body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
35a7abf8b86fc712dbb9e278d0cd7327529043205f45d42d0a25a77d02537848 | def test_loss_function_processing():
'Test loss function processing'
targets = np.random.default_rng(0).integers(3, size=100)
with pytest.raises(ValueError):
ModelAnalyzer(None, None, targets) | Test loss function processing | tests/test_unit.py | test_loss_function_processing | cloudbopper/anamod | 1 | python | def test_loss_function_processing():
targets = np.random.default_rng(0).integers(3, size=100)
with pytest.raises(ValueError):
ModelAnalyzer(None, None, targets) | def test_loss_function_processing():
targets = np.random.default_rng(0).integers(3, size=100)
with pytest.raises(ValueError):
ModelAnalyzer(None, None, targets)<|docstring|>Test loss function processing<|endoftext|> |
8492e6c52251553e681d28f6fd2e0d1b722062f67d1cc45eedce83968676e05f | def find_lib_path():
'Find NNNet dynamic library files.\n\n Returns\n -------\n lib_path : list(string)\n List of all found path to the libraries\n '
if hasattr(__builtin__, 'NNVM_BASE_PATH'):
base_path = __builtin__.NNVM_BASE_PATH
else:
base_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
if hasattr(__builtin__, 'NNVM_LIBRARY_NAME'):
lib_name = __builtin__.NNVM_LIBRARY_NAME
else:
lib_name = ('nnvm_compiler' if sys.platform.startswith('win32') else 'libnnvm_compiler')
api_path = os.path.join(base_path, '..', '..', 'lib')
cmake_build_path_win = os.path.join(base_path, '..', '..', '..', 'build', 'Release')
cmake_build_path = os.path.join(base_path, '..', '..', '..', 'build')
install_path = os.path.join(base_path, '..', '..', '..')
dll_path = [base_path, api_path, cmake_build_path_win, cmake_build_path, install_path]
if (sys.platform.startswith('linux') and os.environ.get('LD_LIBRARY_PATH', None)):
dll_path.extend([p.strip() for p in os.environ['LD_LIBRARY_PATH'].split(':')])
elif (sys.platform.startswith('darwin') and os.environ.get('DYLD_LIBRARY_PATH', None)):
dll_path.extend([p.strip() for p in os.environ['DYLD_LIBRARY_PATH'].split(':')])
elif (sys.platform.startswith('win32') and os.environ.get('PATH', None)):
dll_path.extend([p.strip() for p in os.environ['PATH'].split(';')])
if sys.platform.startswith('win32'):
vs_configuration = 'Release'
if (platform.architecture()[0] == '64bit'):
dll_path.append(os.path.join(base_path, '..', '..', '..', 'build', vs_configuration))
dll_path.append(os.path.join(base_path, '..', '..', '..', 'windows', 'x64', vs_configuration))
else:
dll_path.append(os.path.join(base_path, '..', '..', '..', 'build', vs_configuration))
dll_path.append(os.path.join(base_path, '..', '..', '..', 'windows', vs_configuration))
dll_path = [os.path.join(p, ('%s.dll' % lib_name)) for p in dll_path]
elif sys.platform.startswith('darwin'):
dll_path = [os.path.join(p, ('%s.dylib' % lib_name)) for p in dll_path]
else:
dll_path = [os.path.join(p, ('%s.so' % lib_name)) for p in dll_path]
lib_path = [p for p in dll_path if (os.path.exists(p) and os.path.isfile(p))]
if (not lib_path):
raise RuntimeError((('Cannot find the files.\n' + 'List of candidates:\n') + str('\n'.join(dll_path))))
return lib_path | Find NNNet dynamic library files.
Returns
-------
lib_path : list(string)
List of all found path to the libraries | nnvm/python/nnvm/libinfo.py | find_lib_path | Checkmate50/tvm | 286 | python | def find_lib_path():
'Find NNNet dynamic library files.\n\n Returns\n -------\n lib_path : list(string)\n List of all found path to the libraries\n '
if hasattr(__builtin__, 'NNVM_BASE_PATH'):
base_path = __builtin__.NNVM_BASE_PATH
else:
base_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
if hasattr(__builtin__, 'NNVM_LIBRARY_NAME'):
lib_name = __builtin__.NNVM_LIBRARY_NAME
else:
lib_name = ('nnvm_compiler' if sys.platform.startswith('win32') else 'libnnvm_compiler')
api_path = os.path.join(base_path, '..', '..', 'lib')
cmake_build_path_win = os.path.join(base_path, '..', '..', '..', 'build', 'Release')
cmake_build_path = os.path.join(base_path, '..', '..', '..', 'build')
install_path = os.path.join(base_path, '..', '..', '..')
dll_path = [base_path, api_path, cmake_build_path_win, cmake_build_path, install_path]
if (sys.platform.startswith('linux') and os.environ.get('LD_LIBRARY_PATH', None)):
dll_path.extend([p.strip() for p in os.environ['LD_LIBRARY_PATH'].split(':')])
elif (sys.platform.startswith('darwin') and os.environ.get('DYLD_LIBRARY_PATH', None)):
dll_path.extend([p.strip() for p in os.environ['DYLD_LIBRARY_PATH'].split(':')])
elif (sys.platform.startswith('win32') and os.environ.get('PATH', None)):
dll_path.extend([p.strip() for p in os.environ['PATH'].split(';')])
if sys.platform.startswith('win32'):
vs_configuration = 'Release'
if (platform.architecture()[0] == '64bit'):
dll_path.append(os.path.join(base_path, '..', '..', '..', 'build', vs_configuration))
dll_path.append(os.path.join(base_path, '..', '..', '..', 'windows', 'x64', vs_configuration))
else:
dll_path.append(os.path.join(base_path, '..', '..', '..', 'build', vs_configuration))
dll_path.append(os.path.join(base_path, '..', '..', '..', 'windows', vs_configuration))
dll_path = [os.path.join(p, ('%s.dll' % lib_name)) for p in dll_path]
elif sys.platform.startswith('darwin'):
dll_path = [os.path.join(p, ('%s.dylib' % lib_name)) for p in dll_path]
else:
dll_path = [os.path.join(p, ('%s.so' % lib_name)) for p in dll_path]
lib_path = [p for p in dll_path if (os.path.exists(p) and os.path.isfile(p))]
if (not lib_path):
raise RuntimeError((('Cannot find the files.\n' + 'List of candidates:\n') + str('\n'.join(dll_path))))
return lib_path | def find_lib_path():
'Find NNNet dynamic library files.\n\n Returns\n -------\n lib_path : list(string)\n List of all found path to the libraries\n '
if hasattr(__builtin__, 'NNVM_BASE_PATH'):
base_path = __builtin__.NNVM_BASE_PATH
else:
base_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
if hasattr(__builtin__, 'NNVM_LIBRARY_NAME'):
lib_name = __builtin__.NNVM_LIBRARY_NAME
else:
lib_name = ('nnvm_compiler' if sys.platform.startswith('win32') else 'libnnvm_compiler')
api_path = os.path.join(base_path, '..', '..', 'lib')
cmake_build_path_win = os.path.join(base_path, '..', '..', '..', 'build', 'Release')
cmake_build_path = os.path.join(base_path, '..', '..', '..', 'build')
install_path = os.path.join(base_path, '..', '..', '..')
dll_path = [base_path, api_path, cmake_build_path_win, cmake_build_path, install_path]
if (sys.platform.startswith('linux') and os.environ.get('LD_LIBRARY_PATH', None)):
dll_path.extend([p.strip() for p in os.environ['LD_LIBRARY_PATH'].split(':')])
elif (sys.platform.startswith('darwin') and os.environ.get('DYLD_LIBRARY_PATH', None)):
dll_path.extend([p.strip() for p in os.environ['DYLD_LIBRARY_PATH'].split(':')])
elif (sys.platform.startswith('win32') and os.environ.get('PATH', None)):
dll_path.extend([p.strip() for p in os.environ['PATH'].split(';')])
if sys.platform.startswith('win32'):
vs_configuration = 'Release'
if (platform.architecture()[0] == '64bit'):
dll_path.append(os.path.join(base_path, '..', '..', '..', 'build', vs_configuration))
dll_path.append(os.path.join(base_path, '..', '..', '..', 'windows', 'x64', vs_configuration))
else:
dll_path.append(os.path.join(base_path, '..', '..', '..', 'build', vs_configuration))
dll_path.append(os.path.join(base_path, '..', '..', '..', 'windows', vs_configuration))
dll_path = [os.path.join(p, ('%s.dll' % lib_name)) for p in dll_path]
elif sys.platform.startswith('darwin'):
dll_path = [os.path.join(p, ('%s.dylib' % lib_name)) for p in dll_path]
else:
dll_path = [os.path.join(p, ('%s.so' % lib_name)) for p in dll_path]
lib_path = [p for p in dll_path if (os.path.exists(p) and os.path.isfile(p))]
if (not lib_path):
raise RuntimeError((('Cannot find the files.\n' + 'List of candidates:\n') + str('\n'.join(dll_path))))
return lib_path<|docstring|>Find NNNet dynamic library files.
Returns
-------
lib_path : list(string)
List of all found path to the libraries<|endoftext|> |
6b0a1854e5886e80ed70c2c4a127f89b35775bc79079bb60371842ea42eb76a3 | def lpsd(x, fs=1.0, window='hann', fmin=None, fmax=None, Jdes=1000, Kdes=100, Kmin=1, xi=0.5, scaling='density'):
'\n Compute the LPSD power spectrum estimation with a logarithmic frequency axis.\n\n Parameters\n ----------\n x : array_like\n time series to be transformed. "We assume to have a long stream x(n),\n n=0, ..., N-1 of equally spaced input data sampled with frequency fs. Typical\n values for N range from 10^4 to >10^6" [1]\n\n fs : float\n Sampling frequency of the `x` time series. Defaults to 1.0.\n\n window : str\n Desired window to use. If `window` is a string or tuple, it is passed to\n `scipy.signal.get_window` to generate the window values, which are DFT-even by\n default. See `scipy.signal.get_window` for a list of windows and required\n parameters. Defaults to a Hann window. "Choose a window function w(j, l) to\n reduce spectral leakage within the estimate. ... The computations of the window\n function will be performed when the segment lengths L(j) have been determined."\n [1]\n\n fmin, fmax : float, optional\n Lowest and highest frequency to estimate. Defaults to `fs / len(x)` and the\n Nyquist frequency `fs / 2`, respectively. "... we propose not to use the first\n few frequency bins. The first frequency bin that yields unbiased spectral\n estimates depends on the window function used. The bin is given by the effective\n half-width of the window transfer function." [1].\n\n Jdes : int, optional\n Desired number of Fourier frequencies. Defaults to 1000. "A typical value for J\n is 1000" [1]\n\n Kdes : int, optional\n Desired number of averages. Defaults to 100.\n\n Kmin : int, optional\n Minimum number of averages. Defaults to 1.\n\n xi : float, optional\n Fractional overlap between segments (0 <= xi < 1). Defaults to 0.5. "The\n amount of overlap is a trade-off between computational effort and flatness of\n the data weighting." [1]. See Figures 5 and 6 [1].\n\n scaling : {\'density\', \'spectrum\'}, optional\n Selects between computing the power spectral density (\'density\') where `Pxx` has\n units of V**2/Hz and computing the power spectrum (\'spectrum\') where `Pxx` has\n units of V**2, if `x` is measured in V and `fs` is measured in Hz. Defaults to\n \'density\'.\n\n Returns\n -------\n f : 1-d array\n Vector of frequencies corresponding to Pxx\n Pxx : 1d-array\n Vector of (uncalibrated) power spectrum estimates\n\n Notes\n -----\n The implementation follows references [1] and [2] quite closely; in particular, the\n variable names used in the program generally correspond to the variables in the\n paper; and the corresponding equation numbers are indicated in the comments.\n\n References\n ----------\n [1] Michael Tröbs and Gerhard Heinzel, "Improved spectrum estimation from\n digitized time series on a logarithmic frequency axis" in Measurement, vol 39\n (2006), pp 120-129.\n * http://dx.doi.org/10.1016/j.measurement.2005.10.010\n * http://pubman.mpdl.mpg.de/pubman/item/escidoc:150688:1\n\n [2] Michael Tröbs and Gerhard Heinzel, Corrigendum to "Improved spectrum\n estimation from digitized time series on a logarithmic frequency axis."\n '
assert (scaling in ['density', 'spectrum'])
N = len(x)
jj = np.arange(Jdes, dtype=int)
if (not fmin):
fmin = (fs / N)
if (not fmax):
fmax = (fs / 2)
g = (np.log(fmax) - np.log(fmin))
f = (fmin * np.exp(((jj * g) / (Jdes - 1))))
rp = ((fmin * np.exp(((jj * g) / (Jdes - 1)))) * (np.exp((g / (Jdes - 1))) - 1))
ravg = ((fs / N) * (1 + ((1 - xi) * (Kdes - 1))))
rmin = ((fs / N) * (1 + ((1 - xi) * (Kmin - 1))))
case1 = (rp >= ravg)
case2 = np.logical_and((rp < ravg), (np.sqrt((ravg * rp)) > rmin))
case3 = np.logical_not(np.logical_or(case1, case2))
rpp = np.zeros(Jdes)
rpp[case1] = rp[case1]
rpp[case2] = np.sqrt((ravg * rp[case2]))
rpp[case3] = rmin
L = np.around((fs / rpp)).astype(int)
r = (fs / L)
m = (f / r)
Pxx = np.empty(Jdes)
S1 = np.empty(Jdes)
S2 = np.empty(Jdes)
for jj in range(len(f)):
D = int(np.around(((1 - xi) * L[jj])))
K = int(np.floor((((N - L[jj]) / D) + 1)))
a = np.arange(L[jj])
b = (D * np.arange(K))
ii = (a[(:, np.newaxis)] + b)
data = x[ii]
data -= np.mean(data, axis=0)
w = get_window(window, L[jj])
sinusoid = np.exp((((((- 2j) * np.pi) * np.arange(L[jj])[(:, np.newaxis)]) * m[jj]) / L[jj]))
data = (data * (sinusoid * w[(:, np.newaxis)]))
Pxx[jj] = np.mean((np.abs(np.sum(data, axis=0)) ** 2))
S1[jj] = np.sum(w)
S2[jj] = np.sum((w ** 2))
if (scaling == 'spectrum'):
C = (2.0 / (S1 ** 2))
Pxx = (Pxx * C)
elif (scaling == 'density'):
C = (2.0 / (fs * S2))
Pxx = (Pxx * C)
return (f, Pxx) | Compute the LPSD power spectrum estimation with a logarithmic frequency axis.
Parameters
----------
x : array_like
time series to be transformed. "We assume to have a long stream x(n),
n=0, ..., N-1 of equally spaced input data sampled with frequency fs. Typical
values for N range from 10^4 to >10^6" [1]
fs : float
Sampling frequency of the `x` time series. Defaults to 1.0.
window : str
Desired window to use. If `window` is a string or tuple, it is passed to
`scipy.signal.get_window` to generate the window values, which are DFT-even by
default. See `scipy.signal.get_window` for a list of windows and required
parameters. Defaults to a Hann window. "Choose a window function w(j, l) to
reduce spectral leakage within the estimate. ... The computations of the window
function will be performed when the segment lengths L(j) have been determined."
[1]
fmin, fmax : float, optional
Lowest and highest frequency to estimate. Defaults to `fs / len(x)` and the
Nyquist frequency `fs / 2`, respectively. "... we propose not to use the first
few frequency bins. The first frequency bin that yields unbiased spectral
estimates depends on the window function used. The bin is given by the effective
half-width of the window transfer function." [1].
Jdes : int, optional
Desired number of Fourier frequencies. Defaults to 1000. "A typical value for J
is 1000" [1]
Kdes : int, optional
Desired number of averages. Defaults to 100.
Kmin : int, optional
Minimum number of averages. Defaults to 1.
xi : float, optional
Fractional overlap between segments (0 <= xi < 1). Defaults to 0.5. "The
amount of overlap is a trade-off between computational effort and flatness of
the data weighting." [1]. See Figures 5 and 6 [1].
scaling : {'density', 'spectrum'}, optional
Selects between computing the power spectral density ('density') where `Pxx` has
units of V**2/Hz and computing the power spectrum ('spectrum') where `Pxx` has
units of V**2, if `x` is measured in V and `fs` is measured in Hz. Defaults to
'density'.
Returns
-------
f : 1-d array
Vector of frequencies corresponding to Pxx
Pxx : 1d-array
Vector of (uncalibrated) power spectrum estimates
Notes
-----
The implementation follows references [1] and [2] quite closely; in particular, the
variable names used in the program generally correspond to the variables in the
paper; and the corresponding equation numbers are indicated in the comments.
References
----------
[1] Michael Tröbs and Gerhard Heinzel, "Improved spectrum estimation from
digitized time series on a logarithmic frequency axis" in Measurement, vol 39
(2006), pp 120-129.
* http://dx.doi.org/10.1016/j.measurement.2005.10.010
* http://pubman.mpdl.mpg.de/pubman/item/escidoc:150688:1
[2] Michael Tröbs and Gerhard Heinzel, Corrigendum to "Improved spectrum
estimation from digitized time series on a logarithmic frequency axis." | freqtools/time_data.py | lpsd | bleykauf/freqtools | 0 | python | def lpsd(x, fs=1.0, window='hann', fmin=None, fmax=None, Jdes=1000, Kdes=100, Kmin=1, xi=0.5, scaling='density'):
'\n Compute the LPSD power spectrum estimation with a logarithmic frequency axis.\n\n Parameters\n ----------\n x : array_like\n time series to be transformed. "We assume to have a long stream x(n),\n n=0, ..., N-1 of equally spaced input data sampled with frequency fs. Typical\n values for N range from 10^4 to >10^6" [1]\n\n fs : float\n Sampling frequency of the `x` time series. Defaults to 1.0.\n\n window : str\n Desired window to use. If `window` is a string or tuple, it is passed to\n `scipy.signal.get_window` to generate the window values, which are DFT-even by\n default. See `scipy.signal.get_window` for a list of windows and required\n parameters. Defaults to a Hann window. "Choose a window function w(j, l) to\n reduce spectral leakage within the estimate. ... The computations of the window\n function will be performed when the segment lengths L(j) have been determined."\n [1]\n\n fmin, fmax : float, optional\n Lowest and highest frequency to estimate. Defaults to `fs / len(x)` and the\n Nyquist frequency `fs / 2`, respectively. "... we propose not to use the first\n few frequency bins. The first frequency bin that yields unbiased spectral\n estimates depends on the window function used. The bin is given by the effective\n half-width of the window transfer function." [1].\n\n Jdes : int, optional\n Desired number of Fourier frequencies. Defaults to 1000. "A typical value for J\n is 1000" [1]\n\n Kdes : int, optional\n Desired number of averages. Defaults to 100.\n\n Kmin : int, optional\n Minimum number of averages. Defaults to 1.\n\n xi : float, optional\n Fractional overlap between segments (0 <= xi < 1). Defaults to 0.5. "The\n amount of overlap is a trade-off between computational effort and flatness of\n the data weighting." [1]. See Figures 5 and 6 [1].\n\n scaling : {\'density\', \'spectrum\'}, optional\n Selects between computing the power spectral density (\'density\') where `Pxx` has\n units of V**2/Hz and computing the power spectrum (\'spectrum\') where `Pxx` has\n units of V**2, if `x` is measured in V and `fs` is measured in Hz. Defaults to\n \'density\'.\n\n Returns\n -------\n f : 1-d array\n Vector of frequencies corresponding to Pxx\n Pxx : 1d-array\n Vector of (uncalibrated) power spectrum estimates\n\n Notes\n -----\n The implementation follows references [1] and [2] quite closely; in particular, the\n variable names used in the program generally correspond to the variables in the\n paper; and the corresponding equation numbers are indicated in the comments.\n\n References\n ----------\n [1] Michael Tröbs and Gerhard Heinzel, "Improved spectrum estimation from\n digitized time series on a logarithmic frequency axis" in Measurement, vol 39\n (2006), pp 120-129.\n * http://dx.doi.org/10.1016/j.measurement.2005.10.010\n * http://pubman.mpdl.mpg.de/pubman/item/escidoc:150688:1\n\n [2] Michael Tröbs and Gerhard Heinzel, Corrigendum to "Improved spectrum\n estimation from digitized time series on a logarithmic frequency axis."\n '
assert (scaling in ['density', 'spectrum'])
N = len(x)
jj = np.arange(Jdes, dtype=int)
if (not fmin):
fmin = (fs / N)
if (not fmax):
fmax = (fs / 2)
g = (np.log(fmax) - np.log(fmin))
f = (fmin * np.exp(((jj * g) / (Jdes - 1))))
rp = ((fmin * np.exp(((jj * g) / (Jdes - 1)))) * (np.exp((g / (Jdes - 1))) - 1))
ravg = ((fs / N) * (1 + ((1 - xi) * (Kdes - 1))))
rmin = ((fs / N) * (1 + ((1 - xi) * (Kmin - 1))))
case1 = (rp >= ravg)
case2 = np.logical_and((rp < ravg), (np.sqrt((ravg * rp)) > rmin))
case3 = np.logical_not(np.logical_or(case1, case2))
rpp = np.zeros(Jdes)
rpp[case1] = rp[case1]
rpp[case2] = np.sqrt((ravg * rp[case2]))
rpp[case3] = rmin
L = np.around((fs / rpp)).astype(int)
r = (fs / L)
m = (f / r)
Pxx = np.empty(Jdes)
S1 = np.empty(Jdes)
S2 = np.empty(Jdes)
for jj in range(len(f)):
D = int(np.around(((1 - xi) * L[jj])))
K = int(np.floor((((N - L[jj]) / D) + 1)))
a = np.arange(L[jj])
b = (D * np.arange(K))
ii = (a[(:, np.newaxis)] + b)
data = x[ii]
data -= np.mean(data, axis=0)
w = get_window(window, L[jj])
sinusoid = np.exp((((((- 2j) * np.pi) * np.arange(L[jj])[(:, np.newaxis)]) * m[jj]) / L[jj]))
data = (data * (sinusoid * w[(:, np.newaxis)]))
Pxx[jj] = np.mean((np.abs(np.sum(data, axis=0)) ** 2))
S1[jj] = np.sum(w)
S2[jj] = np.sum((w ** 2))
if (scaling == 'spectrum'):
C = (2.0 / (S1 ** 2))
Pxx = (Pxx * C)
elif (scaling == 'density'):
C = (2.0 / (fs * S2))
Pxx = (Pxx * C)
return (f, Pxx) | def lpsd(x, fs=1.0, window='hann', fmin=None, fmax=None, Jdes=1000, Kdes=100, Kmin=1, xi=0.5, scaling='density'):
'\n Compute the LPSD power spectrum estimation with a logarithmic frequency axis.\n\n Parameters\n ----------\n x : array_like\n time series to be transformed. "We assume to have a long stream x(n),\n n=0, ..., N-1 of equally spaced input data sampled with frequency fs. Typical\n values for N range from 10^4 to >10^6" [1]\n\n fs : float\n Sampling frequency of the `x` time series. Defaults to 1.0.\n\n window : str\n Desired window to use. If `window` is a string or tuple, it is passed to\n `scipy.signal.get_window` to generate the window values, which are DFT-even by\n default. See `scipy.signal.get_window` for a list of windows and required\n parameters. Defaults to a Hann window. "Choose a window function w(j, l) to\n reduce spectral leakage within the estimate. ... The computations of the window\n function will be performed when the segment lengths L(j) have been determined."\n [1]\n\n fmin, fmax : float, optional\n Lowest and highest frequency to estimate. Defaults to `fs / len(x)` and the\n Nyquist frequency `fs / 2`, respectively. "... we propose not to use the first\n few frequency bins. The first frequency bin that yields unbiased spectral\n estimates depends on the window function used. The bin is given by the effective\n half-width of the window transfer function." [1].\n\n Jdes : int, optional\n Desired number of Fourier frequencies. Defaults to 1000. "A typical value for J\n is 1000" [1]\n\n Kdes : int, optional\n Desired number of averages. Defaults to 100.\n\n Kmin : int, optional\n Minimum number of averages. Defaults to 1.\n\n xi : float, optional\n Fractional overlap between segments (0 <= xi < 1). Defaults to 0.5. "The\n amount of overlap is a trade-off between computational effort and flatness of\n the data weighting." [1]. See Figures 5 and 6 [1].\n\n scaling : {\'density\', \'spectrum\'}, optional\n Selects between computing the power spectral density (\'density\') where `Pxx` has\n units of V**2/Hz and computing the power spectrum (\'spectrum\') where `Pxx` has\n units of V**2, if `x` is measured in V and `fs` is measured in Hz. Defaults to\n \'density\'.\n\n Returns\n -------\n f : 1-d array\n Vector of frequencies corresponding to Pxx\n Pxx : 1d-array\n Vector of (uncalibrated) power spectrum estimates\n\n Notes\n -----\n The implementation follows references [1] and [2] quite closely; in particular, the\n variable names used in the program generally correspond to the variables in the\n paper; and the corresponding equation numbers are indicated in the comments.\n\n References\n ----------\n [1] Michael Tröbs and Gerhard Heinzel, "Improved spectrum estimation from\n digitized time series on a logarithmic frequency axis" in Measurement, vol 39\n (2006), pp 120-129.\n * http://dx.doi.org/10.1016/j.measurement.2005.10.010\n * http://pubman.mpdl.mpg.de/pubman/item/escidoc:150688:1\n\n [2] Michael Tröbs and Gerhard Heinzel, Corrigendum to "Improved spectrum\n estimation from digitized time series on a logarithmic frequency axis."\n '
assert (scaling in ['density', 'spectrum'])
N = len(x)
jj = np.arange(Jdes, dtype=int)
if (not fmin):
fmin = (fs / N)
if (not fmax):
fmax = (fs / 2)
g = (np.log(fmax) - np.log(fmin))
f = (fmin * np.exp(((jj * g) / (Jdes - 1))))
rp = ((fmin * np.exp(((jj * g) / (Jdes - 1)))) * (np.exp((g / (Jdes - 1))) - 1))
ravg = ((fs / N) * (1 + ((1 - xi) * (Kdes - 1))))
rmin = ((fs / N) * (1 + ((1 - xi) * (Kmin - 1))))
case1 = (rp >= ravg)
case2 = np.logical_and((rp < ravg), (np.sqrt((ravg * rp)) > rmin))
case3 = np.logical_not(np.logical_or(case1, case2))
rpp = np.zeros(Jdes)
rpp[case1] = rp[case1]
rpp[case2] = np.sqrt((ravg * rp[case2]))
rpp[case3] = rmin
L = np.around((fs / rpp)).astype(int)
r = (fs / L)
m = (f / r)
Pxx = np.empty(Jdes)
S1 = np.empty(Jdes)
S2 = np.empty(Jdes)
for jj in range(len(f)):
D = int(np.around(((1 - xi) * L[jj])))
K = int(np.floor((((N - L[jj]) / D) + 1)))
a = np.arange(L[jj])
b = (D * np.arange(K))
ii = (a[(:, np.newaxis)] + b)
data = x[ii]
data -= np.mean(data, axis=0)
w = get_window(window, L[jj])
sinusoid = np.exp((((((- 2j) * np.pi) * np.arange(L[jj])[(:, np.newaxis)]) * m[jj]) / L[jj]))
data = (data * (sinusoid * w[(:, np.newaxis)]))
Pxx[jj] = np.mean((np.abs(np.sum(data, axis=0)) ** 2))
S1[jj] = np.sum(w)
S2[jj] = np.sum((w ** 2))
if (scaling == 'spectrum'):
C = (2.0 / (S1 ** 2))
Pxx = (Pxx * C)
elif (scaling == 'density'):
C = (2.0 / (fs * S2))
Pxx = (Pxx * C)
return (f, Pxx)<|docstring|>Compute the LPSD power spectrum estimation with a logarithmic frequency axis.
Parameters
----------
x : array_like
time series to be transformed. "We assume to have a long stream x(n),
n=0, ..., N-1 of equally spaced input data sampled with frequency fs. Typical
values for N range from 10^4 to >10^6" [1]
fs : float
Sampling frequency of the `x` time series. Defaults to 1.0.
window : str
Desired window to use. If `window` is a string or tuple, it is passed to
`scipy.signal.get_window` to generate the window values, which are DFT-even by
default. See `scipy.signal.get_window` for a list of windows and required
parameters. Defaults to a Hann window. "Choose a window function w(j, l) to
reduce spectral leakage within the estimate. ... The computations of the window
function will be performed when the segment lengths L(j) have been determined."
[1]
fmin, fmax : float, optional
Lowest and highest frequency to estimate. Defaults to `fs / len(x)` and the
Nyquist frequency `fs / 2`, respectively. "... we propose not to use the first
few frequency bins. The first frequency bin that yields unbiased spectral
estimates depends on the window function used. The bin is given by the effective
half-width of the window transfer function." [1].
Jdes : int, optional
Desired number of Fourier frequencies. Defaults to 1000. "A typical value for J
is 1000" [1]
Kdes : int, optional
Desired number of averages. Defaults to 100.
Kmin : int, optional
Minimum number of averages. Defaults to 1.
xi : float, optional
Fractional overlap between segments (0 <= xi < 1). Defaults to 0.5. "The
amount of overlap is a trade-off between computational effort and flatness of
the data weighting." [1]. See Figures 5 and 6 [1].
scaling : {'density', 'spectrum'}, optional
Selects between computing the power spectral density ('density') where `Pxx` has
units of V**2/Hz and computing the power spectrum ('spectrum') where `Pxx` has
units of V**2, if `x` is measured in V and `fs` is measured in Hz. Defaults to
'density'.
Returns
-------
f : 1-d array
Vector of frequencies corresponding to Pxx
Pxx : 1d-array
Vector of (uncalibrated) power spectrum estimates
Notes
-----
The implementation follows references [1] and [2] quite closely; in particular, the
variable names used in the program generally correspond to the variables in the
paper; and the corresponding equation numbers are indicated in the comments.
References
----------
[1] Michael Tröbs and Gerhard Heinzel, "Improved spectrum estimation from
digitized time series on a logarithmic frequency axis" in Measurement, vol 39
(2006), pp 120-129.
* http://dx.doi.org/10.1016/j.measurement.2005.10.010
* http://pubman.mpdl.mpg.de/pubman/item/escidoc:150688:1
[2] Michael Tröbs and Gerhard Heinzel, Corrigendum to "Improved spectrum
estimation from digitized time series on a logarithmic frequency axis."<|endoftext|> |
66aa909cd4952f294547d330e133c2461be6b7daba0251ce56ee2cabe6abfc8e | def to_oscillator_noise(self, method='welch', window='hann', **kwargs):
'\n Create a OscillatorNoise object using the Welch method.\n\n Parameters\n ----------\n method : {"welch", "lpsd"}, optional\n The method used for calculating the oscillator noise. Defaults to Welch\n method.\n window : str or tuple or array_like, optional\n Desired window to use. If `window` is a string or tuple, it is passed to\n `scipy.signal.get_window` to generate the window values, which are DFT-even\n by default. See `scipy.signal.get_window` for a list of windows and required\n parameters. If `window` is array_like it will be used directly as the window\n and its length must be nperseg. Defaults to a Hann window.\n **kwargs :\n Arguments will be passed to the function used for calculating the oscillator\n noise. Note that `scaling` and `return_onesided` are always set\n automatically for consistency.\n\n Returns\n -------\n OscillatorNoise\n '
assert (method in ['welch', 'lpsd'])
if (method == 'welch'):
(f, Pxx) = welch(self.freqs, self.sample_rate, window=window, return_onesided=True, scaling='density', **kwargs)
elif (method == 'lpsd'):
(f, Pxx) = lpsd(self.freqs, self.sample_rate, window=window, scaling='density', **kwargs)
return OscillatorNoise(f, Pxx, representation='psd_freq', n_sided=1, divide_by=self.divide_by) | Create a OscillatorNoise object using the Welch method.
Parameters
----------
method : {"welch", "lpsd"}, optional
The method used for calculating the oscillator noise. Defaults to Welch
method.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is passed to
`scipy.signal.get_window` to generate the window values, which are DFT-even
by default. See `scipy.signal.get_window` for a list of windows and required
parameters. If `window` is array_like it will be used directly as the window
and its length must be nperseg. Defaults to a Hann window.
**kwargs :
Arguments will be passed to the function used for calculating the oscillator
noise. Note that `scaling` and `return_onesided` are always set
automatically for consistency.
Returns
-------
OscillatorNoise | freqtools/time_data.py | to_oscillator_noise | bleykauf/freqtools | 0 | python | def to_oscillator_noise(self, method='welch', window='hann', **kwargs):
'\n Create a OscillatorNoise object using the Welch method.\n\n Parameters\n ----------\n method : {"welch", "lpsd"}, optional\n The method used for calculating the oscillator noise. Defaults to Welch\n method.\n window : str or tuple or array_like, optional\n Desired window to use. If `window` is a string or tuple, it is passed to\n `scipy.signal.get_window` to generate the window values, which are DFT-even\n by default. See `scipy.signal.get_window` for a list of windows and required\n parameters. If `window` is array_like it will be used directly as the window\n and its length must be nperseg. Defaults to a Hann window.\n **kwargs :\n Arguments will be passed to the function used for calculating the oscillator\n noise. Note that `scaling` and `return_onesided` are always set\n automatically for consistency.\n\n Returns\n -------\n OscillatorNoise\n '
assert (method in ['welch', 'lpsd'])
if (method == 'welch'):
(f, Pxx) = welch(self.freqs, self.sample_rate, window=window, return_onesided=True, scaling='density', **kwargs)
elif (method == 'lpsd'):
(f, Pxx) = lpsd(self.freqs, self.sample_rate, window=window, scaling='density', **kwargs)
return OscillatorNoise(f, Pxx, representation='psd_freq', n_sided=1, divide_by=self.divide_by) | def to_oscillator_noise(self, method='welch', window='hann', **kwargs):
'\n Create a OscillatorNoise object using the Welch method.\n\n Parameters\n ----------\n method : {"welch", "lpsd"}, optional\n The method used for calculating the oscillator noise. Defaults to Welch\n method.\n window : str or tuple or array_like, optional\n Desired window to use. If `window` is a string or tuple, it is passed to\n `scipy.signal.get_window` to generate the window values, which are DFT-even\n by default. See `scipy.signal.get_window` for a list of windows and required\n parameters. If `window` is array_like it will be used directly as the window\n and its length must be nperseg. Defaults to a Hann window.\n **kwargs :\n Arguments will be passed to the function used for calculating the oscillator\n noise. Note that `scaling` and `return_onesided` are always set\n automatically for consistency.\n\n Returns\n -------\n OscillatorNoise\n '
assert (method in ['welch', 'lpsd'])
if (method == 'welch'):
(f, Pxx) = welch(self.freqs, self.sample_rate, window=window, return_onesided=True, scaling='density', **kwargs)
elif (method == 'lpsd'):
(f, Pxx) = lpsd(self.freqs, self.sample_rate, window=window, scaling='density', **kwargs)
return OscillatorNoise(f, Pxx, representation='psd_freq', n_sided=1, divide_by=self.divide_by)<|docstring|>Create a OscillatorNoise object using the Welch method.
Parameters
----------
method : {"welch", "lpsd"}, optional
The method used for calculating the oscillator noise. Defaults to Welch
method.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is passed to
`scipy.signal.get_window` to generate the window values, which are DFT-even
by default. See `scipy.signal.get_window` for a list of windows and required
parameters. If `window` is array_like it will be used directly as the window
and its length must be nperseg. Defaults to a Hann window.
**kwargs :
Arguments will be passed to the function used for calculating the oscillator
noise. Note that `scaling` and `return_onesided` are always set
automatically for consistency.
Returns
-------
OscillatorNoise<|endoftext|> |
67e184d218e42d534c978689871f7a359a9062d1890cbb126138d534ffd482da | def adev(self, scaling=1):
'\n Calculates the Allan deviation of the data.\n\n Parameters\n ----------\n scaling : float (optional)\n normalization factor, i.e. the oscillator frequency ν_0\n\n Returns\n -------\n taus, adev, adeverror : list\n The taus for which the Allan deviation has been calculated, the adev at\n these taus and their statistical error.\n '
freqs = (np.array(self.freqs) * scaling)
tau_max = np.log10(len(self.freqs))
taus = (np.logspace(0, tau_max) / self.sample_rate)
(taus, adev, adeverror, _) = allantools.adev(freqs, data_type='freq', rate=self.sample_rate, taus=taus)
return (taus, adev, adeverror) | Calculates the Allan deviation of the data.
Parameters
----------
scaling : float (optional)
normalization factor, i.e. the oscillator frequency ν_0
Returns
-------
taus, adev, adeverror : list
The taus for which the Allan deviation has been calculated, the adev at
these taus and their statistical error. | freqtools/time_data.py | adev | bleykauf/freqtools | 0 | python | def adev(self, scaling=1):
'\n Calculates the Allan deviation of the data.\n\n Parameters\n ----------\n scaling : float (optional)\n normalization factor, i.e. the oscillator frequency ν_0\n\n Returns\n -------\n taus, adev, adeverror : list\n The taus for which the Allan deviation has been calculated, the adev at\n these taus and their statistical error.\n '
freqs = (np.array(self.freqs) * scaling)
tau_max = np.log10(len(self.freqs))
taus = (np.logspace(0, tau_max) / self.sample_rate)
(taus, adev, adeverror, _) = allantools.adev(freqs, data_type='freq', rate=self.sample_rate, taus=taus)
return (taus, adev, adeverror) | def adev(self, scaling=1):
'\n Calculates the Allan deviation of the data.\n\n Parameters\n ----------\n scaling : float (optional)\n normalization factor, i.e. the oscillator frequency ν_0\n\n Returns\n -------\n taus, adev, adeverror : list\n The taus for which the Allan deviation has been calculated, the adev at\n these taus and their statistical error.\n '
freqs = (np.array(self.freqs) * scaling)
tau_max = np.log10(len(self.freqs))
taus = (np.logspace(0, tau_max) / self.sample_rate)
(taus, adev, adeverror, _) = allantools.adev(freqs, data_type='freq', rate=self.sample_rate, taus=taus)
return (taus, adev, adeverror)<|docstring|>Calculates the Allan deviation of the data.
Parameters
----------
scaling : float (optional)
normalization factor, i.e. the oscillator frequency ν_0
Returns
-------
taus, adev, adeverror : list
The taus for which the Allan deviation has been calculated, the adev at
these taus and their statistical error.<|endoftext|> |
7e630dd71610026ee27bf64e1764b4a12a1754f8487d5c50c4ec7747e9056fca | def plot_time_record(self, ax=None):
'\n Plots the time record of the data.\n\n Parameters\n ----------\n ax : Axis (optional)\n If axis is provided, they will be used for the plot. if not provided, a new\n plot will automatically be created.\n\n Returns\n -------\n fig, ax : Figure and Axis\n The Figure and Axis handles of the plot that was used.\n '
t = np.linspace(0, self.duration, num=self.n_samples)
if (ax is None):
(fig, ax) = plt.subplots()
else:
fig = ax.figure
ax.plot(t, self.freqs, label='Mean frequency: ({:3f}+/-{:3f}) MHz'.format((self.mean_frequency * 1e-06), (np.std(self.freqs) * 1e-06)))
ax.set_xlabel('time t (s)')
ax.set_ylabel('frequency deviation (Hz)')
ax.legend()
plt.grid(b='on', which='minor', axis='both')
plt.box(on='on')
return (fig, ax) | Plots the time record of the data.
Parameters
----------
ax : Axis (optional)
If axis is provided, they will be used for the plot. if not provided, a new
plot will automatically be created.
Returns
-------
fig, ax : Figure and Axis
The Figure and Axis handles of the plot that was used. | freqtools/time_data.py | plot_time_record | bleykauf/freqtools | 0 | python | def plot_time_record(self, ax=None):
'\n Plots the time record of the data.\n\n Parameters\n ----------\n ax : Axis (optional)\n If axis is provided, they will be used for the plot. if not provided, a new\n plot will automatically be created.\n\n Returns\n -------\n fig, ax : Figure and Axis\n The Figure and Axis handles of the plot that was used.\n '
t = np.linspace(0, self.duration, num=self.n_samples)
if (ax is None):
(fig, ax) = plt.subplots()
else:
fig = ax.figure
ax.plot(t, self.freqs, label='Mean frequency: ({:3f}+/-{:3f}) MHz'.format((self.mean_frequency * 1e-06), (np.std(self.freqs) * 1e-06)))
ax.set_xlabel('time t (s)')
ax.set_ylabel('frequency deviation (Hz)')
ax.legend()
plt.grid(b='on', which='minor', axis='both')
plt.box(on='on')
return (fig, ax) | def plot_time_record(self, ax=None):
'\n Plots the time record of the data.\n\n Parameters\n ----------\n ax : Axis (optional)\n If axis is provided, they will be used for the plot. if not provided, a new\n plot will automatically be created.\n\n Returns\n -------\n fig, ax : Figure and Axis\n The Figure and Axis handles of the plot that was used.\n '
t = np.linspace(0, self.duration, num=self.n_samples)
if (ax is None):
(fig, ax) = plt.subplots()
else:
fig = ax.figure
ax.plot(t, self.freqs, label='Mean frequency: ({:3f}+/-{:3f}) MHz'.format((self.mean_frequency * 1e-06), (np.std(self.freqs) * 1e-06)))
ax.set_xlabel('time t (s)')
ax.set_ylabel('frequency deviation (Hz)')
ax.legend()
plt.grid(b='on', which='minor', axis='both')
plt.box(on='on')
return (fig, ax)<|docstring|>Plots the time record of the data.
Parameters
----------
ax : Axis (optional)
If axis is provided, they will be used for the plot. if not provided, a new
plot will automatically be created.
Returns
-------
fig, ax : Figure and Axis
The Figure and Axis handles of the plot that was used.<|endoftext|> |
a6676f0ab287cf8c2386e049fa4d1cd01b5ee7bf1445aeb4e9da8e170adadaf8 | def plot_adev(self, ax=None, **kwargs):
'\n Plots the Allan deviation of the data.\n\n Parameters\n ----------\n ax : Axis (optional)\n If axis is provided, they will be used for the plot. if not provided, a new\n plot will automatically be created.\n **kwargs:\n keyworded arguments passed to `adev()`.\n\n Returns\n -------\n fig, ax : Figure and Axis\n The Figure and Axis handles of the plot that was used.\n '
(taus, adev, adeverror) = self.adev(**kwargs)
if (ax is None):
(fig, ax) = plt.subplots()
else:
fig = ax.figure
ax.set_yscale('log')
ax.set_xscale('log')
ax.errorbar(taus, adev, yerr=adeverror)
ax.set_xlabel('Averaging time t (s)')
ax.set_ylabel('Allan deviation $\\sigma_y(t)$')
plt.grid(b='on', which='minor', axis='both')
plt.box(on='on')
return (fig, ax) | Plots the Allan deviation of the data.
Parameters
----------
ax : Axis (optional)
If axis is provided, they will be used for the plot. if not provided, a new
plot will automatically be created.
**kwargs:
keyworded arguments passed to `adev()`.
Returns
-------
fig, ax : Figure and Axis
The Figure and Axis handles of the plot that was used. | freqtools/time_data.py | plot_adev | bleykauf/freqtools | 0 | python | def plot_adev(self, ax=None, **kwargs):
'\n Plots the Allan deviation of the data.\n\n Parameters\n ----------\n ax : Axis (optional)\n If axis is provided, they will be used for the plot. if not provided, a new\n plot will automatically be created.\n **kwargs:\n keyworded arguments passed to `adev()`.\n\n Returns\n -------\n fig, ax : Figure and Axis\n The Figure and Axis handles of the plot that was used.\n '
(taus, adev, adeverror) = self.adev(**kwargs)
if (ax is None):
(fig, ax) = plt.subplots()
else:
fig = ax.figure
ax.set_yscale('log')
ax.set_xscale('log')
ax.errorbar(taus, adev, yerr=adeverror)
ax.set_xlabel('Averaging time t (s)')
ax.set_ylabel('Allan deviation $\\sigma_y(t)$')
plt.grid(b='on', which='minor', axis='both')
plt.box(on='on')
return (fig, ax) | def plot_adev(self, ax=None, **kwargs):
'\n Plots the Allan deviation of the data.\n\n Parameters\n ----------\n ax : Axis (optional)\n If axis is provided, they will be used for the plot. if not provided, a new\n plot will automatically be created.\n **kwargs:\n keyworded arguments passed to `adev()`.\n\n Returns\n -------\n fig, ax : Figure and Axis\n The Figure and Axis handles of the plot that was used.\n '
(taus, adev, adeverror) = self.adev(**kwargs)
if (ax is None):
(fig, ax) = plt.subplots()
else:
fig = ax.figure
ax.set_yscale('log')
ax.set_xscale('log')
ax.errorbar(taus, adev, yerr=adeverror)
ax.set_xlabel('Averaging time t (s)')
ax.set_ylabel('Allan deviation $\\sigma_y(t)$')
plt.grid(b='on', which='minor', axis='both')
plt.box(on='on')
return (fig, ax)<|docstring|>Plots the Allan deviation of the data.
Parameters
----------
ax : Axis (optional)
If axis is provided, they will be used for the plot. if not provided, a new
plot will automatically be created.
**kwargs:
keyworded arguments passed to `adev()`.
Returns
-------
fig, ax : Figure and Axis
The Figure and Axis handles of the plot that was used.<|endoftext|> |
cae98b213a493a07b2696c0523f87dbbdc388122834b279a998f42a45304d747 | def save_file_to_fe(all_info, spec_fp=settings.JSON_COLORS_DATA_DUMP_FOR_FRONTEND_CHINESE):
'\n 保存信息到前端目录\n :param all_info:\n :param is_chinese:bool,\n :return:\n '
save_fp = get_frontend_path(spec_fp=spec_fp)
bak_fp = '.'.join([save_fp, 'bak'])
print(bak_fp, save_fp)
with open(save_fp, 'w') as jf:
json.dump(all_info, jf, ensure_ascii=False, indent=2)
return 0 | 保存信息到前端目录
:param all_info:
:param is_chinese:bool,
:return: | main.py | save_file_to_fe | imoyao/Traditional-Chinese-Colors | 2 | python | def save_file_to_fe(all_info, spec_fp=settings.JSON_COLORS_DATA_DUMP_FOR_FRONTEND_CHINESE):
'\n 保存信息到前端目录\n :param all_info:\n :param is_chinese:bool,\n :return:\n '
save_fp = get_frontend_path(spec_fp=spec_fp)
bak_fp = '.'.join([save_fp, 'bak'])
print(bak_fp, save_fp)
with open(save_fp, 'w') as jf:
json.dump(all_info, jf, ensure_ascii=False, indent=2)
return 0 | def save_file_to_fe(all_info, spec_fp=settings.JSON_COLORS_DATA_DUMP_FOR_FRONTEND_CHINESE):
'\n 保存信息到前端目录\n :param all_info:\n :param is_chinese:bool,\n :return:\n '
save_fp = get_frontend_path(spec_fp=spec_fp)
bak_fp = '.'.join([save_fp, 'bak'])
print(bak_fp, save_fp)
with open(save_fp, 'w') as jf:
json.dump(all_info, jf, ensure_ascii=False, indent=2)
return 0<|docstring|>保存信息到前端目录
:param all_info:
:param is_chinese:bool,
:return:<|endoftext|> |
4f1b18b9bb0790119fa450d5909825c8a8d2a844904b0d729902d9872caa8f05 | def testStatement(self):
'Test Statement'
pass | Test Statement | test/test_statement.py | testStatement | Factern/factern-client-python | 0 | python | def testStatement(self):
pass | def testStatement(self):
pass<|docstring|>Test Statement<|endoftext|> |
af1bbeffc761af0e7a956725daa214c51a0f539b751aba2a896e0c15f5a18a3b | def pose_dict_to_numpy(pose):
'\n Conert pandaset pose dict to a numpy vector in order to pass it through the network\n '
pose_np = [pose['position']['x'], pose['position']['y'], pose['position']['z'], pose['heading']['w'], pose['heading']['x'], pose['heading']['y'], pose['heading']['z']]
return pose_np | Conert pandaset pose dict to a numpy vector in order to pass it through the network | pcdet/datasets/pandaset/pandaset_dataset.py | pose_dict_to_numpy | CSL-KU/OpenPCDet | 1,984 | python | def pose_dict_to_numpy(pose):
'\n \n '
pose_np = [pose['position']['x'], pose['position']['y'], pose['position']['z'], pose['heading']['w'], pose['heading']['x'], pose['heading']['y'], pose['heading']['z']]
return pose_np | def pose_dict_to_numpy(pose):
'\n \n '
pose_np = [pose['position']['x'], pose['position']['y'], pose['position']['z'], pose['heading']['w'], pose['heading']['x'], pose['heading']['y'], pose['heading']['z']]
return pose_np<|docstring|>Conert pandaset pose dict to a numpy vector in order to pass it through the network<|endoftext|> |
066e2a6591576beb51fe1c992281984a9927f92863202fb40c54f7cf3c9e54a6 | def pose_numpy_to_dict(pose):
'\n Conert pandaset pose dict to a numpy vector in order to pass it through the network\n '
pose_dict = {'position': {'x': pose[0], 'y': pose[1], 'z': pose[2]}, 'heading': {'w': pose[3], 'x': pose[4], 'y': pose[5], 'z': pose[6]}}
return pose_dict | Conert pandaset pose dict to a numpy vector in order to pass it through the network | pcdet/datasets/pandaset/pandaset_dataset.py | pose_numpy_to_dict | CSL-KU/OpenPCDet | 1,984 | python | def pose_numpy_to_dict(pose):
'\n \n '
pose_dict = {'position': {'x': pose[0], 'y': pose[1], 'z': pose[2]}, 'heading': {'w': pose[3], 'x': pose[4], 'y': pose[5], 'z': pose[6]}}
return pose_dict | def pose_numpy_to_dict(pose):
'\n \n '
pose_dict = {'position': {'x': pose[0], 'y': pose[1], 'z': pose[2]}, 'heading': {'w': pose[3], 'x': pose[4], 'y': pose[5], 'z': pose[6]}}
return pose_dict<|docstring|>Conert pandaset pose dict to a numpy vector in order to pass it through the network<|endoftext|> |
255a90d7cdbbaf23010eb4df37faebd5f782e5d2c7265b562a41222a1c3932c4 | def create_pandaset_infos(dataset_cfg, class_names, data_path, save_path):
'\n Create dataset_infos files in order not to have it in a preprocessed pickle\n file with the info for each sample\n See PandasetDataset.get_infos for further details.\n '
dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
for split in ['train', 'val', 'test']:
print('---------------- Start to generate {} data infos ---------------'.format(split))
dataset.set_split(split)
infos = dataset.get_infos()
file_path = os.path.join(save_path, 'pandaset_infos_{}.pkl'.format(split))
with open(file_path, 'wb') as f:
pickle.dump(infos, f)
print('Pandaset info {} file is saved to {}'.format(split, file_path))
print('------------Start create groundtruth database for data augmentation-----------')
dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
dataset.set_split('train')
dataset.create_groundtruth_database(os.path.join(save_path, 'pandaset_infos_train.pkl'), split='train')
print('---------------Data preparation Done---------------') | Create dataset_infos files in order not to have it in a preprocessed pickle
file with the info for each sample
See PandasetDataset.get_infos for further details. | pcdet/datasets/pandaset/pandaset_dataset.py | create_pandaset_infos | CSL-KU/OpenPCDet | 1,984 | python | def create_pandaset_infos(dataset_cfg, class_names, data_path, save_path):
'\n Create dataset_infos files in order not to have it in a preprocessed pickle\n file with the info for each sample\n See PandasetDataset.get_infos for further details.\n '
dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
for split in ['train', 'val', 'test']:
print('---------------- Start to generate {} data infos ---------------'.format(split))
dataset.set_split(split)
infos = dataset.get_infos()
file_path = os.path.join(save_path, 'pandaset_infos_{}.pkl'.format(split))
with open(file_path, 'wb') as f:
pickle.dump(infos, f)
print('Pandaset info {} file is saved to {}'.format(split, file_path))
print('------------Start create groundtruth database for data augmentation-----------')
dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
dataset.set_split('train')
dataset.create_groundtruth_database(os.path.join(save_path, 'pandaset_infos_train.pkl'), split='train')
print('---------------Data preparation Done---------------') | def create_pandaset_infos(dataset_cfg, class_names, data_path, save_path):
'\n Create dataset_infos files in order not to have it in a preprocessed pickle\n file with the info for each sample\n See PandasetDataset.get_infos for further details.\n '
dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
for split in ['train', 'val', 'test']:
print('---------------- Start to generate {} data infos ---------------'.format(split))
dataset.set_split(split)
infos = dataset.get_infos()
file_path = os.path.join(save_path, 'pandaset_infos_{}.pkl'.format(split))
with open(file_path, 'wb') as f:
pickle.dump(infos, f)
print('Pandaset info {} file is saved to {}'.format(split, file_path))
print('------------Start create groundtruth database for data augmentation-----------')
dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
dataset.set_split('train')
dataset.create_groundtruth_database(os.path.join(save_path, 'pandaset_infos_train.pkl'), split='train')
print('---------------Data preparation Done---------------')<|docstring|>Create dataset_infos files in order not to have it in a preprocessed pickle
file with the info for each sample
See PandasetDataset.get_infos for further details.<|endoftext|> |
d1a31f9037c15c6290be1dff7e025adb533f6c7e9de3db73c483f591e487c21a | def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
'\n Args:\n root_path:\n dataset_cfg:\n class_names:\n training:\n logger:\n '
super().__init__(dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger)
if (root_path is None):
root_path = self.dataset_cfg.DATA_PATH
self.dataset = ps.DataSet(os.path.join(root_path, 'dataset'))
self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
self.pandaset_infos = []
self.include_pandaset_infos(self.mode) | Args:
root_path:
dataset_cfg:
class_names:
training:
logger: | pcdet/datasets/pandaset/pandaset_dataset.py | __init__ | CSL-KU/OpenPCDet | 1,984 | python | def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
'\n Args:\n root_path:\n dataset_cfg:\n class_names:\n training:\n logger:\n '
super().__init__(dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger)
if (root_path is None):
root_path = self.dataset_cfg.DATA_PATH
self.dataset = ps.DataSet(os.path.join(root_path, 'dataset'))
self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
self.pandaset_infos = []
self.include_pandaset_infos(self.mode) | def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
'\n Args:\n root_path:\n dataset_cfg:\n class_names:\n training:\n logger:\n '
super().__init__(dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger)
if (root_path is None):
root_path = self.dataset_cfg.DATA_PATH
self.dataset = ps.DataSet(os.path.join(root_path, 'dataset'))
self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
self.pandaset_infos = []
self.include_pandaset_infos(self.mode)<|docstring|>Args:
root_path:
dataset_cfg:
class_names:
training:
logger:<|endoftext|> |
be14a4692c977762fb69e4900402d91de35bc26676158156bc04b8edff36da71 | def __getitem__(self, index):
'\n To support a custom dataset, implement this function to load the raw data (and labels), then transform them to\n the unified normative coordinate (x pointing forward, z pointing upwards) and call the function self.prepare_data() to process the data and send them\n to the model.\n\n Args:\n index:\n\n Returns:\n\n '
info = self.pandaset_infos[index]
seq_idx = info['sequence']
pose = self._get_pose(info)
points = self._get_lidar_points(info, pose)
(boxes, labels, zrot_world_to_ego) = self._get_annotations(info, pose)
pose_np = pose_dict_to_numpy(pose)
input_dict = {'points': points, 'gt_boxes': boxes, 'gt_names': labels, 'sequence': int(seq_idx), 'frame_idx': info['frame_idx'], 'zrot_world_to_ego': zrot_world_to_ego, 'pose': pose_dict_to_numpy(pose)}
data_dict = self.prepare_data(data_dict=input_dict)
return data_dict | To support a custom dataset, implement this function to load the raw data (and labels), then transform them to
the unified normative coordinate (x pointing forward, z pointing upwards) and call the function self.prepare_data() to process the data and send them
to the model.
Args:
index:
Returns: | pcdet/datasets/pandaset/pandaset_dataset.py | __getitem__ | CSL-KU/OpenPCDet | 1,984 | python | def __getitem__(self, index):
'\n To support a custom dataset, implement this function to load the raw data (and labels), then transform them to\n the unified normative coordinate (x pointing forward, z pointing upwards) and call the function self.prepare_data() to process the data and send them\n to the model.\n\n Args:\n index:\n\n Returns:\n\n '
info = self.pandaset_infos[index]
seq_idx = info['sequence']
pose = self._get_pose(info)
points = self._get_lidar_points(info, pose)
(boxes, labels, zrot_world_to_ego) = self._get_annotations(info, pose)
pose_np = pose_dict_to_numpy(pose)
input_dict = {'points': points, 'gt_boxes': boxes, 'gt_names': labels, 'sequence': int(seq_idx), 'frame_idx': info['frame_idx'], 'zrot_world_to_ego': zrot_world_to_ego, 'pose': pose_dict_to_numpy(pose)}
data_dict = self.prepare_data(data_dict=input_dict)
return data_dict | def __getitem__(self, index):
'\n To support a custom dataset, implement this function to load the raw data (and labels), then transform them to\n the unified normative coordinate (x pointing forward, z pointing upwards) and call the function self.prepare_data() to process the data and send them\n to the model.\n\n Args:\n index:\n\n Returns:\n\n '
info = self.pandaset_infos[index]
seq_idx = info['sequence']
pose = self._get_pose(info)
points = self._get_lidar_points(info, pose)
(boxes, labels, zrot_world_to_ego) = self._get_annotations(info, pose)
pose_np = pose_dict_to_numpy(pose)
input_dict = {'points': points, 'gt_boxes': boxes, 'gt_names': labels, 'sequence': int(seq_idx), 'frame_idx': info['frame_idx'], 'zrot_world_to_ego': zrot_world_to_ego, 'pose': pose_dict_to_numpy(pose)}
data_dict = self.prepare_data(data_dict=input_dict)
return data_dict<|docstring|>To support a custom dataset, implement this function to load the raw data (and labels), then transform them to
the unified normative coordinate (x pointing forward, z pointing upwards) and call the function self.prepare_data() to process the data and send them
to the model.
Args:
index:
Returns:<|endoftext|> |
d07aabe767bc70ab5c24e298ed3e1baa675f8cc8c4a65934d3bfa158b22ae910 | def _get_lidar_points(self, info, pose):
'\n Get lidar in the unified normative coordinate system for a given frame\n The intensity is normalized to fit [0-1] range (pandaset intensity is in [0-255] range)\n '
lidar_frame = pd.read_pickle(info['lidar_path'])
device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
if (device != (- 1)):
lidar_frame = lidar_frame[(lidar_frame.d == device)]
world_points = lidar_frame.to_numpy()
del lidar_frame
points_loc = world_points[(:, :3)]
points_int = world_points[(:, 3)]
points_int = (points_int / 255)
ego_points = ps.geometry.lidar_points_to_ego(points_loc, pose)
ego_points = ego_points[(:, [1, 0, 2])]
ego_points[(:, 1)] = (- ego_points[(:, 1)])
return np.append(ego_points, np.expand_dims(points_int, axis=1), axis=1).astype(np.float32) | Get lidar in the unified normative coordinate system for a given frame
The intensity is normalized to fit [0-1] range (pandaset intensity is in [0-255] range) | pcdet/datasets/pandaset/pandaset_dataset.py | _get_lidar_points | CSL-KU/OpenPCDet | 1,984 | python | def _get_lidar_points(self, info, pose):
'\n Get lidar in the unified normative coordinate system for a given frame\n The intensity is normalized to fit [0-1] range (pandaset intensity is in [0-255] range)\n '
lidar_frame = pd.read_pickle(info['lidar_path'])
device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
if (device != (- 1)):
lidar_frame = lidar_frame[(lidar_frame.d == device)]
world_points = lidar_frame.to_numpy()
del lidar_frame
points_loc = world_points[(:, :3)]
points_int = world_points[(:, 3)]
points_int = (points_int / 255)
ego_points = ps.geometry.lidar_points_to_ego(points_loc, pose)
ego_points = ego_points[(:, [1, 0, 2])]
ego_points[(:, 1)] = (- ego_points[(:, 1)])
return np.append(ego_points, np.expand_dims(points_int, axis=1), axis=1).astype(np.float32) | def _get_lidar_points(self, info, pose):
'\n Get lidar in the unified normative coordinate system for a given frame\n The intensity is normalized to fit [0-1] range (pandaset intensity is in [0-255] range)\n '
lidar_frame = pd.read_pickle(info['lidar_path'])
device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
if (device != (- 1)):
lidar_frame = lidar_frame[(lidar_frame.d == device)]
world_points = lidar_frame.to_numpy()
del lidar_frame
points_loc = world_points[(:, :3)]
points_int = world_points[(:, 3)]
points_int = (points_int / 255)
ego_points = ps.geometry.lidar_points_to_ego(points_loc, pose)
ego_points = ego_points[(:, [1, 0, 2])]
ego_points[(:, 1)] = (- ego_points[(:, 1)])
return np.append(ego_points, np.expand_dims(points_int, axis=1), axis=1).astype(np.float32)<|docstring|>Get lidar in the unified normative coordinate system for a given frame
The intensity is normalized to fit [0-1] range (pandaset intensity is in [0-255] range)<|endoftext|> |
c52b9a167fd34559bc2a9aed29d8b12e120e5aadb9bc97a2d6566c0d605ffa1c | def _get_annotations(self, info, pose):
'\n Get box informations in the unified normative coordinate system for a given frame\n '
cuboids = pd.read_pickle(info['cuboids_path'])
device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
if (device != (- 1)):
cuboids = cuboids[(cuboids['cuboids.sensor_id'] != (1 - device))]
xs = cuboids['position.x'].to_numpy()
ys = cuboids['position.y'].to_numpy()
zs = cuboids['position.z'].to_numpy()
dxs = cuboids['dimensions.x'].to_numpy()
dys = cuboids['dimensions.y'].to_numpy()
dzs = cuboids['dimensions.z'].to_numpy()
yaws = cuboids['yaw'].to_numpy()
labels = cuboids['label'].to_numpy()
del cuboids
labels = np.array([self.dataset_cfg.TRAINING_CATEGORIES.get(lab, lab) for lab in labels])
centers = np.vstack([xs, ys, zs]).T
ego_centers = ps.geometry.lidar_points_to_ego(centers, pose)
yaxis_points_from_pose = ps.geometry.lidar_points_to_ego(np.array([[0, 0, 0], [0, 1.0, 0]]), pose)
yaxis_from_pose = (yaxis_points_from_pose[(1, :)] - yaxis_points_from_pose[(0, :)])
if (yaxis_from_pose[(- 1)] >= (10 ** (- 1))):
if (self.logger is not None):
self.logger.warning(("The car's pitch is supposed to be negligible " + 'sin(pitch) is >= 10**-1 ({})'.format(yaxis_from_pose[(- 1)])))
zrot_world_to_ego = np.arctan2((- yaxis_from_pose[0]), yaxis_from_pose[1])
ego_yaws = (yaws + zrot_world_to_ego)
ego_xs = ego_centers[(:, 1)]
ego_ys = (- ego_centers[(:, 0)])
ego_zs = ego_centers[(:, 2)]
ego_dxs = dys
ego_dys = dxs
ego_dzs = dzs
ego_boxes = np.vstack([ego_xs, ego_ys, ego_zs, ego_dxs, ego_dys, ego_dzs, ego_yaws]).T
return (ego_boxes.astype(np.float32), labels, zrot_world_to_ego) | Get box informations in the unified normative coordinate system for a given frame | pcdet/datasets/pandaset/pandaset_dataset.py | _get_annotations | CSL-KU/OpenPCDet | 1,984 | python | def _get_annotations(self, info, pose):
'\n \n '
cuboids = pd.read_pickle(info['cuboids_path'])
device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
if (device != (- 1)):
cuboids = cuboids[(cuboids['cuboids.sensor_id'] != (1 - device))]
xs = cuboids['position.x'].to_numpy()
ys = cuboids['position.y'].to_numpy()
zs = cuboids['position.z'].to_numpy()
dxs = cuboids['dimensions.x'].to_numpy()
dys = cuboids['dimensions.y'].to_numpy()
dzs = cuboids['dimensions.z'].to_numpy()
yaws = cuboids['yaw'].to_numpy()
labels = cuboids['label'].to_numpy()
del cuboids
labels = np.array([self.dataset_cfg.TRAINING_CATEGORIES.get(lab, lab) for lab in labels])
centers = np.vstack([xs, ys, zs]).T
ego_centers = ps.geometry.lidar_points_to_ego(centers, pose)
yaxis_points_from_pose = ps.geometry.lidar_points_to_ego(np.array([[0, 0, 0], [0, 1.0, 0]]), pose)
yaxis_from_pose = (yaxis_points_from_pose[(1, :)] - yaxis_points_from_pose[(0, :)])
if (yaxis_from_pose[(- 1)] >= (10 ** (- 1))):
if (self.logger is not None):
self.logger.warning(("The car's pitch is supposed to be negligible " + 'sin(pitch) is >= 10**-1 ({})'.format(yaxis_from_pose[(- 1)])))
zrot_world_to_ego = np.arctan2((- yaxis_from_pose[0]), yaxis_from_pose[1])
ego_yaws = (yaws + zrot_world_to_ego)
ego_xs = ego_centers[(:, 1)]
ego_ys = (- ego_centers[(:, 0)])
ego_zs = ego_centers[(:, 2)]
ego_dxs = dys
ego_dys = dxs
ego_dzs = dzs
ego_boxes = np.vstack([ego_xs, ego_ys, ego_zs, ego_dxs, ego_dys, ego_dzs, ego_yaws]).T
return (ego_boxes.astype(np.float32), labels, zrot_world_to_ego) | def _get_annotations(self, info, pose):
'\n \n '
cuboids = pd.read_pickle(info['cuboids_path'])
device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
if (device != (- 1)):
cuboids = cuboids[(cuboids['cuboids.sensor_id'] != (1 - device))]
xs = cuboids['position.x'].to_numpy()
ys = cuboids['position.y'].to_numpy()
zs = cuboids['position.z'].to_numpy()
dxs = cuboids['dimensions.x'].to_numpy()
dys = cuboids['dimensions.y'].to_numpy()
dzs = cuboids['dimensions.z'].to_numpy()
yaws = cuboids['yaw'].to_numpy()
labels = cuboids['label'].to_numpy()
del cuboids
labels = np.array([self.dataset_cfg.TRAINING_CATEGORIES.get(lab, lab) for lab in labels])
centers = np.vstack([xs, ys, zs]).T
ego_centers = ps.geometry.lidar_points_to_ego(centers, pose)
yaxis_points_from_pose = ps.geometry.lidar_points_to_ego(np.array([[0, 0, 0], [0, 1.0, 0]]), pose)
yaxis_from_pose = (yaxis_points_from_pose[(1, :)] - yaxis_points_from_pose[(0, :)])
if (yaxis_from_pose[(- 1)] >= (10 ** (- 1))):
if (self.logger is not None):
self.logger.warning(("The car's pitch is supposed to be negligible " + 'sin(pitch) is >= 10**-1 ({})'.format(yaxis_from_pose[(- 1)])))
zrot_world_to_ego = np.arctan2((- yaxis_from_pose[0]), yaxis_from_pose[1])
ego_yaws = (yaws + zrot_world_to_ego)
ego_xs = ego_centers[(:, 1)]
ego_ys = (- ego_centers[(:, 0)])
ego_zs = ego_centers[(:, 2)]
ego_dxs = dys
ego_dys = dxs
ego_dzs = dzs
ego_boxes = np.vstack([ego_xs, ego_ys, ego_zs, ego_dxs, ego_dys, ego_dzs, ego_yaws]).T
return (ego_boxes.astype(np.float32), labels, zrot_world_to_ego)<|docstring|>Get box informations in the unified normative coordinate system for a given frame<|endoftext|> |
fdd9724acfa5c4423cbe677ecf28f248b4fe12bdaff3a955f353c367d9ed74b5 | @staticmethod
def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
'\n To support a custom dataset, implement this function to receive the predicted results from the model, and then\n transform the unified normative coordinate to your required coordinate, and optionally save them to disk.\n\n Args:\n batch_dict: dict of original data from the dataloader\n pred_dicts: dict of predicted results from the model\n pred_boxes: (N, 7), Tensor\n pred_scores: (N), Tensor\n pred_labels: (N), Tensor\n class_names:\n output_path: if it is not None, save the results to this path\n Returns:\n\n '
def generate_single_sample_dataframe(batch_index, box_dict, zrot_world_to_ego, pose):
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
zrot = zrot_world_to_ego.cpu().numpy()
pose_dict = pose_numpy_to_dict(pose.cpu().numpy())
xs = pred_boxes[(:, 0)]
ys = pred_boxes[(:, 1)]
zs = pred_boxes[(:, 2)]
dxs = pred_boxes[(:, 3)]
dys = pred_boxes[(:, 4)]
dzs = pred_boxes[(:, 5)]
yaws = pred_boxes[(:, 6)]
names = np.array(class_names)[(pred_labels - 1)]
ego_xs = (- ys)
ego_ys = xs
ego_zs = zs
ego_dxs = dys
ego_dys = dxs
ego_dzs = dzs
ego_yaws = yaws
world_yaws = (ego_yaws - zrot)
ego_centers = np.vstack([ego_xs, ego_ys, ego_zs]).T
world_centers = ps.geometry.ego_to_lidar_points(ego_centers, pose_dict)
world_xs = world_centers[(:, 0)]
world_ys = world_centers[(:, 1)]
world_zs = world_centers[(:, 2)]
data_dict = {'position.x': world_xs, 'position.y': world_ys, 'position.z': world_zs, 'dimensions.x': ego_dxs, 'dimensions.y': ego_dys, 'dimensions.z': ego_dzs, 'yaw': (world_yaws % (2 * np.pi)), 'label': names, 'score': pred_scores}
return pd.DataFrame(data_dict)
annos = []
for (index, box_dict) in enumerate(pred_dicts):
frame_idx = batch_dict['frame_idx'][index]
seq_idx = batch_dict['sequence'][index]
zrot = batch_dict['zrot_world_to_ego'][index]
pose = batch_dict['pose'][index]
single_pred_df = generate_single_sample_dataframe(index, box_dict, zrot, pose)
single_pred_dict = {'preds': single_pred_df, 'name': single_pred_df['label'].tolist(), 'frame_idx': frame_idx, 'sequence': str(seq_idx).zfill(3)}
if (output_path is not None):
frame_id = str(int(frame_idx)).zfill(2)
seq_id = str(int(seq_idx)).zfill(3)
cur_det_file = os.path.join(output_path, seq_id, 'predictions', 'cuboids', '{}.pkl.gz'.format(frame_id))
os.makedirs(os.path.dirname(cur_det_file), exist_ok=True)
single_pred_df.to_pickle(cur_det_file)
annos.append(single_pred_dict)
return annos | To support a custom dataset, implement this function to receive the predicted results from the model, and then
transform the unified normative coordinate to your required coordinate, and optionally save them to disk.
Args:
batch_dict: dict of original data from the dataloader
pred_dicts: dict of predicted results from the model
pred_boxes: (N, 7), Tensor
pred_scores: (N), Tensor
pred_labels: (N), Tensor
class_names:
output_path: if it is not None, save the results to this path
Returns: | pcdet/datasets/pandaset/pandaset_dataset.py | generate_prediction_dicts | CSL-KU/OpenPCDet | 1,984 | python | @staticmethod
def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
'\n To support a custom dataset, implement this function to receive the predicted results from the model, and then\n transform the unified normative coordinate to your required coordinate, and optionally save them to disk.\n\n Args:\n batch_dict: dict of original data from the dataloader\n pred_dicts: dict of predicted results from the model\n pred_boxes: (N, 7), Tensor\n pred_scores: (N), Tensor\n pred_labels: (N), Tensor\n class_names:\n output_path: if it is not None, save the results to this path\n Returns:\n\n '
def generate_single_sample_dataframe(batch_index, box_dict, zrot_world_to_ego, pose):
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
zrot = zrot_world_to_ego.cpu().numpy()
pose_dict = pose_numpy_to_dict(pose.cpu().numpy())
xs = pred_boxes[(:, 0)]
ys = pred_boxes[(:, 1)]
zs = pred_boxes[(:, 2)]
dxs = pred_boxes[(:, 3)]
dys = pred_boxes[(:, 4)]
dzs = pred_boxes[(:, 5)]
yaws = pred_boxes[(:, 6)]
names = np.array(class_names)[(pred_labels - 1)]
ego_xs = (- ys)
ego_ys = xs
ego_zs = zs
ego_dxs = dys
ego_dys = dxs
ego_dzs = dzs
ego_yaws = yaws
world_yaws = (ego_yaws - zrot)
ego_centers = np.vstack([ego_xs, ego_ys, ego_zs]).T
world_centers = ps.geometry.ego_to_lidar_points(ego_centers, pose_dict)
world_xs = world_centers[(:, 0)]
world_ys = world_centers[(:, 1)]
world_zs = world_centers[(:, 2)]
data_dict = {'position.x': world_xs, 'position.y': world_ys, 'position.z': world_zs, 'dimensions.x': ego_dxs, 'dimensions.y': ego_dys, 'dimensions.z': ego_dzs, 'yaw': (world_yaws % (2 * np.pi)), 'label': names, 'score': pred_scores}
return pd.DataFrame(data_dict)
annos = []
for (index, box_dict) in enumerate(pred_dicts):
frame_idx = batch_dict['frame_idx'][index]
seq_idx = batch_dict['sequence'][index]
zrot = batch_dict['zrot_world_to_ego'][index]
pose = batch_dict['pose'][index]
single_pred_df = generate_single_sample_dataframe(index, box_dict, zrot, pose)
single_pred_dict = {'preds': single_pred_df, 'name': single_pred_df['label'].tolist(), 'frame_idx': frame_idx, 'sequence': str(seq_idx).zfill(3)}
if (output_path is not None):
frame_id = str(int(frame_idx)).zfill(2)
seq_id = str(int(seq_idx)).zfill(3)
cur_det_file = os.path.join(output_path, seq_id, 'predictions', 'cuboids', '{}.pkl.gz'.format(frame_id))
os.makedirs(os.path.dirname(cur_det_file), exist_ok=True)
single_pred_df.to_pickle(cur_det_file)
annos.append(single_pred_dict)
return annos | @staticmethod
def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
'\n To support a custom dataset, implement this function to receive the predicted results from the model, and then\n transform the unified normative coordinate to your required coordinate, and optionally save them to disk.\n\n Args:\n batch_dict: dict of original data from the dataloader\n pred_dicts: dict of predicted results from the model\n pred_boxes: (N, 7), Tensor\n pred_scores: (N), Tensor\n pred_labels: (N), Tensor\n class_names:\n output_path: if it is not None, save the results to this path\n Returns:\n\n '
def generate_single_sample_dataframe(batch_index, box_dict, zrot_world_to_ego, pose):
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
zrot = zrot_world_to_ego.cpu().numpy()
pose_dict = pose_numpy_to_dict(pose.cpu().numpy())
xs = pred_boxes[(:, 0)]
ys = pred_boxes[(:, 1)]
zs = pred_boxes[(:, 2)]
dxs = pred_boxes[(:, 3)]
dys = pred_boxes[(:, 4)]
dzs = pred_boxes[(:, 5)]
yaws = pred_boxes[(:, 6)]
names = np.array(class_names)[(pred_labels - 1)]
ego_xs = (- ys)
ego_ys = xs
ego_zs = zs
ego_dxs = dys
ego_dys = dxs
ego_dzs = dzs
ego_yaws = yaws
world_yaws = (ego_yaws - zrot)
ego_centers = np.vstack([ego_xs, ego_ys, ego_zs]).T
world_centers = ps.geometry.ego_to_lidar_points(ego_centers, pose_dict)
world_xs = world_centers[(:, 0)]
world_ys = world_centers[(:, 1)]
world_zs = world_centers[(:, 2)]
data_dict = {'position.x': world_xs, 'position.y': world_ys, 'position.z': world_zs, 'dimensions.x': ego_dxs, 'dimensions.y': ego_dys, 'dimensions.z': ego_dzs, 'yaw': (world_yaws % (2 * np.pi)), 'label': names, 'score': pred_scores}
return pd.DataFrame(data_dict)
annos = []
for (index, box_dict) in enumerate(pred_dicts):
frame_idx = batch_dict['frame_idx'][index]
seq_idx = batch_dict['sequence'][index]
zrot = batch_dict['zrot_world_to_ego'][index]
pose = batch_dict['pose'][index]
single_pred_df = generate_single_sample_dataframe(index, box_dict, zrot, pose)
single_pred_dict = {'preds': single_pred_df, 'name': single_pred_df['label'].tolist(), 'frame_idx': frame_idx, 'sequence': str(seq_idx).zfill(3)}
if (output_path is not None):
frame_id = str(int(frame_idx)).zfill(2)
seq_id = str(int(seq_idx)).zfill(3)
cur_det_file = os.path.join(output_path, seq_id, 'predictions', 'cuboids', '{}.pkl.gz'.format(frame_id))
os.makedirs(os.path.dirname(cur_det_file), exist_ok=True)
single_pred_df.to_pickle(cur_det_file)
annos.append(single_pred_dict)
return annos<|docstring|>To support a custom dataset, implement this function to receive the predicted results from the model, and then
transform the unified normative coordinate to your required coordinate, and optionally save them to disk.
Args:
batch_dict: dict of original data from the dataloader
pred_dicts: dict of predicted results from the model
pred_boxes: (N, 7), Tensor
pred_scores: (N), Tensor
pred_labels: (N), Tensor
class_names:
output_path: if it is not None, save the results to this path
Returns:<|endoftext|> |
6c65009440eefde283cee95c43d9653d35c6fe6dfbf75c11a6724509f71f9213 | def get_infos(self):
'\n Generate the dataset infos dict for each sample of the dataset.\n For each sample, this dict contains:\n - the sequence index\n - the frame index\n - the path to the lidar data\n - the path to the bounding box annotations\n '
infos = []
for seq in self.sequences:
s = self.dataset[seq]
s.load_lidar()
if (len(s.lidar.data) > 100):
raise ValueError(('The implementation for this dataset assumes that each sequence is ' + 'no longer than 100 frames. The current sequence has {}'.format(len(s.lidar.data))))
info = [{'sequence': seq, 'frame_idx': ii, 'lidar_path': os.path.join(self.root_path, 'dataset', seq, 'lidar', '{:02d}.pkl.gz'.format(ii)), 'cuboids_path': os.path.join(self.root_path, 'dataset', seq, 'annotations', 'cuboids', '{:02d}.pkl.gz'.format(ii))} for ii in range(len(s.lidar.data))]
infos.extend(info)
del self.dataset._sequences[seq]
return infos | Generate the dataset infos dict for each sample of the dataset.
For each sample, this dict contains:
- the sequence index
- the frame index
- the path to the lidar data
- the path to the bounding box annotations | pcdet/datasets/pandaset/pandaset_dataset.py | get_infos | CSL-KU/OpenPCDet | 1,984 | python | def get_infos(self):
'\n Generate the dataset infos dict for each sample of the dataset.\n For each sample, this dict contains:\n - the sequence index\n - the frame index\n - the path to the lidar data\n - the path to the bounding box annotations\n '
infos = []
for seq in self.sequences:
s = self.dataset[seq]
s.load_lidar()
if (len(s.lidar.data) > 100):
raise ValueError(('The implementation for this dataset assumes that each sequence is ' + 'no longer than 100 frames. The current sequence has {}'.format(len(s.lidar.data))))
info = [{'sequence': seq, 'frame_idx': ii, 'lidar_path': os.path.join(self.root_path, 'dataset', seq, 'lidar', '{:02d}.pkl.gz'.format(ii)), 'cuboids_path': os.path.join(self.root_path, 'dataset', seq, 'annotations', 'cuboids', '{:02d}.pkl.gz'.format(ii))} for ii in range(len(s.lidar.data))]
infos.extend(info)
del self.dataset._sequences[seq]
return infos | def get_infos(self):
'\n Generate the dataset infos dict for each sample of the dataset.\n For each sample, this dict contains:\n - the sequence index\n - the frame index\n - the path to the lidar data\n - the path to the bounding box annotations\n '
infos = []
for seq in self.sequences:
s = self.dataset[seq]
s.load_lidar()
if (len(s.lidar.data) > 100):
raise ValueError(('The implementation for this dataset assumes that each sequence is ' + 'no longer than 100 frames. The current sequence has {}'.format(len(s.lidar.data))))
info = [{'sequence': seq, 'frame_idx': ii, 'lidar_path': os.path.join(self.root_path, 'dataset', seq, 'lidar', '{:02d}.pkl.gz'.format(ii)), 'cuboids_path': os.path.join(self.root_path, 'dataset', seq, 'annotations', 'cuboids', '{:02d}.pkl.gz'.format(ii))} for ii in range(len(s.lidar.data))]
infos.extend(info)
del self.dataset._sequences[seq]
return infos<|docstring|>Generate the dataset infos dict for each sample of the dataset.
For each sample, this dict contains:
- the sequence index
- the frame index
- the path to the lidar data
- the path to the bounding box annotations<|endoftext|> |
0dac01ff730f861de3f0dbac17a00b6d35bbcd7882e94becb9d3296625b1e259 | def normal_init(m, mean, stddev, truncated=False):
'\n weight initalizer: truncated normal and random normal.\n '
if truncated:
m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)
else:
m.weight.data.normal_(mean, stddev)
m.bias.data.zero_() | weight initalizer: truncated normal and random normal. | faster_rcnn/lib/model/faster_rcnn/faster_rcnn.py | normal_init | maddie157/BiDet | 161 | python | def normal_init(m, mean, stddev, truncated=False):
'\n \n '
if truncated:
m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)
else:
m.weight.data.normal_(mean, stddev)
m.bias.data.zero_() | def normal_init(m, mean, stddev, truncated=False):
'\n \n '
if truncated:
m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)
else:
m.weight.data.normal_(mean, stddev)
m.bias.data.zero_()<|docstring|>weight initalizer: truncated normal and random normal.<|endoftext|> |
cea191f5bf2abede3a2ebbec8ce0bd938603371e2017cb813a4fd0cb101ae470 | def __init__(self, word_length=50, char_embed_size=100, char_rnn_units=400, char_recurrent_dropout=0.33, recurrent_dropout=0.33, rnn_units=400, embedding_dropout=0.5, main_layer_dropout=0.5, **kwargs):
'\n Deep learning based sequence tagger.\n Consist of:\n - Char embedding (RNN+Attention)\n - RNN\n based on postag model of:\n https://www.aclweb.org/anthology/K17-3002/\n\n :param word_length: int, maximum character length in a token,\n relevant when using cnn\n :param char_embed_size: int, the size of character level embedding,\n relevant when using cnn\n :param char_rnn_units: int, RNN units on char level\n :param char_recurrent_dropout: float, dropout rate in RNN char level\n :param recurrent_dropout: float, dropout rate inside RNN\n :param rnn_units: int, the number of rnn units\n :param embedding_dropout: float, dropout rate after embedding layer\n :param main_layer_dropout: float, dropout rate in between LSTM\n '
super(StackedRNNTagger, self).__init__(**kwargs)
self.word_length = word_length
self.char_embed_size = char_embed_size
self.ed = embedding_dropout
self.rnn_units = rnn_units
self.rd = recurrent_dropout
self.char_rnn_units = char_rnn_units
self.char_rd = char_recurrent_dropout
self.main_layer_dropout = main_layer_dropout | Deep learning based sequence tagger.
Consist of:
- Char embedding (RNN+Attention)
- RNN
based on postag model of:
https://www.aclweb.org/anthology/K17-3002/
:param word_length: int, maximum character length in a token,
relevant when using cnn
:param char_embed_size: int, the size of character level embedding,
relevant when using cnn
:param char_rnn_units: int, RNN units on char level
:param char_recurrent_dropout: float, dropout rate in RNN char level
:param recurrent_dropout: float, dropout rate inside RNN
:param rnn_units: int, the number of rnn units
:param embedding_dropout: float, dropout rate after embedding layer
:param main_layer_dropout: float, dropout rate in between LSTM | model/RNNText/rnn_rnn_tagger.py | __init__ | ajmalkurnia/deeplearning-text-playground | 0 | python | def __init__(self, word_length=50, char_embed_size=100, char_rnn_units=400, char_recurrent_dropout=0.33, recurrent_dropout=0.33, rnn_units=400, embedding_dropout=0.5, main_layer_dropout=0.5, **kwargs):
'\n Deep learning based sequence tagger.\n Consist of:\n - Char embedding (RNN+Attention)\n - RNN\n based on postag model of:\n https://www.aclweb.org/anthology/K17-3002/\n\n :param word_length: int, maximum character length in a token,\n relevant when using cnn\n :param char_embed_size: int, the size of character level embedding,\n relevant when using cnn\n :param char_rnn_units: int, RNN units on char level\n :param char_recurrent_dropout: float, dropout rate in RNN char level\n :param recurrent_dropout: float, dropout rate inside RNN\n :param rnn_units: int, the number of rnn units\n :param embedding_dropout: float, dropout rate after embedding layer\n :param main_layer_dropout: float, dropout rate in between LSTM\n '
super(StackedRNNTagger, self).__init__(**kwargs)
self.word_length = word_length
self.char_embed_size = char_embed_size
self.ed = embedding_dropout
self.rnn_units = rnn_units
self.rd = recurrent_dropout
self.char_rnn_units = char_rnn_units
self.char_rd = char_recurrent_dropout
self.main_layer_dropout = main_layer_dropout | def __init__(self, word_length=50, char_embed_size=100, char_rnn_units=400, char_recurrent_dropout=0.33, recurrent_dropout=0.33, rnn_units=400, embedding_dropout=0.5, main_layer_dropout=0.5, **kwargs):
'\n Deep learning based sequence tagger.\n Consist of:\n - Char embedding (RNN+Attention)\n - RNN\n based on postag model of:\n https://www.aclweb.org/anthology/K17-3002/\n\n :param word_length: int, maximum character length in a token,\n relevant when using cnn\n :param char_embed_size: int, the size of character level embedding,\n relevant when using cnn\n :param char_rnn_units: int, RNN units on char level\n :param char_recurrent_dropout: float, dropout rate in RNN char level\n :param recurrent_dropout: float, dropout rate inside RNN\n :param rnn_units: int, the number of rnn units\n :param embedding_dropout: float, dropout rate after embedding layer\n :param main_layer_dropout: float, dropout rate in between LSTM\n '
super(StackedRNNTagger, self).__init__(**kwargs)
self.word_length = word_length
self.char_embed_size = char_embed_size
self.ed = embedding_dropout
self.rnn_units = rnn_units
self.rd = recurrent_dropout
self.char_rnn_units = char_rnn_units
self.char_rd = char_recurrent_dropout
self.main_layer_dropout = main_layer_dropout<|docstring|>Deep learning based sequence tagger.
Consist of:
- Char embedding (RNN+Attention)
- RNN
based on postag model of:
https://www.aclweb.org/anthology/K17-3002/
:param word_length: int, maximum character length in a token,
relevant when using cnn
:param char_embed_size: int, the size of character level embedding,
relevant when using cnn
:param char_rnn_units: int, RNN units on char level
:param char_recurrent_dropout: float, dropout rate in RNN char level
:param recurrent_dropout: float, dropout rate inside RNN
:param rnn_units: int, the number of rnn units
:param embedding_dropout: float, dropout rate after embedding layer
:param main_layer_dropout: float, dropout rate in between LSTM<|endoftext|> |
c9de942f94686a8a23c230ae62a231b08314c800e78dff4b6c72a0b7e0f8578c | def __get_char_embedding(self):
'\n Initialize character embedding\n '
word_input_layer = Input(shape=(self.word_length,))
embedding_block = Embedding((self.n_chars + 1), self.char_embed_size, input_length=self.word_length, trainable=True, mask_zero=True)(word_input_layer)
rnn_output = LSTM(self.char_rnn_units, recurrent_dropout=self.char_rd, return_sequences=True, return_state=True)(embedding_block)
(embedding_block, h, c) = rnn_output
embedding_block = CharTagAttention(self.char_rnn_units, self.word_length)(embedding_block)
embedding_block = Concatenate()([embedding_block, c])
embedding_block = Dense(self.embedding_size)(embedding_block)
embedding_block = Model(inputs=word_input_layer, outputs=embedding_block)
embedding_block.summary()
seq_inp_layer = Input(shape=(self.seq_length, self.word_length), name='char')
embedding_block = TimeDistributed(embedding_block)(seq_inp_layer)
return (seq_inp_layer, embedding_block) | Initialize character embedding | model/RNNText/rnn_rnn_tagger.py | __get_char_embedding | ajmalkurnia/deeplearning-text-playground | 0 | python | def __get_char_embedding(self):
'\n \n '
word_input_layer = Input(shape=(self.word_length,))
embedding_block = Embedding((self.n_chars + 1), self.char_embed_size, input_length=self.word_length, trainable=True, mask_zero=True)(word_input_layer)
rnn_output = LSTM(self.char_rnn_units, recurrent_dropout=self.char_rd, return_sequences=True, return_state=True)(embedding_block)
(embedding_block, h, c) = rnn_output
embedding_block = CharTagAttention(self.char_rnn_units, self.word_length)(embedding_block)
embedding_block = Concatenate()([embedding_block, c])
embedding_block = Dense(self.embedding_size)(embedding_block)
embedding_block = Model(inputs=word_input_layer, outputs=embedding_block)
embedding_block.summary()
seq_inp_layer = Input(shape=(self.seq_length, self.word_length), name='char')
embedding_block = TimeDistributed(embedding_block)(seq_inp_layer)
return (seq_inp_layer, embedding_block) | def __get_char_embedding(self):
'\n \n '
word_input_layer = Input(shape=(self.word_length,))
embedding_block = Embedding((self.n_chars + 1), self.char_embed_size, input_length=self.word_length, trainable=True, mask_zero=True)(word_input_layer)
rnn_output = LSTM(self.char_rnn_units, recurrent_dropout=self.char_rd, return_sequences=True, return_state=True)(embedding_block)
(embedding_block, h, c) = rnn_output
embedding_block = CharTagAttention(self.char_rnn_units, self.word_length)(embedding_block)
embedding_block = Concatenate()([embedding_block, c])
embedding_block = Dense(self.embedding_size)(embedding_block)
embedding_block = Model(inputs=word_input_layer, outputs=embedding_block)
embedding_block.summary()
seq_inp_layer = Input(shape=(self.seq_length, self.word_length), name='char')
embedding_block = TimeDistributed(embedding_block)(seq_inp_layer)
return (seq_inp_layer, embedding_block)<|docstring|>Initialize character embedding<|endoftext|> |
814d044695658ba319fde481a70f7cfbfcb8dd727243ca1b9949b2ff9d5accc8 | def init_model(self):
'\n Initialize the network model\n '
input_word_layer = Input(shape=(self.seq_length,), name='word')
pre_trained_word_embed = Embedding((self.vocab_size + 1), self.embedding_size, input_length=self.seq_length, embeddings_initializer=self.embedding, mask_zero=True, trainable=False)
pre_trained_word_embed = pre_trained_word_embed(input_word_layer)
learnable_word_embed = Embedding((self.vocab_size + 1), self.embedding_size, input_length=self.seq_length, embeddings_initializer='glorot_uniform', mask_zero=True)(input_word_layer)
(input_char_layer, char_embed_block) = self.__get_char_embedding()
input_layer = [input_char_layer, input_word_layer]
embed_block = Add()([char_embed_block, pre_trained_word_embed, learnable_word_embed])
if (self.ed > 0):
embed_block = Dropout(self.ed)(embed_block)
self.model = embed_block
self.model = Bidirectional(LSTM(self.rnn_units, return_sequences=True, recurrent_dropout=self.rd))(self.model)
self.model = Dropout(self.main_layer_dropout)(self.model)
self.model = Bidirectional(LSTM(self.rnn_units, return_sequences=True, recurrent_dropout=self.rd))(self.model)
self.model = Dense((self.n_label + 1), activation='relu')(self.model)
out = Dense((self.n_label + 1))(self.model)
self.model = Model(input_layer, out)
self.model.summary()
self.model.compile(loss=CategoricalCrossentropy(from_logits=True), optimizer=self.optimizer, metrics=[CategoricalAccuracy()]) | Initialize the network model | model/RNNText/rnn_rnn_tagger.py | init_model | ajmalkurnia/deeplearning-text-playground | 0 | python | def init_model(self):
'\n \n '
input_word_layer = Input(shape=(self.seq_length,), name='word')
pre_trained_word_embed = Embedding((self.vocab_size + 1), self.embedding_size, input_length=self.seq_length, embeddings_initializer=self.embedding, mask_zero=True, trainable=False)
pre_trained_word_embed = pre_trained_word_embed(input_word_layer)
learnable_word_embed = Embedding((self.vocab_size + 1), self.embedding_size, input_length=self.seq_length, embeddings_initializer='glorot_uniform', mask_zero=True)(input_word_layer)
(input_char_layer, char_embed_block) = self.__get_char_embedding()
input_layer = [input_char_layer, input_word_layer]
embed_block = Add()([char_embed_block, pre_trained_word_embed, learnable_word_embed])
if (self.ed > 0):
embed_block = Dropout(self.ed)(embed_block)
self.model = embed_block
self.model = Bidirectional(LSTM(self.rnn_units, return_sequences=True, recurrent_dropout=self.rd))(self.model)
self.model = Dropout(self.main_layer_dropout)(self.model)
self.model = Bidirectional(LSTM(self.rnn_units, return_sequences=True, recurrent_dropout=self.rd))(self.model)
self.model = Dense((self.n_label + 1), activation='relu')(self.model)
out = Dense((self.n_label + 1))(self.model)
self.model = Model(input_layer, out)
self.model.summary()
self.model.compile(loss=CategoricalCrossentropy(from_logits=True), optimizer=self.optimizer, metrics=[CategoricalAccuracy()]) | def init_model(self):
'\n \n '
input_word_layer = Input(shape=(self.seq_length,), name='word')
pre_trained_word_embed = Embedding((self.vocab_size + 1), self.embedding_size, input_length=self.seq_length, embeddings_initializer=self.embedding, mask_zero=True, trainable=False)
pre_trained_word_embed = pre_trained_word_embed(input_word_layer)
learnable_word_embed = Embedding((self.vocab_size + 1), self.embedding_size, input_length=self.seq_length, embeddings_initializer='glorot_uniform', mask_zero=True)(input_word_layer)
(input_char_layer, char_embed_block) = self.__get_char_embedding()
input_layer = [input_char_layer, input_word_layer]
embed_block = Add()([char_embed_block, pre_trained_word_embed, learnable_word_embed])
if (self.ed > 0):
embed_block = Dropout(self.ed)(embed_block)
self.model = embed_block
self.model = Bidirectional(LSTM(self.rnn_units, return_sequences=True, recurrent_dropout=self.rd))(self.model)
self.model = Dropout(self.main_layer_dropout)(self.model)
self.model = Bidirectional(LSTM(self.rnn_units, return_sequences=True, recurrent_dropout=self.rd))(self.model)
self.model = Dense((self.n_label + 1), activation='relu')(self.model)
out = Dense((self.n_label + 1))(self.model)
self.model = Model(input_layer, out)
self.model.summary()
self.model.compile(loss=CategoricalCrossentropy(from_logits=True), optimizer=self.optimizer, metrics=[CategoricalAccuracy()])<|docstring|>Initialize the network model<|endoftext|> |
14b9e2b827841816a8aaf697e12ecb1cd22dbe85f85ed873c836de67ac5c8ced | def vectorize_input(self, inp_seq):
'\n Prepare vector of the input data\n\n :param inp_seq: list of list of string, tokenized input corpus\n :return word_vector: Dictionary, Word and char input vector\n '
input_vector = {'word': self.get_word_vector(inp_seq), 'char': self.get_char_vector(inp_seq)}
return input_vector | Prepare vector of the input data
:param inp_seq: list of list of string, tokenized input corpus
:return word_vector: Dictionary, Word and char input vector | model/RNNText/rnn_rnn_tagger.py | vectorize_input | ajmalkurnia/deeplearning-text-playground | 0 | python | def vectorize_input(self, inp_seq):
'\n Prepare vector of the input data\n\n :param inp_seq: list of list of string, tokenized input corpus\n :return word_vector: Dictionary, Word and char input vector\n '
input_vector = {'word': self.get_word_vector(inp_seq), 'char': self.get_char_vector(inp_seq)}
return input_vector | def vectorize_input(self, inp_seq):
'\n Prepare vector of the input data\n\n :param inp_seq: list of list of string, tokenized input corpus\n :return word_vector: Dictionary, Word and char input vector\n '
input_vector = {'word': self.get_word_vector(inp_seq), 'char': self.get_char_vector(inp_seq)}
return input_vector<|docstring|>Prepare vector of the input data
:param inp_seq: list of list of string, tokenized input corpus
:return word_vector: Dictionary, Word and char input vector<|endoftext|> |
4741385255ee02807daef403e8bbf4de9f96f4a93bf9e7ae3492d88c807d0004 | @staticmethod
def init_from_config(class_param):
'\n Load model from the saved zipfile\n\n :param filepath: path to model zip file\n :return classifier: Loaded model class\n '
constructor_param = {'seq_length': class_param['seq_length'], 'word_length': class_param['word_length']}
classifier = StackedRNNTagger(**constructor_param)
classifier.label2idx = class_param['label2idx']
classifier.word2idx = class_param['word2idx']
classifier.idx2label = class_param['idx2label']
classifier.n_label = len(classifier.label2idx)
classifier.char2idx = class_param['char2idx']
return classifier | Load model from the saved zipfile
:param filepath: path to model zip file
:return classifier: Loaded model class | model/RNNText/rnn_rnn_tagger.py | init_from_config | ajmalkurnia/deeplearning-text-playground | 0 | python | @staticmethod
def init_from_config(class_param):
'\n Load model from the saved zipfile\n\n :param filepath: path to model zip file\n :return classifier: Loaded model class\n '
constructor_param = {'seq_length': class_param['seq_length'], 'word_length': class_param['word_length']}
classifier = StackedRNNTagger(**constructor_param)
classifier.label2idx = class_param['label2idx']
classifier.word2idx = class_param['word2idx']
classifier.idx2label = class_param['idx2label']
classifier.n_label = len(classifier.label2idx)
classifier.char2idx = class_param['char2idx']
return classifier | @staticmethod
def init_from_config(class_param):
'\n Load model from the saved zipfile\n\n :param filepath: path to model zip file\n :return classifier: Loaded model class\n '
constructor_param = {'seq_length': class_param['seq_length'], 'word_length': class_param['word_length']}
classifier = StackedRNNTagger(**constructor_param)
classifier.label2idx = class_param['label2idx']
classifier.word2idx = class_param['word2idx']
classifier.idx2label = class_param['idx2label']
classifier.n_label = len(classifier.label2idx)
classifier.char2idx = class_param['char2idx']
return classifier<|docstring|>Load model from the saved zipfile
:param filepath: path to model zip file
:return classifier: Loaded model class<|endoftext|> |
aa4dbbe5d62c282aad7a67ec560d6b60e3474a58e0f618afca4adebf14419476 | def step(self, action):
'\n\n :param int|None action: move pos=0 ~ 63 (0=top left, 7 top right, 63 bottom right), None is resign\n :return:\n '
assert ((action is None) or (0 <= action <= 63)), f'Illegal action={action}'
if (action is None):
self._resigned()
return (self.board, {})
(own, enemy) = self.get_own_and_enemy()
flipped = calc_flip(action, own, enemy)
if (bit_count(flipped) == 0):
self.illegal_move_to_lose(action)
return (self.board, {})
own ^= flipped
own |= (1 << action)
enemy ^= flipped
self.set_own_and_enemy(own, enemy)
self.turn += 1
if (bit_count(find_correct_moves(enemy, own)) > 0):
self.change_to_next_player()
elif (bit_count(find_correct_moves(own, enemy)) > 0):
pass
else:
self._game_over()
return (self.board, {}) | :param int|None action: move pos=0 ~ 63 (0=top left, 7 top right, 63 bottom right), None is resign
:return: | src/reversi_zero/env/reversi_env.py | step | Jinnrry/reversi-alpha-zero | 699 | python | def step(self, action):
'\n\n :param int|None action: move pos=0 ~ 63 (0=top left, 7 top right, 63 bottom right), None is resign\n :return:\n '
assert ((action is None) or (0 <= action <= 63)), f'Illegal action={action}'
if (action is None):
self._resigned()
return (self.board, {})
(own, enemy) = self.get_own_and_enemy()
flipped = calc_flip(action, own, enemy)
if (bit_count(flipped) == 0):
self.illegal_move_to_lose(action)
return (self.board, {})
own ^= flipped
own |= (1 << action)
enemy ^= flipped
self.set_own_and_enemy(own, enemy)
self.turn += 1
if (bit_count(find_correct_moves(enemy, own)) > 0):
self.change_to_next_player()
elif (bit_count(find_correct_moves(own, enemy)) > 0):
pass
else:
self._game_over()
return (self.board, {}) | def step(self, action):
'\n\n :param int|None action: move pos=0 ~ 63 (0=top left, 7 top right, 63 bottom right), None is resign\n :return:\n '
assert ((action is None) or (0 <= action <= 63)), f'Illegal action={action}'
if (action is None):
self._resigned()
return (self.board, {})
(own, enemy) = self.get_own_and_enemy()
flipped = calc_flip(action, own, enemy)
if (bit_count(flipped) == 0):
self.illegal_move_to_lose(action)
return (self.board, {})
own ^= flipped
own |= (1 << action)
enemy ^= flipped
self.set_own_and_enemy(own, enemy)
self.turn += 1
if (bit_count(find_correct_moves(enemy, own)) > 0):
self.change_to_next_player()
elif (bit_count(find_correct_moves(own, enemy)) > 0):
pass
else:
self._game_over()
return (self.board, {})<|docstring|>:param int|None action: move pos=0 ~ 63 (0=top left, 7 top right, 63 bottom right), None is resign
:return:<|endoftext|> |
05598c252718242c01bcf387e264b8ebc62982405cfccaeb787828be558e2270 | @property
def observation(self):
'\n\n :rtype: Board\n '
return self.board | :rtype: Board | src/reversi_zero/env/reversi_env.py | observation | Jinnrry/reversi-alpha-zero | 699 | python | @property
def observation(self):
'\n\n \n '
return self.board | @property
def observation(self):
'\n\n \n '
return self.board<|docstring|>:rtype: Board<|endoftext|> |
2a5e2e43fbd5fdb87f45c43f4fc9b45002f4019d1cbd9643322d6fb01b152a96 | def _keys_in_sorted(move):
' sort by picking and the responsible for the product the\n move.\n '
return (move.raw_material_production_id.id, move.product_id.responsible_id.id) | sort by picking and the responsible for the product the
move. | odoo-13.0/addons/mrp/models/stock_picking.py | _keys_in_sorted | VaibhavBhujade/Blockchain-ERP-interoperability | 12 | python | def _keys_in_sorted(move):
' sort by picking and the responsible for the product the\n move.\n '
return (move.raw_material_production_id.id, move.product_id.responsible_id.id) | def _keys_in_sorted(move):
' sort by picking and the responsible for the product the\n move.\n '
return (move.raw_material_production_id.id, move.product_id.responsible_id.id)<|docstring|>sort by picking and the responsible for the product the
move.<|endoftext|> |
fb9433215a9007765350a6c5c850a850d08b4da3c371b30311933bbf73a59d5f | def _keys_in_groupby(move):
' group by picking and the responsible for the product the\n move.\n '
return (move.raw_material_production_id, move.product_id.responsible_id) | group by picking and the responsible for the product the
move. | odoo-13.0/addons/mrp/models/stock_picking.py | _keys_in_groupby | VaibhavBhujade/Blockchain-ERP-interoperability | 12 | python | def _keys_in_groupby(move):
' group by picking and the responsible for the product the\n move.\n '
return (move.raw_material_production_id, move.product_id.responsible_id) | def _keys_in_groupby(move):
' group by picking and the responsible for the product the\n move.\n '
return (move.raw_material_production_id, move.product_id.responsible_id)<|docstring|>group by picking and the responsible for the product the
move.<|endoftext|> |
872c13ee8ca863fae21572d7e60b159f9a584eb7c0871084d675418061ddfca8 | def __init__(self, tasker, workflow, timeout=1200, retry_delay=30, insecure=False, secret=None, expect_v2schema2=False):
'\n constructor\n\n :param tasker: DockerTasker instance\n :param workflow: DockerBuildWorkflow instance\n :param timeout: int, maximum number of seconds to wait\n :param retry_delay: int, seconds between pull attempts\n :param insecure: bool, allow non-https pull if true\n :param secret: str, path to secret\n :param expect_v2schema2: bool, require Pulp to return a schema 2 digest and\n retry until it does\n '
super(PulpPullPlugin, self).__init__(tasker, workflow)
self.timeout = timeout
self.retry_delay = retry_delay
self.insecure = insecure
self.secret = secret
self.expect_v2schema2 = (not get_prefer_schema1_digest(workflow, (not expect_v2schema2)))
self.expect_v2schema2list = False
self.expect_v2schema2list_only = False | constructor
:param tasker: DockerTasker instance
:param workflow: DockerBuildWorkflow instance
:param timeout: int, maximum number of seconds to wait
:param retry_delay: int, seconds between pull attempts
:param insecure: bool, allow non-https pull if true
:param secret: str, path to secret
:param expect_v2schema2: bool, require Pulp to return a schema 2 digest and
retry until it does | atomic_reactor/plugins/post_pulp_pull.py | __init__ | jcajka/atomic-reactor | 0 | python | def __init__(self, tasker, workflow, timeout=1200, retry_delay=30, insecure=False, secret=None, expect_v2schema2=False):
'\n constructor\n\n :param tasker: DockerTasker instance\n :param workflow: DockerBuildWorkflow instance\n :param timeout: int, maximum number of seconds to wait\n :param retry_delay: int, seconds between pull attempts\n :param insecure: bool, allow non-https pull if true\n :param secret: str, path to secret\n :param expect_v2schema2: bool, require Pulp to return a schema 2 digest and\n retry until it does\n '
super(PulpPullPlugin, self).__init__(tasker, workflow)
self.timeout = timeout
self.retry_delay = retry_delay
self.insecure = insecure
self.secret = secret
self.expect_v2schema2 = (not get_prefer_schema1_digest(workflow, (not expect_v2schema2)))
self.expect_v2schema2list = False
self.expect_v2schema2list_only = False | def __init__(self, tasker, workflow, timeout=1200, retry_delay=30, insecure=False, secret=None, expect_v2schema2=False):
'\n constructor\n\n :param tasker: DockerTasker instance\n :param workflow: DockerBuildWorkflow instance\n :param timeout: int, maximum number of seconds to wait\n :param retry_delay: int, seconds between pull attempts\n :param insecure: bool, allow non-https pull if true\n :param secret: str, path to secret\n :param expect_v2schema2: bool, require Pulp to return a schema 2 digest and\n retry until it does\n '
super(PulpPullPlugin, self).__init__(tasker, workflow)
self.timeout = timeout
self.retry_delay = retry_delay
self.insecure = insecure
self.secret = secret
self.expect_v2schema2 = (not get_prefer_schema1_digest(workflow, (not expect_v2schema2)))
self.expect_v2schema2list = False
self.expect_v2schema2list_only = False<|docstring|>constructor
:param tasker: DockerTasker instance
:param workflow: DockerBuildWorkflow instance
:param timeout: int, maximum number of seconds to wait
:param retry_delay: int, seconds between pull attempts
:param insecure: bool, allow non-https pull if true
:param secret: str, path to secret
:param expect_v2schema2: bool, require Pulp to return a schema 2 digest and
retry until it does<|endoftext|> |
f4e2c5dc6e63c03362dc210700e01fccbc76801a63063461be1cf1ebefe1b625 | def generate_track_data():
'\n Converts the data from the Spotify Sequential Skip Prediction Challenge dataset to the database format.\n The data needs to be downloaded manually and put in the skip_dataset/data/ folder.\n :return:\n '
folder_name = 'skip_dataset/data/'
all_files = [f for f in listdir(folder_name) if (isfile(join(folder_name, f)) and f.endswith('csv'))]
df1 = pd.read_csv((folder_name + all_files[0]))
df2 = pd.read_csv((folder_name + all_files[1]))
df = pd.concat([df1, df2])
for (index, track) in tqdm(df.iterrows(), total=df.shape[0]):
track_data = TrackData(track_id=track['track_id'], name=track['track_id'], artist='N/A', artist_id='N/A', acousticness=track['acousticness'], danceability=track['danceability'], energy=track['energy'], instrumentalness=track['instrumentalness'], liveness=track['liveness'], loudness=((track['loudness'] + 60) / 60), speechiness=track['speechiness'], valence=track['valence'], tempo=track['tempo'])
track_data.save() | Converts the data from the Spotify Sequential Skip Prediction Challenge dataset to the database format.
The data needs to be downloaded manually and put in the skip_dataset/data/ folder.
:return: | Experiment Processing/skip_dataset/generate_track_data.py | generate_track_data | Austaon/GroupRecommendationThesis | 0 | python | def generate_track_data():
'\n Converts the data from the Spotify Sequential Skip Prediction Challenge dataset to the database format.\n The data needs to be downloaded manually and put in the skip_dataset/data/ folder.\n :return:\n '
folder_name = 'skip_dataset/data/'
all_files = [f for f in listdir(folder_name) if (isfile(join(folder_name, f)) and f.endswith('csv'))]
df1 = pd.read_csv((folder_name + all_files[0]))
df2 = pd.read_csv((folder_name + all_files[1]))
df = pd.concat([df1, df2])
for (index, track) in tqdm(df.iterrows(), total=df.shape[0]):
track_data = TrackData(track_id=track['track_id'], name=track['track_id'], artist='N/A', artist_id='N/A', acousticness=track['acousticness'], danceability=track['danceability'], energy=track['energy'], instrumentalness=track['instrumentalness'], liveness=track['liveness'], loudness=((track['loudness'] + 60) / 60), speechiness=track['speechiness'], valence=track['valence'], tempo=track['tempo'])
track_data.save() | def generate_track_data():
'\n Converts the data from the Spotify Sequential Skip Prediction Challenge dataset to the database format.\n The data needs to be downloaded manually and put in the skip_dataset/data/ folder.\n :return:\n '
folder_name = 'skip_dataset/data/'
all_files = [f for f in listdir(folder_name) if (isfile(join(folder_name, f)) and f.endswith('csv'))]
df1 = pd.read_csv((folder_name + all_files[0]))
df2 = pd.read_csv((folder_name + all_files[1]))
df = pd.concat([df1, df2])
for (index, track) in tqdm(df.iterrows(), total=df.shape[0]):
track_data = TrackData(track_id=track['track_id'], name=track['track_id'], artist='N/A', artist_id='N/A', acousticness=track['acousticness'], danceability=track['danceability'], energy=track['energy'], instrumentalness=track['instrumentalness'], liveness=track['liveness'], loudness=((track['loudness'] + 60) / 60), speechiness=track['speechiness'], valence=track['valence'], tempo=track['tempo'])
track_data.save()<|docstring|>Converts the data from the Spotify Sequential Skip Prediction Challenge dataset to the database format.
The data needs to be downloaded manually and put in the skip_dataset/data/ folder.
:return:<|endoftext|> |
ce78406a062ffc41c99db082eea119819d48dfcb868e4fb93b36498e5f691b13 | def __init__(self, transitions, rewards, horizon=100, init_state=None, seed=None):
'MDP initialization.\n\n Parameters\n ----------\n transitions : array-like\n Array holding transition matrix for each action. The dimension of\n the state and action spaces will be deduced from this array.\n rewards : array-like\n Array holding the reward matrix for each action. It needs to comply\n with the dimensions deduced from the transitions array.\n init_state : int\n Initial state of the process. If None, it will be set to 0.\n '
self.horizon = horizon
self.transitions = transitions
self.rewards = rewards
self.action_space = DiscreteSpace(len(transitions))
self.state_space = DiscreteSpace(len(transitions[0]))
if (init_state is None):
init_state = 0
elif (not self.state_space.contains(init_state)):
raise ValueError('Initial state (%d) is not a valid state.', init_state)
self.init_state = init_state
self.state = init_state
self.random = np.random.RandomState()
if (seed is not None):
self.seed = seed
else:
self._seed = None | MDP initialization.
Parameters
----------
transitions : array-like
Array holding transition matrix for each action. The dimension of
the state and action spaces will be deduced from this array.
rewards : array-like
Array holding the reward matrix for each action. It needs to comply
with the dimensions deduced from the transitions array.
init_state : int
Initial state of the process. If None, it will be set to 0. | SafeRLBench/envs/mdp.py | __init__ | befelix/Safe-RL-Benchmark | 19 | python | def __init__(self, transitions, rewards, horizon=100, init_state=None, seed=None):
'MDP initialization.\n\n Parameters\n ----------\n transitions : array-like\n Array holding transition matrix for each action. The dimension of\n the state and action spaces will be deduced from this array.\n rewards : array-like\n Array holding the reward matrix for each action. It needs to comply\n with the dimensions deduced from the transitions array.\n init_state : int\n Initial state of the process. If None, it will be set to 0.\n '
self.horizon = horizon
self.transitions = transitions
self.rewards = rewards
self.action_space = DiscreteSpace(len(transitions))
self.state_space = DiscreteSpace(len(transitions[0]))
if (init_state is None):
init_state = 0
elif (not self.state_space.contains(init_state)):
raise ValueError('Initial state (%d) is not a valid state.', init_state)
self.init_state = init_state
self.state = init_state
self.random = np.random.RandomState()
if (seed is not None):
self.seed = seed
else:
self._seed = None | def __init__(self, transitions, rewards, horizon=100, init_state=None, seed=None):
'MDP initialization.\n\n Parameters\n ----------\n transitions : array-like\n Array holding transition matrix for each action. The dimension of\n the state and action spaces will be deduced from this array.\n rewards : array-like\n Array holding the reward matrix for each action. It needs to comply\n with the dimensions deduced from the transitions array.\n init_state : int\n Initial state of the process. If None, it will be set to 0.\n '
self.horizon = horizon
self.transitions = transitions
self.rewards = rewards
self.action_space = DiscreteSpace(len(transitions))
self.state_space = DiscreteSpace(len(transitions[0]))
if (init_state is None):
init_state = 0
elif (not self.state_space.contains(init_state)):
raise ValueError('Initial state (%d) is not a valid state.', init_state)
self.init_state = init_state
self.state = init_state
self.random = np.random.RandomState()
if (seed is not None):
self.seed = seed
else:
self._seed = None<|docstring|>MDP initialization.
Parameters
----------
transitions : array-like
Array holding transition matrix for each action. The dimension of
the state and action spaces will be deduced from this array.
rewards : array-like
Array holding the reward matrix for each action. It needs to comply
with the dimensions deduced from the transitions array.
init_state : int
Initial state of the process. If None, it will be set to 0.<|endoftext|> |
8f73a29b2b384b743d857d58e321f8e047eaf18fc605971554ceeb18139416a9 | @property
def seed(self):
'Seed.'
return self._seed | Seed. | SafeRLBench/envs/mdp.py | seed | befelix/Safe-RL-Benchmark | 19 | python | @property
def seed(self):
return self._seed | @property
def seed(self):
return self._seed<|docstring|>Seed.<|endoftext|> |
6ea81e598260403add6bca33c07a4ed68637d94d91bd6b2a62753e4677e7e949 | def _query_to_responses(query: Query, func_response: Callable[([Query], str)]) -> List[search_pb2.SearchResponse]:
'\n\n :param query:\n :param func_response:\n :return:\n '
return [search_pb2.SearchResponse(response=func_response(rec)) for rec in query] | :param query:
:param func_response:
:return: | src/tutorial/grpc/geodatas/rpc/search_servicer.py | _query_to_responses | yoyonel/2017_GRPC_Tutorial | 1 | python | def _query_to_responses(query: Query, func_response: Callable[([Query], str)]) -> List[search_pb2.SearchResponse]:
'\n\n :param query:\n :param func_response:\n :return:\n '
return [search_pb2.SearchResponse(response=func_response(rec)) for rec in query] | def _query_to_responses(query: Query, func_response: Callable[([Query], str)]) -> List[search_pb2.SearchResponse]:
'\n\n :param query:\n :param func_response:\n :return:\n '
return [search_pb2.SearchResponse(response=func_response(rec)) for rec in query]<|docstring|>:param query:
:param func_response:
:return:<|endoftext|> |
ed6937c238b5876b5e11d3e194b1be92ff971b60641dc115c90641d230e6da9a | def _search_thing(lat: float, lng: float, srid: int=(- 1)) -> List[search_pb2.SearchResponse]:
'\n\n :param lat:\n :param lng:\n :param srid:\n :return:\n '
session = session_factory()
query = session.query(Thing).filter(func.ST_Contains(Thing.geom, Position(lat, lng, srid).to_wktelement))
return _query_to_responses(query, (lambda rec: rec.name)) | :param lat:
:param lng:
:param srid:
:return: | src/tutorial/grpc/geodatas/rpc/search_servicer.py | _search_thing | yoyonel/2017_GRPC_Tutorial | 1 | python | def _search_thing(lat: float, lng: float, srid: int=(- 1)) -> List[search_pb2.SearchResponse]:
'\n\n :param lat:\n :param lng:\n :param srid:\n :return:\n '
session = session_factory()
query = session.query(Thing).filter(func.ST_Contains(Thing.geom, Position(lat, lng, srid).to_wktelement))
return _query_to_responses(query, (lambda rec: rec.name)) | def _search_thing(lat: float, lng: float, srid: int=(- 1)) -> List[search_pb2.SearchResponse]:
'\n\n :param lat:\n :param lng:\n :param srid:\n :return:\n '
session = session_factory()
query = session.query(Thing).filter(func.ST_Contains(Thing.geom, Position(lat, lng, srid).to_wktelement))
return _query_to_responses(query, (lambda rec: rec.name))<|docstring|>:param lat:
:param lng:
:param srid:
:return:<|endoftext|> |
8745696f34376870a570dbdd2cf514f86fc4b878e91eebc4df2ca7e17a47928d | def _search_commune(lat: float, lng: float, srid: int=4326) -> List[search_pb2.SearchResponse]:
'\n\n :param lat:\n :param lng:\n :param srid:\n :return:\n '
session = session_factory()
query = session.query(OGRGeoJSON).filter(func.ST_Contains(OGRGeoJSON.wkb_geometry, Position(lat, lng, srid).to_wkbelement))
return _query_to_responses(query, (lambda rec: '{} - {} - {}'.format(rec.insee, rec.nom, rec.wikipedia))) | :param lat:
:param lng:
:param srid:
:return: | src/tutorial/grpc/geodatas/rpc/search_servicer.py | _search_commune | yoyonel/2017_GRPC_Tutorial | 1 | python | def _search_commune(lat: float, lng: float, srid: int=4326) -> List[search_pb2.SearchResponse]:
'\n\n :param lat:\n :param lng:\n :param srid:\n :return:\n '
session = session_factory()
query = session.query(OGRGeoJSON).filter(func.ST_Contains(OGRGeoJSON.wkb_geometry, Position(lat, lng, srid).to_wkbelement))
return _query_to_responses(query, (lambda rec: '{} - {} - {}'.format(rec.insee, rec.nom, rec.wikipedia))) | def _search_commune(lat: float, lng: float, srid: int=4326) -> List[search_pb2.SearchResponse]:
'\n\n :param lat:\n :param lng:\n :param srid:\n :return:\n '
session = session_factory()
query = session.query(OGRGeoJSON).filter(func.ST_Contains(OGRGeoJSON.wkb_geometry, Position(lat, lng, srid).to_wkbelement))
return _query_to_responses(query, (lambda rec: '{} - {} - {}'.format(rec.insee, rec.nom, rec.wikipedia)))<|docstring|>:param lat:
:param lng:
:param srid:
:return:<|endoftext|> |
b13d30e853fc2819faefdeb7c28a59dfc9da5d8d19f29316f0f9efb7b3a10be3 | @stat.timer('search')
def search(self, request: search_pb2.SearchRequest, _context) -> search_pb2.SearchResponses:
'\n\n :param request:\n :param _context:\n :return:\n '
self.stat.incr('search_count')
logger.info(('search (commune) request: ' + str(request)))
responses = _search_commune(request.lat, request.lng)
logger.info(('search responses: ' + str(responses)))
return search_pb2.SearchResponses(responses=responses) | :param request:
:param _context:
:return: | src/tutorial/grpc/geodatas/rpc/search_servicer.py | search | yoyonel/2017_GRPC_Tutorial | 1 | python | @stat.timer('search')
def search(self, request: search_pb2.SearchRequest, _context) -> search_pb2.SearchResponses:
'\n\n :param request:\n :param _context:\n :return:\n '
self.stat.incr('search_count')
logger.info(('search (commune) request: ' + str(request)))
responses = _search_commune(request.lat, request.lng)
logger.info(('search responses: ' + str(responses)))
return search_pb2.SearchResponses(responses=responses) | @stat.timer('search')
def search(self, request: search_pb2.SearchRequest, _context) -> search_pb2.SearchResponses:
'\n\n :param request:\n :param _context:\n :return:\n '
self.stat.incr('search_count')
logger.info(('search (commune) request: ' + str(request)))
responses = _search_commune(request.lat, request.lng)
logger.info(('search responses: ' + str(responses)))
return search_pb2.SearchResponses(responses=responses)<|docstring|>:param request:
:param _context:
:return:<|endoftext|> |
d953044ea21cb667d370d4f7ce78ea8089dc961a1080054875f57446a73ec11b | @stat.timer('search_thing')
def search_thing(self, request: search_pb2.SearchRequest, _context) -> search_pb2.SearchResponses:
'\n\n :param request:\n :param _context:\n :return:\n '
self.stat.incr('search_count')
logger.info(('search (thing) request: ' + str(request)))
responses = _search_thing(request.lat, request.lng)
logger.info(('search responses: ' + str(responses)))
return search_pb2.SearchResponses(responses=responses) | :param request:
:param _context:
:return: | src/tutorial/grpc/geodatas/rpc/search_servicer.py | search_thing | yoyonel/2017_GRPC_Tutorial | 1 | python | @stat.timer('search_thing')
def search_thing(self, request: search_pb2.SearchRequest, _context) -> search_pb2.SearchResponses:
'\n\n :param request:\n :param _context:\n :return:\n '
self.stat.incr('search_count')
logger.info(('search (thing) request: ' + str(request)))
responses = _search_thing(request.lat, request.lng)
logger.info(('search responses: ' + str(responses)))
return search_pb2.SearchResponses(responses=responses) | @stat.timer('search_thing')
def search_thing(self, request: search_pb2.SearchRequest, _context) -> search_pb2.SearchResponses:
'\n\n :param request:\n :param _context:\n :return:\n '
self.stat.incr('search_count')
logger.info(('search (thing) request: ' + str(request)))
responses = _search_thing(request.lat, request.lng)
logger.info(('search responses: ' + str(responses)))
return search_pb2.SearchResponses(responses=responses)<|docstring|>:param request:
:param _context:
:return:<|endoftext|> |
84b6827781e36fd5c29897b8dd393d9aad30e52be116421ab3bac8b9698e89f0 | @stat.timer('monitor')
def monitor(self, _request, _context):
'\n\n :param _request:\n :param _context:\n :return:\n '
session = session_factory()
self.stat.incr('monitor_count')
n_things = session.query(Thing).count()
return search_pb2.MonitorResponse(n_things=n_things) | :param _request:
:param _context:
:return: | src/tutorial/grpc/geodatas/rpc/search_servicer.py | monitor | yoyonel/2017_GRPC_Tutorial | 1 | python | @stat.timer('monitor')
def monitor(self, _request, _context):
'\n\n :param _request:\n :param _context:\n :return:\n '
session = session_factory()
self.stat.incr('monitor_count')
n_things = session.query(Thing).count()
return search_pb2.MonitorResponse(n_things=n_things) | @stat.timer('monitor')
def monitor(self, _request, _context):
'\n\n :param _request:\n :param _context:\n :return:\n '
session = session_factory()
self.stat.incr('monitor_count')
n_things = session.query(Thing).count()
return search_pb2.MonitorResponse(n_things=n_things)<|docstring|>:param _request:
:param _context:
:return:<|endoftext|> |
75f7aa7036495b584bdcb28522d799ebcc6c19aa5135fe9699b4a5aa9fc54934 | def spherical_multiRegion_Green_Arnoldi_Mmn_Uconverge(n, k, RPlist, invchi, gridpts=10000, mpdps=60, klim=25, Taylor_tol=1e-12, Unormtol=1e-08, veclim=3, delveclim=2, maxveclim=40):
"\n generates a representation of the Green's function/Umatrix over spherical region of radius R\n with sub-bases with support in shell regions with boundaries delineated by RPlist\n this is done so that the projection operator for spatial projection based constraints is explicit\n the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]\n RPlist[-1] is the radius of the entire bounding sphere\n the first region is an inner sphere, the other regions are cocentric shells\n note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code\n in the new optimizations U = V^\\dagger-1 - G^\\dagger; here we calculate Uinv, and Uinv = V^-1-G\n "
mp.dps = mpdps
regionnum = len(RPlist)
unitRgdotRglist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitRgdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitImdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
subGmatlist = []
vecnum = 0
subbasis_head_indlist = []
All_unitMvecs = []
rgridlist = []
for i in range(regionnum):
print('M wave Region #', i)
if (i == 0):
subbasis_head_indlist.append(0)
(rmnRgM, rnImM, unitrmnMpols, Uinv) = speedup_Green_Taylor_Arnoldi_RgMmn_Uconverge(n, k, RPlist[0], klim=klim, Taylor_tol=Taylor_tol, invchi=invchi, Unormtol=Unormtol)
unitRgdotRglist[0] = mp.sqrt(rmnMnormsqr_Taylor(n, k, RPlist[0], rmnRgM))
subGmat = ((mp.eye(Uinv.rows) * invchi) - Uinv)
subGmatlist.append(np.array(mpmath.fp.matrix(subGmat.tolist()).tolist()))
vecnum += Uinv.rows
rgrid = np.linspace(0, RPlist[0], gridpts)
rgridlist.append(rgrid)
for i in range((len(unitrmnMpols) - 1)):
All_unitMvecs.append((((k * rgrid) ** n) * po.polyval((k * rgrid), unitrmnMpols[i].coef)))
else:
subbasis_head_indlist.append(vecnum)
try:
(rgrid, rsqrgrid, rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImMmn_Uconverge(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutMgrid = (RgMgrid + (1j * ImMgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Mmn_normsqr(RgMgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Mmn_dot(unitMvecs[0], OutMgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Mmn_dot(unitMvecs[1], OutMgrid, rsqrgrid, rdiffgrid))
except FloatingPointError:
(rgrid, rsqrgrid, rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImMmn_Uconverge_mp(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutMgrid = (RgMgrid + (1j * ImMgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Mmn_normsqr(RgMgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = rgrid_Mmn_dot(unitMvecs[0], OutMgrid, rsqrgrid, rdiffgrid)
unitImdotOutlist[i] = rgrid_Mmn_dot(unitMvecs[1], OutMgrid, rsqrgrid, rdiffgrid)
Gmat = np.array(mpmath.fp.matrix(Gmat.tolist()).tolist())
subGmatlist.append(Gmat)
vecnum += Gmat.shape[0]
All_unitMvecs.extend(unitMvecs[:(- 2)])
rgridlist.append(rgrid)
subbasis_head_indlist.append(vecnum)
Gmat = np.zeros((vecnum, vecnum), dtype=np.complex)
for i in range(regionnum):
indstart = subbasis_head_indlist[i]
indend = subbasis_head_indlist[(i + 1)]
Gmat[(indstart:indend, indstart:indend)] = subGmatlist[i][(:, :)]
jkcubed = (1j * (k ** 3))
for i in range(regionnum):
Rgiind = subbasis_head_indlist[i]
Imiind = (Rgiind + 1)
for j in range(i):
Rgjind = subbasis_head_indlist[j]
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitRgdotOutlist[i]))
Gmat[(Rgjind, Imiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitImdotOutlist[i]))
for j in range((i + 1), regionnum):
Rgjind = subbasis_head_indlist[j]
Imjind = (Rgjind + 1)
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotOutlist[j]) * unitRgdotRglist[i]))
Gmat[(Imjind, Rgiind)] = np.complex(((jkcubed * unitImdotOutlist[j]) * unitRgdotRglist[i]))
Uinv = ((invchi * np.eye(vecnum)) - Gmat)
fullrgrid = rgridlist[0].copy()
rboundaries = [0, gridpts]
for i in range(1, len(rgridlist)):
fullrgrid = np.concatenate((fullrgrid, rgridlist[i][1:]))
rboundaries.append(len(fullrgrid))
All_fullr_unitMvecs = []
for i in range(len(rgridlist)):
for j in range(subbasis_head_indlist[i], subbasis_head_indlist[(i + 1)]):
vecgrid = np.zeros_like(fullrgrid)
if (i == 0):
vecgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitMvecs[j][:]))
else:
vecgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitMvecs[j][1:]))
All_fullr_unitMvecs.append(vecgrid)
return (Gmat, Uinv, unitRgdotRglist, subbasis_head_indlist, fullrgrid, All_fullr_unitMvecs) | generates a representation of the Green's function/Umatrix over spherical region of radius R
with sub-bases with support in shell regions with boundaries delineated by RPlist
this is done so that the projection operator for spatial projection based constraints is explicit
the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]
RPlist[-1] is the radius of the entire bounding sphere
the first region is an inner sphere, the other regions are cocentric shells
note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code
in the new optimizations U = V^\dagger-1 - G^\dagger; here we calculate Uinv, and Uinv = V^-1-G | dualbound/Arnoldi/spherical_multiRegion_Green_Arnoldi.py | spherical_multiRegion_Green_Arnoldi_Mmn_Uconverge | PengningChao/emdb-sphere | 0 | python | def spherical_multiRegion_Green_Arnoldi_Mmn_Uconverge(n, k, RPlist, invchi, gridpts=10000, mpdps=60, klim=25, Taylor_tol=1e-12, Unormtol=1e-08, veclim=3, delveclim=2, maxveclim=40):
"\n generates a representation of the Green's function/Umatrix over spherical region of radius R\n with sub-bases with support in shell regions with boundaries delineated by RPlist\n this is done so that the projection operator for spatial projection based constraints is explicit\n the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]\n RPlist[-1] is the radius of the entire bounding sphere\n the first region is an inner sphere, the other regions are cocentric shells\n note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code\n in the new optimizations U = V^\\dagger-1 - G^\\dagger; here we calculate Uinv, and Uinv = V^-1-G\n "
mp.dps = mpdps
regionnum = len(RPlist)
unitRgdotRglist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitRgdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitImdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
subGmatlist = []
vecnum = 0
subbasis_head_indlist = []
All_unitMvecs = []
rgridlist = []
for i in range(regionnum):
print('M wave Region #', i)
if (i == 0):
subbasis_head_indlist.append(0)
(rmnRgM, rnImM, unitrmnMpols, Uinv) = speedup_Green_Taylor_Arnoldi_RgMmn_Uconverge(n, k, RPlist[0], klim=klim, Taylor_tol=Taylor_tol, invchi=invchi, Unormtol=Unormtol)
unitRgdotRglist[0] = mp.sqrt(rmnMnormsqr_Taylor(n, k, RPlist[0], rmnRgM))
subGmat = ((mp.eye(Uinv.rows) * invchi) - Uinv)
subGmatlist.append(np.array(mpmath.fp.matrix(subGmat.tolist()).tolist()))
vecnum += Uinv.rows
rgrid = np.linspace(0, RPlist[0], gridpts)
rgridlist.append(rgrid)
for i in range((len(unitrmnMpols) - 1)):
All_unitMvecs.append((((k * rgrid) ** n) * po.polyval((k * rgrid), unitrmnMpols[i].coef)))
else:
subbasis_head_indlist.append(vecnum)
try:
(rgrid, rsqrgrid, rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImMmn_Uconverge(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutMgrid = (RgMgrid + (1j * ImMgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Mmn_normsqr(RgMgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Mmn_dot(unitMvecs[0], OutMgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Mmn_dot(unitMvecs[1], OutMgrid, rsqrgrid, rdiffgrid))
except FloatingPointError:
(rgrid, rsqrgrid, rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImMmn_Uconverge_mp(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutMgrid = (RgMgrid + (1j * ImMgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Mmn_normsqr(RgMgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = rgrid_Mmn_dot(unitMvecs[0], OutMgrid, rsqrgrid, rdiffgrid)
unitImdotOutlist[i] = rgrid_Mmn_dot(unitMvecs[1], OutMgrid, rsqrgrid, rdiffgrid)
Gmat = np.array(mpmath.fp.matrix(Gmat.tolist()).tolist())
subGmatlist.append(Gmat)
vecnum += Gmat.shape[0]
All_unitMvecs.extend(unitMvecs[:(- 2)])
rgridlist.append(rgrid)
subbasis_head_indlist.append(vecnum)
Gmat = np.zeros((vecnum, vecnum), dtype=np.complex)
for i in range(regionnum):
indstart = subbasis_head_indlist[i]
indend = subbasis_head_indlist[(i + 1)]
Gmat[(indstart:indend, indstart:indend)] = subGmatlist[i][(:, :)]
jkcubed = (1j * (k ** 3))
for i in range(regionnum):
Rgiind = subbasis_head_indlist[i]
Imiind = (Rgiind + 1)
for j in range(i):
Rgjind = subbasis_head_indlist[j]
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitRgdotOutlist[i]))
Gmat[(Rgjind, Imiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitImdotOutlist[i]))
for j in range((i + 1), regionnum):
Rgjind = subbasis_head_indlist[j]
Imjind = (Rgjind + 1)
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotOutlist[j]) * unitRgdotRglist[i]))
Gmat[(Imjind, Rgiind)] = np.complex(((jkcubed * unitImdotOutlist[j]) * unitRgdotRglist[i]))
Uinv = ((invchi * np.eye(vecnum)) - Gmat)
fullrgrid = rgridlist[0].copy()
rboundaries = [0, gridpts]
for i in range(1, len(rgridlist)):
fullrgrid = np.concatenate((fullrgrid, rgridlist[i][1:]))
rboundaries.append(len(fullrgrid))
All_fullr_unitMvecs = []
for i in range(len(rgridlist)):
for j in range(subbasis_head_indlist[i], subbasis_head_indlist[(i + 1)]):
vecgrid = np.zeros_like(fullrgrid)
if (i == 0):
vecgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitMvecs[j][:]))
else:
vecgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitMvecs[j][1:]))
All_fullr_unitMvecs.append(vecgrid)
return (Gmat, Uinv, unitRgdotRglist, subbasis_head_indlist, fullrgrid, All_fullr_unitMvecs) | def spherical_multiRegion_Green_Arnoldi_Mmn_Uconverge(n, k, RPlist, invchi, gridpts=10000, mpdps=60, klim=25, Taylor_tol=1e-12, Unormtol=1e-08, veclim=3, delveclim=2, maxveclim=40):
"\n generates a representation of the Green's function/Umatrix over spherical region of radius R\n with sub-bases with support in shell regions with boundaries delineated by RPlist\n this is done so that the projection operator for spatial projection based constraints is explicit\n the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]\n RPlist[-1] is the radius of the entire bounding sphere\n the first region is an inner sphere, the other regions are cocentric shells\n note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code\n in the new optimizations U = V^\\dagger-1 - G^\\dagger; here we calculate Uinv, and Uinv = V^-1-G\n "
mp.dps = mpdps
regionnum = len(RPlist)
unitRgdotRglist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitRgdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitImdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
subGmatlist = []
vecnum = 0
subbasis_head_indlist = []
All_unitMvecs = []
rgridlist = []
for i in range(regionnum):
print('M wave Region #', i)
if (i == 0):
subbasis_head_indlist.append(0)
(rmnRgM, rnImM, unitrmnMpols, Uinv) = speedup_Green_Taylor_Arnoldi_RgMmn_Uconverge(n, k, RPlist[0], klim=klim, Taylor_tol=Taylor_tol, invchi=invchi, Unormtol=Unormtol)
unitRgdotRglist[0] = mp.sqrt(rmnMnormsqr_Taylor(n, k, RPlist[0], rmnRgM))
subGmat = ((mp.eye(Uinv.rows) * invchi) - Uinv)
subGmatlist.append(np.array(mpmath.fp.matrix(subGmat.tolist()).tolist()))
vecnum += Uinv.rows
rgrid = np.linspace(0, RPlist[0], gridpts)
rgridlist.append(rgrid)
for i in range((len(unitrmnMpols) - 1)):
All_unitMvecs.append((((k * rgrid) ** n) * po.polyval((k * rgrid), unitrmnMpols[i].coef)))
else:
subbasis_head_indlist.append(vecnum)
try:
(rgrid, rsqrgrid, rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImMmn_Uconverge(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutMgrid = (RgMgrid + (1j * ImMgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Mmn_normsqr(RgMgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Mmn_dot(unitMvecs[0], OutMgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Mmn_dot(unitMvecs[1], OutMgrid, rsqrgrid, rdiffgrid))
except FloatingPointError:
(rgrid, rsqrgrid, rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImMmn_Uconverge_mp(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutMgrid = (RgMgrid + (1j * ImMgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Mmn_normsqr(RgMgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = rgrid_Mmn_dot(unitMvecs[0], OutMgrid, rsqrgrid, rdiffgrid)
unitImdotOutlist[i] = rgrid_Mmn_dot(unitMvecs[1], OutMgrid, rsqrgrid, rdiffgrid)
Gmat = np.array(mpmath.fp.matrix(Gmat.tolist()).tolist())
subGmatlist.append(Gmat)
vecnum += Gmat.shape[0]
All_unitMvecs.extend(unitMvecs[:(- 2)])
rgridlist.append(rgrid)
subbasis_head_indlist.append(vecnum)
Gmat = np.zeros((vecnum, vecnum), dtype=np.complex)
for i in range(regionnum):
indstart = subbasis_head_indlist[i]
indend = subbasis_head_indlist[(i + 1)]
Gmat[(indstart:indend, indstart:indend)] = subGmatlist[i][(:, :)]
jkcubed = (1j * (k ** 3))
for i in range(regionnum):
Rgiind = subbasis_head_indlist[i]
Imiind = (Rgiind + 1)
for j in range(i):
Rgjind = subbasis_head_indlist[j]
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitRgdotOutlist[i]))
Gmat[(Rgjind, Imiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitImdotOutlist[i]))
for j in range((i + 1), regionnum):
Rgjind = subbasis_head_indlist[j]
Imjind = (Rgjind + 1)
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotOutlist[j]) * unitRgdotRglist[i]))
Gmat[(Imjind, Rgiind)] = np.complex(((jkcubed * unitImdotOutlist[j]) * unitRgdotRglist[i]))
Uinv = ((invchi * np.eye(vecnum)) - Gmat)
fullrgrid = rgridlist[0].copy()
rboundaries = [0, gridpts]
for i in range(1, len(rgridlist)):
fullrgrid = np.concatenate((fullrgrid, rgridlist[i][1:]))
rboundaries.append(len(fullrgrid))
All_fullr_unitMvecs = []
for i in range(len(rgridlist)):
for j in range(subbasis_head_indlist[i], subbasis_head_indlist[(i + 1)]):
vecgrid = np.zeros_like(fullrgrid)
if (i == 0):
vecgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitMvecs[j][:]))
else:
vecgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitMvecs[j][1:]))
All_fullr_unitMvecs.append(vecgrid)
return (Gmat, Uinv, unitRgdotRglist, subbasis_head_indlist, fullrgrid, All_fullr_unitMvecs)<|docstring|>generates a representation of the Green's function/Umatrix over spherical region of radius R
with sub-bases with support in shell regions with boundaries delineated by RPlist
this is done so that the projection operator for spatial projection based constraints is explicit
the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]
RPlist[-1] is the radius of the entire bounding sphere
the first region is an inner sphere, the other regions are cocentric shells
note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code
in the new optimizations U = V^\dagger-1 - G^\dagger; here we calculate Uinv, and Uinv = V^-1-G<|endoftext|> |
5f830cb54ada9cb2263479b29b636c0247a8f74483c3343515c10864fbaeb35d | def spherical_multiRegion_Green_Arnoldi_Nmn_Uconverge(n, k, RPlist, invchi, gridpts=10000, mpdps=60, klim=25, Taylor_tol=1e-12, Unormtol=1e-08, veclim=3, delveclim=2, maxveclim=40):
"\n generates a representation of the Green's function/Umatrix over spherical region of radius R\n with sub-bases with support in shell regions with boundaries delineated by RPlist\n this is done so that the projection operator for spatial projection based constraints is explicit\n the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]\n RPlist[-1] is the radius of the entire bounding sphere\n the first region is an inner sphere, the other regions are cocentric shells\n note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code\n in the new optimizations U = V^\\dagger-1 - G^\\dagger; here we calculate Uinv, and Uinv = V^-1-G\n "
mp.dps = mpdps
regionnum = len(RPlist)
unitRgdotRglist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitRgdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitImdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
subGmatlist = []
vecnum = 0
subbasis_head_indlist = []
rgridlist = []
All_unitBvecs = []
All_unitPvecs = []
for i in range(regionnum):
print('N wave Region #', i)
if (i == 0):
subbasis_head_indlist.append(0)
(rmRgN_Bpol, rmRgN_Ppol, rnImN_Bpol, rnImN_Ppol, unitrmnBpols, unitrmnPpols, Uinv) = speedup_Green_Taylor_Arnoldi_RgNmn_Uconverge(n, k, RPlist[0], klim=klim, Taylor_tol=Taylor_tol, invchi=invchi, Unormtol=Unormtol)
unitRgdotRglist[0] = mp.sqrt(rmnNnormsqr_Taylor(n, k, RPlist[0], rmRgN_Bpol, rmRgN_Ppol))
subGmat = ((mp.eye(Uinv.rows) * invchi) - Uinv)
subGmatlist.append(np.array(mpmath.fp.matrix(subGmat.tolist()).tolist()))
vecnum += Uinv.rows
rgrid = np.linspace(0, RPlist[0], gridpts)
rgridlist.append(rgrid)
for i in range((len(unitrmnBpols) - 1)):
All_unitBvecs.append((((k * rgrid) ** (n - 1)) * po.polyval((k * rgrid), unitrmnBpols[i].coef)))
All_unitPvecs.append((((k * rgrid) ** (n - 1)) * po.polyval((k * rgrid), unitrmnPpols[i].coef)))
else:
subbasis_head_indlist.append(vecnum)
try:
(rgrid, rsqrgrid, rdiffgrid, RgBgrid, RgPgrid, ImBgrid, ImPgrid, unitBvecs, unitPvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImNmn_Uconverge(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutBgrid = (RgBgrid + (1j * ImBgrid))
OutPgrid = (RgPgrid + (1j * ImPgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Nmn_normsqr(RgBgrid, RgPgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[0], unitPvecs[0], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[1], unitPvecs[1], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
except FloatingPointError:
(rgrid, rsqrgrid, rdiffgrid, RgBgrid, RgPgrid, ImBgrid, ImPgrid, unitBvecs, unitPvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImNmn_Uconverge_mp(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutBgrid = (RgBgrid + (1j * ImBgrid))
OutPgrid = (RgPgrid + (1j * ImPgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Nmn_normsqr(RgBgrid, RgPgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[0], unitPvecs[0], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[1], unitPvecs[1], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
Gmat = np.array(mpmath.fp.matrix(Gmat.tolist()).tolist())
subGmatlist.append(Gmat)
vecnum += Gmat.shape[0]
rgridlist.append(rgrid)
All_unitBvecs.extend(unitBvecs[:(- 2)])
All_unitPvecs.extend(unitPvecs[:(- 2)])
subbasis_head_indlist.append(vecnum)
Gmat = np.zeros((vecnum, vecnum), dtype=np.complex)
for i in range(regionnum):
indstart = subbasis_head_indlist[i]
indend = subbasis_head_indlist[(i + 1)]
Gmat[(indstart:indend, indstart:indend)] = subGmatlist[i][(:, :)]
jkcubed = (1j * (k ** 3))
for i in range(regionnum):
Rgiind = subbasis_head_indlist[i]
Imiind = (Rgiind + 1)
for j in range(i):
Rgjind = subbasis_head_indlist[j]
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitRgdotOutlist[i]))
Gmat[(Rgjind, Imiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitImdotOutlist[i]))
for j in range((i + 1), regionnum):
Rgjind = subbasis_head_indlist[j]
Imjind = (Rgjind + 1)
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotOutlist[j]) * unitRgdotRglist[i]))
Gmat[(Imjind, Rgiind)] = np.complex(((jkcubed * unitImdotOutlist[j]) * unitRgdotRglist[i]))
Uinv = ((invchi * np.eye(vecnum)) - Gmat)
fullrgrid = rgridlist[0].copy()
rboundaries = [0, gridpts]
for i in range(1, len(rgridlist)):
fullrgrid = np.concatenate((fullrgrid, rgridlist[i][1:]))
rboundaries.append(len(fullrgrid))
All_fullr_unitBvecs = []
All_fullr_unitPvecs = []
for i in range(len(rgridlist)):
for j in range(subbasis_head_indlist[i], subbasis_head_indlist[(i + 1)]):
vecBgrid = np.zeros_like(fullrgrid)
vecPgrid = np.zeros_like(fullrgrid)
if (i == 0):
vecBgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitBvecs[j][:]))
vecPgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitPvecs[j][:]))
else:
vecBgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitBvecs[j][1:]))
vecPgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitPvecs[j][1:]))
All_fullr_unitBvecs.append(vecBgrid)
All_fullr_unitPvecs.append(vecPgrid)
return (Gmat, Uinv, unitRgdotRglist, subbasis_head_indlist, fullrgrid, All_fullr_unitBvecs, All_fullr_unitPvecs) | generates a representation of the Green's function/Umatrix over spherical region of radius R
with sub-bases with support in shell regions with boundaries delineated by RPlist
this is done so that the projection operator for spatial projection based constraints is explicit
the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]
RPlist[-1] is the radius of the entire bounding sphere
the first region is an inner sphere, the other regions are cocentric shells
note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code
in the new optimizations U = V^\dagger-1 - G^\dagger; here we calculate Uinv, and Uinv = V^-1-G | dualbound/Arnoldi/spherical_multiRegion_Green_Arnoldi.py | spherical_multiRegion_Green_Arnoldi_Nmn_Uconverge | PengningChao/emdb-sphere | 0 | python | def spherical_multiRegion_Green_Arnoldi_Nmn_Uconverge(n, k, RPlist, invchi, gridpts=10000, mpdps=60, klim=25, Taylor_tol=1e-12, Unormtol=1e-08, veclim=3, delveclim=2, maxveclim=40):
"\n generates a representation of the Green's function/Umatrix over spherical region of radius R\n with sub-bases with support in shell regions with boundaries delineated by RPlist\n this is done so that the projection operator for spatial projection based constraints is explicit\n the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]\n RPlist[-1] is the radius of the entire bounding sphere\n the first region is an inner sphere, the other regions are cocentric shells\n note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code\n in the new optimizations U = V^\\dagger-1 - G^\\dagger; here we calculate Uinv, and Uinv = V^-1-G\n "
mp.dps = mpdps
regionnum = len(RPlist)
unitRgdotRglist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitRgdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitImdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
subGmatlist = []
vecnum = 0
subbasis_head_indlist = []
rgridlist = []
All_unitBvecs = []
All_unitPvecs = []
for i in range(regionnum):
print('N wave Region #', i)
if (i == 0):
subbasis_head_indlist.append(0)
(rmRgN_Bpol, rmRgN_Ppol, rnImN_Bpol, rnImN_Ppol, unitrmnBpols, unitrmnPpols, Uinv) = speedup_Green_Taylor_Arnoldi_RgNmn_Uconverge(n, k, RPlist[0], klim=klim, Taylor_tol=Taylor_tol, invchi=invchi, Unormtol=Unormtol)
unitRgdotRglist[0] = mp.sqrt(rmnNnormsqr_Taylor(n, k, RPlist[0], rmRgN_Bpol, rmRgN_Ppol))
subGmat = ((mp.eye(Uinv.rows) * invchi) - Uinv)
subGmatlist.append(np.array(mpmath.fp.matrix(subGmat.tolist()).tolist()))
vecnum += Uinv.rows
rgrid = np.linspace(0, RPlist[0], gridpts)
rgridlist.append(rgrid)
for i in range((len(unitrmnBpols) - 1)):
All_unitBvecs.append((((k * rgrid) ** (n - 1)) * po.polyval((k * rgrid), unitrmnBpols[i].coef)))
All_unitPvecs.append((((k * rgrid) ** (n - 1)) * po.polyval((k * rgrid), unitrmnPpols[i].coef)))
else:
subbasis_head_indlist.append(vecnum)
try:
(rgrid, rsqrgrid, rdiffgrid, RgBgrid, RgPgrid, ImBgrid, ImPgrid, unitBvecs, unitPvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImNmn_Uconverge(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutBgrid = (RgBgrid + (1j * ImBgrid))
OutPgrid = (RgPgrid + (1j * ImPgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Nmn_normsqr(RgBgrid, RgPgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[0], unitPvecs[0], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[1], unitPvecs[1], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
except FloatingPointError:
(rgrid, rsqrgrid, rdiffgrid, RgBgrid, RgPgrid, ImBgrid, ImPgrid, unitBvecs, unitPvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImNmn_Uconverge_mp(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutBgrid = (RgBgrid + (1j * ImBgrid))
OutPgrid = (RgPgrid + (1j * ImPgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Nmn_normsqr(RgBgrid, RgPgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[0], unitPvecs[0], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[1], unitPvecs[1], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
Gmat = np.array(mpmath.fp.matrix(Gmat.tolist()).tolist())
subGmatlist.append(Gmat)
vecnum += Gmat.shape[0]
rgridlist.append(rgrid)
All_unitBvecs.extend(unitBvecs[:(- 2)])
All_unitPvecs.extend(unitPvecs[:(- 2)])
subbasis_head_indlist.append(vecnum)
Gmat = np.zeros((vecnum, vecnum), dtype=np.complex)
for i in range(regionnum):
indstart = subbasis_head_indlist[i]
indend = subbasis_head_indlist[(i + 1)]
Gmat[(indstart:indend, indstart:indend)] = subGmatlist[i][(:, :)]
jkcubed = (1j * (k ** 3))
for i in range(regionnum):
Rgiind = subbasis_head_indlist[i]
Imiind = (Rgiind + 1)
for j in range(i):
Rgjind = subbasis_head_indlist[j]
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitRgdotOutlist[i]))
Gmat[(Rgjind, Imiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitImdotOutlist[i]))
for j in range((i + 1), regionnum):
Rgjind = subbasis_head_indlist[j]
Imjind = (Rgjind + 1)
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotOutlist[j]) * unitRgdotRglist[i]))
Gmat[(Imjind, Rgiind)] = np.complex(((jkcubed * unitImdotOutlist[j]) * unitRgdotRglist[i]))
Uinv = ((invchi * np.eye(vecnum)) - Gmat)
fullrgrid = rgridlist[0].copy()
rboundaries = [0, gridpts]
for i in range(1, len(rgridlist)):
fullrgrid = np.concatenate((fullrgrid, rgridlist[i][1:]))
rboundaries.append(len(fullrgrid))
All_fullr_unitBvecs = []
All_fullr_unitPvecs = []
for i in range(len(rgridlist)):
for j in range(subbasis_head_indlist[i], subbasis_head_indlist[(i + 1)]):
vecBgrid = np.zeros_like(fullrgrid)
vecPgrid = np.zeros_like(fullrgrid)
if (i == 0):
vecBgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitBvecs[j][:]))
vecPgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitPvecs[j][:]))
else:
vecBgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitBvecs[j][1:]))
vecPgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitPvecs[j][1:]))
All_fullr_unitBvecs.append(vecBgrid)
All_fullr_unitPvecs.append(vecPgrid)
return (Gmat, Uinv, unitRgdotRglist, subbasis_head_indlist, fullrgrid, All_fullr_unitBvecs, All_fullr_unitPvecs) | def spherical_multiRegion_Green_Arnoldi_Nmn_Uconverge(n, k, RPlist, invchi, gridpts=10000, mpdps=60, klim=25, Taylor_tol=1e-12, Unormtol=1e-08, veclim=3, delveclim=2, maxveclim=40):
"\n generates a representation of the Green's function/Umatrix over spherical region of radius R\n with sub-bases with support in shell regions with boundaries delineated by RPlist\n this is done so that the projection operator for spatial projection based constraints is explicit\n the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]\n RPlist[-1] is the radius of the entire bounding sphere\n the first region is an inner sphere, the other regions are cocentric shells\n note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code\n in the new optimizations U = V^\\dagger-1 - G^\\dagger; here we calculate Uinv, and Uinv = V^-1-G\n "
mp.dps = mpdps
regionnum = len(RPlist)
unitRgdotRglist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitRgdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
unitImdotOutlist = np.zeros(regionnum, dtype=type((1j * mp.one)))
subGmatlist = []
vecnum = 0
subbasis_head_indlist = []
rgridlist = []
All_unitBvecs = []
All_unitPvecs = []
for i in range(regionnum):
print('N wave Region #', i)
if (i == 0):
subbasis_head_indlist.append(0)
(rmRgN_Bpol, rmRgN_Ppol, rnImN_Bpol, rnImN_Ppol, unitrmnBpols, unitrmnPpols, Uinv) = speedup_Green_Taylor_Arnoldi_RgNmn_Uconverge(n, k, RPlist[0], klim=klim, Taylor_tol=Taylor_tol, invchi=invchi, Unormtol=Unormtol)
unitRgdotRglist[0] = mp.sqrt(rmnNnormsqr_Taylor(n, k, RPlist[0], rmRgN_Bpol, rmRgN_Ppol))
subGmat = ((mp.eye(Uinv.rows) * invchi) - Uinv)
subGmatlist.append(np.array(mpmath.fp.matrix(subGmat.tolist()).tolist()))
vecnum += Uinv.rows
rgrid = np.linspace(0, RPlist[0], gridpts)
rgridlist.append(rgrid)
for i in range((len(unitrmnBpols) - 1)):
All_unitBvecs.append((((k * rgrid) ** (n - 1)) * po.polyval((k * rgrid), unitrmnBpols[i].coef)))
All_unitPvecs.append((((k * rgrid) ** (n - 1)) * po.polyval((k * rgrid), unitrmnPpols[i].coef)))
else:
subbasis_head_indlist.append(vecnum)
try:
(rgrid, rsqrgrid, rdiffgrid, RgBgrid, RgPgrid, ImBgrid, ImPgrid, unitBvecs, unitPvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImNmn_Uconverge(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutBgrid = (RgBgrid + (1j * ImBgrid))
OutPgrid = (RgPgrid + (1j * ImPgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Nmn_normsqr(RgBgrid, RgPgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[0], unitPvecs[0], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[1], unitPvecs[1], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
except FloatingPointError:
(rgrid, rsqrgrid, rdiffgrid, RgBgrid, RgPgrid, ImBgrid, ImPgrid, unitBvecs, unitPvecs, Uinv, Gmat) = shell_Green_grid_Arnoldi_RgandImNmn_Uconverge_mp(n, k, RPlist[(i - 1)], RPlist[i], invchi, gridpts=gridpts, Unormtol=Unormtol, maxveclim=maxveclim)
OutBgrid = (RgBgrid + (1j * ImBgrid))
OutPgrid = (RgPgrid + (1j * ImPgrid))
unitRgdotRglist[i] = mp.sqrt(rgrid_Nmn_normsqr(RgBgrid, RgPgrid, rsqrgrid, rdiffgrid))
unitRgdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[0], unitPvecs[0], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
unitImdotOutlist[i] = mp.mpc(rgrid_Nmn_dot(unitBvecs[1], unitPvecs[1], OutBgrid, OutPgrid, rsqrgrid, rdiffgrid))
Gmat = np.array(mpmath.fp.matrix(Gmat.tolist()).tolist())
subGmatlist.append(Gmat)
vecnum += Gmat.shape[0]
rgridlist.append(rgrid)
All_unitBvecs.extend(unitBvecs[:(- 2)])
All_unitPvecs.extend(unitPvecs[:(- 2)])
subbasis_head_indlist.append(vecnum)
Gmat = np.zeros((vecnum, vecnum), dtype=np.complex)
for i in range(regionnum):
indstart = subbasis_head_indlist[i]
indend = subbasis_head_indlist[(i + 1)]
Gmat[(indstart:indend, indstart:indend)] = subGmatlist[i][(:, :)]
jkcubed = (1j * (k ** 3))
for i in range(regionnum):
Rgiind = subbasis_head_indlist[i]
Imiind = (Rgiind + 1)
for j in range(i):
Rgjind = subbasis_head_indlist[j]
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitRgdotOutlist[i]))
Gmat[(Rgjind, Imiind)] = np.complex(((jkcubed * unitRgdotRglist[j]) * unitImdotOutlist[i]))
for j in range((i + 1), regionnum):
Rgjind = subbasis_head_indlist[j]
Imjind = (Rgjind + 1)
Gmat[(Rgjind, Rgiind)] = np.complex(((jkcubed * unitRgdotOutlist[j]) * unitRgdotRglist[i]))
Gmat[(Imjind, Rgiind)] = np.complex(((jkcubed * unitImdotOutlist[j]) * unitRgdotRglist[i]))
Uinv = ((invchi * np.eye(vecnum)) - Gmat)
fullrgrid = rgridlist[0].copy()
rboundaries = [0, gridpts]
for i in range(1, len(rgridlist)):
fullrgrid = np.concatenate((fullrgrid, rgridlist[i][1:]))
rboundaries.append(len(fullrgrid))
All_fullr_unitBvecs = []
All_fullr_unitPvecs = []
for i in range(len(rgridlist)):
for j in range(subbasis_head_indlist[i], subbasis_head_indlist[(i + 1)]):
vecBgrid = np.zeros_like(fullrgrid)
vecPgrid = np.zeros_like(fullrgrid)
if (i == 0):
vecBgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitBvecs[j][:]))
vecPgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitPvecs[j][:]))
else:
vecBgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitBvecs[j][1:]))
vecPgrid[rboundaries[i]:rboundaries[(i + 1)]] = mparr_to_npreal(mp_re(All_unitPvecs[j][1:]))
All_fullr_unitBvecs.append(vecBgrid)
All_fullr_unitPvecs.append(vecPgrid)
return (Gmat, Uinv, unitRgdotRglist, subbasis_head_indlist, fullrgrid, All_fullr_unitBvecs, All_fullr_unitPvecs)<|docstring|>generates a representation of the Green's function/Umatrix over spherical region of radius R
with sub-bases with support in shell regions with boundaries delineated by RPlist
this is done so that the projection operator for spatial projection based constraints is explicit
the sub-regions are 0-RPlist[0], RPlist[0]-RPlist[1], ..., RPlist[-2]-RPlist[-1]
RPlist[-1] is the radius of the entire bounding sphere
the first region is an inner sphere, the other regions are cocentric shells
note here we are still using the old convention for the U matrix to be consistent with older Arnoldi code
in the new optimizations U = V^\dagger-1 - G^\dagger; here we calculate Uinv, and Uinv = V^-1-G<|endoftext|> |
04f42460f5f8ca60bfdd77e93f4a098a6376ad6eb478ce9cb75956be8648f65c | def test_parse_exif_string(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_string` definition.\n '
exif_tag = ExifTag('EXIF', 'Make', 'Canon', '271')
self.assertEqual(parse_exif_string(exif_tag), 'Canon') | Tests :func:`colour_hdri.utilities.exif.parse_exif_string` definition. | colour_hdri/utilities/tests/test_exif.py | test_parse_exif_string | colour-science/colour-hdri | 92 | python | def test_parse_exif_string(self):
'\n \n '
exif_tag = ExifTag('EXIF', 'Make', 'Canon', '271')
self.assertEqual(parse_exif_string(exif_tag), 'Canon') | def test_parse_exif_string(self):
'\n \n '
exif_tag = ExifTag('EXIF', 'Make', 'Canon', '271')
self.assertEqual(parse_exif_string(exif_tag), 'Canon')<|docstring|>Tests :func:`colour_hdri.utilities.exif.parse_exif_string` definition.<|endoftext|> |
0766ae1195add71f482063514e55e065d07c2932c533770cd0b0aef379b367c6 | def test_parse_exif_numeric(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_numeric` definition.\n '
exif_tag = ExifTag('EXIF', 'Focal Length', '16', '37386')
self.assertEqual(parse_exif_numeric(exif_tag), 16)
exif_tag = ExifTag('EXIF', 'Focal Length', '16', '37386')
self.assertIsInstance(parse_exif_numeric(exif_tag, np.int_), np.int_) | Tests :func:`colour_hdri.utilities.exif.parse_exif_numeric` definition. | colour_hdri/utilities/tests/test_exif.py | test_parse_exif_numeric | colour-science/colour-hdri | 92 | python | def test_parse_exif_numeric(self):
'\n \n '
exif_tag = ExifTag('EXIF', 'Focal Length', '16', '37386')
self.assertEqual(parse_exif_numeric(exif_tag), 16)
exif_tag = ExifTag('EXIF', 'Focal Length', '16', '37386')
self.assertIsInstance(parse_exif_numeric(exif_tag, np.int_), np.int_) | def test_parse_exif_numeric(self):
'\n \n '
exif_tag = ExifTag('EXIF', 'Focal Length', '16', '37386')
self.assertEqual(parse_exif_numeric(exif_tag), 16)
exif_tag = ExifTag('EXIF', 'Focal Length', '16', '37386')
self.assertIsInstance(parse_exif_numeric(exif_tag, np.int_), np.int_)<|docstring|>Tests :func:`colour_hdri.utilities.exif.parse_exif_numeric` definition.<|endoftext|> |
b014e78185386a8ea62391ee4c6811d3feab3cb4c4c96016436b3ecefc2b2ed8 | def test_parse_exif_fraction(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_fraction`\n definition.\n '
exif_tag = ExifTag('EXIF', 'Exposure Time', '0.01666666667', '33434')
self.assertAlmostEqual(parse_exif_fraction(exif_tag), 0.01666666, places=7)
exif_tag = ExifTag('EXIF', 'Exposure Time', '10/4000', '33434')
self.assertAlmostEqual(parse_exif_fraction(exif_tag), 0.0025, places=7)
self.assertIsInstance(parse_exif_fraction(exif_tag, np.int_), np.int_) | Tests :func:`colour_hdri.utilities.exif.parse_exif_fraction`
definition. | colour_hdri/utilities/tests/test_exif.py | test_parse_exif_fraction | colour-science/colour-hdri | 92 | python | def test_parse_exif_fraction(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_fraction`\n definition.\n '
exif_tag = ExifTag('EXIF', 'Exposure Time', '0.01666666667', '33434')
self.assertAlmostEqual(parse_exif_fraction(exif_tag), 0.01666666, places=7)
exif_tag = ExifTag('EXIF', 'Exposure Time', '10/4000', '33434')
self.assertAlmostEqual(parse_exif_fraction(exif_tag), 0.0025, places=7)
self.assertIsInstance(parse_exif_fraction(exif_tag, np.int_), np.int_) | def test_parse_exif_fraction(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_fraction`\n definition.\n '
exif_tag = ExifTag('EXIF', 'Exposure Time', '0.01666666667', '33434')
self.assertAlmostEqual(parse_exif_fraction(exif_tag), 0.01666666, places=7)
exif_tag = ExifTag('EXIF', 'Exposure Time', '10/4000', '33434')
self.assertAlmostEqual(parse_exif_fraction(exif_tag), 0.0025, places=7)
self.assertIsInstance(parse_exif_fraction(exif_tag, np.int_), np.int_)<|docstring|>Tests :func:`colour_hdri.utilities.exif.parse_exif_fraction`
definition.<|endoftext|> |
b2a6b188e19105b0ca3150706ae2cbf0b315500c18c9e7cd2d3fc13943f2d5d0 | def test_parse_exif_array(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_array` definition.\n '
exif_tag = ExifTag('EXIF', 'Color Matrix 1', '0.5309 -0.0229 -0.0336 -0.6241 1.3265 0.3337 -0.0817 0.1215 0.6664', '50721')
np.testing.assert_array_equal(parse_exif_array(exif_tag), np.array([0.5309, (- 0.0229), (- 0.0336), (- 0.6241), 1.3265, 0.3337, (- 0.0817), 0.1215, 0.6664]))
np.testing.assert_array_equal(parse_exif_array(exif_tag, shape=(3, 3)), np.array([[0.5309, (- 0.0229), (- 0.0336)], [(- 0.6241), 1.3265, 0.3337], [(- 0.0817), 0.1215, 0.6664]])) | Tests :func:`colour_hdri.utilities.exif.parse_exif_array` definition. | colour_hdri/utilities/tests/test_exif.py | test_parse_exif_array | colour-science/colour-hdri | 92 | python | def test_parse_exif_array(self):
'\n \n '
exif_tag = ExifTag('EXIF', 'Color Matrix 1', '0.5309 -0.0229 -0.0336 -0.6241 1.3265 0.3337 -0.0817 0.1215 0.6664', '50721')
np.testing.assert_array_equal(parse_exif_array(exif_tag), np.array([0.5309, (- 0.0229), (- 0.0336), (- 0.6241), 1.3265, 0.3337, (- 0.0817), 0.1215, 0.6664]))
np.testing.assert_array_equal(parse_exif_array(exif_tag, shape=(3, 3)), np.array([[0.5309, (- 0.0229), (- 0.0336)], [(- 0.6241), 1.3265, 0.3337], [(- 0.0817), 0.1215, 0.6664]])) | def test_parse_exif_array(self):
'\n \n '
exif_tag = ExifTag('EXIF', 'Color Matrix 1', '0.5309 -0.0229 -0.0336 -0.6241 1.3265 0.3337 -0.0817 0.1215 0.6664', '50721')
np.testing.assert_array_equal(parse_exif_array(exif_tag), np.array([0.5309, (- 0.0229), (- 0.0336), (- 0.6241), 1.3265, 0.3337, (- 0.0817), 0.1215, 0.6664]))
np.testing.assert_array_equal(parse_exif_array(exif_tag, shape=(3, 3)), np.array([[0.5309, (- 0.0229), (- 0.0336)], [(- 0.6241), 1.3265, 0.3337], [(- 0.0817), 0.1215, 0.6664]]))<|docstring|>Tests :func:`colour_hdri.utilities.exif.parse_exif_array` definition.<|endoftext|> |
01db84fe224761570bbb95231d8e12e674ad13c9eecf315ee6882e94d57b0538 | def test_parse_exif_data(self):
'\n Tests :func:`colour_hdri.utilities.exif.parse_exif_data` definition.\n '
self.assertListEqual(parse_exif_data('[XMP] - Description :'), ['XMP', '-', 'Description', ''])
self.assertListEqual(parse_exif_data('[EXIF] 296 Resolution Unit : 2'), ['EXIF', '296', 'Resolution Unit', '2'])
self.assertListEqual(parse_exif_data('[ICC_Profile] 8 Profile Version : 528'), ['ICC_Profile', '8', 'Profile Version', '528']) | Tests :func:`colour_hdri.utilities.exif.parse_exif_data` definition. | colour_hdri/utilities/tests/test_exif.py | test_parse_exif_data | colour-science/colour-hdri | 92 | python | def test_parse_exif_data(self):
'\n \n '
self.assertListEqual(parse_exif_data('[XMP] - Description :'), ['XMP', '-', 'Description', ])
self.assertListEqual(parse_exif_data('[EXIF] 296 Resolution Unit : 2'), ['EXIF', '296', 'Resolution Unit', '2'])
self.assertListEqual(parse_exif_data('[ICC_Profile] 8 Profile Version : 528'), ['ICC_Profile', '8', 'Profile Version', '528']) | def test_parse_exif_data(self):
'\n \n '
self.assertListEqual(parse_exif_data('[XMP] - Description :'), ['XMP', '-', 'Description', ])
self.assertListEqual(parse_exif_data('[EXIF] 296 Resolution Unit : 2'), ['EXIF', '296', 'Resolution Unit', '2'])
self.assertListEqual(parse_exif_data('[ICC_Profile] 8 Profile Version : 528'), ['ICC_Profile', '8', 'Profile Version', '528'])<|docstring|>Tests :func:`colour_hdri.utilities.exif.parse_exif_data` definition.<|endoftext|> |
6ff6524531e3f1a825c131585ab57759829398c8397b0edd6112342bdc5f85e6 | def test_read_exif_tags(self):
'\n Tests :func:`colour_hdri.utilities.exif.read_exif_tags` definition.\n '
test_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
exif_data = vivified_to_dict(read_exif_tags(test_jpg_image))
self.assertIsInstance(exif_data, type(dict()))
self.assertListEqual(sorted(exif_data.keys()), ['Composite', 'EXIF', 'ExifTool', 'File', 'ICC_Profile', 'JFIF', 'Photoshop', 'XMP'])
self.assertListEqual(sorted(exif_data['EXIF'].values()), [[ExifTag('EXIF', 'Camera Model Name', 'EOS 5D Mark II', '272')], [ExifTag('EXIF', 'Create Date', '2015:09:19 03:39:20', '36868')], [ExifTag('EXIF', 'Date/Time Original', '2015:09:19 03:39:20', '36867')], [ExifTag('EXIF', 'Exif Image Height', '426', '40963')], [ExifTag('EXIF', 'Exif Image Width', '640', '40962')], [ExifTag('EXIF', 'Exposure Time', '0.125', '33434')], [ExifTag('EXIF', 'F Number', '8', '33437')], [ExifTag('EXIF', 'Focal Length', '16', '37386')], [ExifTag('EXIF', 'ISO', '100', '34855')], [ExifTag('EXIF', 'Make', 'Canon', '271')], [ExifTag('EXIF', 'Modify Date', '2015:09:19 03:39:20', '306')], [ExifTag('EXIF', 'Orientation', '1', '274')], [ExifTag('EXIF', 'Photometric Interpretation', '2', '262')], [ExifTag('EXIF', 'Resolution Unit', '2', '296')], [ExifTag('EXIF', 'Software', 'Photos 1.0.1', '305')], [ExifTag('EXIF', 'X Resolution', '72', '282')], [ExifTag('EXIF', 'Y Resolution', '72', '283')]]) | Tests :func:`colour_hdri.utilities.exif.read_exif_tags` definition. | colour_hdri/utilities/tests/test_exif.py | test_read_exif_tags | colour-science/colour-hdri | 92 | python | def test_read_exif_tags(self):
'\n \n '
test_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
exif_data = vivified_to_dict(read_exif_tags(test_jpg_image))
self.assertIsInstance(exif_data, type(dict()))
self.assertListEqual(sorted(exif_data.keys()), ['Composite', 'EXIF', 'ExifTool', 'File', 'ICC_Profile', 'JFIF', 'Photoshop', 'XMP'])
self.assertListEqual(sorted(exif_data['EXIF'].values()), [[ExifTag('EXIF', 'Camera Model Name', 'EOS 5D Mark II', '272')], [ExifTag('EXIF', 'Create Date', '2015:09:19 03:39:20', '36868')], [ExifTag('EXIF', 'Date/Time Original', '2015:09:19 03:39:20', '36867')], [ExifTag('EXIF', 'Exif Image Height', '426', '40963')], [ExifTag('EXIF', 'Exif Image Width', '640', '40962')], [ExifTag('EXIF', 'Exposure Time', '0.125', '33434')], [ExifTag('EXIF', 'F Number', '8', '33437')], [ExifTag('EXIF', 'Focal Length', '16', '37386')], [ExifTag('EXIF', 'ISO', '100', '34855')], [ExifTag('EXIF', 'Make', 'Canon', '271')], [ExifTag('EXIF', 'Modify Date', '2015:09:19 03:39:20', '306')], [ExifTag('EXIF', 'Orientation', '1', '274')], [ExifTag('EXIF', 'Photometric Interpretation', '2', '262')], [ExifTag('EXIF', 'Resolution Unit', '2', '296')], [ExifTag('EXIF', 'Software', 'Photos 1.0.1', '305')], [ExifTag('EXIF', 'X Resolution', '72', '282')], [ExifTag('EXIF', 'Y Resolution', '72', '283')]]) | def test_read_exif_tags(self):
'\n \n '
test_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
exif_data = vivified_to_dict(read_exif_tags(test_jpg_image))
self.assertIsInstance(exif_data, type(dict()))
self.assertListEqual(sorted(exif_data.keys()), ['Composite', 'EXIF', 'ExifTool', 'File', 'ICC_Profile', 'JFIF', 'Photoshop', 'XMP'])
self.assertListEqual(sorted(exif_data['EXIF'].values()), [[ExifTag('EXIF', 'Camera Model Name', 'EOS 5D Mark II', '272')], [ExifTag('EXIF', 'Create Date', '2015:09:19 03:39:20', '36868')], [ExifTag('EXIF', 'Date/Time Original', '2015:09:19 03:39:20', '36867')], [ExifTag('EXIF', 'Exif Image Height', '426', '40963')], [ExifTag('EXIF', 'Exif Image Width', '640', '40962')], [ExifTag('EXIF', 'Exposure Time', '0.125', '33434')], [ExifTag('EXIF', 'F Number', '8', '33437')], [ExifTag('EXIF', 'Focal Length', '16', '37386')], [ExifTag('EXIF', 'ISO', '100', '34855')], [ExifTag('EXIF', 'Make', 'Canon', '271')], [ExifTag('EXIF', 'Modify Date', '2015:09:19 03:39:20', '306')], [ExifTag('EXIF', 'Orientation', '1', '274')], [ExifTag('EXIF', 'Photometric Interpretation', '2', '262')], [ExifTag('EXIF', 'Resolution Unit', '2', '296')], [ExifTag('EXIF', 'Software', 'Photos 1.0.1', '305')], [ExifTag('EXIF', 'X Resolution', '72', '282')], [ExifTag('EXIF', 'Y Resolution', '72', '283')]])<|docstring|>Tests :func:`colour_hdri.utilities.exif.read_exif_tags` definition.<|endoftext|> |
41dc023123784d4e46888719acf8e62d011c740f49eef70406c3a47d5b7eef23 | def setUp(self):
'\n Initialises common tests attributes.\n '
self._temporary_directory = tempfile.mkdtemp() | Initialises common tests attributes. | colour_hdri/utilities/tests/test_exif.py | setUp | colour-science/colour-hdri | 92 | python | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp() | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp()<|docstring|>Initialises common tests attributes.<|endoftext|> |
ebcbcb9ea0b8083c9de351475f09c7771820d6cd767ff0d307c2cab1efef7028 | def tearDown(self):
'\n After tests actions.\n '
shutil.rmtree(self._temporary_directory) | After tests actions. | colour_hdri/utilities/tests/test_exif.py | tearDown | colour-science/colour-hdri | 92 | python | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory) | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory)<|docstring|>After tests actions.<|endoftext|> |
a6aaa48d996dfb975a12809f6b469adca151082414e0a4d6e4c3191ee6c70962 | def test_copy_exif_tags(self):
'\n Tests :func:`colour_hdri.utilities.exif.copy_exif_tags` definition.\n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '')
copy_exif_tags(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0') | Tests :func:`colour_hdri.utilities.exif.copy_exif_tags` definition. | colour_hdri/utilities/tests/test_exif.py | test_copy_exif_tags | colour-science/colour-hdri | 92 | python | def test_copy_exif_tags(self):
'\n \n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), )
copy_exif_tags(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0') | def test_copy_exif_tags(self):
'\n \n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), )
copy_exif_tags(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')<|docstring|>Tests :func:`colour_hdri.utilities.exif.copy_exif_tags` definition.<|endoftext|> |
41dc023123784d4e46888719acf8e62d011c740f49eef70406c3a47d5b7eef23 | def setUp(self):
'\n Initialises common tests attributes.\n '
self._temporary_directory = tempfile.mkdtemp() | Initialises common tests attributes. | colour_hdri/utilities/tests/test_exif.py | setUp | colour-science/colour-hdri | 92 | python | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp() | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp()<|docstring|>Initialises common tests attributes.<|endoftext|> |
ebcbcb9ea0b8083c9de351475f09c7771820d6cd767ff0d307c2cab1efef7028 | def tearDown(self):
'\n After tests actions.\n '
shutil.rmtree(self._temporary_directory) | After tests actions. | colour_hdri/utilities/tests/test_exif.py | tearDown | colour-science/colour-hdri | 92 | python | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory) | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory)<|docstring|>After tests actions.<|endoftext|> |
bd9979619051a176fbca0f5e631c2a3a97748ae7b46ca94ae10afb798f19503c | def test_update_exif_tags(self):
'\n Tests :func:`colour_hdri.utilities.exif.update_exif_tags` definition.\n '
reference_jpg_images = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))
test_jpg_images = []
for reference_jpg_image in reference_jpg_images:
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '')
test_jpg_images.append(test_jpg_image)
update_exif_tags(zip(reference_jpg_images, test_jpg_images))
for test_jpg_image in test_jpg_images:
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0') | Tests :func:`colour_hdri.utilities.exif.update_exif_tags` definition. | colour_hdri/utilities/tests/test_exif.py | test_update_exif_tags | colour-science/colour-hdri | 92 | python | def test_update_exif_tags(self):
'\n \n '
reference_jpg_images = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))
test_jpg_images = []
for reference_jpg_image in reference_jpg_images:
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), )
test_jpg_images.append(test_jpg_image)
update_exif_tags(zip(reference_jpg_images, test_jpg_images))
for test_jpg_image in test_jpg_images:
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0') | def test_update_exif_tags(self):
'\n \n '
reference_jpg_images = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))
test_jpg_images = []
for reference_jpg_image in reference_jpg_images:
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), )
test_jpg_images.append(test_jpg_image)
update_exif_tags(zip(reference_jpg_images, test_jpg_images))
for test_jpg_image in test_jpg_images:
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')<|docstring|>Tests :func:`colour_hdri.utilities.exif.update_exif_tags` definition.<|endoftext|> |
41dc023123784d4e46888719acf8e62d011c740f49eef70406c3a47d5b7eef23 | def setUp(self):
'\n Initialises common tests attributes.\n '
self._temporary_directory = tempfile.mkdtemp() | Initialises common tests attributes. | colour_hdri/utilities/tests/test_exif.py | setUp | colour-science/colour-hdri | 92 | python | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp() | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp()<|docstring|>Initialises common tests attributes.<|endoftext|> |
ebcbcb9ea0b8083c9de351475f09c7771820d6cd767ff0d307c2cab1efef7028 | def tearDown(self):
'\n After tests actions.\n '
shutil.rmtree(self._temporary_directory) | After tests actions. | colour_hdri/utilities/tests/test_exif.py | tearDown | colour-science/colour-hdri | 92 | python | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory) | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory)<|docstring|>After tests actions.<|endoftext|> |
06ec2cb3f0e1004946e66702e330e3db7485609f4218785a2f1d82053087330c | def test_delete_exif_tags(self):
'\n Tests :func:`colour_hdri.utilities.exif.delete_exif_tags` definition.\n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '') | Tests :func:`colour_hdri.utilities.exif.delete_exif_tags` definition. | colour_hdri/utilities/tests/test_exif.py | test_delete_exif_tags | colour-science/colour-hdri | 92 | python | def test_delete_exif_tags(self):
'\n \n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), ) | def test_delete_exif_tags(self):
'\n \n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
delete_exif_tags(test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), )<|docstring|>Tests :func:`colour_hdri.utilities.exif.delete_exif_tags` definition.<|endoftext|> |
e95b1c3e107f92023b1c9bb103852e3b97ae0e8f8a3c1502ff2d7418d818f826 | def test_read_exif_tag(self):
'\n Tests :func:`colour_hdri.utilities.exif.read_exif_tag` definition.\n '
test_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
self.assertEqual(read_exif_tag(test_jpg_image, 'ExposureTime'), '1/8')
self.assertEqual(read_exif_tag(test_jpg_image, 'ISO'), '100') | Tests :func:`colour_hdri.utilities.exif.read_exif_tag` definition. | colour_hdri/utilities/tests/test_exif.py | test_read_exif_tag | colour-science/colour-hdri | 92 | python | def test_read_exif_tag(self):
'\n \n '
test_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
self.assertEqual(read_exif_tag(test_jpg_image, 'ExposureTime'), '1/8')
self.assertEqual(read_exif_tag(test_jpg_image, 'ISO'), '100') | def test_read_exif_tag(self):
'\n \n '
test_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
self.assertEqual(read_exif_tag(test_jpg_image, 'Aperture'), '8.0')
self.assertEqual(read_exif_tag(test_jpg_image, 'ExposureTime'), '1/8')
self.assertEqual(read_exif_tag(test_jpg_image, 'ISO'), '100')<|docstring|>Tests :func:`colour_hdri.utilities.exif.read_exif_tag` definition.<|endoftext|> |
41dc023123784d4e46888719acf8e62d011c740f49eef70406c3a47d5b7eef23 | def setUp(self):
'\n Initialises common tests attributes.\n '
self._temporary_directory = tempfile.mkdtemp() | Initialises common tests attributes. | colour_hdri/utilities/tests/test_exif.py | setUp | colour-science/colour-hdri | 92 | python | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp() | def setUp(self):
'\n \n '
self._temporary_directory = tempfile.mkdtemp()<|docstring|>Initialises common tests attributes.<|endoftext|> |
ebcbcb9ea0b8083c9de351475f09c7771820d6cd767ff0d307c2cab1efef7028 | def tearDown(self):
'\n After tests actions.\n '
shutil.rmtree(self._temporary_directory) | After tests actions. | colour_hdri/utilities/tests/test_exif.py | tearDown | colour-science/colour-hdri | 92 | python | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory) | def tearDown(self):
'\n \n '
shutil.rmtree(self._temporary_directory)<|docstring|>After tests actions.<|endoftext|> |
fd2c168a70af758fbbd2321d5371be8e7a1532b54395ee27ab82da6caa3be902 | def test_write_exif_tag(self):
'\n Tests :func:`colour_hdri.utilities.exif.write_exif_tag` definition.\n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'FNumber'), '8.0')
write_exif_tag(test_jpg_image, 'FNumber', '16.0')
self.assertEqual(read_exif_tag(test_jpg_image, 'FNumber'), '16.0') | Tests :func:`colour_hdri.utilities.exif.write_exif_tag` definition. | colour_hdri/utilities/tests/test_exif.py | test_write_exif_tag | colour-science/colour-hdri | 92 | python | def test_write_exif_tag(self):
'\n \n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'FNumber'), '8.0')
write_exif_tag(test_jpg_image, 'FNumber', '16.0')
self.assertEqual(read_exif_tag(test_jpg_image, 'FNumber'), '16.0') | def test_write_exif_tag(self):
'\n \n '
reference_jpg_image = filter_files(FROBISHER_001_DIRECTORY, ('jpg',))[0]
test_jpg_image = os.path.join(self._temporary_directory, os.path.basename(reference_jpg_image))
shutil.copyfile(reference_jpg_image, test_jpg_image)
self.assertEqual(read_exif_tag(test_jpg_image, 'FNumber'), '8.0')
write_exif_tag(test_jpg_image, 'FNumber', '16.0')
self.assertEqual(read_exif_tag(test_jpg_image, 'FNumber'), '16.0')<|docstring|>Tests :func:`colour_hdri.utilities.exif.write_exif_tag` definition.<|endoftext|> |
86a9bd2a4b0e87eb76657f749300bccc7c153328ee4d9679752b2dde9cf87ba6 | def __init__(self, **kwargs):
'\n Initializes a new RevocationStatus object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param time_of_revocation:\n The value to assign to the time_of_revocation property of this RevocationStatus.\n :type time_of_revocation: datetime\n\n :param revocation_reason:\n The value to assign to the revocation_reason property of this RevocationStatus.\n Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", \'UNKNOWN_ENUM_VALUE\'.\n Any unrecognized values returned by a service will be mapped to \'UNKNOWN_ENUM_VALUE\'.\n :type revocation_reason: str\n\n '
self.swagger_types = {'time_of_revocation': 'datetime', 'revocation_reason': 'str'}
self.attribute_map = {'time_of_revocation': 'timeOfRevocation', 'revocation_reason': 'revocationReason'}
self._time_of_revocation = None
self._revocation_reason = None | Initializes a new RevocationStatus object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param time_of_revocation:
The value to assign to the time_of_revocation property of this RevocationStatus.
:type time_of_revocation: datetime
:param revocation_reason:
The value to assign to the revocation_reason property of this RevocationStatus.
Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", 'UNKNOWN_ENUM_VALUE'.
Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.
:type revocation_reason: str | src/oci/certificates_management/models/revocation_status.py | __init__ | ezequielramos/oci-python-sdk | 249 | python | def __init__(self, **kwargs):
'\n Initializes a new RevocationStatus object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param time_of_revocation:\n The value to assign to the time_of_revocation property of this RevocationStatus.\n :type time_of_revocation: datetime\n\n :param revocation_reason:\n The value to assign to the revocation_reason property of this RevocationStatus.\n Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", \'UNKNOWN_ENUM_VALUE\'.\n Any unrecognized values returned by a service will be mapped to \'UNKNOWN_ENUM_VALUE\'.\n :type revocation_reason: str\n\n '
self.swagger_types = {'time_of_revocation': 'datetime', 'revocation_reason': 'str'}
self.attribute_map = {'time_of_revocation': 'timeOfRevocation', 'revocation_reason': 'revocationReason'}
self._time_of_revocation = None
self._revocation_reason = None | def __init__(self, **kwargs):
'\n Initializes a new RevocationStatus object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param time_of_revocation:\n The value to assign to the time_of_revocation property of this RevocationStatus.\n :type time_of_revocation: datetime\n\n :param revocation_reason:\n The value to assign to the revocation_reason property of this RevocationStatus.\n Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", \'UNKNOWN_ENUM_VALUE\'.\n Any unrecognized values returned by a service will be mapped to \'UNKNOWN_ENUM_VALUE\'.\n :type revocation_reason: str\n\n '
self.swagger_types = {'time_of_revocation': 'datetime', 'revocation_reason': 'str'}
self.attribute_map = {'time_of_revocation': 'timeOfRevocation', 'revocation_reason': 'revocationReason'}
self._time_of_revocation = None
self._revocation_reason = None<|docstring|>Initializes a new RevocationStatus object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param time_of_revocation:
The value to assign to the time_of_revocation property of this RevocationStatus.
:type time_of_revocation: datetime
:param revocation_reason:
The value to assign to the revocation_reason property of this RevocationStatus.
Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", 'UNKNOWN_ENUM_VALUE'.
Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.
:type revocation_reason: str<|endoftext|> |
91cf74a544e817bf256ac96e032636c209b0f9d669ba4dde482fb245be8a261b | @property
def time_of_revocation(self):
'\n **[Required]** Gets the time_of_revocation of this RevocationStatus.\n The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.\n Example: `2019-04-03T21:10:29.600Z`\n\n __ https://tools.ietf.org/html/rfc3339\n\n\n :return: The time_of_revocation of this RevocationStatus.\n :rtype: datetime\n '
return self._time_of_revocation | **[Required]** Gets the time_of_revocation of this RevocationStatus.
The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.
Example: `2019-04-03T21:10:29.600Z`
__ https://tools.ietf.org/html/rfc3339
:return: The time_of_revocation of this RevocationStatus.
:rtype: datetime | src/oci/certificates_management/models/revocation_status.py | time_of_revocation | ezequielramos/oci-python-sdk | 249 | python | @property
def time_of_revocation(self):
'\n **[Required]** Gets the time_of_revocation of this RevocationStatus.\n The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.\n Example: `2019-04-03T21:10:29.600Z`\n\n __ https://tools.ietf.org/html/rfc3339\n\n\n :return: The time_of_revocation of this RevocationStatus.\n :rtype: datetime\n '
return self._time_of_revocation | @property
def time_of_revocation(self):
'\n **[Required]** Gets the time_of_revocation of this RevocationStatus.\n The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.\n Example: `2019-04-03T21:10:29.600Z`\n\n __ https://tools.ietf.org/html/rfc3339\n\n\n :return: The time_of_revocation of this RevocationStatus.\n :rtype: datetime\n '
return self._time_of_revocation<|docstring|>**[Required]** Gets the time_of_revocation of this RevocationStatus.
The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.
Example: `2019-04-03T21:10:29.600Z`
__ https://tools.ietf.org/html/rfc3339
:return: The time_of_revocation of this RevocationStatus.
:rtype: datetime<|endoftext|> |
3d0930f981f45762f2a15148e5854c43e5b59e88fc2072a15149b1980fa93351 | @time_of_revocation.setter
def time_of_revocation(self, time_of_revocation):
'\n Sets the time_of_revocation of this RevocationStatus.\n The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.\n Example: `2019-04-03T21:10:29.600Z`\n\n __ https://tools.ietf.org/html/rfc3339\n\n\n :param time_of_revocation: The time_of_revocation of this RevocationStatus.\n :type: datetime\n '
self._time_of_revocation = time_of_revocation | Sets the time_of_revocation of this RevocationStatus.
The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.
Example: `2019-04-03T21:10:29.600Z`
__ https://tools.ietf.org/html/rfc3339
:param time_of_revocation: The time_of_revocation of this RevocationStatus.
:type: datetime | src/oci/certificates_management/models/revocation_status.py | time_of_revocation | ezequielramos/oci-python-sdk | 249 | python | @time_of_revocation.setter
def time_of_revocation(self, time_of_revocation):
'\n Sets the time_of_revocation of this RevocationStatus.\n The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.\n Example: `2019-04-03T21:10:29.600Z`\n\n __ https://tools.ietf.org/html/rfc3339\n\n\n :param time_of_revocation: The time_of_revocation of this RevocationStatus.\n :type: datetime\n '
self._time_of_revocation = time_of_revocation | @time_of_revocation.setter
def time_of_revocation(self, time_of_revocation):
'\n Sets the time_of_revocation of this RevocationStatus.\n The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.\n Example: `2019-04-03T21:10:29.600Z`\n\n __ https://tools.ietf.org/html/rfc3339\n\n\n :param time_of_revocation: The time_of_revocation of this RevocationStatus.\n :type: datetime\n '
self._time_of_revocation = time_of_revocation<|docstring|>Sets the time_of_revocation of this RevocationStatus.
The time when the entity was revoked, expressed in `RFC 3339`__ timestamp format.
Example: `2019-04-03T21:10:29.600Z`
__ https://tools.ietf.org/html/rfc3339
:param time_of_revocation: The time_of_revocation of this RevocationStatus.
:type: datetime<|endoftext|> |
40982714f46e6e718dc391b253d5f584554fa1add8e7831c6a1e0495e67c1f6d | @property
def revocation_reason(self):
'\n **[Required]** Gets the revocation_reason of this RevocationStatus.\n The reason the certificate or certificate authority (CA) was revoked.\n\n Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", \'UNKNOWN_ENUM_VALUE\'.\n Any unrecognized values returned by a service will be mapped to \'UNKNOWN_ENUM_VALUE\'.\n\n\n :return: The revocation_reason of this RevocationStatus.\n :rtype: str\n '
return self._revocation_reason | **[Required]** Gets the revocation_reason of this RevocationStatus.
The reason the certificate or certificate authority (CA) was revoked.
Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", 'UNKNOWN_ENUM_VALUE'.
Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.
:return: The revocation_reason of this RevocationStatus.
:rtype: str | src/oci/certificates_management/models/revocation_status.py | revocation_reason | ezequielramos/oci-python-sdk | 249 | python | @property
def revocation_reason(self):
'\n **[Required]** Gets the revocation_reason of this RevocationStatus.\n The reason the certificate or certificate authority (CA) was revoked.\n\n Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", \'UNKNOWN_ENUM_VALUE\'.\n Any unrecognized values returned by a service will be mapped to \'UNKNOWN_ENUM_VALUE\'.\n\n\n :return: The revocation_reason of this RevocationStatus.\n :rtype: str\n '
return self._revocation_reason | @property
def revocation_reason(self):
'\n **[Required]** Gets the revocation_reason of this RevocationStatus.\n The reason the certificate or certificate authority (CA) was revoked.\n\n Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", \'UNKNOWN_ENUM_VALUE\'.\n Any unrecognized values returned by a service will be mapped to \'UNKNOWN_ENUM_VALUE\'.\n\n\n :return: The revocation_reason of this RevocationStatus.\n :rtype: str\n '
return self._revocation_reason<|docstring|>**[Required]** Gets the revocation_reason of this RevocationStatus.
The reason the certificate or certificate authority (CA) was revoked.
Allowed values for this property are: "UNSPECIFIED", "KEY_COMPROMISE", "CA_COMPROMISE", "AFFILIATION_CHANGED", "SUPERSEDED", "CESSATION_OF_OPERATION", "PRIVILEGE_WITHDRAWN", "AA_COMPROMISE", 'UNKNOWN_ENUM_VALUE'.
Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'.
:return: The revocation_reason of this RevocationStatus.
:rtype: str<|endoftext|> |
3ee5b070dfa246189fe995dbd5e72b0a78288f4776bc0e36441a7c4c16f95efa | @revocation_reason.setter
def revocation_reason(self, revocation_reason):
'\n Sets the revocation_reason of this RevocationStatus.\n The reason the certificate or certificate authority (CA) was revoked.\n\n\n :param revocation_reason: The revocation_reason of this RevocationStatus.\n :type: str\n '
allowed_values = ['UNSPECIFIED', 'KEY_COMPROMISE', 'CA_COMPROMISE', 'AFFILIATION_CHANGED', 'SUPERSEDED', 'CESSATION_OF_OPERATION', 'PRIVILEGE_WITHDRAWN', 'AA_COMPROMISE']
if (not value_allowed_none_or_none_sentinel(revocation_reason, allowed_values)):
revocation_reason = 'UNKNOWN_ENUM_VALUE'
self._revocation_reason = revocation_reason | Sets the revocation_reason of this RevocationStatus.
The reason the certificate or certificate authority (CA) was revoked.
:param revocation_reason: The revocation_reason of this RevocationStatus.
:type: str | src/oci/certificates_management/models/revocation_status.py | revocation_reason | ezequielramos/oci-python-sdk | 249 | python | @revocation_reason.setter
def revocation_reason(self, revocation_reason):
'\n Sets the revocation_reason of this RevocationStatus.\n The reason the certificate or certificate authority (CA) was revoked.\n\n\n :param revocation_reason: The revocation_reason of this RevocationStatus.\n :type: str\n '
allowed_values = ['UNSPECIFIED', 'KEY_COMPROMISE', 'CA_COMPROMISE', 'AFFILIATION_CHANGED', 'SUPERSEDED', 'CESSATION_OF_OPERATION', 'PRIVILEGE_WITHDRAWN', 'AA_COMPROMISE']
if (not value_allowed_none_or_none_sentinel(revocation_reason, allowed_values)):
revocation_reason = 'UNKNOWN_ENUM_VALUE'
self._revocation_reason = revocation_reason | @revocation_reason.setter
def revocation_reason(self, revocation_reason):
'\n Sets the revocation_reason of this RevocationStatus.\n The reason the certificate or certificate authority (CA) was revoked.\n\n\n :param revocation_reason: The revocation_reason of this RevocationStatus.\n :type: str\n '
allowed_values = ['UNSPECIFIED', 'KEY_COMPROMISE', 'CA_COMPROMISE', 'AFFILIATION_CHANGED', 'SUPERSEDED', 'CESSATION_OF_OPERATION', 'PRIVILEGE_WITHDRAWN', 'AA_COMPROMISE']
if (not value_allowed_none_or_none_sentinel(revocation_reason, allowed_values)):
revocation_reason = 'UNKNOWN_ENUM_VALUE'
self._revocation_reason = revocation_reason<|docstring|>Sets the revocation_reason of this RevocationStatus.
The reason the certificate or certificate authority (CA) was revoked.
:param revocation_reason: The revocation_reason of this RevocationStatus.
:type: str<|endoftext|> |
b777edfcd80d237c5f5044c0972cd02cba762784b8583b0f1ea52b11c8187ffe | def revision_with_match(pattern=None, prefix=False, all_refs=False, return_unmatched=False):
'Return a description of the current commit\n\n Keyword arguments:\n pattern -- (Default: None) Use only refs that match this pattern.\n prefix -- (Default: False) If True, the pattern is considered a prefix\n and does not require an exact match.\n all_refs -- (Default: False) If True, consider all refs, not just tags.\n return_unmatched -- (Default: False) If False and a pattern is given that\n cannot be matched, return the empty string. If True, return\n the unmatched description nonetheless.\n '
command = REVISION_CMD[:]
if pattern:
command += ['--match', (pattern + ('*' if prefix else ''))]
if all_refs:
command += ['--all', '--long']
description = run(command)
if (pattern and (not return_unmatched) and (not description.startswith(pattern))):
return ''
return description | Return a description of the current commit
Keyword arguments:
pattern -- (Default: None) Use only refs that match this pattern.
prefix -- (Default: False) If True, the pattern is considered a prefix
and does not require an exact match.
all_refs -- (Default: False) If True, consider all refs, not just tags.
return_unmatched -- (Default: False) If False and a pattern is given that
cannot be matched, return the empty string. If True, return
the unmatched description nonetheless. | tools/workspace_status_release.py | revision_with_match | ArsenSysyn/gerrit | 623 | python | def revision_with_match(pattern=None, prefix=False, all_refs=False, return_unmatched=False):
'Return a description of the current commit\n\n Keyword arguments:\n pattern -- (Default: None) Use only refs that match this pattern.\n prefix -- (Default: False) If True, the pattern is considered a prefix\n and does not require an exact match.\n all_refs -- (Default: False) If True, consider all refs, not just tags.\n return_unmatched -- (Default: False) If False and a pattern is given that\n cannot be matched, return the empty string. If True, return\n the unmatched description nonetheless.\n '
command = REVISION_CMD[:]
if pattern:
command += ['--match', (pattern + ('*' if prefix else ))]
if all_refs:
command += ['--all', '--long']
description = run(command)
if (pattern and (not return_unmatched) and (not description.startswith(pattern))):
return
return description | def revision_with_match(pattern=None, prefix=False, all_refs=False, return_unmatched=False):
'Return a description of the current commit\n\n Keyword arguments:\n pattern -- (Default: None) Use only refs that match this pattern.\n prefix -- (Default: False) If True, the pattern is considered a prefix\n and does not require an exact match.\n all_refs -- (Default: False) If True, consider all refs, not just tags.\n return_unmatched -- (Default: False) If False and a pattern is given that\n cannot be matched, return the empty string. If True, return\n the unmatched description nonetheless.\n '
command = REVISION_CMD[:]
if pattern:
command += ['--match', (pattern + ('*' if prefix else ))]
if all_refs:
command += ['--all', '--long']
description = run(command)
if (pattern and (not return_unmatched) and (not description.startswith(pattern))):
return
return description<|docstring|>Return a description of the current commit
Keyword arguments:
pattern -- (Default: None) Use only refs that match this pattern.
prefix -- (Default: False) If True, the pattern is considered a prefix
and does not require an exact match.
all_refs -- (Default: False) If True, consider all refs, not just tags.
return_unmatched -- (Default: False) If False and a pattern is given that
cannot be matched, return the empty string. If True, return
the unmatched description nonetheless.<|endoftext|> |
46de940cb6c13f83a8a5e5f1264d52cfb1a2e097c547bd7f447e7bc5f9ee62fd | @GuildBaseCog.group(name='купить', aliases=['магазин'], invoke_without_command=True)
async def role_shop(self, ctx):
'Displays all of the roles which can be purchased from role shop.'
(await ctx.message.delete(delay=5))
if (not ctx.guild_profile.roleshop):
return (await ctx.send_line(f'{ctx.emotes.imortal_boost.g8} На данный момент в магазине пусто.'))
paginator = ctx.get_field_paginator(ctx.guild_profile.roleshop.roles, entry_parser=self._paginator_parser)
paginator.embed.description = '```css\n Добро пожаловать в лавку !```'
paginator.embed.set_author(name='Магазин ролей', icon_url=ctx.author.avatar_url)
(await paginator.paginate()) | Displays all of the roles which can be purchased from role shop. | cosmos/galaxies/guild/roleshop/base.py | role_shop | MrFreemanser/Bot | 0 | python | @GuildBaseCog.group(name='купить', aliases=['магазин'], invoke_without_command=True)
async def role_shop(self, ctx):
(await ctx.message.delete(delay=5))
if (not ctx.guild_profile.roleshop):
return (await ctx.send_line(f'{ctx.emotes.imortal_boost.g8} На данный момент в магазине пусто.'))
paginator = ctx.get_field_paginator(ctx.guild_profile.roleshop.roles, entry_parser=self._paginator_parser)
paginator.embed.description = '```css\n Добро пожаловать в лавку !```'
paginator.embed.set_author(name='Магазин ролей', icon_url=ctx.author.avatar_url)
(await paginator.paginate()) | @GuildBaseCog.group(name='купить', aliases=['магазин'], invoke_without_command=True)
async def role_shop(self, ctx):
(await ctx.message.delete(delay=5))
if (not ctx.guild_profile.roleshop):
return (await ctx.send_line(f'{ctx.emotes.imortal_boost.g8} На данный момент в магазине пусто.'))
paginator = ctx.get_field_paginator(ctx.guild_profile.roleshop.roles, entry_parser=self._paginator_parser)
paginator.embed.description = '```css\n Добро пожаловать в лавку !```'
paginator.embed.set_author(name='Магазин ролей', icon_url=ctx.author.avatar_url)
(await paginator.paginate())<|docstring|>Displays all of the roles which can be purchased from role shop.<|endoftext|> |
3d3acba93ac4cf45f94ea2f9e285bb35a80aa8f862d92afbcb41c299dc7cbdb9 | def new_nip_project(working_directory):
' Helper for created a bare project '
child = pexpect.spawn('nip init', cwd=working_directory)
child.sendline('')
child.sendline('')
child.sendline('')
child.sendline('')
while child.isalive():
time.sleep(0.2)
return child | Helper for created a bare project | tests/helpers.py | new_nip_project | RopePy/nip | 1 | python | def new_nip_project(working_directory):
' '
child = pexpect.spawn('nip init', cwd=working_directory)
child.sendline()
child.sendline()
child.sendline()
child.sendline()
while child.isalive():
time.sleep(0.2)
return child | def new_nip_project(working_directory):
' '
child = pexpect.spawn('nip init', cwd=working_directory)
child.sendline()
child.sendline()
child.sendline()
child.sendline()
while child.isalive():
time.sleep(0.2)
return child<|docstring|>Helper for created a bare project<|endoftext|> |
7773944bfc7d1048aa0db4f42b85c885df34be3d369ef52a19e20c7cb9c7e33a | def text(img: Image, *, threshold: float=0.8) -> Text:
'Regcognize text line, background color should be black.\n\n Args:\n img (Image): Preprocessed text line.\n\n Returns:\n Text: Text content\n '
reload_on_demand()
ret = ''
(w, h) = (img.width, img.height)
if (img.height < _LINE_HEIGHT):
w = round(((_LINE_HEIGHT / h) * w))
h = _LINE_HEIGHT
img = img.resize((w, h))
cv_img = np.asarray(img.convert('L'))
(_, binary_img) = cv2.threshold(cv_img, 0, 255, cv2.THRESH_OTSU)
(contours, _) = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if (len(contours) == 0):
app.log.image('ocr result is empty', img, level=app.DEBUG)
return ''
contours_with_bbox = sorted(((i, _rect2bbox(cv2.boundingRect(i))) for i in contours), key=(lambda x: x[1][0]))
max_char_width = max(((bbox[2] - bbox[0]) for (_, bbox) in contours_with_bbox))
max_char_height = max(((bbox[3] - bbox[1]) for (_, bbox) in contours_with_bbox))
max_char_width = max((max_char_height + 2), max_char_width)
char_img_list: List[Tuple[(Tuple[(int, int, int, int)], np.ndarray)]] = []
char_parts: List[np.ndarray] = []
char_bbox = contours_with_bbox[0][1]
char_non_zero_bbox = contours_with_bbox[0][1]
def _crop_char(bbox: Tuple[(int, int, int, int)], img: np.ndarray):
non_zero_pos_list = cv2.findNonZero(img)
(l0, t0, r0, b0) = bbox
(_, _, w0, h0) = _bbox2rect(bbox)
non_zero_rect = cv2.boundingRect(non_zero_pos_list)
(_, _, w1, h1) = non_zero_rect
(l1, t1, r1, b1) = _rect2bbox(non_zero_rect)
(ml, mt, mr, mb) = (l1, t1, (w0 - r1), (h0 - b1))
ret = img
if (w1 > (max_char_width * 0.3)):
l0 += ml
r0 -= mr
ret = ret[(:, l1:r1)]
if (h1 > (max_char_height * 0.5)):
t0 += mt
b0 -= mb
ret = ret[t1:b1]
return ((l0, t0, r0, b0), ret)
def _push_char():
if (not char_parts):
return
mask = np.zeros_like(binary_img)
cv2.drawContours(mask, char_parts, (- 1), (255,), thickness=cv2.FILLED)
char_img = cv2.copyTo(binary_img, mask)
(l, t, r, b) = char_bbox
char_img = char_img[(t:b, l:r)]
char_img_list.append((char_bbox, char_img))
def _get_expanded_bbox(index: int) -> Tuple[(int, int, int, int)]:
(_, bbox) = contours_with_bbox[index]
if ((index + 1) < len(contours_with_bbox)):
(_, next_bbox) = contours_with_bbox[(index + 1)]
if ((next_bbox[0] - bbox[2]) < 2):
bbox = _union_bbox(bbox, _get_expanded_bbox((index + 1)))
return bbox
for (index, v) in enumerate(contours_with_bbox):
(i, _) = v
bbox = _get_expanded_bbox(index)
(l, t, r, b) = bbox
is_new_char = (char_parts and (l > char_non_zero_bbox[2]) and (((l - char_non_zero_bbox[0]) > (max_char_width * 0.8)) or ((l - char_non_zero_bbox[2]) > (max_char_width * 0.2)) or ((r - char_non_zero_bbox[0]) > max_char_width) or (((char_non_zero_bbox[3] - char_non_zero_bbox[1]) < (max_char_height * 0.6)) and (((r - l) > (max_char_width * 0.6)) or ((l - char_non_zero_bbox[2]) > (max_char_width * 0.1)))) or (((b - t) < (max_char_height * 0.4)) and (l > (char_non_zero_bbox[2] + 1)) and (l > (char_non_zero_bbox[0] + (max_char_width * 0.3))))) and (not _bbox_contains(_pad_bbox(char_bbox, 2), bbox)))
if is_new_char:
space_w = (l - char_bbox[2])
divide_x = int(((l - (space_w * 0.5)) - 1))
last_r = min(divide_x, (char_bbox[0] + max_char_width))
char_bbox = _union_bbox(char_bbox, (last_r, t, last_r, b))
_push_char()
char_parts = []
char_bbox = (max((last_r + 1), (r - max_char_width)), char_bbox[1], r, int((char_bbox[1] + max_char_height)))
char_non_zero_bbox = bbox
char_parts.append(i)
char_non_zero_bbox = _union_bbox(char_non_zero_bbox, bbox)
char_bbox = _union_bbox(char_bbox, bbox)
_push_char()
cropped_char_img_list = [_crop_char(bbox, img) for (bbox, img) in char_img_list]
if (os.getenv('DEBUG') == __name__):
segmentation_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for i in contours:
(x, y, w, h) = cv2.boundingRect(i)
cv2.rectangle(segmentation_img, (x, y), ((x + w), (y + h)), (0, 0, 255), thickness=1)
chars_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for (bbox, _) in char_img_list:
(l, t, r, b) = bbox
cv2.rectangle(chars_img, (l, t), (r, b), (0, 0, 255), thickness=1)
cropped_chars_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for (bbox, _) in cropped_char_img_list:
(l, t, r, b) = bbox
cv2.rectangle(cropped_chars_img, (l, t), (r, b), (0, 0, 255), thickness=1)
app.log.image('text', cv_img, level=app.DEBUG, layers={'binary': binary_img, 'segmentation': segmentation_img, 'chars': chars_img, 'cropped chars': cropped_chars_img})
else:
app.log.image('text', cv_img, level=app.DEBUG, layers={'binary': binary_img})
for (_, i) in cropped_char_img_list:
ret += _text_from_image(i, threshold)
app.log.text(('ocr result: %s' % ret), level=app.DEBUG)
return ret | Regcognize text line, background color should be black.
Args:
img (Image): Preprocessed text line.
Returns:
Text: Text content | auto_derby/ocr.py | text | gentle-knight-13/auto-derby | 0 | python | def text(img: Image, *, threshold: float=0.8) -> Text:
'Regcognize text line, background color should be black.\n\n Args:\n img (Image): Preprocessed text line.\n\n Returns:\n Text: Text content\n '
reload_on_demand()
ret =
(w, h) = (img.width, img.height)
if (img.height < _LINE_HEIGHT):
w = round(((_LINE_HEIGHT / h) * w))
h = _LINE_HEIGHT
img = img.resize((w, h))
cv_img = np.asarray(img.convert('L'))
(_, binary_img) = cv2.threshold(cv_img, 0, 255, cv2.THRESH_OTSU)
(contours, _) = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if (len(contours) == 0):
app.log.image('ocr result is empty', img, level=app.DEBUG)
return
contours_with_bbox = sorted(((i, _rect2bbox(cv2.boundingRect(i))) for i in contours), key=(lambda x: x[1][0]))
max_char_width = max(((bbox[2] - bbox[0]) for (_, bbox) in contours_with_bbox))
max_char_height = max(((bbox[3] - bbox[1]) for (_, bbox) in contours_with_bbox))
max_char_width = max((max_char_height + 2), max_char_width)
char_img_list: List[Tuple[(Tuple[(int, int, int, int)], np.ndarray)]] = []
char_parts: List[np.ndarray] = []
char_bbox = contours_with_bbox[0][1]
char_non_zero_bbox = contours_with_bbox[0][1]
def _crop_char(bbox: Tuple[(int, int, int, int)], img: np.ndarray):
non_zero_pos_list = cv2.findNonZero(img)
(l0, t0, r0, b0) = bbox
(_, _, w0, h0) = _bbox2rect(bbox)
non_zero_rect = cv2.boundingRect(non_zero_pos_list)
(_, _, w1, h1) = non_zero_rect
(l1, t1, r1, b1) = _rect2bbox(non_zero_rect)
(ml, mt, mr, mb) = (l1, t1, (w0 - r1), (h0 - b1))
ret = img
if (w1 > (max_char_width * 0.3)):
l0 += ml
r0 -= mr
ret = ret[(:, l1:r1)]
if (h1 > (max_char_height * 0.5)):
t0 += mt
b0 -= mb
ret = ret[t1:b1]
return ((l0, t0, r0, b0), ret)
def _push_char():
if (not char_parts):
return
mask = np.zeros_like(binary_img)
cv2.drawContours(mask, char_parts, (- 1), (255,), thickness=cv2.FILLED)
char_img = cv2.copyTo(binary_img, mask)
(l, t, r, b) = char_bbox
char_img = char_img[(t:b, l:r)]
char_img_list.append((char_bbox, char_img))
def _get_expanded_bbox(index: int) -> Tuple[(int, int, int, int)]:
(_, bbox) = contours_with_bbox[index]
if ((index + 1) < len(contours_with_bbox)):
(_, next_bbox) = contours_with_bbox[(index + 1)]
if ((next_bbox[0] - bbox[2]) < 2):
bbox = _union_bbox(bbox, _get_expanded_bbox((index + 1)))
return bbox
for (index, v) in enumerate(contours_with_bbox):
(i, _) = v
bbox = _get_expanded_bbox(index)
(l, t, r, b) = bbox
is_new_char = (char_parts and (l > char_non_zero_bbox[2]) and (((l - char_non_zero_bbox[0]) > (max_char_width * 0.8)) or ((l - char_non_zero_bbox[2]) > (max_char_width * 0.2)) or ((r - char_non_zero_bbox[0]) > max_char_width) or (((char_non_zero_bbox[3] - char_non_zero_bbox[1]) < (max_char_height * 0.6)) and (((r - l) > (max_char_width * 0.6)) or ((l - char_non_zero_bbox[2]) > (max_char_width * 0.1)))) or (((b - t) < (max_char_height * 0.4)) and (l > (char_non_zero_bbox[2] + 1)) and (l > (char_non_zero_bbox[0] + (max_char_width * 0.3))))) and (not _bbox_contains(_pad_bbox(char_bbox, 2), bbox)))
if is_new_char:
space_w = (l - char_bbox[2])
divide_x = int(((l - (space_w * 0.5)) - 1))
last_r = min(divide_x, (char_bbox[0] + max_char_width))
char_bbox = _union_bbox(char_bbox, (last_r, t, last_r, b))
_push_char()
char_parts = []
char_bbox = (max((last_r + 1), (r - max_char_width)), char_bbox[1], r, int((char_bbox[1] + max_char_height)))
char_non_zero_bbox = bbox
char_parts.append(i)
char_non_zero_bbox = _union_bbox(char_non_zero_bbox, bbox)
char_bbox = _union_bbox(char_bbox, bbox)
_push_char()
cropped_char_img_list = [_crop_char(bbox, img) for (bbox, img) in char_img_list]
if (os.getenv('DEBUG') == __name__):
segmentation_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for i in contours:
(x, y, w, h) = cv2.boundingRect(i)
cv2.rectangle(segmentation_img, (x, y), ((x + w), (y + h)), (0, 0, 255), thickness=1)
chars_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for (bbox, _) in char_img_list:
(l, t, r, b) = bbox
cv2.rectangle(chars_img, (l, t), (r, b), (0, 0, 255), thickness=1)
cropped_chars_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for (bbox, _) in cropped_char_img_list:
(l, t, r, b) = bbox
cv2.rectangle(cropped_chars_img, (l, t), (r, b), (0, 0, 255), thickness=1)
app.log.image('text', cv_img, level=app.DEBUG, layers={'binary': binary_img, 'segmentation': segmentation_img, 'chars': chars_img, 'cropped chars': cropped_chars_img})
else:
app.log.image('text', cv_img, level=app.DEBUG, layers={'binary': binary_img})
for (_, i) in cropped_char_img_list:
ret += _text_from_image(i, threshold)
app.log.text(('ocr result: %s' % ret), level=app.DEBUG)
return ret | def text(img: Image, *, threshold: float=0.8) -> Text:
'Regcognize text line, background color should be black.\n\n Args:\n img (Image): Preprocessed text line.\n\n Returns:\n Text: Text content\n '
reload_on_demand()
ret =
(w, h) = (img.width, img.height)
if (img.height < _LINE_HEIGHT):
w = round(((_LINE_HEIGHT / h) * w))
h = _LINE_HEIGHT
img = img.resize((w, h))
cv_img = np.asarray(img.convert('L'))
(_, binary_img) = cv2.threshold(cv_img, 0, 255, cv2.THRESH_OTSU)
(contours, _) = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if (len(contours) == 0):
app.log.image('ocr result is empty', img, level=app.DEBUG)
return
contours_with_bbox = sorted(((i, _rect2bbox(cv2.boundingRect(i))) for i in contours), key=(lambda x: x[1][0]))
max_char_width = max(((bbox[2] - bbox[0]) for (_, bbox) in contours_with_bbox))
max_char_height = max(((bbox[3] - bbox[1]) for (_, bbox) in contours_with_bbox))
max_char_width = max((max_char_height + 2), max_char_width)
char_img_list: List[Tuple[(Tuple[(int, int, int, int)], np.ndarray)]] = []
char_parts: List[np.ndarray] = []
char_bbox = contours_with_bbox[0][1]
char_non_zero_bbox = contours_with_bbox[0][1]
def _crop_char(bbox: Tuple[(int, int, int, int)], img: np.ndarray):
non_zero_pos_list = cv2.findNonZero(img)
(l0, t0, r0, b0) = bbox
(_, _, w0, h0) = _bbox2rect(bbox)
non_zero_rect = cv2.boundingRect(non_zero_pos_list)
(_, _, w1, h1) = non_zero_rect
(l1, t1, r1, b1) = _rect2bbox(non_zero_rect)
(ml, mt, mr, mb) = (l1, t1, (w0 - r1), (h0 - b1))
ret = img
if (w1 > (max_char_width * 0.3)):
l0 += ml
r0 -= mr
ret = ret[(:, l1:r1)]
if (h1 > (max_char_height * 0.5)):
t0 += mt
b0 -= mb
ret = ret[t1:b1]
return ((l0, t0, r0, b0), ret)
def _push_char():
if (not char_parts):
return
mask = np.zeros_like(binary_img)
cv2.drawContours(mask, char_parts, (- 1), (255,), thickness=cv2.FILLED)
char_img = cv2.copyTo(binary_img, mask)
(l, t, r, b) = char_bbox
char_img = char_img[(t:b, l:r)]
char_img_list.append((char_bbox, char_img))
def _get_expanded_bbox(index: int) -> Tuple[(int, int, int, int)]:
(_, bbox) = contours_with_bbox[index]
if ((index + 1) < len(contours_with_bbox)):
(_, next_bbox) = contours_with_bbox[(index + 1)]
if ((next_bbox[0] - bbox[2]) < 2):
bbox = _union_bbox(bbox, _get_expanded_bbox((index + 1)))
return bbox
for (index, v) in enumerate(contours_with_bbox):
(i, _) = v
bbox = _get_expanded_bbox(index)
(l, t, r, b) = bbox
is_new_char = (char_parts and (l > char_non_zero_bbox[2]) and (((l - char_non_zero_bbox[0]) > (max_char_width * 0.8)) or ((l - char_non_zero_bbox[2]) > (max_char_width * 0.2)) or ((r - char_non_zero_bbox[0]) > max_char_width) or (((char_non_zero_bbox[3] - char_non_zero_bbox[1]) < (max_char_height * 0.6)) and (((r - l) > (max_char_width * 0.6)) or ((l - char_non_zero_bbox[2]) > (max_char_width * 0.1)))) or (((b - t) < (max_char_height * 0.4)) and (l > (char_non_zero_bbox[2] + 1)) and (l > (char_non_zero_bbox[0] + (max_char_width * 0.3))))) and (not _bbox_contains(_pad_bbox(char_bbox, 2), bbox)))
if is_new_char:
space_w = (l - char_bbox[2])
divide_x = int(((l - (space_w * 0.5)) - 1))
last_r = min(divide_x, (char_bbox[0] + max_char_width))
char_bbox = _union_bbox(char_bbox, (last_r, t, last_r, b))
_push_char()
char_parts = []
char_bbox = (max((last_r + 1), (r - max_char_width)), char_bbox[1], r, int((char_bbox[1] + max_char_height)))
char_non_zero_bbox = bbox
char_parts.append(i)
char_non_zero_bbox = _union_bbox(char_non_zero_bbox, bbox)
char_bbox = _union_bbox(char_bbox, bbox)
_push_char()
cropped_char_img_list = [_crop_char(bbox, img) for (bbox, img) in char_img_list]
if (os.getenv('DEBUG') == __name__):
segmentation_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for i in contours:
(x, y, w, h) = cv2.boundingRect(i)
cv2.rectangle(segmentation_img, (x, y), ((x + w), (y + h)), (0, 0, 255), thickness=1)
chars_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for (bbox, _) in char_img_list:
(l, t, r, b) = bbox
cv2.rectangle(chars_img, (l, t), (r, b), (0, 0, 255), thickness=1)
cropped_chars_img = cv2.cvtColor(binary_img, cv2.COLOR_GRAY2BGR)
for (bbox, _) in cropped_char_img_list:
(l, t, r, b) = bbox
cv2.rectangle(cropped_chars_img, (l, t), (r, b), (0, 0, 255), thickness=1)
app.log.image('text', cv_img, level=app.DEBUG, layers={'binary': binary_img, 'segmentation': segmentation_img, 'chars': chars_img, 'cropped chars': cropped_chars_img})
else:
app.log.image('text', cv_img, level=app.DEBUG, layers={'binary': binary_img})
for (_, i) in cropped_char_img_list:
ret += _text_from_image(i, threshold)
app.log.text(('ocr result: %s' % ret), level=app.DEBUG)
return ret<|docstring|>Regcognize text line, background color should be black.
Args:
img (Image): Preprocessed text line.
Returns:
Text: Text content<|endoftext|> |
d5d175b4b60185881607655ff4e46fa724fb7aa67adae3f09c08e2244d84944c | def process_mention_pairs_to_output(mention_pairs):
'\n Processes the given mention pairs into a tensor, Y, with the expected output.\n '
Y = np.empty(shape=(len(mention_pairs),), dtype='int32')
for idx in range(len(mention_pairs)):
Y[idx] = (1 if mention_pairs[idx].is_coreferent else 0)
return Y | Processes the given mention pairs into a tensor, Y, with the expected output. | coref/data.py | process_mention_pairs_to_output | AndreFCruz/coref-web-platform | 9 | python | def process_mention_pairs_to_output(mention_pairs):
'\n \n '
Y = np.empty(shape=(len(mention_pairs),), dtype='int32')
for idx in range(len(mention_pairs)):
Y[idx] = (1 if mention_pairs[idx].is_coreferent else 0)
return Y | def process_mention_pairs_to_output(mention_pairs):
'\n \n '
Y = np.empty(shape=(len(mention_pairs),), dtype='int32')
for idx in range(len(mention_pairs)):
Y[idx] = (1 if mention_pairs[idx].is_coreferent else 0)
return Y<|docstring|>Processes the given mention pairs into a tensor, Y, with the expected output.<|endoftext|> |
cdceb48315a20fe1aebccfd9238301e533bc713478faa3be0919a5d3e059a6d9 | def process_mention_pairs_to_distance_features(mention_pairs, num_features=2):
'\n Processes the given mention pairs into a vector of scalar features (e.g.\n sentence distance, mention distance, one-hot encoded pos tags, ...).\n '
bins = [0, 1, 2, 3, 4, 5, 8, 16, 32, 64, math.inf]
bin_distance = (lambda x: bin_scalar(x, bins))
num_mention_pairs = len(mention_pairs)
X_scalar = np.empty(shape=(num_mention_pairs, num_features), dtype='float32')
for (idx, mp) in enumerate(mention_pairs):
features = [bin_distance(mp.sent_dist)]
if (num_features >= 2):
features.append(bin_distance(mp.token_dist))
if (num_features == 3):
pass
elif (num_features > 3):
raise RuntimeWarning('Invalid number of distance features: {}'.format(num_features))
X_scalar[idx] = features
return X_scalar | Processes the given mention pairs into a vector of scalar features (e.g.
sentence distance, mention distance, one-hot encoded pos tags, ...). | coref/data.py | process_mention_pairs_to_distance_features | AndreFCruz/coref-web-platform | 9 | python | def process_mention_pairs_to_distance_features(mention_pairs, num_features=2):
'\n Processes the given mention pairs into a vector of scalar features (e.g.\n sentence distance, mention distance, one-hot encoded pos tags, ...).\n '
bins = [0, 1, 2, 3, 4, 5, 8, 16, 32, 64, math.inf]
bin_distance = (lambda x: bin_scalar(x, bins))
num_mention_pairs = len(mention_pairs)
X_scalar = np.empty(shape=(num_mention_pairs, num_features), dtype='float32')
for (idx, mp) in enumerate(mention_pairs):
features = [bin_distance(mp.sent_dist)]
if (num_features >= 2):
features.append(bin_distance(mp.token_dist))
if (num_features == 3):
pass
elif (num_features > 3):
raise RuntimeWarning('Invalid number of distance features: {}'.format(num_features))
X_scalar[idx] = features
return X_scalar | def process_mention_pairs_to_distance_features(mention_pairs, num_features=2):
'\n Processes the given mention pairs into a vector of scalar features (e.g.\n sentence distance, mention distance, one-hot encoded pos tags, ...).\n '
bins = [0, 1, 2, 3, 4, 5, 8, 16, 32, 64, math.inf]
bin_distance = (lambda x: bin_scalar(x, bins))
num_mention_pairs = len(mention_pairs)
X_scalar = np.empty(shape=(num_mention_pairs, num_features), dtype='float32')
for (idx, mp) in enumerate(mention_pairs):
features = [bin_distance(mp.sent_dist)]
if (num_features >= 2):
features.append(bin_distance(mp.token_dist))
if (num_features == 3):
pass
elif (num_features > 3):
raise RuntimeWarning('Invalid number of distance features: {}'.format(num_features))
X_scalar[idx] = features
return X_scalar<|docstring|>Processes the given mention pairs into a vector of scalar features (e.g.
sentence distance, mention distance, one-hot encoded pos tags, ...).<|endoftext|> |
8bd94dc84fce392206d9518070126af4a5db23e8e9b7e9d08e22cb7fcfe54ebd | def process_mention_pairs_to_indices(mention_pairs, tokenizer, max_mention_length=50):
'\n Processes the given mention pairs into an input dataset, parsing mention tokens\n into their indices in the embedding layer according to the given tokenizer.\n '
num_mention_pairs = len(mention_pairs)
X_m1 = np.empty(shape=(num_mention_pairs, max_mention_length), dtype='int32')
X_m2 = np.empty(shape=(num_mention_pairs, max_mention_length), dtype='int32')
for (idx, mp) in enumerate(mention_pairs):
(seq_m1, seq_m2) = tokenizer.texts_to_sequences([mp.m1.full_mention, mp.m2.full_mention])
(X_m1[idx], X_m2[idx]) = pad_sequences([seq_m1, seq_m2], maxlen=max_mention_length)
return (X_m1, X_m2) | Processes the given mention pairs into an input dataset, parsing mention tokens
into their indices in the embedding layer according to the given tokenizer. | coref/data.py | process_mention_pairs_to_indices | AndreFCruz/coref-web-platform | 9 | python | def process_mention_pairs_to_indices(mention_pairs, tokenizer, max_mention_length=50):
'\n Processes the given mention pairs into an input dataset, parsing mention tokens\n into their indices in the embedding layer according to the given tokenizer.\n '
num_mention_pairs = len(mention_pairs)
X_m1 = np.empty(shape=(num_mention_pairs, max_mention_length), dtype='int32')
X_m2 = np.empty(shape=(num_mention_pairs, max_mention_length), dtype='int32')
for (idx, mp) in enumerate(mention_pairs):
(seq_m1, seq_m2) = tokenizer.texts_to_sequences([mp.m1.full_mention, mp.m2.full_mention])
(X_m1[idx], X_m2[idx]) = pad_sequences([seq_m1, seq_m2], maxlen=max_mention_length)
return (X_m1, X_m2) | def process_mention_pairs_to_indices(mention_pairs, tokenizer, max_mention_length=50):
'\n Processes the given mention pairs into an input dataset, parsing mention tokens\n into their indices in the embedding layer according to the given tokenizer.\n '
num_mention_pairs = len(mention_pairs)
X_m1 = np.empty(shape=(num_mention_pairs, max_mention_length), dtype='int32')
X_m2 = np.empty(shape=(num_mention_pairs, max_mention_length), dtype='int32')
for (idx, mp) in enumerate(mention_pairs):
(seq_m1, seq_m2) = tokenizer.texts_to_sequences([mp.m1.full_mention, mp.m2.full_mention])
(X_m1[idx], X_m2[idx]) = pad_sequences([seq_m1, seq_m2], maxlen=max_mention_length)
return (X_m1, X_m2)<|docstring|>Processes the given mention pairs into an input dataset, parsing mention tokens
into their indices in the embedding layer according to the given tokenizer.<|endoftext|> |
dc7b70183663b90dc8b6129a30d5169b09250c58bc6ab3813b3c22522ffb2d4a | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueUrl'], extension_depth: int=0, max_extension_depth: Optional[int]=2, include_modifierExtension: Optional[bool]=False) -> Union[(StructType, DataType)]:
"\n This resource allows for the definition of various types of plans as a\n sharable, consumable, and executable artifact. The resource is general enough\n to support the description of a broad range of clinical artifacts such as\n clinical decision support rules, order sets and protocols.\n\n\n id: Unique id for the element within a resource (for internal references). This\n may be any string value that does not contain spaces.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the element. To make the use of extensions safe and manageable,\n there is a strict set of governance applied to the definition and use of\n extensions. Though any implementer can define an extension, there is a set of\n requirements that SHALL be met as part of the definition of the extension.\n\n modifierExtension: May be used to represent additional information that is not part of the basic\n definition of the element and that modifies the understanding of the element\n in which it is contained and/or the understanding of the containing element's\n descendants. Usually modifier elements provide negation or qualification. To\n make the use of extensions safe and manageable, there is a strict set of\n governance applied to the definition and use of extensions. Though any\n implementer can define an extension, there is a set of requirements that SHALL\n be met as part of the definition of the extension. Applications processing a\n resource are required to check for modifier extensions.\n\n Modifier extensions SHALL NOT change the meaning of any elements on Resource\n or DomainResource (including cannot change the meaning of modifierExtension\n itself).\n\n path: The path to the element to be customized. This is the path on the resource\n that will hold the result of the calculation defined by the expression. The\n specified path SHALL be a FHIRPath resolveable on the specified target type of\n the ActivityDefinition, and SHALL consist only of identifiers, constant\n indexers, and a restricted subset of functions. The path is allowed to contain\n qualifiers (.) to traverse sub-elements, as well as indexers ([x]) to traverse\n multiple-cardinality sub-elements (see the [Simple FHIRPath\n Profile](fhirpath.html#simple) for full details).\n\n expression: An expression specifying the value of the customized element.\n\n "
from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.r4.complex_types.expression import ExpressionSchema
if ((max_recursion_limit and (nesting_list.count('PlanDefinition_DynamicValue') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['PlanDefinition_DynamicValue'])
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension)), True), StructField('modifierExtension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension)), True), StructField('path', StringType(), True), StructField('expression', ExpressionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
if (not include_modifierExtension):
schema.fields = [(c if (c.name != 'modifierExtension') else StructField('modifierExtension', StringType(), True)) for c in schema.fields]
return schema | This resource allows for the definition of various types of plans as a
sharable, consumable, and executable artifact. The resource is general enough
to support the description of a broad range of clinical artifacts such as
clinical decision support rules, order sets and protocols.
id: Unique id for the element within a resource (for internal references). This
may be any string value that does not contain spaces.
extension: May be used to represent additional information that is not part of the basic
definition of the element. To make the use of extensions safe and manageable,
there is a strict set of governance applied to the definition and use of
extensions. Though any implementer can define an extension, there is a set of
requirements that SHALL be met as part of the definition of the extension.
modifierExtension: May be used to represent additional information that is not part of the basic
definition of the element and that modifies the understanding of the element
in which it is contained and/or the understanding of the containing element's
descendants. Usually modifier elements provide negation or qualification. To
make the use of extensions safe and manageable, there is a strict set of
governance applied to the definition and use of extensions. Though any
implementer can define an extension, there is a set of requirements that SHALL
be met as part of the definition of the extension. Applications processing a
resource are required to check for modifier extensions.
Modifier extensions SHALL NOT change the meaning of any elements on Resource
or DomainResource (including cannot change the meaning of modifierExtension
itself).
path: The path to the element to be customized. This is the path on the resource
that will hold the result of the calculation defined by the expression. The
specified path SHALL be a FHIRPath resolveable on the specified target type of
the ActivityDefinition, and SHALL consist only of identifiers, constant
indexers, and a restricted subset of functions. The path is allowed to contain
qualifiers (.) to traverse sub-elements, as well as indexers ([x]) to traverse
multiple-cardinality sub-elements (see the [Simple FHIRPath
Profile](fhirpath.html#simple) for full details).
expression: An expression specifying the value of the customized element. | spark_fhir_schemas/r4/complex_types/plandefinition_dynamicvalue.py | get_schema | imranq2/SparkFhirSchemas | 2 | python | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueUrl'], extension_depth: int=0, max_extension_depth: Optional[int]=2, include_modifierExtension: Optional[bool]=False) -> Union[(StructType, DataType)]:
"\n This resource allows for the definition of various types of plans as a\n sharable, consumable, and executable artifact. The resource is general enough\n to support the description of a broad range of clinical artifacts such as\n clinical decision support rules, order sets and protocols.\n\n\n id: Unique id for the element within a resource (for internal references). This\n may be any string value that does not contain spaces.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the element. To make the use of extensions safe and manageable,\n there is a strict set of governance applied to the definition and use of\n extensions. Though any implementer can define an extension, there is a set of\n requirements that SHALL be met as part of the definition of the extension.\n\n modifierExtension: May be used to represent additional information that is not part of the basic\n definition of the element and that modifies the understanding of the element\n in which it is contained and/or the understanding of the containing element's\n descendants. Usually modifier elements provide negation or qualification. To\n make the use of extensions safe and manageable, there is a strict set of\n governance applied to the definition and use of extensions. Though any\n implementer can define an extension, there is a set of requirements that SHALL\n be met as part of the definition of the extension. Applications processing a\n resource are required to check for modifier extensions.\n\n Modifier extensions SHALL NOT change the meaning of any elements on Resource\n or DomainResource (including cannot change the meaning of modifierExtension\n itself).\n\n path: The path to the element to be customized. This is the path on the resource\n that will hold the result of the calculation defined by the expression. The\n specified path SHALL be a FHIRPath resolveable on the specified target type of\n the ActivityDefinition, and SHALL consist only of identifiers, constant\n indexers, and a restricted subset of functions. The path is allowed to contain\n qualifiers (.) to traverse sub-elements, as well as indexers ([x]) to traverse\n multiple-cardinality sub-elements (see the [Simple FHIRPath\n Profile](fhirpath.html#simple) for full details).\n\n expression: An expression specifying the value of the customized element.\n\n "
from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.r4.complex_types.expression import ExpressionSchema
if ((max_recursion_limit and (nesting_list.count('PlanDefinition_DynamicValue') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['PlanDefinition_DynamicValue'])
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension)), True), StructField('modifierExtension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension)), True), StructField('path', StringType(), True), StructField('expression', ExpressionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
if (not include_modifierExtension):
schema.fields = [(c if (c.name != 'modifierExtension') else StructField('modifierExtension', StringType(), True)) for c in schema.fields]
return schema | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueUrl'], extension_depth: int=0, max_extension_depth: Optional[int]=2, include_modifierExtension: Optional[bool]=False) -> Union[(StructType, DataType)]:
"\n This resource allows for the definition of various types of plans as a\n sharable, consumable, and executable artifact. The resource is general enough\n to support the description of a broad range of clinical artifacts such as\n clinical decision support rules, order sets and protocols.\n\n\n id: Unique id for the element within a resource (for internal references). This\n may be any string value that does not contain spaces.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the element. To make the use of extensions safe and manageable,\n there is a strict set of governance applied to the definition and use of\n extensions. Though any implementer can define an extension, there is a set of\n requirements that SHALL be met as part of the definition of the extension.\n\n modifierExtension: May be used to represent additional information that is not part of the basic\n definition of the element and that modifies the understanding of the element\n in which it is contained and/or the understanding of the containing element's\n descendants. Usually modifier elements provide negation or qualification. To\n make the use of extensions safe and manageable, there is a strict set of\n governance applied to the definition and use of extensions. Though any\n implementer can define an extension, there is a set of requirements that SHALL\n be met as part of the definition of the extension. Applications processing a\n resource are required to check for modifier extensions.\n\n Modifier extensions SHALL NOT change the meaning of any elements on Resource\n or DomainResource (including cannot change the meaning of modifierExtension\n itself).\n\n path: The path to the element to be customized. This is the path on the resource\n that will hold the result of the calculation defined by the expression. The\n specified path SHALL be a FHIRPath resolveable on the specified target type of\n the ActivityDefinition, and SHALL consist only of identifiers, constant\n indexers, and a restricted subset of functions. The path is allowed to contain\n qualifiers (.) to traverse sub-elements, as well as indexers ([x]) to traverse\n multiple-cardinality sub-elements (see the [Simple FHIRPath\n Profile](fhirpath.html#simple) for full details).\n\n expression: An expression specifying the value of the customized element.\n\n "
from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.r4.complex_types.expression import ExpressionSchema
if ((max_recursion_limit and (nesting_list.count('PlanDefinition_DynamicValue') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['PlanDefinition_DynamicValue'])
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension)), True), StructField('modifierExtension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension)), True), StructField('path', StringType(), True), StructField('expression', ExpressionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
if (not include_modifierExtension):
schema.fields = [(c if (c.name != 'modifierExtension') else StructField('modifierExtension', StringType(), True)) for c in schema.fields]
return schema<|docstring|>This resource allows for the definition of various types of plans as a
sharable, consumable, and executable artifact. The resource is general enough
to support the description of a broad range of clinical artifacts such as
clinical decision support rules, order sets and protocols.
id: Unique id for the element within a resource (for internal references). This
may be any string value that does not contain spaces.
extension: May be used to represent additional information that is not part of the basic
definition of the element. To make the use of extensions safe and manageable,
there is a strict set of governance applied to the definition and use of
extensions. Though any implementer can define an extension, there is a set of
requirements that SHALL be met as part of the definition of the extension.
modifierExtension: May be used to represent additional information that is not part of the basic
definition of the element and that modifies the understanding of the element
in which it is contained and/or the understanding of the containing element's
descendants. Usually modifier elements provide negation or qualification. To
make the use of extensions safe and manageable, there is a strict set of
governance applied to the definition and use of extensions. Though any
implementer can define an extension, there is a set of requirements that SHALL
be met as part of the definition of the extension. Applications processing a
resource are required to check for modifier extensions.
Modifier extensions SHALL NOT change the meaning of any elements on Resource
or DomainResource (including cannot change the meaning of modifierExtension
itself).
path: The path to the element to be customized. This is the path on the resource
that will hold the result of the calculation defined by the expression. The
specified path SHALL be a FHIRPath resolveable on the specified target type of
the ActivityDefinition, and SHALL consist only of identifiers, constant
indexers, and a restricted subset of functions. The path is allowed to contain
qualifiers (.) to traverse sub-elements, as well as indexers ([x]) to traverse
multiple-cardinality sub-elements (see the [Simple FHIRPath
Profile](fhirpath.html#simple) for full details).
expression: An expression specifying the value of the customized element.<|endoftext|> |
6e6aeb1ed70cc7a8289fe93870252446c40e0caa716ae484e61979223fc4d7d2 | def asyncReadDM4(*args, **kwargs):
"\n Calls `readDM4` in a separate thread and executes it in the background.\n\n Parameters\n ----------\n Valid arguments are as for `readDM4()`. \n\n Returns\n -------\n future\n A ``concurrent.futures.Future()`` object. Calling ``future.result()`` \n will halt until the read is finished and returns the image and meta-data\n as per a normal call to `readMRC`. \n\n Example\n -------\n\n worker = asyncReadMRC( 'someones_file.mrc' )\n # Do some work\n mrcImage, mrcMeta = worker.result()\n "
return _asyncExecutor.submit(readDM4, *args, **kwargs) | Calls `readDM4` in a separate thread and executes it in the background.
Parameters
----------
Valid arguments are as for `readDM4()`.
Returns
-------
future
A ``concurrent.futures.Future()`` object. Calling ``future.result()``
will halt until the read is finished and returns the image and meta-data
as per a normal call to `readMRC`.
Example
-------
worker = asyncReadMRC( 'someones_file.mrc' )
# Do some work
mrcImage, mrcMeta = worker.result() | mrcz/ioDM.py | asyncReadDM4 | em-MRCZ/python-mrcz | 13 | python | def asyncReadDM4(*args, **kwargs):
"\n Calls `readDM4` in a separate thread and executes it in the background.\n\n Parameters\n ----------\n Valid arguments are as for `readDM4()`. \n\n Returns\n -------\n future\n A ``concurrent.futures.Future()`` object. Calling ``future.result()`` \n will halt until the read is finished and returns the image and meta-data\n as per a normal call to `readMRC`. \n\n Example\n -------\n\n worker = asyncReadMRC( 'someones_file.mrc' )\n # Do some work\n mrcImage, mrcMeta = worker.result()\n "
return _asyncExecutor.submit(readDM4, *args, **kwargs) | def asyncReadDM4(*args, **kwargs):
"\n Calls `readDM4` in a separate thread and executes it in the background.\n\n Parameters\n ----------\n Valid arguments are as for `readDM4()`. \n\n Returns\n -------\n future\n A ``concurrent.futures.Future()`` object. Calling ``future.result()`` \n will halt until the read is finished and returns the image and meta-data\n as per a normal call to `readMRC`. \n\n Example\n -------\n\n worker = asyncReadMRC( 'someones_file.mrc' )\n # Do some work\n mrcImage, mrcMeta = worker.result()\n "
return _asyncExecutor.submit(readDM4, *args, **kwargs)<|docstring|>Calls `readDM4` in a separate thread and executes it in the background.
Parameters
----------
Valid arguments are as for `readDM4()`.
Returns
-------
future
A ``concurrent.futures.Future()`` object. Calling ``future.result()``
will halt until the read is finished and returns the image and meta-data
as per a normal call to `readMRC`.
Example
-------
worker = asyncReadMRC( 'someones_file.mrc' )
# Do some work
mrcImage, mrcMeta = worker.result()<|endoftext|> |
c9670631ed4cfe8619c85071c041eb08634b023fa3a66925d85612c7660a1964 | def parseTag(self, parent):
' Parse a tag at the given file handle location '
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
if (tag_namelen > 0):
tag_name = self.f.read(tag_namelen)
else:
tag_name = b''
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
loc_tag = self.f.tell()
self.f.read(4)
tag_ninfo = np.fromfile(self.f, dtype='>i8', count=1)[0]
if self.verbose:
print(('Found tag: %s%s with %d elements' % (parent, tag_name, tag_ninfo)))
split_tag_name = (parent + tag_name).split(b'/')
imageIndex = np.int64(split_tag_name[2])
try:
self.im[imageIndex]
except IndexError:
self.im.append(DMImage())
if (split_tag_name[3] == b'ImageData'):
dimCount = 0
if (split_tag_name[4] == b'Calibrations'):
if (split_tag_name[5] == b'Brightness'):
if (split_tag_name[6] == b'Origin'):
self.im[imageIndex].imageInfo['IntensityOrigin'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[6] == b'Scale'):
self.im[imageIndex].imageInfo['IntensityScale'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[6] == b'Units'):
self.im[imageIndex].imageInfo['IntensityUnits'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('utf-8')
elif (split_tag_name[5] == b'Dimension'):
if (np.int64(split_tag_name[6]) == 0):
currDim = 'X'
elif (np.int64(split_tag_name[6]) == 1):
currDim = 'Y'
elif (np.int64(split_tag_name[6]) == 2):
currDim = 'Z'
else:
raise ValueError(((('DM4Import: Unknown dimension ' + str(split_tag_name[6])) + ' at location ') + hex(self.f.tell())))
if (split_tag_name[7] == b'Origin'):
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Origin')] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[7] == b'Scale'):
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Scale')] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[7] == b'Units'):
try:
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Units')] = self.retrieveTagData(tag_ninfo).astype(np.int16).tostring().decode('utf-8')
except:
try:
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Units')] = self.retrieveTagData(tag_ninfo).astype(np.int16).tostring().decode('ISO-8859-1')
except:
pass
pass
elif (split_tag_name[4] == b'Data'):
if self.verbose:
print(((('Found image %d' % imageIndex) + ' at offset : ') + str(self.f.tell())))
self.im[imageIndex].imageData = self.retrieveTagData(tag_ninfo)
pass
elif (split_tag_name[4] == b'Dimensions'):
self.im[imageIndex].shape.append(self.retrieveTagData(tag_ninfo))
dimCount += 1
pass
pass
elif (split_tag_name[3] == b'ImageTags'):
if (split_tag_name[4] == b'Acquisition'):
if ((split_tag_name[(- 1)] == b'Horizontal Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['HorzFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Vertical Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['VertFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Diagonal Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['DiagFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Pixel Size (um)') and (split_tag_name[(- 3)] == b'Device')):
self.im[imageIndex].imageInfo['DetectorPixelSize'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Exposure') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['Exposure'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'hbin') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['HorzBin'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'vbin') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['VertBin'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[4] == b'Calibration'):
if (split_tag_name[5] == b'Dose Rate'):
if (split_tag_name[6] == b'Calibration'):
if (not ('DoseRateCalibration' in self.im[imageIndex].imageInfo)):
self.im[imageIndex].imageInfo['DoseRateCalibration'] = []
try:
self.im[imageIndex].imageInfo['DoseRateCalibration'].append(self.retrieveTagData(tag_ninfo))
except:
print('FAILED TO IMPORT DOSERATECALIBRATION')
elif (split_tag_name[4] == b'Microscope Info'):
if (split_tag_name[5] == b'Actual Magnification'):
self.im[imageIndex].imageInfo['ActualMag'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Indicated Magnification'):
self.im[imageIndex].imageInfo['NominalMag'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Cs(mm)'):
self.im[imageIndex].imageInfo['C3'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Voltage'):
self.im[imageIndex].imageInfo['Voltage'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Mode'):
self.im[imageIndex].imageInfo['MicroscopeMode'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('ascii')
pass
elif (split_tag_name[4] == b'EELS Spectrometer'):
if (split_tag_name[5] == b'Aperture index'):
self.im[imageIndex].imageInfo['EELSApertureIndex'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Aperture label'):
self.im[imageIndex].imageInfo['EELSApertureLabel'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('ascii')
elif (split_tag_name[5] == b'Energy loss (eV)'):
self.im[imageIndex].imageInfo['EELSEnergyLoss'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Slit inserted'):
self.im[imageIndex].imageInfo['EELSSlitIn'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Slit width (eV)'):
self.im[imageIndex].imageInfo['EELSSlitWidth'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[4] == b'Latitude-S'):
if (split_tag_name[5] == b'Distance To Focus Position'):
self.im[imageIndex].imageInfo['LatSDistanceToFocus'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Intended Defocus'):
self.im[imageIndex].imageInfo['LatSIntendedDefocus'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Beam Diameter (nm)'):
self.im[imageIndex].imageInfo['LatSBeamDiameter'] = self.retrieveTagData(tag_ninfo)
self.f.seek((loc_tag + tag_fieldlen))
return | Parse a tag at the given file handle location | mrcz/ioDM.py | parseTag | em-MRCZ/python-mrcz | 13 | python | def parseTag(self, parent):
' '
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
if (tag_namelen > 0):
tag_name = self.f.read(tag_namelen)
else:
tag_name = b
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
loc_tag = self.f.tell()
self.f.read(4)
tag_ninfo = np.fromfile(self.f, dtype='>i8', count=1)[0]
if self.verbose:
print(('Found tag: %s%s with %d elements' % (parent, tag_name, tag_ninfo)))
split_tag_name = (parent + tag_name).split(b'/')
imageIndex = np.int64(split_tag_name[2])
try:
self.im[imageIndex]
except IndexError:
self.im.append(DMImage())
if (split_tag_name[3] == b'ImageData'):
dimCount = 0
if (split_tag_name[4] == b'Calibrations'):
if (split_tag_name[5] == b'Brightness'):
if (split_tag_name[6] == b'Origin'):
self.im[imageIndex].imageInfo['IntensityOrigin'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[6] == b'Scale'):
self.im[imageIndex].imageInfo['IntensityScale'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[6] == b'Units'):
self.im[imageIndex].imageInfo['IntensityUnits'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('utf-8')
elif (split_tag_name[5] == b'Dimension'):
if (np.int64(split_tag_name[6]) == 0):
currDim = 'X'
elif (np.int64(split_tag_name[6]) == 1):
currDim = 'Y'
elif (np.int64(split_tag_name[6]) == 2):
currDim = 'Z'
else:
raise ValueError(((('DM4Import: Unknown dimension ' + str(split_tag_name[6])) + ' at location ') + hex(self.f.tell())))
if (split_tag_name[7] == b'Origin'):
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Origin')] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[7] == b'Scale'):
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Scale')] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[7] == b'Units'):
try:
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Units')] = self.retrieveTagData(tag_ninfo).astype(np.int16).tostring().decode('utf-8')
except:
try:
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Units')] = self.retrieveTagData(tag_ninfo).astype(np.int16).tostring().decode('ISO-8859-1')
except:
pass
pass
elif (split_tag_name[4] == b'Data'):
if self.verbose:
print(((('Found image %d' % imageIndex) + ' at offset : ') + str(self.f.tell())))
self.im[imageIndex].imageData = self.retrieveTagData(tag_ninfo)
pass
elif (split_tag_name[4] == b'Dimensions'):
self.im[imageIndex].shape.append(self.retrieveTagData(tag_ninfo))
dimCount += 1
pass
pass
elif (split_tag_name[3] == b'ImageTags'):
if (split_tag_name[4] == b'Acquisition'):
if ((split_tag_name[(- 1)] == b'Horizontal Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['HorzFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Vertical Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['VertFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Diagonal Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['DiagFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Pixel Size (um)') and (split_tag_name[(- 3)] == b'Device')):
self.im[imageIndex].imageInfo['DetectorPixelSize'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Exposure') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['Exposure'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'hbin') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['HorzBin'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'vbin') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['VertBin'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[4] == b'Calibration'):
if (split_tag_name[5] == b'Dose Rate'):
if (split_tag_name[6] == b'Calibration'):
if (not ('DoseRateCalibration' in self.im[imageIndex].imageInfo)):
self.im[imageIndex].imageInfo['DoseRateCalibration'] = []
try:
self.im[imageIndex].imageInfo['DoseRateCalibration'].append(self.retrieveTagData(tag_ninfo))
except:
print('FAILED TO IMPORT DOSERATECALIBRATION')
elif (split_tag_name[4] == b'Microscope Info'):
if (split_tag_name[5] == b'Actual Magnification'):
self.im[imageIndex].imageInfo['ActualMag'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Indicated Magnification'):
self.im[imageIndex].imageInfo['NominalMag'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Cs(mm)'):
self.im[imageIndex].imageInfo['C3'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Voltage'):
self.im[imageIndex].imageInfo['Voltage'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Mode'):
self.im[imageIndex].imageInfo['MicroscopeMode'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('ascii')
pass
elif (split_tag_name[4] == b'EELS Spectrometer'):
if (split_tag_name[5] == b'Aperture index'):
self.im[imageIndex].imageInfo['EELSApertureIndex'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Aperture label'):
self.im[imageIndex].imageInfo['EELSApertureLabel'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('ascii')
elif (split_tag_name[5] == b'Energy loss (eV)'):
self.im[imageIndex].imageInfo['EELSEnergyLoss'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Slit inserted'):
self.im[imageIndex].imageInfo['EELSSlitIn'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Slit width (eV)'):
self.im[imageIndex].imageInfo['EELSSlitWidth'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[4] == b'Latitude-S'):
if (split_tag_name[5] == b'Distance To Focus Position'):
self.im[imageIndex].imageInfo['LatSDistanceToFocus'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Intended Defocus'):
self.im[imageIndex].imageInfo['LatSIntendedDefocus'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Beam Diameter (nm)'):
self.im[imageIndex].imageInfo['LatSBeamDiameter'] = self.retrieveTagData(tag_ninfo)
self.f.seek((loc_tag + tag_fieldlen))
return | def parseTag(self, parent):
' '
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
if (tag_namelen > 0):
tag_name = self.f.read(tag_namelen)
else:
tag_name = b
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
loc_tag = self.f.tell()
self.f.read(4)
tag_ninfo = np.fromfile(self.f, dtype='>i8', count=1)[0]
if self.verbose:
print(('Found tag: %s%s with %d elements' % (parent, tag_name, tag_ninfo)))
split_tag_name = (parent + tag_name).split(b'/')
imageIndex = np.int64(split_tag_name[2])
try:
self.im[imageIndex]
except IndexError:
self.im.append(DMImage())
if (split_tag_name[3] == b'ImageData'):
dimCount = 0
if (split_tag_name[4] == b'Calibrations'):
if (split_tag_name[5] == b'Brightness'):
if (split_tag_name[6] == b'Origin'):
self.im[imageIndex].imageInfo['IntensityOrigin'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[6] == b'Scale'):
self.im[imageIndex].imageInfo['IntensityScale'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[6] == b'Units'):
self.im[imageIndex].imageInfo['IntensityUnits'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('utf-8')
elif (split_tag_name[5] == b'Dimension'):
if (np.int64(split_tag_name[6]) == 0):
currDim = 'X'
elif (np.int64(split_tag_name[6]) == 1):
currDim = 'Y'
elif (np.int64(split_tag_name[6]) == 2):
currDim = 'Z'
else:
raise ValueError(((('DM4Import: Unknown dimension ' + str(split_tag_name[6])) + ' at location ') + hex(self.f.tell())))
if (split_tag_name[7] == b'Origin'):
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Origin')] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[7] == b'Scale'):
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Scale')] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[7] == b'Units'):
try:
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Units')] = self.retrieveTagData(tag_ninfo).astype(np.int16).tostring().decode('utf-8')
except:
try:
self.im[imageIndex].imageInfo[(('Dim' + currDim) + 'Units')] = self.retrieveTagData(tag_ninfo).astype(np.int16).tostring().decode('ISO-8859-1')
except:
pass
pass
elif (split_tag_name[4] == b'Data'):
if self.verbose:
print(((('Found image %d' % imageIndex) + ' at offset : ') + str(self.f.tell())))
self.im[imageIndex].imageData = self.retrieveTagData(tag_ninfo)
pass
elif (split_tag_name[4] == b'Dimensions'):
self.im[imageIndex].shape.append(self.retrieveTagData(tag_ninfo))
dimCount += 1
pass
pass
elif (split_tag_name[3] == b'ImageTags'):
if (split_tag_name[4] == b'Acquisition'):
if ((split_tag_name[(- 1)] == b'Horizontal Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['HorzFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Vertical Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['VertFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Diagonal Flip') and (split_tag_name[(- 4)] == b'Device')):
self.im[imageIndex].imageInfo['DiagFlip'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Pixel Size (um)') and (split_tag_name[(- 3)] == b'Device')):
self.im[imageIndex].imageInfo['DetectorPixelSize'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'Exposure') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['Exposure'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'hbin') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['HorzBin'] = self.retrieveTagData(tag_ninfo)
elif ((split_tag_name[(- 1)] == b'vbin') and (split_tag_name[(- 2)] == b'Detector')):
self.im[imageIndex].imageInfo['VertBin'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[4] == b'Calibration'):
if (split_tag_name[5] == b'Dose Rate'):
if (split_tag_name[6] == b'Calibration'):
if (not ('DoseRateCalibration' in self.im[imageIndex].imageInfo)):
self.im[imageIndex].imageInfo['DoseRateCalibration'] = []
try:
self.im[imageIndex].imageInfo['DoseRateCalibration'].append(self.retrieveTagData(tag_ninfo))
except:
print('FAILED TO IMPORT DOSERATECALIBRATION')
elif (split_tag_name[4] == b'Microscope Info'):
if (split_tag_name[5] == b'Actual Magnification'):
self.im[imageIndex].imageInfo['ActualMag'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Indicated Magnification'):
self.im[imageIndex].imageInfo['NominalMag'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Cs(mm)'):
self.im[imageIndex].imageInfo['C3'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Voltage'):
self.im[imageIndex].imageInfo['Voltage'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Mode'):
self.im[imageIndex].imageInfo['MicroscopeMode'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('ascii')
pass
elif (split_tag_name[4] == b'EELS Spectrometer'):
if (split_tag_name[5] == b'Aperture index'):
self.im[imageIndex].imageInfo['EELSApertureIndex'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Aperture label'):
self.im[imageIndex].imageInfo['EELSApertureLabel'] = self.retrieveTagData(tag_ninfo).astype(np.uint8).tostring().decode('ascii')
elif (split_tag_name[5] == b'Energy loss (eV)'):
self.im[imageIndex].imageInfo['EELSEnergyLoss'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Slit inserted'):
self.im[imageIndex].imageInfo['EELSSlitIn'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Slit width (eV)'):
self.im[imageIndex].imageInfo['EELSSlitWidth'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[4] == b'Latitude-S'):
if (split_tag_name[5] == b'Distance To Focus Position'):
self.im[imageIndex].imageInfo['LatSDistanceToFocus'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Intended Defocus'):
self.im[imageIndex].imageInfo['LatSIntendedDefocus'] = self.retrieveTagData(tag_ninfo)
elif (split_tag_name[5] == b'Beam Diameter (nm)'):
self.im[imageIndex].imageInfo['LatSBeamDiameter'] = self.retrieveTagData(tag_ninfo)
self.f.seek((loc_tag + tag_fieldlen))
return<|docstring|>Parse a tag at the given file handle location<|endoftext|> |
83ee64f9f602dd4c58614d1b4248efca1f8e2b61acc54e5a653670443a19de9a | def retrieveTagData(self, tag_ninfo):
' Get the actual data from the tag, which is then stored in one of the dicts or imageData '
tag_loc = self.f.tell()
tag_infos = np.fromfile(self.f, dtype='>i8', count=tag_ninfo)
for K in np.arange(0, tag_infos.size):
if (tag_infos[K] == 15):
self.f.seek(((tag_loc + (8 * K)) + 16))
struct_fieldcount = np.fromfile(self.f, dtype='>i8', count=1)
struct_fielddtype = []
struct_fieldval = []
for J in np.arange(0, struct_fieldcount):
np.fromfile(self.f, dtype='>i8', count=1)
struct_fielddtype.append(self.getTagDType(np.fromfile(self.f, dtype='>i8', count=1)))
for J in np.arange(0, struct_fieldcount):
struct_fieldval.append(np.fromfile(self.f, dtype=struct_fielddtype[J], count=1)[0])
return struct_fieldval
elif (tag_infos[K] == 20):
if self.verbose:
print('Found array')
try:
K += 1
array_dtype = self.getTagDType(tag_infos[K])
K += 1
array_ncount = tag_infos[K]
if self.verbose:
print(((('Array dtype: ' + array_dtype) + ', array ncount: ') + str(array_ncount)))
if (array_ncount > 0):
return np.fromfile(self.f, dtype=array_dtype, count=array_ncount)
except IndexError:
pass
pass
elif (tag_infos[K] == 9):
tag_char = self.f.read(1)
if self.verbose:
print(('Found char: ' + tag_char))
pass
elif (tag_infos[K] == 18):
if self.verbose:
print('FIXME Found string')
pass
else:
tag_dtype = self.getTagDType(tag_infos[K])
if (tag_dtype != ''):
tag_data = np.fromfile(self.f, dtype=tag_dtype, count=1)[0]
if self.verbose:
print(((('Singleton: ' + tag_dtype) + ': ') + str(tag_data)))
return tag_data
pass | Get the actual data from the tag, which is then stored in one of the dicts or imageData | mrcz/ioDM.py | retrieveTagData | em-MRCZ/python-mrcz | 13 | python | def retrieveTagData(self, tag_ninfo):
' '
tag_loc = self.f.tell()
tag_infos = np.fromfile(self.f, dtype='>i8', count=tag_ninfo)
for K in np.arange(0, tag_infos.size):
if (tag_infos[K] == 15):
self.f.seek(((tag_loc + (8 * K)) + 16))
struct_fieldcount = np.fromfile(self.f, dtype='>i8', count=1)
struct_fielddtype = []
struct_fieldval = []
for J in np.arange(0, struct_fieldcount):
np.fromfile(self.f, dtype='>i8', count=1)
struct_fielddtype.append(self.getTagDType(np.fromfile(self.f, dtype='>i8', count=1)))
for J in np.arange(0, struct_fieldcount):
struct_fieldval.append(np.fromfile(self.f, dtype=struct_fielddtype[J], count=1)[0])
return struct_fieldval
elif (tag_infos[K] == 20):
if self.verbose:
print('Found array')
try:
K += 1
array_dtype = self.getTagDType(tag_infos[K])
K += 1
array_ncount = tag_infos[K]
if self.verbose:
print(((('Array dtype: ' + array_dtype) + ', array ncount: ') + str(array_ncount)))
if (array_ncount > 0):
return np.fromfile(self.f, dtype=array_dtype, count=array_ncount)
except IndexError:
pass
pass
elif (tag_infos[K] == 9):
tag_char = self.f.read(1)
if self.verbose:
print(('Found char: ' + tag_char))
pass
elif (tag_infos[K] == 18):
if self.verbose:
print('FIXME Found string')
pass
else:
tag_dtype = self.getTagDType(tag_infos[K])
if (tag_dtype != ):
tag_data = np.fromfile(self.f, dtype=tag_dtype, count=1)[0]
if self.verbose:
print(((('Singleton: ' + tag_dtype) + ': ') + str(tag_data)))
return tag_data
pass | def retrieveTagData(self, tag_ninfo):
' '
tag_loc = self.f.tell()
tag_infos = np.fromfile(self.f, dtype='>i8', count=tag_ninfo)
for K in np.arange(0, tag_infos.size):
if (tag_infos[K] == 15):
self.f.seek(((tag_loc + (8 * K)) + 16))
struct_fieldcount = np.fromfile(self.f, dtype='>i8', count=1)
struct_fielddtype = []
struct_fieldval = []
for J in np.arange(0, struct_fieldcount):
np.fromfile(self.f, dtype='>i8', count=1)
struct_fielddtype.append(self.getTagDType(np.fromfile(self.f, dtype='>i8', count=1)))
for J in np.arange(0, struct_fieldcount):
struct_fieldval.append(np.fromfile(self.f, dtype=struct_fielddtype[J], count=1)[0])
return struct_fieldval
elif (tag_infos[K] == 20):
if self.verbose:
print('Found array')
try:
K += 1
array_dtype = self.getTagDType(tag_infos[K])
K += 1
array_ncount = tag_infos[K]
if self.verbose:
print(((('Array dtype: ' + array_dtype) + ', array ncount: ') + str(array_ncount)))
if (array_ncount > 0):
return np.fromfile(self.f, dtype=array_dtype, count=array_ncount)
except IndexError:
pass
pass
elif (tag_infos[K] == 9):
tag_char = self.f.read(1)
if self.verbose:
print(('Found char: ' + tag_char))
pass
elif (tag_infos[K] == 18):
if self.verbose:
print('FIXME Found string')
pass
else:
tag_dtype = self.getTagDType(tag_infos[K])
if (tag_dtype != ):
tag_data = np.fromfile(self.f, dtype=tag_dtype, count=1)[0]
if self.verbose:
print(((('Singleton: ' + tag_dtype) + ': ') + str(tag_data)))
return tag_data
pass<|docstring|>Get the actual data from the tag, which is then stored in one of the dicts or imageData<|endoftext|> |
78bf3ff9c8140b66afed7cbfc0ad3d21fffc52a77ac00e160fedbb4a8ea5d3fd | def discardTag(self):
" Quickly parse to the end of tag that we don't care about its information "
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
self.f.seek((self.f.tell() + tag_namelen))
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
self.f.seek((self.f.tell() + tag_fieldlen))
return | Quickly parse to the end of tag that we don't care about its information | mrcz/ioDM.py | discardTag | em-MRCZ/python-mrcz | 13 | python | def discardTag(self):
" "
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
self.f.seek((self.f.tell() + tag_namelen))
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
self.f.seek((self.f.tell() + tag_fieldlen))
return | def discardTag(self):
" "
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
self.f.seek((self.f.tell() + tag_namelen))
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
self.f.seek((self.f.tell() + tag_fieldlen))
return<|docstring|>Quickly parse to the end of tag that we don't care about its information<|endoftext|> |
c3baee0890559190a66b405c3b6e91b6239cc92e4d41a2b77c4bd9161c7a06aa | def parseTagDir(self, parent):
' Parse a tag directory at the given file handle location '
try:
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
if (tag_namelen > 0):
tag_name = self.f.read(tag_namelen)
else:
tag_name = b''
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
except IndexError:
if self.verbose:
print('Caught IndexError, trying to recover position in file')
return self.f
if (not bool(tag_name)):
if self.verbose:
print('Found empty tag')
tag_depth = int((parent.count(b'/') - 1))
tag_name = bytearray(str(self._unnamedIndices[tag_depth]).encode('ascii'))
self._unnamedIndices[tag_depth] += 1
self._unnamedIndices[(tag_depth + 1):] = 0
loc_tagdir = self.f.tell()
self.f.read(2)
ntags_dir = np.fromfile(self.f, dtype='>i8', count=1)[0]
if self.verbose:
print(((((('Found tag dir ' + str(parent)) + str(tag_name)) + ' with ') + str(ntags_dir)) + ' tags'))
for I in np.arange(0, ntags_dir):
try:
subtag_type = np.fromfile(self.f, dtype='i1', count=1)[0]
except IndexError:
if self.verbose:
print('Caught IndexError, trying to recover')
break
if (subtag_type == 20):
self.parseTagDir(((bytes(parent) + bytes(tag_name)) + b'/'))
elif (subtag_type == 21):
self.parseTag(((bytes(parent) + bytes(tag_name)) + b'/'))
elif (subtag_type == 0):
break
self.f.seek((loc_tagdir + tag_fieldlen))
return | Parse a tag directory at the given file handle location | mrcz/ioDM.py | parseTagDir | em-MRCZ/python-mrcz | 13 | python | def parseTagDir(self, parent):
' '
try:
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
if (tag_namelen > 0):
tag_name = self.f.read(tag_namelen)
else:
tag_name = b
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
except IndexError:
if self.verbose:
print('Caught IndexError, trying to recover position in file')
return self.f
if (not bool(tag_name)):
if self.verbose:
print('Found empty tag')
tag_depth = int((parent.count(b'/') - 1))
tag_name = bytearray(str(self._unnamedIndices[tag_depth]).encode('ascii'))
self._unnamedIndices[tag_depth] += 1
self._unnamedIndices[(tag_depth + 1):] = 0
loc_tagdir = self.f.tell()
self.f.read(2)
ntags_dir = np.fromfile(self.f, dtype='>i8', count=1)[0]
if self.verbose:
print(((((('Found tag dir ' + str(parent)) + str(tag_name)) + ' with ') + str(ntags_dir)) + ' tags'))
for I in np.arange(0, ntags_dir):
try:
subtag_type = np.fromfile(self.f, dtype='i1', count=1)[0]
except IndexError:
if self.verbose:
print('Caught IndexError, trying to recover')
break
if (subtag_type == 20):
self.parseTagDir(((bytes(parent) + bytes(tag_name)) + b'/'))
elif (subtag_type == 21):
self.parseTag(((bytes(parent) + bytes(tag_name)) + b'/'))
elif (subtag_type == 0):
break
self.f.seek((loc_tagdir + tag_fieldlen))
return | def parseTagDir(self, parent):
' '
try:
tag_namelen = np.fromfile(self.f, dtype='>i2', count=1)[0]
if (tag_namelen > 0):
tag_name = self.f.read(tag_namelen)
else:
tag_name = b
tag_fieldlen = np.fromfile(self.f, dtype='>i8', count=1)[0]
except IndexError:
if self.verbose:
print('Caught IndexError, trying to recover position in file')
return self.f
if (not bool(tag_name)):
if self.verbose:
print('Found empty tag')
tag_depth = int((parent.count(b'/') - 1))
tag_name = bytearray(str(self._unnamedIndices[tag_depth]).encode('ascii'))
self._unnamedIndices[tag_depth] += 1
self._unnamedIndices[(tag_depth + 1):] = 0
loc_tagdir = self.f.tell()
self.f.read(2)
ntags_dir = np.fromfile(self.f, dtype='>i8', count=1)[0]
if self.verbose:
print(((((('Found tag dir ' + str(parent)) + str(tag_name)) + ' with ') + str(ntags_dir)) + ' tags'))
for I in np.arange(0, ntags_dir):
try:
subtag_type = np.fromfile(self.f, dtype='i1', count=1)[0]
except IndexError:
if self.verbose:
print('Caught IndexError, trying to recover')
break
if (subtag_type == 20):
self.parseTagDir(((bytes(parent) + bytes(tag_name)) + b'/'))
elif (subtag_type == 21):
self.parseTag(((bytes(parent) + bytes(tag_name)) + b'/'))
elif (subtag_type == 0):
break
self.f.seek((loc_tagdir + tag_fieldlen))
return<|docstring|>Parse a tag directory at the given file handle location<|endoftext|> |
9dbc5a343e409a092eaceab0b2e93f9ff14a9c8cdd0756aa540b411c1f840092 | def execute(self, image):
'Forward propagation.\n \n Args:\n image: images, a array of dimensions (N, 3, 300, 300)\n Return: \n lower-level feature maps conv4_3 and conv7 \n '
out = nn.relu(self.conv1_1(image))
out = nn.relu(self.conv1_2(out))
out = self.pool1(out)
out = nn.relu(self.conv2_1(out))
out = nn.relu(self.conv2_2(out))
out = self.pool2(out)
out = nn.relu(self.conv3_1(out))
out = nn.relu(self.conv3_2(out))
out = nn.relu(self.conv3_3(out))
out = self.pool3(out)
out = nn.relu(self.conv4_1(out))
out = nn.relu(self.conv4_2(out))
out = nn.relu(self.conv4_3(out))
conv4_3_feats = out
out = self.pool4(out)
out = nn.relu(self.conv5_1(out))
out = nn.relu(self.conv5_2(out))
out = nn.relu(self.conv5_3(out))
out = self.pool5(out)
out = nn.relu(self.conv6(out))
conv7_feats = nn.relu(self.conv7(out))
return (conv4_3_feats, conv7_feats) | Forward propagation.
Args:
image: images, a array of dimensions (N, 3, 300, 300)
Return:
lower-level feature maps conv4_3 and conv7 | ssd/model.py | execute | qixuxiang/jittor-models | 4 | python | def execute(self, image):
'Forward propagation.\n \n Args:\n image: images, a array of dimensions (N, 3, 300, 300)\n Return: \n lower-level feature maps conv4_3 and conv7 \n '
out = nn.relu(self.conv1_1(image))
out = nn.relu(self.conv1_2(out))
out = self.pool1(out)
out = nn.relu(self.conv2_1(out))
out = nn.relu(self.conv2_2(out))
out = self.pool2(out)
out = nn.relu(self.conv3_1(out))
out = nn.relu(self.conv3_2(out))
out = nn.relu(self.conv3_3(out))
out = self.pool3(out)
out = nn.relu(self.conv4_1(out))
out = nn.relu(self.conv4_2(out))
out = nn.relu(self.conv4_3(out))
conv4_3_feats = out
out = self.pool4(out)
out = nn.relu(self.conv5_1(out))
out = nn.relu(self.conv5_2(out))
out = nn.relu(self.conv5_3(out))
out = self.pool5(out)
out = nn.relu(self.conv6(out))
conv7_feats = nn.relu(self.conv7(out))
return (conv4_3_feats, conv7_feats) | def execute(self, image):
'Forward propagation.\n \n Args:\n image: images, a array of dimensions (N, 3, 300, 300)\n Return: \n lower-level feature maps conv4_3 and conv7 \n '
out = nn.relu(self.conv1_1(image))
out = nn.relu(self.conv1_2(out))
out = self.pool1(out)
out = nn.relu(self.conv2_1(out))
out = nn.relu(self.conv2_2(out))
out = self.pool2(out)
out = nn.relu(self.conv3_1(out))
out = nn.relu(self.conv3_2(out))
out = nn.relu(self.conv3_3(out))
out = self.pool3(out)
out = nn.relu(self.conv4_1(out))
out = nn.relu(self.conv4_2(out))
out = nn.relu(self.conv4_3(out))
conv4_3_feats = out
out = self.pool4(out)
out = nn.relu(self.conv5_1(out))
out = nn.relu(self.conv5_2(out))
out = nn.relu(self.conv5_3(out))
out = self.pool5(out)
out = nn.relu(self.conv6(out))
conv7_feats = nn.relu(self.conv7(out))
return (conv4_3_feats, conv7_feats)<|docstring|>Forward propagation.
Args:
image: images, a array of dimensions (N, 3, 300, 300)
Return:
lower-level feature maps conv4_3 and conv7<|endoftext|> |
cd45b6f9fce636c73dbd93cc310d49d0d516f2d882d5b436550122cb7695f53b | def init_conv2d(self):
' Initialize convolution parameters. '
for c in self.children():
if isinstance(c, nn.Conv):
init.gauss_(c.weight) | Initialize convolution parameters. | ssd/model.py | init_conv2d | qixuxiang/jittor-models | 4 | python | def init_conv2d(self):
' '
for c in self.children():
if isinstance(c, nn.Conv):
init.gauss_(c.weight) | def init_conv2d(self):
' '
for c in self.children():
if isinstance(c, nn.Conv):
init.gauss_(c.weight)<|docstring|>Initialize convolution parameters.<|endoftext|> |
976d45987a5e09102be9546aef0522c072b973b2d3ad5e677d50dafb13300430 | def execute(self, conv7_feats):
'Forward propagation.\n\n Args:\n conv7_feats: lower-level conv7 feature map, a array of dimensions (N, 1024, 19, 19)\n Return: \n higher-level feature maps conv8_2, conv9_2, conv10_2, and conv11_2\n '
out = nn.relu(self.conv8_1(conv7_feats))
out = nn.relu(self.conv8_2(out))
conv8_2_feats = out
out = nn.relu(self.conv9_1(out))
out = nn.relu(self.conv9_2(out))
conv9_2_feats = out
out = nn.relu(self.conv10_1(out))
out = nn.relu(self.conv10_2(out))
conv10_2_feats = out
out = nn.relu(self.conv11_1(out))
conv11_2_feats = nn.relu(self.conv11_2(out))
return (conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats) | Forward propagation.
Args:
conv7_feats: lower-level conv7 feature map, a array of dimensions (N, 1024, 19, 19)
Return:
higher-level feature maps conv8_2, conv9_2, conv10_2, and conv11_2 | ssd/model.py | execute | qixuxiang/jittor-models | 4 | python | def execute(self, conv7_feats):
'Forward propagation.\n\n Args:\n conv7_feats: lower-level conv7 feature map, a array of dimensions (N, 1024, 19, 19)\n Return: \n higher-level feature maps conv8_2, conv9_2, conv10_2, and conv11_2\n '
out = nn.relu(self.conv8_1(conv7_feats))
out = nn.relu(self.conv8_2(out))
conv8_2_feats = out
out = nn.relu(self.conv9_1(out))
out = nn.relu(self.conv9_2(out))
conv9_2_feats = out
out = nn.relu(self.conv10_1(out))
out = nn.relu(self.conv10_2(out))
conv10_2_feats = out
out = nn.relu(self.conv11_1(out))
conv11_2_feats = nn.relu(self.conv11_2(out))
return (conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats) | def execute(self, conv7_feats):
'Forward propagation.\n\n Args:\n conv7_feats: lower-level conv7 feature map, a array of dimensions (N, 1024, 19, 19)\n Return: \n higher-level feature maps conv8_2, conv9_2, conv10_2, and conv11_2\n '
out = nn.relu(self.conv8_1(conv7_feats))
out = nn.relu(self.conv8_2(out))
conv8_2_feats = out
out = nn.relu(self.conv9_1(out))
out = nn.relu(self.conv9_2(out))
conv9_2_feats = out
out = nn.relu(self.conv10_1(out))
out = nn.relu(self.conv10_2(out))
conv10_2_feats = out
out = nn.relu(self.conv11_1(out))
conv11_2_feats = nn.relu(self.conv11_2(out))
return (conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats)<|docstring|>Forward propagation.
Args:
conv7_feats: lower-level conv7 feature map, a array of dimensions (N, 1024, 19, 19)
Return:
higher-level feature maps conv8_2, conv9_2, conv10_2, and conv11_2<|endoftext|> |
bfc1e940a73cf5bf11a3d63c91a087d873da2cd58520211c59301a54eb543ed3 | def __init__(self, n_classes):
'\n Args:\n n_classes: number of different types of objects\n '
super(PredictionConvolutions, self).__init__()
self.n_classes = n_classes
n_boxes = {'conv4_3': 4, 'conv7': 6, 'conv8_2': 6, 'conv9_2': 6, 'conv10_2': 4, 'conv11_2': 4}
self.loc_conv4_3 = nn.Conv(512, (n_boxes['conv4_3'] * 4), kernel_size=3, padding=1)
self.loc_conv7 = nn.Conv(1024, (n_boxes['conv7'] * 4), kernel_size=3, padding=1)
self.loc_conv8_2 = nn.Conv(512, (n_boxes['conv8_2'] * 4), kernel_size=3, padding=1)
self.loc_conv9_2 = nn.Conv(256, (n_boxes['conv9_2'] * 4), kernel_size=3, padding=1)
self.loc_conv10_2 = nn.Conv(256, (n_boxes['conv10_2'] * 4), kernel_size=3, padding=1)
self.loc_conv11_2 = nn.Conv(256, (n_boxes['conv11_2'] * 4), kernel_size=3, padding=1)
self.cl_conv4_3 = nn.Conv(512, (n_boxes['conv4_3'] * n_classes), kernel_size=3, padding=1)
self.cl_conv7 = nn.Conv(1024, (n_boxes['conv7'] * n_classes), kernel_size=3, padding=1)
self.cl_conv8_2 = nn.Conv(512, (n_boxes['conv8_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv9_2 = nn.Conv(256, (n_boxes['conv9_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv10_2 = nn.Conv(256, (n_boxes['conv10_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv11_2 = nn.Conv(256, (n_boxes['conv11_2'] * n_classes), kernel_size=3, padding=1)
self.init_conv2d() | Args:
n_classes: number of different types of objects | ssd/model.py | __init__ | qixuxiang/jittor-models | 4 | python | def __init__(self, n_classes):
'\n Args:\n n_classes: number of different types of objects\n '
super(PredictionConvolutions, self).__init__()
self.n_classes = n_classes
n_boxes = {'conv4_3': 4, 'conv7': 6, 'conv8_2': 6, 'conv9_2': 6, 'conv10_2': 4, 'conv11_2': 4}
self.loc_conv4_3 = nn.Conv(512, (n_boxes['conv4_3'] * 4), kernel_size=3, padding=1)
self.loc_conv7 = nn.Conv(1024, (n_boxes['conv7'] * 4), kernel_size=3, padding=1)
self.loc_conv8_2 = nn.Conv(512, (n_boxes['conv8_2'] * 4), kernel_size=3, padding=1)
self.loc_conv9_2 = nn.Conv(256, (n_boxes['conv9_2'] * 4), kernel_size=3, padding=1)
self.loc_conv10_2 = nn.Conv(256, (n_boxes['conv10_2'] * 4), kernel_size=3, padding=1)
self.loc_conv11_2 = nn.Conv(256, (n_boxes['conv11_2'] * 4), kernel_size=3, padding=1)
self.cl_conv4_3 = nn.Conv(512, (n_boxes['conv4_3'] * n_classes), kernel_size=3, padding=1)
self.cl_conv7 = nn.Conv(1024, (n_boxes['conv7'] * n_classes), kernel_size=3, padding=1)
self.cl_conv8_2 = nn.Conv(512, (n_boxes['conv8_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv9_2 = nn.Conv(256, (n_boxes['conv9_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv10_2 = nn.Conv(256, (n_boxes['conv10_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv11_2 = nn.Conv(256, (n_boxes['conv11_2'] * n_classes), kernel_size=3, padding=1)
self.init_conv2d() | def __init__(self, n_classes):
'\n Args:\n n_classes: number of different types of objects\n '
super(PredictionConvolutions, self).__init__()
self.n_classes = n_classes
n_boxes = {'conv4_3': 4, 'conv7': 6, 'conv8_2': 6, 'conv9_2': 6, 'conv10_2': 4, 'conv11_2': 4}
self.loc_conv4_3 = nn.Conv(512, (n_boxes['conv4_3'] * 4), kernel_size=3, padding=1)
self.loc_conv7 = nn.Conv(1024, (n_boxes['conv7'] * 4), kernel_size=3, padding=1)
self.loc_conv8_2 = nn.Conv(512, (n_boxes['conv8_2'] * 4), kernel_size=3, padding=1)
self.loc_conv9_2 = nn.Conv(256, (n_boxes['conv9_2'] * 4), kernel_size=3, padding=1)
self.loc_conv10_2 = nn.Conv(256, (n_boxes['conv10_2'] * 4), kernel_size=3, padding=1)
self.loc_conv11_2 = nn.Conv(256, (n_boxes['conv11_2'] * 4), kernel_size=3, padding=1)
self.cl_conv4_3 = nn.Conv(512, (n_boxes['conv4_3'] * n_classes), kernel_size=3, padding=1)
self.cl_conv7 = nn.Conv(1024, (n_boxes['conv7'] * n_classes), kernel_size=3, padding=1)
self.cl_conv8_2 = nn.Conv(512, (n_boxes['conv8_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv9_2 = nn.Conv(256, (n_boxes['conv9_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv10_2 = nn.Conv(256, (n_boxes['conv10_2'] * n_classes), kernel_size=3, padding=1)
self.cl_conv11_2 = nn.Conv(256, (n_boxes['conv11_2'] * n_classes), kernel_size=3, padding=1)
self.init_conv2d()<|docstring|>Args:
n_classes: number of different types of objects<|endoftext|> |
cd45b6f9fce636c73dbd93cc310d49d0d516f2d882d5b436550122cb7695f53b | def init_conv2d(self):
' Initialize convolution parameters. '
for c in self.children():
if isinstance(c, nn.Conv):
init.gauss_(c.weight) | Initialize convolution parameters. | ssd/model.py | init_conv2d | qixuxiang/jittor-models | 4 | python | def init_conv2d(self):
' '
for c in self.children():
if isinstance(c, nn.Conv):
init.gauss_(c.weight) | def init_conv2d(self):
' '
for c in self.children():
if isinstance(c, nn.Conv):
init.gauss_(c.weight)<|docstring|>Initialize convolution parameters.<|endoftext|> |
67dbebcad2b784b2a50cc85f2aa863ab7e0bd696bd4f475adfb9cce74e70a5d7 | def execute(self, conv4_3_feats, conv7_feats, conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats):
' Forward propagation.\n\n Args:\n conv4_3_feats: conv4_3 feature map, a array of dimensions (N, 512, 38, 38)\n conv7_feats: conv7 feature map, a array of dimensions (N, 1024, 19, 19)\n conv8_2_feats: conv8_2 feature map, a array of dimensions (N, 512, 10, 10)\n conv9_2_feats: conv9_2 feature map, a array of dimensions (N, 256, 5, 5)\n conv10_2_feats: conv10_2 feature map, a array of dimensions (N, 256, 3, 3)\n conv11_2_feats: conv11_2 feature map, a array of dimensions (N, 256, 1, 1)\n Return: \n 8732 locations and class scores (i.e. w.r.t each prior box) for each image\n '
batch_size = conv4_3_feats.shape[0]
l_conv4_3 = self.loc_conv4_3(conv4_3_feats)
l_conv4_3 = jt.transpose(l_conv4_3, [0, 2, 3, 1])
l_conv4_3 = jt.reshape(l_conv4_3, [batch_size, (- 1), 4])
l_conv7 = self.loc_conv7(conv7_feats)
l_conv7 = jt.transpose(l_conv7, [0, 2, 3, 1])
l_conv7 = jt.reshape(l_conv7, [batch_size, (- 1), 4])
l_conv8_2 = self.loc_conv8_2(conv8_2_feats)
l_conv8_2 = jt.transpose(l_conv8_2, [0, 2, 3, 1])
l_conv8_2 = jt.reshape(l_conv8_2, [batch_size, (- 1), 4])
l_conv9_2 = self.loc_conv9_2(conv9_2_feats)
l_conv9_2 = jt.transpose(l_conv9_2, [0, 2, 3, 1])
l_conv9_2 = jt.reshape(l_conv9_2, [batch_size, (- 1), 4])
l_conv10_2 = self.loc_conv10_2(conv10_2_feats)
l_conv10_2 = jt.transpose(l_conv10_2, [0, 2, 3, 1])
l_conv10_2 = jt.reshape(l_conv10_2, [batch_size, (- 1), 4])
l_conv11_2 = self.loc_conv11_2(conv11_2_feats)
l_conv11_2 = jt.transpose(l_conv11_2, [0, 2, 3, 1])
l_conv11_2 = jt.reshape(l_conv11_2, [batch_size, (- 1), 4])
c_conv4_3 = self.cl_conv4_3(conv4_3_feats)
c_conv4_3 = jt.transpose(c_conv4_3, [0, 2, 3, 1])
c_conv4_3 = jt.reshape(c_conv4_3, [batch_size, (- 1), self.n_classes])
c_conv7 = self.cl_conv7(conv7_feats)
c_conv7 = jt.transpose(c_conv7, [0, 2, 3, 1])
c_conv7 = jt.reshape(c_conv7, [batch_size, (- 1), self.n_classes])
c_conv8_2 = self.cl_conv8_2(conv8_2_feats)
c_conv8_2 = jt.transpose(c_conv8_2, [0, 2, 3, 1])
c_conv8_2 = jt.reshape(c_conv8_2, [batch_size, (- 1), self.n_classes])
c_conv9_2 = self.cl_conv9_2(conv9_2_feats)
c_conv9_2 = jt.transpose(c_conv9_2, [0, 2, 3, 1])
c_conv9_2 = jt.reshape(c_conv9_2, [batch_size, (- 1), self.n_classes])
c_conv10_2 = self.cl_conv10_2(conv10_2_feats)
c_conv10_2 = jt.transpose(c_conv10_2, [0, 2, 3, 1])
c_conv10_2 = jt.reshape(c_conv10_2, [batch_size, (- 1), self.n_classes])
c_conv11_2 = self.cl_conv11_2(conv11_2_feats)
c_conv11_2 = jt.transpose(c_conv11_2, [0, 2, 3, 1])
c_conv11_2 = jt.reshape(c_conv11_2, [batch_size, (- 1), self.n_classes])
locs = jt.contrib.concat([l_conv4_3, l_conv7, l_conv8_2, l_conv9_2, l_conv10_2, l_conv11_2], dim=1)
classes_scores = jt.contrib.concat([c_conv4_3, c_conv7, c_conv8_2, c_conv9_2, c_conv10_2, c_conv11_2], dim=1)
return (locs, classes_scores) | Forward propagation.
Args:
conv4_3_feats: conv4_3 feature map, a array of dimensions (N, 512, 38, 38)
conv7_feats: conv7 feature map, a array of dimensions (N, 1024, 19, 19)
conv8_2_feats: conv8_2 feature map, a array of dimensions (N, 512, 10, 10)
conv9_2_feats: conv9_2 feature map, a array of dimensions (N, 256, 5, 5)
conv10_2_feats: conv10_2 feature map, a array of dimensions (N, 256, 3, 3)
conv11_2_feats: conv11_2 feature map, a array of dimensions (N, 256, 1, 1)
Return:
8732 locations and class scores (i.e. w.r.t each prior box) for each image | ssd/model.py | execute | qixuxiang/jittor-models | 4 | python | def execute(self, conv4_3_feats, conv7_feats, conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats):
' Forward propagation.\n\n Args:\n conv4_3_feats: conv4_3 feature map, a array of dimensions (N, 512, 38, 38)\n conv7_feats: conv7 feature map, a array of dimensions (N, 1024, 19, 19)\n conv8_2_feats: conv8_2 feature map, a array of dimensions (N, 512, 10, 10)\n conv9_2_feats: conv9_2 feature map, a array of dimensions (N, 256, 5, 5)\n conv10_2_feats: conv10_2 feature map, a array of dimensions (N, 256, 3, 3)\n conv11_2_feats: conv11_2 feature map, a array of dimensions (N, 256, 1, 1)\n Return: \n 8732 locations and class scores (i.e. w.r.t each prior box) for each image\n '
batch_size = conv4_3_feats.shape[0]
l_conv4_3 = self.loc_conv4_3(conv4_3_feats)
l_conv4_3 = jt.transpose(l_conv4_3, [0, 2, 3, 1])
l_conv4_3 = jt.reshape(l_conv4_3, [batch_size, (- 1), 4])
l_conv7 = self.loc_conv7(conv7_feats)
l_conv7 = jt.transpose(l_conv7, [0, 2, 3, 1])
l_conv7 = jt.reshape(l_conv7, [batch_size, (- 1), 4])
l_conv8_2 = self.loc_conv8_2(conv8_2_feats)
l_conv8_2 = jt.transpose(l_conv8_2, [0, 2, 3, 1])
l_conv8_2 = jt.reshape(l_conv8_2, [batch_size, (- 1), 4])
l_conv9_2 = self.loc_conv9_2(conv9_2_feats)
l_conv9_2 = jt.transpose(l_conv9_2, [0, 2, 3, 1])
l_conv9_2 = jt.reshape(l_conv9_2, [batch_size, (- 1), 4])
l_conv10_2 = self.loc_conv10_2(conv10_2_feats)
l_conv10_2 = jt.transpose(l_conv10_2, [0, 2, 3, 1])
l_conv10_2 = jt.reshape(l_conv10_2, [batch_size, (- 1), 4])
l_conv11_2 = self.loc_conv11_2(conv11_2_feats)
l_conv11_2 = jt.transpose(l_conv11_2, [0, 2, 3, 1])
l_conv11_2 = jt.reshape(l_conv11_2, [batch_size, (- 1), 4])
c_conv4_3 = self.cl_conv4_3(conv4_3_feats)
c_conv4_3 = jt.transpose(c_conv4_3, [0, 2, 3, 1])
c_conv4_3 = jt.reshape(c_conv4_3, [batch_size, (- 1), self.n_classes])
c_conv7 = self.cl_conv7(conv7_feats)
c_conv7 = jt.transpose(c_conv7, [0, 2, 3, 1])
c_conv7 = jt.reshape(c_conv7, [batch_size, (- 1), self.n_classes])
c_conv8_2 = self.cl_conv8_2(conv8_2_feats)
c_conv8_2 = jt.transpose(c_conv8_2, [0, 2, 3, 1])
c_conv8_2 = jt.reshape(c_conv8_2, [batch_size, (- 1), self.n_classes])
c_conv9_2 = self.cl_conv9_2(conv9_2_feats)
c_conv9_2 = jt.transpose(c_conv9_2, [0, 2, 3, 1])
c_conv9_2 = jt.reshape(c_conv9_2, [batch_size, (- 1), self.n_classes])
c_conv10_2 = self.cl_conv10_2(conv10_2_feats)
c_conv10_2 = jt.transpose(c_conv10_2, [0, 2, 3, 1])
c_conv10_2 = jt.reshape(c_conv10_2, [batch_size, (- 1), self.n_classes])
c_conv11_2 = self.cl_conv11_2(conv11_2_feats)
c_conv11_2 = jt.transpose(c_conv11_2, [0, 2, 3, 1])
c_conv11_2 = jt.reshape(c_conv11_2, [batch_size, (- 1), self.n_classes])
locs = jt.contrib.concat([l_conv4_3, l_conv7, l_conv8_2, l_conv9_2, l_conv10_2, l_conv11_2], dim=1)
classes_scores = jt.contrib.concat([c_conv4_3, c_conv7, c_conv8_2, c_conv9_2, c_conv10_2, c_conv11_2], dim=1)
return (locs, classes_scores) | def execute(self, conv4_3_feats, conv7_feats, conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats):
' Forward propagation.\n\n Args:\n conv4_3_feats: conv4_3 feature map, a array of dimensions (N, 512, 38, 38)\n conv7_feats: conv7 feature map, a array of dimensions (N, 1024, 19, 19)\n conv8_2_feats: conv8_2 feature map, a array of dimensions (N, 512, 10, 10)\n conv9_2_feats: conv9_2 feature map, a array of dimensions (N, 256, 5, 5)\n conv10_2_feats: conv10_2 feature map, a array of dimensions (N, 256, 3, 3)\n conv11_2_feats: conv11_2 feature map, a array of dimensions (N, 256, 1, 1)\n Return: \n 8732 locations and class scores (i.e. w.r.t each prior box) for each image\n '
batch_size = conv4_3_feats.shape[0]
l_conv4_3 = self.loc_conv4_3(conv4_3_feats)
l_conv4_3 = jt.transpose(l_conv4_3, [0, 2, 3, 1])
l_conv4_3 = jt.reshape(l_conv4_3, [batch_size, (- 1), 4])
l_conv7 = self.loc_conv7(conv7_feats)
l_conv7 = jt.transpose(l_conv7, [0, 2, 3, 1])
l_conv7 = jt.reshape(l_conv7, [batch_size, (- 1), 4])
l_conv8_2 = self.loc_conv8_2(conv8_2_feats)
l_conv8_2 = jt.transpose(l_conv8_2, [0, 2, 3, 1])
l_conv8_2 = jt.reshape(l_conv8_2, [batch_size, (- 1), 4])
l_conv9_2 = self.loc_conv9_2(conv9_2_feats)
l_conv9_2 = jt.transpose(l_conv9_2, [0, 2, 3, 1])
l_conv9_2 = jt.reshape(l_conv9_2, [batch_size, (- 1), 4])
l_conv10_2 = self.loc_conv10_2(conv10_2_feats)
l_conv10_2 = jt.transpose(l_conv10_2, [0, 2, 3, 1])
l_conv10_2 = jt.reshape(l_conv10_2, [batch_size, (- 1), 4])
l_conv11_2 = self.loc_conv11_2(conv11_2_feats)
l_conv11_2 = jt.transpose(l_conv11_2, [0, 2, 3, 1])
l_conv11_2 = jt.reshape(l_conv11_2, [batch_size, (- 1), 4])
c_conv4_3 = self.cl_conv4_3(conv4_3_feats)
c_conv4_3 = jt.transpose(c_conv4_3, [0, 2, 3, 1])
c_conv4_3 = jt.reshape(c_conv4_3, [batch_size, (- 1), self.n_classes])
c_conv7 = self.cl_conv7(conv7_feats)
c_conv7 = jt.transpose(c_conv7, [0, 2, 3, 1])
c_conv7 = jt.reshape(c_conv7, [batch_size, (- 1), self.n_classes])
c_conv8_2 = self.cl_conv8_2(conv8_2_feats)
c_conv8_2 = jt.transpose(c_conv8_2, [0, 2, 3, 1])
c_conv8_2 = jt.reshape(c_conv8_2, [batch_size, (- 1), self.n_classes])
c_conv9_2 = self.cl_conv9_2(conv9_2_feats)
c_conv9_2 = jt.transpose(c_conv9_2, [0, 2, 3, 1])
c_conv9_2 = jt.reshape(c_conv9_2, [batch_size, (- 1), self.n_classes])
c_conv10_2 = self.cl_conv10_2(conv10_2_feats)
c_conv10_2 = jt.transpose(c_conv10_2, [0, 2, 3, 1])
c_conv10_2 = jt.reshape(c_conv10_2, [batch_size, (- 1), self.n_classes])
c_conv11_2 = self.cl_conv11_2(conv11_2_feats)
c_conv11_2 = jt.transpose(c_conv11_2, [0, 2, 3, 1])
c_conv11_2 = jt.reshape(c_conv11_2, [batch_size, (- 1), self.n_classes])
locs = jt.contrib.concat([l_conv4_3, l_conv7, l_conv8_2, l_conv9_2, l_conv10_2, l_conv11_2], dim=1)
classes_scores = jt.contrib.concat([c_conv4_3, c_conv7, c_conv8_2, c_conv9_2, c_conv10_2, c_conv11_2], dim=1)
return (locs, classes_scores)<|docstring|>Forward propagation.
Args:
conv4_3_feats: conv4_3 feature map, a array of dimensions (N, 512, 38, 38)
conv7_feats: conv7 feature map, a array of dimensions (N, 1024, 19, 19)
conv8_2_feats: conv8_2 feature map, a array of dimensions (N, 512, 10, 10)
conv9_2_feats: conv9_2 feature map, a array of dimensions (N, 256, 5, 5)
conv10_2_feats: conv10_2 feature map, a array of dimensions (N, 256, 3, 3)
conv11_2_feats: conv11_2 feature map, a array of dimensions (N, 256, 1, 1)
Return:
8732 locations and class scores (i.e. w.r.t each prior box) for each image<|endoftext|> |
535c7944fa47403869bc381897bcc3ce49a7028df38344aea820765ce7ac13ed | def execute(self, image):
' Forward propagation.\n \n Args:\n image: images, a array of dimensions (N, 3, 300, 300)\n Return: \n 8732 locations and class scores (i.e. w.r.t each prior box) for each image\n '
(conv4_3_feats, conv7_feats) = self.base(image)
norm = conv4_3_feats.sqr().sum(dim=1, keepdims=True).sqrt()
conv4_3_feats = (conv4_3_feats / norm)
conv4_3_feats = (conv4_3_feats * self.rescale_factors)
(conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats) = self.aux_convs(conv7_feats)
(locs, classes_scores) = self.pred_convs(conv4_3_feats, conv7_feats, conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats)
return (locs, classes_scores) | Forward propagation.
Args:
image: images, a array of dimensions (N, 3, 300, 300)
Return:
8732 locations and class scores (i.e. w.r.t each prior box) for each image | ssd/model.py | execute | qixuxiang/jittor-models | 4 | python | def execute(self, image):
' Forward propagation.\n \n Args:\n image: images, a array of dimensions (N, 3, 300, 300)\n Return: \n 8732 locations and class scores (i.e. w.r.t each prior box) for each image\n '
(conv4_3_feats, conv7_feats) = self.base(image)
norm = conv4_3_feats.sqr().sum(dim=1, keepdims=True).sqrt()
conv4_3_feats = (conv4_3_feats / norm)
conv4_3_feats = (conv4_3_feats * self.rescale_factors)
(conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats) = self.aux_convs(conv7_feats)
(locs, classes_scores) = self.pred_convs(conv4_3_feats, conv7_feats, conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats)
return (locs, classes_scores) | def execute(self, image):
' Forward propagation.\n \n Args:\n image: images, a array of dimensions (N, 3, 300, 300)\n Return: \n 8732 locations and class scores (i.e. w.r.t each prior box) for each image\n '
(conv4_3_feats, conv7_feats) = self.base(image)
norm = conv4_3_feats.sqr().sum(dim=1, keepdims=True).sqrt()
conv4_3_feats = (conv4_3_feats / norm)
conv4_3_feats = (conv4_3_feats * self.rescale_factors)
(conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats) = self.aux_convs(conv7_feats)
(locs, classes_scores) = self.pred_convs(conv4_3_feats, conv7_feats, conv8_2_feats, conv9_2_feats, conv10_2_feats, conv11_2_feats)
return (locs, classes_scores)<|docstring|>Forward propagation.
Args:
image: images, a array of dimensions (N, 3, 300, 300)
Return:
8732 locations and class scores (i.e. w.r.t each prior box) for each image<|endoftext|> |
ee21974f84f10e080c0f67c24b85f9974c1b9e26c11ba9d69d0b8f42e4208863 | def create_prior_boxes(self):
' Create the 8732 prior (default) boxes for the SSD300, as defined in the paper.\n Return: prior boxes in center-size coordinates, a array of dimensions (8732, 4)\n '
fmap_dims = {'conv4_3': 38, 'conv7': 19, 'conv8_2': 10, 'conv9_2': 5, 'conv10_2': 3, 'conv11_2': 1}
obj_scales = {'conv4_3': 0.1, 'conv7': 0.2, 'conv8_2': 0.375, 'conv9_2': 0.55, 'conv10_2': 0.725, 'conv11_2': 0.9}
aspect_ratios = {'conv4_3': [1.0, 2.0, 0.5], 'conv7': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv8_2': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv9_2': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv10_2': [1.0, 2.0, 0.5], 'conv11_2': [1.0, 2.0, 0.5]}
fmaps = list(fmap_dims.keys())
prior_boxes = []
for (k, fmap) in enumerate(fmaps):
for i in range(fmap_dims[fmap]):
for j in range(fmap_dims[fmap]):
cx = ((j + 0.5) / fmap_dims[fmap])
cy = ((i + 0.5) / fmap_dims[fmap])
for ratio in aspect_ratios[fmap]:
prior_boxes.append([cx, cy, (obj_scales[fmap] * sqrt(ratio)), (obj_scales[fmap] / sqrt(ratio))])
if (ratio == 1.0):
try:
additional_scale = sqrt((obj_scales[fmap] * obj_scales[fmaps[(k + 1)]]))
except IndexError:
additional_scale = 1.0
prior_boxes.append([cx, cy, additional_scale, additional_scale])
prior_boxes = np.array(prior_boxes)
prior_boxes = np.clip(prior_boxes, 0.0, 1.0)
return prior_boxes | Create the 8732 prior (default) boxes for the SSD300, as defined in the paper.
Return: prior boxes in center-size coordinates, a array of dimensions (8732, 4) | ssd/model.py | create_prior_boxes | qixuxiang/jittor-models | 4 | python | def create_prior_boxes(self):
' Create the 8732 prior (default) boxes for the SSD300, as defined in the paper.\n Return: prior boxes in center-size coordinates, a array of dimensions (8732, 4)\n '
fmap_dims = {'conv4_3': 38, 'conv7': 19, 'conv8_2': 10, 'conv9_2': 5, 'conv10_2': 3, 'conv11_2': 1}
obj_scales = {'conv4_3': 0.1, 'conv7': 0.2, 'conv8_2': 0.375, 'conv9_2': 0.55, 'conv10_2': 0.725, 'conv11_2': 0.9}
aspect_ratios = {'conv4_3': [1.0, 2.0, 0.5], 'conv7': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv8_2': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv9_2': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv10_2': [1.0, 2.0, 0.5], 'conv11_2': [1.0, 2.0, 0.5]}
fmaps = list(fmap_dims.keys())
prior_boxes = []
for (k, fmap) in enumerate(fmaps):
for i in range(fmap_dims[fmap]):
for j in range(fmap_dims[fmap]):
cx = ((j + 0.5) / fmap_dims[fmap])
cy = ((i + 0.5) / fmap_dims[fmap])
for ratio in aspect_ratios[fmap]:
prior_boxes.append([cx, cy, (obj_scales[fmap] * sqrt(ratio)), (obj_scales[fmap] / sqrt(ratio))])
if (ratio == 1.0):
try:
additional_scale = sqrt((obj_scales[fmap] * obj_scales[fmaps[(k + 1)]]))
except IndexError:
additional_scale = 1.0
prior_boxes.append([cx, cy, additional_scale, additional_scale])
prior_boxes = np.array(prior_boxes)
prior_boxes = np.clip(prior_boxes, 0.0, 1.0)
return prior_boxes | def create_prior_boxes(self):
' Create the 8732 prior (default) boxes for the SSD300, as defined in the paper.\n Return: prior boxes in center-size coordinates, a array of dimensions (8732, 4)\n '
fmap_dims = {'conv4_3': 38, 'conv7': 19, 'conv8_2': 10, 'conv9_2': 5, 'conv10_2': 3, 'conv11_2': 1}
obj_scales = {'conv4_3': 0.1, 'conv7': 0.2, 'conv8_2': 0.375, 'conv9_2': 0.55, 'conv10_2': 0.725, 'conv11_2': 0.9}
aspect_ratios = {'conv4_3': [1.0, 2.0, 0.5], 'conv7': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv8_2': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv9_2': [1.0, 2.0, 3.0, 0.5, 0.333], 'conv10_2': [1.0, 2.0, 0.5], 'conv11_2': [1.0, 2.0, 0.5]}
fmaps = list(fmap_dims.keys())
prior_boxes = []
for (k, fmap) in enumerate(fmaps):
for i in range(fmap_dims[fmap]):
for j in range(fmap_dims[fmap]):
cx = ((j + 0.5) / fmap_dims[fmap])
cy = ((i + 0.5) / fmap_dims[fmap])
for ratio in aspect_ratios[fmap]:
prior_boxes.append([cx, cy, (obj_scales[fmap] * sqrt(ratio)), (obj_scales[fmap] / sqrt(ratio))])
if (ratio == 1.0):
try:
additional_scale = sqrt((obj_scales[fmap] * obj_scales[fmaps[(k + 1)]]))
except IndexError:
additional_scale = 1.0
prior_boxes.append([cx, cy, additional_scale, additional_scale])
prior_boxes = np.array(prior_boxes)
prior_boxes = np.clip(prior_boxes, 0.0, 1.0)
return prior_boxes<|docstring|>Create the 8732 prior (default) boxes for the SSD300, as defined in the paper.
Return: prior boxes in center-size coordinates, a array of dimensions (8732, 4)<|endoftext|> |
f9666465928d96cb9fe9a846f5ed5d681f321ab1d067d3c224a19a84bd0aabad | def detect_objects(self, predicted_locs, predicted_scores, min_score, max_overlap, top_k):
" Decipher the 8732 locations and class scores (output of ths SSD300) to detect objects.\n For each class, perform Non-Maximum Suppression (NMS) on boxes that are above a minimum threshold.\n\n Args:\n predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\n predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\n min_score: minimum threshold for a box to be considered a match for a certain class\n max_overlap: maximum overlap two boxes can have so that the one with the lower score is not suppressed via NMS\n top_k: if there are a lot of resulting detection across all classes, keep only the top 'k'\n \n Return: detections (boxes, labels, and scores), lists of length batch_size\n "
batch_size = predicted_locs.shape[0]
n_priors = self.priors_cxcy.shape[0]
predicted_scores = nn.softmax(predicted_scores, dim=2)
all_images_boxes = list()
all_images_labels = list()
all_images_scores = list()
predicted_locs = predicted_locs.data
predicted_scores = predicted_scores.data
assert (n_priors == predicted_locs.shape[1])
for i in range(batch_size):
decoded_locs = cxcy_to_xy(gcxgcy_to_cxcy(predicted_locs[i], self.priors_cxcy))
image_boxes = list()
image_labels = list()
image_scores = list()
for c in range(1, self.n_classes):
class_scores = predicted_scores[i][(:, c)]
score_above_min_score = (class_scores >= min_score)
n_above_min_score = score_above_min_score.sum()
if (n_above_min_score == 0):
continue
class_scores = class_scores[score_above_min_score]
class_decoded_locs = decoded_locs[score_above_min_score]
sort_ind = np.argsort((- class_scores), axis=0)
class_scores = class_scores[sort_ind]
class_decoded_locs = class_decoded_locs[sort_ind]
overlap = find_jaccard_overlap(class_decoded_locs, class_decoded_locs)
suppress = np.zeros(n_above_min_score).astype('int')
for box in range(class_decoded_locs.shape[0]):
if (suppress[box] == 1):
continue
suppress = np.maximum(suppress, (overlap[box] > max_overlap))
suppress[box] = 0
image_boxes.append(class_decoded_locs[(1 - suppress).astype('bool')])
image_labels.append((int((1 - suppress).sum()) * [c]))
image_scores.append(class_scores[(1 - suppress).astype('bool')])
if (len(image_boxes) == 0):
image_boxes.append(np.array([[0.0, 0.0, 1.0, 1.0]]))
image_labels.append(np.array([0]))
image_scores.append(np.array([0.0]))
image_boxes = np.concatenate(image_boxes, 0)
image_labels = np.concatenate(image_labels, 0)
image_scores = np.concatenate(image_scores, 0)
n_objects = image_scores.shape[0]
if (n_objects > top_k):
sort_ind = np.argsort((- image_scores), axis=0)
image_scores = image_scores[sort_ind][:top_k]
image_boxes = image_boxes[sort_ind][:top_k]
image_labels = image_labels[sort_ind][:top_k]
all_images_boxes.append(image_boxes)
all_images_labels.append(image_labels)
all_images_scores.append(image_scores)
return (all_images_boxes, all_images_labels, all_images_scores) | Decipher the 8732 locations and class scores (output of ths SSD300) to detect objects.
For each class, perform Non-Maximum Suppression (NMS) on boxes that are above a minimum threshold.
Args:
predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)
predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)
min_score: minimum threshold for a box to be considered a match for a certain class
max_overlap: maximum overlap two boxes can have so that the one with the lower score is not suppressed via NMS
top_k: if there are a lot of resulting detection across all classes, keep only the top 'k'
Return: detections (boxes, labels, and scores), lists of length batch_size | ssd/model.py | detect_objects | qixuxiang/jittor-models | 4 | python | def detect_objects(self, predicted_locs, predicted_scores, min_score, max_overlap, top_k):
" Decipher the 8732 locations and class scores (output of ths SSD300) to detect objects.\n For each class, perform Non-Maximum Suppression (NMS) on boxes that are above a minimum threshold.\n\n Args:\n predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\n predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\n min_score: minimum threshold for a box to be considered a match for a certain class\n max_overlap: maximum overlap two boxes can have so that the one with the lower score is not suppressed via NMS\n top_k: if there are a lot of resulting detection across all classes, keep only the top 'k'\n \n Return: detections (boxes, labels, and scores), lists of length batch_size\n "
batch_size = predicted_locs.shape[0]
n_priors = self.priors_cxcy.shape[0]
predicted_scores = nn.softmax(predicted_scores, dim=2)
all_images_boxes = list()
all_images_labels = list()
all_images_scores = list()
predicted_locs = predicted_locs.data
predicted_scores = predicted_scores.data
assert (n_priors == predicted_locs.shape[1])
for i in range(batch_size):
decoded_locs = cxcy_to_xy(gcxgcy_to_cxcy(predicted_locs[i], self.priors_cxcy))
image_boxes = list()
image_labels = list()
image_scores = list()
for c in range(1, self.n_classes):
class_scores = predicted_scores[i][(:, c)]
score_above_min_score = (class_scores >= min_score)
n_above_min_score = score_above_min_score.sum()
if (n_above_min_score == 0):
continue
class_scores = class_scores[score_above_min_score]
class_decoded_locs = decoded_locs[score_above_min_score]
sort_ind = np.argsort((- class_scores), axis=0)
class_scores = class_scores[sort_ind]
class_decoded_locs = class_decoded_locs[sort_ind]
overlap = find_jaccard_overlap(class_decoded_locs, class_decoded_locs)
suppress = np.zeros(n_above_min_score).astype('int')
for box in range(class_decoded_locs.shape[0]):
if (suppress[box] == 1):
continue
suppress = np.maximum(suppress, (overlap[box] > max_overlap))
suppress[box] = 0
image_boxes.append(class_decoded_locs[(1 - suppress).astype('bool')])
image_labels.append((int((1 - suppress).sum()) * [c]))
image_scores.append(class_scores[(1 - suppress).astype('bool')])
if (len(image_boxes) == 0):
image_boxes.append(np.array([[0.0, 0.0, 1.0, 1.0]]))
image_labels.append(np.array([0]))
image_scores.append(np.array([0.0]))
image_boxes = np.concatenate(image_boxes, 0)
image_labels = np.concatenate(image_labels, 0)
image_scores = np.concatenate(image_scores, 0)
n_objects = image_scores.shape[0]
if (n_objects > top_k):
sort_ind = np.argsort((- image_scores), axis=0)
image_scores = image_scores[sort_ind][:top_k]
image_boxes = image_boxes[sort_ind][:top_k]
image_labels = image_labels[sort_ind][:top_k]
all_images_boxes.append(image_boxes)
all_images_labels.append(image_labels)
all_images_scores.append(image_scores)
return (all_images_boxes, all_images_labels, all_images_scores) | def detect_objects(self, predicted_locs, predicted_scores, min_score, max_overlap, top_k):
" Decipher the 8732 locations and class scores (output of ths SSD300) to detect objects.\n For each class, perform Non-Maximum Suppression (NMS) on boxes that are above a minimum threshold.\n\n Args:\n predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\n predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\n min_score: minimum threshold for a box to be considered a match for a certain class\n max_overlap: maximum overlap two boxes can have so that the one with the lower score is not suppressed via NMS\n top_k: if there are a lot of resulting detection across all classes, keep only the top 'k'\n \n Return: detections (boxes, labels, and scores), lists of length batch_size\n "
batch_size = predicted_locs.shape[0]
n_priors = self.priors_cxcy.shape[0]
predicted_scores = nn.softmax(predicted_scores, dim=2)
all_images_boxes = list()
all_images_labels = list()
all_images_scores = list()
predicted_locs = predicted_locs.data
predicted_scores = predicted_scores.data
assert (n_priors == predicted_locs.shape[1])
for i in range(batch_size):
decoded_locs = cxcy_to_xy(gcxgcy_to_cxcy(predicted_locs[i], self.priors_cxcy))
image_boxes = list()
image_labels = list()
image_scores = list()
for c in range(1, self.n_classes):
class_scores = predicted_scores[i][(:, c)]
score_above_min_score = (class_scores >= min_score)
n_above_min_score = score_above_min_score.sum()
if (n_above_min_score == 0):
continue
class_scores = class_scores[score_above_min_score]
class_decoded_locs = decoded_locs[score_above_min_score]
sort_ind = np.argsort((- class_scores), axis=0)
class_scores = class_scores[sort_ind]
class_decoded_locs = class_decoded_locs[sort_ind]
overlap = find_jaccard_overlap(class_decoded_locs, class_decoded_locs)
suppress = np.zeros(n_above_min_score).astype('int')
for box in range(class_decoded_locs.shape[0]):
if (suppress[box] == 1):
continue
suppress = np.maximum(suppress, (overlap[box] > max_overlap))
suppress[box] = 0
image_boxes.append(class_decoded_locs[(1 - suppress).astype('bool')])
image_labels.append((int((1 - suppress).sum()) * [c]))
image_scores.append(class_scores[(1 - suppress).astype('bool')])
if (len(image_boxes) == 0):
image_boxes.append(np.array([[0.0, 0.0, 1.0, 1.0]]))
image_labels.append(np.array([0]))
image_scores.append(np.array([0.0]))
image_boxes = np.concatenate(image_boxes, 0)
image_labels = np.concatenate(image_labels, 0)
image_scores = np.concatenate(image_scores, 0)
n_objects = image_scores.shape[0]
if (n_objects > top_k):
sort_ind = np.argsort((- image_scores), axis=0)
image_scores = image_scores[sort_ind][:top_k]
image_boxes = image_boxes[sort_ind][:top_k]
image_labels = image_labels[sort_ind][:top_k]
all_images_boxes.append(image_boxes)
all_images_labels.append(image_labels)
all_images_scores.append(image_scores)
return (all_images_boxes, all_images_labels, all_images_scores)<|docstring|>Decipher the 8732 locations and class scores (output of ths SSD300) to detect objects.
For each class, perform Non-Maximum Suppression (NMS) on boxes that are above a minimum threshold.
Args:
predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)
predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)
min_score: minimum threshold for a box to be considered a match for a certain class
max_overlap: maximum overlap two boxes can have so that the one with the lower score is not suppressed via NMS
top_k: if there are a lot of resulting detection across all classes, keep only the top 'k'
Return: detections (boxes, labels, and scores), lists of length batch_size<|endoftext|> |
5e59bc962f49428a9256cbfe0bee99326f93e7ff328871d1719bca5730c12001 | def execute(self, predicted_locs, predicted_scores, boxes, labels):
' Forward propagation.\n\n Args:\n predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\n predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\n boxes: true object bounding boxes in boundary coordinates, a list of N tensors\n labels: true object labels, a list of N tensors\n Return: multibox loss, a scalar\n '
batch_size = predicted_locs.shape[0]
n_priors = self.priors_cxcy.shape[0]
n_classes = predicted_scores.shape[2]
assert (n_priors == predicted_locs.shape[1] == predicted_scores.shape[1])
true_locs = np.zeros((batch_size, n_priors, 4))
true_classes = np.zeros((batch_size, n_priors))
for i in range(batch_size):
n_objects = boxes[i].shape[0]
overlap = find_jaccard_overlap(boxes[i], self.priors_xy)
(object_for_each_prior, overlap_for_each_prior) = argmax(overlap, axis=0)
(prior_for_each_object, _) = argmax(overlap, axis=1)
object_for_each_prior[prior_for_each_object] = range(n_objects)
overlap_for_each_prior[prior_for_each_object] = 1.0
label_for_each_prior = labels[i][object_for_each_prior]
label_for_each_prior[(overlap_for_each_prior < self.threshold)] = 0
true_classes[i] = label_for_each_prior
true_locs[i] = cxcy_to_gcxgcy(xy_to_cxcy(boxes[i][object_for_each_prior]), self.priors_cxcy)
true_classes = jt.array(true_classes).float32().stop_grad()
true_locs = jt.array(true_locs).float32().stop_grad()
positive_priors = (true_classes != 0)
loc_loss = self.smooth_l1((predicted_locs * positive_priors.broadcast([1, 1, 4], [2])), (true_locs * positive_priors.broadcast([1, 1, 4], [2])))
loc_loss /= (positive_priors.float32().sum() * 4)
n_positives = positive_priors.float32().sum(1)
n_hard_negatives = (self.neg_pos_ratio * n_positives)
conf_loss_all = self.cross_entropy(jt.reshape(predicted_scores, [(- 1), n_classes]), jt.reshape(true_classes, [(- 1)]))
conf_loss_all = jt.reshape(conf_loss_all, [batch_size, n_priors])
conf_loss_pos = (conf_loss_all * positive_priors)
conf_loss_neg = (conf_loss_all * (1 - positive_priors))
(_, conf_loss_neg) = conf_loss_neg.argsort(dim=1, descending=True)
hardness_ranks = jt.array(range(n_priors)).broadcast([conf_loss_neg.shape[0], conf_loss_neg.shape[1]], [0])
hard_negatives = (hardness_ranks < n_hard_negatives.broadcast(hardness_ranks.shape, [1]))
conf_loss_hard_neg = (conf_loss_neg * hard_negatives)
conf_loss = ((conf_loss_hard_neg.sum() + conf_loss_pos.sum()) / n_positives.float32().sum())
return ((conf_loss + (self.alpha * loc_loss)), conf_loss, loc_loss) | Forward propagation.
Args:
predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)
predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)
boxes: true object bounding boxes in boundary coordinates, a list of N tensors
labels: true object labels, a list of N tensors
Return: multibox loss, a scalar | ssd/model.py | execute | qixuxiang/jittor-models | 4 | python | def execute(self, predicted_locs, predicted_scores, boxes, labels):
' Forward propagation.\n\n Args:\n predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\n predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\n boxes: true object bounding boxes in boundary coordinates, a list of N tensors\n labels: true object labels, a list of N tensors\n Return: multibox loss, a scalar\n '
batch_size = predicted_locs.shape[0]
n_priors = self.priors_cxcy.shape[0]
n_classes = predicted_scores.shape[2]
assert (n_priors == predicted_locs.shape[1] == predicted_scores.shape[1])
true_locs = np.zeros((batch_size, n_priors, 4))
true_classes = np.zeros((batch_size, n_priors))
for i in range(batch_size):
n_objects = boxes[i].shape[0]
overlap = find_jaccard_overlap(boxes[i], self.priors_xy)
(object_for_each_prior, overlap_for_each_prior) = argmax(overlap, axis=0)
(prior_for_each_object, _) = argmax(overlap, axis=1)
object_for_each_prior[prior_for_each_object] = range(n_objects)
overlap_for_each_prior[prior_for_each_object] = 1.0
label_for_each_prior = labels[i][object_for_each_prior]
label_for_each_prior[(overlap_for_each_prior < self.threshold)] = 0
true_classes[i] = label_for_each_prior
true_locs[i] = cxcy_to_gcxgcy(xy_to_cxcy(boxes[i][object_for_each_prior]), self.priors_cxcy)
true_classes = jt.array(true_classes).float32().stop_grad()
true_locs = jt.array(true_locs).float32().stop_grad()
positive_priors = (true_classes != 0)
loc_loss = self.smooth_l1((predicted_locs * positive_priors.broadcast([1, 1, 4], [2])), (true_locs * positive_priors.broadcast([1, 1, 4], [2])))
loc_loss /= (positive_priors.float32().sum() * 4)
n_positives = positive_priors.float32().sum(1)
n_hard_negatives = (self.neg_pos_ratio * n_positives)
conf_loss_all = self.cross_entropy(jt.reshape(predicted_scores, [(- 1), n_classes]), jt.reshape(true_classes, [(- 1)]))
conf_loss_all = jt.reshape(conf_loss_all, [batch_size, n_priors])
conf_loss_pos = (conf_loss_all * positive_priors)
conf_loss_neg = (conf_loss_all * (1 - positive_priors))
(_, conf_loss_neg) = conf_loss_neg.argsort(dim=1, descending=True)
hardness_ranks = jt.array(range(n_priors)).broadcast([conf_loss_neg.shape[0], conf_loss_neg.shape[1]], [0])
hard_negatives = (hardness_ranks < n_hard_negatives.broadcast(hardness_ranks.shape, [1]))
conf_loss_hard_neg = (conf_loss_neg * hard_negatives)
conf_loss = ((conf_loss_hard_neg.sum() + conf_loss_pos.sum()) / n_positives.float32().sum())
return ((conf_loss + (self.alpha * loc_loss)), conf_loss, loc_loss) | def execute(self, predicted_locs, predicted_scores, boxes, labels):
' Forward propagation.\n\n Args:\n predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\n predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\n boxes: true object bounding boxes in boundary coordinates, a list of N tensors\n labels: true object labels, a list of N tensors\n Return: multibox loss, a scalar\n '
batch_size = predicted_locs.shape[0]
n_priors = self.priors_cxcy.shape[0]
n_classes = predicted_scores.shape[2]
assert (n_priors == predicted_locs.shape[1] == predicted_scores.shape[1])
true_locs = np.zeros((batch_size, n_priors, 4))
true_classes = np.zeros((batch_size, n_priors))
for i in range(batch_size):
n_objects = boxes[i].shape[0]
overlap = find_jaccard_overlap(boxes[i], self.priors_xy)
(object_for_each_prior, overlap_for_each_prior) = argmax(overlap, axis=0)
(prior_for_each_object, _) = argmax(overlap, axis=1)
object_for_each_prior[prior_for_each_object] = range(n_objects)
overlap_for_each_prior[prior_for_each_object] = 1.0
label_for_each_prior = labels[i][object_for_each_prior]
label_for_each_prior[(overlap_for_each_prior < self.threshold)] = 0
true_classes[i] = label_for_each_prior
true_locs[i] = cxcy_to_gcxgcy(xy_to_cxcy(boxes[i][object_for_each_prior]), self.priors_cxcy)
true_classes = jt.array(true_classes).float32().stop_grad()
true_locs = jt.array(true_locs).float32().stop_grad()
positive_priors = (true_classes != 0)
loc_loss = self.smooth_l1((predicted_locs * positive_priors.broadcast([1, 1, 4], [2])), (true_locs * positive_priors.broadcast([1, 1, 4], [2])))
loc_loss /= (positive_priors.float32().sum() * 4)
n_positives = positive_priors.float32().sum(1)
n_hard_negatives = (self.neg_pos_ratio * n_positives)
conf_loss_all = self.cross_entropy(jt.reshape(predicted_scores, [(- 1), n_classes]), jt.reshape(true_classes, [(- 1)]))
conf_loss_all = jt.reshape(conf_loss_all, [batch_size, n_priors])
conf_loss_pos = (conf_loss_all * positive_priors)
conf_loss_neg = (conf_loss_all * (1 - positive_priors))
(_, conf_loss_neg) = conf_loss_neg.argsort(dim=1, descending=True)
hardness_ranks = jt.array(range(n_priors)).broadcast([conf_loss_neg.shape[0], conf_loss_neg.shape[1]], [0])
hard_negatives = (hardness_ranks < n_hard_negatives.broadcast(hardness_ranks.shape, [1]))
conf_loss_hard_neg = (conf_loss_neg * hard_negatives)
conf_loss = ((conf_loss_hard_neg.sum() + conf_loss_pos.sum()) / n_positives.float32().sum())
return ((conf_loss + (self.alpha * loc_loss)), conf_loss, loc_loss)<|docstring|>Forward propagation.
Args:
predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)
predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)
boxes: true object bounding boxes in boundary coordinates, a list of N tensors
labels: true object labels, a list of N tensors
Return: multibox loss, a scalar<|endoftext|> |
8e67c285bd57da4b0bbe6bb11ba16d43902c55882f0d04e7a1a9038a9290f005 | def index(request):
' A view to render index.html page '
return render(request, 'home/index.html') | A view to render index.html page | home/views.py | index | kevin-ci/janeric2 | 1 | python | def index(request):
' '
return render(request, 'home/index.html') | def index(request):
' '
return render(request, 'home/index.html')<|docstring|>A view to render index.html page<|endoftext|> |
28b5e70f6079a50f8c958ff7ec5abd7d1131ea9b3e44cb8752b4ace919b06dbf | def __virtual__():
'\n Only make these states available if Open vSwitch module is available.\n '
if ('openvswitch.port_add' in __salt__):
return True
return (False, 'openvswitch module could not be loaded') | Only make these states available if Open vSwitch module is available. | salt/states/openvswitch_port.py | __virtual__ | Flowdalic/salt | 9,425 | python | def __virtual__():
'\n \n '
if ('openvswitch.port_add' in __salt__):
return True
return (False, 'openvswitch module could not be loaded') | def __virtual__():
'\n \n '
if ('openvswitch.port_add' in __salt__):
return True
return (False, 'openvswitch module could not be loaded')<|docstring|>Only make these states available if Open vSwitch module is available.<|endoftext|> |
fabed0db39f55d395e1b26ed97fed86012b45148e7e3a2e2faef17c75fb17d49 | def present(name, bridge, tunnel_type=None, id=None, remote=None, dst_port=None, internal=False):
"\n Ensures that the named port exists on bridge, eventually creates it.\n\n Args:\n name: The name of the port.\n bridge: The name of the bridge.\n tunnel_type: Optional type of interface to create, currently supports: vlan, vxlan and gre.\n id: Optional tunnel's key.\n remote: Remote endpoint's IP address.\n dst_port: Port to use when creating tunnelport in the switch.\n internal: Create an internal port if one does not exist\n\n "
ret = {'name': name, 'changes': {}, 'result': False, 'comment': ''}
tunnel_types = ('vlan', 'vxlan', 'gre')
if (tunnel_type and (tunnel_type not in tunnel_types)):
raise TypeError('The optional type argument must be one of these values: {}.'.format(str(tunnel_types)))
bridge_exists = __salt__['openvswitch.bridge_exists'](bridge)
port_list = []
if bridge_exists:
port_list = __salt__['openvswitch.port_list'](bridge)
comments = {}
comments['comment_bridge_notexists'] = 'Bridge {} does not exist.'.format(bridge)
comments['comment_port_exists'] = 'Port {} already exists.'.format(name)
comments['comment_port_created'] = 'Port {} created on bridge {}.'.format(name, bridge)
comments['comment_port_notcreated'] = 'Unable to create port {} on bridge {}.'.format(name, bridge)
comments['changes_port_created'] = {name: {'old': 'No port named {} present.'.format(name), 'new': 'Created port {1} on bridge {0}.'.format(bridge, name)}}
comments['comment_port_internal'] = 'Port {} already exists, but interface type has been changed to internal.'.format(name)
comments['changes_port_internal'] = {'internal': {'old': False, 'new': True}}
comments['comment_port_internal_not_changed'] = 'Port {} already exists, but the interface type could not be changed to internal.'.format(name)
if tunnel_type:
comments['comment_invalid_ip'] = 'Remote is not valid ip address.'
if (tunnel_type == 'vlan'):
comments['comment_vlan_invalid_id'] = 'VLANs id must be between 0 and 4095.'
comments['comment_vlan_invalid_name'] = 'Could not find network interface {}.'.format(name)
comments['comment_vlan_port_exists'] = 'Port {} with access to VLAN {} already exists on bridge {}.'.format(name, id, bridge)
comments['comment_vlan_created'] = 'Created port {} with access to VLAN {} on bridge {}.'.format(name, id, bridge)
comments['comment_vlan_notcreated'] = 'Unable to create port {} with access to VLAN {} on bridge {}.'.format(name, id, bridge)
comments['changes_vlan_created'] = {name: {'old': 'No port named {} with access to VLAN {} present on bridge {} present.'.format(name, id, bridge), 'new': 'Created port {1} with access to VLAN {2} on bridge {0}.'.format(bridge, name, id)}}
elif (tunnel_type == 'gre'):
comments['comment_gre_invalid_id'] = 'Id of GRE tunnel must be an unsigned 32-bit integer.'
comments['comment_gre_interface_exists'] = 'GRE tunnel interface {} with rempte ip {} and key {} already exists on bridge {}.'.format(name, remote, id, bridge)
comments['comment_gre_created'] = 'Created GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)
comments['comment_gre_notcreated'] = 'Unable to create GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)
comments['changes_gre_created'] = {name: {'old': 'No GRE tunnel interface {} with remote ip {} and key {} on bridge {} present.'.format(name, remote, id, bridge), 'new': 'Created GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)}}
elif (tunnel_type == 'vxlan'):
comments['comment_dstport'] = (((' (dst_port' + str(dst_port)) + ')') if (0 < dst_port <= 65535) else '')
comments['comment_vxlan_invalid_id'] = 'Id of VXLAN tunnel must be an unsigned 64-bit integer.'
comments['comment_vxlan_interface_exists'] = 'VXLAN tunnel interface {} with rempte ip {} and key {} already exists on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['comment_vxlan_created'] = 'Created VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['comment_vxlan_notcreated'] = 'Unable to create VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['changes_vxlan_created'] = {name: {'old': 'No VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{} present.'.format(name, remote, id, bridge, comments['comment_dstport']), 'new': 'Created VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])}}
def _check_vlan():
tag = __salt__['openvswitch.port_get_tag'](name)
interfaces = __salt__['network.interfaces']()
if (not (0 <= id <= 4095)):
ret['result'] = False
ret['comment'] = comments['comment_vlan_invalid_id']
elif ((not internal) and (name not in interfaces)):
ret['result'] = False
ret['comment'] = comments['comment_vlan_invalid_name']
elif (tag and (name in port_list)):
try:
if (int(tag[0]) == id):
ret['result'] = True
ret['comment'] = comments['comment_vlan_port_exists']
except (ValueError, KeyError):
pass
def _check_gre():
interface_options = __salt__['openvswitch.interface_get_options'](name)
interface_type = __salt__['openvswitch.interface_get_type'](name)
if (not (0 <= id <= (2 ** 32))):
ret['result'] = False
ret['comment'] = comments['comment_gre_invalid_id']
elif (not __salt__['dig.check_ip'](remote)):
ret['result'] = False
ret['comment'] = comments['comment_invalid_ip']
elif (interface_options and interface_type and (name in port_list)):
interface_attroptions = (((('{key="' + str(id)) + '", remote_ip="') + str(remote)) + '"}')
try:
if ((interface_type[0] == 'gre') and (interface_options[0] == interface_attroptions)):
ret['result'] = True
ret['comment'] = comments['comment_gre_interface_exists']
except KeyError:
pass
def _check_vxlan():
interface_options = __salt__['openvswitch.interface_get_options'](name)
interface_type = __salt__['openvswitch.interface_get_type'](name)
if (not (0 <= id <= (2 ** 64))):
ret['result'] = False
ret['comment'] = comments['comment_vxlan_invalid_id']
elif (not __salt__['dig.check_ip'](remote)):
ret['result'] = False
ret['comment'] = comments['comment_invalid_ip']
elif (interface_options and interface_type and (name in port_list)):
opt_port = ((('dst_port="' + str(dst_port)) + '", ') if (0 < dst_port <= 65535) else '')
interface_attroptions = (((('{{{0}key="'.format(opt_port) + str(id)) + '", remote_ip="') + str(remote)) + '"}')
try:
if ((interface_type[0] == 'vxlan') and (interface_options[0] == interface_attroptions)):
ret['result'] = True
ret['comment'] = comments['comment_vxlan_interface_exists']
except KeyError:
pass
if __opts__['test']:
if bridge_exists:
if (tunnel_type == 'vlan'):
_check_vlan()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_vlan_created']
elif (tunnel_type == 'vxlan'):
_check_vxlan()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_vxlan_created']
elif (tunnel_type == 'gre'):
_check_gre()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_gre_created']
elif (name in port_list):
ret['result'] = True
current_type = __salt__['openvswitch.interface_get_type'](name)
if (internal and (current_type != ['internal'])):
ret['comment'] = comments['comment_port_internal']
else:
ret['comment'] = comments['comment_port_exists']
else:
ret['result'] = None
ret['comment'] = comments['comment_port_created']
else:
ret['result'] = None
ret['comment'] = comments['comment_bridge_notexists']
return ret
if bridge_exists:
if (tunnel_type == 'vlan'):
_check_vlan()
if (not ret['comment']):
port_create_vlan = __salt__['openvswitch.port_create_vlan'](bridge, name, id, internal)
if port_create_vlan:
ret['result'] = True
ret['comment'] = comments['comment_vlan_created']
ret['changes'] = comments['changes_vlan_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_vlan_notcreated']
elif (tunnel_type == 'vxlan'):
_check_vxlan()
if (not ret['comment']):
port_create_vxlan = __salt__['openvswitch.port_create_vxlan'](bridge, name, id, remote, dst_port)
if port_create_vxlan:
ret['result'] = True
ret['comment'] = comments['comment_vxlan_created']
ret['changes'] = comments['changes_vxlan_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_vxlan_notcreated']
elif (tunnel_type == 'gre'):
_check_gre()
if (not ret['comment']):
port_create_gre = __salt__['openvswitch.port_create_gre'](bridge, name, id, remote)
if port_create_gre:
ret['result'] = True
ret['comment'] = comments['comment_gre_created']
ret['changes'] = comments['changes_gre_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_gre_notcreated']
elif (name in port_list):
current_type = __salt__['openvswitch.interface_get_type'](name)
if (internal and (current_type != ['internal'])):
port_add = __salt__['openvswitch.port_add'](bridge, name, may_exist=True, internal=internal)
if port_add:
ret['result'] = True
ret['comment'] = comments['comment_port_internal']
ret['changes'] = comments['changes_port_internal']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_internal_not_changed']
else:
ret['result'] = True
ret['comment'] = comments['comment_port_exists']
else:
port_add = __salt__['openvswitch.port_add'](bridge, name, internal=internal)
if port_add:
ret['result'] = True
ret['comment'] = comments['comment_port_created']
ret['changes'] = comments['changes_port_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_notcreated']
else:
ret['result'] = False
ret['comment'] = comments['comment_bridge_notexists']
return ret | Ensures that the named port exists on bridge, eventually creates it.
Args:
name: The name of the port.
bridge: The name of the bridge.
tunnel_type: Optional type of interface to create, currently supports: vlan, vxlan and gre.
id: Optional tunnel's key.
remote: Remote endpoint's IP address.
dst_port: Port to use when creating tunnelport in the switch.
internal: Create an internal port if one does not exist | salt/states/openvswitch_port.py | present | Flowdalic/salt | 9,425 | python | def present(name, bridge, tunnel_type=None, id=None, remote=None, dst_port=None, internal=False):
"\n Ensures that the named port exists on bridge, eventually creates it.\n\n Args:\n name: The name of the port.\n bridge: The name of the bridge.\n tunnel_type: Optional type of interface to create, currently supports: vlan, vxlan and gre.\n id: Optional tunnel's key.\n remote: Remote endpoint's IP address.\n dst_port: Port to use when creating tunnelport in the switch.\n internal: Create an internal port if one does not exist\n\n "
ret = {'name': name, 'changes': {}, 'result': False, 'comment': }
tunnel_types = ('vlan', 'vxlan', 'gre')
if (tunnel_type and (tunnel_type not in tunnel_types)):
raise TypeError('The optional type argument must be one of these values: {}.'.format(str(tunnel_types)))
bridge_exists = __salt__['openvswitch.bridge_exists'](bridge)
port_list = []
if bridge_exists:
port_list = __salt__['openvswitch.port_list'](bridge)
comments = {}
comments['comment_bridge_notexists'] = 'Bridge {} does not exist.'.format(bridge)
comments['comment_port_exists'] = 'Port {} already exists.'.format(name)
comments['comment_port_created'] = 'Port {} created on bridge {}.'.format(name, bridge)
comments['comment_port_notcreated'] = 'Unable to create port {} on bridge {}.'.format(name, bridge)
comments['changes_port_created'] = {name: {'old': 'No port named {} present.'.format(name), 'new': 'Created port {1} on bridge {0}.'.format(bridge, name)}}
comments['comment_port_internal'] = 'Port {} already exists, but interface type has been changed to internal.'.format(name)
comments['changes_port_internal'] = {'internal': {'old': False, 'new': True}}
comments['comment_port_internal_not_changed'] = 'Port {} already exists, but the interface type could not be changed to internal.'.format(name)
if tunnel_type:
comments['comment_invalid_ip'] = 'Remote is not valid ip address.'
if (tunnel_type == 'vlan'):
comments['comment_vlan_invalid_id'] = 'VLANs id must be between 0 and 4095.'
comments['comment_vlan_invalid_name'] = 'Could not find network interface {}.'.format(name)
comments['comment_vlan_port_exists'] = 'Port {} with access to VLAN {} already exists on bridge {}.'.format(name, id, bridge)
comments['comment_vlan_created'] = 'Created port {} with access to VLAN {} on bridge {}.'.format(name, id, bridge)
comments['comment_vlan_notcreated'] = 'Unable to create port {} with access to VLAN {} on bridge {}.'.format(name, id, bridge)
comments['changes_vlan_created'] = {name: {'old': 'No port named {} with access to VLAN {} present on bridge {} present.'.format(name, id, bridge), 'new': 'Created port {1} with access to VLAN {2} on bridge {0}.'.format(bridge, name, id)}}
elif (tunnel_type == 'gre'):
comments['comment_gre_invalid_id'] = 'Id of GRE tunnel must be an unsigned 32-bit integer.'
comments['comment_gre_interface_exists'] = 'GRE tunnel interface {} with rempte ip {} and key {} already exists on bridge {}.'.format(name, remote, id, bridge)
comments['comment_gre_created'] = 'Created GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)
comments['comment_gre_notcreated'] = 'Unable to create GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)
comments['changes_gre_created'] = {name: {'old': 'No GRE tunnel interface {} with remote ip {} and key {} on bridge {} present.'.format(name, remote, id, bridge), 'new': 'Created GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)}}
elif (tunnel_type == 'vxlan'):
comments['comment_dstport'] = (((' (dst_port' + str(dst_port)) + ')') if (0 < dst_port <= 65535) else )
comments['comment_vxlan_invalid_id'] = 'Id of VXLAN tunnel must be an unsigned 64-bit integer.'
comments['comment_vxlan_interface_exists'] = 'VXLAN tunnel interface {} with rempte ip {} and key {} already exists on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['comment_vxlan_created'] = 'Created VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['comment_vxlan_notcreated'] = 'Unable to create VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['changes_vxlan_created'] = {name: {'old': 'No VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{} present.'.format(name, remote, id, bridge, comments['comment_dstport']), 'new': 'Created VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])}}
def _check_vlan():
tag = __salt__['openvswitch.port_get_tag'](name)
interfaces = __salt__['network.interfaces']()
if (not (0 <= id <= 4095)):
ret['result'] = False
ret['comment'] = comments['comment_vlan_invalid_id']
elif ((not internal) and (name not in interfaces)):
ret['result'] = False
ret['comment'] = comments['comment_vlan_invalid_name']
elif (tag and (name in port_list)):
try:
if (int(tag[0]) == id):
ret['result'] = True
ret['comment'] = comments['comment_vlan_port_exists']
except (ValueError, KeyError):
pass
def _check_gre():
interface_options = __salt__['openvswitch.interface_get_options'](name)
interface_type = __salt__['openvswitch.interface_get_type'](name)
if (not (0 <= id <= (2 ** 32))):
ret['result'] = False
ret['comment'] = comments['comment_gre_invalid_id']
elif (not __salt__['dig.check_ip'](remote)):
ret['result'] = False
ret['comment'] = comments['comment_invalid_ip']
elif (interface_options and interface_type and (name in port_list)):
interface_attroptions = (((('{key="' + str(id)) + '", remote_ip="') + str(remote)) + '"}')
try:
if ((interface_type[0] == 'gre') and (interface_options[0] == interface_attroptions)):
ret['result'] = True
ret['comment'] = comments['comment_gre_interface_exists']
except KeyError:
pass
def _check_vxlan():
interface_options = __salt__['openvswitch.interface_get_options'](name)
interface_type = __salt__['openvswitch.interface_get_type'](name)
if (not (0 <= id <= (2 ** 64))):
ret['result'] = False
ret['comment'] = comments['comment_vxlan_invalid_id']
elif (not __salt__['dig.check_ip'](remote)):
ret['result'] = False
ret['comment'] = comments['comment_invalid_ip']
elif (interface_options and interface_type and (name in port_list)):
opt_port = ((('dst_port="' + str(dst_port)) + '", ') if (0 < dst_port <= 65535) else )
interface_attroptions = (((('{{{0}key="'.format(opt_port) + str(id)) + '", remote_ip="') + str(remote)) + '"}')
try:
if ((interface_type[0] == 'vxlan') and (interface_options[0] == interface_attroptions)):
ret['result'] = True
ret['comment'] = comments['comment_vxlan_interface_exists']
except KeyError:
pass
if __opts__['test']:
if bridge_exists:
if (tunnel_type == 'vlan'):
_check_vlan()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_vlan_created']
elif (tunnel_type == 'vxlan'):
_check_vxlan()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_vxlan_created']
elif (tunnel_type == 'gre'):
_check_gre()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_gre_created']
elif (name in port_list):
ret['result'] = True
current_type = __salt__['openvswitch.interface_get_type'](name)
if (internal and (current_type != ['internal'])):
ret['comment'] = comments['comment_port_internal']
else:
ret['comment'] = comments['comment_port_exists']
else:
ret['result'] = None
ret['comment'] = comments['comment_port_created']
else:
ret['result'] = None
ret['comment'] = comments['comment_bridge_notexists']
return ret
if bridge_exists:
if (tunnel_type == 'vlan'):
_check_vlan()
if (not ret['comment']):
port_create_vlan = __salt__['openvswitch.port_create_vlan'](bridge, name, id, internal)
if port_create_vlan:
ret['result'] = True
ret['comment'] = comments['comment_vlan_created']
ret['changes'] = comments['changes_vlan_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_vlan_notcreated']
elif (tunnel_type == 'vxlan'):
_check_vxlan()
if (not ret['comment']):
port_create_vxlan = __salt__['openvswitch.port_create_vxlan'](bridge, name, id, remote, dst_port)
if port_create_vxlan:
ret['result'] = True
ret['comment'] = comments['comment_vxlan_created']
ret['changes'] = comments['changes_vxlan_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_vxlan_notcreated']
elif (tunnel_type == 'gre'):
_check_gre()
if (not ret['comment']):
port_create_gre = __salt__['openvswitch.port_create_gre'](bridge, name, id, remote)
if port_create_gre:
ret['result'] = True
ret['comment'] = comments['comment_gre_created']
ret['changes'] = comments['changes_gre_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_gre_notcreated']
elif (name in port_list):
current_type = __salt__['openvswitch.interface_get_type'](name)
if (internal and (current_type != ['internal'])):
port_add = __salt__['openvswitch.port_add'](bridge, name, may_exist=True, internal=internal)
if port_add:
ret['result'] = True
ret['comment'] = comments['comment_port_internal']
ret['changes'] = comments['changes_port_internal']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_internal_not_changed']
else:
ret['result'] = True
ret['comment'] = comments['comment_port_exists']
else:
port_add = __salt__['openvswitch.port_add'](bridge, name, internal=internal)
if port_add:
ret['result'] = True
ret['comment'] = comments['comment_port_created']
ret['changes'] = comments['changes_port_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_notcreated']
else:
ret['result'] = False
ret['comment'] = comments['comment_bridge_notexists']
return ret | def present(name, bridge, tunnel_type=None, id=None, remote=None, dst_port=None, internal=False):
"\n Ensures that the named port exists on bridge, eventually creates it.\n\n Args:\n name: The name of the port.\n bridge: The name of the bridge.\n tunnel_type: Optional type of interface to create, currently supports: vlan, vxlan and gre.\n id: Optional tunnel's key.\n remote: Remote endpoint's IP address.\n dst_port: Port to use when creating tunnelport in the switch.\n internal: Create an internal port if one does not exist\n\n "
ret = {'name': name, 'changes': {}, 'result': False, 'comment': }
tunnel_types = ('vlan', 'vxlan', 'gre')
if (tunnel_type and (tunnel_type not in tunnel_types)):
raise TypeError('The optional type argument must be one of these values: {}.'.format(str(tunnel_types)))
bridge_exists = __salt__['openvswitch.bridge_exists'](bridge)
port_list = []
if bridge_exists:
port_list = __salt__['openvswitch.port_list'](bridge)
comments = {}
comments['comment_bridge_notexists'] = 'Bridge {} does not exist.'.format(bridge)
comments['comment_port_exists'] = 'Port {} already exists.'.format(name)
comments['comment_port_created'] = 'Port {} created on bridge {}.'.format(name, bridge)
comments['comment_port_notcreated'] = 'Unable to create port {} on bridge {}.'.format(name, bridge)
comments['changes_port_created'] = {name: {'old': 'No port named {} present.'.format(name), 'new': 'Created port {1} on bridge {0}.'.format(bridge, name)}}
comments['comment_port_internal'] = 'Port {} already exists, but interface type has been changed to internal.'.format(name)
comments['changes_port_internal'] = {'internal': {'old': False, 'new': True}}
comments['comment_port_internal_not_changed'] = 'Port {} already exists, but the interface type could not be changed to internal.'.format(name)
if tunnel_type:
comments['comment_invalid_ip'] = 'Remote is not valid ip address.'
if (tunnel_type == 'vlan'):
comments['comment_vlan_invalid_id'] = 'VLANs id must be between 0 and 4095.'
comments['comment_vlan_invalid_name'] = 'Could not find network interface {}.'.format(name)
comments['comment_vlan_port_exists'] = 'Port {} with access to VLAN {} already exists on bridge {}.'.format(name, id, bridge)
comments['comment_vlan_created'] = 'Created port {} with access to VLAN {} on bridge {}.'.format(name, id, bridge)
comments['comment_vlan_notcreated'] = 'Unable to create port {} with access to VLAN {} on bridge {}.'.format(name, id, bridge)
comments['changes_vlan_created'] = {name: {'old': 'No port named {} with access to VLAN {} present on bridge {} present.'.format(name, id, bridge), 'new': 'Created port {1} with access to VLAN {2} on bridge {0}.'.format(bridge, name, id)}}
elif (tunnel_type == 'gre'):
comments['comment_gre_invalid_id'] = 'Id of GRE tunnel must be an unsigned 32-bit integer.'
comments['comment_gre_interface_exists'] = 'GRE tunnel interface {} with rempte ip {} and key {} already exists on bridge {}.'.format(name, remote, id, bridge)
comments['comment_gre_created'] = 'Created GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)
comments['comment_gre_notcreated'] = 'Unable to create GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)
comments['changes_gre_created'] = {name: {'old': 'No GRE tunnel interface {} with remote ip {} and key {} on bridge {} present.'.format(name, remote, id, bridge), 'new': 'Created GRE tunnel interface {} with remote ip {} and key {} on bridge {}.'.format(name, remote, id, bridge)}}
elif (tunnel_type == 'vxlan'):
comments['comment_dstport'] = (((' (dst_port' + str(dst_port)) + ')') if (0 < dst_port <= 65535) else )
comments['comment_vxlan_invalid_id'] = 'Id of VXLAN tunnel must be an unsigned 64-bit integer.'
comments['comment_vxlan_interface_exists'] = 'VXLAN tunnel interface {} with rempte ip {} and key {} already exists on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['comment_vxlan_created'] = 'Created VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['comment_vxlan_notcreated'] = 'Unable to create VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])
comments['changes_vxlan_created'] = {name: {'old': 'No VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{} present.'.format(name, remote, id, bridge, comments['comment_dstport']), 'new': 'Created VXLAN tunnel interface {} with remote ip {} and key {} on bridge {}{}.'.format(name, remote, id, bridge, comments['comment_dstport'])}}
def _check_vlan():
tag = __salt__['openvswitch.port_get_tag'](name)
interfaces = __salt__['network.interfaces']()
if (not (0 <= id <= 4095)):
ret['result'] = False
ret['comment'] = comments['comment_vlan_invalid_id']
elif ((not internal) and (name not in interfaces)):
ret['result'] = False
ret['comment'] = comments['comment_vlan_invalid_name']
elif (tag and (name in port_list)):
try:
if (int(tag[0]) == id):
ret['result'] = True
ret['comment'] = comments['comment_vlan_port_exists']
except (ValueError, KeyError):
pass
def _check_gre():
interface_options = __salt__['openvswitch.interface_get_options'](name)
interface_type = __salt__['openvswitch.interface_get_type'](name)
if (not (0 <= id <= (2 ** 32))):
ret['result'] = False
ret['comment'] = comments['comment_gre_invalid_id']
elif (not __salt__['dig.check_ip'](remote)):
ret['result'] = False
ret['comment'] = comments['comment_invalid_ip']
elif (interface_options and interface_type and (name in port_list)):
interface_attroptions = (((('{key="' + str(id)) + '", remote_ip="') + str(remote)) + '"}')
try:
if ((interface_type[0] == 'gre') and (interface_options[0] == interface_attroptions)):
ret['result'] = True
ret['comment'] = comments['comment_gre_interface_exists']
except KeyError:
pass
def _check_vxlan():
interface_options = __salt__['openvswitch.interface_get_options'](name)
interface_type = __salt__['openvswitch.interface_get_type'](name)
if (not (0 <= id <= (2 ** 64))):
ret['result'] = False
ret['comment'] = comments['comment_vxlan_invalid_id']
elif (not __salt__['dig.check_ip'](remote)):
ret['result'] = False
ret['comment'] = comments['comment_invalid_ip']
elif (interface_options and interface_type and (name in port_list)):
opt_port = ((('dst_port="' + str(dst_port)) + '", ') if (0 < dst_port <= 65535) else )
interface_attroptions = (((('{{{0}key="'.format(opt_port) + str(id)) + '", remote_ip="') + str(remote)) + '"}')
try:
if ((interface_type[0] == 'vxlan') and (interface_options[0] == interface_attroptions)):
ret['result'] = True
ret['comment'] = comments['comment_vxlan_interface_exists']
except KeyError:
pass
if __opts__['test']:
if bridge_exists:
if (tunnel_type == 'vlan'):
_check_vlan()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_vlan_created']
elif (tunnel_type == 'vxlan'):
_check_vxlan()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_vxlan_created']
elif (tunnel_type == 'gre'):
_check_gre()
if (not ret['comment']):
ret['result'] = None
ret['comment'] = comments['comment_gre_created']
elif (name in port_list):
ret['result'] = True
current_type = __salt__['openvswitch.interface_get_type'](name)
if (internal and (current_type != ['internal'])):
ret['comment'] = comments['comment_port_internal']
else:
ret['comment'] = comments['comment_port_exists']
else:
ret['result'] = None
ret['comment'] = comments['comment_port_created']
else:
ret['result'] = None
ret['comment'] = comments['comment_bridge_notexists']
return ret
if bridge_exists:
if (tunnel_type == 'vlan'):
_check_vlan()
if (not ret['comment']):
port_create_vlan = __salt__['openvswitch.port_create_vlan'](bridge, name, id, internal)
if port_create_vlan:
ret['result'] = True
ret['comment'] = comments['comment_vlan_created']
ret['changes'] = comments['changes_vlan_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_vlan_notcreated']
elif (tunnel_type == 'vxlan'):
_check_vxlan()
if (not ret['comment']):
port_create_vxlan = __salt__['openvswitch.port_create_vxlan'](bridge, name, id, remote, dst_port)
if port_create_vxlan:
ret['result'] = True
ret['comment'] = comments['comment_vxlan_created']
ret['changes'] = comments['changes_vxlan_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_vxlan_notcreated']
elif (tunnel_type == 'gre'):
_check_gre()
if (not ret['comment']):
port_create_gre = __salt__['openvswitch.port_create_gre'](bridge, name, id, remote)
if port_create_gre:
ret['result'] = True
ret['comment'] = comments['comment_gre_created']
ret['changes'] = comments['changes_gre_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_gre_notcreated']
elif (name in port_list):
current_type = __salt__['openvswitch.interface_get_type'](name)
if (internal and (current_type != ['internal'])):
port_add = __salt__['openvswitch.port_add'](bridge, name, may_exist=True, internal=internal)
if port_add:
ret['result'] = True
ret['comment'] = comments['comment_port_internal']
ret['changes'] = comments['changes_port_internal']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_internal_not_changed']
else:
ret['result'] = True
ret['comment'] = comments['comment_port_exists']
else:
port_add = __salt__['openvswitch.port_add'](bridge, name, internal=internal)
if port_add:
ret['result'] = True
ret['comment'] = comments['comment_port_created']
ret['changes'] = comments['changes_port_created']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_notcreated']
else:
ret['result'] = False
ret['comment'] = comments['comment_bridge_notexists']
return ret<|docstring|>Ensures that the named port exists on bridge, eventually creates it.
Args:
name: The name of the port.
bridge: The name of the bridge.
tunnel_type: Optional type of interface to create, currently supports: vlan, vxlan and gre.
id: Optional tunnel's key.
remote: Remote endpoint's IP address.
dst_port: Port to use when creating tunnelport in the switch.
internal: Create an internal port if one does not exist<|endoftext|> |
4b54bb9c56ea45151d5c2ccd3eb6dc03f4b981062102fb13fb80c9c90289f43e | def absent(name, bridge=None):
'\n Ensures that the named port exists on bridge, eventually deletes it.\n If bridge is not set, port is removed from whatever bridge contains it.\n\n Args:\n name: The name of the port.\n bridge: The name of the bridge.\n\n '
ret = {'name': name, 'changes': {}, 'result': False, 'comment': ''}
bridge_exists = False
if bridge:
bridge_exists = __salt__['openvswitch.bridge_exists'](bridge)
if bridge_exists:
port_list = __salt__['openvswitch.port_list'](bridge)
else:
port_list = ()
else:
port_list = [name]
comments = {}
comments['comment_bridge_notexists'] = 'Bridge {} does not exist.'.format(bridge)
comments['comment_port_notexists'] = 'Port {} does not exist on bridge {}.'.format(name, bridge)
comments['comment_port_deleted'] = 'Port {} deleted.'.format(name)
comments['comment_port_notdeleted'] = 'Unable to delete port {}.'.format(name)
comments['changes_port_deleted'] = {name: {'old': 'Port named {} may exist.'.format(name), 'new': 'Deleted port {}.'.format(name)}}
if __opts__['test']:
if (bridge and (not bridge_exists)):
ret['result'] = None
ret['comment'] = comments['comment_bridge_notexists']
elif (name not in port_list):
ret['result'] = True
ret['comment'] = comments['comment_port_notexists']
else:
ret['result'] = None
ret['comment'] = comments['comment_port_deleted']
return ret
if (bridge and (not bridge_exists)):
ret['result'] = False
ret['comment'] = comments['comment_bridge_notexists']
elif (name not in port_list):
ret['result'] = True
ret['comment'] = comments['comment_port_notexists']
else:
if bridge:
port_remove = __salt__['openvswitch.port_remove'](br=bridge, port=name)
else:
port_remove = __salt__['openvswitch.port_remove'](br=None, port=name)
if port_remove:
ret['result'] = True
ret['comment'] = comments['comment_port_deleted']
ret['changes'] = comments['changes_port_deleted']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_notdeleted']
return ret | Ensures that the named port exists on bridge, eventually deletes it.
If bridge is not set, port is removed from whatever bridge contains it.
Args:
name: The name of the port.
bridge: The name of the bridge. | salt/states/openvswitch_port.py | absent | Flowdalic/salt | 9,425 | python | def absent(name, bridge=None):
'\n Ensures that the named port exists on bridge, eventually deletes it.\n If bridge is not set, port is removed from whatever bridge contains it.\n\n Args:\n name: The name of the port.\n bridge: The name of the bridge.\n\n '
ret = {'name': name, 'changes': {}, 'result': False, 'comment': }
bridge_exists = False
if bridge:
bridge_exists = __salt__['openvswitch.bridge_exists'](bridge)
if bridge_exists:
port_list = __salt__['openvswitch.port_list'](bridge)
else:
port_list = ()
else:
port_list = [name]
comments = {}
comments['comment_bridge_notexists'] = 'Bridge {} does not exist.'.format(bridge)
comments['comment_port_notexists'] = 'Port {} does not exist on bridge {}.'.format(name, bridge)
comments['comment_port_deleted'] = 'Port {} deleted.'.format(name)
comments['comment_port_notdeleted'] = 'Unable to delete port {}.'.format(name)
comments['changes_port_deleted'] = {name: {'old': 'Port named {} may exist.'.format(name), 'new': 'Deleted port {}.'.format(name)}}
if __opts__['test']:
if (bridge and (not bridge_exists)):
ret['result'] = None
ret['comment'] = comments['comment_bridge_notexists']
elif (name not in port_list):
ret['result'] = True
ret['comment'] = comments['comment_port_notexists']
else:
ret['result'] = None
ret['comment'] = comments['comment_port_deleted']
return ret
if (bridge and (not bridge_exists)):
ret['result'] = False
ret['comment'] = comments['comment_bridge_notexists']
elif (name not in port_list):
ret['result'] = True
ret['comment'] = comments['comment_port_notexists']
else:
if bridge:
port_remove = __salt__['openvswitch.port_remove'](br=bridge, port=name)
else:
port_remove = __salt__['openvswitch.port_remove'](br=None, port=name)
if port_remove:
ret['result'] = True
ret['comment'] = comments['comment_port_deleted']
ret['changes'] = comments['changes_port_deleted']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_notdeleted']
return ret | def absent(name, bridge=None):
'\n Ensures that the named port exists on bridge, eventually deletes it.\n If bridge is not set, port is removed from whatever bridge contains it.\n\n Args:\n name: The name of the port.\n bridge: The name of the bridge.\n\n '
ret = {'name': name, 'changes': {}, 'result': False, 'comment': }
bridge_exists = False
if bridge:
bridge_exists = __salt__['openvswitch.bridge_exists'](bridge)
if bridge_exists:
port_list = __salt__['openvswitch.port_list'](bridge)
else:
port_list = ()
else:
port_list = [name]
comments = {}
comments['comment_bridge_notexists'] = 'Bridge {} does not exist.'.format(bridge)
comments['comment_port_notexists'] = 'Port {} does not exist on bridge {}.'.format(name, bridge)
comments['comment_port_deleted'] = 'Port {} deleted.'.format(name)
comments['comment_port_notdeleted'] = 'Unable to delete port {}.'.format(name)
comments['changes_port_deleted'] = {name: {'old': 'Port named {} may exist.'.format(name), 'new': 'Deleted port {}.'.format(name)}}
if __opts__['test']:
if (bridge and (not bridge_exists)):
ret['result'] = None
ret['comment'] = comments['comment_bridge_notexists']
elif (name not in port_list):
ret['result'] = True
ret['comment'] = comments['comment_port_notexists']
else:
ret['result'] = None
ret['comment'] = comments['comment_port_deleted']
return ret
if (bridge and (not bridge_exists)):
ret['result'] = False
ret['comment'] = comments['comment_bridge_notexists']
elif (name not in port_list):
ret['result'] = True
ret['comment'] = comments['comment_port_notexists']
else:
if bridge:
port_remove = __salt__['openvswitch.port_remove'](br=bridge, port=name)
else:
port_remove = __salt__['openvswitch.port_remove'](br=None, port=name)
if port_remove:
ret['result'] = True
ret['comment'] = comments['comment_port_deleted']
ret['changes'] = comments['changes_port_deleted']
else:
ret['result'] = False
ret['comment'] = comments['comment_port_notdeleted']
return ret<|docstring|>Ensures that the named port exists on bridge, eventually deletes it.
If bridge is not set, port is removed from whatever bridge contains it.
Args:
name: The name of the port.
bridge: The name of the bridge.<|endoftext|> |
68bb1986b04ed4911b0d83b0937ba427ebc107586157aa445a85521a27c98dbd | def makeOBJFile(points, filename):
'OBJ file creation test that puts cube at each point'
outputFile = open(filename, 'w')
endLine = '\n'
outputFile.write('g cube')
outputFile.write(endLine)
numCubeVertices = len(v)
for p in points:
for i in range(numCubeVertices):
outputFile.write(('v %f %f %f' % ((v[i][0] + p[0]), (v[i][1] + p[1]), (v[i][2] + p[2]))))
outputFile.write(endLine)
for vertexNormalString in vn:
outputFile.write(vertexNormalString)
outputFile.write(endLine)
for cubeIndex in range(len(points)):
vertexIndexOffset = (cubeIndex * numCubeVertices)
for faceIndex in range(len(f)):
face = f[faceIndex].makeFace(vertexIndexOffset)
outputFile.write(('f ' + face.toString()))
outputFile.write(endLine)
outputFile.close() | OBJ file creation test that puts cube at each point | cytoseg/obj_tools.py | makeOBJFile | slash-segmentation/DP2 | 0 | python | def makeOBJFile(points, filename):
outputFile = open(filename, 'w')
endLine = '\n'
outputFile.write('g cube')
outputFile.write(endLine)
numCubeVertices = len(v)
for p in points:
for i in range(numCubeVertices):
outputFile.write(('v %f %f %f' % ((v[i][0] + p[0]), (v[i][1] + p[1]), (v[i][2] + p[2]))))
outputFile.write(endLine)
for vertexNormalString in vn:
outputFile.write(vertexNormalString)
outputFile.write(endLine)
for cubeIndex in range(len(points)):
vertexIndexOffset = (cubeIndex * numCubeVertices)
for faceIndex in range(len(f)):
face = f[faceIndex].makeFace(vertexIndexOffset)
outputFile.write(('f ' + face.toString()))
outputFile.write(endLine)
outputFile.close() | def makeOBJFile(points, filename):
outputFile = open(filename, 'w')
endLine = '\n'
outputFile.write('g cube')
outputFile.write(endLine)
numCubeVertices = len(v)
for p in points:
for i in range(numCubeVertices):
outputFile.write(('v %f %f %f' % ((v[i][0] + p[0]), (v[i][1] + p[1]), (v[i][2] + p[2]))))
outputFile.write(endLine)
for vertexNormalString in vn:
outputFile.write(vertexNormalString)
outputFile.write(endLine)
for cubeIndex in range(len(points)):
vertexIndexOffset = (cubeIndex * numCubeVertices)
for faceIndex in range(len(f)):
face = f[faceIndex].makeFace(vertexIndexOffset)
outputFile.write(('f ' + face.toString()))
outputFile.write(endLine)
outputFile.close()<|docstring|>OBJ file creation test that puts cube at each point<|endoftext|> |
f00ed7b22315b73c5452ee5662e600a15c7cabc91b54de1c25c4c1eb3f036ba7 | def make_mask(cell, coords, relativity=0, shls_slice=None, verbose=None):
'Mask to indicate whether a shell is zero on grid.\n The resultant mask array is an extension to the mask array used in\n molecular code (see also pyscf.dft.numint.make_mask function).\n For given shell ID and block ID, the value of the extended mask array\n means the number of images in Ls that does not vanish.\n '
coords = np.asarray(coords, order='F')
natm = ctypes.c_int(cell._atm.shape[0])
nbas = ctypes.c_int(cell.nbas)
ngrids = len(coords)
if (shls_slice is None):
shls_slice = (0, cell.nbas)
assert (shls_slice == (0, cell.nbas))
Ls = cell.get_lattice_Ls()
Ls = Ls[np.argsort(lib.norm(Ls, axis=1))]
non0tab = np.empty(((((ngrids + BLKSIZE) - 1) // BLKSIZE), cell.nbas), dtype=np.uint8)
libpbc.PBCnr_ao_screen(non0tab.ctypes.data_as(ctypes.c_void_p), coords.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(ngrids), Ls.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(Ls)), cell._atm.ctypes.data_as(ctypes.c_void_p), natm, cell._bas.ctypes.data_as(ctypes.c_void_p), nbas, cell._env.ctypes.data_as(ctypes.c_void_p))
return non0tab | Mask to indicate whether a shell is zero on grid.
The resultant mask array is an extension to the mask array used in
molecular code (see also pyscf.dft.numint.make_mask function).
For given shell ID and block ID, the value of the extended mask array
means the number of images in Ls that does not vanish. | pyscf/pbc/dft/gen_grid.py | make_mask | etaffet/pyscf | 1 | python | def make_mask(cell, coords, relativity=0, shls_slice=None, verbose=None):
'Mask to indicate whether a shell is zero on grid.\n The resultant mask array is an extension to the mask array used in\n molecular code (see also pyscf.dft.numint.make_mask function).\n For given shell ID and block ID, the value of the extended mask array\n means the number of images in Ls that does not vanish.\n '
coords = np.asarray(coords, order='F')
natm = ctypes.c_int(cell._atm.shape[0])
nbas = ctypes.c_int(cell.nbas)
ngrids = len(coords)
if (shls_slice is None):
shls_slice = (0, cell.nbas)
assert (shls_slice == (0, cell.nbas))
Ls = cell.get_lattice_Ls()
Ls = Ls[np.argsort(lib.norm(Ls, axis=1))]
non0tab = np.empty(((((ngrids + BLKSIZE) - 1) // BLKSIZE), cell.nbas), dtype=np.uint8)
libpbc.PBCnr_ao_screen(non0tab.ctypes.data_as(ctypes.c_void_p), coords.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(ngrids), Ls.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(Ls)), cell._atm.ctypes.data_as(ctypes.c_void_p), natm, cell._bas.ctypes.data_as(ctypes.c_void_p), nbas, cell._env.ctypes.data_as(ctypes.c_void_p))
return non0tab | def make_mask(cell, coords, relativity=0, shls_slice=None, verbose=None):
'Mask to indicate whether a shell is zero on grid.\n The resultant mask array is an extension to the mask array used in\n molecular code (see also pyscf.dft.numint.make_mask function).\n For given shell ID and block ID, the value of the extended mask array\n means the number of images in Ls that does not vanish.\n '
coords = np.asarray(coords, order='F')
natm = ctypes.c_int(cell._atm.shape[0])
nbas = ctypes.c_int(cell.nbas)
ngrids = len(coords)
if (shls_slice is None):
shls_slice = (0, cell.nbas)
assert (shls_slice == (0, cell.nbas))
Ls = cell.get_lattice_Ls()
Ls = Ls[np.argsort(lib.norm(Ls, axis=1))]
non0tab = np.empty(((((ngrids + BLKSIZE) - 1) // BLKSIZE), cell.nbas), dtype=np.uint8)
libpbc.PBCnr_ao_screen(non0tab.ctypes.data_as(ctypes.c_void_p), coords.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(ngrids), Ls.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(Ls)), cell._atm.ctypes.data_as(ctypes.c_void_p), natm, cell._bas.ctypes.data_as(ctypes.c_void_p), nbas, cell._env.ctypes.data_as(ctypes.c_void_p))
return non0tab<|docstring|>Mask to indicate whether a shell is zero on grid.
The resultant mask array is an extension to the mask array used in
molecular code (see also pyscf.dft.numint.make_mask function).
For given shell ID and block ID, the value of the extended mask array
means the number of images in Ls that does not vanish.<|endoftext|> |
42e2783803271c97b6d8427b5bf89d5dd34fca36809f99261fbac5cf84330f9d | def gen_becke_grids(cell, atom_grid={}, radi_method=dft.radi.gauss_chebyshev, level=3, prune=nwchem_prune):
'real-space grids using Becke scheme\n\n Args:\n cell : instance of :class:`Cell`\n\n Returns:\n coords : (ngx*ngy*ngz, 3) ndarray\n The real-space grid point coordinates.\n weights : (ngx*ngy*ngz) ndarray\n '
Ls = cell.get_lattice_Ls()
atm_coords = (Ls.reshape((- 1), 1, 3) + cell.atom_coords())
atom_grids_tab = gen_atomic_grids(cell, atom_grid, radi_method, level, prune)
coords_all = []
weights_all = []
b = cell.reciprocal_vectors(norm_to=1)
supatm_idx = []
k = 0
for (iL, L) in enumerate(Ls):
for ia in range(cell.natm):
(coords, vol) = atom_grids_tab[cell.atom_symbol(ia)]
coords = (coords + atm_coords[(iL, ia)])
c = b.dot(coords.T).round(8)
mask = np.ones(c.shape[1], dtype=bool)
if (cell.dimension >= 1):
mask &= ((c[0] >= 0) & (c[0] <= 1))
if (cell.dimension >= 2):
mask &= ((c[1] >= 0) & (c[1] <= 1))
if (cell.dimension == 3):
mask &= ((c[2] >= 0) & (c[2] <= 1))
vol = vol[mask]
if (vol.size > 8):
c = c[(:, mask)]
if (cell.dimension >= 1):
vol[(c[0] == 0)] *= 0.5
vol[(c[0] == 1)] *= 0.5
if (cell.dimension >= 2):
vol[(c[1] == 0)] *= 0.5
vol[(c[1] == 1)] *= 0.5
if (cell.dimension == 3):
vol[(c[2] == 0)] *= 0.5
vol[(c[2] == 1)] *= 0.5
coords = coords[mask]
coords_all.append(coords)
weights_all.append(vol)
supatm_idx.append(k)
k += 1
offs = np.append(0, np.cumsum([w.size for w in weights_all]))
coords_all = np.vstack(coords_all)
weights_all = np.hstack(weights_all)
atm_coords = np.asarray(atm_coords.reshape((- 1), 3)[supatm_idx], order='C')
sup_natm = len(atm_coords)
p_radii_table = lib.c_null_ptr()
fn = dft.gen_grid.libdft.VXCgen_grid
ngrids = weights_all.size
max_memory = (cell.max_memory - lib.current_memory()[0])
blocksize = min(ngrids, max(2000, int((((max_memory * 1000000.0) / 8) / sup_natm))))
displs = lib.misc._blocksize_partition(offs, blocksize)
for (n0, n1) in zip(displs[:(- 1)], displs[1:]):
(p0, p1) = (offs[n0], offs[n1])
pbecke = np.empty((sup_natm, (p1 - p0)))
coords = np.asarray(coords_all[p0:p1], order='F')
fn(pbecke.ctypes.data_as(ctypes.c_void_p), coords.ctypes.data_as(ctypes.c_void_p), atm_coords.ctypes.data_as(ctypes.c_void_p), p_radii_table, ctypes.c_int(sup_natm), ctypes.c_int((p1 - p0)))
weights_all[p0:p1] /= pbecke.sum(axis=0)
for ia in range(n0, n1):
(i0, i1) = (offs[ia], offs[(ia + 1)])
weights_all[i0:i1] *= pbecke[(ia, (i0 - p0):(i1 - p0))]
return (coords_all, weights_all) | real-space grids using Becke scheme
Args:
cell : instance of :class:`Cell`
Returns:
coords : (ngx*ngy*ngz, 3) ndarray
The real-space grid point coordinates.
weights : (ngx*ngy*ngz) ndarray | pyscf/pbc/dft/gen_grid.py | gen_becke_grids | etaffet/pyscf | 1 | python | def gen_becke_grids(cell, atom_grid={}, radi_method=dft.radi.gauss_chebyshev, level=3, prune=nwchem_prune):
'real-space grids using Becke scheme\n\n Args:\n cell : instance of :class:`Cell`\n\n Returns:\n coords : (ngx*ngy*ngz, 3) ndarray\n The real-space grid point coordinates.\n weights : (ngx*ngy*ngz) ndarray\n '
Ls = cell.get_lattice_Ls()
atm_coords = (Ls.reshape((- 1), 1, 3) + cell.atom_coords())
atom_grids_tab = gen_atomic_grids(cell, atom_grid, radi_method, level, prune)
coords_all = []
weights_all = []
b = cell.reciprocal_vectors(norm_to=1)
supatm_idx = []
k = 0
for (iL, L) in enumerate(Ls):
for ia in range(cell.natm):
(coords, vol) = atom_grids_tab[cell.atom_symbol(ia)]
coords = (coords + atm_coords[(iL, ia)])
c = b.dot(coords.T).round(8)
mask = np.ones(c.shape[1], dtype=bool)
if (cell.dimension >= 1):
mask &= ((c[0] >= 0) & (c[0] <= 1))
if (cell.dimension >= 2):
mask &= ((c[1] >= 0) & (c[1] <= 1))
if (cell.dimension == 3):
mask &= ((c[2] >= 0) & (c[2] <= 1))
vol = vol[mask]
if (vol.size > 8):
c = c[(:, mask)]
if (cell.dimension >= 1):
vol[(c[0] == 0)] *= 0.5
vol[(c[0] == 1)] *= 0.5
if (cell.dimension >= 2):
vol[(c[1] == 0)] *= 0.5
vol[(c[1] == 1)] *= 0.5
if (cell.dimension == 3):
vol[(c[2] == 0)] *= 0.5
vol[(c[2] == 1)] *= 0.5
coords = coords[mask]
coords_all.append(coords)
weights_all.append(vol)
supatm_idx.append(k)
k += 1
offs = np.append(0, np.cumsum([w.size for w in weights_all]))
coords_all = np.vstack(coords_all)
weights_all = np.hstack(weights_all)
atm_coords = np.asarray(atm_coords.reshape((- 1), 3)[supatm_idx], order='C')
sup_natm = len(atm_coords)
p_radii_table = lib.c_null_ptr()
fn = dft.gen_grid.libdft.VXCgen_grid
ngrids = weights_all.size
max_memory = (cell.max_memory - lib.current_memory()[0])
blocksize = min(ngrids, max(2000, int((((max_memory * 1000000.0) / 8) / sup_natm))))
displs = lib.misc._blocksize_partition(offs, blocksize)
for (n0, n1) in zip(displs[:(- 1)], displs[1:]):
(p0, p1) = (offs[n0], offs[n1])
pbecke = np.empty((sup_natm, (p1 - p0)))
coords = np.asarray(coords_all[p0:p1], order='F')
fn(pbecke.ctypes.data_as(ctypes.c_void_p), coords.ctypes.data_as(ctypes.c_void_p), atm_coords.ctypes.data_as(ctypes.c_void_p), p_radii_table, ctypes.c_int(sup_natm), ctypes.c_int((p1 - p0)))
weights_all[p0:p1] /= pbecke.sum(axis=0)
for ia in range(n0, n1):
(i0, i1) = (offs[ia], offs[(ia + 1)])
weights_all[i0:i1] *= pbecke[(ia, (i0 - p0):(i1 - p0))]
return (coords_all, weights_all) | def gen_becke_grids(cell, atom_grid={}, radi_method=dft.radi.gauss_chebyshev, level=3, prune=nwchem_prune):
'real-space grids using Becke scheme\n\n Args:\n cell : instance of :class:`Cell`\n\n Returns:\n coords : (ngx*ngy*ngz, 3) ndarray\n The real-space grid point coordinates.\n weights : (ngx*ngy*ngz) ndarray\n '
Ls = cell.get_lattice_Ls()
atm_coords = (Ls.reshape((- 1), 1, 3) + cell.atom_coords())
atom_grids_tab = gen_atomic_grids(cell, atom_grid, radi_method, level, prune)
coords_all = []
weights_all = []
b = cell.reciprocal_vectors(norm_to=1)
supatm_idx = []
k = 0
for (iL, L) in enumerate(Ls):
for ia in range(cell.natm):
(coords, vol) = atom_grids_tab[cell.atom_symbol(ia)]
coords = (coords + atm_coords[(iL, ia)])
c = b.dot(coords.T).round(8)
mask = np.ones(c.shape[1], dtype=bool)
if (cell.dimension >= 1):
mask &= ((c[0] >= 0) & (c[0] <= 1))
if (cell.dimension >= 2):
mask &= ((c[1] >= 0) & (c[1] <= 1))
if (cell.dimension == 3):
mask &= ((c[2] >= 0) & (c[2] <= 1))
vol = vol[mask]
if (vol.size > 8):
c = c[(:, mask)]
if (cell.dimension >= 1):
vol[(c[0] == 0)] *= 0.5
vol[(c[0] == 1)] *= 0.5
if (cell.dimension >= 2):
vol[(c[1] == 0)] *= 0.5
vol[(c[1] == 1)] *= 0.5
if (cell.dimension == 3):
vol[(c[2] == 0)] *= 0.5
vol[(c[2] == 1)] *= 0.5
coords = coords[mask]
coords_all.append(coords)
weights_all.append(vol)
supatm_idx.append(k)
k += 1
offs = np.append(0, np.cumsum([w.size for w in weights_all]))
coords_all = np.vstack(coords_all)
weights_all = np.hstack(weights_all)
atm_coords = np.asarray(atm_coords.reshape((- 1), 3)[supatm_idx], order='C')
sup_natm = len(atm_coords)
p_radii_table = lib.c_null_ptr()
fn = dft.gen_grid.libdft.VXCgen_grid
ngrids = weights_all.size
max_memory = (cell.max_memory - lib.current_memory()[0])
blocksize = min(ngrids, max(2000, int((((max_memory * 1000000.0) / 8) / sup_natm))))
displs = lib.misc._blocksize_partition(offs, blocksize)
for (n0, n1) in zip(displs[:(- 1)], displs[1:]):
(p0, p1) = (offs[n0], offs[n1])
pbecke = np.empty((sup_natm, (p1 - p0)))
coords = np.asarray(coords_all[p0:p1], order='F')
fn(pbecke.ctypes.data_as(ctypes.c_void_p), coords.ctypes.data_as(ctypes.c_void_p), atm_coords.ctypes.data_as(ctypes.c_void_p), p_radii_table, ctypes.c_int(sup_natm), ctypes.c_int((p1 - p0)))
weights_all[p0:p1] /= pbecke.sum(axis=0)
for ia in range(n0, n1):
(i0, i1) = (offs[ia], offs[(ia + 1)])
weights_all[i0:i1] *= pbecke[(ia, (i0 - p0):(i1 - p0))]
return (coords_all, weights_all)<|docstring|>real-space grids using Becke scheme
Args:
cell : instance of :class:`Cell`
Returns:
coords : (ngx*ngy*ngz, 3) ndarray
The real-space grid point coordinates.
weights : (ngx*ngy*ngz) ndarray<|endoftext|> |
ec39ec64be44b8fe0de4c327a703a3ce0c2ad56ce2f7b4d506a2c60b8684ddbc | def __init__(self, db_name: str, db_host: Optional[str], db_port: Optional[int], db_userName: Optional[str], db_password: Optional[str], driverAlias: str, name: str=None) -> None:
'Database connection through a sql driver.'
from .pnsqldrivers import PNSqlDrivers
super(PNConnection, self).__init__()
self.currentSavePoint_ = None
self.driverSql = PNSqlDrivers()
self.connAux: Dict[(str, 'IConnection')] = {}
if (name is None):
self.name = 'default'
else:
self.name = name
self.driverName_ = self.driverSql.aliasToName(driverAlias)
if (name and (name not in ('dbAux', 'Aux'))):
self._isOpen = False
return
if (self.driverName_ and self.driverSql.loadDriver(self.driverName_)):
self.driver_ = self.driverSql.driver()
self.conn = self.conectar(db_name, db_host, db_port, db_userName, db_password)
if (self.conn is False):
return
self._isOpen = True
else:
logger.error("PNConnection.ERROR: No se encontro el driver '%s'", driverAlias)
import sys
sys.exit(0)
self.transaction_ = 0
self.stackSavePoints_ = []
self.queueSavePoints_ = []
self.interactiveGUI_ = True
self.lastActiveCursor_ = None
self.driver().db_ = self | Database connection through a sql driver. | pineboolib/application/database/pnconnection.py | __init__ | deavid/pineboo | 2 | python | def __init__(self, db_name: str, db_host: Optional[str], db_port: Optional[int], db_userName: Optional[str], db_password: Optional[str], driverAlias: str, name: str=None) -> None:
from .pnsqldrivers import PNSqlDrivers
super(PNConnection, self).__init__()
self.currentSavePoint_ = None
self.driverSql = PNSqlDrivers()
self.connAux: Dict[(str, 'IConnection')] = {}
if (name is None):
self.name = 'default'
else:
self.name = name
self.driverName_ = self.driverSql.aliasToName(driverAlias)
if (name and (name not in ('dbAux', 'Aux'))):
self._isOpen = False
return
if (self.driverName_ and self.driverSql.loadDriver(self.driverName_)):
self.driver_ = self.driverSql.driver()
self.conn = self.conectar(db_name, db_host, db_port, db_userName, db_password)
if (self.conn is False):
return
self._isOpen = True
else:
logger.error("PNConnection.ERROR: No se encontro el driver '%s'", driverAlias)
import sys
sys.exit(0)
self.transaction_ = 0
self.stackSavePoints_ = []
self.queueSavePoints_ = []
self.interactiveGUI_ = True
self.lastActiveCursor_ = None
self.driver().db_ = self | def __init__(self, db_name: str, db_host: Optional[str], db_port: Optional[int], db_userName: Optional[str], db_password: Optional[str], driverAlias: str, name: str=None) -> None:
from .pnsqldrivers import PNSqlDrivers
super(PNConnection, self).__init__()
self.currentSavePoint_ = None
self.driverSql = PNSqlDrivers()
self.connAux: Dict[(str, 'IConnection')] = {}
if (name is None):
self.name = 'default'
else:
self.name = name
self.driverName_ = self.driverSql.aliasToName(driverAlias)
if (name and (name not in ('dbAux', 'Aux'))):
self._isOpen = False
return
if (self.driverName_ and self.driverSql.loadDriver(self.driverName_)):
self.driver_ = self.driverSql.driver()
self.conn = self.conectar(db_name, db_host, db_port, db_userName, db_password)
if (self.conn is False):
return
self._isOpen = True
else:
logger.error("PNConnection.ERROR: No se encontro el driver '%s'", driverAlias)
import sys
sys.exit(0)
self.transaction_ = 0
self.stackSavePoints_ = []
self.queueSavePoints_ = []
self.interactiveGUI_ = True
self.lastActiveCursor_ = None
self.driver().db_ = self<|docstring|>Database connection through a sql driver.<|endoftext|> |
df30388ca458a831adcd082ab18dc96de473b2f96991cda72de430e6bde54692 | @decorators.NotImplementedWarn
def finish(self) -> None:
'Set the connection as terminated.'
pass | Set the connection as terminated. | pineboolib/application/database/pnconnection.py | finish | deavid/pineboo | 2 | python | @decorators.NotImplementedWarn
def finish(self) -> None:
pass | @decorators.NotImplementedWarn
def finish(self) -> None:
pass<|docstring|>Set the connection as terminated.<|endoftext|> |
ef15ee5337cfcf0cc8a9bafbf71a664a77c65ace17c86e92c563bdf6ca0ceecc | def connectionName(self) -> Any:
'Get the current connection name for this cursor.'
return self.name | Get the current connection name for this cursor. | pineboolib/application/database/pnconnection.py | connectionName | deavid/pineboo | 2 | python | def connectionName(self) -> Any:
return self.name | def connectionName(self) -> Any:
return self.name<|docstring|>Get the current connection name for this cursor.<|endoftext|> |
8d0abc8b8fdc41222b2ac26088ec723308132e415a110091badf2eaa2c52c9c7 | def useConn(self, name_or_conn: Union[(str, IConnection)]='default') -> IConnection:
'\n Select another connection which can be not the default one.\n\n Allow you to select a connection.\n '
name: str
if isinstance(name_or_conn, IConnection):
name = str(name_or_conn.connectionName())
else:
name = str(name_or_conn)
if (name in ('default', None)):
return self
connection = self.connAux.get(name, None)
if (connection is None):
if (self.driverSql is None):
raise Exception('No driver selected')
connection = PNConnection(self.db_name, self.db_host, self.db_port, self.db_userName, self.db_password, self.driverSql.nameToAlias(self.driverName()), name)
self.connAux[name] = connection
return connection | Select another connection which can be not the default one.
Allow you to select a connection. | pineboolib/application/database/pnconnection.py | useConn | deavid/pineboo | 2 | python | def useConn(self, name_or_conn: Union[(str, IConnection)]='default') -> IConnection:
'\n Select another connection which can be not the default one.\n\n Allow you to select a connection.\n '
name: str
if isinstance(name_or_conn, IConnection):
name = str(name_or_conn.connectionName())
else:
name = str(name_or_conn)
if (name in ('default', None)):
return self
connection = self.connAux.get(name, None)
if (connection is None):
if (self.driverSql is None):
raise Exception('No driver selected')
connection = PNConnection(self.db_name, self.db_host, self.db_port, self.db_userName, self.db_password, self.driverSql.nameToAlias(self.driverName()), name)
self.connAux[name] = connection
return connection | def useConn(self, name_or_conn: Union[(str, IConnection)]='default') -> IConnection:
'\n Select another connection which can be not the default one.\n\n Allow you to select a connection.\n '
name: str
if isinstance(name_or_conn, IConnection):
name = str(name_or_conn.connectionName())
else:
name = str(name_or_conn)
if (name in ('default', None)):
return self
connection = self.connAux.get(name, None)
if (connection is None):
if (self.driverSql is None):
raise Exception('No driver selected')
connection = PNConnection(self.db_name, self.db_host, self.db_port, self.db_userName, self.db_password, self.driverSql.nameToAlias(self.driverName()), name)
self.connAux[name] = connection
return connection<|docstring|>Select another connection which can be not the default one.
Allow you to select a connection.<|endoftext|> |
03fb3742da06223be389a0938a562bc52ec5bcfaa1ea003b596bef6080730a34 | def dictDatabases(self) -> Dict[(str, 'IConnection')]:
'Return dict with own database connections.'
return self.connAux | Return dict with own database connections. | pineboolib/application/database/pnconnection.py | dictDatabases | deavid/pineboo | 2 | python | def dictDatabases(self) -> Dict[(str, 'IConnection')]:
return self.connAux | def dictDatabases(self) -> Dict[(str, 'IConnection')]:
return self.connAux<|docstring|>Return dict with own database connections.<|endoftext|> |
5d358f2b84ef2f463efd97f216a6bd1827ec120e0e4d945831da394e21cd4ec4 | def removeConn(self, name='default') -> bool:
'Delete a connection specified by name.'
try:
conn_ = self.useConn(name).conn
if (conn_ is not None):
conn_.close()
del self.connAux[name]
except Exception:
pass
return True | Delete a connection specified by name. | pineboolib/application/database/pnconnection.py | removeConn | deavid/pineboo | 2 | python | def removeConn(self, name='default') -> bool:
try:
conn_ = self.useConn(name).conn
if (conn_ is not None):
conn_.close()
del self.connAux[name]
except Exception:
pass
return True | def removeConn(self, name='default') -> bool:
try:
conn_ = self.useConn(name).conn
if (conn_ is not None):
conn_.close()
del self.connAux[name]
except Exception:
pass
return True<|docstring|>Delete a connection specified by name.<|endoftext|> |
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