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 |
|---|---|---|---|---|---|---|---|---|---|
9f93183edebcc54aa3fe74e3738a48c11bf7c3374c57327a68d4c943d0f4da1b | def _check_nd_numpy_array(name, array, num_dims):
'Raises an exception if `array` is not a `num_dims`-D numpy array.'
if (len(array.shape) != num_dims):
raise ValueError('The argument {!r} should be a {}D array, not of shape {}'.format(name, num_dims, array.shape)) | Raises an exception if `array` is not a `num_dims`-D numpy array. | surface_distance/metrics.py | _check_nd_numpy_array | capitaltg/surface-distance | 314 | python | def _check_nd_numpy_array(name, array, num_dims):
if (len(array.shape) != num_dims):
raise ValueError('The argument {!r} should be a {}D array, not of shape {}'.format(name, num_dims, array.shape)) | def _check_nd_numpy_array(name, array, num_dims):
if (len(array.shape) != num_dims):
raise ValueError('The argument {!r} should be a {}D array, not of shape {}'.format(name, num_dims, array.shape))<|docstring|>Raises an exception if `array` is not a `num_dims`-D numpy array.<|endoftext|> |
2d0746efa743bf10b0c4d2a693318eb92617d9eb67c6eb407b7b9141649cf77a | def _compute_bounding_box(mask):
"Computes the bounding box of the masks.\n\n This function generalizes to arbitrary number of dimensions great or equal\n to 1.\n\n Args:\n mask: The 2D or 3D numpy mask, where '0' means background and non-zero means\n foreground.\n\n Returns:\n A tuple:\n - The c... | Computes the bounding box of the masks.
This function generalizes to arbitrary number of dimensions great or equal
to 1.
Args:
mask: The 2D or 3D numpy mask, where '0' means background and non-zero means
foreground.
Returns:
A tuple:
- The coordinates of the first point of the bounding box (smallest on al... | surface_distance/metrics.py | _compute_bounding_box | capitaltg/surface-distance | 314 | python | def _compute_bounding_box(mask):
"Computes the bounding box of the masks.\n\n This function generalizes to arbitrary number of dimensions great or equal\n to 1.\n\n Args:\n mask: The 2D or 3D numpy mask, where '0' means background and non-zero means\n foreground.\n\n Returns:\n A tuple:\n - The c... | def _compute_bounding_box(mask):
"Computes the bounding box of the masks.\n\n This function generalizes to arbitrary number of dimensions great or equal\n to 1.\n\n Args:\n mask: The 2D or 3D numpy mask, where '0' means background and non-zero means\n foreground.\n\n Returns:\n A tuple:\n - The c... |
f894cf25bcba26ba7ec3925429273af3215695cbc2b95b668be18005b2c2be7f | def _crop_to_bounding_box(mask, bbox_min, bbox_max):
'Crops a 2D or 3D mask to the bounding box specified by `bbox_{min,max}`.'
cropmask = np.zeros(((bbox_max - bbox_min) + 2), np.uint8)
num_dims = len(mask.shape)
if (num_dims == 2):
cropmask[(0:(- 1), 0:(- 1))] = mask[(bbox_min[0]:(bbox_max[0] ... | Crops a 2D or 3D mask to the bounding box specified by `bbox_{min,max}`. | surface_distance/metrics.py | _crop_to_bounding_box | capitaltg/surface-distance | 314 | python | def _crop_to_bounding_box(mask, bbox_min, bbox_max):
cropmask = np.zeros(((bbox_max - bbox_min) + 2), np.uint8)
num_dims = len(mask.shape)
if (num_dims == 2):
cropmask[(0:(- 1), 0:(- 1))] = mask[(bbox_min[0]:(bbox_max[0] + 1), bbox_min[1]:(bbox_max[1] + 1))]
elif (num_dims == 3):
cr... | def _crop_to_bounding_box(mask, bbox_min, bbox_max):
cropmask = np.zeros(((bbox_max - bbox_min) + 2), np.uint8)
num_dims = len(mask.shape)
if (num_dims == 2):
cropmask[(0:(- 1), 0:(- 1))] = mask[(bbox_min[0]:(bbox_max[0] + 1), bbox_min[1]:(bbox_max[1] + 1))]
elif (num_dims == 3):
cr... |
76f42adfbbc478d7b8d67903275216e84d4f35148422ccd4e07e99a8cc9d1084 | def _sort_distances_surfels(distances, surfel_areas):
'Sorts the two list with respect to the tuple of (distance, surfel_area).\n\n Args:\n distances: The distances from A to B (e.g. `distances_gt_to_pred`).\n surfel_areas: The surfel areas for A (e.g. `surfel_areas_gt`).\n\n Returns:\n A tuple of the so... | Sorts the two list with respect to the tuple of (distance, surfel_area).
Args:
distances: The distances from A to B (e.g. `distances_gt_to_pred`).
surfel_areas: The surfel areas for A (e.g. `surfel_areas_gt`).
Returns:
A tuple of the sorted (distances, surfel_areas). | surface_distance/metrics.py | _sort_distances_surfels | capitaltg/surface-distance | 314 | python | def _sort_distances_surfels(distances, surfel_areas):
'Sorts the two list with respect to the tuple of (distance, surfel_area).\n\n Args:\n distances: The distances from A to B (e.g. `distances_gt_to_pred`).\n surfel_areas: The surfel areas for A (e.g. `surfel_areas_gt`).\n\n Returns:\n A tuple of the so... | def _sort_distances_surfels(distances, surfel_areas):
'Sorts the two list with respect to the tuple of (distance, surfel_area).\n\n Args:\n distances: The distances from A to B (e.g. `distances_gt_to_pred`).\n surfel_areas: The surfel areas for A (e.g. `surfel_areas_gt`).\n\n Returns:\n A tuple of the so... |
5cc76c36e4e8cd5c5a79ef8b1a4515eb6f4ff6f031ff5164f7e229ed3c38aaa2 | def compute_surface_distances(mask_gt, mask_pred, spacing_mm):
'Computes closest distances from all surface points to the other surface.\n\n This function can be applied to 2D or 3D tensors. For 2D, both masks must be\n 2D and `spacing_mm` must be a 2-element list. For 3D, both masks must be 3D\n and `spacing_mm... | Computes closest distances from all surface points to the other surface.
This function can be applied to 2D or 3D tensors. For 2D, both masks must be
2D and `spacing_mm` must be a 2-element list. For 3D, both masks must be 3D
and `spacing_mm` must be a 3-element list. The description is done for the 2D
case, and the f... | surface_distance/metrics.py | compute_surface_distances | capitaltg/surface-distance | 314 | python | def compute_surface_distances(mask_gt, mask_pred, spacing_mm):
'Computes closest distances from all surface points to the other surface.\n\n This function can be applied to 2D or 3D tensors. For 2D, both masks must be\n 2D and `spacing_mm` must be a 2-element list. For 3D, both masks must be 3D\n and `spacing_mm... | def compute_surface_distances(mask_gt, mask_pred, spacing_mm):
'Computes closest distances from all surface points to the other surface.\n\n This function can be applied to 2D or 3D tensors. For 2D, both masks must be\n 2D and `spacing_mm` must be a 2-element list. For 3D, both masks must be 3D\n and `spacing_mm... |
9468613337b6f098551703cf30b04ae134f700e571fa2fd8f141f3ff570a295a | def compute_average_surface_distance(surface_distances):
'Returns the average surface distance.\n\n Computes the average surface distances by correctly taking the area of each\n surface element into account. Call compute_surface_distances(...) before, to\n obtain the `surface_distances` dict.\n\n Args:\n sur... | Returns the average surface distance.
Computes the average surface distances by correctly taking the area of each
surface element into account. Call compute_surface_distances(...) before, to
obtain the `surface_distances` dict.
Args:
surface_distances: dict with "distances_gt_to_pred", "distances_pred_to_gt"
"sur... | surface_distance/metrics.py | compute_average_surface_distance | capitaltg/surface-distance | 314 | python | def compute_average_surface_distance(surface_distances):
'Returns the average surface distance.\n\n Computes the average surface distances by correctly taking the area of each\n surface element into account. Call compute_surface_distances(...) before, to\n obtain the `surface_distances` dict.\n\n Args:\n sur... | def compute_average_surface_distance(surface_distances):
'Returns the average surface distance.\n\n Computes the average surface distances by correctly taking the area of each\n surface element into account. Call compute_surface_distances(...) before, to\n obtain the `surface_distances` dict.\n\n Args:\n sur... |
81e48d4bcad7af1d7b4dccd47fb5635d2a5b9ae63c406306313f2e8f7040c221 | def compute_robust_hausdorff(surface_distances, percent):
'Computes the robust Hausdorff distance.\n\n Computes the robust Hausdorff distance. "Robust", because it uses the\n `percent` percentile of the distances instead of the maximum distance. The\n percentage is computed by correctly taking the area of each s... | Computes the robust Hausdorff distance.
Computes the robust Hausdorff distance. "Robust", because it uses the
`percent` percentile of the distances instead of the maximum distance. The
percentage is computed by correctly taking the area of each surface element
into account.
Args:
surface_distances: dict with "dista... | surface_distance/metrics.py | compute_robust_hausdorff | capitaltg/surface-distance | 314 | python | def compute_robust_hausdorff(surface_distances, percent):
'Computes the robust Hausdorff distance.\n\n Computes the robust Hausdorff distance. "Robust", because it uses the\n `percent` percentile of the distances instead of the maximum distance. The\n percentage is computed by correctly taking the area of each s... | def compute_robust_hausdorff(surface_distances, percent):
'Computes the robust Hausdorff distance.\n\n Computes the robust Hausdorff distance. "Robust", because it uses the\n `percent` percentile of the distances instead of the maximum distance. The\n percentage is computed by correctly taking the area of each s... |
7c753fb5bf1d89af27e9b391e44449f238921f69bd34ec371fb644c6e37c470e | def compute_surface_overlap_at_tolerance(surface_distances, tolerance_mm):
'Computes the overlap of the surfaces at a specified tolerance.\n\n Computes the overlap of the ground truth surface with the predicted surface\n and vice versa allowing a specified tolerance (maximum surface-to-surface\n distance that is... | Computes the overlap of the surfaces at a specified tolerance.
Computes the overlap of the ground truth surface with the predicted surface
and vice versa allowing a specified tolerance (maximum surface-to-surface
distance that is regarded as overlapping). The overlapping fraction is
computed by correctly taking the ar... | surface_distance/metrics.py | compute_surface_overlap_at_tolerance | capitaltg/surface-distance | 314 | python | def compute_surface_overlap_at_tolerance(surface_distances, tolerance_mm):
'Computes the overlap of the surfaces at a specified tolerance.\n\n Computes the overlap of the ground truth surface with the predicted surface\n and vice versa allowing a specified tolerance (maximum surface-to-surface\n distance that is... | def compute_surface_overlap_at_tolerance(surface_distances, tolerance_mm):
'Computes the overlap of the surfaces at a specified tolerance.\n\n Computes the overlap of the ground truth surface with the predicted surface\n and vice versa allowing a specified tolerance (maximum surface-to-surface\n distance that is... |
e00c6527a5ea73360f75ba08c6d5eca202d2480c034e6bbdd689f822eed2523a | def compute_surface_dice_at_tolerance(surface_distances, tolerance_mm):
'Computes the _surface_ DICE coefficient at a specified tolerance.\n\n Computes the _surface_ DICE coefficient at a specified tolerance. Not to be\n confused with the standard _volumetric_ DICE coefficient. The surface DICE\n measures the ov... | Computes the _surface_ DICE coefficient at a specified tolerance.
Computes the _surface_ DICE coefficient at a specified tolerance. Not to be
confused with the standard _volumetric_ DICE coefficient. The surface DICE
measures the overlap of two surfaces instead of two volumes. A surface
element is counted as overlappi... | surface_distance/metrics.py | compute_surface_dice_at_tolerance | capitaltg/surface-distance | 314 | python | def compute_surface_dice_at_tolerance(surface_distances, tolerance_mm):
'Computes the _surface_ DICE coefficient at a specified tolerance.\n\n Computes the _surface_ DICE coefficient at a specified tolerance. Not to be\n confused with the standard _volumetric_ DICE coefficient. The surface DICE\n measures the ov... | def compute_surface_dice_at_tolerance(surface_distances, tolerance_mm):
'Computes the _surface_ DICE coefficient at a specified tolerance.\n\n Computes the _surface_ DICE coefficient at a specified tolerance. Not to be\n confused with the standard _volumetric_ DICE coefficient. The surface DICE\n measures the ov... |
2cd50922da64d10172e99ec023bfb230b220884606d4a99371bf60185138e8bc | def compute_dice_coefficient(mask_gt, mask_pred):
'Computes soerensen-dice coefficient.\n\n compute the soerensen-dice coefficient between the ground truth mask `mask_gt`\n and the predicted mask `mask_pred`.\n\n Args:\n mask_gt: 3-dim Numpy array of type bool. The ground truth mask.\n mask_pred: 3-dim Num... | Computes soerensen-dice coefficient.
compute the soerensen-dice coefficient between the ground truth mask `mask_gt`
and the predicted mask `mask_pred`.
Args:
mask_gt: 3-dim Numpy array of type bool. The ground truth mask.
mask_pred: 3-dim Numpy array of type bool. The predicted mask.
Returns:
the dice coeffcie... | surface_distance/metrics.py | compute_dice_coefficient | capitaltg/surface-distance | 314 | python | def compute_dice_coefficient(mask_gt, mask_pred):
'Computes soerensen-dice coefficient.\n\n compute the soerensen-dice coefficient between the ground truth mask `mask_gt`\n and the predicted mask `mask_pred`.\n\n Args:\n mask_gt: 3-dim Numpy array of type bool. The ground truth mask.\n mask_pred: 3-dim Num... | def compute_dice_coefficient(mask_gt, mask_pred):
'Computes soerensen-dice coefficient.\n\n compute the soerensen-dice coefficient between the ground truth mask `mask_gt`\n and the predicted mask `mask_pred`.\n\n Args:\n mask_gt: 3-dim Numpy array of type bool. The ground truth mask.\n mask_pred: 3-dim Num... |
edd0d652a34d55d4a34dd67da432c61e05d43cd72a73bb5267f004046cfbbf4b | def addUsers(rosterName, users):
'Adds a list of users to an existing roster.\n\n Users are always appended to the end of the roster.\n\n Args:\n rosterName: The name of the roster to modify.\n users: A list of User objects that will be added to the end of\n the roster. User objects c... | Adds a list of users to an existing roster.
Users are always appended to the end of the roster.
Args:
rosterName: The name of the roster to modify.
users: A list of User objects that will be added to the end of
the roster. User objects can be created with the
system.user.getUser and system.use... | src/system/roster.py | addUsers | thecesrom/8.1 | 1 | python | def addUsers(rosterName, users):
'Adds a list of users to an existing roster.\n\n Users are always appended to the end of the roster.\n\n Args:\n rosterName: The name of the roster to modify.\n users: A list of User objects that will be added to the end of\n the roster. User objects c... | def addUsers(rosterName, users):
'Adds a list of users to an existing roster.\n\n Users are always appended to the end of the roster.\n\n Args:\n rosterName: The name of the roster to modify.\n users: A list of User objects that will be added to the end of\n the roster. User objects c... |
260c7a121dbca8cfff88a30ad1fa2ad4642394cc80342d313ed0d8498150d584 | def createRoster(name, description):
'Creates a roster with the given name and description, if it does\n not already exist.\n\n This function was designed to run in the Gateway and in Perspective\n sessions. If creating rosters from Vision clients, use\n system.alarm.createRoster instead.\n\n Args:\n... | Creates a roster with the given name and description, if it does
not already exist.
This function was designed to run in the Gateway and in Perspective
sessions. If creating rosters from Vision clients, use
system.alarm.createRoster instead.
Args:
name: The name of the roster to create.
description: The descr... | src/system/roster.py | createRoster | thecesrom/8.1 | 1 | python | def createRoster(name, description):
'Creates a roster with the given name and description, if it does\n not already exist.\n\n This function was designed to run in the Gateway and in Perspective\n sessions. If creating rosters from Vision clients, use\n system.alarm.createRoster instead.\n\n Args:\n... | def createRoster(name, description):
'Creates a roster with the given name and description, if it does\n not already exist.\n\n This function was designed to run in the Gateway and in Perspective\n sessions. If creating rosters from Vision clients, use\n system.alarm.createRoster instead.\n\n Args:\n... |
0b33251c5cbca4e46257aedb64bcbf8e60786cd3d147bcb4872c61366f5584aa | def deleteRoster(rosterName):
'Deletes a roster with the given name.\n\n Args:\n rosterName: The name of the roster to delete.\n '
print(rosterName) | Deletes a roster with the given name.
Args:
rosterName: The name of the roster to delete. | src/system/roster.py | deleteRoster | thecesrom/8.1 | 1 | python | def deleteRoster(rosterName):
'Deletes a roster with the given name.\n\n Args:\n rosterName: The name of the roster to delete.\n '
print(rosterName) | def deleteRoster(rosterName):
'Deletes a roster with the given name.\n\n Args:\n rosterName: The name of the roster to delete.\n '
print(rosterName)<|docstring|>Deletes a roster with the given name.
Args:
rosterName: The name of the roster to delete.<|endoftext|> |
e2e1b7ab5d7207d6f117031f2a4b80fcac0eebda77dab6c4492c8f3ec354d525 | def getRosters():
'Returns a dictionary of rosters, where the key is the name of the\n roster, and the value is an array list of string user names.\n\n This function was designed to run in the Gateway and in Perspective\n sessions. If creating rosters from Vision clients, use\n system.alarm.getRosters i... | Returns a dictionary of rosters, where the key is the name of the
roster, and the value is an array list of string user names.
This function was designed to run in the Gateway and in Perspective
sessions. If creating rosters from Vision clients, use
system.alarm.getRosters instead.
Returns:
A dictionary that maps... | src/system/roster.py | getRosters | thecesrom/8.1 | 1 | python | def getRosters():
'Returns a dictionary of rosters, where the key is the name of the\n roster, and the value is an array list of string user names.\n\n This function was designed to run in the Gateway and in Perspective\n sessions. If creating rosters from Vision clients, use\n system.alarm.getRosters i... | def getRosters():
'Returns a dictionary of rosters, where the key is the name of the\n roster, and the value is an array list of string user names.\n\n This function was designed to run in the Gateway and in Perspective\n sessions. If creating rosters from Vision clients, use\n system.alarm.getRosters i... |
db39027696da9fa3fe84a9ea41db173b1a6ca3ced68f629602cbe4664ec6f053 | def removeUsers(rosterName, users):
'Removes one or more users from an existing roster.\n\n Args:\n rosterName: The name of the roster to modify.\n users: A list of user objects that will be added to the end of\n the roster. User objects can be created with the\n system.user.g... | Removes one or more users from an existing roster.
Args:
rosterName: The name of the roster to modify.
users: A list of user objects that will be added to the end of
the roster. User objects can be created with the
system.user.getUser and system.user.addUser functions. | src/system/roster.py | removeUsers | thecesrom/8.1 | 1 | python | def removeUsers(rosterName, users):
'Removes one or more users from an existing roster.\n\n Args:\n rosterName: The name of the roster to modify.\n users: A list of user objects that will be added to the end of\n the roster. User objects can be created with the\n system.user.g... | def removeUsers(rosterName, users):
'Removes one or more users from an existing roster.\n\n Args:\n rosterName: The name of the roster to modify.\n users: A list of user objects that will be added to the end of\n the roster. User objects can be created with the\n system.user.g... |
36001eb5586089ab272cdd2a5387f93910e6430bb9075181990fba6cb9fcf20d | def get_strategy(buffer_mem_size, block_size, block_row_size, block_slice_size):
' Get clustered writes best load strategy given the memory available for io optimization.\n\n Returns:\n ---------\n strategy\n '
if (buffer_mem_size < block_size):
raise ValueError('Buffer size too small fo... | Get clustered writes best load strategy given the memory available for io optimization.
Returns:
---------
strategy | repartition_experiments/algorithms/clustered_writes.py | get_strategy | big-data-lab-team/repartition_experiments | 0 | python | def get_strategy(buffer_mem_size, block_size, block_row_size, block_slice_size):
' Get clustered writes best load strategy given the memory available for io optimization.\n\n Returns:\n ---------\n strategy\n '
if (buffer_mem_size < block_size):
raise ValueError('Buffer size too small fo... | def get_strategy(buffer_mem_size, block_size, block_row_size, block_slice_size):
' Get clustered writes best load strategy given the memory available for io optimization.\n\n Returns:\n ---------\n strategy\n '
if (buffer_mem_size < block_size):
raise ValueError('Buffer size too small fo... |
f29d59b77a8911dfd765538bad57e0cce6adf6457c69e718b396eb44134692c9 | def compute_buffers(buffer_mem_size, strategy, origarr_size, cs, block_size, block_row_size, block_slice_size, partition, R, bytes_per_voxel):
'\n partition: partition tuple of R by O = nb chunks per dimension\n '
def get_last_slab():
return
buffers = dict()
index = 0
if (strategy... | partition: partition tuple of R by O = nb chunks per dimension | repartition_experiments/algorithms/clustered_writes.py | compute_buffers | big-data-lab-team/repartition_experiments | 0 | python | def compute_buffers(buffer_mem_size, strategy, origarr_size, cs, block_size, block_row_size, block_slice_size, partition, R, bytes_per_voxel):
'\n \n '
def get_last_slab():
return
buffers = dict()
index = 0
if (strategy == 2):
slices_per_buffer = math.floor((buffer_mem_siz... | def compute_buffers(buffer_mem_size, strategy, origarr_size, cs, block_size, block_row_size, block_slice_size, partition, R, bytes_per_voxel):
'\n \n '
def get_last_slab():
return
buffers = dict()
index = 0
if (strategy == 2):
slices_per_buffer = math.floor((buffer_mem_siz... |
4314f999db689e454cfa129666e17401f101d47a3eaf56dae9a7449bc5ac4fac | def clustered_writes(origarr_filepath, R, cs, bpv, m, ff, outdir_path):
' Implementation of the clustered strategy for splitting a 3D array.\n Output file names are following the following regex: outdir_path/{i}_{j}_{k}.extension\n WARNING: this implementation loads the whole input array in RAM. We had 250GB ... | Implementation of the clustered strategy for splitting a 3D array.
Output file names are following the following regex: outdir_path/{i}_{j}_{k}.extension
WARNING: this implementation loads the whole input array in RAM. We had 250GB of RAM for our experiments so we decided to use it.
Arguments:
----------
R: origi... | repartition_experiments/algorithms/clustered_writes.py | clustered_writes | big-data-lab-team/repartition_experiments | 0 | python | def clustered_writes(origarr_filepath, R, cs, bpv, m, ff, outdir_path):
' Implementation of the clustered strategy for splitting a 3D array.\n Output file names are following the following regex: outdir_path/{i}_{j}_{k}.extension\n WARNING: this implementation loads the whole input array in RAM. We had 250GB ... | def clustered_writes(origarr_filepath, R, cs, bpv, m, ff, outdir_path):
' Implementation of the clustered strategy for splitting a 3D array.\n Output file names are following the following regex: outdir_path/{i}_{j}_{k}.extension\n WARNING: this implementation loads the whole input array in RAM. We had 250GB ... |
4c36d80c2a0c73bb112ac65c161a75393fd8f63bafc38f5ed56c27527810d053 | def kill_proc_tree(pid, sig=signal.SIGTERM, include_parent=True, timeout=None, on_terminate=None):
'Kill a process tree (including grandchildren) with signal\n "sig" and return a (gone, still_alive) tuple.\n "on_terminate", if specified, is a callback function which is\n called as soon as a child terminate... | Kill a process tree (including grandchildren) with signal
"sig" and return a (gone, still_alive) tuple.
"on_terminate", if specified, is a callback function which is
called as soon as a child terminates. | helpers.py | kill_proc_tree | JavaScriptDude/PayPalAuthIntent | 1 | python | def kill_proc_tree(pid, sig=signal.SIGTERM, include_parent=True, timeout=None, on_terminate=None):
'Kill a process tree (including grandchildren) with signal\n "sig" and return a (gone, still_alive) tuple.\n "on_terminate", if specified, is a callback function which is\n called as soon as a child terminate... | def kill_proc_tree(pid, sig=signal.SIGTERM, include_parent=True, timeout=None, on_terminate=None):
'Kill a process tree (including grandchildren) with signal\n "sig" and return a (gone, still_alive) tuple.\n "on_terminate", if specified, is a callback function which is\n called as soon as a child terminate... |
0630056dc6e20e0e56411c5e34a0f74536548b5265e7e08bab921552826e9e99 | def compound_statement(env, node):
'\n Compound statement def for AST.\n interpret - runtime function for Evaluator (interpret first and second statement operators).\n '
node.first.interpret(env)
node.second.interpret(env) | Compound statement def for AST.
interpret - runtime function for Evaluator (interpret first and second statement operators). | src/Interpreter/Eval/common.py | compound_statement | PetukhovVictor/compiler2 | 3 | python | def compound_statement(env, node):
'\n Compound statement def for AST.\n interpret - runtime function for Evaluator (interpret first and second statement operators).\n '
node.first.interpret(env)
node.second.interpret(env) | def compound_statement(env, node):
'\n Compound statement def for AST.\n interpret - runtime function for Evaluator (interpret first and second statement operators).\n '
node.first.interpret(env)
node.second.interpret(env)<|docstring|>Compound statement def for AST.
interpret - runtime function for... |
923c29daab264df1d416469b6ca278e208315ed253467f1172e0f124ba4d0614 | def enumeration(env, node):
"\n 'Enumeration' statement class for AST.\n interpret - runtime function for Evaluator (empty function).\n "
return node.elements | 'Enumeration' statement class for AST.
interpret - runtime function for Evaluator (empty function). | src/Interpreter/Eval/common.py | enumeration | PetukhovVictor/compiler2 | 3 | python | def enumeration(env, node):
"\n 'Enumeration' statement class for AST.\n interpret - runtime function for Evaluator (empty function).\n "
return node.elements | def enumeration(env, node):
"\n 'Enumeration' statement class for AST.\n interpret - runtime function for Evaluator (empty function).\n "
return node.elements<|docstring|>'Enumeration' statement class for AST.
interpret - runtime function for Evaluator (empty function).<|endoftext|> |
4ada6f0e3a97b011d53bfcf6deba879bf95e20ddd730b97345c360c5016e58cb | def update(self):
' Update stats and account information from Slushpool. '
self.stats.update()
if (self.account is not None):
self.account.update() | Update stats and account information from Slushpool. | slushpool/__init__.py | update | RyanMalaspina/slushpool-python | 1 | python | def update(self):
' '
self.stats.update()
if (self.account is not None):
self.account.update() | def update(self):
' '
self.stats.update()
if (self.account is not None):
self.account.update()<|docstring|>Update stats and account information from Slushpool.<|endoftext|> |
f2e4ffae15f87e1c5f14f68ddcf32e7ac1c03391f70a6e0c50e2919d8b1dc565 | def orbit_to_poincare_polar(orbit):
'\n Convert an array of 6D Cartesian positions to Poincaré\n symplectic polar coordinates. These are similar to cylindrical\n coordinates.\n\n Parameters\n ----------\n\n '
if (orbit.norbits > 1):
raise RuntimeError('Can only use with one orbit.')
... | Convert an array of 6D Cartesian positions to Poincaré
symplectic polar coordinates. These are similar to cylindrical
coordinates.
Parameters
---------- | barchaos/experiments/util.py | orbit_to_poincare_polar | adrn/BarChaos | 0 | python | def orbit_to_poincare_polar(orbit):
'\n Convert an array of 6D Cartesian positions to Poincaré\n symplectic polar coordinates. These are similar to cylindrical\n coordinates.\n\n Parameters\n ----------\n\n '
if (orbit.norbits > 1):
raise RuntimeError('Can only use with one orbit.')
... | def orbit_to_poincare_polar(orbit):
'\n Convert an array of 6D Cartesian positions to Poincaré\n symplectic polar coordinates. These are similar to cylindrical\n coordinates.\n\n Parameters\n ----------\n\n '
if (orbit.norbits > 1):
raise RuntimeError('Can only use with one orbit.')
... |
dfa0b6f0903ac1d7538f4c521df1921ca465a7cde9f27545faf7ee38394ea302 | def init_app(self, application: Flask):
'\n Initialize Flask application\n\n :param application: Flask application to initialize with environment variables\n :return:\n '
variables = self._get_vars()
for (key, value) in variables.items():
application.config[key] = value | Initialize Flask application
:param application: Flask application to initialize with environment variables
:return: | flask_py_config_env/Environment.py | init_app | aaronestrada/flask-py-config-env | 0 | python | def init_app(self, application: Flask):
'\n Initialize Flask application\n\n :param application: Flask application to initialize with environment variables\n :return:\n '
variables = self._get_vars()
for (key, value) in variables.items():
application.config[key] = value | def init_app(self, application: Flask):
'\n Initialize Flask application\n\n :param application: Flask application to initialize with environment variables\n :return:\n '
variables = self._get_vars()
for (key, value) in variables.items():
application.config[key] = value<|... |
fbb0d0ca4d31a734c870a3ec505771eded76e52dda4448427aaad679979a3f7a | def get_argparse() -> ArgumentParser:
'\n Get argument parser.\n\n :return: argument parser.\n :rtype: ArgumentParser\n '
parser = ArgumentParser(prog='text-clf')
parser.add_argument('--config', type=str, required=False, default='config.yaml', help='Path to config')
return parser | Get argument parser.
:return: argument parser.
:rtype: ArgumentParser | text_clf/utils.py | get_argparse | igvasilev/text-classification-baseline | 0 | python | def get_argparse() -> ArgumentParser:
'\n Get argument parser.\n\n :return: argument parser.\n :rtype: ArgumentParser\n '
parser = ArgumentParser(prog='text-clf')
parser.add_argument('--config', type=str, required=False, default='config.yaml', help='Path to config')
return parser | def get_argparse() -> ArgumentParser:
'\n Get argument parser.\n\n :return: argument parser.\n :rtype: ArgumentParser\n '
parser = ArgumentParser(prog='text-clf')
parser.add_argument('--config', type=str, required=False, default='config.yaml', help='Path to config')
return parser<|docstring|... |
7e12492a12f725bd95a25838ec2ca90d0f797c86fe03bb3dd4dbf9c50bdab980 | def get_config(path_to_config: str) -> Dict[(str, Any)]:
'\n Get config.\n\n :param str path_to_config: path to config.\n :return: config.\n :rtype: Dict[str, Any]\n '
now = datetime.datetime.now()
with open(path_to_config, mode='r') as fp:
config = yaml.safe_load(fp)
config['path... | Get config.
:param str path_to_config: path to config.
:return: config.
:rtype: Dict[str, Any] | text_clf/utils.py | get_config | igvasilev/text-classification-baseline | 0 | python | def get_config(path_to_config: str) -> Dict[(str, Any)]:
'\n Get config.\n\n :param str path_to_config: path to config.\n :return: config.\n :rtype: Dict[str, Any]\n '
now = datetime.datetime.now()
with open(path_to_config, mode='r') as fp:
config = yaml.safe_load(fp)
config['path... | def get_config(path_to_config: str) -> Dict[(str, Any)]:
'\n Get config.\n\n :param str path_to_config: path to config.\n :return: config.\n :rtype: Dict[str, Any]\n '
now = datetime.datetime.now()
with open(path_to_config, mode='r') as fp:
config = yaml.safe_load(fp)
config['path... |
050609940a60022e6da95f53e1938bd0d260a80271f6b10c9779ac993d047c10 | def get_logger(path_to_logfile: str) -> logging.Logger:
'\n Get logger.\n\n :param str path_to_logfile: path to logfile.\n :return: logger.\n :rtype: logging.Logger\n '
logger = logging.getLogger('text-clf')
logger.setLevel(logging.INFO)
stream_handler = logging.StreamHandler(sys.stdout)
... | Get logger.
:param str path_to_logfile: path to logfile.
:return: logger.
:rtype: logging.Logger | text_clf/utils.py | get_logger | igvasilev/text-classification-baseline | 0 | python | def get_logger(path_to_logfile: str) -> logging.Logger:
'\n Get logger.\n\n :param str path_to_logfile: path to logfile.\n :return: logger.\n :rtype: logging.Logger\n '
logger = logging.getLogger('text-clf')
logger.setLevel(logging.INFO)
stream_handler = logging.StreamHandler(sys.stdout)
... | def get_logger(path_to_logfile: str) -> logging.Logger:
'\n Get logger.\n\n :param str path_to_logfile: path to logfile.\n :return: logger.\n :rtype: logging.Logger\n '
logger = logging.getLogger('text-clf')
logger.setLevel(logging.INFO)
stream_handler = logging.StreamHandler(sys.stdout)
... |
3a76c3fb298c6e0e2e5708a1aa54b66cabe174d2ea66a71d409c6d7c9dadd1ca | def set_seed(seed: int) -> None:
'\n Set seed for reproducibility.\n\n :param int seed: seed.\n '
random.seed(seed)
np.random.seed(seed) | Set seed for reproducibility.
:param int seed: seed. | text_clf/utils.py | set_seed | igvasilev/text-classification-baseline | 0 | python | def set_seed(seed: int) -> None:
'\n Set seed for reproducibility.\n\n :param int seed: seed.\n '
random.seed(seed)
np.random.seed(seed) | def set_seed(seed: int) -> None:
'\n Set seed for reproducibility.\n\n :param int seed: seed.\n '
random.seed(seed)
np.random.seed(seed)<|docstring|>Set seed for reproducibility.
:param int seed: seed.<|endoftext|> |
5531cb446c63e06766af88073ec4312a53ff63e59285c7847ab3a96759ee715e | def __call__(self, output):
' Return the first available format in the priority.\n\n Produces a UserWarning if no compatible mimetype is found.\n\n `output` is dict with structure {mimetype-of-element: value-of-element}\n\n '
metadata = self.metadata.get(self.notebook_path, {})
widgets_... | Return the first available format in the priority.
Produces a UserWarning if no compatible mimetype is found.
`output` is dict with structure {mimetype-of-element: value-of-element} | nbconvert/filters/widgetsdatatypefilter.py | __call__ | TylerAnderson22/nbconvert | 1,367 | python | def __call__(self, output):
' Return the first available format in the priority.\n\n Produces a UserWarning if no compatible mimetype is found.\n\n `output` is dict with structure {mimetype-of-element: value-of-element}\n\n '
metadata = self.metadata.get(self.notebook_path, {})
widgets_... | def __call__(self, output):
' Return the first available format in the priority.\n\n Produces a UserWarning if no compatible mimetype is found.\n\n `output` is dict with structure {mimetype-of-element: value-of-element}\n\n '
metadata = self.metadata.get(self.notebook_path, {})
widgets_... |
54f670c2df206bb498144ad33d9087ae09af2862adf102ce9560def86d747926 | def discardConflictingDocument(couchDbInstance, data, result):
'\n _discardConflictingDocument_\n\n This should be passed to the queue and commit calls of CMSCouch\n in order to tell it what to do with conflicting documents.\n In this case we trash the old one and replace with what we were\n trying t... | _discardConflictingDocument_
This should be passed to the queue and commit calls of CMSCouch
in order to tell it what to do with conflicting documents.
In this case we trash the old one and replace with what we were
trying to commit, this is available in the data argument.
And the result tells us the id of the conflic... | src/python/WMCore/JobStateMachine/ChangeState.py | discardConflictingDocument | hufnagel/WMCore | 1 | python | def discardConflictingDocument(couchDbInstance, data, result):
'\n _discardConflictingDocument_\n\n This should be passed to the queue and commit calls of CMSCouch\n in order to tell it what to do with conflicting documents.\n In this case we trash the old one and replace with what we were\n trying t... | def discardConflictingDocument(couchDbInstance, data, result):
'\n _discardConflictingDocument_\n\n This should be passed to the queue and commit calls of CMSCouch\n in order to tell it what to do with conflicting documents.\n In this case we trash the old one and replace with what we were\n trying t... |
10858f2424b7dde6bc29a06426b69fc4021141e335df1fc6d8ba330665c207e9 | def _connectDatabases(self):
'\n Try connecting to the couchdbs\n '
if ((not hasattr(self, 'jobsdatabase')) or (self.jobsdatabase is None)):
try:
self.jobsdatabase = self.couchdb.connectDatabase(('%s/jobs' % self.dbname), size=250)
except Exception as ex:
lo... | Try connecting to the couchdbs | src/python/WMCore/JobStateMachine/ChangeState.py | _connectDatabases | hufnagel/WMCore | 1 | python | def _connectDatabases(self):
'\n \n '
if ((not hasattr(self, 'jobsdatabase')) or (self.jobsdatabase is None)):
try:
self.jobsdatabase = self.couchdb.connectDatabase(('%s/jobs' % self.dbname), size=250)
except Exception as ex:
logging.error("Error connecting ... | def _connectDatabases(self):
'\n \n '
if ((not hasattr(self, 'jobsdatabase')) or (self.jobsdatabase is None)):
try:
self.jobsdatabase = self.couchdb.connectDatabase(('%s/jobs' % self.dbname), size=250)
except Exception as ex:
logging.error("Error connecting ... |
101b7a9bd45977bd6dcc14d3b34baec3df1c402e585f2201070f60f34f7cfec4 | def propagate(self, jobs, newstate, oldstate, updatesummary=False):
'\n Move the job from a state to another. Book keep the change to CouchDB.\n Report the information to the Dashboard.\n Take a list of job objects (dicts) and the desired state change.\n Return the jobs back, throw asser... | Move the job from a state to another. Book keep the change to CouchDB.
Report the information to the Dashboard.
Take a list of job objects (dicts) and the desired state change.
Return the jobs back, throw assertion error if the state change is not allowed
and other exceptions as appropriate | src/python/WMCore/JobStateMachine/ChangeState.py | propagate | hufnagel/WMCore | 1 | python | def propagate(self, jobs, newstate, oldstate, updatesummary=False):
'\n Move the job from a state to another. Book keep the change to CouchDB.\n Report the information to the Dashboard.\n Take a list of job objects (dicts) and the desired state change.\n Return the jobs back, throw asser... | def propagate(self, jobs, newstate, oldstate, updatesummary=False):
'\n Move the job from a state to another. Book keep the change to CouchDB.\n Report the information to the Dashboard.\n Take a list of job objects (dicts) and the desired state change.\n Return the jobs back, throw asser... |
3028f28c9748ce4ed6b7fd1a2965b93358a4c4d1edaffbdd7b911b3a9d5d8e5c | def check(self, newstate, oldstate):
'\n check that the transition is allowed. return a tuple of the transition\n if it is allowed, throw up an exception if not.\n '
newstate = newstate.lower()
oldstate = oldstate.lower()
transitions = Transitions()
assert (newstate in transitio... | check that the transition is allowed. return a tuple of the transition
if it is allowed, throw up an exception if not. | src/python/WMCore/JobStateMachine/ChangeState.py | check | hufnagel/WMCore | 1 | python | def check(self, newstate, oldstate):
'\n check that the transition is allowed. return a tuple of the transition\n if it is allowed, throw up an exception if not.\n '
newstate = newstate.lower()
oldstate = oldstate.lower()
transitions = Transitions()
assert (newstate in transitio... | def check(self, newstate, oldstate):
'\n check that the transition is allowed. return a tuple of the transition\n if it is allowed, throw up an exception if not.\n '
newstate = newstate.lower()
oldstate = oldstate.lower()
transitions = Transitions()
assert (newstate in transitio... |
53189b6b85f233f6485cfa410447e2547cca886d21f06b75d81483fc35dd28bb | def recordInCouch(self, jobs, newstate, oldstate, updatesummary=False):
'\n _recordInCouch_\n\n Record relevant job information in couch. If the job does not yet exist\n in couch it will be saved as a seperate document. If the job has a FWJR\n attached that will be saved as a seperate d... | _recordInCouch_
Record relevant job information in couch. If the job does not yet exist
in couch it will be saved as a seperate document. If the job has a FWJR
attached that will be saved as a seperate document. | src/python/WMCore/JobStateMachine/ChangeState.py | recordInCouch | hufnagel/WMCore | 1 | python | def recordInCouch(self, jobs, newstate, oldstate, updatesummary=False):
'\n _recordInCouch_\n\n Record relevant job information in couch. If the job does not yet exist\n in couch it will be saved as a seperate document. If the job has a FWJR\n attached that will be saved as a seperate d... | def recordInCouch(self, jobs, newstate, oldstate, updatesummary=False):
'\n _recordInCouch_\n\n Record relevant job information in couch. If the job does not yet exist\n in couch it will be saved as a seperate document. If the job has a FWJR\n attached that will be saved as a seperate d... |
bc3f1125339e113d925a1652ea1d44f5382c8da861d6f92ec3d96e0e5e1a9aa1 | def persist(self, jobs, newstate, oldstate):
'\n _persist_\n\n Update the job state in the database.\n '
if (newstate == 'killed'):
self.incrementRetryDAO.execute(jobs, increment=99999, conn=self.getDBConn(), transaction=self.existingTransaction())
elif ((oldstate == 'submitcool... | _persist_
Update the job state in the database. | src/python/WMCore/JobStateMachine/ChangeState.py | persist | hufnagel/WMCore | 1 | python | def persist(self, jobs, newstate, oldstate):
'\n _persist_\n\n Update the job state in the database.\n '
if (newstate == 'killed'):
self.incrementRetryDAO.execute(jobs, increment=99999, conn=self.getDBConn(), transaction=self.existingTransaction())
elif ((oldstate == 'submitcool... | def persist(self, jobs, newstate, oldstate):
'\n _persist_\n\n Update the job state in the database.\n '
if (newstate == 'killed'):
self.incrementRetryDAO.execute(jobs, increment=99999, conn=self.getDBConn(), transaction=self.existingTransaction())
elif ((oldstate == 'submitcool... |
d536c0a06a707218c88e0f298093f936eb68a1dd3c9af91405498c756d921014 | def reportToDashboard(self, jobs, newstate, oldstate):
'\n _reportToDashboard_\n\n Report job information to the dashboard, completes the job dictionaries\n with any additional information needed\n '
if (newstate == 'created'):
incrementRetry = (True if ('cooloff' in oldstate... | _reportToDashboard_
Report job information to the dashboard, completes the job dictionaries
with any additional information needed | src/python/WMCore/JobStateMachine/ChangeState.py | reportToDashboard | hufnagel/WMCore | 1 | python | def reportToDashboard(self, jobs, newstate, oldstate):
'\n _reportToDashboard_\n\n Report job information to the dashboard, completes the job dictionaries\n with any additional information needed\n '
if (newstate == 'created'):
incrementRetry = (True if ('cooloff' in oldstate... | def reportToDashboard(self, jobs, newstate, oldstate):
'\n _reportToDashboard_\n\n Report job information to the dashboard, completes the job dictionaries\n with any additional information needed\n '
if (newstate == 'created'):
incrementRetry = (True if ('cooloff' in oldstate... |
2e5f0fda478d2608bd1497f3d6864ddc04db44d99b9fe2a91f74722c399120fe | def recordLocationChange(self, jobs):
'\n _recordLocationChange_\n\n Record a location change in couch and WMBS,\n this expects a list of dictionaries with\n jobid and location keys which represent\n the job id in WMBS and new location respectively.\n '
if (not self._co... | _recordLocationChange_
Record a location change in couch and WMBS,
this expects a list of dictionaries with
jobid and location keys which represent
the job id in WMBS and new location respectively. | src/python/WMCore/JobStateMachine/ChangeState.py | recordLocationChange | hufnagel/WMCore | 1 | python | def recordLocationChange(self, jobs):
'\n _recordLocationChange_\n\n Record a location change in couch and WMBS,\n this expects a list of dictionaries with\n jobid and location keys which represent\n the job id in WMBS and new location respectively.\n '
if (not self._co... | def recordLocationChange(self, jobs):
'\n _recordLocationChange_\n\n Record a location change in couch and WMBS,\n this expects a list of dictionaries with\n jobid and location keys which represent\n the job id in WMBS and new location respectively.\n '
if (not self._co... |
32d189fd617411eaa3e12622c2c36bb2e1901bdee96225eb562018037255be07 | def getTicker(pair='btc_idr', session=None):
'\n Retrieve the ticker for the given pair. Returns a Ticker instance.\n\n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
response = get_data(pair, 'ticker', requests_session=session)
ticker = {}
for s in ('high', 'low'... | Retrieve the ticker for the given pair. Returns a Ticker instance.
Arguments:
pair : trading pair
session : vipbtc.Session object | vipbtc/public.py | getTicker | AchmadGozali8/btcid | 8 | python | def getTicker(pair='btc_idr', session=None):
'\n Retrieve the ticker for the given pair. Returns a Ticker instance.\n\n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
response = get_data(pair, 'ticker', requests_session=session)
ticker = {}
for s in ('high', 'low'... | def getTicker(pair='btc_idr', session=None):
'\n Retrieve the ticker for the given pair. Returns a Ticker instance.\n\n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
response = get_data(pair, 'ticker', requests_session=session)
ticker = {}
for s in ('high', 'low'... |
17604bda530d0e1f865ed6e66156e78eccea607e0249cabb1312227d3a2d43e1 | def getDepth(pair='btc_idr', session=None):
'\n Retrieve the depth for the given pair. Returns a dictionary of asks and bids dataframe.\n \n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
depth = get_data(pair, 'depth', requests_session=session)
asks = pd.DataFrame... | Retrieve the depth for the given pair. Returns a dictionary of asks and bids dataframe.
Arguments:
pair : trading pair
session : vipbtc.Session object | vipbtc/public.py | getDepth | AchmadGozali8/btcid | 8 | python | def getDepth(pair='btc_idr', session=None):
'\n Retrieve the depth for the given pair. Returns a dictionary of asks and bids dataframe.\n \n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
depth = get_data(pair, 'depth', requests_session=session)
asks = pd.DataFrame... | def getDepth(pair='btc_idr', session=None):
'\n Retrieve the depth for the given pair. Returns a dictionary of asks and bids dataframe.\n \n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
depth = get_data(pair, 'depth', requests_session=session)
asks = pd.DataFrame... |
5346cadb4da9f0763489b711de0badee7b372e855de42210c5b80e88d67d34ba | def getTradeHistory(pair='btc_idr', session=None):
'\n Retrieve the trade history for the given pair. Returns a pandas dataframe.\n \n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
history = get_data(pair, 'trades', requests_session=session)
df = pd.DataFrame(hist... | Retrieve the trade history for the given pair. Returns a pandas dataframe.
Arguments:
pair : trading pair
session : vipbtc.Session object | vipbtc/public.py | getTradeHistory | AchmadGozali8/btcid | 8 | python | def getTradeHistory(pair='btc_idr', session=None):
'\n Retrieve the trade history for the given pair. Returns a pandas dataframe.\n \n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
history = get_data(pair, 'trades', requests_session=session)
df = pd.DataFrame(hist... | def getTradeHistory(pair='btc_idr', session=None):
'\n Retrieve the trade history for the given pair. Returns a pandas dataframe.\n \n Arguments:\n pair : trading pair\n session : vipbtc.Session object\n '
history = get_data(pair, 'trades', requests_session=session)
df = pd.DataFrame(hist... |
d3a777414ed8a81ac3da6d9b1a6fbc8d9bd684c80ee88cf37eb63a20723e0b2e | def run(ceph_cluster, **kw):
"\n Pre-requisites :\n 1. create fs volume create cephfs and cephfs-ec\n\n Subvolume Group Operations :\n 1. ceph fs subvolumegroup create <vol_name> <group_name> --pool_layout <data_pool_name>\n 2. ceph fs subvolume create <vol_name> <subvol_name> [--size <size_in_bytes>... | Pre-requisites :
1. create fs volume create cephfs and cephfs-ec
Subvolume Group Operations :
1. ceph fs subvolumegroup create <vol_name> <group_name> --pool_layout <data_pool_name>
2. ceph fs subvolume create <vol_name> <subvol_name> [--size <size_in_bytes>] [--group_name <subvol_group_name>]
3. Mount subvolume on bo... | tests/cephfs/cephfs_volume_management/cephfs_vol_mgmt_subvolgroup_pool_layout.py | run | Gopi-Patta/cephci | 21 | python | def run(ceph_cluster, **kw):
"\n Pre-requisites :\n 1. create fs volume create cephfs and cephfs-ec\n\n Subvolume Group Operations :\n 1. ceph fs subvolumegroup create <vol_name> <group_name> --pool_layout <data_pool_name>\n 2. ceph fs subvolume create <vol_name> <subvol_name> [--size <size_in_bytes>... | def run(ceph_cluster, **kw):
"\n Pre-requisites :\n 1. create fs volume create cephfs and cephfs-ec\n\n Subvolume Group Operations :\n 1. ceph fs subvolumegroup create <vol_name> <group_name> --pool_layout <data_pool_name>\n 2. ceph fs subvolume create <vol_name> <subvol_name> [--size <size_in_bytes>... |
cef6c797b7bf019514c6c3960bdc0751f0dd9c122f3f591e9ec676c09485a1db | def _reconstitute_job(self, job_state: dict) -> MySQLJob:
'\n mysql返回的字典解析成 job对象\n :param job_state:\n :return:\n '
job_state['jobstore'] = self
job_state['name'] = '{project_id}-{code_id}'.format(**job_state)
if (job_state['trigger_type'] == 'cron'):
job_state['trig... | mysql返回的字典解析成 job对象
:param job_state:
:return: | bspider/bcron/jobstore.py | _reconstitute_job | littlebai3618/bspider | 3 | python | def _reconstitute_job(self, job_state: dict) -> MySQLJob:
'\n mysql返回的字典解析成 job对象\n :param job_state:\n :return:\n '
job_state['jobstore'] = self
job_state['name'] = '{project_id}-{code_id}'.format(**job_state)
if (job_state['trigger_type'] == 'cron'):
job_state['trig... | def _reconstitute_job(self, job_state: dict) -> MySQLJob:
'\n mysql返回的字典解析成 job对象\n :param job_state:\n :return:\n '
job_state['jobstore'] = self
job_state['name'] = '{project_id}-{code_id}'.format(**job_state)
if (job_state['trigger_type'] == 'cron'):
job_state['trig... |
b3d12b18ce342796119f956c1f6d0a14fcc68b02ee62cd5fd734bb1c1a7b9b61 | def add_job(self, job: MySQLJob):
'因为拆分问题，增加job 的操作交给API模块完成'
update = f"update {self.table_name} set `status`=%s where `id` = '{job.id}';"
self.mysql_client.update(update, (0,))
self.log.info(f'sync job:{job.name} success') | 因为拆分问题，增加job 的操作交给API模块完成 | bspider/bcron/jobstore.py | add_job | littlebai3618/bspider | 3 | python | def add_job(self, job: MySQLJob):
update = f"update {self.table_name} set `status`=%s where `id` = '{job.id}';"
self.mysql_client.update(update, (0,))
self.log.info(f'sync job:{job.name} success') | def add_job(self, job: MySQLJob):
update = f"update {self.table_name} set `status`=%s where `id` = '{job.id}';"
self.mysql_client.update(update, (0,))
self.log.info(f'sync job:{job.name} success')<|docstring|>因为拆分问题，增加job 的操作交给API模块完成<|endoftext|> |
ba585b11893a443e47736ce134849544940bd3b2524f3580c56efd97ebed0a20 | @classmethod
def _defParseDatetime(self, time):
' allow different ways to provide a time and pares it to datetime '
if time:
if isinstance(time, int):
return (datetime.now() + timedelta(minutes=time))
elif isinstance(time, datetime):
return time
elif isinstance(ti... | allow different ways to provide a time and pares it to datetime | lvbRequester/lvbRequester.py | _defParseDatetime | native2k/lvbRequester | 2 | python | @classmethod
def _defParseDatetime(self, time):
' '
if time:
if isinstance(time, int):
return (datetime.now() + timedelta(minutes=time))
elif isinstance(time, datetime):
return time
elif isinstance(time, (timedelta,)):
return (datetime.now() + time)
... | @classmethod
def _defParseDatetime(self, time):
' '
if time:
if isinstance(time, int):
return (datetime.now() + timedelta(minutes=time))
elif isinstance(time, datetime):
return time
elif isinstance(time, (timedelta,)):
return (datetime.now() + time)
... |
6ada37a77c511b0937793c16a04a173c472d8bc3da2f2d85e8b7d48c6c68d3ea | @classmethod
def _encodeRequest(self, request, data=None):
' encode the request parameters in the expected way '
if isinstance(request, (list, tuple)):
request = ''.join(request)
if data:
resStr = (request % data)
else:
resStr = request
res = urllib.parse.quote(resStr).replac... | encode the request parameters in the expected way | lvbRequester/lvbRequester.py | _encodeRequest | native2k/lvbRequester | 2 | python | @classmethod
def _encodeRequest(self, request, data=None):
' '
if isinstance(request, (list, tuple)):
request = .join(request)
if data:
resStr = (request % data)
else:
resStr = request
res = urllib.parse.quote(resStr).replace('%26', '&').replace('%2B', '+').replace('%3D', '=... | @classmethod
def _encodeRequest(self, request, data=None):
' '
if isinstance(request, (list, tuple)):
request = .join(request)
if data:
resStr = (request % data)
else:
resStr = request
res = urllib.parse.quote(resStr).replace('%26', '&').replace('%2B', '+').replace('%3D', '=... |
933b80b2356129c1351c3a0babaf66e5773d8186b0085ff0ea96dca8f8c71890 | @classmethod
def getAutoCompletion(self, station, limit=10):
' retrieves autocomplete result for station.\n\n This should be used to get the correct station name which\n will be needed for getStation and getConnection.\n '
reqData = {'mode': 'autocomplete', 'limit': limit, 'poi': '', 'q': ... | retrieves autocomplete result for station.
This should be used to get the correct station name which
will be needed for getStation and getConnection. | lvbRequester/lvbRequester.py | getAutoCompletion | native2k/lvbRequester | 2 | python | @classmethod
def getAutoCompletion(self, station, limit=10):
' retrieves autocomplete result for station.\n\n This should be used to get the correct station name which\n will be needed for getStation and getConnection.\n '
reqData = {'mode': 'autocomplete', 'limit': limit, 'poi': , 'q': (s... | @classmethod
def getAutoCompletion(self, station, limit=10):
' retrieves autocomplete result for station.\n\n This should be used to get the correct station name which\n will be needed for getStation and getConnection.\n '
reqData = {'mode': 'autocomplete', 'limit': limit, 'poi': , 'q': (s... |
66348791863a63fe445f34efdc7eb82f1aa2a13e9372316ba7bb164141007bcf | @classmethod
def getConnection(self, stationFrom, stationTo, time=None):
' Retrieves connection information to travel from stationFrom to stationTo.\n\n The station name must be completely identical to the one in LVB System.\n You can use getAutoCompletion to retrieve the correct name.\n '
... | Retrieves connection information to travel from stationFrom to stationTo.
The station name must be completely identical to the one in LVB System.
You can use getAutoCompletion to retrieve the correct name. | lvbRequester/lvbRequester.py | getConnection | native2k/lvbRequester | 2 | python | @classmethod
def getConnection(self, stationFrom, stationTo, time=None):
' Retrieves connection information to travel from stationFrom to stationTo.\n\n The station name must be completely identical to the one in LVB System.\n You can use getAutoCompletion to retrieve the correct name.\n '
... | @classmethod
def getConnection(self, stationFrom, stationTo, time=None):
' Retrieves connection information to travel from stationFrom to stationTo.\n\n The station name must be completely identical to the one in LVB System.\n You can use getAutoCompletion to retrieve the correct name.\n '
... |
4e562f96642690398442786c1dd08e9db6219bf659b6c3bb5ab02ef5581dd924 | @classmethod
def _getConnectionParams(self, stationFrom, stationTo, conTime):
' builds parameter structur for connection call '
transport = list(self.TRANSPORTMAP.keys())
res = ['results[5][5][function]=ws_find_connections&results[5][5][data]=[', '{"name":"results[5][5][is_extended]","value":""},', '{"name"... | builds parameter structur for connection call | lvbRequester/lvbRequester.py | _getConnectionParams | native2k/lvbRequester | 2 | python | @classmethod
def _getConnectionParams(self, stationFrom, stationTo, conTime):
' '
transport = list(self.TRANSPORTMAP.keys())
res = ['results[5][5][function]=ws_find_connections&results[5][5][data]=[', '{"name":"results[5][5][is_extended]","value":},', '{"name":"results[5][5][from_opt]","value":"3"},', '{"n... | @classmethod
def _getConnectionParams(self, stationFrom, stationTo, conTime):
' '
transport = list(self.TRANSPORTMAP.keys())
res = ['results[5][5][function]=ws_find_connections&results[5][5][data]=[', '{"name":"results[5][5][is_extended]","value":},', '{"name":"results[5][5][from_opt]","value":"3"},', '{"n... |
218fe26292dafde87b8de0e4c6d787206bd4848b401f3a009f355891cf031faa | @classmethod
def _getConnectionParse(self, result):
' builds connection results '
return result.get('connections', {}) | builds connection results | lvbRequester/lvbRequester.py | _getConnectionParse | native2k/lvbRequester | 2 | python | @classmethod
def _getConnectionParse(self, result):
' '
return result.get('connections', {}) | @classmethod
def _getConnectionParse(self, result):
' '
return result.get('connections', {})<|docstring|>builds connection results<|endoftext|> |
dbedb8c5e19f2fbd45888c0fa09d6231e10c588c327aa34dee137b371fa48c6d | @classmethod
def getStation(self, station, time=None):
' get all exptected Trains at specified station\n\n The station names must be completely identical to the ones in LVB System.\n You can use getAutoCompletion to retrieve the correct names.\n '
params = self._getStationParams(station, se... | get all exptected Trains at specified station
The station names must be completely identical to the ones in LVB System.
You can use getAutoCompletion to retrieve the correct names. | lvbRequester/lvbRequester.py | getStation | native2k/lvbRequester | 2 | python | @classmethod
def getStation(self, station, time=None):
' get all exptected Trains at specified station\n\n The station names must be completely identical to the ones in LVB System.\n You can use getAutoCompletion to retrieve the correct names.\n '
params = self._getStationParams(station, se... | @classmethod
def getStation(self, station, time=None):
' get all exptected Trains at specified station\n\n The station names must be completely identical to the ones in LVB System.\n You can use getAutoCompletion to retrieve the correct names.\n '
params = self._getStationParams(station, se... |
0a086f1837ced462e566037618b6a2d47881f8488038d91e412da9e56df3ca8c | @classmethod
def _getStationParams(self, stop, time):
' build paramter structure for station request '
res = ['results[5][5][function]=ws_info_stop&results[5][5][data]=[', '{"name":"results[5][5][stop]","value":"%(stop)s"},', '{"name":"results[5][5][date]","value":"%(date)s"},', '{"name":"results[5][5][time]","... | build paramter structure for station request | lvbRequester/lvbRequester.py | _getStationParams | native2k/lvbRequester | 2 | python | @classmethod
def _getStationParams(self, stop, time):
' '
res = ['results[5][5][function]=ws_info_stop&results[5][5][data]=[', '{"name":"results[5][5][stop]","value":"%(stop)s"},', '{"name":"results[5][5][date]","value":"%(date)s"},', '{"name":"results[5][5][time]","value":"%(time)s"},', '{"name":"results[5][5... | @classmethod
def _getStationParams(self, stop, time):
' '
res = ['results[5][5][function]=ws_info_stop&results[5][5][data]=[', '{"name":"results[5][5][stop]","value":"%(stop)s"},', '{"name":"results[5][5][date]","value":"%(date)s"},', '{"name":"results[5][5][time]","value":"%(time)s"},', '{"name":"results[5][5... |
cff2c90c1c9c6963d9659ba5d85989fd37431e0dc4a30c6db8e0597d47f93b65 | @classmethod
def _getStationParse(self, result):
' build station results '
return result['connections'] | build station results | lvbRequester/lvbRequester.py | _getStationParse | native2k/lvbRequester | 2 | python | @classmethod
def _getStationParse(self, result):
' '
return result['connections'] | @classmethod
def _getStationParse(self, result):
' '
return result['connections']<|docstring|>build station results<|endoftext|> |
d9f9b0432371689d1e8e8a12b4f7a562cef33499e6e960e38be6ee47da4397cc | def _populate_rules():
"Populate RULES with mappings from rule type to rule subclass.\n\n RULES is a mapping (dict) from rule types to subclasses of Rule.\n A rule's type is the concat of two strings: <str1>-<str2>, where\n str1 denotes whether the rule is arbitrary or not and str2 equals\n the `_data_t... | Populate RULES with mappings from rule type to rule subclass.
RULES is a mapping (dict) from rule types to subclasses of Rule.
A rule's type is the concat of two strings: <str1>-<str2>, where
str1 denotes whether the rule is arbitrary or not and str2 equals
the `_data_type_str` class attribute of the rule, which is si... | src/mist/api/rules/models/main.py | _populate_rules | SpiralUp/mist.api | 6 | python | def _populate_rules():
"Populate RULES with mappings from rule type to rule subclass.\n\n RULES is a mapping (dict) from rule types to subclasses of Rule.\n A rule's type is the concat of two strings: <str1>-<str2>, where\n str1 denotes whether the rule is arbitrary or not and str2 equals\n the `_data_t... | def _populate_rules():
"Populate RULES with mappings from rule type to rule subclass.\n\n RULES is a mapping (dict) from rule types to subclasses of Rule.\n A rule's type is the concat of two strings: <str1>-<str2>, where\n str1 denotes whether the rule is arbitrary or not and str2 equals\n the `_data_t... |
c12486fd813a4fe5991169c307c019a35dd2529d684558fe19749b6ea4a448da | @classmethod
def add(cls, auth_context, title=None, **kwargs):
'Add a new Rule.\n\n New rules should be added by invoking this class method on a Rule\n subclass.\n\n Arguments:\n\n owner: instance of mist.api.users.models.Organization\n title: the name of the rule. This ... | Add a new Rule.
New rules should be added by invoking this class method on a Rule
subclass.
Arguments:
owner: instance of mist.api.users.models.Organization
title: the name of the rule. This must be unique per Organization
kwargs: additional keyword arguments that will be passed to the
corr... | src/mist/api/rules/models/main.py | add | SpiralUp/mist.api | 6 | python | @classmethod
def add(cls, auth_context, title=None, **kwargs):
'Add a new Rule.\n\n New rules should be added by invoking this class method on a Rule\n subclass.\n\n Arguments:\n\n owner: instance of mist.api.users.models.Organization\n title: the name of the rule. This ... | @classmethod
def add(cls, auth_context, title=None, **kwargs):
'Add a new Rule.\n\n New rules should be added by invoking this class method on a Rule\n subclass.\n\n Arguments:\n\n owner: instance of mist.api.users.models.Organization\n title: the name of the rule. This ... |
98919a0b6e26b4eaa852fad816413875e13a19ae2d3b778272893a63cf2d2a31 | @property
def owner(self):
'Return the Organization (instance) owning self.\n\n We refrain from storing the owner as a me.ReferenceField in order to\n avoid automatic/unwanted dereferencing.\n\n '
return Organization.objects.get(id=self.owner_id) | Return the Organization (instance) owning self.
We refrain from storing the owner as a me.ReferenceField in order to
avoid automatic/unwanted dereferencing. | src/mist/api/rules/models/main.py | owner | SpiralUp/mist.api | 6 | python | @property
def owner(self):
'Return the Organization (instance) owning self.\n\n We refrain from storing the owner as a me.ReferenceField in order to\n avoid automatic/unwanted dereferencing.\n\n '
return Organization.objects.get(id=self.owner_id) | @property
def owner(self):
'Return the Organization (instance) owning self.\n\n We refrain from storing the owner as a me.ReferenceField in order to\n avoid automatic/unwanted dereferencing.\n\n '
return Organization.objects.get(id=self.owner_id)<|docstring|>Return the Organization (instanc... |
a152dc7b1f37ab41d5d026edba3ca92c4dff55fe131ed7fa2953fe3d293bf294 | @property
def org(self):
'Return the Organization (instance) owning self.\n\n '
return self.owner | Return the Organization (instance) owning self. | src/mist/api/rules/models/main.py | org | SpiralUp/mist.api | 6 | python | @property
def org(self):
'\n\n '
return self.owner | @property
def org(self):
'\n\n '
return self.owner<|docstring|>Return the Organization (instance) owning self.<|endoftext|> |
2f00e11aa73c9cfe5581511175149a348ee61a5592daa6a393b16c348d57f693 | @property
def plugin(self):
'Return the instance of a backend plugin.\n\n Subclasses MUST define the plugin to be used, instantiated with `self`.\n\n '
return self._backend_plugin(self) | Return the instance of a backend plugin.
Subclasses MUST define the plugin to be used, instantiated with `self`. | src/mist/api/rules/models/main.py | plugin | SpiralUp/mist.api | 6 | python | @property
def plugin(self):
'Return the instance of a backend plugin.\n\n Subclasses MUST define the plugin to be used, instantiated with `self`.\n\n '
return self._backend_plugin(self) | @property
def plugin(self):
'Return the instance of a backend plugin.\n\n Subclasses MUST define the plugin to be used, instantiated with `self`.\n\n '
return self._backend_plugin(self)<|docstring|>Return the instance of a backend plugin.
Subclasses MUST define the plugin to be used, instantiated... |
f17644aa26b634b65ab8707d0fba1d7c07b78333840809e0f83d0eeb6209937a | @property
def name(self):
'Return the name of the task.\n\n '
return ('Org(%s):Rule(%s)' % (self.owner_id, self.id)) | Return the name of the task. | src/mist/api/rules/models/main.py | name | SpiralUp/mist.api | 6 | python | @property
def name(self):
'\n\n '
return ('Org(%s):Rule(%s)' % (self.owner_id, self.id)) | @property
def name(self):
'\n\n '
return ('Org(%s):Rule(%s)' % (self.owner_id, self.id))<|docstring|>Return the name of the task.<|endoftext|> |
a97df2de28b9c3db17b521af0c5e5d69af1d3800212764b642c2eba17cd188fe | @property
def task(self):
'Return the dramatiq task to run.\n\n This is the most basic dramatiq task that should be used for most rule\n evaluations. However, subclasses may provide their own property or\n class attribute based on their needs.\n\n '
return 'mist.api.rules.tasks.evalu... | Return the dramatiq task to run.
This is the most basic dramatiq task that should be used for most rule
evaluations. However, subclasses may provide their own property or
class attribute based on their needs. | src/mist/api/rules/models/main.py | task | SpiralUp/mist.api | 6 | python | @property
def task(self):
'Return the dramatiq task to run.\n\n This is the most basic dramatiq task that should be used for most rule\n evaluations. However, subclasses may provide their own property or\n class attribute based on their needs.\n\n '
return 'mist.api.rules.tasks.evalu... | @property
def task(self):
'Return the dramatiq task to run.\n\n This is the most basic dramatiq task that should be used for most rule\n evaluations. However, subclasses may provide their own property or\n class attribute based on their needs.\n\n '
return 'mist.api.rules.tasks.evalu... |
9d7771049e34c295a7f451b53f1bc45825d829d8c564e98cab1677c7f54f2b28 | @property
def args(self):
'Return the args of the dramatiq task.'
return (self.id,) | Return the args of the dramatiq task. | src/mist/api/rules/models/main.py | args | SpiralUp/mist.api | 6 | python | @property
def args(self):
return (self.id,) | @property
def args(self):
return (self.id,)<|docstring|>Return the args of the dramatiq task.<|endoftext|> |
9dacb201bf4dc210cdcad9886b7141a0f6b69bece472c253b670036d49e4c590 | @property
def kwargs(self):
'Return the kwargs of the dramatiq task.'
return {} | Return the kwargs of the dramatiq task. | src/mist/api/rules/models/main.py | kwargs | SpiralUp/mist.api | 6 | python | @property
def kwargs(self):
return {} | @property
def kwargs(self):
return {}<|docstring|>Return the kwargs of the dramatiq task.<|endoftext|> |
aaef8529e85139fd6844ae7e5f082dd7bc646345bfbacb0b25fc4b0672667bf8 | @property
def expires(self):
'Return None to denote that self is not meant to expire.'
return None | Return None to denote that self is not meant to expire. | src/mist/api/rules/models/main.py | expires | SpiralUp/mist.api | 6 | python | @property
def expires(self):
return None | @property
def expires(self):
return None<|docstring|>Return None to denote that self is not meant to expire.<|endoftext|> |
cb57b4463b679dbec604a49e34cbdd1b5acacdab91020479131e536b6def60c1 | @property
def enabled(self):
'Return True if the dramatiq task is currently enabled.\n\n Subclasses MAY override or extend this property.\n\n '
return (not self.disabled) | Return True if the dramatiq task is currently enabled.
Subclasses MAY override or extend this property. | src/mist/api/rules/models/main.py | enabled | SpiralUp/mist.api | 6 | python | @property
def enabled(self):
'Return True if the dramatiq task is currently enabled.\n\n Subclasses MAY override or extend this property.\n\n '
return (not self.disabled) | @property
def enabled(self):
'Return True if the dramatiq task is currently enabled.\n\n Subclasses MAY override or extend this property.\n\n '
return (not self.disabled)<|docstring|>Return True if the dramatiq task is currently enabled.
Subclasses MAY override or extend this property.<|endoftext... |
458a52a1f54357b50698df823eaa12f31b7b8a3362f701a3d979987bf13e62dc | def is_arbitrary(self):
'Return True if self is arbitrary.\n\n Arbitrary rules lack a list of `selectors` that refer to resources\n either by their UUIDs or by tags. Such a list makes it easy to setup\n rules referencing specific resources without the need to provide the\n raw query expr... | Return True if self is arbitrary.
Arbitrary rules lack a list of `selectors` that refer to resources
either by their UUIDs or by tags. Such a list makes it easy to setup
rules referencing specific resources without the need to provide the
raw query expression. | src/mist/api/rules/models/main.py | is_arbitrary | SpiralUp/mist.api | 6 | python | def is_arbitrary(self):
'Return True if self is arbitrary.\n\n Arbitrary rules lack a list of `selectors` that refer to resources\n either by their UUIDs or by tags. Such a list makes it easy to setup\n rules referencing specific resources without the need to provide the\n raw query expr... | def is_arbitrary(self):
'Return True if self is arbitrary.\n\n Arbitrary rules lack a list of `selectors` that refer to resources\n either by their UUIDs or by tags. Such a list makes it easy to setup\n rules referencing specific resources without the need to provide the\n raw query expr... |
2875a64475021f44de403bc1a7d701e2e97246b36af85805e30100a9831278c5 | def plotImages(images_batch, img_n, classes):
'\n Take as input a batch from the generator and plt a number of images equal to img_n\n Default columns equal to max_c. At least inputs of batch equal two.\n '
max_c = 5
if (img_n <= max_c):
r = 1
c = img_n
else:
r = math.ce... | Take as input a batch from the generator and plt a number of images equal to img_n
Default columns equal to max_c. At least inputs of batch equal two. | utils/visualize.py | plotImages | EscVM/RSC-Wrapper | 2 | python | def plotImages(images_batch, img_n, classes):
'\n Take as input a batch from the generator and plt a number of images equal to img_n\n Default columns equal to max_c. At least inputs of batch equal two.\n '
max_c = 5
if (img_n <= max_c):
r = 1
c = img_n
else:
r = math.ce... | def plotImages(images_batch, img_n, classes):
'\n Take as input a batch from the generator and plt a number of images equal to img_n\n Default columns equal to max_c. At least inputs of batch equal two.\n '
max_c = 5
if (img_n <= max_c):
r = 1
c = img_n
else:
r = math.ce... |
f52f953bffcb9ccf0672d03444c4df1027258f5838b6642f7e963a16540f1306 | def plot_misclassified_images(X_test, y_pred, y_test, labels):
'\n Plot all misclassified images.\n '
y_pred_arg = (logistic.cdf(y_pred) > 0.5)[(..., 0)].astype(np.float32)
errors_indices = np.where((y_pred_arg != y_test))[0]
max_c = 5
r = np.ceil((errors_indices.size / max_c)).astype(np.int32... | Plot all misclassified images. | utils/visualize.py | plot_misclassified_images | EscVM/RSC-Wrapper | 2 | python | def plot_misclassified_images(X_test, y_pred, y_test, labels):
'\n \n '
y_pred_arg = (logistic.cdf(y_pred) > 0.5)[(..., 0)].astype(np.float32)
errors_indices = np.where((y_pred_arg != y_test))[0]
max_c = 5
r = np.ceil((errors_indices.size / max_c)).astype(np.int32)
c = max_c
(fig, axes... | def plot_misclassified_images(X_test, y_pred, y_test, labels):
'\n \n '
y_pred_arg = (logistic.cdf(y_pred) > 0.5)[(..., 0)].astype(np.float32)
errors_indices = np.where((y_pred_arg != y_test))[0]
max_c = 5
r = np.ceil((errors_indices.size / max_c)).astype(np.int32)
c = max_c
(fig, axes... |
c3f26eabb871190e149766541527a7d405918768465183974a3759fd63ffbccd | def differing_ana_field(self, ana1, ana2):
'\n Determine if two analyses with equal number of fields only differ\n in one field, with the possible exception of the gloss field.\n If they do, return the name of the field. If they do not differ\n at all, return empty string. If they have m... | Determine if two analyses with equal number of fields only differ
in one field, with the possible exception of the gloss field.
If they do, return the name of the field. If they do not differ
at all, return empty string. If they have more than one
differing fields, return None. | search/web_app/response_processors.py | differing_ana_field | gisly/evenki-corpus | 0 | python | def differing_ana_field(self, ana1, ana2):
'\n Determine if two analyses with equal number of fields only differ\n in one field, with the possible exception of the gloss field.\n If they do, return the name of the field. If they do not differ\n at all, return empty string. If they have m... | def differing_ana_field(self, ana1, ana2):
'\n Determine if two analyses with equal number of fields only differ\n in one field, with the possible exception of the gloss field.\n If they do, return the name of the field. If they do not differ\n at all, return empty string. If they have m... |
6bbf8490a43e01d010b6e03a42a0b3286e0ce0c0154cc431219a7ba4ca4d29b1 | def join_ana_gloss_variants(self, ana1, ana2):
'\n Check if the gloss field values in the analyses differ only\n in one gloss. If so, return a string with joined glossing, e.g.\n (STEM-PL-GEN) + (STEM-SG-GEN) would give (STEM-PL/SG-GEN). If not,\n return None.\n '
if (('gloss'... | Check if the gloss field values in the analyses differ only
in one gloss. If so, return a string with joined glossing, e.g.
(STEM-PL-GEN) + (STEM-SG-GEN) would give (STEM-PL/SG-GEN). If not,
return None. | search/web_app/response_processors.py | join_ana_gloss_variants | gisly/evenki-corpus | 0 | python | def join_ana_gloss_variants(self, ana1, ana2):
'\n Check if the gloss field values in the analyses differ only\n in one gloss. If so, return a string with joined glossing, e.g.\n (STEM-PL-GEN) + (STEM-SG-GEN) would give (STEM-PL/SG-GEN). If not,\n return None.\n '
if (('gloss'... | def join_ana_gloss_variants(self, ana1, ana2):
'\n Check if the gloss field values in the analyses differ only\n in one gloss. If so, return a string with joined glossing, e.g.\n (STEM-PL-GEN) + (STEM-SG-GEN) would give (STEM-PL/SG-GEN). If not,\n return None.\n '
if (('gloss'... |
92568ac478a0bb07ab4473586079259284f9567106b663589c7b10fb8e83c84b | def simplify_ana(self, analyses, matchingAnalyses):
'\n Collate JSON analyses that only have differences in one field,\n e.g. [(N,sg,gen), (N,pl,gen)] -> (N,sg/pl,gen). Analyses that\n match the query (their indices are stored in matchingAnalyses)\n cannot be collated with those that do ... | Collate JSON analyses that only have differences in one field,
e.g. [(N,sg,gen), (N,pl,gen)] -> (N,sg/pl,gen). Analyses that
match the query (their indices are stored in matchingAnalyses)
cannot be collated with those that do not.
Return a list with simplified analyses and the matching analyses
indices in the new list.... | search/web_app/response_processors.py | simplify_ana | gisly/evenki-corpus | 0 | python | def simplify_ana(self, analyses, matchingAnalyses):
'\n Collate JSON analyses that only have differences in one field,\n e.g. [(N,sg,gen), (N,pl,gen)] -> (N,sg/pl,gen). Analyses that\n match the query (their indices are stored in matchingAnalyses)\n cannot be collated with those that do ... | def simplify_ana(self, analyses, matchingAnalyses):
'\n Collate JSON analyses that only have differences in one field,\n e.g. [(N,sg,gen), (N,pl,gen)] -> (N,sg/pl,gen). Analyses that\n match the query (their indices are stored in matchingAnalyses)\n cannot be collated with those that do ... |
429d99819f6bc22ca0293171f63f22db98ad8fa0e2683ae92715d53b0365232c | def build_gr_ana_part_text(self, grValues, lang):
'\n Build a string with gramtags ordered according to the settings\n for the language specified by lang.\n '
def key_comp(p):
if ('gr_fields_order' not in self.settings['lang_props'][lang]):
return (- 1)
if (p[0]... | Build a string with gramtags ordered according to the settings
for the language specified by lang. | search/web_app/response_processors.py | build_gr_ana_part_text | gisly/evenki-corpus | 0 | python | def build_gr_ana_part_text(self, grValues, lang):
'\n Build a string with gramtags ordered according to the settings\n for the language specified by lang.\n '
def key_comp(p):
if ('gr_fields_order' not in self.settings['lang_props'][lang]):
return (- 1)
if (p[0]... | def build_gr_ana_part_text(self, grValues, lang):
'\n Build a string with gramtags ordered according to the settings\n for the language specified by lang.\n '
def key_comp(p):
if ('gr_fields_order' not in self.settings['lang_props'][lang]):
return (- 1)
if (p[0]... |
6b9abdf7939ffb1711d4d05253fc10dabf39b5e102777a4acfad5a7bc1582a71 | def build_gr_ana_part(self, grValues, lang, gramdic=False):
'\n Build an HTML div with gramtags ordered according to the settings\n for the language specified by lang.\n gramdic == True iff dictionary values (such as gender) are processed. \n '
grAnaPart = self.build_gr_ana_part_text... | Build an HTML div with gramtags ordered according to the settings
for the language specified by lang.
gramdic == True iff dictionary values (such as gender) are processed. | search/web_app/response_processors.py | build_gr_ana_part | gisly/evenki-corpus | 0 | python | def build_gr_ana_part(self, grValues, lang, gramdic=False):
'\n Build an HTML div with gramtags ordered according to the settings\n for the language specified by lang.\n gramdic == True iff dictionary values (such as gender) are processed. \n '
grAnaPart = self.build_gr_ana_part_text... | def build_gr_ana_part(self, grValues, lang, gramdic=False):
'\n Build an HTML div with gramtags ordered according to the settings\n for the language specified by lang.\n gramdic == True iff dictionary values (such as gender) are processed. \n '
grAnaPart = self.build_gr_ana_part_text... |
2882c8c818867b2eff73ceeb98066eb0bf7ea585dbb5e7891636fe8412981621 | def build_ana_div(self, ana, lang, translit=None):
'\n Build the contents of a div with one particular analysis.\n '
def field_sorting_key(x):
if (x['key'] in self.settings['lang_props'][lang]['other_fields_order']):
return (self.settings['lang_props'][lang]['other_fields_orde... | Build the contents of a div with one particular analysis. | search/web_app/response_processors.py | build_ana_div | gisly/evenki-corpus | 0 | python | def build_ana_div(self, ana, lang, translit=None):
'\n \n '
def field_sorting_key(x):
if (x['key'] in self.settings['lang_props'][lang]['other_fields_order']):
return (self.settings['lang_props'][lang]['other_fields_order'].index(x['key']), x['key'])
return (len(self.s... | def build_ana_div(self, ana, lang, translit=None):
'\n \n '
def field_sorting_key(x):
if (x['key'] in self.settings['lang_props'][lang]['other_fields_order']):
return (self.settings['lang_props'][lang]['other_fields_order'].index(x['key']), x['key'])
return (len(self.s... |
332c3b46e1b1ead36ffdd589a3d5514b9363603ec7fda4204abedaca7c9892a3 | def build_ana_popup(self, word, lang, matchingAnalyses=None, translit=None):
'\n Build a string for a popup with the word and its analyses. \n '
if (matchingAnalyses is None):
matchingAnalyses = []
data4template = {'wf': '', 'analyses': []}
if ('wf_display' in word):
data4t... | Build a string for a popup with the word and its analyses. | search/web_app/response_processors.py | build_ana_popup | gisly/evenki-corpus | 0 | python | def build_ana_popup(self, word, lang, matchingAnalyses=None, translit=None):
'\n \n '
if (matchingAnalyses is None):
matchingAnalyses = []
data4template = {'wf': , 'analyses': []}
if ('wf_display' in word):
data4template['wf_display'] = self.transliterate_baseline(word['wf... | def build_ana_popup(self, word, lang, matchingAnalyses=None, translit=None):
'\n \n '
if (matchingAnalyses is None):
matchingAnalyses = []
data4template = {'wf': , 'analyses': []}
if ('wf_display' in word):
data4template['wf_display'] = self.transliterate_baseline(word['wf... |
97498929ff5bbf556c3384a6de0d105c8787ab7dda8e37a7510d63dc16074431 | def prepare_analyses(self, words, indexes, lang, matchWordOffsets=None, translit=None):
'\n Generate viewable analyses for the words with given indexes.\n '
result = ''
for iStr in indexes:
mWordNo = self.rxWordNo.search(iStr)
if (mWordNo is None):
continue
... | Generate viewable analyses for the words with given indexes. | search/web_app/response_processors.py | prepare_analyses | gisly/evenki-corpus | 0 | python | def prepare_analyses(self, words, indexes, lang, matchWordOffsets=None, translit=None):
'\n \n '
result =
for iStr in indexes:
mWordNo = self.rxWordNo.search(iStr)
if (mWordNo is None):
continue
i = int(mWordNo.group(1))
if ((i < 0) or (i >= len(wor... | def prepare_analyses(self, words, indexes, lang, matchWordOffsets=None, translit=None):
'\n \n '
result =
for iStr in indexes:
mWordNo = self.rxWordNo.search(iStr)
if (mWordNo is None):
continue
i = int(mWordNo.group(1))
if ((i < 0) or (i >= len(wor... |
6216fcbca2e40f7472b46e41b67208ae852e51923c1c1e252f0bec8112f5a1bb | def build_span(self, sentSrc, curWords, curStyles, lang, matchWordOffsets, translit=None):
'\n Build a string with a starting span for a word in the baseline.\n '
curClass = ''
if any((wn.startswith('w') for wn in curWords)):
curClass += ' word '
if any((wn.startswith('p') for wn i... | Build a string with a starting span for a word in the baseline. | search/web_app/response_processors.py | build_span | gisly/evenki-corpus | 0 | python | def build_span(self, sentSrc, curWords, curStyles, lang, matchWordOffsets, translit=None):
'\n \n '
curClass =
if any((wn.startswith('w') for wn in curWords)):
curClass += ' word '
if any((wn.startswith('p') for wn in curWords)):
curClass += ' para '
if any((wn.startsw... | def build_span(self, sentSrc, curWords, curStyles, lang, matchWordOffsets, translit=None):
'\n \n '
curClass =
if any((wn.startswith('w') for wn in curWords)):
curClass += ' word '
if any((wn.startswith('p') for wn in curWords)):
curClass += ' para '
if any((wn.startsw... |
52b89ea72848f84132a33c54eee54d9176e1f79c60fe63f9ffebcf96f49874e1 | def add_highlighted_offsets(self, offStarts, offEnds, text):
'\n Find highlighted fragments in source text of the sentence\n and store their offsets in the respective lists.\n '
indexSubtr = 0
for i in range((len(text) - 4)):
if (text[i] != '<'):
continue
if ... | Find highlighted fragments in source text of the sentence
and store their offsets in the respective lists. | search/web_app/response_processors.py | add_highlighted_offsets | gisly/evenki-corpus | 0 | python | def add_highlighted_offsets(self, offStarts, offEnds, text):
'\n Find highlighted fragments in source text of the sentence\n and store their offsets in the respective lists.\n '
indexSubtr = 0
for i in range((len(text) - 4)):
if (text[i] != '<'):
continue
if ... | def add_highlighted_offsets(self, offStarts, offEnds, text):
'\n Find highlighted fragments in source text of the sentence\n and store their offsets in the respective lists.\n '
indexSubtr = 0
for i in range((len(text) - 4)):
if (text[i] != '<'):
continue
if ... |
18234972ac48328a091c5bd9b148ba2efe05581b0c4d682b2afa342d077af4e6 | def process_sentence_header(self, sentSource, format='html'):
'\n Retrieve the metadata of the document the sentence\n belongs to. Return an HTML string with this data that\n can serve as a header for the context on the output page.\n '
if (format == 'csv'):
result = ''
e... | Retrieve the metadata of the document the sentence
belongs to. Return an HTML string with this data that
can serve as a header for the context on the output page. | search/web_app/response_processors.py | process_sentence_header | gisly/evenki-corpus | 0 | python | def process_sentence_header(self, sentSource, format='html'):
'\n Retrieve the metadata of the document the sentence\n belongs to. Return an HTML string with this data that\n can serve as a header for the context on the output page.\n '
if (format == 'csv'):
result =
els... | def process_sentence_header(self, sentSource, format='html'):
'\n Retrieve the metadata of the document the sentence\n belongs to. Return an HTML string with this data that\n can serve as a header for the context on the output page.\n '
if (format == 'csv'):
result =
els... |
9f2baaacbceba3d1400b6265f4cd4e49795ba380d4e92b2e25eb6f6c61242b6d | def get_word_offsets(self, sSource, numSent, matchOffsets=None):
'\n Find at which offsets which word start and end. If macthOffsets\n is not None, find only offsets of the matching words.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets a... | Find at which offsets which word start and end. If macthOffsets
is not None, find only offsets of the matching words.
Return two dicts, one with start offsets and the other with end offsets.
The keys are offsets and the values are the string IDs of the words. | search/web_app/response_processors.py | get_word_offsets | gisly/evenki-corpus | 0 | python | def get_word_offsets(self, sSource, numSent, matchOffsets=None):
'\n Find at which offsets which word start and end. If macthOffsets\n is not None, find only offsets of the matching words.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets a... | def get_word_offsets(self, sSource, numSent, matchOffsets=None):
'\n Find at which offsets which word start and end. If macthOffsets\n is not None, find only offsets of the matching words.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets a... |
7dcdf1dd9f56bd9ef83cc62730652b78105f2691c2843b11048b928303a2c573 | def get_para_offsets(self, sSource):
'\n Find at which offsets which parallel fragments start and end.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets and the values are the string IDs of the fragments.\n '
(offStarts, offEnds) = ({},... | Find at which offsets which parallel fragments start and end.
Return two dicts, one with start offsets and the other with end offsets.
The keys are offsets and the values are the string IDs of the fragments. | search/web_app/response_processors.py | get_para_offsets | gisly/evenki-corpus | 0 | python | def get_para_offsets(self, sSource):
'\n Find at which offsets which parallel fragments start and end.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets and the values are the string IDs of the fragments.\n '
(offStarts, offEnds) = ({},... | def get_para_offsets(self, sSource):
'\n Find at which offsets which parallel fragments start and end.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets and the values are the string IDs of the fragments.\n '
(offStarts, offEnds) = ({},... |
b4d8d0242092d9e02090387c8b74ab328e949539477c6eeb74e02147edadeca5 | def get_src_offsets(self, sSource):
'\n Find at which offsets which sound/video-alignment fragments start and end.\n Return three dicts, one with start offsets, the other with end offsets,\n and the third with the descriptions of the fragments.\n The keys in the first two are offsets and... | Find at which offsets which sound/video-alignment fragments start and end.
Return three dicts, one with start offsets, the other with end offsets,
and the third with the descriptions of the fragments.
The keys in the first two are offsets and the values are the string IDs
of the fragments. | search/web_app/response_processors.py | get_src_offsets | gisly/evenki-corpus | 0 | python | def get_src_offsets(self, sSource):
'\n Find at which offsets which sound/video-alignment fragments start and end.\n Return three dicts, one with start offsets, the other with end offsets,\n and the third with the descriptions of the fragments.\n The keys in the first two are offsets and... | def get_src_offsets(self, sSource):
'\n Find at which offsets which sound/video-alignment fragments start and end.\n Return three dicts, one with start offsets, the other with end offsets,\n and the third with the descriptions of the fragments.\n The keys in the first two are offsets and... |
925bea3a9173e5d167bf03ce2839c8f278be62ad32fd3a1e6c9d9f9ac029300d | def get_style_offsets(self, sSource):
'\n Find spans of text that should be displayed in a non-default style,\n e.g. in italics or in superscript.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets. The values are sets with HTML tags that co... | Find spans of text that should be displayed in a non-default style,
e.g. in italics or in superscript.
Return two dicts, one with start offsets and the other with end offsets.
The keys are offsets. The values are sets with HTML tags that contain
the class and other attributes, such as tooltip text. | search/web_app/response_processors.py | get_style_offsets | gisly/evenki-corpus | 0 | python | def get_style_offsets(self, sSource):
'\n Find spans of text that should be displayed in a non-default style,\n e.g. in italics or in superscript.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets. The values are sets with HTML tags that co... | def get_style_offsets(self, sSource):
'\n Find spans of text that should be displayed in a non-default style,\n e.g. in italics or in superscript.\n Return two dicts, one with start offsets and the other with end offsets.\n The keys are offsets. The values are sets with HTML tags that co... |
15d48520a9a428e76a724412ee60321cda13a36f796872ac5f0258127e3e51bb | def relativize_src_alignment(self, expandedContext, srcFiles):
'\n If the sentences in the expanded context are aligned with the\n neighboring media file fragments rather than with the same fragment,\n re-align them with the same one and recalculate offsets.\n '
srcFiles = set(srcFil... | If the sentences in the expanded context are aligned with the
neighboring media file fragments rather than with the same fragment,
re-align them with the same one and recalculate offsets. | search/web_app/response_processors.py | relativize_src_alignment | gisly/evenki-corpus | 0 | python | def relativize_src_alignment(self, expandedContext, srcFiles):
'\n If the sentences in the expanded context are aligned with the\n neighboring media file fragments rather than with the same fragment,\n re-align them with the same one and recalculate offsets.\n '
srcFiles = set(srcFil... | def relativize_src_alignment(self, expandedContext, srcFiles):
'\n If the sentences in the expanded context are aligned with the\n neighboring media file fragments rather than with the same fragment,\n re-align them with the same one and recalculate offsets.\n '
srcFiles = set(srcFil... |
fcb73eebab460ff06e23199212aba8c7f74937a847c0e763e46ba49cb73d7f91 | def process_sentence_csv(self, sJSON, lang='', translit=None):
"\n Process one sentence taken from response['hits']['hits'].\n Return a CSV string for this sentence.\n "
sDict = self.process_sentence(sJSON, numSent=0, getHeader=False, format='csv', lang=lang, translit=translit)
if (('la... | Process one sentence taken from response['hits']['hits'].
Return a CSV string for this sentence. | search/web_app/response_processors.py | process_sentence_csv | gisly/evenki-corpus | 0 | python | def process_sentence_csv(self, sJSON, lang=, translit=None):
"\n Process one sentence taken from response['hits']['hits'].\n Return a CSV string for this sentence.\n "
sDict = self.process_sentence(sJSON, numSent=0, getHeader=False, format='csv', lang=lang, translit=translit)
if (('lang... | def process_sentence_csv(self, sJSON, lang=, translit=None):
"\n Process one sentence taken from response['hits']['hits'].\n Return a CSV string for this sentence.\n "
sDict = self.process_sentence(sJSON, numSent=0, getHeader=False, format='csv', lang=lang, translit=translit)
if (('lang... |
ce17f466719fad7070d842189a40c41a07365bcdc053fce69be6bc7804c80273 | def view_sentence_meta(self, sSource, format):
'\n If there is a metadata dictionary in the sentence, transform it\n to an HTML span or a text for CSV.\n '
if ('meta' not in sSource):
return ''
meta2show = {k: sSource['meta'][k] for k in sSource['meta'] if (k not in ['sent_analy... | If there is a metadata dictionary in the sentence, transform it
to an HTML span or a text for CSV. | search/web_app/response_processors.py | view_sentence_meta | gisly/evenki-corpus | 0 | python | def view_sentence_meta(self, sSource, format):
'\n If there is a metadata dictionary in the sentence, transform it\n to an HTML span or a text for CSV.\n '
if ('meta' not in sSource):
return
meta2show = {k: sSource['meta'][k] for k in sSource['meta'] if (k not in ['sent_analyse... | def view_sentence_meta(self, sSource, format):
'\n If there is a metadata dictionary in the sentence, transform it\n to an HTML span or a text for CSV.\n '
if ('meta' not in sSource):
return
meta2show = {k: sSource['meta'][k] for k in sSource['meta'] if (k not in ['sent_analyse... |
0faf06d53823638cc4c1328ac717c63783c9d94eb38b3b09c0d6e192c2b7df20 | def process_sentence(self, s, numSent=1, getHeader=False, lang='', langView='', translit=None, format='html'):
"\n Process one sentence taken from response['hits']['hits'].\n If getHeader is True, retrieve the metadata from the database.\n Return dictionary {'header': document header HTML,\n ... | Process one sentence taken from response['hits']['hits'].
If getHeader is True, retrieve the metadata from the database.
Return dictionary {'header': document header HTML,
{'languages': {'<language_name>': {'text': sentence HTML[,
'img': related image name,
... | search/web_app/response_processors.py | process_sentence | gisly/evenki-corpus | 0 | python | def process_sentence(self, s, numSent=1, getHeader=False, lang=, langView=, translit=None, format='html'):
"\n Process one sentence taken from response['hits']['hits'].\n If getHeader is True, retrieve the metadata from the database.\n Return dictionary {'header': document header HTML,\n ... | def process_sentence(self, s, numSent=1, getHeader=False, lang=, langView=, translit=None, format='html'):
"\n Process one sentence taken from response['hits']['hits'].\n If getHeader is True, retrieve the metadata from the database.\n Return dictionary {'header': document header HTML,\n ... |
36d27e4f4a41ddd5b4a8a199ca46c04acec1ba6892a8bf16ccdf6f132dae8e92 | def get_glossed_sentence(self, s, getHeader=True, lang='', translit=None, skipNonGlossed=False):
"\n Process one sentence taken from response['hits']['hits'].\n If getHeader is True, retrieve the metadata from the database.\n Return tab-delimited text version of the sentence that could be inser... | Process one sentence taken from response['hits']['hits'].
If getHeader is True, retrieve the metadata from the database.
Return tab-delimited text version of the sentence that could be inserted
either as a simple text example or as a glossed example in a
linguistic paper. | search/web_app/response_processors.py | get_glossed_sentence | gisly/evenki-corpus | 0 | python | def get_glossed_sentence(self, s, getHeader=True, lang=, translit=None, skipNonGlossed=False):
"\n Process one sentence taken from response['hits']['hits'].\n If getHeader is True, retrieve the metadata from the database.\n Return tab-delimited text version of the sentence that could be inserte... | def get_glossed_sentence(self, s, getHeader=True, lang=, translit=None, skipNonGlossed=False):
"\n Process one sentence taken from response['hits']['hits'].\n If getHeader is True, retrieve the metadata from the database.\n Return tab-delimited text version of the sentence that could be inserte... |
cebd26bec5c7eb398d46a27a1aabbdbd74138027ea920b184667eba1abc5099a | def count_word_subcorpus_stats(self, w, docIDs):
'\n Return statistics about the given word in the subcorpus\n specified by the list of document IDs.\n This function is currently unused and will probably be deleted.\n '
query = {'bool': {'must': [{'term': {'w_id': w['_id']}}, {'terms... | Return statistics about the given word in the subcorpus
specified by the list of document IDs.
This function is currently unused and will probably be deleted. | search/web_app/response_processors.py | count_word_subcorpus_stats | gisly/evenki-corpus | 0 | python | def count_word_subcorpus_stats(self, w, docIDs):
'\n Return statistics about the given word in the subcorpus\n specified by the list of document IDs.\n This function is currently unused and will probably be deleted.\n '
query = {'bool': {'must': [{'term': {'w_id': w['_id']}}, {'terms... | def count_word_subcorpus_stats(self, w, docIDs):
'\n Return statistics about the given word in the subcorpus\n specified by the list of document IDs.\n This function is currently unused and will probably be deleted.\n '
query = {'bool': {'must': [{'term': {'w_id': w['_id']}}, {'terms... |
35ca884d6392549a9580685910a161d2a249e49f3f0d3b31102d9584e2095312 | def process_word(self, w, lang, searchType='word', translit=None):
"\n Process one word taken from response['hits']['hits'].\n "
if ('_source' not in w):
return ''
wSource = w['_source']
freq = str(wSource['freq'])
rank = str(wSource['rank'])
nDocs = str(wSource['n_docs'])
... | Process one word taken from response['hits']['hits']. | search/web_app/response_processors.py | process_word | gisly/evenki-corpus | 0 | python | def process_word(self, w, lang, searchType='word', translit=None):
"\n \n "
if ('_source' not in w):
return
wSource = w['_source']
freq = str(wSource['freq'])
rank = str(wSource['rank'])
nDocs = str(wSource['n_docs'])
otherFields = []
if (searchType == 'word'):
... | def process_word(self, w, lang, searchType='word', translit=None):
"\n \n "
if ('_source' not in w):
return
wSource = w['_source']
freq = str(wSource['freq'])
rank = str(wSource['rank'])
nDocs = str(wSource['n_docs'])
otherFields = []
if (searchType == 'word'):
... |
a2b51948da1f797639e30a2cf5c23963f30c20b9b7766dcf81ac4533680c6267 | def process_word_subcorpus(self, w, nDocuments, freq, lang, translit=None):
"\n Process one word taken from response['hits']['hits'] for subcorpus\n queries (where frequency data comes separately from the aggregations).\n "
if ('_source' not in w):
return ''
wSource = w['_source... | Process one word taken from response['hits']['hits'] for subcorpus
queries (where frequency data comes separately from the aggregations). | search/web_app/response_processors.py | process_word_subcorpus | gisly/evenki-corpus | 0 | python | def process_word_subcorpus(self, w, nDocuments, freq, lang, translit=None):
"\n Process one word taken from response['hits']['hits'] for subcorpus\n queries (where frequency data comes separately from the aggregations).\n "
if ('_source' not in w):
return
wSource = w['_source']... | def process_word_subcorpus(self, w, nDocuments, freq, lang, translit=None):
"\n Process one word taken from response['hits']['hits'] for subcorpus\n queries (where frequency data comes separately from the aggregations).\n "
if ('_source' not in w):
return
wSource = w['_source']... |
dd453f05ae11ccdf63b5bfffe956c38bd0536f057ca3fa22d5fa2ac5893bf12c | def filter_multi_word_highlight_iter(self, hit, nWords=1, negWords=None, keepOnlyFirst=False):
'\n Remove those of the highlights that are empty or which do\n not constitute a full set of search terms. If keepOnlyFirst\n is True, remove highlights for all non-first query words.\n negWord... | Remove those of the highlights that are empty or which do
not constitute a full set of search terms. If keepOnlyFirst
is True, remove highlights for all non-first query words.
negWords is a list of words whose query was negative: they will be
absent from the highlighting.
Iterate over filtered inner hits. | search/web_app/response_processors.py | filter_multi_word_highlight_iter | gisly/evenki-corpus | 0 | python | def filter_multi_word_highlight_iter(self, hit, nWords=1, negWords=None, keepOnlyFirst=False):
'\n Remove those of the highlights that are empty or which do\n not constitute a full set of search terms. If keepOnlyFirst\n is True, remove highlights for all non-first query words.\n negWord... | def filter_multi_word_highlight_iter(self, hit, nWords=1, negWords=None, keepOnlyFirst=False):
'\n Remove those of the highlights that are empty or which do\n not constitute a full set of search terms. If keepOnlyFirst\n is True, remove highlights for all non-first query words.\n negWord... |
25f97ba3ad0a77e26c37c3ccfb2cac332e668f4b8e38ee001336328383410630 | def filter_multi_word_highlight(self, hit, nWords=1, negWords=None, keepOnlyFirst=False):
"\n Non-iterative version of filter_multi_word_highlight_iter whic\n replaces hits['inner_hits'] dictionary.\n "
if (('inner_hits' not in hit) or (nWords <= 1)):
return
hit['inner_hits'] = ... | Non-iterative version of filter_multi_word_highlight_iter whic
replaces hits['inner_hits'] dictionary. | search/web_app/response_processors.py | filter_multi_word_highlight | gisly/evenki-corpus | 0 | python | def filter_multi_word_highlight(self, hit, nWords=1, negWords=None, keepOnlyFirst=False):
"\n Non-iterative version of filter_multi_word_highlight_iter whic\n replaces hits['inner_hits'] dictionary.\n "
if (('inner_hits' not in hit) or (nWords <= 1)):
return
hit['inner_hits'] = ... | def filter_multi_word_highlight(self, hit, nWords=1, negWords=None, keepOnlyFirst=False):
"\n Non-iterative version of filter_multi_word_highlight_iter whic\n replaces hits['inner_hits'] dictionary.\n "
if (('inner_hits' not in hit) or (nWords <= 1)):
return
hit['inner_hits'] = ... |
911324332a77ddf30749da317081ef9c3e53bc1d3e47c0e66ae54d2347ada457 | def add_word_from_sentence(self, hitsProcessed, hit, nWords=1):
'\n Extract word data from the highlighted w1 in the sentence and\n add it to the dictionary hitsProcessed.\n '
if (('_source' not in hit) or ('inner_hits' not in hit)):
return
(langID, lang) = self.get_lang_from_hi... | Extract word data from the highlighted w1 in the sentence and
add it to the dictionary hitsProcessed. | search/web_app/response_processors.py | add_word_from_sentence | gisly/evenki-corpus | 0 | python | def add_word_from_sentence(self, hitsProcessed, hit, nWords=1):
'\n Extract word data from the highlighted w1 in the sentence and\n add it to the dictionary hitsProcessed.\n '
if (('_source' not in hit) or ('inner_hits' not in hit)):
return
(langID, lang) = self.get_lang_from_hi... | def add_word_from_sentence(self, hitsProcessed, hit, nWords=1):
'\n Extract word data from the highlighted w1 in the sentence and\n add it to the dictionary hitsProcessed.\n '
if (('_source' not in hit) or ('inner_hits' not in hit)):
return
(langID, lang) = self.get_lang_from_hi... |
0c95b8037b508e80b888e39dbb4d3aebb45c39a19123e1f1b34c9e56a043986f | def get_lemma(self, word):
'\n Join all lemmata in the JSON representation of a word with\n an analysis and return them as a string.\n '
if ('ana' not in word):
return ''
if (('keep_lemma_order' not in self.settings) or (not self.settings['keep_lemma_order'])):
curLemmat... | Join all lemmata in the JSON representation of a word with
an analysis and return them as a string. | search/web_app/response_processors.py | get_lemma | gisly/evenki-corpus | 0 | python | def get_lemma(self, word):
'\n Join all lemmata in the JSON representation of a word with\n an analysis and return them as a string.\n '
if ('ana' not in word):
return
if (('keep_lemma_order' not in self.settings) or (not self.settings['keep_lemma_order'])):
curLemmata ... | def get_lemma(self, word):
'\n Join all lemmata in the JSON representation of a word with\n an analysis and return them as a string.\n '
if ('ana' not in word):
return
if (('keep_lemma_order' not in self.settings) or (not self.settings['keep_lemma_order'])):
curLemmata ... |
99ecf55529b46665c20ca36e4ec0b53bb0efb7d2c76834dc923a39db164f7969 | def get_gramm(self, word, lang):
'\n Join all grammar tags strings in the JSON representation of a word with\n an analysis and return them as a string.\n '
if ('ana' not in word):
return ''
if (('keep_lemma_order' not in self.settings) or (not self.settings['keep_lemma_order']))... | Join all grammar tags strings in the JSON representation of a word with
an analysis and return them as a string. | search/web_app/response_processors.py | get_gramm | gisly/evenki-corpus | 0 | python | def get_gramm(self, word, lang):
'\n Join all grammar tags strings in the JSON representation of a word with\n an analysis and return them as a string.\n '
if ('ana' not in word):
return
if (('keep_lemma_order' not in self.settings) or (not self.settings['keep_lemma_order'])):
... | def get_gramm(self, word, lang):
'\n Join all grammar tags strings in the JSON representation of a word with\n an analysis and return them as a string.\n '
if ('ana' not in word):
return
if (('keep_lemma_order' not in self.settings) or (not self.settings['keep_lemma_order'])):
... |
f22e32e7420eb09f62c95af130b7bd94276b8fd0ca8997f4691c7bb98cf278b8 | def get_word_table_fields(self, word):
'\n Return a list with values of fields that have to be displayed\n in a word search hits table, along with wordform and lemma.\n '
if ('word_table_fields' not in self.settings):
return []
wordTableValues = []
for field in self.settings... | Return a list with values of fields that have to be displayed
in a word search hits table, along with wordform and lemma. | search/web_app/response_processors.py | get_word_table_fields | gisly/evenki-corpus | 0 | python | def get_word_table_fields(self, word):
'\n Return a list with values of fields that have to be displayed\n in a word search hits table, along with wordform and lemma.\n '
if ('word_table_fields' not in self.settings):
return []
wordTableValues = []
for field in self.settings... | def get_word_table_fields(self, word):
'\n Return a list with values of fields that have to be displayed\n in a word search hits table, along with wordform and lemma.\n '
if ('word_table_fields' not in self.settings):
return []
wordTableValues = []
for field in self.settings... |
f088a724c9371fdad8dd963577de7f2bd343949040a1ec51f1deda293c2d0fb7 | def process_words_collected_from_sentences(self, hitsProcessed, sortOrder='freq', pageSize=10):
'\n Process all words collected from the sentences with a multi-word query.\n '
for (wID, freqData) in hitsProcessed['word_ids'].items():
word = {'w_id': wID, '_source': {'wf': freqData['wf']}}
... | Process all words collected from the sentences with a multi-word query. | search/web_app/response_processors.py | process_words_collected_from_sentences | gisly/evenki-corpus | 0 | python | def process_words_collected_from_sentences(self, hitsProcessed, sortOrder='freq', pageSize=10):
'\n \n '
for (wID, freqData) in hitsProcessed['word_ids'].items():
word = {'w_id': wID, '_source': {'wf': freqData['wf']}}
word['_source']['freq'] = freqData['n_occurrences']
wor... | def process_words_collected_from_sentences(self, hitsProcessed, sortOrder='freq', pageSize=10):
'\n \n '
for (wID, freqData) in hitsProcessed['word_ids'].items():
word = {'w_id': wID, '_source': {'wf': freqData['wf']}}
word['_source']['freq'] = freqData['n_occurrences']
wor... |
2b3fb857dd26759c7b02af7b763cdf0180445395d10b147543c5e3c40e325960 | def calculate_ranks(self, hitsProcessed):
"\n Calculate frequency ranks of the words collected from sentences based\n on their frequency in the hitsProcessed list.\n For each word, store results in word['_source']['rank']. Return nothing.\n "
freqsSorted = [w['_source']['freq'] for w... | Calculate frequency ranks of the words collected from sentences based
on their frequency in the hitsProcessed list.
For each word, store results in word['_source']['rank']. Return nothing. | search/web_app/response_processors.py | calculate_ranks | gisly/evenki-corpus | 0 | python | def calculate_ranks(self, hitsProcessed):
"\n Calculate frequency ranks of the words collected from sentences based\n on their frequency in the hitsProcessed list.\n For each word, store results in word['_source']['rank']. Return nothing.\n "
freqsSorted = [w['_source']['freq'] for w... | def calculate_ranks(self, hitsProcessed):
"\n Calculate frequency ranks of the words collected from sentences based\n on their frequency in the hitsProcessed list.\n For each word, store results in word['_source']['rank']. Return nothing.\n "
freqsSorted = [w['_source']['freq'] for w... |
1a14dcc5c97fafdb15ed1fe7bc342cf291de817f440938ee08e84507f80e6372 | def process_doc(self, d, exclude=None):
"\n Process one document taken from response['hits']['hits'].\n "
if ('_source' not in d):
return ''
dSource = d['_source']
dID = d['_id']
doc = {'fields': [], 'excluded': ((exclude is not None) and (int(dID) in exclude)), 'id': dID}
... | Process one document taken from response['hits']['hits']. | search/web_app/response_processors.py | process_doc | gisly/evenki-corpus | 0 | python | def process_doc(self, d, exclude=None):
"\n \n "
if ('_source' not in d):
return
dSource = d['_source']
dID = d['_id']
doc = {'fields': [], 'excluded': ((exclude is not None) and (int(dID) in exclude)), 'id': dID}
dateDisplayed = '-'
if ('year_from' in dSource):
... | def process_doc(self, d, exclude=None):
"\n \n "
if ('_source' not in d):
return
dSource = d['_source']
dID = d['_id']
doc = {'fields': [], 'excluded': ((exclude is not None) and (int(dID) in exclude)), 'id': dID}
dateDisplayed = '-'
if ('year_from' in dSource):
... |
ba30afb702ec7e4e0d2d1945bc46118d366dcaf57f47e03b9ed9f1cc358d93e7 | def retrieve_highlighted_words(self, sentence, numSent, queryWordID=''):
'\n Explore the inner_hits part of the response to find the\n offsets of the words that matched the word-level query\n and offsets of the respective analyses, if any.\n Search for word offsets recursively, so that t... | Explore the inner_hits part of the response to find the
offsets of the words that matched the word-level query
and offsets of the respective analyses, if any.
Search for word offsets recursively, so that the procedure
does not depend excatly on the response structure.
Return a dictionary where keys are offsets of highl... | search/web_app/response_processors.py | retrieve_highlighted_words | gisly/evenki-corpus | 0 | python | def retrieve_highlighted_words(self, sentence, numSent, queryWordID=):
'\n Explore the inner_hits part of the response to find the\n offsets of the words that matched the word-level query\n and offsets of the respective analyses, if any.\n Search for word offsets recursively, so that the... | def retrieve_highlighted_words(self, sentence, numSent, queryWordID=):
'\n Explore the inner_hits part of the response to find the\n offsets of the words that matched the word-level query\n and offsets of the respective analyses, if any.\n Search for word offsets recursively, so that the... |
cdddeecdb7b5e3d2a66ab6b84a40b40a784c8a8375f254b0b915fc6da4aed0be | def get_lang_from_hit(self, hit):
'\n Return the ID and the name of the language of the current hit\n taken from ES response.\n '
if ('lang' in hit['_source']):
langID = hit['_source']['lang']
else:
langID = 0
lang = self.settings['languages'][langID]
return (lan... | Return the ID and the name of the language of the current hit
taken from ES response. | search/web_app/response_processors.py | get_lang_from_hit | gisly/evenki-corpus | 0 | python | def get_lang_from_hit(self, hit):
'\n Return the ID and the name of the language of the current hit\n taken from ES response.\n '
if ('lang' in hit['_source']):
langID = hit['_source']['lang']
else:
langID = 0
lang = self.settings['languages'][langID]
return (lan... | def get_lang_from_hit(self, hit):
'\n Return the ID and the name of the language of the current hit\n taken from ES response.\n '
if ('lang' in hit['_source']):
langID = hit['_source']['lang']
else:
langID = 0
lang = self.settings['languages'][langID]
return (lan... |
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