hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b0607d53868c5168be9e11e7881aa22c929be4fc
| 95
|
py
|
Python
|
Scrapers/setup.py
|
TLTFinancialConsulting/Stock-Analysis
|
2abb3d7849acc675efe52ccc7979ef14e82dae41
|
[
"MIT"
] | null | null | null |
Scrapers/setup.py
|
TLTFinancialConsulting/Stock-Analysis
|
2abb3d7849acc675efe52ccc7979ef14e82dae41
|
[
"MIT"
] | null | null | null |
Scrapers/setup.py
|
TLTFinancialConsulting/Stock-Analysis
|
2abb3d7849acc675efe52ccc7979ef14e82dae41
|
[
"MIT"
] | 1
|
2021-09-05T13:56:52.000Z
|
2021-09-05T13:56:52.000Z
|
from distutils.core import setup
import py2exe
setup(console=['Single Stock Scraper.py'])
| 19
| 43
| 0.757895
| 13
| 95
| 5.538462
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012346
| 0.147368
| 95
| 4
| 44
| 23.75
| 0.876543
| 0
| 0
| 0
| 0
| 0
| 0.252747
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c669da8c85a9ad97e2316f863823e6affc4076e8
| 52
|
py
|
Python
|
autos/googleapi/__init__.py
|
hans-t/autos
|
4cb370187cb7a8104cedb1942a4c159033729677
|
[
"MIT"
] | 1
|
2016-08-17T15:34:15.000Z
|
2016-08-17T15:34:15.000Z
|
autos/googleapi/__init__.py
|
hans-t/autos
|
4cb370187cb7a8104cedb1942a4c159033729677
|
[
"MIT"
] | 6
|
2016-08-17T15:34:55.000Z
|
2021-04-30T20:38:05.000Z
|
autos/googleapi/__init__.py
|
hans-t/autos
|
4cb370187cb7a8104cedb1942a4c159033729677
|
[
"MIT"
] | null | null | null |
from .drive import Drive
from .sheets import Sheets
| 17.333333
| 26
| 0.807692
| 8
| 52
| 5.25
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.153846
| 52
| 2
| 27
| 26
| 0.954545
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| 0
| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c68a4b8268cd38312278416900497b8abe84bff8
| 349
|
py
|
Python
|
aliyun_rocketmq_provider/__init__.py
|
Ed-XCF/airflow-provider-rocketmq
|
4d6b9c91d6981f6439ad37b8c0302fe1a7df6e82
|
[
"Apache-2.0"
] | null | null | null |
aliyun_rocketmq_provider/__init__.py
|
Ed-XCF/airflow-provider-rocketmq
|
4d6b9c91d6981f6439ad37b8c0302fe1a7df6e82
|
[
"Apache-2.0"
] | null | null | null |
aliyun_rocketmq_provider/__init__.py
|
Ed-XCF/airflow-provider-rocketmq
|
4d6b9c91d6981f6439ad37b8c0302fe1a7df6e82
|
[
"Apache-2.0"
] | null | null | null |
def get_provider_info():
return {
"package-name": "airflow-providers-aliyun-rocketmq",
"name": "Aliyun RocketMQ Airflow Provider",
"description": "Airflow provider for aliyun rocketmq",
"hook-class-names": ["aliyun_rocketmq_provider.hooks.aliyun_rocketmq.AliyunRocketMQHook"],
"versions": ["0.1.2"]
}
| 38.777778
| 98
| 0.659026
| 36
| 349
| 6.25
| 0.611111
| 0.311111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010753
| 0.200573
| 349
| 8
| 99
| 43.625
| 0.795699
| 0
| 0
| 0
| 0
| 0
| 0.636103
| 0.280802
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| true
| 0
| 0
| 0.125
| 0.25
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
c6b85532df7e50b9b99d76edc5eb3952977abc72
| 156
|
py
|
Python
|
pyfibre_analysis_tools/__init__.py
|
franklongford/pyfibre_analysis_scripts
|
75b43017c2a532100a77a920068b15ad4574d5bb
|
[
"MIT"
] | null | null | null |
pyfibre_analysis_tools/__init__.py
|
franklongford/pyfibre_analysis_scripts
|
75b43017c2a532100a77a920068b15ad4574d5bb
|
[
"MIT"
] | 2
|
2021-01-30T17:25:05.000Z
|
2021-07-25T22:16:21.000Z
|
pyfibre_analysis_tools/__init__.py
|
franklongford/pyfibre_analysis_scripts
|
75b43017c2a532100a77a920068b15ad4574d5bb
|
[
"MIT"
] | null | null | null |
from .analysis_tools import load_databases # noqa: E501
from .plotting import confidence_ellipse, scatter, plot_roc_curve, plot_lda_analysis # noqa: E501
| 52
| 98
| 0.820513
| 22
| 156
| 5.5
| 0.727273
| 0.132231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043796
| 0.121795
| 156
| 2
| 99
| 78
| 0.839416
| 0.134615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c6cfc9ee7d8ad047bcf7137c60c3b646a3992b91
| 86,991
|
py
|
Python
|
spinsim/__init__.py
|
rpanderson/spinsim
|
8f93b7dd1964290e2cc85ae1c15e73ca31a34bdc
|
[
"BSD-3-Clause"
] | null | null | null |
spinsim/__init__.py
|
rpanderson/spinsim
|
8f93b7dd1964290e2cc85ae1c15e73ca31a34bdc
|
[
"BSD-3-Clause"
] | null | null | null |
spinsim/__init__.py
|
rpanderson/spinsim
|
8f93b7dd1964290e2cc85ae1c15e73ca31a34bdc
|
[
"BSD-3-Clause"
] | null | null | null |
"""
"""
# from . import utilities
from enum import Enum
import numpy as np
import numba as nb
from numba import cuda
from numba import roc
import math
sqrt2 = math.sqrt(2)
sqrt3 = math.sqrt(3)
class SpinQuantumNumber(Enum):
"""
Options for the spin quantum number of a system.
Parameters
----------
value : :obj:`float`
The numerical value of the spin quantum number.
dimension : :obj:`int`
Dimension of the hilbert space the states with this spin belong to.
label : :obj:`str`
A text label that can be used for archiving.
"""
def __init__(self, value, dimension, label):
super().__init__()
self._value_ = value
self.dimension = dimension
self.label = label
HALF = (1/2, 2, "half")
"""
For two level systems.
"""
ONE = (1, 3, "one")
"""
For three level systems.
"""
class IntegrationMethod(Enum):
"""
Options for describing which method is used during the integration.
Parameters
----------
value : :obj:`str`
A text label that can be used for archiving.
"""
MAGNUS_CF4 = "magnus_cf4"
"""
Commutator free, fourth order Magnus based integrator.
"""
MIDPOINT_SAMPLE = "midpoint_sample"
"""
Euler integration method.
"""
HALF_STEP = "half_step"
"""
Integration method from AtomicPy. Makes two Euler integration steps, one sampling the field from the start of the time step, one sampling the field from the end of the time step. The equivalent of the trapezoidal method.
"""
class ExponentiationMethod(Enum):
"""
The implementation to use for matrix exponentiation within the integrator.
Parameters
----------
value : :obj:`str`
A text label that can be used for archiving.
index : :obj:`int`
A reference number, used when compiling the integrator, where higher level objects like enums cannot be interpreted.
"""
def __init__(self, value, index):
super().__init__()
self._value_ = value
self.index = index
ANALYTIC = ("analytic", 0)
"""
Analytic expression of the matrix exponential. For spin half :obj:`SpinQuantumNumber.HALF` systems only.
"""
LIE_TROTTER = ("lie_trotter", 1)
"""
Approximation using the Lie Trotter theorem.
"""
class Device(Enum):
"""
The target device that the integrator is being compiled for.
.. _Supported Python features: http://numba.pydata.org/numba-doc/latest/reference/pysupported.html
.. _Supported Numpy features: http://numba.pydata.org/numba-doc/latest/reference/numpysupported.html
.. _Supported CUDA Python features: http://numba.pydata.org/numba-doc/latest/cuda/cudapysupported.html
"""
def __init__(self, value, index):
super().__init__()
self._value_ = value
self.index = index
if value == "python":
def jit_host(template, max_registers):
def jit_host(func):
return func
return jit_host
self.jit_host = jit_host
def jit_device(func):
return func
self.jit_device = jit_device
def jit_device_template(template):
def jit_device_template(func):
return func
return jit_device_template
self.jit_device_template = jit_device_template
elif value == "cpu_single":
def jit_host(template, max_registers):
def jit_host(func):
return nb.njit(template)(func)
return jit_host
self.jit_host = jit_host
def jit_device(func):
return nb.njit()(func)
self.jit_device = jit_device
def jit_device_template(template):
def jit_device_template(func):
return nb.njit(template)(func)
return jit_device_template
self.jit_device_template = jit_device_template
elif value == "cpu":
def jit_host(template, max_registers):
def jit_host(func):
return nb.njit(template, parallel = True)(func)
return jit_host
self.jit_host = jit_host
def jit_device(func):
return nb.njit()(func)
self.jit_device = jit_device
def jit_device_template(template):
def jit_device_template(func):
return nb.njit(template)(func)
return jit_device_template
self.jit_device_template = jit_device_template
elif value == "cuda":
def jit_host(template, max_registers):
def jit_host(func):
return cuda.jit(template, debug = False, max_registers = max_registers)(func)
return jit_host
self.jit_host = jit_host
def jit_device(func):
return cuda.jit(device = True, inline = True)(func)
self.jit_device = jit_device
def jit_device_template(template):
def jit_device_template(func):
return cuda.jit(template, device = True, inline = True)(func)
return jit_device_template
self.jit_device_template = jit_device_template
elif value == "roc":
def jit_host(template, max_registers):
def jit_host(func):
return roc.jit(template)(func)
return jit_host
self.jit_host = jit_host
def jit_device(func):
return roc.jit(device = True)(func)
self.jit_device = jit_device
def jit_device_template(template):
def jit_device_template(func):
return roc.jit(template, device = True)(func)
return jit_device_template
self.jit_device_template = jit_device_template
PYTHON = ("python", 0)
"""
Use pure python interpreted code for the integrator, ie, don't compile the integrator.
"""
CPU_SINGLE = ("cpu_single", 0)
"""
Use the :func:`numba.jit()` LLVM compiler to compile the integrator to run on a single CPU core.
.. note ::
To use this device option, the user defined field function must be :func:`numba.jit()` compilable. See `Supported Python features`_ for compilable python features, and `Supported Numpy features`_ for compilable numpy features.
"""
CPU = ("cpu", 0)
"""
Use the :func:`numba.jit()` LLVM compiler to compile the integrator to run on all CPU cores, in parallel.
.. note ::
To use this device option, the user defined field function must be :func:`numba.jit()` compilable. See `Supported Python features`_ for compilable python features, and `Supported Numpy features`_ for compilable numpy features.
"""
CUDA = ("cuda", 1)
"""
Use the :func:`numba.cuda.jit()` LLVM compiler to compile the integrator to run on an Nvidia cuda compatible GPU, in parallel.
.. note ::
To use this device option, the user defined field function must be :func:`numba.cuda.jit()` compilable. See `Supported CUDA Python features`_ for compilable python features.
"""
ROC = ("roc", 2)
"""
Use the :func:`numba.roc.jit()` LLVM compiler to compile the integrator to run on an AMD ROCm compatible GPU, in parallel.
.. warning ::
Work in progress, not currently functional!
"""
class Results:
"""
The results of a an evaluation of the integrator.
Attributes
----------
time : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index)
The times that `state` was evaluated at.
time_evolution : :obj:`numpy.ndarray` of :obj:`numpy.float128` (time_index, y_index, x_index)
The evaluated time evolution operator between each time step. See :ref:`architecture` for some information.
state : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)
The evaluated quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction.
spin : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)
The expected spin projection (Bloch vector) over time. This is calculated just in time using the JITed :obj:`callable` `spin_calculator`.
spin_calculator : :obj:`callable`
Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. Used to calculate `spin` the first time it is referenced by the user.
Parameters:
* **state** (:obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)) - The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction.
Returns:
* **spin** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)) - The expected spin projection (Bloch vector) over time.
"""
def __init__(self, time, time_evolution, state, spin_calculator):
"""
Parameters
----------
time : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index)
The times that `state` was evaluated at.
time_evolution : :obj:`numpy.ndarray` of :obj:`numpy.float128` (time_index, y_index, x_index)
The evaluated time evolution operator between each time step. See :ref:`architecture` for some information.
state : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)
The evaluated quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction.
spin_calculator : :obj:`callable`
Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. Used to calculate `spin` the first time it is referenced by the user.
Parameters:
* **state** (:obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)) - The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction.
Returns:
* **spin** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)) - The expected spin projection (Bloch vector) over time.
"""
self.time = time
self.time_evolution = time_evolution
self.state = state
self.spin_calculator = spin_calculator
def __getattr__(self, attr_name):
if attr_name == "spin":
spin = self.spin_calculator(self.state)
setattr(self, attr_name, spin)
return self.spin
raise AttributeError("{} has no attribute called {}.".format(self, attr_name))
class Simulator:
"""
Attributes
----------
spin_quantum_number : :obj:`SpinQuantumNumber`
The option to select whether the simulator will integrate a spin half :obj:`SpinQuantumNumber.HALF`, or spin one :obj:`SpinQuantumNumber.ONE` quantum system.
threads_per_block : :obj:`int`
The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models.
device : :obj:`Device`
The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details.
get_time_evolution_raw : :obj:`callable`
The internal function for evaluating the time evolution operator in parallel. Compiled for chosen device on object constrution.
Parameters:
* **sweep_parameter** (:obj:`float`) - The input to the `get_field` function supplied by the user. Modifies the field function so the integrator can be used for many experiments, without the need for slow recompilation. For example, if the `sweep_parameter` is used to define the bias field strength in `get_field`, then one can run many simulations, sweeping through bias values, by calling this method multiple times, each time varying `sweep_parameter`.
* **time_coarse** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index)) - The times that `state` was evaluated at.
* **time_end_points** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (start/end)) - The time offset that the experiment is to start at, and the time that the experiment is to finish at. Measured in s.
* **time_step_integration** (:obj:`float`) - The integration time step. Measured in s.
* **time_step_output** (:obj:`float`) - The sample resolution of the output timeseries for the state. Must be a whole number multiple of `time_step_integration`. Measured in s.
* **time_evolution_coarse** (:obj:`numpy.ndarray` of :obj:`numpy.float128` (time_index, y_index, x_index)) - The evaluated time evolution operator between each time step. See :ref:`architecture` for some information.
spin_calculator : :obj:`callable`
Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. This :obj:`callable` is passed to the :obj:`Results` object returned from :func:`Simulator.evaluate()`, and is executed there just in time if the `spin` property is needed. Compiled for chosen device on object constrution.
Parameters:
* **state** (:obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)) - The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction.
Returns:
* **spin** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)) - The expected spin projection (Bloch vector) over time.
"""
def __init__(self, get_field, spin_quantum_number, device = None, exponentiation_method = None, use_rotating_frame = True, integration_method = IntegrationMethod.MAGNUS_CF4, trotter_cutoff = 32, threads_per_block = 64, max_registers = 63):
"""
.. _Achieved Occupancy: https://docs.nvidia.com/gameworks/content/developertools/desktop/analysis/report/cudaexperiments/kernellevel/achievedoccupancy.htm
Parameters
----------
get_field : :obj:`callable`
A python function that describes the field that the spin system is being put under. It must have three arguments:
* **time_sample** (:obj:`float`) - the time to sample the field at, in units of s.
* **simulation_index** (:obj:`int`) - a parameter that can be swept over when multiple simulations need to be run. For example, it is used to sweep over dressing frequencies during the simulations that `spinsim` was designed for.
* **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64` (spatial_index)) the returned value of the field. This is a four dimensional vector, with the first three entries being x, y, z spatial directions (to model a magnetic field, for example), and the fourth entry being the amplitude of the quadratic shift (only appearing, and required, in spin one systems).
.. note::
This function must be compilable for the device that the integrator is being compiled for. See :class:`Device` for more information and links.
spin_quantum_number : :obj:`SpinQuantumNumber`
The option to select whether the simulator will integrate a spin half :obj:`SpinQuantumNumber.HALF`, or spin one :obj:`SpinQuantumNumber.ONE` quantum system.
device : :obj:`Device`
The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details.
exponentiation_method : :obj:`ExponentiationMethod`
Which method to use for matrix exponentiation in the integration algorithm. Defaults to :obj:`ExponentiationMethod.LIE_TROTTER` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.ONE`, and defaults to :obj:`ExponentiationMethod.ANALYTIC` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.HALF`. See :obj:`ExponentiationMethod` for more details.
use_rotating_frame : :obj:`bool`
Whether or not to use the rotating frame optimisation. Defaults to :obj:`True`. If set to :obj:`True`, the integrator moves into a frame rotating in the z axis by an amount defined by the field in the z direction. This removes the (possibly large) z component of the field, which increases the accuracy of the output since the integrator will on average take smaller steps.
.. note ::
The use of a rotating frame is commonly associated with the use of a rotating wave approximation, a technique used to get approximate analytic solutions of spin system dynamics. This is not done when this option is set to :obj:`True` - no such approximations are made, and the output state in given out of the rotating frame. One can, of course, use :mod:`spinsim` to integrate states in the rotating frame, using the rating wave approximation: just define `get_field()` with field functions that use the rotating wave approximation in the rotating frame.
integration_method : :obj:`IntegrationMethod`
Which integration method to use in the integration. Defaults to :obj:`IntegrationMethod.MAGNUS_CF4`. See :obj:`IntegrationMethod` for more details.
trotter_cutoff : :obj:`int`
The number of squares made by the matrix exponentiator, if :obj:`ExponentiationMethod.LIE_TROTTER` is chosen.
threads_per_block : :obj:`int`
The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models.
max_registers : :obj:`int`
The maximum number of registers allocated per thread when using :obj:`Device.CUDA` as the target device, and can be modified to increase the execution speed for a specific GPU model. Defaults to 63 (optimal for GTX1070, the device used for testing. Note that one extra register per thread is always added to the number specified for control, so really this number is 64).
Raising this value allocates more registers (fast memory) to each thread, out of a maximum number for the whole GPU, for each specific GPU model. This means that if more registers are allocated than are available for the GPU model, the GPU must run fewer threads concurrently than it has Cuda cores, meaning some cores are inactive, and the GPU is said to have less occupancy. Lowering the value increases GPU occupancy, meaning more threads run concurrently, at the expense of fewer resgiters being avaliable to each thread, meaning slower memory must be used. Thus, there will be an optimal value of `max_registers` for each model of GPU running :mod:`spinsim`, balancing more threads vs faster running threads, and changing this value could increase performance for your GPU. See `Achieved Occupancy`_ for Nvidia's official explanation.
"""
if not device:
if cuda.is_available():
device = Device.CUDA
else:
device = Device.CPU
self.threads_per_block = threads_per_block
self.spin_quantum_number = spin_quantum_number
self.device = device
self.get_time_evolution_raw = None
self.get_spin_raw = None
try:
self.compile_time_evolver(get_field, spin_quantum_number, device, use_rotating_frame, integration_method, exponentiation_method, trotter_cutoff, threads_per_block, max_registers)
except:
print("\033[31mspinsim error: numba could not jit get_field function into a device function.\033[0m\n")
raise
def compile_time_evolver(self, get_field, spin_quantum_number, device, use_rotating_frame = True, integration_method = IntegrationMethod.MAGNUS_CF4, exponentiation_method = None, trotter_cutoff:int = 28, threads_per_block = 64, max_registers = 63):
"""
Compiles the integrator and spin calculation functions of the simulator.
Parameters
----------
get_field : :obj:`callable`
A python function that describes the field that the spin system is being put under. It must have three arguments:
* **time_sample** (:obj:`float`) - the time to sample the field at, in units of s.
* **simulation_index** (:obj:`int`) - a parameter that can be swept over when multiple simulations need to be run. For example, it is used to sweep over dressing frequencies during the simulations that `spinsim` was designed for.
* **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64` (spatial_index)) the returned value of the field. This is a four dimensional vector, with the first three entries being x, y, z spatial directions (to model a magnetic field, for example), and the fourth entry being the amplitude of the quadratic shift (only appearing, and required, in spin one systems).
.. note::
This function must be compilable for the device that the integrator is being compiled for. See :class:`Device` for more information and links.
spin_quantum_number : :obj:`SpinQuantumNumber`
The option to select whether the simulator will integrate a spin half :obj:`SpinQuantumNumber.HALF`, or spin one :obj:`SpinQuantumNumber.ONE` quantum system.
device : :obj:`Device`
The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details.
exponentiation_method : :obj:`ExponentiationMethod`
Which method to use for matrix exponentiation in the integration algorithm. Defaults to :obj:`ExponentiationMethod.LIE_TROTTER` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.ONE`, and defaults to :obj:`ExponentiationMethod.ANALYTIC` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.HALF`. See :obj:`ExponentiationMethod` for more details.
use_rotating_frame : :obj:`bool`
Whether or not to use the rotating frame optimisation. Defaults to :obj:`True`. If set to :obj:`True`, the integrator moves into a frame rotating in the z axis by an amount defined by the field in the z direction. This removes the (possibly large) z component of the field, which increases the accuracy of the output since the integrator will on average take smaller steps.
.. note ::
The use of a rotating frame is commonly associated with the use of a rotating wave approximation, a technique used to get approximate analytic solutions of spin system dynamics. This is not done when this option is set to :obj:`True` - no such approximations are made, and the output state in given out of the rotating frame. One can, of course, use :mod:`spinsim` to integrate states in the rotating frame, using the rating wave approximation: just define `get_field()` with field functions that use the rotating wave approximation in the rotating frame.
integration_method : :obj:`IntegrationMethod`
Which integration method to use in the integration. Defaults to :obj:`IntegrationMethod.MAGNUS_CF4`. See :obj:`IntegrationMethod` for more details.
trotter_cutoff : :obj:`int`
The number of squares made by the matrix exponentiator, if :obj:`ExponentiationMethod.LIE_TROTTER` is chosen.
threads_per_block : :obj:`int`
The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models.
max_registers : :obj:`int`
The maximum number of registers allocated per thread when using :obj:`Device.CUDA` as the target device, and can be modified to increase the execution speed for a specific GPU model. Defaults to 63 (optimal for GTX1070, the device used for testing. Note that one extra register per thread is always added to the number specified for control, so really this number is 64).
Raising this value allocates more registers (fast memory) to each thread, out of a maximum number for the whole GPU, for each specific GPU model. This means that if more registers are allocated than are available for the GPU model, the GPU must run fewer threads concurrently than it has Cuda cores, meaning some cores are inactive, and the GPU is said to have less occupancy. Lowering the value increases GPU occupancy, meaning more threads run concurrently, at the expense of fewer resgiters being avaliable to each thread, meaning slower memory must be used. Thus, there will be an optimal value of `max_registers` for each model of GPU running :mod:`spinsim`, balancing more threads vs faster running threads, and changing this value could increase performance for your GPU. See `Achieved Occupancy`_ for Nvidia's official explanation.
"""
utilities = Utilities(spin_quantum_number, device, threads_per_block)
conj = utilities.conj
complex_abs = utilities.complex_abs
norm2 = utilities.norm2
inner = utilities.inner
set_to = utilities.set_to
set_to_one = utilities.set_to_one
set_to_zero = utilities.set_to_zero
matrix_multiply = utilities.matrix_multiply
adjoint = utilities.adjoint
matrix_exponential_analytic = utilities.matrix_exponential_analytic
matrix_exponential_lie_trotter = utilities.matrix_exponential_lie_trotter
jit_host = device.jit_host
jit_device = device.jit_device
jit_device_template = device.jit_device_template
device_index = device.index
dimension = spin_quantum_number.dimension
lie_dimension = dimension + 1
# utility_set = spin_quantum_number.utility_set
if not exponentiation_method:
if spin_quantum_number == SpinQuantumNumber.ONE:
exponentiation_method = ExponentiationMethod.LIE_TROTTER
elif spin_quantum_number == SpinQuantumNumber.HALF:
exponentiation_method = ExponentiationMethod.ANALYTIC
if integration_method == IntegrationMethod.MAGNUS_CF4:
sample_index_max = 3
sample_index_end = 4
elif integration_method == IntegrationMethod.HALF_STEP:
sample_index_max = 3
sample_index_end = 4
elif integration_method == IntegrationMethod.MIDPOINT_SAMPLE:
sample_index_max = 1
sample_index_end = 1
exponentiation_method_index = exponentiation_method.index
if (exponentiation_method == ExponentiationMethod.ANALYTIC) and (spin_quantum_number != SpinQuantumNumber.HALF):
print("\033[31mspinsim warning!!!\n_attempting to use an analytic exponentiation method outside of spin half. Switching to a Lie Trotter method.\033[0m")
exponentiation_method = ExponentiationMethod.LIE_TROTTER
exponentiation_method_index = 1
@jit_device_template("(float64[:], complex128[:, :], complex128[:, :])")
def append_exponentiation(field_sample, time_evolution_fine, time_evolution_coarse):
if device_index == 0:
time_evolution_old = np.empty((dimension, dimension), dtype = np.complex128)
elif device_index == 1:
time_evolution_old = cuda.local.array((dimension, dimension), dtype = np.complex128)
elif device_index == 2:
time_evolution_old_group = roc.shared.array((threads_per_block, dimension, dimension), dtype = np.complex128)
time_evolution_old = time_evolution_old_group[roc.get_local_id(1), :, :]
# Calculate the exponential
if exponentiation_method_index == 0:
matrix_exponential_analytic(field_sample, time_evolution_fine)
elif exponentiation_method_index == 1:
matrix_exponential_lie_trotter(field_sample, time_evolution_fine, trotter_cutoff)
# Premultiply to the exitsing time evolution operator
set_to(time_evolution_coarse, time_evolution_old)
matrix_multiply(time_evolution_fine, time_evolution_old, time_evolution_coarse)
if use_rotating_frame:
if dimension == 3:
@jit_device_template("(float64[:], float64, complex128)")
def transform_frame_spin_one_rotating(field_sample, rotating_wave, rotating_wave_winding):
X = (field_sample[0] + 1j*field_sample[1])/rotating_wave_winding
field_sample[0] = X.real
field_sample[1] = X.imag
field_sample[2] = field_sample[2] - rotating_wave
transform_frame = transform_frame_spin_one_rotating
else:
@jit_device_template("(float64[:], float64, complex128)")
def transform_frame_spin_half_rotating(field_sample, rotating_wave, rotating_wave_winding):
X = (field_sample[0] + 1j*field_sample[1])/(rotating_wave_winding**2)
field_sample[0] = X.real
field_sample[1] = X.imag
field_sample[2] = field_sample[2] - 2*rotating_wave
transform_frame = transform_frame_spin_half_rotating
else:
@jit_device_template("(float64[:], float64, complex128)")
def transform_frame_lab(field_sample, rotating_wave, rotating_wave_winding):
return
transform_frame = transform_frame_lab
get_field_jit = jit_device(get_field)
if integration_method == IntegrationMethod.MAGNUS_CF4:
@jit_device_template("(float64, float64, float64, float64, float64[:, :], float64, complex128[:])")
def get_field_integration_magnus_cf4(sweep_parameter, time_fine, time_coarse, time_step_integration, field_sample, rotating_wave, rotating_wave_winding):
time_sample = ((time_fine + 0.5*time_step_integration*(1 - 1/sqrt3)) - time_coarse)
rotating_wave_winding[0] = math.cos(math.tau*rotating_wave*time_sample) + 1j*math.sin(math.tau*rotating_wave*time_sample)
time_sample += time_coarse
get_field_jit(time_sample, sweep_parameter, field_sample[0, :])
time_sample = ((time_fine + 0.5*time_step_integration*(1 + 1/sqrt3)) - time_coarse)
rotating_wave_winding[1] = math.cos(math.tau*rotating_wave*time_sample) + 1j*math.sin(math.tau*rotating_wave*time_sample)
time_sample += time_coarse
get_field_jit(time_sample, sweep_parameter, field_sample[1, :])
@jit_device_template("(complex128[:, :], complex128[:, :], float64[:, :], float64, float64, complex128[:])")
def append_exponentiation_integration_magnus_cf4(time_evolution_fine, time_evolution_coarse, field_sample, time_step_integration, rotating_wave, rotating_wave_winding):
transform_frame(field_sample[0, :], rotating_wave, rotating_wave_winding[0])
transform_frame(field_sample[1, :], rotating_wave, rotating_wave_winding[1])
w0 = (1.5 + sqrt3)/6
w1 = (1.5 - sqrt3)/6
field_sample[2, 0] = math.tau*time_step_integration*(w0*field_sample[0, 0] + w1*field_sample[1, 0])
field_sample[2, 1] = math.tau*time_step_integration*(w0*field_sample[0, 1] + w1*field_sample[1, 1])
field_sample[2, 2] = math.tau*time_step_integration*(w0*field_sample[0, 2] + w1*field_sample[1, 2])
if dimension > 2:
field_sample[2, 3] = math.tau*time_step_integration*(w0*field_sample[0, 3] + w1*field_sample[1, 3])
append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_coarse)
field_sample[2, 0] = math.tau*time_step_integration*(w1*field_sample[0, 0] + w0*field_sample[1, 0])
field_sample[2, 1] = math.tau*time_step_integration*(w1*field_sample[0, 1] + w0*field_sample[1, 1])
field_sample[2, 2] = math.tau*time_step_integration*(w1*field_sample[0, 2] + w0*field_sample[1, 2])
if dimension > 2:
field_sample[2, 3] = math.tau*time_step_integration*(w1*field_sample[0, 3] + w0*field_sample[1, 3])
append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_coarse)
get_field_integration = get_field_integration_magnus_cf4
append_exponentiation_integration = append_exponentiation_integration_magnus_cf4
elif integration_method == IntegrationMethod.HALF_STEP:
@jit_device_template("(float64, float64, float64, float64, float64[:, :], float64, complex128[:])")
def get_field_integration_half_step(sweep_parameter, time_fine, time_coarse, time_step_integration, field_sample, rotating_wave, rotating_wave_winding):
time_sample = time_fine - time_coarse
rotating_wave_winding[0] = math.cos(math.tau*rotating_wave*time_sample) + 1j*math.sin(math.tau*rotating_wave*time_sample)
time_sample += time_coarse
get_field_jit(time_sample, sweep_parameter, field_sample[0, :])
time_sample = time_fine + time_step_integration - time_coarse
rotating_wave_winding[1] = math.cos(math.tau*rotating_wave*time_sample) + 1j*math.sin(math.tau*rotating_wave*time_sample)
time_sample += time_coarse
get_field_jit(time_sample, sweep_parameter, field_sample[1, :])
@jit_device_template("(complex128[:, :], complex128[:, :], float64[:, :], float64, float64, complex128[:])")
def append_exponentiation_integration_half_step(time_evolution_fine, time_evolution_coarse, field_sample, time_step_integration, rotating_wave, rotating_wave_winding):
transform_frame(field_sample[0, :], rotating_wave, rotating_wave_winding[0])
transform_frame(field_sample[1, :], rotating_wave, rotating_wave_winding[1])
field_sample[2, 0] = math.tau*time_step_integration*field_sample[0, 0]/2
field_sample[2, 1] = math.tau*time_step_integration*field_sample[0, 1]/2
field_sample[2, 2] = math.tau*time_step_integration*field_sample[0, 2]/2
if dimension > 2:
field_sample[2, 3] = math.tau*time_step_integration*field_sample[0, 3]/2
append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_coarse)
field_sample[2, 0] = math.tau*time_step_integration*field_sample[1, 0]/2
field_sample[2, 1] = math.tau*time_step_integration*field_sample[1, 1]/2
field_sample[2, 2] = math.tau*time_step_integration*field_sample[1, 2]/2
if dimension > 2:
field_sample[2, 3] = math.tau*time_step_integration*field_sample[1, 3]/2
append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_coarse)
get_field_integration = get_field_integration_half_step
append_exponentiation_integration = append_exponentiation_integration_half_step
elif integration_method == IntegrationMethod.MIDPOINT_SAMPLE:
@jit_device_template("(float64, float64, float64, float64, float64[:, :], float64, complex128[:])")
def get_field_integration_midpoint(sweep_parameter, time_fine, time_coarse, time_step_integration, field_sample, rotating_wave, rotating_wave_winding):
time_sample = time_fine + 0.5*time_step_integration - time_coarse
rotating_wave_winding[0] = math.cos(math.tau*rotating_wave*time_sample) + 1j*math.sin(math.tau*rotating_wave*time_sample)
time_sample += time_coarse
get_field_jit(time_sample, sweep_parameter, field_sample[0, :])
@jit_device_template("(complex128[:, :], complex128[:, :], float64[:, :], float64, float64, complex128[:])")
def append_exponentiation_integration_midpoint(time_evolution_fine, time_evolution_coarse, field_sample, time_step_integration, rotating_wave, rotating_wave_winding):
transform_frame(field_sample[0, :], rotating_wave, rotating_wave_winding[0])
field_sample[0, 0] = math.tau*time_step_integration*field_sample[0, 0]
field_sample[0, 1] = math.tau*time_step_integration*field_sample[0, 1]
field_sample[0, 2] = math.tau*time_step_integration*field_sample[0, 2]
if dimension > 2:
field_sample[0, 3] = math.tau*time_step_integration*field_sample[0, 3]
append_exponentiation(field_sample[0, :], time_evolution_fine, time_evolution_coarse)
get_field_integration = get_field_integration_midpoint
append_exponentiation_integration = append_exponentiation_integration_midpoint
@jit_device_template("(int64, float64[:], float64, float64, float64[:], complex128[:, :, :], float64)")
def get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_coarse, sweep_parameter):
# Declare variables
if device_index == 0:
time_evolution_fine = np.empty((dimension, dimension), dtype = np.complex128)
field_sample = np.empty((sample_index_max, lie_dimension), dtype = np.float64)
rotating_wave_winding = np.empty(sample_index_end, dtype = np.complex128)
elif device_index == 1:
time_evolution_fine = cuda.local.array((dimension, dimension), dtype = np.complex128)
field_sample = cuda.local.array((sample_index_max, lie_dimension), dtype = np.float64)
rotating_wave_winding = cuda.local.array(sample_index_end, dtype = np.complex128)
elif device_index == 2:
time_evolution_fine_group = roc.shared.array((threads_per_block, dimension, dimension), dtype = np.complex128)
time_evolution_fine = time_evolution_fine_group[roc.get_local_id(1), :, :]
field_sample_group = roc.shared.array((threads_per_block, sample_index_max, lie_dimension), dtype = np.float64)
field_sample = field_sample_group[roc.get_local_id(1), :, :]
rotating_wave_winding_group = roc.shared.array((threads_per_block, sample_index_end), dtype = np.complex128)
rotating_wave_winding = rotating_wave_winding_group[roc.get_local_id(1), :]
time_coarse[time_index] = time_end_points[0] + time_step_output*time_index
time_fine = time_coarse[time_index]
# Initialise time evolution operator to 1
set_to_one(time_evolution_coarse[time_index, :])
field_sample[0, 2] = 0
if use_rotating_frame:
time_sample = time_coarse[time_index] + time_step_output/2
get_field_jit(time_sample, sweep_parameter, field_sample[0, :])
rotating_wave = field_sample[0, 2]
if dimension == 2:
rotating_wave /= 2
# For every fine step
for time_fine_index in range(math.floor(time_step_output/time_step_integration + 0.5)):
get_field_integration(sweep_parameter, time_fine, time_coarse[time_index], time_step_integration, field_sample, rotating_wave, rotating_wave_winding)
append_exponentiation_integration(time_evolution_fine, time_evolution_coarse[time_index, :], field_sample, time_step_integration, rotating_wave, rotating_wave_winding)
time_fine += time_step_integration
if use_rotating_frame:
# Take out of rotating frame
rotating_wave_winding[0] = math.cos(math.tau*rotating_wave*time_step_output) + 1j*math.sin(math.tau*rotating_wave*time_step_output)
time_evolution_coarse[time_index, 0, 0] /= rotating_wave_winding[0]
time_evolution_coarse[time_index, 0, 1] /= rotating_wave_winding[0]
if dimension > 2:
time_evolution_coarse[time_index, 0, 2] /= rotating_wave_winding[0]
time_evolution_coarse[time_index, 2, 0] *= rotating_wave_winding[0]
time_evolution_coarse[time_index, 2, 1] *= rotating_wave_winding[0]
time_evolution_coarse[time_index, 2, 2] *= rotating_wave_winding[0]
else:
time_evolution_coarse[time_index, 1, 0] *= rotating_wave_winding[0]
time_evolution_coarse[time_index, 1, 1] *= rotating_wave_winding[0]
@jit_host("(float64, float64[:], float64[:], float64, float64, complex128[:, :, :])", max_registers)
def get_time_evolution(sweep_parameter, time_coarse, time_end_points, time_step_integration, time_step_output, time_evolution_coarse):
"""
Find the stepwise time evolution opperator.
Parameters
----------
sweep_parameter : :obj:`float`
time_coarse : :class:`numpy.ndarray` of :class:`numpy.float64` (time_index)
A coarse grained list of time samples that the time evolution operator is found for. In units of s. This is an output, so use an empty :class:`numpy.ndarray` with :func:`numpy.empty()`, or declare a :class:`numpy.ndarray` using :func:`numba.cuda.device_array_like()`.
time_end_points : :class:`numpy.ndarray` of :class:`numpy.float64` (start time (0) or end time (1))
The time values for when the experiment is to start and finishes. In units of s.
time_step_integration : :obj:`float`
The time step used within the integration algorithm. In units of s.
time_step_output : :obj:`float`
The time difference between each element of `time_coarse`. In units of s. Determines the sample rate of the outputs `time_coarse` and `time_evolution_coarse`.
time_evolution_coarse : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, bra_state_index, ket_state_index)
Time evolution operator (matrix) between the current and next timesteps, for each time sampled. See :math:`U(t)` in :ref:`overview_of_simulation_method`. This is an output, so use an empty :class:`numpy.ndarray` with :func:`numpy.empty()`, or declare a :class:`numpy.ndarray` using :func:`numba.cuda.device_array_like()`.
"""
if device_index == 0:
for time_index in nb.prange(time_coarse.size):
get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_coarse, sweep_parameter)
elif device_index == 1:
# Run calculation for each coarse timestep in parallel
time_index = cuda.grid(1)
if time_index < time_coarse.size:
get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_coarse, sweep_parameter)
elif device_index == 2:
# Run calculation for each coarse timestep in parallel
time_index = roc.get_global_id(1)
if time_index < time_coarse.size:
get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_coarse, sweep_parameter)
return
@jit_host("(complex128[:, :], float64[:, :])", max_registers = max_registers)
def get_spin(state, spin):
"""
Calculate each expected spin value in parallel.
For spin half:
.. math::
\\begin{align*}
\\langle F\\rangle(t) = \\begin{pmatrix}
\\Re(\\psi_{+\\frac{1}{2}}(t)\\psi_{-\\frac{1}{2}}(t)^*)\\\\
-\\Im(\\psi_{+\\frac{1}{2}}(t)\\psi_{-\\frac{1}{2}}(t)^*)\\\\
\\frac{1}{2}(|\\psi_{+\\frac{1}{2}}(t)|^2 - |\\psi_{-\\frac{1}{2}}(t)|^2)
\\end{pmatrix}
\\end{align*}
For spin one:
.. math::
\\begin{align*}
\\langle F\\rangle(t) = \\begin{pmatrix}
\\Re(\\sqrt{2}\\psi_{0}(t)^*(\\psi_{+1}(t) + \\psi_{-1}(t))\\\\
-\\Im(\\sqrt{2}\\psi_{0}(t)^*(\\psi_{+1}(t) - \\psi_{-1}(t))\\\\
|\\psi_{+1}(t)|^2 - |\\psi_{-1}(t)|^2
\\end{pmatrix}
\\end{align*}
Parameters
----------
state : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, state_index)
The state (wavefunction) of the spin system in the lab frame, for each time sampled. See :math:`\\psi(t)` in :ref:`overview_of_simulation_method`.
spin : :class:`numpy.ndarray` of :class:`numpy.float64` (time_index, spatial_index)
The expected value for hyperfine spin of the spin system in the lab frame, for each time sampled. Units of :math:`\\hbar`. This is an output, so use an empty :class:`numpy.ndarray` with :func:`numpy.empty()`, or declare a :class:`numpy.ndarray` using :func:`numba.cuda.device_array_like()`.
"""
if device_index == 0:
for time_index in nb.prange(spin.shape[0]):
if dimension == 2:
spin[time_index, 0] = (state[time_index, 0]*conj(state[time_index, 1])).real
spin[time_index, 1] = (1j*state[time_index, 0]*conj(state[time_index, 1])).real
spin[time_index, 2] = 0.5*(state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 1].real**2 - state[time_index, 1].imag**2)
else:
spin[time_index, 0] = (2*conj(state[time_index, 1])*(state[time_index, 0] + state[time_index, 2])/sqrt2).real
spin[time_index, 1] = (2j*conj(state[time_index, 1])*(state[time_index, 0] - state[time_index, 2])/sqrt2).real
spin[time_index, 2] = state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 2].real**2 - state[time_index, 2].imag**2
elif device_index > 0:
if device_index == 1:
time_index = cuda.grid(1)
elif device_index == 1:
time_index = roc.get_global_id(1)
if time_index < spin.shape[0]:
if dimension == 2:
spin[time_index, 0] = (state[time_index, 0]*conj(state[time_index, 1])).real
spin[time_index, 1] = (1j*state[time_index, 0]*conj(state[time_index, 1])).real
spin[time_index, 2] = 0.5*(state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 1].real**2 - state[time_index, 1].imag**2)
else:
spin[time_index, 0] = (2*conj(state[time_index, 1])*(state[time_index, 0] + state[time_index, 2])/sqrt2).real
spin[time_index, 1] = (2j*conj(state[time_index, 1])*(state[time_index, 0] - state[time_index, 2])/sqrt2).real
spin[time_index, 2] = state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 2].real**2 - state[time_index, 2].imag**2
return
def spin_calculator(state):
"""
Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state.
Parameters
----------
state : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)
The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction.
Returns
-------
spin : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)
The expected spin projection (Bloch vector) over time.
"""
if device.index == 0:
spin = np.empty((state.shape[0], 3), np.float64)
get_spin(state, spin)
elif device == Device.CUDA:
spin = cuda.device_array((state.shape[0], 3), np.float64)
blocks_per_grid = (state.shape[0] + (threads_per_block - 1)) // threads_per_block
get_spin[blocks_per_grid, threads_per_block](cuda.to_device(state), spin)
spin = spin.copy_to_host()
elif device == Device.ROC:
spin = roc.device_array((state.shape[0], 3), np.float64)
blocks_per_grid = (state.shape[0] + (threads_per_block - 1)) // threads_per_block
get_spin[blocks_per_grid, threads_per_block](roc.to_device(state), spin)
spin = spin.copy_to_host()
return spin
self.get_time_evolution_raw = get_time_evolution
self.spin_calculator = spin_calculator
def evaluate(self, sweep_parameter, time_start, time_end, time_step_integration, time_step_output, state_init):
"""
Integrates the time dependent Schroedinger equation and returns the quantum state of the spin system over time.
Parameters
----------
sweep_parameter : :obj:`float`
The input to the `get_field` function supplied by the user. Modifies the field function so the integrator can be used for many experiments, without the need for slow recompilation. For example, if the `sweep_parameter` is used to define the bias field strength in `get_field`, then one can run many simulations, sweeping through bias values, by calling this method multiple times, each time varying `sweep_parameter`.
time_start : :obj:`float`
The time offset that the experiment is to start at. Measured in s.
time_end : :obj:`float`
The time that the experiment is to finish at. Measured in s. The duration of the experiment is `time_end - time_start`.
time_step_integration : :obj:`float`
The integration time step. Measured in s.
time_step_output : :obj:`float`
The sample resolution of the output timeseries for the state. Must be a whole number multiple of `time_step_integration`. Measured in s.
state_init : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (magnetic_quantum_number)
The initial quantum state of the spin system, written in terms of the eigenstates of the spin projection operator in the z direction.
Returns
-------
results : :obj:`Results`
An object containing the results of the simulation.
"""
if math.fabs(time_step_output/time_step_integration - round(time_step_output/time_step_integration)) > 1e-6:
print(f"\033[33mspinsim warning: time_step_output not an integer multiple of time_step_integration. Resetting time_step_integration to {time_step_output/round(time_step_output/time_step_integration):8.4e}.\033[0m\n")
time_step_integration = time_step_output/round(time_step_output/time_step_integration)
time_end_points = np.asarray([time_start, time_end], np.float64)
state_init = np.asarray(state_init, np.complex128)
time_index_max = int((time_end_points[1] - time_end_points[0])/time_step_output)
if self.device.index == 0:
time = np.empty(time_index_max, np.float64)
time_evolution_coarse = np.empty((time_index_max, self.spin_quantum_number.dimension, self.spin_quantum_number.dimension), np.complex128)
self.get_time_evolution_raw(sweep_parameter, time, time_end_points, time_step_integration, time_step_output, time_evolution_coarse)
elif self.device == Device.CUDA:
time = cuda.device_array(time_index_max, np.float64)
time_evolution_coarse = cuda.device_array((time_index_max, self.spin_quantum_number.dimension, self.spin_quantum_number.dimension), np.complex128)
blocks_per_grid = (time.size + (self.threads_per_block - 1)) // self.threads_per_block
try:
self.get_time_evolution_raw[blocks_per_grid, self.threads_per_block](sweep_parameter, time, time_end_points, time_step_integration, time_step_output, time_evolution_coarse)
except:
print("\033[31mspinsim error: numba.cuda could not jit get_field function into a cuda device function.\033[0m\n")
raise
time_evolution_coarse = time_evolution_coarse.copy_to_host()
time = time.copy_to_host()
elif self.device == Device.ROC:
time = roc.device_array(time_index_max, np.float64)
time_evolution_coarse = roc.device_array((time_index_max, self.spin_quantum_number.dimension, self.spin_quantum_number.dimension), np.complex128)
blocks_per_grid = (time.size + (self.threads_per_block - 1)) // self.threads_per_block
try:
self.get_time_evolution_raw[blocks_per_grid, self.threads_per_block](sweep_parameter, time, time_end_points, time_step_integration, time_step_output, time_evolution_coarse)
except:
print("\033[31mspinsim error: numba.roc could not jit get_field function into a roc device function.\033[0m\n")
raise
time_evolution_coarse = time_evolution_coarse.copy_to_host()
time = time.copy_to_host()
state = np.empty((time_index_max, self.spin_quantum_number.dimension), np.complex128)
self.get_state(state_init, state, time_evolution_coarse)
results = Results(time, time_evolution_coarse, state, self.spin_calculator)
return results
@staticmethod
@nb.njit
def get_state(state_init, state, time_evolution):
"""
Use the stepwise time evolution operators in succession to find the quantum state timeseries of the 3 level atom.
Parameters
----------
state_init : :class:`numpy.ndarray` of :class:`numpy.complex128`
The state (spin wavefunction) of the system at the start of the simulation.
state : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, state_index)
The state (wavefunction) of the spin system in the lab frame, for each time sampled.
time_evolution : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, bra_state_index, ket_state_index)
The evaluated time evolution operator between each time step. See :ref:`architecture` for some information.
"""
for time_index in range(state.shape[0]):
# State = time evolution * previous state
for x_index in nb.prange(state.shape[1]):
state[time_index, x_index] = 0
if time_index > 0:
for z_index in range(state.shape[1]):
state[time_index, x_index] += time_evolution[time_index - 1, x_index, z_index]*state[time_index - 1, z_index]
else:
state[time_index, x_index] += state_init[x_index]
sqrt2 = math.sqrt(2)
sqrt3 = math.sqrt(3)
machine_epsilon = np.finfo(np.float64).eps*1000
class Utilities:
"""
A on object that contains definitions of all of the device functions (functions compiled for use on the target device) used in the integrator. These device functions are compiled for the chosen target device on construction of the object.
Attributes
----------
conj(z) : :obj:`callable`
Conjugate of a complex number.
.. math::
\\begin{align*}
(a + ib)^* &= a - ib\\\\
a, b &\\in \\mathbb{R}
\\end{align*}
Parameters:
* **z** (:class:`numpy.complex128`) - The complex number to take the conjugate of.
Returns
* **cz** (:class:`numpy.complex128`) - The conjugate of z.
complex_abs(z) : :obj:`callable`
The absolute value of a complex number.
.. math::
\\begin{align*}
|a + ib| &= \\sqrt{a^2 + b^2}\\\\
a, b &\\in \\mathbb{R}
\\end{align*}
Parameters:
* **z** (:class:`numpy.complex128`) - The complex number to take the absolute value of.
Returns
* **az** (:class:`numpy.float64`) - The absolute value of z.
norm2(z) : :obj:`callable`
The 2 norm of a complex vector.
.. math::
\|a + ib\|_2 = \\sqrt {\\left(\\sum_i a_i^2 + b_i^2\\right)}
Parameters:
* **z** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (index)) - The vector to take the 2 norm of.
Returns
* **nz** (:class:`numpy.float64`) - The 2 norm of z.
inner(left, right) : :obj:`callable`
The inner (maths convention dot) product between two complex vectors.
.. note::
The mathematics definition is used here rather than the physics definition, so the left vector is conjugated. Thus the inner product of two orthogonal vectors is 0.
.. math::
\\begin{align*}
l \\cdot r &\\equiv \\langle l, r \\rangle\\\\
l \\cdot r &= \\sum_i (l_i)^* r_i
\\end{align*}
Parameters:
* **left** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (index)) - The vector to left multiply in the inner product.
* **right** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (index)) - The vector to right multiply in the inner product.
Returns
* **d** (:class:`numpy.complex128`) - The inner product of l and r.
set_to(operator, result) : :obj:`callable`
Copy the contents of one matrix into another.
.. math::
(A)_{i, j} = (B)_{i, j}
Parameters:
* **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to copy from.
* **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to copy to.
set_to_one(operator) : :obj:`callable`
Make a matrix the multiplicative identity, ie, :math:`1`.
.. math::
\\begin{align*}
(A)_{i, j} &= \\delta_{i, j}\\\\
&= \\begin{cases}
1,&i = j\\\\
0,&i\\neq j
\\end{cases}
\\end{align*}
Parameters:
* **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to set to :math:`1`.
set_to_zero(operator) : :obj:`callable`
Make a matrix the additive identity, ie, :math:`0`.
.. math::
\\begin{align*}
(A)_{i, j} = 0
\\end{align*}
Parameters:
* **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to set to :math:`0`.
matrix_multiply(left, right, result) : :obj:`callable`
Multiply matrices left and right together, to be returned in result.
.. math::
\\begin{align*}
(LR)_{i,k} = \\sum_j (L)_{i,j} (R)_{j,k}
\\end{align*}
Parameters:
* **left** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to left multiply by.
* **right** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to right multiply by.
* **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - A matrix to be filled with the result of the product.
adjoint(operator) : :obj:`callable`
Takes the hermitian adjoint of a matrix.
.. math::
\\begin{align*}
A^\\dagger &\\equiv A^H\\\\
(A^\\dagger)_{y,x} &= ((A)_{x,y})^*
\\end{align*}
Matrix can be in :math:`\\mathbb{C}^{2\\times2}` or :math:`\\mathbb{C}^{3\\times3}`.
Parameters:
* **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The operator to take the adjoint of.
* **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - An array to write the resultant adjoint to.
matrix_exponential_analytic(field_sample, result) : :obj:`callable`
Calculates a :math:`\\mathfrak{su}(2)` matrix exponential based on its analytic form.
.. warning::
Only available for use with spin half systems. Will not work with spin one systems.
Assumes the exponent is an imaginary linear combination of :math:`\\mathfrak{su}(2)`, being,
.. math::
\\begin{align*}
A &= -i(x J_x + y J_y + z J_z),
\\end{align*}
with
.. math::
\\begin{align*}
J_x &= \\frac{1}{2}\\begin{pmatrix}
0 & 1 \\\\
1 & 0
\\end{pmatrix},&
J_y &= \\frac{1}{2}\\begin{pmatrix}
0 & -i \\\\
i & 0
\\end{pmatrix},&
J_z &= \\frac{1}{2}\\begin{pmatrix}
1 & 0 \\\\
0 & -1
\\end{pmatrix}
\\end{align*}
Then the exponential can be calculated as
.. math::
\\begin{align*}
\\exp(A) &= \\exp(-ix J_x - iy J_y - iz J_z)\\\\
&= \\begin{pmatrix}
\\cos(\\frac{r}{2}) - i\\frac{z}{r}\\sin(\\frac{r}{2}) & -\\frac{y + ix}{r}\\sin(\\frac{r}{2})\\\\
\\frac{y - ix}{r}\\sin(\\frac{r}{2}) & \\cos(\\frac{r}{2}) + i\\frac{z}{r}\\sin(\\frac{r}{2})
\\end{pmatrix}
\\end{align*}
with :math:`r = \\sqrt{x^2 + y^2 + z^2}`.
Parameters:
* **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64`, (y_index, x_index)) - The values of x, y and z respectively, as described above.
* **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix which the result of the exponentiation is to be written to.
matrix_exponential_lie_trotter(field_sample, result) : :obj:`callable`
Calculates a matrix exponential based on the Lie Product Formula,
.. math::
\\exp(A + B) = \\lim_{c \\to \\infty} \\left(\\exp\\left(\\frac{1}{c}A\\right) \\exp\\left(\\frac{1}{c}B\\right)\\right)^c.
**For spin half systems:**
Assumes the exponent is an imaginary linear combination of a subspace of :math:`\\mathfrak{su}(2)`, being,
.. math::
\\begin{align*}
A &= -i(x J_x + y J_y + z J_z),
\\end{align*}
with
.. math::
\\begin{align*}
J_x &= \\frac{1}{2}\\begin{pmatrix}
0 & 1 \\\\
1 & 0
\\end{pmatrix},&
J_y &= \\frac{1}{2}\\begin{pmatrix}
0 & -i \\\\
i & 0
\\end{pmatrix},&
J_z &= \\frac{1}{2}\\begin{pmatrix}
1 & 0 \\\\
0 & -1
\\end{pmatrix}
\\end{align*}
Then the exponential can be approximated as, for large :math:`\\tau`,
.. math::
\\begin{align*}
\\exp(A) &= \\exp(-ix J_x - iy J_y - iz J_z)\\\\
&= \\exp(2^{-\\tau}(-ix J_x - iy J_y - iz J_z))^{2^\\tau}\\\\
&\\approx (\\exp(-i(2^{-\\tau} x) J_x) \\exp(-i(2^{-\\tau} y) J_y) \\exp(-i(2^{-\\tau} z) J_z)^{2^\\tau}\\\\
&= \\begin{pmatrix}
(c_Xc_Y - is_Xs_Y) e^{-iZ} &
-(c_Xs_Y + is_Xc_Y) e^{iZ} \\\\
(c_Xs_Y - is_Xc_Y) e^{-iZ} &
(c_Xc_Y + is_Xs_Y) e^{iZ}
\\end{pmatrix}^{2^\\tau}\\\\
&= T^{2^\\tau},
\\end{align*}
with
.. math::
\\begin{align*}
X &= \\frac{1}{2}2^{-\\tau}x,\\\\
Y &= \\frac{1}{2}2^{-\\tau}y,\\\\
Z &= \\frac{1}{2}2^{-\\tau}z,\\\\
c_{\\theta} &= \\cos(\\theta),\\\\
s_{\\theta} &= \\sin(\\theta).
\\end{align*}
**For spin one systems**
Assumes the exponent is an imaginary linear combination of a subspace of :math:`\\mathfrak{su}(3)`, being,
.. math::
\\begin{align*}
A &= -i(x J_x + y J_y + z J_z + q J_q),
\\end{align*}
with
.. math::
\\begin{align*}
J_x &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix}
0 & 1 & 0 \\\\
1 & 0 & 1 \\\\
0 & 1 & 0
\\end{pmatrix},&
J_y &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix}
0 & -i & 0 \\\\
i & 0 & -i \\\\
0 & i & 0
\\end{pmatrix},\\\\
J_z &= \\begin{pmatrix}
1 & 0 & 0 \\\\
0 & 0 & 0 \\\\
0 & 0 & -1
\\end{pmatrix},&
J_q &= \\frac{1}{3}\\begin{pmatrix}
1 & 0 & 0 \\\\
0 & -2 & 0 \\\\
0 & 0 & 1
\\end{pmatrix}
\\end{align*}
Then the exponential can be approximated as, for large :math:`\\tau`,
.. math::
\\begin{align*}
\\exp(A) &= \\exp(-ix J_x - iy J_y - iz J_z - iq J_q)\\\\
&= \\exp(2^{-\\tau}(-ix J_x - iy J_y - iz J_z - iq J_q))^{2^\\tau}\\\\
&\\approx (\\exp(-i(2^{-\\tau} x) J_x) \\exp(-i(2^{-\\tau} y) J_y) \\exp(-i(2^{-\\tau} z J_z + (2^{-\\tau} q) J_q)))^{2^\\tau}\\\\
&= \\begin{pmatrix}
\\frac{e^{-i\\left(Z + \\frac{Q}{3}\\right)}(c_X + c_Y - i s_Xs_Y)}{2} & \\frac{e^{i\\frac{2Q}{3}} (-s_Y -i c_Y s_X)}{\\sqrt{2}} & \\frac{e^{-i\\left(-Z + \\frac{Q}{3}\\right)}(c_X - c_Y + i s_Xs_Y)}{2} \\\\
\\frac{e^{-i\\left(Z + \\frac{Q}{3}\\right)} (-i s_X + c_X s_Y)}{\\sqrt{2}} & e^{i\\frac{2Q}{3}} c_X c_Y & \\frac{e^{-i(Z - \\frac{Q}{3})} (-i s_X - c_X s_Y)}{\\sqrt{2}} \\\\
\\frac{e^{-i\\left(Z + \\frac{Q}{3}\\right)}(c_X - c_Y - i s_Xs_Y)}{2} & \\frac{e^{i\\frac{2Q}{3}} (s_Y -i c_Y s_X)}{\\sqrt{2}} & \\frac{e^{-i\\left(-Z + \\frac{Q}{3}\\right)}(c_X + c_Y + i s_Xs_Y)}{2}
\\end{pmatrix}^{2^\\tau}\\\\
&= T^{2^\\tau},
\\end{align*}
with
.. math::
\\begin{align*}
X &= 2^{-\\tau}x,\\\\
Y &= 2^{-\\tau}y,\\\\
Z &= 2^{-\\tau}z,\\\\
Q &= 2^{-\\tau}q,\\\\
c_{\\theta} &= \\cos(\\theta),\\\\
s_{\\theta} &= \\sin(\\theta).
\\end{align*}
Once :math:`T` is calculated, it is then recursively squared :math:`\\tau` times to obtain :math:`\\exp(A)`.
Parameters:
* **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64`, (y_index, x_index)) - The values of x, y and z (and q for spin one) respectively, as described above.
* **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix which the result of the exponentiation is to be written to.
* **trotter_cutoff** (:obj:`int`) - The number of squares to make to the approximate matrix (:math:`\\tau` above).
"""
def __init__(self, spin_quantum_number, device, threads_per_block):
"""
Parameters
----------
spin_quantum_number : :obj:`SpinQuantumNumber`
The option to select whether the simulator will integrate a spin half :obj:`SpinQuantumNumber.HALF`, or spin one :obj:`SpinQuantumNumber.ONE` quantum system.
device : :obj:`Device`
The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details.
threads_per_block : :obj:`int`
The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models.
"""
jit_device = device.jit_device
device_index = device.index
@jit_device
def conj(z):
return (z.real - 1j*z.imag)
@jit_device
def complex_abs(z):
return math.sqrt(z.real**2 + z.imag**2)
if spin_quantum_number == SpinQuantumNumber.HALF:
@jit_device
def norm2(z):
return math.sqrt(z[0].real**2 + z[0].imag**2 + z[1].real**2 + z[1].imag**2)
@jit_device
def inner(left, right):
return conj(left[0])*right[0] + conj(left[1])*right[1]
@jit_device
def set_to(operator, result):
result[0, 0] = operator[0, 0]
result[1, 0] = operator[1, 0]
result[0, 1] = operator[0, 1]
result[1, 1] = operator[1, 1]
@jit_device
def set_to_one(operator):
operator[0, 0] = 1
operator[1, 0] = 0
operator[0, 1] = 0
operator[1, 1] = 1
@jit_device
def set_to_zero(operator):
operator[0, 0] = 0
operator[1, 0] = 0
operator[0, 1] = 0
operator[1, 1] = 0
@jit_device
def matrix_multiply(left, right, result):
result[0, 0] = left[0, 0]*right[0, 0] + left[0, 1]*right[1, 0]
result[1, 0] = left[1, 0]*right[0, 0] + left[1, 1]*right[1, 0]
result[0, 1] = left[0, 0]*right[0, 1] + left[0, 1]*right[1, 1]
result[1, 1] = left[1, 0]*right[0, 1] + left[1, 1]*right[1, 1]
@jit_device
def matrix_square_residual(operator, result):
result[0, 0] = (2 + operator[0, 0])*operator[0, 0] + operator[0, 1]*operator[1, 0]
result[1, 0] = operator[1, 0]*operator[0, 0] + (2 + operator[1, 1])*operator[1, 0]
result[0, 1] = (2 + operator[0, 0])*operator[0, 1] + operator[0, 1]*operator[1, 1]
result[1, 1] = operator[1, 0]*operator[0, 1] + (2 + operator[1, 1])*operator[1, 1]
@jit_device
def adjoint(operator, result):
result[0, 0] = conj(operator[0, 0])
result[1, 0] = conj(operator[0, 1])
result[0, 1] = conj(operator[1, 0])
result[1, 1] = conj(operator[1, 1])
@jit_device
def matrix_exponential_analytic(field_sample, result):
x = field_sample[0]
y = field_sample[1]
z = field_sample[2]
r = math.sqrt(x**2 + y**2 + z**2)
if r > 0:
x /= r
y /= r
z /= r
c = math.cos(r/2)
s = math.sin(r/2)
result[0, 0] = c - 1j*z*s
result[1, 0] = (y - 1j*x)*s
result[0, 1] = -(y + 1j*x)*s
result[1, 1] = c + 1j*z*s
else:
result[0, 0] = 1
result[1, 0] = 0
result[0, 1] = 0
result[1, 1] = 1
@jit_device
def matrix_exponential_lie_trotter(field_sample, result, trotter_cutoff):
hyper_cube_amount = math.ceil(trotter_cutoff/2)
if hyper_cube_amount < 0:
hyper_cube_amount = 0
precision = 4**hyper_cube_amount
a = math.sqrt(field_sample[0]*field_sample[0] + field_sample[1]*field_sample[1])
if a > 0:
ep = (field_sample[0] + 1j*field_sample[1])/a
else:
ep = 1
a = a/precision
Ca = math.cos(a/2)
Sa = -1j*math.sin(a/2)
ez = field_sample[2]/(2*precision)
ez = math.cos(ez) + 1j*math.sin(ez)
# eq = field_sample[3]/(6*precision)
# eq = math.cos(eq) + 1j*math.sin(eq)
result[0, 0] = Ca/ez - 1
result[1, 0] = Sa*ep
result[0, 1] = Sa/ep
result[1, 1] = Ca*ez - 1
if device_index == 0:
temporary = np.empty((2, 2), dtype = np.complex128)
elif device_index == 1:
temporary = cuda.local.array((2, 2), dtype = np.complex128)
elif device_index == 2:
temporary_group = roc.shared.array((threads_per_block, 2, 2), dtype = np.complex128)
temporary = temporary_group[roc.get_local_id(1), :, :]
for power_index in range(hyper_cube_amount):
matrix_square_residual(result, temporary)
matrix_square_residual(temporary, result)
# matrix_multiply(result, result, temporary)
# matrix_multiply(temporary, temporary, result)
result[0, 0] += 1
result[1, 1] += 1
# @jit_device
# def matrix_exponential_lie_trotter(field_sample, result, trotter_cutoff):
# hyper_cube_amount = math.ceil(trotter_cutoff/2)
# if hyper_cube_amount < 0:
# hyper_cube_amount = 0
# precision = 4**hyper_cube_amount
# x = field_sample[0]/(2*precision)
# y = field_sample[1]/(2*precision)
# z = field_sample[2]/(2*precision)
# cx = math.cos(x)
# sx = math.sin(x)
# cy = math.cos(y)
# sy = math.sin(y)
# cisz = math.cos(z) + 1j*math.sin(z)
# result[0, 0] = (cx*cy - 1j*sx*sy)/cisz
# result[1, 0] = (cx*sy -1j*sx*cy)/cisz
# result[0, 1] = -(cx*sy + 1j*sx*cy)*cisz
# result[1, 1] = (cx*cy + 1j*sx*sy)*cisz
# if device_index == 0:
# temporary = np.empty((2, 2), dtype = np.complex128)
# elif device_index == 1:
# temporary = cuda.local.array((2, 2), dtype = np.complex128)
# elif device_index == 2:
# temporary_group = roc.shared.array((threads_per_block, 2, 2), dtype = np.complex128)
# temporary = temporary_group[roc.get_local_id(1), :, :]
# for power_index in range(hyper_cube_amount):
# matrix_multiply(result, result, temporary)
# matrix_multiply(temporary, temporary, result)
else:
@jit_device
def norm2(z):
return math.sqrt(z[0].real**2 + z[0].imag**2 + z[1].real**2 + z[1].imag**2 + z[2].real**2 + z[2].imag**2)
@jit_device
def cross(left, right, result):
result[0] = conj(left[1]*right[2] - left[2]*right[1])
result[1] = conj(left[2]*right[0] - left[0]*right[2])
result[2] = conj(left[0]*right[1] - left[1]*right[0])
@jit_device
def inner(left, right):
return conj(left[0])*right[0] + conj(left[1])*right[1] + conj(left[2])*right[2]
@jit_device
def set_to(operator, result):
result[0, 0] = operator[0, 0]
result[1, 0] = operator[1, 0]
result[2, 0] = operator[2, 0]
result[0, 1] = operator[0, 1]
result[1, 1] = operator[1, 1]
result[2, 1] = operator[2, 1]
result[0, 2] = operator[0, 2]
result[1, 2] = operator[1, 2]
result[2, 2] = operator[2, 2]
@jit_device
def set_to_one(operator):
operator[0, 0] = 1
operator[1, 0] = 0
operator[2, 0] = 0
operator[0, 1] = 0
operator[1, 1] = 1
operator[2, 1] = 0
operator[0, 2] = 0
operator[1, 2] = 0
operator[2, 2] = 1
@jit_device
def set_to_zero(operator):
operator[0, 0] = 0
operator[1, 0] = 0
operator[2, 0] = 0
operator[0, 1] = 0
operator[1, 1] = 0
operator[2, 1] = 0
operator[0, 2] = 0
operator[1, 2] = 0
operator[2, 2] = 0
@jit_device
def matrix_multiply(left, right, result):
result[0, 0] = left[0, 0]*right[0, 0] + left[0, 1]*right[1, 0] + left[0, 2]*right[2, 0]
result[1, 0] = left[1, 0]*right[0, 0] + left[1, 1]*right[1, 0] + left[1, 2]*right[2, 0]
result[2, 0] = left[2, 0]*right[0, 0] + left[2, 1]*right[1, 0] + left[2, 2]*right[2, 0]
result[0, 1] = left[0, 0]*right[0, 1] + left[0, 1]*right[1, 1] + left[0, 2]*right[2, 1]
result[1, 1] = left[1, 0]*right[0, 1] + left[1, 1]*right[1, 1] + left[1, 2]*right[2, 1]
result[2, 1] = left[2, 0]*right[0, 1] + left[2, 1]*right[1, 1] + left[2, 2]*right[2, 1]
result[0, 2] = left[0, 0]*right[0, 2] + left[0, 1]*right[1, 2] + left[0, 2]*right[2, 2]
result[1, 2] = left[1, 0]*right[0, 2] + left[1, 1]*right[1, 2] + left[1, 2]*right[2, 2]
result[2, 2] = left[2, 0]*right[0, 2] + left[2, 1]*right[1, 2] + left[2, 2]*right[2, 2]
@jit_device
def matrix_square_residual(operator, result):
result[0, 0] = (2 + operator[0, 0])*operator[0, 0] + operator[0, 1]*operator[1, 0] + operator[0, 2]*operator[2, 0]
result[1, 0] = operator[1, 0]*operator[0, 0] + (2 + operator[1, 1])*operator[1, 0] + operator[1, 2]*operator[2, 0]
result[2, 0] = operator[2, 0]*operator[0, 0] + operator[2, 1]*operator[1, 0] + (2 + operator[2, 2])*operator[2, 0]
result[0, 1] = (2 + operator[0, 0])*operator[0, 1] + operator[0, 1]*operator[1, 1] + operator[0, 2]*operator[2, 1]
result[1, 1] = operator[1, 0]*operator[0, 1] + (2 + operator[1, 1])*operator[1, 1] + operator[1, 2]*operator[2, 1]
result[2, 1] = operator[2, 0]*operator[0, 1] + operator[2, 1]*operator[1, 1] + (2 + operator[2, 2])*operator[2, 1]
result[0, 2] = (2 + operator[0, 0])*operator[0, 2] + operator[0, 1]*operator[1, 2] + operator[0, 2]*operator[2, 2]
result[1, 2] = operator[1, 0]*operator[0, 2] + (2 + operator[1, 1])*operator[1, 2] + operator[1, 2]*operator[2, 2]
result[2, 2] = operator[2, 0]*operator[0, 2] + operator[2, 1]*operator[1, 2] + (2 + operator[2, 2])*operator[2, 2]
@jit_device
def adjoint(operator, result):
result[0, 0] = conj(operator[0, 0])
result[1, 0] = conj(operator[0, 1])
result[2, 0] = conj(operator[0, 2])
result[0, 1] = conj(operator[1, 0])
result[1, 1] = conj(operator[1, 1])
result[2, 1] = conj(operator[1, 2])
result[0, 2] = conj(operator[2, 0])
result[1, 2] = conj(operator[2, 1])
result[2, 2] = conj(operator[2, 2])
@jit_device
def matrix_exponential_analytic(field_sample, result, trotter_cutoff):
pass
@jit_device
def matrix_exponential_lie_trotter(field_sample, result, trotter_cutoff):
hyper_cube_amount = math.ceil(trotter_cutoff/2)
if hyper_cube_amount < 0:
hyper_cube_amount = 0
precision = 4**hyper_cube_amount
a = math.sqrt(field_sample[0]*field_sample[0] + field_sample[1]*field_sample[1])
if a > 0:
ep = (field_sample[0] + 1j*field_sample[1])/a
else:
ep = 1
a = a/precision
Ca = math.cos(a/2)
Sa = math.sin(a/2)
ca = math.cos(a)
sa = -1j*math.sin(a)/sqrt2
ez = field_sample[2]/(2*precision)
ez = math.cos(ez) + 1j*math.sin(ez)
eq = field_sample[3]/(6*precision)
eq = math.cos(eq) + 1j*math.sin(eq)
# Ca = 1
# Sa = a/2
# ca = 1
# sa = -1j*a/sqrt2
# ez = field_sample[2]/(2*precision)
# ez = 1 + 1j*ez
# eq = field_sample[3]/(6*precision)
# eq = 1 + 1j*eq
result[0, 0] = (Ca/(eq*ez))*(Ca/(eq*ez)) - 1
result[1, 0] = sa*eq*ep/ez
result[2, 0] = -((Sa*ep/eq)*(Sa*ep/eq))
result[0, 1] = sa*eq/(ez*ep)
result[1, 1] = ca*(eq*eq*eq*eq) - 1
result[2, 1] = sa*eq*ez*ep
result[0, 2] = -((Sa*eq/ep)*(Sa*eq/ep))
result[1, 2] = sa*eq*ez/ep
result[2, 2] = (Ca*ez/eq)*(Ca*ez/eq) - 1
if device_index == 0:
temporary = np.empty((3, 3), dtype = np.complex128)
elif device_index == 1:
temporary = cuda.local.array((3, 3), dtype = np.complex128)
elif device_index == 2:
temporary_group = roc.shared.array((threads_per_block, 3, 3), dtype = np.complex128)
temporary = temporary_group[roc.get_local_id(1), :, :]
for power_index in range(hyper_cube_amount):
matrix_square_residual(result, temporary)
matrix_square_residual(temporary, result)
result[0, 0] += 1
result[1, 1] += 1
result[2, 2] += 1
# @jit_device
# def matrix_exponential_lie_trotter(field_sample, result, trotter_cutoff):
# hyper_cube_amount = math.ceil(trotter_cutoff/2)
# if hyper_cube_amount < 0:
# hyper_cube_amount = 0
# precision = 4**hyper_cube_amount
# x = field_sample[0]/precision
# y = field_sample[1]/precision
# z = field_sample[2]/precision
# q = field_sample[3]/precision
# cx = math.cos(x)
# sx = math.sin(x)
# cy = math.cos(y)
# sy = math.sin(y)
# cisz = math.cos(z + q/3) - 1j*math.sin(z + q/3)
# result[0, 0] = 0.5*cisz*(cx + cy - 1j*sx*sy)
# result[1, 0] = cisz*(-1j*sx + cx*sy)/sqrt2
# result[2, 0] = 0.5*cisz*(cx - cy - 1j*sx*sy)
# cisz = math.cos(2*q/3) + 1j*math.sin(2*q/3)
# result[0, 1] = cisz*(-sy - 1j*cy*sx)/sqrt2
# result[1, 1] = cisz*cx*cy
# result[2, 1] = cisz*(sy - 1j*cy*sx)/sqrt2
# cisz = math.cos(z - q/3) + 1j*math.sin(z - q/3)
# result[0, 2] = 0.5*cisz*(cx - cy + 1j*sx*sy)
# result[1, 2] = cisz*(-1j*sx - cx*sy)/sqrt2
# result[2, 2] = 0.5*cisz*(cx + cy + 1j*sx*sy)
# if device_index == 0:
# temporary = np.empty((3, 3), dtype = np.complex128)
# elif device_index == 1:
# temporary = cuda.local.array((3, 3), dtype = np.complex128)
# elif device_index == 2:
# temporary_group = roc.shared.array((threads_per_block, 3, 3), dtype = np.complex128)
# temporary = temporary_group[roc.get_local_id(1), :, :]
# for power_index in range(hyper_cube_amount):
# matrix_multiply(result, result, temporary)
# matrix_multiply(temporary, temporary, result)
self.conj = conj
self.complex_abs = complex_abs
self.norm2 = norm2
self.inner = inner
self.set_to = set_to
self.set_to_one = set_to_one
self.set_to_zero = set_to_zero
self.matrix_multiply = matrix_multiply
self.adjoint = adjoint
self.matrix_exponential_analytic = matrix_exponential_analytic
self.matrix_exponential_lie_trotter = matrix_exponential_lie_trotter
self.matrix_square_residual = matrix_square_residual
| 53.108059
| 851
| 0.594533
| 11,351
| 86,991
| 4.386133
| 0.060523
| 0.030269
| 0.020989
| 0.009922
| 0.815976
| 0.768675
| 0.730472
| 0.704059
| 0.687228
| 0.660895
| 0
| 0.030821
| 0.290225
| 86,991
| 1,638
| 852
| 53.108059
| 0.775525
| 0.436585
| 0
| 0.483163
| 0
| 0.007321
| 0.03686
| 0.003332
| 0
| 0
| 0
| 0
| 0
| 1
| 0.105417
| false
| 0.001464
| 0.008785
| 0.032211
| 0.196193
| 0.007321
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
05cd5b57f0a4fc900a153814359c496aef92a9b8
| 322
|
py
|
Python
|
fnss/adapters/__init__.py
|
brucespang/fnss
|
8e1d95744347afa77383092e6f144980d84e222d
|
[
"BSD-2-Clause"
] | 114
|
2015-01-19T14:15:07.000Z
|
2022-02-22T01:47:19.000Z
|
fnss/adapters/__init__.py
|
brucespang/fnss
|
8e1d95744347afa77383092e6f144980d84e222d
|
[
"BSD-2-Clause"
] | 15
|
2016-02-11T09:09:02.000Z
|
2021-04-05T12:57:09.000Z
|
fnss/adapters/__init__.py
|
brucespang/fnss
|
8e1d95744347afa77383092e6f144980d84e222d
|
[
"BSD-2-Clause"
] | 36
|
2015-02-08T12:28:04.000Z
|
2021-11-19T06:08:17.000Z
|
"""Tools for exporting and importing FNSS data structures (topologies,
event schedules and traffic matrices) to/from other simulators or emulators
"""
from fnss.adapters.autonetkit import *
from fnss.adapters.mn import *
from fnss.adapters.ns2 import *
from fnss.adapters.omnetpp import *
from fnss.adapters.jfed import *
| 35.777778
| 75
| 0.798137
| 45
| 322
| 5.711111
| 0.577778
| 0.155642
| 0.311284
| 0.342412
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003546
| 0.124224
| 322
| 8
| 76
| 40.25
| 0.907801
| 0.444099
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
05eb0737013725c24915e89b0934dc5569241fb4
| 1,708
|
py
|
Python
|
tests/data/sample/__init__.py
|
SD2E/python-datacatalog
|
51ab366639505fb6e8a14cd6b446de37080cd20d
|
[
"CNRI-Python"
] | null | null | null |
tests/data/sample/__init__.py
|
SD2E/python-datacatalog
|
51ab366639505fb6e8a14cd6b446de37080cd20d
|
[
"CNRI-Python"
] | 2
|
2019-07-25T15:39:04.000Z
|
2019-10-21T15:31:46.000Z
|
tests/data/sample/__init__.py
|
SD2E/python-datacatalog
|
51ab366639505fb6e8a14cd6b446de37080cd20d
|
[
"CNRI-Python"
] | 1
|
2019-10-15T14:33:44.000Z
|
2019-10-15T14:33:44.000Z
|
CREATES = [
('expt1', {'sample_id': 'biofab.sample.900000', 'child_of': ['102edd93-29d6-5483-b60b-8dfd4d094b9c']}, '103dfcc6-7dd8-54a1-b5d7-c36129511173'),
('expt2', {'sample_id': 'ginkgo.sample.ABCDEFG', 'child_of': ['102d8a08-034d-5c27-8d9c-a24bfcc94858']}, '1031e39a-ac24-57b6-8fb3-b8fb65708654'),
('expt3', {'sample_id': 'tacc.sample.8675309', 'child_of': ['1027aa77-d524-5359-a802-a8008adaecb5']}, '103b4050-f7dc-5680-8445-cd14e092445a')
]
CLEAN = [
('expt41', {'sample_id': 'sample1.lab.experiment.lab.4', 'control_type': 'HIGH_FITC', 'standard_type':'BEAD_FLUORESCENCE', 'child_of': ['102edd93-29d6-5483-b60b-8dfd4d094b9c']}, '1dcbf4fc-63f0-403d-9a3b-a4d9838b00b0'),
('expt42', {'sample_id': 'sample2.lab.experiment.lab.4', 'child_of': ['102edd93-29d6-5483-b60b-8dfd4d094b9c']}, '596521f3-a72c-4b05-a315-34b51cde25de'),
('expt51', {'sample_id': 'sample1.lab.experiment.lab.5', 'child_of': ['102d8a08-034d-5c27-8d9c-a24bfcc94858']}, '22d9781d-3a53-4258-8d0b-948c28fc02f6'),
('expt52', {'sample_id': 'sample2.lab.experiment.lab.5', 'child_of': ['102d8a08-034d-5c27-8d9c-a24bfcc94858']}, '84ce0cfb-5240-4e4a-9642-0d37df354337')
]
DELETES = CREATES
UPDATES = [
('expt1', {'sample_id': 'biofab.sample.900000', 'replicate': 0, 'child_of': ['102edd93-29d6-5483-b60b-8dfd4d094b9c']}, '103dfcc6-7dd8-54a1-b5d7-c36129511173'),
('expt2', {'sample_id': 'ginkgo.sample.ABCDEFG', 'control_type': 'BASELINE', 'child_of': ['102d8a08-034d-5c27-8d9c-a24bfcc94858']},
'1031e39a-ac24-57b6-8fb3-b8fb65708654'),
('expt3', {'sample_id': 'tacc.sample.8675309', 'replicate': 3, 'child_of': ['1027aa77-d524-5359-a802-a8008adaecb5']}, '103b4050-f7dc-5680-8445-cd14e092445a')
]
| 77.636364
| 222
| 0.697892
| 208
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| 0
|
0
| 5
|
af60d548011be7f3019d8d47e83e0eda9e3da842
| 26
|
py
|
Python
|
src/kivy_garden/qrcode/version.py
|
kivy-garden/qrcode
|
a4a636bee8a2aca2b2a98ac196ef93793c9f8295
|
[
"MIT"
] | 7
|
2019-10-25T00:46:16.000Z
|
2022-03-19T20:47:14.000Z
|
src/kivy_garden/qrcode/version.py
|
kivy-garden/qrcode
|
a4a636bee8a2aca2b2a98ac196ef93793c9f8295
|
[
"MIT"
] | 16
|
2019-10-26T13:30:40.000Z
|
2021-03-14T11:32:57.000Z
|
src/kivy_garden/qrcode/version.py
|
kivy-garden/qrcode
|
a4a636bee8a2aca2b2a98ac196ef93793c9f8295
|
[
"MIT"
] | 4
|
2019-10-25T00:48:09.000Z
|
2022-01-27T16:49:24.000Z
|
__version__ = '2021.0314'
| 13
| 25
| 0.730769
| 3
| 26
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.347826
| 0.115385
| 26
| 1
| 26
| 26
| 0.304348
| 0
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| 0
| 0.346154
| 0
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| 0
| 0
| 0
| 1
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| false
| 0
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| 1
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| null | 0
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| 1
| 0
| 0
| 1
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| 0
| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
af61a51da0b66eaa0861cbc30d931523b7417957
| 78
|
py
|
Python
|
firetail/extensions/fleet_up/__init__.py
|
evidex/Firetail
|
408211db92af0a2d57ae856657e3b6d8b7efe835
|
[
"MIT"
] | 29
|
2017-11-22T21:56:51.000Z
|
2021-09-20T13:08:05.000Z
|
firetail/extensions/fleet_up/__init__.py
|
evidex/Firetail
|
408211db92af0a2d57ae856657e3b6d8b7efe835
|
[
"MIT"
] | 43
|
2017-11-22T10:57:32.000Z
|
2022-03-08T07:20:36.000Z
|
firetail/extensions/fleet_up/__init__.py
|
evidex/Firetail
|
408211db92af0a2d57ae856657e3b6d8b7efe835
|
[
"MIT"
] | 25
|
2017-12-13T22:14:37.000Z
|
2021-08-12T03:58:53.000Z
|
from .fleet_up import FleetUp
def setup(bot):
bot.add_cog(FleetUp(bot))
| 13
| 29
| 0.717949
| 13
| 78
| 4.153846
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 78
| 5
| 30
| 15.6
| 0.830769
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
af69763d707c060693a71270569f9c457180ff1c
| 80
|
py
|
Python
|
src/phat/learn/__init__.py
|
rskene/phat
|
84a946e1e638642f36ce5fd81dc85aa89f7b66f0
|
[
"MIT"
] | 2
|
2021-07-23T11:34:21.000Z
|
2022-01-09T17:22:45.000Z
|
src/phat/learn/__init__.py
|
rjskene/phat
|
84a946e1e638642f36ce5fd81dc85aa89f7b66f0
|
[
"MIT"
] | 3
|
2022-01-18T09:27:16.000Z
|
2022-01-18T09:28:43.000Z
|
src/phat/learn/__init__.py
|
rskene/phat
|
84a946e1e638642f36ce5fd81dc85aa89f7b66f0
|
[
"MIT"
] | null | null | null |
from .phatnet import PhatNet, PhatLoss, PhatMetric
from .utils import DataSplit
| 26.666667
| 50
| 0.825
| 10
| 80
| 6.6
| 0.7
| 0
| 0
| 0
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| 0
| 0.125
| 80
| 2
| 51
| 40
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| 1
| 0
|
0
| 5
|
af6d841bf62bcc1fb7340b1afe24f647759ad772
| 160
|
py
|
Python
|
main/calendar-module/calendar-module.py
|
EliahKagan/old-practice-snapshot
|
1b53897eac6902f8d867c8f154ce2a489abb8133
|
[
"0BSD"
] | null | null | null |
main/calendar-module/calendar-module.py
|
EliahKagan/old-practice-snapshot
|
1b53897eac6902f8d867c8f154ce2a489abb8133
|
[
"0BSD"
] | null | null | null |
main/calendar-module/calendar-module.py
|
EliahKagan/old-practice-snapshot
|
1b53897eac6902f8d867c8f154ce2a489abb8133
|
[
"0BSD"
] | null | null | null |
#!/usr/bin/env python3
from calendar import day_name, weekday
month, day, year = map(int, input().split())
print(day_name[weekday(year, month, day)].upper())
| 22.857143
| 50
| 0.7125
| 25
| 160
| 4.48
| 0.72
| 0.125
| 0.25
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| 0
| 0
| 0
| 0.007042
| 0.1125
| 160
| 6
| 51
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|
0
| 5
|
af96c657d4f8c911032770e6adbb2edee5f6a778
| 6,497
|
py
|
Python
|
snakemake/wrappers/.snakemake.w0wlzoye.merge_bams_wrapper.py
|
saketkc/EE-546-project
|
fb7eacd90f6c0a2cb3061837ec5427a14f521aa5
|
[
"BSD-2-Clause"
] | 1
|
2020-11-02T07:05:09.000Z
|
2020-11-02T07:05:09.000Z
|
snakemake/wrappers/.snakemake.w0wlzoye.merge_bams_wrapper.py
|
saketkc/EE-546-project
|
fb7eacd90f6c0a2cb3061837ec5427a14f521aa5
|
[
"BSD-2-Clause"
] | null | null | null |
snakemake/wrappers/.snakemake.w0wlzoye.merge_bams_wrapper.py
|
saketkc/EE-546-project
|
fb7eacd90f6c0a2cb3061837ec5427a14f521aa5
|
[
"BSD-2-Clause"
] | null | null | null |
######## Snakemake header ########
import sys; sys.path.append("/home/cmb-panasas2/skchoudh/software_frozen/anaconda27/envs/riboraptor/lib/python3.5/site-packages"); import pickle; snakemake = pickle.loads(b'\x80\x03csnakemake.script\nSnakemake\nq\x00)\x81q\x01}q\x02(X\x05\x00\x00\x00inputq\x03csnakemake.io\nInputFiles\nq\x04)\x81q\x05(X\x17\x00\x00\x00bams_srr/SRR1062455.bamq\x06X\x17\x00\x00\x00bams_srr/SRR1062456.bamq\x07X\x17\x00\x00\x00bams_srr/SRR1062457.bamq\x08X\x17\x00\x00\x00bams_srr/SRR1062458.bamq\tX\x17\x00\x00\x00bams_srr/SRR1062459.bamq\nX\x17\x00\x00\x00bams_srr/SRR1062460.bamq\x0bX\x17\x00\x00\x00bams_srr/SRR1062461.bamq\x0cX\x17\x00\x00\x00bams_srr/SRR1062462.bamq\rX\x17\x00\x00\x00bams_srr/SRR1062463.bamq\x0eX\x17\x00\x00\x00bams_srr/SRR1062464.bamq\x0fX\x17\x00\x00\x00bams_srr/SRR1062465.bamq\x10X\x17\x00\x00\x00bams_srr/SRR1062466.bamq\x11X\x17\x00\x00\x00bams_srr/SRR1062467.bamq\x12X\x17\x00\x00\x00bams_srr/SRR1062468.bamq\x13X\x17\x00\x00\x00bams_srr/SRR1062469.bamq\x14X\x17\x00\x00\x00bams_srr/SRR1062470.bamq\x15X\x17\x00\x00\x00bams_srr/SRR1062471.bamq\x16X\x17\x00\x00\x00bams_srr/SRR1062472.bamq\x17X\x17\x00\x00\x00bams_srr/SRR1062473.bamq\x18X\x17\x00\x00\x00bams_srr/SRR1062474.bamq\x19X\x17\x00\x00\x00bams_srr/SRR1062475.bamq\x1aX\x17\x00\x00\x00bams_srr/SRR1062476.bamq\x1bX\x17\x00\x00\x00bams_srr/SRR1062477.bamq\x1cX\x17\x00\x00\x00bams_srr/SRR1062478.bamq\x1dX\x17\x00\x00\x00bams_srr/SRR1062479.bamq\x1eX\x17\x00\x00\x00bams_srr/SRR1062480.bamq\x1fX\x17\x00\x00\x00bams_srr/SRR1062481.bamq X\x17\x00\x00\x00bams_srr/SRR1062482.bamq!X\x17\x00\x00\x00bams_srr/SRR1062483.bamq"X\x17\x00\x00\x00bams_srr/SRR1062484.bamq#X\x17\x00\x00\x00bams_srr/SRR1062485.bamq$X\x17\x00\x00\x00bams_srr/SRR1062486.bamq%X\x17\x00\x00\x00bams_srr/SRR1062487.bamq&X\x17\x00\x00\x00bams_srr/SRR1062488.bamq\'X\x17\x00\x00\x00bams_srr/SRR1062489.bamq(X\x17\x00\x00\x00bams_srr/SRR1062490.bamq)X\x17\x00\x00\x00bams_srr/SRR1062491.bamq*X\x17\x00\x00\x00bams_srr/SRR1062492.bamq+X\x17\x00\x00\x00bams_srr/SRR1062493.bamq,X\x17\x00\x00\x00bams_srr/SRR1062494.bamq-X\x17\x00\x00\x00bams_srr/SRR1062495.bamq.X\x17\x00\x00\x00bams_srr/SRR1062496.bamq/X\x17\x00\x00\x00bams_srr/SRR1062497.bamq0X\x17\x00\x00\x00bams_srr/SRR1062498.bamq1X\x17\x00\x00\x00bams_srr/SRR1062499.bamq2X\x17\x00\x00\x00bams_srr/SRR1062500.bamq3X\x17\x00\x00\x00bams_srr/SRR1062501.bamq4X\x17\x00\x00\x00bams_srr/SRR1062502.bamq5X\x17\x00\x00\x00bams_srr/SRR1062503.bamq6X\x17\x00\x00\x00bams_srr/SRR1062504.bamq7X\x17\x00\x00\x00bams_srr/SRR1062505.bamq8X\x17\x00\x00\x00bams_srr/SRR1062506.bamq9X\x17\x00\x00\x00bams_srr/SRR1062507.bamq:X\x17\x00\x00\x00bams_srr/SRR1062508.bamq;X\x17\x00\x00\x00bams_srr/SRR1062509.bamq<X\x17\x00\x00\x00bams_srr/SRR1062510.bamq=X\x17\x00\x00\x00bams_srr/SRR1062511.bamq>X\x17\x00\x00\x00bams_srr/SRR1062512.bamq?X\x17\x00\x00\x00bams_srr/SRR1062513.bamq@X\x17\x00\x00\x00bams_srr/SRR1062514.bamqAX\x17\x00\x00\x00bams_srr/SRR1062515.bamqBX\x17\x00\x00\x00bams_srr/SRR1062516.bamqCX\x17\x00\x00\x00bams_srr/SRR1062517.bamqDX\x17\x00\x00\x00bams_srr/SRR1062518.bamqEX\x17\x00\x00\x00bams_srr/SRR1062519.bamqFX\x17\x00\x00\x00bams_srr/SRR1062520.bamqGX\x17\x00\x00\x00bams_srr/SRR1062521.bamqHX\x17\x00\x00\x00bams_srr/SRR1062522.bamqIX\x17\x00\x00\x00bams_srr/SRR1062523.bamqJX\x17\x00\x00\x00bams_srr/SRR1062524.bamqKX\x17\x00\x00\x00bams_srr/SRR1062525.bamqLX\x17\x00\x00\x00bams_srr/SRR1062526.bamqMX\x17\x00\x00\x00bams_srr/SRR1062527.bamqNX\x17\x00\x00\x00bams_srr/SRR1062528.bamqOX\x17\x00\x00\x00bams_srr/SRR1062529.bamqPX\x17\x00\x00\x00bams_srr/SRR1062530.bamqQX\x17\x00\x00\x00bams_srr/SRR1062531.bamqRX\x17\x00\x00\x00bams_srr/SRR1062532.bamqSX\x17\x00\x00\x00bams_srr/SRR1062533.bamqTX\x17\x00\x00\x00bams_srr/SRR1062534.bamqUX\x17\x00\x00\x00bams_srr/SRR1062535.bamqVX\x17\x00\x00\x00bams_srr/SRR1062536.bamqWX\x17\x00\x00\x00bams_srr/SRR1062537.bamqXX\x17\x00\x00\x00bams_srr/SRR1062538.bamqYX\x17\x00\x00\x00bams_srr/SRR1062539.bamqZX\x17\x00\x00\x00bams_srr/SRR1062540.bamq[X\x17\x00\x00\x00bams_srr/SRR1062541.bamq\\X\x17\x00\x00\x00bams_srr/SRR1062542.bamq]X\x17\x00\x00\x00bams_srr/SRR1062543.bamq^X\x17\x00\x00\x00bams_srr/SRR1062544.bamq_X\x17\x00\x00\x00bams_srr/SRR1062545.bamq`X\x17\x00\x00\x00bams_srr/SRR1062546.bamqaX\x17\x00\x00\x00bams_srr/SRR1062547.bamqbX\x17\x00\x00\x00bams_srr/SRR1062548.bamqcX\x17\x00\x00\x00bams_srr/SRR1062549.bamqdX\x17\x00\x00\x00bams_srr/SRR1062550.bamqeX\x17\x00\x00\x00bams_srr/SRR1062551.bamqfX\x17\x00\x00\x00bams_srr/SRR1062552.bamqgX\x17\x00\x00\x00bams_srr/SRR1062553.bamqhX\x17\x00\x00\x00bams_srr/SRR1062554.bamqie}qjX\x06\x00\x00\x00_namesqk}qlsbX\t\x00\x00\x00wildcardsqmcsnakemake.io\nWildcards\nqn)\x81qoX\t\x00\x00\x00SRX399822qpa}qq(hk}qrX\x06\x00\x00\x00sampleqsK\x00N\x86qtsX\x06\x00\x00\x00samplequhpubX\x06\x00\x00\x00outputqvcsnakemake.io\nOutputFiles\nqw)\x81qxX\x12\x00\x00\x00bams/SRX399822.bamqya}qzhk}q{sbX\x03\x00\x00\x00logq|csnakemake.io\nLog\nq})\x81q~}q\x7fhk}q\x80sbX\x07\x00\x00\x00threadsq\x81K\x01X\t\x00\x00\x00resourcesq\x82csnakemake.io\nResources\nq\x83)\x81q\x84(K\x01K\x01e}q\x85(X\x06\x00\x00\x00_nodesq\x86K\x01X\x06\x00\x00\x00_coresq\x87K\x01hk}q\x88(h\x86K\x00N\x86q\x89h\x87K\x01N\x86q\x8auubX\x06\x00\x00\x00paramsq\x8bcsnakemake.io\nParams\nq\x8c)\x81q\x8dX\x04\x00\x00\x00/tmpq\x8ea}q\x8f(hk}q\x90X\x07\x00\x00\x00tmp_dirq\x91K\x00N\x86q\x92sh\x91h\x8eubX\x04\x00\x00\x00ruleq\x93X\n\x00\x00\x00merge_bamsq\x94X\x06\x00\x00\x00configq\x95}q\x96X\x0b\x00\x00\x00config_pathq\x97X\x1b\x00\x00\x00configs/GRCz10_SRP034750.pyq\x98sub.'); from snakemake.logging import logger; logger.printshellcmds = True
######## Original script #########
import os
import tempfile
from snakemake.shell import shell
if len(snakemake.input) > 1:
with tempfile.TemporaryDirectory(dir=snakemake.params.tmp_dir) as temp_dir:
cmd = ' -in '.join(snakemake.input)
shell(r'''bamtools merge -in {cmd} -out {snakemake.output}.unsorted \
&& samtools sort -@ {snakemake.threads} \
-T {temp_dir}/{snakemake.wildcards.sample}_merge_bam \
-o {snakemake.output} {snakemake.output}.unsorted \
&& samtools index {snakemake.output} \
&& yes | rm -rf {snakemake.output}.unsorted''')
elif len(snakemake.input) == 1:
source = os.path.abspath(str(snakemake.input[0]))
destination = os.path.abspath(str(snakemake.output))
shell('''cp {source} {destination} && cp {source}.bai {destination}.bai''')
| 295.318182
| 5,604
| 0.808989
| 1,087
| 6,497
| 4.730451
| 0.316467
| 0.145858
| 0.255348
| 0.311163
| 0.426293
| 0.217036
| 0.135356
| 0
| 0
| 0
| 0
| 0.292063
| 0.030322
| 6,497
| 21
| 5,605
| 309.380952
| 0.524127
| 0.005079
| 0
| 0
| 0
| 0.117647
| 0.335408
| 0.288892
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.235294
| 0
| 0.235294
| 0.058824
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
afb5d8165c3d9acb6bda824d49384a6f2a95e9aa
| 95
|
py
|
Python
|
terrascript/tls/d.py
|
amlodzianowski/python-terrascript
|
1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49
|
[
"BSD-2-Clause"
] | null | null | null |
terrascript/tls/d.py
|
amlodzianowski/python-terrascript
|
1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49
|
[
"BSD-2-Clause"
] | null | null | null |
terrascript/tls/d.py
|
amlodzianowski/python-terrascript
|
1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49
|
[
"BSD-2-Clause"
] | null | null | null |
# terrascript/tls/d.py
import terrascript
class tls_public_key(terrascript.Data):
pass
| 11.875
| 39
| 0.757895
| 13
| 95
| 5.384615
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 95
| 7
| 40
| 13.571429
| 0.875
| 0.210526
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
afbefab62b9d1777239eb667c1ac94cf05de0f46
| 44
|
py
|
Python
|
src/visualization/__init__.py
|
vvrahul11/sentiment_analysis
|
1bd22a96467d72865d65f9ab4a15d46cc97b7bba
|
[
"MIT"
] | null | null | null |
src/visualization/__init__.py
|
vvrahul11/sentiment_analysis
|
1bd22a96467d72865d65f9ab4a15d46cc97b7bba
|
[
"MIT"
] | null | null | null |
src/visualization/__init__.py
|
vvrahul11/sentiment_analysis
|
1bd22a96467d72865d65f9ab4a15d46cc97b7bba
|
[
"MIT"
] | null | null | null |
from .visualize import plot_confusion_matrix
| 44
| 44
| 0.909091
| 6
| 44
| 6.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068182
| 44
| 1
| 44
| 44
| 0.926829
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
afcf3e8b6fc76f7eb80f9ccc7a90b613ad9bfc9c
| 1,900
|
py
|
Python
|
FewShotPreprocessing.py
|
ahirsharan/MTL_Segmentation
|
f49f4ae4b7fdc12e83e5f4e5058e819cdd335b98
|
[
"MIT"
] | 9
|
2020-04-29T07:45:19.000Z
|
2021-05-04T14:39:57.000Z
|
FewShotPreprocessing.py
|
ahirsharan/MTL_Segmentation
|
f49f4ae4b7fdc12e83e5f4e5058e819cdd335b98
|
[
"MIT"
] | null | null | null |
FewShotPreprocessing.py
|
ahirsharan/MTL_Segmentation
|
f49f4ae4b7fdc12e83e5f4e5058e819cdd335b98
|
[
"MIT"
] | 4
|
2020-05-08T10:51:29.000Z
|
2021-06-25T08:39:49.000Z
|
import os
import os.path as osp
from PIL import Image
PATH='../Fewshot/Fewshot/'
classes= os.listdir(PATH)
trainp='../Fewshot/train/'
valp='../Fewshot/val/'
testp='../Fewshot/test/'
for classv in classes:
if classv[0]=='.':
continue
pathn=osp.join(PATH,classv)
pathn=pathn+'/'
folders=os.listdir(pathn)
path1=osp.join(trainp,'images/')
path1=osp.join(path1,classv)
os.mkdir(path1)
path1 =path1 +'/'
path2=osp.join(trainp,'labels/')
path2=osp.join(path2,classv)
os.mkdir(path2)
path2=path2+'/'
for i in range(0,8,1):
p=osp.join(pathn,folders[i])
im=Image.open(p)
if(i%2==0):
p1=osp.join(path1,folders[i])
im.save(p1)
else:
p2=osp.join(path2,folders[i])
im.save(p2)
path1=osp.join(valp,'images/')
path1=osp.join(path1,classv)
os.mkdir(path1)
path1 =path1 +'/'
path2=osp.join(valp,'labels/')
path2=osp.join(path2,classv)
os.mkdir(path2)
path2=path2+'/'
for i in range(8,16,1):
p=osp.join(pathn,folders[i])
im=Image.open(p)
if(i%2==0):
p1=osp.join(path1,folders[i])
im.save(p1)
else:
p2=osp.join(path2,folders[i])
im.save(p2)
path1=osp.join(testp,'images/')
path1=osp.join(path1,classv)
os.mkdir(path1)
path1=path1+'/'
path2=osp.join(testp,'labels/')
path2=osp.join(path2,classv)
os.mkdir(path2)
path2=path2+'/'
for i in range(16,20,1):
p=osp.join(pathn,folders[i])
im=Image.open(p)
if(i%2==0):
p1=osp.join(path1,folders[i])
im.save(p1)
else:
p2=osp.join(path2,folders[i])
im.save(p2)
| 23.45679
| 41
| 0.513684
| 256
| 1,900
| 3.8125
| 0.175781
| 0.157787
| 0.092213
| 0.086066
| 0.72541
| 0.72541
| 0.72541
| 0.72541
| 0.72541
| 0.72541
| 0
| 0.056545
| 0.320526
| 1,900
| 80
| 42
| 23.75
| 0.699458
| 0
| 0
| 0.646154
| 0
| 0
| 0.061611
| 0
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| 0
| 0
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| 0
| 1
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| false
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| 0.046154
| 0
| 0.046154
| 0
| 0
| 0
| 0
| null | 0
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| 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bb86997b5357e6cd3c7be720e4153aa8762103db
| 200
|
py
|
Python
|
saleor/lib/python3.7/site-packages/django_prices_openexchangerates/apps.py
|
cxsper/saleor
|
5566ddcdaf8f72ba872eca869798e66eb9cdae44
|
[
"BSD-3-Clause"
] | 34
|
2015-01-28T20:56:58.000Z
|
2021-09-08T18:14:49.000Z
|
saleor/lib/python3.7/site-packages/django_prices_openexchangerates/apps.py
|
cxsper/saleor
|
5566ddcdaf8f72ba872eca869798e66eb9cdae44
|
[
"BSD-3-Clause"
] | 33
|
2015-01-27T10:29:39.000Z
|
2019-12-06T15:45:05.000Z
|
myvenv/lib/python3.6/site-packages/django_prices_openexchangerates/apps.py
|
yog240597/saleor
|
b75a23827a4ec2ce91637f0afe6808c9d09da00a
|
[
"CC-BY-4.0"
] | 15
|
2015-01-27T10:27:23.000Z
|
2021-12-21T16:10:21.000Z
|
from django.apps import AppConfig
class DjangoPricesOpenExchangeRatesConfig(AppConfig):
name = 'django_prices_openexchangerates'
verbose_name = "Django prices openexchangerates integration"
| 28.571429
| 64
| 0.825
| 18
| 200
| 9
| 0.666667
| 0.123457
| 0.197531
| 0.407407
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 200
| 6
| 65
| 33.333333
| 0.925714
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| 0
| 0.37
| 0.155
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| 1
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| false
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| 0.25
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| 1
| 0
|
0
| 5
|
bb9fc31bdbcc3c5cedf0718808657c1863046fe6
| 155
|
py
|
Python
|
mercury/plugin/service/__init__.py
|
greenlsi/mercury_mso_framework
|
8b9639e5cb4b2c526a65861c93a9fe9db2460ea4
|
[
"Apache-2.0"
] | 1
|
2020-07-21T11:22:39.000Z
|
2020-07-21T11:22:39.000Z
|
mercury/plugin/service/__init__.py
|
greenlsi/mercury_mso_framework
|
8b9639e5cb4b2c526a65861c93a9fe9db2460ea4
|
[
"Apache-2.0"
] | 2
|
2021-08-25T16:09:58.000Z
|
2022-02-10T02:21:03.000Z
|
mercury/plugin/service/__init__.py
|
greenlsi/mercury_mso_framework
|
8b9639e5cb4b2c526a65861c93a9fe9db2460ea4
|
[
"Apache-2.0"
] | 1
|
2021-02-24T15:54:09.000Z
|
2021-02-24T15:54:09.000Z
|
from .request_profile import ServiceRequestProfile
from .session_duration import ServiceSessionDuration
from .session_profile import ServiceSessionProfile
| 38.75
| 52
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| 15
| 155
| 9.133333
| 0.6
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0
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|
bbb60e5feac428852f8a4a9814c7d81298a22738
| 48
|
py
|
Python
|
src/ufdl/annotations_plugin/image/object_detection/__init__.py
|
waikato-ufdl/ufdl-annotations-plugin
|
9eb3d807e35215ad9cfbd4aa651d7f7142e83efe
|
[
"Apache-2.0"
] | null | null | null |
src/ufdl/annotations_plugin/image/object_detection/__init__.py
|
waikato-ufdl/ufdl-annotations-plugin
|
9eb3d807e35215ad9cfbd4aa651d7f7142e83efe
|
[
"Apache-2.0"
] | 4
|
2020-07-29T04:09:13.000Z
|
2020-11-22T20:52:18.000Z
|
src/ufdl/annotations_plugin/image/object_detection/__init__.py
|
waikato-ufdl/ufdl-annotations-plugin
|
9eb3d807e35215ad9cfbd4aa651d7f7142e83efe
|
[
"Apache-2.0"
] | null | null | null |
"""
The image object-detection data domain.
"""
| 12
| 39
| 0.6875
| 6
| 48
| 5.5
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| 0.145833
| 48
| 3
| 40
| 16
| 0.804878
| 0.8125
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0
| 5
|
bbd51eb45c7f5628083f05be82e4a0bc26ee198c
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/urllib3/exceptions.py
|
GiulianaPola/select_repeats
|
17a0d053d4f874e42cf654dd142168c2ec8fbd11
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/urllib3/exceptions.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/urllib3/exceptions.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/d0/c9/e7/a372874cd7d745f63beb7f0db9f38f9146fa9973a6f8baa3fb8c76c3c0
| 96
| 96
| 0.895833
| 9
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| 9.555556
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| 96
| 0.520833
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0
| 5
|
a52577b4981a2ff9a3a5e8ca0265e497121ae73f
| 219
|
py
|
Python
|
iblog/api_server/model.py
|
openjw/blog
|
dac1f4835fef8a3448d5f2b71bb29ea7e5d74288
|
[
"MIT"
] | null | null | null |
iblog/api_server/model.py
|
openjw/blog
|
dac1f4835fef8a3448d5f2b71bb29ea7e5d74288
|
[
"MIT"
] | 10
|
2020-09-11T07:54:06.000Z
|
2020-09-26T09:17:38.000Z
|
iblog/api_server/model.py
|
openjw/blog
|
dac1f4835fef8a3448d5f2b71bb29ea7e5d74288
|
[
"MIT"
] | null | null | null |
from dataclasses import dataclass, field
from typing import Any
@dataclass
class Response(object):
ok: bool = field(default=False)
data: Any = field(default=None)
message: str = field(default='')
| 21.9
| 41
| 0.684932
| 27
| 219
| 5.555556
| 0.666667
| 0.24
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| 219
| 9
| 42
| 24.333333
| 0.872093
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| 0
| 1
| 0
|
0
| 5
|
a5297b8f2d7f2c7bd83c6051630dcff677b71047
| 1,192
|
py
|
Python
|
montreal_forced_aligner/multiprocessing/__init__.py
|
ai-zahran/Montreal-Forced-Aligner
|
decbacfe86f81703022da4e95fd109eb94e7686d
|
[
"MIT"
] | null | null | null |
montreal_forced_aligner/multiprocessing/__init__.py
|
ai-zahran/Montreal-Forced-Aligner
|
decbacfe86f81703022da4e95fd109eb94e7686d
|
[
"MIT"
] | null | null | null |
montreal_forced_aligner/multiprocessing/__init__.py
|
ai-zahran/Montreal-Forced-Aligner
|
decbacfe86f81703022da4e95fd109eb94e7686d
|
[
"MIT"
] | null | null | null |
"""Multiprocessing functions and classes for Montreal Forced Aligner"""
from .alignment import acc_stats # noqa
from .alignment import align # noqa
from .alignment import calc_fmllr # noqa
from .alignment import calc_lda_mllt # noqa
from .alignment import compile_information # noqa
from .alignment import compile_train_graphs # noqa
from .alignment import compute_alignment_improvement # noqa
from .alignment import convert_ali_to_textgrids # noqa
from .alignment import convert_alignments # noqa
from .alignment import create_align_model # noqa
from .alignment import lda_acc_stats # noqa
from .alignment import mono_align_equal # noqa
from .alignment import train_map # noqa
from .alignment import tree_stats # noqa; noqa
from .helper import Counter, Stopped, run_mp, run_non_mp # noqa
from .ivector import acc_global_stats # noqa
from .ivector import acc_ivector_stats # noqa
from .ivector import extract_ivectors # noqa
from .ivector import gauss_to_post # noqa
from .ivector import gmm_gselect # noqa
from .ivector import segment_vad # noqa
from .pronunciations import generate_pronunciations # noqa
from .transcription import transcribe, transcribe_fmllr # noqa
| 47.68
| 71
| 0.806208
| 163
| 1,192
| 5.680982
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| 0.190065
| 0.287257
| 0.322894
| 0.340173
| 0.066955
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| 1,192
| 24
| 72
| 49.666667
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|
0
| 5
|
a550741bd2e519583a406da32bf6be195ea749cf
| 308
|
py
|
Python
|
blacktape/util.py
|
carascap/blacktape
|
52e0b912f4c67899911d10d2d6e3770671db02fa
|
[
"MIT"
] | null | null | null |
blacktape/util.py
|
carascap/blacktape
|
52e0b912f4c67899911d10d2d6e3770671db02fa
|
[
"MIT"
] | 1
|
2022-02-22T19:45:27.000Z
|
2022-02-22T19:45:27.000Z
|
blacktape/util.py
|
carascap/blacktape
|
52e0b912f4c67899911d10d2d6e3770671db02fa
|
[
"MIT"
] | null | null | null |
import signal
def worker_init():
"""
Initializer for worker processes that makes them ignore interrupt signals
https://docs.python.org/3/library/signal.html#signal.signal
https://docs.python.org/3/library/signal.html#signal.SIG_IGN
"""
signal.signal(signal.SIGINT, signal.SIG_IGN)
| 25.666667
| 77
| 0.727273
| 43
| 308
| 5.139535
| 0.55814
| 0.162896
| 0.135747
| 0.162896
| 0.380091
| 0.380091
| 0.380091
| 0.380091
| 0.380091
| 0
| 0
| 0.007663
| 0.152597
| 308
| 11
| 78
| 28
| 0.83908
| 0.62987
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a5676d68f98f91cfa01613d2160d7005e2f62b6b
| 103
|
pyw
|
Python
|
run.pyw
|
xue0228/keyboard
|
dcb0def1d87a9197676c0f405b980a67e128ab24
|
[
"MIT"
] | null | null | null |
run.pyw
|
xue0228/keyboard
|
dcb0def1d87a9197676c0f405b980a67e128ab24
|
[
"MIT"
] | null | null | null |
run.pyw
|
xue0228/keyboard
|
dcb0def1d87a9197676c0f405b980a67e128ab24
|
[
"MIT"
] | null | null | null |
from xue_macro import macro
if __name__ == '__main__':
# macro.run(fg='#ECB1AC')
macro.run()
| 14.714286
| 29
| 0.640777
| 14
| 103
| 4.071429
| 0.714286
| 0.280702
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012195
| 0.203884
| 103
| 6
| 30
| 17.166667
| 0.682927
| 0.213592
| 0
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| 0
| 0
| 0.102564
| 0
| 0
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| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
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| null | 1
| 0
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| 0
| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
a5898b7a209d56a17c5b29e36003176dbecb22c1
| 109
|
py
|
Python
|
src/vcslinks/tests/test_py_typed.py
|
tkf/vcslinks
|
0d3d20ba7766285d28a2a72ec089b7a0347c9fbe
|
[
"MIT"
] | 1
|
2019-05-11T09:33:12.000Z
|
2019-05-11T09:33:12.000Z
|
src/vcslinks/tests/test_py_typed.py
|
tkf/vcslink
|
0d3d20ba7766285d28a2a72ec089b7a0347c9fbe
|
[
"MIT"
] | 8
|
2020-08-02T04:28:22.000Z
|
2020-09-17T02:47:18.000Z
|
src/vcslinks/tests/test_py_typed.py
|
tkf/vcslinks
|
0d3d20ba7766285d28a2a72ec089b7a0347c9fbe
|
[
"MIT"
] | null | null | null |
from pathlib import Path
def test_py_typed():
assert (Path(__file__).parents[1] / "py.typed").exists()
| 18.166667
| 60
| 0.697248
| 16
| 109
| 4.375
| 0.8125
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010753
| 0.146789
| 109
| 5
| 61
| 21.8
| 0.741935
| 0
| 0
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| 0
| 0
| 0.073395
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
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| null | 0
| 0
| 0
| 0
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| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
a5afcd5db1cddf50398cf0d215956a1afb89ad6d
| 83
|
py
|
Python
|
mimic/utils/exceptions.py
|
Jimmy2027/MoPoE-MIMIC
|
d167719b0dc7ba002b7421eb82a83e47d2437795
|
[
"MIT"
] | 1
|
2021-09-30T07:56:46.000Z
|
2021-09-30T07:56:46.000Z
|
mimic/utils/exceptions.py
|
Jimmy2027/MoPoE-MIMIC
|
d167719b0dc7ba002b7421eb82a83e47d2437795
|
[
"MIT"
] | null | null | null |
mimic/utils/exceptions.py
|
Jimmy2027/MoPoE-MIMIC
|
d167719b0dc7ba002b7421eb82a83e47d2437795
|
[
"MIT"
] | null | null | null |
class NaNInLatent(Exception):
pass
class CudaOutOfMemory(Exception):
pass
| 13.833333
| 33
| 0.746988
| 8
| 83
| 7.75
| 0.625
| 0.419355
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.180723
| 83
| 6
| 34
| 13.833333
| 0.911765
| 0
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| 0.5
| 0
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| 1
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| true
| 0.5
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| 0.5
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| null | 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
3c0793128a0aec79d43d5faa628fdb5217f086a5
| 200
|
py
|
Python
|
staging/commands/dev/repos/push.py
|
cligraphy/cligraphy
|
f4214f544d8c8a9b1dd6d12a4e2c8950a0f6cd56
|
[
"Apache-2.0"
] | 5
|
2017-07-30T15:29:04.000Z
|
2021-01-31T13:04:28.000Z
|
staging/commands/dev/repos/push.py
|
Netflix-Skunkworks/cligraphy
|
f84cc5b834f997762313f94f1015373875178f5e
|
[
"Apache-2.0"
] | 1
|
2019-09-20T23:09:49.000Z
|
2020-02-11T22:01:25.000Z
|
staging/commands/dev/repos/push.py
|
cligraphy/cligraphy
|
f4214f544d8c8a9b1dd6d12a4e2c8950a0f6cd56
|
[
"Apache-2.0"
] | 1
|
2017-08-14T17:36:14.000Z
|
2017-08-14T17:36:14.000Z
|
#!/usr/bin/env python
# Copyright 2013 Netflix
"""Push all repos to stash
"""
from nflx_oc.commands.dev.repos import run_for_all_repos
def main():
run_for_all_repos('git push origin master')
| 15.384615
| 56
| 0.735
| 33
| 200
| 4.242424
| 0.757576
| 0.171429
| 0.128571
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.023669
| 0.155
| 200
| 12
| 57
| 16.666667
| 0.804734
| 0.335
| 0
| 0
| 0
| 0
| 0.176
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3c1d13f890f957a4aef5185e5e69d9025f886ee5
| 3,188
|
py
|
Python
|
paper2tmb/tests/test_manipulator.py
|
sotetsuk/paper2img
|
bf441f58d7ff1b6e4532d8b0bbee47f51243ea2e
|
[
"MIT"
] | 1
|
2020-12-15T15:47:04.000Z
|
2020-12-15T15:47:04.000Z
|
paper2tmb/tests/test_manipulator.py
|
sotetsuk/paper2img
|
bf441f58d7ff1b6e4532d8b0bbee47f51243ea2e
|
[
"MIT"
] | 2
|
2016-05-20T08:09:12.000Z
|
2016-05-20T08:09:52.000Z
|
paper2tmb/tests/test_manipulator.py
|
sotetsuk/paper2img
|
bf441f58d7ff1b6e4532d8b0bbee47f51243ea2e
|
[
"MIT"
] | null | null | null |
import os
import unittest
import subprocess
from paper2tmb.manipulator import Manipulator
class TestManipulator(unittest.TestCase):
def test_init(self):
with Manipulator('test.pdf') as m:
self.assertTrue(os.path.isdir(m.dirname))
def test_pdf2png(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png()
for i in range(12):
self.assertTrue(os.path.exists(os.path.join(m.dirname, "pdf2png-{}.png".format(i))))
self.assertTrue(m._last == os.path.join(m.dirname, "pdf2png.png"))
def test_pdf2png_trim(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png(trim="100x100")
for i in range(12):
self.assertTrue(os.path.exists(os.path.join(m.dirname, "pdf2png-{}.png".format(i))))
self.assertTrue(m._last == os.path.join(m.dirname, "pdf2png.png"))
def test_pdf2png_density(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png(density="20")
for i in range(12):
self.assertTrue(os.path.exists(os.path.join(m.dirname, "pdf2png-{}.png".format(i))))
self.assertTrue(m._last == os.path.join(m.dirname, "pdf2png.png"))
def test_pdf2png_both_trim_density(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png(trim="300x300", density="10")
for i in range(12):
self.assertTrue(os.path.exists(os.path.join(m.dirname, "pdf2png-{}.png".format(i))))
self.assertTrue(m._last == os.path.join(m.dirname, "pdf2png.png"))
def test_stack(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png()
m.stack(4, 2)
self.assertTrue(os.path.exists(os.path.join(m.dirname, "stack_row_0.png")))
self.assertTrue(os.path.exists(os.path.join(m.dirname, "stack_row_1.png")))
self.assertTrue(os.path.exists(os.path.join(m.dirname, "stack.png")))
self.assertTrue(m._last == os.path.join(m.dirname, "stack.png"))
def test_stack(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png(trim="100x60")
m.stack(6, 2)
m.resize("x400")
self.assertTrue(os.path.exists(os.path.join(m.dirname, "resize_x400.png")))
self.assertTrue(m._last == os.path.join(m.dirname, "resize_x400.png"))
def test_top(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
m.pdf2png(trim="400x240", density="300x300")
m.top("60%")
self.assertTrue(os.path.exists(os.path.join(m.dirname, "top_60%-0.png")))
self.assertTrue(m._last == os.path.join(m.dirname, "top_60%-0.png"))
def test_out(self):
with Manipulator("paper2tmb/tests/testdata/1412.6785v2.pdf") as m:
target = "paper2tmb/tests/testdata/out.pdf"
m.out(target)
self.assertTrue(os.path.exists(target))
subprocess.call(["rm", target])
| 37.069767
| 100
| 0.614806
| 433
| 3,188
| 4.459584
| 0.140878
| 0.083894
| 0.082859
| 0.091144
| 0.792853
| 0.779389
| 0.779389
| 0.7768
| 0.764889
| 0.757121
| 0
| 0.066991
| 0.227415
| 3,188
| 85
| 101
| 37.505882
| 0.717012
| 0
| 0
| 0.40678
| 0
| 0
| 0.191656
| 0.110414
| 0
| 0
| 0
| 0
| 0.305085
| 1
| 0.152542
| false
| 0
| 0.067797
| 0
| 0.237288
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3c317fd08a51e339c4d1d723e5fbd63e072df552
| 81
|
wsgi
|
Python
|
site/index.wsgi
|
jiqiang/vagrant-python-flask
|
3eeb9314f0ff585b02605522253778915498d5ae
|
[
"MIT"
] | null | null | null |
site/index.wsgi
|
jiqiang/vagrant-python-flask
|
3eeb9314f0ff585b02605522253778915498d5ae
|
[
"MIT"
] | null | null | null |
site/index.wsgi
|
jiqiang/vagrant-python-flask
|
3eeb9314f0ff585b02605522253778915498d5ae
|
[
"MIT"
] | null | null | null |
import sys
sys.path.insert(0, "/srv/site/")
from index import app as application
| 20.25
| 36
| 0.753086
| 14
| 81
| 4.357143
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014085
| 0.123457
| 81
| 3
| 37
| 27
| 0.84507
| 0
| 0
| 0
| 0
| 0
| 0.123457
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3c9d19e4b4dc167bd7507239cb978299822d6487
| 6,205
|
py
|
Python
|
eland/tests/field_mappings/test_metric_source_fields_pytest.py
|
redNixon/eland
|
1b9cb1db6d30f0662fe3679c7bb31e2c0865f0c3
|
[
"Apache-2.0"
] | null | null | null |
eland/tests/field_mappings/test_metric_source_fields_pytest.py
|
redNixon/eland
|
1b9cb1db6d30f0662fe3679c7bb31e2c0865f0c3
|
[
"Apache-2.0"
] | null | null | null |
eland/tests/field_mappings/test_metric_source_fields_pytest.py
|
redNixon/eland
|
1b9cb1db6d30f0662fe3679c7bb31e2c0865f0c3
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2019 Elasticsearch BV
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# File called _pytest for PyCharm compatability
import numpy as np
import eland as ed
from eland.tests import ES_TEST_CLIENT, ECOMMERCE_INDEX_NAME, FLIGHTS_INDEX_NAME
from eland.tests.common import TestData
class TestMetricSourceFields(TestData):
def test_flights_all_metric_source_fields(self):
ed_field_mappings = ed.FieldMappings(
client=ed.Client(ES_TEST_CLIENT), index_pattern=FLIGHTS_INDEX_NAME
)
pd_flights = self.pd_flights()
ed_dtypes, ed_fields, es_date_formats = ed_field_mappings.metric_source_fields()
pd_metric = pd_flights.select_dtypes(include=np.number)
assert pd_metric.dtypes.to_list() == ed_dtypes
assert pd_metric.columns.to_list() == ed_fields
assert len(es_date_formats) == len(ed_dtypes)
assert set(es_date_formats) == {None}
def test_flights_all_metric_source_fields_and_bool(self):
ed_field_mappings = ed.FieldMappings(
client=ed.Client(ES_TEST_CLIENT), index_pattern=FLIGHTS_INDEX_NAME
)
pd_flights = self.pd_flights()
ed_dtypes, ed_fields, es_date_formats = ed_field_mappings.metric_source_fields(
include_bool=True
)
pd_metric = pd_flights.select_dtypes(include=[np.number, "bool"])
assert pd_metric.dtypes.to_list() == ed_dtypes
assert pd_metric.columns.to_list() == ed_fields
assert len(es_date_formats) == len(ed_dtypes)
assert set(es_date_formats) == {None}
def test_flights_all_metric_source_fields_bool_and_timestamp(self):
ed_field_mappings = ed.FieldMappings(
client=ed.Client(ES_TEST_CLIENT), index_pattern=FLIGHTS_INDEX_NAME
)
pd_flights = self.pd_flights()
ed_dtypes, ed_fields, es_date_formats = ed_field_mappings.metric_source_fields(
include_bool=True, include_timestamp=True
)
pd_metric = pd_flights.select_dtypes(include=[np.number, "bool", "datetime"])
assert pd_metric.dtypes.to_list() == ed_dtypes
assert pd_metric.columns.to_list() == ed_fields
assert len(es_date_formats) == len(ed_dtypes)
assert set(es_date_formats) == set(
{"strict_date_hour_minute_second", None}
) # TODO - test position of date_format
def test_ecommerce_selected_non_metric_source_fields(self):
field_names = [
"category",
"currency",
"customer_birth_date",
"customer_first_name",
"user",
]
"""
Note: non of there are metric
category object
currency object
customer_birth_date datetime64[ns]
customer_first_name object
user object
"""
ed_field_mappings = ed.FieldMappings(
client=ed.Client(ES_TEST_CLIENT),
index_pattern=ECOMMERCE_INDEX_NAME,
display_names=field_names,
)
pd_ecommerce = self.pd_ecommerce()[field_names]
ed_dtypes, ed_fields, es_date_formats = ed_field_mappings.metric_source_fields()
pd_metric = pd_ecommerce.select_dtypes(include=np.number)
assert pd_metric.dtypes.to_list() == ed_dtypes
assert pd_metric.columns.to_list() == ed_fields
assert len(es_date_formats) == len(ed_dtypes)
assert set(es_date_formats) == set()
def test_ecommerce_selected_mixed_metric_source_fields(self):
field_names = [
"category",
"currency",
"customer_birth_date",
"customer_first_name",
"total_quantity",
"user",
]
"""
Note: one is metric
category object
currency object
customer_birth_date datetime64[ns]
customer_first_name object
total_quantity int64
user object
"""
ed_field_mappings = ed.FieldMappings(
client=ed.Client(ES_TEST_CLIENT),
index_pattern=ECOMMERCE_INDEX_NAME,
display_names=field_names,
)
pd_ecommerce = self.pd_ecommerce()[field_names]
ed_dtypes, ed_fields, es_date_formats = ed_field_mappings.metric_source_fields()
pd_metric = pd_ecommerce.select_dtypes(include=np.number)
assert len(es_date_formats) == len(ed_dtypes)
assert set(es_date_formats) == {None}
assert pd_metric.dtypes.to_list() == ed_dtypes
assert pd_metric.columns.to_list() == ed_fields
def test_ecommerce_selected_all_metric_source_fields(self):
field_names = ["total_quantity", "taxful_total_price", "taxless_total_price"]
"""
Note: all are metric
total_quantity int64
taxful_total_price float64
taxless_total_price float64
"""
ed_field_mappings = ed.FieldMappings(
client=ed.Client(ES_TEST_CLIENT),
index_pattern=ECOMMERCE_INDEX_NAME,
display_names=field_names,
)
pd_ecommerce = self.pd_ecommerce()[field_names]
ed_dtypes, ed_fields, es_date_formats = ed_field_mappings.metric_source_fields()
pd_metric = pd_ecommerce.select_dtypes(include=np.number)
assert pd_metric.dtypes.to_list() == ed_dtypes
assert pd_metric.columns.to_list() == ed_fields
assert len(es_date_formats) == len(ed_dtypes)
assert set(es_date_formats) == {None}
| 39.025157
| 88
| 0.646575
| 754
| 6,205
| 4.950928
| 0.190981
| 0.038575
| 0.062684
| 0.027324
| 0.725422
| 0.724618
| 0.716046
| 0.70667
| 0.70667
| 0.704795
| 0
| 0.004454
| 0.27639
| 6,205
| 158
| 89
| 39.272152
| 0.826949
| 0.109589
| 0
| 0.676768
| 0
| 0
| 0.04783
| 0.006321
| 0
| 0
| 0
| 0.006329
| 0.242424
| 1
| 0.060606
| false
| 0
| 0.040404
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
5930749703f2f85e5b805d03b801c6c8d1b05b84
| 171
|
py
|
Python
|
sipam/serializers/__init__.py
|
Selfnet/sipam
|
32d7fde288cf7200cde170eadbd6b3541fa730fe
|
[
"Apache-2.0"
] | 2
|
2020-04-19T20:00:32.000Z
|
2022-01-01T21:00:06.000Z
|
sipam/serializers/__init__.py
|
Selfnet/sipam
|
32d7fde288cf7200cde170eadbd6b3541fa730fe
|
[
"Apache-2.0"
] | 7
|
2020-06-05T22:41:24.000Z
|
2022-02-28T01:42:45.000Z
|
sipam/serializers/__init__.py
|
Selfnet/sipam
|
32d7fde288cf7200cde170eadbd6b3541fa730fe
|
[
"Apache-2.0"
] | null | null | null |
from .assignment import AssignmentSerializer
from .cidr import CIDRSerializer, RecursiveCIDRSerializer
from .pool import PoolSerializer
from .label import LabelSerializer
| 34.2
| 57
| 0.871345
| 17
| 171
| 8.764706
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.099415
| 171
| 4
| 58
| 42.75
| 0.967532
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3caa48ade5f074f5b232a3b1022672537c7cd852
| 2,050
|
py
|
Python
|
meridian/channels/gallbladder.py
|
sinotradition/meridian
|
8c6c1762b204b72346be4bbfb74dedd792ae3024
|
[
"Apache-2.0"
] | 5
|
2015-12-14T15:14:23.000Z
|
2022-02-09T10:15:33.000Z
|
meridian/channels/gallbladder.py
|
sinotradition/meridian
|
8c6c1762b204b72346be4bbfb74dedd792ae3024
|
[
"Apache-2.0"
] | null | null | null |
meridian/channels/gallbladder.py
|
sinotradition/meridian
|
8c6c1762b204b72346be4bbfb74dedd792ae3024
|
[
"Apache-2.0"
] | 3
|
2015-11-27T05:23:49.000Z
|
2020-11-28T09:01:56.000Z
|
#!/usr/bin/python
#coding=utf-8
'''
@author: sheng
@license:
'''
from meridian.acupoints import tongziliao232
from meridian.acupoints import tinghui14
from meridian.acupoints import shangguan41
from meridian.acupoints import heyan24
from meridian.acupoints import xuanlu22
from meridian.acupoints import xuanli22
from meridian.acupoints import qubin14
from meridian.acupoints import shuaigu43
from meridian.acupoints import tianchong11
from meridian.acupoints import fubai22
from meridian.acupoints import touqiaoyin241
from meridian.acupoints import wangu23
from meridian.acupoints import benshen32
from meridian.acupoints import yangbai22
from meridian.acupoints import toulinqi221
from meridian.acupoints import muchuang41
from meridian.acupoints import zhengying42
from meridian.acupoints import chengling22
from meridian.acupoints import naokong31
from meridian.acupoints import fengchi12
from meridian.acupoints import jianjing13
from meridian.acupoints import yuanye14
from meridian.acupoints import zhejin21
from meridian.acupoints import riyue44
from meridian.acupoints import jingmen12
from meridian.acupoints import daimai44
from meridian.acupoints import wushu31
from meridian.acupoints import weidao24
from meridian.acupoints import juliao12
from meridian.acupoints import huantiao24
from meridian.acupoints import fengshi14
from meridian.acupoints import zhongdu12
from meridian.acupoints import xiyangguan121
from meridian.acupoints import yanglingquan222
from meridian.acupoints import yangjiao21
from meridian.acupoints import waiqiu41
from meridian.acupoints import guangming12
from meridian.acupoints import yangfu23
from meridian.acupoints import xuanzhong21
from meridian.acupoints import qiuxu11
from meridian.acupoints import zulinqi224
from meridian.acupoints import diwuhui434
from meridian.acupoints import xiaxi21
from meridian.acupoints import zuqiaoyin241
SPELL=u'zúshàoyángdǎnjīng'
CN=u'足少阳胆经'
ABBR=u'GB'
NAME='gallbladder'
FULLNAME='GallbladderChannelofFoot-Shaoyang'
SEQ=8
if __name__ == '__main__':
pass
| 30.597015
| 46
| 0.858049
| 249
| 2,050
| 7.032129
| 0.293173
| 0.301542
| 0.527698
| 0.678469
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053174
| 0.100976
| 2,050
| 66
| 47
| 31.060606
| 0.896907
| 0.026341
| 0
| 0
| 0
| 0
| 0.038249
| 0.016608
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.019231
| 0.846154
| 0
| 0.846154
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3cc0dc589d60eab725ed7fba20be4f5f092d223e
| 591
|
py
|
Python
|
djmodels/contrib/gis/utils/__init__.py
|
iMerica/dj-models
|
fbe4a55ac362f9355a2298f58aa0deb0b6082e19
|
[
"BSD-3-Clause"
] | 5
|
2019-02-15T16:47:50.000Z
|
2021-12-26T18:52:23.000Z
|
djmodels/contrib/gis/utils/__init__.py
|
iMerica/dj-models
|
fbe4a55ac362f9355a2298f58aa0deb0b6082e19
|
[
"BSD-3-Clause"
] | null | null | null |
djmodels/contrib/gis/utils/__init__.py
|
iMerica/dj-models
|
fbe4a55ac362f9355a2298f58aa0deb0b6082e19
|
[
"BSD-3-Clause"
] | 2
|
2021-08-09T02:29:09.000Z
|
2021-08-20T03:30:11.000Z
|
"""
This module contains useful utilities for GeoDjango.
"""
from djmodels.contrib.gis.utils.ogrinfo import ogrinfo # NOQA
from djmodels.contrib.gis.utils.ogrinspect import mapping, ogrinspect # NOQA
from djmodels.contrib.gis.utils.srs import add_srs_entry # NOQA
from djmodels.core.exceptions import ImproperlyConfigured
try:
# LayerMapping requires DJMODELS_SETTINGS_MODULE to be set,
# and ImproperlyConfigured is raised if that's not the case.
from djmodels.contrib.gis.utils.layermapping import LayerMapping, LayerMapError # NOQA
except ImproperlyConfigured:
pass
| 39.4
| 91
| 0.796954
| 75
| 591
| 6.226667
| 0.56
| 0.12848
| 0.162741
| 0.188437
| 0.248394
| 0.132762
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138748
| 591
| 14
| 92
| 42.214286
| 0.917485
| 0.321489
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.125
| 0.625
| 0
| 0.625
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
3cdbf08b5f5fff9387cf356b7ff390a157b056b6
| 91
|
py
|
Python
|
backend/reservas/admin.py
|
ES2-UFPI/404-portal
|
ac673d341a5a215441859fcd6184ff1e22a3fab4
|
[
"Apache-2.0"
] | 1
|
2019-03-21T19:53:55.000Z
|
2019-03-21T19:53:55.000Z
|
backend/reservas/admin.py
|
ES2-UFPI/404-portal
|
ac673d341a5a215441859fcd6184ff1e22a3fab4
|
[
"Apache-2.0"
] | 46
|
2019-03-28T14:34:19.000Z
|
2021-09-22T19:02:11.000Z
|
backend/reservas/admin.py
|
ES2-UFPI/404-portal
|
ac673d341a5a215441859fcd6184ff1e22a3fab4
|
[
"Apache-2.0"
] | 1
|
2022-02-17T16:51:04.000Z
|
2022-02-17T16:51:04.000Z
|
from django.contrib import admin
from .models import Reserva
admin.site.register(Reserva)
| 18.2
| 32
| 0.824176
| 13
| 91
| 5.769231
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10989
| 91
| 4
| 33
| 22.75
| 0.925926
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
5953f7d667b442f547a3963748e76fd82869bab9
| 205
|
py
|
Python
|
haptools/data/__init__.py
|
aryarm/admixtools
|
9b4393aa2985e1a3471485a892da724c52e1bca9
|
[
"MIT"
] | null | null | null |
haptools/data/__init__.py
|
aryarm/admixtools
|
9b4393aa2985e1a3471485a892da724c52e1bca9
|
[
"MIT"
] | 3
|
2021-12-20T06:25:46.000Z
|
2022-02-05T00:33:19.000Z
|
haptools/data/__init__.py
|
aryarm/admixtools
|
9b4393aa2985e1a3471485a892da724c52e1bca9
|
[
"MIT"
] | null | null | null |
from .data import Data
from .genotypes import Genotypes, GenotypesRefAlt
from .phenotypes import Phenotypes
from .covariates import Covariates
from .haplotypes import Extra, Variant, Haplotype, Haplotypes
| 34.166667
| 61
| 0.839024
| 24
| 205
| 7.166667
| 0.458333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117073
| 205
| 5
| 62
| 41
| 0.950276
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
595b1162da14ef59b0f22a913681ef6fc35e7c01
| 45
|
py
|
Python
|
wave/wave/freq_old/domain/__init__.py
|
jedhsu/wave
|
a05d8f4b0a96722bdc2f5a514646c7a44681982b
|
[
"Apache-2.0"
] | null | null | null |
wave/wave/freq_old/domain/__init__.py
|
jedhsu/wave
|
a05d8f4b0a96722bdc2f5a514646c7a44681982b
|
[
"Apache-2.0"
] | null | null | null |
wave/wave/freq_old/domain/__init__.py
|
jedhsu/wave
|
a05d8f4b0a96722bdc2f5a514646c7a44681982b
|
[
"Apache-2.0"
] | null | null | null |
from .param import *
from .spectrum import *
| 15
| 23
| 0.733333
| 6
| 45
| 5.5
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177778
| 45
| 2
| 24
| 22.5
| 0.891892
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
596890ffe3e3cc8b3be9c28d0dbf0315b87054e0
| 53
|
py
|
Python
|
docs/source/plots/var_plot_forecast.py
|
madhushree14/statsmodels
|
04f00006a7aeb1c93d6894caa420698400da6c33
|
[
"BSD-3-Clause"
] | 6,931
|
2015-01-01T11:41:55.000Z
|
2022-03-31T17:03:24.000Z
|
docs/source/plots/var_plot_forecast.py
|
madhushree14/statsmodels
|
04f00006a7aeb1c93d6894caa420698400da6c33
|
[
"BSD-3-Clause"
] | 6,137
|
2015-01-01T00:33:45.000Z
|
2022-03-31T22:53:17.000Z
|
docs/source/plots/var_plot_forecast.py
|
madhushree14/statsmodels
|
04f00006a7aeb1c93d6894caa420698400da6c33
|
[
"BSD-3-Clause"
] | 2,608
|
2015-01-02T21:32:31.000Z
|
2022-03-31T07:38:30.000Z
|
from var_plots import plot_forecast
plot_forecast()
| 13.25
| 35
| 0.849057
| 8
| 53
| 5.25
| 0.75
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113208
| 53
| 3
| 36
| 17.666667
| 0.893617
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
59821f20d513c2ecfc35aeb284a1603cce8d3463
| 777
|
py
|
Python
|
minydra/console.py
|
pg2455/minydra
|
c1ac4987808b57e5dd0dfc332252623495ce4b15
|
[
"MIT"
] | null | null | null |
minydra/console.py
|
pg2455/minydra
|
c1ac4987808b57e5dd0dfc332252623495ce4b15
|
[
"MIT"
] | null | null | null |
minydra/console.py
|
pg2455/minydra
|
c1ac4987808b57e5dd0dfc332252623495ce4b15
|
[
"MIT"
] | null | null | null |
class bcolors:
HEADER = "\033[95m"
OKBLUE = "\033[94m"
OKGREEN = "\033[92m"
WARNING = "\033[93m"
FAIL = "\033[91m"
ENDC = "\033[0m"
BOLD = "\033[1m"
UNDERLINE = "\033[4m"
class MinyConsole:
def _end(self, key, *args):
return f"{key}{' '.join(args)}{bcolors.ENDC}"
def okblue(self, *args):
return self._end(bcolors.OKBLUE, *args)
def warn(self, *args):
return self._end(bcolors.WARNING, *args)
def okgreen(self, *args):
return self._end(bcolors.OKGREEN, *args)
def fail(self, *args):
return self._end(bcolors.FAIL, *args)
def bold(self, *args):
return self._end(bcolors.BOLD, *args)
def underline(self, *args):
return self._end(bcolors.UNDERLINE, *args)
| 23.545455
| 53
| 0.584299
| 100
| 777
| 4.47
| 0.29
| 0.1566
| 0.187919
| 0.241611
| 0.375839
| 0.375839
| 0
| 0
| 0
| 0
| 0
| 0.063574
| 0.250965
| 777
| 32
| 54
| 24.28125
| 0.704467
| 0
| 0
| 0
| 0
| 0
| 0.123552
| 0.034749
| 0
| 0
| 0
| 0
| 0
| 1
| 0.291667
| false
| 0
| 0
| 0.291667
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
59884f8d1b85980e98171ff305ebedead302adca
| 198
|
py
|
Python
|
doba/structures/__init__.py
|
marverix/doba
|
2ba1551be2bd891ece70d4ead1adea5b83f7486e
|
[
"Apache-2.0"
] | null | null | null |
doba/structures/__init__.py
|
marverix/doba
|
2ba1551be2bd891ece70d4ead1adea5b83f7486e
|
[
"Apache-2.0"
] | null | null | null |
doba/structures/__init__.py
|
marverix/doba
|
2ba1551be2bd891ece70d4ead1adea5b83f7486e
|
[
"Apache-2.0"
] | null | null | null |
from .BackupPath import BackupPath
from .Container import Container
from .DobaContainersConfig import DobaContainersConfig
from .Image import Image
from .Port import Port
from .Volume import Volume
| 28.285714
| 54
| 0.848485
| 24
| 198
| 7
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 198
| 6
| 55
| 33
| 0.965517
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
599967b5c70e420cafb5f200f50543c068f04a01
| 88
|
py
|
Python
|
app/module_load/__init__.py
|
B02902008/TaipeiWater
|
7364ce0bdfafddb7448cd8943c0c048f1a199dda
|
[
"MIT"
] | null | null | null |
app/module_load/__init__.py
|
B02902008/TaipeiWater
|
7364ce0bdfafddb7448cd8943c0c048f1a199dda
|
[
"MIT"
] | null | null | null |
app/module_load/__init__.py
|
B02902008/TaipeiWater
|
7364ce0bdfafddb7448cd8943c0c048f1a199dda
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
blue_load = Blueprint('load', __name__)
from . import view
| 17.6
| 39
| 0.772727
| 12
| 88
| 5.25
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147727
| 88
| 5
| 40
| 17.6
| 0.84
| 0
| 0
| 0
| 0
| 0
| 0.044944
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0.666667
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 5
|
59b01082e2e5bc03379549faaca4737d8b437504
| 24
|
py
|
Python
|
week 2/datatype.py
|
marksikaundi/Computer-Programming-with-Python
|
136fec0196a246eb47e3802337fb3fad38d441bb
|
[
"Intel"
] | 2
|
2022-01-18T09:13:36.000Z
|
2022-01-18T09:41:05.000Z
|
week 2/datatype.py
|
marksikaundi/Computer-Programming-with-Python
|
136fec0196a246eb47e3802337fb3fad38d441bb
|
[
"Intel"
] | null | null | null |
week 2/datatype.py
|
marksikaundi/Computer-Programming-with-Python
|
136fec0196a246eb47e3802337fb3fad38d441bb
|
[
"Intel"
] | null | null | null |
print("this is dataype")
| 24
| 24
| 0.75
| 4
| 24
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 24
| 1
| 24
| 24
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
59bcee36fa1749592bf27dc1effeefa6c48b950e
| 82
|
py
|
Python
|
mathfun/primes/__init__.py
|
lsbardel/mathfun
|
98e7c210409c2b5777e91059c3651cef4f3045dd
|
[
"BSD-3-Clause"
] | null | null | null |
mathfun/primes/__init__.py
|
lsbardel/mathfun
|
98e7c210409c2b5777e91059c3651cef4f3045dd
|
[
"BSD-3-Clause"
] | null | null | null |
mathfun/primes/__init__.py
|
lsbardel/mathfun
|
98e7c210409c2b5777e91059c3651cef4f3045dd
|
[
"BSD-3-Clause"
] | null | null | null |
from .gcd import xgcd
from .prime_numbers import factors, is_prime, prime_factors
| 27.333333
| 59
| 0.829268
| 13
| 82
| 5
| 0.615385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121951
| 82
| 2
| 60
| 41
| 0.902778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
abfb8a59f2c5a921f7dfdacb014f216ec2dd9879
| 39
|
py
|
Python
|
djsubject/__init__.py
|
ttngu207/canonical-colony-management
|
0bf0016f6836b3c6d90fe4b38871928e16dc0b9f
|
[
"MIT"
] | null | null | null |
djsubject/__init__.py
|
ttngu207/canonical-colony-management
|
0bf0016f6836b3c6d90fe4b38871928e16dc0b9f
|
[
"MIT"
] | null | null | null |
djsubject/__init__.py
|
ttngu207/canonical-colony-management
|
0bf0016f6836b3c6d90fe4b38871928e16dc0b9f
|
[
"MIT"
] | 3
|
2020-05-06T23:44:26.000Z
|
2020-05-21T23:05:08.000Z
|
from .subject import schema as subject
| 19.5
| 38
| 0.820513
| 6
| 39
| 5.333333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 39
| 1
| 39
| 39
| 0.969697
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
0501fdb7ce164c9cfa4a45dfed386b7e996ed439
| 3,701
|
py
|
Python
|
Icons/fu4028.py
|
friedc/fu
|
2f457ebb3b6f98a3a9838ec791e88044594c9bca
|
[
"BSD-2-Clause"
] | null | null | null |
Icons/fu4028.py
|
friedc/fu
|
2f457ebb3b6f98a3a9838ec791e88044594c9bca
|
[
"BSD-2-Clause"
] | null | null | null |
Icons/fu4028.py
|
friedc/fu
|
2f457ebb3b6f98a3a9838ec791e88044594c9bca
|
[
"BSD-2-Clause"
] | null | null | null |
#----------------------------------------------------------------------
# This file was generated by C:\Python27\Scripts\img2py
#
from wx.lib.embeddedimage import PyEmbeddedImage
fu4028 = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAACgAAAAcCAYAAAATFf3WAAAAAXNSR0IArs4c6QAAAARnQU1B"
"AACxjwv8YQUAAAAJcEhZcwAADsIAAA7CARUoSoAAAAh8SURBVFhHzVgLbFTHFT379r/rXa/t"
"XZvFv1ITjCGJaQsEEFSiqQrBNIUAhrZpcFJIqqZRKpGojUgl1OZD2gQQARGpQQ0NpJBGClIk"
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"AAAASUVORK5CYII=")
getfu4028Data = fu4028.GetData
getfu4028Image = fu4028.GetImage
getfu4028Bitmap = fu4028.GetBitmap
| 68.537037
| 79
| 0.851932
| 141
| 3,701
| 22.361702
| 0.964539
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138579
| 0.079708
| 3,701
| 53
| 80
| 69.830189
| 0.78714
| 0.033504
| 0
| 0
| 1
| 0
| 0.863636
| 0.859091
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0.020833
| 0.020833
| 0
| 0.020833
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| 1
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| 1
| 1
| null | 1
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| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
050f6e8862f46f19f26b98bf9249d85c71cdf4c8
| 177
|
py
|
Python
|
Buzznauts/__init__.py
|
eduardojdiniz/Buzznauts
|
8ac242a8d5309b4090a0f0b148ec275cac762bc0
|
[
"MIT"
] | 2
|
2021-08-03T15:07:04.000Z
|
2022-03-02T15:10:07.000Z
|
Buzznauts/__init__.py
|
eduardojdiniz/Buzznauts
|
8ac242a8d5309b4090a0f0b148ec275cac762bc0
|
[
"MIT"
] | 8
|
2021-08-04T14:21:14.000Z
|
2021-08-16T21:07:12.000Z
|
Buzznauts/__init__.py
|
eduardojdiniz/Buzznauts
|
8ac242a8d5309b4090a0f0b148ec275cac762bc0
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# coding=utf-8
from __future__ import absolute_import, division, print_function
from .version import __version__ # noqa
from .Buzznauts import * # noqa
| 25.285714
| 64
| 0.774011
| 24
| 177
| 5.291667
| 0.708333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006579
| 0.141243
| 177
| 6
| 65
| 29.5
| 0.828947
| 0.242938
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
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| 1
| 0.333333
| 1
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| 0
| null | 0
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| 0
| 0
| 0
| 0
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| 0
| 1
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
055b6d75c35fa7f14482be9ba1095496a0d3ec94
| 81
|
py
|
Python
|
jaseci_core/jaseci/actions/vector.py
|
Gim3l/jaseci
|
cca187ed3e6aae31514c6c0353a7844f7703d039
|
[
"MIT"
] | null | null | null |
jaseci_core/jaseci/actions/vector.py
|
Gim3l/jaseci
|
cca187ed3e6aae31514c6c0353a7844f7703d039
|
[
"MIT"
] | null | null | null |
jaseci_core/jaseci/actions/vector.py
|
Gim3l/jaseci
|
cca187ed3e6aae31514c6c0353a7844f7703d039
|
[
"MIT"
] | null | null | null |
"""Built in actions for Jaseci"""
from .module.vector_actions import * # noqa
| 16.2
| 44
| 0.703704
| 11
| 81
| 5.090909
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.17284
| 81
| 4
| 45
| 20.25
| 0.835821
| 0.407407
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
056c390c7cc74e9f86f6fb364220f0bf7d143093
| 30
|
py
|
Python
|
powerline_owmweather/__init__.py
|
suoto/powerline-owmweather
|
529b6928bdcd43f731e665c03227158f36872624
|
[
"MIT"
] | null | null | null |
powerline_owmweather/__init__.py
|
suoto/powerline-owmweather
|
529b6928bdcd43f731e665c03227158f36872624
|
[
"MIT"
] | null | null | null |
powerline_owmweather/__init__.py
|
suoto/powerline-owmweather
|
529b6928bdcd43f731e665c03227158f36872624
|
[
"MIT"
] | null | null | null |
from .weather import weather
| 10
| 28
| 0.8
| 4
| 30
| 6
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 30
| 2
| 29
| 15
| 0.96
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
0591bc17586cf19a6f9f80eb8f6a0726273e6f12
| 32
|
py
|
Python
|
problem/01000~09999/04999/4999.py3.py
|
njw1204/BOJ-AC
|
1de41685725ae4657a7ff94e413febd97a888567
|
[
"MIT"
] | 1
|
2019-04-19T16:37:44.000Z
|
2019-04-19T16:37:44.000Z
|
problem/01000~09999/04999/4999.py3.py
|
njw1204/BOJ-AC
|
1de41685725ae4657a7ff94e413febd97a888567
|
[
"MIT"
] | 1
|
2019-04-20T11:42:44.000Z
|
2019-04-20T11:42:44.000Z
|
problem/01000~09999/04999/4999.py3.py
|
njw1204/BOJ-AC
|
1de41685725ae4657a7ff94e413febd97a888567
|
[
"MIT"
] | 3
|
2019-04-19T16:37:47.000Z
|
2021-10-25T00:45:00.000Z
|
print('gn'[input()>input()]+'o')
| 32
| 32
| 0.5625
| 5
| 32
| 3.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 32
| 1
| 32
| 32
| 0.5625
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
5538e2681589f52e8e442691ef1dc410b9be755e
| 42
|
py
|
Python
|
scripts/lastdata.py
|
winkste/rki2_scraper
|
e8ad3e9ab2530a5b9a91886e263ac7e137030ce6
|
[
"MIT"
] | null | null | null |
scripts/lastdata.py
|
winkste/rki2_scraper
|
e8ad3e9ab2530a5b9a91886e263ac7e137030ce6
|
[
"MIT"
] | null | null | null |
scripts/lastdata.py
|
winkste/rki2_scraper
|
e8ad3e9ab2530a5b9a91886e263ac7e137030ce6
|
[
"MIT"
] | null | null | null |
celle_last_inc =121.53
noh_last_inc =73.25
| 21
| 22
| 0.833333
| 10
| 42
| 3.1
| 0.8
| 0.451613
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.230769
| 0.071429
| 42
| 2
| 23
| 21
| 0.564103
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
5545884a3f92092201b6771a732a3c7e12b36068
| 34
|
py
|
Python
|
reth/reth/algorithm/dqn/__init__.py
|
sosp2021/Reth
|
10c032f44a25049355ebdd97a2cb3299e8c3fb82
|
[
"MIT"
] | null | null | null |
reth/reth/algorithm/dqn/__init__.py
|
sosp2021/Reth
|
10c032f44a25049355ebdd97a2cb3299e8c3fb82
|
[
"MIT"
] | 1
|
2021-08-10T02:58:58.000Z
|
2021-08-10T02:58:58.000Z
|
reth/reth/algorithm/dqn/__init__.py
|
sosp2021/reth
|
10c032f44a25049355ebdd97a2cb3299e8c3fb82
|
[
"MIT"
] | null | null | null |
from .dqn_solver import DQNSolver
| 17
| 33
| 0.852941
| 5
| 34
| 5.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 34
| 1
| 34
| 34
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
554b34eafe0ff961a9ed8a5ad7beef54aa4a65fe
| 63
|
py
|
Python
|
aerokit/aero/riemann.py
|
PierreMignerot/aerokit
|
78717288d840ef5cb3939b44e967cf8f250dc270
|
[
"MIT"
] | null | null | null |
aerokit/aero/riemann.py
|
PierreMignerot/aerokit
|
78717288d840ef5cb3939b44e967cf8f250dc270
|
[
"MIT"
] | null | null | null |
aerokit/aero/riemann.py
|
PierreMignerot/aerokit
|
78717288d840ef5cb3939b44e967cf8f250dc270
|
[
"MIT"
] | null | null | null |
# backward compatibility
from aerokit.instance.riemann import *
| 31.5
| 38
| 0.84127
| 7
| 63
| 7.571429
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 63
| 2
| 38
| 31.5
| 0.929825
| 0.349206
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
555b4334789b104c3cb3ee0b3b11a1d01f452cfe
| 36,708
|
py
|
Python
|
komle/bindings/v1411/write/_gmd.py
|
HemersonRafael/komle
|
01fb03447e063130c6a2c6830e07bbd220518996
|
[
"Apache-2.0"
] | null | null | null |
komle/bindings/v1411/write/_gmd.py
|
HemersonRafael/komle
|
01fb03447e063130c6a2c6830e07bbd220518996
|
[
"Apache-2.0"
] | null | null | null |
komle/bindings/v1411/write/_gmd.py
|
HemersonRafael/komle
|
01fb03447e063130c6a2c6830e07bbd220518996
|
[
"Apache-2.0"
] | null | null | null |
# komle/bindings/v1411/write/_gmd.py
# -*- coding: utf-8 -*-
# PyXB bindings for NM:410705b36885cae3fa86a581e054e9b962463ed8
# Generated 2020-05-05 12:37:19.643594 by PyXB version 1.2.6 using Python 3.8.2.final.0
# Namespace http://www.isotc211.org/2005/gmd [xmlns:gmd]
from __future__ import unicode_literals
import io
import sys
import pyxb
import pyxb.binding
import pyxb.binding.saxer
import pyxb.utils.domutils
import pyxb.utils.six as _six
import pyxb.utils.utility
# Unique identifier for bindings created at the same time
_GenerationUID = pyxb.utils.utility.UniqueIdentifier(
'urn:uuid:23c8451a-8ebc-11ea-ae29-f507f064c4f5'
)
# Version of PyXB used to generate the bindings
_PyXBVersion = '1.2.6'
# Generated bindings are not compatible across PyXB versions
if pyxb.__version__ != _PyXBVersion:
raise pyxb.PyXBVersionError(_PyXBVersion)
# A holder for module-level binding classes so we can access them from
# inside class definitions where property names may conflict.
_module_typeBindings = pyxb.utils.utility.Object()
# Import bindings for namespaces imported into schema
import komle.bindings.v1411.write._nsgroup as _ImportedBinding_bindings_v1411_write__nsgroup
# NOTE: All namespace declarations are reserved within the binding
Namespace = pyxb.namespace.NamespaceForURI(
'http://www.isotc211.org/2005/gmd', create_if_missing=True
)
Namespace.configureCategories(['typeBinding', 'elementBinding'])
def CreateFromDocument(xml_text, default_namespace=None, location_base=None):
"""Parse the given XML and use the document element to create a
Python instance.
@param xml_text An XML document. This should be data (Python 2
str or Python 3 bytes), or a text (Python 2 unicode or Python 3
str) in the L{pyxb._InputEncoding} encoding.
@keyword default_namespace The L{pyxb.Namespace} instance to use as the
default namespace where there is no default namespace in scope.
If unspecified or C{None}, the namespace of the module containing
this function will be used.
@keyword location_base: An object to be recorded as the base of all
L{pyxb.utils.utility.Location} instances associated with events and
objects handled by the parser. You might pass the URI from which
the document was obtained.
"""
if pyxb.XMLStyle_saxer != pyxb._XMLStyle:
dom = pyxb.utils.domutils.StringToDOM(xml_text)
return CreateFromDOM(
dom.documentElement, default_namespace=default_namespace
)
if default_namespace is None:
default_namespace = Namespace.fallbackNamespace()
saxer = pyxb.binding.saxer.make_parser(
fallback_namespace=default_namespace, location_base=location_base
)
handler = saxer.getContentHandler()
xmld = xml_text
if isinstance(xmld, _six.text_type):
xmld = xmld.encode(pyxb._InputEncoding)
saxer.parse(io.BytesIO(xmld))
instance = handler.rootObject()
return instance
def CreateFromDOM(node, default_namespace=None):
"""Create a Python instance from the given DOM node.
The node tag must correspond to an element declaration in this module.
@deprecated: Forcing use of DOM interface is unnecessary; use L{CreateFromDocument}."""
if default_namespace is None:
default_namespace = Namespace.fallbackNamespace()
return pyxb.binding.basis.element.AnyCreateFromDOM(node, default_namespace)
from komle.bindings.v1411.write._nsgroup import ( # {http://www.isotc211.org/2005/gmd}URL; {http://www.isotc211.org/2005/gmd}CI_RoleCode; {http://www.isotc211.org/2005/gmd}CI_PresentationFormCode; {http://www.isotc211.org/2005/gmd}CI_OnLineFunctionCode; {http://www.isotc211.org/2005/gmd}CI_DateTypeCode; {http://www.isotc211.org/2005/gmd}MD_ClassificationCode; {http://www.isotc211.org/2005/gmd}MD_RestrictionCode; {http://www.isotc211.org/2005/gmd}MD_CoverageContentTypeCode; {http://www.isotc211.org/2005/gmd}MD_ImagingConditionCode; {http://www.isotc211.org/2005/gmd}DQ_EvaluationMethodTypeCode; {http://www.isotc211.org/2005/gmd}MD_DistributionUnits; {http://www.isotc211.org/2005/gmd}MD_MediumFormatCode; {http://www.isotc211.org/2005/gmd}MD_MediumNameCode; {http://www.isotc211.org/2005/gmd}LocalisedCharacterString; {http://www.isotc211.org/2005/gmd}PT_LocaleContainer; {http://www.isotc211.org/2005/gmd}LanguageCode; {http://www.isotc211.org/2005/gmd}Country; {http://www.isotc211.org/2005/gmd}MD_Resolution; {http://www.isotc211.org/2005/gmd}MD_TopicCategoryCode; {http://www.isotc211.org/2005/gmd}MD_CharacterSetCode; {http://www.isotc211.org/2005/gmd}MD_SpatialRepresentationTypeCode; {http://www.isotc211.org/2005/gmd}MD_ProgressCode; {http://www.isotc211.org/2005/gmd}MD_KeywordTypeCode; {http://www.isotc211.org/2005/gmd}DS_AssociationTypeCode; {http://www.isotc211.org/2005/gmd}DS_InitiativeTypeCode; {http://www.isotc211.org/2005/gmd}MD_ScopeDescription; {http://www.isotc211.org/2005/gmd}MD_MaintenanceFrequencyCode; {http://www.isotc211.org/2005/gmd}MD_ScopeCode; {http://www.isotc211.org/2005/gmd}MD_ObligationCode; {http://www.isotc211.org/2005/gmd}MD_DatatypeCode; {http://www.isotc211.org/2005/gmd}MD_PixelOrientationCode; {http://www.isotc211.org/2005/gmd}MD_TopologyLevelCode; {http://www.isotc211.org/2005/gmd}MD_GeometricObjectTypeCode; {http://www.isotc211.org/2005/gmd}MD_CellGeometryCode; {http://www.isotc211.org/2005/gmd}MD_DimensionNameTypeCode; {http://www.isotc211.org/2005/gmd}MD_ApplicationSchemaInformation; {http://www.isotc211.org/2005/gmd}CI_ResponsibleParty; {http://www.isotc211.org/2005/gmd}CI_Citation; {http://www.isotc211.org/2005/gmd}CI_Address; {http://www.isotc211.org/2005/gmd}CI_OnlineResource; {http://www.isotc211.org/2005/gmd}CI_Contact; {http://www.isotc211.org/2005/gmd}CI_Telephone; {http://www.isotc211.org/2005/gmd}CI_Date; {http://www.isotc211.org/2005/gmd}CI_Series; {http://www.isotc211.org/2005/gmd}MD_Constraints; {http://www.isotc211.org/2005/gmd}AbstractMD_ContentInformation; {http://www.isotc211.org/2005/gmd}MD_RangeDimension; {http://www.isotc211.org/2005/gmd}LI_ProcessStep; {http://www.isotc211.org/2005/gmd}LI_Source; {http://www.isotc211.org/2005/gmd}LI_Lineage; {http://www.isotc211.org/2005/gmd}AbstractDQ_Result; {http://www.isotc211.org/2005/gmd}AbstractDQ_Element; {http://www.isotc211.org/2005/gmd}DQ_DataQuality; {http://www.isotc211.org/2005/gmd}DQ_Scope; {http://www.isotc211.org/2005/gmd}MD_Medium; {http://www.isotc211.org/2005/gmd}MD_DigitalTransferOptions; {http://www.isotc211.org/2005/gmd}MD_StandardOrderProcess; {http://www.isotc211.org/2005/gmd}MD_Distributor; {http://www.isotc211.org/2005/gmd}MD_Distribution; {http://www.isotc211.org/2005/gmd}MD_Format; {http://www.isotc211.org/2005/gmd}EX_TemporalExtent; {http://www.isotc211.org/2005/gmd}EX_VerticalExtent; {http://www.isotc211.org/2005/gmd}EX_Extent; {http://www.isotc211.org/2005/gmd}AbstractEX_GeographicExtent; {http://www.isotc211.org/2005/gmd}PT_FreeText; {http://www.isotc211.org/2005/gmd}PT_Locale; {http://www.isotc211.org/2005/gmd}AbstractMD_Identification; {http://www.isotc211.org/2005/gmd}MD_BrowseGraphic; {http://www.isotc211.org/2005/gmd}MD_RepresentativeFraction; {http://www.isotc211.org/2005/gmd}MD_Usage; {http://www.isotc211.org/2005/gmd}MD_Keywords; {http://www.isotc211.org/2005/gmd}DS_Association; {http://www.isotc211.org/2005/gmd}MD_AggregateInformation; {http://www.isotc211.org/2005/gmd}MD_MaintenanceInformation; {http://www.isotc211.org/2005/gmd}AbstractDS_Aggregate; {http://www.isotc211.org/2005/gmd}DS_DataSet; {http://www.isotc211.org/2005/gmd}MD_Metadata; {http://www.isotc211.org/2005/gmd}MD_ExtendedElementInformation; {http://www.isotc211.org/2005/gmd}MD_MetadataExtensionInformation; {http://www.isotc211.org/2005/gmd}MD_PortrayalCatalogueReference; {http://www.isotc211.org/2005/gmd}MD_ReferenceSystem; {http://www.isotc211.org/2005/gmd}MD_Identifier; {http://www.isotc211.org/2005/gmd}AbstractRS_ReferenceSystem; {http://www.isotc211.org/2005/gmd}AbstractMD_SpatialRepresentation; {http://www.isotc211.org/2005/gmd}MD_Dimension; {http://www.isotc211.org/2005/gmd}MD_GeometricObjects; {http://www.isotc211.org/2005/gmd}MD_LegalConstraints; {http://www.isotc211.org/2005/gmd}MD_SecurityConstraints; {http://www.isotc211.org/2005/gmd}MD_FeatureCatalogueDescription; {http://www.isotc211.org/2005/gmd}MD_CoverageDescription; {http://www.isotc211.org/2005/gmd}MD_Band; {http://www.isotc211.org/2005/gmd}DQ_ConformanceResult; {http://www.isotc211.org/2005/gmd}DQ_QuantitativeResult; {http://www.isotc211.org/2005/gmd}AbstractDQ_TemporalAccuracy; {http://www.isotc211.org/2005/gmd}AbstractDQ_ThematicAccuracy; {http://www.isotc211.org/2005/gmd}AbstractDQ_PositionalAccuracy; {http://www.isotc211.org/2005/gmd}AbstractDQ_LogicalConsistency; {http://www.isotc211.org/2005/gmd}AbstractDQ_Completeness; {http://www.isotc211.org/2005/gmd}EX_BoundingPolygon; {http://www.isotc211.org/2005/gmd}EX_GeographicBoundingBox; {http://www.isotc211.org/2005/gmd}EX_SpatialTemporalExtent; {http://www.isotc211.org/2005/gmd}EX_GeographicDescription; {http://www.isotc211.org/2005/gmd}MD_DataIdentification; {http://www.isotc211.org/2005/gmd}MD_ServiceIdentification; {http://www.isotc211.org/2005/gmd}DS_OtherAggregate; {http://www.isotc211.org/2005/gmd}DS_Series; {http://www.isotc211.org/2005/gmd}DS_Initiative; {http://www.isotc211.org/2005/gmd}RS_Identifier; {http://www.isotc211.org/2005/gmd}MD_GridSpatialRepresentation; {http://www.isotc211.org/2005/gmd}MD_VectorSpatialRepresentation; {http://www.isotc211.org/2005/gmd}MD_ImageDescription; {http://www.isotc211.org/2005/gmd}DQ_TemporalValidity; {http://www.isotc211.org/2005/gmd}DQ_TemporalConsistency; {http://www.isotc211.org/2005/gmd}DQ_AccuracyOfATimeMeasurement; {http://www.isotc211.org/2005/gmd}DQ_QuantitativeAttributeAccuracy; {http://www.isotc211.org/2005/gmd}DQ_NonQuantitativeAttributeAccuracy; {http://www.isotc211.org/2005/gmd}DQ_ThematicClassificationCorrectness; {http://www.isotc211.org/2005/gmd}DQ_RelativeInternalPositionalAccuracy; {http://www.isotc211.org/2005/gmd}DQ_GriddedDataPositionalAccuracy; {http://www.isotc211.org/2005/gmd}DQ_AbsoluteExternalPositionalAccuracy; {http://www.isotc211.org/2005/gmd}DQ_TopologicalConsistency; {http://www.isotc211.org/2005/gmd}DQ_FormatConsistency; {http://www.isotc211.org/2005/gmd}DQ_DomainConsistency; {http://www.isotc211.org/2005/gmd}DQ_ConceptualConsistency; {http://www.isotc211.org/2005/gmd}DQ_CompletenessOmission; {http://www.isotc211.org/2005/gmd}DQ_CompletenessCommission; {http://www.isotc211.org/2005/gmd}DS_Platform; {http://www.isotc211.org/2005/gmd}DS_Sensor; {http://www.isotc211.org/2005/gmd}DS_ProductionSeries; {http://www.isotc211.org/2005/gmd}DS_StereoMate; {http://www.isotc211.org/2005/gmd}MD_Georeferenceable; {http://www.isotc211.org/2005/gmd}MD_Georectified; {http://www.isotc211.org/2005/gmd}LocalisedCharacterString_Type; {http://www.isotc211.org/2005/gmd}PT_LocaleContainer_Type; {http://www.isotc211.org/2005/gmd}MD_Resolution_Type; {http://www.isotc211.org/2005/gmd}MD_TopicCategoryCode_Type; {http://www.isotc211.org/2005/gmd}MD_ScopeDescription_Type; {http://www.isotc211.org/2005/gmd}MD_ObligationCode_Type; {http://www.isotc211.org/2005/gmd}MD_PixelOrientationCode_Type; {http://www.isotc211.org/2005/gmd}MD_ApplicationSchemaInformation_Type; {http://www.isotc211.org/2005/gmd}CI_ResponsibleParty_Type; {http://www.isotc211.org/2005/gmd}CI_Citation_Type; {http://www.isotc211.org/2005/gmd}CI_Address_Type; {http://www.isotc211.org/2005/gmd}CI_OnlineResource_Type; {http://www.isotc211.org/2005/gmd}CI_Contact_Type; {http://www.isotc211.org/2005/gmd}CI_Telephone_Type; {http://www.isotc211.org/2005/gmd}CI_Date_Type; {http://www.isotc211.org/2005/gmd}CI_Series_Type; {http://www.isotc211.org/2005/gmd}MD_Constraints_Type; {http://www.isotc211.org/2005/gmd}AbstractMD_ContentInformation_Type; {http://www.isotc211.org/2005/gmd}MD_RangeDimension_Type; {http://www.isotc211.org/2005/gmd}LI_ProcessStep_Type; {http://www.isotc211.org/2005/gmd}LI_Source_Type; {http://www.isotc211.org/2005/gmd}LI_Lineage_Type; {http://www.isotc211.org/2005/gmd}AbstractDQ_Result_Type; {http://www.isotc211.org/2005/gmd}AbstractDQ_Element_Type; {http://www.isotc211.org/2005/gmd}DQ_DataQuality_Type; {http://www.isotc211.org/2005/gmd}DQ_Scope_Type; {http://www.isotc211.org/2005/gmd}MD_Medium_Type; {http://www.isotc211.org/2005/gmd}MD_DigitalTransferOptions_Type; {http://www.isotc211.org/2005/gmd}MD_StandardOrderProcess_Type; {http://www.isotc211.org/2005/gmd}MD_Distributor_Type; {http://www.isotc211.org/2005/gmd}MD_Distribution_Type; {http://www.isotc211.org/2005/gmd}MD_Format_Type; {http://www.isotc211.org/2005/gmd}EX_TemporalExtent_Type; {http://www.isotc211.org/2005/gmd}EX_VerticalExtent_Type; {http://www.isotc211.org/2005/gmd}EX_Extent_Type; {http://www.isotc211.org/2005/gmd}AbstractEX_GeographicExtent_Type; {http://www.isotc211.org/2005/gmd}PT_FreeText_Type; {http://www.isotc211.org/2005/gmd}PT_Locale_Type; {http://www.isotc211.org/2005/gmd}AbstractMD_Identification_Type; {http://www.isotc211.org/2005/gmd}MD_BrowseGraphic_Type; {http://www.isotc211.org/2005/gmd}MD_RepresentativeFraction_Type; {http://www.isotc211.org/2005/gmd}MD_Usage_Type; {http://www.isotc211.org/2005/gmd}MD_Keywords_Type; {http://www.isotc211.org/2005/gmd}DS_Association_Type; {http://www.isotc211.org/2005/gmd}MD_AggregateInformation_Type; {http://www.isotc211.org/2005/gmd}MD_MaintenanceInformation_Type; {http://www.isotc211.org/2005/gmd}AbstractDS_Aggregate_Type; {http://www.isotc211.org/2005/gmd}DS_DataSet_Type; {http://www.isotc211.org/2005/gmd}MD_Metadata_Type; {http://www.isotc211.org/2005/gmd}MD_ExtendedElementInformation_Type; {http://www.isotc211.org/2005/gmd}MD_MetadataExtensionInformation_Type; {http://www.isotc211.org/2005/gmd}MD_PortrayalCatalogueReference_Type; {http://www.isotc211.org/2005/gmd}MD_ReferenceSystem_Type; {http://www.isotc211.org/2005/gmd}MD_Identifier_Type; {http://www.isotc211.org/2005/gmd}AbstractRS_ReferenceSystem_Type; {http://www.isotc211.org/2005/gmd}AbstractMD_SpatialRepresentation_Type; {http://www.isotc211.org/2005/gmd}MD_Dimension_Type; {http://www.isotc211.org/2005/gmd}MD_GeometricObjects_Type; {http://www.isotc211.org/2005/gmd}MD_ApplicationSchemaInformation_PropertyType; {http://www.isotc211.org/2005/gmd}CI_ResponsibleParty_PropertyType; {http://www.isotc211.org/2005/gmd}CI_Citation_PropertyType; {http://www.isotc211.org/2005/gmd}CI_Address_PropertyType; {http://www.isotc211.org/2005/gmd}CI_OnlineResource_PropertyType; {http://www.isotc211.org/2005/gmd}CI_Contact_PropertyType; {http://www.isotc211.org/2005/gmd}CI_Telephone_PropertyType; {http://www.isotc211.org/2005/gmd}CI_Date_PropertyType; {http://www.isotc211.org/2005/gmd}CI_Series_PropertyType; {http://www.isotc211.org/2005/gmd}URL_PropertyType; {http://www.isotc211.org/2005/gmd}CI_RoleCode_PropertyType; {http://www.isotc211.org/2005/gmd}CI_PresentationFormCode_PropertyType; {http://www.isotc211.org/2005/gmd}CI_OnLineFunctionCode_PropertyType; {http://www.isotc211.org/2005/gmd}CI_DateTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Constraints_PropertyType; {http://www.isotc211.org/2005/gmd}MD_LegalConstraints_Type; {http://www.isotc211.org/2005/gmd}MD_LegalConstraints_PropertyType; {http://www.isotc211.org/2005/gmd}MD_SecurityConstraints_Type; {http://www.isotc211.org/2005/gmd}MD_SecurityConstraints_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ClassificationCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_RestrictionCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_FeatureCatalogueDescription_Type; {http://www.isotc211.org/2005/gmd}MD_FeatureCatalogueDescription_PropertyType; {http://www.isotc211.org/2005/gmd}MD_CoverageDescription_Type; {http://www.isotc211.org/2005/gmd}MD_CoverageDescription_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ImageDescription_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ContentInformation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_RangeDimension_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Band_Type; {http://www.isotc211.org/2005/gmd}MD_Band_PropertyType; {http://www.isotc211.org/2005/gmd}MD_CoverageContentTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ImagingConditionCode_PropertyType; {http://www.isotc211.org/2005/gmd}LI_ProcessStep_PropertyType; {http://www.isotc211.org/2005/gmd}LI_Source_PropertyType; {http://www.isotc211.org/2005/gmd}LI_Lineage_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_ConformanceResult_Type; {http://www.isotc211.org/2005/gmd}DQ_ConformanceResult_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_QuantitativeResult_Type; {http://www.isotc211.org/2005/gmd}DQ_QuantitativeResult_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_Result_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_TemporalValidity_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_TemporalConsistency_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_AccuracyOfATimeMeasurement_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_QuantitativeAttributeAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_NonQuantitativeAttributeAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_ThematicClassificationCorrectness_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_RelativeInternalPositionalAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_GriddedDataPositionalAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_AbsoluteExternalPositionalAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_TopologicalConsistency_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_FormatConsistency_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_DomainConsistency_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_ConceptualConsistency_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_CompletenessOmission_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_CompletenessCommission_PropertyType; {http://www.isotc211.org/2005/gmd}AbstractDQ_TemporalAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_TemporalAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}AbstractDQ_ThematicAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_ThematicAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}AbstractDQ_PositionalAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_PositionalAccuracy_PropertyType; {http://www.isotc211.org/2005/gmd}AbstractDQ_LogicalConsistency_Type; {http://www.isotc211.org/2005/gmd}DQ_LogicalConsistency_PropertyType; {http://www.isotc211.org/2005/gmd}AbstractDQ_Completeness_Type; {http://www.isotc211.org/2005/gmd}DQ_Completeness_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_Element_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_DataQuality_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_Scope_PropertyType; {http://www.isotc211.org/2005/gmd}DQ_EvaluationMethodTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Medium_PropertyType; {http://www.isotc211.org/2005/gmd}MD_DigitalTransferOptions_PropertyType; {http://www.isotc211.org/2005/gmd}MD_StandardOrderProcess_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Distributor_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Distribution_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Format_PropertyType; {http://www.isotc211.org/2005/gmd}MD_DistributionUnits_PropertyType; {http://www.isotc211.org/2005/gmd}MD_MediumFormatCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_MediumNameCode_PropertyType; {http://www.isotc211.org/2005/gmd}EX_TemporalExtent_PropertyType; {http://www.isotc211.org/2005/gmd}EX_VerticalExtent_PropertyType; {http://www.isotc211.org/2005/gmd}EX_BoundingPolygon_Type; {http://www.isotc211.org/2005/gmd}EX_BoundingPolygon_PropertyType; {http://www.isotc211.org/2005/gmd}EX_Extent_PropertyType; {http://www.isotc211.org/2005/gmd}EX_GeographicExtent_PropertyType; {http://www.isotc211.org/2005/gmd}EX_GeographicBoundingBox_Type; {http://www.isotc211.org/2005/gmd}EX_GeographicBoundingBox_PropertyType; {http://www.isotc211.org/2005/gmd}EX_SpatialTemporalExtent_Type; {http://www.isotc211.org/2005/gmd}EX_SpatialTemporalExtent_PropertyType; {http://www.isotc211.org/2005/gmd}EX_GeographicDescription_Type; {http://www.isotc211.org/2005/gmd}EX_GeographicDescription_PropertyType; {http://www.isotc211.org/2005/gmd}PT_Locale_PropertyType; {http://www.isotc211.org/2005/gmd}PT_LocaleContainer_PropertyType; {http://www.isotc211.org/2005/gmd}LanguageCode_PropertyType; {http://www.isotc211.org/2005/gmd}Country_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Identification_PropertyType; {http://www.isotc211.org/2005/gmd}MD_BrowseGraphic_PropertyType; {http://www.isotc211.org/2005/gmd}MD_DataIdentification_Type; {http://www.isotc211.org/2005/gmd}MD_DataIdentification_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ServiceIdentification_Type; {http://www.isotc211.org/2005/gmd}MD_ServiceIdentification_PropertyType; {http://www.isotc211.org/2005/gmd}MD_RepresentativeFraction_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Usage_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Keywords_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Association_PropertyType; {http://www.isotc211.org/2005/gmd}MD_AggregateInformation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Resolution_PropertyType; {http://www.isotc211.org/2005/gmd}MD_TopicCategoryCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_CharacterSetCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_SpatialRepresentationTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ProgressCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_KeywordTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}DS_AssociationTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}DS_InitiativeTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_MaintenanceInformation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ScopeDescription_PropertyType; {http://www.isotc211.org/2005/gmd}MD_MaintenanceFrequencyCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ScopeCode_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Aggregate_PropertyType; {http://www.isotc211.org/2005/gmd}DS_DataSet_PropertyType; {http://www.isotc211.org/2005/gmd}DS_OtherAggregate_Type; {http://www.isotc211.org/2005/gmd}DS_OtherAggregate_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Series_Type; {http://www.isotc211.org/2005/gmd}DS_Series_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Initiative_Type; {http://www.isotc211.org/2005/gmd}DS_Initiative_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Platform_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Sensor_PropertyType; {http://www.isotc211.org/2005/gmd}DS_ProductionSeries_PropertyType; {http://www.isotc211.org/2005/gmd}DS_StereoMate_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Metadata_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ExtendedElementInformation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_MetadataExtensionInformation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ObligationCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_DatatypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_PortrayalCatalogueReference_PropertyType; {http://www.isotc211.org/2005/gmd}RS_Identifier_Type; {http://www.isotc211.org/2005/gmd}RS_Identifier_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ReferenceSystem_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Identifier_PropertyType; {http://www.isotc211.org/2005/gmd}RS_ReferenceSystem_PropertyType; {http://www.isotc211.org/2005/gmd}MD_GridSpatialRepresentation_Type; {http://www.isotc211.org/2005/gmd}MD_GridSpatialRepresentation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_VectorSpatialRepresentation_Type; {http://www.isotc211.org/2005/gmd}MD_VectorSpatialRepresentation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_SpatialRepresentation_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Georeferenceable_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Dimension_PropertyType; {http://www.isotc211.org/2005/gmd}MD_Georectified_PropertyType; {http://www.isotc211.org/2005/gmd}MD_GeometricObjects_PropertyType; {http://www.isotc211.org/2005/gmd}MD_PixelOrientationCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_TopologyLevelCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_GeometricObjectTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_CellGeometryCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_DimensionNameTypeCode_PropertyType; {http://www.isotc211.org/2005/gmd}MD_ImageDescription_Type; {http://www.isotc211.org/2005/gmd}DQ_TemporalValidity_Type; {http://www.isotc211.org/2005/gmd}DQ_TemporalConsistency_Type; {http://www.isotc211.org/2005/gmd}DQ_AccuracyOfATimeMeasurement_Type; {http://www.isotc211.org/2005/gmd}DQ_QuantitativeAttributeAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_NonQuantitativeAttributeAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_ThematicClassificationCorrectness_Type; {http://www.isotc211.org/2005/gmd}DQ_RelativeInternalPositionalAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_GriddedDataPositionalAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_AbsoluteExternalPositionalAccuracy_Type; {http://www.isotc211.org/2005/gmd}DQ_TopologicalConsistency_Type; {http://www.isotc211.org/2005/gmd}DQ_FormatConsistency_Type; {http://www.isotc211.org/2005/gmd}DQ_DomainConsistency_Type; {http://www.isotc211.org/2005/gmd}DQ_ConceptualConsistency_Type; {http://www.isotc211.org/2005/gmd}DQ_CompletenessOmission_Type; {http://www.isotc211.org/2005/gmd}DQ_CompletenessCommission_Type; {http://www.isotc211.org/2005/gmd}PT_FreeText_PropertyType; {http://www.isotc211.org/2005/gmd}LocalisedCharacterString_PropertyType; {http://www.isotc211.org/2005/gmd}DS_Platform_Type; {http://www.isotc211.org/2005/gmd}DS_Sensor_Type; {http://www.isotc211.org/2005/gmd}DS_ProductionSeries_Type; {http://www.isotc211.org/2005/gmd}DS_StereoMate_Type; {http://www.isotc211.org/2005/gmd}MD_Georeferenceable_Type; {http://www.isotc211.org/2005/gmd}MD_Georectified_Type
URL, AbstractDQ_Completeness, AbstractDQ_Completeness_Type,
AbstractDQ_Element, AbstractDQ_Element_Type, AbstractDQ_LogicalConsistency,
AbstractDQ_LogicalConsistency_Type, AbstractDQ_PositionalAccuracy,
AbstractDQ_PositionalAccuracy_Type, AbstractDQ_Result,
AbstractDQ_Result_Type, AbstractDQ_TemporalAccuracy,
AbstractDQ_TemporalAccuracy_Type, AbstractDQ_ThematicAccuracy,
AbstractDQ_ThematicAccuracy_Type, AbstractDS_Aggregate,
AbstractDS_Aggregate_Type, AbstractEX_GeographicExtent,
AbstractEX_GeographicExtent_Type, AbstractMD_ContentInformation,
AbstractMD_ContentInformation_Type, AbstractMD_Identification,
AbstractMD_Identification_Type, AbstractMD_SpatialRepresentation,
AbstractMD_SpatialRepresentation_Type, AbstractRS_ReferenceSystem,
AbstractRS_ReferenceSystem_Type, CI_Address, CI_Address_PropertyType,
CI_Address_Type, CI_Citation, CI_Citation_PropertyType, CI_Citation_Type,
CI_Contact, CI_Contact_PropertyType, CI_Contact_Type, CI_Date,
CI_Date_PropertyType, CI_Date_Type, CI_DateTypeCode,
CI_DateTypeCode_PropertyType, CI_OnLineFunctionCode,
CI_OnLineFunctionCode_PropertyType, CI_OnlineResource,
CI_OnlineResource_PropertyType, CI_OnlineResource_Type,
CI_PresentationFormCode, CI_PresentationFormCode_PropertyType,
CI_ResponsibleParty, CI_ResponsibleParty_PropertyType,
CI_ResponsibleParty_Type, CI_RoleCode, CI_RoleCode_PropertyType, CI_Series,
CI_Series_PropertyType, CI_Series_Type, CI_Telephone,
CI_Telephone_PropertyType, CI_Telephone_Type, Country,
Country_PropertyType, DQ_AbsoluteExternalPositionalAccuracy,
DQ_AbsoluteExternalPositionalAccuracy_PropertyType,
DQ_AbsoluteExternalPositionalAccuracy_Type, DQ_AccuracyOfATimeMeasurement,
DQ_AccuracyOfATimeMeasurement_PropertyType,
DQ_AccuracyOfATimeMeasurement_Type, DQ_Completeness_PropertyType,
DQ_CompletenessCommission, DQ_CompletenessCommission_PropertyType,
DQ_CompletenessCommission_Type, DQ_CompletenessOmission,
DQ_CompletenessOmission_PropertyType, DQ_CompletenessOmission_Type,
DQ_ConceptualConsistency, DQ_ConceptualConsistency_PropertyType,
DQ_ConceptualConsistency_Type, DQ_ConformanceResult,
DQ_ConformanceResult_PropertyType, DQ_ConformanceResult_Type,
DQ_DataQuality, DQ_DataQuality_PropertyType, DQ_DataQuality_Type,
DQ_DomainConsistency, DQ_DomainConsistency_PropertyType,
DQ_DomainConsistency_Type, DQ_Element_PropertyType,
DQ_EvaluationMethodTypeCode, DQ_EvaluationMethodTypeCode_PropertyType,
DQ_FormatConsistency, DQ_FormatConsistency_PropertyType,
DQ_FormatConsistency_Type, DQ_GriddedDataPositionalAccuracy,
DQ_GriddedDataPositionalAccuracy_PropertyType,
DQ_GriddedDataPositionalAccuracy_Type, DQ_LogicalConsistency_PropertyType,
DQ_NonQuantitativeAttributeAccuracy,
DQ_NonQuantitativeAttributeAccuracy_PropertyType,
DQ_NonQuantitativeAttributeAccuracy_Type,
DQ_PositionalAccuracy_PropertyType, DQ_QuantitativeAttributeAccuracy,
DQ_QuantitativeAttributeAccuracy_PropertyType,
DQ_QuantitativeAttributeAccuracy_Type, DQ_QuantitativeResult,
DQ_QuantitativeResult_PropertyType, DQ_QuantitativeResult_Type,
DQ_RelativeInternalPositionalAccuracy,
DQ_RelativeInternalPositionalAccuracy_PropertyType,
DQ_RelativeInternalPositionalAccuracy_Type, DQ_Result_PropertyType,
DQ_Scope, DQ_Scope_PropertyType, DQ_Scope_Type,
DQ_TemporalAccuracy_PropertyType, DQ_TemporalConsistency,
DQ_TemporalConsistency_PropertyType, DQ_TemporalConsistency_Type,
DQ_TemporalValidity, DQ_TemporalValidity_PropertyType,
DQ_TemporalValidity_Type, DQ_ThematicAccuracy_PropertyType,
DQ_ThematicClassificationCorrectness,
DQ_ThematicClassificationCorrectness_PropertyType,
DQ_ThematicClassificationCorrectness_Type, DQ_TopologicalConsistency,
DQ_TopologicalConsistency_PropertyType, DQ_TopologicalConsistency_Type,
DS_Aggregate_PropertyType, DS_Association, DS_Association_PropertyType,
DS_Association_Type, DS_AssociationTypeCode,
DS_AssociationTypeCode_PropertyType, DS_DataSet, DS_DataSet_PropertyType,
DS_DataSet_Type, DS_Initiative, DS_Initiative_PropertyType,
DS_Initiative_Type, DS_InitiativeTypeCode,
DS_InitiativeTypeCode_PropertyType, DS_OtherAggregate,
DS_OtherAggregate_PropertyType, DS_OtherAggregate_Type, DS_Platform,
DS_Platform_PropertyType, DS_Platform_Type, DS_ProductionSeries,
DS_ProductionSeries_PropertyType, DS_ProductionSeries_Type, DS_Sensor,
DS_Sensor_PropertyType, DS_Sensor_Type, DS_Series, DS_Series_PropertyType,
DS_Series_Type, DS_StereoMate, DS_StereoMate_PropertyType,
DS_StereoMate_Type, EX_BoundingPolygon, EX_BoundingPolygon_PropertyType,
EX_BoundingPolygon_Type, EX_Extent, EX_Extent_PropertyType, EX_Extent_Type,
EX_GeographicBoundingBox, EX_GeographicBoundingBox_PropertyType,
EX_GeographicBoundingBox_Type, EX_GeographicDescription,
EX_GeographicDescription_PropertyType, EX_GeographicDescription_Type,
EX_GeographicExtent_PropertyType, EX_SpatialTemporalExtent,
EX_SpatialTemporalExtent_PropertyType, EX_SpatialTemporalExtent_Type,
EX_TemporalExtent, EX_TemporalExtent_PropertyType, EX_TemporalExtent_Type,
EX_VerticalExtent, EX_VerticalExtent_PropertyType, EX_VerticalExtent_Type,
LanguageCode, LanguageCode_PropertyType, LI_Lineage,
LI_Lineage_PropertyType, LI_Lineage_Type, LI_ProcessStep,
LI_ProcessStep_PropertyType, LI_ProcessStep_Type, LI_Source,
LI_Source_PropertyType, LI_Source_Type, LocalisedCharacterString,
LocalisedCharacterString_PropertyType, LocalisedCharacterString_Type,
MD_AggregateInformation, MD_AggregateInformation_PropertyType,
MD_AggregateInformation_Type, MD_ApplicationSchemaInformation,
MD_ApplicationSchemaInformation_PropertyType,
MD_ApplicationSchemaInformation_Type, MD_Band, MD_Band_PropertyType,
MD_Band_Type, MD_BrowseGraphic, MD_BrowseGraphic_PropertyType,
MD_BrowseGraphic_Type, MD_CellGeometryCode,
MD_CellGeometryCode_PropertyType, MD_CharacterSetCode,
MD_CharacterSetCode_PropertyType, MD_ClassificationCode,
MD_ClassificationCode_PropertyType, MD_Constraints,
MD_Constraints_PropertyType, MD_Constraints_Type,
MD_ContentInformation_PropertyType, MD_CoverageContentTypeCode,
MD_CoverageContentTypeCode_PropertyType, MD_CoverageDescription,
MD_CoverageDescription_PropertyType, MD_CoverageDescription_Type,
MD_DataIdentification, MD_DataIdentification_PropertyType,
MD_DataIdentification_Type, MD_DatatypeCode, MD_DatatypeCode_PropertyType,
MD_DigitalTransferOptions, MD_DigitalTransferOptions_PropertyType,
MD_DigitalTransferOptions_Type, MD_Dimension, MD_Dimension_PropertyType,
MD_Dimension_Type, MD_DimensionNameTypeCode,
MD_DimensionNameTypeCode_PropertyType, MD_Distribution,
MD_Distribution_PropertyType, MD_Distribution_Type, MD_DistributionUnits,
MD_DistributionUnits_PropertyType, MD_Distributor,
MD_Distributor_PropertyType, MD_Distributor_Type,
MD_ExtendedElementInformation, MD_ExtendedElementInformation_PropertyType,
MD_ExtendedElementInformation_Type, MD_FeatureCatalogueDescription,
MD_FeatureCatalogueDescription_PropertyType,
MD_FeatureCatalogueDescription_Type, MD_Format, MD_Format_PropertyType,
MD_Format_Type, MD_GeometricObjects, MD_GeometricObjects_PropertyType,
MD_GeometricObjects_Type, MD_GeometricObjectTypeCode,
MD_GeometricObjectTypeCode_PropertyType, MD_Georectified,
MD_Georectified_PropertyType, MD_Georectified_Type, MD_Georeferenceable,
MD_Georeferenceable_PropertyType, MD_Georeferenceable_Type,
MD_GridSpatialRepresentation, MD_GridSpatialRepresentation_PropertyType,
MD_GridSpatialRepresentation_Type, MD_Identification_PropertyType,
MD_Identifier, MD_Identifier_PropertyType, MD_Identifier_Type,
MD_ImageDescription, MD_ImageDescription_PropertyType,
MD_ImageDescription_Type, MD_ImagingConditionCode,
MD_ImagingConditionCode_PropertyType, MD_Keywords,
MD_Keywords_PropertyType, MD_Keywords_Type, MD_KeywordTypeCode,
MD_KeywordTypeCode_PropertyType, MD_LegalConstraints,
MD_LegalConstraints_PropertyType, MD_LegalConstraints_Type,
MD_MaintenanceFrequencyCode, MD_MaintenanceFrequencyCode_PropertyType,
MD_MaintenanceInformation, MD_MaintenanceInformation_PropertyType,
MD_MaintenanceInformation_Type, MD_Medium, MD_Medium_PropertyType,
MD_Medium_Type, MD_MediumFormatCode, MD_MediumFormatCode_PropertyType,
MD_MediumNameCode, MD_MediumNameCode_PropertyType, MD_Metadata,
MD_Metadata_PropertyType, MD_Metadata_Type,
MD_MetadataExtensionInformation,
MD_MetadataExtensionInformation_PropertyType,
MD_MetadataExtensionInformation_Type, MD_ObligationCode,
MD_ObligationCode_PropertyType, MD_ObligationCode_Type,
MD_PixelOrientationCode, MD_PixelOrientationCode_PropertyType,
MD_PixelOrientationCode_Type, MD_PortrayalCatalogueReference,
MD_PortrayalCatalogueReference_PropertyType,
MD_PortrayalCatalogueReference_Type, MD_ProgressCode,
MD_ProgressCode_PropertyType, MD_RangeDimension,
MD_RangeDimension_PropertyType, MD_RangeDimension_Type, MD_ReferenceSystem,
MD_ReferenceSystem_PropertyType, MD_ReferenceSystem_Type,
MD_RepresentativeFraction, MD_RepresentativeFraction_PropertyType,
MD_RepresentativeFraction_Type, MD_Resolution, MD_Resolution_PropertyType,
MD_Resolution_Type, MD_RestrictionCode, MD_RestrictionCode_PropertyType,
MD_ScopeCode, MD_ScopeCode_PropertyType, MD_ScopeDescription,
MD_ScopeDescription_PropertyType, MD_ScopeDescription_Type,
MD_SecurityConstraints, MD_SecurityConstraints_PropertyType,
MD_SecurityConstraints_Type, MD_ServiceIdentification,
MD_ServiceIdentification_PropertyType, MD_ServiceIdentification_Type,
MD_SpatialRepresentation_PropertyType, MD_SpatialRepresentationTypeCode,
MD_SpatialRepresentationTypeCode_PropertyType, MD_StandardOrderProcess,
MD_StandardOrderProcess_PropertyType, MD_StandardOrderProcess_Type,
MD_TopicCategoryCode, MD_TopicCategoryCode_PropertyType,
MD_TopicCategoryCode_Type, MD_TopologyLevelCode,
MD_TopologyLevelCode_PropertyType, MD_Usage, MD_Usage_PropertyType,
MD_Usage_Type, MD_VectorSpatialRepresentation,
MD_VectorSpatialRepresentation_PropertyType,
MD_VectorSpatialRepresentation_Type, PT_FreeText, PT_FreeText_PropertyType,
PT_FreeText_Type, PT_Locale, PT_Locale_PropertyType, PT_Locale_Type,
PT_LocaleContainer, PT_LocaleContainer_PropertyType,
PT_LocaleContainer_Type, RS_Identifier, RS_Identifier_PropertyType,
RS_Identifier_Type, RS_ReferenceSystem_PropertyType, URL_PropertyType)
| 142.27907
| 22,602
| 0.829138
| 4,600
| 36,708
| 6.346087
| 0.073696
| 0.088723
| 0.190121
| 0.228145
| 0.615751
| 0.613696
| 0.611983
| 0.599651
| 0.021924
| 0
| 0
| 0.077377
| 0.05364
| 36,708
| 257
| 22,603
| 142.832685
| 0.762947
| 0.657541
| 0
| 0.019139
| 1
| 0
| 0.008569
| 0.003604
| 0
| 0
| 0
| 0
| 0
| 1
| 0.009569
| false
| 0
| 0.052632
| 0
| 0.076555
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
5564987b0ec54b45b52b845518aab9ffc474385a
| 154
|
py
|
Python
|
metrics/metrics_interface.py
|
DominikSpiljak/imdb-review-classifier
|
7f89d411966632d16d8bfed3548d47eb1f914020
|
[
"Apache-2.0"
] | null | null | null |
metrics/metrics_interface.py
|
DominikSpiljak/imdb-review-classifier
|
7f89d411966632d16d8bfed3548d47eb1f914020
|
[
"Apache-2.0"
] | null | null | null |
metrics/metrics_interface.py
|
DominikSpiljak/imdb-review-classifier
|
7f89d411966632d16d8bfed3548d47eb1f914020
|
[
"Apache-2.0"
] | null | null | null |
class Metric:
def initialize(self):
pass
def log_batch(self, predicted, ground_truth):
pass
def compute(self):
pass
| 15.4
| 49
| 0.590909
| 18
| 154
| 4.944444
| 0.666667
| 0.179775
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.331169
| 154
| 9
| 50
| 17.111111
| 0.864078
| 0
| 0
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0.428571
| 0
| 0
| 0.571429
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
e965640c569c3e22c1b551a4decb1a1ac48a30ab
| 161
|
py
|
Python
|
audiorename/utils.py
|
Josef-Friedrich/mutagen-renamer
|
3df494f04dc74b4ed5a70150502756ba80a81cd4
|
[
"MIT"
] | null | null | null |
audiorename/utils.py
|
Josef-Friedrich/mutagen-renamer
|
3df494f04dc74b4ed5a70150502756ba80a81cd4
|
[
"MIT"
] | null | null | null |
audiorename/utils.py
|
Josef-Friedrich/mutagen-renamer
|
3df494f04dc74b4ed5a70150502756ba80a81cd4
|
[
"MIT"
] | null | null | null |
import re
def indent(text: str) -> str:
return ' ' + re.sub(r'\n', '\n ', text)
def read_file(path: str) -> str:
return open(path, 'r').read()
| 16.1
| 49
| 0.534161
| 25
| 161
| 3.4
| 0.56
| 0.141176
| 0.282353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.254658
| 161
| 9
| 50
| 17.888889
| 0.708333
| 0
| 0
| 0
| 0
| 0
| 0.080745
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.2
| 0.4
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
e96c727c86f49695cf042faff7e60f902055f05f
| 125
|
py
|
Python
|
flask_api/app/resources/__init__.py
|
brennanhfredericks/network-monitor-server
|
7c811d7851aee5d069569306c46dff39d8d52400
|
[
"MIT"
] | null | null | null |
flask_api/app/resources/__init__.py
|
brennanhfredericks/network-monitor-server
|
7c811d7851aee5d069569306c46dff39d8d52400
|
[
"MIT"
] | null | null | null |
flask_api/app/resources/__init__.py
|
brennanhfredericks/network-monitor-server
|
7c811d7851aee5d069569306c46dff39d8d52400
|
[
"MIT"
] | null | null | null |
from .packet_endpoints import (
Packet_EP,
Packet_Table_EP,
Packet_Table_Counts_EP,
Packet_Table_Views_EP,
)
| 17.857143
| 31
| 0.744
| 17
| 125
| 4.882353
| 0.470588
| 0.289157
| 0.46988
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 125
| 6
| 32
| 20.833333
| 0.83
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.166667
| 0
| 0.166667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e9c15e337ba78228c400dea800cc3b186d86f49d
| 55
|
py
|
Python
|
tests/__init__.py
|
pyj4104/FuncToWav
|
f6076cf71a51ac8f2612f3a26f35ca1efca0811d
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
pyj4104/FuncToWav
|
f6076cf71a51ac8f2612f3a26f35ca1efca0811d
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
pyj4104/FuncToWav
|
f6076cf71a51ac8f2612f3a26f35ca1efca0811d
|
[
"MIT"
] | null | null | null |
import sys
sys.path.append('~/Func2Wav/FuncToWav/src')
| 18.333333
| 43
| 0.763636
| 8
| 55
| 5.25
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019231
| 0.054545
| 55
| 2
| 44
| 27.5
| 0.788462
| 0
| 0
| 0
| 0
| 0
| 0.436364
| 0.436364
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
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| null | 0
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| 0
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
e9d85fb7192ec175a27c9e1f5f71288fe27f8035
| 203
|
py
|
Python
|
searchtube/db.py
|
dermasmid/searchtube
|
68d740b37b990d00c35e9eec4fa30cc24affe954
|
[
"MIT"
] | 11
|
2021-06-17T06:12:29.000Z
|
2022-02-17T14:54:08.000Z
|
searchtube/db.py
|
dermasmid/searchtube
|
68d740b37b990d00c35e9eec4fa30cc24affe954
|
[
"MIT"
] | null | null | null |
searchtube/db.py
|
dermasmid/searchtube
|
68d740b37b990d00c35e9eec4fa30cc24affe954
|
[
"MIT"
] | null | null | null |
from pymongo import MongoClient
import os
def get_client():
client = MongoClient('searchtube_mongo', 27017, username=os.environ['DB_USERNAME'], password=os.environ['DB_PASSWORD'])
return client
| 29
| 123
| 0.763547
| 26
| 203
| 5.807692
| 0.615385
| 0.119205
| 0.145695
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02809
| 0.123153
| 203
| 6
| 124
| 33.833333
| 0.820225
| 0
| 0
| 0
| 0
| 0
| 0.187192
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0.2
| 0.4
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
756a5e7cc869a0c3e6f927a064fd2bbad38f34d6
| 6,769
|
py
|
Python
|
uw_canvas/external_tools.py
|
uw-it-aca/uw-restclients-canvas
|
2c54d7676649ec18129817992890878ace1ec6c6
|
[
"Apache-2.0"
] | 1
|
2019-11-26T21:38:50.000Z
|
2019-11-26T21:38:50.000Z
|
uw_canvas/external_tools.py
|
uw-it-aca/uw-restclients-canvas
|
2c54d7676649ec18129817992890878ace1ec6c6
|
[
"Apache-2.0"
] | 135
|
2017-04-04T23:11:26.000Z
|
2021-05-28T17:00:20.000Z
|
uw_canvas/external_tools.py
|
uw-it-aca/uw-restclients-canvas
|
2c54d7676649ec18129817992890878ace1ec6c6
|
[
"Apache-2.0"
] | 2
|
2020-05-20T20:36:55.000Z
|
2022-03-05T00:23:44.000Z
|
# Copyright 2021 UW-IT, University of Washington
# SPDX-License-Identifier: Apache-2.0
from uw_canvas import Canvas
from uw_canvas.accounts import ACCOUNTS_API
from uw_canvas.courses import COURSES_API
class ExternalToolsException(Exception):
pass
class ExternalTools(Canvas):
def get_external_tools_in_account(self, account_id, params={}):
"""
Return external tools for the passed canvas account id.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.index
"""
url = ACCOUNTS_API.format(account_id) + "/external_tools"
external_tools = []
for data in self._get_paged_resource(url, params=params):
external_tools.append(data)
return external_tools
def get_external_tools_in_account_by_sis_id(self, sis_id):
"""
Return external tools for given account sis id.
"""
return self.get_external_tools_in_account(self._sis_id(sis_id,
"account"))
def get_external_tools_in_course(self, course_id, params={}):
"""
Return external tools for the passed canvas course id.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.index
"""
url = COURSES_API.format(course_id) + "/external_tools"
external_tools = []
for data in self._get_paged_resource(url, params=params):
external_tools.append(data)
return external_tools
def get_external_tools_in_course_by_sis_id(self, sis_id):
"""
Return external tools for given course sis id.
"""
return self.get_external_tools_in_course(self._sis_id(sis_id,
"course"))
def create_external_tool_in_course(self, course_id, json_data):
return self._create_external_tool(COURSES_API, course_id, json_data)
def create_external_tool_in_account(self, account_id, json_data):
return self._create_external_tool(ACCOUNTS_API, account_id, json_data)
def _create_external_tool(self, context, context_id, json_data):
"""
Create an external tool using the passed json_data.
context is either COURSES_API or ACCOUNTS_API.
context_id is the Canvas course_id or account_id, depending on context.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.create
"""
url = context.format(context_id) + "/external_tools"
return self._post_resource(url, body=json_data)
def update_external_tool_in_course(self, course_id, external_tool_id,
json_data):
return self._update_external_tool(COURSES_API, course_id,
external_tool_id, json_data)
def update_external_tool_in_account(self, account_id, external_tool_id,
json_data):
return self._update_external_tool(ACCOUNTS_API, account_id,
external_tool_id, json_data)
def _update_external_tool(self, context, context_id, external_tool_id,
json_data):
"""
Update the external tool identified by external_tool_id with the passed
json data.
context is either COURSES_API or ACCOUNTS_API.
context_id is the course_id or account_id, depending on context
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.update
"""
url = context.format(context_id) + "/external_tools/{}".format(
external_tool_id)
return self._put_resource(url, body=json_data)
def delete_external_tool_in_course(self, course_id, external_tool_id):
return self._delete_external_tool(COURSES_API, course_id,
external_tool_id)
def delete_external_tool_in_account(self, account_id, external_tool_id):
return self._delete_external_tool(ACCOUNTS_API, account_id,
external_tool_id)
def _delete_external_tool(self, context, context_id, external_tool_id):
"""
Delete the external tool identified by external_tool_id.
context is either COURSES_API or ACCOUNTS_API.
context_id is the course_id or account_id, depending on context
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.destroy
"""
url = context.format(context_id) + "/external_tools/{}".format(
external_tool_id)
response = self._delete_resource(url)
return True
def _get_sessionless_launch_url(self, context, context_id, tool_id):
"""
Get a sessionless launch url for an external tool.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.generate_sessionless_launch
"""
url = context.format(context_id) + "/external_tools/sessionless_launch"
params = {"id": tool_id}
return self._get_resource(url, params)
def get_sessionless_launch_url_from_account(self, tool_id, account_id):
"""
Get a sessionless launch url for an external tool.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.generate_sessionless_launch
"""
return self._get_sessionless_launch_url(
ACCOUNTS_API, account_id, tool_id)
def get_sessionless_launch_url_from_account_sis_id(
self, tool_id, account_sis_id):
"""
Get a sessionless launch url for an external tool.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.generate_sessionless_launch
"""
return self.get_sessionless_launch_url_from_account(
tool_id, self._sis_id(account_sis_id, "account"))
def get_sessionless_launch_url_from_course(self, tool_id, course_id):
"""
Get a sessionless launch url for an external tool.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.generate_sessionless_launch
"""
return self._get_sessionless_launch_url(
COURSES_API, course_id, tool_id)
def get_sessionless_launch_url_from_course_sis_id(
self, tool_id, course_sis_id):
"""
Get a sessionless launch url for an external tool.
https://canvas.instructure.com/doc/api/external_tools.html#method.external_tools.generate_sessionless_launch
"""
return self.get_sessionless_launch_url_from_course(
tool_id, self._sis_id(course_sis_id, "course"))
| 40.777108
| 116
| 0.670114
| 851
| 6,769
| 4.962397
| 0.092832
| 0.129292
| 0.07104
| 0.0592
| 0.852001
| 0.813403
| 0.762965
| 0.694056
| 0.636751
| 0.541558
| 0
| 0.001187
| 0.253361
| 6,769
| 165
| 117
| 41.024242
| 0.834389
| 0.306397
| 0
| 0.287671
| 0
| 0
| 0.033403
| 0.007942
| 0
| 0
| 0
| 0
| 0
| 1
| 0.246575
| false
| 0.013699
| 0.041096
| 0.082192
| 0.561644
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
758d7ae97eca22789ab13746cb71c59220fd4ee8
| 129
|
py
|
Python
|
easyPlog/__init__.py
|
whuhit/easyPlog
|
a09e94f3619bb5f3a059a292cc19e7d1f4fb443a
|
[
"MIT"
] | null | null | null |
easyPlog/__init__.py
|
whuhit/easyPlog
|
a09e94f3619bb5f3a059a292cc19e7d1f4fb443a
|
[
"MIT"
] | null | null | null |
easyPlog/__init__.py
|
whuhit/easyPlog
|
a09e94f3619bb5f3a059a292cc19e7d1f4fb443a
|
[
"MIT"
] | null | null | null |
"""
@author: yangqiang
@contact: whuhit2020@gmail.com
@file: __init__.py.py
@time: 2020/4/3 14:49
"""
from .easyPlog import Plog
| 16.125
| 30
| 0.713178
| 20
| 129
| 4.4
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122807
| 0.116279
| 129
| 7
| 31
| 18.428571
| 0.649123
| 0.72093
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
758fc65a148479b0a1df117e202f4813587271d6
| 151
|
py
|
Python
|
models/__init__.py
|
theorenck/pm-bot
|
798d1b573db0bd470fe7007f97d4f84148093f8b
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
theorenck/pm-bot
|
798d1b573db0bd470fe7007f97d4f84148093f8b
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
theorenck/pm-bot
|
798d1b573db0bd470fe7007f97d4f84148093f8b
|
[
"MIT"
] | null | null | null |
from .colors import Color, Colors
from .grid import Location, Annotation, Annotations, Grid
from .minimap import Minimap
from .astar import AStarSearch
| 37.75
| 57
| 0.821192
| 20
| 151
| 6.2
| 0.55
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125828
| 151
| 4
| 58
| 37.75
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
f933e64f330919ecd9b43196ea6f6afe010ecbf4
| 264
|
py
|
Python
|
src/cautious_invention/utils.py
|
MerktSimon/cautious-invention
|
fb04b1afd57f4d8c9ca3431d777f2f59dc30b4e9
|
[
"MIT"
] | null | null | null |
src/cautious_invention/utils.py
|
MerktSimon/cautious-invention
|
fb04b1afd57f4d8c9ca3431d777f2f59dc30b4e9
|
[
"MIT"
] | null | null | null |
src/cautious_invention/utils.py
|
MerktSimon/cautious-invention
|
fb04b1afd57f4d8c9ca3431d777f2f59dc30b4e9
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""Contain the utilities of cautious-invention."""
my_awesome_constant = 4
def hello_world():
"""Print hello World."""
print("Hello, I am a cautious invention")
def add(x, y):
"""Add two numbers together."""
return x+y
| 17.6
| 50
| 0.625
| 37
| 264
| 4.378378
| 0.72973
| 0.209877
| 0.185185
| 0.246914
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009524
| 0.204545
| 264
| 14
| 51
| 18.857143
| 0.761905
| 0.424242
| 0
| 0
| 0
| 0
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0
| 0.6
| 0.2
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
f93d3dd242ef0631e597d0dcaf621d352179618f
| 25
|
py
|
Python
|
silverpieces/__init__.py
|
fareedmirza/silverpieces
|
930edb9d962cb322a19e66aa3cc29eebe6d1d037
|
[
"MIT"
] | 1
|
2019-07-17T04:30:37.000Z
|
2019-07-17T04:30:37.000Z
|
silverpieces/__init__.py
|
fareedmirza/silverpieces
|
930edb9d962cb322a19e66aa3cc29eebe6d1d037
|
[
"MIT"
] | 5
|
2019-07-17T03:49:19.000Z
|
2019-07-18T04:41:20.000Z
|
silverpieces/__init__.py
|
fareedmirza/silverpieces
|
930edb9d962cb322a19e66aa3cc29eebe6d1d037
|
[
"MIT"
] | 3
|
2020-03-01T22:15:52.000Z
|
2020-07-29T05:44:54.000Z
|
# required for python2?
| 8.333333
| 23
| 0.72
| 3
| 25
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05
| 0.2
| 25
| 2
| 24
| 12.5
| 0.85
| 0.84
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f97ca2643ecffa66e061e771160e087bf20e75fd
| 250
|
py
|
Python
|
safe/safe_dict.py
|
andreclaudino/python-safe-dict
|
b0bfdbd47c68810d9f8741cc1ca69e6a3c584ca5
|
[
"MIT"
] | null | null | null |
safe/safe_dict.py
|
andreclaudino/python-safe-dict
|
b0bfdbd47c68810d9f8741cc1ca69e6a3c584ca5
|
[
"MIT"
] | null | null | null |
safe/safe_dict.py
|
andreclaudino/python-safe-dict
|
b0bfdbd47c68810d9f8741cc1ca69e6a3c584ca5
|
[
"MIT"
] | null | null | null |
class SafeDict(dict):
def __init__(self, dictionary:dict = {}, default=None, **kwargs):
super().__init__(**dictionary, **kwargs)
self.default = default
def __getitem__(self, key):
return self.get(key, self.default)
| 25
| 69
| 0.632
| 28
| 250
| 5.214286
| 0.535714
| 0.150685
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.22
| 250
| 9
| 70
| 27.777778
| 0.748718
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.166667
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
f98296934b3b5b93f9b88830c6700ed8f9f5dc91
| 48
|
py
|
Python
|
dog_cmd.py
|
vilsmeier/SummerSession_2021
|
308430a5ec16828ee193a9b1ca2f09b888492ce8
|
[
"MIT"
] | 1
|
2021-08-17T12:37:43.000Z
|
2021-08-17T12:37:43.000Z
|
dog_cmd.py
|
vilsmeier/SummerSession_2021
|
308430a5ec16828ee193a9b1ca2f09b888492ce8
|
[
"MIT"
] | null | null | null |
dog_cmd.py
|
vilsmeier/SummerSession_2021
|
308430a5ec16828ee193a9b1ca2f09b888492ce8
|
[
"MIT"
] | null | null | null |
from DogService import *
add_heart_rate(2,89)
| 9.6
| 24
| 0.770833
| 8
| 48
| 4.375
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073171
| 0.145833
| 48
| 4
| 25
| 12
| 0.780488
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
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| 0.5
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| 1
| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| null | 0
| 0
| 0
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| 0
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| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
f99b5b1c515470f3471e9568df015375fdcc2cf8
| 152
|
py
|
Python
|
controller/server.py
|
uniaim-event-team/watch-link
|
0ad2665fd88cba2fdb7e7c8f533bb5a8e6d91b31
|
[
"MIT"
] | 2
|
2020-05-05T14:53:00.000Z
|
2020-05-05T14:53:13.000Z
|
controller/server.py
|
uniaim-event-team/watch-link
|
0ad2665fd88cba2fdb7e7c8f533bb5a8e6d91b31
|
[
"MIT"
] | 1
|
2021-03-01T02:00:11.000Z
|
2021-03-01T02:00:11.000Z
|
controller/server.py
|
uniaim-event-team/watch-link
|
0ad2665fd88cba2fdb7e7c8f533bb5a8e6d91b31
|
[
"MIT"
] | null | null | null |
from flask import Blueprint, render_template
app = Blueprint(__name__, "server")
@app.route('/')
def route():
return render_template('top.html')
| 16.888889
| 44
| 0.717105
| 19
| 152
| 5.421053
| 0.736842
| 0.271845
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138158
| 152
| 8
| 45
| 19
| 0.78626
| 0
| 0
| 0
| 0
| 0
| 0.098684
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.2
| 0.2
| 0.6
| 0.4
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
f9e70a190b61b5a6f43c58d9f3ad467639c468de
| 166
|
py
|
Python
|
torchlit/utils/__init__.py
|
himanshu-dutta/torchlit
|
74bb7f07e9c10fc2dbd04d26217a0be767192992
|
[
"MIT"
] | 1
|
2021-03-08T14:03:55.000Z
|
2021-03-08T14:03:55.000Z
|
torchlit/utils/__init__.py
|
himanshu-dutta/torchlit
|
74bb7f07e9c10fc2dbd04d26217a0be767192992
|
[
"MIT"
] | null | null | null |
torchlit/utils/__init__.py
|
himanshu-dutta/torchlit
|
74bb7f07e9c10fc2dbd04d26217a0be767192992
|
[
"MIT"
] | null | null | null |
def to_device(data, device):
if isinstance(data, (list, tuple)):
return [to_device(x, device) for x in data]
return data.to(device, non_blocking=True)
| 41.5
| 51
| 0.680723
| 26
| 166
| 4.230769
| 0.576923
| 0.218182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.192771
| 166
| 4
| 52
| 41.5
| 0.820896
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
ddb14f05a0f7c875347127deb355039c607f56fb
| 275
|
py
|
Python
|
inspecta/__init__.py
|
grimen/python-inspecta
|
67dd442f549304e1c2634e6a769ea364141a3577
|
[
"MIT"
] | null | null | null |
inspecta/__init__.py
|
grimen/python-inspecta
|
67dd442f549304e1c2634e6a769ea364141a3577
|
[
"MIT"
] | null | null | null |
inspecta/__init__.py
|
grimen/python-inspecta
|
67dd442f549304e1c2634e6a769ea364141a3577
|
[
"MIT"
] | null | null | null |
# =========================================
# IMPORTS
# --------------------------------------
import rootpath
rootpath.append()
# =========================================
# EXPORTS
# --------------------------------------
from inspecta.inspector import *
| 17.1875
| 43
| 0.250909
| 10
| 275
| 6.9
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134545
| 275
| 15
| 44
| 18.333333
| 0.289916
| 0.687273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
ddbf4a67f4b86cbc3a99610fa00f5f44add9683f
| 57
|
py
|
Python
|
call_sibling.py
|
sahlinet/tumbo-demoapp
|
2353a656fc236d992a45729c8b6105260b48b6a9
|
[
"MIT"
] | null | null | null |
call_sibling.py
|
sahlinet/tumbo-demoapp
|
2353a656fc236d992a45729c8b6105260b48b6a9
|
[
"MIT"
] | null | null | null |
call_sibling.py
|
sahlinet/tumbo-demoapp
|
2353a656fc236d992a45729c8b6105260b48b6a9
|
[
"MIT"
] | null | null | null |
def func(self):
return self.siblings.sibling_A(self)
| 19
| 40
| 0.736842
| 9
| 57
| 4.555556
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140351
| 57
| 2
| 41
| 28.5
| 0.836735
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
ddd5a0222f3d5977939485bdcd5bbadbf98977d8
| 61
|
py
|
Python
|
penut/__init__.py
|
penut85420/Penut
|
834fa188e936697c1724f478e22b3b2a1c9041d1
|
[
"MIT"
] | null | null | null |
penut/__init__.py
|
penut85420/Penut
|
834fa188e936697c1724f478e22b3b2a1c9041d1
|
[
"MIT"
] | null | null | null |
penut/__init__.py
|
penut85420/Penut
|
834fa188e936697c1724f478e22b3b2a1c9041d1
|
[
"MIT"
] | null | null | null |
from .utils import TimeCost, walk_dir, td2s, timedelta2string
| 61
| 61
| 0.836066
| 8
| 61
| 6.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.036364
| 0.098361
| 61
| 1
| 61
| 61
| 0.872727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
ddd8c76827f80072100905335e8afc94540c053d
| 363
|
py
|
Python
|
src/models/ffhq_1024_haar/__init__.py
|
YorkUCVIL/Wavelet-Flow
|
8d6d63fa116ec44299c32f37e66817594510f644
|
[
"MIT"
] | 59
|
2020-10-28T03:09:05.000Z
|
2022-01-29T22:10:04.000Z
|
src/models/ffhq_1024_haar/__init__.py
|
YorkUCVIL/Wavelet-Flow
|
8d6d63fa116ec44299c32f37e66817594510f644
|
[
"MIT"
] | 4
|
2020-12-24T11:00:40.000Z
|
2021-05-22T06:14:27.000Z
|
src/models/ffhq_1024_haar/__init__.py
|
YorkUCVIL/Wavelet-Flow
|
8d6d63fa116ec44299c32f37e66817594510f644
|
[
"MIT"
] | 2
|
2020-10-29T01:15:03.000Z
|
2021-04-20T11:55:51.000Z
|
from models.ffhq_1024_haar.Training_data import *
from models.ffhq_1024_haar.Validation_data import *
from models.ffhq_1024_haar.Network_body import *
from models.ffhq_1024_haar.Conditioning_network import *
import models.shared.routines as routines
from models.ffhq_1024_haar.build_training_graph import *
model_config_path = 'data/ffhq_1024_haar/config.hjson'
| 40.333333
| 56
| 0.859504
| 56
| 363
| 5.214286
| 0.375
| 0.164384
| 0.246575
| 0.308219
| 0.465753
| 0.315068
| 0.219178
| 0
| 0
| 0
| 0
| 0.071642
| 0.077135
| 363
| 8
| 57
| 45.375
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0.088154
| 0.088154
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.857143
| 0
| 0.857143
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
ddfd7c2e62761183b7b29182595343c0b7c0bd31
| 88
|
py
|
Python
|
signaling-games/algs/model_agents/__init__.py
|
vbhatt-cs/inference-based-messaging
|
323438a2ff5814712faf1be80048459c1a556d72
|
[
"MIT"
] | 3
|
2021-03-10T15:22:26.000Z
|
2021-09-18T02:25:46.000Z
|
signaling-games/algs/model_agents/__init__.py
|
vbhatt-cs/inference-based-messaging
|
323438a2ff5814712faf1be80048459c1a556d72
|
[
"MIT"
] | null | null | null |
signaling-games/algs/model_agents/__init__.py
|
vbhatt-cs/inference-based-messaging
|
323438a2ff5814712faf1be80048459c1a556d72
|
[
"MIT"
] | 1
|
2021-04-05T05:26:55.000Z
|
2021-04-05T05:26:55.000Z
|
from .model_s import ModelS
from .model_r import ModelR
__all__ = ["ModelR", "ModelS"]
| 17.6
| 30
| 0.738636
| 13
| 88
| 4.538462
| 0.615385
| 0.305085
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147727
| 88
| 4
| 31
| 22
| 0.786667
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
fb0e36e0c441d3755c689f994145e928e7b8d698
| 77
|
py
|
Python
|
i18nparse/__init__.py
|
nueh/i18nparse
|
eee8ba8da4aedeff25fba8258b0e4face2ce9289
|
[
"MIT"
] | 5
|
2019-12-07T18:55:53.000Z
|
2022-01-25T08:22:49.000Z
|
i18nparse/__init__.py
|
nueh/i18nparse
|
eee8ba8da4aedeff25fba8258b0e4face2ce9289
|
[
"MIT"
] | 3
|
2018-11-15T10:52:26.000Z
|
2021-06-06T21:28:02.000Z
|
i18nparse/__init__.py
|
nueh/i18nparse
|
eee8ba8da4aedeff25fba8258b0e4face2ce9289
|
[
"MIT"
] | 4
|
2020-03-25T19:34:25.000Z
|
2021-08-05T08:21:16.000Z
|
from .version import __version__
from .i18nparse import activate, deactivate
| 25.666667
| 43
| 0.844156
| 9
| 77
| 6.777778
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029412
| 0.116883
| 77
| 2
| 44
| 38.5
| 0.867647
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
fb328e35760fdc117e59d8f252695e30e3ae3eb0
| 125
|
py
|
Python
|
HackerRank/Python3/set_union.py
|
santoshgawande/DS-Algorithms
|
eb1de229fd3336d862bd4787295f208a4424d0bb
|
[
"Apache-2.0"
] | null | null | null |
HackerRank/Python3/set_union.py
|
santoshgawande/DS-Algorithms
|
eb1de229fd3336d862bd4787295f208a4424d0bb
|
[
"Apache-2.0"
] | null | null | null |
HackerRank/Python3/set_union.py
|
santoshgawande/DS-Algorithms
|
eb1de229fd3336d862bd4787295f208a4424d0bb
|
[
"Apache-2.0"
] | null | null | null |
am = int(input())
a = set(map(int,input().split()))
bm = int(input())
b =set(map(int,input().split()))
print(len(a.union(b)))
| 25
| 33
| 0.6
| 23
| 125
| 3.26087
| 0.521739
| 0.426667
| 0.24
| 0.373333
| 0.506667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088
| 125
| 5
| 34
| 25
| 0.657895
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
fb3d6cc6625bd5b1d1886cac12d840367075b01b
| 30
|
py
|
Python
|
src/hello_world.py
|
DreamMazeTeam/BootSrc
|
e24e8a234c66e7ae71565f7047db177f0402da72
|
[
"MIT"
] | null | null | null |
src/hello_world.py
|
DreamMazeTeam/BootSrc
|
e24e8a234c66e7ae71565f7047db177f0402da72
|
[
"MIT"
] | null | null | null |
src/hello_world.py
|
DreamMazeTeam/BootSrc
|
e24e8a234c66e7ae71565f7047db177f0402da72
|
[
"MIT"
] | null | null | null |
import module1
module1.hello()
| 15
| 15
| 0.833333
| 4
| 30
| 6.25
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 0.066667
| 30
| 2
| 15
| 15
| 0.821429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
34d1620d480a8eb06884d68fb99586549ab08464
| 17,733
|
py
|
Python
|
smda/intel/MnemonicTfIdf.py
|
jcrussell/smda
|
331cb68f511d55ef6db276b81ae6cedea40dd3eb
|
[
"BSD-2-Clause"
] | 142
|
2018-07-12T04:54:39.000Z
|
2022-03-25T09:41:23.000Z
|
smda/intel/MnemonicTfIdf.py
|
jcrussell/smda
|
331cb68f511d55ef6db276b81ae6cedea40dd3eb
|
[
"BSD-2-Clause"
] | 23
|
2018-07-01T14:11:43.000Z
|
2022-03-22T18:20:46.000Z
|
smda/intel/MnemonicTfIdf.py
|
jcrussell/smda
|
331cb68f511d55ef6db276b81ae6cedea40dd3eb
|
[
"BSD-2-Clause"
] | 25
|
2018-09-13T10:49:55.000Z
|
2022-03-07T13:53:39.000Z
|
import math
from collections import Counter
from copy import deepcopy
class MnemonicTfIdf(object):
idf = {}
# derived using groundtruth by Andriesse, Bao, and Plohmann
# all_histograms_x86 = Counter({'num_functions': 572387, 'mov': 511833, 'ret': 500172, 'push': 452213, 'add': 423033, 'call': 414348, 'sub': 380594, 'pop': 370950, 'jmp': 270366, 'cmp': 242071, 'je': 235712, 'lea': 207601, 'test': 207200, 'jne': 189321, 'xor': 169864, 'movzx': 91671, 'leave': 70744, 'and': 70236, 'nop': 65414, 'inc': 64094, 'shl': 48804, 'jle': 48688, 'or': 42761, 'ja': 38606, 'sar': 37170, 'jb': 34838, 'jbe': 34209, 'jl': 34110, 'imul': 32911, 'jae': 31808, 'dec': 31575, 'jg': 29916, 'shr': 27972, 'jge': 27333, 'fstp': 26961, 'fld': 22244, 'movsd': 21956, 'sete': 21729, 'setne': 20976, 'js': 18670, 'movsx': 16361, 'sbb': 13209, 'jns': 13139, 'neg': 13012, 'cmovne': 11240, 'cdq': 10162, 'cmove': 9813, 'fldz': 8900, 'fmul': 7975, 'not': 7944, 'mulsd': 7897, 'addsd': 6613, 'fxch': 6562, 'idiv': 6468, 'xorps': 6422, 'ucomisd': 5855, 'subsd': 5532, 'fadd': 5396, 'fst': 5214, 'movapd': 5213, 'fucomip': 5203, 'bt': 5030, 'movss': 5030, 'xorpd': 4854, 'faddp': 4800, 'adc': 4784, 'fild': 4194, 'jp': 3873, 'setg': 3828, 'divsd': 3794, 'fmulp': 3596, 'fucomi': 3580, 'fld1': 3346, 'rep movsd': 3194, 'setb': 3092, 'div': 3020, 'cmovb': 2966, 'movaps': 2903, 'cmova': 2754, 'seta': 2754, 'fsub': 2741, 'cvtss2sd': 2741, 'mul': 2599, 'cvtsi2sd': 2497, 'jnp': 2473, 'fnstcw': 2470, 'movd': 2442, 'fchs': 2341, 'fldcw': 2290, 'cmovg': 2264, 'fsubp': 2233, 'shld': 2210, 'fdiv': 2199, 'fistp': 2124, 'rep stosd': 2092, 'fsubr': 2074, 'cmovle': 1971, 'cvttsd2si': 1963, 'cvtsd2ss': 1944, 'cmovae': 1830, 'cmovl': 1666, 'cmovns': 1604, 'cmovs': 1551, 'setl': 1521, 'fdivp': 1521, 'int3': 1502, 'andpd': 1488, 'shrd': 1478, 'fsubrp': 1438, 'cmovge': 1431, 'orpd': 1384, 'fdivr': 1348, 'addss': 1331, 'cmovbe': 1313, 'setle': 1305, 'movupd': 1282, 'movups': 1233, 'fabs': 1221, 'mulss': 1214, 'repne scasb': 1162, 'setbe': 1136, 'rep movsb': 1118, 'fdivrp': 1107, 'sqrtsd': 1088, 'setge': 1057, 'movlpd': 1028, 'ucomiss': 1018, 'setae': 960, 'mulpd': 956, 'movlhps': 922, 'fsqrt': 813, 'cmovno': 772, 'addpd': 771, 'movdqu': 751, 'cwde': 748, 'divss': 708, 'subss': 694, 'shufpd': 692, 'rol': 688, 'cvtsi2ss': 612, 'movdqa': 537, 'repe cmpsb': 527, 'setp': 522, 'andnpd': 522, 'fcmovbe': 521, 'subpd': 508, 'pshufd': 489, 'cmpltsd': 457, 'sets': 451, 'minsd': 445, 'unpcklpd': 438, 'fcmovnbe': 424, 'maxsd': 408, 'rep stosb': 404, 'fnstsw': 389, 'stmxcsr': 367, 'stosd': 360, 'pxor': 341, 'setnp': 333, 'ud2': 321, 'por': 310, 'bswap': 291, 'xchg': 289, 'setns': 275, 'wait': 271, 'paddd': 266, 'cld': 265, 'movq': 264, 'cmovo': 264, 'punpckldq': 254, 'cvttss2si': 252, 'ror': 250, 'movhpd': 247, 'cmpneqsd': 224, 'hlt': 206, 'bsr': 195, 'rcr': 193, 'pushfd': 184, 'andps': 177, 'fimul': 163, 'shufps': 156, 'movsb': 153, 'movsw': 148, 'divpd': 145, 'pand': 144, 'std': 139, 'cmpeqsd': 133, 'seto': 132, 'bts': 118, 'clc': 118, 'pcmpeqd': 116, 'cvtps2pd': 113, 'fidiv': 107, 'fist': 107, 'fcomp': 106, 'fucompp': 105, 'fiadd': 105, 'mulps': 103, 'lock xadd': 101, 'stosb': 100, 'movhlps': 100, 'maxss': 99, 'fidivr': 97, 'ldmxcsr': 96, 'sqrtss': 95, 'pslldq': 94, 'pandn': 94, 'stosw': 93, 'frndint': 92, 'fcmovne': 83, 'addps': 79, 'fnclex': 77, 'unpcklps': 76, 'fcmove': 70, 'orps': 70, 'loop': 63, 'popal': 61, 'andnps': 60, 'popfd': 57, 'pushal': 56, 'psrad': 55, 'cmpltss': 48, 'lock cmpxchg': 47, 'psllq': 47, 'cmplesd': 47, 'unpckhpd': 46, 'punpcklbw': 45, 'paddq': 44, 'cmpnlesd': 44, 'pslld': 43, 'punpcklwd': 42, 'lodsb': 41, 'cmovp': 41, 'movlps': 39, 'minpd': 39, 'maxpd': 37, 'fxam': 36, 'pshufhw': 36, 'cvtpd2ps': 35, 'psubd': 34, 'pshuflw': 34, 'cbw': 33, 'minss': 33, 'ljmp': 31, 'cpuid': 30, 'psrlq': 30, 'pmuludq': 30, 'psrld': 28, 'lodsd': 27, 'fcmovb': 27, 'int': 26, 'cmpltpd': 26, 'fldpi': 25, 'pcmpgtd': 24, 'fisubr': 23, 'packuswb': 23, 'retf': 22, 'fucomp': 19, 'psrldq': 19, 'fisub': 19, 'cmpneqss': 19, 'cvttps2dq': 19, 'fcom': 18, 'fcompp': 18, 'subps': 17, 'jecxz': 16, 'divps': 15, 'xlatb': 14, 'punpcklqdq': 12, 'punpckhbw': 12, 'rdtsc': 11, 'repe cmpsd': 9, 'cmpnlepd': 9, 'fcmovnb': 8, 'cmpnless': 8, 'f2xm1': 8, 'fscale': 8, 'lodsw': 7, 'paddb': 7, 'insb': 6, 'outsb': 6, 'cmpeqss': 6, 'pcmpeqw': 6, 'pmulld': 6, 'in': 5, 'repne scasw': 5, 'lcall': 5, 'das': 5, 'outsd': 5, 'cmpneqpd': 5, 'cmpnltsd': 5, 'psubusb': 5, 'pcmpeqb': 5, 'fninit': 4, 'psraw': 4, 'pause': 4, 'lock inc': 3, 'lock dec': 3, 'arpl': 3, 'rcl': 3, 'loopne': 3, 'comisd': 3, 'maxps': 3, 'paddw': 3, 'pextrw': 3, 'btr': 2, 'sahf': 2, 'sti': 2, 'lock xchg': 2, 'pshufb': 2, 'outsw': 2, 'aas': 2, 'aaa': 2, 'jo': 2, 'xcryptcbc': 2, 'repne scasd': 2, 'bound': 2, 'cmpneqps': 2, 'psubusw': 2, 'pmaxub': 2, 'psllw': 2, 'pcmpgtb': 2, 'cvtdq2ps': 2, 'lock or': 2, 'pinsrw': 2, 'mfence': 2, 'cmpsd': 2, 'sal': 1, 'fisttp': 1, 'fcomip': 1, 'lahf': 1, 'pushf': 1, 'scasb': 1, 'cli': 1, 'jcxz': 1, 'repe cmpsw': 1, 'emms': 1, 'aeskeygenassist': 1, 'aesenc': 1, 'aesenclast': 1, 'pcmpeqq': 1, 'psubq': 1, 'aam': 1, 'lock bts': 1, 'repne movsd': 1, 'repne movsb': 1, 'repne stosd': 1, 'repne stosb': 1, 'les': 1, 'sldt': 1, 'fbstp': 1, 'rep movsw': 1, 'cmovnp': 1, 'lock add': 1, 'lock sub': 1})
# all_histograms_x64 = Counter({'num_functions': 542822, 'mov': 477831, 'ret': 445568, 'call': 377492, 'push': 369944, 'pop': 323101, 'add': 317721, 'sub': 299270, 'jmp': 286751, 'cmp': 226990, 'je': 222952, 'test': 202138, 'xor': 194796, 'lea': 189520, 'jne': 180164, 'movzx': 90750, 'nop': 86672, 'movsxd': 83369, 'and': 62323, 'inc': 58805, 'shl': 53526, 'jle': 48907, 'or': 40966, 'leave': 39261, 'ja': 37600, 'dec': 34033, 'sar': 33707, 'shr': 32676, 'jb': 32671, 'movsd': 32183, 'jl': 31845, 'jae': 31477, 'jbe': 31477, 'imul': 31134, 'movabs': 30275, 'jg': 29521, 'jge': 26152, 'sete': 21347, 'setne': 19565, 'cdqe': 19150, 'js': 19061, 'mulsd': 15938, 'movsx': 15902, 'cmovne': 15163, 'xorps': 14001, 'cmove': 13750, 'addsd': 13636, 'not': 12319, 'jns': 11984, 'movapd': 11263, 'ucomisd': 11193, 'neg': 10266, 'movss': 9929, 'movups': 9808, 'subsd': 9541, 'pxor': 9005, 'movaps': 8936, 'cdq': 7844, 'bt': 7742, 'divsd': 7623, 'cvtsi2sd': 6825, 'sbb': 6685, 'xorpd': 6542, 'idiv': 6373, 'repne scasb': 6186, 'cvtss2sd': 5508, 'cmovg': 4297, 'cvttsd2si': 3944, 'jp': 3909, 'cvtsd2ss': 3860, 'cmovl': 3749, 'setg': 3521, 'movq': 3385, 'cmovb': 3348, 'setb': 3343, 'cmova': 3082, 'div': 2821, 'addss': 2653, 'andpd': 2430, 'cmovs': 2418, 'mulss': 2409, 'jnp': 2375, 'seta': 2331, 'cmovle': 2248, 'int3': 2242, 'ucomiss': 2142, 'movupd': 2069, 'sqrtsd': 1973, 'cmovge': 1678, 'cmovns': 1622, 'cmovae': 1621, 'divss': 1457, 'cmovbe': 1436, 'cvtsi2ss': 1414, 'subss': 1364, 'setl': 1258, 'cqo': 1217, 'movd': 1208, 'repe cmpsb': 1157, 'movdqa': 1132, 'mul': 1129, 'bts': 1088, 'addpd': 1051, 'setbe': 1040, 'rep movsb': 1011, 'mulpd': 1005, 'movdqu': 1003, 'ud2': 985, 'adc': 966, 'setle': 918, 'rep stosq': 872, 'pshufd': 868, 'movlhps': 867, 'setge': 846, 'setae': 843, 'subpd': 767, 'seto': 765, 'rol': 755, 'btr': 746, 'maxsd': 734, 'minsd': 700, 'andnpd': 685, 'rep stosd': 684, 'fld': 661, 'shufpd': 648, 'rep movsd': 636, 'fstp': 622, 'orpd': 614, 'cmpltsd': 599, 'andps': 588, 'lock dec': 576, 'unpcklpd': 547, 'setp': 533, 'movlpd': 515, 'cvttss2si': 509, 'cwde': 503, 'rep movsq': 457, 'punpckldq': 418, 'ror': 397, 'syscall': 359, 'cmovno': 357, 'setnp': 353, 'bswap': 342, 'cvtdq2pd': 329, 'paddd': 322, 'punpcklqdq': 307, 'rep stosb': 303, 'lock cmpxchg': 296, 'shufps': 247, 'cmovo': 245, 'cmpxchg': 238, 'fldz': 232, 'faddp': 228, 'cmpneqsd': 218, 'movhpd': 218, 'fmul': 215, 'setns': 207, 'hlt': 205, 'lock inc': 201, 'maxss': 197, 'comisd': 194, 'pand': 181, 'sqrtss': 171, 'pslldq': 169, 'xchg': 158, 'divpd': 150, 'orps': 146, 'cvtps2pd': 141, 'andnps': 138, 'cmpeqsd': 134, 'bsr': 131, 'fmulp': 128, 'por': 126, 'mulps': 123, 'pcmpeqd': 114, 'movhlps': 112, 'punpcklwd': 106, 'cmpnltsd': 100, 'unpcklps': 100, 'psrldq': 98, 'addps': 95, 'paddq': 93, 'pandn': 88, 'lock or': 87, 'fxch': 87, 'movnti': 84, 'cmplesd': 83, 'punpcklbw': 78, 'movlps': 74, 'lock xadd': 72, 'minss': 71, 'sets': 70, 'fild': 68, 'cmpltss': 68, 'punpckhdq': 67, 'psubd': 60, 'psrad': 57, 'repne scasd': 51, 'prefetchnta': 50, 'lock add': 49, 'cmpnlesd': 49, 'cvtpd2ps': 47, 'shld': 45, 'pcmpeqb': 44, 'punpckhwd': 42, 'pmovmskb': 42, 'pcmpgtd': 41, 'fucomip': 40, 'pslld': 35, 'punpckhbw': 34, 'unpckhpd': 34, 'psrlq': 32, 'cld': 32, 'fdivp': 31, 'fadd': 31, 'psubq': 30, 'shrd': 30, 'fucomi': 29, 'cmovp': 29, 'pshuflw': 29, 'pshufhw': 29, 'btc': 29, 'fld1': 28, 'psllq': 28, 'bsf': 27, 'psrld': 26, 'packuswb': 25, 'fabs': 24, 'cpuid': 23, 'fdivrp': 21, 'maxpd': 21, 'int': 21, 'movhps': 20, 'pmuludq': 20, 'fsubrp': 19, 'cmpltpd': 19, 'subps': 19, 'divps': 19, 'cmpneqss': 19, 'minpd': 19, 'cmpnless': 18, 'stmxcsr': 18, 'ldmxcsr': 18, 'fsubp': 17, 'fchs': 17, 'pcmpgtb': 17, 'cbw': 17, 'cvttpd2dq': 17, 'pinsrw': 17, 'cmpnltss': 16, 'cvtpd2dq': 16, 'pushfq': 15, 'popfq': 15, 'pminub': 15, 'psubb': 13, 'lock sub': 12, 'pcmpgtw': 10, 'paddb': 10, 'fsqrt': 9, 'fnstcw': 9, 'palignr': 9, 'fdiv': 8, 'fsub': 8, 'fsubr': 8, 'sfence': 8, 'fxam': 8, 'wait': 8, 'fnstsw': 8, 'fnclex': 8, 'fprem': 8, 'cvtsd2si': 8, 'pmulld': 8, 'fdivr': 7, 'fcmovnbe': 7, 'pcmpistri': 7, 'cmpneqpd': 6, 'cmpeqss': 6, 'pcmpeqw': 6, 'psrlw': 6, 'pause': 6, 'vmovdqa': 6, 'vmovdqu': 6, 'psubusb': 5, 'pmaxub': 5, 'prefetcht0': 5, 'cmpneqps': 4, 'psraw': 4, 'mfence': 4, 'vzeroupper': 4, 'repe cmpsd': 3, 'maxps': 3, 'cvtdq2ps': 3, 'pextrw': 3, 'fldenv': 3, 'pshufb': 3, 'vpxor': 3, 'vmovntdq': 3, 'movntdq': 3, 'comiss': 3, 'lock bts': 2, 'fcmovbe': 2, 'psubusw': 2, 'outsb': 2, 'outsd': 2, 'insd': 2, 'jo': 2, 'fst': 2, 'psllw': 2, 'fnstenv': 2, 'jrcxz': 2, 'rdtsc': 2, 'prefetchw': 2, 'vpcmpgtb': 2, 'vpandn': 2, 'vpand': 2, 'vpor': 2, 'vpcmpeqb': 2, 'vpsubb': 2, 'vpmovmskb': 2, 'vpcmpistri': 2, 'lfence': 2, 'ptest': 2, 'cmovnp': 1, 'insb': 1, 'outsw': 1, 'vpminsb': 1, 'vpminsd': 1, 'cvttps2dq': 1, 'fscale': 1, 'fldcw': 1, 'tzcnt': 1, 'lock and': 1, 'popcnt': 1, 'vmovd': 1, 'vpshufb': 1, 'vmovq': 1, 'vinserti128': 1})
# msvc specific, using groundtruth by Bao and Plohmann
all_histograms_x86 = Counter({'num_functions': 129538, 'ret': 107683, 'mov': 106382, 'push': 101507, 'pop': 93246, 'call': 90833, 'add': 75808, 'jmp': 70397, 'cmp': 70245, 'je': 64126, 'jne': 60986, 'test': 58090, 'xor': 56252, 'lea': 50181, 'sub': 48433, 'inc': 29640, 'and': 28883, 'or': 19398, 'movzx': 18699, 'jle': 16280, 'dec': 15928, 'jl': 14663, 'leave': 12343, 'jge': 11945, 'jg': 9261, 'jb': 9202, 'shl': 8954, 'sbb': 8001, 'ja': 7944, 'neg': 7654, 'jae': 6880, 'jbe': 6870, 'shr': 6600, 'sar': 5880, 'movsx': 5462, 'js': 5252, 'imul': 5098, 'jns': 5082, 'setne': 4494, 'cdq': 4281, 'sete': 4021, 'nop': 3381, 'rep movsd': 1839, 'not': 1664, 'idiv': 1515, 'int3': 1502, 'adc': 1325, 'div': 1179, 'rep stosd': 838, 'rep movsb': 771, 'fstp': 755, 'setg': 713, 'fld': 667, 'setl': 496, 'mul': 474, 'setge': 450, 'movsd': 430, 'fldz': 413, 'setle': 401, 'stmxcsr': 367, 'fnstsw': 365, 'stosd': 360, 'rol': 332, 'fnstcw': 321, 'fild': 303, 'xchg': 280, 'wait': 271, 'shld': 241, 'cld': 239, 'cmove': 234, 'cmovne': 223, 'ror': 201, 'rep stosb': 200, 'rcr': 193, 'xorps': 186, 'movdqu': 184, 'jp': 173, 'shrd': 172, 'movq': 171, 'movlpd': 168, 'repne scasb': 156, 'movsb': 153, 'movsw': 148, 'fst': 148, 'pushfd': 141, 'fldcw': 141, 'std': 139, 'movdqa': 139, 'sets': 130, 'setns': 129, 'bts': 118, 'clc': 118, 'bt': 114, 'fadd': 108, 'fcomp': 106, 'fucompp': 105, 'stosb': 100, 'pxor': 100, 'cwde': 98, 'ldmxcsr': 96, 'stosw': 93, 'lock xadd': 91, 'fnclex': 77, 'cmova': 70, 'fmul': 69, 'jnp': 64, 'fchs': 63, 'loop': 63, 'fld1': 63, 'popal': 61, 'fistp': 61, 'cmovb': 61, 'pushal': 56, 'movd': 52, 'fxch': 44, 'lodsb': 41, 'fdiv': 37, 'fmulp': 36, 'fsub': 33, 'paddd': 33, 'ljmp': 31, 'fdivp': 30, 'xorpd': 29, 'cpuid': 28, 'lock cmpxchg': 28, 'lodsd': 27, 'cmovae': 27, 'setb': 26, 'fdivrp': 26, 'int': 26, 'fldpi': 25, 'psubd': 24, 'fsubp': 22, 'retf': 22, 'cvttsd2si': 21, 'faddp': 20, 'movapd': 19, 'frndint': 19, 'fucomp': 19, 'psrldq': 19, 'fabs': 19, 'psrlq': 18, 'psllq': 18, 'cmovl': 18, 'fcom': 18, 'fcompp': 18, 'repe cmpsb': 17, 'cmovbe': 17, 'andpd': 17, 'ucomisd': 17, 'bswap': 17, 'jecxz': 16, 'cmovns': 16, 'pshufd': 16, 'popfd': 14, 'cmovg': 14, 'xlatb': 14, 'fsubr': 14, 'seto': 13, 'cbw': 12, 'fxam': 12, 'rdtsc': 11, 'cmovge': 11, 'cmovs': 11, 'pslld': 11, 'fdivr': 10, 'addsd': 10, 'repe cmpsd': 9, 'cmpnlepd': 9, 'psrld': 9, 'subsd': 9, 'pand': 8, 'cmpltpd': 8, 'orpd': 8, 'f2xm1': 8, 'fscale': 8, 'lodsw': 7, 'cmovle': 6, 'hlt': 6, 'insb': 6, 'outsb': 6, 'fidiv': 6, 'pmulld': 6, 'in': 5, 'repne scasw': 5, 'lcall': 5, 'das': 5, 'outsd': 5, 'fninit': 4, 'seta': 4, 'punpckldq': 4, 'arpl': 3, 'rcl': 3, 'loopne': 3, 'comisd': 3, 'lock inc': 2, 'lock dec': 2, 'btr': 2, 'sahf': 2, 'sti': 2, 'lock xchg': 2, 'pshufb': 2, 'outsw': 2, 'aas': 2, 'aaa': 2, 'jo': 2, 'xcryptcbc': 2, 'cvtsi2sd': 2, 'repne scasd': 2, 'bound': 2, 'cmpsd': 2, 'sal': 1, 'fisttp': 1, 'fcomip': 1, 'fucomip': 1, 'lahf': 1, 'pushf': 1, 'scasb': 1, 'cli': 1, 'jcxz': 1, 'repe cmpsw': 1, 'emms': 1, 'por': 1, 'setae': 1, 'pslldq': 1, 'aeskeygenassist': 1, 'aesenc': 1, 'aesenclast': 1, 'paddq': 1, 'pcmpeqq': 1, 'punpcklqdq': 1, 'psubq': 1, 'aam': 1, 'lock bts': 1, 'movups': 1, 'repne movsd': 1, 'repne movsb': 1, 'repne stosd': 1, 'repne stosb': 1, 'les': 1, 'sldt': 1, 'fimul': 1, 'fiadd': 1, 'fbstp': 1, 'rep movsw': 1})
all_histograms_x64 = Counter({'num_functions': 106192, 'mov': 86807, 'ret': 81053, 'sub': 74410, 'add': 74329, 'call': 70083, 'jmp': 66833, 'cmp': 55737, 'xor': 53815, 'lea': 53661, 'je': 51419, 'test': 50093, 'jne': 49506, 'push': 45546, 'pop': 44960, 'inc': 25204, 'movzx': 23127, 'movsxd': 22975, 'or': 18761, 'and': 18466, 'dec': 17530, 'nop': 17467, 'jle': 15478, 'jl': 12598, 'jge': 11000, 'jg': 8178, 'shl': 7005, 'jae': 6419, 'imul': 6393, 'jb': 6359, 'not': 6213, 'sar': 5901, 'shr': 5792, 'ja': 5711, 'js': 5236, 'repne scasb': 5180, 'movsx': 4911, 'cmove': 4713, 'cmovne': 4609, 'cdqe': 4576, 'jns': 4120, 'jbe': 3947, 'neg': 3609, 'sete': 3540, 'cdq': 3396, 'setne': 3283, 'sbb': 2567, 'int3': 2242, 'cmovl': 1938, 'cmovg': 1827, 'movabs': 1307, 'bt': 1186, 'bts': 1087, 'idiv': 1078, 'cmovs': 908, 'movsd': 860, 'div': 854, 'movaps': 839, 'btr': 746, 'rep movsb': 663, 'repe cmpsb': 629, 'cmova': 546, 'cmovb': 432, 'movups': 415, 'setg': 362, 'movd': 362, 'setl': 341, 'movdqa': 338, 'lock dec': 336, 'cmovle': 324, 'setge': 319, 'cvtdq2pd': 318, 'xorpd': 290, 'movapd': 281, 'rep stosd': 279, 'mulsd': 269, 'rol': 256, 'mul': 236, 'cmovge': 231, 'seta': 229, 'addsd': 214, 'lock inc': 196, 'comisd': 194, 'ror': 189, 'subsd': 189, 'setb': 185, 'movdqu': 152, 'setns': 137, 'xchg': 128, 'divsd': 124, 'cmovae': 123, 'movq': 119, 'pxor': 115, 'cvttsd2si': 114, 'adc': 110, 'rep stosb': 103, 'setle': 85, 'cqo': 84, 'ucomisd': 83, 'movnti': 82, 'lock or': 82, 'setbe': 79, 'cvtsi2sd': 75, 'jp': 69, 'cmovbe': 60, 'lock xadd': 60, 'cmovns': 58, 'movss': 54, 'repne scasd': 51, 'setae': 50, 'paddd': 48, 'cmovo': 46, 'lock add': 44, 'cwde': 44, 'prefetchnta': 41, 'bswap': 39, 'sets': 39, 'psrldq': 36, 'andpd': 33, 'pand': 32, 'psubd': 32, 'lock cmpxchg': 30, 'btc': 29, 'xorps': 28, 'psrlq': 24, 'psubq': 24, 'por': 24, 'int': 21, 'sqrtsd': 20, 'cvtps2pd': 20, 'cpuid': 16, 'stmxcsr': 16, 'ldmxcsr': 16, 'cvttpd2dq': 16, 'cvtpd2dq': 16, 'orpd': 16, 'pshufd': 16, 'cvtsd2ss': 12, 'pslld': 12, 'unpcklps': 9, 'rep movsd': 9, 'fld': 8, 'fxam': 8, 'wait': 8, 'fnstsw': 8, 'fnclex': 8, 'fprem': 8, 'fstp': 8, 'movlpd': 8, 'cvtsd2si': 8, 'psrld': 8, 'pmulld': 8, 'jnp': 6, 'rep movsq': 6, 'punpckldq': 4, 'rep stosq': 4, 'repe cmpsd': 3, 'comiss': 3, 'lock bts': 2})
def __init__(self, bitness=32):
counts = deepcopy(self.all_histograms_x86) if bitness == 32 else deepcopy(self.all_histograms_x64)
num_documents = counts.pop("num_functions")
for term, term_count in counts.items():
self.idf[term] = self._calculateIdf(num_documents, term_count) if term_count else self._calculateIdf(num_documents, 1)
def getTfIdfFromBlocks(self, blocks):
term_counts = Counter()
for _, block in blocks.items():
for ins in block:
term_counts[str(ins[2])] += 1
return self.tfidf(term_counts)
def tfidf(self, term_counts):
score = 0
sum_term_counts = sum(term_counts.values())
max_count = max(term_counts.values())
for term, term_count in term_counts.items():
score += self._calculateTf(sum_term_counts, term_count, max_count) * self.getFrequency(term)
return score
def _calculateTf(self, num_terms, term_count, max_term_count=0):
# raw count
return term_count
# term frequency
# return term_count / num_terms if num_terms else 0
# double normal 0.5
# return 0.5 + 0.5 * (term_count / max_term_count) if max_term_count else 0.5
# log normal
# return math.log(1 + term_count)
# binary
# return 1 if term_count else 0
def _calculateIdf(self, num_documents, value_count):
# idf probabilistic
return math.log(1.0 * (num_documents - value_count) / value_count)
# smooth
# return math.log(num_documents / (1 + value_count)) + 1
# idf
# return math.log(num_documents / (value_count))
def getFrequency(self, term):
# if we don't have that word in our collection, use the least observed frequency
return self.idf[term] if term in self.idf else max(self.idf.values())
| 295.55
| 5,104
| 0.580556
| 2,580
| 17,733
| 3.962403
| 0.418992
| 0.012325
| 0.007434
| 0.006456
| 0.116013
| 0.09283
| 0.083439
| 0.065734
| 0.057615
| 0.043236
| 0
| 0.209044
| 0.160774
| 17,733
| 59
| 5,105
| 300.559322
| 0.477893
| 0.595725
| 0
| 0
| 0
| 0
| 0.288292
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.193548
| false
| 0
| 0.096774
| 0.096774
| 0.580645
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
34fb973f7565c2abf16ca28aee303ff9836a614c
| 39
|
py
|
Python
|
main.py
|
digitaltembo/kismet
|
60c6ed470f3a891bad0aee90659bd40cea36d993
|
[
"MIT"
] | null | null | null |
main.py
|
digitaltembo/kismet
|
60c6ed470f3a891bad0aee90659bd40cea36d993
|
[
"MIT"
] | null | null | null |
main.py
|
digitaltembo/kismet
|
60c6ed470f3a891bad0aee90659bd40cea36d993
|
[
"MIT"
] | null | null | null |
from application import app
app = app
| 9.75
| 27
| 0.769231
| 6
| 39
| 5
| 0.666667
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205128
| 39
| 3
| 28
| 13
| 0.967742
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
9b9de1215241b49f4f40d0f68243f9ac55d823de
| 145
|
py
|
Python
|
tail.py
|
doctoryes/python-morsels
|
4899cea21f4758a6ec8640e2152f40b3b9145dee
|
[
"MIT"
] | null | null | null |
tail.py
|
doctoryes/python-morsels
|
4899cea21f4758a6ec8640e2152f40b3b9145dee
|
[
"MIT"
] | null | null | null |
tail.py
|
doctoryes/python-morsels
|
4899cea21f4758a6ec8640e2152f40b3b9145dee
|
[
"MIT"
] | null | null | null |
def tail(things, num_items):
if num_items <= 0:
return []
x = list(things)
return [i for i in x[max(len(x)-num_items, 0):]]
| 20.714286
| 52
| 0.565517
| 25
| 145
| 3.16
| 0.6
| 0.303797
| 0.227848
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019048
| 0.275862
| 145
| 6
| 53
| 24.166667
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
9baa52cd5cbc0e67d8f49ca281e2d95346630e39
| 118
|
py
|
Python
|
rawdisk/scheme/__init__.py
|
dariusbakunas/rawdisk
|
d4c72d37b2c1cf27bf65e7b2c2923f14d00939c4
|
[
"BSD-3-Clause"
] | 3
|
2017-05-21T12:50:05.000Z
|
2019-09-29T11:05:58.000Z
|
rawdisk/scheme/__init__.py
|
dariusbakunas/rawdisk
|
d4c72d37b2c1cf27bf65e7b2c2923f14d00939c4
|
[
"BSD-3-Clause"
] | 149
|
2016-02-16T09:36:46.000Z
|
2021-05-21T11:04:14.000Z
|
rawdisk/scheme/__init__.py
|
dariusbakunas/rawdisk
|
d4c72d37b2c1cf27bf65e7b2c2923f14d00939c4
|
[
"BSD-3-Clause"
] | 3
|
2016-02-16T10:11:39.000Z
|
2020-01-18T11:46:50.000Z
|
# -*- coding: utf-8 -*-
__all__ = ['mbr', 'gpt', 'common']
from . import common
from . import mbr
from . import gpt
| 14.75
| 34
| 0.601695
| 16
| 118
| 4.1875
| 0.5625
| 0.447761
| 0.477612
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010638
| 0.20339
| 118
| 7
| 35
| 16.857143
| 0.702128
| 0.177966
| 0
| 0
| 0
| 0
| 0.126316
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
32dac51b5428a7e11fc25b807a262ab5cc1e9b16
| 79
|
py
|
Python
|
__init__.py
|
Sleemanmunk/approximate-randomization
|
96c854b5f49e693c7b085096abf3743d4547e3c5
|
[
"Apache-2.0"
] | 5
|
2020-04-29T06:46:49.000Z
|
2022-01-20T14:58:08.000Z
|
__init__.py
|
Sleemanmunk/approximate-randomization
|
96c854b5f49e693c7b085096abf3743d4547e3c5
|
[
"Apache-2.0"
] | null | null | null |
__init__.py
|
Sleemanmunk/approximate-randomization
|
96c854b5f49e693c7b085096abf3743d4547e3c5
|
[
"Apache-2.0"
] | 1
|
2022-02-15T07:30:48.000Z
|
2022-02-15T07:30:48.000Z
|
from .approximate_randomization import meandiff, meanlt, meangt, chanceByChance
| 79
| 79
| 0.873418
| 8
| 79
| 8.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075949
| 79
| 1
| 79
| 79
| 0.931507
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
32fe96a3d4e0a8b98ab3036cb4d04465fed94bec
| 43
|
py
|
Python
|
beluga/continuation/ContinuationSolution.py
|
Rapid-Design-of-Systems-Laboratory/beluga-legacy
|
d14713d8211b64293c4427005cf02fbd58630598
|
[
"MIT"
] | 1
|
2019-03-26T03:00:03.000Z
|
2019-03-26T03:00:03.000Z
|
beluga/continuation/ContinuationSolution.py
|
Rapid-Design-of-Systems-Laboratory/beluga-legacy
|
d14713d8211b64293c4427005cf02fbd58630598
|
[
"MIT"
] | null | null | null |
beluga/continuation/ContinuationSolution.py
|
Rapid-Design-of-Systems-Laboratory/beluga-legacy
|
d14713d8211b64293c4427005cf02fbd58630598
|
[
"MIT"
] | 1
|
2019-07-14T22:53:52.000Z
|
2019-07-14T22:53:52.000Z
|
class ContinuationSolution(list):
pass
| 14.333333
| 33
| 0.767442
| 4
| 43
| 8.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.162791
| 43
| 2
| 34
| 21.5
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
fd0da107a6990d875937e2736ae19585dfa93cb9
| 63
|
py
|
Python
|
mfgp/task1_new/utils/has_duplicates.py
|
kunalghosh/Multi_Fidelity_Prediction_GP
|
c858554f5c1f0c4aafa12cf7c441bd2d56b115f5
|
[
"BSD-3-Clause"
] | null | null | null |
mfgp/task1_new/utils/has_duplicates.py
|
kunalghosh/Multi_Fidelity_Prediction_GP
|
c858554f5c1f0c4aafa12cf7c441bd2d56b115f5
|
[
"BSD-3-Clause"
] | 3
|
2021-08-31T08:53:49.000Z
|
2021-10-05T15:10:42.000Z
|
mfgp/task1_new/utils/has_duplicates.py
|
kunalghosh/Multi_Fidelity_Prediction_GP
|
c858554f5c1f0c4aafa12cf7c441bd2d56b115f5
|
[
"BSD-3-Clause"
] | null | null | null |
def has_duplicates(seq):
return len(seq) != len(set(seq))
| 15.75
| 36
| 0.650794
| 10
| 63
| 4
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.174603
| 63
| 3
| 37
| 21
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
fd3853969cfe50e2484f5c049ca9429633441f3e
| 492
|
py
|
Python
|
test_palindrome.py
|
zhonchik/python_examples
|
c9f54d27c4ae27d06630f679b7fef53bdf057bd9
|
[
"MIT"
] | null | null | null |
test_palindrome.py
|
zhonchik/python_examples
|
c9f54d27c4ae27d06630f679b7fef53bdf057bd9
|
[
"MIT"
] | null | null | null |
test_palindrome.py
|
zhonchik/python_examples
|
c9f54d27c4ae27d06630f679b7fef53bdf057bd9
|
[
"MIT"
] | null | null | null |
from palindrome import is_palindrome
def test_is_palindrome_1():
assert is_palindrome('a')
def test_is_palindrome_2():
assert is_palindrome('aa')
assert not is_palindrome('ab')
def test_is_palindrome_3():
assert is_palindrome('aba')
assert not is_palindrome('abc')
def test_is_palindrome_4():
assert is_palindrome('abba')
assert not is_palindrome('abcd')
def test_is_palindrome_5():
assert is_palindrome('abcba')
assert not is_palindrome('abcde')
| 19.68
| 37
| 0.731707
| 70
| 492
| 4.785714
| 0.314286
| 0.537313
| 0.134328
| 0.283582
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012165
| 0.164634
| 492
| 24
| 38
| 20.5
| 0.80292
| 0
| 0
| 0
| 0
| 0
| 0.058943
| 0
| 0
| 0
| 0
| 0
| 0.6
| 1
| 0.333333
| true
| 0
| 0.066667
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
fd56547ef7cecbb947c927c5d9b403eef818fcc1
| 190
|
py
|
Python
|
PIP/Class Program/ClassQuestion1.py
|
ankitrajbiswal/SEM_5
|
db716e242e77149a4091e0e564356ddc724aeff0
|
[
"Apache-2.0"
] | 10
|
2021-04-24T11:46:48.000Z
|
2022-01-17T05:14:37.000Z
|
PIP/Class Program/ClassQuestion1.py
|
ankitrajbiswal/SEM_5
|
db716e242e77149a4091e0e564356ddc724aeff0
|
[
"Apache-2.0"
] | 2
|
2021-06-28T11:51:50.000Z
|
2021-11-01T08:21:53.000Z
|
PIP/Class Program/ClassQuestion1.py
|
ankitrajbiswal/SEM_5
|
db716e242e77149a4091e0e564356ddc724aeff0
|
[
"Apache-2.0"
] | 16
|
2021-04-24T11:46:58.000Z
|
2022-03-02T05:08:19.000Z
|
def rectangle(breadth,length):
return breadth*length,2*(length+breadth)
x,y=rectangle(float(input("Enter breatdh ")),float(input("Enter length ")))
print("Area ",x,"Perimeter ",y)
| 31.666667
| 76
| 0.694737
| 26
| 190
| 5.076923
| 0.576923
| 0.19697
| 0.227273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005952
| 0.115789
| 190
| 5
| 77
| 38
| 0.779762
| 0
| 0
| 0
| 0
| 0
| 0.228261
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 0.5
| 0.25
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
fd62496e51a3663de8de06bfd16ca4a16948e42a
| 132
|
py
|
Python
|
mmflow/__init__.py
|
gaotongxiao/mmflow
|
92f0fb951d0d5b3a1aa75f42c35f447803c31ded
|
[
"Apache-2.0"
] | 1
|
2021-11-16T12:32:54.000Z
|
2021-11-16T12:32:54.000Z
|
mmflow/__init__.py
|
xiaokekeke/mmflow
|
c9ab798cec832d3472cbb06f04b2d64299802168
|
[
"Apache-2.0"
] | null | null | null |
mmflow/__init__.py
|
xiaokekeke/mmflow
|
c9ab798cec832d3472cbb06f04b2d64299802168
|
[
"Apache-2.0"
] | 1
|
2022-03-24T06:46:05.000Z
|
2022-03-24T06:46:05.000Z
|
from .version import __version__, parse_version_info, version_info
__all__ = ['__version__', 'version_info', 'parse_version_info']
| 33
| 66
| 0.80303
| 16
| 132
| 5.5
| 0.375
| 0.5
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 132
| 3
| 67
| 44
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0.310606
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
b5f9174e540cdeb9dce27335725b85298ea4036b
| 211
|
py
|
Python
|
src/onapsdk/msb/k8s/__init__.py
|
krasm/python-onapsdk
|
87cd3017fc542a8afd3be51fbd89934ed87ed3a7
|
[
"Apache-2.0"
] | 4
|
2020-06-13T04:51:27.000Z
|
2021-01-06T15:00:51.000Z
|
src/onapsdk/msb/k8s/__init__.py
|
krasm/python-onapsdk
|
87cd3017fc542a8afd3be51fbd89934ed87ed3a7
|
[
"Apache-2.0"
] | 10
|
2021-09-20T15:42:47.000Z
|
2021-09-23T12:49:51.000Z
|
src/onapsdk/msb/k8s/__init__.py
|
krasm/python-onapsdk
|
87cd3017fc542a8afd3be51fbd89934ed87ed3a7
|
[
"Apache-2.0"
] | 8
|
2020-08-28T10:56:02.000Z
|
2022-02-11T17:06:03.000Z
|
"""K8s package."""
from .definition import Definition, Profile, ConfigurationTemplate
from .connectivity_info import ConnectivityInfo
from .instance import InstantiationParameter, InstantiationRequest, Instance
| 42.2
| 76
| 0.848341
| 19
| 211
| 9.368421
| 0.684211
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005181
| 0.085308
| 211
| 4
| 77
| 52.75
| 0.917098
| 0.056872
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bd273ee2464d11dbcac02d0275e9db91e84c4141
| 16
|
py
|
Python
|
util/__init__.py
|
atlas-calo-ml/GraphNets4Pions_LLNL
|
fb25259124711526cc4110461f09db1d03a669f9
|
[
"Apache-2.0"
] | 1
|
2021-11-02T00:40:19.000Z
|
2021-11-02T00:40:19.000Z
|
util/__init__.py
|
atlas-calo-ml/GraphNets4Pions_LLNL
|
fb25259124711526cc4110461f09db1d03a669f9
|
[
"Apache-2.0"
] | null | null | null |
util/__init__.py
|
atlas-calo-ml/GraphNets4Pions_LLNL
|
fb25259124711526cc4110461f09db1d03a669f9
|
[
"Apache-2.0"
] | null | null | null |
# i am an import
| 16
| 16
| 0.6875
| 4
| 16
| 2.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 16
| 1
| 16
| 16
| 0.916667
| 0.875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1faeffa5e6348cc8468a219d5a739a425edc8c65
| 28
|
py
|
Python
|
image_trasnformations/__init__.py
|
Mauricio-xxi/image_transformations
|
7474b9d475ca8547869c8c6687b3b4696d69906a
|
[
"MIT"
] | null | null | null |
image_trasnformations/__init__.py
|
Mauricio-xxi/image_transformations
|
7474b9d475ca8547869c8c6687b3b4696d69906a
|
[
"MIT"
] | null | null | null |
image_trasnformations/__init__.py
|
Mauricio-xxi/image_transformations
|
7474b9d475ca8547869c8c6687b3b4696d69906a
|
[
"MIT"
] | null | null | null |
from hello import say_hello
| 14
| 27
| 0.857143
| 5
| 28
| 4.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 28
| 1
| 28
| 28
| 0.958333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
9531611c9d8eeddaa184669dc9aff0e5cac306a9
| 33
|
py
|
Python
|
efundsapi/views/__init__.py
|
code-scaffold/django
|
b149cbdee87adb7e45998d86d4caa986a784814d
|
[
"Apache-2.0"
] | 1
|
2021-09-30T07:17:29.000Z
|
2021-09-30T07:17:29.000Z
|
efundsapi/views/__init__.py
|
code-scaffold/django
|
b149cbdee87adb7e45998d86d4caa986a784814d
|
[
"Apache-2.0"
] | null | null | null |
efundsapi/views/__init__.py
|
code-scaffold/django
|
b149cbdee87adb7e45998d86d4caa986a784814d
|
[
"Apache-2.0"
] | null | null | null |
from .demo import (DemoViewSet,)
| 16.5
| 32
| 0.757576
| 4
| 33
| 6.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.862069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
20f6f46d484d529155973c3619a40c35ca719418
| 239
|
py
|
Python
|
hypemaths/exceptions/exceptions.py
|
janaSunrise/HypeMaths
|
4a9afe1bb0c765e273fb3f1a54ab1d7489421db1
|
[
"MIT"
] | 8
|
2020-12-25T07:02:07.000Z
|
2021-02-06T07:06:07.000Z
|
hypemaths/exceptions/exceptions.py
|
Deep-Alchemy/HypeMaths
|
4a9afe1bb0c765e273fb3f1a54ab1d7489421db1
|
[
"MIT"
] | 2
|
2020-12-27T07:57:06.000Z
|
2021-03-10T14:44:23.000Z
|
hypemaths/exceptions/exceptions.py
|
Deep-Alchemy/HypeMaths
|
4a9afe1bb0c765e273fb3f1a54ab1d7489421db1
|
[
"MIT"
] | 2
|
2020-12-27T02:25:43.000Z
|
2021-03-08T05:44:14.000Z
|
class InvalidMatrixError(Exception):
pass
class MatrixDimensionError(Exception):
pass
class MatrixNotSquare(Exception):
pass
class InvalidVectorError(Exception):
pass
class VectorDimensionError(Exception):
pass
| 12.578947
| 38
| 0.757322
| 20
| 239
| 9.05
| 0.4
| 0.359116
| 0.39779
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179916
| 239
| 18
| 39
| 13.277778
| 0.923469
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
1f793aceca8b4b35f9c37048849f73528c05496c
| 287
|
py
|
Python
|
part_2/week_14/bubbleSort.py
|
eduardovivi/Intro-to-Computer-Science-with-Python-Part-1-and-2-IME-USP-Coursera
|
af65ba8af0f07bcc36c4bc03ee23023b829966dc
|
[
"MIT"
] | 16
|
2019-06-27T23:03:54.000Z
|
2022-03-05T00:22:37.000Z
|
part_2/week_14/bubbleSort.py
|
eduardovivi/Intro-to-Computer-Science-with-Python-Part-1-and-2-IME-USP-Coursera
|
af65ba8af0f07bcc36c4bc03ee23023b829966dc
|
[
"MIT"
] | null | null | null |
part_2/week_14/bubbleSort.py
|
eduardovivi/Intro-to-Computer-Science-with-Python-Part-1-and-2-IME-USP-Coursera
|
af65ba8af0f07bcc36c4bc03ee23023b829966dc
|
[
"MIT"
] | 16
|
2019-09-23T13:44:31.000Z
|
2021-11-16T17:20:37.000Z
|
def bubble_sort(lista):
for passnum in range(len(lista)-1,0,-1):
for i in range(passnum):
if lista[i]>lista[i+1]:
temp = lista[i]
lista[i] = lista[i+1]
lista[i+1] = temp
print(lista)
return lista
| 31.888889
| 44
| 0.470383
| 40
| 287
| 3.35
| 0.4
| 0.268657
| 0.246269
| 0.268657
| 0.19403
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034884
| 0.400697
| 287
| 9
| 45
| 31.888889
| 0.744186
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0.222222
| 0
| 0
| 0.222222
| 0.111111
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
2f2b136607cee5292b7c5b41ab13d40f95f2f493
| 19
|
py
|
Python
|
main.py
|
suanyouyou/Hello-world
|
6447f05e8895f6b3fd2e03f28c1b62bbfe1518cc
|
[
"MIT"
] | null | null | null |
main.py
|
suanyouyou/Hello-world
|
6447f05e8895f6b3fd2e03f28c1b62bbfe1518cc
|
[
"MIT"
] | null | null | null |
main.py
|
suanyouyou/Hello-world
|
6447f05e8895f6b3fd2e03f28c1b62bbfe1518cc
|
[
"MIT"
] | null | null | null |
print("your name")
| 9.5
| 18
| 0.684211
| 3
| 19
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 19
| 1
| 19
| 19
| 0.764706
| 0
| 0
| 0
| 0
| 0
| 0.473684
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
2f583336b166c3b17ea691c3a2a33e8cebb2bee7
| 194
|
py
|
Python
|
violation/fields/rule.py
|
adepeter/django-violations
|
92f6052a11594a66a7a963abb04cb17e00412bcc
|
[
"MIT"
] | 1
|
2020-05-24T20:46:20.000Z
|
2020-05-24T20:46:20.000Z
|
violation/fields/rule.py
|
adepeter/django-violations
|
92f6052a11594a66a7a963abb04cb17e00412bcc
|
[
"MIT"
] | null | null | null |
violation/fields/rule.py
|
adepeter/django-violations
|
92f6052a11594a66a7a963abb04cb17e00412bcc
|
[
"MIT"
] | null | null | null |
from django import forms
class RulesModelMultipleChoiceField(forms.ModelMultipleChoiceField):
def label_from_instance(self, obj):
return '%(rule_name)s' % {'rule_name': obj.name}
| 24.25
| 68
| 0.747423
| 22
| 194
| 6.409091
| 0.727273
| 0.113475
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.149485
| 194
| 7
| 69
| 27.714286
| 0.854545
| 0
| 0
| 0
| 0
| 0
| 0.113402
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
2f835ca18838a72a56575fc6f9415b160e090c5a
| 70
|
py
|
Python
|
ML/Pytorch/GANs/5. ProGAN/test.py
|
ZonePG/Machine-Learning-Collection
|
85f1e761fab85b61d4dbd44285d6483b75ba649c
|
[
"MIT"
] | 9
|
2021-03-29T13:55:35.000Z
|
2021-12-24T11:45:39.000Z
|
ML/Pytorch/GANs/5. ProGAN/test.py
|
Mubasshir-Ali/Machine-Learning-Collection
|
ee0b0f0718fac7810bb660713618605c58eb282e
|
[
"MIT"
] | null | null | null |
ML/Pytorch/GANs/5. ProGAN/test.py
|
Mubasshir-Ali/Machine-Learning-Collection
|
ee0b0f0718fac7810bb660713618605c58eb282e
|
[
"MIT"
] | 3
|
2021-05-06T06:53:43.000Z
|
2021-12-04T13:09:13.000Z
|
def func(x=1, y=2, **kwargs):
print(x, y)
print(func(x=3, y=4))
| 11.666667
| 29
| 0.528571
| 16
| 70
| 2.3125
| 0.625
| 0.27027
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 0.2
| 70
| 5
| 30
| 14
| 0.589286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.333333
| 0.666667
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
c81b1b749a6afa98fd2b62c243bf9f3e3d3bc3f0
| 55
|
py
|
Python
|
math/stochastic_modeling/program.py
|
spideynolove/Other-repo
|
34066f177994415d031183ab9dd219d787e6e13a
|
[
"MIT"
] | null | null | null |
math/stochastic_modeling/program.py
|
spideynolove/Other-repo
|
34066f177994415d031183ab9dd219d787e6e13a
|
[
"MIT"
] | null | null | null |
math/stochastic_modeling/program.py
|
spideynolove/Other-repo
|
34066f177994415d031183ab9dd219d787e6e13a
|
[
"MIT"
] | null | null | null |
import numpy as np
print("Hello stochastic_modeling!")
| 18.333333
| 35
| 0.8
| 8
| 55
| 5.375
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109091
| 55
| 3
| 35
| 18.333333
| 0.877551
| 0
| 0
| 0
| 0
| 0
| 0.464286
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
c836f5c3e41f2f3150b831af630246de04b3803b
| 484
|
py
|
Python
|
ex009.py
|
vinisantos7/PythonExercicios
|
bc8f38e03a606d6b0216632a93affeab0792e534
|
[
"MIT"
] | 2
|
2021-11-04T21:09:11.000Z
|
2021-11-08T09:42:10.000Z
|
ex009.py
|
vinisantos7/PythonExercicios
|
bc8f38e03a606d6b0216632a93affeab0792e534
|
[
"MIT"
] | null | null | null |
ex009.py
|
vinisantos7/PythonExercicios
|
bc8f38e03a606d6b0216632a93affeab0792e534
|
[
"MIT"
] | null | null | null |
print("Bem-Vindo a Tabuada v1.0!")
num = (int(input("Digite um número para a tabuada: ")))
print('-'*12)
print(f"{num} x {1:2} = {num * 1}")
print(f"{num} x {2:2} = {num * 2}")
print(f"{num} x {3:2} = {num * 3}")
print(f"{num} x {4:2} = {num * 4}")
print(f"{num} x {5:2} = {num * 5}")
print(f"{num} x {6:2} = {num * 6}")
print(f"{num} x {7:2} = {num * 7}")
print(f"{num} x {8:2} = {num * 8}")
print(f"{num} x {9:2} = {num * 9}")
print(f"{num} x {10:2} = {num * 10}")
print("-"*12)
| 26.888889
| 55
| 0.489669
| 100
| 484
| 2.37
| 0.27
| 0.253165
| 0.379747
| 0.421941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09596
| 0.181818
| 484
| 17
| 56
| 28.470588
| 0.502525
| 0
| 0
| 0
| 0
| 0
| 0.644628
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.928571
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
c8588ae903205a44df03000e2180c59ad6c22133
| 34
|
py
|
Python
|
src/jupyter_contrib_nbextensions/hello.py
|
satishv21/jupyter_contrib_nbextensions
|
22735bf4e6be6e59f551b542ec75f366133af6d6
|
[
"BSD-3-Clause-Clear"
] | null | null | null |
src/jupyter_contrib_nbextensions/hello.py
|
satishv21/jupyter_contrib_nbextensions
|
22735bf4e6be6e59f551b542ec75f366133af6d6
|
[
"BSD-3-Clause-Clear"
] | null | null | null |
src/jupyter_contrib_nbextensions/hello.py
|
satishv21/jupyter_contrib_nbextensions
|
22735bf4e6be6e59f551b542ec75f366133af6d6
|
[
"BSD-3-Clause-Clear"
] | null | null | null |
def main(str):
print("nonce")
| 11.333333
| 18
| 0.588235
| 5
| 34
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 34
| 2
| 19
| 17
| 0.740741
| 0
| 0
| 0
| 0
| 0
| 0.147059
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
c07a8e800a976fd14cdab0b71abfc26f29e384ac
| 44
|
py
|
Python
|
tests/__init__.py
|
crashfrog/peewee-pymssql
|
23b5cdf4a9c3d13bec0663ccff3a2e8e1bf83b1c
|
[
"Unlicense"
] | null | null | null |
tests/__init__.py
|
crashfrog/peewee-pymssql
|
23b5cdf4a9c3d13bec0663ccff3a2e8e1bf83b1c
|
[
"Unlicense"
] | null | null | null |
tests/__init__.py
|
crashfrog/peewee-pymssql
|
23b5cdf4a9c3d13bec0663ccff3a2e8e1bf83b1c
|
[
"Unlicense"
] | null | null | null |
"""Unit test package for peewee_pymssql."""
| 22
| 43
| 0.727273
| 6
| 44
| 5.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113636
| 44
| 1
| 44
| 44
| 0.794872
| 0.840909
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
c0e97b221652cd6c1ba631ac8b060e16f774978d
| 88
|
py
|
Python
|
dreamerv2/training/__init__.py
|
baecm/dreamerv2
|
80f7f124a3ef572ac3feb590d789852ed4d7d76f
|
[
"MIT"
] | null | null | null |
dreamerv2/training/__init__.py
|
baecm/dreamerv2
|
80f7f124a3ef572ac3feb590d789852ed4d7d76f
|
[
"MIT"
] | null | null | null |
dreamerv2/training/__init__.py
|
baecm/dreamerv2
|
80f7f124a3ef572ac3feb590d789852ed4d7d76f
|
[
"MIT"
] | null | null | null |
from .trainer import Trainer
from .config import Config
from .evaluator import Evaluator
| 29.333333
| 32
| 0.840909
| 12
| 88
| 6.166667
| 0.416667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 88
| 3
| 32
| 29.333333
| 0.961039
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
8d08246b7ef1b63961d5047cfb40819835e5ddbf
| 107
|
py
|
Python
|
src/model/__init__.py
|
LaikaIztech/ProjectLaikaServer
|
483d4ee43b6ca31ac07d4037f68e8394ad66119f
|
[
"MIT"
] | null | null | null |
src/model/__init__.py
|
LaikaIztech/ProjectLaikaServer
|
483d4ee43b6ca31ac07d4037f68e8394ad66119f
|
[
"MIT"
] | null | null | null |
src/model/__init__.py
|
LaikaIztech/ProjectLaikaServer
|
483d4ee43b6ca31ac07d4037f68e8394ad66119f
|
[
"MIT"
] | null | null | null |
__all__ = ["create_positives", "create_positive_annotations", "create_negative_annotations", "train_ready"]
| 107
| 107
| 0.82243
| 11
| 107
| 7.090909
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046729
| 107
| 1
| 107
| 107
| 0.764706
| 0
| 0
| 0
| 0
| 0
| 0.75
| 0.5
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
8d09e4301864b924b4de01a025501d05b410b6ef
| 45
|
py
|
Python
|
tests/test-root/conf.py
|
kai687/sphinxawesome-sampdirective
|
d5955b823967db52747df3c329cec76592ec612b
|
[
"MIT"
] | 1
|
2020-07-30T10:40:52.000Z
|
2020-07-30T10:40:52.000Z
|
tests/test-root/conf.py
|
kai687/sphinxawesome-sampdirective
|
d5955b823967db52747df3c329cec76592ec612b
|
[
"MIT"
] | 79
|
2020-06-09T11:41:51.000Z
|
2022-03-27T08:18:57.000Z
|
tests/test-root/conf.py
|
kai687/sphinxawesome-sampdirective
|
d5955b823967db52747df3c329cec76592ec612b
|
[
"MIT"
] | null | null | null |
"""Sphinx configuration file for testing."""
| 22.5
| 44
| 0.733333
| 5
| 45
| 6.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 45
| 1
| 45
| 45
| 0.825
| 0.844444
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
23d7c93f118113c9fbe319e8035d5e344c3c1aa4
| 66
|
py
|
Python
|
src/allennlp/fortex/allennlp/__init__.py
|
Piyush13y/forte-wrappers
|
250df428a8705f769d53eb070f89c3f66e713015
|
[
"Apache-2.0"
] | 3
|
2021-06-17T18:52:00.000Z
|
2022-01-11T19:15:21.000Z
|
src/allennlp/fortex/allennlp/__init__.py
|
Piyush13y/forte-wrappers
|
250df428a8705f769d53eb070f89c3f66e713015
|
[
"Apache-2.0"
] | 66
|
2021-03-30T15:04:11.000Z
|
2022-03-24T04:35:11.000Z
|
src/allennlp/fortex/allennlp/__init__.py
|
Piyush13y/forte-wrappers
|
250df428a8705f769d53eb070f89c3f66e713015
|
[
"Apache-2.0"
] | 10
|
2021-03-16T19:48:31.000Z
|
2022-03-01T05:48:17.000Z
|
from fortex.allennlp.allennlp_processors import AllenNLPProcessor
| 33
| 65
| 0.909091
| 7
| 66
| 8.428571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.060606
| 66
| 1
| 66
| 66
| 0.951613
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
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