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Neurosim-lab/netpyne | doc/source/code/HHCellFile.py | Cell.createNetcon | def createNetcon(self, thresh=10):
""" created netcon to record spikes """
nc = h.NetCon(self.soma(0.5)._ref_v, None, sec = self.soma)
nc.threshold = thresh
return nc | python | def createNetcon(self, thresh=10):
""" created netcon to record spikes """
nc = h.NetCon(self.soma(0.5)._ref_v, None, sec = self.soma)
nc.threshold = thresh
return nc | [
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Neurosim-lab/netpyne | doc/source/code/HHCellFile.py | HHCellClass.createSections | def createSections(self):
"""Create the sections of the cell."""
self.soma = h.Section(name='soma', cell=self)
self.dend = h.Section(name='dend', cell=self) | python | def createSections(self):
"""Create the sections of the cell."""
self.soma = h.Section(name='soma', cell=self)
self.dend = h.Section(name='dend', cell=self) | [
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Neurosim-lab/netpyne | doc/source/code/HHCellFile.py | HHCellClass.defineGeometry | def defineGeometry(self):
"""Set the 3D geometry of the cell."""
self.soma.L = 18.8
self.soma.diam = 18.8
self.soma.Ra = 123.0
self.dend.L = 200.0
self.dend.diam = 1.0
self.dend.Ra = 100.0 | python | def defineGeometry(self):
"""Set the 3D geometry of the cell."""
self.soma.L = 18.8
self.soma.diam = 18.8
self.soma.Ra = 123.0
self.dend.L = 200.0
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Neurosim-lab/netpyne | doc/source/code/HHCellFile.py | HHCellClass.defineBiophysics | def defineBiophysics(self):
"""Assign the membrane properties across the cell."""
# Insert active Hodgkin-Huxley current in the soma
self.soma.insert('hh')
self.soma.gnabar_hh = 0.12 # Sodium conductance in S/cm2
self.soma.gkbar_hh = 0.036 # Potassium conductance in S/cm2
... | python | def defineBiophysics(self):
"""Assign the membrane properties across the cell."""
# Insert active Hodgkin-Huxley current in the soma
self.soma.insert('hh')
self.soma.gnabar_hh = 0.12 # Sodium conductance in S/cm2
self.soma.gkbar_hh = 0.036 # Potassium conductance in S/cm2
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Neurosim-lab/netpyne | netpyne/support/morphology.py | shapeplot | def shapeplot(h,ax,sections=None,order='pre',cvals=None,\
clim=None,cmap=cm.YlOrBr_r, legend=True, **kwargs): # meanLineWidth=1.0, maxLineWidth=10.0,
"""
Plots a 3D shapeplot
Args:
h = hocObject to interface with neuron
ax = matplotlib axis for plotting
sections = li... | python | def shapeplot(h,ax,sections=None,order='pre',cvals=None,\
clim=None,cmap=cm.YlOrBr_r, legend=True, **kwargs): # meanLineWidth=1.0, maxLineWidth=10.0,
"""
Plots a 3D shapeplot
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h = hocObject to interface with neuron
ax = matplotlib axis for plotting
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Neurosim-lab/netpyne | netpyne/support/morphology.py | shapeplot_animate | def shapeplot_animate(v,lines,nframes=None,tscale='linear',\
clim=[-80,50],cmap=cm.YlOrBr_r):
""" Returns animate function which updates color of shapeplot """
if nframes is None:
nframes = v.shape[0]
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i_t = int((i/nfram... | python | def shapeplot_animate(v,lines,nframes=None,tscale='linear',\
clim=[-80,50],cmap=cm.YlOrBr_r):
""" Returns animate function which updates color of shapeplot """
if nframes is None:
nframes = v.shape[0]
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Neurosim-lab/netpyne | netpyne/support/morphology.py | mark_locations | def mark_locations(h,section,locs,markspec='or',**kwargs):
"""
Marks one or more locations on along a section. Could be used to
mark the location of a recording or electrical stimulation.
Args:
h = hocObject to interface with neuron
section = reference to section
locs = float be... | python | def mark_locations(h,section,locs,markspec='or',**kwargs):
"""
Marks one or more locations on along a section. Could be used to
mark the location of a recording or electrical stimulation.
Args:
h = hocObject to interface with neuron
section = reference to section
locs = float be... | [
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Neurosim-lab/netpyne | netpyne/support/morphology.py | root_sections | def root_sections(h):
"""
Returns a list of all sections that have no parent.
"""
roots = []
for section in h.allsec():
sref = h.SectionRef(sec=section)
# has_parent returns a float... cast to bool
if sref.has_parent() < 0.9:
roots.append(section)
return roots | python | def root_sections(h):
"""
Returns a list of all sections that have no parent.
"""
roots = []
for section in h.allsec():
sref = h.SectionRef(sec=section)
# has_parent returns a float... cast to bool
if sref.has_parent() < 0.9:
roots.append(section)
return roots | [
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Neurosim-lab/netpyne | netpyne/support/morphology.py | leaf_sections | def leaf_sections(h):
"""
Returns a list of all sections that have no children.
"""
leaves = []
for section in h.allsec():
sref = h.SectionRef(sec=section)
# nchild returns a float... cast to bool
if sref.nchild() < 0.9:
leaves.append(section)
return leaves | python | def leaf_sections(h):
"""
Returns a list of all sections that have no children.
"""
leaves = []
for section in h.allsec():
sref = h.SectionRef(sec=section)
# nchild returns a float... cast to bool
if sref.nchild() < 0.9:
leaves.append(section)
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Neurosim-lab/netpyne | netpyne/support/morphology.py | root_indices | def root_indices(sec_list):
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Returns the index of all sections without a parent.
"""
roots = []
for i,section in enumerate(sec_list):
sref = h.SectionRef(sec=section)
# has_parent returns a float... cast to bool
if sref.has_parent() < 0.9:
roots.append(i)
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"""
Returns the index of all sections without a parent.
"""
roots = []
for i,section in enumerate(sec_list):
sref = h.SectionRef(sec=section)
# has_parent returns a float... cast to bool
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roots.append(i)
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Neurosim-lab/netpyne | netpyne/support/morphology.py | branch_order | def branch_order(h,section, path=[]):
"""
Returns the branch order of a section
"""
path.append(section)
sref = h.SectionRef(sec=section)
# has_parent returns a float... cast to bool
if sref.has_parent() < 0.9:
return 0 # section is a root
else:
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"""
Returns the branch order of a section
"""
path.append(section)
sref = h.SectionRef(sec=section)
# has_parent returns a float... cast to bool
if sref.has_parent() < 0.9:
return 0 # section is a root
else:
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Neurosim-lab/netpyne | netpyne/network/pop.py | Pop.createCells | def createCells(self):
'''Function to instantiate Cell objects based on the characteristics of this population'''
# add individual cells
if 'cellsList' in self.tags:
cells = self.createCellsList()
# create cells based on fixed number of cells
elif 'numCells' in self.... | python | def createCells(self):
'''Function to instantiate Cell objects based on the characteristics of this population'''
# add individual cells
if 'cellsList' in self.tags:
cells = self.createCellsList()
# create cells based on fixed number of cells
elif 'numCells' in self.... | [
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Neurosim-lab/netpyne | netpyne/network/pop.py | Pop.createCellsList | def createCellsList (self):
''' Create population cells based on list of individual cells'''
from .. import sim
cells = []
self.tags['numCells'] = len(self.tags['cellsList'])
for i in self._distributeCells(len(self.tags['cellsList']))[sim.rank]:
#if 'cellMode... | python | def createCellsList (self):
''' Create population cells based on list of individual cells'''
from .. import sim
cells = []
self.tags['numCells'] = len(self.tags['cellsList'])
for i in self._distributeCells(len(self.tags['cellsList']))[sim.rank]:
#if 'cellMode... | [
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Neurosim-lab/netpyne | netpyne/sim/wrappers.py | create | def create (netParams=None, simConfig=None, output=False):
''' Sequence of commands to create network '''
from .. import sim
import __main__ as top
if not netParams: netParams = top.netParams
if not simConfig: simConfig = top.simConfig
sim.initialize(netParams, simConfig) # create network obje... | python | def create (netParams=None, simConfig=None, output=False):
''' Sequence of commands to create network '''
from .. import sim
import __main__ as top
if not netParams: netParams = top.netParams
if not simConfig: simConfig = top.simConfig
sim.initialize(netParams, simConfig) # create network obje... | [
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Neurosim-lab/netpyne | netpyne/sim/wrappers.py | intervalSimulate | def intervalSimulate (interval):
''' Sequence of commands to simulate network '''
from .. import sim
sim.runSimWithIntervalFunc(interval, sim.intervalSave) # run parallel Neuron simulation
#this gather is justa merging of files
sim.fileGather() | python | def intervalSimulate (interval):
''' Sequence of commands to simulate network '''
from .. import sim
sim.runSimWithIntervalFunc(interval, sim.intervalSave) # run parallel Neuron simulation
#this gather is justa merging of files
sim.fileGather() | [
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Neurosim-lab/netpyne | netpyne/sim/wrappers.py | load | def load (filename, simConfig=None, output=False, instantiate=True, createNEURONObj=True):
''' Sequence of commands load, simulate and analyse network '''
from .. import sim
sim.initialize() # create network object and set cfg and net params
sim.cfg.createNEURONObj = createNEURONObj
sim.loadAll(fil... | python | def load (filename, simConfig=None, output=False, instantiate=True, createNEURONObj=True):
''' Sequence of commands load, simulate and analyse network '''
from .. import sim
sim.initialize() # create network object and set cfg and net params
sim.cfg.createNEURONObj = createNEURONObj
sim.loadAll(fil... | [
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Neurosim-lab/netpyne | netpyne/sim/wrappers.py | createExportNeuroML2 | def createExportNeuroML2 (netParams=None, simConfig=None, reference=None, connections=True, stimulations=True, output=False, format='xml'):
''' Sequence of commands to create and export network to NeuroML2 '''
from .. import sim
import __main__ as top
if not netParams: netParams = top.netParams
if n... | python | def createExportNeuroML2 (netParams=None, simConfig=None, reference=None, connections=True, stimulations=True, output=False, format='xml'):
''' Sequence of commands to create and export network to NeuroML2 '''
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Neurosim-lab/netpyne | netpyne/analysis/utils.py | exception | def exception(function):
"""
A decorator that wraps the passed in function and prints exception should one occur
"""
@functools.wraps(function)
def wrapper(*args, **kwargs):
try:
return function(*args, **kwargs)
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# print
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"""
A decorator that wraps the passed in function and prints exception should one occur
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@functools.wraps(function)
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Neurosim-lab/netpyne | netpyne/analysis/utils.py | getSpktSpkid | def getSpktSpkid(cellGids=[], timeRange=None, allCells=False):
'''return spike ids and times; with allCells=True just need to identify slice of time so can omit cellGids'''
from .. import sim
import pandas as pd
try: # Pandas 0.24 and later
from pandas import _lib as pandaslib
except: #... | python | def getSpktSpkid(cellGids=[], timeRange=None, allCells=False):
'''return spike ids and times; with allCells=True just need to identify slice of time so can omit cellGids'''
from .. import sim
import pandas as pd
try: # Pandas 0.24 and later
from pandas import _lib as pandaslib
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Neurosim-lab/netpyne | netpyne/support/recxelectrode.py | RecXElectrode.calcTransferResistance | def calcTransferResistance(self, gid, seg_coords):
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sigma = 0.3 # mS/mm
# Value used in NEURON extracellular recording example ("extracellular_stim_and_rec")
# rho = 35.4 # ohm cm, squid axon cytoplasm = 2.8249e-2 S/cm = 0.0... | python | def calcTransferResistance(self, gid, seg_coords):
"""Precompute mapping from segment to electrode locations"""
sigma = 0.3 # mS/mm
# Value used in NEURON extracellular recording example ("extracellular_stim_and_rec")
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Neurosim-lab/netpyne | netpyne/conversion/excel.py | importConnFromExcel | def importConnFromExcel (fileName, sheetName):
''' Import connectivity rules from Excel sheet'''
import openpyxl as xl
# set columns
colPreTags = 0 # 'A'
colPostTags = 1 # 'B'
colConnFunc = 2 # 'C'
colSyn = 3 # 'D'
colProb = 5 # 'F'
colWeight = 6 # 'G'
colAnnot = 8 # 'I'
o... | python | def importConnFromExcel (fileName, sheetName):
''' Import connectivity rules from Excel sheet'''
import openpyxl as xl
# set columns
colPreTags = 0 # 'A'
colPostTags = 1 # 'B'
colConnFunc = 2 # 'C'
colSyn = 3 # 'D'
colProb = 5 # 'F'
colWeight = 6 # 'G'
colAnnot = 8 # 'I'
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zerwes/hiyapyco | hiyapyco/odyldo.py | safe_dump | def safe_dump(data, stream=None, **kwds):
"""implementation of safe dumper using Ordered Dict Yaml Dumper"""
return yaml.dump(data, stream=stream, Dumper=ODYD, **kwds) | python | def safe_dump(data, stream=None, **kwds):
"""implementation of safe dumper using Ordered Dict Yaml Dumper"""
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zerwes/hiyapyco | hiyapyco/__init__.py | dump | def dump(data, **kwds):
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andycasey/ads | ads/search.py | Article.bibtex | def bibtex(self):
"""Return a BiBTeX entry for the current article."""
warnings.warn("bibtex should be queried with ads.ExportQuery(); You will "
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"""Return a BiBTeX entry for the current article."""
warnings.warn("bibtex should be queried with ads.ExportQuery(); You will "
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andycasey/ads | examples/monthly-institute-publications/stromlo.py | get_pdf | def get_pdf(article, debug=False):
"""
Download an article PDF from arXiv.
:param article:
The ADS article to retrieve.
:type article:
:class:`ads.search.Article`
:returns:
The binary content of the requested PDF.
"""
print('Retrieving {0}'.format(article))
i... | python | def get_pdf(article, debug=False):
"""
Download an article PDF from arXiv.
:param article:
The ADS article to retrieve.
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:class:`ads.search.Article`
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The binary content of the requested PDF.
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andycasey/ads | examples/monthly-institute-publications/stromlo.py | summarise_pdfs | def summarise_pdfs(pdfs):
"""
Collate the first page from each of the PDFs provided into a single PDF.
:param pdfs:
The contents of several PDF files.
:type pdfs:
list of str
:returns:
The contents of single PDF, which can be written directly to disk.
"""
# Ignore... | python | def summarise_pdfs(pdfs):
"""
Collate the first page from each of the PDFs provided into a single PDF.
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The contents of several PDF files.
:type pdfs:
list of str
:returns:
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andycasey/ads | ads/metrics.py | MetricsQuery.execute | def execute(self):
"""
Execute the http request to the metrics service
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self.response = MetricsResponse.load_http_response(
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return self.response.metrics | python | def execute(self):
"""
Execute the http request to the metrics service
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self.response = MetricsResponse.load_http_response(
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andycasey/ads | ads/base.py | _Singleton.get_info | def get_info(cls):
"""
Print all of the instantiated Singletons
"""
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[str(cls._instances[key]) for key in cls._instances]
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Print all of the instantiated Singletons
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andycasey/ads | ads/base.py | APIResponse.load_http_response | def load_http_response(cls, http_response):
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"""
if not http_response.ok:
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andycasey/ads | ads/base.py | BaseQuery.token | def token(self):
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set the instance attribute `token` following the following logic,
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- environment variables TOKEN_ENVIRON_VARS
- file containing plaintext as the contents in TOKEN_FILES
... | python | def token(self):
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set the instance attribute `token` following the following logic,
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- file containing plaintext as the contents in TOKEN_FILES
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andycasey/ads | ads/base.py | BaseQuery.session | def session(self):
"""
http session interface, transparent proxy to requests.session
"""
if self._session is None:
self._session = requests.session()
self._session.headers.update(
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"""
http session interface, transparent proxy to requests.session
"""
if self._session is None:
self._session = requests.session()
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.from_csv | def from_csv(input_csv_pattern, headers=None, schema_file=None):
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input_csv_pattern: Path to Csv file pattern (with no header). Can be local or GCS path.
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sql: A BigQuery table name or a query.
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"""Create a Metrics instance from a bigquery query or table.
Returns:
a Metrics instance.
Args:
sql: A BigQuery table name or a query.
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics._get_data_from_csv_files | def _get_data_from_csv_files(self):
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all_df = []
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics._get_data_from_bigquery | def _get_data_from_bigquery(self, queries):
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googledatalab/pydatalab | google/datalab/bigquery/_udf.py | UDF._expanded_sql | def _expanded_sql(self):
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | BucketMetadata.created_on | def created_on(self):
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Bucket.metadata | def metadata(self):
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Raises:
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Returns:
A BucketMetadata instance with information about this bucket.
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Args:
key: the key of the object within the bucket.
Returns:
An Object instance representing the specified key.
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Bucket.delete | def delete(self):
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Raises:
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"""
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Buckets.contains | def contains(self, name):
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Args:
name: the name of the bucket to lookup.
Returns:
True if the bucket exists; False otherwise.
Raises:
Exception if there was an error requesting information about the bucket.
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"""Checks if the specified bucket exists.
Args:
name: the name of the bucket to lookup.
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key: the key of the item within the bucket.
Returns:
An Item instance representing the specified key.
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key: the key of the item within the bucket.
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googledatalab/pydatalab | datalab/storage/_bucket.py | Bucket.create | def create(self, project_id=None):
"""Creates the bucket.
Args:
project_id: the project in which to create the bucket.
Returns:
The bucket.
Raises:
Exception if there was an error creating the bucket.
"""
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"""Creates the bucket.
Args:
project_id: the project in which to create the bucket.
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googledatalab/pydatalab | datalab/storage/_bucket.py | Buckets.create | def create(self, name):
"""Creates a new bucket.
Args:
name: a unique name for the new bucket.
Returns:
The newly created bucket.
Raises:
Exception if there was an error creating the bucket.
"""
return Bucket(name, context=self._context).create(self._project_id) | python | def create(self, name):
"""Creates a new bucket.
Args:
name: a unique name for the new bucket.
Returns:
The newly created bucket.
Raises:
Exception if there was an error creating the bucket.
"""
return Bucket(name, context=self._context).create(self._project_id) | [
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/regression/dnn/_regression_dnn.py | train | def train(train_dataset,
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num_epochs=None,
train_batch_size=100,
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min_eval_frequency=100,
learning_rate=0.01,
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googledatalab/pydatalab | google/datalab/storage/commands/_storage.py | _gcs_list_buckets | def _gcs_list_buckets(project, pattern):
""" List all Google Cloud Storage buckets that match a pattern. """
data = [{'Bucket': 'gs://' + bucket.name, 'Created': bucket.metadata.created_on}
for bucket in google.datalab.storage.Buckets(_make_context(project))
if fnmatch.fnmatch(bucket.name, patte... | python | def _gcs_list_buckets(project, pattern):
""" List all Google Cloud Storage buckets that match a pattern. """
data = [{'Bucket': 'gs://' + bucket.name, 'Created': bucket.metadata.created_on}
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googledatalab/pydatalab | google/datalab/storage/commands/_storage.py | _gcs_list_keys | def _gcs_list_keys(bucket, pattern):
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | prepare_image_transforms | def prepare_image_transforms(element, image_columns):
"""Replace an images url with its jpeg bytes.
Args:
element: one input row, as a dict
image_columns: list of columns that are image paths
Return:
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"""
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im... | python | def prepare_image_transforms(element, image_columns):
"""Replace an images url with its jpeg bytes.
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csv_string: a csv string. May contain missing values "a,,c"
column_names: list of column names
Returns:
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | encode_csv | def encode_csv(data_dict, column_names):
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Args:
data_dict: dict of {column_name: 1 value}
column_names: list of column names
Returns:
A csv string version of data_dict
"""
import csv
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values = [str(data_dict[x]) for x in column_names]
str_buff = six.StringI... | python | def encode_csv(data_dict, column_names):
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Args:
data_dict: dict of {column_name: 1 value}
column_names: list of column names
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | serialize_example | def serialize_example(transformed_json_data, info_dict):
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Args:
transformed_json_data: dict of transformed data.
info_dict: output of feature_transforms.get_transfrormed_feature_info()
Returns:
The serialized tf.example version of transformed_json_data.
"""
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3) build a csv input string with images paths replaced with base64. This
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | main | def main(argv=None):
"""Run Preprocessing as a Dataflow."""
args = parse_arguments(sys.argv if argv is None else argv)
temp_dir = os.path.join(args.output, 'tmp')
if args.cloud:
pipeline_name = 'DataflowRunner'
else:
pipeline_name = 'DirectRunner'
# Suppress TF warnings.
os.environ['TF_CPP_MI... | python | def main(argv=None):
"""Run Preprocessing as a Dataflow."""
args = parse_arguments(sys.argv if argv is None else argv)
temp_dir = os.path.join(args.output, 'tmp')
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | TransformFeaturesDoFn.start_bundle | def start_bundle(self, element=None):
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session = tf.Session(graph=g)
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googledatalab/pydatalab | google/datalab/bigquery/_parser.py | Parser.parse_row | def parse_row(schema, data):
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schema: the array of fields defining the schema of the data.
data: the JSON row from a query result.
Returns:
The parsed row object.
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _tf_predict | def _tf_predict(model_dir, input_csvlines):
"""Prediction with a tf savedmodel.
Args:
model_dir: directory that contains a saved model
input_csvlines: list of csv strings
Returns:
Dict in the form tensor_name:prediction_list. Note that the value is always
a list, even if there was only 1 row... | python | def _tf_predict(model_dir, input_csvlines):
"""Prediction with a tf savedmodel.
Args:
model_dir: directory that contains a saved model
input_csvlines: list of csv strings
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"""Download images given image columns."""
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _get_predicton_csv_lines | def _get_predicton_csv_lines(data, headers, images):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _get_display_data_with_images | def _get_display_data_with_images(data, images):
"""Create display data by converting image urls to base64 strings."""
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return data
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"""Create display data by converting image urls to base64 strings."""
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | get_model_schema_and_features | def get_model_schema_and_features(model_dir):
"""Get a local model's schema and features config.
Args:
model_dir: local or GCS path of a model.
Returns:
A tuple of schema (list) and features config (dict).
"""
schema_file = os.path.join(model_dir, 'assets.extra', 'schema.json')
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"""Get a local model's schema and features config.
Args:
model_dir: local or GCS path of a model.
Returns:
A tuple of schema (list) and features config (dict).
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | get_prediction_results | def get_prediction_results(model_dir_or_id, data, headers, img_cols=None,
cloud=False, with_source=True, show_image=True):
""" Predict with a specified model.
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | get_probs_for_labels | def get_probs_for_labels(labels, prediction_results):
""" Given ML Workbench prediction results, get probs of each label for each instance.
The prediction results are like:
[
{'predicted': 'daisy', 'probability': 0.8, 'predicted_2': 'rose', 'probability_2': 0.1},
{'predicted': 'sunflower', 'probability':... | python | def get_probs_for_labels(labels, prediction_results):
""" Given ML Workbench prediction results, get probs of each label for each instance.
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{'predicted': 'daisy', 'probability': 0.8, 'predicted_2': 'rose', 'probability_2': 0.1},
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | local_batch_predict | def local_batch_predict(model_dir, csv_file_pattern, output_dir, output_format, batch_size=100):
""" Batch Predict with a specified model.
It does batch prediction, saves results to output files and also creates an output
schema file. The output file names are input file names prepended by 'predict_results_'.
... | python | def local_batch_predict(model_dir, csv_file_pattern, output_dir, output_format, batch_size=100):
""" Batch Predict with a specified model.
It does batch prediction, saves results to output files and also creates an output
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googledatalab/pydatalab | google/datalab/ml/_job.py | Job.submit_training | def submit_training(job_request, job_id=None):
"""Submit a training job.
Args:
job_request: the arguments of the training job in a dict. For example,
{
'package_uris': 'gs://my-bucket/iris/trainer-0.1.tar.gz',
'python_module': 'trainer.task',
'scale_tier': '... | python | def submit_training(job_request, job_id=None):
"""Submit a training job.
Args:
job_request: the arguments of the training job in a dict. For example,
{
'package_uris': 'gs://my-bucket/iris/trainer-0.1.tar.gz',
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googledatalab/pydatalab | google/datalab/ml/_job.py | Job.submit_batch_prediction | def submit_batch_prediction(job_request, job_id=None):
"""Submit a batch prediction job.
Args:
job_request: the arguments of the training job in a dict. For example,
{
'version_name': 'projects/my-project/models/my-model/versions/my-version',
'data_format': 'TEXT',
... | python | def submit_batch_prediction(job_request, job_id=None):
"""Submit a batch prediction job.
Args:
job_request: the arguments of the training job in a dict. For example,
{
'version_name': 'projects/my-project/models/my-model/versions/my-version',
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_inceptionlib.py | _reduced_kernel_size_for_small_input | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
"""Define kernel size which is automatically reduced for small input.
If the shape of the input images is unknown at graph construction time this
function assumes that the input images are is large enough.
Args:
input_tensor: input tenso... | python | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_inceptionlib.py | inception_v3_arg_scope | def inception_v3_arg_scope(weight_decay=0.00004,
stddev=0.1,
batch_norm_var_collection='moving_vars'):
"""Defines the default InceptionV3 arg scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
stddev: The standard deviation o... | python | def inception_v3_arg_scope(weight_decay=0.00004,
stddev=0.1,
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"""Defines the default InceptionV3 arg scope.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.preprocess | def preprocess(train_dataset, output_dir, eval_dataset, checkpoint):
"""Preprocess data locally."""
import apache_beam as beam
from google.datalab.utils import LambdaJob
from . import _preprocess
if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
job_id = ('preprocess-im... | python | def preprocess(train_dataset, output_dir, eval_dataset, checkpoint):
"""Preprocess data locally."""
import apache_beam as beam
from google.datalab.utils import LambdaJob
from . import _preprocess
if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
job_id = ('preprocess-im... | [
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.train | def train(input_dir, batch_size, max_steps, output_dir, checkpoint):
"""Train model locally."""
from google.datalab.utils import LambdaJob
if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
labels = _util.get_labels(input_dir)
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... | python | def train(input_dir, batch_size, max_steps, output_dir, checkpoint):
"""Train model locally."""
from google.datalab.utils import LambdaJob
if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
labels = _util.get_labels(input_dir)
model = _model.Model(labels, 0.5, checkpoint)
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.predict | def predict(model_dir, image_files, resize, show_image):
"""Predict using an model in a local or GCS directory."""
from . import _predictor
images = _util.load_images(image_files, resize=resize)
labels_and_scores = _predictor.predict(model_dir, images)
results = zip(image_files, images, labels_and... | python | def predict(model_dir, image_files, resize, show_image):
"""Predict using an model in a local or GCS directory."""
from . import _predictor
images = _util.load_images(image_files, resize=resize)
labels_and_scores = _predictor.predict(model_dir, images)
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.batch_predict | def batch_predict(dataset, model_dir, output_csv, output_bq_table):
"""Batch predict running locally."""
import apache_beam as beam
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from . import _predictor
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"""Batch predict running locally."""
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googledatalab/pydatalab | datalab/utils/_job.py | Job.state | def state(self):
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""" Describe the state of a Job.
Returns: A string describing the job's state.
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googledatalab/pydatalab | datalab/utils/_job.py | Job.wait_any | def wait_any(jobs, timeout=None):
""" Return when at least one of the specified jobs has completed or timeout expires.
Args:
jobs: a Job or list of Jobs to wait on.
timeout: a timeout in seconds to wait for. None (the default) means no timeout.
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""" Return when at least one of the specified jobs has completed or timeout expires.
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googledatalab/pydatalab | datalab/utils/_job.py | Job.wait_all | def wait_all(jobs, timeout=None):
""" Return when at all of the specified jobs have completed or timeout expires.
Args:
jobs: a Job or list of Jobs to wait on.
timeout: a timeout in seconds to wait for. None (the default) means no timeout.
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""" Return when at all of the specified jobs have completed or timeout expires.
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jobs: a Job or list of Jobs to wait on.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Evaluator.evaluate | def evaluate(self, num_eval_batches=None):
"""Run one round of evaluation, return loss and accuracy."""
num_eval_batches = num_eval_batches or self.num_eval_batches
with tf.Graph().as_default() as graph:
self.tensors = self.model.build_eval_graph(self.eval_data_paths,
... | python | def evaluate(self, num_eval_batches=None):
"""Run one round of evaluation, return loss and accuracy."""
num_eval_batches = num_eval_batches or self.num_eval_batches
with tf.Graph().as_default() as graph:
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Trainer.log | def log(self, session):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Trainer.eval | def eval(self, session):
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"""Runs evaluation loop."""
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googledatalab/pydatalab | google/datalab/ml/_feature_slice_view.py | FeatureSliceView.plot | def plot(self, data):
""" Plots a featire slice view on given data.
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data: Can be one of:
A string of sql query.
A sql query module defined by "%%sql --module module_name".
A pandas DataFrame.
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... | python | def plot(self, data):
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/preprocess/cloud_preprocess.py | run_analysis | def run_analysis(args):
"""Builds an analysis file for training.
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Args:
args: command line args
Raises:
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"""
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table = bq.Table(args.bigquery_table)... | python | def run_analysis(args):
"""Builds an analysis file for training.
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args: command line args
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ValueError if schema contains unknown types.
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googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.from_csv | def from_csv(input_csv, headers=None, schema_file=None):
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input_csv: Path to a Csv file (with no header). Can be local or GCS path.
headers: Csv headers. If present, it must include 'target' and 'predicted'.
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A Bigquery table string.
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googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.to_dataframe | def to_dataframe(self):
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A DataFrame with "target", "predicted", "count" columns.
"""
data = []
for target_index, target_row in enumerate(self._cm):
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data.append((self._l... | python | def to_dataframe(self):
"""Convert the confusion matrix to a dataframe.
Returns:
A DataFrame with "target", "predicted", "count" columns.
"""
data = []
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googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.plot | def plot(self, figsize=None, rotation=45):
"""Plot the confusion matrix.
Args:
figsize: tuple (x, y) of ints. Sets the size of the figure
rotation: the rotation angle of the labels on the x-axis.
"""
fig, ax = plt.subplots(figsize=figsize)
plt.imshow(self._cm, interpolation='nearest',... | python | def plot(self, figsize=None, rotation=45):
"""Plot the confusion matrix.
Args:
figsize: tuple (x, y) of ints. Sets the size of the figure
rotation: the rotation angle of the labels on the x-axis.
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googledatalab/pydatalab | google/datalab/contrib/pipeline/composer/_api.py | Api.get_environment_details | def get_environment_details(zone, environment):
""" Issues a request to Composer to get the environment details.
Args:
zone: GCP zone of the composer environment
environment: name of the Composer environment
Returns:
A parsed result object.
Raises:
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""" Issues a request to Composer to get the environment details.
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zone: GCP zone of the composer environment
environment: name of the Composer environment
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A parsed result object.
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googledatalab/pydatalab | google/datalab/storage/_api.py | Api.buckets_delete | def buckets_delete(self, bucket):
"""Issues a request to delete a bucket.
Args:
bucket: the name of the bucket.
Raises:
Exception if there is an error performing the operation.
"""
url = Api._ENDPOINT + (Api._BUCKET_PATH % bucket)
google.datalab.utils.Http.request(url, method='DELET... | python | def buckets_delete(self, bucket):
"""Issues a request to delete a bucket.
Args:
bucket: the name of the bucket.
Raises:
Exception if there is an error performing the operation.
"""
url = Api._ENDPOINT + (Api._BUCKET_PATH % bucket)
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googledatalab/pydatalab | google/datalab/storage/_api.py | Api.buckets_get | def buckets_get(self, bucket, projection='noAcl'):
"""Issues a request to retrieve information about a bucket.
Args:
bucket: the name of the bucket.
projection: the projection of the bucket information to retrieve.
Returns:
A parsed bucket information dictionary.
Raises:
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"""Issues a request to retrieve information about a bucket.
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bucket: the name of the bucket.
projection: the projection of the bucket information to retrieve.
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A parsed bucket information dictionary.
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googledatalab/pydatalab | google/datalab/storage/_api.py | Api.buckets_list | def buckets_list(self, projection='noAcl', max_results=0, page_token=None, project_id=None):
"""Issues a request to retrieve the list of buckets.
Args:
projection: the projection of the bucket information to retrieve.
max_results: an optional maximum number of objects to retrieve.
page_token:... | python | def buckets_list(self, projection='noAcl', max_results=0, page_token=None, project_id=None):
"""Issues a request to retrieve the list of buckets.
Args:
projection: the projection of the bucket information to retrieve.
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googledatalab/pydatalab | google/datalab/storage/_api.py | Api.object_download | def object_download(self, bucket, key, start_offset=0, byte_count=None):
"""Reads the contents of an object as text.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be read.
start_offset: the start offset of bytes to read.
byte_count: the number... | python | def object_download(self, bucket, key, start_offset=0, byte_count=None):
"""Reads the contents of an object as text.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be read.
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googledatalab/pydatalab | google/datalab/storage/_api.py | Api.object_upload | def object_upload(self, bucket, key, content, content_type):
"""Writes text content to the object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be written.
content: the text content to be written.
content_type: the type of text content.
R... | python | def object_upload(self, bucket, key, content, content_type):
"""Writes text content to the object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be written.
content: the text content to be written.
content_type: the type of text content.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | preprocess_async | def preprocess_async(train_dataset, output_dir, eval_dataset=None, checkpoint=None, cloud=None):
"""Preprocess data. Produce output that can be used by training efficiently.
Args:
train_dataset: training data source to preprocess. Can be CsvDataset or BigQueryDataSet.
If eval_dataset is None, the pipel... | python | def preprocess_async(train_dataset, output_dir, eval_dataset=None, checkpoint=None, cloud=None):
"""Preprocess data. Produce output that can be used by training efficiently.
Args:
train_dataset: training data source to preprocess. Can be CsvDataset or BigQueryDataSet.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | train_async | def train_async(input_dir, batch_size, max_steps, output_dir, checkpoint=None, cloud=None):
"""Train model. The output can be used for batch prediction or online deployment.
Args:
input_dir: A directory path containing preprocessed results. Can be local or GCS path.
batch_size: size of batch used for train... | python | def train_async(input_dir, batch_size, max_steps, output_dir, checkpoint=None, cloud=None):
"""Train model. The output can be used for batch prediction or online deployment.
Args:
input_dir: A directory path containing preprocessed results. Can be local or GCS path.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | batch_predict_async | def batch_predict_async(dataset, model_dir, output_csv=None, output_bq_table=None, cloud=None):
"""Batch prediction with an offline model.
Args:
dataset: CsvDataSet or BigQueryDataSet for batch prediction input. Can contain either
one column 'image_url', or two columns with another being 'label'.
m... | python | def batch_predict_async(dataset, model_dir, output_csv=None, output_bq_table=None, cloud=None):
"""Batch prediction with an offline model.
Args:
dataset: CsvDataSet or BigQueryDataSet for batch prediction input. Can contain either
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googledatalab/pydatalab | google/datalab/bigquery/_job.py | Job._refresh_state | def _refresh_state(self):
""" Get the state of a job. If the job is complete this does nothing;
otherwise it gets a refreshed copy of the job resource.
"""
# TODO(gram): should we put a choke on refreshes? E.g. if the last call was less than
# a second ago should we return the cached value?
... | python | def _refresh_state(self):
""" Get the state of a job. If the job is complete this does nothing;
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"""
# TODO(gram): should we put a choke on refreshes? E.g. if the last call was less than
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