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burnash/gspread | gspread/models.py | Worksheet.update_cells | def update_cells(self, cell_list, value_input_option='RAW'):
"""Updates many cells at once.
:param cell_list: List of :class:`Cell` objects to update.
:param value_input_option: (optional) Determines how input data should
be interpreted. See `ValueInputOption... | python | def update_cells(self, cell_list, value_input_option='RAW'):
"""Updates many cells at once.
:param cell_list: List of :class:`Cell` objects to update.
:param value_input_option: (optional) Determines how input data should
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burnash/gspread | gspread/models.py | Worksheet.resize | def resize(self, rows=None, cols=None):
"""Resizes the worksheet. Specify one of ``rows`` or ``cols``.
:param rows: (optional) New number of rows.
:type rows: int
:param cols: (optional) New number columns.
:type cols: int
"""
grid_properties = {}
if row... | python | def resize(self, rows=None, cols=None):
"""Resizes the worksheet. Specify one of ``rows`` or ``cols``.
:param rows: (optional) New number of rows.
:type rows: int
:param cols: (optional) New number columns.
:type cols: int
"""
grid_properties = {}
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burnash/gspread | gspread/models.py | Worksheet.update_title | def update_title(self, title):
"""Renames the worksheet.
:param title: A new title.
:type title: str
"""
body = {
'requests': [{
'updateSheetProperties': {
'properties': {
'sheetId': self.id,
... | python | def update_title(self, title):
"""Renames the worksheet.
:param title: A new title.
:type title: str
"""
body = {
'requests': [{
'updateSheetProperties': {
'properties': {
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burnash/gspread | gspread/models.py | Worksheet.append_row | def append_row(self, values, value_input_option='RAW'):
"""Adds a row to the worksheet and populates it with values.
Widens the worksheet if there are more values than columns.
:param values: List of values for the new row.
:param value_input_option: (optional) Determines how input data... | python | def append_row(self, values, value_input_option='RAW'):
"""Adds a row to the worksheet and populates it with values.
Widens the worksheet if there are more values than columns.
:param values: List of values for the new row.
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burnash/gspread | gspread/models.py | Worksheet.insert_row | def insert_row(
self,
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index=1,
value_input_option='RAW'
):
"""Adds a row to the worksheet at the specified index
and populates it with values.
Widens the worksheet if there are more values than columns.
:param values: List of values for the n... | python | def insert_row(
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index=1,
value_input_option='RAW'
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burnash/gspread | gspread/models.py | Worksheet.delete_row | def delete_row(self, index):
""""Deletes the row from the worksheet at the specified index.
:param index: Index of a row for deletion.
:type index: int
"""
body = {
"requests": [{
"deleteDimension": {
"range": {
... | python | def delete_row(self, index):
""""Deletes the row from the worksheet at the specified index.
:param index: Index of a row for deletion.
:type index: int
"""
body = {
"requests": [{
"deleteDimension": {
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burnash/gspread | gspread/models.py | Worksheet.find | def find(self, query):
"""Finds the first cell matching the query.
:param query: A literal string to match or compiled regular expression.
:type query: str, :py:class:`re.RegexObject`
"""
try:
return self._finder(finditem, query)
except StopIteration:
... | python | def find(self, query):
"""Finds the first cell matching the query.
:param query: A literal string to match or compiled regular expression.
:type query: str, :py:class:`re.RegexObject`
"""
try:
return self._finder(finditem, query)
except StopIteration:
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burnash/gspread | gspread/models.py | Worksheet.duplicate | def duplicate(
self,
insert_sheet_index=None,
new_sheet_id=None,
new_sheet_name=None
):
"""Duplicate the sheet.
:param int insert_sheet_index: (optional) The zero-based index
where the new sheet should be inserted.
... | python | def duplicate(
self,
insert_sheet_index=None,
new_sheet_id=None,
new_sheet_name=None
):
"""Duplicate the sheet.
:param int insert_sheet_index: (optional) The zero-based index
where the new sheet should be inserted.
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burnash/gspread | gspread/utils.py | numericise | def numericise(value, empty2zero=False, default_blank="", allow_underscores_in_numeric_literals=False):
"""Returns a value that depends on the input string:
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- Integer if input can be converted to integer
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burnash/gspread | gspread/utils.py | numericise_all | def numericise_all(input, empty2zero=False, default_blank="", allow_underscores_in_numeric_literals=False):
"""Returns a list of numericised values from strings"""
return [numericise(s, empty2zero, default_blank, allow_underscores_in_numeric_literals) for s in input] | python | def numericise_all(input, empty2zero=False, default_blank="", allow_underscores_in_numeric_literals=False):
"""Returns a list of numericised values from strings"""
return [numericise(s, empty2zero, default_blank, allow_underscores_in_numeric_literals) for s in input] | [
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burnash/gspread | gspread/utils.py | rowcol_to_a1 | def rowcol_to_a1(row, col):
"""Translates a row and column cell address to A1 notation.
:param row: The row of the cell to be converted.
Rows start at index 1.
:type row: int, str
:param col: The column of the cell to be converted.
Columns start at index 1.
:type ro... | python | def rowcol_to_a1(row, col):
"""Translates a row and column cell address to A1 notation.
:param row: The row of the cell to be converted.
Rows start at index 1.
:type row: int, str
:param col: The column of the cell to be converted.
Columns start at index 1.
:type ro... | [
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burnash/gspread | gspread/utils.py | a1_to_rowcol | def a1_to_rowcol(label):
"""Translates a cell's address in A1 notation to a tuple of integers.
:param label: A cell label in A1 notation, e.g. 'B1'.
Letter case is ignored.
:type label: str
:returns: a tuple containing `row` and `column` numbers. Both indexed
from 1 (on... | python | def a1_to_rowcol(label):
"""Translates a cell's address in A1 notation to a tuple of integers.
:param label: A cell label in A1 notation, e.g. 'B1'.
Letter case is ignored.
:type label: str
:returns: a tuple containing `row` and `column` numbers. Both indexed
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burnash/gspread | gspread/utils.py | cast_to_a1_notation | def cast_to_a1_notation(method):
"""
Decorator function casts wrapped arguments to A1 notation
in range method calls.
"""
@wraps(method)
def wrapper(self, *args, **kwargs):
try:
if len(args):
int(args[0])
# Convert to A1 notation
range... | python | def cast_to_a1_notation(method):
"""
Decorator function casts wrapped arguments to A1 notation
in range method calls.
"""
@wraps(method)
def wrapper(self, *args, **kwargs):
try:
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burnash/gspread | gspread/utils.py | wid_to_gid | def wid_to_gid(wid):
"""Calculate gid of a worksheet from its wid."""
widval = wid[1:] if len(wid) > 3 else wid
xorval = 474 if len(wid) > 3 else 31578
return str(int(widval, 36) ^ xorval) | python | def wid_to_gid(wid):
"""Calculate gid of a worksheet from its wid."""
widval = wid[1:] if len(wid) > 3 else wid
xorval = 474 if len(wid) > 3 else 31578
return str(int(widval, 36) ^ xorval) | [
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burnash/gspread | gspread/client.py | Client.login | def login(self):
"""Authorize client."""
if not self.auth.access_token or \
(hasattr(self.auth, 'access_token_expired') and self.auth.access_token_expired):
import httplib2
http = httplib2.Http()
self.auth.refresh(http)
self.session.headers.u... | python | def login(self):
"""Authorize client."""
if not self.auth.access_token or \
(hasattr(self.auth, 'access_token_expired') and self.auth.access_token_expired):
import httplib2
http = httplib2.Http()
self.auth.refresh(http)
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burnash/gspread | gspread/client.py | Client.open | def open(self, title):
"""Opens a spreadsheet.
:param title: A title of a spreadsheet.
:type title: str
:returns: a :class:`~gspread.models.Spreadsheet` instance.
If there's more than one spreadsheet with same title the first one
will be opened.
:raises gsprea... | python | def open(self, title):
"""Opens a spreadsheet.
:param title: A title of a spreadsheet.
:type title: str
:returns: a :class:`~gspread.models.Spreadsheet` instance.
If there's more than one spreadsheet with same title the first one
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:raises gsprea... | [
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burnash/gspread | gspread/client.py | Client.openall | def openall(self, title=None):
"""Opens all available spreadsheets.
:param title: (optional) If specified can be used to filter
spreadsheets by title.
:type title: str
:returns: a list of :class:`~gspread.models.Spreadsheet` instances.
"""
spreads... | python | def openall(self, title=None):
"""Opens all available spreadsheets.
:param title: (optional) If specified can be used to filter
spreadsheets by title.
:type title: str
:returns: a list of :class:`~gspread.models.Spreadsheet` instances.
"""
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burnash/gspread | gspread/client.py | Client.create | def create(self, title):
"""Creates a new spreadsheet.
:param title: A title of a new spreadsheet.
:type title: str
:returns: a :class:`~gspread.models.Spreadsheet` instance.
.. note::
In order to use this method, you need to add
``https://www.googleapis... | python | def create(self, title):
"""Creates a new spreadsheet.
:param title: A title of a new spreadsheet.
:type title: str
:returns: a :class:`~gspread.models.Spreadsheet` instance.
.. note::
In order to use this method, you need to add
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burnash/gspread | gspread/client.py | Client.copy | def copy(self, file_id, title=None, copy_permissions=False):
"""Copies a spreadsheet.
:param file_id: A key of a spreadsheet to copy.
:type title: str
:param title: (optional) A title for the new spreadsheet.
:type title: str
:param copy_permissions: (optional) If True... | python | def copy(self, file_id, title=None, copy_permissions=False):
"""Copies a spreadsheet.
:param file_id: A key of a spreadsheet to copy.
:type title: str
:param title: (optional) A title for the new spreadsheet.
:type title: str
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burnash/gspread | gspread/client.py | Client.del_spreadsheet | def del_spreadsheet(self, file_id):
"""Deletes a spreadsheet.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
"""
url = '{0}/{1}'.format(
DRIVE_FILES_API_V2_URL,
file_id
)
self.request('delete', url) | python | def del_spreadsheet(self, file_id):
"""Deletes a spreadsheet.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
"""
url = '{0}/{1}'.format(
DRIVE_FILES_API_V2_URL,
file_id
)
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burnash/gspread | gspread/client.py | Client.import_csv | def import_csv(self, file_id, data):
"""Imports data into the first page of the spreadsheet.
:param str data: A CSV string of data.
Example:
.. code::
# Read CSV file contents
content = open('file_to_import.csv', 'r').read()
gc.import_csv(spreadsh... | python | def import_csv(self, file_id, data):
"""Imports data into the first page of the spreadsheet.
:param str data: A CSV string of data.
Example:
.. code::
# Read CSV file contents
content = open('file_to_import.csv', 'r').read()
gc.import_csv(spreadsh... | [
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Example:
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content = open('file_to_import.csv', 'r').read()
gc.import_csv(spreadsheet.id, content)
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burnash/gspread | gspread/client.py | Client.list_permissions | def list_permissions(self, file_id):
"""Retrieve a list of permissions for a file.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
"""
url = '{0}/{1}/permissions'.format(DRIVE_FILES_API_V2_URL, file_id)
r = self.request('get', url)
return r.j... | python | def list_permissions(self, file_id):
"""Retrieve a list of permissions for a file.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
"""
url = '{0}/{1}/permissions'.format(DRIVE_FILES_API_V2_URL, file_id)
r = self.request('get', url)
return r.j... | [
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burnash/gspread | gspread/client.py | Client.insert_permission | def insert_permission(
self,
file_id,
value,
perm_type,
role,
notify=True,
email_message=None,
with_link=False
):
"""Creates a new permission for a file.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
... | python | def insert_permission(
self,
file_id,
value,
perm_type,
role,
notify=True,
email_message=None,
with_link=False
):
"""Creates a new permission for a file.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
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burnash/gspread | gspread/client.py | Client.remove_permission | def remove_permission(self, file_id, permission_id):
"""Deletes a permission from a file.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
:param permission_id: an ID for the permission.
:type permission_id: str
"""
url = '{0}/{1}/permissions/{2... | python | def remove_permission(self, file_id, permission_id):
"""Deletes a permission from a file.
:param file_id: a spreadsheet ID (aka file ID.)
:type file_id: str
:param permission_id: an ID for the permission.
:type permission_id: str
"""
url = '{0}/{1}/permissions/{2... | [
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lmcinnes/umap | umap/utils.py | tau_rand_int | def tau_rand_int(state):
"""A fast (pseudo)-random number generator.
Parameters
----------
state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
A (pseudo)-random int32 value
"""
state[0] = (((state[0] & 4294967294) << 12) & 0xffffffff) ^ (
... | python | def tau_rand_int(state):
"""A fast (pseudo)-random number generator.
Parameters
----------
state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
A (pseudo)-random int32 value
"""
state[0] = (((state[0] & 4294967294) << 12) & 0xffffffff) ^ (
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lmcinnes/umap | umap/utils.py | norm | def norm(vec):
"""Compute the (standard l2) norm of a vector.
Parameters
----------
vec: array of shape (dim,)
Returns
-------
The l2 norm of vec.
"""
result = 0.0
for i in range(vec.shape[0]):
result += vec[i] ** 2
return np.sqrt(result) | python | def norm(vec):
"""Compute the (standard l2) norm of a vector.
Parameters
----------
vec: array of shape (dim,)
Returns
-------
The l2 norm of vec.
"""
result = 0.0
for i in range(vec.shape[0]):
result += vec[i] ** 2
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lmcinnes/umap | umap/utils.py | rejection_sample | def rejection_sample(n_samples, pool_size, rng_state):
"""Generate n_samples many integers from 0 to pool_size such that no
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rejection sampling.
Parameters
----------
n_samples: int
The number of random samples to sele... | python | def rejection_sample(n_samples, pool_size, rng_state):
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----------
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lmcinnes/umap | umap/utils.py | make_heap | def make_heap(n_points, size):
"""Constructor for the numba enabled heap objects. The heaps are used
for approximate nearest neighbor search, maintaining a list of potential
neighbors sorted by their distance. We also flag if potential neighbors
are newly added to the list or not. Internally this is sto... | python | def make_heap(n_points, size):
"""Constructor for the numba enabled heap objects. The heaps are used
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lmcinnes/umap | umap/utils.py | siftdown | def siftdown(heap1, heap2, elt):
"""Restore the heap property for a heap with an out of place element
at position ``elt``. This works with a heap pair where heap1 carries
the weights and heap2 holds the corresponding elements."""
while elt * 2 + 1 < heap1.shape[0]:
left_child = elt * 2 + 1
... | python | def siftdown(heap1, heap2, elt):
"""Restore the heap property for a heap with an out of place element
at position ``elt``. This works with a heap pair where heap1 carries
the weights and heap2 holds the corresponding elements."""
while elt * 2 + 1 < heap1.shape[0]:
left_child = elt * 2 + 1
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lmcinnes/umap | umap/utils.py | deheap_sort | def deheap_sort(heap):
"""Given an array of heaps (of indices and weights), unpack the heap
out to give and array of sorted lists of indices and weights by increasing
weight. This is effectively just the second half of heap sort (the first
half not being required since we already have the data in a heap... | python | def deheap_sort(heap):
"""Given an array of heaps (of indices and weights), unpack the heap
out to give and array of sorted lists of indices and weights by increasing
weight. This is effectively just the second half of heap sort (the first
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lmcinnes/umap | umap/utils.py | smallest_flagged | def smallest_flagged(heap, row):
"""Search the heap for the smallest element that is
still flagged.
Parameters
----------
heap: array of shape (3, n_samples, n_neighbors)
The heaps to search
row: int
Which of the heaps to search
Returns
-------
index: int
T... | python | def smallest_flagged(heap, row):
"""Search the heap for the smallest element that is
still flagged.
Parameters
----------
heap: array of shape (3, n_samples, n_neighbors)
The heaps to search
row: int
Which of the heaps to search
Returns
-------
index: int
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lmcinnes/umap | umap/utils.py | build_candidates | def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):
"""Build a heap of candidate neighbors for nearest neighbor descent. For
each vertex the candidate neighbors are any current neighbors, and any
vertices that have the vertex as one of their nearest neighbors.
Param... | python | def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):
"""Build a heap of candidate neighbors for nearest neighbor descent. For
each vertex the candidate neighbors are any current neighbors, and any
vertices that have the vertex as one of their nearest neighbors.
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lmcinnes/umap | umap/utils.py | new_build_candidates | def new_build_candidates(
current_graph, n_vertices, n_neighbors, max_candidates, rng_state, rho=0.5
): # pragma: no cover
"""Build a heap of candidate neighbors for nearest neighbor descent. For
each vertex the candidate neighbors are any current neighbors, and any
vertices that have the vertex as one... | python | def new_build_candidates(
current_graph, n_vertices, n_neighbors, max_candidates, rng_state, rho=0.5
): # pragma: no cover
"""Build a heap of candidate neighbors for nearest neighbor descent. For
each vertex the candidate neighbors are any current neighbors, and any
vertices that have the vertex as one... | [
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lmcinnes/umap | umap/utils.py | submatrix | def submatrix(dmat, indices_col, n_neighbors):
"""Return a submatrix given an orginal matrix and the indices to keep.
Parameters
----------
mat: array, shape (n_samples, n_samples)
Original matrix.
indices_col: array, shape (n_samples, n_neighbors)
Indices to keep. Each row consist... | python | def submatrix(dmat, indices_col, n_neighbors):
"""Return a submatrix given an orginal matrix and the indices to keep.
Parameters
----------
mat: array, shape (n_samples, n_samples)
Original matrix.
indices_col: array, shape (n_samples, n_neighbors)
Indices to keep. Each row consist... | [
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lmcinnes/umap | umap/distances.py | euclidean | def euclidean(x, y):
"""Standard euclidean distance.
..math::
D(x, y) = \sqrt{\sum_i (x_i - y_i)^2}
"""
result = 0.0
for i in range(x.shape[0]):
result += (x[i] - y[i]) ** 2
return np.sqrt(result) | python | def euclidean(x, y):
"""Standard euclidean distance.
..math::
D(x, y) = \sqrt{\sum_i (x_i - y_i)^2}
"""
result = 0.0
for i in range(x.shape[0]):
result += (x[i] - y[i]) ** 2
return np.sqrt(result) | [
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lmcinnes/umap | umap/distances.py | standardised_euclidean | def standardised_euclidean(x, y, sigma=_mock_ones):
"""Euclidean distance standardised against a vector of standard
deviations per coordinate.
..math::
D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
"""
result = 0.0
for i in range(x.shape[0]):
result += ((x[i] - y[i]) ** 2) ... | python | def standardised_euclidean(x, y, sigma=_mock_ones):
"""Euclidean distance standardised against a vector of standard
deviations per coordinate.
..math::
D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
"""
result = 0.0
for i in range(x.shape[0]):
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lmcinnes/umap | umap/distances.py | manhattan | def manhattan(x, y):
"""Manhatten, taxicab, or l1 distance.
..math::
D(x, y) = \sum_i |x_i - y_i|
"""
result = 0.0
for i in range(x.shape[0]):
result += np.abs(x[i] - y[i])
return result | python | def manhattan(x, y):
"""Manhatten, taxicab, or l1 distance.
..math::
D(x, y) = \sum_i |x_i - y_i|
"""
result = 0.0
for i in range(x.shape[0]):
result += np.abs(x[i] - y[i])
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lmcinnes/umap | umap/distances.py | chebyshev | def chebyshev(x, y):
"""Chebyshev or l-infinity distance.
..math::
D(x, y) = \max_i |x_i - y_i|
"""
result = 0.0
for i in range(x.shape[0]):
result = max(result, np.abs(x[i] - y[i]))
return result | python | def chebyshev(x, y):
"""Chebyshev or l-infinity distance.
..math::
D(x, y) = \max_i |x_i - y_i|
"""
result = 0.0
for i in range(x.shape[0]):
result = max(result, np.abs(x[i] - y[i]))
return result | [
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lmcinnes/umap | umap/distances.py | minkowski | def minkowski(x, y, p=2):
"""Minkowski distance.
..math::
D(x, y) = \left(\sum_i |x_i - y_i|^p\right)^{\frac{1}{p}}
This is a general distance. For p=1 it is equivalent to
manhattan distance, for p=2 it is Euclidean distance, and
for p=infinity it is Chebyshev distance. In general it is be... | python | def minkowski(x, y, p=2):
"""Minkowski distance.
..math::
D(x, y) = \left(\sum_i |x_i - y_i|^p\right)^{\frac{1}{p}}
This is a general distance. For p=1 it is equivalent to
manhattan distance, for p=2 it is Euclidean distance, and
for p=infinity it is Chebyshev distance. In general it is be... | [
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lmcinnes/umap | umap/nndescent.py | make_nn_descent | def make_nn_descent(dist, dist_args):
"""Create a numba accelerated version of nearest neighbor descent
specialised for the given distance metric and metric arguments. Numba
doesn't support higher order functions directly, but we can instead JIT
compile the version of NN-descent for any given metric.
... | python | def make_nn_descent(dist, dist_args):
"""Create a numba accelerated version of nearest neighbor descent
specialised for the given distance metric and metric arguments. Numba
doesn't support higher order functions directly, but we can instead JIT
compile the version of NN-descent for any given metric.
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lmcinnes/umap | umap/spectral.py | component_layout | def component_layout(
data, n_components, component_labels, dim, metric="euclidean", metric_kwds={}
):
"""Provide a layout relating the separate connected components. This is done
by taking the centroid of each component and then performing a spectral embedding
of the centroids.
Parameters
----... | python | def component_layout(
data, n_components, component_labels, dim, metric="euclidean", metric_kwds={}
):
"""Provide a layout relating the separate connected components. This is done
by taking the centroid of each component and then performing a spectral embedding
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lmcinnes/umap | umap/spectral.py | multi_component_layout | def multi_component_layout(
data,
graph,
n_components,
component_labels,
dim,
random_state,
metric="euclidean",
metric_kwds={},
):
"""Specialised layout algorithm for dealing with graphs with many connected components.
This will first fid relative positions for the components by ... | python | def multi_component_layout(
data,
graph,
n_components,
component_labels,
dim,
random_state,
metric="euclidean",
metric_kwds={},
):
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lmcinnes/umap | umap/spectral.py | spectral_layout | def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_kwds={}):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
normalized laplacian.
Parameters
----------
data: array of shape ... | python | def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_kwds={}):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
normalized laplacian.
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lmcinnes/umap | umap/rp_tree.py | sparse_angular_random_projection_split | def sparse_angular_random_projection_split(inds, indptr, data, indices, rng_state):
"""Given a set of ``indices`` for data points from a sparse data set
presented in csr sparse format as inds, indptr and data, create
a random hyperplane to split the data, returning two arrays indices
that fall on either... | python | def sparse_angular_random_projection_split(inds, indptr, data, indices, rng_state):
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lmcinnes/umap | umap/rp_tree.py | sparse_euclidean_random_projection_split | def sparse_euclidean_random_projection_split(inds, indptr, data, indices, rng_state):
"""Given a set of ``indices`` for data points from a sparse data set
presented in csr sparse format as inds, indptr and data, create
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lmcinnes/umap | umap/rp_tree.py | make_tree | def make_tree(data, rng_state, leaf_size=30, angular=False):
"""Construct a random projection tree based on ``data`` with leaves
of size at most ``leaf_size``.
Parameters
----------
data: array of shape (n_samples, n_features)
The original data to be split
rng_state: array of int64, shap... | python | def make_tree(data, rng_state, leaf_size=30, angular=False):
"""Construct a random projection tree based on ``data`` with leaves
of size at most ``leaf_size``.
Parameters
----------
data: array of shape (n_samples, n_features)
The original data to be split
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lmcinnes/umap | umap/rp_tree.py | num_nodes | def num_nodes(tree):
"""Determine the number of nodes in a tree"""
if tree.is_leaf:
return 1
else:
return 1 + num_nodes(tree.left_child) + num_nodes(tree.right_child) | python | def num_nodes(tree):
"""Determine the number of nodes in a tree"""
if tree.is_leaf:
return 1
else:
return 1 + num_nodes(tree.left_child) + num_nodes(tree.right_child) | [
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lmcinnes/umap | umap/rp_tree.py | num_leaves | def num_leaves(tree):
"""Determine the number of leaves in a tree"""
if tree.is_leaf:
return 1
else:
return num_leaves(tree.left_child) + num_leaves(tree.right_child) | python | def num_leaves(tree):
"""Determine the number of leaves in a tree"""
if tree.is_leaf:
return 1
else:
return num_leaves(tree.left_child) + num_leaves(tree.right_child) | [
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lmcinnes/umap | umap/rp_tree.py | max_sparse_hyperplane_size | def max_sparse_hyperplane_size(tree):
"""Determine the most number on non zeros in a hyperplane entry"""
if tree.is_leaf:
return 0
else:
return max(
tree.hyperplane.shape[1],
max_sparse_hyperplane_size(tree.left_child),
max_sparse_hyperplane_size(tree.righ... | python | def max_sparse_hyperplane_size(tree):
"""Determine the most number on non zeros in a hyperplane entry"""
if tree.is_leaf:
return 0
else:
return max(
tree.hyperplane.shape[1],
max_sparse_hyperplane_size(tree.left_child),
max_sparse_hyperplane_size(tree.righ... | [
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lmcinnes/umap | umap/rp_tree.py | make_forest | def make_forest(data, n_neighbors, n_trees, rng_state, angular=False):
"""Build a random projection forest with ``n_trees``.
Parameters
----------
data
n_neighbors
n_trees
rng_state
angular
Returns
-------
forest: list
A list of random projection trees.
"""
... | python | def make_forest(data, n_neighbors, n_trees, rng_state, angular=False):
"""Build a random projection forest with ``n_trees``.
Parameters
----------
data
n_neighbors
n_trees
rng_state
angular
Returns
-------
forest: list
A list of random projection trees.
"""
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lmcinnes/umap | umap/rp_tree.py | rptree_leaf_array | def rptree_leaf_array(rp_forest):
"""Generate an array of sets of candidate nearest neighbors by
constructing a random projection forest and taking the leaves of all the
trees. Any given tree has leaves that are a set of potential nearest
neighbors. Given enough trees the set of all such leaves gives a ... | python | def rptree_leaf_array(rp_forest):
"""Generate an array of sets of candidate nearest neighbors by
constructing a random projection forest and taking the leaves of all the
trees. Any given tree has leaves that are a set of potential nearest
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lmcinnes/umap | umap/umap_.py | smooth_knn_dist | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0):
"""Compute a continuous version of the distance to the kth nearest
neighbor. That is, this is similar to knn-distance but allows continuous
k values rather than requiring an integral k. In esscence we are simply
computi... | python | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0):
"""Compute a continuous version of the distance to the kth nearest
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lmcinnes/umap | umap/umap_.py | nearest_neighbors | def nearest_neighbors(
X, n_neighbors, metric, metric_kwds, angular, random_state, verbose=False
):
"""Compute the ``n_neighbors`` nearest points for each data point in ``X``
under ``metric``. This may be exact, but more likely is approximated via
nearest neighbor descent.
Parameters
----------... | python | def nearest_neighbors(
X, n_neighbors, metric, metric_kwds, angular, random_state, verbose=False
):
"""Compute the ``n_neighbors`` nearest points for each data point in ``X``
under ``metric``. This may be exact, but more likely is approximated via
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lmcinnes/umap | umap/umap_.py | compute_membership_strengths | def compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos):
"""Construct the membership strength data for the 1-skeleton of each local
fuzzy simplicial set -- this is formed as a sparse matrix where each row is
a local fuzzy simplicial set, with a membership strength for the
1-simplex to eac... | python | def compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos):
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lmcinnes/umap | umap/umap_.py | fuzzy_simplicial_set | def fuzzy_simplicial_set(
X,
n_neighbors,
random_state,
metric,
metric_kwds={},
knn_indices=None,
knn_dists=None,
angular=False,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
verbose=False,
):
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co... | python | def fuzzy_simplicial_set(
X,
n_neighbors,
random_state,
metric,
metric_kwds={},
knn_indices=None,
knn_dists=None,
angular=False,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
verbose=False,
):
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lmcinnes/umap | umap/umap_.py | fast_intersection | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0):
"""Under the assumption of categorical distance for the intersecting
simplicial set perform a fast intersection.
Parameters
----------
rows: array
An array of the row of each non-zero in the sparse matrix
... | python | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0):
"""Under the assumption of categorical distance for the intersecting
simplicial set perform a fast intersection.
Parameters
----------
rows: array
An array of the row of each non-zero in the sparse matrix
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lmcinnes/umap | umap/umap_.py | reset_local_connectivity | def reset_local_connectivity(simplicial_set):
"""Reset the local connectivity requirement -- each data sample should
have complete confidence in at least one 1-simplex in the simplicial set.
We can enforce this by locally rescaling confidences, and then remerging the
different local simplicial sets toge... | python | def reset_local_connectivity(simplicial_set):
"""Reset the local connectivity requirement -- each data sample should
have complete confidence in at least one 1-simplex in the simplicial set.
We can enforce this by locally rescaling confidences, and then remerging the
different local simplicial sets toge... | [
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lmcinnes/umap | umap/umap_.py | categorical_simplicial_set_intersection | def categorical_simplicial_set_intersection(
simplicial_set, target, unknown_dist=1.0, far_dist=5.0
):
"""Combine a fuzzy simplicial set with another fuzzy simplicial set
generated from categorical data using categorical distances. The target
data is assumed to be categorical label data (a vector of lab... | python | def categorical_simplicial_set_intersection(
simplicial_set, target, unknown_dist=1.0, far_dist=5.0
):
"""Combine a fuzzy simplicial set with another fuzzy simplicial set
generated from categorical data using categorical distances. The target
data is assumed to be categorical label data (a vector of lab... | [
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lmcinnes/umap | umap/umap_.py | make_epochs_per_sample | def make_epochs_per_sample(weights, n_epochs):
"""Given a set of weights and number of epochs generate the number of
epochs per sample for each weight.
Parameters
----------
weights: array of shape (n_1_simplices)
The weights ofhow much we wish to sample each 1-simplex.
n_epochs: int
... | python | def make_epochs_per_sample(weights, n_epochs):
"""Given a set of weights and number of epochs generate the number of
epochs per sample for each weight.
Parameters
----------
weights: array of shape (n_1_simplices)
The weights ofhow much we wish to sample each 1-simplex.
n_epochs: int
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lmcinnes/umap | umap/umap_.py | rdist | def rdist(x, y):
"""Reduced Euclidean distance.
Parameters
----------
x: array of shape (embedding_dim,)
y: array of shape (embedding_dim,)
Returns
-------
The squared euclidean distance between x and y
"""
result = 0.0
for i in range(x.shape[0]):
result += (x[i] - ... | python | def rdist(x, y):
"""Reduced Euclidean distance.
Parameters
----------
x: array of shape (embedding_dim,)
y: array of shape (embedding_dim,)
Returns
-------
The squared euclidean distance between x and y
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lmcinnes/umap | umap/umap_.py | optimize_layout | def optimize_layout(
head_embedding,
tail_embedding,
head,
tail,
n_epochs,
n_vertices,
epochs_per_sample,
a,
b,
rng_state,
gamma=1.0,
initial_alpha=1.0,
negative_sample_rate=5.0,
verbose=False,
):
"""Improve an embedding using stochastic gradient descent to mi... | python | def optimize_layout(
head_embedding,
tail_embedding,
head,
tail,
n_epochs,
n_vertices,
epochs_per_sample,
a,
b,
rng_state,
gamma=1.0,
initial_alpha=1.0,
negative_sample_rate=5.0,
verbose=False,
):
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lmcinnes/umap | umap/umap_.py | simplicial_set_embedding | def simplicial_set_embedding(
data,
graph,
n_components,
initial_alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init,
random_state,
metric,
metric_kwds,
verbose,
):
"""Perform a fuzzy simplicial set embedding, using a specified
initialisation method a... | python | def simplicial_set_embedding(
data,
graph,
n_components,
initial_alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init,
random_state,
metric,
metric_kwds,
verbose,
):
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lmcinnes/umap | umap/umap_.py | init_transform | def init_transform(indices, weights, embedding):
"""Given indices and weights and an original embeddings
initialize the positions of new points relative to the
indices and weights (of their neighbors in the source data).
Parameters
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"""Given indices and weights and an original embeddings
initialize the positions of new points relative to the
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lmcinnes/umap | umap/umap_.py | find_ab_params | def find_ab_params(spread, min_dist):
"""Fit a, b params for the differentiable curve used in lower
dimensional fuzzy simplicial complex construction. We want the
smooth curve (from a pre-defined family with simple gradient) that
best matches an offset exponential decay.
"""
def curve(x, a, b):... | python | def find_ab_params(spread, min_dist):
"""Fit a, b params for the differentiable curve used in lower
dimensional fuzzy simplicial complex construction. We want the
smooth curve (from a pre-defined family with simple gradient) that
best matches an offset exponential decay.
"""
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lmcinnes/umap | umap/umap_.py | UMAP.fit | def fit(self, X, y=None):
"""Fit X into an embedded space.
Optionally use y for supervised dimension reduction.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
... | python | def fit(self, X, y=None):
"""Fit X into an embedded space.
Optionally use y for supervised dimension reduction.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
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lmcinnes/umap | umap/umap_.py | UMAP.fit_transform | def fit_transform(self, X, y=None):
"""Fit X into an embedded space and return that transformed
output.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
mat... | python | def fit_transform(self, X, y=None):
"""Fit X into an embedded space and return that transformed
output.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
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lmcinnes/umap | umap/umap_.py | UMAP.transform | def transform(self, X):
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----------
X : array, shape (n_samples, n_features)
New data to be transformed.
Returns
-------
X_new : array, shape (n_sam... | python | def transform(self, X):
"""Transform X into the existing embedded space and return that
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X : array, shape (n_samples, n_features)
New data to be transformed.
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lmcinnes/umap | umap/sparse.py | make_sparse_nn_descent | def make_sparse_nn_descent(sparse_dist, dist_args):
"""Create a numba accelerated version of nearest neighbor descent
specialised for the given distance metric and metric arguments on sparse
matrix data provided in CSR ind, indptr and data format. Numba
doesn't support higher order functions directly, b... | python | def make_sparse_nn_descent(sparse_dist, dist_args):
"""Create a numba accelerated version of nearest neighbor descent
specialised for the given distance metric and metric arguments on sparse
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deepmind/pysc2 | pysc2/bin/benchmark_observe.py | interface_options | def interface_options(score=False, raw=False, features=None, rgb=None):
"""Get an InterfaceOptions for the config."""
interface = sc_pb.InterfaceOptions()
interface.score = score
interface.raw = raw
if features:
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = features
int... | python | def interface_options(score=False, raw=False, features=None, rgb=None):
"""Get an InterfaceOptions for the config."""
interface = sc_pb.InterfaceOptions()
interface.score = score
interface.raw = raw
if features:
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = features
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deepmind/pysc2 | pysc2/lib/point_flag.py | DEFINE_point | def DEFINE_point(name, default, help): # pylint: disable=invalid-name,redefined-builtin
"""Registers a flag whose value parses as a point."""
flags.DEFINE(PointParser(), name, default, help) | python | def DEFINE_point(name, default, help): # pylint: disable=invalid-name,redefined-builtin
"""Registers a flag whose value parses as a point."""
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deepmind/pysc2 | pysc2/lib/actions.py | spatial | def spatial(action, action_space):
"""Choose the action space for the action proto."""
if action_space == ActionSpace.FEATURES:
return action.action_feature_layer
elif action_space == ActionSpace.RGB:
return action.action_render
else:
raise ValueError("Unexpected value for action_space: %s" % action... | python | def spatial(action, action_space):
"""Choose the action space for the action proto."""
if action_space == ActionSpace.FEATURES:
return action.action_feature_layer
elif action_space == ActionSpace.RGB:
return action.action_render
else:
raise ValueError("Unexpected value for action_space: %s" % action... | [
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deepmind/pysc2 | pysc2/lib/actions.py | move_camera | def move_camera(action, action_space, minimap):
"""Move the camera."""
minimap.assign_to(spatial(action, action_space).camera_move.center_minimap) | python | def move_camera(action, action_space, minimap):
"""Move the camera."""
minimap.assign_to(spatial(action, action_space).camera_move.center_minimap) | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_point | def select_point(action, action_space, select_point_act, screen):
"""Select a unit at a point."""
select = spatial(action, action_space).unit_selection_point
screen.assign_to(select.selection_screen_coord)
select.type = select_point_act | python | def select_point(action, action_space, select_point_act, screen):
"""Select a unit at a point."""
select = spatial(action, action_space).unit_selection_point
screen.assign_to(select.selection_screen_coord)
select.type = select_point_act | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_rect | def select_rect(action, action_space, select_add, screen, screen2):
"""Select units within a rectangle."""
select = spatial(action, action_space).unit_selection_rect
out_rect = select.selection_screen_coord.add()
screen_rect = point.Rect(screen, screen2)
screen_rect.tl.assign_to(out_rect.p0)
screen_rect.br.... | python | def select_rect(action, action_space, select_add, screen, screen2):
"""Select units within a rectangle."""
select = spatial(action, action_space).unit_selection_rect
out_rect = select.selection_screen_coord.add()
screen_rect = point.Rect(screen, screen2)
screen_rect.tl.assign_to(out_rect.p0)
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deepmind/pysc2 | pysc2/lib/actions.py | select_idle_worker | def select_idle_worker(action, action_space, select_worker):
"""Select an idle worker."""
del action_space
action.action_ui.select_idle_worker.type = select_worker | python | def select_idle_worker(action, action_space, select_worker):
"""Select an idle worker."""
del action_space
action.action_ui.select_idle_worker.type = select_worker | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_army | def select_army(action, action_space, select_add):
"""Select the entire army."""
del action_space
action.action_ui.select_army.selection_add = select_add | python | def select_army(action, action_space, select_add):
"""Select the entire army."""
del action_space
action.action_ui.select_army.selection_add = select_add | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_warp_gates | def select_warp_gates(action, action_space, select_add):
"""Select all warp gates."""
del action_space
action.action_ui.select_warp_gates.selection_add = select_add | python | def select_warp_gates(action, action_space, select_add):
"""Select all warp gates."""
del action_space
action.action_ui.select_warp_gates.selection_add = select_add | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_unit | def select_unit(action, action_space, select_unit_act, select_unit_id):
"""Select a specific unit from the multi-unit selection."""
del action_space
select = action.action_ui.multi_panel
select.type = select_unit_act
select.unit_index = select_unit_id | python | def select_unit(action, action_space, select_unit_act, select_unit_id):
"""Select a specific unit from the multi-unit selection."""
del action_space
select = action.action_ui.multi_panel
select.type = select_unit_act
select.unit_index = select_unit_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | control_group | def control_group(action, action_space, control_group_act, control_group_id):
"""Act on a control group, selecting, setting, etc."""
del action_space
select = action.action_ui.control_group
select.action = control_group_act
select.control_group_index = control_group_id | python | def control_group(action, action_space, control_group_act, control_group_id):
"""Act on a control group, selecting, setting, etc."""
del action_space
select = action.action_ui.control_group
select.action = control_group_act
select.control_group_index = control_group_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | unload | def unload(action, action_space, unload_id):
"""Unload a unit from a transport/bunker/nydus/etc."""
del action_space
action.action_ui.cargo_panel.unit_index = unload_id | python | def unload(action, action_space, unload_id):
"""Unload a unit from a transport/bunker/nydus/etc."""
del action_space
action.action_ui.cargo_panel.unit_index = unload_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | build_queue | def build_queue(action, action_space, build_queue_id):
"""Cancel a unit in the build queue."""
del action_space
action.action_ui.production_panel.unit_index = build_queue_id | python | def build_queue(action, action_space, build_queue_id):
"""Cancel a unit in the build queue."""
del action_space
action.action_ui.production_panel.unit_index = build_queue_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | cmd_quick | def cmd_quick(action, action_space, ability_id, queued):
"""Do a quick command like 'Stop' or 'Stim'."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued | python | def cmd_quick(action, action_space, ability_id, queued):
"""Do a quick command like 'Stop' or 'Stim'."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued | [
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deepmind/pysc2 | pysc2/lib/actions.py | cmd_screen | def cmd_screen(action, action_space, ability_id, queued, screen):
"""Do a command that needs a point on the screen."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
screen.assign_to(action_cmd.target_screen_coord) | python | def cmd_screen(action, action_space, ability_id, queued, screen):
"""Do a command that needs a point on the screen."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
screen.assign_to(action_cmd.target_screen_coord) | [
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deepmind/pysc2 | pysc2/lib/actions.py | cmd_minimap | def cmd_minimap(action, action_space, ability_id, queued, minimap):
"""Do a command that needs a point on the minimap."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
minimap.assign_to(action_cmd.target_minimap_coord) | python | def cmd_minimap(action, action_space, ability_id, queued, minimap):
"""Do a command that needs a point on the minimap."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
minimap.assign_to(action_cmd.target_minimap_coord) | [
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deepmind/pysc2 | pysc2/lib/actions.py | autocast | def autocast(action, action_space, ability_id):
"""Toggle autocast."""
del action_space
action.action_ui.toggle_autocast.ability_id = ability_id | python | def autocast(action, action_space, ability_id):
"""Toggle autocast."""
del action_space
action.action_ui.toggle_autocast.ability_id = ability_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | ArgumentType.enum | def enum(cls, options, values):
"""Create an ArgumentType where you choose one of a set of known values."""
names, real = zip(*options)
del names # unused
def factory(i, name):
return cls(i, name, (len(real),), lambda a: real[a[0]], values)
return factory | python | def enum(cls, options, values):
"""Create an ArgumentType where you choose one of a set of known values."""
names, real = zip(*options)
del names # unused
def factory(i, name):
return cls(i, name, (len(real),), lambda a: real[a[0]], values)
return factory | [
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deepmind/pysc2 | pysc2/lib/actions.py | ArgumentType.scalar | def scalar(cls, value):
"""Create an ArgumentType with a single scalar in range(value)."""
return lambda i, name: cls(i, name, (value,), lambda a: a[0], None) | python | def scalar(cls, value):
"""Create an ArgumentType with a single scalar in range(value)."""
return lambda i, name: cls(i, name, (value,), lambda a: a[0], None) | [
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deepmind/pysc2 | pysc2/lib/actions.py | ArgumentType.point | def point(cls): # No range because it's unknown at this time.
"""Create an ArgumentType that is represented by a point.Point."""
def factory(i, name):
return cls(i, name, (0, 0), lambda a: point.Point(*a).floor(), None)
return factory | python | def point(cls): # No range because it's unknown at this time.
"""Create an ArgumentType that is represented by a point.Point."""
def factory(i, name):
return cls(i, name, (0, 0), lambda a: point.Point(*a).floor(), None)
return factory | [
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deepmind/pysc2 | pysc2/lib/actions.py | Arguments.types | def types(cls, **kwargs):
"""Create an Arguments of the possible Types."""
named = {name: factory(Arguments._fields.index(name), name)
for name, factory in six.iteritems(kwargs)}
return cls(**named) | python | def types(cls, **kwargs):
"""Create an Arguments of the possible Types."""
named = {name: factory(Arguments._fields.index(name), name)
for name, factory in six.iteritems(kwargs)}
return cls(**named) | [
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deepmind/pysc2 | pysc2/lib/actions.py | Function.ui_func | def ui_func(cls, id_, name, function_type, avail_fn=always):
"""Define a function representing a ui action."""
return cls(id_, name, 0, 0, function_type, FUNCTION_TYPES[function_type],
avail_fn) | python | def ui_func(cls, id_, name, function_type, avail_fn=always):
"""Define a function representing a ui action."""
return cls(id_, name, 0, 0, function_type, FUNCTION_TYPES[function_type],
avail_fn) | [
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deepmind/pysc2 | pysc2/lib/actions.py | Function.ability | def ability(cls, id_, name, function_type, ability_id, general_id=0):
"""Define a function represented as a game ability."""
assert function_type in ABILITY_FUNCTIONS
return cls(id_, name, ability_id, general_id, function_type,
FUNCTION_TYPES[function_type], None) | python | def ability(cls, id_, name, function_type, ability_id, general_id=0):
"""Define a function represented as a game ability."""
assert function_type in ABILITY_FUNCTIONS
return cls(id_, name, ability_id, general_id, function_type,
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deepmind/pysc2 | pysc2/lib/actions.py | Function.str | def str(self, space=False):
"""String version. Set space=True to line them all up nicely."""
return "%s/%s (%s)" % (str(int(self.id)).rjust(space and 4),
self.name.ljust(space and 50),
"; ".join(str(a) for a in self.args)) | python | def str(self, space=False):
"""String version. Set space=True to line them all up nicely."""
return "%s/%s (%s)" % (str(int(self.id)).rjust(space and 4),
self.name.ljust(space and 50),
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deepmind/pysc2 | pysc2/lib/actions.py | FunctionCall.init_with_validation | def init_with_validation(cls, function, arguments):
"""Return a `FunctionCall` given some validation for the function and args.
Args:
function: A function name or id, to be converted into a function id enum.
arguments: An iterable of function arguments. Arguments that are enum
types can b... | python | def init_with_validation(cls, function, arguments):
"""Return a `FunctionCall` given some validation for the function and args.
Args:
function: A function name or id, to be converted into a function id enum.
arguments: An iterable of function arguments. Arguments that are enum
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deepmind/pysc2 | pysc2/lib/actions.py | FunctionCall.all_arguments | def all_arguments(cls, function, arguments):
"""Helper function for creating `FunctionCall`s with `Arguments`.
Args:
function: The value to store for the action function.
arguments: The values to store for the arguments of the action. Can either
be an `Arguments` object, a `dict`, or an ite... | python | def all_arguments(cls, function, arguments):
"""Helper function for creating `FunctionCall`s with `Arguments`.
Args:
function: The value to store for the action function.
arguments: The values to store for the arguments of the action. Can either
be an `Arguments` object, a `dict`, or an ite... | [
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deepmind/pysc2 | pysc2/bin/valid_actions.py | main | def main(unused_argv):
"""Print the valid actions."""
feats = features.Features(
# Actually irrelevant whether it's feature or rgb size.
features.AgentInterfaceFormat(
feature_dimensions=features.Dimensions(
screen=FLAGS.screen_size,
minimap=FLAGS.minimap_size)))
... | python | def main(unused_argv):
"""Print the valid actions."""
feats = features.Features(
# Actually irrelevant whether it's feature or rgb size.
features.AgentInterfaceFormat(
feature_dimensions=features.Dimensions(
screen=FLAGS.screen_size,
minimap=FLAGS.minimap_size)))
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deepmind/pysc2 | pysc2/lib/portspicker.py | pick_unused_ports | def pick_unused_ports(num_ports, retry_interval_secs=3, retry_attempts=5):
"""Reserves and returns a list of `num_ports` unused ports."""
ports = set()
for _ in range(retry_attempts):
ports.update(
portpicker.pick_unused_port() for _ in range(num_ports - len(ports)))
ports.discard(None) # portpic... | python | def pick_unused_ports(num_ports, retry_interval_secs=3, retry_attempts=5):
"""Reserves and returns a list of `num_ports` unused ports."""
ports = set()
for _ in range(retry_attempts):
ports.update(
portpicker.pick_unused_port() for _ in range(num_ports - len(ports)))
ports.discard(None) # portpic... | [
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deepmind/pysc2 | pysc2/lib/portspicker.py | pick_contiguous_unused_ports | def pick_contiguous_unused_ports(
num_ports,
retry_interval_secs=3,
retry_attempts=5):
"""Reserves and returns a list of `num_ports` contiguous unused ports."""
for _ in range(retry_attempts):
start_port = portpicker.pick_unused_port()
if start_port is not None:
ports = [start_port + p for... | python | def pick_contiguous_unused_ports(
num_ports,
retry_interval_secs=3,
retry_attempts=5):
"""Reserves and returns a list of `num_ports` contiguous unused ports."""
for _ in range(retry_attempts):
start_port = portpicker.pick_unused_port()
if start_port is not None:
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deepmind/pysc2 | pysc2/bin/agent.py | run_thread | def run_thread(agent_classes, players, map_name, visualize):
"""Run one thread worth of the environment with agents."""
with sc2_env.SC2Env(
map_name=map_name,
players=players,
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=FLAGS.feature_screen_size,
... | python | def run_thread(agent_classes, players, map_name, visualize):
"""Run one thread worth of the environment with agents."""
with sc2_env.SC2Env(
map_name=map_name,
players=players,
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=FLAGS.feature_screen_size,
... | [
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deepmind/pysc2 | pysc2/bin/agent.py | main | def main(unused_argv):
"""Run an agent."""
stopwatch.sw.enabled = FLAGS.profile or FLAGS.trace
stopwatch.sw.trace = FLAGS.trace
map_inst = maps.get(FLAGS.map)
agent_classes = []
players = []
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
agent_cls = getattr(importlib.import_module(agent_module... | python | def main(unused_argv):
"""Run an agent."""
stopwatch.sw.enabled = FLAGS.profile or FLAGS.trace
stopwatch.sw.trace = FLAGS.trace
map_inst = maps.get(FLAGS.map)
agent_classes = []
players = []
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
agent_cls = getattr(importlib.import_module(agent_module... | [
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] | df4cc4b00f07a2242be9ba153d4a7f4ad2017897 | https://github.com/deepmind/pysc2/blob/df4cc4b00f07a2242be9ba153d4a7f4ad2017897/pysc2/bin/agent.py#L106-L146 | train | Run an agent. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
deepmind/pysc2 | pysc2/lib/remote_controller.py | check_error | def check_error(res, error_enum):
"""Raise if the result has an error, otherwise return the result."""
if res.HasField("error"):
enum_name = error_enum.DESCRIPTOR.full_name
error_name = error_enum.Name(res.error)
details = getattr(res, "error_details", "<none>")
raise RequestError("%s.%s: '%s'" % (e... | python | def check_error(res, error_enum):
"""Raise if the result has an error, otherwise return the result."""
if res.HasField("error"):
enum_name = error_enum.DESCRIPTOR.full_name
error_name = error_enum.Name(res.error)
details = getattr(res, "error_details", "<none>")
raise RequestError("%s.%s: '%s'" % (e... | [
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