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src/aosat/fftx.py | mfeldt/AOSAT | 2 | 6623151 | <filename>src/aosat/fftx.py
# -*- coding: utf-8 -*-
"""
This selects the best (i.e. fastest) FFT routine to be used in AOSAT.
The module will look first for CUDA, then for OpenCL, and fall back
to numpy FFTs if neither is available.
"""
import logging
log = logging.getLogger('aosat_logger')
log.addHandler(logging.NullHandler())
##
##
## Check for GPU support and select FFT function
##
##
from pip._internal.utils.misc import get_installed_distributions
if any(["cupy" in str(f) for f in get_installed_distributions()]):
import cupy as np
else:
import numpy as np
#import numpy as np
from packaging import version
from aosat import aosat_cfg
avl_pkgs = [f.project_name for f in get_installed_distributions()]
avl_vrss = [f.version for f in get_installed_distributions()]
cuda_vrs = avl_vrss[avl_pkgs.index('pycuda')] if 'pycuda' in avl_pkgs else "0.0"
opcl_vrs = avl_vrss[avl_pkgs.index('pyopencl')] if 'pyopencl' in avl_pkgs else "0.0"
FORCE_NUMPY = aosat_cfg.CFG_SETTINGS['aosat_fft_forcenumpy']
if (FORCE_NUMPY or (version.parse(opcl_vrs) < version.parse("2018.2.4") and version.parse(cuda_vrs) < version.parse("0.94"))):
##
## use numpy fftx
##
log.debug("Neither CUDA nor OpenCL found or not desired!")
log.info("Using numpy FFTs!")
def FFTmakeplan(samplearr):
"""Make a plan for FFTs
Parameters
----------
arrshape : numpy array
the shape of the arrays to be transformed
Returns
-------
plan
FFT plan to be executed upon calls.
In the current case where numpy FFTs
are used, plan is None!
Examples
-------
>>> plan = FFTmakeplan(np.zeros((1024,1024)).shape)
>>> plan == None
True
"""
return(None)
def FFTmakeout(inarr):
"""Make an output for FFTs
Parameters
----------
inarr : numpy array
Then input array
Returns
-------
plan
FFT plan to be executed upon calls.
In the current case where numpy FFTs
are used, plan is None!
Examples
-------
>>> out = FFTmakeout(np.zeros((1024,1024)))
>>> out == None
True
"""
return(None)
def FFTprepare(inarr):
"""Prepare an array for input int FFTforward or
FFTinverse
Parameters
----------
inarr : numpy array
The array to be transformed
Returns
-------
numpy array
In the current case of numpy FFTs, the array itself
Examples
-------
>>> arr = np.zeros((1024,1024))
>>> iarr = FFTprepare(arr)
>>> np.array_equal(arr,iarr)
True
"""
return(inarr)
def FFTforward(plan,outarr,inarr):
"""Perform forward FFT.
Parameters
----------
plan : fft plan
In this case of numpy FFTs, plan should be None.
outarr : device-suitable output array
The device-suitable output. Should be None.
inarr : numpy array
The array to be transformed. Should be square.
Returns
-------
numpy array
The transformed array
Examples
-------
>>> arr = np.zeros((1024,1024))
>>> oarr = FFTforward(None,None,arr)
>>> oarr.dtype
dtype('complex128')
>>> oarr.shape
(1024, 1024)
"""
return(np.fft.fft2(inarr))
def FFTinverse(plan,outarr, inarr):
"""Perform inverse FFT.
Parameters
----------
plan : fft plan
In this case of numpy FFTs, plan should be None.
outarr : device-suitable output array
The device-suitable output. Should be None.
inarr : numpy array
The array to be transformed. Should be square.
Returns
-------
numpy array
The transformed array
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> farr = FFTforward(None,None,arr)
>>> oarr = FFTinverse(None,None,farr)
>>> np.abs((np.real(oarr)-arr)).sum() < 2e-10
True
>>> oarr.dtype
dtype('complex128')
>>> oarr.shape
(1024, 1024)
"""
return(np.fft.ifft2(inarr))
def FFTshift(inarr):
"""Shift array transformed by FFT such that
the zero component is centered
Parameters
----------
inarr : numpy array
Output of an FFT transform
Returns
-------
numpy array
Centered array.
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> farr = np.abs(FFTshift(FFTforward(None,None,arr)))
>>> np.unravel_index(np.argmax(farr, axis=None), farr.shape)
(512, 512)
"""
return(np.fft.fftshift(inarr))
else:
##
## use one of the CLUDA FFTs
##
if version.parse(cuda_vrs) >= version.parse("0.94"):
log.debug("CUDA version %s found" % cuda_vrs)
log.info("Using CUDA FFTs!")
from reikna.cluda import dtypes, cuda_api
FFTapi = cuda_api()
else:
log.debug("OpenCL version %s found" % opcl_vrs)
log.info("Using OpenCL FFTs!")
from reikna.cluda import dtypes, ocl_api
FFTapi = ocl_api()
import reikna.fft as cludaFFT
FFTthr = FFTapi.Thread.create()
def FFTmakeplan(samplearr):
"""Make a plan for FFTs
Parameters
----------
samplearr : numpy array
an array of the same type and size as the ones
you're going to transform a lot later on
Returns
-------
plan
FFT plan to be executed upon calls.
Examples
-------
>>> plan = FFTmakeplan(np.zeros((1024,1024),dtype=np.float64))
>>> plan
<reikna.core.computation.ComputationCallable object at ...
"""
fft = cludaFFT.FFT(samplearr.astype(np.complex128))
return(fft.compile(FFTthr))
def FFTmakeout(inarr):
"""Make a suitable output object for FFTs
Parameters
----------
inarr : numpy array, preferably np.complex64
Sample of input array, same size and type which you
are going to transform a lot later on.
Returns
-------
FFTout object
The output object which must be passed to FFTplan
later on. Will not be used explicitly
Examples
-------
>>> inarr = np.zeros((1024,1024))
>>> outobj = FFTmakeout(inarr)
>>> type(outobj)
<class 'reikna.cluda.ocl.Array'>
"""
return(FFTthr.array(inarr.shape, dtype=np.complex128))
def FFTprepare(inarr):
"""Prepare an input array to suit machine/GPU architecture
Parameters
----------
inarr : numpy array
The input array
Returns
-------
inarr_dev
device dependent input array representation
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> arr_dev = FFTprepare(arr)
>>> type(arr_dev)
<class 'reikna.cluda.ocl.Array'>
"""
return(FFTthr.to_device(inarr.astype(np.complex128)))
def FFTforward(plan,outarr,inarr_dev):
"""Perform forward FFT
Parameters
----------
plan : fft plan
The plan produced by FFTmakeplan.
outarr : reikna.cluda.ocl.Array
Output array, produced by FFTmakeout
inarr_dev : reikna.cluda.ocl.Array
Device-suitable input array representation,
produced by FFTprepare
Returns
-------
numpy array
FFT transform of input array
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> plan = FFTmakeplan(arr)
>>> arr_dev = FFTprepare(arr)
>>> arr_out = FFTmakeout(arr)
>>> arr_fft = FFTforward(plan,arr_out,arr_dev)
>>> ref_fft = np.fft.fft2(arr)
>>> print(np.testing.assert_almost_equal(arr_fft,ref_fft))
None
You can also time the execution to see if it's really faster than the standard numpy FFT:
>>> import time
>>> start=time.time()
>>> for i in range(100):
... arr_fft = FFTforward(plan,arr_out,arr_dev)
...
>>> end=time.time()
>>> print("100 CLUDA FFTs: ",end-start)
100 CLUDA FFTs: ...
>>> start=time.time()
>>> for i in range(100):
... ref_fft = np.fft.fft2(arr)
...
>>> end=time.time()
>>> print("100 numpy FFTs: ",end-start)
100 numpy FFTs: ...
If that's not the case, you can force the use of numpy's FFTs by putting the line
force_np_fft = True
in any configuration or report file!
"""
plan(outarr, inarr_dev, inverse=0)
return(outarr.get())
def FFTinverse(plan,outarr,inarr_dev):
"""Perform inverse FFT
Parameters
----------
plan : fft plan
The plan produced by FFTmakeplan.
outarr : reikna.cluda.ocl.Array
Output array, produced by FFTmakeout
inarr_dev : reikna.cluda.ocl.Array
Device-suitable input array representation,
produced by FFTprepare
Returns
-------
numpy array
FFT inverse transform of input array
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> plan = FFTmakeplan(arr)
>>> arr_dev = FFTprepare(arr)
>>> arr_out = FFTmakeout(arr)
>>> arr_fft = FFTinverse(plan,arr_out,arr_dev)
>>> ref_fft = np.fft.ifft2(arr)
>>> print(np.testing.assert_almost_equal(arr_fft,ref_fft))
None
"""
plan(outarr, inarr_dev, inverse=1)
return(outarr.get())
def FFTshift(inarr):
"""Shift array transformed by FFT such that
the zero component is centered
Parameters
----------
inarr : numpy array
Output of an FFT transform
Returns
-------
numpy array
Centered array.
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> plan = FFTmakeplan(arr)
>>> arr_dev = FFTprepare(arr)
>>> arr_out = FFTmakeout(arr)
>>> farr = FFTshift(FFTforward(plan,arr_out,arr_dev))
>>> np.unravel_index(np.argmax(farr, axis=None), farr.shape)
(512, 512)
"""
return(np.fft.fftshift(inarr))
def fftSpeedTest(max_res=13):
"""Test of speed of currently selected FFT vs. numpy.
This is intended to verify that the selected OpenCL / CUDA
FFTs are indeed faster than numpy. For older GPUs, that
may not always be the case.
When called, fftSpeedtest will test a series of array sizes
and print the ratio of execution times against standardnumpy
implementations. If you see a lot of number smaller than one,
in particular in the line for the array size of interest,
make sure that the golbal aosat_cfg.CFG_SETTINGS contains the
entry CFG_SETTINGS['aosat_fft_forcenumpy'] = True .
In aosat, this can be achieved by calling
aosat.aosat_cfg.configure(setupfile)
where the setupfile contains the line
aosat_fft_forcenumpy = True
The setup (or "report") fiel is also an argument of many
helper and convenience functions.
After an execution of configure, issue a
reload(aosat.fftx)
This will redefine the fftx.FFT* functions!
Parameters
----------
Returns
-------
nothing
Examples
-------
>>> fftSpeedTest(max_res=4)
array size = 4 x 4 : gpu speedup = ...
"""
from time import time
dtype = np.complex128
resolutions = range(2,max_res)
Nloops = 20
rtol = 1e-7
atol = 0
for n in resolutions:
shape, axes = (2**n,2**n), (0,1)
data = np.random.rand(*shape).astype(dtype)
plan = FFTmakeplan(data)
rtime, ntime = 0., 0.
for nloop in range(Nloops):
data = np.random.rand(*shape).astype(dtype)
# forward
t0 = time()
data_dev = FFTprepare(data)
fwd = FFTforward(plan,data_dev,data_dev)
rtime += time() - t0
t0 = time()
fwd_ref = np.fft.fft2(data, axes=axes).astype(dtype)
ntime += time() - t0
actualf = np.real(fwd * np.conj(fwd))
desiredf = np.real(fwd_ref * np.conj(fwd_ref))
# inverse
t0 = time()
data_dev = FFTprepare(data)
inv = FFTinverse(plan,data_dev,data_dev)
rtime += time() - t0
t0 = time()
inv_ref = np.fft.ifft2(data, axes=axes).astype(dtype)
ntime += time() - t0
actuali = np.real(inv * np.conj(inv))
desiredi = np.real(inv_ref * np.conj(inv_ref))
np.testing.assert_allclose(desiredf, actualf, rtol, atol)
np.testing.assert_allclose(desiredi, actuali, rtol, atol)
print ('array size = %5d x %5d : gpu speedup = %g' % (2**n, 2**n, ntime / rtime))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True, optionflags=doctest.ELLIPSIS)
| <filename>src/aosat/fftx.py
# -*- coding: utf-8 -*-
"""
This selects the best (i.e. fastest) FFT routine to be used in AOSAT.
The module will look first for CUDA, then for OpenCL, and fall back
to numpy FFTs if neither is available.
"""
import logging
log = logging.getLogger('aosat_logger')
log.addHandler(logging.NullHandler())
##
##
## Check for GPU support and select FFT function
##
##
from pip._internal.utils.misc import get_installed_distributions
if any(["cupy" in str(f) for f in get_installed_distributions()]):
import cupy as np
else:
import numpy as np
#import numpy as np
from packaging import version
from aosat import aosat_cfg
avl_pkgs = [f.project_name for f in get_installed_distributions()]
avl_vrss = [f.version for f in get_installed_distributions()]
cuda_vrs = avl_vrss[avl_pkgs.index('pycuda')] if 'pycuda' in avl_pkgs else "0.0"
opcl_vrs = avl_vrss[avl_pkgs.index('pyopencl')] if 'pyopencl' in avl_pkgs else "0.0"
FORCE_NUMPY = aosat_cfg.CFG_SETTINGS['aosat_fft_forcenumpy']
if (FORCE_NUMPY or (version.parse(opcl_vrs) < version.parse("2018.2.4") and version.parse(cuda_vrs) < version.parse("0.94"))):
##
## use numpy fftx
##
log.debug("Neither CUDA nor OpenCL found or not desired!")
log.info("Using numpy FFTs!")
def FFTmakeplan(samplearr):
"""Make a plan for FFTs
Parameters
----------
arrshape : numpy array
the shape of the arrays to be transformed
Returns
-------
plan
FFT plan to be executed upon calls.
In the current case where numpy FFTs
are used, plan is None!
Examples
-------
>>> plan = FFTmakeplan(np.zeros((1024,1024)).shape)
>>> plan == None
True
"""
return(None)
def FFTmakeout(inarr):
"""Make an output for FFTs
Parameters
----------
inarr : numpy array
Then input array
Returns
-------
plan
FFT plan to be executed upon calls.
In the current case where numpy FFTs
are used, plan is None!
Examples
-------
>>> out = FFTmakeout(np.zeros((1024,1024)))
>>> out == None
True
"""
return(None)
def FFTprepare(inarr):
"""Prepare an array for input int FFTforward or
FFTinverse
Parameters
----------
inarr : numpy array
The array to be transformed
Returns
-------
numpy array
In the current case of numpy FFTs, the array itself
Examples
-------
>>> arr = np.zeros((1024,1024))
>>> iarr = FFTprepare(arr)
>>> np.array_equal(arr,iarr)
True
"""
return(inarr)
def FFTforward(plan,outarr,inarr):
"""Perform forward FFT.
Parameters
----------
plan : fft plan
In this case of numpy FFTs, plan should be None.
outarr : device-suitable output array
The device-suitable output. Should be None.
inarr : numpy array
The array to be transformed. Should be square.
Returns
-------
numpy array
The transformed array
Examples
-------
>>> arr = np.zeros((1024,1024))
>>> oarr = FFTforward(None,None,arr)
>>> oarr.dtype
dtype('complex128')
>>> oarr.shape
(1024, 1024)
"""
return(np.fft.fft2(inarr))
def FFTinverse(plan,outarr, inarr):
"""Perform inverse FFT.
Parameters
----------
plan : fft plan
In this case of numpy FFTs, plan should be None.
outarr : device-suitable output array
The device-suitable output. Should be None.
inarr : numpy array
The array to be transformed. Should be square.
Returns
-------
numpy array
The transformed array
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> farr = FFTforward(None,None,arr)
>>> oarr = FFTinverse(None,None,farr)
>>> np.abs((np.real(oarr)-arr)).sum() < 2e-10
True
>>> oarr.dtype
dtype('complex128')
>>> oarr.shape
(1024, 1024)
"""
return(np.fft.ifft2(inarr))
def FFTshift(inarr):
"""Shift array transformed by FFT such that
the zero component is centered
Parameters
----------
inarr : numpy array
Output of an FFT transform
Returns
-------
numpy array
Centered array.
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> farr = np.abs(FFTshift(FFTforward(None,None,arr)))
>>> np.unravel_index(np.argmax(farr, axis=None), farr.shape)
(512, 512)
"""
return(np.fft.fftshift(inarr))
else:
##
## use one of the CLUDA FFTs
##
if version.parse(cuda_vrs) >= version.parse("0.94"):
log.debug("CUDA version %s found" % cuda_vrs)
log.info("Using CUDA FFTs!")
from reikna.cluda import dtypes, cuda_api
FFTapi = cuda_api()
else:
log.debug("OpenCL version %s found" % opcl_vrs)
log.info("Using OpenCL FFTs!")
from reikna.cluda import dtypes, ocl_api
FFTapi = ocl_api()
import reikna.fft as cludaFFT
FFTthr = FFTapi.Thread.create()
def FFTmakeplan(samplearr):
"""Make a plan for FFTs
Parameters
----------
samplearr : numpy array
an array of the same type and size as the ones
you're going to transform a lot later on
Returns
-------
plan
FFT plan to be executed upon calls.
Examples
-------
>>> plan = FFTmakeplan(np.zeros((1024,1024),dtype=np.float64))
>>> plan
<reikna.core.computation.ComputationCallable object at ...
"""
fft = cludaFFT.FFT(samplearr.astype(np.complex128))
return(fft.compile(FFTthr))
def FFTmakeout(inarr):
"""Make a suitable output object for FFTs
Parameters
----------
inarr : numpy array, preferably np.complex64
Sample of input array, same size and type which you
are going to transform a lot later on.
Returns
-------
FFTout object
The output object which must be passed to FFTplan
later on. Will not be used explicitly
Examples
-------
>>> inarr = np.zeros((1024,1024))
>>> outobj = FFTmakeout(inarr)
>>> type(outobj)
<class 'reikna.cluda.ocl.Array'>
"""
return(FFTthr.array(inarr.shape, dtype=np.complex128))
def FFTprepare(inarr):
"""Prepare an input array to suit machine/GPU architecture
Parameters
----------
inarr : numpy array
The input array
Returns
-------
inarr_dev
device dependent input array representation
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> arr_dev = FFTprepare(arr)
>>> type(arr_dev)
<class 'reikna.cluda.ocl.Array'>
"""
return(FFTthr.to_device(inarr.astype(np.complex128)))
def FFTforward(plan,outarr,inarr_dev):
"""Perform forward FFT
Parameters
----------
plan : fft plan
The plan produced by FFTmakeplan.
outarr : reikna.cluda.ocl.Array
Output array, produced by FFTmakeout
inarr_dev : reikna.cluda.ocl.Array
Device-suitable input array representation,
produced by FFTprepare
Returns
-------
numpy array
FFT transform of input array
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> plan = FFTmakeplan(arr)
>>> arr_dev = FFTprepare(arr)
>>> arr_out = FFTmakeout(arr)
>>> arr_fft = FFTforward(plan,arr_out,arr_dev)
>>> ref_fft = np.fft.fft2(arr)
>>> print(np.testing.assert_almost_equal(arr_fft,ref_fft))
None
You can also time the execution to see if it's really faster than the standard numpy FFT:
>>> import time
>>> start=time.time()
>>> for i in range(100):
... arr_fft = FFTforward(plan,arr_out,arr_dev)
...
>>> end=time.time()
>>> print("100 CLUDA FFTs: ",end-start)
100 CLUDA FFTs: ...
>>> start=time.time()
>>> for i in range(100):
... ref_fft = np.fft.fft2(arr)
...
>>> end=time.time()
>>> print("100 numpy FFTs: ",end-start)
100 numpy FFTs: ...
If that's not the case, you can force the use of numpy's FFTs by putting the line
force_np_fft = True
in any configuration or report file!
"""
plan(outarr, inarr_dev, inverse=0)
return(outarr.get())
def FFTinverse(plan,outarr,inarr_dev):
"""Perform inverse FFT
Parameters
----------
plan : fft plan
The plan produced by FFTmakeplan.
outarr : reikna.cluda.ocl.Array
Output array, produced by FFTmakeout
inarr_dev : reikna.cluda.ocl.Array
Device-suitable input array representation,
produced by FFTprepare
Returns
-------
numpy array
FFT inverse transform of input array
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> plan = FFTmakeplan(arr)
>>> arr_dev = FFTprepare(arr)
>>> arr_out = FFTmakeout(arr)
>>> arr_fft = FFTinverse(plan,arr_out,arr_dev)
>>> ref_fft = np.fft.ifft2(arr)
>>> print(np.testing.assert_almost_equal(arr_fft,ref_fft))
None
"""
plan(outarr, inarr_dev, inverse=1)
return(outarr.get())
def FFTshift(inarr):
"""Shift array transformed by FFT such that
the zero component is centered
Parameters
----------
inarr : numpy array
Output of an FFT transform
Returns
-------
numpy array
Centered array.
Examples
-------
>>> arr = np.random.rand(1024,1024)
>>> plan = FFTmakeplan(arr)
>>> arr_dev = FFTprepare(arr)
>>> arr_out = FFTmakeout(arr)
>>> farr = FFTshift(FFTforward(plan,arr_out,arr_dev))
>>> np.unravel_index(np.argmax(farr, axis=None), farr.shape)
(512, 512)
"""
return(np.fft.fftshift(inarr))
def fftSpeedTest(max_res=13):
"""Test of speed of currently selected FFT vs. numpy.
This is intended to verify that the selected OpenCL / CUDA
FFTs are indeed faster than numpy. For older GPUs, that
may not always be the case.
When called, fftSpeedtest will test a series of array sizes
and print the ratio of execution times against standardnumpy
implementations. If you see a lot of number smaller than one,
in particular in the line for the array size of interest,
make sure that the golbal aosat_cfg.CFG_SETTINGS contains the
entry CFG_SETTINGS['aosat_fft_forcenumpy'] = True .
In aosat, this can be achieved by calling
aosat.aosat_cfg.configure(setupfile)
where the setupfile contains the line
aosat_fft_forcenumpy = True
The setup (or "report") fiel is also an argument of many
helper and convenience functions.
After an execution of configure, issue a
reload(aosat.fftx)
This will redefine the fftx.FFT* functions!
Parameters
----------
Returns
-------
nothing
Examples
-------
>>> fftSpeedTest(max_res=4)
array size = 4 x 4 : gpu speedup = ...
"""
from time import time
dtype = np.complex128
resolutions = range(2,max_res)
Nloops = 20
rtol = 1e-7
atol = 0
for n in resolutions:
shape, axes = (2**n,2**n), (0,1)
data = np.random.rand(*shape).astype(dtype)
plan = FFTmakeplan(data)
rtime, ntime = 0., 0.
for nloop in range(Nloops):
data = np.random.rand(*shape).astype(dtype)
# forward
t0 = time()
data_dev = FFTprepare(data)
fwd = FFTforward(plan,data_dev,data_dev)
rtime += time() - t0
t0 = time()
fwd_ref = np.fft.fft2(data, axes=axes).astype(dtype)
ntime += time() - t0
actualf = np.real(fwd * np.conj(fwd))
desiredf = np.real(fwd_ref * np.conj(fwd_ref))
# inverse
t0 = time()
data_dev = FFTprepare(data)
inv = FFTinverse(plan,data_dev,data_dev)
rtime += time() - t0
t0 = time()
inv_ref = np.fft.ifft2(data, axes=axes).astype(dtype)
ntime += time() - t0
actuali = np.real(inv * np.conj(inv))
desiredi = np.real(inv_ref * np.conj(inv_ref))
np.testing.assert_allclose(desiredf, actualf, rtol, atol)
np.testing.assert_allclose(desiredi, actuali, rtol, atol)
print ('array size = %5d x %5d : gpu speedup = %g' % (2**n, 2**n, ntime / rtime))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True, optionflags=doctest.ELLIPSIS)
| en | 0.586954 | # -*- coding: utf-8 -*- This selects the best (i.e. fastest) FFT routine to be used in AOSAT. The module will look first for CUDA, then for OpenCL, and fall back to numpy FFTs if neither is available. ## ## ## Check for GPU support and select FFT function ## ## #import numpy as np ## ## use numpy fftx ## Make a plan for FFTs Parameters ---------- arrshape : numpy array the shape of the arrays to be transformed Returns ------- plan FFT plan to be executed upon calls. In the current case where numpy FFTs are used, plan is None! Examples ------- >>> plan = FFTmakeplan(np.zeros((1024,1024)).shape) >>> plan == None True Make an output for FFTs Parameters ---------- inarr : numpy array Then input array Returns ------- plan FFT plan to be executed upon calls. In the current case where numpy FFTs are used, plan is None! Examples ------- >>> out = FFTmakeout(np.zeros((1024,1024))) >>> out == None True Prepare an array for input int FFTforward or FFTinverse Parameters ---------- inarr : numpy array The array to be transformed Returns ------- numpy array In the current case of numpy FFTs, the array itself Examples ------- >>> arr = np.zeros((1024,1024)) >>> iarr = FFTprepare(arr) >>> np.array_equal(arr,iarr) True Perform forward FFT. Parameters ---------- plan : fft plan In this case of numpy FFTs, plan should be None. outarr : device-suitable output array The device-suitable output. Should be None. inarr : numpy array The array to be transformed. Should be square. Returns ------- numpy array The transformed array Examples ------- >>> arr = np.zeros((1024,1024)) >>> oarr = FFTforward(None,None,arr) >>> oarr.dtype dtype('complex128') >>> oarr.shape (1024, 1024) Perform inverse FFT. Parameters ---------- plan : fft plan In this case of numpy FFTs, plan should be None. outarr : device-suitable output array The device-suitable output. Should be None. inarr : numpy array The array to be transformed. Should be square. Returns ------- numpy array The transformed array Examples ------- >>> arr = np.random.rand(1024,1024) >>> farr = FFTforward(None,None,arr) >>> oarr = FFTinverse(None,None,farr) >>> np.abs((np.real(oarr)-arr)).sum() < 2e-10 True >>> oarr.dtype dtype('complex128') >>> oarr.shape (1024, 1024) Shift array transformed by FFT such that the zero component is centered Parameters ---------- inarr : numpy array Output of an FFT transform Returns ------- numpy array Centered array. Examples ------- >>> arr = np.random.rand(1024,1024) >>> farr = np.abs(FFTshift(FFTforward(None,None,arr))) >>> np.unravel_index(np.argmax(farr, axis=None), farr.shape) (512, 512) ## ## use one of the CLUDA FFTs ## Make a plan for FFTs Parameters ---------- samplearr : numpy array an array of the same type and size as the ones you're going to transform a lot later on Returns ------- plan FFT plan to be executed upon calls. Examples ------- >>> plan = FFTmakeplan(np.zeros((1024,1024),dtype=np.float64)) >>> plan <reikna.core.computation.ComputationCallable object at ... Make a suitable output object for FFTs Parameters ---------- inarr : numpy array, preferably np.complex64 Sample of input array, same size and type which you are going to transform a lot later on. Returns ------- FFTout object The output object which must be passed to FFTplan later on. Will not be used explicitly Examples ------- >>> inarr = np.zeros((1024,1024)) >>> outobj = FFTmakeout(inarr) >>> type(outobj) <class 'reikna.cluda.ocl.Array'> Prepare an input array to suit machine/GPU architecture Parameters ---------- inarr : numpy array The input array Returns ------- inarr_dev device dependent input array representation Examples ------- >>> arr = np.random.rand(1024,1024) >>> arr_dev = FFTprepare(arr) >>> type(arr_dev) <class 'reikna.cluda.ocl.Array'> Perform forward FFT Parameters ---------- plan : fft plan The plan produced by FFTmakeplan. outarr : reikna.cluda.ocl.Array Output array, produced by FFTmakeout inarr_dev : reikna.cluda.ocl.Array Device-suitable input array representation, produced by FFTprepare Returns ------- numpy array FFT transform of input array Examples ------- >>> arr = np.random.rand(1024,1024) >>> plan = FFTmakeplan(arr) >>> arr_dev = FFTprepare(arr) >>> arr_out = FFTmakeout(arr) >>> arr_fft = FFTforward(plan,arr_out,arr_dev) >>> ref_fft = np.fft.fft2(arr) >>> print(np.testing.assert_almost_equal(arr_fft,ref_fft)) None You can also time the execution to see if it's really faster than the standard numpy FFT: >>> import time >>> start=time.time() >>> for i in range(100): ... arr_fft = FFTforward(plan,arr_out,arr_dev) ... >>> end=time.time() >>> print("100 CLUDA FFTs: ",end-start) 100 CLUDA FFTs: ... >>> start=time.time() >>> for i in range(100): ... ref_fft = np.fft.fft2(arr) ... >>> end=time.time() >>> print("100 numpy FFTs: ",end-start) 100 numpy FFTs: ... If that's not the case, you can force the use of numpy's FFTs by putting the line force_np_fft = True in any configuration or report file! Perform inverse FFT Parameters ---------- plan : fft plan The plan produced by FFTmakeplan. outarr : reikna.cluda.ocl.Array Output array, produced by FFTmakeout inarr_dev : reikna.cluda.ocl.Array Device-suitable input array representation, produced by FFTprepare Returns ------- numpy array FFT inverse transform of input array Examples ------- >>> arr = np.random.rand(1024,1024) >>> plan = FFTmakeplan(arr) >>> arr_dev = FFTprepare(arr) >>> arr_out = FFTmakeout(arr) >>> arr_fft = FFTinverse(plan,arr_out,arr_dev) >>> ref_fft = np.fft.ifft2(arr) >>> print(np.testing.assert_almost_equal(arr_fft,ref_fft)) None Shift array transformed by FFT such that the zero component is centered Parameters ---------- inarr : numpy array Output of an FFT transform Returns ------- numpy array Centered array. Examples ------- >>> arr = np.random.rand(1024,1024) >>> plan = FFTmakeplan(arr) >>> arr_dev = FFTprepare(arr) >>> arr_out = FFTmakeout(arr) >>> farr = FFTshift(FFTforward(plan,arr_out,arr_dev)) >>> np.unravel_index(np.argmax(farr, axis=None), farr.shape) (512, 512) Test of speed of currently selected FFT vs. numpy. This is intended to verify that the selected OpenCL / CUDA FFTs are indeed faster than numpy. For older GPUs, that may not always be the case. When called, fftSpeedtest will test a series of array sizes and print the ratio of execution times against standardnumpy implementations. If you see a lot of number smaller than one, in particular in the line for the array size of interest, make sure that the golbal aosat_cfg.CFG_SETTINGS contains the entry CFG_SETTINGS['aosat_fft_forcenumpy'] = True . In aosat, this can be achieved by calling aosat.aosat_cfg.configure(setupfile) where the setupfile contains the line aosat_fft_forcenumpy = True The setup (or "report") fiel is also an argument of many helper and convenience functions. After an execution of configure, issue a reload(aosat.fftx) This will redefine the fftx.FFT* functions! Parameters ---------- Returns ------- nothing Examples ------- >>> fftSpeedTest(max_res=4) array size = 4 x 4 : gpu speedup = ... # forward # inverse | 2.235471 | 2 |
src/data.py | nikikilbertus/general-iv-models | 9 | 6623152 | <filename>src/data.py
"""Data loading and pre-processing utilities."""
from typing import Tuple, Callable, Sequence, Text, Dict, Union
import os
from absl import logging
import jax.numpy as np
from jax import random
import numpy as onp
import pandas as pd
from scipy.stats import norm
import utils
DataSynth = Tuple[Dict[Text, Union[np.ndarray, float, None]],
np.ndarray, np.ndarray]
DataReal = Dict[Text, Union[np.ndarray, float, None]]
ArrayTup = Tuple[np.ndarray, np.ndarray]
Equations = Dict[Text, Callable[..., np.ndarray]]
# =============================================================================
# NOISE SOURCES
# =============================================================================
def std_normal_1d(key: np.ndarray, num: int) -> np.ndarray:
"""Generate a Gaussian for the confounder."""
return random.normal(key, (num,))
def std_normal_2d(key: np.ndarray, num: int) -> ArrayTup:
"""Generate a multivariate Gaussian for the noises e_X, e_Y."""
key1, key2 = random.split(key)
return random.normal(key1, (num,)), random.normal(key2, (num,))
# =============================================================================
# SYNTHETIC STRUCTURAL EQUATIONS
# =============================================================================
structural_equations = {
"lin1": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 0.5 * z + 3 * c + ex,
"f_y": lambda x, c, ey: x - 6 * c + ey,
},
"lin2": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 3.0 * z + 0.5 * c + ex,
"f_y": lambda x, c, ey: x - 6 * c + ey,
},
"quad1": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 0.5 * z + 3 * c + ex,
"f_y": lambda x, c, ey: 0.3 * x ** 2 - 1.5 * x * c + ey,
},
"quad2": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 3.0 * z + 0.5 * c + ex,
"f_y": lambda x, c, ey: 0.3 * x ** 2 - 1.5 * x * c + ey,
},
}
# =============================================================================
# DATA GENERATORS
# =============================================================================
def whiten(
inputs: Dict[Text, np.ndarray]
) -> Dict[Text, Union[float, np.ndarray, None]]:
"""Whiten each input."""
res = {}
for k, v in inputs.items():
if v is not None:
mu = np.mean(v, 0)
std = np.maximum(np.std(v, 0), 1e-7)
res[k + "_mu"] = mu
res[k + "_std"] = std
res[k] = (v - mu) / std
else:
res[k] = v
return res
def whiten_with_mu_std(val: np.ndarray, mu: float, std: float) -> np.ndarray:
return (val - mu) / std
def get_synth_data(
key: np.ndarray,
num: int,
equations: Text,
num_xstar: int = 100,
external_equations: Equations = None,
disconnect_instrument: bool = False
) -> DataSynth:
"""Generate some synthetic data.
Args:
key: A JAX random key.
num: The number of examples to generate.
equations: Which structural equations to choose for x and y. Default: 1
num_xstar: Size of grid for interventions on x.
external_equations: A dictionary that must contain the keys 'f_x' and
'f_y' mapping to callables as values that take two np.ndarrays as
arguments and produce another np.ndarray. These are the structural
equations for X and Y in the graph Z -> X -> Y.
If this argument is not provided, the `equation` argument selects
structural equations from the pre-defined dict `structural_equations`.
disconnect_instrument: Whether to regenerate random (standard Gaussian)
values for the instrument after the data has been generated. This
serves for diagnostic purposes, i.e., looking at the same x, y data,
Returns:
A 3-tuple (values, xstar, ystar) consisting a dictionary `values`
containing values for x, y, z, confounder, ex, ey as well as two
array xstar, ystar containing values for the true cause-effect.
"""
if external_equations is not None:
eqs = external_equations
elif equations == "np":
return get_newey_powell(key, num, num_xstar)
else:
eqs = structural_equations[equations]
key, subkey = random.split(key)
ex, ey = eqs["noise"](subkey, num)
key, subkey = random.split(key)
confounder = eqs["confounder"](subkey, num)
key, subkey = random.split(key)
z = eqs["f_z"](subkey, num)
x = eqs["f_x"](z, confounder, ex)
y = eqs["f_y"](x, confounder, ey)
values = whiten({'x': x, 'y': y, 'z': z, 'confounder': confounder,
'ex': ex, 'ey': ey})
# Evaluate E[ Y | do(x^*)] empirically
xmin, xmax = np.min(x), np.max(x)
xstar = np.linspace(xmin, xmax, num_xstar)
ystar = []
for _ in range(500):
key, subkey = random.split(key)
tmpey = eqs["noise"](subkey, num_xstar)[1]
key, subkey = random.split(key)
tmpconf = eqs["confounder"](subkey, num_xstar)
tmp_ystar = whiten_with_mu_std(
eqs["f_y"](xstar, tmpconf, tmpey), values["y_mu"], values["y_std"])
ystar.append(tmp_ystar)
ystar = np.array(ystar)
xstar = whiten_with_mu_std(xstar, values["x_mu"], values["x_std"])
if disconnect_instrument:
key, subkey = random.split(key)
values['z'] = random.normal(subkey, shape=z.shape)
return values, xstar, ystar
def get_colonial_origins(data_dir: Text = "../data") -> DataReal:
"""Load data from colonial origins paper of Acemoglu."""
stata_path = os.path.join(data_dir, "colonial_origins", "data.dta")
df = pd.read_stata(stata_path)
ycol = 'logpgp95'
zcol = 'logem4'
xcol = 'avexpr'
df = df[[zcol, xcol, ycol]].dropna()
z, x, y = df[zcol].values, df[xcol].values, df[ycol].values
data = {'x': x, 'y': y, 'z': z, 'confounder': None, 'ex': None, 'ey': None}
return whiten(data)
def get_newey_powell(key: np.ndarray,
num: int,
num_xstar: int = 100) -> DataSynth:
"""Get simulated Newey Powell (sigmoid design) data from KIV paper."""
def np_true(vals: np.ndarray):
return np.log(np.abs(16. * vals - 8) + 1) * np.sign(vals - 0.5)
xstar = np.linspace(0, 1, num_xstar)
ystar = np_true(xstar)
mu = np.zeros(3)
sigma = np.array([[1., 0.5, 0.], [0.5, 1., 0.], [0., 0., 1.]])
r = random.multivariate_normal(key, mu, sigma, shape=(num,))
u, t, w = r[:, 0], r[:, 1], r[:, 2]
x = w + t
x = norm.cdf(x / np.sqrt(2.))
z = norm.cdf(w)
e = u
y = np_true(x) + e
values = whiten({'x': x, 'y': y, 'z': z, 'ex': e, 'ey': e})
xstar = whiten_with_mu_std(xstar, values['x_mu'], values['x_std'])
ystar = whiten_with_mu_std(ystar, values['y_mu'], values['y_std'])
values['confounder'] = None
return values, xstar, ystar
# =============================================================================
# DISCRETIZATION AND CDF HANDLING
# =============================================================================
def ecdf(vals: np.ndarray, num_points: int = None) -> ArrayTup:
"""Evaluate the empirical distribution function on fixed number of points."""
if num_points is None:
num_points = len(vals)
cdf = np.linspace(0, 1, num_points)
t = np.quantile(vals, cdf)
return t, cdf
def cdf_inv(vals: np.ndarray,
num_points: int = None) -> Callable[..., np.ndarray]:
"""Compute an interpolation function of the (empirical) inverse cdf."""
t, cdf = ecdf(vals, num_points)
return lambda x: onp.interp(x, cdf, t)
def get_cdf_invs(val: np.ndarray,
bin_ids: np.ndarray,
num_z: int) -> Sequence[Callable[..., np.ndarray]]:
"""Compute a list of interpolated inverse CDFs of val at each z in Z grid."""
cdf_invs = []
for i in range(num_z):
cdf_invs.append(cdf_inv(val[bin_ids == i]))
return cdf_invs
def get_z_bin_assigment(z: np.ndarray, z_grid: np.ndarray) -> np.ndarray:
"""Assignment of values in z to the respective bin in z_grid."""
bins = np.concatenate((np.array([-np.inf]),
z_grid[1:-1],
np.array([np.inf])))
hist = onp.digitize(z, bins=bins, right=True) - 1
return hist
def get_x_samples(x: np.ndarray,
bin_ids: np.ndarray,
num_z: int,
num_sample: int) -> ArrayTup:
"""Pre-compute samples from p(x | z^{(i)}) for each gridpoint zi."""
x_cdf_invs = get_cdf_invs(x, bin_ids, num_z)
tmp = np.linspace(0, 1, num_sample + 2)[1:-1]
tmp0 = utils.normal_cdf_inv(tmp, np.array([0]), np.array([0]))
return tmp0, np.array([x_cdf_inv(tmp) for x_cdf_inv in x_cdf_invs])
def get_y_pre(y: np.ndarray,
bin_ids: np.ndarray,
num_z: int,
num_points: int) -> np.ndarray:
"""Compute the grid of y points for constraint approach y."""
y_cdf_invs = get_cdf_invs(y, bin_ids, num_z)
grid = np.linspace(0, 1, num_points + 2)[1:-1]
return np.array([y_cdf_inv(grid) for y_cdf_inv in y_cdf_invs])
def make_zgrid_and_binids(z: np.ndarray, num_z: int) -> ArrayTup:
"""Discretize instrument Z and assign all z points to corresponding bins."""
if num_z <= 0:
logging.info("Discrete instrument specified, checking for values.")
z_grid = np.sort(onp.unique(z))
if len(z_grid) > 50:
logging.info("Found more than 50 unique values for z. This is not a "
"discrete instrument. Aborting!")
raise RuntimeError("Discrete instrument specified, but not found.")
logging.info(f"Found {len(z_grid)} unique values for discrete instrument.")
bin_ids = - onp.ones_like(z)
for i, zpoint in enumerate(z_grid):
bin_ids[z == zpoint] = i
if onp.any(bin_ids < 0):
raise ValueError(f"Found negative value in bin_ids. "
"Couldn't discretize instrument.")
bin_ids = np.array(bin_ids).astype(int)
else:
z_grid = ecdf(z, num_z + 1)[0]
bin_ids = get_z_bin_assigment(z, z_grid)
z_grid = (z_grid[:-1] + z_grid[1:]) / 2
return z_grid, bin_ids
| <filename>src/data.py
"""Data loading and pre-processing utilities."""
from typing import Tuple, Callable, Sequence, Text, Dict, Union
import os
from absl import logging
import jax.numpy as np
from jax import random
import numpy as onp
import pandas as pd
from scipy.stats import norm
import utils
DataSynth = Tuple[Dict[Text, Union[np.ndarray, float, None]],
np.ndarray, np.ndarray]
DataReal = Dict[Text, Union[np.ndarray, float, None]]
ArrayTup = Tuple[np.ndarray, np.ndarray]
Equations = Dict[Text, Callable[..., np.ndarray]]
# =============================================================================
# NOISE SOURCES
# =============================================================================
def std_normal_1d(key: np.ndarray, num: int) -> np.ndarray:
"""Generate a Gaussian for the confounder."""
return random.normal(key, (num,))
def std_normal_2d(key: np.ndarray, num: int) -> ArrayTup:
"""Generate a multivariate Gaussian for the noises e_X, e_Y."""
key1, key2 = random.split(key)
return random.normal(key1, (num,)), random.normal(key2, (num,))
# =============================================================================
# SYNTHETIC STRUCTURAL EQUATIONS
# =============================================================================
structural_equations = {
"lin1": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 0.5 * z + 3 * c + ex,
"f_y": lambda x, c, ey: x - 6 * c + ey,
},
"lin2": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 3.0 * z + 0.5 * c + ex,
"f_y": lambda x, c, ey: x - 6 * c + ey,
},
"quad1": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 0.5 * z + 3 * c + ex,
"f_y": lambda x, c, ey: 0.3 * x ** 2 - 1.5 * x * c + ey,
},
"quad2": {
"noise": std_normal_2d,
"confounder": std_normal_1d,
"f_z": std_normal_1d,
"f_x": lambda z, c, ex: 3.0 * z + 0.5 * c + ex,
"f_y": lambda x, c, ey: 0.3 * x ** 2 - 1.5 * x * c + ey,
},
}
# =============================================================================
# DATA GENERATORS
# =============================================================================
def whiten(
inputs: Dict[Text, np.ndarray]
) -> Dict[Text, Union[float, np.ndarray, None]]:
"""Whiten each input."""
res = {}
for k, v in inputs.items():
if v is not None:
mu = np.mean(v, 0)
std = np.maximum(np.std(v, 0), 1e-7)
res[k + "_mu"] = mu
res[k + "_std"] = std
res[k] = (v - mu) / std
else:
res[k] = v
return res
def whiten_with_mu_std(val: np.ndarray, mu: float, std: float) -> np.ndarray:
return (val - mu) / std
def get_synth_data(
key: np.ndarray,
num: int,
equations: Text,
num_xstar: int = 100,
external_equations: Equations = None,
disconnect_instrument: bool = False
) -> DataSynth:
"""Generate some synthetic data.
Args:
key: A JAX random key.
num: The number of examples to generate.
equations: Which structural equations to choose for x and y. Default: 1
num_xstar: Size of grid for interventions on x.
external_equations: A dictionary that must contain the keys 'f_x' and
'f_y' mapping to callables as values that take two np.ndarrays as
arguments and produce another np.ndarray. These are the structural
equations for X and Y in the graph Z -> X -> Y.
If this argument is not provided, the `equation` argument selects
structural equations from the pre-defined dict `structural_equations`.
disconnect_instrument: Whether to regenerate random (standard Gaussian)
values for the instrument after the data has been generated. This
serves for diagnostic purposes, i.e., looking at the same x, y data,
Returns:
A 3-tuple (values, xstar, ystar) consisting a dictionary `values`
containing values for x, y, z, confounder, ex, ey as well as two
array xstar, ystar containing values for the true cause-effect.
"""
if external_equations is not None:
eqs = external_equations
elif equations == "np":
return get_newey_powell(key, num, num_xstar)
else:
eqs = structural_equations[equations]
key, subkey = random.split(key)
ex, ey = eqs["noise"](subkey, num)
key, subkey = random.split(key)
confounder = eqs["confounder"](subkey, num)
key, subkey = random.split(key)
z = eqs["f_z"](subkey, num)
x = eqs["f_x"](z, confounder, ex)
y = eqs["f_y"](x, confounder, ey)
values = whiten({'x': x, 'y': y, 'z': z, 'confounder': confounder,
'ex': ex, 'ey': ey})
# Evaluate E[ Y | do(x^*)] empirically
xmin, xmax = np.min(x), np.max(x)
xstar = np.linspace(xmin, xmax, num_xstar)
ystar = []
for _ in range(500):
key, subkey = random.split(key)
tmpey = eqs["noise"](subkey, num_xstar)[1]
key, subkey = random.split(key)
tmpconf = eqs["confounder"](subkey, num_xstar)
tmp_ystar = whiten_with_mu_std(
eqs["f_y"](xstar, tmpconf, tmpey), values["y_mu"], values["y_std"])
ystar.append(tmp_ystar)
ystar = np.array(ystar)
xstar = whiten_with_mu_std(xstar, values["x_mu"], values["x_std"])
if disconnect_instrument:
key, subkey = random.split(key)
values['z'] = random.normal(subkey, shape=z.shape)
return values, xstar, ystar
def get_colonial_origins(data_dir: Text = "../data") -> DataReal:
"""Load data from colonial origins paper of Acemoglu."""
stata_path = os.path.join(data_dir, "colonial_origins", "data.dta")
df = pd.read_stata(stata_path)
ycol = 'logpgp95'
zcol = 'logem4'
xcol = 'avexpr'
df = df[[zcol, xcol, ycol]].dropna()
z, x, y = df[zcol].values, df[xcol].values, df[ycol].values
data = {'x': x, 'y': y, 'z': z, 'confounder': None, 'ex': None, 'ey': None}
return whiten(data)
def get_newey_powell(key: np.ndarray,
num: int,
num_xstar: int = 100) -> DataSynth:
"""Get simulated Newey Powell (sigmoid design) data from KIV paper."""
def np_true(vals: np.ndarray):
return np.log(np.abs(16. * vals - 8) + 1) * np.sign(vals - 0.5)
xstar = np.linspace(0, 1, num_xstar)
ystar = np_true(xstar)
mu = np.zeros(3)
sigma = np.array([[1., 0.5, 0.], [0.5, 1., 0.], [0., 0., 1.]])
r = random.multivariate_normal(key, mu, sigma, shape=(num,))
u, t, w = r[:, 0], r[:, 1], r[:, 2]
x = w + t
x = norm.cdf(x / np.sqrt(2.))
z = norm.cdf(w)
e = u
y = np_true(x) + e
values = whiten({'x': x, 'y': y, 'z': z, 'ex': e, 'ey': e})
xstar = whiten_with_mu_std(xstar, values['x_mu'], values['x_std'])
ystar = whiten_with_mu_std(ystar, values['y_mu'], values['y_std'])
values['confounder'] = None
return values, xstar, ystar
# =============================================================================
# DISCRETIZATION AND CDF HANDLING
# =============================================================================
def ecdf(vals: np.ndarray, num_points: int = None) -> ArrayTup:
"""Evaluate the empirical distribution function on fixed number of points."""
if num_points is None:
num_points = len(vals)
cdf = np.linspace(0, 1, num_points)
t = np.quantile(vals, cdf)
return t, cdf
def cdf_inv(vals: np.ndarray,
num_points: int = None) -> Callable[..., np.ndarray]:
"""Compute an interpolation function of the (empirical) inverse cdf."""
t, cdf = ecdf(vals, num_points)
return lambda x: onp.interp(x, cdf, t)
def get_cdf_invs(val: np.ndarray,
bin_ids: np.ndarray,
num_z: int) -> Sequence[Callable[..., np.ndarray]]:
"""Compute a list of interpolated inverse CDFs of val at each z in Z grid."""
cdf_invs = []
for i in range(num_z):
cdf_invs.append(cdf_inv(val[bin_ids == i]))
return cdf_invs
def get_z_bin_assigment(z: np.ndarray, z_grid: np.ndarray) -> np.ndarray:
"""Assignment of values in z to the respective bin in z_grid."""
bins = np.concatenate((np.array([-np.inf]),
z_grid[1:-1],
np.array([np.inf])))
hist = onp.digitize(z, bins=bins, right=True) - 1
return hist
def get_x_samples(x: np.ndarray,
bin_ids: np.ndarray,
num_z: int,
num_sample: int) -> ArrayTup:
"""Pre-compute samples from p(x | z^{(i)}) for each gridpoint zi."""
x_cdf_invs = get_cdf_invs(x, bin_ids, num_z)
tmp = np.linspace(0, 1, num_sample + 2)[1:-1]
tmp0 = utils.normal_cdf_inv(tmp, np.array([0]), np.array([0]))
return tmp0, np.array([x_cdf_inv(tmp) for x_cdf_inv in x_cdf_invs])
def get_y_pre(y: np.ndarray,
bin_ids: np.ndarray,
num_z: int,
num_points: int) -> np.ndarray:
"""Compute the grid of y points for constraint approach y."""
y_cdf_invs = get_cdf_invs(y, bin_ids, num_z)
grid = np.linspace(0, 1, num_points + 2)[1:-1]
return np.array([y_cdf_inv(grid) for y_cdf_inv in y_cdf_invs])
def make_zgrid_and_binids(z: np.ndarray, num_z: int) -> ArrayTup:
"""Discretize instrument Z and assign all z points to corresponding bins."""
if num_z <= 0:
logging.info("Discrete instrument specified, checking for values.")
z_grid = np.sort(onp.unique(z))
if len(z_grid) > 50:
logging.info("Found more than 50 unique values for z. This is not a "
"discrete instrument. Aborting!")
raise RuntimeError("Discrete instrument specified, but not found.")
logging.info(f"Found {len(z_grid)} unique values for discrete instrument.")
bin_ids = - onp.ones_like(z)
for i, zpoint in enumerate(z_grid):
bin_ids[z == zpoint] = i
if onp.any(bin_ids < 0):
raise ValueError(f"Found negative value in bin_ids. "
"Couldn't discretize instrument.")
bin_ids = np.array(bin_ids).astype(int)
else:
z_grid = ecdf(z, num_z + 1)[0]
bin_ids = get_z_bin_assigment(z, z_grid)
z_grid = (z_grid[:-1] + z_grid[1:]) / 2
return z_grid, bin_ids
| en | 0.696818 | Data loading and pre-processing utilities. # ============================================================================= # NOISE SOURCES # ============================================================================= Generate a Gaussian for the confounder. Generate a multivariate Gaussian for the noises e_X, e_Y. # ============================================================================= # SYNTHETIC STRUCTURAL EQUATIONS # ============================================================================= # ============================================================================= # DATA GENERATORS # ============================================================================= Whiten each input. Generate some synthetic data. Args: key: A JAX random key. num: The number of examples to generate. equations: Which structural equations to choose for x and y. Default: 1 num_xstar: Size of grid for interventions on x. external_equations: A dictionary that must contain the keys 'f_x' and 'f_y' mapping to callables as values that take two np.ndarrays as arguments and produce another np.ndarray. These are the structural equations for X and Y in the graph Z -> X -> Y. If this argument is not provided, the `equation` argument selects structural equations from the pre-defined dict `structural_equations`. disconnect_instrument: Whether to regenerate random (standard Gaussian) values for the instrument after the data has been generated. This serves for diagnostic purposes, i.e., looking at the same x, y data, Returns: A 3-tuple (values, xstar, ystar) consisting a dictionary `values` containing values for x, y, z, confounder, ex, ey as well as two array xstar, ystar containing values for the true cause-effect. # Evaluate E[ Y | do(x^*)] empirically Load data from colonial origins paper of Acemoglu. Get simulated Newey Powell (sigmoid design) data from KIV paper. # ============================================================================= # DISCRETIZATION AND CDF HANDLING # ============================================================================= Evaluate the empirical distribution function on fixed number of points. Compute an interpolation function of the (empirical) inverse cdf. Compute a list of interpolated inverse CDFs of val at each z in Z grid. Assignment of values in z to the respective bin in z_grid. Pre-compute samples from p(x | z^{(i)}) for each gridpoint zi. Compute the grid of y points for constraint approach y. Discretize instrument Z and assign all z points to corresponding bins. | 2.752634 | 3 |
Interact with the API/get-blobs.py | KevoLoyal/youngrockets | 0 | 6623153 | ########### Blob Interaction ###########
import http.client, urllib.request, urllib.parse, urllib.error, base64, requests, json
# Get all modules for the Blob Storage
from azure.storage.blob import BlockBlobService
from azure.storage.blob import PublicAccess
from azure.storage.blob import ContentSettings
from os import path # Import OS Path Function
# Python Classes Involved
# BlobServiceClient: The BlobServiceClient class allows you to manipulate Azure Storage resources and blob containers.
# ContainerClient: The ContainerClient class allows you to manipulate Azure Storage containers and their blobs.
# BlobClient: The BlobClient class allows you to manipulate Azure Storage blobs.
# Info to Blob
blob_storage_key = 'Key'
connect_str = 'String'
storage_account = 'account name'
container_name = input("To what container you want to list? ")
print("***********************************************")
# local_file_name = input("What is the name of the file to upload? ")
try:
block_blob_service = BlockBlobService(account_name=storage_account, account_key=blob_storage_key)
generator = block_blob_service.list_blobs(container_name)
except Exception as ex:
print('Exception:')
print(ex)
for blob in generator:
print(blob.name)
| ########### Blob Interaction ###########
import http.client, urllib.request, urllib.parse, urllib.error, base64, requests, json
# Get all modules for the Blob Storage
from azure.storage.blob import BlockBlobService
from azure.storage.blob import PublicAccess
from azure.storage.blob import ContentSettings
from os import path # Import OS Path Function
# Python Classes Involved
# BlobServiceClient: The BlobServiceClient class allows you to manipulate Azure Storage resources and blob containers.
# ContainerClient: The ContainerClient class allows you to manipulate Azure Storage containers and their blobs.
# BlobClient: The BlobClient class allows you to manipulate Azure Storage blobs.
# Info to Blob
blob_storage_key = 'Key'
connect_str = 'String'
storage_account = 'account name'
container_name = input("To what container you want to list? ")
print("***********************************************")
# local_file_name = input("What is the name of the file to upload? ")
try:
block_blob_service = BlockBlobService(account_name=storage_account, account_key=blob_storage_key)
generator = block_blob_service.list_blobs(container_name)
except Exception as ex:
print('Exception:')
print(ex)
for blob in generator:
print(blob.name)
| en | 0.674102 | ########### Blob Interaction ########### # Get all modules for the Blob Storage # Import OS Path Function # Python Classes Involved # BlobServiceClient: The BlobServiceClient class allows you to manipulate Azure Storage resources and blob containers. # ContainerClient: The ContainerClient class allows you to manipulate Azure Storage containers and their blobs. # BlobClient: The BlobClient class allows you to manipulate Azure Storage blobs. # Info to Blob # local_file_name = input("What is the name of the file to upload? ") | 3.033523 | 3 |
by-session/ta-921/j3/number2.py | amiraliakbari/sharif-mabani-python | 2 | 6623154 | print 'A'
x = 3
y = 2
print 'B'
if x > 10:
print "1"
else:
print x / y
print 'C'
print x * 1.0 / y
print 2 / (y - 2)
print 'D'
x = 3.0
y = 2.0
print x / y
| print 'A'
x = 3
y = 2
print 'B'
if x > 10:
print "1"
else:
print x / y
print 'C'
print x * 1.0 / y
print 2 / (y - 2)
print 'D'
x = 3.0
y = 2.0
print x / y
| none | 1 | 3.838209 | 4 | |
solution/11655(ROT13).py | OMEGA-Y/CodingTest-sol | 0 | 6623155 | import sys
data = sys.stdin.readline().rstrip()
output = ""
for i in data:
if i.islower():
output += chr(97+int((ord(i)-ord('a')+13)%26))
elif i.isupper():
output += chr(65+int((ord(i)-ord('A')+13)%26))
else:
output += i
print(output) | import sys
data = sys.stdin.readline().rstrip()
output = ""
for i in data:
if i.islower():
output += chr(97+int((ord(i)-ord('a')+13)%26))
elif i.isupper():
output += chr(65+int((ord(i)-ord('A')+13)%26))
else:
output += i
print(output) | none | 1 | 3.363043 | 3 | |
problems/OF/auto/problem62_OF.py | sunandita/ICAPS_Summer_School_RAE_2020 | 5 | 6623156 | <reponame>sunandita/ICAPS_Summer_School_RAE_2020
__author__ = 'mason'
from domain_orderFulfillment import *
from timer import DURATION
from state import state
import numpy as np
'''
This is a randomly generated problem
'''
def GetCostOfMove(id, r, loc1, loc2, dist):
return 1 + dist
def GetCostOfLookup(id, item):
return max(1, np.random.beta(2, 2))
def GetCostOfWrap(id, orderName, m, item):
return max(1, np.random.normal(5, .5))
def GetCostOfPickup(id, r, item):
return max(1, np.random.normal(4, 1))
def GetCostOfPutdown(id, r, item):
return max(1, np.random.normal(4, 1))
def GetCostOfLoad(id, orderName, r, m, item):
return max(1, np.random.normal(3, .5))
DURATION.TIME = {
'lookupDB': GetCostOfLookup,
'wrap': GetCostOfWrap,
'pickup': GetCostOfPickup,
'putdown': GetCostOfPutdown,
'loadMachine': GetCostOfLoad,
'moveRobot': GetCostOfMove,
'acquireRobot': 1,
'freeRobot': 1,
'wait': 5
}
DURATION.COUNTER = {
'lookupDB': GetCostOfLookup,
'wrap': GetCostOfWrap,
'pickup': GetCostOfPickup,
'putdown': GetCostOfPutdown,
'loadMachine': GetCostOfLoad,
'moveRobot': GetCostOfMove,
'acquireRobot': 1,
'freeRobot': 1,
'wait': 5
}
rv.LOCATIONS = [0, 1, 2, 3, 4, 200]
rv.FACTORY1 = frozenset({0, 1, 2, 3, 4, 200})
rv.FACTORY_UNION = rv.FACTORY1
rv.SHIPPING_DOC = {rv.FACTORY1: 0}
rv.GROUND_EDGES = {0: [1, 2, 4, 200, 3], 1: [2, 4, 0, 3, 200], 2: [4, 0, 1], 3: [0, 1, 4], 4: [3, 0, 1, 2], 200: [1, 0]}
rv.GROUND_WEIGHTS = {(0, 1): 8.339734977426263, (0, 2): 12.782471204845653, (0, 4): 1.4573925753056773, (0, 200): 1.9054250683218728, (0, 3): 4.193557905999012, (1, 2): 15.777006308874313, (1, 4): 10.742111749991842, (1, 3): 7.534473584229431, (1, 200): 6.632985362706415, (2, 4): 5.1167109230895, (3, 4): 1.2180650914526048}
rv.ROBOTS = { 'r0': rv.FACTORY1, }
rv.ROBOT_CAPACITY = {'r0': 4.642981441266107}
rv.MACHINES = { 'm0': rv.FACTORY1, 'm1': rv.FACTORY1, 'm2': rv.FACTORY1, }
rv.PALLETS = { 'p0', }
def ResetState():
state.OBJECTS = { 'o0': True, 'o1': True, 'o2': True, 'o3': True, 'o4': True, 'o5': True, 'o6': True, 'o7': True, 'o8': True, }
state.OBJ_WEIGHT = {'o0': 4.642981441266107, 'o1': 4.642981441266107, 'o2': 4.642981441266107, 'o3': 3.978811029733769, 'o4': 4.642981441266107, 'o5': 4.642981441266107, 'o6': 4.642981441266107, 'o7': 3.9377798836061166, 'o8': 4.642981441266107}
state.OBJ_CLASS = {'type0': ['o0', 'o1'], 'type1': ['o2', 'o3', 'o4'], 'type2': ['o5'], 'type3': ['o6', 'o7', 'o8']}
state.loc = { 'r0': 0, 'm0': 4, 'm1': 1, 'm2': 200, 'p0': 2, 'o0': 4, 'o1': 3, 'o2': 1, 'o3': 200, 'o4': 3, 'o5': 0, 'o6': 2, 'o7': 2, 'o8': 3,}
state.load = { 'r0': NIL,}
state.busy = {'r0': False, 'm0': False, 'm1': False, 'm2': False}
state.numUses = {'m0': 4, 'm1': 9, 'm2': 13}
state.var1 = {'temp': 'r0', 'temp1': 'r0', 'temp2': 1, 'redoId': 0}
state.shouldRedo = {}
tasks = {
15: [['orderStart', ['type0']]],
12: [['orderStart', ['type0']]],
}
eventsEnv = {
} | __author__ = 'mason'
from domain_orderFulfillment import *
from timer import DURATION
from state import state
import numpy as np
'''
This is a randomly generated problem
'''
def GetCostOfMove(id, r, loc1, loc2, dist):
return 1 + dist
def GetCostOfLookup(id, item):
return max(1, np.random.beta(2, 2))
def GetCostOfWrap(id, orderName, m, item):
return max(1, np.random.normal(5, .5))
def GetCostOfPickup(id, r, item):
return max(1, np.random.normal(4, 1))
def GetCostOfPutdown(id, r, item):
return max(1, np.random.normal(4, 1))
def GetCostOfLoad(id, orderName, r, m, item):
return max(1, np.random.normal(3, .5))
DURATION.TIME = {
'lookupDB': GetCostOfLookup,
'wrap': GetCostOfWrap,
'pickup': GetCostOfPickup,
'putdown': GetCostOfPutdown,
'loadMachine': GetCostOfLoad,
'moveRobot': GetCostOfMove,
'acquireRobot': 1,
'freeRobot': 1,
'wait': 5
}
DURATION.COUNTER = {
'lookupDB': GetCostOfLookup,
'wrap': GetCostOfWrap,
'pickup': GetCostOfPickup,
'putdown': GetCostOfPutdown,
'loadMachine': GetCostOfLoad,
'moveRobot': GetCostOfMove,
'acquireRobot': 1,
'freeRobot': 1,
'wait': 5
}
rv.LOCATIONS = [0, 1, 2, 3, 4, 200]
rv.FACTORY1 = frozenset({0, 1, 2, 3, 4, 200})
rv.FACTORY_UNION = rv.FACTORY1
rv.SHIPPING_DOC = {rv.FACTORY1: 0}
rv.GROUND_EDGES = {0: [1, 2, 4, 200, 3], 1: [2, 4, 0, 3, 200], 2: [4, 0, 1], 3: [0, 1, 4], 4: [3, 0, 1, 2], 200: [1, 0]}
rv.GROUND_WEIGHTS = {(0, 1): 8.339734977426263, (0, 2): 12.782471204845653, (0, 4): 1.4573925753056773, (0, 200): 1.9054250683218728, (0, 3): 4.193557905999012, (1, 2): 15.777006308874313, (1, 4): 10.742111749991842, (1, 3): 7.534473584229431, (1, 200): 6.632985362706415, (2, 4): 5.1167109230895, (3, 4): 1.2180650914526048}
rv.ROBOTS = { 'r0': rv.FACTORY1, }
rv.ROBOT_CAPACITY = {'r0': 4.642981441266107}
rv.MACHINES = { 'm0': rv.FACTORY1, 'm1': rv.FACTORY1, 'm2': rv.FACTORY1, }
rv.PALLETS = { 'p0', }
def ResetState():
state.OBJECTS = { 'o0': True, 'o1': True, 'o2': True, 'o3': True, 'o4': True, 'o5': True, 'o6': True, 'o7': True, 'o8': True, }
state.OBJ_WEIGHT = {'o0': 4.642981441266107, 'o1': 4.642981441266107, 'o2': 4.642981441266107, 'o3': 3.978811029733769, 'o4': 4.642981441266107, 'o5': 4.642981441266107, 'o6': 4.642981441266107, 'o7': 3.9377798836061166, 'o8': 4.642981441266107}
state.OBJ_CLASS = {'type0': ['o0', 'o1'], 'type1': ['o2', 'o3', 'o4'], 'type2': ['o5'], 'type3': ['o6', 'o7', 'o8']}
state.loc = { 'r0': 0, 'm0': 4, 'm1': 1, 'm2': 200, 'p0': 2, 'o0': 4, 'o1': 3, 'o2': 1, 'o3': 200, 'o4': 3, 'o5': 0, 'o6': 2, 'o7': 2, 'o8': 3,}
state.load = { 'r0': NIL,}
state.busy = {'r0': False, 'm0': False, 'm1': False, 'm2': False}
state.numUses = {'m0': 4, 'm1': 9, 'm2': 13}
state.var1 = {'temp': 'r0', 'temp1': 'r0', 'temp2': 1, 'redoId': 0}
state.shouldRedo = {}
tasks = {
15: [['orderStart', ['type0']]],
12: [['orderStart', ['type0']]],
}
eventsEnv = {
} | en | 0.887912 | This is a randomly generated problem | 2.265882 | 2 |
2_PG/2-2_A2C/model.py | stella-moon323/Reinforcement_Learning | 1 | 6623157 | import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_nums, output_nums, hidden_nums):
super(Net, self).__init__()
self.layers = nn.Sequential(
nn.Linear(input_nums, hidden_nums),
nn.ReLU(inplace=True),
nn.Linear(hidden_nums, hidden_nums),
nn.ReLU(inplace=True),
)
self.fc_actor = nn.Linear(hidden_nums, output_nums)
self.fc_critic = nn.Linear(hidden_nums, 1)
# 重みの初期化
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
m.bias.data.zero_()
def forward(self, x):
hid = self.layers(x)
actor = self.fc_actor(hid)
critic = self.fc_critic(hid)
return actor, critic
| import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_nums, output_nums, hidden_nums):
super(Net, self).__init__()
self.layers = nn.Sequential(
nn.Linear(input_nums, hidden_nums),
nn.ReLU(inplace=True),
nn.Linear(hidden_nums, hidden_nums),
nn.ReLU(inplace=True),
)
self.fc_actor = nn.Linear(hidden_nums, output_nums)
self.fc_critic = nn.Linear(hidden_nums, 1)
# 重みの初期化
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
m.bias.data.zero_()
def forward(self, x):
hid = self.layers(x)
actor = self.fc_actor(hid)
critic = self.fc_critic(hid)
return actor, critic
| none | 1 | 3.224252 | 3 | |
postcorrection/seq2seq_tester.py | shrutirij/ocr-post-correction | 35 | 6623158 | <reponame>shrutirij/ocr-post-correction<gh_stars>10-100
"""Module with functions for using a trained post-correction model to produce predictions on unseen input data.
The module can be used with a test set in order to get a text output and CER/WER metrics (lines 26).
It can also be used without a target prediction to only get the predicted output (line 40).
Copyright (c) 2021, <NAME>
All rights reserved.
This source code is licensed under the BSD-style license found in the
LICENSE file in the root directory of this source tree.
"""
import dynet as dy
from utils import DataReader, ErrorMetrics
class Seq2SeqTester:
def __init__(self, model, output_name):
self.model = model
self.datareader = DataReader()
self.metrics = ErrorMetrics()
self.output_name = output_name
def test(self, src1, src2, tgt):
if tgt:
data = self.datareader.read_parallel_data(self.model, src1, src2, tgt)
output_name = "{}_{}".format(self.output_name, src1.split("/")[-1])
cer, wer = self.metrics.get_average_cer(
self.model,
data,
output_file=open(
"{}.output".format(output_name), "w", encoding="utf-8"
),
write_pgens=False,
)
with open("{}.metrics".format(output_name), "w") as output_file:
output_file.write("TEST CER: %0.4f\n" % (cer))
output_file.write("TEST WER: %0.4f\n" % (wer))
else:
output_file = open(
"{}_{}.output".format(self.output_name, src1.split("/")[-1]),
"w",
encoding="utf8",
)
data = self.datareader.read_test_data(self.model, src1, src2)
for src1, src2 in data:
if len(src1) == 0 or len(src2) == 0:
output_file.write("\n")
continue
dy.renew_cg()
output, _ = self.model.generate_beam(src1, src2)
output_file.write(str(output) + "\n")
output_file.close()
| """Module with functions for using a trained post-correction model to produce predictions on unseen input data.
The module can be used with a test set in order to get a text output and CER/WER metrics (lines 26).
It can also be used without a target prediction to only get the predicted output (line 40).
Copyright (c) 2021, <NAME>
All rights reserved.
This source code is licensed under the BSD-style license found in the
LICENSE file in the root directory of this source tree.
"""
import dynet as dy
from utils import DataReader, ErrorMetrics
class Seq2SeqTester:
def __init__(self, model, output_name):
self.model = model
self.datareader = DataReader()
self.metrics = ErrorMetrics()
self.output_name = output_name
def test(self, src1, src2, tgt):
if tgt:
data = self.datareader.read_parallel_data(self.model, src1, src2, tgt)
output_name = "{}_{}".format(self.output_name, src1.split("/")[-1])
cer, wer = self.metrics.get_average_cer(
self.model,
data,
output_file=open(
"{}.output".format(output_name), "w", encoding="utf-8"
),
write_pgens=False,
)
with open("{}.metrics".format(output_name), "w") as output_file:
output_file.write("TEST CER: %0.4f\n" % (cer))
output_file.write("TEST WER: %0.4f\n" % (wer))
else:
output_file = open(
"{}_{}.output".format(self.output_name, src1.split("/")[-1]),
"w",
encoding="utf8",
)
data = self.datareader.read_test_data(self.model, src1, src2)
for src1, src2 in data:
if len(src1) == 0 or len(src2) == 0:
output_file.write("\n")
continue
dy.renew_cg()
output, _ = self.model.generate_beam(src1, src2)
output_file.write(str(output) + "\n")
output_file.close() | en | 0.817666 | Module with functions for using a trained post-correction model to produce predictions on unseen input data. The module can be used with a test set in order to get a text output and CER/WER metrics (lines 26). It can also be used without a target prediction to only get the predicted output (line 40). Copyright (c) 2021, <NAME> All rights reserved. This source code is licensed under the BSD-style license found in the LICENSE file in the root directory of this source tree. | 2.839709 | 3 |
src/pyndf/gui/widgets/control.py | Guillaume-Guardia/ndf-python | 0 | 6623159 | <filename>src/pyndf/gui/widgets/control.py
# -*- coding: utf-8 -*-
from pyndf.qtlib import QtWidgets
class ControlButtons(QtWidgets.QWidget):
"""Widget adding in status bar to control the execution of the thread
Args:
QtWidgets (QT):
"""
def __init__(self, windows, *args, **kwargs):
super().__init__(*args, **kwargs)
self.windows = windows
self.buttons = {}
# Create vertical layout
layout = QtWidgets.QHBoxLayout()
# Cancel button
icon = "" # TODO find icon pause
text = self.tr("&Cancel") # Alt + C
self.buttons["cancel"] = QtWidgets.QPushButton(text, parent=self)
self.buttons["cancel"].clicked.connect(self.cancel)
layout.addWidget(self.buttons["cancel"])
self.setLayout(layout)
def cancel(self):
"""Cancel method which stop the thread execution safely with the set of a flag."""
if self.windows.process is not None:
self.windows.process.flags.cancel = True
| <filename>src/pyndf/gui/widgets/control.py
# -*- coding: utf-8 -*-
from pyndf.qtlib import QtWidgets
class ControlButtons(QtWidgets.QWidget):
"""Widget adding in status bar to control the execution of the thread
Args:
QtWidgets (QT):
"""
def __init__(self, windows, *args, **kwargs):
super().__init__(*args, **kwargs)
self.windows = windows
self.buttons = {}
# Create vertical layout
layout = QtWidgets.QHBoxLayout()
# Cancel button
icon = "" # TODO find icon pause
text = self.tr("&Cancel") # Alt + C
self.buttons["cancel"] = QtWidgets.QPushButton(text, parent=self)
self.buttons["cancel"].clicked.connect(self.cancel)
layout.addWidget(self.buttons["cancel"])
self.setLayout(layout)
def cancel(self):
"""Cancel method which stop the thread execution safely with the set of a flag."""
if self.windows.process is not None:
self.windows.process.flags.cancel = True
| en | 0.777384 | # -*- coding: utf-8 -*- Widget adding in status bar to control the execution of the thread Args: QtWidgets (QT): # Create vertical layout # Cancel button # TODO find icon pause # Alt + C Cancel method which stop the thread execution safely with the set of a flag. | 2.91059 | 3 |
bacaml/conftest.py | phetdam/bac-advanced-ml | 0 | 6623160 | <reponame>phetdam/bac-advanced-ml<filename>bacaml/conftest.py<gh_stars>0
"""pytest fixtures required by all unit test subpackages.
.. codeauthor:: <NAME> <<EMAIL>>
"""
import pytest
@pytest.fixture(scope="session")
def global_seed():
"""Universal seed value to be reused by all test methods.
Returns
-------
int
"""
return 7 | """pytest fixtures required by all unit test subpackages.
.. codeauthor:: <NAME> <<EMAIL>>
"""
import pytest
@pytest.fixture(scope="session")
def global_seed():
"""Universal seed value to be reused by all test methods.
Returns
-------
int
"""
return 7 | en | 0.590198 | pytest fixtures required by all unit test subpackages. .. codeauthor:: <NAME> <<EMAIL>> Universal seed value to be reused by all test methods. Returns ------- int | 1.937557 | 2 |
tests/fixtures/q_functions/__init__.py | blacksph3re/garage | 1,500 | 6623161 | <filename>tests/fixtures/q_functions/__init__.py
from tests.fixtures.q_functions.simple_q_function import SimpleQFunction
__all__ = ['SimpleQFunction']
| <filename>tests/fixtures/q_functions/__init__.py
from tests.fixtures.q_functions.simple_q_function import SimpleQFunction
__all__ = ['SimpleQFunction']
| none | 1 | 1.24167 | 1 | |
Walkthru_10/ex_10_01.py | Witziger/Walkthru-Python | 0 | 6623162 | name = input("Enter file:")
if len(name) < 1 : name = "mbox-short.txt"
handle = open(name)
counts = dict()
for line in handle:
if not line.startswith("From ") : continue
line = line.rstrip()
line = line.split()
time = line[5]
hour, minute, second = time.split(':')
counts[hour] = counts.get(hour,0) + 1
#print (counts)
t = list(counts.items())
t.sort()
for key, val in t:
print(key, val)
| name = input("Enter file:")
if len(name) < 1 : name = "mbox-short.txt"
handle = open(name)
counts = dict()
for line in handle:
if not line.startswith("From ") : continue
line = line.rstrip()
line = line.split()
time = line[5]
hour, minute, second = time.split(':')
counts[hour] = counts.get(hour,0) + 1
#print (counts)
t = list(counts.items())
t.sort()
for key, val in t:
print(key, val)
| en | 0.573618 | #print (counts) | 3.429411 | 3 |
openstack_dashboard/test/integration_tests/pages/admin/system/flavorspage.py | ankur-gupta91/block_storage | 3 | 6623163 | # Licensed 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.
from openstack_dashboard.test.integration_tests.pages import basepage
from openstack_dashboard.test.integration_tests.regions import forms
from openstack_dashboard.test.integration_tests.regions import tables
class FlavorsTable(tables.TableRegion):
name = "flavors"
CREATE_FLAVOR_FORM_FIELDS = (("name", "flavor_id", "vcpus", "memory_mb",
"disk_gb", "eph_gb", "swap_mb"),
("all_projects", "selected_projects"))
@tables.bind_table_action('create')
def create_flavor(self, create_button):
create_button.click()
return forms.TabbedFormRegion(
self.driver, self.conf,
field_mappings=self.CREATE_FLAVOR_FORM_FIELDS)
@tables.bind_table_action('delete')
def delete_flavor(self, delete_button):
delete_button.click()
return forms.BaseFormRegion(self.driver, self.conf, None)
class FlavorsPage(basepage.BaseNavigationPage):
DEFAULT_ID = "auto"
FLAVORS_TABLE_NAME_COLUMN = 'name'
def __init__(self, driver, conf):
super(FlavorsPage, self).__init__(driver, conf)
self._page_title = "Flavors"
@property
def flavors_table(self):
return FlavorsTable(self.driver, self.conf)
def _get_flavor_row(self, name):
return self.flavors_table.get_row(self.FLAVORS_TABLE_NAME_COLUMN, name)
def create_flavor(self, name, id_=DEFAULT_ID, vcpus=None, ram=None,
root_disk=None, ephemeral_disk=None, swap_disk=None):
create_flavor_form = self.flavors_table.create_flavor()
create_flavor_form.name.text = name
if id_ is not None:
create_flavor_form.flavor_id.text = id_
create_flavor_form.vcpus.value = vcpus
create_flavor_form.memory_mb.value = ram
create_flavor_form.disk_gb.value = root_disk
create_flavor_form.eph_gb.value = ephemeral_disk
create_flavor_form.swap_mb.value = swap_disk
create_flavor_form.submit()
def delete_flavor(self, name):
row = self._get_flavor_row(name)
row.mark()
confirm_delete_flavors_form = self.flavors_table.delete_flavor()
confirm_delete_flavors_form.submit()
def is_flavor_present(self, name):
return bool(self._get_flavor_row(name))
| # Licensed 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.
from openstack_dashboard.test.integration_tests.pages import basepage
from openstack_dashboard.test.integration_tests.regions import forms
from openstack_dashboard.test.integration_tests.regions import tables
class FlavorsTable(tables.TableRegion):
name = "flavors"
CREATE_FLAVOR_FORM_FIELDS = (("name", "flavor_id", "vcpus", "memory_mb",
"disk_gb", "eph_gb", "swap_mb"),
("all_projects", "selected_projects"))
@tables.bind_table_action('create')
def create_flavor(self, create_button):
create_button.click()
return forms.TabbedFormRegion(
self.driver, self.conf,
field_mappings=self.CREATE_FLAVOR_FORM_FIELDS)
@tables.bind_table_action('delete')
def delete_flavor(self, delete_button):
delete_button.click()
return forms.BaseFormRegion(self.driver, self.conf, None)
class FlavorsPage(basepage.BaseNavigationPage):
DEFAULT_ID = "auto"
FLAVORS_TABLE_NAME_COLUMN = 'name'
def __init__(self, driver, conf):
super(FlavorsPage, self).__init__(driver, conf)
self._page_title = "Flavors"
@property
def flavors_table(self):
return FlavorsTable(self.driver, self.conf)
def _get_flavor_row(self, name):
return self.flavors_table.get_row(self.FLAVORS_TABLE_NAME_COLUMN, name)
def create_flavor(self, name, id_=DEFAULT_ID, vcpus=None, ram=None,
root_disk=None, ephemeral_disk=None, swap_disk=None):
create_flavor_form = self.flavors_table.create_flavor()
create_flavor_form.name.text = name
if id_ is not None:
create_flavor_form.flavor_id.text = id_
create_flavor_form.vcpus.value = vcpus
create_flavor_form.memory_mb.value = ram
create_flavor_form.disk_gb.value = root_disk
create_flavor_form.eph_gb.value = ephemeral_disk
create_flavor_form.swap_mb.value = swap_disk
create_flavor_form.submit()
def delete_flavor(self, name):
row = self._get_flavor_row(name)
row.mark()
confirm_delete_flavors_form = self.flavors_table.delete_flavor()
confirm_delete_flavors_form.submit()
def is_flavor_present(self, name):
return bool(self._get_flavor_row(name))
| en | 0.859654 | # Licensed 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. | 1.840577 | 2 |
setup.py | TylerPflueger/CSCI4900 | 0 | 6623164 | <gh_stars>0
from setuptools import setup
_domaven_version = '0.0.1'
'''
'flake8',
'pyflakes',
'mccabe',
'pep8',
'dosocs2'
'''
install_requires = [
'treelib'
]
tests_require = [
'pytest'
]
setup(
name='domaven',
version=_domaven_version,
description='Connector between DoSOCSv2 and Maven for relationships',
long_description='',
url='https://github.com/tpflueger/CSCI4900',
author='<NAME>, <NAME>',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Software Development :: Documentation',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT',
'Programming Language :: Python :: 2.7'
'Environment :: Console'
],
keywords='spdx licenses maven dosocs2',
packages=['scripts'],
install_requires=install_requires,
tests_require=tests_require,
extras_require={
'tests': install_requires + tests_require
},
entry_points={'console_scripts': ['domaven=scripts.main:main']},
test_suite='py.test',
zip_safe=False
)
| from setuptools import setup
_domaven_version = '0.0.1'
'''
'flake8',
'pyflakes',
'mccabe',
'pep8',
'dosocs2'
'''
install_requires = [
'treelib'
]
tests_require = [
'pytest'
]
setup(
name='domaven',
version=_domaven_version,
description='Connector between DoSOCSv2 and Maven for relationships',
long_description='',
url='https://github.com/tpflueger/CSCI4900',
author='<NAME>, <NAME>',
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Software Development :: Documentation',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT',
'Programming Language :: Python :: 2.7'
'Environment :: Console'
],
keywords='spdx licenses maven dosocs2',
packages=['scripts'],
install_requires=install_requires,
tests_require=tests_require,
extras_require={
'tests': install_requires + tests_require
},
entry_points={'console_scripts': ['domaven=scripts.main:main']},
test_suite='py.test',
zip_safe=False
) | en | 0.087476 | 'flake8', 'pyflakes', 'mccabe', 'pep8', 'dosocs2' | 1.283001 | 1 |
cla_backend/apps/checker/migrations/0001_initial.py | uk-gov-mirror/ministryofjustice.cla_backend | 3 | 6623165 | # coding=utf-8
from __future__ import unicode_literals
from django.db import models, migrations
import django.utils.timezone
import model_utils.fields
import uuidfield.fields
class Migration(migrations.Migration):
dependencies = [("legalaid", "0003_eod_details")]
operations = [
migrations.CreateModel(
name="ReasonForContacting",
fields=[
("id", models.AutoField(verbose_name="ID", serialize=False, auto_created=True, primary_key=True)),
(
"created",
model_utils.fields.AutoCreatedField(
default=django.utils.timezone.now, verbose_name="created", editable=False
),
),
(
"modified",
model_utils.fields.AutoLastModifiedField(
default=django.utils.timezone.now, verbose_name="modified", editable=False
),
),
("reference", uuidfield.fields.UUIDField(unique=True, max_length=32, editable=False, blank=True)),
("other_reasons", models.TextField(blank=True)),
("referrer", models.CharField(max_length=255, blank=True)),
("user_agent", models.CharField(max_length=255, blank=True)),
("case", models.ForeignKey(blank=True, to="legalaid.Case", null=True)),
],
options={"ordering": ("-created",), "verbose_name_plural": "reasons for contacting"},
bases=(models.Model,),
),
migrations.CreateModel(
name="ReasonForContactingCategory",
fields=[
("id", models.AutoField(verbose_name="ID", serialize=False, auto_created=True, primary_key=True)),
(
"category",
models.CharField(
max_length=20,
choices=[
(b"CANT_ANSWER", "I don\u2019t know how to answer a question"),
(b"MISSING_PAPERWORK", "I don\u2019t have the paperwork I need"),
(b"PREFER_SPEAKING", "I\u2019d prefer to speak to someone"),
(b"DIFFICULTY_ONLINE", "I have trouble using online services"),
(b"HOW_SERVICE_HELPS", "I don\u2019t understand how this service can help me"),
(b"AREA_NOT_COVERED", "My problem area isn\u2019t covered"),
(b"PNS", "I\u2019d prefer not to say"),
(b"OTHER", "Another reason"),
],
),
),
("reason_for_contacting", models.ForeignKey(related_name="reasons", to="checker.ReasonForContacting")),
],
options={"verbose_name": "category", "verbose_name_plural": "categories"},
bases=(models.Model,),
),
]
| # coding=utf-8
from __future__ import unicode_literals
from django.db import models, migrations
import django.utils.timezone
import model_utils.fields
import uuidfield.fields
class Migration(migrations.Migration):
dependencies = [("legalaid", "0003_eod_details")]
operations = [
migrations.CreateModel(
name="ReasonForContacting",
fields=[
("id", models.AutoField(verbose_name="ID", serialize=False, auto_created=True, primary_key=True)),
(
"created",
model_utils.fields.AutoCreatedField(
default=django.utils.timezone.now, verbose_name="created", editable=False
),
),
(
"modified",
model_utils.fields.AutoLastModifiedField(
default=django.utils.timezone.now, verbose_name="modified", editable=False
),
),
("reference", uuidfield.fields.UUIDField(unique=True, max_length=32, editable=False, blank=True)),
("other_reasons", models.TextField(blank=True)),
("referrer", models.CharField(max_length=255, blank=True)),
("user_agent", models.CharField(max_length=255, blank=True)),
("case", models.ForeignKey(blank=True, to="legalaid.Case", null=True)),
],
options={"ordering": ("-created",), "verbose_name_plural": "reasons for contacting"},
bases=(models.Model,),
),
migrations.CreateModel(
name="ReasonForContactingCategory",
fields=[
("id", models.AutoField(verbose_name="ID", serialize=False, auto_created=True, primary_key=True)),
(
"category",
models.CharField(
max_length=20,
choices=[
(b"CANT_ANSWER", "I don\u2019t know how to answer a question"),
(b"MISSING_PAPERWORK", "I don\u2019t have the paperwork I need"),
(b"PREFER_SPEAKING", "I\u2019d prefer to speak to someone"),
(b"DIFFICULTY_ONLINE", "I have trouble using online services"),
(b"HOW_SERVICE_HELPS", "I don\u2019t understand how this service can help me"),
(b"AREA_NOT_COVERED", "My problem area isn\u2019t covered"),
(b"PNS", "I\u2019d prefer not to say"),
(b"OTHER", "Another reason"),
],
),
),
("reason_for_contacting", models.ForeignKey(related_name="reasons", to="checker.ReasonForContacting")),
],
options={"verbose_name": "category", "verbose_name_plural": "categories"},
bases=(models.Model,),
),
]
| en | 0.644078 | # coding=utf-8 | 1.934536 | 2 |
router_change_ip.py | hbvj99/vianet-scripts | 2 | 6623166 | <filename>router_change_ip.py
# ISCOM HT803-1GE EPON Home Terminal
# Generate new public IP through reconnect
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.options import Options
from selenium.common.exceptions import NoSuchElementException, NoSuchFrameException
from requests import get
import sys
import credentials as c
chrome_options = Options()
chrome_options.add_argument("--headless")
driver = webdriver.Chrome(options=chrome_options)
def LoginRouter():
driver.get('http://192.168.1.1/admin/login.asp')
driver.find_element(By.ID, 'username').click()
driver.find_element(By.ID, 'username').click()
element = driver.find_element(By.ID, 'username')
actions = ActionChains(driver)
actions.double_click(element).perform()
driver.find_element(By.ID, 'username').send_keys(c.router_usr)
driver.find_element(By.ID, 'psd').send_keys(c.router_psw)
driver.find_element(By.CSS_SELECTOR, '.button:nth-child(1)').click()
try:
driver.find_element(By.CSS_SELECTOR, 'body > blockquote > form > table > tbody > tr:nth-child(1) > td > h4')
sys.exit('ERROR! Superadmin login credential incorrect')
except NoSuchElementException:
pass
driver.switch_to.frame(0)
def NewIp():
driver.find_element(By.CSS_SELECTOR, 'td:nth-child(2) > p').click()
driver.find_element(By.CSS_SELECTOR, 'td:nth-child(2) span').click()
element = driver.find_element(By.CSS_SELECTOR, 'td:nth-child(2) span')
actions = ActionChains(driver)
actions.move_to_element(element).perform()
element = driver.find_element(By.CSS_SELECTOR, 'body')
actions = ActionChains(driver)
driver.switch_to.default_content()
driver.switch_to.frame(2)
element = driver.find_element(By.CSS_SELECTOR, '.button:nth-child(39)')
actions = ActionChains(driver)
actions.move_to_element(element).release().perform()
driver.find_element(By.CSS_SELECTOR, '.button:nth-child(39)').click()
driver.find_element(By.CSS_SELECTOR, 'input').click()
geo = get('http://ip-api.com/json').json()
print('SUCCESS! New public IP is '+geo['query']+' by '+geo['isp'])
if __name__ == '__main__':
LoginRouter()
NewIp()
driver.quit() | <filename>router_change_ip.py
# ISCOM HT803-1GE EPON Home Terminal
# Generate new public IP through reconnect
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.options import Options
from selenium.common.exceptions import NoSuchElementException, NoSuchFrameException
from requests import get
import sys
import credentials as c
chrome_options = Options()
chrome_options.add_argument("--headless")
driver = webdriver.Chrome(options=chrome_options)
def LoginRouter():
driver.get('http://192.168.1.1/admin/login.asp')
driver.find_element(By.ID, 'username').click()
driver.find_element(By.ID, 'username').click()
element = driver.find_element(By.ID, 'username')
actions = ActionChains(driver)
actions.double_click(element).perform()
driver.find_element(By.ID, 'username').send_keys(c.router_usr)
driver.find_element(By.ID, 'psd').send_keys(c.router_psw)
driver.find_element(By.CSS_SELECTOR, '.button:nth-child(1)').click()
try:
driver.find_element(By.CSS_SELECTOR, 'body > blockquote > form > table > tbody > tr:nth-child(1) > td > h4')
sys.exit('ERROR! Superadmin login credential incorrect')
except NoSuchElementException:
pass
driver.switch_to.frame(0)
def NewIp():
driver.find_element(By.CSS_SELECTOR, 'td:nth-child(2) > p').click()
driver.find_element(By.CSS_SELECTOR, 'td:nth-child(2) span').click()
element = driver.find_element(By.CSS_SELECTOR, 'td:nth-child(2) span')
actions = ActionChains(driver)
actions.move_to_element(element).perform()
element = driver.find_element(By.CSS_SELECTOR, 'body')
actions = ActionChains(driver)
driver.switch_to.default_content()
driver.switch_to.frame(2)
element = driver.find_element(By.CSS_SELECTOR, '.button:nth-child(39)')
actions = ActionChains(driver)
actions.move_to_element(element).release().perform()
driver.find_element(By.CSS_SELECTOR, '.button:nth-child(39)').click()
driver.find_element(By.CSS_SELECTOR, 'input').click()
geo = get('http://ip-api.com/json').json()
print('SUCCESS! New public IP is '+geo['query']+' by '+geo['isp'])
if __name__ == '__main__':
LoginRouter()
NewIp()
driver.quit() | en | 0.692329 | # ISCOM HT803-1GE EPON Home Terminal # Generate new public IP through reconnect | 2.478469 | 2 |
marlin-firmware/buildroot/share/PlatformIO/scripts/common-cxxflags.py | voicevon/gogame_bot | 6 | 6623167 | <reponame>voicevon/gogame_bot
#
# common-cxxflags.py
# Convenience script to apply customizations to CPP flags
#
Import("env")
env.Append(CXXFLAGS=[
"-Wno-register"
#"-Wno-incompatible-pointer-types",
#"-Wno-unused-const-variable",
#"-Wno-maybe-uninitialized",
#"-Wno-sign-compare"
])
| #
# common-cxxflags.py
# Convenience script to apply customizations to CPP flags
#
Import("env")
env.Append(CXXFLAGS=[
"-Wno-register"
#"-Wno-incompatible-pointer-types",
#"-Wno-unused-const-variable",
#"-Wno-maybe-uninitialized",
#"-Wno-sign-compare"
]) | en | 0.57934 | # # common-cxxflags.py # Convenience script to apply customizations to CPP flags # #"-Wno-incompatible-pointer-types", #"-Wno-unused-const-variable", #"-Wno-maybe-uninitialized", #"-Wno-sign-compare" | 1.487318 | 1 |
aghlam/migrations/0007_aghlam_external.py | mablue/Specialized-Procurement-and-Sales-Management-System-for-East-Azarbaijan-Gas-Company | 30 | 6623168 | <filename>aghlam/migrations/0007_aghlam_external.py
# Generated by Django 2.2.2 on 2019-07-21 11:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('aghlam', '0006_auto_20190721_1122'),
]
operations = [
migrations.AddField(
model_name='aghlam',
name='external',
field=models.BooleanField(default=1, verbose_name='خارجی'),
preserve_default=False,
),
]
| <filename>aghlam/migrations/0007_aghlam_external.py
# Generated by Django 2.2.2 on 2019-07-21 11:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('aghlam', '0006_auto_20190721_1122'),
]
operations = [
migrations.AddField(
model_name='aghlam',
name='external',
field=models.BooleanField(default=1, verbose_name='خارجی'),
preserve_default=False,
),
]
| en | 0.763552 | # Generated by Django 2.2.2 on 2019-07-21 11:25 | 1.272019 | 1 |
tests/test_make_dataset.py | Hannemit/data_science_projects | 0 | 6623169 | <filename>tests/test_make_dataset.py
import pandas as pd
CHOROPLETH_FILE = "./tests/data/choropleth_df.csv"
CONVENIENT_FILE = "./tests/data/year_country_data.csv"
def test_make_df_nicer_format():
"""
Perform some checks on whether we didn't change anything weird in making the more convenient dataframe to work with
It should contain the same information as the choropleth dataframe, just differently structured. We're here just
checking that they're similar
"""
try:
df_choropleth = pd.read_csv(CHOROPLETH_FILE)
df_convenient = pd.read_csv(CONVENIENT_FILE)
except FileNotFoundError:
raise FileNotFoundError("Before running this test, make sure the data files are there.. these are the same ones"
"that are created from running the make_dataset.py file")
both = df_choropleth.groupby(['year', 'country']).agg(population=pd.NamedAgg(column="population", aggfunc=sum),
suicides_no=pd.NamedAgg(column="suicides_no", aggfunc=sum),
code=pd.NamedAgg(column="code", aggfunc=lambda x: x[0]),
).reset_index()
both["overall_rate"] = both['suicides_no'] / both['population'] * 100000
right = df_convenient[["year", "country", "code", "population", "overall_rate", "suicides_no"]]
pd.testing.assert_frame_equal(both.sort_index(axis=1), right.sort_index(axis=1)) # sort_index to get same col order
left = df_choropleth.loc[df_choropleth["sex"] == "female"].drop(columns=["sex"], axis=1)
right = df_convenient[["year", "country", "code", "female_pop", "female_rate", "suicide_num_f"]]
right.index = list(range(len(right)))
left.index = list(range(len(left)))
right = right.rename(columns={"female_rate": "suicides per 100,000",
"suicide_num_f": "suicides_no",
"female_pop": "population"})
pd.testing.assert_frame_equal(left.sort_index(axis=1), right.sort_index(axis=1))
left = df_choropleth.loc[df_choropleth["sex"] == "male"].drop(columns=["sex"], axis=1)
right = df_convenient[["year", "country", "code", "male_pop", "male_rate", "suicide_num_m"]]
right.index = list(range(len(right)))
left.index = list(range(len(left)))
right = right.rename(columns={"male_rate": "suicides per 100,000",
"suicide_num_m": "suicides_no",
"male_pop": "population"})
pd.testing.assert_frame_equal(left.sort_index(axis=1), right.sort_index(axis=1))
def test_prepare_data_for_choropleth():
try:
df_choropleth = pd.read_csv(CHOROPLETH_FILE)
except FileNotFoundError:
raise FileNotFoundError("Before running this test, run make_dataset.py to create our processed datafiles")
both = df_choropleth.groupby(['year', 'country']).agg(population=pd.NamedAgg(column="population", aggfunc=sum),
suicides_no=pd.NamedAgg(column="suicides_no", aggfunc=sum),
code=pd.NamedAgg(column="code", aggfunc=lambda x: x[0]),
).reset_index()
females = df_choropleth[df_choropleth["sex"] == "female"]
males = df_choropleth[df_choropleth["sex"] == "male"]
both["suicides per 100,000"] = both['suicides_no'] / both['population'] * 100000
# pick some random countries and check that male and female population add up to total population
countries = df_choropleth["country"].unique()
for country in countries[:50]:
female_pop = females.loc[(females["country"] == country) & (females["year"] == 1992), "population"].values
if len(female_pop) == 0:
continue # might not have data for this country in the specific year we're looking at
male_pop = males.loc[(males["country"] == country) & (males["year"] == 1992), "population"].values
combined_pop = both.loc[(both["country"] == country) & (both["year"] == 1992), "population"].values
assert combined_pop == female_pop + male_pop
| <filename>tests/test_make_dataset.py
import pandas as pd
CHOROPLETH_FILE = "./tests/data/choropleth_df.csv"
CONVENIENT_FILE = "./tests/data/year_country_data.csv"
def test_make_df_nicer_format():
"""
Perform some checks on whether we didn't change anything weird in making the more convenient dataframe to work with
It should contain the same information as the choropleth dataframe, just differently structured. We're here just
checking that they're similar
"""
try:
df_choropleth = pd.read_csv(CHOROPLETH_FILE)
df_convenient = pd.read_csv(CONVENIENT_FILE)
except FileNotFoundError:
raise FileNotFoundError("Before running this test, make sure the data files are there.. these are the same ones"
"that are created from running the make_dataset.py file")
both = df_choropleth.groupby(['year', 'country']).agg(population=pd.NamedAgg(column="population", aggfunc=sum),
suicides_no=pd.NamedAgg(column="suicides_no", aggfunc=sum),
code=pd.NamedAgg(column="code", aggfunc=lambda x: x[0]),
).reset_index()
both["overall_rate"] = both['suicides_no'] / both['population'] * 100000
right = df_convenient[["year", "country", "code", "population", "overall_rate", "suicides_no"]]
pd.testing.assert_frame_equal(both.sort_index(axis=1), right.sort_index(axis=1)) # sort_index to get same col order
left = df_choropleth.loc[df_choropleth["sex"] == "female"].drop(columns=["sex"], axis=1)
right = df_convenient[["year", "country", "code", "female_pop", "female_rate", "suicide_num_f"]]
right.index = list(range(len(right)))
left.index = list(range(len(left)))
right = right.rename(columns={"female_rate": "suicides per 100,000",
"suicide_num_f": "suicides_no",
"female_pop": "population"})
pd.testing.assert_frame_equal(left.sort_index(axis=1), right.sort_index(axis=1))
left = df_choropleth.loc[df_choropleth["sex"] == "male"].drop(columns=["sex"], axis=1)
right = df_convenient[["year", "country", "code", "male_pop", "male_rate", "suicide_num_m"]]
right.index = list(range(len(right)))
left.index = list(range(len(left)))
right = right.rename(columns={"male_rate": "suicides per 100,000",
"suicide_num_m": "suicides_no",
"male_pop": "population"})
pd.testing.assert_frame_equal(left.sort_index(axis=1), right.sort_index(axis=1))
def test_prepare_data_for_choropleth():
try:
df_choropleth = pd.read_csv(CHOROPLETH_FILE)
except FileNotFoundError:
raise FileNotFoundError("Before running this test, run make_dataset.py to create our processed datafiles")
both = df_choropleth.groupby(['year', 'country']).agg(population=pd.NamedAgg(column="population", aggfunc=sum),
suicides_no=pd.NamedAgg(column="suicides_no", aggfunc=sum),
code=pd.NamedAgg(column="code", aggfunc=lambda x: x[0]),
).reset_index()
females = df_choropleth[df_choropleth["sex"] == "female"]
males = df_choropleth[df_choropleth["sex"] == "male"]
both["suicides per 100,000"] = both['suicides_no'] / both['population'] * 100000
# pick some random countries and check that male and female population add up to total population
countries = df_choropleth["country"].unique()
for country in countries[:50]:
female_pop = females.loc[(females["country"] == country) & (females["year"] == 1992), "population"].values
if len(female_pop) == 0:
continue # might not have data for this country in the specific year we're looking at
male_pop = males.loc[(males["country"] == country) & (males["year"] == 1992), "population"].values
combined_pop = both.loc[(both["country"] == country) & (both["year"] == 1992), "population"].values
assert combined_pop == female_pop + male_pop
| en | 0.938221 | Perform some checks on whether we didn't change anything weird in making the more convenient dataframe to work with It should contain the same information as the choropleth dataframe, just differently structured. We're here just checking that they're similar # sort_index to get same col order # pick some random countries and check that male and female population add up to total population # might not have data for this country in the specific year we're looking at | 3.641819 | 4 |
examples/jetson_nano/example_technichub_jetson_nano.py | AlexandrePoisson/pylgbst | 0 | 6623170 | <reponame>AlexandrePoisson/pylgbst
from pylgbst.hub import TechnicHub
from pylgbst import get_connection_gattool
from pylgbst.peripherals import Motor,EncodedMotor
import time
import random
def callback(value):
print("Voltage: %s" % value)
conn = get_connection_gattool(hub_mac='90:84:2B:5F:33:35') #auto connect does not work
hub = TechnicHub(conn)
for device in hub.peripherals:
print(device)
direction_motor = Motor(hub, hub.PORT_B)
power_motor = Motor(hub, hub.PORT_D)
while True:
#hub.connection.notification_delayed('050082030a', 0.1)
power_motor.start_power(random.uniform(0, 1.0)) #here motor really moves
direction_motor.start_power(random.uniform(-0.2, 0.2)) #here motor really moves
time.sleep(0.5)
#hub.connection.notification_delayed('050082030a', 0.1)
power_motor.stop() #here motor really stops
print("Goodbye")
"""
Output
0
50 => 0x32
59 => 0x3B
60 => 0x3C
Goodbye
"""
| from pylgbst.hub import TechnicHub
from pylgbst import get_connection_gattool
from pylgbst.peripherals import Motor,EncodedMotor
import time
import random
def callback(value):
print("Voltage: %s" % value)
conn = get_connection_gattool(hub_mac='90:84:2B:5F:33:35') #auto connect does not work
hub = TechnicHub(conn)
for device in hub.peripherals:
print(device)
direction_motor = Motor(hub, hub.PORT_B)
power_motor = Motor(hub, hub.PORT_D)
while True:
#hub.connection.notification_delayed('050082030a', 0.1)
power_motor.start_power(random.uniform(0, 1.0)) #here motor really moves
direction_motor.start_power(random.uniform(-0.2, 0.2)) #here motor really moves
time.sleep(0.5)
#hub.connection.notification_delayed('050082030a', 0.1)
power_motor.stop() #here motor really stops
print("Goodbye")
"""
Output
0
50 => 0x32
59 => 0x3B
60 => 0x3C
Goodbye
""" | en | 0.697338 | #auto connect does not work #hub.connection.notification_delayed('050082030a', 0.1) #here motor really moves #here motor really moves #hub.connection.notification_delayed('050082030a', 0.1) #here motor really stops Output 0 50 => 0x32 59 => 0x3B 60 => 0x3C Goodbye | 2.494912 | 2 |
servers/bazarr/scripts/findRename.py | beakerflo/nas_synology_docker-compose | 3 | 6623171 | <reponame>beakerflo/nas_synology_docker-compose
import os
from datetime import datetime
dateString = (datetime.now()).strftime("%Y%m%d_%H%M%S")
os.rename('/volume1/containers/services/bazarr/scripts/rename.sh', '/volume1/containers/services/bazarr/scripts/rename.sh_' + dateString + '.txt')
renameSrt = open('/volume1/containers/services/bazarr/scripts/rename.sh','a')
movies = '/volume2/movies'
for movieLanguage in os.listdir(movies):
if movieLanguage != '.DS_Store':
for movieFolder in os.listdir(movies + '/' + movieLanguage):
if ' (' in movieFolder:
for movieFile in os.listdir(movies + '/' + movieLanguage + '/' + movieFolder):
if 'srt' in movieFile:
if 'synced' in movieFile:
srtfile = '"' + movies + '/' + movieLanguage + '/' + movieFolder + '/' + movieFile + '"'
syncedSrtFile = srtfile
outOfSync = srtfile.replace('synced.','')
newNameForOutOfSync = srtfile.replace('synced','downloaded')
newNameForSyncedSrtFile = outOfSync
renameSrt.write('mv ' + outOfSync + ' ' + newNameForOutOfSync + '\n')
renameSrt.write('mv ' + syncedSrtFile + ' ' + newNameForSyncedSrtFile + '\n')
series = '/volume2/tvshows'
for serieLanguage in os.listdir(series):
if serieLanguage != '.DS_Store' and serieLanguage != '@eaDir':
for serieFolder in os.listdir(series + '/' + serieLanguage):
if serieFolder != '.DS_Store' and serieFolder != '@eaDir':
for serieSeason in os.listdir(series + '/' + serieLanguage + '/' + serieFolder):
if serieSeason != '.DS_Store' and serieSeason != '@eaDir':
for serieFile in os.listdir(series + '/' + serieLanguage + '/' + serieFolder + '/' + serieSeason):
if 'synced' in serieFile:
srtfile = '"' + series + '/' + serieLanguage + '/' + serieFolder + '/' + serieSeason + '/' + serieFile + '"'
syncedSrtFile = srtfile
outOfSync = srtfile.replace('synced.','')
newNameForOutOfSync = srtfile.replace('synced','downloaded')
newNameForSyncedSrtFile = outOfSync
renameSrt.write('mv ' + outOfSync + ' ' + newNameForOutOfSync + '\n')
renameSrt.write('mv ' + syncedSrtFile + ' ' + newNameForSyncedSrtFile + '\n')
| import os
from datetime import datetime
dateString = (datetime.now()).strftime("%Y%m%d_%H%M%S")
os.rename('/volume1/containers/services/bazarr/scripts/rename.sh', '/volume1/containers/services/bazarr/scripts/rename.sh_' + dateString + '.txt')
renameSrt = open('/volume1/containers/services/bazarr/scripts/rename.sh','a')
movies = '/volume2/movies'
for movieLanguage in os.listdir(movies):
if movieLanguage != '.DS_Store':
for movieFolder in os.listdir(movies + '/' + movieLanguage):
if ' (' in movieFolder:
for movieFile in os.listdir(movies + '/' + movieLanguage + '/' + movieFolder):
if 'srt' in movieFile:
if 'synced' in movieFile:
srtfile = '"' + movies + '/' + movieLanguage + '/' + movieFolder + '/' + movieFile + '"'
syncedSrtFile = srtfile
outOfSync = srtfile.replace('synced.','')
newNameForOutOfSync = srtfile.replace('synced','downloaded')
newNameForSyncedSrtFile = outOfSync
renameSrt.write('mv ' + outOfSync + ' ' + newNameForOutOfSync + '\n')
renameSrt.write('mv ' + syncedSrtFile + ' ' + newNameForSyncedSrtFile + '\n')
series = '/volume2/tvshows'
for serieLanguage in os.listdir(series):
if serieLanguage != '.DS_Store' and serieLanguage != '@eaDir':
for serieFolder in os.listdir(series + '/' + serieLanguage):
if serieFolder != '.DS_Store' and serieFolder != '@eaDir':
for serieSeason in os.listdir(series + '/' + serieLanguage + '/' + serieFolder):
if serieSeason != '.DS_Store' and serieSeason != '@eaDir':
for serieFile in os.listdir(series + '/' + serieLanguage + '/' + serieFolder + '/' + serieSeason):
if 'synced' in serieFile:
srtfile = '"' + series + '/' + serieLanguage + '/' + serieFolder + '/' + serieSeason + '/' + serieFile + '"'
syncedSrtFile = srtfile
outOfSync = srtfile.replace('synced.','')
newNameForOutOfSync = srtfile.replace('synced','downloaded')
newNameForSyncedSrtFile = outOfSync
renameSrt.write('mv ' + outOfSync + ' ' + newNameForOutOfSync + '\n')
renameSrt.write('mv ' + syncedSrtFile + ' ' + newNameForSyncedSrtFile + '\n') | none | 1 | 2.424469 | 2 | |
src/main.py | loreloc/exoplanet-detection | 5 | 6623172 | import numpy as np
import sklearn as sk
import sklearn.model_selection
from rfc_worker import RFCWorker
from hb_optimizer import HBOptimizer
from metrics import compute_metrics
from koi_dataset import load_koi_dataset
# Set the LOCALHOST, PROJECT_NAME constants
LOCALHOST = '127.0.0.1'
PROJECT_NAME = 'exoplanet-detection'
# Set the parameters for hyperparameters optimization
eta = 3
min_budget = 8
max_budget = 216
n_iterations = 8
n_workers = 4
n_repetitions = 10
# Load the dataset
x_data, y_data = load_koi_dataset()
(n_samples, n_features) = x_data.shape
# Initialize the optimizer
optimizer = HBOptimizer(
LOCALHOST, PROJECT_NAME, RFCWorker,
eta, min_budget, max_budget, n_iterations
)
metrics = {
'precision': 0.0, 'recall': 0.0, 'f1': 0.0,
'confusion': [[0, 0], [0, 0]], 'importances': np.zeros(n_features)
}
# Repeat multiple times the test
for _ in range(n_repetitions):
# Split the dataset in train set and test set
x_train, x_test, y_train, y_test = sk.model_selection.train_test_split(
x_data, y_data, test_size=0.20, stratify=y_data
)
# Start the optimizer
optimizer.start()
# Run the optimizer
config = optimizer.run(n_workers, x_train, y_train)
# Build and train the best model
rfc = RFCWorker.build(config, max_budget)
rfc.fit(x_train, y_train)
# Compute some evaluation metrics
scores = compute_metrics(rfc, x_test, y_test)
for k in metrics:
metrics[k] = metrics[k] + scores[k]
# Close the optimizer
optimizer.close()
# Normalize the metrics
for k in metrics:
metrics[k] = metrics[k] / n_repetitions
# Print the metrics
print(metrics)
| import numpy as np
import sklearn as sk
import sklearn.model_selection
from rfc_worker import RFCWorker
from hb_optimizer import HBOptimizer
from metrics import compute_metrics
from koi_dataset import load_koi_dataset
# Set the LOCALHOST, PROJECT_NAME constants
LOCALHOST = '127.0.0.1'
PROJECT_NAME = 'exoplanet-detection'
# Set the parameters for hyperparameters optimization
eta = 3
min_budget = 8
max_budget = 216
n_iterations = 8
n_workers = 4
n_repetitions = 10
# Load the dataset
x_data, y_data = load_koi_dataset()
(n_samples, n_features) = x_data.shape
# Initialize the optimizer
optimizer = HBOptimizer(
LOCALHOST, PROJECT_NAME, RFCWorker,
eta, min_budget, max_budget, n_iterations
)
metrics = {
'precision': 0.0, 'recall': 0.0, 'f1': 0.0,
'confusion': [[0, 0], [0, 0]], 'importances': np.zeros(n_features)
}
# Repeat multiple times the test
for _ in range(n_repetitions):
# Split the dataset in train set and test set
x_train, x_test, y_train, y_test = sk.model_selection.train_test_split(
x_data, y_data, test_size=0.20, stratify=y_data
)
# Start the optimizer
optimizer.start()
# Run the optimizer
config = optimizer.run(n_workers, x_train, y_train)
# Build and train the best model
rfc = RFCWorker.build(config, max_budget)
rfc.fit(x_train, y_train)
# Compute some evaluation metrics
scores = compute_metrics(rfc, x_test, y_test)
for k in metrics:
metrics[k] = metrics[k] + scores[k]
# Close the optimizer
optimizer.close()
# Normalize the metrics
for k in metrics:
metrics[k] = metrics[k] / n_repetitions
# Print the metrics
print(metrics)
| en | 0.544526 | # Set the LOCALHOST, PROJECT_NAME constants # Set the parameters for hyperparameters optimization # Load the dataset # Initialize the optimizer # Repeat multiple times the test # Split the dataset in train set and test set # Start the optimizer # Run the optimizer # Build and train the best model # Compute some evaluation metrics # Close the optimizer # Normalize the metrics # Print the metrics | 2.546557 | 3 |
app/views/display_tasks_view.py | namuan/task-rider | 0 | 6623173 | <filename>app/views/display_tasks_view.py
import logging
from PyQt5 import QtWidgets
from PyQt5.QtCore import Qt, QModelIndex
from PyQt5.QtWidgets import QMenu, QAction
from app.widgets.completed_task_item_widget import CompletedTaskItemWidget
from app.widgets.task_item_widget import TaskItemWidget
class DisplayTasksView:
def __init__(self, main_window):
self.main_window = main_window
def setup_item_edit_handler(self, on_edit_selected):
self.main_window.lst_tasks.itemDoubleClicked.connect(on_edit_selected)
def setup_context_menu(self, on_delete_selected):
delete_action = QAction("Delete", self.main_window.lst_tasks)
delete_action.triggered.connect(on_delete_selected)
self.menu = QMenu()
self.menu.addAction(delete_action)
self.main_window.lst_tasks.setContextMenuPolicy(Qt.CustomContextMenu)
self.main_window.lst_tasks.customContextMenuRequested.connect(
self.on_display_context_menu
)
def on_display_context_menu(self, position):
index: QModelIndex = self.main_window.lst_tasks.indexAt(position)
if not index.isValid():
return
global_position = self.main_window.lst_tasks.viewport().mapToGlobal(position)
self.menu.exec_(global_position)
def clear(self):
self.main_window.lst_tasks.clear()
def task_from_widget(self, item_widget):
return self.main_window.lst_tasks.itemWidget(item_widget)
def selected_task_widget(self):
item_widget = self.main_window.lst_tasks.currentItem()
if item_widget:
t = self.task_from_widget(item_widget)
return t.get_task_id()
else:
return None
def widget_iterator(self):
for i in range(self.main_window.lst_tasks.count()):
task_widget = self.task_from_widget(self.main_window.lst_tasks.item(i))
yield i, task_widget
def show_task_editor(self, item_widget):
task_widget = self.task_from_widget(item_widget)
task_widget.edit_task()
def render_task_entity(self, task_entity, on_btn_task_done=None, on_task_save=None):
logging.info("Adding a new task widget for {}".format(task_entity))
task_widget = TaskItemWidget(
self.main_window, task_entity, on_btn_task_done, on_task_save
)
task_widget_item = QtWidgets.QListWidgetItem(self.main_window.lst_tasks)
task_widget_item.setSizeHint(task_widget.sizeHint())
self.main_window.lst_tasks.addItem(task_widget_item)
self.main_window.lst_tasks.setItemWidget(task_widget_item, task_widget)
def render_completed_task_entity(self, task_entity, callback=None):
logging.info("Adding a new completed task widget for {}".format(task_entity))
task_widget = CompletedTaskItemWidget(self.main_window, task_entity, callback)
task_widget_item = QtWidgets.QListWidgetItem(self.main_window.lst_tasks)
task_widget_item.setSizeHint(task_widget.sizeHint())
self.main_window.lst_tasks.addItem(task_widget_item)
self.main_window.lst_tasks.setItemWidget(task_widget_item, task_widget)
| <filename>app/views/display_tasks_view.py
import logging
from PyQt5 import QtWidgets
from PyQt5.QtCore import Qt, QModelIndex
from PyQt5.QtWidgets import QMenu, QAction
from app.widgets.completed_task_item_widget import CompletedTaskItemWidget
from app.widgets.task_item_widget import TaskItemWidget
class DisplayTasksView:
def __init__(self, main_window):
self.main_window = main_window
def setup_item_edit_handler(self, on_edit_selected):
self.main_window.lst_tasks.itemDoubleClicked.connect(on_edit_selected)
def setup_context_menu(self, on_delete_selected):
delete_action = QAction("Delete", self.main_window.lst_tasks)
delete_action.triggered.connect(on_delete_selected)
self.menu = QMenu()
self.menu.addAction(delete_action)
self.main_window.lst_tasks.setContextMenuPolicy(Qt.CustomContextMenu)
self.main_window.lst_tasks.customContextMenuRequested.connect(
self.on_display_context_menu
)
def on_display_context_menu(self, position):
index: QModelIndex = self.main_window.lst_tasks.indexAt(position)
if not index.isValid():
return
global_position = self.main_window.lst_tasks.viewport().mapToGlobal(position)
self.menu.exec_(global_position)
def clear(self):
self.main_window.lst_tasks.clear()
def task_from_widget(self, item_widget):
return self.main_window.lst_tasks.itemWidget(item_widget)
def selected_task_widget(self):
item_widget = self.main_window.lst_tasks.currentItem()
if item_widget:
t = self.task_from_widget(item_widget)
return t.get_task_id()
else:
return None
def widget_iterator(self):
for i in range(self.main_window.lst_tasks.count()):
task_widget = self.task_from_widget(self.main_window.lst_tasks.item(i))
yield i, task_widget
def show_task_editor(self, item_widget):
task_widget = self.task_from_widget(item_widget)
task_widget.edit_task()
def render_task_entity(self, task_entity, on_btn_task_done=None, on_task_save=None):
logging.info("Adding a new task widget for {}".format(task_entity))
task_widget = TaskItemWidget(
self.main_window, task_entity, on_btn_task_done, on_task_save
)
task_widget_item = QtWidgets.QListWidgetItem(self.main_window.lst_tasks)
task_widget_item.setSizeHint(task_widget.sizeHint())
self.main_window.lst_tasks.addItem(task_widget_item)
self.main_window.lst_tasks.setItemWidget(task_widget_item, task_widget)
def render_completed_task_entity(self, task_entity, callback=None):
logging.info("Adding a new completed task widget for {}".format(task_entity))
task_widget = CompletedTaskItemWidget(self.main_window, task_entity, callback)
task_widget_item = QtWidgets.QListWidgetItem(self.main_window.lst_tasks)
task_widget_item.setSizeHint(task_widget.sizeHint())
self.main_window.lst_tasks.addItem(task_widget_item)
self.main_window.lst_tasks.setItemWidget(task_widget_item, task_widget)
| none | 1 | 2.184412 | 2 | |
model.py | Information-Fusion-Lab-Umass/NoisyInjectiveFlows | 0 | 6623174 | <filename>model.py
import os
import glob
import jax
import jax.numpy as jnp
import staxplusplus as spp
import normalizing_flows as nf
import jax.nn.initializers as jaxinit
from jax.tree_util import tree_flatten
import util
import non_dim_preserving as ndp
from functools import partial
######################################################################################################################################################
class Model():
def __init__(self, dataset_name, x_shape):
self.x_shape = x_shape
self.dataset_name = dataset_name
self.init_fun, self.forward, self.inverse = None, None, None
self.names, self.z_shape, self.params, self.state = None, None, None, None
self.n_params = None
def get_architecture(self, init_key=None):
assert 0, 'unimplemented'
def get_prior(self):
assert 0, 'unimplemented'
#####################################################################
def build_model(self, quantize_level_bits, init_key=None):
architecture = self.get_architecture(init_key=init_key)
prior = self.get_prior()
# Use uniform dequantization to build our model
flow = nf.sequential_flow(nf.Dequantization(scale=2**quantize_level_bits),
nf.Logit(),
architecture,
nf.Flatten(),
prior)
self.init_fun, self.forward, self.inverse = flow
#####################################################################
def initialize_model(self, key):
assert self.init_fun is not None, 'Need to call build_model'
self.names, self.z_shape, self.params, self.state = self.init_fun(key, self.x_shape, ())
self.n_params = jax.flatten_util.ravel_pytree(self.params)[0].shape[0]
print('Total number of parameters:', self.n_params)
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
meta = {'x_shape' : list(self.x_shape),
'dataset_name': self.dataset_name,
'model' : None}
return meta
@classmethod
def initialize_from_meta_data(cls, meta):
assert 0, 'unimplemented'
#####################################################################
def save_state(self, path):
util.save_pytree_to_file(self.state, path)
def load_state_from_file(self, path):
self.state = util.load_pytree_from_file(self.state, path)
######################################################################################################################################################
class GLOW(Model):
def __init__(self, dataset_name, x_shape, n_filters=256, n_blocks=16, n_multiscale=5, data_init_iterations=1000):
super().__init__(dataset_name, x_shape)
self.n_filters = n_filters
self.n_blocks = n_blocks
self.n_multiscale = n_multiscale
self.data_init_iterations = data_init_iterations
#####################################################################
def get_architecture(self, init_key=None):
""" Build the architecture from GLOW https://arxiv.org/pdf/1807.03039.pdf """
def GLOWNet(out_shape, n_filters):
""" Transformation used inside affine coupling """
_, _, channels = out_shape
return spp.sequential(spp.Conv(n_filters, filter_shape=(3, 3), padding=((1, 1), (1, 1)), bias=True, weightnorm=False),
spp.Relu(),
spp.Conv(n_filters, filter_shape=(1, 1), padding=((0, 0), (0, 0)), bias=True, weightnorm=False),
spp.Relu(),
spp.Conv(2*channels, filter_shape=(3, 3), padding=((1, 1), (1, 1)), bias=True, weightnorm=False, W_init=jaxinit.zeros, b_init=jaxinit.zeros),
spp.Split(2, axis=-1),
spp.parallel(spp.Tanh(), spp.Identity())) # log_s, t
def GLOWComponent(name_iter, n_filters, n_blocks):
""" Compose glow blocks """
layers = [nf.GLOWBlock(partial(GLOWNet, n_filters=n_filters),
masked=False,
name=next(name_iter),
additive_coupling=False)]*n_blocks
return nf.sequential_flow(nf.Debug(''), *layers)
# To initialize our model, we want a debugger to print out the size of the network at each multiscale
debug_kwargs = dict(print_init_shape=True, print_forward_shape=False, print_inverse_shape=False, compare_vals=False)
# We want to name the glow blocks so that we can do data dependent initialization
name_iter = iter(['glow_%d'%i for i in range(400)])
# The multiscale architecture factors out pixels
def multi_scale(i, flow):
if(isinstance(self.n_filters, int)):
n_filters = self.n_filters
else:
n_filters = self.n_filters[i]
if(isinstance(self.n_blocks, int)):
n_blocks = self.n_blocks
else:
n_blocks = self.n_blocks[i]
return nf.sequential_flow(nf.Squeeze(),
GLOWComponent(name_iter, n_filters, n_blocks),
nf.FactorOut(2),
nf.factored_flow(flow, nf.Identity()),
nf.FanInConcat(2),
nf.UnSqueeze())
flow = nf.Identity()
for i in range(self.n_multiscale):
flow = multi_scale(i, flow)
if(init_key is not None):
# Add the ability to ensure that things arae initialized together
flow = nf.key_wrap(flow, init_key)
return flow
def get_prior(self):
return nf.UnitGaussianPrior()
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
meta = {'n_filters' : self.n_filters,
'n_blocks' : self.n_blocks,
'n_multiscale' : self.n_multiscale,
'data_init_iterations': self.data_init_iterations,
'model' : 'GLOW'}
parent_meta = super().gen_meta_data()
parent_meta.update(meta)
return parent_meta
@classmethod
def initialize_from_meta_data(cls, meta):
""" Using a meta data, construct an instance of this model """
dataset_name = meta['dataset_name']
x_shape = tuple(meta['x_shape'])
n_filters = meta['n_filters']
n_blocks = meta['n_blocks']
n_multiscale = meta['n_multiscale']
data_init_iterations = meta.get('data_init_iterations', 1000)
return GLOW(dataset_name, x_shape, n_filters, n_blocks, n_multiscale, data_init_iterations)
#####################################################################
def data_dependent_init(self, key, data_loader, batch_size=64):
actnorm_names = [name for name in tree_flatten(self.names)[0] if 'act_norm' in name]
flow_model = (self.names, self.z_shape, self.params, self.state), self.forward, self.inverse
params = nf.multistep_flow_data_dependent_init(None,
actnorm_names,
flow_model,
(),
'actnorm_seed',
key,
data_loader=data_loader,
n_seed_examples=self.data_init_iterations,
batch_size=batch_size,
notebook=False)
self.params = params
######################################################################################################################################################
class SimpleNIF(GLOW):
def __init__(self, dataset_name, x_shape, z_dim, n_filters=256, n_blocks=16, n_multiscale=5, data_init_iterations=1000):
super().__init__(dataset_name, x_shape, n_filters, n_blocks, n_multiscale, data_init_iterations)
self.z_dim = z_dim
def get_prior(self):
return ndp.AffineGaussianPriorDiagCov(self.z_dim)
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
parent_meta = super().gen_meta_data()
meta = {'z_dim': self.z_dim,
'model': 'SimpleNIF'}
parent_meta.update(meta)
return parent_meta
@classmethod
def initialize_from_meta_data(cls, meta):
""" Using a meta data, construct an instance of this model """
dataset_name = meta['dataset_name']
x_shape = tuple(meta['x_shape'])
n_filters = meta['n_filters']
n_blocks = meta['n_blocks']
n_multiscale = meta['n_multiscale']
z_dim = meta['z_dim']
data_init_iterations = meta.get('data_init_iterations', 1000)
return SimpleNIF(dataset_name, x_shape, z_dim, n_filters, n_blocks, n_multiscale, data_init_iterations)
######################################################################################################################################################
class NIF(GLOW):
def __init__(self, dataset_name, x_shape, z_dim, n_filters=256, n_blocks=16, n_multiscale=5, n_hidden_layers=3, layer_size=1024, n_flat_layers=5, n_importance_samples=16):
super().__init__(dataset_name, x_shape, n_filters, n_blocks, n_multiscale)
self.z_dim = z_dim
self.n_hidden_layers = n_hidden_layers
self.layer_size = layer_size
self.n_flat_layers = n_flat_layers
self.n_importance_samples = n_importance_samples
def get_prior(self):
an_names = iter(['flat_act_norm_%d'%i for i in range(100)])
def FlatTransform(out_shape):
dense_layers = [spp.Dense(self.layer_size), spp.Relu()]*self.n_hidden_layers
return spp.sequential(*dense_layers,
spp.Dense(out_shape[-1]*2),
spp.Split(2, axis=-1),
spp.parallel(spp.Tanh(), spp.Identity())) # log_s, t
layers = [nf.AffineCoupling(FlatTransform), nf.ActNorm(name=next(an_names)), nf.Reverse()]*self.n_flat_layers
prior_flow = nf.sequential_flow(*layers, nf.UnitGaussianPrior())
return ndp.TallAffineDiagCov(prior_flow, self.z_dim, n_training_importance_samples=self.n_importance_samples)
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
parent_meta = super().gen_meta_data()
meta = {'z_dim' : self.z_dim,
'n_hidden_layers' : self.n_hidden_layers,
'layer_size' : self.layer_size,
'n_flat_layers' : self.n_flat_layers,
'n_importance_samples': self.n_importance_samples,
'model' : 'NIF'}
parent_meta.update(meta)
return parent_meta
@classmethod
def initialize_from_meta_data(cls, meta):
""" Using a meta data, construct an instance of this model """
dataset_name = meta['dataset_name']
x_shape = tuple(meta['x_shape'])
n_filters = meta['n_filters']
n_blocks = meta['n_blocks']
n_multiscale = meta['n_multiscale']
z_dim = meta['z_dim']
n_hidden_layers = meta['n_hidden_layers']
layer_size = meta['layer_size']
n_flat_layers = meta['n_flat_layers']
n_importance_samples = meta['n_importance_samples']
return NIF(dataset_name, x_shape, z_dim, n_filters, n_blocks, n_multiscale, n_hidden_layers, layer_size, n_flat_layers, n_importance_samples)
######################################################################################################################################################
# Use a global to make loading easy
MODEL_LIST = {'GLOW' : GLOW,
'SimpleNIF': SimpleNIF,
'NIF' : NIF}
| <filename>model.py
import os
import glob
import jax
import jax.numpy as jnp
import staxplusplus as spp
import normalizing_flows as nf
import jax.nn.initializers as jaxinit
from jax.tree_util import tree_flatten
import util
import non_dim_preserving as ndp
from functools import partial
######################################################################################################################################################
class Model():
def __init__(self, dataset_name, x_shape):
self.x_shape = x_shape
self.dataset_name = dataset_name
self.init_fun, self.forward, self.inverse = None, None, None
self.names, self.z_shape, self.params, self.state = None, None, None, None
self.n_params = None
def get_architecture(self, init_key=None):
assert 0, 'unimplemented'
def get_prior(self):
assert 0, 'unimplemented'
#####################################################################
def build_model(self, quantize_level_bits, init_key=None):
architecture = self.get_architecture(init_key=init_key)
prior = self.get_prior()
# Use uniform dequantization to build our model
flow = nf.sequential_flow(nf.Dequantization(scale=2**quantize_level_bits),
nf.Logit(),
architecture,
nf.Flatten(),
prior)
self.init_fun, self.forward, self.inverse = flow
#####################################################################
def initialize_model(self, key):
assert self.init_fun is not None, 'Need to call build_model'
self.names, self.z_shape, self.params, self.state = self.init_fun(key, self.x_shape, ())
self.n_params = jax.flatten_util.ravel_pytree(self.params)[0].shape[0]
print('Total number of parameters:', self.n_params)
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
meta = {'x_shape' : list(self.x_shape),
'dataset_name': self.dataset_name,
'model' : None}
return meta
@classmethod
def initialize_from_meta_data(cls, meta):
assert 0, 'unimplemented'
#####################################################################
def save_state(self, path):
util.save_pytree_to_file(self.state, path)
def load_state_from_file(self, path):
self.state = util.load_pytree_from_file(self.state, path)
######################################################################################################################################################
class GLOW(Model):
def __init__(self, dataset_name, x_shape, n_filters=256, n_blocks=16, n_multiscale=5, data_init_iterations=1000):
super().__init__(dataset_name, x_shape)
self.n_filters = n_filters
self.n_blocks = n_blocks
self.n_multiscale = n_multiscale
self.data_init_iterations = data_init_iterations
#####################################################################
def get_architecture(self, init_key=None):
""" Build the architecture from GLOW https://arxiv.org/pdf/1807.03039.pdf """
def GLOWNet(out_shape, n_filters):
""" Transformation used inside affine coupling """
_, _, channels = out_shape
return spp.sequential(spp.Conv(n_filters, filter_shape=(3, 3), padding=((1, 1), (1, 1)), bias=True, weightnorm=False),
spp.Relu(),
spp.Conv(n_filters, filter_shape=(1, 1), padding=((0, 0), (0, 0)), bias=True, weightnorm=False),
spp.Relu(),
spp.Conv(2*channels, filter_shape=(3, 3), padding=((1, 1), (1, 1)), bias=True, weightnorm=False, W_init=jaxinit.zeros, b_init=jaxinit.zeros),
spp.Split(2, axis=-1),
spp.parallel(spp.Tanh(), spp.Identity())) # log_s, t
def GLOWComponent(name_iter, n_filters, n_blocks):
""" Compose glow blocks """
layers = [nf.GLOWBlock(partial(GLOWNet, n_filters=n_filters),
masked=False,
name=next(name_iter),
additive_coupling=False)]*n_blocks
return nf.sequential_flow(nf.Debug(''), *layers)
# To initialize our model, we want a debugger to print out the size of the network at each multiscale
debug_kwargs = dict(print_init_shape=True, print_forward_shape=False, print_inverse_shape=False, compare_vals=False)
# We want to name the glow blocks so that we can do data dependent initialization
name_iter = iter(['glow_%d'%i for i in range(400)])
# The multiscale architecture factors out pixels
def multi_scale(i, flow):
if(isinstance(self.n_filters, int)):
n_filters = self.n_filters
else:
n_filters = self.n_filters[i]
if(isinstance(self.n_blocks, int)):
n_blocks = self.n_blocks
else:
n_blocks = self.n_blocks[i]
return nf.sequential_flow(nf.Squeeze(),
GLOWComponent(name_iter, n_filters, n_blocks),
nf.FactorOut(2),
nf.factored_flow(flow, nf.Identity()),
nf.FanInConcat(2),
nf.UnSqueeze())
flow = nf.Identity()
for i in range(self.n_multiscale):
flow = multi_scale(i, flow)
if(init_key is not None):
# Add the ability to ensure that things arae initialized together
flow = nf.key_wrap(flow, init_key)
return flow
def get_prior(self):
return nf.UnitGaussianPrior()
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
meta = {'n_filters' : self.n_filters,
'n_blocks' : self.n_blocks,
'n_multiscale' : self.n_multiscale,
'data_init_iterations': self.data_init_iterations,
'model' : 'GLOW'}
parent_meta = super().gen_meta_data()
parent_meta.update(meta)
return parent_meta
@classmethod
def initialize_from_meta_data(cls, meta):
""" Using a meta data, construct an instance of this model """
dataset_name = meta['dataset_name']
x_shape = tuple(meta['x_shape'])
n_filters = meta['n_filters']
n_blocks = meta['n_blocks']
n_multiscale = meta['n_multiscale']
data_init_iterations = meta.get('data_init_iterations', 1000)
return GLOW(dataset_name, x_shape, n_filters, n_blocks, n_multiscale, data_init_iterations)
#####################################################################
def data_dependent_init(self, key, data_loader, batch_size=64):
actnorm_names = [name for name in tree_flatten(self.names)[0] if 'act_norm' in name]
flow_model = (self.names, self.z_shape, self.params, self.state), self.forward, self.inverse
params = nf.multistep_flow_data_dependent_init(None,
actnorm_names,
flow_model,
(),
'actnorm_seed',
key,
data_loader=data_loader,
n_seed_examples=self.data_init_iterations,
batch_size=batch_size,
notebook=False)
self.params = params
######################################################################################################################################################
class SimpleNIF(GLOW):
def __init__(self, dataset_name, x_shape, z_dim, n_filters=256, n_blocks=16, n_multiscale=5, data_init_iterations=1000):
super().__init__(dataset_name, x_shape, n_filters, n_blocks, n_multiscale, data_init_iterations)
self.z_dim = z_dim
def get_prior(self):
return ndp.AffineGaussianPriorDiagCov(self.z_dim)
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
parent_meta = super().gen_meta_data()
meta = {'z_dim': self.z_dim,
'model': 'SimpleNIF'}
parent_meta.update(meta)
return parent_meta
@classmethod
def initialize_from_meta_data(cls, meta):
""" Using a meta data, construct an instance of this model """
dataset_name = meta['dataset_name']
x_shape = tuple(meta['x_shape'])
n_filters = meta['n_filters']
n_blocks = meta['n_blocks']
n_multiscale = meta['n_multiscale']
z_dim = meta['z_dim']
data_init_iterations = meta.get('data_init_iterations', 1000)
return SimpleNIF(dataset_name, x_shape, z_dim, n_filters, n_blocks, n_multiscale, data_init_iterations)
######################################################################################################################################################
class NIF(GLOW):
def __init__(self, dataset_name, x_shape, z_dim, n_filters=256, n_blocks=16, n_multiscale=5, n_hidden_layers=3, layer_size=1024, n_flat_layers=5, n_importance_samples=16):
super().__init__(dataset_name, x_shape, n_filters, n_blocks, n_multiscale)
self.z_dim = z_dim
self.n_hidden_layers = n_hidden_layers
self.layer_size = layer_size
self.n_flat_layers = n_flat_layers
self.n_importance_samples = n_importance_samples
def get_prior(self):
an_names = iter(['flat_act_norm_%d'%i for i in range(100)])
def FlatTransform(out_shape):
dense_layers = [spp.Dense(self.layer_size), spp.Relu()]*self.n_hidden_layers
return spp.sequential(*dense_layers,
spp.Dense(out_shape[-1]*2),
spp.Split(2, axis=-1),
spp.parallel(spp.Tanh(), spp.Identity())) # log_s, t
layers = [nf.AffineCoupling(FlatTransform), nf.ActNorm(name=next(an_names)), nf.Reverse()]*self.n_flat_layers
prior_flow = nf.sequential_flow(*layers, nf.UnitGaussianPrior())
return ndp.TallAffineDiagCov(prior_flow, self.z_dim, n_training_importance_samples=self.n_importance_samples)
#####################################################################
def gen_meta_data(self):
""" Create a dictionary that will tell us exactly how to create this model """
parent_meta = super().gen_meta_data()
meta = {'z_dim' : self.z_dim,
'n_hidden_layers' : self.n_hidden_layers,
'layer_size' : self.layer_size,
'n_flat_layers' : self.n_flat_layers,
'n_importance_samples': self.n_importance_samples,
'model' : 'NIF'}
parent_meta.update(meta)
return parent_meta
@classmethod
def initialize_from_meta_data(cls, meta):
""" Using a meta data, construct an instance of this model """
dataset_name = meta['dataset_name']
x_shape = tuple(meta['x_shape'])
n_filters = meta['n_filters']
n_blocks = meta['n_blocks']
n_multiscale = meta['n_multiscale']
z_dim = meta['z_dim']
n_hidden_layers = meta['n_hidden_layers']
layer_size = meta['layer_size']
n_flat_layers = meta['n_flat_layers']
n_importance_samples = meta['n_importance_samples']
return NIF(dataset_name, x_shape, z_dim, n_filters, n_blocks, n_multiscale, n_hidden_layers, layer_size, n_flat_layers, n_importance_samples)
######################################################################################################################################################
# Use a global to make loading easy
MODEL_LIST = {'GLOW' : GLOW,
'SimpleNIF': SimpleNIF,
'NIF' : NIF}
| de | 0.650966 | ###################################################################################################################################################### ##################################################################### # Use uniform dequantization to build our model ##################################################################### ##################################################################### Create a dictionary that will tell us exactly how to create this model ##################################################################### ###################################################################################################################################################### ##################################################################### Build the architecture from GLOW https://arxiv.org/pdf/1807.03039.pdf Transformation used inside affine coupling # log_s, t Compose glow blocks # To initialize our model, we want a debugger to print out the size of the network at each multiscale # We want to name the glow blocks so that we can do data dependent initialization # The multiscale architecture factors out pixels # Add the ability to ensure that things arae initialized together ##################################################################### Create a dictionary that will tell us exactly how to create this model Using a meta data, construct an instance of this model ##################################################################### ###################################################################################################################################################### ##################################################################### Create a dictionary that will tell us exactly how to create this model Using a meta data, construct an instance of this model ###################################################################################################################################################### # log_s, t ##################################################################### Create a dictionary that will tell us exactly how to create this model Using a meta data, construct an instance of this model ###################################################################################################################################################### # Use a global to make loading easy | 2.285354 | 2 |
Printer.py | dgirzadas/Pulse-of-the-City | 2 | 6623175 | def loading_bar(percentage):
if percentage < 100:
print("[" + "-" * percentage + " " * (100 - percentage) + "] " + str(percentage) + "%", end='\r')
else:
print("[" + "-" * percentage + " " * (100 - percentage) + "] " + "Done!") | def loading_bar(percentage):
if percentage < 100:
print("[" + "-" * percentage + " " * (100 - percentage) + "] " + str(percentage) + "%", end='\r')
else:
print("[" + "-" * percentage + " " * (100 - percentage) + "] " + "Done!") | none | 1 | 3.08356 | 3 | |
flowgraph/config.py | Bhaskers-Blu-Org1/pyflowgraph | 17 | 6623176 | <reponame>Bhaskers-Blu-Org1/pyflowgraph
c.RemoteAnnotationDB.api_url = "https://api.datascienceontology.org"
| c.RemoteAnnotationDB.api_url = "https://api.datascienceontology.org" | none | 1 | 1.247719 | 1 | |
bax_insertion/util/error_propagation.py | johnbachman/bax_insertion_paper | 0 | 6623177 | <reponame>johnbachman/bax_insertion_paper
import numpy as np
def calc_ratio_sd(numer_mean, numer_sd, denom_mean, denom_sd,
num_samples=10000):
"""Calculates the variance of a ratio of two normal distributions with
the given means and standard deviations."""
numer_samples = numer_mean + (numer_sd * np.random.randn(num_samples))
denom_samples = denom_mean + (denom_sd * np.random.randn(num_samples))
ratio_samples = numer_samples / denom_samples
return np.std(ratio_samples)
def calc_ratio_mean_sd(numer_mean, numer_sd, denom_mean, denom_sd,
num_samples=10000):
"""Calculates the variance of a ratio of two normal distributions with
the given means and standard deviations."""
# If we're dealing with a numpy array:
if isinstance(numer_mean, np.ndarray) and \
isinstance(denom_mean, np.ndarray) and \
isinstance(numer_sd, np.ndarray) and \
isinstance(denom_sd, np.ndarray):
num_pts = numer_mean.shape[0]
numer_samples = numer_mean + (numer_sd *
np.random.randn(num_samples, num_pts))
denom_samples = denom_mean + (denom_sd *
np.random.randn(num_samples, num_pts))
# Otherwise, assume we're dealing with a number
else:
numer_samples = numer_mean + (numer_sd *
np.random.randn(num_samples))
denom_samples = denom_mean + (denom_sd *
np.random.randn(num_samples))
ratio_samples = numer_samples / denom_samples
return (np.mean(ratio_samples, axis=0), np.std(ratio_samples, axis=0))
| import numpy as np
def calc_ratio_sd(numer_mean, numer_sd, denom_mean, denom_sd,
num_samples=10000):
"""Calculates the variance of a ratio of two normal distributions with
the given means and standard deviations."""
numer_samples = numer_mean + (numer_sd * np.random.randn(num_samples))
denom_samples = denom_mean + (denom_sd * np.random.randn(num_samples))
ratio_samples = numer_samples / denom_samples
return np.std(ratio_samples)
def calc_ratio_mean_sd(numer_mean, numer_sd, denom_mean, denom_sd,
num_samples=10000):
"""Calculates the variance of a ratio of two normal distributions with
the given means and standard deviations."""
# If we're dealing with a numpy array:
if isinstance(numer_mean, np.ndarray) and \
isinstance(denom_mean, np.ndarray) and \
isinstance(numer_sd, np.ndarray) and \
isinstance(denom_sd, np.ndarray):
num_pts = numer_mean.shape[0]
numer_samples = numer_mean + (numer_sd *
np.random.randn(num_samples, num_pts))
denom_samples = denom_mean + (denom_sd *
np.random.randn(num_samples, num_pts))
# Otherwise, assume we're dealing with a number
else:
numer_samples = numer_mean + (numer_sd *
np.random.randn(num_samples))
denom_samples = denom_mean + (denom_sd *
np.random.randn(num_samples))
ratio_samples = numer_samples / denom_samples
return (np.mean(ratio_samples, axis=0), np.std(ratio_samples, axis=0)) | en | 0.885717 | Calculates the variance of a ratio of two normal distributions with the given means and standard deviations. Calculates the variance of a ratio of two normal distributions with the given means and standard deviations. # If we're dealing with a numpy array: # Otherwise, assume we're dealing with a number | 3.641943 | 4 |
selenium/spider.py | andrewsmedina/scrap-tools-benchmarking | 2 | 6623178 | from selenium import webdriver
driver = webdriver.Firefox()
journal_url = "http://www.rondonopolis.mt.gov.br/diario-oficial/"
driver.get(journal_url)
pdf_links = driver.find_elements_by_css_selector("table a")
for link in pdf_links:
print(link.get_attribute("href"))
driver.close() | from selenium import webdriver
driver = webdriver.Firefox()
journal_url = "http://www.rondonopolis.mt.gov.br/diario-oficial/"
driver.get(journal_url)
pdf_links = driver.find_elements_by_css_selector("table a")
for link in pdf_links:
print(link.get_attribute("href"))
driver.close() | none | 1 | 2.794113 | 3 | |
train_arguments.py | kun193/ransomware-classification | 7 | 6623179 | import argparse
import os
class Arguments():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('name', type=str, help='experiment name.')
parser.add_argument('--phase', default='train', type=str, choices=['train', 'test'], help='determining whether the model is being trained or used for inference. Since this is the train_arguments file, this needs to set to train!!')
parser.add_argument('--data_root', default='../../Data/ransom_ware/train', type=str, help='path to the training data directory.')
parser.add_argument('--num_classes', default=50, type=int, help='number of classes in the classification task.')
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--num_steps', default=24000, type=int, help='number of steps for which the model is trained.')
parser.add_argument('--break_count', default=600, type=int, help='how many steps to before training is stopped when the loss value does not change.')
parser.add_argument('--arch', type=str, default='AmirNet', help='which architecture is used to create the classifier', choices=['inception', 'resnet34', 'resnet50', 'resnet101', 'resnext50', 'resnext101', 'densenet161', 'densenet169', 'densenet201', 'vgg16_bn', 'vgg19_bn', 'squeezenet', 'shufflenet', 'mobilenet', 'AmirNet', 'AmirNet_DO', 'AmirNet_CDO', 'AmirNet_VDO'])
parser.add_argument('--augs', nargs='+', help='which augmentations are used to help in the training process', choices=['rotate', 'vflip', 'hflip', 'contrast', 'brightness', 'noise', 'occlusion', 'regularblur', 'defocusblur', 'motionblur', 'perspective', 'gray', 'colorjitter'])
parser.add_argument('--input_size', type=int, default=128, help='size of the input image.')
parser.add_argument('--pretrained', action='store_true', help='the model is initialized with weights pre-trained on imagenet.')
parser.add_argument('--num_workers', default=2, type=int, help='number of workers used in the dataloader.')
parser.add_argument('--lr', type=float, default=5e-4, help='learning rate.')
parser.add_argument('--weight_decay', type=float, default=1e-5, help='weight decay.')
parser.add_argument('--resume', action='store_true', help='resume from a checkpoint')
parser.add_argument('--which_checkpoint', type=str, default='latest', help='the checkpoint to be loaded to resume training. Checkpoints are identified and saved by the number of steps passed during training.')
parser.add_argument('--checkpoints_dir', type=str, default='checkpoints', help='the path to where the model is saved.')
parser.add_argument('--print_freq', default=50, type=int, help='how many steps before printing the loss values to the standard output for inspection purposes only.')
parser.add_argument('--display', action='store_true', help='display the results periodically via visdom')
parser.add_argument('--display_winsize', type=int, default=256, help='display window size for visdom.')
parser.add_argument('--display_freq', type=int, default=50, help='frequency of showing training results on screen using visdom.')
parser.add_argument('--display_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
parser.add_argument('--display_id', type=int, default=1, help='window id of the web display.')
parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display.')
parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main").')
parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display.')
parser.add_argument('--save_checkpoint_freq', default=5000, type=int, help='how many steps before saving one sequence of images to disk for inspection purposes only.')
self.initialized = True
return parser
def get_args(self):
if not self.initialized:
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
self.parser = parser
return parser.parse_args()
def print_args(self, args):
txt = '\n'
txt += '-------------------- Arguments --------------------\n'
for k, v in sorted(vars(args).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
txt += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
txt += '----------------------- End -----------------------'
txt += '\n'
print(txt)
def parse(self):
args = self.get_args()
self.print_args(args)
self.args = args
return self.args
| import argparse
import os
class Arguments():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('name', type=str, help='experiment name.')
parser.add_argument('--phase', default='train', type=str, choices=['train', 'test'], help='determining whether the model is being trained or used for inference. Since this is the train_arguments file, this needs to set to train!!')
parser.add_argument('--data_root', default='../../Data/ransom_ware/train', type=str, help='path to the training data directory.')
parser.add_argument('--num_classes', default=50, type=int, help='number of classes in the classification task.')
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--num_steps', default=24000, type=int, help='number of steps for which the model is trained.')
parser.add_argument('--break_count', default=600, type=int, help='how many steps to before training is stopped when the loss value does not change.')
parser.add_argument('--arch', type=str, default='AmirNet', help='which architecture is used to create the classifier', choices=['inception', 'resnet34', 'resnet50', 'resnet101', 'resnext50', 'resnext101', 'densenet161', 'densenet169', 'densenet201', 'vgg16_bn', 'vgg19_bn', 'squeezenet', 'shufflenet', 'mobilenet', 'AmirNet', 'AmirNet_DO', 'AmirNet_CDO', 'AmirNet_VDO'])
parser.add_argument('--augs', nargs='+', help='which augmentations are used to help in the training process', choices=['rotate', 'vflip', 'hflip', 'contrast', 'brightness', 'noise', 'occlusion', 'regularblur', 'defocusblur', 'motionblur', 'perspective', 'gray', 'colorjitter'])
parser.add_argument('--input_size', type=int, default=128, help='size of the input image.')
parser.add_argument('--pretrained', action='store_true', help='the model is initialized with weights pre-trained on imagenet.')
parser.add_argument('--num_workers', default=2, type=int, help='number of workers used in the dataloader.')
parser.add_argument('--lr', type=float, default=5e-4, help='learning rate.')
parser.add_argument('--weight_decay', type=float, default=1e-5, help='weight decay.')
parser.add_argument('--resume', action='store_true', help='resume from a checkpoint')
parser.add_argument('--which_checkpoint', type=str, default='latest', help='the checkpoint to be loaded to resume training. Checkpoints are identified and saved by the number of steps passed during training.')
parser.add_argument('--checkpoints_dir', type=str, default='checkpoints', help='the path to where the model is saved.')
parser.add_argument('--print_freq', default=50, type=int, help='how many steps before printing the loss values to the standard output for inspection purposes only.')
parser.add_argument('--display', action='store_true', help='display the results periodically via visdom')
parser.add_argument('--display_winsize', type=int, default=256, help='display window size for visdom.')
parser.add_argument('--display_freq', type=int, default=50, help='frequency of showing training results on screen using visdom.')
parser.add_argument('--display_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
parser.add_argument('--display_id', type=int, default=1, help='window id of the web display.')
parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display.')
parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main").')
parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display.')
parser.add_argument('--save_checkpoint_freq', default=5000, type=int, help='how many steps before saving one sequence of images to disk for inspection purposes only.')
self.initialized = True
return parser
def get_args(self):
if not self.initialized:
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
self.parser = parser
return parser.parse_args()
def print_args(self, args):
txt = '\n'
txt += '-------------------- Arguments --------------------\n'
for k, v in sorted(vars(args).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
txt += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
txt += '----------------------- End -----------------------'
txt += '\n'
print(txt)
def parse(self):
args = self.get_args()
self.print_args(args)
self.args = args
return self.args
| none | 1 | 2.903597 | 3 | |
foundation/press/views.py | pilnujemy/foundation-manager | 1 | 6623180 | from braces.views import SelectRelatedMixin
from django.views.generic.dates import ArchiveIndexView
from django.views.generic.dates import YearArchiveView
from django.views.generic.dates import MonthArchiveView
from django.views.generic.dates import DayArchiveView
from django.views.generic import DetailView
from django.shortcuts import get_object_or_404
from .models import Post, Tag
class PostArchiveMixin(SelectRelatedMixin):
model = Post
date_field = "published"
select_related = ['user', ]
make_object_list = True
month_format = '%m'
paginate_by = 25
def get_context_data(self, **kwargs):
context = super(PostArchiveMixin, self).get_context_data(**kwargs)
context['month_list'] = self.model.objects.datetimes('published', 'month')
return context
class PostArchiveIndexView(PostArchiveMixin, ArchiveIndexView):
pass
class PostTagIndexView(PostArchiveMixin, ArchiveIndexView):
template_name_suffix = '_archive_tag'
def get_queryset(self, *args, **kwargs):
self.tag = get_object_or_404(Tag, slug=self.kwargs['slug'])
qs = super(PostTagIndexView, self).get_queryset(*args, **kwargs)
return qs.filter(tags=self.tag)
def get_context_data(self, *args, **kwargs):
context = super(PostTagIndexView, self).get_context_data(*args, **kwargs)
context['tag'] = self.tag
return context
class PostYearArchiveView(PostArchiveMixin, YearArchiveView):
pass
class PostMonthArchiveView(PostArchiveMixin, MonthArchiveView):
pass
class PostDayArchiveView(PostArchiveMixin, DayArchiveView):
pass
class PostDetailView(PostArchiveMixin, DetailView):
def get_queryset(self, *args, **kwargs):
qs = super(PostDetailView, self).get_queryset(*args, **kwargs)
return qs.published()
| from braces.views import SelectRelatedMixin
from django.views.generic.dates import ArchiveIndexView
from django.views.generic.dates import YearArchiveView
from django.views.generic.dates import MonthArchiveView
from django.views.generic.dates import DayArchiveView
from django.views.generic import DetailView
from django.shortcuts import get_object_or_404
from .models import Post, Tag
class PostArchiveMixin(SelectRelatedMixin):
model = Post
date_field = "published"
select_related = ['user', ]
make_object_list = True
month_format = '%m'
paginate_by = 25
def get_context_data(self, **kwargs):
context = super(PostArchiveMixin, self).get_context_data(**kwargs)
context['month_list'] = self.model.objects.datetimes('published', 'month')
return context
class PostArchiveIndexView(PostArchiveMixin, ArchiveIndexView):
pass
class PostTagIndexView(PostArchiveMixin, ArchiveIndexView):
template_name_suffix = '_archive_tag'
def get_queryset(self, *args, **kwargs):
self.tag = get_object_or_404(Tag, slug=self.kwargs['slug'])
qs = super(PostTagIndexView, self).get_queryset(*args, **kwargs)
return qs.filter(tags=self.tag)
def get_context_data(self, *args, **kwargs):
context = super(PostTagIndexView, self).get_context_data(*args, **kwargs)
context['tag'] = self.tag
return context
class PostYearArchiveView(PostArchiveMixin, YearArchiveView):
pass
class PostMonthArchiveView(PostArchiveMixin, MonthArchiveView):
pass
class PostDayArchiveView(PostArchiveMixin, DayArchiveView):
pass
class PostDetailView(PostArchiveMixin, DetailView):
def get_queryset(self, *args, **kwargs):
qs = super(PostDetailView, self).get_queryset(*args, **kwargs)
return qs.published()
| none | 1 | 2.020676 | 2 | |
server.py | keithnull/ace-power | 4 | 6623181 | from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import entry
app = FastAPI()
origins = [
"http://localhost:8080",
]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/search/{author_name}")
def read_author(author_name: str):
return entry.query(author_name)
| from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import entry
app = FastAPI()
origins = [
"http://localhost:8080",
]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/search/{author_name}")
def read_author(author_name: str):
return entry.query(author_name)
| none | 1 | 2.542464 | 3 | |
django/bosscore/lookup.py | ArnaudGallardo/boss | 20 | 6623182 | <gh_stars>10-100
# Copyright 2016 The Johns Hopkins University Applied Physics Laboratory
#
# Licensed 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.
import re
from .serializers import BossLookupSerializer
from .models import BossLookup
from .error import BossError, ErrorCodes
class LookUpKey:
"""
Bosskey manager
"""
@staticmethod
def add_lookup(lookup_key, boss_key, collection_name, experiment_name=None,
channel_name=None):
"""
Add the lookup key that correspond to a data model object
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
experiment_name: Experiment name . Matches the experiment in the bosskey
channel_name: Channel name . Matches the channel in the bosskey
Returns: None
"""
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key,
'collection_name': collection_name,
'experiment_name': experiment_name,
'channel_name': channel_name
}
serializer = BossLookupSerializer(data=lookup_data)
if serializer.is_valid():
serializer.save()
@staticmethod
def get_lookup_key(bkey):
"""
Get the lookup keys for a request
Args:
bkey: Bosskey that corresponds to a request
Returns:
Lookup key
"""
lookup_obj = BossLookup.objects.get(boss_key=bkey)
return lookup_obj
@staticmethod
def delete_lookup_key(collection, experiment=None, channel=None):
"""
Delete a lookupkey for a specific bosskey
Args:
collection: Collection Name
experiment : Experiment Name
channel : Channel name
Returns:
None
"""
try:
if channel and experiment and collection:
lookup_obj = BossLookup.objects.get(collection_name=collection, experiment_name=experiment,
channel_name=channel)
lookup_obj.delete()
elif experiment and collection:
lookup_obj = BossLookup.objects.get(collection_name=collection, experiment_name=experiment)
lookup_obj.delete()
elif collection:
lookup_obj = BossLookup.objects.get(collection_name=collection)
lookup_obj.delete()
else:
raise BossError(404, "Cannot delete lookupkey", 30000)
except BossLookup.DoesNotExist:
raise BossError(404, "Cannot find a lookup key for bosskey", 30000)
@staticmethod
def update_lookup(lookup_key, boss_key, collection_name, experiment_name=None, channel_name=None):
"""
Update the fields that correspond to a lookupkey
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
experiment_name: Experiment name . Matches the experiment in the bosskey
channel_name: Channel name . Matches the channel in the bosskey
Returns: None
"""
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key,
'collection_name': collection_name,
'experiment_name': experiment_name,
'channel_name': channel_name
}
lookup_obj = BossLookup.objects.get(lookup_key=lookup_key)
serializer = BossLookupSerializer(lookup_obj, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
@staticmethod
def update_lookup_collection(lookup_key, boss_key, collection_name):
"""
Update the fields that correspond to a lookupkey
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
Returns: None
"""
try:
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key,
'collection_name': collection_name,
}
lookup_obj = BossLookup.objects.get(lookup_key=lookup_key)
old_collection_name = lookup_obj.collection_name
serializer = BossLookupSerializer(lookup_obj, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key"
.format(serializer.errors), ErrorCodes.INVALID_POST_ARGUMENT)
# update all object that reference this collection
all_lookup_objs = BossLookup.objects.filter(collection_name=old_collection_name)\
.exclude(lookup_key=lookup_key)
for item in all_lookup_objs:
split_key = item.boss_key.split('&')
split_key[0] = collection_name
boss_key = '&'.join(split_key)
lookup_data = {'lookup_key': item.lookup_key, 'boss_key': boss_key,
'collection_name': collection_name
}
serializer = BossLookupSerializer(item, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key".
format(serializer.errors), ErrorCodes.INVALID_POST_ARGUMENT)
except BossLookup.DoesNotExist:
raise BossError("Cannot update the lookup key", ErrorCodes.UNABLE_TO_VALIDATE)
@staticmethod
def update_lookup_experiment(lookup_key, boss_key, collection_name, experiment_name):
"""
Update the fields that correspond to a lookupkey
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
experiment_name: Collection name . Matches the collection in the bosskey
Returns: None
"""
try:
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key, 'collection_name': collection_name,
'experiment_name': experiment_name,
}
lookup_obj = BossLookup.objects.get(lookup_key=lookup_key)
old_experiment_name = lookup_obj.experiment_name
serializer = BossLookupSerializer(lookup_obj, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key".format(serializer.errors),
ErrorCodes.INVALID_POST_ARGUMENT)
# update all channels that reference this experiment
all_lookup_objs = BossLookup.objects.filter(
collection_name=collection_name, experiment_name=old_experiment_name).exclude(
lookup_key=lookup_key)
for item in all_lookup_objs:
split_key = item.boss_key.split('&')
split_key[1] = experiment_name
boss_key = '&'.join(split_key)
#boss_key = re.sub(old_experiment_name, experiment_name, item.boss_key)
lookup_data = {'lookup_key': item.lookup_key, 'boss_key': boss_key,
'experiment_name': experiment_name
}
serializer = BossLookupSerializer(item, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key".
format(serializer.errors), ErrorCodes.INVALID_POST_ARGUMENT)
except BossLookup.DoesNotExist:
raise BossError("Cannot update the lookup key", ErrorCodes.UNABLE_TO_VALIDATE)
| # Copyright 2016 The Johns Hopkins University Applied Physics Laboratory
#
# Licensed 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.
import re
from .serializers import BossLookupSerializer
from .models import BossLookup
from .error import BossError, ErrorCodes
class LookUpKey:
"""
Bosskey manager
"""
@staticmethod
def add_lookup(lookup_key, boss_key, collection_name, experiment_name=None,
channel_name=None):
"""
Add the lookup key that correspond to a data model object
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
experiment_name: Experiment name . Matches the experiment in the bosskey
channel_name: Channel name . Matches the channel in the bosskey
Returns: None
"""
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key,
'collection_name': collection_name,
'experiment_name': experiment_name,
'channel_name': channel_name
}
serializer = BossLookupSerializer(data=lookup_data)
if serializer.is_valid():
serializer.save()
@staticmethod
def get_lookup_key(bkey):
"""
Get the lookup keys for a request
Args:
bkey: Bosskey that corresponds to a request
Returns:
Lookup key
"""
lookup_obj = BossLookup.objects.get(boss_key=bkey)
return lookup_obj
@staticmethod
def delete_lookup_key(collection, experiment=None, channel=None):
"""
Delete a lookupkey for a specific bosskey
Args:
collection: Collection Name
experiment : Experiment Name
channel : Channel name
Returns:
None
"""
try:
if channel and experiment and collection:
lookup_obj = BossLookup.objects.get(collection_name=collection, experiment_name=experiment,
channel_name=channel)
lookup_obj.delete()
elif experiment and collection:
lookup_obj = BossLookup.objects.get(collection_name=collection, experiment_name=experiment)
lookup_obj.delete()
elif collection:
lookup_obj = BossLookup.objects.get(collection_name=collection)
lookup_obj.delete()
else:
raise BossError(404, "Cannot delete lookupkey", 30000)
except BossLookup.DoesNotExist:
raise BossError(404, "Cannot find a lookup key for bosskey", 30000)
@staticmethod
def update_lookup(lookup_key, boss_key, collection_name, experiment_name=None, channel_name=None):
"""
Update the fields that correspond to a lookupkey
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
experiment_name: Experiment name . Matches the experiment in the bosskey
channel_name: Channel name . Matches the channel in the bosskey
Returns: None
"""
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key,
'collection_name': collection_name,
'experiment_name': experiment_name,
'channel_name': channel_name
}
lookup_obj = BossLookup.objects.get(lookup_key=lookup_key)
serializer = BossLookupSerializer(lookup_obj, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
@staticmethod
def update_lookup_collection(lookup_key, boss_key, collection_name):
"""
Update the fields that correspond to a lookupkey
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
Returns: None
"""
try:
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key,
'collection_name': collection_name,
}
lookup_obj = BossLookup.objects.get(lookup_key=lookup_key)
old_collection_name = lookup_obj.collection_name
serializer = BossLookupSerializer(lookup_obj, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key"
.format(serializer.errors), ErrorCodes.INVALID_POST_ARGUMENT)
# update all object that reference this collection
all_lookup_objs = BossLookup.objects.filter(collection_name=old_collection_name)\
.exclude(lookup_key=lookup_key)
for item in all_lookup_objs:
split_key = item.boss_key.split('&')
split_key[0] = collection_name
boss_key = '&'.join(split_key)
lookup_data = {'lookup_key': item.lookup_key, 'boss_key': boss_key,
'collection_name': collection_name
}
serializer = BossLookupSerializer(item, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key".
format(serializer.errors), ErrorCodes.INVALID_POST_ARGUMENT)
except BossLookup.DoesNotExist:
raise BossError("Cannot update the lookup key", ErrorCodes.UNABLE_TO_VALIDATE)
@staticmethod
def update_lookup_experiment(lookup_key, boss_key, collection_name, experiment_name):
"""
Update the fields that correspond to a lookupkey
Args:
lookup_key: Lookup key for the object that was created
boss_key: Bosskey for the objec that we created
collection_name: Collection name . Matches the collection in the bosskey
experiment_name: Collection name . Matches the collection in the bosskey
Returns: None
"""
try:
# Create the boss lookup key
lookup_data = {'lookup_key': lookup_key, 'boss_key': boss_key, 'collection_name': collection_name,
'experiment_name': experiment_name,
}
lookup_obj = BossLookup.objects.get(lookup_key=lookup_key)
old_experiment_name = lookup_obj.experiment_name
serializer = BossLookupSerializer(lookup_obj, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key".format(serializer.errors),
ErrorCodes.INVALID_POST_ARGUMENT)
# update all channels that reference this experiment
all_lookup_objs = BossLookup.objects.filter(
collection_name=collection_name, experiment_name=old_experiment_name).exclude(
lookup_key=lookup_key)
for item in all_lookup_objs:
split_key = item.boss_key.split('&')
split_key[1] = experiment_name
boss_key = '&'.join(split_key)
#boss_key = re.sub(old_experiment_name, experiment_name, item.boss_key)
lookup_data = {'lookup_key': item.lookup_key, 'boss_key': boss_key,
'experiment_name': experiment_name
}
serializer = BossLookupSerializer(item, data=lookup_data, partial=True)
if serializer.is_valid():
serializer.save()
else:
raise BossError("{}. Error updating the collection name in the lookup key".
format(serializer.errors), ErrorCodes.INVALID_POST_ARGUMENT)
except BossLookup.DoesNotExist:
raise BossError("Cannot update the lookup key", ErrorCodes.UNABLE_TO_VALIDATE) | en | 0.793337 | # Copyright 2016 The Johns Hopkins University Applied Physics Laboratory # # Licensed 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. Bosskey manager Add the lookup key that correspond to a data model object Args: lookup_key: Lookup key for the object that was created boss_key: Bosskey for the objec that we created collection_name: Collection name . Matches the collection in the bosskey experiment_name: Experiment name . Matches the experiment in the bosskey channel_name: Channel name . Matches the channel in the bosskey Returns: None # Create the boss lookup key Get the lookup keys for a request Args: bkey: Bosskey that corresponds to a request Returns: Lookup key Delete a lookupkey for a specific bosskey Args: collection: Collection Name experiment : Experiment Name channel : Channel name Returns: None Update the fields that correspond to a lookupkey Args: lookup_key: Lookup key for the object that was created boss_key: Bosskey for the objec that we created collection_name: Collection name . Matches the collection in the bosskey experiment_name: Experiment name . Matches the experiment in the bosskey channel_name: Channel name . Matches the channel in the bosskey Returns: None # Create the boss lookup key Update the fields that correspond to a lookupkey Args: lookup_key: Lookup key for the object that was created boss_key: Bosskey for the objec that we created collection_name: Collection name . Matches the collection in the bosskey Returns: None # Create the boss lookup key # update all object that reference this collection Update the fields that correspond to a lookupkey Args: lookup_key: Lookup key for the object that was created boss_key: Bosskey for the objec that we created collection_name: Collection name . Matches the collection in the bosskey experiment_name: Collection name . Matches the collection in the bosskey Returns: None # Create the boss lookup key # update all channels that reference this experiment #boss_key = re.sub(old_experiment_name, experiment_name, item.boss_key) | 2.278519 | 2 |
process/honda-label/ProjectGPSonVideo.py | sameeptandon/sail-car-log | 1 | 6623183 | import pickle
import sys, os
from GPSReader import *
from VideoReader import *
from WGS84toENU import *
from GPSReprojection import *
from transformations import euler_matrix
from numpy import array, dot, zeros, around, divide, nonzero, float32, maximum
import numpy as np
from cv2 import imshow, waitKey, resize, warpPerspective, getPerspectiveTransform, transpose, Canny, namedWindow
import cv
import cv2
import time
from generate_lane_labels import *
def on_mouse(event, x, y, flags, params):
if event == cv.CV_EVENT_LBUTTONDOWN:
print 'click: ', (x,y)
print 'color: ', I[y,x,:]
if __name__ == '__main__':
video_filename = sys.argv[1]
path, vfname = os.path.split(video_filename)
vidname = vfname.split('.')[0]
cam_num = int(vidname[-1])
gps_filename = path + '/' + vidname[0:-1] + '_gps.out'
#gps_filename = sys.argv[2]
cv2.namedWindow('video')
cv.SetMouseCallback('video', on_mouse, 0)
num_imgs_fwd = 200;
video_reader = VideoReader(video_filename)
gps_reader = GPSReader(gps_filename)
gps_dat = gps_reader.getNumericData()
cam = pickle.load(open('cam_params.pickle'))[cam_num - 1]
#framenum = 1926;
#framenum = 29000
framenum = 0
lastTime = time.time()
lastCols = [None, None]
lastLine = [None, None, None, None]
video_reader.setFrame(framenum)
while True:
framenum = framenum + 1;
(success, I) = video_reader.getNextFrame()
if success == False:
break
#if framenum % 10 != 0:
# continue
if framenum % 100 == 0:
print framenum
M = GPSMask(gps_dat[framenum:framenum+num_imgs_fwd,:], cam, width=1);
I = np.minimum(M,I)
"""
src = array([[0,0],[1280,0],[1280,960],[0,960]], float32)
minX = 0
minY = 0
maxX = 1200
maxY = 960
dst = array([[-1000,200],[2280,200],[780,960],[500,960]], float32)
#src = array([(567, 759), (896, 756), (919, 791), (555, 793)], float32)
src = array([(570, 737), (864, 737), (881, 761), (564, 761)], float32)
#src = array([(520, 727), (916, 733), (950, 775), (497, 771)], float32)
#src = array([[499,597],[972,597],[1112,661],[448,678]], float32) #good one
#src = array([[528,560],[861,557],[1244,759],[271,773]], float32)
#src = array([[550,524],[840,523],[1019,613],[496,612]], float32)
##src = array([(378, 604), (742, 601), (967, 802), (79, 784)], float32)
##src = array( [(445, 521), (729, 527), (1159, 819), (27, 747)], float32)
##src = array([(386, 521), (829, 517), (1190, 681), (92, 663)], float32)
rx = 38
ry = 24
sx = 150
sy = 100
dst = array([[sx,sy],[sx+rx,sy],[sx+rx,sy+ry],[sx,sy+ry]],float32)
#dst = array([[320,320],[960,320],[960,640],[320,640]], float32)
#dst[:,0] += 960
imsize = (320,240)
I = resize(I, imsize)
I[:, :5] = [255, 0, 0]
I[:, -5:] = [255, 0, 0]
I[-5:, :] = [0, 255, 0]
src = src / 4;
dst = dst ;
#dst = array([[0,0],[1280,0],[1280,960],[0,960]], float32)
P = getPerspectiveTransform(src,dst)
WARP = warpPerspective(I, P, imsize);
#WARP = resize(WARP, imsize)
#I[0:480,:,:]=0
I = WARP
if lastCols[0] is None:
M = 255 - resize(M, imsize)
warped_M = np.nonzero(warpPerspective(M, P, imsize))
col_avg = np.mean(warped_M[1])
lastCols[0] = col_avg - 50
lastCols[1] = col_avg + 50
if lastLine[0] is None:
lastLine[0] = 0
lastLine[1] = lastCols[0]
lastLine[2] = 0
lastLine[3] = lastCols[1]
(WARP, lastCols, lastLine) = findLanes(WARP, (imsize[1], imsize[0]), lastCols, lastLine)
WARP = warpPerspective(WARP, P, imsize,flags=cv.CV_WARP_INVERSE_MAP);
I_t = np.zeros((imsize[1], imsize[0], 3))
I_t[239/2, :, 0] = 255
I_t = warpPerspective(I_t, P, imsize, flags=cv.CV_WARP_INVERSE_MAP)
I[WARP[:,:,0] > 0, 0] = 0
I[WARP[:,:,0] > 0, 1] = 0
I[WARP[:,:,0] > 0, 2] = 255
#I[I_t[:, :, 0] > 0, 0] = 255
#I[I_t[:, :, 0] > 0, 1] = 0
#I[I_t[:, :, 0] > 0, 2] = 0
if lastCols[0] is not None and lastCols[1] is not None:
I[:,lastCols[0],:] = 0
I[:,lastCols[1],:] = 0
I[:,(lastCols[0]+lastCols[1])/2,:] = 0
"""
#I = warpPerspective(I, P, imsize, flags=cv.CV_WARP_INVERSE_MAP)
I = resize(I, (640, 480))
imshow('video', I )
key = (waitKey(2) & 255)
if key == ord('q'):
break;
currentTime = time.time();
if (currentTime - lastTime > 1):
lastTime = currentTime
| import pickle
import sys, os
from GPSReader import *
from VideoReader import *
from WGS84toENU import *
from GPSReprojection import *
from transformations import euler_matrix
from numpy import array, dot, zeros, around, divide, nonzero, float32, maximum
import numpy as np
from cv2 import imshow, waitKey, resize, warpPerspective, getPerspectiveTransform, transpose, Canny, namedWindow
import cv
import cv2
import time
from generate_lane_labels import *
def on_mouse(event, x, y, flags, params):
if event == cv.CV_EVENT_LBUTTONDOWN:
print 'click: ', (x,y)
print 'color: ', I[y,x,:]
if __name__ == '__main__':
video_filename = sys.argv[1]
path, vfname = os.path.split(video_filename)
vidname = vfname.split('.')[0]
cam_num = int(vidname[-1])
gps_filename = path + '/' + vidname[0:-1] + '_gps.out'
#gps_filename = sys.argv[2]
cv2.namedWindow('video')
cv.SetMouseCallback('video', on_mouse, 0)
num_imgs_fwd = 200;
video_reader = VideoReader(video_filename)
gps_reader = GPSReader(gps_filename)
gps_dat = gps_reader.getNumericData()
cam = pickle.load(open('cam_params.pickle'))[cam_num - 1]
#framenum = 1926;
#framenum = 29000
framenum = 0
lastTime = time.time()
lastCols = [None, None]
lastLine = [None, None, None, None]
video_reader.setFrame(framenum)
while True:
framenum = framenum + 1;
(success, I) = video_reader.getNextFrame()
if success == False:
break
#if framenum % 10 != 0:
# continue
if framenum % 100 == 0:
print framenum
M = GPSMask(gps_dat[framenum:framenum+num_imgs_fwd,:], cam, width=1);
I = np.minimum(M,I)
"""
src = array([[0,0],[1280,0],[1280,960],[0,960]], float32)
minX = 0
minY = 0
maxX = 1200
maxY = 960
dst = array([[-1000,200],[2280,200],[780,960],[500,960]], float32)
#src = array([(567, 759), (896, 756), (919, 791), (555, 793)], float32)
src = array([(570, 737), (864, 737), (881, 761), (564, 761)], float32)
#src = array([(520, 727), (916, 733), (950, 775), (497, 771)], float32)
#src = array([[499,597],[972,597],[1112,661],[448,678]], float32) #good one
#src = array([[528,560],[861,557],[1244,759],[271,773]], float32)
#src = array([[550,524],[840,523],[1019,613],[496,612]], float32)
##src = array([(378, 604), (742, 601), (967, 802), (79, 784)], float32)
##src = array( [(445, 521), (729, 527), (1159, 819), (27, 747)], float32)
##src = array([(386, 521), (829, 517), (1190, 681), (92, 663)], float32)
rx = 38
ry = 24
sx = 150
sy = 100
dst = array([[sx,sy],[sx+rx,sy],[sx+rx,sy+ry],[sx,sy+ry]],float32)
#dst = array([[320,320],[960,320],[960,640],[320,640]], float32)
#dst[:,0] += 960
imsize = (320,240)
I = resize(I, imsize)
I[:, :5] = [255, 0, 0]
I[:, -5:] = [255, 0, 0]
I[-5:, :] = [0, 255, 0]
src = src / 4;
dst = dst ;
#dst = array([[0,0],[1280,0],[1280,960],[0,960]], float32)
P = getPerspectiveTransform(src,dst)
WARP = warpPerspective(I, P, imsize);
#WARP = resize(WARP, imsize)
#I[0:480,:,:]=0
I = WARP
if lastCols[0] is None:
M = 255 - resize(M, imsize)
warped_M = np.nonzero(warpPerspective(M, P, imsize))
col_avg = np.mean(warped_M[1])
lastCols[0] = col_avg - 50
lastCols[1] = col_avg + 50
if lastLine[0] is None:
lastLine[0] = 0
lastLine[1] = lastCols[0]
lastLine[2] = 0
lastLine[3] = lastCols[1]
(WARP, lastCols, lastLine) = findLanes(WARP, (imsize[1], imsize[0]), lastCols, lastLine)
WARP = warpPerspective(WARP, P, imsize,flags=cv.CV_WARP_INVERSE_MAP);
I_t = np.zeros((imsize[1], imsize[0], 3))
I_t[239/2, :, 0] = 255
I_t = warpPerspective(I_t, P, imsize, flags=cv.CV_WARP_INVERSE_MAP)
I[WARP[:,:,0] > 0, 0] = 0
I[WARP[:,:,0] > 0, 1] = 0
I[WARP[:,:,0] > 0, 2] = 255
#I[I_t[:, :, 0] > 0, 0] = 255
#I[I_t[:, :, 0] > 0, 1] = 0
#I[I_t[:, :, 0] > 0, 2] = 0
if lastCols[0] is not None and lastCols[1] is not None:
I[:,lastCols[0],:] = 0
I[:,lastCols[1],:] = 0
I[:,(lastCols[0]+lastCols[1])/2,:] = 0
"""
#I = warpPerspective(I, P, imsize, flags=cv.CV_WARP_INVERSE_MAP)
I = resize(I, (640, 480))
imshow('video', I )
key = (waitKey(2) & 255)
if key == ord('q'):
break;
currentTime = time.time();
if (currentTime - lastTime > 1):
lastTime = currentTime
| en | 0.404959 | #gps_filename = sys.argv[2] #framenum = 1926; #framenum = 29000 #if framenum % 10 != 0: # continue src = array([[0,0],[1280,0],[1280,960],[0,960]], float32) minX = 0 minY = 0 maxX = 1200 maxY = 960 dst = array([[-1000,200],[2280,200],[780,960],[500,960]], float32) #src = array([(567, 759), (896, 756), (919, 791), (555, 793)], float32) src = array([(570, 737), (864, 737), (881, 761), (564, 761)], float32) #src = array([(520, 727), (916, 733), (950, 775), (497, 771)], float32) #src = array([[499,597],[972,597],[1112,661],[448,678]], float32) #good one #src = array([[528,560],[861,557],[1244,759],[271,773]], float32) #src = array([[550,524],[840,523],[1019,613],[496,612]], float32) ##src = array([(378, 604), (742, 601), (967, 802), (79, 784)], float32) ##src = array( [(445, 521), (729, 527), (1159, 819), (27, 747)], float32) ##src = array([(386, 521), (829, 517), (1190, 681), (92, 663)], float32) rx = 38 ry = 24 sx = 150 sy = 100 dst = array([[sx,sy],[sx+rx,sy],[sx+rx,sy+ry],[sx,sy+ry]],float32) #dst = array([[320,320],[960,320],[960,640],[320,640]], float32) #dst[:,0] += 960 imsize = (320,240) I = resize(I, imsize) I[:, :5] = [255, 0, 0] I[:, -5:] = [255, 0, 0] I[-5:, :] = [0, 255, 0] src = src / 4; dst = dst ; #dst = array([[0,0],[1280,0],[1280,960],[0,960]], float32) P = getPerspectiveTransform(src,dst) WARP = warpPerspective(I, P, imsize); #WARP = resize(WARP, imsize) #I[0:480,:,:]=0 I = WARP if lastCols[0] is None: M = 255 - resize(M, imsize) warped_M = np.nonzero(warpPerspective(M, P, imsize)) col_avg = np.mean(warped_M[1]) lastCols[0] = col_avg - 50 lastCols[1] = col_avg + 50 if lastLine[0] is None: lastLine[0] = 0 lastLine[1] = lastCols[0] lastLine[2] = 0 lastLine[3] = lastCols[1] (WARP, lastCols, lastLine) = findLanes(WARP, (imsize[1], imsize[0]), lastCols, lastLine) WARP = warpPerspective(WARP, P, imsize,flags=cv.CV_WARP_INVERSE_MAP); I_t = np.zeros((imsize[1], imsize[0], 3)) I_t[239/2, :, 0] = 255 I_t = warpPerspective(I_t, P, imsize, flags=cv.CV_WARP_INVERSE_MAP) I[WARP[:,:,0] > 0, 0] = 0 I[WARP[:,:,0] > 0, 1] = 0 I[WARP[:,:,0] > 0, 2] = 255 #I[I_t[:, :, 0] > 0, 0] = 255 #I[I_t[:, :, 0] > 0, 1] = 0 #I[I_t[:, :, 0] > 0, 2] = 0 if lastCols[0] is not None and lastCols[1] is not None: I[:,lastCols[0],:] = 0 I[:,lastCols[1],:] = 0 I[:,(lastCols[0]+lastCols[1])/2,:] = 0 #I = warpPerspective(I, P, imsize, flags=cv.CV_WARP_INVERSE_MAP) | 2.28926 | 2 |
memery/core.py | wkrettek/memery | 0 | 6623184 | # Builtins
import time
from pathlib import Path
import logging
# Dependencies
import torch
from torch import Tensor, device
from torchvision.transforms import Compose
from PIL import Image
# Local imports
from memery import loader, crafter, encoder, indexer, ranker
class Memery():
def __init__(self, root: str = '.'):
self.index_file = 'memery.ann'
self.db_file = 'memery.pt'
self.root = root
self.index = None
self.db = None
self.model = None
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def index_flow(self, root: str, num_workers=0) -> tuple[str, str]:
'''Indexes images in path, returns the location of save files'''
start_time = time.time()
if self.root != root:
self.root = root
self.reset_state()
path = Path(root)
if not path.is_dir():
logging.error("Invalid path: %s", root)
return
device = self.device
# Check if we should re-index the files
print("Checking files...")
dbpath = path/self.db_file
db = self.get_db(str(dbpath))
treepath = path/self.index_file
treemap = self.get_index(str(treepath))
filepaths = loader.get_valid_images(path)
db_set = set([o['hash'] for o in db.values()])
fp_set = set([o for _, o in filepaths])
if treemap == None or db_set != fp_set:
archive_db = {}
archive_db, new_files = loader.archive_loader(filepaths, db)
print(f"Loaded {len(archive_db)} encodings")
print(f"Encoding {len(new_files)} new images")
# Crafting and encoding
crafted_files = crafter.crafter(new_files, device, num_workers=num_workers)
model = self.get_model()
new_embeddings = encoder.image_encoder(crafted_files, device, model)
# Reindexing
db = indexer.join_all(archive_db, new_files, new_embeddings)
print("Building treemap")
treemap = indexer.build_treemap(db)
print(f"Saving {len(db)} encodings")
save_paths = indexer.save_archives(path, treemap, db)
else:
save_paths = (str(dbpath), str(treepath))
self.reset_state()
print(f"Done in {time.time() - start_time} seconds")
return(save_paths)
def query_flow(self, root: str, query: str=None, image_query: str=None, reindex: bool=False) -> list[str]:
'''
Indexes a folder and returns file paths ranked by query.
Parameters:
path (str): Folder to search
query (str): Search query text
image_query (Tensor): Search query image(s)
reindex (bool): Reindex the folder if True
Returns:
list of file paths ranked by query
'''
start_time = time.time()
if self.root != root:
self.root = root
self.reset_state()
path = Path(root)
if not path.is_dir():
logging.error("Invalid path: %s", root)
return
device = self.device
dbpath = path/self.db_file
treepath = path/self.index_file
treemap = self.get_index(treepath)
db = self.get_db(dbpath)
# Rebuild the tree if it doesn't exist
if reindex==True or len(db) == 0 or treemap == None:
print('Indexing')
dbpath, treepath = self.index_flow(path)
self.reset_state()
treemap = self.get_index(treepath)
db = self.get_db(dbpath)
model = self.get_model()
# Convert queries to vector
print('Converting query')
if image_query:
image_query = Image.open(image_query).convert('RGB')
img = crafter.preproc(image_query)
if query and image_query:
text_vec = encoder.text_encoder(query, device, model)
image_vec = encoder.image_query_encoder(img, device, model)
query_vec = text_vec + image_vec
elif query:
query_vec = encoder.text_encoder(query, device, model)
elif image_query:
query_vec = encoder.image_query_encoder(img, device, model)
else:
print('No query!')
return ""
# Rank db by query
print(f"Searching {len(db)} images")
indexes = ranker.ranker(query_vec, treemap)
ranked_files = ranker.nns_to_files(db, indexes)
print(f"Done in {time.time() - start_time} seconds")
return(ranked_files)
def clean(self, root: str) -> None:
'''
Removes all files produced by Memery
'''
path = Path(root)
if not path.is_dir():
logging.error("Invalid path: %s", root)
db_path = path/Path(self.db_file)
treemap_path = path/Path(self.index_file)
db_path.unlink(missing_ok=True), treemap_path.unlink(missing_ok=True)
def get_model(self):
'''
Gets a new clip model if not initialized
'''
if self.model == None:
self.model = encoder.load_model(self.device)
return self.model
def get_index(self, treepath: str):
'''
Gets a new index if not initialized
Parameters:
path (str): Path to index
'''
if self.index == None:
self.index = loader.treemap_loader(treepath)
return self.index
def get_db(self, dbpath: str):
'''
Gets a new db if not initialized
Parameters:
path (str): Path to db
'''
if self.db == None:
self.db = loader.db_loader(dbpath, self.device)
return self.db
def reset_state(self) -> None:
'''
Resets the index and db
'''
self.index = None
self.db = None | # Builtins
import time
from pathlib import Path
import logging
# Dependencies
import torch
from torch import Tensor, device
from torchvision.transforms import Compose
from PIL import Image
# Local imports
from memery import loader, crafter, encoder, indexer, ranker
class Memery():
def __init__(self, root: str = '.'):
self.index_file = 'memery.ann'
self.db_file = 'memery.pt'
self.root = root
self.index = None
self.db = None
self.model = None
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def index_flow(self, root: str, num_workers=0) -> tuple[str, str]:
'''Indexes images in path, returns the location of save files'''
start_time = time.time()
if self.root != root:
self.root = root
self.reset_state()
path = Path(root)
if not path.is_dir():
logging.error("Invalid path: %s", root)
return
device = self.device
# Check if we should re-index the files
print("Checking files...")
dbpath = path/self.db_file
db = self.get_db(str(dbpath))
treepath = path/self.index_file
treemap = self.get_index(str(treepath))
filepaths = loader.get_valid_images(path)
db_set = set([o['hash'] for o in db.values()])
fp_set = set([o for _, o in filepaths])
if treemap == None or db_set != fp_set:
archive_db = {}
archive_db, new_files = loader.archive_loader(filepaths, db)
print(f"Loaded {len(archive_db)} encodings")
print(f"Encoding {len(new_files)} new images")
# Crafting and encoding
crafted_files = crafter.crafter(new_files, device, num_workers=num_workers)
model = self.get_model()
new_embeddings = encoder.image_encoder(crafted_files, device, model)
# Reindexing
db = indexer.join_all(archive_db, new_files, new_embeddings)
print("Building treemap")
treemap = indexer.build_treemap(db)
print(f"Saving {len(db)} encodings")
save_paths = indexer.save_archives(path, treemap, db)
else:
save_paths = (str(dbpath), str(treepath))
self.reset_state()
print(f"Done in {time.time() - start_time} seconds")
return(save_paths)
def query_flow(self, root: str, query: str=None, image_query: str=None, reindex: bool=False) -> list[str]:
'''
Indexes a folder and returns file paths ranked by query.
Parameters:
path (str): Folder to search
query (str): Search query text
image_query (Tensor): Search query image(s)
reindex (bool): Reindex the folder if True
Returns:
list of file paths ranked by query
'''
start_time = time.time()
if self.root != root:
self.root = root
self.reset_state()
path = Path(root)
if not path.is_dir():
logging.error("Invalid path: %s", root)
return
device = self.device
dbpath = path/self.db_file
treepath = path/self.index_file
treemap = self.get_index(treepath)
db = self.get_db(dbpath)
# Rebuild the tree if it doesn't exist
if reindex==True or len(db) == 0 or treemap == None:
print('Indexing')
dbpath, treepath = self.index_flow(path)
self.reset_state()
treemap = self.get_index(treepath)
db = self.get_db(dbpath)
model = self.get_model()
# Convert queries to vector
print('Converting query')
if image_query:
image_query = Image.open(image_query).convert('RGB')
img = crafter.preproc(image_query)
if query and image_query:
text_vec = encoder.text_encoder(query, device, model)
image_vec = encoder.image_query_encoder(img, device, model)
query_vec = text_vec + image_vec
elif query:
query_vec = encoder.text_encoder(query, device, model)
elif image_query:
query_vec = encoder.image_query_encoder(img, device, model)
else:
print('No query!')
return ""
# Rank db by query
print(f"Searching {len(db)} images")
indexes = ranker.ranker(query_vec, treemap)
ranked_files = ranker.nns_to_files(db, indexes)
print(f"Done in {time.time() - start_time} seconds")
return(ranked_files)
def clean(self, root: str) -> None:
'''
Removes all files produced by Memery
'''
path = Path(root)
if not path.is_dir():
logging.error("Invalid path: %s", root)
db_path = path/Path(self.db_file)
treemap_path = path/Path(self.index_file)
db_path.unlink(missing_ok=True), treemap_path.unlink(missing_ok=True)
def get_model(self):
'''
Gets a new clip model if not initialized
'''
if self.model == None:
self.model = encoder.load_model(self.device)
return self.model
def get_index(self, treepath: str):
'''
Gets a new index if not initialized
Parameters:
path (str): Path to index
'''
if self.index == None:
self.index = loader.treemap_loader(treepath)
return self.index
def get_db(self, dbpath: str):
'''
Gets a new db if not initialized
Parameters:
path (str): Path to db
'''
if self.db == None:
self.db = loader.db_loader(dbpath, self.device)
return self.db
def reset_state(self) -> None:
'''
Resets the index and db
'''
self.index = None
self.db = None | en | 0.708312 | # Builtins # Dependencies # Local imports Indexes images in path, returns the location of save files # Check if we should re-index the files # Crafting and encoding # Reindexing Indexes a folder and returns file paths ranked by query. Parameters: path (str): Folder to search query (str): Search query text image_query (Tensor): Search query image(s) reindex (bool): Reindex the folder if True Returns: list of file paths ranked by query # Rebuild the tree if it doesn't exist # Convert queries to vector # Rank db by query Removes all files produced by Memery Gets a new clip model if not initialized Gets a new index if not initialized Parameters: path (str): Path to index Gets a new db if not initialized Parameters: path (str): Path to db Resets the index and db | 2.095909 | 2 |
JTScheduler/JTScheduler_main.py | MaciejGGH/JTDataOrchestrator | 0 | 6623185 | <filename>JTScheduler/JTScheduler_main.py
# -*- coding: utf-8 -*
#-------------------------------------------------------------------------------
# Name: Informatica Scheduler
# Purpose:
#
# Author: <NAME>
#
# Created: 22.02.2017
# Copyright: (c) macie 2017
# Licence: <your licence>
#-------------------------------------------------------------------------------
import cherrypy
from jinja2 import Environment, FileSystemLoader
from os.path import join, dirname, abspath, sep
from os import getcwd
import ConfigParser
class job():
def __init__(self, jobId, executorName, name, description, groupId, statusId, type, expectedBy, maxRunTime, maxRetryCnt, maxThreads, updatedBy, updatedOn, version):
self.jobId = jobId.strip()
self.executorName = executorName.strip()
self.name = name.strip()
self.description = description
self.groupId = groupId.strip()
self.statusId = statusId.strip()
self.type = type.strip()
self.expectedBy = expectedBy.strip()
self.maxRunTime = maxRunTime.strip()
self.maxRetryCnt = maxRetryCnt.strip()
self.maxThreads = maxThreads.strip()
self.updatedBy = updatedBy.strip()
self.updatedOn = updatedOn.strip()
self.version = version.strip()
def __str__(self):
return 'Job: {0}, running on: {1}'.format(self.name, self.executorName)
class filematcher():
def __init__(self, filematcherId, jobId, type, value):
self.filematcherId = filematcherId.strip()
self.jobId = jobId.strip()
self.type = type.strip()
self.value = value.strip()
def __str__(self):
return 'jobId: {0}, type: {1}, value: {2}'.format(self.jobId, self.type, self.value)
class JTSchedulerWebService(object):
@cherrypy.tools.accept(media='text/plain')
def __init__(self):
self.executors = {}
self.affectedFiles = [] #list of files coming from FileMatchers
self.jobs = {}
self.filematchers = {}
# get config
self.registerJobs()
self.registerFileMatchers()
self.registerExecutors()
self.filematcherseparator = ';'
def registerExecutors(self):
dir = dirname(__file__)
configFileName = join(dir, r'cfg\scheduler.cfg')
config = ConfigParser.RawConfigParser()
config.optionxform = lambda option: option
config.read(configFileName)
ExecutorList = config.get('Common', 'ExecutorList').split(',')
for executorName in ExecutorList:
executorAddress = config.get('Executors', executorName)
self.executors[executorName] = executorAddress
registerExecutors.exposed = True
def registerJobs(self):
dir = dirname(__file__)
for jobLine in open(join(dir, r'job_config\jobs.cfg'), 'r').readlines()[1:]:
jobId, executorName, name, description, groupId, statusId, type, expectedBy, maxRunTime, maxRetryCnt, maxThreads, updatedBy, updatedOn, version = jobLine.replace('\n', '').split(',')
self.jobs[jobId.strip()] = job(jobId, executorName, name, description, groupId, statusId, type, expectedBy, maxRunTime, maxRetryCnt, maxThreads, updatedBy, updatedOn, version)
registerJobs.exposed = True
def registerFileMatchers(self):
dir = dirname(__file__)
for fmLine in open(join(dir, r'job_config\filematchers.cfg'), 'r').readlines()[1:]:
filematcherId, jobId, type, value = fmLine.replace('\n', '').split(',')
newFM = filematcher(filematcherId, jobId, type, value)
try:
self.filematchers[jobId.strip()] += [newFM]
except:
self.filematchers[jobId.strip()] = [newFM]
registerFileMatchers.exposed = True
def listRegistrations(self):
return '<ul>' + ''.join('<li>{0}</li>'.format(f) for f in self.affectedFiles) + '</ul>'
listRegistrations.exposed = True
def viewJobs(self):
print self.jobs
return '<ul>' + ''.join('<li>{0}</li>'.format(self.jobs[k]) for k in self.jobs) + '</ul>'
viewJobs.exposed = True
def viewFilematchers(self):
fmList = '<ul>'
for jobId in self.filematchers:
print '>{0}<'.format(jobId.strip())
fmList += '<li>{0}</li>'.format(self.jobs[jobId].name)
fmList += '<ul>' + ''.join('<li>{0}</li>'.format(fm) for fm in self.filematchers[jobId]) + '</ul>'
fmList += '</ul>'
return fmList
#return '<ul>' + ''.join('<li>{0}</li>'.format(self.filematchers[jobId]) for jobId in self.filematchers) + '</ul>'
viewFilematchers.exposed = True
def viewExecutors(self):
print self.executors
return '<ul>' + ''.join('<li>{0}:{1}</li>'.format(k, self.executors[k]) for k in self.executors) + '</ul>'
viewExecutors.exposed = True
def status(self):
template_dir = join(dirname(__file__), 'templates')
jinja_env = Environment(loader=FileSystemLoader(template_dir), autoescape=True)
template = jinja_env.get_template('JTSchedulerIndex.html')
return template.render(affectedFiles = self.affectedFiles)
status.exposed = True
def registerFile(self, fileName, testMode = 0):
def checkJobsToRun(fileName):
jobsToRun = []
for jobId in self.filematchers:
activateJob = True
for fm in self.filematchers[jobId]:
#print fm
if fm.type == 'FS':
if fileName[:len(fm.value)] != fm.value: activateJob = False
elif fm.type == 'FE':
if fileName[-1*len(fm.value):] != fm.value: activateJob = False
elif fm.type == 'FM':
if fileName != fm.value: activateJob = False
elif fm.type == 'FSL':
matchFound = False
for val in fm.value.split(self.filematcherseparator):
if fileName[:len(val)] == val: matchFound = True
if not matchFound: activateJob = False
elif fm.type == 'FEL':
matchFound = False
for val in fm.value.split(self.filematcherseparator):
if fileName[-1*len(val):] == val: matchFound = True
if not matchFound: activateJob = False
elif fm.type == 'FML':
matchFound = False
for val in fm.value.split(self.filematcherseparator):
if fileName == val: matchFound = True
if not matchFound: activateJob = False
if activateJob:
jobsToRun += [jobId]
return jobsToRun
self.affectedFiles += [fileName]
print cherrypy.request.params['fileName']
jobsToRun = checkJobsToRun(fileName)
if testMode:
li = ''.join('<li>{0}</li>'.format(self.jobs[jobId]) for jobId in jobsToRun)
return '<ul>{0}</ul>'.format(li)
else:
#TODO: rum the jobs
# check filematchers for jobs to be activated
# send notification to Executor if FM found
return fileName
# check filematchers for jobs to be activated
# send notification to Executor if FM found
return fileName
registerFile.exposed = True
def requestCheck(self):
## #def PUT(self, fileName):
# check filematchers for jobs to be activated
# send notification to Executor if FM found
s = cherrypy.request.headers
print s
return ''.join(k + ': ' + s[k] + '<br>' for k in s)
requestCheck.exposed = True
## def DELETE(self):
## cherrypy.session.pop('mystring', None)
def index(self):
return self.affectedFiles
index.exposed = True
if __name__ == '__main__':
# On Startup
current_dir = dirname(abspath(__file__)) + sep
config = {
'global': {
## 'environment': 'production',
'log.screen': True,
'server.socket_host': '127.0.0.1',
'server.socket_port': 8080,
'engine.autoreload_on': True,
'log.error_file': join(current_dir, 'errors.log'),
'log.access_file': join(current_dir, 'access.log'),
},
'/registerFile': {
#'request.dispatch': cherrypy.dispatch.MethodDispatcher(),
'tools.sessions.on': True,
'tools.response_headers.on': True,
#'tools.response_headers.headers': [('Content-Type', 'text/plain')],
},
'/css': {
'tools.staticdir.on': True,
'tools.staticdir.dir': '/css'
},
'/job_config': {
'tools.staticfile.debug': True,
'tools.staticdir.on': True,
'tools.staticdir.dir': '/job_config'
},
'/css/mystyle.css': {
'tools.staticfile.debug': True,
'tools.staticfile.on': True,
'tools.staticfile.filename': join(join(current_dir, 'css'), 'mystyle.css')
},
}
#cherrypy.quickstart(JTSchedulerWebService(), '/', config)
cherrypy.tree.mount(JTSchedulerWebService(), '/', config)
cherrypy.engine.start()
cherrypy.engine.block()
| <filename>JTScheduler/JTScheduler_main.py
# -*- coding: utf-8 -*
#-------------------------------------------------------------------------------
# Name: Informatica Scheduler
# Purpose:
#
# Author: <NAME>
#
# Created: 22.02.2017
# Copyright: (c) macie 2017
# Licence: <your licence>
#-------------------------------------------------------------------------------
import cherrypy
from jinja2 import Environment, FileSystemLoader
from os.path import join, dirname, abspath, sep
from os import getcwd
import ConfigParser
class job():
def __init__(self, jobId, executorName, name, description, groupId, statusId, type, expectedBy, maxRunTime, maxRetryCnt, maxThreads, updatedBy, updatedOn, version):
self.jobId = jobId.strip()
self.executorName = executorName.strip()
self.name = name.strip()
self.description = description
self.groupId = groupId.strip()
self.statusId = statusId.strip()
self.type = type.strip()
self.expectedBy = expectedBy.strip()
self.maxRunTime = maxRunTime.strip()
self.maxRetryCnt = maxRetryCnt.strip()
self.maxThreads = maxThreads.strip()
self.updatedBy = updatedBy.strip()
self.updatedOn = updatedOn.strip()
self.version = version.strip()
def __str__(self):
return 'Job: {0}, running on: {1}'.format(self.name, self.executorName)
class filematcher():
def __init__(self, filematcherId, jobId, type, value):
self.filematcherId = filematcherId.strip()
self.jobId = jobId.strip()
self.type = type.strip()
self.value = value.strip()
def __str__(self):
return 'jobId: {0}, type: {1}, value: {2}'.format(self.jobId, self.type, self.value)
class JTSchedulerWebService(object):
@cherrypy.tools.accept(media='text/plain')
def __init__(self):
self.executors = {}
self.affectedFiles = [] #list of files coming from FileMatchers
self.jobs = {}
self.filematchers = {}
# get config
self.registerJobs()
self.registerFileMatchers()
self.registerExecutors()
self.filematcherseparator = ';'
def registerExecutors(self):
dir = dirname(__file__)
configFileName = join(dir, r'cfg\scheduler.cfg')
config = ConfigParser.RawConfigParser()
config.optionxform = lambda option: option
config.read(configFileName)
ExecutorList = config.get('Common', 'ExecutorList').split(',')
for executorName in ExecutorList:
executorAddress = config.get('Executors', executorName)
self.executors[executorName] = executorAddress
registerExecutors.exposed = True
def registerJobs(self):
dir = dirname(__file__)
for jobLine in open(join(dir, r'job_config\jobs.cfg'), 'r').readlines()[1:]:
jobId, executorName, name, description, groupId, statusId, type, expectedBy, maxRunTime, maxRetryCnt, maxThreads, updatedBy, updatedOn, version = jobLine.replace('\n', '').split(',')
self.jobs[jobId.strip()] = job(jobId, executorName, name, description, groupId, statusId, type, expectedBy, maxRunTime, maxRetryCnt, maxThreads, updatedBy, updatedOn, version)
registerJobs.exposed = True
def registerFileMatchers(self):
dir = dirname(__file__)
for fmLine in open(join(dir, r'job_config\filematchers.cfg'), 'r').readlines()[1:]:
filematcherId, jobId, type, value = fmLine.replace('\n', '').split(',')
newFM = filematcher(filematcherId, jobId, type, value)
try:
self.filematchers[jobId.strip()] += [newFM]
except:
self.filematchers[jobId.strip()] = [newFM]
registerFileMatchers.exposed = True
def listRegistrations(self):
return '<ul>' + ''.join('<li>{0}</li>'.format(f) for f in self.affectedFiles) + '</ul>'
listRegistrations.exposed = True
def viewJobs(self):
print self.jobs
return '<ul>' + ''.join('<li>{0}</li>'.format(self.jobs[k]) for k in self.jobs) + '</ul>'
viewJobs.exposed = True
def viewFilematchers(self):
fmList = '<ul>'
for jobId in self.filematchers:
print '>{0}<'.format(jobId.strip())
fmList += '<li>{0}</li>'.format(self.jobs[jobId].name)
fmList += '<ul>' + ''.join('<li>{0}</li>'.format(fm) for fm in self.filematchers[jobId]) + '</ul>'
fmList += '</ul>'
return fmList
#return '<ul>' + ''.join('<li>{0}</li>'.format(self.filematchers[jobId]) for jobId in self.filematchers) + '</ul>'
viewFilematchers.exposed = True
def viewExecutors(self):
print self.executors
return '<ul>' + ''.join('<li>{0}:{1}</li>'.format(k, self.executors[k]) for k in self.executors) + '</ul>'
viewExecutors.exposed = True
def status(self):
template_dir = join(dirname(__file__), 'templates')
jinja_env = Environment(loader=FileSystemLoader(template_dir), autoescape=True)
template = jinja_env.get_template('JTSchedulerIndex.html')
return template.render(affectedFiles = self.affectedFiles)
status.exposed = True
def registerFile(self, fileName, testMode = 0):
def checkJobsToRun(fileName):
jobsToRun = []
for jobId in self.filematchers:
activateJob = True
for fm in self.filematchers[jobId]:
#print fm
if fm.type == 'FS':
if fileName[:len(fm.value)] != fm.value: activateJob = False
elif fm.type == 'FE':
if fileName[-1*len(fm.value):] != fm.value: activateJob = False
elif fm.type == 'FM':
if fileName != fm.value: activateJob = False
elif fm.type == 'FSL':
matchFound = False
for val in fm.value.split(self.filematcherseparator):
if fileName[:len(val)] == val: matchFound = True
if not matchFound: activateJob = False
elif fm.type == 'FEL':
matchFound = False
for val in fm.value.split(self.filematcherseparator):
if fileName[-1*len(val):] == val: matchFound = True
if not matchFound: activateJob = False
elif fm.type == 'FML':
matchFound = False
for val in fm.value.split(self.filematcherseparator):
if fileName == val: matchFound = True
if not matchFound: activateJob = False
if activateJob:
jobsToRun += [jobId]
return jobsToRun
self.affectedFiles += [fileName]
print cherrypy.request.params['fileName']
jobsToRun = checkJobsToRun(fileName)
if testMode:
li = ''.join('<li>{0}</li>'.format(self.jobs[jobId]) for jobId in jobsToRun)
return '<ul>{0}</ul>'.format(li)
else:
#TODO: rum the jobs
# check filematchers for jobs to be activated
# send notification to Executor if FM found
return fileName
# check filematchers for jobs to be activated
# send notification to Executor if FM found
return fileName
registerFile.exposed = True
def requestCheck(self):
## #def PUT(self, fileName):
# check filematchers for jobs to be activated
# send notification to Executor if FM found
s = cherrypy.request.headers
print s
return ''.join(k + ': ' + s[k] + '<br>' for k in s)
requestCheck.exposed = True
## def DELETE(self):
## cherrypy.session.pop('mystring', None)
def index(self):
return self.affectedFiles
index.exposed = True
if __name__ == '__main__':
# On Startup
current_dir = dirname(abspath(__file__)) + sep
config = {
'global': {
## 'environment': 'production',
'log.screen': True,
'server.socket_host': '127.0.0.1',
'server.socket_port': 8080,
'engine.autoreload_on': True,
'log.error_file': join(current_dir, 'errors.log'),
'log.access_file': join(current_dir, 'access.log'),
},
'/registerFile': {
#'request.dispatch': cherrypy.dispatch.MethodDispatcher(),
'tools.sessions.on': True,
'tools.response_headers.on': True,
#'tools.response_headers.headers': [('Content-Type', 'text/plain')],
},
'/css': {
'tools.staticdir.on': True,
'tools.staticdir.dir': '/css'
},
'/job_config': {
'tools.staticfile.debug': True,
'tools.staticdir.on': True,
'tools.staticdir.dir': '/job_config'
},
'/css/mystyle.css': {
'tools.staticfile.debug': True,
'tools.staticfile.on': True,
'tools.staticfile.filename': join(join(current_dir, 'css'), 'mystyle.css')
},
}
#cherrypy.quickstart(JTSchedulerWebService(), '/', config)
cherrypy.tree.mount(JTSchedulerWebService(), '/', config)
cherrypy.engine.start()
cherrypy.engine.block()
| en | 0.361317 | # -*- coding: utf-8 -* #------------------------------------------------------------------------------- # Name: Informatica Scheduler # Purpose: # # Author: <NAME> # # Created: 22.02.2017 # Copyright: (c) macie 2017 # Licence: <your licence> #------------------------------------------------------------------------------- #list of files coming from FileMatchers # get config #return '<ul>' + ''.join('<li>{0}</li>'.format(self.filematchers[jobId]) for jobId in self.filematchers) + '</ul>' #print fm #TODO: rum the jobs # check filematchers for jobs to be activated # send notification to Executor if FM found # check filematchers for jobs to be activated # send notification to Executor if FM found ## #def PUT(self, fileName): # check filematchers for jobs to be activated # send notification to Executor if FM found ## def DELETE(self): ## cherrypy.session.pop('mystring', None) # On Startup ## 'environment': 'production', #'request.dispatch': cherrypy.dispatch.MethodDispatcher(), #'tools.response_headers.headers': [('Content-Type', 'text/plain')], #cherrypy.quickstart(JTSchedulerWebService(), '/', config) | 2.091833 | 2 |
sandbox/ex2/exps/doom.py | sokol1412/rllab_hierarchical_rl | 0 | 6623186 | from sandbox.ex2.algos.trpo import TRPO
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import stub, run_experiment_lite
from rllab.policies.categorical_conv_policy import CategoricalConvPolicy
import lasagne.nonlinearities as NL
from sandbox.ex2.envs.cropped_gym_env import CroppedGymEnv
from sandbox.ex2.models.exemplar import Exemplar
from sandbox.ex2.models.siamese import SiameseConv
from sandbox.ex2.utils.log_utils import get_time_stamp
from sandbox.ex2.parallel_trpo.linear_feature_baseline import ZeroBaseline
import os
stub(globals())
from rllab.misc.instrument import VariantGenerator, variant
class VG(VariantGenerator):
@variant
def seed(self):
return [0, 100, 200, 300, 400]
@variant
def env(self):
return ['doommaze']
@variant
def bonus_coeff(self):
return [2e-4]
@variant
def entropy_bonus(self):
return [1e-5]
@variant
def train_itrs(self):
return [1000]
@variant
def n_parallel(self):
return [4]
@variant
def reset_freq(self):
return [0]
@variant
def hidden_sizes(self):
return [(32,)]
@variant
def exemplar_learning_rate(self):
return [1e-4]
@variant
def bonus_form(self):
return ["-log(p)"]
@variant
def use_actions(self):
return [False]
@variant
def kl_weight(self):
return [0.001]
@variant
def eval_first(self):
return [False]
variants = VG().variants()
for v in variants:
exp_index = os.path.basename(__file__).split('.')[0]
exp_prefix = "trpo/" + exp_index + "-" + v["env"]
exp_name = "{exp_index}_{time}_{env}".format(
exp_index=exp_index,
time=get_time_stamp(),
env=v["env"],
)
img_width = 32
img_height = 32
env = CroppedGymEnv(env_name='ex2/DoomMyWayHomeCustom-v0',
screen_height=img_height, screen_width=img_width ,
record_video=True, frame_skip=10, conv=True,
transpose_output=True,
doom_actionspace=None)
env = normalize(env)
network_args = dict(
conv_filters=[16,16],
conv_filter_sizes=[4,4],
conv_strides=[4,4],
conv_pads=[(0,0)]*2,
hidden_sizes=[32],
hidden_nonlinearity=NL.rectify,
output_nonlinearity=NL.softmax,
)
policy = CategoricalConvPolicy(
env_spec=env.spec,
name="policy",
**network_args
)
batch_size = 4000
max_path_length = batch_size
baseline = ZeroBaseline(env_spec=env.spec)
channel_size = 3
model_args = dict(input_size=channel_size*img_width * img_height, img_width=img_width, img_height=img_height,
channel_size=channel_size,
feature_dim=v['hidden_sizes'][-1]//2,
hidden_sizes=v['hidden_sizes'],
l2_reg=0,
learning_rate=v['exemplar_learning_rate'],
hidden_act=NL.tanh,
kl_weight=v['kl_weight'],
set_norm_constant=1,
conv_args=dict(filter_sizes=((4, 4), (4, 4)),
num_filters=(16, 16),
strides=((2, 2), (2, 2)),
hidden_act=NL.tanh) # TODO Try Relu
)
exemplar_args = dict(state_dim=env.observation_space.flat_dim, #1,
replay_state_dim=env.observation_space.flat_dim,
n_action=env.action_space.flat_dim,
model_cls=SiameseConv,
model_args=model_args,
replay_size=5000*50,
min_replay_size=4000*2,
train_itrs=v["train_itrs"],
first_train_itrs=2000,
bonus_form=v["bonus_form"],
use_actions=v["use_actions"],
)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=batch_size,
#whole_paths=False,
n_parallel=v['n_parallel'],
n_itr=300,
discount=0.99,
step_size=0.01,
exemplar_cls=Exemplar,
exemplar_args=exemplar_args,
bonus_coeff=v['bonus_coeff'],
entropy_bonus=v['entropy_bonus'],
eval_first=v['eval_first']
#sampler_cls=BatchSampler,
#sampler_arg=sampler_args,
)
run_experiment_lite(
algo.train(),
#use_gpu=True,
exp_prefix=exp_prefix,
exp_name=exp_name,
seed=v["seed"],
variant=v,
mode='local',
sync_s3_log=True,
use_cloudpickle=False,
sync_log_on_termination=True,
sync_all_data_node_to_s3=True,
snapshot_mode='gap',
snapshot_gap=50
)
| from sandbox.ex2.algos.trpo import TRPO
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import stub, run_experiment_lite
from rllab.policies.categorical_conv_policy import CategoricalConvPolicy
import lasagne.nonlinearities as NL
from sandbox.ex2.envs.cropped_gym_env import CroppedGymEnv
from sandbox.ex2.models.exemplar import Exemplar
from sandbox.ex2.models.siamese import SiameseConv
from sandbox.ex2.utils.log_utils import get_time_stamp
from sandbox.ex2.parallel_trpo.linear_feature_baseline import ZeroBaseline
import os
stub(globals())
from rllab.misc.instrument import VariantGenerator, variant
class VG(VariantGenerator):
@variant
def seed(self):
return [0, 100, 200, 300, 400]
@variant
def env(self):
return ['doommaze']
@variant
def bonus_coeff(self):
return [2e-4]
@variant
def entropy_bonus(self):
return [1e-5]
@variant
def train_itrs(self):
return [1000]
@variant
def n_parallel(self):
return [4]
@variant
def reset_freq(self):
return [0]
@variant
def hidden_sizes(self):
return [(32,)]
@variant
def exemplar_learning_rate(self):
return [1e-4]
@variant
def bonus_form(self):
return ["-log(p)"]
@variant
def use_actions(self):
return [False]
@variant
def kl_weight(self):
return [0.001]
@variant
def eval_first(self):
return [False]
variants = VG().variants()
for v in variants:
exp_index = os.path.basename(__file__).split('.')[0]
exp_prefix = "trpo/" + exp_index + "-" + v["env"]
exp_name = "{exp_index}_{time}_{env}".format(
exp_index=exp_index,
time=get_time_stamp(),
env=v["env"],
)
img_width = 32
img_height = 32
env = CroppedGymEnv(env_name='ex2/DoomMyWayHomeCustom-v0',
screen_height=img_height, screen_width=img_width ,
record_video=True, frame_skip=10, conv=True,
transpose_output=True,
doom_actionspace=None)
env = normalize(env)
network_args = dict(
conv_filters=[16,16],
conv_filter_sizes=[4,4],
conv_strides=[4,4],
conv_pads=[(0,0)]*2,
hidden_sizes=[32],
hidden_nonlinearity=NL.rectify,
output_nonlinearity=NL.softmax,
)
policy = CategoricalConvPolicy(
env_spec=env.spec,
name="policy",
**network_args
)
batch_size = 4000
max_path_length = batch_size
baseline = ZeroBaseline(env_spec=env.spec)
channel_size = 3
model_args = dict(input_size=channel_size*img_width * img_height, img_width=img_width, img_height=img_height,
channel_size=channel_size,
feature_dim=v['hidden_sizes'][-1]//2,
hidden_sizes=v['hidden_sizes'],
l2_reg=0,
learning_rate=v['exemplar_learning_rate'],
hidden_act=NL.tanh,
kl_weight=v['kl_weight'],
set_norm_constant=1,
conv_args=dict(filter_sizes=((4, 4), (4, 4)),
num_filters=(16, 16),
strides=((2, 2), (2, 2)),
hidden_act=NL.tanh) # TODO Try Relu
)
exemplar_args = dict(state_dim=env.observation_space.flat_dim, #1,
replay_state_dim=env.observation_space.flat_dim,
n_action=env.action_space.flat_dim,
model_cls=SiameseConv,
model_args=model_args,
replay_size=5000*50,
min_replay_size=4000*2,
train_itrs=v["train_itrs"],
first_train_itrs=2000,
bonus_form=v["bonus_form"],
use_actions=v["use_actions"],
)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=batch_size,
#whole_paths=False,
n_parallel=v['n_parallel'],
n_itr=300,
discount=0.99,
step_size=0.01,
exemplar_cls=Exemplar,
exemplar_args=exemplar_args,
bonus_coeff=v['bonus_coeff'],
entropy_bonus=v['entropy_bonus'],
eval_first=v['eval_first']
#sampler_cls=BatchSampler,
#sampler_arg=sampler_args,
)
run_experiment_lite(
algo.train(),
#use_gpu=True,
exp_prefix=exp_prefix,
exp_name=exp_name,
seed=v["seed"],
variant=v,
mode='local',
sync_s3_log=True,
use_cloudpickle=False,
sync_log_on_termination=True,
sync_all_data_node_to_s3=True,
snapshot_mode='gap',
snapshot_gap=50
)
| en | 0.438431 | # TODO Try Relu #1, #whole_paths=False, #sampler_cls=BatchSampler, #sampler_arg=sampler_args, #use_gpu=True, | 1.710401 | 2 |
generators/examples/hosts.py | davidyshuang/python3 | 0 | 6623187 | # hosts.py
#
# Find unique host IP addresses
from linesdir import lines_from_dir
from apachelog import apache_log
lines = lines_from_dir("access-log*","www")
log = apache_log(lines)
hosts = set(r['host'] for r in log)
for h in hosts:
print(h)
| # hosts.py
#
# Find unique host IP addresses
from linesdir import lines_from_dir
from apachelog import apache_log
lines = lines_from_dir("access-log*","www")
log = apache_log(lines)
hosts = set(r['host'] for r in log)
for h in hosts:
print(h)
| en | 0.738287 | # hosts.py # # Find unique host IP addresses | 2.713811 | 3 |
finder.py | bckhm/EmailMatcher | 1 | 6623188 | import re
import pandas as pd
# Function that searches data.txt for email/phone numbers before returning a dictionary
def find_data(pattern, column_name):
with open('data.txt', 'r') as file:
contents = file.read()
matches = pattern.findall(contents)
matches_dict = {column_name: matches}
return matches_dict
# Function that converts aobve dictionary to excel
def save_excel(matches, filename):
df = pd.DataFrame(data=matches)
df.to_excel(filename)
print(f"{filename} has been created.")
print("--- Ensure that you have copied the text into data.txt---")
# Dictionary that gives varying patterns. column names and file names based on user input
choice_dict = {'e': [r'[\w._-]*@[\w._-]*\.\w+', 'Email Addresses', 'emails.xlsx'], 'n': [r'\d{4}\s*\d{4}', 'Numbers', 'numbers.xlsx']}
try:
# Allows users to select between finding email addresses/phone numbers
choice = input("Type N to find phone numbers and E to find emails: ")
choice_lower = choice.lower()
pattern = re.compile(choice_dict[choice_lower][0])
except KeyError as e:
print(f"You typed {e}, that's not 'E' or 'N'!")
except Exception:
print("Something went wrong")
else:
matches = find_data(pattern, choice_dict[choice_lower][1])
save_excel(matches, choice_dict[choice_lower][2])
finally:
exit = input("Press Enter key to exit...")
| import re
import pandas as pd
# Function that searches data.txt for email/phone numbers before returning a dictionary
def find_data(pattern, column_name):
with open('data.txt', 'r') as file:
contents = file.read()
matches = pattern.findall(contents)
matches_dict = {column_name: matches}
return matches_dict
# Function that converts aobve dictionary to excel
def save_excel(matches, filename):
df = pd.DataFrame(data=matches)
df.to_excel(filename)
print(f"{filename} has been created.")
print("--- Ensure that you have copied the text into data.txt---")
# Dictionary that gives varying patterns. column names and file names based on user input
choice_dict = {'e': [r'[\w._-]*@[\w._-]*\.\w+', 'Email Addresses', 'emails.xlsx'], 'n': [r'\d{4}\s*\d{4}', 'Numbers', 'numbers.xlsx']}
try:
# Allows users to select between finding email addresses/phone numbers
choice = input("Type N to find phone numbers and E to find emails: ")
choice_lower = choice.lower()
pattern = re.compile(choice_dict[choice_lower][0])
except KeyError as e:
print(f"You typed {e}, that's not 'E' or 'N'!")
except Exception:
print("Something went wrong")
else:
matches = find_data(pattern, choice_dict[choice_lower][1])
save_excel(matches, choice_dict[choice_lower][2])
finally:
exit = input("Press Enter key to exit...")
| en | 0.827275 | # Function that searches data.txt for email/phone numbers before returning a dictionary # Function that converts aobve dictionary to excel # Dictionary that gives varying patterns. column names and file names based on user input # Allows users to select between finding email addresses/phone numbers | 4.136726 | 4 |
benchmarks/sample_fsm/Test_Utilities.py | nuprl/retic_performance | 3 | 6623189 | import pytest
from Untyped.Utilities import relative_average, accumulated_s
def test_relative_average():
assert relative_average([1, 2, 3], 1) == 2
assert relative_average([1, 2, 3], 2) == 1
def test_accumulated_s():
assert accumulated_s([1]) == [1]
assert accumulated_s([2, 2]) == [.5, 1]
assert accumulated_s([2, 8]) == [.2, 1]
| import pytest
from Untyped.Utilities import relative_average, accumulated_s
def test_relative_average():
assert relative_average([1, 2, 3], 1) == 2
assert relative_average([1, 2, 3], 2) == 1
def test_accumulated_s():
assert accumulated_s([1]) == [1]
assert accumulated_s([2, 2]) == [.5, 1]
assert accumulated_s([2, 8]) == [.2, 1]
| none | 1 | 2.678093 | 3 | |
__init__.py | manahter/tarag | 3 | 6623190 | """
Info:
module : Tarag
description : Ağdaki cihazları bulur
author : Manahter
Files:
getmac : IP adresini kullanarak MAC adresini bulmamıza yardımcı olur.
get_mac_address(ip=ip)
main source: "https://github.com/GhostofGoes/getmac",
getvendor : MAC adresini kullanarak Üretici Markayı bulmamızı sağlar.
get_mac_vendor(mac)
rapor : Ağı tarama esnasında ekrana çıktı veren modüldür.
INPROCESS : Tarag modül dizininde INPROCESS isimli bir dosyanın varlığı, Tarama işleminde olduğunun göstergesidir.
Tarama bittiğinde dosya otomatik olarak silinir. Bu yüzden dosya bazen görünür bazen kaybolur.
"" -> İçi boş dosya
RESULTS : Çıktıların biriktiği dosyadır
{ IP: { "MAC": MAC, "VENDOR": VENDOR}, ... }
Methods:
tarag.start() -> None -> Arka planda ağ taraması başlatılır
tarag.scan() -> None -> start aynı
tarag.inprocess -> bool -> Tarama işleminde olup olmadığı sorgulanır.
tarag.result -> dict -> { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... } -> Bulunan cihaz bilgileri
tarag.devices( -> list -> [ ("IP", "MAC", "VENDOR"), ... ] -> Bulunan cihaz bilgileri liste şeklinde
only_esp -> bool -> Sadece bulunan ESPressif ürünlerini döndür
tarag.wait( -> bool -> Tarama bitene kadar bekler. True-> İşlem bitti. False-> Zaman aşımı sonucu döndüm
timeout -> float -> Zaman aşımı
Example:
# İçe aktarma
from .Tarag import tarag
# Arkaplanda Ağ Taramayı başlat
tarag.scan()
# Tarama bitene kadar bekle
tarag.wait()
# Tarama sonuçlarını kullanabilirsin
results = tarag.result
# Bulunan cihaz bilgilerini yazdır
print(*tarag.devices(), sep="\n")
"""
import subprocess
import time
import json
import sys
import os
# Sabitler
MAC = "MAC"
VENDOR = "VENDOR"
RESULTS = "RESULTS"
INPROCESS = "INPROCESS"
dirname = os.path.dirname(__file__)
PATH_RESULTS = os.path.join(dirname, RESULTS)
PATH_INPROCESS = os.path.join(dirname, INPROCESS)
# Ağ tarama işleminde olup olmadığı, INPROCESS isimli dosyanın olup olmadığına bağlanmıştır.
# Bu yüzden, modül import edilirken, bu dosya önceden kalmışsa silinir
if INPROCESS in os.listdir(dirname):
os.remove(PATH_INPROCESS)
class TarAg:
def __init__(self):
self.inprocess = False
def start(self):
self.scan()
def scan(self):
"""Ağdaki diğer cihazları tarar"""
if self.inprocess:
return
self.inprocess = True
@property
def inprocess(self):
return INPROCESS in os.listdir(dirname)
@inprocess.setter
def inprocess(self, value):
# İşleme başlansın isteniyorsa..
if value:
subprocess.Popen([
# Python konumu
sys.executable,
# Script konumu
dirname,
# Parametre
"-p"
])
# İşlem bitirilsin isteniyorsa
elif self.inprocess:
os.remove(PATH_INPROCESS)
@property
def result(self) -> dict:
"""Sonuçları çağır.
:return { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... }
"""
if RESULTS not in os.listdir(dirname):
return {}
with open(PATH_RESULTS, "r") as f:
result = json.load(f)
return result
@result.setter
def result(self, data):
"""Sonuçları kaydet"""
# Veri girildiyse veriyi dosyaya yaz.
if data:
with open(PATH_RESULTS, "w") as f:
json.dump(data, f)
# Veri girilmediyse ve dosyada varsa, dosyayı sil
elif self.result:
os.remove(PATH_RESULTS)
# Tarama işleminin bittiğini kaydet
self.inprocess = False
@staticmethod
def devices(only_esp=False):
"""Ağda bulunan cihazları döndürür
:param only_esp: bool: Sadece ESPressif Cihazlarını Döndür
:return list: [ ("IP", "MAC", "VENDOR"), (...) ... ]
"""
data = tarag.result
if only_esp:
for i in data.copy():
if not data[i].get(VENDOR, "").lower().startswith("espressif"):
data.pop(i)
return [(ip, data[ip].get(MAC), data[ip].get(VENDOR)) for ip in data]
def wait(self, timeout=20) -> bool:
"""İşlem bitene kadar bekle.
:param timeout: int: Zaman aşımı. Bu kadar bekledikten sonra hala işlem bitmediyse daha bekleme
:return bool: True -> İşlem bitti
False-> Zaman aşımı
"""
# 1 sn'lik bekleme süresi ekliyoruz. Böylece __main__.py başlayıp INPROCESS dosyasını oluşturabilir.
time.sleep(1)
# timeout'u sorgulama için başlangıç zamanı kaydedilir.
start_t = time.time()
while self.inprocess:
# zaman aşımı olduysa işlemi bitir
if time.time() - start_t > timeout:
return False
return True
# Diğer modüllerden işlemler bu değişken üzerinden yapılır.
tarag = TarAg()
| """
Info:
module : Tarag
description : Ağdaki cihazları bulur
author : Manahter
Files:
getmac : IP adresini kullanarak MAC adresini bulmamıza yardımcı olur.
get_mac_address(ip=ip)
main source: "https://github.com/GhostofGoes/getmac",
getvendor : MAC adresini kullanarak Üretici Markayı bulmamızı sağlar.
get_mac_vendor(mac)
rapor : Ağı tarama esnasında ekrana çıktı veren modüldür.
INPROCESS : Tarag modül dizininde INPROCESS isimli bir dosyanın varlığı, Tarama işleminde olduğunun göstergesidir.
Tarama bittiğinde dosya otomatik olarak silinir. Bu yüzden dosya bazen görünür bazen kaybolur.
"" -> İçi boş dosya
RESULTS : Çıktıların biriktiği dosyadır
{ IP: { "MAC": MAC, "VENDOR": VENDOR}, ... }
Methods:
tarag.start() -> None -> Arka planda ağ taraması başlatılır
tarag.scan() -> None -> start aynı
tarag.inprocess -> bool -> Tarama işleminde olup olmadığı sorgulanır.
tarag.result -> dict -> { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... } -> Bulunan cihaz bilgileri
tarag.devices( -> list -> [ ("IP", "MAC", "VENDOR"), ... ] -> Bulunan cihaz bilgileri liste şeklinde
only_esp -> bool -> Sadece bulunan ESPressif ürünlerini döndür
tarag.wait( -> bool -> Tarama bitene kadar bekler. True-> İşlem bitti. False-> Zaman aşımı sonucu döndüm
timeout -> float -> Zaman aşımı
Example:
# İçe aktarma
from .Tarag import tarag
# Arkaplanda Ağ Taramayı başlat
tarag.scan()
# Tarama bitene kadar bekle
tarag.wait()
# Tarama sonuçlarını kullanabilirsin
results = tarag.result
# Bulunan cihaz bilgilerini yazdır
print(*tarag.devices(), sep="\n")
"""
import subprocess
import time
import json
import sys
import os
# Sabitler
MAC = "MAC"
VENDOR = "VENDOR"
RESULTS = "RESULTS"
INPROCESS = "INPROCESS"
dirname = os.path.dirname(__file__)
PATH_RESULTS = os.path.join(dirname, RESULTS)
PATH_INPROCESS = os.path.join(dirname, INPROCESS)
# Ağ tarama işleminde olup olmadığı, INPROCESS isimli dosyanın olup olmadığına bağlanmıştır.
# Bu yüzden, modül import edilirken, bu dosya önceden kalmışsa silinir
if INPROCESS in os.listdir(dirname):
os.remove(PATH_INPROCESS)
class TarAg:
def __init__(self):
self.inprocess = False
def start(self):
self.scan()
def scan(self):
"""Ağdaki diğer cihazları tarar"""
if self.inprocess:
return
self.inprocess = True
@property
def inprocess(self):
return INPROCESS in os.listdir(dirname)
@inprocess.setter
def inprocess(self, value):
# İşleme başlansın isteniyorsa..
if value:
subprocess.Popen([
# Python konumu
sys.executable,
# Script konumu
dirname,
# Parametre
"-p"
])
# İşlem bitirilsin isteniyorsa
elif self.inprocess:
os.remove(PATH_INPROCESS)
@property
def result(self) -> dict:
"""Sonuçları çağır.
:return { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... }
"""
if RESULTS not in os.listdir(dirname):
return {}
with open(PATH_RESULTS, "r") as f:
result = json.load(f)
return result
@result.setter
def result(self, data):
"""Sonuçları kaydet"""
# Veri girildiyse veriyi dosyaya yaz.
if data:
with open(PATH_RESULTS, "w") as f:
json.dump(data, f)
# Veri girilmediyse ve dosyada varsa, dosyayı sil
elif self.result:
os.remove(PATH_RESULTS)
# Tarama işleminin bittiğini kaydet
self.inprocess = False
@staticmethod
def devices(only_esp=False):
"""Ağda bulunan cihazları döndürür
:param only_esp: bool: Sadece ESPressif Cihazlarını Döndür
:return list: [ ("IP", "MAC", "VENDOR"), (...) ... ]
"""
data = tarag.result
if only_esp:
for i in data.copy():
if not data[i].get(VENDOR, "").lower().startswith("espressif"):
data.pop(i)
return [(ip, data[ip].get(MAC), data[ip].get(VENDOR)) for ip in data]
def wait(self, timeout=20) -> bool:
"""İşlem bitene kadar bekle.
:param timeout: int: Zaman aşımı. Bu kadar bekledikten sonra hala işlem bitmediyse daha bekleme
:return bool: True -> İşlem bitti
False-> Zaman aşımı
"""
# 1 sn'lik bekleme süresi ekliyoruz. Böylece __main__.py başlayıp INPROCESS dosyasını oluşturabilir.
time.sleep(1)
# timeout'u sorgulama için başlangıç zamanı kaydedilir.
start_t = time.time()
while self.inprocess:
# zaman aşımı olduysa işlemi bitir
if time.time() - start_t > timeout:
return False
return True
# Diğer modüllerden işlemler bu değişken üzerinden yapılır.
tarag = TarAg()
| tr | 0.980864 | Info: module : Tarag description : Ağdaki cihazları bulur author : Manahter Files: getmac : IP adresini kullanarak MAC adresini bulmamıza yardımcı olur. get_mac_address(ip=ip) main source: "https://github.com/GhostofGoes/getmac", getvendor : MAC adresini kullanarak Üretici Markayı bulmamızı sağlar. get_mac_vendor(mac) rapor : Ağı tarama esnasında ekrana çıktı veren modüldür. INPROCESS : Tarag modül dizininde INPROCESS isimli bir dosyanın varlığı, Tarama işleminde olduğunun göstergesidir. Tarama bittiğinde dosya otomatik olarak silinir. Bu yüzden dosya bazen görünür bazen kaybolur. "" -> İçi boş dosya RESULTS : Çıktıların biriktiği dosyadır { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... } Methods: tarag.start() -> None -> Arka planda ağ taraması başlatılır tarag.scan() -> None -> start aynı tarag.inprocess -> bool -> Tarama işleminde olup olmadığı sorgulanır. tarag.result -> dict -> { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... } -> Bulunan cihaz bilgileri tarag.devices( -> list -> [ ("IP", "MAC", "VENDOR"), ... ] -> Bulunan cihaz bilgileri liste şeklinde only_esp -> bool -> Sadece bulunan ESPressif ürünlerini döndür tarag.wait( -> bool -> Tarama bitene kadar bekler. True-> İşlem bitti. False-> Zaman aşımı sonucu döndüm timeout -> float -> Zaman aşımı Example: # İçe aktarma from .Tarag import tarag # Arkaplanda Ağ Taramayı başlat tarag.scan() # Tarama bitene kadar bekle tarag.wait() # Tarama sonuçlarını kullanabilirsin results = tarag.result # Bulunan cihaz bilgilerini yazdır print(*tarag.devices(), sep="\n") # Sabitler # Ağ tarama işleminde olup olmadığı, INPROCESS isimli dosyanın olup olmadığına bağlanmıştır. # Bu yüzden, modül import edilirken, bu dosya önceden kalmışsa silinir Ağdaki diğer cihazları tarar # İşleme başlansın isteniyorsa.. # Python konumu # Script konumu # Parametre # İşlem bitirilsin isteniyorsa Sonuçları çağır. :return { IP: { "MAC": MAC, "VENDOR": VENDOR}, ... } Sonuçları kaydet # Veri girildiyse veriyi dosyaya yaz. # Veri girilmediyse ve dosyada varsa, dosyayı sil # Tarama işleminin bittiğini kaydet Ağda bulunan cihazları döndürür :param only_esp: bool: Sadece ESPressif Cihazlarını Döndür :return list: [ ("IP", "MAC", "VENDOR"), (...) ... ] İşlem bitene kadar bekle. :param timeout: int: Zaman aşımı. Bu kadar bekledikten sonra hala işlem bitmediyse daha bekleme :return bool: True -> İşlem bitti False-> Zaman aşımı # 1 sn'lik bekleme süresi ekliyoruz. Böylece __main__.py başlayıp INPROCESS dosyasını oluşturabilir. # timeout'u sorgulama için başlangıç zamanı kaydedilir. # zaman aşımı olduysa işlemi bitir # Diğer modüllerden işlemler bu değişken üzerinden yapılır. | 2.360084 | 2 |
sdbms/app/views.py | xSkyripper/simple-fs-dbms | 0 | 6623191 | <reponame>xSkyripper/simple-fs-dbms<gh_stars>0
from flask import Blueprint, render_template, request
from sdbms.app.service.builder import QueryBuilder
from sdbms.core._manager import DbManager
from sdbms.core._parser import QueryParser, CommandError
"""
Web api that builds the query and sends it to the query manager.
Each route will represent a different CRUD operation and for each operation you will need
to send a form with the certain data, and as result we will receive a html page.
1.'/' we will display the menu that will give you all the hiperlinks for each operation.
2.'/select' page used for building the select query. Some fields of this form ar optional.
As a result, you will receive a page witch contains the selected rows.
3.'/insert' page used for building the insert query. For this one you will need to press the
'+' button and fill the fields. As a response, you will receive a success message or an exception if something went wrong
4.'/delete' page use for deleting a row. As a response you will receive success or expcetion.
5.'/update' page used for updating a row. You will need to fill the labels, conditions and values.
As a response you will receive a successs message or an exception.
"""
root_path = "/Users/cernescustefan/Documents/Facultate/db"
main_api = Blueprint('main', __name__,
template_folder='templates')
@main_api.route('/', methods=['GET'])
def index():
return render_template('index.html')
@main_api.route('/select', methods=['GET'])
def select():
return render_template('select.html')
@main_api.route('/insert', methods=['GET'])
def insert():
return render_template('insert.html')
@main_api.route('/delete', methods=['GET'])
def delete():
return render_template('delete.html')
@main_api.route('/update', methods=['GET'])
def update():
return render_template('update.html')
@main_api.route('/result', methods=['POST', 'GET'])
def result():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
query = queryBuilder.build_select(result)
print(result)
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
context = list(rv)
return render_template('result.html', context=context)
@main_api.route('/insertResult', methods=['POST', 'GET'])
def insertResult():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
query = queryBuilder.build_insert(result)
print(result)
assert result
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
return render_template('insertResutl.html')
@main_api.route('/deleteResult', methods=['POST', 'GET'])
def deleteResult():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
query = queryBuilder.build_delete(result)
print(result)
assert result
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
return render_template('deleteResult.html')
@main_api.route('/updateResult', methods=['POST', 'GET'])
def updateResult():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
print(result)
assert result
query = queryBuilder.build_update(result)
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
return render_template('updateResult.html')
| from flask import Blueprint, render_template, request
from sdbms.app.service.builder import QueryBuilder
from sdbms.core._manager import DbManager
from sdbms.core._parser import QueryParser, CommandError
"""
Web api that builds the query and sends it to the query manager.
Each route will represent a different CRUD operation and for each operation you will need
to send a form with the certain data, and as result we will receive a html page.
1.'/' we will display the menu that will give you all the hiperlinks for each operation.
2.'/select' page used for building the select query. Some fields of this form ar optional.
As a result, you will receive a page witch contains the selected rows.
3.'/insert' page used for building the insert query. For this one you will need to press the
'+' button and fill the fields. As a response, you will receive a success message or an exception if something went wrong
4.'/delete' page use for deleting a row. As a response you will receive success or expcetion.
5.'/update' page used for updating a row. You will need to fill the labels, conditions and values.
As a response you will receive a successs message or an exception.
"""
root_path = "/Users/cernescustefan/Documents/Facultate/db"
main_api = Blueprint('main', __name__,
template_folder='templates')
@main_api.route('/', methods=['GET'])
def index():
return render_template('index.html')
@main_api.route('/select', methods=['GET'])
def select():
return render_template('select.html')
@main_api.route('/insert', methods=['GET'])
def insert():
return render_template('insert.html')
@main_api.route('/delete', methods=['GET'])
def delete():
return render_template('delete.html')
@main_api.route('/update', methods=['GET'])
def update():
return render_template('update.html')
@main_api.route('/result', methods=['POST', 'GET'])
def result():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
query = queryBuilder.build_select(result)
print(result)
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
context = list(rv)
return render_template('result.html', context=context)
@main_api.route('/insertResult', methods=['POST', 'GET'])
def insertResult():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
query = queryBuilder.build_insert(result)
print(result)
assert result
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
return render_template('insertResutl.html')
@main_api.route('/deleteResult', methods=['POST', 'GET'])
def deleteResult():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
query = queryBuilder.build_delete(result)
print(result)
assert result
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
return render_template('deleteResult.html')
@main_api.route('/updateResult', methods=['POST', 'GET'])
def updateResult():
if request.method == 'POST':
result = request.form
queryBuilder = QueryBuilder()
set_db = queryBuilder.use_db(result)
print(result)
assert result
query = queryBuilder.build_update(result)
db_manager = DbManager(root_path)
parser = QueryParser()
cmd = parser.parse(set_db)
rv = cmd.execute(db_manager)
cmd = parser.parse(query)
rv = cmd.execute(db_manager)
return render_template('updateResult.html') | en | 0.870266 | Web api that builds the query and sends it to the query manager. Each route will represent a different CRUD operation and for each operation you will need to send a form with the certain data, and as result we will receive a html page. 1.'/' we will display the menu that will give you all the hiperlinks for each operation. 2.'/select' page used for building the select query. Some fields of this form ar optional. As a result, you will receive a page witch contains the selected rows. 3.'/insert' page used for building the insert query. For this one you will need to press the '+' button and fill the fields. As a response, you will receive a success message or an exception if something went wrong 4.'/delete' page use for deleting a row. As a response you will receive success or expcetion. 5.'/update' page used for updating a row. You will need to fill the labels, conditions and values. As a response you will receive a successs message or an exception. | 2.844118 | 3 |
extraPackages/Pillow-6.0.0/Tests/test_uploader.py | dolboBobo/python3_ios | 130 | 6623192 | <filename>extraPackages/Pillow-6.0.0/Tests/test_uploader.py
from .helper import PillowTestCase, hopper
class TestUploader(PillowTestCase):
def check_upload_equal(self):
result = hopper('P').convert('RGB')
target = hopper('RGB')
self.assert_image_equal(result, target)
def check_upload_similar(self):
result = hopper('P').convert('RGB')
target = hopper('RGB')
self.assert_image_similar(result, target, 0)
| <filename>extraPackages/Pillow-6.0.0/Tests/test_uploader.py
from .helper import PillowTestCase, hopper
class TestUploader(PillowTestCase):
def check_upload_equal(self):
result = hopper('P').convert('RGB')
target = hopper('RGB')
self.assert_image_equal(result, target)
def check_upload_similar(self):
result = hopper('P').convert('RGB')
target = hopper('RGB')
self.assert_image_similar(result, target, 0)
| none | 1 | 2.27951 | 2 | |
Behavioral-Patterns/Interpreter/lexing_and_parsing.py | PratikRamdasi/Design-Patterns-in-Python | 0 | 6623193 | # Evaluate numerical expression
from enum import Enum, auto
class Token:
# Token can be anything - numeric INT value or brackets
class Type(Enum):
INTEGER = auto()
PLUS = auto()
MINUS = auto()
LPAREN = auto() # left paranthesis
RPAREN = auto() # right paranthesis
def __init__(self, type, text):
# takes the type of token and associated text with it
# (for printing the text mainly)
self.type = type
self.text = text
def __str__(self):
return f'`{self.text}`' # see whitespace by using ` `
def lex(input):
# input is a string
result = []
i = 0
while(i < len(input)):
# check every char - 1 char
if input[i] == '+':
result.append(Token(Token.Type.PLUS, '+'))
elif input[i] == '-':
result.append(Token(Token.Type.MINUS, '-'))
elif input[i] == '(':
result.append(Token(Token.Type.LPAREN, '('))
elif input[i] == ')':
result.append(Token(Token.Type.RPAREN, ')'))
# integer - more than 1 char
else:
digits = [input[i]]
for j in range(i+1, len(input)):
if input[j].isdigit():
digits.append(input[j])
i += 1
else:
# digit parsing done, generate token for it -> 13, 4, 12, 1
result.append(Token(Token.Type.INTEGER, ''.join(digits)))
break
i += 1
return result
## Parsing ##
# takes tokens and returns objcect oriented structure
# integer object
class Integer:
def __init__(self, value):
self.value = value
# binary expression -> +, - etc
class BinaryExpression:
class Type(Enum):
ADDITION = 0
SUBTRACTION = 1
def __init__(self):
self.type = None
self.left = None # left side of expression
self.right = None # right side of expression
# calculate value of expression
@property
def value(self):
if self.type == self.Type.ADDITION:
return self.left.value + self.right.value
else:
return self.left.value - self.right.value
# Assume final expression is always binary expression
def parse(tokens):
result = BinaryExpression()
# flag to put at LHS or RHS
have_lhs = False
i = 0
while i < len(tokens):
token = tokens[i]
if token.type == Token.Type.INTEGER:
# put integer value into Integer object defined above
integer = Integer(int(token.text))
if not have_lhs:
result.left = integer
have_lhs = True
else:
result.right = integer
elif token.type == Token.Type.PLUS:
# modify current binary expression
result.type = BinaryExpression.Type.ADDITION
elif token.type == Token.Type.MINUS:
# modify current binary expression
result.type = BinaryExpression.Type.SUBTRACTION
# open and close paranthesis
# need to get sub expression and assign at proper left/right location
elif token.type == Token.Type.LPAREN:
# find right paranthesis to get the subex
j = i
while j < len(tokens):
if tokens[j].type == Token.Type.RPAREN:
break
j += 1
subexpression = tokens[i+1:j]
# parse subexpression recursively
element = parse(subexpression)
if not have_lhs:
result.left = element
have_lhs = True
else:
result.right = element
i = j # to the starting of next subexpression
i += 1
return result
def calc(input):
# split input into tokens
tokens = lex(input)
# turn tokens as string
print(' '.join(map(str, tokens)))
# parse the tokens
parsed = parse(tokens)
# print final result
print(f'{input} = {parsed.value}')
if __name__ == "__main__":
calc('(13+4)-(12+1)') # 4
| # Evaluate numerical expression
from enum import Enum, auto
class Token:
# Token can be anything - numeric INT value or brackets
class Type(Enum):
INTEGER = auto()
PLUS = auto()
MINUS = auto()
LPAREN = auto() # left paranthesis
RPAREN = auto() # right paranthesis
def __init__(self, type, text):
# takes the type of token and associated text with it
# (for printing the text mainly)
self.type = type
self.text = text
def __str__(self):
return f'`{self.text}`' # see whitespace by using ` `
def lex(input):
# input is a string
result = []
i = 0
while(i < len(input)):
# check every char - 1 char
if input[i] == '+':
result.append(Token(Token.Type.PLUS, '+'))
elif input[i] == '-':
result.append(Token(Token.Type.MINUS, '-'))
elif input[i] == '(':
result.append(Token(Token.Type.LPAREN, '('))
elif input[i] == ')':
result.append(Token(Token.Type.RPAREN, ')'))
# integer - more than 1 char
else:
digits = [input[i]]
for j in range(i+1, len(input)):
if input[j].isdigit():
digits.append(input[j])
i += 1
else:
# digit parsing done, generate token for it -> 13, 4, 12, 1
result.append(Token(Token.Type.INTEGER, ''.join(digits)))
break
i += 1
return result
## Parsing ##
# takes tokens and returns objcect oriented structure
# integer object
class Integer:
def __init__(self, value):
self.value = value
# binary expression -> +, - etc
class BinaryExpression:
class Type(Enum):
ADDITION = 0
SUBTRACTION = 1
def __init__(self):
self.type = None
self.left = None # left side of expression
self.right = None # right side of expression
# calculate value of expression
@property
def value(self):
if self.type == self.Type.ADDITION:
return self.left.value + self.right.value
else:
return self.left.value - self.right.value
# Assume final expression is always binary expression
def parse(tokens):
result = BinaryExpression()
# flag to put at LHS or RHS
have_lhs = False
i = 0
while i < len(tokens):
token = tokens[i]
if token.type == Token.Type.INTEGER:
# put integer value into Integer object defined above
integer = Integer(int(token.text))
if not have_lhs:
result.left = integer
have_lhs = True
else:
result.right = integer
elif token.type == Token.Type.PLUS:
# modify current binary expression
result.type = BinaryExpression.Type.ADDITION
elif token.type == Token.Type.MINUS:
# modify current binary expression
result.type = BinaryExpression.Type.SUBTRACTION
# open and close paranthesis
# need to get sub expression and assign at proper left/right location
elif token.type == Token.Type.LPAREN:
# find right paranthesis to get the subex
j = i
while j < len(tokens):
if tokens[j].type == Token.Type.RPAREN:
break
j += 1
subexpression = tokens[i+1:j]
# parse subexpression recursively
element = parse(subexpression)
if not have_lhs:
result.left = element
have_lhs = True
else:
result.right = element
i = j # to the starting of next subexpression
i += 1
return result
def calc(input):
# split input into tokens
tokens = lex(input)
# turn tokens as string
print(' '.join(map(str, tokens)))
# parse the tokens
parsed = parse(tokens)
# print final result
print(f'{input} = {parsed.value}')
if __name__ == "__main__":
calc('(13+4)-(12+1)') # 4
| en | 0.71128 | # Evaluate numerical expression # Token can be anything - numeric INT value or brackets # left paranthesis # right paranthesis # takes the type of token and associated text with it # (for printing the text mainly) # see whitespace by using ` ` # input is a string # check every char - 1 char # integer - more than 1 char # digit parsing done, generate token for it -> 13, 4, 12, 1 ## Parsing ## # takes tokens and returns objcect oriented structure # integer object # binary expression -> +, - etc # left side of expression # right side of expression # calculate value of expression # Assume final expression is always binary expression # flag to put at LHS or RHS # put integer value into Integer object defined above # modify current binary expression # modify current binary expression # open and close paranthesis # need to get sub expression and assign at proper left/right location # find right paranthesis to get the subex # parse subexpression recursively # to the starting of next subexpression # split input into tokens # turn tokens as string # parse the tokens # print final result # 4 | 3.860278 | 4 |
btdata.py | bstitt79/muzero-general | 0 | 6623194 | <reponame>bstitt79/muzero-general<filename>btdata.py<gh_stars>0
companies = [
'OEDV',
'AAPL',
'BAC',
'AMZN',
'T',
'GOOG',
'MO',
'DAL',
'AA',
'AXP',
'DD',
'BABA',
'ABT',
'UA',
'AMAT',
'AMGN',
'AAL',
'AIG',
'ALL',
'ADBE',
'GOOGL',
'ACN',
'ABBV',
'MT',
'LLY',
'AGN',
'APA',
'ADP',
'APC',
'AKAM',
'NLY',
'ABX',
'ATVI',
'ADSK',
'ADM',
'BMH.AX',
'WBA',
'ARNA',
'LUV',
'ACAD',
'PANW',
'AMD',
'AET',
'AEP',
'ALXN',
'CLMS',
'AVGO',
'EA',
'DB',
'RAI',
'AEM',
'APD',
'AMBA',
'NVS',
'APOL',
'ANF',
'LULU',
'RAD',
'BRK.AX',
'ARRY',
'AGNC',
'JBLU',
'A',
'ORLY',
'FOLD',
'AZO',
'ATML',
'AN',
'AZN',
'AES',
'GAS',
'BUD',
'ARR',
'BDX',
'AKS',
'AB',
'ACOR',
'CS',
'AFL',
'ADI',
'AEGR',
'ACIW',
'AMP',
'AVP',
'AMTD',
'AEO',
'AWK',
'NVO',
'ALTR',
'ALK',
'PAA',
'MTU.AX',
'ARCC',
'AAP',
'NAT',
'FNMA',
'FAV',
'AIV',
'AGIO',
'AEE',
'UBS',
'AVXL',
'ARLP',
'ANTM',
'AGU',
'AG',
'AFSI',
'ABC',
'STO',
'ATI',
'ADT',
'AVB',
'ATW',
'ALNY',
'LH',
'AVY',
'AUY',
'ASH',
'ARMH',
'ARIA',
'ANR',
'AINV',
'ACXM',
'ACHN',
'ACET',
'ABMD',
'ABM',
'VA',
'LIFE',
'ATO',
'ARP',
'AON',
'ADXS',
'ADC',
'APU',
'SAVE',
'AV',
'AKRX',
'ADS',
'ABAX',
'AYI',
'AWH',
'ASML',
'AMT',
'ALDR',
'ACM',
'DWA',
'ATRS',
'ARW',
'ARI',
'ARG',
'AR',
'AMCC',
'AMC',
'AL',
'AGEN',
'AAN',
'WTR',
'FCAU',
'BAH',
'AXAS',
'AVT',
'ALB',
'AIZ',
'SAIC',
'CAR',
'AXLL',
'AU',
'ARO',
'APH',
'ANTH',
'AMX',
'AMDA',
'AI',
'ABCO',
'WMC',
'MPX.AX',
'JKHY',
'AVAV',
'AMKR',
'ALJ',
'ACH',
'GPH.AX',
'ERC',
'APPY',
'ANAC',
'AEIS',
'Y',
'MTGE',
'CENX',
'ASPS',
'AMRN',
'AMPE',
'AMAG',
'ALKS',
'AFFX',
'ADES',
'ACAT',
'AAON',
'XLRN',
'VRSK',
'VJET',
'OA',
'ATLS',
'APTS',
'APO',
'ALSK',
'ALG',
'AHC',
'ACTG',
'ACAS',
'RBA',
'MAA',
'BAM',
'ATHN',
'AT',
'ASX',
'ARCO',
'ANET',
'ANCX',
'AIR',
'AF',
'WAB',
'RS',
'PKG',
'CSH',
'AXDX',
'AVHI',
'AVA',
'ATHX',
'ARWR',
'ANGI',
'AMG',
'ALSN',
'ALGN',
'AKBA',
'AGO',
'AEZS',
'ACRX',
'ROK',
'GLPI',
'DNI',
'AZZ',
'ATRC',
'ARRS',
'ARMK',
'AOS',
'ANFI',
'AMID',
'AMCX',
'ALIM',
'ALE',
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'ZX',
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'PAHC',
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'CHLN',
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'6599.KL',
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'ASLM',
'ASH.L',
'ASETEK.OL',
'ASDOF',
'ASB-PB',
'ARVSF',
'ARREF',
'ARRB.DE',
'AROFF',
'ARMHF',
'ARL.TO',
'ARET',
'ARE.TO',
'APYI',
'APS.TO',
'APLD',
'RY4A.HM',
'APELY',
'APCX',
'APBRA.BA',
'AP2.DE',
'AOT.V',
'AOMFF',
'AOBI',
'ANZ.NZ',
'ANFGY',
'ANDR',
'ANCKF',
'AN.V',
'AMZ.DE',
'AMX.V',
'KKL.AX',
'AMT-PB',
'AMSYF',
'AMSU',
'AMS.SW',
'AMNL',
'AMMX',
'AMMS',
'AMLM',
'AML.L',
'AMIH',
'AMG.F',
'AMFL',
'AMEN',
'AMC.AX',
'AMBCW',
'AMADY',
'ALY.V',
'ALU.AX',
'ALT.PA',
'ALPMY',
'ALPHA.AT',
'ALOCT.PA',
'ALMONDZ.NS',
'ALMMF',
'ALMDG.PA',
'ALLVF',
'ALLN',
'ALL-H.V',
'ALIOY',
'ALIAF',
'ALFVY',
'ALFE',
'ALFAA.MX',
'ALDA',
'ALAWP',
'ALAST.PA',
'AKKVY',
'AJL.AX',
'AJACD',
'AIW',
'AIVI',
'AIRYY',
'AIRW',
'AIPUY',
'AHT-PE',
'AHMS',
'AHLA.DE',
'AHIID',
'AHCHY',
'AHCG.L',
'AHBIF',
'AGTMF',
'AGS.BR',
'SLED.L',
'AGO.V',
'AGN.AS',
'AGN-PA',
'AGM.V',
'AGI.AX',
'AGGZF',
'AGESF',
'AGE.V',
'AFYG',
'AFT',
'AFCMF',
'AFBA',
'AF-B.ST',
'AEZ.TO',
'AEYIF',
'AETUF',
'AEI.TO',
'AE.MI',
'ADW-B.TO',
'ADTR',
'ADSK.MX',
'ADRNF',
'ADP.PA',
'ADINATH.BO',
'ADIA',
'ADHLY',
'ACUS',
'ACSEF',
'ACRB',
'ACOPF',
'ACMUY',
'ACGFF',
'ACAR',
'ABX.BA',
'ABTO',
'ABRW',
'ABR.V',
'ABJ.DE',
'ABGPF',
'ABGLY',
'ABF.L',
'ABE.V',
'ABC.L',
'ABC.AX',
'ABBN.VX',
'AAV.TO',
'AAST',
'AASL',
'AAD.DE',
'AABVF',
'AABB',
'A2A.MI',
'7579.KL',
'7162.KL',
'6AT.BE',
'6432.KL',
'REMO',
'5IB.SI',
'5014.KL',
'4162.KL',
'LEON',
'0104.HK',
] | none | 1 | 1.301624 | 1 | |
scripts/shared/file_utils.py | cric96/scala-native-benchmarks | 13 | 6623195 | <reponame>cric96/scala-native-benchmarks
import os
import errno
import subprocess as subp
def mkdir(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def touch(path):
open(path, 'w+').close()
def slurp(path):
with open(path) as f:
return f.read().strip()
def where(cmd):
if os.path.isfile(cmd):
return cmd
else:
paths = os.environ['PATH'].split(os.pathsep)
for p in paths:
f = os.path.join(p, cmd)
if os.path.isfile(f):
return f
else:
return None
def run(cmd):
print(">>> " + str(cmd))
return subp.check_output(cmd)
def dict_from_file(settings_file):
kv = {}
with open(settings_file) as settings:
for line in settings.readlines():
key, raw_value = line.split('=')
value = raw_value.strip()
kv[key] = value
return kv
def dict_to_file(settings_file, kv):
with open(settings_file, 'w+') as settings:
for k, v in kv.items():
settings.write('{}={}\n'.format(k, v))
sbt = where('sbt')
| import os
import errno
import subprocess as subp
def mkdir(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def touch(path):
open(path, 'w+').close()
def slurp(path):
with open(path) as f:
return f.read().strip()
def where(cmd):
if os.path.isfile(cmd):
return cmd
else:
paths = os.environ['PATH'].split(os.pathsep)
for p in paths:
f = os.path.join(p, cmd)
if os.path.isfile(f):
return f
else:
return None
def run(cmd):
print(">>> " + str(cmd))
return subp.check_output(cmd)
def dict_from_file(settings_file):
kv = {}
with open(settings_file) as settings:
for line in settings.readlines():
key, raw_value = line.split('=')
value = raw_value.strip()
kv[key] = value
return kv
def dict_to_file(settings_file, kv):
with open(settings_file, 'w+') as settings:
for k, v in kv.items():
settings.write('{}={}\n'.format(k, v))
sbt = where('sbt') | en | 0.298393 | # Python >2.5 | 2.761861 | 3 |
scripts/utils/make_template.py | mozilla-releng/staging-mozilla-vpn-client | 0 | 6623196 | <filename>scripts/utils/make_template.py
#! /usr/bin/env python3
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import sys
import argparse
# Parse arguments to determine what to do.
parser = argparse.ArgumentParser(
description='Generate a file from a template')
parser.add_argument('template', metavar='TEMPLATE', type=str, action='store',
help='Template file to process')
parser.add_argument('-o', '--output', metavar='FILENAME', type=str, action='store',
help='Output file to write')
parser.add_argument('-k', '--keyword', metavar='KEY=VALUE', type=str, action='append', default=[],
help='Keyword to replace, and the value to replace it with')
parser.add_argument('-f', '--keyfile', metavar='KEY=FILE', type=str, action='append', default=[],
help='Keyword to replace, and the file to source its value from')
args = parser.parse_args()
# Build up a dictionary of keywords and their replacement values
keywords = {}
for keyval in args.keyword:
kvsplit = keyval.split("=", 1)
if len(kvsplit) != 2:
print('Unable to parse KEY=VALUE from: ' + keyval)
sys.exit(1)
keywords[kvsplit[0]] = kvsplit[1]
for keyfile in args.keyfile:
kfsplit = keyfile.split("=", 1)
if len(kfsplit) != 2:
print('Unable to parse KEY=FILE from: ' + keyfile)
sys.exit(1)
with open(kfsplit[1]) as fp:
keywords[kfsplit[0]] = fp.read()
# Scan through the string for each of the keywords, replacing them
# as they are found, while taking care not to apply transformations
# to any already-transformed text.
def transform(text):
start = 0
while start < len(text):
# Find the next matching keyword, if any.
matchIdx = -1
matchKey = ""
for key in keywords:
x = text.find(key, start)
if (matchIdx < 0) or (x < matchIdx):
matchIdx = x
matchKey = key
# If there are no matches, we can return.
if matchIdx < 0:
return text
# Substitute the keyword and adjust the start.
value = keywords[matchKey]
start = matchIdx + len(value)
text = text[0:matchIdx] + value + text[matchIdx+len(matchKey):]
# Open the output file
if args.output is None:
fout = sys.stdout
else:
fout = open(args.output, "w")
# Read through the input file and apply variable substitutions.
with open(args.template) as fin:
fout.write(transform(fin.read()))
fout.flush()
fout.close()
| <filename>scripts/utils/make_template.py
#! /usr/bin/env python3
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import sys
import argparse
# Parse arguments to determine what to do.
parser = argparse.ArgumentParser(
description='Generate a file from a template')
parser.add_argument('template', metavar='TEMPLATE', type=str, action='store',
help='Template file to process')
parser.add_argument('-o', '--output', metavar='FILENAME', type=str, action='store',
help='Output file to write')
parser.add_argument('-k', '--keyword', metavar='KEY=VALUE', type=str, action='append', default=[],
help='Keyword to replace, and the value to replace it with')
parser.add_argument('-f', '--keyfile', metavar='KEY=FILE', type=str, action='append', default=[],
help='Keyword to replace, and the file to source its value from')
args = parser.parse_args()
# Build up a dictionary of keywords and their replacement values
keywords = {}
for keyval in args.keyword:
kvsplit = keyval.split("=", 1)
if len(kvsplit) != 2:
print('Unable to parse KEY=VALUE from: ' + keyval)
sys.exit(1)
keywords[kvsplit[0]] = kvsplit[1]
for keyfile in args.keyfile:
kfsplit = keyfile.split("=", 1)
if len(kfsplit) != 2:
print('Unable to parse KEY=FILE from: ' + keyfile)
sys.exit(1)
with open(kfsplit[1]) as fp:
keywords[kfsplit[0]] = fp.read()
# Scan through the string for each of the keywords, replacing them
# as they are found, while taking care not to apply transformations
# to any already-transformed text.
def transform(text):
start = 0
while start < len(text):
# Find the next matching keyword, if any.
matchIdx = -1
matchKey = ""
for key in keywords:
x = text.find(key, start)
if (matchIdx < 0) or (x < matchIdx):
matchIdx = x
matchKey = key
# If there are no matches, we can return.
if matchIdx < 0:
return text
# Substitute the keyword and adjust the start.
value = keywords[matchKey]
start = matchIdx + len(value)
text = text[0:matchIdx] + value + text[matchIdx+len(matchKey):]
# Open the output file
if args.output is None:
fout = sys.stdout
else:
fout = open(args.output, "w")
# Read through the input file and apply variable substitutions.
with open(args.template) as fin:
fout.write(transform(fin.read()))
fout.flush()
fout.close()
| en | 0.843204 | #! /usr/bin/env python3 # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. # Parse arguments to determine what to do. # Build up a dictionary of keywords and their replacement values # Scan through the string for each of the keywords, replacing them # as they are found, while taking care not to apply transformations # to any already-transformed text. # Find the next matching keyword, if any. # If there are no matches, we can return. # Substitute the keyword and adjust the start. # Open the output file # Read through the input file and apply variable substitutions. | 2.502912 | 3 |
examples/spot/mining/mining_hashrate_resale_request.py | Banging12/binance-connector-python | 512 | 6623197 | #!/usr/bin/env python
import logging
from binance.spot import Spot as Client
from binance.lib.utils import config_logging
config_logging(logging, logging.DEBUG)
key = ""
secret = ""
params = {
"algo": "sha256",
"userName": "user_name",
"startDate": 1607659086000,
"endDate": 1617659086000,
"toPoolUser": "pool_user_name",
"hashRate": "100000000",
}
client = Client(key, secret)
logging.info(client.mining_hashrate_resale_request(**params))
| #!/usr/bin/env python
import logging
from binance.spot import Spot as Client
from binance.lib.utils import config_logging
config_logging(logging, logging.DEBUG)
key = ""
secret = ""
params = {
"algo": "sha256",
"userName": "user_name",
"startDate": 1607659086000,
"endDate": 1617659086000,
"toPoolUser": "pool_user_name",
"hashRate": "100000000",
}
client = Client(key, secret)
logging.info(client.mining_hashrate_resale_request(**params))
| ru | 0.26433 | #!/usr/bin/env python | 2.050379 | 2 |
mapss/static/packages/arches/arches/app/models/concept.py | MPI-MAPSS/MAPSS | 0 | 6623198 | """
ARCHES - a program developed to inventory and manage immovable cultural heritage.
Copyright (C) 2013 <NAME> and World Monuments Fund
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import re
import uuid
import copy
from operator import itemgetter
from operator import methodcaller
from django.db import transaction, connection
from django.db.models import Q
from arches.app.models import models
from arches.app.models.system_settings import settings
from arches.app.search.search_engine_factory import SearchEngineInstance as se
from arches.app.search.elasticsearch_dsl_builder import Term, Query, Bool, Match, Terms
from arches.app.search.mappings import CONCEPTS_INDEX
from arches.app.utils.betterJSONSerializer import JSONSerializer, JSONDeserializer
from django.utils.translation import ugettext as _
from django.utils.translation import get_language
from django.db import IntegrityError
import logging
logger = logging.getLogger(__name__)
CORE_CONCEPTS = (
"00000000-0000-0000-0000-000000000001",
"00000000-0000-0000-0000-000000000004",
"00000000-0000-0000-0000-000000000005",
"00000000-0000-0000-0000-000000000006",
)
class Concept(object):
def __init__(self, *args, **kwargs):
self.id = ""
self.nodetype = ""
self.legacyoid = ""
self.relationshiptype = ""
self.values = []
self.subconcepts = []
self.parentconcepts = []
self.relatedconcepts = []
self.hassubconcepts = False
if len(args) != 0:
if isinstance(args[0], str):
try:
uuid.UUID(args[0])
self.get(args[0])
except (ValueError):
self.load(JSONDeserializer().deserialize(args[0]))
elif isinstance(args[0], dict):
self.load(args[0])
elif isinstance(args[0], object):
self.load(args[0])
def __unicode__(self):
return ("%s - %s") % (self.get_preflabel().value, self.id)
def __hash__(self):
return hash(self.id)
def __eq__(self, x):
return hash(self) == hash(x)
def __ne__(self, x):
return hash(self) != hash(x)
def load(self, value):
if isinstance(value, dict):
self.id = str(value["id"]) if "id" in value else ""
self.nodetype = value["nodetype"] if "nodetype" in value else ""
self.legacyoid = value["legacyoid"] if "legacyoid" in value else ""
self.relationshiptype = value["relationshiptype"] if "relationshiptype" in value else ""
if "values" in value:
for val in value["values"]:
self.addvalue(val)
if "subconcepts" in value:
for subconcept in value["subconcepts"]:
self.addsubconcept(subconcept)
if "parentconcepts" in value:
for parentconcept in value["parentconcepts"]:
self.addparent(parentconcept)
if "relatedconcepts" in value:
for relatedconcept in value["relatedconcepts"]:
self.addrelatedconcept(relatedconcept)
if isinstance(value, models.Concept):
self.id = str(value.pk)
self.nodetype = value.nodetype_id
self.legacyoid = value.legacyoid
def get(
self,
id="",
legacyoid="",
include_subconcepts=False,
include_parentconcepts=False,
include_relatedconcepts=False,
exclude=[],
include=[],
depth_limit=None,
up_depth_limit=None,
lang=settings.LANGUAGE_CODE,
semantic=True,
pathway_filter=None,
**kwargs,
):
if id != "":
self.load(models.Concept.objects.get(pk=id))
elif legacyoid != "":
self.load(models.Concept.objects.get(legacyoid=legacyoid))
_cache = kwargs.pop("_cache", {})
_cache[self.id] = self.__class__(
{"id": self.id, "nodetype": self.nodetype, "legacyoid": self.legacyoid, "relationshiptype": self.relationshiptype}
)
if semantic == True:
pathway_filter = (
pathway_filter
if pathway_filter
else Q(relationtype__category="Semantic Relations") | Q(relationtype__category="Properties")
)
else:
pathway_filter = pathway_filter if pathway_filter else Q(relationtype="member") | Q(relationtype="hasCollection")
if self.id != "":
nodetype = kwargs.pop("nodetype", self.nodetype)
uplevel = kwargs.pop("uplevel", 0)
downlevel = kwargs.pop("downlevel", 0)
depth_limit = depth_limit if depth_limit is None else int(depth_limit)
up_depth_limit = up_depth_limit if up_depth_limit is None else int(up_depth_limit)
if include is not None:
if len(include) > 0 and len(exclude) > 0:
raise Exception(_("Only include values for include or exclude, but not both"))
include = (
include if len(include) != 0 else models.DValueType.objects.distinct("category").values_list("category", flat=True)
)
include = set(include).difference(exclude)
exclude = []
if len(include) > 0:
values = models.Value.objects.filter(concept=self.id)
for value in values:
if value.valuetype.category in include:
self.values.append(ConceptValue(value))
hassubconcepts = models.Relation.objects.filter(Q(conceptfrom=self.id), pathway_filter, ~Q(relationtype="related"))[0:1]
if len(hassubconcepts) > 0:
self.hassubconcepts = True
if include_subconcepts:
conceptrealations = models.Relation.objects.filter(Q(conceptfrom=self.id), pathway_filter, ~Q(relationtype="related"))
if depth_limit is None or downlevel < depth_limit:
if depth_limit is not None:
downlevel = downlevel + 1
for relation in conceptrealations:
subconcept = (
_cache[str(relation.conceptto_id)]
if str(relation.conceptto_id) in _cache
else self.__class__().get(
id=relation.conceptto_id,
include_subconcepts=include_subconcepts,
include_parentconcepts=include_parentconcepts,
include_relatedconcepts=include_relatedconcepts,
exclude=exclude,
include=include,
depth_limit=depth_limit,
up_depth_limit=up_depth_limit,
downlevel=downlevel,
uplevel=uplevel,
nodetype=nodetype,
semantic=semantic,
pathway_filter=pathway_filter,
_cache=_cache.copy(),
lang=lang,
)
)
subconcept.relationshiptype = relation.relationtype_id
self.subconcepts.append(subconcept)
self.subconcepts = sorted(
self.subconcepts, key=lambda concept: self.natural_keys(concept.get_sortkey(lang)), reverse=False
)
# self.subconcepts = sorted(self.subconcepts, key=methodcaller(
# 'get_sortkey', lang=lang), reverse=False)
if include_parentconcepts:
conceptrealations = models.Relation.objects.filter(Q(conceptto=self.id), pathway_filter, ~Q(relationtype="related"))
if up_depth_limit is None or uplevel < up_depth_limit:
if up_depth_limit is not None:
uplevel = uplevel + 1
for relation in conceptrealations:
parentconcept = (
_cache[str(relation.conceptfrom_id)]
if str(relation.conceptfrom_id) in _cache
else self.__class__().get(
id=relation.conceptfrom_id,
include_subconcepts=False,
include_parentconcepts=include_parentconcepts,
include_relatedconcepts=include_relatedconcepts,
exclude=exclude,
include=include,
depth_limit=depth_limit,
up_depth_limit=up_depth_limit,
downlevel=downlevel,
uplevel=uplevel,
nodetype=nodetype,
semantic=semantic,
pathway_filter=pathway_filter,
_cache=_cache.copy(),
lang=lang,
)
)
parentconcept.relationshiptype = relation.relationtype_id
self.parentconcepts.append(parentconcept)
if include_relatedconcepts:
conceptrealations = models.Relation.objects.filter(
Q(relationtype="related") | Q(relationtype__category="Mapping Properties"),
Q(conceptto=self.id) | Q(conceptfrom=self.id),
)
relations = []
for relation in conceptrealations:
if str(relation.conceptto_id) != self.id and str(relation.relationid) not in relations:
relations.append(str(relation.relationid))
relatedconcept = self.__class__().get(relation.conceptto_id, include=["label"], lang=lang)
relatedconcept.relationshiptype = relation.relationtype_id
self.relatedconcepts.append(relatedconcept)
if str(relation.conceptfrom_id) != self.id and str(relation.relationid) not in relations:
relations.append(str(relation.relationid))
relatedconcept = self.__class__().get(relation.conceptfrom_id, include=["label"], lang=lang)
relatedconcept.relationshiptype = relation.relationtype_id
self.relatedconcepts.append(relatedconcept)
return self
def save(self):
self.id = self.id if (self.id != "" and self.id is not None) else str(uuid.uuid4())
concept, created = models.Concept.objects.get_or_create(
pk=self.id, defaults={"legacyoid": self.legacyoid if self.legacyoid != "" else self.id, "nodetype_id": self.nodetype}
)
for value in self.values:
if not isinstance(value, ConceptValue):
value = ConceptValue(value)
value.conceptid = self.id
value.save()
for parentconcept in self.parentconcepts:
parentconcept.save()
parentconcept.add_relation(self, parentconcept.relationshiptype)
for subconcept in self.subconcepts:
subconcept.save()
self.add_relation(subconcept, subconcept.relationshiptype)
# if we're moving a Concept Scheme below another Concept or Concept Scheme
if len(self.parentconcepts) > 0 and concept.nodetype_id == "ConceptScheme":
concept.nodetype_id = "Concept"
concept.save()
self.load(concept)
for relation in models.Relation.objects.filter(conceptfrom=concept, relationtype_id="hasTopConcept"):
relation.relationtype_id = "narrower"
relation.save()
for relatedconcept in self.relatedconcepts:
self.add_relation(relatedconcept, relatedconcept.relationshiptype)
if relatedconcept.relationshiptype == "member":
child_concepts = relatedconcept.get(include_subconcepts=True)
def applyRelationship(concept):
for subconcept in concept.subconcepts:
concept.add_relation(subconcept, relatedconcept.relationshiptype)
child_concepts.traverse(applyRelationship)
return concept
def delete(self, delete_self=False):
"""
Deletes any subconcepts associated with this concept and additionally this concept if 'delete_self' is True
If any parentconcepts or relatedconcepts are included then it will only delete the relationship to those concepts but not the concepts themselves
If any values are passed, then those values as well as the relationship to those values will be deleted
Note, django will automatically take care of deleting any db models that have a foreign key relationship to the model being deleted
(eg: deleting a concept model will also delete all values and relationships), but because we need to manage deleting
parent concepts and related concepts and values we have to do that here too
"""
for subconcept in self.subconcepts:
concepts_to_delete = Concept.gather_concepts_to_delete(subconcept)
for key, concept in concepts_to_delete.items():
models.Concept.objects.get(pk=key).delete()
for parentconcept in self.parentconcepts:
relations_filter = (
(Q(relationtype__category="Semantic Relations") | Q(relationtype="hasTopConcept"))
& Q(conceptfrom=parentconcept.id)
& Q(conceptto=self.id)
)
conceptrelations = models.Relation.objects.filter(relations_filter)
for relation in conceptrelations:
relation.delete()
if models.Relation.objects.filter(relations_filter).count() == 0:
# we've removed all parent concepts so now this concept needs to be promoted to a Concept Scheme
concept = models.Concept.objects.get(pk=self.id)
concept.nodetype_id = "ConceptScheme"
concept.save()
self.load(concept)
for relation in models.Relation.objects.filter(conceptfrom=concept, relationtype_id="narrower"):
relation.relationtype_id = "hasTopConcept"
relation.save()
deletedrelatedconcepts = []
for relatedconcept in self.relatedconcepts:
conceptrelations = models.Relation.objects.filter(
Q(relationtype="related") | Q(relationtype="member") | Q(relationtype__category="Mapping Properties"),
conceptto=relatedconcept.id,
conceptfrom=self.id,
)
for relation in conceptrelations:
relation.delete()
deletedrelatedconcepts.append(relatedconcept)
conceptrelations = models.Relation.objects.filter(
Q(relationtype="related") | Q(relationtype="member") | Q(relationtype__category="Mapping Properties"),
conceptfrom=relatedconcept.id,
conceptto=self.id,
)
for relation in conceptrelations:
relation.delete()
deletedrelatedconcepts.append(relatedconcept)
for deletedrelatedconcept in deletedrelatedconcepts:
if deletedrelatedconcept in self.relatedconcepts:
self.relatedconcepts.remove(deletedrelatedconcept)
for value in self.values:
if not isinstance(value, ConceptValue):
value = ConceptValue(value)
value.delete()
if delete_self:
concepts_to_delete = Concept.gather_concepts_to_delete(self)
for key, concept in concepts_to_delete.items():
# delete only member relationships if the nodetype == Collection
if concept.nodetype == "Collection":
concept = Concept().get(
id=concept.id,
include_subconcepts=True,
include_parentconcepts=True,
include=["label"],
up_depth_limit=1,
semantic=False,
)
def find_concepts(concept):
if len(concept.parentconcepts) <= 1:
for subconcept in concept.subconcepts:
conceptrelation = models.Relation.objects.get(
conceptfrom=concept.id, conceptto=subconcept.id, relationtype="member"
)
conceptrelation.delete()
find_concepts(subconcept)
find_concepts(concept)
# if the concept is a collection, loop through the nodes and delete their rdmCollection values
for node in models.Node.objects.filter(config__rdmCollection=concept.id):
node.config["rdmCollection"] = None
node.save()
models.Concept.objects.get(pk=key).delete()
return
def add_relation(self, concepttorelate, relationtype):
"""
Relates this concept to 'concepttorelate' via the relationtype
"""
relation, created = models.Relation.objects.get_or_create(
conceptfrom_id=self.id, conceptto_id=concepttorelate.id, relationtype_id=relationtype
)
return relation
@staticmethod
def gather_concepts_to_delete(concept, lang=settings.LANGUAGE_CODE):
"""
Gets a dictionary of all the concepts ids to delete
The values of the dictionary keys differ somewhat depending on the node type being deleted
If the nodetype == 'Concept' then return ConceptValue objects keyed to the concept id
If the nodetype == 'ConceptScheme' then return a ConceptValue object with the value set to any ONE prefLabel keyed to the concept id
We do this because it takes so long to gather the ids of the concepts when deleting a Scheme or Group
"""
concepts_to_delete = {}
# Here we have to worry about making sure we don't delete nodes that have more than 1 parent
if concept.nodetype == "Concept":
concept = Concept().get(
id=concept.id, include_subconcepts=True, include_parentconcepts=True, include=["label"], up_depth_limit=1
)
def find_concepts(concept):
if len(concept.parentconcepts) <= 1:
concepts_to_delete[concept.id] = concept
for subconcept in concept.subconcepts:
find_concepts(subconcept)
find_concepts(concept)
return concepts_to_delete
# here we can just delete everything and so use a recursive CTE to get the concept ids much more quickly
if concept.nodetype == "ConceptScheme":
concepts_to_delete[concept.id] = concept
rows = Concept().get_child_concepts(concept.id)
for row in rows:
if row[0] not in concepts_to_delete:
concepts_to_delete[row[0]] = Concept({"id": row[0]})
concepts_to_delete[row[0]].addvalue({"id": row[2], "conceptid": row[0], "value": row[1]})
if concept.nodetype == "Collection":
concepts_to_delete[concept.id] = concept
rows = Concept().get_child_collections(concept.id)
for row in rows:
if row[0] not in concepts_to_delete:
concepts_to_delete[row[0]] = Concept({"id": row[0]})
concepts_to_delete[row[0]].addvalue({"id": row[2], "conceptid": row[0], "value": row[1]})
return concepts_to_delete
def get_child_collections_hierarchically(self, conceptid, child_valuetypes=None, offset=0, limit=50, query=None):
child_valuetypes = child_valuetypes if child_valuetypes else ["prefLabel"]
columns = "valueidto::text, conceptidto::text, valueto, valuetypeto, depth, count(*) OVER() AS full_count, collector"
return self.get_child_edges(
conceptid, ["member"], child_valuetypes, offset=offset, limit=limit, order_hierarchically=True, query=query, columns=columns
)
def get_child_collections(self, conceptid, child_valuetypes=None, parent_valuetype="prefLabel", columns=None, depth_limit=""):
child_valuetypes = child_valuetypes if child_valuetypes else ["prefLabel"]
columns = columns if columns else "conceptidto::text, valueto, valueidto::text"
return self.get_child_edges(conceptid, ["member"], child_valuetypes, parent_valuetype, columns, depth_limit)
def get_child_concepts(self, conceptid, child_valuetypes=None, parent_valuetype="prefLabel", columns=None, depth_limit=""):
columns = columns if columns else "conceptidto::text, valueto, valueidto::text"
return self.get_child_edges(conceptid, ["narrower", "hasTopConcept"], child_valuetypes, parent_valuetype, columns, depth_limit)
def get_child_concepts_for_indexing(self, conceptid, child_valuetypes=None, parent_valuetype="prefLabel", depth_limit=""):
columns = "valueidto::text, conceptidto::text, valuetypeto, categoryto, valueto, languageto"
data = self.get_child_edges(conceptid, ["narrower", "hasTopConcept"], child_valuetypes, parent_valuetype, columns, depth_limit)
return [dict(list(zip(["id", "conceptid", "type", "category", "value", "language"], d)), top_concept="") for d in data]
def get_child_edges(
self,
conceptid,
relationtypes,
child_valuetypes=None,
parent_valuetype="prefLabel",
columns=None,
depth_limit=None,
offset=None,
limit=20,
order_hierarchically=False,
query=None,
languageid=None,
):
"""
Recursively builds a list of concept relations for a given concept and all it's subconcepts based on its relationship type and valuetypes.
"""
languageid = get_language() if languageid is None else languageid
relationtypes = " or ".join(["r.relationtype = '%s'" % (relationtype) for relationtype in relationtypes])
depth_limit = "and depth < %s" % depth_limit if depth_limit else ""
child_valuetypes = ("','").join(
child_valuetypes if child_valuetypes else models.DValueType.objects.filter(category="label").values_list("valuetype", flat=True)
)
limit_clause = " limit %s offset %s" % (limit, offset) if offset is not None else ""
if order_hierarchically:
sql = """
WITH RECURSIVE
ordered_relationships AS (
(
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, (
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) as valuesto,
(
SELECT value::int
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('sortorder')
limit 1
) as sortorder,
(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('collector')
limit 1
) as collector
FROM relations r
WHERE r.conceptidfrom = '{conceptid}'
and ({relationtypes})
ORDER BY sortorder, valuesto
)
UNION
(
SELECT r.conceptidfrom, r.conceptidto, r.relationtype,(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) as valuesto,
(
SELECT value::int
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('sortorder')
limit 1
) as sortorder,
(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('collector')
limit 1
) as collector
FROM relations r
JOIN ordered_relationships b ON(b.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
ORDER BY sortorder, valuesto
)
),
children AS (
SELECT r.conceptidfrom, r.conceptidto,
to_char(row_number() OVER (), 'fm000000') as row,
r.collector,
1 AS depth ---|NonRecursive Part
FROM ordered_relationships r
WHERE r.conceptidfrom = '{conceptid}'
and ({relationtypes})
UNION
SELECT r.conceptidfrom, r.conceptidto,
row || '-' || to_char(row_number() OVER (), 'fm000000'),
r.collector,
depth+1 ---|RecursivePart
FROM ordered_relationships r
JOIN children b ON(b.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
{depth_limit}
)
{subquery}
SELECT
(
select row_to_json(d)
FROM (
SELECT *
FROM values
WHERE conceptid={recursive_table}.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) d
) as valueto,
depth, collector, count(*) OVER() AS full_count
FROM {recursive_table} order by row {limit_clause};
"""
subquery = (
""", results as (
SELECT c.conceptidfrom, c.conceptidto, c.row, c.depth, c.collector
FROM children c
JOIN values ON(values.conceptid = c.conceptidto)
WHERE LOWER(values.value) like '%%%s%%'
AND values.valuetype in ('prefLabel')
UNION
SELECT c.conceptidfrom, c.conceptidto, c.row, c.depth, c.collector
FROM children c
JOIN results r on (r.conceptidfrom=c.conceptidto)
)"""
% query.lower()
if query is not None
else ""
)
recursive_table = "results" if query else "children"
sql = sql.format(
conceptid=conceptid,
relationtypes=relationtypes,
child_valuetypes=child_valuetypes,
parent_valuetype=parent_valuetype,
depth_limit=depth_limit,
limit_clause=limit_clause,
subquery=subquery,
recursive_table=recursive_table,
languageid=languageid,
short_languageid=languageid.split("-")[0],
default_languageid=settings.LANGUAGE_CODE,
)
else:
sql = """
WITH RECURSIVE
children AS (
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, 1 AS depth
FROM relations r
WHERE r.conceptidfrom = '{conceptid}'
AND ({relationtypes})
UNION
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, depth+1
FROM relations r
JOIN children c ON(c.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
{depth_limit}
),
results AS (
SELECT
valuefrom.value as valuefrom, valueto.value as valueto,
valuefrom.valueid as valueidfrom, valueto.valueid as valueidto,
valuefrom.valuetype as valuetypefrom, valueto.valuetype as valuetypeto,
valuefrom.languageid as languagefrom, valueto.languageid as languageto,
dtypesfrom.category as categoryfrom, dtypesto.category as categoryto,
c.conceptidfrom, c.conceptidto
FROM values valueto
JOIN d_value_types dtypesto ON(dtypesto.valuetype = valueto.valuetype)
JOIN children c ON(c.conceptidto = valueto.conceptid)
JOIN values valuefrom ON(c.conceptidfrom = valuefrom.conceptid)
JOIN d_value_types dtypesfrom ON(dtypesfrom.valuetype = valuefrom.valuetype)
WHERE valueto.valuetype in ('{child_valuetypes}')
AND valuefrom.valuetype in ('{child_valuetypes}')
)
SELECT distinct {columns}
FROM results {limit_clause}
"""
if not columns:
columns = """
conceptidfrom::text, conceptidto::text,
valuefrom, valueto,
valueidfrom::text, valueidto::text,
valuetypefrom, valuetypeto,
languagefrom, languageto,
categoryfrom, categoryto
"""
sql = sql.format(
conceptid=conceptid,
relationtypes=relationtypes,
child_valuetypes=child_valuetypes,
columns=columns,
depth_limit=depth_limit,
limit_clause=limit_clause,
)
cursor = connection.cursor()
cursor.execute(sql)
rows = cursor.fetchall()
return rows
def traverse(self, func, direction="down", scope=None, **kwargs):
"""
Traverses a concept graph from self to leaf (direction='down') or root (direction='up') calling
the given function on each node, passes an optional scope to each function
Return a value from the function to prematurely end the traversal
"""
_cache = kwargs.pop("_cache", [])
if self.id not in _cache:
_cache.append(self.id)
if scope is None:
ret = func(self, **kwargs)
else:
ret = func(self, scope, **kwargs)
# break out of the traversal if the function returns a value
if ret is not None:
return ret
if direction == "down":
for subconcept in self.subconcepts:
ret = subconcept.traverse(func, direction, scope, _cache=_cache, **kwargs)
if ret is not None:
return ret
else:
for parentconcept in self.parentconcepts:
ret = parentconcept.traverse(func, direction, scope, _cache=_cache, **kwargs)
if ret is not None:
return ret
def get_sortkey(self, lang=settings.LANGUAGE_CODE):
for value in self.values:
if value.type == "sortorder":
try:
return float(value.value)
except:
return None
return self.get_preflabel(lang=lang).value
def natural_keys(self, text):
"""
alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com/blog/200712/human_sorting.html
(See Toothy's implementation in the comments)
float regex comes from https://stackoverflow.com/a/12643073/190597
"""
def atof(text):
try:
retval = float(text)
except ValueError:
retval = text
return retval
return [atof(c) for c in re.split(r"[+-]?([0-9]+(?:[.][0-9]*)?|[.][0-9]+)", str(text))]
def get_preflabel(self, lang=settings.LANGUAGE_CODE):
score = 0
ranked_labels = []
if self.values == []:
concept = Concept().get(id=self.id, include_subconcepts=False, include_parentconcepts=False, include=["label"])
else:
concept = self
for value in concept.values:
ranked_label = {"weight": 1, "value": value}
if value.type == "prefLabel":
ranked_label["weight"] = ranked_label["weight"] * 10
elif value.type == "altLabel":
ranked_label["weight"] = ranked_label["weight"] * 4
if value.language == lang:
ranked_label["weight"] = ranked_label["weight"] * 10
elif value.language.split("-")[0] == lang.split("-")[0]:
ranked_label["weight"] = ranked_label["weight"] * 5
ranked_labels.append(ranked_label)
ranked_labels = sorted(ranked_labels, key=lambda label: label["weight"], reverse=True)
if len(ranked_labels) == 0:
ranked_labels.append({"weight": 1, "value": ConceptValue()})
return ranked_labels[0]["value"]
def flatten(self, ret=None):
"""
Flattens the graph into a unordered list of concepts
"""
if ret is None:
ret = []
ret.append(self)
for subconcept in self.subconcepts:
subconcept.flatten(ret)
return ret
def addparent(self, value):
if isinstance(value, dict):
self.parentconcepts.append(Concept(value))
elif isinstance(value, Concept):
self.parentconcepts.append(value)
else:
raise Exception("Invalid parent concept definition: %s" % (value))
def addsubconcept(self, value):
if isinstance(value, dict):
self.subconcepts.append(Concept(value))
elif isinstance(value, Concept):
self.subconcepts.append(value)
else:
raise Exception(_("Invalid subconcept definition: %s") % (value))
def addrelatedconcept(self, value):
if isinstance(value, dict):
self.relatedconcepts.append(Concept(value))
elif isinstance(value, Concept):
self.relatedconcepts.append(value)
else:
raise Exception(_("Invalid related concept definition: %s") % (value))
def addvalue(self, value):
if isinstance(value, dict):
value["conceptid"] = self.id
self.values.append(ConceptValue(value))
elif isinstance(value, ConceptValue):
self.values.append(value)
elif isinstance(value, models.Value):
self.values.append(ConceptValue(value))
else:
raise Exception(_("Invalid value definition: %s") % (value))
def index(self, scheme=None):
if scheme is None:
scheme = self.get_context()
for value in self.values:
value.index(scheme=scheme)
if self.nodetype == "ConceptScheme":
scheme = None
for subconcept in self.subconcepts:
subconcept.index(scheme=scheme)
def bulk_index(self):
concept_docs = []
if self.nodetype == "ConceptScheme":
concept = Concept().get(id=self.id, values=["label"])
concept.index()
for topConcept in self.get_child_concepts_for_indexing(self.id, depth_limit=1):
concept = Concept().get(id=topConcept["conceptid"])
scheme = concept.get_context()
topConcept["top_concept"] = scheme.id
concept_docs.append(se.create_bulk_item(index=CONCEPTS_INDEX, id=topConcept["id"], data=topConcept))
for childConcept in concept.get_child_concepts_for_indexing(topConcept["conceptid"]):
childConcept["top_concept"] = scheme.id
concept_docs.append(se.create_bulk_item(index=CONCEPTS_INDEX, id=childConcept["id"], data=childConcept))
if self.nodetype == "Concept":
concept = Concept().get(id=self.id, values=["label"])
scheme = concept.get_context()
concept.index(scheme)
for childConcept in concept.get_child_concepts_for_indexing(self.id):
childConcept["top_concept"] = scheme.id
concept_docs.append(se.create_bulk_item(index=CONCEPTS_INDEX, id=childConcept["id"], data=childConcept))
se.bulk_index(concept_docs)
def delete_index(self, delete_self=False):
def delete_concept_values_index(concepts_to_delete):
for concept in concepts_to_delete.values():
query = Query(se, start=0, limit=10000)
term = Term(field="conceptid", term=concept.id)
query.add_query(term)
query.delete(index=CONCEPTS_INDEX)
if delete_self:
concepts_to_delete = Concept.gather_concepts_to_delete(self)
delete_concept_values_index(concepts_to_delete)
else:
for subconcept in self.subconcepts:
concepts_to_delete = Concept.gather_concepts_to_delete(subconcept)
delete_concept_values_index(concepts_to_delete)
def concept_tree(
self, top_concept="00000000-0000-0000-0000-000000000001", lang=settings.LANGUAGE_CODE, mode="semantic",
):
class concept(object):
def __init__(self, *args, **kwargs):
self.label = ""
self.labelid = ""
self.id = ""
self.sortorder = None
self.load_on_demand = False
self.children = []
def _findNarrowerConcept(conceptid, depth_limit=None, level=0):
labels = models.Value.objects.filter(concept=conceptid)
ret = concept()
temp = Concept()
for label in labels:
temp.addvalue(label)
if label.valuetype_id == "sortorder":
try:
ret.sortorder = float(label.value)
except:
ret.sortorder = None
label = temp.get_preflabel(lang=lang)
ret.label = label.value
ret.id = label.conceptid
ret.labelid = label.id
if mode == "semantic":
conceptrealations = models.Relation.objects.filter(
Q(conceptfrom=conceptid), Q(relationtype__category="Semantic Relations") | Q(relationtype__category="Properties")
)
if mode == "collections":
conceptrealations = models.Relation.objects.filter(
Q(conceptfrom=conceptid), Q(relationtype="member") | Q(relationtype="hasCollection")
)
if depth_limit is not None and len(conceptrealations) > 0 and level >= depth_limit:
ret.load_on_demand = True
else:
if depth_limit is not None:
level = level + 1
for relation in conceptrealations:
ret.children.append(_findNarrowerConcept(relation.conceptto_id, depth_limit=depth_limit, level=level))
ret.children = sorted(
ret.children,
key=lambda concept: self.natural_keys(concept.sortorder if concept.sortorder else concept.label),
reverse=False,
)
return ret
def _findBroaderConcept(conceptid, child_concept, depth_limit=None, level=0):
conceptrealations = models.Relation.objects.filter(
Q(conceptto=conceptid), ~Q(relationtype="related"), ~Q(relationtype__category="Mapping Properties")
)
if len(conceptrealations) > 0 and conceptid != top_concept:
labels = models.Value.objects.filter(concept=conceptrealations[0].conceptfrom_id)
ret = concept()
temp = Concept()
for label in labels:
temp.addvalue(label)
label = temp.get_preflabel(lang=lang)
ret.label = label.value
ret.id = label.conceptid
ret.labelid = label.id
ret.children.append(child_concept)
return _findBroaderConcept(conceptrealations[0].conceptfrom_id, ret, depth_limit=depth_limit, level=level)
else:
return child_concept
graph = []
if self.id is None or self.id == "" or self.id == "None" or self.id == top_concept:
if mode == "semantic":
concepts = models.Concept.objects.filter(nodetype="ConceptScheme")
for conceptmodel in concepts:
graph.append(_findNarrowerConcept(conceptmodel.pk, depth_limit=1))
if mode == "collections":
concepts = models.Concept.objects.filter(nodetype="Collection")
for conceptmodel in concepts:
graph.append(_findNarrowerConcept(conceptmodel.pk, depth_limit=0))
graph = sorted(graph, key=lambda concept: concept.label)
# graph = _findNarrowerConcept(concepts[0].pk, depth_limit=1).children
else:
graph = _findNarrowerConcept(self.id, depth_limit=1).children
# concepts = _findNarrowerConcept(self.id, depth_limit=1)
# graph = [_findBroaderConcept(self.id, concepts, depth_limit=1)]
return graph
def get_paths(self, lang=settings.LANGUAGE_CODE):
def graph_to_paths(current_concept, path=[], path_list=[], _cache=[]):
if len(path) == 0:
current_path = []
else:
current_path = path[:]
current_path.insert(
0,
{
"label": current_concept.get_preflabel(lang=lang).value,
"relationshiptype": current_concept.relationshiptype,
"id": current_concept.id,
},
)
if len(current_concept.parentconcepts) == 0 or current_concept.id in _cache:
path_list.append(current_path[:])
else:
_cache.append(current_concept.id)
for parent in current_concept.parentconcepts:
ret = graph_to_paths(parent, current_path, path_list, _cache)
return path_list
# def graph_to_paths(current_concept, **kwargs):
# path = kwargs.get('path', [])
# path_list = kwargs.get('path_list', [])
# if len(path) == 0:
# current_path = []
# else:
# current_path = path[:]
# current_path.insert(0, {'label': current_concept.get_preflabel(lang=lang).value, 'relationshiptype': current_concept.relationshiptype, 'id': current_concept.id})
# if len(current_concept.parentconcepts) == 0:
# path_list.append(current_path[:])
# # else:
# # for parent in current_concept.parentconcepts:
# # ret = graph_to_paths(parent, current_path, path_list, _cache)
# #return path_list
# self.traverse(graph_to_paths, direction='up')
return graph_to_paths(self)
def get_node_and_links(self, lang=settings.LANGUAGE_CODE):
nodes = [{"concept_id": self.id, "name": self.get_preflabel(lang=lang).value, "type": "Current"}]
links = []
def get_parent_nodes_and_links(current_concept, _cache=[]):
if current_concept.id not in _cache:
_cache.append(current_concept.id)
parents = current_concept.parentconcepts
for parent in parents:
nodes.append(
{
"concept_id": parent.id,
"name": parent.get_preflabel(lang=lang).value,
"type": "Root" if len(parent.parentconcepts) == 0 else "Ancestor",
}
)
links.append(
{"target": current_concept.id, "source": parent.id, "relationship": "broader", }
)
get_parent_nodes_and_links(parent, _cache)
get_parent_nodes_and_links(self)
# def get_parent_nodes_and_links(current_concept):
# parents = current_concept.parentconcepts
# for parent in parents:
# nodes.append({'concept_id': parent.id, 'name': parent.get_preflabel(lang=lang).value, 'type': 'Root' if len(parent.parentconcepts) == 0 else 'Ancestor'})
# links.append({'target': current_concept.id, 'source': parent.id, 'relationship': 'broader' })
# self.traverse(get_parent_nodes_and_links, direction='up')
for child in self.subconcepts:
nodes.append(
{"concept_id": child.id, "name": child.get_preflabel(lang=lang).value, "type": "Descendant", }
)
links.append({"source": self.id, "target": child.id, "relationship": "narrower"})
for related in self.relatedconcepts:
nodes.append(
{"concept_id": related.id, "name": related.get_preflabel(lang=lang).value, "type": "Related", }
)
links.append({"source": self.id, "target": related.id, "relationship": "related"})
# get unique node list and assign unique integer ids for each node (required by d3)
nodes = list({node["concept_id"]: node for node in nodes}.values())
for i in range(len(nodes)):
nodes[i]["id"] = i
for link in links:
link["source"] = i if link["source"] == nodes[i]["concept_id"] else link["source"]
link["target"] = i if link["target"] == nodes[i]["concept_id"] else link["target"]
return {"nodes": nodes, "links": links}
def get_context(self):
"""
get the Top Concept that the Concept particpates in
"""
if self.nodetype == "Concept" or self.nodetype == "Collection":
concept = Concept().get(id=self.id, include_parentconcepts=True, include=None)
def get_scheme_id(concept):
for parentconcept in concept.parentconcepts:
if parentconcept.relationshiptype == "hasTopConcept":
return concept
if len(concept.parentconcepts) > 0:
return concept.traverse(get_scheme_id, direction="up")
else:
return self
else: # like ConceptScheme or EntityType
return self
def get_scheme(self):
"""
get the ConceptScheme that the Concept particpates in
"""
topConcept = self.get_context()
if len(topConcept.parentconcepts) == 1:
if topConcept.parentconcepts[0].nodetype == "ConceptScheme":
return topConcept.parentconcepts[0]
return None
def check_if_concept_in_use(self):
"""Checks if a concept or any of its subconcepts is in use by a resource instance"""
in_use = False
cursor = connection.cursor()
for value in self.values:
sql = (
"""
SELECT count(*) from tiles t, jsonb_each_text(t.tiledata) as json_data
WHERE json_data.value = '%s'
"""
% value.id
)
cursor.execute(sql)
rows = cursor.fetchall()
if rows[0][0] > 0:
in_use = True
break
if in_use is not True:
for subconcept in self.subconcepts:
in_use = subconcept.check_if_concept_in_use()
if in_use == True:
return in_use
return in_use
def get_e55_domain(self, conceptid):
"""
For a given entitytypeid creates a dictionary representing that entitytypeid's concept graph (member pathway) formatted to support
select2 dropdowns
"""
cursor = connection.cursor()
sql = """
WITH RECURSIVE children AS (
SELECT d.conceptidfrom, d.conceptidto, c2.value, c2.valueid as valueid, c.value as valueto, c.valueid as valueidto, c.valuetype as vtype, 1 AS depth, array[d.conceptidto] AS conceptpath, array[c.valueid] AS idpath ---|NonRecursive Part
FROM relations d
JOIN values c ON(c.conceptid = d.conceptidto)
JOIN values c2 ON(c2.conceptid = d.conceptidfrom)
WHERE d.conceptidfrom = '{0}'
and c2.valuetype = 'prefLabel'
and c.valuetype in ('prefLabel', 'sortorder', 'collector')
and (d.relationtype = 'member' or d.relationtype = 'hasTopConcept')
UNION
SELECT d.conceptidfrom, d.conceptidto, v2.value, v2.valueid as valueid, v.value as valueto, v.valueid as valueidto, v.valuetype as vtype, depth+1, (conceptpath || d.conceptidto), (idpath || v.valueid) ---|RecursivePart
FROM relations d
JOIN children b ON(b.conceptidto = d.conceptidfrom)
JOIN values v ON(v.conceptid = d.conceptidto)
JOIN values v2 ON(v2.conceptid = d.conceptidfrom)
WHERE v2.valuetype = 'prefLabel'
and v.valuetype in ('prefLabel','sortorder', 'collector')
and (d.relationtype = 'member' or d.relationtype = 'hasTopConcept')
) SELECT conceptidfrom::text, conceptidto::text, value, valueid::text, valueto, valueidto::text, depth, idpath::text, conceptpath::text, vtype FROM children ORDER BY depth, conceptpath;
""".format(
conceptid
)
column_names = [
"conceptidfrom",
"conceptidto",
"value",
"valueid",
"valueto",
"valueidto",
"depth",
"idpath",
"conceptpath",
"vtype",
]
cursor.execute(sql)
rows = cursor.fetchall()
class Val(object):
def __init__(self, conceptid):
self.text = ""
self.conceptid = conceptid
self.id = ""
self.sortorder = ""
self.collector = ""
self.children = []
result = Val(conceptid)
def _findNarrower(val, path, rec):
for conceptid in path:
childids = [child.conceptid for child in val.children]
if conceptid not in childids:
new_val = Val(rec["conceptidto"])
if rec["vtype"] == "sortorder":
new_val.sortorder = rec["valueto"]
elif rec["vtype"] == "prefLabel":
new_val.text = rec["valueto"]
new_val.id = rec["valueidto"]
elif rec["vtype"] == "collector":
new_val.collector = "collector"
val.children.append(new_val)
else:
for child in val.children:
if conceptid == child.conceptid:
if conceptid == path[-1]:
if rec["vtype"] == "sortorder":
child.sortorder = rec["valueto"]
elif rec["vtype"] == "prefLabel":
child.text = rec["valueto"]
child.id = rec["valueidto"]
elif rec["vtype"] == "collector":
child.collector = "collector"
path.pop(0)
_findNarrower(child, path, rec)
val.children.sort(key=lambda x: (x.sortorder, x.text))
for row in rows:
rec = dict(list(zip(column_names, row)))
path = rec["conceptpath"][1:-1].split(",")
_findNarrower(result, path, rec)
return JSONSerializer().serializeToPython(result)["children"]
def make_collection(self):
if len(self.values) == 0:
raise Exception(_("Need to include values when creating a collection"))
values = JSONSerializer().serializeToPython(self.values)
for value in values:
value["id"] = ""
collection_concept = Concept({"nodetype": "Collection", "values": values})
def create_collection(conceptfrom):
for relation in models.Relation.objects.filter(
Q(conceptfrom_id=conceptfrom.id),
Q(relationtype__category="Semantic Relations") | Q(relationtype__category="Properties"),
~Q(relationtype="related"),
):
conceptto = Concept(relation.conceptto)
if conceptfrom == self:
collection_concept.add_relation(conceptto, "member")
else:
conceptfrom.add_relation(conceptto, "member")
create_collection(conceptto)
with transaction.atomic():
collection_concept.save()
create_collection(self)
return collection_concept
class ConceptValue(object):
def __init__(self, *args, **kwargs):
self.id = ""
self.conceptid = ""
self.type = ""
self.category = ""
self.value = ""
self.language = ""
if len(args) != 0:
if isinstance(args[0], str):
try:
uuid.UUID(args[0])
self.get(args[0])
except (ValueError):
self.load(JSONDeserializer().deserialize(args[0]))
elif isinstance(args[0], object):
self.load(args[0])
def __repr__(self):
return ('%s: %s = "%s" in lang %s') % (self.__class__, self.type, self.value, self.language)
def get(self, id=""):
self.load(models.Value.objects.get(pk=id))
return self
def save(self):
if self.value.strip() != "":
self.id = self.id if (self.id != "" and self.id is not None) else str(uuid.uuid4())
value = models.Value()
value.pk = self.id
value.value = self.value
value.concept_id = self.conceptid # models.Concept.objects.get(pk=self.conceptid)
value.valuetype_id = self.type # models.DValueType.objects.get(pk=self.type)
if self.language != "":
# need to normalize language ids to the form xx-XX
lang_parts = self.language.lower().replace("_", "-").split("-")
try:
lang_parts[1] = lang_parts[1].upper()
except:
pass
self.language = "-".join(lang_parts)
value.language_id = self.language # models.DLanguage.objects.get(pk=self.language)
else:
value.language_id = settings.LANGUAGE_CODE
value.save()
self.category = value.valuetype.category
def delete(self):
if self.id != "":
newvalue = models.Value.objects.get(pk=self.id)
if newvalue.valuetype.valuetype == "image":
newvalue = models.FileValue.objects.get(pk=self.id)
newvalue.delete()
self = ConceptValue()
return self
def load(self, value):
if isinstance(value, models.Value):
self.id = str(value.pk)
self.conceptid = str(value.concept_id)
self.type = value.valuetype_id
self.category = value.valuetype.category
self.value = value.value
self.language = value.language_id
if isinstance(value, dict):
self.id = str(value["id"]) if "id" in value else ""
self.conceptid = str(value["conceptid"]) if "conceptid" in value else ""
self.type = value["type"] if "type" in value else ""
self.category = value["category"] if "category" in value else ""
self.value = value["value"] if "value" in value else ""
self.language = value["language"] if "language" in value else ""
def index(self, scheme=None):
if self.category == "label":
data = JSONSerializer().serializeToPython(self)
if scheme is None:
scheme = self.get_scheme_id()
if scheme is None:
raise Exception(_("Index of label failed. Index type (scheme id) could not be derived from the label."))
data["top_concept"] = scheme.id
se.index_data(index=CONCEPTS_INDEX, body=data, idfield="id")
def delete_index(self):
query = Query(se, start=0, limit=10000)
term = Term(field="id", term=self.id)
query.add_query(term)
query.delete(index=CONCEPTS_INDEX)
def get_scheme_id(self):
result = se.search(index=CONCEPTS_INDEX, id=self.id)
if result["found"]:
return Concept(result["top_concept"])
else:
return None
def get_preflabel_from_conceptid(conceptid, lang):
ret = None
default = {
"category": "",
"conceptid": "",
"language": "",
"value": "",
"type": "",
"id": "",
}
query = Query(se)
bool_query = Bool()
bool_query.must(Match(field="type", query="prefLabel", type="phrase"))
bool_query.filter(Terms(field="conceptid", terms=[conceptid]))
query.add_query(bool_query)
preflabels = query.search(index=CONCEPTS_INDEX)["hits"]["hits"]
for preflabel in preflabels:
default = preflabel["_source"]
if preflabel["_source"]["language"] is not None and lang is not None:
# get the label in the preferred language, otherwise get the label in the default language
if preflabel["_source"]["language"] == lang:
return preflabel["_source"]
if preflabel["_source"]["language"].split("-")[0] == lang.split("-")[0]:
ret = preflabel["_source"]
if preflabel["_source"]["language"] == settings.LANGUAGE_CODE and ret is None:
ret = preflabel["_source"]
return default if ret is None else ret
def get_valueids_from_concept_label(label, conceptid=None, lang=None):
def exact_val_match(val, conceptid=None):
# exact term match, don't care about relevance ordering.
# due to language formating issues, and with (hopefully) small result sets
# easier to have filter logic in python than to craft it in dsl
if conceptid is None:
return {"query": {"bool": {"filter": {"match_phrase": {"value": val}}}}}
else:
return {
"query": {
"bool": {"filter": [{"match_phrase": {"value": val}}, {"term": {"conceptid": conceptid}}, ]}
}
}
concept_label_results = se.search(index=CONCEPTS_INDEX, body=exact_val_match(label, conceptid))
if concept_label_results is None:
print("Found no matches for label:'{0}' and concept_id: '{1}'".format(label, conceptid))
return
return [
res["_source"]
for res in concept_label_results["hits"]["hits"]
if lang is None or res["_source"]["language"].lower() == lang.lower()
]
def get_preflabel_from_valueid(valueid, lang):
concept_label = se.search(index=CONCEPTS_INDEX, id=valueid)
if concept_label["found"]:
return get_preflabel_from_conceptid(concept_label["_source"]["conceptid"], lang)
| """
ARCHES - a program developed to inventory and manage immovable cultural heritage.
Copyright (C) 2013 <NAME> and World Monuments Fund
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import re
import uuid
import copy
from operator import itemgetter
from operator import methodcaller
from django.db import transaction, connection
from django.db.models import Q
from arches.app.models import models
from arches.app.models.system_settings import settings
from arches.app.search.search_engine_factory import SearchEngineInstance as se
from arches.app.search.elasticsearch_dsl_builder import Term, Query, Bool, Match, Terms
from arches.app.search.mappings import CONCEPTS_INDEX
from arches.app.utils.betterJSONSerializer import JSONSerializer, JSONDeserializer
from django.utils.translation import ugettext as _
from django.utils.translation import get_language
from django.db import IntegrityError
import logging
logger = logging.getLogger(__name__)
CORE_CONCEPTS = (
"00000000-0000-0000-0000-000000000001",
"00000000-0000-0000-0000-000000000004",
"00000000-0000-0000-0000-000000000005",
"00000000-0000-0000-0000-000000000006",
)
class Concept(object):
def __init__(self, *args, **kwargs):
self.id = ""
self.nodetype = ""
self.legacyoid = ""
self.relationshiptype = ""
self.values = []
self.subconcepts = []
self.parentconcepts = []
self.relatedconcepts = []
self.hassubconcepts = False
if len(args) != 0:
if isinstance(args[0], str):
try:
uuid.UUID(args[0])
self.get(args[0])
except (ValueError):
self.load(JSONDeserializer().deserialize(args[0]))
elif isinstance(args[0], dict):
self.load(args[0])
elif isinstance(args[0], object):
self.load(args[0])
def __unicode__(self):
return ("%s - %s") % (self.get_preflabel().value, self.id)
def __hash__(self):
return hash(self.id)
def __eq__(self, x):
return hash(self) == hash(x)
def __ne__(self, x):
return hash(self) != hash(x)
def load(self, value):
if isinstance(value, dict):
self.id = str(value["id"]) if "id" in value else ""
self.nodetype = value["nodetype"] if "nodetype" in value else ""
self.legacyoid = value["legacyoid"] if "legacyoid" in value else ""
self.relationshiptype = value["relationshiptype"] if "relationshiptype" in value else ""
if "values" in value:
for val in value["values"]:
self.addvalue(val)
if "subconcepts" in value:
for subconcept in value["subconcepts"]:
self.addsubconcept(subconcept)
if "parentconcepts" in value:
for parentconcept in value["parentconcepts"]:
self.addparent(parentconcept)
if "relatedconcepts" in value:
for relatedconcept in value["relatedconcepts"]:
self.addrelatedconcept(relatedconcept)
if isinstance(value, models.Concept):
self.id = str(value.pk)
self.nodetype = value.nodetype_id
self.legacyoid = value.legacyoid
def get(
self,
id="",
legacyoid="",
include_subconcepts=False,
include_parentconcepts=False,
include_relatedconcepts=False,
exclude=[],
include=[],
depth_limit=None,
up_depth_limit=None,
lang=settings.LANGUAGE_CODE,
semantic=True,
pathway_filter=None,
**kwargs,
):
if id != "":
self.load(models.Concept.objects.get(pk=id))
elif legacyoid != "":
self.load(models.Concept.objects.get(legacyoid=legacyoid))
_cache = kwargs.pop("_cache", {})
_cache[self.id] = self.__class__(
{"id": self.id, "nodetype": self.nodetype, "legacyoid": self.legacyoid, "relationshiptype": self.relationshiptype}
)
if semantic == True:
pathway_filter = (
pathway_filter
if pathway_filter
else Q(relationtype__category="Semantic Relations") | Q(relationtype__category="Properties")
)
else:
pathway_filter = pathway_filter if pathway_filter else Q(relationtype="member") | Q(relationtype="hasCollection")
if self.id != "":
nodetype = kwargs.pop("nodetype", self.nodetype)
uplevel = kwargs.pop("uplevel", 0)
downlevel = kwargs.pop("downlevel", 0)
depth_limit = depth_limit if depth_limit is None else int(depth_limit)
up_depth_limit = up_depth_limit if up_depth_limit is None else int(up_depth_limit)
if include is not None:
if len(include) > 0 and len(exclude) > 0:
raise Exception(_("Only include values for include or exclude, but not both"))
include = (
include if len(include) != 0 else models.DValueType.objects.distinct("category").values_list("category", flat=True)
)
include = set(include).difference(exclude)
exclude = []
if len(include) > 0:
values = models.Value.objects.filter(concept=self.id)
for value in values:
if value.valuetype.category in include:
self.values.append(ConceptValue(value))
hassubconcepts = models.Relation.objects.filter(Q(conceptfrom=self.id), pathway_filter, ~Q(relationtype="related"))[0:1]
if len(hassubconcepts) > 0:
self.hassubconcepts = True
if include_subconcepts:
conceptrealations = models.Relation.objects.filter(Q(conceptfrom=self.id), pathway_filter, ~Q(relationtype="related"))
if depth_limit is None or downlevel < depth_limit:
if depth_limit is not None:
downlevel = downlevel + 1
for relation in conceptrealations:
subconcept = (
_cache[str(relation.conceptto_id)]
if str(relation.conceptto_id) in _cache
else self.__class__().get(
id=relation.conceptto_id,
include_subconcepts=include_subconcepts,
include_parentconcepts=include_parentconcepts,
include_relatedconcepts=include_relatedconcepts,
exclude=exclude,
include=include,
depth_limit=depth_limit,
up_depth_limit=up_depth_limit,
downlevel=downlevel,
uplevel=uplevel,
nodetype=nodetype,
semantic=semantic,
pathway_filter=pathway_filter,
_cache=_cache.copy(),
lang=lang,
)
)
subconcept.relationshiptype = relation.relationtype_id
self.subconcepts.append(subconcept)
self.subconcepts = sorted(
self.subconcepts, key=lambda concept: self.natural_keys(concept.get_sortkey(lang)), reverse=False
)
# self.subconcepts = sorted(self.subconcepts, key=methodcaller(
# 'get_sortkey', lang=lang), reverse=False)
if include_parentconcepts:
conceptrealations = models.Relation.objects.filter(Q(conceptto=self.id), pathway_filter, ~Q(relationtype="related"))
if up_depth_limit is None or uplevel < up_depth_limit:
if up_depth_limit is not None:
uplevel = uplevel + 1
for relation in conceptrealations:
parentconcept = (
_cache[str(relation.conceptfrom_id)]
if str(relation.conceptfrom_id) in _cache
else self.__class__().get(
id=relation.conceptfrom_id,
include_subconcepts=False,
include_parentconcepts=include_parentconcepts,
include_relatedconcepts=include_relatedconcepts,
exclude=exclude,
include=include,
depth_limit=depth_limit,
up_depth_limit=up_depth_limit,
downlevel=downlevel,
uplevel=uplevel,
nodetype=nodetype,
semantic=semantic,
pathway_filter=pathway_filter,
_cache=_cache.copy(),
lang=lang,
)
)
parentconcept.relationshiptype = relation.relationtype_id
self.parentconcepts.append(parentconcept)
if include_relatedconcepts:
conceptrealations = models.Relation.objects.filter(
Q(relationtype="related") | Q(relationtype__category="Mapping Properties"),
Q(conceptto=self.id) | Q(conceptfrom=self.id),
)
relations = []
for relation in conceptrealations:
if str(relation.conceptto_id) != self.id and str(relation.relationid) not in relations:
relations.append(str(relation.relationid))
relatedconcept = self.__class__().get(relation.conceptto_id, include=["label"], lang=lang)
relatedconcept.relationshiptype = relation.relationtype_id
self.relatedconcepts.append(relatedconcept)
if str(relation.conceptfrom_id) != self.id and str(relation.relationid) not in relations:
relations.append(str(relation.relationid))
relatedconcept = self.__class__().get(relation.conceptfrom_id, include=["label"], lang=lang)
relatedconcept.relationshiptype = relation.relationtype_id
self.relatedconcepts.append(relatedconcept)
return self
def save(self):
self.id = self.id if (self.id != "" and self.id is not None) else str(uuid.uuid4())
concept, created = models.Concept.objects.get_or_create(
pk=self.id, defaults={"legacyoid": self.legacyoid if self.legacyoid != "" else self.id, "nodetype_id": self.nodetype}
)
for value in self.values:
if not isinstance(value, ConceptValue):
value = ConceptValue(value)
value.conceptid = self.id
value.save()
for parentconcept in self.parentconcepts:
parentconcept.save()
parentconcept.add_relation(self, parentconcept.relationshiptype)
for subconcept in self.subconcepts:
subconcept.save()
self.add_relation(subconcept, subconcept.relationshiptype)
# if we're moving a Concept Scheme below another Concept or Concept Scheme
if len(self.parentconcepts) > 0 and concept.nodetype_id == "ConceptScheme":
concept.nodetype_id = "Concept"
concept.save()
self.load(concept)
for relation in models.Relation.objects.filter(conceptfrom=concept, relationtype_id="hasTopConcept"):
relation.relationtype_id = "narrower"
relation.save()
for relatedconcept in self.relatedconcepts:
self.add_relation(relatedconcept, relatedconcept.relationshiptype)
if relatedconcept.relationshiptype == "member":
child_concepts = relatedconcept.get(include_subconcepts=True)
def applyRelationship(concept):
for subconcept in concept.subconcepts:
concept.add_relation(subconcept, relatedconcept.relationshiptype)
child_concepts.traverse(applyRelationship)
return concept
def delete(self, delete_self=False):
"""
Deletes any subconcepts associated with this concept and additionally this concept if 'delete_self' is True
If any parentconcepts or relatedconcepts are included then it will only delete the relationship to those concepts but not the concepts themselves
If any values are passed, then those values as well as the relationship to those values will be deleted
Note, django will automatically take care of deleting any db models that have a foreign key relationship to the model being deleted
(eg: deleting a concept model will also delete all values and relationships), but because we need to manage deleting
parent concepts and related concepts and values we have to do that here too
"""
for subconcept in self.subconcepts:
concepts_to_delete = Concept.gather_concepts_to_delete(subconcept)
for key, concept in concepts_to_delete.items():
models.Concept.objects.get(pk=key).delete()
for parentconcept in self.parentconcepts:
relations_filter = (
(Q(relationtype__category="Semantic Relations") | Q(relationtype="hasTopConcept"))
& Q(conceptfrom=parentconcept.id)
& Q(conceptto=self.id)
)
conceptrelations = models.Relation.objects.filter(relations_filter)
for relation in conceptrelations:
relation.delete()
if models.Relation.objects.filter(relations_filter).count() == 0:
# we've removed all parent concepts so now this concept needs to be promoted to a Concept Scheme
concept = models.Concept.objects.get(pk=self.id)
concept.nodetype_id = "ConceptScheme"
concept.save()
self.load(concept)
for relation in models.Relation.objects.filter(conceptfrom=concept, relationtype_id="narrower"):
relation.relationtype_id = "hasTopConcept"
relation.save()
deletedrelatedconcepts = []
for relatedconcept in self.relatedconcepts:
conceptrelations = models.Relation.objects.filter(
Q(relationtype="related") | Q(relationtype="member") | Q(relationtype__category="Mapping Properties"),
conceptto=relatedconcept.id,
conceptfrom=self.id,
)
for relation in conceptrelations:
relation.delete()
deletedrelatedconcepts.append(relatedconcept)
conceptrelations = models.Relation.objects.filter(
Q(relationtype="related") | Q(relationtype="member") | Q(relationtype__category="Mapping Properties"),
conceptfrom=relatedconcept.id,
conceptto=self.id,
)
for relation in conceptrelations:
relation.delete()
deletedrelatedconcepts.append(relatedconcept)
for deletedrelatedconcept in deletedrelatedconcepts:
if deletedrelatedconcept in self.relatedconcepts:
self.relatedconcepts.remove(deletedrelatedconcept)
for value in self.values:
if not isinstance(value, ConceptValue):
value = ConceptValue(value)
value.delete()
if delete_self:
concepts_to_delete = Concept.gather_concepts_to_delete(self)
for key, concept in concepts_to_delete.items():
# delete only member relationships if the nodetype == Collection
if concept.nodetype == "Collection":
concept = Concept().get(
id=concept.id,
include_subconcepts=True,
include_parentconcepts=True,
include=["label"],
up_depth_limit=1,
semantic=False,
)
def find_concepts(concept):
if len(concept.parentconcepts) <= 1:
for subconcept in concept.subconcepts:
conceptrelation = models.Relation.objects.get(
conceptfrom=concept.id, conceptto=subconcept.id, relationtype="member"
)
conceptrelation.delete()
find_concepts(subconcept)
find_concepts(concept)
# if the concept is a collection, loop through the nodes and delete their rdmCollection values
for node in models.Node.objects.filter(config__rdmCollection=concept.id):
node.config["rdmCollection"] = None
node.save()
models.Concept.objects.get(pk=key).delete()
return
def add_relation(self, concepttorelate, relationtype):
"""
Relates this concept to 'concepttorelate' via the relationtype
"""
relation, created = models.Relation.objects.get_or_create(
conceptfrom_id=self.id, conceptto_id=concepttorelate.id, relationtype_id=relationtype
)
return relation
@staticmethod
def gather_concepts_to_delete(concept, lang=settings.LANGUAGE_CODE):
"""
Gets a dictionary of all the concepts ids to delete
The values of the dictionary keys differ somewhat depending on the node type being deleted
If the nodetype == 'Concept' then return ConceptValue objects keyed to the concept id
If the nodetype == 'ConceptScheme' then return a ConceptValue object with the value set to any ONE prefLabel keyed to the concept id
We do this because it takes so long to gather the ids of the concepts when deleting a Scheme or Group
"""
concepts_to_delete = {}
# Here we have to worry about making sure we don't delete nodes that have more than 1 parent
if concept.nodetype == "Concept":
concept = Concept().get(
id=concept.id, include_subconcepts=True, include_parentconcepts=True, include=["label"], up_depth_limit=1
)
def find_concepts(concept):
if len(concept.parentconcepts) <= 1:
concepts_to_delete[concept.id] = concept
for subconcept in concept.subconcepts:
find_concepts(subconcept)
find_concepts(concept)
return concepts_to_delete
# here we can just delete everything and so use a recursive CTE to get the concept ids much more quickly
if concept.nodetype == "ConceptScheme":
concepts_to_delete[concept.id] = concept
rows = Concept().get_child_concepts(concept.id)
for row in rows:
if row[0] not in concepts_to_delete:
concepts_to_delete[row[0]] = Concept({"id": row[0]})
concepts_to_delete[row[0]].addvalue({"id": row[2], "conceptid": row[0], "value": row[1]})
if concept.nodetype == "Collection":
concepts_to_delete[concept.id] = concept
rows = Concept().get_child_collections(concept.id)
for row in rows:
if row[0] not in concepts_to_delete:
concepts_to_delete[row[0]] = Concept({"id": row[0]})
concepts_to_delete[row[0]].addvalue({"id": row[2], "conceptid": row[0], "value": row[1]})
return concepts_to_delete
def get_child_collections_hierarchically(self, conceptid, child_valuetypes=None, offset=0, limit=50, query=None):
child_valuetypes = child_valuetypes if child_valuetypes else ["prefLabel"]
columns = "valueidto::text, conceptidto::text, valueto, valuetypeto, depth, count(*) OVER() AS full_count, collector"
return self.get_child_edges(
conceptid, ["member"], child_valuetypes, offset=offset, limit=limit, order_hierarchically=True, query=query, columns=columns
)
def get_child_collections(self, conceptid, child_valuetypes=None, parent_valuetype="prefLabel", columns=None, depth_limit=""):
child_valuetypes = child_valuetypes if child_valuetypes else ["prefLabel"]
columns = columns if columns else "conceptidto::text, valueto, valueidto::text"
return self.get_child_edges(conceptid, ["member"], child_valuetypes, parent_valuetype, columns, depth_limit)
def get_child_concepts(self, conceptid, child_valuetypes=None, parent_valuetype="prefLabel", columns=None, depth_limit=""):
columns = columns if columns else "conceptidto::text, valueto, valueidto::text"
return self.get_child_edges(conceptid, ["narrower", "hasTopConcept"], child_valuetypes, parent_valuetype, columns, depth_limit)
def get_child_concepts_for_indexing(self, conceptid, child_valuetypes=None, parent_valuetype="prefLabel", depth_limit=""):
columns = "valueidto::text, conceptidto::text, valuetypeto, categoryto, valueto, languageto"
data = self.get_child_edges(conceptid, ["narrower", "hasTopConcept"], child_valuetypes, parent_valuetype, columns, depth_limit)
return [dict(list(zip(["id", "conceptid", "type", "category", "value", "language"], d)), top_concept="") for d in data]
def get_child_edges(
self,
conceptid,
relationtypes,
child_valuetypes=None,
parent_valuetype="prefLabel",
columns=None,
depth_limit=None,
offset=None,
limit=20,
order_hierarchically=False,
query=None,
languageid=None,
):
"""
Recursively builds a list of concept relations for a given concept and all it's subconcepts based on its relationship type and valuetypes.
"""
languageid = get_language() if languageid is None else languageid
relationtypes = " or ".join(["r.relationtype = '%s'" % (relationtype) for relationtype in relationtypes])
depth_limit = "and depth < %s" % depth_limit if depth_limit else ""
child_valuetypes = ("','").join(
child_valuetypes if child_valuetypes else models.DValueType.objects.filter(category="label").values_list("valuetype", flat=True)
)
limit_clause = " limit %s offset %s" % (limit, offset) if offset is not None else ""
if order_hierarchically:
sql = """
WITH RECURSIVE
ordered_relationships AS (
(
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, (
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) as valuesto,
(
SELECT value::int
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('sortorder')
limit 1
) as sortorder,
(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('collector')
limit 1
) as collector
FROM relations r
WHERE r.conceptidfrom = '{conceptid}'
and ({relationtypes})
ORDER BY sortorder, valuesto
)
UNION
(
SELECT r.conceptidfrom, r.conceptidto, r.relationtype,(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) as valuesto,
(
SELECT value::int
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('sortorder')
limit 1
) as sortorder,
(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('collector')
limit 1
) as collector
FROM relations r
JOIN ordered_relationships b ON(b.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
ORDER BY sortorder, valuesto
)
),
children AS (
SELECT r.conceptidfrom, r.conceptidto,
to_char(row_number() OVER (), 'fm000000') as row,
r.collector,
1 AS depth ---|NonRecursive Part
FROM ordered_relationships r
WHERE r.conceptidfrom = '{conceptid}'
and ({relationtypes})
UNION
SELECT r.conceptidfrom, r.conceptidto,
row || '-' || to_char(row_number() OVER (), 'fm000000'),
r.collector,
depth+1 ---|RecursivePart
FROM ordered_relationships r
JOIN children b ON(b.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
{depth_limit}
)
{subquery}
SELECT
(
select row_to_json(d)
FROM (
SELECT *
FROM values
WHERE conceptid={recursive_table}.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) d
) as valueto,
depth, collector, count(*) OVER() AS full_count
FROM {recursive_table} order by row {limit_clause};
"""
subquery = (
""", results as (
SELECT c.conceptidfrom, c.conceptidto, c.row, c.depth, c.collector
FROM children c
JOIN values ON(values.conceptid = c.conceptidto)
WHERE LOWER(values.value) like '%%%s%%'
AND values.valuetype in ('prefLabel')
UNION
SELECT c.conceptidfrom, c.conceptidto, c.row, c.depth, c.collector
FROM children c
JOIN results r on (r.conceptidfrom=c.conceptidto)
)"""
% query.lower()
if query is not None
else ""
)
recursive_table = "results" if query else "children"
sql = sql.format(
conceptid=conceptid,
relationtypes=relationtypes,
child_valuetypes=child_valuetypes,
parent_valuetype=parent_valuetype,
depth_limit=depth_limit,
limit_clause=limit_clause,
subquery=subquery,
recursive_table=recursive_table,
languageid=languageid,
short_languageid=languageid.split("-")[0],
default_languageid=settings.LANGUAGE_CODE,
)
else:
sql = """
WITH RECURSIVE
children AS (
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, 1 AS depth
FROM relations r
WHERE r.conceptidfrom = '{conceptid}'
AND ({relationtypes})
UNION
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, depth+1
FROM relations r
JOIN children c ON(c.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
{depth_limit}
),
results AS (
SELECT
valuefrom.value as valuefrom, valueto.value as valueto,
valuefrom.valueid as valueidfrom, valueto.valueid as valueidto,
valuefrom.valuetype as valuetypefrom, valueto.valuetype as valuetypeto,
valuefrom.languageid as languagefrom, valueto.languageid as languageto,
dtypesfrom.category as categoryfrom, dtypesto.category as categoryto,
c.conceptidfrom, c.conceptidto
FROM values valueto
JOIN d_value_types dtypesto ON(dtypesto.valuetype = valueto.valuetype)
JOIN children c ON(c.conceptidto = valueto.conceptid)
JOIN values valuefrom ON(c.conceptidfrom = valuefrom.conceptid)
JOIN d_value_types dtypesfrom ON(dtypesfrom.valuetype = valuefrom.valuetype)
WHERE valueto.valuetype in ('{child_valuetypes}')
AND valuefrom.valuetype in ('{child_valuetypes}')
)
SELECT distinct {columns}
FROM results {limit_clause}
"""
if not columns:
columns = """
conceptidfrom::text, conceptidto::text,
valuefrom, valueto,
valueidfrom::text, valueidto::text,
valuetypefrom, valuetypeto,
languagefrom, languageto,
categoryfrom, categoryto
"""
sql = sql.format(
conceptid=conceptid,
relationtypes=relationtypes,
child_valuetypes=child_valuetypes,
columns=columns,
depth_limit=depth_limit,
limit_clause=limit_clause,
)
cursor = connection.cursor()
cursor.execute(sql)
rows = cursor.fetchall()
return rows
def traverse(self, func, direction="down", scope=None, **kwargs):
"""
Traverses a concept graph from self to leaf (direction='down') or root (direction='up') calling
the given function on each node, passes an optional scope to each function
Return a value from the function to prematurely end the traversal
"""
_cache = kwargs.pop("_cache", [])
if self.id not in _cache:
_cache.append(self.id)
if scope is None:
ret = func(self, **kwargs)
else:
ret = func(self, scope, **kwargs)
# break out of the traversal if the function returns a value
if ret is not None:
return ret
if direction == "down":
for subconcept in self.subconcepts:
ret = subconcept.traverse(func, direction, scope, _cache=_cache, **kwargs)
if ret is not None:
return ret
else:
for parentconcept in self.parentconcepts:
ret = parentconcept.traverse(func, direction, scope, _cache=_cache, **kwargs)
if ret is not None:
return ret
def get_sortkey(self, lang=settings.LANGUAGE_CODE):
for value in self.values:
if value.type == "sortorder":
try:
return float(value.value)
except:
return None
return self.get_preflabel(lang=lang).value
def natural_keys(self, text):
"""
alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com/blog/200712/human_sorting.html
(See Toothy's implementation in the comments)
float regex comes from https://stackoverflow.com/a/12643073/190597
"""
def atof(text):
try:
retval = float(text)
except ValueError:
retval = text
return retval
return [atof(c) for c in re.split(r"[+-]?([0-9]+(?:[.][0-9]*)?|[.][0-9]+)", str(text))]
def get_preflabel(self, lang=settings.LANGUAGE_CODE):
score = 0
ranked_labels = []
if self.values == []:
concept = Concept().get(id=self.id, include_subconcepts=False, include_parentconcepts=False, include=["label"])
else:
concept = self
for value in concept.values:
ranked_label = {"weight": 1, "value": value}
if value.type == "prefLabel":
ranked_label["weight"] = ranked_label["weight"] * 10
elif value.type == "altLabel":
ranked_label["weight"] = ranked_label["weight"] * 4
if value.language == lang:
ranked_label["weight"] = ranked_label["weight"] * 10
elif value.language.split("-")[0] == lang.split("-")[0]:
ranked_label["weight"] = ranked_label["weight"] * 5
ranked_labels.append(ranked_label)
ranked_labels = sorted(ranked_labels, key=lambda label: label["weight"], reverse=True)
if len(ranked_labels) == 0:
ranked_labels.append({"weight": 1, "value": ConceptValue()})
return ranked_labels[0]["value"]
def flatten(self, ret=None):
"""
Flattens the graph into a unordered list of concepts
"""
if ret is None:
ret = []
ret.append(self)
for subconcept in self.subconcepts:
subconcept.flatten(ret)
return ret
def addparent(self, value):
if isinstance(value, dict):
self.parentconcepts.append(Concept(value))
elif isinstance(value, Concept):
self.parentconcepts.append(value)
else:
raise Exception("Invalid parent concept definition: %s" % (value))
def addsubconcept(self, value):
if isinstance(value, dict):
self.subconcepts.append(Concept(value))
elif isinstance(value, Concept):
self.subconcepts.append(value)
else:
raise Exception(_("Invalid subconcept definition: %s") % (value))
def addrelatedconcept(self, value):
if isinstance(value, dict):
self.relatedconcepts.append(Concept(value))
elif isinstance(value, Concept):
self.relatedconcepts.append(value)
else:
raise Exception(_("Invalid related concept definition: %s") % (value))
def addvalue(self, value):
if isinstance(value, dict):
value["conceptid"] = self.id
self.values.append(ConceptValue(value))
elif isinstance(value, ConceptValue):
self.values.append(value)
elif isinstance(value, models.Value):
self.values.append(ConceptValue(value))
else:
raise Exception(_("Invalid value definition: %s") % (value))
def index(self, scheme=None):
if scheme is None:
scheme = self.get_context()
for value in self.values:
value.index(scheme=scheme)
if self.nodetype == "ConceptScheme":
scheme = None
for subconcept in self.subconcepts:
subconcept.index(scheme=scheme)
def bulk_index(self):
concept_docs = []
if self.nodetype == "ConceptScheme":
concept = Concept().get(id=self.id, values=["label"])
concept.index()
for topConcept in self.get_child_concepts_for_indexing(self.id, depth_limit=1):
concept = Concept().get(id=topConcept["conceptid"])
scheme = concept.get_context()
topConcept["top_concept"] = scheme.id
concept_docs.append(se.create_bulk_item(index=CONCEPTS_INDEX, id=topConcept["id"], data=topConcept))
for childConcept in concept.get_child_concepts_for_indexing(topConcept["conceptid"]):
childConcept["top_concept"] = scheme.id
concept_docs.append(se.create_bulk_item(index=CONCEPTS_INDEX, id=childConcept["id"], data=childConcept))
if self.nodetype == "Concept":
concept = Concept().get(id=self.id, values=["label"])
scheme = concept.get_context()
concept.index(scheme)
for childConcept in concept.get_child_concepts_for_indexing(self.id):
childConcept["top_concept"] = scheme.id
concept_docs.append(se.create_bulk_item(index=CONCEPTS_INDEX, id=childConcept["id"], data=childConcept))
se.bulk_index(concept_docs)
def delete_index(self, delete_self=False):
def delete_concept_values_index(concepts_to_delete):
for concept in concepts_to_delete.values():
query = Query(se, start=0, limit=10000)
term = Term(field="conceptid", term=concept.id)
query.add_query(term)
query.delete(index=CONCEPTS_INDEX)
if delete_self:
concepts_to_delete = Concept.gather_concepts_to_delete(self)
delete_concept_values_index(concepts_to_delete)
else:
for subconcept in self.subconcepts:
concepts_to_delete = Concept.gather_concepts_to_delete(subconcept)
delete_concept_values_index(concepts_to_delete)
def concept_tree(
self, top_concept="00000000-0000-0000-0000-000000000001", lang=settings.LANGUAGE_CODE, mode="semantic",
):
class concept(object):
def __init__(self, *args, **kwargs):
self.label = ""
self.labelid = ""
self.id = ""
self.sortorder = None
self.load_on_demand = False
self.children = []
def _findNarrowerConcept(conceptid, depth_limit=None, level=0):
labels = models.Value.objects.filter(concept=conceptid)
ret = concept()
temp = Concept()
for label in labels:
temp.addvalue(label)
if label.valuetype_id == "sortorder":
try:
ret.sortorder = float(label.value)
except:
ret.sortorder = None
label = temp.get_preflabel(lang=lang)
ret.label = label.value
ret.id = label.conceptid
ret.labelid = label.id
if mode == "semantic":
conceptrealations = models.Relation.objects.filter(
Q(conceptfrom=conceptid), Q(relationtype__category="Semantic Relations") | Q(relationtype__category="Properties")
)
if mode == "collections":
conceptrealations = models.Relation.objects.filter(
Q(conceptfrom=conceptid), Q(relationtype="member") | Q(relationtype="hasCollection")
)
if depth_limit is not None and len(conceptrealations) > 0 and level >= depth_limit:
ret.load_on_demand = True
else:
if depth_limit is not None:
level = level + 1
for relation in conceptrealations:
ret.children.append(_findNarrowerConcept(relation.conceptto_id, depth_limit=depth_limit, level=level))
ret.children = sorted(
ret.children,
key=lambda concept: self.natural_keys(concept.sortorder if concept.sortorder else concept.label),
reverse=False,
)
return ret
def _findBroaderConcept(conceptid, child_concept, depth_limit=None, level=0):
conceptrealations = models.Relation.objects.filter(
Q(conceptto=conceptid), ~Q(relationtype="related"), ~Q(relationtype__category="Mapping Properties")
)
if len(conceptrealations) > 0 and conceptid != top_concept:
labels = models.Value.objects.filter(concept=conceptrealations[0].conceptfrom_id)
ret = concept()
temp = Concept()
for label in labels:
temp.addvalue(label)
label = temp.get_preflabel(lang=lang)
ret.label = label.value
ret.id = label.conceptid
ret.labelid = label.id
ret.children.append(child_concept)
return _findBroaderConcept(conceptrealations[0].conceptfrom_id, ret, depth_limit=depth_limit, level=level)
else:
return child_concept
graph = []
if self.id is None or self.id == "" or self.id == "None" or self.id == top_concept:
if mode == "semantic":
concepts = models.Concept.objects.filter(nodetype="ConceptScheme")
for conceptmodel in concepts:
graph.append(_findNarrowerConcept(conceptmodel.pk, depth_limit=1))
if mode == "collections":
concepts = models.Concept.objects.filter(nodetype="Collection")
for conceptmodel in concepts:
graph.append(_findNarrowerConcept(conceptmodel.pk, depth_limit=0))
graph = sorted(graph, key=lambda concept: concept.label)
# graph = _findNarrowerConcept(concepts[0].pk, depth_limit=1).children
else:
graph = _findNarrowerConcept(self.id, depth_limit=1).children
# concepts = _findNarrowerConcept(self.id, depth_limit=1)
# graph = [_findBroaderConcept(self.id, concepts, depth_limit=1)]
return graph
def get_paths(self, lang=settings.LANGUAGE_CODE):
def graph_to_paths(current_concept, path=[], path_list=[], _cache=[]):
if len(path) == 0:
current_path = []
else:
current_path = path[:]
current_path.insert(
0,
{
"label": current_concept.get_preflabel(lang=lang).value,
"relationshiptype": current_concept.relationshiptype,
"id": current_concept.id,
},
)
if len(current_concept.parentconcepts) == 0 or current_concept.id in _cache:
path_list.append(current_path[:])
else:
_cache.append(current_concept.id)
for parent in current_concept.parentconcepts:
ret = graph_to_paths(parent, current_path, path_list, _cache)
return path_list
# def graph_to_paths(current_concept, **kwargs):
# path = kwargs.get('path', [])
# path_list = kwargs.get('path_list', [])
# if len(path) == 0:
# current_path = []
# else:
# current_path = path[:]
# current_path.insert(0, {'label': current_concept.get_preflabel(lang=lang).value, 'relationshiptype': current_concept.relationshiptype, 'id': current_concept.id})
# if len(current_concept.parentconcepts) == 0:
# path_list.append(current_path[:])
# # else:
# # for parent in current_concept.parentconcepts:
# # ret = graph_to_paths(parent, current_path, path_list, _cache)
# #return path_list
# self.traverse(graph_to_paths, direction='up')
return graph_to_paths(self)
def get_node_and_links(self, lang=settings.LANGUAGE_CODE):
nodes = [{"concept_id": self.id, "name": self.get_preflabel(lang=lang).value, "type": "Current"}]
links = []
def get_parent_nodes_and_links(current_concept, _cache=[]):
if current_concept.id not in _cache:
_cache.append(current_concept.id)
parents = current_concept.parentconcepts
for parent in parents:
nodes.append(
{
"concept_id": parent.id,
"name": parent.get_preflabel(lang=lang).value,
"type": "Root" if len(parent.parentconcepts) == 0 else "Ancestor",
}
)
links.append(
{"target": current_concept.id, "source": parent.id, "relationship": "broader", }
)
get_parent_nodes_and_links(parent, _cache)
get_parent_nodes_and_links(self)
# def get_parent_nodes_and_links(current_concept):
# parents = current_concept.parentconcepts
# for parent in parents:
# nodes.append({'concept_id': parent.id, 'name': parent.get_preflabel(lang=lang).value, 'type': 'Root' if len(parent.parentconcepts) == 0 else 'Ancestor'})
# links.append({'target': current_concept.id, 'source': parent.id, 'relationship': 'broader' })
# self.traverse(get_parent_nodes_and_links, direction='up')
for child in self.subconcepts:
nodes.append(
{"concept_id": child.id, "name": child.get_preflabel(lang=lang).value, "type": "Descendant", }
)
links.append({"source": self.id, "target": child.id, "relationship": "narrower"})
for related in self.relatedconcepts:
nodes.append(
{"concept_id": related.id, "name": related.get_preflabel(lang=lang).value, "type": "Related", }
)
links.append({"source": self.id, "target": related.id, "relationship": "related"})
# get unique node list and assign unique integer ids for each node (required by d3)
nodes = list({node["concept_id"]: node for node in nodes}.values())
for i in range(len(nodes)):
nodes[i]["id"] = i
for link in links:
link["source"] = i if link["source"] == nodes[i]["concept_id"] else link["source"]
link["target"] = i if link["target"] == nodes[i]["concept_id"] else link["target"]
return {"nodes": nodes, "links": links}
def get_context(self):
"""
get the Top Concept that the Concept particpates in
"""
if self.nodetype == "Concept" or self.nodetype == "Collection":
concept = Concept().get(id=self.id, include_parentconcepts=True, include=None)
def get_scheme_id(concept):
for parentconcept in concept.parentconcepts:
if parentconcept.relationshiptype == "hasTopConcept":
return concept
if len(concept.parentconcepts) > 0:
return concept.traverse(get_scheme_id, direction="up")
else:
return self
else: # like ConceptScheme or EntityType
return self
def get_scheme(self):
"""
get the ConceptScheme that the Concept particpates in
"""
topConcept = self.get_context()
if len(topConcept.parentconcepts) == 1:
if topConcept.parentconcepts[0].nodetype == "ConceptScheme":
return topConcept.parentconcepts[0]
return None
def check_if_concept_in_use(self):
"""Checks if a concept or any of its subconcepts is in use by a resource instance"""
in_use = False
cursor = connection.cursor()
for value in self.values:
sql = (
"""
SELECT count(*) from tiles t, jsonb_each_text(t.tiledata) as json_data
WHERE json_data.value = '%s'
"""
% value.id
)
cursor.execute(sql)
rows = cursor.fetchall()
if rows[0][0] > 0:
in_use = True
break
if in_use is not True:
for subconcept in self.subconcepts:
in_use = subconcept.check_if_concept_in_use()
if in_use == True:
return in_use
return in_use
def get_e55_domain(self, conceptid):
"""
For a given entitytypeid creates a dictionary representing that entitytypeid's concept graph (member pathway) formatted to support
select2 dropdowns
"""
cursor = connection.cursor()
sql = """
WITH RECURSIVE children AS (
SELECT d.conceptidfrom, d.conceptidto, c2.value, c2.valueid as valueid, c.value as valueto, c.valueid as valueidto, c.valuetype as vtype, 1 AS depth, array[d.conceptidto] AS conceptpath, array[c.valueid] AS idpath ---|NonRecursive Part
FROM relations d
JOIN values c ON(c.conceptid = d.conceptidto)
JOIN values c2 ON(c2.conceptid = d.conceptidfrom)
WHERE d.conceptidfrom = '{0}'
and c2.valuetype = 'prefLabel'
and c.valuetype in ('prefLabel', 'sortorder', 'collector')
and (d.relationtype = 'member' or d.relationtype = 'hasTopConcept')
UNION
SELECT d.conceptidfrom, d.conceptidto, v2.value, v2.valueid as valueid, v.value as valueto, v.valueid as valueidto, v.valuetype as vtype, depth+1, (conceptpath || d.conceptidto), (idpath || v.valueid) ---|RecursivePart
FROM relations d
JOIN children b ON(b.conceptidto = d.conceptidfrom)
JOIN values v ON(v.conceptid = d.conceptidto)
JOIN values v2 ON(v2.conceptid = d.conceptidfrom)
WHERE v2.valuetype = 'prefLabel'
and v.valuetype in ('prefLabel','sortorder', 'collector')
and (d.relationtype = 'member' or d.relationtype = 'hasTopConcept')
) SELECT conceptidfrom::text, conceptidto::text, value, valueid::text, valueto, valueidto::text, depth, idpath::text, conceptpath::text, vtype FROM children ORDER BY depth, conceptpath;
""".format(
conceptid
)
column_names = [
"conceptidfrom",
"conceptidto",
"value",
"valueid",
"valueto",
"valueidto",
"depth",
"idpath",
"conceptpath",
"vtype",
]
cursor.execute(sql)
rows = cursor.fetchall()
class Val(object):
def __init__(self, conceptid):
self.text = ""
self.conceptid = conceptid
self.id = ""
self.sortorder = ""
self.collector = ""
self.children = []
result = Val(conceptid)
def _findNarrower(val, path, rec):
for conceptid in path:
childids = [child.conceptid for child in val.children]
if conceptid not in childids:
new_val = Val(rec["conceptidto"])
if rec["vtype"] == "sortorder":
new_val.sortorder = rec["valueto"]
elif rec["vtype"] == "prefLabel":
new_val.text = rec["valueto"]
new_val.id = rec["valueidto"]
elif rec["vtype"] == "collector":
new_val.collector = "collector"
val.children.append(new_val)
else:
for child in val.children:
if conceptid == child.conceptid:
if conceptid == path[-1]:
if rec["vtype"] == "sortorder":
child.sortorder = rec["valueto"]
elif rec["vtype"] == "prefLabel":
child.text = rec["valueto"]
child.id = rec["valueidto"]
elif rec["vtype"] == "collector":
child.collector = "collector"
path.pop(0)
_findNarrower(child, path, rec)
val.children.sort(key=lambda x: (x.sortorder, x.text))
for row in rows:
rec = dict(list(zip(column_names, row)))
path = rec["conceptpath"][1:-1].split(",")
_findNarrower(result, path, rec)
return JSONSerializer().serializeToPython(result)["children"]
def make_collection(self):
if len(self.values) == 0:
raise Exception(_("Need to include values when creating a collection"))
values = JSONSerializer().serializeToPython(self.values)
for value in values:
value["id"] = ""
collection_concept = Concept({"nodetype": "Collection", "values": values})
def create_collection(conceptfrom):
for relation in models.Relation.objects.filter(
Q(conceptfrom_id=conceptfrom.id),
Q(relationtype__category="Semantic Relations") | Q(relationtype__category="Properties"),
~Q(relationtype="related"),
):
conceptto = Concept(relation.conceptto)
if conceptfrom == self:
collection_concept.add_relation(conceptto, "member")
else:
conceptfrom.add_relation(conceptto, "member")
create_collection(conceptto)
with transaction.atomic():
collection_concept.save()
create_collection(self)
return collection_concept
class ConceptValue(object):
def __init__(self, *args, **kwargs):
self.id = ""
self.conceptid = ""
self.type = ""
self.category = ""
self.value = ""
self.language = ""
if len(args) != 0:
if isinstance(args[0], str):
try:
uuid.UUID(args[0])
self.get(args[0])
except (ValueError):
self.load(JSONDeserializer().deserialize(args[0]))
elif isinstance(args[0], object):
self.load(args[0])
def __repr__(self):
return ('%s: %s = "%s" in lang %s') % (self.__class__, self.type, self.value, self.language)
def get(self, id=""):
self.load(models.Value.objects.get(pk=id))
return self
def save(self):
if self.value.strip() != "":
self.id = self.id if (self.id != "" and self.id is not None) else str(uuid.uuid4())
value = models.Value()
value.pk = self.id
value.value = self.value
value.concept_id = self.conceptid # models.Concept.objects.get(pk=self.conceptid)
value.valuetype_id = self.type # models.DValueType.objects.get(pk=self.type)
if self.language != "":
# need to normalize language ids to the form xx-XX
lang_parts = self.language.lower().replace("_", "-").split("-")
try:
lang_parts[1] = lang_parts[1].upper()
except:
pass
self.language = "-".join(lang_parts)
value.language_id = self.language # models.DLanguage.objects.get(pk=self.language)
else:
value.language_id = settings.LANGUAGE_CODE
value.save()
self.category = value.valuetype.category
def delete(self):
if self.id != "":
newvalue = models.Value.objects.get(pk=self.id)
if newvalue.valuetype.valuetype == "image":
newvalue = models.FileValue.objects.get(pk=self.id)
newvalue.delete()
self = ConceptValue()
return self
def load(self, value):
if isinstance(value, models.Value):
self.id = str(value.pk)
self.conceptid = str(value.concept_id)
self.type = value.valuetype_id
self.category = value.valuetype.category
self.value = value.value
self.language = value.language_id
if isinstance(value, dict):
self.id = str(value["id"]) if "id" in value else ""
self.conceptid = str(value["conceptid"]) if "conceptid" in value else ""
self.type = value["type"] if "type" in value else ""
self.category = value["category"] if "category" in value else ""
self.value = value["value"] if "value" in value else ""
self.language = value["language"] if "language" in value else ""
def index(self, scheme=None):
if self.category == "label":
data = JSONSerializer().serializeToPython(self)
if scheme is None:
scheme = self.get_scheme_id()
if scheme is None:
raise Exception(_("Index of label failed. Index type (scheme id) could not be derived from the label."))
data["top_concept"] = scheme.id
se.index_data(index=CONCEPTS_INDEX, body=data, idfield="id")
def delete_index(self):
query = Query(se, start=0, limit=10000)
term = Term(field="id", term=self.id)
query.add_query(term)
query.delete(index=CONCEPTS_INDEX)
def get_scheme_id(self):
result = se.search(index=CONCEPTS_INDEX, id=self.id)
if result["found"]:
return Concept(result["top_concept"])
else:
return None
def get_preflabel_from_conceptid(conceptid, lang):
ret = None
default = {
"category": "",
"conceptid": "",
"language": "",
"value": "",
"type": "",
"id": "",
}
query = Query(se)
bool_query = Bool()
bool_query.must(Match(field="type", query="prefLabel", type="phrase"))
bool_query.filter(Terms(field="conceptid", terms=[conceptid]))
query.add_query(bool_query)
preflabels = query.search(index=CONCEPTS_INDEX)["hits"]["hits"]
for preflabel in preflabels:
default = preflabel["_source"]
if preflabel["_source"]["language"] is not None and lang is not None:
# get the label in the preferred language, otherwise get the label in the default language
if preflabel["_source"]["language"] == lang:
return preflabel["_source"]
if preflabel["_source"]["language"].split("-")[0] == lang.split("-")[0]:
ret = preflabel["_source"]
if preflabel["_source"]["language"] == settings.LANGUAGE_CODE and ret is None:
ret = preflabel["_source"]
return default if ret is None else ret
def get_valueids_from_concept_label(label, conceptid=None, lang=None):
def exact_val_match(val, conceptid=None):
# exact term match, don't care about relevance ordering.
# due to language formating issues, and with (hopefully) small result sets
# easier to have filter logic in python than to craft it in dsl
if conceptid is None:
return {"query": {"bool": {"filter": {"match_phrase": {"value": val}}}}}
else:
return {
"query": {
"bool": {"filter": [{"match_phrase": {"value": val}}, {"term": {"conceptid": conceptid}}, ]}
}
}
concept_label_results = se.search(index=CONCEPTS_INDEX, body=exact_val_match(label, conceptid))
if concept_label_results is None:
print("Found no matches for label:'{0}' and concept_id: '{1}'".format(label, conceptid))
return
return [
res["_source"]
for res in concept_label_results["hits"]["hits"]
if lang is None or res["_source"]["language"].lower() == lang.lower()
]
def get_preflabel_from_valueid(valueid, lang):
concept_label = se.search(index=CONCEPTS_INDEX, id=valueid)
if concept_label["found"]:
return get_preflabel_from_conceptid(concept_label["_source"]["conceptid"], lang)
| en | 0.574605 | ARCHES - a program developed to inventory and manage immovable cultural heritage.
Copyright (C) 2013 <NAME> and World Monuments Fund
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>. # self.subconcepts = sorted(self.subconcepts, key=methodcaller( # 'get_sortkey', lang=lang), reverse=False) # if we're moving a Concept Scheme below another Concept or Concept Scheme Deletes any subconcepts associated with this concept and additionally this concept if 'delete_self' is True
If any parentconcepts or relatedconcepts are included then it will only delete the relationship to those concepts but not the concepts themselves
If any values are passed, then those values as well as the relationship to those values will be deleted
Note, django will automatically take care of deleting any db models that have a foreign key relationship to the model being deleted
(eg: deleting a concept model will also delete all values and relationships), but because we need to manage deleting
parent concepts and related concepts and values we have to do that here too # we've removed all parent concepts so now this concept needs to be promoted to a Concept Scheme # delete only member relationships if the nodetype == Collection # if the concept is a collection, loop through the nodes and delete their rdmCollection values Relates this concept to 'concepttorelate' via the relationtype Gets a dictionary of all the concepts ids to delete
The values of the dictionary keys differ somewhat depending on the node type being deleted
If the nodetype == 'Concept' then return ConceptValue objects keyed to the concept id
If the nodetype == 'ConceptScheme' then return a ConceptValue object with the value set to any ONE prefLabel keyed to the concept id
We do this because it takes so long to gather the ids of the concepts when deleting a Scheme or Group # Here we have to worry about making sure we don't delete nodes that have more than 1 parent # here we can just delete everything and so use a recursive CTE to get the concept ids much more quickly Recursively builds a list of concept relations for a given concept and all it's subconcepts based on its relationship type and valuetypes. WITH RECURSIVE
ordered_relationships AS (
(
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, (
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) as valuesto,
(
SELECT value::int
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('sortorder')
limit 1
) as sortorder,
(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('collector')
limit 1
) as collector
FROM relations r
WHERE r.conceptidfrom = '{conceptid}'
and ({relationtypes})
ORDER BY sortorder, valuesto
)
UNION
(
SELECT r.conceptidfrom, r.conceptidto, r.relationtype,(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) as valuesto,
(
SELECT value::int
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('sortorder')
limit 1
) as sortorder,
(
SELECT value
FROM values
WHERE conceptid=r.conceptidto
AND valuetype in ('collector')
limit 1
) as collector
FROM relations r
JOIN ordered_relationships b ON(b.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
ORDER BY sortorder, valuesto
)
),
children AS (
SELECT r.conceptidfrom, r.conceptidto,
to_char(row_number() OVER (), 'fm000000') as row,
r.collector,
1 AS depth ---|NonRecursive Part
FROM ordered_relationships r
WHERE r.conceptidfrom = '{conceptid}'
and ({relationtypes})
UNION
SELECT r.conceptidfrom, r.conceptidto,
row || '-' || to_char(row_number() OVER (), 'fm000000'),
r.collector,
depth+1 ---|RecursivePart
FROM ordered_relationships r
JOIN children b ON(b.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
{depth_limit}
)
{subquery}
SELECT
(
select row_to_json(d)
FROM (
SELECT *
FROM values
WHERE conceptid={recursive_table}.conceptidto
AND valuetype in ('prefLabel')
ORDER BY (
CASE WHEN languageid = '{languageid}' THEN 10
WHEN languageid like '{short_languageid}%' THEN 5
WHEN languageid like '{default_languageid}%' THEN 2
ELSE 0
END
) desc limit 1
) d
) as valueto,
depth, collector, count(*) OVER() AS full_count
FROM {recursive_table} order by row {limit_clause}; , results as (
SELECT c.conceptidfrom, c.conceptidto, c.row, c.depth, c.collector
FROM children c
JOIN values ON(values.conceptid = c.conceptidto)
WHERE LOWER(values.value) like '%%%s%%'
AND values.valuetype in ('prefLabel')
UNION
SELECT c.conceptidfrom, c.conceptidto, c.row, c.depth, c.collector
FROM children c
JOIN results r on (r.conceptidfrom=c.conceptidto)
) WITH RECURSIVE
children AS (
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, 1 AS depth
FROM relations r
WHERE r.conceptidfrom = '{conceptid}'
AND ({relationtypes})
UNION
SELECT r.conceptidfrom, r.conceptidto, r.relationtype, depth+1
FROM relations r
JOIN children c ON(c.conceptidto = r.conceptidfrom)
WHERE ({relationtypes})
{depth_limit}
),
results AS (
SELECT
valuefrom.value as valuefrom, valueto.value as valueto,
valuefrom.valueid as valueidfrom, valueto.valueid as valueidto,
valuefrom.valuetype as valuetypefrom, valueto.valuetype as valuetypeto,
valuefrom.languageid as languagefrom, valueto.languageid as languageto,
dtypesfrom.category as categoryfrom, dtypesto.category as categoryto,
c.conceptidfrom, c.conceptidto
FROM values valueto
JOIN d_value_types dtypesto ON(dtypesto.valuetype = valueto.valuetype)
JOIN children c ON(c.conceptidto = valueto.conceptid)
JOIN values valuefrom ON(c.conceptidfrom = valuefrom.conceptid)
JOIN d_value_types dtypesfrom ON(dtypesfrom.valuetype = valuefrom.valuetype)
WHERE valueto.valuetype in ('{child_valuetypes}')
AND valuefrom.valuetype in ('{child_valuetypes}')
)
SELECT distinct {columns}
FROM results {limit_clause} conceptidfrom::text, conceptidto::text,
valuefrom, valueto,
valueidfrom::text, valueidto::text,
valuetypefrom, valuetypeto,
languagefrom, languageto,
categoryfrom, categoryto Traverses a concept graph from self to leaf (direction='down') or root (direction='up') calling
the given function on each node, passes an optional scope to each function
Return a value from the function to prematurely end the traversal # break out of the traversal if the function returns a value alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com/blog/200712/human_sorting.html
(See Toothy's implementation in the comments)
float regex comes from https://stackoverflow.com/a/12643073/190597 Flattens the graph into a unordered list of concepts # graph = _findNarrowerConcept(concepts[0].pk, depth_limit=1).children # concepts = _findNarrowerConcept(self.id, depth_limit=1) # graph = [_findBroaderConcept(self.id, concepts, depth_limit=1)] # def graph_to_paths(current_concept, **kwargs): # path = kwargs.get('path', []) # path_list = kwargs.get('path_list', []) # if len(path) == 0: # current_path = [] # else: # current_path = path[:] # current_path.insert(0, {'label': current_concept.get_preflabel(lang=lang).value, 'relationshiptype': current_concept.relationshiptype, 'id': current_concept.id}) # if len(current_concept.parentconcepts) == 0: # path_list.append(current_path[:]) # # else: # # for parent in current_concept.parentconcepts: # # ret = graph_to_paths(parent, current_path, path_list, _cache) # #return path_list # self.traverse(graph_to_paths, direction='up') # def get_parent_nodes_and_links(current_concept): # parents = current_concept.parentconcepts # for parent in parents: # nodes.append({'concept_id': parent.id, 'name': parent.get_preflabel(lang=lang).value, 'type': 'Root' if len(parent.parentconcepts) == 0 else 'Ancestor'}) # links.append({'target': current_concept.id, 'source': parent.id, 'relationship': 'broader' }) # self.traverse(get_parent_nodes_and_links, direction='up') # get unique node list and assign unique integer ids for each node (required by d3) get the Top Concept that the Concept particpates in # like ConceptScheme or EntityType get the ConceptScheme that the Concept particpates in Checks if a concept or any of its subconcepts is in use by a resource instance SELECT count(*) from tiles t, jsonb_each_text(t.tiledata) as json_data
WHERE json_data.value = '%s' For a given entitytypeid creates a dictionary representing that entitytypeid's concept graph (member pathway) formatted to support
select2 dropdowns WITH RECURSIVE children AS (
SELECT d.conceptidfrom, d.conceptidto, c2.value, c2.valueid as valueid, c.value as valueto, c.valueid as valueidto, c.valuetype as vtype, 1 AS depth, array[d.conceptidto] AS conceptpath, array[c.valueid] AS idpath ---|NonRecursive Part
FROM relations d
JOIN values c ON(c.conceptid = d.conceptidto)
JOIN values c2 ON(c2.conceptid = d.conceptidfrom)
WHERE d.conceptidfrom = '{0}'
and c2.valuetype = 'prefLabel'
and c.valuetype in ('prefLabel', 'sortorder', 'collector')
and (d.relationtype = 'member' or d.relationtype = 'hasTopConcept')
UNION
SELECT d.conceptidfrom, d.conceptidto, v2.value, v2.valueid as valueid, v.value as valueto, v.valueid as valueidto, v.valuetype as vtype, depth+1, (conceptpath || d.conceptidto), (idpath || v.valueid) ---|RecursivePart
FROM relations d
JOIN children b ON(b.conceptidto = d.conceptidfrom)
JOIN values v ON(v.conceptid = d.conceptidto)
JOIN values v2 ON(v2.conceptid = d.conceptidfrom)
WHERE v2.valuetype = 'prefLabel'
and v.valuetype in ('prefLabel','sortorder', 'collector')
and (d.relationtype = 'member' or d.relationtype = 'hasTopConcept')
) SELECT conceptidfrom::text, conceptidto::text, value, valueid::text, valueto, valueidto::text, depth, idpath::text, conceptpath::text, vtype FROM children ORDER BY depth, conceptpath; # models.Concept.objects.get(pk=self.conceptid) # models.DValueType.objects.get(pk=self.type) # need to normalize language ids to the form xx-XX # models.DLanguage.objects.get(pk=self.language) # get the label in the preferred language, otherwise get the label in the default language # exact term match, don't care about relevance ordering. # due to language formating issues, and with (hopefully) small result sets # easier to have filter logic in python than to craft it in dsl | 2.15739 | 2 |
Jobs/Propensity_net_NN.py | Shantanu48114860/DPN-SA | 2 | 6623199 | """
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import torch.nn as nn
import torch.nn.functional as F
# phase = ["train", "eval"]
class Propensity_net_NN(nn.Module):
def __init__(self, phase, input_nodes):
super(Propensity_net_NN, self).__init__()
self.phase = phase
self.fc1 = nn.Linear(in_features=input_nodes, out_features=25)
# nn.init.xavier_uniform_(self.fc1.weight)
self.fc2 = nn.Linear(in_features=25, out_features=25)
# nn.init.xavier_uniform_(self.fc2.weight)
self.ps_out = nn.Linear(in_features=25, out_features=2)
def forward(self, x):
# if torch.cuda.is_available():
# x = x.float().cuda()
# else:
# x = x.float()
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.ps_out(x)
if self.phase == "eval":
return F.softmax(x, dim=1)
else:
return x
| """
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import torch.nn as nn
import torch.nn.functional as F
# phase = ["train", "eval"]
class Propensity_net_NN(nn.Module):
def __init__(self, phase, input_nodes):
super(Propensity_net_NN, self).__init__()
self.phase = phase
self.fc1 = nn.Linear(in_features=input_nodes, out_features=25)
# nn.init.xavier_uniform_(self.fc1.weight)
self.fc2 = nn.Linear(in_features=25, out_features=25)
# nn.init.xavier_uniform_(self.fc2.weight)
self.ps_out = nn.Linear(in_features=25, out_features=2)
def forward(self, x):
# if torch.cuda.is_available():
# x = x.float().cuda()
# else:
# x = x.float()
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.ps_out(x)
if self.phase == "eval":
return F.softmax(x, dim=1)
else:
return x
| en | 0.717884 | MIT License Copyright (c) 2020 <NAME> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # phase = ["train", "eval"] # nn.init.xavier_uniform_(self.fc1.weight) # nn.init.xavier_uniform_(self.fc2.weight) # if torch.cuda.is_available(): # x = x.float().cuda() # else: # x = x.float() | 1.863243 | 2 |
tests/utils.py | riptano/argus | 2 | 6623200 | # Copyright 2018 DataStax, Inc.
#
# Licensed 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.
import os
import shutil
from configparser import ConfigParser
from src.utils import TEST_DIR
def clean_test_files():
"""
Deletes any files that could have been created by running tests
"""
test_conf_path = os.path.join(TEST_DIR, 'conf')
test_data_path = os.path.join(TEST_DIR, 'data')
deleted_folders = False
if os.path.exists(test_conf_path):
print('Removing path {} from previous test'.format(test_conf_path))
deleted_folders = True
shutil.rmtree(test_conf_path)
if os.path.exists(test_data_path):
print('Removing path {} from previous test'.format(test_data_path))
deleted_folders = True
shutil.rmtree(test_data_path)
if not deleted_folders:
print('Test directory, "{}", is clean')
print('No files removed')
def parser_to_dict(filename):
if not os.path.exists(filename):
raise Exception('{} does not exist'.format(filename))
cp = ConfigParser()
cp.read(filename)
data = {}
for section in cp.sections():
data[section] = {}
for option in cp.options(section):
data[section].update({option: cp.get(section, option)})
return data
def csv_to_list(row):
return sorted(filter(None, [r for r in row.split(',')]))
| # Copyright 2018 DataStax, Inc.
#
# Licensed 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.
import os
import shutil
from configparser import ConfigParser
from src.utils import TEST_DIR
def clean_test_files():
"""
Deletes any files that could have been created by running tests
"""
test_conf_path = os.path.join(TEST_DIR, 'conf')
test_data_path = os.path.join(TEST_DIR, 'data')
deleted_folders = False
if os.path.exists(test_conf_path):
print('Removing path {} from previous test'.format(test_conf_path))
deleted_folders = True
shutil.rmtree(test_conf_path)
if os.path.exists(test_data_path):
print('Removing path {} from previous test'.format(test_data_path))
deleted_folders = True
shutil.rmtree(test_data_path)
if not deleted_folders:
print('Test directory, "{}", is clean')
print('No files removed')
def parser_to_dict(filename):
if not os.path.exists(filename):
raise Exception('{} does not exist'.format(filename))
cp = ConfigParser()
cp.read(filename)
data = {}
for section in cp.sections():
data[section] = {}
for option in cp.options(section):
data[section].update({option: cp.get(section, option)})
return data
def csv_to_list(row):
return sorted(filter(None, [r for r in row.split(',')]))
| en | 0.896665 | # Copyright 2018 DataStax, Inc. # # Licensed 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. Deletes any files that could have been created by running tests | 2.41546 | 2 |
Clase_1/snippets/hint_q1.py | uncrayon/python-para-sistemas | 0 | 6623201 | # Recuerda que el radio es la mitad del diámetro.
# Por otro lado, si nosotros quisieramos saber cuanto es 51 entre 17 lo podemos guardar en una variable
# Resultado_Sorprendente = 51 / 17
# Y luego esa podemos usarla para otros calculos como:
# Resultado_Sorprendete_2 = (Resultado_Sorprendente ** 2) * 11 | # Recuerda que el radio es la mitad del diámetro.
# Por otro lado, si nosotros quisieramos saber cuanto es 51 entre 17 lo podemos guardar en una variable
# Resultado_Sorprendente = 51 / 17
# Y luego esa podemos usarla para otros calculos como:
# Resultado_Sorprendete_2 = (Resultado_Sorprendente ** 2) * 11 | es | 0.876126 | # Recuerda que el radio es la mitad del diámetro. # Por otro lado, si nosotros quisieramos saber cuanto es 51 entre 17 lo podemos guardar en una variable # Resultado_Sorprendente = 51 / 17 # Y luego esa podemos usarla para otros calculos como: # Resultado_Sorprendete_2 = (Resultado_Sorprendente ** 2) * 11 | 1.94188 | 2 |
tests/test_nptorch.py | guitargeek/geeksw | 2 | 6623202 | <reponame>guitargeek/geeksw
import unittest
import numpy as np
import torch
import geeksw.nptorch as nt
class Test(unittest.TestCase):
def test_nptorch(self):
a = np.random.uniform(size=10)
t = torch.tensor(a)
def test_f(f, f_ref):
np.testing.assert_array_almost_equal(f(a), f(t).numpy())
np.testing.assert_array_almost_equal(f_ref(a), f(a))
test_f(nt.exp, np.exp)
test_f(nt.cos, np.cos)
test_f(nt.sin, np.sin)
test_f(nt.tan, np.tan)
test_f(nt.sqrt, np.sqrt)
if __name__ == "__main__":
unittest.main(verbosity=2)
| import unittest
import numpy as np
import torch
import geeksw.nptorch as nt
class Test(unittest.TestCase):
def test_nptorch(self):
a = np.random.uniform(size=10)
t = torch.tensor(a)
def test_f(f, f_ref):
np.testing.assert_array_almost_equal(f(a), f(t).numpy())
np.testing.assert_array_almost_equal(f_ref(a), f(a))
test_f(nt.exp, np.exp)
test_f(nt.cos, np.cos)
test_f(nt.sin, np.sin)
test_f(nt.tan, np.tan)
test_f(nt.sqrt, np.sqrt)
if __name__ == "__main__":
unittest.main(verbosity=2) | none | 1 | 2.66131 | 3 | |
tests/test_schematic_upload.py | Frumple/mrt-file-server | 2 | 6623203 | <reponame>Frumple/mrt-file-server
from test_schematic_base import TestSchematicBase
from unittest.mock import call, patch
from werkzeug.datastructures import OrderedMultiDict
from io import BytesIO
import os
import pytest
class TestSchematicUpload(TestSchematicBase):
def setup(self):
TestSchematicBase.setup(self)
self.uploads_dir = self.app.config["SCHEMATIC_UPLOADS_DIR"]
self.clean_schematic_uploads_dir()
def teardown(self):
TestSchematicBase.teardown(self)
self.clean_schematic_uploads_dir()
# Tests
@patch("mrt_file_server.utils.log_utils.log_adapter")
@pytest.mark.parametrize("filename", [
("mrt_v5_final_elevated_centre_station.schem"),
("mrt_v5_final_elevated_centre_station.schematic")
])
def test_upload_single_file_should_be_successful(self, mock_logger, filename):
username = "Frumple"
uploaded_filename = self.uploaded_filename(username, filename)
original_file_content = self.load_test_data_file(filename)
message_key = "SCHEMATIC_UPLOAD_SUCCESS"
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_file_content(self.uploads_dir, uploaded_filename, original_file_content)
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
mock_logger.info.assert_called_with(self.get_log_message(message_key), uploaded_filename, username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_multiple_files_should_be_successful(self, mock_logger):
username = "Frumple"
message_key = "SCHEMATIC_UPLOAD_SUCCESS"
# Upload 5 files
filenames = [
"mrt_v5_final_elevated_centre_station.schem",
"mrt_v5_final_elevated_side_station.schematic",
"mrt_v5_final_elevated_single_track.schematic",
"mrt_v5_final_elevated_double_track.schematic",
"mrt_v5_final_elevated_double_curve.schematic"]
original_files = self.load_test_data_files(filenames)
data = OrderedMultiDict()
data.add("userName", username)
for filename in original_files:
data.add("schematic", (BytesIO(original_files[filename]), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
logger_calls = []
for filename in original_files:
uploaded_filename = self.uploaded_filename(username, filename)
self.verify_file_content(self.uploads_dir, uploaded_filename, original_files[filename])
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
logger_calls.append(call(self.get_log_message(message_key), uploaded_filename, username))
mock_logger.info.assert_has_calls(logger_calls, any_order = True)
@patch("mrt_file_server.utils.log_utils.log_adapter")
@pytest.mark.parametrize("username, message_key", [
("", "SCHEMATIC_UPLOAD_USERNAME_EMPTY"),
("Eris The Eagle", "SCHEMATIC_UPLOAD_USERNAME_WHITESPACE")
])
def test_upload_with_invalid_username_should_fail(self, mock_logger, username, message_key):
filename = "mrt_v5_final_elevated_centre_station.schematic"
original_file_content = self.load_test_data_file(filename)
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data)
if username:
mock_logger.warn.assert_called_with(self.get_log_message(message_key), username)
else:
mock_logger.warn.assert_called_with(self.get_log_message(message_key))
@patch("mrt_file_server.utils.log_utils.log_adapter")
@pytest.mark.parametrize("filename, message_key", [
("admod.schematic", "SCHEMATIC_UPLOAD_FILE_TOO_LARGE"),
("this file has spaces.schematic", "SCHEMATIC_UPLOAD_FILENAME_WHITESPACE"),
("this_file_has_the_wrong_extension.dat", "SCHEMATIC_UPLOAD_FILENAME_EXTENSION")
])
def test_upload_with_invalid_file_should_fail(self, mock_logger, filename, message_key):
username = "Frumple"
uploaded_filename = self.uploaded_filename(username, filename)
original_file_content = self.load_test_data_file(filename)
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), uploaded_filename, username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_with_no_files_should_fail(self, mock_logger):
username = "Frumple"
message_key = "SCHEMATIC_UPLOAD_NO_FILES"
data = OrderedMultiDict()
data.add("userName", username)
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_with_too_many_files_should_fail(self, mock_logger):
username = "Frumple"
message_key = "SCHEMATIC_UPLOAD_TOO_MANY_FILES"
# Upload 12 files, over the limit of 10.
filenames = [
"mrt_v5_final_elevated_centre_station.schematic",
"mrt_v5_final_elevated_side_station.schematic",
"mrt_v5_final_elevated_single_track.schematic",
"mrt_v5_final_elevated_double_track.schematic",
"mrt_v5_final_elevated_double_curve.schematic",
"mrt_v5_final_ground_centre_station.schematic",
"mrt_v5_final_ground_side_station.schematic",
"mrt_v5_final_ground_single_track.schematic",
"mrt_v5_final_ground_double_track.schematic",
"mrt_v5_final_ground_double_curve.schematic",
"mrt_v5_final_subground_centre_station.schematic",
"mrt_v5_final_subground_side_station.schematic"]
original_files = self.load_test_data_files(filenames)
data = OrderedMultiDict()
data.add("userName", username)
for filename in original_files:
data.add("schematic", (BytesIO(original_files[filename]), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_file_that_already_exists_should_fail(self, mock_logger):
username = "Frumple"
filename = "mrt_v5_final_elevated_centre_station.schematic"
uploaded_filename = self.uploaded_filename(username, filename)
impostor_filename = "mrt_v5_final_underground_single_track.schematic"
message_key = "SCHEMATIC_UPLOAD_FILE_EXISTS"
# Copy an impostor file with different content to the uploads directory with the same name as the file to upload
self.copy_test_data_file(impostor_filename, self.uploads_dir, uploaded_filename)
original_file_content = self.load_test_data_file(filename)
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
# Verify that the uploads directory has only the impostor file, and the file has not been modified
files = os.listdir(self.uploads_dir)
assert len(files) == 1
impostor_file_content = self.load_test_data_file(impostor_filename)
self.verify_file_content(self.uploads_dir, uploaded_filename, impostor_file_content)
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), uploaded_filename, username)
# Helper Functions
def perform_upload(self, data):
return self.client.post("/schematic/upload", content_type = "multipart/form-data", data = data)
def clean_schematic_uploads_dir(self):
self.remove_files(self.uploads_dir, "schematic")
self.remove_files(self.uploads_dir, "schem")
def uploaded_filename(self, username, filename):
return "{}-{}".format(username, filename)
def verify_schematic_uploads_dir_is_empty(self):
assert os.listdir(self.uploads_dir) == []
| from test_schematic_base import TestSchematicBase
from unittest.mock import call, patch
from werkzeug.datastructures import OrderedMultiDict
from io import BytesIO
import os
import pytest
class TestSchematicUpload(TestSchematicBase):
def setup(self):
TestSchematicBase.setup(self)
self.uploads_dir = self.app.config["SCHEMATIC_UPLOADS_DIR"]
self.clean_schematic_uploads_dir()
def teardown(self):
TestSchematicBase.teardown(self)
self.clean_schematic_uploads_dir()
# Tests
@patch("mrt_file_server.utils.log_utils.log_adapter")
@pytest.mark.parametrize("filename", [
("mrt_v5_final_elevated_centre_station.schem"),
("mrt_v5_final_elevated_centre_station.schematic")
])
def test_upload_single_file_should_be_successful(self, mock_logger, filename):
username = "Frumple"
uploaded_filename = self.uploaded_filename(username, filename)
original_file_content = self.load_test_data_file(filename)
message_key = "SCHEMATIC_UPLOAD_SUCCESS"
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_file_content(self.uploads_dir, uploaded_filename, original_file_content)
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
mock_logger.info.assert_called_with(self.get_log_message(message_key), uploaded_filename, username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_multiple_files_should_be_successful(self, mock_logger):
username = "Frumple"
message_key = "SCHEMATIC_UPLOAD_SUCCESS"
# Upload 5 files
filenames = [
"mrt_v5_final_elevated_centre_station.schem",
"mrt_v5_final_elevated_side_station.schematic",
"mrt_v5_final_elevated_single_track.schematic",
"mrt_v5_final_elevated_double_track.schematic",
"mrt_v5_final_elevated_double_curve.schematic"]
original_files = self.load_test_data_files(filenames)
data = OrderedMultiDict()
data.add("userName", username)
for filename in original_files:
data.add("schematic", (BytesIO(original_files[filename]), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
logger_calls = []
for filename in original_files:
uploaded_filename = self.uploaded_filename(username, filename)
self.verify_file_content(self.uploads_dir, uploaded_filename, original_files[filename])
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
logger_calls.append(call(self.get_log_message(message_key), uploaded_filename, username))
mock_logger.info.assert_has_calls(logger_calls, any_order = True)
@patch("mrt_file_server.utils.log_utils.log_adapter")
@pytest.mark.parametrize("username, message_key", [
("", "SCHEMATIC_UPLOAD_USERNAME_EMPTY"),
("Eris The Eagle", "SCHEMATIC_UPLOAD_USERNAME_WHITESPACE")
])
def test_upload_with_invalid_username_should_fail(self, mock_logger, username, message_key):
filename = "mrt_v5_final_elevated_centre_station.schematic"
original_file_content = self.load_test_data_file(filename)
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data)
if username:
mock_logger.warn.assert_called_with(self.get_log_message(message_key), username)
else:
mock_logger.warn.assert_called_with(self.get_log_message(message_key))
@patch("mrt_file_server.utils.log_utils.log_adapter")
@pytest.mark.parametrize("filename, message_key", [
("admod.schematic", "SCHEMATIC_UPLOAD_FILE_TOO_LARGE"),
("this file has spaces.schematic", "SCHEMATIC_UPLOAD_FILENAME_WHITESPACE"),
("this_file_has_the_wrong_extension.dat", "SCHEMATIC_UPLOAD_FILENAME_EXTENSION")
])
def test_upload_with_invalid_file_should_fail(self, mock_logger, filename, message_key):
username = "Frumple"
uploaded_filename = self.uploaded_filename(username, filename)
original_file_content = self.load_test_data_file(filename)
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), uploaded_filename, username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_with_no_files_should_fail(self, mock_logger):
username = "Frumple"
message_key = "SCHEMATIC_UPLOAD_NO_FILES"
data = OrderedMultiDict()
data.add("userName", username)
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_with_too_many_files_should_fail(self, mock_logger):
username = "Frumple"
message_key = "SCHEMATIC_UPLOAD_TOO_MANY_FILES"
# Upload 12 files, over the limit of 10.
filenames = [
"mrt_v5_final_elevated_centre_station.schematic",
"mrt_v5_final_elevated_side_station.schematic",
"mrt_v5_final_elevated_single_track.schematic",
"mrt_v5_final_elevated_double_track.schematic",
"mrt_v5_final_elevated_double_curve.schematic",
"mrt_v5_final_ground_centre_station.schematic",
"mrt_v5_final_ground_side_station.schematic",
"mrt_v5_final_ground_single_track.schematic",
"mrt_v5_final_ground_double_track.schematic",
"mrt_v5_final_ground_double_curve.schematic",
"mrt_v5_final_subground_centre_station.schematic",
"mrt_v5_final_subground_side_station.schematic"]
original_files = self.load_test_data_files(filenames)
data = OrderedMultiDict()
data.add("userName", username)
for filename in original_files:
data.add("schematic", (BytesIO(original_files[filename]), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
self.verify_schematic_uploads_dir_is_empty()
self.verify_flash_message_by_key(message_key, response.data)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), username)
@patch("mrt_file_server.utils.log_utils.log_adapter")
def test_upload_file_that_already_exists_should_fail(self, mock_logger):
username = "Frumple"
filename = "mrt_v5_final_elevated_centre_station.schematic"
uploaded_filename = self.uploaded_filename(username, filename)
impostor_filename = "mrt_v5_final_underground_single_track.schematic"
message_key = "SCHEMATIC_UPLOAD_FILE_EXISTS"
# Copy an impostor file with different content to the uploads directory with the same name as the file to upload
self.copy_test_data_file(impostor_filename, self.uploads_dir, uploaded_filename)
original_file_content = self.load_test_data_file(filename)
data = OrderedMultiDict()
data.add("userName", username)
data.add("schematic", (BytesIO(original_file_content), filename))
response = self.perform_upload(data)
assert response.status_code == 200
assert response.mimetype == "text/html"
# Verify that the uploads directory has only the impostor file, and the file has not been modified
files = os.listdir(self.uploads_dir)
assert len(files) == 1
impostor_file_content = self.load_test_data_file(impostor_filename)
self.verify_file_content(self.uploads_dir, uploaded_filename, impostor_file_content)
self.verify_flash_message_by_key(message_key, response.data, uploaded_filename)
mock_logger.warn.assert_called_with(self.get_log_message(message_key), uploaded_filename, username)
# Helper Functions
def perform_upload(self, data):
return self.client.post("/schematic/upload", content_type = "multipart/form-data", data = data)
def clean_schematic_uploads_dir(self):
self.remove_files(self.uploads_dir, "schematic")
self.remove_files(self.uploads_dir, "schem")
def uploaded_filename(self, username, filename):
return "{}-{}".format(username, filename)
def verify_schematic_uploads_dir_is_empty(self):
assert os.listdir(self.uploads_dir) == [] | en | 0.941261 | # Tests # Upload 5 files # Upload 12 files, over the limit of 10. # Copy an impostor file with different content to the uploads directory with the same name as the file to upload # Verify that the uploads directory has only the impostor file, and the file has not been modified # Helper Functions | 2.229117 | 2 |
main.py | grimmpp/cloudFoundryServiceBroker | 0 | 6623204 | import sys
sys.path.append('cfBroker')
from cfBroker.app import App
App().start() | import sys
sys.path.append('cfBroker')
from cfBroker.app import App
App().start() | none | 1 | 1.225134 | 1 | |
commonutils.py | troxel/t_mon | 0 | 6623205 | import pprint
import os
import os.path
import subprocess
import cherrypy
class Utils:
# Common utils to mostly handle ro/rw issues and other common needs
def __init__(self):
version = 1.0;
self.is_ro = self.is_filesys_ro()
# -----------------------
# Write file to a ro filesystem
def write_sysfile(self,fspec,contents):
# If filesystem is currently ro then umount ro and remount rw
# Otherwise leave alone
self.rw()
with open(fspec, 'w+') as fid:
fid.write(contents)
self.ro()
# ------------------------
def rw(self):
rtn = os.system('mount -o rw,remount /')
if rtn != 0:
raise SystemError("Cannot remount rw root partition")
# ------------------------
def ro(self):
os.sync() # Forces write from buffer to disk...
# If the filesystem was originally in ro mode leave in ro mode
# Otherwise leave alone - a development feature...
if self.is_ro:
rtn = os.system('mount -o ro,remount /')
if rtn != 0:
raise SystemError("Cannot remount ro root partition")
# ------------------------
def is_filesys_ro(self):
rtn = os.system('egrep "\sro[\s,]" /proc/mounts | egrep "\s+/\s+"')
if rtn == 0:
return(True)
else:
return(False)
# ------------------------
# Removes dir contents
def rm_dir(self,dspec):
cnt = dspec.count("/")
if cnt < 2:
print("Refusing to remove low level directory {}".format(dspec), file=sys.stderr)
return(False)
self.rw()
rtn = os.system('rm -rf {}'.format(dspec))
self.ro()
if rtn == 0:
return(True)
else:
return(False)
# ------------------------
def url_gen(self,path,from_page=''):
# Cannot do relative url redirects when working with proxy
# as cherrpy isn't aware of the protocol
host = cherrypy.request.headers.get('Host')
proto = cherrypy.request.headers.get('X-Scheme')
if proto is None: proto = 'http'
if from_page:
from_page = "?from_page={}".format(from_page)
url = "{}://{}{}{}".format(proto,host,path,from_page)
return(url)
| import pprint
import os
import os.path
import subprocess
import cherrypy
class Utils:
# Common utils to mostly handle ro/rw issues and other common needs
def __init__(self):
version = 1.0;
self.is_ro = self.is_filesys_ro()
# -----------------------
# Write file to a ro filesystem
def write_sysfile(self,fspec,contents):
# If filesystem is currently ro then umount ro and remount rw
# Otherwise leave alone
self.rw()
with open(fspec, 'w+') as fid:
fid.write(contents)
self.ro()
# ------------------------
def rw(self):
rtn = os.system('mount -o rw,remount /')
if rtn != 0:
raise SystemError("Cannot remount rw root partition")
# ------------------------
def ro(self):
os.sync() # Forces write from buffer to disk...
# If the filesystem was originally in ro mode leave in ro mode
# Otherwise leave alone - a development feature...
if self.is_ro:
rtn = os.system('mount -o ro,remount /')
if rtn != 0:
raise SystemError("Cannot remount ro root partition")
# ------------------------
def is_filesys_ro(self):
rtn = os.system('egrep "\sro[\s,]" /proc/mounts | egrep "\s+/\s+"')
if rtn == 0:
return(True)
else:
return(False)
# ------------------------
# Removes dir contents
def rm_dir(self,dspec):
cnt = dspec.count("/")
if cnt < 2:
print("Refusing to remove low level directory {}".format(dspec), file=sys.stderr)
return(False)
self.rw()
rtn = os.system('rm -rf {}'.format(dspec))
self.ro()
if rtn == 0:
return(True)
else:
return(False)
# ------------------------
def url_gen(self,path,from_page=''):
# Cannot do relative url redirects when working with proxy
# as cherrpy isn't aware of the protocol
host = cherrypy.request.headers.get('Host')
proto = cherrypy.request.headers.get('X-Scheme')
if proto is None: proto = 'http'
if from_page:
from_page = "?from_page={}".format(from_page)
url = "{}://{}{}{}".format(proto,host,path,from_page)
return(url)
| en | 0.764125 | # Common utils to mostly handle ro/rw issues and other common needs # ----------------------- # Write file to a ro filesystem # If filesystem is currently ro then umount ro and remount rw # Otherwise leave alone # ------------------------ # ------------------------ # Forces write from buffer to disk... # If the filesystem was originally in ro mode leave in ro mode # Otherwise leave alone - a development feature... # ------------------------ # ------------------------ # Removes dir contents # ------------------------ # Cannot do relative url redirects when working with proxy # as cherrpy isn't aware of the protocol | 2.270466 | 2 |
subset.py | andrewgryan/swift-testbed | 0 | 6623206 | <filename>subset.py
#!/usr/bin/env python
'''
Read data from a netCDF file, cut out a sub-region and save to a new file
'''
import sys
import os
import iris
import argparse
def parse_args(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("in_file",
help="file to extract region from")
parser.add_argument("out_file",
help="file to write to")
parser.add_argument("--north", default=30, type=int)
parser.add_argument("--south", default=-18, type=int)
parser.add_argument("--west", default=90, type=int)
parser.add_argument("--east", default=154, type=int)
return parser.parse_args(args=argv)
def main(argv=None):
args = parse_args(argv=argv)
constraint = iris.Constraint(
latitude=lambda cell: args.south < cell < args.north,
longitude=lambda cell: args.west < cell < args.east)
print("Reading data from {}".format(args.in_file))
cubes = iris.load(args.in_file)
print("N, S, E, W: {}".format(
(args.north, args.south, args.east, args.west)))
# Cut out a domain
small_cubes = []
for cube in cubes:
print(cube.name())
try:
small_cube = cube.intersection(
longitude=(args.west, args.east),
latitude=(args.south, args.north))
except:
small_cube = cube.extract(constraint)
if small_cube is not None:
print(small_cube)
small_cubes.append(small_cube)
print("Writing subset to {}".format(args.out_file))
iris.save(small_cubes, args.out_file)
if __name__ == '__main__':
main()
| <filename>subset.py
#!/usr/bin/env python
'''
Read data from a netCDF file, cut out a sub-region and save to a new file
'''
import sys
import os
import iris
import argparse
def parse_args(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("in_file",
help="file to extract region from")
parser.add_argument("out_file",
help="file to write to")
parser.add_argument("--north", default=30, type=int)
parser.add_argument("--south", default=-18, type=int)
parser.add_argument("--west", default=90, type=int)
parser.add_argument("--east", default=154, type=int)
return parser.parse_args(args=argv)
def main(argv=None):
args = parse_args(argv=argv)
constraint = iris.Constraint(
latitude=lambda cell: args.south < cell < args.north,
longitude=lambda cell: args.west < cell < args.east)
print("Reading data from {}".format(args.in_file))
cubes = iris.load(args.in_file)
print("N, S, E, W: {}".format(
(args.north, args.south, args.east, args.west)))
# Cut out a domain
small_cubes = []
for cube in cubes:
print(cube.name())
try:
small_cube = cube.intersection(
longitude=(args.west, args.east),
latitude=(args.south, args.north))
except:
small_cube = cube.extract(constraint)
if small_cube is not None:
print(small_cube)
small_cubes.append(small_cube)
print("Writing subset to {}".format(args.out_file))
iris.save(small_cubes, args.out_file)
if __name__ == '__main__':
main()
| en | 0.727173 | #!/usr/bin/env python Read data from a netCDF file, cut out a sub-region and save to a new file # Cut out a domain | 3.389354 | 3 |
visualization/dashapp/index.py | Lchuang/yews | 0 | 6623207 | #!/Users/lindsaychuang/miniconda3/envs/obspy/bin/python
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
from app import app
from apps import geomaps, Continuous_WF
# ---- 02. Page contents
app.layout = html.Div([
dcc.Location(id='url', refresh=False),
html.Div(id='page-content')])
@app.callback(Output('page-content', 'children'),
[Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/apps/Continuous_WF':
return Continuous_WF.layout
elif pathname == '/':
return geomaps.layout
if __name__ == '__main__':
app.run_server(debug=True) | #!/Users/lindsaychuang/miniconda3/envs/obspy/bin/python
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
from app import app
from apps import geomaps, Continuous_WF
# ---- 02. Page contents
app.layout = html.Div([
dcc.Location(id='url', refresh=False),
html.Div(id='page-content')])
@app.callback(Output('page-content', 'children'),
[Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/apps/Continuous_WF':
return Continuous_WF.layout
elif pathname == '/':
return geomaps.layout
if __name__ == '__main__':
app.run_server(debug=True) | en | 0.445499 | #!/Users/lindsaychuang/miniconda3/envs/obspy/bin/python # ---- 02. Page contents | 2.073131 | 2 |
pop_tools/datasets.py | dcherian/pop-tools | 0 | 6623208 | <filename>pop_tools/datasets.py
"""
Functions to load sample data
"""
import os
import pkg_resources
import pooch
DATASETS = pooch.create(
path=['~', '.pop_tools', 'data'],
version_dev='master',
base_url='ftp://ftp.cgd.ucar.edu/archive/aletheia-data/cesm-data/ocn/',
)
DATASETS.load_registry(pkg_resources.resource_stream('pop_tools', 'data_registry.txt'))
| <filename>pop_tools/datasets.py
"""
Functions to load sample data
"""
import os
import pkg_resources
import pooch
DATASETS = pooch.create(
path=['~', '.pop_tools', 'data'],
version_dev='master',
base_url='ftp://ftp.cgd.ucar.edu/archive/aletheia-data/cesm-data/ocn/',
)
DATASETS.load_registry(pkg_resources.resource_stream('pop_tools', 'data_registry.txt'))
| en | 0.811216 | Functions to load sample data | 1.721295 | 2 |
xcube_hub/models/user_user_metadata.py | bcdev/xcube-hub | 3 | 6623209 | # coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from xcube_hub.models.base_model_ import Model
from xcube_hub import util
from xcube_hub.models.subscription import Subscription
class UserUserMetadata(Model):
"""NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
Do not edit the class manually.
"""
def __init__(self, client_id=None, client_secret=None, subscriptions=None):
"""UserUserMetadata - a model defined in OpenAPI
:param client_id: The client_id of this UserUserMetadata. # noqa: E501
:type client_id: str
:param client_secret: The client_secret of this UserUserMetadata. # noqa: E501
:type client_secret: str
"""
self.openapi_types = {
'client_id': str,
'client_secret': str,
'subscriptions': Dict[str, Subscription]
}
self.attribute_map = {
'client_id': 'client_id',
'client_secret': 'client_secret',
'subscriptions': 'subscriptions',
}
self._client_id = client_id
self._client_secret = client_secret
self._subscriptions = subscriptions
@classmethod
def from_dict(cls, dikt) -> 'UserUserMetadata':
"""Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The User_user_metadata of this UserUserMetadata. # noqa: E501
:rtype: UserUserMetadata
"""
return util.deserialize_model(dikt, cls)
@property
def client_id(self):
"""Gets the client_id of this UserUserMetadata.
:return: The client_id of this UserUserMetadata.
:rtype: str
"""
return self._client_id
@client_id.setter
def client_id(self, client_id):
"""Sets the client_id of this UserUserMetadata.
:param client_id: The client_id of this UserUserMetadata.
:type client_id: str
"""
self._client_id = client_id
@property
def client_secret(self):
"""Gets the client_secret of this UserUserMetadata.
:return: The client_secret of this UserUserMetadata.
:rtype: str
"""
return self._client_secret
@client_secret.setter
def client_secret(self, client_secret):
"""Sets the client_secret of this UserUserMetadata.
:param client_secret: The client_secret of this UserUserMetadata.
:type client_secret: str
"""
self._client_secret = client_secret
@property
def subscriptions(self):
"""Gets the subscriptions of this UserUserMetadata.
:return: The subscriptions of this UserUserMetadata.
:rtype: float
"""
return self._subscriptions
@subscriptions.setter
def subscriptions(self, subscriptions):
"""Sets the subscriptions of this UserUserMetadata.
:param subscriptions: The punits of this UserUserMetadata.
:type subscriptions: float
"""
self._subscriptions = subscriptions
| # coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from xcube_hub.models.base_model_ import Model
from xcube_hub import util
from xcube_hub.models.subscription import Subscription
class UserUserMetadata(Model):
"""NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
Do not edit the class manually.
"""
def __init__(self, client_id=None, client_secret=None, subscriptions=None):
"""UserUserMetadata - a model defined in OpenAPI
:param client_id: The client_id of this UserUserMetadata. # noqa: E501
:type client_id: str
:param client_secret: The client_secret of this UserUserMetadata. # noqa: E501
:type client_secret: str
"""
self.openapi_types = {
'client_id': str,
'client_secret': str,
'subscriptions': Dict[str, Subscription]
}
self.attribute_map = {
'client_id': 'client_id',
'client_secret': 'client_secret',
'subscriptions': 'subscriptions',
}
self._client_id = client_id
self._client_secret = client_secret
self._subscriptions = subscriptions
@classmethod
def from_dict(cls, dikt) -> 'UserUserMetadata':
"""Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The User_user_metadata of this UserUserMetadata. # noqa: E501
:rtype: UserUserMetadata
"""
return util.deserialize_model(dikt, cls)
@property
def client_id(self):
"""Gets the client_id of this UserUserMetadata.
:return: The client_id of this UserUserMetadata.
:rtype: str
"""
return self._client_id
@client_id.setter
def client_id(self, client_id):
"""Sets the client_id of this UserUserMetadata.
:param client_id: The client_id of this UserUserMetadata.
:type client_id: str
"""
self._client_id = client_id
@property
def client_secret(self):
"""Gets the client_secret of this UserUserMetadata.
:return: The client_secret of this UserUserMetadata.
:rtype: str
"""
return self._client_secret
@client_secret.setter
def client_secret(self, client_secret):
"""Sets the client_secret of this UserUserMetadata.
:param client_secret: The client_secret of this UserUserMetadata.
:type client_secret: str
"""
self._client_secret = client_secret
@property
def subscriptions(self):
"""Gets the subscriptions of this UserUserMetadata.
:return: The subscriptions of this UserUserMetadata.
:rtype: float
"""
return self._subscriptions
@subscriptions.setter
def subscriptions(self, subscriptions):
"""Sets the subscriptions of this UserUserMetadata.
:param subscriptions: The punits of this UserUserMetadata.
:type subscriptions: float
"""
self._subscriptions = subscriptions
| en | 0.501917 | # coding: utf-8 # noqa: F401 # noqa: F401 NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). Do not edit the class manually. UserUserMetadata - a model defined in OpenAPI :param client_id: The client_id of this UserUserMetadata. # noqa: E501 :type client_id: str :param client_secret: The client_secret of this UserUserMetadata. # noqa: E501 :type client_secret: str Returns the dict as a model :param dikt: A dict. :type: dict :return: The User_user_metadata of this UserUserMetadata. # noqa: E501 :rtype: UserUserMetadata Gets the client_id of this UserUserMetadata. :return: The client_id of this UserUserMetadata. :rtype: str Sets the client_id of this UserUserMetadata. :param client_id: The client_id of this UserUserMetadata. :type client_id: str Gets the client_secret of this UserUserMetadata. :return: The client_secret of this UserUserMetadata. :rtype: str Sets the client_secret of this UserUserMetadata. :param client_secret: The client_secret of this UserUserMetadata. :type client_secret: str Gets the subscriptions of this UserUserMetadata. :return: The subscriptions of this UserUserMetadata. :rtype: float Sets the subscriptions of this UserUserMetadata. :param subscriptions: The punits of this UserUserMetadata. :type subscriptions: float | 2.19699 | 2 |
bioprocs/scripts/tsv/pTsv2Xlsx.py | pwwang/biopipen | 2 | 6623210 | <gh_stars>1-10
import csv
from os import path
from openpyxl import Workbook
infile = {{i.infile | quote}}
outfile = {{o.outfile | quote}}
fn2sheet = {{args.fn2sheet}}
def tsv2sheet(wb, tsvfile):
ws = wb.create_sheet('Sheet1')
with open(tsvfile) as f:
reader = csv.reader(f, delimiter = "\t")
for row in reader:
ws.append(row)
wb = Workbook()
wb.remove(wb.active) # remove default sheet
tsv2sheet(wb, infile)
wb.save(outfile)
| import csv
from os import path
from openpyxl import Workbook
infile = {{i.infile | quote}}
outfile = {{o.outfile | quote}}
fn2sheet = {{args.fn2sheet}}
def tsv2sheet(wb, tsvfile):
ws = wb.create_sheet('Sheet1')
with open(tsvfile) as f:
reader = csv.reader(f, delimiter = "\t")
for row in reader:
ws.append(row)
wb = Workbook()
wb.remove(wb.active) # remove default sheet
tsv2sheet(wb, infile)
wb.save(outfile) | uk | 0.055452 | # remove default sheet | 2.966441 | 3 |
createManifest.py | Kaseaa/Tera-Manifest-Auto-Updater | 0 | 6623211 | import json
import os
from Crypto.Hash import SHA256
from multiprocessing.pool import ThreadPool as pool
DEF_SYNTAXES = [
"dispatch.toServer",
"dispatch.toClient",
"dispatch.hook"
]
IGNORE_FILES = [
'createManifest.py',
'createManifest.exe',
'manifest.json',
'module.json'
]
NUMBER_OF_THREADS = 6
INDENTING = 4
def getFilePathsFor(path, excluded):
""" Get all the files for a given filepath
:param path: The parent directory
:param excluded: An array of excluded file names
:return: An array of filepaths
"""
filePaths = []
for dir, b, files in os.walk(path):
if dir.count('.git') != 0: continue
for file in files:
if file not in excluded:
filePaths.append((dir + '/' + file).replace('\\\\', '\\').replace('\\', '/'))
return filePaths
def sha1(data):
""" Get the SHA256 value of a byte string """
s = SHA256.new()
s.update(data)
return s.hexdigest()
def getDefForSyntax(data, syntax):
""" Gets the definition version and name using a given syntax
:param data: the data to look for this information in
:param syntax: for example "dispatch.toServer"
:return: A dictionary with the key as the packet name and a list with the number
"""
ret = {}
syntaxLen = len(syntax)
s = data.find(syntax)
# While we can't find anymore
while s != -1 and len(data) > s + 1:
s += syntaxLen + 1
# We make sure it's an actual string and not a variable
if data[s] in ['"', "'"]:
s += 1
e = data.find(data[s-1], s)
# We have the packet name
packetName = data[s:e]
# While s isn't a number we increment it by 1
while len(data) > s + 1 and not data[s].isdigit(): s+= 1
# Make sure we didn't pass a new line, a { or a ( to find that digit
if s > data.find('\n', e) or s > data.find('{', e) or s > data.find('(', e): continue
e = s
# while e is a number we increment it by 1
while data[e].isdigit(): e += 1
# We have the def number -- assuming it's not more than one because of complexity reasons
packetVersion = int(data[s:e])
# Make sure we didn't pass a new line aswell as a { and a (
if e > data.find('\n', s) or e > data.find('{', s) or e > data.find('(', s): continue
# If the packet name already exist in our dict, we append it
if ret.get(packetName, False): ret[packetName].append(packetVersion)
else: ret[packetName] = [packetVersion]
# Find the next occurrence
s = data.find(syntax, s)
return ret
def getDefsForFile(path):
""" Get the definitions for a file path """
defs = {}
try:
data = open(path, "r").read()
except:
return {}
for syntax in DEF_SYNTAXES:
# Get the defintions found using this syntax
ret = getDefForSyntax(data, syntax)
# Remove duplicates
for key, value in ret.items():
# If the key already exist in our defs variable add them together.
if defs.get(key, False): defs[key] = defs[key] + value
# If it didn't exist, create the entry
else: defs[key] = value
return defs
def getFinalDefs(oldDefs, newDefs):
""" Combines old and new defs and returns it """
# make sure we include all manually added defs
for k, v in oldDefs.items():
if newDefs.get(k, False) is False: newDefs[k] = v
# merge old and new def
newDefs[k] = list(set(newDefs[k] + oldDefs[k]))
return newDefs
def createManifest(newData):
oldManifest = {}
# Load the old manifest
try: oldManifest = json.loads(open('manifest.json', 'r').read())
except: pass
newManifest = { "files": newData.get('files', {}), "defs": getFinalDefs(oldManifest.get('defs', {}), newData.get('defs', {})) }
for key, value in oldManifest.get('files', {}).items():
# If the file isn't part of the new manifest ignore it
if newManifest['files'].get(key, None) is None: continue
# If it has settings we want to keep
if isinstance(value, dict):
value['hash'] = newManifest['files'][key]
newManifest['files'][key] = value
else: newManifest['files'][key] = newManifest['files'][key]
# Write/create the new manifest
open('manifest.json', 'w').write(json.dumps(newManifest, indent=INDENTING))
def getFileInfo(path):
return {
"hash": sha1(open(path, "rb").read()),
"path": path,
"defs": getDefsForFile(path)
}
def main():
with pool(NUMBER_OF_THREADS) as p:
files = p.map(getFileInfo, getFilePathsFor('.', IGNORE_FILES))
data = { "files": {}, "defs": {} }
for file in files:
# Get the hash value for the file path
data['files'][file['path'][2:]] = file['hash']
# Get the defs from the file
for key, value in file['defs'].items():
# If the packet already exists in our data[defs] append them, else create it
if data['defs'].get(key, False): data['defs'][key] += value
else: data['defs'][key] = value
# To assure there are no duplicates we add it into a set, then back into a list
data['defs'][key] = list(set(data['defs'][key]))
createManifest(data)
if __name__ == '__main__':
main()
| import json
import os
from Crypto.Hash import SHA256
from multiprocessing.pool import ThreadPool as pool
DEF_SYNTAXES = [
"dispatch.toServer",
"dispatch.toClient",
"dispatch.hook"
]
IGNORE_FILES = [
'createManifest.py',
'createManifest.exe',
'manifest.json',
'module.json'
]
NUMBER_OF_THREADS = 6
INDENTING = 4
def getFilePathsFor(path, excluded):
""" Get all the files for a given filepath
:param path: The parent directory
:param excluded: An array of excluded file names
:return: An array of filepaths
"""
filePaths = []
for dir, b, files in os.walk(path):
if dir.count('.git') != 0: continue
for file in files:
if file not in excluded:
filePaths.append((dir + '/' + file).replace('\\\\', '\\').replace('\\', '/'))
return filePaths
def sha1(data):
""" Get the SHA256 value of a byte string """
s = SHA256.new()
s.update(data)
return s.hexdigest()
def getDefForSyntax(data, syntax):
""" Gets the definition version and name using a given syntax
:param data: the data to look for this information in
:param syntax: for example "dispatch.toServer"
:return: A dictionary with the key as the packet name and a list with the number
"""
ret = {}
syntaxLen = len(syntax)
s = data.find(syntax)
# While we can't find anymore
while s != -1 and len(data) > s + 1:
s += syntaxLen + 1
# We make sure it's an actual string and not a variable
if data[s] in ['"', "'"]:
s += 1
e = data.find(data[s-1], s)
# We have the packet name
packetName = data[s:e]
# While s isn't a number we increment it by 1
while len(data) > s + 1 and not data[s].isdigit(): s+= 1
# Make sure we didn't pass a new line, a { or a ( to find that digit
if s > data.find('\n', e) or s > data.find('{', e) or s > data.find('(', e): continue
e = s
# while e is a number we increment it by 1
while data[e].isdigit(): e += 1
# We have the def number -- assuming it's not more than one because of complexity reasons
packetVersion = int(data[s:e])
# Make sure we didn't pass a new line aswell as a { and a (
if e > data.find('\n', s) or e > data.find('{', s) or e > data.find('(', s): continue
# If the packet name already exist in our dict, we append it
if ret.get(packetName, False): ret[packetName].append(packetVersion)
else: ret[packetName] = [packetVersion]
# Find the next occurrence
s = data.find(syntax, s)
return ret
def getDefsForFile(path):
""" Get the definitions for a file path """
defs = {}
try:
data = open(path, "r").read()
except:
return {}
for syntax in DEF_SYNTAXES:
# Get the defintions found using this syntax
ret = getDefForSyntax(data, syntax)
# Remove duplicates
for key, value in ret.items():
# If the key already exist in our defs variable add them together.
if defs.get(key, False): defs[key] = defs[key] + value
# If it didn't exist, create the entry
else: defs[key] = value
return defs
def getFinalDefs(oldDefs, newDefs):
""" Combines old and new defs and returns it """
# make sure we include all manually added defs
for k, v in oldDefs.items():
if newDefs.get(k, False) is False: newDefs[k] = v
# merge old and new def
newDefs[k] = list(set(newDefs[k] + oldDefs[k]))
return newDefs
def createManifest(newData):
oldManifest = {}
# Load the old manifest
try: oldManifest = json.loads(open('manifest.json', 'r').read())
except: pass
newManifest = { "files": newData.get('files', {}), "defs": getFinalDefs(oldManifest.get('defs', {}), newData.get('defs', {})) }
for key, value in oldManifest.get('files', {}).items():
# If the file isn't part of the new manifest ignore it
if newManifest['files'].get(key, None) is None: continue
# If it has settings we want to keep
if isinstance(value, dict):
value['hash'] = newManifest['files'][key]
newManifest['files'][key] = value
else: newManifest['files'][key] = newManifest['files'][key]
# Write/create the new manifest
open('manifest.json', 'w').write(json.dumps(newManifest, indent=INDENTING))
def getFileInfo(path):
return {
"hash": sha1(open(path, "rb").read()),
"path": path,
"defs": getDefsForFile(path)
}
def main():
with pool(NUMBER_OF_THREADS) as p:
files = p.map(getFileInfo, getFilePathsFor('.', IGNORE_FILES))
data = { "files": {}, "defs": {} }
for file in files:
# Get the hash value for the file path
data['files'][file['path'][2:]] = file['hash']
# Get the defs from the file
for key, value in file['defs'].items():
# If the packet already exists in our data[defs] append them, else create it
if data['defs'].get(key, False): data['defs'][key] += value
else: data['defs'][key] = value
# To assure there are no duplicates we add it into a set, then back into a list
data['defs'][key] = list(set(data['defs'][key]))
createManifest(data)
if __name__ == '__main__':
main()
| en | 0.886336 | Get all the files for a given filepath
:param path: The parent directory
:param excluded: An array of excluded file names
:return: An array of filepaths Get the SHA256 value of a byte string Gets the definition version and name using a given syntax
:param data: the data to look for this information in
:param syntax: for example "dispatch.toServer"
:return: A dictionary with the key as the packet name and a list with the number # While we can't find anymore # We make sure it's an actual string and not a variable # We have the packet name # While s isn't a number we increment it by 1 # Make sure we didn't pass a new line, a { or a ( to find that digit # while e is a number we increment it by 1 # We have the def number -- assuming it's not more than one because of complexity reasons # Make sure we didn't pass a new line aswell as a { and a ( # If the packet name already exist in our dict, we append it # Find the next occurrence Get the definitions for a file path # Get the defintions found using this syntax # Remove duplicates # If the key already exist in our defs variable add them together. # If it didn't exist, create the entry Combines old and new defs and returns it # make sure we include all manually added defs # merge old and new def # Load the old manifest # If the file isn't part of the new manifest ignore it # If it has settings we want to keep # Write/create the new manifest # Get the hash value for the file path # Get the defs from the file # If the packet already exists in our data[defs] append them, else create it # To assure there are no duplicates we add it into a set, then back into a list | 2.623403 | 3 |
extras/wordlist.py | i1470s/IVRY | 3 | 6623212 | words = ['simp', 'SIMP', 'fag', 'FAG', 'faggot', 'FAGGOT', 'nigger', 'NIGGER'] | words = ['simp', 'SIMP', 'fag', 'FAG', 'faggot', 'FAGGOT', 'nigger', 'NIGGER'] | none | 1 | 2.071484 | 2 | |
app/models.py | rahulraj6000/E-commerce | 0 | 6623213 | from django.db import models
from django.contrib.auth.models import User
from django.core.validators import MaxValueValidator, MinValueValidator
STATE_CHOICES = (
('Andaman & Nicobar Island ', 'Andaman & Nicobar Islands'),
('Andhra Pradesh', 'Andhra Pradesh'),
('Arunachal Pradesh', 'Arunachal Pradesh'),
('Assam', 'Assam'),
('Bihar', 'Bihar'),
('Chandigarh', 'Chandigarh'),
('Chhattisgarh', 'Chhattisgarh')
)
class Customer(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
name = models.CharField(max_length=200)
locality = models.IntegerField()
city = models.CharField(max_length=50)
zipcode = models.IntegerField()
state = models.CharField(choices=STATE_CHOICES, max_length=50)
def __str__(self):
return str(self.id)
CATEGORY_CHOICES = (
('M', 'Mobile'),
('L', 'Laptop'),
('TW', 'Top Wear'),
('BW', 'Bottom Wear'),
)
class Product(models.Model):
title = models.CharField(max_length=100)
selling_price = models.FloatField()
discounted_price = models.FloatField()
description = models.TextField()
brand = models.CharField(max_length=100)
category = models.CharField(choices=CATEGORY_CHOICES, max_length=2)
product_image = models.ImageField(upload_to='productimg')
def __str__(self):
return str(self.id)
class Cart(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
product = models.ForeignKey(Product, on_delete=models.CASCADE)
quantity = models.PositiveIntegerField(default=1)
def __str__(self):
return str(self.id)
@property
def total_cost(self):
return self.quantity * self.product.discounted_price
STATUS_CHOICES = (
('Accepted', 'Accepted'),
('Packed', 'Packed'),
('On The Way', 'On The Way'),
('Delivered', 'Delivered'),
('Cancel', 'Cancel')
)
class OrderPlaced(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
product = models.ForeignKey(Product, on_delete=models.CASCADE)
quantity = models.PositiveIntegerField(default=1)
ordered_date = models.DateField(auto_now_add=True)
status = models.CharField(
max_length=50, choices=STATUS_CHOICES, default='Pending')
@property
def total_cost(self):
return self.quantity * self.product.discounted_price
| from django.db import models
from django.contrib.auth.models import User
from django.core.validators import MaxValueValidator, MinValueValidator
STATE_CHOICES = (
('Andaman & Nicobar Island ', 'Andaman & Nicobar Islands'),
('Andhra Pradesh', 'Andhra Pradesh'),
('Arunachal Pradesh', 'Arunachal Pradesh'),
('Assam', 'Assam'),
('Bihar', 'Bihar'),
('Chandigarh', 'Chandigarh'),
('Chhattisgarh', 'Chhattisgarh')
)
class Customer(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
name = models.CharField(max_length=200)
locality = models.IntegerField()
city = models.CharField(max_length=50)
zipcode = models.IntegerField()
state = models.CharField(choices=STATE_CHOICES, max_length=50)
def __str__(self):
return str(self.id)
CATEGORY_CHOICES = (
('M', 'Mobile'),
('L', 'Laptop'),
('TW', 'Top Wear'),
('BW', 'Bottom Wear'),
)
class Product(models.Model):
title = models.CharField(max_length=100)
selling_price = models.FloatField()
discounted_price = models.FloatField()
description = models.TextField()
brand = models.CharField(max_length=100)
category = models.CharField(choices=CATEGORY_CHOICES, max_length=2)
product_image = models.ImageField(upload_to='productimg')
def __str__(self):
return str(self.id)
class Cart(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
product = models.ForeignKey(Product, on_delete=models.CASCADE)
quantity = models.PositiveIntegerField(default=1)
def __str__(self):
return str(self.id)
@property
def total_cost(self):
return self.quantity * self.product.discounted_price
STATUS_CHOICES = (
('Accepted', 'Accepted'),
('Packed', 'Packed'),
('On The Way', 'On The Way'),
('Delivered', 'Delivered'),
('Cancel', 'Cancel')
)
class OrderPlaced(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
product = models.ForeignKey(Product, on_delete=models.CASCADE)
quantity = models.PositiveIntegerField(default=1)
ordered_date = models.DateField(auto_now_add=True)
status = models.CharField(
max_length=50, choices=STATUS_CHOICES, default='Pending')
@property
def total_cost(self):
return self.quantity * self.product.discounted_price
| none | 1 | 2.412412 | 2 | |
apps/locations/forms.py | ExpoAshique/ProveBanking__s | 0 | 6623214 | from django import forms
from django.utils.translation import ugettext_lazy as _
from med_social.forms.base import DeletableFieldsetForm
from med_social.utils import slugify
from .models import Location
from med_social.forms.mixins import FieldsetMixin
class LocationCreateForm(forms.ModelForm, FieldsetMixin):
fieldsets = (
('', { 'rows':(
('city',),
),
}),
)
class Meta:
model = Location
fields = ('city',)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request', None)
super(LocationCreateForm, self).__init__(*args, **kwargs)
self.__setup_fieldsets__()
def clean_city(self):
city = self.cleaned_data.get("city")
slug = slugify(city.strip())
if self._meta.model.objects.filter(slug=slug).exists():
raise forms.ValidationError(
_("location '{}' already exists in database").format(city))
return city
class LocationEditForm(DeletableFieldsetForm, FieldsetMixin):
fieldsets = (
('', { 'rows':(
('city',),
),
}),
)
class Meta:
model = Location
fields = ('city',)
deletable = False
def __init__(self, *args, **kwargs):
super(LocationEditForm, self).__init__(*args, **kwargs) | from django import forms
from django.utils.translation import ugettext_lazy as _
from med_social.forms.base import DeletableFieldsetForm
from med_social.utils import slugify
from .models import Location
from med_social.forms.mixins import FieldsetMixin
class LocationCreateForm(forms.ModelForm, FieldsetMixin):
fieldsets = (
('', { 'rows':(
('city',),
),
}),
)
class Meta:
model = Location
fields = ('city',)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request', None)
super(LocationCreateForm, self).__init__(*args, **kwargs)
self.__setup_fieldsets__()
def clean_city(self):
city = self.cleaned_data.get("city")
slug = slugify(city.strip())
if self._meta.model.objects.filter(slug=slug).exists():
raise forms.ValidationError(
_("location '{}' already exists in database").format(city))
return city
class LocationEditForm(DeletableFieldsetForm, FieldsetMixin):
fieldsets = (
('', { 'rows':(
('city',),
),
}),
)
class Meta:
model = Location
fields = ('city',)
deletable = False
def __init__(self, *args, **kwargs):
super(LocationEditForm, self).__init__(*args, **kwargs) | none | 1 | 2.042417 | 2 | |
cointrol/utils.py | fakegit/cointrol | 967 | 6623215 | import json as _json
from decimal import Decimal
from functools import partial
import rest_framework.utils.encoders
class JSONEncoder(rest_framework.utils.encoders.JSONEncoder):
def default(self, o):
if isinstance(o, Decimal):
return float(o)
return super().default(o)
class json:
dumps = partial(_json.dumps, cls=JSONEncoder)
loads = partial(_json.loads)
| import json as _json
from decimal import Decimal
from functools import partial
import rest_framework.utils.encoders
class JSONEncoder(rest_framework.utils.encoders.JSONEncoder):
def default(self, o):
if isinstance(o, Decimal):
return float(o)
return super().default(o)
class json:
dumps = partial(_json.dumps, cls=JSONEncoder)
loads = partial(_json.loads)
| none | 1 | 2.465285 | 2 | |
zilean/datasets/basics.py | A-Hilaly/zilean | 0 | 6623216 | <filename>zilean/datasets/basics.py
from greww.data.mysql import MysqlPen as M
class BasicTable(object):
"""
Basic Table Modelisation Sample
"""
__slots__ = ["_data"]
db = ""
table = ""
fields = []
def __init__(self):
"""
Initialise class instance with table content as data
====================================================
"""
self._data = M.table_content(self.db, self.table)
def update(self):
"""
Call __init__() in order to rewrite _data attribute
with the newest table
====================================================
"""
self.__init__()
@property
def data(self):
return self._data
@classmethod
def DATA(cls):
obj = object.__new__(cls)
obj.__init__()
return obj._data
@classmethod
def _quantify(cls, line=0):
"""
Return a Dict (Json type) with fields as keys and line
as values
=====================================================
"""
return dict(zip(self.fields, line))
class ZileanCache(BasicTable):
"""
Zilean Cache Tables
"""
__slots__ = ["_data"]
db = "zileancache"
table = ""
fields = []
class ZileanSys(BasicTable):
"""
Zilean System Tables
"""
__slots__ = ["_data"]
db = "zileansys"
table = ""
fields = []
| <filename>zilean/datasets/basics.py
from greww.data.mysql import MysqlPen as M
class BasicTable(object):
"""
Basic Table Modelisation Sample
"""
__slots__ = ["_data"]
db = ""
table = ""
fields = []
def __init__(self):
"""
Initialise class instance with table content as data
====================================================
"""
self._data = M.table_content(self.db, self.table)
def update(self):
"""
Call __init__() in order to rewrite _data attribute
with the newest table
====================================================
"""
self.__init__()
@property
def data(self):
return self._data
@classmethod
def DATA(cls):
obj = object.__new__(cls)
obj.__init__()
return obj._data
@classmethod
def _quantify(cls, line=0):
"""
Return a Dict (Json type) with fields as keys and line
as values
=====================================================
"""
return dict(zip(self.fields, line))
class ZileanCache(BasicTable):
"""
Zilean Cache Tables
"""
__slots__ = ["_data"]
db = "zileancache"
table = ""
fields = []
class ZileanSys(BasicTable):
"""
Zilean System Tables
"""
__slots__ = ["_data"]
db = "zileansys"
table = ""
fields = []
| en | 0.636711 | Basic Table Modelisation Sample Initialise class instance with table content as data ==================================================== Call __init__() in order to rewrite _data attribute with the newest table ==================================================== Return a Dict (Json type) with fields as keys and line as values ===================================================== Zilean Cache Tables Zilean System Tables | 2.615186 | 3 |
setup.py | dani-garcia/multiview_gpu | 5 | 6623217 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="multiview_gpu",
version="0.1.0",
author="<NAME>",
author_email="<EMAIL>",
description="GPU-accelerated multiview clustering and dimensionality reduction",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/dani-garcia/multiview_gpu",
keywords=["multiview", "clustering", "dimensionality reduction"],
packages=setuptools.find_packages(),
install_requires=[
'numpy',
],
extras_require={
"tf": ["tensorflow>=1.12.0"],
"tf_gpu": ["tensorflow-gpu>=1.12.0"],
},
setup_requires=[
"pytest-runner"
],
tests_require=[
"pytest",
"pytest-benchmark"
],
classifiers=[
"Programming Language :: Python",
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
"Development Status :: 3 - Alpha",
"Environment :: Other Environment",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
) | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="multiview_gpu",
version="0.1.0",
author="<NAME>",
author_email="<EMAIL>",
description="GPU-accelerated multiview clustering and dimensionality reduction",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/dani-garcia/multiview_gpu",
keywords=["multiview", "clustering", "dimensionality reduction"],
packages=setuptools.find_packages(),
install_requires=[
'numpy',
],
extras_require={
"tf": ["tensorflow>=1.12.0"],
"tf_gpu": ["tensorflow-gpu>=1.12.0"],
},
setup_requires=[
"pytest-runner"
],
tests_require=[
"pytest",
"pytest-benchmark"
],
classifiers=[
"Programming Language :: Python",
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
"Development Status :: 3 - Alpha",
"Environment :: Other Environment",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
) | none | 1 | 1.402467 | 1 | |
scripts/ebook_name_fix.py | mcxiaoke/python-labs | 7 | 6623218 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: mcxiaoke
# @Date: 2017-05-29 15:01:41
# @Last Modified by: mcxiaoke
# @Last Modified time: 2017-06-27 17:09:59
from __future__ import print_function
import sys
import os
import codecs
import re
import string
import shutil
from datetime import datetime
ISO_DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
FORMATS = ('.pdf', '.epub', '.mobi', '.azw3', '.djv', '.txt')
INVALID_CHARS = '~!@#$%^&*()+,._[]{}<>?`【】《》：”‘，。？'
processed = []
def log(x):
print(x)
def _replace_invalid(s):
for c in INVALID_CHARS:
if c in s:
s = s.replace(c, " ")
s = s.replace(' ', ' ')
s = s.replace(' ', ' ')
return s.strip()
def nomalize_name(old_name):
'''
1. strip (xxx) at name start
(Wiley Finance 019)Portfolio Theory and Performance Analysis.pdf
2. strip 20_xxx at name start
10_Novel Sensors for Food Inspection_Modelling,Fabrication and Experimentation 2014.pdf
101 Ready-to-Use Excel Formulas-John Wiley & Sons(2014).pdf
06.Head First Python.PDF
3. strip press company name
Addison-Wesley Starting Out with Visual Basic 2012 6th (2014).pdf
ADDISON.WESLEY.DATA.JUST.RIGHT.2014.pdf
4. repalce special chars and strip
[] 【】 _
5. capitalize characters
04 - Seven Concurrency Models in Seven Weeks_When Threads (2014).epub
'''
# print('original: {}'.format(base))
new_name = old_name
# pass 1
p = re.compile(r'(?:\(.+?\))\s*(.+)', re.I)
m = p.match(old_name)
if m:
new_name = m.group(1)
# print('pass1: {}'.format(new_base))
# pass 2
p = re.compile(r'\d+[-_\.](.+)', re.I)
m = p.match(new_name)
if m:
new_name = m.group(1)
# print('pass2: {}'.format(new_base))
# pass 4
new_name = _replace_invalid(new_name)
# print('pass4: {}'.format(new_base))
# pass 5
# new_base = string.capwords(new_base)
# print('pass5: {}'.format(new_base))
return (old_name, new_name)
def fix_fileanme(old_path, dry_run=False):
curdir = os.path.dirname(old_path)
# log('file: {}'.format(old_path))
old_name = os.path.basename(old_path)
base, ext = os.path.splitext(old_name)
if not ext:
return old_path
if ext.lower() not in FORMATS:
return old_path
# print(name)
old_base, new_base = nomalize_name(base)
if old_base == new_base:
return old_path
new_name = '{}{}'.format(new_base, ext.lower())
new_path = os.path.join(curdir, new_name)
# print(type(old_path), type(new_path))
# print(repr(old_path)[1:-1])
if not os.path.exists(old_path):
log('Error: {}'.format(old_path))
return old_path
if new_path != old_path:
if not os.path.exists(new_path):
log('Rename: {} -> {}'.format(old_name, new_name))
processed.append((old_path, new_path))
if not dry_run:
shutil.move(old_path, new_path)
return new_path
else:
log('Exists: {}'.format(new_path))
else:
log('NoNeed: {}'.format(new_path))
return old_path
def rename_ebooks(root, dry_run=False):
for curdir, subdirs, filenames in os.walk(root, topdown=True):
log('-- {} --'.format(curdir))
for name in filenames:
filename = os.path.join(curdir, name)
fix_fileanme(filename, dry_run)
logfile = os.path.join(root, 'logs.txt')
log('processed count: {}'.format(len(processed)))
with codecs.open(logfile, 'w', 'utf-8') as f:
timestamp = datetime.strftime(datetime.now(), ISO_DATE_FORMAT)
f.write('--- Time: {} ---\n'.format(timestamp))
f.write('--- Root: {} ---\n'.format(root))
if dry_run:
f.write('--- Mode: dry run mode, no files will be renamed. ---\n')
for (o, n) in processed:
f.write('{} -> {}\n'.format(o, n))
f.flush()
def contains_cjk(text):
cjk_pattern = re.compile('[\u4e00-\u9fa5]+')
return cjk_pattern.search(text)
def remove_cjk(root, dry_run=False):
for curdir, subdirs, filenames in os.walk(root, topdown=True):
log('-- {} --'.format(curdir))
for name in filenames:
if contains_cjk(name):
log('Delete {}'.format(name))
os.remove(os.path.join(curdir, name))
if __name__ == '__main__':
sys.path.insert(1, os.path.dirname(
os.path.dirname(os.path.realpath(__file__))))
print(sys.argv)
if len(sys.argv) < 2:
log('Usage: {} target_dir -n'.format(sys.argv[0]))
sys.exit(1)
dry_run = False
if len(sys.argv) == 3 and sys.argv[2] == '-n':
dry_run = True
log(u"Mode: dry run mode, no files will be renamed.")
root = os.path.abspath(sys.argv[1])
log('Root: {}'.format(root))
rename_ebooks(root, dry_run)
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: mcxiaoke
# @Date: 2017-05-29 15:01:41
# @Last Modified by: mcxiaoke
# @Last Modified time: 2017-06-27 17:09:59
from __future__ import print_function
import sys
import os
import codecs
import re
import string
import shutil
from datetime import datetime
ISO_DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
FORMATS = ('.pdf', '.epub', '.mobi', '.azw3', '.djv', '.txt')
INVALID_CHARS = '~!@#$%^&*()+,._[]{}<>?`【】《》：”‘，。？'
processed = []
def log(x):
print(x)
def _replace_invalid(s):
for c in INVALID_CHARS:
if c in s:
s = s.replace(c, " ")
s = s.replace(' ', ' ')
s = s.replace(' ', ' ')
return s.strip()
def nomalize_name(old_name):
'''
1. strip (xxx) at name start
(Wiley Finance 019)Portfolio Theory and Performance Analysis.pdf
2. strip 20_xxx at name start
10_Novel Sensors for Food Inspection_Modelling,Fabrication and Experimentation 2014.pdf
101 Ready-to-Use Excel Formulas-John Wiley & Sons(2014).pdf
06.Head First Python.PDF
3. strip press company name
Addison-Wesley Starting Out with Visual Basic 2012 6th (2014).pdf
ADDISON.WESLEY.DATA.JUST.RIGHT.2014.pdf
4. repalce special chars and strip
[] 【】 _
5. capitalize characters
04 - Seven Concurrency Models in Seven Weeks_When Threads (2014).epub
'''
# print('original: {}'.format(base))
new_name = old_name
# pass 1
p = re.compile(r'(?:\(.+?\))\s*(.+)', re.I)
m = p.match(old_name)
if m:
new_name = m.group(1)
# print('pass1: {}'.format(new_base))
# pass 2
p = re.compile(r'\d+[-_\.](.+)', re.I)
m = p.match(new_name)
if m:
new_name = m.group(1)
# print('pass2: {}'.format(new_base))
# pass 4
new_name = _replace_invalid(new_name)
# print('pass4: {}'.format(new_base))
# pass 5
# new_base = string.capwords(new_base)
# print('pass5: {}'.format(new_base))
return (old_name, new_name)
def fix_fileanme(old_path, dry_run=False):
curdir = os.path.dirname(old_path)
# log('file: {}'.format(old_path))
old_name = os.path.basename(old_path)
base, ext = os.path.splitext(old_name)
if not ext:
return old_path
if ext.lower() not in FORMATS:
return old_path
# print(name)
old_base, new_base = nomalize_name(base)
if old_base == new_base:
return old_path
new_name = '{}{}'.format(new_base, ext.lower())
new_path = os.path.join(curdir, new_name)
# print(type(old_path), type(new_path))
# print(repr(old_path)[1:-1])
if not os.path.exists(old_path):
log('Error: {}'.format(old_path))
return old_path
if new_path != old_path:
if not os.path.exists(new_path):
log('Rename: {} -> {}'.format(old_name, new_name))
processed.append((old_path, new_path))
if not dry_run:
shutil.move(old_path, new_path)
return new_path
else:
log('Exists: {}'.format(new_path))
else:
log('NoNeed: {}'.format(new_path))
return old_path
def rename_ebooks(root, dry_run=False):
for curdir, subdirs, filenames in os.walk(root, topdown=True):
log('-- {} --'.format(curdir))
for name in filenames:
filename = os.path.join(curdir, name)
fix_fileanme(filename, dry_run)
logfile = os.path.join(root, 'logs.txt')
log('processed count: {}'.format(len(processed)))
with codecs.open(logfile, 'w', 'utf-8') as f:
timestamp = datetime.strftime(datetime.now(), ISO_DATE_FORMAT)
f.write('--- Time: {} ---\n'.format(timestamp))
f.write('--- Root: {} ---\n'.format(root))
if dry_run:
f.write('--- Mode: dry run mode, no files will be renamed. ---\n')
for (o, n) in processed:
f.write('{} -> {}\n'.format(o, n))
f.flush()
def contains_cjk(text):
cjk_pattern = re.compile('[\u4e00-\u9fa5]+')
return cjk_pattern.search(text)
def remove_cjk(root, dry_run=False):
for curdir, subdirs, filenames in os.walk(root, topdown=True):
log('-- {} --'.format(curdir))
for name in filenames:
if contains_cjk(name):
log('Delete {}'.format(name))
os.remove(os.path.join(curdir, name))
if __name__ == '__main__':
sys.path.insert(1, os.path.dirname(
os.path.dirname(os.path.realpath(__file__))))
print(sys.argv)
if len(sys.argv) < 2:
log('Usage: {} target_dir -n'.format(sys.argv[0]))
sys.exit(1)
dry_run = False
if len(sys.argv) == 3 and sys.argv[2] == '-n':
dry_run = True
log(u"Mode: dry run mode, no files will be renamed.")
root = os.path.abspath(sys.argv[1])
log('Root: {}'.format(root))
rename_ebooks(root, dry_run)
| en | 0.45803 | #!/usr/bin/env python # -*- coding: utf-8 -*- # @Author: mcxiaoke # @Date: 2017-05-29 15:01:41 # @Last Modified by: mcxiaoke # @Last Modified time: 2017-06-27 17:09:59 #$%^&*()+,._[]{}<>?`【】《》：”‘，。？' 1. strip (xxx) at name start (Wiley Finance 019)Portfolio Theory and Performance Analysis.pdf 2. strip 20_xxx at name start 10_Novel Sensors for Food Inspection_Modelling,Fabrication and Experimentation 2014.pdf 101 Ready-to-Use Excel Formulas-John Wiley & Sons(2014).pdf 06.Head First Python.PDF 3. strip press company name Addison-Wesley Starting Out with Visual Basic 2012 6th (2014).pdf ADDISON.WESLEY.DATA.JUST.RIGHT.2014.pdf 4. repalce special chars and strip [] 【】 _ 5. capitalize characters 04 - Seven Concurrency Models in Seven Weeks_When Threads (2014).epub # print('original: {}'.format(base)) # pass 1 # print('pass1: {}'.format(new_base)) # pass 2 # print('pass2: {}'.format(new_base)) # pass 4 # print('pass4: {}'.format(new_base)) # pass 5 # new_base = string.capwords(new_base) # print('pass5: {}'.format(new_base)) # log('file: {}'.format(old_path)) # print(name) # print(type(old_path), type(new_path)) # print(repr(old_path)[1:-1]) | 2.763051 | 3 |
python/tests/unit/test_ledger_tls.py | DACH-NY/dazl-client | 0 | 6623219 | <reponame>DACH-NY/dazl-client<filename>python/tests/unit/test_ledger_tls.py<gh_stars>0
# Copyright (c) 2017-2022 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
from asyncio import sleep
from dazl import connect, testing
import pytest
from .config import daml_sdk_versions
@pytest.mark.asyncio
@pytest.mark.parametrize("daml_sdk_version", daml_sdk_versions())
async def test_tls(daml_sdk_version):
with testing.sandbox(version=daml_sdk_version, use_tls=True) as sandbox:
async with connect(url=sandbox.url, admin=True, cert=sandbox.public_cert) as conn:
# the result of this call is not particularly interesting;
# we just need to make sure it doesn't crash
await conn.list_package_ids()
await sleep(1)
| # Copyright (c) 2017-2022 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
from asyncio import sleep
from dazl import connect, testing
import pytest
from .config import daml_sdk_versions
@pytest.mark.asyncio
@pytest.mark.parametrize("daml_sdk_version", daml_sdk_versions())
async def test_tls(daml_sdk_version):
with testing.sandbox(version=daml_sdk_version, use_tls=True) as sandbox:
async with connect(url=sandbox.url, admin=True, cert=sandbox.public_cert) as conn:
# the result of this call is not particularly interesting;
# we just need to make sure it doesn't crash
await conn.list_package_ids()
await sleep(1) | en | 0.875815 | # Copyright (c) 2017-2022 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # the result of this call is not particularly interesting; # we just need to make sure it doesn't crash | 1.851376 | 2 |
apps/jobs/migrations/0004_auto_20201028_1104.py | iamjackwachira/wwfh | 0 | 6623220 | # Generated by Django 3.1.2 on 2020-10-28 11:04
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('jobs', '0003_auto_20201028_1035'),
]
operations = [
migrations.RenameField(
model_name='jobpost',
old_name='regional_restrictions',
new_name='job_category',
),
migrations.RemoveField(
model_name='jobpost',
name='category',
),
migrations.DeleteModel(
name='Category',
),
]
| # Generated by Django 3.1.2 on 2020-10-28 11:04
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('jobs', '0003_auto_20201028_1035'),
]
operations = [
migrations.RenameField(
model_name='jobpost',
old_name='regional_restrictions',
new_name='job_category',
),
migrations.RemoveField(
model_name='jobpost',
name='category',
),
migrations.DeleteModel(
name='Category',
),
]
| en | 0.824272 | # Generated by Django 3.1.2 on 2020-10-28 11:04 | 1.540819 | 2 |
base/autoencoder.py | RichardLeeK/MachineLearning | 1 | 6623221 | from keras.layers import Input, Dense
from keras.models import Model
import numpy as np
encoding_dim = 32
input_img = Input(shape=(16384,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(16384, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
encoder = Model(input_img, encoded)
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
file = open('D:/Richard/CBFV/Auto-encoder/001040SE_interpolated.csv')
lines = file.readlines()
file.close()
signals = []
for line in lines:
sl = line.split(',')
cur_sig = []
for v in sl[1:]:
cur_sig.append(float(v))
signals.append(cur_sig)
imgs = []
for sig in signals:
dim = len(sig)
img = np.zeros((dim, dim))
for i in range(dim):
idx = round(sig[i] * (dim - 1))
img[dim-idx-1][i] = 255
if idx > 0:
img[dim-idx][i] = 125
if idx < dim - 1:
img[dim-idx-2][i] = 125
imgs.append(img)
x = np.array(imgs).astype('float32')/255
x = x.reshape((len(x), np.prod(x.shape[1:])))
autoencoder.fit(x, x, epochs=200, batch_size=100, shuffle=True)
encoded_imgs = encoder.predict(x)
decoded_imgs = decoder.predict(encoded_imgs)
"""
from keras.datasets import mnist
import numpy as np
(x, _), (x2, _) = mnist.load_data()
x = x[:10]
x2 = x2[:10]
x = x.astype('float32')/255
x2 = x2.astype('float32')/255
x = x.reshape((len(x), np.prod(x.shape[1:])))
x2 = x2.reshape((len(x2), np.prod(x2.shape[1:])))
print (x.shape)
print(x2.shape)
autoencoder.fit(x, x, epochs=30, batch_size=100, shuffle=True, validation_data=(x2, x2))
encoded_imgs = encoder.predict(x2)
decoded_imgs = decoder.predict(encoded_imgs)
"""
import matplotlib.pyplot as plt
n = 8
plt.figure(figsize=(20,4))
for i in range(n):
ax = plt.subplot(2, n, i+1)
plt.imshow(x[i].reshape(128, 128))
#plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(128, 128))
#plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
| from keras.layers import Input, Dense
from keras.models import Model
import numpy as np
encoding_dim = 32
input_img = Input(shape=(16384,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(16384, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
encoder = Model(input_img, encoded)
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
file = open('D:/Richard/CBFV/Auto-encoder/001040SE_interpolated.csv')
lines = file.readlines()
file.close()
signals = []
for line in lines:
sl = line.split(',')
cur_sig = []
for v in sl[1:]:
cur_sig.append(float(v))
signals.append(cur_sig)
imgs = []
for sig in signals:
dim = len(sig)
img = np.zeros((dim, dim))
for i in range(dim):
idx = round(sig[i] * (dim - 1))
img[dim-idx-1][i] = 255
if idx > 0:
img[dim-idx][i] = 125
if idx < dim - 1:
img[dim-idx-2][i] = 125
imgs.append(img)
x = np.array(imgs).astype('float32')/255
x = x.reshape((len(x), np.prod(x.shape[1:])))
autoencoder.fit(x, x, epochs=200, batch_size=100, shuffle=True)
encoded_imgs = encoder.predict(x)
decoded_imgs = decoder.predict(encoded_imgs)
"""
from keras.datasets import mnist
import numpy as np
(x, _), (x2, _) = mnist.load_data()
x = x[:10]
x2 = x2[:10]
x = x.astype('float32')/255
x2 = x2.astype('float32')/255
x = x.reshape((len(x), np.prod(x.shape[1:])))
x2 = x2.reshape((len(x2), np.prod(x2.shape[1:])))
print (x.shape)
print(x2.shape)
autoencoder.fit(x, x, epochs=30, batch_size=100, shuffle=True, validation_data=(x2, x2))
encoded_imgs = encoder.predict(x2)
decoded_imgs = decoder.predict(encoded_imgs)
"""
import matplotlib.pyplot as plt
n = 8
plt.figure(figsize=(20,4))
for i in range(n):
ax = plt.subplot(2, n, i+1)
plt.imshow(x[i].reshape(128, 128))
#plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(128, 128))
#plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
| en | 0.425148 | from keras.datasets import mnist import numpy as np (x, _), (x2, _) = mnist.load_data() x = x[:10] x2 = x2[:10] x = x.astype('float32')/255 x2 = x2.astype('float32')/255 x = x.reshape((len(x), np.prod(x.shape[1:]))) x2 = x2.reshape((len(x2), np.prod(x2.shape[1:]))) print (x.shape) print(x2.shape) autoencoder.fit(x, x, epochs=30, batch_size=100, shuffle=True, validation_data=(x2, x2)) encoded_imgs = encoder.predict(x2) decoded_imgs = decoder.predict(encoded_imgs) #plt.gray() #plt.gray() | 2.639742 | 3 |
utils/download.py | IanDesuyo/AIKyaru | 11 | 6623222 | <filename>utils/download.py<gh_stars>10-100
import logging
from aiofile import async_open
import aiohttp
import re
import json
import os
import brotli
HEADER = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36"
}
async def rank() -> dict:
async with async_open("db/RecommendedRank.json", "r") as f:
data = json.loads(await f.read())
return data
async def GSheet(key: str, gid: str, sql: str = "select%20*"):
"""
Download google sheet and convert to a dict.
Args:
key (str): Between the slashes after spreadsheets/d.
gid (str): The gid value at query.
sql (str, optional): Query sql. Defaults to "select%20*".
Raises:
e: Exceptions caused by ClientSession.
Returns:
A converted dict.
"""
url = f"https://docs.google.com/spreadsheets/u/0/d/{key}/gviz/tq?gid={gid}&tqx=out:json&tq={sql}"
async with aiohttp.ClientSession() as session:
try:
fetchData = await session.get(url, headers=HEADER, timeout=aiohttp.ClientTimeout(total=10.0))
data = re.search(r"(\{.*\})", await fetchData.text())
data = json.loads(data.group(1))
rows = data["table"]["rows"]
cols = [i["label"].strip() for i in data["table"]["cols"]]
result = {}
for row in rows:
temp = {}
for i in range(1, len(cols)):
if isinstance(row["c"][i]["v"], float):
temp[cols[i]] = int(row["c"][i]["v"])
else:
temp[cols[i]] = row["c"][i]["v"]
result[int(row["c"][0]["v"])] = temp
return result
except Exception as e:
logging.error(f"Download Google Sheets failed. {key}({gid})")
raise e
async def gameDB(url: str, filename: str, isBrotli: bool = True):
"""
Download game database from url.
Args:
url (str): Url of the file.
filename (str): The name that should be saved as.
isBrotli (bool, optional): Should it be decompressed by brotli. Defaults to True.
Raises:
e: Exceptions caused by ClientSession.
"""
async with aiohttp.ClientSession() as session:
try:
fetch = await session.get(url, headers=HEADER, timeout=aiohttp.ClientTimeout(total=10.0))
async with async_open(os.path.join("./gameDB", filename), "wb+") as f:
if isBrotli:
await f.write(brotli.decompress(await fetch.content.read()))
else:
await f.write(await fetch.content.read())
except Exception as e:
logging.error(f"Download gameDB failed. ({filename}, {url})")
raise e
async def json_(url: str):
async with aiohttp.ClientSession() as session:
try:
f = await session.get(url, headers=HEADER, timeout=aiohttp.ClientTimeout(total=10.0))
return await f.json()
except Exception as e:
logging.error(f"Download json failed. ({url})")
raise e
| <filename>utils/download.py<gh_stars>10-100
import logging
from aiofile import async_open
import aiohttp
import re
import json
import os
import brotli
HEADER = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36"
}
async def rank() -> dict:
async with async_open("db/RecommendedRank.json", "r") as f:
data = json.loads(await f.read())
return data
async def GSheet(key: str, gid: str, sql: str = "select%20*"):
"""
Download google sheet and convert to a dict.
Args:
key (str): Between the slashes after spreadsheets/d.
gid (str): The gid value at query.
sql (str, optional): Query sql. Defaults to "select%20*".
Raises:
e: Exceptions caused by ClientSession.
Returns:
A converted dict.
"""
url = f"https://docs.google.com/spreadsheets/u/0/d/{key}/gviz/tq?gid={gid}&tqx=out:json&tq={sql}"
async with aiohttp.ClientSession() as session:
try:
fetchData = await session.get(url, headers=HEADER, timeout=aiohttp.ClientTimeout(total=10.0))
data = re.search(r"(\{.*\})", await fetchData.text())
data = json.loads(data.group(1))
rows = data["table"]["rows"]
cols = [i["label"].strip() for i in data["table"]["cols"]]
result = {}
for row in rows:
temp = {}
for i in range(1, len(cols)):
if isinstance(row["c"][i]["v"], float):
temp[cols[i]] = int(row["c"][i]["v"])
else:
temp[cols[i]] = row["c"][i]["v"]
result[int(row["c"][0]["v"])] = temp
return result
except Exception as e:
logging.error(f"Download Google Sheets failed. {key}({gid})")
raise e
async def gameDB(url: str, filename: str, isBrotli: bool = True):
"""
Download game database from url.
Args:
url (str): Url of the file.
filename (str): The name that should be saved as.
isBrotli (bool, optional): Should it be decompressed by brotli. Defaults to True.
Raises:
e: Exceptions caused by ClientSession.
"""
async with aiohttp.ClientSession() as session:
try:
fetch = await session.get(url, headers=HEADER, timeout=aiohttp.ClientTimeout(total=10.0))
async with async_open(os.path.join("./gameDB", filename), "wb+") as f:
if isBrotli:
await f.write(brotli.decompress(await fetch.content.read()))
else:
await f.write(await fetch.content.read())
except Exception as e:
logging.error(f"Download gameDB failed. ({filename}, {url})")
raise e
async def json_(url: str):
async with aiohttp.ClientSession() as session:
try:
f = await session.get(url, headers=HEADER, timeout=aiohttp.ClientTimeout(total=10.0))
return await f.json()
except Exception as e:
logging.error(f"Download json failed. ({url})")
raise e
| en | 0.710662 | Download google sheet and convert to a dict. Args: key (str): Between the slashes after spreadsheets/d. gid (str): The gid value at query. sql (str, optional): Query sql. Defaults to "select%20*". Raises: e: Exceptions caused by ClientSession. Returns: A converted dict. Download game database from url. Args: url (str): Url of the file. filename (str): The name that should be saved as. isBrotli (bool, optional): Should it be decompressed by brotli. Defaults to True. Raises: e: Exceptions caused by ClientSession. | 2.638176 | 3 |
QuestionsBidding/bidding/admin.py | Athi223/Questions-Bidding | 0 | 6623223 | from django.contrib import admin
from .models import Question, Allotment
import json
import random
# Register your models here.
admin.site.register(Question)
@admin.register(Allotment)
class AllotmentAdmin(admin.ModelAdmin):
def save_model(self, request, obj, form, change):
allotments = {}
for i in range(int(form.cleaned_data.get('allotments'))):
allotments[i] = []
obj.allotments = json.dumps(allotments)
obj.room_id = random.randint(1, 2147483647)
obj.scores = json.dumps({})
return super().save_model(request, obj, form, change) | from django.contrib import admin
from .models import Question, Allotment
import json
import random
# Register your models here.
admin.site.register(Question)
@admin.register(Allotment)
class AllotmentAdmin(admin.ModelAdmin):
def save_model(self, request, obj, form, change):
allotments = {}
for i in range(int(form.cleaned_data.get('allotments'))):
allotments[i] = []
obj.allotments = json.dumps(allotments)
obj.room_id = random.randint(1, 2147483647)
obj.scores = json.dumps({})
return super().save_model(request, obj, form, change) | en | 0.968259 | # Register your models here. | 2.192819 | 2 |
rta.py | fact-project/rta_frontend | 1 | 6623224 | <reponame>fact-project/rta_frontend
from flask import Flask
from flask import render_template, Response, request
from datetime import datetime
import lightcurve
from dateutil import parser
from dateutil.relativedelta import relativedelta
app = Flask(__name__)
def _make_response_for_invalid_request(message):
return Response(
response=message,
status=400,
mimetype='application/json'
)
@app.route('/v1/excess', methods=['GET'])
def rta():
args = request.args
try:
d = args.get('start_date', None)
if d:
start_date = parser.parse(d, fuzzy=True).isoformat()
else:
start_date = (datetime.now() - relativedelta(hours=12)).isoformat()
except ValueError:
return _make_response_for_invalid_request('Could not parse start date')
try:
latest_date = parser.parse(args.get('latest_date', datetime.now().isoformat()), fuzzy=True)
latest_date = latest_date.isoformat()
except ValueError:
return _make_response_for_invalid_request('Could not parse latest date')
try:
bin_width = int(args.get('bin_width', 20))
except ValueError:
return _make_response_for_invalid_request('Could not parse bin width')
source = args.get('source', None)
print(start_date)
runs, events = lightcurve.fetch_data(start=start_date, end=latest_date, source=source)
if len(runs) == 0:
return Response(
response='[]',
status=200,
mimetype="application/json"
)
excess = lightcurve.excess(runs, events, bin_width_minutes=bin_width)
excess = excess.drop(['run_start', 'run_stop', 'night'], axis=1)
excess['bin_start'] = excess.time_mean - excess.time_width * 0.5
excess['bin_end'] = excess.time_mean + excess.time_width * 0.5
excess = excess.drop(['time_mean', 'time_width'], axis=1)
resp = Response(
response=excess.to_json(orient='records', date_format='iso'),
status=200,
mimetype="application/json"
)
return resp
@app.route('/')
def hello():
title = 'FACT Real Time Analysis'
return render_template('index.html', title=title)
| from flask import Flask
from flask import render_template, Response, request
from datetime import datetime
import lightcurve
from dateutil import parser
from dateutil.relativedelta import relativedelta
app = Flask(__name__)
def _make_response_for_invalid_request(message):
return Response(
response=message,
status=400,
mimetype='application/json'
)
@app.route('/v1/excess', methods=['GET'])
def rta():
args = request.args
try:
d = args.get('start_date', None)
if d:
start_date = parser.parse(d, fuzzy=True).isoformat()
else:
start_date = (datetime.now() - relativedelta(hours=12)).isoformat()
except ValueError:
return _make_response_for_invalid_request('Could not parse start date')
try:
latest_date = parser.parse(args.get('latest_date', datetime.now().isoformat()), fuzzy=True)
latest_date = latest_date.isoformat()
except ValueError:
return _make_response_for_invalid_request('Could not parse latest date')
try:
bin_width = int(args.get('bin_width', 20))
except ValueError:
return _make_response_for_invalid_request('Could not parse bin width')
source = args.get('source', None)
print(start_date)
runs, events = lightcurve.fetch_data(start=start_date, end=latest_date, source=source)
if len(runs) == 0:
return Response(
response='[]',
status=200,
mimetype="application/json"
)
excess = lightcurve.excess(runs, events, bin_width_minutes=bin_width)
excess = excess.drop(['run_start', 'run_stop', 'night'], axis=1)
excess['bin_start'] = excess.time_mean - excess.time_width * 0.5
excess['bin_end'] = excess.time_mean + excess.time_width * 0.5
excess = excess.drop(['time_mean', 'time_width'], axis=1)
resp = Response(
response=excess.to_json(orient='records', date_format='iso'),
status=200,
mimetype="application/json"
)
return resp
@app.route('/')
def hello():
title = 'FACT Real Time Analysis'
return render_template('index.html', title=title) | none | 1 | 2.409988 | 2 | |
tsl/datasets/prototypes/mixin.py | TorchSpatiotemporal/tsl | 4 | 6623225 | import numpy as np
import pandas as pd
from tsl.ops.dataframe import to_numpy
from . import checks
from ...typing import FrameArray
from ...utils.python_utils import ensure_list
class PandasParsingMixin:
def _parse_dataframe(self, df: pd.DataFrame, node_level: bool = True):
assert checks.is_datetime_like_index(df.index)
if node_level:
df = checks.to_nodes_channels_columns(df)
else:
df = checks.to_channels_columns(df)
df = checks.cast_df(df, precision=self.precision)
return df
def _to_indexed_df(self, array: np.ndarray):
if array.ndim == 1:
array = array[..., None]
# check shape equivalence
time, channels = array.shape
if time != self.length:
raise ValueError("Cannot match temporal dimensions {} and {}"
.format(time, self.length))
return pd.DataFrame(array, self.index)
def _to_primary_df_schema(self, array: np.ndarray):
array = np.asarray(array)
while array.ndim < 3:
array = array[..., None]
# check shape equivalence
time, nodes, channels = array.shape
if time != self.length:
raise ValueError("Cannot match temporal dimensions {} and {}"
.format(time, self.length))
if nodes != self.n_nodes:
raise ValueError("Cannot match nodes dimensions {} and {}"
.format(nodes, self.n_nodes))
array = array.reshape(time, nodes * channels)
columns = self.columns(channels=pd.RangeIndex(channels))
return pd.DataFrame(array, self.index, columns)
def _synch_with_primary(self, df: pd.DataFrame):
assert hasattr(self, 'df'), \
"Cannot call this method before setting primary dataframe."
if df.columns.nlevels == 2:
nodes = set(df.columns.unique(0))
channels = list(df.columns.unique(1))
assert nodes.issubset(self.nodes), \
"You are trying to add an exogenous dataframe with nodes that" \
" are not in the dataset."
columns = self.columns(channels=channels)
df = df.reindex(index=self.index, columns=columns)
elif df.columns.nlevels == 1:
df = df.reindex(index=self.index)
else:
raise ValueError("Input dataframe must have either 1 ('nodes' or "
"'channels') or 2 ('nodes', 'channels') column "
"levels.")
return df
def _check_name(self, name: str, check_type: str):
assert check_type in ['exogenous', 'attribute']
invalid_names = set(dir(self))
if check_type == 'exogenous':
invalid_names.update(self._attributes)
else:
invalid_names.update(self._exogenous)
if name in invalid_names:
raise ValueError(f"Cannot set {check_type} with name '{name}', "
f"{self.__class__.__name__} contains already an "
f"attribute named '{name}'.")
class TemporalFeaturesMixin:
def datetime_encoded(self, units):
units = ensure_list(units)
mapping = {un: pd.to_timedelta('1' + un).delta
for un in ['day', 'hour', 'minute', 'second',
'millisecond', 'microsecond', 'nanosecond']}
mapping['week'] = pd.to_timedelta('1W').delta
mapping['year'] = 365.2425 * 24 * 60 * 60 * 10 ** 9
index_nano = self.index.view(np.int64)
datetime = dict()
for unit in units:
if unit not in mapping:
raise ValueError()
nano_sec = index_nano * (2 * np.pi / mapping[unit])
datetime[unit + '_sin'] = np.sin(nano_sec)
datetime[unit + '_cos'] = np.cos(nano_sec)
return pd.DataFrame(datetime, index=self.index, dtype=np.float32)
def datetime_onehot(self, units):
units = ensure_list(units)
datetime = dict()
for unit in units:
if hasattr(self.index.__dict__, unit):
raise ValueError()
datetime[unit] = getattr(self.index, unit)
dummies = pd.get_dummies(pd.DataFrame(datetime, index=self.index),
columns=units)
return dummies
def holidays_onehot(self, country, subdiv=None):
"""Returns a DataFrame to indicate if dataset timestamps is holiday.
See https://python-holidays.readthedocs.io/en/latest/
Args:
country (str): country for which holidays have to be checked, e.g.,
"CH" for Switzerland.
subdiv (dict, optional): optional country sub-division (state,
region, province, canton), e.g., "TI" for Ticino, Switzerland.
Returns:
pandas.DataFrame: DataFrame with one column ("holiday") as one-hot
encoding (1 if the timestamp is in a holiday, 0 otherwise).
"""
try:
import holidays
except ModuleNotFoundError:
raise RuntimeError("You should install optional dependency "
"'holidays' to call 'datetime_holidays'.")
years = np.unique(self.index.year.values)
h = holidays.country_holidays(country, subdiv=subdiv, years=years)
# label all the timestamps, whether holiday or not
out = pd.DataFrame(0, dtype=np.uint8,
index=self.index.normalize(), columns=['holiday'])
for date in h.keys():
try:
out.loc[[date]] = 1
except KeyError:
pass
out.index = self.index
return out
class MissingValuesMixin:
eval_mask: np.ndarray
def set_eval_mask(self, eval_mask: FrameArray):
if isinstance(eval_mask, pd.DataFrame):
eval_mask = to_numpy(self._parse_dataframe(eval_mask))
if eval_mask.ndim == 2:
eval_mask = eval_mask[..., None]
assert eval_mask.shape == self.shape
eval_mask = eval_mask.astype(self.mask.dtype) & self.mask
self.eval_mask = eval_mask
@property
def training_mask(self):
if hasattr(self, 'eval_mask') and self.eval_mask is not None:
return self.mask & (1 - self.eval_mask)
return self.mask
| import numpy as np
import pandas as pd
from tsl.ops.dataframe import to_numpy
from . import checks
from ...typing import FrameArray
from ...utils.python_utils import ensure_list
class PandasParsingMixin:
def _parse_dataframe(self, df: pd.DataFrame, node_level: bool = True):
assert checks.is_datetime_like_index(df.index)
if node_level:
df = checks.to_nodes_channels_columns(df)
else:
df = checks.to_channels_columns(df)
df = checks.cast_df(df, precision=self.precision)
return df
def _to_indexed_df(self, array: np.ndarray):
if array.ndim == 1:
array = array[..., None]
# check shape equivalence
time, channels = array.shape
if time != self.length:
raise ValueError("Cannot match temporal dimensions {} and {}"
.format(time, self.length))
return pd.DataFrame(array, self.index)
def _to_primary_df_schema(self, array: np.ndarray):
array = np.asarray(array)
while array.ndim < 3:
array = array[..., None]
# check shape equivalence
time, nodes, channels = array.shape
if time != self.length:
raise ValueError("Cannot match temporal dimensions {} and {}"
.format(time, self.length))
if nodes != self.n_nodes:
raise ValueError("Cannot match nodes dimensions {} and {}"
.format(nodes, self.n_nodes))
array = array.reshape(time, nodes * channels)
columns = self.columns(channels=pd.RangeIndex(channels))
return pd.DataFrame(array, self.index, columns)
def _synch_with_primary(self, df: pd.DataFrame):
assert hasattr(self, 'df'), \
"Cannot call this method before setting primary dataframe."
if df.columns.nlevels == 2:
nodes = set(df.columns.unique(0))
channels = list(df.columns.unique(1))
assert nodes.issubset(self.nodes), \
"You are trying to add an exogenous dataframe with nodes that" \
" are not in the dataset."
columns = self.columns(channels=channels)
df = df.reindex(index=self.index, columns=columns)
elif df.columns.nlevels == 1:
df = df.reindex(index=self.index)
else:
raise ValueError("Input dataframe must have either 1 ('nodes' or "
"'channels') or 2 ('nodes', 'channels') column "
"levels.")
return df
def _check_name(self, name: str, check_type: str):
assert check_type in ['exogenous', 'attribute']
invalid_names = set(dir(self))
if check_type == 'exogenous':
invalid_names.update(self._attributes)
else:
invalid_names.update(self._exogenous)
if name in invalid_names:
raise ValueError(f"Cannot set {check_type} with name '{name}', "
f"{self.__class__.__name__} contains already an "
f"attribute named '{name}'.")
class TemporalFeaturesMixin:
def datetime_encoded(self, units):
units = ensure_list(units)
mapping = {un: pd.to_timedelta('1' + un).delta
for un in ['day', 'hour', 'minute', 'second',
'millisecond', 'microsecond', 'nanosecond']}
mapping['week'] = pd.to_timedelta('1W').delta
mapping['year'] = 365.2425 * 24 * 60 * 60 * 10 ** 9
index_nano = self.index.view(np.int64)
datetime = dict()
for unit in units:
if unit not in mapping:
raise ValueError()
nano_sec = index_nano * (2 * np.pi / mapping[unit])
datetime[unit + '_sin'] = np.sin(nano_sec)
datetime[unit + '_cos'] = np.cos(nano_sec)
return pd.DataFrame(datetime, index=self.index, dtype=np.float32)
def datetime_onehot(self, units):
units = ensure_list(units)
datetime = dict()
for unit in units:
if hasattr(self.index.__dict__, unit):
raise ValueError()
datetime[unit] = getattr(self.index, unit)
dummies = pd.get_dummies(pd.DataFrame(datetime, index=self.index),
columns=units)
return dummies
def holidays_onehot(self, country, subdiv=None):
"""Returns a DataFrame to indicate if dataset timestamps is holiday.
See https://python-holidays.readthedocs.io/en/latest/
Args:
country (str): country for which holidays have to be checked, e.g.,
"CH" for Switzerland.
subdiv (dict, optional): optional country sub-division (state,
region, province, canton), e.g., "TI" for Ticino, Switzerland.
Returns:
pandas.DataFrame: DataFrame with one column ("holiday") as one-hot
encoding (1 if the timestamp is in a holiday, 0 otherwise).
"""
try:
import holidays
except ModuleNotFoundError:
raise RuntimeError("You should install optional dependency "
"'holidays' to call 'datetime_holidays'.")
years = np.unique(self.index.year.values)
h = holidays.country_holidays(country, subdiv=subdiv, years=years)
# label all the timestamps, whether holiday or not
out = pd.DataFrame(0, dtype=np.uint8,
index=self.index.normalize(), columns=['holiday'])
for date in h.keys():
try:
out.loc[[date]] = 1
except KeyError:
pass
out.index = self.index
return out
class MissingValuesMixin:
eval_mask: np.ndarray
def set_eval_mask(self, eval_mask: FrameArray):
if isinstance(eval_mask, pd.DataFrame):
eval_mask = to_numpy(self._parse_dataframe(eval_mask))
if eval_mask.ndim == 2:
eval_mask = eval_mask[..., None]
assert eval_mask.shape == self.shape
eval_mask = eval_mask.astype(self.mask.dtype) & self.mask
self.eval_mask = eval_mask
@property
def training_mask(self):
if hasattr(self, 'eval_mask') and self.eval_mask is not None:
return self.mask & (1 - self.eval_mask)
return self.mask
| en | 0.737801 | # check shape equivalence # check shape equivalence Returns a DataFrame to indicate if dataset timestamps is holiday. See https://python-holidays.readthedocs.io/en/latest/ Args: country (str): country for which holidays have to be checked, e.g., "CH" for Switzerland. subdiv (dict, optional): optional country sub-division (state, region, province, canton), e.g., "TI" for Ticino, Switzerland. Returns: pandas.DataFrame: DataFrame with one column ("holiday") as one-hot encoding (1 if the timestamp is in a holiday, 0 otherwise). # label all the timestamps, whether holiday or not | 2.5037 | 3 |
testslide/import_profiler.py | Flameeyes/TestSlide | 0 | 6623226 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import time
class ImportedModule(object):
"""
A module that was imported with __import__.
"""
def __init__(self, name, globals, level, parent=None):
self.name = name
self.globals = globals
self.level = level
self.parent = parent
self.children = []
self.time = None
if parent:
parent.children.append(self)
def __eq__(self, value):
return str(self) == str(value)
@property
def all_children(self):
children = []
for child in self.children:
children.append(child)
children.extend(child.all_children)
return children
@property
def own_time(self):
"""
How many seconds it took to import this module, minus all child imports.
"""
return self.time - sum(child.time for child in self.children)
def __str__(self):
if self.globals and self.level:
if self.level == 1:
prefix = self.globals["__package__"]
else:
end = -1 * (self.level - 1)
prefix = ".".join(self.globals["__package__"].split(".")[:end]) + "."
else:
prefix = ""
return "{}{}".format(prefix, self.name)
def __enter__(self):
self._start_time = time.time()
def __exit__(self, exc_type, exc_val, exc_tb):
self.time = time.time() - self._start_time
class ImportProfiler(object):
"""
Experimental!
Quick'n dirty profiler for module import times.
Usage:
from testslide.import_profiler import ImportProfiler
with ImportProfiler() as import_profiler:
import everything.here
import_profiler.print_stats(100)
This will print the dependency tree for imported modules that took more than 100ms
to be imported.
"""
def __init__(self):
self._original_import = __builtins__["__import__"]
def __enter__(self):
__builtins__["__import__"] = self._profiled_import
self._top_imp_modules = []
self._import_stack = []
self.total_time = None
self._start_time = time.time()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.total_time = time.time() - self._start_time
__builtins__["__import__"] = self._original_import
# def _profiled_import(self, name, globals=None, locals=None, fromlist=(), level=0):
def _profiled_import(self, name, globals=None, locals=None, fromlist=(), level=0):
# print('Importing {}'.format(repr(name)))
imp_mod = ImportedModule(
name=name,
globals=globals,
level=level,
parent=self._import_stack[-1] if self._import_stack else None,
)
if not self._import_stack:
self._top_imp_modules.append(imp_mod)
self._import_stack.append(imp_mod)
with imp_mod:
try:
return self._original_import(name, globals, locals, fromlist, level)
finally:
self._import_stack.pop()
def print_stats(self, threshold_ms=0):
def print_imp_mod(imp_mod, indent=0):
own_ms = int(imp_mod.own_time * 1000)
if own_ms >= threshold_ms or any(
child
for child in imp_mod.all_children
if child.own_time * 1000 >= threshold_ms
):
print("{}{}: {}ms".format(" " * indent, imp_mod, own_ms))
for child_imp_mod in imp_mod.children:
print_imp_mod(child_imp_mod, indent + 1)
for imp_mod in self._top_imp_modules:
print_imp_mod(imp_mod)
print()
print("Total import time: {}ms".format(int(self.total_time * 1000)))
| #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import time
class ImportedModule(object):
"""
A module that was imported with __import__.
"""
def __init__(self, name, globals, level, parent=None):
self.name = name
self.globals = globals
self.level = level
self.parent = parent
self.children = []
self.time = None
if parent:
parent.children.append(self)
def __eq__(self, value):
return str(self) == str(value)
@property
def all_children(self):
children = []
for child in self.children:
children.append(child)
children.extend(child.all_children)
return children
@property
def own_time(self):
"""
How many seconds it took to import this module, minus all child imports.
"""
return self.time - sum(child.time for child in self.children)
def __str__(self):
if self.globals and self.level:
if self.level == 1:
prefix = self.globals["__package__"]
else:
end = -1 * (self.level - 1)
prefix = ".".join(self.globals["__package__"].split(".")[:end]) + "."
else:
prefix = ""
return "{}{}".format(prefix, self.name)
def __enter__(self):
self._start_time = time.time()
def __exit__(self, exc_type, exc_val, exc_tb):
self.time = time.time() - self._start_time
class ImportProfiler(object):
"""
Experimental!
Quick'n dirty profiler for module import times.
Usage:
from testslide.import_profiler import ImportProfiler
with ImportProfiler() as import_profiler:
import everything.here
import_profiler.print_stats(100)
This will print the dependency tree for imported modules that took more than 100ms
to be imported.
"""
def __init__(self):
self._original_import = __builtins__["__import__"]
def __enter__(self):
__builtins__["__import__"] = self._profiled_import
self._top_imp_modules = []
self._import_stack = []
self.total_time = None
self._start_time = time.time()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.total_time = time.time() - self._start_time
__builtins__["__import__"] = self._original_import
# def _profiled_import(self, name, globals=None, locals=None, fromlist=(), level=0):
def _profiled_import(self, name, globals=None, locals=None, fromlist=(), level=0):
# print('Importing {}'.format(repr(name)))
imp_mod = ImportedModule(
name=name,
globals=globals,
level=level,
parent=self._import_stack[-1] if self._import_stack else None,
)
if not self._import_stack:
self._top_imp_modules.append(imp_mod)
self._import_stack.append(imp_mod)
with imp_mod:
try:
return self._original_import(name, globals, locals, fromlist, level)
finally:
self._import_stack.pop()
def print_stats(self, threshold_ms=0):
def print_imp_mod(imp_mod, indent=0):
own_ms = int(imp_mod.own_time * 1000)
if own_ms >= threshold_ms or any(
child
for child in imp_mod.all_children
if child.own_time * 1000 >= threshold_ms
):
print("{}{}: {}ms".format(" " * indent, imp_mod, own_ms))
for child_imp_mod in imp_mod.children:
print_imp_mod(child_imp_mod, indent + 1)
for imp_mod in self._top_imp_modules:
print_imp_mod(imp_mod)
print()
print("Total import time: {}ms".format(int(self.total_time * 1000)))
| en | 0.800996 | #!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. A module that was imported with __import__. How many seconds it took to import this module, minus all child imports. Experimental! Quick'n dirty profiler for module import times. Usage: from testslide.import_profiler import ImportProfiler with ImportProfiler() as import_profiler: import everything.here import_profiler.print_stats(100) This will print the dependency tree for imported modules that took more than 100ms to be imported. # def _profiled_import(self, name, globals=None, locals=None, fromlist=(), level=0): # print('Importing {}'.format(repr(name))) | 2.565533 | 3 |
companion/src/cycle.py | kreako/soklaki | 0 | 6623227 | <filename>companion/src/cycle.py
from datetime import date, timedelta
YEARS_6 = timedelta(days=6 * 365 + 1)
YEARS_9 = timedelta(days=9 * 365 + 2)
YEARS_12 = timedelta(days=12 * 365 + 3)
def estimate_cycle(birthdate, evaluation_date):
# First estimate scholar year of the evaluation
if evaluation_date.month > 8:
scholar_year = evaluation_date.year
else:
scholar_year = evaluation_date.year - 1
# the date corresponding to the end of the year in the scholar year
end_of_year = date(scholar_year, 12, 31)
age = end_of_year - birthdate
if age < YEARS_6:
return "c1"
elif age < YEARS_9:
return "c2"
elif age < YEARS_12:
return "c3"
else:
return "c4"
| <filename>companion/src/cycle.py
from datetime import date, timedelta
YEARS_6 = timedelta(days=6 * 365 + 1)
YEARS_9 = timedelta(days=9 * 365 + 2)
YEARS_12 = timedelta(days=12 * 365 + 3)
def estimate_cycle(birthdate, evaluation_date):
# First estimate scholar year of the evaluation
if evaluation_date.month > 8:
scholar_year = evaluation_date.year
else:
scholar_year = evaluation_date.year - 1
# the date corresponding to the end of the year in the scholar year
end_of_year = date(scholar_year, 12, 31)
age = end_of_year - birthdate
if age < YEARS_6:
return "c1"
elif age < YEARS_9:
return "c2"
elif age < YEARS_12:
return "c3"
else:
return "c4"
| en | 0.846065 | # First estimate scholar year of the evaluation # the date corresponding to the end of the year in the scholar year | 3.684935 | 4 |
wizzer/__init__.py | mabolhasani/wizzer | 0 | 6623228 | <reponame>mabolhasani/wizzer
#module file: __init__.py
"""This module is a wizard builder for setting up parameters
[ e.g. variable(s) / configuration(s) ] to run a service."""
| #module file: __init__.py
"""This module is a wizard builder for setting up parameters
[ e.g. variable(s) / configuration(s) ] to run a service.""" | en | 0.426073 | #module file: __init__.py This module is a wizard builder for setting up parameters [ e.g. variable(s) / configuration(s) ] to run a service. | 1.665951 | 2 |
vlm/param.py | woojeongjin/vokenization | 173 | 6623229 | <reponame>woojeongjin/vokenization
import argparse
def process_args():
parser = argparse.ArgumentParser()
# Datasets
parser.add_argument(
"--train_data_file", default=None, type=str,
help="The input training data file (a text file).")
parser.add_argument(
"--eval_data_file", default=None, type=str,
help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
# Data loader
parser.add_argument("--col_data", action="store_true", help="Using the specific dataset object in data.py")
parser.add_argument("--split_sent", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument("--shuffle", action="store_true", help="Shuffle the training dataset")
parser.add_argument(
"--block_size", default=-1, type=int,
help="Optional input sequence length after tokenization."
"The training dataset will be truncated in block of this size for training."
"Default to the model max input length for single sentence inputs (take into account special tokens).",
)
# Logging and Saving
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
parser.add_argument(
"--output_dir", type=str,
help="The output directory where the model predictions and checkpoints will be written.",)
parser.add_argument(
"--overwrite_output_dir", action="store_true",
help="Overwrite the content of the output directory")
# Model types
parser.add_argument(
"--model_type", type=str, help="The model architecture to be trained or fine-tuned.",)
parser.add_argument(
"--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir")
parser.add_argument(
"--model_name_or_path", default=None, type=str,
help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",)
parser.add_argument(
"--config_name", default=None, type=str,
help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",)
parser.add_argument(
"--tokenizer_name", default=None, type=str,
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",)
parser.add_argument(
"--cache_dir", default=None, type=str,
help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",)
parser.add_argument(
"--overwrite_cache", action="store_true",
help="Overwrite the cached training and evaluation sets")
# MLM tasks
parser.add_argument(
"--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling.")
parser.add_argument(
"--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss")
parser.add_argument(
"--mlm_ratio", type=float, default=1., help="The ratio of mlm loss in the total loss.")
# VLM related params
parser.add_argument("--voken_dir", type=str, default='snap1/coco_hinge05_dim64_resxt101_robertal4/vokens',
help='Where the vokens are saved')
parser.add_argument("--voken_suffix", type=str, default='vg_nococo.10000',
help='The suffix after the voken file, e.g., en.train.raw.{suffix} where suffix==vgcoco.1000')
parser.add_argument("--voken_labels", type=str, default='all',
help='all: Calculate voken loss for all tokens;'
'mask: Calculate voken loss for masked tokens.'
'nonmask: Calculate voken loss for non-masked tokens.')
parser.add_argument("--voken_feat_dir", type=str, default=None,
help='Where the vokens are saved')
parser.add_argument("--do_voken_cls", action='store_true', help='Will do voken classification task')
parser.add_argument("--do_voken_reg", action='store_true', help='Will do voken regression task (not used in this paper)')
parser.add_argument("--do_voken_ctr", action='store_true', help='Will do voken contrastive task (not used in this paper)')
parser.add_argument("--shared_head", action='store_true', help='Share the head if more than one tasks (e.g., cls, reg, ctr) are used (not used in this paper)')
# Batch Size and Training Steps
parser.add_argument("--seed", type=int, default=95, help="random seed for initialization")
parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=4, type=int, help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--gradient_accumulation_steps", type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",)
parser.add_argument("--num_train_epochs", default=1.0, type=float, help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",)
# Optimizer
parser.add_argument("--lamb", action="store_true", help='Use the LAMB optimizer in apex')
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--warmup_ratio", default=0., type=float, help="Linear warmup over warmup_steps.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.01, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
# Distributed Training
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("--nodes", type=int, default=1)
parser.add_argument("--nr", type=int, default=0)
# Half Precision
parser.add_argument(
"--fp16", action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",)
parser.add_argument(
"--fp16_opt_level", type=str, default="O1",
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",)
# Ablation Study
parser.add_argument("--voken_ablation", default=None,
help="random, shuffle, reverse, token")
args = parser.parse_args()
return args
| import argparse
def process_args():
parser = argparse.ArgumentParser()
# Datasets
parser.add_argument(
"--train_data_file", default=None, type=str,
help="The input training data file (a text file).")
parser.add_argument(
"--eval_data_file", default=None, type=str,
help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
# Data loader
parser.add_argument("--col_data", action="store_true", help="Using the specific dataset object in data.py")
parser.add_argument("--split_sent", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument("--shuffle", action="store_true", help="Shuffle the training dataset")
parser.add_argument(
"--block_size", default=-1, type=int,
help="Optional input sequence length after tokenization."
"The training dataset will be truncated in block of this size for training."
"Default to the model max input length for single sentence inputs (take into account special tokens).",
)
# Logging and Saving
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
parser.add_argument(
"--output_dir", type=str,
help="The output directory where the model predictions and checkpoints will be written.",)
parser.add_argument(
"--overwrite_output_dir", action="store_true",
help="Overwrite the content of the output directory")
# Model types
parser.add_argument(
"--model_type", type=str, help="The model architecture to be trained or fine-tuned.",)
parser.add_argument(
"--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir")
parser.add_argument(
"--model_name_or_path", default=None, type=str,
help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",)
parser.add_argument(
"--config_name", default=None, type=str,
help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",)
parser.add_argument(
"--tokenizer_name", default=None, type=str,
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",)
parser.add_argument(
"--cache_dir", default=None, type=str,
help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",)
parser.add_argument(
"--overwrite_cache", action="store_true",
help="Overwrite the cached training and evaluation sets")
# MLM tasks
parser.add_argument(
"--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling.")
parser.add_argument(
"--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss")
parser.add_argument(
"--mlm_ratio", type=float, default=1., help="The ratio of mlm loss in the total loss.")
# VLM related params
parser.add_argument("--voken_dir", type=str, default='snap1/coco_hinge05_dim64_resxt101_robertal4/vokens',
help='Where the vokens are saved')
parser.add_argument("--voken_suffix", type=str, default='vg_nococo.10000',
help='The suffix after the voken file, e.g., en.train.raw.{suffix} where suffix==vgcoco.1000')
parser.add_argument("--voken_labels", type=str, default='all',
help='all: Calculate voken loss for all tokens;'
'mask: Calculate voken loss for masked tokens.'
'nonmask: Calculate voken loss for non-masked tokens.')
parser.add_argument("--voken_feat_dir", type=str, default=None,
help='Where the vokens are saved')
parser.add_argument("--do_voken_cls", action='store_true', help='Will do voken classification task')
parser.add_argument("--do_voken_reg", action='store_true', help='Will do voken regression task (not used in this paper)')
parser.add_argument("--do_voken_ctr", action='store_true', help='Will do voken contrastive task (not used in this paper)')
parser.add_argument("--shared_head", action='store_true', help='Share the head if more than one tasks (e.g., cls, reg, ctr) are used (not used in this paper)')
# Batch Size and Training Steps
parser.add_argument("--seed", type=int, default=95, help="random seed for initialization")
parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=4, type=int, help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--gradient_accumulation_steps", type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",)
parser.add_argument("--num_train_epochs", default=1.0, type=float, help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",)
# Optimizer
parser.add_argument("--lamb", action="store_true", help='Use the LAMB optimizer in apex')
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--warmup_ratio", default=0., type=float, help="Linear warmup over warmup_steps.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.01, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
# Distributed Training
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("--nodes", type=int, default=1)
parser.add_argument("--nr", type=int, default=0)
# Half Precision
parser.add_argument(
"--fp16", action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",)
parser.add_argument(
"--fp16_opt_level", type=str, default="O1",
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",)
# Ablation Study
parser.add_argument("--voken_ablation", default=None,
help="random, shuffle, reverse, token")
args = parser.parse_args()
return args | en | 0.798648 | # Datasets # Data loader # Logging and Saving # Model types # MLM tasks # VLM related params # Batch Size and Training Steps # Optimizer # Distributed Training # Half Precision # Ablation Study | 2.803858 | 3 |
rough_work/test.py | ndey96/spiking-actor-critic | 1 | 6623230 | <gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
import nengo
import nengo_ocl
# define the model
with nengo.Network() as model:
stim = nengo.Node(np.sin)
a = nengo.Ensemble(100, 1)
b = nengo.Ensemble(100, 1)
nengo.Connection(stim, a)
nengo.Connection(a, b, function=lambda x: x**2)
probe_a = nengo.Probe(a, synapse=0.01)
probe_b = nengo.Probe(b, synapse=0.01)
# build and run the model
with nengo_ocl.Simulator(model) as sim:
sim.run(10)
# plot the results
#plt.plot(sim.trange(), sim.data[probe_a])
#plt.plot(sim.trange(), sim.data[probe_b])
#plt.show()
| import numpy as np
import matplotlib.pyplot as plt
import nengo
import nengo_ocl
# define the model
with nengo.Network() as model:
stim = nengo.Node(np.sin)
a = nengo.Ensemble(100, 1)
b = nengo.Ensemble(100, 1)
nengo.Connection(stim, a)
nengo.Connection(a, b, function=lambda x: x**2)
probe_a = nengo.Probe(a, synapse=0.01)
probe_b = nengo.Probe(b, synapse=0.01)
# build and run the model
with nengo_ocl.Simulator(model) as sim:
sim.run(10)
# plot the results
#plt.plot(sim.trange(), sim.data[probe_a])
#plt.plot(sim.trange(), sim.data[probe_b])
#plt.show() | en | 0.101077 | # define the model # build and run the model # plot the results #plt.plot(sim.trange(), sim.data[probe_a]) #plt.plot(sim.trange(), sim.data[probe_b]) #plt.show() | 2.711178 | 3 |
chat/migrations/0002_auto_20190625_1304.py | lokesh1729/chatapp | 1 | 6623231 | # Generated by Django 2.0.13 on 2019-06-25 07:34
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('chat', '0001_initial'),
]
operations = [
migrations.RenameField(
model_name='participant',
old_name='room',
new_name='rooms',
),
]
| # Generated by Django 2.0.13 on 2019-06-25 07:34
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('chat', '0001_initial'),
]
operations = [
migrations.RenameField(
model_name='participant',
old_name='room',
new_name='rooms',
),
]
| en | 0.728945 | # Generated by Django 2.0.13 on 2019-06-25 07:34 | 1.828832 | 2 |
2019/day2/2.py | tomhel/AoC_2019 | 1 | 6623232 | <filename>2019/day2/2.py
#!/usr/bin/env python3
def execute(prog):
pc = 0
while True:
op = prog[pc]
if op == 1:
r = prog[prog[pc + 1]] + prog[prog[pc + 2]]
prog[prog[pc + 3]] = r
pc += 4
elif op == 2:
r = prog[prog[pc + 1]] * prog[prog[pc + 2]]
prog[prog[pc + 3]] = r
pc += 4
elif op == 99:
break
else:
raise Exception("unknown op: %d" % op)
def find_init_values(prog):
for noun in range(100):
for verb in range(100):
p = prog[:]
p[1] = noun
p[2] = verb
execute(p)
if p[0] == 19690720:
return noun, verb
program = [int(i) for i in open("input").read().split(",")]
print("%02d%02d" % find_init_values(program))
| <filename>2019/day2/2.py
#!/usr/bin/env python3
def execute(prog):
pc = 0
while True:
op = prog[pc]
if op == 1:
r = prog[prog[pc + 1]] + prog[prog[pc + 2]]
prog[prog[pc + 3]] = r
pc += 4
elif op == 2:
r = prog[prog[pc + 1]] * prog[prog[pc + 2]]
prog[prog[pc + 3]] = r
pc += 4
elif op == 99:
break
else:
raise Exception("unknown op: %d" % op)
def find_init_values(prog):
for noun in range(100):
for verb in range(100):
p = prog[:]
p[1] = noun
p[2] = verb
execute(p)
if p[0] == 19690720:
return noun, verb
program = [int(i) for i in open("input").read().split(",")]
print("%02d%02d" % find_init_values(program))
| fr | 0.221828 | #!/usr/bin/env python3 | 3.467883 | 3 |
config.py | SnewbieChen/YunWeiBlog | 1 | 6623233 | # -*- coding: utf-8 -*-
# @Author : YunWei.Chen
# @Site : https://chen.yunwei.space
import os
import datetime
class Config:
DEBUG = False # 是否开启调试模式
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://yunwei:chen@yunwei#space@localhost/myspace' # 数据库URI
SECRET_KEY = 'Chen@YunWei#Space->Blog' # session加密密钥
JSON_AS_ASCII = False # 让JSON字符串显示中文
PERMANENT_SESSION_LIFETIME = datetime.timedelta(days=3) # 设置session过期时间为3天
SQLALCHEMY_COMMIT_ON_TEARDOWN = True
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_RECORD_QUERIES = True
UPLOAD_DIR = 'uploads' # 文件保存文件夹名
UPLOAD_FOLDER = os.path.join(os.path.dirname(os.path.abspath(__file__)), UPLOAD_DIR) # 文件上传目录
| # -*- coding: utf-8 -*-
# @Author : YunWei.Chen
# @Site : https://chen.yunwei.space
import os
import datetime
class Config:
DEBUG = False # 是否开启调试模式
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://yunwei:chen@yunwei#space@localhost/myspace' # 数据库URI
SECRET_KEY = 'Chen@YunWei#Space->Blog' # session加密密钥
JSON_AS_ASCII = False # 让JSON字符串显示中文
PERMANENT_SESSION_LIFETIME = datetime.timedelta(days=3) # 设置session过期时间为3天
SQLALCHEMY_COMMIT_ON_TEARDOWN = True
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_RECORD_QUERIES = True
UPLOAD_DIR = 'uploads' # 文件保存文件夹名
UPLOAD_FOLDER = os.path.join(os.path.dirname(os.path.abspath(__file__)), UPLOAD_DIR) # 文件上传目录
| zh | 0.636533 | # -*- coding: utf-8 -*- # @Author : YunWei.Chen # @Site : https://chen.yunwei.space # 是否开启调试模式 #space@localhost/myspace' # 数据库URI #Space->Blog' # session加密密钥 # 让JSON字符串显示中文 # 设置session过期时间为3天 # 文件保存文件夹名 # 文件上传目录 | 2.07028 | 2 |
app.py | rishabh99-rc/Student-Feedback-Sentimental-Analysis | 0 | 6623234 | <filename>app.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import faculty
import drawFigure
from flask import Flask , render_template , redirect , request
app = Flask(__name__)
with open('feedback1.json') as file:
json_string = file.read()
documents1 = json.loads(json_string)
with open('feedback2.json') as file:
json_string = file.read()
documents2 = json.loads(json_string)
with open('feedback3.json') as file:
json_string = file.read()
documents3 = json.loads(json_string)
with open('feedback4.json') as file:
json_string = file.read()
documents4 = json.loads(json_string)
with open('feedback5.json') as file:
json_string = file.read()
documents5 = json.loads(json_string)
with open('feedback6.json') as file:
json_string = file.read()
documents6 = json.loads(json_string)
label2category = {1: 'positive' , 0: 'neutral' , -1: 'negative'}
category2label = {cat:label for label , cat in label2category.items()}
categories1 = [category2label[category] for doc , category in documents1]
categories2 = [category2label[category] for doc , category in documents2]
categories3 = [category2label[category] for doc , category in documents3]
categories4 = [category2label[category] for doc , category in documents4]
categories5 = [category2label[category] for doc , category in documents5]
categories6 = [category2label[category] for doc , category in documents6]
corpus1 = [' '.join(document) for document , cat in documents1]
corpus2 = [' '.join(document) for document , cat in documents2]
corpus3 = [' '.join(document) for document , cat in documents3]
corpus4 = [' '.join(document) for document , cat in documents4]
corpus5 = [' '.join(document) for document , cat in documents5]
corpus6 = [' '.join(document) for document , cat in documents6]
@app.route('/')
def display():
return render_template('index.html')
@app.route('/' , methods=['POST'])
def caption():
if request.method == 'POST':
f = request.files["file_name"]
path = "./static/{}".format(f.filename)
f.save(path)
category_no = int(request.form['Cate'])
df = pd.read_csv(path)
cols1 = []
cols2 = []
cols3 = []
cols4 = []
cols5 = []
cols6 = []
substring1 = ['teacher' , 'faculty' , 'feedback' , 'effectiveness' , 'teaching' , 'knowledge' , 'delivery' , 'content' , 'quality' ,
'lecture' , 'subject' , 'syllabus' , 'review' , 'assessment']
substring2 = ['course' , 'content' , 'syllabus' , 'review' , 'evaluation' , 'curriculum' , 'syllabi' , 'contents' , 'level' ,
'difficulty' , 'lecture' , 'outline']
substring3 = ['exam' , 'examination' , 'pattern' , 'conduct' , 'question' , 'paper' , 'level' , 'outline']
substring4 = ['laboratory' , 'laboratories' , 'lab' , 'facility' , 'facilities' , 'review' , 'feedback' , 'rate' , 'learning' ]
substring5 = ['library' , 'facilities' , 'books' , 'availability' , 'facility' , 'material' , 'rate' , 'feedback' , 'review']
substring6 = ['extra' , 'curricular' , 'activity' , 'activities']
for i in list(df.columns):
for j in substring1:
if j.casefold() in i.casefold():
cols1.append(df.columns.get_loc(i))
if cols1 != []:
break
for i in list(df.columns):
for j in substring2:
if j.casefold() in i.casefold():
cols2.append(df.columns.get_loc(i))
if cols2 != []:
break
for i in list(df.columns):
for j in substring3:
if j.casefold() in i.casefold():
cols3.append(df.columns.get_loc(i))
if cols3 != []:
break
for i in list(df.columns):
for j in substring4:
if j.casefold() in i.casefold():
cols4.append(df.columns.get_loc(i))
if cols4 != []:
break
for i in list(df.columns):
for j in substring5:
if j.casefold() in i.casefold():
cols5.append(df.columns.get_loc(i))
if cols5 != []:
break
for i in list(df.columns):
for j in substring6:
if j.casefold() in i.casefold():
cols6.append(df.columns.get_loc(i))
if cols6 != []:
break
cols = cols1+cols2+cols3+cols4+cols5+cols6
cols = list(set(cols))
df_form = pd.read_csv(path , usecols = cols)
reviews = np.array(df_form)
pos1 , n1 , neg1 = faculty.predict(corpus1 , categories1 , reviews[: , 0])
pos2 , n2 , neg2 = faculty.predict(corpus1 , categories1 , reviews[: , 1])
pos3 , n3 , neg3 = faculty.predict(corpus1 , categories1 , reviews[: , 2])
pos4 , n4 , neg4 = faculty.predict(corpus1 , categories1 , reviews[: , 3])
pos5 , n5 , neg5 = faculty.predict(corpus1 , categories1 , reviews[: , 4])
pos6 , n6 , neg6 = faculty.predict(corpus1 , categories1 , reviews[: , 5])
results = {
'f1' : 'Teacher Feedback',
'pos1' : pos1,
'n1' : n1,
'neg1' : neg1,
'f2' : 'Course Content',
'pos2' : pos2,
'n2' : n2,
'neg1' : neg2,
'f3' : 'Examination pattern',
'pos3' : pos3,
'n3' : n3,
'neg3' : neg3,
'f4' : 'Laboratory',
'pos4' : pos4,
'n4' : n4,
'neg4' : neg4,
'f5' : 'Library Facilities',
'pos5' : pos5,
'n5' : n5,
'neg5' : neg5,
'f6' : 'Extra Co-Curricular Activities',
'pos6' : pos6,
'n6' : n6,
'neg6' : neg6,
}
values = list([[pos1 , n1 , neg1], [pos2 , n2 , neg2], [pos3 , n3 , neg3], [pos4 , n4 , neg4], [pos5 , n5 , neg5], [pos6 , n6 , neg6]])
labels = list(['Teacher Feedback', 'Course Content', 'Examination pattern','Laboratory','Library Facilities', 'Extra Co-Curricular Activities'])
print(values[category_no-1] , labels[category_no-1] , category_no , category_no-1)
if category_no == 1:
results_1 = {
'f1' : 'Teacher Feedback',
'pos1' : pos1,
'n1' : n1,
'neg1' : neg1
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result1 = results_1 , cat = category_no)
elif category_no == 2:
results_2 = {
'f2' : 'Course Content',
'pos2' : pos2,
'n2' : n2,
'neg2' : neg2
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result2 = results_2 , cat = category_no)
elif category_no == 3:
results_3 = {
'f3' : 'Examination pattern',
'pos3' : pos3,
'n3' : n3,
'neg3' : neg3
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result3 = results_3 , cat = category_no)
elif category_no == 4:
results_4 = {
'f4' : 'Laboratory',
'pos4' : pos4,
'n4' : n4,
'neg4' : neg4
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result4 = results_4 , cat = category_no)
elif category_no == 5:
results_5 = {
'f5' : 'Library Facilities',
'pos5' : pos5,
'n5' : n5,
'neg5' : neg5
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result5 = results_5 , cat = category_no)
elif category_no == 6:
results_6 = {
'f6' : 'Extra Co-Curricular Activities',
'pos6' : pos6,
'n6' : n6,
'neg6' : neg6
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result6 = results_6 , cat = category_no)
else:
for i in range(0 , 6):
fig = plt.figure(figsize=(8,8) , edgecolor='red' , linewidth=10)
plt.bar(x = ['Positive' , 'Neutral' , 'Negative'] , height = values[i] , color=['blue','gold','red'])
plt.title(labels[i], fontsize = 24, weight = 'demibold', pad = 15, fontstyle = 'italic' , family = 'cursive')
plt.xticks(rotation=0 , fontsize=16)
plt.yticks([])
plt.xlabel('Feedback Type',fontsize = 18, labelpad=17, weight= 550 , family = 'cursive')
plt.ylabel('')
fig.subplots_adjust(bottom = 0.14)
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
for p in ax.patches:
ax.annotate("%.1f%%" % (100*float(p.get_height()/sum(values[i]))), (p.get_x() + p.get_width() / 2., abs(p.get_height())),
ha='center', va='bottom', color='black', xytext=(0, 5),rotation = 'horizontal',
textcoords='offset points', fontsize = 16 , fontweight = 'medium')
plt.savefig(f'./static/plot{i+10}.jpg')
return render_template('index1.html' , result = results)
else:
return render_template('error.html')
if __name__ == '__main__':
app.run(debug=True) | <filename>app.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import faculty
import drawFigure
from flask import Flask , render_template , redirect , request
app = Flask(__name__)
with open('feedback1.json') as file:
json_string = file.read()
documents1 = json.loads(json_string)
with open('feedback2.json') as file:
json_string = file.read()
documents2 = json.loads(json_string)
with open('feedback3.json') as file:
json_string = file.read()
documents3 = json.loads(json_string)
with open('feedback4.json') as file:
json_string = file.read()
documents4 = json.loads(json_string)
with open('feedback5.json') as file:
json_string = file.read()
documents5 = json.loads(json_string)
with open('feedback6.json') as file:
json_string = file.read()
documents6 = json.loads(json_string)
label2category = {1: 'positive' , 0: 'neutral' , -1: 'negative'}
category2label = {cat:label for label , cat in label2category.items()}
categories1 = [category2label[category] for doc , category in documents1]
categories2 = [category2label[category] for doc , category in documents2]
categories3 = [category2label[category] for doc , category in documents3]
categories4 = [category2label[category] for doc , category in documents4]
categories5 = [category2label[category] for doc , category in documents5]
categories6 = [category2label[category] for doc , category in documents6]
corpus1 = [' '.join(document) for document , cat in documents1]
corpus2 = [' '.join(document) for document , cat in documents2]
corpus3 = [' '.join(document) for document , cat in documents3]
corpus4 = [' '.join(document) for document , cat in documents4]
corpus5 = [' '.join(document) for document , cat in documents5]
corpus6 = [' '.join(document) for document , cat in documents6]
@app.route('/')
def display():
return render_template('index.html')
@app.route('/' , methods=['POST'])
def caption():
if request.method == 'POST':
f = request.files["file_name"]
path = "./static/{}".format(f.filename)
f.save(path)
category_no = int(request.form['Cate'])
df = pd.read_csv(path)
cols1 = []
cols2 = []
cols3 = []
cols4 = []
cols5 = []
cols6 = []
substring1 = ['teacher' , 'faculty' , 'feedback' , 'effectiveness' , 'teaching' , 'knowledge' , 'delivery' , 'content' , 'quality' ,
'lecture' , 'subject' , 'syllabus' , 'review' , 'assessment']
substring2 = ['course' , 'content' , 'syllabus' , 'review' , 'evaluation' , 'curriculum' , 'syllabi' , 'contents' , 'level' ,
'difficulty' , 'lecture' , 'outline']
substring3 = ['exam' , 'examination' , 'pattern' , 'conduct' , 'question' , 'paper' , 'level' , 'outline']
substring4 = ['laboratory' , 'laboratories' , 'lab' , 'facility' , 'facilities' , 'review' , 'feedback' , 'rate' , 'learning' ]
substring5 = ['library' , 'facilities' , 'books' , 'availability' , 'facility' , 'material' , 'rate' , 'feedback' , 'review']
substring6 = ['extra' , 'curricular' , 'activity' , 'activities']
for i in list(df.columns):
for j in substring1:
if j.casefold() in i.casefold():
cols1.append(df.columns.get_loc(i))
if cols1 != []:
break
for i in list(df.columns):
for j in substring2:
if j.casefold() in i.casefold():
cols2.append(df.columns.get_loc(i))
if cols2 != []:
break
for i in list(df.columns):
for j in substring3:
if j.casefold() in i.casefold():
cols3.append(df.columns.get_loc(i))
if cols3 != []:
break
for i in list(df.columns):
for j in substring4:
if j.casefold() in i.casefold():
cols4.append(df.columns.get_loc(i))
if cols4 != []:
break
for i in list(df.columns):
for j in substring5:
if j.casefold() in i.casefold():
cols5.append(df.columns.get_loc(i))
if cols5 != []:
break
for i in list(df.columns):
for j in substring6:
if j.casefold() in i.casefold():
cols6.append(df.columns.get_loc(i))
if cols6 != []:
break
cols = cols1+cols2+cols3+cols4+cols5+cols6
cols = list(set(cols))
df_form = pd.read_csv(path , usecols = cols)
reviews = np.array(df_form)
pos1 , n1 , neg1 = faculty.predict(corpus1 , categories1 , reviews[: , 0])
pos2 , n2 , neg2 = faculty.predict(corpus1 , categories1 , reviews[: , 1])
pos3 , n3 , neg3 = faculty.predict(corpus1 , categories1 , reviews[: , 2])
pos4 , n4 , neg4 = faculty.predict(corpus1 , categories1 , reviews[: , 3])
pos5 , n5 , neg5 = faculty.predict(corpus1 , categories1 , reviews[: , 4])
pos6 , n6 , neg6 = faculty.predict(corpus1 , categories1 , reviews[: , 5])
results = {
'f1' : 'Teacher Feedback',
'pos1' : pos1,
'n1' : n1,
'neg1' : neg1,
'f2' : 'Course Content',
'pos2' : pos2,
'n2' : n2,
'neg1' : neg2,
'f3' : 'Examination pattern',
'pos3' : pos3,
'n3' : n3,
'neg3' : neg3,
'f4' : 'Laboratory',
'pos4' : pos4,
'n4' : n4,
'neg4' : neg4,
'f5' : 'Library Facilities',
'pos5' : pos5,
'n5' : n5,
'neg5' : neg5,
'f6' : 'Extra Co-Curricular Activities',
'pos6' : pos6,
'n6' : n6,
'neg6' : neg6,
}
values = list([[pos1 , n1 , neg1], [pos2 , n2 , neg2], [pos3 , n3 , neg3], [pos4 , n4 , neg4], [pos5 , n5 , neg5], [pos6 , n6 , neg6]])
labels = list(['Teacher Feedback', 'Course Content', 'Examination pattern','Laboratory','Library Facilities', 'Extra Co-Curricular Activities'])
print(values[category_no-1] , labels[category_no-1] , category_no , category_no-1)
if category_no == 1:
results_1 = {
'f1' : 'Teacher Feedback',
'pos1' : pos1,
'n1' : n1,
'neg1' : neg1
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result1 = results_1 , cat = category_no)
elif category_no == 2:
results_2 = {
'f2' : 'Course Content',
'pos2' : pos2,
'n2' : n2,
'neg2' : neg2
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result2 = results_2 , cat = category_no)
elif category_no == 3:
results_3 = {
'f3' : 'Examination pattern',
'pos3' : pos3,
'n3' : n3,
'neg3' : neg3
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result3 = results_3 , cat = category_no)
elif category_no == 4:
results_4 = {
'f4' : 'Laboratory',
'pos4' : pos4,
'n4' : n4,
'neg4' : neg4
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result4 = results_4 , cat = category_no)
elif category_no == 5:
results_5 = {
'f5' : 'Library Facilities',
'pos5' : pos5,
'n5' : n5,
'neg5' : neg5
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result5 = results_5 , cat = category_no)
elif category_no == 6:
results_6 = {
'f6' : 'Extra Co-Curricular Activities',
'pos6' : pos6,
'n6' : n6,
'neg6' : neg6
}
drawFigure.make(values[category_no-1] , labels[category_no-1] , category_no)
return render_template('index1.html' , result6 = results_6 , cat = category_no)
else:
for i in range(0 , 6):
fig = plt.figure(figsize=(8,8) , edgecolor='red' , linewidth=10)
plt.bar(x = ['Positive' , 'Neutral' , 'Negative'] , height = values[i] , color=['blue','gold','red'])
plt.title(labels[i], fontsize = 24, weight = 'demibold', pad = 15, fontstyle = 'italic' , family = 'cursive')
plt.xticks(rotation=0 , fontsize=16)
plt.yticks([])
plt.xlabel('Feedback Type',fontsize = 18, labelpad=17, weight= 550 , family = 'cursive')
plt.ylabel('')
fig.subplots_adjust(bottom = 0.14)
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
for p in ax.patches:
ax.annotate("%.1f%%" % (100*float(p.get_height()/sum(values[i]))), (p.get_x() + p.get_width() / 2., abs(p.get_height())),
ha='center', va='bottom', color='black', xytext=(0, 5),rotation = 'horizontal',
textcoords='offset points', fontsize = 16 , fontweight = 'medium')
plt.savefig(f'./static/plot{i+10}.jpg')
return render_template('index1.html' , result = results)
else:
return render_template('error.html')
if __name__ == '__main__':
app.run(debug=True) | none | 1 | 2.617895 | 3 | |
RASPI-stuff/python-codeline/Nokia5110/rpiMonitor.py | siliconchris1973/fairytale | 3 | 6623235 | <filename>RASPI-stuff/python-codeline/Nokia5110/rpiMonitor.py
#!/usr/bin/env python
import httplib, time, os, sys, json
import pcd8544.lcd as lcd
# class Process dedicated to process data get from Client
# and send information to LCD and console
class Process:
# Process constructor
def __init__(self):
# Initialize LCD
lcd.init()
# Turn the backlight on
lcd.backlight(1)
def run(self, jsonString):
# Parse data as json
data = json.loads( jsonString )
# Try to get data from json or return default value
try:
rpi_temperature = data['living_room_temp']
except:
rpi_temperature="--.---"
try:
rpi_humidity = data['humidity']
except:
rpi_humidity = "--"
# Construct string to be displayed on screens
temperature = "Temp: %s C" % rpi_temperature
humidity = "Humidity: %s %%" % rpi_humidity
lcd.gotorc(0,1)
lcd.text("RPi-Monitor")
lcd.gotorc(2,0)
lcd.text(temperature)
lcd.gotorc(3,0)
lcd.text(humidity)
# Also print string in console
os.system("clear")
print " RPi-Monitor "
print
print temperature
print humidity
print
time.sleep(1)
# Class client design to work as web client and get information
# from RPi-Monitor embedded web server
class Client:
# Client constructor
def __init__(self):
# Create a Process object
self.process = Process()
def run(self):
# Infinite loop
while True:
try:
# Initiate a connection to RPi-Monitor embedded server
connection = httplib.HTTPConnection("localhost", 8888)
# Get the file dynamic.json
connection.request("GET","/dynamic.json")
# Get the server response
response = connection.getresponse()
if ( response.status == 200 ):
# If response is OK, read data
data = response.read()
# Run process object on extracted data
self.process.run(data)
# Close the connection to RPi-Monitor embedded server
connection.close()
finally:
# Wait 5 secondes before restarting the loop
time.sleep(5)
# Main function
def main():
try:
# Create a Client object
client = Client()
# Run it
client.run()
except KeyboardInterrupt:
# if Ctrl+C has been pressed
# turn off the lcd backlight
lcd.backlight(0);
# exit from the program
sys.exit(0)
# Execute main if the script is directly called
if __name__ == "__main__":
main()
| <filename>RASPI-stuff/python-codeline/Nokia5110/rpiMonitor.py
#!/usr/bin/env python
import httplib, time, os, sys, json
import pcd8544.lcd as lcd
# class Process dedicated to process data get from Client
# and send information to LCD and console
class Process:
# Process constructor
def __init__(self):
# Initialize LCD
lcd.init()
# Turn the backlight on
lcd.backlight(1)
def run(self, jsonString):
# Parse data as json
data = json.loads( jsonString )
# Try to get data from json or return default value
try:
rpi_temperature = data['living_room_temp']
except:
rpi_temperature="--.---"
try:
rpi_humidity = data['humidity']
except:
rpi_humidity = "--"
# Construct string to be displayed on screens
temperature = "Temp: %s C" % rpi_temperature
humidity = "Humidity: %s %%" % rpi_humidity
lcd.gotorc(0,1)
lcd.text("RPi-Monitor")
lcd.gotorc(2,0)
lcd.text(temperature)
lcd.gotorc(3,0)
lcd.text(humidity)
# Also print string in console
os.system("clear")
print " RPi-Monitor "
print
print temperature
print humidity
print
time.sleep(1)
# Class client design to work as web client and get information
# from RPi-Monitor embedded web server
class Client:
# Client constructor
def __init__(self):
# Create a Process object
self.process = Process()
def run(self):
# Infinite loop
while True:
try:
# Initiate a connection to RPi-Monitor embedded server
connection = httplib.HTTPConnection("localhost", 8888)
# Get the file dynamic.json
connection.request("GET","/dynamic.json")
# Get the server response
response = connection.getresponse()
if ( response.status == 200 ):
# If response is OK, read data
data = response.read()
# Run process object on extracted data
self.process.run(data)
# Close the connection to RPi-Monitor embedded server
connection.close()
finally:
# Wait 5 secondes before restarting the loop
time.sleep(5)
# Main function
def main():
try:
# Create a Client object
client = Client()
# Run it
client.run()
except KeyboardInterrupt:
# if Ctrl+C has been pressed
# turn off the lcd backlight
lcd.backlight(0);
# exit from the program
sys.exit(0)
# Execute main if the script is directly called
if __name__ == "__main__":
main()
| en | 0.787954 | #!/usr/bin/env python # class Process dedicated to process data get from Client # and send information to LCD and console # Process constructor # Initialize LCD # Turn the backlight on # Parse data as json # Try to get data from json or return default value # Construct string to be displayed on screens # Also print string in console # Class client design to work as web client and get information # from RPi-Monitor embedded web server # Client constructor # Create a Process object # Infinite loop # Initiate a connection to RPi-Monitor embedded server # Get the file dynamic.json # Get the server response # If response is OK, read data # Run process object on extracted data # Close the connection to RPi-Monitor embedded server # Wait 5 secondes before restarting the loop # Main function # Create a Client object # Run it # if Ctrl+C has been pressed # turn off the lcd backlight # exit from the program # Execute main if the script is directly called | 3.168679 | 3 |
demo.py | sseemayer/msacounts | 1 | 6623236 | #!/usr/bin/env python
"""msacounts demo
Compile the C extensions using `python setup.py build_ext --inplace` before running this!
"""
import msacounts
import sys
import numpy as np
def main():
aln = msacounts.read_msa('data/1atzA.aln')
counts = msacounts.pair_counts(aln)
pwm = msacounts.pwm(counts)
np.savetxt('pwm.txt', pwm)
if __name__ == '__main__':
main()
| #!/usr/bin/env python
"""msacounts demo
Compile the C extensions using `python setup.py build_ext --inplace` before running this!
"""
import msacounts
import sys
import numpy as np
def main():
aln = msacounts.read_msa('data/1atzA.aln')
counts = msacounts.pair_counts(aln)
pwm = msacounts.pwm(counts)
np.savetxt('pwm.txt', pwm)
if __name__ == '__main__':
main()
| en | 0.443132 | #!/usr/bin/env python msacounts demo Compile the C extensions using `python setup.py build_ext --inplace` before running this! | 2.000182 | 2 |
scripts/shared_options.py | shaypal5/hollywood_crawler | 7 | 6623237 | <filename>scripts/shared_options.py
"""Shared holcrawl cli options."""
import click
_SHARED_OPTIONS = [
click.option('--verbose/--silent', default=True,
help="Turn printing progress to screen on or off.")
]
def _shared_options(func):
for option in reversed(_SHARED_OPTIONS):
func = option(func)
return func
| <filename>scripts/shared_options.py
"""Shared holcrawl cli options."""
import click
_SHARED_OPTIONS = [
click.option('--verbose/--silent', default=True,
help="Turn printing progress to screen on or off.")
]
def _shared_options(func):
for option in reversed(_SHARED_OPTIONS):
func = option(func)
return func
| en | 0.402392 | Shared holcrawl cli options. | 2.54079 | 3 |
Python/homework/hw03/my_gray_scaler.py | LucasChangcoding/USTC-2018-Smester-1 | 32 | 6623238 | from graphics import *
class MyGrayScaler(object):
# 构造函数, 注意 graphics 只支持 gif 和 ppm 格式的图片, 默认的图像文件名为 'color.gif'
def __init__(self, filename='color.gif'):
# 图像中心位于(200, 200)
self.img = Image(Point(200, 200), filename)
width = self.img.getWidth()
height = self.img.getHeight()
# 新建一个背景窗口, 长款分别为彩色图像的 2 倍
self.winImage = GraphWin('Color Image', width*2, height*2)
# 显示图像
def showImg(self):
# 先撤掉(可能)已经画过的图像
self.img.undraw()
# 然后在背景窗口中画出 img
self.img.draw(self.winImage)
# 进行灰度转换
def convert(self):
text = self.setHint('在窗口内单击鼠标进行灰度转换')
self.winImage.getMouse()
text.undraw()
text = self.setHint('转换中...')
img = self.img
# 根据公式进行灰度转换
for x in range(img.getHeight()):
for y in range(img.getWidth()):
r, g, b = img.getPixel(x, y)
grayscale = int(round(0.299*r + 0.587*g + 0.114*b))
img.setPixel(x, y, color_rgb(grayscale, grayscale, grayscale))
text.undraw()
text = self.setHint('转换完成, 单击鼠标进入保存窗口')
self.winImage.getMouse()
# 设置提示
def setHint(self, hint=''):
text = Text(Point(200, 50), hint)
text.draw(self.winImage)
return text
# 保存图像
def saveImg(self):
win2 = GraphWin('另存为', 400, 400)
Text(Point(200, 150), '请输入文件名').draw(win2)
Text(Point(200, 250), '然后单击空白处退出').draw(win2)
inputText = Entry(Point(200, 200), 10)
Text(Point(250, 200), '.gif').draw(win2)
inputText.setText("gray image")
inputText.draw(win2)
win2.getMouse()
filename = inputText.getText()
self.img.save(filename+'.gif')
if __name__ == '__main__':
# 创建对象
mgs = MyGrayScaler()
# 显示彩色图像
mgs.showImg()
# 转换为灰度图
mgs.convert()
# 保存图像
mgs.saveImg() | from graphics import *
class MyGrayScaler(object):
# 构造函数, 注意 graphics 只支持 gif 和 ppm 格式的图片, 默认的图像文件名为 'color.gif'
def __init__(self, filename='color.gif'):
# 图像中心位于(200, 200)
self.img = Image(Point(200, 200), filename)
width = self.img.getWidth()
height = self.img.getHeight()
# 新建一个背景窗口, 长款分别为彩色图像的 2 倍
self.winImage = GraphWin('Color Image', width*2, height*2)
# 显示图像
def showImg(self):
# 先撤掉(可能)已经画过的图像
self.img.undraw()
# 然后在背景窗口中画出 img
self.img.draw(self.winImage)
# 进行灰度转换
def convert(self):
text = self.setHint('在窗口内单击鼠标进行灰度转换')
self.winImage.getMouse()
text.undraw()
text = self.setHint('转换中...')
img = self.img
# 根据公式进行灰度转换
for x in range(img.getHeight()):
for y in range(img.getWidth()):
r, g, b = img.getPixel(x, y)
grayscale = int(round(0.299*r + 0.587*g + 0.114*b))
img.setPixel(x, y, color_rgb(grayscale, grayscale, grayscale))
text.undraw()
text = self.setHint('转换完成, 单击鼠标进入保存窗口')
self.winImage.getMouse()
# 设置提示
def setHint(self, hint=''):
text = Text(Point(200, 50), hint)
text.draw(self.winImage)
return text
# 保存图像
def saveImg(self):
win2 = GraphWin('另存为', 400, 400)
Text(Point(200, 150), '请输入文件名').draw(win2)
Text(Point(200, 250), '然后单击空白处退出').draw(win2)
inputText = Entry(Point(200, 200), 10)
Text(Point(250, 200), '.gif').draw(win2)
inputText.setText("gray image")
inputText.draw(win2)
win2.getMouse()
filename = inputText.getText()
self.img.save(filename+'.gif')
if __name__ == '__main__':
# 创建对象
mgs = MyGrayScaler()
# 显示彩色图像
mgs.showImg()
# 转换为灰度图
mgs.convert()
# 保存图像
mgs.saveImg() | zh | 0.995467 | # 构造函数, 注意 graphics 只支持 gif 和 ppm 格式的图片, 默认的图像文件名为 'color.gif' # 图像中心位于(200, 200) # 新建一个背景窗口, 长款分别为彩色图像的 2 倍 # 显示图像 # 先撤掉(可能)已经画过的图像 # 然后在背景窗口中画出 img # 进行灰度转换 # 根据公式进行灰度转换 # 设置提示 # 保存图像 # 创建对象 # 显示彩色图像 # 转换为灰度图 # 保存图像 | 3.299703 | 3 |
Rozdzial_1/r1_06.py | xinulsw/helion-python | 1 | 6623239 | <filename>Rozdzial_1/r1_06.py
# program r1_06.py
# Test modyfikacji obiektu typu list
list_object = [11, 22, 33, "A", "B", "C"]
print(f"Dla ID = {id(list_object)} wartość: {list_object}")
# Do obiektu możemy dodać wartość
list_object.append("Nowa")
print(f"Dla ID = {id(list_object)} wartość: {list_object}")
# lub zmienić wartość w środku
list_object[2] = "Inna wartość"
print(f"Dla ID = {id(list_object)} wartość: {list_object}")
# Test modyfikacji obiektu typu dict
dict_object = {1: "Pierwszy element"}
print(f"Dla ID = {id(dict_object)} wartość: {dict_object}")
# Do obiektu możemy dodać wartość
dict_object[2] = "Drugi element"
dict_object[3] = "Trzeci element"
print(f"Dla ID = {id(dict_object)} wartość: {dict_object}")
# lub zmienić wartość w środku
dict_object[2] = "Inna wartość"
print(f"Dla ID = {id(dict_object)} wartość: {dict_object}")
| <filename>Rozdzial_1/r1_06.py
# program r1_06.py
# Test modyfikacji obiektu typu list
list_object = [11, 22, 33, "A", "B", "C"]
print(f"Dla ID = {id(list_object)} wartość: {list_object}")
# Do obiektu możemy dodać wartość
list_object.append("Nowa")
print(f"Dla ID = {id(list_object)} wartość: {list_object}")
# lub zmienić wartość w środku
list_object[2] = "Inna wartość"
print(f"Dla ID = {id(list_object)} wartość: {list_object}")
# Test modyfikacji obiektu typu dict
dict_object = {1: "Pierwszy element"}
print(f"Dla ID = {id(dict_object)} wartość: {dict_object}")
# Do obiektu możemy dodać wartość
dict_object[2] = "Drugi element"
dict_object[3] = "Trzeci element"
print(f"Dla ID = {id(dict_object)} wartość: {dict_object}")
# lub zmienić wartość w środku
dict_object[2] = "Inna wartość"
print(f"Dla ID = {id(dict_object)} wartość: {dict_object}")
| pl | 0.999329 | # program r1_06.py # Test modyfikacji obiektu typu list # Do obiektu możemy dodać wartość # lub zmienić wartość w środku # Test modyfikacji obiektu typu dict # Do obiektu możemy dodać wartość # lub zmienić wartość w środku | 3.169746 | 3 |
python/vast/voidfinder/viz/load_results.py | DESI-UR/VoidFinder | 5 | 6623240 | <filename>python/vast/voidfinder/viz/load_results.py
import numpy
import h5py
from astropy.table import Table
import matplotlib
import matplotlib.pyplot as plt
#from vast.voidfinder.absmag_comovingdist_functions import Distance
from vast.voidfinder.distance import z_to_comoving_dist
from vast.voidfinder.preprocessing import load_data_to_Table
# Constants
c = 3e5
DtoR = numpy.pi/180.
RtoD = 180./numpy.pi
distance_metric = 'comoving'
#distance_metric = 'redshift'
Omega_M = 0.3
h = 1.0
if __name__ == "__main__":
infilename1 = "vollim_dr7_cbp_102709_holes.txt"
infilename2 = "vollim_dr7_cbp_102709_maximal.txt"
infilename3 = "vollim_dr7_cbp_102709.dat"
############################################################################
# load hole locations
# keys are 'x' 'y' 'z' 'radius' 'flag'
#---------------------------------------------------------------------------
holes_data = Table.read(infilename1, format='ascii.commented_header')
############################################################################
############################################################################
# Load galaxy data and convert coordinates to xyz
#---------------------------------------------------------------------------
galaxy_data = Table.read(infilename3, format='ascii.commented_header')
if distance_metric == 'comoving':
r_gal = galaxy_data['Rgal']
else:
if 'redshift' in galaxy_data.colnames:
z_column = 'redshift'
elif 'REDSHIFT' in galaxy_data.colnames:
z_column = 'REDSHIFT'
elif 'z' in galaxy_data.colnames:
z_column = 'z'
else:
print('Redshift column not known. Please rename column to "redshift".')
r_gal = c*galaxy_data[z_column]/(100*h)
xin = r_gal*numpy.cos(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
yin = r_gal*numpy.sin(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
zin = r_gal*numpy.sin(galaxy_data['dec']*DtoR)
xyz_galaxy_data = Table([xin, yin, zin], names=('x','y','z'))
############################################################################
print(xyz_galaxy_data)
print(holes_data)
def load_void_data(infilename):
'''
Load voids as formatted for VoidFinder
Parameters
==========
infilename : string
path to desired data file
Returns
=======
holes_xyz : numpy.ndarray shape (N,3)
the xyz centers of the holes
holes_radii : numpy.ndarray shape (N,)
the radii of the holes
hole_flags : numpy.ndarray shape (N,)
the VoidFinder 'flag' output representing
which void group a hole belongs to
'''
holes_data = load_data_to_Table(infilename)
num_rows = len(holes_data)
holes_xyz = numpy.empty((num_rows, 3), dtype=numpy.float64)
hole_radii = numpy.empty(num_rows, dtype=numpy.float64)
hole_flags = numpy.empty(num_rows, dtype=numpy.int32)
holes_xyz[:,0] = holes_data['x']
holes_xyz[:,1] = holes_data['y']
holes_xyz[:,2] = holes_data['z']
hole_radii[:] = holes_data["radius"]
hole_flags[:] = holes_data["flag"]
return holes_xyz, hole_radii, hole_flags
def load_galaxy_data(infilename):
"""
Load a table of galaxies for use in VoidRender
Parameters
==========
infilename : string
path to desired data file
intended to be an astropy table output from VoidFinder
with columns 'ra', 'dec', 'redshift', and possibly 'Rgal'
Returns
=======
galaxy_data_xyz : numpy.ndarray shape (N,3)
xyz coordinates of galaxies from the data table
"""
galaxy_data = load_data_to_Table(infilename)
if all([name in galaxy_data.colnames for name in ['x', 'y', 'z']]):
num_rows = len(galaxy_data)
galaxy_data_xyz = numpy.empty((num_rows, 3), dtype=numpy.float64)
galaxy_data_xyz[:,0] = galaxy_data['x']
galaxy_data_xyz[:,1] = galaxy_data['y']
galaxy_data_xyz[:,2] = galaxy_data['z']
else:
############################################################################
# Identify the redshift column label
#---------------------------------------------------------------------------
if 'redshift' in galaxy_data.colnames:
z_column = 'redshift'
elif 'REDSHIFT' in galaxy_data.colnames:
z_column = 'REDSHIFT'
elif 'z' in galaxy_data.colnames:
z_column = 'z'
else:
print('Redshift column not known. Please rename column to "redshift".')
############################################################################
############################################################################
# Calculate the distance to the galaxies
#---------------------------------------------------------------------------
if distance_metric == 'comoving' and 'Rgal' not in galaxy_data.columns:
r_gal = z_to_comoving_dist(galaxy_data[z_column].data.astype(numpy.float32),
Omega_M,
h)
elif distance_metric == 'comoving':
r_gal = galaxy_data['Rgal']
else:
r_gal = c*galaxy_data[z_column]/(100*h)
############################################################################
############################################################################
# Convert sky coordinates to Cartesian coordinates
#---------------------------------------------------------------------------
xin = r_gal*numpy.cos(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
yin = r_gal*numpy.sin(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
zin = r_gal*numpy.sin(galaxy_data['dec']*DtoR)
############################################################################
############################################################################
# Create output array
#---------------------------------------------------------------------------
#xyz_galaxy_table = Table([xin, yin, zin], names=('x','y','z'))
num_rows = len(galaxy_data)
galaxy_data_xyz = numpy.empty((num_rows, 3), dtype=numpy.float64)
galaxy_data_xyz[:,0] = xin
galaxy_data_xyz[:,1] = yin
galaxy_data_xyz[:,2] = zin
############################################################################
return galaxy_data_xyz
| <filename>python/vast/voidfinder/viz/load_results.py
import numpy
import h5py
from astropy.table import Table
import matplotlib
import matplotlib.pyplot as plt
#from vast.voidfinder.absmag_comovingdist_functions import Distance
from vast.voidfinder.distance import z_to_comoving_dist
from vast.voidfinder.preprocessing import load_data_to_Table
# Constants
c = 3e5
DtoR = numpy.pi/180.
RtoD = 180./numpy.pi
distance_metric = 'comoving'
#distance_metric = 'redshift'
Omega_M = 0.3
h = 1.0
if __name__ == "__main__":
infilename1 = "vollim_dr7_cbp_102709_holes.txt"
infilename2 = "vollim_dr7_cbp_102709_maximal.txt"
infilename3 = "vollim_dr7_cbp_102709.dat"
############################################################################
# load hole locations
# keys are 'x' 'y' 'z' 'radius' 'flag'
#---------------------------------------------------------------------------
holes_data = Table.read(infilename1, format='ascii.commented_header')
############################################################################
############################################################################
# Load galaxy data and convert coordinates to xyz
#---------------------------------------------------------------------------
galaxy_data = Table.read(infilename3, format='ascii.commented_header')
if distance_metric == 'comoving':
r_gal = galaxy_data['Rgal']
else:
if 'redshift' in galaxy_data.colnames:
z_column = 'redshift'
elif 'REDSHIFT' in galaxy_data.colnames:
z_column = 'REDSHIFT'
elif 'z' in galaxy_data.colnames:
z_column = 'z'
else:
print('Redshift column not known. Please rename column to "redshift".')
r_gal = c*galaxy_data[z_column]/(100*h)
xin = r_gal*numpy.cos(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
yin = r_gal*numpy.sin(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
zin = r_gal*numpy.sin(galaxy_data['dec']*DtoR)
xyz_galaxy_data = Table([xin, yin, zin], names=('x','y','z'))
############################################################################
print(xyz_galaxy_data)
print(holes_data)
def load_void_data(infilename):
'''
Load voids as formatted for VoidFinder
Parameters
==========
infilename : string
path to desired data file
Returns
=======
holes_xyz : numpy.ndarray shape (N,3)
the xyz centers of the holes
holes_radii : numpy.ndarray shape (N,)
the radii of the holes
hole_flags : numpy.ndarray shape (N,)
the VoidFinder 'flag' output representing
which void group a hole belongs to
'''
holes_data = load_data_to_Table(infilename)
num_rows = len(holes_data)
holes_xyz = numpy.empty((num_rows, 3), dtype=numpy.float64)
hole_radii = numpy.empty(num_rows, dtype=numpy.float64)
hole_flags = numpy.empty(num_rows, dtype=numpy.int32)
holes_xyz[:,0] = holes_data['x']
holes_xyz[:,1] = holes_data['y']
holes_xyz[:,2] = holes_data['z']
hole_radii[:] = holes_data["radius"]
hole_flags[:] = holes_data["flag"]
return holes_xyz, hole_radii, hole_flags
def load_galaxy_data(infilename):
"""
Load a table of galaxies for use in VoidRender
Parameters
==========
infilename : string
path to desired data file
intended to be an astropy table output from VoidFinder
with columns 'ra', 'dec', 'redshift', and possibly 'Rgal'
Returns
=======
galaxy_data_xyz : numpy.ndarray shape (N,3)
xyz coordinates of galaxies from the data table
"""
galaxy_data = load_data_to_Table(infilename)
if all([name in galaxy_data.colnames for name in ['x', 'y', 'z']]):
num_rows = len(galaxy_data)
galaxy_data_xyz = numpy.empty((num_rows, 3), dtype=numpy.float64)
galaxy_data_xyz[:,0] = galaxy_data['x']
galaxy_data_xyz[:,1] = galaxy_data['y']
galaxy_data_xyz[:,2] = galaxy_data['z']
else:
############################################################################
# Identify the redshift column label
#---------------------------------------------------------------------------
if 'redshift' in galaxy_data.colnames:
z_column = 'redshift'
elif 'REDSHIFT' in galaxy_data.colnames:
z_column = 'REDSHIFT'
elif 'z' in galaxy_data.colnames:
z_column = 'z'
else:
print('Redshift column not known. Please rename column to "redshift".')
############################################################################
############################################################################
# Calculate the distance to the galaxies
#---------------------------------------------------------------------------
if distance_metric == 'comoving' and 'Rgal' not in galaxy_data.columns:
r_gal = z_to_comoving_dist(galaxy_data[z_column].data.astype(numpy.float32),
Omega_M,
h)
elif distance_metric == 'comoving':
r_gal = galaxy_data['Rgal']
else:
r_gal = c*galaxy_data[z_column]/(100*h)
############################################################################
############################################################################
# Convert sky coordinates to Cartesian coordinates
#---------------------------------------------------------------------------
xin = r_gal*numpy.cos(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
yin = r_gal*numpy.sin(galaxy_data['ra']*DtoR)*numpy.cos(galaxy_data['dec']*DtoR)
zin = r_gal*numpy.sin(galaxy_data['dec']*DtoR)
############################################################################
############################################################################
# Create output array
#---------------------------------------------------------------------------
#xyz_galaxy_table = Table([xin, yin, zin], names=('x','y','z'))
num_rows = len(galaxy_data)
galaxy_data_xyz = numpy.empty((num_rows, 3), dtype=numpy.float64)
galaxy_data_xyz[:,0] = xin
galaxy_data_xyz[:,1] = yin
galaxy_data_xyz[:,2] = zin
############################################################################
return galaxy_data_xyz
| de | 0.331728 | #from vast.voidfinder.absmag_comovingdist_functions import Distance # Constants #distance_metric = 'redshift' ############################################################################ # load hole locations # keys are 'x' 'y' 'z' 'radius' 'flag' #--------------------------------------------------------------------------- ############################################################################ ############################################################################ # Load galaxy data and convert coordinates to xyz #--------------------------------------------------------------------------- ############################################################################ Load voids as formatted for VoidFinder Parameters ========== infilename : string path to desired data file Returns ======= holes_xyz : numpy.ndarray shape (N,3) the xyz centers of the holes holes_radii : numpy.ndarray shape (N,) the radii of the holes hole_flags : numpy.ndarray shape (N,) the VoidFinder 'flag' output representing which void group a hole belongs to Load a table of galaxies for use in VoidRender Parameters ========== infilename : string path to desired data file intended to be an astropy table output from VoidFinder with columns 'ra', 'dec', 'redshift', and possibly 'Rgal' Returns ======= galaxy_data_xyz : numpy.ndarray shape (N,3) xyz coordinates of galaxies from the data table ############################################################################ # Identify the redshift column label #--------------------------------------------------------------------------- ############################################################################ ############################################################################ # Calculate the distance to the galaxies #--------------------------------------------------------------------------- ############################################################################ ############################################################################ # Convert sky coordinates to Cartesian coordinates #--------------------------------------------------------------------------- ############################################################################ ############################################################################ # Create output array #--------------------------------------------------------------------------- #xyz_galaxy_table = Table([xin, yin, zin], names=('x','y','z')) ############################################################################ | 2.224967 | 2 |
Backend/Pozyx/pypozyx/structures/generic.py | osoc21/Safe-Crossing | 2 | 6623241 | <filename>Backend/Pozyx/pypozyx/structures/generic.py
#!/usr/bin/env python
# TODO move this in the RST files.
"""
pypozyx.structures.generic - introduces generic data structures derived from ByteStructure
Generic Structures
As the name implies, contains generic structures whose specific use is up to the
user. You should use SingleRegister where applicable when reading/writing
a single register, and use Data for larger data structures.
Structures contained:
Data
THE generic data structure, a powerful way of constructing arbitrarily
formed packed data structures
XYZ
A generic XYZ data structure that is used in much 3D sensor data
SingleRegister
Data resembling a single register. Can choose size and whether signed.
UniformData
A variation on Data with all data being a uniform format. Questionably useful.
The use of Data:
Data creates a packed data structure with size and format that is entirely the user's choice.
The format follows the one used in struct, where b is a byte, h is a 2-byte int, and
i is a default-sized integer, and f is a float. In capitals, these are signed.
So, to create a custom construct consisting of 4 uint16 and a single int, the
following code can be used.
>>> d = Data([0] * 5, 'HHHHi')
or
>>> data_format = 'HHHHi'
>>> d = Data([0] * len(data_format), data_format)
"""
from pypozyx.structures.byte_structure import ByteStructure
def is_reg_readable(reg):
"""Returns whether a Pozyx register is readable."""
if (0x00 <= reg < 0x07) or (0x10 <= reg < 0x12) or (0x14 <= reg < 0x22) or (0x22 <= reg <= 0x24) or (
0x26 <= reg < 0x2B) or (0x30 <= reg < 0x48) or (0x4E <= reg < 0x89):
return True
return False
def is_reg_writable(reg):
"""Returns whether a Pozyx register is writeable."""
if (0x10 <= reg < 0x12) or (0x14 <= reg < 0x22) or (0x22 <= reg <= 0x24) or (0x26 <= reg < 0x2B) or (
0x30 <= reg < 0x3C) or (0x85 <= reg < 0x89):
return True
return False
def is_functioncall(reg):
"""Returns whether a Pozyx register is a Pozyx function."""
if (0xB0 <= reg <= 0xBC) or (0xC0 <= reg < 0xC9):
return True
return False
def dataCheck(data):
"""Returns whether an object is part of the ByteStructure-derived classes or not.
The function checks the base classes of the passed data object. This function enables
many library functions to be passed along its data as either an int/list or the properly
intended data structure. For example, the following code will result in the
same behaviour::
>>> p.setCoordinates([0, 0, 0])
>>> # or
>>> coords = Coordinates()
>>> p.setCoordinates(coords)
AND
>>> p.setNetworkId(0x6000)
>>> # or
>>> n = NetworkID(0x6000)
>>> p.setNetworkId(n)
Note that this only works for functions where you change one of the Pozyx's
settings. When reading data from the Pozyx, you have to pass along the correct
data structure.
Using dataCheck:
You might want to use this in your own function, as it makes it more robust
to whether an int or list gets sent as a parameter to your function, or a
ByteStructure-like object. If so, you can perform::
>>> if not dataCheck(sample): # assume a is an int but you want it to be a SingleRegister
>>> sample = SingleRegister(sample)
"""
if not(Data in type(data).__bases__ or ByteStructure in type(data).__bases__ or Data is type(data) or XYZ in type(data).__bases__ or SingleRegister in type(data).__bases__):
return False
return True
class XYZ(ByteStructure):
"""
Generic XYZ data structure consisting of 3 integers x, y, and z.
Not recommended to use in practice, as relevant sensor data classes are derived from this.
"""
physical_convert = 1
byte_size = 12
data_format = 'iii'
def __init__(self, x=0, y=0, z=0):
"""Initializes the XYZ or XYZ-derived object."""
self.data = [x, y, z]
def load(self, data, convert=True):
self.data = data
def __str__(self):
return 'X: {}, Y: {}, Z: {}'.format(self.x, self.y, self.z)
@property
def x(self):
return self.data[0] / self.physical_convert
@x.setter
def x(self, value):
self.data[0] = value * self.physical_convert
@property
def y(self):
return self.data[1] / self.physical_convert
@y.setter
def y(self, value):
self.data[1] = value * self.physical_convert
@property
def z(self):
return self.data[2] / self.physical_convert
@z.setter
def z(self, value):
self.data[2] = value * self.physical_convert
# TODO maybe use asdict()? Move to dataclasses?
def to_dict(self):
return {
"x": self.x,
"y": self.y,
"z": self.z,
}
class Data(ByteStructure):
"""Data allows the user to define arbitrary data structures to use with Pozyx.
The Leatherman of ByteStructure-derived classes, Data allows you to create your own
library-compatible packed data structures. Also for empty data, this is used.
The use of Data:
Data creates a packed data structure with size and format that is entirely the user's choice.
The format follows the one used in struct, where b is a byte, h is a 2-byte int, and
i is a default-sized integer, and f is a float. In capitals, these are unsigned.
So, to create a custom construct consisting of 4 uint16 and a single int, the
following code can be used.
>>> d = Data([0] * 5, 'HHHHi')
or
>>> data_format = 'HHHHi'
>>> d = Data([0] * len(data_format), data_format)
Args:
data (optional): Data contained in the data structure. When no data_format is passed, these are assumed UInt8 values.
data_format (optional): Custom data format for the data passed.
"""
def __init__(self, data=None, data_format=None):
if data is None:
data = []
self.data = data
if data_format is None:
data_format = 'B' * len(data)
self.data_format = data_format
self.set_packed_size()
self.byte_data = '00' * self.byte_size
def load(self, data, convert=True):
self.data = data
class SingleRegister(Data):
""" SingleRegister is container for the data from a single Pozyx register.
By default, this represents a UInt8 register. Used for both reading and writing.
The size and whether the data is a 'signed' integer are both changeable by the
user using the size and signed keyword arguments.
Args:
value (optional): Value of the register.
size (optional): Size of the register. 1, 2, or 4. Default 1.
signed (optional): Whether the data is signed. unsigned by default.
print_hex (optional): How to print the register output. Hex by default. Special options are 'hex' and 'bin'
other things, such as 'dec', will return decimal output.
"""
byte_size = 1
data_format = 'B'
def __init__(self, value=0, size=1, signed=False, print_style='hex'):
self.print_style = print_style
if size == 1:
data_format = 'b'
elif size == 2:
data_format = 'h'
elif size == 4:
data_format = 'i'
else:
raise ValueError("Size should be 1, 2, or 4")
if not signed:
data_format = data_format.capitalize()
Data.__init__(self, [value], data_format)
def load(self, data, convert=True):
self.data = data
@property
def value(self):
return self.data[0]
@value.setter
def value(self, new_value):
self.data[0] = new_value
def __str__(self):
if self.print_style is 'hex':
return hex(self.value).capitalize()
elif self.print_style is 'bin':
return bin(self.value)
else:
return str(self.value)
def __eq__(self, other):
if type(other) == SingleRegister:
return self.value == other.value
elif type(other) == int:
return self.value == other
else:
raise ValueError("Can't compare SingleRegister value with non-integer values or registers")
def __le__(self, other):
if type(other) == SingleRegister:
return self.value <= other.value
elif type(other) == int:
return self.value <= other
else:
raise ValueError("Can't compare SingleRegister value with non-integer values or registers")
def __lt__(self, other):
if type(other) == SingleRegister:
return self.value < other.value
elif type(other) == int:
return self.value < other
else:
raise ValueError("Can't compare SingleRegister value with non-integer values or registers")
def __gt__(self, other):
return not self.__le__(other)
def __ge__(self, other):
return not self.__lt__(other)
class SingleSensorValue(ByteStructure):
"""
Generic Single Sensor Value data structure.
Not recommended to use in practice, as relevant sensor data classes are derived from this.
"""
physical_convert = 1
byte_size = 4
data_format = 'i'
def __init__(self, value=0):
"""Initializes the XYZ or XYZ-derived object."""
self.data = [0]
self.load([value])
@property
def value(self):
return self.data[0]
@value.setter
def value(self, new_value):
self.data[0] = new_value
def load(self, data=None, convert=True):
self.data = [0] if data is None else data
if convert:
self.data[0] = float(self.data[0]) / self.physical_convert
def __str__(self):
return 'Value: {}'.format(self.value)
| <filename>Backend/Pozyx/pypozyx/structures/generic.py
#!/usr/bin/env python
# TODO move this in the RST files.
"""
pypozyx.structures.generic - introduces generic data structures derived from ByteStructure
Generic Structures
As the name implies, contains generic structures whose specific use is up to the
user. You should use SingleRegister where applicable when reading/writing
a single register, and use Data for larger data structures.
Structures contained:
Data
THE generic data structure, a powerful way of constructing arbitrarily
formed packed data structures
XYZ
A generic XYZ data structure that is used in much 3D sensor data
SingleRegister
Data resembling a single register. Can choose size and whether signed.
UniformData
A variation on Data with all data being a uniform format. Questionably useful.
The use of Data:
Data creates a packed data structure with size and format that is entirely the user's choice.
The format follows the one used in struct, where b is a byte, h is a 2-byte int, and
i is a default-sized integer, and f is a float. In capitals, these are signed.
So, to create a custom construct consisting of 4 uint16 and a single int, the
following code can be used.
>>> d = Data([0] * 5, 'HHHHi')
or
>>> data_format = 'HHHHi'
>>> d = Data([0] * len(data_format), data_format)
"""
from pypozyx.structures.byte_structure import ByteStructure
def is_reg_readable(reg):
"""Returns whether a Pozyx register is readable."""
if (0x00 <= reg < 0x07) or (0x10 <= reg < 0x12) or (0x14 <= reg < 0x22) or (0x22 <= reg <= 0x24) or (
0x26 <= reg < 0x2B) or (0x30 <= reg < 0x48) or (0x4E <= reg < 0x89):
return True
return False
def is_reg_writable(reg):
"""Returns whether a Pozyx register is writeable."""
if (0x10 <= reg < 0x12) or (0x14 <= reg < 0x22) or (0x22 <= reg <= 0x24) or (0x26 <= reg < 0x2B) or (
0x30 <= reg < 0x3C) or (0x85 <= reg < 0x89):
return True
return False
def is_functioncall(reg):
"""Returns whether a Pozyx register is a Pozyx function."""
if (0xB0 <= reg <= 0xBC) or (0xC0 <= reg < 0xC9):
return True
return False
def dataCheck(data):
"""Returns whether an object is part of the ByteStructure-derived classes or not.
The function checks the base classes of the passed data object. This function enables
many library functions to be passed along its data as either an int/list or the properly
intended data structure. For example, the following code will result in the
same behaviour::
>>> p.setCoordinates([0, 0, 0])
>>> # or
>>> coords = Coordinates()
>>> p.setCoordinates(coords)
AND
>>> p.setNetworkId(0x6000)
>>> # or
>>> n = NetworkID(0x6000)
>>> p.setNetworkId(n)
Note that this only works for functions where you change one of the Pozyx's
settings. When reading data from the Pozyx, you have to pass along the correct
data structure.
Using dataCheck:
You might want to use this in your own function, as it makes it more robust
to whether an int or list gets sent as a parameter to your function, or a
ByteStructure-like object. If so, you can perform::
>>> if not dataCheck(sample): # assume a is an int but you want it to be a SingleRegister
>>> sample = SingleRegister(sample)
"""
if not(Data in type(data).__bases__ or ByteStructure in type(data).__bases__ or Data is type(data) or XYZ in type(data).__bases__ or SingleRegister in type(data).__bases__):
return False
return True
class XYZ(ByteStructure):
"""
Generic XYZ data structure consisting of 3 integers x, y, and z.
Not recommended to use in practice, as relevant sensor data classes are derived from this.
"""
physical_convert = 1
byte_size = 12
data_format = 'iii'
def __init__(self, x=0, y=0, z=0):
"""Initializes the XYZ or XYZ-derived object."""
self.data = [x, y, z]
def load(self, data, convert=True):
self.data = data
def __str__(self):
return 'X: {}, Y: {}, Z: {}'.format(self.x, self.y, self.z)
@property
def x(self):
return self.data[0] / self.physical_convert
@x.setter
def x(self, value):
self.data[0] = value * self.physical_convert
@property
def y(self):
return self.data[1] / self.physical_convert
@y.setter
def y(self, value):
self.data[1] = value * self.physical_convert
@property
def z(self):
return self.data[2] / self.physical_convert
@z.setter
def z(self, value):
self.data[2] = value * self.physical_convert
# TODO maybe use asdict()? Move to dataclasses?
def to_dict(self):
return {
"x": self.x,
"y": self.y,
"z": self.z,
}
class Data(ByteStructure):
"""Data allows the user to define arbitrary data structures to use with Pozyx.
The Leatherman of ByteStructure-derived classes, Data allows you to create your own
library-compatible packed data structures. Also for empty data, this is used.
The use of Data:
Data creates a packed data structure with size and format that is entirely the user's choice.
The format follows the one used in struct, where b is a byte, h is a 2-byte int, and
i is a default-sized integer, and f is a float. In capitals, these are unsigned.
So, to create a custom construct consisting of 4 uint16 and a single int, the
following code can be used.
>>> d = Data([0] * 5, 'HHHHi')
or
>>> data_format = 'HHHHi'
>>> d = Data([0] * len(data_format), data_format)
Args:
data (optional): Data contained in the data structure. When no data_format is passed, these are assumed UInt8 values.
data_format (optional): Custom data format for the data passed.
"""
def __init__(self, data=None, data_format=None):
if data is None:
data = []
self.data = data
if data_format is None:
data_format = 'B' * len(data)
self.data_format = data_format
self.set_packed_size()
self.byte_data = '00' * self.byte_size
def load(self, data, convert=True):
self.data = data
class SingleRegister(Data):
""" SingleRegister is container for the data from a single Pozyx register.
By default, this represents a UInt8 register. Used for both reading and writing.
The size and whether the data is a 'signed' integer are both changeable by the
user using the size and signed keyword arguments.
Args:
value (optional): Value of the register.
size (optional): Size of the register. 1, 2, or 4. Default 1.
signed (optional): Whether the data is signed. unsigned by default.
print_hex (optional): How to print the register output. Hex by default. Special options are 'hex' and 'bin'
other things, such as 'dec', will return decimal output.
"""
byte_size = 1
data_format = 'B'
def __init__(self, value=0, size=1, signed=False, print_style='hex'):
self.print_style = print_style
if size == 1:
data_format = 'b'
elif size == 2:
data_format = 'h'
elif size == 4:
data_format = 'i'
else:
raise ValueError("Size should be 1, 2, or 4")
if not signed:
data_format = data_format.capitalize()
Data.__init__(self, [value], data_format)
def load(self, data, convert=True):
self.data = data
@property
def value(self):
return self.data[0]
@value.setter
def value(self, new_value):
self.data[0] = new_value
def __str__(self):
if self.print_style is 'hex':
return hex(self.value).capitalize()
elif self.print_style is 'bin':
return bin(self.value)
else:
return str(self.value)
def __eq__(self, other):
if type(other) == SingleRegister:
return self.value == other.value
elif type(other) == int:
return self.value == other
else:
raise ValueError("Can't compare SingleRegister value with non-integer values or registers")
def __le__(self, other):
if type(other) == SingleRegister:
return self.value <= other.value
elif type(other) == int:
return self.value <= other
else:
raise ValueError("Can't compare SingleRegister value with non-integer values or registers")
def __lt__(self, other):
if type(other) == SingleRegister:
return self.value < other.value
elif type(other) == int:
return self.value < other
else:
raise ValueError("Can't compare SingleRegister value with non-integer values or registers")
def __gt__(self, other):
return not self.__le__(other)
def __ge__(self, other):
return not self.__lt__(other)
class SingleSensorValue(ByteStructure):
"""
Generic Single Sensor Value data structure.
Not recommended to use in practice, as relevant sensor data classes are derived from this.
"""
physical_convert = 1
byte_size = 4
data_format = 'i'
def __init__(self, value=0):
"""Initializes the XYZ or XYZ-derived object."""
self.data = [0]
self.load([value])
@property
def value(self):
return self.data[0]
@value.setter
def value(self, new_value):
self.data[0] = new_value
def load(self, data=None, convert=True):
self.data = [0] if data is None else data
if convert:
self.data[0] = float(self.data[0]) / self.physical_convert
def __str__(self):
return 'Value: {}'.format(self.value)
| en | 0.81027 | #!/usr/bin/env python # TODO move this in the RST files. pypozyx.structures.generic - introduces generic data structures derived from ByteStructure Generic Structures As the name implies, contains generic structures whose specific use is up to the user. You should use SingleRegister where applicable when reading/writing a single register, and use Data for larger data structures. Structures contained: Data THE generic data structure, a powerful way of constructing arbitrarily formed packed data structures XYZ A generic XYZ data structure that is used in much 3D sensor data SingleRegister Data resembling a single register. Can choose size and whether signed. UniformData A variation on Data with all data being a uniform format. Questionably useful. The use of Data: Data creates a packed data structure with size and format that is entirely the user's choice. The format follows the one used in struct, where b is a byte, h is a 2-byte int, and i is a default-sized integer, and f is a float. In capitals, these are signed. So, to create a custom construct consisting of 4 uint16 and a single int, the following code can be used. >>> d = Data([0] * 5, 'HHHHi') or >>> data_format = 'HHHHi' >>> d = Data([0] * len(data_format), data_format) Returns whether a Pozyx register is readable. Returns whether a Pozyx register is writeable. Returns whether a Pozyx register is a Pozyx function. Returns whether an object is part of the ByteStructure-derived classes or not. The function checks the base classes of the passed data object. This function enables many library functions to be passed along its data as either an int/list or the properly intended data structure. For example, the following code will result in the same behaviour:: >>> p.setCoordinates([0, 0, 0]) >>> # or >>> coords = Coordinates() >>> p.setCoordinates(coords) AND >>> p.setNetworkId(0x6000) >>> # or >>> n = NetworkID(0x6000) >>> p.setNetworkId(n) Note that this only works for functions where you change one of the Pozyx's settings. When reading data from the Pozyx, you have to pass along the correct data structure. Using dataCheck: You might want to use this in your own function, as it makes it more robust to whether an int or list gets sent as a parameter to your function, or a ByteStructure-like object. If so, you can perform:: >>> if not dataCheck(sample): # assume a is an int but you want it to be a SingleRegister >>> sample = SingleRegister(sample) Generic XYZ data structure consisting of 3 integers x, y, and z. Not recommended to use in practice, as relevant sensor data classes are derived from this. Initializes the XYZ or XYZ-derived object. # TODO maybe use asdict()? Move to dataclasses? Data allows the user to define arbitrary data structures to use with Pozyx. The Leatherman of ByteStructure-derived classes, Data allows you to create your own library-compatible packed data structures. Also for empty data, this is used. The use of Data: Data creates a packed data structure with size and format that is entirely the user's choice. The format follows the one used in struct, where b is a byte, h is a 2-byte int, and i is a default-sized integer, and f is a float. In capitals, these are unsigned. So, to create a custom construct consisting of 4 uint16 and a single int, the following code can be used. >>> d = Data([0] * 5, 'HHHHi') or >>> data_format = 'HHHHi' >>> d = Data([0] * len(data_format), data_format) Args: data (optional): Data contained in the data structure. When no data_format is passed, these are assumed UInt8 values. data_format (optional): Custom data format for the data passed. SingleRegister is container for the data from a single Pozyx register. By default, this represents a UInt8 register. Used for both reading and writing. The size and whether the data is a 'signed' integer are both changeable by the user using the size and signed keyword arguments. Args: value (optional): Value of the register. size (optional): Size of the register. 1, 2, or 4. Default 1. signed (optional): Whether the data is signed. unsigned by default. print_hex (optional): How to print the register output. Hex by default. Special options are 'hex' and 'bin' other things, such as 'dec', will return decimal output. Generic Single Sensor Value data structure. Not recommended to use in practice, as relevant sensor data classes are derived from this. Initializes the XYZ or XYZ-derived object. | 2.331866 | 2 |
Protheus_WebApp/Modules/SIGAGTP/GTPA042TestCase.py | 98llm/tir-script-samples | 17 | 6623242 | from tir import Webapp
import unittest
class GTPA042(unittest.TestCase):
@classmethod
def setUpClass(inst):
inst.oHelper = Webapp()
inst.oHelper.Setup('SIGAGTP', '14/08/2020', 'T1', 'D MG 01 ')
inst.oHelper.Program('GTPA042')
# Efetua o cadastro de evento para envio de e-mail
print('CT001 - inclui evento para Envio de e-mails')
def test_GTPA042_CT001(self):
self.oHelper.SetButton('Sim')
self.oHelper.SetButton('Incluir')
self.oHelper.SetValue('GZ8_DESEVE', 'AUTOMACAO ENVIO DE EMAIL')
self.oHelper.SetValue('GZ8_TEXTO', 'AUTOMACAO ENVIO DE EMAIL TEXTO E-MAIL')
self.oHelper.SetValue('GZ8_STATUS', '1')
self.oHelper.SetValue('GZ8_TITULO', 'AUTOMACAO ENVIO DE EMAIL TITULO')
self.oHelper.SetValue('GZ8_RECOR', '2')
self.oHelper.SetValue('GZ6_CODIGO', '000002')
self.oHelper.SetButton('Outras Ações','Automação')
self.oHelper.SetValue('GY5_ENTIDA', 'G57')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetValue('GY6_CAMPO1', 'G57_AGENCI')
self.oHelper.SetValue('GY6_CONTEU', '000050')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetButton('Visualizar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetButton('Outras Ações','Excluir')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.AssertTrue()
@classmethod
def tearDownClass(inst):
inst.oHelper.TearDown()
if __name__ == '__main__':
unittest.main()
| from tir import Webapp
import unittest
class GTPA042(unittest.TestCase):
@classmethod
def setUpClass(inst):
inst.oHelper = Webapp()
inst.oHelper.Setup('SIGAGTP', '14/08/2020', 'T1', 'D MG 01 ')
inst.oHelper.Program('GTPA042')
# Efetua o cadastro de evento para envio de e-mail
print('CT001 - inclui evento para Envio de e-mails')
def test_GTPA042_CT001(self):
self.oHelper.SetButton('Sim')
self.oHelper.SetButton('Incluir')
self.oHelper.SetValue('GZ8_DESEVE', 'AUTOMACAO ENVIO DE EMAIL')
self.oHelper.SetValue('GZ8_TEXTO', 'AUTOMACAO ENVIO DE EMAIL TEXTO E-MAIL')
self.oHelper.SetValue('GZ8_STATUS', '1')
self.oHelper.SetValue('GZ8_TITULO', 'AUTOMACAO ENVIO DE EMAIL TITULO')
self.oHelper.SetValue('GZ8_RECOR', '2')
self.oHelper.SetValue('GZ6_CODIGO', '000002')
self.oHelper.SetButton('Outras Ações','Automação')
self.oHelper.SetValue('GY5_ENTIDA', 'G57')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetValue('GY6_CAMPO1', 'G57_AGENCI')
self.oHelper.SetValue('GY6_CONTEU', '000050')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetButton('Visualizar')
self.oHelper.SetButton('Fechar')
self.oHelper.SetButton('Outras Ações','Excluir')
self.oHelper.SetButton('Confirmar')
self.oHelper.SetButton('Fechar')
self.oHelper.AssertTrue()
@classmethod
def tearDownClass(inst):
inst.oHelper.TearDown()
if __name__ == '__main__':
unittest.main()
| es | 0.553952 | # Efetua o cadastro de evento para envio de e-mail | 2.524049 | 3 |
src/support/__init__.py | TauferLab/UrbanTrafficFramework_20 | 0 | 6623243 | from . import roadnet, simsio, utm, mappings, linkvolio, heatmap, emissions
| from . import roadnet, simsio, utm, mappings, linkvolio, heatmap, emissions
| none | 1 | 0.925337 | 1 | |
server/tests/base.py | ZoiksScoob/SimpleEvents | 1 | 6623244 | import json
from flask_testing import TestCase
from simple_events.app import app
from simple_events.models import db
class BaseTestCase(TestCase):
""" Base Tests """
def create_app(self):
app.config.from_object('simple_events.config.TestingConfig')
return app
def setUp(self):
db.create_all()
db.session.commit()
def tearDown(self):
db.session.remove()
db.drop_all()
def register_user(self, username, password):
return self.client.post(
'auth/register',
data=json.dumps(dict(
username=username,
password=password
)),
content_type='application/json',
)
def login_user(self, username, password):
return self.client.post(
'auth/login',
data=json.dumps(dict(
username=username,
password=password
)),
content_type='application/json',
)
| import json
from flask_testing import TestCase
from simple_events.app import app
from simple_events.models import db
class BaseTestCase(TestCase):
""" Base Tests """
def create_app(self):
app.config.from_object('simple_events.config.TestingConfig')
return app
def setUp(self):
db.create_all()
db.session.commit()
def tearDown(self):
db.session.remove()
db.drop_all()
def register_user(self, username, password):
return self.client.post(
'auth/register',
data=json.dumps(dict(
username=username,
password=password
)),
content_type='application/json',
)
def login_user(self, username, password):
return self.client.post(
'auth/login',
data=json.dumps(dict(
username=username,
password=password
)),
content_type='application/json',
)
| en | 0.806644 | Base Tests | 2.429068 | 2 |
fjord/settings/base.py | joshua-s/fjord | 0 | 6623245 | # This is your project's main settings file that can be committed to
# your repo. If you need to override a setting locally, use
# settings_local.py
from funfactory.settings_base import *
# Name of the top-level module where you put all your apps. If you
# did not install Playdoh with the funfactory installer script you may
# need to edit this value. See the docs about installing from a clone.
PROJECT_MODULE = 'fjord'
# Defines the views served for root URLs.
ROOT_URLCONF = '%s.urls' % PROJECT_MODULE
# This is the list of languages that are active for non-DEV
# environments. Add languages here to allow users to see the site in
# that locale and additionally submit feedback in that locale.
PROD_LANGUAGES = [
'ach',
'af',
'ak',
'am-et',
'an',
'ar',
'as',
'ast',
'az',
'be',
'bg',
'bn-BD',
'bn-IN',
'br',
'bs',
'ca',
'cs',
'csb',
'cy',
'da',
'dbg',
'de',
'de-AT',
'de-CH',
'de-DE',
'dsb',
'el',
'en-AU',
'en-CA',
'en-GB',
'en-NZ',
'en-US',
'en-ZA',
'eo',
'es',
'es-AR',
'es-CL',
'es-ES',
'es-MX',
'et',
'eu',
'fa',
'ff',
'fi',
'fj-FJ',
'fr',
'fur-IT',
'fy',
'fy-NL',
'ga',
'ga-IE',
'gd',
'gl',
'gu-IN',
'he',
'hi',
'hi-IN',
'hr',
'hsb',
'hu',
'hy-AM',
'id',
'is',
'it',
'ja',
'ka',
'kk',
'km',
'kn',
'ko',
'ku',
'la',
'lg',
'lij',
'lt',
'lv',
'mai',
'mg',
'mi',
'mk',
'ml',
'mn',
'mr',
'ms',
'my',
'nb-NO',
'ne-NP',
'nl',
'nn-NO',
'nr',
'nso',
'oc',
'or',
'pa-IN',
'pl',
'pt',
'pt-BR',
'pt-PT',
'rm',
'ro',
'ru',
'rw',
'sa',
'sah',
'si',
'sk',
'sl',
'son',
'sq',
'sr',
'sr-Latn',
'ss',
'st',
'sv-SE',
'sw',
'ta',
'ta-IN',
'ta-LK',
'te',
'th',
'tn',
'tr',
'ts',
'tt-RU',
'uk',
'ur',
've',
'vi',
'wo',
'xh',
'zh-CN',
'zh-TW',
'zu'
]
DEV_LANGUAGES = PROD_LANGUAGES
INSTALLED_APPS = get_apps(
exclude=(
'compressor',
),
append=(
# south has to come early, otherwise tests fail.
'south',
'django_browserid',
'adminplus',
'django.contrib.admin',
'django_extensions',
'django_nose',
'djcelery',
'eadred',
'jingo_minify',
'dennis.django_dennis',
'fjord.analytics',
'fjord.base',
'fjord.feedback',
'fjord.search',
'fjord.translations',
))
MIDDLEWARE_CLASSES = get_middleware(
exclude=(
# We do mobile detection ourselves.
'mobility.middleware.DetectMobileMiddleware',
'mobility.middleware.XMobileMiddleware',
),
append=(
'fjord.base.middleware.UserAgentMiddleware',
'fjord.base.middleware.MobileQueryStringMiddleware',
'fjord.base.middleware.MobileMiddleware',
'django_statsd.middleware.GraphiteMiddleware',
'django_statsd.middleware.GraphiteRequestTimingMiddleware',
))
LOCALE_PATHS = (
os.path.join(ROOT, PROJECT_MODULE, 'locale'),
)
SUPPORTED_NONLOCALES += (
'robots.txt',
'services',
'api',
)
# Because Jinja2 is the default template loader, add any non-Jinja
# templated apps here:
JINGO_EXCLUDE_APPS = [
'admin',
'adminplus',
'registration',
'browserid',
]
MINIFY_BUNDLES = {
'css': {
'base': (
'css/lib/normalize.css',
'css/fjord.less',
),
'generic_feedback': (
'css/lib/normalize.css',
'css/lib/brick-1.0.0.byob.min.css',
# FIXME - This should become feedback.less and move out of
# mobile/.
'css/mobile/base.less',
'css/generic_feedback.less',
),
'dashboard': (
'css/ui-lightness/jquery-ui.css',
'css/lib/normalize.css',
'css/fjord.less',
'css/dashboard.less',
),
'stage': (
'css/stage.less',
),
'thanks': (
'css/lib/normalize.css',
'css/thanks.less',
),
'mobile/base': (
'css/lib/normalize.css',
'css/mobile/base.less',
),
'mobile/fxos_feedback': (
'css/lib/normalize.css',
'css/lib/brick-1.0.0.byob.min.css',
'css/mobile/base.less',
'css/mobile/fxos_feedback.less',
),
'mobile/thanks': (
'css/lib/normalize.css',
'css/mobile/base.less',
'css/mobile/thanks.less',
)
},
'js': {
'base': (
'js/lib/jquery.min.js',
'browserid/browserid.js',
'js/init.js',
'js/ga.js',
),
'singlecard': (
'js/lib/jquery.min.js',
'js/ga.js',
),
'generic_feedback': (
'js/lib/jquery.min.js',
'js/common_feedback.js',
'js/generic_feedback.js',
'js/ga.js',
),
'dashboard': (
'js/lib/jquery.min.js',
'js/lib/jquery-ui.min.js',
'js/init.js',
'js/lib/excanvas.js',
'js/lib/jquery.flot.js',
'js/lib/jquery.flot.time.js',
'js/lib/jquery.flot.resize.js',
'js/dashboard.js',
'browserid/browserid.js',
'js/ga.js',
),
'thanks': (
'js/lib/jquery.min.js',
'js/init.js',
'js/ga.js',
),
'mobile/base': (
'js/lib/jquery.min.js',
'js/ga.js',
),
'mobile/fxos_feedback': (
'js/lib/jquery.min.js',
'js/common_feedback.js',
'js/mobile/fxos_feedback.js',
'js/ga.js',
),
}
}
LESS_PREPROCESS = True
JINGO_MINIFY_USE_STATIC = True
LESS_BIN = 'lessc'
JAVA_BIN = 'java'
AUTHENTICATION_BACKENDS = [
'django.contrib.auth.backends.ModelBackend',
'django_browserid.auth.BrowserIDBackend',
]
BROWSERID_VERIFY_CLASS = 'fjord.base.browserid.FjordVerify'
BROWSERID_AUDIENCES = ['http://127.0.0.1:8000', 'http://localhost:8000']
LOGIN_URL = '/'
LOGIN_REDIRECT_URL = '/'
LOGIN_REDIRECT_URL_FAILURE = '/login-failure'
TEMPLATE_CONTEXT_PROCESSORS = get_template_context_processors(
exclude=(),
append=(
'django_browserid.context_processors.browserid',
))
# Should robots.txt deny everything or disallow a calculated list of
# URLs we don't want to be crawled? Default is false, disallow
# everything. Also see
# http://www.google.com/support/webmasters/bin/answer.py?answer=93710
ENGAGE_ROBOTS = False
# Always generate a CSRF token for anonymous users.
ANON_ALWAYS = True
# CSRF error page
CSRF_FAILURE_VIEW = 'fjord.base.views.csrf_failure'
# Tells the extract script what files to look for L10n in and what
# function handles the extraction. The Tower library expects this.
DOMAIN_METHODS['messages'] = [
('%s/**.py' % PROJECT_MODULE,
'tower.management.commands.extract.extract_tower_python'),
('%s/**/templates/**.html' % PROJECT_MODULE,
'tower.management.commands.extract.extract_tower_template'),
('templates/**.html',
'tower.management.commands.extract.extract_tower_template'),
]
# # Use this if you have localizable HTML files:
# DOMAIN_METHODS['lhtml'] = [
# ('**/templates/**.lhtml',
# 'tower.management.commands.extract.extract_tower_template'),
# ]
# # Use this if you have localizable JS files:
# DOMAIN_METHODS['javascript'] = [
# # Make sure that this won't pull in strings from external
# # libraries you may use.
# ('media/js/**.js', 'javascript'),
# ]
# When set to True, this will cause a message to be displayed on all
# pages that this is not production.
SHOW_STAGE_NOTICE = False
# Explicitly set this because the one from funfactory includes
# django-compressor which we don't use.
# STATICFILES_FINDERS = (
# 'django.contrib.staticfiles.finders.FileSystemFinder',
# 'django.contrib.staticfiles.finders.AppDirectoriesFinder',
# )
# ElasticSearch settings.
# List of host urls for the ES hosts we should connect to.
ES_URLS = ['http://localhost:9200']
# Dict of mapping-type-name -> index-name to use. Input pretty much
# uses one index, so this should be some variation of:
# {'default': 'inputindex'}.
ES_INDEXES = {'default': 'inputindex'}
# Prefix for the index. This allows -dev and -stage to share the same
# ES cluster, but not bump into each other.
ES_INDEX_PREFIX = 'input'
# When True, objects that belong in the index will get automatically
# indexed and deindexed when created and destroyed.
ES_LIVE_INDEX = True
ES_TIMEOUT = 10
# Time in seconds before celery.exceptions.SoftTimeLimitExceeded is raised.
# The task can catch that and recover but should exit ASAP.
CELERYD_TASK_SOFT_TIME_LIMIT = 60 * 10
# Configuration for API views.
REST_FRAMEWORK = {
'DEFAULT_THROTTLE_CLASSES': (
'fjord.base.util.MeasuredAnonRateThrottle',
),
'DEFAULT_THROTTLE_RATES': {
'anon': '100/hour',
},
'DEFAULT_RENDERER_CLASSES': (
'rest_framework.renderers.JSONRenderer',
)
}
| # This is your project's main settings file that can be committed to
# your repo. If you need to override a setting locally, use
# settings_local.py
from funfactory.settings_base import *
# Name of the top-level module where you put all your apps. If you
# did not install Playdoh with the funfactory installer script you may
# need to edit this value. See the docs about installing from a clone.
PROJECT_MODULE = 'fjord'
# Defines the views served for root URLs.
ROOT_URLCONF = '%s.urls' % PROJECT_MODULE
# This is the list of languages that are active for non-DEV
# environments. Add languages here to allow users to see the site in
# that locale and additionally submit feedback in that locale.
PROD_LANGUAGES = [
'ach',
'af',
'ak',
'am-et',
'an',
'ar',
'as',
'ast',
'az',
'be',
'bg',
'bn-BD',
'bn-IN',
'br',
'bs',
'ca',
'cs',
'csb',
'cy',
'da',
'dbg',
'de',
'de-AT',
'de-CH',
'de-DE',
'dsb',
'el',
'en-AU',
'en-CA',
'en-GB',
'en-NZ',
'en-US',
'en-ZA',
'eo',
'es',
'es-AR',
'es-CL',
'es-ES',
'es-MX',
'et',
'eu',
'fa',
'ff',
'fi',
'fj-FJ',
'fr',
'fur-IT',
'fy',
'fy-NL',
'ga',
'ga-IE',
'gd',
'gl',
'gu-IN',
'he',
'hi',
'hi-IN',
'hr',
'hsb',
'hu',
'hy-AM',
'id',
'is',
'it',
'ja',
'ka',
'kk',
'km',
'kn',
'ko',
'ku',
'la',
'lg',
'lij',
'lt',
'lv',
'mai',
'mg',
'mi',
'mk',
'ml',
'mn',
'mr',
'ms',
'my',
'nb-NO',
'ne-NP',
'nl',
'nn-NO',
'nr',
'nso',
'oc',
'or',
'pa-IN',
'pl',
'pt',
'pt-BR',
'pt-PT',
'rm',
'ro',
'ru',
'rw',
'sa',
'sah',
'si',
'sk',
'sl',
'son',
'sq',
'sr',
'sr-Latn',
'ss',
'st',
'sv-SE',
'sw',
'ta',
'ta-IN',
'ta-LK',
'te',
'th',
'tn',
'tr',
'ts',
'tt-RU',
'uk',
'ur',
've',
'vi',
'wo',
'xh',
'zh-CN',
'zh-TW',
'zu'
]
DEV_LANGUAGES = PROD_LANGUAGES
INSTALLED_APPS = get_apps(
exclude=(
'compressor',
),
append=(
# south has to come early, otherwise tests fail.
'south',
'django_browserid',
'adminplus',
'django.contrib.admin',
'django_extensions',
'django_nose',
'djcelery',
'eadred',
'jingo_minify',
'dennis.django_dennis',
'fjord.analytics',
'fjord.base',
'fjord.feedback',
'fjord.search',
'fjord.translations',
))
MIDDLEWARE_CLASSES = get_middleware(
exclude=(
# We do mobile detection ourselves.
'mobility.middleware.DetectMobileMiddleware',
'mobility.middleware.XMobileMiddleware',
),
append=(
'fjord.base.middleware.UserAgentMiddleware',
'fjord.base.middleware.MobileQueryStringMiddleware',
'fjord.base.middleware.MobileMiddleware',
'django_statsd.middleware.GraphiteMiddleware',
'django_statsd.middleware.GraphiteRequestTimingMiddleware',
))
LOCALE_PATHS = (
os.path.join(ROOT, PROJECT_MODULE, 'locale'),
)
SUPPORTED_NONLOCALES += (
'robots.txt',
'services',
'api',
)
# Because Jinja2 is the default template loader, add any non-Jinja
# templated apps here:
JINGO_EXCLUDE_APPS = [
'admin',
'adminplus',
'registration',
'browserid',
]
MINIFY_BUNDLES = {
'css': {
'base': (
'css/lib/normalize.css',
'css/fjord.less',
),
'generic_feedback': (
'css/lib/normalize.css',
'css/lib/brick-1.0.0.byob.min.css',
# FIXME - This should become feedback.less and move out of
# mobile/.
'css/mobile/base.less',
'css/generic_feedback.less',
),
'dashboard': (
'css/ui-lightness/jquery-ui.css',
'css/lib/normalize.css',
'css/fjord.less',
'css/dashboard.less',
),
'stage': (
'css/stage.less',
),
'thanks': (
'css/lib/normalize.css',
'css/thanks.less',
),
'mobile/base': (
'css/lib/normalize.css',
'css/mobile/base.less',
),
'mobile/fxos_feedback': (
'css/lib/normalize.css',
'css/lib/brick-1.0.0.byob.min.css',
'css/mobile/base.less',
'css/mobile/fxos_feedback.less',
),
'mobile/thanks': (
'css/lib/normalize.css',
'css/mobile/base.less',
'css/mobile/thanks.less',
)
},
'js': {
'base': (
'js/lib/jquery.min.js',
'browserid/browserid.js',
'js/init.js',
'js/ga.js',
),
'singlecard': (
'js/lib/jquery.min.js',
'js/ga.js',
),
'generic_feedback': (
'js/lib/jquery.min.js',
'js/common_feedback.js',
'js/generic_feedback.js',
'js/ga.js',
),
'dashboard': (
'js/lib/jquery.min.js',
'js/lib/jquery-ui.min.js',
'js/init.js',
'js/lib/excanvas.js',
'js/lib/jquery.flot.js',
'js/lib/jquery.flot.time.js',
'js/lib/jquery.flot.resize.js',
'js/dashboard.js',
'browserid/browserid.js',
'js/ga.js',
),
'thanks': (
'js/lib/jquery.min.js',
'js/init.js',
'js/ga.js',
),
'mobile/base': (
'js/lib/jquery.min.js',
'js/ga.js',
),
'mobile/fxos_feedback': (
'js/lib/jquery.min.js',
'js/common_feedback.js',
'js/mobile/fxos_feedback.js',
'js/ga.js',
),
}
}
LESS_PREPROCESS = True
JINGO_MINIFY_USE_STATIC = True
LESS_BIN = 'lessc'
JAVA_BIN = 'java'
AUTHENTICATION_BACKENDS = [
'django.contrib.auth.backends.ModelBackend',
'django_browserid.auth.BrowserIDBackend',
]
BROWSERID_VERIFY_CLASS = 'fjord.base.browserid.FjordVerify'
BROWSERID_AUDIENCES = ['http://127.0.0.1:8000', 'http://localhost:8000']
LOGIN_URL = '/'
LOGIN_REDIRECT_URL = '/'
LOGIN_REDIRECT_URL_FAILURE = '/login-failure'
TEMPLATE_CONTEXT_PROCESSORS = get_template_context_processors(
exclude=(),
append=(
'django_browserid.context_processors.browserid',
))
# Should robots.txt deny everything or disallow a calculated list of
# URLs we don't want to be crawled? Default is false, disallow
# everything. Also see
# http://www.google.com/support/webmasters/bin/answer.py?answer=93710
ENGAGE_ROBOTS = False
# Always generate a CSRF token for anonymous users.
ANON_ALWAYS = True
# CSRF error page
CSRF_FAILURE_VIEW = 'fjord.base.views.csrf_failure'
# Tells the extract script what files to look for L10n in and what
# function handles the extraction. The Tower library expects this.
DOMAIN_METHODS['messages'] = [
('%s/**.py' % PROJECT_MODULE,
'tower.management.commands.extract.extract_tower_python'),
('%s/**/templates/**.html' % PROJECT_MODULE,
'tower.management.commands.extract.extract_tower_template'),
('templates/**.html',
'tower.management.commands.extract.extract_tower_template'),
]
# # Use this if you have localizable HTML files:
# DOMAIN_METHODS['lhtml'] = [
# ('**/templates/**.lhtml',
# 'tower.management.commands.extract.extract_tower_template'),
# ]
# # Use this if you have localizable JS files:
# DOMAIN_METHODS['javascript'] = [
# # Make sure that this won't pull in strings from external
# # libraries you may use.
# ('media/js/**.js', 'javascript'),
# ]
# When set to True, this will cause a message to be displayed on all
# pages that this is not production.
SHOW_STAGE_NOTICE = False
# Explicitly set this because the one from funfactory includes
# django-compressor which we don't use.
# STATICFILES_FINDERS = (
# 'django.contrib.staticfiles.finders.FileSystemFinder',
# 'django.contrib.staticfiles.finders.AppDirectoriesFinder',
# )
# ElasticSearch settings.
# List of host urls for the ES hosts we should connect to.
ES_URLS = ['http://localhost:9200']
# Dict of mapping-type-name -> index-name to use. Input pretty much
# uses one index, so this should be some variation of:
# {'default': 'inputindex'}.
ES_INDEXES = {'default': 'inputindex'}
# Prefix for the index. This allows -dev and -stage to share the same
# ES cluster, but not bump into each other.
ES_INDEX_PREFIX = 'input'
# When True, objects that belong in the index will get automatically
# indexed and deindexed when created and destroyed.
ES_LIVE_INDEX = True
ES_TIMEOUT = 10
# Time in seconds before celery.exceptions.SoftTimeLimitExceeded is raised.
# The task can catch that and recover but should exit ASAP.
CELERYD_TASK_SOFT_TIME_LIMIT = 60 * 10
# Configuration for API views.
REST_FRAMEWORK = {
'DEFAULT_THROTTLE_CLASSES': (
'fjord.base.util.MeasuredAnonRateThrottle',
),
'DEFAULT_THROTTLE_RATES': {
'anon': '100/hour',
},
'DEFAULT_RENDERER_CLASSES': (
'rest_framework.renderers.JSONRenderer',
)
}
| en | 0.823802 | # This is your project's main settings file that can be committed to # your repo. If you need to override a setting locally, use # settings_local.py # Name of the top-level module where you put all your apps. If you # did not install Playdoh with the funfactory installer script you may # need to edit this value. See the docs about installing from a clone. # Defines the views served for root URLs. # This is the list of languages that are active for non-DEV # environments. Add languages here to allow users to see the site in # that locale and additionally submit feedback in that locale. # south has to come early, otherwise tests fail. # We do mobile detection ourselves. # Because Jinja2 is the default template loader, add any non-Jinja # templated apps here: # FIXME - This should become feedback.less and move out of # mobile/. # Should robots.txt deny everything or disallow a calculated list of # URLs we don't want to be crawled? Default is false, disallow # everything. Also see # http://www.google.com/support/webmasters/bin/answer.py?answer=93710 # Always generate a CSRF token for anonymous users. # CSRF error page # Tells the extract script what files to look for L10n in and what # function handles the extraction. The Tower library expects this. # # Use this if you have localizable HTML files: # DOMAIN_METHODS['lhtml'] = [ # ('**/templates/**.lhtml', # 'tower.management.commands.extract.extract_tower_template'), # ] # # Use this if you have localizable JS files: # DOMAIN_METHODS['javascript'] = [ # # Make sure that this won't pull in strings from external # # libraries you may use. # ('media/js/**.js', 'javascript'), # ] # When set to True, this will cause a message to be displayed on all # pages that this is not production. # Explicitly set this because the one from funfactory includes # django-compressor which we don't use. # STATICFILES_FINDERS = ( # 'django.contrib.staticfiles.finders.FileSystemFinder', # 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # ) # ElasticSearch settings. # List of host urls for the ES hosts we should connect to. # Dict of mapping-type-name -> index-name to use. Input pretty much # uses one index, so this should be some variation of: # {'default': 'inputindex'}. # Prefix for the index. This allows -dev and -stage to share the same # ES cluster, but not bump into each other. # When True, objects that belong in the index will get automatically # indexed and deindexed when created and destroyed. # Time in seconds before celery.exceptions.SoftTimeLimitExceeded is raised. # The task can catch that and recover but should exit ASAP. # Configuration for API views. | 1.598789 | 2 |
tests/test_utils.py | pauleveritt/wired_injector | 1 | 6623246 | from wired_injector.utils import caller_package, caller_module
def test_caller_package():
result = caller_package()
assert '_pytest' == result.__name__
def test_caller_module():
result = caller_module()
assert '_pytest.python' == result.__name__
| from wired_injector.utils import caller_package, caller_module
def test_caller_package():
result = caller_package()
assert '_pytest' == result.__name__
def test_caller_module():
result = caller_module()
assert '_pytest.python' == result.__name__
| none | 1 | 2.066433 | 2 | |
pca_utils.py | akashpalrecha/computational-linear-algebra | 1 | 6623247 | import torch
import torchvision
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D, proj3d
from matplotlib.patches import FancyArrowPatch
from datetime import datetime
from pdb import set_trace
MIN_16, MAX_16 = torch.finfo(torch.float16).min, torch.finfo(torch.float16).max
MIN_32, MAX_32 = torch.finfo(torch.float32).min, torch.finfo(torch.float32).max
def stats(*args):
for x in args:
print("Type : ", type(x))
print("Shape: ", x.shape)
print("Sum : ", x.sum())
print("Mean : ", x.mean())
print("STD : ", x.std())
print()
def torchCov(matrix:torch.Tensor, transposed=False, debug=False):
"Transposed = True if individual samples are columns and not rows"
if not isinstance(matrix, torch.Tensor): matrix = torch.tensor(matrix)
if torch.cuda.is_available(): matrix = matrix.cuda()
m = matrix.T if transposed else matrix
if debug: set_trace()
n = m.shape[0]
MAX = torch.finfo(m.dtype).max
mean = m.mean(axis=0, keepdim=True)
m.sub_(mean)
product = (m.T @ m).clamp(0, MAX)
product[torch.isnan(product)] = 0
product[torch.isinf(product)] = MAX
return product / (n-1)
def torchPCA(matrix:torch.Tensor, k=2, transposed=False, fp16=True, debug=False):
# Convert to tensor, cuda, half precision
if debug: set_trace()
dtype = torch.float16 if (fp16 and torch.cuda.is_available()) else torch.float32
if not isinstance(matrix, torch.Tensor): matrix = torch.tensor(matrix).type(dtype)
if torch.cuda.is_available():
torch.cuda.set_device(0)
matrix = matrix.cuda()
# Make sure samples are rows and not columns
m = matrix.T.type(dtype) if transposed else matrix.type(dtype)
# PCA Computations
now = datetime.now()
cov_mat = torchCov(m, False, debug=debug).type(torch.float32)
eig_vals, eig_vecs = cov_mat.eig(eigenvectors=True)
eig_vals = eig_vals[:, 0] # Ignoring the complex part [:, 1]
# Getting the top k eigen vectors
order = eig_vals.argsort(descending=True)
top_k = eig_vecs[:, order[:k]].type_as(m)
# Reducing the matrix
res = m @ top_k, top_k
total_time = datetime.now() - now
return res, total_time.microseconds / 1e6
# def torchCov(x, rowvar=False, bias=False, ddof=None, aweights=None):
# """Estimates covariance matrix like numpy.cov"""
# # ensure at least 2D
# if x.dim() == 1: x = x.view(-1, 1)
# # treat each column as a data point, each row as a variable
# if rowvar and x.shape[0] != 1:
# x = x.t()
# if ddof is None:
# if bias == 0: ddof = 1
# else: ddof = 0
# w = aweights
# if w is not None:
# if not torch.is_tensor(w): w = torch.tensor(w, dtype=torch.float)
# w_sum = torch.sum(w)
# avg = torch.sum(x * (w/w_sum)[:,None], 0)
# else:
# avg = torch.mean(x, 0)
# # Determine the normalization
# if w is None: fact = x.shape[0] - ddof
# elif ddof == 0: fact = w_sum
# elif aweights is None: fact = w_sum - ddof
# else: fact = w_sum - ddof * torch.sum(w * w) / w_sum
# xm = x.sub(avg.expand_as(x))
# if w is None: X_T = xm.t()
# else: X_T = torch.mm(torch.diag(w), xm).t()
# c = torch.mm(X_T, xm)
# c = c / fact
# return c.squeeze()
class Arrow3D(FancyArrowPatch):
def __init__(self, xs, ys, zs, *args, **kwargs):
FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)
self._verts3d = xs, ys, zs
def draw(self, renderer):
xs3d, ys3d, zs3d = self._verts3d
xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
FancyArrowPatch.draw(self, renderer)
def visualize3dData(matrix:torch.Tensor, labels=None, transposed=False):
if not isinstance(matrix, torch.Tensor): matrix = torch.tensor(matrix)
m = matrix.clone().T if not transposed else matrix.clone()
assert m.shape[0] == 3
if labels is None:
labels = torch.zeros(m.shape[1])
else:
if not isinstance(labels, torch.Tensor): labels = torch.tensor(labels)
fig = plt.figure(figsize=(16,16))
ax = fig.add_subplot(111, projection='3d')
plt.rcParams['legend.fontsize'] = 10
classes = torch.unique(labels)
for label in classes:
data = m[:, labels == label]
ax.plot(data[0, :], data[1, :], data[2, :],
'o', markersize=8, alpha=0.4, label="Class 1")
mean_vector = m.mean(dim=1, keepdim=True)
cov_mat = torchCov(m, True)
eig_vals, eig_vecs = cov_mat.eig(eigenvectors=True)
eig_vals = eig_vals[:, 0] # Ignoring the complex part [:, 1]
scaled_eig_vecs = (eig_vecs * eig_vals).cpu()
means = mean_vector.cpu()
for v in scaled_eig_vecs.T:
a = Arrow3D([means[0].item(), v[0]], [means[1].item(), v[1]], [means[2].item(), v[2]],
mutation_scale=20, lw=3, arrowstyle="-|>", color="black")
ax.add_artist(a)
ax.set_xlabel('x_values')
ax.set_ylabel('y_values')
ax.set_zlabel('z_values')
plt.title('Eigenvectors')
plt.show()
return fig
| import torch
import torchvision
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D, proj3d
from matplotlib.patches import FancyArrowPatch
from datetime import datetime
from pdb import set_trace
MIN_16, MAX_16 = torch.finfo(torch.float16).min, torch.finfo(torch.float16).max
MIN_32, MAX_32 = torch.finfo(torch.float32).min, torch.finfo(torch.float32).max
def stats(*args):
for x in args:
print("Type : ", type(x))
print("Shape: ", x.shape)
print("Sum : ", x.sum())
print("Mean : ", x.mean())
print("STD : ", x.std())
print()
def torchCov(matrix:torch.Tensor, transposed=False, debug=False):
"Transposed = True if individual samples are columns and not rows"
if not isinstance(matrix, torch.Tensor): matrix = torch.tensor(matrix)
if torch.cuda.is_available(): matrix = matrix.cuda()
m = matrix.T if transposed else matrix
if debug: set_trace()
n = m.shape[0]
MAX = torch.finfo(m.dtype).max
mean = m.mean(axis=0, keepdim=True)
m.sub_(mean)
product = (m.T @ m).clamp(0, MAX)
product[torch.isnan(product)] = 0
product[torch.isinf(product)] = MAX
return product / (n-1)
def torchPCA(matrix:torch.Tensor, k=2, transposed=False, fp16=True, debug=False):
# Convert to tensor, cuda, half precision
if debug: set_trace()
dtype = torch.float16 if (fp16 and torch.cuda.is_available()) else torch.float32
if not isinstance(matrix, torch.Tensor): matrix = torch.tensor(matrix).type(dtype)
if torch.cuda.is_available():
torch.cuda.set_device(0)
matrix = matrix.cuda()
# Make sure samples are rows and not columns
m = matrix.T.type(dtype) if transposed else matrix.type(dtype)
# PCA Computations
now = datetime.now()
cov_mat = torchCov(m, False, debug=debug).type(torch.float32)
eig_vals, eig_vecs = cov_mat.eig(eigenvectors=True)
eig_vals = eig_vals[:, 0] # Ignoring the complex part [:, 1]
# Getting the top k eigen vectors
order = eig_vals.argsort(descending=True)
top_k = eig_vecs[:, order[:k]].type_as(m)
# Reducing the matrix
res = m @ top_k, top_k
total_time = datetime.now() - now
return res, total_time.microseconds / 1e6
# def torchCov(x, rowvar=False, bias=False, ddof=None, aweights=None):
# """Estimates covariance matrix like numpy.cov"""
# # ensure at least 2D
# if x.dim() == 1: x = x.view(-1, 1)
# # treat each column as a data point, each row as a variable
# if rowvar and x.shape[0] != 1:
# x = x.t()
# if ddof is None:
# if bias == 0: ddof = 1
# else: ddof = 0
# w = aweights
# if w is not None:
# if not torch.is_tensor(w): w = torch.tensor(w, dtype=torch.float)
# w_sum = torch.sum(w)
# avg = torch.sum(x * (w/w_sum)[:,None], 0)
# else:
# avg = torch.mean(x, 0)
# # Determine the normalization
# if w is None: fact = x.shape[0] - ddof
# elif ddof == 0: fact = w_sum
# elif aweights is None: fact = w_sum - ddof
# else: fact = w_sum - ddof * torch.sum(w * w) / w_sum
# xm = x.sub(avg.expand_as(x))
# if w is None: X_T = xm.t()
# else: X_T = torch.mm(torch.diag(w), xm).t()
# c = torch.mm(X_T, xm)
# c = c / fact
# return c.squeeze()
class Arrow3D(FancyArrowPatch):
def __init__(self, xs, ys, zs, *args, **kwargs):
FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)
self._verts3d = xs, ys, zs
def draw(self, renderer):
xs3d, ys3d, zs3d = self._verts3d
xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
FancyArrowPatch.draw(self, renderer)
def visualize3dData(matrix:torch.Tensor, labels=None, transposed=False):
if not isinstance(matrix, torch.Tensor): matrix = torch.tensor(matrix)
m = matrix.clone().T if not transposed else matrix.clone()
assert m.shape[0] == 3
if labels is None:
labels = torch.zeros(m.shape[1])
else:
if not isinstance(labels, torch.Tensor): labels = torch.tensor(labels)
fig = plt.figure(figsize=(16,16))
ax = fig.add_subplot(111, projection='3d')
plt.rcParams['legend.fontsize'] = 10
classes = torch.unique(labels)
for label in classes:
data = m[:, labels == label]
ax.plot(data[0, :], data[1, :], data[2, :],
'o', markersize=8, alpha=0.4, label="Class 1")
mean_vector = m.mean(dim=1, keepdim=True)
cov_mat = torchCov(m, True)
eig_vals, eig_vecs = cov_mat.eig(eigenvectors=True)
eig_vals = eig_vals[:, 0] # Ignoring the complex part [:, 1]
scaled_eig_vecs = (eig_vecs * eig_vals).cpu()
means = mean_vector.cpu()
for v in scaled_eig_vecs.T:
a = Arrow3D([means[0].item(), v[0]], [means[1].item(), v[1]], [means[2].item(), v[2]],
mutation_scale=20, lw=3, arrowstyle="-|>", color="black")
ax.add_artist(a)
ax.set_xlabel('x_values')
ax.set_ylabel('y_values')
ax.set_zlabel('z_values')
plt.title('Eigenvectors')
plt.show()
return fig
| en | 0.696656 | # Convert to tensor, cuda, half precision # Make sure samples are rows and not columns # PCA Computations # Ignoring the complex part [:, 1] # Getting the top k eigen vectors # Reducing the matrix # def torchCov(x, rowvar=False, bias=False, ddof=None, aweights=None): # """Estimates covariance matrix like numpy.cov""" # # ensure at least 2D # if x.dim() == 1: x = x.view(-1, 1) # # treat each column as a data point, each row as a variable # if rowvar and x.shape[0] != 1: # x = x.t() # if ddof is None: # if bias == 0: ddof = 1 # else: ddof = 0 # w = aweights # if w is not None: # if not torch.is_tensor(w): w = torch.tensor(w, dtype=torch.float) # w_sum = torch.sum(w) # avg = torch.sum(x * (w/w_sum)[:,None], 0) # else: # avg = torch.mean(x, 0) # # Determine the normalization # if w is None: fact = x.shape[0] - ddof # elif ddof == 0: fact = w_sum # elif aweights is None: fact = w_sum - ddof # else: fact = w_sum - ddof * torch.sum(w * w) / w_sum # xm = x.sub(avg.expand_as(x)) # if w is None: X_T = xm.t() # else: X_T = torch.mm(torch.diag(w), xm).t() # c = torch.mm(X_T, xm) # c = c / fact # return c.squeeze() # Ignoring the complex part [:, 1] | 2.297537 | 2 |
kdbtest/__main__.py | SiMylo/kmel_db | 0 | 6623248 | <gh_stars>0
"""Python's unittest main entry point, extended to include coverage"""
import os
import sys
import unittest
try:
import coverage
HAVE_COVERAGE = True
except ImportError:
HAVE_COVERAGE = False
if sys.argv[0].endswith("__main__.py"):
# We change sys.argv[0] to make help message more useful
# use executable without path, unquoted
# (it's just a hint anyway)
# (if you have spaces in your executable you get what you deserve!)
executable = os.path.basename(sys.executable)
sys.argv[0] = executable + " -m unittest"
__unittest = True
html_dir = 'test_coverage'
cov = None
if HAVE_COVERAGE:
cov = coverage.Coverage(branch=True)
cov._warn_no_data = False
cov.exclude(r'\@abc\.abstract', 'partial')
cov.start()
try:
loader = unittest.TestLoader()
_current_dir = os.path.dirname(__file__)
suite = loader.discover(_current_dir + '/../tests')
runner = unittest.TextTestRunner()
runner.run(suite)
finally:
if cov is not None:
cov.stop()
cov.save()
cov.html_report(directory=html_dir, title='DapGen test coverage')
| """Python's unittest main entry point, extended to include coverage"""
import os
import sys
import unittest
try:
import coverage
HAVE_COVERAGE = True
except ImportError:
HAVE_COVERAGE = False
if sys.argv[0].endswith("__main__.py"):
# We change sys.argv[0] to make help message more useful
# use executable without path, unquoted
# (it's just a hint anyway)
# (if you have spaces in your executable you get what you deserve!)
executable = os.path.basename(sys.executable)
sys.argv[0] = executable + " -m unittest"
__unittest = True
html_dir = 'test_coverage'
cov = None
if HAVE_COVERAGE:
cov = coverage.Coverage(branch=True)
cov._warn_no_data = False
cov.exclude(r'\@abc\.abstract', 'partial')
cov.start()
try:
loader = unittest.TestLoader()
_current_dir = os.path.dirname(__file__)
suite = loader.discover(_current_dir + '/../tests')
runner = unittest.TextTestRunner()
runner.run(suite)
finally:
if cov is not None:
cov.stop()
cov.save()
cov.html_report(directory=html_dir, title='DapGen test coverage') | en | 0.887393 | Python's unittest main entry point, extended to include coverage # We change sys.argv[0] to make help message more useful # use executable without path, unquoted # (it's just a hint anyway) # (if you have spaces in your executable you get what you deserve!) | 2.448614 | 2 |
app/views/product.py | LP-Dev-Web/LeBonRecoin | 0 | 6623249 | <gh_stars>0
from django.contrib import messages
from django.contrib.messages.views import SuccessMessageMixin
from django.http import HttpResponseRedirect
from django.urls import reverse_lazy
from django.views.generic import (
DetailView,
RedirectView,
CreateView,
UpdateView,
DeleteView,
ListView,
)
from django.utils.translation import gettext_lazy as _
from app.forms.product import AdForm, PictureForm, EditPictureForm
from app.models import Product, Picture, Address, Favorite
class NewAdView(CreateView):
template_name = "account/form.html"
model = Product
form_class = AdForm
def get(self, request, *args, **kwargs):
if Address.objects.filter(user_id=self.request.user.pk).exists():
return super().get(request)
else:
return HttpResponseRedirect(reverse_lazy("new-address"))
def get_context_data(self, **kwargs):
data = super(NewAdView, self).get_context_data(**kwargs)
data["title"] = _("Add your ad")
data["link"] = "index"
data["button"] = _("Next")
return data
def form_valid(self, form):
new_ad = form.save(commit=False)
new_ad.user_id = self.request.user.pk
new_ad.save()
return super().form_valid(form)
def get_success_url(self):
product = Product.objects.filter(user_id=self.request.user.pk).latest("pk")
return reverse_lazy("new-ad-picture", kwargs={"pk": product.pk})
class NewPictureView(SuccessMessageMixin, CreateView):
success_url = reverse_lazy("profil-ads")
template_name = "account/form.html"
model = Picture
form_class = PictureForm
success_message = _("Your ad has been published.")
def get_context_data(self, **kwargs):
data = super(NewPictureView, self).get_context_data(**kwargs)
data["title"] = _("Add your pictures")
data["back"] = "index"
return data
def form_valid(self, form):
new_picture = form.save(commit=False)
new_picture.product_id = self.kwargs["pk"]
new_picture.save()
return super().form_valid(form)
class ListAdView(ListView):
template_name = "account/list.html"
model = Product
def get_context_data(self, **kwargs):
data = super(ListAdView, self).get_context_data(**kwargs)
data["title"] = _("Your ads")
data["message"] = _("You have not yet published any ads.")
data["view"] = "view-ad"
data["edit"] = "edit-ad"
data["delete"] = "delete-ad"
return data
def get_queryset(self):
return Picture.objects.filter(product__user_id=self.request.user.pk).all()
class ViewAdView(DetailView):
template_name = "product/ad.html"
model = Product
def get(self, request, *args, **kwargs):
if Product.objects.filter(
user_id=self.request.user.pk, id=self.kwargs["pk"]
).exists():
return super().get(request)
else:
lastest = Product.objects.filter(user_id=self.request.user.pk).first()
return HttpResponseRedirect(
reverse_lazy("view-ad", kwargs={"pk": lastest.pk})
)
def get_context_data(self, **kwargs):
data = super(ViewAdView, self).get_context_data(**kwargs)
data["pictures"] = Picture.objects.filter(product_id=self.kwargs["pk"]).first()
return data
class EditAdView(SuccessMessageMixin, UpdateView):
template_name = "account/form.html"
model = Product
form_class = AdForm
success_message = _("The ad has been modified.")
def get(self, request, *args, **kwargs):
if Product.objects.filter(
user_id=self.request.user.pk, id=self.kwargs["pk"]
).exists():
return super().get(request)
else:
lastest = Product.objects.filter(user_id=self.request.user.pk).first()
return HttpResponseRedirect(
reverse_lazy("edit-ad", kwargs={"pk": lastest.pk})
)
def get_context_data(self, **kwargs):
data = super(EditAdView, self).get_context_data(**kwargs)
data["pictures"] = Picture.objects.filter(product_id=self.kwargs["pk"]).first()
data["extra"] = True
data["title"] = _("Edit your ad")
data["link"] = "profil-ads"
data["button"] = _("Edit")
return data
def get_success_url(self):
pk = self.kwargs["pk"]
return reverse_lazy("view-ad", kwargs={"pk": pk})
class EditPictureView(SuccessMessageMixin, UpdateView):
template_name = "account/form.html"
model = Picture
form_class = EditPictureForm
success_message = _("The image(s) have been modified.")
def get(self, request, *args, **kwargs):
product = Product.objects.filter(
picture=self.kwargs["pk"], user_id=self.request.user.pk
).first()
if Picture.objects.filter(product_id=product, id=self.kwargs["pk"]).exists():
return super().get(request)
else:
product = Product.objects.filter(user_id=self.request.user.pk).first()
lastest = Picture.objects.get(product_id=product)
return HttpResponseRedirect(
reverse_lazy("edit-ad-picture", kwargs={"pk": lastest.pk})
)
def get_context_data(self, **kwargs):
data = super(EditPictureView, self).get_context_data(**kwargs)
data["product"] = Product.objects.filter(picture=self.kwargs["pk"]).first()
data["title"] = _("Edit your ad")
data["link"] = "edit-ad"
data["value"] = data["product"].id
data["button"] = _("Edit")
return data
def get_success_url(self):
product = Product.objects.get(picture=self.kwargs["pk"])
return reverse_lazy("view-ad", kwargs={"pk": product.pk})
class DeleteAdView(DeleteView):
success_url = reverse_lazy("profil-ads")
template_name = "account/delete.html"
model = Product
def get(self, request, *args, **kwargs):
if Product.objects.filter(
user_id=self.request.user.pk, id=self.kwargs["pk"]
).exists():
return super().get(request)
else:
lastest = Product.objects.filter(user_id=self.request.user.pk).first()
return HttpResponseRedirect(
reverse_lazy("delete-ad", kwargs={"pk": lastest.pk})
)
def delete(self, request, *args, **kwargs):
messages.success(request, _("The ad has been removed."))
return super().delete(request)
class AdView(DetailView):
template_name = "product/ad.html"
model = Product
def get_context_data(self, **kwargs):
data = super(AdView, self).get_context_data(**kwargs)
data["pictures"] = Picture.objects.filter(product_id=self.kwargs["pk"]).first()
user_id = Product.objects.get(id=self.kwargs["pk"]).user.pk
data["address"] = Address.objects.filter(user_id=user_id).first()
data["products"] = (
Picture.objects.filter(product__categorie_id=data["product"].categorie.id)
.all()
.exclude(product_id=self.kwargs["pk"])[:4]
)
if (
not Picture.objects.filter(
product__categorie_id=data["product"].categorie.id
)
.all()
.exclude(product_id=self.kwargs["pk"])
.exists()
):
data["others"] = Picture.objects.all().exclude(
product_id=self.kwargs["pk"]
)[:4]
data["favorite"] = Favorite.objects.filter(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
).exists()
data["edit"] = Product.objects.filter(
pk=self.kwargs["pk"], user_id=self.request.user.pk
).exists()
return data
class OfferAdView(DetailView):
template_name = "product/offer.html"
model = Product
def get_context_data(self, **kwargs):
data = super(OfferAdView, self).get_context_data(**kwargs)
user_id = Product.objects.get(id=self.kwargs["pk"]).user.pk
data["address"] = Address.objects.filter(user_id=user_id).first()
return data
class FavoriteView(RedirectView):
def get(self, request, *args, **kwargs):
if Favorite.objects.filter(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
).exists():
Favorite.objects.get(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
).delete()
else:
Favorite.objects.create(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
)
return HttpResponseRedirect(
reverse_lazy("ad", kwargs={"pk": self.kwargs["pk"]})
)
| from django.contrib import messages
from django.contrib.messages.views import SuccessMessageMixin
from django.http import HttpResponseRedirect
from django.urls import reverse_lazy
from django.views.generic import (
DetailView,
RedirectView,
CreateView,
UpdateView,
DeleteView,
ListView,
)
from django.utils.translation import gettext_lazy as _
from app.forms.product import AdForm, PictureForm, EditPictureForm
from app.models import Product, Picture, Address, Favorite
class NewAdView(CreateView):
template_name = "account/form.html"
model = Product
form_class = AdForm
def get(self, request, *args, **kwargs):
if Address.objects.filter(user_id=self.request.user.pk).exists():
return super().get(request)
else:
return HttpResponseRedirect(reverse_lazy("new-address"))
def get_context_data(self, **kwargs):
data = super(NewAdView, self).get_context_data(**kwargs)
data["title"] = _("Add your ad")
data["link"] = "index"
data["button"] = _("Next")
return data
def form_valid(self, form):
new_ad = form.save(commit=False)
new_ad.user_id = self.request.user.pk
new_ad.save()
return super().form_valid(form)
def get_success_url(self):
product = Product.objects.filter(user_id=self.request.user.pk).latest("pk")
return reverse_lazy("new-ad-picture", kwargs={"pk": product.pk})
class NewPictureView(SuccessMessageMixin, CreateView):
success_url = reverse_lazy("profil-ads")
template_name = "account/form.html"
model = Picture
form_class = PictureForm
success_message = _("Your ad has been published.")
def get_context_data(self, **kwargs):
data = super(NewPictureView, self).get_context_data(**kwargs)
data["title"] = _("Add your pictures")
data["back"] = "index"
return data
def form_valid(self, form):
new_picture = form.save(commit=False)
new_picture.product_id = self.kwargs["pk"]
new_picture.save()
return super().form_valid(form)
class ListAdView(ListView):
template_name = "account/list.html"
model = Product
def get_context_data(self, **kwargs):
data = super(ListAdView, self).get_context_data(**kwargs)
data["title"] = _("Your ads")
data["message"] = _("You have not yet published any ads.")
data["view"] = "view-ad"
data["edit"] = "edit-ad"
data["delete"] = "delete-ad"
return data
def get_queryset(self):
return Picture.objects.filter(product__user_id=self.request.user.pk).all()
class ViewAdView(DetailView):
template_name = "product/ad.html"
model = Product
def get(self, request, *args, **kwargs):
if Product.objects.filter(
user_id=self.request.user.pk, id=self.kwargs["pk"]
).exists():
return super().get(request)
else:
lastest = Product.objects.filter(user_id=self.request.user.pk).first()
return HttpResponseRedirect(
reverse_lazy("view-ad", kwargs={"pk": lastest.pk})
)
def get_context_data(self, **kwargs):
data = super(ViewAdView, self).get_context_data(**kwargs)
data["pictures"] = Picture.objects.filter(product_id=self.kwargs["pk"]).first()
return data
class EditAdView(SuccessMessageMixin, UpdateView):
template_name = "account/form.html"
model = Product
form_class = AdForm
success_message = _("The ad has been modified.")
def get(self, request, *args, **kwargs):
if Product.objects.filter(
user_id=self.request.user.pk, id=self.kwargs["pk"]
).exists():
return super().get(request)
else:
lastest = Product.objects.filter(user_id=self.request.user.pk).first()
return HttpResponseRedirect(
reverse_lazy("edit-ad", kwargs={"pk": lastest.pk})
)
def get_context_data(self, **kwargs):
data = super(EditAdView, self).get_context_data(**kwargs)
data["pictures"] = Picture.objects.filter(product_id=self.kwargs["pk"]).first()
data["extra"] = True
data["title"] = _("Edit your ad")
data["link"] = "profil-ads"
data["button"] = _("Edit")
return data
def get_success_url(self):
pk = self.kwargs["pk"]
return reverse_lazy("view-ad", kwargs={"pk": pk})
class EditPictureView(SuccessMessageMixin, UpdateView):
template_name = "account/form.html"
model = Picture
form_class = EditPictureForm
success_message = _("The image(s) have been modified.")
def get(self, request, *args, **kwargs):
product = Product.objects.filter(
picture=self.kwargs["pk"], user_id=self.request.user.pk
).first()
if Picture.objects.filter(product_id=product, id=self.kwargs["pk"]).exists():
return super().get(request)
else:
product = Product.objects.filter(user_id=self.request.user.pk).first()
lastest = Picture.objects.get(product_id=product)
return HttpResponseRedirect(
reverse_lazy("edit-ad-picture", kwargs={"pk": lastest.pk})
)
def get_context_data(self, **kwargs):
data = super(EditPictureView, self).get_context_data(**kwargs)
data["product"] = Product.objects.filter(picture=self.kwargs["pk"]).first()
data["title"] = _("Edit your ad")
data["link"] = "edit-ad"
data["value"] = data["product"].id
data["button"] = _("Edit")
return data
def get_success_url(self):
product = Product.objects.get(picture=self.kwargs["pk"])
return reverse_lazy("view-ad", kwargs={"pk": product.pk})
class DeleteAdView(DeleteView):
success_url = reverse_lazy("profil-ads")
template_name = "account/delete.html"
model = Product
def get(self, request, *args, **kwargs):
if Product.objects.filter(
user_id=self.request.user.pk, id=self.kwargs["pk"]
).exists():
return super().get(request)
else:
lastest = Product.objects.filter(user_id=self.request.user.pk).first()
return HttpResponseRedirect(
reverse_lazy("delete-ad", kwargs={"pk": lastest.pk})
)
def delete(self, request, *args, **kwargs):
messages.success(request, _("The ad has been removed."))
return super().delete(request)
class AdView(DetailView):
template_name = "product/ad.html"
model = Product
def get_context_data(self, **kwargs):
data = super(AdView, self).get_context_data(**kwargs)
data["pictures"] = Picture.objects.filter(product_id=self.kwargs["pk"]).first()
user_id = Product.objects.get(id=self.kwargs["pk"]).user.pk
data["address"] = Address.objects.filter(user_id=user_id).first()
data["products"] = (
Picture.objects.filter(product__categorie_id=data["product"].categorie.id)
.all()
.exclude(product_id=self.kwargs["pk"])[:4]
)
if (
not Picture.objects.filter(
product__categorie_id=data["product"].categorie.id
)
.all()
.exclude(product_id=self.kwargs["pk"])
.exists()
):
data["others"] = Picture.objects.all().exclude(
product_id=self.kwargs["pk"]
)[:4]
data["favorite"] = Favorite.objects.filter(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
).exists()
data["edit"] = Product.objects.filter(
pk=self.kwargs["pk"], user_id=self.request.user.pk
).exists()
return data
class OfferAdView(DetailView):
template_name = "product/offer.html"
model = Product
def get_context_data(self, **kwargs):
data = super(OfferAdView, self).get_context_data(**kwargs)
user_id = Product.objects.get(id=self.kwargs["pk"]).user.pk
data["address"] = Address.objects.filter(user_id=user_id).first()
return data
class FavoriteView(RedirectView):
def get(self, request, *args, **kwargs):
if Favorite.objects.filter(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
).exists():
Favorite.objects.get(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
).delete()
else:
Favorite.objects.create(
product_id=self.kwargs["pk"], user_id=self.request.user.pk
)
return HttpResponseRedirect(
reverse_lazy("ad", kwargs={"pk": self.kwargs["pk"]})
) | none | 1 | 2.136909 | 2 | |
AutoRegression.py | sercangul/AutoMachineLearning | 0 | 6623250 | <reponame>sercangul/AutoMachineLearning<gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# This notebook explains the steps to develop an Automated Supervised Machine Learning Regression program, which automatically tunes the hyperparameters and prints out the final accuracy results as a tables together with feature importance results.
# Let's import all libraries.
# In[1]:
import pandas as pd
import numpy as np
from xgboost import XGBRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.svm import SVR
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import explained_variance_score
from sklearn.metrics import max_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.metrics import median_absolute_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_poisson_deviance
from sklearn.metrics import mean_gamma_deviance
from sklearn.metrics import mean_absolute_percentage_error
from itertools import repeat
import matplotlib.pyplot as plt
# Lets import our dataset from the csv files as a dataframe.
# In[2]:
df = pd.read_csv('data.csv')
# Let's take a look at dataset. I like using df.describe() function to have some statistics about each column.
# In[3]:
df.describe().T
# Let's define the features as X and the column we want to predict (column F) as y.
# In[4]:
n = len(df.columns)
X = df.iloc[:,0:n-1].to_numpy()
y = df.iloc[:,n-1].to_numpy()
# This defines X as all the values except the last column (columns A,B,C,D,E), and y as the last column (column numbers start from zero, hence: 0 - A, 1 - B, 2 - C, 3 - D,4 - E, 5 -F).
# Some algorithms provide better accuracies with the standard scaling of the input features (i.e. normalization). Let's normalize the data.
# In[5]:
scaler = StandardScaler()
scaler.fit(X)
X= scaler.transform(X)
# We have to split our dataset as train and test data. For this we can use train_test_split by sklearn.model_selection. Test size of 0.20 means that 20% of the data will be used as test data and 80% of the data will be used for training.
# In[6]:
X_train, X_test, y_train, y_test= train_test_split(X,y,test_size = 0.20)
# We might not always want to tune the parameters of models, or only tune for some models. For this I have defined a basic input. When it is set to "True", the program will perform the tuning for all the models.
# In[7]:
Perform_tuning = True
Lassotuning, Ridgetuning, randomforestparametertuning, XGboostparametertuning, SVMparametertuning, MLPparametertuning = repeat(Perform_tuning,6)
# Let's define the grid search function to be used with our models. The values of grid might need to be changed regarding the problem (i.e., some problems might require higher values of n_estimators, while some might require lower ranges).
# In[8]:
def grid_search(model,grid):
# Instantiate the grid search model
print ("Performing gridsearch for {}".format(model))
grid_search = GridSearchCV(estimator = model(), param_grid=grid,
cv = 3, n_jobs = -1, verbose = 2)
# Fit the grid search to the data
grid_search.fit(X_train, y_train)
print("Grid Search Best Parameters for {}".format(model))
print (grid_search.best_params_)
return grid_search.best_params_
# Performing Lasso parameter tuning.
# In[9]:
if Lassotuning:
# Create the parameter grid based on the results of random search
grid = {
'alpha': [1,0.9,0.75,0.5,0.1,0.01,0.001,0.0001] ,
"fit_intercept": [True, False]
}
Lasso_bestparam = grid_search(Lasso,grid)
# Performing Ridge parameter tuning.
# In[10]:
if Ridgetuning:
# Create the parameter grid based on the results of random search
grid = {
'alpha': [1,0.9,0.75,0.5,0.1,0.01,0.001,0.0001] ,
"fit_intercept": [True, False]
}
Ridge_bestparam = grid_search(Ridge,grid)
# Performing Random Forest parameter tuning.
# In[11]:
if randomforestparametertuning:
# Create the parameter grid based on the results of random search
grid = {
'bootstrap': [True,False],
'max_depth': [40, 50, 60, 70],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1,2,3,],
'min_samples_split': [3, 4, 5,6,7],
'n_estimators': [5,10,15]
}
RF_bestparam = grid_search(RandomForestRegressor,grid)
# Performing XGBoost parameter tuning.
# In[12]:
if XGboostparametertuning:
# Create the parameter grid based on the results of random search
grid = {'colsample_bytree': [0.9,0.7],
'gamma': [2,5],
'learning_rate': [0.1,0.2,0.3],
'max_depth': [8,10,12],
'n_estimators': [5,10],
'subsample': [0.8,1],
'reg_alpha': [15,20],
'min_child_weight':[3,5]}
XGB_bestparam = grid_search(XGBRegressor,grid)
# Performing SVM parameter tuning.
# In[13]:
#SVM Parameter Tuning----------------------------------------------------------
if SVMparametertuning:
grid = {'gamma': 10. ** np.arange(-5, 3),
'C': 10. ** np.arange(-3, 3)}
SVR_bestparam = grid_search(SVR,grid)
# Performing MLP parameter tuning.
# In[14]:
if MLPparametertuning:
grid = {
'hidden_layer_sizes': [2,5,8,10],
'activation': ['identity','logistic','tanh','relu'],
'solver': ['lbfgs', 'sgd','adam'],
'learning_rate': ['constant','invscaling','adaptive']}
MLP_bestparam = grid_search(MLPRegressor,grid)
# Now we obtained the best parameters for all the models using the training data. Let's define the error metrics that will be used in analyzing the accuracy of each model.
# In[15]:
error_metrics = (
explained_variance_score,
max_error,
mean_absolute_error,
mean_squared_error,
mean_squared_log_error,
median_absolute_error,
r2_score,
mean_poisson_deviance,
mean_gamma_deviance,
mean_absolute_percentage_error
)
# Let's define fit_model function to predict the results, and analyze the error metrics for each model.
# In[16]:
def fit_model(model,X_train, X_test, y_train, y_test,error_metrics):
fitted_model = model.fit(X_train,y_train)
y_predicted = fitted_model.predict(X_test)
calculations = []
for metric in error_metrics:
calc = metric(y_test, y_predicted)
calculations.append(calc)
return calculations
# Provide a summary of each model and their GridSearch best parameter results. If tuning is not performed, the script will use the default values as best parameters.
# In[17]:
try:
trainingmodels = (
LinearRegression(),
Ridge(**Ridge_bestparam),
RandomForestRegressor(**RF_bestparam),
XGBRegressor(**XGB_bestparam),
Lasso(**Lasso_bestparam),
SVR(**SVR_bestparam),
MLPRegressor(**MLP_bestparam)
)
except:
trainingmodels = (
LinearRegression(),
Ridge(),
RandomForestRegressor(),
XGBRegressor(),
Lasso(),
SVR(),
MLPRegressor()
)
calculations = []
# Below loop performes training, testing and error metrics calculations for each model.
# In[18]:
for trainmodel in trainingmodels:
errors = fit_model(trainmodel,X_train, X_test, y_train, y_test,error_metrics)
calculations.append(errors)
# Let's organize these results, and summarize them all in a dataframe.
# In[19]:
errors = (
'Explained variance score',
'Max error',
'Mean absolute error',
'Mean squared error',
'Mean squared log error',
'Median absolute error',
'r2 score',
'Mean poisson deviance',
'Mean gamma deviance',
'Mean absolute percentage error'
)
model_names = (
'LinearRegression',
'Ridge',
'RandomForestRegressor',
'XGBRegressor',
'Lasso',
'SVR',
'MLPRegressor'
)
df_error = pd.DataFrame(calculations, columns=errors)
df_error["Model"] = model_names
cols = df_error.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_error = df_error[cols]
df_error = df_error.sort_values(by=['Mean squared error'],ascending=True)
df_error = (df_error.set_index('Model')
.astype(float)
.applymap('{:,.3f}'.format))
df_error.to_csv("errors.csv")
df_error
# Moreover, we can analyze the feature importance results using the Random Forest regressor.
# In[20]:
#Principal Component Analysis
features = df.columns[:-1]
try:
randreg = RandomForestRegressor(**RF_bestparam).fit(X,y)
except:
randreg = RandomForestRegressor().fit(X,y)
importances = randreg.feature_importances_
indices = np.argsort(importances)
plt.figure(3) #the axis number
plt.title('Feature Importance')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), features[indices])
plt.xlabel('Relative Importance')
plt.savefig('Feature Importance.png',
bbox_inches='tight', dpi = 500)
# In[ ]:
| #!/usr/bin/env python
# coding: utf-8
# This notebook explains the steps to develop an Automated Supervised Machine Learning Regression program, which automatically tunes the hyperparameters and prints out the final accuracy results as a tables together with feature importance results.
# Let's import all libraries.
# In[1]:
import pandas as pd
import numpy as np
from xgboost import XGBRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.svm import SVR
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import explained_variance_score
from sklearn.metrics import max_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.metrics import median_absolute_error
from sklearn.metrics import r2_score
from sklearn.metrics import mean_poisson_deviance
from sklearn.metrics import mean_gamma_deviance
from sklearn.metrics import mean_absolute_percentage_error
from itertools import repeat
import matplotlib.pyplot as plt
# Lets import our dataset from the csv files as a dataframe.
# In[2]:
df = pd.read_csv('data.csv')
# Let's take a look at dataset. I like using df.describe() function to have some statistics about each column.
# In[3]:
df.describe().T
# Let's define the features as X and the column we want to predict (column F) as y.
# In[4]:
n = len(df.columns)
X = df.iloc[:,0:n-1].to_numpy()
y = df.iloc[:,n-1].to_numpy()
# This defines X as all the values except the last column (columns A,B,C,D,E), and y as the last column (column numbers start from zero, hence: 0 - A, 1 - B, 2 - C, 3 - D,4 - E, 5 -F).
# Some algorithms provide better accuracies with the standard scaling of the input features (i.e. normalization). Let's normalize the data.
# In[5]:
scaler = StandardScaler()
scaler.fit(X)
X= scaler.transform(X)
# We have to split our dataset as train and test data. For this we can use train_test_split by sklearn.model_selection. Test size of 0.20 means that 20% of the data will be used as test data and 80% of the data will be used for training.
# In[6]:
X_train, X_test, y_train, y_test= train_test_split(X,y,test_size = 0.20)
# We might not always want to tune the parameters of models, or only tune for some models. For this I have defined a basic input. When it is set to "True", the program will perform the tuning for all the models.
# In[7]:
Perform_tuning = True
Lassotuning, Ridgetuning, randomforestparametertuning, XGboostparametertuning, SVMparametertuning, MLPparametertuning = repeat(Perform_tuning,6)
# Let's define the grid search function to be used with our models. The values of grid might need to be changed regarding the problem (i.e., some problems might require higher values of n_estimators, while some might require lower ranges).
# In[8]:
def grid_search(model,grid):
# Instantiate the grid search model
print ("Performing gridsearch for {}".format(model))
grid_search = GridSearchCV(estimator = model(), param_grid=grid,
cv = 3, n_jobs = -1, verbose = 2)
# Fit the grid search to the data
grid_search.fit(X_train, y_train)
print("Grid Search Best Parameters for {}".format(model))
print (grid_search.best_params_)
return grid_search.best_params_
# Performing Lasso parameter tuning.
# In[9]:
if Lassotuning:
# Create the parameter grid based on the results of random search
grid = {
'alpha': [1,0.9,0.75,0.5,0.1,0.01,0.001,0.0001] ,
"fit_intercept": [True, False]
}
Lasso_bestparam = grid_search(Lasso,grid)
# Performing Ridge parameter tuning.
# In[10]:
if Ridgetuning:
# Create the parameter grid based on the results of random search
grid = {
'alpha': [1,0.9,0.75,0.5,0.1,0.01,0.001,0.0001] ,
"fit_intercept": [True, False]
}
Ridge_bestparam = grid_search(Ridge,grid)
# Performing Random Forest parameter tuning.
# In[11]:
if randomforestparametertuning:
# Create the parameter grid based on the results of random search
grid = {
'bootstrap': [True,False],
'max_depth': [40, 50, 60, 70],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1,2,3,],
'min_samples_split': [3, 4, 5,6,7],
'n_estimators': [5,10,15]
}
RF_bestparam = grid_search(RandomForestRegressor,grid)
# Performing XGBoost parameter tuning.
# In[12]:
if XGboostparametertuning:
# Create the parameter grid based on the results of random search
grid = {'colsample_bytree': [0.9,0.7],
'gamma': [2,5],
'learning_rate': [0.1,0.2,0.3],
'max_depth': [8,10,12],
'n_estimators': [5,10],
'subsample': [0.8,1],
'reg_alpha': [15,20],
'min_child_weight':[3,5]}
XGB_bestparam = grid_search(XGBRegressor,grid)
# Performing SVM parameter tuning.
# In[13]:
#SVM Parameter Tuning----------------------------------------------------------
if SVMparametertuning:
grid = {'gamma': 10. ** np.arange(-5, 3),
'C': 10. ** np.arange(-3, 3)}
SVR_bestparam = grid_search(SVR,grid)
# Performing MLP parameter tuning.
# In[14]:
if MLPparametertuning:
grid = {
'hidden_layer_sizes': [2,5,8,10],
'activation': ['identity','logistic','tanh','relu'],
'solver': ['lbfgs', 'sgd','adam'],
'learning_rate': ['constant','invscaling','adaptive']}
MLP_bestparam = grid_search(MLPRegressor,grid)
# Now we obtained the best parameters for all the models using the training data. Let's define the error metrics that will be used in analyzing the accuracy of each model.
# In[15]:
error_metrics = (
explained_variance_score,
max_error,
mean_absolute_error,
mean_squared_error,
mean_squared_log_error,
median_absolute_error,
r2_score,
mean_poisson_deviance,
mean_gamma_deviance,
mean_absolute_percentage_error
)
# Let's define fit_model function to predict the results, and analyze the error metrics for each model.
# In[16]:
def fit_model(model,X_train, X_test, y_train, y_test,error_metrics):
fitted_model = model.fit(X_train,y_train)
y_predicted = fitted_model.predict(X_test)
calculations = []
for metric in error_metrics:
calc = metric(y_test, y_predicted)
calculations.append(calc)
return calculations
# Provide a summary of each model and their GridSearch best parameter results. If tuning is not performed, the script will use the default values as best parameters.
# In[17]:
try:
trainingmodels = (
LinearRegression(),
Ridge(**Ridge_bestparam),
RandomForestRegressor(**RF_bestparam),
XGBRegressor(**XGB_bestparam),
Lasso(**Lasso_bestparam),
SVR(**SVR_bestparam),
MLPRegressor(**MLP_bestparam)
)
except:
trainingmodels = (
LinearRegression(),
Ridge(),
RandomForestRegressor(),
XGBRegressor(),
Lasso(),
SVR(),
MLPRegressor()
)
calculations = []
# Below loop performes training, testing and error metrics calculations for each model.
# In[18]:
for trainmodel in trainingmodels:
errors = fit_model(trainmodel,X_train, X_test, y_train, y_test,error_metrics)
calculations.append(errors)
# Let's organize these results, and summarize them all in a dataframe.
# In[19]:
errors = (
'Explained variance score',
'Max error',
'Mean absolute error',
'Mean squared error',
'Mean squared log error',
'Median absolute error',
'r2 score',
'Mean poisson deviance',
'Mean gamma deviance',
'Mean absolute percentage error'
)
model_names = (
'LinearRegression',
'Ridge',
'RandomForestRegressor',
'XGBRegressor',
'Lasso',
'SVR',
'MLPRegressor'
)
df_error = pd.DataFrame(calculations, columns=errors)
df_error["Model"] = model_names
cols = df_error.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_error = df_error[cols]
df_error = df_error.sort_values(by=['Mean squared error'],ascending=True)
df_error = (df_error.set_index('Model')
.astype(float)
.applymap('{:,.3f}'.format))
df_error.to_csv("errors.csv")
df_error
# Moreover, we can analyze the feature importance results using the Random Forest regressor.
# In[20]:
#Principal Component Analysis
features = df.columns[:-1]
try:
randreg = RandomForestRegressor(**RF_bestparam).fit(X,y)
except:
randreg = RandomForestRegressor().fit(X,y)
importances = randreg.feature_importances_
indices = np.argsort(importances)
plt.figure(3) #the axis number
plt.title('Feature Importance')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), features[indices])
plt.xlabel('Relative Importance')
plt.savefig('Feature Importance.png',
bbox_inches='tight', dpi = 500)
# In[ ]: | en | 0.739494 | #!/usr/bin/env python # coding: utf-8 # This notebook explains the steps to develop an Automated Supervised Machine Learning Regression program, which automatically tunes the hyperparameters and prints out the final accuracy results as a tables together with feature importance results. # Let's import all libraries. # In[1]: # Lets import our dataset from the csv files as a dataframe. # In[2]: # Let's take a look at dataset. I like using df.describe() function to have some statistics about each column. # In[3]: # Let's define the features as X and the column we want to predict (column F) as y. # In[4]: # This defines X as all the values except the last column (columns A,B,C,D,E), and y as the last column (column numbers start from zero, hence: 0 - A, 1 - B, 2 - C, 3 - D,4 - E, 5 -F). # Some algorithms provide better accuracies with the standard scaling of the input features (i.e. normalization). Let's normalize the data. # In[5]: # We have to split our dataset as train and test data. For this we can use train_test_split by sklearn.model_selection. Test size of 0.20 means that 20% of the data will be used as test data and 80% of the data will be used for training. # In[6]: # We might not always want to tune the parameters of models, or only tune for some models. For this I have defined a basic input. When it is set to "True", the program will perform the tuning for all the models. # In[7]: # Let's define the grid search function to be used with our models. The values of grid might need to be changed regarding the problem (i.e., some problems might require higher values of n_estimators, while some might require lower ranges). # In[8]: # Instantiate the grid search model # Fit the grid search to the data # Performing Lasso parameter tuning. # In[9]: # Create the parameter grid based on the results of random search # Performing Ridge parameter tuning. # In[10]: # Create the parameter grid based on the results of random search # Performing Random Forest parameter tuning. # In[11]: # Create the parameter grid based on the results of random search # Performing XGBoost parameter tuning. # In[12]: # Create the parameter grid based on the results of random search # Performing SVM parameter tuning. # In[13]: #SVM Parameter Tuning---------------------------------------------------------- # Performing MLP parameter tuning. # In[14]: # Now we obtained the best parameters for all the models using the training data. Let's define the error metrics that will be used in analyzing the accuracy of each model. # In[15]: # Let's define fit_model function to predict the results, and analyze the error metrics for each model. # In[16]: # Provide a summary of each model and their GridSearch best parameter results. If tuning is not performed, the script will use the default values as best parameters. # In[17]: # Below loop performes training, testing and error metrics calculations for each model. # In[18]: # Let's organize these results, and summarize them all in a dataframe. # In[19]: # Moreover, we can analyze the feature importance results using the Random Forest regressor. # In[20]: #Principal Component Analysis #the axis number # In[ ]: | 3.557041 | 4 |