content stringlengths 1 1.04M | input_ids listlengths 1 774k | ratio_char_token float64 0.38 22.9 | token_count int64 1 774k |
|---|---|---|---|
import sys
import os
import signal
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from munch import Munch
'''
Signals
munch.subscription.create -> Create a new subscription
munch.subscription.delete -> Deletes an existing subscription
munch.subscription.update -> Updates an existing subscription
munch.subscription.list -> Returns a list of all the available subscriptions
munch.appInstanceId.subscriptions.ui -> Notifies UI about events that have happened in the backend
'''
'''
Notifications
Message Format -> JSON
Sample Incoming Message
{
appInstanceId: 'XXXXX'
data: {
id: ...
username: ...
}
}
Sample Notification Message
{
status: 1,
message: 'Some context information'
data: {
}
}
'''
# Create instance of munch using your publish and subscribe keys
munch = Munch(
'pub-c-a2ca7a5e-f2c4-4682-93fa-4d95929e2c3f',
'sub-c-c84d1450-0df9-11e6-bbd9-02ee2ddab7fe'
)
@munch.consumes('munch.subscription.list')
@munch.consumes('munch.subscription.create')
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3... | 2.670918 | 392 |
# Definition for a Node.
from typing import Optional
s = Solution()
print(s.flatten(Node(None, None, None, None)))
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] | 3.216216 | 37 |
import io
import os
import tarfile
from core.DockerClient import DockerClient
from core.LineBuffer import LineBuffer
from core.TarUtils import make_tarfile
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"""Routines for streaming cell data"""
import os
import warnings
import logging
logging.captureWarnings(True)
if __name__ == "__main__":
warnings.warn("to be implemented")
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import sae
import os
import sys
from driving import wsgi
app_root = os.path.dirname(__file__)
sys.path.insert(0, os.path.join(app_root, 'django-filter-0.9.2'))
application = sae.create_wsgi_app(wsgi.application) | [
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 00:12:01 2021
@author: ali_d
"""
#3D graphs
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
x = [1,2,3,4,5,6,7,8,9,10]
y = [5,6,7,8,2,5,6,3,7,2]
z = [1,2,6,3,2,7,3,3,7,2]
ax1.plot(x,y,z)
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
#%%
style.use("fivethirtyeight")
fig = plt.figure()
axis1 = fig.add_subplot(111,projection="3d")
x = [1,2,3,4,5,6,7,8,9,10]
y = [5,2,7,1,5,8,6,9,2,1]
z = [2,4,7,8,5,3,8,7,4,9]
axis1.plot(x,y,z)
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
#%%
#3D Scatter Plot
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
x = [1,2,3,4,5,6,7,8,9,10]
y = [5,6,7,8,2,5,6,3,7,2]
z = [1,2,6,3,2,7,3,3,7,2]
x2 = [-1,-2,-3,-4,-5,-6,-7,-8,-9,-10]
y2 = [-5,-6,-7,-8,-2,-5,-6,-3,-7,-2]
z2 = [1,2,6,3,2,7,3,3,7,2]
ax1.scatter(x, y, z, c='g', marker='o')
ax1.scatter(x2, y2, z2, c ='r', marker='o')
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
#%%
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fast')
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
x = [4,3,7,8,5,8,2,6,11,10]
y = [5,3,7,9,8,5,5,2,7,1]
z = [4,2,6,3,2,7,3,3,7,2]
x2 = [-1,-2,-3,-4,-5,-6,-7,-8,-9,-10]
y2 = [-5,-6,-7,-8,-2,-5,-6,-3,-7,-2]
z2 = [1,2,6,3,2,7,3,3,7,2]
ax1.scatter(x, y, z, c='g', marker='o')
ax1.scatter(x2, y2, z2, c ='r', marker='o')
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
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# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full license information.
# ==============================================================================
from . import cntk_py
from .cntk_py import default_param_init_scale as DefaultParamInitScale,\
sentinel_value_for_infer_param_init_rank as SentinelValueForInferParamInitRank,\
sentinel_value_for_auto_select_random_seed as SentinelValueForAutoSelectRandomSeed
def uniform(scale=DefaultParamInitScale, seed=None):
'''
Uniform initializer
Args:
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to uniform distribution between `scale*[-0.05, 0.05]`
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.uniform_initializer(scale, seed)
def gaussian(output_rank=SentinelValueForInferParamInitRank, filter_rank=SentinelValueForInferParamInitRank, scale=DefaultParamInitScale, seed=None):
'''
Gaussian initializer
Args:
output_rank (`int`): output rank
filter_rank (`int`): filter rank
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to Gaussian distribution with mean `0` and standard deviation `scale*0.2/sqrt(fanIn))`.
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.gaussian_initializer(output_rank, filter_rank, scale, seed)
def xavier(output_rank=SentinelValueForInferParamInitRank, filter_rank=SentinelValueForInferParamInitRank, scale=DefaultParamInitScale, seed=None):
'''
Xavier initializer
Args:
output_rank (`int`): output rank
filter_rank (`int`): filter rank
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to Gaussian distribution with mean `0` and standard deviation `scale*sqrt(3.0/fanIn)`
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.xavier_initializer(output_rank, filter_rank, scale, seed)
def glorot_uniform(output_rank=SentinelValueForInferParamInitRank, filter_rank=SentinelValueForInferParamInitRank, scale=DefaultParamInitScale, seed=None):
'''
Glorot initializer
Args:
output_rank (`int`): output rank
filter_rank (`int`): filter rank
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to uniform distribution between `scale*sqrt(6.0/(fanIn+fanOut))*[-1,1]`
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.glorot_uniform_initializer(output_rank, filter_rank, scale, seed)
def glorot_normal(output_rank=SentinelValueForInferParamInitRank, filter_rank=SentinelValueForInferParamInitRank, scale=DefaultParamInitScale, seed=None):
'''
initializer
Args:
output_rank (`int`): output rank
filter_rank (`int`): filter rank
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to Gaussian distribution with mean `0` and standard deviation `scale*sqrt(2.0/(fanIn+fanOut))`
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.glorot_normal_initializer(output_rank, filter_rank, scale, seed)
def he_uniform(output_rank=SentinelValueForInferParamInitRank, filter_rank=SentinelValueForInferParamInitRank, scale=DefaultParamInitScale, seed=None):
'''
initializer
Args:
output_rank (`int`): output rank
filter_rank (`int`): filter rank
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to uniform distribution between `scale*sqrt(6.0/fanIn)*[-1,1]`
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.he_uniform_initializer(output_rank, filter_rank, scale, seed)
def he_normal(output_rank=SentinelValueForInferParamInitRank, filter_rank=SentinelValueForInferParamInitRank, scale=DefaultParamInitScale, seed=None):
'''
initializer
Args:
output_rank (`int`): output rank
filter_rank (`int`): filter rank
scale (`float`): scale
seed (`int`): random seed
Returns:
initializer for :class:`cntk.variables.Parameter`
initialized to Gaussian distribution with mean `0` and standard deviation `scale*sqrt(2.0/fanIn)`
'''
if seed is None:
seed = SentinelValueForAutoSelectRandomSeed
return cntk_py.he_normal_initializer(output_rank, filter_rank, scale, seed)
def bilinear(kernel_width, kernel_height):
'''
initializer
Args:
kernel_width (`int`): kernel width
kernel_height (`int`): kernel height
Returns:
initializer for :class:`cntk.variables.Parameter`
useful for deconvolution layer
'''
return cntk_py.bilinear_initializer(kernel_width, kernel_height)
def initializer_with_rank(initializer, output_rank=None, filter_rank=None):
'''
override output_rank and filter_rank specification in a random initializer
constructed without an explciti output_rank and filter_rank specification
Args:'
initializer: initializer whose output_rank and filter_rank parameters are to be overriden
output_rank (`int`): new output rank value
filter_rank (`int`): new filter rank value
Returns:
new initializer for `:class:cntk.variables.Parameter` with specified output_rank and filter_rank
'''
if output_rank is None:
output_rank = SentinelValueForInferParamInitRank
if filter_rank is None:
filter_rank = SentinelValueForInferParamInitRank
return cntk_py.random_initializer_with_rank(initializer, output_rank, filter_rank)
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functions = {
'AddCDAQSyncConnection': {
'parameters': [
{
'direction': 'in',
'name': 'portList',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'AddGlobalChansToTask': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelNames',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'AddNetworkDevice': {
'parameters': [
{
'direction': 'in',
'name': 'ipAddress',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'attemptReservation',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'deviceNameOut',
'size': {
'mechanism': 'ivi-dance',
'value': 'deviceNameOutBufferSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'deviceNameOutBufferSize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'AreConfiguredCDAQSyncPortsDisconnected': {
'parameters': [
{
'direction': 'in',
'name': 'chassisDevicesPorts',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'disconnectedPortsExist',
'type': 'bool32'
}
],
'returns': 'int32'
},
'AutoConfigureCDAQSyncConnections': {
'parameters': [
{
'direction': 'in',
'name': 'chassisDevicesPorts',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
}
],
'returns': 'int32'
},
'CalculateReversePolyCoeff': {
'parameters': [
{
'direction': 'in',
'name': 'forwardCoeffs',
'size': {
'mechanism': 'len',
'value': 'numForwardCoeffsIn'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numForwardCoeffsIn',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'minValX',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxValX',
'type': 'float64'
},
{
'direction': 'in',
'name': 'numPointsToCompute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'reversePolyOrder',
'type': 'int32'
},
{
'direction': 'out',
'name': 'reverseCoeffs',
'size': {
'mechanism': 'custom-code',
'value': '(reversePolyOrder < 0) ? numForwardCoeffsIn : reversePolyOrder + 1'
},
'type': 'float64[]'
}
],
'returns': 'int32'
},
'CfgAnlgEdgeRefTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Slope1',
'name': 'triggerSlope',
'type': 'int32'
},
{
'direction': 'in',
'name': 'triggerLevel',
'type': 'float64'
},
{
'direction': 'in',
'name': 'pretriggerSamples',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgAnlgEdgeStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Slope1',
'name': 'triggerSlope',
'type': 'int32'
},
{
'direction': 'in',
'name': 'triggerLevel',
'type': 'float64'
}
],
'returns': 'int32'
},
'CfgAnlgMultiEdgeRefTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSources',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'triggerSlopeArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'triggerLevelArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'pretriggerSamples',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'arraySize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgAnlgMultiEdgeStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSources',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'triggerSlopeArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'triggerLevelArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'arraySize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgAnlgWindowRefTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'WindowTriggerCondition1',
'name': 'triggerWhen',
'type': 'int32'
},
{
'direction': 'in',
'name': 'windowTop',
'type': 'float64'
},
{
'direction': 'in',
'name': 'windowBottom',
'type': 'float64'
},
{
'direction': 'in',
'name': 'pretriggerSamples',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgAnlgWindowStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'WindowTriggerCondition1',
'name': 'triggerWhen',
'type': 'int32'
},
{
'direction': 'in',
'name': 'windowTop',
'type': 'float64'
},
{
'direction': 'in',
'name': 'windowBottom',
'type': 'float64'
}
],
'returns': 'int32'
},
'CfgBurstHandshakingTimingExportClock': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
},
{
'direction': 'in',
'name': 'sampleClkRate',
'type': 'float64'
},
{
'direction': 'in',
'name': 'sampleClkOutpTerm',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Polarity2',
'name': 'sampleClkPulsePolarity',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Level1',
'name': 'pauseWhen',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Polarity2',
'name': 'readyEventActiveLevel',
'type': 'int32'
}
],
'returns': 'int32'
},
'CfgBurstHandshakingTimingImportClock': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
},
{
'direction': 'in',
'name': 'sampleClkRate',
'type': 'float64'
},
{
'direction': 'in',
'name': 'sampleClkSrc',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'sampleClkActiveEdge',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Level1',
'name': 'pauseWhen',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Polarity2',
'name': 'readyEventActiveLevel',
'type': 'int32'
}
],
'returns': 'int32'
},
'CfgChangeDetectionTiming': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'risingEdgeChan',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'fallingEdgeChan',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
}
],
'returns': 'int32'
},
'CfgDigEdgeRefTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'triggerEdge',
'type': 'int32'
},
{
'direction': 'in',
'name': 'pretriggerSamples',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgDigEdgeStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'triggerEdge',
'type': 'int32'
}
],
'returns': 'int32'
},
'CfgDigPatternRefTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'triggerPattern',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'DigitalPatternCondition1',
'name': 'triggerWhen',
'type': 'int32'
},
{
'direction': 'in',
'name': 'pretriggerSamples',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgDigPatternStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'triggerSource',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'triggerPattern',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'DigitalPatternCondition1',
'name': 'triggerWhen',
'type': 'int32'
}
],
'returns': 'int32'
},
'CfgHandshakingTiming': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
}
],
'returns': 'int32'
},
'CfgImplicitTiming': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
}
],
'returns': 'int32'
},
'CfgInputBuffer': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgOutputBuffer': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgPipelinedSampClkTiming': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'source',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'rate',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'activeEdge',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
}
],
'returns': 'int32'
},
'CfgSampClkTiming': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'source',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'rate',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'activeEdge',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'AcquisitionType',
'name': 'sampleMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sampsPerChan',
'type': 'uInt64'
}
],
'returns': 'int32'
},
'CfgTimeStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'when',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'enum': 'Timescale2',
'name': 'timescale',
'type': 'int32'
}
],
'returns': 'int32'
},
'CfgWatchdogAOExpirStates': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelNames',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'expirStateArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'enum': 'WatchdogAOOutputType',
'name': 'outputTypeArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'arraySize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgWatchdogCOExpirStates': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelNames',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'WatchdogCOExpirState',
'name': 'expirStateArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'arraySize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'CfgWatchdogDOExpirStates': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelNames',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'DigitalLineState',
'name': 'expirStateArray',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'arraySize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'ClearTEDS': {
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'ClearTask': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'ConfigureLogging': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'filePath',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'LoggingMode',
'name': 'loggingMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'groupName',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'LoggingOperation',
'name': 'operation',
'type': 'int32'
}
],
'returns': 'int32'
},
'ConfigureTEDS': {
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'filePath',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'ConnectTerms': {
'parameters': [
{
'direction': 'in',
'name': 'sourceTerminal',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'destinationTerminal',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InvertPolarity',
'name': 'signalModifiers',
'type': 'int32'
}
],
'returns': 'int32'
},
'ControlWatchdogTask': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'WatchdogControlAction',
'name': 'action',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateAIAccel4WireDCVoltageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelSensitivityUnits1',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'useExcitForScaling',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIAccelChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelSensitivityUnits1',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIAccelChargeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelChargeSensitivityUnits',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIBridgeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgeUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIChargeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ChargeUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAICurrentChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'CurrentUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CurrentShuntResistorLocationWithDefault',
'name': 'shuntResistorLoc',
'type': 'int32'
},
{
'direction': 'in',
'name': 'extShuntResistorVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAICurrentRMSChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'CurrentUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CurrentShuntResistorLocationWithDefault',
'name': 'shuntResistorLoc',
'type': 'int32'
},
{
'direction': 'in',
'name': 'extShuntResistorVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIForceBridgePolynomialChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'forwardCoeffs',
'size': {
'mechanism': 'len',
'value': 'numForwardCoeffs'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numForwardCoeffs',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'reverseCoeffs',
'size': {
'mechanism': 'len',
'value': 'numReverseCoeffs'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numReverseCoeffs',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIForceBridgeTableChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'electricalVals',
'size': {
'mechanism': 'len',
'value': 'numElectricalVals'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numElectricalVals',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'physicalVals',
'size': {
'mechanism': 'len',
'value': 'numPhysicalVals'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numPhysicalVals',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIForceBridgeTwoPointLinChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'firstElectricalVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'secondElectricalVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'firstPhysicalVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'secondPhysicalVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIForceIEPEChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceIEPEUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceIEPESensorSensitivityUnits',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIFreqVoltageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'FrequencyUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'thresholdLevel',
'type': 'float64'
},
{
'direction': 'in',
'name': 'hysteresis',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIMicrophoneChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'SoundPressureUnits1',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'micSensitivity',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxSndPressLevel',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIPosEddyCurrProxProbeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'LengthUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'EddyCurrentProxProbeSensitivityUnits',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIPosLVDTChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'LengthUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'LVDTSensitivityUnits1',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'voltageExcitFreq',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ACExcitWireMode',
'name': 'acExcitWireMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIPosRVDTChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AngleUnits1',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'RVDTSensitivityUnits1',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'voltageExcitFreq',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ACExcitWireMode',
'name': 'acExcitWireMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIPressureBridgePolynomialChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'PressureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'forwardCoeffs',
'size': {
'mechanism': 'len',
'value': 'numForwardCoeffs'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numForwardCoeffs',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'reverseCoeffs',
'size': {
'mechanism': 'len',
'value': 'numReverseCoeffs'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numReverseCoeffs',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIPressureBridgeTableChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'PressureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'electricalVals',
'size': {
'mechanism': 'len',
'value': 'numElectricalVals'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numElectricalVals',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'physicalVals',
'size': {
'mechanism': 'len',
'value': 'numPhysicalVals'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numPhysicalVals',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIPressureBridgeTwoPointLinChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'PressureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'firstElectricalVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'secondElectricalVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'firstPhysicalVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'secondPhysicalVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIRTDChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'RTDType1',
'name': 'rtdType',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'r0',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateAIResistanceChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ResistanceUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIRosetteStrainGageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'StrainGageRosetteType',
'name': 'rosetteType',
'type': 'int32'
},
{
'direction': 'in',
'name': 'gageOrientation',
'type': 'float64'
},
{
'direction': 'in',
'name': 'rosetteMeasTypes',
'size': {
'mechanism': 'len',
'value': 'numRosetteMeasTypes'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'numRosetteMeasTypes',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'StrainGageBridgeType1',
'name': 'strainConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'gageFactor',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalGageResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'poissonRatio',
'type': 'float64'
},
{
'direction': 'in',
'name': 'leadWireResistance',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateAIStrainGageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'StrainUnits1',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'StrainGageBridgeType1',
'name': 'strainConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'gageFactor',
'type': 'float64'
},
{
'direction': 'in',
'name': 'initialBridgeVoltage',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalGageResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'poissonRatio',
'type': 'float64'
},
{
'direction': 'in',
'name': 'leadWireResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAITempBuiltInSensorChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateAIThrmcplChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ThermocoupleType1',
'name': 'thermocoupleType',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CJCSource1',
'name': 'cjcSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'cjcVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'cjcChannel',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIThrmstrChanIex': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'a',
'type': 'float64'
},
{
'direction': 'in',
'name': 'b',
'type': 'float64'
},
{
'direction': 'in',
'name': 'c',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateAIThrmstrChanVex': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'a',
'type': 'float64'
},
{
'direction': 'in',
'name': 'b',
'type': 'float64'
},
{
'direction': 'in',
'name': 'c',
'type': 'float64'
},
{
'direction': 'in',
'name': 'r1',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateAITorqueBridgePolynomialChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TorqueUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'forwardCoeffs',
'size': {
'mechanism': 'len',
'value': 'numForwardCoeffs'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numForwardCoeffs',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'reverseCoeffs',
'size': {
'mechanism': 'len',
'value': 'numReverseCoeffs'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numReverseCoeffs',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAITorqueBridgeTableChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TorqueUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'electricalVals',
'size': {
'mechanism': 'len',
'value': 'numElectricalVals'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numElectricalVals',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'physicalVals',
'size': {
'mechanism': 'len',
'value': 'numPhysicalVals'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numPhysicalVals',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAITorqueBridgeTwoPointLinChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TorqueUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'nominalBridgeResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'firstElectricalVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'secondElectricalVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgeElectricalUnits',
'name': 'electricalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'firstPhysicalVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'secondPhysicalVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'BridgePhysicalUnits',
'name': 'physicalUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIVelocityIEPEChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'VelocityUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'sensitivity',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'VelocityIEPESensorSensitivityUnits',
'name': 'sensitivityUnits',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIVoltageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'VoltageUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIVoltageChanWithExcit': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'VoltageUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'BridgeConfiguration1',
'name': 'bridgeConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'useExcitForScaling',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAIVoltageRMSChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'VoltageUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAOCurrentChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'CurrentUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateAOFuncGenChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'FuncGenType',
'name': 'type',
'type': 'int32'
},
{
'direction': 'in',
'name': 'freq',
'type': 'float64'
},
{
'direction': 'in',
'name': 'amplitude',
'type': 'float64'
},
{
'direction': 'in',
'name': 'offset',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateAOVoltageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'VoltageUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCIAngEncoderChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'EncoderType2',
'name': 'decodingType',
'type': 'int32'
},
{
'direction': 'in',
'name': 'zidxEnable',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'zidxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'EncoderZIndexPhase1',
'name': 'zidxPhase',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'AngleUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'pulsesPerRev',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'initialAngle',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCIAngVelocityChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'EncoderType2',
'name': 'decodingType',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'AngularVelocityUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'pulsesPerRev',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCICountEdgesChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'edge',
'type': 'int32'
},
{
'direction': 'in',
'name': 'initialCount',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'CountDirection1',
'name': 'countDirection',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateCIDutyCycleChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minFreq',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxFreq',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'edge',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCIFreqChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'FrequencyUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'edge',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CounterFrequencyMethod',
'name': 'measMethod',
'type': 'int32'
},
{
'direction': 'in',
'name': 'measTime',
'type': 'float64'
},
{
'direction': 'in',
'name': 'divisor',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCIGPSTimestampChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'TimeUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'GpsSignalType1',
'name': 'syncMethod',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCILinEncoderChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'EncoderType2',
'name': 'decodingType',
'type': 'int32'
},
{
'direction': 'in',
'name': 'zidxEnable',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'zidxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'EncoderZIndexPhase1',
'name': 'zidxPhase',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'LengthUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'distPerPulse',
'type': 'float64'
},
{
'direction': 'in',
'name': 'initialPos',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCILinVelocityChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'EncoderType2',
'name': 'decodingType',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'VelocityUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'distPerPulse',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCIPeriodChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TimeUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'edge',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CounterFrequencyMethod',
'name': 'measMethod',
'type': 'int32'
},
{
'direction': 'in',
'name': 'measTime',
'type': 'float64'
},
{
'direction': 'in',
'name': 'divisor',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCIPulseChanFreq': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'FrequencyUnits2',
'name': 'units',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateCIPulseChanTicks': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'sourceTerminal',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateCIPulseChanTime': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'DigitalWidthUnits3',
'name': 'units',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateCIPulseWidthChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TimeUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'startingEdge',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCISemiPeriodChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TimeUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCITwoEdgeSepChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TimeUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'firstEdge',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Edge1',
'name': 'secondEdge',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateCOPulseChanFreq': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'FrequencyUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Level1',
'name': 'idleState',
'type': 'int32'
},
{
'direction': 'in',
'name': 'initialDelay',
'type': 'float64'
},
{
'direction': 'in',
'name': 'freq',
'type': 'float64'
},
{
'direction': 'in',
'name': 'dutyCycle',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateCOPulseChanTicks': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'sourceTerminal',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'Level1',
'name': 'idleState',
'type': 'int32'
},
{
'direction': 'in',
'name': 'initialDelay',
'type': 'int32'
},
{
'direction': 'in',
'name': 'lowTicks',
'type': 'int32'
},
{
'direction': 'in',
'name': 'highTicks',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateCOPulseChanTime': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'counter',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'DigitalWidthUnits3',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'Level1',
'name': 'idleState',
'type': 'int32'
},
{
'direction': 'in',
'name': 'initialDelay',
'type': 'float64'
},
{
'direction': 'in',
'name': 'lowTime',
'type': 'float64'
},
{
'direction': 'in',
'name': 'highTime',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateDIChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToLines',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'LineGrouping',
'name': 'lineGrouping',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateDOChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToLines',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'LineGrouping',
'name': 'lineGrouping',
'type': 'int32'
}
],
'returns': 'int32'
},
'CreateLinScale': {
'parameters': [
{
'direction': 'in',
'name': 'name',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'slope',
'type': 'float64'
},
{
'direction': 'in',
'name': 'yIntercept',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'UnitsPreScaled',
'name': 'preScaledUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'scaledUnits',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateMapScale': {
'parameters': [
{
'direction': 'in',
'name': 'name',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'prescaledMin',
'type': 'float64'
},
{
'direction': 'in',
'name': 'prescaledMax',
'type': 'float64'
},
{
'direction': 'in',
'name': 'scaledMin',
'type': 'float64'
},
{
'direction': 'in',
'name': 'scaledMax',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'UnitsPreScaled',
'name': 'preScaledUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'scaledUnits',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreatePolynomialScale': {
'parameters': [
{
'direction': 'in',
'name': 'name',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'forwardCoeffs',
'size': {
'mechanism': 'len',
'value': 'numForwardCoeffsIn'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numForwardCoeffsIn',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'reverseCoeffs',
'size': {
'mechanism': 'len',
'value': 'numReverseCoeffsIn'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numReverseCoeffsIn',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'UnitsPreScaled',
'name': 'preScaledUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'scaledUnits',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIAccelChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AccelUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIBridgeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TEDSUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAICurrentChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TEDSUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CurrentShuntResistorLocationWithDefault',
'name': 'shuntResistorLoc',
'type': 'int32'
},
{
'direction': 'in',
'name': 'extShuntResistorVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIForceBridgeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIForceIEPEChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ForceIEPEUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIMicrophoneChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'SoundPressureUnits1',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'maxSndPressLevel',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIPosLVDTChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'LengthUnits2',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'voltageExcitFreq',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ACExcitWireMode',
'name': 'acExcitWireMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIPosRVDTChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'AngleUnits1',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'voltageExcitFreq',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'ACExcitWireMode',
'name': 'acExcitWireMode',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIPressureBridgeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'PressureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIRTDChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateTEDSAIResistanceChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TEDSUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIStrainGageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'StrainUnits1',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'initialBridgeVoltage',
'type': 'float64'
},
{
'direction': 'in',
'name': 'leadWireResistance',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIThrmcplChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'CJCSource1',
'name': 'cjcSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'cjcVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'cjcChannel',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIThrmstrChanIex': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'currentExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'currentExcitVal',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateTEDSAIThrmstrChanVex': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TemperatureUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ResistanceConfiguration',
'name': 'resistanceConfig',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'r1',
'type': 'float64'
}
],
'returns': 'int32'
},
'CreateTEDSAITorqueBridgeChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TorqueUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIVoltageChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TEDSUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTEDSAIVoltageChanWithExcit': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'nameToAssignToChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'InputTermCfgWithDefault',
'name': 'terminalConfig',
'type': 'int32'
},
{
'direction': 'in',
'name': 'minVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'maxVal',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'TEDSUnits',
'name': 'units',
'type': 'int32'
},
{
'direction': 'in',
'enum': 'ExcitationSource',
'name': 'voltageExcitSource',
'type': 'int32'
},
{
'direction': 'in',
'name': 'voltageExcitVal',
'type': 'float64'
},
{
'direction': 'in',
'name': 'customScaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTableScale': {
'parameters': [
{
'direction': 'in',
'name': 'name',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'prescaledVals',
'size': {
'mechanism': 'len',
'value': 'numPrescaledValsIn'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numPrescaledValsIn',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'scaledVals',
'size': {
'mechanism': 'len',
'value': 'numScaledValsIn'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'numScaledValsIn',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'UnitsPreScaled',
'name': 'preScaledUnits',
'type': 'int32'
},
{
'direction': 'in',
'name': 'scaledUnits',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'CreateTask': {
'init_method': True,
'parameters': [
{
'direction': 'in',
'is_session_name': True,
'name': 'sessionName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'CreateWatchdogTimerTask': {
'init_method': True,
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'is_session_name': True,
'name': 'sessionName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'lines',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'DigitalLineState',
'include_in_proto': False,
'name': 'expState',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated WatchdogExpChannelsAndState',
'is_compound_type': True,
'max_length': 96,
'name': 'expStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'CreateWatchdogTimerTaskEx': {
'init_method': True,
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'is_session_name': True,
'name': 'sessionName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
}
],
'returns': 'int32'
},
'DeleteNetworkDevice': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'DeleteSavedGlobalChan': {
'parameters': [
{
'direction': 'in',
'name': 'channelName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'DeleteSavedScale': {
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'DeleteSavedTask': {
'parameters': [
{
'direction': 'in',
'name': 'taskName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'DeviceSupportsCal': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'calSupported',
'type': 'bool32'
}
],
'returns': 'int32'
},
'DisableRefTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'DisableStartTrig': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'DisconnectTerms': {
'parameters': [
{
'direction': 'in',
'name': 'sourceTerminal',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'destinationTerminal',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'ExportSignal': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'Signal',
'name': 'signalID',
'type': 'int32'
},
{
'direction': 'in',
'name': 'outputTerminal',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'GetAIChanCalCalDate': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'year',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'month',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'day',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'hour',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'minute',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetAIChanCalExpDate': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'year',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'month',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'day',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'hour',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'minute',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetAnalogPowerUpStates': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'channelName',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'out',
'include_in_proto': False,
'name': 'state',
'repeating_argument': True,
'type': 'float64'
},
{
'direction': 'in',
'enum': 'PowerUpChannelType',
'include_in_proto': False,
'name': 'channelType',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated AnalogPowerUpChannelAndType',
'is_compound_type': True,
'max_length': 96,
'name': 'channels',
'repeated_var_args': True
},
{
'direction': 'out',
'grpc_type': 'repeated double',
'max_length': 96,
'name': 'powerUpStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'GetArmStartTrigTimestampVal': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetArmStartTrigTrigWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetAutoConfiguredCDAQSyncConnections': {
'parameters': [
{
'direction': 'out',
'name': 'portList',
'size': {
'mechanism': 'ivi-dance',
'value': 'portListSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'portListSize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetBufferAttributeUInt32': {
'cname': 'DAQmxGetBufferAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'BufferAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetCalInfoAttributeBool': {
'cname': 'DAQmxGetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetCalInfoAttributeDouble': {
'cname': 'DAQmxGetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetCalInfoAttributeString': {
'cname': 'DAQmxGetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetCalInfoAttributeUInt32': {
'cname': 'DAQmxGetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetChanAttributeBool': {
'cname': 'DAQmxGetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetChanAttributeDouble': {
'cname': 'DAQmxGetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetChanAttributeDoubleArray': {
'cname': 'DAQmxGetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetChanAttributeInt32': {
'cname': 'DAQmxGetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetChanAttributeString': {
'cname': 'DAQmxGetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetChanAttributeUInt32': {
'cname': 'DAQmxGetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeBool': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeDouble': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeDoubleArray': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeInt32': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeInt32Array': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'int32[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeString': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeUInt32': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDeviceAttributeUInt32Array': {
'cname': 'DAQmxGetDeviceAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'DeviceAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetDigitalLogicFamilyPowerUpState': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'logicFamily',
'type': 'int32'
}
],
'returns': 'int32'
},
'GetDigitalPowerUpStates': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'channelName',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'out',
'enum': 'PowerUpStates',
'include_in_proto': False,
'name': 'state',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated string',
'max_length': 96,
'name': 'channelName',
'repeated_var_args': True
},
{
'direction': 'out',
'grpc_type': 'repeated PowerUpStates',
'max_length': 96,
'name': 'powerUpStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'GetDigitalPullUpPullDownStates': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'channelName',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'out',
'enum': 'ResistorState',
'include_in_proto': False,
'name': 'state',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated string',
'max_length': 96,
'name': 'channelName',
'repeated_var_args': True
},
{
'direction': 'out',
'grpc_type': 'repeated ResistorState',
'max_length': 96,
'name': 'pullUpPullDownStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'GetDisconnectedCDAQSyncPorts': {
'parameters': [
{
'direction': 'out',
'name': 'portList',
'size': {
'mechanism': 'ivi-dance',
'value': 'portListSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'portListSize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetErrorString': {
'parameters': [
{
'direction': 'in',
'name': 'errorCode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'errorString',
'size': {
'mechanism': 'ivi-dance',
'value': 'bufferSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'bufferSize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetExportedSignalAttributeBool': {
'cname': 'DAQmxGetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetExportedSignalAttributeDouble': {
'cname': 'DAQmxGetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetExportedSignalAttributeInt32': {
'cname': 'DAQmxGetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetExportedSignalAttributeString': {
'cname': 'DAQmxGetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetExportedSignalAttributeUInt32': {
'cname': 'DAQmxGetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetExtendedErrorInfo': {
'parameters': [
{
'direction': 'out',
'name': 'errorString',
'size': {
'mechanism': 'ivi-dance',
'value': 'bufferSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'bufferSize',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetFirstSampClkWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetFirstSampTimestampVal': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetNthTaskChannel': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'index',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'buffer',
'size': {
'mechanism': 'ivi-dance',
'value': 'bufferSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'bufferSize',
'type': 'int32'
}
],
'returns': 'int32'
},
'GetNthTaskDevice': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'index',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'buffer',
'size': {
'mechanism': 'ivi-dance',
'value': 'bufferSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'bufferSize',
'type': 'int32'
}
],
'returns': 'int32'
},
'GetNthTaskReadChannel': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'index',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'buffer',
'size': {
'mechanism': 'ivi-dance',
'value': 'bufferSize'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'bufferSize',
'type': 'int32'
}
],
'returns': 'int32'
},
'GetPersistedChanAttributeBool': {
'cname': 'DAQmxGetPersistedChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PersistedChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPersistedChanAttributeString': {
'cname': 'DAQmxGetPersistedChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PersistedChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPersistedScaleAttributeBool': {
'cname': 'DAQmxGetPersistedScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PersistedScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPersistedScaleAttributeString': {
'cname': 'DAQmxGetPersistedScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PersistedScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPersistedTaskAttributeBool': {
'cname': 'DAQmxGetPersistedTaskAttribute',
'parameters': [
{
'direction': 'in',
'name': 'taskName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PersistedTaskAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPersistedTaskAttributeString': {
'cname': 'DAQmxGetPersistedTaskAttribute',
'parameters': [
{
'direction': 'in',
'name': 'taskName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PersistedTaskAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeBool': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeBytes': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'uInt8[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeDouble': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeDoubleArray': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeInt32': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeInt32Array': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'int32[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeString': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeUInt32': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetPhysicalChanAttributeUInt32Array': {
'cname': 'DAQmxGetPhysicalChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'PhysicalChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetReadAttributeBool': {
'cname': 'DAQmxGetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetReadAttributeDouble': {
'cname': 'DAQmxGetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetReadAttributeInt32': {
'cname': 'DAQmxGetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetReadAttributeString': {
'cname': 'DAQmxGetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetReadAttributeUInt32': {
'cname': 'DAQmxGetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetReadAttributeUInt64': {
'cname': 'DAQmxGetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetRealTimeAttributeBool': {
'cname': 'DAQmxGetRealTimeAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetRealTimeAttributeInt32': {
'cname': 'DAQmxGetRealTimeAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetRealTimeAttributeUInt32': {
'cname': 'DAQmxGetRealTimeAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetRefTrigTimestampVal': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetScaleAttributeDouble': {
'cname': 'DAQmxGetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetScaleAttributeDoubleArray': {
'cname': 'DAQmxGetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetScaleAttributeInt32': {
'cname': 'DAQmxGetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetScaleAttributeString': {
'cname': 'DAQmxGetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetSelfCalLastDateAndTime': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'year',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'month',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'day',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'hour',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'minute',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetStartTrigTimestampVal': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetStartTrigTrigWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetSyncPulseTimeWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'GetSystemInfoAttributeString': {
'cname': 'DAQmxGetSystemInfoAttribute',
'parameters': [
{
'direction': 'in',
'grpc_type': 'SystemAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetSystemInfoAttributeUInt32': {
'cname': 'DAQmxGetSystemInfoAttribute',
'parameters': [
{
'direction': 'in',
'grpc_type': 'SystemAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTaskAttributeBool': {
'cname': 'DAQmxGetTaskAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TaskAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTaskAttributeString': {
'cname': 'DAQmxGetTaskAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TaskAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTaskAttributeUInt32': {
'cname': 'DAQmxGetTaskAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TaskAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeBool': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeDouble': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExBool': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExDouble': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExInt32': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExString': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExTimestamp': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExUInt32': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeExUInt64': {
'cname': 'DAQmxGetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeInt32': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeString': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeTimestamp': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeUInt32': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTimingAttributeUInt64': {
'cname': 'DAQmxGetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeBool': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeDouble': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeDoubleArray': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeInt32': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeInt32Array': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'int32[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeString': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeTimestamp': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetTrigAttributeUInt32': {
'cname': 'DAQmxGetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWatchdogAttributeBool': {
'cname': 'DAQmxGetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWatchdogAttributeDouble': {
'cname': 'DAQmxGetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWatchdogAttributeInt32': {
'cname': 'DAQmxGetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWatchdogAttributeString': {
'cname': 'DAQmxGetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWriteAttributeBool': {
'cname': 'DAQmxGetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWriteAttributeDouble': {
'cname': 'DAQmxGetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWriteAttributeInt32': {
'cname': 'DAQmxGetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWriteAttributeString': {
'cname': 'DAQmxGetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'size': {
'mechanism': 'ivi-dance',
'value': 'size'
},
'type': 'char[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWriteAttributeUInt32': {
'cname': 'DAQmxGetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'GetWriteAttributeUInt64': {
'cname': 'DAQmxGetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'IsTaskDone': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'isTaskDone',
'type': 'bool32'
}
],
'returns': 'int32'
},
'LoadTask': {
'init_method': True,
'parameters': [
{
'direction': 'in',
'is_session_name': True,
'name': 'sessionName',
'type': 'const char[]'
},
{
'direction': 'out',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'ReadAnalogF64': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadAnalogScalarF64': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadBinaryI16': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'coerced': True,
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'int16[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadBinaryI32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'int32[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadBinaryU16': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'coerced': True,
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt16[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadBinaryU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCounterF64': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCounterF64Ex': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCounterScalarF64': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCounterScalarU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCounterU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCounterU32Ex': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCtrFreq': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'interleaved',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArrayFrequency',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'out',
'name': 'readArrayDutyCycle',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCtrFreqScalar': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'frequency',
'type': 'float64'
},
{
'direction': 'out',
'name': 'dutyCycle',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCtrTicks': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'interleaved',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArrayHighTicks',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt32[]'
},
{
'direction': 'out',
'name': 'readArrayLowTicks',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCtrTicksScalar': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'highTicks',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'lowTicks',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCtrTime': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'interleaved',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArrayHighTime',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'out',
'name': 'readArrayLowTime',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'float64[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadCtrTimeScalar': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'highTime',
'type': 'float64'
},
{
'direction': 'out',
'name': 'lowTime',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadDigitalLines': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInBytes'
},
'type': 'uInt8[]'
},
{
'direction': 'in',
'name': 'arraySizeInBytes',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'out',
'name': 'numBytesPerSamp',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadDigitalScalarU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadDigitalU16': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'coerced': True,
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt16[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadDigitalU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt32[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadDigitalU8': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'fillMode',
'type': 'int32'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInSamps'
},
'type': 'uInt8[]'
},
{
'direction': 'in',
'name': 'arraySizeInSamps',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsPerChanRead',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'ReadRaw': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'readArray',
'size': {
'mechanism': 'passed-in',
'value': 'arraySizeInBytes'
},
'type': 'uInt8[]'
},
{
'direction': 'in',
'name': 'arraySizeInBytes',
'type': 'uInt32'
},
{
'direction': 'out',
'name': 'sampsRead',
'type': 'int32'
},
{
'direction': 'out',
'name': 'numBytesPerSamp',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'RegisterDoneEvent': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'options',
'type': 'uInt32'
},
{
'callback_params': [
{
'direction': 'out',
'include_in_proto': False,
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'status',
'type': 'int32'
}
],
'direction': 'in',
'include_in_proto': False,
'name': 'callbackFunction',
'type': 'DAQmxDoneEventCallbackPtr'
},
{
'callback_token': True,
'direction': 'in',
'include_in_proto': False,
'name': 'callbackData',
'pointer': True,
'type': 'void'
}
],
'returns': 'int32',
'stream_response': True
},
'RegisterEveryNSamplesEvent': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'EveryNSamplesEventType',
'name': 'everyNSamplesEventType',
'type': 'int32'
},
{
'direction': 'in',
'name': 'nSamples',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'options',
'type': 'uInt32'
},
{
'callback_params': [
{
'direction': 'out',
'include_in_proto': False,
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'enum': 'EveryNSamplesEventType',
'name': 'everyNSamplesEventType',
'type': 'int32'
},
{
'direction': 'out',
'name': 'nSamples',
'type': 'uInt32'
}
],
'direction': 'in',
'include_in_proto': False,
'name': 'callbackFunction',
'type': 'DAQmxEveryNSamplesEventCallbackPtr'
},
{
'callback_token': True,
'direction': 'in',
'include_in_proto': False,
'name': 'callbackData',
'pointer': True,
'type': 'void'
}
],
'returns': 'int32',
'stream_response': True
},
'RegisterSignalEvent': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'Signal2',
'name': 'signalID',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'options',
'type': 'uInt32'
},
{
'callback_params': [
{
'direction': 'out',
'include_in_proto': False,
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'out',
'name': 'signalID',
'type': 'int32'
}
],
'direction': 'in',
'include_in_proto': False,
'name': 'callbackFunction',
'type': 'DAQmxSignalEventCallbackPtr'
},
{
'callback_token': True,
'direction': 'in',
'include_in_proto': False,
'name': 'callbackData',
'pointer': True,
'type': 'void'
}
],
'returns': 'int32',
'stream_response': True
},
'RemoveCDAQSyncConnection': {
'parameters': [
{
'direction': 'in',
'name': 'portList',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'ReserveNetworkDevice': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'overrideReservation',
'type': 'bool32'
}
],
'returns': 'int32'
},
'ResetBufferAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'BufferAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetChanAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetDevice': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'ResetExportedSignalAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetReadAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetRealTimeAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetTimingAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetTimingAttributeEx': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetTrigAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetWatchdogAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'ResetWriteAttribute': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
}
],
'returns': 'int32'
},
'SaveGlobalChan': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelName',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'saveAs',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'author',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'SaveOptions',
'name': 'options',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SaveScale': {
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'saveAs',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'author',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'SaveOptions',
'name': 'options',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SaveTask': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'saveAs',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'author',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'SaveOptions',
'name': 'options',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SelfCal': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'SelfTestDevice': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'SetAIChanCalCalDate': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelName',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'year',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'month',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'day',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'hour',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'minute',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetAIChanCalExpDate': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channelName',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'year',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'month',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'day',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'hour',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'minute',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetAnalogPowerUpStates': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'channelNames',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'state',
'repeating_argument': True,
'type': 'float64'
},
{
'direction': 'in',
'enum': 'PowerUpChannelType',
'include_in_proto': False,
'name': 'channelType',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated AnalogPowerUpChannelsAndState',
'is_compound_type': True,
'max_length': 96,
'name': 'powerUpStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'SetArmStartTrigTrigWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'SetBufferAttributeUInt32': {
'cname': 'DAQmxSetBufferAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'BufferAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetCalInfoAttributeBool': {
'cname': 'DAQmxSetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetCalInfoAttributeDouble': {
'cname': 'DAQmxSetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetCalInfoAttributeString': {
'cname': 'DAQmxSetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetCalInfoAttributeUInt32': {
'cname': 'DAQmxSetCalInfoAttribute',
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'CalibrationInfoAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetChanAttributeBool': {
'cname': 'DAQmxSetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetChanAttributeDouble': {
'cname': 'DAQmxSetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetChanAttributeDoubleArray': {
'cname': 'DAQmxSetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'size': {
'mechanism': 'len',
'value': 'size'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetChanAttributeInt32': {
'cname': 'DAQmxSetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetChanAttributeString': {
'cname': 'DAQmxSetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetChanAttributeUInt32': {
'cname': 'DAQmxSetChanAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'channel',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ChannelAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetDigitalLogicFamilyPowerUpState': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'LogicFamily',
'name': 'logicFamily',
'type': 'int32'
}
],
'returns': 'int32'
},
'SetDigitalPowerUpStates': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'channelNames',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'PowerUpStates',
'include_in_proto': False,
'name': 'state',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated DigitalPowerUpChannelsAndState',
'is_compound_type': True,
'max_length': 96,
'name': 'powerUpStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'SetDigitalPullUpPullDownStates': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
},
{
'direction': 'in',
'include_in_proto': False,
'name': 'channelNames',
'repeating_argument': True,
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'ResistorState',
'include_in_proto': False,
'name': 'state',
'repeating_argument': True,
'type': 'int32'
},
{
'direction': 'in',
'grpc_type': 'repeated DigitalPullUpPullDownChannelsAndState',
'is_compound_type': True,
'max_length': 96,
'name': 'pullUpPullDownStates',
'repeated_var_args': True
}
],
'returns': 'int32'
},
'SetExportedSignalAttributeBool': {
'cname': 'DAQmxSetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetExportedSignalAttributeDouble': {
'cname': 'DAQmxSetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetExportedSignalAttributeInt32': {
'cname': 'DAQmxSetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetExportedSignalAttributeString': {
'cname': 'DAQmxSetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetExportedSignalAttributeUInt32': {
'cname': 'DAQmxSetExportedSignalAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ExportSignalAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetFirstSampClkWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'SetReadAttributeBool': {
'cname': 'DAQmxSetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetReadAttributeDouble': {
'cname': 'DAQmxSetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetReadAttributeInt32': {
'cname': 'DAQmxSetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetReadAttributeString': {
'cname': 'DAQmxSetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetReadAttributeUInt32': {
'cname': 'DAQmxSetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetReadAttributeUInt64': {
'cname': 'DAQmxSetReadAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'ReadAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetRealTimeAttributeBool': {
'cname': 'DAQmxSetRealTimeAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetRealTimeAttributeInt32': {
'cname': 'DAQmxSetRealTimeAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetRealTimeAttributeUInt32': {
'cname': 'DAQmxSetRealTimeAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'RealTimeAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetScaleAttributeDouble': {
'cname': 'DAQmxSetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetScaleAttributeDoubleArray': {
'cname': 'DAQmxSetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'size': {
'mechanism': 'len',
'value': 'size'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetScaleAttributeInt32': {
'cname': 'DAQmxSetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetScaleAttributeString': {
'cname': 'DAQmxSetScaleAttribute',
'parameters': [
{
'direction': 'in',
'name': 'scaleName',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'ScaleAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetStartTrigTrigWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'SetSyncPulseTimeWhen': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'data',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'SetTimingAttributeBool': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeDouble': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExBool': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExDouble': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExInt32': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExString': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExTimestamp': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExUInt32': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeExUInt64': {
'cname': 'DAQmxSetTimingAttributeEx',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'deviceNames',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeInt32': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeString': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeTimestamp': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeUInt32': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTimingAttributeUInt64': {
'cname': 'DAQmxSetTimingAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TimingAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeBool': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeDouble': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeDoubleArray': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'size': {
'mechanism': 'len',
'value': 'size'
},
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeInt32': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeInt32Array': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'size': {
'mechanism': 'len',
'value': 'size'
},
'type': 'const int32[]'
},
{
'direction': 'in',
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeString': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeTimestamp': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'CVIAbsoluteTime'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetTrigAttributeUInt32': {
'cname': 'DAQmxSetTrigAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'TriggerAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWatchdogAttributeBool': {
'cname': 'DAQmxSetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWatchdogAttributeDouble': {
'cname': 'DAQmxSetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWatchdogAttributeInt32': {
'cname': 'DAQmxSetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWatchdogAttributeString': {
'cname': 'DAQmxSetWatchdogAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'lines',
'type': 'const char[]'
},
{
'direction': 'in',
'grpc_type': 'WatchdogAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWriteAttributeBool': {
'cname': 'DAQmxSetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'bool32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWriteAttributeDouble': {
'cname': 'DAQmxSetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWriteAttributeInt32': {
'cname': 'DAQmxSetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWriteAttributeString': {
'cname': 'DAQmxSetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'const char[]'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWriteAttributeUInt32': {
'cname': 'DAQmxSetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'SetWriteAttributeUInt64': {
'cname': 'DAQmxSetWriteAttribute',
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'grpc_type': 'WriteAttributes',
'name': 'attribute',
'type': 'int32'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt64'
},
{
'direction': 'in',
'hardcoded_value': '0U',
'include_in_proto': False,
'name': 'size',
'type': 'uInt32'
}
],
'returns': 'int32'
},
'StartNewFile': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'filePath',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'StartTask': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'StopTask': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
}
],
'returns': 'int32'
},
'TaskControl': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'TaskControlAction',
'name': 'action',
'type': 'int32'
}
],
'returns': 'int32'
},
'TristateOutputTerm': {
'parameters': [
{
'direction': 'in',
'name': 'outputTerminal',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'UnreserveNetworkDevice': {
'parameters': [
{
'direction': 'in',
'name': 'deviceName',
'type': 'const char[]'
}
],
'returns': 'int32'
},
'WaitForNextSampleClock': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'isLate',
'type': 'bool32'
}
],
'returns': 'int32'
},
'WaitForValidTimestamp': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'enum': 'TimestampEvent',
'name': 'timestampEvent',
'type': 'int32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'out',
'name': 'timestamp',
'type': 'CVIAbsoluteTime'
}
],
'returns': 'int32'
},
'WaitUntilTaskDone': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'timeToWait',
'type': 'float64'
}
],
'returns': 'int32'
},
'WriteAnalogF64': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const float64[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteAnalogScalarF64': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'name': 'value',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteBinaryI16': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'coerced': True,
'direction': 'in',
'name': 'writeArray',
'type': 'const int16[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteBinaryI32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const int32[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteBinaryU16': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'coerced': True,
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt16[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteBinaryU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt32[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteCtrFreq': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'frequency',
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'dutyCycle',
'type': 'const float64[]'
},
{
'direction': 'out',
'name': 'numSampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteCtrFreqScalar': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'name': 'frequency',
'type': 'float64'
},
{
'direction': 'in',
'name': 'dutyCycle',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteCtrTicks': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'highTicks',
'type': 'const uInt32[]'
},
{
'direction': 'in',
'name': 'lowTicks',
'type': 'const uInt32[]'
},
{
'direction': 'out',
'name': 'numSampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteCtrTicksScalar': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'name': 'highTicks',
'type': 'uInt32'
},
{
'direction': 'in',
'name': 'lowTicks',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteCtrTime': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'highTime',
'type': 'const float64[]'
},
{
'direction': 'in',
'name': 'lowTime',
'type': 'const float64[]'
},
{
'direction': 'out',
'name': 'numSampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteCtrTimeScalar': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'name': 'highTime',
'type': 'float64'
},
{
'direction': 'in',
'name': 'lowTime',
'type': 'float64'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteDigitalLines': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt8[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteDigitalScalarU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'name': 'value',
'type': 'uInt32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteDigitalU16': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'coerced': True,
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt16[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteDigitalU32': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt32[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteDigitalU8': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSampsPerChan',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'enum': 'GroupBy',
'name': 'dataLayout',
'type': 'int32'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt8[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteRaw': {
'parameters': [
{
'direction': 'in',
'name': 'task',
'type': 'TaskHandle'
},
{
'direction': 'in',
'name': 'numSamps',
'type': 'int32'
},
{
'direction': 'in',
'name': 'autoStart',
'type': 'bool32'
},
{
'direction': 'in',
'name': 'timeout',
'type': 'float64'
},
{
'direction': 'in',
'name': 'writeArray',
'type': 'const uInt8[]'
},
{
'direction': 'out',
'name': 'sampsPerChanWritten',
'type': 'int32'
},
{
'direction': 'in',
'hardcoded_value': 'nullptr',
'include_in_proto': False,
'name': 'reserved',
'pointer': True,
'type': 'bool32'
}
],
'returns': 'int32'
},
'WriteToTEDSFromArray': {
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'bitStream',
'size': {
'mechanism': 'len',
'value': 'arraySize'
},
'type': 'const uInt8[]'
},
{
'direction': 'in',
'name': 'arraySize',
'type': 'uInt32'
},
{
'direction': 'in',
'enum': 'WriteBasicTEDSOptions',
'name': 'basicTEDSOptions',
'type': 'int32'
}
],
'returns': 'int32'
},
'WriteToTEDSFromFile': {
'parameters': [
{
'direction': 'in',
'name': 'physicalChannel',
'type': 'const char[]'
},
{
'direction': 'in',
'name': 'filePath',
'type': 'const char[]'
},
{
'direction': 'in',
'enum': 'WriteBasicTEDSOptions',
'name': 'basicTEDSOptions',
'type': 'int32'
}
],
'returns': 'int32'
}
}
| [
12543,
2733,
796,
1391,
198,
220,
220,
220,
705,
4550,
34,
46640,
28985,
32048,
10354,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
705,
17143,
7307,
10354,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
198... | 1.501789 | 255,145 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
x = abs(100)
y = abs(-20)
print(x, y)
print()
# 以下函数可以接收任意多个参数
print('max(1, 2, 3) =', max(1, 2, 3))
print('min(1, 2, 3) =', min(1, 2, 3))
print('sum([1, 2, 3]) =', sum([1, 2, 3]))
print()
# 数据类型转换
print(int('123'))
print(int(12.34))
print(float('12.34'))
print(str(1.23))
print(str(100))
print(bool(1))
print(bool(0))
print(bool('he'))
print(bool(''))
print(bool(None))
# 函数名其实就是指向一个函数对象的引用，完全可以把函数名赋给一个变量，相当于给这个函数起了一个“别名”
a = abs # 变量a指向abs函数
print(a(-1)) # 所以也可以通过a调用abs函数
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
87,
796,
2352,
7,
3064,
8,
198,
88,
796,
2352,
32590,
1238,
8,
198,
4798,
7,
87,
11,
331,
8,
198,
198,
... | 1.273381 | 417 |
### Comparison between drivers
##
import time
import board
import busio
### Adafruit MPU6050 (similar chip)
import adafruit_mpu6050
### RM Forked Driver
from robohat_mpu9250.mpu9250 import MPU9250 as RM9250
from robohat_mpu9250.mpu6500 import MPU6500 as RM6500
from robohat_mpu9250.ak8963 import AK8963 as RM8963
### RM CircuitPython Driver
import roboticsmasters_mpu6500
import roboticsmasters_mpu9250
### i2c
i2c = busio.I2C(board.SCL, board.SDA)
### adafruit driver
mpu = adafruit_mpu6050.MPU6050(i2c, address=0x69)
##
### RM Forked
#rm_mpu = RM6500(i2c, address=0x69)
#rm_ak = RM8963(i2c)
#sensor = RM9250(rm_mpu, rm_ak)
### New Driver
#nmpu = roboticsmasters_mpu6500.MPU6500(i2c, address=0x69)
npmu = roboticsmasters_mpu9250.MPU9250(i2c)
time.sleep(1)
##while True:
## print("=============")
## print("Acceleration:")
## print("X:%.2f, Y: %.2f, Z: %.2f m/s^2"%(mpu.acceleration))
## print("X:{0:0.2f}, Y: {1:0.2f}, Z: {2:0.2f} m/s^2".format(*sensor.read_acceleration()))
## print("X:%.2f, Y: %.2f, Z: %.2f m/s^2"%(nmpu.acceleration))
## print("Gyro:")
## print("X:%.2f, Y: %.2f, Z: %.2f degrees/s"%(mpu.gyro))
## print("X:{0:0.2f}, Y: {1:0.2f}, Z: {2:0.2f} degrees/s".format(*sensor.read_gyro()))
## print("X:%.2f, Y: %.2f, Z: %.2f degrees/s"%(nmpu.gyro))
## print("Temperature:")
## print("%.2f C"%mpu.temperature)
## print("%.2f C"%sensor.read_temperature())
## print("%.2f C"%nmpu.temperature)
## print("")
## time.sleep(2)
while not i2c.try_lock():
pass
while True:
print("I2C addresses found:", [hex(device_address)
for device_address in i2c.scan()])
time.sleep(2)
| [
21017,
220,
34420,
1022,
6643,
198,
2235,
198,
11748,
640,
198,
11748,
3096,
198,
11748,
1323,
952,
198,
198,
21017,
1215,
1878,
4872,
4904,
52,
1899,
1120,
357,
38610,
11594,
8,
198,
11748,
512,
1878,
4872,
62,
3149,
84,
1899,
1120,
... | 2.037576 | 825 |
__all__ = ['create_build_dir']
from pathlib import Path
from typing import Optional
from error import *
from file_structure import *
from ..find_build_dir import *
| [
834,
439,
834,
796,
37250,
17953,
62,
11249,
62,
15908,
20520,
198,
198,
6738,
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198,
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1330,
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198,
198,
6738,
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1330,
1635,
198,
6738,
2393,
62,
301,
5620,
1330,
1635,
198,
6738,
11485,
19796... | 3.479167 | 48 |
import os
from util.YamlConfig import YamlConfig
if __name__ == "__main__":
yaml_test()
| [
11748,
28686,
198,
198,
6738,
7736,
13,
56,
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1330,
14063,
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16934,
628,
198,
198,
361,
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3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
331,
43695,
62,
9288,
3419,
198
] | 2.594595 | 37 |
from pathlib import Path
from fhir.resources.valueset import ValueSet as _ValueSet
from oops_fhir.utils import ValueSet
from oops_fhir.r4.code_system.v3_act_code import v3ActCode
__all__ = ["v3SecurityPolicy"]
_resource = _ValueSet.parse_file(Path(__file__).with_suffix(".json"))
class v3SecurityPolicy(v3ActCode):
"""
V3 Value SetSecurityPolicy
Types of security policies that further specify the ActClassPolicy
value set. Examples: encrypt prohibit redisclosure without consent
directive
Status: active - Version: 2014-03-26
http://terminology.hl7.org/ValueSet/v3-SecurityPolicy
"""
| [
6738,
3108,
8019,
1330,
10644,
198,
198,
6738,
277,
71,
343,
13,
37540,
13,
27160,
316,
1330,
11052,
7248,
355,
4808,
11395,
7248,
198,
198,
6738,
267,
2840,
62,
69,
71,
343,
13,
26791,
1330,
11052,
7248,
628,
198,
6738,
267,
2840,
... | 2.966825 | 211 |
from __future__ import absolute_import
from sentry import http
from sentry.plugins.bases.data_forwarding import DataForwardingPlugin
from test_only_plugins.base import CorePluginMixin
from test_only_plugins.utils import get_secret_field_config
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
198,
198,
6738,
1908,
563,
1330,
2638,
198,
6738,
1908,
563,
13,
37390,
13,
65,
1386,
13,
7890,
62,
11813,
278,
1330,
6060,
39746,
278,
37233,
198,
198,
6738,
1332,
62,
8807,
62,
37390,
... | 3.686567 | 67 |
import pytest
import os
from contextualized_topic_models.models.ctm import ZeroShotTM
from contextualized_topic_models.evaluation.measures import CoherenceNPMI, CoherenceCV, InvertedRBO, TopicDiversity
from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
@pytest.fixture
@pytest.fixture
@pytest.fixture
| [
11748,
12972,
9288,
198,
11748,
28686,
198,
198,
6738,
38356,
1143,
62,
26652,
62,
27530,
13,
27530,
13,
310,
76,
1330,
12169,
28512,
15972,
198,
6738,
38356,
1143,
62,
26652,
62,
27530,
13,
18206,
2288,
13,
47336,
1330,
1766,
23545,
45... | 3.317308 | 104 |
# !/usr/bin/env python3
# -*-coding:utf-8-*-
# @file: setup.py
# @brief:
# @author: Changjiang Cai, ccai1@stevens.edu, caicj5351@gmail.com
# @version: 0.0.1
# @creation date: 28-10-2019
# @last modified: Tue 29 Oct 2019 02:00:37 PM EDT
from distutils.core import setup
from Cython.Build import cythonize
from distutils.extension import Extension
ext_modules = [Extension('writeKT15FalseColor', ['writeKT15FalseColor.pyx'])]
setup(
ext_modules = cythonize(ext_modules)
)
| [
2,
5145,
14,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
66,
7656,
25,
40477,
12,
23,
12,
9,
12,
198,
2,
2488,
7753,
25,
9058,
13,
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198,
2,
2488,
65,
3796,
25,
198,
2,
2488,
9800,
25,
22597,
39598,
327,
1... | 2.512953 | 193 |
#--------------------------------
# demo of convolution of 2D image - laplacian kernel
#--------------------------------
import numpy as np
import cv2
if __name__ == '__main__':
UseWebCam()
| [
201,
198,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198,
2,
3880,
201,
198,
2,
13605,
286,
3063,
2122,
286,
362,
35,
2939,
532,
8591,
489,
330,
666,
9720,
220,
201,
198,
2,
3880,
201,
198,
201,
198,
201,
198,
11748,
299,
3215... | 2.290909 | 110 |
"""
N Queens
==========
Place N queens on a NxN chessboard such that no queens come under attack.
Give all possible placements of N queens.
SOLUTION
- Backtracking to find all possible placements
- Grow the state space tree by placing a queen on one row at a time
- Bounding function: no two queens share the same row, column and diagonal.
Time O(N!)
The worst case “brute force” solution for the N-queens puzzle has an O(n^n) time complexity.
This means it will look through every position on an NxN board, N times, for N queens.
It is by far the slowest and most impractical method.
If you refactor and prevent it from checking queens occupying the same row as each other,
it will still be brute force,
but the possible board states drop from 16,777,216 to a little over 40,000 and has a time complexity of O(n!).
"""
from typing import NamedTuple, List, Tuple, Set
| [
37811,
201,
198,
45,
14045,
201,
198,
2559,
855,
201,
198,
27271,
399,
38771,
319,
257,
399,
87,
45,
19780,
3526,
884,
326,
645,
38771,
1282,
739,
1368,
13,
201,
198,
23318,
477,
1744,
21957,
3196,
286,
399,
38771,
13,
201,
198,
201... | 3.440613 | 261 |
# in not in 重载
# contains
l1 = MyList([1, 2, 3])
print(1 in l1) # True
print(1 not in l1) # False
print(4 in l1) # False
| [
2,
287,
407,
287,
16268,
229,
235,
164,
121,
121,
198,
2,
4909,
628,
198,
198,
75,
16,
796,
2011,
8053,
26933,
16,
11,
362,
11,
513,
12962,
198,
4798,
7,
16,
287,
300,
16,
8,
220,
1303,
6407,
198,
4798,
7,
16,
407,
287,
300,
... | 2.015873 | 63 |
use_list_comprehension()
print(use_zip_for_list_processing()) | [
198,
1904,
62,
4868,
62,
785,
3866,
5135,
295,
3419,
198,
4798,
7,
1904,
62,
13344,
62,
1640,
62,
4868,
62,
36948,
28955
] | 2.695652 | 23 |
import json
import uuid
import factory
from django.db import connection
from django.test import TestCase
from django.test import override_settings
from django.utils import timezone
from facility_profile.models import Facility
import mock
import pytest
from ..helpers import create_buffer_and_store_dummy_data
from ..helpers import create_dummy_store_data
from morango.constants import transfer_statuses
from morango.errors import MorangoLimitExceeded
from morango.models.core import Buffer
from morango.models.core import DatabaseIDModel
from morango.models.core import InstanceIDModel
from morango.models.core import RecordMaxCounter
from morango.models.core import RecordMaxCounterBuffer
from morango.models.core import Store
from morango.models.core import SyncSession
from morango.models.core import TransferSession
from morango.sync.backends.utils import load_backend
from morango.sync.context import LocalSessionContext
from morango.sync.controller import MorangoProfileController
from morango.sync.controller import SessionController
from morango.sync.operations import _dequeue_into_store
from morango.sync.operations import _queue_into_buffer
from morango.sync.operations import CleanupOperation
from morango.sync.operations import ReceiverDequeueOperation
from morango.sync.operations import ProducerDequeueOperation
from morango.sync.operations import ReceiverDeserializeOperation
from morango.sync.operations import InitializeOperation
from morango.sync.operations import ProducerQueueOperation
from morango.sync.operations import ReceiverQueueOperation
from morango.sync.syncsession import TransferClient
DBBackend = load_backend(connection).SQLWrapper()
@override_settings(MORANGO_SERIALIZE_BEFORE_QUEUING=False)
@override_settings(MORANGO_DESERIALIZE_AFTER_DEQUEUING=False)
| [
11748,
33918,
198,
11748,
334,
27112,
198,
198,
11748,
8860,
198,
6738,
42625,
14208,
13,
9945,
1330,
4637,
198,
6738,
42625,
14208,
13,
9288,
1330,
6208,
20448,
198,
6738,
42625,
14208,
13,
9288,
1330,
20957,
62,
33692,
198,
6738,
42625,... | 3.74375 | 480 |
'''
Koko loves to eat bananas. There are n piles of bananas, the ith pile has piles[i] bananas. The guards have gone and will come back in h hours.
Koko can decide her bananas-per-hour eating speed of k. Each hour, she chooses some pile of bananas and eats k bananas from that pile.
If the pile has less than k bananas, she eats all of them instead and will not eat any more bananas during this hour.
Koko likes to eat slowly but still wants to finish eating all the bananas before the guards return.
Return the minimum integer k such that she can eat all the bananas within h hours.
Example 1:
Input: piles = [3,6,7,11], h = 8
Output: 4
Example 2:
Input: piles = [30,11,23,4,20], h = 5
Output: 30
Example 3:
Input: piles = [30,11,23,4,20], h = 6
Output: 23
'''
# low, high = 1, max(piles)
# mid = low + high / 2 that is 6 in given example
# 1 + 1 + 2 + 2 = 6 hours to finish but we need to optimise
# low = 1, high = 6 + 1 =
# mid = 4 now
#
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160... | 3.176667 | 300 |
#!/usr/bin/env python
#coding=utf-8
import sys
import db_opr
################################# main program##################################
if __name__ == '__main__':
print 'start initdb...'
db_init = DBInit()
db_init.run()
del db_init
print 'end initdb...'
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... | 3.045455 | 88 |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytext.config.field_config import CharFeatConfig
from pytext.data.utils import Vocabulary
from pytext.fields import FieldMeta
from pytext.utils.usage import log_class_usage
from .embedding_base import EmbeddingBase
class CharacterEmbedding(EmbeddingBase):
"""
Module for character aware CNN embeddings for tokens. It uses convolution
followed by max-pooling over character embeddings to obtain an embedding
vector for each token.
Implementation is loosely based on https://arxiv.org/abs/1508.06615.
Args:
num_embeddings (int): Total number of characters (vocabulary size).
embed_dim (int): Size of character embeddings to be passed to convolutions.
out_channels (int): Number of output channels.
kernel_sizes (List[int]): Dimension of input Tensor passed to MLP.
highway_layers (int): Number of highway layers applied to pooled output.
projection_dim (int): If specified, size of output embedding for token, via
a linear projection from convolution output.
Attributes:
char_embed (nn.Embedding): Character embedding table.
convs (nn.ModuleList): Convolution layers that operate on character
embeddings.
highway_layers (nn.Module): Highway layers on top of convolution output.
projection (nn.Module): Final linear layer to token embedding.
embedding_dim (int): Dimension of the final token embedding produced.
"""
Config = CharFeatConfig
@classmethod
def from_config(
cls,
config: CharFeatConfig,
metadata: Optional[FieldMeta] = None,
vocab_size: Optional[int] = None,
):
"""Factory method to construct an instance of CharacterEmbedding from
the module's config object and the field's metadata object.
Args:
config (CharFeatConfig): Configuration object specifying all the
parameters of CharacterEmbedding.
metadata (FieldMeta): Object containing this field's metadata.
Returns:
type: An instance of CharacterEmbedding.
"""
if vocab_size is None:
vocab_size = metadata.vocab_size
return cls(
vocab_size,
config.embed_dim,
config.cnn.kernel_num,
config.cnn.kernel_sizes,
config.highway_layers,
config.projection_dim,
)
def forward(self, chars: torch.Tensor) -> torch.Tensor:
"""
Given a batch of sentences such that tokens are broken into character ids,
produce token embedding vectors for each sentence in the batch.
Args:
chars (torch.Tensor): Batch of sentences where each token is broken
into characters.
Dimension: batch size X maximum sentence length X maximum word length
Returns:
torch.Tensor: Embedded batch of sentences. Dimension:
batch size X maximum sentence length, token embedding size.
Token embedding size = `out_channels * len(self.convs))`
"""
batch_size = chars.size(0)
max_sent_length = chars.size(1)
max_word_length = chars.size(2)
chars = chars.view(batch_size * max_sent_length, max_word_length)
# char_embedding: (bsize * max_sent_length, max_word_length, embed_dim)
char_embedding = self.char_embed(chars)
# conv_inp dim: (bsize * max_sent_length, emb_size, max_word_length)
conv_inp = char_embedding.transpose(1, 2)
char_conv_outs = [F.relu(conv(conv_inp)) for conv in self.convs]
# Apply max pooling
# char_pool_out[i] dims: (bsize * max_sent_length, out_channels)
char_pool_outs = [torch.max(out, dim=2)[0] for out in char_conv_outs]
# Concat different feature maps together
# char_pool_out dim: (bsize * max_sent_length, out_channel * num_kernels)
char_out = torch.cat(char_pool_outs, 1)
# Highway layers, preserves dims
if self.highway is not None:
char_out = self.highway(char_out)
if self.projection is not None:
# Linear map back to final embedding size:
# (bsize * max_sent_length, projection_dim)
char_out = self.projection(char_out)
# Reshape to (bsize, max_sent_length, "output_dim")
return char_out.view(batch_size, max_sent_length, -1)
class Highway(nn.Module):
"""
A `Highway layer <https://arxiv.org/abs/1505.00387>`.
Adopted from the AllenNLP implementation.
"""
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204... | 2.540757 | 1,877 |
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
from time import sleep
from .xpath import xpath
from .browser import explicit_wait_visibility_of_element_located
MODE_INSTAGRAM = "mode=instagram"
def go_to_instagram_Tab(browser):
"""Go to instragram mode"""
if MODE_INSTAGRAM in browser.current_url:
print("Already in Instragram Mode")
return
try:
instagram_button = browser.find_element_by_xpath(xpath[go_to_instagram_Tab.__name__]["instagram_button"])
except:
print("Unable to find Instagram Button")
ActionChains(browser).move_to_element(instagram_button).click().perform()
if MODE_INSTAGRAM in browser.current_url:
print("Switched to Instagram Mode")
return
def create_post(browser, account, message, file, schedule_options=None):
"""Create a post."""
explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["create_post_button"])
create_post_button = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["create_post_button"])
ActionChains(browser).move_to_element(create_post_button).click().perform()
sleep(2)
explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["instagram_feed_button"])
instagram_feed_button = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["instagram_feed_button"])
ActionChains(browser).move_to_element(instagram_feed_button).click().perform()
sleep(1)
try:
explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["input_account"])
input_account = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["input_account"])
for character in account:
ActionChains(browser).move_to_element(input_account).click().send_keys(character).perform()
sleep(1)
list_account = explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["list_account"].format(account), timeout=1)
if list_account is not None:
ActionChains(browser).move_to_element(list_account).click().perform()
sleep(1)
break
sleep(2)
except:
pass
# First we load the content
explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["add_content_button"])
add_content_button = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["add_content_button"])
browser.execute_script("arguments[0].scrollIntoView();", add_content_button)
ActionChains(browser).move_to_element(add_content_button).click().perform()
sleep(2)
input_file = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["input_file"])
input_file.send_keys(file)
sleep(1)
input_message = explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["input_message"])
browser.execute_script("arguments[0].scrollIntoView();", input_message)
ActionChains(browser).move_to_element(input_message).click().send_keys(message).perform()
sleep(1)
if schedule_options is not None:
schedule_options_hour, schedule_options_minutes, schedule_options_am_pm, = schedule_options["time"].replace(":", " ").split()
options_publish_button = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["options_publish_button"])
ActionChains(browser).move_to_element(options_publish_button).click().perform()
sleep(1)
schedule_option = explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["schedule_option"])
ActionChains(browser).move_to_element(schedule_option).click().perform()
sleep(1)
input_date = browser.find_element_by_xpath(xpath["instagram"][create_post.__name__]["input_date"])
ActionChains(browser).move_to_element(input_date).click().send_keys(schedule_options["date"]).perform()
sleep(1)
hour, minutes, am_pm = browser.find_elements_by_xpath(xpath["instagram"][create_post.__name__]["input_time"])
hour.send_keys(schedule_options_hour)
sleep(1)
minutes.send_keys(schedule_options_minutes)
sleep(1)
am_pm.send_keys(schedule_options_am_pm)
sleep(1)
print("Watting for load finish.")
load_finished = explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["load_complete"])
if load_finished is None:
print("Load not finish. Aborting...")
return
send_post_button = explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["send_post_button"])
browser.execute_script("arguments[0].scrollIntoView();", send_post_button)
ActionChains(browser).move_to_element(send_post_button).click().perform()
sleep(1)
success_message = explicit_wait_visibility_of_element_located(browser, xpath["instagram"][create_post.__name__]["success_message"])
if success_message is None:
print("Error Creating Post")
return False
print("Post Created.")
return True
| [
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198,
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640,
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198,
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6738,
764,
87,
6... | 2.585873 | 2,067 |
# Copyright (C) 2019-2021 Ruhr West University of Applied Sciences, Bottrop, Germany
# AND Elektronische Fahrwerksysteme GmbH, Gaimersheim Germany
#
# This Source Code Form is subject to the terms of the Apache License 2.0
# If a copy of the APL2 was not distributed with this
# file, You can obtain one at https://www.apache.org/licenses/LICENSE-2.0.txt.
from typing import Tuple
from collections import OrderedDict
import numpy as np
import torch
import torch.distributions.constraints as constraints
import pyro
import pyro.distributions as dist
from netcal.scaling import AbstractLogisticRegression
class LogisticCalibrationDependent(AbstractLogisticRegression):
"""
This calibration method is for detection only and uses multivariate normal distributions to obtain a
calibration mapping by means of the confidence as well as additional features. This calibration scheme
tries to model several dependencies in the variables given by the input ``X`` [1]_.
It is necessary to provide all data in input parameter ``X`` as an NumPy array of shape ``(n_samples, n_features)``,
whereas the confidence must be the first feature given in the input array. The ground-truth samples ``y``
must be an array of shape ``(n_samples,)`` consisting of binary labels :math:`y \\in \\{0, 1\\}`. Those
labels indicate if the according sample has matched a ground truth box :math:`\\text{m}=1` or is a false
prediction :math:`\\text{m}=0`.
**Mathematical background:** For confidence calibration in classification tasks, a
confidence mapping :math:`g` is applied on top of a miscalibrated scoring classifier :math:`\\hat{p} = h(x)` to
deliver a calibrated confidence score :math:`\\hat{q} = g(h(x))`.
For detection calibration, we can also use the additional box regression output which we denote as
:math:`\\hat{r} \\in [0, 1]^J` with :math:`J` as the number of dimensions used for the box encoding (e.g.
:math:`J=4` for x position, y position, width and height).
Therefore, the calibration map is not only a function of the confidence score, but also of :math:`\\hat{r}`.
To define a general calibration map for binary problems, we use the logistic function and the combined
input :math:`s = (\\hat{p}, \\hat{r})` of size K by
.. math::
g(s) = \\frac{1}{1 + \\exp(-z(s))} ,
According to [2]_, we can interpret the logit :math:`z` as the logarithm of the posterior odds
.. math::
z(s) = \\log \\frac{f(\\text{m}=1 | s)}{f(\\text{m}=0 | s)} \\approx
\\log \\frac{f(s | \\text{m}=1)}{f(s | \\text{m}=1)} = \\ell r(s)
Inserting multivariate normal density distributions into this framework with
:math:`\\mu^+, \\mu^- \\in \\mathbb{R}^K` and :math:`\\Sigma^+, \\Sigma^- \\in \\mathbb{R}^{K \\times K}`
as the mean vectors and covariance matrices for :math:`\\text{m}=1` and
:math:`\\text{m}=0`, respectively, we get a likelihood ratio of
.. math::
\\ell r(s) = \\log \\frac{\\Sigma^-}{\\Sigma^+}
+ \\frac{1}{2} (s_-^T \\Sigma_-^{-1}s^-) - (s_+^T \\Sigma_+^{-1}s^+),
with :math:`s^+ = s - \\mu^+` and :math:`s^- = s - \\mu^-`.
To keep the restrictions to covariance matrices (symmetric and positive semidefinit), we optimize a decomposed
matrix V as
.. math::
\\Sigma = V^T V
instead of estimating :math:`\\Sigma` directly. This guarantees both requirements.
Parameters
----------
method : str, default: "mle"
Method that is used to obtain a calibration mapping:
- 'mle': Maximum likelihood estimate without uncertainty using a convex optimizer.
- 'momentum': MLE estimate using Momentum optimizer for non-convex optimization.
- 'variational': Variational Inference with uncertainty.
- 'mcmc': Markov-Chain Monte-Carlo sampling with uncertainty.
momentum_epochs : int, optional, default: 1000
Number of epochs used by momentum optimizer.
mcmc_steps : int, optional, default: 20
Number of weight samples obtained by MCMC sampling.
mcmc_chains : int, optional, default: 1
Number of Markov-chains used in parallel for MCMC sampling (this will result
in mcmc_steps * mcmc_chains samples).
mcmc_warmup_steps : int, optional, default: 100
Warmup steps used for MCMC sampling.
vi_epochs : int, optional, default: 1000
Number of epochs used for ELBO optimization.
independent_probabilities : bool, optional, default: False
Boolean for multi class probabilities.
If set to True, the probability estimates for each
class are treated as independent of each other (sigmoid).
use_cuda : str or bool, optional, default: False
Specify if CUDA should be used. If str, you can also specify the device
number like 'cuda:0', etc.
References
----------
.. [1] Fabian Küppers, Jan Kronenberger, Amirhossein Shantia and Anselm Haselhoff:
"Multivariate Confidence Calibration for Object Detection."
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
.. [2] Kull, Meelis, Telmo Silva Filho, and Peter Flach:
"Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers"
Artificial Intelligence and Statistics, PMLR 54:623-631, 2017
`Get source online <http://proceedings.mlr.press/v54/kull17a/kull17a.pdf>`_
.. [3] Fabian Küppers, Jan Kronenberger, Jonas Schneider and Anselm Haselhoff:
"Bayesian Confidence Calibration for Epistemic Uncertainty Modelling."
2021 IEEE Intelligent Vehicles Symposium (IV), 2021
"""
def __init__(self, *args, **kwargs):
""" Create an instance of `LogisticCalibrationDependent`. Detailed parameter description given in class docs. """
# an instance of this class is definitely of type detection
if 'detection' in kwargs and kwargs['detection'] == False:
print("WARNING: On LogisticCalibrationDependent, attribute \'detection\' must be True.")
kwargs['detection'] = True
super().__init__(*args, **kwargs)
# -------------------------------------------------
@property
def intercept(self) -> float:
""" Getter for intercept of dependent logistic calibration. """
if self._sites is None:
raise ValueError("Intercept is None. You have to call the method 'fit' first.")
return self._sites['bias']['values']
@property
def means(self) -> Tuple[np.ndarray, np.ndarray]:
""" Getter for mean vectors of dependent logistic calibration. """
if self._sites is None:
raise ValueError("Weights is None. You have to call the method 'fit' first.")
index_1 = 2 * (self.num_features ** 2)
index_2 = index_1 + self.num_features
weights = self._sites['weights']['values']
return weights[index_1:index_2], weights[index_2:]
@property
def covariances(self) -> Tuple[np.ndarray, np.ndarray]:
""" Getter for covariance matrices of dependent logistic calibration. """
if self._sites is None:
raise ValueError("Weights is None. You have to call the method 'fit' first.")
index_1 = self.num_features ** 2
index_2 = index_1 + self.num_features ** 2
weights = self._sites['weights']['values']
decomposed_inv_cov_pos = np.reshape(weights[:index_1], (self.num_features, self.num_features))
decomposed_inv_cov_neg = np.reshape(weights[index_1:index_2], (self.num_features, self.num_features))
inv_cov_pos = np.matmul(decomposed_inv_cov_pos.T, decomposed_inv_cov_pos.T)
inv_cov_neg = np.matmul(decomposed_inv_cov_neg.T, decomposed_inv_cov_neg.T)
cov_pos = np.linalg.inv(inv_cov_pos)
cov_neg = np.linalg.inv(inv_cov_neg)
return cov_pos, cov_neg
# -------------------------------------------------
def prepare(self, X: np.ndarray) -> torch.Tensor:
"""
Preprocessing of input data before called at the beginning of the fit-function.
Parameters
----------
X : np.ndarray, shape=(n_samples, [n_classes]) or (n_samples, [n_box_features])
NumPy array with confidence values for each prediction on classification with shapes
1-D for binary classification, 2-D for multi class (softmax).
On detection, this array must have 2 dimensions with number of additional box features in last dim.
Returns
-------
torch.Tensor
Prepared data vector X as torch tensor.
"""
assert self.detection, "Detection mode must be enabled for dependent logistic calibration."
if len(X.shape) == 1:
X = np.reshape(X, (-1, 1))
# on detection mode, convert confidence to sigmoid and append the remaining features
data_input = np.concatenate((self._inverse_sigmoid(X[:, 0]).reshape(-1, 1), X[:, 1:]), axis=1)
return torch.Tensor(data_input)
def prior(self):
"""
Prior definition of the weights used for log regression. This function has to set the
variables 'self.weight_prior_dist', 'self.weight_mean_init' and 'self.weight_stddev_init'.
"""
# number of weights
num_weights = 2 * (self.num_features ** 2 + self.num_features)
# prior estimates for decomposed inverse covariance matrices and mean vectors
decomposed_inv_cov_prior = torch.diag(torch.ones(self.num_features))
mean_mean_prior = torch.ones(self.num_features)
# initial stddev for all weights is always the same
weights_mean_prior = torch.cat((decomposed_inv_cov_prior.flatten(),
decomposed_inv_cov_prior.flatten(),
mean_mean_prior.flatten(),
mean_mean_prior.flatten()))
self._sites = OrderedDict()
# set properties for "weights"
self._sites['weights'] = {
'values': None,
'constraint': constraints.real,
'init': {
'mean': weights_mean_prior,
'scale': torch.ones(num_weights)
},
'prior': dist.Normal(weights_mean_prior, 10 * torch.ones(num_weights), validate_args=True),
}
# set properties for "bias"
self._sites['bias'] = {
'values': None,
'constraint': constraints.real,
'init': {
'mean': torch.zeros(1),
'scale': torch.ones(1)
},
'prior': dist.Normal(torch.zeros(1), 10 * torch.ones(1), validate_args=True),
}
def model(self, X: torch.Tensor = None, y: torch.Tensor = None) -> torch.Tensor:
"""
Definition of the log regression model.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_log_regression_features)
Input data that has been prepared by "self.prepare" function call.
y : torch.Tensor, shape=(n_samples, [n_classes])
Torch tensor with ground truth labels.
Either as label vector (1-D) or as one-hot encoded ground truth array (2-D) (for multiclass MLE only).
Returns
-------
torch.Tensor, shape=(n_samples, [n_classes])
Logit of the log regression model.
"""
# get indices of weights
index_1 = int(np.power(self.num_features, 2))
index_2 = index_1 + int(np.power(self.num_features, 2))
index_3 = index_2 + self.num_features
# sample from prior - on MLE, this weight will be set as conditional
bias = pyro.sample("bias", self._sites["bias"]["prior"])
weights = pyro.sample("weights", self._sites["weights"]["prior"])
# the first dimension of the given input data is the "independent" sample dimension
with pyro.plate("data", X.shape[0]):
# get weights of decomposed cov matrices V^(-1)
decomposed_inv_cov_pos = torch.reshape(weights[:index_1], (self.num_features, self.num_features))
decomposed_inv_cov_neg = torch.reshape(weights[index_1:index_2], (self.num_features, self.num_features))
mean_pos = weights[index_2:index_3]
mean_neg = weights[index_3:]
# get logits by calculating gaussian ratio between both distributions
# calculate covariance matrices
# COV^(-1) = V^(-1) * V^(-1,T)
inverse_cov_pos = torch.matmul(decomposed_inv_cov_pos, decomposed_inv_cov_pos.transpose(1, 0))
inverse_cov_neg = torch.matmul(decomposed_inv_cov_neg, decomposed_inv_cov_neg.transpose(1, 0))
# calculate data without means
difference_pos = X - mean_pos
difference_neg = X - mean_neg
# add a new dimensions. This is necessary for torch to distribute dot product
difference_pos = torch.unsqueeze(difference_pos, 2)
difference_neg = torch.unsqueeze(difference_neg, 2)
logit = 0.5 * (torch.matmul(difference_neg.transpose(2, 1),
torch.matmul(inverse_cov_neg, difference_neg)) -
torch.matmul(difference_pos.transpose(2, 1),
torch.matmul(inverse_cov_pos, difference_pos))
)
# remove unnecessary dimensions
logit = torch.squeeze(logit)
# add bias ratio to logit
logit = bias + logit
# if MLE, (slow) sampling is not necessary. However, this is needed for 'variational' and 'mcmc'
if self.method in ['variational', 'mcmc']:
probs = torch.sigmoid(logit)
pyro.sample("obs", dist.Bernoulli(probs=probs, validate_args=True), obs=y)
return logit
| [
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2059,
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364,
9096,... | 2.510612 | 5,560 |
"""Show the current status information for each of the selected lambda targets."""
import argparse
import textwrap
import typing
import yaml
from botocore.client import BaseClient
from reviser import definitions
from reviser import interactivity
from reviser import servicer
def get_completions(
completer: "interactivity.ShellCompleter",
) -> typing.List[str]:
"""Get shell auto-completes for this command."""
return []
def populate_subparser(parser: argparse.ArgumentParser):
"""Populate parser for the status command."""
parser.add_argument(
"qualifier",
nargs="?",
help="""
Specifies a version or alias to show status for.
If not specified, $LATEST will be used for functions
and the latest version will be dynamically determined
for layers.
""",
)
def _get_layer_version_info(
client: BaseClient,
layer_reference: "definitions.LambdaLayerReference",
) -> dict:
"""Fetch layer information for display."""
if not layer_reference.unversioned_arn:
return {}
versions = servicer.get_layer_versions(
client,
layer_reference.unversioned_arn,
)
current = next((v for v in versions if v.arn == layer_reference.arn), None)
if current is None:
return {}
out = {
"name": current.name,
"version": current.version or "UNKNOWN",
"created": current.created.isoformat("T"),
"runtimes": ", ".join(current.runtimes),
"arn": layer_reference.arn or "UNKNOWN",
}
latest = versions[-1]
if latest != current:
out["status"] = f"Newer version {latest.version} exists."
else:
out["status"] = "Is latest version."
return out
def _display_function_info(
client: BaseClient,
name: str,
qualifier: str,
):
"""Display the response lambda function information."""
lambda_function = servicer.get_function_version(
lambda_client=client,
function_name=name,
qualifier=qualifier,
)
data = {
"modified": lambda_function.modified,
"description": lambda_function.description,
"arn": lambda_function.arn,
"runtime": lambda_function.runtime,
"role": lambda_function.role,
"handler": lambda_function.handler,
"size": lambda_function.size,
"timeout": lambda_function.timeout,
"memory": lambda_function.memory,
"version": lambda_function.version,
"environment": lambda_function.environment,
"revision_id": lambda_function.revision_id,
"layers": [
{
**_get_layer_version_info(client, item),
"size": item.size,
}
for item in lambda_function.layers
],
"status": lambda_function.status.to_dict(),
"update_status": lambda_function.status.to_dict(),
}
suffix = qualifier or "$LATEST"
print(f"\n--- {lambda_function.name}:{suffix} ---")
print(textwrap.indent(yaml.safe_dump(data), prefix=" "))
print("\n")
def _display_layer_info(
client: BaseClient,
name: str,
qualifier: str,
) -> None:
"""Display layer version information."""
try:
version = int(qualifier)
except (ValueError, TypeError):
version = servicer.get_layer_versions(client, name)[-1].version or 0
layer = servicer.get_layer_version(
lambda_client=client,
layer_name=name,
version=version,
)
if layer is None:
return
print(f"\n--- {layer.name}:{version} ---")
data = {
"arn": layer.arn,
"version": layer.version,
"created": layer.created.isoformat(),
"description": layer.description,
"size": layer.size,
"runtimes": ", ".join(layer.runtimes),
}
print(textwrap.indent(yaml.safe_dump(data), prefix=" "))
def run(ex: "interactivity.Execution") -> "interactivity.Execution":
"""Display the current configuration of the lambda target(s)."""
selected = ex.shell.context.get_selected_targets(ex.shell.selection)
qualifier = ex.args.get("qualifier") or "$LATEST"
items: typing.List[typing.Tuple[definitions.Target, str]]
items = [(t, n) for t in selected.function_targets for n in t.names]
for target, name in items:
_display_function_info(target.client("lambda"), name, qualifier)
items = [(t, n) for t in selected.layer_targets for n in t.names]
for target, name in items:
_display_layer_info(target.client("lambda"), name, qualifier)
return ex.finalize(
status="SUCCESS",
message="Status reports have been display.",
echo=False,
)
| [
37811,
15307,
262,
1459,
3722,
1321,
329,
1123,
286,
262,
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37456,
6670,
526,
15931,
198,
11748,
1822,
29572,
198,
11748,
2420,
37150,
198,
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19720,
198,
198,
11748,
331,
43695,
198,
6738,
10214,
420,
382,
13,
16366,
1330,
7308,
... | 2.529348 | 1,857 |
ff = open('LINEAS', 'r')
flines = ff.readlines()
ff.close()
ff = open('LINEAS.csv', 'w')
for l in flines:
tmp = l.split()
idx, element = tmp[0].split('=')
ff.write('{0},{1},{2},{3}\n'.format(idx, element,tmp[1],tmp[2]))
ff.close()
| [
198,
198,
487,
796,
1280,
10786,
24027,
1921,
3256,
705,
81,
11537,
198,
2704,
1127,
796,
31246,
13,
961,
6615,
3419,
198,
487,
13,
19836,
3419,
198,
198,
487,
796,
1280,
10786,
24027,
1921,
13,
40664,
3256,
705,
86,
11537,
198,
1640,... | 2.084034 | 119 |
import argparse, logging
| [
11748,
1822,
29572,
11,
18931,
198
] | 4.166667 | 6 |
from pygame.sprite import RenderClear
from subpixelsurface import *
# This class keeps an ordered list of sprites in addition to the dict,
# so we can draw in the order the sprites were added.
# Some quick benchmarks show that [:] is the fastest way to get a
# shallow copy of a list.
# This is kind of a wart -- the actual RenderUpdates class doesn't
# use add_internal in its add method, so just overriding
# add_internal won't work.
| [
6738,
12972,
6057,
13,
34975,
578,
1330,
46722,
19856,
198,
6738,
22718,
14810,
333,
2550,
1330,
1635,
198,
198,
2,
770,
1398,
7622,
281,
6149,
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286,
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287,
3090,
284,
262,
8633,
11,
198,
2,
523,
356,
460,
3197,
287,
262,
... | 3.805085 | 118 |
from logging import getLogger
log = getLogger('msn.nssb')
import msn
from msn import MSNTextMessage
from util import callsback
from util.primitives.funcs import get
from util.Events import EventMixin
class NSSBAdapter(EventMixin):
'''
Chatting with federated (yahoo) buddies happens over the NS protocol, but
MSNConversations are made to work with Switchboard protocol implementations.
This class exists to provide a switchboard interface to the NS.
'''
_instances = []
events = EventMixin.events | set ((
'on_buddy_join',
'on_buddy_leave',
'on_buddy_timeout',
'on_conn_success',
'on_authenticate',
'disconnect',
'contact_alias',
'needs_auth',
'recv_error',
'recv_text_msg',
'send_text_msg',
'typing_info',
'recv_action',
'recv_p2p_msg',
'transport_error',
))
@property
@property
@property
@callsback
@callsback
def send_typing_status(self, name, status):
'''
UUM 0 bob@yahoo.com 32 2 87\r\n
MIME-Version: 1.0\r\n
Content-Type: text/x-msmsgscontrol\r\n
TypingUser: alice@live.com\r\n
\r\n
'''
payload = []
line = lambda k,v: '%s: %s' % (k,v)
add = payload.append
add(line('MIME-Version', '1.0'))
add(line('Content-Type', 'text/x-msmsgscontrol'))
add(line('TypingUser', name))
add('')
add('')
payload = '\r\n'.join(payload)
netid = 32
msg = msn.Message('UUM', self.__chatbuddy, netid, 2, payload = payload)
self.ns.socket.send(msg, trid=True, callback=sentinel)
def recv_msg_plain(self, msg):
'''
msg_plain(msg, src_account, src_display)
this is called when a msg comes in with type='text/plain'
@param socket: the socket it arrived from (better be self.socket!)
@param msg: the rfc822 object representing the MIME headers and such
@param src_account: the email address/passport this comes from
@param src_display: the display name of the buddy who sent it
@param *params: more stuff!
'''
name, nick = msg.args[:2]
msg = MSNTextMessage.from_net(msg.payload)
# self.event('contact_alias', name, nick)
self.event('recv_text_msg', name, msg)
def recv_msg_control(self, msg):
'''
msg_control(msg, src_account, src_display)
This is called when a message comes in with type='text/x-msmsgscontrol'
Generally, these are typing indicators.
@param msg: msnmessage
'''
name = msg.args[0]
self.event('typing_info', name, bool(msg.payload.get('TypingUser', False)))
@classmethod
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201,
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20... | 2.062281 | 1,429 |
import urllib
from django.shortcuts import get_object_or_404
from django.utils.translation import ugettext
from olympia.amo.templatetags import jinja_helpers
from olympia.amo.feeds import NonAtomicFeed
from olympia.addons.models import Addon, Review
| [
11748,
2956,
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571,
198,
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198,
198,
6738,
267,
6760,
544,
13,
18811,
13,
1149... | 3.048193 | 83 |
default_app_config = 'wechat_ggfilm_backend.apps.WechatGgfilmBackendConfig' | [
12286,
62,
1324,
62,
11250,
796,
705,
732,
17006,
62,
1130,
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62,
1891,
437,
13,
18211,
13,
1135,
17006,
38,
70,
26240,
7282,
437,
16934,
6
] | 2.777778 | 27 |
import logging
import mimetypes
from concurrent.futures import ThreadPoolExecutor
from http.client import RemoteDisconnected
from io import BytesIO
from typing import Iterator, Callable
from urllib.parse import urlparse
import pymongo
import urllib3
from bson import ObjectId
from requests import HTTPError
from tsing_spider.porn.xarthunter import (
XarthunterItemPage,
XarthunterVideoIndexPage,
XarthunterImageIndexPage
)
from tsing_spider.util import http_get
from ghs.spiders.base import BaseSpiderTaskGenerator
from ghs.utils.storage import create_s3_client, create_mongodb_client, bucket_name, put_json
log = logging.getLogger(__file__)
item_thread_pool = ThreadPoolExecutor(max_workers=8)
mongodb_client = create_mongodb_client()
collection = mongodb_client.get_database("resman").get_collection("spider_xart")
urllib3.disable_warnings()
s3_client = create_s3_client()
def initialize():
"""
Initialize mongodb and s3
:return:
"""
log.info("Initializing database")
collection.create_index([("published", pymongo.ASCENDING)])
collection.create_index([("type", pymongo.ASCENDING)])
collection.create_index([("url", pymongo.ASCENDING)])
def image_item_processor(item: XarthunterItemPage):
"""
Download all images to S3 storage and append item details to mongodb
:param item:
:return:
"""
_id = ObjectId()
doc = item.json
doc["_id"] = _id
s3_path_list = []
for i, image_url in enumerate(item.image_urls):
try:
image_data = http_get(image_url, headers={"Referer": item.url})
url_path = urlparse(image_url).path
mime_type = mimetypes.guess_type(url_path)[0]
file_suffix = url_path.split(".")[-1]
s3_path = f"xart/images/{str(_id)}/{i}.{file_suffix}"
s3_path_list.append(s3_path)
with BytesIO(image_data) as fp:
s3_client.put_object(
bucket_name=bucket_name,
object_name=s3_path,
data=fp,
length=len(image_data),
content_type=mime_type
)
except HTTPError as he:
if 400 <= he.response.status_code < 500:
log.warning(f"Can't download image {image_url} since resource is not able to access (4xx).")
else:
raise he
doc["url"] = item.url
doc["type"] = "image"
doc["published"] = False
put_json(s3_client, doc, f"xart/images/{str(_id)}/meta.json")
collection.insert_one(doc)
log.info(f"Image {item.url} written.")
def video_item_processor(item: XarthunterItemPage):
"""
Download video to S3 storage and append item details to mongodb
:param item:
:return:
"""
_id = ObjectId()
doc = item.json
doc["_id"] = _id
video_data = http_get(item.mp4_video_url, headers={"Referer": item.url})
with BytesIO(video_data) as fp:
s3_client.put_object(
bucket_name=bucket_name,
object_name=f"xart/videos/{str(_id)}/video.mp4",
data=fp,
length=len(video_data)
)
if item.preview_image_url is not None:
preview_image_data = http_get(item.preview_image_url, headers={"Referer": item.url})
with BytesIO(preview_image_data) as fp:
s3_client.put_object(
bucket_name=bucket_name,
object_name=f"xart/videos/{str(_id)}/preview.jpg",
data=fp,
length=len(preview_image_data),
content_type="image/jpeg"
)
doc["url"] = item.url
doc["type"] = "video"
doc["published"] = False
put_json(s3_client, doc, f"xart/videos/{str(_id)}/meta.json")
collection.insert_one(doc)
log.info(f"Video {item.url} written.")
| [
11748,
18931,
198,
11748,
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2963,
12272,
198,
6738,
24580,
13,
69,
315,
942,
1330,
14122,
27201,
23002,
38409,
198,
6738,
2638,
13,
16366,
1330,
21520,
7279,
15236,
198,
6738,
33245,
1330,
2750,
4879,
9399,
198,
6738,
19720,
1330,
40... | 2.196787 | 1,743 |
#!/usr/bin/env python
#import roslib; roslib.load_manifest('Phoebe')
import rospy
from nav_msgs.msg import Odometry
# print("All data", completePose)
if __name__ == "__main__":
rospy.init_node('getPoseTTBot', anonymous=False) #make node
rospy.Subscriber('odom', Odometry, callback)
rospy.spin()
| [
2,
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14629,
14,
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198,
2,
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686,
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686,
2777,
88,
198,
198,
6738,
6812,
62,
907,
14542,
13,
19662,
133... | 2.449612 | 129 |
import datetime
import io
import math
import os.path
import subprocess
WGRIB_BIN = os.path.expanduser('bin/osx/wgrib2')
| [
11748,
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8800,
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418,
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14,
86,
70... | 2.595745 | 47 |
# -*- coding: utf-8 -*-
# Copyright (c) 2021, Noah Jacob and Contributors
# See license.txt
from __future__ import unicode_literals
import frappe
from frappe.utils import nowdate,flt
import unittest
| [
2,
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37443,
834,
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1098,
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874,
198,
19... | 2.802632 | 76 |
from nltk.tokenize import SExprTokenizer
from apodeixi.util.a6i_error import ApodeixiError
from apodeixi.util.dataframe_utils import DataFrameUtils
from apodeixi.util.formatting_utils import ListUtils, StringUtils
class IntervalSpec():
'''
Abstract helper class used to construct Interval objects. This is needed because sometimes all columns in an Interval
are not known at configuration time, and are only known at runtime.
For example, perhaps at configuration time we know where an interval starts, but not where it ends, since the
end user might add columns to an Excel spreadsheet that quality as part of the interval. Thus, only at runtime
in the context of a particular set of Excel columns (a "linear space") can it be determined which are the columns
that qualify as belonging to an interval.
Example: Say an interval spec is: "All columns from A to F, not inclusive". Then if the linear space is
[Q, R, A, T, Y, U, F, G, W], the application of the spec to the linear space yields the Interval [A, T, Y, U]
Concrete classes implement different "spec" algorithms, so this particular class is just an abstract class.
'''
def buildIntervals(self, parent_trace, linear_space):
'''
Implemented by concrete derived classes.
Must return a list of Interval objects, constructed by applying the concrete class's semantics
to the specificity of the linear_space given.
Example: Say an interval spec is:
"One interval is the list of all columns up to A (non inclusive, then columns from A to F, not inclusive, and the remaining
columns form the last interval".
Then if the linear space is
[Q, R, A, T, Y, U, F, G, W], the application of the spec to the linear space yields these 3 Intervals:
* [Q, R]
* [A, T, Y, U]
* [F, G, W]
'''
raise NotImplementedError("Class " + str(self.__class__) + " forgot to implement method buildInterval")
class GreedyIntervalSpec(IntervalSpec):
'''
Concrete interval spec class which builds a list consisting of a
single interval taking all the columns found in Excel (i.e., all the 'linear space')
Example: Say the linear space is [Q, R, A, T, Y, U, F, G, W].
Then this class returns the list containing just one interval: [Q, R, A, T, Y, U, F, G, W]
'''
def buildIntervals(self, parent_trace, linear_space):
'''
'''
#if self.entity_name == None:
# Overwrite self.entity_name to be consistent with the linear space given
self.entity_name = IntervalUtils().infer_first_entity(parent_trace, linear_space)
my_trace = parent_trace.doing("Validating mandatory columns are present")
missing_cols = [col for col in self.mandatory_columns if not col in linear_space]
if len(missing_cols) > 0:
raise ApodeixiError(my_trace, "Posting lacks some mandatory columns",
data = { 'Missing columns': missing_cols,
'Posted columns': linear_space})
return [Interval(parent_trace, linear_space, self.entity_name)]
class MinimalistIntervalSpec(IntervalSpec):
'''
Concrete interval spec class which builds minimalist intervals, where each interval has exactly 1 non-UID column
from the linear space.
For example, if the linear space is
['UID', 'Big Rock', 'UID.1', 'Breakdown 1', 'UID.2', 'Breakdown 2'], then calling the
`buildIntervals` method will produce these intervals:
* ['UID', 'Big Rock']
* ['UID.1', 'Breakdown 1']
* ['UID.2', 'Breakdown 2']
'''
def buildIntervals(self, parent_trace, linear_space):
'''
'''
if self.entity_name == None:
self.entity_name = IntervalUtils().infer_first_entity(parent_trace, linear_space)
my_trace = parent_trace.doing("Validating mandatory columns are present")
missing_cols = [col for col in self.mandatory_columns if not col in linear_space]
if len(missing_cols) > 0:
raise ApodeixiError(my_trace, "Posting lacks some mandatory columns",
data = { 'Missing columns': missing_cols,
'Posted columns': linear_space})
interval_columns = []
interval_entity = None
intervals_list = []
#for col in linear_space[start_idx:]:
current_interval_cols = []
for idx in range(len(linear_space)):
loop_trace = parent_trace.doing("Looping through linear space to build intervals",
data = { 'linear_space': str(linear_space),
'idx in loop': str(idx),
'current_interval_cols': str(current_interval_cols)})
col = linear_space[idx]
if IntervalUtils().is_a_UID_column(loop_trace, col): # append all UIDs until you hit a non-UID, and stop there
current_interval_cols.append(col)
continue
else: # This is the end of the interval
current_interval_cols.append(col)
interval_entity = col
intervals_list.append(Interval(loop_trace, current_interval_cols, interval_entity))
# Reset for next interval to process
current_interval_cols = []
continue
return intervals_list
class ClosedOpenIntervalSpec(IntervalSpec):
'''
Concrete interval spec class which builds a list of interval based on knowing the intervals' endpoints, where each endpoint
is the start of an interval (and not the end of the previous interval).
Example: Say an interval spec is:
"Split the linear space at [A, F]"
Then if the linear space is
[Q, R, A, T, Y, U, F, G, W], the application of the spec to the linear space yields these 3 Intervals:
* [Q, R]
* [A, T, Y, U]
* [F, G, W]
@param splitting_columns The columns inside the interval that partition it. In the above example,
that would be [A, F]
@param may_ignore_tail A boolean. It determines whether it is OK to not have a "tail" of splitting columns,
i.e., for a posting's columns to only include a subset of splitting columns up to an index
in self.splitting_columns. By default it is False, which means that all splitting columns are
mandatory.
In the above example, if the linear space is [Q, R, A, T, Y, U] and the splitting columsn are [A, F],
then this class will error out when building intervals unless `may_ignore_tail` is True, in which
case it will result in these intervals:
* [Q, R]
* [A, T, Y, U]
'''
def buildIntervals(self, parent_trace, linear_space):
'''
'''
if self.entity_name == None:
self.entity_name = IntervalUtils().infer_first_entity(parent_trace, linear_space)
my_trace = parent_trace.doing("Checking splitting columns all belong to linear_spac_")
if True:
filtered_splitting_columns = self.splitting_columns.copy()
missing = [col for col in self.splitting_columns if not col in linear_space]
if len(missing) > 0:
if not self.may_ignore_tail: # Error out
raise ApodeixiError(my_trace, "Can't build intervals because some splitting columns are not in linear space. "
+ "\n\t=> This sometimes happens if the ranges in the Posting Label don't cover all "
+ "the data.",
data = { 'linear_space': str(linear_space),
'splitting_columns': str(self.splitting_columns),
'missing in linear space': str(missing) })
else: # Check if the missing is a tail, which would be OK
missing_start_idx = min([self.splitting_columns.index(col) for col in missing])
after_misses = [col for col in self.splitting_columns if not col in missing
and self.splitting_columns.index(col) > missing_start_idx]
if len(after_misses) == 0: # We are missing a tail, which is allowed
filtered_splitting_columns = self.splitting_columns[:missing_start_idx].copy()
else: # error out
raise ApodeixiError(my_trace, "Can't build intervals because some non-blank splitting columns"
+ " are 'present after misses', i.e., they lie after some "
+ " of the splitting columns missing in linear space",
data = { 'linear_space': str(linear_space),
'splitting_columns': str(self.splitting_columns),
'missing in linear space': str(missing),
'present after misses': str(after_misses)})
my_trace = parent_trace.doing("Splitting linear space",
data = { 'linear_space': str(linear_space),
'splitting_columns': str(self.splitting_columns)})
if True:
intervals_list = []
remaining_cols = linear_space
# We add a synthetic extra point at the end for loop to work, since if there are N splitting columns we
# will end up with N+1 intervals, so we need to loop through N+1 cycles, not N
# That makes the loop below work (otherwise the last interval is not produced, which is a bug)
class _PointAtInfinity():
'''
Helper class to represent an additional object "after" all the others.
'''
# Add POINT_AT_INFINITY for loop to work
# for loop to work, since if there are N splitting columns we
# will end up with N+1 intervals, so we need to loop through N+1 cycles, not N
# That makes the loop below work (otherwise the last interval is not produced, which is a bug)
interval_endpoints = filtered_splitting_columns.copy()
interval_endpoints. append(Interval.POINT_AT_INFINITY)
for col in interval_endpoints:
loop_trace = my_trace.doing("Cycle in loop for one of the splitting_columns",
data = {'col': str(col)})
if col != Interval.POINT_AT_INFINITY: # We split by 'col' if it is not the POINT_AT_INFINITY
#
# GOTCHA: if the submitted form has UIDs, then there probably is a UID column to the left
# of `col`. If so, include such UID if it exists, else it will erroneously be considered part
# of the entity to the left of `col`, which might then error out thinking it has two UID
# columns, which is not legal.
#
col_idx = linear_space.index(col)
if col_idx > 0 and IntervalUtils().is_a_UID_column(loop_trace, linear_space[col_idx - 1]):
split_by = [linear_space[col_idx-1]]
else:
split_by = [col]
check, pre_cols, post_cols = ListUtils().is_sublist( parent_trace = loop_trace,
super_list = remaining_cols,
alleged_sub_list = split_by)
if not check:
raise ApodeixiError(loop_trace, "Strange internal error: couldn't split columns by column",
data = { 'columns_to_split': remaining_cols,
'splitting_column': col})
interval_entity = IntervalUtils().infer_first_entity(loop_trace, remaining_cols)
intervals_list.append(Interval( parent_trace = loop_trace,
columns = pre_cols,
entity_name = interval_entity))
# Initialize data for next cycle in loop
remaining_cols = split_by
remaining_cols.extend(post_cols)
else: # This is the last interval, splitting by the POINT_AT_INFINITY
interval_entity = IntervalUtils().infer_first_entity(loop_trace, remaining_cols)
intervals_list.append(Interval( parent_trace = loop_trace,
columns = remaining_cols,
entity_name = interval_entity))
return intervals_list
class Interval():
'''
Helper class used as part of the configuration for parsing a table in an Excel spreadsheet. It represents
a list of string-valued column names in Excel, ordered from left to right, all for a given entity.
Additionally, it indicates which of those column names is the name of the entity (as opposed to a property of)
the entity.
'''
UID = 'UID' # Field name for anything that is a UID
# Sometimes we need a synthetic extra point at the end of an interval.
class _PointAtInfinity():
'''
Helper class to represent an additional object "after" all the others.
'''
POINT_AT_INFINITY = _PointAtInfinity()
def is_subset(self, columns):
'''
UID-aware method to test if this Interval is a subset of the given columns. By "UID-aware" we mean
that the method ignores any UID column when determining subset condition.
For example, ['UID', 'Car', 'Make'] would be considered a subset of ['Car', 'Make', 'Driver']
For internal reasons, it also has POINT_AT_INFINITY awareness
'''
me = set(self.columns).difference(set([Interval.UID])).difference(set([Interval.POINT_AT_INFINITY]))
them = set(columns)
return me.issubset(them)
def non_entity_cols(self):
'''
Returns a list of strings, corresponding to the Interval's columns that are not the entity type
'''
#GOTCHA: Don't compare column names to the entity name directly, since there might be spurious
# differences due to lower/upper case. Instead, format as a yaml field to have a standard
# policy on case, space, hyphens, etc. prior to comparison
FMT = StringUtils().format_as_yaml_fieldname
result = [col for col in self.columns if FMT(col) != FMT(self.entity_name)]
return result
| [
198,
6738,
299,
2528,
74,
13,
30001,
1096,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1330,
311,
3109,
1050,
30642,
7509,
220,
... | 2.02957 | 8,184 |
"""Manage assets.
Usage:
./manage.py assets rebuild
Rebuild all known assets; this requires tracking to be enabled:
Only assets that have previously been built and tracked are
considered "known".
./manage.py assets rebuild --parse-templates
Try to find as many of the project's templates (hopefully all),
and check them for the use of assets. Rebuild all the assets
discovered in this way. If tracking is enabled, the tracking
database will be replaced by the newly found assets.
"""
import os, sys, imp
import time
from optparse import make_option
from django.core.management.base import BaseCommand, CommandError
from django import template
from django_assets.conf import settings
from django_assets.templatetags.assets import AssetsNode as AssetsNodeOriginal
try:
from django.templatetags.assets import AssetsNode as AssetsNodeMapped
except ImportError:
# Since Django #12295, custom templatetags are no longer mapped into
# the Django namespace. Support both versions.
AssetsNodeMapped = None
from django_assets import registry, Bundle
from django_assets.merge import abspath
from django_assets.bundle import BuildError
try:
import jinja2
except:
jinja2 = None
else:
jinja2_envs = []
from django_assets.jinja2.extension import AssetsExtension
# Prepare a Jinja2 environment we can later use for parsing.
# If not specified by the user, put in there at least our own
# extension, which we will need most definitely to achieve anything.
_jinja2_extensions = getattr(settings, 'ASSETS_JINJA2_EXTENSIONS')
if not _jinja2_extensions:
_jinja2_extensions = [AssetsExtension.identifier]
jinja2_envs.append(jinja2.Environment(extensions=_jinja2_extensions))
try:
from coffin.common import get_env as get_coffin_env
except:
pass
else:
jinja2_envs.append(get_coffin_env())
def _shortpath(abspath):
"""Make an absolute path relative to the project's settings module,
which would usually be the project directory."""
b = os.path.dirname(os.path.normpath(sys.modules[settings.SETTINGS_MODULE].__file__))
p = os.path.normpath(abspath)
return p[len(os.path.commonprefix([b, p])):]
from django.utils import autoreload | [
37811,
5124,
496,
6798,
13,
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198,
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25,
628,
220,
220,
220,
24457,
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6798,
17884,
628,
220,
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220,
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1900,
6798,
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9646,
284,
307,
9343,
25,
198,
220,
2... | 2.962677 | 777 |
import torch
from ..Objective import Objective
| [
11748,
28034,
198,
198,
6738,
11485,
10267,
425,
1330,
37092,
628
] | 4.454545 | 11 |
import imaplib
import re
import warnings
from typing import Iterable, List
from nehushtan.logger.NehushtanFileLogger import NehushtanFileLogger
from nehushtan.logger.NehushtanLogging import NehushtanLogging
from nehushtan.mail.rfc3501.SearchCommandKit import SearchCommandKit
from nehushtan.mail.rfc822.NehushtanEmailMessage import NehushtanEmailMessage
class IMAPAgent:
"""
Greatly Changed between 0.4.7 and 0.4.8
"""
FETCH_METHOD_RFC822 = '(RFC822)'
FETCH_METHOD_RFC822_HEADER = '(RFC822.HEADER)'
FETCH_METHOD_RFC822_WITH_UID = '(UID RFC822)'
FETCH_METHOD_RFC822_HEADER_WITH_UID = '(UID RFC822.HEADER)'
"""
Since 0.1.13
[Experimental, Not Fully Completed]
"""
STATUS_NAME_MESSAGES = 'MESSAGES' # 邮箱中的邮件数。
STATUS_NAME_RECENT = 'RECENT' # 设置了 .ecent 标志的消息数。
STATUS_NAME_UIDNEXT = 'UIDNEXT' # 邮箱的下一个唯一标识符值。
STATUS_NAME_UIDVALIDITY = 'UIDVALIDITY' # 邮箱的唯一标识符有效性值。
STATUS_NAME_UNSEEN = 'UNSEEN' # 没有设置 .een 标志的消息数。
def list_mail_boxes(self, directory='""', pattern='*'):
"""
检索帐户可用的邮箱
:param directory:
:param pattern:
:return: 一个字符串序列，包含每个邮箱的 `标志`，`层次结构分隔符` 和 `邮箱名称`
"""
response_code, boxes = self._connection.list(directory, pattern)
if response_code != 'OK':
raise Exception(f'IMAPAgent cannot fetch box list. Code = {response_code} Data = {boxes}')
return boxes
@staticmethod
def parse_box_string_to_tuple(box_string):
"""
:param box_string:
:return: A tuple with parsed components (flags, delimiter, mailbox_name)
"""
list_response_pattern = re.compile(
r'.(?P<flags>.*?). "(?P<delimiter>.*)" (?P<name>.*)'
)
match = list_response_pattern.match(box_string.decode('utf-8'))
flags, delimiter, mailbox_name = match.groups()
mailbox_name = mailbox_name.strip('"')
return flags, delimiter, mailbox_name
def select_mailbox(self, box: str, readonly: bool = False):
"""
:param box:
:param readonly:
:return: The Number of Total Mails
"""
response_code, data = self._connection.select(box, readonly)
if response_code != 'OK':
raise Exception(f"IMAPAgent select_mailbox {box} failed: {response_code} with Data: {data}")
return int(data[0])
def search_mails_for_message_id(self, criteria, charset=None):
"""
:param criteria:
:param charset:
:return: Message ID array
"""
warnings.warn('NOT FRIENDLY WITH NON-ASCII')
response_code, data = self._connection.search(charset, criteria)
if response_code != 'OK':
raise Exception(f"IMAPAgent search_mails_in_mailbox failed: {response_code} with Data: {data}")
message_id_array = data[0].decode('utf-8').split(' ')
return message_id_array
def fetch_mail_with_message_id(self, message_id: str, message_parts: str) -> list:
"""
:param message_id:
:param message_parts: such as '(BODY.PEEK[HEADER] FLAGS)' for subject
:return:
"""
response_code, data = self._connection.fetch(message_id, message_parts)
if response_code != 'OK':
raise Exception(f"IMAPAgent fetch_mail failed: {response_code} with Data: {data}")
return data
# # 字符编码转换
# @staticmethod
# def decode_str(str_in):
# value, charset = decode_header(str_in)[0]
# if charset:
# value = value.decode(charset)
# return value
def fetch_mail_with_message_id_as_nem(
self,
message_id: str,
headers_only: bool = False
) -> NehushtanEmailMessage:
"""
Since 0.4.8
"""
fetch_method = self.FETCH_METHOD_RFC822
if headers_only:
fetch_method = self.FETCH_METHOD_RFC822_HEADER
rfc822 = self.fetch_mail_with_message_id(message_id, fetch_method)
raw_mail_text: bytes = rfc822[0][1]
return NehushtanEmailMessage.parse_bytes(raw_mail_text)
def fetch_mail_with_uid_as_nem(
self,
uid: str,
headers_only: bool = False
) -> NehushtanEmailMessage:
"""
Since 0.4.8
"""
fetch_method = self.FETCH_METHOD_RFC822_WITH_UID
if headers_only:
fetch_method = self.FETCH_METHOD_RFC822_HEADER_WITH_UID
rfc822 = self.fetch_mail_with_uid(uid, fetch_method)
raw_mail_text: bytes = rfc822[0][1]
return NehushtanEmailMessage.parse_bytes(raw_mail_text)
| [
11748,
545,
64,
489,
571,
198,
11748,
302,
198,
11748,
14601,
198,
6738,
19720,
1330,
40806,
540,
11,
7343,
198,
198,
6738,
497,
71,
1530,
38006,
13,
6404,
1362,
13,
8199,
71,
1530,
38006,
8979,
11187,
1362,
1330,
44470,
1530,
38006,
... | 2.025405 | 2,283 |
"""Version file."""
VERSION = "0.0.38"
| [
37811,
14815,
2393,
526,
15931,
198,
43717,
796,
366,
15,
13,
15,
13,
2548,
1,
198
] | 2.4375 | 16 |
# Copyright Contributors to the Amundsen project.
# SPDX-License-Identifier: Apache-2.0
import logging
import unittest
from collections import OrderedDict
from mock import patch
from pyhocon import ConfigFactory
from typing import Any
from cassandra.metadata import ColumnMetadata as CassandraColumnMetadata
from databuilder.extractor.cassandra_extractor import CassandraExtractor
from databuilder.models.table_metadata import TableMetadata, ColumnMetadata
# patch whole class to avoid actually calling for boto3.client during tests
@patch('cassandra.cluster.Cluster.connect', lambda x: None)
if __name__ == '__main__':
unittest.main()
| [
2,
15069,
25767,
669,
284,
262,
1703,
917,
6248,
1628,
13,
198,
2,
30628,
55,
12,
34156,
12,
33234,
7483,
25,
24843,
12,
17,
13,
15,
198,
198,
11748,
18931,
198,
11748,
555,
715,
395,
198,
6738,
17268,
1330,
14230,
1068,
35,
713,
... | 3.594444 | 180 |
import os
import io
import re
import pytest
from contextlib import redirect_stdout
import numpy as np
from sklearn.neighbors import KDTree
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import normalize
import pickle
import joblib
import scipy
from pynndescent import NNDescent, PyNNDescentTransformer
@pytest.mark.skipif(
list(map(int, scipy.version.version.split("."))) < [1, 3, 0],
reason="requires scipy >= 1.3.0",
)
@pytest.mark.skipif(
list(map(int, scipy.version.version.split("."))) < [1, 3, 0],
reason="requires scipy >= 1.3.0",
)
# This tests a recursion error on cosine metric reported at:
# https://github.com/lmcinnes/umap/issues/99
# graph_data used is a cut-down version of that provided by @scharron
# It contains lots of all-zero vectors and some other duplicates
# same as the previous two test, but this time using the PyNNDescentTransformer
# interface
@pytest.mark.parametrize("metric", ["euclidean", "cosine"])
@pytest.mark.parametrize("metric", ["euclidean", "cosine"])
@pytest.mark.parametrize("metric", ["euclidean", "cosine"])
@pytest.mark.parametrize("metric", ["manhattan"])
@pytest.mark.parametrize("n_trees", [1, 2, 3, 10])
@pytest.mark.parametrize("metric", ["euclidean", "cosine"])
@pytest.mark.parametrize(
"metric", ["euclidean", "manhattan"]
) # cosine makes no sense for 1D
@pytest.mark.parametrize("metric", ["euclidean", "cosine"])
| [
11748,
28686,
198,
11748,
33245,
198,
11748,
302,
198,
11748,
12972,
9288,
198,
6738,
4732,
8019,
1330,
18941,
62,
19282,
448,
198,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
1341,
35720,
13,
710,
394,
32289,
1330,
509,
24544,
631,
... | 2.675824 | 546 |
from django.test import SimpleTestCase
from django.utils.deprecation import CallableFalse, CallableTrue
| [
6738,
42625,
14208,
13,
9288,
1330,
17427,
14402,
20448,
198,
6738,
42625,
14208,
13,
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13,
10378,
8344,
341,
1330,
4889,
540,
25101,
11,
4889,
540,
17821,
628
] | 3.75 | 28 |
"""Parallelized, single-point launch script to run DSO on a set of benchmarks."""
import os
import sys
import time
import multiprocessing
from copy import deepcopy
from datetime import datetime
import click
from dso import DeepSymbolicOptimizer
from dso.logeval import LogEval
from dso.config import load_config
from dso.utils import safe_update_summary
def train_dso(config):
"""Trains DSO and returns dict of reward, expression, and traversal"""
print("\n== TRAINING SEED {} START ============".format(config["experiment"]["seed"]))
# For some reason, for the control task, the environment needs to be instantiated
# before creating the pool. Otherwise, gym.make() hangs during the pool initializer
if config["task"]["task_type"] == "control" and config["training"]["n_cores_batch"] > 1:
import gym
import dso.task.control # Registers custom and third-party environments
gym.make(config["task"]["env"])
# Train the model
model = DeepSymbolicOptimizer(deepcopy(config))
start = time.time()
result = model.train()
result["t"] = time.time() - start
result.pop("program")
save_path = model.config_experiment["save_path"]
summary_path = os.path.join(save_path, "summary.csv")
print("== TRAINING SEED {} END ==============".format(config["experiment"]["seed"]))
return result, summary_path
@click.command()
@click.argument('config_template', default="")
@click.option('--runs', '--r', default=1, type=int, help="Number of independent runs with different seeds")
@click.option('--n_cores_task', '--n', default=1, help="Number of cores to spread out across tasks")
@click.option('--seed', '--s', default=None, type=int, help="Starting seed (overwrites seed in config), incremented for each independent run")
@click.option('--benchmark', '--b', default=None, type=str, help="Name of benchmark")
def main(config_template, runs, n_cores_task, seed, benchmark):
"""Runs DSO in parallel across multiple seeds using multiprocessing."""
messages = []
# Load the experiment config
config_template = config_template if config_template != "" else None
config = load_config(config_template)
# Overwrite named benchmark (for tasks that support them)
task_type = config["task"]["task_type"]
if benchmark is not None:
# For regression, --b overwrites config["task"]["dataset"]
if task_type == "regression":
config["task"]["dataset"] = benchmark
# For control, --b overwrites config["task"]["env"]
elif task_type == "control":
config["task"]["env"] = benchmark
else:
raise ValueError("--b is not supported for task {}.".format(task_type))
# Overwrite config seed, if specified
if seed is not None:
if config["experiment"]["seed"] is not None:
messages.append(
"INFO: Replacing config seed {} with command-line seed {}.".format(
config["experiment"]["seed"], seed))
config["experiment"]["seed"] = seed
# Save starting seed and run command
config["experiment"]["starting_seed"] = config["experiment"]["seed"]
config["experiment"]["cmd"] = " ".join(sys.argv)
# Set timestamp once to be used by all workers
timestamp = datetime.now().strftime("%Y-%m-%d-%H%M%S")
config["experiment"]["timestamp"] = timestamp
# Fix incompatible configurations
if n_cores_task == -1:
n_cores_task = multiprocessing.cpu_count()
if n_cores_task > runs:
messages.append(
"INFO: Setting 'n_cores_task' to {} because there are only {} runs.".format(
runs, runs))
n_cores_task = runs
if config["training"]["verbose"] and n_cores_task > 1:
messages.append(
"INFO: Setting 'verbose' to False for parallelized run.")
config["training"]["verbose"] = False
if config["training"]["n_cores_batch"] != 1 and n_cores_task > 1:
messages.append(
"INFO: Setting 'n_cores_batch' to 1 to avoid nested child processes.")
config["training"]["n_cores_batch"] = 1
# Start training
print_summary(config, runs, messages)
# Generate configs (with incremented seeds) for each run
configs = [deepcopy(config) for _ in range(runs)]
for i, config in enumerate(configs):
config["experiment"]["seed"] += i
# Farm out the work
if n_cores_task > 1:
pool = multiprocessing.Pool(n_cores_task)
for i, (result, summary_path) in enumerate(pool.imap_unordered(train_dso, configs)):
if not safe_update_summary(summary_path, result):
print("Warning: Could not update summary stats at {}".format(summary_path))
print("INFO: Completed run {} of {} in {:.0f} s".format(i + 1, runs, result["t"]))
else:
for i, config in enumerate(configs):
result, summary_path = train_dso(config)
if not safe_update_summary(summary_path, result):
print("Warning: Could not update summary stats at {}".format(summary_path))
print("INFO: Completed run {} of {} in {:.0f} s".format(i + 1, runs, result["t"]))
# Evaluate the log files
print("\n== POST-PROCESS START =================")
log = LogEval(config_path=os.path.dirname(summary_path))
log.analyze_log(
show_count=config["postprocess"]["show_count"],
show_hof=config["training"]["hof"] is not None and config["training"]["hof"] > 0,
show_pf=config["training"]["save_pareto_front"],
save_plots=config["postprocess"]["save_plots"])
print("== POST-PROCESS END ===================")
if __name__ == "__main__":
main()
| [
37811,
10044,
29363,
1143,
11,
2060,
12,
4122,
4219,
4226,
284,
1057,
360,
15821,
319,
257,
900,
286,
31747,
526,
15931,
198,
198,
11748,
28686,
198,
11748,
25064,
198,
11748,
640,
198,
11748,
18540,
305,
919,
278,
198,
6738,
4866,
1330... | 2.656467 | 2,157 |
import cvxpy as cp
import matplotlib.pyplot as matplt
from utils import *
from ddpg_alg_spinup import ddpg
import tensorflow as tf
from env_mra import ResourceEnv
import numpy as np
import time
import pickle
from parameters import *
if __name__ == "__main__":
with open("saved_alpha.pickle", "wb") as fileop:
pickle.dump(alpha, fileop)
with open("saved_weight.pickle", "wb") as fileop:
pickle.dump(weight, fileop)
########################################################################################################################
########################################## Main Training #############################################
########################################################################################################################
start_time = time.time()
utility = np.zeros(SliceNum)
x = np.zeros([UENum, maxTime], dtype=np.float32)
for i in range(SliceNum):
ac_kwargs = dict(hidden_sizes=hidden_sizes, activation=tf.nn.relu, output_activation=tf.nn.sigmoid)
logger_kwargs = dict(output_dir=str(RESNum)+'slice'+str(i), exp_name=str(RESNum)+'slice_exp'+str(i))
env = ResourceEnv(alpha=alpha[i], weight=weight[i],
num_res=RESNum, num_user=UENum,
max_time=maxTime, min_reward=minReward,
rho=rho, test_env=False)
utility[i], _ = ddpg(env=env, ac_kwargs=ac_kwargs,
steps_per_epoch=steps_per_epoch,
epochs=epochs, pi_lr=pi_lr, q_lr=q_lr,
start_steps=start_steps, batch_size=batch_size,
seed=seed, replay_size=replay_size, max_ep_len=maxTime,
logger_kwargs=logger_kwargs, fresh_learn_idx=True)
print('slice' + str(i) + 'training completed.')
end_time = time.time()
print('Training Time is ' + str(end_time - start_time))
##################################### result ploting ###############################################
with open("saved_alpha.pickle", "rb") as fileop:
load_alpha = pickle.load(fileop)
with open("saved_weight.pickle", "rb") as fileop:
load_weight = pickle.load(fileop)
#print(weight)
#matplt.subplot(2, 1, 1)
#matplt.plot(sum_utility)
#matplt.subplot(2, 1, 2)
#matplt.plot(sum_x)
matplt.show()
print('done')
| [
11748,
269,
85,
87,
9078,
355,
31396,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
2603,
489,
83,
198,
6738,
3384,
4487,
1330,
1635,
198,
6738,
49427,
6024,
62,
14016,
62,
39706,
929,
1330,
49427,
6024,
198,
11748,
11192,
273,
... | 2.325234 | 1,070 |
import numpy as np
import neat.parallel
| [
11748,
299,
32152,
355,
45941,
198,
198,
11748,
15049,
13,
1845,
29363,
628
] | 3.230769 | 13 |
import httpx
| [
11748,
2638,
87,
628
] | 3.5 | 4 |
from YTPlaylistRanking.YTPlaylistRanking import SortType, main
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=("Gets statistics about videos in a YouTube playlist using the playlist's id to write to a file"
" the list of videos in ascending order according to a certain criteria (ex view count)."
"\nWarning: the output file is overwritten with each run!!"),
epilog="https://github.com/TheDigitalPhoenixX/YTPlaylistRanking"
)
parser.add_argument("id", help="Playlist ID")
parser.add_argument("-d", "--debug",
help="write intermediate api responses to files(default: %(default)s)",
action="store_true")
parser.add_argument("-vc", "--viewCount", dest="sortTypes",
help="Sort according to ViewCount(default)",
action="append_const", const=SortType.ViewCount)
parser.add_argument("-lc", "--likeCount", dest="sortTypes",
help="Sort according to Like Count",
action="append_const", const=SortType.LikeCount)
parser.add_argument("-dlc", "--dislikeCount", dest="sortTypes",
help="Sort according to Dislike Count",
action="append_const", const=SortType.DislikeCount)
parser.add_argument("-ltvc", "--likeToViewCount", dest="sortTypes",
help="Sort according to LikeToViewCount",
action="append_const", const=SortType.LikeToViewCount)
parser.add_argument("-ltdlc", "--likeToDislikeCount", dest="sortTypes",
help="Sort according to LikeToDislikeCount",
action="append_const", const=SortType.LikeToDislikeCount)
parser.add_argument("-r", "--reverse",
help="reverse the order of the list(default: %(default)s)",
action="store_true")
parser.add_argument("-rn", "--removeNumbering",
help="remove numbering(default: %(default)s)",
action="store_true")
args = parser.parse_args()
main(args.id, args.debug, args.sortTypes if args.sortTypes != None else [SortType.ViewCount],
not args.reverse, args.removeNumbering)
| [
6738,
575,
51,
11002,
4868,
49,
15230,
13,
56,
51,
11002,
4868,
49,
15230,
1330,
33947,
6030,
11,
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201,
198,
11748,
1822,
29572,
201,
198,
201,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
201,
198,
220,
220,
2... | 2.23546 | 1,066 |
# Bot token goes here
BOT_TOKEN = ''
# Manually specify Tesseract path
TESSERACT_PATH = ''
| [
2,
18579,
11241,
2925,
994,
198,
33,
2394,
62,
10468,
43959,
796,
10148,
198,
198,
2,
1869,
935,
11986,
39412,
263,
529,
3108,
198,
51,
7597,
1137,
10659,
62,
34219,
796,
10148,
198
] | 2.787879 | 33 |
#!/usr/bin/env python
import time
from netmiko import ConnectHandler, redispatch
net_connect = ConnectHandler(
device_type='terminal_server',
ip='10.10.10.10',
username='admin',
password='admin123',
secret='secret123')
# Manually handle interaction in the Terminal Server (fictional example, but
# hopefully you see the pattern)
net_connect.write_channel("\r\n")
time.sleep(1)
net_connect.write_channel("\r\n")
time.sleep(1)
output = net_connect.read_channel()
# Should hopefully see the terminal server prompt
print(output)
# Login to end device from terminal server
net_connect.write_channel("connect 1\r\n")
time.sleep(1)
# Manually handle the Username and Password
max_loops = 10
i = 1
while i <= max_loops:
output = net_connect.read_channel()
if 'Username' in output:
net_connect.write_channel(net_connect.username + '\r\n')
time.sleep(1)
output = net_connect.read_channel()
# Search for password pattern / send password
if 'Password' in output:
net_connect.write_channel(net_connect.password + '\r\n')
time.sleep(.5)
output = net_connect.read_channel()
# Did we successfully login
if '>' in output or '#' in output:
break
net_connect.write_channel('\r\n')
time.sleep(.5)
i += 1
# We are now logged into the end device
# Dynamically reset the class back to the proper Netmiko class
redispatch(net_connect, device_type='cisco_ios')
# Now just do your normal Netmiko operations
new_output = net_connect.send_command("show ip int brief")
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
11748,
640,
198,
6738,
2010,
76,
12125,
1330,
8113,
25060,
11,
2266,
8802,
963,
198,
198,
3262,
62,
8443,
796,
8113,
25060,
7,
198,
220,
220,
220,
3335,
62,
4906,
11639,
23705,
282,
... | 2.750871 | 574 |
import numpy as np
from scipy.stats import norm
from .mixed_optimiser import MVO
from scipy.optimize import shgo, differential_evolution, dual_annealing
import scipy as stats
class MVMOO(MVO):
"""
Multi variate mixed variable optimisation
"""
def __init__(self, input_dim=1, num_qual=0, num_obj=2, bounds=None, k_type='matern3', dist='manhattan', scale='bounds'):
"""
Initialisation of the class
"""
super().__init__(input_dim=input_dim, num_qual=num_qual, bounds=bounds, dist=dist, k_type=k_type)
self.num_obj = num_obj
self.scale = scale
def generatemodels(self, X, Y, scale=True, variance=1.0):
"""
Generate a list containing the models for each of the objectives
"""
self.nsamples, nobj = np.shape(Y)
models = []
if scale is True:
self.Yscaled = self.scaley(Y)
self.Xscaled = self.scaleX(X,mode=self.scale)
for i in range(nobj):
self.fitmodel(self.Xscaled, self.Yscaled[:,i].reshape((-1,1)), variance=variance)
models.append(self.model)
return models
for i in range(nobj):
self.fitmodel(X, Y[:,i].reshape((-1,1)))
models.append(self.model)
return models
def is_pareto_efficient(self, costs, return_mask = True):
"""
Find the pareto-efficient points for minimisation problem
:param costs: An (n_points, n_costs) array
:param return_mask: True to return a mask
:return: An array of indices of pareto-efficient points.
If return_mask is True, this will be an (n_points, ) boolean array
Otherwise it will be a (n_efficient_points, ) integer array of indices.
"""
is_efficient = np.arange(costs.shape[0])
n_points = costs.shape[0]
next_point_index = 0 # Next index in the is_efficient array to search for
while next_point_index<len(costs):
nondominated_point_mask = np.any(costs<costs[next_point_index], axis=1)
nondominated_point_mask[next_point_index] = True
is_efficient = is_efficient[nondominated_point_mask] # Remove dominated points
costs = costs[nondominated_point_mask]
next_point_index = np.sum(nondominated_point_mask[:next_point_index])+1
if return_mask:
is_efficient_mask = np.zeros(n_points, dtype = bool)
is_efficient_mask[is_efficient] = True
return is_efficient_mask
else:
return is_efficient
def paretofront(self, Y):
"""
Return an array of the pareto front for the system, set up for a minimising
"""
ind = self.is_pareto_efficient(Y, return_mask=False)
return Y[ind,:]
def EIM(self, X, mode='euclidean'):
"""
Calculate the expected improvment matrix for a candidate point
@ARTICLE{7908974,
author={D. {Zhan} and Y. {Cheng} and J. {Liu}},
journal={IEEE Transactions on Evolutionary Computation},
title={Expected Improvement Matrix-Based Infill Criteria for Expensive Multiobjective Optimization},
year={2017},
volume={21},
number={6},
pages={956-975},
doi={10.1109/TEVC.2017.2697503},
ISSN={1089-778X},
month={Dec}}
"""
f = self.currentfront
nfx = np.shape(f)[0]
nobj = np.shape(f)[1]
nx = np.shape(X)[0]
r = 1.1 * np.ones((1, nobj))
y = np.zeros((nx, 1))
ulist = []
varlist = []
X = self.scaleX(X, mode='bounds')
for iobj in range(nobj):
u, var = self.models[iobj].predict_y(X)
ulist.append(u)
varlist.append(var)
u = np.concatenate(ulist, axis=1)
var = np.concatenate(varlist, axis=1)
std = np.sqrt(np.maximum(0,var))
u_matrix = np.reshape(u.T,(1,nobj,nx)) * np.ones((nfx,1,1))
s_matrix = np.reshape(std.T,(1,nobj,nx)) * np.ones((nfx,1,1))
f_matrix = f.reshape((nfx,nobj,1)) * np.ones((1,1,nx))
Z_matrix = (f_matrix - u_matrix) / s_matrix
EI_matrix = np.multiply((f_matrix - u_matrix), norm.cdf(Z_matrix)) + np.multiply(s_matrix, norm.pdf(Z_matrix))
if mode == 'euclidean':
y = np.min(np.sqrt(np.sum(EI_matrix**2,axis=1)),axis=0).reshape(-1,1)
elif mode == 'hypervolume':
y = np.min(np.prod(r.reshape(1,2,1) - f_matrix + EI_matrix, axis=1) - np.prod(r - f, axis=1).reshape((-1,1)),axis=0).reshape((-1,1))
elif mode == 'maxmin':
y = np.min(np.max(EI_matrix,axis=1),axis=0).reshape(-1,1)
elif mode == 'combine':
y = np.min(np.sqrt(np.sum(EI_matrix**2,axis=1)),axis=0).reshape(-1,1) +\
np.min(np.prod(r.reshape(1,2,1) - f_matrix + EI_matrix, axis=1) - \
np.prod(r - f, axis=1).reshape((-1,1)),axis=0).reshape((-1,1))
else:
y1 = np.min(np.sqrt(np.sum(EI_matrix**2,axis=1)),axis=0).reshape(-1,1)
y2 = np.min(np.prod(r.reshape(1,2,1) - f_matrix + EI_matrix, axis=1) - np.prod(r - f, axis=1).reshape((-1,1)),axis=0).reshape((-1,1))
#y3 = np.min(np.max(EI_matrix,axis=1),axis=0).reshape(-1,1)
return np.hstack((y1,y2))
return y
def CEIM_Hypervolume(self, X):
"""
Calculate the expected improvment matrix for a candidate point, given constraints
@ARTICLE{7908974,
author={D. {Zhan} and Y. {Cheng} and J. {Liu}},
journal={IEEE Transactions on Evolutionary Computation},
title={Expected Improvement Matrix-Based Infill Criteria for Expensive Multiobjective Optimization},
year={2017},
volume={21},
number={6},
pages={956-975},
doi={10.1109/TEVC.2017.2697503},
ISSN={1089-778X},
month={Dec}}
"""
f = self.currentfront
nobj = np.shape(f)[1]
nx = np.shape(X)[0]
r = 1.1 * np.ones((1, nobj))
y = np.zeros((nx, 1))
ulist = []
varlist = []
for iobj in range(nobj):
u, var = self.models[iobj].predict_y(X)
ulist.append(u)
varlist.append(var)
u = np.concatenate(ulist, axis=1)
var = np.concatenate(varlist, axis=1)
std = np.sqrt(np.maximum(0,var))
for ix in range(nx):
Z = (f - u[ix,:]) / std[ix,:]
EIM = np.multiply((f - u[ix,:]), norm.cdf(Z)) + np.multiply(std[ix,:], norm.pdf(Z))
y[ix] = np.min(np.prod(r - f + EIM, axis=1) - np.prod(r - f, axis=1))
# Constraints
ncon = len(self.constrainedmodels)
uconlist = []
varconlist = []
for iobj in range(ncon):
ucon, varcon = self.constrainedmodels[iobj].predict_y(X)
uconlist.append(ucon)
varconlist.append(varcon)
ucon = np.concatenate(uconlist, axis=1)
varcon = np.concatenate(varconlist, axis=1)
stdcon = np.sqrt(np.maximum(0,varcon))
PoF = np.prod(norm.cdf((0 - ucon) / stdcon), axis=1).reshape(-1,1)
return y * PoF
def AEIM_Hypervolume(self, X):
"""
Calculate the adaptive expected improvment matrix for a candidate point
Adaptive addition based on https://arxiv.org/pdf/1807.01279.pdf
"""
f = self.currentfront
c = self.contextual
nfx = np.shape(f)[0]
nobj = np.shape(f)[1]
nx = np.shape(X)[0]
r = 1.1 * np.ones((1, nobj))
y = np.zeros((nx, 1))
ulist = []
varlist = []
for iobj in range(nobj):
u, var = self.models[iobj].predict_y(X)
ulist.append(u)
varlist.append(var)
u = np.concatenate(ulist, axis=1)
var = np.concatenate(varlist, axis=1)
std = np.sqrt(np.maximum(0,var))
u_matrix = np.reshape(u.T,(1,nobj,nx)) * np.ones((nfx,1,1))
s_matrix = np.reshape(std.T,(1,nobj,nx)) * np.ones((nfx,1,1))
f_matrix = f.reshape((nfx,nobj,1)) * np.ones((1,1,nx))
c_matrix = c.reshape((nfx,nobj,1)) * np.ones((1,1,nx))
Z_matrix = (f_matrix - u_matrix - c_matrix) / s_matrix
EI_matrix = np.multiply((f_matrix - u_matrix), norm.cdf(Z_matrix)) + np.multiply(s_matrix, norm.pdf(Z_matrix))
y = np.min(np.prod(r.reshape(1,2,1) - f_matrix + EI_matrix, axis=1) - np.prod(r - f, axis=1).reshape((-1,1)),axis=0).reshape((-1,1))
#for ix in range(nx):
# Z = (f - u[ix,:] - c) / std[ix,:]
# EIM = np.multiply((f - u[ix,:]), norm.cdf(Z)) + np.multiply(std[ix,:], norm.pdf(Z))
# y[ix] = np.min(np.prod(r - f + EIM, axis=1) - np.prod(r - f, axis=1))
return y
def AEIM_Euclidean(self, X):
"""
Calculate the expected improvment matrix for a candidate point
@ARTICLE{7908974,
author={D. {Zhan} and Y. {Cheng} and J. {Liu}},
journal={IEEE Transactions on Evolutionary Computation},
title={Expected Improvement Matrix-Based Infill Criteria for Expensive Multiobjective Optimization},
year={2017},
volume={21},
number={6},
pages={956-975},
doi={10.1109/TEVC.2017.2697503},
ISSN={1089-778X},
month={Dec}}
"""
f = self.currentfront
c = self.contextual
nfx = np.shape(f)[0]
nobj = np.shape(f)[1]
nx = np.shape(X)[0]
y = np.zeros((nx, 1))
ulist = []
varlist = []
X = self.scaleX(X, mode='bounds')
for iobj in range(nobj):
u, var = self.models[iobj].predict_f(X)
ulist.append(u)
varlist.append(var)
u = np.concatenate(ulist, axis=1)
var = np.concatenate(varlist, axis=1)
std = np.sqrt(np.maximum(0,var))
u_matrix = np.reshape(u.T,(1,nobj,nx)) * np.ones((nfx,1,1))
s_matrix = np.reshape(std.T,(1,nobj,nx)) * np.ones((nfx,1,1))
f_matrix = f.reshape((nfx,nobj,1)) * np.ones((1,1,nx))
c_matrix = c.reshape((nfx,nobj,1)) * np.ones((1,1,nx))
Z_matrix = (f_matrix - u_matrix - c_matrix) / s_matrix
EI_matrix = np.multiply((f_matrix - u_matrix), norm.cdf(Z_matrix)) + np.multiply(s_matrix, norm.pdf(Z_matrix))
y = np.min(np.sqrt(np.sum(EI_matrix**2,axis=1)),axis=0).reshape(-1,1)
return y
def EIMmixedoptimiser(self, constraints, algorithm='Random Local', values=None, mode='euclidean'):
"""
Optimise EI search whole domain
"""
if algorithm == 'Random':
Xsamples = self.sample_design(samples=10000, design='halton')
if constraints is False:
fvals = self.EIM(Xsamples, mode=mode)
else:
fvals = self.CEIM_Hypervolume(Xsamples)
fmax = np.amax(fvals)
indymax = np.argmax(fvals)
xmax = Xsamples[indymax,:]
if values is None:
return fmax, xmax
return fmax, xmax, fvals, Xsamples
elif algorithm == 'Random Local':
Xsamples = self.sample_design(samples=10000, design='halton')
if constraints is False:
fvals = self.EIM(Xsamples, mode=mode)
else:
fvals = self.CEIM_Hypervolume(Xsamples)
if mode == 'all':
fmax = np.max(fvals,axis=0)
print(fvals.shape)
print(fmax.shape)
indmax = np.argmax(fvals,axis=0)
print(indmax)
xmax = Xsamples[indmax,:]
qual = xmax[:,-self.num_qual:].reshape(-1)
bnd = list(self.bounds[:,:self.num_quant].T)
bndlist = []
for element in bnd:
bndlist.append(tuple(element))
modes = ['euclidean', 'hypervolume']
results = []
for i in range(2):
results.append(stats.optimize.minimize(self.EIMoptimiserWrapper, xmax[i,:-self.num_qual].reshape(-1), args=(qual[i],constraints,modes[i]), bounds=bndlist,method='SLSQP'))
xmax = np.concatenate((results[0].x, qual[0]),axis=None)
xmax = np.vstack((xmax,np.concatenate((results[1].x, qual[1]),axis=None)))
fmax = np.array((results[0].fun,results[1].fun))
return fmax, xmax
fmax = np.amax(fvals)
indymax = np.argmax(fvals)
xmax = Xsamples[indymax,:]
qual = xmax[-self.num_qual:]
bnd = list(self.bounds[:,:self.num_quant].T)
bndlist = []
for element in bnd:
bndlist.append(tuple(element))
result = stats.optimize.minimize(self.EIMoptimiserWrapper, xmax[:-self.num_qual].reshape(-1), args=(qual,constraints,mode), bounds=bndlist,method='SLSQP')
if values is None:
return result.fun, np.concatenate((result.x, qual),axis=None)
return fmax, xmax, fvals, Xsamples
else:
raise NotImplementedError()
def multinextcondition(self, X, Y, constraints=False, values=None, method='EIM', mode='euclidean'):
"""
Suggest the next condition for evaluation
"""
if constraints is False:
try:
self.k_type = 'matern3'
self.models = self.generatemodels(X, Y)
except:
print('Initial model optimisation failed, retrying with new kernel')
try:
self.k_type = 'matern5'
self.models = self.generatemodels(X, Y)
except:
print('Model optimisation failed, retrying with new value of variance')
for variance in [0.1,1,2,10]:
try:
self.models = self.generatemodels(X, Y, variance=variance)
except:
print('Model optimisation failed, retrying with new value of variance')
self.currentfront = self.paretofront(self.Yscaled)
means = []
for model in self.models:
mean, _ = model.predict_y(self.sample_design(samples=2, design='halton'))
means.append(mean.numpy())
if np.any(means == np.nan):
print("Retraining model with new starting variance")
self.models = self.generatemodels(X, Y, variance=0.1)
if method == 'AEIM':
fmax, xmax = self.AEIMmixedoptimiser(constraints, algorithm='Random Local')
else:
fmax, xmax = self.EIMmixedoptimiser(constraints, algorithm='Random Local',mode=mode)
if values is None and mode != 'all':
return xmax.reshape(1,-1), fmax
elif values is None and mode == 'all':
if np.allclose(xmax[0,:],xmax[1,:], rtol=1e-3, atol=1e-5):
return xmax[0,:].reshape(1,-1), fmax[0]
return np.unique(xmax.round(6),axis=0), fmax
self.models = self.generatemodels(X,Y)
self.currentfront = self.paretofront(self.Yscaled)
self.constrainedmodels = self.generatemodels(X, constraints, scale=False)
fmax, xmax = self.EIMmixedoptimiser(constraints, algorithm='Simplical')
if values is None:
return xmax.reshape(1,-1), fmax | [
11748,
299,
32152,
355,
45941,
198,
6738,
629,
541,
88,
13,
34242,
1330,
2593,
198,
6738,
764,
76,
2966,
62,
40085,
5847,
1330,
337,
29516,
198,
6738,
629,
541,
88,
13,
40085,
1096,
1330,
427,
2188,
11,
22577,
62,
1990,
2122,
11,
10... | 1.872631 | 8,495 |
number_of_dragons = int(input())
dragons_dict = {}
for index in range(number_of_dragons):
data = input().split()
type = data[0]
name = data[1]
damage = data[2]
health = data[3]
armor = data[4]
if damage == 'null':
damage = 45
damage = int(damage)
if health == 'null':
health = 250
health = int(health)
if armor == 'null':
armor = 10
armor = int(armor)
if type not in dragons_dict:
dragons_dict[type] = {}
dragons_dict[type][name] = {'damage': damage, 'health': health, 'armor': armor}
elif type in dragons_dict and name in dragons_dict[type]:
dragons_dict[type][name]['damage'] = damage
dragons_dict[type][name]['health'] = health
dragons_dict[type][name]['armor'] = armor
elif type in dragons_dict and name not in dragons_dict[type]:
dragons_dict[type][name] = {'damage': damage, 'health': health, 'armor': armor}
# but dragons are sorted alphabetically by their name
# average damage, health, and armor of the dragons
for type, value in dragons_dict.items():
total_damage = 0
total_health = 0
total_armor = 0
sorted_dragons = dict(sorted(value.items(), key=lambda kvp: kvp[0]))
for name, values in sorted_dragons.items():
total_damage += values['damage']
total_health += values['health']
total_armor += values['armor']
average_damage = total_damage / len(value)
average_health = total_health / len(value)
average_armor = total_armor / len(value)
print(f"{type}::({average_damage:.2f}/{average_health:.2f}/{average_armor:.2f})")
for name, values in sorted_dragons.items():
print(f"-{name} -> damage: {values['damage']}, health: {values['health']}, armor: {values['armor']}") | [
17618,
62,
1659,
62,
7109,
34765,
796,
493,
7,
15414,
28955,
198,
7109,
34765,
62,
11600,
796,
23884,
198,
1640,
6376,
287,
2837,
7,
17618,
62,
1659,
62,
7109,
34765,
2599,
198,
220,
220,
220,
1366,
796,
5128,
22446,
35312,
3419,
198,... | 2.59708 | 685 |
# Copyright (c) Andrey Sobolev, 2020. Distributed under MIT license, see LICENSE file.
import os
from jinja2 import Environment, FileSystemLoader
| [
2,
220,
220,
15069,
357,
66,
8,
220,
843,
4364,
36884,
2305,
85,
11,
12131,
13,
4307,
6169,
739,
17168,
5964,
11,
766,
38559,
24290,
2393,
13,
198,
198,
11748,
28686,
198,
6738,
474,
259,
6592,
17,
1330,
9344,
11,
9220,
11964,
17401... | 3.431818 | 44 |
import json
import os
import sys
from src.gitHubApiRequest import performRequest
from src.utils import createFolderIfDoesntExist
if __name__ == '__main__':
try:
sys.argv[1]
except IndexError:
print('pass your github token as parameter ('
'https://help.github.com/en/articles/creating-a-personal-access-token-for-the-command-line)')
exit(1)
# devList = ['rafaelfranca', 'eileencodes', 'lifo']
path = '{}\\{}'.format(os.path.dirname(os.path.abspath(__file__)), 'data')
createFolderIfDoesntExist(path)
devsByRepositorysLanguage(['JavaScript', 'Ruby', 'c'], 500, 5)
f = open('data/ProjWithUser.json', )
data = json.load(f)
for lang in data:
print(lang)
numberContByLang = 0
qtdCount = 0
for repositories in data[lang]:
numberContByRep = 0
for rep in repositories:
print('--', rep)
for dev in repositories[rep]:
numberContByRep += dev['contributions']
# print('----', dev['contributions'])
# print('\n\n')
print(numberContByRep)
print('\n\n')
numberContByLang += numberContByRep
print(numberContByLang)
| [
11748,
33918,
198,
11748,
28686,
198,
11748,
25064,
198,
198,
6738,
12351,
13,
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32,
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41092,
1532,
13921,
429,
3109,
396,
628,
628,
198,
361,
11593,
3672... | 2.10802 | 611 |
from django import template
from django.template.base import FilterExpression
from django.template.loader import get_template
from django.conf import settings
from ..exceptions import SiteMessageConfigurationError
register = template.Library()
@register.tag
def detect_clause(parser, clause_name, tokens):
"""Helper function detects a certain clause in tag tokens list.
Returns its value.
"""
if clause_name in tokens:
t_index = tokens.index(clause_name)
clause_value = parser.compile_filter(tokens[t_index + 1])
del tokens[t_index:t_index + 2]
else:
clause_value = None
return clause_value
| [
6738,
42625,
14208,
1330,
11055,
198,
6738,
42625,
14208,
13,
28243,
13,
8692,
1330,
25853,
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198,
6738,
42625,
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13,
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198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
198,
6738,
... | 3.009174 | 218 |
# -*- coding: utf-8 -*-
"""
Defines loader objects that parse config files and return functions that provide
inputs to TensorFlow Estimator objects.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__all__ = ["ContinuousSequenceBatchLoader", "DiscreteSequenceBatchLoader",
"IndependentBatchLoader"]
import json
import tensorflow as tf
from os import path, walk
from nnetmaker.loader.parsers import *
from nnetmaker.loader.processors import *
from nnetmaker.loader.secondaries import *
from nnetmaker.util import *
_PARSERS = {"raw": RawParser,
"int": Int64Parser,
"float": FloatParser,
"string": StringParser}
_SECONDARIES = {"ones": OnesFeature}
_PROCESSORS = {"slice": SliceProcessor,
"onehot": OneHotProcessor,
"reshape": ReshapeProcessor}
_EPSILON = float(1e-8)
def _parse_and_validate_manifest(manifest_filename):
"""Reads parameters from the specified __manifest__.json file, validates the
entries, and returns a dictionary of record parser objects for each feature."""
# Strip comments while keeping line numbers.
s = ""
with open(manifest_filename, "r") as f_in:
for line in f_in:
comment_pos = line.find("//")
s += line[:comment_pos] + "\n"
manifest = json.loads(s)
manifest_val = ArgumentsValidator(manifest, "Dataset manifest")
with manifest_val:
compression_type = manifest_val.get("compression", [ATYPE_NONE, ATYPE_STRING], True)
if compression_type is not None:
compression_type = compression_type.upper()
if compression_type not in ["ZLIB", "GZIP"]:
raise ValueError("Unsupported compression type: %s" % compression_type)
allow_var_len = manifest_val.get("allow_var_len", ATYPE_BOOL, True)
features_list = manifest_val.get("features", ATYPE_DICTS_LIST, True)
# Validate each feature and create parser objects.
feat_parsers = {}
feat_shapes = {}
feat_dtypes = {}
for feat in features_list:
feat_val = ArgumentsValidator(feat, "Dataset feature")
with feat_val:
name = feat_val.get("name", ATYPE_STRING, True)
dtype = tf.as_dtype(feat_val.get("dtype", ATYPE_STRING, True))
shape = feat_val.get("shape", ATYPE_INTS_LIST, True)
deserialize_type = feat_val.get("deserialize_type", ATYPE_STRING, True)
deserialize_args = feat_val.get("deserialize_args", ATYPE_DICT, False, default={})
var_len = feat_val.get("var_len", ATYPE_BOOL, allow_var_len, default=False)
if var_len and not allow_var_len:
raise ValueError("Variable length features not allowed for this dataset.")
try:
shape = [int(x) for x in list(shape)]
except:
raise ValueError("Invalid shape for feature `%s`: %s" % (name, shape))
try:
feat_parsers[name] = _PARSERS[deserialize_type](shape, dtype, deserialize_args, var_len)
except KeyError:
raise ValueError("Unsupported deserialization type: %s" % deserialize_type)
if var_len:
feat_shapes[name] = [-1] + shape
else:
feat_shapes[name] = shape
feat_dtypes[name] = dtype
return compression_type, allow_var_len, feat_parsers, feat_shapes, feat_dtypes
def _parse_dataset_dict(dataset_dict):
"""Parses the dataset dictionary and loads the list of filenames, parsers, and
manifest info."""
dataset_val = ArgumentsValidator(dataset_dict, "Dataset loader")
with dataset_val:
dataset_type = dataset_val.get("type", ATYPE_STRING, True)
dataset_args = dataset_val.get("args", ATYPE_DICT, True)
if dataset_type == "dir":
# Recursively get all TFRecords files from the specified data dir into a sorted list.
dataset_val = ArgumentsValidator(dataset_args, "Dataset dir loader")
with dataset_val:
data_dir = path.realpath(dataset_val.get("data_dir", ATYPE_STRING, True))
manifest_filename = path.join(data_dir, "__manifest__.json")
all_filenames = []
for root_dir, _, filenames in walk(data_dir):
for f in filenames:
if f.lower().endswith(".tfrecords"):
all_filenames.append(path.join(root_dir, f))
all_filenames = list(sorted(all_filenames))
if len(all_filenames) == 0:
raise ValueError("No .tfrecords files found in %s" % data_dir)
elif dataset_type == "list":
# Use the user-specified file that explicitly lists data source locations.
dataset_val = ArgumentsValidator(dataset_args, "Dataset list loader")
with dataset_val:
list_filename = path.realpath(dataset_val.get("list_file", ATYPE_STRING, True))
manifest_filename = path.realpath(dataset_val.get("manifest_file", ATYPE_STRING, True))
with open(list_filename) as f_in:
all_filenames = [line.strip() for line in f_in if len(line.strip()) > 0]
if len(all_filenames) == 0:
raise ValueError("No filenames found in %s" % list_filename)
else:
raise ValueError("Unsupported datatype type: %s" % dataset_type)
return tuple([all_filenames] + list(_parse_and_validate_manifest(manifest_filename)))
class BaseLoader(object):
"""Base class for data loader objects."""
@property
def target_batch_size(self):
"""The target batch size of the loader."""
return self._target_batch_size
class IndependentBatchLoader(BaseLoader):
"""A loader for iterating over batches of independent examples stored as
TFRecord files in the specified data directory."""
class ContinuousSequenceBatchLoader(BaseLoader):
"""A loader for iterating over batches of subsequences selected from large
continuous sequences of data. Each ``TFRecord`` file stores a sequence of contiguous
data chunks that together comprise a sequence containing no boundaries within.
Each file is considered an independent source, and batch subsequences are selected by
concatenating all chunks along the first dimension then selecting windows of the
concatenated data. All features for each chunk must have the same length
along the first dimension."""
class DiscreteSequenceBatchLoader(BaseLoader):
"""A loader for iterating over batches of subsequences selected from large
discrete sequences of data. Each TFRecord file stores a sequence of contiguous
subsequences that each represent a single unit and together comprise a longer
sequence. Each file is considered an independent source, and batch
subsequences are selected by concatenating all unit subsequences along the
first dimension then selecting a number of contiguous units, aligned at unit
subsequence boundaries. Unit subsequence features may have different lengths
along the first dimension."""
pass
| [
2,
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9,
12,
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25,
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69,
12,
23,
532,
9,
12,
198,
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198,
7469,
1127,
40213,
5563,
326,
21136,
4566,
3696,
290,
1441,
5499,
326,
2148,
198,
15414,
82,
284,
309,
22854,
37535,
10062,
320,
1352,
5563,
13,
198,
37811,
... | 2.913495 | 2,312 |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Configurable VTA Hareware Environment scope."""
# pylint: disable=invalid-name, exec-used
from __future__ import absolute_import as _abs
import os
import json
import copy
import tvm
from tvm import te
from . import intrin
def get_vta_hw_path():
"""Get the VTA HW path."""
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
vta_hw_default = os.path.abspath(os.path.join(curr_path, "../../../3rdparty/vta-hw"))
VTA_HW_PATH = os.getenv('VTA_HW_PATH', vta_hw_default)
return os.path.abspath(VTA_HW_PATH)
def pkg_config(cfg):
"""Returns PkgConfig pkg config object."""
pkg_config_py = os.path.join(get_vta_hw_path(), "config/pkg_config.py")
libpkg = {"__file__": pkg_config_py}
exec(compile(open(pkg_config_py, "rb").read(), pkg_config_py, "exec"), libpkg, libpkg)
PkgConfig = libpkg["PkgConfig"]
return PkgConfig(cfg)
class DevContext(object):
"""Internal development context
This contains all the non-user facing compiler
internal context that is hold by the Environment.
Parameters
----------
env : Environment
The environment hosting the DevContext
Note
----
This class is introduced so we have a clear separation
of developer related, and user facing attributes.
"""
# Memory id for DMA
MEM_ID_UOP = 0
MEM_ID_WGT = 1
MEM_ID_INP = 2
MEM_ID_ACC = 3
MEM_ID_OUT = 4
# VTA ALU Opcodes
ALU_OPCODE_MIN = 0
ALU_OPCODE_MAX = 1
ALU_OPCODE_ADD = 2
ALU_OPCODE_SHR = 3
# Task queue id (pipeline stage)
QID_LOAD_INP = 1
QID_LOAD_WGT = 1
QID_LOAD_OUT = 2
QID_STORE_OUT = 3
QID_COMPUTE = 2
def get_task_qid(self, qid):
"""Get transformed queue index."""
return 1 if self.DEBUG_NO_SYNC else qid
class Environment(object):
"""Hardware configuration object.
This object contains all the information
needed for compiling to a specific VTA backend.
Parameters
----------
cfg : dict of str to value.
The configuration parameters.
Example
--------
.. code-block:: python
# the following code reconfigures the environment
# temporarily to attributes specified in new_cfg.json
new_cfg = json.load(json.load(open("new_cfg.json")))
with vta.Environment(new_cfg):
# env works on the new environment
env = vta.get_env()
"""
current = None
# constants
MAX_XFER = 1 << 22
# debug flags
DEBUG_DUMP_INSN = (1 << 1)
DEBUG_DUMP_UOP = (1 << 2)
DEBUG_SKIP_READ_BARRIER = (1 << 3)
DEBUG_SKIP_WRITE_BARRIER = (1 << 4)
# memory scopes
inp_scope = "local.inp_buffer"
wgt_scope = "local.wgt_buffer"
acc_scope = "local.acc_buffer"
# initialization function
@property
@property
def dev(self):
"""Developer context"""
if self._dev_ctx is None:
self._dev_ctx = DevContext(self)
return self._dev_ctx
@property
def mock(self):
"""A mock version of the Environment
The ALU, dma_copy and intrinsics will be
mocked to be nop.
"""
if self.mock_mode:
return self
if self._mock_env is None:
self._mock_env = copy.copy(self)
self._mock_env._dev_ctx = None
self._mock_env.mock_mode = True
return self._mock_env
@property
def dma_copy(self):
"""DMA copy pragma"""
return ("dma_copy"
if not self.mock_mode
else "skip_dma_copy")
@property
def alu(self):
"""ALU pragma"""
return ("alu"
if not self.mock_mode
else "skip_alu")
@property
def gemm(self):
"""GEMM intrinsic"""
return self.dev.gemm
@property
@property
def target_host(self):
"""The target host"""
if self.TARGET in ["pynq", "de10nano", "zc706"]:
return "llvm -target=armv7-none-linux-gnueabihf"
if self.TARGET == "ultra96":
return "llvm -target=aarch64-linux-gnu"
if self.TARGET in ["sim", "tsim"]:
return "llvm"
raise ValueError("Unknown target %s" % self.TARGET)
@property
def get_env():
"""Get the current VTA Environment.
Returns
-------
env : Environment
The current environment.
"""
return Environment.current
# The memory information for the compiler
@tvm.register_func("tvm.info.mem.%s" % Environment.inp_scope)
@tvm.register_func("tvm.info.mem.%s" % Environment.wgt_scope)
@tvm.register_func("tvm.info.mem.%s" % Environment.acc_scope)
# TVM related registration
@tvm.register_func("tvm.intrin.rule.default.vta.coproc_sync")
@tvm.register_func("tvm.intrin.rule.default.vta.coproc_dep_push")
@tvm.register_func("tvm.intrin.rule.default.vta.coproc_dep_pop")
def _init_env():
"""Initialize the default global env"""
config_path = os.path.join(get_vta_hw_path(), "config/vta_config.json")
if not os.path.exists(config_path):
raise RuntimeError("Cannot find config in %s" % str(config_path))
cfg = json.load(open(config_path))
return Environment(cfg)
Environment.current = _init_env()
| [
2,
49962,
284,
262,
24843,
10442,
5693,
357,
1921,
37,
8,
739,
530,
198,
2,
393,
517,
18920,
5964,
11704,
13,
220,
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262,
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2393,
198,
2,
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351,
428,
670,
329,
3224,
1321,
198,
2,
5115,
6634,
9238,
13,
220,
383,
7054,... | 2.45551 | 2,450 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os.path
import sys
try:
import apiai
except ImportError:
sys.path.append(
os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir)
)
import apiai
CLIENT_ACCESS_TOKEN = '73a62055c012487b9312db1d7ac7de61'
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
11748,
28686,
13,
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198,
11748,
25064,
198,
198,
28311,
25,
198,
220,
220,
220,
1330,
2471,
544,
72,
198,
16... | 2.091549 | 142 |
#!/usr/bin/env python
import codecs
import cStringIO
import unittest
from chirp.library import bulk_tagging_form
TEST_FORM_1 = """
00123--------- A Perfect Circle
a7314af4 [ 1 ] 192 12 Mer de Noms
e2ebb0f2 [ 1 ] 128 12 Mer de Noms
55d1c5a4 [ ] 192 12 Thirteenth Step
A line to skip
1234abcd [ x ] 192 10 To Be Deleted
abcd1234 [ ? ] 222 7 What You Talkin' About Willis?
00665--------- Audioslave
00085fb1 [ 1 ] 128 14 Audioslave
660414cd [ 1 ] 192 14 Audioslave
52c55fc7 [ ] 228 12 Out of Exile
11111111 [ 2 ] 228 13 TALB
22222222 [ 2 ] 228 13 MISMATCH
"""
EXPECTED_RESULTS_1 = {
"a7314af4": (bulk_tagging_form.VERIFIED,
"A Perfect Circle", "Mer de Noms"),
"e2ebb0f2": (bulk_tagging_form.DUPLICATE,
"A Perfect Circle", "Mer de Noms"),
"55d1c5a4": (bulk_tagging_form.VERIFIED,
"A Perfect Circle", "Thirteenth Step"),
"1234abcd": (bulk_tagging_form.DELETE,
"A Perfect Circle", "To Be Deleted"),
"abcd1234": (bulk_tagging_form.QUESTION,
"A Perfect Circle", "What You Talkin' About Willis?"),
"00085fb1": (bulk_tagging_form.DUPLICATE,
"Audioslave", "Audioslave"),
"660414cd": (bulk_tagging_form.VERIFIED,
"Audioslave", "Audioslave"),
"52c55fc7": (bulk_tagging_form.VERIFIED,
"Audioslave", "Out of Exile"),
"11111111": (bulk_tagging_form.TALB_MISMATCH,
"Audioslave", "TALB", '"MISMATCH" vs. "TALB"'),
"22222222": (bulk_tagging_form.TALB_MISMATCH,
"Audioslave", "MISMATCH", '"MISMATCH" vs. "TALB"'),
}
if __name__ == "__main__":
unittest.main()
| [
2,
48443,
14629,
14,
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14,
24330,
21015,
198,
198,
11748,
40481,
82,
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198,
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555,
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13,
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1330,
11963,
62,
12985,
2667,
62,
687,
628,
198,
51,
6465,
62... | 2.045288 | 817 |
from environs import Env
env = Env()
env.read_env()
BOT_TOKEN = env.str("BOT_TOKEN")
ADMIN = env.str("ADMIN")
IP = env.str("ip") | [
6738,
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43959,
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... | 2.20339 | 59 |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2020 Vinay M. Sajip. See LICENSE for licensing information.
#
# Part of the test harness for sarge: Subprocess Allegedly Rewards Good Encapsulation :-)
#
import sys
import time
if __name__ == '__main__':
try:
rc = main()
except Exception as e:
print(e)
rc = 9
sys.exit(rc)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
198,
2,
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357,
34,
8,
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11820,
323,
337,
13,
311,
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541,
13,
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24290,
329,
15665,
1321,
13,
198,
2,
198,
2,
2142,
286,
262,
1332,
19356,
... | 2.496454 | 141 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# PQN-NNLS algorithm (Kim, Sra, Dhillon 2006)
import numpy as np
def pqn_nnls(A, b, err, limit = 300):
"""return x which is >= 0 and minimizes ||Ax - b||"""
m, n = A.shape;
AtA = np.dot(A.T, A)
Atb = np.dot(A.T, b)
curS = np.eye(n)
x = np.zeros(n, 1)
for iteration in range(limit):
# gradient for current x
grad = AtA*x - Atb
fixed_set = []
free_set = []
for i in range(n):
if (abs(x[i]) < err) and (grad[i] > err):
fixed_set.append(i)
else:
free_set.append(i)
cur_y = x[free_set]
grad_y = grad[free_set];
subS = curS[free_set, free_set]
subA = A(:, free_set);
# using APA rule
alpha = 1;
sigma = 1;
s = 1/2;
tau = 1/4;
m = 0;
storedgamma = gamma(s^m*sigma)
while (obj(cur_y)-obj(storedgamma)) < tau*grad_y.T * (cur_y - storedgamma)
m = m+1;
storedgamma = gamma(s^m*sigma);
d = storedgamma - cur_y;
u = x;
u[free_set] = alpha*d;
u[fixed_set] = 0;
pre_b = np.dot(A, u)
temp2 = np.dot(At, pre_b)
temp3 = np.dot(u, u.T)
temp4 = np.dot(pre_b.T, pre_b)
temp5 = np.dot(curS, temp2, u.T)
curS = ((1 + temp2.T*curS*temp2/temp4)*temp3 - ...
(temp5+temp5.\'))/temp4 + curS;
curs +=
if norm(x[free_set] - cur_y - alpha*d) < err
break;
x[free_set] = cur_y + alpha*d;
return x; | [
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8,
201,
... | 1.662351 | 1,004 |
from django.urls import path
from . import views
app_name = 'memberships'
urlpatterns = [
path('register/', views.RegistrationView.as_view(), name='registration'),
path('login/', views.LoginView.as_view(), name='login'),
path('logout/', views.logout_view, name='logout'),
path('forget-pass/', views.ForgetPassView.as_view(), name='forget_pass'),
path('activation/<str:id>/<str:code>', views.ActivationView.as_view(), name='activation'),
path('reset-code/<str:email>/<str:code>', views.ResetCodeView.as_view(), name='reset_code'),
]
| [
6738,
42625,
14208,
13,
6371,
82,
1330,
3108,
198,
198,
6738,
764,
1330,
5009,
198,
198,
1324,
62,
3672,
796,
705,
30814,
5748,
6,
198,
6371,
33279,
82,
796,
685,
198,
220,
220,
220,
3108,
10786,
30238,
14,
3256,
5009,
13,
47133,
76... | 2.748768 | 203 |
from conceptnet_rocks.database import load_dump_into_database
from graph_garden import arangodb
from pathlib import Path
from typing import Optional
import typer
app = typer.Typer()
@app.command()
| [
6738,
3721,
3262,
62,
305,
4657,
13,
48806,
1330,
3440,
62,
39455,
62,
20424,
62,
48806,
198,
6738,
4823,
62,
70,
5872,
1330,
610,
648,
375,
65,
198,
6738,
3108,
8019,
1330,
10644,
198,
6738,
19720,
1330,
32233,
198,
11748,
1259,
525,... | 3.35 | 60 |
import logging
import os
import sys
import traceback
import time
import argparse
from . import config, util, server
from .writer import _rebuild, _check_output
logger = logging.getLogger(__name__)
if __name__ == "__main__":
main()
| [
11748,
18931,
198,
11748,
28686,
198,
11748,
25064,
198,
11748,
12854,
1891,
198,
11748,
640,
198,
11748,
1822,
29572,
198,
198,
6738,
764,
1330,
4566,
11,
7736,
11,
4382,
198,
6738,
764,
16002,
1330,
4808,
260,
11249,
11,
4808,
9122,
6... | 3.102564 | 78 |
from sklearn.feature_extraction.text import CountVectorizer
examples = [
"apple ball cat",
"ball cat dog",
]
# vectorizer = CountVectorizer()
# X = vectorizer.fit_transform(examples)
# print(vectorizer.get_feature_names_out())
# print(X.toarray())
max_features = 3
ngrams = 2
vectorizer = CountVectorizer(max_features=max_features, ngram_range=(1, ngrams))
X = vectorizer.fit_transform(examples)
print(vectorizer.get_feature_names_out())
print(X.toarray())
| [
6738,
1341,
35720,
13,
30053,
62,
2302,
7861,
13,
5239,
1330,
2764,
38469,
7509,
628,
198,
1069,
12629,
796,
685,
198,
220,
220,
220,
366,
18040,
2613,
3797,
1600,
198,
220,
220,
220,
366,
1894,
3797,
3290,
1600,
198,
60,
198,
198,
... | 2.838323 | 167 |
#coding=utf-8
import sys
import gzip
import json
import hashlib
import re
import urllib2
import xml.dom.minidom
import zlib
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.99 Safari/537.36'
APPKEY = '85eb6835b0a1034e'
APPSEC = '2ad42749773c441109bdc0191257a664'
if __name__ == '__main__':
if len(sys.argv) == 1:
print('输入视频播放地址')
else:
media_urls = GetBilibiliUrl(sys.argv[1])
for media_url in media_urls:
print media_url
| [
2,
66,
7656,
28,
40477,
12,
23,
198,
198,
11748,
25064,
198,
11748,
308,
13344,
198,
11748,
33918,
198,
11748,
12234,
8019,
198,
11748,
302,
198,
11748,
2956,
297,
571,
17,
198,
11748,
35555,
13,
3438,
13,
1084,
312,
296,
198,
11748,
... | 1.98893 | 271 |
from flask import Flask, render_template #this has changed
import plotly
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import json
app = Flask(__name__)
@app.route('/')
if __name__ == '__main__':
app.run()
| [
6738,
42903,
1330,
46947,
11,
8543,
62,
28243,
1303,
5661,
468,
3421,
198,
11748,
7110,
306,
198,
11748,
7110,
306,
13,
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62,
672,
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355,
467,
198,
198,
11748,
19798,
292,
355,
279,
67,
198,
11748,
299,
32152,
355,
45941,
198... | 2.86747 | 83 |
XXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXX XXXXX XXXXXXX XXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXX
XXXX XX XXXXX
XXXXXX XXXXX XXXXXX
X XXXXXXXX XXXXXXXXXXXXX XXXX XXXXXX XXXXX XXXXXXXXX XXXXX XX XXXXX
XXXXXXXXXXXXXXXXXX
X X XXXXXXXX XXXXX XXXX XXXXXXX XXXXXXXXXX XX XXXXX XXXXXXXXXXXXXXXXXX
X XXX XXXX XX XXXXXXXXXXXXXX XXXXXX XXXXXX XXXXXXXXXX XXXXXX XXXXX XXXXXXXXXX
XX XXXXX XXXXXXXXX XXXXXXXXXXXXXXXXXX
| [
24376,
24376,
24376,
24376,
24376,
24376,
8051,
198,
24376,
8051,
1395,
24376,
1395,
24376,
8051,
1395,
24376,
198,
24376,
24376,
24376,
24376,
24376,
24376,
8051,
198,
198,
24376,
21044,
1395,
24376,
198,
198,
24376,
8051,
1395,
24376,
1395,... | 2.726027 | 146 |
import numbers
from typing import Optional, Union, Any, Callable, Sequence
import torch
from ignite.metrics import Metric, MetricsLambda
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced
__all__ = ["ConfusionMatrix", "mIoU", "IoU", "DiceCoefficient", "cmAccuracy", "cmPrecision", "cmRecall"]
class ConfusionMatrix(Metric):
"""Calculates confusion matrix for multi-class data.
- `update` must receive output of the form `(y_pred, y)` or `{'y_pred': y_pred, 'y': y}`.
- `y_pred` must contain logits and has the following shape (batch_size, num_categories, ...)
- `y` should have the following shape (batch_size, ...) and contains ground-truth class indices
with or without the background class. During the computation, argmax of `y_pred` is taken to determine
predicted classes.
Args:
num_classes (int): number of classes. See notes for more details.
average (str, optional): confusion matrix values averaging schema: None, "samples", "recall", "precision".
Default is None. If `average="samples"` then confusion matrix values are normalized by the number of seen
samples. If `average="recall"` then confusion matrix values are normalized such that diagonal values
represent class recalls. If `average="precision"` then confusion matrix values are normalized such that
diagonal values represent class precisions.
output_transform (callable, optional): a callable that is used to transform the
:class:`~ignite.engine.Engine`'s `process_function`'s output into the
form expected by the metric. This can be useful if, for example, you have a multi-output model and
you want to compute the metric with respect to one of the outputs.
device (str of torch.device, optional): device specification in case of distributed computation usage.
In most of the cases, it can be defined as "cuda:local_rank" or "cuda"
if already set `torch.cuda.set_device(local_rank)`. By default, if a distributed process group is
initialized and available, device is set to `cuda`.
Note:
In case of the targets `y` in `(batch_size, ...)` format, target indices between 0 and `num_classes` only
contribute to the confusion matrix and others are neglected. For example, if `num_classes=20` and target index
equal 255 is encountered, then it is filtered out.
"""
@reinit__is_reduced
@reinit__is_reduced
@sync_all_reduce("confusion_matrix", "_num_examples")
def IoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda:
"""Calculates Intersection over Union using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
Examples:
.. code-block:: python
train_evaluator = ...
cm = ConfusionMatrix(num_classes=num_classes)
IoU(cm, ignore_index=0).attach(train_evaluator, 'IoU')
state = train_evaluator.run(train_dataset)
# state.metrics['IoU'] -> tensor of shape (num_classes - 1, )
"""
if not isinstance(cm, ConfusionMatrix):
raise TypeError("Argument cm should be instance of ConfusionMatrix, but given {}".format(type(cm)))
if ignore_index is not None:
if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes):
raise ValueError("ignore_index should be non-negative integer, but given {}".format(ignore_index))
# Increase floating point precision and pass to CPU
cm = cm.type(torch.DoubleTensor)
iou = cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag() + 1e-15)
if ignore_index is not None:
return MetricsLambda(ignore_index_fn, iou)
else:
return iou
def mIoU(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda:
"""Calculates mean Intersection over Union using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
Returns:
MetricsLambda
Examples:
.. code-block:: python
train_evaluator = ...
cm = ConfusionMatrix(num_classes=num_classes)
mIoU(cm, ignore_index=0).attach(train_evaluator, 'mean IoU')
state = train_evaluator.run(train_dataset)
# state.metrics['mean IoU'] -> scalar
"""
return IoU(cm=cm, ignore_index=ignore_index).mean()
def cmAccuracy(cm: ConfusionMatrix) -> MetricsLambda:
"""Calculates accuracy using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
Returns:
MetricsLambda
"""
# Increase floating point precision and pass to CPU
cm = cm.type(torch.DoubleTensor)
return cm.diag().sum() / (cm.sum() + 1e-15)
def cmPrecision(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda:
"""Calculates precision using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
Returns:
MetricsLambda
"""
# Increase floating point precision and pass to CPU
cm = cm.type(torch.DoubleTensor)
precision = cm.diag() / (cm.sum(dim=0) + 1e-15)
if average:
return precision.mean()
return precision
def cmRecall(cm: ConfusionMatrix, average: bool = True) -> MetricsLambda:
"""
Calculates recall using :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
average (bool, optional): if True metric value is averaged over all classes
Returns:
MetricsLambda
"""
# Increase floating point precision and pass to CPU
cm = cm.type(torch.DoubleTensor)
recall = cm.diag() / (cm.sum(dim=1) + 1e-15)
if average:
return recall.mean()
return recall
def DiceCoefficient(cm: ConfusionMatrix, ignore_index: Optional[int] = None) -> MetricsLambda:
"""Calculates Dice Coefficient for a given :class:`~ignite.metrics.ConfusionMatrix` metric.
Args:
cm (ConfusionMatrix): instance of confusion matrix metric
ignore_index (int, optional): index to ignore, e.g. background index
"""
if not isinstance(cm, ConfusionMatrix):
raise TypeError("Argument cm should be instance of ConfusionMatrix, but given {}".format(type(cm)))
if ignore_index is not None:
if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes):
raise ValueError("ignore_index should be non-negative integer, but given {}".format(ignore_index))
# Increase floating point precision and pass to CPU
cm = cm.type(torch.DoubleTensor)
dice = 2.0 * cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) + 1e-15)
if ignore_index is not None:
return MetricsLambda(ignore_index_fn, dice)
else:
return dice
| [
11748,
3146,
198,
6738,
19720,
1330,
32233,
11,
4479,
11,
4377,
11,
4889,
540,
11,
45835,
198,
198,
11748,
28034,
198,
198,
6738,
44794,
13,
4164,
10466,
1330,
3395,
1173,
11,
3395,
10466,
43,
4131,
6814,
198,
6738,
44794,
13,
1069,
1... | 2.780136 | 2,638 |
import arcade
from arcade import load_texture
from arcade.gui import UIManager
from arcade.gui.widgets import UITextArea, UIInputText, UITexturePane
LOREM_IPSUM = (
"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent eget pellentesque velit. "
"Nam eu rhoncus nulla. Fusce ornare libero eget ex vulputate, vitae mattis orci eleifend. "
"Donec quis volutpat arcu. Proin lacinia velit id imperdiet ultrices. Fusce porta magna leo, "
"non maximus justo facilisis vel. Duis pretium sem ut eros scelerisque, a dignissim ante "
"pellentesque. Cras rutrum aliquam fermentum. Donec id mollis mi.\n"
"\n"
"Nullam vitae nunc aliquet, lobortis purus eget, porttitor purus. Curabitur feugiat purus sit "
"amet finibus accumsan. Proin varius, enim in pretium pulvinar, augue erat pellentesque ipsum, "
"sit amet varius leo risus quis tellus. Donec posuere ligula risus, et scelerisque nibh cursus "
"ac. Mauris feugiat tortor turpis, vitae imperdiet mi euismod aliquam. Fusce vel ligula volutpat, "
"finibus sapien in, lacinia lorem. Proin tincidunt gravida nisl in pellentesque. Aenean sed "
"arcu ipsum. Vivamus quam arcu, elementum nec auctor non, convallis non elit. Maecenas id "
"scelerisque lectus. Vivamus eget sem tristique, dictum lorem eget, maximus leo. Mauris lorem "
"tellus, molestie eu orci ut, porta aliquam est. Nullam lobortis tempor magna, egestas lacinia lectus.\n"
)
window = MyWindow()
arcade.run()
| [
11748,
27210,
198,
6738,
27210,
1330,
3440,
62,
41293,
198,
6738,
27210,
13,
48317,
1330,
471,
3955,
272,
3536,
198,
6738,
27210,
13,
48317,
13,
28029,
11407,
1330,
471,
2043,
2302,
30547,
11,
12454,
20560,
8206,
11,
471,
2043,
2302,
49... | 2.56304 | 579 |
#########################
# 演示元组
#########################
# 元组： 元组是一序列不可修改的元素的集合
dimensions = (1, 2, 3, 4, 5)
print(dimensions)
print(type(dimensions))
| [
14468,
7804,
2,
198,
2,
10545,
120,
242,
163,
97,
118,
17739,
225,
163,
119,
226,
198,
14468,
7804,
2,
628,
198,
2,
10263,
227,
225,
163,
119,
226,
171,
120,
248,
10263,
227,
225,
163,
119,
226,
42468,
31660,
41753,
237,
26344,
24... | 1.666667 | 93 |
# Generated by Django 1.11.8 on 2018-01-24 15:48
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
352,
13,
1157,
13,
23,
319,
2864,
12,
486,
12,
1731,
1315,
25,
2780,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.875 | 32 |
from django.shortcuts import render
# Create your views here.
from rest_framework import generics, status, mixins
from rest_framework.permissions import AllowAny
from rest_framework.response import Response
from rest_framework.views import APIView
from measure.models import Measures
from .serializers import MeasuresSerializer
import datetime as dt
from attack_information.models import Attackinformation
from django.db.models import Count, Sum, Q, F, Avg
| [
6738,
42625,
14208,
13,
19509,
23779,
1330,
8543,
198,
198,
2,
13610,
534,
5009,
994,
13,
198,
6738,
1334,
62,
30604,
1330,
1152,
873,
11,
3722,
11,
5022,
1040,
198,
6738,
1334,
62,
30604,
13,
525,
8481,
1330,
22507,
7149,
198,
6738,
... | 4.034783 | 115 |
import torch.nn as nn
| [
11748,
28034,
13,
20471,
355,
299,
77,
201,
198,
201,
198
] | 2.272727 | 11 |
from abc import ABC, abstractmethod
| [
6738,
450,
66,
1330,
9738,
11,
12531,
24396,
198
] | 4 | 9 |
import numpy, itertools, tqdm, json
scanners = []
new = []
with open("data.txt", "r") as fh:
lines = fh.readlines()
number = 0
for line in lines:
line = line.strip()
# print(line)
if "scanner" in line:
if new:
scanners.append({"coords_raw":new, "id": number})
number = int(line.split(" scanner ")[1].split(" ")[0])
new = []
elif line:
new.append(tuple([int(i) for i in line.split(",")]))
number += 1
scanners.append({"coords_raw":new, "id": number})
# print(scanners)
unsolved = list(range(len(scanners)))
print("yet to solve:",unsolved)
# start = unsolved.pop(0)
start = unsolved[0]
print("starting with",scanners[start]["id"])
scanners[start]["offset"] = (0,0,0)
scanners[start]["rotations"] = (0,0,0)
scanners[start]["range"] = ((-1000, 1000), (-1000, 1000), (-1000, 1000))
scanners[start]["coords_parsed"] = scanners[start]["coords_raw"].copy()
solved = []
absoluteprobes = set(scanners[start]["coords_parsed"])
while len(unsolved) > 0:
print("solved",[i["id"] for i in solved],"unsolved",unsolved)
candidate_index = unsolved.pop(0)
candidate = scanners[candidate_index]
# solved_so_far = list(solved.keys())
candidate["matched"] = 11
got_it = False
# print("looking at", candidate, "vs", absoluteprobes)
for hypothesis_matchA, hypothesis_matchB in tqdm.tqdm(itertools.product(candidate["coords_raw"],absoluteprobes)):
# print("trying",hypothesis_matchA,"vs",hypothesis_matchB)
for rotx, roty, rotz in itertools.product(range(4), range(4), range(4)):
# print(rotx,roty,rotz)
matched = 1
offset = [ hypothesis_matchB[i] - rotate(hypothesis_matchA, (rotx, roty, rotz))[i] for i in range(3) ]
for coord in candidate["coords_raw"]:
it_would_be = rotate(coord, (rotx, roty, rotz))
# print("coord",coord,"rotated to",it_would_be)
it_would_be = offset_coord(it_would_be, offset)
# print("coord",coord,"offset to",it_would_be)
if tuple(it_would_be) in absoluteprobes:
# print("found!")
matched += 1
# if matched > 1:
# print("offset", offset, "matched", matched)
if matched > candidate["matched"]:
# simplistic...
got_it = True
candidate["rotation"] = (rotx, roty, rotz)
candidate["offset"] = offset
candidate["range"] = ((-1000 + offset[0], 1000 + offset[0]),(-1000 + offset[1], 1000 + offset[1]), (-1000 + offset[2], 1000 + offset[2]))
candidate["matched"] = matched
parsed = []
for i in candidate["coords_raw"]:
parsed.append(offset_coord(rotate(i, (rotx, roty, rotz)), offset))
candidate["coords_parsed"] = parsed
# print("an improved position was found:", candidate)
if not got_it:
unsolved.append(candidate_index)
else:
solved.append(candidate)
absoluteprobes.update(candidate["coords_parsed"])
# print("solved", candidate)
# print("current view of all probes:", absoluteprobes)
print(len(absoluteprobes),"probes found.")
with open("probes.json", "w") as fh:
fh.write(json.dumps(list(absoluteprobes)))
with open("scanners.json", "w") as fh:
fh.write(json.dumps(scanners))
print("Locations:", absoluteprobes)
print("full table dump:", solved)
| [
11748,
299,
32152,
11,
340,
861,
10141,
11,
256,
80,
36020,
11,
33918,
198,
198,
1416,
15672,
796,
17635,
198,
3605,
796,
17635,
198,
198,
4480,
1280,
7203,
7890,
13,
14116,
1600,
366,
81,
4943,
355,
277,
71,
25,
198,
220,
220,
220,... | 2.143623 | 1,678 |
from typing import TypeVar, Generic, Optional, Dict, Any
from abc import abstractmethod
from dataclasses import dataclass
T = TypeVar("T")
@dataclass
| [
6738,
19720,
1330,
5994,
19852,
11,
42044,
11,
32233,
11,
360,
713,
11,
4377,
198,
6738,
450,
66,
1330,
12531,
24396,
198,
6738,
4818,
330,
28958,
1330,
4818,
330,
31172,
628,
198,
51,
796,
5994,
19852,
7203,
51,
4943,
628,
198,
31,
... | 3.369565 | 46 |
import dask.array as da
import dask.dataframe as dd
def apply_parallel(
function,
array,
chunks=4,
drop_axis=None,
new_axis=None,
dtype=float,
*args,
**kwargs
):
"""Apply the function in parallel using Dask.
See https://docs.dask.org/en/latest/array-api.html#dask.array.map_blocks
for more information.
Parameters
----------
function
function to apply
array : ndarray
data to apply function against
chunks : int, optional (default: 4)
number of chunks to break data into in order to run
drop_axis : int or list-like, optional (default: None)
If present, is the number or tuple of numbers of axis to drop
new_axis : int or list-like, optional (default: None)
If present, is the axis number or tuple of numbers of axes to add
dtype : [type], optional (default: float)
output dtype
Returns
-------
ndarray of dtype
"""
return (
da.from_array(array, chunks=chunks, name=False)
.map_blocks(
function,
*args,
**kwargs,
dtype=dtype,
drop_axis=drop_axis,
new_axis=new_axis,
)
.compute()
)
def apply_parallel_predicate(function, array1, array2, chunks=4, *args, **kwargs):
"""Apply the pygeos predicate function in parallel using Dask.
See https://docs.dask.org/en/latest/array-api.html#dask.array.map_blocks
for more information.
Parameters
----------
function
predicate function to apply, takes 2 input arrays
array1 : ndarray
array2 : ndarray
chunks : int, optional (default: 4)
number of chunks to break data into in order to run
dtype : [type], optional (default: float)
output dtype
Returns
-------
ndarray(bool)
"""
return da.map_blocks(
function,
da.from_array(array1, chunks=chunks, name=False),
da.from_array(array2, chunks=chunks, name=False),
dtype="bool",
).compute()
def apply_parallel_dataframe(function, df, partitions=4, *args, **kwargs):
"""Apply the function in parallel using Dask.
See https://docs.dask.org/en/latest/array-api.html#dask.array.map_blocks
for more information.
Parameters
----------
function
function to apply
df : DataFrame
data frame to apply function against
partitions : int, optional (default: 4)
number of partitions to break data into in order to run
drop_axis : int or list-like, optional (default: None)
If present, is the number or tuple of numbers of axis to drop
new_axis : int or list-like, optional (default: None)
If present, is the axis number or tuple of numbers of axes to add
Returns
-------
ndarray of dtype
"""
return (
dd.from_pandas(df, npartitions=partitions)
.map_partitions(function, *args, **kwargs)
.compute()
)
| [
11748,
288,
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13,
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11748,
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13,
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14535,
355,
49427,
628,
198,
4299,
4174,
62,
1845,
29363,
7,
198,
220,
220,
220,
2163,
11,
198,
220,
220,
220,
7177,
11,
198,
220,
220,
220,
22716,
28,
19,
... | 2.492077 | 1,199 |
'''
Zephyr: Open-source seismic waveform modelling and inversion code written in Python
'''
__version__ = 'devel'
__author__ = 'Brendan Smithyman'
__license__ = 'MIT'
__copyright__ = 'Copyright 2015 Brendan Smithyman'
| [
7061,
6,
198,
57,
27446,
2417,
25,
4946,
12,
10459,
37463,
6769,
687,
38591,
290,
287,
9641,
2438,
3194,
287,
11361,
198,
7061,
6,
198,
198,
834,
9641,
834,
220,
220,
796,
705,
2934,
626,
6,
198,
834,
9800,
834,
220,
220,
220,
796... | 2.986842 | 76 |
# -*- coding: utf-8 -*-
"""
example.routes.py
"""
from webapp2_extras.routes import HandlerPrefixRoute, RedirectRoute
# --------------------------------------------------------------------
# Routes
# --------------------------------------------------------------------
routes = [
HandlerPrefixRoute('example.handlers.', [
RedirectRoute('/',
name = 'index',
handler = 'ExampleHandler',
strict_slash = True,
),
RedirectRoute('/populate',
name = 'populate',
handler = 'DebugHandler:populate',
strict_slash = True,
),
RedirectRoute('/update',
name = 'update',
handler = 'DebugHandler:update',
strict_slash = True,
),
RedirectRoute('/delete',
name = 'delete',
handler = 'DebugHandler:delete',
strict_slash = True,
),
])
]
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
20688,
13,
81,
448,
274,
13,
9078,
198,
198,
37811,
198,
198,
6738,
3992,
1324,
17,
62,
2302,
8847,
13,
81,
448,
274,
1330,
32412,
36698,
844,
43401,
11,
... | 2.218391 | 435 |
from django.conf.urls.static import static
from django.contrib import admin
from django.urls import path, include
from django.conf import settings
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('django.contrib.auth.urls')),
path('', include('social_django.urls', namespace='social')),
path('', include('social_auth_app.urls')),
]
if settings.DEBUG:
urlpatterns += static(settings.STATIC_URL,
document_root=settings.STATIC_ROOT)
urlpatterns += static(settings.MEDIA_URL,
document_root=settings.MEDIA_ROOT)
| [
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
13,
12708,
1330,
9037,
198,
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
198,
6738,
42625,
14208,
13,
6371,
82,
1330,
3108,
11,
2291,
198,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
... | 2.45122 | 246 |
# -*- coding: utf-8 -*-
#! /usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from six.moves import range
# plain plotting from values
def plot_conn(values, name='', fs=1, ylim=None, xlim=None, show=True):
'''
Plot connectivity estimation results. Allows to plot your results
without using *Data* class.
Args:
*values* : numpy.array
connectivity estimation values in shape (fq, k, k) where fq -
frequency, k - number of channels
*name* = '' : str
title of the plot
*fs* = 1 : int
sampling frequency
*ylim* = None : list
range of y-axis values shown, e.g. [0,1]
*None* means that default values of given estimator are taken
into account
*xlim* = None : list [from (int), to (int)]
range of y-axis values shown, if None it is from 0 to Nyquist frequency
*show* = True : boolean
show the plot or not
'''
fq, k, k = values.shape
fig, axes = plt.subplots(k, k)
freqs = np.linspace(0, fs//2, fq)
if not xlim:
xlim = [0, np.max(freqs)]
if not ylim:
ylim = [np.min(values), np.max(values)]
for i in range(k):
for j in range(k):
axes[i, j].fill_between(freqs, values[:, i, j], 0)
axes[i, j].set_xlim(xlim)
axes[i, j].set_ylim(ylim)
plt.suptitle(name, y=0.98)
plt.tight_layout()
plt.subplots_adjust(top=0.92)
if show:
plt.show()
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
0,
1220,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
6738,
2237,
... | 2.153957 | 695 |
from typing import Optional, List
from pydantic import BaseModel
| [
6738,
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11,
7343,
198,
198,
6738,
279,
5173,
5109,
1330,
7308,
17633,
628,
628,
198
] | 3.888889 | 18 |
import librosa
import os
import numpy as np
import scipy.io.wavfile as wavfile
audio_range = (0, 20)
if not os.path.exists('./norm_audio_train'):
os.mkdir('./norm_audio_train')
for idx in range(audio_range[0], audio_range[1]):
print('Processing audio %s'%idx)
path = './audio_train/trim_audio_train%s.wav' % idx
norm = './norm_audio_train/trim_audio_train%s.wav' % idx
if os.path.exists(path):
audio, _ = librosa.load(path, sr=16000)
max = np.max(np.abs(audio))
norm_audio = np.divide(audio, max)
wavfile.write(norm,16000,norm_audio)
| [
11748,
9195,
4951,
64,
198,
11748,
28686,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
629,
541,
88,
13,
952,
13,
45137,
7753,
355,
266,
615,
7753,
198,
198,
24051,
62,
9521,
796,
357,
15,
11,
1160,
8,
198,
198,
361,
407,
28686,
... | 2.180812 | 271 |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 29 12:08:34 2021
@author: BKG
"""
import sqlite3
from sqlite3 import Error
import pandas as pd
import PySimpleGUI as sg
import re
from re import search
from datetime import datetime
from fpdf import FPDF
def _db_connection():
'''
Connects to the .db file
Returns
-------
connection : sqlite db connection
'''
try:
connection = sqlite3.connect('Data\\UIF_Alumni_DB.db')
except Error:
print(Error)
return connection
if __name__ == "__main__":
main() | [
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198,
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198,
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11748,
44161,
578,
18,
198,
6738... | 2.587156 | 218 |
"""isort:skip_file."""
import argparse
import os
import sys
sys.path.append("../")
from torch.utils.data import DataLoader
from tqdm import tqdm
from beta_rec.core.eval_engine import SeqEvalEngine
from beta_rec.core.train_engine import TrainEngine
from beta_rec.datasets.seq_data_utils import (
SeqDataset,
collate_fn,
create_seq_db,
dataset_to_seq_target_format,
load_dataset,
reindex_items,
)
from beta_rec.models.narm import NARMEngine
from beta_rec.utils.monitor import Monitor
def parse_args():
"""Parse args from command line.
Returns:
args object.
"""
parser = argparse.ArgumentParser(description="Run NARM..")
parser.add_argument(
"--config_file",
nargs="?",
type=str,
default="../configs/narm_default.json",
help="Specify the config file name. Only accept a file from ../configs/",
)
# If the following settings are specified with command line,
# these settings will be updated.
parser.add_argument(
"--dataset",
nargs="?",
type=str,
help="Options are: tafeng, dunnhunmby and instacart",
)
parser.add_argument(
"--data_split",
nargs="?",
type=str,
help="Options are: leave_one_out and temporal",
)
parser.add_argument("--root_dir", nargs="?", type=str, help="working directory")
parser.add_argument(
"--n_sample", nargs="?", type=int, help="Number of sampled triples."
)
parser.add_argument("--sub_set", nargs="?", type=int, help="Subset of dataset.")
parser.add_argument(
"--temp_train",
nargs="?",
type=int,
help="IF value >0, then the model will be trained based on the temporal feeding, else use normal trainning.",
)
parser.add_argument(
"--emb_dim", nargs="?", type=int, help="Dimension of the embedding."
)
parser.add_argument(
"--late_dim", nargs="?", type=int, help="Dimension of the latent layers.",
)
parser.add_argument("--lr", nargs="?", type=float, help="Intial learning rate.")
parser.add_argument("--num_epoch", nargs="?", type=int, help="Number of max epoch.")
parser.add_argument(
"--batch_size", nargs="?", type=int, help="Batch size for training."
)
parser.add_argument("--optimizer", nargs="?", type=str, help="OPTI")
parser.add_argument("--activator", nargs="?", type=str, help="activator")
parser.add_argument("--alpha", nargs="?", type=float, help="ALPHA")
return parser.parse_args()
class NARM_train(TrainEngine):
"""An instance class from the TrainEngine base class."""
def __init__(self, config):
"""Initialize NARM_trian Class.
Args:
config (dict): All the parameters for the model.
"""
self.config = config
super(NARM_train, self).__init__(self.config)
self.load_dataset_seq()
self.build_data_loader()
self.engine = NARMEngine(self.config)
self.seq_eval_engine = SeqEvalEngine(self.config)
def load_dataset_seq(self):
"""Build a dataset for model."""
# ml = Movielens_100k()
# ml.download()
# ml.load_interaction()
# self.dataset = ml.make_temporal_split(n_negative=0, n_test=0)
ld_dataset = load_dataset(self.config)
ld_dataset.download()
ld_dataset.load_interaction()
self.dataset = ld_dataset.make_temporal_split(n_negative=0, n_test=0)
self.train_data = self.dataset[self.dataset.col_flag == "train"]
self.valid_data = self.dataset[self.dataset.col_flag == "validate"]
self.test_data = self.dataset[self.dataset.col_flag == "test"]
# self.dataset = Dataset(self.config)
self.config["dataset"]["n_users"] = self.train_data.col_user.nunique()
self.config["dataset"]["n_items"] = self.train_data.col_item.nunique() + 1
def build_data_loader(self):
"""Convert users' interactions to sequences.
Returns:
load_train_data (DataLoader): training set.
"""
# reindex items from 1
self.train_data, self.valid_data, self.test_data = reindex_items(
self.train_data, self.valid_data, self.test_data
)
# data to sequences
self.valid_data = create_seq_db(self.valid_data)
self.test_data = create_seq_db(self.test_data)
# convert interactions to sequences
seq_train_data = create_seq_db(self.train_data)
# convert sequences to (seq, target) format
load_train_data = dataset_to_seq_target_format(seq_train_data)
# define pytorch Dataset class for sequential datasets
load_train_data = SeqDataset(load_train_data)
# pad the sequences with 0
self.load_train_data = DataLoader(
load_train_data,
batch_size=self.config["model"]["batch_size"],
shuffle=False,
collate_fn=collate_fn,
)
return self.load_train_data
def _train(self, engine, train_loader, save_dir):
"""Train the model with epochs."""
epoch_bar = tqdm(range(self.config["model"]["max_epoch"]), file=sys.stdout)
for epoch in epoch_bar:
print("Epoch {} starts !".format(epoch))
print("-" * 80)
if self.check_early_stop(engine, save_dir, epoch):
break
engine.train_an_epoch(train_loader, epoch=epoch)
"""evaluate model on validation and test sets"""
# evaluation
self.seq_eval_engine.train_eval_seq(
self.valid_data, self.test_data, engine, epoch
)
def train(self):
"""Train and test NARM."""
self.monitor = Monitor(
log_dir=self.config["system"]["run_dir"], delay=1, gpu_id=self.gpu_id
)
train_loader = self.load_train_data
self.engine = NARMEngine(self.config)
self.narm_save_dir = os.path.join(
self.config["system"]["model_save_dir"], self.config["model"]["save_name"]
)
self._train(self.engine, train_loader, self.narm_save_dir)
self.config["run_time"] = self.monitor.stop()
self.seq_eval_engine.test_eval_seq(self.test_data, self.engine)
if __name__ == "__main__":
args = parse_args()
narm = NARM_train(args)
narm.train()
# narm.test() have already implemented in train()
| [
37811,
271,
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25,
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526,
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1822,
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198,
11748,
28686,
198,
11748,
25064,
198,
198,
17597,
13,
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13,
33295,
7203,
40720,
4943,
198,
198,
6738,
28034,
13,
26791,
13,
7890,
1330,
6060,
17401,
198... | 2.300644 | 2,794 |
import logging
from pathlib import Path
from flask import Flask
from config import config
from model.model import get_detector
from views.views import detector_blueprint
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = Flask(__name__)
app.secret_key = config.get("secret_key", "secret")
app.config["UPLOAD_FOLDER"] = config.get("upload_folder", "static")
app.config["MAX_CONTENT_LENGTH"] = config.get("max_content_length", 16 * 1024 * 1024)
app.config["ALLOWED_IMAGE_EXTENSIONS"] = config.get(
"allowed_image_extensions", ["JPEG", "JPG", "PNG"]
)
app.config["CONFIDENCE"] = config.get("default_confidence", 0.90)
app.register_blueprint(detector_blueprint)
# Creating upload folder in case it doesn't exist
Path(app.config["UPLOAD_FOLDER"]).mkdir(exist_ok=True)
# Preloading model
get_detector()
if __name__ == "__main__":
logger.info("Starting app")
app.run(port=config.get("serving_port", 8000))
| [
11748,
18931,
198,
6738,
3108,
8019,
1330,
10644,
198,
198,
6738,
42903,
1330,
46947,
198,
198,
6738,
4566,
1330,
4566,
198,
6738,
2746,
13,
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1330,
651,
62,
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9250,
198,
6738,
5009,
13,
33571,
1330,
31029,
62,
17585,
4798,
19... | 2.893293 | 328 |
from flask import Flask, jsonify, request
from flask_restful import Api, Resource
from pymongo import MongoClient
import bcrypt
import logging
logging.basicConfig(level=logging.DEBUG,format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] [%(thread)d] - %(message)s',datefmt='%d/%m/%Y %H:%M:%S',filename='flask.log')
from logging.handlers import TimedRotatingFileHandler
app = Flask(__name__)
api = Api(app)
client = MongoClient("mongodb://my_db:27017")
db = client.projectDB
users = db["Users"]
"""
HELPER FUNCTIONS
"""
"""
RESOURCES
"""
api.add_resource(Hello, '/hello')
api.add_resource(Register, '/register')
api.add_resource(Retrieve, '/retrieve')
api.add_resource(Save, '/save')
if __name__ == "__main__":
app.run(host='0.0.0.0', debug=False)
| [
6738,
42903,
1330,
46947,
11,
33918,
1958,
11,
2581,
198,
6738,
42903,
62,
2118,
913,
1330,
5949,
72,
11,
20857,
198,
6738,
279,
4948,
25162,
1330,
42591,
11792,
198,
11748,
275,
29609,
198,
11748,
18931,
198,
6404,
2667,
13,
35487,
169... | 2.607383 | 298 |
score_list = []
score_list_file = open("score.txt")
for score in score_list_file:
score = score.rstrip().split(",")
score_list.append([score[0],int(score[1])])
score_list_file.close()
print (score_list)
| [
26675,
62,
4868,
796,
17635,
198,
198,
26675,
62,
4868,
62,
7753,
796,
1280,
7203,
26675,
13,
14116,
4943,
198,
198,
1640,
4776,
287,
4776,
62,
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62,
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25,
198,
220,
220,
220,
4776,
796,
4776,
13,
81,
36311,
22446,
35312,
7,... | 2.529412 | 85 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/02/25 16:30
# @Author : niuliangtao
# @Site :
# @File : MachineLearninginAction.py
# @Software: PyCharm
import csv
import random
import socket
import time
import http.client
import http.client
import requests
from bs4 import BeautifulSoup
if __name__ == '__main__':
url = "https://github.com/apachecn/MachineLearning"
req = requests.get(url)
soup = BeautifulSoup(req.content.decode('gbk', 'ignore'), 'lxml')
print req.text.encode("utf-8")
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
2488,
7575,
220,
220,
220,
1058,
2864,
14,
2999,
14,
1495,
1467,
25,
1270,
198,
2,
2488,
13838,
220,
1058,
37628,
... | 2.607843 | 204 |
from mercury import *
b64_cert = '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'
print(parse_cert(b64_cert))
b64_dns = '1e2BgAABAAAAAQAABGxpdmUGZ2l0aHViA2NvbQAAHAABwBEABgABAAABzQBIB25zLTE3MDcJYXdzZG5zLTIxAmNvAnVrABFhd3NkbnMtaG9zdG1hc3RlcgZhbWF6b27AGAAAAAEAABwgAAADhAASdQAAAVGA'
print(parse_dns(b64_dns))
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4... | 1.34532 | 3,782 |
import os
import random
import string
import uuid
from dotenv import load_dotenv
from flask import request, g, jsonify, make_response
from flask_cors import CORS
from flask_sqlalchemy import SQLAlchemy as _SQLAlchemy
from flask_marshmallow import Marshmallow
from flask_migrate import Migrate
from time import monotonic
from notifications_utils.clients.zendesk.zendesk_client import ZendeskClient
from notifications_utils.clients.statsd.statsd_client import StatsdClient
from notifications_utils.clients.redis.redis_client import RedisClient
from notifications_utils import logging, request_helper
from werkzeug.exceptions import HTTPException as WerkzeugHTTPException, RequestEntityTooLarge
from werkzeug.local import LocalProxy
from app.callback.sqs_client import SQSClient
from app.celery.celery import NotifyCelery
from app.clients import Clients
from app.clients.email.aws_ses import AwsSesClient
from app.clients.sms.firetext import FiretextClient
from app.clients.sms.loadtesting import LoadtestingClient
from app.clients.sms.mmg import MMGClient
from app.clients.sms.aws_sns import AwsSnsClient
from app.clients.sms.twilio import TwilioSMSClient
from app.clients.sms.aws_pinpoint import AwsPinpointClient
from app.clients.performance_platform.performance_platform_client import PerformancePlatformClient
from app.oauth.registry import oauth_registry
from app.va.va_profile import VAProfileClient
from app.va.mpi import MpiClient
from app.encryption import Encryption
from app.attachments.store import AttachmentStore
DATETIME_FORMAT = "%Y-%m-%dT%H:%M:%S.%fZ"
DATE_FORMAT = "%Y-%m-%d"
load_dotenv()
class SQLAlchemy(_SQLAlchemy):
"""We need to subclass SQLAlchemy in order to override create_engine options"""
db = SQLAlchemy()
migrate = Migrate()
ma = Marshmallow()
notify_celery = NotifyCelery()
encryption = Encryption()
firetext_client = FiretextClient()
loadtest_client = LoadtestingClient()
mmg_client = MMGClient()
aws_ses_client = AwsSesClient()
from app.clients.email.govdelivery_client import GovdeliveryClient # noqa
govdelivery_client = GovdeliveryClient()
aws_sns_client = AwsSnsClient()
twilio_sms_client = TwilioSMSClient(
account_sid=os.getenv('TWILIO_ACCOUNT_SID'),
auth_token=os.getenv('TWILIO_AUTH_TOKEN'),
from_number=os.getenv('TWILIO_FROM_NUMBER'),
)
aws_pinpoint_client = AwsPinpointClient()
sqs_client = SQSClient()
zendesk_client = ZendeskClient()
statsd_client = StatsdClient()
redis_store = RedisClient()
performance_platform_client = PerformancePlatformClient()
va_profile_client = VAProfileClient()
mpi_client = MpiClient()
attachment_store = AttachmentStore()
clients = Clients()
from app.oauth.jwt_manager import jwt # noqa
from app.provider_details.provider_service import ProviderService # noqa
provider_service = ProviderService()
api_user = LocalProxy(lambda: g.api_user)
authenticated_service = LocalProxy(lambda: g.authenticated_service)
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... | 3.071504 | 951 |
from flask import Flask, request, Response, jsonify
#import pprint
import json
import spacy
app = Flask(__name__)
print("Loading spacy SM")
_nlp = spacy.load("en_core_web_sm")
#_nlp = spacy.load("en_core_web_trf") # performs better
# Run with
# <s>flask run --port=5002</s>
# TODO Didn't work with spaCy, use
# python app.py
# Test with
# curl http://127.0.0.1:5002/ --header "Content-Type: application/json" --request POST -d '{"doc" : {"text": "Napoleon was the emperor of the First French Empire."}}'
@app.route('/', methods=['get', 'post'])
# Run at flask startup (https://stackoverflow.com/a/55573732)
with app.app_context():
pass
if __name__ == '__main__':
port = 5001
print("Running app... on port: ", port)
app.wsgi_app = LoggingMiddleware(app.wsgi_app)
#app.run(host='0.0.0.0', port=80)
# expose 0.0.0.0 - esp. important for docker
app.run(host='0.0.0.0', port=port)
#app.run()
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599,
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... | 2.523035 | 369 |
REGISTRY = {}
from .simple_agent import SimpleAgent
from .cql_agent import CQLAgent
REGISTRY['simple'] = SimpleAgent
REGISTRY['cql'] = CQLAgent | [
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40... | 3 | 48 |
import time_gen
assert time_gen.get_file_list(None,['fits']) == True
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] | 2.730769 | 26 |
from random import *
data = range(10000)
# the original, naive version
# "final" version
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] | 3.241379 | 29 |
if __name__=="__main__":
import sys
CpGLocationFileName = sys.argv[1]
CpGsInfoFileName = sys.argv[2]
outputFileName = sys.argv[3]
CpGToLocationDict = makeCpGLocationDict(CpGLocationFileName)
convertCpGsToLocations(CpGToLocationDict, CpGsInfoFileName, outputFileName) | [
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198,
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79,
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12360,
8979,
5376,
796,... | 2.295082 | 122 |