec2.html>`__ in the *AWS Snowball Developer Guide* .\n - **AmiId** *(string) --* \n The unique identifier for an individual Snowball Edge AMI.\n - **Name** *(string) --* \n The optional name of a compatible image.\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_compatible_images`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListCompatibleImages>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'CompatibleImages': [
{
'AmiId': 'string',
'Name': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **CompatibleImages** *(list) --*
A JSON-formatted object that describes a compatible AMI.
- *(dict) --*
A JSON-formatted object that describes a compatible Amazon Machine Image (AMI). For more information on compatible AMIs, see `Using Amazon EC2 Compute Instances <http://docs.aws.amazon.com/snowball/latest/developer-guide/using-ec2.html>`__ in the *AWS Snowball Developer Guide* .
- **AmiId** *(string) --*
The unique identifier for an individual Snowball Edge AMI.
- **Name** *(string) --*
The optional name of a compatible image.
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns: | boto3_type_annotations_with_docs/boto3_type_annotations/snowball/paginator.py | paginate | ybastide/boto3_type_annotations | 119 | python | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_compatible_images`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListCompatibleImages>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'CompatibleImages': [\n {\n 'AmiId': 'string',\n 'Name': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **CompatibleImages** *(list) --* \n A JSON-formatted object that describes a compatible AMI.\n - *(dict) --* \n A JSON-formatted object that describes a compatible Amazon Machine Image (AMI). For more information on compatible AMIs, see `Using Amazon EC2 Compute Instances <http://docs.aws.amazon.com/snowball/latest/developer-guide/using-ec2.html>`__ in the *AWS Snowball Developer Guide* .\n - **AmiId** *(string) --* \n The unique identifier for an individual Snowball Edge AMI.\n - **Name** *(string) --* \n The optional name of a compatible image.\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_compatible_images`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListCompatibleImages>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'CompatibleImages': [\n {\n 'AmiId': 'string',\n 'Name': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **CompatibleImages** *(list) --* \n A JSON-formatted object that describes a compatible AMI.\n - *(dict) --* \n A JSON-formatted object that describes a compatible Amazon Machine Image (AMI). For more information on compatible AMIs, see `Using Amazon EC2 Compute Instances <http://docs.aws.amazon.com/snowball/latest/developer-guide/using-ec2.html>`__ in the *AWS Snowball Developer Guide* .\n - **AmiId** *(string) --* \n The unique identifier for an individual Snowball Edge AMI.\n - **Name** *(string) --* \n The optional name of a compatible image.\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass<|docstring|>Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_compatible_images`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListCompatibleImages>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'CompatibleImages': [
{
'AmiId': 'string',
'Name': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **CompatibleImages** *(list) --*
A JSON-formatted object that describes a compatible AMI.
- *(dict) --*
A JSON-formatted object that describes a compatible Amazon Machine Image (AMI). For more information on compatible AMIs, see `Using Amazon EC2 Compute Instances <http://docs.aws.amazon.com/snowball/latest/developer-guide/using-ec2.html>`__ in the *AWS Snowball Developer Guide* .
- **AmiId** *(string) --*
The unique identifier for an individual Snowball Edge AMI.
- **Name** *(string) --*
The optional name of a compatible image.
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:<|endoftext|> |
fa966a47d64edefe045d1d16fdaecd124a15cfedeace6ef7185df66a7d2133cb | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_jobs`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListJobs>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'JobListEntries': [\n {\n 'JobId': 'string',\n 'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',\n 'IsMaster': True|False,\n 'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',\n 'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **JobListEntries** *(list) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs. \n - *(dict) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.\n - **JobId** *(string) --* \n The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .\n - **JobState** *(string) --* \n The current state of this job.\n - **IsMaster** *(boolean) --* \n A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.\n - **JobType** *(string) --* \n The type of job.\n - **SnowballType** *(string) --* \n The type of device used with this job.\n - **CreationDate** *(datetime) --* \n The creation date for this job.\n - **Description** *(string) --* \n The optional description of this specific job, for example ``Important Photos 2016-08-11`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_jobs`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListJobs>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'JobListEntries': [
{
'JobId': 'string',
'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
'IsMaster': True|False,
'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',
'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',
'CreationDate': datetime(2015, 1, 1),
'Description': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **JobListEntries** *(list) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs.
- *(dict) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.
- **JobId** *(string) --*
The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .
- **JobState** *(string) --*
The current state of this job.
- **IsMaster** *(boolean) --*
A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.
- **JobType** *(string) --*
The type of job.
- **SnowballType** *(string) --*
The type of device used with this job.
- **CreationDate** *(datetime) --*
The creation date for this job.
- **Description** *(string) --*
The optional description of this specific job, for example ``Important Photos 2016-08-11`` .
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns: | boto3_type_annotations_with_docs/boto3_type_annotations/snowball/paginator.py | paginate | ybastide/boto3_type_annotations | 119 | python | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_jobs`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListJobs>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'JobListEntries': [\n {\n 'JobId': 'string',\n 'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',\n 'IsMaster': True|False,\n 'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',\n 'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **JobListEntries** *(list) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs. \n - *(dict) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.\n - **JobId** *(string) --* \n The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .\n - **JobState** *(string) --* \n The current state of this job.\n - **IsMaster** *(boolean) --* \n A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.\n - **JobType** *(string) --* \n The type of job.\n - **SnowballType** *(string) --* \n The type of device used with this job.\n - **CreationDate** *(datetime) --* \n The creation date for this job.\n - **Description** *(string) --* \n The optional description of this specific job, for example ``Important Photos 2016-08-11`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass | def paginate(self, PaginationConfig: Dict=None) -> Dict:
"\n Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_jobs`.\n See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListJobs>`_\n \n **Request Syntax**\n ::\n response_iterator = paginator.paginate(\n PaginationConfig={\n 'MaxItems': 123,\n 'PageSize': 123,\n 'StartingToken': 'string'\n }\n )\n \n **Response Syntax**\n ::\n {\n 'JobListEntries': [\n {\n 'JobId': 'string',\n 'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',\n 'IsMaster': True|False,\n 'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',\n 'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',\n 'CreationDate': datetime(2015, 1, 1),\n 'Description': 'string'\n },\n ],\n }\n \n **Response Structure**\n - *(dict) --* \n - **JobListEntries** *(list) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs. \n - *(dict) --* \n Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.\n - **JobId** *(string) --* \n The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .\n - **JobState** *(string) --* \n The current state of this job.\n - **IsMaster** *(boolean) --* \n A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.\n - **JobType** *(string) --* \n The type of job.\n - **SnowballType** *(string) --* \n The type of device used with this job.\n - **CreationDate** *(datetime) --* \n The creation date for this job.\n - **Description** *(string) --* \n The optional description of this specific job, for example ``Important Photos 2016-08-11`` .\n :type PaginationConfig: dict\n :param PaginationConfig:\n A dictionary that provides parameters to control pagination.\n - **MaxItems** *(integer) --*\n The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.\n - **PageSize** *(integer) --*\n The size of each page.\n - **StartingToken** *(string) --*\n A token to specify where to start paginating. This is the ``NextToken`` from a previous response.\n :rtype: dict\n :returns:\n "
pass<|docstring|>Creates an iterator that will paginate through responses from :py:meth:`Snowball.Client.list_jobs`.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/snowball-2016-06-30/ListJobs>`_
**Request Syntax**
::
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
**Response Syntax**
::
{
'JobListEntries': [
{
'JobId': 'string',
'JobState': 'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWSSortingFacility'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
'IsMaster': True|False,
'JobType': 'IMPORT'|'EXPORT'|'LOCAL_USE',
'SnowballType': 'STANDARD'|'EDGE'|'EDGE_C'|'EDGE_CG',
'CreationDate': datetime(2015, 1, 1),
'Description': 'string'
},
],
}
**Response Structure**
- *(dict) --*
- **JobListEntries** *(list) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of export jobs.
- *(dict) --*
Each ``JobListEntry`` object contains a job's state, a job's ID, and a value that indicates whether the job is a job part, in the case of an export job.
- **JobId** *(string) --*
The automatically generated ID for a job, for example ``JID123e4567-e89b-12d3-a456-426655440000`` .
- **JobState** *(string) --*
The current state of this job.
- **IsMaster** *(boolean) --*
A value that indicates that this job is a master job. A master job represents a successful request to create an export job. Master jobs aren't associated with any Snowballs. Instead, each master job will have at least one job part, and each job part is associated with a Snowball. It might take some time before the job parts associated with a particular master job are listed, because they are created after the master job is created.
- **JobType** *(string) --*
The type of job.
- **SnowballType** *(string) --*
The type of device used with this job.
- **CreationDate** *(datetime) --*
The creation date for this job.
- **Description** *(string) --*
The optional description of this specific job, for example ``Important Photos 2016-08-11`` .
:type PaginationConfig: dict
:param PaginationConfig:
A dictionary that provides parameters to control pagination.
- **MaxItems** *(integer) --*
The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination.
- **PageSize** *(integer) --*
The size of each page.
- **StartingToken** *(string) --*
A token to specify where to start paginating. This is the ``NextToken`` from a previous response.
:rtype: dict
:returns:<|endoftext|> |
bdce3aae62fa855c1f3fe6280af1aab1d54c53af4e85fa39173ca4316827d991 | def report(self) -> None:
'\n Print a summary report about self.graph\n '
log.info('Total nodes in {}: {}'.format((self.graph.name or 'graph'), len(self.graph.nodes())))
log.info('Total edges in {}: {}'.format((self.graph.name or 'graph'), len(self.graph.edges()))) | Print a summary report about self.graph | kgx/transformers/transformer.py | report | stevencox/kgx | 0 | python | def report(self) -> None:
'\n \n '
log.info('Total nodes in {}: {}'.format((self.graph.name or 'graph'), len(self.graph.nodes())))
log.info('Total edges in {}: {}'.format((self.graph.name or 'graph'), len(self.graph.edges()))) | def report(self) -> None:
'\n \n '
log.info('Total nodes in {}: {}'.format((self.graph.name or 'graph'), len(self.graph.nodes())))
log.info('Total edges in {}: {}'.format((self.graph.name or 'graph'), len(self.graph.edges())))<|docstring|>Print a summary report about self.graph<|endoftext|> |
f7c98250692cba67c835385c89de398d6d8cf0a968116422d9e3217188d162c5 | def is_empty(self) -> bool:
'\n Check whether self.graph is empty.\n\n Returns\n -------\n bool\n A boolean value asserting whether the graph is empty or not\n\n '
return ((len(self.graph.nodes()) == 0) and (len(self.graph.edges()) == 0)) | Check whether self.graph is empty.
Returns
-------
bool
A boolean value asserting whether the graph is empty or not | kgx/transformers/transformer.py | is_empty | stevencox/kgx | 0 | python | def is_empty(self) -> bool:
'\n Check whether self.graph is empty.\n\n Returns\n -------\n bool\n A boolean value asserting whether the graph is empty or not\n\n '
return ((len(self.graph.nodes()) == 0) and (len(self.graph.edges()) == 0)) | def is_empty(self) -> bool:
'\n Check whether self.graph is empty.\n\n Returns\n -------\n bool\n A boolean value asserting whether the graph is empty or not\n\n '
return ((len(self.graph.nodes()) == 0) and (len(self.graph.edges()) == 0))<|docstring|>Check whether self.graph is empty.
Returns
-------
bool
A boolean value asserting whether the graph is empty or not<|endoftext|> |
1a353784ac4276ebe674da9505ae073e6e8684a5cccb0d7ec92db8627c1e253c | def set_node_filter(self, key: str, value: Union[(str, set)]) -> None:
"\n Set a node filter, as defined by a key and value pair.\n These filters are used to create a subgraph or reduce the\n search space when fetching nodes from a source.\n\n .. note::\n When defining the 'category' filter, the value should be of type ``set``.\n This method also sets the 'subject_category' and 'object_category'\n edge filters, to get a consistent set of nodes in the subgraph.\n\n Parameters\n ----------\n key: str\n The key for node filter\n value: Union[str, set]\n The value for the node filter. Can be either a string or a set.\n\n "
if (key == 'category'):
if isinstance(value, set):
if ('subject_category' in self.edge_filters):
self.edge_filters['subject_category'].update(value)
else:
self.edge_filters['subject_category'] = value
if ('object_category' in self.edge_filters):
self.edge_filters['object_category'].update(value)
else:
self.edge_filters['object_category'] = value
else:
raise TypeError("'category' node filter should have a value of type 'set'")
if (key in self.node_filters):
self.node_filters[key].update(value)
else:
self.node_filters[key] = value | Set a node filter, as defined by a key and value pair.
These filters are used to create a subgraph or reduce the
search space when fetching nodes from a source.
.. note::
When defining the 'category' filter, the value should be of type ``set``.
This method also sets the 'subject_category' and 'object_category'
edge filters, to get a consistent set of nodes in the subgraph.
Parameters
----------
key: str
The key for node filter
value: Union[str, set]
The value for the node filter. Can be either a string or a set. | kgx/transformers/transformer.py | set_node_filter | stevencox/kgx | 0 | python | def set_node_filter(self, key: str, value: Union[(str, set)]) -> None:
"\n Set a node filter, as defined by a key and value pair.\n These filters are used to create a subgraph or reduce the\n search space when fetching nodes from a source.\n\n .. note::\n When defining the 'category' filter, the value should be of type ``set``.\n This method also sets the 'subject_category' and 'object_category'\n edge filters, to get a consistent set of nodes in the subgraph.\n\n Parameters\n ----------\n key: str\n The key for node filter\n value: Union[str, set]\n The value for the node filter. Can be either a string or a set.\n\n "
if (key == 'category'):
if isinstance(value, set):
if ('subject_category' in self.edge_filters):
self.edge_filters['subject_category'].update(value)
else:
self.edge_filters['subject_category'] = value
if ('object_category' in self.edge_filters):
self.edge_filters['object_category'].update(value)
else:
self.edge_filters['object_category'] = value
else:
raise TypeError("'category' node filter should have a value of type 'set'")
if (key in self.node_filters):
self.node_filters[key].update(value)
else:
self.node_filters[key] = value | def set_node_filter(self, key: str, value: Union[(str, set)]) -> None:
"\n Set a node filter, as defined by a key and value pair.\n These filters are used to create a subgraph or reduce the\n search space when fetching nodes from a source.\n\n .. note::\n When defining the 'category' filter, the value should be of type ``set``.\n This method also sets the 'subject_category' and 'object_category'\n edge filters, to get a consistent set of nodes in the subgraph.\n\n Parameters\n ----------\n key: str\n The key for node filter\n value: Union[str, set]\n The value for the node filter. Can be either a string or a set.\n\n "
if (key == 'category'):
if isinstance(value, set):
if ('subject_category' in self.edge_filters):
self.edge_filters['subject_category'].update(value)
else:
self.edge_filters['subject_category'] = value
if ('object_category' in self.edge_filters):
self.edge_filters['object_category'].update(value)
else:
self.edge_filters['object_category'] = value
else:
raise TypeError("'category' node filter should have a value of type 'set'")
if (key in self.node_filters):
self.node_filters[key].update(value)
else:
self.node_filters[key] = value<|docstring|>Set a node filter, as defined by a key and value pair.
These filters are used to create a subgraph or reduce the
search space when fetching nodes from a source.
.. note::
When defining the 'category' filter, the value should be of type ``set``.
This method also sets the 'subject_category' and 'object_category'
edge filters, to get a consistent set of nodes in the subgraph.
Parameters
----------
key: str
The key for node filter
value: Union[str, set]
The value for the node filter. Can be either a string or a set.<|endoftext|> |
37b148d5981923da2c29dbbfefd33cebee26bf4dbc65f6e5f6923922eba7da5e | def set_edge_filter(self, key: str, value: set) -> None:
"\n Set an edge filter, as defined by a key and value pair.\n These filters are used to create a subgraph or reduce the\n search space when fetching edges from a source.\n\n .. note::\n When defining the 'subject_category' or 'object_category' filter,\n the value should be of type ``set``.\n This method also sets the 'category' node filter, to get a\n consistent set of nodes in the subgraph.\n\n Parameters\n ----------\n key: str\n The key for edge filter\n value: Union[str, set]\n The value for the edge filter. Can be either a string or a set.\n\n "
if (key in {'subject_category', 'object_category'}):
if isinstance(value, set):
if ('category' in self.node_filters):
self.node_filters['category'].update(value)
else:
self.node_filters['category'] = value
else:
raise TypeError(f"'{key}' edge filter should have a value of type 'set'")
if (key in self.edge_filters):
self.edge_filters[key].update(value)
else:
self.edge_filters[key] = value | Set an edge filter, as defined by a key and value pair.
These filters are used to create a subgraph or reduce the
search space when fetching edges from a source.
.. note::
When defining the 'subject_category' or 'object_category' filter,
the value should be of type ``set``.
This method also sets the 'category' node filter, to get a
consistent set of nodes in the subgraph.
Parameters
----------
key: str
The key for edge filter
value: Union[str, set]
The value for the edge filter. Can be either a string or a set. | kgx/transformers/transformer.py | set_edge_filter | stevencox/kgx | 0 | python | def set_edge_filter(self, key: str, value: set) -> None:
"\n Set an edge filter, as defined by a key and value pair.\n These filters are used to create a subgraph or reduce the\n search space when fetching edges from a source.\n\n .. note::\n When defining the 'subject_category' or 'object_category' filter,\n the value should be of type ``set``.\n This method also sets the 'category' node filter, to get a\n consistent set of nodes in the subgraph.\n\n Parameters\n ----------\n key: str\n The key for edge filter\n value: Union[str, set]\n The value for the edge filter. Can be either a string or a set.\n\n "
if (key in {'subject_category', 'object_category'}):
if isinstance(value, set):
if ('category' in self.node_filters):
self.node_filters['category'].update(value)
else:
self.node_filters['category'] = value
else:
raise TypeError(f"'{key}' edge filter should have a value of type 'set'")
if (key in self.edge_filters):
self.edge_filters[key].update(value)
else:
self.edge_filters[key] = value | def set_edge_filter(self, key: str, value: set) -> None:
"\n Set an edge filter, as defined by a key and value pair.\n These filters are used to create a subgraph or reduce the\n search space when fetching edges from a source.\n\n .. note::\n When defining the 'subject_category' or 'object_category' filter,\n the value should be of type ``set``.\n This method also sets the 'category' node filter, to get a\n consistent set of nodes in the subgraph.\n\n Parameters\n ----------\n key: str\n The key for edge filter\n value: Union[str, set]\n The value for the edge filter. Can be either a string or a set.\n\n "
if (key in {'subject_category', 'object_category'}):
if isinstance(value, set):
if ('category' in self.node_filters):
self.node_filters['category'].update(value)
else:
self.node_filters['category'] = value
else:
raise TypeError(f"'{key}' edge filter should have a value of type 'set'")
if (key in self.edge_filters):
self.edge_filters[key].update(value)
else:
self.edge_filters[key] = value<|docstring|>Set an edge filter, as defined by a key and value pair.
These filters are used to create a subgraph or reduce the
search space when fetching edges from a source.
.. note::
When defining the 'subject_category' or 'object_category' filter,
the value should be of type ``set``.
This method also sets the 'category' node filter, to get a
consistent set of nodes in the subgraph.
Parameters
----------
key: str
The key for edge filter
value: Union[str, set]
The value for the edge filter. Can be either a string or a set.<|endoftext|> |
c8b92d885bc45bf6e300e2b62653972aa76eeeaf1fc4e359cf473415f5d986c1 | @staticmethod
def serialize(g: nx.MultiDiGraph) -> Dict:
'\n Convert networkx.MultiDiGraph as a dictionary.\n\n Parameters\n ----------\n g: networkx.MultiDiGraph\n Graph to convert as a dictionary\n\n Returns\n -------\n dict\n A dictionary\n\n '
data = json_graph.node_link_data(g)
return data | Convert networkx.MultiDiGraph as a dictionary.
Parameters
----------
g: networkx.MultiDiGraph
Graph to convert as a dictionary
Returns
-------
dict
A dictionary | kgx/transformers/transformer.py | serialize | stevencox/kgx | 0 | python | @staticmethod
def serialize(g: nx.MultiDiGraph) -> Dict:
'\n Convert networkx.MultiDiGraph as a dictionary.\n\n Parameters\n ----------\n g: networkx.MultiDiGraph\n Graph to convert as a dictionary\n\n Returns\n -------\n dict\n A dictionary\n\n '
data = json_graph.node_link_data(g)
return data | @staticmethod
def serialize(g: nx.MultiDiGraph) -> Dict:
'\n Convert networkx.MultiDiGraph as a dictionary.\n\n Parameters\n ----------\n g: networkx.MultiDiGraph\n Graph to convert as a dictionary\n\n Returns\n -------\n dict\n A dictionary\n\n '
data = json_graph.node_link_data(g)
return data<|docstring|>Convert networkx.MultiDiGraph as a dictionary.
Parameters
----------
g: networkx.MultiDiGraph
Graph to convert as a dictionary
Returns
-------
dict
A dictionary<|endoftext|> |
f89754ca11ad97681e35d0f75bf71a0d47d54853b8373a96bf0fe846f48dd9d4 | @staticmethod
def dump_to_file(g: nx.MultiDiGraph, filename: str) -> None:
'\n Serialize networkx.MultiDiGraph as JSON and write to file.\n\n Parameters\n ----------\n g: networkx.MultiDiGraph\n Graph to convert as a dictionary\n filename: str\n File to write the JSON\n\n '
FH = open(filename, 'w')
json_data = Transformer.serialize(g)
FH.write(json.dumps(json_data))
FH.close() | Serialize networkx.MultiDiGraph as JSON and write to file.
Parameters
----------
g: networkx.MultiDiGraph
Graph to convert as a dictionary
filename: str
File to write the JSON | kgx/transformers/transformer.py | dump_to_file | stevencox/kgx | 0 | python | @staticmethod
def dump_to_file(g: nx.MultiDiGraph, filename: str) -> None:
'\n Serialize networkx.MultiDiGraph as JSON and write to file.\n\n Parameters\n ----------\n g: networkx.MultiDiGraph\n Graph to convert as a dictionary\n filename: str\n File to write the JSON\n\n '
FH = open(filename, 'w')
json_data = Transformer.serialize(g)
FH.write(json.dumps(json_data))
FH.close() | @staticmethod
def dump_to_file(g: nx.MultiDiGraph, filename: str) -> None:
'\n Serialize networkx.MultiDiGraph as JSON and write to file.\n\n Parameters\n ----------\n g: networkx.MultiDiGraph\n Graph to convert as a dictionary\n filename: str\n File to write the JSON\n\n '
FH = open(filename, 'w')
json_data = Transformer.serialize(g)
FH.write(json.dumps(json_data))
FH.close()<|docstring|>Serialize networkx.MultiDiGraph as JSON and write to file.
Parameters
----------
g: networkx.MultiDiGraph
Graph to convert as a dictionary
filename: str
File to write the JSON<|endoftext|> |
0621074e1ed14823a229c9f7aeb92d09a7bcf92acf170468b736554f86ad0281 | @staticmethod
def deserialize(data: Dict) -> nx.MultiDiGraph:
'\n Deserialize a networkx.MultiDiGraph from a dictionary.\n\n Parameters\n ----------\n data: dict\n Dictionary containing nodes and edges\n\n Returns\n -------\n networkx.MultiDiGraph\n A networkx.MultiDiGraph representation\n\n '
g = json_graph.node_link_graph(data)
return g | Deserialize a networkx.MultiDiGraph from a dictionary.
Parameters
----------
data: dict
Dictionary containing nodes and edges
Returns
-------
networkx.MultiDiGraph
A networkx.MultiDiGraph representation | kgx/transformers/transformer.py | deserialize | stevencox/kgx | 0 | python | @staticmethod
def deserialize(data: Dict) -> nx.MultiDiGraph:
'\n Deserialize a networkx.MultiDiGraph from a dictionary.\n\n Parameters\n ----------\n data: dict\n Dictionary containing nodes and edges\n\n Returns\n -------\n networkx.MultiDiGraph\n A networkx.MultiDiGraph representation\n\n '
g = json_graph.node_link_graph(data)
return g | @staticmethod
def deserialize(data: Dict) -> nx.MultiDiGraph:
'\n Deserialize a networkx.MultiDiGraph from a dictionary.\n\n Parameters\n ----------\n data: dict\n Dictionary containing nodes and edges\n\n Returns\n -------\n networkx.MultiDiGraph\n A networkx.MultiDiGraph representation\n\n '
g = json_graph.node_link_graph(data)
return g<|docstring|>Deserialize a networkx.MultiDiGraph from a dictionary.
Parameters
----------
data: dict
Dictionary containing nodes and edges
Returns
-------
networkx.MultiDiGraph
A networkx.MultiDiGraph representation<|endoftext|> |
88ac1b7703448ea7c0ed285b9a742f15dbed559a8df4b55835d9365907b0dc11 | @staticmethod
def restore_from_file(filename) -> nx.MultiDiGraph:
'\n Deserialize a networkx.MultiDiGraph from a JSON file.\n\n Parameters\n ----------\n filename: str\n File to read from\n\n Returns\n -------\n networkx.MultiDiGraph\n A networkx.MultiDiGraph representation\n\n '
FH = open(filename, 'r')
data = FH.read()
g = Transformer.deserialize(json.loads(data))
return g | Deserialize a networkx.MultiDiGraph from a JSON file.
Parameters
----------
filename: str
File to read from
Returns
-------
networkx.MultiDiGraph
A networkx.MultiDiGraph representation | kgx/transformers/transformer.py | restore_from_file | stevencox/kgx | 0 | python | @staticmethod
def restore_from_file(filename) -> nx.MultiDiGraph:
'\n Deserialize a networkx.MultiDiGraph from a JSON file.\n\n Parameters\n ----------\n filename: str\n File to read from\n\n Returns\n -------\n networkx.MultiDiGraph\n A networkx.MultiDiGraph representation\n\n '
FH = open(filename, 'r')
data = FH.read()
g = Transformer.deserialize(json.loads(data))
return g | @staticmethod
def restore_from_file(filename) -> nx.MultiDiGraph:
'\n Deserialize a networkx.MultiDiGraph from a JSON file.\n\n Parameters\n ----------\n filename: str\n File to read from\n\n Returns\n -------\n networkx.MultiDiGraph\n A networkx.MultiDiGraph representation\n\n '
FH = open(filename, 'r')
data = FH.read()
g = Transformer.deserialize(json.loads(data))
return g<|docstring|>Deserialize a networkx.MultiDiGraph from a JSON file.
Parameters
----------
filename: str
File to read from
Returns
-------
networkx.MultiDiGraph
A networkx.MultiDiGraph representation<|endoftext|> |
2349970a52b08e8af228beda8c586d72d9522355b6ddff3a064404f2cba2d12b | @staticmethod
def validate_node(node: dict) -> dict:
'\n Given a node as a dictionary, check for required properties.\n This method will return the node dictionary with default assumptions applied, if any.\n\n Parameters\n ----------\n node: dict\n A node represented as a dict\n\n Returns\n -------\n dict\n A node represented as a dict, with default assumptions applied.\n\n '
if (len(node) == 0):
log.debug('Empty node encountered: {}'.format(node))
return node
if ('id' not in node):
raise KeyError("node does not have 'id' property: {}".format(node))
if ('name' not in node):
log.debug("node does not have 'name' property: {}".format(node))
if ('category' not in node):
log.debug("node does not have 'category' property: {}\nUsing {} as default".format(node, Transformer.DEFAULT_NODE_CATEGORY))
node['category'] = [Transformer.DEFAULT_NODE_CATEGORY]
return node | Given a node as a dictionary, check for required properties.
This method will return the node dictionary with default assumptions applied, if any.
Parameters
----------
node: dict
A node represented as a dict
Returns
-------
dict
A node represented as a dict, with default assumptions applied. | kgx/transformers/transformer.py | validate_node | stevencox/kgx | 0 | python | @staticmethod
def validate_node(node: dict) -> dict:
'\n Given a node as a dictionary, check for required properties.\n This method will return the node dictionary with default assumptions applied, if any.\n\n Parameters\n ----------\n node: dict\n A node represented as a dict\n\n Returns\n -------\n dict\n A node represented as a dict, with default assumptions applied.\n\n '
if (len(node) == 0):
log.debug('Empty node encountered: {}'.format(node))
return node
if ('id' not in node):
raise KeyError("node does not have 'id' property: {}".format(node))
if ('name' not in node):
log.debug("node does not have 'name' property: {}".format(node))
if ('category' not in node):
log.debug("node does not have 'category' property: {}\nUsing {} as default".format(node, Transformer.DEFAULT_NODE_CATEGORY))
node['category'] = [Transformer.DEFAULT_NODE_CATEGORY]
return node | @staticmethod
def validate_node(node: dict) -> dict:
'\n Given a node as a dictionary, check for required properties.\n This method will return the node dictionary with default assumptions applied, if any.\n\n Parameters\n ----------\n node: dict\n A node represented as a dict\n\n Returns\n -------\n dict\n A node represented as a dict, with default assumptions applied.\n\n '
if (len(node) == 0):
log.debug('Empty node encountered: {}'.format(node))
return node
if ('id' not in node):
raise KeyError("node does not have 'id' property: {}".format(node))
if ('name' not in node):
log.debug("node does not have 'name' property: {}".format(node))
if ('category' not in node):
log.debug("node does not have 'category' property: {}\nUsing {} as default".format(node, Transformer.DEFAULT_NODE_CATEGORY))
node['category'] = [Transformer.DEFAULT_NODE_CATEGORY]
return node<|docstring|>Given a node as a dictionary, check for required properties.
This method will return the node dictionary with default assumptions applied, if any.
Parameters
----------
node: dict
A node represented as a dict
Returns
-------
dict
A node represented as a dict, with default assumptions applied.<|endoftext|> |
e0280523e66b8c6f85699eba3c98a7f06d6f0baed2b9aec9f5de72d258e3a8d0 | @staticmethod
def validate_edge(edge: dict) -> dict:
'\n Given an edge as a dictionary, check for required properties.\n This method will return the edge dictionary with default assumptions applied, if any.\n\n Parameters\n ----------\n edge: dict\n An edge represented as a dict\n\n Returns\n -------\n dict\n An edge represented as a dict, with default assumptions applied.\n '
if ('subject' not in edge):
raise KeyError("edge does not have 'subject' property: {}".format(edge))
if ('edge_label' not in edge):
raise KeyError("edge does not have 'edge_label' property: {}".format(edge))
if ('object' not in edge):
raise KeyError("edge does not have 'object' property: {}".format(edge))
return edge | Given an edge as a dictionary, check for required properties.
This method will return the edge dictionary with default assumptions applied, if any.
Parameters
----------
edge: dict
An edge represented as a dict
Returns
-------
dict
An edge represented as a dict, with default assumptions applied. | kgx/transformers/transformer.py | validate_edge | stevencox/kgx | 0 | python | @staticmethod
def validate_edge(edge: dict) -> dict:
'\n Given an edge as a dictionary, check for required properties.\n This method will return the edge dictionary with default assumptions applied, if any.\n\n Parameters\n ----------\n edge: dict\n An edge represented as a dict\n\n Returns\n -------\n dict\n An edge represented as a dict, with default assumptions applied.\n '
if ('subject' not in edge):
raise KeyError("edge does not have 'subject' property: {}".format(edge))
if ('edge_label' not in edge):
raise KeyError("edge does not have 'edge_label' property: {}".format(edge))
if ('object' not in edge):
raise KeyError("edge does not have 'object' property: {}".format(edge))
return edge | @staticmethod
def validate_edge(edge: dict) -> dict:
'\n Given an edge as a dictionary, check for required properties.\n This method will return the edge dictionary with default assumptions applied, if any.\n\n Parameters\n ----------\n edge: dict\n An edge represented as a dict\n\n Returns\n -------\n dict\n An edge represented as a dict, with default assumptions applied.\n '
if ('subject' not in edge):
raise KeyError("edge does not have 'subject' property: {}".format(edge))
if ('edge_label' not in edge):
raise KeyError("edge does not have 'edge_label' property: {}".format(edge))
if ('object' not in edge):
raise KeyError("edge does not have 'object' property: {}".format(edge))
return edge<|docstring|>Given an edge as a dictionary, check for required properties.
This method will return the edge dictionary with default assumptions applied, if any.
Parameters
----------
edge: dict
An edge represented as a dict
Returns
-------
dict
An edge represented as a dict, with default assumptions applied.<|endoftext|> |
1933c7766ae770161f28ef1b510d2d0fc999c2ed8e3353e83f4c46367f4e4f52 | def compute_convolution(I_o, T, stride=1):
'\n This function takes an image <I> and a template <T> (both numpy arrays) \n and returns a heatmap where each grid represents the output produced by \n convolution at each location. You can add optional parameters (e.g. stride, \n window_size, padding) to create additional functionality. \n '
(n_rows, n_cols, n_channels) = np.shape(I_o)
(n_rows_T, n_cols_T, n_channels_T) = np.shape(T)
nrTh = int(((n_rows_T - 1) / 2))
ncTh = int(((n_cols_T - 1) / 2))
I = np.pad(I_o, ((nrTh, nrTh), (ncTh, ncTh), (0, 0)), 'constant')
heatmap_rgb = np.zeros((n_rows, n_cols, n_channels))
T_n = np.zeros((n_rows_T, n_cols_T, n_channels_T))
for ch in range(n_channels_T):
norm_T = np.linalg.norm(T[(:, :, ch)])
T_n[(:, :, ch)] = (T[(:, :, ch)] / norm_T)
for i in range(n_rows):
for j in range(n_cols):
for ch in range(n_channels):
norm_I = np.linalg.norm(I[(i:(i + n_rows_T), j:(j + n_cols_T), ch)])
I_cropped_n = (I[(i:(i + n_rows_T), j:(j + n_cols_T), ch)] / norm_I)
heatmap_rgb[i][j][ch] = np.sum((T_n[(:, :, ch)] * I_cropped_n))
heatmap = (((0.8 * heatmap_rgb[(:, :, 0)]) + (0.1 * heatmap_rgb[(:, :, 1)])) + (0.1 * heatmap_rgb[(:, :, 2)]))
return heatmap | This function takes an image <I> and a template <T> (both numpy arrays)
and returns a heatmap where each grid represents the output produced by
convolution at each location. You can add optional parameters (e.g. stride,
window_size, padding) to create additional functionality. | run_predictions.py | compute_convolution | MingshuLiang/caltech-ee148-spring2020-hw02 | 0 | python | def compute_convolution(I_o, T, stride=1):
'\n This function takes an image <I> and a template <T> (both numpy arrays) \n and returns a heatmap where each grid represents the output produced by \n convolution at each location. You can add optional parameters (e.g. stride, \n window_size, padding) to create additional functionality. \n '
(n_rows, n_cols, n_channels) = np.shape(I_o)
(n_rows_T, n_cols_T, n_channels_T) = np.shape(T)
nrTh = int(((n_rows_T - 1) / 2))
ncTh = int(((n_cols_T - 1) / 2))
I = np.pad(I_o, ((nrTh, nrTh), (ncTh, ncTh), (0, 0)), 'constant')
heatmap_rgb = np.zeros((n_rows, n_cols, n_channels))
T_n = np.zeros((n_rows_T, n_cols_T, n_channels_T))
for ch in range(n_channels_T):
norm_T = np.linalg.norm(T[(:, :, ch)])
T_n[(:, :, ch)] = (T[(:, :, ch)] / norm_T)
for i in range(n_rows):
for j in range(n_cols):
for ch in range(n_channels):
norm_I = np.linalg.norm(I[(i:(i + n_rows_T), j:(j + n_cols_T), ch)])
I_cropped_n = (I[(i:(i + n_rows_T), j:(j + n_cols_T), ch)] / norm_I)
heatmap_rgb[i][j][ch] = np.sum((T_n[(:, :, ch)] * I_cropped_n))
heatmap = (((0.8 * heatmap_rgb[(:, :, 0)]) + (0.1 * heatmap_rgb[(:, :, 1)])) + (0.1 * heatmap_rgb[(:, :, 2)]))
return heatmap | def compute_convolution(I_o, T, stride=1):
'\n This function takes an image <I> and a template <T> (both numpy arrays) \n and returns a heatmap where each grid represents the output produced by \n convolution at each location. You can add optional parameters (e.g. stride, \n window_size, padding) to create additional functionality. \n '
(n_rows, n_cols, n_channels) = np.shape(I_o)
(n_rows_T, n_cols_T, n_channels_T) = np.shape(T)
nrTh = int(((n_rows_T - 1) / 2))
ncTh = int(((n_cols_T - 1) / 2))
I = np.pad(I_o, ((nrTh, nrTh), (ncTh, ncTh), (0, 0)), 'constant')
heatmap_rgb = np.zeros((n_rows, n_cols, n_channels))
T_n = np.zeros((n_rows_T, n_cols_T, n_channels_T))
for ch in range(n_channels_T):
norm_T = np.linalg.norm(T[(:, :, ch)])
T_n[(:, :, ch)] = (T[(:, :, ch)] / norm_T)
for i in range(n_rows):
for j in range(n_cols):
for ch in range(n_channels):
norm_I = np.linalg.norm(I[(i:(i + n_rows_T), j:(j + n_cols_T), ch)])
I_cropped_n = (I[(i:(i + n_rows_T), j:(j + n_cols_T), ch)] / norm_I)
heatmap_rgb[i][j][ch] = np.sum((T_n[(:, :, ch)] * I_cropped_n))
heatmap = (((0.8 * heatmap_rgb[(:, :, 0)]) + (0.1 * heatmap_rgb[(:, :, 1)])) + (0.1 * heatmap_rgb[(:, :, 2)]))
return heatmap<|docstring|>This function takes an image <I> and a template <T> (both numpy arrays)
and returns a heatmap where each grid represents the output produced by
convolution at each location. You can add optional parameters (e.g. stride,
window_size, padding) to create additional functionality.<|endoftext|> |
28cbddeae2866753bc8ed83a0678a1972a72c80f54cf156cf07ec7708530b382 | def predict_boxes(heatmap_o, n_rows_T, n_cols_T):
'\n This function takes heatmap and returns the bounding boxes and associated\n confidence scores.\n '
n_rows = np.shape(heatmap_o)[0]
n_cols = np.shape(heatmap_o)[1]
output = []
'\n BEGIN YOUR CODE\n '
nrTh = int(((n_rows_T - 1) / 2))
ncTh = int(((n_cols_T - 1) / 2))
heatmap = np.where((heatmap_o > 0.92), heatmap_o, 0)
while np.any((heatmap != 0)):
score = np.amax(heatmap)
idx = np.where((heatmap == score))
c_row = idx[0].item(0)
c_col = idx[1].item(0)
tl_row = (c_row - nrTh)
tl_col = (c_col - ncTh)
br_row = (tl_row + 7)
br_col = (tl_col + 7)
output.append([tl_row, tl_col, br_row, br_col, score])
top = np.max([(c_row - n_rows_T), 0])
bottom = np.min([(c_row + n_rows_T), n_rows])
left = np.max([(c_col - n_cols_T), 0])
right = np.min([(c_col + n_cols_T), n_cols])
heatmap[(top:bottom, left:right)] = 0
'\n END YOUR CODE\n '
return output | This function takes heatmap and returns the bounding boxes and associated
confidence scores. | run_predictions.py | predict_boxes | MingshuLiang/caltech-ee148-spring2020-hw02 | 0 | python | def predict_boxes(heatmap_o, n_rows_T, n_cols_T):
'\n This function takes heatmap and returns the bounding boxes and associated\n confidence scores.\n '
n_rows = np.shape(heatmap_o)[0]
n_cols = np.shape(heatmap_o)[1]
output = []
'\n BEGIN YOUR CODE\n '
nrTh = int(((n_rows_T - 1) / 2))
ncTh = int(((n_cols_T - 1) / 2))
heatmap = np.where((heatmap_o > 0.92), heatmap_o, 0)
while np.any((heatmap != 0)):
score = np.amax(heatmap)
idx = np.where((heatmap == score))
c_row = idx[0].item(0)
c_col = idx[1].item(0)
tl_row = (c_row - nrTh)
tl_col = (c_col - ncTh)
br_row = (tl_row + 7)
br_col = (tl_col + 7)
output.append([tl_row, tl_col, br_row, br_col, score])
top = np.max([(c_row - n_rows_T), 0])
bottom = np.min([(c_row + n_rows_T), n_rows])
left = np.max([(c_col - n_cols_T), 0])
right = np.min([(c_col + n_cols_T), n_cols])
heatmap[(top:bottom, left:right)] = 0
'\n END YOUR CODE\n '
return output | def predict_boxes(heatmap_o, n_rows_T, n_cols_T):
'\n This function takes heatmap and returns the bounding boxes and associated\n confidence scores.\n '
n_rows = np.shape(heatmap_o)[0]
n_cols = np.shape(heatmap_o)[1]
output = []
'\n BEGIN YOUR CODE\n '
nrTh = int(((n_rows_T - 1) / 2))
ncTh = int(((n_cols_T - 1) / 2))
heatmap = np.where((heatmap_o > 0.92), heatmap_o, 0)
while np.any((heatmap != 0)):
score = np.amax(heatmap)
idx = np.where((heatmap == score))
c_row = idx[0].item(0)
c_col = idx[1].item(0)
tl_row = (c_row - nrTh)
tl_col = (c_col - ncTh)
br_row = (tl_row + 7)
br_col = (tl_col + 7)
output.append([tl_row, tl_col, br_row, br_col, score])
top = np.max([(c_row - n_rows_T), 0])
bottom = np.min([(c_row + n_rows_T), n_rows])
left = np.max([(c_col - n_cols_T), 0])
right = np.min([(c_col + n_cols_T), n_cols])
heatmap[(top:bottom, left:right)] = 0
'\n END YOUR CODE\n '
return output<|docstring|>This function takes heatmap and returns the bounding boxes and associated
confidence scores.<|endoftext|> |
886fe39d6ba61563f6bec3e7e103855d650a23660794817d85f07389d0332fdd | def detect_red_light_mf(I):
'\n This function takes a numpy array <I> and returns a list <output>.\n The length of <output> is the number of bounding boxes predicted for <I>. \n Each entry of <output> is a list <[row_TL,col_TL,row_BR,col_BR,score]>. \n The first four entries are four integers specifying a bounding box \n (the row and column index of the top left corner and the row and column \n index of the bottom right corner).\n <score> is a confidence score ranging from 0 to 1. \n\n Note that PIL loads images in RGB order, so:\n I[:,:,0] is the red channel\n I[:,:,1] is the green channel\n I[:,:,2] is the blue channel\n '
'\n BEGIN YOUR CODE\n '
T_path = 'data/kernel'
T_names = sorted(os.listdir(T_path))
T_names = [f for f in T_names if ('.jpg' in f)]
T = np.zeros((21, 9, 3))
for i in range(len(T_names)):
T_c = Image.open(os.path.join(T_path, T_names[i]))
T += np.asarray(T_c)
T /= len(T_names)
heatmap = compute_convolution(I, T)
(n_rows_T, n_cols_T, n_channels_T) = np.shape(T)
output = predict_boxes(heatmap, n_rows_T, n_cols_T)
'\n END YOUR CODE\n '
for i in range(len(output)):
assert (len(output[i]) == 5)
assert ((output[i][4] >= 0.0) and (output[i][4] <= 1.0))
return output | This function takes a numpy array <I> and returns a list <output>.
The length of <output> is the number of bounding boxes predicted for <I>.
Each entry of <output> is a list <[row_TL,col_TL,row_BR,col_BR,score]>.
The first four entries are four integers specifying a bounding box
(the row and column index of the top left corner and the row and column
index of the bottom right corner).
<score> is a confidence score ranging from 0 to 1.
Note that PIL loads images in RGB order, so:
I[:,:,0] is the red channel
I[:,:,1] is the green channel
I[:,:,2] is the blue channel | run_predictions.py | detect_red_light_mf | MingshuLiang/caltech-ee148-spring2020-hw02 | 0 | python | def detect_red_light_mf(I):
'\n This function takes a numpy array <I> and returns a list <output>.\n The length of <output> is the number of bounding boxes predicted for <I>. \n Each entry of <output> is a list <[row_TL,col_TL,row_BR,col_BR,score]>. \n The first four entries are four integers specifying a bounding box \n (the row and column index of the top left corner and the row and column \n index of the bottom right corner).\n <score> is a confidence score ranging from 0 to 1. \n\n Note that PIL loads images in RGB order, so:\n I[:,:,0] is the red channel\n I[:,:,1] is the green channel\n I[:,:,2] is the blue channel\n '
'\n BEGIN YOUR CODE\n '
T_path = 'data/kernel'
T_names = sorted(os.listdir(T_path))
T_names = [f for f in T_names if ('.jpg' in f)]
T = np.zeros((21, 9, 3))
for i in range(len(T_names)):
T_c = Image.open(os.path.join(T_path, T_names[i]))
T += np.asarray(T_c)
T /= len(T_names)
heatmap = compute_convolution(I, T)
(n_rows_T, n_cols_T, n_channels_T) = np.shape(T)
output = predict_boxes(heatmap, n_rows_T, n_cols_T)
'\n END YOUR CODE\n '
for i in range(len(output)):
assert (len(output[i]) == 5)
assert ((output[i][4] >= 0.0) and (output[i][4] <= 1.0))
return output | def detect_red_light_mf(I):
'\n This function takes a numpy array <I> and returns a list <output>.\n The length of <output> is the number of bounding boxes predicted for <I>. \n Each entry of <output> is a list <[row_TL,col_TL,row_BR,col_BR,score]>. \n The first four entries are four integers specifying a bounding box \n (the row and column index of the top left corner and the row and column \n index of the bottom right corner).\n <score> is a confidence score ranging from 0 to 1. \n\n Note that PIL loads images in RGB order, so:\n I[:,:,0] is the red channel\n I[:,:,1] is the green channel\n I[:,:,2] is the blue channel\n '
'\n BEGIN YOUR CODE\n '
T_path = 'data/kernel'
T_names = sorted(os.listdir(T_path))
T_names = [f for f in T_names if ('.jpg' in f)]
T = np.zeros((21, 9, 3))
for i in range(len(T_names)):
T_c = Image.open(os.path.join(T_path, T_names[i]))
T += np.asarray(T_c)
T /= len(T_names)
heatmap = compute_convolution(I, T)
(n_rows_T, n_cols_T, n_channels_T) = np.shape(T)
output = predict_boxes(heatmap, n_rows_T, n_cols_T)
'\n END YOUR CODE\n '
for i in range(len(output)):
assert (len(output[i]) == 5)
assert ((output[i][4] >= 0.0) and (output[i][4] <= 1.0))
return output<|docstring|>This function takes a numpy array <I> and returns a list <output>.
The length of <output> is the number of bounding boxes predicted for <I>.
Each entry of <output> is a list <[row_TL,col_TL,row_BR,col_BR,score]>.
The first four entries are four integers specifying a bounding box
(the row and column index of the top left corner and the row and column
index of the bottom right corner).
<score> is a confidence score ranging from 0 to 1.
Note that PIL loads images in RGB order, so:
I[:,:,0] is the red channel
I[:,:,1] is the green channel
I[:,:,2] is the blue channel<|endoftext|> |
67c7d8a53bb7acf46c905080238afc4088fc3305d7fc11db82ff5dc0422f074b | def make_promotion_mask_up(step_size, data_format):
'\n 右上、右、上、左上、左方向に移動した時の成りの領域のマスク\n 成れる場所ならばTrue、それ以外はFalse\n\n :param step_size:\n :param data_format:\n :return:\n '
name = 'black_promotion_mask_up'
collection = tf.get_collection_ref(name)
if len(collection):
return collection[0]
if (data_format == 'NCHW'):
mask = np.zeros((1, 1, 9, 9), dtype=np.bool)
mask[(:, :, :, :3)] = True
else:
mask = np.zeros((1, 9, 9, 1), dtype=np.bool)
mask[(:, :, :3, :)] = True
mask = tf.constant(mask, dtype=tf.bool)
tf.add_to_collection(name, mask)
return mask | 右上、右、上、左上、左方向に移動した時の成りの領域のマスク
成れる場所ならばTrue、それ以外はFalse
:param step_size:
:param data_format:
:return: | annotation/black_action/promotion_mask.py | make_promotion_mask_up | windfall-shogi/feature-annotation | 0 | python | def make_promotion_mask_up(step_size, data_format):
'\n 右上、右、上、左上、左方向に移動した時の成りの領域のマスク\n 成れる場所ならばTrue、それ以外はFalse\n\n :param step_size:\n :param data_format:\n :return:\n '
name = 'black_promotion_mask_up'
collection = tf.get_collection_ref(name)
if len(collection):
return collection[0]
if (data_format == 'NCHW'):
mask = np.zeros((1, 1, 9, 9), dtype=np.bool)
mask[(:, :, :, :3)] = True
else:
mask = np.zeros((1, 9, 9, 1), dtype=np.bool)
mask[(:, :, :3, :)] = True
mask = tf.constant(mask, dtype=tf.bool)
tf.add_to_collection(name, mask)
return mask | def make_promotion_mask_up(step_size, data_format):
'\n 右上、右、上、左上、左方向に移動した時の成りの領域のマスク\n 成れる場所ならばTrue、それ以外はFalse\n\n :param step_size:\n :param data_format:\n :return:\n '
name = 'black_promotion_mask_up'
collection = tf.get_collection_ref(name)
if len(collection):
return collection[0]
if (data_format == 'NCHW'):
mask = np.zeros((1, 1, 9, 9), dtype=np.bool)
mask[(:, :, :, :3)] = True
else:
mask = np.zeros((1, 9, 9, 1), dtype=np.bool)
mask[(:, :, :3, :)] = True
mask = tf.constant(mask, dtype=tf.bool)
tf.add_to_collection(name, mask)
return mask<|docstring|>右上、右、上、左上、左方向に移動した時の成りの領域のマスク
成れる場所ならばTrue、それ以外はFalse
:param step_size:
:param data_format:
:return:<|endoftext|> |
9a43e60ed0f1f55738e75243208ad2b9a5d565dc7972119b9a3a229c13887e0f | def make_promotion_mask_down(step_size, data_format):
'\n 右下、下、左下方向に移動した時の成りの領域のマスク\n 成れる場所ならばTrue、それ以外はFalse\n\n :param step_size:\n :param data_format:\n :return:\n '
name = 'black_promotion_mask_down{}'.format(step_size)
collection = tf.get_collection_ref(name)
if len(collection):
return collection[0]
if (data_format == 'NCHW'):
mask = np.zeros((1, 1, 9, 9), dtype=np.bool)
mask[(:, :, :, step_size:(step_size + 3))] = True
else:
mask = np.zeros((1, 9, 9, 1), dtype=np.bool)
mask[(:, :, step_size:(step_size + 3), :)] = True
mask = tf.constant(mask, dtype=tf.bool)
tf.add_to_collection(name, mask)
return mask | 右下、下、左下方向に移動した時の成りの領域のマスク
成れる場所ならばTrue、それ以外はFalse
:param step_size:
:param data_format:
:return: | annotation/black_action/promotion_mask.py | make_promotion_mask_down | windfall-shogi/feature-annotation | 0 | python | def make_promotion_mask_down(step_size, data_format):
'\n 右下、下、左下方向に移動した時の成りの領域のマスク\n 成れる場所ならばTrue、それ以外はFalse\n\n :param step_size:\n :param data_format:\n :return:\n '
name = 'black_promotion_mask_down{}'.format(step_size)
collection = tf.get_collection_ref(name)
if len(collection):
return collection[0]
if (data_format == 'NCHW'):
mask = np.zeros((1, 1, 9, 9), dtype=np.bool)
mask[(:, :, :, step_size:(step_size + 3))] = True
else:
mask = np.zeros((1, 9, 9, 1), dtype=np.bool)
mask[(:, :, step_size:(step_size + 3), :)] = True
mask = tf.constant(mask, dtype=tf.bool)
tf.add_to_collection(name, mask)
return mask | def make_promotion_mask_down(step_size, data_format):
'\n 右下、下、左下方向に移動した時の成りの領域のマスク\n 成れる場所ならばTrue、それ以外はFalse\n\n :param step_size:\n :param data_format:\n :return:\n '
name = 'black_promotion_mask_down{}'.format(step_size)
collection = tf.get_collection_ref(name)
if len(collection):
return collection[0]
if (data_format == 'NCHW'):
mask = np.zeros((1, 1, 9, 9), dtype=np.bool)
mask[(:, :, :, step_size:(step_size + 3))] = True
else:
mask = np.zeros((1, 9, 9, 1), dtype=np.bool)
mask[(:, :, step_size:(step_size + 3), :)] = True
mask = tf.constant(mask, dtype=tf.bool)
tf.add_to_collection(name, mask)
return mask<|docstring|>右下、下、左下方向に移動した時の成りの領域のマスク
成れる場所ならばTrue、それ以外はFalse
:param step_size:
:param data_format:
:return:<|endoftext|> |
cec5f3b8ab927ef73702eed50a905fad1735ac0aca43ecf3161be039f9bc708b | def word_tokenize(text: str, separator: str='-', return_tokens: bool=True) -> Union[(List, str)]:
'Khmer language word tokenization\n\n Args:\n text(str): Raw text\n separator(str, optional): Token - separator in case return_tokens = True. Defaults to "-".\n return_tokens(bool, optional): Whether return a tokenized text or a list of tokens. Defaults to True.\n\n Returns:\n Union[List, str]: Tokens or tokenized text, separated by the separator\n '
text = cleanup_str(text)
skcc = seg_kcc(text)
features = create_kcc_features(skcc)
pred = crf_model.predict([features])
tkcc = []
for k in features:
tkcc.append(k['kcc'])
complete = ''
tokens = []
for (i, p) in enumerate(pred[0]):
if ((p == '1') or (i == 0)):
tokens.append(tkcc[i])
else:
tokens[(- 1)] += tkcc[i]
if return_tokens:
return tokens
complete = separator.join(tokens)
complete = complete.replace(((separator + ' ') + separator), ' ')
return complete | Khmer language word tokenization
Args:
text(str): Raw text
separator(str, optional): Token - separator in case return_tokens = True. Defaults to "-".
return_tokens(bool, optional): Whether return a tokenized text or a list of tokens. Defaults to True.
Returns:
Union[List, str]: Tokens or tokenized text, separated by the separator | khmernltk/word_tokenize/__init__.py | word_tokenize | VietHoang1710/khmer_nltk | 18 | python | def word_tokenize(text: str, separator: str='-', return_tokens: bool=True) -> Union[(List, str)]:
'Khmer language word tokenization\n\n Args:\n text(str): Raw text\n separator(str, optional): Token - separator in case return_tokens = True. Defaults to "-".\n return_tokens(bool, optional): Whether return a tokenized text or a list of tokens. Defaults to True.\n\n Returns:\n Union[List, str]: Tokens or tokenized text, separated by the separator\n '
text = cleanup_str(text)
skcc = seg_kcc(text)
features = create_kcc_features(skcc)
pred = crf_model.predict([features])
tkcc = []
for k in features:
tkcc.append(k['kcc'])
complete =
tokens = []
for (i, p) in enumerate(pred[0]):
if ((p == '1') or (i == 0)):
tokens.append(tkcc[i])
else:
tokens[(- 1)] += tkcc[i]
if return_tokens:
return tokens
complete = separator.join(tokens)
complete = complete.replace(((separator + ' ') + separator), ' ')
return complete | def word_tokenize(text: str, separator: str='-', return_tokens: bool=True) -> Union[(List, str)]:
'Khmer language word tokenization\n\n Args:\n text(str): Raw text\n separator(str, optional): Token - separator in case return_tokens = True. Defaults to "-".\n return_tokens(bool, optional): Whether return a tokenized text or a list of tokens. Defaults to True.\n\n Returns:\n Union[List, str]: Tokens or tokenized text, separated by the separator\n '
text = cleanup_str(text)
skcc = seg_kcc(text)
features = create_kcc_features(skcc)
pred = crf_model.predict([features])
tkcc = []
for k in features:
tkcc.append(k['kcc'])
complete =
tokens = []
for (i, p) in enumerate(pred[0]):
if ((p == '1') or (i == 0)):
tokens.append(tkcc[i])
else:
tokens[(- 1)] += tkcc[i]
if return_tokens:
return tokens
complete = separator.join(tokens)
complete = complete.replace(((separator + ' ') + separator), ' ')
return complete<|docstring|>Khmer language word tokenization
Args:
text(str): Raw text
separator(str, optional): Token - separator in case return_tokens = True. Defaults to "-".
return_tokens(bool, optional): Whether return a tokenized text or a list of tokens. Defaults to True.
Returns:
Union[List, str]: Tokens or tokenized text, separated by the separator<|endoftext|> |
6106380a028375fd8d045de90d8dd242dc0441cbcdb601933068b6e2c065ca7e | def load(filename):
'Load an image from the given filename.\n\n Parameters\n ----------\n filename : string\n Should resolve to a complete filename path.\n\n Returns\n -------\n image : An `Image` object\n If successful, a new `Image` object is returned.\n\n See Also\n --------\n save_image : function for saving images\n fromarray : function for creating images from numpy arrays\n\n Examples\n --------\n\n >>> from nipy.io.api import load_image\n >>> from nipy.testing import anatfile\n >>> img = load_image(anatfile)\n >>> img.shape\n (33, 41, 25)\n '
img = formats.load(filename)
aff = img.get_affine()
shape = img.get_shape()
hdr = img.get_header()
try:
fps = hdr.get_dim_info()
except (TypeError, AttributeError):
fps = (None, None, None)
ijk = ijk_from_fps(fps)
try:
zooms = hdr.get_zooms()
except AttributeError:
zooms = np.ones(len(shape))
aff = _match_affine(aff, len(shape), zooms)
coordmap = coordmap_from_affine(aff, ijk)
img = Image(img.get_data(), coordmap)
img.header = hdr
return img | Load an image from the given filename.
Parameters
----------
filename : string
Should resolve to a complete filename path.
Returns
-------
image : An `Image` object
If successful, a new `Image` object is returned.
See Also
--------
save_image : function for saving images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> from nipy.io.api import load_image
>>> from nipy.testing import anatfile
>>> img = load_image(anatfile)
>>> img.shape
(33, 41, 25) | nipy/io/files.py | load | yarikoptic/NiPy-OLD | 1 | python | def load(filename):
'Load an image from the given filename.\n\n Parameters\n ----------\n filename : string\n Should resolve to a complete filename path.\n\n Returns\n -------\n image : An `Image` object\n If successful, a new `Image` object is returned.\n\n See Also\n --------\n save_image : function for saving images\n fromarray : function for creating images from numpy arrays\n\n Examples\n --------\n\n >>> from nipy.io.api import load_image\n >>> from nipy.testing import anatfile\n >>> img = load_image(anatfile)\n >>> img.shape\n (33, 41, 25)\n '
img = formats.load(filename)
aff = img.get_affine()
shape = img.get_shape()
hdr = img.get_header()
try:
fps = hdr.get_dim_info()
except (TypeError, AttributeError):
fps = (None, None, None)
ijk = ijk_from_fps(fps)
try:
zooms = hdr.get_zooms()
except AttributeError:
zooms = np.ones(len(shape))
aff = _match_affine(aff, len(shape), zooms)
coordmap = coordmap_from_affine(aff, ijk)
img = Image(img.get_data(), coordmap)
img.header = hdr
return img | def load(filename):
'Load an image from the given filename.\n\n Parameters\n ----------\n filename : string\n Should resolve to a complete filename path.\n\n Returns\n -------\n image : An `Image` object\n If successful, a new `Image` object is returned.\n\n See Also\n --------\n save_image : function for saving images\n fromarray : function for creating images from numpy arrays\n\n Examples\n --------\n\n >>> from nipy.io.api import load_image\n >>> from nipy.testing import anatfile\n >>> img = load_image(anatfile)\n >>> img.shape\n (33, 41, 25)\n '
img = formats.load(filename)
aff = img.get_affine()
shape = img.get_shape()
hdr = img.get_header()
try:
fps = hdr.get_dim_info()
except (TypeError, AttributeError):
fps = (None, None, None)
ijk = ijk_from_fps(fps)
try:
zooms = hdr.get_zooms()
except AttributeError:
zooms = np.ones(len(shape))
aff = _match_affine(aff, len(shape), zooms)
coordmap = coordmap_from_affine(aff, ijk)
img = Image(img.get_data(), coordmap)
img.header = hdr
return img<|docstring|>Load an image from the given filename.
Parameters
----------
filename : string
Should resolve to a complete filename path.
Returns
-------
image : An `Image` object
If successful, a new `Image` object is returned.
See Also
--------
save_image : function for saving images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> from nipy.io.api import load_image
>>> from nipy.testing import anatfile
>>> img = load_image(anatfile)
>>> img.shape
(33, 41, 25)<|endoftext|> |
89f29a007e6c197096ab81c4448135cbe53d97edf5e46c44eadbab52dc00c628 | def _match_affine(aff, ndim, zooms=None):
' Fill or prune affine to given number of dimensions\n\n >>> aff = np.arange(16).reshape(4,4)\n >>> _match_affine(aff, 3)\n array([[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11],\n [12, 13, 14, 15]])\n >>> _match_affine(aff, 2)\n array([[ 0., 1., 3.],\n [ 4., 5., 7.],\n [ 0., 0., 1.]])\n >>> _match_affine(aff, 4)\n array([[ 0., 1., 2., 0., 3.],\n [ 4., 5., 6., 0., 7.],\n [ 8., 9., 10., 0., 11.],\n [ 0., 0., 0., 1., 0.],\n [ 0., 0., 0., 0., 1.]])\n >>> aff = np.arange(9).reshape(3,3)\n >>> _match_affine(aff, 2)\n array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])\n '
if (aff.shape[0] != aff.shape[1]):
raise ValueError('Need square affine')
aff_dim = (aff.shape[0] - 1)
if (ndim == aff_dim):
return aff
aff_diag = np.ones((ndim + 1))
if (not (zooms is None)):
n = min(len(zooms), ndim)
aff_diag[:n] = zooms[:n]
mod_aff = np.diag(aff_diag)
n = min(ndim, aff_dim)
mod_aff[(:n, :n)] = aff[(:n, :n)]
mod_aff[(:n, (- 1))] = aff[(:n, (- 1))]
return mod_aff | Fill or prune affine to given number of dimensions
>>> aff = np.arange(16).reshape(4,4)
>>> _match_affine(aff, 3)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> _match_affine(aff, 2)
array([[ 0., 1., 3.],
[ 4., 5., 7.],
[ 0., 0., 1.]])
>>> _match_affine(aff, 4)
array([[ 0., 1., 2., 0., 3.],
[ 4., 5., 6., 0., 7.],
[ 8., 9., 10., 0., 11.],
[ 0., 0., 0., 1., 0.],
[ 0., 0., 0., 0., 1.]])
>>> aff = np.arange(9).reshape(3,3)
>>> _match_affine(aff, 2)
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]) | nipy/io/files.py | _match_affine | yarikoptic/NiPy-OLD | 1 | python | def _match_affine(aff, ndim, zooms=None):
' Fill or prune affine to given number of dimensions\n\n >>> aff = np.arange(16).reshape(4,4)\n >>> _match_affine(aff, 3)\n array([[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11],\n [12, 13, 14, 15]])\n >>> _match_affine(aff, 2)\n array([[ 0., 1., 3.],\n [ 4., 5., 7.],\n [ 0., 0., 1.]])\n >>> _match_affine(aff, 4)\n array([[ 0., 1., 2., 0., 3.],\n [ 4., 5., 6., 0., 7.],\n [ 8., 9., 10., 0., 11.],\n [ 0., 0., 0., 1., 0.],\n [ 0., 0., 0., 0., 1.]])\n >>> aff = np.arange(9).reshape(3,3)\n >>> _match_affine(aff, 2)\n array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])\n '
if (aff.shape[0] != aff.shape[1]):
raise ValueError('Need square affine')
aff_dim = (aff.shape[0] - 1)
if (ndim == aff_dim):
return aff
aff_diag = np.ones((ndim + 1))
if (not (zooms is None)):
n = min(len(zooms), ndim)
aff_diag[:n] = zooms[:n]
mod_aff = np.diag(aff_diag)
n = min(ndim, aff_dim)
mod_aff[(:n, :n)] = aff[(:n, :n)]
mod_aff[(:n, (- 1))] = aff[(:n, (- 1))]
return mod_aff | def _match_affine(aff, ndim, zooms=None):
' Fill or prune affine to given number of dimensions\n\n >>> aff = np.arange(16).reshape(4,4)\n >>> _match_affine(aff, 3)\n array([[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11],\n [12, 13, 14, 15]])\n >>> _match_affine(aff, 2)\n array([[ 0., 1., 3.],\n [ 4., 5., 7.],\n [ 0., 0., 1.]])\n >>> _match_affine(aff, 4)\n array([[ 0., 1., 2., 0., 3.],\n [ 4., 5., 6., 0., 7.],\n [ 8., 9., 10., 0., 11.],\n [ 0., 0., 0., 1., 0.],\n [ 0., 0., 0., 0., 1.]])\n >>> aff = np.arange(9).reshape(3,3)\n >>> _match_affine(aff, 2)\n array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])\n '
if (aff.shape[0] != aff.shape[1]):
raise ValueError('Need square affine')
aff_dim = (aff.shape[0] - 1)
if (ndim == aff_dim):
return aff
aff_diag = np.ones((ndim + 1))
if (not (zooms is None)):
n = min(len(zooms), ndim)
aff_diag[:n] = zooms[:n]
mod_aff = np.diag(aff_diag)
n = min(ndim, aff_dim)
mod_aff[(:n, :n)] = aff[(:n, :n)]
mod_aff[(:n, (- 1))] = aff[(:n, (- 1))]
return mod_aff<|docstring|>Fill or prune affine to given number of dimensions
>>> aff = np.arange(16).reshape(4,4)
>>> _match_affine(aff, 3)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> _match_affine(aff, 2)
array([[ 0., 1., 3.],
[ 4., 5., 7.],
[ 0., 0., 1.]])
>>> _match_affine(aff, 4)
array([[ 0., 1., 2., 0., 3.],
[ 4., 5., 6., 0., 7.],
[ 8., 9., 10., 0., 11.],
[ 0., 0., 0., 1., 0.],
[ 0., 0., 0., 0., 1.]])
>>> aff = np.arange(9).reshape(3,3)
>>> _match_affine(aff, 2)
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])<|endoftext|> |
8f29f7f063a677667e97a2020fe507b364cab514afdb32012cd500a793707ef0 | def save(img, filename, dtype=None):
'Write the image to a file.\n\n Parameters\n ----------\n img : An `Image` object\n filename : string\n Should be a valid filename.\n\n Returns\n -------\n image : An `Image` object\n\n See Also\n --------\n load_image : function for loading images\n fromarray : function for creating images from numpy arrays\n\n Examples\n --------\n\n >>> import os\n >>> import numpy as np\n >>> from tempfile import mkstemp\n >>> from nipy.core.api import fromarray\n >>> from nipy.io.api import save_image\n >>> data = np.zeros((91,109,91), dtype=np.uint8)\n >>> img = fromarray(data, \'kji\', \'zxy\')\n >>> fd, fname = mkstemp(suffix=\'.nii.gz\')\n >>> saved_img = save_image(img, fname)\n >>> saved_img.shape\n (91, 109, 91)\n >>> os.unlink(fname)\n >>> fd, fname = mkstemp(suffix=\'.img.gz\')\n >>> saved_img = save_image(img, fname)\n >>> saved_img.shape\n (91, 109, 91)\n >>> os.unlink(fname)\n >>> fname = \'test.mnc\'\n >>> saved_image = save_image(img, fname)\n Traceback (most recent call last):\n ...\n ValueError: Cannot save file type "minc"\n \n Notes\n -----\n Filetype is determined by the file extension in \'filename\'. Currently the\n following filetypes are supported:\n \n * Nifti single file : [\'.nii\', \'.nii.gz\']\n * Nifti file pair : [\'.hdr\', \'.hdr.gz\']\n * Analyze file pair : [\'.img\', \'img.gz\']\n '
try:
original_hdr = img.header
except AttributeError:
original_hdr = None
(newcmap, order) = coerce_coordmap(img.coordmap)
Fimg = Image(np.transpose(np.asarray(img), order), newcmap)
rzs = Fimg.affine[(:(- 1), :(- 1))]
zooms = np.sqrt(np.sum((rzs * rzs), axis=0))
aff = _match_affine(Fimg.affine, 3, zooms)
ftype = _type_from_filename(filename)
if ftype.startswith('nifti1'):
klass = formats.Nifti1Image
elif (ftype == 'analyze'):
klass = formats.Spm2AnalyzeImage
else:
raise ValueError(('Cannot save file type "%s"' % ftype))
out_img = klass(data=np.asarray(Fimg), affine=aff, header=original_hdr)
hdr = out_img.get_header()
ijk = newcmap.input_coords.coord_names
fps = fps_from_ijk(ijk)
try:
hdr.set_dim_info(*fps)
except AttributeError:
pass
hdr.set_zooms(zooms)
out_img.to_filename(filename)
return Fimg | Write the image to a file.
Parameters
----------
img : An `Image` object
filename : string
Should be a valid filename.
Returns
-------
image : An `Image` object
See Also
--------
load_image : function for loading images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> import os
>>> import numpy as np
>>> from tempfile import mkstemp
>>> from nipy.core.api import fromarray
>>> from nipy.io.api import save_image
>>> data = np.zeros((91,109,91), dtype=np.uint8)
>>> img = fromarray(data, 'kji', 'zxy')
>>> fd, fname = mkstemp(suffix='.nii.gz')
>>> saved_img = save_image(img, fname)
>>> saved_img.shape
(91, 109, 91)
>>> os.unlink(fname)
>>> fd, fname = mkstemp(suffix='.img.gz')
>>> saved_img = save_image(img, fname)
>>> saved_img.shape
(91, 109, 91)
>>> os.unlink(fname)
>>> fname = 'test.mnc'
>>> saved_image = save_image(img, fname)
Traceback (most recent call last):
...
ValueError: Cannot save file type "minc"
Notes
-----
Filetype is determined by the file extension in 'filename'. Currently the
following filetypes are supported:
* Nifti single file : ['.nii', '.nii.gz']
* Nifti file pair : ['.hdr', '.hdr.gz']
* Analyze file pair : ['.img', 'img.gz'] | nipy/io/files.py | save | yarikoptic/NiPy-OLD | 1 | python | def save(img, filename, dtype=None):
'Write the image to a file.\n\n Parameters\n ----------\n img : An `Image` object\n filename : string\n Should be a valid filename.\n\n Returns\n -------\n image : An `Image` object\n\n See Also\n --------\n load_image : function for loading images\n fromarray : function for creating images from numpy arrays\n\n Examples\n --------\n\n >>> import os\n >>> import numpy as np\n >>> from tempfile import mkstemp\n >>> from nipy.core.api import fromarray\n >>> from nipy.io.api import save_image\n >>> data = np.zeros((91,109,91), dtype=np.uint8)\n >>> img = fromarray(data, \'kji\', \'zxy\')\n >>> fd, fname = mkstemp(suffix=\'.nii.gz\')\n >>> saved_img = save_image(img, fname)\n >>> saved_img.shape\n (91, 109, 91)\n >>> os.unlink(fname)\n >>> fd, fname = mkstemp(suffix=\'.img.gz\')\n >>> saved_img = save_image(img, fname)\n >>> saved_img.shape\n (91, 109, 91)\n >>> os.unlink(fname)\n >>> fname = \'test.mnc\'\n >>> saved_image = save_image(img, fname)\n Traceback (most recent call last):\n ...\n ValueError: Cannot save file type "minc"\n \n Notes\n -----\n Filetype is determined by the file extension in \'filename\'. Currently the\n following filetypes are supported:\n \n * Nifti single file : [\'.nii\', \'.nii.gz\']\n * Nifti file pair : [\'.hdr\', \'.hdr.gz\']\n * Analyze file pair : [\'.img\', \'img.gz\']\n '
try:
original_hdr = img.header
except AttributeError:
original_hdr = None
(newcmap, order) = coerce_coordmap(img.coordmap)
Fimg = Image(np.transpose(np.asarray(img), order), newcmap)
rzs = Fimg.affine[(:(- 1), :(- 1))]
zooms = np.sqrt(np.sum((rzs * rzs), axis=0))
aff = _match_affine(Fimg.affine, 3, zooms)
ftype = _type_from_filename(filename)
if ftype.startswith('nifti1'):
klass = formats.Nifti1Image
elif (ftype == 'analyze'):
klass = formats.Spm2AnalyzeImage
else:
raise ValueError(('Cannot save file type "%s"' % ftype))
out_img = klass(data=np.asarray(Fimg), affine=aff, header=original_hdr)
hdr = out_img.get_header()
ijk = newcmap.input_coords.coord_names
fps = fps_from_ijk(ijk)
try:
hdr.set_dim_info(*fps)
except AttributeError:
pass
hdr.set_zooms(zooms)
out_img.to_filename(filename)
return Fimg | def save(img, filename, dtype=None):
'Write the image to a file.\n\n Parameters\n ----------\n img : An `Image` object\n filename : string\n Should be a valid filename.\n\n Returns\n -------\n image : An `Image` object\n\n See Also\n --------\n load_image : function for loading images\n fromarray : function for creating images from numpy arrays\n\n Examples\n --------\n\n >>> import os\n >>> import numpy as np\n >>> from tempfile import mkstemp\n >>> from nipy.core.api import fromarray\n >>> from nipy.io.api import save_image\n >>> data = np.zeros((91,109,91), dtype=np.uint8)\n >>> img = fromarray(data, \'kji\', \'zxy\')\n >>> fd, fname = mkstemp(suffix=\'.nii.gz\')\n >>> saved_img = save_image(img, fname)\n >>> saved_img.shape\n (91, 109, 91)\n >>> os.unlink(fname)\n >>> fd, fname = mkstemp(suffix=\'.img.gz\')\n >>> saved_img = save_image(img, fname)\n >>> saved_img.shape\n (91, 109, 91)\n >>> os.unlink(fname)\n >>> fname = \'test.mnc\'\n >>> saved_image = save_image(img, fname)\n Traceback (most recent call last):\n ...\n ValueError: Cannot save file type "minc"\n \n Notes\n -----\n Filetype is determined by the file extension in \'filename\'. Currently the\n following filetypes are supported:\n \n * Nifti single file : [\'.nii\', \'.nii.gz\']\n * Nifti file pair : [\'.hdr\', \'.hdr.gz\']\n * Analyze file pair : [\'.img\', \'img.gz\']\n '
try:
original_hdr = img.header
except AttributeError:
original_hdr = None
(newcmap, order) = coerce_coordmap(img.coordmap)
Fimg = Image(np.transpose(np.asarray(img), order), newcmap)
rzs = Fimg.affine[(:(- 1), :(- 1))]
zooms = np.sqrt(np.sum((rzs * rzs), axis=0))
aff = _match_affine(Fimg.affine, 3, zooms)
ftype = _type_from_filename(filename)
if ftype.startswith('nifti1'):
klass = formats.Nifti1Image
elif (ftype == 'analyze'):
klass = formats.Spm2AnalyzeImage
else:
raise ValueError(('Cannot save file type "%s"' % ftype))
out_img = klass(data=np.asarray(Fimg), affine=aff, header=original_hdr)
hdr = out_img.get_header()
ijk = newcmap.input_coords.coord_names
fps = fps_from_ijk(ijk)
try:
hdr.set_dim_info(*fps)
except AttributeError:
pass
hdr.set_zooms(zooms)
out_img.to_filename(filename)
return Fimg<|docstring|>Write the image to a file.
Parameters
----------
img : An `Image` object
filename : string
Should be a valid filename.
Returns
-------
image : An `Image` object
See Also
--------
load_image : function for loading images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> import os
>>> import numpy as np
>>> from tempfile import mkstemp
>>> from nipy.core.api import fromarray
>>> from nipy.io.api import save_image
>>> data = np.zeros((91,109,91), dtype=np.uint8)
>>> img = fromarray(data, 'kji', 'zxy')
>>> fd, fname = mkstemp(suffix='.nii.gz')
>>> saved_img = save_image(img, fname)
>>> saved_img.shape
(91, 109, 91)
>>> os.unlink(fname)
>>> fd, fname = mkstemp(suffix='.img.gz')
>>> saved_img = save_image(img, fname)
>>> saved_img.shape
(91, 109, 91)
>>> os.unlink(fname)
>>> fname = 'test.mnc'
>>> saved_image = save_image(img, fname)
Traceback (most recent call last):
...
ValueError: Cannot save file type "minc"
Notes
-----
Filetype is determined by the file extension in 'filename'. Currently the
following filetypes are supported:
* Nifti single file : ['.nii', '.nii.gz']
* Nifti file pair : ['.hdr', '.hdr.gz']
* Analyze file pair : ['.img', 'img.gz']<|endoftext|> |
efb835e8e37a6bfece8178c404e1ad6d42fb566b0f1caea93198b327615404df | def _type_from_filename(filename):
" Return image type determined from filename\n \n Filetype is determined by the file extension in 'filename'.\n Currently the following filetypes are supported:\n \n * Nifti single file : ['.nii', '.nii.gz']\n * Nifti file pair : ['.hdr', '.hdr.gz']\n * Analyze file pair : ['.img', '.img.gz']\n\n >>> _type_from_filename('test.nii')\n 'nifti1single'\n >>> _type_from_filename('test')\n 'nifti1single'\n >>> _type_from_filename('test.hdr')\n 'nifti1pair'\n >>> _type_from_filename('test.hdr.gz')\n 'nifti1pair'\n >>> _type_from_filename('test.img.gz')\n 'analyze'\n >>> _type_from_filename('test.mnc')\n 'minc'\n "
if filename.endswith('.gz'):
filename = filename[:(- 3)]
elif filename.endswith('.bz2'):
filename = filename[:(- 4)]
(_, ext) = os.path.splitext(filename)
if (ext in ('', '.nii')):
return 'nifti1single'
if (ext == '.hdr'):
return 'nifti1pair'
if (ext == '.img'):
return 'analyze'
if (ext == '.mnc'):
return 'minc'
raise ValueError(('Strange file extension "%s"' % ext)) | Return image type determined from filename
Filetype is determined by the file extension in 'filename'.
Currently the following filetypes are supported:
* Nifti single file : ['.nii', '.nii.gz']
* Nifti file pair : ['.hdr', '.hdr.gz']
* Analyze file pair : ['.img', '.img.gz']
>>> _type_from_filename('test.nii')
'nifti1single'
>>> _type_from_filename('test')
'nifti1single'
>>> _type_from_filename('test.hdr')
'nifti1pair'
>>> _type_from_filename('test.hdr.gz')
'nifti1pair'
>>> _type_from_filename('test.img.gz')
'analyze'
>>> _type_from_filename('test.mnc')
'minc' | nipy/io/files.py | _type_from_filename | yarikoptic/NiPy-OLD | 1 | python | def _type_from_filename(filename):
" Return image type determined from filename\n \n Filetype is determined by the file extension in 'filename'.\n Currently the following filetypes are supported:\n \n * Nifti single file : ['.nii', '.nii.gz']\n * Nifti file pair : ['.hdr', '.hdr.gz']\n * Analyze file pair : ['.img', '.img.gz']\n\n >>> _type_from_filename('test.nii')\n 'nifti1single'\n >>> _type_from_filename('test')\n 'nifti1single'\n >>> _type_from_filename('test.hdr')\n 'nifti1pair'\n >>> _type_from_filename('test.hdr.gz')\n 'nifti1pair'\n >>> _type_from_filename('test.img.gz')\n 'analyze'\n >>> _type_from_filename('test.mnc')\n 'minc'\n "
if filename.endswith('.gz'):
filename = filename[:(- 3)]
elif filename.endswith('.bz2'):
filename = filename[:(- 4)]
(_, ext) = os.path.splitext(filename)
if (ext in (, '.nii')):
return 'nifti1single'
if (ext == '.hdr'):
return 'nifti1pair'
if (ext == '.img'):
return 'analyze'
if (ext == '.mnc'):
return 'minc'
raise ValueError(('Strange file extension "%s"' % ext)) | def _type_from_filename(filename):
" Return image type determined from filename\n \n Filetype is determined by the file extension in 'filename'.\n Currently the following filetypes are supported:\n \n * Nifti single file : ['.nii', '.nii.gz']\n * Nifti file pair : ['.hdr', '.hdr.gz']\n * Analyze file pair : ['.img', '.img.gz']\n\n >>> _type_from_filename('test.nii')\n 'nifti1single'\n >>> _type_from_filename('test')\n 'nifti1single'\n >>> _type_from_filename('test.hdr')\n 'nifti1pair'\n >>> _type_from_filename('test.hdr.gz')\n 'nifti1pair'\n >>> _type_from_filename('test.img.gz')\n 'analyze'\n >>> _type_from_filename('test.mnc')\n 'minc'\n "
if filename.endswith('.gz'):
filename = filename[:(- 3)]
elif filename.endswith('.bz2'):
filename = filename[:(- 4)]
(_, ext) = os.path.splitext(filename)
if (ext in (, '.nii')):
return 'nifti1single'
if (ext == '.hdr'):
return 'nifti1pair'
if (ext == '.img'):
return 'analyze'
if (ext == '.mnc'):
return 'minc'
raise ValueError(('Strange file extension "%s"' % ext))<|docstring|>Return image type determined from filename
Filetype is determined by the file extension in 'filename'.
Currently the following filetypes are supported:
* Nifti single file : ['.nii', '.nii.gz']
* Nifti file pair : ['.hdr', '.hdr.gz']
* Analyze file pair : ['.img', '.img.gz']
>>> _type_from_filename('test.nii')
'nifti1single'
>>> _type_from_filename('test')
'nifti1single'
>>> _type_from_filename('test.hdr')
'nifti1pair'
>>> _type_from_filename('test.hdr.gz')
'nifti1pair'
>>> _type_from_filename('test.img.gz')
'analyze'
>>> _type_from_filename('test.mnc')
'minc'<|endoftext|> |
dcca314eaa3cc42efa393a27a5ef3cb7e3d858b0dfed24eb208a76e0d54c444f | def as_image(image_input):
' Load image from filename or pass through image instance\n\n Parameters\n ----------\n image_input : str or Image instance\n image or string filename of image. If a string, load image and\n return. If an image, pass through without modification\n\n Returns\n -------\n img : Image or Image-like instance\n Input object if `image_input` seemed to be an image, loaded Image\n object if `image_input` was a string.\n\n Raises\n ------\n TypeError : if neither string nor image-like passed\n\n Examples\n --------\n >>> from nipy.testing import anatfile\n >>> from nipy.io.api import load_image\n >>> img = as_image(anatfile)\n >>> img2 = as_image(img)\n >>> img2 is img\n True\n '
if is_image(image_input):
return image_input
if isinstance(image_input, basestring):
return load(image_input)
raise TypeError('Expecting an image-like object or filename string') | Load image from filename or pass through image instance
Parameters
----------
image_input : str or Image instance
image or string filename of image. If a string, load image and
return. If an image, pass through without modification
Returns
-------
img : Image or Image-like instance
Input object if `image_input` seemed to be an image, loaded Image
object if `image_input` was a string.
Raises
------
TypeError : if neither string nor image-like passed
Examples
--------
>>> from nipy.testing import anatfile
>>> from nipy.io.api import load_image
>>> img = as_image(anatfile)
>>> img2 = as_image(img)
>>> img2 is img
True | nipy/io/files.py | as_image | yarikoptic/NiPy-OLD | 1 | python | def as_image(image_input):
' Load image from filename or pass through image instance\n\n Parameters\n ----------\n image_input : str or Image instance\n image or string filename of image. If a string, load image and\n return. If an image, pass through without modification\n\n Returns\n -------\n img : Image or Image-like instance\n Input object if `image_input` seemed to be an image, loaded Image\n object if `image_input` was a string.\n\n Raises\n ------\n TypeError : if neither string nor image-like passed\n\n Examples\n --------\n >>> from nipy.testing import anatfile\n >>> from nipy.io.api import load_image\n >>> img = as_image(anatfile)\n >>> img2 = as_image(img)\n >>> img2 is img\n True\n '
if is_image(image_input):
return image_input
if isinstance(image_input, basestring):
return load(image_input)
raise TypeError('Expecting an image-like object or filename string') | def as_image(image_input):
' Load image from filename or pass through image instance\n\n Parameters\n ----------\n image_input : str or Image instance\n image or string filename of image. If a string, load image and\n return. If an image, pass through without modification\n\n Returns\n -------\n img : Image or Image-like instance\n Input object if `image_input` seemed to be an image, loaded Image\n object if `image_input` was a string.\n\n Raises\n ------\n TypeError : if neither string nor image-like passed\n\n Examples\n --------\n >>> from nipy.testing import anatfile\n >>> from nipy.io.api import load_image\n >>> img = as_image(anatfile)\n >>> img2 = as_image(img)\n >>> img2 is img\n True\n '
if is_image(image_input):
return image_input
if isinstance(image_input, basestring):
return load(image_input)
raise TypeError('Expecting an image-like object or filename string')<|docstring|>Load image from filename or pass through image instance
Parameters
----------
image_input : str or Image instance
image or string filename of image. If a string, load image and
return. If an image, pass through without modification
Returns
-------
img : Image or Image-like instance
Input object if `image_input` seemed to be an image, loaded Image
object if `image_input` was a string.
Raises
------
TypeError : if neither string nor image-like passed
Examples
--------
>>> from nipy.testing import anatfile
>>> from nipy.io.api import load_image
>>> img = as_image(anatfile)
>>> img2 = as_image(img)
>>> img2 is img
True<|endoftext|> |
c573cd038586b5ec59e2422eb77715f22e309bbf3f740e3f03447f2bd7025185 | def parse_read_options(form, prefix=''):
"Extract read options from form data.\n\n Arguments:\n form (obj): Form object\n\n Keyword Arguments:\n prefix (str): prefix for the form fields (default: {''})\n\n Returns:\n (dict): Read options key - value dictionary.\n "
read_options = {'encoding': getattr(form, (prefix + 'encoding')).data, 'delimiter': getattr(form, (prefix + 'delimiter')).data}
geom = getattr(form, (prefix + 'geom'))
lat = getattr(form, (prefix + 'lat'))
lon = getattr(form, (prefix + 'lon'))
if (geom.data != ''):
read_options['geom'] = geom.data
elif ((lat.data != '') and (lon.data != '')):
read_options['lat'] = lat.data
read_options['lon'] = lon.data
return read_options | Extract read options from form data.
Arguments:
form (obj): Form object
Keyword Arguments:
prefix (str): prefix for the form fields (default: {''})
Returns:
(dict): Read options key - value dictionary. | geometry_service/api/helpers.py | parse_read_options | OpertusMundi/geometry-service | 0 | python | def parse_read_options(form, prefix=):
"Extract read options from form data.\n\n Arguments:\n form (obj): Form object\n\n Keyword Arguments:\n prefix (str): prefix for the form fields (default: {})\n\n Returns:\n (dict): Read options key - value dictionary.\n "
read_options = {'encoding': getattr(form, (prefix + 'encoding')).data, 'delimiter': getattr(form, (prefix + 'delimiter')).data}
geom = getattr(form, (prefix + 'geom'))
lat = getattr(form, (prefix + 'lat'))
lon = getattr(form, (prefix + 'lon'))
if (geom.data != ):
read_options['geom'] = geom.data
elif ((lat.data != ) and (lon.data != )):
read_options['lat'] = lat.data
read_options['lon'] = lon.data
return read_options | def parse_read_options(form, prefix=):
"Extract read options from form data.\n\n Arguments:\n form (obj): Form object\n\n Keyword Arguments:\n prefix (str): prefix for the form fields (default: {})\n\n Returns:\n (dict): Read options key - value dictionary.\n "
read_options = {'encoding': getattr(form, (prefix + 'encoding')).data, 'delimiter': getattr(form, (prefix + 'delimiter')).data}
geom = getattr(form, (prefix + 'geom'))
lat = getattr(form, (prefix + 'lat'))
lon = getattr(form, (prefix + 'lon'))
if (geom.data != ):
read_options['geom'] = geom.data
elif ((lat.data != ) and (lon.data != )):
read_options['lat'] = lat.data
read_options['lon'] = lon.data
return read_options<|docstring|>Extract read options from form data.
Arguments:
form (obj): Form object
Keyword Arguments:
prefix (str): prefix for the form fields (default: {''})
Returns:
(dict): Read options key - value dictionary.<|endoftext|> |
609611b9b301b8cfe6a349baba825ea19a5b584e946cd4b59a5365ca5ba73e53 | def send_file(file):
'Create a send file response.\n\n Arguments:\n file (str): Path of the file.\n\n Returns:\n (obj): Flask response\n '
from flask import send_file as flask_send_file
file_content = open(file, 'rb')
filename = os.path.basename(file)
response = flask_send_file(file_content, attachment_filename=filename, as_attachment=True)
response.headers['Content-Length'] = str(os.path.getsize(file))
return response | Create a send file response.
Arguments:
file (str): Path of the file.
Returns:
(obj): Flask response | geometry_service/api/helpers.py | send_file | OpertusMundi/geometry-service | 0 | python | def send_file(file):
'Create a send file response.\n\n Arguments:\n file (str): Path of the file.\n\n Returns:\n (obj): Flask response\n '
from flask import send_file as flask_send_file
file_content = open(file, 'rb')
filename = os.path.basename(file)
response = flask_send_file(file_content, attachment_filename=filename, as_attachment=True)
response.headers['Content-Length'] = str(os.path.getsize(file))
return response | def send_file(file):
'Create a send file response.\n\n Arguments:\n file (str): Path of the file.\n\n Returns:\n (obj): Flask response\n '
from flask import send_file as flask_send_file
file_content = open(file, 'rb')
filename = os.path.basename(file)
response = flask_send_file(file_content, attachment_filename=filename, as_attachment=True)
response.headers['Content-Length'] = str(os.path.getsize(file))
return response<|docstring|>Create a send file response.
Arguments:
file (str): Path of the file.
Returns:
(obj): Flask response<|endoftext|> |
eedd498d1b3b90ab04232b18a94b4ab9441b9e08e2d79cc186aae2718fe9aeae | def copy_to_output(file, ticket):
'Copy file to output dir, after creating the containing path.\n\n Arguments:\n file (str): Path of the file.\n ticket (str): Request ticket.\n\n Returns:\n (str): Relative to output dir path of the copied file.\n '
from datetime import datetime
from shutil import copyfile
filename = os.path.basename(file)
output_path = os.path.join(datetime.now().strftime('%y%m'), ticket)
output_file = os.path.join(output_path, filename)
full_output = os.path.join(os.environ['OUTPUT_DIR'], output_path)
os.makedirs(full_output, exist_ok=True)
copyfile(file, os.path.join(full_output, filename))
return output_file | Copy file to output dir, after creating the containing path.
Arguments:
file (str): Path of the file.
ticket (str): Request ticket.
Returns:
(str): Relative to output dir path of the copied file. | geometry_service/api/helpers.py | copy_to_output | OpertusMundi/geometry-service | 0 | python | def copy_to_output(file, ticket):
'Copy file to output dir, after creating the containing path.\n\n Arguments:\n file (str): Path of the file.\n ticket (str): Request ticket.\n\n Returns:\n (str): Relative to output dir path of the copied file.\n '
from datetime import datetime
from shutil import copyfile
filename = os.path.basename(file)
output_path = os.path.join(datetime.now().strftime('%y%m'), ticket)
output_file = os.path.join(output_path, filename)
full_output = os.path.join(os.environ['OUTPUT_DIR'], output_path)
os.makedirs(full_output, exist_ok=True)
copyfile(file, os.path.join(full_output, filename))
return output_file | def copy_to_output(file, ticket):
'Copy file to output dir, after creating the containing path.\n\n Arguments:\n file (str): Path of the file.\n ticket (str): Request ticket.\n\n Returns:\n (str): Relative to output dir path of the copied file.\n '
from datetime import datetime
from shutil import copyfile
filename = os.path.basename(file)
output_path = os.path.join(datetime.now().strftime('%y%m'), ticket)
output_file = os.path.join(output_path, filename)
full_output = os.path.join(os.environ['OUTPUT_DIR'], output_path)
os.makedirs(full_output, exist_ok=True)
copyfile(file, os.path.join(full_output, filename))
return output_file<|docstring|>Copy file to output dir, after creating the containing path.
Arguments:
file (str): Path of the file.
ticket (str): Request ticket.
Returns:
(str): Relative to output dir path of the copied file.<|endoftext|> |
f5d9dff121006a6558dd07cf04793450aab2fe4509543b2797b77c02c750e37f | def burnin_action_func():
'Select random actions until model is updated one or more times.'
return np.random.uniform(action_space.low, action_space.high).astype(np.float32) | Select random actions until model is updated one or more times. | examples/mujoco/reproduction/td3/train_td3.py | burnin_action_func | tarokiritani/pfrl | 824 | python | def burnin_action_func():
return np.random.uniform(action_space.low, action_space.high).astype(np.float32) | def burnin_action_func():
return np.random.uniform(action_space.low, action_space.high).astype(np.float32)<|docstring|>Select random actions until model is updated one or more times.<|endoftext|> |
5bb2556645933927abf01d34def954c0e70a74a72bc97c975fefc8695d8c785a | @with_feature_flags(ALERT_REPORTS=False)
@pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule_disabled(self):
'\n ReportSchedule Api: Test get report schedule 404s when feature is disabled\n '
report_schedule = db.session.query(ReportSchedule).filter((ReportSchedule.name == 'name1')).first()
self.login(username='admin')
uri = f'api/v1/report/{report_schedule.id}'
rv = self.client.get(uri)
assert (rv.status_code == 404) | ReportSchedule Api: Test get report schedule 404s when feature is disabled | tests/integration_tests/reports/api_tests.py | test_get_report_schedule_disabled | CodeingBoy/superset | 2 | python | @with_feature_flags(ALERT_REPORTS=False)
@pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule_disabled(self):
'\n \n '
report_schedule = db.session.query(ReportSchedule).filter((ReportSchedule.name == 'name1')).first()
self.login(username='admin')
uri = f'api/v1/report/{report_schedule.id}'
rv = self.client.get(uri)
assert (rv.status_code == 404) | @with_feature_flags(ALERT_REPORTS=False)
@pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule_disabled(self):
'\n \n '
report_schedule = db.session.query(ReportSchedule).filter((ReportSchedule.name == 'name1')).first()
self.login(username='admin')
uri = f'api/v1/report/{report_schedule.id}'
rv = self.client.get(uri)
assert (rv.status_code == 404)<|docstring|>ReportSchedule Api: Test get report schedule 404s when feature is disabled<|endoftext|> |
682468918c6f242615652b91c8b03f17a77b829f61aac356c395b81262fdd350 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule(self):
'\n ReportSchedule Api: Test get report schedule\n '
report_schedule = db.session.query(ReportSchedule).filter((ReportSchedule.name == 'name1')).first()
self.login(username='admin')
uri = f'api/v1/report/{report_schedule.id}'
rv = self.get_assert_metric(uri, 'get')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 200)
expected_result = {'active': report_schedule.active, 'chart': {'id': report_schedule.chart.id, 'slice_name': report_schedule.chart.slice_name, 'viz_type': report_schedule.chart.viz_type}, 'context_markdown': report_schedule.context_markdown, 'crontab': report_schedule.crontab, 'dashboard': None, 'database': {'id': report_schedule.database.id, 'database_name': report_schedule.database.database_name}, 'description': report_schedule.description, 'grace_period': report_schedule.grace_period, 'id': report_schedule.id, 'last_eval_dttm': report_schedule.last_eval_dttm, 'last_state': report_schedule.last_state, 'last_value': report_schedule.last_value, 'last_value_row_json': report_schedule.last_value_row_json, 'log_retention': report_schedule.log_retention, 'name': report_schedule.name, 'recipients': [{'id': report_schedule.recipients[0].id, 'recipient_config_json': '{"target": "example@example.com"}', 'type': 'Email'}], 'timezone': report_schedule.timezone, 'type': report_schedule.type, 'validator_config_json': report_schedule.validator_config_json, 'validator_type': report_schedule.validator_type}
for key in expected_result:
assert (data['result'][key] == expected_result[key])
assert ({'first_name': 'admin', 'id': 1, 'last_name': 'user'} in data['result']['owners'])
assert ({'first_name': 'alpha', 'id': 5, 'last_name': 'user'} in data['result']['owners'])
assert (len(data['result']['owners']) == 2) | ReportSchedule Api: Test get report schedule | tests/integration_tests/reports/api_tests.py | test_get_report_schedule | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule(self):
'\n \n '
report_schedule = db.session.query(ReportSchedule).filter((ReportSchedule.name == 'name1')).first()
self.login(username='admin')
uri = f'api/v1/report/{report_schedule.id}'
rv = self.get_assert_metric(uri, 'get')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 200)
expected_result = {'active': report_schedule.active, 'chart': {'id': report_schedule.chart.id, 'slice_name': report_schedule.chart.slice_name, 'viz_type': report_schedule.chart.viz_type}, 'context_markdown': report_schedule.context_markdown, 'crontab': report_schedule.crontab, 'dashboard': None, 'database': {'id': report_schedule.database.id, 'database_name': report_schedule.database.database_name}, 'description': report_schedule.description, 'grace_period': report_schedule.grace_period, 'id': report_schedule.id, 'last_eval_dttm': report_schedule.last_eval_dttm, 'last_state': report_schedule.last_state, 'last_value': report_schedule.last_value, 'last_value_row_json': report_schedule.last_value_row_json, 'log_retention': report_schedule.log_retention, 'name': report_schedule.name, 'recipients': [{'id': report_schedule.recipients[0].id, 'recipient_config_json': '{"target": "example@example.com"}', 'type': 'Email'}], 'timezone': report_schedule.timezone, 'type': report_schedule.type, 'validator_config_json': report_schedule.validator_config_json, 'validator_type': report_schedule.validator_type}
for key in expected_result:
assert (data['result'][key] == expected_result[key])
assert ({'first_name': 'admin', 'id': 1, 'last_name': 'user'} in data['result']['owners'])
assert ({'first_name': 'alpha', 'id': 5, 'last_name': 'user'} in data['result']['owners'])
assert (len(data['result']['owners']) == 2) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule(self):
'\n \n '
report_schedule = db.session.query(ReportSchedule).filter((ReportSchedule.name == 'name1')).first()
self.login(username='admin')
uri = f'api/v1/report/{report_schedule.id}'
rv = self.get_assert_metric(uri, 'get')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 200)
expected_result = {'active': report_schedule.active, 'chart': {'id': report_schedule.chart.id, 'slice_name': report_schedule.chart.slice_name, 'viz_type': report_schedule.chart.viz_type}, 'context_markdown': report_schedule.context_markdown, 'crontab': report_schedule.crontab, 'dashboard': None, 'database': {'id': report_schedule.database.id, 'database_name': report_schedule.database.database_name}, 'description': report_schedule.description, 'grace_period': report_schedule.grace_period, 'id': report_schedule.id, 'last_eval_dttm': report_schedule.last_eval_dttm, 'last_state': report_schedule.last_state, 'last_value': report_schedule.last_value, 'last_value_row_json': report_schedule.last_value_row_json, 'log_retention': report_schedule.log_retention, 'name': report_schedule.name, 'recipients': [{'id': report_schedule.recipients[0].id, 'recipient_config_json': '{"target": "example@example.com"}', 'type': 'Email'}], 'timezone': report_schedule.timezone, 'type': report_schedule.type, 'validator_config_json': report_schedule.validator_config_json, 'validator_type': report_schedule.validator_type}
for key in expected_result:
assert (data['result'][key] == expected_result[key])
assert ({'first_name': 'admin', 'id': 1, 'last_name': 'user'} in data['result']['owners'])
assert ({'first_name': 'alpha', 'id': 5, 'last_name': 'user'} in data['result']['owners'])
assert (len(data['result']['owners']) == 2)<|docstring|>ReportSchedule Api: Test get report schedule<|endoftext|> |
c19c08ba7e8d6d4da0784249af909de3f89060b69169b91958818207270232a4 | def test_info_report_schedule(self):
'\n ReportSchedule API: Test info\n '
self.login(username='admin')
uri = f'api/v1/report/_info'
rv = self.get_assert_metric(uri, 'info')
assert (rv.status_code == 200) | ReportSchedule API: Test info | tests/integration_tests/reports/api_tests.py | test_info_report_schedule | CodeingBoy/superset | 2 | python | def test_info_report_schedule(self):
'\n \n '
self.login(username='admin')
uri = f'api/v1/report/_info'
rv = self.get_assert_metric(uri, 'info')
assert (rv.status_code == 200) | def test_info_report_schedule(self):
'\n \n '
self.login(username='admin')
uri = f'api/v1/report/_info'
rv = self.get_assert_metric(uri, 'info')
assert (rv.status_code == 200)<|docstring|>ReportSchedule API: Test info<|endoftext|> |
a3807f4d5f448ea1cd13eddace9e133c4bc92d0cd0c270e0672fec46f5e24100 | def test_info_security_report(self):
'\n ReportSchedule API: Test info security\n '
self.login(username='admin')
params = {'keys': ['permissions']}
uri = f'api/v1/report/_info?q={prison.dumps(params)}'
rv = self.get_assert_metric(uri, 'info')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 200)
assert ('can_read' in data['permissions'])
assert ('can_write' in data['permissions'])
assert (len(data['permissions']) == 2) | ReportSchedule API: Test info security | tests/integration_tests/reports/api_tests.py | test_info_security_report | CodeingBoy/superset | 2 | python | def test_info_security_report(self):
'\n \n '
self.login(username='admin')
params = {'keys': ['permissions']}
uri = f'api/v1/report/_info?q={prison.dumps(params)}'
rv = self.get_assert_metric(uri, 'info')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 200)
assert ('can_read' in data['permissions'])
assert ('can_write' in data['permissions'])
assert (len(data['permissions']) == 2) | def test_info_security_report(self):
'\n \n '
self.login(username='admin')
params = {'keys': ['permissions']}
uri = f'api/v1/report/_info?q={prison.dumps(params)}'
rv = self.get_assert_metric(uri, 'info')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 200)
assert ('can_read' in data['permissions'])
assert ('can_write' in data['permissions'])
assert (len(data['permissions']) == 2)<|docstring|>ReportSchedule API: Test info security<|endoftext|> |
18fc55324cc9418046c391abdfef9eaf3990d82bd7610d3509acdcb16ab4f349 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule_not_found(self):
'\n ReportSchedule Api: Test get report schedule not found\n '
max_id = db.session.query(func.max(ReportSchedule.id)).scalar()
self.login(username='admin')
uri = f'api/v1/report/{(max_id + 1)}'
rv = self.get_assert_metric(uri, 'get')
assert (rv.status_code == 404) | ReportSchedule Api: Test get report schedule not found | tests/integration_tests/reports/api_tests.py | test_get_report_schedule_not_found | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule_not_found(self):
'\n \n '
max_id = db.session.query(func.max(ReportSchedule.id)).scalar()
self.login(username='admin')
uri = f'api/v1/report/{(max_id + 1)}'
rv = self.get_assert_metric(uri, 'get')
assert (rv.status_code == 404) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_report_schedule_not_found(self):
'\n \n '
max_id = db.session.query(func.max(ReportSchedule.id)).scalar()
self.login(username='admin')
uri = f'api/v1/report/{(max_id + 1)}'
rv = self.get_assert_metric(uri, 'get')
assert (rv.status_code == 404)<|docstring|>ReportSchedule Api: Test get report schedule not found<|endoftext|> |
4eb2b32cd95b443f4953a23e299060c5542dae7af26f7184431b1902efebf78e | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule(self):
'\n ReportSchedule Api: Test get list report schedules\n '
self.login(username='admin')
uri = f'api/v1/report/'
rv = self.get_assert_metric(uri, 'get_list')
expected_fields = ['active', 'changed_by', 'changed_on', 'changed_on_delta_humanized', 'chart_id', 'created_by', 'created_on', 'creation_method', 'crontab', 'crontab_humanized', 'dashboard_id', 'description', 'id', 'last_eval_dttm', 'last_state', 'name', 'owners', 'recipients', 'timezone', 'type']
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)
data_keys = sorted(list(data['result'][0].keys()))
assert (expected_fields == data_keys)
expected_owners_fields = ['first_name', 'id', 'last_name']
data_keys = sorted(list(data['result'][0]['owners'][0].keys()))
assert (expected_owners_fields == data_keys)
expected_recipients_fields = ['id', 'type']
data_keys = sorted(list(data['result'][1]['recipients'][0].keys()))
assert (expected_recipients_fields == data_keys) | ReportSchedule Api: Test get list report schedules | tests/integration_tests/reports/api_tests.py | test_get_list_report_schedule | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule(self):
'\n \n '
self.login(username='admin')
uri = f'api/v1/report/'
rv = self.get_assert_metric(uri, 'get_list')
expected_fields = ['active', 'changed_by', 'changed_on', 'changed_on_delta_humanized', 'chart_id', 'created_by', 'created_on', 'creation_method', 'crontab', 'crontab_humanized', 'dashboard_id', 'description', 'id', 'last_eval_dttm', 'last_state', 'name', 'owners', 'recipients', 'timezone', 'type']
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)
data_keys = sorted(list(data['result'][0].keys()))
assert (expected_fields == data_keys)
expected_owners_fields = ['first_name', 'id', 'last_name']
data_keys = sorted(list(data['result'][0]['owners'][0].keys()))
assert (expected_owners_fields == data_keys)
expected_recipients_fields = ['id', 'type']
data_keys = sorted(list(data['result'][1]['recipients'][0].keys()))
assert (expected_recipients_fields == data_keys) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule(self):
'\n \n '
self.login(username='admin')
uri = f'api/v1/report/'
rv = self.get_assert_metric(uri, 'get_list')
expected_fields = ['active', 'changed_by', 'changed_on', 'changed_on_delta_humanized', 'chart_id', 'created_by', 'created_on', 'creation_method', 'crontab', 'crontab_humanized', 'dashboard_id', 'description', 'id', 'last_eval_dttm', 'last_state', 'name', 'owners', 'recipients', 'timezone', 'type']
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)
data_keys = sorted(list(data['result'][0].keys()))
assert (expected_fields == data_keys)
expected_owners_fields = ['first_name', 'id', 'last_name']
data_keys = sorted(list(data['result'][0]['owners'][0].keys()))
assert (expected_owners_fields == data_keys)
expected_recipients_fields = ['id', 'type']
data_keys = sorted(list(data['result'][1]['recipients'][0].keys()))
assert (expected_recipients_fields == data_keys)<|docstring|>ReportSchedule Api: Test get list report schedules<|endoftext|> |
07dd9c4b5e398cea0eb2c1ca7e954a9320dd0a19f4cf6a18c083b74d06b33e59 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_sorting(self):
'\n ReportSchedule Api: Test sorting on get list report schedules\n '
self.login(username='admin')
uri = 'api/v1/report/'
order_columns = ['active', 'created_by.first_name', 'changed_by.first_name', 'changed_on', 'changed_on_delta_humanized', 'created_on', 'crontab', 'description', 'last_eval_dttm', 'name', 'type', 'crontab_humanized']
for order_column in order_columns:
arguments = {'order_column': order_column, 'order_direction': 'asc'}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200) | ReportSchedule Api: Test sorting on get list report schedules | tests/integration_tests/reports/api_tests.py | test_get_list_report_schedule_sorting | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_sorting(self):
'\n \n '
self.login(username='admin')
uri = 'api/v1/report/'
order_columns = ['active', 'created_by.first_name', 'changed_by.first_name', 'changed_on', 'changed_on_delta_humanized', 'created_on', 'crontab', 'description', 'last_eval_dttm', 'name', 'type', 'crontab_humanized']
for order_column in order_columns:
arguments = {'order_column': order_column, 'order_direction': 'asc'}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_sorting(self):
'\n \n '
self.login(username='admin')
uri = 'api/v1/report/'
order_columns = ['active', 'created_by.first_name', 'changed_by.first_name', 'changed_on', 'changed_on_delta_humanized', 'created_on', 'crontab', 'description', 'last_eval_dttm', 'name', 'type', 'crontab_humanized']
for order_column in order_columns:
arguments = {'order_column': order_column, 'order_direction': 'asc'}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)<|docstring|>ReportSchedule Api: Test sorting on get list report schedules<|endoftext|> |
4f416f348a3a0bfc1b4076b5b8b282805ebf7914824a1afbc9594f0a831d4577 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_name(self):
'\n ReportSchedule Api: Test filter name on get list report schedules\n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'name', 'opr': 'ct', 'value': '2'}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
expected_result = {'name': 'name2'}
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 1)
assert (data['result'][0] == expected_result) | ReportSchedule Api: Test filter name on get list report schedules | tests/integration_tests/reports/api_tests.py | test_get_list_report_schedule_filter_name | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_name(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'name', 'opr': 'ct', 'value': '2'}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
expected_result = {'name': 'name2'}
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 1)
assert (data['result'][0] == expected_result) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_name(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'name', 'opr': 'ct', 'value': '2'}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
expected_result = {'name': 'name2'}
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 1)
assert (data['result'][0] == expected_result)<|docstring|>ReportSchedule Api: Test filter name on get list report schedules<|endoftext|> |
acf7ec0dbd9c41d3c705e2dbf59bdf56b1474206692824f64e39f4768a955859 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_custom(self):
'\n ReportSchedule Api: Test custom filter on get list report schedules\n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'name', 'opr': 'report_all_text', 'value': 'table3'}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
expected_result = {'name': 'name3'}
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 1)
assert (data['result'][0] == expected_result) | ReportSchedule Api: Test custom filter on get list report schedules | tests/integration_tests/reports/api_tests.py | test_get_list_report_schedule_filter_custom | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_custom(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'name', 'opr': 'report_all_text', 'value': 'table3'}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
expected_result = {'name': 'name3'}
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 1)
assert (data['result'][0] == expected_result) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_custom(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'name', 'opr': 'report_all_text', 'value': 'table3'}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
expected_result = {'name': 'name3'}
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 1)
assert (data['result'][0] == expected_result)<|docstring|>ReportSchedule Api: Test custom filter on get list report schedules<|endoftext|> |
001725c054ff96f270f96bc7fa4717d1619f48087114fb9bddb785c72ec9eba7 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_active(self):
'\n ReportSchedule Api: Test active filter on get list report schedules\n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'active', 'opr': 'eq', 'value': True}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT) | ReportSchedule Api: Test active filter on get list report schedules | tests/integration_tests/reports/api_tests.py | test_get_list_report_schedule_filter_active | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_active(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'active', 'opr': 'eq', 'value': True}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_active(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'active', 'opr': 'eq', 'value': True}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)<|docstring|>ReportSchedule Api: Test active filter on get list report schedules<|endoftext|> |
9a1c01b6da0886a1660e85bc83f4873ea9aff9243a5c0e749e4398afe9b8f6e8 | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_type(self):
'\n ReportSchedule Api: Test type filter on get list report schedules\n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'type', 'opr': 'eq', 'value': ReportScheduleType.ALERT}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)
arguments = {'columns': ['name'], 'filters': [{'col': 'type', 'opr': 'eq', 'value': ReportScheduleType.REPORT}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 0) | ReportSchedule Api: Test type filter on get list report schedules | tests/integration_tests/reports/api_tests.py | test_get_list_report_schedule_filter_type | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_type(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'type', 'opr': 'eq', 'value': ReportScheduleType.ALERT}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)
arguments = {'columns': ['name'], 'filters': [{'col': 'type', 'opr': 'eq', 'value': ReportScheduleType.REPORT}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 0) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_list_report_schedule_filter_type(self):
'\n \n '
self.login(username='admin')
arguments = {'columns': ['name'], 'filters': [{'col': 'type', 'opr': 'eq', 'value': ReportScheduleType.ALERT}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == REPORTS_COUNT)
arguments = {'columns': ['name'], 'filters': [{'col': 'type', 'opr': 'eq', 'value': ReportScheduleType.REPORT}]}
uri = f'api/v1/report/?q={prison.dumps(arguments)}'
rv = self.get_assert_metric(uri, 'get_list')
assert (rv.status_code == 200)
data = json.loads(rv.data.decode('utf-8'))
assert (data['count'] == 0)<|docstring|>ReportSchedule Api: Test type filter on get list report schedules<|endoftext|> |
3e99aba5e6c425cae320ec0241cf0df05c3c59fb566bd9066135e5eb3dcfab0d | @pytest.mark.usefixtures('create_report_schedules')
def test_get_related_report_schedule(self):
'\n ReportSchedule Api: Test get releated report schedule\n '
self.login(username='admin')
related_columns = ['created_by', 'chart', 'dashboard', 'database']
for related_column in related_columns:
uri = f'api/v1/report/related/{related_column}'
rv = self.client.get(uri)
assert (rv.status_code == 200) | ReportSchedule Api: Test get releated report schedule | tests/integration_tests/reports/api_tests.py | test_get_related_report_schedule | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_get_related_report_schedule(self):
'\n \n '
self.login(username='admin')
related_columns = ['created_by', 'chart', 'dashboard', 'database']
for related_column in related_columns:
uri = f'api/v1/report/related/{related_column}'
rv = self.client.get(uri)
assert (rv.status_code == 200) | @pytest.mark.usefixtures('create_report_schedules')
def test_get_related_report_schedule(self):
'\n \n '
self.login(username='admin')
related_columns = ['created_by', 'chart', 'dashboard', 'database']
for related_column in related_columns:
uri = f'api/v1/report/related/{related_column}'
rv = self.client.get(uri)
assert (rv.status_code == 200)<|docstring|>ReportSchedule Api: Test get releated report schedule<|endoftext|> |
644877590ef9918b88872eb6a21b8e0e46fa05a730bf8e44e2b1cb827f45e9e9 | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices')
def test_create_report_schedule(self):
'\n ReportSchedule Api: Test create report schedule\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'grace_period': 14400, 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
created_model = db.session.query(ReportSchedule).get(data.get('id'))
assert (created_model is not None)
assert (created_model.name == report_schedule_data['name'])
assert (created_model.grace_period == report_schedule_data['grace_period'])
assert (created_model.working_timeout == report_schedule_data['working_timeout'])
assert (created_model.description == report_schedule_data['description'])
assert (created_model.crontab == report_schedule_data['crontab'])
assert (created_model.chart.id == report_schedule_data['chart'])
assert (created_model.database.id == report_schedule_data['database'])
assert (created_model.creation_method == report_schedule_data['creation_method'])
db.session.delete(created_model)
db.session.commit() | ReportSchedule Api: Test create report schedule | tests/integration_tests/reports/api_tests.py | test_create_report_schedule | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices')
def test_create_report_schedule(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'grace_period': 14400, 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
created_model = db.session.query(ReportSchedule).get(data.get('id'))
assert (created_model is not None)
assert (created_model.name == report_schedule_data['name'])
assert (created_model.grace_period == report_schedule_data['grace_period'])
assert (created_model.working_timeout == report_schedule_data['working_timeout'])
assert (created_model.description == report_schedule_data['description'])
assert (created_model.crontab == report_schedule_data['crontab'])
assert (created_model.chart.id == report_schedule_data['chart'])
assert (created_model.database.id == report_schedule_data['database'])
assert (created_model.creation_method == report_schedule_data['creation_method'])
db.session.delete(created_model)
db.session.commit() | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices')
def test_create_report_schedule(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'grace_period': 14400, 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
created_model = db.session.query(ReportSchedule).get(data.get('id'))
assert (created_model is not None)
assert (created_model.name == report_schedule_data['name'])
assert (created_model.grace_period == report_schedule_data['grace_period'])
assert (created_model.working_timeout == report_schedule_data['working_timeout'])
assert (created_model.description == report_schedule_data['description'])
assert (created_model.crontab == report_schedule_data['crontab'])
assert (created_model.chart.id == report_schedule_data['chart'])
assert (created_model.database.id == report_schedule_data['database'])
assert (created_model.creation_method == report_schedule_data['creation_method'])
db.session.delete(created_model)
db.session.commit()<|docstring|>ReportSchedule Api: Test create report schedule<|endoftext|> |
59d0ea5547f01f35e06b23d6e6c499d1c5601865a5edef50b5d21979bc776bca | @pytest.mark.usefixtures('create_report_schedules')
def test_create_report_schedule_uniqueness(self):
'\n ReportSchedule Api: Test create report schedule uniqueness\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 422)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'name': ['An alert named "name3" already exists']}})
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.client.post(uri, json=report_schedule_data)
assert (rv.status_code == 201)
data = json.loads(rv.data.decode('utf-8'))
created_model = db.session.query(ReportSchedule).get(data.get('id'))
db.session.delete(created_model)
db.session.commit() | ReportSchedule Api: Test create report schedule uniqueness | tests/integration_tests/reports/api_tests.py | test_create_report_schedule_uniqueness | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('create_report_schedules')
def test_create_report_schedule_uniqueness(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 422)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'name': ['An alert named "name3" already exists']}})
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.client.post(uri, json=report_schedule_data)
assert (rv.status_code == 201)
data = json.loads(rv.data.decode('utf-8'))
created_model = db.session.query(ReportSchedule).get(data.get('id'))
db.session.delete(created_model)
db.session.commit() | @pytest.mark.usefixtures('create_report_schedules')
def test_create_report_schedule_uniqueness(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 422)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'name': ['An alert named "name3" already exists']}})
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.client.post(uri, json=report_schedule_data)
assert (rv.status_code == 201)
data = json.loads(rv.data.decode('utf-8'))
created_model = db.session.query(ReportSchedule).get(data.get('id'))
db.session.delete(created_model)
db.session.commit()<|docstring|>ReportSchedule Api: Test create report schedule uniqueness<|endoftext|> |
b028e210f2665eb913180d69681779beb206cceec0cd9493c4e690da12784c08 | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_report_schedule_schema(self):
'\n ReportSchedule Api: Test create report schedule schema check\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': (- 10), 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'grace_period': (- 10), 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new4', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': chart.id, 'dashboard': None, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': None, 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': None, 'dashboard': dashboard.id, 'database': example_db.id}
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'timezone': ['Field may not be null.']}})
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': 'this is not a timezone', 'dashboard': dashboard.id, 'database': example_db.id}
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'timezone': [f"Must be one of: {', '.join(pytz.all_timezones)}."]}})
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new6', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': 'America/Los_Angeles', 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (data['result']['timezone'] == 'America/Los_Angeles')
assert (rv.status_code == 201) | ReportSchedule Api: Test create report schedule schema check | tests/integration_tests/reports/api_tests.py | test_create_report_schedule_schema | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_report_schedule_schema(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': (- 10), 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'grace_period': (- 10), 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new4', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': chart.id, 'dashboard': None, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': None, 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': None, 'dashboard': dashboard.id, 'database': example_db.id}
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'timezone': ['Field may not be null.']}})
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': 'this is not a timezone', 'dashboard': dashboard.id, 'database': example_db.id}
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'timezone': [f"Must be one of: {', '.join(pytz.all_timezones)}."]}})
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new6', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': 'America/Los_Angeles', 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (data['result']['timezone'] == 'America/Los_Angeles')
assert (rv.status_code == 201) | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_report_schedule_schema(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': (- 10), 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'grace_period': (- 10), 'working_timeout': 3600, 'chart': chart.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new4', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': chart.id, 'dashboard': None, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'chart': None, 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': None, 'dashboard': dashboard.id, 'database': example_db.id}
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'timezone': ['Field may not be null.']}})
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new5', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': 'this is not a timezone', 'dashboard': dashboard.id, 'database': example_db.id}
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 400)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'timezone': [f"Must be one of: {', '.join(pytz.all_timezones)}."]}})
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new6', 'description': 'description', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'crontab': '0 9 * * *', 'recipients': [{'type': ReportRecipientType.EMAIL, 'recipient_config_json': {'target': 'example@example.com'}}, {'type': ReportRecipientType.SLACK, 'recipient_config_json': {'target': 'channel'}}], 'working_timeout': 3600, 'timezone': 'America/Los_Angeles', 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (data['result']['timezone'] == 'America/Los_Angeles')
assert (rv.status_code == 201)<|docstring|>ReportSchedule Api: Test create report schedule schema check<|endoftext|> |
6277081b4d7257df6228db371b948cbc68ff63a1318ba33fc9eb3cb03d7cdb7c | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_unsaved_report_schedule_schema(self):
'\n ReportSchedule Api: Test create report schedule with unsaved chart\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'chart': 0}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 422)
assert (data['message']['chart'] == 'Please save your chart first, then try creating a new email report.') | ReportSchedule Api: Test create report schedule with unsaved chart | tests/integration_tests/reports/api_tests.py | test_unsaved_report_schedule_schema | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_unsaved_report_schedule_schema(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'chart': 0}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 422)
assert (data['message']['chart'] == 'Please save your chart first, then try creating a new email report.') | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_unsaved_report_schedule_schema(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'chart': 0}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 422)
assert (data['message']['chart'] == 'Please save your chart first, then try creating a new email report.')<|docstring|>ReportSchedule Api: Test create report schedule with unsaved chart<|endoftext|> |
48e17eb16276f3b8d85fe67038576e33089408f075be7f2f613b63d4c51b7445 | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_no_dashboard_report_schedule_schema(self):
'\n ReportSchedule Api: Test create report schedule with no dashboard id\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *'}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 422)
assert (data['message']['dashboard'] == 'Please save your dashboard first, then try creating a new email report.') | ReportSchedule Api: Test create report schedule with no dashboard id | tests/integration_tests/reports/api_tests.py | test_no_dashboard_report_schedule_schema | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_no_dashboard_report_schedule_schema(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *'}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 422)
assert (data['message']['dashboard'] == 'Please save your dashboard first, then try creating a new email report.') | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_no_dashboard_report_schedule_schema(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name3', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *'}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 422)
assert (data['message']['dashboard'] == 'Please save your dashboard first, then try creating a new email report.')<|docstring|>ReportSchedule Api: Test create report schedule with no dashboard id<|endoftext|> |
60e351f37cf30e7e208a7b044403691354c516b1e012ed224554ab76881f2966 | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_multiple_creation_method_report_schedule_charts(self):
'\n ReportSchedule Api: Test create multiple reports with the same creation method\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name4', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name5', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 409)
assert (data == {'errors': [{'message': 'Resource already has an attached report.', 'error_type': 'GENERIC_COMMAND_ERROR', 'level': 'warning', 'extra': {'issue_codes': [{'code': 1010, 'message': 'Issue 1010 - Superset encountered an error while running a command.'}]}}]}) | ReportSchedule Api: Test create multiple reports with the same creation method | tests/integration_tests/reports/api_tests.py | test_create_multiple_creation_method_report_schedule_charts | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_multiple_creation_method_report_schedule_charts(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name4', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name5', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 409)
assert (data == {'errors': [{'message': 'Resource already has an attached report.', 'error_type': 'GENERIC_COMMAND_ERROR', 'level': 'warning', 'extra': {'issue_codes': [{'code': 1010, 'message': 'Issue 1010 - Superset encountered an error while running a command.'}]}}]}) | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_multiple_creation_method_report_schedule_charts(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name4', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name5', 'description': 'description', 'creation_method': ReportCreationMethod.CHARTS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'chart': chart.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 409)
assert (data == {'errors': [{'message': 'Resource already has an attached report.', 'error_type': 'GENERIC_COMMAND_ERROR', 'level': 'warning', 'extra': {'issue_codes': [{'code': 1010, 'message': 'Issue 1010 - Superset encountered an error while running a command.'}]}}]})<|docstring|>ReportSchedule Api: Test create multiple reports with the same creation method<|endoftext|> |
511a33f2a59390a2922f1658ad64e0232d02eb1e2cfa26f3968411f282fa2024 | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_multiple_creation_method_report_schedule_dashboards(self):
'\n ReportSchedule Api: Test create multiple reports with the same creation method\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name4', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'dashboard': dashboard.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name5', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'dashboard': dashboard.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 409)
assert (data == {'errors': [{'message': 'Resource already has an attached report.', 'error_type': 'GENERIC_COMMAND_ERROR', 'level': 'warning', 'extra': {'issue_codes': [{'code': 1010, 'message': 'Issue 1010 - Superset encountered an error while running a command.'}]}}]}) | ReportSchedule Api: Test create multiple reports with the same creation method | tests/integration_tests/reports/api_tests.py | test_create_multiple_creation_method_report_schedule_dashboards | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_multiple_creation_method_report_schedule_dashboards(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name4', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'dashboard': dashboard.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name5', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'dashboard': dashboard.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 409)
assert (data == {'errors': [{'message': 'Resource already has an attached report.', 'error_type': 'GENERIC_COMMAND_ERROR', 'level': 'warning', 'extra': {'issue_codes': [{'code': 1010, 'message': 'Issue 1010 - Superset encountered an error while running a command.'}]}}]}) | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices', 'create_report_schedules')
def test_create_multiple_creation_method_report_schedule_dashboards(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name4', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'dashboard': dashboard.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 201)
report_schedule_data = {'type': ReportScheduleType.REPORT, 'name': 'name5', 'description': 'description', 'creation_method': ReportCreationMethod.DASHBOARDS, 'crontab': '0 9 * * *', 'working_timeout': 3600, 'dashboard': dashboard.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
data = json.loads(rv.data.decode('utf-8'))
assert (rv.status_code == 409)
assert (data == {'errors': [{'message': 'Resource already has an attached report.', 'error_type': 'GENERIC_COMMAND_ERROR', 'level': 'warning', 'extra': {'issue_codes': [{'code': 1010, 'message': 'Issue 1010 - Superset encountered an error while running a command.'}]}}]})<|docstring|>ReportSchedule Api: Test create multiple reports with the same creation method<|endoftext|> |
1251472d87687708eb701cdb3ca56a2c8b65356d984d28ad356aff22d022a4a0 | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices')
def test_create_report_schedule_chart_dash_validation(self):
'\n ReportSchedule Api: Test create report schedule chart and dashboard validation\n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'chart': chart.id, 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 422)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'chart': 'Choose a chart or dashboard not both'}}) | ReportSchedule Api: Test create report schedule chart and dashboard validation | tests/integration_tests/reports/api_tests.py | test_create_report_schedule_chart_dash_validation | CodeingBoy/superset | 2 | python | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices')
def test_create_report_schedule_chart_dash_validation(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'chart': chart.id, 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 422)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'chart': 'Choose a chart or dashboard not both'}}) | @pytest.mark.usefixtures('load_birth_names_dashboard_with_slices')
def test_create_report_schedule_chart_dash_validation(self):
'\n \n '
self.login(username='admin')
chart = db.session.query(Slice).first()
dashboard = db.session.query(Dashboard).first()
example_db = get_example_database()
report_schedule_data = {'type': ReportScheduleType.ALERT, 'name': 'new3', 'description': 'description', 'crontab': '0 9 * * *', 'creation_method': ReportCreationMethod.ALERTS_REPORTS, 'chart': chart.id, 'dashboard': dashboard.id, 'database': example_db.id}
uri = 'api/v1/report/'
rv = self.post_assert_metric(uri, report_schedule_data, 'post')
assert (rv.status_code == 422)
data = json.loads(rv.data.decode('utf-8'))
assert (data == {'message': {'chart': 'Choose a chart or dashboard not both'}})<|docstring|>ReportSchedule Api: Test create report schedule chart and dashboard validation<|endoftext|> |
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