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revasbot/revas_calculations.py | iwo-strzebonski/SeleBot | 0 | 176568 | class RevasCalculations:
'''Functions related to calculations go there
'''
pass
| [
1,
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biosimulators_test_suite/results/data_model.py | Ryannjordan/Biosimulators_test_suite | 0 | 17389 | """ Data model for results of test cases
:Author: <NAME> <<EMAIL>>
:Date: 2021-01-01
:Copyright: 2021, Center for Reproducible Biomedical Modeling
:License: MIT
"""
from .._version import __version__
from ..warnings import TestCaseWarning # noqa: F401
import enum
__all__ = [
'TestCaseResultType',
'TestCaseResult',
'TestResultsReport',
]
class TestCaseResultType(str, enum.Enum):
""" Type of test case result """
passed = 'passed'
failed = 'failed'
skipped = 'skipped'
class TestCaseResult(object):
""" A result of executing a test case
Attributes:
case (:obj:`TestCase`): test case
type (:obj:`obj:`TestCaseResultType`): type
duration (:obj:`float`): execution duration in seconds
exception (:obj:`Exception`): exception
warnings (:obj:`list` of :obj:`TestCaseWarning`): warnings
skip_reason (:obj:`Exception`): Exception which explains reason for skip
log (:obj:`str`): log of execution
"""
def __init__(self, case=None, type=None, duration=None, exception=None, warnings=None, skip_reason=None, log=None):
"""
Args:
case (:obj:`TestCase`, optional): test case
type (:obj:`obj:`TestCaseResultType`, optional): type
duration (:obj:`float`, optional): execution duration in seconds
exception (:obj:`Exception`, optional): exception
warnings (:obj:`list` of :obj:`TestCaseWarning`, optional): warnings
skip_reason (:obj:`Exception`, optional): Exception which explains reason for skip
log (:obj:`str`, optional): log of execution
"""
self.case = case
self.type = type
self.duration = duration
self.exception = exception
self.warnings = warnings or []
self.skip_reason = skip_reason
self.log = log
def to_dict(self):
""" Generate a dictionary representation e.g., for export to JSON
Returns:
:obj:`dict`: dictionary representation
"""
return {
'case': {
'id': self.case.id,
'description': self.case.description,
},
'resultType': self.type.value,
'duration': self.duration,
'exception': {
'category': self.exception.__class__.__name__,
'message': str(self.exception),
} if self.exception else None,
'warnings': [{'category': warning.category.__name__, 'message': str(warning.message)}
for warning in self.warnings],
'skipReason': {
'category': self.skip_reason.__class__.__name__,
'message': str(self.skip_reason),
} if self.skip_reason else None,
'log': self.log,
}
class TestResultsReport(object):
""" A report of the results of executing the test suite with a simulation tool
Attributes:
test_suite_version (:obj:`str`): version of the test suite which was executed
results (:obj:`list` of :obj:`TestCaseResult`): results of the test cases of the test suite
gh_issue (:obj:`int`): GitHub issue for which the test suite was executed
gh_action_run (:obj:`int`): GitHub action run in which the test suite was executed
"""
def __init__(self, test_suite_version=__version__, results=None, gh_issue=None, gh_action_run=None):
"""
Args:
test_suite_version (:obj:`str`, optional): version of the test suite which was executed
results (:obj:`list` of :obj:`TestCaseResult`, optional): results of the test cases of the test suite
gh_issue (:obj:`int`, optional): GitHub issue for which the test suite was executed
gh_action_run (:obj:`int`, optional): GitHub action run in which the test suite was executed
"""
self.test_suite_version = test_suite_version
self.results = results or []
self.gh_issue = gh_issue
self.gh_action_run = gh_action_run
def to_dict(self):
""" Generate a dictionary representation e.g., for export to JSON
Returns:
:obj:`dict`: dictionary representation
"""
return {
'testSuiteVersion': self.test_suite_version,
'results': [result.to_dict() for result in self.results],
'ghIssue': self.gh_issue,
'ghActionRun': self.gh_action_run,
}
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django_sqlprint_middleware/__init__.py | thebjorn/django-sqlprint-middleware | 1 | 1616599 | <gh_stars>1-10
# -*- coding: utf-8 -*-
"""
==========================
django-sqlprint-middleware
==========================
Middleware that pretty prints Django's sql statements.
Installation
------------
Install from PyPI::
pip install django-sqlprint-middleware
then add it to the `MIDDLEWARE_CLASSES` settings in your `settings.py` file::
MIDDLEWARE_CLASSES = (
...
'django_sqlprint_middleware.SqlPrintMiddleware',
...
)
This middleware is not order sensitive so you can put it at any position in
the list of classes.
Settings
--------
The following settings control the behavior (all of these have defaults).
DEBUG or TESTING
Either of these need to be True for the middleware to run.
SQLPRINT_MIDDLEWARE
Set to False to skip middleware without removing it.
X_DB_HITS
Set to false to prevent db hits being sent in the response header 'X-DB-hits'
SQLPRINT_MAX_QUERIES
Raise an exception if the number of queries for a view is greater than this.
Default is 1200 (zero means no check).
SQLPRINT_MIN_QUERIES
For less output you can set this to the number of queries a view can make before
querires are printed (default is zero).
"""
__version__ = '0.1.3'
from .django_sqlprint_middleware import SqlPrintMiddleware
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] |
cursor/device.py | brendanhowell/cursor | 3 | 74391 | <gh_stars>1-10
from cursor import data
from cursor import renderer
from enum import Enum
import wasabi
import inspect
import hashlib
log = wasabi.Printer()
class MinMax:
def __init__(self, minx: int, maxx: int, miny: int, maxy: int):
self.minx = minx
self.maxx = maxx
self.miny = miny
self.maxy = maxy
def center(self):
return (self.minx + self.maxx) / 2, (self.miny + self.maxy) / 2
def tuple(self):
return self.minx, self.maxx, self.miny, self.maxy
class PlotterType(Enum):
ROLAND_DPX3300 = 0
DIY_PLOTTER = 1
AXIDRAW = 2
HP_7475A_A4 = 3
HP_7475A_A3 = 4
ROLAND_DXY1200 = 5
ROLAND_DXY980 = 6
HP_7595A = 7
ROLAND_PNC1000 = 8
ROLAND_DPX3300_A2 = 9
ROLAND_DPX3300_A3 = 10
HP_7595A_A3 = 11
class ExportFormat(Enum):
JPG = 0
SVG = 1
GCODE = 2
HPGL = 3
class ExportFormatMappings:
maps = {
PlotterType.ROLAND_DPX3300: ExportFormat.HPGL,
PlotterType.ROLAND_DPX3300_A2: ExportFormat.HPGL,
PlotterType.ROLAND_DPX3300_A3: ExportFormat.HPGL,
PlotterType.DIY_PLOTTER: ExportFormat.GCODE,
PlotterType.AXIDRAW: ExportFormat.SVG,
PlotterType.HP_7475A_A3: ExportFormat.HPGL,
PlotterType.HP_7475A_A4: ExportFormat.HPGL,
PlotterType.ROLAND_DXY1200: ExportFormat.HPGL,
PlotterType.ROLAND_DXY980: ExportFormat.HPGL,
PlotterType.HP_7595A: ExportFormat.HPGL,
PlotterType.ROLAND_PNC1000: ExportFormat.HPGL,
PlotterType.HP_7595A_A3: ExportFormat.HPGL,
}
class MinmaxMapping:
maps = {
PlotterType.ROLAND_DPX3300: MinMax(-16920, 16340, -11180, 11180),
PlotterType.ROLAND_DPX3300_A2: MinMax(-16920, 5440, -11180, 4629),
PlotterType.ROLAND_DPX3300_A3: MinMax(-16920, -1112, -11180, -3276),
PlotterType.DIY_PLOTTER: MinMax(0, 3350, 0, -1715),
PlotterType.AXIDRAW: MinMax(0, 0, 0, -0), # todo: missing real bounds
PlotterType.HP_7475A_A4: MinMax(0, 11040, 0, 7721),
PlotterType.HP_7475A_A3: MinMax(0, 16158, 0, 11040),
PlotterType.ROLAND_DXY1200: MinMax(0, 0, 0, 0), # todo: missing real bounds
PlotterType.ROLAND_DXY980: MinMax(0, 16158, 0, 11040),
PlotterType.HP_7595A: MinMax(-23160, 23160, -17602, 17602),
PlotterType.ROLAND_PNC1000: MinMax(0, 0, 17200, 40000), # actually unlimited y
PlotterType.HP_7595A_A3: MinMax(-7728, 7728 + 960, -5752, 5752),
}
class PaperSize(Enum):
PORTRAIT_36_48 = 0
LANDSCAPE_48_36 = 1
PORTRAIT_42_56 = 2
LANDSCAPE_56_42 = 3
PORTRAIT_50_70 = 4
LANDSCAPE_70_50 = 5
SQUARE_70_70 = 6
PORTRAIT_70_100 = 7
LANDSCAPE_100_70 = 8
LANDSCAPE_A4 = 9
PORTRAIT_A4 = 10
LANDSCAPE_A1 = 11
LANDSCAPE_A0 = 12
PORTRAIT_A3 = 13
LANDSCAPE_A3 = 14
LANDSCAPE_80_50 = 15
PORTRAIT_50_80 = 16
LANDSCAPE_A2 = 17
SQUARE_59_59 = 18
class PlotterName:
names = {
PlotterType.ROLAND_DPX3300: "dpx3300",
PlotterType.ROLAND_DPX3300_A2: "dpx3300_a2",
PlotterType.ROLAND_DPX3300_A3: "dpx3300_a3",
PlotterType.AXIDRAW: "axidraw",
PlotterType.DIY_PLOTTER: "custom",
PlotterType.HP_7475A_A3: "hp7475a_a3",
PlotterType.HP_7475A_A4: "hp7475a_a4",
PlotterType.ROLAND_DXY1200: "dxy1200",
PlotterType.ROLAND_DXY980: "dxy980",
PlotterType.HP_7595A: "hp7595a_draftmaster_sx",
PlotterType.ROLAND_PNC1000: "roland_camm1",
PlotterType.HP_7595A_A3: "hp7595a_draftmaster_sx_a3",
}
class PaperSizeName:
names = {
PaperSize.PORTRAIT_36_48: "portrait_36_48",
PaperSize.LANDSCAPE_48_36: "landscape_48_36",
PaperSize.PORTRAIT_42_56: "portrait_42_56",
PaperSize.LANDSCAPE_56_42: "landscape_56_42",
PaperSize.PORTRAIT_50_70: "portrait_50_70",
PaperSize.LANDSCAPE_70_50: "landscape_70_50",
PaperSize.SQUARE_70_70: "square_70_70",
PaperSize.PORTRAIT_70_100: "portrait_70_100",
PaperSize.LANDSCAPE_100_70: "landscape_100_70",
PaperSize.LANDSCAPE_A4: "landscape_a4",
PaperSize.PORTRAIT_A4: "portrait_a4",
PaperSize.LANDSCAPE_A1: "landscape_a1",
PaperSize.LANDSCAPE_A0: "landscape_a0",
PaperSize.PORTRAIT_A3: "portrait_a3",
PaperSize.LANDSCAPE_A3: "landscape_a3",
PaperSize.LANDSCAPE_80_50: "landscape_80x50",
PaperSize.PORTRAIT_50_80: "portrait_50_80",
PaperSize.LANDSCAPE_A2: "landscape_a2",
PaperSize.SQUARE_59_59: "square_59_59",
}
class Paper:
sizes = {
PaperSize.PORTRAIT_36_48: (360, 480),
PaperSize.LANDSCAPE_48_36: (480, 360),
PaperSize.PORTRAIT_42_56: (420, 560),
PaperSize.LANDSCAPE_56_42: (560, 420),
PaperSize.PORTRAIT_50_70: (500, 700),
PaperSize.LANDSCAPE_70_50: (700, 500),
PaperSize.SQUARE_70_70: (700, 700),
PaperSize.PORTRAIT_70_100: (700, 1000),
PaperSize.LANDSCAPE_100_70: (1000, 700),
PaperSize.LANDSCAPE_A4: (297, 210),
PaperSize.PORTRAIT_A4: (210, 297),
PaperSize.LANDSCAPE_A1: (841, 594),
PaperSize.LANDSCAPE_A0: (1189, 841),
PaperSize.PORTRAIT_A3: (297, 420),
PaperSize.LANDSCAPE_A3: (420, 297),
PaperSize.LANDSCAPE_80_50: (800, 500),
PaperSize.PORTRAIT_50_80: (500, 800),
PaperSize.LANDSCAPE_A2: (594, 420),
PaperSize.SQUARE_59_59: (590, 590),
}
class XYFactors:
fac = {
PlotterType.ROLAND_DPX3300: (40, 40),
PlotterType.ROLAND_DPX3300_A2: (40, 40),
PlotterType.ROLAND_DPX3300_A3: (40, 40),
PlotterType.DIY_PLOTTER: (2.85714, 2.90572),
PlotterType.AXIDRAW: (3.704, 3.704),
PlotterType.HP_7475A_A3: (40, 40),
PlotterType.HP_7475A_A4: (40, 40),
PlotterType.ROLAND_DXY1200: (40, 40),
PlotterType.ROLAND_DXY980: (40, 40),
PlotterType.HP_7595A: (40, 40),
PlotterType.ROLAND_PNC1000: (40, 40),
PlotterType.HP_7595A_A3: (37, 37),
}
class Cfg:
def __init__(self):
self.__type = None
self.__dimensions = None
self.__margin = None
self.__cutoff = None
@property
def type(self) -> PlotterType:
return self.__type
@type.setter
def type(self, t: PlotterType) -> None:
self.__type = t
@property
def dimension(self) -> PaperSize:
return self.__dimensions
@dimension.setter
def dimension(self, t: PaperSize):
self.__dimensions = t
@property
def margin(self) -> int:
return self.__margin
@margin.setter
def margin(self, t: int):
self.__margin = t
@property
def cutoff(self) -> int:
return self.__cutoff
@cutoff.setter
def cutoff(self, t: int):
self.__cutoff = t
class Exporter:
from cursor import path
def __init__(self):
self.__paths = None
self.__cfg = None
self.__name = None
self.__suffix = None
self.__gcode_speed = None
self.__layer_pen_mapping = None
self.__linetype_mapping = None
@property
def paths(self) -> path.PathCollection:
return self.__paths
@paths.setter
def paths(self, t: path.PathCollection) -> None:
self.__paths = t
@property
def cfg(self) -> Cfg:
return self.__cfg
@cfg.setter
def cfg(self, t: Cfg) -> None:
self.__cfg = t
@property
def name(self) -> str:
return self.__name
@name.setter
def name(self, t: str) -> None:
self.__name = t
@property
def suffix(self) -> str:
return self.__suffix
@suffix.setter
def suffix(self, t: str) -> None:
self.__suffix = t
@property
def gcode_speed(self) -> int:
return self.__gcode_speed
@gcode_speed.setter
def gcode_speed(self, t: int) -> None:
self.__gcode_speed = t
@property
def layer_pen_mapping(self) -> dict:
return self.__layer_pen_mapping
@layer_pen_mapping.setter
def layer_pen_mapping(self, m: dict) -> None:
self.__layer_pen_mapping = m
@property
def linetype_mapping(self) -> dict:
return self.__linetype_mapping
@linetype_mapping.setter
def linetype_mapping(self, m: dict) -> None:
self.__linetype_mapping = m
def run(self, jpg: bool = False, source: bool = False) -> None:
if self.cfg is None or self.paths is None or self.name is None:
log.fail("Config, Name or Paths is None. Not exporting anything")
return
# jpeg fitting roughly
self.paths.fit(
Paper.sizes[self.cfg.dimension],
padding_mm=self.cfg.margin,
cutoff_mm=self.cfg.cutoff,
)
stack = inspect.stack()
frame = stack[2]
module = inspect.getmodule(frame[0])
ms = inspect.getsource(module)
if jpg:
separate_layers = self.paths.get_layers()
for layer, pc in separate_layers.items():
sizename = PaperSizeName.names[self.cfg.dimension]
machinename = PlotterName.names[self.cfg.type]
fname = (
f"{self.name}_{self.suffix}_{sizename}_{machinename}_{layer}_"
f"{hashlib.sha256(ms.encode('utf-8')).hexdigest()}"
)
jpeg_folder = data.DataDirHandler().jpg(self.name)
jpeg_renderer = renderer.JpegRenderer(jpeg_folder)
jpeg_renderer.render(pc, scale=4.0)
jpeg_renderer.save(f"{fname}")
if source:
source_folder = data.DataDirHandler().source(self.name)
sizename = PaperSizeName.names[self.cfg.dimension]
machinename = PlotterName.names[self.cfg.type]
fname = (
f"{self.name}_{self.suffix}_{sizename}_{machinename}_"
f"{hashlib.sha256(ms.encode('utf-8')).hexdigest()}.py"
)
import pathlib
pathlib.Path(source_folder).mkdir(parents=True, exist_ok=True)
log.good(f"Saved source to {source_folder / fname}")
with open(source_folder / fname, "w") as file:
file.write(ms)
self.paths.fit(
Paper.sizes[self.cfg.dimension],
xy_factor=XYFactors.fac[self.cfg.type],
padding_mm=self.cfg.margin,
output_bounds=MinmaxMapping.maps[self.cfg.type].tuple(),
cutoff_mm=self.cfg.cutoff,
)
sizename = PaperSizeName.names[self.cfg.dimension]
machinename = PlotterName.names[self.cfg.type]
fname = f"{self.name}_{self.suffix}_{sizename}_{machinename}_{hashlib.sha256(ms.encode('utf-8')).hexdigest()}"
format = ExportFormatMappings.maps[self.cfg.type]
if self.linetype_mapping and format is ExportFormat.HPGL:
hpgl_folder = data.DataDirHandler().hpgl(self.name)
hpgl_renderer = renderer.HPGLRenderer(
hpgl_folder, line_type_mapping=self.linetype_mapping
)
hpgl_renderer.render(self.paths)
hpgl_renderer.save(f"{fname}")
if self.layer_pen_mapping is not None:
if format is ExportFormat.HPGL:
hpgl_folder = data.DataDirHandler().hpgl(self.name)
hpgl_renderer = renderer.HPGLRenderer(
hpgl_folder, layer_pen_mapping=self.layer_pen_mapping
)
hpgl_renderer.render(self.paths)
hpgl_renderer.save(f"{fname}")
else:
separate_layers = self.paths.get_layers()
for layer, pc in separate_layers.items():
if format is ExportFormat.HPGL:
hpgl_folder = data.DataDirHandler().hpgl(self.name)
hpgl_renderer = renderer.HPGLRenderer(hpgl_folder)
hpgl_renderer.render(pc)
hpgl_renderer.save(f"{fname}_{layer}")
if format is ExportFormat.SVG:
svg_dir = data.DataDirHandler().svg(self.name)
svg_renderer = renderer.SvgRenderer(svg_dir)
svg_renderer.render(pc)
svg_renderer.save(f"{fname}_{layer}")
if format is ExportFormat.GCODE:
gcode_folder = data.DataDirHandler().gcode(self.name)
if self.gcode_speed:
gcode_renderer = renderer.GCodeRenderer(
gcode_folder, z_down=4.5
)
else:
gcode_renderer = renderer.GCodeRenderer(
gcode_folder, feedrate_xy=self.gcode_speed, z_down=4.5
)
gcode_renderer.render(pc)
gcode_renderer.save(f"{layer}_{fname}")
class SimpleExportWrapper:
from cursor import path
def ex(
self,
paths: path.PathCollection,
ptype: PlotterType,
psize: PaperSize,
margin: int,
name: str = "output_name",
suffix: str = "",
cutoff: int = None,
gcode_speed: int = None,
hpgl_pen_layer_mapping=None,
hpgl_linetype_mapping=None,
):
cfg = Cfg()
cfg.type = ptype
cfg.dimension = psize
cfg.margin = margin
cfg.cutoff = cutoff
# paths.clean()
exp = Exporter()
exp.cfg = cfg
exp.paths = paths
exp.name = name
exp.suffix = str(suffix)
exp.gcode_speed = gcode_speed
exp.layer_pen_mapping = hpgl_pen_layer_mapping
exp.linetype_mapping = hpgl_linetype_mapping
exp.run(True, True)
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20698,
13,
4706,
1518,
29889,
3389,
29898,
5574,
29892,
5852,
29897,
13,
2
] |
sms/backends/__init__.py | sms77io/django-sms | 29 | 1610806 | # SMS backends shipped with django-sms
| [
1,
396,
317,
4345,
1250,
1975,
528,
16242,
411,
9557,
29899,
29879,
1516,
13,
2
] |
Practice/Problem Solving/BirthdayChocolate.py | avantikasharma/HackerRank-Solutions | 1 | 190656 | #!/bin/python
import sys
def solve(n, s, d, m):
total=0
for i in range(n-m+1):
sum=0
for j in range(m):
sum=sum+s[i+j]
if sum==d:
total=total+1
return total
n = int(raw_input().strip())
s = map(int, raw_input().strip().split(' '))
d, m = raw_input().strip().split(' ')
d, m = [int(d), int(m)]
result = solve(n, s, d, m)
print(result)
| [
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269,
29892,
270,
29892,
286,
29897,
13,
2158,
29898,
2914,
29897,
13,
2
] |
statuscheck/services/bases/_statusio.py | amureki/statuscheck | 35 | 102057 | import httpx
from statuscheck.services.bases._base import BaseServiceAPI
from statuscheck.services.models.generic import (
COMPONENT_TYPE_DEGRADED,
COMPONENT_TYPE_GOOD,
COMPONENT_TYPE_MAINTENANCE,
COMPONENT_TYPE_MAJOR_OUTAGE,
COMPONENT_TYPE_PARTIAL_OUTAGE,
COMPONENT_TYPE_SECURITY,
COMPONENT_TYPE_UNKNOWN,
TYPE_GOOD,
TYPE_INCIDENT,
TYPE_MAINTENANCE,
TYPE_OUTAGE,
TYPE_SECURITY,
Component,
Incident,
Status,
Summary,
)
STATUS_OK = 100
STATUS_PLANNED_MAINTENANCE = 200
STATUS_DEGRADED_PERFORMANCE = 300
STATUS_PARTIAL_SERVICE_DISRUPTION = 400
STATUS_MAJOR = 500
STATUS_SECURITY = 600
STATUS_TYPE_MAPPING = {
STATUS_OK: TYPE_GOOD,
STATUS_PLANNED_MAINTENANCE: TYPE_MAINTENANCE,
STATUS_DEGRADED_PERFORMANCE: TYPE_INCIDENT,
STATUS_PARTIAL_SERVICE_DISRUPTION: TYPE_INCIDENT,
STATUS_MAJOR: TYPE_OUTAGE,
STATUS_SECURITY: TYPE_SECURITY,
}
COMPONENT_STATUS_GOOD = 100
COMPONENT_STATUS_MAINTENANCE = 200
COMPONENT_STATUS_DEGRADED = 300
COMPONENT_STATUS_PARTIAL_OUTAGE = 400
COMPONENT_STATUS_MAJOR_OUTAGE = 500
COMPONENT_STATUS_SECURITY = 600
COMPONENT_STATUS_UNKNOWN = ""
COMPONENT_STATUS_MAPPING = {
COMPONENT_STATUS_GOOD: COMPONENT_TYPE_GOOD,
COMPONENT_STATUS_MAINTENANCE: COMPONENT_TYPE_MAINTENANCE,
COMPONENT_STATUS_DEGRADED: COMPONENT_TYPE_DEGRADED,
COMPONENT_STATUS_PARTIAL_OUTAGE: COMPONENT_TYPE_PARTIAL_OUTAGE,
COMPONENT_STATUS_MAJOR_OUTAGE: COMPONENT_TYPE_MAJOR_OUTAGE,
COMPONENT_STATUS_SECURITY: COMPONENT_TYPE_SECURITY,
COMPONENT_STATUS_UNKNOWN: COMPONENT_TYPE_UNKNOWN,
}
class BaseStatusIOAPI(BaseServiceAPI):
"""
Status.io pages API handler.
Documentation: https://statusio.docs.apiary.io
Public status API: https://kb.status.io/developers/public-status-api/
"""
domain_id: str = ""
def _get_base_url(self) -> str:
if not self.domain_id:
raise NotImplementedError("Please, add domain id")
return f"https://api.status.io/1.0/status/{self.domain_id}"
def get_summary(self) -> Summary:
url = self._get_base_url()
response_json = httpx.get(url).json()
status_dict = response_json["result"]["status_overall"]
status = Status(
code=str(status_dict["status_code"]),
name=STATUS_TYPE_MAPPING[status_dict["status_code"]],
description=status_dict["status"],
is_ok=status_dict["status_code"] == 100,
)
components = [
Component(
id=component["id"],
name=component["name"],
status=COMPONENT_STATUS_MAPPING[component["status_code"]],
extra_data=component,
)
for component in response_json["result"]["status"]
]
# incident payload contains trimmed component data, so we need to enrich it
components_dict = {component.id: component for component in components}
incidents = [
Incident(
id=incident["_id"],
name=incident["name"],
components=[
Component(
id=component["_id"],
name=component["name"],
status=components_dict[component["_id"]].status,
)
for component in incident.get("components_affected", [])
],
extra_data=incident,
)
for incident in response_json["result"]["incidents"]
]
self.summary = Summary(status, components, incidents)
return self.summary
| [
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13,
13,
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29928,
29901,
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29954,
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1164,
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29918,
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29918,
26092,
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12015,
10461,
29901,
4810,
3580,
1164,
3919,
29918,
11116,
29918,
26092,
25758,
29918,
12015,
10461,
29892,
13,
1678,
4810,
3580,
1164,
3919,
29918,
27047,
29918,
1529,
29967,
1955,
29918,
12015,
10461,
29901,
4810,
3580,
1164,
3919,
29918,
11116,
29918,
1529,
29967,
1955,
29918,
12015,
10461,
29892,
13,
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4810,
3580,
1164,
3919,
29918,
27047,
29918,
1660,
22484,
11937,
29901,
4810,
3580,
1164,
3919,
29918,
11116,
29918,
1660,
22484,
11937,
29892,
13,
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4810,
3580,
1164,
3919,
29918,
27047,
29918,
3904,
29968,
6632,
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29901,
4810,
3580,
1164,
3919,
29918,
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29918,
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13,
13,
13,
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13,
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362,
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601,
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2754,
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601,
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29889,
601,
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17426,
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3597,
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13,
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13,
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597,
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4882,
29889,
601,
29914,
29896,
29889,
29900,
29914,
4882,
19248,
1311,
29889,
7247,
29918,
333,
5038,
13,
13,
1678,
822,
679,
29918,
7727,
29898,
1311,
29897,
1599,
6991,
5219,
29901,
13,
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3142,
353,
1583,
3032,
657,
29918,
3188,
29918,
2271,
580,
13,
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2933,
29918,
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353,
1732,
29916,
29889,
657,
29898,
2271,
467,
3126,
580,
13,
4706,
4660,
29918,
8977,
353,
2933,
29918,
3126,
3366,
2914,
3108,
3366,
4882,
29918,
957,
497,
3108,
13,
4706,
4660,
353,
16034,
29898,
13,
9651,
775,
29922,
710,
29898,
4882,
29918,
8977,
3366,
4882,
29918,
401,
3108,
511,
13,
9651,
1024,
29922,
27047,
29918,
11116,
29918,
1529,
18009,
4214,
29961,
4882,
29918,
8977,
3366,
4882,
29918,
401,
3108,
1402,
13,
9651,
6139,
29922,
4882,
29918,
8977,
3366,
4882,
12436,
13,
9651,
338,
29918,
554,
29922,
4882,
29918,
8977,
3366,
4882,
29918,
401,
3108,
1275,
29871,
29896,
29900,
29900,
29892,
13,
4706,
1723,
13,
13,
4706,
7117,
353,
518,
13,
9651,
15924,
29898,
13,
18884,
1178,
29922,
9700,
3366,
333,
12436,
13,
18884,
1024,
29922,
9700,
3366,
978,
12436,
13,
18884,
4660,
29922,
21514,
1164,
3919,
29918,
27047,
29918,
1529,
18009,
4214,
29961,
9700,
3366,
4882,
29918,
401,
3108,
1402,
13,
18884,
4805,
29918,
1272,
29922,
9700,
29892,
13,
9651,
1723,
13,
9651,
363,
4163,
297,
2933,
29918,
3126,
3366,
2914,
3108,
3366,
4882,
3108,
13,
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13,
13,
4706,
396,
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20092,
3743,
17151,
2168,
4163,
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577,
591,
817,
304,
427,
4018,
372,
13,
4706,
7117,
29918,
8977,
353,
426,
9700,
29889,
333,
29901,
4163,
363,
4163,
297,
7117,
29913,
13,
4706,
5528,
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353,
518,
13,
9651,
9266,
1693,
29898,
13,
18884,
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9700,
3366,
29918,
333,
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1822,
4882,
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13,
462,
1678,
1723,
13,
462,
1678,
363,
4163,
297,
15134,
29889,
657,
703,
14036,
29918,
3470,
26458,
613,
518,
2314,
13,
18884,
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] |
tests/test_util.py | rampage644/densecap-tensorflow | 10 | 1614766 | import densecap.util
def test_IoU_overlap():
box0 = (0, 0, 100, 100)
box1 = (50, 50, 100, 100)
box2 = (0, 50, 100, 100)
box3 = (50, 0, 100, 100)
assert densecap.util.iou(box0, box1) == 50 ** 2 / (2 * 100 ** 2 - 50 ** 2)
assert densecap.util.iou(box0, box2) == (50 * 100) / (2 * 100 ** 2 - 50 * 100)
assert densecap.util.iou(box0, box3) == (50 * 100) / (2 * 100 ** 2 - 50 * 100)
def test_IoU_no_overlap():
box1 = (0, 0, 100, 100)
box2 = (100, 100, 20, 20)
box3 = (0, 120, 40, 40)
assert densecap.util.iou(box1, box2) == 0.0
assert densecap.util.iou(box1, box3) == 0.0
def test_IoU_inside():
box1 = (0, 0, 100, 100)
box2 = (10, 10, 20, 20)
box3 = (0, 30, 40, 40)
assert densecap.util.iou(box1, box2) == 20 ** 2 / 100 ** 2
assert densecap.util.iou(box2, box1) == 20 ** 2 / 100 ** 2
assert densecap.util.iou(box3, box1) == 40 ** 2 / 100 ** 2
def test_IoU_with_self():
box1 = (0, 0, 10, 10)
assert densecap.util.iou(box1, box1) == 1.0
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Model/lookalike-model/lookalike_model/trainer/train_multi_gpu.py | rangaswamymr/incubator-bluemarlin | 21 | 138478 | <filename>Model/lookalike-model/lookalike_model/trainer/train_multi_gpu.py
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1,2,3"
import time
import pickle
import random
import numpy as np
import tensorflow as tf
import sys
from input import DataInput, DataInputTest
from model_multi_gpu import Model, DIN
from sklearn.metrics import roc_auc_score
random.seed(1234)
np.random.seed(1234)
tf.set_random_seed(1234)
train_batch_size = 2048 #1024
test_batch_size = 2048 #1024
predict_batch_size = 32
predict_users_num = 1000
# predict_ads_num = 6
predict_ads_num = 30
time_interval_num = 10
gpu_num = 3
with open('ad_dataset_lookalike_jimmy_' + str(time_interval_num) + '.pkl', 'rb') as f:
train_set = pickle.load(f)
test_set = pickle.load(f)
cate_list = pickle.load(f)
user_count, item_count, cate_count = pickle.load(f)
with open('label_lookalike_jimmy_' + str(time_interval_num) + '.pkl', 'rb') as f:
test_lb = pickle.load(f)
test_set_with_label = []
for i in range(len(test_lb)):
test_set_with_label.append(tuple([test_set[i][0], test_set[i][1], test_set[i][2][0], test_lb[i]]))
best_auc = 0.0
best_auc_train = 0.0
best_auc_test = 0.0
def calc_auc(raw_arr):
"""Summary
Args:
raw_arr (TYPE): Description
Returns:
TYPE: Description
"""
# sort by pred value, from small to big
arr = sorted(raw_arr, key=lambda d:d[2])
auc = 0.0
fp1, tp1, fp2, tp2 = 0.0, 0.0, 0.0, 0.0
for record in arr:
fp2 += record[0] # noclick
tp2 += record[1] # click
auc += (fp2 - fp1) * (tp2 + tp1)
fp1, tp1 = fp2, tp2
# if all nonclick or click, disgard
threshold = len(arr) - 1e-3
if tp2 > threshold or fp2 > threshold:
return -0.5
if tp2 * fp2 > 0.0: # normal auc
return (1.0 - auc / (2.0 * tp2 * fp2))
else:
return None
def _auc_arr(score):
score_p = score[:,0]
score_n = score[:,1]
#print "============== p ============="
#print score_p
#print "============== n ============="
#print score_n
score_arr = []
for s in score_p.tolist():
score_arr.append([0, 1, s])
for s in score_n.tolist():
score_arr.append([1, 0, s])
return score_arr
def _eval(sess, model):
auc_sum = 0.0
score_arr = []
for _, uij in DataInputTest(test_set, test_batch_size):
auc_, score_ = model.eval(sess, uij)
score_arr += _auc_arr(score_)
auc_sum += auc_ * len(uij[0])
test_gauc = auc_sum / len(test_set)
Auc = calc_auc(score_arr)
global best_auc
if best_auc < test_gauc:
best_auc = test_gauc
model.save(sess, 'save_path/ckpt')
return test_gauc, Auc
def _eval_logdata(sess, model, dataset, batch_size, type):
score_arr = []
y = []
for _, uij in DataInput(dataset, batch_size):
score_ = model.eval_logdata(sess, uij)
score_arr.append(np.squeeze(score_[0]))
y.append(np.asarray(uij[2]))
# score_arr = np.vstack(score_arr)
score_arr = np.hstack(score_arr)
y = np.hstack(np.asarray(y))
Auc = roc_auc_score(y, score_arr)
global best_auc_train
global best_auc_test
if type=='train' and best_auc_train<Auc:
best_auc_train = Auc
if type=='test' and best_auc_test<Auc:
best_auc_test = Auc
model.save(sess, 'save_path/ckpt')
return Auc
def _eval_logdata_improve(sess, dataset, batch_size, type):
score_arr = []
y = []
for _, uij in DataInput(dataset, batch_size):
score_ = sess.run([batch_logits_test], feed_dict={
i_batch: uij[1],
j_batch: uij[2],
y_batch: uij[2],
hist_i_batch: uij[3],
sl_batch: uij[4]
})
score_arr.append(np.squeeze(score_[0]))
y.append(np.asarray(uij[2]))
score_arr = np.hstack(score_arr)
y = np.hstack(np.asarray(y))
Auc = roc_auc_score(y[:len(score_arr)], score_arr)
global best_auc_train
global best_auc_test
if type=='train' and best_auc_train<Auc:
best_auc_train = Auc
if type=='test' and best_auc_test<Auc:
best_auc_test = Auc
saver = tf.train.Saver()
saver.save(sess, save_path='save_path/ckpt')
return Auc
def _test(sess, model):
auc_sum = 0.0
score_arr = []
predicted_users_num = 0
print("test sub items")
for _, uij in DataInputTest(test_set, predict_batch_size):
if predicted_users_num >= predict_users_num:
break
score_ = model.test(sess, uij)
score_arr.append(score_)
predicted_users_num += predict_batch_size
return score_[0]
PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable']
def assign_to_device(device, ps_device='/cpu:0'):
def _assign(op):
node_def = op if isinstance(op, tf.NodeDef) else op.node_def
if node_def.op in PS_OPS:
return "/" + ps_device
else:
return device
return _assign
def average_gradients(tower_grads):
average_grads, cnt = [], 0
for grad_and_vars in zip(*tower_grads):
grads = []
for g, _ in grad_and_vars:
if g is not None:
expanded_g = tf.expand_dims(g, 0)
else:
expanded_g = None
grads.append(expanded_g)
if all(x is None for x in grads):
grad = None
else:
grad = tf.concat(grads, 0)
grad = tf.reduce_mean(grad, 0)
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
cnt += 1
rlt_grads = [v[0] for v in average_grads]
rlt_vars = [v[1] for v in average_grads]
return average_grads
# return rlt_grads, rlt_vars
if min(cate_list)>0:
item_count += 1
lr = 1.0 # 0.1
reuse_vars = False
tf.reset_default_graph()
with tf.device('/cpu:0'):
tower_grads, tower_logits_test = [], []
reuse_vars = False
# tf Graph input
i_batch = tf.placeholder(tf.int32, [None,]) # [B]
j_batch = tf.placeholder(tf.int32, [None,]) # [B]
y_batch = tf.placeholder(tf.int32, [None,]) # [B]
hist_i_batch = tf.placeholder(tf.int32, [None, None]) # [B, T]
sl_batch = tf.placeholder(tf.int32, [None,]) # [B]
for i in range(gpu_num):
with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')):
delta = tf.shape(i_batch)[0] // gpu_num
start, end = i*delta, (i+1)*delta
i = i_batch[start:end]
j = j_batch[start:end]
y = y_batch[start:end]
hist_i = hist_i_batch[start:end,:]
sl = sl_batch[start:end]
logits_train = DIN(i, j, y, hist_i, sl,
item_count, cate_count, cate_list, reuse=reuse_vars, is_training=True)
logits_test = DIN(i, j, y, hist_i, sl,
item_count, cate_count, cate_list, reuse=True, is_training=False)
loss_op = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_train, labels=tf.cast(y, tf.float32)))
trainable_params = tf.trainable_variables()
gradients = tf.gradients(loss_op, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(gradients, 5)
grads = [(v, trainable_params[ind]) for ind, v in enumerate(clip_gradients)]
reuse_vars = True
tower_grads.append(grads)
tower_logits_test.append(logits_test)
batch_logits_test = tf.concat(tower_logits_test, 0)
average_grads = average_gradients(tower_grads)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=lr)
train_op = optimizer.apply_gradients(average_grads)
init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(allow_growth=True)
# with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, device_count={'GPU': 2})) as sess:
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True, gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(init)
epoch2display = 2
start_time = time.time()
for epoch in range(10):
random.shuffle(train_set)
epoch_total_loss, cnt = 0.0, 0
for _, uij in DataInput(train_set, train_batch_size*gpu_num):
loss_, _ = sess.run([loss_op, train_op], feed_dict={
i_batch: uij[1],
j_batch: uij[2],
y_batch: uij[2],
hist_i_batch: uij[3],
sl_batch: uij[4]
})
epoch_total_loss += loss_
cnt += 1
epoch_mean_loss = epoch_total_loss / float(cnt)
if (epoch>0) and ((epoch+1 % epoch2display==0) or (epoch==9)):
auc_train = _eval_logdata_improve(sess, train_set, train_batch_size*gpu_num, 'train')
auc_test = _eval_logdata_improve(sess, test_set_with_label, test_batch_size*gpu_num, 'test')
print(f'Epoch {epoch} DONE\tCost time: {time.time()-start_time}\ttrain epoch average loss: {epoch_mean_loss}\ttrain auc: {auc_train}\ttest auc: {auc_test}')
sys.stdout.flush()
print('best train_gauc %.4f\ttest_gauc: %.4f' % (best_auc_train, best_auc_test))
sys.stdout.flush()
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Codigos Python/String_to_int.py | BrunoHarlis/Solucoes_LeetCode | 0 | 32210 | <gh_stars>0
# Fonte: https://leetcode.com/problems/string-to-integer-atoi/
# Autor: <NAME>
# Data: 03/08/2021
"""
Implemente a função myAtoi(string s), que converte uma string em um
inteiro assinado de 32 bits (semelhante à função C / C ++ atoi).
O algoritmo para myAtoi(string s) é o seguinte:
Leia e ignore qualquer espaço em branco à esquerda.
Verifique se o próximo caractere (se ainda não estiver no final da string)
é '-' ou '+'. Leia este caractere se for algum. Isso determina se o resultado
final é negativo ou positivo, respectivamente. Suponha que o resultado seja
positivo se nenhum estiver presente.
Leia a seguir os caracteres até que o próximo caractere não digitado ou o
final da entrada seja alcançado. O resto da string é ignorado.
Converta esses dígitos em um número inteiro (ou seja "123" -> 123, "0032" -> 32).
Se nenhum dígito foi lido, o número inteiro é 0. Altere o sinal conforme
necessário (da etapa 2).
Se o inteiro estiver fora do intervalo de inteiros com sinal de 32 bits, fixe
o inteiro para que ele permaneça no intervalo. Especificamente, números inteiros
menores do que deveriam ser fixados e inteiros maiores do que deveriam ser fixados.
[-231, 231 - 1]-231-231231 - 1231 - 1
Retorne o inteiro como o resultado final.
Observação:
Apenas o caractere de espaço ' ' é considerado um caractere de espaço em branco.
Não ignore nenhum caractere além do espaço em branco inicial ou o resto da string após os dígitos.
Tempo de execução : 36 ms, mais rápido que 60,05 % dos envios.
Uso da memória : 14,5 MB, menos de 26,10 % dos envios.
"""
def myAtoi(s):
s = s.strip()
negativo = False
i = 0
r = 0
if len(s) == 0:
return 0
if s[i] == '-':
negativo = True
i += 1
elif s[i] == '+':
i += 1
while i < len(s):
if s[i].isnumeric():
temp = int(s[i:i+1])
r = r * 10 + temp
if negativo:
if r * -1 < -2**31:
return -2**31
elif r > 2**31 - 1:
return 2**31 - 1
i += 1
else:
break
return r * -1 if negativo else r
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] |
dl.py | onoira/instawow-installer | 0 | 1604605 | <reponame>onoira/instawow-installer
from __future__ import annotations
import json
import os
import os.path as path
import re
import shutil
import zipfile
from configparser import ConfigParser
from glob import glob
import requests
_RE_NAME_GUI = re.compile(
r'\b\binstawow-desktop.+\.exe\b',
flags=re.IGNORECASE
)
_RE_NAME_CLI = re.compile(
r'\binstawow-(?:cli-)?windows.+\.zip\b',
flags=re.IGNORECASE
)
DOWNLOAD_DIRECTORY = './download'
REPO_PATH = "layday/instawow"
class GitHubClient():
def get_asset(self, url: str) -> bytes:
return self._get(url).content
def get_manifest(self, repo_path: str) -> dict:
owner, repo = repo_path.split('/')
resp = self._get(
f'https://api.github.com/repos/{owner}/{repo}/releases/latest'
)
jso = json.loads(resp.content)
return jso
def _get(self, url):
return requests.get(url, headers=self.headers)
def __init__(self, headers: dict = None):
self.headers = {
'Accept': "application/vnd.github.v3+json"} | (headers or dict())
def collect_assets(assets: list[dict]) -> dict[str, str]:
assets = {
'instawow-gui.exe': next(
i['browser_download_url'] for i in assets if _RE_NAME_GUI.match(i['name'])
),
'.instawow.zip': next(
i['browser_download_url'] for i in assets if _RE_NAME_CLI.match(i['name'])
)
}
return assets
def download_assets(
client: GitHubClient,
assets: list[dict[str, str]],
download_dir: str
) -> None:
for name, url in assets.items():
file = path.join(download_dir, name)
print(f"Downloading '{url}' (-> {file})...")
content = client.get_asset(url)
with open(path.join(download_dir, name), 'wb') as fp:
fp.write(content)
if name.endswith('.zip'):
print(f"Unzipping '{file}' (-> {download_dir})")
with zipfile.ZipFile(file, 'r') as zr:
zr.extractall(download_dir)
def copy_assets(
files: list[str],
download_dir: str,
install_dir: str
) -> None:
for src in files:
dst = path.join(install_dir, path.basename(src))
if path.exists(dst):
print(f"Backing up '{dst}'")
shutil.move(dst, dst + '.bak')
print(f"Copying '{src}' -> '{dst}'")
shutil.copy(src, dst)
def main():
config = ConfigParser()
config.read('dl.cfg')
install_dir = config['default']['install_dir']
print(f"Using install directory '{install_dir}'")
download_dir = path.abspath(DOWNLOAD_DIRECTORY)
if not path.exists(download_dir):
print(f"Creating directory '{download_dir}'")
os.makedirs(download_dir)
client = GitHubClient()
manifest = client.get_manifest(REPO_PATH)
version = manifest['tag_name']
print(f"Collecting instawow-{version}...")
assets = collect_assets(manifest['assets'])
download_assets(client, assets, download_dir)
files = [i for i in glob(path.join(download_dir, '*.exe'))]
copy_assets(files, download_dir, install_dir)
print("Done.")
if __name__ == '__main__':
try:
main()
except KeyboardInterrupt:
pass
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Scripts/mapOutline_AGP2.py | usgs/gems-tools-pro | 15 | 194849 | <filename>Scripts/mapOutline_AGP2.py
# MapOutline.py
# generates rectangular (in lat-long) map outline and
# appropriate tics in projection of user's choosing. Result is
# stored in an existing geodatabase
#
# For complex map outlines, try several runs and intersect the results.
#
# <NAME>, U.S. Geological Survey
# <EMAIL>
#
# July 23, 2019: updated to work with Python 3 in ArcGIS Pro 2
# Only had to make some syntax edits.
# Renamed from mapOutline_Arc10.py to mapOutline_AGP2.py
# <NAME>
import arcpy, sys, os
versionString = 'mapOutline_AGP2.py, version of 23 July 2019'
rawurl = 'https://raw.githubusercontent.com/usgs/gems-tools-pro/master/Scripts/mapOutline_AGP2.py'
checkVersion(versionString, rawurl, 'gems-tools-pro')
"""
INPUTS
maxLongStr # in D M S, separated by spaces. Decimals OK.
# Note that west values must be negative
# -122.625 = -122 37 30
# if value contains spaces it should be quoted
minLatStr # DITTO
dLong # in decimal degrees OR decimal minutes
# values <= 5 are assumed to be degrees
# values > 5 are assumed to be minutes
dLat # DITTO
# default values of dLong and dLat are 7.5
ticInterval # in decimal minutes! Default value is 2.5
isNAD27 # NAD27 or NAD83 for lat-long locations
outgdb # existing geodatabase to host output feature classes
outSpRef # output spatial reference system
scratch # scratch folder, must be writable
"""
c = ','
degreeSymbol = '°'
minuteSymbol = "'"
secondSymbol = '"'
def addMsgAndPrint(msg, severity=0):
# prints msg to screen and adds msg to the geoprocessor (in case this is run as a tool)
# print msg
try:
for string in msg.split('\n'):
# Add appropriate geoprocessing message
if severity == 0:
arcpy.AddMessage(string)
elif severity == 1:
arcpy.AddWarning(string)
elif severity == 2:
arcpy.AddError(string)
except:
pass
def dmsStringToDD(dmsString):
dms = dmsString.split()
dd = abs(float(dms[0]))
if len(dms) > 1:
dd = dd + float(dms[1])/60.0
if len(dms) > 2:
dd = dd + float(dms[2])/3600.0
if dms[0][0] == '-':
dd = 0 - dd
return(dd)
def ddToDmsString(dd):
dd = abs(dd)
degrees = int(dd)
minutes = int((dd-degrees)* 60)
seconds = int(round((dd-degrees-(minutes/60.0))* 3600))
if seconds == 60:
minutes = minutes+1
seconds = 0
dmsString = str(degrees)+degreeSymbol
dmsString = dmsString+str(minutes)+minuteSymbol
if seconds != 0:
dmsString = dmsString+str(seconds)+secondSymbol
return dmsString
addMsgAndPrint(versionString)
## MAP BOUNDARY
# get and check inputs
SELongStr = sys.argv[1]
SELatStr = sys.argv[2]
dLong = float(sys.argv[3])
dLat = float(sys.argv[4])
ticInterval = float(sys.argv[5])
if sys.argv[6] == 'true':
isNAD27 = True
else:
isNAD27 = False
if isNAD27:
xycs = 'GEOGCS["GCS_North_American_1927",DATUM["D_North_American_1927",SPHEROID["Clarke_1866",6378206.4,294.9786982]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4267]]'
else:
xycs = 'GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4269]]'
outgdb = sys.argv[7]
outSpRef = sys.argv[8]
scratch = sys.argv[9]
# set workspace
arcpy.env.workspace = outgdb
arcpy.env.scratchWorkspace = scratch
# calculate maxLong and minLat, dLat, dLong, minLong, maxLat
maxLong = dmsStringToDD(SELongStr)
minLat = dmsStringToDD(SELatStr)
if dLong > 5:
dLong = dLong/60.0
if dLat > 5:
dLat = dLat/60.0
minLong = maxLong - dLong
maxLat = minLat + dLat
# test for and delete any feature classes to be created
for xx in ['xxMapOutline','MapOutline','xxTics','Tics']:
if arcpy.Exists(xx):
arcpy.Delete_management(xx)
addMsgAndPrint(' deleted feature class {}'.format(xx))
## MAP OUTLINE
# make XY file for map outline
addMsgAndPrint(' writing map outline file')
genf = open(os.path.join(scratch, 'xxxbox.csv'), 'w')
genf.write('LONGITUDE,LATITUDE\n')
genf.write('{},{}\n'.format(str(minLong), str(maxLat)))
genf.write('{},{}\n'.format(str(maxLong), str(maxLat)))
genf.write('{},{}\n'.format(str(maxLong), str(minLat)))
genf.write('{},{}\n'.format(str(minLong), str(minLat)))
genf.write('{},{}\n'.format(str(minLong), str(maxLat)))
genf.close()
# convert XY file to .dbf table
boxdbf = arcpy.CreateScratchName('xxx','.dbf','',scratch)
boxdbf = os.path.basename(boxdbf)
arcpy.TableToTable_conversion(os.path.join(scratch,'xxxbox.csv'), scratch,boxdbf)
# make XY event layer from .dbf table
arcpy.MakeXYEventLayer_management(os.path.join(scratch, boxdbf),'LONGITUDE','LATITUDE','boxlayer',xycs)
# convert event layer to preliminary line feature class with PointsToLine_management
arcpy.PointsToLine_management('boxlayer', 'xxMapOutline')
# densify MapOutline
arcpy.Densify_edit('xxMapOutline', 'DISTANCE', 0.0001)
# project to correct spatial reference
### THIS ASSUMES THAT OUTPUT COORDINATE SYSTEM IS HARN AND WE ARE IN OREGON OR WASHINGTON!!
if isNAD27:
geotransformation = 'NAD_1927_To_NAD_1983_NADCON;NAD_1983_To_HARN_OR_WA'
else:
geotransformation = 'NAD_1983_To_HARN_OR_WA'
geotransformation = ''
arcpy.Project_management('xxMapOutline', 'MapOutline', outSpRef, geotransformation, xycs)
## TICS
# calculate minTicLong, minTicLat, maxTicLong, maxTiclat
ticInterval = ticInterval / 60.0 # convert minutes to degrees
minTicLong = int(round(0.1 + minLong // ticInterval))
maxTicLong = int(round(1.1 + maxLong // ticInterval))
minTicLat = int(round(0.1 + minLat // ticInterval))
maxTicLat = int(round(1.1 + maxLat // ticInterval))
if minTicLong < 0:
minTicLong = minTicLong + 1
if maxTicLong < 0:
maxTicLong = maxTicLong + 1
# make xy file for tics
addMsgAndPrint(' writing tic file')
genf = open(os.path.join(scratch, 'xxxtics.csv'),'w')
genf.write('ID,LONGITUDE,LATITUDE\n')
nTic = 1
for y in range(minTicLat, maxTicLat):
ticLat = y * ticInterval
for x in range(minTicLong, maxTicLong):
ticLong = x * ticInterval
genf.write(str(nTic) + c + str(ticLong) + c + str(ticLat) + '\n')
nTic = nTic+1
genf.close()
# convert to dbf
ticdbf = arcpy.CreateScratchName('xxx','.dbf','',scratch)
print(ticdbf)
ticdbf = os.path.basename(ticdbf)
print(ticdbf)
arcpy.TableToTable_conversion(os.path.join(scratch, 'xxxtics.csv'), scratch, ticdbf)
# make XY event layer from table
arcpy.MakeXYEventLayer_management(os.path.join(scratch, ticdbf), 'LONGITUDE', 'LATITUDE', 'ticlayer', xycs)
# copy to point featureclass
arcpy.FeatureToPoint_management('ticlayer', 'xxtics')
# project to correct coordinate system
arcpy.Project_management('xxtics', 'tics', outSpRef, geotransformation,xycs)
# add attributes
for fld in ['Easting','Northing']:
arcpy.AddField_management('tics',fld,'DOUBLE')
for fld in ['LatDMS','LongDMS']:
arcpy.AddField_management('tics',fld,'TEXT',"","",20)
arcpy.AddXY_management('tics')
# calc Easting = Point_X, Northing = Point_Y
arcpy.CalculateField_management('tics','Easting','!Point_X!','PYTHON')
arcpy.CalculateField_management('tics','Northing','!Point_Y!','PYTHON')
# create update cursor, cycle through tics, and add LatDMS and LongDMS
addMsgAndPrint(' adding lat-long text strings')
rows = arcpy.UpdateCursor('tics')
for row in rows:
row.LatDMS = ddToDmsString(row.LATITUDE)
row.LongDMS = ddToDmsString(row.LONGITUDE)
rows.updateRow(row)
del row
del rows
# delete csv files, dbf files, and preliminary featureclasses
addMsgAndPrint(' cleaning up scratch workspace')
for xx in [boxdbf,boxdbf+'.xml',ticdbf,ticdbf+'.xml']: #,'xxxbox.csv','xxxtics.csv']:
os.remove(os.path.join(scratch, xx))
addMsgAndPrint(' deleting temporary feature classes')
arcpy.Delete_management('xxtics')
arcpy.Delete_management('xxMapOutline')
#sys.exit() # force exit with failure
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project1b.py | kaamila42/project1 | 0 | 92253 | <gh_stars>0
filename = input("Input the Filename: " ,"py = python", "txt = text", ...)
f_extns = filename.split(".")
print(f " The extension of the file is '{f_extns[]}'")
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python/cursoemvideo/040.py | Gustavo-Martins/learning | 0 | 160305 | # Reads two grades and prints average
grade1 = float(input('Digite a primeira nota: '))
grade2 = float(input('Digite a segunda nota: '))
grade_average = (grade1 + grade2) / 2
print('Tirando {:.1f} e {:.1f}, a média é {:.1f}'.format(grade1, grade2, grade_average))
if grade_average < 5:
print('O aluno(a) está REPROVADO')
elif grade_average >= 7:
print('O aluno(a) está APROVADO')
else:
print('O aluno(a) está em RECUPERAÇÃO')
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piefuzz/__init__.py | timeyyy/piefuzz | 7 | 186810 | from piefuzz.piefuzz import *
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1,
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BlogSite-Flask/flaskblog/__init__.py | Mihir008/Web-Ideas | 10 | 167414 | from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_bcrypt import Bcrypt
from flask_login import LoginManager
app = Flask(__name__)
app.config['SECRET_KEY'] = '<KEY>'
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db'
db = SQLAlchemy(app)
bcrypt = Bcrypt(app)
login_manager = LoginManager(app)
login_manager.login_view= 'login'
login_manager.login_message_category= 'info'
from flaskblog import routes
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] |
person_detector.py | RocketMan112/STEM_Mentorship | 0 | 156800 | import logging
log = logging.getLogger(__name__)
class PersonDetectorBase:
def initialize(self):
pass
def name(self):
return "Error - Base PersonDetector class, not for direct use"
class FakeFixedPersonDetector(PersonDetectorBase):
def initialize(self):
pass
def name(self):
return "FakeFixedPersonDetector"
class ComputerVisionPersonDetector(PersonDetectorBase):
def initialize(self):
pass
def name(self):
return "ComputerVisionPersonDetector"
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bcs-ui/backend/tests/resources/test_resource.py | laodiu/bk-bcs | 1 | 1616915 | <reponame>laodiu/bk-bcs
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2021 TH<NAME>, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
import pytest
from backend.container_service.clusters.base.models import CtxCluster
from backend.resources.resource import ResourceClient, ResourceList, ResourceObj
from backend.resources.utils.format import ResourceDefaultFormatter
from backend.tests.conftest import TEST_NAMESPACE
@pytest.fixture
def ctx_cluster(project_id, cluster_id):
return CtxCluster.create(cluster_id, project_id, token='token')
class PodResourceObj(ResourceObj):
@property
def image_name(self) -> str:
return self.data.spec.containers[0]['image']
class MyPod(ResourceClient):
kind = 'Pod'
formatter = ResourceDefaultFormatter()
result_type = PodResourceObj
@pytest.fixture(
params=[
# method kwargs, expected_type
# default `is_format` value is True
({}, dict),
({'is_format': True}, dict),
({'is_format': False}, PodResourceObj),
]
)
def type_assertion_pair(request):
return request.param
class TestResourceClient:
@pytest.fixture(autouse=True)
def init_resources(self, random_name, ctx_cluster):
MyPod(ctx_cluster).update_or_create(
namespace=TEST_NAMESPACE, name=random_name, body=make_pod_body(random_name)
)
yield
MyPod(ctx_cluster).delete(namespace=TEST_NAMESPACE, name=random_name)
def test_list_formatted(self, random_name, ctx_cluster):
pods = MyPod(ctx_cluster).list(namespace=TEST_NAMESPACE, is_format=True)
assert isinstance(pods, list)
assert isinstance(pods[0], dict)
def test_list_not_formatted(self, random_name, ctx_cluster):
pods = MyPod(ctx_cluster).list(namespace=TEST_NAMESPACE, is_format=False)
assert isinstance(pods, ResourceList)
assert isinstance(pods.metadata, dict)
assert random_name in [pod.name for pod in pods.items]
for pod in pods.items:
if pod.name == random_name:
assert pod.image_name == 'busybox'
def test_get_is_format(self, type_assertion_pair, random_name, ctx_cluster):
kwargs, expected_type = type_assertion_pair
pod = MyPod(ctx_cluster).get(namespace=TEST_NAMESPACE, name=random_name, **kwargs)
assert isinstance(pod, expected_type)
def test_get_none(self, random_name, ctx_cluster):
pod = MyPod(ctx_cluster).get(namespace=TEST_NAMESPACE, name=random_name + '-non-existent')
assert pod is None
def test_patch(self, type_assertion_pair, random_name, ctx_cluster):
body = make_pod_body(random_name)
body['metadata']['labels'] = {'foo': 'bar'}
kwargs, expected_type = type_assertion_pair
pod = MyPod(ctx_cluster).patch(namespace=TEST_NAMESPACE, name=random_name, body=body, **kwargs)
assert isinstance(pod, expected_type)
class TestResourceClientCreation:
def test_create(self, type_assertion_pair, random_name, ctx_cluster):
kwargs, expected_type = type_assertion_pair
pod = MyPod(ctx_cluster).create(
namespace=TEST_NAMESPACE, name=random_name, body=make_pod_body(random_name), **kwargs
)
assert isinstance(pod, expected_type)
def test_update_or_create(self, type_assertion_pair, random_name, ctx_cluster):
kwargs, expected_type = type_assertion_pair
pod, created = MyPod(ctx_cluster).update_or_create(
namespace=TEST_NAMESPACE, name=random_name, body=make_pod_body(random_name), **kwargs
)
assert isinstance(created, bool)
assert isinstance(pod, expected_type)
def make_pod_body(name: str):
"""Make simple Pod body for testing"""
return {
'apiVersion': 'v1',
'kind': 'Pod',
'metadata': {'name': name},
'spec': {'containers': [{'name': "main", 'image': "busybox"}]},
}
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MiScan/model.py | QuKunLab/MiScan | 3 | 1613529 | from keras import Model
from keras.layers import Dropout, Input
from keras.optimizers import adam
from .layers import dense, activation
def build_dense_model(input_dim=13885,
lr=1e-6,
dropout=0.25,
acti='leakyrelu'
):
input_x = Input(shape=(input_dim,), name="input", dtype='float32')
x = dense(256)(input_x)
x = activation(type=acti)(x)
x = Dropout(dropout)(x)
x = dense(128)(x)
x = activation(type=acti)(x)
x = Dropout(dropout)(x)
x = dense(64)(x)
x = activation(type=acti)(x)
x = Dropout(dropout)(x)
last = dense(2, activation='softmax')(x)
model = Model(inputs=input_x, outputs=last)
model.compile(loss='categorical_crossentropy', optimizer=adam(lr=lr), metrics=['accuracy'])
return model
| [
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Reader.py | noahwc/gtfs-pyparse | 0 | 115246 | # This class parses a gtfs text file
class Reader:
def __init__(self, file):
self.fields = []
self.fp = open(file, "r")
self.fields.extend(self.fp.readline().rstrip().split(","))
def get_line(self):
data = {}
line = self.fp.readline().rstrip().split(",")
for el, field in zip (line, self.fields):
data[field] = el
if len(data) == 1:
return None
else:
return data
def end(self):
self.fp.close() | [
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PyTorch/loader.py | avinashsai/Gated-CNN-for-ABSA | 0 | 81148 | <reponame>avinashsai/Gated-CNN-for-ABSA
import sys
import os
import re
import ast
from ast import literal_eval
label = {'negative':0,'positive':1,'neutral':2}
def preprocess(string):
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip()
def load_data(dataset):
temp=open(dataset+"atsa_train.json","r",encoding="ISO-8859-1").read()
train=literal_eval(temp)
train_sentence=[]
train_aspect=[]
train_sentiment=[]
for i in train:
if(i['sentiment']!='conflict'):
train_sentence.append(preprocess(i["sentence"]))
train_aspect.append(preprocess(i["aspect"]))
train_sentiment.append(label[i["sentiment"]])
temp=open(dataset+"atsa_test.json","r",encoding="ISO-8859-1").read()
test=literal_eval(temp)
test_sentence=[]
test_aspect=[]
test_sentiment=[]
for i in test:
if(i['sentiment']!='conflict'):
test_sentence.append(preprocess(i["sentence"]))
test_aspect.append(preprocess(i["aspect"]))
test_sentiment.append(label[i["sentiment"]])
return train_sentence,test_sentence,train_aspect,test_aspect,train_sentiment,test_sentiment | [
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templogboss/myapp/models.py | Bossabossy/Templog | 0 | 57578 | <gh_stars>0
from __future__ import unicode_literals
from django.db import models
class Node(models.Model):
node_name=models.CharField(max_length=100)
description=models.CharField(max_length=100)
def __str__(self):
return "{}:{}:{}".format(self.id, self.node_name, self.description)
class Record(models.Model):
created=models.DateTimeField(auto_now_add=True)
updated=models.DateTimeField(auto_now=True)
node=models.ForeignKey(Node, on_delete=models.CASCADE)
value=models.FloatField(default=0.0)
class Attribute(models.Model):
attribute=models.CharField(max_length=100)
description=models.CharField(max_length=100)
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2
] |
semisupervised/label_spreading.py | romanorac/ml | 4 | 109737 | # coding=utf8
import numpy as np
class LabelSpreading:
def __init__(self, alpha=0.2, max_iter=30, tol=1e-3):
"""
:param alpha: clamping factor between (0,1)
:param max_iter: maximum number of iterations
:param tol: convergence tolerance
"""
self.alpha = alpha
self.max_iter = max_iter
self.tol = tol
self.dist = None
def fit(self, w, y):
"""
fit label spreading algorithm
:param w: similarity matrix of n x n shape with n samples
:param y: labels of n x c shape with c labels, where 1
denotes label of x_i or 0 otherwise. Unlabeled samples
have labels set to 0.
"""
if type(w) != np.ndarray or type(y) != np.ndarray or len(w) != len(y):
raise Exception("w and y should be numpy array with equal length")
if 0 > self.alpha > 1 or self.max_iter < 0 or self.tol < 0:
raise Exception("Parameters are set incorrectly")
# construct the matrix S
d = np.sum(w, axis=1)
d[d == 0] = 1
np.power(d, -1 / 2., d)
d = np.diag(d)
s = np.dot(np.dot(d, w), d)
# Iterate F(t+1) until convergence
cur_iter = 0
err = self.tol
f0 = y
f1 = None
while cur_iter < self.max_iter and err >= self.tol:
f1 = self.alpha * np.dot(s, f0) + (1 - self.alpha) * y
err = np.max(np.abs(f1 - f0))
f0 = f1
cur_iter += 1
self.dist = f1 # set distributions
return self
def predict(self, y):
"""
use model to create predictions
:param y: labels of n x c shape with c labels, where 1
denotes label of x_i or 0 otherwise. Unlabeled samples
have labels set to 0.
:return: list with predictions
"""
if not np.any(y):
raise Exception("Please fit model first")
if type(y) != np.ndarray:
raise Exception("y should be numpy array")
predictions = []
for i, labels in enumerate(y):
index = np.where(labels == 1)[0]
if len(index) == 1:
# was labeled before
predictions.append(index[0])
else:
# use label with highest score
predictions.append(np.argmax(self.dist[i]))
return predictions
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2
] |
build/lib/myrest/myparser.py | xiaoyexu/myrestengine | 0 | 1602638 | <filename>build/lib/myrest/myparser.py
# -*- coding: UTF-8 -*-
import re
class StringReader(object):
def __init__(self, text):
self.text = text
self.maxlength = len(text)
self.currentPosition = 0
def isOverLength(self, length):
return length >= self.maxlength or length < 0
def isCurrentPositionValid(self):
return self.isPositionValid(self.currentPosition)
def isPositionValid(self, p):
return p >= 0 and p < self.maxlength
def peek(self, length):
if not self.isCurrentPositionValid():
return None
m = self.currentPosition
n = m + length
if self.isOverLength(n):
return self.text[m:]
else:
return self.text[m:n]
def peekNext(self):
return self.peek(1)
def read(self, length):
if not self.isCurrentPositionValid():
return None
m = self.currentPosition
n = m + length
if self.isOverLength(n):
self.currentPosition = -1
return self.text[m:]
else:
self.currentPosition += length
return self.text[m:n]
def readNext(self):
return self.read(1)
def skipBlank(self):
if not self.isCurrentPositionValid():
return
while self.peekNext() == ' ':
self.readNext()
def readStringPatternWord(self, pattern):
if not self.isCurrentPositionValid():
return None
l = self.peekNext()
if not l:
return None
result = ''
while l and re.compile(pattern).match(l):
result += self.readNext()
l = self.peekNext()
return result
def readStringVariable(self):
if not self.isCurrentPositionValid():
return None
m = self.currentPosition
text = self.text[m:]
v = self.text[m]
if v == '"' or v == "'":
try:
idx = text.index(v, 1)
n = idx + 1
self.currentPosition += n
if not self.isCurrentPositionValid():
self.currentPosition = -1
return text[:n]
except ValueError as e:
return None
else:
return None
def isEnd(self):
return self.currentPosition == -1
class Parser(object):
def __init__(self, text):
self.text = text
self.textStack = []
self.parseStack = []
self.priorityStack = []
self.reader = StringReader(text)
self.pair = {
')': '('
}
self.syntax_definition = {
'condition': [
(1, '\('),
(1, '\)'),
(1, '\|'),
(1, ',')
],
'operator': [
(1, '='), # equal
(2, '!='), # not equal
(1, '@'), # between
(2, '!@'), # not between
(1, '%'), # contains(not case sensitive)
(2, '!%'), # not contains(not case sensitive)
(2, '%%'), # contains(case sensitive)
(3, '!%%'), # not contains(case sensitive)
(1, '>'), # great than
(1, '<'), # less than
(2, '>='), # great equal than
(2, '<=') # less equal than
]
}
self.string_definition = {
'string': [(1, """["']""")]
}
self.variable_definiation = {
'variable': [(1, """[A-Za-z0-9_]""")]
}
self.convertToStack()
def checkDefinition(self, table, char):
# result = None
for k, v in table.items():
for l, p in v:
if len(char) == l and re.compile(p).match(char):
return (k, p)
return (None, None)
def convertToStack(self):
self.reader.skipBlank()
while not self.reader.isEnd():
if self.reader.peekNext() == ' ':
self.reader.skipBlank()
else:
char3 = self.reader.peek(3)
(syntaxDef, pattern) = self.checkDefinition(self.syntax_definition, char3)
if syntaxDef:
self.reader.read(3)
self.textStack.insert(0, (syntaxDef, char3))
continue
char2 = self.reader.peek(2)
(syntaxDef, pattern) = self.checkDefinition(self.syntax_definition, char2)
if syntaxDef:
self.reader.read(2)
self.textStack.insert(0, (syntaxDef, char2))
continue
char = self.reader.peekNext()
(syntaxDef, pattern) = self.checkDefinition(self.syntax_definition, char)
if syntaxDef:
self.reader.readNext()
self.textStack.insert(0, (syntaxDef, char))
continue
(stringDef, pattern) = self.checkDefinition(self.string_definition, char)
if stringDef:
word = self.reader.readStringVariable()
self.textStack.insert(0, (stringDef, word))
continue
(varDef, pattern) = self.checkDefinition(self.variable_definiation, char)
if varDef:
word = self.reader.readStringPatternWord(pattern)
self.textStack.insert(0, (varDef, word))
continue
self.raiseError('Scan error, char not allow', (None, self.reader.currentPosition))
def raiseError(self, desc, node):
raise Exception('%s at %s' % (desc, node[1]))
def parsePush(self, node, status, parseFunc, parseEndStatus):
self.textStack.pop()
status.append(node[1])
result = parseFunc(status)
status[-1] = parseEndStatus
return result
def parsePop(self, node, status, parseEndStatus):
previousChar = self.pair.get(node[1], None)
if not previousChar:
return
if len(status) > 2 and status[-2] == previousChar:
self.textStack.pop()
status.pop()
status[-1] = parseEndStatus
else:
self.raiseError('Parse error, mismatch %s' % node[1], node)
def parseSingleCondition(self, status):
operator = None
fieldName = None
value = None
status.append('S')
while len(self.textStack) > 0 and status[-1] != 'E':
node = self.textStack[-1]
if status[-1] == 'S':
if node[0] == 'variable':
status[-1] = 'S1'
fieldName = node[1]
self.textStack.pop()
else:
self.raiseError('Parse error, should be name', node)
elif status[-1] == 'S1':
if node[0] == 'operator':
status[-1] = 'S2'
operator = node[1]
self.textStack.pop()
else:
self.raiseError('Parse error, should be operator', node)
elif status[-1] == 'S2':
if node[0] == 'string':
value = node[1]
status[-1] = 'E'
self.textStack.pop()
elif node[0] == 'variable' and node[1] == 'None':
value = None
status[-1] = 'E'
self.textStack.pop()
else:
self.raiseError('Parse error, should be value', node)
status.pop()
return ('condition', (operator, fieldName, value))
def parseCondition(self, status):
status.append('S')
left_result = None
right_result = None
operator = None
while len(self.textStack) > 0 and status[-1] != 'E':
node = self.textStack[-1]
if status[-1] == 'S':
if node[0] == 'condition' and node[1] == '(':
left_result = self.parsePush(node, status, self.parseCondition, 'S1')
elif node[0] == 'variable':
left_result = self.parseSingleCondition(status)
status[-1] = 'S1'
else:
self.raiseError('Parse error, should be nest condition or condition', node)
elif status[-1] == 'S1':
right_result = None
if node[0] == 'condition' and node[1] == '|':
status[-1] = 'S'
operator = 'or'
self.textStack.pop()
self.priorityStack.append(('or', left_result))
elif node[0] == 'condition' and node[1] == ',':
status[-1] = 'S2'
operator = 'and'
self.textStack.pop()
elif node[0] == 'condition' and node[1] == ')':
self.parsePop(node, status, 'E')
else:
self.raiseError('Parse error, should be and or bracket', node)
elif status[-1] == 'S2':
if node[0] == 'condition' and node[1] == '(':
right_result = self.parsePush(node, status, self.parseCondition, 'S3')
elif node[0] == 'variable':
right_result = self.parseSingleCondition(status)
status[-1] = 'S3'
else:
self.raiseError('Parse error, should be nest condition or condition', node)
elif status[-1] == 'S3':
if node[0] == 'condition' and node[1] == ')':
self.parsePop(node, status, 'E')
elif node[0] == 'condition':
if len(status) > 0:
status[-1] = 'S1'
left_result = (operator, left_result, right_result)
else:
self.raiseError('Parse error, not be any text here', node)
status.pop()
if operator and right_result:
finalResult = ('andor', (operator, left_result, right_result))
else:
finalResult = left_result
while len(self.priorityStack) > 0:
(left_operator, left_result_in_stack) = self.priorityStack.pop()
finalResult = (left_operator, left_result_in_stack, finalResult)
return finalResult
def parse(self):
status = []
return self.parseCondition(status)
def loop(self, result):
(type, node) = result[0], result[1]
if type == 'andor':
return self.loop(node)
elif type == 'condition':
return self.loop(node)
n, l, r = result[0], result[1], result[2]
if n == 'and' or n == 'or':
left = self.loop(l)
right = self.loop(r)
if n == 'and':
return '(%s %s %s)' % (left, ' && ', right)
else:
return '(%s %s %s)' % (left, ' || ', right)
else:
return '%s %s %s' % (l, n, r)
def toDict(self, result):
(type, node) = result[0], result[1]
if type == 'andor':
dict = self.toDict(node)
return dict
elif type == 'condition':
dict = self.toDict(node)
return dict
n, l, r = result[0], result[1], result[2]
if n == 'and' or n == 'or':
left = self.toDict(l)
right = self.toDict(r)
return {'opt': n, 'left': left, 'right': right}
else:
# Remove padding ' ", but allow special value None
r = r[1:-1] if r else None
return {'opt': n, 'field': l, 'value': r}
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] |
conductor/conductor/tests/unit/common/models/test_order_lock_history.py | aalsudais/optf-has | 4 | 147055 | #
# -------------------------------------------------------------------------
# Copyright (C) 2019 IBM.
#
# 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.
#
# -------------------------------------------------------------------------
#
"""Test class for model order_lock_history"""
import unittest
from conductor.common.models.order_lock_history import OrderLockHistory
class TestOrderLockHistory(unittest.TestCase):
def setUp(self):
self.orderLockHistory = OrderLockHistory()
def test_OrderLockHistory(self):
self.assertEqual(True, self.orderLockHistory.atomic())
self.assertEqual("id", self.orderLockHistory.pk_name())
self.assertEqual(None, self.orderLockHistory.pk_value())
self.value = {'is_spinup_completed': False, 'conflict_id': None, 'spinup_completed_timestamp': None, 'plans': None}
self.assertEqual(self.value, self.orderLockHistory.values())
self.assertEqual("text", self.orderLockHistory.schema().get("conflict_id"))
self.assertEqual(None, self.orderLockHistory.__json__().get("plans"))
if __name__ == '__main__':
unittest.main()
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] |
Bio/UniGene/UniGene.py | zachcp/biopython | 5 | 1611651 | # Copyright 2001 by <NAME>. All Rights Reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
from __future__ import print_function
import warnings
from Bio import BiopythonDeprecationWarning
warnings.warn("The module Bio.UniGene.UniGene is now deprecated, "
"and will be removed in a future release of Biopython."
"To parse UniGene flat files, please use the parser in "
"Bio.UniGene instead",
BiopythonDeprecationWarning)
import string
import sgmllib
import UserDict
import Bio.File
class UniGeneParser( sgmllib.SGMLParser ):
def reset( self ):
sgmllib.SGMLParser.reset( self )
self.text = ''
self.queue = UserDict.UserDict()
self.open_tag_stack = []
self.open_tag = 'open_html'
self.key_waiting = ''
self.master_key = ''
self.context = 'general_info'
def parse( self, handle ):
self.reset()
self.feed( handle )
for key in self.queue:
if( self.queue[ key ] == {} ):
if( key[ :15 ] == 'UniGene Cluster' ):
self.queue[ 'UniGene Cluster' ] = key[ 16: ]
del self.queue[ key ]
return self.queue
#
# Assumes an empty line between records
#
def feed( self, handle ):
if isinstance(handle, Bio.File.UndoHandle):
uhandle = handle
else:
uhandle = Bio.File.UndoHandle(handle)
text = ''
while True:
line = uhandle.readline()
line = string.strip( line )
if( line == '' ):
break
text = text + ' ' + line
sgmllib.SGMLParser.feed( self, text )
def handle_data(self, newtext ):
newtext = string.strip( newtext )
self.text = self.text + newtext
def start_a( self, attrs ):
if( self.context == 'seq_info' ):
if( self.open_tag != 'open_b' ):
self.text = ''
# self.queue.append( attrs )
def end_a( self ):
if( self.context == 'seq_info' ):
if( self.open_tag != 'open_b' ):
if( self.key_waiting == '' ):
self.key_waiting = self.text
self.text = ''
def start_b( self, attrs ):
self.open_tag_stack.append( self.open_tag )
self.open_tag = 'open_b'
if( self.key_waiting == '' ):
self.text = ''
def end_b( self ):
if( self.text[ :15 ] == 'UniGene Cluster' ):
self.queue[ 'UniGene Cluster' ] = self.text[ 16: ]
self.text = ''
elif( self.key_waiting == '' ):
self.extract_key()
def extract_key( self ):
text = string.strip( self.text )
key = string.join( string.split( text ) )
words = string.split( key )
key = string.join( words[ :2 ] )
self.text = ''
try:
self.open_tag = self.open_tag_stack.pop()
except:
self.open_tag = 'open_html'
if( self.open_tag == 'open_table_data' ):
if( self.context == 'general_info' ):
if( self.key_waiting == '' ):
self.key_waiting = key
self.text = ''
elif( self.context == 'seq_info' ):
if( text == 'Key to Symbols' ):
self.context = 'legend'
self.master_key = key
elif( self.context == 'general_info' ):
self.master_key = key
if 'SEQUENCE' in key:
self.context = 'seq_info'
self.queue[ key ] = UserDict.UserDict()
elif( self.context == 'seq_info' ):
self.queue[ key ] = UserDict.UserDict()
self.master_key = key
def start_table( self, attrs ):
self.open_tag_stack.append( self.open_tag )
self.open_tag = 'open_table'
def end_table( self ):
try:
self.open_tag = self.open_tag_stack.pop()
except:
self.open_tag = 'open_html'
self.key_waiting = ''
def start_tr( self, attrs ):
self.open_tag_stack.append( self.open_tag )
self.open_tag = 'open_table_row'
self.text = ''
def end_tr( self ):
try:
self.open_tag = self.open_tag_stack.pop()
except:
self.open_tag = 'open_html'
text = self.text
if text:
self.text = ''
if( text[ 0 ] == ':' ):
text = text[ 1: ]
text = string.join( string.split( text ) )
if self.context == 'general_info' or \
self.context == 'seq_info':
try:
contents = self.queue[ self.master_key ][ self.key_waiting ]
if isinstance(contents, list):
contents.append( text )
else:
self.queue[ self.master_key ][ self.key_waiting ] = \
[ contents, text ]
except:
self.queue[ self.master_key ][ self.key_waiting ] = text
self.key_waiting = ''
def start_td( self, attrs ):
self.open_tag_stack.append( self.open_tag )
self.open_tag = 'open_table_data'
def end_td( self ):
try:
self.open_tag = self.open_tag_stack.pop()
except:
self.open_tag = 'open_html'
if( self.context == 'seq_info' ):
self.text = self.text + ' '
def print_item( self, item, level = 1 ):
indent = ' '
for j in range( 0, level ):
indent = indent + ' '
if isinstance(item, str):
if( item != '' ):
print('%s%s' % ( indent, item ))
elif isinstance(item, list):
for subitem in item:
self.print_item( subitem, level + 1 )
elif( isinstance( item, UserDict.UserDict ) ):
for subitem in item:
print('%skey is %s' % ( indent, subitem ))
self.print_item( item[ subitem ], level + 1 )
else:
print(item)
def print_tags( self ):
for key in self.queue:
print('key %s' % key)
self.print_item( self.queue[ key ] )
if( __name__ == '__main__' ):
with open( 'Hs13225.htm') as handle:
undo_handle = Bio.File.UndoHandle( handle )
unigene_parser = UniGeneParser()
unigene_parser.parse( handle )
unigene_parser.print_tags()
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api/urls.py | alaasalman/aussieshopper | 0 | 146476 | <reponame>alaasalman/aussieshopper
from django.conf.urls import url, include
from rest_framework import routers
from api import views
app_name = 'api'
router = routers.SimpleRouter()
router.register(r'stats', views.StatsViewSet)
urlpatterns = [
url(r'bot/handle/',
views.HandleChatMessage.as_view(),
name='api-handle-message'),
url(r'^', include(router.urls))
]
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dolosse/hardware/xia/pixie16/list_mode_data_decoder.py | Tobias2023/dolosse | 0 | 88220 | """
file: list_mode_data_decoder.py
brief: Decodes binary data produced by an XIA Pixie16 module
author: <NAME>
date: January 17, 2019
"""
from enum import Enum
from functools import partial
from io import BytesIO
from struct import unpack
from dolosse.constants.data import WORD, HALF_WORD
class HeaderCodes(Enum):
"""
Defines the various header values that we expect. If we get something that's not one of these
then we have a problem.
"""
STATS_BLOCK = 1
HEADER = 4
HEADER_W_ETS = 6
HEADER_W_ESUM = 8
HEADER_W_ESUM_ETS = 10
HEADER_W_QDC = 12
HEADER_W_QDC_ETS = 14
HEADER_W_ESUM_QDC = 16
HEADER_W_ESUM_QDC_ETS = 18
def decode_word_zero(word, mask):
"""
Decodes the
* Channel
* Slot
* Crate
* Header Length (Base 4 words + options)
* Event Length (Header + Trace Length / 2)
* Finish Code
:param word: The word that we're going to decode
:param mask: The mask that we'll be using.
:return: A dictionary containing the decoded information.
"""
return {
'channel': (word & mask.channel()[0]) >> mask.channel()[1],
'slot': (word & mask.slot()[0]) >> mask.slot()[1],
'crate': (word & mask.crate()[0]) >> mask.crate()[1],
'header_length': (word & mask.header_length()[0]) >> mask.header_length()[1],
'event_length': (word & mask.event_length()[0]) >> mask.event_length()[1],
'finish_code': (word & mask.finish_code()[0]) >> mask.finish_code()[1]
}
def decode_word_two(word, mask):
"""
Decodes the second word in the standard 4 word header.
:param word: The word that we're going to decode.
:param mask: The mask we'll use to decode that word.
:return: A dictionary containing the decoded information
"""
return {
'event_time_high': (word & mask.event_time_high()[0]) >> mask.event_time_high()[1],
'cfd_fractional_time': (word & mask.cfd_fractional_time()[0]) >>
mask.cfd_fractional_time()[1],
'cfd_trigger_source_bit': (word & mask.cfd_trigger_source()[0]) >>
mask.cfd_trigger_source()[1],
'cfd_forced_trigger_bit': (word & mask.cfd_forced_trigger()[0]) >>
mask.cfd_forced_trigger()[1]
}
def decode_word_three(word, mask):
"""
The final word of the standard header.
:param word: The word that we're going to decode.
:param mask: The mask we'll use to decode that word.
:return: A dictionary containing the decoded information
"""
return {
'energy': (word & mask.energy()[0]) >> mask.energy()[1],
'trace_length': (word & mask.trace_length()[0]) >> mask.trace_length()[1],
'trace_out_of_range': (word & mask.trace_out_of_range()[0]) >> mask.trace_out_of_range()[1]
}
def decode_energy_sums(buf):
"""
The first three words are the leading edge, flattop, and falling edge of the trapezoidal filter.
The last word is the baseline encoded in IEEE 754 format. We need to decode this set of data
in a different way than the other header words.
https://en.wikipedia.org/wiki/IEEE_754#IEEE_754-2008
https://stackoverflow.com/questions/39593087/double-conversion-to-decimal-value-ieee-754-in-python
:param buf: The buffer containing the encoded information
:return: An array with the elements we decoded.
"""
return [unpack('I', buf.read(WORD))[0],
unpack('I', buf.read(WORD))[0],
unpack('I', buf.read(WORD))[0],
unpack(b'<f', buf.read(WORD))[0]]
def decode_external_timestamp(buf, mask):
"""
External time stamps come in from the front panel, we will need to be able to decode them so that
we can do cross system time correlations. This time works exactly like the channel event time
in the header.
:param buf: The buffer we'll be decoding
:param mask: The mask we need to decode the data.
:return:
"""
return [unpack('I', buf.read(WORD))[0],
(unpack('I', buf.read(WORD))[0] & mask.event_time_high()[0])
>> mask.event_time_high()[1]]
def decode_qdc(buf):
"""
The Pixie-16 modules collect QDC Sums of each signal they process. They're stored in 8 words.
:param buf: Buffer containing the QDC data
:return: An array containing the QDC data
"""
qdc = []
for chunk in iter(partial(buf.read, WORD), b''):
qdc.append(unpack('I', chunk)[0])
return qdc
def decode_trace(buf):
"""
Pixie-16 stores traces with two samples per word. This is about the only place we need to use
a half word to decode the data structure.
:param buf: The buffer containing the trace.
:return:
"""
trc = []
for chunk in iter(partial(buf.read, HALF_WORD), b''):
trc.append(unpack('H', chunk)[0])
return trc
def process_optional_header_data(buf, header_length, mask):
"""
Process additional header data if they exist. We need to execute the buffer reads in order due
to how the data structure works. The order is always ESUMS -> QDC -> EXTERNAL_TIMESTAMP (ETS).
If one of the three is missing everything else shifts up.
:param buf: The buffer that we'll be reading from
:param header_length: The header length so that we know what the buffer contains.
:param mask: The mask that we need to know how many words each component has.
:return: A dictionary containing the decoded data.
"""
data = {}
if HeaderCodes(header_length) in [code for code in HeaderCodes if "ESUM" in code.name]:
data['esums'] = \
decode_energy_sums(BytesIO(buf.read(WORD * mask.number_of_energy_sum_words())))
if HeaderCodes(header_length) in [code for code in HeaderCodes if "QDC" in code.name]:
data['qdc'] = decode_qdc(BytesIO(buf.read(WORD * mask.number_of_qdc_words())))
if HeaderCodes(header_length) in [code for code in HeaderCodes if "ETS" in code.name]:
data['external_timestamp'] = decode_external_timestamp(
BytesIO(buf.read(WORD * mask.number_of_external_timestamp_words())), mask)
return data
def decode_listmode_data(stream, mask):
"""
Decodes data from Pixie16 binary data stream. We'll have an unknown number of events in the
data buffer. Therefore, we'll need to loop over the buffer and decode the events as we go. We
store the decoded events as a dictionary, and put those dictionaries into an array.
:param stream: The data stream that we'll be decoding
:param mask: The binary data mask that we'll need to decode the data.
"""
decoded_data_list = []
for chunk in iter(partial(stream.read, WORD), b''):
decoded_data = decode_word_zero(unpack('I', chunk)[0], mask)
decoded_data.update({
'event_time_low': unpack('I', stream.read(WORD))[0]})
decoded_data.update(
decode_word_two(unpack('I', stream.read(WORD))[0], mask))
decoded_data.update(
decode_word_three(unpack('I', stream.read(WORD))[0], mask))
try:
decoded_data.update(
process_optional_header_data(stream, decoded_data['header_length'], mask))
except ValueError:
raise BufferError('Unexpected Header Length: %s\n\tCRATE:SLOT:CHAN = %s:%s:%s'
% (decoded_data['header_length'], decoded_data['crate'],
decoded_data['slot'], decoded_data['channel']))
if decoded_data['trace_length'] != 0:
if decoded_data['event_length'] - decoded_data['header_length'] \
== decoded_data['trace_length'] * 0.5:
decoded_data.update({'trace': decode_trace(BytesIO(stream.read(
WORD * int(decoded_data['trace_length'] * 0.5))))})
else:
raise ValueError(
'Event Length (%s) does not match with Header(%s) + Trace (%s) Length !' % (
decoded_data['event_length'], decoded_data['header_length'],
decoded_data['trace_length']))
decoded_data_list.append(decoded_data)
return decoded_data_list
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deepcompton/scripts/CreateDatasetWithUncertainties.py | vuillaut/DeepIntegralCompton | 1 | 27703 | <gh_stars>1-10
# Deep Learning applique a l'imagerie Compton avec les donnees du satellite INTEGRAL
# Hackatlon AstroInfo 2021
##___ Importations
import numpy as np
import matplotlib.pyplot as plt
import Utilitaires_Compton as compton
import pickle as pkl
from os import listdir
from sys import setrecursionlimit
# ___ Constantes
Ee = 511
z_isgri = 0 # position plan ISGRI -> 0
z_picsit = -8.68 # position plan PICSIT -> distance entre les deux plans
r = 100000000000000.
precision = 5000.
## ___ Fonctions
def angular_separation(colat1, long1, colat2, long2):
"""
Compute the angular separation in radians
between two pointing direction given with lat-long
Parameters
----------
lat1: 1d `numpy.ndarray` , latitude of the first pointing direction
long1: 1d `numpy.ndarray` longitude of the first pointing direction
lat2: 1d `numpy.ndarray`, latitude of the second pointing direction
long2: 1d `numpy.ndarray`, longitude of the second pointing direction
Returns
-------
1d `numpy.ndarray`, angular separation
"""
cosdelta = np.sin(colat1) * np.sin(colat2) * np.cos(
(long1 - long2)) + np.cos(colat1) * np.cos(colat2)
cosdelta[cosdelta > 1] = 1.
cosdelta[cosdelta < -1] = -1.
ang_sep = np.arccos(cosdelta)
return ang_sep
def AddFunction_DistCone(f1,f2):
"""Somme de deux fonctions produisant une distribution
pour un ou plusieurs cones.
"""
return lambda l,c : f1(l,c) + f2(l,c)
def Cone_param(pos1x,pos1y,energ1,pos2x,pos2y,energ2) :
""" Retourne les parametres theta, phi et cottheta d'un cone
a partir des observations pos1x,pos1y,energ1,pos2x,pos2y,energ2.
retourne None si l'energie limite est atteinte.
-----------
Attention :
indice 1 : ISGRI, indice 2 : PICsIT
"""
x1cur = z_isgri
y1cur = pos1y
z1cur = -pos1x
x2cur = z_picsit
y2cur = pos2y
z2cur = -pos2x
E0 = energ1 + energ2
Ec = E0/(1+2*E0/Ee)
if (energ2 >= Ec) and (energ1 <= E0 - Ec):
cotheta = compton.cottheta(energ1,E0 - energ1)
A = np.array([x1cur,y1cur,z1cur])
B = np.array([x2cur,y2cur,z2cur])
theta = compton.colatitudeaxe(B,A)
phi = compton.longitudeaxe(B,A)
return theta, phi, cotheta
else :
return None
def Normal(delta_x,sigma):
"""Loi normale
"""
return 1/(sigma * np.sqrt(np.pi)) * np.exp(-1/2*(delta_x /sigma)**2)
def DistriCone_v3_2D(theta, phi, cottheta, sigma ):
""" Retourne la distribution associee au cone de parametres theta, phi, cottheta.
c'est-a-dire le cone plus une incertitude gaussienne de parametre sigma.
"""
def f_2D(l,c) :
"""l et c 2D
output 2D
"""
theta_list = np.full_like(c, theta)
phi_list = np.full_like(l, phi)
e = angular_separation(theta_list, phi_list,
np.radians(c), np.radians(l))
delta = e - np.arctan(1/cottheta)
return Normal(delta,np.radians(sigma))
return f_2D
def Create_image(data, n_cones, sigma = 2, grid_longit = np.linspace(0, 359, 180), grid_colat = np.linspace(0, 89, 45) ):
""" A partir des data simulees ou observees, creation d'une image sur la grille (grid_longit, grid_colat).
Pour cela on applique sur cette grille la fonction cumulant n_cones distributions produites avec n_cones evenements
de data, tires aleatoirement. Ces distributions gaussiennes ont une incertitude sigma.
"""
# ___ Load data
s = data.shape[0]
# ISGRI -> 1
E1_list = data[:,0]
Y1_list = data[:,6]
Z1_list = data[:,7]
#PICsIT -> 2
E2_list = data[:,1]
Y2_list = data[:,8]
Z2_list = data[:,9]
# ___ Create distribution
f_v3 = lambda l,c : 0
for i in np.random.randint(0,s,size = n_cones) :
L = Cone_param(Y1_list[i],Z1_list[i],E1_list[i],Y2_list[i],Z2_list[i],E2_list[i])
if L!= None :
theta, phi, cottheta = L
f_new = DistriCone_v3_2D(theta, phi, cottheta, sigma = sigma)
f_v3 = AddFunction_DistCone(f_v3,f_new)
# ___ Application on image
l, c = np.meshgrid(grid_colat, grid_longit)
im = f_v3(c,l)
return im
def Create_dataset(name_file_list,path, name_pkl, n_file_max = -1, n_image_file = 50, n_cones_min = 100, n_cones_max = 2000, sigma = 2):
""" Creation d'un dataset a partir d'une liste de fichiers path+name_file_list.
A partir de n_file_max fichiers, on tire n_image_file images par fichier
avec un nombre de cones variant aleatoirement entre n_cones_min et n_cones_max.
Le dataset est enregistre dans un pkl nomme name_pkl.
"""
y,x=[],[]
i=0
for name_file in name_file_list[:n_file_max] :
i+=1
_,theta,_,phi=name_file.replace(".npy","").split("_")
j=0
for n_cones in np.random.randint(n_cones_min,n_cones_max+1,size = n_image_file) :
data = np.load(path+name_file).astype("float64")
if len(data)>0:
#print(f"\r file n°{i}/{n_file_max}, image n°{j}/{n_image_file} ", end='', flush=True)
data = Create_image(data, n_cones, sigma = sigma)
y.append([float(theta),float(phi),n_cones])
x.append(data)
j+=1
if len(y)%100 == 0:
nx,ny = pkl.load(open(name_pkl, "rb"))
nx+=x
ny+=y
pkl.dump((nx,ny), open(name_pkl, "wb"))
x.clear()
y.clear()
nx,ny = pkl.load(open(name_pkl, "rb"))
nx+=x
ny+=y
pkl.dump((nx,ny), open(name_pkl, "wb"))
return 0
## ___ Script
path = '/Users/lg265853/Documents/AstroInfo/save_Compton'
name_file_list = listdir(path)
n_file_max = -1
n_image_file = 100
n_cones_min = 100
n_cones_max = 2000
sigma = 2
name_pkl = f'dataset_{n_image_file}images_{n_file_max}files_sig{sigma}_ncones{n_cones_min}-{n_cones_max}.pkl'
setrecursionlimit(n_cones_max)
Create_dataset(name_file_list,path+'/',name_pkl,n_file_max, n_image_file, n_cones_min, n_cones_max, sigma)
## ___ Verification
#x,y = pkl.load(open(name_pkl, "rb"))
#print(len(x), len(y))
#
#plt.figure(figsize=(20,20))
#k = 1
#for i in np.random.randint(0,len(x),size = 30):
# plt.subplot(3,10,k)
# plt.imshow(x[i], origin='lower', cmap = 'CMRmap_r')
# plt.scatter(y[i][0]/2,y[i][1]/2)
# plt.title(str(y[i][2]) + ' cones')
# plt.xlabel('colatitude') ; plt.ylabel('longitude')
# k+=1
#
#plt.show()
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api/src/controller/feature/FeatureDataController.py | SamuelJansen/FeatureManager | 1 | 46706 | from python_framework import Controller, ControllerMethod, HttpStatus
from Role import *
from dto.FeatureDataDto import *
@Controller(url = '/feature-datas', tag='FeatureData', description='Single FeatureData controller')
class FeatureDataController:
@ControllerMethod(url='/<string:featureKey>/<string:sampleKey>',
responseClass=FeatureDataResponseDto,
roleRequired=[USER, ADMIN, API])
def get(self, featureKey, sampleKey):
return self.service.featureData.queryByFeatureKeyAndSampleKey(featureKey, sampleKey), HttpStatus.OK
@ControllerMethod(url='/<string:featureKey>/<string:sampleKey>',
roleRequired=[USER, ADMIN, API])
def delete(self, featureKey, sampleKey):
self.service.featureData.deleteByFeatureKeyAndSampleKey(featureKey, sampleKey), HttpStatus.NO_CONTENT
return {}, HttpStatus.NO_CONTENT
@Controller(url = '/feature-datas/batch', tag='FeatureData', description='Multiple FeatureData controller')
class FeatureDataBatchController:
@ControllerMethod(url='/<string:featureKey>',
responseClass=[[FeatureDataResponseDto]],
roleRequired=[ADMIN, API])
def get(self, featureKey):
return self.service.featureData.queryAllByFeatureKey(featureKey), HttpStatus.OK
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inlineedit/apps.py | VolkerGoeschl/django-inlineedit | 0 | 60419 | <reponame>VolkerGoeschl/django-inlineedit<filename>inlineedit/apps.py
from django.apps import AppConfig
class InlineeditConfig(AppConfig):
label = "inlineedit"
name = 'inlineedit'
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pyvisdk/enums/file_system_interface_version.py | Infinidat/pyvisdk | 0 | 99683 |
########################################
# Automatically generated, do not edit.
########################################
from pyvisdk.thirdparty import Enum
FileSystemInterfaceVersion = Enum(
'NFSV3_0',
)
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client/Agent/stare.py | zhanglei/Qalarm | 1 | 81087 | from os import stat
import json
import signal
import socket
import time
from module import conf
# Report
def report(error, module):
if report.fp is None:
report.fp = open(conf.qalarm_file, 'a')
# Level
if 'level' not in error:
error['level'] = 'default'
else:
error['level'] = error['level'].lower()
# Only mode
if ('only_levels' in conf.check_list[module] and
error['level'] not in conf.check_list[module]['only_levels']):
return
# Ignore mode
if ('ignore_levels' in conf.check_list[module] and
error['level'] in conf.check_list[module]['ignore_levels']):
return
data = {
'project': 'stare',
'module': module,
'code': None,
'env': 'prod',
'time': error['timestamp'],
'level': error['level'],
'server_ip': socket.gethostname(),
'client_ip': None,
'script': error['file'],
'message': '\t'.join(error['lines']),
}
report.fp.write(json.dumps(data, report.fp) + '\n')
report.fp.flush()
# init report file handler
report.fp = None
# Config & record
def load_records():
try:
data = json.load(open(conf.record_file, 'r'))
except (IOError, ValueError):
data = {}
for module, item in conf.check_list.items():
if item['path'] not in data:
data[item['path']] = {'inode': 0, 'offset': 0}
# Always update module name
data[item['path']]['module'] = module
return data
def save_records(data):
return json.dump(data, open(conf.record_file, 'w'), indent=2)
# Heartbeat
def heartbeat():
fp = open(conf.heartbeat_file, 'a')
data = {
'k': 'qalarm|heartbeat|201|prod',
'i': 'localhost',
't': int(time.time()),
'type': 'h'
}
fp.write(json.dumps(data) + '\n')
fp.close()
def main():
print 'begin staring...'
# Load records
records = load_records()
for path in records:
print path, records[path]
last_heartbeat_time = None
# Run as daemon
while not main.stop:
# Heartbeat per 5 minute
now = time.time()
if last_heartbeat_time is None or (now - last_heartbeat_time) > 300:
last_heartbeat_time = now
heartbeat()
for path, record in records.items():
# Check existence, inode
try:
res = stat(path)
except OSError:
continue
# New file, jump to the end
if record['inode'] == 0 and record['offset'] == 0:
print 'New file added, jump to the end %s@%d:%d' % (path, res.st_ino, res.st_size)
record['inode'] = res.st_ino
record['offset'] = res.st_size
continue
# No new data
elif res.st_ino == record['inode'] and res.st_size == record['offset']:
continue
# Another file
elif res.st_ino != record['inode'] or res.st_size < record['offset']:
print 'Open new file %s@%d:%d' % (path, res.st_ino, record['offset'])
record['inode'] = res.st_ino
record['offset'] = 0
# Open then set offset
print 'Log file processed %s@%d:%d' % (path, res.st_ino, record['offset'])
fp = open(path, 'r')
fp.seek(record['offset'])
# Process
module = record['module']
action = conf.check_list[module]['action']
action(fp, module, report)
# Save offset
record['offset'] = fp.tell()
fp.close()
save_records(records)
time.sleep(1)
print 'finish staring'
# set stop flag
main.stop = False
def sig_handler(signum, frame):
print 'Signal handler called with signal', signum
main.stop = True
if __name__ == '__main__':
signal.signal(signal.SIGTERM, sig_handler)
main()
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player.py | Drayux/Battlematus | 0 | 1189 | # PLAYER
class player:
def __init__(self):
| [
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hwt/interfaces/agents/signal.py | mgielda/hwt | 0 | 146339 | from collections import deque
from hwt.hdl.constants import Time
from hwt.simulator.agentBase import AgentBase, SyncAgentBase
from hwt.synthesizer.exceptions import IntfLvlConfErr
# 100 MHz
DEFAULT_CLOCK = 10 * Time.ns
class SignalAgent(SyncAgentBase):
"""
Agent for signal interface, it can use clock and reset interface
for synchronization or can be synchronized by delay
:attention: clock synchronization has higher priority
"""
def __init__(self, intf, delay=DEFAULT_CLOCK):
AgentBase.__init__(self, intf)
self.delay = delay
self.initDelay = 0
# resolve clk and rstn
try:
self.clk = self.intf._getAssociatedClk()
except IntfLvlConfErr:
self.clk = None
self._discoverReset(True)
self.data = deque()
self.initPending = True
if self.clk is not None:
if self.initDelay:
raise NotImplementedError("initDelay only without clock")
c = self.SELECTED_EDGE_CALLBACK
self.monitor = c(self.clk, self.monitor, self.getEnable)
self.driver = c(self.clk, self.driver, self.getEnable)
def getDrivers(self):
d = SyncAgentBase.getDrivers(self)
if self.clk is None:
return d
else:
return d + [self.driverInit_nonClk]
def driverInit_nonClk(self, sim):
try:
d = self.data[0]
except IndexError:
d = None
self.doWrite(sim, d)
return
yield
def doRead(self, s):
return s.read(self.intf)
def doWrite(self, s, data):
s.write(data, self.intf)
def driver(self, sim):
if self.clk is None:
if self.initPending:
if self.initDelay:
yield sim.wait(self.initDelay)
self.initPending = False
# if clock is specified this function is periodically called every
# clk tick
while True:
if self._enabled and self.data and self.notReset(sim):
d = self.data.popleft()
self.doWrite(sim, d)
yield sim.wait(self.delay)
else:
# if clock is specified this function is periodically called every
# clk tick, when agent is enabled
if self.data and self.notReset(sim):
d = self.data.popleft()
self.doWrite(sim, d)
def monitor(self, sim):
if self.clk is None:
if self.initPending and self.initDelay:
yield sim.wait(self.initDelay)
self.initPending = False
# if there is no clk, we have to manage periodic call by our selfs
while True:
if self._enabled and self.notReset(sim):
yield sim.waitOnCombUpdate()
d = self.doRead(sim)
self.data.append(d)
yield sim.wait(self.delay)
else:
# if clock is specified this function is periodically called every
# clk tick, when agent is enabled
yield sim.waitOnCombUpdate()
if self.notReset(sim):
d = self.doRead(sim)
self.data.append(d)
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] |
src/test/unit/testgamer.py | alok1974/pychess | 2 | 73347 | import unittest
from pychess.core.gamer import Game
from pychess.element.squarer import Square
from pychess.element.piecer import Piece
from pychess import constant as c
from pychess.core.mover import Move
def _check_win(game):
if game.is_game_over:
winner = str(game.winner.name).capitalize()
print(f'{winner} wins!')
class TestGamer(unittest.TestCase):
@classmethod
def setUpClass(cls):
# Some short hands for fitting one move in one line
p = c.PieceType.pawn
b = c.PieceType.bishop
r = c.PieceType.rook
q = c.PieceType.queen
wt = c.Color.white
bl = c.Color.black
s = Square
cls.expected_move_history = []
# A simple gameplay (even a noob would not play so bad ;)
cls.game = Game()
# Illegal Move - White rook trying to move diagonally
cls.game.move(('a1h8', None))
_check_win(cls.game)
# Move 1 - White opening with d4
cls.game.move(('d2d4', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(p, wt, 3), s('d2'), s('d4'))
)
# Move 2 - Black opening with c6
cls.game.move(('c7c6', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(p, bl, 2), s('c7'), s('c6'))
)
# Illegal Move - Trying to capture white pawn at d4 by white queen
cls.game.move(('d1d4', None))
_check_win(cls.game)
# Move 3 - White Queen to d3
cls.game.move(('d1d3', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(q, wt, 0), s('d1'), s('d3'))
)
# Illegal Move - Queen trying to jump over white pawn at d4
# nothing will happen
cls.game.move(('d3d5', None))
_check_win(cls.game)
# Move 4 - Black moves pawn a6
cls.game.move(('a7a6', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(p, bl, 0), s('a7'), s('a6'))
)
# Move 5 - Black pawn at h7 captured by the white queen
cls.game.move(('d3h7', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(q, wt, 0), s('d3'), s('h7'))
)
# Move 6 - Black pawn to a5
cls.game.move(('a6a5', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(p, bl, 0), s('a6'), s('a5'))
)
# Move 7 - White queen to f5
cls.game.move(('h7f5', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(q, wt, 0), s('h7'), s('f5'))
)
# Move 8 - Black rook to h6, where it comes under attack
# by the black bishop at c1
cls.game.move(('h8h6', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(r, bl, 1), s('h8'), s('h6'))
)
# Move 9 - White pawn to e4
cls.game.move(('e2e4', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(p, wt, 4), s('e2'), s('e4'))
)
# Move 10 - Black pawn to a4
cls.game.move(('a5a4', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(p, bl, 0), s('a5'), s('a4'))
)
# Move 11 - White rook to c4
cls.game.move(('f1c4', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(b, wt, 1), s('f1'), s('c4'))
)
# Move 12 - White rook to h5 attacking the queen
cls.game.move(('h6h5', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(r, bl, 1), s('h6'), s('h5'))
)
# Move 13 - White queen to f7, it's a mate!
cls.game.move(('f5f7', None))
_check_win(cls.game)
cls.expected_move_history.append(
Move(Piece(q, wt, 0), s('f5'), s('f7'))
)
def test_board(self):
expected_result = (
' a b c d e f g h\n'
' 8 |r|n|b|q|k|b|n|#| 8\n'
' 7 |#|p|#|p|p|Q|p|_| 7\n'
' 6 |_|#|p|#|_|#|_|#| 6\n'
' 5 |#|_|#|_|#|_|#|r| 5\n'
' 4 |p|#|B|P|P|#|_|#| 4\n'
' 3 |#|_|#|_|#|_|#|_| 3\n'
' 2 |P|P|P|#|_|P|P|P| 2\n'
' 1 |R|N|B|_|K|_|N|R| 1\n'
' a b c d e f g h'
)
self.assertEqual(repr(self.game.board), expected_result)
def test_captured_black(self):
expected_result = [
Piece(c.PieceType.pawn, c.Color.black, order=7),
Piece(c.PieceType.pawn, c.Color.black, order=5),
]
self.assertEqual(self.game.captured_black, expected_result)
def test_captured_white(self):
expected_result = []
self.assertEqual(self.game.captured_white, expected_result)
def test_white_points(self):
expected_result = 2
self.assertEqual(self.game.white_points, expected_result)
def test_black_points(self):
expected_result = 0
self.assertEqual(self.game.black_points, expected_result)
def test_leader(self):
expected_result = c.Color.white
self.assertEqual(self.game.leader, expected_result)
def test_lead(self):
expected_result = 2
self.assertEqual(self.game.lead, expected_result)
def test_pieces_checking_black(self):
expected_result = [Piece(c.PieceType.queen, c.Color.white)]
self.assertEqual(self.game.pieces_checking_black, expected_result)
def test_pieces_checking_white(self):
expected_result = []
self.assertEqual(self.game.pieces_checking_white, expected_result)
def test_move_history(self):
self.assertEqual(self.game.move_history, self.expected_move_history)
def test_capturables(self):
expected_result = {
c.Color.white: {
Piece(c.PieceType.king, c.Color.black): [
Piece(c.PieceType.queen, c.Color.white)
],
Piece(c.PieceType.rook, c.Color.black, 1): [
Piece(c.PieceType.pawn, c.Color.white, 7)
]
},
c.Color.black: {
Piece(c.PieceType.queen, c.Color.white): [
Piece(c.PieceType.pawn, c.Color.black, 4),
Piece(c.PieceType.king, c.Color.black),
Piece(c.PieceType.bishop, c.Color.black, 1),
Piece(c.PieceType.pawn, c.Color.black, 6),
Piece(c.PieceType.knight, c.Color.black, 1),
Piece(c.PieceType.rook, c.Color.black, 1),
]
}
}
self.assertEqual(self.game.capturables, expected_result)
def test_winner(self):
expected_result = c.Color.white
self.assertEqual(self.game.winner, expected_result)
def test_parse_move_spec_1(self):
move_spec = 'a1h8'
expected_result = (Square('a1'), Square('h8'))
self.assertEqual(Game.parse_move_spec(move_spec), expected_result)
def test_parse_move_spec_2(self):
move_spec = ('a1', 'h8')
expected_result = (Square('a1'), Square('h8'))
self.assertEqual(Game.parse_move_spec(move_spec), expected_result)
def test_parse_move_spec_3(self):
move_spec = ((0, 0), (0, 7))
expected_result = (Square((0, 0)), Square((0, 7)))
self.assertEqual(Game.parse_move_spec(move_spec), expected_result)
def test_parse_move_spec_4(self):
move_spec = [1, 2, 3, 4]
exc_str = (
f'`move_spec={move_spec}` should either be a tuple or a string'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_5(self):
move_spec = (1, 2, 3)
exc_str = (
f'{move_spec} tuple should have exactly two elements!'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_6(self):
addr_1 = 'aa'
addr_2 = '11'
move_spec = (addr_1, addr_2)
exc_str = (
f'Malformed address! The address {addr_1} supplied '
f'in arg `move_spec={move_spec}` should be of '
'the format \'<alpha><number>\' where alpha is '
'one of [a, b, c, d, e, f, g, h] and number '
'is from 1 to 8'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_7(self):
addr_1 = 'a1'
addr_2 = '11'
move_spec = (addr_1, addr_2)
exc_str = (
f'Malformed address! The address {addr_2} supplied '
f'in arg `move_spec={move_spec}` should be of '
'the format \'<alpha><number>\' where alpha is '
'one of [a, b, c, d, e, f, g, h] and number '
'is from 1 to 8'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_8(self):
addr_1 = (10, 30)
addr_2 = (12, 40)
move_spec = (addr_1, addr_2)
exc_str = (
f'The address {addr_1} supplied in arg '
f'`move_spec={move_spec}` should exactly be a '
'tuple of the form (a, b) where both a and b '
'are one of the numbers 0 to 7'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_9(self):
addr_1 = (0, 5)
addr_2 = (12, 40)
move_spec = (addr_1, addr_2)
exc_str = (
f'The address {addr_2} supplied in arg '
f'`move_spec={move_spec}` should exactly be a '
'tuple of the form (a, b) where both a and b '
'are one of the numbers 0 to 7'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_10(self):
move_spec = ((0, 5), (0, 5))
exc_str = (
f'`move_spec={move_spec}` should have distinct source and '
'destination'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_11(self):
move_spec = ('a1', 'a1')
exc_str = (
f'`move_spec={move_spec}` should have distinct source and '
'destination'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_12(self):
move_spec = 'c6c6'
exc_str = (
f'`move_spec={move_spec}` should have distinct source and '
'destination'
)
with self.assertRaises(Exception) as context:
Game.parse_move_spec(move_spec)
self.assertTrue(exc_str in str(context.exception))
def test_parse_move_spec_13(self):
move_spec = (Square('e1'), Square('e4'))
expected_result = move_spec
self.assertEqual(Game.parse_move_spec(move_spec), expected_result)
if __name__ == '__main__':
unittest.main()
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FabricUI/widget/ConfigWidget.py | shuaih7/FabricUI | 0 | 194782 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Created on 11.25.2020
Updated on 04.12.2020
Author: <EMAIL>
"""
import os
import sys
import json
from PyQt5.uic import loadUi
from PyQt5.QtGui import QIntValidator
from PyQt5.QtCore import Qt, pyqtSignal, pyqtSlot
from PyQt5.QtWidgets import QTabWidget, QFileDialog
abs_path = os.path.abspath(os.path.dirname(__file__))
cfg_path = os.path.abspath(os.path.join(abs_path, ".."))
def dtype_cast(value, dtype):
if dtype == 'int':
return int(value)
elif dtype == 'float':
return float(value)
elif dtype == 'str':
return str(value)
elif dtype in ['list', 'dict']:
return eval(value)
else:
return value
class ConfigWidget(QTabWidget):
configSignal = pyqtSignal(str)
def __init__(self, config_matrix):
super(ConfigWidget, self).__init__()
loadUi(os.path.join(os.path.abspath(os.path.dirname(__file__)), "ConfigWidget.ui"), self)
self.setWindowFlags(Qt.WindowStaysOnTopHint)
self.config_matrix = config_matrix
self.func_dict = {
'General': {
'save_mode': [self.getSaveMode, self.setSaveMode, 'str'],
'save_dir': [self.lnSaveDir.text, self.lnSaveDir.setText, 'str']
},
'Camera': {
'serial_number': [self.lnSN.text, self.lnSN.setText, 'str'],
'exposure_time': [self.lnExpose.text, self.lnExpose.setText, 'int'],
'gain': [self.lnGain.text, self.lnGain.setText, 'int'],
'binning': [self.lnBinning.text, self.lnBinning.setText, 'int']
},
'Light': {
},
'Model': {
'offsets': [self.getOffsets, self.setOffsets, 'list'],
'input_h': [self.lnInputHeight.text, self.lnInputHeight.setText, 'int'],
'input_w': [self.lnInputWidth.text, self.lnInputWidth.setText, 'int'],
'obj_threshold': [self.lnThresh.text, self.lnThresh.setText, 'float'],
'nms_threshold': [self.lnNMS.text, self.lnNMS.setText, 'float']
}
}
def showConfig(self):
for module in self.func_dict:
params = self.config_matrix[module]
funcs = self.func_dict[module]
self.loadConfig(params, funcs)
self.show()
def loadConfig(self, params, funcs):
for item in funcs:
value = params[item]
func_get, func_set, data_type = funcs[item]
func_set(str(value))
@pyqtSlot()
def updateConfig(self):
for module in self.func_dict:
params = self.config_matrix[module]
funcs = self.func_dict[module]
self.setConfig(params, funcs, module)
self.exitConfig()
def setConfig(self, params, funcs, module):
flag = False
for item in funcs:
func_get, func_set, data_type = funcs[item]
pre_value = str(params[item])
cur_value = str(func_get())
if pre_value != cur_value:
params[item] = dtype_cast(cur_value, data_type)
flag = True
if flag:
self.writeConfig()
self.configSignal.emit(module)
@pyqtSlot()
def exitConfig(self):
self.close()
def writeConfig(self):
json_file = os.path.join(cfg_path, "config.json")
with open(json_file, "w", encoding="utf-8") as f:
cfg_obj = json.dumps(self.config_matrix, indent=4)
f.write(cfg_obj)
f.close()
@pyqtSlot()
def setSaveDir(self):
save_dir = QFileDialog.getExistingDirectory()
if not os.path.exists(save_dir):
save_dir = self.config_matrix['General']['save_dir']
self.lnSaveDir.setText(save_dir)
def setSaveMode(self, mode="1"):
"""Set the save mode: 0 for not saving, 1 for saving all, 2 for saving the defect images
"""
self.save_mode = mode
if str(mode) == "1":
self.btnSaveAll.setChecked(True)
self.btnSaveDef.setChecked(False)
elif str(mode) == "2":
self.btnSaveAll.setChecked(False)
self.btnSaveDef.setChecked(True)
else:
self.btnSaveAll.setChecked(False)
self.btnSaveDef.setChecked(False)
def getSaveMode(self):
mode = "0"
if self.btnSaveDef.isChecked(): mode = "2"
elif self.btnSaveAll.isChecked(): mode = "1"
return mode
@pyqtSlot()
def setSaveAll(self):
if str(self.save_mode) == "1": self.setSaveMode("0")
else: self.setSaveMode("1")
@pyqtSlot()
def setSaveDefect(self):
if str(self.save_mode) == "2": self.setSaveMode("0")
else: self.setSaveMode("2")
def getOffsets(self):
offLeft = int(self.lnOffLeft.text())
offRight = int(self.lnOffRight.text())
offTop = int(self.lnOffTop.text())
offBottom = int(self.lnOffBottom.text())
return str([offLeft, offRight, offTop, offBottom])
def setOffsets(self, offsets):
if isinstance(offsets, str):
offsets = eval(offsets)
offLeft, offRight, offTop, offBottom = offsets
self.lnOffLeft.setText(str(offLeft))
self.lnOffRight.setText(str(offRight))
self.lnOffTop.setText(str(offTop))
self.lnOffBottom.setText(str(offBottom))
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] |
Preprocessing/randomSampler.py | kstisser/DeepLearningPointCloud | 0 | 78934 | <reponame>kstisser/DeepLearningPointCloud<filename>Preprocessing/randomSampler.py
import numpy as np
class RandomSampler:
def __init__(self):
print("Opened random sampler")
def randomlySamplePoints(self, points, downsampleNumber):
rows = points.shape[0]
randomIndices = (np.random.choice(rows, size=downsampleNumber, replace=False))
downsampled = points[randomIndices,:]
print("Downsampled shape: ", downsampled.shape)
return downsampled
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tests/auth/auth_test.py | oslokommune/okdata-sdk-python | 2 | 1604066 | import json
import logging
import re
import pytest
from okdata.sdk.auth.auth import Authenticate
from okdata.sdk.auth.credentials.client_credentials import ClientCredentialsProvider
from okdata.sdk.config import Config
from okdata.sdk.exceptions import ApiAuthenticateError
from freezegun import freeze_time
from tests.auth.client_credentials_test_utils import (
from_cache_not_expired_token,
from_cache_expired_token,
utc_now,
)
from tests.test_utils import (
client_credentials_response,
client_credentials_response_no_refresh,
)
logging.basicConfig(level=logging.INFO)
config = Config(env="prod")
token_endpoint = "https://login.oslo.kommune.no/auth/realms/api-catalog/protocol/openid-connect/token"
@pytest.fixture(scope="function")
def mock_home_dir(monkeypatch, tmp_path):
monkeypatch.setenv("HOME", str(tmp_path))
@freeze_time(utc_now)
class TestAuthenticate:
def test_authenticate_cache_disabled(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = False
response = json.dumps(client_credentials_response)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
auth.login()
assert auth._access_token == client_credentials_response["access_token"]
assert auth._refresh_token == client_credentials_response["refresh_token"]
def test_authenticat_no_cache(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
response = json.dumps(client_credentials_response)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
auth.login()
assert auth._access_token == client_credentials_response["access_token"]
assert auth._refresh_token == client_credentials_response["refresh_token"]
def test_authenticate_cached_credentials(self, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
cached_credentials = {
"provider": "ClientCredentialsProvider",
"access_token": from_cache_not_expired_token,
"refresh_token": from_cache_not_expired_token,
}
auth.file_cache.write_credentials(json.dumps(cached_credentials))
auth.login()
assert auth._access_token == cached_credentials["access_token"]
assert auth._refresh_token == cached_credentials["refresh_token"]
def test_authenticate_refresh_credentials(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
cached_credentials = {
"provider": "ClientCredentialsProvider",
"access_token": from_cache_not_expired_token,
"refresh_token": from_cache_not_expired_token,
}
auth.file_cache.write_credentials(json.dumps(cached_credentials))
response = json.dumps(client_credentials_response)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
auth.login()
assert auth._access_token == cached_credentials["access_token"]
assert auth._refresh_token == cached_credentials["refresh_token"]
def test_authenticate_expired_tokens(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
cached_credentials = {
"provider": "TokenServiceProvider",
"access_token": from_cache_expired_token,
"refresh_token": from_cache_expired_token,
}
auth.file_cache.write_credentials(json.dumps(cached_credentials))
response = json.dumps(client_credentials_response)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
auth.login()
print(from_cache_not_expired_token)
print(from_cache_expired_token)
assert auth._access_token == client_credentials_response["access_token"]
assert auth._refresh_token == client_credentials_response["access_token"]
def test_authenticate_expired_access_token(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
cached_credentials = {
"provider": "TokenServiceProvider",
"access_token": from_cache_expired_token,
"refresh_token": from_cache_not_expired_token,
}
auth.file_cache.write_credentials(json.dumps(cached_credentials))
response = json.dumps(client_credentials_response)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
auth.login()
assert auth._access_token == from_cache_not_expired_token
assert auth._refresh_token == cached_credentials["refresh_token"]
def test_authenticate_fail(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(
config, client_id="wrong_id"
)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
response = json.dumps(
{"error": "authentication error", "error_description": "No such client"}
)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
try:
auth.login()
except ApiAuthenticateError:
assert True
def test_refresh_inactive_session(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
cached_credentials = {
"provider": "TokenServiceProvider",
"access_token": from_cache_expired_token,
"refresh_token": from_cache_not_expired_token,
}
auth.file_cache.write_credentials(json.dumps(cached_credentials))
error_msg = {
"error": "invalid_grant",
"error_description": "Session not active",
}
refresh_response = {"text": json.dumps(error_msg), "status_code": 400}
login_response = {
"text": json.dumps(client_credentials_response),
"status_code": 200,
}
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, [refresh_response, login_response])
auth.login()
assert auth._access_token == from_cache_not_expired_token
assert auth._refresh_token == cached_credentials["refresh_token"]
def test_refresh_no_refresh_token(self, requests_mock, mock_home_dir):
client_credentials_provider = ClientCredentialsProvider(config)
auth = Authenticate(config=config, token_provider=client_credentials_provider)
auth.file_cache.credentials_cache_enabled = True
cached_credentials = {
"provider": "TokenServiceProvider",
"access_token": from_cache_expired_token,
}
auth.file_cache.write_credentials(json.dumps(cached_credentials))
response = json.dumps(client_credentials_response_no_refresh)
matcher = re.compile(token_endpoint)
requests_mock.register_uri("POST", matcher, text=response, status_code=200)
auth.login()
assert auth._access_token == from_cache_not_expired_token
assert auth._refresh_token is None
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] |
Easy/1629_slowestKey.py | a-shah8/LeetCode | 1 | 129234 | class Solution:
def slowestKey(self, releaseTimes: List[int], keysPressed: str) -> str:
# Time Complexity: O(N)
# Space Complexity: O(1)
longest = releaseTimes[0]
slowestKey = keysPressed[0]
for i in range(1, len(releaseTimes)):
cur = releaseTimes[i] - releaseTimes[i-1]
if (cur > longest or (cur==longest and keysPressed[i] > slowestKey)):
longest = cur
slowestKey = keysPressed[i]
return slowestKey
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lib/main.py | gammaforceio/infrascript | 0 | 56916 | #!/usr/bin/env python3
# vim: set ft=python:sw=4:ts=4
import os
import sys
# This location is set within the Dockerfile.
sys.path.insert(0, '/opt/infra/lib')
from infra import (
load_definitions_file,
parse_args,
get_org_repo,
cleanup_boilerplate,
write_tf_backend_file,
write_tfvars_file,
run_terraform,
save_outputs,
write_awstf_file,
)
if __name__ == '__main__':
# TODO: Ensure the AWS envvars are set
GLOBALS, SECTIONS = load_definitions_file()
args = parse_args(
legal_sections=SECTIONS.keys(),
)
# TODO: Handle the None,None and the x,'' cases
org, repo = get_org_repo()
# Set ourselves in the right directory. This simplifies the rest of the code
# The directory is either specified in the SECTIONS definition or defaults
# to the section name.
os.chdir(SECTIONS[args.section].get('subdir', args.section))
cleanup_boilerplate()
# There are a very few cases where we don't want to write a TF backend file.
# Specifically, when we're creating the TF backend in the first place.
if not args.no_backend:
write_tf_backend_file(
region=GLOBALS['region'],
bucket=GLOBALS['backend']['bucket_name'],
dynamodb_table=GLOBALS['backend']['dynamodb_table'],
org=org,
repo=repo,
environment=args.environment,
section=args.section,
)
section_values = SECTIONS.get(args.section, {}).get('inputs', {})
tfvars_filename = write_tfvars_file(
GLOBALS=GLOBALS,
# These are the values that all sections must handle
global_values={
"environment": args.environment,
# This will be used by the boilerplate aws.tf file
"region": section_values.get('region', GLOBALS['region']),
},
section_values=section_values,
org=org,
repo=repo,
environment=args.environment,
)
write_awstf_file()
# TODO: Generate the boilerplate aws.tf file with the region variable
# The output subcommand's STDOUT needs to be parseable as JSON.
suppress_verbiage = False
if args.subcmd == 'output':
suppress_verbiage = True
# Always run "terraform init". This is safe.
run_terraform('init',
reconfigure=args.reconfigure,
tfvars_filename=tfvars_filename,
suppress_verbiage=suppress_verbiage,
)
options = []
suppress_input = True
# Force -auto-approve otherwise terraform apply/destroy will error out.
if args.subcmd == 'apply':
options.append('-auto-approve')
elif args.subcmd == 'destroy':
options.append('-auto-approve')
elif args.subcmd == 'output':
# The output subcommand cannot handle the -var-file parameter.
tfvars_filename = None
suppress_input = False
# Always display outputs in JSON
options.append('-json')
# Run the command we were asked to run.
rv = run_terraform(args.subcmd,
options=options,
suppress_input=suppress_input,
tfvars_filename=tfvars_filename,
suppress_verbiage=suppress_verbiage,
)
# TODO: Do something here with rv - it's a CompletedProcess object
# q.v. https://docs.python.org/3/library/subprocess.html#subprocess.CompletedProcess
# TODO: Add a remove_outputs() to be called when destroying
# TODO: Add a read_outputs() to be used when reading
if args.subcmd == 'apply':
save_outputs(
bucket=GLOBALS['backend']['bucket_name'],
org=org,
repo=repo,
environment=args.environment,
section=args.section,
)
cleanup_boilerplate()
# Scripts should be clear when they succeed. A visual statement is helpful.
if not suppress_verbiage:
print("Ok", flush=True)
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] |
cryptopanic-producer/src/utils/fetch_utils.py | bietdoikiem/pipeline-asm-1 | 0 | 69587 | import requests
from requests.models import Response
from asyncio.queues import Queue
import asyncio
async def producer(queue: Queue, arg, callback):
result: Response
try:
result = await callback(arg)
await asyncio.sleep(0.2)
await queue.put(result.json())
except Exception as error:
print("Producer failed (%s)" % error)
return result
async def consumer(queue: Queue, agg_result: list):
result = await queue.get()
queue.task_done()
agg_result.append(result)
async def fetch(url):
try:
r = requests.get(url)
return r
except Exception as error:
print("Request to CryptoPanic failed (%s)" % error)
return None
async def batch_async_fetch(url_list):
agg_result = []
queue = asyncio.Queue()
# fire up both producers and consumers
producers = [
asyncio.create_task(producer(queue, url, fetch)) for url in url_list
]
consumers = [
asyncio.create_task(consumer(queue, agg_result))
for _ in range(len(url_list))
]
# with both producers and consumers running, wait for
# the producers to finish
await asyncio.gather(*producers)
# wait for the remaining tasks to be processed
await queue.join()
# cancel the consumers, which are idle
for c in consumers:
c.cancel()
return agg_result
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mrplot/modules/design/variable_design.py | enzofabricio/mrplot | 0 | 24126 | <reponame>enzofabricio/mrplot
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'variable_design.ui'
#
# Created by: PyQt5 UI code generator 5.11.3
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.setWindowModality(QtCore.Qt.WindowModal)
MainWindow.resize(929, 461)
MainWindow.setMinimumSize(QtCore.QSize(929, 461))
MainWindow.setMaximumSize(QtCore.QSize(929, 461))
MainWindow.setStyleSheet("background-color: rgb(70, 70, 70);")
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.x_variable_lineEdit = QtWidgets.QLineEdit(self.centralwidget)
self.x_variable_lineEdit.setGeometry(QtCore.QRect(10, 40, 651, 51))
font = QtGui.QFont()
font.setFamily("Courier")
font.setPointSize(10)
self.x_variable_lineEdit.setFont(font)
self.x_variable_lineEdit.setStyleSheet("color: rgb(255, 255, 255);\n"
"background-color: rgb(53, 53, 53);\n"
"\n"
"")
self.x_variable_lineEdit.setObjectName("x_variable_lineEdit")
self.newVariableButton = QtWidgets.QPushButton(self.centralwidget)
self.newVariableButton.setGeometry(QtCore.QRect(10, 380, 35, 35))
font = QtGui.QFont()
font.setPointSize(8)
self.newVariableButton.setFont(font)
self.newVariableButton.setStyleSheet("color: rgb(255, 255, 255);")
self.newVariableButton.setText("")
self.newVariableButton.setObjectName("newVariableButton")
self.variablesList_treeWidget = QtWidgets.QTreeWidget(self.centralwidget)
self.variablesList_treeWidget.setGeometry(QtCore.QRect(10, 190, 651, 181))
self.variablesList_treeWidget.setStyleSheet("color: rgb(1, 1, 1);\n"
"background-color: rgb(120, 120, 120);")
self.variablesList_treeWidget.setObjectName("variablesList_treeWidget")
self.variablesList_treeWidget.header().setDefaultSectionSize(120)
self.RemoveVar_Button = QtWidgets.QPushButton(self.centralwidget)
self.RemoveVar_Button.setGeometry(QtCore.QRect(50, 380, 35, 35))
self.RemoveVar_Button.setStyleSheet("color: rgb(255, 255, 255);")
self.RemoveVar_Button.setText("")
self.RemoveVar_Button.setObjectName("RemoveVar_Button")
self.label = QtWidgets.QLabel(self.centralwidget)
self.label.setGeometry(QtCore.QRect(10, 10, 91, 16))
font = QtGui.QFont()
font.setPointSize(8)
self.label.setFont(font)
self.label.setStyleSheet("color: rgb(255, 255, 255);")
self.label.setObjectName("label")
self.x_expr_lineEdit = QtWidgets.QLineEdit(self.centralwidget)
self.x_expr_lineEdit.setGeometry(QtCore.QRect(110, 10, 551, 20))
font = QtGui.QFont()
font.setFamily("Courier")
self.x_expr_lineEdit.setFont(font)
self.x_expr_lineEdit.setStyleSheet("color: rgb(204, 204, 204);")
self.x_expr_lineEdit.setReadOnly(True)
self.x_expr_lineEdit.setObjectName("x_expr_lineEdit")
self.y_variable_lineEdit = QtWidgets.QLineEdit(self.centralwidget)
self.y_variable_lineEdit.setGeometry(QtCore.QRect(10, 130, 651, 51))
font = QtGui.QFont()
font.setFamily("Courier")
font.setPointSize(10)
self.y_variable_lineEdit.setFont(font)
self.y_variable_lineEdit.setStyleSheet("color: rgb(255, 255, 255);\n"
"background-color: rgb(53, 53, 53);\n"
"\n"
"")
self.y_variable_lineEdit.setObjectName("y_variable_lineEdit")
self.label_2 = QtWidgets.QLabel(self.centralwidget)
self.label_2.setGeometry(QtCore.QRect(10, 100, 91, 16))
font = QtGui.QFont()
font.setPointSize(8)
self.label_2.setFont(font)
self.label_2.setStyleSheet("color: rgb(255, 255, 255);")
self.label_2.setObjectName("label_2")
self.y_expr_lineEdit = QtWidgets.QLineEdit(self.centralwidget)
self.y_expr_lineEdit.setGeometry(QtCore.QRect(110, 100, 551, 20))
font = QtGui.QFont()
font.setFamily("Courier")
self.y_expr_lineEdit.setFont(font)
self.y_expr_lineEdit.setStyleSheet("color: rgb(204, 204, 204);")
self.y_expr_lineEdit.setReadOnly(True)
self.y_expr_lineEdit.setObjectName("y_expr_lineEdit")
self.apply_pushButton = QtWidgets.QPushButton(self.centralwidget)
self.apply_pushButton.setGeometry(QtCore.QRect(500, 380, 75, 35))
self.apply_pushButton.setStyleSheet("color: rgb(255, 255, 255);")
self.apply_pushButton.setObjectName("apply_pushButton")
self.cancel_pushButton = QtWidgets.QPushButton(self.centralwidget)
self.cancel_pushButton.setGeometry(QtCore.QRect(580, 380, 75, 35))
self.cancel_pushButton.setStyleSheet("color: rgb(255, 255, 255);")
self.cancel_pushButton.setObjectName("cancel_pushButton")
self.ok_btn = QtWidgets.QPushButton(self.centralwidget)
self.ok_btn.setGeometry(QtCore.QRect(420, 380, 75, 35))
self.ok_btn.setStyleSheet("color: rgb(255, 255, 255);")
self.ok_btn.setObjectName("ok_btn")
self.groupBox = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox.setGeometry(QtCore.QRect(670, 0, 251, 181))
self.groupBox.setStyleSheet("color: rgb(200, 200, 200);")
self.groupBox.setObjectName("groupBox")
self.grab_gbl_btn = QtWidgets.QPushButton(self.groupBox)
self.grab_gbl_btn.setGeometry(QtCore.QRect(90, 150, 75, 23))
self.grab_gbl_btn.setObjectName("grab_gbl_btn")
self.rm_gbl_btn = QtWidgets.QPushButton(self.groupBox)
self.rm_gbl_btn.setGeometry(QtCore.QRect(170, 150, 71, 23))
self.rm_gbl_btn.setObjectName("rm_gbl_btn")
self.global_treeview = QtWidgets.QTreeView(self.groupBox)
self.global_treeview.setGeometry(QtCore.QRect(10, 20, 231, 121))
self.global_treeview.setObjectName("global_treeview")
self.set_gbl_btn = QtWidgets.QPushButton(self.groupBox)
self.set_gbl_btn.setGeometry(QtCore.QRect(10, 150, 75, 23))
self.set_gbl_btn.setToolTip("")
self.set_gbl_btn.setObjectName("set_gbl_btn")
self.groupBox_2 = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_2.setGeometry(QtCore.QRect(670, 190, 251, 221))
self.groupBox_2.setStyleSheet("color: rgb(200, 200, 200);")
self.groupBox_2.setObjectName("groupBox_2")
self.search_alf_le = QtWidgets.QLineEdit(self.groupBox_2)
self.search_alf_le.setGeometry(QtCore.QRect(10, 190, 110, 20))
self.search_alf_le.setStyleSheet("color: rgb(200, 200, 200);")
self.search_alf_le.setObjectName("search_alf_le")
self.search_num_le = QtWidgets.QLineEdit(self.groupBox_2)
self.search_num_le.setGeometry(QtCore.QRect(130, 190, 110, 20))
self.search_num_le.setStyleSheet("color: rgb(200, 200, 200);")
self.search_num_le.setObjectName("search_num_le")
self.num_view = QtWidgets.QListView(self.groupBox_2)
self.num_view.setGeometry(QtCore.QRect(130, 60, 110, 121))
self.num_view.setStyleSheet("color: rgb(150, 150, 150);\n"
"background-color: rgb(60, 60, 60);\n"
"")
self.num_view.setObjectName("num_view")
self.alf_view = QtWidgets.QListView(self.groupBox_2)
self.alf_view.setGeometry(QtCore.QRect(10, 60, 110, 121))
self.alf_view.setStyleSheet("color: rgb(150, 150, 150);\n"
"background-color: rgb(60, 60, 60);\n"
"")
self.alf_view.setObjectName("alf_view")
self.alf_label_2 = QtWidgets.QLabel(self.groupBox_2)
self.alf_label_2.setGeometry(QtCore.QRect(140, 40, 51, 16))
font = QtGui.QFont()
font.setPointSize(8)
self.alf_label_2.setFont(font)
self.alf_label_2.setStyleSheet("color: rgb(255, 255, 255);")
self.alf_label_2.setObjectName("alf_label_2")
self.alf_label = QtWidgets.QLabel(self.groupBox_2)
self.alf_label.setGeometry(QtCore.QRect(10, 40, 51, 16))
font = QtGui.QFont()
font.setPointSize(8)
self.alf_label.setFont(font)
self.alf_label.setStyleSheet("color: rgb(255, 255, 255);")
self.alf_label.setObjectName("alf_label")
self.add2Y_checkBox = QtWidgets.QCheckBox(self.groupBox_2)
self.add2Y_checkBox.setGeometry(QtCore.QRect(90, 20, 61, 17))
self.add2Y_checkBox.setStyleSheet("color: rgb(255, 255, 255);")
self.add2Y_checkBox.setObjectName("add2Y_checkBox")
self.add2XcheckBox = QtWidgets.QCheckBox(self.groupBox_2)
self.add2XcheckBox.setGeometry(QtCore.QRect(10, 20, 70, 17))
self.add2XcheckBox.setStyleSheet("color: rgb(255, 255, 255);")
self.add2XcheckBox.setObjectName("add2XcheckBox")
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 929, 21))
self.menubar.setObjectName("menubar")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "Variables"))
self.variablesList_treeWidget.headerItem().setText(0, _translate("MainWindow", "Variable Name"))
self.variablesList_treeWidget.headerItem().setText(1, _translate("MainWindow", "Label"))
self.variablesList_treeWidget.headerItem().setText(2, _translate("MainWindow", "x"))
self.variablesList_treeWidget.headerItem().setText(3, _translate("MainWindow", "y"))
self.variablesList_treeWidget.headerItem().setText(4, _translate("MainWindow", "Restart"))
self.variablesList_treeWidget.headerItem().setText(5, _translate("MainWindow", "csv"))
self.variablesList_treeWidget.headerItem().setText(6, _translate("MainWindow", "Plot"))
self.label.setText(_translate("MainWindow", "Current xVariable"))
self.label_2.setText(_translate("MainWindow", "Current yVariable"))
self.apply_pushButton.setText(_translate("MainWindow", "Apply"))
self.cancel_pushButton.setText(_translate("MainWindow", "Cancel"))
self.ok_btn.setText(_translate("MainWindow", "Ok"))
self.groupBox.setTitle(_translate("MainWindow", "Global expressions"))
self.grab_gbl_btn.setText(_translate("MainWindow", "grab"))
self.rm_gbl_btn.setText(_translate("MainWindow", "remove"))
self.set_gbl_btn.setText(_translate("MainWindow", "set"))
self.groupBox_2.setTitle(_translate("MainWindow", "Plot_alf | Plot_num"))
self.search_alf_le.setPlaceholderText(_translate("MainWindow", "find plot_alfa"))
self.search_num_le.setPlaceholderText(_translate("MainWindow", "find plot_num"))
self.alf_label_2.setText(_translate("MainWindow", "plot_num"))
self.alf_label.setText(_translate("MainWindow", "plot_alf"))
self.add2Y_checkBox.setText(_translate("MainWindow", "Add to Y"))
self.add2XcheckBox.setText(_translate("MainWindow", "Add to X"))
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
MainWindow = QtWidgets.QMainWindow()
ui = Ui_MainWindow()
ui.setupUi(MainWindow)
MainWindow.show()
sys.exit(app.exec_())
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src/rrt/rrt.py | hjp-muser/rrt-algorithms | 0 | 132241 | from src.rrt.rrt_base import RRTBase
class RRT(RRTBase):
def __init__(self, X, Q, x_init, x_goal, max_samples, r, prc=0.01):
"""
Template RRT planner
:param X: Search Space
:param Q: list of lengths of edges added to tree
:param x_init: tuple, initial location
:param x_goal: tuple, goal location
:param max_samples: max number of samples to take
:param r: resolution of points to sample along edge when checking for collisions
:param prc: probability of checking whether there is a solution
"""
super().__init__(X, Q, x_init, x_goal, max_samples, r, prc)
def rrt_search(self):
"""
Create and return a Rapidly-exploring Random Tree, keeps expanding until can connect to goal
https://en.wikipedia.org/wiki/Rapidly-exploring_random_tree
:return: list representation of path, dict representing edges of tree in form E[child] = parent
"""
self.add_vertex(0, self.x_init)
self.add_edge(0, self.x_init, None)
while True:
for q in self.Q: # iterate over different edge lengths until solution found or time out
for i in range(q[1]): # iterate over number of edges of given length to add
x_new, x_nearest = self.new_and_near(0, q)
if x_new is None:
continue
# connect shortest valid edge
self.connect_to_point(0, x_nearest, x_new)
solution = self.check_solution()
if solution[0]:
return solution
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29961,
29900,
5387,
13,
462,
4706,
736,
1650,
13,
2
] |
Textbook/Chapter 3/statsFunctions2.py | hunterluepke/Learn-Python-for-Stats-and-Econ | 16 | 42574 | #statsFunctions2.py
def total(list_obj):
total = 0
n = len(list_obj)
for i in range(n):
total += list_obj[i]
return total
def mean(list_obj):
n = len(list_obj)
mean = total(list_obj) / n
return mean
list1 = [3, 6, 9, 12, 15]
total_list1 = total(list1)
print(total_list1)
mean_list1 = mean(list1)
print("Mean of list1:", mean_list1)
| [
1,
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613,
2099,
29918,
1761,
29896,
29897,
13,
13,
2
] |
insta/migrations/0003_auto_20200106_0720.py | Josephat-n/instagram | 0 | 186712 | # Generated by Django 2.1 on 2020-01-06 04:20
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('insta', '0002_profile_name'),
]
operations = [
migrations.AlterField(
model_name='image',
name='image_comments',
field=models.TextField(blank=True),
),
migrations.AlterField(
model_name='image',
name='image_likes',
field=models.IntegerField(blank=True),
),
]
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13,
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10353,
13,
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4514,
13,
2
] |
vedro/plugins/slicer/__init__.py | iri6e4k0/vedro | 2 | 172943 | <gh_stars>1-10
from ._slicer import Slicer
__all__ = ("Slicer",)
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1,
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libary/yml_wrapper.py | NekoFanatic/kaiji | 0 | 19221 | from typing import Union
import yaml
class ConfigReader:
def __init__(self):
with open("config.yml", "r") as f:
data = yaml.safe_load(f)
self.data = data
def __getattr__(self, __name: str):
s = __name.split("_")
data = self.data
try:
for i in s:
data = data[i]
return data
except KeyError:
raise Exception("Can't find object")
class TextReader:
def __init__(self):
with open("text.yml", "r") as f:
data = yaml.safe_load(f)
self.data = data
def __getattr__(self, __name: str):
s = __name.split("_")
data = self.data
try:
for i in s:
data = data[i]
return data
except KeyError:
raise Exception("Can't find object")
def find(self, string: str) -> Union[str, list]:
s = string.split("_")
data = self.data
try:
for i in s:
data = data[i]
return data
except KeyError:
raise Exception("Can't find object")
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29915,
29873,
1284,
1203,
1159,
13,
2
] |
bdbc/lib/python3.5/site-packages/bigchaindb_driver/crypto.py | entropyx/fiduchain-blockchain-interface | 0 | 5693 | from collections import namedtuple
from cryptoconditions import crypto
CryptoKeypair = namedtuple('CryptoKeypair', ('signing_key', 'verifying_key'))
def generate_keypair():
"""Generates a cryptographic key pair.
Returns:
:class:`~bigchaindb_driver.crypto.CryptoKeypair`: A
:obj:`collections.namedtuple` with named fields
:attr:`~bigchaindb_driver.crypto.CryptoKeypair.signing_key` and
:attr:`~bigchaindb_driver.crypto.CryptoKeypair.verifying_key`.
"""
return CryptoKeypair(
*(k.decode() for k in crypto.ed25519_generate_key_pair()))
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29929,
29918,
17158,
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1989,
29918,
18784,
22130,
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2
] |
rltf/agents/pg_agent.py | psFournier/rltf | 90 | 188132 | import gym
import numpy as np
from rltf.agents import LoggingAgent
from rltf.memory import PGBuffer
from rltf.monitoring import Monitor
class AgentPG(LoggingAgent):
def __init__(self,
env_maker,
model,
gamma,
lam,
rollout_len,
stop_step,
vf_iters=1,
stack_frames=3,
**agent_kwargs
):
"""
Args:
env_maker: callable. Function that takes the mode of an env and retruns a new environment instance
gamma: float. Discount factor for GAE(gamma, lambda)
lam: float. Lambda value for GAE(gamma, lambda)
rollout_len: int. Number of agent steps before taking a policy gradient step
stop_step: int. Total number of agent steps
vf_iters: int. Number of value function training steps in a single epoch
"""
super().__init__(**agent_kwargs)
assert self.log_period % rollout_len == 0, "Log period must be divisible by rollout length"
if self.eval_len > 0:
assert self.eval_period % rollout_len == 0, "Eval period must be divisible by rollout length"
if self.save_period > np.inf:
assert self.save_period % rollout_len == 0, "Save period must be divisible by rollout length"
self.env_train = Monitor(
env=env_maker('t'),
log_dir=self.model_dir,
mode='t',
log_period=None,
video_spec=self.video_period,
)
self.env_eval = Monitor(
env=env_maker('e'),
log_dir=self.model_dir,
mode='e',
log_period=self.eval_len,
video_spec=self.video_period,
eval_period=self.eval_period,
)
self.rollout_len = rollout_len
self.stop_step = stop_step
self.epochs = self.stop_step // self.rollout_len
self.vf_iters = vf_iters
self.gamma = gamma
self.lam = lam
# Get environment specs
obs_shape, obs_dtype, act_shape, act_dtype, obs_len = self._state_action_spec(stack_frames)
# Initialize the model and the experience buffer
self.model = model(obs_shape=obs_shape, act_space=self.env_train.action_space, **self.model_kwargs)
self.buffer = PGBuffer(self.rollout_len, obs_shape, obs_dtype, act_shape, act_dtype, obs_len)
def _train(self):
# Get the function that generates trajectories
run_policy = self._trajectory_generator(self.rollout_len)
for t in range(self.agent_step+1, self.epochs+1):
if self._terminate:
break
step = t * self.rollout_len
# Collect experience in the environment
run_policy()
# Train the model
self._run_train_step(t)
if step % self.log_period == 0:
self.env_train.monitor.log_stats()
# Stop and run evaluation procedure
if self.eval_len > 0 and step % self.eval_period == 0:
self._eval_agent()
# Update the agent step - corresponds to number of epochs
self.agent_step = step
# Save **after** agent step is correct and completed
if step % self.save_period == 0:
self.save()
def _trajectory_generator(self, horizon):
"""
Args:
horizon: int. Number of steps to run before yielding the trajectories
Returns:
A function which generates trajectories
"""
obs = self.reset()
def run_env():
nonlocal obs
# Clear the buffer to avoid using old data
self.buffer.reset()
for _ in range(horizon):
if self._terminate:
return
# Get an action to run and the value function estimate of this state
action, vf, logp = self._action_train(obs)
# Run action
next_obs, reward, done, info = self.env_train.step(action)
# Store the effect of the action taken upon obs
self.buffer.store(obs, action, reward, done, vf, logp)
# Reset the environment if end of episode
if done:
next_obs = self.reset()
obs = next_obs
# Store the value function for the next state. Needed to compute GAE(lambda)
if not done:
_, next_vf, _ = self._action_train(obs)
else:
next_vf = 0
# Compute GAE(gamma, lambda) and TD(lambda)
self.buffer.compute_estimates(self.gamma, self.lam, next_vf)
return run_env
def _get_feed_dict(self, batch, t):
feed_dict = {
self.model.obs_ph: batch["obs"],
self.model.act_ph: batch["act"],
self.model.adv_ph: batch["adv"],
self.model.ret_ph: batch["ret"],
self.model.old_logp_ph: batch["logp"],
self.model.pi_opt_conf.lr_ph: self.model.pi_opt_conf.lr_value(t),
self.model.vf_opt_conf.lr_ph: self.model.vf_opt_conf.lr_value(t),
}
return feed_dict
def _run_summary_op(self, t, feed_dict):
if t * self.rollout_len % self.log_period == 0:
self.summary = self.sess.run(self.summary_op, feed_dict=feed_dict)
def _run_train_step(self, t):
batch = self.buffer.get_data()
feed_dict = self._get_feed_dict(batch, t)
train_pi = self.model.ops_dict["train_pi"]
train_vf = self.model.ops_dict["train_vf"]
# Run a policy gradient step and a value function training step
self.sess.run([train_pi, train_vf], feed_dict=feed_dict)
# self.sess.run([self.model.train_op], feed_dict=feed_dict)
# Run a policy gradient step
# self.sess.run(train_pi, feed_dict=feed_dict)
# Train the value function additionally
for _ in range(self.vf_iters-1):
if self._terminate:
break
self.sess.run(train_vf, feed_dict=feed_dict)
# Run the summary op to log the changes from the update if necessary
self._run_summary_op(t, feed_dict)
def _action_train(self, state):
data = self.model.action_train_ops(self.sess, state)
action = data["action"][0]
vf = data["vf"][0]
logp = data["logp"][0]
return action, vf, logp
def _action_eval(self, state):
data = self.model.action_eval_ops(self.sess, state)
action = data["action"][0]
return action
def _save_allowed(self):
# Prevent saving if the process was terminated - state is most likely inconsistent
return not self._terminate
def _state_action_spec(self, stack_frames):
assert isinstance(self.env_train.observation_space, gym.spaces.Box)
# Get environment specs
act_shape = list(self.env_train.action_space.shape)
obs_shape = list(self.env_train.observation_space.shape)
# Get obs_shape and obs_dtype
if len(obs_shape) == 3:
assert stack_frames > 1
obs_dtype = np.uint8
obs_len = stack_frames
else:
obs_dtype = np.float32
obs_len = 1
# Get act_shape and act_dtype
if isinstance(self.env_train.action_space, gym.spaces.Box):
act_shape = list(self.env_train.action_space.shape)
act_dtype = np.float32
elif isinstance(self.env_train.action_space, gym.spaces.Discrete):
act_shape = []
act_dtype = np.uint8
else:
raise ValueError("Unsupported action space")
return obs_shape, obs_dtype, act_shape, act_dtype, obs_len
def _reset(self):
pass
def _append_log_spec(self):
return [ ( "pi_learn_rate", "f", self.model.pi_opt_conf.lr_value), ]
| [
1,
1053,
330,
962,
13,
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13,
13,
13,
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376,
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1631,
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3670,
29918,
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29889,
29212,
29918,
1767,
511,
4514,
13,
2
] |
pyportable_installer/__main__.py | wuye999/pyportable-installer | 20 | 101022 | from lk_utils import relpath
from rich_click import Path
from ._vendor.rich_click_ext import click
@click.grp(help='PyPortable Installer command line interface.')
def cli():
pass
@click.cmd()
@click.arg('directory', default='.', type=Path())
def init(directory='.'):
"""
Initialize project with template config file.
args:
directory:
Create a [cyan]"pyproject.json"[/] under this directory.
If directory not exists, will create it.
If directory parameter is not given, will use [magenta]current
directory[/].
"""
import os
from shutil import copyfile
file_i = relpath('./template/pyproject.json')
file_o = f'{directory}/pyproject.json'
if not os.path.exists(directory):
os.mkdir(directory)
elif os.path.exists(file_o):
os.remove(file_o)
copyfile(file_i, file_o)
@click.cmd()
@click.arg('pyproject-file', default='./pyproject.json', type=Path(exists=True))
def build(pyproject_file='./pyproject.json'):
"""
Start building application from pyproject config file.
args:
pyproject-file:
Choose a pyproject config file. Accepts ".json", ".yaml", ".toml"
formats.
If parameter is not given, will use [magenta]"./pyproject.json"[/]
as default.
"""
from .main import full_build
full_build(pyproject_file)
@click.cmd()
def gui():
"""
Launch PyPortable Installer GUI. [dim](experimental feature)[/]
"""
from .user_interface import gui_on_psg
gui_on_psg.main()
if __name__ == '__main__':
cli()
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613,
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13,
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29918,
4282,
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13,
13,
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29992,
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9006,
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13,
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997,
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2799,
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518,
6229,
850,
735,
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4682,
9601,
29914,
29962,
13,
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9995,
13,
1678,
515,
869,
1792,
29918,
13248,
1053,
1410,
29875,
29918,
265,
29918,
567,
29887,
13,
1678,
1410,
29875,
29918,
265,
29918,
567,
29887,
29889,
3396,
580,
13,
13,
13,
361,
4770,
978,
1649,
1275,
525,
1649,
3396,
1649,
2396,
13,
1678,
9335,
580,
13,
2
] |
main.py | CaioMar/dsutils | 1 | 168200 | """
Author: <NAME>
Since: 2019-12
"""
import json
import querybuilder as qb
import importlib
importlib.reload(qb)
with open('./config.json', 'rb') as file:
CONFIG = json.load(fp=file)
SAFRAS = ['201901']
QUERY_DICT = CONFIG["123456"]
print(qb.ConfigQueryReader(SAFRAS, QUERY_DICT)\
.build())
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13,
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13,
13,
2
] |
tests/test_misc.py | untom/scikits.cuda | 0 | 1607981 | <gh_stars>0
#!/usr/bin/env python
"""
Unit tests for scikits.cuda.misc
"""
from unittest import main, TestCase, TestSuite
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import numpy as np
from numpy.testing import assert_raises
import skcuda.misc as misc
class test_misc(TestCase):
def setUp(self):
np.random.seed(0)
misc.init()
def test_maxabs_float32(self):
x = np.array([-1, 2, -3], np.float32)
x_gpu = gpuarray.to_gpu(x)
m_gpu = misc.maxabs(x_gpu)
assert np.allclose(m_gpu.get(), np.max(np.abs(x)))
def test_maxabs_float64(self):
x = np.array([-1, 2, -3], np.float64)
x_gpu = gpuarray.to_gpu(x)
m_gpu = misc.maxabs(x_gpu)
assert np.allclose(m_gpu.get(), np.max(np.abs(x)))
def test_maxabs_complex64(self):
x = np.array([-1j, 2, -3j], np.complex64)
x_gpu = gpuarray.to_gpu(x)
m_gpu = misc.maxabs(x_gpu)
assert np.allclose(m_gpu.get(), np.max(np.abs(x)))
def test_maxabs_complex128(self):
x = np.array([-1j, 2, -3j], np.complex128)
x_gpu = gpuarray.to_gpu(x)
m_gpu = misc.maxabs(x_gpu)
assert np.allclose(m_gpu.get(), np.max(np.abs(x)))
def test_cumsum_float32(self):
x = np.array([1, 4, 3, 2, 8], np.float32)
x_gpu = gpuarray.to_gpu(x)
c_gpu = misc.cumsum(x_gpu)
assert np.allclose(c_gpu.get(), np.cumsum(x))
def test_cumsum_float64(self):
x = np.array([1, 4, 3, 2, 8], np.float64)
x_gpu = gpuarray.to_gpu(x)
c_gpu = misc.cumsum(x_gpu)
assert np.allclose(c_gpu.get(), np.cumsum(x))
def test_cumsum_complex64(self):
x = np.array([1, 4j, 3, 2j, 8], np.complex64)
x_gpu = gpuarray.to_gpu(x)
c_gpu = misc.cumsum(x_gpu)
assert np.allclose(c_gpu.get(), np.cumsum(x))
def test_cumsum_complex128(self):
x = np.array([1, 4j, 3, 2j, 8], np.complex128)
x_gpu = gpuarray.to_gpu(x)
c_gpu = misc.cumsum(x_gpu)
assert np.allclose(c_gpu.get(), np.cumsum(x))
def test_diff_float32(self):
x = np.array([1.3, 2.7, 4.9, 5.1], np.float32)
x_gpu = gpuarray.to_gpu(x)
y_gpu = misc.diff(x_gpu)
assert np.allclose(y_gpu.get(), np.diff(x))
def test_diff_float64(self):
x = np.array([1.3, 2.7, 4.9, 5.1], np.float64)
x_gpu = gpuarray.to_gpu(x)
y_gpu = misc.diff(x_gpu)
assert np.allclose(y_gpu.get(), np.diff(x))
def test_diff_complex64(self):
x = np.array([1.3+2.0j, 2.7-3.9j, 4.9+1.0j, 5.1-9.0j], np.complex64)
x_gpu = gpuarray.to_gpu(x)
y_gpu = misc.diff(x_gpu)
assert np.allclose(y_gpu.get(), np.diff(x))
def test_diff_complex128(self):
x = np.array([1.3+2.0j, 2.7-3.9j, 4.9+1.0j, 5.1-9.0j], np.complex128)
x_gpu = gpuarray.to_gpu(x)
y_gpu = misc.diff(x_gpu)
assert np.allclose(y_gpu.get(), np.diff(x))
def test_get_by_index_float32(self):
src = np.random.rand(5).astype(np.float32)
src_gpu = gpuarray.to_gpu(src)
ind = gpuarray.to_gpu(np.array([0, 2, 4]))
res_gpu = misc.get_by_index(src_gpu, ind)
assert np.allclose(res_gpu.get(), src[[0, 2, 4]])
ind = gpuarray.to_gpu(np.array([], np.int64))
res_gpu = misc.get_by_index(src_gpu, ind)
assert len(res_gpu) == 0
def test_get_by_index_float64(self):
src = np.random.rand(5).astype(np.float64)
src_gpu = gpuarray.to_gpu(src)
ind = gpuarray.to_gpu(np.array([0, 2, 4]))
res_gpu = misc.get_by_index(src_gpu, ind)
assert np.allclose(res_gpu.get(), src[[0, 2, 4]])
ind = gpuarray.to_gpu(np.array([], np.int64))
res_gpu = misc.get_by_index(src_gpu, ind)
assert len(res_gpu) == 0
def test_set_by_index_dest_float32(self):
dest_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.float32))
ind = gpuarray.to_gpu(np.array([0, 2, 4]))
src_gpu = gpuarray.to_gpu(np.array([1, 1, 1], dtype=np.float32))
misc.set_by_index(dest_gpu, ind, src_gpu, 'dest')
assert np.allclose(dest_gpu.get(),
np.array([1, 1, 1, 3, 1], dtype=np.float32))
dest_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.float32))
ind = gpuarray.to_gpu(np.array([], np.int64))
src_gpu = gpuarray.to_gpu(np.array([1, 1, 1], dtype=np.float32))
misc.set_by_index(dest_gpu, ind, src_gpu, 'dest')
assert np.allclose(dest_gpu.get(),
np.arange(5, dtype=np.float32))
def test_set_by_index_dest_float64(self):
dest_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.double))
ind = gpuarray.to_gpu(np.array([0, 2, 4]))
src_gpu = gpuarray.to_gpu(np.array([1, 1, 1], dtype=np.double))
misc.set_by_index(dest_gpu, ind, src_gpu, 'dest')
assert np.allclose(dest_gpu.get(),
np.array([1, 1, 1, 3, 1], dtype=np.double))
dest_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.double))
ind = gpuarray.to_gpu(np.array([], np.int64))
src_gpu = gpuarray.to_gpu(np.array([1, 1, 1], dtype=np.double))
misc.set_by_index(dest_gpu, ind, src_gpu, 'dest')
assert np.allclose(dest_gpu.get(),
np.arange(5, dtype=np.double))
def test_set_by_index_src_float32(self):
dest_gpu = gpuarray.to_gpu(np.zeros(3, dtype=np.float32))
ind = gpuarray.to_gpu(np.array([0, 2, 4]))
src_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.float32))
misc.set_by_index(dest_gpu, ind, src_gpu, 'src')
assert np.allclose(dest_gpu.get(),
np.array([0, 2, 4], dtype=np.float32))
dest_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.float32))
ind = gpuarray.to_gpu(np.array([], np.int64))
src_gpu = gpuarray.to_gpu(np.array([1, 1, 1], dtype=np.float32))
misc.set_by_index(dest_gpu, ind, src_gpu, 'src')
assert np.allclose(dest_gpu.get(),
np.arange(5, dtype=np.float32))
def test_set_by_index_src_float64(self):
dest_gpu = gpuarray.to_gpu(np.zeros(3, dtype=np.double))
ind = gpuarray.to_gpu(np.array([0, 2, 4]))
src_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.double))
misc.set_by_index(dest_gpu, ind, src_gpu, 'src')
assert np.allclose(dest_gpu.get(),
np.array([0, 2, 4], dtype=np.double))
dest_gpu = gpuarray.to_gpu(np.arange(5, dtype=np.double))
ind = gpuarray.to_gpu(np.array([], np.int64))
src_gpu = gpuarray.to_gpu(np.array([1, 1, 1], dtype=np.double))
misc.set_by_index(dest_gpu, ind, src_gpu, 'src')
assert np.allclose(dest_gpu.get(),
np.arange(5, dtype=np.double))
def impl_test_binaryop_matvec(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5)).astype(dtype)
a = np.random.normal(scale=5.0, size=(1, 5)).astype(dtype)
b = np.random.normal(scale=5.0, size=(3, 1)).astype(dtype)
# the following two test correct broadcasting on 0D vectors
c = np.random.normal(scale=5.0, size=(5, )).astype(dtype)
d = np.random.normal(scale=5.0, size=(3, )).astype(dtype)
x_gpu = gpuarray.to_gpu(x)
a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
c_gpu = gpuarray.to_gpu(c)
d_gpu = gpuarray.to_gpu(d)
out = gpuarray.empty(x.shape, dtype=dtype)
# addition
res = misc.add_matvec(x_gpu, a_gpu, out=out).get()
assert np.allclose(res, x+a)
assert np.allclose(misc.add_matvec(x_gpu, b_gpu).get(), x+b)
assert np.allclose(misc.add_matvec(x_gpu, c_gpu).get(), x+c)
assert_raises(ValueError, misc.add_matvec, x_gpu, d_gpu)
# multiplication
res = misc.mult_matvec(x_gpu, a_gpu, out=out).get()
assert np.allclose(res, x*a)
assert np.allclose(misc.mult_matvec(x_gpu, b_gpu).get(), x*b)
assert np.allclose(misc.mult_matvec(x_gpu, c_gpu).get(), x*c)
assert_raises(ValueError, misc.mult_matvec, x_gpu, d_gpu)
# division
res = misc.div_matvec(x_gpu, a_gpu, out=out).get()
assert np.allclose(res, x/a)
assert np.allclose(misc.div_matvec(x_gpu, b_gpu).get(), x/b)
assert np.allclose(misc.div_matvec(x_gpu, c_gpu).get(), x/c)
assert_raises(ValueError, misc.div_matvec, x_gpu, d_gpu)
def test_binaryop_matvec_float32(self):
self.impl_test_binaryop_matvec(np.float32)
def test_binaryop_matvec_float64(self):
self.impl_test_binaryop_matvec(np.float64)
def test_binaryop_matvec_complex64(self):
self.impl_test_binaryop_matvec(np.complex64)
def test_binaryop_matvec_complex128(self):
self.impl_test_binaryop_matvec(np.complex128)
def impl_test_sum(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5))
x = x.astype(dtype=dtype, order='C')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.sum(x_gpu).get(), x.sum())
assert np.allclose(misc.sum(x_gpu, axis=0).get(), x.sum(axis=0))
assert np.allclose(misc.sum(x_gpu, axis=1).get(), x.sum(axis=1))
x = x.astype(dtype=dtype, order='F')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.sum(x_gpu).get(), x.sum())
assert np.allclose(misc.sum(x_gpu, axis=0).get(), x.sum(axis=0))
assert np.allclose(misc.sum(x_gpu, axis=1).get(), x.sum(axis=1))
def test_sum_float32(self):
self.impl_test_sum(np.float32)
def test_sum_float64(self):
self.impl_test_sum(np.float64)
def test_sum_complex64(self):
self.impl_test_sum(np.complex64)
def test_sum_complex128(self):
self.impl_test_sum(np.complex128)
def impl_test_mean(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5))
x = x.astype(dtype=dtype, order='C')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.mean(x_gpu).get(), x.mean())
assert np.allclose(misc.mean(x_gpu, axis=0).get(), x.mean(axis=0))
assert np.allclose(misc.mean(x_gpu, axis=1).get(), x.mean(axis=1))
x = x.astype(dtype=dtype, order='F')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.mean(x_gpu).get(), x.mean())
assert np.allclose(misc.mean(x_gpu, axis=-1).get(), x.mean(axis=-1))
assert np.allclose(misc.mean(x_gpu, axis=-2).get(), x.mean(axis=-2))
def test_mean_float32(self):
self.impl_test_mean(np.float32)
def test_mean_float64(self):
self.impl_test_mean(np.float64)
def test_mean_complex64(self):
self.impl_test_mean(np.complex64)
def test_mean_complex128(self):
self.impl_test_mean(np.complex128)
def impl_test_var(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5))
x = x.astype(dtype=dtype, order='C')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.var(x_gpu).get(), x.var())
assert np.allclose(misc.var(x_gpu, axis=0).get(), x.var(axis=0))
assert np.allclose(misc.var(x_gpu, axis=1).get(), x.var(axis=1))
# Currently not working due to a bug in PyCUDA, see Issue #92
#x = x.astype(dtype=dtype, order='F')
#x_gpu = gpuarray.to_gpu(x)
#assert np.allclose(misc.var(x_gpu).get(), x.var())
#assert np.allclose(misc.var(x_gpu, axis=-1).get(), x.var(axis=-1))
#assert np.allclose(misc.var(x_gpu, axis=-2).get(), x.var(axis=-2))
def test_var_float32(self):
self.impl_test_var(np.float32)
def test_var_float64(self):
self.impl_test_var(np.float64)
def test_var_complex64(self):
self.impl_test_var(np.complex64)
def test_var_complex128(self):
self.impl_test_var(np.complex128)
def impl_test_std(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5))
x = x.astype(dtype=dtype, order='C')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.std(x_gpu).get(), x.std())
assert np.allclose(misc.std(x_gpu, axis=0).get(), x.std(axis=0))
assert np.allclose(misc.std(x_gpu, axis=1).get(), x.std(axis=1))
# Currently not working due to a bug in PyCUDA, see Issue #92
#x = x.astype(dtype=dtype, order='F')
#x_gpu = gpuarray.to_gpu(x)
#assert np.allclose(misc.std(x_gpu).get(), x.std())
#assert np.allclose(misc.std(x_gpu, axis=-1).get(), x.std(axis=-1))
#assert np.allclose(misc.std(x_gpu, axis=-2).get(), x.std(axis=-2))
def test_std_float32(self):
self.impl_test_std(np.float32)
def test_std_float64(self):
self.impl_test_std(np.float64)
def test_std_complex64(self):
self.impl_test_std(np.complex64)
def test_std_complex128(self):
self.impl_test_std(np.complex128)
def _impl_test_minmax(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5))
x = x.astype(dtype=dtype, order='C')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.max(x_gpu, axis=0).get(), x.max(axis=0))
assert np.allclose(misc.max(x_gpu, axis=1).get(), x.max(axis=1))
assert np.allclose(misc.min(x_gpu, axis=0).get(), x.min(axis=0))
assert np.allclose(misc.min(x_gpu, axis=1).get(), x.min(axis=1))
x = x.astype(dtype=dtype, order='F')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.max(x_gpu, axis=0).get(), x.max(axis=0))
assert np.allclose(misc.max(x_gpu, axis=1).get(), x.max(axis=1))
assert np.allclose(misc.min(x_gpu, axis=0).get(), x.min(axis=0))
assert np.allclose(misc.min(x_gpu, axis=1).get(), x.min(axis=1))
def test_minmax_float32(self):
self._impl_test_minmax(np.float32)
def test_minmax_float64(self):
self._impl_test_minmax(np.float64)
def _impl_test_argminmax(self, dtype):
x = np.random.normal(scale=5.0, size=(3, 5))
x = x.astype(dtype=dtype, order='C')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.argmax(x_gpu, axis=0).get(), x.argmax(axis=0))
assert np.allclose(misc.argmax(x_gpu, axis=1).get(), x.argmax(axis=1))
assert np.allclose(misc.argmin(x_gpu, axis=0).get(), x.argmin(axis=0))
assert np.allclose(misc.argmin(x_gpu, axis=1).get(), x.argmin(axis=1))
x = x.astype(dtype=dtype, order='F')
x_gpu = gpuarray.to_gpu(x)
assert np.allclose(misc.argmax(x_gpu, axis=0).get(), x.argmax(axis=0))
assert np.allclose(misc.argmax(x_gpu, axis=1).get(), x.argmax(axis=1))
assert np.allclose(misc.argmin(x_gpu, axis=0).get(), x.argmin(axis=0))
assert np.allclose(misc.argmin(x_gpu, axis=1).get(), x.argmin(axis=1))
def test_argminmax_float32(self):
self._impl_test_argminmax(np.float32)
def test_argminmax_float64(self):
self._impl_test_argminmax(np.float64)
def suite():
s = TestSuite()
s.addTest(test_misc('test_maxabs_float32'))
s.addTest(test_misc('test_maxabs_complex64'))
s.addTest(test_misc('test_cumsum_float32'))
s.addTest(test_misc('test_cumsum_complex64'))
s.addTest(test_misc('test_diff_float32'))
s.addTest(test_misc('test_diff_complex64'))
s.addTest(test_misc('test_get_by_index_float32'))
s.addTest(test_misc('test_set_by_index_dest_float32'))
s.addTest(test_misc('test_set_by_index_src_float32'))
s.addTest(test_misc('test_binaryop_matvec_float32'))
s.addTest(test_misc('test_binaryop_matvec_complex64'))
s.addTest(test_misc('test_sum_float32'))
s.addTest(test_misc('test_sum_complex64'))
s.addTest(test_misc('test_mean_float32'))
s.addTest(test_misc('test_mean_complex64'))
s.addTest(test_misc('test_var_float32'))
s.addTest(test_misc('test_var_complex64'))
s.addTest(test_misc('test_std_float32'))
s.addTest(test_misc('test_std_complex64'))
s.addTest(test_misc('test_minmax_float32'))
s.addTest(test_misc('test_argminmax_float32'))
if misc.get_compute_capability(pycuda.autoinit.device) >= 1.3:
s.addTest(test_misc('test_maxabs_float64'))
s.addTest(test_misc('test_maxabs_complex128'))
s.addTest(test_misc('test_cumsum_float64'))
s.addTest(test_misc('test_cumsum_complex128'))
s.addTest(test_misc('test_diff_float64'))
s.addTest(test_misc('test_diff_complex128'))
s.addTest(test_misc('test_get_by_index_float32'))
s.addTest(test_misc('test_set_by_index_dest_float64'))
s.addTest(test_misc('test_set_by_index_src_float64'))
s.addTest(test_misc('test_sum_float64'))
s.addTest(test_misc('test_sum_complex128'))
s.addTest(test_misc('test_mean_float64'))
s.addTest(test_misc('test_mean_complex128'))
s.addTest(test_misc('test_binaryop_matvec_float64'))
s.addTest(test_misc('test_binaryop_matvec_complex128'))
s.addTest(test_misc('test_var_float64'))
s.addTest(test_misc('test_var_complex128'))
s.addTest(test_misc('test_std_float64'))
s.addTest(test_misc('test_std_complex128'))
s.addTest(test_misc('test_minmax_float64'))
s.addTest(test_misc('test_argminmax_float64'))
return s
if __name__ == '__main__':
main(defaultTest = 'suite')
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2003,
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2003,
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2003,
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2003,
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2003,
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4713,
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2922,
2003,
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396,
8542,
13,
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4563,
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2922,
2003,
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263,
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2003,
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4563,
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2922,
2003,
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2003,
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270,
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13,
13,
13,
1678,
822,
1243,
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19541,
459,
29918,
2922,
2003,
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7411,
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1311,
1125,
13,
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1583,
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13699,
29918,
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2003,
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19541,
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29918,
2922,
2003,
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7411,
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1311,
1125,
13,
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13699,
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1688,
29918,
19541,
459,
29918,
2922,
2003,
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29889,
7411,
29953,
29946,
29897,
13,
13,
1678,
822,
1243,
29918,
19541,
459,
29918,
2922,
2003,
29918,
19676,
29953,
29946,
29898,
1311,
1125,
13,
4706,
1583,
29889,
13699,
29918,
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test/integration/ggrc/proposal/api/test_property.py | j0gurt/ggrc-core | 1 | 196711 | <gh_stars>1-10
# Copyright (C) 2018 Google Inc.
# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>
"""TestCase for proposal property api."""
from ggrc.models import all_models
from integration.ggrc.models import factories
from integration.ggrc.proposal.api import base
class TestPropoertyProposals(base.BaseTestProposalApi):
"""Test case for proposal property api."""
def test_simple_get_proposal(self):
"""Test simple get proposal."""
with factories.single_commit():
control = factories.ControlFactory()
proposal = factories.ProposalFactory(instance=control,
content={"field": "a"},
agenda="agenda content")
instance_dict = {"id": control.id, "type": control.type}
resp = self.api.get(all_models.Proposal, proposal.id)
self.assert200(resp)
self.assertIn("proposal", resp.json)
data = resp.json["proposal"]
self.assertIn("content", data)
self.assertIn("instance", data)
self.assertIn("agenda", data)
self.assertDictEqual(instance_dict, data["instance"])
self.assertDictEqual({"field": "a"}, data["content"])
self.assertEqual("agenda content", data["agenda"])
def test_simple_create_proposal(self):
"""Test simple create proposal."""
new_title = "2"
control = factories.ControlFactory(title="1")
control_id = control.id
control.title = new_title
self.assertEqual(0, len(control.comments))
self.create_proposal(control,
full_instance_content=control.log_json(),
agenda="update title from 1 to 2",
context=None)
control = all_models.Control.query.get(control_id)
self.assertEqual(1, len(control.proposals))
self.assertIn("fields", control.proposals[0].content)
self.assertEqual({"title": "2"}, control.proposals[0].content["fields"])
self.assertEqual(1, len(control.comments))
def test_simple_apply_status(self):
"""Test simple apply status."""
with factories.single_commit():
control = factories.ControlFactory(title="1")
proposal = factories.ProposalFactory(
instance=control,
content={"fields": {"title": "2"}},
agenda="agenda content")
control_id = control.id
proposal_id = proposal.id
self.assertEqual(proposal.STATES.PROPOSED, proposal.status)
self.assertEqual(0, len(control.comments))
with self.number_obj_revisions_for(control):
self.apply_proposal(proposal, apply_reason="approved")
control = all_models.Control.query.get(control_id)
proposal = all_models.Proposal.query.get(proposal_id)
self.assertEqual(proposal.STATES.APPLIED, proposal.status)
self.assertEqual("2", control.title)
self.assertEqual("2", self.latest_revision_for(control).content['title'])
self.assertEqual(1, len(control.comments))
def test_simple_decline_status(self):
"""Test simple decline status."""
with factories.single_commit():
control = factories.ControlFactory(title="1")
proposal = factories.ProposalFactory(
instance=control,
content={"fields": {"title": "2"}},
agenda="agenda content")
control_id = control.id
proposal_id = proposal.id
self.assertEqual(proposal.STATES.PROPOSED, proposal.status)
self.assertEqual(0, len(control.comments))
with self.number_obj_revisions_for(control, increase_on=0):
self.decline_proposal(proposal, decline_reason="declined bla")
control = all_models.Control.query.get(control_id)
proposal = all_models.Proposal.query.get(proposal_id)
self.assertEqual(proposal.STATES.DECLINED, proposal.status)
self.assertEqual("1", control.title)
self.assertEqual(1, len(control.comments))
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] |
tests/test_patch.py | ElMehdi19/flask-mux | 1 | 100899 | from flask.testing import FlaskClient
import pytest
from flask import Flask
from flask_mux import Mux
from testing import test_router
from testing.test_cases.middlewares import test_mws_router
@pytest.fixture
def client():
app = Flask(__name__)
mux = Mux(app)
mux.use('/', test_mws_router)
return app.test_client()
def test_basic(client: FlaskClient):
return test_router.test_basic(client, 'patch')
def test_one_mw(client: FlaskClient):
return test_router.test_one_mw(client, 'patch')
def test_one_mw_failing(client: FlaskClient):
return test_router.test_one_mw_failing(client, 'patch')
def test_multi_mws(client: FlaskClient):
return test_router.test_multi_mws(client, 'patch')
def test_multi_mws_failing(client: FlaskClient):
return test_router.test_multi_mws_failing(client, 'patch')
def test_extra_mws(client: FlaskClient):
return test_router.test_extra_mws(client, 'patch')
def test_extra_mws_failing_1(client: FlaskClient):
return test_router.test_extra_mws_failing_1(client, 'patch')
def test_extra_mws_failing_2(client: FlaskClient):
return test_router.test_extra_mws_failing_2(client, 'patch')
def test_extra_mws_failing_3(client: FlaskClient):
return test_router.test_extra_mws_failing_3(client, 'patch')
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modular_exp.py | a-l-r1/cryptography | 0 | 1609294 | <reponame>a-l-r1/cryptography
def square_modular_exp(x:int, power:int, modulus:int) -> int:
if (modulus <= 0):
raise ValueError("modular_pow: modulus must be " + \
"positive: (%d ** %d) %% %d" % (x, power, modulus))
result = 1
bit_list = []
while (power != 0):
bit_list.insert(0, power % 2)
power //= 2
for i in bit_list:
result = (result * result) % modulus
if (i == 1):
result = (result * x) % modulus
return result
modular_exp = square_modular_exp
def generic_modular_exp(x:int, power:int, modulus:int) -> int:
if (modulus <= 0):
raise ValueError("modular_pow: modulus must be " + \
"positive: (%d ** %d) %% %d" % (x, power, modulus))
result = 1
for i in range(power):
result = (result * x) % modulus
return result
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lib/desconst.py | elp2/des | 20 | 67565 |
# Applied once at the beginning of the algorithm.
INITIAL_PERMUTATION = [
58, 50, 42, 34, 26, 18, 10, 2,
60, 52, 44, 36, 28, 20, 12, 4,
62, 54, 46, 38, 30, 22, 14, 6,
64, 56, 48, 40, 32, 24, 16, 8,
57, 49, 41, 33, 25, 17, 9, 1,
59, 51, 43, 35, 27, 19, 11, 3,
61, 53, 45, 37, 29, 21, 13, 5,
63, 55, 47, 39, 31, 23, 15, 7,
]
# Inverse of INITIAL_PERMUTATION. Applied once at the end of the algorithm.
FINAL_PERMUTATION = [
40, 8, 48, 16, 56, 24, 64, 32,
39, 7, 47, 15, 55, 23, 63, 31,
38, 6, 46, 14, 54, 22, 62, 30,
37, 5, 45, 13, 53, 21, 61, 29,
36, 4, 44, 12, 52, 20, 60, 28,
35, 3, 43, 11, 51, 19, 59, 27,
34, 2, 42, 10, 50, 18, 58, 26,
33, 1, 41, 9, 49, 17, 57, 25,
]
# Applied to the half-block at the beginning of the Fiestel function.
EXPANSION = [
32, 1, 2, 3, 4, 5,
4, 5, 6, 7, 8, 9,
8, 9, 10, 11, 12, 13,
12, 13, 14, 15, 16, 17,
16, 17, 18, 19, 20, 21,
20, 21, 22, 23, 24, 25,
24, 25, 26, 27, 28, 29,
28, 29, 30, 31, 32, 1,
]
# Applied at the end of the Feistel function.
PERMUTATION = [
16, 7, 20, 21, 29, 12, 28, 17,
1, 15, 23, 26, 5, 18, 31, 10,
2, 8, 24, 14, 32, 27, 3, 9,
19, 13, 30, 6, 22, 11, 4, 25,
]
# Converts from full 64-bit key to two key halves: left and right. Only 48
# bits from the original key are used.
PERMUTED_CHOICE_1_LEFT = [
57, 49, 41, 33, 25, 17, 9,
1, 58, 50, 42, 34, 26, 18,
10, 2, 59, 51, 43, 35, 27,
19, 11, 3, 60, 52, 44, 36,
]
PERMUTED_CHOICE_1_RIGHT = [
63, 55, 47, 39, 31, 23, 15,
7, 62, 54, 46, 38, 30, 22,
14, 6, 61, 53, 45, 37, 29,
21, 13, 5, 28, 20, 12, 4,
]
# Converts the shifted right and left key halves (concatenated together) into
# the subkey for the round (input into Feistel function).
PERMUTED_CHOICE_2 = [
14, 17, 11, 24, 1, 5, 3, 28,
15, 6, 21, 10, 23, 19, 12, 4,
26, 8, 16, 7, 27, 20, 13, 2,
41, 52, 31, 37, 47, 55, 30, 40,
51, 45, 33, 48, 44, 49, 39, 56,
34, 53, 46, 42, 50, 36, 29, 32,
]
# S-Boxes
# SBOX[outer 2 bits][inner 4 bits]
# Each value represents 4 bits that the 6-bit input is mapped to.
SBOX_1 = [
[14, 4, 13, 1, 2, 15, 11, 8, 3, 10, 6, 12, 5, 9, 0, 7],
[0, 15, 7, 4, 14, 2, 13, 1, 10, 6, 12, 11, 9, 5, 3, 8],
[4, 1, 14, 8, 13, 6, 2, 11, 15, 12, 9, 7, 3, 10, 5, 0],
[15, 12, 8, 2, 4, 9, 1, 7, 5, 11, 3, 14, 10, 0, 6, 13],
]
SBOX_2 = [
[15, 1, 8, 14, 6, 11, 3, 4, 9, 7, 2, 13, 12, 0, 5, 10],
[3, 13, 4, 7, 15, 2, 8, 14, 12, 0, 1, 10, 6, 9, 11, 5],
[0, 14, 7, 11, 10, 4, 13, 1, 5, 8, 12, 6, 9, 3, 2, 15],
[13, 8, 10, 1, 3, 15, 4, 2, 11, 6, 7, 12, 0, 5, 14, 9],
]
SBOX_3 = [
[10, 0, 9, 14, 6, 3, 15, 5, 1, 13, 12, 7, 11, 4, 2, 8],
[13, 7, 0, 9, 3, 4, 6, 10, 2, 8, 5, 14, 12, 11, 15, 1],
[13, 6, 4, 9, 8, 15, 3, 0, 11, 1, 2, 12, 5, 10, 14, 7],
[1, 10, 13, 0, 6, 9, 8, 7, 4, 15, 14, 3, 11, 5, 2, 12],
]
SBOX_4 = [
[7, 13, 14, 3, 0, 6, 9, 10, 1, 2, 8, 5, 11, 12, 4, 15],
[13, 8, 11, 5, 6, 15, 0, 3, 4, 7, 2, 12, 1, 10, 14, 9],
[10, 6, 9, 0, 12, 11, 7, 13, 15, 1, 3, 14, 5, 2, 8, 4],
[3, 15, 0, 6, 10, 1, 13, 8, 9, 4, 5, 11, 12, 7, 2, 14],
]
SBOX_5 = [
[2, 12, 4, 1, 7, 10, 11, 6, 8, 5, 3, 15, 13, 0, 14, 9],
[14, 11, 2, 12, 4, 7, 13, 1, 5, 0, 15, 10, 3, 9, 8, 6],
[4, 2, 1, 11, 10, 13, 7, 8, 15, 9, 12, 5, 6, 3, 0, 14],
[11, 8, 12, 7, 1, 14, 2, 13, 6, 15, 0, 9, 10, 4, 5, 3],
]
SBOX_6 = [
[12, 1, 10, 15, 9, 2, 6, 8, 0, 13, 3, 4, 14, 7, 5, 11],
[10, 15, 4, 2, 7, 12, 9, 5, 6, 1, 13, 14, 0, 11, 3, 8],
[9, 14, 15, 5, 2, 8, 12, 3, 7, 0, 4, 10, 1, 13, 11, 6],
[4, 3, 2, 12, 9, 5, 15, 10, 11, 14, 1, 7, 6, 0, 8, 13],
]
SBOX_7 = [
[4, 11, 2, 14, 15, 0, 8, 13, 3, 12, 9, 7, 5, 10, 6, 1],
[13, 0, 11, 7, 4, 9, 1, 10, 14, 3, 5, 12, 2, 15, 8, 6],
[1, 4, 11, 13, 12, 3, 7, 14, 10, 15, 6, 8, 0, 5, 9, 2],
[6, 11, 13, 8, 1, 4, 10, 7, 9, 5, 0, 15, 14, 2, 3, 12],
]
SBOX_8 = [
[13, 2, 8, 4, 6, 15, 11, 1, 10, 9, 3, 14, 5, 0, 12, 7],
[1, 15, 13, 8, 10, 3, 7, 4, 12, 5, 6, 11, 0, 14, 9, 2],
[7, 11, 4, 1, 9, 12, 14, 2, 0, 6, 10, 13, 15, 3, 5, 8],
[2, 1, 14, 7, 4, 10, 8, 13, 15, 12, 9, 0, 3, 5, 6, 11],
]
SBOXES = [SBOX_1, SBOX_2, SBOX_3, SBOX_4, SBOX_5, SBOX_6, SBOX_7, SBOX_8]
# How much the left and right key halves are shifted every round.
KEY_SHIFT_AMOUNTS = [1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1]
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] |
src/tests/assembly/structured_config/assembled_config/test_assembled_config.py | fabio-d/fuchsia-stardock | 5 | 29848 | <gh_stars>1-10
# Copyright 2022 The Fuchsia Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import argparse
import pathlib
import subprocess
import sys
from run_assembly import run_product_assembly
def main():
parser = argparse.ArgumentParser(
description=
"Ensure that ffx assembly product adds structured config with the right ffx config."
)
parser.add_argument(
"--ffx-bin",
type=pathlib.Path,
required=True,
help="Path to ffx binary.")
parser.add_argument(
"--product-assembly-config",
type=pathlib.Path,
required=True,
help="Path to product assembly configuration input.")
parser.add_argument(
"--input-bundles-dir",
type=pathlib.Path,
required=True,
help="Path to input bundles directory.")
parser.add_argument(
"--outdir",
type=pathlib.Path,
required=True,
help="Path to output directory.")
parser.add_argument(
"--stamp",
type=pathlib.Path,
required=True,
help="Path to stampfile for telling ninja we're done.")
args = parser.parse_args()
output = run_product_assembly(
ffx_bin=args.ffx_bin,
product=args.product_assembly_config,
input_bundles=args.input_bundles_dir,
outdir=args.outdir,
extra_config=["assembly_example_enabled=true"])
output.check_returncode()
with open(args.stamp, 'w') as f:
pass # creates the file
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] |
wflow-water-index/wflow-water-index.py | Fondus/Fondus-Python-SDK | 0 | 26305 | <filename>wflow-water-index/wflow-water-index.py
import argparse
import datetime
import math
from netCDF4 import Dataset
import numpy as np
import threading
import queue
# Worker 類別，負責處理資料
class Worker(threading.Thread):
def __init__(self, req, res, num):
threading.Thread.__init__(self)
self.req = req
self.res = res
self.num = num
self.running = True
def run(self):
while self.running or not self.req.empty():
try:
data = self.req.get(timeout=1)
x, y, cx, cy, array = data
res.put((x, y, np.max(array), np.count_nonzero(array > 0.01)))
req.task_done()
# print("Worker %d: %f,%f" % (self.num, x, y))
except queue.Empty:
print("Worker %d: Nothing to do, wait 1 second" % (self.num))
def stop(self):
self.running = False
def get_range(v, low, high):
dim = v.get_dims()[0].size
d = (v[1]-v[0])/2
i0 = None
i1 = dim
for i in range(0, dim):
if i0 is None and v[i]+d >= low:
i0 = i
if v[i]-d <= high:
i1 = i
if not i0:
i0 = 0
return [i0, i1]
def get_skip_index(v_time):
dim = v_time.get_dims()[0].size
n0 = v_time[0]
for i in range(1, dim):
if (v_time[i] - n0) >= 1440:
return i
return 0
parser = argparse.ArgumentParser()
parser.add_argument("--input", help="source NC filename")
parser.add_argument("--output", help="output XYM ascii filename")
parser.add_argument(
"--bbox", help="bounding box to filter grids, format as x1,y1,x2,y2")
parser.add_argument(
"--skip", help="skip data of first N steps when model is warming up, default = 24 hour")
args = parser.parse_args()
input_filename = args.input if args.input else "wflow.nc"
print("Input file:", input_filename)
output_filename = args.output if args.output else "flow.asc"
print("Output file:", output_filename)
bbox = list(map(lambda s: float(s), args.bbox.split(','))
) if args.bbox else [-180.0, -90.0, 180, 90.0]
print("Bounding box:", bbox)
wflow = Dataset(input_filename, "r")
print("NETCDF metadata")
# print(wflow.dimensions)
# print(wflow.variables)
v_time = wflow.variables['time']
# print(v_time[:])
skip = args.skip if args.skip else get_skip_index(v_time)
print("Will skip first", skip, "steps")
v_lon = wflow.variables['x']
v_lat = wflow.variables['y']
dx = (v_lon[1]-v_lon[0])/2
dy = (v_lat[1]-v_lat[0])/2
lon_range = get_range(v_lon, bbox[0], bbox[2])
lat_range = get_range(v_lat, bbox[1], bbox[3])
v_data = wflow.variables['flow_simulated']
print("Loading data...")
# cache all data in memory
time = v_time[:]
lon = v_lon[:]
lat = v_lat[:]
data = v_data[:, lat_range[0]:lat_range[1], lon_range[0]:lon_range[1]]
print("Start processing...")
dim_t = wflow.dimensions['time'].size
tt = dim_t - skip
max_v = np.empty([lon.size, lat.size])
count_0_01_v = np.empty([lon.size, lat.size])
progress = dict(processed=0, queued=0, total_grid=(
lon_range[1]-lon_range[0])*(lat_range[1]-lat_range[0]))
# prepare for multithread
req = queue.Queue()
res = queue.Queue()
workers = [Worker(req, res, 1), Worker(req, res, 2)]
for i in range(0, len(workers)):
workers[i].start()
def processing_res():
while workers[0].is_alive() or workers[1].is_alive() or not res.empty():
try:
result = res.get(timeout=1)
(x, y, m, c) = result
max_v[x, y] = m
count_0_01_v[x, y] = 1.0 * c / tt
progress["processed"] += 1
res.task_done()
except queue.Empty:
print("Response worker: Nothing to do, wait 1 second")
if (progress["processed"] % 1000 == 0):
print("processed:", progress)
print("processed:", progress)
threading.Thread(target=processing_res, daemon=True).start()
for x in range(lon_range[0], lon_range[1]):
cx = lon[x]
print(cx, "queued:", progress)
for y in range(lat_range[0], lat_range[1]):
cy = lat[y]
id = 'x'+f'{cx:.4f}'+'y'+f'{cy:.4f}'
vt = np.array(data[skip:dim_t, y, x])
req.put((x, y, cx, cy, vt))
progress["queued"] += 1
# max_v[x,y]=np.max(vt)
# count_0_1_v=np.count_nonzero(vt>0.01)
wflow.close()
for i in range(0, len(workers)):
workers[i].stop()
for i in range(0, len(workers)):
workers[i].join()
res.join()
print("grid with flow value: ",np.count_nonzero(max_v > 0))
print("count with flow value: ",np.count_nonzero(count_0_01_v > 0))
with open(output_filename, 'w') as out_file:
for x in range(lon_range[0], lon_range[1]):
cx = lon[x]
for y in range(lat_range[0], lat_range[1]):
cy = lat[y]
m = max_v[x, y]
c = count_0_01_v[x, y]
if m > 0 or c > 0:
out_file.write("%f,%f,%f,%f\n" % (cx, cy, m, c))
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parser_app/api/serializers.py | prantoamt/django-parser-app | 3 | 99187 | from rest_framework import serializers
from rest_framework import serializers
from rest_framework.fields import SkipField
from rest_framework.fields import ChoiceField
import six
from parser_app.models import RegisteredModel
class ChoiceDisplayField(ChoiceField):
def __init__(self, *args, **kwargs):
super(ChoiceDisplayField, self).__init__(*args, **kwargs)
self.choice_strings_to_display = {
six.text_type(key): value for key, value in self.choices.items()
}
def to_representation(self, value):
if value is None:
return value
return {
'value': self.choice_strings_to_values.get(six.text_type(value), value),
'name': self.choice_strings_to_display.get(six.text_type(value), value),
}
class DefaultModelSerializer(serializers.ModelSerializer):
serializer_choice_field = ChoiceDisplayField
class RegisteredModelSerializer(DefaultModelSerializer):
class Meta:
model = RegisteredModel
fields = '__all__'
class MappingErrorSerializer(serializers.Serializer):
detail = serializers.CharField(max_length=256, required=False)
model_columns = serializers.ListField(child=serializers.CharField())
file_columns = serializers.ListField(child=serializers.CharField())
required_fields = serializers.ListField(child=serializers.CharField())
foreign_key_fields = serializers.ListField(child=serializers.CharField())
class ValidationErrorSerializer(serializers.Serializer):
detail = serializers.ListField(child=serializers.JSONField()) | [
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glymur/lib/openjpeg.py | jarrelscy/glymur | 0 | 197350 | """Wraps library calls to openjpeg.
"""
import ctypes
import sys
from ..config import glymur_config
_, OPENJPEG = glymur_config()
# Maximum number of tile parts expected by JPWL: increase at your will
JPWL_MAX_NO_TILESPECS = 16
J2K_MAXRLVLS = 33 # Number of maximum resolution level authorized
PATH_LEN = 4096 # maximum allowed size for filenames
def version():
"""Wrapper for opj_version library routine."""
OPENJPEG.opj_version.restype = ctypes.c_char_p
library_version = OPENJPEG.opj_version()
if sys.hexversion >= 0x03000000:
return library_version.decode('utf-8')
else:
return library_version
# Need to get the minor version, make sure we are at least at 1.4.x
if OPENJPEG is not None:
_MINOR = version().split('.')[1]
else:
# Does not really matter. But version should not be called if there is no
# OpenJPEG library found.
_MINOR = 0
class EventMgrType(ctypes.Structure):
"""Message handler object.
Corresponds to event_mgr_t type in openjpeg headers.
"""
_fields_ = [("error_handler", ctypes.c_void_p),
("warning_handler", ctypes.c_void_p),
("info_handler", ctypes.c_void_p)]
class CommonStructType(ctypes.Structure):
"""Common fields between JPEG 2000 compression and decompression contextx.
"""
_fields_ = [("event_mgr", ctypes.POINTER(EventMgrType)),
("client_data", ctypes.c_void_p),
("is_decompressor", ctypes.c_bool),
("codec_format", ctypes.c_int),
("j2k_handle", ctypes.c_void_p),
("jp2_handle", ctypes.c_void_p),
("mj2_handle", ctypes.c_void_p)]
STREAM_READ = 0x0001 # The stream was opened for reading.
STREAM_WRITE = 0x0002 # The stream was opened for writing.
class CioType(ctypes.Structure):
"""Byte input-output stream (CIO)
Corresponds to cio_t in openjpeg headers.
"""
_fields_ = [("cinfo", ctypes.POINTER(CommonStructType)), # codec context
# STREAM_READ or STREAM_WRITE
("openmode", ctypes.c_int),
# pointer to start of buffer
("buffer", ctypes.POINTER(ctypes.c_char)),
# buffer size in bytes
("length", ctypes.c_int),
# pointer to start of stream
("start", ctypes.c_char_p),
# pointer to end of stream
("end", ctypes.c_char_p),
# pointer to current position
("bp", ctypes.c_char_p)]
class CompressionInfoType(CommonStructType):
"""Common fields between JPEG-2000 compression and decompression contexts.
This is for compression contexts. Corresponds to common_struct_t.
"""
pass
class PocType(ctypes.Structure):
"""Progression order changes."""
_fields_ = [("resno", ctypes.c_int),
# Resolution num start, Component num start, given by POC
("compno0", ctypes.c_int),
# Layer num end,Resolution num end, Component num end, given
# by POC
("layno1", ctypes.c_int),
("resno1", ctypes.c_int),
("compno1", ctypes.c_int),
# Layer num start,Precinct num start, Precinct num end
("layno0", ctypes.c_int),
("precno0", ctypes.c_int),
("precno1", ctypes.c_int),
# Progression order enum
# OPJ_PROG_ORDER prg1,prg;
("prg1", ctypes.c_int),
("prg", ctypes.c_int),
# Progression order string
# char progorder[5];
("progorder", ctypes.c_char * 5),
# Tile number
# int tile;
("tile", ctypes.c_int),
("tx0", ctypes.c_int),
("tx1", ctypes.c_int),
("ty0", ctypes.c_int),
("ty1", ctypes.c_int),
("layS", ctypes.c_int),
("resS", ctypes.c_int),
("compS", ctypes.c_int),
("prcS", ctypes.c_int),
("layE", ctypes.c_int),
("resE", ctypes.c_int),
("compE", ctypes.c_int),
("prcE", ctypes.c_int),
("txS", ctypes.c_int),
("txE", ctypes.c_int),
("tyS", ctypes.c_int),
("tyE", ctypes.c_int),
("dx", ctypes.c_int),
("dy", ctypes.c_int),
("lay_t", ctypes.c_int),
("res_t", ctypes.c_int),
("comp_t", ctypes.c_int),
("prc_t", ctypes.c_int),
("tx0_t", ctypes.c_int),
("ty0_t", ctypes.c_int)]
class CompressionParametersType(ctypes.Structure):
"""Compression parameters.
Corresponds to cparameters_t type in openjp2 headers.
"""
_fields_ = [
# size of tile:
# tile_size_on = false (not in argument) or
# = true (in argument)
("tile_size_on", ctypes.c_int),
# XTOsiz, YTOsiz
("cp_tx0", ctypes.c_int),
("cp_ty0", ctypes.c_int),
# XTsiz, YTsiz
("cp_tdx", ctypes.c_int),
("cp_tdy", ctypes.c_int),
# allocation by rate/distortion
("cp_disto_alloc", ctypes.c_int),
# allocation by fixed layer
("cp_fixed_alloc", ctypes.c_int),
# add fixed_quality
("cp_fixed_quality", ctypes.c_int),
# fixed layer
("cp_matrice", ctypes.c_void_p),
# comment for coding
("cp_comment", ctypes.c_char_p),
# csty : coding style
("csty", ctypes.c_int),
# progression order (default OPJ_LRCP)
("prog_order", ctypes.c_int),
# progression order changes
("poc", PocType * 32),
# number of progression order changes (POC), default to 0
("numpocs", ctypes.c_uint),
# number of layers
("tcp_numlayers", ctypes.c_int),
# rates of layers
("tcp_rates", ctypes.c_float * 100),
# different psnr for successive layers
("tcp_distoratio", ctypes.c_float * 100),
# number of resolutions
("numresolution", ctypes.c_int),
# initial code block width, default to 64
("cblockw_init", ctypes.c_int),
# initial code block height, default to 64
("cblockh_init", ctypes.c_int),
# mode switch (cblk_style)
("mode", ctypes.c_int),
# 1 : use the irreversible DWT 9-7
# 0 : use lossless compression (default)
("irreversible", ctypes.c_int),
# region of interest: affected component in [0..3], -1 means no ROI
("roi_compno", ctypes.c_int),
# region of interest: upshift value
("roi_shift", ctypes.c_int),
# number of precinct size specifications
("res_spec", ctypes.c_int),
# initial precinct width
("prcw_init", ctypes.c_int * J2K_MAXRLVLS),
# initial precinct height
("prch_init", ctypes.c_int * J2K_MAXRLVLS),
# input file name
("infile", ctypes.c_char * PATH_LEN),
# output file name
("outfile", ctypes.c_char * PATH_LEN),
# DEPRECATED.
("index_on", ctypes.c_int),
# DEPRECATED.
("index", ctypes.c_char * PATH_LEN),
# subimage encoding: origin image offset in x direction
# subimage encoding: origin image offset in y direction
("image_offset_x0", ctypes.c_int),
("image_offset_y0", ctypes.c_int),
# subsampling value for dx
# subsampling value for dy
("subsampling_dx", ctypes.c_int),
("subsampling_dy", ctypes.c_int),
# input file format 0: PGX, 1: PxM, 2: BMP 3:TIF
# output file format 0: J2K, 1: JP2, 2: JPT
("decod_format", ctypes.c_int),
("cod_format", ctypes.c_int),
# JPWL encoding parameters
# enables writing of EPC in MH, thus activating JPWL
("jpwl_epc_on", ctypes.c_int),
# error protection method for MH (0,1,16,32,37-128)
("jpwl_hprot_mh", ctypes.c_int),
# tile number of header protection specification (>=0)
("jpwl_hprot_tph_tileno", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# error protection methods for TPHs (0,1,16,32,37-128)
("jpwl_hprot_tph", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# tile number of packet protection specification (>=0)
("jpwl_pprot_tileno", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# packet number of packet protection specification (>=0)
("jpwl_pprot_packno", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# error protection methods for packets (0,1,16,32,37-128)
("jpwl_pprot", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# enables writing of ESD, (0=no/1/2 bytes)
("jpwl_sens_size", ctypes.c_int),
# sensitivity addressing size (0=auto/2/4 bytes)
("jpwl_sens_addr", ctypes.c_int),
# sensitivity range (0-3)
("jpwl_sens_range", ctypes.c_int),
# sensitivity method for MH (-1=no,0-7)
("jpwl_sens_mh", ctypes.c_int),
# tile number of sensitivity specification (>=0)
("jpwl_sens_tph_tileno", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# sensitivity methods for TPHs (-1=no,0-7)
("jpwl_sens_tph", ctypes.c_int * JPWL_MAX_NO_TILESPECS),
# Digital Cinema compliance 0-not compliant, 1-compliant
("cp_cinema", ctypes.c_int),
# Maximum rate for each component.
# If == 0, component size limitation is not considered
("max_comp_size", ctypes.c_int),
# Profile name
("cp_rsiz", ctypes.c_int),
# Tile part generation
("tp_on", ctypes.c_uint8),
# Flag for Tile part generation
("tp_flag", ctypes.c_uint8),
# MCT (multiple component transform)
("tcp_mct", ctypes.c_uint8),
# Enable JPIP indexing
("jpip_on", ctypes.c_int)]
class DecompressionInfoType(ctypes.Structure):
"""This is for decompression contexts.
Corresponds to dinfo_t type in openjpeg headers.
"""
pass
class DecompressionParametersType(ctypes.Structure):
"""Decompression parameters.
Corresponds to dparameters_t type in openjpeg headers.
"""
# cp_reduce: the number of highest resolution levels to be discarded
_fields_ = [("cp_reduce", ctypes.c_int),
# cp_layer: the maximum number of quality layers to decode
("cp_layer", ctypes.c_int),
# infile: input file name
("infile", ctypes.c_char * PATH_LEN),
# outfile: output file name
("outfile", ctypes.c_char * PATH_LEN),
# decod_format: input file format 0: J2K, 1: JP2, 2: JPT
("decod_format", ctypes.c_int),
# cod_format: output file format 0: PGX, 1: PxM, 2: BMP
("cod_format", ctypes.c_int),
# jpwl_correct: activates the JPWL correction capabilities
("jpwl_correct", ctypes.c_bool),
# jpwl_exp_comps: expected number of components
("jpwl_exp_comps", ctypes.c_int),
# jpwl_max_tiles: maximum number of tiles
("jpwl_max_tiles", ctypes.c_int),
# cp_limit_decoding: whether decoding should be done on the
# entire codestream or be limited to the main header
("cp_limit_decoding", ctypes.c_int)]
if _MINOR == '5':
_fields_.append(("flags", ctypes.c_uint))
class ImageComptParmType(ctypes.Structure):
"""Component parameters structure used by the opj_image_create function.
"""
_fields_ = [("dx", ctypes.c_int),
# XRsiz: horizontal separation of a sample of ith component
# with respect to the reference grid
# YRsiz: vertical separation of a sample of ith component with
# respect to the reference grid */
("dy", ctypes.c_int),
# data width, height
("w", ctypes.c_int),
("h", ctypes.c_int),
# x component offset compared to the whole image
# y component offset compared to the whole image
("x0", ctypes.c_int),
("y0", ctypes.c_int),
# precision
('prec', ctypes.c_int),
# image depth in bits
('bpp', ctypes.c_int),
# signed (1) / unsigned (0)
('sgnd', ctypes.c_int)]
class ImageCompType(ctypes.Structure):
"""Defines a single image component. """
_fields_ = [("dx", ctypes.c_int),
("dy", ctypes.c_int),
("w", ctypes.c_int),
("h", ctypes.c_int),
("x0", ctypes.c_int),
("y0", ctypes.c_int),
("prec", ctypes.c_int),
("bpp", ctypes.c_int),
("sgnd", ctypes.c_int),
("resno_decoded", ctypes.c_int),
("factor", ctypes.c_int),
("data", ctypes.POINTER(ctypes.c_int))]
class ImageType(ctypes.Structure):
"""Defines image data and characteristics.
Corresponds to image_t type in openjpeg headers.
"""
_fields_ = [("x0", ctypes.c_int),
("y0", ctypes.c_int),
("x1", ctypes.c_int),
("y1", ctypes.c_int),
("numcomps", ctypes.c_int),
("color_space", ctypes.c_int),
("comps", ctypes.POINTER(ImageCompType)),
("icc_profile_buf", ctypes.c_char_p),
("icc_profile_len", ctypes.c_int)]
def cio_open(cinfo, src=None):
"""Wrapper for openjpeg library function opj_cio_open."""
argtypes = [ctypes.POINTER(CommonStructType), ctypes.c_char_p,
ctypes.c_int]
OPENJPEG.opj_cio_open.argtypes = argtypes
OPENJPEG.opj_cio_open.restype = ctypes.POINTER(CioType)
if src is None:
length = 0
else:
length = len(src)
cio = OPENJPEG.opj_cio_open(ctypes.cast(cinfo,
ctypes.POINTER(CommonStructType)),
src,
length)
return cio
def cio_close(cio):
"""Wraps openjpeg library function cio_close.
"""
OPENJPEG.opj_cio_close.argtypes = [ctypes.POINTER(CioType)]
OPENJPEG.opj_cio_close(cio)
def cio_tell(cio):
"""Get position in byte stream."""
OPENJPEG.cio_tell.argtypes = [ctypes.POINTER(CioType)]
OPENJPEG.cio_tell.restype = ctypes.c_int
pos = OPENJPEG.cio_tell(cio)
return pos
def create_compress(fmt):
"""Wrapper for openjpeg library function opj_create_compress.
Creates a J2K/JPT/JP2 compression structure.
"""
OPENJPEG.opj_create_compress.argtypes = [ctypes.c_int]
OPENJPEG.opj_create_compress.restype = ctypes.POINTER(CompressionInfoType)
cinfo = OPENJPEG.opj_create_compress(fmt)
return cinfo
def create_decompress(fmt):
"""Wraps openjpeg library function opj_create_decompress.
"""
OPENJPEG.opj_create_decompress.argtypes = [ctypes.c_int]
restype = ctypes.POINTER(DecompressionInfoType)
OPENJPEG.opj_create_decompress.restype = restype
dinfo = OPENJPEG.opj_create_decompress(fmt)
return dinfo
def decode(dinfo, cio):
"""Wrapper for opj_decode.
"""
argtypes = [ctypes.POINTER(DecompressionInfoType), ctypes.POINTER(CioType)]
OPENJPEG.opj_decode.argtypes = argtypes
OPENJPEG.opj_decode.restype = ctypes.POINTER(ImageType)
image = OPENJPEG.opj_decode(dinfo, cio)
return image
def destroy_compress(cinfo):
"""Wrapper for openjpeg library function opj_destroy_compress.
Release resources for a compressor handle.
"""
argtypes = [ctypes.POINTER(CompressionInfoType)]
OPENJPEG.opj_destroy_compress.argtypes = argtypes
OPENJPEG.opj_destroy_compress(cinfo)
def encode(cinfo, cio, image):
"""Wrapper for openjpeg library function opj_encode.
Encodes an image into a JPEG-2000 codestream.
Parameters
----------
cinfo : compression handle
cio : output buffer stream
image : image to encode
"""
argtypes = [ctypes.POINTER(CompressionInfoType),
ctypes.POINTER(CioType),
ctypes.POINTER(ImageType)]
OPENJPEG.opj_encode.argtypes = argtypes
OPENJPEG.opj_encode.restype = ctypes.c_int
status = OPENJPEG.opj_encode(cinfo, cio, image)
return status
def destroy_decompress(dinfo):
"""Wraps openjpeg library function opj_destroy_decompress."""
argtypes = [ctypes.POINTER(DecompressionInfoType)]
OPENJPEG.opj_destroy_decompress.argtypes = argtypes
OPENJPEG.opj_destroy_decompress(dinfo)
def image_create(cmptparms, cspace):
"""Wrapper for openjpeg library function opj_image_create.
"""
lst = [ctypes.c_int, ctypes.POINTER(ImageComptParmType), ctypes.c_int]
OPENJPEG.opj_image_create.argtypes = lst
OPENJPEG.opj_image_create.restype = ctypes.POINTER(ImageType)
image = OPENJPEG.opj_image_create(len(cmptparms), cmptparms, cspace)
return(image)
def image_destroy(image):
"""Wraps openjpeg library function opj_image_destroy."""
OPENJPEG.opj_image_destroy.argtypes = [ctypes.POINTER(ImageType)]
OPENJPEG.opj_image_destroy(image)
def set_default_encoder_parameters():
"""Wrapper for openjpeg library function opj_set_default_encoder_parameters.
"""
cparams = CompressionParametersType()
argtypes = [ctypes.POINTER(CompressionParametersType)]
OPENJPEG.opj_set_default_encoder_parameters.argtypes = argtypes
OPENJPEG.opj_set_default_encoder_parameters(ctypes.byref(cparams))
return cparams
def set_default_decoder_parameters(dparams_p):
"""Wrapper for opj_set_default_decoder_parameters.
"""
argtypes = [ctypes.POINTER(DecompressionParametersType)]
OPENJPEG.opj_set_default_decoder_parameters.argtypes = argtypes
OPENJPEG.opj_set_default_decoder_parameters(dparams_p)
def set_event_mgr(dinfo, event_mgr, context=None):
"""Wrapper for openjpeg library function opj_set_event_mgr.
"""
argtypes = [ctypes.POINTER(CommonStructType),
ctypes.POINTER(EventMgrType),
ctypes.c_void_p]
OPENJPEG.opj_set_event_mgr.argtypes = argtypes
OPENJPEG.opj_set_event_mgr(ctypes.cast(dinfo,
ctypes.POINTER(CommonStructType)),
event_mgr, context)
def setup_encoder(cinfo, cparameters, image):
"""Wrapper for openjpeg library function opj_setup_decoder."""
argtypes = [ctypes.POINTER(CompressionInfoType),
ctypes.POINTER(CompressionParametersType),
ctypes.POINTER(ImageType)]
OPENJPEG.opj_setup_encoder.argtypes = argtypes
OPENJPEG.opj_setup_encoder(cinfo, cparameters, image)
def setup_decoder(dinfo, dparams):
"""Wrapper for openjpeg library function opj_setup_decoder."""
argtypes = [ctypes.POINTER(DecompressionInfoType),
ctypes.POINTER(DecompressionParametersType)]
OPENJPEG.opj_setup_decoder.argtypes = argtypes
OPENJPEG.opj_setup_decoder(dinfo, dparams)
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env.py | Reeche/metacognitive_learning_part23 | 0 | 81790 | import numpy as np
from heuristics import lex, equalweight
from utils import select_p_based_on_experiment
class environment():
def __init__(self, x1, y1, x2, y2, p, experiment):
"""
The environment is initiated with different parameters of x1, x2, y1, y2 and p.
:param x1: uniform[-10, 10]
:param y1: uniform[-10, 10]
:param x2: uniform[-10, 10]
:param y2: uniform[-10, 10]
:param p: uniform[0.5, 1]
:param experiment: which experiment is currently run, 1 denotes part II.a,
2 denotes part II.b, 3 denotes part II.c, 4 denotes the part III
"""
# gamble 1
self.x1 = x1
self.y1 = y1
# gamble 2
self.x2 = x2
self.y2 = y2
self.p = p
self.experiment = experiment
def step(self, strategy: int):
"""
The environment receives the selected strategy/heuristic and outputs the selected outcome (x1, y1, x2 or y2).
It also selects the next p.
:param strategy: the selected heuristic/strategy (LEX or EQW) by the RL agent
:return: the selected outcome (x1, y1, x2, y2), the execution time of the selected strategy and next p
"""
# creates a dictionary combining the payoffs and their corresponding probabilities
probability_dict = {"x1": self.p,
"y1": 1 - self.p,
"x2": self.p,
"y2": 1 - self.p}
# creates a dictionary combining the payoffs and their corresponding values
values_dict = {"x1": self.x1,
"y1": self.y1,
"x2": self.x2,
"y2": self.y2}
if strategy == 0: #LEX
outcome, execution_time = lex(probability_dict, values_dict)
else: #EQW
outcome, execution_time = equalweight(values_dict)
# get p
next_p = select_p_based_on_experiment(self.experiment)
return outcome, execution_time, next_p
def reward(self, selected_outcome: str, execution_time: float):
"""
The selected outcome determines whether gamble 1 or gamble 2 is played
(if x1 or y1 is chosen -> gamble 1; if x2 or y2 is chosen -> gamble 2).
Based on the gamble, the reward is calculated based on the uniformly generated values and the probabilities.
:param selected_outcome: the selected outcome (x1, y1, x2, y2)
:param execution_time: the execution time of the used strategy
:return:
"""
if selected_outcome.endswith("1"): #selected gamble 1
reward = np.random.choice([float(self.x1), float(self.y1)], 1, p=[self.p, 1 - self.p])
else: #selected gamble 2
reward = np.random.choice([float(self.x2), float(self.y2)], 1, p=[self.p, 1 - self.p])
return reward - execution_time
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venv/lib/python3.6/site-packages/ansible_collections/dellemc/openmanage/tests/unit/plugins/modules/test_dellemc_configure_idrac_eventing.py | usegalaxy-no/usegalaxy | 1 | 172225 | # -*- coding: utf-8 -*-
#
# Dell EMC OpenManage Ansible Modules
# Version 3.0.0
# Copyright (C) 2020-2021 Dell Inc. or its subsidiaries. All Rights Reserved.
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
#
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import pytest
from ansible_collections.dellemc.openmanage.plugins.modules import dellemc_configure_idrac_eventing
from ansible_collections.dellemc.openmanage.tests.unit.plugins.modules.common import FakeAnsibleModule, Constants
from ansible_collections.dellemc.openmanage.tests.unit.compat.mock import MagicMock, patch, Mock, PropertyMock
from pytest import importorskip
importorskip("omsdk.sdkfile")
importorskip("omsdk.sdkcreds")
class TestConfigureEventing(FakeAnsibleModule):
module = dellemc_configure_idrac_eventing
@pytest.fixture
def idrac_configure_eventing_mock(self, mocker):
omsdk_mock = MagicMock()
idrac_obj = MagicMock()
omsdk_mock.file_share_manager = idrac_obj
omsdk_mock.config_mgr = idrac_obj
type(idrac_obj).create_share_obj = Mock(return_value="Status")
type(idrac_obj).set_liason_share = Mock(return_value="Status")
return idrac_obj
@pytest.fixture
def idrac_file_manager_config_eventing_mock(self, mocker):
try:
file_manager_obj = mocker.patch(
'ansible_collections.dellemc.openmanage.plugins.modules.dellemc_configure_idrac_eventing.file_share_manager')
except AttributeError:
file_manager_obj = MagicMock()
obj = MagicMock()
file_manager_obj.create_share_obj.return_value = obj
return file_manager_obj
@pytest.fixture
def is_changes_applicable_eventing_mock(self, mocker):
try:
changes_applicable_obj = mocker.patch(
'ansible_collections.dellemc.openmanage.plugins.modules.dellemc_configure_idrac_eventing.config_mgr')
except AttributeError:
changes_applicable_obj = MagicMock()
obj = MagicMock()
changes_applicable_obj.is_change_applicable.return_value = obj
return changes_applicable_obj
@pytest.fixture
def idrac_connection_configure_eventing_mock(self, mocker, idrac_configure_eventing_mock):
idrac_conn_class_mock = mocker.patch('ansible_collections.dellemc.openmanage.plugins.modules.'
'dellemc_configure_idrac_eventing.iDRACConnection',
return_value=idrac_configure_eventing_mock)
idrac_conn_class_mock.return_value.__enter__.return_value = idrac_configure_eventing_mock
return idrac_configure_eventing_mock
def test_main_configure_eventing_success_case01(self, idrac_connection_configure_eventing_mock, idrac_default_args,
mocker, idrac_file_manager_config_eventing_mock):
idrac_default_args.update({"share_name": "sharename", 'share_password': None, "destination_number": 1,
"destination": "1.1.1.1", 'share_mnt': None, 'share_user': None})
message = {'msg': 'Successfully configured the idrac eventing settings.',
'eventing_status': {"Id": "JID_12345123456", "JobState": "Completed"},
'changed': True}
mocker.patch('ansible_collections.dellemc.openmanage.plugins.modules.'
'dellemc_configure_idrac_eventing.run_idrac_eventing_config', return_value=message)
result = self._run_module(idrac_default_args)
assert result["msg"] == "Successfully configured the iDRAC eventing settings."
def test_run_idrac_eventing_config_success_case01(self, idrac_connection_configure_eventing_mock,
idrac_file_manager_config_eventing_mock, idrac_default_args,
is_changes_applicable_eventing_mock):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1, "destination": "1.1.1.1",
"snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4,
"email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test",
"enable_alerts": "Enabled", "authentication": "Enabled",
"smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname",
"password": "<PASSWORD>"})
message = {"changes_applicable": True, "message": "Changes found to commit!"}
idrac_connection_configure_eventing_mock.config_mgr.is_change_applicable.return_value = message
f_module = self.get_module_mock(params=idrac_default_args, check_mode=True)
with pytest.raises(Exception) as ex:
self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert "Changes found to commit!" == ex.value.args[0]
def test_run_idrac_eventing_config_success_case02(self, idrac_connection_configure_eventing_mock,
idrac_file_manager_config_eventing_mock, idrac_default_args):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1, "destination": "1.1.1.1",
"snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4,
"email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test",
"enable_alerts": "Enabled", "authentication": "Enabled",
"smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname",
"password": "<PASSWORD>"})
message = {"changes_applicable": True, "message": "changes found to commit!", "changed": True,
"Status": "Success"}
idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
f_module.check_mode = False
result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert result['message'] == 'changes found to commit!'
def test_run_idrac_eventing_config_success_case03(self, idrac_connection_configure_eventing_mock,
idrac_file_manager_config_eventing_mock, idrac_default_args):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1,
"destination": "1.1.1.1", "snmp_v3_username": "snmpuser",
"snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled",
"address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled",
"authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443,
"username": "uname", "password": "<PASSWORD>"})
message = {"changes_applicable": False, "Message": "No changes found to commit!", "changed": False,
"Status": "Success"}
idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
f_module.check_mode = False
result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert result["Message"] == 'No changes found to commit!'
def test_run_idrac_eventing_config_success_case04(self, idrac_connection_configure_eventing_mock,
idrac_default_args, idrac_file_manager_config_eventing_mock):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1, "destination": "1.1.1.1",
"snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4,
"email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test",
"enable_alerts": "Enabled", "authentication": "Enabled",
"smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname",
"password": "<PASSWORD>"})
message = {"changes_applicable": False, "Message": "No changes were applied", "changed": False,
"Status": "Success"}
idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
f_module.check_mode = False
result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert result['Message'] == 'No changes were applied'
def test_run_idrac_eventing_config_success_case05(self, idrac_connection_configure_eventing_mock,
idrac_file_manager_config_eventing_mock, idrac_default_args):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": None, "destination": None,
"snmp_v3_username": None, "snmp_trap_state": None, "alert_number": None,
"email_alert_state": None, "address": None, "custom_message": None,
"enable_alerts": None, "authentication": None,
"smtp_ip_address": None, "smtp_port": None, "username": None,
"password": <PASSWORD>})
message = {"changes_applicable": False, "Message": "No changes were applied", "changed": False,
"Status": "Success"}
obj = MagicMock()
idrac_connection_configure_eventing_mock.config_mgr = obj
type(obj).configure_snmp_trap_destination = PropertyMock(return_value=message)
type(obj).configure_email_alerts = PropertyMock(return_value=message)
type(obj).configure_idrac_alerts = PropertyMock(return_value=message)
type(obj).configure_smtp_server_settings = PropertyMock(return_value=message)
idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
f_module.check_mode = False
result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert result['Message'] == 'No changes were applied'
def test_run_idrac_eventing_config_failed_case01(self, idrac_connection_configure_eventing_mock,
idrac_file_manager_config_eventing_mock, idrac_default_args):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1, "destination": "1.1.1.1",
"snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4,
"email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test",
"enable_alerts": "Enabled", "authentication": "Enabled",
"smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname",
"password": "<PASSWORD>"})
message = {'Status': 'Failed', "Data": {'Message': 'status failed in checking Data'}}
idrac_connection_configure_eventing_mock.file_share_manager.create_share_obj.return_value = "mnt/iso"
idrac_connection_configure_eventing_mock.config_mgr.set_liason_share.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
with pytest.raises(Exception) as ex:
self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert ex.value.args[0] == 'status failed in checking Data'
def test_run_idrac_eventing_config_failed_case02(self, idrac_connection_configure_eventing_mock,
idrac_default_args, idrac_file_manager_config_eventing_mock):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1, "destination": "1.1.1.1",
"snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4,
"email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test",
"enable_alerts": "Enabled", "authentication": "Enabled",
"smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname",
"password": "<PASSWORD>"})
message = {"changes_applicable": False, "Message": "No changes were applied", "changed": False,
"Status": "failed"}
idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
f_module.check_mode = False
result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert result['Message'] == 'No changes were applied'
def test_run_idrac_eventing_config_failed_case03(self, idrac_connection_configure_eventing_mock,
idrac_default_args, idrac_file_manager_config_eventing_mock):
idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser",
"share_password": "<PASSWORD>", "destination_number": 1,
"destination": "1.1.1.1", "snmp_v3_username": "snmpuser",
"snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled",
"address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled",
"authentication": "Enabled", "smtp_ip_address": "192.168.0.1",
"smtp_port": 443, "username": "uname", "password": "<PASSWORD>"})
message = {'Status': 'Failed', "Data": {'Message': "Failed to found changes"}}
idrac_connection_configure_eventing_mock.file_share_manager.create_share_obj.return_value = "mnt/iso"
idrac_connection_configure_eventing_mock.config_mgr.set_liason_share.return_value = message
f_module = self.get_module_mock(params=idrac_default_args)
with pytest.raises(Exception) as ex:
self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module)
assert ex.value.args[0] == 'Failed to found changes'
@pytest.mark.parametrize("exc_type", [ImportError, ValueError, RuntimeError])
def test_main_configure_eventing_exception_handling_case(self, exc_type, mocker, idrac_default_args,
idrac_connection_configure_eventing_mock,
idrac_file_manager_config_eventing_mock):
idrac_default_args.update({"share_name": "sharename", 'share_password': <PASSWORD>,
'share_mnt': None, 'share_user': None})
mocker.patch('ansible_collections.dellemc.openmanage.plugins.modules.'
'dellemc_configure_idrac_eventing.run_idrac_eventing_config', side_effect=exc_type('test'))
result = self._run_module_with_fail_json(idrac_default_args)
assert 'msg' in result
assert result['failed'] is True
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pose_sync_pytorch/video_pose_landmarks.py | lilly9117/Cross-Cutting | 40 | 57765 | from torchvision import models
import numpy as np
import torch
import os
from moviepy.editor import VideoFileClip
SKIP_FRAME_RATE = 10
MINIMAX_FRAME = 4
# 함수에서 documentaiton 읽기
model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()
os.environ['KMP_DUPLICATE_LIB_OK']='True'
def extract_boxes(reference_clip, compare_clip):
clips = [reference_clip, compare_clip]
clips_frame_info = []
for clip in clips:
i = 0
every_frame_info = []
# loop over the frames from the video stream
while True:
i+=SKIP_FRAME_RATE # 1초에 60 fps가 있으므로 몇개는 skip해도 될거 같음!
if (i*1.0/clip.fps)> clip.duration:
break
frame = clip.get_frame(i*1.0/clip.fps)
frame = frame/255 # image, and should be in ``0-1`` range.
frame = np.transpose(frame, (2,0,1)) # HWC -> CHW(그 위치에 몇차원 애를 넣을거냔?)
x = [torch.from_numpy(frame).float()]
# label list https://github.com/tensorflow/models/blob/master/research/object_detection/data/mscoco_label_map.pbtxt
predictions = model(x)
prediction= predictions[0]
each_box_list = zip(prediction['boxes'].tolist(), prediction['labels'].tolist(), prediction['scores'].tolist())
# 0.95 정도 올려야 까맣게 보이는 관중이 없어짐!
filtered_box_list = filter(lambda x: x[1]==1 and x[2] >= 0.95, each_box_list)
filtered_center_dot_list = list(map(lambda x: [(x[0][0]+x[0][2])/2, (x[0][1]+x[0][3])/2], filtered_box_list))
# x좌표로 정렬하기(대형이 가로로 늘어져 있다고 가정하고 순서대로 정렬)
sorted_dot_list = sorted(filtered_center_dot_list, key = lambda x: x[0])
every_frame_info.append(sorted_dot_list) # 프레임별 정보
clips_frame_info.append(np.array(every_frame_info)) # 각 영상별로 붙이기
return clips_frame_info
def calculate_pose_distance(reference_clip, compare_clip):
clips_frame_info = extract_boxes(reference_clip, compare_clip) # 모든 프레임마다 길이 계산해줌
min_size = min(len(clips_frame_info[0]),len(clips_frame_info[1]))
dist_arr = list()
# Calculate distance (by frame)
for i in range(min_size):
if len(clips_frame_info[0][i])>0 and len(clips_frame_info[1][i])>0: # 둘다 있으면
# x축 값이 가장 가까운걸로 찾고 그거랑 비교(어차피 대형이 중요한거니까)
ref_frame_dots = clips_frame_info[0][i] # 해당 frame의 정보
compare_frame_dots = clips_frame_info[1][i] # 해당 frame의 정보
min_dot_num = min(len(ref_frame_dots), len(compare_frame_dots)) # reference 기준으로 계산할거양
penalty = ((reference_clip.w **2 + reference_clip.h**2)**0.5) * abs(len(ref_frame_dots)-len(compare_frame_dots)) # 개수가 다를때 주는 패널티
total_diff = penalty
for dot_idx in range(min_dot_num):
ref_frame_dots[dot_idx] and compare_frame_dots[dot_idx]
total_diff += ((ref_frame_dots[dot_idx][0] - compare_frame_dots[dot_idx][0])**2 + (ref_frame_dots[dot_idx][1] - compare_frame_dots[dot_idx][1])**2)**0.5
dist_arr.append(total_diff)
else:
dist_arr.append(None)
# Minimize max distance in (minimax_frames) frames
min_diff = np.float('Inf')
min_idx = 0
max_dist = []
for i in range(min_size-(MINIMAX_FRAME-1)):
if None in dist_arr[i:i+MINIMAX_FRAME]:
max_dist.append(None)
else:
tmp_max = np.max(dist_arr[i:i+MINIMAX_FRAME])
max_dist.append(tmp_max)
if min_diff > tmp_max:
min_diff = tmp_max
min_idx = i
# return distance, second, additional_info
return min_diff, (min_idx*SKIP_FRAME_RATE)/reference_clip.fps, {} | [
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] |
esphome/components/zyaura/sensor.py | OttoWinter/esphomeyaml | 249 | 117170 | import esphome.codegen as cg
import esphome.config_validation as cv
from esphome import pins
from esphome.components import sensor
from esphome.const import (
CONF_ID,
CONF_CLOCK_PIN,
CONF_DATA_PIN,
CONF_CO2,
CONF_TEMPERATURE,
CONF_HUMIDITY,
DEVICE_CLASS_CARBON_DIOXIDE,
DEVICE_CLASS_HUMIDITY,
DEVICE_CLASS_TEMPERATURE,
STATE_CLASS_MEASUREMENT,
UNIT_PARTS_PER_MILLION,
UNIT_CELSIUS,
UNIT_PERCENT,
ICON_MOLECULE_CO2,
)
from esphome.cpp_helpers import gpio_pin_expression
zyaura_ns = cg.esphome_ns.namespace("zyaura")
ZyAuraSensor = zyaura_ns.class_("ZyAuraSensor", cg.PollingComponent)
CONFIG_SCHEMA = cv.Schema(
{
cv.GenerateID(): cv.declare_id(ZyAuraSensor),
cv.Required(CONF_CLOCK_PIN): cv.All(pins.internal_gpio_input_pin_schema),
cv.Required(CONF_DATA_PIN): cv.All(pins.internal_gpio_input_pin_schema),
cv.Optional(CONF_CO2): sensor.sensor_schema(
unit_of_measurement=UNIT_PARTS_PER_MILLION,
icon=ICON_MOLECULE_CO2,
accuracy_decimals=0,
device_class=DEVICE_CLASS_CARBON_DIOXIDE,
state_class=STATE_CLASS_MEASUREMENT,
),
cv.Optional(CONF_TEMPERATURE): sensor.sensor_schema(
unit_of_measurement=UNIT_CELSIUS,
accuracy_decimals=1,
device_class=DEVICE_CLASS_TEMPERATURE,
state_class=STATE_CLASS_MEASUREMENT,
),
cv.Optional(CONF_HUMIDITY): sensor.sensor_schema(
unit_of_measurement=UNIT_PERCENT,
accuracy_decimals=1,
device_class=DEVICE_CLASS_HUMIDITY,
state_class=STATE_CLASS_MEASUREMENT,
),
}
).extend(cv.polling_component_schema("60s"))
async def to_code(config):
var = cg.new_Pvariable(config[CONF_ID])
await cg.register_component(var, config)
pin_clock = await gpio_pin_expression(config[CONF_CLOCK_PIN])
cg.add(var.set_pin_clock(pin_clock))
pin_data = await gpio_pin_expression(config[CONF_DATA_PIN])
cg.add(var.set_pin_data(pin_data))
if CONF_CO2 in config:
sens = await sensor.new_sensor(config[CONF_CO2])
cg.add(var.set_co2_sensor(sens))
if CONF_TEMPERATURE in config:
sens = await sensor.new_sensor(config[CONF_TEMPERATURE])
cg.add(var.set_temperature_sensor(sens))
if CONF_HUMIDITY in config:
sens = await sensor.new_sensor(config[CONF_HUMIDITY])
cg.add(var.set_humidity_sensor(sens))
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] |
recsys/recommender/utils.py | shenghuiliuu/recsys | 50 | 1615750 | import numpy as np
import pandas as pd
from copy import deepcopy
def super_str(x):
if isinstance(x,np.int64):
x=float(x)
if isinstance(x,int):
x=float(x)
ans=str(x)
return ans
def convert_to_array(x):
if isinstance(x, np.ndarray):
return x
else:
return np.array(x)
def special_sort(a, order='ascending'):
n=len(a)
if order=='ascending':
for i in range(1,n):
j=deepcopy(i)
while j>0 and a[j][1]<a[j-1][1]:
temp=a[j-1]
a[j-1]=a[j]
a[j]=temp
j=j-1
elif order=='descending':
for i in range(1,n):
j=deepcopy(i)
while j>0 and a[j][1]>a[j-1][1]:
temp=a[j-1]
a[j-1]=a[j]
a[j]=temp
j=j-1
return a
def dissimilarity(arr1, arr2, weighted):
n=arr1.shape[0]
s=0
if weighted==True:
for i in range(0,n):
diff=abs(arr1[i]-arr2[i])
s = s + (diff*(n-i)/n)
else:
for i in range(0,n):
diff=abs(arr1[i]-arr2[i])
s = s + (diff)
return s
def create_utility_matrix(data, formatizer = {'user':0, 'item': 1, 'value': 2}):
"""
:param data: pandas dataframe, 2D, nx3
:param formatizer: dict having the column name or ids for users, items and ratings/values
:return: 1. the utility matrix. (2D, n x m, n=users, m=items)
2. list of users (in order with the utility matrix rows)
3. list of items (in order with the utility matrix columns)
"""
itemField = formatizer['item']
userField = formatizer['user']
valueField = formatizer['value']
userList = data.ix[:,userField].tolist()
itemList = data.ix[:,itemField].tolist()
valueList = data.ix[:,valueField].tolist()
users = list(set(data.ix[:,userField]))
items = list(set(data.ix[:,itemField]))
users_index = {users[i]: i for i in range(len(users))}
pd_dict = {item: [np.nan for i in range(len(users))] for item in items}
for i in range(0,len(data)):
item = itemList[i]
user = userList[i]
value = valueList[i]
pd_dict[item][users_index[user]] = value
X = pd.DataFrame(pd_dict)
X.index = users
users = list(X.index)
items = list(X.columns)
return np.array(X), users, items | [
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Scripts/Prepare/generate_tif.py | broxtopd/Map-Tiling-App | 0 | 1605149 | <gh_stars>0
# Script create an image (tif) file from GIS data
#
###############################################################################
# Copyright (c) 2018, <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
###############################################################################
import sys,os
import zipfile
import gdal, osr
import subprocess
from PIL import Image
import tempfile
import shutil
import urllib
(major,minor,micro,releaselevel,serial) = sys.version_info
if major == 2:
from cStringIO import StringIO
elif major == 3:
from io import StringIO
from cgi import parse_qs, escape
import numpy as np
import multiprocessing
from itertools import repeat
resampling_list = ('average','near','bilinear','cubic','cubicspline','lanczos','antialias')
__version__ = "0.1"
scriptdir = os.path.dirname(os.path.realpath(__file__))
def optparse_init():
"""Prepare the option parser for input (argv)"""
from optparse import OptionParser, OptionGroup
usage = 'Usage: %prog [options] input_file(s) [output]'
p = OptionParser(usage, version='%prog '+ __version__)
p.add_option('-r', '--resample', dest='resample', type='choice', choices=resampling_list,
help='Resampling method (%s) - default "average"' % ','.join(resampling_list))
p.add_option('-s', '--shpfile', dest='shpfilename',
help='Shapefile used to clip the raster')
p.add_option('-o', '--outsideMask', dest='outsideMask', action='store_true',
help='If specified, specifies that everything outside of shape is masked')
p.add_option('-c', '--clrfile', dest='clrfilename',
help='Color file to be used to make the file into a color file')
p.add_option('-f', '--bgfile', dest='bgfilename',
help='Background data to be blended with foreground data')
p.add_option('-b', '--blend', dest='blend',
help='Fractional blend between background and foreground file (0-1)')
p.add_option('-p', '--pct', dest='pct', action='store_true',
help='If speficied, creates a pct file from an rgb file')
p.add_option('-a', '--s_srs', dest='s_srs',
help='Coordinate System')
p.add_option('-d', '--t_srs', dest='t_srs',
help='Coordinate System')
p.add_option('-e', '--te', dest='te',
help='Map Bounds')
p.add_option('-q', '--tr', dest='tr',
help='Map Resolution')
p.add_option('-t', '--ts', dest='ts',
help='Map Scale')
p.add_option('-x', '--ExactColorEntry', dest='ExactColorEntry', action='store_true',
help='Exact Color Entry')
p.add_option('-n', '--NearestColorEntry', dest='NearestColorEntry', action='store_true',
help='Nearest Color Entry')
p.add_option('-g', '--GISOptimizedImage', dest='GISOptimizedImage', action='store_true',
help='Tiled image, GIS Optimized')
p.set_defaults(resample='near', blend='0.5', shpfilename='', s_srs = '', t_srs = '', te = '', tr = '', ts = '', overlayName = '', FType='', outsideMask=False, clrfilename='', bgfilename='', pct=False,ExactColorEntry=False,NearestColorEntry=False,GISOptimizedImage=False)
return p
if __name__ == '__main__':
# Parse the command line arguments
argv = gdal.GeneralCmdLineProcessor( sys.argv )
parser = optparse_init()
options,args = parser.parse_args(args=argv[1:])
inraster = args[0]
inpath, infile = os.path.split(inraster)
outraster = args[1]
resample = options.resample
shpfilename = options.shpfilename
outsideMask = options.outsideMask
clrfilename = options.clrfilename
bgfilename = options.bgfilename
blend = options.blend
pct = options.pct
GISOptimizedImage = options.GISOptimizedImage
s_srs = options.s_srs
te = options.te
ts = options.ts
tr = options.tr
t_srs = options.t_srs
ExactColorEntry = options.ExactColorEntry
NearestColorEntry = options.NearestColorEntry
working_dir = outraster.replace('.tif','_' + os.path.basename(tempfile.mktemp()))
if ~os.path.exists(working_dir):
os.makedirs(working_dir)
tempfilename_web = working_dir + '/prepare_web'
tempfilename_vrt = working_dir + '/prepare_vrt'
tempfilename_raster = working_dir + '/prepare_raster' + infile[-4:]
tempfilename_a = working_dir + '/prepare_a.tif'
tempfilename_clr = working_dir + '/prepare_clr.vrt'
tempfilename_shp = working_dir + '/prepare_shp.tif'
tempfilename_bg = working_dir + '/prepare_bg.tif'
tempfilename_bg2 = working_dir + '/prepare_bg2.tif'
tempfilename_pct = working_dir + '/prepare_pct.vrt'
ifname = inraster
if '.zip' in inraster:
zf,zp = inraster.split('.zip')
fh = open(zf + '.zip', 'rb')
z = zipfile.ZipFile(fh)
outfile = open(tempfilename_raster, 'wb')
outfile.write(z.read(zp[1:]))
outfile.close()
fh.close()
ifname = tempfilename_raster
if clrfilename != '':
command = 'gdaldem color-relief -alpha -of vrt'
if NearestColorEntry == True:
command += ' -nearest_color_entry'
elif ExactColorEntry == True:
commmand += ' -exact_color_entry'
command += ' "' + ifname + '" "' + clrfilename + '" "' + tempfilename_clr + '"'
print(command)
subprocess.call(command, shell=True)
ifname = tempfilename_clr
else:
in_ds = gdal.Open(ifname)
bands = in_ds.RasterCount
del in_ds
if bands == 1:
command = 'gdal_translate -of vrt "' + ifname + '" "' + tempfilename_clr + '"'
subprocess.call(command, shell=True)
ifname = tempfilename_clr
args = ''
if s_srs != '':
args += ' -s_srs "' + s_srs + '"'
if te != '':
args += ' -te ' + te
if ts != '':
args += ' -ts ' + ts
if tr!= '':
args += ' -tr ' + tr
if t_srs!= '':
args += ' -t_srs "' + t_srs + '"'
in_ds = gdal.Open(ifname)
bands = in_ds.RasterCount
del in_ds
if bands == 3 or bands == 1:
args += ' -dstalpha'
command = 'gdalwarp' + args + ' -multi -r ' + resample + ' -overwrite "' + ifname + '" "' + tempfilename_a + '"'
print(command)
subprocess.call(command, shell=True)
ifname = tempfilename_a
if shpfilename != '':
path, shpfile = os.path.split(shpfilename)
shplayer = shpfile.replace('.shp','')
os.rename(ifname,tempfilename_shp)
if outsideMask == True:
command = 'gdal_rasterize -b 4 -burn 0 -l "' + shplayer + '" "' + shpfilename + '" "' + tempfilename_shp + '"'
print(command)
subprocess.call(command, shell=True)
ifname = tempfilename_shp
else:
command = 'gdal_rasterize -i -b 4 -burn 0 -l "' + shplayer + '" "' + shpfilename + '" "' + tempfilename_shp + '"'
print(command)
subprocess.call(command, shell=True)
ifname = tempfilename_shp
if bgfilename != '':
in_ds = gdal.Open(ifname)
projection = in_ds.GetProjection()
geotransform = in_ds.GetGeoTransform()
minx = geotransform[0]
maxy = geotransform[3]
maxx = minx + geotransform[1]*in_ds.RasterXSize
miny = maxy + geotransform[5]*in_ds.RasterYSize
te_str = str(minx) + ' ' + str(miny) + ' ' + str(maxx) + ' ' + str(maxy)
ts_str = str(in_ds.RasterXSize) + ' ' + str(in_ds.RasterYSize)
srs = osr.SpatialReference()
srs.ImportFromWkt(projection)
t_srs = srs.ExportToProj4()
del in_ds
command = 'gdalwarp -multi -r ' + resample + ' -overwrite -te ' + te_str + ' -ts ' + ts_str + ' -t_srs "' + t_srs + '" "' + bgfilename + '" "' + tempfilename_bg + '"'
print(command)
subprocess.call(command, shell=True)
command = 'python "' + scriptdir + os.path.sep + 'rgb_merge.py" "' + ifname + '" "' + tempfilename_bg + '" ' + blend + ' "' + tempfilename_bg2 + '"'
print(command)
subprocess.call(command, shell=True)
ifname = tempfilename_bg2
if pct == True:
command = 'python "' + scriptdir + os.path.sep + 'rgb2pct.py" -of VRT "' + ifname + '" "' + tempfilename_pct + '"'
print(command)
subprocess.call(command, shell=True)
ifname = tempfilename_pct
if GISOptimizedImage == True:
command = 'gdalwarp -co COMPRESS=LZW -co TILED=YES -co BIGTIFF=IF_SAFER -multi -overwrite "' + ifname + '" "' + outraster + '"'
print(command)
subprocess.call(command, shell=True)
command = 'gdaladdo --config COMPRESS_OVERVIEW LZW --config INTERLEAVE_OVERVIEW PIXEL -r average "' + outraster + '" 2 4 8 16 32 64'
print(command)
subprocess.call(command, shell=True)
else:
command = 'gdalwarp -co COMPRESS=JPEG -multi -overwrite "' + ifname + '" "' + outraster + '"'
print(command)
subprocess.call(command, shell=True)
# Remove temporary files
shutil.rmtree(working_dir)
print('DONE CREATING MAP!')
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] |
threadingHelpers.py | Mimieam/NMouse | 0 | 71914 | from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
import traceback, sys
import logging
# Make this threadsafe
class QtHandler(logging.Handler):
def __init__(self, output_widget: QTextEdit):
logging.Handler.__init__(self)
self.widget: QTextEdit = output_widget
self.widget.setReadOnly(True)
def emit(self, record):
try:
msg = self.format(record)
print(f"emiting: {msg}")
self.widget.appendPlainText(msg)
except:
print("something bad happened")
def setup_logs(logger, output_widget=None):
# to Gui
qtLogHandler = QtHandler(output_widget)
qtLogHandler.setFormatter(logging.Formatter("%(levelname)s: %(message)s"))
logger.addHandler(qtLogHandler)
logger = logging.getLogger(__name__)
# to console
syslog = logging.StreamHandler()
formatter = logging.Formatter('[%(asctime)s.%(msecs)03d %(threadName)10s] -- %(funcName)20s() -> %(levelname)5s: %(message)s',"%H:%M:%S")
syslog.setFormatter(formatter)
logger.addHandler(syslog)
logger.setLevel(logging.DEBUG)
# thanks https://www.learnpyqt.com/courses/concurrent-execution/multithreading-pyqt-applications-qthreadpool/
class WorkerSignals(QObject):
'''
Defines the signals available from a running worker thread.
Supported signals are:
finished
No data
error
`tuple` (exctype, value, traceback.format_exc() )
result
`object` data returned from processing, anything
progress
`int` indicating % progress
'''
finished = pyqtSignal()
error = pyqtSignal(tuple)
result = pyqtSignal(object)
progress = pyqtSignal(int)
class Worker(QRunnable):
'''
Worker thread
Inherits from QRunnable to handler worker thread setup, signals and wrap-up.
:param callback: The function callback to run on this worker thread. Supplied args and
kwargs will be passed through to the runner.
:type callback: function
:param args: Arguments to pass to the callback function
:param kwargs: Keywords to pass to the callback function
'''
def __init__(self, fn, *args, **kwargs):
super(Worker, self).__init__()
# Store constructor arguments (re-used for processing)
self.fn = fn
self.args = args
self.kwargs = kwargs
self.signals = WorkerSignals()
# Add the callback to our kwargs
# self.kwargs['progress_callback'] = self.signals.progress
@pyqtSlot()
def run(self):
'''
Initialise the runner function with passed args, kwargs.
'''
# Retrieve args/kwargs here; and fire processing using them
try:
print(self.fn, self.args, self.kwargs)
result = self.fn(*self.args, **self.kwargs)
except:
traceback.print_exc()
exctype, value = sys.exc_info()[:2]
self.signals.error.emit((exctype, value, traceback.format_exc()))
else:
self.signals.result.emit(result) # Return the result of the processing
finally:
self.signals.finished.emit() # Done
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580,
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396,
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308,
13,
2
] |
dm_control/scripts/print_profile.py | mhauskn/dm_control | 0 | 1601647 | <reponame>mhauskn/dm_control
import sys
import pstats
p = pstats.Stats(sys.argv[1])
p.sort_stats('cumtime').print_stats(50) | [
1,
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] |
contests_atcoder/arc113/arc113_d_stupid.py | takelifetime/competitive-programming | 0 | 149513 | <filename>contests_atcoder/arc113/arc113_d_stupid.py
from itertools import product
from collections import defaultdict
MOD = 998244353
n, m, k = map(int, input().split())
def stupid(n, m, k):
ret = defaultdict(int)
for content in product(range(1, k + 1), repeat=n * m):
a = []
b = [0] * m
for i in range(n):
for j in range(m):
b[j] = max(b[j], content[i * m + j])
a.append(min(content[i * m:(i + 1) * m]))
ret[tuple(a + b)] = 1
return ret.keys()
ret = stupid(n, m, k)
print(*sorted(ret))
print(len(ret)) | [
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2
] |
portal/capstone/simulator.py | LDSSA/portal | 2 | 178642 | <reponame>LDSSA/portal<gh_stars>1-10
import json
import logging
import queue
import random
import time
import traceback
from concurrent.futures import ThreadPoolExecutor as PoolExecutor
from datetime import datetime, timezone
import requests
from django.db import transaction, close_old_connections
from django.conf import settings
from portal.capstone import models
logger = logging.getLogger(__name__)
def run():
# Queue
submissions = queue.Queue()
# Start consumer pool
with PoolExecutor(max_workers=50) as executor:
# Start simulator
logger.info("Starting simulator..")
executor.submit(run_simulator)
# Start producer
logger.info("Starting producer...")
executor.submit(run_producer, submissions)
logger.info("Starting consumer...")
while True:
# Consume queue
id_ = submissions.get(block=True)
executor.submit(consume, id_)
def run_simulator():
while True:
logger.debug("Simulator cycle...")
close_old_connections()
try:
with transaction.atomic():
simulators = models.Simulator.objects.select_for_update().all()
for simulator in simulators:
simulator.reset()
simulator.start()
except Exception:
logger.exception("Exception in simulator")
time.sleep(settings.SIMULATOR_INTERVAL)
def run_producer(submissions):
# Prevent thundering herd
time.sleep(2 * random.random())
while True:
logger.debug("Producer cycle...")
close_old_connections()
try:
# Retrieve a block of due datapoints
with transaction.atomic():
# Lock due datapoints
# prevent multiple producers from repeating datapoints
now = datetime.now(timezone.utc)
qs = (
models.DueDatapoint.objects.select_for_update()
.filter(simulator__status="started")
.filter(state="due")
.filter(due__lte=now)
)[: settings.BLOCK_SIZE]
items = []
for due_datapoint in qs:
items.append(due_datapoint.id)
due_datapoint.status = "queued"
due_datapoint.save()
# Queue due datapoints outside lock
if items:
logger.info("Queuing due datapoints")
for item in items:
logger.info("Producing %s", item)
submissions.put(item)
logger.debug("Queue size: %s", submissions.qsize())
except Exception:
logger.exception("Exception in producer")
time.sleep(settings.PRODUCER_INTERVAL)
# TODO mark as due any unconsumed due datapoints
def consume(id_):
close_old_connections()
logger.info("Consuming %s", id_)
try:
with transaction.atomic():
due_datapoint = (
models.DueDatapoint.objects.select_related("datapoint")
.select_for_update()
.get(id=id_)
)
try:
logger.info("Posting %s", id_)
data = json.loads(due_datapoint.datapoint.data)
response = requests.post(
due_datapoint.url, json=data, timeout=settings.TIMEOUT
)
except requests.exceptions.RequestException as exc:
logger.info("Request Exception %s", id_, exc_info=True)
logger.debug(exc.__class__.__name__)
logger.debug(traceback.format_tb(exc.__traceback__))
due_datapoint.state = "fail"
due_datapoint.response_exception = exc.__class__.__name__
due_datapoint.response_traceback = traceback.format_tb(
exc.__traceback__
)
if isinstance(exc, requests.exceptions.Timeout):
due_datapoint.response_timeout = True
due_datapoint.save()
return
try:
response.raise_for_status()
except requests.exceptions.HTTPError as exc:
logger.info("HTTP Exception %s", id_, exc_info=True)
due_datapoint.state = "fail"
due_datapoint.response_exception = exc.__class__.__name__
due_datapoint.response_traceback = traceback.format_tb(
exc.__traceback__
)
due_datapoint.response_status = response.status_code
due_datapoint.response_elapsed = (
response.elapsed.total_seconds()
)
due_datapoint.response_content = response.text
due_datapoint.save()
return
try:
response.json()
content = response.text
except json.JSONDecodeError as exc:
logger.info("Response Exception %s", id_, exc_info=True)
due_datapoint.state = "fail"
due_datapoint.response_exception = exc.__class__.__name__
due_datapoint.response_traceback = traceback.format_tb(
exc.__traceback__
)
due_datapoint.response_status = response.status_code
due_datapoint.response_elapsed = (
response.elapsed.total_seconds()
)
due_datapoint.response_content = response.text
due_datapoint.save()
return
else:
logger.info("Success %s", id_)
due_datapoint.state = "success"
due_datapoint.response_content = content
due_datapoint.response_status = response.status_code
due_datapoint.response_elapsed = (
response.elapsed.total_seconds()
)
due_datapoint.save()
except Exception:
logger.exception("Exception consuming %s", id_)
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flink-python/pyflink/examples/datastream/windowing/sliding_time_window.py | rudikershaw/flink | 9 | 146909 | ################################################################################
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
import sys
import argparse
from typing import Iterable
from pyflink.datastream.connectors import FileSink, OutputFileConfig, RollingPolicy
from pyflink.common import Types, WatermarkStrategy, Time, Encoder
from pyflink.common.watermark_strategy import TimestampAssigner
from pyflink.datastream import StreamExecutionEnvironment, ProcessWindowFunction
from pyflink.datastream.window import SlidingEventTimeWindows, TimeWindow
class MyTimestampAssigner(TimestampAssigner):
def extract_timestamp(self, value, record_timestamp) -> int:
return int(value[1])
class CountWindowProcessFunction(ProcessWindowFunction[tuple, tuple, str, TimeWindow]):
def process(self,
key: str,
context: ProcessWindowFunction.Context[TimeWindow],
elements: Iterable[tuple]) -> Iterable[tuple]:
return [(key, context.window().start, context.window().end, len([e for e in elements]))]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--output',
dest='output',
required=False,
help='Output file to write results to.')
argv = sys.argv[1:]
known_args, _ = parser.parse_known_args(argv)
output_path = known_args.output
env = StreamExecutionEnvironment.get_execution_environment()
# write all the data to one file
env.set_parallelism(1)
# define the source
data_stream = env.from_collection([
('hi', 1), ('hi', 2), ('hi', 3), ('hi', 4), ('hi', 5), ('hi', 8), ('hi', 9), ('hi', 15)],
type_info=Types.TUPLE([Types.STRING(), Types.INT()]))
# define the watermark strategy
watermark_strategy = WatermarkStrategy.for_monotonous_timestamps() \
.with_timestamp_assigner(MyTimestampAssigner())
ds = data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
.key_by(lambda x: x[0], key_type=Types.STRING()) \
.window(SlidingEventTimeWindows.of(Time.milliseconds(5), Time.milliseconds(2))) \
.process(CountWindowProcessFunction(),
Types.TUPLE([Types.STRING(), Types.INT(), Types.INT(), Types.INT()]))
# define the sink
if output_path is not None:
ds.sink_to(
sink=FileSink.for_row_format(
base_path=output_path,
encoder=Encoder.simple_string_encoder())
.with_output_file_config(
OutputFileConfig.builder()
.with_part_prefix("prefix")
.with_part_suffix(".ext")
.build())
.with_rolling_policy(RollingPolicy.default_rolling_policy())
.build()
)
else:
print("Printing result to stdout. Use --output to specify output path.")
ds.print()
# submit for execution
env.execute()
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src/preprocessing/compute_moments.py | thomasjo/nemo-redux | 0 | 102371 | from argparse import ArgumentParser
from pathlib import Path
import torch
from nemo.datasets import classification_dataloaders, detection_dataloaders
from nemo.utils import ensure_reproducibility
DATASET_TYPES = [
"classification",
"detection",
]
class RunningAverage:
def __init__(self, num_channels=3):
self.num_channels = 3
self.average = torch.zeros(num_channels)
self.num_samples = 0
def update(self, values):
batch_size, num_channels = values.size()
if num_channels != self.num_channels:
raise RuntimeError("num_channels mismatch")
updated_num_samples = self.num_samples + batch_size
correction_factor = self.num_samples / updated_num_samples
updated_average = self.average * correction_factor
updated_average += torch.sum(values, dim=0) / updated_num_samples
self.average = updated_average
self.num_samples = updated_num_samples
def tolist(self):
return self.average.detach().tolist()
def __str__(self):
return "[" + ", ".join([f"{value:.3f}" for value in self.tolist()]) + "]"
def main(args):
ensure_reproducibility(seed=42)
if args.type == DATASET_TYPES[0]:
train_loader, _, _ = classification_dataloaders(
args.data_dir,
no_augmentation=True,
num_workers=args.num_workers,
)
elif args.type == DATASET_TYPES[1]:
train_loader, _, _ = detection_dataloaders(
args.data_dir,
no_augmentation=True,
num_workers=args.num_workers,
)
running_mean = RunningAverage()
running_std = RunningAverage()
with torch.no_grad():
for batch_idx, (image, _) in enumerate(train_loader):
print("Processing batch {}".format(batch_idx + 1))
if isinstance(image, list):
image = torch.stack(image)
image_flattened = torch.flatten(image, start_dim=2)
mean = torch.mean(image_flattened, dim=2)
running_mean.update(mean)
std = torch.std(image_flattened, dim=2)
running_std.update(std)
print(f"mean={running_mean}, std={running_std}")
def parse_args():
parser = ArgumentParser()
parser.add_argument("--data-dir", type=Path, metavar="PATH", required=True, help="path to partitioned data directory")
parser.add_argument("--type", type=str, metavar="NAME", choices=DATASET_TYPES, default=DATASET_TYPES[0], help="type of dataset to process")
parser.add_argument("--batch-size", type=int, metavar="N", default=2, help="number of examples per batch")
parser.add_argument("--num-workers", type=int, metavar="N", default=2, help="number of workers to use for preparing batches of data")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
main(args)
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tests/functional/test_cmd_line_call.py | fevral13/django-migration-linter | 0 | 86542 | # Copyright 2019 3YOURMIND GmbH
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import unittest
from subprocess import Popen, PIPE
from django_migration_linter import utils, DEFAULT_CACHE_PATH
from tests import fixtures
import sys
class CallLinterFromCommandLineTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(CallLinterFromCommandLineTest, cls).setUpClass()
linter_name = 'django-migration-linter'
cls.linter_exec = '{0}/bin/{1}'.format(sys.prefix, linter_name) if hasattr(sys, 'real_prefix') else linter_name
def test_call_linter_cmd_line_working(self):
cmd = '{0} --no-cache {1}'.format(self.linter_exec, fixtures.CORRECT_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 6)
self.assertEqual(
sorted(lines[:4]),
sorted([
"(test_app1, 0001_initial)... OK",
"(test_app1, 0002_a_new_null_field)... OK",
"(test_app2, 0001_foo)... OK",
"(test_app3, 0001_initial)... OK",
])
)
def test_call_linter_cmd_line_errors(self):
cmd = '{0} --no-cache {1}'.format(
self.linter_exec,
fixtures.ADD_NOT_NULL_COLUMN_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertNotEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 5)
self.assertTrue(lines[0].endswith('OK'))
self.assertTrue(lines[1].endswith('ERR'))
self.assertTrue('RENAMING tables' in lines[2])
def test_call_linter_cmd_line_exclude_apps(self):
cmd = '{0} --no-cache {1} --exclude-apps test_app2'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 6)
self.assertEqual(
sorted(lines[:4]),
sorted([
"(test_app1, 0001_initial)... OK",
"(test_app1, 0002_a_new_null_field)... OK",
"(test_app2, 0001_foo)... IGNORE",
"(test_app3, 0001_initial)... OK",
])
)
def test_call_linter_cmd_line_include_apps(self):
cmd = '{0} --no-cache {1} --include-apps test_app2'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 6)
self.assertEqual(
sorted(lines[:4]),
sorted([
"(test_app1, 0001_initial)... IGNORE",
"(test_app1, 0002_a_new_null_field)... IGNORE",
"(test_app2, 0001_foo)... OK",
"(test_app3, 0001_initial)... IGNORE",
])
)
def test_call_linter_cmd_line_ignore_name(self):
cmd = '{0} --no-cache {1} --ignore-name 0001_initial'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 6)
self.assertEqual(
sorted(lines[:4]),
sorted([
"(test_app1, 0001_initial)... IGNORE",
"(test_app1, 0002_a_new_null_field)... OK",
"(test_app2, 0001_foo)... OK",
"(test_app3, 0001_initial)... IGNORE",
])
)
def test_call_linter_cmd_line_ignore_name_contains(self):
cmd = '{0} --no-cache {1} --ignore-name-contains 0001'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 6)
self.assertEqual(
sorted(lines[:4]),
sorted([
"(test_app1, 0001_initial)... IGNORE",
"(test_app1, 0002_a_new_null_field)... OK",
"(test_app2, 0001_foo)... IGNORE",
"(test_app3, 0001_initial)... IGNORE",
])
)
def test_call_linter_cmd_line_cache(self):
cache_file = os.path.join(DEFAULT_CACHE_PATH, 'test_correct_project.pickle')
if os.path.exists(cache_file):
os.remove(cache_file)
cmd = '{0} {1}'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT
)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertTrue(os.path.exists(DEFAULT_CACHE_PATH))
def test_call_linter_cmd_line_cache_path(self):
if os.path.exists('/tmp/migration-linter-cache-tests/'):
shutil.rmtree('/tmp/migration-linter-cache-tests/')
cmd = '{0} {1} --cache-path={2}'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT,
'/tmp/migration-linter-cache-tests/'
)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
self.assertTrue(os.path.exists('/tmp/migration-linter-cache-tests'))
def test_call_linter_cmd_line_no_cache(self):
cache_file = os.path.join(DEFAULT_CACHE_PATH, 'test_correct_project.pickle')
if os.path.exists(cache_file):
os.remove(cache_file)
cmd = '{0} --no-cache {1}'.format(
self.linter_exec,
fixtures.CORRECT_PROJECT
)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
self.assertFalse(os.path.exists(cache_file))
def test_call_linter_cmd_line_git_id(self):
cmd = '{0} --no-cache {1} d7125d5f4f0cc9623f670a66c54f131acc50032d'.format(
self.linter_exec,
fixtures.MULTI_COMMIT_PROJECT)
fixtures.prepare_git_project(fixtures.MULTI_COMMIT_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 3)
self.assertTrue(lines[0].endswith('OK'))
self.assertTrue(lines[1].startswith('*** Summary'))
def test_call_linter_with_deleted_migrations(self):
cmd = '{0} --no-cache {1} 154ecf6119325cc3b1f3f5a4e709bfbd61a4a4ba'.format(
self.linter_exec,
fixtures.DELETED_MIGRATION_PROJECT)
fixtures.prepare_git_project(fixtures.DELETED_MIGRATION_PROJECT)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 3)
self.assertTrue(lines[0].endswith('OK'))
self.assertTrue(lines[1].startswith('*** Summary'))
def test_call_from_within_project(self):
cmd = 'cd {0} && {1} --no-cache .'.format(
fixtures.CORRECT_PROJECT,
self.linter_exec)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 6)
self.assertEqual(
sorted(lines[:4]),
sorted([
"(test_app1, 0001_initial)... OK",
"(test_app1, 0002_a_new_null_field)... OK",
"(test_app2, 0001_foo)... OK",
"(test_app3, 0001_initial)... OK",
])
)
def test_call_project_non_git_root(self):
# could use tag: version_ok (but doesn't work in Travis)
cmd = '{0} --no-cache {1} 2c46f438a89972f9ce2d5151a825a5bfb7f7db4b'.format(
self.linter_exec,
fixtures.NON_GIT_ROOT_DJANGO_PROJECT)
fixtures.prepare_git_project(fixtures.NON_GIT_ROOT_GIT_FOLDER)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 3)
self.assertTrue(lines[0].endswith('OK'))
self.assertTrue(lines[1].startswith('*** Summary'))
def test_call_project_non_git_root_ko(self):
# could use tag: version_ko (but doesn't work in Travis)
cmd = '{0} --no-cache {1} 0140e142724fc58944797f9ddc2ebf964146339a'.format(
self.linter_exec,
fixtures.NON_GIT_ROOT_DJANGO_PROJECT)
fixtures.prepare_git_project(fixtures.NON_GIT_ROOT_GIT_FOLDER)
process = Popen(
cmd, shell=True, stdout=PIPE, stderr=PIPE)
process.wait()
self.assertNotEqual(process.returncode, 0)
lines = list(map(utils.clean_bytes_to_str, process.stdout.readlines()))
self.assertEqual(len(lines), 5)
self.assertTrue(lines[0].endswith('ERR'))
self.assertTrue(lines[2].endswith('OK'))
self.assertTrue(lines[3].startswith('*** Summary'))
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] |
estimate_nislands_params.py | willyrv/estimate_islands | 1 | 96495 | <reponame>willyrv/estimate_islands<filename>estimate_nislands_params.py
# -*- coding: utf-8 -*-
"""
Created on Mon May 2 14:48:39 2016
@author: willy
"""
import re
import argparse
from nisland_model import max_llk_estim_hist_theta_variable
parser = argparse.ArgumentParser(description='Estimates the parameters of an n-island model.')
# The file with the data (.ndiff)
parser.add_argument('input_file', type=str,
help='Full path to the input data.')
parameters = parser.parse_args()
INPUT_FILENAME = parameters.input_file
# Reading the file
with open(INPUT_FILENAME, 'r') as f:
text = f.read()
(header, obs_text) = text.split('# Value|count\n')
# Get the observed values
obs = []
pattern = '[0-9]*\|[0-9]*\n'
p = re.compile(pattern)
for line in p.findall(obs_text):
obs.append(int(line.split('|')[1]))
# Do the maximum likelihood estimation
[n, M, theta, llk] = max_llk_estim_hist_theta_variable(obs)
# Print the results
print("Number of islands: {}. Migration rate: {} ".format(n, M))
print("theta: {}".format(theta))
print("Value of the log-likelihood function: {}".format(llk))
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] |
SENN/scripts/main_beer.py | ricokoff/FACT-AI | 1 | 92831 | ''' Code for training and evaluating Self-Explaining Neural Networks.
Copyright (C) 2018 <NAME> <<EMAIL>>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License,
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
'''
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] |
app/lib/duplication_check/compare.py | WHUT-XGP/ASoulCnki | 0 | 1609614 | <reponame>WHUT-XGP/ASoulCnki
# -*- encoding: utf-8 -*-
"""
Filename :compare.py
Description :1:1文章比较
Time :2021/06/22 22:54:18
Author :hwa
Version :1.0
"""
def article_compare(article1, article2):
red_list = []
for i in range(0, len(article1)):
if article1[i:i + 3] in article2:
red_list = red_list + [i, i + 1, i + 2, i + 3]
count = 0
for index, c in enumerate(article1):
if index in red_list:
# print("\033[31m" + c + "\033[0m", end="")
count += 1
# else:
# print(c, end="")
return count
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] |
dirbot/items.py | lihongqiang/crawl-image | 1 | 39076 | from scrapy.item import Item, Field
class MyImage(Item):
image_urls = Field()
images = Field()
image_paths = Field()
image_label = Field()
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caos/_internal/utils/working_directory.py | caotic-co/caos | 0 | 84890 | <filename>caos/_internal/utils/working_directory.py
import os
def is_dev_environment() -> bool:
"""Returns True if the project source code structure is found in the working directory"""
return os.path.isdir("caos") and \
os.path.isdir("caos/_cli_commands") and \
os.path.isfile("caos/_cli.py") and \
os.path.isdir("docs") and\
os.path.isdir("tests") and\
os.path.isfile("LICENSE") and\
os.path.isfile("caos.py")
def get_current_dir() -> str:
if is_dev_environment():
if not os.path.isdir("tmp"):
os.mkdir("tmp")
return os.path.abspath(os.getcwd()+"/tmp")
return os.path.abspath(os.getcwd()) | [
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atlas/auth/voms/models.py | kiae-grid/panda-bigmon-atlas | 0 | 187810 | <reponame>kiae-grid/panda-bigmon-atlas
__author__ = 'sbel'
from django.db import models
from django.utils import timezone
class VomsUser(models.Model):
id = models.AutoField(db_column='ID', primary_key=True)
username = models.CharField(max_length=60, db_column='USERNAME', db_index=True)
dn = models.CharField(max_length=255, db_column='DN', db_index=True)
ca = models.CharField(max_length=255, db_column='CA')
added_on = models.DateTimeField(auto_now_add=True, db_column='ADDED_ON')
def save(self, *args, **kwargs):
if not self.added_on:
self.added_on = timezone.now()
super(VomsUser, self).save(*args, **kwargs)
class Meta:
managed = True
db_table = u'VOMS_USERS_MAP'
app_label = 'auth'
unique_together = (("username", "dn"),)
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pqu/Check/check.py | brown170/fudge | 14 | 36702 | <filename>pqu/Check/check.py
# <<BEGIN-copyright>>
# Copyright 2021, Lawrence Livermore National Security, LLC.
# See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
# <<END-copyright>>
import sys
import glob, os, shutil, filecmp
PYTHON = sys.executable
files = sorted( glob.glob( 't*.py' ) )
if( os.path.exists( 'Out' ) ) : shutil.rmtree( 'Out' )
os.mkdir( 'Out' )
for file in files :
base = file[:-3]
status = os.system( '%s %s > Out/%s.out' % ( PYTHON, file, base ) )
if( status ) : print( '=========== %s ===========' % file )
outs = sorted( glob.glob( 'Out/t*.out' ) )
for out in outs :
file = os.path.basename( out )
if( not( filecmp.cmp( os.path.join( 'Out.checked', file ), out ) ) ) : print( 'ERROR: %s' % out )
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src/ganground/metric/kernel.py | tsirif/ganground | 1 | 147789 | # -*- coding: utf-8 -*-
r"""
:mod:`ganground.metric.kernel` -- Kernel function definitions
=============================================================
.. module:: kernel
:platform: Unix
:synopsis: Helper functions for Maximum Mean Discrepancy, plus kernel
function definitions.
"""
from abc import abstractmethod
import logging
import torch
import torch.nn.functional as F
from ganground.utils import (AbstractSingletonType, SingletonFactory)
logger = logging.getLogger(__name__)
__all__ = [
'mmd2', 'cross_mean_kernel_wrap',
'_pairwise_dot', '_pairwise_dist', '_pairwise_pow_dist',
'AbstractKernel', 'Kernel', 'Gaussian', 'Laplacian', 'Exp',
'Linear', 'Poly', 'Tanh', 'IMQ'
]
def mmd2(PPk, QQk, PQk):
"""Calculate squared Maximum Mean Discrepancy distance.
Args:
PPk: None, scalar torch tensor containing the mean PPk
or the full pairwise distance matrix
QQk: scalar torch tensor containing the mean QQk
or the full pairwise distance matrix
PQk: scalar torch tensor containing the mean PQk
or the full pairwise distance matrix
"""
assert(PQk is not None)
# Allow `PPk` to be None, if we want to compute mmd2 for the generator
if PPk is None:
PPk_ = 0
elif len(PPk.shape) == 2:
m = PPk.size(0)
PPk_ = (PPk.sum() - PPk.trace()) / (m**2 - m) if m != 1 else 0
elif len(PPk.shape) == 1:
PPk_ = PPk.mean()
elif len(PPk.shape) == 0:
PPk_ = PPk
else:
raise ValueError("Not supported `PPk`.")
if QQk is None:
QQk_ = 0
elif len(QQk.shape) == 2:
n = QQk.size(0)
QQk_ = (QQk.sum() - QQk.trace()) / (n**2 - n) if n != 1 else 0
elif len(QQk.shape) == 1:
QQk_ = QQk.mean()
elif len(QQk.shape) == 0:
QQk_ = QQk
else:
raise ValueError("Not supported `QQk`.")
if PQk.size():
PQk_ = PQk.mean()
else:
PQk_ = PQk
return PPk_ + QQk_ - 2 * PQk_
################################################################################
# Helper Kernel Functions #
################################################################################
def _find_tensor_device(tensor):
try:
dev = tensor.get_device()
except RuntimeError as e:
if 'CPU backend' in str(e):
return 'cpu'
else:
raise
if dev == -1:
return 'cpu'
return 'cuda:' + str(dev)
def cross_mean_kernel_wrap(kernel_fn, calcpp=True, calcqq=True,
try_pdist=False):
def kernel(cp, cq, **kernel_args):
m = cp.size(0)
n = cq.size(0)
cp_cq_len = m + n
if try_pdist:
# logger.debug("Using pytorch.pdist with concatenation to calc cross means.")
# It will use F.pdist to calculate cpp, cpq, and cqq
cp_cq = torch.cat((cp, cq), dim=0).contiguous()
res_type = cp_cq.dtype
res_device = _find_tensor_device(cp_cq)
res = kernel_fn(cp_cq, cp_cq, **kernel_args).to(torch.float64)
inds = torch.triu_indices(cp_cq_len, cp_cq_len, offset=1,
dtype=torch.float64, device=res_device)
inds_r = inds[0, :]
inds_c = inds[1, :]
# Split to get Ecxx, Ecxy, and Ecyy
if calcpp:
mask = inds_c.sub(m).mul_(-1).clamp_(0, 1)
n = mask.sum()
cpp = mask.mul_(res).sum().div_(n).to(res_type)
else:
cpp = None
if calcqq:
mask = inds_r.sub(m - 1).clamp_(0, 1)
n = mask.sum()
cqq = mask.mul_(res).sum().div_(n).to(res_type)
else:
cqq = None
mask = inds_r.sub(m).mul_(-1).clamp_(0, 1)
mask = inds_c.sub(m - 1).clamp_(0, 1).mul_(mask)
n = mask.sum()
cpq = mask.mul_(res).sum().div_(n).to(res_type)
else:
# logger.debug("Invoking three times the kernel to calc cross means.")
if calcpp:
cpp = kernel_fn(cp, cp, **kernel_args)
else:
cpp = None
if calcqq:
cqq = kernel_fn(cq, cq, **kernel_args)
else:
cqq = None
cpq = kernel_fn(cp, cq, **kernel_args)
return cpp, cqq, cpq
return kernel
def _pairwise_dist(cx, cy, p=2, _pow_flag=False):
"""Compute pairwise distances between two Tensors of size m x `shape`
and n x `shape`.
This should be done as efficiently as possible. Discussions:
https://github.com/pytorch/pytorch/issues/9406
"""
def _are_equal(cx, cy):
if cx is cy:
return True
return torch.equal(cx, cy)
res = None
m = cx.size(0)
n = cy.size(0)
imsize = cx.view(m, -1).size(-1)
cx_eq_cy = _are_equal(cx, cy)
if cx_eq_cy:
# logger.debug("Calc pairwise distance with pytorch.pdist.")
# Calculate only triangular, fast, cheaper, stable. Looks like this:
# torch.cat([torch.full((n - i - 1,), i, dtype=torch.int64) for i in range(n)])
res = F.pdist(cx.view(m, -1), p=p)
elif p == 2 and m * n * imsize * (torch.finfo(cx.dtype).bits // 8) > 4 * 1024**2:
# logger.debug("Calc pairwise distance with quadratic expansion.")
# If more than 4MB needed to repr a full matrix
# Faster and cheaper, but less stable (quadratic expansion)
# Still slower than the first choice
cx_ = cx.view(m, -1)
cy_ = cy.view(n, -1)
cx_norm = cx_.pow(2).sum(dim=-1, keepdim=True)
cy_norm = cy_.pow(2).sum(dim=-1, keepdim=True).transpose(-2, -1)
res = cx_norm + cy_norm - 2 * cx_.matmul(cy_.transpose(-2, -1))
if cx_eq_cy:
# Ensure zero diagonal
diag_inds = torch.arange(m)
res[diag_inds, diag_inds] = 0
# Zero out negative values
res.clamp_min_(0)
if _pow_flag:
_pow_flag[0] = True
else:
res = res.sqrt()
else:
# logger.debug("Calc pairwise distance with naive broadcasting.")
# More expensive - Θ(n^2 d), but numerically more stable
cx_ = cx.view(m, 1, -1)
cy_ = cy.view(1, n, -1)
# XXX does not support broadcasting yet #15918 and #15901
# res = F.pairwise_distance(cx_, cy_, p=p, eps=1e-8)
res = torch.norm(cx_ - cy_, p=p, dim=-1)
return res
def _pairwise_pow_dist(cx, cy, p=2):
pow_flag = [False]
res = _pairwise_dist(cx, cy, p=p, _pow_flag=pow_flag)
if pow_flag[0]:
return res
return res.pow(p)
def _pairwise_dot(cx, cy):
# TODO Find a more memory efficient way to do this
# Can pytorch cosine similarity be used perhaps??
m = cx.size(0)
n = cy.size(0)
cx_ = cx.view(m, 1, -1)
cy_ = cy.view(1, n, -1)
return cx_.mul(cy_).sum(dim=-1)
################################################################################
# Kernel Function Definitions #
################################################################################
class AbstractKernel(object, metaclass=AbstractSingletonType):
@abstractmethod
def __call__(self, cx, cy, **kernel_kwargs):
pass
class Gaussian(AbstractKernel):
def __call__(self, cx, cy, sigma=1):
XY = _pairwise_pow_dist(cx, cy)
return XY.div_(- 2 * sigma**2).exp_()
class Laplacian(AbstractKernel):
def __call__(self, cx, cy, p=2, sigma=1):
XY = _pairwise_dist(cx, cy, p=p)
return XY.div_(-sigma).exp_()
class Exp(AbstractKernel):
def __call__(self, cx, cy, sigma=1):
XY = _pairwise_dot(cx, cy)
return XY.div_(sigma).exp_()
class Linear(AbstractKernel):
def __call__(self, cx, cy, c=0):
return _pairwise_dot(cx - c, cy - c)
class Poly(AbstractKernel):
def __call__(self, cx, cy, a=1, c=1, d=2):
XY = _pairwise_dot(cx, cy)
return XY.mul_(a).add_(c).pow_(d)
class Tanh(AbstractKernel):
def __call__(self, cx, cy, a=1, c=0):
XY = _pairwise_dot(cx, cy)
return XY.mul_(a).add_(c).tanh_()
class IMQ(AbstractKernel):
def __call__(self, cx, cy, c=1, d=2, sigma=1):
XY = _pairwise_pow_dist(cx, cy)
return XY.div_(sigma**2).add_(c).pow_(-d / 2)
class Kernel(AbstractKernel, metaclass=SingletonFactory):
rbf = ('gaussian', 'exp', 'laplacian', 'imq')
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13,
1678,
364,
1635,
353,
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29887,
17019,
742,
525,
4548,
742,
525,
6984,
433,
28445,
742,
525,
326,
29939,
1495,
13,
2
] |
allennlp/nn/parallel/__init__.py | MSLars/allennlp | 11,433 | 88956 | <filename>allennlp/nn/parallel/__init__.py
from allennlp.nn.parallel.sharded_module_mixin import ShardedModuleMixin
from allennlp.nn.parallel.ddp_accelerator import (
DdpAccelerator,
DdpWrappedModel,
TorchDdpAccelerator,
)
from allennlp.nn.parallel.fairscale_fsdp_accelerator import (
FairScaleFsdpAccelerator,
FairScaleFsdpWrappedModel,
)
| [
1,
529,
9507,
29958,
497,
2108,
22833,
29914,
15755,
29914,
23482,
29914,
1649,
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26914,
2272,
13,
3166,
599,
2108,
22833,
29889,
15755,
29889,
23482,
29889,
845,
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29918,
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262,
1053,
1383,
25600,
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861,
262,
13,
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313,
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7052,
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29888,
4928,
29886,
29918,
562,
2242,
261,
1061,
1053,
313,
13,
1678,
13822,
17185,
29943,
4928,
29886,
7504,
7367,
1061,
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13,
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17185,
29943,
4928,
29886,
29956,
336,
2986,
3195,
29892,
13,
29897,
13,
2
] |
aries_cloudcontroller/model/v10_credential_exchange.py | didx-xyz/aries-cloudcontroller-pyton | 5 | 150921 | <gh_stars>1-10
# coding: utf-8
from __future__ import annotations
from datetime import date, datetime # noqa: F401
import re # noqa: F401
from typing import Any, Dict, List, Optional, Union, Literal # noqa: F401
from pydantic import AnyUrl, BaseModel, EmailStr, validator, Field, Extra # noqa: F401
from aries_cloudcontroller.model.credential_offer import CredentialOffer
from aries_cloudcontroller.model.credential_proposal import CredentialProposal
from aries_cloudcontroller.model.indy_cred_abstract import IndyCredAbstract
from aries_cloudcontroller.model.indy_cred_info import IndyCredInfo
from aries_cloudcontroller.model.indy_cred_request import IndyCredRequest
from aries_cloudcontroller.model.indy_credential import IndyCredential
class V10CredentialExchange(BaseModel):
"""NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
Do not edit the class manually.
V10CredentialExchange - a model defined in OpenAPI
auto_issue: Issuer choice to issue to request in this credential exchange [Optional].
auto_offer: Holder choice to accept offer in this credential exchange [Optional].
auto_remove: Issuer choice to remove this credential exchange record when complete [Optional].
connection_id: Connection identifier [Optional].
created_at: Time of record creation [Optional].
credential: Credential as stored [Optional].
credential_definition_id: Credential definition identifier [Optional].
credential_exchange_id: Credential exchange identifier [Optional].
credential_id: Credential identifier [Optional].
credential_offer: (Indy) credential offer [Optional].
credential_offer_dict: Credential offer message [Optional].
credential_proposal_dict: Credential proposal message [Optional].
credential_request: (Indy) credential request [Optional].
credential_request_metadata: (Indy) credential request metadata [Optional].
error_msg: Error message [Optional].
initiator: Issue-credential exchange initiator: self or external [Optional].
parent_thread_id: Parent thread identifier [Optional].
raw_credential: Credential as received, prior to storage in holder wallet [Optional].
revoc_reg_id: Revocation registry identifier [Optional].
revocation_id: Credential identifier within revocation registry [Optional].
role: Issue-credential exchange role: holder or issuer [Optional].
schema_id: Schema identifier [Optional].
state: Issue-credential exchange state [Optional].
thread_id: Thread identifier [Optional].
trace: Record trace information, based on agent configuration [Optional].
updated_at: Time of last record update [Optional].
"""
auto_issue: Optional[bool] = None
auto_offer: Optional[bool] = None
auto_remove: Optional[bool] = None
connection_id: Optional[str] = None
created_at: Optional[str] = None
credential: Optional[IndyCredInfo] = None
credential_definition_id: Optional[str] = None
credential_exchange_id: Optional[str] = None
credential_id: Optional[str] = None
credential_offer: Optional[IndyCredAbstract] = None
credential_offer_dict: Optional[CredentialOffer] = None
credential_proposal_dict: Optional[CredentialProposal] = None
credential_request: Optional[IndyCredRequest] = None
credential_request_metadata: Optional[Dict[str, Any]] = None
error_msg: Optional[str] = None
initiator: Optional[Literal["self", "external"]] = None
parent_thread_id: Optional[str] = None
raw_credential: Optional[IndyCredential] = None
revoc_reg_id: Optional[str] = None
revocation_id: Optional[str] = None
role: Optional[Literal["holder", "issuer"]] = None
schema_id: Optional[str] = None
state: Optional[str] = None
thread_id: Optional[str] = None
trace: Optional[bool] = None
updated_at: Optional[str] = None
def __init__(
self,
*,
auto_issue: Optional[bool] = None,
auto_offer: Optional[bool] = None,
auto_remove: Optional[bool] = None,
connection_id: Optional[str] = None,
created_at: Optional[str] = None,
credential: Optional[IndyCredInfo] = None,
credential_definition_id: Optional[str] = None,
credential_exchange_id: Optional[str] = None,
credential_id: Optional[str] = None,
credential_offer: Optional[IndyCredAbstract] = None,
credential_offer_dict: Optional[CredentialOffer] = None,
credential_proposal_dict: Optional[CredentialProposal] = None,
credential_request: Optional[IndyCredRequest] = None,
credential_request_metadata: Optional[Dict[str, Any]] = None,
error_msg: Optional[str] = None,
initiator: Optional[Literal["self", "external"]] = None,
parent_thread_id: Optional[str] = None,
raw_credential: Optional[IndyCredential] = None,
revoc_reg_id: Optional[str] = None,
revocation_id: Optional[str] = None,
role: Optional[Literal["holder", "issuer"]] = None,
schema_id: Optional[str] = None,
state: Optional[str] = None,
thread_id: Optional[str] = None,
trace: Optional[bool] = None,
updated_at: Optional[str] = None,
**kwargs,
):
super().__init__(
auto_issue=auto_issue,
auto_offer=auto_offer,
auto_remove=auto_remove,
connection_id=connection_id,
created_at=created_at,
credential=credential,
credential_definition_id=credential_definition_id,
credential_exchange_id=credential_exchange_id,
credential_id=credential_id,
credential_offer=credential_offer,
credential_offer_dict=credential_offer_dict,
credential_proposal_dict=credential_proposal_dict,
credential_request=credential_request,
credential_request_metadata=credential_request_metadata,
error_msg=error_msg,
initiator=initiator,
parent_thread_id=parent_thread_id,
raw_credential=raw_credential,
revoc_reg_id=revoc_reg_id,
revocation_id=revocation_id,
role=role,
schema_id=schema_id,
state=state,
thread_id=thread_id,
trace=trace,
updated_at=updated_at,
**kwargs,
)
@validator("created_at")
def created_at_pattern(cls, value):
# Property is optional
if value is None:
return
pattern = r"^\d{4}-\d\d-\d\d[T ]\d\d:\d\d(?:\:(?:\d\d(?:\.\d{1,6})?))?(?:[+-]\d\d:?\d\d|Z|)$"
if not re.match(pattern, value):
raise ValueError(
f"Value of created_at does not match regex pattern ('{pattern}')"
)
return value
@validator("credential_definition_id")
def credential_definition_id_pattern(cls, value):
# Property is optional
if value is None:
return
pattern = r"^([123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz]{21,22}):3:CL:(([1-9][0-9]*)|([123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz]{21,22}:2:.+:[0-9.]+)):(.+)?$"
if not re.match(pattern, value):
raise ValueError(
f"Value of credential_definition_id does not match regex pattern ('{pattern}')"
)
return value
@validator("schema_id")
def schema_id_pattern(cls, value):
# Property is optional
if value is None:
return
pattern = r"^[123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz]{21,22}:2:.+:[0-9.]+$"
if not re.match(pattern, value):
raise ValueError(
f"Value of schema_id does not match regex pattern ('{pattern}')"
)
return value
@validator("updated_at")
def updated_at_pattern(cls, value):
# Property is optional
if value is None:
return
pattern = r"^\d{4}-\d\d-\d\d[T ]\d\d:\d\d(?:\:(?:\d\d(?:\.\d{1,6})?))?(?:[+-]\d\d:?\d\d|Z|)$"
if not re.match(pattern, value):
raise ValueError(
f"Value of updated_at does not match regex pattern ('{pattern}')"
)
return value
class Config:
allow_population_by_field_name = True
V10CredentialExchange.update_forward_refs()
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] |
python/TrackExtrapolateTool.py | Plamenna/proba | 0 | 178466 | import ROOT
import shipunit as u
minNdf = 20
parallelToZ = ROOT.TVector3(0., 0., 1.)
top = ROOT.gGeoManager.GetTopVolume()
z_ecal = top.GetNode('Ecal_1').GetMatrix().GetTranslation()[2]
def extrapolateToPlane(fT,z):
# etrapolate to a plane perpendicular to beam direction (z)
rc,pos,mom = False,None,None
fst = fT.getFitStatus()
if fst.isFitConverged() and fst.getNdf() > minNdf:
fstate0,fstate1 = fT.getFittedState(0),fT.getFittedState(1)
fPos0,fPos1 = fstate0.getPos(),fstate1.getPos()
if abs(z-fPos0.z()) < abs(z-fPos1.z()): fstate = fstate0
else: fstate = fstate1
zs = min(z,z_ecal)
NewPosition = ROOT.TVector3(0., 0., zs)
rep = ROOT.genfit.RKTrackRep(13*cmp(fstate.getPDG(),0) )
state = ROOT.genfit.StateOnPlane(rep)
pos,mom = fstate.getPos(),fstate.getMom()
rep.setPosMom(state,pos,mom)
try:
rep.extrapolateToPlane(state, NewPosition, parallelToZ )
pos,mom = state.getPos(),state.getMom()
rc = True
except:
print 'error with extrapolation: z=',z/u.m,'m'
if not rc or z>z_ecal:
# use linear extrapolation
px,py,pz = mom.X(),mom.Y(),mom.Z()
lam = (z-pos.Z())/pz
pos = ROOT.TVector3( pos.X()+lam*px, pos.Y()+lam*py, z )
return rc,pos,mom
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] |
src/hydra_zen/experimental/_implementations.py | rlbellaire/hydra-zen | 0 | 165891 | # Copyright (c) 2021 Massachusetts Institute of Technology
# SPDX-License-Identifier: MIT
from pathlib import Path
from typing import Any, Callable, List, Mapping, Optional, Union
from hydra._internal.callbacks import Callbacks
from hydra._internal.hydra import Hydra
from hydra._internal.utils import create_config_search_path
from hydra.core.config_store import ConfigStore
from hydra.core.global_hydra import GlobalHydra
from hydra.core.utils import JobReturn
from hydra.plugins.sweeper import Sweeper
from hydra.types import HydraContext, RunMode
from omegaconf import DictConfig, OmegaConf
from .._hydra_overloads import instantiate
from ..typing import DataClass
def _store_config(
cfg: Union[DataClass, DictConfig, Mapping], config_name: str = "hydra_launch"
) -> str:
"""Stores configuration object in Hydra's ConfigStore.
Parameters
----------
cfg: Union[DataClass, DictConfig, Mapping]
A configuration as a dataclass, configuration object, or a dictionary.
config_name: str (default: hydra_launch)
The configuration name used to store the configuration.
Returns
-------
config_name: str
The configuration name used to store the default configuration.
Notes
-----
The input configuration is registered in the Hydra ConfigStore [1]_ using a
user-provided config name.
References
----------
.. [1] https://hydra.cc/docs/tutorials/structured_config/config_store
"""
cs = ConfigStore().instance()
cs.store(name=config_name, node=cfg)
return config_name
def hydra_run(
config: Union[DataClass, DictConfig, Mapping],
task_function: Callable[[DictConfig], Any],
overrides: Optional[List[str]] = None,
config_dir: Optional[Union[str, Path]] = None,
config_name: str = "hydra_run",
job_name: str = "hydra_run",
with_log_configuration: bool = True,
) -> JobReturn:
"""Launch a Hydra job defined by `task_function` using the configuration
provided in `config`.
Similar to how Hydra CLI works, `overrides` are a string list of configuration
values to use for a given experiment run. For example, the Hydra CLI provided by::
$ python -m job.task_function job/group=group_name job.group.param=1
would be::
>>> job = hydra_run(config, task_function, overrides=["job/group=group_name", "job.group.param=1"])
This functions executes Hydra and therefore creates its own working directory. See Configuring Hydra [2]_ for more
details on customizing Hydra.
Parameters
----------
config: Union[DataClass, DictConfig, Mapping]
A configuration as a dataclass, configuration object, or a dictionary.
task_function: Callable[[DictConfig], Any]
The function Hydra will execute with the given configuration.
overrides: Optional[List[str]] (default: None)
If provided, overrides default configurations, see [1]_ and [2]_.
config_dir: Optional[Union[str, Path]] (default: None)
Add configuration directories if needed.
config_name: str (default: "hydra_run")
Name of the stored configuration in Hydra's ConfigStore API.
job_name: str (default: "hydra_run")
with_log_configuration: bool (default: True)
Flag to configure logging subsystem from the loaded config
Returns
-------
result: JobReturn
The object storing the results of the Hydra experiment.
- overrides: From `overrides` and `multirun_overrides`
- return_value: The return value of the task function
- cfg: The configuration object sent to the task function
- hydra_cfg: The hydra configuration object
- working_dir: The experiment working directory
- task_name: The task name of the Hydra job
References
----------
.. [1] https://hydra.cc/docs/next/advanced/override_grammar/basic
.. [2] https://hydra.cc/docs/next/configure_hydra/intro
Examples
--------
Simple Hydra run:
>>> from hydra_zen import instantiate, builds
>>> from hydra_zen.experimental import hydra_run
>>> job = hydra_run(builds(dict, a=1, b=1), task_function=instantiate)
>>> job.return_value
{'a': 1, 'b': 1}
Using a more complex task function:
>>> from hydra_zen.experimental import hydra_run
>>> from hydra_zen import builds, instantiate
>>> cfg = dict(f=builds(pow, exp=2, hydra_partial=True), x=10)
>>> def task_function(cfg):
... return instantiate(cfg.f)(cfg.x)
Launch a job to evaluate the function using the given configuration:
>>> job = hydra_run(cfg, task_function)
>>> job.return_value
100
An example using PyTorch:
>>> from torch.optim import Adam
>>> from torch.nn import Linear
>>> AdamConfig = builds(Adam, lr=0.001, hydra_partial=True)
>>> ModelConfig = builds(Linear, in_features=1, out_features=1)
>>> cfg = dict(optim=AdamConfig(), model=ModelConfig())
>>> def task_function(cfg):
... cfg = instantiate(cfg)
... optim = cfg.optim(model.parameters())
... loss = cfg.model(torch.ones(1)).mean()
... optim.zero_grad()
... loss.backward()
... optim.step()
... return loss.item()
>>> jobs = hydra_run(cfg, task_function, overrides=["optim.lr=0.1"])
>>> j.return_value
0.3054758310317993
"""
config_name = _store_config(config, config_name)
if config_dir is not None:
config_dir = str(Path(config_dir).absolute())
search_path = create_config_search_path(config_dir)
hydra = Hydra.create_main_hydra2(task_name=job_name, config_search_path=search_path)
try:
job = hydra.run(
config_name=config_name,
task_function=task_function,
overrides=overrides if overrides is not None else [],
with_log_configuration=with_log_configuration,
)
finally:
GlobalHydra.instance().clear()
return job
def hydra_multirun(
config: Union[DataClass, DictConfig, Mapping],
task_function: Callable[[DictConfig], Any],
overrides: Optional[List[str]] = None,
config_dir: Optional[Union[str, Path]] = None,
config_name: str = "hydra_multirun",
job_name: str = "hydra_multirun",
with_log_configuration: bool = True,
) -> Any:
"""Launch a Hydra multi-run ([1]_) job defined by `task_function` using the configuration
provided in `config`.
Similar to how Hydra CLI works, `overrides` are a string list of configuration
values to use for a given experiment run. For example, the Hydra CLI provided by::
$ python -m job.task_function job/group=group_name job.group.param=1 --multirun
would be::
>>> job = hydra_multirun(config, task_function, overrides=["job/group=group_name", "job.group.param=1"])
To sweep over parameters the Hydra CLI provided by::
$ python -m job.task_function job/group=group_name job.group.param=1,2,3 --multirun
would be::
>>> job = hydra_multirun(config, task_function, overrides=["job/group=group_name", "job.group.param=1,2,3"])
This functions executes Hydra and therefore creates its own working directory. See Configuring Hydra [3]_ for more
details on customizing Hydra.
Parameters
----------
config: Union[DataClass, DictConfig]
A configuration as a dataclass, configuration object, or a dictionary.
task_function: Callable[[DictConfig], Any]
The function Hydra will execute with the given configuration.
overrides: Optional[List[str]] (default: None)
If provided, overrides default configurations, see [2]_ and [3]_.
config_dir: Optional[Union[str, Path]] (default: None)
Add configuration directories if needed.
config_name: str (default: "hydra_run")
Name of the stored configuration in Hydra's ConfigStore API.
job_name: str (default: "hydra_multirun")
with_log_configuration: bool (default: True)
Flag to configure logging subsystem from the loaded config
Returns
-------
result: Any
The return values of all launched jobs (depends on the Sweeper implementation).
References
----------
.. [1] https://hydra.cc/docs/tutorials/basic/running_your_app/multi-run
.. [2] https://hydra.cc/docs/next/advanced/override_grammar/basic
.. [3] https://hydra.cc/docs/next/configure_hydra/intro
Examples
--------
Simple Hydra multirun:
>>> job = hydra_multirun(
... builds(dict, a=1, b=1),
... task_function=instantiate,
... overrides=["a=1,2"],
... )
>>> [j.return_value for j in job[0]]
[{'a': 1, 'b': 1}, {'a': 2, 'b': 1}]
Using a more complex `task_function`
>>> from hydra_zen import builds, instantiate
>>> cfg = dict(f=builds(pow, exp=2, hydra_partial=True), x=1)
>>> def task_function(cfg):
... return instantiate(cfg.f)(cfg.x)
Launch a multi-run over a list of different `x` values using Hydra's override syntax `range`:
>>> jobs = hydra_multirun(cfg, task_function, overrides=["x=range(-2,3)"])
>>> [j.return_value for j in jobs[0]]
[4, 1, 0, 1, 4]
An example using PyTorch
>>> from torch.optim import Adam
>>> from torch.nn import Linear
>>> AdamConfig = builds(Adam, lr=0.001, hydra_partial=True)
>>> ModelConfig = builds(Linear, in_features=1, out_features=1)
>>> cfg = dict(optim=AdamConfig(), model=ModelConfig())
>>> def task_function(cfg):
... cfg = instantiate(cfg)
... optim = cfg.optim(model.parameters())
... loss = cfg.model(torch.ones(1)).mean()
... optim.zero_grad()
... loss.backward()
... optim.step()
... return loss.item()
Evaluate the function for different learning rates
>>> jobs = hydra_multirun(cfg, task_function, overrides=["optim.lr=0.1,1.0"])
>>> [j.return_value for j in jobs[0]]
[0.3054758310317993, 0.28910207748413086]
"""
config_name = _store_config(config, config_name)
if config_dir is not None:
config_dir = str(Path(config_dir).absolute())
search_path = create_config_search_path(config_dir)
hydra = Hydra.create_main_hydra2(task_name=job_name, config_search_path=search_path)
try:
cfg = hydra.compose_config(
config_name=config_name,
overrides=overrides if overrides is not None else [],
with_log_configuration=with_log_configuration,
run_mode=RunMode.MULTIRUN,
)
callbacks = Callbacks(cfg)
callbacks.on_multirun_start(config=cfg, config_name=config_name)
# Instantiate sweeper without using Hydra's Plugin discovery (Zen!)
sweeper = instantiate(cfg.hydra.sweeper)
assert isinstance(sweeper, Sweeper)
sweeper.setup(
config=cfg,
hydra_context=HydraContext(
config_loader=hydra.config_loader, callbacks=callbacks
),
task_function=task_function,
)
task_overrides = OmegaConf.to_container(cfg.hydra.overrides.task, resolve=False)
assert isinstance(task_overrides, list)
job = sweeper.sweep(arguments=task_overrides)
callbacks.on_multirun_end(config=cfg, config_name=config_name)
finally:
GlobalHydra.instance().clear()
return job
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] |
exercicios/agenda_de_contatos.py | ricardoluizbb/python-codes | 1 | 98516 | <reponame>ricardoluizbb/python-codes<gh_stars>1-10
AGENDA = {}
def exibir_contatos():
if len(AGENDA) > 0:
for contato in AGENDA:
buscar_contato(contato)
print('-' * 30)
else:
print('\nA sua agenda está vazia. Adicione alguns contatos!\n')
def buscar_contato(contato):
try:
print(f'Nome: {contato}')
print('Celular:', AGENDA[contato]['celular'])
print('E-mail:', AGENDA[contato]['email'])
except KeyError:
print(f'\nO contato {contato} não existe na agenda.\n')
except:
print('\nDesculpe, aconteceu um erro inesperado.\n')
# Verifica se o nome do contato digitado já existe na agenda. Linha 87 e 97 #
def verificando_se_existe(contato):
if contato in AGENDA:
return True
return False
def incluir_contato(contato, celular, email, verificacao_incluir_editar = True):
AGENDA[contato] = {
'celular': celular,
'email': email
}
# Verificação para retornar a mensagem correta. #
if verificacao_incluir_editar:
print(f'\nO contato {contato} foi incluido.\n')
else:
print(f'\nO contato {contato} foi editado.\n')
def editar_contato(contato, celular, email):
incluir_contato(contato, celular, email, verificacao_incluir_editar = False)
def deletar_contato(contato):
try:
AGENDA.pop(contato)
print(f'\nO contato {contato} foi deletado.\n')
except KeyError:
print(f'\nO contato {contato} não existe na agenda.\n')
# Função que exporta contatos para um arquivo txt #
def exportar_contatos():
try:
with open('agenda.txt', 'w') as arquivo:
arquivo.write('Nome, Celular, E-mail\n')
for contato in AGENDA:
celular = AGENDA[contato]['celular']
email = AGENDA[contato]['email']
arquivo.write(f'{contato}, {celular}, {email}\n')
print('\nSua agenda foi exportada com sucesso!\n')
except Exception as error:
print('\nDesculpe, aconteceu um erro inesperado.\n')
def imprimir_menu():
print('\n1 - Exibir contatos')
print('2 - Buscar um contato')
print('3 - Incluir um contato')
print('4 - Editar um contato')
print('5 - Deletar um contato')
print('6 - Exportar agenda para um arquivo txt')
print('0 - Fechar agenda\n')
while True:
imprimir_menu()
action = (input('Digite o que deseja fazer: '))
print()
if action == '1':
exibir_contatos()
elif action == '2':
contato = input('Digite o nome do contato: ')
print('-' * 30)
buscar_contato(contato)
print('-' * 30)
elif action == '3':
contato = input('Digite o nome do contato: ')
if verificando_se_existe(contato):
print(f'\nO contato {contato} já existe na agenda. Tente adicioná-lo com outro nome ou sobrenome.\n')
else:
celular = input('Digite o número do celular do contato: ')
email = input('Digite o e-mail do contato: ')
incluir_contato(contato, celular, email)
elif action == '4':
contato = input('Digite o nome do contato que deseja editar: ')
if verificando_se_existe(contato):
celular = input('Digite o número do celular do contato: ')
email = input('Digite o e-mail do contato: ')
editar_contato(contato, celular, email)
else:
print(f'\nO contato {contato} não existe na agenda.\n')
elif action == '5':
contato = input('Digite o nome do contato que deseja deletar: ')
deletar_contato(contato)
elif action == '6':
exportar_contatos()
elif action == '0':
print('A agenda foi fechada.')
break
else:
print('Por favor, dhigite um comando válido.') | [
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] |
difftorch/util.py | gbaydin/difftorch | 30 | 169092 | import torch
import typing
def check_vector(x, name):
if not torch.is_tensor(x):
raise RuntimeError('{} needs to be a Tensor'.format(name))
if x.dim() != 1:
raise RuntimeError('{} needs to be a vector (one-dimensional Tensor)'.format(name))
def check_scalar(x, name):
if not torch.is_tensor(x):
raise RuntimeError('{} needs to be a Tensor'.format(name))
if x.dim() != 0:
raise RuntimeError('{} needs to be a scalar (zero-dimensional Tensor)'.format(name))
def check_same_shape(x, xname, y, yname):
if x.shape != y.shape:
raise RuntimeError('Expecting {}.shape ({}) and {}.shape ({}) to be the same'.format(xname, x.shape, yname, y.shape))
def onehot_like(x, i):
ret = torch.zeros_like(x)
ret[i] = 1.
return ret
def unzip(lst):
return zip(*lst)
def check_tensor(value):
if not torch.is_tensor(value):
raise ValueError('Expecting value to be a tensor')
def check_list_or_tuple_of_tensors(value):
if not (isinstance(value, typing.List) or isinstance(value, typing.Tuple)):
raise ValueError('Expecting value to be a list or tuple')
for v in value:
check_tensor(v)
def flatten(tensors):
if torch.is_tensor(tensors):
return tensors.reshape(-1)
check_list_or_tuple_of_tensors(tensors)
return torch.cat([t.reshape(-1) for t in tensors])
def unflatten_as(tensor, tensors):
if torch.is_tensor(tensors):
return tensor.reshape(tensors.shape)
check_tensor(tensor)
check_list_or_tuple_of_tensors(tensors)
shapes = [t.shape for t in tensors]
nelements = [t.nelement() for t in tensors]
ts = torch.split(tensor.reshape(-1), nelements)
return [ts[i].reshape(shapes[i]) for i in range(len(tensors))]
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363,
474,
297,
3464,
29898,
2435,
29898,
29873,
575,
943,
28166,
13,
2
] |
Multidimensional-Arrays/Lab-Multidimensional-Lists/primary_diagonal.py | dechevh/Python-Advanced | 2 | 137333 | <filename>Multidimensional-Arrays/Lab-Multidimensional-Lists/primary_diagonal.py<gh_stars>1-10
n = int(input())
matrix = [list(map(int, input().split(" "))) for _ in range(n)]
total = 0
for i in range(n):
value = matrix[i][i]
total += value
print(total) | [
1,
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13,
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2158,
29898,
7827,
29897,
2
] |
jentry/entry/script/file.py | HansBug/jentry | 0 | 147173 | import codecs
from typing import Iterator, Tuple
import javalang
from javalang.ast import Node
from javalang.tree import MethodDeclaration, PackageDeclaration, ClassDeclaration
from ...model import JavaEntry
def is_entry_method(node: Node) -> bool:
"""
Determine if the given method node is an entry or not.
:param node: Given method node.
:return: Valid or not.
"""
if 'public' in node.modifiers and 'static' in node.modifiers \
and node.name == 'main' and node.return_type is None and len(node.parameters) == 1:
param = node.parameters[0]
if param.type.name == 'String' and (
(not param.varargs and param.type.dimensions == [None]) or
(param.varargs and param.type.dimensions == [])
):
return True
return False
def load_entries_from_file(filename: str) -> Iterator[JavaEntry]:
"""
Load entries from one java source code file.
:param filename: File name.
:return: Iterator of :class:`jentry.model.entry.JavaEntry` objects.
"""
with codecs.open(filename, 'r') as cf:
for package, clazz in load_entry_classes_from_code(cf.read()):
yield JavaEntry(filename, package, clazz)
def load_entry_classes_from_code(code: str) -> Iterator[Tuple[str, str]]:
"""
Load entry classes from one java source code.
:param code: Java source code string.
:return: Iterator of entry class tuples.
"""
parsed_code = javalang.parse.parse(code)
package = None
for pkgpath, pkgnode in parsed_code.filter(PackageDeclaration):
if isinstance(pkgnode, PackageDeclaration):
package = pkgnode.name
for clspath, clsnode in parsed_code.filter(ClassDeclaration):
if len(clspath) == 2 and 'private' not in clsnode.modifiers:
is_entry = False
for mthpath, mthnode in clsnode.filter(MethodDeclaration):
if len(mthpath) == 2 and is_entry_method(mthnode):
is_entry = True
break
if is_entry:
yield package, clsnode.name
| [
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353,
432,
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29889,
5510,
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401,
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13,
13,
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13,
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363,
282,
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282,
9415,
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297,
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401,
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565,
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29898,
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3177,
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22029,
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23838,
1125,
13,
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353,
282,
9415,
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29889,
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13,
13,
1678,
363,
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1028,
493,
29892,
1067,
29879,
3177,
297,
21213,
29918,
401,
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4572,
29898,
2385,
6185,
23838,
1125,
13,
4706,
565,
7431,
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695,
1028,
493,
29897,
1275,
29871,
29906,
322,
525,
9053,
29915,
451,
297,
1067,
29879,
3177,
29889,
1545,
14903,
29901,
13,
9651,
338,
29918,
8269,
353,
7700,
13,
9651,
363,
286,
386,
2084,
29892,
286,
386,
3177,
297,
1067,
29879,
3177,
29889,
4572,
29898,
4062,
6185,
23838,
1125,
13,
18884,
565,
7431,
29898,
29885,
386,
2084,
29897,
1275,
29871,
29906,
322,
338,
29918,
8269,
29918,
5696,
29898,
29885,
386,
3177,
1125,
13,
462,
1678,
338,
29918,
8269,
353,
5852,
13,
462,
1678,
2867,
13,
13,
9651,
565,
338,
29918,
8269,
29901,
13,
18884,
7709,
3577,
29892,
1067,
29879,
3177,
29889,
978,
13,
2
] |
src/asphalt/serialization/marshalling.py | Asphalt-framework/asphalt-serialization | 0 | 48131 | <filename>src/asphalt/serialization/marshalling.py
from __future__ import annotations
from typing import Any
from asphalt.core import qualified_name
def default_marshaller(obj: Any) -> Any:
"""
Retrieve the state of the given object.
Calls the ``__getstate__()`` method of the object if available, otherwise returns
the ``__dict__`` of the object.
:param obj: the object to marshal
:return: the marshalled object state
"""
if hasattr(obj, "__getstate__"):
return obj.__getstate__()
try:
return obj.__dict__
except AttributeError:
raise TypeError(
f"{qualified_name(obj.__class__)!r} has no __dict__ attribute and does not "
f"implement __getstate__()"
) from None
def default_unmarshaller(instance: Any, state: Any) -> None:
"""
Restore the state of an object.
If the ``__setstate__()`` method exists on the instance, it is called with the state
object as the argument. Otherwise, the instance's ``__dict__`` is replaced with
``state``.
:param instance: an uninitialized instance
:param state: the state object, as returned by :func:`default_marshaller`
"""
if hasattr(instance, "__setstate__"):
instance.__setstate__(state)
else:
try:
instance.__dict__.update(state)
except AttributeError:
raise TypeError(
f"{qualified_name(instance.__class__)!r} has no __dict__ attribute and "
f"does not implement __setstate__()"
) from None
| [
1,
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29958,
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294,
561,
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326,
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1723,
515,
6213,
13,
13,
13,
1753,
2322,
29918,
348,
3034,
845,
12572,
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29901,
3139,
29892,
2106,
29901,
3139,
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1599,
6213,
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443,
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278,
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408,
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491,
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13,
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13,
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756,
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13,
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285,
29908,
29912,
15380,
2164,
29918,
978,
29898,
8758,
17255,
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20198,
29878,
29913,
756,
694,
4770,
8977,
1649,
5352,
322,
376,
13,
18884,
285,
29908,
13221,
451,
2334,
4770,
842,
3859,
1649,
25318,
13,
9651,
1723,
515,
6213,
13,
2
] |
src/plotting/TATA_enrichment_plots.py | Switham1/PromoterArchitecture | 0 | 139785 | <filename>src/plotting/TATA_enrichment_plots.py<gh_stars>0
import argparse
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def parse_args(args):
"""define arguments"""
parser = argparse.ArgumentParser(description="TATA_enrichment_plots")
parser.add_argument(
"file_names",
type=str,
help="Name of folder and filenames for the promoters extracted",
)
parser.add_argument(
"gat_TATA_constitutive_output",
type=str,
help="Location of constitutive promoter gat analysis output",
)
parser.add_argument(
"gat_TATA_variable_output",
type=str,
help="Location of variable promoter gat analysis output",
)
parser.add_argument(
"output_prefix",
type=str,
help="Output prefix to add to plot file name",
)
parser.add_argument(
"output_folder_name",
type=str,
help="Optional output folder name ending in a forward slash",
default="",
nargs="?",
)
parser.add_argument(
"variable1_name",
type=str,
help="Optional replacement name for 2nd variable eg. non-specific",
default="constitutive",
nargs="?",
)
parser.add_argument(
"variable2_name",
type=str,
help="Optional replacement name for 2nd variable eg. tissue_specific",
default="variable",
nargs="?",
)
parser.add_argument(
"palette",
type=str,
help="Optional replacement colour palette for plots",
default=None,
nargs="?",
)
return parser.parse_args(
args
) # let argparse grab args from sys.argv itself to allow for testing in module import
def create_plot(
file_names,
output_folder_name,
output_prefix,
gat_TATA_constitutive_output,
gat_TATA_variable_output,
palette,
variable1_name,
variable2_name,
):
"""import and process the raw outputs after running gat (Genomic association tester). Then create barplot of constitutive and variable gene TATA enrichment"""
# import gat output files as dfs
constitutive = pd.read_table(
gat_TATA_constitutive_output, sep="\t", header=0
)
variable = pd.read_table(gat_TATA_variable_output, sep="\t", header=0)
# merge dfs
merged = pd.concat([constitutive, variable], ignore_index=True)
# set style to ticks
sns.set(style="ticks", color_codes=True)
# set colour palette
colours = sns.color_palette(palette)
# bar chart, 95% confidence intervals
plot = sns.barplot(
x="annotation",
y="l2fold",
data=merged,
order=[variable1_name, variable2_name],
palette=colours,
)
plot.axhline(0, color="black")
plt.xlabel("Gene type")
plt.ylabel("Log2-fold enrichment over background").get_figure().savefig(
f"../../data/output/{file_names}/TATA/{output_folder_name}plots/{output_prefix}_log2fold.pdf",
format="pdf",
)
def main(args):
# parse arguments
args = parse_args(args)
# make directory for the plots to be exported to
dirName = (
f"../../data/output/{args.file_names}/TATA/{args.output_folder_name}"
)
try:
# Create target Directory
os.mkdir(dirName)
print("Directory ", dirName, " created")
except FileExistsError:
print("Directory ", dirName, " already exists")
# make directory for the plots to be exported to
dirName = f"../../data/output/{args.file_names}/TATA/{args.output_folder_name}/plots"
try:
# Create target Directory
os.mkdir(dirName)
print("Directory ", dirName, " created")
except FileExistsError:
print("Directory ", dirName, " already exists")
# Create barplot
create_plot(
args.file_names,
args.output_folder_name,
args.output_prefix,
args.gat_TATA_constitutive_output,
args.gat_TATA_variable_output,
args.palette,
args.variable1_name,
args.variable2_name,
)
if __name__ == "__main__":
import sys
main(sys.argv[1:])
| [
1,
529,
9507,
29958,
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] |
tensorflow_addons/optimizers/tests/moving_average_test.py | leandro-gracia-gil/addons | 2 | 66529 | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for MovingAverage optimizers."""
import numpy as np
import pytest
import tensorflow as tf
from tensorflow_addons.optimizers import MovingAverage
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_run():
var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0, 4.0])
grads0 = tf.constant([0.1, 0.1])
grads1 = tf.constant([0.01, 0.01])
grads_and_vars = list(zip([grads0, grads1], [var0, var1]))
opt = MovingAverage(tf.keras.optimizers.SGD(lr=2.0), average_decay=0.5,)
opt.apply_gradients(grads_and_vars)
opt.apply_gradients(grads_and_vars)
np.testing.assert_allclose(var0.read_value(), [0.6, 1.6])
np.testing.assert_allclose(var1.read_value(), [2.96, 3.96])
ema_var0 = opt.get_slot(var0, "average")
ema_var1 = opt.get_slot(var1, "average")
np.testing.assert_allclose(ema_var0.read_value(), [0.75, 1.75])
np.testing.assert_allclose(ema_var1.read_value(), [2.975, 3.975])
_ = opt.assign_average_vars([var0, var1])
np.testing.assert_allclose(var0.read_value(), [0.75, 1.75])
np.testing.assert_allclose(var1.read_value(), [2.975, 3.975])
var0.assign_add([1.0, 1.0]),
var1.assign_add([2.0, 2.0]),
ema_var0.assign_add([3.0, 3.0]),
ema_var1.assign_add([4.0, 4.0]),
np.testing.assert_allclose(var0.read_value(), [1.75, 2.75])
np.testing.assert_allclose(var1.read_value(), [4.975, 5.975])
np.testing.assert_allclose(ema_var0.read_value(), [3.75, 4.75])
np.testing.assert_allclose(ema_var1.read_value(), [6.975, 7.975])
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_opt_failure():
base_opt = None
with pytest.raises(TypeError):
MovingAverage(base_opt, 0.5)
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_model_weights_update():
grad = tf.Variable([[0.1]])
model = tf.keras.Sequential(
[
tf.keras.layers.Dense(
1,
kernel_initializer=tf.keras.initializers.Constant([[1.0]]),
use_bias=False,
)
]
)
model.build(input_shape=[1, 1])
opt = MovingAverage(tf.keras.optimizers.SGD(lr=2.0), average_decay=0.5)
_ = opt.apply_gradients(list(zip([grad], model.variables)))
np.testing.assert_allclose(model.variables[0].read_value(), [[0.8]])
_ = opt.assign_average_vars(model.variables)
np.testing.assert_allclose(model.variables[0].read_value(), [[0.9]])
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_model_dynamic_lr():
grad = tf.Variable([[0.1]])
model = tf.keras.Sequential(
[
tf.keras.layers.Dense(
1,
kernel_initializer=tf.keras.initializers.Constant([[1.0]]),
use_bias=False,
)
]
)
model.build(input_shape=[1, 1])
opt = MovingAverage(tf.keras.optimizers.SGD(lr=1e-3), average_decay=0.5)
_ = opt.apply_gradients(list(zip([grad], model.variables)))
np.testing.assert_allclose(opt.lr.read_value(), 1e-3)
opt.lr = 1e-4
np.testing.assert_allclose(opt.lr.read_value(), 1e-4)
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_optimizer_string():
_ = MovingAverage("adam")
def test_config():
sgd_opt = tf.keras.optimizers.SGD(lr=2.0, nesterov=True, momentum=0.3, decay=0.1)
opt = MovingAverage(
sgd_opt, average_decay=0.5, num_updates=None, start_step=5, dynamic_decay=True,
)
config = opt.get_config()
assert config["average_decay"] == 0.5
assert config["num_updates"] is None
assert config["start_step"] == 5
assert config["dynamic_decay"] is True
new_opt = MovingAverage.from_config(config)
old_sgd_config = opt._optimizer.get_config()
new_sgd_config = new_opt._optimizer.get_config()
for k1, k2 in zip(old_sgd_config, new_sgd_config):
assert old_sgd_config[k1] == new_sgd_config[k2]
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_fit_simple_linear_model():
seed = 0x2019
np.random.seed(seed)
tf.random.set_seed(seed)
num_examples = 5000
x = np.random.standard_normal((num_examples, 3))
w = np.random.standard_normal((3, 1))
y = np.dot(x, w) + np.random.standard_normal((num_examples, 1)) * 1e-4
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(input_shape=(3,), units=1))
opt = MovingAverage("sgd")
model.compile(opt, loss="mse")
model.fit(x, y, epochs=5)
opt.assign_average_vars(model.variables)
x = np.random.standard_normal((100, 3))
y = np.dot(x, w)
predicted = model.predict(x)
max_abs_diff = np.max(np.abs(predicted - y))
assert max_abs_diff < 5e-3
def test_serialization():
sgd_opt = tf.keras.optimizers.SGD(lr=2.0, nesterov=True, momentum=0.3, decay=0.1)
optimizer = MovingAverage(
sgd_opt, average_decay=0.5, num_updates=None, start_step=5, dynamic_decay=True,
)
config = tf.keras.optimizers.serialize(optimizer)
new_optimizer = tf.keras.optimizers.deserialize(config)
assert new_optimizer.get_config() == optimizer.get_config()
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_start_step():
var0 = tf.Variable([1.0, 2.0])
grads0 = tf.constant([0.1, 0.1])
grads_and_vars = [(grads0, var0)]
opt = MovingAverage(
tf.keras.optimizers.SGD(lr=1.0), average_decay=0.5, start_step=1,
)
opt.apply_gradients(grads_and_vars)
np.testing.assert_allclose(var0.read_value(), [0.9, 1.9])
ema_var0 = opt.get_slot(var0, "average")
opt.apply_gradients(grads_and_vars)
np.testing.assert_allclose(var0.read_value(), [0.8, 1.8])
np.testing.assert_allclose(ema_var0.read_value(), [0.85, 1.85])
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_dynamic_decay():
var0 = tf.Variable([1.0, 2.0])
grads0 = tf.constant([0.1, 0.1])
grads_and_vars = [(grads0, var0)]
opt = MovingAverage(
tf.keras.optimizers.SGD(lr=2.0), average_decay=0.5, dynamic_decay=True,
)
opt.apply_gradients(grads_and_vars)
opt.apply_gradients(grads_and_vars)
np.testing.assert_allclose(var0.read_value(), [0.6, 1.6])
ema_var0 = opt.get_slot(var0, "average")
np.testing.assert_allclose(ema_var0.read_value(), [0.64, 1.64])
@pytest.mark.usefixtures("maybe_run_functions_eagerly")
@pytest.mark.with_device([tf.distribute.MirroredStrategy])
def test_swap_weights(device):
with device.scope():
var = tf.Variable([1.0, 2.0])
grads = tf.constant([0.1, 0.1])
opt = MovingAverage(tf.keras.optimizers.SGD(lr=2.0), average_decay=0.5,)
@tf.function
def apply_gradients():
opt.apply_gradients([(grads, var)])
device.run(apply_gradients)
np.testing.assert_allclose(var.read_value(), [0.8, 1.8])
ema_var = opt.get_slot(var, "average")
np.testing.assert_allclose(ema_var.read_value(), [0.85, 1.85])
with device.scope():
opt.shadow_copy([var])
opt.swap_weights()
np.testing.assert_allclose(ema_var.read_value(), [0.8, 1.8])
np.testing.assert_allclose(var.read_value(), [0.85, 1.85])
with device.scope():
opt.swap_weights()
np.testing.assert_allclose(var.read_value(), [0.8, 1.8])
np.testing.assert_allclose(ema_var.read_value(), [0.85, 1.85])
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2314,
13,
2
] |
app/auth/passwords.py | LambArchie/Competition-Manager | 0 | 115230 | <reponame>LambArchie/Competition-Manager<gh_stars>0
"""
Controls passwords checks
"""
from hashlib import sha1
from urllib.request import Request, urlopen
from flask import current_app
PWNED_PASSWORD_API = "https://api.pwnedpasswords.com/range/"
def strength_check(password):
"""Checks if password reaches minimum requirement
Returns True if meets requirements, or string with error if issue"""
check = length_check(password)
if check != 0:
return check
if current_app.config['HIBP_PW_CHECK'] is True:
check = pwned_passwords_check(password)
if check != 0:
return "Password has been leaked {} times before".format(check)
return 0
def pwned_passwords_check(password):
"""Uses pwned passwords api"""
pw_hash = sha1(password.encode()).hexdigest()
hash_prefix = pw_hash[:5]
hash_suffix = pw_hash[5:].upper()
response = Request(url=PWNED_PASSWORD_API + hash_prefix, headers={
'User-Agent': 'Competition Manager'})
for line in urlopen(response).read().decode().split('\r\n'):
suffix, count = line.split(":")
if suffix == hash_suffix:
return count
return 0
def length_check(password):
"""Checks minimum length, gets value from config"""
if len(password) < int(current_app.config['MIN_LENGTH']):
return "Needs to be {} characters long".format(current_app.config['MIN_LENGTH'])
return 0
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imblearn/utils/tests/test_validation.py | fengtongtong/imbalanced-learning | 1 | 83223 | <reponame>fengtongtong/imbalanced-learning
"""Test for the validation helper"""
# Authors: <NAME> <<EMAIL>>
# <NAME>
# License: MIT
from collections import Counter
import numpy as np
import pytest
from pytest import raises
from sklearn.neighbors.base import KNeighborsMixin
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import check_random_state
from sklearn.externals import joblib
from sklearn.utils.testing import assert_array_equal
from imblearn.utils.testing import warns
from imblearn.utils import check_neighbors_object
from imblearn.utils import check_ratio
from imblearn.utils import hash_X_y
from imblearn.utils import check_target_type
def test_check_neighbors_object():
name = 'n_neighbors'
n_neighbors = 1
estimator = check_neighbors_object(name, n_neighbors)
assert issubclass(type(estimator), KNeighborsMixin)
assert estimator.n_neighbors == 1
estimator = check_neighbors_object(name, n_neighbors, 1)
assert issubclass(type(estimator), KNeighborsMixin)
assert estimator.n_neighbors == 2
estimator = NearestNeighbors(n_neighbors)
assert estimator is check_neighbors_object(name, estimator)
n_neighbors = 'rnd'
with raises(ValueError, match="has to be one of"):
check_neighbors_object(name, n_neighbors)
@pytest.mark.parametrize(
"target, output_target",
[(np.array([0, 1, 1]), np.array([0, 1, 1])),
(np.array([0, 1, 2]), np.array([0, 1, 2])),
(np.array([[0, 1], [1, 0]]), np.array([1, 0]))]
)
def test_check_target_type(target, output_target):
converted_target = check_target_type(target.astype(int))
assert_array_equal(converted_target, output_target.astype(int))
@pytest.mark.parametrize(
"target, output_target, is_ova",
[(np.array([0, 1, 1]), np.array([0, 1, 1]), False),
(np.array([0, 1, 2]), np.array([0, 1, 2]), False),
(np.array([[0, 1], [1, 0]]), np.array([1, 0]), True)]
)
def test_check_target_type_ova(target, output_target, is_ova):
converted_target, binarize_target = check_target_type(
target.astype(int), indicate_one_vs_all=True)
assert_array_equal(converted_target, output_target.astype(int))
assert binarize_target == is_ova
def test_check_target_warning():
target = np.arange(4).reshape((2, 2))
with pytest.warns(UserWarning, match='should be of types'):
check_target_type(target)
def test_check_ratio_error():
with raises(ValueError, match="'sampling_type' should be one of"):
check_ratio('auto', np.array([1, 2, 3]), 'rnd')
error_regex = "The target 'y' needs to have more than 1 class."
with raises(ValueError, match=error_regex):
check_ratio('auto', np.ones((10, )), 'over-sampling')
error_regex = "When 'ratio' is a string, it needs to be one of"
with raises(ValueError, match=error_regex):
check_ratio('rnd', np.array([1, 2, 3]), 'over-sampling')
def test_ratio_all_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
for each in ('all', 'auto'):
assert check_ratio(each, y, 'over-sampling') == {1: 50, 2: 0, 3: 75}
def test_ratio_all_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('all', y, 'under-sampling')
assert ratio == {1: 25, 2: 25, 3: 25}
def test_ratio_majority_over_sampling():
error_regex = "'ratio'='majority' cannot be used with over-sampler."
with raises(ValueError, match=error_regex):
check_ratio('majority', np.array([1, 2, 3]), 'over-sampling')
def test_ratio_majority_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('majority', y, 'under-sampling')
assert ratio == {2: 25}
def test_ratio_not_minority_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('not minority', y, 'over-sampling')
assert ratio == {1: 50, 2: 0}
def test_ratio_not_minority_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('not minority', y, 'under-sampling')
assert ratio == {1: 25, 2: 25}
ratio = check_ratio('auto', y, 'under-sampling')
assert ratio == {1: 25, 2: 25}
def test_ratio_minority_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('minority', y, 'over-sampling')
assert ratio == {3: 75}
def test_ratio_minority_under_sampling():
error_regex = "'ratio'='minority' cannot be used with under-sampler."
with raises(ValueError, match=error_regex):
check_ratio('minority', np.array([1, 2, 3]), 'under-sampling')
def test_ratio_dict_error():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: -100, 2: 50, 3: 25}
with raises(ValueError, match="in a class cannot be negative."):
check_ratio(ratio, y, 'under-sampling')
ratio = {10: 10}
with raises(ValueError, match="are not present in the data."):
check_ratio(ratio, y, 'over-sampling')
ratio = {1: 45, 2: 100, 3: 70}
error_regex = ("With over-sampling methods, the number of samples in a"
" class should be greater or equal to the original number"
" of samples. Originally, there is 50 samples and 45"
" samples are asked.")
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'over-sampling')
error_regex = ("With under-sampling methods, the number of samples in a"
" class should be less or equal to the original number of"
" samples. Originally, there is 25 samples and 70 samples"
" are asked.")
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'under-sampling')
def test_ratio_dict_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: 70, 2: 100, 3: 70}
ratio_ = check_ratio(ratio, y, 'over-sampling')
assert ratio_ == {1: 20, 2: 0, 3: 45}
ratio = {1: 70, 2: 140, 3: 70}
expected_msg = ("After over-sampling, the number of samples \(140\) in"
" class 2 will be larger than the number of samples in the"
" majority class \(class #2 -> 100\)")
with warns(UserWarning, expected_msg):
check_ratio(ratio, y, 'over-sampling')
def test_ratio_dict_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: 30, 2: 45, 3: 25}
ratio_ = check_ratio(ratio, y, 'under-sampling')
assert ratio_ == ratio
def test_ratio_callable():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
def ratio_func(y):
# this function could create an equal number of samples
target_stats = Counter(y)
n_samples = max(target_stats.values())
return {key: int(n_samples)
for key in target_stats.keys()}
ratio_ = check_ratio(ratio_func, y, 'over-sampling')
assert ratio_ == {1: 50, 2: 0, 3: 75}
def test_ratio_callable_args():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
multiplier = {1: 1.5, 2: 1, 3: 3}
def ratio_func(y, multiplier):
"""samples such that each class will be affected by the multiplier."""
target_stats = Counter(y)
return {key: int(values * multiplier[key])
for key, values in target_stats.items()}
ratio_ = check_ratio(ratio_func, y, 'over-sampling',
multiplier=multiplier)
assert ratio_ == {1: 25, 2: 0, 3: 50}
def test_hash_X_y():
rng = check_random_state(0)
X = rng.randn(2000, 20)
y = np.array([0] * 500 + [1] * 1500)
assert hash_X_y(X, y, 10, 10) == (joblib.hash(X[::200, ::2]),
joblib.hash(y[::200]))
X = rng.randn(5, 2)
y = np.array([0] * 2 + [1] * 3)
# all data will be used in this case
assert hash_X_y(X, y) == (joblib.hash(X), joblib.hash(y))
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] |
Chapter 4/restful_python_chapter_04_05/gamesapi/games/admin.py | Mohamed2011-bit/Building-RESTful-Python-Web-Services | 116 | 130133 | <gh_stars>100-1000
"""
Book: Building RESTful Python Web Services
Chapter 4: Throttling, Filtering, Testing and Deploying an API with Django
Author: <NAME> - Twitter.com/gastonhillar
Publisher: Packt Publishing Ltd. - http://www.packtpub.com
"""
from django.contrib import admin
# Register your models here.
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setup.py | sdu-cfei/modest-py | 37 | 8804 | from setuptools import setup
setup(
name='modestpy',
version='0.1',
description='FMI-compliant model identification package',
url='https://github.com/sdu-cfei/modest-py',
keywords='fmi fmu optimization model identification estimation',
author='<NAME>, Center for Energy Informatics SDU',
author_email='<EMAIL>, <EMAIL>',
license='BSD',
platforms=['Windows', 'Linux'],
packages=[
'modestpy',
'modestpy.estim',
'modestpy.estim.ga_parallel',
'modestpy.estim.ga',
'modestpy.estim.ps',
'modestpy.estim.scipy',
'modestpy.fmi',
'modestpy.utilities',
'modestpy.test'],
include_package_data=True,
install_requires=[
'fmpy[complete]',
'scipy',
'pandas',
'matplotlib',
'numpy',
'pyDOE',
'modestga'
],
classifiers=[
'Programming Language :: Python :: 3'
]
)
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] |
farmer/version.py | jstaf/farmer | 0 | 1600758 | <reponame>jstaf/farmer
import click
import pkg_resources
def get_package_versions():
distributions = [dist for dist in pkg_resources.working_set if dist.project_name.startswith(__package__)]
return {dist.project_name: dist.version for dist in distributions}
def print_package_versions():
for package, version in sorted(get_package_versions().items()):
click.echo('{} {}'.format(package, version))
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] |
main.py | ANOVAProjectUSYD/trading-bot | 0 | 68707 | import os, argparse
import datetime
import time
from poloniex import Poloniex
def read_ticker():
#TODO: should add a check to see if user input is in the list of valid options
ticker = input("Enter the ticker pair, e.g. BTC_ETH \n")
return ticker
def continueOption():
'''Allows user to be able to view option menu again'''
cont = input("Would you like to see the options again? Please enter yes or no \n")
if cont.lower() == "yes":
operating = True
elif cont.lower() == "no":
operating = False
else:
print("Invalid option. Terminating program.")
quit()
return operating
def clean_arguments(time_period):
'''Validates if time period is allowed.'''
if int(time_period) in [300,900,1800,7200,14400,86400]:
validPeriod = time_period
else:
print("Issue with time argument. Program terminating.")
quit()
return validPeriod
def connect_api():
'''Easiest way to connect to Poloniex API.'''
api_key = '<KEY>'
secret = '6bd7cf3f563edded66f6f73782fed44d0e9cc0b345d5a290a4abf60f7c9713e32a718f94a665237bc2d444320bb33bf98e3911dd547d2c7acdf7a464bc37ae53'
polo = Poloniex(api_key, secret) # connects to API with key.
return polo
def option(result, api_obj, ticker):
rdict = {'1': 'You chose to view live prices', '2': 'You chose to view 24 hour volume', '3': 'You chose to view Percentage change', '4': 'You chose to view 24 hour high/low', '5':'Program terminating'}
print (rdict[result])
if result == '1':
time_period = input("Enter the time period. Valid time periods are 300, 900, 1800, 7200, 14400 and 86400. Press Control-Z to terminate. \n")
validPeriod = clean_arguments(time_period)
stream_data(api_obj, ticker, validPeriod)
elif result == '2':
get_daily_vol(api_obj, ticker)
operating = continueOption()
return(operating)
elif result == '3':
get_percent_change(api_obj,ticker)
operating = continueOption()
return(operating)
elif result == '4':
get_high_low(api_obj,ticker)
operating = continueOption()
return(operating)
elif result == '5':
quit()
else:
print("Please enter a valid number")
def get_daily_vol(polo, ticker):
'''Computes the 24 hour volume.'''
polo_data = polo.returnTicker()[ticker]
volume = polo_data['baseVolume']
print("The 24 hour volume is {} BTC".format(volume))
def get_percent_change(polo, ticker):
'''Computes the 24 hour percentage change.'''
polo_data = polo.returnTicker()[ticker]
percentageChange = polo_data['percentChange']
print("The 24 hour percentage change is {}".format(percentageChange))
def get_high_low(polo, ticker):
'''Prints out the high and low of the ticker.'''
polo_data = polo.returnTicker()[ticker]
high = polo_data['high24hr']
low = polo_data['low24hr']
print("The 24 hour high is {} and the 24 hour low is {}".format(high,low))
def stream_data(polo, ticker, ticker_time):
'''Streams data regarding cryptocurrency.'''
while True:
polo_data = polo.returnTicker()[ticker] # polo_data is a dictionary containing info about the ticker.
#print(type polo_data) or #print(polo_data.keys())
date = datetime.datetime.now()
last_price = polo_data['last'] # read documentation section in README file to see other data we can get.
print("Time: {date} Last price: {last_price}".format(date = date, last_price=last_price))
new_time = float("." + str(ticker_time))
time.sleep(new_time) # pauses program and then grabs price after some time.
def main():
print("Please enter a trading pair, e.g. BTC_ETH")
ticker = read_ticker()
operating = True
api_obj = connect_api()
# While loop so user gets the choice to see options again
while operating:
result = input("""
Please enter one of the following number options:
1 Check live prices
2 Check 24 hour volume
3 Check 24 hour percentage change
4 Check 24 hour high/low
5 exit \n
""")
operating = option(result, api_obj, ticker)
# this executes the main method
if __name__ == "__main__":
main()
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] |
ceilometer/network/notifications.py | rackerlabs/instrumented-ceilometer | 3 | 16540 | # -*- encoding: utf-8 -*-
#
# Copyright © 2012 New Dream Network, LLC (DreamHost)
#
# Author: <NAME> <<EMAIL>>
#
# 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.
"""Handler for producing network counter messages from Neutron notification
events.
"""
from oslo.config import cfg
from ceilometer.openstack.common.gettextutils import _ # noqa
from ceilometer.openstack.common import log
from ceilometer import plugin
from ceilometer import sample
OPTS = [
cfg.StrOpt('neutron_control_exchange',
default='neutron',
help="Exchange name for Neutron notifications",
deprecated_name='quantum_control_exchange'),
]
cfg.CONF.register_opts(OPTS)
LOG = log.getLogger(__name__)
class NetworkNotificationBase(plugin.NotificationBase):
resource_name = None
@property
def event_types(self):
return [
# NOTE(flwang): When the *.create.start notification sending,
# there is no resource id assigned by Neutron yet. So we ignore
# the *.create.start notification for now and only listen the
# *.create.end to make sure the resource id is existed.
'%s.create.end' % (self.resource_name),
'%s.update.*' % (self.resource_name),
'%s.exists' % (self.resource_name),
# FIXME(dhellmann): Neutron delete notifications do
# not include the same metadata as the other messages,
# so we ignore them for now. This isn't ideal, since
# it may mean we miss charging for some amount of time,
# but it is better than throwing away the existing
# metadata for a resource when it is deleted.
##'%s.delete.start' % (self.resource_name),
]
@staticmethod
def get_exchange_topics(conf):
"""Return a sequence of ExchangeTopics defining the exchange and topics
to be connected for this plugin.
"""
return [
plugin.ExchangeTopics(
exchange=conf.neutron_control_exchange,
topics=set(topic + ".info"
for topic in conf.notification_topics)),
]
def process_notification(self, message):
LOG.info(_('network notification %r') % message)
message['payload'] = message['payload'][self.resource_name]
counter_name = getattr(self, 'counter_name', self.resource_name)
unit_value = getattr(self, 'unit', self.resource_name)
yield sample.Sample.from_notification(
name=counter_name,
type=sample.TYPE_GAUGE,
unit=unit_value,
volume=1,
user_id=message['_context_user_id'],
project_id=message['payload']['tenant_id'],
resource_id=message['payload']['id'],
message=message)
event_type_split = message['event_type'].split('.')
if len(event_type_split) > 2:
yield sample.Sample.from_notification(
name=counter_name
+ "." + event_type_split[1],
type=sample.TYPE_DELTA,
unit=unit_value,
volume=1,
user_id=message['_context_user_id'],
project_id=message['payload']['tenant_id'],
resource_id=message['payload']['id'],
message=message)
class Network(NetworkNotificationBase):
"""Listen for Neutron network notifications in order to mediate with the
metering framework.
"""
resource_name = 'network'
class Subnet(NetworkNotificationBase):
"""Listen for Neutron notifications in order to mediate with the
metering framework.
"""
resource_name = 'subnet'
class Port(NetworkNotificationBase):
"""Listen for Neutron notifications in order to mediate with the
metering framework.
"""
resource_name = 'port'
class Router(NetworkNotificationBase):
"""Listen for Neutron notifications in order to mediate with the
metering framework.
"""
resource_name = 'router'
class FloatingIP(NetworkNotificationBase):
"""Listen for Neutron notifications in order to mediate with the
metering framework.
"""
resource_name = 'floatingip'
counter_name = 'ip.floating'
unit = 'ip'
class Bandwidth(NetworkNotificationBase):
"""Listen for Neutron notifications in order to mediate with the
metering framework.
"""
event_types = ['l3.meter']
def process_notification(self, message):
yield sample.Sample.from_notification(
name='bandwidth',
type=sample.TYPE_DELTA,
unit='B',
volume=message['payload']['bytes'],
user_id=None,
project_id=message['payload']['tenant_id'],
resource_id=message['payload']['label_id'],
message=message)
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] |
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