repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 | partition stringclasses 1
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btimby/fulltext | fulltext/data/winmake.py | rm | def rm(pattern):
"""Recursively remove a file or dir by pattern."""
paths = glob.glob(pattern)
for path in paths:
if path.startswith('.git/'):
continue
if os.path.isdir(path):
def onerror(fun, path, excinfo):
exc = excinfo[1]
if exc.err... | python | def rm(pattern):
"""Recursively remove a file or dir by pattern."""
paths = glob.glob(pattern)
for path in paths:
if path.startswith('.git/'):
continue
if os.path.isdir(path):
def onerror(fun, path, excinfo):
exc = excinfo[1]
if exc.err... | [
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btimby/fulltext | fulltext/data/winmake.py | help | def help():
"""Print this help"""
safe_print('Run "make [-p <PYTHON>] <target>" where <target> is one of:')
for name in sorted(_cmds):
safe_print(
" %-20s %s" % (name.replace('_', '-'), _cmds[name] or ''))
sys.exit(1) | python | def help():
"""Print this help"""
safe_print('Run "make [-p <PYTHON>] <target>" where <target> is one of:')
for name in sorted(_cmds):
safe_print(
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btimby/fulltext | fulltext/data/winmake.py | clean | def clean():
"""Deletes dev files"""
rm("$testfn*")
rm("*.bak")
rm("*.core")
rm("*.egg-info")
rm("*.orig")
rm("*.pyc")
rm("*.pyd")
rm("*.pyo")
rm("*.rej")
rm("*.so")
rm("*.~")
rm("*__pycache__")
rm(".coverage")
rm(".tox")
rm(".coverage")
rm("build")
... | python | def clean():
"""Deletes dev files"""
rm("$testfn*")
rm("*.bak")
rm("*.core")
rm("*.egg-info")
rm("*.orig")
rm("*.pyc")
rm("*.pyd")
rm("*.pyo")
rm("*.rej")
rm("*.so")
rm("*.~")
rm("*__pycache__")
rm(".coverage")
rm(".tox")
rm(".coverage")
rm("build")
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btimby/fulltext | fulltext/data/winmake.py | lint | def lint():
"""Run flake8 against all py files"""
py_files = subprocess.check_output("git ls-files")
if PY3:
py_files = py_files.decode()
py_files = [x for x in py_files.split() if x.endswith('.py')]
py_files = ' '.join(py_files)
sh("%s -m flake8 %s" % (PYTHON, py_files), nolog=True) | python | def lint():
"""Run flake8 against all py files"""
py_files = subprocess.check_output("git ls-files")
if PY3:
py_files = py_files.decode()
py_files = [x for x in py_files.split() if x.endswith('.py')]
py_files = ' '.join(py_files)
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btimby/fulltext | fulltext/data/winmake.py | coverage | def coverage():
"""Run coverage tests."""
# Note: coverage options are controlled by .coveragerc file
install()
test_setup()
sh("%s -m coverage run %s" % (PYTHON, TEST_SCRIPT))
sh("%s -m coverage report" % PYTHON)
sh("%s -m coverage html" % PYTHON)
sh("%s -m webbrowser -t htmlcov/index.h... | python | def coverage():
"""Run coverage tests."""
# Note: coverage options are controlled by .coveragerc file
install()
test_setup()
sh("%s -m coverage run %s" % (PYTHON, TEST_SCRIPT))
sh("%s -m coverage report" % PYTHON)
sh("%s -m coverage html" % PYTHON)
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btimby/fulltext | fulltext/data/winmake.py | venv | def venv():
"""Install venv + deps."""
try:
import virtualenv # NOQA
except ImportError:
sh("%s -m pip install virtualenv" % PYTHON)
if not os.path.isdir("venv"):
sh("%s -m virtualenv venv" % PYTHON)
sh("venv\\Scripts\\pip install -r %s" % (REQUIREMENTS_TXT)) | python | def venv():
"""Install venv + deps."""
try:
import virtualenv # NOQA
except ImportError:
sh("%s -m pip install virtualenv" % PYTHON)
if not os.path.isdir("venv"):
sh("%s -m virtualenv venv" % PYTHON)
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pschmitt/pykeepass | pykeepass/kdbx_parsing/kdbx4.py | compute_header_hmac_hash | def compute_header_hmac_hash(context):
"""Compute HMAC-SHA256 hash of header.
Used to prevent header tampering."""
return hmac.new(
hashlib.sha512(
b'\xff' * 8 +
hashlib.sha512(
context._.header.value.dynamic_header.master_seed.data +
context.... | python | def compute_header_hmac_hash(context):
"""Compute HMAC-SHA256 hash of header.
Used to prevent header tampering."""
return hmac.new(
hashlib.sha512(
b'\xff' * 8 +
hashlib.sha512(
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pschmitt/pykeepass | pykeepass/kdbx_parsing/kdbx4.py | compute_payload_block_hash | def compute_payload_block_hash(this):
"""Compute hash of each payload block.
Used to prevent payload corruption and tampering."""
return hmac.new(
hashlib.sha512(
struct.pack('<Q', this._index) +
hashlib.sha512(
this._._.header.value.dynamic_header.master_see... | python | def compute_payload_block_hash(this):
"""Compute hash of each payload block.
Used to prevent payload corruption and tampering."""
return hmac.new(
hashlib.sha512(
struct.pack('<Q', this._index) +
hashlib.sha512(
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pschmitt/pykeepass | pykeepass/kdbx_parsing/pytwofish.py | Twofish.decrypt | def decrypt(self, block):
"""Decrypt blocks."""
if len(block) % 16:
raise ValueError("block size must be a multiple of 16")
plaintext = b''
while block:
a, b, c, d = struct.unpack("<4L", block[:16])
temp = [a, b, c, d]
decrypt(self.conte... | python | def decrypt(self, block):
"""Decrypt blocks."""
if len(block) % 16:
raise ValueError("block size must be a multiple of 16")
plaintext = b''
while block:
a, b, c, d = struct.unpack("<4L", block[:16])
temp = [a, b, c, d]
decrypt(self.conte... | [
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pschmitt/pykeepass | pykeepass/kdbx_parsing/pytwofish.py | Twofish.encrypt | def encrypt(self, block):
"""Encrypt blocks."""
if len(block) % 16:
raise ValueError("block size must be a multiple of 16")
ciphertext = b''
while block:
a, b, c, d = struct.unpack("<4L", block[0:16])
temp = [a, b, c, d]
encrypt(self.con... | python | def encrypt(self, block):
"""Encrypt blocks."""
if len(block) % 16:
raise ValueError("block size must be a multiple of 16")
ciphertext = b''
while block:
a, b, c, d = struct.unpack("<4L", block[0:16])
temp = [a, b, c, d]
encrypt(self.con... | [
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pschmitt/pykeepass | pykeepass/kdbx_parsing/common.py | aes_kdf | def aes_kdf(key, rounds, password=None, keyfile=None):
"""Set up a context for AES128-ECB encryption to find transformed_key"""
cipher = AES.new(key, AES.MODE_ECB)
key_composite = compute_key_composite(
password=password,
keyfile=keyfile
)
# get the number of rounds from the header... | python | def aes_kdf(key, rounds, password=None, keyfile=None):
"""Set up a context for AES128-ECB encryption to find transformed_key"""
cipher = AES.new(key, AES.MODE_ECB)
key_composite = compute_key_composite(
password=password,
keyfile=keyfile
)
# get the number of rounds from the header... | [
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pschmitt/pykeepass | pykeepass/kdbx_parsing/common.py | compute_key_composite | def compute_key_composite(password=None, keyfile=None):
"""Compute composite key.
Used in header verification and payload decryption."""
# hash the password
if password:
password_composite = hashlib.sha256(password.encode('utf-8')).digest()
else:
password_composite = b''
# hash ... | python | def compute_key_composite(password=None, keyfile=None):
"""Compute composite key.
Used in header verification and payload decryption."""
# hash the password
if password:
password_composite = hashlib.sha256(password.encode('utf-8')).digest()
else:
password_composite = b''
# hash ... | [
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pschmitt/pykeepass | pykeepass/kdbx_parsing/common.py | compute_master | def compute_master(context):
"""Computes master key from transformed key and master seed.
Used in payload decryption."""
# combine the transformed key with the header master seed to find the master_key
master_key = hashlib.sha256(
context._.header.value.dynamic_header.master_seed.data +
... | python | def compute_master(context):
"""Computes master key from transformed key and master seed.
Used in payload decryption."""
# combine the transformed key with the header master seed to find the master_key
master_key = hashlib.sha256(
context._.header.value.dynamic_header.master_seed.data +
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pschmitt/pykeepass | pykeepass/kdbx_parsing/common.py | Unprotect | def Unprotect(protected_stream_id, protected_stream_key, subcon):
"""Select stream cipher based on protected_stream_id"""
return Switch(
protected_stream_id,
{'arcfourvariant': ARCFourVariantStream(protected_stream_key, subcon),
'salsa20': Salsa20Stream(protected_stream_key, subcon),
... | python | def Unprotect(protected_stream_id, protected_stream_key, subcon):
"""Select stream cipher based on protected_stream_id"""
return Switch(
protected_stream_id,
{'arcfourvariant': ARCFourVariantStream(protected_stream_key, subcon),
'salsa20': Salsa20Stream(protected_stream_key, subcon),
... | [
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pschmitt/pykeepass | pykeepass/kdbx_parsing/twofish.py | BlockCipher.encrypt | def encrypt(self,plaintext,n=''):
"""Encrypt some plaintext
plaintext = a string of binary data
n = the 'tweak' value when the chaining mode is XTS
The encrypt function will encrypt the supplied plaintext.
The behavior varies slightly depending on the chaini... | python | def encrypt(self,plaintext,n=''):
"""Encrypt some plaintext
plaintext = a string of binary data
n = the 'tweak' value when the chaining mode is XTS
The encrypt function will encrypt the supplied plaintext.
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pschmitt/pykeepass | pykeepass/kdbx_parsing/twofish.py | BlockCipher.decrypt | def decrypt(self,ciphertext,n=''):
"""Decrypt some ciphertext
ciphertext = a string of binary data
n = the 'tweak' value when the chaining mode is XTS
The decrypt function will decrypt the supplied ciphertext.
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padfct = padding function
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pschmitt/pykeepass | pykeepass/baseelement.py | BaseElement._datetime_to_utc | def _datetime_to_utc(self, dt):
"""Convert naive datetimes to UTC"""
if not dt.tzinfo:
dt = dt.replace(tzinfo=tz.gettz())
return dt.astimezone(tz.gettz('UTC')) | python | def _datetime_to_utc(self, dt):
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thunder-project/thunder | thunder/images/readers.py | fromrdd | def fromrdd(rdd, dims=None, nrecords=None, dtype=None, labels=None, ordered=False):
"""
Load images from a Spark RDD.
Input RDD must be a collection of key-value pairs
where keys are singleton tuples indexing images,
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Parameters
----------
rdd : SparkRD... | python | def fromrdd(rdd, dims=None, nrecords=None, dtype=None, labels=None, ordered=False):
"""
Load images from a Spark RDD.
Input RDD must be a collection of key-value pairs
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thunder-project/thunder | thunder/images/readers.py | fromarray | def fromarray(values, labels=None, npartitions=None, engine=None):
"""
Load images from an array.
First dimension will be used to index images,
so remaining dimensions after the first should
be the dimensions of the images,
e.g. (3, 100, 200) for 3 x (100, 200) images
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------... | python | def fromarray(values, labels=None, npartitions=None, engine=None):
"""
Load images from an array.
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thunder-project/thunder | thunder/images/readers.py | fromlist | def fromlist(items, accessor=None, keys=None, dims=None, dtype=None, labels=None, npartitions=None, engine=None):
"""
Load images from a list of items using the given accessor.
Parameters
----------
accessor : function
Apply to each item from the list to yield an image.
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"""
Load images from a list of items using the given accessor.
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thunder-project/thunder | thunder/images/readers.py | frompath | def frompath(path, accessor=None, ext=None, start=None, stop=None, recursive=False, npartitions=None, dims=None, dtype=None, labels=None, recount=False, engine=None, credentials=None):
"""
Load images from a path using the given accessor.
Supports both local and remote filesystems.
Parameters
----... | python | def frompath(path, accessor=None, ext=None, start=None, stop=None, recursive=False, npartitions=None, dims=None, dtype=None, labels=None, recount=False, engine=None, credentials=None):
"""
Load images from a path using the given accessor.
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thunder-project/thunder | thunder/images/readers.py | fromtif | def fromtif(path, ext='tif', start=None, stop=None, recursive=False, nplanes=None, npartitions=None, labels=None, engine=None, credentials=None, discard_extra=False):
"""
Loads images from single or multi-page TIF files.
Parameters
----------
path : str
Path to data files or directory, spec... | python | def fromtif(path, ext='tif', start=None, stop=None, recursive=False, nplanes=None, npartitions=None, labels=None, engine=None, credentials=None, discard_extra=False):
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thunder-project/thunder | thunder/images/readers.py | frompng | def frompng(path, ext='png', start=None, stop=None, recursive=False, npartitions=None, labels=None, engine=None, credentials=None):
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Load images from PNG files.
Parameters
----------
path : str
Path to data files or directory, specified as either a local filesystem path
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thunder-project/thunder | thunder/images/readers.py | fromrandom | def fromrandom(shape=(10, 50, 50), npartitions=1, seed=42, engine=None):
"""
Generate random image data.
Parameters
----------
shape : tuple, optional, default=(10, 50, 50)
Dimensions of images.
npartitions : int, optional, default=1
Number of partitions.
seed : int, optio... | python | def fromrandom(shape=(10, 50, 50), npartitions=1, seed=42, engine=None):
"""
Generate random image data.
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----------
shape : tuple, optional, default=(10, 50, 50)
Dimensions of images.
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thunder-project/thunder | thunder/images/readers.py | fromexample | def fromexample(name=None, engine=None):
"""
Load example image data.
Data are downloaded from S3, so this method requires an internet connection.
Parameters
----------
name : str
Name of dataset, if not specified will print options.
engine : object, default = None
Computa... | python | def fromexample(name=None, engine=None):
"""
Load example image data.
Data are downloaded from S3, so this method requires an internet connection.
Parameters
----------
name : str
Name of dataset, if not specified will print options.
engine : object, default = None
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thunder-project/thunder | thunder/blocks/local.py | LocalChunks.unchunk | def unchunk(self):
"""
Reconstitute the chunked array back into a full ndarray.
Returns
-------
ndarray
"""
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shape = self.values.shape
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"""
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ndarray
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thunder-project/thunder | thunder/blocks/local.py | LocalChunks.chunk | def chunk(arr, chunk_size="150", padding=None):
"""
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----------
array : ndarray
Array that will be broken into chunks
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thunder-project/thunder | thunder/base.py | Data.filter | def filter(self, func):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
along a single axis or multiple axes. Array will be
aligned so that the desired set of axes are in the
keys, which may require a transpose/reshape.
Param... | python | def filter(self, func):
"""
Filter array along an axis.
Applies a function which should evaluate to boolean,
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aligned so that the desired set of axes are in the
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thunder-project/thunder | thunder/base.py | Data.map | def map(self, func, value_shape=None, dtype=None, with_keys=False):
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Apply an array -> array function across an axis.
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thunder-project/thunder | thunder/base.py | Data._reduce | def _reduce(self, func, axis=0):
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Reduce an array along an axis.
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thunder-project/thunder | thunder/base.py | Data.element_wise | def element_wise(self, other, op):
"""
Apply an elementwise operation to data.
Both self and other data must have the same mode.
If self is in local mode, other can also be a numpy array.
Self and other must have the same shape, or other must be a scalar.
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"""
Apply an elementwise operation to data.
Both self and other data must have the same mode.
If self is in local mode, other can also be a numpy array.
Self and other must have the same shape, or other must be a scalar.
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thunder-project/thunder | thunder/base.py | Data.clip | def clip(self, min=None, max=None):
"""
Clip values above and below.
Parameters
----------
min : scalar or array-like
Minimum value. If array, will be broadcasted
max : scalar or array-like
Maximum value. If array, will be broadcasted.
""... | python | def clip(self, min=None, max=None):
"""
Clip values above and below.
Parameters
----------
min : scalar or array-like
Minimum value. If array, will be broadcasted
max : scalar or array-like
Maximum value. If array, will be broadcasted.
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thunder-project/thunder | thunder/series/readers.py | fromrdd | def fromrdd(rdd, nrecords=None, shape=None, index=None, labels=None, dtype=None, ordered=False):
"""
Load series data from a Spark RDD.
Assumes keys are tuples with increasing and unique indices,
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Load series data from a Spark RDD.
Assumes keys are tuples with increasing and unique indices,
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thunder-project/thunder | thunder/series/readers.py | fromarray | def fromarray(values, index=None, labels=None, npartitions=None, engine=None):
"""
Load series data from an array.
Assumes that all but final dimension index the records,
and the size of the final dimension is the length of each record,
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"""
Load series data from an array.
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thunder-project/thunder | thunder/series/readers.py | fromlist | def fromlist(items, accessor=None, index=None, labels=None, dtype=None, npartitions=None, engine=None):
"""
Load series data from a list with an optional accessor function.
Will call accessor function on each item from the list,
providing a generic interface for data loading.
Parameters
------... | python | def fromlist(items, accessor=None, index=None, labels=None, dtype=None, npartitions=None, engine=None):
"""
Load series data from a list with an optional accessor function.
Will call accessor function on each item from the list,
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thunder-project/thunder | thunder/series/readers.py | fromtext | def fromtext(path, ext='txt', dtype='float64', skip=0, shape=None, index=None, labels=None, npartitions=None, engine=None, credentials=None):
"""
Loads series data from text files.
Assumes data are formatted as rows, where each record is a row
of numbers separated by spaces e.g. 'v v v v v'. You can
... | python | def fromtext(path, ext='txt', dtype='float64', skip=0, shape=None, index=None, labels=None, npartitions=None, engine=None, credentials=None):
"""
Loads series data from text files.
Assumes data are formatted as rows, where each record is a row
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thunder-project/thunder | thunder/series/readers.py | frombinary | def frombinary(path, ext='bin', conf='conf.json', dtype=None, shape=None, skip=0, index=None, labels=None, engine=None, credentials=None):
"""
Load series data from flat binary files.
Parameters
----------
path : string URI or local filesystem path
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"""
Load series data from flat binary files.
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path : string URI or local filesystem path
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thunder-project/thunder | thunder/series/readers.py | _binaryconfig | def _binaryconfig(path, conf, dtype=None, shape=None, credentials=None):
"""
Collects parameters to use for binary series loading.
"""
import json
from thunder.readers import get_file_reader, FileNotFoundError
reader = get_file_reader(path)(credentials=credentials)
try:
buf = reader... | python | def _binaryconfig(path, conf, dtype=None, shape=None, credentials=None):
"""
Collects parameters to use for binary series loading.
"""
import json
from thunder.readers import get_file_reader, FileNotFoundError
reader = get_file_reader(path)(credentials=credentials)
try:
buf = reader... | [
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thunder-project/thunder | thunder/series/readers.py | fromexample | def fromexample(name=None, engine=None):
"""
Load example series data.
Data are downloaded from S3, so this method requires an internet connection.
Parameters
----------
name : str
Name of dataset, options include 'iris' | 'mouse' | 'fish'.
If not specified will print options.
... | python | def fromexample(name=None, engine=None):
"""
Load example series data.
Data are downloaded from S3, so this method requires an internet connection.
Parameters
----------
name : str
Name of dataset, options include 'iris' | 'mouse' | 'fish'.
If not specified will print options.
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thunder-project/thunder | thunder/series/writers.py | tobinary | def tobinary(series, path, prefix='series', overwrite=False, credentials=None):
"""
Writes out data to binary format.
Parameters
----------
series : Series
The data to write
path : string path or URI to directory to be created
Output files will be written underneath path.
... | python | def tobinary(series, path, prefix='series', overwrite=False, credentials=None):
"""
Writes out data to binary format.
Parameters
----------
series : Series
The data to write
path : string path or URI to directory to be created
Output files will be written underneath path.
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thunder-project/thunder | thunder/series/writers.py | write_config | def write_config(path, shape=None, dtype=None, name="conf.json", overwrite=True, credentials=None):
"""
Write a conf.json file with required information to load Series binary data.
"""
import json
from thunder.writers import get_file_writer
writer = get_file_writer(path)
conf = {'shape': sh... | python | def write_config(path, shape=None, dtype=None, name="conf.json", overwrite=True, credentials=None):
"""
Write a conf.json file with required information to load Series binary data.
"""
import json
from thunder.writers import get_file_writer
writer = get_file_writer(path)
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thunder-project/thunder | thunder/images/images.py | Images.toblocks | def toblocks(self, chunk_size='auto', padding=None):
"""
Convert to blocks which represent subdivisions of the images data.
Parameters
----------
chunk_size : str or tuple, size of image chunk used during conversion, default = 'auto'
String interpreted as memory size... | python | def toblocks(self, chunk_size='auto', padding=None):
"""
Convert to blocks which represent subdivisions of the images data.
Parameters
----------
chunk_size : str or tuple, size of image chunk used during conversion, default = 'auto'
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thunder-project/thunder | thunder/images/images.py | Images.toseries | def toseries(self, chunk_size='auto'):
"""
Converts to series data.
This method is equivalent to images.toblocks(size).toSeries().
Parameters
----------
chunk_size : str or tuple, size of image chunk used during conversion, default = 'auto'
String interprete... | python | def toseries(self, chunk_size='auto'):
"""
Converts to series data.
This method is equivalent to images.toblocks(size).toSeries().
Parameters
----------
chunk_size : str or tuple, size of image chunk used during conversion, default = 'auto'
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String interpreted as memory size (in kilobytes, e.g. '64').
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thunder-project/thunder | thunder/images/images.py | Images.tospark | def tospark(self, engine=None):
"""
Convert to distributed spark mode.
"""
from thunder.images.readers import fromarray
if self.mode == 'spark':
logging.getLogger('thunder').warn('images already in spark mode')
pass
if engine is None:
... | python | def tospark(self, engine=None):
"""
Convert to distributed spark mode.
"""
from thunder.images.readers import fromarray
if self.mode == 'spark':
logging.getLogger('thunder').warn('images already in spark mode')
pass
if engine is None:
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thunder-project/thunder | thunder/images/images.py | Images.foreach | def foreach(self, func):
"""
Execute a function on each image.
Functions can have side effects. There is no return value.
"""
if self.mode == 'spark':
self.values.tordd().map(lambda kv: (kv[0][0], kv[1])).foreach(func)
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[func(kv) for kv in en... | python | def foreach(self, func):
"""
Execute a function on each image.
Functions can have side effects. There is no return value.
"""
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thunder-project/thunder | thunder/images/images.py | Images.sample | def sample(self, nsamples=100, seed=None):
"""
Extract a random sample of images.
Parameters
----------
nsamples : int, optional, default = 100
The number of data points to sample.
seed : int, optional, default = None
Random seed.
"""
... | python | def sample(self, nsamples=100, seed=None):
"""
Extract a random sample of images.
Parameters
----------
nsamples : int, optional, default = 100
The number of data points to sample.
seed : int, optional, default = None
Random seed.
"""
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thunder-project/thunder | thunder/images/images.py | Images.var | def var(self):
"""
Compute the variance across images.
"""
return self._constructor(self.values.var(axis=0, keepdims=True)) | python | def var(self):
"""
Compute the variance across images.
"""
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thunder-project/thunder | thunder/images/images.py | Images.std | def std(self):
"""
Compute the standard deviation across images.
"""
return self._constructor(self.values.std(axis=0, keepdims=True)) | python | def std(self):
"""
Compute the standard deviation across images.
"""
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thunder-project/thunder | thunder/images/images.py | Images.squeeze | def squeeze(self):
"""
Remove single-dimensional axes from images.
"""
axis = tuple(range(1, len(self.shape) - 1)) if prod(self.shape[1:]) == 1 else None
return self.map(lambda x: x.squeeze(axis=axis)) | python | def squeeze(self):
"""
Remove single-dimensional axes from images.
"""
axis = tuple(range(1, len(self.shape) - 1)) if prod(self.shape[1:]) == 1 else None
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thunder-project/thunder | thunder/images/images.py | Images.max_projection | def max_projection(self, axis=2):
"""
Compute maximum projections of images along a dimension.
Parameters
----------
axis : int, optional, default = 2
Which axis to compute projection along.
"""
if axis >= size(self.value_shape):
raise Exc... | python | def max_projection(self, axis=2):
"""
Compute maximum projections of images along a dimension.
Parameters
----------
axis : int, optional, default = 2
Which axis to compute projection along.
"""
if axis >= size(self.value_shape):
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thunder-project/thunder | thunder/images/images.py | Images.max_min_projection | def max_min_projection(self, axis=2):
"""
Compute maximum-minimum projection along a dimension.
This computes the sum of the maximum and minimum values.
Parameters
----------
axis : int, optional, default = 2
Which axis to compute projection along.
"... | python | def max_min_projection(self, axis=2):
"""
Compute maximum-minimum projection along a dimension.
This computes the sum of the maximum and minimum values.
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----------
axis : int, optional, default = 2
Which axis to compute projection along.
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thunder-project/thunder | thunder/images/images.py | Images.subsample | def subsample(self, factor):
"""
Downsample images by an integer factor.
Parameters
----------
factor : positive int or tuple of positive ints
Stride to use in subsampling. If a single int is passed,
each dimension of the image will be downsampled by this... | python | def subsample(self, factor):
"""
Downsample images by an integer factor.
Parameters
----------
factor : positive int or tuple of positive ints
Stride to use in subsampling. If a single int is passed,
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thunder-project/thunder | thunder/images/images.py | Images.gaussian_filter | def gaussian_filter(self, sigma=2, order=0):
"""
Spatially smooth images with a gaussian filter.
Filtering will be applied to every image in the collection.
Parameters
----------
sigma : scalar or sequence of scalars, default = 2
Size of the filter size as s... | python | def gaussian_filter(self, sigma=2, order=0):
"""
Spatially smooth images with a gaussian filter.
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----------
sigma : scalar or sequence of scalars, default = 2
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thunder-project/thunder | thunder/images/images.py | Images._image_filter | def _image_filter(self, filter=None, size=2):
"""
Generic function for maping a filtering operation over images.
Parameters
----------
filter : string
Which filter to use.
size : int or tuple
Size parameter for filter.
"""
from nu... | python | def _image_filter(self, filter=None, size=2):
"""
Generic function for maping a filtering operation over images.
Parameters
----------
filter : string
Which filter to use.
size : int or tuple
Size parameter for filter.
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thunder-project/thunder | thunder/images/images.py | Images.localcorr | def localcorr(self, size=2):
"""
Correlate every pixel in an image sequence to the average of its local neighborhood.
This algorithm computes, for every pixel, the correlation coefficient
between the sequence of values for that pixel, and the average of all pixels
in a local nei... | python | def localcorr(self, size=2):
"""
Correlate every pixel in an image sequence to the average of its local neighborhood.
This algorithm computes, for every pixel, the correlation coefficient
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thunder-project/thunder | thunder/images/images.py | Images.subtract | def subtract(self, val):
"""
Subtract a constant value or an image from all images.
Parameters
----------
val : int, float, or ndarray
Value to subtract.
"""
if isinstance(val, ndarray):
if val.shape != self.value_shape:
ra... | python | def subtract(self, val):
"""
Subtract a constant value or an image from all images.
Parameters
----------
val : int, float, or ndarray
Value to subtract.
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thunder-project/thunder | thunder/images/images.py | Images.topng | def topng(self, path, prefix='image', overwrite=False):
"""
Write 2d images as PNG files.
Files will be written into a newly-created directory.
Three-dimensional data will be treated as RGB channels.
Parameters
----------
path : string
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"""
Write 2d images as PNG files.
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Three-dimensional data will be treated as RGB channels.
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path : string
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thunder-project/thunder | thunder/images/images.py | Images.map_as_series | def map_as_series(self, func, value_size=None, dtype=None, chunk_size='auto'):
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Efficiently apply a function to images as series data.
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thunder-project/thunder | thunder/blocks/blocks.py | Blocks.count | def count(self):
"""
Explicit count of the number of items.
For lazy or distributed data, will force a computation.
"""
if self.mode == 'spark':
return self.tordd().count()
if self.mode == 'local':
return prod(self.values.values.shape) | python | def count(self):
"""
Explicit count of the number of items.
For lazy or distributed data, will force a computation.
"""
if self.mode == 'spark':
return self.tordd().count()
if self.mode == 'local':
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thunder-project/thunder | thunder/blocks/blocks.py | Blocks.collect_blocks | def collect_blocks(self):
"""
Collect the blocks in a list
"""
if self.mode == 'spark':
return self.values.tordd().sortByKey().values().collect()
if self.mode == 'local':
return self.values.values.flatten().tolist() | python | def collect_blocks(self):
"""
Collect the blocks in a list
"""
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thunder-project/thunder | thunder/blocks/blocks.py | Blocks.map | def map(self, func, value_shape=None, dtype=None):
"""
Apply an array -> array function to each block
"""
mapped = self.values.map(func, value_shape=value_shape, dtype=dtype)
return self._constructor(mapped).__finalize__(self, noprop=('dtype',)) | python | def map(self, func, value_shape=None, dtype=None):
"""
Apply an array -> array function to each block
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mapped = self.values.map(func, value_shape=value_shape, dtype=dtype)
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thunder-project/thunder | thunder/blocks/blocks.py | Blocks.toimages | def toimages(self):
"""
Convert blocks to images.
"""
from thunder.images.images import Images
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if self.mode == 'local':
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return Im... | python | def toimages(self):
"""
Convert blocks to images.
"""
from thunder.images.images import Images
if self.mode == 'spark':
values = self.values.values_to_keys((0,)).unchunk()
if self.mode == 'local':
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thunder-project/thunder | thunder/blocks/blocks.py | Blocks.toseries | def toseries(self):
"""
Converts blocks to series.
"""
from thunder.series.series import Series
if self.mode == 'spark':
values = self.values.values_to_keys(tuple(range(1, len(self.shape)))).unchunk()
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"""
Converts blocks to series.
"""
from thunder.series.series import Series
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values = self.values.values_to_keys(tuple(range(1, len(self.shape)))).unchunk()
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thunder-project/thunder | thunder/blocks/blocks.py | Blocks.toarray | def toarray(self):
"""
Convert blocks to local ndarray
"""
if self.mode == 'spark':
return self.values.unchunk().toarray()
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return self.values.unchunk() | python | def toarray(self):
"""
Convert blocks to local ndarray
"""
if self.mode == 'spark':
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thunder-project/thunder | thunder/series/series.py | Series.flatten | def flatten(self):
"""
Reshape all dimensions but the last into a single dimension
"""
size = prod(self.shape[:-1])
return self.reshape(size, self.shape[-1]) | python | def flatten(self):
"""
Reshape all dimensions but the last into a single dimension
"""
size = prod(self.shape[:-1])
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thunder-project/thunder | thunder/series/series.py | Series.tospark | def tospark(self, engine=None):
"""
Convert to spark mode.
"""
from thunder.series.readers import fromarray
if self.mode == 'spark':
logging.getLogger('thunder').warn('images already in local mode')
pass
if engine is None:
raise Value... | python | def tospark(self, engine=None):
"""
Convert to spark mode.
"""
from thunder.series.readers import fromarray
if self.mode == 'spark':
logging.getLogger('thunder').warn('images already in local mode')
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thunder-project/thunder | thunder/series/series.py | Series.sample | def sample(self, n=100, seed=None):
"""
Extract random sample of records.
Parameters
----------
n : int, optional, default = 100
The number of data points to sample.
seed : int, optional, default = None
Random seed.
"""
if n < 1:
... | python | def sample(self, n=100, seed=None):
"""
Extract random sample of records.
Parameters
----------
n : int, optional, default = 100
The number of data points to sample.
seed : int, optional, default = None
Random seed.
"""
if n < 1:
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thunder-project/thunder | thunder/series/series.py | Series.map | def map(self, func, index=None, value_shape=None, dtype=None, with_keys=False):
"""
Map an array -> array function over each record.
Parameters
----------
func : function
A function of a single record.
index : array-like, optional, default = None
... | python | def map(self, func, index=None, value_shape=None, dtype=None, with_keys=False):
"""
Map an array -> array function over each record.
Parameters
----------
func : function
A function of a single record.
index : array-like, optional, default = None
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thunder-project/thunder | thunder/series/series.py | Series.mean | def mean(self):
"""
Compute the mean across records
"""
return self._constructor(self.values.mean(axis=self.baseaxes, keepdims=True)) | python | def mean(self):
"""
Compute the mean across records
"""
return self._constructor(self.values.mean(axis=self.baseaxes, keepdims=True)) | [
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thunder-project/thunder | thunder/series/series.py | Series.sum | def sum(self):
"""
Compute the sum across records.
"""
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"""
Compute the sum across records.
"""
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thunder-project/thunder | thunder/series/series.py | Series.max | def max(self):
"""
Compute the max across records.
"""
return self._constructor(self.values.max(axis=self.baseaxes, keepdims=True)) | python | def max(self):
"""
Compute the max across records.
"""
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thunder-project/thunder | thunder/series/series.py | Series.min | def min(self):
"""
Compute the min across records.
"""
return self._constructor(self.values.min(axis=self.baseaxes, keepdims=True)) | python | def min(self):
"""
Compute the min across records.
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thunder-project/thunder | thunder/series/series.py | Series.reshape | def reshape(self, *shape):
"""
Reshape the Series object
Cannot change the last dimension.
Parameters
----------
shape: one or more ints
New shape
"""
if prod(self.shape) != prod(shape):
raise ValueError("Reshaping must leave the ... | python | def reshape(self, *shape):
"""
Reshape the Series object
Cannot change the last dimension.
Parameters
----------
shape: one or more ints
New shape
"""
if prod(self.shape) != prod(shape):
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thunder-project/thunder | thunder/series/series.py | Series.between | def between(self, left, right):
"""
Select subset of values within the given index range.
Inclusive on the left; exclusive on the right.
Parameters
----------
left : int
Left-most index in the desired range.
right: int
Right-most index i... | python | def between(self, left, right):
"""
Select subset of values within the given index range.
Inclusive on the left; exclusive on the right.
Parameters
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left : int
Left-most index in the desired range.
right: int
Right-most index i... | [
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thunder-project/thunder | thunder/series/series.py | Series.select | def select(self, crit):
"""
Select subset of values that match a given index criterion.
Parameters
----------
crit : function, list, str, int
Criterion function to map to indices, specific index value,
or list of indices.
"""
import types
... | python | def select(self, crit):
"""
Select subset of values that match a given index criterion.
Parameters
----------
crit : function, list, str, int
Criterion function to map to indices, specific index value,
or list of indices.
"""
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thunder-project/thunder | thunder/series/series.py | Series.center | def center(self, axis=1):
"""
Subtract the mean either within or across records.
Parameters
----------
axis : int, optional, default = 1
Which axis to center along, within (1) or across (0) records.
"""
if axis == 1:
return self.map(lambda... | python | def center(self, axis=1):
"""
Subtract the mean either within or across records.
Parameters
----------
axis : int, optional, default = 1
Which axis to center along, within (1) or across (0) records.
"""
if axis == 1:
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thunder-project/thunder | thunder/series/series.py | Series.standardize | def standardize(self, axis=1):
"""
Divide by standard deviation either within or across records.
Parameters
----------
axis : int, optional, default = 0
Which axis to standardize along, within (1) or across (0) records
"""
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re... | python | def standardize(self, axis=1):
"""
Divide by standard deviation either within or across records.
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axis : int, optional, default = 0
Which axis to standardize along, within (1) or across (0) records
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thunder-project/thunder | thunder/series/series.py | Series.zscore | def zscore(self, axis=1):
"""
Subtract the mean and divide by standard deviation within or across records.
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----------
axis : int, optional, default = 0
Which axis to zscore along, within (1) or across (0) records
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... | python | def zscore(self, axis=1):
"""
Subtract the mean and divide by standard deviation within or across records.
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axis : int, optional, default = 0
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thunder-project/thunder | thunder/series/series.py | Series.squelch | def squelch(self, threshold):
"""
Set all records that do not exceed the given threhsold to 0.
Parameters
----------
threshold : scalar
Level below which to set records to zero
"""
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"""
Set all records that do not exceed the given threhsold to 0.
Parameters
----------
threshold : scalar
Level below which to set records to zero
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thunder-project/thunder | thunder/series/series.py | Series.correlate | def correlate(self, signal):
"""
Correlate records against one or many one-dimensional arrays.
Parameters
----------
signal : array-like
One or more signals to correlate against.
"""
s = asarray(signal)
if s.ndim == 1:
if size(s) ... | python | def correlate(self, signal):
"""
Correlate records against one or many one-dimensional arrays.
Parameters
----------
signal : array-like
One or more signals to correlate against.
"""
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thunder-project/thunder | thunder/series/series.py | Series._check_panel | def _check_panel(self, length):
"""
Check that given fixed panel length evenly divides index.
Parameters
----------
length : int
Fixed length with which to subdivide index
"""
n = len(self.index)
if divmod(n, length)[1] != 0:
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"""
Check that given fixed panel length evenly divides index.
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----------
length : int
Fixed length with which to subdivide index
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thunder-project/thunder | thunder/series/series.py | Series.mean_by_panel | def mean_by_panel(self, length):
"""
Compute the mean across fixed sized panels of each record.
Splits each record into panels of size `length`,
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Panel length must subdivide record exactly.
Parameters
----------
l... | python | def mean_by_panel(self, length):
"""
Compute the mean across fixed sized panels of each record.
Splits each record into panels of size `length`,
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Panel length must subdivide record exactly.
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thunder-project/thunder | thunder/series/series.py | Series._makemasks | def _makemasks(self, index=None, level=0):
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Internal function for generating masks for selecting values based on multi-index values.
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... | python | def _makemasks(self, index=None, level=0):
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Internal function for generating masks for selecting values based on multi-index values.
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thunder-project/thunder | thunder/series/series.py | Series._map_by_index | def _map_by_index(self, function, level=0):
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thunder-project/thunder | thunder/series/series.py | Series.aggregate_by_index | def aggregate_by_index(self, function, level=0):
"""
Aggregrate data in each record, grouping by index values.
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indexed by that value. Returns a Series indexed by those unique values.
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"""
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thunder-project/thunder | thunder/series/series.py | Series.gramian | def gramian(self):
"""
Compute gramian of a distributed matrix.
The gramian is defined as the product of the matrix
with its transpose, i.e. A^T * A.
"""
if self.mode == 'spark':
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from pyspark.accumulators import Accumula... | python | def gramian(self):
"""
Compute gramian of a distributed matrix.
The gramian is defined as the product of the matrix
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"""
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thunder-project/thunder | thunder/series/series.py | Series.times | def times(self, other):
"""
Multiply a matrix by another one.
Other matrix must be a numpy array, a scalar,
or another matrix in local mode.
Parameters
----------
other : Matrix, scalar, or numpy array
A matrix to multiply with
"""
if... | python | def times(self, other):
"""
Multiply a matrix by another one.
Other matrix must be a numpy array, a scalar,
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----------
other : Matrix, scalar, or numpy array
A matrix to multiply with
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thunder-project/thunder | thunder/series/series.py | Series._makewindows | def _makewindows(self, indices, window):
"""
Make masks used by windowing functions
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one per window, that index into the target array
Parameters
----------
... | python | def _makewindows(self, indices, window):
"""
Make masks used by windowing functions
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thunder-project/thunder | thunder/series/series.py | Series.mean_by_window | def mean_by_window(self, indices, window):
"""
Average series across multiple windows specified by their centers.
Parameters
----------
indices : array-like
List of times specifying window centers
window : int
Window size
"""
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Average series across multiple windows specified by their centers.
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indices : array-like
List of times specifying window centers
window : int
Window size
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thunder-project/thunder | thunder/series/series.py | Series.subsample | def subsample(self, sample_factor=2):
"""
Subsample series by an integer factor.
Parameters
----------
sample_factor : positive integer, optional, default=2
Factor for downsampling.
"""
if sample_factor < 0:
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"""
Subsample series by an integer factor.
Parameters
----------
sample_factor : positive integer, optional, default=2
Factor for downsampling.
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thunder-project/thunder | thunder/series/series.py | Series.downsample | def downsample(self, sample_factor=2):
"""
Downsample series by an integer factor by averaging.
Parameters
----------
sample_factor : positive integer, optional, default=2
Factor for downsampling.
"""
if sample_factor < 0:
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sample_factor : positive integer, optional, default=2
Factor for downsampling.
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Parameters
----------
sample_factor : positive integer, optional, default=2
Factor for downsampling. | [
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thunder-project/thunder | thunder/series/series.py | Series.fourier | def fourier(self, freq=None):
"""
Compute statistics of a Fourier decomposition on series data.
Parameters
----------
freq : int
Digital frequency at which to compute coherence and phase
"""
def get(y, freq):
y = y - mean(y)
nf... | python | def fourier(self, freq=None):
"""
Compute statistics of a Fourier decomposition on series data.
Parameters
----------
freq : int
Digital frequency at which to compute coherence and phase
"""
def get(y, freq):
y = y - mean(y)
nf... | [
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Digital frequency at which to compute coherence and phase | [
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thunder-project/thunder | thunder/series/series.py | Series.convolve | def convolve(self, signal, mode='full'):
"""
Convolve series data against another signal.
Parameters
----------
signal : array
Signal to convolve with (must be 1D)
mode : str, optional, default='full'
Mode of convolution, options are 'full', 'sam... | python | def convolve(self, signal, mode='full'):
"""
Convolve series data against another signal.
Parameters
----------
signal : array
Signal to convolve with (must be 1D)
mode : str, optional, default='full'
Mode of convolution, options are 'full', 'sam... | [
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signal : array
Signal to convolve with (must be 1D)
mode : str, optional, default='full'
Mode of convolution, options are 'full', 'same', and 'valid' | [
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thunder-project/thunder | thunder/series/series.py | Series.crosscorr | def crosscorr(self, signal, lag=0):
"""
Cross correlate series data against another signal.
Parameters
----------
signal : array
Signal to correlate against (must be 1D).
lag : int
Range of lags to consider, will cover (-lag, +lag).
"""
... | python | def crosscorr(self, signal, lag=0):
"""
Cross correlate series data against another signal.
Parameters
----------
signal : array
Signal to correlate against (must be 1D).
lag : int
Range of lags to consider, will cover (-lag, +lag).
"""
... | [
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Parameters
----------
signal : array
Signal to correlate against (must be 1D).
lag : int
Range of lags to consider, will cover (-lag, +lag). | [
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thunder-project/thunder | thunder/series/series.py | Series.detrend | def detrend(self, method='linear', order=5):
"""
Detrend series data with linear or nonlinear detrending.
Preserve intercept so that subsequent operations can adjust the baseline.
Parameters
----------
method : str, optional, default = 'linear'
Detrending me... | python | def detrend(self, method='linear', order=5):
"""
Detrend series data with linear or nonlinear detrending.
Preserve intercept so that subsequent operations can adjust the baseline.
Parameters
----------
method : str, optional, default = 'linear'
Detrending me... | [
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Preserve intercept so that subsequent operations can adjust the baseline.
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method : str, optional, default = 'linear'
Detrending method
order : int, optional, default = 5
Orde... | [
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thunder-project/thunder | thunder/series/series.py | Series.normalize | def normalize(self, method='percentile', window=None, perc=20, offset=0.1):
"""
Normalize by subtracting and dividing by a baseline.
Baseline can be derived from a global mean or percentile,
or a smoothed percentile estimated within a rolling window.
Windowed baselines may only ... | python | def normalize(self, method='percentile', window=None, perc=20, offset=0.1):
"""
Normalize by subtracting and dividing by a baseline.
Baseline can be derived from a global mean or percentile,
or a smoothed percentile estimated within a rolling window.
Windowed baselines may only ... | [
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Baseline can be derived from a global mean or percentile,
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Windowed baselines may only be well-defined for
temporal series data.
Parameters
----------
... | [
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] | 967ff8f3e7c2fabe1705743d95eb2746d4329786 | https://github.com/thunder-project/thunder/blob/967ff8f3e7c2fabe1705743d95eb2746d4329786/thunder/series/series.py#L1024-L1081 | train |
thunder-project/thunder | thunder/series/series.py | Series.toimages | def toimages(self, chunk_size='auto'):
"""
Converts to images data.
This method is equivalent to series.toblocks(size).toimages().
Parameters
----------
chunk_size : str or tuple, size of series chunk used during conversion, default = 'auto'
String interpret... | python | def toimages(self, chunk_size='auto'):
"""
Converts to images data.
This method is equivalent to series.toblocks(size).toimages().
Parameters
----------
chunk_size : str or tuple, size of series chunk used during conversion, default = 'auto'
String interpret... | [
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This method is equivalent to series.toblocks(size).toimages().
Parameters
----------
chunk_size : str or tuple, size of series chunk used during conversion, default = 'auto'
String interpreted as memory size (in kilobytes, e.g. '64').
Th... | [
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] | 967ff8f3e7c2fabe1705743d95eb2746d4329786 | https://github.com/thunder-project/thunder/blob/967ff8f3e7c2fabe1705743d95eb2746d4329786/thunder/series/series.py#L1083-L1108 | train |
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