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1c4407450f29ef110bf3b72088dfff08feacca6b
| 5,291
|
py
|
Python
|
src/arg_utils.py
|
Nicolas-Reyland/python-polygon
|
2847bebc58d50219ae8fc7eb5cc14d6b8d1161ed
|
[
"MIT"
] | 1
|
2021-09-03T08:17:11.000Z
|
2021-09-03T08:17:11.000Z
|
src/arg_utils.py
|
Nicolas-Reyland/python-polygon
|
2847bebc58d50219ae8fc7eb5cc14d6b8d1161ed
|
[
"MIT"
] | null | null | null |
src/arg_utils.py
|
Nicolas-Reyland/python-polygon
|
2847bebc58d50219ae8fc7eb5cc14d6b8d1161ed
|
[
"MIT"
] | null | null | null |
from __future__ import annotations
from argparse import ArgumentParser
import os
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
DEFAULT_ARGS_JSON_FILE_PATH = "default-args.json"
class ArgumentError(Exception):
"""
Simple class, representing an commandline-argument error.
"""
pass
def gen_arg_parser() -> ArgumentParser:
"""
Generate argument parser.
Generate an ArgumentParser for all Chaikin3D arguments.
Generally, there is a short and long argument (short: -char, long: --words).
Returns:
ArgumentParser instance
"""
parser = ArgumentParser(
description="Apply the Chaikin algorithm, expanded to the 3D space"
)
# polyhedron
parser.add_argument("-i", "--input", type=str, help="input file", required=True)
parser.add_argument(
"-rm",
"--rotate-mesh",
help="Rotate the mesh when loading a file",
action="store_true",
)
# chaikin algorithm
parser.add_argument(
"-cg",
"--chaikin-generations",
type=int,
default=0,
help="number of chaikin generations",
)
parser.add_argument(
"-cc", "--chaikin-coef", type=float, default=4.0, help="Chaikin coefficient"
)
parser.add_argument(
"-oe",
"--order-edges",
type=str,
default="none",
help='Order edges ["none", "first", "all"]',
)
parser.add_argument("-v", "--verbose", help="verbose mode", action="store_true")
parser.add_argument("-vv", "--vverbose", help="very-verbose", action="store_true")
# what to plot
parser.add_argument(
"-r",
"--renderer",
type=str,
default="plotly",
help='renderer ["plotly", "mpl"]',
)
parser.add_argument(
"-p",
"--plot",
type=str,
default="simple",
help='plot type ["none", "simple", "full", "evolution", "animation"]',
)
parser.add_argument(
"-hme",
"--hide-main-edges",
help='Hide the main edges (for plots: "simple", "full" and "evolution")',
action="store_true",
)
parser.add_argument(
"-sge",
"--show-graphical-edges",
help='Show the graphical edges (for plots: "simple", "full" and "evolution")',
action="store_true",
)
# how to plot
parser.add_argument(
"-a",
"--alpha",
type=float,
default=0.8,
help="Alpha/Opacity value for mesh rendering",
)
parser.add_argument(
"-pc", "--polygon-color", type=str, default="lightblue", help="Polygon color"
)
parser.add_argument(
"-nc", "--node-color", type=str, default="green", help="Node color"
)
parser.add_argument(
"-mec",
"--main-edge-color",
type=str,
default="darkred",
help="Main edge color",
)
parser.add_argument(
"-gec",
"--graphical-edge-color",
type=str,
default="black",
help="Graphical edge",
)
# output
parser.add_argument(
"-o",
"--output",
type=str,
default=None,
help="Output file path (wavefront '.obj' or '.html' format)",
)
return parser
def read_args(arg_parser: ArgumentParser) -> dict[str, str | bool]:
"""
Read and parse the command-line arguments.
Args:
arg_parser (ArgumentParser): Argument parser.
Returns:
A:
Instance of class 'A', created inside this function.
You can access the elements of this class by variable name or by using
bracket notation (value = a["key"]).
The keys are the command line arguments (spaces are used instead of '-'/'_').
Raises:
ArgumentError: The specified renderer is not known
"""
# parse the command line arguments
args = vars(arg_parser.parse_args())
args = dict(
map(
lambda kvpair: (kvpair[0].replace("_", " ").replace("-", " "), kvpair[1]),
args.items(),
)
)
# order-edges
assert args["order edges"] in ("none", "first", "all"), ArgumentError(
f'Invalid value for "order-edges" option: {args["order edges"]}'
)
# output file
if args["output"] is not None:
assert args["output"].endswith(".obj") or args["output"].endswith(
".html"
), f"Invalid file extension: '{args['output']}'. Must end with '.obj' or '.html'"
# verbosity level
if args["vverbose"]:
args["verbosity"] = 2
args["verbose"] = True
elif args["verbose"]:
args["verbosity"] = 1
else:
args["verbosity"] = 0
# add 'show-main-edges' value, based on 'hide-main-edges'
args["show main edges"] = not args["hide main edges"]
# renderer
if args["renderer"] == "plotly":
from plotly_renderer import Renderer
elif args["renderer"] == "mpl":
from mpl_renderer import Renderer
else:
raise ArgumentError(f'Unkown renderer: {args["renderer"]}')
args["renderer class"] = Renderer
A = type(
"A",
(),
dict((k.replace(" ", "_"), v) for k, v in args.items())
| {"__getitem__": lambda self, value: args[value]},
)
return A()
| 26.994898
| 89
| 0.567757
|
from __future__ import annotations
from argparse import ArgumentParser
import os
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
DEFAULT_ARGS_JSON_FILE_PATH = "default-args.json"
class ArgumentError(Exception):
pass
def gen_arg_parser() -> ArgumentParser:
parser = ArgumentParser(
description="Apply the Chaikin algorithm, expanded to the 3D space"
)
parser.add_argument("-i", "--input", type=str, help="input file", required=True)
parser.add_argument(
"-rm",
"--rotate-mesh",
help="Rotate the mesh when loading a file",
action="store_true",
)
parser.add_argument(
"-cg",
"--chaikin-generations",
type=int,
default=0,
help="number of chaikin generations",
)
parser.add_argument(
"-cc", "--chaikin-coef", type=float, default=4.0, help="Chaikin coefficient"
)
parser.add_argument(
"-oe",
"--order-edges",
type=str,
default="none",
help='Order edges ["none", "first", "all"]',
)
parser.add_argument("-v", "--verbose", help="verbose mode", action="store_true")
parser.add_argument("-vv", "--vverbose", help="very-verbose", action="store_true")
parser.add_argument(
"-r",
"--renderer",
type=str,
default="plotly",
help='renderer ["plotly", "mpl"]',
)
parser.add_argument(
"-p",
"--plot",
type=str,
default="simple",
help='plot type ["none", "simple", "full", "evolution", "animation"]',
)
parser.add_argument(
"-hme",
"--hide-main-edges",
help='Hide the main edges (for plots: "simple", "full" and "evolution")',
action="store_true",
)
parser.add_argument(
"-sge",
"--show-graphical-edges",
help='Show the graphical edges (for plots: "simple", "full" and "evolution")',
action="store_true",
)
parser.add_argument(
"-a",
"--alpha",
type=float,
default=0.8,
help="Alpha/Opacity value for mesh rendering",
)
parser.add_argument(
"-pc", "--polygon-color", type=str, default="lightblue", help="Polygon color"
)
parser.add_argument(
"-nc", "--node-color", type=str, default="green", help="Node color"
)
parser.add_argument(
"-mec",
"--main-edge-color",
type=str,
default="darkred",
help="Main edge color",
)
parser.add_argument(
"-gec",
"--graphical-edge-color",
type=str,
default="black",
help="Graphical edge",
)
parser.add_argument(
"-o",
"--output",
type=str,
default=None,
help="Output file path (wavefront '.obj' or '.html' format)",
)
return parser
def read_args(arg_parser: ArgumentParser) -> dict[str, str | bool]:
args = vars(arg_parser.parse_args())
args = dict(
map(
lambda kvpair: (kvpair[0].replace("_", " ").replace("-", " "), kvpair[1]),
args.items(),
)
)
assert args["order edges"] in ("none", "first", "all"), ArgumentError(
f'Invalid value for "order-edges" option: {args["order edges"]}'
)
if args["output"] is not None:
assert args["output"].endswith(".obj") or args["output"].endswith(
".html"
), f"Invalid file extension: '{args['output']}'. Must end with '.obj' or '.html'"
if args["vverbose"]:
args["verbosity"] = 2
args["verbose"] = True
elif args["verbose"]:
args["verbosity"] = 1
else:
args["verbosity"] = 0
args["show main edges"] = not args["hide main edges"]
if args["renderer"] == "plotly":
from plotly_renderer import Renderer
elif args["renderer"] == "mpl":
from mpl_renderer import Renderer
else:
raise ArgumentError(f'Unkown renderer: {args["renderer"]}')
args["renderer class"] = Renderer
A = type(
"A",
(),
dict((k.replace(" ", "_"), v) for k, v in args.items())
| {"__getitem__": lambda self, value: args[value]},
)
return A()
| true
| true
|
1c4407904b26ff7709bfbd7dfc2fb50b553a83f6
| 25,483
|
py
|
Python
|
tensorflow/python/autograph/operators/control_flow_test.py
|
fwtan/tensorflow
|
efa3fb28d94b7937edaafb5874c191ad0e2149ca
|
[
"Apache-2.0"
] | 1
|
2020-05-14T03:53:01.000Z
|
2020-05-14T03:53:01.000Z
|
tensorflow/python/autograph/operators/control_flow_test.py
|
fwtan/tensorflow
|
efa3fb28d94b7937edaafb5874c191ad0e2149ca
|
[
"Apache-2.0"
] | 2
|
2021-08-25T16:05:52.000Z
|
2022-02-10T01:51:12.000Z
|
tensorflow/python/autograph/operators/control_flow_test.py
|
taotesea/tensorflow
|
5e6479904941624cf7ce58ab3d236375c8012ef4
|
[
"Apache-2.0"
] | 1
|
2020-08-07T12:49:50.000Z
|
2020-08-07T12:49:50.000Z
|
# Lint as: python3
# Copyright 2017 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 control_flow module."""
# Unfortunately pylint has false positives when nonlocal is present.
# pylint:disable=unused-variable
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import sys
import numpy as np
import six
from tensorflow.python.autograph.operators import control_flow
from tensorflow.python.autograph.operators import variables as variable_operators
from tensorflow.python.autograph.utils import ag_logging
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import func_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.ops.ragged import ragged_factory_ops
from tensorflow.python.platform import test
@test_util.run_all_in_graph_and_eager_modes
class ForLoopTest(test.TestCase):
def test_tensor(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
constant_op.constant([1, 2, 3, 4]),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (1234,))
def test_range_tensor(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
math_ops.range(5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (1234,))
def test_range_tensor_explicit_limit_delta(self):
def body(i):
nonlocal s
s = s * 100 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
math_ops.range(-17, -3, 5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (-171207,))
def test_range_tensor_explicit_limit_negative_delta(self):
def body(i):
nonlocal s
s = s * 100 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
math_ops.range(17, 3, -5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (171207,))
def test_range_tensor_random_delta(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
random_one = random_ops.random_uniform((), 1, 2, dtype=dtypes.int32)
control_flow.for_stmt(
math_ops.range(0, 5, random_one),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (1234,))
def test_range_tensor_random_negative_delta(self):
def body(i):
nonlocal s
s = s * 100 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
random_neg_five = random_ops.random_uniform((), -5, -4, dtype=dtypes.int32)
control_flow.for_stmt(
math_ops.range(17, 3, random_neg_five),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (171207,))
def test_tensor_with_extra_test_object_vars(self):
class MutableObject(object):
field_1 = constant_op.constant(0, dtype=dtypes.int32)
field_2 = constant_op.constant(1, dtype=dtypes.int32)
state = MutableObject()
def body(i):
state.field_1 += i
state.field_2 *= i
def get_state():
return state.field_1, state.field_2
def set_state(loop_vars):
state.field_1, state.field_2 = loop_vars
control_flow.for_stmt(
iter_=constant_op.constant([1, 2, 3, 4]),
body=body,
extra_test=lambda: state.field_1 < 6,
get_state=get_state,
set_state=set_state,
symbol_names=('state.field_1', 'state.field_2'),
opts={})
self.assertEqual(self.evaluate((state.field_1, state.field_2)), (6, 6))
def test_python(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
range(5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(s, 1234)
def test_python_generator_with_extra_test(self):
def new_generator():
for i in range(1, 5):
yield i
gen = new_generator()
def run_loop():
s = 0
c = 0
def body(i):
nonlocal s, c
s = s * 10 + i
c += 1
control_flow.for_stmt(
gen,
extra_test=lambda: c == 0, # Break after first iteration
body=body,
get_state=None,
set_state=None,
symbol_names=('s', 'c'),
opts={})
return s, c
self.assertEqual(run_loop(), (1, 1))
self.assertEqual(run_loop(), (2, 1))
self.assertEqual(run_loop(), (3, 1))
self.assertEqual(next(gen), 4)
def test_python_generator_with_extra_test_no_iterations(self):
def new_generator():
for i in range(5):
yield i
gen = new_generator()
def run_loop():
s = 0
def body(i):
nonlocal s
s = s * 10 + i
control_flow.for_stmt(
gen,
extra_test=lambda: False, # Break before loop
body=body,
get_state=None,
set_state=None,
symbol_names=('s',),
opts={})
return s
self.assertEqual(run_loop(), 0)
self.assertEqual(run_loop(), 0)
self.assertEqual(next(gen), 0)
def test_tf_dataset(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (1234,))
def test_dataset_with_extra_test(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=lambda: s < 3,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (12,))
def test_dataset_with_extra_test_collection_vars(self):
def body(i):
nonlocal s
l[0] += i
s += i
def set_state(loop_vars):
nonlocal s
l[0], s = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
l = [constant_op.constant(0, dtype=dtypes.int64)]
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=lambda: s < 3,
body=body,
get_state=lambda: (l[0], s),
set_state=set_state,
symbol_names=('l[0]', 's'),
opts={})
self.assertEqual(self.evaluate((l[0], s)), (3, 3))
def test_dataset_with_extra_test_iteration_limiting(self):
def body(it):
nonlocal i
with ops.control_dependencies((control_flow_ops.Assert(i < 3, (i,)),)):
i = it
def set_state(loop_vars):
nonlocal i
i, = loop_vars
i = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=lambda: i < 3,
body=body,
get_state=lambda: (i,),
set_state=set_state,
symbol_names=('i',),
opts={})
self.assertEqual(self.evaluate(i), (3,))
def test_tf_dataset_no_loop_vars(self):
def body(i):
v.assign(v.read_value() * 10 + i)
v = variables.Variable(0, dtype=dtypes.int64)
self.evaluate(v.initializer)
# tf.function required for the automatic control dependencies, and because
# ops test for its presence.
@def_function.function
def test_fn():
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=None,
body=body,
get_state=lambda: (),
set_state=lambda _: None,
symbol_names=(),
opts={})
self.evaluate(test_fn())
self.assertEqual(self.evaluate(v.read_value()), 1234)
def test_tf_iterator(self):
# graph-mode iterators are only supported inside tf.function.
@def_function.function
def test_fn():
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
iter(dataset_ops.Dataset.range(5)),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
return s
self.assertAllEqual(test_fn(), 1234)
def test_tf_iterator_shape_invariants(self):
# graph-mode iterators are only supported inside tf.function.
@def_function.function
def test_fn():
def body(i):
nonlocal s
s = array_ops.concat([s, [i]], 0)
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant([], dtype=dtypes.int64)
control_flow.for_stmt(
iter(dataset_ops.Dataset.range(5)),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'shape_invariants': [(s, tensor_shape.TensorShape([None]))]})
return s
self.assertAllEqual(test_fn(), [0, 1, 2, 3, 4])
def test_tf_iterator_no_loop_vars(self):
def body(i):
v.assign(v.read_value() * 10 + i)
v = variables.Variable(0, dtype=dtypes.int64)
self.evaluate(v.initializer)
# tf.function required for the automatic control dependencies.
@def_function.function
def test_fn():
control_flow.for_stmt(
iter(dataset_ops.Dataset.range(5)),
extra_test=None,
body=body,
get_state=lambda: (),
set_state=lambda _: None,
symbol_names=(),
opts={})
self.evaluate(test_fn())
self.assertEqual(self.evaluate(v.read_value()), 1234)
def test_tf_ragged_tensor(self):
def body(i):
nonlocal s
s = s * 10 + i[0]
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
ragged_factory_ops.constant([[1], [2, 4], [3]]),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (123,))
def test_tf_ragged_tensor_higher_dimensional(self):
def body(i):
nonlocal s
s = s * 10 + i[0][0]
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
ragged_3d = [
[[1], [1, 1], [1]],
[[2], [2]],
]
control_flow.for_stmt(
ragged_factory_ops.constant(ragged_3d),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (12,))
def test_tf_ragged_tensor_no_loop_vars(self):
v = variables.Variable(0, dtype=dtypes.int32)
self.evaluate(v.initializer)
def body(i):
v.assign(v.read_value() * 10 + i[0])
# tf.function required for the automatic control dependencies.
@def_function.function(autograph=False)
def test_fn():
control_flow.for_stmt(
ragged_factory_ops.constant([[1], [2, 4], [3]]),
extra_test=None,
body=body,
get_state=lambda: (),
set_state=lambda _: None,
symbol_names=(),
opts={})
self.evaluate(test_fn())
# Note: 123 = ((0*10 + 1)*10+2)*10+3 (first element of each row).
self.assertEqual(self.evaluate(v.read_value()), 123)
def _basic_loop(self, init_value, body_fn):
def body(i):
nonlocal s
s = body_fn(i, s)
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = init_value
control_flow.for_stmt(
constant_op.constant([1, 2, 3, 4]),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
return s
def test_tensor_illegal_input(self):
with self.assertRaisesRegex(ValueError, '"s" may not be None'):
self._basic_loop(None, lambda i, s: s)
with self.assertRaisesRegex(ValueError, '"s" must be defined'):
self._basic_loop(variable_operators.Undefined(''), lambda i, s: s)
def test_tensor_none_output(self):
with self.assertRaisesRegex(ValueError, '"s" is None at the end'):
self._basic_loop(0, lambda i, s: None)
def test_tensor_dtype_change(self):
with self.assertRaisesRegex(TypeError, '"s".* dtype float32 after'):
self._basic_loop(0, lambda i, s: 1.0)
def test_tensor_shape_change(self):
with self.assertRaisesRegex(ValueError, r'"s".* shape \(1,\) after'):
self._basic_loop(0, lambda i, s: np.array([1], dtype=np.int32))
@test_util.run_all_in_graph_and_eager_modes
class WhileLoopTest(test.TestCase):
def test_tensor(self):
def body():
nonlocal i, s
s = s * 10 + i
i += 1
def set_state(loop_vars):
nonlocal i, s
i, s = loop_vars
i = 0
n = constant_op.constant(5)
s = 0
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=lambda: (i, s),
set_state=set_state,
symbol_names=('i', 's'),
opts={})
self.assertEqual(self.evaluate((i, s)), (5, 1234))
def test_tensor_with_side_effecting_condition(self):
v = variables.Variable(0)
# tf.function required for the automatic control dependencies.
@def_function.function
def test_fn():
def cond():
v.assign(v.read_value() * 10 + i)
return i < n
def body():
nonlocal i
i += 1
def set_state(loop_vars):
nonlocal i
i, = loop_vars
i = 0
n = constant_op.constant(5)
control_flow.while_stmt(
test=cond,
body=body,
get_state=lambda: (i,),
set_state=set_state,
symbol_names=('i',),
opts={})
return i
self.evaluate(v.initializer)
self.assertEqual(self.evaluate(test_fn()), (5,))
self.assertEqual(self.evaluate(v), (12345,))
def test_tensor_with_python_state(self):
class MutableObject(object):
field = constant_op.constant(0, dtype=dtypes.int32)
state = MutableObject()
def body():
nonlocal i
state.field = state.field * 10 + i
i += 1
def set_state(loop_vars):
nonlocal i
i, state.field = loop_vars
i = 0
n = constant_op.constant(5)
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=lambda: (i, state.field),
set_state=set_state,
symbol_names=('i', 'state.field'),
opts={})
self.assertEqual(self.evaluate((i, state.field)), (5, 1234))
def test_python(self):
def body():
nonlocal i, s
s = s * 10 + i
i += 1
i = 0
s = 0
n = 5
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=None,
set_state=None,
symbol_names=('i', 's'),
opts={})
self.assertEqual(s, 1234)
def test_python_with_tensor_state(self):
def body():
nonlocal i, s
s = s * 10 + i
i += 1
i = 0
s = constant_op.constant(0)
n = 5
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=None,
set_state=None,
symbol_names=('i', 's'),
opts={})
self.assertEqual(i, 5)
self.assertEqual(self.evaluate(s), 1234)
def test_python_while_infinite(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(control_flow, 'PYTHON_MAX_ITERATIONS', 100):
with self.assertRaisesRegexp(ValueError, 'iteration limit'):
control_flow.while_stmt(
test=lambda: True,
body=lambda: None,
get_state=None,
set_state=None,
symbol_names=(),
opts={})
def test_python_for_infinite(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(control_flow, 'PYTHON_MAX_ITERATIONS', 100):
with self.assertRaisesRegexp(ValueError, 'iteration limit'):
control_flow.for_stmt(
iter_=range(101),
extra_test=None,
body=lambda i: None,
get_state=None,
set_state=None,
symbol_names=(),
opts={})
def test_python_while_large_unroll_warning(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(
control_flow, 'INEFFICIENT_UNROLL_MIN_ITERATIONS', 10):
with ops.Graph().as_default():
out_capturer = six.StringIO()
with test.mock.patch.object(sys, 'stdout', out_capturer):
with test.mock.patch.object(ag_logging, 'echo_log_to_stdout', True):
def custom_iterator():
for i in range(11):
c = constant_op.constant(i)
yield c
i = 0
control_flow.for_stmt(
iter_=custom_iterator(),
extra_test=None,
body=lambda i: None,
get_state=None,
set_state=None,
symbol_names=(),
opts={})
self.assertTrue(re.match(
r'.* Large unrolled loop.*Const.*', out_capturer.getvalue()))
def test_python_for_large_unroll_warning(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(
control_flow, 'INEFFICIENT_UNROLL_MIN_ITERATIONS', 10):
with ops.Graph().as_default():
out_capturer = six.StringIO()
with test.mock.patch.object(sys, 'stdout', out_capturer):
with test.mock.patch.object(ag_logging, 'echo_log_to_stdout', True):
def body():
nonlocal i
gen_math_ops.add(i, 1)
i += 1
i = 0
control_flow.while_stmt(
test=lambda: i < 100,
body=body,
get_state=None,
set_state=None,
symbol_names=('i',),
opts={})
self.assertTrue(re.match(
r'.* Large unrolled loop.*Add.*', out_capturer.getvalue()))
def _basic_loop(self, init_value, body_fn):
def body():
nonlocal i, s
s = body_fn(i, s)
i += 1
def set_state(loop_vars):
nonlocal i, s
i, s = loop_vars
i = 0
n = constant_op.constant(5)
s = init_value
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=lambda: (i, s),
set_state=set_state,
symbol_names=('i', 's'),
opts={})
return s
def test_tensor_illegal_input(self):
with self.assertRaisesRegex(ValueError, '"s" may not be None'):
self._basic_loop(None, lambda i, s: s)
with self.assertRaisesRegex(ValueError, '"s" must be defined'):
self._basic_loop(variable_operators.Undefined(''), lambda i, s: s)
def test_tensor_none_output(self):
with self.assertRaisesRegex(ValueError, '"s" is None at the end'):
self._basic_loop(0, lambda i, s: None)
def test_tensor_dtype_change(self):
with self.assertRaisesRegex(TypeError, '"s".* dtype float32 after'):
self._basic_loop(0, lambda i, s: 1.0)
def test_tensor_shape_change(self):
with self.assertRaisesRegex(ValueError, r'"s".* shape \(1,\) after'):
self._basic_loop(0, lambda i, s: np.array([1], dtype=np.int32))
@test_util.run_all_in_graph_and_eager_modes
class IfStmtTest(test.TestCase):
def test_tensor(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: constant_op.constant(1),
orelse=lambda: constant_op.constant(-1),
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual(1, self.evaluate(test_fn(constant_op.constant(True))))
self.assertEqual(-1, self.evaluate(test_fn(constant_op.constant(False))))
def test_tensor_multiple_returns(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: (constant_op.constant(1), constant_op.constant(2)),
orelse=lambda: (constant_op.constant(-1), constant_op.constant(-2)),
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual((1, 2), self.evaluate(test_fn(constant_op.constant(True))))
self.assertEqual((-1, -2),
self.evaluate(test_fn(constant_op.constant(False))))
def test_python(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: 1,
orelse=lambda: -1,
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual(1, test_fn(True))
self.assertEqual(-1, test_fn(False))
def test_python_multiple_returns(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: (1, 2),
orelse=lambda: (-1, -2),
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual((1, 2), test_fn(True))
self.assertEqual((-1, -2), test_fn(False))
def _basic_cond(self, true_value, false_value):
# Eager cond had different semantics, we don't test those here.
with func_graph.FuncGraph('tmp').as_default():
return control_flow.if_stmt(
cond=constant_op.constant(True),
body=true_value,
orelse=false_value,
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('s',),
composite_symbol_names=())
def test_tensor_none_output(self):
with self.assertRaisesRegex(
ValueError, '"s" is None at the end of the TRUE branch'):
self._basic_cond(lambda: None, lambda: 1)
with self.assertRaisesRegex(
ValueError, '"s" is None at the end of the FALSE branch'):
self._basic_cond(lambda: 1, lambda: None)
def test_tensor_undefined_output(self):
with self.assertRaisesRegex(
ValueError, "must also be initialized in the if.*'s'"):
self._basic_cond(lambda: variable_operators.Undefined('s'), lambda: 1)
with self.assertRaisesRegex(
ValueError, "must also be initialized in the else.*'s'"):
self._basic_cond(lambda: 1, lambda: variable_operators.Undefined('s'))
def test_tensor_dtype_change(self):
with self.assertRaisesRegex(TypeError, '"s" has dtype int32.*but.*float32'):
self._basic_cond(lambda: 1, lambda: 1.0)
if __name__ == '__main__':
test.main()
| 28.251663
| 81
| 0.608563
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import sys
import numpy as np
import six
from tensorflow.python.autograph.operators import control_flow
from tensorflow.python.autograph.operators import variables as variable_operators
from tensorflow.python.autograph.utils import ag_logging
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import func_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.ops.ragged import ragged_factory_ops
from tensorflow.python.platform import test
@test_util.run_all_in_graph_and_eager_modes
class ForLoopTest(test.TestCase):
def test_tensor(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
constant_op.constant([1, 2, 3, 4]),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (1234,))
def test_range_tensor(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
math_ops.range(5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (1234,))
def test_range_tensor_explicit_limit_delta(self):
def body(i):
nonlocal s
s = s * 100 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
math_ops.range(-17, -3, 5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (-171207,))
def test_range_tensor_explicit_limit_negative_delta(self):
def body(i):
nonlocal s
s = s * 100 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
math_ops.range(17, 3, -5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (171207,))
def test_range_tensor_random_delta(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
random_one = random_ops.random_uniform((), 1, 2, dtype=dtypes.int32)
control_flow.for_stmt(
math_ops.range(0, 5, random_one),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (1234,))
def test_range_tensor_random_negative_delta(self):
def body(i):
nonlocal s
s = s * 100 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
random_neg_five = random_ops.random_uniform((), -5, -4, dtype=dtypes.int32)
control_flow.for_stmt(
math_ops.range(17, 3, random_neg_five),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'iterate_names': 'i'})
self.assertEqual(self.evaluate(s), (171207,))
def test_tensor_with_extra_test_object_vars(self):
class MutableObject(object):
field_1 = constant_op.constant(0, dtype=dtypes.int32)
field_2 = constant_op.constant(1, dtype=dtypes.int32)
state = MutableObject()
def body(i):
state.field_1 += i
state.field_2 *= i
def get_state():
return state.field_1, state.field_2
def set_state(loop_vars):
state.field_1, state.field_2 = loop_vars
control_flow.for_stmt(
iter_=constant_op.constant([1, 2, 3, 4]),
body=body,
extra_test=lambda: state.field_1 < 6,
get_state=get_state,
set_state=set_state,
symbol_names=('state.field_1', 'state.field_2'),
opts={})
self.assertEqual(self.evaluate((state.field_1, state.field_2)), (6, 6))
def test_python(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
range(5),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(s, 1234)
def test_python_generator_with_extra_test(self):
def new_generator():
for i in range(1, 5):
yield i
gen = new_generator()
def run_loop():
s = 0
c = 0
def body(i):
nonlocal s, c
s = s * 10 + i
c += 1
control_flow.for_stmt(
gen,
extra_test=lambda: c == 0,
body=body,
get_state=None,
set_state=None,
symbol_names=('s', 'c'),
opts={})
return s, c
self.assertEqual(run_loop(), (1, 1))
self.assertEqual(run_loop(), (2, 1))
self.assertEqual(run_loop(), (3, 1))
self.assertEqual(next(gen), 4)
def test_python_generator_with_extra_test_no_iterations(self):
def new_generator():
for i in range(5):
yield i
gen = new_generator()
def run_loop():
s = 0
def body(i):
nonlocal s
s = s * 10 + i
control_flow.for_stmt(
gen,
extra_test=lambda: False,
body=body,
get_state=None,
set_state=None,
symbol_names=('s',),
opts={})
return s
self.assertEqual(run_loop(), 0)
self.assertEqual(run_loop(), 0)
self.assertEqual(next(gen), 0)
def test_tf_dataset(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (1234,))
def test_dataset_with_extra_test(self):
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=lambda: s < 3,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (12,))
def test_dataset_with_extra_test_collection_vars(self):
def body(i):
nonlocal s
l[0] += i
s += i
def set_state(loop_vars):
nonlocal s
l[0], s = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
l = [constant_op.constant(0, dtype=dtypes.int64)]
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=lambda: s < 3,
body=body,
get_state=lambda: (l[0], s),
set_state=set_state,
symbol_names=('l[0]', 's'),
opts={})
self.assertEqual(self.evaluate((l[0], s)), (3, 3))
def test_dataset_with_extra_test_iteration_limiting(self):
def body(it):
nonlocal i
with ops.control_dependencies((control_flow_ops.Assert(i < 3, (i,)),)):
i = it
def set_state(loop_vars):
nonlocal i
i, = loop_vars
i = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=lambda: i < 3,
body=body,
get_state=lambda: (i,),
set_state=set_state,
symbol_names=('i',),
opts={})
self.assertEqual(self.evaluate(i), (3,))
def test_tf_dataset_no_loop_vars(self):
def body(i):
v.assign(v.read_value() * 10 + i)
v = variables.Variable(0, dtype=dtypes.int64)
self.evaluate(v.initializer)
@def_function.function
def test_fn():
control_flow.for_stmt(
dataset_ops.Dataset.range(5),
extra_test=None,
body=body,
get_state=lambda: (),
set_state=lambda _: None,
symbol_names=(),
opts={})
self.evaluate(test_fn())
self.assertEqual(self.evaluate(v.read_value()), 1234)
def test_tf_iterator(self):
@def_function.function
def test_fn():
def body(i):
nonlocal s
s = s * 10 + i
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant(0, dtype=dtypes.int64)
control_flow.for_stmt(
iter(dataset_ops.Dataset.range(5)),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
return s
self.assertAllEqual(test_fn(), 1234)
def test_tf_iterator_shape_invariants(self):
@def_function.function
def test_fn():
def body(i):
nonlocal s
s = array_ops.concat([s, [i]], 0)
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = constant_op.constant([], dtype=dtypes.int64)
control_flow.for_stmt(
iter(dataset_ops.Dataset.range(5)),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={'shape_invariants': [(s, tensor_shape.TensorShape([None]))]})
return s
self.assertAllEqual(test_fn(), [0, 1, 2, 3, 4])
def test_tf_iterator_no_loop_vars(self):
def body(i):
v.assign(v.read_value() * 10 + i)
v = variables.Variable(0, dtype=dtypes.int64)
self.evaluate(v.initializer)
@def_function.function
def test_fn():
control_flow.for_stmt(
iter(dataset_ops.Dataset.range(5)),
extra_test=None,
body=body,
get_state=lambda: (),
set_state=lambda _: None,
symbol_names=(),
opts={})
self.evaluate(test_fn())
self.assertEqual(self.evaluate(v.read_value()), 1234)
def test_tf_ragged_tensor(self):
def body(i):
nonlocal s
s = s * 10 + i[0]
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
control_flow.for_stmt(
ragged_factory_ops.constant([[1], [2, 4], [3]]),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (123,))
def test_tf_ragged_tensor_higher_dimensional(self):
def body(i):
nonlocal s
s = s * 10 + i[0][0]
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = 0
ragged_3d = [
[[1], [1, 1], [1]],
[[2], [2]],
]
control_flow.for_stmt(
ragged_factory_ops.constant(ragged_3d),
extra_test=None,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
self.assertEqual(self.evaluate(s), (12,))
def test_tf_ragged_tensor_no_loop_vars(self):
v = variables.Variable(0, dtype=dtypes.int32)
self.evaluate(v.initializer)
def body(i):
v.assign(v.read_value() * 10 + i[0])
@def_function.function(autograph=False)
def test_fn():
control_flow.for_stmt(
ragged_factory_ops.constant([[1], [2, 4], [3]]),
extra_test=None,
body=body,
get_state=lambda: (),
set_state=lambda _: None,
symbol_names=(),
opts={})
self.evaluate(test_fn())
self.assertEqual(self.evaluate(v.read_value()), 123)
def _basic_loop(self, init_value, body_fn):
def body(i):
nonlocal s
s = body_fn(i, s)
def set_state(loop_vars):
nonlocal s
s, = loop_vars
s = init_value
control_flow.for_stmt(
constant_op.constant([1, 2, 3, 4]),
extra_test=lambda: True,
body=body,
get_state=lambda: (s,),
set_state=set_state,
symbol_names=('s',),
opts={})
return s
def test_tensor_illegal_input(self):
with self.assertRaisesRegex(ValueError, '"s" may not be None'):
self._basic_loop(None, lambda i, s: s)
with self.assertRaisesRegex(ValueError, '"s" must be defined'):
self._basic_loop(variable_operators.Undefined(''), lambda i, s: s)
def test_tensor_none_output(self):
with self.assertRaisesRegex(ValueError, '"s" is None at the end'):
self._basic_loop(0, lambda i, s: None)
def test_tensor_dtype_change(self):
with self.assertRaisesRegex(TypeError, '"s".* dtype float32 after'):
self._basic_loop(0, lambda i, s: 1.0)
def test_tensor_shape_change(self):
with self.assertRaisesRegex(ValueError, r'"s".* shape \(1,\) after'):
self._basic_loop(0, lambda i, s: np.array([1], dtype=np.int32))
@test_util.run_all_in_graph_and_eager_modes
class WhileLoopTest(test.TestCase):
def test_tensor(self):
def body():
nonlocal i, s
s = s * 10 + i
i += 1
def set_state(loop_vars):
nonlocal i, s
i, s = loop_vars
i = 0
n = constant_op.constant(5)
s = 0
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=lambda: (i, s),
set_state=set_state,
symbol_names=('i', 's'),
opts={})
self.assertEqual(self.evaluate((i, s)), (5, 1234))
def test_tensor_with_side_effecting_condition(self):
v = variables.Variable(0)
@def_function.function
def test_fn():
def cond():
v.assign(v.read_value() * 10 + i)
return i < n
def body():
nonlocal i
i += 1
def set_state(loop_vars):
nonlocal i
i, = loop_vars
i = 0
n = constant_op.constant(5)
control_flow.while_stmt(
test=cond,
body=body,
get_state=lambda: (i,),
set_state=set_state,
symbol_names=('i',),
opts={})
return i
self.evaluate(v.initializer)
self.assertEqual(self.evaluate(test_fn()), (5,))
self.assertEqual(self.evaluate(v), (12345,))
def test_tensor_with_python_state(self):
class MutableObject(object):
field = constant_op.constant(0, dtype=dtypes.int32)
state = MutableObject()
def body():
nonlocal i
state.field = state.field * 10 + i
i += 1
def set_state(loop_vars):
nonlocal i
i, state.field = loop_vars
i = 0
n = constant_op.constant(5)
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=lambda: (i, state.field),
set_state=set_state,
symbol_names=('i', 'state.field'),
opts={})
self.assertEqual(self.evaluate((i, state.field)), (5, 1234))
def test_python(self):
def body():
nonlocal i, s
s = s * 10 + i
i += 1
i = 0
s = 0
n = 5
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=None,
set_state=None,
symbol_names=('i', 's'),
opts={})
self.assertEqual(s, 1234)
def test_python_with_tensor_state(self):
def body():
nonlocal i, s
s = s * 10 + i
i += 1
i = 0
s = constant_op.constant(0)
n = 5
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=None,
set_state=None,
symbol_names=('i', 's'),
opts={})
self.assertEqual(i, 5)
self.assertEqual(self.evaluate(s), 1234)
def test_python_while_infinite(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(control_flow, 'PYTHON_MAX_ITERATIONS', 100):
with self.assertRaisesRegexp(ValueError, 'iteration limit'):
control_flow.while_stmt(
test=lambda: True,
body=lambda: None,
get_state=None,
set_state=None,
symbol_names=(),
opts={})
def test_python_for_infinite(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(control_flow, 'PYTHON_MAX_ITERATIONS', 100):
with self.assertRaisesRegexp(ValueError, 'iteration limit'):
control_flow.for_stmt(
iter_=range(101),
extra_test=None,
body=lambda i: None,
get_state=None,
set_state=None,
symbol_names=(),
opts={})
def test_python_while_large_unroll_warning(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(
control_flow, 'INEFFICIENT_UNROLL_MIN_ITERATIONS', 10):
with ops.Graph().as_default():
out_capturer = six.StringIO()
with test.mock.patch.object(sys, 'stdout', out_capturer):
with test.mock.patch.object(ag_logging, 'echo_log_to_stdout', True):
def custom_iterator():
for i in range(11):
c = constant_op.constant(i)
yield c
i = 0
control_flow.for_stmt(
iter_=custom_iterator(),
extra_test=None,
body=lambda i: None,
get_state=None,
set_state=None,
symbol_names=(),
opts={})
self.assertTrue(re.match(
r'.* Large unrolled loop.*Const.*', out_capturer.getvalue()))
def test_python_for_large_unroll_warning(self):
if not __debug__:
self.skipTest('Feature disabled in optimized mode.')
with test.mock.patch.object(
control_flow, 'INEFFICIENT_UNROLL_MIN_ITERATIONS', 10):
with ops.Graph().as_default():
out_capturer = six.StringIO()
with test.mock.patch.object(sys, 'stdout', out_capturer):
with test.mock.patch.object(ag_logging, 'echo_log_to_stdout', True):
def body():
nonlocal i
gen_math_ops.add(i, 1)
i += 1
i = 0
control_flow.while_stmt(
test=lambda: i < 100,
body=body,
get_state=None,
set_state=None,
symbol_names=('i',),
opts={})
self.assertTrue(re.match(
r'.* Large unrolled loop.*Add.*', out_capturer.getvalue()))
def _basic_loop(self, init_value, body_fn):
def body():
nonlocal i, s
s = body_fn(i, s)
i += 1
def set_state(loop_vars):
nonlocal i, s
i, s = loop_vars
i = 0
n = constant_op.constant(5)
s = init_value
control_flow.while_stmt(
test=lambda: i < n,
body=body,
get_state=lambda: (i, s),
set_state=set_state,
symbol_names=('i', 's'),
opts={})
return s
def test_tensor_illegal_input(self):
with self.assertRaisesRegex(ValueError, '"s" may not be None'):
self._basic_loop(None, lambda i, s: s)
with self.assertRaisesRegex(ValueError, '"s" must be defined'):
self._basic_loop(variable_operators.Undefined(''), lambda i, s: s)
def test_tensor_none_output(self):
with self.assertRaisesRegex(ValueError, '"s" is None at the end'):
self._basic_loop(0, lambda i, s: None)
def test_tensor_dtype_change(self):
with self.assertRaisesRegex(TypeError, '"s".* dtype float32 after'):
self._basic_loop(0, lambda i, s: 1.0)
def test_tensor_shape_change(self):
with self.assertRaisesRegex(ValueError, r'"s".* shape \(1,\) after'):
self._basic_loop(0, lambda i, s: np.array([1], dtype=np.int32))
@test_util.run_all_in_graph_and_eager_modes
class IfStmtTest(test.TestCase):
def test_tensor(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: constant_op.constant(1),
orelse=lambda: constant_op.constant(-1),
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual(1, self.evaluate(test_fn(constant_op.constant(True))))
self.assertEqual(-1, self.evaluate(test_fn(constant_op.constant(False))))
def test_tensor_multiple_returns(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: (constant_op.constant(1), constant_op.constant(2)),
orelse=lambda: (constant_op.constant(-1), constant_op.constant(-2)),
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual((1, 2), self.evaluate(test_fn(constant_op.constant(True))))
self.assertEqual((-1, -2),
self.evaluate(test_fn(constant_op.constant(False))))
def test_python(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: 1,
orelse=lambda: -1,
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual(1, test_fn(True))
self.assertEqual(-1, test_fn(False))
def test_python_multiple_returns(self):
def test_fn(cond):
return control_flow.if_stmt(
cond=cond,
body=lambda: (1, 2),
orelse=lambda: (-1, -2),
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('_',),
composite_symbol_names=())
self.assertEqual((1, 2), test_fn(True))
self.assertEqual((-1, -2), test_fn(False))
def _basic_cond(self, true_value, false_value):
with func_graph.FuncGraph('tmp').as_default():
return control_flow.if_stmt(
cond=constant_op.constant(True),
body=true_value,
orelse=false_value,
get_state=lambda: (),
set_state=lambda _: None,
basic_symbol_names=('s',),
composite_symbol_names=())
def test_tensor_none_output(self):
with self.assertRaisesRegex(
ValueError, '"s" is None at the end of the TRUE branch'):
self._basic_cond(lambda: None, lambda: 1)
with self.assertRaisesRegex(
ValueError, '"s" is None at the end of the FALSE branch'):
self._basic_cond(lambda: 1, lambda: None)
def test_tensor_undefined_output(self):
with self.assertRaisesRegex(
ValueError, "must also be initialized in the if.*'s'"):
self._basic_cond(lambda: variable_operators.Undefined('s'), lambda: 1)
with self.assertRaisesRegex(
ValueError, "must also be initialized in the else.*'s'"):
self._basic_cond(lambda: 1, lambda: variable_operators.Undefined('s'))
def test_tensor_dtype_change(self):
with self.assertRaisesRegex(TypeError, '"s" has dtype int32.*but.*float32'):
self._basic_cond(lambda: 1, lambda: 1.0)
if __name__ == '__main__':
test.main()
| true
| true
|
1c440820431ca1ad195527bd8221ac39c820de89
| 1,174
|
py
|
Python
|
pcdet/models/model_utils/layers.py
|
collector-m/H-23D_R-CNN
|
40c89c7a6910b738f7e4ed1d0dbb32b1ca99a016
|
[
"Apache-2.0"
] | 49
|
2021-08-02T02:04:32.000Z
|
2022-03-31T03:24:23.000Z
|
pcdet/models/model_utils/layers.py
|
collector-m/H-23D_R-CNN
|
40c89c7a6910b738f7e4ed1d0dbb32b1ca99a016
|
[
"Apache-2.0"
] | 5
|
2021-08-11T06:29:14.000Z
|
2022-01-23T02:59:29.000Z
|
pcdet/models/model_utils/layers.py
|
collector-m/H-23D_R-CNN
|
40c89c7a6910b738f7e4ed1d0dbb32b1ca99a016
|
[
"Apache-2.0"
] | 3
|
2021-08-08T12:11:31.000Z
|
2021-11-30T15:07:32.000Z
|
import torch
import torch.nn as nn
class ConvBNReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, eps=1e-3, momentum=0.01):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=kernel_size//2, bias=False),
nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum),
nn.ReLU(inplace=True)
)
def forward(self, x):
out = self.block(x)
return out
class SeparateConvBNReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, eps=1e-3, momentum=0.01):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, \
padding=kernel_size//2, groups=in_channels, bias=False),
nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum),
nn.ReLU(inplace=True)
)
def forward(self, x):
out = self.block(x)
return out
| 32.611111
| 107
| 0.681431
|
import torch
import torch.nn as nn
class ConvBNReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, eps=1e-3, momentum=0.01):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=kernel_size//2, bias=False),
nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum),
nn.ReLU(inplace=True)
)
def forward(self, x):
out = self.block(x)
return out
class SeparateConvBNReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, eps=1e-3, momentum=0.01):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, \
padding=kernel_size//2, groups=in_channels, bias=False),
nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum),
nn.ReLU(inplace=True)
)
def forward(self, x):
out = self.block(x)
return out
| true
| true
|
1c440acc35d3ac6fc5cec55840701662ea24566a
| 2,257
|
py
|
Python
|
lib/GenomeImporter/GenomeImporterClient.py
|
ModelSEED/GenomeImporter
|
c7af3e37e194315efa59276eed026373b13af658
|
[
"MIT"
] | null | null | null |
lib/GenomeImporter/GenomeImporterClient.py
|
ModelSEED/GenomeImporter
|
c7af3e37e194315efa59276eed026373b13af658
|
[
"MIT"
] | null | null | null |
lib/GenomeImporter/GenomeImporterClient.py
|
ModelSEED/GenomeImporter
|
c7af3e37e194315efa59276eed026373b13af658
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
############################################################
#
# Autogenerated by the KBase type compiler -
# any changes made here will be overwritten
#
############################################################
from __future__ import print_function
# the following is a hack to get the baseclient to import whether we're in a
# package or not. This makes pep8 unhappy hence the annotations.
try:
# baseclient and this client are in a package
from .baseclient import BaseClient as _BaseClient # @UnusedImport
except:
# no they aren't
from baseclient import BaseClient as _BaseClient # @Reimport
class GenomeImporter(object):
def __init__(
self, url=None, timeout=30 * 60, user_id=None,
password=None, token=None, ignore_authrc=False,
trust_all_ssl_certificates=False,
auth_svc='https://kbase.us/services/authorization/Sessions/Login'):
if url is None:
raise ValueError('A url is required')
self._service_ver = None
self._client = _BaseClient(
url, timeout=timeout, user_id=user_id, password=password,
token=token, ignore_authrc=ignore_authrc,
trust_all_ssl_certificates=trust_all_ssl_certificates,
auth_svc=auth_svc)
def import_external_genome(self, params, context=None):
"""
Function to import a list of genomes from a specified source
:param params: instance of type "ImportGenomeParams" (Input
parameters for the import_external_genome function) -> structure:
parameter "genome_ids" of String, parameter "source" of String,
parameter "workspace" of String
:returns: instance of type "ImportGenomeResults" (Output structure
for the import_external_genome function) -> structure: parameter
"report_name" of String, parameter "report_ref" of String
"""
return self._client.call_method(
'GenomeImporter.import_external_genome',
[params], self._service_ver, context)
def status(self, context=None):
return self._client.call_method('GenomeImporter.status',
[], self._service_ver, context)
| 41.796296
| 79
| 0.633584
| true
| true
|
|
1c440ae2da9545be2fbe11a7cf25b19d6daad111
| 2,515
|
py
|
Python
|
alttprbot_discord/util/embed_formatter.py
|
skyscooby/sahasrahbot
|
16fce824bd024f6357a8f260e2447ba477dcdac2
|
[
"MIT"
] | 15
|
2019-10-15T21:35:59.000Z
|
2022-03-31T19:49:39.000Z
|
alttprbot_discord/util/embed_formatter.py
|
skyscooby/sahasrahbot
|
16fce824bd024f6357a8f260e2447ba477dcdac2
|
[
"MIT"
] | 12
|
2019-10-06T01:33:13.000Z
|
2022-03-10T14:35:16.000Z
|
alttprbot_discord/util/embed_formatter.py
|
skyscooby/sahasrahbot
|
16fce824bd024f6357a8f260e2447ba477dcdac2
|
[
"MIT"
] | 28
|
2019-11-25T23:49:56.000Z
|
2022-03-10T04:03:31.000Z
|
import discord
def config(ctx, configdict):
embed = discord.Embed(
title="Server Configuration",
description="List of configuration parameters for this server.",
color=discord.Colour.teal())
for item in configdict:
embed.add_field(name=item['parameter'], value=item['value'])
return embed
async def reaction_group_list(ctx, reaction_groups):
embed = discord.Embed(
title="Server Reaction Groups",
description="List of server reaction groups.",
color=discord.Colour.gold())
for item in reaction_groups:
channel = ctx.guild.get_channel(item['channel_id'])
message = await channel.fetch_message(item['message_id'])
name = '{id}: {name}'.format(
id=item['id'],
name=item['name']
)
value = 'Description: {description}\n\nChannel: {channel}\nMessage Link: {messagelink}\nBot Managed: {botmanaged}'.format(
description=item['description'], channel=channel.mention, messagelink=message.jump_url, botmanaged='something')
embed.add_field(name=name, value=value, inline=False)
return embed
def reaction_role_list(ctx, reaction_roles):
embed = discord.Embed(
title="Reaction Roles for Group",
description="List of reaction roles for specified group.",
color=discord.Colour.gold())
for item in reaction_roles:
role_obj = ctx.guild.get_role(item['role_id'])
name = '{id}: {name}'.format(
id=item['id'],
name=item['name']
)
value = 'Role: {role}\nDescription: {description}\nEmoji: {emoji}\nProtected: {protected}'.format(
role=role_obj, description=item['description'], emoji=item['emoji'], protected=bool(
item['protect_mentions']))
embed.add_field(name=name, value=value, inline=False)
return embed
def reaction_menu(ctx, group, roles):
embed = discord.Embed(
title=group['name'],
description=group['description'],
color=discord.Colour.green(),
timestamp=discord.utils.utcnow()
)
value = ''
for role in roles:
value = value + '{emoji} `{name}`: {description}\n'.format(
emoji=role['emoji'],
name=role['name'],
description=role['description']
)
embed.add_field(name='Roles', value=value, inline=False)
embed.set_footer(text=group['id'])
return embed
| 37.537313
| 131
| 0.611531
|
import discord
def config(ctx, configdict):
embed = discord.Embed(
title="Server Configuration",
description="List of configuration parameters for this server.",
color=discord.Colour.teal())
for item in configdict:
embed.add_field(name=item['parameter'], value=item['value'])
return embed
async def reaction_group_list(ctx, reaction_groups):
embed = discord.Embed(
title="Server Reaction Groups",
description="List of server reaction groups.",
color=discord.Colour.gold())
for item in reaction_groups:
channel = ctx.guild.get_channel(item['channel_id'])
message = await channel.fetch_message(item['message_id'])
name = '{id}: {name}'.format(
id=item['id'],
name=item['name']
)
value = 'Description: {description}\n\nChannel: {channel}\nMessage Link: {messagelink}\nBot Managed: {botmanaged}'.format(
description=item['description'], channel=channel.mention, messagelink=message.jump_url, botmanaged='something')
embed.add_field(name=name, value=value, inline=False)
return embed
def reaction_role_list(ctx, reaction_roles):
embed = discord.Embed(
title="Reaction Roles for Group",
description="List of reaction roles for specified group.",
color=discord.Colour.gold())
for item in reaction_roles:
role_obj = ctx.guild.get_role(item['role_id'])
name = '{id}: {name}'.format(
id=item['id'],
name=item['name']
)
value = 'Role: {role}\nDescription: {description}\nEmoji: {emoji}\nProtected: {protected}'.format(
role=role_obj, description=item['description'], emoji=item['emoji'], protected=bool(
item['protect_mentions']))
embed.add_field(name=name, value=value, inline=False)
return embed
def reaction_menu(ctx, group, roles):
embed = discord.Embed(
title=group['name'],
description=group['description'],
color=discord.Colour.green(),
timestamp=discord.utils.utcnow()
)
value = ''
for role in roles:
value = value + '{emoji} `{name}`: {description}\n'.format(
emoji=role['emoji'],
name=role['name'],
description=role['description']
)
embed.add_field(name='Roles', value=value, inline=False)
embed.set_footer(text=group['id'])
return embed
| true
| true
|
1c440b1e1de464dfdff22caf8ef6161d4e39e699
| 5,094
|
py
|
Python
|
tests/python/contrib/test_cmsisnn/test_pooling.py
|
jwfromm/relax
|
f120282007778706199243ee88b50697c2b9550c
|
[
"Apache-2.0"
] | 2,084
|
2020-11-25T02:31:53.000Z
|
2022-03-31T11:33:47.000Z
|
tests/python/contrib/test_cmsisnn/test_pooling.py
|
jwfromm/relax
|
f120282007778706199243ee88b50697c2b9550c
|
[
"Apache-2.0"
] | 3,022
|
2020-11-24T14:02:31.000Z
|
2022-03-31T23:55:31.000Z
|
tests/python/contrib/test_cmsisnn/test_pooling.py
|
jwfromm/relax
|
f120282007778706199243ee88b50697c2b9550c
|
[
"Apache-2.0"
] | 977
|
2020-11-25T00:54:52.000Z
|
2022-03-31T12:47:08.000Z
|
# 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.
"""CMSIS-NN integration tests: Conv2D"""
import itertools
import numpy as np
import pytest
import tvm
from tvm import relay
from tvm.relay.op.contrib import cmsisnn
from tests.python.relay.aot.aot_test_utils import (
AOTTestModel,
AOT_CORSTONE300_RUNNER,
AOT_DEFAULT_RUNNER,
generate_ref_data,
compile_and_run,
)
from utils import (
skip_if_no_reference_system,
make_module,
count_num_calls,
get_range_for_dtype_str,
get_same_padding,
get_conv2d_qnn_params,
make_qnn_relu,
)
def make_model(pool_op, shape, pool_size, strides, padding, dtype, scale, zero_point, relu_type):
"""Return a model and any parameters it may have"""
op = relay.var("input", shape=shape, dtype=dtype)
pad_ = (0, 0, 0, 0)
if padding == "SAME":
dilation = (1, 1)
pad_ = get_same_padding((shape[1], shape[2]), pool_size, dilation, strides)
op = relay.nn.pad(
op,
pad_width=[(0, 0), (pad_[0], pad_[2]), (pad_[1], pad_[3]), (0, 0)],
pad_value=zero_point,
pad_mode="constant",
)
if pool_op == relay.nn.avg_pool2d:
op = relay.cast(op, "int32")
op = pool_op(
op, pool_size=pool_size, strides=strides, padding=pad_, ceil_mode=True, layout="NHWC"
)
if pool_op == relay.nn.avg_pool2d:
op = relay.cast(op, dtype)
op = make_qnn_relu(op, relu_type, scale, zero_point, dtype)
return op
@tvm.testing.requires_cmsisnn
@pytest.mark.parametrize("in_shape", [(1, 28, 28, 12), (1, 64, 100, 4)])
@pytest.mark.parametrize(
"pool_size, strides, padding", [((3, 3), (2, 2), "SAME"), ((2, 2), (1, 1), "VALID")]
)
@pytest.mark.parametrize("relu_type", ["RELU"])
@pytest.mark.parametrize("pool_type", [relay.nn.max_pool2d, relay.nn.avg_pool2d])
@pytest.mark.parametrize("zero_point, scale", [(-34, 0.0256)])
def test_op_int8(
in_shape,
pool_size,
strides,
padding,
relu_type,
pool_type,
zero_point,
scale,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_CORSTONE300_RUNNER
dtype = "int8"
model = make_model(
pool_type,
in_shape,
pool_size,
strides,
padding,
dtype,
scale,
zero_point,
relu_type,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
# validate pattern matching
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert any(attrs), "At least one function with external attributes was expected."
compilers = [
key == "Compiler" and value == "cmsis-nn" for attr in attrs for key, value in attr.items()
]
assert any(compilers), "Module does not contain function for cmsisnn target."
assert count_num_calls(orig_mod) == count_num_calls(
cmsisnn_mod
), "Number of calls changed during partitioning"
# validate the output
in_min, in_max = get_range_for_dtype_str(dtype)
np.random.seed(0)
inputs = {
"input": np.random.randint(in_min, high=in_max, size=in_shape, dtype="int8"),
}
output_list = generate_ref_data(orig_mod["main"], inputs)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=None,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
@tvm.testing.requires_cmsisnn
def test_invalid_parameters():
model = make_model(
pool_op=relay.nn.avg_pool2d,
shape=(1, 28, 28, 12),
pool_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype="uint8",
scale=1,
zero_point=-33,
relu_type="RELU",
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
# validate pattern matching
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert not any(attrs), "No function should have an external attribute."
if __name__ == "__main__":
sys.exit(pytest.main([__file__] + sys.argv[1:]))
| 29.275862
| 98
| 0.65371
|
import itertools
import numpy as np
import pytest
import tvm
from tvm import relay
from tvm.relay.op.contrib import cmsisnn
from tests.python.relay.aot.aot_test_utils import (
AOTTestModel,
AOT_CORSTONE300_RUNNER,
AOT_DEFAULT_RUNNER,
generate_ref_data,
compile_and_run,
)
from utils import (
skip_if_no_reference_system,
make_module,
count_num_calls,
get_range_for_dtype_str,
get_same_padding,
get_conv2d_qnn_params,
make_qnn_relu,
)
def make_model(pool_op, shape, pool_size, strides, padding, dtype, scale, zero_point, relu_type):
op = relay.var("input", shape=shape, dtype=dtype)
pad_ = (0, 0, 0, 0)
if padding == "SAME":
dilation = (1, 1)
pad_ = get_same_padding((shape[1], shape[2]), pool_size, dilation, strides)
op = relay.nn.pad(
op,
pad_width=[(0, 0), (pad_[0], pad_[2]), (pad_[1], pad_[3]), (0, 0)],
pad_value=zero_point,
pad_mode="constant",
)
if pool_op == relay.nn.avg_pool2d:
op = relay.cast(op, "int32")
op = pool_op(
op, pool_size=pool_size, strides=strides, padding=pad_, ceil_mode=True, layout="NHWC"
)
if pool_op == relay.nn.avg_pool2d:
op = relay.cast(op, dtype)
op = make_qnn_relu(op, relu_type, scale, zero_point, dtype)
return op
@tvm.testing.requires_cmsisnn
@pytest.mark.parametrize("in_shape", [(1, 28, 28, 12), (1, 64, 100, 4)])
@pytest.mark.parametrize(
"pool_size, strides, padding", [((3, 3), (2, 2), "SAME"), ((2, 2), (1, 1), "VALID")]
)
@pytest.mark.parametrize("relu_type", ["RELU"])
@pytest.mark.parametrize("pool_type", [relay.nn.max_pool2d, relay.nn.avg_pool2d])
@pytest.mark.parametrize("zero_point, scale", [(-34, 0.0256)])
def test_op_int8(
in_shape,
pool_size,
strides,
padding,
relu_type,
pool_type,
zero_point,
scale,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_CORSTONE300_RUNNER
dtype = "int8"
model = make_model(
pool_type,
in_shape,
pool_size,
strides,
padding,
dtype,
scale,
zero_point,
relu_type,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert any(attrs), "At least one function with external attributes was expected."
compilers = [
key == "Compiler" and value == "cmsis-nn" for attr in attrs for key, value in attr.items()
]
assert any(compilers), "Module does not contain function for cmsisnn target."
assert count_num_calls(orig_mod) == count_num_calls(
cmsisnn_mod
), "Number of calls changed during partitioning"
in_min, in_max = get_range_for_dtype_str(dtype)
np.random.seed(0)
inputs = {
"input": np.random.randint(in_min, high=in_max, size=in_shape, dtype="int8"),
}
output_list = generate_ref_data(orig_mod["main"], inputs)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=None,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
@tvm.testing.requires_cmsisnn
def test_invalid_parameters():
model = make_model(
pool_op=relay.nn.avg_pool2d,
shape=(1, 28, 28, 12),
pool_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype="uint8",
scale=1,
zero_point=-33,
relu_type="RELU",
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert not any(attrs), "No function should have an external attribute."
if __name__ == "__main__":
sys.exit(pytest.main([__file__] + sys.argv[1:]))
| true
| true
|
1c440b708248398fce0129be28a565447b2b4b8c
| 8,025
|
py
|
Python
|
pyEX/tests/test_refdata.py
|
andrescevp/pyEX
|
4c8daa411b01133a292d341a78f6e1b80cc2be99
|
[
"Apache-2.0"
] | null | null | null |
pyEX/tests/test_refdata.py
|
andrescevp/pyEX
|
4c8daa411b01133a292d341a78f6e1b80cc2be99
|
[
"Apache-2.0"
] | null | null | null |
pyEX/tests/test_refdata.py
|
andrescevp/pyEX
|
4c8daa411b01133a292d341a78f6e1b80cc2be99
|
[
"Apache-2.0"
] | null | null | null |
# for Coverage
from mock import patch, MagicMock
class TestAll:
def setup(self):
pass
# setup() before each test method
def teardown(self):
pass
# teardown() after each test method
@classmethod
def setup_class(cls):
pass
# setup_class() before any methods in this class
@classmethod
def teardown_class(cls):
pass
# teardown_class() after any methods in this class
def test_symbols(self):
from pyEX.refdata import (
symbols,
iexSymbols,
mutualFundSymbols,
otcSymbols,
internationalSymbols,
fxSymbols,
)
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
symbols()
iexSymbols()
mutualFundSymbols()
otcSymbols()
internationalSymbols()
internationalSymbols("GB")
internationalSymbols(exchange="test")
mock.return_value.json = MagicMock(
return_value={"currencies": [], "pairs": []}
)
fxSymbols()
def test_symbolsDF(self):
from pyEX.refdata import symbolsDF
from pyEX import Client
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
c = Client(version="sandbox")
symbolsDF()
c.iexSymbolsDF()
c.mutualFundSymbolsDF()
c.otcSymbolsDF()
c.internationalSymbolsDF()
c.internationalSymbolsDF("GB")
c.internationalSymbolsDF(exchange="test")
c.symbolsList()
c.iexSymbolsList()
c.mutualFundSymbolsList()
c.otcSymbolsList()
c.internationalSymbolsList()
c.internationalSymbolsList("GB")
c.internationalSymbolsList(exchange="test")
mock.return_value.json = MagicMock(
return_value={"currencies": [], "pairs": []}
)
c.fxSymbolsDF()
c.fxSymbolsList()
def test_calendar(self):
from pyEX import Client
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
c = Client(version="sandbox")
c.calendar()
c.holidays()
def test_calendarDF(self):
from pyEX import Client
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
c = Client(version="sandbox")
c.calendarDF()
c.holidaysDF()
def test_corporateActions(self):
from pyEX.refdata import corporateActions
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
corporateActions()
corporateActions("20170202")
def test_corporateActionsDF(self):
from pyEX.refdata import corporateActionsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
corporateActionsDF()
def test_dividends(self):
from pyEX.refdata import refDividends
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
refDividends()
refDividends("20170202")
def test_dividendsDF(self):
from pyEX.refdata import refDividendsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
refDividendsDF()
def test_nextDayExtDate(self):
from pyEX.refdata import nextDayExtDate
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
nextDayExtDate()
nextDayExtDate("20170202")
def test_nextDayExtDateDF(self):
from pyEX.refdata import nextDayExtDateDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
nextDayExtDateDF()
def test_directory(self):
from pyEX.refdata import directory
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
directory()
directory("20170202")
def test_directoryDF(self):
from pyEX.refdata import directoryDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
directoryDF()
def test_sectors(self):
from pyEX.refdata import sectors
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
sectors()
def test_sectorsDF(self):
from pyEX.refdata import sectorsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
sectorsDF()
def test_exchanges(self):
from pyEX.refdata import exchanges, internationalExchanges
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
exchanges()
internationalExchanges()
def test_exchangesDF(self):
from pyEX.refdata import exchangesDF, internationalExchangesDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
exchangesDF()
internationalExchangesDF()
def test_figi(self):
from pyEX.refdata import figi
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
figi("")
def test_figiDF(self):
from pyEX.refdata import figiDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
figiDF("")
def test_tags(self):
from pyEX.refdata import tags
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
tags()
def test_tagsDF(self):
from pyEX.refdata import tagsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
tagsDF()
| 33.024691
| 70
| 0.594891
|
from mock import patch, MagicMock
class TestAll:
def setup(self):
pass
def teardown(self):
pass
@classmethod
def setup_class(cls):
pass
@classmethod
def teardown_class(cls):
pass
def test_symbols(self):
from pyEX.refdata import (
symbols,
iexSymbols,
mutualFundSymbols,
otcSymbols,
internationalSymbols,
fxSymbols,
)
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
symbols()
iexSymbols()
mutualFundSymbols()
otcSymbols()
internationalSymbols()
internationalSymbols("GB")
internationalSymbols(exchange="test")
mock.return_value.json = MagicMock(
return_value={"currencies": [], "pairs": []}
)
fxSymbols()
def test_symbolsDF(self):
from pyEX.refdata import symbolsDF
from pyEX import Client
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
c = Client(version="sandbox")
symbolsDF()
c.iexSymbolsDF()
c.mutualFundSymbolsDF()
c.otcSymbolsDF()
c.internationalSymbolsDF()
c.internationalSymbolsDF("GB")
c.internationalSymbolsDF(exchange="test")
c.symbolsList()
c.iexSymbolsList()
c.mutualFundSymbolsList()
c.otcSymbolsList()
c.internationalSymbolsList()
c.internationalSymbolsList("GB")
c.internationalSymbolsList(exchange="test")
mock.return_value.json = MagicMock(
return_value={"currencies": [], "pairs": []}
)
c.fxSymbolsDF()
c.fxSymbolsList()
def test_calendar(self):
from pyEX import Client
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
c = Client(version="sandbox")
c.calendar()
c.holidays()
def test_calendarDF(self):
from pyEX import Client
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
c = Client(version="sandbox")
c.calendarDF()
c.holidaysDF()
def test_corporateActions(self):
from pyEX.refdata import corporateActions
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
corporateActions()
corporateActions("20170202")
def test_corporateActionsDF(self):
from pyEX.refdata import corporateActionsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
corporateActionsDF()
def test_dividends(self):
from pyEX.refdata import refDividends
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
refDividends()
refDividends("20170202")
def test_dividendsDF(self):
from pyEX.refdata import refDividendsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
refDividendsDF()
def test_nextDayExtDate(self):
from pyEX.refdata import nextDayExtDate
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
nextDayExtDate()
nextDayExtDate("20170202")
def test_nextDayExtDateDF(self):
from pyEX.refdata import nextDayExtDateDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
nextDayExtDateDF()
def test_directory(self):
from pyEX.refdata import directory
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
directory()
directory("20170202")
def test_directoryDF(self):
from pyEX.refdata import directoryDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
directoryDF()
def test_sectors(self):
from pyEX.refdata import sectors
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
sectors()
def test_sectorsDF(self):
from pyEX.refdata import sectorsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
sectorsDF()
def test_exchanges(self):
from pyEX.refdata import exchanges, internationalExchanges
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
exchanges()
internationalExchanges()
def test_exchangesDF(self):
from pyEX.refdata import exchangesDF, internationalExchangesDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
exchangesDF()
internationalExchangesDF()
def test_figi(self):
from pyEX.refdata import figi
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
figi("")
def test_figiDF(self):
from pyEX.refdata import figiDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
figiDF("")
def test_tags(self):
from pyEX.refdata import tags
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
tags()
def test_tagsDF(self):
from pyEX.refdata import tagsDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
tagsDF()
| true
| true
|
1c440b889b1465133c187d8b8d2d064e1d116e83
| 6,186
|
py
|
Python
|
pandas/tests/resample/test_timedelta.py
|
CJL89/pandas
|
6210077d32a9e9675526ea896e6d1f9189629d4a
|
[
"BSD-3-Clause"
] | 603
|
2020-12-23T13:49:32.000Z
|
2022-03-31T23:38:03.000Z
|
pandas/tests/resample/test_timedelta.py
|
CJL89/pandas
|
6210077d32a9e9675526ea896e6d1f9189629d4a
|
[
"BSD-3-Clause"
] | 387
|
2020-12-15T14:54:04.000Z
|
2022-03-31T07:00:21.000Z
|
pandas/tests/resample/test_timedelta.py
|
CJL89/pandas
|
6210077d32a9e9675526ea896e6d1f9189629d4a
|
[
"BSD-3-Clause"
] | 35
|
2021-03-26T03:12:04.000Z
|
2022-03-23T10:15:10.000Z
|
from datetime import timedelta
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series
import pandas._testing as tm
from pandas.core.indexes.timedeltas import timedelta_range
def test_asfreq_bug():
df = DataFrame(data=[1, 3], index=[timedelta(), timedelta(minutes=3)])
result = df.resample("1T").asfreq()
expected = DataFrame(
data=[1, np.nan, np.nan, 3],
index=timedelta_range("0 day", periods=4, freq="1T"),
)
tm.assert_frame_equal(result, expected)
def test_resample_with_nat():
# GH 13223
index = pd.to_timedelta(["0s", pd.NaT, "2s"])
result = DataFrame({"value": [2, 3, 5]}, index).resample("1s").mean()
expected = DataFrame(
{"value": [2.5, np.nan, 5.0]},
index=timedelta_range("0 day", periods=3, freq="1S"),
)
tm.assert_frame_equal(result, expected)
def test_resample_as_freq_with_subperiod():
# GH 13022
index = timedelta_range("00:00:00", "00:10:00", freq="5T")
df = DataFrame(data={"value": [1, 5, 10]}, index=index)
result = df.resample("2T").asfreq()
expected_data = {"value": [1, np.nan, np.nan, np.nan, np.nan, 10]}
expected = DataFrame(
data=expected_data, index=timedelta_range("00:00:00", "00:10:00", freq="2T")
)
tm.assert_frame_equal(result, expected)
def test_resample_with_timedeltas():
expected = DataFrame({"A": np.arange(1480)})
expected = expected.groupby(expected.index // 30).sum()
expected.index = pd.timedelta_range("0 days", freq="30T", periods=50)
df = DataFrame(
{"A": np.arange(1480)}, index=pd.to_timedelta(np.arange(1480), unit="T")
)
result = df.resample("30T").sum()
tm.assert_frame_equal(result, expected)
s = df["A"]
result = s.resample("30T").sum()
tm.assert_series_equal(result, expected["A"])
def test_resample_single_period_timedelta():
s = Series(list(range(5)), index=pd.timedelta_range("1 day", freq="s", periods=5))
result = s.resample("2s").sum()
expected = Series(
[1, 5, 4], index=pd.timedelta_range("1 day", freq="2s", periods=3)
)
tm.assert_series_equal(result, expected)
def test_resample_timedelta_idempotency():
# GH 12072
index = pd.timedelta_range("0", periods=9, freq="10L")
series = Series(range(9), index=index)
result = series.resample("10L").mean()
expected = series
tm.assert_series_equal(result, expected)
def test_resample_offset_with_timedeltaindex():
# GH 10530 & 31809
rng = timedelta_range(start="0s", periods=25, freq="s")
ts = Series(np.random.randn(len(rng)), index=rng)
with_base = ts.resample("2s", offset="5s").mean()
without_base = ts.resample("2s").mean()
exp_without_base = timedelta_range(start="0s", end="25s", freq="2s")
exp_with_base = timedelta_range(start="5s", end="29s", freq="2s")
tm.assert_index_equal(without_base.index, exp_without_base)
tm.assert_index_equal(with_base.index, exp_with_base)
def test_resample_categorical_data_with_timedeltaindex():
# GH #12169
df = DataFrame({"Group_obj": "A"}, index=pd.to_timedelta(list(range(20)), unit="s"))
df["Group"] = df["Group_obj"].astype("category")
result = df.resample("10s").agg(lambda x: (x.value_counts().index[0]))
expected = DataFrame(
{"Group_obj": ["A", "A"], "Group": ["A", "A"]},
index=pd.TimedeltaIndex([0, 10], unit="s", freq="10s"),
)
expected = expected.reindex(["Group_obj", "Group"], axis=1)
expected["Group"] = expected["Group_obj"]
tm.assert_frame_equal(result, expected)
def test_resample_timedelta_values():
# GH 13119
# check that timedelta dtype is preserved when NaT values are
# introduced by the resampling
times = timedelta_range("1 day", "6 day", freq="4D")
df = DataFrame({"time": times}, index=times)
times2 = timedelta_range("1 day", "6 day", freq="2D")
exp = Series(times2, index=times2, name="time")
exp.iloc[1] = pd.NaT
res = df.resample("2D").first()["time"]
tm.assert_series_equal(res, exp)
res = df["time"].resample("2D").first()
tm.assert_series_equal(res, exp)
@pytest.mark.parametrize(
"start, end, freq, resample_freq",
[
("8H", "21h59min50s", "10S", "3H"), # GH 30353 example
("3H", "22H", "1H", "5H"),
("527D", "5006D", "3D", "10D"),
("1D", "10D", "1D", "2D"), # GH 13022 example
# tests that worked before GH 33498:
("8H", "21h59min50s", "10S", "2H"),
("0H", "21h59min50s", "10S", "3H"),
("10D", "85D", "D", "2D"),
],
)
def test_resample_timedelta_edge_case(start, end, freq, resample_freq):
# GH 33498
# check that the timedelta bins does not contains an extra bin
idx = pd.timedelta_range(start=start, end=end, freq=freq)
s = Series(np.arange(len(idx)), index=idx)
result = s.resample(resample_freq).min()
expected_index = pd.timedelta_range(freq=resample_freq, start=start, end=end)
tm.assert_index_equal(result.index, expected_index)
assert result.index.freq == expected_index.freq
assert not np.isnan(result[-1])
def test_resample_with_timedelta_yields_no_empty_groups():
# GH 10603
df = DataFrame(
np.random.normal(size=(10000, 4)),
index=pd.timedelta_range(start="0s", periods=10000, freq="3906250n"),
)
result = df.loc["1s":, :].resample("3s").apply(lambda x: len(x))
expected = DataFrame(
[[768.0] * 4] * 12 + [[528.0] * 4],
index=pd.timedelta_range(start="1s", periods=13, freq="3s"),
)
tm.assert_frame_equal(result, expected)
def test_resample_quantile_timedelta():
# GH: 29485
df = DataFrame(
{"value": pd.to_timedelta(np.arange(4), unit="s")},
index=pd.date_range("20200101", periods=4, tz="UTC"),
)
result = df.resample("2D").quantile(0.99)
expected = DataFrame(
{
"value": [
pd.Timedelta("0 days 00:00:00.990000"),
pd.Timedelta("0 days 00:00:02.990000"),
]
},
index=pd.date_range("20200101", periods=2, tz="UTC", freq="2D"),
)
tm.assert_frame_equal(result, expected)
| 33.080214
| 88
| 0.631911
|
from datetime import timedelta
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series
import pandas._testing as tm
from pandas.core.indexes.timedeltas import timedelta_range
def test_asfreq_bug():
df = DataFrame(data=[1, 3], index=[timedelta(), timedelta(minutes=3)])
result = df.resample("1T").asfreq()
expected = DataFrame(
data=[1, np.nan, np.nan, 3],
index=timedelta_range("0 day", periods=4, freq="1T"),
)
tm.assert_frame_equal(result, expected)
def test_resample_with_nat():
index = pd.to_timedelta(["0s", pd.NaT, "2s"])
result = DataFrame({"value": [2, 3, 5]}, index).resample("1s").mean()
expected = DataFrame(
{"value": [2.5, np.nan, 5.0]},
index=timedelta_range("0 day", periods=3, freq="1S"),
)
tm.assert_frame_equal(result, expected)
def test_resample_as_freq_with_subperiod():
index = timedelta_range("00:00:00", "00:10:00", freq="5T")
df = DataFrame(data={"value": [1, 5, 10]}, index=index)
result = df.resample("2T").asfreq()
expected_data = {"value": [1, np.nan, np.nan, np.nan, np.nan, 10]}
expected = DataFrame(
data=expected_data, index=timedelta_range("00:00:00", "00:10:00", freq="2T")
)
tm.assert_frame_equal(result, expected)
def test_resample_with_timedeltas():
expected = DataFrame({"A": np.arange(1480)})
expected = expected.groupby(expected.index // 30).sum()
expected.index = pd.timedelta_range("0 days", freq="30T", periods=50)
df = DataFrame(
{"A": np.arange(1480)}, index=pd.to_timedelta(np.arange(1480), unit="T")
)
result = df.resample("30T").sum()
tm.assert_frame_equal(result, expected)
s = df["A"]
result = s.resample("30T").sum()
tm.assert_series_equal(result, expected["A"])
def test_resample_single_period_timedelta():
s = Series(list(range(5)), index=pd.timedelta_range("1 day", freq="s", periods=5))
result = s.resample("2s").sum()
expected = Series(
[1, 5, 4], index=pd.timedelta_range("1 day", freq="2s", periods=3)
)
tm.assert_series_equal(result, expected)
def test_resample_timedelta_idempotency():
index = pd.timedelta_range("0", periods=9, freq="10L")
series = Series(range(9), index=index)
result = series.resample("10L").mean()
expected = series
tm.assert_series_equal(result, expected)
def test_resample_offset_with_timedeltaindex():
rng = timedelta_range(start="0s", periods=25, freq="s")
ts = Series(np.random.randn(len(rng)), index=rng)
with_base = ts.resample("2s", offset="5s").mean()
without_base = ts.resample("2s").mean()
exp_without_base = timedelta_range(start="0s", end="25s", freq="2s")
exp_with_base = timedelta_range(start="5s", end="29s", freq="2s")
tm.assert_index_equal(without_base.index, exp_without_base)
tm.assert_index_equal(with_base.index, exp_with_base)
def test_resample_categorical_data_with_timedeltaindex():
f = DataFrame({"Group_obj": "A"}, index=pd.to_timedelta(list(range(20)), unit="s"))
df["Group"] = df["Group_obj"].astype("category")
result = df.resample("10s").agg(lambda x: (x.value_counts().index[0]))
expected = DataFrame(
{"Group_obj": ["A", "A"], "Group": ["A", "A"]},
index=pd.TimedeltaIndex([0, 10], unit="s", freq="10s"),
)
expected = expected.reindex(["Group_obj", "Group"], axis=1)
expected["Group"] = expected["Group_obj"]
tm.assert_frame_equal(result, expected)
def test_resample_timedelta_values():
times = timedelta_range("1 day", "6 day", freq="4D")
df = DataFrame({"time": times}, index=times)
times2 = timedelta_range("1 day", "6 day", freq="2D")
exp = Series(times2, index=times2, name="time")
exp.iloc[1] = pd.NaT
res = df.resample("2D").first()["time"]
tm.assert_series_equal(res, exp)
res = df["time"].resample("2D").first()
tm.assert_series_equal(res, exp)
@pytest.mark.parametrize(
"start, end, freq, resample_freq",
[
("8H", "21h59min50s", "10S", "3H"),
("3H", "22H", "1H", "5H"),
("527D", "5006D", "3D", "10D"),
("1D", "10D", "1D", "2D"),
("8H", "21h59min50s", "10S", "2H"),
("0H", "21h59min50s", "10S", "3H"),
("10D", "85D", "D", "2D"),
],
)
def test_resample_timedelta_edge_case(start, end, freq, resample_freq):
idx = pd.timedelta_range(start=start, end=end, freq=freq)
s = Series(np.arange(len(idx)), index=idx)
result = s.resample(resample_freq).min()
expected_index = pd.timedelta_range(freq=resample_freq, start=start, end=end)
tm.assert_index_equal(result.index, expected_index)
assert result.index.freq == expected_index.freq
assert not np.isnan(result[-1])
def test_resample_with_timedelta_yields_no_empty_groups():
df = DataFrame(
np.random.normal(size=(10000, 4)),
index=pd.timedelta_range(start="0s", periods=10000, freq="3906250n"),
)
result = df.loc["1s":, :].resample("3s").apply(lambda x: len(x))
expected = DataFrame(
[[768.0] * 4] * 12 + [[528.0] * 4],
index=pd.timedelta_range(start="1s", periods=13, freq="3s"),
)
tm.assert_frame_equal(result, expected)
def test_resample_quantile_timedelta():
df = DataFrame(
{"value": pd.to_timedelta(np.arange(4), unit="s")},
index=pd.date_range("20200101", periods=4, tz="UTC"),
)
result = df.resample("2D").quantile(0.99)
expected = DataFrame(
{
"value": [
pd.Timedelta("0 days 00:00:00.990000"),
pd.Timedelta("0 days 00:00:02.990000"),
]
},
index=pd.date_range("20200101", periods=2, tz="UTC", freq="2D"),
)
tm.assert_frame_equal(result, expected)
| true
| true
|
1c440bf67138afa263b8887f4363d386e850994f
| 1,395
|
py
|
Python
|
azure-mgmt-compute/azure/mgmt/compute/models/storage_profile.py
|
CharaD7/azure-sdk-for-python
|
9fdf0aac0cec8a15a5bb2a0ea27dd331dbfa2f5c
|
[
"MIT"
] | null | null | null |
azure-mgmt-compute/azure/mgmt/compute/models/storage_profile.py
|
CharaD7/azure-sdk-for-python
|
9fdf0aac0cec8a15a5bb2a0ea27dd331dbfa2f5c
|
[
"MIT"
] | null | null | null |
azure-mgmt-compute/azure/mgmt/compute/models/storage_profile.py
|
CharaD7/azure-sdk-for-python
|
9fdf0aac0cec8a15a5bb2a0ea27dd331dbfa2f5c
|
[
"MIT"
] | null | null | null |
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class StorageProfile(Model):
"""Describes a storage profile.
:param image_reference: the image reference.
:type image_reference: :class:`ImageReference
<azure.mgmt.compute.models.ImageReference>`
:param os_disk: the OS disk.
:type os_disk: :class:`OSDisk <azure.mgmt.compute.models.OSDisk>`
:param data_disks: the data disks.
:type data_disks: list of :class:`DataDisk
<azure.mgmt.compute.models.DataDisk>`
"""
_attribute_map = {
'image_reference': {'key': 'imageReference', 'type': 'ImageReference'},
'os_disk': {'key': 'osDisk', 'type': 'OSDisk'},
'data_disks': {'key': 'dataDisks', 'type': '[DataDisk]'},
}
def __init__(self, image_reference=None, os_disk=None, data_disks=None):
self.image_reference = image_reference
self.os_disk = os_disk
self.data_disks = data_disks
| 36.710526
| 79
| 0.615771
|
from msrest.serialization import Model
class StorageProfile(Model):
_attribute_map = {
'image_reference': {'key': 'imageReference', 'type': 'ImageReference'},
'os_disk': {'key': 'osDisk', 'type': 'OSDisk'},
'data_disks': {'key': 'dataDisks', 'type': '[DataDisk]'},
}
def __init__(self, image_reference=None, os_disk=None, data_disks=None):
self.image_reference = image_reference
self.os_disk = os_disk
self.data_disks = data_disks
| true
| true
|
1c440ead8b5d13c3647b3df37feee6ea8b6383e4
| 492
|
py
|
Python
|
weather-forecast-api/weather_forecast/tasks/receive_weather_forecast_failure.py
|
dalmarcogd/weather-forecast
|
f0987009c5691e46d9b8b6ba6f4408688ebec944
|
[
"Apache-2.0"
] | null | null | null |
weather-forecast-api/weather_forecast/tasks/receive_weather_forecast_failure.py
|
dalmarcogd/weather-forecast
|
f0987009c5691e46d9b8b6ba6f4408688ebec944
|
[
"Apache-2.0"
] | null | null | null |
weather-forecast-api/weather_forecast/tasks/receive_weather_forecast_failure.py
|
dalmarcogd/weather-forecast
|
f0987009c5691e46d9b8b6ba6f4408688ebec944
|
[
"Apache-2.0"
] | null | null | null |
from typing import Dict
from celery.task import Task
from weather_forecast.database import queries
from weather_forecast.database.models.weather_forecast import WeatherForecastStatus
class ReceiveWeatherForecastFailureTask(Task):
name = "receive-weather-forecast-failure"
ignore_result = True
def run(self, result: Dict) -> Dict:
return queries.update_weather_forecast(
{"id": result["weatherForecastId"], **result}, WeatherForecastStatus.error
)
| 28.941176
| 86
| 0.754065
|
from typing import Dict
from celery.task import Task
from weather_forecast.database import queries
from weather_forecast.database.models.weather_forecast import WeatherForecastStatus
class ReceiveWeatherForecastFailureTask(Task):
name = "receive-weather-forecast-failure"
ignore_result = True
def run(self, result: Dict) -> Dict:
return queries.update_weather_forecast(
{"id": result["weatherForecastId"], **result}, WeatherForecastStatus.error
)
| true
| true
|
1c44110674bac1be1a91e7868849d9425da2b31e
| 27,746
|
py
|
Python
|
anchore_engine/utils.py
|
ballad86/anchore-engine
|
51f784dbb697586083bce023e2e6a708a25f1797
|
[
"Apache-2.0"
] | 1,484
|
2017-09-11T19:08:42.000Z
|
2022-03-29T07:47:44.000Z
|
anchore_engine/utils.py
|
ballad86/anchore-engine
|
51f784dbb697586083bce023e2e6a708a25f1797
|
[
"Apache-2.0"
] | 913
|
2017-09-27T20:37:53.000Z
|
2022-03-29T17:21:28.000Z
|
anchore_engine/utils.py
|
PhoenixRedflash/anchore-engine
|
4192eba02bb91cf0eebebe32e8134b27b06feefe
|
[
"Apache-2.0"
] | 294
|
2017-09-12T16:54:03.000Z
|
2022-03-14T01:28:51.000Z
|
"""
Generic utilities
"""
import decimal
import os
import platform
import re
import shlex
import subprocess
import threading
import time
import uuid
from contextlib import contextmanager
from operator import itemgetter
from ijson import common as ijcommon
from ijson.backends import python as ijpython
from anchore_engine.subsys import logger
SANITIZE_CMD_ERROR_MESSAGE = "bad character in shell input"
PIPED_CMD_VALUE_ERROR_MESSAGE = "Piped command cannot be None or empty"
K_BYTES = 1024
M_BYTES = 1024 * K_BYTES
G_BYTES = 1024 * M_BYTES
T_BYTES = 1024 * G_BYTES
SIZE_UNITS = {"kb": K_BYTES, "mb": M_BYTES, "gb": G_BYTES, "tb": T_BYTES}
BYTES_REGEX = re.compile("^([0-9]+)([kmgt]b)?$")
def process_cve_status(old_cves_result=None, new_cves_result=None):
"""
Returns the diff of two cve results. Only compares two valid results, if either is None or empty, will return empty.
:param cve_record:
:return: dict with diff results: {'added': [], 'updated': [], 'removed': []}
"""
if not new_cves_result or not old_cves_result:
return {} # Nothing to do
try:
if "multi" in old_cves_result:
old_cve_header = old_cves_result["multi"]["result"]["header"]
old_cve_rows = old_cves_result["multi"]["result"]["rows"]
else:
# element 0 is the image id
old_cve_header = old_cves_result[0]["result"]["header"]
old_cve_rows = old_cves_result[0]["result"]["rows"]
except:
old_cve_header = None
old_cve_rows = None
try:
if "multi" in new_cves_result:
new_cve_header = new_cves_result["multi"]["result"]["header"]
new_cve_rows = new_cves_result["multi"]["result"]["rows"]
else:
# element 0 is the image id
new_cve_header = new_cves_result[0]["result"]["header"]
new_cve_rows = new_cves_result[0]["result"]["rows"]
except:
new_cve_header = None
new_cve_rows = None
summary_elements = [
"CVE_ID",
"Severity",
"Vulnerable_Package",
"Fix_Available",
"URL",
"Package_Name",
"Package_Version",
"Package_Type",
"Feed",
"Feed_Group",
]
if new_cve_rows is None or old_cve_rows is None:
return {}
new_cves = pivot_rows_to_keys(
new_cve_header,
new_cve_rows,
key_names=["CVE_ID", "Vulnerable_Package"],
whitelist_headers=summary_elements,
)
old_cves = pivot_rows_to_keys(
old_cve_header,
old_cve_rows,
key_names=["CVE_ID", "Vulnerable_Package"],
whitelist_headers=summary_elements,
)
diff = item_diffs(old_cves, new_cves)
return diff
def item_diffs(old_items=None, new_items=None):
"""
Given previous cve-scan output and new cve-scan output for the same image, return a diff as a map.
Keys:
{
'added': [],
'removed': [],
'updated': []
}
:param old_cves: mapped cve results (from map_rows() result) from previous value
:param new_cves: mapped cve results (from map_rows() result) from current_value
:return: dictionary object with results
"""
if not old_items:
old_items = {}
if not new_items:
new_items = {}
new_ids = set(new_items.keys())
old_ids = set(old_items.keys())
added = [new_items[x] for x in new_ids.difference(old_ids)]
removed = [old_items[x] for x in old_ids.difference(new_ids)]
intersected_ids = new_ids.intersection(old_ids)
updated = [
new_items[x]
for x in [x for x in intersected_ids if new_items[x] != old_items[x]]
]
return {"added": added, "removed": removed, "updated": updated}
def list_to_map(item_list, key_name):
"""
Given a list of dicts/objects return a dict mapping item[key_name] -> item
:param item_list:
:param key_name:
:return:
"""
return {x.pop(key_name): x for x in item_list}
def map_rows(header_list, row_list):
"""
:param header_list: list of names ordered to match row data, provides names for each row
:param row_list: list of row tuples/lists with each tuple/list in same order as header_list
:return: list of dicts with named values instead of tuples
"""
header_map = {v: header_list.index(v) for v in header_list}
mapped = [{key: item[header_map[key]] for key in header_map} for item in row_list]
return mapped
def pivot_rows_to_keys(header_list, row_list, key_names=[], whitelist_headers=None):
"""
Slightly more direct converter for header,row combo into a dict of objects
:param header_list:
:param row_list:
:param key_name:
:return:
"""
header_map = {
v: header_list.index(v)
for v in [
x
for x in header_list
if not whitelist_headers or x in whitelist_headers or x in key_names
]
}
key_idxs = []
for key_name in key_names:
key_idxs.append(header_map[key_name])
# key_idx = header_map[key_name]
# return {"{}{}".format(x[key_idx],x[keya_idx]): {k: x[v] for k, v in list(header_map.items())} for x in row_list}
return {
":".join(itemgetter(*key_idxs)(x)): {
k: x[v] for k, v in list(header_map.items())
}
for x in row_list
}
def filter_record_keys(record_list, whitelist_keys):
"""
Filter the list records to remove verbose entries and make it suitable for notification format
:param record_dict: dict containing values to process
:param whitelist_keys: keys to leave in the record dicts
:return: a new list with dicts that only contain the whitelisted elements
"""
filtered = [
{k: v for k, v in [y for y in list(x.items()) if y[0] in whitelist_keys]}
for x in record_list
]
return filtered
def run_sanitize(cmd_list):
def shellcheck(x):
if not re.search("[;&<>]", x):
return x
else:
raise Exception(SANITIZE_CMD_ERROR_MESSAGE)
return [x for x in cmd_list if shellcheck(x)]
def run_command_list_with_piped_input(
cmd_list,
input_data,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE,
**kwargs
):
"""
Pipe the input data to the command list and run it with optional environment and return a tuple (rc, stdout_str, stderr_str)
:param cmd_list: list of command e.g. ['ls', '/tmp']
:param input_data: string or bytes to be piped to cmd_list
:param stdin:
:param stdout:
:param stderr:
:return: tuple (rc_int, stdout_str, stderr_str)
"""
try:
input_data = input_data.encode("utf-8")
except AttributeError:
# it is a str already, no need to encode
pass
cmd_list = run_sanitize(cmd_list)
pipes = subprocess.Popen(
cmd_list, **dict(stdout=stdout, stderr=stderr, stdin=stdin, **kwargs)
)
stdout_result, stderr_result = pipes.communicate(input=input_data)
return pipes.returncode, stdout_result, stderr_result
def run_command_list(
cmd_list, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs
):
"""
Run a command from a list with optional environment and return a tuple (rc, stdout_str, stderr_str)
:param cmd_list: list of command e.g. ['ls', '/tmp']
:param env: dict of env vars for the environment if desired. will replace normal env, not augment
:return: tuple (rc_int, stdout_str, stderr_str)
"""
cmd_list = run_sanitize(cmd_list)
pipes = subprocess.Popen(cmd_list, **dict(stdout=stdout, stderr=stderr, **kwargs))
stdout_result, stderr_result = pipes.communicate()
return pipes.returncode, stdout_result, stderr_result
def run_check(cmd, input_data=None, log_level="debug", **kwargs):
"""
Run a command (input required to be a list), log the output, and raise an
exception if a non-zero exit status code is returned.
"""
cmd = run_sanitize(cmd)
try:
if input_data is not None:
logger.debug("running cmd: %s with piped input", " ".join(cmd))
code, stdout, stderr = run_command_list_with_piped_input(
cmd, input_data, **kwargs
)
else:
logger.debug("running cmd: %s", " ".join(cmd))
code, stdout, stderr = run_command_list(cmd, **kwargs)
except FileNotFoundError:
msg = "unable to run command. Executable does not exist or not availabe in path"
raise CommandException(cmd, 1, "", "", msg=msg)
try:
stdout = stdout.decode("utf-8")
stderr = stderr.decode("utf-8")
except AttributeError:
# it is a str already, no need to decode
pass
stdout_stream = stdout.splitlines()
stderr_stream = stderr.splitlines()
if log_level == "spew":
# Some commands (like grype scanning) will generate enough output here that we
# need to try to limit the impact of debug logging on system performance
for line in stdout_stream:
logger.spew("stdout: %s" % line) # safe formatting not available for spew
for line in stderr_stream:
logger.spew("stderr: %s" % line)
else: # Always log stdout and stderr as debug, unless spew is specified
for line in stdout_stream:
logger.debug("stdout: %s", line)
for line in stderr_stream:
logger.debug("stderr: %s", line)
if code != 0:
# When non-zero exit status returns, log stderr as error, but only when
# the log level is higher (lower in Engine's interpretation) than debug.
# XXX: engine mangles the logger, so this way of checking the level is
# non-standard. This line should be:
# if logger.level > logging.debug:
if logger.log_level < logger.log_level_map["DEBUG"]:
for line in stderr_stream:
logger.error(line)
raise CommandException(cmd, code, stdout, stderr)
return stdout, stderr
def run_command(cmdstr, **kwargs):
return run_command_list(shlex.split(cmdstr), **kwargs)
def get_threadbased_id(guarantee_uniq=False):
"""
Returns a string for use with acquire() calls optionally. Constructs a consistent id from the platform node, process_id and thread_id
:param guarantee_uniq: bool to have the id generate a uuid suffix to guarantee uniqeness between invocations even in the same thread
:return: string
"""
return "{}:{}:{}:{}".format(
platform.node(),
os.getpid(),
str(threading.get_ident()),
uuid.uuid4().hex if guarantee_uniq else "",
)
class AnchoreException(Exception):
def to_dict(self):
return {
self.__class__.__name__: dict(
(key, value)
for key, value in vars(self).items()
if not key.startswith("_")
)
}
class CommandException(Exception):
"""
An exception raised when subprocess.Popen calls have non-zero exit status.
Capture useful information as part of the exception raised
"""
def __init__(self, cmd, code, stdout, stderr, msg=None):
self.msg = msg or "Non-zero exit status code when running subprocess"
self.cmd = " ".join(cmd) if isinstance(cmd, list) else cmd
self.code = code
self.stderr = stderr
self.stdout = stdout
def __repr__(self):
return "{}: cmd={}, rc={}".format(self.msg, self.cmd, self.code)
def __str__(self):
return "{}: cmd={}, rc={}".format(self.msg, self.cmd, self.code)
def ensure_bytes(obj):
return obj.encode("utf-8") if type(obj) != bytes else obj
def ensure_str(obj):
return str(obj, "utf-8") if type(obj) != str else obj
def convert_bytes_size(size_str):
"""
Converts a size string to an int. Allows trailing units
e.g. "10" -> 10, "1kb" -> 1024, "1gb" -> 1024*1024*1024
:param size_str:
:return:
"""
m = BYTES_REGEX.fullmatch(size_str.lower())
if m:
number = int(m.group(1))
if m.group(2) is not None:
unit = m.group(2)
conversion = SIZE_UNITS.get(unit)
if conversion:
return conversion * number
return number
else:
raise ValueError("Invalid size string: {}".format(size_str))
CPE_SPECIAL_CHAR_ENCODER = {
"!": "%21",
'"': "%22",
"#": "%23",
"$": "%24",
"%": "%25",
"&": "%26",
"'": "%27",
"(": "%28",
")": "%29",
"*": "%2a",
"+": "%2b",
",": "%2c",
# '-': '-', # not affected by transformation between formatted string and uri, only impacts wfn
# '.': '.', # not affected by transformation between formatted string and uri, only impacts wfn
"/": "%2f",
":": "%3a",
";": "%3b",
"<": "%3c",
"=": "%3d",
">": "%3e",
"?": "%3f",
"@": "%40",
"[": "%5b",
"\\": "%5c",
"]": "%5d",
"^": "%5e",
"`": "%60",
"{": "%7b",
"|": "%7c",
"}": "%7d",
"~": "%7e",
}
class CPE(object):
"""
A helper class for converting CPE 2.3 formatted string into CPE 2.2 URI and matching CPE 2.3 formatted strings
"""
def __init__(
self,
part=None,
vendor=None,
product=None,
version=None,
update=None,
edition=None,
language=None,
sw_edition=None,
target_sw=None,
target_hw=None,
other=None,
):
self.part = part
self.vendor = vendor
self.product = product
self.version = version
self.update = update
self.edition = edition
self.language = language
self.sw_edition = sw_edition
self.target_sw = target_sw
self.target_hw = target_hw
self.other = other
def __hash__(self):
return hash(
(
self.part,
self.vendor,
self.product,
self.version,
self.update,
self.edition,
self.language,
self.sw_edition,
self.target_sw,
self.target_hw,
self.other,
)
)
def __eq__(self, other):
return other and self == other
def __repr__(self):
return "CPE: part={}, vendor={}, product={}, version={}, update={}, edition={}, language={}, sw_edition={}, target_sw={}, target_hw={}, other={}".format(
self.part,
self.vendor,
self.product,
self.version,
self.update,
self.edition,
self.language,
self.sw_edition,
self.target_sw,
self.target_hw,
self.other,
)
def copy(self):
return CPE(
part=self.part,
vendor=self.vendor,
product=self.product,
version=self.version,
update=self.update,
edition=self.edition,
language=self.language,
sw_edition=self.sw_edition,
target_sw=self.target_sw,
target_hw=self.target_hw,
other=self.other,
)
@staticmethod
def from_cpe23_fs(cpe23_fs):
"""
Takes a CPE 2.3 formatted string and returns a CPE object. This is the only supported method to create an instance of this class
This is not entirely true to the spec, it does not unbind all the elements as wfn representation is not used.
All of unbinding logic is concentrated in the conversion from wfn to uri format in as_cpe22_uri()
:param cpe23_fs: cpe:2.3:part:vendor:product:version:update:edition:language:sw_edition:target_sw:target_hw:other
:return:
"""
cpe_parts = cpe23_fs.split(":")
if cpe_parts and len(cpe_parts) == 13:
return CPE(
part=cpe_parts[2],
vendor=cpe_parts[3],
product=cpe_parts[4],
version=cpe_parts[5],
update=cpe_parts[6],
edition=cpe_parts[7],
language=cpe_parts[8],
sw_edition=cpe_parts[9],
target_sw=cpe_parts[10],
target_hw=cpe_parts[11],
other=cpe_parts[12],
)
elif len(cpe_parts) > 13:
# logger.debug('{} did not split nicely into 13 parts'.format(cpe23_fs))
adjusted_cpe_parts = []
counter = 1
# start from the third element in the list and iterate through the penultimate element
while counter < len(cpe_parts) - 1:
counter += 1
part = cpe_parts[counter]
# if the element ends with a '\', good chance its an escape for ':', concatenate the elements together
if part.endswith("\\"):
new_part = part
while counter < len(cpe_parts) - 1:
counter += 1
part = cpe_parts[counter]
new_part += ":" + part
if part.endswith("\\"):
continue
else:
break
adjusted_cpe_parts.append(new_part)
else:
adjusted_cpe_parts.append(part)
if len(adjusted_cpe_parts) == 11:
# logger.debug('Adjusted cpe components: {}'.format(adjusted_cpe_parts))
return CPE(
part=adjusted_cpe_parts[0],
vendor=adjusted_cpe_parts[1],
product=adjusted_cpe_parts[2],
version=adjusted_cpe_parts[3],
update=adjusted_cpe_parts[4],
edition=adjusted_cpe_parts[5],
language=adjusted_cpe_parts[6],
sw_edition=adjusted_cpe_parts[7],
target_sw=adjusted_cpe_parts[8],
target_hw=adjusted_cpe_parts[9],
other=adjusted_cpe_parts[10],
)
else:
raise Exception(
"Cannot convert cpe 2.3 formatted string {} into wfn".format(
cpe23_fs
)
)
else:
raise Exception(
"Invalid cpe 2.3 formatted string {} Splitting with : delimiter resulted in less than 13 elements".format(
cpe23_fs
)
)
def as_cpe23_fs(self):
return "cpe:2.3:{}".format(
":".join(
[
self.part,
self.vendor,
self.product,
self.version,
self.update,
self.edition,
self.language,
self.sw_edition,
self.target_sw,
self.target_hw,
self.other,
]
)
)
def update_version(self, version):
"""
Helper method for escaping the
Ensures that resulting version is CPE 2.3 formatted string compliant, this is necessary for as_cpe22_uri() to do its thing
affected version data in nvd json data which is usually unescaped. Converts the supplied version
:param version:
:return:
"""
self.version = CPE.escape_for_cpe23_fs(version)
@staticmethod
def escape_for_cpe23_fs(element):
"""
Helper method for escaping special characters as per the CPE 2.3 formatted string spec
:param element:
:return: escaped element string as per CPE 2.3 formatted string spec
"""
if not isinstance(element, str):
raise Exception("Value to be escaped is not a string")
if element in ["*", "-", ""]: # let these pass through as they are
return element
elif any(char in CPE_SPECIAL_CHAR_ENCODER.keys() for char in element):
new_element = str()
pos = 0
while pos < len(element):
char = element[pos]
if (
char == "\\"
): # this might be an escape character, check to see if the next character requires escape
pos += 1
if pos < len(element):
n_char = element[pos]
if (
n_char in CPE_SPECIAL_CHAR_ENCODER
): # definitely an escaped sequence, preserve it as it is
new_element += char + n_char
else: # just a \ that needs to be escaped
new_element += "\\" + char + n_char
else: # last char is unescaped \, just add an escape
new_element += "\\" + char
elif char in CPE_SPECIAL_CHAR_ENCODER:
new_element += "\\" + char
else:
new_element += char
pos += 1
return new_element
else:
return element
@staticmethod
def bind_for_cpe22_uri(element):
if not isinstance(element, str):
raise Exception("Value to be bound in URI format is not a string")
if element == "*":
return ""
elif element in ["-", ""]:
return element
else:
result = str()
pos = -1
while pos < (len(element) - 1):
pos += 1
char = element[pos]
if char == "\\": # an escaped character, percent encode it if possible
if pos != (
len(element) - 1
): # check the next character and transform into percent encoded string
pos += 1
n_char = element[pos]
encoded = CPE_SPECIAL_CHAR_ENCODER.get(n_char, None)
if encoded:
result += encoded
else: # no encoding found, let it go through as it is
logger.warn(
"No encoding found for {}{}".format(char, n_char)
)
result += char + n_char
else: # this is the last char, nothing to percent encode
logger.warn(
"{} is the last char, skipping percent encoded transformation".format(
char
)
)
result += char
elif char == "?": # bind the unescaped ? to %01
result += "%01"
elif char == "*": # bind the unescaped * to %02
result += "%02"
else:
result += char
return result
def as_cpe22_uri(self):
"""
Transforms this CPE object into a CPE 2.2 URI. Based on the specification in https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nistir7695.pdf
:return: CPE 2.2 URI string
"""
# part:vendor:product:version:update:edition:language:sw_edition:target_sw:target_hw:other
# 0 1 2 3 4 5 6 7 8 9 10
# |-------------cpe 2.2 attributes----------- |------------new in cpe 2.3----------|
e = CPE.bind_for_cpe22_uri(self.edition)
sw_e = CPE.bind_for_cpe22_uri(self.sw_edition)
t_sw = CPE.bind_for_cpe22_uri(self.target_sw)
t_hw = CPE.bind_for_cpe22_uri(self.target_hw)
o = CPE.bind_for_cpe22_uri(self.other)
if sw_e or t_sw or t_hw or o:
edition = "~{}~{}~{}~{}~{}".format(e, sw_e, t_sw, t_hw, o)
else:
edition = e
uri_parts = [
"cpe",
"/" + self.part,
CPE.bind_for_cpe22_uri(self.vendor),
CPE.bind_for_cpe22_uri(self.product),
CPE.bind_for_cpe22_uri(self.version),
CPE.bind_for_cpe22_uri(self.update),
edition,
CPE.bind_for_cpe22_uri(self.language),
]
uri = ":".join(uri_parts)
uri = uri.strip(":") # remove any trailing :
return uri
def is_match(self, other_cpe):
"""
This is a very limited implementation of cpe matching. other_cpe is a wildcard ridden base cpe used by range descriptors
other_cpe checked against this cpe for an exact match of part and vendor.
For all the remaining components a match is positive if the other cpe is an exact match or contains the wild char
:param other_cpe:
:return:
"""
if not isinstance(other_cpe, CPE):
return False
if self.part == other_cpe.part and self.vendor == other_cpe.vendor:
if other_cpe.product not in ["*", self.product]:
return False
if other_cpe.version not in ["*", self.version]:
return False
if other_cpe.update not in ["*", self.update]:
return False
if other_cpe.edition not in ["*", self.edition]:
return False
if other_cpe.language not in ["*", self.language]:
return False
if other_cpe.sw_edition not in ["*", self.sw_edition]:
return False
if other_cpe.target_sw not in ["*", self.target_sw]:
return False
if other_cpe.target_hw not in ["*", self.target_hw]:
return False
if other_cpe.other not in ["*", self.other]:
return False
return True
else:
return False
@contextmanager
def timer(label, log_level="debug"):
t = time.time()
try:
yield
finally:
log_level = log_level.lower()
if log_level == "info":
logger.info(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
elif log_level == "warn":
logger.warn(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
elif log_level == "spew":
logger.spew(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
else:
logger.debug(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
# Generally we're not dealing with high precision floats in feed data, so this shouldn't result in any loss of precision
def ijson_decimal_to_float(event):
"""
Event handler for use with ijson parsers to output floats instead of Decimals for better json serializability downstream.
:param event:
:return:
"""
if event[1] == "number" and isinstance(event[2], decimal.Decimal):
return event[0], event[1], float(event[2])
else:
return event
def mapped_parser_item_iterator(input_stream, item_path):
"""
Boilerplate function to setup the event mapper to ensure floats instead of decimals for use with ijson
:param input_stream:
:param item_path:
:return:
"""
events = map(ijson_decimal_to_float, ijpython.parse(input_stream))
return ijcommon.items(events, item_path)
def bytes_to_mb(value, round_to=None):
mb = value / M_BYTES
if round_to:
mb = round(mb, round_to)
return mb
| 32.225319
| 161
| 0.558026
|
import decimal
import os
import platform
import re
import shlex
import subprocess
import threading
import time
import uuid
from contextlib import contextmanager
from operator import itemgetter
from ijson import common as ijcommon
from ijson.backends import python as ijpython
from anchore_engine.subsys import logger
SANITIZE_CMD_ERROR_MESSAGE = "bad character in shell input"
PIPED_CMD_VALUE_ERROR_MESSAGE = "Piped command cannot be None or empty"
K_BYTES = 1024
M_BYTES = 1024 * K_BYTES
G_BYTES = 1024 * M_BYTES
T_BYTES = 1024 * G_BYTES
SIZE_UNITS = {"kb": K_BYTES, "mb": M_BYTES, "gb": G_BYTES, "tb": T_BYTES}
BYTES_REGEX = re.compile("^([0-9]+)([kmgt]b)?$")
def process_cve_status(old_cves_result=None, new_cves_result=None):
if not new_cves_result or not old_cves_result:
return {}
try:
if "multi" in old_cves_result:
old_cve_header = old_cves_result["multi"]["result"]["header"]
old_cve_rows = old_cves_result["multi"]["result"]["rows"]
else:
old_cve_header = old_cves_result[0]["result"]["header"]
old_cve_rows = old_cves_result[0]["result"]["rows"]
except:
old_cve_header = None
old_cve_rows = None
try:
if "multi" in new_cves_result:
new_cve_header = new_cves_result["multi"]["result"]["header"]
new_cve_rows = new_cves_result["multi"]["result"]["rows"]
else:
new_cve_header = new_cves_result[0]["result"]["header"]
new_cve_rows = new_cves_result[0]["result"]["rows"]
except:
new_cve_header = None
new_cve_rows = None
summary_elements = [
"CVE_ID",
"Severity",
"Vulnerable_Package",
"Fix_Available",
"URL",
"Package_Name",
"Package_Version",
"Package_Type",
"Feed",
"Feed_Group",
]
if new_cve_rows is None or old_cve_rows is None:
return {}
new_cves = pivot_rows_to_keys(
new_cve_header,
new_cve_rows,
key_names=["CVE_ID", "Vulnerable_Package"],
whitelist_headers=summary_elements,
)
old_cves = pivot_rows_to_keys(
old_cve_header,
old_cve_rows,
key_names=["CVE_ID", "Vulnerable_Package"],
whitelist_headers=summary_elements,
)
diff = item_diffs(old_cves, new_cves)
return diff
def item_diffs(old_items=None, new_items=None):
if not old_items:
old_items = {}
if not new_items:
new_items = {}
new_ids = set(new_items.keys())
old_ids = set(old_items.keys())
added = [new_items[x] for x in new_ids.difference(old_ids)]
removed = [old_items[x] for x in old_ids.difference(new_ids)]
intersected_ids = new_ids.intersection(old_ids)
updated = [
new_items[x]
for x in [x for x in intersected_ids if new_items[x] != old_items[x]]
]
return {"added": added, "removed": removed, "updated": updated}
def list_to_map(item_list, key_name):
return {x.pop(key_name): x for x in item_list}
def map_rows(header_list, row_list):
header_map = {v: header_list.index(v) for v in header_list}
mapped = [{key: item[header_map[key]] for key in header_map} for item in row_list]
return mapped
def pivot_rows_to_keys(header_list, row_list, key_names=[], whitelist_headers=None):
header_map = {
v: header_list.index(v)
for v in [
x
for x in header_list
if not whitelist_headers or x in whitelist_headers or x in key_names
]
}
key_idxs = []
for key_name in key_names:
key_idxs.append(header_map[key_name])
return {
":".join(itemgetter(*key_idxs)(x)): {
k: x[v] for k, v in list(header_map.items())
}
for x in row_list
}
def filter_record_keys(record_list, whitelist_keys):
filtered = [
{k: v for k, v in [y for y in list(x.items()) if y[0] in whitelist_keys]}
for x in record_list
]
return filtered
def run_sanitize(cmd_list):
def shellcheck(x):
if not re.search("[;&<>]", x):
return x
else:
raise Exception(SANITIZE_CMD_ERROR_MESSAGE)
return [x for x in cmd_list if shellcheck(x)]
def run_command_list_with_piped_input(
cmd_list,
input_data,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE,
**kwargs
):
try:
input_data = input_data.encode("utf-8")
except AttributeError:
pass
cmd_list = run_sanitize(cmd_list)
pipes = subprocess.Popen(
cmd_list, **dict(stdout=stdout, stderr=stderr, stdin=stdin, **kwargs)
)
stdout_result, stderr_result = pipes.communicate(input=input_data)
return pipes.returncode, stdout_result, stderr_result
def run_command_list(
cmd_list, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs
):
cmd_list = run_sanitize(cmd_list)
pipes = subprocess.Popen(cmd_list, **dict(stdout=stdout, stderr=stderr, **kwargs))
stdout_result, stderr_result = pipes.communicate()
return pipes.returncode, stdout_result, stderr_result
def run_check(cmd, input_data=None, log_level="debug", **kwargs):
cmd = run_sanitize(cmd)
try:
if input_data is not None:
logger.debug("running cmd: %s with piped input", " ".join(cmd))
code, stdout, stderr = run_command_list_with_piped_input(
cmd, input_data, **kwargs
)
else:
logger.debug("running cmd: %s", " ".join(cmd))
code, stdout, stderr = run_command_list(cmd, **kwargs)
except FileNotFoundError:
msg = "unable to run command. Executable does not exist or not availabe in path"
raise CommandException(cmd, 1, "", "", msg=msg)
try:
stdout = stdout.decode("utf-8")
stderr = stderr.decode("utf-8")
except AttributeError:
pass
stdout_stream = stdout.splitlines()
stderr_stream = stderr.splitlines()
if log_level == "spew":
for line in stdout_stream:
logger.spew("stdout: %s" % line)
for line in stderr_stream:
logger.spew("stderr: %s" % line)
else:
for line in stdout_stream:
logger.debug("stdout: %s", line)
for line in stderr_stream:
logger.debug("stderr: %s", line)
if code != 0:
# XXX: engine mangles the logger, so this way of checking the level is
# non-standard. This line should be:
# if logger.level > logging.debug:
if logger.log_level < logger.log_level_map["DEBUG"]:
for line in stderr_stream:
logger.error(line)
raise CommandException(cmd, code, stdout, stderr)
return stdout, stderr
def run_command(cmdstr, **kwargs):
return run_command_list(shlex.split(cmdstr), **kwargs)
def get_threadbased_id(guarantee_uniq=False):
return "{}:{}:{}:{}".format(
platform.node(),
os.getpid(),
str(threading.get_ident()),
uuid.uuid4().hex if guarantee_uniq else "",
)
class AnchoreException(Exception):
def to_dict(self):
return {
self.__class__.__name__: dict(
(key, value)
for key, value in vars(self).items()
if not key.startswith("_")
)
}
class CommandException(Exception):
def __init__(self, cmd, code, stdout, stderr, msg=None):
self.msg = msg or "Non-zero exit status code when running subprocess"
self.cmd = " ".join(cmd) if isinstance(cmd, list) else cmd
self.code = code
self.stderr = stderr
self.stdout = stdout
def __repr__(self):
return "{}: cmd={}, rc={}".format(self.msg, self.cmd, self.code)
def __str__(self):
return "{}: cmd={}, rc={}".format(self.msg, self.cmd, self.code)
def ensure_bytes(obj):
return obj.encode("utf-8") if type(obj) != bytes else obj
def ensure_str(obj):
return str(obj, "utf-8") if type(obj) != str else obj
def convert_bytes_size(size_str):
m = BYTES_REGEX.fullmatch(size_str.lower())
if m:
number = int(m.group(1))
if m.group(2) is not None:
unit = m.group(2)
conversion = SIZE_UNITS.get(unit)
if conversion:
return conversion * number
return number
else:
raise ValueError("Invalid size string: {}".format(size_str))
CPE_SPECIAL_CHAR_ENCODER = {
"!": "%21",
'"': "%22",
"#": "%23",
"$": "%24",
"%": "%25",
"&": "%26",
"'": "%27",
"(": "%28",
")": "%29",
"*": "%2a",
"+": "%2b",
",": "%2c",
# '-': '-', # not affected by transformation between formatted string and uri, only impacts wfn
# '.': '.', # not affected by transformation between formatted string and uri, only impacts wfn
"/": "%2f",
":": "%3a",
";": "%3b",
"<": "%3c",
"=": "%3d",
">": "%3e",
"?": "%3f",
"@": "%40",
"[": "%5b",
"\\": "%5c",
"]": "%5d",
"^": "%5e",
"`": "%60",
"{": "%7b",
"|": "%7c",
"}": "%7d",
"~": "%7e",
}
class CPE(object):
def __init__(
self,
part=None,
vendor=None,
product=None,
version=None,
update=None,
edition=None,
language=None,
sw_edition=None,
target_sw=None,
target_hw=None,
other=None,
):
self.part = part
self.vendor = vendor
self.product = product
self.version = version
self.update = update
self.edition = edition
self.language = language
self.sw_edition = sw_edition
self.target_sw = target_sw
self.target_hw = target_hw
self.other = other
def __hash__(self):
return hash(
(
self.part,
self.vendor,
self.product,
self.version,
self.update,
self.edition,
self.language,
self.sw_edition,
self.target_sw,
self.target_hw,
self.other,
)
)
def __eq__(self, other):
return other and self == other
def __repr__(self):
return "CPE: part={}, vendor={}, product={}, version={}, update={}, edition={}, language={}, sw_edition={}, target_sw={}, target_hw={}, other={}".format(
self.part,
self.vendor,
self.product,
self.version,
self.update,
self.edition,
self.language,
self.sw_edition,
self.target_sw,
self.target_hw,
self.other,
)
def copy(self):
return CPE(
part=self.part,
vendor=self.vendor,
product=self.product,
version=self.version,
update=self.update,
edition=self.edition,
language=self.language,
sw_edition=self.sw_edition,
target_sw=self.target_sw,
target_hw=self.target_hw,
other=self.other,
)
@staticmethod
def from_cpe23_fs(cpe23_fs):
cpe_parts = cpe23_fs.split(":")
if cpe_parts and len(cpe_parts) == 13:
return CPE(
part=cpe_parts[2],
vendor=cpe_parts[3],
product=cpe_parts[4],
version=cpe_parts[5],
update=cpe_parts[6],
edition=cpe_parts[7],
language=cpe_parts[8],
sw_edition=cpe_parts[9],
target_sw=cpe_parts[10],
target_hw=cpe_parts[11],
other=cpe_parts[12],
)
elif len(cpe_parts) > 13:
# logger.debug('{} did not split nicely into 13 parts'.format(cpe23_fs))
adjusted_cpe_parts = []
counter = 1
# start from the third element in the list and iterate through the penultimate element
while counter < len(cpe_parts) - 1:
counter += 1
part = cpe_parts[counter]
# if the element ends with a '\', good chance its an escape for ':', concatenate the elements together
if part.endswith("\\"):
new_part = part
while counter < len(cpe_parts) - 1:
counter += 1
part = cpe_parts[counter]
new_part += ":" + part
if part.endswith("\\"):
continue
else:
break
adjusted_cpe_parts.append(new_part)
else:
adjusted_cpe_parts.append(part)
if len(adjusted_cpe_parts) == 11:
# logger.debug('Adjusted cpe components: {}'.format(adjusted_cpe_parts))
return CPE(
part=adjusted_cpe_parts[0],
vendor=adjusted_cpe_parts[1],
product=adjusted_cpe_parts[2],
version=adjusted_cpe_parts[3],
update=adjusted_cpe_parts[4],
edition=adjusted_cpe_parts[5],
language=adjusted_cpe_parts[6],
sw_edition=adjusted_cpe_parts[7],
target_sw=adjusted_cpe_parts[8],
target_hw=adjusted_cpe_parts[9],
other=adjusted_cpe_parts[10],
)
else:
raise Exception(
"Cannot convert cpe 2.3 formatted string {} into wfn".format(
cpe23_fs
)
)
else:
raise Exception(
"Invalid cpe 2.3 formatted string {} Splitting with : delimiter resulted in less than 13 elements".format(
cpe23_fs
)
)
def as_cpe23_fs(self):
return "cpe:2.3:{}".format(
":".join(
[
self.part,
self.vendor,
self.product,
self.version,
self.update,
self.edition,
self.language,
self.sw_edition,
self.target_sw,
self.target_hw,
self.other,
]
)
)
def update_version(self, version):
self.version = CPE.escape_for_cpe23_fs(version)
@staticmethod
def escape_for_cpe23_fs(element):
if not isinstance(element, str):
raise Exception("Value to be escaped is not a string")
if element in ["*", "-", ""]: # let these pass through as they are
return element
elif any(char in CPE_SPECIAL_CHAR_ENCODER.keys() for char in element):
new_element = str()
pos = 0
while pos < len(element):
char = element[pos]
if (
char == "\\"
): # this might be an escape character, check to see if the next character requires escape
pos += 1
if pos < len(element):
n_char = element[pos]
if (
n_char in CPE_SPECIAL_CHAR_ENCODER
): # definitely an escaped sequence, preserve it as it is
new_element += char + n_char
else: # just a \ that needs to be escaped
new_element += "\\" + char + n_char
else: # last char is unescaped \, just add an escape
new_element += "\\" + char
elif char in CPE_SPECIAL_CHAR_ENCODER:
new_element += "\\" + char
else:
new_element += char
pos += 1
return new_element
else:
return element
@staticmethod
def bind_for_cpe22_uri(element):
if not isinstance(element, str):
raise Exception("Value to be bound in URI format is not a string")
if element == "*":
return ""
elif element in ["-", ""]:
return element
else:
result = str()
pos = -1
while pos < (len(element) - 1):
pos += 1
char = element[pos]
if char == "\\": # an escaped character, percent encode it if possible
if pos != (
len(element) - 1
): # check the next character and transform into percent encoded string
pos += 1
n_char = element[pos]
encoded = CPE_SPECIAL_CHAR_ENCODER.get(n_char, None)
if encoded:
result += encoded
else: # no encoding found, let it go through as it is
logger.warn(
"No encoding found for {}{}".format(char, n_char)
)
result += char + n_char
else: # this is the last char, nothing to percent encode
logger.warn(
"{} is the last char, skipping percent encoded transformation".format(
char
)
)
result += char
elif char == "?": # bind the unescaped ? to %01
result += "%01"
elif char == "*": # bind the unescaped * to %02
result += "%02"
else:
result += char
return result
def as_cpe22_uri(self):
# part:vendor:product:version:update:edition:language:sw_edition:target_sw:target_hw:other
# 0 1 2 3 4 5 6 7 8 9 10
# |-------------cpe 2.2 attributes----------- |------------new in cpe 2.3----------|
e = CPE.bind_for_cpe22_uri(self.edition)
sw_e = CPE.bind_for_cpe22_uri(self.sw_edition)
t_sw = CPE.bind_for_cpe22_uri(self.target_sw)
t_hw = CPE.bind_for_cpe22_uri(self.target_hw)
o = CPE.bind_for_cpe22_uri(self.other)
if sw_e or t_sw or t_hw or o:
edition = "~{}~{}~{}~{}~{}".format(e, sw_e, t_sw, t_hw, o)
else:
edition = e
uri_parts = [
"cpe",
"/" + self.part,
CPE.bind_for_cpe22_uri(self.vendor),
CPE.bind_for_cpe22_uri(self.product),
CPE.bind_for_cpe22_uri(self.version),
CPE.bind_for_cpe22_uri(self.update),
edition,
CPE.bind_for_cpe22_uri(self.language),
]
uri = ":".join(uri_parts)
uri = uri.strip(":") # remove any trailing :
return uri
def is_match(self, other_cpe):
if not isinstance(other_cpe, CPE):
return False
if self.part == other_cpe.part and self.vendor == other_cpe.vendor:
if other_cpe.product not in ["*", self.product]:
return False
if other_cpe.version not in ["*", self.version]:
return False
if other_cpe.update not in ["*", self.update]:
return False
if other_cpe.edition not in ["*", self.edition]:
return False
if other_cpe.language not in ["*", self.language]:
return False
if other_cpe.sw_edition not in ["*", self.sw_edition]:
return False
if other_cpe.target_sw not in ["*", self.target_sw]:
return False
if other_cpe.target_hw not in ["*", self.target_hw]:
return False
if other_cpe.other not in ["*", self.other]:
return False
return True
else:
return False
@contextmanager
def timer(label, log_level="debug"):
t = time.time()
try:
yield
finally:
log_level = log_level.lower()
if log_level == "info":
logger.info(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
elif log_level == "warn":
logger.warn(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
elif log_level == "spew":
logger.spew(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
else:
logger.debug(
"Execution of {} took: {} seconds".format(label, time.time() - t)
)
# Generally we're not dealing with high precision floats in feed data, so this shouldn't result in any loss of precision
def ijson_decimal_to_float(event):
if event[1] == "number" and isinstance(event[2], decimal.Decimal):
return event[0], event[1], float(event[2])
else:
return event
def mapped_parser_item_iterator(input_stream, item_path):
events = map(ijson_decimal_to_float, ijpython.parse(input_stream))
return ijcommon.items(events, item_path)
def bytes_to_mb(value, round_to=None):
mb = value / M_BYTES
if round_to:
mb = round(mb, round_to)
return mb
| true
| true
|
1c44111cc007cf80d7930ed6a4faa8477866dacd
| 12,036
|
py
|
Python
|
src/docker_code/docker_face_detect_v0.py
|
yuhaoluo/facenet
|
d3a3087f52ae1a17a77a1dadb81c53911be97b4b
|
[
"MIT"
] | null | null | null |
src/docker_code/docker_face_detect_v0.py
|
yuhaoluo/facenet
|
d3a3087f52ae1a17a77a1dadb81c53911be97b4b
|
[
"MIT"
] | null | null | null |
src/docker_code/docker_face_detect_v0.py
|
yuhaoluo/facenet
|
d3a3087f52ae1a17a77a1dadb81c53911be97b4b
|
[
"MIT"
] | null | null | null |
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy import misc
import sys
import os
import argparse
import tensorflow as tf
import numpy as np
import align.detect_face
import time
import imageio
import requests
#import skimage
import json
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
minsize = 20 # minimum size of face
threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
factor = 0.709 # scale factor
ai_type = 'face-detect'
config_path = '/data/configure.json'
job_path = '/data/job/job.json'
log_path = '/data/job/logs.log'
log_path = '/home/luoyuhao/Datasets/Docker/logs/logs.log'
test_config_path = '/home/luoyuhao/Datasets/Docker/configure.json'
test_job_path = '/home/luoyuhao/Datasets/Docker/job.json'
def load_mtcnn_model(args):
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
start_time = time.time();
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
print('create and load mtcnn model time: ', (time.time() - start_time))
return pnet,rnet,onet
def crop_face(img,bounding_boxes,margin,image_size):
nrof_faces = bounding_boxes.shape[0]
if nrof_faces>0:
det = bounding_boxes[:,0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces>1:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
else:
det_arr.append(np.squeeze(det))
face_res = []
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img_size[1])
bb[3] = np.minimum(det[3]+margin/2, img_size[0])
cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
#scaled = skimage.transform.resize(cropped, (args.image_size, args.image_size), interp='bilinear')
scaled = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
face_res.append(scaled)
return face_res
def save_faces(res,output_filename):
if(len(res)>0):
filename_base, file_extension = os.path.splitext(output_filename)
for i in range(len(res)):
if (len(res)>1):
output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
else:
output_filename_n = "{}{}".format(filename_base, file_extension)
imageio.imwrite(output_filename_n, res[i])
def img_resize(img,scale):
img_resize = misc.imresize(img, (int(img.shape[0]/scale), int(img.shape[1]/scale)), interp='bilinear')
return img_resize
def detectFace(args,img,pnet,rnet,onet,output_filename=None,isDrawFace=False,isPrintTimeInfo=False):
## reseize img to detect
if args.scale>1:
img_input = img_resize(img,args.scale)
else:
img_input = img
detect_time_start = time.time()
bounding_boxes, _ = align.detect_face.detect_face(img_input, minsize, pnet, rnet, onet, threshold, factor)
if args.scale>1:
bounding_boxes[:,0:4] = args.scale * bounding_boxes[:,0:4]
detect_time = time.time() - detect_time_start
if isPrintTimeInfo:
print('detect_face_time: ', detect_time)
faces = crop_face(img,bounding_boxes,args.margin,args.image_size)
if(output_filename is not None):
save_time_start = time.time()
save_faces(faces,output_filename)
if isPrintTimeInfo:
print('save_face_time: ', time.time() - save_time_start)
if isDrawFace:
draw = align.detect_face.drawBoxes(img,bounding_boxes)
filename_base, file_extension = os.path.splitext(output_filename)
imageio.imwrite(filename_base+'_res'+file_extension,draw)
#print(bounding_boxes)
return faces,bounding_boxes
def read_config(config_path):
#TODO
try:
f = open(config_path,encoding='utf-8')
json_read = f.read()
dic = json.loads(json_read)
f.close()
except Exception as e:
print(e)
input_url = dic["input"]
output_url = dic['output']
logs_info = dic['logs']
return input_url,output_url,logs_info
def read_state(job_path):
#TODO
try:
f = open(job_path,encoding='utf-8')
json_read = f.read()
dic = json.loads(json_read)
state = dic['run']
f.close()
except Exception as e:
print(e)
if state == 'true':
return True
else:
return False
def read_input(input_url):
#TODO
try:
r = requests.get(input_url)
res_dic = r.json() #dic
except Exception as e:
#errorMessage = '{}: {}'.format(input_url, e)
print(e)
if r.raise_for_status() is None:
try:
taskJson_dic = res_dic['taskJson']
except Exception as e:
print("no face detect job.")
taskJson_dic = []
errorCode = res_dic['errorCode']
errorMsg = res_dic['errorMsg']
else:
taskJson_dic = []
errorCode = []
errorMsg = []
return taskJson_dic, errorCode, errorMsg
def push_output(input_dic,output_url,faces,bounding_boxes):
#TODO
for i in range(len(faces)):
save_path = '/home/luoyuhao/Datasets/Docker/saveface/'
save_path = save_path + str(time.time())+".png"
#imageio.imwrite(save_path,faces[i])
storage = 1
avatar = save_path
box = bounding_boxes[i,0:4]
location = [int(box[0]),int(box[1]),int(box[2]),int(box[3])]
out_dic = {"storage":storage,"avatar":avatar,'location':str(location),\
"camId":input_dic["camId"],"capTs":input_dic["capTs"]}
requests.post(output_url, data=out_dic)
def read_img_from_taskJson(task_dic,tsb):
storage = task_dic['storage']
img = []
if storage == 1:
img_path = task_dic['imagePath']
try:
img = imageio.imread(img_path)
except Exception as e:
msg = 'Face-detect failed. Wrong picture format'
write_logs(log_path,ai_type,task_dic,msg,tsb,time.time())
img = []
errorMessage = '{}: {}'.format(img_path, e)
print(errorMessage)
# =============================================================================
# else if storage == '2':
# img = []
# else if storage == '3':
# img = []
#
# =============================================================================
return img
def write_logs(log_path,ai_type,taskJson_dic,msg,tsb,tse):
storage = taskJson_dic['storage']
img_path = taskJson_dic['imagePath']
cam_id = taskJson_dic['camId']
cap_ts = taskJson_dic['capTs']
(filepath,tempfilename) = os.path.split(log_path)
if not os.path.exists(filepath):
os.mkdir(filepath)
with open(log_path,'at') as f:
f.write('tsb:%s\ttype:%s\tstorage:%d\timagePath:%s\tcamId:%d\tcapTs:%d\tmsg:%s\ttse:%s\n' % (tsb, ai_type,storage, img_path,\
cam_id,cap_ts,msg,tse))
########################################################################################################
def main(args):
# =============================================================================
# output_dir = args.output_dir
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# #
# image_path = args.image_path
# img = imageio.imread(image_path)
# filename = os.path.splitext(os.path.split(image_path)[1])[0]
# output_filename = os.path.join(output_dir, filename+'.png')
#
# =============================================================================
input_url,output_url,logs_info = read_config(config_path)
pnet,rnet,onet = load_mtcnn_model(args)
while(1):
time.sleep(2)
if read_state(job_path):
tsb = time.time()
taskJson_dic, errorCode, errorMsg = read_input(input_url)
if len(taskJson_dic)!=4:
continue
img = read_img_from_taskJson(taskJson_dic,tsb)
if len(img)>0:
try:
faces, bounding_boxes = detectFace(args,img,pnet,rnet,onet,None,False,True)
if len(faces)>0:
nums = len(faces)
msg = 'Face-detect success. find {} faces'.format(nums)
write_logs(log_path,ai_type,taskJson_dic,msg,tsb,time.time())
print("detect face success.")
else:
msg = "Face-detect success. find 0 faces."
write_logs(log_path,ai_type,taskJson_dic,msg,tsb,time.time())
print("detect no face.")
push_output(taskJson_dic,output_url,faces,bounding_boxes)
except Exception as e:
msg = "Face-detect failed. System exception"
write_logs(log_path,ai_type,taskJson_dic,msg,tsb,time.time())
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('image_path', type=str, help='Directory with unaligned images.')
parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
parser.add_argument('--detect_multiple_faces', type=bool,
help='Detect and align multiple faces per image.', default=True)
parser.add_argument('--scale', type=int,
help='the height and width will resize to height/scale and width/scale to detect faces.', default=2)
return parser.parse_args(argv)
if __name__ == '__main__':
img_path = '/home/luoyuhao/Datasets/Align/10.jpg'
output_dir = '/home/luoyuhao/Datasets/Align/res'
args = [img_path,output_dir,'--scale','2']
main(parse_arguments(args))
#main(parse_arguments(sys.argv[1:]))
| 36.695122
| 133
| 0.601196
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy import misc
import sys
import os
import argparse
import tensorflow as tf
import numpy as np
import align.detect_face
import time
import imageio
import requests
import json
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
minsize = 20
threshold = [ 0.6, 0.7, 0.7 ]
factor = 0.709 # scale factor
ai_type = 'face-detect'
config_path = '/data/configure.json'
job_path = '/data/job/job.json'
log_path = '/data/job/logs.log'
log_path = '/home/luoyuhao/Datasets/Docker/logs/logs.log'
test_config_path = '/home/luoyuhao/Datasets/Docker/configure.json'
test_job_path = '/home/luoyuhao/Datasets/Docker/job.json'
def load_mtcnn_model(args):
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
start_time = time.time();
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
print('create and load mtcnn model time: ', (time.time() - start_time))
return pnet,rnet,onet
def crop_face(img,bounding_boxes,margin,image_size):
nrof_faces = bounding_boxes.shape[0]
if nrof_faces>0:
det = bounding_boxes[:,0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces>1:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
else:
det_arr.append(np.squeeze(det))
face_res = []
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img_size[1])
bb[3] = np.minimum(det[3]+margin/2, img_size[0])
cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
#scaled = skimage.transform.resize(cropped, (args.image_size, args.image_size), interp='bilinear')
scaled = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
face_res.append(scaled)
return face_res
def save_faces(res,output_filename):
if(len(res)>0):
filename_base, file_extension = os.path.splitext(output_filename)
for i in range(len(res)):
if (len(res)>1):
output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
else:
output_filename_n = "{}{}".format(filename_base, file_extension)
imageio.imwrite(output_filename_n, res[i])
def img_resize(img,scale):
img_resize = misc.imresize(img, (int(img.shape[0]/scale), int(img.shape[1]/scale)), interp='bilinear')
return img_resize
def detectFace(args,img,pnet,rnet,onet,output_filename=None,isDrawFace=False,isPrintTimeInfo=False):
## reseize img to detect
if args.scale>1:
img_input = img_resize(img,args.scale)
else:
img_input = img
detect_time_start = time.time()
bounding_boxes, _ = align.detect_face.detect_face(img_input, minsize, pnet, rnet, onet, threshold, factor)
if args.scale>1:
bounding_boxes[:,0:4] = args.scale * bounding_boxes[:,0:4]
detect_time = time.time() - detect_time_start
if isPrintTimeInfo:
print('detect_face_time: ', detect_time)
faces = crop_face(img,bounding_boxes,args.margin,args.image_size)
if(output_filename is not None):
save_time_start = time.time()
save_faces(faces,output_filename)
if isPrintTimeInfo:
print('save_face_time: ', time.time() - save_time_start)
if isDrawFace:
draw = align.detect_face.drawBoxes(img,bounding_boxes)
filename_base, file_extension = os.path.splitext(output_filename)
imageio.imwrite(filename_base+'_res'+file_extension,draw)
#print(bounding_boxes)
return faces,bounding_boxes
def read_config(config_path):
#TODO
try:
f = open(config_path,encoding='utf-8')
json_read = f.read()
dic = json.loads(json_read)
f.close()
except Exception as e:
print(e)
input_url = dic["input"]
output_url = dic['output']
logs_info = dic['logs']
return input_url,output_url,logs_info
def read_state(job_path):
#TODO
try:
f = open(job_path,encoding='utf-8')
json_read = f.read()
dic = json.loads(json_read)
state = dic['run']
f.close()
except Exception as e:
print(e)
if state == 'true':
return True
else:
return False
def read_input(input_url):
#TODO
try:
r = requests.get(input_url)
res_dic = r.json() #dic
except Exception as e:
#errorMessage = '{}: {}'.format(input_url, e)
print(e)
if r.raise_for_status() is None:
try:
taskJson_dic = res_dic['taskJson']
except Exception as e:
print("no face detect job.")
taskJson_dic = []
errorCode = res_dic['errorCode']
errorMsg = res_dic['errorMsg']
else:
taskJson_dic = []
errorCode = []
errorMsg = []
return taskJson_dic, errorCode, errorMsg
def push_output(input_dic,output_url,faces,bounding_boxes):
#TODO
for i in range(len(faces)):
save_path = '/home/luoyuhao/Datasets/Docker/saveface/'
save_path = save_path + str(time.time())+".png"
#imageio.imwrite(save_path,faces[i])
storage = 1
avatar = save_path
box = bounding_boxes[i,0:4]
location = [int(box[0]),int(box[1]),int(box[2]),int(box[3])]
out_dic = {"storage":storage,"avatar":avatar,'location':str(location),\
"camId":input_dic["camId"],"capTs":input_dic["capTs"]}
requests.post(output_url, data=out_dic)
def read_img_from_taskJson(task_dic,tsb):
storage = task_dic['storage']
img = []
if storage == 1:
img_path = task_dic['imagePath']
try:
img = imageio.imread(img_path)
except Exception as e:
msg = 'Face-detect failed. Wrong picture format'
write_logs(log_path,ai_type,task_dic,msg,tsb,time.time())
img = []
errorMessage = '{}: {}'.format(img_path, e)
print(errorMessage)
# =============================================================================
# else if storage == '2':
# img = []
# else if storage == '3':
# img = []
#
# =============================================================================
return img
def write_logs(log_path,ai_type,taskJson_dic,msg,tsb,tse):
storage = taskJson_dic['storage']
img_path = taskJson_dic['imagePath']
cam_id = taskJson_dic['camId']
cap_ts = taskJson_dic['capTs']
(filepath,tempfilename) = os.path.split(log_path)
if not os.path.exists(filepath):
os.mkdir(filepath)
with open(log_path,'at') as f:
f.write('tsb:%s\ttype:%s\tstorage:%d\timagePath:%s\tcamId:%d\tcapTs:%d\tmsg:%s\ttse:%s\n' % (tsb, ai_type,storage, img_path,\
cam_id,cap_ts,msg,tse))
########################################################################################################
def main(args):
# =============================================================================
# output_dir = args.output_dir
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# #
# image_path = args.image_path
# img = imageio.imread(image_path)
# filename = os.path.splitext(os.path.split(image_path)[1])[0]
# output_filename = os.path.join(output_dir, filename+'.png')
#
# =============================================================================
input_url,output_url,logs_info = read_config(config_path)
pnet,rnet,onet = load_mtcnn_model(args)
while(1):
time.sleep(2)
if read_state(job_path):
tsb = time.time()
taskJson_dic, errorCode, errorMsg = read_input(input_url)
if len(taskJson_dic)!=4:
continue
img = read_img_from_taskJson(taskJson_dic,tsb)
if len(img)>0:
try:
faces, bounding_boxes = detectFace(args,img,pnet,rnet,onet,None,False,True)
if len(faces)>0:
nums = len(faces)
msg = 'Face-detect success. find {} faces'.format(nums)
write_logs(log_path,ai_type,taskJson_dic,msg,tsb,time.time())
print("detect face success.")
else:
msg = "Face-detect success. find 0 faces."
write_logs(log_path,ai_type,taskJson_dic,msg,tsb,time.time())
print("detect no face.")
push_output(taskJson_dic,output_url,faces,bounding_boxes)
except Exception as e:
msg = "Face-detect failed. System exception"
write_logs(log_path,ai_type,taskJson_dic,msg,tsb,time.time())
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('image_path', type=str, help='Directory with unaligned images.')
parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
parser.add_argument('--detect_multiple_faces', type=bool,
help='Detect and align multiple faces per image.', default=True)
parser.add_argument('--scale', type=int,
help='the height and width will resize to height/scale and width/scale to detect faces.', default=2)
return parser.parse_args(argv)
if __name__ == '__main__':
img_path = '/home/luoyuhao/Datasets/Align/10.jpg'
output_dir = '/home/luoyuhao/Datasets/Align/res'
args = [img_path,output_dir,'--scale','2']
main(parse_arguments(args))
#main(parse_arguments(sys.argv[1:]))
| true
| true
|
1c4411906a6862cd76a4a9ab8fdaf4a537918ff5
| 62,520
|
py
|
Python
|
isi_sdk/api/fsa_results_api.py
|
robzim/isilon_sdk_python
|
3c2efcae7002f8ad25c0cfcb42a53b4d83e826d7
|
[
"MIT"
] | null | null | null |
isi_sdk/api/fsa_results_api.py
|
robzim/isilon_sdk_python
|
3c2efcae7002f8ad25c0cfcb42a53b4d83e826d7
|
[
"MIT"
] | null | null | null |
isi_sdk/api/fsa_results_api.py
|
robzim/isilon_sdk_python
|
3c2efcae7002f8ad25c0cfcb42a53b4d83e826d7
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
Isilon SDK
Isilon SDK - Language bindings for the OneFS API # noqa: E501
OpenAPI spec version: 3
Contact: sdk@isilon.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from isi_sdk_8_0.api_client import ApiClient
class FsaResultsApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def get_histogram_stat_by(self, id, stat, **kwargs): # noqa: E501
"""get_histogram_stat_by # noqa: E501
This resource retrieves a histogram breakout for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_histogram_stat_by(id, stat, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:param str stat: (required)
:return: HistogramStatBy
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_histogram_stat_by_with_http_info(id, stat, **kwargs) # noqa: E501
else:
(data) = self.get_histogram_stat_by_with_http_info(id, stat, **kwargs) # noqa: E501
return data
def get_histogram_stat_by_with_http_info(self, id, stat, **kwargs): # noqa: E501
"""get_histogram_stat_by # noqa: E501
This resource retrieves a histogram breakout for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_histogram_stat_by_with_http_info(id, stat, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:param str stat: (required)
:return: HistogramStatBy
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'stat'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_histogram_stat_by" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_histogram_stat_by`") # noqa: E501
# verify the required parameter 'stat' is set
if ('stat' not in params or
params['stat'] is None):
raise ValueError("Missing the required parameter `stat` when calling `get_histogram_stat_by`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
if 'stat' in params:
path_params['Stat'] = params['stat'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram/{Stat}/by', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HistogramStatBy', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_histogram_stat_by_breakout(self, histogram_stat_by_breakout, id, stat, **kwargs): # noqa: E501
"""get_histogram_stat_by_breakout # noqa: E501
This resource retrieves a histogram breakout for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_histogram_stat_by_breakout(histogram_stat_by_breakout, id, stat, async=True)
>>> result = thread.get()
:param async bool
:param str histogram_stat_by_breakout: This resource retrieves a histogram breakout for an individual FSA result set. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str stat: (required)
:param str directory_filter: Filter according to a specific directory, which includes all of its subdirectories.
:param str attribute_filter: Filter according to the name of a file user attribute.
:param str node_pool_filter: Filter according to the name of a node pool, which is a set of disk pools that belong to nodes of the same equivalence class.
:param str disk_pool_filter: Filter according to the name of a disk pool, which is a set of drives that represent an independent failure domain.
:param str tier_filter: Filter according to the name of a storage tier, which is a user-created set of node pools.
:param int comp_report: Result set identifier for comparison of database results.
:param int log_size_filter: Filter according to file logical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by logical size. The list of valid log_size filter values may be found by performing a histogram breakout by log_size and viewing the resulting key values.
:param int phys_size_filter: Filter according to file physical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by physical size. The list of valid phys_size filter values may be found by performing a histogram breakout by phys_size and viewing the resulting key values.
:param int limit: Limit the number of breakout results.
:param str path_ext_filter: Filter according to the name of a single file extension.
:param int ctime_filter: Filter according to file modified time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid ctime filter values may be found by performing a histogram breakout by ctime and viewing the resulting key values.
:param int atime_filter: Filter according to file accessed time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid atime filter values may be found by performing a histogram breakout by atime and viewing the resulting key values.
:return: HistogramStatBy
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_histogram_stat_by_breakout_with_http_info(histogram_stat_by_breakout, id, stat, **kwargs) # noqa: E501
else:
(data) = self.get_histogram_stat_by_breakout_with_http_info(histogram_stat_by_breakout, id, stat, **kwargs) # noqa: E501
return data
def get_histogram_stat_by_breakout_with_http_info(self, histogram_stat_by_breakout, id, stat, **kwargs): # noqa: E501
"""get_histogram_stat_by_breakout # noqa: E501
This resource retrieves a histogram breakout for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_histogram_stat_by_breakout_with_http_info(histogram_stat_by_breakout, id, stat, async=True)
>>> result = thread.get()
:param async bool
:param str histogram_stat_by_breakout: This resource retrieves a histogram breakout for an individual FSA result set. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str stat: (required)
:param str directory_filter: Filter according to a specific directory, which includes all of its subdirectories.
:param str attribute_filter: Filter according to the name of a file user attribute.
:param str node_pool_filter: Filter according to the name of a node pool, which is a set of disk pools that belong to nodes of the same equivalence class.
:param str disk_pool_filter: Filter according to the name of a disk pool, which is a set of drives that represent an independent failure domain.
:param str tier_filter: Filter according to the name of a storage tier, which is a user-created set of node pools.
:param int comp_report: Result set identifier for comparison of database results.
:param int log_size_filter: Filter according to file logical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by logical size. The list of valid log_size filter values may be found by performing a histogram breakout by log_size and viewing the resulting key values.
:param int phys_size_filter: Filter according to file physical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by physical size. The list of valid phys_size filter values may be found by performing a histogram breakout by phys_size and viewing the resulting key values.
:param int limit: Limit the number of breakout results.
:param str path_ext_filter: Filter according to the name of a single file extension.
:param int ctime_filter: Filter according to file modified time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid ctime filter values may be found by performing a histogram breakout by ctime and viewing the resulting key values.
:param int atime_filter: Filter according to file accessed time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid atime filter values may be found by performing a histogram breakout by atime and viewing the resulting key values.
:return: HistogramStatBy
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['histogram_stat_by_breakout', 'id', 'stat', 'directory_filter', 'attribute_filter', 'node_pool_filter', 'disk_pool_filter', 'tier_filter', 'comp_report', 'log_size_filter', 'phys_size_filter', 'limit', 'path_ext_filter', 'ctime_filter', 'atime_filter'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_histogram_stat_by_breakout" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'histogram_stat_by_breakout' is set
if ('histogram_stat_by_breakout' not in params or
params['histogram_stat_by_breakout'] is None):
raise ValueError("Missing the required parameter `histogram_stat_by_breakout` when calling `get_histogram_stat_by_breakout`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_histogram_stat_by_breakout`") # noqa: E501
# verify the required parameter 'stat' is set
if ('stat' not in params or
params['stat'] is None):
raise ValueError("Missing the required parameter `stat` when calling `get_histogram_stat_by_breakout`") # noqa: E501
collection_formats = {}
path_params = {}
if 'histogram_stat_by_breakout' in params:
path_params['HistogramStatByBreakout'] = params['histogram_stat_by_breakout'] # noqa: E501
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
if 'stat' in params:
path_params['Stat'] = params['stat'] # noqa: E501
query_params = []
if 'directory_filter' in params:
query_params.append(('directory_filter', params['directory_filter'])) # noqa: E501
if 'attribute_filter' in params:
query_params.append(('attribute_filter', params['attribute_filter'])) # noqa: E501
if 'node_pool_filter' in params:
query_params.append(('node_pool_filter', params['node_pool_filter'])) # noqa: E501
if 'disk_pool_filter' in params:
query_params.append(('disk_pool_filter', params['disk_pool_filter'])) # noqa: E501
if 'tier_filter' in params:
query_params.append(('tier_filter', params['tier_filter'])) # noqa: E501
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report'])) # noqa: E501
if 'log_size_filter' in params:
query_params.append(('log_size_filter', params['log_size_filter'])) # noqa: E501
if 'phys_size_filter' in params:
query_params.append(('phys_size_filter', params['phys_size_filter'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'path_ext_filter' in params:
query_params.append(('path_ext_filter', params['path_ext_filter'])) # noqa: E501
if 'ctime_filter' in params:
query_params.append(('ctime_filter', params['ctime_filter'])) # noqa: E501
if 'atime_filter' in params:
query_params.append(('atime_filter', params['atime_filter'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram/{Stat}/by/{HistogramStatByBreakout}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HistogramStatBy', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_directories(self, id, **kwargs): # noqa: E501
"""get_result_directories # noqa: E501
This resource retrieves directory information. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_directories(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param str path: Primary directory path to report usage information, which may be specified instead of a LIN.
:param int limit: Limit the number of reported subdirectories.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultDirectories
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_directories_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.get_result_directories_with_http_info(id, **kwargs) # noqa: E501
return data
def get_result_directories_with_http_info(self, id, **kwargs): # noqa: E501
"""get_result_directories # noqa: E501
This resource retrieves directory information. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_directories_with_http_info(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param str path: Primary directory path to report usage information, which may be specified instead of a LIN.
:param int limit: Limit the number of reported subdirectories.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultDirectories
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'sort', 'path', 'limit', 'comp_report', 'dir'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_directories" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_directories`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort'])) # noqa: E501
if 'path' in params:
query_params.append(('path', params['path'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report'])) # noqa: E501
if 'dir' in params:
query_params.append(('dir', params['dir'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/directories', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultDirectories', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_directory(self, result_directory_id, id, **kwargs): # noqa: E501
"""get_result_directory # noqa: E501
This resource retrieves directory information. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_directory(result_directory_id, id, async=True)
>>> result = thread.get()
:param async bool
:param int result_directory_id: This resource retrieves directory information. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param int limit: Limit the number of reported subdirectories.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultDirectories
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_directory_with_http_info(result_directory_id, id, **kwargs) # noqa: E501
else:
(data) = self.get_result_directory_with_http_info(result_directory_id, id, **kwargs) # noqa: E501
return data
def get_result_directory_with_http_info(self, result_directory_id, id, **kwargs): # noqa: E501
"""get_result_directory # noqa: E501
This resource retrieves directory information. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_directory_with_http_info(result_directory_id, id, async=True)
>>> result = thread.get()
:param async bool
:param int result_directory_id: This resource retrieves directory information. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param int limit: Limit the number of reported subdirectories.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultDirectories
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['result_directory_id', 'id', 'sort', 'limit', 'comp_report', 'dir'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_directory" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'result_directory_id' is set
if ('result_directory_id' not in params or
params['result_directory_id'] is None):
raise ValueError("Missing the required parameter `result_directory_id` when calling `get_result_directory`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_directory`") # noqa: E501
collection_formats = {}
path_params = {}
if 'result_directory_id' in params:
path_params['ResultDirectoryId'] = params['result_directory_id'] # noqa: E501
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report'])) # noqa: E501
if 'dir' in params:
query_params.append(('dir', params['dir'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/directories/{ResultDirectoryId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultDirectories', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_histogram(self, id, **kwargs): # noqa: E501
"""get_result_histogram # noqa: E501
This resource retrieves a histogram of file counts for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_histogram(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:return: ResultHistogram
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_histogram_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.get_result_histogram_with_http_info(id, **kwargs) # noqa: E501
return data
def get_result_histogram_with_http_info(self, id, **kwargs): # noqa: E501
"""get_result_histogram # noqa: E501
This resource retrieves a histogram of file counts for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_histogram_with_http_info(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:return: ResultHistogram
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_histogram" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_histogram`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultHistogram', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_histogram_stat(self, result_histogram_stat, id, **kwargs): # noqa: E501
"""get_result_histogram_stat # noqa: E501
This resource retrieves a histogram of file counts for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_histogram_stat(result_histogram_stat, id, async=True)
>>> result = thread.get()
:param async bool
:param str result_histogram_stat: This resource retrieves a histogram of file counts for an individual FSA result set. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str directory_filter: Filter according to a specific directory, which includes all of its subdirectories.
:param str attribute_filter: Filter according to the name of a file user attribute.
:param str node_pool_filter: Filter according to the name of a node pool, which is a set of disk pools that belong to nodes of the same equivalence class.
:param str disk_pool_filter: Filter according to the name of a disk pool, which is a set of drives that represent an independent failure domain.
:param str tier_filter: Filter according to the name of a storage tier, which is a user-created set of node pools.
:param int comp_report: Result set identifier for comparison of database results.
:param int log_size_filter: Filter according to file logical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by logical size. The list of valid log_size filter values may be found by performing a histogram breakout by log_size and viewing the resulting key values.
:param int phys_size_filter: Filter according to file physical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by physical size. The list of valid phys_size filter values may be found by performing a histogram breakout by phys_size and viewing the resulting key values.
:param str path_ext_filter: Filter according to the name of a single file extension.
:param int ctime_filter: Filter according to file modified time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid ctime filter values may be found by performing a histogram breakout by ctime and viewing the resulting key values.
:param int atime_filter: Filter according to file accessed time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid atime filter values may be found by performing a histogram breakout by atime and viewing the resulting key values.
:return: ResultHistogram
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_histogram_stat_with_http_info(result_histogram_stat, id, **kwargs) # noqa: E501
else:
(data) = self.get_result_histogram_stat_with_http_info(result_histogram_stat, id, **kwargs) # noqa: E501
return data
def get_result_histogram_stat_with_http_info(self, result_histogram_stat, id, **kwargs): # noqa: E501
"""get_result_histogram_stat # noqa: E501
This resource retrieves a histogram of file counts for an individual FSA result set. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_histogram_stat_with_http_info(result_histogram_stat, id, async=True)
>>> result = thread.get()
:param async bool
:param str result_histogram_stat: This resource retrieves a histogram of file counts for an individual FSA result set. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str directory_filter: Filter according to a specific directory, which includes all of its subdirectories.
:param str attribute_filter: Filter according to the name of a file user attribute.
:param str node_pool_filter: Filter according to the name of a node pool, which is a set of disk pools that belong to nodes of the same equivalence class.
:param str disk_pool_filter: Filter according to the name of a disk pool, which is a set of drives that represent an independent failure domain.
:param str tier_filter: Filter according to the name of a storage tier, which is a user-created set of node pools.
:param int comp_report: Result set identifier for comparison of database results.
:param int log_size_filter: Filter according to file logical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by logical size. The list of valid log_size filter values may be found by performing a histogram breakout by log_size and viewing the resulting key values.
:param int phys_size_filter: Filter according to file physical size, where the filter value specifies the lower bound in bytes to a set of files that have been grouped by physical size. The list of valid phys_size filter values may be found by performing a histogram breakout by phys_size and viewing the resulting key values.
:param str path_ext_filter: Filter according to the name of a single file extension.
:param int ctime_filter: Filter according to file modified time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid ctime filter values may be found by performing a histogram breakout by ctime and viewing the resulting key values.
:param int atime_filter: Filter according to file accessed time, where the filter value specifies a negative number of seconds representing a time before the begin time of the report. The list of valid atime filter values may be found by performing a histogram breakout by atime and viewing the resulting key values.
:return: ResultHistogram
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['result_histogram_stat', 'id', 'directory_filter', 'attribute_filter', 'node_pool_filter', 'disk_pool_filter', 'tier_filter', 'comp_report', 'log_size_filter', 'phys_size_filter', 'path_ext_filter', 'ctime_filter', 'atime_filter'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_histogram_stat" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'result_histogram_stat' is set
if ('result_histogram_stat' not in params or
params['result_histogram_stat'] is None):
raise ValueError("Missing the required parameter `result_histogram_stat` when calling `get_result_histogram_stat`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_histogram_stat`") # noqa: E501
collection_formats = {}
path_params = {}
if 'result_histogram_stat' in params:
path_params['ResultHistogramStat'] = params['result_histogram_stat'] # noqa: E501
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
if 'directory_filter' in params:
query_params.append(('directory_filter', params['directory_filter'])) # noqa: E501
if 'attribute_filter' in params:
query_params.append(('attribute_filter', params['attribute_filter'])) # noqa: E501
if 'node_pool_filter' in params:
query_params.append(('node_pool_filter', params['node_pool_filter'])) # noqa: E501
if 'disk_pool_filter' in params:
query_params.append(('disk_pool_filter', params['disk_pool_filter'])) # noqa: E501
if 'tier_filter' in params:
query_params.append(('tier_filter', params['tier_filter'])) # noqa: E501
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report'])) # noqa: E501
if 'log_size_filter' in params:
query_params.append(('log_size_filter', params['log_size_filter'])) # noqa: E501
if 'phys_size_filter' in params:
query_params.append(('phys_size_filter', params['phys_size_filter'])) # noqa: E501
if 'path_ext_filter' in params:
query_params.append(('path_ext_filter', params['path_ext_filter'])) # noqa: E501
if 'ctime_filter' in params:
query_params.append(('ctime_filter', params['ctime_filter'])) # noqa: E501
if 'atime_filter' in params:
query_params.append(('atime_filter', params['atime_filter'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram/{ResultHistogramStat}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultHistogram', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_dir(self, result_top_dir_id, id, **kwargs): # noqa: E501
"""get_result_top_dir # noqa: E501
This resource retrieves the top directories. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_dir(result_top_dir_id, id, async=True)
>>> result = thread.get()
:param async bool
:param str result_top_dir_id: This resource retrieves the top directories. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param int start: Starting index for results. Default value of 0.
:param int limit: Number of results from start index. Default value of 1000.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultTopDirs
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_dir_with_http_info(result_top_dir_id, id, **kwargs) # noqa: E501
else:
(data) = self.get_result_top_dir_with_http_info(result_top_dir_id, id, **kwargs) # noqa: E501
return data
def get_result_top_dir_with_http_info(self, result_top_dir_id, id, **kwargs): # noqa: E501
"""get_result_top_dir # noqa: E501
This resource retrieves the top directories. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_dir_with_http_info(result_top_dir_id, id, async=True)
>>> result = thread.get()
:param async bool
:param str result_top_dir_id: This resource retrieves the top directories. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param int start: Starting index for results. Default value of 0.
:param int limit: Number of results from start index. Default value of 1000.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultTopDirs
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['result_top_dir_id', 'id', 'sort', 'start', 'limit', 'comp_report', 'dir'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_dir" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'result_top_dir_id' is set
if ('result_top_dir_id' not in params or
params['result_top_dir_id'] is None):
raise ValueError("Missing the required parameter `result_top_dir_id` when calling `get_result_top_dir`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_dir`") # noqa: E501
collection_formats = {}
path_params = {}
if 'result_top_dir_id' in params:
path_params['ResultTopDirId'] = params['result_top_dir_id'] # noqa: E501
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort'])) # noqa: E501
if 'start' in params:
query_params.append(('start', params['start'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report'])) # noqa: E501
if 'dir' in params:
query_params.append(('dir', params['dir'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-dirs/{ResultTopDirId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopDirs', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_dirs(self, id, **kwargs): # noqa: E501
"""get_result_top_dirs # noqa: E501
This resource retrieves the top directories. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_dirs(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:return: ResultTopDirs
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_dirs_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.get_result_top_dirs_with_http_info(id, **kwargs) # noqa: E501
return data
def get_result_top_dirs_with_http_info(self, id, **kwargs): # noqa: E501
"""get_result_top_dirs # noqa: E501
This resource retrieves the top directories. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_dirs_with_http_info(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:return: ResultTopDirs
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_dirs" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_dirs`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-dirs', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopDirs', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_file(self, result_top_file_id, id, **kwargs): # noqa: E501
"""get_result_top_file # noqa: E501
This resource retrieves the top files. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_file(result_top_file_id, id, async=True)
>>> result = thread.get()
:param async bool
:param str result_top_file_id: This resource retrieves the top files. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param int start: Starting index for results. Default value of 0.
:param int limit: Number of results from start index. Default value of 1000.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultTopFiles
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_file_with_http_info(result_top_file_id, id, **kwargs) # noqa: E501
else:
(data) = self.get_result_top_file_with_http_info(result_top_file_id, id, **kwargs) # noqa: E501
return data
def get_result_top_file_with_http_info(self, result_top_file_id, id, **kwargs): # noqa: E501
"""get_result_top_file # noqa: E501
This resource retrieves the top files. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_file_with_http_info(result_top_file_id, id, async=True)
>>> result = thread.get()
:param async bool
:param str result_top_file_id: This resource retrieves the top files. ID in the resource path is the result set ID. (required)
:param str id: (required)
:param str sort: The field that will be used for sorting.
:param int start: Starting index for results. Default value of 0.
:param int limit: Number of results from start index. Default value of 1000.
:param int comp_report: Result set identifier for comparison of database results.
:param str dir: The direction of the sort.
:return: ResultTopFiles
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['result_top_file_id', 'id', 'sort', 'start', 'limit', 'comp_report', 'dir'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_file" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'result_top_file_id' is set
if ('result_top_file_id' not in params or
params['result_top_file_id'] is None):
raise ValueError("Missing the required parameter `result_top_file_id` when calling `get_result_top_file`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_file`") # noqa: E501
collection_formats = {}
path_params = {}
if 'result_top_file_id' in params:
path_params['ResultTopFileId'] = params['result_top_file_id'] # noqa: E501
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort'])) # noqa: E501
if 'start' in params:
query_params.append(('start', params['start'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report'])) # noqa: E501
if 'dir' in params:
query_params.append(('dir', params['dir'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-files/{ResultTopFileId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopFiles', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_files(self, id, **kwargs): # noqa: E501
"""get_result_top_files # noqa: E501
This resource retrieves the top files. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_files(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:return: ResultTopFiles
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_files_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.get_result_top_files_with_http_info(id, **kwargs) # noqa: E501
return data
def get_result_top_files_with_http_info(self, id, **kwargs): # noqa: E501
"""get_result_top_files # noqa: E501
This resource retrieves the top files. ID in the resource path is the result set ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_result_top_files_with_http_info(id, async=True)
>>> result = thread.get()
:param async bool
:param str id: (required)
:return: ResultTopFiles
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_files" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_files`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-files', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopFiles', # noqa: E501
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 50.056045
| 334
| 0.644962
|
from __future__ import absolute_import
import re
import six
from isi_sdk_8_0.api_client import ApiClient
class FsaResultsApi(object):
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def get_histogram_stat_by(self, id, stat, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_histogram_stat_by_with_http_info(id, stat, **kwargs)
else:
(data) = self.get_histogram_stat_by_with_http_info(id, stat, **kwargs)
return data
def get_histogram_stat_by_with_http_info(self, id, stat, **kwargs):
all_params = ['id', 'stat']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_histogram_stat_by" % key
)
params[key] = val
del params['kwargs']
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_histogram_stat_by`")
if ('stat' not in params or
params['stat'] is None):
raise ValueError("Missing the required parameter `stat` when calling `get_histogram_stat_by`")
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id']
if 'stat' in params:
path_params['Stat'] = params['stat']
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram/{Stat}/by', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HistogramStatBy',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_histogram_stat_by_breakout(self, histogram_stat_by_breakout, id, stat, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_histogram_stat_by_breakout_with_http_info(histogram_stat_by_breakout, id, stat, **kwargs)
else:
(data) = self.get_histogram_stat_by_breakout_with_http_info(histogram_stat_by_breakout, id, stat, **kwargs)
return data
def get_histogram_stat_by_breakout_with_http_info(self, histogram_stat_by_breakout, id, stat, **kwargs):
all_params = ['histogram_stat_by_breakout', 'id', 'stat', 'directory_filter', 'attribute_filter', 'node_pool_filter', 'disk_pool_filter', 'tier_filter', 'comp_report', 'log_size_filter', 'phys_size_filter', 'limit', 'path_ext_filter', 'ctime_filter', 'atime_filter']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_histogram_stat_by_breakout" % key
)
params[key] = val
del params['kwargs']
if ('histogram_stat_by_breakout' not in params or
params['histogram_stat_by_breakout'] is None):
raise ValueError("Missing the required parameter `histogram_stat_by_breakout` when calling `get_histogram_stat_by_breakout`")
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_histogram_stat_by_breakout`")
if ('stat' not in params or
params['stat'] is None):
raise ValueError("Missing the required parameter `stat` when calling `get_histogram_stat_by_breakout`")
collection_formats = {}
path_params = {}
if 'histogram_stat_by_breakout' in params:
path_params['HistogramStatByBreakout'] = params['histogram_stat_by_breakout']
if 'id' in params:
path_params['Id'] = params['id']
if 'stat' in params:
path_params['Stat'] = params['stat']
query_params = []
if 'directory_filter' in params:
query_params.append(('directory_filter', params['directory_filter']))
if 'attribute_filter' in params:
query_params.append(('attribute_filter', params['attribute_filter']))
if 'node_pool_filter' in params:
query_params.append(('node_pool_filter', params['node_pool_filter']))
if 'disk_pool_filter' in params:
query_params.append(('disk_pool_filter', params['disk_pool_filter']))
if 'tier_filter' in params:
query_params.append(('tier_filter', params['tier_filter']))
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report']))
if 'log_size_filter' in params:
query_params.append(('log_size_filter', params['log_size_filter']))
if 'phys_size_filter' in params:
query_params.append(('phys_size_filter', params['phys_size_filter']))
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'path_ext_filter' in params:
query_params.append(('path_ext_filter', params['path_ext_filter']))
if 'ctime_filter' in params:
query_params.append(('ctime_filter', params['ctime_filter']))
if 'atime_filter' in params:
query_params.append(('atime_filter', params['atime_filter']))
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram/{Stat}/by/{HistogramStatByBreakout}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HistogramStatBy',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_directories(self, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_directories_with_http_info(id, **kwargs)
else:
(data) = self.get_result_directories_with_http_info(id, **kwargs)
return data
def get_result_directories_with_http_info(self, id, **kwargs):
all_params = ['id', 'sort', 'path', 'limit', 'comp_report', 'dir']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_directories" % key
)
params[key] = val
del params['kwargs']
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_directories`")
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort']))
if 'path' in params:
query_params.append(('path', params['path']))
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report']))
if 'dir' in params:
query_params.append(('dir', params['dir']))
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/directories', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultDirectories',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_directory(self, result_directory_id, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_directory_with_http_info(result_directory_id, id, **kwargs)
else:
(data) = self.get_result_directory_with_http_info(result_directory_id, id, **kwargs)
return data
def get_result_directory_with_http_info(self, result_directory_id, id, **kwargs):
all_params = ['result_directory_id', 'id', 'sort', 'limit', 'comp_report', 'dir']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_directory" % key
)
params[key] = val
del params['kwargs']
if ('result_directory_id' not in params or
params['result_directory_id'] is None):
raise ValueError("Missing the required parameter `result_directory_id` when calling `get_result_directory`")
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_directory`")
collection_formats = {}
path_params = {}
if 'result_directory_id' in params:
path_params['ResultDirectoryId'] = params['result_directory_id']
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort']))
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report']))
if 'dir' in params:
query_params.append(('dir', params['dir']))
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/directories/{ResultDirectoryId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultDirectories',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_histogram(self, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_histogram_with_http_info(id, **kwargs)
else:
(data) = self.get_result_histogram_with_http_info(id, **kwargs)
return data
def get_result_histogram_with_http_info(self, id, **kwargs):
all_params = ['id']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_histogram" % key
)
params[key] = val
del params['kwargs']
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_histogram`")
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultHistogram',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_histogram_stat(self, result_histogram_stat, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_histogram_stat_with_http_info(result_histogram_stat, id, **kwargs)
else:
(data) = self.get_result_histogram_stat_with_http_info(result_histogram_stat, id, **kwargs)
return data
def get_result_histogram_stat_with_http_info(self, result_histogram_stat, id, **kwargs):
all_params = ['result_histogram_stat', 'id', 'directory_filter', 'attribute_filter', 'node_pool_filter', 'disk_pool_filter', 'tier_filter', 'comp_report', 'log_size_filter', 'phys_size_filter', 'path_ext_filter', 'ctime_filter', 'atime_filter']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_histogram_stat" % key
)
params[key] = val
del params['kwargs']
if ('result_histogram_stat' not in params or
params['result_histogram_stat'] is None):
raise ValueError("Missing the required parameter `result_histogram_stat` when calling `get_result_histogram_stat`")
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_histogram_stat`")
collection_formats = {}
path_params = {}
if 'result_histogram_stat' in params:
path_params['ResultHistogramStat'] = params['result_histogram_stat']
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
if 'directory_filter' in params:
query_params.append(('directory_filter', params['directory_filter']))
if 'attribute_filter' in params:
query_params.append(('attribute_filter', params['attribute_filter']))
if 'node_pool_filter' in params:
query_params.append(('node_pool_filter', params['node_pool_filter']))
if 'disk_pool_filter' in params:
query_params.append(('disk_pool_filter', params['disk_pool_filter']))
if 'tier_filter' in params:
query_params.append(('tier_filter', params['tier_filter']))
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report']))
if 'log_size_filter' in params:
query_params.append(('log_size_filter', params['log_size_filter']))
if 'phys_size_filter' in params:
query_params.append(('phys_size_filter', params['phys_size_filter']))
if 'path_ext_filter' in params:
query_params.append(('path_ext_filter', params['path_ext_filter']))
if 'ctime_filter' in params:
query_params.append(('ctime_filter', params['ctime_filter']))
if 'atime_filter' in params:
query_params.append(('atime_filter', params['atime_filter']))
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/histogram/{ResultHistogramStat}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultHistogram',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_dir(self, result_top_dir_id, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_dir_with_http_info(result_top_dir_id, id, **kwargs)
else:
(data) = self.get_result_top_dir_with_http_info(result_top_dir_id, id, **kwargs)
return data
def get_result_top_dir_with_http_info(self, result_top_dir_id, id, **kwargs):
all_params = ['result_top_dir_id', 'id', 'sort', 'start', 'limit', 'comp_report', 'dir']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_dir" % key
)
params[key] = val
del params['kwargs']
if ('result_top_dir_id' not in params or
params['result_top_dir_id'] is None):
raise ValueError("Missing the required parameter `result_top_dir_id` when calling `get_result_top_dir`")
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_dir`")
collection_formats = {}
path_params = {}
if 'result_top_dir_id' in params:
path_params['ResultTopDirId'] = params['result_top_dir_id']
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort']))
if 'start' in params:
query_params.append(('start', params['start']))
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report']))
if 'dir' in params:
query_params.append(('dir', params['dir']))
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-dirs/{ResultTopDirId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopDirs',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_dirs(self, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_dirs_with_http_info(id, **kwargs)
else:
(data) = self.get_result_top_dirs_with_http_info(id, **kwargs)
return data
def get_result_top_dirs_with_http_info(self, id, **kwargs):
all_params = ['id']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_dirs" % key
)
params[key] = val
del params['kwargs']
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_dirs`")
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-dirs', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopDirs',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_file(self, result_top_file_id, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_file_with_http_info(result_top_file_id, id, **kwargs)
else:
(data) = self.get_result_top_file_with_http_info(result_top_file_id, id, **kwargs)
return data
def get_result_top_file_with_http_info(self, result_top_file_id, id, **kwargs):
all_params = ['result_top_file_id', 'id', 'sort', 'start', 'limit', 'comp_report', 'dir']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_file" % key
)
params[key] = val
del params['kwargs']
if ('result_top_file_id' not in params or
params['result_top_file_id'] is None):
raise ValueError("Missing the required parameter `result_top_file_id` when calling `get_result_top_file`")
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_file`")
collection_formats = {}
path_params = {}
if 'result_top_file_id' in params:
path_params['ResultTopFileId'] = params['result_top_file_id']
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
if 'sort' in params:
query_params.append(('sort', params['sort']))
if 'start' in params:
query_params.append(('start', params['start']))
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'comp_report' in params:
query_params.append(('comp_report', params['comp_report']))
if 'dir' in params:
query_params.append(('dir', params['dir']))
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-files/{ResultTopFileId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopFiles',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_result_top_files(self, id, **kwargs):
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_result_top_files_with_http_info(id, **kwargs)
else:
(data) = self.get_result_top_files_with_http_info(id, **kwargs)
return data
def get_result_top_files_with_http_info(self, id, **kwargs):
all_params = ['id']
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_result_top_files" % key
)
params[key] = val
del params['kwargs']
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_result_top_files`")
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['Id'] = params['id']
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept(
['application/json'])
header_params['Content-Type'] = self.api_client.select_header_content_type(
['application/json'])
auth_settings = ['basicAuth']
return self.api_client.call_api(
'/platform/3/fsa/results/{Id}/top-files', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResultTopFiles',
auth_settings=auth_settings,
myAsync=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| true
| true
|
1c4412a8c09bdeeffe088f019f7889bfd861cd4d
| 1,918
|
py
|
Python
|
command/box.py
|
DrLarck/DragonBotZ
|
eab773d6e55f7f5f325828fe249800193120abaf
|
[
"MIT"
] | 3
|
2020-05-01T07:38:38.000Z
|
2020-06-02T12:03:40.000Z
|
command/box.py
|
DrLarck/DragonBotZ
|
eab773d6e55f7f5f325828fe249800193120abaf
|
[
"MIT"
] | 19
|
2020-11-01T22:15:57.000Z
|
2021-09-08T15:28:30.000Z
|
command/box.py
|
DrLarck/DragonBotZ
|
eab773d6e55f7f5f325828fe249800193120abaf
|
[
"MIT"
] | 1
|
2021-03-05T04:51:21.000Z
|
2021-03-05T04:51:21.000Z
|
"""
Box command
--
Author : Drlarck
Last update : 25/12/20 by DrLarck
"""
from discord.ext import commands
# util
from utility.command.checker import CommandChecker
from utility.entity.player import Player
from utility.global_tool import GlobalTool
# tool
from utility.command.tool.tool_box import ToolBox
class CommandBox(commands.Cog):
def __init__(self, client):
# Public
self.client = client
@commands.check(CommandChecker.game_ready)
@commands.check(CommandChecker.register)
@commands.cooldown(1, 10, commands.BucketType.user)
@commands.group(invoke_without_command=True)
async def box(self, context, rarity: str = None):
# Log
await self.client.logger.log(context)
# Init
player = Player(context, self.client, context.message.author)
box_tool = ToolBox(self.client, context)
global_tool = GlobalTool()
# Normal box
if rarity is None:
await box_tool.box_manager(player)
else:
# Get the rarity value
value = await global_tool.get_rarity_value(rarity)
if value is not None:
await box_tool.box_manager(player, rarity=value)
else:
await context.send(f"Sorry, I can't find the rarity `{rarity}`")
@commands.check(CommandChecker.game_ready)
@commands.check(CommandChecker.register)
@commands.cooldown(1, 10, commands.BucketType.user)
@box.command(aliases=["u"])
async def unique(self, context, reference: int):
# Log
await self.client.logger.log(context)
# Init
player = Player(context, self.client, context.message.author)
box_tool = ToolBox(self.client, context)
# Display the unique box
await box_tool.box_manager(player, unique_reference=reference)
def setup(client):
client.add_cog(CommandBox(client))
| 26.273973
| 80
| 0.661627
|
from discord.ext import commands
from utility.command.checker import CommandChecker
from utility.entity.player import Player
from utility.global_tool import GlobalTool
from utility.command.tool.tool_box import ToolBox
class CommandBox(commands.Cog):
def __init__(self, client):
self.client = client
@commands.check(CommandChecker.game_ready)
@commands.check(CommandChecker.register)
@commands.cooldown(1, 10, commands.BucketType.user)
@commands.group(invoke_without_command=True)
async def box(self, context, rarity: str = None):
await self.client.logger.log(context)
player = Player(context, self.client, context.message.author)
box_tool = ToolBox(self.client, context)
global_tool = GlobalTool()
if rarity is None:
await box_tool.box_manager(player)
else:
value = await global_tool.get_rarity_value(rarity)
if value is not None:
await box_tool.box_manager(player, rarity=value)
else:
await context.send(f"Sorry, I can't find the rarity `{rarity}`")
@commands.check(CommandChecker.game_ready)
@commands.check(CommandChecker.register)
@commands.cooldown(1, 10, commands.BucketType.user)
@box.command(aliases=["u"])
async def unique(self, context, reference: int):
# Log
await self.client.logger.log(context)
# Init
player = Player(context, self.client, context.message.author)
box_tool = ToolBox(self.client, context)
# Display the unique box
await box_tool.box_manager(player, unique_reference=reference)
def setup(client):
client.add_cog(CommandBox(client))
| true
| true
|
1c4412e0c4f9a993f9fd8586877ecb41b54a8605
| 2,297
|
py
|
Python
|
mlens/parallel/tests/test_a_learner_subset.py
|
mehrdad-shokri/mlens
|
6cbc11354b5f9500a33d9cefb700a1bba9d3199a
|
[
"MIT"
] | 760
|
2017-03-13T10:11:45.000Z
|
2022-03-30T20:59:20.000Z
|
mlens/parallel/tests/test_a_learner_subset.py
|
rahulsaini/mlens
|
6cbc11354b5f9500a33d9cefb700a1bba9d3199a
|
[
"MIT"
] | 115
|
2017-01-18T22:10:33.000Z
|
2022-03-17T12:42:34.000Z
|
mlens/parallel/tests/test_a_learner_subset.py
|
rahulsaini/mlens
|
6cbc11354b5f9500a33d9cefb700a1bba9d3199a
|
[
"MIT"
] | 96
|
2017-03-13T10:12:48.000Z
|
2022-02-23T17:12:39.000Z
|
""""ML-ENSEMBLE
Testing suite for Learner and Transformer
"""
from mlens.testing import Data, EstimatorContainer, get_learner, run_learner
def test_fit():
"""[Parallel | Learner | Subset | No Proba | No Prep] test fit"""
args = get_learner('fit', 'subsemble', False, False)
run_learner(*args)
def test_predict():
"""[Parallel | Learner | Subset | No Proba | No Prep] test predict"""
args = get_learner('predict', 'subsemble', False, False)
run_learner(*args)
def test_transform():
"""[Parallel | Learner | Subset | No Proba | No Prep] test transform"""
args = get_learner('transform', 'subsemble', False, False)
run_learner(*args)
def test_fit_prep():
"""[Parallel | Learner | Subset | No Proba | Prep] test fit"""
args = get_learner('fit', 'subsemble', False, True)
run_learner(*args)
def test_predict_prep():
"""[Parallel | Learner | Subset | No Proba | Prep] test predict"""
args = get_learner('predict', 'subsemble', False, True)
run_learner(*args)
def test_transform_prep():
"""[Parallel | Learner | Subset | No Proba | Prep] test transform"""
args = get_learner('transform', 'subsemble', False, True)
run_learner(*args)
def test_fit_proba():
"""[Parallel | Learner | Subset | Proba | No Prep] test fit"""
args = get_learner('fit', 'subsemble', True, False)
run_learner(*args)
def test_predict_proba():
"""[Parallel | Learner | Subset | Proba | No Prep] test predict"""
args = get_learner('predict', 'subsemble', True, False)
run_learner(*args)
def test_transform_proba():
"""[Parallel | Learner | Subset | Proba | No Prep] test transform"""
args = get_learner('transform', 'subsemble', True, False)
run_learner(*args)
def test_fit_prep_proba():
"""[Parallel | Learner | Subset | Proba | Prep] test fit"""
args = get_learner('fit', 'subsemble', True, True)
run_learner(*args)
def test_predict_prep_proba():
"""[Parallel | Learner | Subset | Proba | No Prep] test predict"""
args = get_learner('predict', 'subsemble', True, True)
run_learner(*args)
def test_transform_prep_proba():
"""[Parallel | Learner | Subset | Proba | Prep] test transform"""
args = get_learner('transform', 'subsemble', True, True)
run_learner(*args)
| 29.448718
| 76
| 0.65825
|
from mlens.testing import Data, EstimatorContainer, get_learner, run_learner
def test_fit():
args = get_learner('fit', 'subsemble', False, False)
run_learner(*args)
def test_predict():
args = get_learner('predict', 'subsemble', False, False)
run_learner(*args)
def test_transform():
args = get_learner('transform', 'subsemble', False, False)
run_learner(*args)
def test_fit_prep():
args = get_learner('fit', 'subsemble', False, True)
run_learner(*args)
def test_predict_prep():
args = get_learner('predict', 'subsemble', False, True)
run_learner(*args)
def test_transform_prep():
args = get_learner('transform', 'subsemble', False, True)
run_learner(*args)
def test_fit_proba():
args = get_learner('fit', 'subsemble', True, False)
run_learner(*args)
def test_predict_proba():
args = get_learner('predict', 'subsemble', True, False)
run_learner(*args)
def test_transform_proba():
args = get_learner('transform', 'subsemble', True, False)
run_learner(*args)
def test_fit_prep_proba():
args = get_learner('fit', 'subsemble', True, True)
run_learner(*args)
def test_predict_prep_proba():
args = get_learner('predict', 'subsemble', True, True)
run_learner(*args)
def test_transform_prep_proba():
args = get_learner('transform', 'subsemble', True, True)
run_learner(*args)
| true
| true
|
1c4413a8fd4db352cd8e11d757e9ebd5c2024042
| 3,019
|
py
|
Python
|
donations/management/commands/export_translations.py
|
diffractive/newstream
|
cf1a1f230e18d01c63b50ab9d360aa44ac5a486f
|
[
"MIT"
] | 1
|
2020-05-03T12:33:42.000Z
|
2020-05-03T12:33:42.000Z
|
donations/management/commands/export_translations.py
|
diffractive/newstream
|
cf1a1f230e18d01c63b50ab9d360aa44ac5a486f
|
[
"MIT"
] | 14
|
2020-07-06T20:05:57.000Z
|
2022-03-12T00:39:11.000Z
|
donations/management/commands/export_translations.py
|
diffractive/newstream
|
cf1a1f230e18d01c63b50ab9d360aa44ac5a486f
|
[
"MIT"
] | null | null | null |
import csv
import os
import re
from zipfile import ZipFile
from django.core.management.base import BaseCommand
from django.apps import apps
from pages.models import HomePage
class Command(BaseCommand):
help = 'updates .po files, compiles all i18n fields into a csv file and zips them all into one zip file'
def handle(self, *args, **options):
# Get all i18nfields values from all models + page content
with open('translation_fields.csv', 'w', newline='') as f:
writer = csv.writer(f)
# header
writer.writerow(['Field/Model Name', 'Field Value'])
for model in apps.get_models():
i18n_fields = []
for field in model._meta.get_fields():
# relational fields cannot be deconstructed, we don't have translated relational fields anyway
if hasattr(field, 'deconstruct'):
# more on deconstruct: https://docs.djangoproject.com/en/3.1/ref/models/fields/#django.db.models.Field.deconstruct
field_info = field.deconstruct()
field_name = field_info[0]
import_path = field_info[1]
if re.search(r'(I18nCharField|I18nTextField|I18nRichTextField)$', import_path):
i18n_fields.append(field_name)
# loop over all instances to get all english values from the i18n_fields
if len(i18n_fields):
# First make a row for this model
writer.writerow(['[Model] %s' % model.__name__])
for obj in model.objects.all():
for field in i18n_fields:
writer.writerow(['[Field] #%i - %s' % (obj.id, field), getattr(obj, field).localize('en')])
# loop over all pages (HomePage)
writer.writerow(['[Model] %s' % 'HomePage'])
for page in HomePage.objects.all():
# the locale field might still hasn't been set yet if the page wasn't saved after the locales are introduced
if str(page.locale) == 'English' or not page.locale:
writer.writerow(['[Field] #%i - %s' % (page.id, 'body'), page.body])
# Get all the .po files
file_paths = []
for root, directories, files in os.walk('./'):
if not re.search(r'^\./(venv|virtualenv|node_modules|\.git)', root):
for filename in files:
if 'django.po' in filename:
# join the two strings in order to form the full filepath.
filepath = os.path.join(root, filename)
file_paths.append(filepath)
# zip all the files together
with ZipFile('translation_files.zip', 'w') as zip:
for file in file_paths:
zip.write(file)
zip.write('translation_fields.csv')
print('Zip file generated')
| 47.920635
| 138
| 0.561444
|
import csv
import os
import re
from zipfile import ZipFile
from django.core.management.base import BaseCommand
from django.apps import apps
from pages.models import HomePage
class Command(BaseCommand):
help = 'updates .po files, compiles all i18n fields into a csv file and zips them all into one zip file'
def handle(self, *args, **options):
with open('translation_fields.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['Field/Model Name', 'Field Value'])
for model in apps.get_models():
i18n_fields = []
for field in model._meta.get_fields():
if hasattr(field, 'deconstruct'):
# more on deconstruct: https://docs.djangoproject.com/en/3.1/ref/models/fields/#django.db.models.Field.deconstruct
field_info = field.deconstruct()
field_name = field_info[0]
import_path = field_info[1]
if re.search(r'(I18nCharField|I18nTextField|I18nRichTextField)$', import_path):
i18n_fields.append(field_name)
# loop over all instances to get all english values from the i18n_fields
if len(i18n_fields):
# First make a row for this model
writer.writerow(['[Model] %s' % model.__name__])
for obj in model.objects.all():
for field in i18n_fields:
writer.writerow(['[Field]
# loop over all pages (HomePage)
writer.writerow(['[Model] %s' % 'HomePage'])
for page in HomePage.objects.all():
# the locale field might still hasn't been set yet if the page wasn't saved after the locales are introduced
if str(page.locale) == 'English' or not page.locale:
writer.writerow(['[Field]
# Get all the .po files
file_paths = []
for root, directories, files in os.walk('./'):
if not re.search(r'^\./(venv|virtualenv|node_modules|\.git)', root):
for filename in files:
if 'django.po' in filename:
# join the two strings in order to form the full filepath.
filepath = os.path.join(root, filename)
file_paths.append(filepath)
# zip all the files together
with ZipFile('translation_files.zip', 'w') as zip:
for file in file_paths:
zip.write(file)
zip.write('translation_fields.csv')
print('Zip file generated')
| true
| true
|
1c4414e04d2fcbc39108f6767504885c71b48ec0
| 1,927
|
py
|
Python
|
modules/AIWorker/backend/celery_interface.py
|
junxnone/aerial_wildlife_detection
|
0eebed2aaf926ceb212b6a2b7a75bb0a82b28a88
|
[
"MIT"
] | 1
|
2021-04-26T22:50:52.000Z
|
2021-04-26T22:50:52.000Z
|
modules/AIWorker/backend/celery_interface.py
|
junxnone/aerial_wildlife_detection
|
0eebed2aaf926ceb212b6a2b7a75bb0a82b28a88
|
[
"MIT"
] | null | null | null |
modules/AIWorker/backend/celery_interface.py
|
junxnone/aerial_wildlife_detection
|
0eebed2aaf926ceb212b6a2b7a75bb0a82b28a88
|
[
"MIT"
] | 2
|
2021-04-15T17:26:40.000Z
|
2021-04-15T17:26:53.000Z
|
'''
Wrapper for the Celery message broker concerning
the AIWorker(s).
2019-20 Benjamin Kellenberger
'''
import os
from celery import current_app
from kombu.common import Broadcast
from constants.version import AIDE_VERSION
from modules.AIWorker.app import AIWorker
from util.configDef import Config
# init AIWorker
modules = os.environ['AIDE_MODULES']
passiveMode = (os.environ['PASSIVE_MODE']=='1' if 'PASSIVE_MODE' in os.environ else False) or not('aiworker' in modules.lower())
worker = AIWorker(Config(), passiveMode)
@current_app.task(name='AIWorker.aide_internal_notify')
def aide_internal_notify(message):
return worker.aide_internal_notify(message)
@current_app.task(name='AIWorker.call_train', rate_limit=1)
def call_train(data, index, epoch, numEpochs, project):
is_subset = (len(data) > 1)
if index < len(data):
return worker.call_train(data[index], epoch, numEpochs, project, is_subset)
else:
# worker not needed
print("[{}] Subset {} requested, but only {} chunk(s) provided. Skipping...".format(
project,
index, len(data)
))
return 0
@current_app.task(name='AIWorker.call_average_model_states', rate_limit=1)
def call_average_model_states(blank, epoch, numEpochs, project, *args):
return worker.call_average_model_states(epoch, numEpochs, project)
@current_app.task(name='AIWorker.call_inference')
def call_inference(data, index, epoch, numEpochs, project):
if index < len(data):
return worker.call_inference(data[index], epoch, numEpochs, project)
else:
# worker not needed
print("[{}] Subset {} requested, but only {} chunk(s) provided. Skipping...".format(
project,
index, len(data)
))
return 0
@current_app.task(name='AIWorker.verify_model_state')
def verify_model_state(project):
return worker.verify_model_state(project)
| 31.080645
| 128
| 0.705241
|
import os
from celery import current_app
from kombu.common import Broadcast
from constants.version import AIDE_VERSION
from modules.AIWorker.app import AIWorker
from util.configDef import Config
modules = os.environ['AIDE_MODULES']
passiveMode = (os.environ['PASSIVE_MODE']=='1' if 'PASSIVE_MODE' in os.environ else False) or not('aiworker' in modules.lower())
worker = AIWorker(Config(), passiveMode)
@current_app.task(name='AIWorker.aide_internal_notify')
def aide_internal_notify(message):
return worker.aide_internal_notify(message)
@current_app.task(name='AIWorker.call_train', rate_limit=1)
def call_train(data, index, epoch, numEpochs, project):
is_subset = (len(data) > 1)
if index < len(data):
return worker.call_train(data[index], epoch, numEpochs, project, is_subset)
else:
print("[{}] Subset {} requested, but only {} chunk(s) provided. Skipping...".format(
project,
index, len(data)
))
return 0
@current_app.task(name='AIWorker.call_average_model_states', rate_limit=1)
def call_average_model_states(blank, epoch, numEpochs, project, *args):
return worker.call_average_model_states(epoch, numEpochs, project)
@current_app.task(name='AIWorker.call_inference')
def call_inference(data, index, epoch, numEpochs, project):
if index < len(data):
return worker.call_inference(data[index], epoch, numEpochs, project)
else:
print("[{}] Subset {} requested, but only {} chunk(s) provided. Skipping...".format(
project,
index, len(data)
))
return 0
@current_app.task(name='AIWorker.verify_model_state')
def verify_model_state(project):
return worker.verify_model_state(project)
| true
| true
|
1c44168c82761f7500b7377a312882fa34c63c3c
| 2,479
|
py
|
Python
|
scripts/empirical/generate_hcp_surrogates.py
|
netneurolab/markello_spatialnulls
|
06eb614af626791d55be0e1c8fc3694fa0771c67
|
[
"BSD-3-Clause"
] | 8
|
2020-08-17T13:00:26.000Z
|
2022-01-09T05:37:44.000Z
|
scripts/empirical/generate_hcp_surrogates.py
|
netneurolab/markello_spatialnulls
|
06eb614af626791d55be0e1c8fc3694fa0771c67
|
[
"BSD-3-Clause"
] | 1
|
2021-02-24T19:28:37.000Z
|
2021-02-24T19:28:37.000Z
|
scripts/empirical/generate_hcp_surrogates.py
|
netneurolab/markello_spatialnulls
|
06eb614af626791d55be0e1c8fc3694fa0771c67
|
[
"BSD-3-Clause"
] | 3
|
2020-08-27T20:00:04.000Z
|
2021-01-30T01:55:53.000Z
|
# -*- coding: utf-8 -*-
"""
Creates surrogate maps for HCP myelin data using Burt 2018 + 2020 methods. In
both cases, surrogate maps are stored as resampling arrays of the original maps
and are saved to `data/derivatives/surrogates/<atlas>/<method>/hcp`.
"""
from pathlib import Path
from joblib import Parallel, delayed
from parspin import surrogates, utils as putils
ROIDIR = Path('./data/raw/rois').resolve()
HCPDIR = Path('./data/derivatives/hcp').resolve()
DISTDIR = Path('./data/derivatives/geodesic').resolve()
SURRDIR = Path('./data/derivatives/surrogates').resolve()
SEED = 1234
N_PROC = 36
N_PERM = 10000
def burt2018_surrogates(name, scale):
"""
Generates surrogates according to Burt et al., 2018, Nat Neuro
Parameters
----------
atlas : {'atl-cammoun2012', 'atl-schaefer2018'}, str
Name of atlas for which to load data
scale : str
Scale of atlas to use
"""
fn = SURRDIR / name / 'burt2018' / 'hcp' / f'{scale}_surrogates.csv'
if fn.exists():
return
# load data + distance matrix for given parcellation
lh, rh = surrogates.load_data(HCPDIR, name, scale)[:-1]
dlh, drh = surrogates.load_dist(DISTDIR, name, scale)
# generate surrogates and save to disk
surrogates.burt2018_surrogates(lh, rh, dlh, drh, fname=fn, n_perm=N_PERM)
def burt2020_surrogates(name, scale):
"""
Generates surrogates according to Burt et al., 2020, NeuroImage
Parameters
----------
atlas : {'atl-cammoun2012', 'atl-schaefer2018'}, str
Name of atlas for which to load data
scale : str
Scale of atlas to use
"""
fn = SURRDIR / name / 'burt2020' / 'hcp' / f'{scale}_surrogates.csv'
if fn.exists():
return
# load data + distance matrix for given parcellation
lh, rh = surrogates.load_data(HCPDIR, name, scale)[:-1]
dlh, drh = surrogates.load_dist(DISTDIR, name, scale)
# generate surrogates and save to disk
surrogates.burt2020_surrogates(lh, rh, dlh, drh, fname=fn,
n_perm=N_PERM, seed=SEED)
if __name__ == '__main__':
# get cammoun + schaefer parcellations
parcellations = putils.get_cammoun_schaefer(data_dir=ROIDIR)
for func in (burt2018_surrogates, burt2020_surrogates):
Parallel(n_jobs=N_PROC)(
delayed(func)(name, scale)
for (name, annotations) in parcellations.items()
for (scale, annot) in annotations.items()
)
| 30.231707
| 79
| 0.657523
|
from pathlib import Path
from joblib import Parallel, delayed
from parspin import surrogates, utils as putils
ROIDIR = Path('./data/raw/rois').resolve()
HCPDIR = Path('./data/derivatives/hcp').resolve()
DISTDIR = Path('./data/derivatives/geodesic').resolve()
SURRDIR = Path('./data/derivatives/surrogates').resolve()
SEED = 1234
N_PROC = 36
N_PERM = 10000
def burt2018_surrogates(name, scale):
fn = SURRDIR / name / 'burt2018' / 'hcp' / f'{scale}_surrogates.csv'
if fn.exists():
return
lh, rh = surrogates.load_data(HCPDIR, name, scale)[:-1]
dlh, drh = surrogates.load_dist(DISTDIR, name, scale)
surrogates.burt2018_surrogates(lh, rh, dlh, drh, fname=fn, n_perm=N_PERM)
def burt2020_surrogates(name, scale):
fn = SURRDIR / name / 'burt2020' / 'hcp' / f'{scale}_surrogates.csv'
if fn.exists():
return
lh, rh = surrogates.load_data(HCPDIR, name, scale)[:-1]
dlh, drh = surrogates.load_dist(DISTDIR, name, scale)
surrogates.burt2020_surrogates(lh, rh, dlh, drh, fname=fn,
n_perm=N_PERM, seed=SEED)
if __name__ == '__main__':
parcellations = putils.get_cammoun_schaefer(data_dir=ROIDIR)
for func in (burt2018_surrogates, burt2020_surrogates):
Parallel(n_jobs=N_PROC)(
delayed(func)(name, scale)
for (name, annotations) in parcellations.items()
for (scale, annot) in annotations.items()
)
| true
| true
|
1c4416da16e0e24c7acaf69acc78211ad072d992
| 3,296
|
py
|
Python
|
update_windows_mappings.py
|
jean/tzlocal
|
37b49de83103f81c5e3f414eacf265972b85f9af
|
[
"MIT"
] | null | null | null |
update_windows_mappings.py
|
jean/tzlocal
|
37b49de83103f81c5e3f414eacf265972b85f9af
|
[
"MIT"
] | null | null | null |
update_windows_mappings.py
|
jean/tzlocal
|
37b49de83103f81c5e3f414eacf265972b85f9af
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# This script generates the mapping between MS Windows timezone names and
# tzdata/Olsen timezone names, by retrieving a file:
# http://unicode.org/cldr/data/common/supplemental/supplementalData.xml
# and parsing it, and from this generating the file windows_tz.py.
#
# It must be run with Python 3.
import ftplib
import logging
from io import BytesIO
from pprint import pprint
import tarfile
from urllib.parse import urlparse
from urllib.request import urlopen
from xml.dom import minidom
WIN_ZONES_URL = 'http://unicode.org/repos/cldr/trunk/common/supplemental/windowsZones.xml'
ZONEINFO_URL = 'ftp://ftp.iana.org/tz/tzdata-latest.tar.gz'
logging.basicConfig(level=logging.INFO)
log = logging.getLogger()
def update_old_names():
"""Fetches the list of old tz names and returns a mapping"""
url = urlparse(ZONEINFO_URL)
log.info('Connecting to %s' % url.netloc)
ftp = ftplib.FTP(url.netloc)
ftp.login()
gzfile = BytesIO()
log.info('Fetching zoneinfo database')
ftp.retrbinary('RETR ' + url.path, gzfile.write)
gzfile.seek(0)
log.info('Extracting backwards data')
archive = tarfile.open(mode="r:gz", fileobj=gzfile)
backward = {}
for line in archive.extractfile('backward').readlines():
if line[0] == '#':
continue
if len(line.strip()) == 0:
continue
parts = line.split()
if parts[0] != b'Link':
continue
backward[parts[2].decode('ascii')] = parts[1].decode('ascii')
return backward
def update_windows_zones():
backward = update_old_names()
log.info('Fetching Windows mapping info from unicode.org')
source = urlopen(WIN_ZONES_URL).read()
dom = minidom.parseString(source)
for element in dom.getElementsByTagName('mapTimezones'):
if element.getAttribute('type') == 'windows':
break
log.info('Making windows mapping')
win_tz = {}
tz_win = {}
for mapping in element.getElementsByTagName('mapZone'):
if mapping.getAttribute('territory') == '001':
win_tz[mapping.getAttribute('other')] = mapping.getAttribute('type').split(' ')[0]
if win_tz[mapping.getAttribute('other')].startswith('Etc'):
print (win_tz[mapping.getAttribute('other')], mapping.getAttribute('type').split(' ')[0])
for tz_name in mapping.getAttribute('type').split(' '):
tz_win[tz_name] = mapping.getAttribute('other')
log.info('Adding backwards data')
# Map in the backwards compatible zone names
for backward_compat_name, standard_name in backward.items():
win_zone = tz_win.get(standard_name, None)
if win_zone:
tz_win[backward_compat_name] = win_zone
# Etc/UTC is a common but non-standard alias for Etc/GMT:
tz_win['Etc/UTC'] = 'UTC'
log.info('Writing mapping')
with open('tzlocal/windows_tz.py', "wt") as out:
out.write("# This file is autogenerated by the update_windows_mapping.py script\n"
"# Do not edit.\nwin_tz = ")
pprint(win_tz, out)
out.write("\n# Old name for the win_tz variable:\ntz_names = win_tz\n\ntz_win = ")
pprint(tz_win, out)
log.info('Done')
if __name__ == '__main__':
update_windows_zones()
| 32.313725
| 105
| 0.664138
|
import ftplib
import logging
from io import BytesIO
from pprint import pprint
import tarfile
from urllib.parse import urlparse
from urllib.request import urlopen
from xml.dom import minidom
WIN_ZONES_URL = 'http://unicode.org/repos/cldr/trunk/common/supplemental/windowsZones.xml'
ZONEINFO_URL = 'ftp://ftp.iana.org/tz/tzdata-latest.tar.gz'
logging.basicConfig(level=logging.INFO)
log = logging.getLogger()
def update_old_names():
url = urlparse(ZONEINFO_URL)
log.info('Connecting to %s' % url.netloc)
ftp = ftplib.FTP(url.netloc)
ftp.login()
gzfile = BytesIO()
log.info('Fetching zoneinfo database')
ftp.retrbinary('RETR ' + url.path, gzfile.write)
gzfile.seek(0)
log.info('Extracting backwards data')
archive = tarfile.open(mode="r:gz", fileobj=gzfile)
backward = {}
for line in archive.extractfile('backward').readlines():
if line[0] == '#':
continue
if len(line.strip()) == 0:
continue
parts = line.split()
if parts[0] != b'Link':
continue
backward[parts[2].decode('ascii')] = parts[1].decode('ascii')
return backward
def update_windows_zones():
backward = update_old_names()
log.info('Fetching Windows mapping info from unicode.org')
source = urlopen(WIN_ZONES_URL).read()
dom = minidom.parseString(source)
for element in dom.getElementsByTagName('mapTimezones'):
if element.getAttribute('type') == 'windows':
break
log.info('Making windows mapping')
win_tz = {}
tz_win = {}
for mapping in element.getElementsByTagName('mapZone'):
if mapping.getAttribute('territory') == '001':
win_tz[mapping.getAttribute('other')] = mapping.getAttribute('type').split(' ')[0]
if win_tz[mapping.getAttribute('other')].startswith('Etc'):
print (win_tz[mapping.getAttribute('other')], mapping.getAttribute('type').split(' ')[0])
for tz_name in mapping.getAttribute('type').split(' '):
tz_win[tz_name] = mapping.getAttribute('other')
log.info('Adding backwards data')
for backward_compat_name, standard_name in backward.items():
win_zone = tz_win.get(standard_name, None)
if win_zone:
tz_win[backward_compat_name] = win_zone
tz_win['Etc/UTC'] = 'UTC'
log.info('Writing mapping')
with open('tzlocal/windows_tz.py', "wt") as out:
out.write("# This file is autogenerated by the update_windows_mapping.py script\n"
"# Do not edit.\nwin_tz = ")
pprint(win_tz, out)
out.write("\n# Old name for the win_tz variable:\ntz_names = win_tz\n\ntz_win = ")
pprint(tz_win, out)
log.info('Done')
if __name__ == '__main__':
update_windows_zones()
| true
| true
|
1c44181b13c6e5605edca9644c493c62871b48d8
| 900
|
py
|
Python
|
todoism/apis/v1/errors.py
|
zhaofangfang1991/airsupport-
|
b2599091dae6105ad7a01444acb9ab53d273675e
|
[
"MIT"
] | null | null | null |
todoism/apis/v1/errors.py
|
zhaofangfang1991/airsupport-
|
b2599091dae6105ad7a01444acb9ab53d273675e
|
[
"MIT"
] | null | null | null |
todoism/apis/v1/errors.py
|
zhaofangfang1991/airsupport-
|
b2599091dae6105ad7a01444acb9ab53d273675e
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from flask import jsonify
from werkzeug.http import HTTP_STATUS_CODES
from todoism.apis.v1 import api_v1
def api_abort(code, message=None, **kwargs):
if message is None:
message = HTTP_STATUS_CODES.get(code, '')
response = jsonify(code=code, message=message, **kwargs)
response.status_code = code
return response # You can also just return (response, code) tuple
def invalid_token():
response = api_abort(401, error='invalid_token', error_description='Either the token was expired or invalid.')
response.headers['WWW-Authenticate'] = 'Bearer'
return response
def token_missing():
response = api_abort(401)
response.headers['WWW-Authenticate'] = 'Bearer'
return response
class ValidationError(ValueError):
pass
@api_v1.errorhandler(ValidationError)
def validation_error(e):
return api_abort(400, e.args[0])
| 24.324324
| 114
| 0.718889
|
from flask import jsonify
from werkzeug.http import HTTP_STATUS_CODES
from todoism.apis.v1 import api_v1
def api_abort(code, message=None, **kwargs):
if message is None:
message = HTTP_STATUS_CODES.get(code, '')
response = jsonify(code=code, message=message, **kwargs)
response.status_code = code
return response
def invalid_token():
response = api_abort(401, error='invalid_token', error_description='Either the token was expired or invalid.')
response.headers['WWW-Authenticate'] = 'Bearer'
return response
def token_missing():
response = api_abort(401)
response.headers['WWW-Authenticate'] = 'Bearer'
return response
class ValidationError(ValueError):
pass
@api_v1.errorhandler(ValidationError)
def validation_error(e):
return api_abort(400, e.args[0])
| true
| true
|
1c441909547247dc21e1764be21febe8946d6c2e
| 6,424
|
py
|
Python
|
global_directions/cog_predict.py
|
bfirsh/StyleCLIP
|
164fa8497ea91ea184c0488fcc5e3e14f709561a
|
[
"MIT"
] | null | null | null |
global_directions/cog_predict.py
|
bfirsh/StyleCLIP
|
164fa8497ea91ea184c0488fcc5e3e14f709561a
|
[
"MIT"
] | null | null | null |
global_directions/cog_predict.py
|
bfirsh/StyleCLIP
|
164fa8497ea91ea184c0488fcc5e3e14f709561a
|
[
"MIT"
] | null | null | null |
import tempfile
from pathlib import Path
import os
from argparse import Namespace
import time
import dlib
import os
import sys
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
import tensorflow as tf
import numpy as np
import torch
import clip
from PIL import Image
import pickle
import copy
import matplotlib.pyplot as plt
from MapTS import GetFs, GetBoundary, GetDt
from manipulate import Manipulator
from dnnlib import tflib
sys.path.insert(0, "/content")
sys.path.insert(0, "/content/encoder4editing")
from encoder4editing.utils.common import tensor2im
from encoder4editing.utils.alignment import align_face
from encoder4editing.models.psp import pSp
import cog
class Model(cog.Model):
def setup(self):
print("starting setup")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model, self.preprocess = clip.load(
"ViT-B/32", device=self.device, jit=False
)
self.graph = tf.get_default_graph()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
self.sess = tf.Session(
graph=self.graph, config=tf.ConfigProto(gpu_options=gpu_options)
)
experiment_type = "ffhq_encode"
self.experiment_args = {"model_path": "e4e_ffhq_encode.pt"}
self.experiment_args["transform"] = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
self.resize_dims = (256, 256)
model_path = self.experiment_args["model_path"]
ckpt = torch.load(model_path, map_location="cpu")
opts = ckpt["opts"]
# pprint.pprint(opts) # Display full options used
# update the training options
opts["checkpoint_path"] = model_path
opts = Namespace(**opts)
self.net = pSp(opts)
self.net.eval()
self.net.cuda()
self.shape_predictor = dlib.shape_predictor(
"/content/shape_predictor_68_face_landmarks.dat"
)
with self.graph.as_default(), self.sess.as_default():
#tflib.init_tf()
self.M = Manipulator(dataset_name="ffhq", sess=self.sess)
self.fs3 = np.load("./npy/ffhq/fs3.npy")
np.set_printoptions(suppress=True)
print("setup complete")
@cog.input("input", type=Path, help="Input image")
@cog.input("neutral", type=str, help="Neutral image description")
@cog.input("target", type=str, help="Target image description")
@cog.input(
"manipulation_strength",
type=float,
min=-10,
max=10,
default=4.1,
help="The higher the manipulation strength, the closer the generated image becomes to the target description. Negative values moves the generated image further from the target description",
)
@cog.input(
"disentanglement_threshold",
type=float,
min=0.08,
max=0.3,
default=0.15,
help="The higher the disentanglement threshold, the more specific the changes are to the target attribute. Lower values mean that broader changes are made to the input image",
)
def predict(
self,
input,
neutral,
target,
manipulation_strength,
disentanglement_threshold,
):
# @title Align image
original_image = Image.open(str(input))
original_image = original_image.convert("RGB")
input_image = self.run_alignment(str(input))
input_image = original_image
input_image.resize(self.resize_dims)
img_transforms = self.experiment_args["transform"]
transformed_image = img_transforms(input_image)
with torch.no_grad():
images, latents = self.run_on_batch(transformed_image.unsqueeze(0))
result_image, latent = images[0], latents[0]
print("latents", latents)
print(transformed_image.shape, result_image.shape)
w_plus = latents.cpu().detach().numpy()
with self.graph.as_default(), self.sess.as_default():
dlatents_loaded = self.M.W2S(w_plus)
#print("w_plus, dlatents_loaded", w_plus, dlatents_loaded)
img_index = 0
w_plus=latents.cpu().detach().numpy()
with self.graph.as_default(), self.sess.as_default():
dlatents_loaded=self.M.W2S(w_plus)
img_indexs=[img_index]
dlatent_tmp=[tmp[img_indexs] for tmp in dlatents_loaded]
with self.graph.as_default(), self.sess.as_default():
self.M.num_images = len(img_indexs)
self.M.alpha = [0]
self.M.manipulate_layers = [0]
with self.graph.as_default(), self.sess.as_default():
codes, out = self.M.EditOneC(0, dlatent_tmp)
original = Image.fromarray(out[0, 0]).resize((512, 512))
with self.graph.as_default(), self.sess.as_default():
self.M.manipulate_layers = None
classnames = [target, neutral]
dt = GetDt(classnames, self.model)
with self.graph.as_default(), self.sess.as_default():
self.M.alpha = [manipulation_strength]
boundary_tmp2, c = GetBoundary(
self.fs3, dt, self.M, threshold=disentanglement_threshold
)
codes = self.M.MSCode(dlatent_tmp, boundary_tmp2)
out = self.M.GenerateImg(codes)
generated = Image.fromarray(out[0, 0]) # .resize((512,512))
out_path = Path(tempfile.mkdtemp()) / "out.jpg"
generated.save(str(out_path))
return out_path
def run_alignment(self, image_path):
aligned_image = align_face(filepath=image_path, predictor=self.shape_predictor)
print("Aligned image has shape: {}".format(aligned_image.size))
return aligned_image
def run_on_batch(self, inputs):
images, latents = self.net(
inputs.to("cuda").float(), randomize_noise=False, return_latents=True
)
return images, latents
def concat_images(*images):
width = 0
for im in images:
width += im.width
height = max([im.height for im in images])
concat = Image.new("RGB", (width, height))
offset = 0
for im in images:
concat.paste(im, (offset, 0))
offset += im.width
return concat
| 32.444444
| 197
| 0.636986
|
import tempfile
from pathlib import Path
import os
from argparse import Namespace
import time
import dlib
import os
import sys
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
import tensorflow as tf
import numpy as np
import torch
import clip
from PIL import Image
import pickle
import copy
import matplotlib.pyplot as plt
from MapTS import GetFs, GetBoundary, GetDt
from manipulate import Manipulator
from dnnlib import tflib
sys.path.insert(0, "/content")
sys.path.insert(0, "/content/encoder4editing")
from encoder4editing.utils.common import tensor2im
from encoder4editing.utils.alignment import align_face
from encoder4editing.models.psp import pSp
import cog
class Model(cog.Model):
def setup(self):
print("starting setup")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model, self.preprocess = clip.load(
"ViT-B/32", device=self.device, jit=False
)
self.graph = tf.get_default_graph()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
self.sess = tf.Session(
graph=self.graph, config=tf.ConfigProto(gpu_options=gpu_options)
)
experiment_type = "ffhq_encode"
self.experiment_args = {"model_path": "e4e_ffhq_encode.pt"}
self.experiment_args["transform"] = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
self.resize_dims = (256, 256)
model_path = self.experiment_args["model_path"]
ckpt = torch.load(model_path, map_location="cpu")
opts = ckpt["opts"]
ckpoint_path"] = model_path
opts = Namespace(**opts)
self.net = pSp(opts)
self.net.eval()
self.net.cuda()
self.shape_predictor = dlib.shape_predictor(
"/content/shape_predictor_68_face_landmarks.dat"
)
with self.graph.as_default(), self.sess.as_default():
self.M = Manipulator(dataset_name="ffhq", sess=self.sess)
self.fs3 = np.load("./npy/ffhq/fs3.npy")
np.set_printoptions(suppress=True)
print("setup complete")
@cog.input("input", type=Path, help="Input image")
@cog.input("neutral", type=str, help="Neutral image description")
@cog.input("target", type=str, help="Target image description")
@cog.input(
"manipulation_strength",
type=float,
min=-10,
max=10,
default=4.1,
help="The higher the manipulation strength, the closer the generated image becomes to the target description. Negative values moves the generated image further from the target description",
)
@cog.input(
"disentanglement_threshold",
type=float,
min=0.08,
max=0.3,
default=0.15,
help="The higher the disentanglement threshold, the more specific the changes are to the target attribute. Lower values mean that broader changes are made to the input image",
)
def predict(
self,
input,
neutral,
target,
manipulation_strength,
disentanglement_threshold,
):
original_image = Image.open(str(input))
original_image = original_image.convert("RGB")
input_image = self.run_alignment(str(input))
input_image = original_image
input_image.resize(self.resize_dims)
img_transforms = self.experiment_args["transform"]
transformed_image = img_transforms(input_image)
with torch.no_grad():
images, latents = self.run_on_batch(transformed_image.unsqueeze(0))
result_image, latent = images[0], latents[0]
print("latents", latents)
print(transformed_image.shape, result_image.shape)
w_plus = latents.cpu().detach().numpy()
with self.graph.as_default(), self.sess.as_default():
dlatents_loaded = self.M.W2S(w_plus)
img_index = 0
w_plus=latents.cpu().detach().numpy()
with self.graph.as_default(), self.sess.as_default():
dlatents_loaded=self.M.W2S(w_plus)
img_indexs=[img_index]
dlatent_tmp=[tmp[img_indexs] for tmp in dlatents_loaded]
with self.graph.as_default(), self.sess.as_default():
self.M.num_images = len(img_indexs)
self.M.alpha = [0]
self.M.manipulate_layers = [0]
with self.graph.as_default(), self.sess.as_default():
codes, out = self.M.EditOneC(0, dlatent_tmp)
original = Image.fromarray(out[0, 0]).resize((512, 512))
with self.graph.as_default(), self.sess.as_default():
self.M.manipulate_layers = None
classnames = [target, neutral]
dt = GetDt(classnames, self.model)
with self.graph.as_default(), self.sess.as_default():
self.M.alpha = [manipulation_strength]
boundary_tmp2, c = GetBoundary(
self.fs3, dt, self.M, threshold=disentanglement_threshold
)
codes = self.M.MSCode(dlatent_tmp, boundary_tmp2)
out = self.M.GenerateImg(codes)
generated = Image.fromarray(out[0, 0])
out_path = Path(tempfile.mkdtemp()) / "out.jpg"
generated.save(str(out_path))
return out_path
def run_alignment(self, image_path):
aligned_image = align_face(filepath=image_path, predictor=self.shape_predictor)
print("Aligned image has shape: {}".format(aligned_image.size))
return aligned_image
def run_on_batch(self, inputs):
images, latents = self.net(
inputs.to("cuda").float(), randomize_noise=False, return_latents=True
)
return images, latents
def concat_images(*images):
width = 0
for im in images:
width += im.width
height = max([im.height for im in images])
concat = Image.new("RGB", (width, height))
offset = 0
for im in images:
concat.paste(im, (offset, 0))
offset += im.width
return concat
| true
| true
|
1c4419cfc5241033119415ac6f09947cc75e8ab2
| 40,341
|
py
|
Python
|
superset/security/manager.py
|
GodelTech/superset
|
da170aa57e94053cf715f7b41b09901c813a149a
|
[
"Apache-2.0"
] | 44
|
2021-04-14T10:53:36.000Z
|
2021-09-11T00:29:50.000Z
|
superset/security/manager.py
|
GodelTech/superset
|
da170aa57e94053cf715f7b41b09901c813a149a
|
[
"Apache-2.0"
] | 60
|
2021-04-09T08:17:13.000Z
|
2022-03-04T07:41:38.000Z
|
superset/security/manager.py
|
GodelTech/superset
|
da170aa57e94053cf715f7b41b09901c813a149a
|
[
"Apache-2.0"
] | 11
|
2021-06-09T08:30:57.000Z
|
2021-11-30T03:16:14.000Z
|
# 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.
# pylint: disable=too-few-public-methods
"""A set of constants and methods to manage permissions and security"""
import logging
import re
from typing import Any, Callable, cast, List, Optional, Set, Tuple, TYPE_CHECKING, Union
from flask import current_app, g
from flask_appbuilder import Model
from flask_appbuilder.security.sqla.manager import SecurityManager
from flask_appbuilder.security.sqla.models import (
assoc_permissionview_role,
assoc_user_role,
PermissionView,
Role,
User,
)
from flask_appbuilder.security.views import (
PermissionModelView,
PermissionViewModelView,
RoleModelView,
UserModelView,
ViewMenuModelView,
)
from flask_appbuilder.widgets import ListWidget
from sqlalchemy import and_, or_
from sqlalchemy.engine.base import Connection
from sqlalchemy.orm import Session
from sqlalchemy.orm.mapper import Mapper
from sqlalchemy.orm.query import Query as SqlaQuery
from superset import sql_parse
from superset.connectors.connector_registry import ConnectorRegistry
from superset.constants import RouteMethod
from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
from superset.exceptions import SupersetSecurityException
from superset.utils.core import DatasourceName, RowLevelSecurityFilterType
if TYPE_CHECKING:
from superset.common.query_context import QueryContext
from superset.connectors.base.models import BaseDatasource
from superset.connectors.druid.models import DruidCluster
from superset.models.dashboard import Dashboard
from superset.models.core import Database
from superset.models.sql_lab import Query
from superset.sql_parse import Table
from superset.viz import BaseViz
logger = logging.getLogger(__name__)
class SupersetSecurityListWidget(ListWidget):
"""
Redeclaring to avoid circular imports
"""
template = "superset/fab_overrides/list.html"
class SupersetRoleListWidget(ListWidget):
"""
Role model view from FAB already uses a custom list widget override
So we override the override
"""
template = "superset/fab_overrides/list_role.html"
def __init__(self, **kwargs: Any) -> None:
kwargs["appbuilder"] = current_app.appbuilder
super().__init__(**kwargs)
UserModelView.list_widget = SupersetSecurityListWidget
RoleModelView.list_widget = SupersetRoleListWidget
PermissionViewModelView.list_widget = SupersetSecurityListWidget
PermissionModelView.list_widget = SupersetSecurityListWidget
# Limiting routes on FAB model views
UserModelView.include_route_methods = RouteMethod.CRUD_SET | {
RouteMethod.ACTION,
RouteMethod.API_READ,
RouteMethod.ACTION_POST,
"userinfo",
}
RoleModelView.include_route_methods = RouteMethod.CRUD_SET
PermissionViewModelView.include_route_methods = {RouteMethod.LIST}
PermissionModelView.include_route_methods = {RouteMethod.LIST}
ViewMenuModelView.include_route_methods = {RouteMethod.LIST}
RoleModelView.list_columns = ["name"]
RoleModelView.edit_columns = ["name", "permissions", "user"]
RoleModelView.related_views = []
class SupersetSecurityManager( # pylint: disable=too-many-public-methods
SecurityManager
):
userstatschartview = None
READ_ONLY_MODEL_VIEWS = {"Database", "DruidClusterModelView", "DynamicPlugin"}
USER_MODEL_VIEWS = {
"UserDBModelView",
"UserLDAPModelView",
"UserOAuthModelView",
"UserOIDModelView",
"UserRemoteUserModelView",
}
GAMMA_READ_ONLY_MODEL_VIEWS = {
"Dataset",
"DruidColumnInlineView",
"DruidDatasourceModelView",
"DruidMetricInlineView",
"Datasource",
} | READ_ONLY_MODEL_VIEWS
ADMIN_ONLY_VIEW_MENUS = {
"AccessRequestsModelView",
"SQL Lab",
"Refresh Druid Metadata",
"ResetPasswordView",
"RoleModelView",
"Log",
"Security",
"Row Level Security",
"Row Level Security Filters",
"RowLevelSecurityFiltersModelView",
} | USER_MODEL_VIEWS
ALPHA_ONLY_VIEW_MENUS = {
"Manage",
"CSS Templates",
"Queries",
"Import dashboards",
"Upload a CSV",
}
ADMIN_ONLY_PERMISSIONS = {
"can_sql_json", # TODO: move can_sql_json to sql_lab role
"can_override_role_permissions",
"can_sync_druid_source",
"can_override_role_permissions",
"can_approve",
"can_update_role",
"all_query_access",
}
READ_ONLY_PERMISSION = {
"can_show",
"can_list",
"can_get",
"can_external_metadata",
"can_read",
}
ALPHA_ONLY_PERMISSIONS = {
"muldelete",
"all_database_access",
"all_datasource_access",
}
OBJECT_SPEC_PERMISSIONS = {
"database_access",
"schema_access",
"datasource_access",
"metric_access",
}
ACCESSIBLE_PERMS = {"can_userinfo", "resetmypassword"}
data_access_permissions = (
"database_access",
"schema_access",
"datasource_access",
"all_datasource_access",
"all_database_access",
"all_query_access",
)
def get_schema_perm( # pylint: disable=no-self-use
self, database: Union["Database", str], schema: Optional[str] = None
) -> Optional[str]:
"""
Return the database specific schema permission.
:param database: The Superset database or database name
:param schema: The Superset schema name
:return: The database specific schema permission
"""
if schema:
return f"[{database}].[{schema}]"
return None
def unpack_schema_perm( # pylint: disable=no-self-use
self, schema_permission: str
) -> Tuple[str, str]:
# [database_name].[schema_name]
schema_name = schema_permission.split(".")[1][1:-1]
database_name = schema_permission.split(".")[0][1:-1]
return database_name, schema_name
def can_access(self, permission_name: str, view_name: str) -> bool:
"""
Return True if the user can access the FAB permission/view, False otherwise.
Note this method adds protection from has_access failing from missing
permission/view entries.
:param permission_name: The FAB permission name
:param view_name: The FAB view-menu name
:returns: Whether the user can access the FAB permission/view
"""
user = g.user
if user.is_anonymous:
return self.is_item_public(permission_name, view_name)
return self._has_view_access(user, permission_name, view_name)
def can_access_all_queries(self) -> bool:
"""
Return True if the user can access all SQL Lab queries, False otherwise.
:returns: Whether the user can access all queries
"""
return self.can_access("all_query_access", "all_query_access")
def can_access_all_datasources(self) -> bool:
"""
Return True if the user can fully access all the Superset datasources, False
otherwise.
:returns: Whether the user can fully access all Superset datasources
"""
return self.can_access("all_datasource_access", "all_datasource_access")
def can_access_all_databases(self) -> bool:
"""
Return True if the user can fully access all the Superset databases, False
otherwise.
:returns: Whether the user can fully access all Superset databases
"""
return self.can_access("all_database_access", "all_database_access")
def can_access_database(self, database: Union["Database", "DruidCluster"]) -> bool:
"""
Return True if the user can fully access the Superset database, False otherwise.
Note for Druid the database is akin to the Druid cluster.
:param database: The Superset database
:returns: Whether the user can fully access the Superset database
"""
return (
self.can_access_all_datasources()
or self.can_access_all_databases()
or self.can_access("database_access", database.perm) # type: ignore
)
def can_access_schema(self, datasource: "BaseDatasource") -> bool:
"""
Return True if the user can fully access the schema associated with the Superset
datasource, False otherwise.
Note for Druid datasources the database and schema are akin to the Druid cluster
and datasource name prefix respectively, i.e., [schema.]datasource.
:param datasource: The Superset datasource
:returns: Whether the user can fully access the datasource's schema
"""
return (
self.can_access_all_datasources()
or self.can_access_database(datasource.database)
or self.can_access("schema_access", datasource.schema_perm or "")
)
def can_access_datasource(self, datasource: "BaseDatasource") -> bool:
"""
Return True if the user can fully access of the Superset datasource, False
otherwise.
:param datasource: The Superset datasource
:returns: Whether the user can fully access the Superset datasource
"""
try:
self.raise_for_access(datasource=datasource)
except SupersetSecurityException:
return False
return True
@staticmethod
def get_datasource_access_error_msg(datasource: "BaseDatasource") -> str:
"""
Return the error message for the denied Superset datasource.
:param datasource: The denied Superset datasource
:returns: The error message
"""
return f"""This endpoint requires the datasource {datasource.name}, database or
`all_datasource_access` permission"""
@staticmethod
def get_datasource_access_link( # pylint: disable=unused-argument
datasource: "BaseDatasource",
) -> Optional[str]:
"""
Return the link for the denied Superset datasource.
:param datasource: The denied Superset datasource
:returns: The access URL
"""
from superset import conf
return conf.get("PERMISSION_INSTRUCTIONS_LINK")
def get_datasource_access_error_object( # pylint: disable=invalid-name
self, datasource: "BaseDatasource"
) -> SupersetError:
"""
Return the error object for the denied Superset datasource.
:param datasource: The denied Superset datasource
:returns: The error object
"""
return SupersetError(
error_type=SupersetErrorType.DATASOURCE_SECURITY_ACCESS_ERROR,
message=self.get_datasource_access_error_msg(datasource),
level=ErrorLevel.ERROR,
extra={
"link": self.get_datasource_access_link(datasource),
"datasource": datasource.name,
},
)
def get_table_access_error_msg( # pylint: disable=no-self-use
self, tables: Set["Table"]
) -> str:
"""
Return the error message for the denied SQL tables.
:param tables: The set of denied SQL tables
:returns: The error message
"""
quoted_tables = [f"`{table}`" for table in tables]
return f"""You need access to the following tables: {", ".join(quoted_tables)},
`all_database_access` or `all_datasource_access` permission"""
def get_table_access_error_object(self, tables: Set["Table"]) -> SupersetError:
"""
Return the error object for the denied SQL tables.
:param tables: The set of denied SQL tables
:returns: The error object
"""
return SupersetError(
error_type=SupersetErrorType.TABLE_SECURITY_ACCESS_ERROR,
message=self.get_table_access_error_msg(tables),
level=ErrorLevel.ERROR,
extra={
"link": self.get_table_access_link(tables),
"tables": [str(table) for table in tables],
},
)
def get_table_access_link( # pylint: disable=unused-argument,no-self-use
self, tables: Set["Table"]
) -> Optional[str]:
"""
Return the access link for the denied SQL tables.
:param tables: The set of denied SQL tables
:returns: The access URL
"""
from superset import conf
return conf.get("PERMISSION_INSTRUCTIONS_LINK")
def can_access_table(self, database: "Database", table: "Table") -> bool:
"""
Return True if the user can access the SQL table, False otherwise.
:param database: The SQL database
:param table: The SQL table
:returns: Whether the user can access the SQL table
"""
try:
self.raise_for_access(database=database, table=table)
except SupersetSecurityException:
return False
return True
def user_view_menu_names(self, permission_name: str) -> Set[str]:
base_query = (
self.get_session.query(self.viewmenu_model.name)
.join(self.permissionview_model)
.join(self.permission_model)
.join(assoc_permissionview_role)
.join(self.role_model)
)
if not g.user.is_anonymous:
# filter by user id
view_menu_names = (
base_query.join(assoc_user_role)
.join(self.user_model)
.filter(self.user_model.id == g.user.id)
.filter(self.permission_model.name == permission_name)
).all()
return {s.name for s in view_menu_names}
# Properly treat anonymous user
public_role = self.get_public_role()
if public_role:
# filter by public role
view_menu_names = (
base_query.filter(self.role_model.id == public_role.id).filter(
self.permission_model.name == permission_name
)
).all()
return {s.name for s in view_menu_names}
return set()
def get_schemas_accessible_by_user(
self, database: "Database", schemas: List[str], hierarchical: bool = True
) -> List[str]:
"""
Return the list of SQL schemas accessible by the user.
:param database: The SQL database
:param schemas: The list of eligible SQL schemas
:param hierarchical: Whether to check using the hierarchical permission logic
:returns: The list of accessible SQL schemas
"""
from superset.connectors.sqla.models import SqlaTable
if hierarchical and self.can_access_database(database):
return schemas
# schema_access
accessible_schemas = {
self.unpack_schema_perm(s)[1]
for s in self.user_view_menu_names("schema_access")
if s.startswith(f"[{database}].")
}
# datasource_access
perms = self.user_view_menu_names("datasource_access")
if perms:
tables = (
self.get_session.query(SqlaTable.schema)
.filter(SqlaTable.database_id == database.id)
.filter(SqlaTable.schema.isnot(None))
.filter(SqlaTable.schema != "")
.filter(or_(SqlaTable.perm.in_(perms)))
.distinct()
)
accessible_schemas.update([table.schema for table in tables])
return [s for s in schemas if s in accessible_schemas]
def get_datasources_accessible_by_user( # pylint: disable=invalid-name
self,
database: "Database",
datasource_names: List[DatasourceName],
schema: Optional[str] = None,
) -> List[DatasourceName]:
"""
Return the list of SQL tables accessible by the user.
:param database: The SQL database
:param datasource_names: The list of eligible SQL tables w/ schema
:param schema: The fallback SQL schema if not present in the table name
:returns: The list of accessible SQL tables w/ schema
"""
if self.can_access_database(database):
return datasource_names
if schema:
schema_perm = self.get_schema_perm(database, schema)
if schema_perm and self.can_access("schema_access", schema_perm):
return datasource_names
user_perms = self.user_view_menu_names("datasource_access")
schema_perms = self.user_view_menu_names("schema_access")
user_datasources = ConnectorRegistry.query_datasources_by_permissions(
self.get_session, database, user_perms, schema_perms
)
if schema:
names = {d.table_name for d in user_datasources if d.schema == schema}
return [d for d in datasource_names if d in names]
full_names = {d.full_name for d in user_datasources}
return [d for d in datasource_names if f"[{database}].[{d}]" in full_names]
def merge_perm(self, permission_name: str, view_menu_name: str) -> None:
"""
Add the FAB permission/view-menu.
:param permission_name: The FAB permission name
:param view_menu_names: The FAB view-menu name
:see: SecurityManager.add_permission_view_menu
"""
logger.warning(
"This method 'merge_perm' is deprecated use add_permission_view_menu"
)
self.add_permission_view_menu(permission_name, view_menu_name)
def _is_user_defined_permission(self, perm: Model) -> bool:
"""
Return True if the FAB permission is user defined, False otherwise.
:param perm: The FAB permission
:returns: Whether the FAB permission is user defined
"""
return perm.permission.name in self.OBJECT_SPEC_PERMISSIONS
def create_custom_permissions(self) -> None:
"""
Create custom FAB permissions.
"""
self.add_permission_view_menu("all_datasource_access", "all_datasource_access")
self.add_permission_view_menu("all_database_access", "all_database_access")
self.add_permission_view_menu("all_query_access", "all_query_access")
def create_missing_perms(self) -> None:
"""
Creates missing FAB permissions for datasources, schemas and metrics.
"""
from superset.models import core as models
logger.info("Fetching a set of all perms to lookup which ones are missing")
all_pvs = set()
for pv in self.get_session.query(self.permissionview_model).all():
if pv.permission and pv.view_menu:
all_pvs.add((pv.permission.name, pv.view_menu.name))
def merge_pv(view_menu: str, perm: str) -> None:
"""Create permission view menu only if it doesn't exist"""
if view_menu and perm and (view_menu, perm) not in all_pvs:
self.add_permission_view_menu(view_menu, perm)
logger.info("Creating missing datasource permissions.")
datasources = ConnectorRegistry.get_all_datasources(self.get_session)
for datasource in datasources:
merge_pv("datasource_access", datasource.get_perm())
merge_pv("schema_access", datasource.get_schema_perm())
logger.info("Creating missing database permissions.")
databases = self.get_session.query(models.Database).all()
for database in databases:
merge_pv("database_access", database.perm)
def clean_perms(self) -> None:
"""
Clean up the FAB faulty permissions.
"""
logger.info("Cleaning faulty perms")
sesh = self.get_session
pvms = sesh.query(PermissionView).filter(
or_(
PermissionView.permission # pylint: disable=singleton-comparison
== None,
PermissionView.view_menu # pylint: disable=singleton-comparison
== None,
)
)
deleted_count = pvms.delete()
sesh.commit()
if deleted_count:
logger.info("Deleted %i faulty permissions", deleted_count)
def sync_role_definitions(self) -> None:
"""
Initialize the Superset application with security roles and such.
"""
from superset import conf
logger.info("Syncing role definition")
self.create_custom_permissions()
# Creating default roles
self.set_role("Admin", self._is_admin_pvm)
self.set_role("Alpha", self._is_alpha_pvm)
self.set_role("Gamma", self._is_gamma_pvm)
self.set_role("granter", self._is_granter_pvm)
self.set_role("sql_lab", self._is_sql_lab_pvm)
# Configure public role
if conf["PUBLIC_ROLE_LIKE"]:
self.copy_role(conf["PUBLIC_ROLE_LIKE"], self.auth_role_public, merge=True)
if conf.get("PUBLIC_ROLE_LIKE_GAMMA", False):
logger.warning(
"The config `PUBLIC_ROLE_LIKE_GAMMA` is deprecated and will be removed "
"in Superset 1.0. Please use `PUBLIC_ROLE_LIKE` instead."
)
self.copy_role("Gamma", self.auth_role_public, merge=True)
self.create_missing_perms()
# commit role and view menu updates
self.get_session.commit()
self.clean_perms()
def _get_pvms_from_builtin_role(self, role_name: str) -> List[PermissionView]:
"""
Gets a list of model PermissionView permissions infered from a builtin role
definition
"""
role_from_permissions_names = self.builtin_roles.get(role_name, [])
all_pvms = self.get_session.query(PermissionView).all()
role_from_permissions = []
for pvm_regex in role_from_permissions_names:
view_name_regex = pvm_regex[0]
permission_name_regex = pvm_regex[1]
for pvm in all_pvms:
if re.match(view_name_regex, pvm.view_menu.name) and re.match(
permission_name_regex, pvm.permission.name
):
if pvm not in role_from_permissions:
role_from_permissions.append(pvm)
return role_from_permissions
def find_roles_by_id(self, role_ids: List[int]) -> List[Role]:
"""
Find a List of models by a list of ids, if defined applies `base_filter`
"""
query = self.get_session.query(Role).filter(Role.id.in_(role_ids))
return query.all()
def copy_role(
self, role_from_name: str, role_to_name: str, merge: bool = True
) -> None:
"""
Copies permissions from a role to another.
Note: Supports regex defined builtin roles
:param role_from_name: The FAB role name from where the permissions are taken
:param role_to_name: The FAB role name from where the permissions are copied to
:param merge: If merge is true, keep data access permissions
if they already exist on the target role
"""
logger.info("Copy/Merge %s to %s", role_from_name, role_to_name)
# If it's a builtin role extract permissions from it
if role_from_name in self.builtin_roles:
role_from_permissions = self._get_pvms_from_builtin_role(role_from_name)
else:
role_from_permissions = list(self.find_role(role_from_name).permissions)
role_to = self.add_role(role_to_name)
# If merge, recover existing data access permissions
if merge:
for permission_view in role_to.permissions:
if (
permission_view not in role_from_permissions
and permission_view.permission.name in self.data_access_permissions
):
role_from_permissions.append(permission_view)
role_to.permissions = role_from_permissions
self.get_session.merge(role_to)
self.get_session.commit()
def set_role(
self, role_name: str, pvm_check: Callable[[PermissionView], bool]
) -> None:
"""
Set the FAB permission/views for the role.
:param role_name: The FAB role name
:param pvm_check: The FAB permission/view check
"""
logger.info("Syncing %s perms", role_name)
pvms = self.get_session.query(PermissionView).all()
pvms = [p for p in pvms if p.permission and p.view_menu]
role = self.add_role(role_name)
role_pvms = [
permission_view for permission_view in pvms if pvm_check(permission_view)
]
role.permissions = role_pvms
self.get_session.merge(role)
self.get_session.commit()
def _is_admin_only(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is accessible to only Admin users,
False otherwise.
Note readonly operations on read only model views are allowed only for admins.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is accessible to only Admin users
"""
if (
pvm.view_menu.name in self.READ_ONLY_MODEL_VIEWS
and pvm.permission.name not in self.READ_ONLY_PERMISSION
):
return True
return (
pvm.view_menu.name in self.ADMIN_ONLY_VIEW_MENUS
or pvm.permission.name in self.ADMIN_ONLY_PERMISSIONS
)
def _is_alpha_only(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is accessible to only Alpha users,
False otherwise.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is accessible to only Alpha users
"""
if (
pvm.view_menu.name in self.GAMMA_READ_ONLY_MODEL_VIEWS
and pvm.permission.name not in self.READ_ONLY_PERMISSION
):
return True
return (
pvm.view_menu.name in self.ALPHA_ONLY_VIEW_MENUS
or pvm.permission.name in self.ALPHA_ONLY_PERMISSIONS
)
def _is_accessible_to_all(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is accessible to all, False
otherwise.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is accessible to all users
"""
return pvm.permission.name in self.ACCESSIBLE_PERMS
def _is_admin_pvm(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is Admin user related, False
otherwise.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is Admin related
"""
return not self._is_user_defined_permission(pvm)
def _is_alpha_pvm(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is Alpha user related, False
otherwise.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is Alpha related
"""
return not (
self._is_user_defined_permission(pvm) or self._is_admin_only(pvm)
) or self._is_accessible_to_all(pvm)
def _is_gamma_pvm(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is Gamma user related, False
otherwise.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is Gamma related
"""
return not (
self._is_user_defined_permission(pvm)
or self._is_admin_only(pvm)
or self._is_alpha_only(pvm)
) or self._is_accessible_to_all(pvm)
def _is_sql_lab_pvm(self, pvm: PermissionView) -> bool:
"""
Return True if the FAB permission/view is SQL Lab related, False
otherwise.
:param pvm: The FAB permission/view
:returns: Whether the FAB object is SQL Lab related
"""
return (
pvm.view_menu.name
in {"SQL Lab", "SQL Editor", "Query Search", "Saved Queries"}
or pvm.permission.name
in {
"can_sql_json",
"can_csv",
"can_search_queries",
"can_sqllab_viz",
"can_sqllab_table_viz",
"can_sqllab",
}
or (
pvm.view_menu.name in self.USER_MODEL_VIEWS
and pvm.permission.name == "can_list"
)
)
def _is_granter_pvm( # pylint: disable=no-self-use
self, pvm: PermissionView
) -> bool:
"""
Return True if the user can grant the FAB permission/view, False
otherwise.
:param pvm: The FAB permission/view
:returns: Whether the user can grant the FAB permission/view
"""
return pvm.permission.name in {"can_override_role_permissions", "can_approve"}
def set_perm( # pylint: disable=no-self-use,unused-argument
self, mapper: Mapper, connection: Connection, target: "BaseDatasource"
) -> None:
"""
Set the datasource permissions.
:param mapper: The table mapper
:param connection: The DB-API connection
:param target: The mapped instance being persisted
"""
link_table = target.__table__ # pylint: disable=no-member
if target.perm != target.get_perm():
connection.execute(
link_table.update()
.where(link_table.c.id == target.id)
.values(perm=target.get_perm())
)
if (
hasattr(target, "schema_perm")
and target.schema_perm != target.get_schema_perm()
):
connection.execute(
link_table.update()
.where(link_table.c.id == target.id)
.values(schema_perm=target.get_schema_perm())
)
pvm_names = []
if target.__tablename__ in {"dbs", "clusters"}:
pvm_names.append(("database_access", target.get_perm()))
else:
pvm_names.append(("datasource_access", target.get_perm()))
if target.schema:
pvm_names.append(("schema_access", target.get_schema_perm()))
# TODO(bogdan): modify slice permissions as well.
for permission_name, view_menu_name in pvm_names:
permission = self.find_permission(permission_name)
view_menu = self.find_view_menu(view_menu_name)
pv = None
if not permission:
permission_table = (
self.permission_model.__table__ # pylint: disable=no-member
)
connection.execute(
permission_table.insert().values(name=permission_name)
)
permission = self.find_permission(permission_name)
if not view_menu:
view_menu_table = (
self.viewmenu_model.__table__ # pylint: disable=no-member
)
connection.execute(view_menu_table.insert().values(name=view_menu_name))
view_menu = self.find_view_menu(view_menu_name)
if permission and view_menu:
pv = (
self.get_session.query(self.permissionview_model)
.filter_by(permission=permission, view_menu=view_menu)
.first()
)
if not pv and permission and view_menu:
permission_view_table = (
self.permissionview_model.__table__ # pylint: disable=no-member
)
connection.execute(
permission_view_table.insert().values(
permission_id=permission.id, view_menu_id=view_menu.id
)
)
def raise_for_access( # pylint: disable=too-many-arguments,too-many-branches
self,
database: Optional["Database"] = None,
datasource: Optional["BaseDatasource"] = None,
query: Optional["Query"] = None,
query_context: Optional["QueryContext"] = None,
table: Optional["Table"] = None,
viz: Optional["BaseViz"] = None,
) -> None:
"""
Raise an exception if the user cannot access the resource.
:param database: The Superset database
:param datasource: The Superset datasource
:param query: The SQL Lab query
:param query_context: The query context
:param table: The Superset table (requires database)
:param viz: The visualization
:raises SupersetSecurityException: If the user cannot access the resource
"""
from superset.connectors.sqla.models import SqlaTable
from superset.sql_parse import Table
if database and table or query:
if query:
database = query.database
database = cast("Database", database)
if self.can_access_database(database):
return
if query:
tables = {
Table(table_.table, table_.schema or query.schema)
for table_ in sql_parse.ParsedQuery(query.sql).tables
}
elif table:
tables = {table}
denied = set()
for table_ in tables:
schema_perm = self.get_schema_perm(database, schema=table_.schema)
if not (schema_perm and self.can_access("schema_access", schema_perm)):
datasources = SqlaTable.query_datasources_by_name(
self.get_session, database, table_.table, schema=table_.schema
)
# Access to any datasource is suffice.
for datasource_ in datasources:
if self.can_access("datasource_access", datasource_.perm):
break
else:
denied.add(table_)
if denied:
raise SupersetSecurityException(
self.get_table_access_error_object(denied)
)
if datasource or query_context or viz:
if query_context:
datasource = query_context.datasource
elif viz:
datasource = viz.datasource
assert datasource
if not (
self.can_access_schema(datasource)
or self.can_access("datasource_access", datasource.perm or "")
):
raise SupersetSecurityException(
self.get_datasource_access_error_object(datasource)
)
def get_user_by_username(
self, username: str, session: Session = None
) -> Optional[User]:
"""
Retrieves a user by it's username case sensitive. Optional session parameter
utility method normally useful for celery tasks where the session
need to be scoped
"""
session = session or self.get_session
return (
session.query(self.user_model)
.filter(self.user_model.username == username)
.one_or_none()
)
def get_rls_filters(self, table: "BaseDatasource") -> List[SqlaQuery]:
"""
Retrieves the appropriate row level security filters for the current user and
the passed table.
:param table: The table to check against
:returns: A list of filters
"""
if hasattr(g, "user") and hasattr(g.user, "id"):
from superset.connectors.sqla.models import (
RLSFilterRoles,
RLSFilterTables,
RowLevelSecurityFilter,
)
user_roles = (
self.get_session.query(assoc_user_role.c.role_id)
.filter(assoc_user_role.c.user_id == g.user.id)
.subquery()
)
regular_filter_roles = (
self.get_session.query(RLSFilterRoles.c.rls_filter_id)
.join(RowLevelSecurityFilter)
.filter(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.REGULAR
)
.filter(RLSFilterRoles.c.role_id.in_(user_roles))
.subquery()
)
base_filter_roles = (
self.get_session.query(RLSFilterRoles.c.rls_filter_id)
.join(RowLevelSecurityFilter)
.filter(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.BASE
)
.filter(RLSFilterRoles.c.role_id.in_(user_roles))
.subquery()
)
filter_tables = (
self.get_session.query(RLSFilterTables.c.rls_filter_id)
.filter(RLSFilterTables.c.table_id == table.id)
.subquery()
)
query = (
self.get_session.query(
RowLevelSecurityFilter.id,
RowLevelSecurityFilter.group_key,
RowLevelSecurityFilter.clause,
)
.filter(RowLevelSecurityFilter.id.in_(filter_tables))
.filter(
or_(
and_(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.REGULAR,
RowLevelSecurityFilter.id.in_(regular_filter_roles),
),
and_(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.BASE,
RowLevelSecurityFilter.id.notin_(base_filter_roles),
),
)
)
)
return query.all()
return []
def get_rls_ids(self, table: "BaseDatasource") -> List[int]:
"""
Retrieves the appropriate row level security filters IDs for the current user
and the passed table.
:param table: The table to check against
:returns: A list of IDs
"""
ids = [f.id for f in self.get_rls_filters(table)]
ids.sort() # Combinations rather than permutations
return ids
# pylint: disable=no-self-use
def raise_for_dashboard_access(self, dashboard: "Dashboard") -> None:
"""
Raise an exception if the user cannot access the dashboard.
:param dashboard: Dashboard the user wants access to
:raises DashboardAccessDeniedError: If the user cannot access the resource
"""
from superset.dashboards.commands.exceptions import DashboardAccessDeniedError
from superset.views.base import get_user_roles, is_user_admin
from superset.views.utils import is_owner
from superset import is_feature_enabled
if is_feature_enabled("DASHBOARD_RBAC"):
has_rbac_access = any(
dashboard_role.id in [user_role.id for user_role in get_user_roles()]
for dashboard_role in dashboard.roles
)
can_access = (
is_user_admin()
or is_owner(dashboard, g.user)
or (dashboard.published and has_rbac_access)
)
if not can_access:
raise DashboardAccessDeniedError()
| 35.763298
| 88
| 0.620634
|
import logging
import re
from typing import Any, Callable, cast, List, Optional, Set, Tuple, TYPE_CHECKING, Union
from flask import current_app, g
from flask_appbuilder import Model
from flask_appbuilder.security.sqla.manager import SecurityManager
from flask_appbuilder.security.sqla.models import (
assoc_permissionview_role,
assoc_user_role,
PermissionView,
Role,
User,
)
from flask_appbuilder.security.views import (
PermissionModelView,
PermissionViewModelView,
RoleModelView,
UserModelView,
ViewMenuModelView,
)
from flask_appbuilder.widgets import ListWidget
from sqlalchemy import and_, or_
from sqlalchemy.engine.base import Connection
from sqlalchemy.orm import Session
from sqlalchemy.orm.mapper import Mapper
from sqlalchemy.orm.query import Query as SqlaQuery
from superset import sql_parse
from superset.connectors.connector_registry import ConnectorRegistry
from superset.constants import RouteMethod
from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
from superset.exceptions import SupersetSecurityException
from superset.utils.core import DatasourceName, RowLevelSecurityFilterType
if TYPE_CHECKING:
from superset.common.query_context import QueryContext
from superset.connectors.base.models import BaseDatasource
from superset.connectors.druid.models import DruidCluster
from superset.models.dashboard import Dashboard
from superset.models.core import Database
from superset.models.sql_lab import Query
from superset.sql_parse import Table
from superset.viz import BaseViz
logger = logging.getLogger(__name__)
class SupersetSecurityListWidget(ListWidget):
template = "superset/fab_overrides/list.html"
class SupersetRoleListWidget(ListWidget):
template = "superset/fab_overrides/list_role.html"
def __init__(self, **kwargs: Any) -> None:
kwargs["appbuilder"] = current_app.appbuilder
super().__init__(**kwargs)
UserModelView.list_widget = SupersetSecurityListWidget
RoleModelView.list_widget = SupersetRoleListWidget
PermissionViewModelView.list_widget = SupersetSecurityListWidget
PermissionModelView.list_widget = SupersetSecurityListWidget
UserModelView.include_route_methods = RouteMethod.CRUD_SET | {
RouteMethod.ACTION,
RouteMethod.API_READ,
RouteMethod.ACTION_POST,
"userinfo",
}
RoleModelView.include_route_methods = RouteMethod.CRUD_SET
PermissionViewModelView.include_route_methods = {RouteMethod.LIST}
PermissionModelView.include_route_methods = {RouteMethod.LIST}
ViewMenuModelView.include_route_methods = {RouteMethod.LIST}
RoleModelView.list_columns = ["name"]
RoleModelView.edit_columns = ["name", "permissions", "user"]
RoleModelView.related_views = []
class SupersetSecurityManager(
SecurityManager
):
userstatschartview = None
READ_ONLY_MODEL_VIEWS = {"Database", "DruidClusterModelView", "DynamicPlugin"}
USER_MODEL_VIEWS = {
"UserDBModelView",
"UserLDAPModelView",
"UserOAuthModelView",
"UserOIDModelView",
"UserRemoteUserModelView",
}
GAMMA_READ_ONLY_MODEL_VIEWS = {
"Dataset",
"DruidColumnInlineView",
"DruidDatasourceModelView",
"DruidMetricInlineView",
"Datasource",
} | READ_ONLY_MODEL_VIEWS
ADMIN_ONLY_VIEW_MENUS = {
"AccessRequestsModelView",
"SQL Lab",
"Refresh Druid Metadata",
"ResetPasswordView",
"RoleModelView",
"Log",
"Security",
"Row Level Security",
"Row Level Security Filters",
"RowLevelSecurityFiltersModelView",
} | USER_MODEL_VIEWS
ALPHA_ONLY_VIEW_MENUS = {
"Manage",
"CSS Templates",
"Queries",
"Import dashboards",
"Upload a CSV",
}
ADMIN_ONLY_PERMISSIONS = {
"can_sql_json",
"can_override_role_permissions",
"can_sync_druid_source",
"can_override_role_permissions",
"can_approve",
"can_update_role",
"all_query_access",
}
READ_ONLY_PERMISSION = {
"can_show",
"can_list",
"can_get",
"can_external_metadata",
"can_read",
}
ALPHA_ONLY_PERMISSIONS = {
"muldelete",
"all_database_access",
"all_datasource_access",
}
OBJECT_SPEC_PERMISSIONS = {
"database_access",
"schema_access",
"datasource_access",
"metric_access",
}
ACCESSIBLE_PERMS = {"can_userinfo", "resetmypassword"}
data_access_permissions = (
"database_access",
"schema_access",
"datasource_access",
"all_datasource_access",
"all_database_access",
"all_query_access",
)
def get_schema_perm(
self, database: Union["Database", str], schema: Optional[str] = None
) -> Optional[str]:
if schema:
return f"[{database}].[{schema}]"
return None
def unpack_schema_perm(
self, schema_permission: str
) -> Tuple[str, str]:
schema_name = schema_permission.split(".")[1][1:-1]
database_name = schema_permission.split(".")[0][1:-1]
return database_name, schema_name
def can_access(self, permission_name: str, view_name: str) -> bool:
user = g.user
if user.is_anonymous:
return self.is_item_public(permission_name, view_name)
return self._has_view_access(user, permission_name, view_name)
def can_access_all_queries(self) -> bool:
return self.can_access("all_query_access", "all_query_access")
def can_access_all_datasources(self) -> bool:
return self.can_access("all_datasource_access", "all_datasource_access")
def can_access_all_databases(self) -> bool:
return self.can_access("all_database_access", "all_database_access")
def can_access_database(self, database: Union["Database", "DruidCluster"]) -> bool:
return (
self.can_access_all_datasources()
or self.can_access_all_databases()
or self.can_access("database_access", database.perm)
)
def can_access_schema(self, datasource: "BaseDatasource") -> bool:
return (
self.can_access_all_datasources()
or self.can_access_database(datasource.database)
or self.can_access("schema_access", datasource.schema_perm or "")
)
def can_access_datasource(self, datasource: "BaseDatasource") -> bool:
try:
self.raise_for_access(datasource=datasource)
except SupersetSecurityException:
return False
return True
@staticmethod
def get_datasource_access_error_msg(datasource: "BaseDatasource") -> str:
return f"""This endpoint requires the datasource {datasource.name}, database or
`all_datasource_access` permission"""
@staticmethod
def get_datasource_access_link(
datasource: "BaseDatasource",
) -> Optional[str]:
from superset import conf
return conf.get("PERMISSION_INSTRUCTIONS_LINK")
def get_datasource_access_error_object(
self, datasource: "BaseDatasource"
) -> SupersetError:
return SupersetError(
error_type=SupersetErrorType.DATASOURCE_SECURITY_ACCESS_ERROR,
message=self.get_datasource_access_error_msg(datasource),
level=ErrorLevel.ERROR,
extra={
"link": self.get_datasource_access_link(datasource),
"datasource": datasource.name,
},
)
def get_table_access_error_msg(
self, tables: Set["Table"]
) -> str:
quoted_tables = [f"`{table}`" for table in tables]
return f"""You need access to the following tables: {", ".join(quoted_tables)},
`all_database_access` or `all_datasource_access` permission"""
def get_table_access_error_object(self, tables: Set["Table"]) -> SupersetError:
return SupersetError(
error_type=SupersetErrorType.TABLE_SECURITY_ACCESS_ERROR,
message=self.get_table_access_error_msg(tables),
level=ErrorLevel.ERROR,
extra={
"link": self.get_table_access_link(tables),
"tables": [str(table) for table in tables],
},
)
def get_table_access_link(
self, tables: Set["Table"]
) -> Optional[str]:
from superset import conf
return conf.get("PERMISSION_INSTRUCTIONS_LINK")
def can_access_table(self, database: "Database", table: "Table") -> bool:
try:
self.raise_for_access(database=database, table=table)
except SupersetSecurityException:
return False
return True
def user_view_menu_names(self, permission_name: str) -> Set[str]:
base_query = (
self.get_session.query(self.viewmenu_model.name)
.join(self.permissionview_model)
.join(self.permission_model)
.join(assoc_permissionview_role)
.join(self.role_model)
)
if not g.user.is_anonymous:
view_menu_names = (
base_query.join(assoc_user_role)
.join(self.user_model)
.filter(self.user_model.id == g.user.id)
.filter(self.permission_model.name == permission_name)
).all()
return {s.name for s in view_menu_names}
public_role = self.get_public_role()
if public_role:
view_menu_names = (
base_query.filter(self.role_model.id == public_role.id).filter(
self.permission_model.name == permission_name
)
).all()
return {s.name for s in view_menu_names}
return set()
def get_schemas_accessible_by_user(
self, database: "Database", schemas: List[str], hierarchical: bool = True
) -> List[str]:
from superset.connectors.sqla.models import SqlaTable
if hierarchical and self.can_access_database(database):
return schemas
accessible_schemas = {
self.unpack_schema_perm(s)[1]
for s in self.user_view_menu_names("schema_access")
if s.startswith(f"[{database}].")
}
perms = self.user_view_menu_names("datasource_access")
if perms:
tables = (
self.get_session.query(SqlaTable.schema)
.filter(SqlaTable.database_id == database.id)
.filter(SqlaTable.schema.isnot(None))
.filter(SqlaTable.schema != "")
.filter(or_(SqlaTable.perm.in_(perms)))
.distinct()
)
accessible_schemas.update([table.schema for table in tables])
return [s for s in schemas if s in accessible_schemas]
def get_datasources_accessible_by_user(
self,
database: "Database",
datasource_names: List[DatasourceName],
schema: Optional[str] = None,
) -> List[DatasourceName]:
if self.can_access_database(database):
return datasource_names
if schema:
schema_perm = self.get_schema_perm(database, schema)
if schema_perm and self.can_access("schema_access", schema_perm):
return datasource_names
user_perms = self.user_view_menu_names("datasource_access")
schema_perms = self.user_view_menu_names("schema_access")
user_datasources = ConnectorRegistry.query_datasources_by_permissions(
self.get_session, database, user_perms, schema_perms
)
if schema:
names = {d.table_name for d in user_datasources if d.schema == schema}
return [d for d in datasource_names if d in names]
full_names = {d.full_name for d in user_datasources}
return [d for d in datasource_names if f"[{database}].[{d}]" in full_names]
def merge_perm(self, permission_name: str, view_menu_name: str) -> None:
logger.warning(
"This method 'merge_perm' is deprecated use add_permission_view_menu"
)
self.add_permission_view_menu(permission_name, view_menu_name)
def _is_user_defined_permission(self, perm: Model) -> bool:
return perm.permission.name in self.OBJECT_SPEC_PERMISSIONS
def create_custom_permissions(self) -> None:
self.add_permission_view_menu("all_datasource_access", "all_datasource_access")
self.add_permission_view_menu("all_database_access", "all_database_access")
self.add_permission_view_menu("all_query_access", "all_query_access")
def create_missing_perms(self) -> None:
from superset.models import core as models
logger.info("Fetching a set of all perms to lookup which ones are missing")
all_pvs = set()
for pv in self.get_session.query(self.permissionview_model).all():
if pv.permission and pv.view_menu:
all_pvs.add((pv.permission.name, pv.view_menu.name))
def merge_pv(view_menu: str, perm: str) -> None:
if view_menu and perm and (view_menu, perm) not in all_pvs:
self.add_permission_view_menu(view_menu, perm)
logger.info("Creating missing datasource permissions.")
datasources = ConnectorRegistry.get_all_datasources(self.get_session)
for datasource in datasources:
merge_pv("datasource_access", datasource.get_perm())
merge_pv("schema_access", datasource.get_schema_perm())
logger.info("Creating missing database permissions.")
databases = self.get_session.query(models.Database).all()
for database in databases:
merge_pv("database_access", database.perm)
def clean_perms(self) -> None:
logger.info("Cleaning faulty perms")
sesh = self.get_session
pvms = sesh.query(PermissionView).filter(
or_(
PermissionView.permission
== None,
PermissionView.view_menu
== None,
)
)
deleted_count = pvms.delete()
sesh.commit()
if deleted_count:
logger.info("Deleted %i faulty permissions", deleted_count)
def sync_role_definitions(self) -> None:
from superset import conf
logger.info("Syncing role definition")
self.create_custom_permissions()
self.set_role("Admin", self._is_admin_pvm)
self.set_role("Alpha", self._is_alpha_pvm)
self.set_role("Gamma", self._is_gamma_pvm)
self.set_role("granter", self._is_granter_pvm)
self.set_role("sql_lab", self._is_sql_lab_pvm)
if conf["PUBLIC_ROLE_LIKE"]:
self.copy_role(conf["PUBLIC_ROLE_LIKE"], self.auth_role_public, merge=True)
if conf.get("PUBLIC_ROLE_LIKE_GAMMA", False):
logger.warning(
"The config `PUBLIC_ROLE_LIKE_GAMMA` is deprecated and will be removed "
"in Superset 1.0. Please use `PUBLIC_ROLE_LIKE` instead."
)
self.copy_role("Gamma", self.auth_role_public, merge=True)
self.create_missing_perms()
self.get_session.commit()
self.clean_perms()
def _get_pvms_from_builtin_role(self, role_name: str) -> List[PermissionView]:
role_from_permissions_names = self.builtin_roles.get(role_name, [])
all_pvms = self.get_session.query(PermissionView).all()
role_from_permissions = []
for pvm_regex in role_from_permissions_names:
view_name_regex = pvm_regex[0]
permission_name_regex = pvm_regex[1]
for pvm in all_pvms:
if re.match(view_name_regex, pvm.view_menu.name) and re.match(
permission_name_regex, pvm.permission.name
):
if pvm not in role_from_permissions:
role_from_permissions.append(pvm)
return role_from_permissions
def find_roles_by_id(self, role_ids: List[int]) -> List[Role]:
query = self.get_session.query(Role).filter(Role.id.in_(role_ids))
return query.all()
def copy_role(
self, role_from_name: str, role_to_name: str, merge: bool = True
) -> None:
logger.info("Copy/Merge %s to %s", role_from_name, role_to_name)
if role_from_name in self.builtin_roles:
role_from_permissions = self._get_pvms_from_builtin_role(role_from_name)
else:
role_from_permissions = list(self.find_role(role_from_name).permissions)
role_to = self.add_role(role_to_name)
# If merge, recover existing data access permissions
if merge:
for permission_view in role_to.permissions:
if (
permission_view not in role_from_permissions
and permission_view.permission.name in self.data_access_permissions
):
role_from_permissions.append(permission_view)
role_to.permissions = role_from_permissions
self.get_session.merge(role_to)
self.get_session.commit()
def set_role(
self, role_name: str, pvm_check: Callable[[PermissionView], bool]
) -> None:
logger.info("Syncing %s perms", role_name)
pvms = self.get_session.query(PermissionView).all()
pvms = [p for p in pvms if p.permission and p.view_menu]
role = self.add_role(role_name)
role_pvms = [
permission_view for permission_view in pvms if pvm_check(permission_view)
]
role.permissions = role_pvms
self.get_session.merge(role)
self.get_session.commit()
def _is_admin_only(self, pvm: PermissionView) -> bool:
if (
pvm.view_menu.name in self.READ_ONLY_MODEL_VIEWS
and pvm.permission.name not in self.READ_ONLY_PERMISSION
):
return True
return (
pvm.view_menu.name in self.ADMIN_ONLY_VIEW_MENUS
or pvm.permission.name in self.ADMIN_ONLY_PERMISSIONS
)
def _is_alpha_only(self, pvm: PermissionView) -> bool:
if (
pvm.view_menu.name in self.GAMMA_READ_ONLY_MODEL_VIEWS
and pvm.permission.name not in self.READ_ONLY_PERMISSION
):
return True
return (
pvm.view_menu.name in self.ALPHA_ONLY_VIEW_MENUS
or pvm.permission.name in self.ALPHA_ONLY_PERMISSIONS
)
def _is_accessible_to_all(self, pvm: PermissionView) -> bool:
return pvm.permission.name in self.ACCESSIBLE_PERMS
def _is_admin_pvm(self, pvm: PermissionView) -> bool:
return not self._is_user_defined_permission(pvm)
def _is_alpha_pvm(self, pvm: PermissionView) -> bool:
return not (
self._is_user_defined_permission(pvm) or self._is_admin_only(pvm)
) or self._is_accessible_to_all(pvm)
def _is_gamma_pvm(self, pvm: PermissionView) -> bool:
return not (
self._is_user_defined_permission(pvm)
or self._is_admin_only(pvm)
or self._is_alpha_only(pvm)
) or self._is_accessible_to_all(pvm)
def _is_sql_lab_pvm(self, pvm: PermissionView) -> bool:
return (
pvm.view_menu.name
in {"SQL Lab", "SQL Editor", "Query Search", "Saved Queries"}
or pvm.permission.name
in {
"can_sql_json",
"can_csv",
"can_search_queries",
"can_sqllab_viz",
"can_sqllab_table_viz",
"can_sqllab",
}
or (
pvm.view_menu.name in self.USER_MODEL_VIEWS
and pvm.permission.name == "can_list"
)
)
def _is_granter_pvm( # pylint: disable=no-self-use
self, pvm: PermissionView
) -> bool:
return pvm.permission.name in {"can_override_role_permissions", "can_approve"}
def set_perm( # pylint: disable=no-self-use,unused-argument
self, mapper: Mapper, connection: Connection, target: "BaseDatasource"
) -> None:
link_table = target.__table__ # pylint: disable=no-member
if target.perm != target.get_perm():
connection.execute(
link_table.update()
.where(link_table.c.id == target.id)
.values(perm=target.get_perm())
)
if (
hasattr(target, "schema_perm")
and target.schema_perm != target.get_schema_perm()
):
connection.execute(
link_table.update()
.where(link_table.c.id == target.id)
.values(schema_perm=target.get_schema_perm())
)
pvm_names = []
if target.__tablename__ in {"dbs", "clusters"}:
pvm_names.append(("database_access", target.get_perm()))
else:
pvm_names.append(("datasource_access", target.get_perm()))
if target.schema:
pvm_names.append(("schema_access", target.get_schema_perm()))
# TODO(bogdan): modify slice permissions as well.
for permission_name, view_menu_name in pvm_names:
permission = self.find_permission(permission_name)
view_menu = self.find_view_menu(view_menu_name)
pv = None
if not permission:
permission_table = (
self.permission_model.__table__ # pylint: disable=no-member
)
connection.execute(
permission_table.insert().values(name=permission_name)
)
permission = self.find_permission(permission_name)
if not view_menu:
view_menu_table = (
self.viewmenu_model.__table__ # pylint: disable=no-member
)
connection.execute(view_menu_table.insert().values(name=view_menu_name))
view_menu = self.find_view_menu(view_menu_name)
if permission and view_menu:
pv = (
self.get_session.query(self.permissionview_model)
.filter_by(permission=permission, view_menu=view_menu)
.first()
)
if not pv and permission and view_menu:
permission_view_table = (
self.permissionview_model.__table__ # pylint: disable=no-member
)
connection.execute(
permission_view_table.insert().values(
permission_id=permission.id, view_menu_id=view_menu.id
)
)
def raise_for_access( # pylint: disable=too-many-arguments,too-many-branches
self,
database: Optional["Database"] = None,
datasource: Optional["BaseDatasource"] = None,
query: Optional["Query"] = None,
query_context: Optional["QueryContext"] = None,
table: Optional["Table"] = None,
viz: Optional["BaseViz"] = None,
) -> None:
from superset.connectors.sqla.models import SqlaTable
from superset.sql_parse import Table
if database and table or query:
if query:
database = query.database
database = cast("Database", database)
if self.can_access_database(database):
return
if query:
tables = {
Table(table_.table, table_.schema or query.schema)
for table_ in sql_parse.ParsedQuery(query.sql).tables
}
elif table:
tables = {table}
denied = set()
for table_ in tables:
schema_perm = self.get_schema_perm(database, schema=table_.schema)
if not (schema_perm and self.can_access("schema_access", schema_perm)):
datasources = SqlaTable.query_datasources_by_name(
self.get_session, database, table_.table, schema=table_.schema
)
# Access to any datasource is suffice.
for datasource_ in datasources:
if self.can_access("datasource_access", datasource_.perm):
break
else:
denied.add(table_)
if denied:
raise SupersetSecurityException(
self.get_table_access_error_object(denied)
)
if datasource or query_context or viz:
if query_context:
datasource = query_context.datasource
elif viz:
datasource = viz.datasource
assert datasource
if not (
self.can_access_schema(datasource)
or self.can_access("datasource_access", datasource.perm or "")
):
raise SupersetSecurityException(
self.get_datasource_access_error_object(datasource)
)
def get_user_by_username(
self, username: str, session: Session = None
) -> Optional[User]:
session = session or self.get_session
return (
session.query(self.user_model)
.filter(self.user_model.username == username)
.one_or_none()
)
def get_rls_filters(self, table: "BaseDatasource") -> List[SqlaQuery]:
if hasattr(g, "user") and hasattr(g.user, "id"):
from superset.connectors.sqla.models import (
RLSFilterRoles,
RLSFilterTables,
RowLevelSecurityFilter,
)
user_roles = (
self.get_session.query(assoc_user_role.c.role_id)
.filter(assoc_user_role.c.user_id == g.user.id)
.subquery()
)
regular_filter_roles = (
self.get_session.query(RLSFilterRoles.c.rls_filter_id)
.join(RowLevelSecurityFilter)
.filter(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.REGULAR
)
.filter(RLSFilterRoles.c.role_id.in_(user_roles))
.subquery()
)
base_filter_roles = (
self.get_session.query(RLSFilterRoles.c.rls_filter_id)
.join(RowLevelSecurityFilter)
.filter(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.BASE
)
.filter(RLSFilterRoles.c.role_id.in_(user_roles))
.subquery()
)
filter_tables = (
self.get_session.query(RLSFilterTables.c.rls_filter_id)
.filter(RLSFilterTables.c.table_id == table.id)
.subquery()
)
query = (
self.get_session.query(
RowLevelSecurityFilter.id,
RowLevelSecurityFilter.group_key,
RowLevelSecurityFilter.clause,
)
.filter(RowLevelSecurityFilter.id.in_(filter_tables))
.filter(
or_(
and_(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.REGULAR,
RowLevelSecurityFilter.id.in_(regular_filter_roles),
),
and_(
RowLevelSecurityFilter.filter_type
== RowLevelSecurityFilterType.BASE,
RowLevelSecurityFilter.id.notin_(base_filter_roles),
),
)
)
)
return query.all()
return []
def get_rls_ids(self, table: "BaseDatasource") -> List[int]:
ids = [f.id for f in self.get_rls_filters(table)]
ids.sort() # Combinations rather than permutations
return ids
# pylint: disable=no-self-use
def raise_for_dashboard_access(self, dashboard: "Dashboard") -> None:
from superset.dashboards.commands.exceptions import DashboardAccessDeniedError
from superset.views.base import get_user_roles, is_user_admin
from superset.views.utils import is_owner
from superset import is_feature_enabled
if is_feature_enabled("DASHBOARD_RBAC"):
has_rbac_access = any(
dashboard_role.id in [user_role.id for user_role in get_user_roles()]
for dashboard_role in dashboard.roles
)
can_access = (
is_user_admin()
or is_owner(dashboard, g.user)
or (dashboard.published and has_rbac_access)
)
if not can_access:
raise DashboardAccessDeniedError()
| true
| true
|
1c441a02c742a3dfe6abbfd727f960c9fef4f1d5
| 552
|
py
|
Python
|
network/migrations/0007_auto_20181013_1141.py
|
pawangeek/PollsChain
|
6059796c671d3250f2cd8bb36171bf54013d176e
|
[
"MIT"
] | null | null | null |
network/migrations/0007_auto_20181013_1141.py
|
pawangeek/PollsChain
|
6059796c671d3250f2cd8bb36171bf54013d176e
|
[
"MIT"
] | null | null | null |
network/migrations/0007_auto_20181013_1141.py
|
pawangeek/PollsChain
|
6059796c671d3250f2cd8bb36171bf54013d176e
|
[
"MIT"
] | null | null | null |
# Generated by Django 2.1 on 2018-10-13 11:41
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('network', '0006_transaction_transaction_id'),
]
operations = [
migrations.RemoveField(
model_name='block',
name='transaction',
),
migrations.AddField(
model_name='block',
name='transaction_id',
field=models.CharField(default=0, max_length=100),
preserve_default=False,
),
]
| 23
| 62
| 0.586957
|
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('network', '0006_transaction_transaction_id'),
]
operations = [
migrations.RemoveField(
model_name='block',
name='transaction',
),
migrations.AddField(
model_name='block',
name='transaction_id',
field=models.CharField(default=0, max_length=100),
preserve_default=False,
),
]
| true
| true
|
1c441a48841857af47bd57c1c28d22667ebaeb0d
| 542
|
py
|
Python
|
A_mecanica_das_Classes_e_Instancias/09-Instance-Methods.py
|
nnsdtr/OOP-Python
|
3b739966c9b35c32a2bd934574f6421b1470eb23
|
[
"MIT"
] | null | null | null |
A_mecanica_das_Classes_e_Instancias/09-Instance-Methods.py
|
nnsdtr/OOP-Python
|
3b739966c9b35c32a2bd934574f6421b1470eb23
|
[
"MIT"
] | null | null | null |
A_mecanica_das_Classes_e_Instancias/09-Instance-Methods.py
|
nnsdtr/OOP-Python
|
3b739966c9b35c32a2bd934574f6421b1470eb23
|
[
"MIT"
] | null | null | null |
# Exemplo 1:
class Person(object):
greeting = '\nHello there!'
joe = Person()
print(joe.greeting)
print('\n')
# Exemplo 2:
# Método interno à classe utiliza self como 1º parâmetro, sempre.
class Something(object):
def call_this_method(self):
print('Verificação de john == john.call_this_method() resulta em:')
return self
# Criando instância da classe 'Something'
john = Something()
# Verificação de igualdade
print(john == john.call_this_method()) # Logo, a instância 'john' é o próprio parâmetro 'self'!
| 20.846154
| 98
| 0.693727
|
class Person(object):
greeting = '\nHello there!'
joe = Person()
print(joe.greeting)
print('\n')
class Something(object):
def call_this_method(self):
print('Verificação de john == john.call_this_method() resulta em:')
return self
john = Something()
print(john == john.call_this_method())
| true
| true
|
1c441b172e4ee117cc58cbc80d255338d7e0f552
| 2,835
|
py
|
Python
|
tests/test_queries.py
|
klen/aio-databases
|
395edcc810598e1639ccd9727aecb4d97cf04df9
|
[
"MIT"
] | 6
|
2021-08-13T16:17:47.000Z
|
2022-02-04T01:22:02.000Z
|
tests/test_queries.py
|
klen/aio-databases
|
395edcc810598e1639ccd9727aecb4d97cf04df9
|
[
"MIT"
] | null | null | null |
tests/test_queries.py
|
klen/aio-databases
|
395edcc810598e1639ccd9727aecb4d97cf04df9
|
[
"MIT"
] | null | null | null |
import pytest
from pypika import Parameter
@pytest.fixture
async def schema(pool, User, manager):
UserManager = manager(User)
await pool.execute(UserManager.create_table().if_not_exists())
yield
await pool.execute(UserManager.drop_table().if_exists())
async def test_base(db):
await db.execute("select %s", '1')
res = await db.fetchall("select (2 * %s) res", 2)
assert [tuple(r) for r in res] == [(4,)]
res = await db.fetchmany(10, "select (2 * %s) res", 2)
assert [tuple(r) for r in res] == [(4,)]
res = await db.fetchone("select (2 * %s) res", 2)
assert tuple(res) == (4,)
res = await db.fetchval("select 2 + %s", 2)
assert res == 4
async def test_all(db, User, manager, schema):
UserManager = manager(User)
await db.execute(UserManager.delete())
async with db.transaction() as main_trans:
assert main_trans
res = await db.execute(UserManager.insert(name='Jim', fullname='Jim Jones'))
assert res
async with db.transaction() as trans2:
assert trans2
res = await db.execute(UserManager.insert(name='Tom', fullname='Tom Smith'))
assert res
res = await db.fetchall(UserManager.select())
assert res
assert len(res) == 2
await trans2.rollback()
res = await db.fetchall(UserManager.select())
assert res
assert len(res) == 1
[user] = res
assert user
assert user['id']
assert user['name'] == 'Jim'
assert user['fullname'] == 'Jim Jones'
res = await db.fetchone(UserManager.select().where(User.id == 100))
assert res is None
async def test_execute(db, User, manager, schema):
UserManager = manager(User)
await db.execute(UserManager.insert(name='Jim', fullname='Tom Smith'))
await db.execute(UserManager.insert(name='Jim', fullname='Tom Smith'))
updated, lastid = await db.execute(UserManager.update().set(User.name, 'Tom'))
assert updated == 2
@pytest.mark.parametrize('backend', ['aiomysql'])
async def test_execute_many(db, User, manager, schema):
UserManager = manager(User)
await db.execute(UserManager.delete())
qs = UserManager.insert(name=Parameter('%s'), fullname=Parameter('%s'))
await db.executemany(qs, ('Jim', 'Jim Jones'), ('Tom', 'Tom Smith'))
res = await db.fetchall(UserManager.select())
assert res
assert len(res) == 2
u1, u2 = res
assert u1['name'] == 'Jim'
assert u2['name'] == 'Tom'
async def test_iterate(db, User, manager, schema):
UserManager = manager(User)
qs = UserManager.insert(name=Parameter('%s'), fullname=Parameter('%s'))
await db.executemany(qs, ('Jim', 'Jim Jones'), ('Tom', 'Tom Smith'))
async for rec in db.iterate(UserManager.select()):
assert rec['name'] in {'Jim', 'Tom'}
| 29.842105
| 88
| 0.631393
|
import pytest
from pypika import Parameter
@pytest.fixture
async def schema(pool, User, manager):
UserManager = manager(User)
await pool.execute(UserManager.create_table().if_not_exists())
yield
await pool.execute(UserManager.drop_table().if_exists())
async def test_base(db):
await db.execute("select %s", '1')
res = await db.fetchall("select (2 * %s) res", 2)
assert [tuple(r) for r in res] == [(4,)]
res = await db.fetchmany(10, "select (2 * %s) res", 2)
assert [tuple(r) for r in res] == [(4,)]
res = await db.fetchone("select (2 * %s) res", 2)
assert tuple(res) == (4,)
res = await db.fetchval("select 2 + %s", 2)
assert res == 4
async def test_all(db, User, manager, schema):
UserManager = manager(User)
await db.execute(UserManager.delete())
async with db.transaction() as main_trans:
assert main_trans
res = await db.execute(UserManager.insert(name='Jim', fullname='Jim Jones'))
assert res
async with db.transaction() as trans2:
assert trans2
res = await db.execute(UserManager.insert(name='Tom', fullname='Tom Smith'))
assert res
res = await db.fetchall(UserManager.select())
assert res
assert len(res) == 2
await trans2.rollback()
res = await db.fetchall(UserManager.select())
assert res
assert len(res) == 1
[user] = res
assert user
assert user['id']
assert user['name'] == 'Jim'
assert user['fullname'] == 'Jim Jones'
res = await db.fetchone(UserManager.select().where(User.id == 100))
assert res is None
async def test_execute(db, User, manager, schema):
UserManager = manager(User)
await db.execute(UserManager.insert(name='Jim', fullname='Tom Smith'))
await db.execute(UserManager.insert(name='Jim', fullname='Tom Smith'))
updated, lastid = await db.execute(UserManager.update().set(User.name, 'Tom'))
assert updated == 2
@pytest.mark.parametrize('backend', ['aiomysql'])
async def test_execute_many(db, User, manager, schema):
UserManager = manager(User)
await db.execute(UserManager.delete())
qs = UserManager.insert(name=Parameter('%s'), fullname=Parameter('%s'))
await db.executemany(qs, ('Jim', 'Jim Jones'), ('Tom', 'Tom Smith'))
res = await db.fetchall(UserManager.select())
assert res
assert len(res) == 2
u1, u2 = res
assert u1['name'] == 'Jim'
assert u2['name'] == 'Tom'
async def test_iterate(db, User, manager, schema):
UserManager = manager(User)
qs = UserManager.insert(name=Parameter('%s'), fullname=Parameter('%s'))
await db.executemany(qs, ('Jim', 'Jim Jones'), ('Tom', 'Tom Smith'))
async for rec in db.iterate(UserManager.select()):
assert rec['name'] in {'Jim', 'Tom'}
| true
| true
|
1c441ba3d757d278aee135f301e462d85e7c43f4
| 2,447
|
py
|
Python
|
python/GafferAppleseedUI/AppleseedRenderUI.py
|
ddesmond/gaffer
|
4f25df88103b7893df75865ea919fb035f92bac0
|
[
"BSD-3-Clause"
] | 561
|
2016-10-18T04:30:48.000Z
|
2022-03-30T06:52:04.000Z
|
python/GafferAppleseedUI/AppleseedRenderUI.py
|
ddesmond/gaffer
|
4f25df88103b7893df75865ea919fb035f92bac0
|
[
"BSD-3-Clause"
] | 1,828
|
2016-10-14T19:01:46.000Z
|
2022-03-30T16:07:19.000Z
|
python/GafferAppleseedUI/AppleseedRenderUI.py
|
ddesmond/gaffer
|
4f25df88103b7893df75865ea919fb035f92bac0
|
[
"BSD-3-Clause"
] | 120
|
2016-10-18T15:19:13.000Z
|
2021-12-20T16:28:23.000Z
|
##########################################################################
#
# Copyright (c) 2014, Esteban Tovagliari. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above
# copyright notice, this list of conditions and the following
# disclaimer.
#
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with
# the distribution.
#
# * Neither the name of John Haddon nor the names of
# any other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
##########################################################################
import IECore
import Gaffer
import GafferUI
import GafferAppleseed
Gaffer.Metadata.registerNode(
GafferAppleseed.AppleseedRender,
"description",
"""
Performs offline batch rendering using the
appleseed renderer, or optionally generates
appleseed projects for later rendering using a SystemCommand
node.
""",
plugs = {
"fileName" : [
"description",
"""
The name of the appleseed project file to be generated.
""",
"nodule:type", "",
"plugValueWidget:type", "GafferUI.FileSystemPathPlugValueWidget",
"path:leaf", True,
"path:bookmarks", "appleseed",
"fileSystemPath:extensions", "appleseed",
],
}
)
| 32.626667
| 77
| 0.688598
| true
| true
|
|
1c441bb44de6f92ddbdce2bba4858f65eb61b169
| 3,559
|
py
|
Python
|
bindings/python/ensmallen/datasets/string/coriobacteriumglomerans.py
|
AnacletoLAB/ensmallen_graph
|
b2c1b18fb1e5801712852bcc239f239e03076f09
|
[
"MIT"
] | 5
|
2021-02-17T00:44:45.000Z
|
2021-08-09T16:41:47.000Z
|
bindings/python/ensmallen/datasets/string/coriobacteriumglomerans.py
|
AnacletoLAB/ensmallen_graph
|
b2c1b18fb1e5801712852bcc239f239e03076f09
|
[
"MIT"
] | 18
|
2021-01-07T16:47:39.000Z
|
2021-08-12T21:51:32.000Z
|
bindings/python/ensmallen/datasets/string/coriobacteriumglomerans.py
|
AnacletoLAB/ensmallen
|
b2c1b18fb1e5801712852bcc239f239e03076f09
|
[
"MIT"
] | 3
|
2021-01-14T02:20:59.000Z
|
2021-08-04T19:09:52.000Z
|
"""
This file offers the methods to automatically retrieve the graph Coriobacterium glomerans.
The graph is automatically retrieved from the STRING repository.
References
---------------------
Please cite the following if you use the data:
```bib
@article{szklarczyk2019string,
title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets},
author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others},
journal={Nucleic acids research},
volume={47},
number={D1},
pages={D607--D613},
year={2019},
publisher={Oxford University Press}
}
```
"""
from typing import Dict
from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph
from ...ensmallen import Graph # pylint: disable=import-error
def CoriobacteriumGlomerans(
directed: bool = False,
preprocess: bool = True,
load_nodes: bool = True,
verbose: int = 2,
cache: bool = True,
cache_path: str = "graphs/string",
version: str = "links.v11.5",
**additional_graph_kwargs: Dict
) -> Graph:
"""Return new instance of the Coriobacterium glomerans graph.
The graph is automatically retrieved from the STRING repository.
Parameters
-------------------
directed: bool = False
Wether to load the graph as directed or undirected.
By default false.
preprocess: bool = True
Whether to preprocess the graph to be loaded in
optimal time and memory.
load_nodes: bool = True,
Whether to load the nodes vocabulary or treat the nodes
simply as a numeric range.
verbose: int = 2,
Wether to show loading bars during the retrieval and building
of the graph.
cache: bool = True
Whether to use cache, i.e. download files only once
and preprocess them only once.
cache_path: str = "graphs"
Where to store the downloaded graphs.
version: str = "links.v11.5"
The version of the graph to retrieve.
The available versions are:
- homology.v11.0
- homology.v11.5
- physical.links.v11.0
- physical.links.v11.5
- links.v11.0
- links.v11.5
additional_graph_kwargs: Dict
Additional graph kwargs.
Returns
-----------------------
Instace of Coriobacterium glomerans graph.
References
---------------------
Please cite the following if you use the data:
```bib
@article{szklarczyk2019string,
title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets},
author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others},
journal={Nucleic acids research},
volume={47},
number={D1},
pages={D607--D613},
year={2019},
publisher={Oxford University Press}
}
```
"""
return AutomaticallyRetrievedGraph(
graph_name="CoriobacteriumGlomerans",
repository="string",
version=version,
directed=directed,
preprocess=preprocess,
load_nodes=load_nodes,
verbose=verbose,
cache=cache,
cache_path=cache_path,
additional_graph_kwargs=additional_graph_kwargs
)()
| 32.953704
| 223
| 0.678561
|
from typing import Dict
from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph
from ...ensmallen import Graph
def CoriobacteriumGlomerans(
directed: bool = False,
preprocess: bool = True,
load_nodes: bool = True,
verbose: int = 2,
cache: bool = True,
cache_path: str = "graphs/string",
version: str = "links.v11.5",
**additional_graph_kwargs: Dict
) -> Graph:
return AutomaticallyRetrievedGraph(
graph_name="CoriobacteriumGlomerans",
repository="string",
version=version,
directed=directed,
preprocess=preprocess,
load_nodes=load_nodes,
verbose=verbose,
cache=cache,
cache_path=cache_path,
additional_graph_kwargs=additional_graph_kwargs
)()
| true
| true
|
1c441bde02d7e435b5ed2bc275e134215dc9609b
| 54,472
|
py
|
Python
|
fmpy/gui/MainWindow.py
|
CSchulzeTLK/FMPy
|
fde192346c36eb69dbaca60a96e80cdc8ef37b89
|
[
"CC-BY-3.0",
"CC-BY-4.0"
] | 1
|
2021-03-17T14:24:08.000Z
|
2021-03-17T14:24:08.000Z
|
fmpy/gui/MainWindow.py
|
CSchulzeTLK/FMPy
|
fde192346c36eb69dbaca60a96e80cdc8ef37b89
|
[
"CC-BY-3.0",
"CC-BY-4.0"
] | null | null | null |
fmpy/gui/MainWindow.py
|
CSchulzeTLK/FMPy
|
fde192346c36eb69dbaca60a96e80cdc8ef37b89
|
[
"CC-BY-3.0",
"CC-BY-4.0"
] | null | null | null |
""" Entry point for the graphical user interface """
try:
from . import compile_resources
compile_resources()
except Exception as e:
print("Failed to compiled resources. %s" % e)
import os
import sys
from PyQt5.QtCore import QCoreApplication, QDir, Qt, pyqtSignal, QUrl, QSettings, QPoint, QTimer, QStandardPaths, \
QPointF, QBuffer, QIODevice
from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QLineEdit, QComboBox, QFileDialog, QLabel, QVBoxLayout, QMenu, QMessageBox, QProgressDialog, QProgressBar, QDialog, QGraphicsScene, QGraphicsItemGroup, QGraphicsRectItem, QGraphicsTextItem, QGraphicsPathItem
from PyQt5.QtGui import QDesktopServices, QPixmap, QIcon, QDoubleValidator, QColor, QFont, QPen, QFontMetricsF, QPolygonF, QPainterPath
from fmpy.gui.generated.MainWindow import Ui_MainWindow
import fmpy
from fmpy import read_model_description, supported_platforms, platform
from fmpy.model_description import ScalarVariable
from fmpy.util import can_simulate
from fmpy.gui.model import VariablesTableModel, VariablesTreeModel, VariablesModel, VariablesFilterModel
from fmpy.gui.log import Log, LogMessagesFilterProxyModel
QCoreApplication.setApplicationVersion(fmpy.__version__)
QCoreApplication.setOrganizationName("CATIA-Systems")
QCoreApplication.setApplicationName("FMPy")
import pyqtgraph as pg
pg.setConfigOptions(background='w', foreground='k', antialias=True)
COLLAPSABLE_COLUMNS = ['Type', 'Value Reference', 'Initial', 'Causality', 'Variability', 'Min', 'Max']
class ClickableLabel(QLabel):
""" A QLabel that shows a pointing hand cursor and emits a *clicked* event when clicked """
clicked = pyqtSignal()
def __init__(self, parent=None):
super(ClickableLabel, self).__init__(parent)
self.setCursor(Qt.PointingHandCursor)
def mousePressEvent(self, ev):
self.clicked.emit()
super(ClickableLabel, self).mousePressEvent(ev)
class AboutDialog(QDialog):
def __init__(self, parent=None):
super(AboutDialog, self).__init__(parent)
from .generated.AboutDialog import Ui_Dialog
from .. import __version__, platform, __file__
import sys
import os
self.ui = Ui_Dialog()
self.ui.setupUi(self)
# hide the question mark button
flags = self.windowFlags()
flags &= ~Qt.WindowContextHelpButtonHint
flags |= Qt.MSWindowsFixedSizeDialogHint
self.setWindowFlags(flags)
self.ui.fmpyVersionLabel.setText(__version__)
self.ui.fmiPlatformLabel.setText(platform)
self.ui.installationPathLabel.setText(os.path.dirname(__file__))
self.ui.pythonInterpreterLabel.setText(sys.executable)
self.ui.pythonVersionLabel.setText(sys.version)
class MainWindow(QMainWindow):
variableSelected = pyqtSignal(ScalarVariable, name='variableSelected')
variableDeselected = pyqtSignal(ScalarVariable, name='variableDeselected')
windows = []
windowOffset = QPoint()
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
# save from garbage collection
self.windows.append(self)
# state
self.filename = None
self.result = None
self.modelDescription = None
self.variables = dict()
self.selectedVariables = set()
self.startValues = dict()
self.simulationThread = None
# self.progressDialog = None
self.plotUpdateTimer = QTimer(self)
self.plotUpdateTimer.timeout.connect(self.updatePlotData)
self.curves = []
# UI
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.showColumnActions = {}
# use a smaller default font size on Mac and Linux
if sys.platform in ['darwin', 'linux']:
defaultFont = QFont()
defaultFont.setPixelSize(11)
QApplication.setFont(defaultFont)
self.setStyleSheet("QWidget { font-size: 11px; }")
self.ui.treeView.setAttribute(Qt.WA_MacShowFocusRect, False)
self.ui.tableView.setAttribute(Qt.WA_MacShowFocusRect, False)
self.ui.logTreeView.setAttribute(Qt.WA_MacShowFocusRect, False)
# set the window size to 85% of the available space
geo = QApplication.desktop().availableGeometry()
width = min(geo.width() * 0.85, 1100.0)
height = min(geo.height() * 0.85, 900.0)
self.resize(int(width), int(height))
# hide the variables
self.ui.dockWidget.hide()
# toolbar
self.stopTimeLineEdit = QLineEdit("1")
self.stopTimeLineEdit.setToolTip("Stop time")
self.stopTimeLineEdit.setFixedWidth(50)
self.stopTimeValidator = QDoubleValidator(self)
self.stopTimeValidator.setBottom(0)
self.stopTimeLineEdit.setValidator(self.stopTimeValidator)
self.ui.toolBar.addWidget(self.stopTimeLineEdit)
spacer = QWidget(self)
spacer.setFixedWidth(10)
self.ui.toolBar.addWidget(spacer)
self.fmiTypeComboBox = QComboBox(self)
self.fmiTypeComboBox.addItem("Co-Simulation")
self.fmiTypeComboBox.setToolTip("FMI type")
self.fmiTypeComboBox.setSizeAdjustPolicy(QComboBox.AdjustToContents)
self.ui.toolBar.addWidget(self.fmiTypeComboBox)
# disable widgets
self.ui.actionLoadStartValues.setEnabled(False)
self.ui.actionReload.setEnabled(False)
self.ui.actionSettings.setEnabled(False)
self.ui.actionShowLog.setEnabled(False)
self.ui.actionShowResults.setEnabled(False)
self.ui.actionSimulate.setEnabled(False)
self.ui.actionSaveResult.setEnabled(False)
self.ui.actionSavePlottedResult.setEnabled(False)
self.stopTimeLineEdit.setEnabled(False)
self.fmiTypeComboBox.setEnabled(False)
# hide the dock's title bar
self.ui.dockWidget.setTitleBarWidget(QWidget())
self.ui.dockWidgetContents.setMinimumWidth(500)
self.tableModel = VariablesTableModel(self.selectedVariables, self.startValues)
self.tableFilterModel = VariablesFilterModel()
self.tableFilterModel.setSourceModel(self.tableModel)
self.tableFilterModel.setFilterCaseSensitivity(Qt.CaseInsensitive)
self.ui.tableView.setModel(self.tableFilterModel)
self.treeModel = VariablesTreeModel(self.selectedVariables, self.startValues)
self.treeFilterModel = VariablesFilterModel()
self.treeFilterModel.setSourceModel(self.treeModel)
self.treeFilterModel.setFilterCaseSensitivity(Qt.CaseInsensitive)
self.ui.treeView.setModel(self.treeFilterModel)
for i, (w, n) in enumerate(zip(VariablesModel.COLUMN_WIDTHS, VariablesModel.COLUMN_NAMES)):
self.ui.treeView.setColumnWidth(i, w)
self.ui.tableView.setColumnWidth(i, w)
self.hideAllColumns()
# populate the recent files list
settings = QSettings()
recent_files = settings.value("recentFiles", defaultValue=[])
recent_files = self.removeDuplicates(recent_files)
vbox = QVBoxLayout()
if recent_files:
added = set()
for file in recent_files[:5]:
link = QLabel('<a href="%s" style="text-decoration: none">%s</a>' % (file, os.path.basename(file)))
link.setToolTip(file)
link.linkActivated.connect(self.load)
vbox.addWidget(link)
added.add(file)
self.ui.recentFilesGroupBox.setLayout(vbox)
self.ui.recentFilesGroupBox.setVisible(len(recent_files) > 0)
# settings page
self.inputFileMenu = QMenu()
self.inputFileMenu.addAction("New input file...", self.createInputFile)
self.inputFileMenu.addSeparator()
self.inputFileMenu.addAction("Show in Explorer", self.showInputFileInExplorer)
self.inputFileMenu.addAction("Open in default application", self.openInputFile)
self.ui.selectInputButton.setMenu(self.inputFileMenu)
# log page
self.log = Log(self)
self.logFilterModel = LogMessagesFilterProxyModel(self)
self.logFilterModel.setSourceModel(self.log)
self.logFilterModel.setFilterCaseSensitivity(Qt.CaseInsensitive)
self.ui.logTreeView.setModel(self.logFilterModel)
self.ui.clearLogButton.clicked.connect(self.log.clear)
self.log.numberOfDebugMessagesChanged.connect(lambda n: self.ui.showDebugMessagesButton.setText(str(n)))
self.log.numberOfInfoMessagesChanged.connect(lambda n: self.ui.showInfoMessagesButton.setText(str(n)))
self.log.numberOfWarningMessagesChanged.connect(lambda n: self.ui.showWarningMessagesButton.setText(str(n)))
self.log.numberOfErrorMessagesChanged.connect(lambda n: self.ui.showErrorMessagesButton.setText(str(n)))
self.ui.logFilterLineEdit.textChanged.connect(self.logFilterModel.setFilterFixedString)
self.ui.showDebugMessagesButton.toggled.connect(self.logFilterModel.setShowDebugMessages)
self.ui.showInfoMessagesButton.toggled.connect(self.logFilterModel.setShowInfoMessages)
self.ui.showWarningMessagesButton.toggled.connect(self.logFilterModel.setShowWarningMessages)
self.ui.showErrorMessagesButton.toggled.connect(self.logFilterModel.setShowErrorMessages)
# context menu
self.contextMenu = QMenu()
self.actionExpandAll = self.contextMenu.addAction("Expand all")
self.actionExpandAll.triggered.connect(self.ui.treeView.expandAll)
self.actionCollapseAll = self.contextMenu.addAction("Collapse all")
self.actionCollapseAll.triggered.connect(self.ui.treeView.collapseAll)
self.contextMenu.addSeparator()
self.actionCopyVariableName = self.contextMenu.addAction("Copy Variable Name", self.copyVariableName)
self.actionCopyValueReference = self.contextMenu.addAction("Copy Value Reference", self.copyValueReference)
self.contextMenu.addSeparator()
self.actionEditTable = self.contextMenu.addAction("Edit Table", self.editTable)
self.contextMenu.addSeparator()
self.columnsMenu = self.contextMenu.addMenu('Columns')
action = self.columnsMenu.addAction('Show All')
action.triggered.connect(self.showAllColumns)
action = self.columnsMenu.addAction('Hide All')
action.triggered.connect(self.hideAllColumns)
self.columnsMenu.addSeparator()
for column in COLLAPSABLE_COLUMNS:
action = self.columnsMenu.addAction(column)
action.setCheckable(True)
action.toggled.connect(lambda show, col=column: self.showColumn(col, show))
self.showColumnActions[column] = action
self.contextMenu.addSeparator()
self.actionClearPlots = self.contextMenu.addAction("Clear Plots", self.clearPlots)
# file menu
self.ui.actionExit.triggered.connect(QApplication.closeAllWindows)
self.ui.actionLoadStartValues.triggered.connect(self.loadStartValues)
self.ui.actionReload.triggered.connect(lambda: self.load(self.filename))
self.ui.actionSaveChanges.triggered.connect(self.saveChanges)
# tools menu
self.ui.actionValidateFMU.triggered.connect(self.validateFMU)
self.ui.actionCompileDarwinBinary.triggered.connect(lambda: self.compilePlatformBinary('darwin64'))
self.ui.actionCompileLinuxBinary.triggered.connect(lambda: self.compilePlatformBinary('linux64'))
self.ui.actionCompileWin32Binary.triggered.connect(lambda: self.compilePlatformBinary('win32'))
self.ui.actionCompileWin64Binary.triggered.connect(lambda: self.compilePlatformBinary('win64'))
self.ui.actionCreateJupyterNotebook.triggered.connect(self.createJupyterNotebook)
self.ui.actionCreateCMakeProject.triggered.connect(self.createCMakeProject)
self.ui.actionAddWindows32Remoting.triggered.connect(lambda: self.addRemotingBinaries('win64', 'win32'))
self.ui.actionAddLinux64Remoting.triggered.connect(lambda: self.addRemotingBinaries('linux64', 'win64'))
self.ui.actionAddCoSimulationWrapper.triggered.connect(self.addCoSimulationWrapper)
# help menu
self.ui.actionOpenFMI1SpecCS.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://fmi-standard.org/assets/releases/FMI_for_CoSimulation_v1.0.1.pdf')))
self.ui.actionOpenFMI1SpecME.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://fmi-standard.org/assets/releases/FMI_for_ModelExchange_v1.0.1.pdf')))
self.ui.actionOpenFMI2Spec.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://github.com/modelica/fmi-standard/releases/download/v2.0.3/FMI-Specification-2.0.3.pdf')))
self.ui.actionOpenTestFMUs.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://github.com/modelica/fmi-cross-check/tree/master/fmus')))
self.ui.actionOpenWebsite.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://github.com/CATIA-Systems/FMPy')))
self.ui.actionShowReleaseNotes.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://fmpy.readthedocs.io/en/latest/changelog/')))
# filter menu
self.filterMenu = QMenu()
self.filterMenu.addAction(self.ui.actionFilterInputs)
self.filterMenu.addAction(self.ui.actionFilterOutputs)
self.filterMenu.addAction(self.ui.actionFilterParameters)
self.filterMenu.addAction(self.ui.actionFilterCalculatedParameters)
self.filterMenu.addAction(self.ui.actionFilterIndependentVariables)
self.filterMenu.addAction(self.ui.actionFilterLocalVariables)
self.ui.filterToolButton.setMenu(self.filterMenu)
# status bar
self.statusIconLabel = ClickableLabel(self)
self.statusIconLabel.setStyleSheet("QLabel { margin-left: 5px; }")
self.statusIconLabel.clicked.connect(lambda: self.setCurrentPage(self.ui.logPage))
self.ui.statusBar.addPermanentWidget(self.statusIconLabel)
self.statusTextLabel = ClickableLabel(self)
self.statusTextLabel.setMinimumWidth(10)
self.statusTextLabel.clicked.connect(lambda: self.setCurrentPage(self.ui.logPage))
self.ui.statusBar.addPermanentWidget(self.statusTextLabel)
self.ui.statusBar.addPermanentWidget(QWidget(self), 1) # spacer
self.simulationProgressBar = QProgressBar(self)
self.simulationProgressBar.setFixedHeight(18)
self.ui.statusBar.addPermanentWidget(self.simulationProgressBar)
self.simulationProgressBar.setVisible(False)
# connect signals and slots
self.ui.actionNewWindow.triggered.connect(self.newWindow)
self.ui.openButton.clicked.connect(self.open)
self.ui.actionOpen.triggered.connect(self.open)
self.ui.actionSaveResult.triggered.connect(self.saveResult)
self.ui.actionSavePlottedResult.triggered.connect(lambda: self.saveResult(plotted=True))
self.ui.actionSimulate.triggered.connect(self.startSimulation)
self.ui.actionSettings.triggered.connect(lambda: self.setCurrentPage(self.ui.settingsPage))
self.ui.actionShowLog.triggered.connect(lambda: self.setCurrentPage(self.ui.logPage))
self.ui.actionShowResults.triggered.connect(lambda: self.setCurrentPage(self.ui.resultPage))
self.fmiTypeComboBox.currentTextChanged.connect(self.updateSimulationSettings)
self.ui.solverComboBox.currentTextChanged.connect(self.updateSimulationSettings)
self.variableSelected.connect(self.updatePlotLayout)
self.variableDeselected.connect(self.updatePlotLayout)
self.tableModel.variableSelected.connect(self.selectVariable)
self.tableModel.variableDeselected.connect(self.deselectVariable)
self.treeModel.variableSelected.connect(self.selectVariable)
self.treeModel.variableDeselected.connect(self.deselectVariable)
self.ui.filterLineEdit.textChanged.connect(self.treeFilterModel.setFilterFixedString)
self.ui.filterLineEdit.textChanged.connect(self.tableFilterModel.setFilterFixedString)
self.ui.filterToolButton.toggled.connect(self.treeFilterModel.setFilterByCausality)
self.ui.filterToolButton.toggled.connect(self.tableFilterModel.setFilterByCausality)
self.log.currentMessageChanged.connect(self.setStatusMessage)
self.ui.selectInputButton.clicked.connect(self.selectInputFile)
self.ui.actionShowAboutDialog.triggered.connect(self.showAboutDialog)
if os.name == 'nt':
self.ui.actionCreateDesktopShortcut.triggered.connect(self.createDesktopShortcut)
self.ui.actionAddFileAssociation.triggered.connect(self.addFileAssociation)
else:
self.ui.actionCreateDesktopShortcut.setEnabled(False)
self.ui.actionAddFileAssociation.setEnabled(False)
self.ui.tableViewToolButton.toggled.connect(lambda show: self.ui.variablesStackedWidget.setCurrentWidget(self.ui.tablePage if show else self.ui.treePage))
for model in [self.treeFilterModel, self.tableFilterModel]:
self.ui.actionFilterInputs.triggered.connect(model.setFilterInputs)
self.ui.actionFilterOutputs.triggered.connect(model.setFilterOutputs)
self.ui.actionFilterParameters.triggered.connect(model.setFilterParameters)
self.ui.actionFilterCalculatedParameters.triggered.connect(model.setFilterCalculatedParameters)
self.ui.actionFilterIndependentVariables.triggered.connect(model.setFilterIndependentVariables)
self.ui.actionFilterLocalVariables.triggered.connect(model.setFilterLocalVariables)
self.ui.treeView.customContextMenuRequested.connect(self.showContextMenu)
self.ui.tableView.customContextMenuRequested.connect(self.showContextMenu)
def newWindow(self):
window = MainWindow()
window.show()
def show(self):
super(MainWindow, self).show()
self.move(self.frameGeometry().topLeft() + self.windowOffset)
self.windowOffset += QPoint(20, 20)
def showContextMenu(self, point):
""" Update and show the variables context menu """
from .TableDialog import TableDialog
if self.ui.variablesStackedWidget.currentWidget() == self.ui.treePage:
currentView = self.ui.treeView
else:
currentView = self.ui.tableView
self.actionExpandAll.setEnabled(currentView == self.ui.treeView)
self.actionCollapseAll.setEnabled(currentView == self.ui.treeView)
selected = self.getSelectedVariables()
self.actionEditTable.setEnabled(len(selected) == 1 and TableDialog.canEdit(selected[0]))
can_copy = len(selected) > 0
self.actionCopyVariableName.setEnabled(can_copy)
self.actionCopyValueReference.setEnabled(can_copy)
self.contextMenu.exec_(currentView.mapToGlobal(point))
def load(self, filename):
import zipfile
if not self.isVisible():
self.show()
try:
self.modelDescription = md = read_model_description(filename)
except Exception as e:
QMessageBox.warning(self, "Failed to load FMU", "Failed to load %s. %s" % (filename, e))
return
# show model.png
try:
pixmap = QPixmap()
# load the model.png
with zipfile.ZipFile(filename, 'r') as zf:
pixmap.loadFromData(zf.read('model.png'), format='PNG')
# show the unscaled version in tooltip
buffer = QBuffer()
buffer.open(QIODevice.WriteOnly)
pixmap.save(buffer, "PNG", quality=100)
image = bytes(buffer.data().toBase64()).decode()
html = '<img src="data:image/png;base64,{}">'.format(image)
self.ui.modelImageLabel.setToolTip(html)
# show a scaled preview in "Model Info"
pixmap = pixmap.scaled(200, 200, Qt.KeepAspectRatio, Qt.SmoothTransformation)
self.ui.modelImageLabel.setPixmap(pixmap)
except:
self.ui.modelImageLabel.setPixmap(QPixmap())
self.ui.modelImageLabel.setToolTip(None)
self.filename = filename
platforms = supported_platforms(self.filename)
self.variables.clear()
self.selectedVariables.clear()
self.startValues.clear()
for v in md.modelVariables:
self.variables[v.name] = v
if v.causality == 'output' and not v.dimensions:
self.selectedVariables.add(v)
fmi_types = []
if md.coSimulation:
fmi_types.append('Co-Simulation')
if md.modelExchange:
fmi_types.append('Model Exchange')
experiment = md.defaultExperiment
# toolbar
if experiment is not None and experiment.stopTime is not None:
self.stopTimeLineEdit.setText(str(experiment.stopTime))
# actions
self.ui.actionValidateFMU.setEnabled(True)
can_compile = md.fmiVersion == '2.0' and 'c-code' in platforms
self.ui.actionCompileDarwinBinary.setEnabled(can_compile and fmpy.system == 'darwin')
self.ui.actionCompileLinuxBinary.setEnabled(can_compile and fmpy.system in ['linux', 'windows'])
self.ui.actionCompileWin32Binary.setEnabled(can_compile and fmpy.system == 'windows')
self.ui.actionCompileWin64Binary.setEnabled(can_compile and fmpy.system == 'windows')
self.ui.actionCreateCMakeProject.setEnabled(can_compile)
self.ui.actionCreateJupyterNotebook.setEnabled(True)
self.ui.actionAddWindows32Remoting.setEnabled(md.fmiVersion == '2.0' and 'win32' in platforms and 'win64' not in platforms)
self.ui.actionAddLinux64Remoting.setEnabled(md.fmiVersion == '2.0' and 'win64' in platforms and 'linux64' not in platforms)
can_add_cswrapper = md.fmiVersion == '2.0' and md.coSimulation is None and md.modelExchange is not None
self.ui.actionAddCoSimulationWrapper.setEnabled(can_add_cswrapper)
# variables view
self.treeModel.setModelDescription(md)
self.tableModel.setModelDescription(md)
self.treeFilterModel.invalidate()
self.tableFilterModel.invalidate()
self.ui.treeView.reset()
self.ui.tableView.reset()
# settings page
self.ui.fmiVersionLabel.setText(md.fmiVersion)
self.ui.fmiTypeLabel.setText(', '.join(fmi_types))
self.ui.platformsLabel.setText(', '.join(platforms))
self.ui.modelNameLabel.setText(md.modelName)
self.ui.descriptionLabel.setText(md.description)
self.ui.numberOfContinuousStatesLabel.setText(str(md.numberOfContinuousStates))
self.ui.numberOfEventIndicatorsLabel.setText(str(md.numberOfEventIndicators))
self.ui.numberOfVariablesLabel.setText(str(len(md.modelVariables)))
self.ui.generationToolLabel.setText(md.generationTool)
self.ui.generationDateAndTimeLabel.setText(md.generationDateAndTime)
# relative tolerance
if experiment is not None and experiment.tolerance is not None:
relative_tolerance = experiment.tolerance
else:
relative_tolerance = 1e-6
self.ui.relativeToleranceLineEdit.setText(str(relative_tolerance))
# output interval
if experiment is not None and experiment.stepSize is not None:
output_interval = float(experiment.stepSize)
while output_interval > 1000:
output_interval *= 0.5
else:
output_interval = float(self.stopTimeLineEdit.text()) / 500
self.ui.outputIntervalLineEdit.setText(str(output_interval))
self.fmiTypeComboBox.clear()
self.fmiTypeComboBox.addItems(fmi_types)
self.updateSimulationSettings()
self.setCurrentPage(self.ui.settingsPage)
self.ui.dockWidget.show()
self.ui.actionReload.setEnabled(True)
self.ui.actionSettings.setEnabled(True)
self.ui.actionShowLog.setEnabled(True)
self.ui.actionShowResults.setEnabled(False)
can_sim, _ = can_simulate(platforms)
self.ui.actionLoadStartValues.setEnabled(can_sim)
self.ui.actionSimulate.setEnabled(can_sim)
self.stopTimeLineEdit.setEnabled(can_sim)
self.fmiTypeComboBox.setEnabled(can_sim and len(fmi_types) > 1)
self.ui.settingsGroupBox.setEnabled(can_sim)
settings = QSettings()
recent_files = settings.value("recentFiles", defaultValue=[])
recent_files = self.removeDuplicates([filename] + recent_files)
# save the 10 most recent files
settings.setValue('recentFiles', recent_files[:10])
self.setWindowTitle("%s - FMPy" % os.path.normpath(filename))
self.createGraphics()
def open(self):
start_dir = QDir.homePath()
settings = QSettings()
recent_files = settings.value("recentFiles", defaultValue=[])
for filename in recent_files:
dirname = os.path.dirname(filename)
if os.path.isdir(dirname):
start_dir = dirname
break
filename, _ = QFileDialog.getOpenFileName(parent=self,
caption="Open File",
directory=start_dir,
filter="FMUs (*.fmu);;All Files (*.*)")
if filename:
self.load(filename)
def setCurrentPage(self, widget):
""" Set the current page and the actions """
# block the signals during the update
self.ui.actionSettings.blockSignals(True)
self.ui.actionShowLog.blockSignals(True)
self.ui.actionShowResults.blockSignals(True)
self.ui.stackedWidget.setCurrentWidget(widget)
# toggle the actions
self.ui.actionSettings.setChecked(widget == self.ui.settingsPage)
self.ui.actionShowLog.setChecked(widget == self.ui.logPage)
self.ui.actionShowResults.setChecked(widget == self.ui.resultPage)
# un-block the signals during the update
self.ui.actionSettings.blockSignals(False)
self.ui.actionShowLog.blockSignals(False)
self.ui.actionShowResults.blockSignals(False)
def selectInputFile(self):
start_dir = os.path.dirname(self.filename)
filename, _ = QFileDialog.getOpenFileName(parent=self,
caption="Select Input File",
directory=start_dir,
filter="FMUs (*.csv);;All Files (*.*)")
if filename:
self.ui.inputFilenameLineEdit.setText(filename)
def createInputFile(self):
""" Create an input file based on the input variables in the model description """
input_variables = []
for variable in self.modelDescription.modelVariables:
if variable.causality == 'input':
input_variables.append(variable)
if len(input_variables) == 0:
QMessageBox.warning(self,
"Cannot create input file",
"The input file cannot be created because the model has no input variables")
return
filename, _ = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(parent=self,
caption="Save Input File",
directory=filename + '_in.csv',
filter="Comma Separated Values (*.csv);;All Files (*.*)")
if not filename:
return
with open(filename, 'w') as f:
# column names
f.write('"time"')
for variable in input_variables:
f.write(',"%s"' % variable.name)
f.write('\n')
# example data
f.write(','.join(['0'] * (len(input_variables) + 1)) + '\n')
self.ui.inputFilenameLineEdit.setText(filename)
def showInputFileInExplorer(self):
""" Reveal the input file in the file browser """
filename = self.ui.inputFilenameLineEdit.text()
if not os.path.isfile(filename):
QMessageBox.warning(self, "Cannot show input file", "The input file does not exist")
return
QDesktopServices.openUrl(QUrl.fromLocalFile(os.path.dirname(filename)))
def openInputFile(self):
""" Open the input file in the default application """
filename = self.ui.inputFilenameLineEdit.text()
if not os.path.isfile(filename):
QMessageBox.warning(self, "Cannot open input file", "The input file does not exist")
return
QDesktopServices.openUrl(QUrl.fromLocalFile(filename))
def updateSimulationSettings(self):
if self.fmiTypeComboBox.currentText() == 'Co-Simulation':
self.ui.solverComboBox.setEnabled(False)
self.ui.stepSizeLineEdit.setEnabled(False)
self.ui.relativeToleranceLineEdit.setEnabled(True)
else:
self.ui.solverComboBox.setEnabled(True)
fixed_step = self.ui.solverComboBox.currentText() == 'Fixed-step'
self.ui.stepSizeLineEdit.setEnabled(fixed_step)
self.ui.relativeToleranceLineEdit.setEnabled(not fixed_step)
def selectVariable(self, variable):
self.selectedVariables.add(variable)
self.variableSelected.emit(variable)
def deselectVariable(self, variable):
self.selectedVariables.remove(variable)
self.variableDeselected.emit(variable)
def startSimulation(self):
from fmpy.gui.simulation import SimulationThread
try:
stop_time = float(self.stopTimeLineEdit.text())
step_size = float(self.ui.stepSizeLineEdit.text())
relative_tolerance = float(self.ui.relativeToleranceLineEdit.text())
if self.ui.outputIntervalRadioButton.isChecked():
output_interval = float(self.ui.outputIntervalLineEdit.text())
else:
max_samples = float(self.ui.maxSamplesLineEdit.text())
output_interval = stop_time / max_samples
except Exception as ex:
self.log.log('error', "Failed to start simulation: %s" % ex)
self.ui.stackedWidget.setCurrentWidget(self.ui.logPage)
return
step_size = min(step_size, output_interval)
if self.ui.solverComboBox.currentText() == 'Fixed-step':
solver = 'Euler'
else:
solver = 'CVode'
if self.ui.inputCheckBox.isChecked():
input_variables = []
for variable in self.modelDescription.modelVariables:
if variable.causality == 'input':
input_variables.append(variable.name)
try:
from fmpy.util import read_csv
filename = self.ui.inputFilenameLineEdit.text()
input = read_csv(filename, variable_names=input_variables)
except Exception as e:
self.log.log('error', "Failed to load input from '%s'. %s" % (filename, e))
return
else:
input = None
output = []
for variable in self.modelDescription.modelVariables:
output.append(variable.name)
fmi_type = 'CoSimulation' if self.fmiTypeComboBox.currentText() == 'Co-Simulation' else 'ModelExchange'
self.simulationThread = SimulationThread(filename=self.filename,
fmiType=fmi_type,
stopTime=stop_time,
solver=solver,
stepSize=step_size,
relativeTolerance=relative_tolerance,
outputInterval=output_interval,
startValues=self.startValues,
applyDefaultStartValues=self.ui.applyDefaultStartValuesCheckBox.isChecked(),
input=input,
output=output,
debugLogging=self.ui.debugLoggingCheckBox.isChecked(),
fmiLogging=self.ui.logFMICallsCheckBox.isChecked())
self.ui.actionSimulate.setIcon(QIcon(':/icons/stop.png'))
self.ui.actionSimulate.setToolTip("Stop simulation")
self.ui.actionSimulate.triggered.disconnect(self.startSimulation)
self.ui.actionSimulate.triggered.connect(self.simulationThread.stop)
self.simulationProgressBar.setVisible(True)
self.simulationThread.messageChanged.connect(self.log.log)
self.simulationThread.progressChanged.connect(self.simulationProgressBar.setValue)
self.simulationThread.finished.connect(self.simulationFinished)
if self.ui.clearLogOnStartButton.isChecked():
self.log.clear()
self.setCurrentPage(self.ui.resultPage)
self.simulationThread.start()
self.plotUpdateTimer.start(100)
self.updatePlotLayout()
def simulationFinished(self):
# update UI
self.ui.actionSimulate.triggered.disconnect(self.simulationThread.stop)
self.ui.actionSimulate.triggered.connect(self.startSimulation)
self.ui.actionSimulate.setIcon(QIcon(':/icons/play.png'))
self.ui.actionSimulate.setToolTip("Start simulation")
self.plotUpdateTimer.stop()
self.simulationProgressBar.setVisible(False)
self.ui.actionShowResults.setEnabled(True)
self.ui.actionSettings.setEnabled(True)
self.setCurrentPage(self.ui.resultPage)
self.updatePlotLayout()
if self.result is None:
self.setCurrentPage(self.ui.logPage)
else:
self.ui.actionSaveResult.setEnabled(True)
self.ui.actionSavePlottedResult.setEnabled(True)
self.result = self.simulationThread.result
self.simulationThread = None
self.updatePlotData()
def updatePlotData(self):
import numpy as np
if self.simulationThread is not None and len(self.simulationThread.rows) > 1:
# get results from current simulation
self.result = np.array(self.simulationThread.rows, dtype=np.dtype(self.simulationThread.cols))
if self.result is None:
return # no results available yet
time = self.result['time']
for variable, curve in self.curves:
if variable.name not in self.result.dtype.names:
continue
y = self.result[variable.name]
if variable.type == 'Real':
curve.setData(x=time, y=y)
else:
curve.setData(x=np.repeat(time, 2)[1:], y=np.repeat(y, 2)[:-1])
def updatePlotLayout(self):
self.ui.plotWidget.clear()
self.curves[:] = []
if self.simulationThread is not None:
stop_time = self.simulationThread.stopTime
elif self.result is not None:
stop_time = self.result['time'][-1]
else:
stop_time = 1.0
pen = (0, 0, 255)
for variable in self.selectedVariables:
self.ui.plotWidget.nextRow()
plot = self.ui.plotWidget.addPlot()
if variable.type == 'Real':
curve = plot.plot(pen=pen)
else:
if variable.type == 'Boolean':
plot.setYRange(0, 1, padding=0.2)
plot.getAxis('left').setTicks([[(0, 'false'), (1, 'true')], []])
curve = plot.plot(pen=pen, fillLevel=0, fillBrush=(0, 0, 255, 50), antialias=False)
else:
curve = plot.plot(pen=pen, antialias=False)
plot.setXRange(0, stop_time, padding=0.05)
plot.setLabel('left', variable.name)
plot.showGrid(x=True, y=True, alpha=0.25)
# hide the auto-scale button and disable context menu and mouse interaction
plot.hideButtons()
plot.setMouseEnabled(False, False)
plot.setMenuEnabled(False)
self.curves.append((variable, curve))
self.updatePlotData()
def showColumn(self, name, show):
if name in self.showColumnActions:
self.showColumnActions[name].setChecked(show)
i = VariablesModel.COLUMN_NAMES.index(name)
self.ui.treeView.setColumnHidden(i, not show)
self.ui.tableView.setColumnHidden(i, not show)
def showAllColumns(self):
for name in COLLAPSABLE_COLUMNS:
self.showColumn(name, True)
def hideAllColumns(self):
for name in COLLAPSABLE_COLUMNS:
self.showColumn(name, False)
def setStatusMessage(self, level, text):
if level in ['debug', 'info', 'warning', 'error']:
self.statusIconLabel.setPixmap(QPixmap(':/icons/%s-16x16.png' % level))
else:
self.statusIconLabel.setPixmap(QPixmap())
self.statusTextLabel.setText(text)
def dragEnterEvent(self, event):
for url in event.mimeData().urls():
if not url.isLocalFile():
return
event.acceptProposedAction()
def dropEvent(self, event):
urls = event.mimeData().urls()
for url in urls:
if url == urls[0]:
window = self
else:
window = MainWindow()
window.load(url.toLocalFile())
def saveResult(self, plotted=False):
filename, _ = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(parent=self,
caption="Save Result",
directory=filename + '_out.csv',
filter="Comma Separated Values (*.csv);;All Files (*.*)")
if filename:
from ..util import write_csv
if plotted:
columns = [variable.name for variable in self.selectedVariables]
else:
columns = None
try:
write_csv(filename=filename, result=self.result, columns=columns)
except Exception as e:
QMessageBox.critical(self, "Failed to write result", '"Failed to write "%s". %s' % (filename, e))
def createDesktopShortcut(self):
""" Create a desktop shortcut to start the GUI """
import os
from win32com.client import Dispatch
import sys
env = os.environ.get('CONDA_DEFAULT_ENV')
if env is None:
target_path = sys.executable
root, ext = os.path.splitext(target_path)
pythonw = root + 'w' + ext
if os.path.isfile(pythonw):
target_path = pythonw
arguments = '-m fmpy.gui'
else:
for path in os.environ["PATH"].split(os.pathsep):
activate = os.path.join(path, 'activate.bat')
if os.path.isfile(activate):
break
target_path = r'%windir%\System32\cmd.exe'
arguments = '/C ""%s" %s && python -m fmpy.gui"' % (activate, env)
file_path = os.path.dirname(__file__)
icon = os.path.join(file_path, 'icons', 'app_icon.ico')
desktop_locations = QStandardPaths.standardLocations(QStandardPaths.DesktopLocation)
shortcut_path = os.path.join(desktop_locations[0], "FMPy GUI.lnk")
shell = Dispatch('WScript.Shell')
shortcut = shell.CreateShortCut(shortcut_path)
shortcut.Targetpath = target_path
shortcut.Arguments = arguments
# shortcut.WorkingDirectory = ...
shortcut.IconLocation = icon
shortcut.save()
def showAboutDialog(self):
dialog = AboutDialog(self)
dialog.show()
@staticmethod
def removeDuplicates(seq):
""" Remove duplicates from a sequence """
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
def validateFMU(self):
from ..validation import validate_fmu
problems = validate_fmu(self.filename)
if problems:
button = QMessageBox.question(self, "Validation failed", "%d problems have been found. Save validation messages?" % len(problems))
if button == QMessageBox.Yes:
filename, _ = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(parent=self,
caption="Save validation messages",
directory=filename + '_validation.txt',
filter="Text Files (*.txt);;All Files (*.*)")
if filename:
with open(filename, 'w') as f:
f.writelines(problems)
else:
QMessageBox.information(self, "Validation successful", "No problems have been found.")
def addFileAssociation(self):
""" Associate *.fmu with the FMPy GUI """
try:
from winreg import HKEY_CURRENT_USER, KEY_WRITE, REG_SZ, OpenKey, CreateKey, SetValueEx, CloseKey
env = os.environ.get('CONDA_DEFAULT_ENV_')
if env is None:
python = sys.executable
root, ext = os.path.splitext(python)
pythonw = root + 'w' + ext
if os.path.isfile(pythonw):
python = pythonw
target = '"%s" -m fmpy.gui "%%1"' % python
else:
# activate the conda environment
for path in os.environ["PATH"].split(os.pathsep):
activate = os.path.join(path, 'activate.bat')
if os.path.isfile(activate):
break
windir = os.environ['WINDIR']
cmd = os.path.join(windir, 'System32', 'cmd.exe')
target = r'%s /C ""%s" %s && python -m fmpy.gui %%1"' % (cmd, activate, env)
key_path = r'Software\Classes\fmpy.gui\shell\open\command'
CreateKey(HKEY_CURRENT_USER, key_path)
key = OpenKey(HKEY_CURRENT_USER, key_path, 0, KEY_WRITE)
SetValueEx(key, '', 0, REG_SZ, target)
CloseKey(key)
key_path = r'SOFTWARE\Classes\.fmu'
CreateKey(HKEY_CURRENT_USER, key_path)
key = OpenKey(HKEY_CURRENT_USER, key_path, 0, KEY_WRITE)
SetValueEx(key, '', 0, REG_SZ, 'fmpy.gui')
CloseKey(key)
QMessageBox.information(self, "File association added", "The file association for *.fmu has been added")
except Exception as e:
QMessageBox.critical(self, "File association failed", "The file association for *.fmu could not be added. %s" % e)
def copyValueReference(self):
""" Copy the value references of the selected variables to the clipboard """
text = '\n'.join([str(v.valueReference) for v in self.getSelectedVariables()])
QApplication.clipboard().setText(text)
def copyVariableName(self):
""" Copy the names of the selected variables to the clipboard """
text = '\n'.join([str(v.name) for v in self.getSelectedVariables()])
QApplication.clipboard().setText(text)
def getSelectedVariables(self):
""" Returns a list of selected variables in the current view """
variables = []
if self.ui.variablesStackedWidget.currentWidget() == self.ui.treePage:
for index in self.ui.treeView.selectionModel().selectedRows():
sourceIndex = self.treeFilterModel.mapToSource(index)
treeItem = sourceIndex.internalPointer()
if treeItem.variable is not None:
variables.append(treeItem.variable)
else:
for index in self.ui.tableView.selectionModel().selectedRows():
sourceIndex = self.tableFilterModel.mapToSource(index)
variable = sourceIndex.internalPointer()
variables.append(variable)
return variables
def clearPlots(self):
""" Clear all plots """
self.selectedVariables.clear()
self.updatePlotLayout()
def createGraphics(self):
""" Create the graphical representation of the FMU's inputs and outputs """
def variableColor(variable):
if variable.type.startswith(('Float', 'Real')):
return QColor.fromRgb(26, 77, 179)
elif variable.type.startswith(('Enumeration', 'Int', 'UInt')):
return QColor.fromRgb(179, 77, 26)
elif variable.type == 'Boolean':
return QColor.fromRgb(255, 0, 255)
elif variable.type == 'String':
return QColor.fromRgb(26, 114, 16)
elif variable.type == 'Binary':
return QColor.fromRgb(81, 81, 81)
else:
return QColor.fromRgb(0, 0, 0)
inputVariables = []
outputVariables = []
maxInputLabelWidth = 0
maxOutputLabelWidth = 0
textItem = QGraphicsTextItem()
fontMetrics = QFontMetricsF(textItem.font())
for variable in self.modelDescription.modelVariables:
if variable.causality == 'input':
inputVariables.append(variable)
elif variable.causality == 'output':
outputVariables.append(variable)
for variable in inputVariables:
maxInputLabelWidth = max(maxInputLabelWidth, fontMetrics.width(variable.name))
for variable in outputVariables:
maxOutputLabelWidth = max(maxOutputLabelWidth, fontMetrics.width(variable.name))
from math import floor
scene = QGraphicsScene()
self.ui.graphicsView.setScene(scene)
group = QGraphicsItemGroup()
scene.addItem(group)
group.setPos(200.5, -50.5)
lh = 15 # line height
w = max(150., maxInputLabelWidth + maxOutputLabelWidth + 20)
h = max(50., 10 + lh * max(len(inputVariables), len(outputVariables)))
block = QGraphicsRectItem(0, 0, w, h, group)
block.setPen(QColor.fromRgb(0, 0, 0))
pen = QPen()
pen.setWidthF(1)
font = QFont()
font.setPixelSize(10)
# inputs
y = floor((h - len(inputVariables) * lh) / 2 - 2)
for variable in inputVariables:
text = QGraphicsTextItem(variable.name, group)
text.setDefaultTextColor(QColor.fromRgb(0, 0, 0))
text.setFont(font)
text.setX(3)
text.setY(y)
polygon = QPolygonF([QPointF(-8, y + 7.5), QPointF(-1, y + 11), QPointF(-8, y + 14.5)])
path = QPainterPath()
path.addPolygon(polygon)
path.closeSubpath()
contour = QGraphicsPathItem(path, group)
contour.setPen(QPen(Qt.NoPen))
contour.setBrush(variableColor(variable))
pen = QPen()
pen.setColor(variableColor(variable))
pen.setJoinStyle(Qt.MiterJoin)
contour.setPen(pen)
y += lh
# outputs
y = floor((h - len(outputVariables) * lh) / 2 - 2)
for variable in outputVariables:
text = QGraphicsTextItem(variable.name, group)
text.setDefaultTextColor(QColor.fromRgb(0, 0, 0))
text.setFont(font)
text.setX(w - 3 - text.boundingRect().width())
text.setY(y)
polygon = QPolygonF([QPointF(w + 1, y + 7.5), QPointF(w + 8, y + 11), QPointF(w + 1, y + 14.5)])
path = QPainterPath()
path.addPolygon(polygon)
path.closeSubpath()
contour = QGraphicsPathItem(path, group)
contour.setPen(QPen(Qt.NoPen))
contour.setBrush(variableColor(variable))
pen = QPen()
pen.setColor(variableColor(variable))
pen.setJoinStyle(Qt.MiterJoin)
contour.setPen(pen)
y += lh
def saveChanges(self):
from ..util import change_fmu
output_file, _ = QFileDialog.getSaveFileName(parent=self,
caption='Save Changed FMU',
directory=self.filename,
filter='FMUs (*.fmu)')
if output_file:
change_fmu(input_file=self.filename, output_file=output_file, start_values=self.startValues)
def loadStartValues(self):
from ..util import get_start_values
start_values = get_start_values(self.filename)
self.startValues.update(start_values)
self.ui.treeView.reset()
self.ui.tableView.reset()
def editTable(self):
""" Open the table dialog """
from .TableDialog import TableDialog
variables = self.getSelectedVariables()
if len(variables) == 1:
start_values = self.startValues.copy()
dialog = TableDialog(modelVariables=self.modelDescription.modelVariables,
variable=variables[0],
startValues=start_values)
if dialog.exec_() == QDialog.Accepted:
self.startValues.clear()
self.startValues.update(start_values)
def compilePlatformBinary(self, target_platform):
""" Compile the platform binary """
from ..util import compile_platform_binary
platforms = supported_platforms(self.filename)
if target_platform in platforms:
button = QMessageBox.question(self, "Platform binary already exists",
f'The FMU already contains a binary for the platform "{target_platform}".'
' Do you want to compile and overwrite the existing binary?')
if button == QMessageBox.No:
return
try:
compile_platform_binary(self.filename, target_platform=target_platform)
except Exception as e:
QMessageBox.critical(self, "Failed to compile platform binaries", str(e))
return
self.load(self.filename)
def createJupyterNotebook(self):
""" Create a Juypyter Notebook to simulate the FMU """
from fmpy.util import create_jupyter_notebook
filename, ext = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(
parent=self,
directory=filename + '.ipynb',
filter='Jupyter Notebooks (*.ipynb);;All Files (*)'
)
if filename:
try:
create_jupyter_notebook(self.filename, filename)
except Exception as e:
QMessageBox.critical(self, "Failed to create Jupyter Notebook", str(e))
return
if QMessageBox.question(self, "Open Jupyter Notebook?", f"Start Jupyter and open {filename}?") == QMessageBox.Yes:
from subprocess import run, CREATE_NEW_CONSOLE
try:
run(['jupyter', 'notebook', filename], creationflags=CREATE_NEW_CONSOLE)
except Exception as e:
QMessageBox.critical(self, "Failed to start Jupyter", str(e))
def createCMakeProject(self):
""" Create a CMake project from a C code FMU """
from fmpy.util import create_cmake_project
project_dir = QFileDialog.getExistingDirectory(
parent=self,
caption='Select CMake Project Folder',
directory=os.path.dirname(self.filename))
if project_dir:
create_cmake_project(self.filename, project_dir)
def addRemotingBinaries(self, host_platform, remote_platform):
from ..util import add_remoting
try:
add_remoting(self.filename, host_platform, remote_platform)
except Exception as e:
QMessageBox.warning(self, "Failed to add Remoting Binaries",
f"Failed to add remoting binaries to {self.filename}. {e}")
self.load(self.filename)
def addCoSimulationWrapper(self):
""" Add the Co-Simulation Wrapper to the FMU """
from ..cswrapper import add_cswrapper
try:
add_cswrapper(self.filename)
except Exception as e:
QMessageBox.warning(self, "Failed to add Co-Simulation Wrapper",
"Failed to add Co-Simulation Wrapper %s. %s" % (self.filename, e))
self.load(self.filename)
| 41.966102
| 272
| 0.62265
|
try:
from . import compile_resources
compile_resources()
except Exception as e:
print("Failed to compiled resources. %s" % e)
import os
import sys
from PyQt5.QtCore import QCoreApplication, QDir, Qt, pyqtSignal, QUrl, QSettings, QPoint, QTimer, QStandardPaths, \
QPointF, QBuffer, QIODevice
from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QLineEdit, QComboBox, QFileDialog, QLabel, QVBoxLayout, QMenu, QMessageBox, QProgressDialog, QProgressBar, QDialog, QGraphicsScene, QGraphicsItemGroup, QGraphicsRectItem, QGraphicsTextItem, QGraphicsPathItem
from PyQt5.QtGui import QDesktopServices, QPixmap, QIcon, QDoubleValidator, QColor, QFont, QPen, QFontMetricsF, QPolygonF, QPainterPath
from fmpy.gui.generated.MainWindow import Ui_MainWindow
import fmpy
from fmpy import read_model_description, supported_platforms, platform
from fmpy.model_description import ScalarVariable
from fmpy.util import can_simulate
from fmpy.gui.model import VariablesTableModel, VariablesTreeModel, VariablesModel, VariablesFilterModel
from fmpy.gui.log import Log, LogMessagesFilterProxyModel
QCoreApplication.setApplicationVersion(fmpy.__version__)
QCoreApplication.setOrganizationName("CATIA-Systems")
QCoreApplication.setApplicationName("FMPy")
import pyqtgraph as pg
pg.setConfigOptions(background='w', foreground='k', antialias=True)
COLLAPSABLE_COLUMNS = ['Type', 'Value Reference', 'Initial', 'Causality', 'Variability', 'Min', 'Max']
class ClickableLabel(QLabel):
clicked = pyqtSignal()
def __init__(self, parent=None):
super(ClickableLabel, self).__init__(parent)
self.setCursor(Qt.PointingHandCursor)
def mousePressEvent(self, ev):
self.clicked.emit()
super(ClickableLabel, self).mousePressEvent(ev)
class AboutDialog(QDialog):
def __init__(self, parent=None):
super(AboutDialog, self).__init__(parent)
from .generated.AboutDialog import Ui_Dialog
from .. import __version__, platform, __file__
import sys
import os
self.ui = Ui_Dialog()
self.ui.setupUi(self)
flags = self.windowFlags()
flags &= ~Qt.WindowContextHelpButtonHint
flags |= Qt.MSWindowsFixedSizeDialogHint
self.setWindowFlags(flags)
self.ui.fmpyVersionLabel.setText(__version__)
self.ui.fmiPlatformLabel.setText(platform)
self.ui.installationPathLabel.setText(os.path.dirname(__file__))
self.ui.pythonInterpreterLabel.setText(sys.executable)
self.ui.pythonVersionLabel.setText(sys.version)
class MainWindow(QMainWindow):
variableSelected = pyqtSignal(ScalarVariable, name='variableSelected')
variableDeselected = pyqtSignal(ScalarVariable, name='variableDeselected')
windows = []
windowOffset = QPoint()
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
self.windows.append(self)
self.filename = None
self.result = None
self.modelDescription = None
self.variables = dict()
self.selectedVariables = set()
self.startValues = dict()
self.simulationThread = None
self.plotUpdateTimer = QTimer(self)
self.plotUpdateTimer.timeout.connect(self.updatePlotData)
self.curves = []
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.showColumnActions = {}
if sys.platform in ['darwin', 'linux']:
defaultFont = QFont()
defaultFont.setPixelSize(11)
QApplication.setFont(defaultFont)
self.setStyleSheet("QWidget { font-size: 11px; }")
self.ui.treeView.setAttribute(Qt.WA_MacShowFocusRect, False)
self.ui.tableView.setAttribute(Qt.WA_MacShowFocusRect, False)
self.ui.logTreeView.setAttribute(Qt.WA_MacShowFocusRect, False)
geo = QApplication.desktop().availableGeometry()
width = min(geo.width() * 0.85, 1100.0)
height = min(geo.height() * 0.85, 900.0)
self.resize(int(width), int(height))
self.ui.dockWidget.hide()
self.stopTimeLineEdit = QLineEdit("1")
self.stopTimeLineEdit.setToolTip("Stop time")
self.stopTimeLineEdit.setFixedWidth(50)
self.stopTimeValidator = QDoubleValidator(self)
self.stopTimeValidator.setBottom(0)
self.stopTimeLineEdit.setValidator(self.stopTimeValidator)
self.ui.toolBar.addWidget(self.stopTimeLineEdit)
spacer = QWidget(self)
spacer.setFixedWidth(10)
self.ui.toolBar.addWidget(spacer)
self.fmiTypeComboBox = QComboBox(self)
self.fmiTypeComboBox.addItem("Co-Simulation")
self.fmiTypeComboBox.setToolTip("FMI type")
self.fmiTypeComboBox.setSizeAdjustPolicy(QComboBox.AdjustToContents)
self.ui.toolBar.addWidget(self.fmiTypeComboBox)
self.ui.actionLoadStartValues.setEnabled(False)
self.ui.actionReload.setEnabled(False)
self.ui.actionSettings.setEnabled(False)
self.ui.actionShowLog.setEnabled(False)
self.ui.actionShowResults.setEnabled(False)
self.ui.actionSimulate.setEnabled(False)
self.ui.actionSaveResult.setEnabled(False)
self.ui.actionSavePlottedResult.setEnabled(False)
self.stopTimeLineEdit.setEnabled(False)
self.fmiTypeComboBox.setEnabled(False)
self.ui.dockWidget.setTitleBarWidget(QWidget())
self.ui.dockWidgetContents.setMinimumWidth(500)
self.tableModel = VariablesTableModel(self.selectedVariables, self.startValues)
self.tableFilterModel = VariablesFilterModel()
self.tableFilterModel.setSourceModel(self.tableModel)
self.tableFilterModel.setFilterCaseSensitivity(Qt.CaseInsensitive)
self.ui.tableView.setModel(self.tableFilterModel)
self.treeModel = VariablesTreeModel(self.selectedVariables, self.startValues)
self.treeFilterModel = VariablesFilterModel()
self.treeFilterModel.setSourceModel(self.treeModel)
self.treeFilterModel.setFilterCaseSensitivity(Qt.CaseInsensitive)
self.ui.treeView.setModel(self.treeFilterModel)
for i, (w, n) in enumerate(zip(VariablesModel.COLUMN_WIDTHS, VariablesModel.COLUMN_NAMES)):
self.ui.treeView.setColumnWidth(i, w)
self.ui.tableView.setColumnWidth(i, w)
self.hideAllColumns()
# populate the recent files list
settings = QSettings()
recent_files = settings.value("recentFiles", defaultValue=[])
recent_files = self.removeDuplicates(recent_files)
vbox = QVBoxLayout()
if recent_files:
added = set()
for file in recent_files[:5]:
link = QLabel('<a href="%s" style="text-decoration: none">%s</a>' % (file, os.path.basename(file)))
link.setToolTip(file)
link.linkActivated.connect(self.load)
vbox.addWidget(link)
added.add(file)
self.ui.recentFilesGroupBox.setLayout(vbox)
self.ui.recentFilesGroupBox.setVisible(len(recent_files) > 0)
# settings page
self.inputFileMenu = QMenu()
self.inputFileMenu.addAction("New input file...", self.createInputFile)
self.inputFileMenu.addSeparator()
self.inputFileMenu.addAction("Show in Explorer", self.showInputFileInExplorer)
self.inputFileMenu.addAction("Open in default application", self.openInputFile)
self.ui.selectInputButton.setMenu(self.inputFileMenu)
# log page
self.log = Log(self)
self.logFilterModel = LogMessagesFilterProxyModel(self)
self.logFilterModel.setSourceModel(self.log)
self.logFilterModel.setFilterCaseSensitivity(Qt.CaseInsensitive)
self.ui.logTreeView.setModel(self.logFilterModel)
self.ui.clearLogButton.clicked.connect(self.log.clear)
self.log.numberOfDebugMessagesChanged.connect(lambda n: self.ui.showDebugMessagesButton.setText(str(n)))
self.log.numberOfInfoMessagesChanged.connect(lambda n: self.ui.showInfoMessagesButton.setText(str(n)))
self.log.numberOfWarningMessagesChanged.connect(lambda n: self.ui.showWarningMessagesButton.setText(str(n)))
self.log.numberOfErrorMessagesChanged.connect(lambda n: self.ui.showErrorMessagesButton.setText(str(n)))
self.ui.logFilterLineEdit.textChanged.connect(self.logFilterModel.setFilterFixedString)
self.ui.showDebugMessagesButton.toggled.connect(self.logFilterModel.setShowDebugMessages)
self.ui.showInfoMessagesButton.toggled.connect(self.logFilterModel.setShowInfoMessages)
self.ui.showWarningMessagesButton.toggled.connect(self.logFilterModel.setShowWarningMessages)
self.ui.showErrorMessagesButton.toggled.connect(self.logFilterModel.setShowErrorMessages)
# context menu
self.contextMenu = QMenu()
self.actionExpandAll = self.contextMenu.addAction("Expand all")
self.actionExpandAll.triggered.connect(self.ui.treeView.expandAll)
self.actionCollapseAll = self.contextMenu.addAction("Collapse all")
self.actionCollapseAll.triggered.connect(self.ui.treeView.collapseAll)
self.contextMenu.addSeparator()
self.actionCopyVariableName = self.contextMenu.addAction("Copy Variable Name", self.copyVariableName)
self.actionCopyValueReference = self.contextMenu.addAction("Copy Value Reference", self.copyValueReference)
self.contextMenu.addSeparator()
self.actionEditTable = self.contextMenu.addAction("Edit Table", self.editTable)
self.contextMenu.addSeparator()
self.columnsMenu = self.contextMenu.addMenu('Columns')
action = self.columnsMenu.addAction('Show All')
action.triggered.connect(self.showAllColumns)
action = self.columnsMenu.addAction('Hide All')
action.triggered.connect(self.hideAllColumns)
self.columnsMenu.addSeparator()
for column in COLLAPSABLE_COLUMNS:
action = self.columnsMenu.addAction(column)
action.setCheckable(True)
action.toggled.connect(lambda show, col=column: self.showColumn(col, show))
self.showColumnActions[column] = action
self.contextMenu.addSeparator()
self.actionClearPlots = self.contextMenu.addAction("Clear Plots", self.clearPlots)
# file menu
self.ui.actionExit.triggered.connect(QApplication.closeAllWindows)
self.ui.actionLoadStartValues.triggered.connect(self.loadStartValues)
self.ui.actionReload.triggered.connect(lambda: self.load(self.filename))
self.ui.actionSaveChanges.triggered.connect(self.saveChanges)
# tools menu
self.ui.actionValidateFMU.triggered.connect(self.validateFMU)
self.ui.actionCompileDarwinBinary.triggered.connect(lambda: self.compilePlatformBinary('darwin64'))
self.ui.actionCompileLinuxBinary.triggered.connect(lambda: self.compilePlatformBinary('linux64'))
self.ui.actionCompileWin32Binary.triggered.connect(lambda: self.compilePlatformBinary('win32'))
self.ui.actionCompileWin64Binary.triggered.connect(lambda: self.compilePlatformBinary('win64'))
self.ui.actionCreateJupyterNotebook.triggered.connect(self.createJupyterNotebook)
self.ui.actionCreateCMakeProject.triggered.connect(self.createCMakeProject)
self.ui.actionAddWindows32Remoting.triggered.connect(lambda: self.addRemotingBinaries('win64', 'win32'))
self.ui.actionAddLinux64Remoting.triggered.connect(lambda: self.addRemotingBinaries('linux64', 'win64'))
self.ui.actionAddCoSimulationWrapper.triggered.connect(self.addCoSimulationWrapper)
# help menu
self.ui.actionOpenFMI1SpecCS.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://fmi-standard.org/assets/releases/FMI_for_CoSimulation_v1.0.1.pdf')))
self.ui.actionOpenFMI1SpecME.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://fmi-standard.org/assets/releases/FMI_for_ModelExchange_v1.0.1.pdf')))
self.ui.actionOpenFMI2Spec.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://github.com/modelica/fmi-standard/releases/download/v2.0.3/FMI-Specification-2.0.3.pdf')))
self.ui.actionOpenTestFMUs.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://github.com/modelica/fmi-cross-check/tree/master/fmus')))
self.ui.actionOpenWebsite.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://github.com/CATIA-Systems/FMPy')))
self.ui.actionShowReleaseNotes.triggered.connect(lambda: QDesktopServices.openUrl(QUrl('https://fmpy.readthedocs.io/en/latest/changelog/')))
# filter menu
self.filterMenu = QMenu()
self.filterMenu.addAction(self.ui.actionFilterInputs)
self.filterMenu.addAction(self.ui.actionFilterOutputs)
self.filterMenu.addAction(self.ui.actionFilterParameters)
self.filterMenu.addAction(self.ui.actionFilterCalculatedParameters)
self.filterMenu.addAction(self.ui.actionFilterIndependentVariables)
self.filterMenu.addAction(self.ui.actionFilterLocalVariables)
self.ui.filterToolButton.setMenu(self.filterMenu)
# status bar
self.statusIconLabel = ClickableLabel(self)
self.statusIconLabel.setStyleSheet("QLabel { margin-left: 5px; }")
self.statusIconLabel.clicked.connect(lambda: self.setCurrentPage(self.ui.logPage))
self.ui.statusBar.addPermanentWidget(self.statusIconLabel)
self.statusTextLabel = ClickableLabel(self)
self.statusTextLabel.setMinimumWidth(10)
self.statusTextLabel.clicked.connect(lambda: self.setCurrentPage(self.ui.logPage))
self.ui.statusBar.addPermanentWidget(self.statusTextLabel)
self.ui.statusBar.addPermanentWidget(QWidget(self), 1) # spacer
self.simulationProgressBar = QProgressBar(self)
self.simulationProgressBar.setFixedHeight(18)
self.ui.statusBar.addPermanentWidget(self.simulationProgressBar)
self.simulationProgressBar.setVisible(False)
# connect signals and slots
self.ui.actionNewWindow.triggered.connect(self.newWindow)
self.ui.openButton.clicked.connect(self.open)
self.ui.actionOpen.triggered.connect(self.open)
self.ui.actionSaveResult.triggered.connect(self.saveResult)
self.ui.actionSavePlottedResult.triggered.connect(lambda: self.saveResult(plotted=True))
self.ui.actionSimulate.triggered.connect(self.startSimulation)
self.ui.actionSettings.triggered.connect(lambda: self.setCurrentPage(self.ui.settingsPage))
self.ui.actionShowLog.triggered.connect(lambda: self.setCurrentPage(self.ui.logPage))
self.ui.actionShowResults.triggered.connect(lambda: self.setCurrentPage(self.ui.resultPage))
self.fmiTypeComboBox.currentTextChanged.connect(self.updateSimulationSettings)
self.ui.solverComboBox.currentTextChanged.connect(self.updateSimulationSettings)
self.variableSelected.connect(self.updatePlotLayout)
self.variableDeselected.connect(self.updatePlotLayout)
self.tableModel.variableSelected.connect(self.selectVariable)
self.tableModel.variableDeselected.connect(self.deselectVariable)
self.treeModel.variableSelected.connect(self.selectVariable)
self.treeModel.variableDeselected.connect(self.deselectVariable)
self.ui.filterLineEdit.textChanged.connect(self.treeFilterModel.setFilterFixedString)
self.ui.filterLineEdit.textChanged.connect(self.tableFilterModel.setFilterFixedString)
self.ui.filterToolButton.toggled.connect(self.treeFilterModel.setFilterByCausality)
self.ui.filterToolButton.toggled.connect(self.tableFilterModel.setFilterByCausality)
self.log.currentMessageChanged.connect(self.setStatusMessage)
self.ui.selectInputButton.clicked.connect(self.selectInputFile)
self.ui.actionShowAboutDialog.triggered.connect(self.showAboutDialog)
if os.name == 'nt':
self.ui.actionCreateDesktopShortcut.triggered.connect(self.createDesktopShortcut)
self.ui.actionAddFileAssociation.triggered.connect(self.addFileAssociation)
else:
self.ui.actionCreateDesktopShortcut.setEnabled(False)
self.ui.actionAddFileAssociation.setEnabled(False)
self.ui.tableViewToolButton.toggled.connect(lambda show: self.ui.variablesStackedWidget.setCurrentWidget(self.ui.tablePage if show else self.ui.treePage))
for model in [self.treeFilterModel, self.tableFilterModel]:
self.ui.actionFilterInputs.triggered.connect(model.setFilterInputs)
self.ui.actionFilterOutputs.triggered.connect(model.setFilterOutputs)
self.ui.actionFilterParameters.triggered.connect(model.setFilterParameters)
self.ui.actionFilterCalculatedParameters.triggered.connect(model.setFilterCalculatedParameters)
self.ui.actionFilterIndependentVariables.triggered.connect(model.setFilterIndependentVariables)
self.ui.actionFilterLocalVariables.triggered.connect(model.setFilterLocalVariables)
self.ui.treeView.customContextMenuRequested.connect(self.showContextMenu)
self.ui.tableView.customContextMenuRequested.connect(self.showContextMenu)
def newWindow(self):
window = MainWindow()
window.show()
def show(self):
super(MainWindow, self).show()
self.move(self.frameGeometry().topLeft() + self.windowOffset)
self.windowOffset += QPoint(20, 20)
def showContextMenu(self, point):
from .TableDialog import TableDialog
if self.ui.variablesStackedWidget.currentWidget() == self.ui.treePage:
currentView = self.ui.treeView
else:
currentView = self.ui.tableView
self.actionExpandAll.setEnabled(currentView == self.ui.treeView)
self.actionCollapseAll.setEnabled(currentView == self.ui.treeView)
selected = self.getSelectedVariables()
self.actionEditTable.setEnabled(len(selected) == 1 and TableDialog.canEdit(selected[0]))
can_copy = len(selected) > 0
self.actionCopyVariableName.setEnabled(can_copy)
self.actionCopyValueReference.setEnabled(can_copy)
self.contextMenu.exec_(currentView.mapToGlobal(point))
def load(self, filename):
import zipfile
if not self.isVisible():
self.show()
try:
self.modelDescription = md = read_model_description(filename)
except Exception as e:
QMessageBox.warning(self, "Failed to load FMU", "Failed to load %s. %s" % (filename, e))
return
# show model.png
try:
pixmap = QPixmap()
# load the model.png
with zipfile.ZipFile(filename, 'r') as zf:
pixmap.loadFromData(zf.read('model.png'), format='PNG')
# show the unscaled version in tooltip
buffer = QBuffer()
buffer.open(QIODevice.WriteOnly)
pixmap.save(buffer, "PNG", quality=100)
image = bytes(buffer.data().toBase64()).decode()
html = '<img src="data:image/png;base64,{}">'.format(image)
self.ui.modelImageLabel.setToolTip(html)
# show a scaled preview in "Model Info"
pixmap = pixmap.scaled(200, 200, Qt.KeepAspectRatio, Qt.SmoothTransformation)
self.ui.modelImageLabel.setPixmap(pixmap)
except:
self.ui.modelImageLabel.setPixmap(QPixmap())
self.ui.modelImageLabel.setToolTip(None)
self.filename = filename
platforms = supported_platforms(self.filename)
self.variables.clear()
self.selectedVariables.clear()
self.startValues.clear()
for v in md.modelVariables:
self.variables[v.name] = v
if v.causality == 'output' and not v.dimensions:
self.selectedVariables.add(v)
fmi_types = []
if md.coSimulation:
fmi_types.append('Co-Simulation')
if md.modelExchange:
fmi_types.append('Model Exchange')
experiment = md.defaultExperiment
# toolbar
if experiment is not None and experiment.stopTime is not None:
self.stopTimeLineEdit.setText(str(experiment.stopTime))
# actions
self.ui.actionValidateFMU.setEnabled(True)
can_compile = md.fmiVersion == '2.0' and 'c-code' in platforms
self.ui.actionCompileDarwinBinary.setEnabled(can_compile and fmpy.system == 'darwin')
self.ui.actionCompileLinuxBinary.setEnabled(can_compile and fmpy.system in ['linux', 'windows'])
self.ui.actionCompileWin32Binary.setEnabled(can_compile and fmpy.system == 'windows')
self.ui.actionCompileWin64Binary.setEnabled(can_compile and fmpy.system == 'windows')
self.ui.actionCreateCMakeProject.setEnabled(can_compile)
self.ui.actionCreateJupyterNotebook.setEnabled(True)
self.ui.actionAddWindows32Remoting.setEnabled(md.fmiVersion == '2.0' and 'win32' in platforms and 'win64' not in platforms)
self.ui.actionAddLinux64Remoting.setEnabled(md.fmiVersion == '2.0' and 'win64' in platforms and 'linux64' not in platforms)
can_add_cswrapper = md.fmiVersion == '2.0' and md.coSimulation is None and md.modelExchange is not None
self.ui.actionAddCoSimulationWrapper.setEnabled(can_add_cswrapper)
# variables view
self.treeModel.setModelDescription(md)
self.tableModel.setModelDescription(md)
self.treeFilterModel.invalidate()
self.tableFilterModel.invalidate()
self.ui.treeView.reset()
self.ui.tableView.reset()
# settings page
self.ui.fmiVersionLabel.setText(md.fmiVersion)
self.ui.fmiTypeLabel.setText(', '.join(fmi_types))
self.ui.platformsLabel.setText(', '.join(platforms))
self.ui.modelNameLabel.setText(md.modelName)
self.ui.descriptionLabel.setText(md.description)
self.ui.numberOfContinuousStatesLabel.setText(str(md.numberOfContinuousStates))
self.ui.numberOfEventIndicatorsLabel.setText(str(md.numberOfEventIndicators))
self.ui.numberOfVariablesLabel.setText(str(len(md.modelVariables)))
self.ui.generationToolLabel.setText(md.generationTool)
self.ui.generationDateAndTimeLabel.setText(md.generationDateAndTime)
# relative tolerance
if experiment is not None and experiment.tolerance is not None:
relative_tolerance = experiment.tolerance
else:
relative_tolerance = 1e-6
self.ui.relativeToleranceLineEdit.setText(str(relative_tolerance))
# output interval
if experiment is not None and experiment.stepSize is not None:
output_interval = float(experiment.stepSize)
while output_interval > 1000:
output_interval *= 0.5
else:
output_interval = float(self.stopTimeLineEdit.text()) / 500
self.ui.outputIntervalLineEdit.setText(str(output_interval))
self.fmiTypeComboBox.clear()
self.fmiTypeComboBox.addItems(fmi_types)
self.updateSimulationSettings()
self.setCurrentPage(self.ui.settingsPage)
self.ui.dockWidget.show()
self.ui.actionReload.setEnabled(True)
self.ui.actionSettings.setEnabled(True)
self.ui.actionShowLog.setEnabled(True)
self.ui.actionShowResults.setEnabled(False)
can_sim, _ = can_simulate(platforms)
self.ui.actionLoadStartValues.setEnabled(can_sim)
self.ui.actionSimulate.setEnabled(can_sim)
self.stopTimeLineEdit.setEnabled(can_sim)
self.fmiTypeComboBox.setEnabled(can_sim and len(fmi_types) > 1)
self.ui.settingsGroupBox.setEnabled(can_sim)
settings = QSettings()
recent_files = settings.value("recentFiles", defaultValue=[])
recent_files = self.removeDuplicates([filename] + recent_files)
# save the 10 most recent files
settings.setValue('recentFiles', recent_files[:10])
self.setWindowTitle("%s - FMPy" % os.path.normpath(filename))
self.createGraphics()
def open(self):
start_dir = QDir.homePath()
settings = QSettings()
recent_files = settings.value("recentFiles", defaultValue=[])
for filename in recent_files:
dirname = os.path.dirname(filename)
if os.path.isdir(dirname):
start_dir = dirname
break
filename, _ = QFileDialog.getOpenFileName(parent=self,
caption="Open File",
directory=start_dir,
filter="FMUs (*.fmu);;All Files (*.*)")
if filename:
self.load(filename)
def setCurrentPage(self, widget):
# block the signals during the update
self.ui.actionSettings.blockSignals(True)
self.ui.actionShowLog.blockSignals(True)
self.ui.actionShowResults.blockSignals(True)
self.ui.stackedWidget.setCurrentWidget(widget)
# toggle the actions
self.ui.actionSettings.setChecked(widget == self.ui.settingsPage)
self.ui.actionShowLog.setChecked(widget == self.ui.logPage)
self.ui.actionShowResults.setChecked(widget == self.ui.resultPage)
# un-block the signals during the update
self.ui.actionSettings.blockSignals(False)
self.ui.actionShowLog.blockSignals(False)
self.ui.actionShowResults.blockSignals(False)
def selectInputFile(self):
start_dir = os.path.dirname(self.filename)
filename, _ = QFileDialog.getOpenFileName(parent=self,
caption="Select Input File",
directory=start_dir,
filter="FMUs (*.csv);;All Files (*.*)")
if filename:
self.ui.inputFilenameLineEdit.setText(filename)
def createInputFile(self):
input_variables = []
for variable in self.modelDescription.modelVariables:
if variable.causality == 'input':
input_variables.append(variable)
if len(input_variables) == 0:
QMessageBox.warning(self,
"Cannot create input file",
"The input file cannot be created because the model has no input variables")
return
filename, _ = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(parent=self,
caption="Save Input File",
directory=filename + '_in.csv',
filter="Comma Separated Values (*.csv);;All Files (*.*)")
if not filename:
return
with open(filename, 'w') as f:
# column names
f.write('"time"')
for variable in input_variables:
f.write(',"%s"' % variable.name)
f.write('\n')
# example data
f.write(','.join(['0'] * (len(input_variables) + 1)) + '\n')
self.ui.inputFilenameLineEdit.setText(filename)
def showInputFileInExplorer(self):
filename = self.ui.inputFilenameLineEdit.text()
if not os.path.isfile(filename):
QMessageBox.warning(self, "Cannot show input file", "The input file does not exist")
return
QDesktopServices.openUrl(QUrl.fromLocalFile(os.path.dirname(filename)))
def openInputFile(self):
filename = self.ui.inputFilenameLineEdit.text()
if not os.path.isfile(filename):
QMessageBox.warning(self, "Cannot open input file", "The input file does not exist")
return
QDesktopServices.openUrl(QUrl.fromLocalFile(filename))
def updateSimulationSettings(self):
if self.fmiTypeComboBox.currentText() == 'Co-Simulation':
self.ui.solverComboBox.setEnabled(False)
self.ui.stepSizeLineEdit.setEnabled(False)
self.ui.relativeToleranceLineEdit.setEnabled(True)
else:
self.ui.solverComboBox.setEnabled(True)
fixed_step = self.ui.solverComboBox.currentText() == 'Fixed-step'
self.ui.stepSizeLineEdit.setEnabled(fixed_step)
self.ui.relativeToleranceLineEdit.setEnabled(not fixed_step)
def selectVariable(self, variable):
self.selectedVariables.add(variable)
self.variableSelected.emit(variable)
def deselectVariable(self, variable):
self.selectedVariables.remove(variable)
self.variableDeselected.emit(variable)
def startSimulation(self):
from fmpy.gui.simulation import SimulationThread
try:
stop_time = float(self.stopTimeLineEdit.text())
step_size = float(self.ui.stepSizeLineEdit.text())
relative_tolerance = float(self.ui.relativeToleranceLineEdit.text())
if self.ui.outputIntervalRadioButton.isChecked():
output_interval = float(self.ui.outputIntervalLineEdit.text())
else:
max_samples = float(self.ui.maxSamplesLineEdit.text())
output_interval = stop_time / max_samples
except Exception as ex:
self.log.log('error', "Failed to start simulation: %s" % ex)
self.ui.stackedWidget.setCurrentWidget(self.ui.logPage)
return
step_size = min(step_size, output_interval)
if self.ui.solverComboBox.currentText() == 'Fixed-step':
solver = 'Euler'
else:
solver = 'CVode'
if self.ui.inputCheckBox.isChecked():
input_variables = []
for variable in self.modelDescription.modelVariables:
if variable.causality == 'input':
input_variables.append(variable.name)
try:
from fmpy.util import read_csv
filename = self.ui.inputFilenameLineEdit.text()
input = read_csv(filename, variable_names=input_variables)
except Exception as e:
self.log.log('error', "Failed to load input from '%s'. %s" % (filename, e))
return
else:
input = None
output = []
for variable in self.modelDescription.modelVariables:
output.append(variable.name)
fmi_type = 'CoSimulation' if self.fmiTypeComboBox.currentText() == 'Co-Simulation' else 'ModelExchange'
self.simulationThread = SimulationThread(filename=self.filename,
fmiType=fmi_type,
stopTime=stop_time,
solver=solver,
stepSize=step_size,
relativeTolerance=relative_tolerance,
outputInterval=output_interval,
startValues=self.startValues,
applyDefaultStartValues=self.ui.applyDefaultStartValuesCheckBox.isChecked(),
input=input,
output=output,
debugLogging=self.ui.debugLoggingCheckBox.isChecked(),
fmiLogging=self.ui.logFMICallsCheckBox.isChecked())
self.ui.actionSimulate.setIcon(QIcon(':/icons/stop.png'))
self.ui.actionSimulate.setToolTip("Stop simulation")
self.ui.actionSimulate.triggered.disconnect(self.startSimulation)
self.ui.actionSimulate.triggered.connect(self.simulationThread.stop)
self.simulationProgressBar.setVisible(True)
self.simulationThread.messageChanged.connect(self.log.log)
self.simulationThread.progressChanged.connect(self.simulationProgressBar.setValue)
self.simulationThread.finished.connect(self.simulationFinished)
if self.ui.clearLogOnStartButton.isChecked():
self.log.clear()
self.setCurrentPage(self.ui.resultPage)
self.simulationThread.start()
self.plotUpdateTimer.start(100)
self.updatePlotLayout()
def simulationFinished(self):
# update UI
self.ui.actionSimulate.triggered.disconnect(self.simulationThread.stop)
self.ui.actionSimulate.triggered.connect(self.startSimulation)
self.ui.actionSimulate.setIcon(QIcon(':/icons/play.png'))
self.ui.actionSimulate.setToolTip("Start simulation")
self.plotUpdateTimer.stop()
self.simulationProgressBar.setVisible(False)
self.ui.actionShowResults.setEnabled(True)
self.ui.actionSettings.setEnabled(True)
self.setCurrentPage(self.ui.resultPage)
self.updatePlotLayout()
if self.result is None:
self.setCurrentPage(self.ui.logPage)
else:
self.ui.actionSaveResult.setEnabled(True)
self.ui.actionSavePlottedResult.setEnabled(True)
self.result = self.simulationThread.result
self.simulationThread = None
self.updatePlotData()
def updatePlotData(self):
import numpy as np
if self.simulationThread is not None and len(self.simulationThread.rows) > 1:
# get results from current simulation
self.result = np.array(self.simulationThread.rows, dtype=np.dtype(self.simulationThread.cols))
if self.result is None:
return # no results available yet
time = self.result['time']
for variable, curve in self.curves:
if variable.name not in self.result.dtype.names:
continue
y = self.result[variable.name]
if variable.type == 'Real':
curve.setData(x=time, y=y)
else:
curve.setData(x=np.repeat(time, 2)[1:], y=np.repeat(y, 2)[:-1])
def updatePlotLayout(self):
self.ui.plotWidget.clear()
self.curves[:] = []
if self.simulationThread is not None:
stop_time = self.simulationThread.stopTime
elif self.result is not None:
stop_time = self.result['time'][-1]
else:
stop_time = 1.0
pen = (0, 0, 255)
for variable in self.selectedVariables:
self.ui.plotWidget.nextRow()
plot = self.ui.plotWidget.addPlot()
if variable.type == 'Real':
curve = plot.plot(pen=pen)
else:
if variable.type == 'Boolean':
plot.setYRange(0, 1, padding=0.2)
plot.getAxis('left').setTicks([[(0, 'false'), (1, 'true')], []])
curve = plot.plot(pen=pen, fillLevel=0, fillBrush=(0, 0, 255, 50), antialias=False)
else:
curve = plot.plot(pen=pen, antialias=False)
plot.setXRange(0, stop_time, padding=0.05)
plot.setLabel('left', variable.name)
plot.showGrid(x=True, y=True, alpha=0.25)
# hide the auto-scale button and disable context menu and mouse interaction
plot.hideButtons()
plot.setMouseEnabled(False, False)
plot.setMenuEnabled(False)
self.curves.append((variable, curve))
self.updatePlotData()
def showColumn(self, name, show):
if name in self.showColumnActions:
self.showColumnActions[name].setChecked(show)
i = VariablesModel.COLUMN_NAMES.index(name)
self.ui.treeView.setColumnHidden(i, not show)
self.ui.tableView.setColumnHidden(i, not show)
def showAllColumns(self):
for name in COLLAPSABLE_COLUMNS:
self.showColumn(name, True)
def hideAllColumns(self):
for name in COLLAPSABLE_COLUMNS:
self.showColumn(name, False)
def setStatusMessage(self, level, text):
if level in ['debug', 'info', 'warning', 'error']:
self.statusIconLabel.setPixmap(QPixmap(':/icons/%s-16x16.png' % level))
else:
self.statusIconLabel.setPixmap(QPixmap())
self.statusTextLabel.setText(text)
def dragEnterEvent(self, event):
for url in event.mimeData().urls():
if not url.isLocalFile():
return
event.acceptProposedAction()
def dropEvent(self, event):
urls = event.mimeData().urls()
for url in urls:
if url == urls[0]:
window = self
else:
window = MainWindow()
window.load(url.toLocalFile())
def saveResult(self, plotted=False):
filename, _ = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(parent=self,
caption="Save Result",
directory=filename + '_out.csv',
filter="Comma Separated Values (*.csv);;All Files (*.*)")
if filename:
from ..util import write_csv
if plotted:
columns = [variable.name for variable in self.selectedVariables]
else:
columns = None
try:
write_csv(filename=filename, result=self.result, columns=columns)
except Exception as e:
QMessageBox.critical(self, "Failed to write result", '"Failed to write "%s". %s' % (filename, e))
def createDesktopShortcut(self):
import os
from win32com.client import Dispatch
import sys
env = os.environ.get('CONDA_DEFAULT_ENV')
if env is None:
target_path = sys.executable
root, ext = os.path.splitext(target_path)
pythonw = root + 'w' + ext
if os.path.isfile(pythonw):
target_path = pythonw
arguments = '-m fmpy.gui'
else:
for path in os.environ["PATH"].split(os.pathsep):
activate = os.path.join(path, 'activate.bat')
if os.path.isfile(activate):
break
target_path = r'%windir%\System32\cmd.exe'
arguments = '/C ""%s" %s && python -m fmpy.gui"' % (activate, env)
file_path = os.path.dirname(__file__)
icon = os.path.join(file_path, 'icons', 'app_icon.ico')
desktop_locations = QStandardPaths.standardLocations(QStandardPaths.DesktopLocation)
shortcut_path = os.path.join(desktop_locations[0], "FMPy GUI.lnk")
shell = Dispatch('WScript.Shell')
shortcut = shell.CreateShortCut(shortcut_path)
shortcut.Targetpath = target_path
shortcut.Arguments = arguments
# shortcut.WorkingDirectory = ...
shortcut.IconLocation = icon
shortcut.save()
def showAboutDialog(self):
dialog = AboutDialog(self)
dialog.show()
@staticmethod
def removeDuplicates(seq):
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
def validateFMU(self):
from ..validation import validate_fmu
problems = validate_fmu(self.filename)
if problems:
button = QMessageBox.question(self, "Validation failed", "%d problems have been found. Save validation messages?" % len(problems))
if button == QMessageBox.Yes:
filename, _ = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(parent=self,
caption="Save validation messages",
directory=filename + '_validation.txt',
filter="Text Files (*.txt);;All Files (*.*)")
if filename:
with open(filename, 'w') as f:
f.writelines(problems)
else:
QMessageBox.information(self, "Validation successful", "No problems have been found.")
def addFileAssociation(self):
try:
from winreg import HKEY_CURRENT_USER, KEY_WRITE, REG_SZ, OpenKey, CreateKey, SetValueEx, CloseKey
env = os.environ.get('CONDA_DEFAULT_ENV_')
if env is None:
python = sys.executable
root, ext = os.path.splitext(python)
pythonw = root + 'w' + ext
if os.path.isfile(pythonw):
python = pythonw
target = '"%s" -m fmpy.gui "%%1"' % python
else:
# activate the conda environment
for path in os.environ["PATH"].split(os.pathsep):
activate = os.path.join(path, 'activate.bat')
if os.path.isfile(activate):
break
windir = os.environ['WINDIR']
cmd = os.path.join(windir, 'System32', 'cmd.exe')
target = r'%s /C ""%s" %s && python -m fmpy.gui %%1"' % (cmd, activate, env)
key_path = r'Software\Classes\fmpy.gui\shell\open\command'
CreateKey(HKEY_CURRENT_USER, key_path)
key = OpenKey(HKEY_CURRENT_USER, key_path, 0, KEY_WRITE)
SetValueEx(key, '', 0, REG_SZ, target)
CloseKey(key)
key_path = r'SOFTWARE\Classes\.fmu'
CreateKey(HKEY_CURRENT_USER, key_path)
key = OpenKey(HKEY_CURRENT_USER, key_path, 0, KEY_WRITE)
SetValueEx(key, '', 0, REG_SZ, 'fmpy.gui')
CloseKey(key)
QMessageBox.information(self, "File association added", "The file association for *.fmu has been added")
except Exception as e:
QMessageBox.critical(self, "File association failed", "The file association for *.fmu could not be added. %s" % e)
def copyValueReference(self):
text = '\n'.join([str(v.valueReference) for v in self.getSelectedVariables()])
QApplication.clipboard().setText(text)
def copyVariableName(self):
text = '\n'.join([str(v.name) for v in self.getSelectedVariables()])
QApplication.clipboard().setText(text)
def getSelectedVariables(self):
variables = []
if self.ui.variablesStackedWidget.currentWidget() == self.ui.treePage:
for index in self.ui.treeView.selectionModel().selectedRows():
sourceIndex = self.treeFilterModel.mapToSource(index)
treeItem = sourceIndex.internalPointer()
if treeItem.variable is not None:
variables.append(treeItem.variable)
else:
for index in self.ui.tableView.selectionModel().selectedRows():
sourceIndex = self.tableFilterModel.mapToSource(index)
variable = sourceIndex.internalPointer()
variables.append(variable)
return variables
def clearPlots(self):
self.selectedVariables.clear()
self.updatePlotLayout()
def createGraphics(self):
def variableColor(variable):
if variable.type.startswith(('Float', 'Real')):
return QColor.fromRgb(26, 77, 179)
elif variable.type.startswith(('Enumeration', 'Int', 'UInt')):
return QColor.fromRgb(179, 77, 26)
elif variable.type == 'Boolean':
return QColor.fromRgb(255, 0, 255)
elif variable.type == 'String':
return QColor.fromRgb(26, 114, 16)
elif variable.type == 'Binary':
return QColor.fromRgb(81, 81, 81)
else:
return QColor.fromRgb(0, 0, 0)
inputVariables = []
outputVariables = []
maxInputLabelWidth = 0
maxOutputLabelWidth = 0
textItem = QGraphicsTextItem()
fontMetrics = QFontMetricsF(textItem.font())
for variable in self.modelDescription.modelVariables:
if variable.causality == 'input':
inputVariables.append(variable)
elif variable.causality == 'output':
outputVariables.append(variable)
for variable in inputVariables:
maxInputLabelWidth = max(maxInputLabelWidth, fontMetrics.width(variable.name))
for variable in outputVariables:
maxOutputLabelWidth = max(maxOutputLabelWidth, fontMetrics.width(variable.name))
from math import floor
scene = QGraphicsScene()
self.ui.graphicsView.setScene(scene)
group = QGraphicsItemGroup()
scene.addItem(group)
group.setPos(200.5, -50.5)
lh = 15 # line height
w = max(150., maxInputLabelWidth + maxOutputLabelWidth + 20)
h = max(50., 10 + lh * max(len(inputVariables), len(outputVariables)))
block = QGraphicsRectItem(0, 0, w, h, group)
block.setPen(QColor.fromRgb(0, 0, 0))
pen = QPen()
pen.setWidthF(1)
font = QFont()
font.setPixelSize(10)
# inputs
y = floor((h - len(inputVariables) * lh) / 2 - 2)
for variable in inputVariables:
text = QGraphicsTextItem(variable.name, group)
text.setDefaultTextColor(QColor.fromRgb(0, 0, 0))
text.setFont(font)
text.setX(3)
text.setY(y)
polygon = QPolygonF([QPointF(-8, y + 7.5), QPointF(-1, y + 11), QPointF(-8, y + 14.5)])
path = QPainterPath()
path.addPolygon(polygon)
path.closeSubpath()
contour = QGraphicsPathItem(path, group)
contour.setPen(QPen(Qt.NoPen))
contour.setBrush(variableColor(variable))
pen = QPen()
pen.setColor(variableColor(variable))
pen.setJoinStyle(Qt.MiterJoin)
contour.setPen(pen)
y += lh
# outputs
y = floor((h - len(outputVariables) * lh) / 2 - 2)
for variable in outputVariables:
text = QGraphicsTextItem(variable.name, group)
text.setDefaultTextColor(QColor.fromRgb(0, 0, 0))
text.setFont(font)
text.setX(w - 3 - text.boundingRect().width())
text.setY(y)
polygon = QPolygonF([QPointF(w + 1, y + 7.5), QPointF(w + 8, y + 11), QPointF(w + 1, y + 14.5)])
path = QPainterPath()
path.addPolygon(polygon)
path.closeSubpath()
contour = QGraphicsPathItem(path, group)
contour.setPen(QPen(Qt.NoPen))
contour.setBrush(variableColor(variable))
pen = QPen()
pen.setColor(variableColor(variable))
pen.setJoinStyle(Qt.MiterJoin)
contour.setPen(pen)
y += lh
def saveChanges(self):
from ..util import change_fmu
output_file, _ = QFileDialog.getSaveFileName(parent=self,
caption='Save Changed FMU',
directory=self.filename,
filter='FMUs (*.fmu)')
if output_file:
change_fmu(input_file=self.filename, output_file=output_file, start_values=self.startValues)
def loadStartValues(self):
from ..util import get_start_values
start_values = get_start_values(self.filename)
self.startValues.update(start_values)
self.ui.treeView.reset()
self.ui.tableView.reset()
def editTable(self):
from .TableDialog import TableDialog
variables = self.getSelectedVariables()
if len(variables) == 1:
start_values = self.startValues.copy()
dialog = TableDialog(modelVariables=self.modelDescription.modelVariables,
variable=variables[0],
startValues=start_values)
if dialog.exec_() == QDialog.Accepted:
self.startValues.clear()
self.startValues.update(start_values)
def compilePlatformBinary(self, target_platform):
from ..util import compile_platform_binary
platforms = supported_platforms(self.filename)
if target_platform in platforms:
button = QMessageBox.question(self, "Platform binary already exists",
f'The FMU already contains a binary for the platform "{target_platform}".'
' Do you want to compile and overwrite the existing binary?')
if button == QMessageBox.No:
return
try:
compile_platform_binary(self.filename, target_platform=target_platform)
except Exception as e:
QMessageBox.critical(self, "Failed to compile platform binaries", str(e))
return
self.load(self.filename)
def createJupyterNotebook(self):
from fmpy.util import create_jupyter_notebook
filename, ext = os.path.splitext(self.filename)
filename, _ = QFileDialog.getSaveFileName(
parent=self,
directory=filename + '.ipynb',
filter='Jupyter Notebooks (*.ipynb);;All Files (*)'
)
if filename:
try:
create_jupyter_notebook(self.filename, filename)
except Exception as e:
QMessageBox.critical(self, "Failed to create Jupyter Notebook", str(e))
return
if QMessageBox.question(self, "Open Jupyter Notebook?", f"Start Jupyter and open {filename}?") == QMessageBox.Yes:
from subprocess import run, CREATE_NEW_CONSOLE
try:
run(['jupyter', 'notebook', filename], creationflags=CREATE_NEW_CONSOLE)
except Exception as e:
QMessageBox.critical(self, "Failed to start Jupyter", str(e))
def createCMakeProject(self):
from fmpy.util import create_cmake_project
project_dir = QFileDialog.getExistingDirectory(
parent=self,
caption='Select CMake Project Folder',
directory=os.path.dirname(self.filename))
if project_dir:
create_cmake_project(self.filename, project_dir)
def addRemotingBinaries(self, host_platform, remote_platform):
from ..util import add_remoting
try:
add_remoting(self.filename, host_platform, remote_platform)
except Exception as e:
QMessageBox.warning(self, "Failed to add Remoting Binaries",
f"Failed to add remoting binaries to {self.filename}. {e}")
self.load(self.filename)
def addCoSimulationWrapper(self):
from ..cswrapper import add_cswrapper
try:
add_cswrapper(self.filename)
except Exception as e:
QMessageBox.warning(self, "Failed to add Co-Simulation Wrapper",
"Failed to add Co-Simulation Wrapper %s. %s" % (self.filename, e))
self.load(self.filename)
| true
| true
|
1c441c536b6f0ba2ae48a33a2e97137a6527f44f
| 17,494
|
py
|
Python
|
lib/python2.7/site-packages/matplotlib/tests/test_dates.py
|
wfehrnstrom/harmonize
|
e5661d24b2021739e8ac4bf1d3a530eda4e155b3
|
[
"MIT"
] | 1
|
2017-12-05T15:35:47.000Z
|
2017-12-05T15:35:47.000Z
|
lib/python2.7/site-packages/matplotlib/tests/test_dates.py
|
wfehrnstrom/harmonize
|
e5661d24b2021739e8ac4bf1d3a530eda4e155b3
|
[
"MIT"
] | 10
|
2017-07-13T00:24:03.000Z
|
2017-07-17T07:39:03.000Z
|
lib/python2.7/site-packages/matplotlib/tests/test_dates.py
|
wfehrnstrom/harmonize
|
e5661d24b2021739e8ac4bf1d3a530eda4e155b3
|
[
"MIT"
] | 7
|
2017-08-01T04:02:07.000Z
|
2018-10-06T21:07:20.000Z
|
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import map
import datetime
import warnings
import tempfile
import dateutil
import pytz
try:
# mock in python 3.3+
from unittest import mock
except ImportError:
import mock
from nose.tools import assert_raises, assert_equal
from nose.plugins.skip import SkipTest
from matplotlib.testing.decorators import image_comparison, cleanup
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
@image_comparison(baseline_images=['date_empty'], extensions=['png'])
def test_date_empty():
# make sure mpl does the right thing when told to plot dates even
# if no date data has been presented, cf
# http://sourceforge.net/tracker/?func=detail&aid=2850075&group_id=80706&atid=560720
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.xaxis_date()
@image_comparison(baseline_images=['date_axhspan'], extensions=['png'])
def test_date_axhspan():
# test ax hspan with date inputs
t0 = datetime.datetime(2009, 1, 20)
tf = datetime.datetime(2009, 1, 21)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axhspan(t0, tf, facecolor="blue", alpha=0.25)
ax.set_ylim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
fig.subplots_adjust(left=0.25)
@image_comparison(baseline_images=['date_axvspan'], extensions=['png'])
def test_date_axvspan():
# test ax hspan with date inputs
t0 = datetime.datetime(2000, 1, 20)
tf = datetime.datetime(2010, 1, 21)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axvspan(t0, tf, facecolor="blue", alpha=0.25)
ax.set_xlim(t0 - datetime.timedelta(days=720),
tf + datetime.timedelta(days=720))
fig.autofmt_xdate()
@image_comparison(baseline_images=['date_axhline'],
extensions=['png'])
def test_date_axhline():
# test ax hline with date inputs
t0 = datetime.datetime(2009, 1, 20)
tf = datetime.datetime(2009, 1, 31)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axhline(t0, color="blue", lw=3)
ax.set_ylim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
fig.subplots_adjust(left=0.25)
@image_comparison(baseline_images=['date_axvline'],
extensions=['png'])
def test_date_axvline():
# test ax hline with date inputs
t0 = datetime.datetime(2000, 1, 20)
tf = datetime.datetime(2000, 1, 21)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axvline(t0, color="red", lw=3)
ax.set_xlim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
fig.autofmt_xdate()
@cleanup
def test_too_many_date_ticks():
# Attempt to test SF 2715172, see
# https://sourceforge.net/tracker/?func=detail&aid=2715172&group_id=80706&atid=560720
# setting equal datetimes triggers and expander call in
# transforms.nonsingular which results in too many ticks in the
# DayLocator. This should trigger a Locator.MAXTICKS RuntimeError
warnings.filterwarnings(
'ignore',
'Attempting to set identical left==right results\\nin singular '
'transformations; automatically expanding.\\nleft=\d*\.\d*, '
'right=\d*\.\d*',
UserWarning, module='matplotlib.axes')
t0 = datetime.datetime(2000, 1, 20)
tf = datetime.datetime(2000, 1, 20)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_xlim((t0, tf), auto=True)
ax.plot([], [])
ax.xaxis.set_major_locator(mdates.DayLocator())
assert_raises(RuntimeError, fig.savefig, 'junk.png')
@image_comparison(baseline_images=['RRuleLocator_bounds'], extensions=['png'])
def test_RRuleLocator():
import matplotlib.testing.jpl_units as units
units.register()
# This will cause the RRuleLocator to go out of bounds when it tries
# to add padding to the limits, so we make sure it caps at the correct
# boundary values.
t0 = datetime.datetime(1000, 1, 1)
tf = datetime.datetime(6000, 1, 1)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_autoscale_on(True)
ax.plot([t0, tf], [0.0, 1.0], marker='o')
rrule = mdates.rrulewrapper(dateutil.rrule.YEARLY, interval=500)
locator = mdates.RRuleLocator(rrule)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(locator))
ax.autoscale_view()
fig.autofmt_xdate()
@image_comparison(baseline_images=['DateFormatter_fractionalSeconds'],
extensions=['png'])
def test_DateFormatter():
import matplotlib.testing.jpl_units as units
units.register()
# Lets make sure that DateFormatter will allow us to have tick marks
# at intervals of fractional seconds.
t0 = datetime.datetime(2001, 1, 1, 0, 0, 0)
tf = datetime.datetime(2001, 1, 1, 0, 0, 1)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_autoscale_on(True)
ax.plot([t0, tf], [0.0, 1.0], marker='o')
# rrule = mpldates.rrulewrapper( dateutil.rrule.YEARLY, interval=500 )
# locator = mpldates.RRuleLocator( rrule )
# ax.xaxis.set_major_locator( locator )
# ax.xaxis.set_major_formatter( mpldates.AutoDateFormatter(locator) )
ax.autoscale_view()
fig.autofmt_xdate()
def test_date_formatter_strftime():
"""
Tests that DateFormatter matches datetime.strftime,
check microseconds for years before 1900 for bug #3179
as well as a few related issues for years before 1900.
"""
def test_strftime_fields(dt):
"""For datetime object dt, check DateFormatter fields"""
# Note: the last couple of %%s are to check multiple %s are handled
# properly; %% should get replaced by %.
formatter = mdates.DateFormatter("%w %d %m %y %Y %H %I %M %S %%%f %%x")
# Compute date fields without using datetime.strftime,
# since datetime.strftime does not work before year 1900
formatted_date_str = (
"{weekday} {day:02d} {month:02d} {year:02d} {full_year:04d} "
"{hour24:02d} {hour12:02d} {minute:02d} {second:02d} "
"%{microsecond:06d} %x"
.format(
# weeknum=dt.isocalendar()[1], # %U/%W {weeknum:02d}
# %w Sunday=0, weekday() Monday=0
weekday=str((dt.weekday() + 1) % 7),
day=dt.day,
month=dt.month,
year=dt.year % 100,
full_year=dt.year,
hour24=dt.hour,
hour12=((dt.hour-1) % 12) + 1,
minute=dt.minute,
second=dt.second,
microsecond=dt.microsecond))
assert_equal(formatter.strftime(dt), formatted_date_str)
try:
# Test strftime("%x") with the current locale.
import locale # Might not exist on some platforms, such as Windows
locale_formatter = mdates.DateFormatter("%x")
locale_d_fmt = locale.nl_langinfo(locale.D_FMT)
expanded_formatter = mdates.DateFormatter(locale_d_fmt)
assert_equal(locale_formatter.strftime(dt),
expanded_formatter.strftime(dt))
except (ImportError, AttributeError):
pass
for year in range(1, 3000, 71):
# Iterate through random set of years
test_strftime_fields(datetime.datetime(year, 1, 1))
test_strftime_fields(datetime.datetime(year, 2, 3, 4, 5, 6, 12345))
def test_date_formatter_callable():
scale = -11
locator = mock.Mock(_get_unit=mock.Mock(return_value=scale))
callable_formatting_function = (lambda dates, _:
[dt.strftime('%d-%m//%Y') for dt in dates])
formatter = mdates.AutoDateFormatter(locator)
formatter.scaled[-10] = callable_formatting_function
assert_equal(formatter([datetime.datetime(2014, 12, 25)]),
['25-12//2014'])
def test_drange():
"""
This test should check if drange works as expected, and if all the
rounding errors are fixed
"""
start = datetime.datetime(2011, 1, 1, tzinfo=mdates.UTC)
end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
delta = datetime.timedelta(hours=1)
# We expect 24 values in drange(start, end, delta), because drange returns
# dates from an half open interval [start, end)
assert_equal(24, len(mdates.drange(start, end, delta)))
# if end is a little bit later, we expect the range to contain one element
# more
end = end + datetime.timedelta(microseconds=1)
assert_equal(25, len(mdates.drange(start, end, delta)))
# reset end
end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
# and tst drange with "complicated" floats:
# 4 hours = 1/6 day, this is an "dangerous" float
delta = datetime.timedelta(hours=4)
daterange = mdates.drange(start, end, delta)
assert_equal(6, len(daterange))
assert_equal(mdates.num2date(daterange[-1]), end - delta)
@cleanup
def test_empty_date_with_year_formatter():
# exposes sf bug 2861426:
# https://sourceforge.net/tracker/?func=detail&aid=2861426&group_id=80706&atid=560720
# update: I am no longer believe this is a bug, as I commented on
# the tracker. The question is now: what to do with this test
import matplotlib.dates as dates
fig = plt.figure()
ax = fig.add_subplot(111)
yearFmt = dates.DateFormatter('%Y')
ax.xaxis.set_major_formatter(yearFmt)
with tempfile.TemporaryFile() as fh:
assert_raises(ValueError, fig.savefig, fh)
def test_auto_date_locator():
def _create_auto_date_locator(date1, date2):
locator = mdates.AutoDateLocator()
locator.create_dummy_axis()
locator.set_view_interval(mdates.date2num(date1),
mdates.date2num(date2))
return locator
d1 = datetime.datetime(1990, 1, 1)
results = ([datetime.timedelta(weeks=52 * 200),
['1990-01-01 00:00:00+00:00', '2010-01-01 00:00:00+00:00',
'2030-01-01 00:00:00+00:00', '2050-01-01 00:00:00+00:00',
'2070-01-01 00:00:00+00:00', '2090-01-01 00:00:00+00:00',
'2110-01-01 00:00:00+00:00', '2130-01-01 00:00:00+00:00',
'2150-01-01 00:00:00+00:00', '2170-01-01 00:00:00+00:00']
],
[datetime.timedelta(weeks=52),
['1990-01-01 00:00:00+00:00', '1990-02-01 00:00:00+00:00',
'1990-03-01 00:00:00+00:00', '1990-04-01 00:00:00+00:00',
'1990-05-01 00:00:00+00:00', '1990-06-01 00:00:00+00:00',
'1990-07-01 00:00:00+00:00', '1990-08-01 00:00:00+00:00',
'1990-09-01 00:00:00+00:00', '1990-10-01 00:00:00+00:00',
'1990-11-01 00:00:00+00:00', '1990-12-01 00:00:00+00:00']
],
[datetime.timedelta(days=141),
['1990-01-05 00:00:00+00:00', '1990-01-26 00:00:00+00:00',
'1990-02-16 00:00:00+00:00', '1990-03-09 00:00:00+00:00',
'1990-03-30 00:00:00+00:00', '1990-04-20 00:00:00+00:00',
'1990-05-11 00:00:00+00:00']
],
[datetime.timedelta(days=40),
['1990-01-03 00:00:00+00:00', '1990-01-10 00:00:00+00:00',
'1990-01-17 00:00:00+00:00', '1990-01-24 00:00:00+00:00',
'1990-01-31 00:00:00+00:00', '1990-02-07 00:00:00+00:00']
],
[datetime.timedelta(hours=40),
['1990-01-01 00:00:00+00:00', '1990-01-01 04:00:00+00:00',
'1990-01-01 08:00:00+00:00', '1990-01-01 12:00:00+00:00',
'1990-01-01 16:00:00+00:00', '1990-01-01 20:00:00+00:00',
'1990-01-02 00:00:00+00:00', '1990-01-02 04:00:00+00:00',
'1990-01-02 08:00:00+00:00', '1990-01-02 12:00:00+00:00',
'1990-01-02 16:00:00+00:00']
],
[datetime.timedelta(minutes=20),
['1990-01-01 00:00:00+00:00', '1990-01-01 00:05:00+00:00',
'1990-01-01 00:10:00+00:00', '1990-01-01 00:15:00+00:00',
'1990-01-01 00:20:00+00:00']
],
[datetime.timedelta(seconds=40),
['1990-01-01 00:00:00+00:00', '1990-01-01 00:00:05+00:00',
'1990-01-01 00:00:10+00:00', '1990-01-01 00:00:15+00:00',
'1990-01-01 00:00:20+00:00', '1990-01-01 00:00:25+00:00',
'1990-01-01 00:00:30+00:00', '1990-01-01 00:00:35+00:00',
'1990-01-01 00:00:40+00:00']
],
[datetime.timedelta(microseconds=1500),
['1989-12-31 23:59:59.999507+00:00',
'1990-01-01 00:00:00+00:00',
'1990-01-01 00:00:00.000502+00:00',
'1990-01-01 00:00:00.001005+00:00',
'1990-01-01 00:00:00.001508+00:00']
],
)
for t_delta, expected in results:
d2 = d1 + t_delta
locator = _create_auto_date_locator(d1, d2)
assert_equal(list(map(str, mdates.num2date(locator()))),
expected)
@image_comparison(baseline_images=['date_inverted_limit'],
extensions=['png'])
def test_date_inverted_limit():
# test ax hline with date inputs
t0 = datetime.datetime(2009, 1, 20)
tf = datetime.datetime(2009, 1, 31)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axhline(t0, color="blue", lw=3)
ax.set_ylim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
ax.invert_yaxis()
fig.subplots_adjust(left=0.25)
def _test_date2num_dst(date_range, tz_convert):
# Timezones
BRUSSELS = pytz.timezone('Europe/Brussels')
UTC = pytz.UTC
# Create a list of timezone-aware datetime objects in UTC
# Interval is 0b0.0000011 days, to prevent float rounding issues
dtstart = datetime.datetime(2014, 3, 30, 0, 0, tzinfo=UTC)
interval = datetime.timedelta(minutes=33, seconds=45)
interval_days = 0.0234375 # 2025 / 86400 seconds
N = 8
dt_utc = date_range(start=dtstart, freq=interval, periods=N)
dt_bxl = tz_convert(dt_utc, BRUSSELS)
expected_ordinalf = [735322.0 + (i * interval_days) for i in range(N)]
actual_ordinalf = list(mdates.date2num(dt_bxl))
assert_equal(actual_ordinalf, expected_ordinalf)
def test_date2num_dst():
# Test for github issue #3896, but in date2num around DST transitions
# with a timezone-aware pandas date_range object.
class dt_tzaware(datetime.datetime):
"""
This bug specifically occurs because of the normalization behavior of
pandas Timestamp objects, so in order to replicate it, we need a
datetime-like object that applies timezone normalization after
subtraction.
"""
def __sub__(self, other):
r = super(dt_tzaware, self).__sub__(other)
tzinfo = getattr(r, 'tzinfo', None)
if tzinfo is not None:
localizer = getattr(tzinfo, 'normalize', None)
if localizer is not None:
r = tzinfo.normalize(r)
if isinstance(r, datetime.datetime):
r = self.mk_tzaware(r)
return r
def __add__(self, other):
return self.mk_tzaware(super(dt_tzaware, self).__add__(other))
def astimezone(self, tzinfo):
dt = super(dt_tzaware, self).astimezone(tzinfo)
return self.mk_tzaware(dt)
@classmethod
def mk_tzaware(cls, datetime_obj):
kwargs = {}
attrs = ('year',
'month',
'day',
'hour',
'minute',
'second',
'microsecond',
'tzinfo')
for attr in attrs:
val = getattr(datetime_obj, attr, None)
if val is not None:
kwargs[attr] = val
return cls(**kwargs)
# Define a date_range function similar to pandas.date_range
def date_range(start, freq, periods):
dtstart = dt_tzaware.mk_tzaware(start)
return [dtstart + (i * freq) for i in range(periods)]
# Define a tz_convert function that converts a list to a new time zone.
def tz_convert(dt_list, tzinfo):
return [d.astimezone(tzinfo) for d in dt_list]
_test_date2num_dst(date_range, tz_convert)
def test_date2num_dst_pandas():
# Test for github issue #3896, but in date2num around DST transitions
# with a timezone-aware pandas date_range object.
try:
import pandas as pd
except ImportError:
raise SkipTest('pandas not installed')
def tz_convert(*args):
return pd.DatetimeIndex.tz_convert(*args).astype(object)
_test_date2num_dst(pd.date_range, tz_convert)
def test_DayLocator():
assert_raises(ValueError, mdates.DayLocator, interval=-1)
assert_raises(ValueError, mdates.DayLocator, interval=-1.5)
assert_raises(ValueError, mdates.DayLocator, interval=0)
assert_raises(ValueError, mdates.DayLocator, interval=1.3)
mdates.DayLocator(interval=1.0)
def test_tz_utc():
dt = datetime.datetime(1970, 1, 1, tzinfo=mdates.UTC)
dt.tzname()
if __name__ == '__main__':
import nose
nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
| 36.752101
| 89
| 0.613124
|
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import map
import datetime
import warnings
import tempfile
import dateutil
import pytz
try:
from unittest import mock
except ImportError:
import mock
from nose.tools import assert_raises, assert_equal
from nose.plugins.skip import SkipTest
from matplotlib.testing.decorators import image_comparison, cleanup
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
@image_comparison(baseline_images=['date_empty'], extensions=['png'])
def test_date_empty():
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.xaxis_date()
@image_comparison(baseline_images=['date_axhspan'], extensions=['png'])
def test_date_axhspan():
t0 = datetime.datetime(2009, 1, 20)
tf = datetime.datetime(2009, 1, 21)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axhspan(t0, tf, facecolor="blue", alpha=0.25)
ax.set_ylim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
fig.subplots_adjust(left=0.25)
@image_comparison(baseline_images=['date_axvspan'], extensions=['png'])
def test_date_axvspan():
t0 = datetime.datetime(2000, 1, 20)
tf = datetime.datetime(2010, 1, 21)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axvspan(t0, tf, facecolor="blue", alpha=0.25)
ax.set_xlim(t0 - datetime.timedelta(days=720),
tf + datetime.timedelta(days=720))
fig.autofmt_xdate()
@image_comparison(baseline_images=['date_axhline'],
extensions=['png'])
def test_date_axhline():
t0 = datetime.datetime(2009, 1, 20)
tf = datetime.datetime(2009, 1, 31)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axhline(t0, color="blue", lw=3)
ax.set_ylim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
fig.subplots_adjust(left=0.25)
@image_comparison(baseline_images=['date_axvline'],
extensions=['png'])
def test_date_axvline():
t0 = datetime.datetime(2000, 1, 20)
tf = datetime.datetime(2000, 1, 21)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axvline(t0, color="red", lw=3)
ax.set_xlim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
fig.autofmt_xdate()
@cleanup
def test_too_many_date_ticks():
warnings.filterwarnings(
'ignore',
'Attempting to set identical left==right results\\nin singular '
'transformations; automatically expanding.\\nleft=\d*\.\d*, '
'right=\d*\.\d*',
UserWarning, module='matplotlib.axes')
t0 = datetime.datetime(2000, 1, 20)
tf = datetime.datetime(2000, 1, 20)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_xlim((t0, tf), auto=True)
ax.plot([], [])
ax.xaxis.set_major_locator(mdates.DayLocator())
assert_raises(RuntimeError, fig.savefig, 'junk.png')
@image_comparison(baseline_images=['RRuleLocator_bounds'], extensions=['png'])
def test_RRuleLocator():
import matplotlib.testing.jpl_units as units
units.register()
t0 = datetime.datetime(1000, 1, 1)
tf = datetime.datetime(6000, 1, 1)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_autoscale_on(True)
ax.plot([t0, tf], [0.0, 1.0], marker='o')
rrule = mdates.rrulewrapper(dateutil.rrule.YEARLY, interval=500)
locator = mdates.RRuleLocator(rrule)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(locator))
ax.autoscale_view()
fig.autofmt_xdate()
@image_comparison(baseline_images=['DateFormatter_fractionalSeconds'],
extensions=['png'])
def test_DateFormatter():
import matplotlib.testing.jpl_units as units
units.register()
t0 = datetime.datetime(2001, 1, 1, 0, 0, 0)
tf = datetime.datetime(2001, 1, 1, 0, 0, 1)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_autoscale_on(True)
ax.plot([t0, tf], [0.0, 1.0], marker='o')
ax.autoscale_view()
fig.autofmt_xdate()
def test_date_formatter_strftime():
def test_strftime_fields(dt):
formatter = mdates.DateFormatter("%w %d %m %y %Y %H %I %M %S %%%f %%x")
formatted_date_str = (
"{weekday} {day:02d} {month:02d} {year:02d} {full_year:04d} "
"{hour24:02d} {hour12:02d} {minute:02d} {second:02d} "
"%{microsecond:06d} %x"
.format(
weekday=str((dt.weekday() + 1) % 7),
day=dt.day,
month=dt.month,
year=dt.year % 100,
full_year=dt.year,
hour24=dt.hour,
hour12=((dt.hour-1) % 12) + 1,
minute=dt.minute,
second=dt.second,
microsecond=dt.microsecond))
assert_equal(formatter.strftime(dt), formatted_date_str)
try:
import locale
locale_formatter = mdates.DateFormatter("%x")
locale_d_fmt = locale.nl_langinfo(locale.D_FMT)
expanded_formatter = mdates.DateFormatter(locale_d_fmt)
assert_equal(locale_formatter.strftime(dt),
expanded_formatter.strftime(dt))
except (ImportError, AttributeError):
pass
for year in range(1, 3000, 71):
test_strftime_fields(datetime.datetime(year, 1, 1))
test_strftime_fields(datetime.datetime(year, 2, 3, 4, 5, 6, 12345))
def test_date_formatter_callable():
scale = -11
locator = mock.Mock(_get_unit=mock.Mock(return_value=scale))
callable_formatting_function = (lambda dates, _:
[dt.strftime('%d-%m//%Y') for dt in dates])
formatter = mdates.AutoDateFormatter(locator)
formatter.scaled[-10] = callable_formatting_function
assert_equal(formatter([datetime.datetime(2014, 12, 25)]),
['25-12//2014'])
def test_drange():
start = datetime.datetime(2011, 1, 1, tzinfo=mdates.UTC)
end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
delta = datetime.timedelta(hours=1)
assert_equal(24, len(mdates.drange(start, end, delta)))
end = end + datetime.timedelta(microseconds=1)
assert_equal(25, len(mdates.drange(start, end, delta)))
end = datetime.datetime(2011, 1, 2, tzinfo=mdates.UTC)
delta = datetime.timedelta(hours=4)
daterange = mdates.drange(start, end, delta)
assert_equal(6, len(daterange))
assert_equal(mdates.num2date(daterange[-1]), end - delta)
@cleanup
def test_empty_date_with_year_formatter():
import matplotlib.dates as dates
fig = plt.figure()
ax = fig.add_subplot(111)
yearFmt = dates.DateFormatter('%Y')
ax.xaxis.set_major_formatter(yearFmt)
with tempfile.TemporaryFile() as fh:
assert_raises(ValueError, fig.savefig, fh)
def test_auto_date_locator():
def _create_auto_date_locator(date1, date2):
locator = mdates.AutoDateLocator()
locator.create_dummy_axis()
locator.set_view_interval(mdates.date2num(date1),
mdates.date2num(date2))
return locator
d1 = datetime.datetime(1990, 1, 1)
results = ([datetime.timedelta(weeks=52 * 200),
['1990-01-01 00:00:00+00:00', '2010-01-01 00:00:00+00:00',
'2030-01-01 00:00:00+00:00', '2050-01-01 00:00:00+00:00',
'2070-01-01 00:00:00+00:00', '2090-01-01 00:00:00+00:00',
'2110-01-01 00:00:00+00:00', '2130-01-01 00:00:00+00:00',
'2150-01-01 00:00:00+00:00', '2170-01-01 00:00:00+00:00']
],
[datetime.timedelta(weeks=52),
['1990-01-01 00:00:00+00:00', '1990-02-01 00:00:00+00:00',
'1990-03-01 00:00:00+00:00', '1990-04-01 00:00:00+00:00',
'1990-05-01 00:00:00+00:00', '1990-06-01 00:00:00+00:00',
'1990-07-01 00:00:00+00:00', '1990-08-01 00:00:00+00:00',
'1990-09-01 00:00:00+00:00', '1990-10-01 00:00:00+00:00',
'1990-11-01 00:00:00+00:00', '1990-12-01 00:00:00+00:00']
],
[datetime.timedelta(days=141),
['1990-01-05 00:00:00+00:00', '1990-01-26 00:00:00+00:00',
'1990-02-16 00:00:00+00:00', '1990-03-09 00:00:00+00:00',
'1990-03-30 00:00:00+00:00', '1990-04-20 00:00:00+00:00',
'1990-05-11 00:00:00+00:00']
],
[datetime.timedelta(days=40),
['1990-01-03 00:00:00+00:00', '1990-01-10 00:00:00+00:00',
'1990-01-17 00:00:00+00:00', '1990-01-24 00:00:00+00:00',
'1990-01-31 00:00:00+00:00', '1990-02-07 00:00:00+00:00']
],
[datetime.timedelta(hours=40),
['1990-01-01 00:00:00+00:00', '1990-01-01 04:00:00+00:00',
'1990-01-01 08:00:00+00:00', '1990-01-01 12:00:00+00:00',
'1990-01-01 16:00:00+00:00', '1990-01-01 20:00:00+00:00',
'1990-01-02 00:00:00+00:00', '1990-01-02 04:00:00+00:00',
'1990-01-02 08:00:00+00:00', '1990-01-02 12:00:00+00:00',
'1990-01-02 16:00:00+00:00']
],
[datetime.timedelta(minutes=20),
['1990-01-01 00:00:00+00:00', '1990-01-01 00:05:00+00:00',
'1990-01-01 00:10:00+00:00', '1990-01-01 00:15:00+00:00',
'1990-01-01 00:20:00+00:00']
],
[datetime.timedelta(seconds=40),
['1990-01-01 00:00:00+00:00', '1990-01-01 00:00:05+00:00',
'1990-01-01 00:00:10+00:00', '1990-01-01 00:00:15+00:00',
'1990-01-01 00:00:20+00:00', '1990-01-01 00:00:25+00:00',
'1990-01-01 00:00:30+00:00', '1990-01-01 00:00:35+00:00',
'1990-01-01 00:00:40+00:00']
],
[datetime.timedelta(microseconds=1500),
['1989-12-31 23:59:59.999507+00:00',
'1990-01-01 00:00:00+00:00',
'1990-01-01 00:00:00.000502+00:00',
'1990-01-01 00:00:00.001005+00:00',
'1990-01-01 00:00:00.001508+00:00']
],
)
for t_delta, expected in results:
d2 = d1 + t_delta
locator = _create_auto_date_locator(d1, d2)
assert_equal(list(map(str, mdates.num2date(locator()))),
expected)
@image_comparison(baseline_images=['date_inverted_limit'],
extensions=['png'])
def test_date_inverted_limit():
t0 = datetime.datetime(2009, 1, 20)
tf = datetime.datetime(2009, 1, 31)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.axhline(t0, color="blue", lw=3)
ax.set_ylim(t0 - datetime.timedelta(days=5),
tf + datetime.timedelta(days=5))
ax.invert_yaxis()
fig.subplots_adjust(left=0.25)
def _test_date2num_dst(date_range, tz_convert):
BRUSSELS = pytz.timezone('Europe/Brussels')
UTC = pytz.UTC
dtstart = datetime.datetime(2014, 3, 30, 0, 0, tzinfo=UTC)
interval = datetime.timedelta(minutes=33, seconds=45)
interval_days = 0.0234375
N = 8
dt_utc = date_range(start=dtstart, freq=interval, periods=N)
dt_bxl = tz_convert(dt_utc, BRUSSELS)
expected_ordinalf = [735322.0 + (i * interval_days) for i in range(N)]
actual_ordinalf = list(mdates.date2num(dt_bxl))
assert_equal(actual_ordinalf, expected_ordinalf)
def test_date2num_dst():
):
def __sub__(self, other):
r = super(dt_tzaware, self).__sub__(other)
tzinfo = getattr(r, 'tzinfo', None)
if tzinfo is not None:
localizer = getattr(tzinfo, 'normalize', None)
if localizer is not None:
r = tzinfo.normalize(r)
if isinstance(r, datetime.datetime):
r = self.mk_tzaware(r)
return r
def __add__(self, other):
return self.mk_tzaware(super(dt_tzaware, self).__add__(other))
def astimezone(self, tzinfo):
dt = super(dt_tzaware, self).astimezone(tzinfo)
return self.mk_tzaware(dt)
@classmethod
def mk_tzaware(cls, datetime_obj):
kwargs = {}
attrs = ('year',
'month',
'day',
'hour',
'minute',
'second',
'microsecond',
'tzinfo')
for attr in attrs:
val = getattr(datetime_obj, attr, None)
if val is not None:
kwargs[attr] = val
return cls(**kwargs)
def date_range(start, freq, periods):
dtstart = dt_tzaware.mk_tzaware(start)
return [dtstart + (i * freq) for i in range(periods)]
def tz_convert(dt_list, tzinfo):
return [d.astimezone(tzinfo) for d in dt_list]
_test_date2num_dst(date_range, tz_convert)
def test_date2num_dst_pandas():
except ImportError:
raise SkipTest('pandas not installed')
def tz_convert(*args):
return pd.DatetimeIndex.tz_convert(*args).astype(object)
_test_date2num_dst(pd.date_range, tz_convert)
def test_DayLocator():
assert_raises(ValueError, mdates.DayLocator, interval=-1)
assert_raises(ValueError, mdates.DayLocator, interval=-1.5)
assert_raises(ValueError, mdates.DayLocator, interval=0)
assert_raises(ValueError, mdates.DayLocator, interval=1.3)
mdates.DayLocator(interval=1.0)
def test_tz_utc():
dt = datetime.datetime(1970, 1, 1, tzinfo=mdates.UTC)
dt.tzname()
if __name__ == '__main__':
import nose
nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
| true
| true
|
1c441f687a15c8a847fbb5b8238c9fec5fcdeb16
| 728
|
py
|
Python
|
icekit/plugins/child_pages/migrations/0001_initial.py
|
ic-labs/django-icekit
|
c507ea5b1864303732c53ad7c5800571fca5fa94
|
[
"MIT"
] | 52
|
2016-09-13T03:50:58.000Z
|
2022-02-23T16:25:08.000Z
|
icekit/plugins/child_pages/migrations/0001_initial.py
|
ic-labs/django-icekit
|
c507ea5b1864303732c53ad7c5800571fca5fa94
|
[
"MIT"
] | 304
|
2016-08-11T14:17:30.000Z
|
2020-07-22T13:35:18.000Z
|
icekit/plugins/child_pages/migrations/0001_initial.py
|
ic-labs/django-icekit
|
c507ea5b1864303732c53ad7c5800571fca5fa94
|
[
"MIT"
] | 12
|
2016-09-21T18:46:35.000Z
|
2021-02-15T19:37:50.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('fluent_contents', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='ChildPageItem',
fields=[
('contentitem_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='fluent_contents.ContentItem')),
],
options={
'db_table': 'contentitem_child_pages_childpageitem',
'verbose_name': 'Child Page',
},
bases=('fluent_contents.contentitem',),
),
]
| 28
| 164
| 0.593407
|
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('fluent_contents', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='ChildPageItem',
fields=[
('contentitem_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='fluent_contents.ContentItem')),
],
options={
'db_table': 'contentitem_child_pages_childpageitem',
'verbose_name': 'Child Page',
},
bases=('fluent_contents.contentitem',),
),
]
| true
| true
|
1c4421bf3400323db69447583130a58df30bd901
| 1,811
|
py
|
Python
|
src/embedding/auxiliary/factory.py
|
chengemily/Distributional-Signatures
|
7ef96f9cfc8aeb2fb54e117e3968e4390aaad819
|
[
"MIT"
] | 243
|
2019-08-15T18:34:09.000Z
|
2022-03-31T11:51:00.000Z
|
src/embedding/auxiliary/factory.py
|
phymucs/460d60d2c25a118c67dcbfdd37f27d6c
|
cd7e4659fc9761a8af046e824853aa338b22f2f6
|
[
"MIT"
] | 34
|
2019-10-22T08:11:28.000Z
|
2022-03-19T08:03:30.000Z
|
src/embedding/auxiliary/factory.py
|
phymucs/460d60d2c25a118c67dcbfdd37f27d6c
|
cd7e4659fc9761a8af046e824853aa338b22f2f6
|
[
"MIT"
] | 54
|
2019-08-19T16:11:49.000Z
|
2022-03-31T05:36:01.000Z
|
import datetime
import torch
import torch.nn as nn
import torch.nn.functional as F
from embedding.auxiliary.pos import POS
def get_embedding(args):
'''
@return AUX module with aggregated embeddings or None if args.aux
did not provide additional embeddings
'''
print("{}, Building augmented embedding".format(
datetime.datetime.now().strftime('%02y/%02m/%02d %H:%M:%S')))
aux = []
for ebd in args.auxiliary:
if ebd == 'pos':
aux.append(POS(args))
else:
raise ValueError('Invalid argument for auxiliary ebd')
if args.cuda != -1:
aux = [a.cuda(args.cuda) for a in aux]
model = AUX(aux, args)
if args.cuda != -1:
return model.cuda(args.cuda)
else:
return model
class AUX(nn.Module):
'''
Wrapper around combination of auxiliary embeddings
'''
def __init__(self, aux, args):
super(AUX, self).__init__()
self.args = args
# this is a list of nn.Module
self.aux = nn.ModuleList(aux)
# this is 0 if self.aux is empty
self.embedding_dim = sum(a.embedding_dim for a in self.aux)
def forward(self, data, weights=None):
# torch.cat will discard the empty tensor
if len(self.aux) == 0:
if self.args.cuda != -1:
return torch.FloatTensor().cuda(self.args.cuda)
return torch.FloatTensor()
# aggregate results from each auxiliary module
results = [aux(data, weights) for aux in self.aux]
# aux embeddings should only be used with cnn, meta or meta_mlp.
# concatenate together with word embeddings
assert (self.args.embedding in ['cnn', 'meta', 'meta_mlp', 'lstmatt'])
x = torch.cat(results, dim=2)
return x
| 27.861538
| 78
| 0.605191
|
import datetime
import torch
import torch.nn as nn
import torch.nn.functional as F
from embedding.auxiliary.pos import POS
def get_embedding(args):
print("{}, Building augmented embedding".format(
datetime.datetime.now().strftime('%02y/%02m/%02d %H:%M:%S')))
aux = []
for ebd in args.auxiliary:
if ebd == 'pos':
aux.append(POS(args))
else:
raise ValueError('Invalid argument for auxiliary ebd')
if args.cuda != -1:
aux = [a.cuda(args.cuda) for a in aux]
model = AUX(aux, args)
if args.cuda != -1:
return model.cuda(args.cuda)
else:
return model
class AUX(nn.Module):
def __init__(self, aux, args):
super(AUX, self).__init__()
self.args = args
self.aux = nn.ModuleList(aux)
self.embedding_dim = sum(a.embedding_dim for a in self.aux)
def forward(self, data, weights=None):
if len(self.aux) == 0:
if self.args.cuda != -1:
return torch.FloatTensor().cuda(self.args.cuda)
return torch.FloatTensor()
results = [aux(data, weights) for aux in self.aux]
assert (self.args.embedding in ['cnn', 'meta', 'meta_mlp', 'lstmatt'])
x = torch.cat(results, dim=2)
return x
| true
| true
|
1c44232a092f5ad3d514e0826917e96a5a8c0b13
| 1,278
|
py
|
Python
|
leads/urls.py
|
tmbyers1102/djcrm
|
7a2830a3d1867a223a748b5cb9b771fcc45577e4
|
[
"MIT"
] | null | null | null |
leads/urls.py
|
tmbyers1102/djcrm
|
7a2830a3d1867a223a748b5cb9b771fcc45577e4
|
[
"MIT"
] | null | null | null |
leads/urls.py
|
tmbyers1102/djcrm
|
7a2830a3d1867a223a748b5cb9b771fcc45577e4
|
[
"MIT"
] | null | null | null |
from django.urls import path
from .views import (
LeadListView, LeadDetailView, LeadCreateView, LeadUpdateView,
LeadDeleteView, AssignAgentView, CategoryListView, CategoryDetailView,
LeadCategoryUpdateView, CategoryCreateView, CategoryUpdateView, CategoryDeleteView
)
app_name = "leads"
urlpatterns = [
path('', LeadListView.as_view(), name='lead-list'),
path('<int:pk>/', LeadDetailView.as_view(), name='lead-detail'),
path('<int:pk>/update/', LeadUpdateView.as_view(), name='lead-update'),
path('<int:pk>/delete/', LeadDeleteView.as_view(), name='lead-delete'),
path('<int:pk>/assign-agent/', AssignAgentView.as_view(), name='assign-agent'),
path('<int:pk>/category/', LeadCategoryUpdateView.as_view(), name='lead-category-update'),
path('create/', LeadCreateView.as_view(), name='lead-create'),
path('categories/', CategoryListView.as_view(), name='category-list'),
path('categories/<int:pk>/', CategoryDetailView.as_view(), name='category-detail'),
path('create-category/', CategoryCreateView.as_view(), name='category-create'),
path('categories/<int:pk>/update/', CategoryUpdateView.as_view(), name='category-update'),
path('categories/<int:pk>/delete/', CategoryDeleteView.as_view(), name='category-delete'),
]
| 55.565217
| 94
| 0.717527
|
from django.urls import path
from .views import (
LeadListView, LeadDetailView, LeadCreateView, LeadUpdateView,
LeadDeleteView, AssignAgentView, CategoryListView, CategoryDetailView,
LeadCategoryUpdateView, CategoryCreateView, CategoryUpdateView, CategoryDeleteView
)
app_name = "leads"
urlpatterns = [
path('', LeadListView.as_view(), name='lead-list'),
path('<int:pk>/', LeadDetailView.as_view(), name='lead-detail'),
path('<int:pk>/update/', LeadUpdateView.as_view(), name='lead-update'),
path('<int:pk>/delete/', LeadDeleteView.as_view(), name='lead-delete'),
path('<int:pk>/assign-agent/', AssignAgentView.as_view(), name='assign-agent'),
path('<int:pk>/category/', LeadCategoryUpdateView.as_view(), name='lead-category-update'),
path('create/', LeadCreateView.as_view(), name='lead-create'),
path('categories/', CategoryListView.as_view(), name='category-list'),
path('categories/<int:pk>/', CategoryDetailView.as_view(), name='category-detail'),
path('create-category/', CategoryCreateView.as_view(), name='category-create'),
path('categories/<int:pk>/update/', CategoryUpdateView.as_view(), name='category-update'),
path('categories/<int:pk>/delete/', CategoryDeleteView.as_view(), name='category-delete'),
]
| true
| true
|
1c4423c0dbd6723411c958372c1c8fc474df25d0
| 819
|
py
|
Python
|
examples/loading_images.py
|
FPEPOSHI/PyBoof
|
00c67c0689be35019b65d5e5dc1a8a5a8e471d9d
|
[
"Apache-2.0"
] | 1
|
2022-02-10T04:18:28.000Z
|
2022-02-10T04:18:28.000Z
|
examples/loading_images.py
|
yuantailing/PyBoof
|
a5a0ffba6adfeb8d97c099c2470995553466eeaa
|
[
"Apache-2.0"
] | null | null | null |
examples/loading_images.py
|
yuantailing/PyBoof
|
a5a0ffba6adfeb8d97c099c2470995553466eeaa
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
import numpy as np
import cv2
import pyboof as pb
# Enable use of memory mapped files for MUCH faster conversion of images between java and python
pb.init_memmap()
image_path = '../data/example/outdoors01.jpg'
# Can load an image using OpenCV then convert it into BoofCV
ndarray_img = cv2.imread(image_path, 0)
boof_cv = pb.ndarray_to_boof(ndarray_img)
# Can also use BoofCV to load the image directly
boof_gray = pb.load_single_band(image_path, np.uint8)
boof_color = pb.load_planar(image_path, np.uint8)
# Let's display all 3 of them in Java
# display the results in a single window as a list
image_list = [(boof_cv, "OpenCV"),
(boof_gray, "Gray Scale"),
(boof_color, "Color")]
pb.swing.show_list(image_list, title="Images")
input("Press any key to exit")
| 26.419355
| 96
| 0.73138
|
import numpy as np
import cv2
import pyboof as pb
pb.init_memmap()
image_path = '../data/example/outdoors01.jpg'
ndarray_img = cv2.imread(image_path, 0)
boof_cv = pb.ndarray_to_boof(ndarray_img)
boof_gray = pb.load_single_band(image_path, np.uint8)
boof_color = pb.load_planar(image_path, np.uint8)
# display the results in a single window as a list
image_list = [(boof_cv, "OpenCV"),
(boof_gray, "Gray Scale"),
(boof_color, "Color")]
pb.swing.show_list(image_list, title="Images")
input("Press any key to exit")
| true
| true
|
1c4423ccd33c81a933965b4db4ae0ecdcaf8a624
| 1,563
|
py
|
Python
|
app/urls.py
|
victoriadrake/django-starter
|
4fbb423edf79bf4e512a4a6c578072c539d00b9d
|
[
"MIT"
] | 6
|
2021-08-25T12:06:29.000Z
|
2022-02-16T12:36:58.000Z
|
app/urls.py
|
victoriadrake/django-starter
|
4fbb423edf79bf4e512a4a6c578072c539d00b9d
|
[
"MIT"
] | null | null | null |
app/urls.py
|
victoriadrake/django-starter
|
4fbb423edf79bf4e512a4a6c578072c539d00b9d
|
[
"MIT"
] | 2
|
2021-08-28T07:50:16.000Z
|
2022-02-21T09:47:46.000Z
|
"""URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
import app.views as views
from django.conf import settings
from django.urls import re_path
from django.views.static import serve
from django.contrib import admin
from django.urls import include, path
urlpatterns = [path("admin/", admin.site.urls), path("", views.Welcome.as_view())]
if settings.DEBUG:
from django.conf.urls.static import static
# Will serve files from /media/ in development mode
# https://docs.djangoproject.com/en/3.2/ref/views/#serving-files-in-development
urlpatterns += [
re_path(r"^media/(?P<path>.*)$", serve, {"document_root": settings.MEDIA_ROOT})
] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
if "debug_toolbar" in settings.INSTALLED_APPS:
# Displays the Debug Toolbar
# https://django-debug-toolbar.readthedocs.io/en/latest/index.html
import debug_toolbar
urlpatterns += [path("__debug__/", include(debug_toolbar.urls))]
| 39.075
| 87
| 0.715291
|
import app.views as views
from django.conf import settings
from django.urls import re_path
from django.views.static import serve
from django.contrib import admin
from django.urls import include, path
urlpatterns = [path("admin/", admin.site.urls), path("", views.Welcome.as_view())]
if settings.DEBUG:
from django.conf.urls.static import static
re_path(r"^media/(?P<path>.*)$", serve, {"document_root": settings.MEDIA_ROOT})
] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
if "debug_toolbar" in settings.INSTALLED_APPS:
import debug_toolbar
urlpatterns += [path("__debug__/", include(debug_toolbar.urls))]
| true
| true
|
1c4423e641fedf13db70ea243a100a5ec19fb37b
| 711
|
py
|
Python
|
dodo.py
|
Thirty-OneR/Astr496_assignment03
|
e5c8c842906fa9d6c141fb92a0fc9e134810ec64
|
[
"BSD-3-Clause"
] | null | null | null |
dodo.py
|
Thirty-OneR/Astr496_assignment03
|
e5c8c842906fa9d6c141fb92a0fc9e134810ec64
|
[
"BSD-3-Clause"
] | null | null | null |
dodo.py
|
Thirty-OneR/Astr496_assignment03
|
e5c8c842906fa9d6c141fb92a0fc9e134810ec64
|
[
"BSD-3-Clause"
] | null | null | null |
from doit.tools import run_once
import h5py
import numpy as np
import matplotlib.pyplot as plt
def task_generate_gaussian():
N = 32**3
seed = 0x4d3d3d3
fn = "gaussian.h5"
def _generate():
np.random.seed(seed)
pos = np.random.normal(loc = [0.5, 0.5, 0.5], scale = 0.2, size = (N, 3))
vel = np.random.random(size = (N, 3)) * 10.0 - 5.0
with h5py.File(fn, "w") as f:
f.create_dataset("/particle_positions", data = pos)
f.create_dataset("/particle_velocities", data = vel)
f.create_dataset("/particle_masses", data = np.ones(N))
return {'actions': [_generate],
'targets': [fn],
'uptodate': [run_once]}
| 33.857143
| 81
| 0.583685
|
from doit.tools import run_once
import h5py
import numpy as np
import matplotlib.pyplot as plt
def task_generate_gaussian():
N = 32**3
seed = 0x4d3d3d3
fn = "gaussian.h5"
def _generate():
np.random.seed(seed)
pos = np.random.normal(loc = [0.5, 0.5, 0.5], scale = 0.2, size = (N, 3))
vel = np.random.random(size = (N, 3)) * 10.0 - 5.0
with h5py.File(fn, "w") as f:
f.create_dataset("/particle_positions", data = pos)
f.create_dataset("/particle_velocities", data = vel)
f.create_dataset("/particle_masses", data = np.ones(N))
return {'actions': [_generate],
'targets': [fn],
'uptodate': [run_once]}
| true
| true
|
1c4423f3e839538adbe798b9a8ec37bc7802b9d8
| 4,881
|
py
|
Python
|
ciscoisesdk/models/validators/v3_1_0/jsd_df9ab8ff636353279d5c787585dcb6af.py
|
CiscoISE/ciscoisesdk
|
860b0fc7cc15d0c2a39c64608195a7ab3d5f4885
|
[
"MIT"
] | 36
|
2021-05-18T16:24:19.000Z
|
2022-03-05T13:44:41.000Z
|
ciscoisesdk/models/validators/v3_0_0/jsd_df9ab8ff636353279d5c787585dcb6af.py
|
CiscoISE/ciscoisesdk
|
860b0fc7cc15d0c2a39c64608195a7ab3d5f4885
|
[
"MIT"
] | 15
|
2021-06-08T19:03:37.000Z
|
2022-02-25T14:47:33.000Z
|
ciscoisesdk/models/validators/v3_1_0/jsd_df9ab8ff636353279d5c787585dcb6af.py
|
CiscoISE/ciscoisesdk
|
860b0fc7cc15d0c2a39c64608195a7ab3d5f4885
|
[
"MIT"
] | 6
|
2021-06-10T09:32:01.000Z
|
2022-01-12T08:34:39.000Z
|
# -*- coding: utf-8 -*-
"""Identity Services Engine updateRadiusServerSequenceById data model.
Copyright (c) 2021 Cisco and/or its affiliates.
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.
"""
from __future__ import (
absolute_import,
division,
print_function,
unicode_literals,
)
import fastjsonschema
import json
from ciscoisesdk.exceptions import MalformedRequest
from builtins import *
class JSONSchemaValidatorDf9Ab8Ff636353279D5C787585Dcb6Af(object):
"""updateRadiusServerSequenceById request schema definition."""
def __init__(self):
super(JSONSchemaValidatorDf9Ab8Ff636353279D5C787585Dcb6Af, self).__init__()
self._validator = fastjsonschema.compile(json.loads(
'''{
"$schema": "http://json-schema.org/draft-04/schema#",
"properties": {
"RadiusServerSequence": {
"properties": {
"BeforeAcceptAttrManipulatorsList": {
"items": {
"properties": {
"action": {
"type": "string"
},
"attributeName": {
"type": "string"
},
"changedVal": {
"type": "string"
},
"dictionaryName": {
"type": "string"
},
"value": {
"type": "string"
}
},
"type": "object"
},
"type": "array"
},
"OnRequestAttrManipulatorList": {
"items": {
"properties": {
"action": {
"type": "string"
},
"attributeName": {
"type": "string"
},
"changedVal": {
"type": "string"
},
"dictionaryName": {
"type": "string"
},
"value": {
"type": "string"
}
},
"type": "object"
},
"type": "array"
},
"RadiusServerList": {
"items": {
"type": "string"
},
"type": "array"
},
"continueAuthorzPolicy": {
"type": "boolean"
},
"description":
{
"type": "string"
},
"id": {
"type": "string"
},
"localAccounting": {
"type": "boolean"
},
"name": {
"type": "string"
},
"prefixSeparator": {
"type": "string"
},
"remoteAccounting": {
"type": "boolean"
},
"stripPrefix": {
"type": "boolean"
},
"stripSuffix": {
"type": "boolean"
},
"suffixSeparator": {
"type": "string"
},
"useAttrSetBeforeAcc": {
"type": "boolean"
},
"useAttrSetOnRequest": {
"type": "boolean"
}
},
"type": "object"
}
},
"type": "object"
}'''.replace("\n" + ' ' * 16, '')
))
def validate(self, request):
try:
self._validator(request)
except fastjsonschema.exceptions.JsonSchemaException as e:
raise MalformedRequest(
'{} is invalid. Reason: {}'.format(request, e.message)
)
| 31.694805
| 83
| 0.461995
|
from __future__ import (
absolute_import,
division,
print_function,
unicode_literals,
)
import fastjsonschema
import json
from ciscoisesdk.exceptions import MalformedRequest
from builtins import *
class JSONSchemaValidatorDf9Ab8Ff636353279D5C787585Dcb6Af(object):
def __init__(self):
super(JSONSchemaValidatorDf9Ab8Ff636353279D5C787585Dcb6Af, self).__init__()
self._validator = fastjsonschema.compile(json.loads(
'''{
"$schema": "http://json-schema.org/draft-04/schema#",
"properties": {
"RadiusServerSequence": {
"properties": {
"BeforeAcceptAttrManipulatorsList": {
"items": {
"properties": {
"action": {
"type": "string"
},
"attributeName": {
"type": "string"
},
"changedVal": {
"type": "string"
},
"dictionaryName": {
"type": "string"
},
"value": {
"type": "string"
}
},
"type": "object"
},
"type": "array"
},
"OnRequestAttrManipulatorList": {
"items": {
"properties": {
"action": {
"type": "string"
},
"attributeName": {
"type": "string"
},
"changedVal": {
"type": "string"
},
"dictionaryName": {
"type": "string"
},
"value": {
"type": "string"
}
},
"type": "object"
},
"type": "array"
},
"RadiusServerList": {
"items": {
"type": "string"
},
"type": "array"
},
"continueAuthorzPolicy": {
"type": "boolean"
},
"description":
{
"type": "string"
},
"id": {
"type": "string"
},
"localAccounting": {
"type": "boolean"
},
"name": {
"type": "string"
},
"prefixSeparator": {
"type": "string"
},
"remoteAccounting": {
"type": "boolean"
},
"stripPrefix": {
"type": "boolean"
},
"stripSuffix": {
"type": "boolean"
},
"suffixSeparator": {
"type": "string"
},
"useAttrSetBeforeAcc": {
"type": "boolean"
},
"useAttrSetOnRequest": {
"type": "boolean"
}
},
"type": "object"
}
},
"type": "object"
}'''.replace("\n" + ' ' * 16, '')
))
def validate(self, request):
try:
self._validator(request)
except fastjsonschema.exceptions.JsonSchemaException as e:
raise MalformedRequest(
'{} is invalid. Reason: {}'.format(request, e.message)
)
| true
| true
|
1c44240d49c67657b76398b4c928871217f7814a
| 1,365
|
py
|
Python
|
blog/migrations/0001_initial.py
|
albertmil97/django_AlbertMIliano
|
9d1c1763e9061083d1cc1e389a77423cfd2e7daf
|
[
"MIT"
] | null | null | null |
blog/migrations/0001_initial.py
|
albertmil97/django_AlbertMIliano
|
9d1c1763e9061083d1cc1e389a77423cfd2e7daf
|
[
"MIT"
] | null | null | null |
blog/migrations/0001_initial.py
|
albertmil97/django_AlbertMIliano
|
9d1c1763e9061083d1cc1e389a77423cfd2e7daf
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.0.7 on 2020-06-08 04:47
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=250)),
('slug', models.SlugField(max_length=250, unique_for_date='publish')),
('body', models.TextField()),
('publish', models.DateTimeField(default=django.utils.timezone.now)),
('created', models.DateTimeField(auto_now_add=True)),
('updated', models.DateTimeField(auto_now=True)),
('status', models.CharField(choices=[('draft', 'Draft'), ('published', 'Published')], default='draft', max_length=10)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='blog_post', to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ('-publish',),
},
),
]
| 37.916667
| 146
| 0.606593
|
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=250)),
('slug', models.SlugField(max_length=250, unique_for_date='publish')),
('body', models.TextField()),
('publish', models.DateTimeField(default=django.utils.timezone.now)),
('created', models.DateTimeField(auto_now_add=True)),
('updated', models.DateTimeField(auto_now=True)),
('status', models.CharField(choices=[('draft', 'Draft'), ('published', 'Published')], default='draft', max_length=10)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='blog_post', to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ('-publish',),
},
),
]
| true
| true
|
1c4424f6ccb062b4183b7bc73cbbf4c674e6bcfd
| 25,179
|
py
|
Python
|
hw/vendor/lowrisc_ibex/vendor/google_riscv-dv/pygen/pygen_src/isa/riscv_cov_instr.py
|
msfschaffner/opentitan-bak
|
de4cb1bb9e7b707a3ca2a6882d83af7ed2aa1ab8
|
[
"Apache-2.0"
] | 1
|
2021-12-15T09:23:09.000Z
|
2021-12-15T09:23:09.000Z
|
hw/vendor/lowrisc_ibex/vendor/google_riscv-dv/pygen/pygen_src/isa/riscv_cov_instr.py
|
msfschaffner/opentitan-bak
|
de4cb1bb9e7b707a3ca2a6882d83af7ed2aa1ab8
|
[
"Apache-2.0"
] | 3
|
2020-05-29T13:12:25.000Z
|
2020-06-19T13:07:23.000Z
|
hw/vendor/lowrisc_ibex/vendor/google_riscv-dv/pygen/pygen_src/isa/riscv_cov_instr.py
|
msfschaffner/opentitan-bak
|
de4cb1bb9e7b707a3ca2a6882d83af7ed2aa1ab8
|
[
"Apache-2.0"
] | 1
|
2021-08-28T16:19:23.000Z
|
2021-08-28T16:19:23.000Z
|
"""Copyright 2020 Google LLC
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 vsc
import logging
from importlib import import_module
from enum import IntEnum, auto
from pygen_src.riscv_instr_pkg import *
from pygen_src.riscv_instr_gen_config import cfg
rcs = import_module("pygen_src.target." + cfg.argv.target + ".riscv_core_setting")
class operand_sign_e(IntEnum):
POSITIVE = 0
NEGATIVE = auto()
class div_result_e(IntEnum):
DIV_NORMAL = 0
DIV_BY_ZERO = auto()
DIV_OVERFLOW = auto()
class div_result_ex_overflow_e(IntEnum):
DIV_NORMAL = 0
DIV_BY_ZERO = auto()
class compare_result_e(IntEnum):
EQUAL = 0
LARGER = auto()
SMALLER = auto()
class logical_similarity_e(IntEnum):
IDENTICAL = 0
OPPOSITE = auto()
SIMILAR = auto()
DIFFERENT = auto()
class special_val_e(IntEnum):
NORMAL_VAL = 0
MIN_VAL = auto()
MAX_VAL = auto()
ZERO_VAL = auto()
class riscv_cov_instr:
""" Class for a riscv instruction in functional coverage phase;
data parsed from the CSV file fill different fields of an instruction """
# class attr. to keep track of reg_name:reg_value throughout the program
gpr_state = {}
def __init__(self):
# Program counter (PC) of the instruction
self.pc = vsc.bit_t(rcs.XLEN)
self.instr = None
# self.gpr = None # destination operand of the instruction
self.binary = vsc.bit_t(32) # Instruction binary
# self.mode = None # Instruction mode
self.trace = "None" # String representation of the instruction
# self.operands = "None" # Instruction operands (srcss/dests)
# self.pad = None # Not used
self.rs1_value = vsc.bit_t(rcs.XLEN)
self.rs2_value = vsc.bit_t(rcs.XLEN)
self.rs3_value = vsc.bit_t(rcs.XLEN)
self.rd_value = vsc.bit_t(rcs.XLEN)
self.fs1_value = vsc.bit_t(rcs.XLEN)
self.fs2_value = vsc.bit_t(rcs.XLEN)
self.fs3_value = vsc.bit_t(rcs.XLEN)
self.fd_value = vsc.bit_t(rcs.XLEN)
self.mem_addr = vsc.int_t(rcs.XLEN)
self.unaligned_pc = 0
self.unaligned_mem_access = 0
self.compressed = 0
self.branch_hit = 0
self.div_result = None
self.rs1_sign = 0
self.rs2_sign = 0
self.rs3_sign = 0
self.fs1_sign = 0
self.fs2_sign = 0
self.fs3_sign = 0
self.imm_sign = 0
self.rd_sign = 0
self.fd_sign = 0
self.gpr_hazard = hazard_e.NO_HAZARD
self.lsu_hazard = hazard_e.NO_HAZARD
self.rs1_special_value = 0
self.rs2_special_value = 0
self.rs3_special_value = 0
self.rd_special_value = 0
self.imm_special_value = 0
self.compare_result = 0
self.logical_similarity = 0
self.group = None
self.format = None
self.category = None
self.imm_type = None
self.csr = vsc.bit_t(12)
''' TODO: rs2, rs1, rd, group, format, category, imm_type
fs1, fs2, fs3, fd will be changed to vsc.enum_t once
the issue with set/get_val is fixed '''
self.rs2 = 0
self.rs1 = 0
self.rd = 0
self.imm = vsc.int_t(32)
self.has_rs1 = 1
self.has_rs2 = 1
self.has_rd = 1
self.has_imm = 1
self.imm_len = 0
self.has_fs1 = 1
self.has_fs2 = 1
self.has_fs3 = 0
self.has_fd = 1
self.fs1 = 0
self.fs2 = 0
self.fs3 = 0
self.fd = 0
def assign_attributes(self):
attr_list = get_attr_list(self.instr)
self.format = attr_list[0]
self.category = attr_list[1]
self.group = attr_list[2]
self.imm_type = imm_t.IMM
if len(attr_list) > 3:
self.imm_type = attr_list[3]
self.set_imm_len()
self.set_mode()
if self.group.name in ["RV32D", "RV32F"]:
self.set_fd_mode()
def set_imm_len(self):
if self.format.name in ["U_FORMAT", "J_FORMAT"]:
self.imm_len = 20
elif self.format.name in ["I_FORMAT", "S_FORMAT", "B_FORMAT"]:
if self.imm_type.name == "UIMM":
self.imm_len = 5
else:
self.imm_len = 12
def set_mode(self):
# mode setting for Instruction Format
if self.format.name == "R_FORMAT":
self.has_imm = 0
if self.format.name == "I_FORMAT":
self.has_rs2 = 0
if self.format.name in ["S_FORMAT", "B_FORMAT"]:
self.has_rd = 0
if self.format.name in ["U_FORMAT", "J_FORMAT"]:
self.has_rs1 = 0
self.has_rs2 = 0
# mode setting for Instruction Category
if self.category.name == "CSR":
self.has_rs2 = 0
if self.format.name == "I_FORMAT":
self.has_rs1 = 0
# mode setting for F and D Instruction
def set_fd_mode(self):
if self.format == riscv_instr_format_t.I_FORMAT:
self.has_fs2 = 0
if self.category == riscv_instr_category_t.LOAD:
self.has_imm = 1
elif self.instr.name in ['FMV_X_W', 'FMV_X_D', 'FCVT_W_S', 'FCVT_WU_S',
'FCVT_L_S', 'FCVT_LU_S', 'FCVT_L_D', 'FCVT_LU_D',
'FCVT_LU_S', 'FCVT_W_D', 'FCVT_WU_D']:
self.has_fd = 0
self.has_rd = 1
elif self.instr.name in ['FMV_W_X', 'FMV_D_X', 'FCVT_S_W', 'FCVT_S_WU',
'FCVT_S_L', 'FCVT_D_L', 'FCVT_S_LU', 'FCVT_D_W',
'FCVT_D_LU', 'FCVT_D_WU']:
self.has_rs1 = 1
self.has_fs1 = 0
elif self.format == riscv_instr_format_t.S_FORMAT:
self.has_imm = 1
self.has_rs1 = 1
self.has_fs1 = 0
self.has_fs3 = 0
elif self.format == riscv_instr_format_t.R_FORMAT:
if self.category == riscv_instr_category_t.COMPARE:
self.has_rd = 1
self.has_fd = 0
elif self.instr.name in ['FCLASS_S', 'FCLASS_D']:
self.has_rd = 1
self.has_fd = 0
self.has_fs2 = 0
elif self.format == riscv_instr_format_t.R4_FORMAT:
self.has_fs3 = 1
elif self.format == riscv_instr_format_t.CL_FORMAT:
self.has_imm = 1
self.has_rs1 = 1
self.has_fs1 = 0
self.has_fs2 = 0
elif self.format == riscv_instr_format_t.CS_FORMAT:
self.has_imm = 1
self.has_rs1 = 1
self.has_fs1 = 0
self.has_fd = 0
else:
logging.info("Unsupported format {}".format(self.format.name))
def pre_sample(self):
unaligned_pc = self.pc.get_val() % 4 != 0
self.rs1_sign = self.get_operand_sign(self.rs1_value)
self.rs2_sign = self.get_operand_sign(self.rs2_value)
self.rs3_sign = self.get_operand_sign(self.rs3_value)
self.rd_sign = self.get_operand_sign(self.rd_value)
self.fs1_sign = self.get_operand_sign(self.fs1_value)
self.fs2_sign = self.get_operand_sign(self.fs2_value)
self.fs3_sign = self.get_operand_sign(self.fs3_value)
self.fd_sign = self.get_operand_sign(self.fd_value)
self.imm_sign = self.get_imm_sign(self.imm)
self.rs1_special_value = self.get_operand_special_value(self.rs1_value)
self.rd_special_value = self.get_operand_special_value(self.rd_value)
self.rs2_special_value = self.get_operand_special_value(self.rs2_value)
self.rs3_special_value = self.get_operand_special_value(self.rs3_value)
if self.format.name not in ["R_FORMAT", "CR_FORMAT"]:
self.imm_special_value = self.get_imm_special_val(self.imm)
if self.category.name in ["COMPARE", "BRANCH"]:
self.compare_result = self.get_compare_result()
if self.category.name in ["LOAD", "STORE"]:
self.mem_addr.set_val(self.rs1_value.get_val() +
self.imm.get_val())
self.unaligned_mem_access = self.is_unaligned_mem_access()
if self.unaligned_mem_access:
logging.info("Unaligned: {}, mem_addr: {}".format(
self.instr.name, self.mem_addr.get_val()))
if self.category.name == "LOGICAL":
self.logical_similarity = self.get_logical_similarity()
if self.category.name == "BRANCH":
self.branch_hit = self.is_branch_hit()
if self.instr.name in ["DIV", "DIVU", "REM", "REMU", "DIVW", "DIVUW",
"REMW", "REMUW"]:
self.div_result = self.get_div_result()
@staticmethod
def get_operand_sign(operand):
# TODO: Currently handled using string formatting as part select
# isn't yet supported for global vsc variables
operand_bin = format(operand.get_val(), '#0{}b'.format(rcs.XLEN + 2))
# "0b" is the prefix, so operand_bin[2] is the sign bit
if operand_bin[2] == "0":
return operand_sign_e["POSITIVE"]
else:
return operand_sign_e["NEGATIVE"]
def is_unaligned_mem_access(self):
if (self.instr.name in ["LWU", "LD", "SD", "C_LD", "C_SD"] and
self.mem_addr.get_val() % 8 != 0):
return 1
elif (self.instr.name in ["LW", "SW", "C_LW", "C_SW"] and
self.mem_addr.get_val() % 4 != 0):
return 1
elif (self.instr.name in ["LH", "LHU", "SH"] and
self.mem_addr.get_val() % 2 != 0):
return 1
return 0
@staticmethod
def get_imm_sign(imm):
# TODO: Currently handled using string formatting as part select
# isn't yet supported for global vsc variables
imm_bin = format(imm.get_val(), '#0{}b'.format(rcs.XLEN + 2))
# "0b" is the prefix, so imm_bin[2] is the sign bit
if imm_bin[2] == "0":
return operand_sign_e["POSITIVE"]
else:
return operand_sign_e["NEGATIVE"]
def get_div_result(self):
if self.rs2_value.get_val() == 0:
return div_result_e["DIV_BY_ZERO"]
elif (self.rs2_value.get_val() == 1
and self.rs1_value.get_val() == (1 << (rcs.XLEN - 1))):
return div_result_e["DIV_OVERFLOW"]
else:
return div_result_e["DIV_NORMAL"]
@staticmethod
def get_operand_special_value(operand):
if operand.get_val() == 0:
return special_val_e["ZERO_VAL"]
elif operand.get_val() == 1 << (rcs.XLEN - 1):
return special_val_e["MIN_VAL"]
elif operand.get_val() == 1 >> 1:
return special_val_e["MAX_VAL"]
else:
return special_val_e["NORMAL_VAL"]
def get_imm_special_val(self, imm):
if imm.get_val() == 0:
return special_val_e["ZERO_VAL"]
elif self.format == riscv_instr_format_t.U_FORMAT:
# unsigned immediate value
max_val = vsc.int_t(32, (1 << self.imm_len) - 1)
if imm.get_val() == 0:
return special_val_e["MIN_VAL"]
if imm.get_val() == max_val.get_val():
return special_val_e["MAX_VAL"]
else:
# signed immediate value
max_val = vsc.int_t(32, (2 ** (self.imm_len - 1)) - 1)
min_val = vsc.int_t(32, -2 ** (self.imm_len - 1))
if min_val.get_val() == imm.get_val():
return special_val_e["MIN_VAL"]
if max_val.get_val() == imm.get_val():
return special_val_e["MAX_VAL"]
return special_val_e["NORMAL_VAL"]
def get_compare_result(self):
val1 = vsc.int_t(rcs.XLEN, self.rs1_value.get_val())
val2 = vsc.int_t(rcs.XLEN, self.imm.get_val() if (
self.format == riscv_instr_format_t.I_FORMAT) else
self.rs2_value.val)
if val1.get_val() == val2.get_val():
return compare_result_e["EQUAL"]
elif val1.get_val() < val2.get_val():
return compare_result_e["SMALLER"]
else:
return compare_result_e["LARGER"]
def is_branch_hit(self):
if self.instr.name == "BEQ":
return int(self.rs1_value.get_val() == self.rs2_value.get_val())
elif self.instr.name == "C_BEQZ":
return int(self.rs1_value.get_val() == 0)
elif self.instr.name == "BNE":
return int(self.rs1_value.get_val() != self.rs2_value.get_val())
elif self.instr.name == "C_BNEZ":
return int(self.rs1_value.get_val() != 0)
elif self.instr.name == "BLT" or self.instr.name == "BLTU":
return int(self.rs1_value.get_val() < self.rs2_value.get_val())
elif self.instr.name == "BGE" or self.instr.name == "BGEU":
return int(self.rs1_value.get_val() >= self.rs2_value.get_val())
else:
logging.error("Unexpected instruction {}".format(self.instr.name))
def get_logical_similarity(self):
val1 = vsc.int_t(rcs.XLEN, self.rs1_value.get_val())
val2 = vsc.int_t(rcs.XLEN, (self.imm.get_val() if
self.format == riscv_instr_format_t.I_FORMAT
else self.rs2_value.val))
temp = bin(val1.get_val() ^ val2.get_val())
bit_difference = len([[ones for ones in temp[2:] if ones == '1']])
if val1.get_val() == val2.get_val():
return logical_similarity_e["IDENTICAL"]
elif bit_difference == 32:
return logical_similarity_e["OPPOSITE"]
elif bit_difference < 5:
return logical_similarity_e["SIMILAR"]
else:
return logical_similarity_e["DIFFERENT"]
def check_hazard_condition(self, pre_instr):
'''TODO: There are cases where instruction actually has destination but
ovpsim doesn't log it because of no change in its value. Hence,
the result of the check_hazard_condition won't be accurate. Need to
explicitly extract the destination register from the operands '''
if pre_instr.has_rd:
if ((self.has_rs1 and (self.rs1 == pre_instr.rd)) or
(self.has_rs2 and (self.rs2 == pre_instr.rd))):
logging.info("pre_instr {}".format(pre_instr.instr.name))
self.gpr_hazard = hazard_e["RAW_HAZARD"]
elif self.has_rd and (self.rd == pre_instr.rd):
self.gpr_hazard = hazard_e["WAW_HAZARD"]
elif (self.has_rd and
((pre_instr.has_rs1 and (pre_instr.rs1 == self.rd)) or
(pre_instr.has_rs2 and (pre_instr.rs2 == self.rd)))):
self.gpr_hazard = hazard_e["WAR_HAZARD"]
else:
self.gpr_hazard = hazard_e["NO_HAZARD"]
if self.category == riscv_instr_category_t.LOAD:
if (pre_instr.category == riscv_instr_category_t.STORE and
(pre_instr.mem_addr.get_val() == self.mem_addr.get_val())):
self.lsu_hazard = hazard_e["RAW_HAZARD"]
else:
self.lsu_hazard = hazard_e["NO_HAZARD"]
if self.category == riscv_instr_category_t.STORE:
if (pre_instr.category == riscv_instr_category_t.STORE and
(pre_instr.mem_addr.get_val() == self.mem_addr.get_val())):
self.lsu_hazard = hazard_e["WAW_HAZARD"]
elif (pre_instr.category == riscv_instr_category_t.LOAD and
(pre_instr.mem_addr.get_val() == self.mem_addr.get_val())):
self.lsu_hazard = hazard_e["WAR_HAZARD"]
else:
self.lsu_hazard = hazard_e["NO_HAZARD"]
# Hazard Condition check for RV32D and RV32F instructions
if pre_instr.has_fd:
if ((self.has_fs1 and (self.fs1 == pre_instr.fd)) or
(self.has_fs2 and (self.fs2 == pre_instr.fd)) or
(self.has_fs3 and (self.fs3 == pre_instr.fd))):
self.gpr_hazard = hazard_e["RAW_HAZARD"]
elif (self.has_fd and (self.fd == pre_instr.fd)):
self.gpr_hazard = hazard_e["WAW_HAZARD"]
elif (self.has_fd and ((pre_instr.has_fs1 and (pre_instr.fs1 == self.fd)) or
(pre_instr.has_fs2 and (pre_instr.fs2 == self.fd)) or
(pre_instr.has_fs3 and (pre_instr.fs3 == self.fd)))):
self.gpr_hazard = hazard_e["WAR_HAZARD"]
else:
self.gpr_hazard = hazard_e["NO_HAZARD"]
logging.debug("Pre PC/name: {}/{}, Cur PC/name: {}/{}, "
"Hazard: {}/{}".format(pre_instr.pc.get_val(),
pre_instr.instr.name,
self.pc.get_val(),
self.instr.name,
self.gpr_hazard.name,
self.lsu_hazard.name))
def get_instr_name(self):
get_instr_name = self.instr.name
for i in get_instr_name:
if i == "_":
get_instr_name = get_instr_name.replace(i, ".")
return get_instr_name
def update_src_regs(self, operands):
if self.format.name in ["J_FORMAT", "U_FORMAT"]:
# instr rd,imm
assert len(operands) == 2
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "I_FORMAT":
assert len(operands) == 3
if self.category.name == "LOAD":
# load rd, imm(rs1)
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
self.imm.set_val(get_val(operands[1]))
elif self.category.name == "CSR":
# csrrwi rd, csr, imm
self.imm.set_val(get_val(operands[2]))
if operands[1].upper() in privileged_reg_t.__members__:
self.csr.set_val(
privileged_reg_t[operands[1].upper()].value)
else:
self.csr.set_val(get_val(operands[1]))
else:
# addi rd, rs1, imm
self.rs1 = self.get_gpr(operands[1])
self.rs1_value.set_val(self.get_gpr_state(operands[1]))
self.imm.set_val(get_val(operands[2]))
elif self.format.name in ["S_FORMAT", "B_FORMAT"]:
assert len(operands) == 3
if self.category.name == "STORE":
self.rs2 = self.get_gpr(operands[0])
self.rs2_value.set_val(self.get_gpr_state(operands[0]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
self.imm.set_val(get_val(operands[1]))
else:
# bne rs1, rs2, imm
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
self.rs2 = self.get_gpr(operands[1])
self.rs2_value.set_val(self.get_gpr_state(operands[1]))
self.imm.set_val(get_val(operands[2]))
elif self.format.name == "R_FORMAT":
if self.has_rs2 or self.category.name == "CSR":
assert len(operands) == 3
else:
assert len(operands) == 2
if self.category.name == "CSR":
# csrrw rd, csr, rs1
if operands[1].upper() in privileged_reg_t.__members__:
self.csr.set_val(
privileged_reg_t[operands[1].upper()].value)
else:
self.csr.set_val(get_val(operands[1]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
else:
# add rd, rs1, rs2
self.rs1 = self.get_gpr(operands[1])
self.rs1_value.set_val(self.get_gpr_state(operands[1]))
if self.has_rs2:
self.rs2 = self.get_gpr(operands[2])
self.rs2_value.set_val(self.get_gpr_state(operands[2]))
elif self.format.name == "R4_FORMAT":
assert len(operands) == 4
self.rs1 = self.get_gpr(operands[1])
self.rs1_value.set_val(self.get_gpr_state(operands[1]))
self.rs2 = self.get_gpr(operands[2])
self.rs2_value.set_val(self.get_gpr_state(operands[2]))
self.rs2 = self.get_gpr(operands[3])
self.rs2_value.set_val(self.get_gpr_state(operands[3]))
elif self.format.name in ["CI_FORMAT", "CIW_FORMAT"]:
if self.instr.name == "C_ADDI16SP":
self.imm.set_val(get_val(operands[1]))
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
elif self.instr.name == "C_ADDI4SPN":
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
elif self.instr.name in ["C_LDSP", "C_LWSP", "C_LQSP"]:
# c.ldsp rd, imm
self.imm.set_val(get_val(operands[1]))
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
else:
# c.lui rd, imm
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CL_FORMAT":
# c.lw rd, imm(rs1)
self.imm.set_val(get_val(operands[1]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
elif self.format.name == "CS_FORMAT":
# c.sw rs2,imm(rs1)
self.rs2 = self.get_gpr(operands[0])
self.rs2_value.set_val(self.get_gpr_state(operands[0]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CA_FORMAT":
# c.and rd, rs2 (rs1 == rd)
self.rs2 = self.get_gpr(operands[1])
self.rs2_value.set_val(self.get_gpr_state(operands[1]))
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
elif self.format.name == "CB_FORMAT":
# c.beqz rs1, imm
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CSS_FORMAT":
# c.swsp rs2, imm
self.rs2 = self.get_gpr(operands[0])
self.rs2_value.set_val(self.get_gpr_state(operands[0]))
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CR_FORMAT":
if self.instr.name in ["C_JR", "C_JALR"]:
# c.jalr rs1
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
else:
# c.add rd, rs2
self.rs2 = self.get_gpr(operands[1])
self.rs2_value.set_val(self.get_gpr_state(operands[1]))
elif self.format.name == "CJ_FORMAT":
# c.j imm
self.imm.set_val(get_val(operands[0]))
else:
logging.error("Unsupported format {}".format(self.format.name))
def update_dst_regs(self, reg_name, val_str):
riscv_cov_instr.gpr_state[reg_name] = get_val(val_str, hexa=1)
self.rd = self.get_gpr(reg_name)
self.rd_value.set_val(self.get_gpr_state(reg_name))
@staticmethod
def get_gpr(reg_name):
reg_name = reg_name.upper()
if reg_name not in riscv_reg_t.__members__:
logging.error("Cannot convert {} to GPR".format(reg_name))
return riscv_reg_t[reg_name]
@staticmethod
def get_gpr_state(name):
if name in ["zero", "x0"]:
return 0
elif name in riscv_cov_instr.gpr_state:
return riscv_cov_instr.gpr_state[name]
else:
logging.warning(
"Cannot find GPR state: {}; initialize to 0".format(name))
if name.upper() in riscv_reg_t.__members__:
riscv_cov_instr.gpr_state[name] = 0
return 0
| 42.604061
| 91
| 0.569959
|
import vsc
import logging
from importlib import import_module
from enum import IntEnum, auto
from pygen_src.riscv_instr_pkg import *
from pygen_src.riscv_instr_gen_config import cfg
rcs = import_module("pygen_src.target." + cfg.argv.target + ".riscv_core_setting")
class operand_sign_e(IntEnum):
POSITIVE = 0
NEGATIVE = auto()
class div_result_e(IntEnum):
DIV_NORMAL = 0
DIV_BY_ZERO = auto()
DIV_OVERFLOW = auto()
class div_result_ex_overflow_e(IntEnum):
DIV_NORMAL = 0
DIV_BY_ZERO = auto()
class compare_result_e(IntEnum):
EQUAL = 0
LARGER = auto()
SMALLER = auto()
class logical_similarity_e(IntEnum):
IDENTICAL = 0
OPPOSITE = auto()
SIMILAR = auto()
DIFFERENT = auto()
class special_val_e(IntEnum):
NORMAL_VAL = 0
MIN_VAL = auto()
MAX_VAL = auto()
ZERO_VAL = auto()
class riscv_cov_instr:
gpr_state = {}
def __init__(self):
self.pc = vsc.bit_t(rcs.XLEN)
self.instr = None
e = "None"
rcs.XLEN)
self.rs2_value = vsc.bit_t(rcs.XLEN)
self.rs3_value = vsc.bit_t(rcs.XLEN)
self.rd_value = vsc.bit_t(rcs.XLEN)
self.fs1_value = vsc.bit_t(rcs.XLEN)
self.fs2_value = vsc.bit_t(rcs.XLEN)
self.fs3_value = vsc.bit_t(rcs.XLEN)
self.fd_value = vsc.bit_t(rcs.XLEN)
self.mem_addr = vsc.int_t(rcs.XLEN)
self.unaligned_pc = 0
self.unaligned_mem_access = 0
self.compressed = 0
self.branch_hit = 0
self.div_result = None
self.rs1_sign = 0
self.rs2_sign = 0
self.rs3_sign = 0
self.fs1_sign = 0
self.fs2_sign = 0
self.fs3_sign = 0
self.imm_sign = 0
self.rd_sign = 0
self.fd_sign = 0
self.gpr_hazard = hazard_e.NO_HAZARD
self.lsu_hazard = hazard_e.NO_HAZARD
self.rs1_special_value = 0
self.rs2_special_value = 0
self.rs3_special_value = 0
self.rd_special_value = 0
self.imm_special_value = 0
self.compare_result = 0
self.logical_similarity = 0
self.group = None
self.format = None
self.category = None
self.imm_type = None
self.csr = vsc.bit_t(12)
self.rs2 = 0
self.rs1 = 0
self.rd = 0
self.imm = vsc.int_t(32)
self.has_rs1 = 1
self.has_rs2 = 1
self.has_rd = 1
self.has_imm = 1
self.imm_len = 0
self.has_fs1 = 1
self.has_fs2 = 1
self.has_fs3 = 0
self.has_fd = 1
self.fs1 = 0
self.fs2 = 0
self.fs3 = 0
self.fd = 0
def assign_attributes(self):
attr_list = get_attr_list(self.instr)
self.format = attr_list[0]
self.category = attr_list[1]
self.group = attr_list[2]
self.imm_type = imm_t.IMM
if len(attr_list) > 3:
self.imm_type = attr_list[3]
self.set_imm_len()
self.set_mode()
if self.group.name in ["RV32D", "RV32F"]:
self.set_fd_mode()
def set_imm_len(self):
if self.format.name in ["U_FORMAT", "J_FORMAT"]:
self.imm_len = 20
elif self.format.name in ["I_FORMAT", "S_FORMAT", "B_FORMAT"]:
if self.imm_type.name == "UIMM":
self.imm_len = 5
else:
self.imm_len = 12
def set_mode(self):
if self.format.name == "R_FORMAT":
self.has_imm = 0
if self.format.name == "I_FORMAT":
self.has_rs2 = 0
if self.format.name in ["S_FORMAT", "B_FORMAT"]:
self.has_rd = 0
if self.format.name in ["U_FORMAT", "J_FORMAT"]:
self.has_rs1 = 0
self.has_rs2 = 0
if self.category.name == "CSR":
self.has_rs2 = 0
if self.format.name == "I_FORMAT":
self.has_rs1 = 0
def set_fd_mode(self):
if self.format == riscv_instr_format_t.I_FORMAT:
self.has_fs2 = 0
if self.category == riscv_instr_category_t.LOAD:
self.has_imm = 1
elif self.instr.name in ['FMV_X_W', 'FMV_X_D', 'FCVT_W_S', 'FCVT_WU_S',
'FCVT_L_S', 'FCVT_LU_S', 'FCVT_L_D', 'FCVT_LU_D',
'FCVT_LU_S', 'FCVT_W_D', 'FCVT_WU_D']:
self.has_fd = 0
self.has_rd = 1
elif self.instr.name in ['FMV_W_X', 'FMV_D_X', 'FCVT_S_W', 'FCVT_S_WU',
'FCVT_S_L', 'FCVT_D_L', 'FCVT_S_LU', 'FCVT_D_W',
'FCVT_D_LU', 'FCVT_D_WU']:
self.has_rs1 = 1
self.has_fs1 = 0
elif self.format == riscv_instr_format_t.S_FORMAT:
self.has_imm = 1
self.has_rs1 = 1
self.has_fs1 = 0
self.has_fs3 = 0
elif self.format == riscv_instr_format_t.R_FORMAT:
if self.category == riscv_instr_category_t.COMPARE:
self.has_rd = 1
self.has_fd = 0
elif self.instr.name in ['FCLASS_S', 'FCLASS_D']:
self.has_rd = 1
self.has_fd = 0
self.has_fs2 = 0
elif self.format == riscv_instr_format_t.R4_FORMAT:
self.has_fs3 = 1
elif self.format == riscv_instr_format_t.CL_FORMAT:
self.has_imm = 1
self.has_rs1 = 1
self.has_fs1 = 0
self.has_fs2 = 0
elif self.format == riscv_instr_format_t.CS_FORMAT:
self.has_imm = 1
self.has_rs1 = 1
self.has_fs1 = 0
self.has_fd = 0
else:
logging.info("Unsupported format {}".format(self.format.name))
def pre_sample(self):
unaligned_pc = self.pc.get_val() % 4 != 0
self.rs1_sign = self.get_operand_sign(self.rs1_value)
self.rs2_sign = self.get_operand_sign(self.rs2_value)
self.rs3_sign = self.get_operand_sign(self.rs3_value)
self.rd_sign = self.get_operand_sign(self.rd_value)
self.fs1_sign = self.get_operand_sign(self.fs1_value)
self.fs2_sign = self.get_operand_sign(self.fs2_value)
self.fs3_sign = self.get_operand_sign(self.fs3_value)
self.fd_sign = self.get_operand_sign(self.fd_value)
self.imm_sign = self.get_imm_sign(self.imm)
self.rs1_special_value = self.get_operand_special_value(self.rs1_value)
self.rd_special_value = self.get_operand_special_value(self.rd_value)
self.rs2_special_value = self.get_operand_special_value(self.rs2_value)
self.rs3_special_value = self.get_operand_special_value(self.rs3_value)
if self.format.name not in ["R_FORMAT", "CR_FORMAT"]:
self.imm_special_value = self.get_imm_special_val(self.imm)
if self.category.name in ["COMPARE", "BRANCH"]:
self.compare_result = self.get_compare_result()
if self.category.name in ["LOAD", "STORE"]:
self.mem_addr.set_val(self.rs1_value.get_val() +
self.imm.get_val())
self.unaligned_mem_access = self.is_unaligned_mem_access()
if self.unaligned_mem_access:
logging.info("Unaligned: {}, mem_addr: {}".format(
self.instr.name, self.mem_addr.get_val()))
if self.category.name == "LOGICAL":
self.logical_similarity = self.get_logical_similarity()
if self.category.name == "BRANCH":
self.branch_hit = self.is_branch_hit()
if self.instr.name in ["DIV", "DIVU", "REM", "REMU", "DIVW", "DIVUW",
"REMW", "REMUW"]:
self.div_result = self.get_div_result()
@staticmethod
def get_operand_sign(operand):
operand_bin = format(operand.get_val(), '
# "0b" is the prefix, so operand_bin[2] is the sign bit
if operand_bin[2] == "0":
return operand_sign_e["POSITIVE"]
else:
return operand_sign_e["NEGATIVE"]
def is_unaligned_mem_access(self):
if (self.instr.name in ["LWU", "LD", "SD", "C_LD", "C_SD"] and
self.mem_addr.get_val() % 8 != 0):
return 1
elif (self.instr.name in ["LW", "SW", "C_LW", "C_SW"] and
self.mem_addr.get_val() % 4 != 0):
return 1
elif (self.instr.name in ["LH", "LHU", "SH"] and
self.mem_addr.get_val() % 2 != 0):
return 1
return 0
@staticmethod
def get_imm_sign(imm):
# TODO: Currently handled using string formatting as part select
# isn't yet supported for global vsc variables
imm_bin = format(imm.get_val(), '#0{}b'.format(rcs.XLEN + 2))
if imm_bin[2] == "0":
return operand_sign_e["POSITIVE"]
else:
return operand_sign_e["NEGATIVE"]
def get_div_result(self):
if self.rs2_value.get_val() == 0:
return div_result_e["DIV_BY_ZERO"]
elif (self.rs2_value.get_val() == 1
and self.rs1_value.get_val() == (1 << (rcs.XLEN - 1))):
return div_result_e["DIV_OVERFLOW"]
else:
return div_result_e["DIV_NORMAL"]
@staticmethod
def get_operand_special_value(operand):
if operand.get_val() == 0:
return special_val_e["ZERO_VAL"]
elif operand.get_val() == 1 << (rcs.XLEN - 1):
return special_val_e["MIN_VAL"]
elif operand.get_val() == 1 >> 1:
return special_val_e["MAX_VAL"]
else:
return special_val_e["NORMAL_VAL"]
def get_imm_special_val(self, imm):
if imm.get_val() == 0:
return special_val_e["ZERO_VAL"]
elif self.format == riscv_instr_format_t.U_FORMAT:
max_val = vsc.int_t(32, (1 << self.imm_len) - 1)
if imm.get_val() == 0:
return special_val_e["MIN_VAL"]
if imm.get_val() == max_val.get_val():
return special_val_e["MAX_VAL"]
else:
max_val = vsc.int_t(32, (2 ** (self.imm_len - 1)) - 1)
min_val = vsc.int_t(32, -2 ** (self.imm_len - 1))
if min_val.get_val() == imm.get_val():
return special_val_e["MIN_VAL"]
if max_val.get_val() == imm.get_val():
return special_val_e["MAX_VAL"]
return special_val_e["NORMAL_VAL"]
def get_compare_result(self):
val1 = vsc.int_t(rcs.XLEN, self.rs1_value.get_val())
val2 = vsc.int_t(rcs.XLEN, self.imm.get_val() if (
self.format == riscv_instr_format_t.I_FORMAT) else
self.rs2_value.val)
if val1.get_val() == val2.get_val():
return compare_result_e["EQUAL"]
elif val1.get_val() < val2.get_val():
return compare_result_e["SMALLER"]
else:
return compare_result_e["LARGER"]
def is_branch_hit(self):
if self.instr.name == "BEQ":
return int(self.rs1_value.get_val() == self.rs2_value.get_val())
elif self.instr.name == "C_BEQZ":
return int(self.rs1_value.get_val() == 0)
elif self.instr.name == "BNE":
return int(self.rs1_value.get_val() != self.rs2_value.get_val())
elif self.instr.name == "C_BNEZ":
return int(self.rs1_value.get_val() != 0)
elif self.instr.name == "BLT" or self.instr.name == "BLTU":
return int(self.rs1_value.get_val() < self.rs2_value.get_val())
elif self.instr.name == "BGE" or self.instr.name == "BGEU":
return int(self.rs1_value.get_val() >= self.rs2_value.get_val())
else:
logging.error("Unexpected instruction {}".format(self.instr.name))
def get_logical_similarity(self):
val1 = vsc.int_t(rcs.XLEN, self.rs1_value.get_val())
val2 = vsc.int_t(rcs.XLEN, (self.imm.get_val() if
self.format == riscv_instr_format_t.I_FORMAT
else self.rs2_value.val))
temp = bin(val1.get_val() ^ val2.get_val())
bit_difference = len([[ones for ones in temp[2:] if ones == '1']])
if val1.get_val() == val2.get_val():
return logical_similarity_e["IDENTICAL"]
elif bit_difference == 32:
return logical_similarity_e["OPPOSITE"]
elif bit_difference < 5:
return logical_similarity_e["SIMILAR"]
else:
return logical_similarity_e["DIFFERENT"]
def check_hazard_condition(self, pre_instr):
if pre_instr.has_rd:
if ((self.has_rs1 and (self.rs1 == pre_instr.rd)) or
(self.has_rs2 and (self.rs2 == pre_instr.rd))):
logging.info("pre_instr {}".format(pre_instr.instr.name))
self.gpr_hazard = hazard_e["RAW_HAZARD"]
elif self.has_rd and (self.rd == pre_instr.rd):
self.gpr_hazard = hazard_e["WAW_HAZARD"]
elif (self.has_rd and
((pre_instr.has_rs1 and (pre_instr.rs1 == self.rd)) or
(pre_instr.has_rs2 and (pre_instr.rs2 == self.rd)))):
self.gpr_hazard = hazard_e["WAR_HAZARD"]
else:
self.gpr_hazard = hazard_e["NO_HAZARD"]
if self.category == riscv_instr_category_t.LOAD:
if (pre_instr.category == riscv_instr_category_t.STORE and
(pre_instr.mem_addr.get_val() == self.mem_addr.get_val())):
self.lsu_hazard = hazard_e["RAW_HAZARD"]
else:
self.lsu_hazard = hazard_e["NO_HAZARD"]
if self.category == riscv_instr_category_t.STORE:
if (pre_instr.category == riscv_instr_category_t.STORE and
(pre_instr.mem_addr.get_val() == self.mem_addr.get_val())):
self.lsu_hazard = hazard_e["WAW_HAZARD"]
elif (pre_instr.category == riscv_instr_category_t.LOAD and
(pre_instr.mem_addr.get_val() == self.mem_addr.get_val())):
self.lsu_hazard = hazard_e["WAR_HAZARD"]
else:
self.lsu_hazard = hazard_e["NO_HAZARD"]
if pre_instr.has_fd:
if ((self.has_fs1 and (self.fs1 == pre_instr.fd)) or
(self.has_fs2 and (self.fs2 == pre_instr.fd)) or
(self.has_fs3 and (self.fs3 == pre_instr.fd))):
self.gpr_hazard = hazard_e["RAW_HAZARD"]
elif (self.has_fd and (self.fd == pre_instr.fd)):
self.gpr_hazard = hazard_e["WAW_HAZARD"]
elif (self.has_fd and ((pre_instr.has_fs1 and (pre_instr.fs1 == self.fd)) or
(pre_instr.has_fs2 and (pre_instr.fs2 == self.fd)) or
(pre_instr.has_fs3 and (pre_instr.fs3 == self.fd)))):
self.gpr_hazard = hazard_e["WAR_HAZARD"]
else:
self.gpr_hazard = hazard_e["NO_HAZARD"]
logging.debug("Pre PC/name: {}/{}, Cur PC/name: {}/{}, "
"Hazard: {}/{}".format(pre_instr.pc.get_val(),
pre_instr.instr.name,
self.pc.get_val(),
self.instr.name,
self.gpr_hazard.name,
self.lsu_hazard.name))
def get_instr_name(self):
get_instr_name = self.instr.name
for i in get_instr_name:
if i == "_":
get_instr_name = get_instr_name.replace(i, ".")
return get_instr_name
def update_src_regs(self, operands):
if self.format.name in ["J_FORMAT", "U_FORMAT"]:
assert len(operands) == 2
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "I_FORMAT":
assert len(operands) == 3
if self.category.name == "LOAD":
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
self.imm.set_val(get_val(operands[1]))
elif self.category.name == "CSR":
self.imm.set_val(get_val(operands[2]))
if operands[1].upper() in privileged_reg_t.__members__:
self.csr.set_val(
privileged_reg_t[operands[1].upper()].value)
else:
self.csr.set_val(get_val(operands[1]))
else:
self.rs1 = self.get_gpr(operands[1])
self.rs1_value.set_val(self.get_gpr_state(operands[1]))
self.imm.set_val(get_val(operands[2]))
elif self.format.name in ["S_FORMAT", "B_FORMAT"]:
assert len(operands) == 3
if self.category.name == "STORE":
self.rs2 = self.get_gpr(operands[0])
self.rs2_value.set_val(self.get_gpr_state(operands[0]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
self.imm.set_val(get_val(operands[1]))
else:
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
self.rs2 = self.get_gpr(operands[1])
self.rs2_value.set_val(self.get_gpr_state(operands[1]))
self.imm.set_val(get_val(operands[2]))
elif self.format.name == "R_FORMAT":
if self.has_rs2 or self.category.name == "CSR":
assert len(operands) == 3
else:
assert len(operands) == 2
if self.category.name == "CSR":
if operands[1].upper() in privileged_reg_t.__members__:
self.csr.set_val(
privileged_reg_t[operands[1].upper()].value)
else:
self.csr.set_val(get_val(operands[1]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
else:
self.rs1 = self.get_gpr(operands[1])
self.rs1_value.set_val(self.get_gpr_state(operands[1]))
if self.has_rs2:
self.rs2 = self.get_gpr(operands[2])
self.rs2_value.set_val(self.get_gpr_state(operands[2]))
elif self.format.name == "R4_FORMAT":
assert len(operands) == 4
self.rs1 = self.get_gpr(operands[1])
self.rs1_value.set_val(self.get_gpr_state(operands[1]))
self.rs2 = self.get_gpr(operands[2])
self.rs2_value.set_val(self.get_gpr_state(operands[2]))
self.rs2 = self.get_gpr(operands[3])
self.rs2_value.set_val(self.get_gpr_state(operands[3]))
elif self.format.name in ["CI_FORMAT", "CIW_FORMAT"]:
if self.instr.name == "C_ADDI16SP":
self.imm.set_val(get_val(operands[1]))
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
elif self.instr.name == "C_ADDI4SPN":
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
elif self.instr.name in ["C_LDSP", "C_LWSP", "C_LQSP"]:
self.imm.set_val(get_val(operands[1]))
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
else:
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CL_FORMAT":
self.imm.set_val(get_val(operands[1]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
elif self.format.name == "CS_FORMAT":
self.rs2 = self.get_gpr(operands[0])
self.rs2_value.set_val(self.get_gpr_state(operands[0]))
self.rs1 = self.get_gpr(operands[2])
self.rs1_value.set_val(self.get_gpr_state(operands[2]))
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CA_FORMAT":
self.rs2 = self.get_gpr(operands[1])
self.rs2_value.set_val(self.get_gpr_state(operands[1]))
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
elif self.format.name == "CB_FORMAT":
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CSS_FORMAT":
self.rs2 = self.get_gpr(operands[0])
self.rs2_value.set_val(self.get_gpr_state(operands[0]))
self.rs1 = riscv_reg_t.SP
self.rs1_value.set_val(self.get_gpr_state("sp"))
self.imm.set_val(get_val(operands[1]))
elif self.format.name == "CR_FORMAT":
if self.instr.name in ["C_JR", "C_JALR"]:
self.rs1 = self.get_gpr(operands[0])
self.rs1_value.set_val(self.get_gpr_state(operands[0]))
else:
self.rs2 = self.get_gpr(operands[1])
self.rs2_value.set_val(self.get_gpr_state(operands[1]))
elif self.format.name == "CJ_FORMAT":
self.imm.set_val(get_val(operands[0]))
else:
logging.error("Unsupported format {}".format(self.format.name))
def update_dst_regs(self, reg_name, val_str):
riscv_cov_instr.gpr_state[reg_name] = get_val(val_str, hexa=1)
self.rd = self.get_gpr(reg_name)
self.rd_value.set_val(self.get_gpr_state(reg_name))
@staticmethod
def get_gpr(reg_name):
reg_name = reg_name.upper()
if reg_name not in riscv_reg_t.__members__:
logging.error("Cannot convert {} to GPR".format(reg_name))
return riscv_reg_t[reg_name]
@staticmethod
def get_gpr_state(name):
if name in ["zero", "x0"]:
return 0
elif name in riscv_cov_instr.gpr_state:
return riscv_cov_instr.gpr_state[name]
else:
logging.warning(
"Cannot find GPR state: {}; initialize to 0".format(name))
if name.upper() in riscv_reg_t.__members__:
riscv_cov_instr.gpr_state[name] = 0
return 0
| true
| true
|
1c4425361079a914aa13fb002ab4d66acd3e4a30
| 104
|
py
|
Python
|
poetry/__main__.py
|
uda/poetry
|
30e3d7e33c20cbe2af8eda06e0db4888275caaa1
|
[
"MIT"
] | 12,347
|
2019-12-12T07:07:32.000Z
|
2022-03-31T21:08:50.000Z
|
poetry/__main__.py
|
uda/poetry
|
30e3d7e33c20cbe2af8eda06e0db4888275caaa1
|
[
"MIT"
] | 3,483
|
2019-12-11T20:20:20.000Z
|
2022-03-31T23:18:18.000Z
|
poetry/__main__.py
|
uda/poetry
|
30e3d7e33c20cbe2af8eda06e0db4888275caaa1
|
[
"MIT"
] | 1,399
|
2019-12-12T12:27:46.000Z
|
2022-03-31T09:12:53.000Z
|
import sys
if __name__ == "__main__":
from .console.application import main
sys.exit(main())
| 13
| 41
| 0.673077
|
import sys
if __name__ == "__main__":
from .console.application import main
sys.exit(main())
| true
| true
|
1c44263ae8800ae610a0ea2b221f9c8a0ffd43f6
| 911
|
py
|
Python
|
handlers/InputHandler.py
|
sachio222/socketchat_v3
|
cd62b892842f6708055359fa2384269038f425dc
|
[
"MIT"
] | 1
|
2020-11-30T03:54:35.000Z
|
2020-11-30T03:54:35.000Z
|
handlers/InputHandler.py
|
sachio222/socketchat_v3
|
cd62b892842f6708055359fa2384269038f425dc
|
[
"MIT"
] | null | null | null |
handlers/InputHandler.py
|
sachio222/socketchat_v3
|
cd62b892842f6708055359fa2384269038f425dc
|
[
"MIT"
] | null | null | null |
import socket
from sys import prefix
from chatutils import utils
import config.filepaths as paths
from handlers import EncryptionHandler, ClientMsgHandler
prefixes = utils.JSONLoader(paths.prefix_path)
def dispatch(sock: socket, msg: str) -> bytes:
"""Splits input data between commands and transmissions.
Message type - (prefix)
1. Input command - ("/") for control, not messaging.
2. Default - Sent as encrypted message.
"""
if len(msg):
if msg[0] == '/': # Check for command
msg = ClientMsgHandler.command_router(sock=sock, msg=msg)
msg_type = None
else:
msg = EncryptionHandler.message_router(msg)
msg_type = prefixes.dict["client"]["chat"]["msg"]
else:
# Send new line on enter press.
msg = b"\n"
msg_type = prefixes.dict["client"]["chat"]["newLine"]
return msg, msg_type
| 30.366667
| 69
| 0.637761
|
import socket
from sys import prefix
from chatutils import utils
import config.filepaths as paths
from handlers import EncryptionHandler, ClientMsgHandler
prefixes = utils.JSONLoader(paths.prefix_path)
def dispatch(sock: socket, msg: str) -> bytes:
if len(msg):
if msg[0] == '/':
msg = ClientMsgHandler.command_router(sock=sock, msg=msg)
msg_type = None
else:
msg = EncryptionHandler.message_router(msg)
msg_type = prefixes.dict["client"]["chat"]["msg"]
else:
msg = b"\n"
msg_type = prefixes.dict["client"]["chat"]["newLine"]
return msg, msg_type
| true
| true
|
1c4427a91f89d5195c34bc012f9a3d3cfc65f9b3
| 3,355
|
py
|
Python
|
Octopus/app/__init__.py
|
zhnlk/octopus
|
4deb502eebc655ed512273a330b885d77bb8e32a
|
[
"MIT"
] | null | null | null |
Octopus/app/__init__.py
|
zhnlk/octopus
|
4deb502eebc655ed512273a330b885d77bb8e32a
|
[
"MIT"
] | null | null | null |
Octopus/app/__init__.py
|
zhnlk/octopus
|
4deb502eebc655ed512273a330b885d77bb8e32a
|
[
"MIT"
] | null | null | null |
# Import flask and template operators
import logging
import traceback
import apscheduler
from apscheduler.schedulers.background import BackgroundScheduler
from flask import Flask
from flask import jsonify
from flask_basicauth import BasicAuth
from flask_restful import Api
from flask_restful_swagger import swagger
from flask_sqlalchemy import SQLAlchemy
from werkzeug.exceptions import HTTPException
import Octopus
from Octopus import config
# Define the WSGI application object
app = Flask(__name__)
# Configurations
app.config.from_object(config)
# Logging
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
app.logger.setLevel(app.config.get('LOG_LEVEL', "INFO"))
app.logger.addHandler(handler)
# swagger
api = swagger.docs(Api(app), apiVersion=Octopus.__version__, api_spec_url="/api",
description='Octopus')
# Define the database object which is imported
# by modules and controllers
db = SQLAlchemy(app, session_options=dict(autocommit=False, autoflush=True))
@app.teardown_request
def teardown_request(exception):
if exception:
db.session.rollback()
db.session.remove()
db.session.remove()
# Define apscheduler
scheduler = BackgroundScheduler()
class Base(db.Model):
__abstract__ = True
id = db.Column(db.Integer, primary_key=True)
date_created = db.Column(db.DateTime, default=db.func.current_timestamp())
date_modified = db.Column(db.DateTime, default=db.func.current_timestamp(),
onupdate=db.func.current_timestamp())
# Sample HTTP error handling
# @app.errorhandler(404)
# def not_found(error):
# abort(404)
@app.errorhandler(Exception)
def handle_error(e):
code = 500
if isinstance(e, HTTPException):
code = e.code
app.logger.error(traceback.print_exc())
return jsonify({
'code': code,
'success': False,
'msg': str(e),
'data': None
})
# Build the database:
from Octopus.app.spider.model import *
def init_database():
db.init_app(app)
db.create_all()
# regist spider service proxy
from Octopus.app.proxy.spiderctrl import SpiderAgent
from Octopus.app.proxy.contrib.scrapy import ScrapydProxy
agent = SpiderAgent()
def regist_server():
if app.config.get('SERVER_TYPE') == 'scrapyd':
for server in app.config.get("SERVERS"):
agent.regist(ScrapydProxy(server))
from Octopus.app.spider.controller import api_spider_bp
# Register blueprint(s)
app.register_blueprint(api_spider_bp)
# start sync job status scheduler
from Octopus.app.schedulers.common import sync_job_execution_status_job, sync_spiders, \
reload_runnable_spider_job_execution
scheduler.add_job(sync_job_execution_status_job, 'interval', seconds=5, id='sys_sync_status')
scheduler.add_job(sync_spiders, 'interval', seconds=10, id='sys_sync_spiders')
scheduler.add_job(reload_runnable_spider_job_execution, 'interval', seconds=30, id='sys_reload_job')
def start_scheduler():
scheduler.start()
def init_basic_auth():
if not app.config.get('NO_AUTH'):
basic_auth = BasicAuth(app)
def initialize():
init_database()
regist_server()
start_scheduler()
init_basic_auth()
| 25.807692
| 100
| 0.739493
|
import logging
import traceback
import apscheduler
from apscheduler.schedulers.background import BackgroundScheduler
from flask import Flask
from flask import jsonify
from flask_basicauth import BasicAuth
from flask_restful import Api
from flask_restful_swagger import swagger
from flask_sqlalchemy import SQLAlchemy
from werkzeug.exceptions import HTTPException
import Octopus
from Octopus import config
app = Flask(__name__)
app.config.from_object(config)
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
app.logger.setLevel(app.config.get('LOG_LEVEL', "INFO"))
app.logger.addHandler(handler)
api = swagger.docs(Api(app), apiVersion=Octopus.__version__, api_spec_url="/api",
description='Octopus')
db = SQLAlchemy(app, session_options=dict(autocommit=False, autoflush=True))
@app.teardown_request
def teardown_request(exception):
if exception:
db.session.rollback()
db.session.remove()
db.session.remove()
scheduler = BackgroundScheduler()
class Base(db.Model):
__abstract__ = True
id = db.Column(db.Integer, primary_key=True)
date_created = db.Column(db.DateTime, default=db.func.current_timestamp())
date_modified = db.Column(db.DateTime, default=db.func.current_timestamp(),
onupdate=db.func.current_timestamp())
@app.errorhandler(Exception)
def handle_error(e):
code = 500
if isinstance(e, HTTPException):
code = e.code
app.logger.error(traceback.print_exc())
return jsonify({
'code': code,
'success': False,
'msg': str(e),
'data': None
})
from Octopus.app.spider.model import *
def init_database():
db.init_app(app)
db.create_all()
from Octopus.app.proxy.spiderctrl import SpiderAgent
from Octopus.app.proxy.contrib.scrapy import ScrapydProxy
agent = SpiderAgent()
def regist_server():
if app.config.get('SERVER_TYPE') == 'scrapyd':
for server in app.config.get("SERVERS"):
agent.regist(ScrapydProxy(server))
from Octopus.app.spider.controller import api_spider_bp
app.register_blueprint(api_spider_bp)
from Octopus.app.schedulers.common import sync_job_execution_status_job, sync_spiders, \
reload_runnable_spider_job_execution
scheduler.add_job(sync_job_execution_status_job, 'interval', seconds=5, id='sys_sync_status')
scheduler.add_job(sync_spiders, 'interval', seconds=10, id='sys_sync_spiders')
scheduler.add_job(reload_runnable_spider_job_execution, 'interval', seconds=30, id='sys_reload_job')
def start_scheduler():
scheduler.start()
def init_basic_auth():
if not app.config.get('NO_AUTH'):
basic_auth = BasicAuth(app)
def initialize():
init_database()
regist_server()
start_scheduler()
init_basic_auth()
| true
| true
|
1c4427b25aca05f214bd3202fd7bba4c40a1e7a0
| 87,751
|
py
|
Python
|
typhon/plots/cm/_cmocean.py
|
tmieslinger/typhon
|
588539e5c4831ee18753d7ead5b2f2736e922bb1
|
[
"MIT"
] | 53
|
2017-09-19T06:40:37.000Z
|
2022-03-21T07:59:30.000Z
|
typhon/plots/cm/_cmocean.py
|
tmieslinger/typhon
|
588539e5c4831ee18753d7ead5b2f2736e922bb1
|
[
"MIT"
] | 96
|
2017-09-18T12:01:42.000Z
|
2021-12-17T13:54:45.000Z
|
typhon/plots/cm/_cmocean.py
|
tmieslinger/typhon
|
588539e5c4831ee18753d7ead5b2f2736e922bb1
|
[
"MIT"
] | 32
|
2017-09-07T09:09:21.000Z
|
2021-10-01T03:54:23.000Z
|
# -*- coding: utf-8 -*-
"""
It is a subset of the cmocean package [0] provided by Kristen M. Thyng.
The MIT License (MIT)
Copyright (c) 2015 Kristen M. Thyng
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.
[0] http://matplotlib.org/cmocean/
"""
_density_data = [
[0.90220216, 0.94417980, 0.94380273],
[0.89544454, 0.94095789, 0.94106488],
[0.88868558, 0.93774038, 0.93840987],
[0.88192751, 0.93452625, 0.93583728],
[0.87517248, 0.93131455, 0.93334654],
[0.86842255, 0.92810438, 0.93093690],
[0.86167970, 0.92489490, 0.92860749],
[0.85494584, 0.92168533, 0.92635732],
[0.84822279, 0.91847494, 0.92418529],
[0.84151229, 0.91526306, 0.92209023],
[0.83481598, 0.91204906, 0.92007088],
[0.82813543, 0.90883237, 0.91812595],
[0.82147215, 0.90561245, 0.91625407],
[0.81482754, 0.90238881, 0.91445386],
[0.80820295, 0.89916100, 0.91272391],
[0.80159965, 0.89592859, 0.91106281],
[0.79501887, 0.89269119, 0.90946910],
[0.78846179, 0.88944845, 0.90794134],
[0.78192947, 0.88620003, 0.90647814],
[0.77542295, 0.88294564, 0.90507810],
[0.76894320, 0.87968498, 0.90373988],
[0.76249117, 0.87641781, 0.90246212],
[0.75606778, 0.87314387, 0.90124351],
[0.74967389, 0.86986292, 0.90008279],
[0.74331039, 0.86657473, 0.89897869],
[0.73697811, 0.86327910, 0.89793000],
[0.73067788, 0.85997580, 0.89693553],
[0.72441053, 0.85666464, 0.89599414],
[0.71817690, 0.85334541, 0.89510469],
[0.71197782, 0.85001791, 0.89426610],
[0.70581423, 0.84668191, 0.89347718],
[0.69968688, 0.84333724, 0.89273702],
[0.69359663, 0.83998369, 0.89204464],
[0.68754438, 0.83662105, 0.89139904],
[0.68153106, 0.83324911, 0.89079928],
[0.67555762, 0.82986764, 0.89024441],
[0.66962506, 0.82647642, 0.88973352],
[0.66373440, 0.82307522, 0.88926568],
[0.65788673, 0.81966379, 0.88884001],
[0.65208315, 0.81624189, 0.88845562],
[0.64632485, 0.81280925, 0.88811162],
[0.64061303, 0.80936562, 0.88780715],
[0.63494897, 0.80591071, 0.88754133],
[0.62933401, 0.80244424, 0.88731328],
[0.62376953, 0.79896592, 0.88712212],
[0.61825699, 0.79547544, 0.88696697],
[0.61279785, 0.79197249, 0.88684700],
[0.60739371, 0.78845676, 0.88676131],
[0.60204624, 0.78492789, 0.88670892],
[0.59675713, 0.78138555, 0.88668891],
[0.59152819, 0.77782940, 0.88670031],
[0.58636126, 0.77425906, 0.88674212],
[0.58125826, 0.77067417, 0.88681333],
[0.57622118, 0.76707435, 0.88691290],
[0.57125208, 0.76345922, 0.88703974],
[0.56635309, 0.75982839, 0.88719273],
[0.56152638, 0.75618144, 0.88737072],
[0.55677422, 0.75251799, 0.88757251],
[0.55209890, 0.74883762, 0.88779685],
[0.54750281, 0.74513991, 0.88804246],
[0.54298830, 0.74142444, 0.88830811],
[0.53855780, 0.73769077, 0.88859267],
[0.53421391, 0.73393850, 0.88889434],
[0.52995915, 0.73016720, 0.88921160],
[0.52579610, 0.72637645, 0.88954287],
[0.52172732, 0.72256583, 0.88988650],
[0.51775541, 0.71873493, 0.89024078],
[0.51388294, 0.71488334, 0.89060393],
[0.51011248, 0.71101066, 0.89097410],
[0.50644655, 0.70711649, 0.89134936],
[0.50288765, 0.70320047, 0.89172772],
[0.49943822, 0.69926217, 0.89210744],
[0.49610063, 0.69530122, 0.89248647],
[0.49287720, 0.69131735, 0.89286215],
[0.48977007, 0.68731025, 0.89323218],
[0.48678129, 0.68327963, 0.89359417],
[0.48391278, 0.67922522, 0.89394566],
[0.48116628, 0.67514678, 0.89428415],
[0.47854335, 0.67104413, 0.89460702],
[0.47604536, 0.66691708, 0.89491161],
[0.47367364, 0.66276538, 0.89519579],
[0.47142903, 0.65858902, 0.89545627],
[0.46931216, 0.65438798, 0.89569008],
[0.46732345, 0.65016224, 0.89589433],
[0.46546305, 0.64591186, 0.89606608],
[0.46373081, 0.64163690, 0.89620239],
[0.46212629, 0.63733750, 0.89630027],
[0.46064879, 0.63301382, 0.89635684],
[0.45929732, 0.62866605, 0.89636919],
[0.45807033, 0.62429458, 0.89633399],
[0.45696613, 0.61989976, 0.89624827],
[0.45598269, 0.61548199, 0.89610905],
[0.45511768, 0.61104174, 0.89591342],
[0.45436847, 0.60657953, 0.89565850],
[0.45373211, 0.60209592, 0.89534144],
[0.45320531, 0.59759159, 0.89495941],
[0.45278458, 0.59306722, 0.89450974],
[0.45246621, 0.58852353, 0.89398987],
[0.45224622, 0.58396130, 0.89339735],
[0.45212047, 0.57938135, 0.89272981],
[0.45208461, 0.57478456, 0.89198505],
[0.45213406, 0.57017185, 0.89116092],
[0.45226366, 0.56554440, 0.89025507],
[0.45246908, 0.56090297, 0.88926607],
[0.45274551, 0.55624857, 0.88819230],
[0.45308803, 0.55158223, 0.88703226],
[0.45349173, 0.54690499, 0.88578463],
[0.45395166, 0.54221790, 0.88444824],
[0.45446291, 0.53752203, 0.88302210],
[0.45502044, 0.53281852, 0.88150529],
[0.45561856, 0.52810881, 0.87989676],
[0.45625342, 0.52339359, 0.87819649],
[0.45692037, 0.51867392, 0.87640412],
[0.45761487, 0.51395086, 0.87451947],
[0.45833250, 0.50922545, 0.87254251],
[0.45906898, 0.50449872, 0.87047333],
[0.45982017, 0.49977167, 0.86831217],
[0.46058208, 0.49504528, 0.86605942],
[0.46135090, 0.49032052, 0.86371555],
[0.46212297, 0.48559831, 0.86128120],
[0.46289481, 0.48087955, 0.85875708],
[0.46366216, 0.47616550, 0.85614385],
[0.46442285, 0.47145661, 0.85344263],
[0.46517394, 0.46675366, 0.85065444],
[0.46591265, 0.46205743, 0.84778034],
[0.46663636, 0.45736864, 0.84482148],
[0.46734263, 0.45268799, 0.84177905],
[0.46802917, 0.44801616, 0.83865432],
[0.46869383, 0.44335376, 0.83544858],
[0.46933466, 0.43870139, 0.83216318],
[0.46994980, 0.43405962, 0.82879947],
[0.47053758, 0.42942898, 0.82535887],
[0.47109642, 0.42480997, 0.82184277],
[0.47162490, 0.42020304, 0.81825263],
[0.47212171, 0.41560865, 0.81458986],
[0.47258566, 0.41102721, 0.81085591],
[0.47301567, 0.40645908, 0.80705223],
[0.47341074, 0.40190463, 0.80318024],
[0.47376999, 0.39736419, 0.79924139],
[0.47409263, 0.39283807, 0.79523708],
[0.47437795, 0.38832654, 0.79116870],
[0.47462530, 0.38382989, 0.78703766],
[0.47483412, 0.37934834, 0.78284530],
[0.47500393, 0.37488213, 0.77859297],
[0.47513427, 0.37043147, 0.77428199],
[0.47522479, 0.36599656, 0.76991363],
[0.47527514, 0.36157758, 0.76548918],
[0.47528466, 0.35717487, 0.76101004],
[0.47525316, 0.35278857, 0.75647742],
[0.47518080, 0.34841867, 0.75189235],
[0.47506742, 0.34406531, 0.74725597],
[0.47491290, 0.33972863, 0.74256938],
[0.47471715, 0.33540873, 0.73783366],
[0.47448009, 0.33110575, 0.73304987],
[0.47420168, 0.32681981, 0.72821900],
[0.47388188, 0.32255100, 0.72334205],
[0.47352067, 0.31829946, 0.71841996],
[0.47311806, 0.31406530, 0.71345366],
[0.47267406, 0.30984863, 0.70844403],
[0.47218867, 0.30564956, 0.70339194],
[0.47166137, 0.30146848, 0.69829868],
[0.47109270, 0.29730529, 0.69316468],
[0.47048281, 0.29316004, 0.68799058],
[0.46983176, 0.28903289, 0.68277713],
[0.46913959, 0.28492398, 0.67752502],
[0.46840632, 0.28083345, 0.67223496],
[0.46763200, 0.27676148, 0.66690758],
[0.46681665, 0.27270825, 0.66154354],
[0.46596031, 0.26867393, 0.65614343],
[0.46506286, 0.26465880, 0.65070800],
[0.46412426, 0.26066310, 0.64523790],
[0.46314479, 0.25668693, 0.63973335],
[0.46212445, 0.25273054, 0.63419487],
[0.46106323, 0.24879421, 0.62862296],
[0.45996110, 0.24487823, 0.62301809],
[0.45881803, 0.24098289, 0.61738074],
[0.45763398, 0.23710854, 0.61171135],
[0.45640887, 0.23325554, 0.60601037],
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_speed_data = _velocity_g_data
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_vorticity_data = _vorticity_t_data + _vorticity_p_data
_difference_data = [
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datad = {
'density': _density_data,
'phase': _phase_data,
'speed': _speed_data,
'temperature': _temperature_data,
'velocity': _velocity_data,
'vorticity': _vorticity_data,
'difference': _difference_data,
}
| 41.548769
| 78
| 0.645337
|
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[0.84622998, 0.86496746, 0.79968738],
[0.85182123, 0.86823244, 0.80497972],
[0.85739886, 0.87151457, 0.81030033],
[0.86296333, 0.87481394, 0.81564861],
[0.86851129, 0.87813251, 0.82102020],
[0.87404693, 0.88146862, 0.82641787],
[0.87957085, 0.88482234, 0.83184102],
[0.88508363, 0.88819370, 0.83728899],
[0.89058366, 0.89158388, 0.84275871],
[0.89607183, 0.89499282, 0.84824952],
[0.90155093, 0.89841950, 0.85376285],
[0.90702184, 0.90186385, 0.85929802],
[0.91248330, 0.90532691, 0.86485172],
[0.91793653, 0.90880848, 0.87042326],
[0.92338518, 0.91230711, 0.87601483],
[0.92883088, 0.91582235, 0.88162632],
[0.93427164, 0.91935565, 0.88725314],
[0.93971244, 0.92290477, 0.89289983],
[0.94515638, 0.92646829, 0.89856977],
[0.95060356, 0.93004618, 0.90426466],
[0.95605310, 0.93363854, 0.90998868],
[0.96150666, 0.93724342, 0.91575606],
[0.96695738, 0.94086247, 0.92157953],
[0.97238707, 0.94450246, 0.92746703],
[0.97777857, 0.94816875, 0.93343649],
[0.98310507, 0.95187277, 0.93948603],
[0.98834174, 0.95562829, 0.94558363],
[0.99349184, 0.95943756, 0.95169832],
[0.99857633, 0.96329654, 0.95778950]]
_vorticity_data = _vorticity_t_data + _vorticity_p_data
_difference_data = [
[0.09317630, 0.11117333, 0.26151239],
[0.09697152, 0.11687021, 0.27309631],
[0.10096885, 0.12239315, 0.28491036],
[0.10499270, 0.12782437, 0.29687381],
[0.10898740, 0.13319350, 0.30895384],
[0.11292230, 0.13851314, 0.32115284],
[0.11677874, 0.14379072, 0.33347631],
[0.12054634, 0.14903305, 0.34591927],
[0.12421597, 0.15424490, 0.35848249],
[0.12777784, 0.15942930, 0.37117372],
[0.13122438, 0.16458993, 0.38399390],
[0.13454904, 0.16973078, 0.39694025],
[0.13774403, 0.17485525, 0.41001322],
[0.14079963, 0.17996609, 0.42321738],
[0.14370728, 0.18506718, 0.43655022],
[0.14645608, 0.19016193, 0.45001310],
[0.14903482, 0.19525448, 0.46360362],
[0.15143093, 0.20034937, 0.47731853],
[0.15362665, 0.20545038, 0.49116235],
[0.15560730, 0.21056390, 0.50512420],
[0.15735349, 0.21569621, 0.51919772],
[0.15884111, 0.22085406, 0.53337810],
[0.16003963, 0.22604481, 0.54766269],
[0.16092053, 0.23127922, 0.56203080],
[0.16144553, 0.23656876, 0.57646619],
[0.16156876, 0.24192717, 0.59094728],
[0.16123528, 0.24737119, 0.60544468],
[0.16037934, 0.25292130, 0.61991794],
[0.15891657, 0.25860221, 0.63431839],
[0.15673874, 0.26444442, 0.64858326],
[0.15374227, 0.27048752, 0.66259429],
[0.14975399, 0.27677729, 0.67624289],
[0.14461903, 0.28337034, 0.68933032],
[0.13815067, 0.29032903, 0.70161667],
[0.13019375, 0.29771375, 0.71279281],
[0.12070563, 0.30556233, 0.72251014],
[0.10982269, 0.31386519, 0.73046944],
[0.09795843, 0.32254310, 0.73652716],
[0.08570024, 0.33146616, 0.74077181],
[0.07367689, 0.34049675, 0.74346934],
[0.06252233, 0.34951974, 0.74495630],
[0.05283258, 0.35846008, 0.74554518],
[0.04520939, 0.36727493, 0.74548891],
[0.04023631, 0.37594185, 0.74498414],
[0.03831576, 0.38445368, 0.74417463],
[0.03948350, 0.39281375, 0.74315813],
[0.04345844, 0.40102371, 0.74202430],
[0.04962432, 0.40909532, 0.74081668],
[0.05739800, 0.41703470, 0.73958775],
[0.06626018, 0.42485215, 0.73836501],
[0.07582789, 0.43255722, 0.73716979],
[0.08583710, 0.44015834, 0.73602068],
[0.09611024, 0.44766357, 0.73493125],
[0.10653039, 0.45508042, 0.73391177],
[0.11702165, 0.46241591, 0.73296937],
[0.12753580, 0.46967672, 0.73210750],
[0.13804321, 0.47686868, 0.73132951],
[0.14852667, 0.48399707, 0.73063745],
[0.15897744, 0.49106666, 0.73003226],
[0.16939271, 0.49808168, 0.72951410],
[0.17977386, 0.50504587, 0.72908242],
[0.19012529, 0.51196251, 0.72873614],
[0.20045367, 0.51883439, 0.72847377],
[0.21076743, 0.52566384, 0.72829343],
[0.22107637, 0.53245276, 0.72819301],
[0.23139144, 0.53920256, 0.72817020],
[0.24172459, 0.54591421, 0.72822258],
[0.25208860, 0.55258823, 0.72834769],
[0.26249697, 0.55922467, 0.72854317],
[0.27296382, 0.56582312, 0.72880681],
[0.28350369, 0.57238271, 0.72913673],
[0.29413138, 0.57890214, 0.72953149],
[0.30486166, 0.58537967, 0.72999025],
[0.31570894, 0.59181322, 0.73051298],
[0.32668681, 0.59820041, 0.73110060],
[0.33780753, 0.60453865, 0.73175521],
[0.34908180, 0.61082531, 0.73247968],
[0.36051697, 0.61705790, 0.73327922],
[0.37211638, 0.62323432, 0.73416123],
[0.38387988, 0.62935306, 0.73513375],
[0.39580281, 0.63541344, 0.73620602],
[0.40787554, 0.64141586, 0.73738846],
[0.42008256, 0.64736207, 0.73869308],
[0.43240665, 0.65325485, 0.74012945],
[0.44482701, 0.65909826, 0.74170693],
[0.45732109, 0.66489739, 0.74343353],
[0.46986588, 0.67065815, 0.74531538],
[0.48243916, 0.67638699, 0.74735656],
[0.49502055, 0.68209061, 0.74955890],
[0.50759227, 0.68777566, 0.75192214],
[0.52013952, 0.69344853, 0.75444409],
[0.53265066, 0.69911520, 0.75712102],
[0.54511511, 0.70478153, 0.75994930],
[0.55751816, 0.71045432, 0.76292914],
[0.56986354, 0.71613651, 0.76604883],
[0.58214859, 0.72183221, 0.76930236],
[0.59436024, 0.72754780, 0.77269163],
[0.60650809, 0.73328445, 0.77620411],
[0.61858647, 0.73904668, 0.77983837],
[0.63059636, 0.74483734, 0.78358922],
[0.64254033, 0.75065877, 0.78745106],
[0.65441860, 0.75651373, 0.79142043],
[0.66623153, 0.76240488, 0.79549418],
[0.67798504, 0.76833329, 0.79966600],
[0.68967550, 0.77430243, 0.80393610],
[0.70131075, 0.78031263, 0.80829755],
[0.71288853, 0.78636686, 0.81275022],
[0.72441124, 0.79246672, 0.81729113],
[0.73588370, 0.79861307, 0.82191590],
[0.74730444, 0.80480845, 0.82662434],
[0.75867599, 0.81105425, 0.83141375],
[0.77000070, 0.81735182, 0.83628157],
[0.78127895, 0.82370305, 0.84122655],
[0.79251162, 0.83010967, 0.84624710],
[0.80369975, 0.83657330, 0.85134141],
[0.81484311, 0.84309597, 0.85650845],
[0.82594216, 0.84967946, 0.86174654],
[0.83699675, 0.85632577, 0.86705416],
[0.84800383, 0.86303779, 0.87243156],
[0.85896284, 0.86981769, 0.87787684],
[0.86987188, 0.87666811, 0.88338843],
[0.88072260, 0.88359376, 0.88896898],
[0.89151256, 0.89059766, 0.89461579],
[0.90223219, 0.89768518, 0.90033034],
[0.91286295, 0.90486468, 0.90612019],
[0.92339067, 0.91214345, 0.91198727],
[0.93376907, 0.91953891, 0.91796043],
[0.94387686, 0.92709058, 0.92414784],
[0.94502413, 0.92672740, 0.92320173],
[0.94017715, 0.91750110, 0.91273530],
[0.93577882, 0.90818496, 0.90199590],
[0.93161955, 0.89884559, 0.89112473],
[0.92763669, 0.88950153, 0.88016251],
[0.92379637, 0.88016199, 0.86913066],
[0.92007678, 0.87083231, 0.85804265],
[0.91646227, 0.86151587, 0.84690792],
[0.91294097, 0.85221483, 0.83573351],
[0.90950339, 0.84293065, 0.82452495],
[0.90614135, 0.83366435, 0.81328703],
[0.90284824, 0.82441650, 0.80202350],
[0.89961835, 0.81518734, 0.79073759],
[0.89644640, 0.80597704, 0.77943233],
[0.89332810, 0.79678541, 0.76811006],
[0.89025854, 0.78761252, 0.75677380],
[0.88723415, 0.77845801, 0.74542553],
[0.88425152, 0.76932142, 0.73406721],
[0.88130757, 0.76020224, 0.72270059],
[0.87839898, 0.75110001, 0.71132772],
[0.87552202, 0.74201448, 0.69995114],
[0.87267498, 0.73294464, 0.68857164],
[0.86985472, 0.72389001, 0.67719140],
[0.86705966, 0.71484951, 0.66581120],
[0.86428554, 0.70582315, 0.65443443],
[0.86153122, 0.69680968, 0.64306165],
[0.85879456, 0.68780825, 0.63169442],
[0.85607377, 0.67881784, 0.62033401],
[0.85336475, 0.66983845, 0.60898403],
[0.85066718, 0.66086845, 0.59764454],
[0.84798038, 0.65190630, 0.58631596],
[0.84529929, 0.64295243, 0.57500309],
[0.84262333, 0.63400527, 0.56370649],
[0.83995267, 0.62506283, 0.55242594],
[0.83728236, 0.61612552, 0.54116651],
[0.83461150, 0.60719184, 0.52992929],
[0.83194170, 0.59825898, 0.51871286],
[0.82926584, 0.58932842, 0.50752486],
[0.82658652, 0.58039681, 0.49636308],
[0.82390077, 0.57146348, 0.48523112],
[0.82120472, 0.56252830, 0.47413374],
[0.81850284, 0.55358664, 0.46306700],
[0.81578580, 0.54464120, 0.45204172],
[0.81305988, 0.53568615, 0.44105231],
[0.81031573, 0.52672425, 0.43011000],
[0.80756008, 0.51774912, 0.41920905],
[0.80478300, 0.50876385, 0.40836176],
[0.80199136, 0.49976163, 0.39756263],
[0.79917761, 0.49074411, 0.38682195],
[0.79634125, 0.48170863, 0.37614268],
[0.79348534, 0.47265010, 0.36552429],
[0.79060209, 0.46357018, 0.35497832],
[0.78769221, 0.45446502, 0.34450752],
[0.78475844, 0.44532901, 0.33411300],
[0.78179438, 0.43616264, 0.32380621],
[0.77879860, 0.42696282, 0.31359382],
[0.77577003, 0.41772599, 0.30348288],
[0.77270738, 0.40844842, 0.29348145],
[0.76960910, 0.39912628, 0.28359883],
[0.76647331, 0.38975562, 0.27384573],
[0.76329775, 0.38033247, 0.26423455],
[0.76007961, 0.37085287, 0.25477962],
[0.75681552, 0.36131299, 0.24549744],
[0.75350142, 0.35170920, 0.23640702],
[0.75013244, 0.34203820, 0.22753009],
[0.74670837, 0.33229173, 0.21888681],
[0.74322062, 0.32246895, 0.21050730],
[0.73966264, 0.31256672, 0.20242263],
[0.73602766, 0.30258171, 0.19466697],
[0.73230751, 0.29251159, 0.18727887],
[0.72848868, 0.28235996, 0.18030258],
[0.72455978, 0.27212835, 0.17378304],
[0.72050526, 0.26182377, 0.16776811],
[0.71630775, 0.25145685, 0.16230504],
[0.71194775, 0.24104317, 0.15743769],
[0.70740461, 0.23060296, 0.15320278],
[0.70265778, 0.22016032, 0.14962642],
[0.69768564, 0.20974625, 0.14671934],
[0.69247010, 0.19939235, 0.14447576],
[0.68699563, 0.18913262, 0.14287203],
[0.68125094, 0.17900057, 0.14186849],
[0.67522904, 0.16902840, 0.14141262],
[0.66892723, 0.15924601, 0.14144362],
[0.66234629, 0.14968150, 0.14189670],
[0.65548946, 0.14036226, 0.14270608],
[0.64836184, 0.13131545, 0.14380938],
[0.64096909, 0.12257090, 0.14514666],
[0.63331719, 0.11416164, 0.14666355],
[0.62541177, 0.10612632, 0.14830833],
[0.61725809, 0.09850956, 0.15003428],
[0.60886121, 0.09136271, 0.15179467],
[0.60022601, 0.08474269, 0.15354719],
[0.59135807, 0.07870938, 0.15524760],
[0.58226407, 0.07332111, 0.15685285],
[0.57295232, 0.06862870, 0.15832095],
[0.56343372, 0.06466696, 0.15960971],
[0.55372241, 0.06144627, 0.16067796],
[0.54383514, 0.05894915, 0.16148955],
[0.53379121, 0.05712858, 0.16201381],
[0.52361200, 0.05591095, 0.16222653],
[0.51332147, 0.05519900, 0.16210827],
[0.50294151, 0.05489269, 0.16165212],
[0.49249206, 0.05489498, 0.16085946],
[0.48199704, 0.05509874, 0.15972947],
[0.47146853, 0.05543753, 0.15828036],
[0.46092984, 0.05581817, 0.15651615],
[0.45038823, 0.05620093, 0.15446036],
[0.43985504, 0.05653967, 0.15212964],
[0.42933959, 0.05679893, 0.14954130],
[0.41884881, 0.05695351, 0.14671310],
[0.40838748, 0.05698680, 0.14366262],
[0.39795842, 0.05688899, 0.14040679],
[0.38756618, 0.05664745, 0.13695962],
[0.37721626, 0.05624859, 0.13333329],
[0.36690097, 0.05571028, 0.12954563],
[0.35662892, 0.05501236, 0.12560413],
[0.34639461, 0.05416713, 0.12152211],
[0.33620182, 0.05316577, 0.11730740],
[0.32604244, 0.05202498, 0.11297139],
[0.31592786, 0.05071934, 0.10851752],
[0.30584405, 0.04927678, 0.10395692],
[0.29579063, 0.04769543, 0.09929501],
[0.28577617, 0.04595517, 0.09453470],
[0.27578839, 0.04407730, 0.08968329],
[0.26582514, 0.04206161, 0.08474472],
[0.25588406, 0.03990047, 0.07972236],
[0.24596222, 0.03762595, 0.07461914]]
datad = {
'density': _density_data,
'phase': _phase_data,
'speed': _speed_data,
'temperature': _temperature_data,
'velocity': _velocity_data,
'vorticity': _vorticity_data,
'difference': _difference_data,
}
| true
| true
|
1c442889a1b7474a639e574b6d8ff896dff9adeb
| 7,809
|
py
|
Python
|
instrumentation/opentelemetry-instrumentation-asgi/src/opentelemetry/instrumentation/asgi/__init__.py
|
stschenk/opentelemetry-python-contrib
|
28c1331e571d386baab74f5028e3268e4bfda4cd
|
[
"Apache-2.0",
"BSD-3-Clause"
] | 1
|
2020-03-17T05:37:21.000Z
|
2020-03-17T05:37:21.000Z
|
instrumentation/opentelemetry-instrumentation-asgi/src/opentelemetry/instrumentation/asgi/__init__.py
|
stschenk/opentelemetry-python-contrib
|
28c1331e571d386baab74f5028e3268e4bfda4cd
|
[
"Apache-2.0",
"BSD-3-Clause"
] | 3
|
2019-08-26T13:06:36.000Z
|
2020-02-21T21:44:02.000Z
|
instrumentation/opentelemetry-instrumentation-asgi/src/opentelemetry/instrumentation/asgi/__init__.py
|
stschenk/opentelemetry-python-contrib
|
28c1331e571d386baab74f5028e3268e4bfda4cd
|
[
"Apache-2.0",
"BSD-3-Clause"
] | 1
|
2020-10-22T20:13:37.000Z
|
2020-10-22T20:13:37.000Z
|
# Copyright The OpenTelemetry Authors
#
# 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.
"""
The opentelemetry-instrumentation-asgi package provides an ASGI middleware that can be used
on any ASGI framework (such as Django-channels / Quart) to track requests
timing through OpenTelemetry.
"""
import operator
import typing
import urllib
from functools import wraps
from typing import Tuple
from asgiref.compatibility import guarantee_single_callable
from opentelemetry import context, propagators, trace
from opentelemetry.instrumentation.asgi.version import __version__ # noqa
from opentelemetry.instrumentation.utils import http_status_to_status_code
from opentelemetry.trace.propagation.textmap import DictGetter
from opentelemetry.trace.status import Status, StatusCode
class CarrierGetter(DictGetter):
def get(self, carrier: dict, key: str) -> typing.List[str]:
"""Getter implementation to retrieve a HTTP header value from the ASGI
scope.
Args:
carrier: ASGI scope object
key: header name in scope
Returns:
A list with a single string with the header value if it exists,
else an empty list.
"""
headers = carrier.get("headers")
return [
_value.decode("utf8")
for (_key, _value) in headers
if _key.decode("utf8") == key
]
carrier_getter = CarrierGetter()
def collect_request_attributes(scope):
"""Collects HTTP request attributes from the ASGI scope and returns a
dictionary to be used as span creation attributes."""
server = scope.get("server") or ["0.0.0.0", 80]
port = server[1]
server_host = server[0] + (":" + str(port) if port != 80 else "")
full_path = scope.get("root_path", "") + scope.get("path", "")
http_url = scope.get("scheme", "http") + "://" + server_host + full_path
query_string = scope.get("query_string")
if query_string and http_url:
if isinstance(query_string, bytes):
query_string = query_string.decode("utf8")
http_url = http_url + ("?" + urllib.parse.unquote(query_string))
result = {
"component": scope["type"],
"http.scheme": scope.get("scheme"),
"http.host": server_host,
"host.port": port,
"http.flavor": scope.get("http_version"),
"http.target": scope.get("path"),
"http.url": http_url,
}
http_method = scope.get("method")
if http_method:
result["http.method"] = http_method
http_host_value = ",".join(carrier_getter.get(scope, "host"))
if http_host_value:
result["http.server_name"] = http_host_value
http_user_agent = carrier_getter.get(scope, "user-agent")
if len(http_user_agent) > 0:
result["http.user_agent"] = http_user_agent[0]
if "client" in scope and scope["client"] is not None:
result["net.peer.ip"] = scope.get("client")[0]
result["net.peer.port"] = scope.get("client")[1]
# remove None values
result = {k: v for k, v in result.items() if v is not None}
return result
def set_status_code(span, status_code):
"""Adds HTTP response attributes to span using the status_code argument."""
if not span.is_recording():
return
try:
status_code = int(status_code)
except ValueError:
span.set_status(
Status(
StatusCode.ERROR,
"Non-integer HTTP status: " + repr(status_code),
)
)
else:
span.set_attribute("http.status_code", status_code)
span.set_status(Status(http_status_to_status_code(status_code)))
def get_default_span_details(scope: dict) -> Tuple[str, dict]:
"""Default implementation for span_details_callback
Args:
scope: the asgi scope dictionary
Returns:
a tuple of the span, and any attributes to attach to the
span.
"""
method_or_path = scope.get("method") or scope.get("path")
return method_or_path, {}
class OpenTelemetryMiddleware:
"""The ASGI application middleware.
This class is an ASGI middleware that starts and annotates spans for any
requests it is invoked with.
Args:
app: The ASGI application callable to forward requests to.
span_details_callback: Callback which should return a string
and a tuple, representing the desired span name and a
dictionary with any additional span attributes to set.
Optional: Defaults to get_default_span_details.
"""
def __init__(self, app, span_details_callback=None):
self.app = guarantee_single_callable(app)
self.tracer = trace.get_tracer(__name__, __version__)
self.span_details_callback = (
span_details_callback or get_default_span_details
)
async def __call__(self, scope, receive, send):
"""The ASGI application
Args:
scope: A ASGI environment.
receive: An awaitable callable yielding dictionaries
send: An awaitable callable taking a single dictionary as argument.
"""
if scope["type"] not in ("http", "websocket"):
return await self.app(scope, receive, send)
token = context.attach(propagators.extract(carrier_getter, scope))
span_name, additional_attributes = self.span_details_callback(scope)
try:
with self.tracer.start_as_current_span(
span_name + " asgi", kind=trace.SpanKind.SERVER,
) as span:
if span.is_recording():
attributes = collect_request_attributes(scope)
attributes.update(additional_attributes)
for key, value in attributes.items():
span.set_attribute(key, value)
@wraps(receive)
async def wrapped_receive():
with self.tracer.start_as_current_span(
span_name + " asgi." + scope["type"] + ".receive"
) as receive_span:
message = await receive()
if receive_span.is_recording():
if message["type"] == "websocket.receive":
set_status_code(receive_span, 200)
receive_span.set_attribute("type", message["type"])
return message
@wraps(send)
async def wrapped_send(message):
with self.tracer.start_as_current_span(
span_name + " asgi." + scope["type"] + ".send"
) as send_span:
if send_span.is_recording():
if message["type"] == "http.response.start":
status_code = message["status"]
set_status_code(send_span, status_code)
elif message["type"] == "websocket.send":
set_status_code(send_span, 200)
send_span.set_attribute("type", message["type"])
await send(message)
await self.app(scope, wrapped_receive, wrapped_send)
finally:
context.detach(token)
| 37.363636
| 91
| 0.619029
|
import operator
import typing
import urllib
from functools import wraps
from typing import Tuple
from asgiref.compatibility import guarantee_single_callable
from opentelemetry import context, propagators, trace
from opentelemetry.instrumentation.asgi.version import __version__
from opentelemetry.instrumentation.utils import http_status_to_status_code
from opentelemetry.trace.propagation.textmap import DictGetter
from opentelemetry.trace.status import Status, StatusCode
class CarrierGetter(DictGetter):
def get(self, carrier: dict, key: str) -> typing.List[str]:
headers = carrier.get("headers")
return [
_value.decode("utf8")
for (_key, _value) in headers
if _key.decode("utf8") == key
]
carrier_getter = CarrierGetter()
def collect_request_attributes(scope):
server = scope.get("server") or ["0.0.0.0", 80]
port = server[1]
server_host = server[0] + (":" + str(port) if port != 80 else "")
full_path = scope.get("root_path", "") + scope.get("path", "")
http_url = scope.get("scheme", "http") + "://" + server_host + full_path
query_string = scope.get("query_string")
if query_string and http_url:
if isinstance(query_string, bytes):
query_string = query_string.decode("utf8")
http_url = http_url + ("?" + urllib.parse.unquote(query_string))
result = {
"component": scope["type"],
"http.scheme": scope.get("scheme"),
"http.host": server_host,
"host.port": port,
"http.flavor": scope.get("http_version"),
"http.target": scope.get("path"),
"http.url": http_url,
}
http_method = scope.get("method")
if http_method:
result["http.method"] = http_method
http_host_value = ",".join(carrier_getter.get(scope, "host"))
if http_host_value:
result["http.server_name"] = http_host_value
http_user_agent = carrier_getter.get(scope, "user-agent")
if len(http_user_agent) > 0:
result["http.user_agent"] = http_user_agent[0]
if "client" in scope and scope["client"] is not None:
result["net.peer.ip"] = scope.get("client")[0]
result["net.peer.port"] = scope.get("client")[1]
result = {k: v for k, v in result.items() if v is not None}
return result
def set_status_code(span, status_code):
if not span.is_recording():
return
try:
status_code = int(status_code)
except ValueError:
span.set_status(
Status(
StatusCode.ERROR,
"Non-integer HTTP status: " + repr(status_code),
)
)
else:
span.set_attribute("http.status_code", status_code)
span.set_status(Status(http_status_to_status_code(status_code)))
def get_default_span_details(scope: dict) -> Tuple[str, dict]:
method_or_path = scope.get("method") or scope.get("path")
return method_or_path, {}
class OpenTelemetryMiddleware:
def __init__(self, app, span_details_callback=None):
self.app = guarantee_single_callable(app)
self.tracer = trace.get_tracer(__name__, __version__)
self.span_details_callback = (
span_details_callback or get_default_span_details
)
async def __call__(self, scope, receive, send):
if scope["type"] not in ("http", "websocket"):
return await self.app(scope, receive, send)
token = context.attach(propagators.extract(carrier_getter, scope))
span_name, additional_attributes = self.span_details_callback(scope)
try:
with self.tracer.start_as_current_span(
span_name + " asgi", kind=trace.SpanKind.SERVER,
) as span:
if span.is_recording():
attributes = collect_request_attributes(scope)
attributes.update(additional_attributes)
for key, value in attributes.items():
span.set_attribute(key, value)
@wraps(receive)
async def wrapped_receive():
with self.tracer.start_as_current_span(
span_name + " asgi." + scope["type"] + ".receive"
) as receive_span:
message = await receive()
if receive_span.is_recording():
if message["type"] == "websocket.receive":
set_status_code(receive_span, 200)
receive_span.set_attribute("type", message["type"])
return message
@wraps(send)
async def wrapped_send(message):
with self.tracer.start_as_current_span(
span_name + " asgi." + scope["type"] + ".send"
) as send_span:
if send_span.is_recording():
if message["type"] == "http.response.start":
status_code = message["status"]
set_status_code(send_span, status_code)
elif message["type"] == "websocket.send":
set_status_code(send_span, 200)
send_span.set_attribute("type", message["type"])
await send(message)
await self.app(scope, wrapped_receive, wrapped_send)
finally:
context.detach(token)
| true
| true
|
1c4429efcf1bf8e61716fe3bf3416c05614bb251
| 6,071
|
py
|
Python
|
tests/falcon_test.py
|
titaux12/falcon-apispec
|
2f680622ddfb2af57685903578f9d4dccba72a6b
|
[
"MIT"
] | null | null | null |
tests/falcon_test.py
|
titaux12/falcon-apispec
|
2f680622ddfb2af57685903578f9d4dccba72a6b
|
[
"MIT"
] | null | null | null |
tests/falcon_test.py
|
titaux12/falcon-apispec
|
2f680622ddfb2af57685903578f9d4dccba72a6b
|
[
"MIT"
] | 1
|
2021-03-25T17:13:09.000Z
|
2021-03-25T17:13:09.000Z
|
import logging
import falcon
import pytest
from apispec import APISpec
from apispec.exceptions import APISpecError
from falcon_apispec import FalconPlugin
logging.basicConfig(level="DEBUG")
@pytest.fixture()
def spec_factory():
def _spec(app):
return APISpec(
title="Swagger Petstore",
version="1.0.0",
openapi_version="3.0.2",
description="This is a sample Petstore server. You can find out "
'more about Swagger at <a href="https://swagger.io"> '
"http://swagger.wordnik.com</a> or on irc.freenode.net, #swagger."
'For this sample, you can use the api key "special-key" to test '
"the authorization filters",
plugins=[FalconPlugin(app)],
)
return _spec
@pytest.fixture()
def app():
falcon_app = falcon.API()
return falcon_app
class TestPathHelpers:
def test_gettable_resource(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
"""A greeting endpoint.
---
description: get a greeting
responses:
200:
description: said hi
"""
return "dummy"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource = HelloResource()
app.add_route("/hi", hello_resource)
spec = spec_factory(app)
spec.path(resource=hello_resource)
assert spec._paths["/hi"]["get"] == expected
def test_posttable_resource(self, app, spec_factory):
class HelloResource:
def on_post(self, req, resp):
"""A greeting endpoint.
---
description: get a greeting
responses:
201:
description: posted something
"""
return "hi"
expected = {
"description": "get a greeting",
"responses": {"201": {"description": "posted something"}},
}
hello_resource = HelloResource()
app.add_route("/hi", hello_resource)
spec = spec_factory(app)
spec.path(resource=hello_resource)
assert spec._paths["/hi"]["post"] == expected
def test_resource_with_metadata(self, app, spec_factory):
class HelloResource:
"""Greeting API.
---
x-extension: global metadata
"""
hello_resource = HelloResource()
app.add_route("/hi", hello_resource)
spec = spec_factory(app)
spec.path(resource=hello_resource)
assert spec._paths["/hi"]["x-extension"] == "global metadata"
def test_unredundant_basepath_resource_with_slash(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
"""A greeting endpoint.
---
description: get a greeting
responses:
200:
description: said hi
"""
return "dummy"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource = HelloResource()
app.add_route("/v1/foo/v1", hello_resource)
spec = spec_factory(app)
base_path = '/v1'
spec.path(resource=hello_resource, base_path=base_path)
assert spec._paths["/foo/v1"]["get"] == expected
def test_unredundant_basepath_resource_wo_slash(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
"""A greeting endpoint.
---
description: get a greeting
responses:
200:
description: said hi
"""
return "dummy"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource = HelloResource()
app.add_route("/v1/foo/v1", hello_resource)
spec = spec_factory(app)
base_path = 'v1'
spec.path(resource=hello_resource, base_path=base_path)
assert spec._paths["/foo/v1"]["get"] == expected
def test_path_with_suffix(self, app, spec_factory):
class HelloResource:
def on_get_hello(self):
"""A greeting endpoint.
---
description: get a greeting
responses:
200:
description: said hi
"""
return "dummy"
def on_get(self):
"""An invalid method.
---
description: this should not pass
responses:
200:
description: said hi
"""
return "invalid"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource_with_suffix = HelloResource()
app.add_route("/hi", hello_resource_with_suffix, suffix="hello")
spec = spec_factory(app)
spec.path(resource=hello_resource_with_suffix)
assert spec._paths["/hi"]["get"] == expected
def test_resource_without_endpoint(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
"""A greeting endpoint.
---
description: get a greeting
responses:
200:
description: said hi
"""
return "dummy"
hello_resource = HelloResource()
spec = spec_factory(app)
with pytest.raises(APISpecError):
spec.path(resource=hello_resource)
| 30.661616
| 79
| 0.51919
|
import logging
import falcon
import pytest
from apispec import APISpec
from apispec.exceptions import APISpecError
from falcon_apispec import FalconPlugin
logging.basicConfig(level="DEBUG")
@pytest.fixture()
def spec_factory():
def _spec(app):
return APISpec(
title="Swagger Petstore",
version="1.0.0",
openapi_version="3.0.2",
description="This is a sample Petstore server. You can find out "
'more about Swagger at <a href="https://swagger.io"> '
"http://swagger.wordnik.com</a> or on irc.freenode.net, #swagger."
'For this sample, you can use the api key "special-key" to test '
"the authorization filters",
plugins=[FalconPlugin(app)],
)
return _spec
@pytest.fixture()
def app():
falcon_app = falcon.API()
return falcon_app
class TestPathHelpers:
def test_gettable_resource(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
return "dummy"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource = HelloResource()
app.add_route("/hi", hello_resource)
spec = spec_factory(app)
spec.path(resource=hello_resource)
assert spec._paths["/hi"]["get"] == expected
def test_posttable_resource(self, app, spec_factory):
class HelloResource:
def on_post(self, req, resp):
return "hi"
expected = {
"description": "get a greeting",
"responses": {"201": {"description": "posted something"}},
}
hello_resource = HelloResource()
app.add_route("/hi", hello_resource)
spec = spec_factory(app)
spec.path(resource=hello_resource)
assert spec._paths["/hi"]["post"] == expected
def test_resource_with_metadata(self, app, spec_factory):
class HelloResource:
hello_resource = HelloResource()
app.add_route("/hi", hello_resource)
spec = spec_factory(app)
spec.path(resource=hello_resource)
assert spec._paths["/hi"]["x-extension"] == "global metadata"
def test_unredundant_basepath_resource_with_slash(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
return "dummy"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource = HelloResource()
app.add_route("/v1/foo/v1", hello_resource)
spec = spec_factory(app)
base_path = '/v1'
spec.path(resource=hello_resource, base_path=base_path)
assert spec._paths["/foo/v1"]["get"] == expected
def test_unredundant_basepath_resource_wo_slash(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
return "dummy"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource = HelloResource()
app.add_route("/v1/foo/v1", hello_resource)
spec = spec_factory(app)
base_path = 'v1'
spec.path(resource=hello_resource, base_path=base_path)
assert spec._paths["/foo/v1"]["get"] == expected
def test_path_with_suffix(self, app, spec_factory):
class HelloResource:
def on_get_hello(self):
return "dummy"
def on_get(self):
return "invalid"
expected = {
"description": "get a greeting",
"responses": {"200": {"description": "said hi"}},
}
hello_resource_with_suffix = HelloResource()
app.add_route("/hi", hello_resource_with_suffix, suffix="hello")
spec = spec_factory(app)
spec.path(resource=hello_resource_with_suffix)
assert spec._paths["/hi"]["get"] == expected
def test_resource_without_endpoint(self, app, spec_factory):
class HelloResource:
def on_get(self, req, resp):
return "dummy"
hello_resource = HelloResource()
spec = spec_factory(app)
with pytest.raises(APISpecError):
spec.path(resource=hello_resource)
| true
| true
|
1c442aa98df653b95bdf0ffef94696a49f90a158
| 22,175
|
py
|
Python
|
sdk/python/pulumi_azure_native/network/v20200501/express_route_port.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/network/v20200501/express_route_port.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/network/v20200501/express_route_port.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['ExpressRoutePortArgs', 'ExpressRoutePort']
@pulumi.input_type
class ExpressRoutePortArgs:
def __init__(__self__, *,
resource_group_name: pulumi.Input[str],
bandwidth_in_gbps: Optional[pulumi.Input[int]] = None,
encapsulation: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]] = None,
express_route_port_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
identity: Optional[pulumi.Input['ManagedServiceIdentityArgs']] = None,
links: Optional[pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]]] = None,
location: Optional[pulumi.Input[str]] = None,
peering_location: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):
"""
The set of arguments for constructing a ExpressRoutePort resource.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[int] bandwidth_in_gbps: Bandwidth of procured ports in Gbps.
:param pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']] encapsulation: Encapsulation method on physical ports.
:param pulumi.Input[str] express_route_port_name: The name of the ExpressRoutePort resource.
:param pulumi.Input[str] id: Resource ID.
:param pulumi.Input['ManagedServiceIdentityArgs'] identity: The identity of ExpressRoutePort, if configured.
:param pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]] links: The set of physical links of the ExpressRoutePort resource.
:param pulumi.Input[str] location: Resource location.
:param pulumi.Input[str] peering_location: The name of the peering location that the ExpressRoutePort is mapped to physically.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags.
"""
pulumi.set(__self__, "resource_group_name", resource_group_name)
if bandwidth_in_gbps is not None:
pulumi.set(__self__, "bandwidth_in_gbps", bandwidth_in_gbps)
if encapsulation is not None:
pulumi.set(__self__, "encapsulation", encapsulation)
if express_route_port_name is not None:
pulumi.set(__self__, "express_route_port_name", express_route_port_name)
if id is not None:
pulumi.set(__self__, "id", id)
if identity is not None:
pulumi.set(__self__, "identity", identity)
if links is not None:
pulumi.set(__self__, "links", links)
if location is not None:
pulumi.set(__self__, "location", location)
if peering_location is not None:
pulumi.set(__self__, "peering_location", peering_location)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
The name of the resource group.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="bandwidthInGbps")
def bandwidth_in_gbps(self) -> Optional[pulumi.Input[int]]:
"""
Bandwidth of procured ports in Gbps.
"""
return pulumi.get(self, "bandwidth_in_gbps")
@bandwidth_in_gbps.setter
def bandwidth_in_gbps(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "bandwidth_in_gbps", value)
@property
@pulumi.getter
def encapsulation(self) -> Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]]:
"""
Encapsulation method on physical ports.
"""
return pulumi.get(self, "encapsulation")
@encapsulation.setter
def encapsulation(self, value: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]]):
pulumi.set(self, "encapsulation", value)
@property
@pulumi.getter(name="expressRoutePortName")
def express_route_port_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the ExpressRoutePort resource.
"""
return pulumi.get(self, "express_route_port_name")
@express_route_port_name.setter
def express_route_port_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "express_route_port_name", value)
@property
@pulumi.getter
def id(self) -> Optional[pulumi.Input[str]]:
"""
Resource ID.
"""
return pulumi.get(self, "id")
@id.setter
def id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "id", value)
@property
@pulumi.getter
def identity(self) -> Optional[pulumi.Input['ManagedServiceIdentityArgs']]:
"""
The identity of ExpressRoutePort, if configured.
"""
return pulumi.get(self, "identity")
@identity.setter
def identity(self, value: Optional[pulumi.Input['ManagedServiceIdentityArgs']]):
pulumi.set(self, "identity", value)
@property
@pulumi.getter
def links(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]]]:
"""
The set of physical links of the ExpressRoutePort resource.
"""
return pulumi.get(self, "links")
@links.setter
def links(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]]]):
pulumi.set(self, "links", value)
@property
@pulumi.getter
def location(self) -> Optional[pulumi.Input[str]]:
"""
Resource location.
"""
return pulumi.get(self, "location")
@location.setter
def location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "location", value)
@property
@pulumi.getter(name="peeringLocation")
def peering_location(self) -> Optional[pulumi.Input[str]]:
"""
The name of the peering location that the ExpressRoutePort is mapped to physically.
"""
return pulumi.get(self, "peering_location")
@peering_location.setter
def peering_location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "peering_location", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
Resource tags.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
class ExpressRoutePort(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
bandwidth_in_gbps: Optional[pulumi.Input[int]] = None,
encapsulation: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]] = None,
express_route_port_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
identity: Optional[pulumi.Input[pulumi.InputType['ManagedServiceIdentityArgs']]] = None,
links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteLinkArgs']]]]] = None,
location: Optional[pulumi.Input[str]] = None,
peering_location: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
"""
ExpressRoutePort resource definition.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[int] bandwidth_in_gbps: Bandwidth of procured ports in Gbps.
:param pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']] encapsulation: Encapsulation method on physical ports.
:param pulumi.Input[str] express_route_port_name: The name of the ExpressRoutePort resource.
:param pulumi.Input[str] id: Resource ID.
:param pulumi.Input[pulumi.InputType['ManagedServiceIdentityArgs']] identity: The identity of ExpressRoutePort, if configured.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteLinkArgs']]]] links: The set of physical links of the ExpressRoutePort resource.
:param pulumi.Input[str] location: Resource location.
:param pulumi.Input[str] peering_location: The name of the peering location that the ExpressRoutePort is mapped to physically.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ExpressRoutePortArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
ExpressRoutePort resource definition.
:param str resource_name: The name of the resource.
:param ExpressRoutePortArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ExpressRoutePortArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
bandwidth_in_gbps: Optional[pulumi.Input[int]] = None,
encapsulation: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]] = None,
express_route_port_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
identity: Optional[pulumi.Input[pulumi.InputType['ManagedServiceIdentityArgs']]] = None,
links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteLinkArgs']]]]] = None,
location: Optional[pulumi.Input[str]] = None,
peering_location: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ExpressRoutePortArgs.__new__(ExpressRoutePortArgs)
__props__.__dict__["bandwidth_in_gbps"] = bandwidth_in_gbps
__props__.__dict__["encapsulation"] = encapsulation
__props__.__dict__["express_route_port_name"] = express_route_port_name
__props__.__dict__["id"] = id
__props__.__dict__["identity"] = identity
__props__.__dict__["links"] = links
__props__.__dict__["location"] = location
__props__.__dict__["peering_location"] = peering_location
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["tags"] = tags
__props__.__dict__["allocation_date"] = None
__props__.__dict__["circuits"] = None
__props__.__dict__["etag"] = None
__props__.__dict__["ether_type"] = None
__props__.__dict__["mtu"] = None
__props__.__dict__["name"] = None
__props__.__dict__["provisioned_bandwidth_in_gbps"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["resource_guid"] = None
__props__.__dict__["type"] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20200501:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20180801:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20181001:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20181101:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20181201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190401:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190401:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190601:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190701:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190801:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190901:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20191101:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20191201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200301:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200401:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200601:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200701:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200801:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200801:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20201101:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20201101:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20210201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20210201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20210301:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20210301:ExpressRoutePort")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(ExpressRoutePort, __self__).__init__(
'azure-native:network/v20200501:ExpressRoutePort',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ExpressRoutePort':
"""
Get an existing ExpressRoutePort resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = ExpressRoutePortArgs.__new__(ExpressRoutePortArgs)
__props__.__dict__["allocation_date"] = None
__props__.__dict__["bandwidth_in_gbps"] = None
__props__.__dict__["circuits"] = None
__props__.__dict__["encapsulation"] = None
__props__.__dict__["etag"] = None
__props__.__dict__["ether_type"] = None
__props__.__dict__["identity"] = None
__props__.__dict__["links"] = None
__props__.__dict__["location"] = None
__props__.__dict__["mtu"] = None
__props__.__dict__["name"] = None
__props__.__dict__["peering_location"] = None
__props__.__dict__["provisioned_bandwidth_in_gbps"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["resource_guid"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["type"] = None
return ExpressRoutePort(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="allocationDate")
def allocation_date(self) -> pulumi.Output[str]:
"""
Date of the physical port allocation to be used in Letter of Authorization.
"""
return pulumi.get(self, "allocation_date")
@property
@pulumi.getter(name="bandwidthInGbps")
def bandwidth_in_gbps(self) -> pulumi.Output[Optional[int]]:
"""
Bandwidth of procured ports in Gbps.
"""
return pulumi.get(self, "bandwidth_in_gbps")
@property
@pulumi.getter
def circuits(self) -> pulumi.Output[Sequence['outputs.SubResourceResponse']]:
"""
Reference the ExpressRoute circuit(s) that are provisioned on this ExpressRoutePort resource.
"""
return pulumi.get(self, "circuits")
@property
@pulumi.getter
def encapsulation(self) -> pulumi.Output[Optional[str]]:
"""
Encapsulation method on physical ports.
"""
return pulumi.get(self, "encapsulation")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[str]:
"""
A unique read-only string that changes whenever the resource is updated.
"""
return pulumi.get(self, "etag")
@property
@pulumi.getter(name="etherType")
def ether_type(self) -> pulumi.Output[str]:
"""
Ether type of the physical port.
"""
return pulumi.get(self, "ether_type")
@property
@pulumi.getter
def identity(self) -> pulumi.Output[Optional['outputs.ManagedServiceIdentityResponse']]:
"""
The identity of ExpressRoutePort, if configured.
"""
return pulumi.get(self, "identity")
@property
@pulumi.getter
def links(self) -> pulumi.Output[Optional[Sequence['outputs.ExpressRouteLinkResponse']]]:
"""
The set of physical links of the ExpressRoutePort resource.
"""
return pulumi.get(self, "links")
@property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
"""
Resource location.
"""
return pulumi.get(self, "location")
@property
@pulumi.getter
def mtu(self) -> pulumi.Output[str]:
"""
Maximum transmission unit of the physical port pair(s).
"""
return pulumi.get(self, "mtu")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Resource name.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="peeringLocation")
def peering_location(self) -> pulumi.Output[Optional[str]]:
"""
The name of the peering location that the ExpressRoutePort is mapped to physically.
"""
return pulumi.get(self, "peering_location")
@property
@pulumi.getter(name="provisionedBandwidthInGbps")
def provisioned_bandwidth_in_gbps(self) -> pulumi.Output[float]:
"""
Aggregate Gbps of associated circuit bandwidths.
"""
return pulumi.get(self, "provisioned_bandwidth_in_gbps")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
"""
The provisioning state of the express route port resource.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="resourceGuid")
def resource_guid(self) -> pulumi.Output[str]:
"""
The resource GUID property of the express route port resource.
"""
return pulumi.get(self, "resource_guid")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource tags.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Resource type.
"""
return pulumi.get(self, "type")
| 47.997835
| 3,108
| 0.670891
|
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['ExpressRoutePortArgs', 'ExpressRoutePort']
@pulumi.input_type
class ExpressRoutePortArgs:
def __init__(__self__, *,
resource_group_name: pulumi.Input[str],
bandwidth_in_gbps: Optional[pulumi.Input[int]] = None,
encapsulation: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]] = None,
express_route_port_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
identity: Optional[pulumi.Input['ManagedServiceIdentityArgs']] = None,
links: Optional[pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]]] = None,
location: Optional[pulumi.Input[str]] = None,
peering_location: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):
pulumi.set(__self__, "resource_group_name", resource_group_name)
if bandwidth_in_gbps is not None:
pulumi.set(__self__, "bandwidth_in_gbps", bandwidth_in_gbps)
if encapsulation is not None:
pulumi.set(__self__, "encapsulation", encapsulation)
if express_route_port_name is not None:
pulumi.set(__self__, "express_route_port_name", express_route_port_name)
if id is not None:
pulumi.set(__self__, "id", id)
if identity is not None:
pulumi.set(__self__, "identity", identity)
if links is not None:
pulumi.set(__self__, "links", links)
if location is not None:
pulumi.set(__self__, "location", location)
if peering_location is not None:
pulumi.set(__self__, "peering_location", peering_location)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="bandwidthInGbps")
def bandwidth_in_gbps(self) -> Optional[pulumi.Input[int]]:
return pulumi.get(self, "bandwidth_in_gbps")
@bandwidth_in_gbps.setter
def bandwidth_in_gbps(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "bandwidth_in_gbps", value)
@property
@pulumi.getter
def encapsulation(self) -> Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]]:
return pulumi.get(self, "encapsulation")
@encapsulation.setter
def encapsulation(self, value: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]]):
pulumi.set(self, "encapsulation", value)
@property
@pulumi.getter(name="expressRoutePortName")
def express_route_port_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "express_route_port_name")
@express_route_port_name.setter
def express_route_port_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "express_route_port_name", value)
@property
@pulumi.getter
def id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "id")
@id.setter
def id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "id", value)
@property
@pulumi.getter
def identity(self) -> Optional[pulumi.Input['ManagedServiceIdentityArgs']]:
return pulumi.get(self, "identity")
@identity.setter
def identity(self, value: Optional[pulumi.Input['ManagedServiceIdentityArgs']]):
pulumi.set(self, "identity", value)
@property
@pulumi.getter
def links(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]]]:
return pulumi.get(self, "links")
@links.setter
def links(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ExpressRouteLinkArgs']]]]):
pulumi.set(self, "links", value)
@property
@pulumi.getter
def location(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "location")
@location.setter
def location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "location", value)
@property
@pulumi.getter(name="peeringLocation")
def peering_location(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "peering_location")
@peering_location.setter
def peering_location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "peering_location", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
class ExpressRoutePort(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
bandwidth_in_gbps: Optional[pulumi.Input[int]] = None,
encapsulation: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]] = None,
express_route_port_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
identity: Optional[pulumi.Input[pulumi.InputType['ManagedServiceIdentityArgs']]] = None,
links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteLinkArgs']]]]] = None,
location: Optional[pulumi.Input[str]] = None,
peering_location: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
...
@overload
def __init__(__self__,
resource_name: str,
args: ExpressRoutePortArgs,
opts: Optional[pulumi.ResourceOptions] = None):
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ExpressRoutePortArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
bandwidth_in_gbps: Optional[pulumi.Input[int]] = None,
encapsulation: Optional[pulumi.Input[Union[str, 'ExpressRoutePortsEncapsulation']]] = None,
express_route_port_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
identity: Optional[pulumi.Input[pulumi.InputType['ManagedServiceIdentityArgs']]] = None,
links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteLinkArgs']]]]] = None,
location: Optional[pulumi.Input[str]] = None,
peering_location: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ExpressRoutePortArgs.__new__(ExpressRoutePortArgs)
__props__.__dict__["bandwidth_in_gbps"] = bandwidth_in_gbps
__props__.__dict__["encapsulation"] = encapsulation
__props__.__dict__["express_route_port_name"] = express_route_port_name
__props__.__dict__["id"] = id
__props__.__dict__["identity"] = identity
__props__.__dict__["links"] = links
__props__.__dict__["location"] = location
__props__.__dict__["peering_location"] = peering_location
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["tags"] = tags
__props__.__dict__["allocation_date"] = None
__props__.__dict__["circuits"] = None
__props__.__dict__["etag"] = None
__props__.__dict__["ether_type"] = None
__props__.__dict__["mtu"] = None
__props__.__dict__["name"] = None
__props__.__dict__["provisioned_bandwidth_in_gbps"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["resource_guid"] = None
__props__.__dict__["type"] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20200501:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20180801:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20181001:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20181101:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20181201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190401:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190401:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190601:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190701:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190801:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20190901:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20191101:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20191201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200301:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200401:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200601:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200701:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20200801:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20200801:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20201101:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20201101:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20210201:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20210201:ExpressRoutePort"), pulumi.Alias(type_="azure-native:network/v20210301:ExpressRoutePort"), pulumi.Alias(type_="azure-nextgen:network/v20210301:ExpressRoutePort")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(ExpressRoutePort, __self__).__init__(
'azure-native:network/v20200501:ExpressRoutePort',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ExpressRoutePort':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = ExpressRoutePortArgs.__new__(ExpressRoutePortArgs)
__props__.__dict__["allocation_date"] = None
__props__.__dict__["bandwidth_in_gbps"] = None
__props__.__dict__["circuits"] = None
__props__.__dict__["encapsulation"] = None
__props__.__dict__["etag"] = None
__props__.__dict__["ether_type"] = None
__props__.__dict__["identity"] = None
__props__.__dict__["links"] = None
__props__.__dict__["location"] = None
__props__.__dict__["mtu"] = None
__props__.__dict__["name"] = None
__props__.__dict__["peering_location"] = None
__props__.__dict__["provisioned_bandwidth_in_gbps"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["resource_guid"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["type"] = None
return ExpressRoutePort(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="allocationDate")
def allocation_date(self) -> pulumi.Output[str]:
return pulumi.get(self, "allocation_date")
@property
@pulumi.getter(name="bandwidthInGbps")
def bandwidth_in_gbps(self) -> pulumi.Output[Optional[int]]:
return pulumi.get(self, "bandwidth_in_gbps")
@property
@pulumi.getter
def circuits(self) -> pulumi.Output[Sequence['outputs.SubResourceResponse']]:
return pulumi.get(self, "circuits")
@property
@pulumi.getter
def encapsulation(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "encapsulation")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[str]:
return pulumi.get(self, "etag")
@property
@pulumi.getter(name="etherType")
def ether_type(self) -> pulumi.Output[str]:
return pulumi.get(self, "ether_type")
@property
@pulumi.getter
def identity(self) -> pulumi.Output[Optional['outputs.ManagedServiceIdentityResponse']]:
return pulumi.get(self, "identity")
@property
@pulumi.getter
def links(self) -> pulumi.Output[Optional[Sequence['outputs.ExpressRouteLinkResponse']]]:
return pulumi.get(self, "links")
@property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "location")
@property
@pulumi.getter
def mtu(self) -> pulumi.Output[str]:
return pulumi.get(self, "mtu")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter(name="peeringLocation")
def peering_location(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "peering_location")
@property
@pulumi.getter(name="provisionedBandwidthInGbps")
def provisioned_bandwidth_in_gbps(self) -> pulumi.Output[float]:
return pulumi.get(self, "provisioned_bandwidth_in_gbps")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="resourceGuid")
def resource_guid(self) -> pulumi.Output[str]:
return pulumi.get(self, "resource_guid")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
return pulumi.get(self, "type")
| true
| true
|
1c442ab2c8f5c84fc82c2d5fdb56f5553c96b1cb
| 14,164
|
py
|
Python
|
reversion/revisions.py
|
baffolobill/django-reversion
|
e0e12ce00f91043ba9c828dc47cbe0e57d3cbc36
|
[
"BSD-3-Clause"
] | null | null | null |
reversion/revisions.py
|
baffolobill/django-reversion
|
e0e12ce00f91043ba9c828dc47cbe0e57d3cbc36
|
[
"BSD-3-Clause"
] | null | null | null |
reversion/revisions.py
|
baffolobill/django-reversion
|
e0e12ce00f91043ba9c828dc47cbe0e57d3cbc36
|
[
"BSD-3-Clause"
] | null | null | null |
from collections import namedtuple, defaultdict
from contextlib import contextmanager
from functools import wraps
from threading import local
from django.apps import apps
from django.core import serializers
from django.core.exceptions import ObjectDoesNotExist
from django.db import models, transaction, router
from django.db.models.query import QuerySet
from django.db.models.signals import post_save, m2m_changed
from django.utils.encoding import force_str
from django.utils import timezone
from reversion.errors import RevisionManagementError, RegistrationError
from reversion.signals import pre_revision_commit, post_revision_commit
_VersionOptions = namedtuple("VersionOptions", (
"fields",
"follow",
"format",
"for_concrete_model",
"ignore_duplicates",
"use_natural_foreign_keys",
))
_StackFrame = namedtuple("StackFrame", (
"manage_manually",
"user",
"comment",
"date_created",
"db_versions",
"meta",
"extra_data",
))
class _Local(local):
def __init__(self):
self.stack = ()
_local = _Local()
def is_active():
return bool(_local.stack)
def _current_frame():
if not is_active():
raise RevisionManagementError("There is no active revision for this thread")
return _local.stack[-1]
def _copy_db_versions(db_versions):
return {
db: versions.copy()
for db, versions
in db_versions.items()
}
def _push_frame(manage_manually, using):
if is_active():
current_frame = _current_frame()
db_versions = _copy_db_versions(current_frame.db_versions)
db_versions.setdefault(using, {})
stack_frame = current_frame._replace(
manage_manually=manage_manually,
db_versions=db_versions,
)
else:
stack_frame = _StackFrame(
manage_manually=manage_manually,
user=None,
comment="",
date_created=timezone.now(),
db_versions={using: {}},
meta=(),
extra_data=None,
)
_local.stack += (stack_frame,)
def _update_frame(**kwargs):
_local.stack = _local.stack[:-1] + (_current_frame()._replace(**kwargs),)
def _pop_frame():
prev_frame = _current_frame()
_local.stack = _local.stack[:-1]
if is_active():
current_frame = _current_frame()
db_versions = {
db: prev_frame.db_versions[db]
for db
in current_frame.db_versions.keys()
}
_update_frame(
user=prev_frame.user,
comment=prev_frame.comment,
date_created=prev_frame.date_created,
db_versions=db_versions,
meta=prev_frame.meta,
extra_data=prev_frame.extra_data,
)
def is_manage_manually():
return _current_frame().manage_manually
def set_extra_data(extra_data):
_update_frame(extra_data=extra_data)
def get_extra_data():
return _current_frame().extra_data
def set_user(user):
_update_frame(user=user)
def get_user():
return _current_frame().user
def set_comment(comment):
_update_frame(comment=comment)
def get_comment():
return _current_frame().comment
def set_date_created(date_created):
_update_frame(date_created=date_created)
def get_date_created():
return _current_frame().date_created
def add_meta(model, **values):
_update_frame(meta=_current_frame().meta + ((model, values),))
def _follow_relations(obj):
version_options = _get_options(obj.__class__)
for follow_name in version_options.follow:
try:
follow_obj = getattr(obj, follow_name)
except ObjectDoesNotExist:
continue
if isinstance(follow_obj, models.Model):
yield follow_obj
elif isinstance(follow_obj, (models.Manager, QuerySet)):
for follow_obj_instance in follow_obj.all():
yield follow_obj_instance
elif follow_obj is not None:
raise RegistrationError("{name}.{follow_name} should be a Model or QuerySet".format(
name=obj.__class__.__name__,
follow_name=follow_name,
))
def _follow_relations_recursive(obj):
def do_follow(obj):
if obj not in relations:
relations.add(obj)
for related in _follow_relations(obj):
do_follow(related)
relations = set()
do_follow(obj)
return relations
def _add_to_revision(obj, using, model_db, explicit):
from reversion.models import Version
# Exit early if the object is not fully-formed.
if obj.pk is None:
return
version_options = _get_options(obj.__class__)
content_type = _get_content_type(obj.__class__, using)
object_id = force_str(obj.pk)
version_key = (content_type, object_id)
# If the obj is already in the revision, stop now.
db_versions = _current_frame().db_versions
versions = db_versions[using]
if version_key in versions and not explicit:
return
# Get the version data.
version = Version(
content_type=content_type,
object_id=object_id,
db=model_db,
format=version_options.format,
serialized_data=serializers.serialize(
version_options.format,
(obj,),
fields=version_options.fields,
use_natural_foreign_keys=version_options.use_natural_foreign_keys,
),
object_repr=force_str(obj),
)
# If the version is a duplicate, stop now.
if version_options.ignore_duplicates and explicit:
previous_version = Version.objects.using(using).get_for_object(obj, model_db=model_db).first()
if previous_version and previous_version._local_field_dict == version._local_field_dict:
return
# Store the version.
db_versions = _copy_db_versions(db_versions)
db_versions[using][version_key] = version
_update_frame(db_versions=db_versions)
# Follow relations.
for follow_obj in _follow_relations(obj):
_add_to_revision(follow_obj, using, model_db, False)
def add_to_revision(obj, model_db=None):
model_db = model_db or router.db_for_write(obj.__class__, instance=obj)
for db in _current_frame().db_versions.keys():
_add_to_revision(obj, db, model_db, True)
def _find_parent_version(version):
from reversion.models import Version
try:
# return Version.objects.get_for_object(version.object)[0]
return Version.objects\
.get_for_object_reference(version._model, version.object_id)\
.filter(reverted_at__isnull=True)[0]
except IndexError:
return None
def _save_revision(versions, user=None, comment="", meta=(), date_created=None, using=None, extra_data=None):
from reversion.models import Revision
# Only save versions that exist in the database.
# Use _base_manager so we don't have problems when _default_manager is overriden
model_db_pks = defaultdict(lambda: defaultdict(set))
for version in versions:
model_db_pks[version._model][version.db].add(version.object_id)
model_db_existing_pks = {
model: {
db: frozenset(map(
force_str,
model._base_manager.using(db).filter(pk__in=pks).values_list("pk", flat=True),
))
for db, pks in db_pks.items()
}
for model, db_pks in model_db_pks.items()
}
versions = [
version for version in versions
if version.object_id in model_db_existing_pks[version._model][version.db]
]
# Bail early if there are no objects to save.
if not versions:
return
# Save a new revision.
revision = Revision(
date_created=date_created,
user=user,
comment=comment,
extra_data=extra_data,
)
# Send the pre_revision_commit signal.
pre_revision_commit.send(
sender=create_revision,
revision=revision,
versions=versions,
)
# Save the revision.
revision.save(using=using)
# Save version models.
for version in versions:
version.parent = _find_parent_version(version)
version.revision = revision
version.save(using=using)
# Save the meta information.
for meta_model, meta_fields in meta:
meta_model._base_manager.db_manager(using=using).create(
revision=revision,
**meta_fields
)
# Send the post_revision_commit signal.
post_revision_commit.send(
sender=create_revision,
revision=revision,
versions=versions,
)
@contextmanager
def _dummy_context():
yield
@contextmanager
def _create_revision_context(manage_manually, using, atomic):
context = transaction.atomic(using=using) if atomic else _dummy_context()
with context:
_push_frame(manage_manually, using)
try:
yield
# Only save for a db if that's the last stack frame for that db.
if not any(using in frame.db_versions for frame in _local.stack[:-1]):
current_frame = _current_frame()
_save_revision(
versions=current_frame.db_versions[using].values(),
user=current_frame.user,
comment=current_frame.comment,
meta=current_frame.meta,
date_created=current_frame.date_created,
using=using,
extra_data=current_frame.extra_data,
)
finally:
_pop_frame()
def create_revision(manage_manually=False, using=None, atomic=True):
from reversion.models import Revision
using = using or router.db_for_write(Revision)
return _ContextWrapper(_create_revision_context, (manage_manually, using, atomic))
class _ContextWrapper(object):
def __init__(self, func, args):
self._func = func
self._args = args
self._context = func(*args)
def __enter__(self):
return self._context.__enter__()
def __exit__(self, exc_type, exc_value, traceback):
return self._context.__exit__(exc_type, exc_value, traceback)
def __call__(self, func):
@wraps(func)
def do_revision_context(*args, **kwargs):
with self._func(*self._args):
return func(*args, **kwargs)
return do_revision_context
def _post_save_receiver(sender, instance, using, **kwargs):
if is_registered(sender) and is_active() and not is_manage_manually():
add_to_revision(instance, model_db=using)
def _m2m_changed_receiver(instance, using, action, model, reverse, **kwargs):
if action.startswith("post_") and not reverse:
if is_registered(instance) and is_active() and not is_manage_manually():
add_to_revision(instance, model_db=using)
def _get_registration_key(model):
return (model._meta.app_label, model._meta.model_name)
_registered_models = {}
def is_registered(model):
return _get_registration_key(model) in _registered_models
def get_registered_models():
return (apps.get_model(*key) for key in _registered_models.keys())
def _get_senders_and_signals(model):
yield model, post_save, _post_save_receiver
opts = model._meta.concrete_model._meta
for field in opts.local_many_to_many:
m2m_model = field.remote_field.through
if isinstance(m2m_model, str):
if "." not in m2m_model:
m2m_model = "{app_label}.{m2m_model}".format(
app_label=opts.app_label,
m2m_model=m2m_model
)
yield m2m_model, m2m_changed, _m2m_changed_receiver
def register(model=None, fields=None, exclude=(), follow=(), format="json",
for_concrete_model=True, ignore_duplicates=False, use_natural_foreign_keys=False):
def register(model):
# Prevent multiple registration.
if is_registered(model):
raise RegistrationError("{model} has already been registered with django-reversion".format(
model=model,
))
# Parse fields.
opts = model._meta.concrete_model._meta
version_options = _VersionOptions(
fields=tuple(
field_name
for field_name
in ([
field.name
for field
in opts.local_fields + opts.local_many_to_many
] if fields is None else fields)
if field_name not in exclude
),
follow=tuple(follow),
format=format,
for_concrete_model=for_concrete_model,
ignore_duplicates=ignore_duplicates,
use_natural_foreign_keys=use_natural_foreign_keys,
)
# Register the model.
_registered_models[_get_registration_key(model)] = version_options
# Connect signals.
for sender, signal, signal_receiver in _get_senders_and_signals(model):
signal.connect(signal_receiver, sender=sender)
# All done!
return model
# Return a class decorator if model is not given
if model is None:
return register
# Register the model.
return register(model)
def _assert_registered(model):
if not is_registered(model):
raise RegistrationError("{model} has not been registered with django-reversion".format(
model=model,
))
def _get_options(model):
_assert_registered(model)
return _registered_models[_get_registration_key(model)]
def unregister(model):
_assert_registered(model)
del _registered_models[_get_registration_key(model)]
# Disconnect signals.
for sender, signal, signal_receiver in _get_senders_and_signals(model):
signal.disconnect(signal_receiver, sender=sender)
def _get_content_type(model, using):
from django.contrib.contenttypes.models import ContentType
version_options = _get_options(model)
return ContentType.objects.db_manager(using).get_for_model(
model,
for_concrete_model=version_options.for_concrete_model,
)
| 30.724512
| 109
| 0.662666
|
from collections import namedtuple, defaultdict
from contextlib import contextmanager
from functools import wraps
from threading import local
from django.apps import apps
from django.core import serializers
from django.core.exceptions import ObjectDoesNotExist
from django.db import models, transaction, router
from django.db.models.query import QuerySet
from django.db.models.signals import post_save, m2m_changed
from django.utils.encoding import force_str
from django.utils import timezone
from reversion.errors import RevisionManagementError, RegistrationError
from reversion.signals import pre_revision_commit, post_revision_commit
_VersionOptions = namedtuple("VersionOptions", (
"fields",
"follow",
"format",
"for_concrete_model",
"ignore_duplicates",
"use_natural_foreign_keys",
))
_StackFrame = namedtuple("StackFrame", (
"manage_manually",
"user",
"comment",
"date_created",
"db_versions",
"meta",
"extra_data",
))
class _Local(local):
def __init__(self):
self.stack = ()
_local = _Local()
def is_active():
return bool(_local.stack)
def _current_frame():
if not is_active():
raise RevisionManagementError("There is no active revision for this thread")
return _local.stack[-1]
def _copy_db_versions(db_versions):
return {
db: versions.copy()
for db, versions
in db_versions.items()
}
def _push_frame(manage_manually, using):
if is_active():
current_frame = _current_frame()
db_versions = _copy_db_versions(current_frame.db_versions)
db_versions.setdefault(using, {})
stack_frame = current_frame._replace(
manage_manually=manage_manually,
db_versions=db_versions,
)
else:
stack_frame = _StackFrame(
manage_manually=manage_manually,
user=None,
comment="",
date_created=timezone.now(),
db_versions={using: {}},
meta=(),
extra_data=None,
)
_local.stack += (stack_frame,)
def _update_frame(**kwargs):
_local.stack = _local.stack[:-1] + (_current_frame()._replace(**kwargs),)
def _pop_frame():
prev_frame = _current_frame()
_local.stack = _local.stack[:-1]
if is_active():
current_frame = _current_frame()
db_versions = {
db: prev_frame.db_versions[db]
for db
in current_frame.db_versions.keys()
}
_update_frame(
user=prev_frame.user,
comment=prev_frame.comment,
date_created=prev_frame.date_created,
db_versions=db_versions,
meta=prev_frame.meta,
extra_data=prev_frame.extra_data,
)
def is_manage_manually():
return _current_frame().manage_manually
def set_extra_data(extra_data):
_update_frame(extra_data=extra_data)
def get_extra_data():
return _current_frame().extra_data
def set_user(user):
_update_frame(user=user)
def get_user():
return _current_frame().user
def set_comment(comment):
_update_frame(comment=comment)
def get_comment():
return _current_frame().comment
def set_date_created(date_created):
_update_frame(date_created=date_created)
def get_date_created():
return _current_frame().date_created
def add_meta(model, **values):
_update_frame(meta=_current_frame().meta + ((model, values),))
def _follow_relations(obj):
version_options = _get_options(obj.__class__)
for follow_name in version_options.follow:
try:
follow_obj = getattr(obj, follow_name)
except ObjectDoesNotExist:
continue
if isinstance(follow_obj, models.Model):
yield follow_obj
elif isinstance(follow_obj, (models.Manager, QuerySet)):
for follow_obj_instance in follow_obj.all():
yield follow_obj_instance
elif follow_obj is not None:
raise RegistrationError("{name}.{follow_name} should be a Model or QuerySet".format(
name=obj.__class__.__name__,
follow_name=follow_name,
))
def _follow_relations_recursive(obj):
def do_follow(obj):
if obj not in relations:
relations.add(obj)
for related in _follow_relations(obj):
do_follow(related)
relations = set()
do_follow(obj)
return relations
def _add_to_revision(obj, using, model_db, explicit):
from reversion.models import Version
if obj.pk is None:
return
version_options = _get_options(obj.__class__)
content_type = _get_content_type(obj.__class__, using)
object_id = force_str(obj.pk)
version_key = (content_type, object_id)
db_versions = _current_frame().db_versions
versions = db_versions[using]
if version_key in versions and not explicit:
return
version = Version(
content_type=content_type,
object_id=object_id,
db=model_db,
format=version_options.format,
serialized_data=serializers.serialize(
version_options.format,
(obj,),
fields=version_options.fields,
use_natural_foreign_keys=version_options.use_natural_foreign_keys,
),
object_repr=force_str(obj),
)
if version_options.ignore_duplicates and explicit:
previous_version = Version.objects.using(using).get_for_object(obj, model_db=model_db).first()
if previous_version and previous_version._local_field_dict == version._local_field_dict:
return
db_versions = _copy_db_versions(db_versions)
db_versions[using][version_key] = version
_update_frame(db_versions=db_versions)
for follow_obj in _follow_relations(obj):
_add_to_revision(follow_obj, using, model_db, False)
def add_to_revision(obj, model_db=None):
model_db = model_db or router.db_for_write(obj.__class__, instance=obj)
for db in _current_frame().db_versions.keys():
_add_to_revision(obj, db, model_db, True)
def _find_parent_version(version):
from reversion.models import Version
try:
return Version.objects\
.get_for_object_reference(version._model, version.object_id)\
.filter(reverted_at__isnull=True)[0]
except IndexError:
return None
def _save_revision(versions, user=None, comment="", meta=(), date_created=None, using=None, extra_data=None):
from reversion.models import Revision
model_db_pks = defaultdict(lambda: defaultdict(set))
for version in versions:
model_db_pks[version._model][version.db].add(version.object_id)
model_db_existing_pks = {
model: {
db: frozenset(map(
force_str,
model._base_manager.using(db).filter(pk__in=pks).values_list("pk", flat=True),
))
for db, pks in db_pks.items()
}
for model, db_pks in model_db_pks.items()
}
versions = [
version for version in versions
if version.object_id in model_db_existing_pks[version._model][version.db]
]
# Bail early if there are no objects to save.
if not versions:
return
# Save a new revision.
revision = Revision(
date_created=date_created,
user=user,
comment=comment,
extra_data=extra_data,
)
# Send the pre_revision_commit signal.
pre_revision_commit.send(
sender=create_revision,
revision=revision,
versions=versions,
)
# Save the revision.
revision.save(using=using)
# Save version models.
for version in versions:
version.parent = _find_parent_version(version)
version.revision = revision
version.save(using=using)
# Save the meta information.
for meta_model, meta_fields in meta:
meta_model._base_manager.db_manager(using=using).create(
revision=revision,
**meta_fields
)
# Send the post_revision_commit signal.
post_revision_commit.send(
sender=create_revision,
revision=revision,
versions=versions,
)
@contextmanager
def _dummy_context():
yield
@contextmanager
def _create_revision_context(manage_manually, using, atomic):
context = transaction.atomic(using=using) if atomic else _dummy_context()
with context:
_push_frame(manage_manually, using)
try:
yield
# Only save for a db if that's the last stack frame for that db.
if not any(using in frame.db_versions for frame in _local.stack[:-1]):
current_frame = _current_frame()
_save_revision(
versions=current_frame.db_versions[using].values(),
user=current_frame.user,
comment=current_frame.comment,
meta=current_frame.meta,
date_created=current_frame.date_created,
using=using,
extra_data=current_frame.extra_data,
)
finally:
_pop_frame()
def create_revision(manage_manually=False, using=None, atomic=True):
from reversion.models import Revision
using = using or router.db_for_write(Revision)
return _ContextWrapper(_create_revision_context, (manage_manually, using, atomic))
class _ContextWrapper(object):
def __init__(self, func, args):
self._func = func
self._args = args
self._context = func(*args)
def __enter__(self):
return self._context.__enter__()
def __exit__(self, exc_type, exc_value, traceback):
return self._context.__exit__(exc_type, exc_value, traceback)
def __call__(self, func):
@wraps(func)
def do_revision_context(*args, **kwargs):
with self._func(*self._args):
return func(*args, **kwargs)
return do_revision_context
def _post_save_receiver(sender, instance, using, **kwargs):
if is_registered(sender) and is_active() and not is_manage_manually():
add_to_revision(instance, model_db=using)
def _m2m_changed_receiver(instance, using, action, model, reverse, **kwargs):
if action.startswith("post_") and not reverse:
if is_registered(instance) and is_active() and not is_manage_manually():
add_to_revision(instance, model_db=using)
def _get_registration_key(model):
return (model._meta.app_label, model._meta.model_name)
_registered_models = {}
def is_registered(model):
return _get_registration_key(model) in _registered_models
def get_registered_models():
return (apps.get_model(*key) for key in _registered_models.keys())
def _get_senders_and_signals(model):
yield model, post_save, _post_save_receiver
opts = model._meta.concrete_model._meta
for field in opts.local_many_to_many:
m2m_model = field.remote_field.through
if isinstance(m2m_model, str):
if "." not in m2m_model:
m2m_model = "{app_label}.{m2m_model}".format(
app_label=opts.app_label,
m2m_model=m2m_model
)
yield m2m_model, m2m_changed, _m2m_changed_receiver
def register(model=None, fields=None, exclude=(), follow=(), format="json",
for_concrete_model=True, ignore_duplicates=False, use_natural_foreign_keys=False):
def register(model):
if is_registered(model):
raise RegistrationError("{model} has already been registered with django-reversion".format(
model=model,
))
opts = model._meta.concrete_model._meta
version_options = _VersionOptions(
fields=tuple(
field_name
for field_name
in ([
field.name
for field
in opts.local_fields + opts.local_many_to_many
] if fields is None else fields)
if field_name not in exclude
),
follow=tuple(follow),
format=format,
for_concrete_model=for_concrete_model,
ignore_duplicates=ignore_duplicates,
use_natural_foreign_keys=use_natural_foreign_keys,
)
_registered_models[_get_registration_key(model)] = version_options
for sender, signal, signal_receiver in _get_senders_and_signals(model):
signal.connect(signal_receiver, sender=sender)
return model
if model is None:
return register
return register(model)
def _assert_registered(model):
if not is_registered(model):
raise RegistrationError("{model} has not been registered with django-reversion".format(
model=model,
))
def _get_options(model):
_assert_registered(model)
return _registered_models[_get_registration_key(model)]
def unregister(model):
_assert_registered(model)
del _registered_models[_get_registration_key(model)]
for sender, signal, signal_receiver in _get_senders_and_signals(model):
signal.disconnect(signal_receiver, sender=sender)
def _get_content_type(model, using):
from django.contrib.contenttypes.models import ContentType
version_options = _get_options(model)
return ContentType.objects.db_manager(using).get_for_model(
model,
for_concrete_model=version_options.for_concrete_model,
)
| true
| true
|
1c442c4ca43be27ff4775ef2715d2c6c62226955
| 3,211
|
py
|
Python
|
rfxcom/protocol/lighting2.py
|
d0ugal-archive/python-rfxcom
|
2eb87f85e5f5a04d00f32f25e0f010edfefbde0d
|
[
"BSD-3-Clause"
] | 3
|
2015-07-16T13:33:13.000Z
|
2017-09-17T13:11:42.000Z
|
rfxcom/protocol/lighting2.py
|
d0ugal/python-rfxcom
|
2eb87f85e5f5a04d00f32f25e0f010edfefbde0d
|
[
"BSD-3-Clause"
] | 6
|
2015-07-20T21:50:36.000Z
|
2017-06-05T06:06:25.000Z
|
rfxcom/protocol/lighting2.py
|
d0ugal-archive/python-rfxcom
|
2eb87f85e5f5a04d00f32f25e0f010edfefbde0d
|
[
"BSD-3-Clause"
] | 6
|
2015-07-21T07:47:25.000Z
|
2017-03-03T05:11:03.000Z
|
"""
Lighting 5
==========
"""
from rfxcom.protocol.base import BasePacketHandler
from rfxcom.protocol.rfxpacketutils import RfxPacketUtils
SUB_TYPE_COMMANDS = {
0x00: {
0x00: 'Off',
0x01: 'On',
0x02: 'Set level',
0x03: 'Group Off',
0x04: 'Group On',
0x05: 'Set Group Level',
},
0x01: {
0x00: "Off",
0x01: "On",
0x02: "Learn",
},
0x02: {
0x00: "Off",
0x01: "On",
0x02: "Group Off",
0x03: "Group On",
}
}
DIM_LEVEL_TO_PERCENT = {
0x00: 0,
0x01: 6,
0x02: 12,
0x03: 18,
0x04: 24,
0x05: 30,
0x06: 36,
0x07: 42,
0x08: 48,
0x09: 54,
0x0A: 60,
0x0B: 66,
0x0C: 72,
0x0D: 78,
0x0E: 84,
0x0F: 100
}
class Lighting2(BasePacketHandler):
"""The Lighting2 protocol is a 12 byte packet used by a number of lighting
systems. For example Lightwave devices use this protocol.
==== ====
Byte Meaning
==== ====
0 Packet Length, 0x0C (excludes this byte)
1 Packet Type, 0x11
2 Sub Type
3 Sequence Number
4 ID 1
5 ID 2
6 ID 3
7 ID 4
8 Unit Code
9 Command
10 Dim Level
11 RSSI and Filler
==== ====
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.PACKET_TYPES = {
0x11: "Lighting2 sensors"
}
self.PACKET_SUBTYPES = {
0x00: 'AC',
0x01: 'HomeEasy EU',
0x02: 'Anslut'
}
def parse(self, data):
"""Parse a 12 bytes packet in the Lighting2 format and return a
dictionary containing the data extracted. An example of a return value
would be:
.. code-block:: python
{
'id': "0x111F342",
'packet_length': 10,
'packet_type': 17,
'packet_type_name': 'Humidity sensors',
'sequence_number': 19,
'packet_subtype': 0,
'packet_subtype_name': "AC",
'unit_code': 10,
'command': 1,
'command_text': "Off",
'level': 7,
'signal_level': 9,
}
:param data: bytearray to be parsed
:type data: bytearray
:return: Data dictionary containing the parsed values
:rtype: dict
"""
self.validate_packet(data)
results = self.parse_header_part(data)
sub_type = results['packet_subtype']
id_ = self.dump_hex(data[4:8])
unit_code = data[8]
command = data[9]
command_text = SUB_TYPE_COMMANDS.get(sub_type, {}).get(command)
dim_level = DIM_LEVEL_TO_PERCENT.get(data[10], '--??--')
sensor_specific = {
'id': id_,
'unit_code': unit_code,
'command': command,
'command_text': command_text,
'dim_level': dim_level
}
results.update(RfxPacketUtils.parse_signal_upper(data[11]))
results.update(sensor_specific)
return results
| 22.612676
| 78
| 0.50109
|
from rfxcom.protocol.base import BasePacketHandler
from rfxcom.protocol.rfxpacketutils import RfxPacketUtils
SUB_TYPE_COMMANDS = {
0x00: {
0x00: 'Off',
0x01: 'On',
0x02: 'Set level',
0x03: 'Group Off',
0x04: 'Group On',
0x05: 'Set Group Level',
},
0x01: {
0x00: "Off",
0x01: "On",
0x02: "Learn",
},
0x02: {
0x00: "Off",
0x01: "On",
0x02: "Group Off",
0x03: "Group On",
}
}
DIM_LEVEL_TO_PERCENT = {
0x00: 0,
0x01: 6,
0x02: 12,
0x03: 18,
0x04: 24,
0x05: 30,
0x06: 36,
0x07: 42,
0x08: 48,
0x09: 54,
0x0A: 60,
0x0B: 66,
0x0C: 72,
0x0D: 78,
0x0E: 84,
0x0F: 100
}
class Lighting2(BasePacketHandler):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.PACKET_TYPES = {
0x11: "Lighting2 sensors"
}
self.PACKET_SUBTYPES = {
0x00: 'AC',
0x01: 'HomeEasy EU',
0x02: 'Anslut'
}
def parse(self, data):
self.validate_packet(data)
results = self.parse_header_part(data)
sub_type = results['packet_subtype']
id_ = self.dump_hex(data[4:8])
unit_code = data[8]
command = data[9]
command_text = SUB_TYPE_COMMANDS.get(sub_type, {}).get(command)
dim_level = DIM_LEVEL_TO_PERCENT.get(data[10], '--??--')
sensor_specific = {
'id': id_,
'unit_code': unit_code,
'command': command,
'command_text': command_text,
'dim_level': dim_level
}
results.update(RfxPacketUtils.parse_signal_upper(data[11]))
results.update(sensor_specific)
return results
| true
| true
|
1c442c763412ef5363e2973cbb581f3531ffb93c
| 32,879
|
py
|
Python
|
mmdet/datasets/pipelines/transforms.py
|
ktw361/Local-Mid-Propagation
|
0a99e82cccf8c35bc5f6989af2702203def4c7a4
|
[
"Apache-2.0"
] | 10
|
2020-08-13T17:51:20.000Z
|
2021-05-23T08:31:50.000Z
|
mmdet/datasets/pipelines/transforms.py
|
ktw361/Local-Mid-Propagation
|
0a99e82cccf8c35bc5f6989af2702203def4c7a4
|
[
"Apache-2.0"
] | null | null | null |
mmdet/datasets/pipelines/transforms.py
|
ktw361/Local-Mid-Propagation
|
0a99e82cccf8c35bc5f6989af2702203def4c7a4
|
[
"Apache-2.0"
] | 2
|
2020-09-07T08:33:43.000Z
|
2020-12-22T12:28:26.000Z
|
import inspect
import albumentations
import mmcv
import numpy as np
from albumentations import Compose
from imagecorruptions import corrupt
from numpy import random
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from ..registry import PIPELINES
@PIPELINES.register_module
class Resize(object):
"""Resize images & bbox & mask.
This transform resizes the input image to some scale. Bboxes and masks are
then resized with the same scale factor. If the input dict contains the key
"scale", then the scale in the input dict is used, otherwise the specified
scale in the init method is used.
`img_scale` can either be a tuple (single-scale) or a list of tuple
(multi-scale). There are 3 multiscale modes:
- `ratio_range` is not None: randomly sample a ratio from the ratio range
and multiply it with the image scale.
- `ratio_range` is None and `multiscale_mode` == "range": randomly sample a
scale from the a range.
- `ratio_range` is None and `multiscale_mode` == "value": randomly sample a
scale from multiple scales.
Args:
img_scale (tuple or list[tuple]): Images scales for resizing.
multiscale_mode (str): Either "range" or "value".
ratio_range (tuple[float]): (min_ratio, max_ratio)
keep_ratio (bool): Whether to keep the aspect ratio when resizing the
image.
"""
def __init__(self,
img_scale=None,
multiscale_mode='range',
ratio_range=None,
keep_ratio=True):
if img_scale is None:
self.img_scale = None
else:
if isinstance(img_scale, list):
self.img_scale = img_scale
else:
self.img_scale = [img_scale]
assert mmcv.is_list_of(self.img_scale, tuple)
if ratio_range is not None:
# mode 1: given a scale and a range of image ratio
assert len(self.img_scale) == 1
else:
# mode 2: given multiple scales or a range of scales
assert multiscale_mode in ['value', 'range']
self.multiscale_mode = multiscale_mode
self.ratio_range = ratio_range
self.keep_ratio = keep_ratio
@staticmethod
def random_select(img_scales):
assert mmcv.is_list_of(img_scales, tuple)
scale_idx = np.random.randint(len(img_scales))
img_scale = img_scales[scale_idx]
return img_scale, scale_idx
@staticmethod
def random_sample(img_scales):
assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
long_edge = np.random.randint(
min(img_scale_long),
max(img_scale_long) + 1)
short_edge = np.random.randint(
min(img_scale_short),
max(img_scale_short) + 1)
img_scale = (long_edge, short_edge)
return img_scale, None
@staticmethod
def random_sample_ratio(img_scale, ratio_range):
assert isinstance(img_scale, tuple) and len(img_scale) == 2
min_ratio, max_ratio = ratio_range
assert min_ratio <= max_ratio
ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
return scale, None
def _random_scale(self, results):
if self.ratio_range is not None:
scale, scale_idx = self.random_sample_ratio(
self.img_scale[0], self.ratio_range)
elif len(self.img_scale) == 1:
scale, scale_idx = self.img_scale[0], 0
elif self.multiscale_mode == 'range':
scale, scale_idx = self.random_sample(self.img_scale)
elif self.multiscale_mode == 'value':
scale, scale_idx = self.random_select(self.img_scale)
else:
raise NotImplementedError
results['scale'] = scale
results['scale_idx'] = scale_idx
def _resize_img(self, results):
if self.keep_ratio:
img, scale_factor = mmcv.imrescale(
results['img'], results['scale'], return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
results['img'], results['scale'], return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
results['img'] = img
results['img_shape'] = img.shape
results['pad_shape'] = img.shape # in case that there is no padding
results['scale_factor'] = scale_factor
results['keep_ratio'] = self.keep_ratio
def _resize_bboxes(self, results):
img_shape = results['img_shape']
for key in results.get('bbox_fields', []):
bboxes = results[key] * results['scale_factor']
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0] - 1)
results[key] = bboxes
def _resize_masks(self, results):
for key in results.get('mask_fields', []):
if results[key] is None:
continue
if self.keep_ratio:
masks = [
mmcv.imrescale(
mask, results['scale_factor'], interpolation='nearest')
for mask in results[key]
]
else:
mask_size = (results['img_shape'][1], results['img_shape'][0])
masks = [
mmcv.imresize(mask, mask_size, interpolation='nearest')
for mask in results[key]
]
results[key] = masks
def __call__(self, results):
if 'scale' not in results:
self._random_scale(results)
self._resize_img(results)
self._resize_bboxes(results)
self._resize_masks(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += ('(img_scale={}, multiscale_mode={}, ratio_range={}, '
'keep_ratio={})').format(self.img_scale,
self.multiscale_mode,
self.ratio_range,
self.keep_ratio)
return repr_str
@PIPELINES.register_module
class RandomFlip(object):
"""Flip the image & bbox & mask.
If the input dict contains the key "flip", then the flag will be used,
otherwise it will be randomly decided by a ratio specified in the init
method.
Args:
flip_ratio (float, optional): The flipping probability.
"""
def __init__(self, flip_ratio=None):
self.flip_ratio = flip_ratio
if flip_ratio is not None:
assert flip_ratio >= 0 and flip_ratio <= 1
def bbox_flip(self, bboxes, img_shape):
"""Flip bboxes horizontally.
Args:
bboxes(ndarray): shape (..., 4*k)
img_shape(tuple): (height, width)
"""
assert bboxes.shape[-1] % 4 == 0
w = img_shape[1]
flipped = bboxes.copy()
flipped[..., 0::4] = w - bboxes[..., 2::4] - 1
flipped[..., 2::4] = w - bboxes[..., 0::4] - 1
return flipped
def __call__(self, results):
if 'flip' not in results:
flip = True if np.random.rand() < self.flip_ratio else False
results['flip'] = flip
if results['flip']:
# flip image
results['img'] = mmcv.imflip(results['img'])
# flip bboxes
for key in results.get('bbox_fields', []):
results[key] = self.bbox_flip(results[key],
results['img_shape'])
# flip masks
for key in results.get('mask_fields', []):
results[key] = [mask[:, ::-1] for mask in results[key]]
return results
def __repr__(self):
return self.__class__.__name__ + '(flip_ratio={})'.format(
self.flip_ratio)
@PIPELINES.register_module
class Pad(object):
"""Pad the image & mask.
There are two padding modes: (1) pad to a fixed size and (2) pad to the
minimum size that is divisible by some number.
Args:
size (tuple, optional): Fixed padding size.
size_divisor (int, optional): The divisor of padded size.
pad_val (float, optional): Padding value, 0 by default.
"""
def __init__(self, size=None, size_divisor=None, pad_val=0):
self.size = size
self.size_divisor = size_divisor
self.pad_val = pad_val
# only one of size and size_divisor should be valid
assert size is not None or size_divisor is not None
assert size is None or size_divisor is None
def _pad_img(self, results):
if self.size is not None:
padded_img = mmcv.impad(results['img'], self.size)
elif self.size_divisor is not None:
padded_img = mmcv.impad_to_multiple(
results['img'], self.size_divisor, pad_val=self.pad_val)
results['img'] = padded_img
results['pad_shape'] = padded_img.shape
results['pad_fixed_size'] = self.size
results['pad_size_divisor'] = self.size_divisor
def _pad_masks(self, results):
pad_shape = results['pad_shape'][:2]
for key in results.get('mask_fields', []):
padded_masks = [
mmcv.impad(mask, pad_shape, pad_val=self.pad_val)
for mask in results[key]
]
results[key] = np.stack(padded_masks, axis=0)
def __call__(self, results):
self._pad_img(results)
self._pad_masks(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(size={}, size_divisor={}, pad_val={})'.format(
self.size, self.size_divisor, self.pad_val)
return repr_str
@PIPELINES.register_module
class Normalize(object):
"""Normalize the image.
Args:
mean (sequence): Mean values of 3 channels.
std (sequence): Std values of 3 channels.
to_rgb (bool): Whether to convert the image from BGR to RGB,
default is true.
"""
def __init__(self, mean, std, to_rgb=True):
self.mean = np.array(mean, dtype=np.float32)
self.std = np.array(std, dtype=np.float32)
self.to_rgb = to_rgb
def __call__(self, results):
results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
self.to_rgb)
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(mean={}, std={}, to_rgb={})'.format(
self.mean, self.std, self.to_rgb)
return repr_str
@PIPELINES.register_module
class RandomCrop(object):
"""Random crop the image & bboxes & masks.
Args:
crop_size (tuple): Expected size after cropping, (h, w).
"""
def __init__(self, crop_size):
self.crop_size = crop_size
def __call__(self, results):
img = results['img']
margin_h = max(img.shape[0] - self.crop_size[0], 0)
margin_w = max(img.shape[1] - self.crop_size[1], 0)
offset_h = np.random.randint(0, margin_h + 1)
offset_w = np.random.randint(0, margin_w + 1)
crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
# crop the image
img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
# crop bboxes accordingly and clip to the image boundary
for key in results.get('bbox_fields', []):
bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
dtype=np.float32)
bboxes = results[key] - bbox_offset
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0] - 1)
results[key] = bboxes
# filter out the gt bboxes that are completely cropped
if 'gt_bboxes' in results:
gt_bboxes = results['gt_bboxes']
valid_inds = (gt_bboxes[:, 2] > gt_bboxes[:, 0]) & (
gt_bboxes[:, 3] > gt_bboxes[:, 1])
# if no gt bbox remains after cropping, just skip this image
if not np.any(valid_inds):
return None
results['gt_bboxes'] = gt_bboxes[valid_inds, :]
if 'gt_labels' in results:
results['gt_labels'] = results['gt_labels'][valid_inds]
# filter and crop the masks
if 'gt_masks' in results:
valid_gt_masks = []
for i in np.where(valid_inds)[0]:
gt_mask = results['gt_masks'][i][crop_y1:crop_y2, crop_x1:
crop_x2]
valid_gt_masks.append(gt_mask)
results['gt_masks'] = valid_gt_masks
return results
def __repr__(self):
return self.__class__.__name__ + '(crop_size={})'.format(
self.crop_size)
@PIPELINES.register_module
class RandomRatioCrop(object):
"""Random crop the image & bboxes by a ratio.
Args:
min_crop_ratio (tuple): Expected ratio after cropping, (h, w).
max_crop_ratio (tuple of float).
"""
def __init__(self, min_crop_ratio, max_crop_ratio):
self.min_crop_ratio = min_crop_ratio
self.max_crop_ratio = max_crop_ratio
def __call__(self, results):
img = results['img']
crop_ratio = (random.uniform(self.min_crop_ratio[0], self.max_crop_ratio[0]),
random.uniform(self.min_crop_ratio[1], self.max_crop_ratio[1]))
crop_size = (int(crop_ratio[0] * img.shape[0]),
int(crop_ratio[1] * img.shape[1]))
margin_h = max(img.shape[0] - crop_size[0], 0)
margin_w = max(img.shape[1] - crop_size[1], 0)
offset_h = np.random.randint(0, margin_h + 1)
offset_w = np.random.randint(0, margin_w + 1)
crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]
# crop the image
img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
# crop bboxes accordingly and clip to the image boundary
for key in results.get('bbox_fields', []):
bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
dtype=np.float32)
bboxes = results[key] - bbox_offset
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0] - 1)
results[key] = bboxes
# filter out the gt bboxes that are completely cropped
if 'gt_bboxes' in results:
gt_bboxes = results['gt_bboxes']
valid_inds = (gt_bboxes[:, 2] > gt_bboxes[:, 0]) & (
gt_bboxes[:, 3] > gt_bboxes[:, 1])
# if no gt bbox remains after cropping, just skip this image
if not np.any(valid_inds):
return None
results['gt_bboxes'] = gt_bboxes[valid_inds, :]
if 'gt_labels' in results:
results['gt_labels'] = results['gt_labels'][valid_inds]
# filter and crop the masks
if 'gt_masks' in results:
valid_gt_masks = []
for i in valid_inds:
gt_mask = results['gt_masks'][i][crop_y1:crop_y2, crop_x1:
crop_x2]
valid_gt_masks.append(gt_mask)
results['gt_masks'] = valid_gt_masks
return results
def __repr__(self):
return self.__class__.__name__ + \
f'(min_crop_ratio={self.min_crop_ratio}, max_crop_ratio={self.max_crop_ratio})'
@PIPELINES.register_module
class SegResizeFlipPadRescale(object):
"""A sequential transforms to semantic segmentation maps.
The same pipeline as input images is applied to the semantic segmentation
map, and finally rescale it by some scale factor. The transforms include:
1. resize
2. flip
3. pad
4. rescale (so that the final size can be different from the image size)
Args:
scale_factor (float): The scale factor of the final output.
"""
def __init__(self, scale_factor=1):
self.scale_factor = scale_factor
def __call__(self, results):
if results['keep_ratio']:
gt_seg = mmcv.imrescale(
results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
else:
gt_seg = mmcv.imresize(
results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
if results['flip']:
gt_seg = mmcv.imflip(gt_seg)
if gt_seg.shape != results['pad_shape']:
gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
if self.scale_factor != 1:
gt_seg = mmcv.imrescale(
gt_seg, self.scale_factor, interpolation='nearest')
results['gt_semantic_seg'] = gt_seg
return results
def __repr__(self):
return self.__class__.__name__ + '(scale_factor={})'.format(
self.scale_factor)
@PIPELINES.register_module
class PhotoMetricDistortion(object):
"""Apply photometric distortion to image sequentially, every transformation
is applied with a probability of 0.5. The position of random contrast is in
second or second to last.
1. random brightness
2. random contrast (mode 0)
3. convert color from BGR to HSV
4. random saturation
5. random hue
6. convert color from HSV to BGR
7. random contrast (mode 1)
8. randomly swap channels
Args:
brightness_delta (int): delta of brightness.
contrast_range (tuple): range of contrast.
saturation_range (tuple): range of saturation.
hue_delta (int): delta of hue.
"""
def __init__(self,
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18):
self.brightness_delta = brightness_delta
self.contrast_lower, self.contrast_upper = contrast_range
self.saturation_lower, self.saturation_upper = saturation_range
self.hue_delta = hue_delta
def __call__(self, results):
img = results['img']
# random brightness
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
# mode == 0 --> do random contrast first
# mode == 1 --> do random contrast last
mode = random.randint(2)
if mode == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
# convert color from BGR to HSV
img = mmcv.bgr2hsv(img)
# random saturation
if random.randint(2):
img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
# random hue
if random.randint(2):
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
# convert color from HSV to BGR
img = mmcv.hsv2bgr(img)
# random contrast
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
# randomly swap channels
if random.randint(2):
img = img[..., random.permutation(3)]
results['img'] = img
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += ('(brightness_delta={}, contrast_range={}, '
'saturation_range={}, hue_delta={})').format(
self.brightness_delta, self.contrast_range,
self.saturation_range, self.hue_delta)
return repr_str
@PIPELINES.register_module
class Expand(object):
"""Random expand the image & bboxes.
Randomly place the original image on a canvas of 'ratio' x original image
size filled with mean values. The ratio is in the range of ratio_range.
Args:
mean (tuple): mean value of dataset.
to_rgb (bool): if need to convert the order of mean to align with RGB.
ratio_range (tuple): range of expand ratio.
"""
def __init__(self,
mean=(0, 0, 0),
to_rgb=True,
ratio_range=(1, 4),
seg_ignore_label=None):
self.to_rgb = to_rgb
self.ratio_range = ratio_range
if to_rgb:
self.mean = mean[::-1]
else:
self.mean = mean
self.min_ratio, self.max_ratio = ratio_range
self.seg_ignore_label = seg_ignore_label
def __call__(self, results):
if random.randint(2):
return results
img, boxes = [results[k] for k in ('img', 'gt_bboxes')]
h, w, c = img.shape
ratio = random.uniform(self.min_ratio, self.max_ratio)
expand_img = np.full((int(h * ratio), int(w * ratio), c),
self.mean).astype(img.dtype)
left = int(random.uniform(0, w * ratio - w))
top = int(random.uniform(0, h * ratio - h))
expand_img[top:top + h, left:left + w] = img
boxes = boxes + np.tile((left, top), 2).astype(boxes.dtype)
results['img'] = expand_img
results['gt_bboxes'] = boxes
if 'gt_masks' in results:
expand_gt_masks = []
for mask in results['gt_masks']:
expand_mask = np.full((int(h * ratio), int(w * ratio)),
0).astype(mask.dtype)
expand_mask[top:top + h, left:left + w] = mask
expand_gt_masks.append(expand_mask)
results['gt_masks'] = expand_gt_masks
# not tested
if 'gt_semantic_seg' in results:
assert self.seg_ignore_label is not None
gt_seg = results['gt_semantic_seg']
expand_gt_seg = np.full((int(h * ratio), int(w * ratio)),
self.seg_ignore_label).astype(gt_seg.dtype)
expand_gt_seg[top:top + h, left:left + w] = gt_seg
results['gt_semantic_seg'] = expand_gt_seg
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(mean={}, to_rgb={}, ratio_range={}, ' \
'seg_ignore_label={})'.format(
self.mean, self.to_rgb, self.ratio_range,
self.seg_ignore_label)
return repr_str
@PIPELINES.register_module
class MinIoURandomCrop(object):
"""Random crop the image & bboxes, the cropped patches have minimum IoU
requirement with original image & bboxes, the IoU threshold is randomly
selected from min_ious.
Args:
min_ious (tuple): minimum IoU threshold
crop_size (tuple): Expected size after cropping, (h, w).
"""
def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3):
# 1: return ori img
self.sample_mode = (1, *min_ious, 0)
self.min_crop_size = min_crop_size
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
# Force return origin for no annotation.
if len(boxes) == 0:
mode = 1
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = ((center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) *
(center[:, 0] < patch[2]) * (center[:, 1] < patch[3]))
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
if 'gt_masks' in results:
valid_masks = [
results['gt_masks'][i] for i in range(len(mask))
if mask[i]
]
results['gt_masks'] = [
gt_mask[patch[1]:patch[3], patch[0]:patch[2]]
for gt_mask in valid_masks
]
# not tested
if 'gt_semantic_seg' in results:
results['gt_semantic_seg'] = results['gt_semantic_seg'][
patch[1]:patch[3], patch[0]:patch[2]]
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(min_ious={}, min_crop_size={})'.format(
self.min_ious, self.min_crop_size)
return repr_str
@PIPELINES.register_module
class Corrupt(object):
def __init__(self, corruption, severity=1):
self.corruption = corruption
self.severity = severity
def __call__(self, results):
results['img'] = corrupt(
results['img'].astype(np.uint8),
corruption_name=self.corruption,
severity=self.severity)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(corruption={}, severity={})'.format(
self.corruption, self.severity)
return repr_str
@PIPELINES.register_module
class Albu(object):
def __init__(self,
transforms,
bbox_params=None,
keymap=None,
update_pad_shape=False,
skip_img_without_anno=False):
"""
Adds custom transformations from Albumentations lib.
Please, visit `https://albumentations.readthedocs.io`
to get more information.
transforms (list): list of albu transformations
bbox_params (dict): bbox_params for albumentation `Compose`
keymap (dict): contains {'input key':'albumentation-style key'}
skip_img_without_anno (bool): whether to skip the image
if no ann left after aug
"""
self.transforms = transforms
self.filter_lost_elements = False
self.update_pad_shape = update_pad_shape
self.skip_img_without_anno = skip_img_without_anno
# A simple workaround to remove masks without boxes
if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
and 'filter_lost_elements' in bbox_params):
self.filter_lost_elements = True
self.origin_label_fields = bbox_params['label_fields']
bbox_params['label_fields'] = ['idx_mapper']
del bbox_params['filter_lost_elements']
self.bbox_params = (
self.albu_builder(bbox_params) if bbox_params else None)
self.aug = Compose([self.albu_builder(t) for t in self.transforms],
bbox_params=self.bbox_params)
if not keymap:
self.keymap_to_albu = {
'img': 'image',
'gt_masks': 'masks',
'gt_bboxes': 'bboxes'
}
else:
self.keymap_to_albu = keymap
self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
def albu_builder(self, cfg):
"""Import a module from albumentations.
Inherits some of `build_from_cfg` logic.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
Returns:
obj: The constructed object.
"""
assert isinstance(cfg, dict) and "type" in cfg
args = cfg.copy()
obj_type = args.pop("type")
if mmcv.is_str(obj_type):
obj_cls = getattr(albumentations, obj_type)
elif inspect.isclass(obj_type):
obj_cls = obj_type
else:
raise TypeError(
'type must be a str or valid type, but got {}'.format(
type(obj_type)))
if 'transforms' in args:
args['transforms'] = [
self.albu_builder(transform)
for transform in args['transforms']
]
return obj_cls(**args)
@staticmethod
def mapper(d, keymap):
"""
Dictionary mapper.
Renames keys according to keymap provided.
Args:
d (dict): old dict
keymap (dict): {'old_key':'new_key'}
Returns:
dict: new dict.
"""
updated_dict = {}
for k, v in zip(d.keys(), d.values()):
new_k = keymap.get(k, k)
updated_dict[new_k] = d[k]
return updated_dict
def __call__(self, results):
# dict to albumentations format
results = self.mapper(results, self.keymap_to_albu)
if 'bboxes' in results:
# to list of boxes
if isinstance(results['bboxes'], np.ndarray):
results['bboxes'] = [x for x in results['bboxes']]
# add pseudo-field for filtration
if self.filter_lost_elements:
results['idx_mapper'] = np.arange(len(results['bboxes']))
results = self.aug(**results)
if 'bboxes' in results:
if isinstance(results['bboxes'], list):
results['bboxes'] = np.array(
results['bboxes'], dtype=np.float32)
# filter label_fields
if self.filter_lost_elements:
results['idx_mapper'] = np.arange(len(results['bboxes']))
for label in self.origin_label_fields:
results[label] = np.array(
[results[label][i] for i in results['idx_mapper']])
if 'masks' in results:
results['masks'] = [
results['masks'][i] for i in results['idx_mapper']
]
if (not len(results['idx_mapper'])
and self.skip_img_without_anno):
return None
if 'gt_labels' in results:
if isinstance(results['gt_labels'], list):
results['gt_labels'] = np.array(results['gt_labels'])
# back to the original format
results = self.mapper(results, self.keymap_back)
# update final shape
if self.update_pad_shape:
results['pad_shape'] = results['img'].shape
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(transformations={})'.format(self.transformations)
return repr_str
| 36.370575
| 94
| 0.559962
|
import inspect
import albumentations
import mmcv
import numpy as np
from albumentations import Compose
from imagecorruptions import corrupt
from numpy import random
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from ..registry import PIPELINES
@PIPELINES.register_module
class Resize(object):
def __init__(self,
img_scale=None,
multiscale_mode='range',
ratio_range=None,
keep_ratio=True):
if img_scale is None:
self.img_scale = None
else:
if isinstance(img_scale, list):
self.img_scale = img_scale
else:
self.img_scale = [img_scale]
assert mmcv.is_list_of(self.img_scale, tuple)
if ratio_range is not None:
assert len(self.img_scale) == 1
else:
assert multiscale_mode in ['value', 'range']
self.multiscale_mode = multiscale_mode
self.ratio_range = ratio_range
self.keep_ratio = keep_ratio
@staticmethod
def random_select(img_scales):
assert mmcv.is_list_of(img_scales, tuple)
scale_idx = np.random.randint(len(img_scales))
img_scale = img_scales[scale_idx]
return img_scale, scale_idx
@staticmethod
def random_sample(img_scales):
assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
long_edge = np.random.randint(
min(img_scale_long),
max(img_scale_long) + 1)
short_edge = np.random.randint(
min(img_scale_short),
max(img_scale_short) + 1)
img_scale = (long_edge, short_edge)
return img_scale, None
@staticmethod
def random_sample_ratio(img_scale, ratio_range):
assert isinstance(img_scale, tuple) and len(img_scale) == 2
min_ratio, max_ratio = ratio_range
assert min_ratio <= max_ratio
ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
return scale, None
def _random_scale(self, results):
if self.ratio_range is not None:
scale, scale_idx = self.random_sample_ratio(
self.img_scale[0], self.ratio_range)
elif len(self.img_scale) == 1:
scale, scale_idx = self.img_scale[0], 0
elif self.multiscale_mode == 'range':
scale, scale_idx = self.random_sample(self.img_scale)
elif self.multiscale_mode == 'value':
scale, scale_idx = self.random_select(self.img_scale)
else:
raise NotImplementedError
results['scale'] = scale
results['scale_idx'] = scale_idx
def _resize_img(self, results):
if self.keep_ratio:
img, scale_factor = mmcv.imrescale(
results['img'], results['scale'], return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
results['img'], results['scale'], return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
results['img'] = img
results['img_shape'] = img.shape
results['pad_shape'] = img.shape
results['scale_factor'] = scale_factor
results['keep_ratio'] = self.keep_ratio
def _resize_bboxes(self, results):
img_shape = results['img_shape']
for key in results.get('bbox_fields', []):
bboxes = results[key] * results['scale_factor']
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0] - 1)
results[key] = bboxes
def _resize_masks(self, results):
for key in results.get('mask_fields', []):
if results[key] is None:
continue
if self.keep_ratio:
masks = [
mmcv.imrescale(
mask, results['scale_factor'], interpolation='nearest')
for mask in results[key]
]
else:
mask_size = (results['img_shape'][1], results['img_shape'][0])
masks = [
mmcv.imresize(mask, mask_size, interpolation='nearest')
for mask in results[key]
]
results[key] = masks
def __call__(self, results):
if 'scale' not in results:
self._random_scale(results)
self._resize_img(results)
self._resize_bboxes(results)
self._resize_masks(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += ('(img_scale={}, multiscale_mode={}, ratio_range={}, '
'keep_ratio={})').format(self.img_scale,
self.multiscale_mode,
self.ratio_range,
self.keep_ratio)
return repr_str
@PIPELINES.register_module
class RandomFlip(object):
def __init__(self, flip_ratio=None):
self.flip_ratio = flip_ratio
if flip_ratio is not None:
assert flip_ratio >= 0 and flip_ratio <= 1
def bbox_flip(self, bboxes, img_shape):
assert bboxes.shape[-1] % 4 == 0
w = img_shape[1]
flipped = bboxes.copy()
flipped[..., 0::4] = w - bboxes[..., 2::4] - 1
flipped[..., 2::4] = w - bboxes[..., 0::4] - 1
return flipped
def __call__(self, results):
if 'flip' not in results:
flip = True if np.random.rand() < self.flip_ratio else False
results['flip'] = flip
if results['flip']:
results['img'] = mmcv.imflip(results['img'])
for key in results.get('bbox_fields', []):
results[key] = self.bbox_flip(results[key],
results['img_shape'])
for key in results.get('mask_fields', []):
results[key] = [mask[:, ::-1] for mask in results[key]]
return results
def __repr__(self):
return self.__class__.__name__ + '(flip_ratio={})'.format(
self.flip_ratio)
@PIPELINES.register_module
class Pad(object):
def __init__(self, size=None, size_divisor=None, pad_val=0):
self.size = size
self.size_divisor = size_divisor
self.pad_val = pad_val
assert size is not None or size_divisor is not None
assert size is None or size_divisor is None
def _pad_img(self, results):
if self.size is not None:
padded_img = mmcv.impad(results['img'], self.size)
elif self.size_divisor is not None:
padded_img = mmcv.impad_to_multiple(
results['img'], self.size_divisor, pad_val=self.pad_val)
results['img'] = padded_img
results['pad_shape'] = padded_img.shape
results['pad_fixed_size'] = self.size
results['pad_size_divisor'] = self.size_divisor
def _pad_masks(self, results):
pad_shape = results['pad_shape'][:2]
for key in results.get('mask_fields', []):
padded_masks = [
mmcv.impad(mask, pad_shape, pad_val=self.pad_val)
for mask in results[key]
]
results[key] = np.stack(padded_masks, axis=0)
def __call__(self, results):
self._pad_img(results)
self._pad_masks(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(size={}, size_divisor={}, pad_val={})'.format(
self.size, self.size_divisor, self.pad_val)
return repr_str
@PIPELINES.register_module
class Normalize(object):
def __init__(self, mean, std, to_rgb=True):
self.mean = np.array(mean, dtype=np.float32)
self.std = np.array(std, dtype=np.float32)
self.to_rgb = to_rgb
def __call__(self, results):
results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
self.to_rgb)
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(mean={}, std={}, to_rgb={})'.format(
self.mean, self.std, self.to_rgb)
return repr_str
@PIPELINES.register_module
class RandomCrop(object):
def __init__(self, crop_size):
self.crop_size = crop_size
def __call__(self, results):
img = results['img']
margin_h = max(img.shape[0] - self.crop_size[0], 0)
margin_w = max(img.shape[1] - self.crop_size[1], 0)
offset_h = np.random.randint(0, margin_h + 1)
offset_w = np.random.randint(0, margin_w + 1)
crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
for key in results.get('bbox_fields', []):
bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
dtype=np.float32)
bboxes = results[key] - bbox_offset
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0] - 1)
results[key] = bboxes
if 'gt_bboxes' in results:
gt_bboxes = results['gt_bboxes']
valid_inds = (gt_bboxes[:, 2] > gt_bboxes[:, 0]) & (
gt_bboxes[:, 3] > gt_bboxes[:, 1])
if not np.any(valid_inds):
return None
results['gt_bboxes'] = gt_bboxes[valid_inds, :]
if 'gt_labels' in results:
results['gt_labels'] = results['gt_labels'][valid_inds]
if 'gt_masks' in results:
valid_gt_masks = []
for i in np.where(valid_inds)[0]:
gt_mask = results['gt_masks'][i][crop_y1:crop_y2, crop_x1:
crop_x2]
valid_gt_masks.append(gt_mask)
results['gt_masks'] = valid_gt_masks
return results
def __repr__(self):
return self.__class__.__name__ + '(crop_size={})'.format(
self.crop_size)
@PIPELINES.register_module
class RandomRatioCrop(object):
def __init__(self, min_crop_ratio, max_crop_ratio):
self.min_crop_ratio = min_crop_ratio
self.max_crop_ratio = max_crop_ratio
def __call__(self, results):
img = results['img']
crop_ratio = (random.uniform(self.min_crop_ratio[0], self.max_crop_ratio[0]),
random.uniform(self.min_crop_ratio[1], self.max_crop_ratio[1]))
crop_size = (int(crop_ratio[0] * img.shape[0]),
int(crop_ratio[1] * img.shape[1]))
margin_h = max(img.shape[0] - crop_size[0], 0)
margin_w = max(img.shape[1] - crop_size[1], 0)
offset_h = np.random.randint(0, margin_h + 1)
offset_w = np.random.randint(0, margin_w + 1)
crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]
img = img[crop_y1:crop_y2, crop_x1:crop_x2, :]
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
for key in results.get('bbox_fields', []):
bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
dtype=np.float32)
bboxes = results[key] - bbox_offset
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0] - 1)
results[key] = bboxes
if 'gt_bboxes' in results:
gt_bboxes = results['gt_bboxes']
valid_inds = (gt_bboxes[:, 2] > gt_bboxes[:, 0]) & (
gt_bboxes[:, 3] > gt_bboxes[:, 1])
if not np.any(valid_inds):
return None
results['gt_bboxes'] = gt_bboxes[valid_inds, :]
if 'gt_labels' in results:
results['gt_labels'] = results['gt_labels'][valid_inds]
if 'gt_masks' in results:
valid_gt_masks = []
for i in valid_inds:
gt_mask = results['gt_masks'][i][crop_y1:crop_y2, crop_x1:
crop_x2]
valid_gt_masks.append(gt_mask)
results['gt_masks'] = valid_gt_masks
return results
def __repr__(self):
return self.__class__.__name__ + \
f'(min_crop_ratio={self.min_crop_ratio}, max_crop_ratio={self.max_crop_ratio})'
@PIPELINES.register_module
class SegResizeFlipPadRescale(object):
def __init__(self, scale_factor=1):
self.scale_factor = scale_factor
def __call__(self, results):
if results['keep_ratio']:
gt_seg = mmcv.imrescale(
results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
else:
gt_seg = mmcv.imresize(
results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
if results['flip']:
gt_seg = mmcv.imflip(gt_seg)
if gt_seg.shape != results['pad_shape']:
gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
if self.scale_factor != 1:
gt_seg = mmcv.imrescale(
gt_seg, self.scale_factor, interpolation='nearest')
results['gt_semantic_seg'] = gt_seg
return results
def __repr__(self):
return self.__class__.__name__ + '(scale_factor={})'.format(
self.scale_factor)
@PIPELINES.register_module
class PhotoMetricDistortion(object):
def __init__(self,
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18):
self.brightness_delta = brightness_delta
self.contrast_lower, self.contrast_upper = contrast_range
self.saturation_lower, self.saturation_upper = saturation_range
self.hue_delta = hue_delta
def __call__(self, results):
img = results['img']
if random.randint(2):
delta = random.uniform(-self.brightness_delta,
self.brightness_delta)
img += delta
mode = random.randint(2)
if mode == 1:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
img = mmcv.bgr2hsv(img)
if random.randint(2):
img[..., 1] *= random.uniform(self.saturation_lower,
self.saturation_upper)
if random.randint(2):
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
img[..., 0][img[..., 0] > 360] -= 360
img[..., 0][img[..., 0] < 0] += 360
img = mmcv.hsv2bgr(img)
if mode == 0:
if random.randint(2):
alpha = random.uniform(self.contrast_lower,
self.contrast_upper)
img *= alpha
if random.randint(2):
img = img[..., random.permutation(3)]
results['img'] = img
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += ('(brightness_delta={}, contrast_range={}, '
'saturation_range={}, hue_delta={})').format(
self.brightness_delta, self.contrast_range,
self.saturation_range, self.hue_delta)
return repr_str
@PIPELINES.register_module
class Expand(object):
def __init__(self,
mean=(0, 0, 0),
to_rgb=True,
ratio_range=(1, 4),
seg_ignore_label=None):
self.to_rgb = to_rgb
self.ratio_range = ratio_range
if to_rgb:
self.mean = mean[::-1]
else:
self.mean = mean
self.min_ratio, self.max_ratio = ratio_range
self.seg_ignore_label = seg_ignore_label
def __call__(self, results):
if random.randint(2):
return results
img, boxes = [results[k] for k in ('img', 'gt_bboxes')]
h, w, c = img.shape
ratio = random.uniform(self.min_ratio, self.max_ratio)
expand_img = np.full((int(h * ratio), int(w * ratio), c),
self.mean).astype(img.dtype)
left = int(random.uniform(0, w * ratio - w))
top = int(random.uniform(0, h * ratio - h))
expand_img[top:top + h, left:left + w] = img
boxes = boxes + np.tile((left, top), 2).astype(boxes.dtype)
results['img'] = expand_img
results['gt_bboxes'] = boxes
if 'gt_masks' in results:
expand_gt_masks = []
for mask in results['gt_masks']:
expand_mask = np.full((int(h * ratio), int(w * ratio)),
0).astype(mask.dtype)
expand_mask[top:top + h, left:left + w] = mask
expand_gt_masks.append(expand_mask)
results['gt_masks'] = expand_gt_masks
if 'gt_semantic_seg' in results:
assert self.seg_ignore_label is not None
gt_seg = results['gt_semantic_seg']
expand_gt_seg = np.full((int(h * ratio), int(w * ratio)),
self.seg_ignore_label).astype(gt_seg.dtype)
expand_gt_seg[top:top + h, left:left + w] = gt_seg
results['gt_semantic_seg'] = expand_gt_seg
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(mean={}, to_rgb={}, ratio_range={}, ' \
'seg_ignore_label={})'.format(
self.mean, self.to_rgb, self.ratio_range,
self.seg_ignore_label)
return repr_str
@PIPELINES.register_module
class MinIoURandomCrop(object):
def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3):
self.sample_mode = (1, *min_ious, 0)
self.min_crop_size = min_crop_size
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if len(boxes) == 0:
mode = 1
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = ((center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) *
(center[:, 0] < patch[2]) * (center[:, 1] < patch[3]))
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
if 'gt_masks' in results:
valid_masks = [
results['gt_masks'][i] for i in range(len(mask))
if mask[i]
]
results['gt_masks'] = [
gt_mask[patch[1]:patch[3], patch[0]:patch[2]]
for gt_mask in valid_masks
]
if 'gt_semantic_seg' in results:
results['gt_semantic_seg'] = results['gt_semantic_seg'][
patch[1]:patch[3], patch[0]:patch[2]]
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(min_ious={}, min_crop_size={})'.format(
self.min_ious, self.min_crop_size)
return repr_str
@PIPELINES.register_module
class Corrupt(object):
def __init__(self, corruption, severity=1):
self.corruption = corruption
self.severity = severity
def __call__(self, results):
results['img'] = corrupt(
results['img'].astype(np.uint8),
corruption_name=self.corruption,
severity=self.severity)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(corruption={}, severity={})'.format(
self.corruption, self.severity)
return repr_str
@PIPELINES.register_module
class Albu(object):
def __init__(self,
transforms,
bbox_params=None,
keymap=None,
update_pad_shape=False,
skip_img_without_anno=False):
self.transforms = transforms
self.filter_lost_elements = False
self.update_pad_shape = update_pad_shape
self.skip_img_without_anno = skip_img_without_anno
if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
and 'filter_lost_elements' in bbox_params):
self.filter_lost_elements = True
self.origin_label_fields = bbox_params['label_fields']
bbox_params['label_fields'] = ['idx_mapper']
del bbox_params['filter_lost_elements']
self.bbox_params = (
self.albu_builder(bbox_params) if bbox_params else None)
self.aug = Compose([self.albu_builder(t) for t in self.transforms],
bbox_params=self.bbox_params)
if not keymap:
self.keymap_to_albu = {
'img': 'image',
'gt_masks': 'masks',
'gt_bboxes': 'bboxes'
}
else:
self.keymap_to_albu = keymap
self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
def albu_builder(self, cfg):
assert isinstance(cfg, dict) and "type" in cfg
args = cfg.copy()
obj_type = args.pop("type")
if mmcv.is_str(obj_type):
obj_cls = getattr(albumentations, obj_type)
elif inspect.isclass(obj_type):
obj_cls = obj_type
else:
raise TypeError(
'type must be a str or valid type, but got {}'.format(
type(obj_type)))
if 'transforms' in args:
args['transforms'] = [
self.albu_builder(transform)
for transform in args['transforms']
]
return obj_cls(**args)
@staticmethod
def mapper(d, keymap):
updated_dict = {}
for k, v in zip(d.keys(), d.values()):
new_k = keymap.get(k, k)
updated_dict[new_k] = d[k]
return updated_dict
def __call__(self, results):
results = self.mapper(results, self.keymap_to_albu)
if 'bboxes' in results:
if isinstance(results['bboxes'], np.ndarray):
results['bboxes'] = [x for x in results['bboxes']]
if self.filter_lost_elements:
results['idx_mapper'] = np.arange(len(results['bboxes']))
results = self.aug(**results)
if 'bboxes' in results:
if isinstance(results['bboxes'], list):
results['bboxes'] = np.array(
results['bboxes'], dtype=np.float32)
if self.filter_lost_elements:
results['idx_mapper'] = np.arange(len(results['bboxes']))
for label in self.origin_label_fields:
results[label] = np.array(
[results[label][i] for i in results['idx_mapper']])
if 'masks' in results:
results['masks'] = [
results['masks'][i] for i in results['idx_mapper']
]
if (not len(results['idx_mapper'])
and self.skip_img_without_anno):
return None
if 'gt_labels' in results:
if isinstance(results['gt_labels'], list):
results['gt_labels'] = np.array(results['gt_labels'])
results = self.mapper(results, self.keymap_back)
if self.update_pad_shape:
results['pad_shape'] = results['img'].shape
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += '(transformations={})'.format(self.transformations)
return repr_str
| true
| true
|
1c442d2fada5240fc6d9343528a6ef23452beded
| 7,694
|
py
|
Python
|
nova/api/openstack/compute/contrib/baremetal_nodes.py
|
bopopescu/nova-39
|
36c7a819582b838b7bbab11d55ca3d991a587405
|
[
"Apache-2.0"
] | 1
|
2021-04-08T10:13:03.000Z
|
2021-04-08T10:13:03.000Z
|
nova/api/openstack/compute/contrib/baremetal_nodes.py
|
bopopescu/nova-39
|
36c7a819582b838b7bbab11d55ca3d991a587405
|
[
"Apache-2.0"
] | null | null | null |
nova/api/openstack/compute/contrib/baremetal_nodes.py
|
bopopescu/nova-39
|
36c7a819582b838b7bbab11d55ca3d991a587405
|
[
"Apache-2.0"
] | 1
|
2020-07-24T09:39:47.000Z
|
2020-07-24T09:39:47.000Z
|
# Copyright (c) 2013 NTT DOCOMO, INC.
# 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.
"""The bare-metal admin extension."""
import webob
from nova.api.openstack import extensions
from nova.api.openstack import wsgi
from nova.api.openstack import xmlutil
from nova import exception
from nova.virt.baremetal import db
authorize = extensions.extension_authorizer('compute', 'baremetal_nodes')
node_fields = ['id', 'cpus', 'local_gb', 'memory_mb', 'pm_address',
'pm_user',
'service_host', 'terminal_port', 'instance_uuid',
]
interface_fields = ['id', 'address', 'datapath_id', 'port_no']
def _node_dict(node_ref):
d = {}
for f in node_fields:
d[f] = node_ref.get(f)
return d
def _interface_dict(interface_ref):
d = {}
for f in interface_fields:
d[f] = interface_ref.get(f)
return d
def _make_node_elem(elem):
for f in node_fields:
elem.set(f)
def _make_interface_elem(elem):
for f in interface_fields:
elem.set(f)
class NodeTemplate(xmlutil.TemplateBuilder):
def construct(self):
node_elem = xmlutil.TemplateElement('node', selector='node')
_make_node_elem(node_elem)
ifs_elem = xmlutil.TemplateElement('interfaces')
if_elem = xmlutil.SubTemplateElement(ifs_elem, 'interface',
selector='interfaces')
_make_interface_elem(if_elem)
node_elem.append(ifs_elem)
return xmlutil.MasterTemplate(node_elem, 1)
class NodesTemplate(xmlutil.TemplateBuilder):
def construct(self):
root = xmlutil.TemplateElement('nodes')
node_elem = xmlutil.SubTemplateElement(root, 'node', selector='nodes')
_make_node_elem(node_elem)
ifs_elem = xmlutil.TemplateElement('interfaces')
if_elem = xmlutil.SubTemplateElement(ifs_elem, 'interface',
selector='interfaces')
_make_interface_elem(if_elem)
node_elem.append(ifs_elem)
return xmlutil.MasterTemplate(root, 1)
class InterfaceTemplate(xmlutil.TemplateBuilder):
def construct(self):
root = xmlutil.TemplateElement('interface', selector='interface')
_make_interface_elem(root)
return xmlutil.MasterTemplate(root, 1)
class BareMetalNodeController(wsgi.Controller):
"""The Bare-Metal Node API controller for the OpenStack API."""
@wsgi.serializers(xml=NodesTemplate)
def index(self, req):
context = req.environ['nova.context']
authorize(context)
nodes_from_db = db.bm_node_get_all(context)
nodes = []
for node_from_db in nodes_from_db:
try:
ifs = db.bm_interface_get_all_by_bm_node_id(
context, node_from_db['id'])
except exception.NodeNotFound:
ifs = []
node = _node_dict(node_from_db)
node['interfaces'] = [_interface_dict(i) for i in ifs]
nodes.append(node)
return {'nodes': nodes}
@wsgi.serializers(xml=NodeTemplate)
def show(self, req, id):
context = req.environ['nova.context']
authorize(context)
try:
node = db.bm_node_get(context, id)
except exception.NodeNotFound:
raise webob.exc.HTTPNotFound
try:
ifs = db.bm_interface_get_all_by_bm_node_id(context, id)
except exception.NodeNotFound:
ifs = []
node = _node_dict(node)
node['interfaces'] = [_interface_dict(i) for i in ifs]
return {'node': node}
@wsgi.serializers(xml=NodeTemplate)
def create(self, req, body):
context = req.environ['nova.context']
authorize(context)
values = body['node'].copy()
prov_mac_address = values.pop('prov_mac_address', None)
node = db.bm_node_create(context, values)
node = _node_dict(node)
if prov_mac_address:
if_id = db.bm_interface_create(context,
bm_node_id=node['id'],
address=prov_mac_address,
datapath_id=None,
port_no=None)
if_ref = db.bm_interface_get(context, if_id)
node['interfaces'] = [_interface_dict(if_ref)]
else:
node['interfaces'] = []
return {'node': node}
def delete(self, req, id):
context = req.environ['nova.context']
authorize(context)
try:
db.bm_node_destroy(context, id)
except exception.NodeNotFound:
raise webob.exc.HTTPNotFound
return webob.Response(status_int=202)
def _check_node_exists(self, context, node_id):
try:
db.bm_node_get(context, node_id)
except exception.NodeNotFound:
raise webob.exc.HTTPNotFound
@wsgi.serializers(xml=InterfaceTemplate)
@wsgi.action('add_interface')
def _add_interface(self, req, id, body):
context = req.environ['nova.context']
authorize(context)
self._check_node_exists(context, id)
body = body['add_interface']
address = body['address']
datapath_id = body.get('datapath_id')
port_no = body.get('port_no')
if_id = db.bm_interface_create(context,
bm_node_id=id,
address=address,
datapath_id=datapath_id,
port_no=port_no)
if_ref = db.bm_interface_get(context, if_id)
return {'interface': _interface_dict(if_ref)}
@wsgi.response(202)
@wsgi.action('remove_interface')
def _remove_interface(self, req, id, body):
context = req.environ['nova.context']
authorize(context)
self._check_node_exists(context, id)
body = body['remove_interface']
if_id = body.get('id')
address = body.get('address')
if not if_id and not address:
raise webob.exc.HTTPBadRequest(
explanation=_("Must specify id or address"))
ifs = db.bm_interface_get_all_by_bm_node_id(context, id)
for i in ifs:
if if_id and if_id != i['id']:
continue
if address and address != i['address']:
continue
db.bm_interface_destroy(context, i['id'])
return webob.Response(status_int=202)
raise webob.exc.HTTPNotFound
class Baremetal_nodes(extensions.ExtensionDescriptor):
"""Admin-only bare-metal node administration."""
name = "BareMetalNodes"
alias = "os-baremetal-nodes"
namespace = "http://docs.openstack.org/compute/ext/baremetal_nodes/api/v2"
updated = "2013-01-04T00:00:00+00:00"
def get_resources(self):
resources = []
res = extensions.ResourceExtension('os-baremetal-nodes',
BareMetalNodeController(),
member_actions={"action": "POST", })
resources.append(res)
return resources
| 35.13242
| 78
| 0.610216
|
import webob
from nova.api.openstack import extensions
from nova.api.openstack import wsgi
from nova.api.openstack import xmlutil
from nova import exception
from nova.virt.baremetal import db
authorize = extensions.extension_authorizer('compute', 'baremetal_nodes')
node_fields = ['id', 'cpus', 'local_gb', 'memory_mb', 'pm_address',
'pm_user',
'service_host', 'terminal_port', 'instance_uuid',
]
interface_fields = ['id', 'address', 'datapath_id', 'port_no']
def _node_dict(node_ref):
d = {}
for f in node_fields:
d[f] = node_ref.get(f)
return d
def _interface_dict(interface_ref):
d = {}
for f in interface_fields:
d[f] = interface_ref.get(f)
return d
def _make_node_elem(elem):
for f in node_fields:
elem.set(f)
def _make_interface_elem(elem):
for f in interface_fields:
elem.set(f)
class NodeTemplate(xmlutil.TemplateBuilder):
def construct(self):
node_elem = xmlutil.TemplateElement('node', selector='node')
_make_node_elem(node_elem)
ifs_elem = xmlutil.TemplateElement('interfaces')
if_elem = xmlutil.SubTemplateElement(ifs_elem, 'interface',
selector='interfaces')
_make_interface_elem(if_elem)
node_elem.append(ifs_elem)
return xmlutil.MasterTemplate(node_elem, 1)
class NodesTemplate(xmlutil.TemplateBuilder):
def construct(self):
root = xmlutil.TemplateElement('nodes')
node_elem = xmlutil.SubTemplateElement(root, 'node', selector='nodes')
_make_node_elem(node_elem)
ifs_elem = xmlutil.TemplateElement('interfaces')
if_elem = xmlutil.SubTemplateElement(ifs_elem, 'interface',
selector='interfaces')
_make_interface_elem(if_elem)
node_elem.append(ifs_elem)
return xmlutil.MasterTemplate(root, 1)
class InterfaceTemplate(xmlutil.TemplateBuilder):
def construct(self):
root = xmlutil.TemplateElement('interface', selector='interface')
_make_interface_elem(root)
return xmlutil.MasterTemplate(root, 1)
class BareMetalNodeController(wsgi.Controller):
@wsgi.serializers(xml=NodesTemplate)
def index(self, req):
context = req.environ['nova.context']
authorize(context)
nodes_from_db = db.bm_node_get_all(context)
nodes = []
for node_from_db in nodes_from_db:
try:
ifs = db.bm_interface_get_all_by_bm_node_id(
context, node_from_db['id'])
except exception.NodeNotFound:
ifs = []
node = _node_dict(node_from_db)
node['interfaces'] = [_interface_dict(i) for i in ifs]
nodes.append(node)
return {'nodes': nodes}
@wsgi.serializers(xml=NodeTemplate)
def show(self, req, id):
context = req.environ['nova.context']
authorize(context)
try:
node = db.bm_node_get(context, id)
except exception.NodeNotFound:
raise webob.exc.HTTPNotFound
try:
ifs = db.bm_interface_get_all_by_bm_node_id(context, id)
except exception.NodeNotFound:
ifs = []
node = _node_dict(node)
node['interfaces'] = [_interface_dict(i) for i in ifs]
return {'node': node}
@wsgi.serializers(xml=NodeTemplate)
def create(self, req, body):
context = req.environ['nova.context']
authorize(context)
values = body['node'].copy()
prov_mac_address = values.pop('prov_mac_address', None)
node = db.bm_node_create(context, values)
node = _node_dict(node)
if prov_mac_address:
if_id = db.bm_interface_create(context,
bm_node_id=node['id'],
address=prov_mac_address,
datapath_id=None,
port_no=None)
if_ref = db.bm_interface_get(context, if_id)
node['interfaces'] = [_interface_dict(if_ref)]
else:
node['interfaces'] = []
return {'node': node}
def delete(self, req, id):
context = req.environ['nova.context']
authorize(context)
try:
db.bm_node_destroy(context, id)
except exception.NodeNotFound:
raise webob.exc.HTTPNotFound
return webob.Response(status_int=202)
def _check_node_exists(self, context, node_id):
try:
db.bm_node_get(context, node_id)
except exception.NodeNotFound:
raise webob.exc.HTTPNotFound
@wsgi.serializers(xml=InterfaceTemplate)
@wsgi.action('add_interface')
def _add_interface(self, req, id, body):
context = req.environ['nova.context']
authorize(context)
self._check_node_exists(context, id)
body = body['add_interface']
address = body['address']
datapath_id = body.get('datapath_id')
port_no = body.get('port_no')
if_id = db.bm_interface_create(context,
bm_node_id=id,
address=address,
datapath_id=datapath_id,
port_no=port_no)
if_ref = db.bm_interface_get(context, if_id)
return {'interface': _interface_dict(if_ref)}
@wsgi.response(202)
@wsgi.action('remove_interface')
def _remove_interface(self, req, id, body):
context = req.environ['nova.context']
authorize(context)
self._check_node_exists(context, id)
body = body['remove_interface']
if_id = body.get('id')
address = body.get('address')
if not if_id and not address:
raise webob.exc.HTTPBadRequest(
explanation=_("Must specify id or address"))
ifs = db.bm_interface_get_all_by_bm_node_id(context, id)
for i in ifs:
if if_id and if_id != i['id']:
continue
if address and address != i['address']:
continue
db.bm_interface_destroy(context, i['id'])
return webob.Response(status_int=202)
raise webob.exc.HTTPNotFound
class Baremetal_nodes(extensions.ExtensionDescriptor):
name = "BareMetalNodes"
alias = "os-baremetal-nodes"
namespace = "http://docs.openstack.org/compute/ext/baremetal_nodes/api/v2"
updated = "2013-01-04T00:00:00+00:00"
def get_resources(self):
resources = []
res = extensions.ResourceExtension('os-baremetal-nodes',
BareMetalNodeController(),
member_actions={"action": "POST", })
resources.append(res)
return resources
| true
| true
|
1c442e3d79417cf8362146c9eef72af711a9391e
| 1,130
|
py
|
Python
|
demo/example/wsgi.py
|
afahounko/django-plans
|
089c90486ead3b8ab69b8b119f33d5ef923ca08e
|
[
"MIT"
] | 13
|
2016-01-19T15:45:32.000Z
|
2018-06-21T22:51:56.000Z
|
demo/example/wsgi.py
|
afahounko/django-plans
|
089c90486ead3b8ab69b8b119f33d5ef923ca08e
|
[
"MIT"
] | 11
|
2015-12-01T20:01:06.000Z
|
2018-07-07T05:12:17.000Z
|
demo/example/wsgi.py
|
afahounko/django-plans
|
089c90486ead3b8ab69b8b119f33d5ef923ca08e
|
[
"MIT"
] | 8
|
2015-11-17T02:12:36.000Z
|
2018-08-01T22:17:12.000Z
|
"""
WSGI config for xmpl project.
This module contains the WSGI application used by Django's development server
and any production WSGI deployments. It should expose a module-level variable
named ``application``. Django's ``runserver`` and ``runfcgi`` commands discover
this application via the ``WSGI_APPLICATION`` setting.
Usually you will have the standard Django WSGI application here, but it also
might make sense to replace the whole Django WSGI application with a custom one
that later delegates to the Django one. For example, you could introduce WSGI
middleware here, or combine a Django application with an application of another
framework.
"""
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "xmpl.settings")
# This application object is used by any WSGI server configured to use this
# file. This includes Django's development server, if the WSGI_APPLICATION
# setting points here.
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
# Apply WSGI middleware here.
# from helloworld.wsgi import HelloWorldApplication
# application = HelloWorldApplication(application)
| 38.965517
| 79
| 0.806195
|
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "xmpl.settings")
# setting points here.
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
# Apply WSGI middleware here.
# from helloworld.wsgi import HelloWorldApplication
# application = HelloWorldApplication(application)
| true
| true
|
1c442f847b28080303990953647216aee7c6607f
| 11,205
|
py
|
Python
|
python_src/adaptive_formation/gradient_interactive.py
|
tkortz/motion_planning_rt
|
08e914642b802f7217a8ad0f6153d41ccdce8c7d
|
[
"MIT"
] | null | null | null |
python_src/adaptive_formation/gradient_interactive.py
|
tkortz/motion_planning_rt
|
08e914642b802f7217a8ad0f6153d41ccdce8c7d
|
[
"MIT"
] | null | null | null |
python_src/adaptive_formation/gradient_interactive.py
|
tkortz/motion_planning_rt
|
08e914642b802f7217a8ad0f6153d41ccdce8c7d
|
[
"MIT"
] | null | null | null |
# In order to launch execute:
# python3 gradient_interactive.py
import numpy as np
from numpy.linalg import norm
import matplotlib.pyplot as plt
from matplotlib import collections
from scipy.ndimage.morphology import distance_transform_edt as bwdist
from math import *
import random
from impedance_modeles import *
import time
from progress.bar import FillingCirclesBar
from tasks import *
from threading import Thread
from multiprocessing import Process
import os
import liblitmus
def poly_area(x,y):
# https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
# https://en.wikipedia.org/wiki/Shoelace_formula
return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))
def meters2grid(pose_m, nrows=500, ncols=500):
# [0, 0](m) -> [250, 250]
# [1, 0](m) -> [250+100, 250]
# [0,-1](m) -> [250, 250-100]
pose_on_grid = np.array(pose_m)*100 + np.array([ncols/2, nrows/2])
return np.array( pose_on_grid, dtype=int)
def grid2meters(pose_grid, nrows=500, ncols=500):
# [250, 250] -> [0, 0](m)
# [250+100, 250] -> [1, 0](m)
# [250, 250-100] -> [0,-1](m)
pose_meters = ( np.array(pose_grid) - np.array([ncols/2, nrows/2]) ) / 100.0
return pose_meters
def gradient_planner(f, current_point, ncols=500, nrows=500, movement_rate=0.06):
"""
GradientBasedPlanner : This function computes the next_point
given current location, goal location and potential map, f.
It also returns mean velocity, V, of the gradient map in current point.
"""
[gy, gx] = np.gradient(-f);
iy, ix = np.array( meters2grid(current_point), dtype=int )
w = 30 # smoothing window size for gradient-velocity
vx = np.mean(gx[ix-int(w/2) : ix+int(w/2), iy-int(w/2) : iy+int(w/2)])
vy = np.mean(gy[ix-int(w/2) : ix+int(w/2), iy-int(w/2) : iy+int(w/2)])
V = np.array([vx, vy])
dt = 0.06 / norm(V);
next_point = current_point + dt*V;
return next_point, V
def combined_potential(obstacles_poses, R_obstacles, goal, nrows=500, ncols=500):
""" Repulsive potential """
obstacles_map = map(obstacles_poses, R_obstacles)
goal = meters2grid(goal)
d = bwdist(obstacles_map==0);
d2 = (d/100.) + 1; # Rescale and transform distances
d0 = 2;
nu = 200;
repulsive = nu*((1./d2 - 1./d0)**2);
repulsive [d2 > d0] = 0;
""" Attractive potential """
[x, y] = np.meshgrid(np.arange(ncols), np.arange(nrows))
xi = 1/700.;
attractive = xi * ( (x - goal[0])**2 + (y - goal[1])**2 );
""" Combine terms """
f = attractive + repulsive;
return f
def map(obstacles_poses, R_obstacles, nrows=500, ncols=500):
""" Obstacles map """
obstacles_map = np.zeros((nrows, ncols));
[x, y] = np.meshgrid(np.arange(ncols), np.arange(nrows))
for pose in obstacles_poses:
pose = meters2grid(pose)
x0 = pose[0]; y0 = pose[1]
# cylindrical obstacles
t = ((x - x0)**2 + (y - y0)**2) < (100*R_obstacles)**2
obstacles_map[t] = 1;
# rectangular obstacles
obstacles_map[400:, 130:150] = 1;
obstacles_map[130:150, :200] = 1;
obstacles_map[330:380, 300:] = 1;
return obstacles_map
def move_obstacles(obstacles_poses, obstacles_goal_poses):
""" All of the obstacles tend to go to the origin, (0,0) - point """
# for pose in obstacles_poses:
# dx = random.uniform(0, 0.03); dy = random.uniform(0,0.03);
# pose[0] -= np.sign(pose[0])*dx; pose[1] -= np.sign(pose[1])*dy;
""" Each obstacles tends to go to its selected goal point with random speed """
for p in range(len(obstacles_poses)):
pose = obstacles_poses[p]; goal = obstacles_goal_poses[p]
dx, dy = (goal - pose) / norm(goal-pose) * 0.05#random.uniform(0,0.05)
pose[0] += dx; pose[1] += dy;
return obstacles_poses
def formation(num_robots, leader_des, v, R_swarm):
if num_robots<=1: return []
u = np.array([-v[1], v[0]])
des4 = leader_des - v*R_swarm*sqrt(3) # follower
if num_robots==2: return [des4]
des2 = leader_des - v*R_swarm*sqrt(3)/2 + u*R_swarm/2 # follower
des3 = leader_des - v*R_swarm*sqrt(3)/2 - u*R_swarm/2 # follower
if num_robots==3: return [des2, des3]
return [des2, des3, des4]
def gradient_interactive():
""" initialization """
animate = 1 # show 1-each frame or 0-just final configuration
random_obstacles = 1 # randomly distributed obstacles on the map
num_random_obstacles = 8 # number of random circular obstacles on the map
num_robots = 4 # <=4, number of drones in formation
moving_obstacles = 1 # 0-static or 1-dynamic obstacles
impedance = 0 # impedance links between the leader and followers (leader's velocity)
formation_gradient = 1 # followers are attracting to their formation position and repelling from obstacles
draw_gradients = 1 # 1-gradients plot, 0-grid
postprocessing = 0 # show processed data figures after the flight
""" human guided swarm params """
interactive = 0 # 1-human guided swarm (requires MoCap system), 0-potential fields as a planner to goal pose
human_name = 'palm' # vicon mocap object
pos_coef = 3.0 # scale of the leader's movement relatively to the human operator
initialized = False # is always inits with False: for relative position control
max_its = 500 if interactive else 120 # max number of allowed iters for formation to reach the goal
VISUALIZE = False
# movie writer
if VISUALIZE:
progress_bar = FillingCirclesBar('Number of Iterations', max=max_its)
should_write_movie = 0; movie_file_name = os.getcwd()+'/videos/output.avi'
movie_writer = get_movie_writer(should_write_movie, 'Simulation Potential Fields', movie_fps=10., plot_pause_len=0.01)
R_obstacles = 0.05 # [m]
R_swarm = 0.3 # [m]
start = np.array([-1.8, 1.8]); goal = np.array([1.8, -1.8])
V0 = (goal - start) / norm(goal-start) # initial movement direction, |V0| = 1
U0 = np.array([-V0[1], V0[0]]) / norm(V0) # perpendicular to initial movement direction, |U0|=1
imp_pose_prev = np.array([0, 0])
imp_vel_prev = np.array([0, 0])
imp_time_prev = time.time()
if random_obstacles:
obstacles_poses = np.random.uniform(low=-2.5, high=2.5, size=(num_random_obstacles,2)) # randomly located obstacles
obstacles_goal_poses = np.random.uniform(low=-1.3, high=1.3, size=(num_random_obstacles,2)) # randomly located obstacles goal poses
else:
obstacles_poses = np.array([[-2, 1], [1.5, 0.5], [-1.0, 1.5], [0.1, 0.1], [1, -2], [-1.8, -1.8]]) # 2D - coordinates [m]
obstacles_goal_poses = np.array([[-0, 0], [0.0, 0.0], [ 0.0, 0.0], [0.0, 0.0], [0, 0], [ 0.0, 0.0]])
""" Main loop """
# drones polygonal formation
route1 = start # leader
current_point1 = start
robots_poses = [start] + formation(num_robots, start, V0, R_swarm)
routes = [route1] + robots_poses[1:]
centroid_route = [ sum([p[0] for p in robots_poses])/len(robots_poses), sum([p[1] for p in robots_poses])/len(robots_poses) ]
des_poses = robots_poses
vels = []
for r in range(num_robots): vels.append([])
norm_vels = []
for r in range(num_robots): norm_vels.append([])
# variables for postprocessing and performance estimation
area_array = []
start_time = time.time()
fig = plt.figure(figsize=(10, 10))
with get_dummy_context_mgr():
for i in range(max_its):
if moving_obstacles: obstacles_poses = move_obstacles(obstacles_poses, obstacles_goal_poses)
""" Leader's pose update """
f1 = combined_potential(obstacles_poses, R_obstacles, goal)
des_poses[0], vels[0] = gradient_planner(f1, current_point1)
direction = ( goal - des_poses[0] ) / norm(goal - des_poses[0])
norm_vels[0].append(norm(vels[0]))
# drones polygonal formation
# direction = ( goal - des_poses[0] ) / norm(goal - des_poses[0])
des_poses[1:] = formation(num_robots, des_poses[0], direction, R_swarm)
v = direction; u = np.array([-v[1], v[0]])
if formation_gradient:
# following drones are attracting to desired points - vertices of the polygonal formation
for p in range(1, num_robots):
""" including another robots in formation in obstacles array: """
robots_obstacles = [x for i,x in enumerate(robots_poses) if i!=p]
# obstacles_poses1 = np.array(robots_obstacles + obstacles_poses.tolist())
# f = combined_potential(obstacles_poses1, des_poses[p])
f = combined_potential(obstacles_poses, R_obstacles, des_poses[p])
des_poses[p], vels[p] = gradient_planner(f, des_poses[p])
norm_vels[p].append(norm(vels[p]))
for r in range(num_robots):
routes[r] = np.vstack([routes[r], des_poses[r]])
current_point1 = des_poses[0] # update current point of the leader
pp = des_poses
centroid = [ sum([p[0] for p in pp])/len(pp), sum([p[1] for p in pp])/len(pp) ]
centroid_route = np.vstack([centroid_route, centroid])
dist_to_goal = norm(centroid - goal)
if dist_to_goal < 1.5*R_swarm:
print('\nReached the goal')
break
if VISUALIZE:
progress_bar.next()
plt.cla()
draw_map(start, goal, obstacles_poses, R_obstacles, f1, draw_gradients=draw_gradients)
draw_robots(current_point1, routes, num_robots, robots_poses, centroid, vels[0])
if animate:
plt.draw()
plt.pause(0.01)
# print('Current simulation time: ', time.time()-start_time)
# Wait for the next period
global jobs
jobs += 1
liblitmus.call_sleep_next_period()
if VISUALIZE:
print('\nDone')
progress_bar.finish()
plt.show()
end_time = time.time()
print('Simulation execution time: ', round(end_time-start_time,2))
if __name__ == "__main__":
wcet = 150
period = 200
deadline = 200
phase = 0
early = False
numReps = 35
jobs = 0
# Make this thread a real-time task
liblitmus.call_set_rt_task_param(wcet, period, deadline, phase, early)
print("\nFinished setting rt params.\n")
liblitmus.call_init_litmus()
print("\nCalled init_litmus.\n")
liblitmus.set_task_mode_litmusrt()
print("\nNow a real-time task.\n")
print("\nAbout to wait for synchronous release.\n")
liblitmus.call_wait_for_ts_release()
# Do the work
for i in range(numReps):
gradient_interactive()
# Make it not a real-time task anymore
liblitmus.set_task_mode_background()
print("\nNow a background task again.\n")
print("Number of jobs:", jobs)
| 40.745455
| 139
| 0.619188
|
import numpy as np
from numpy.linalg import norm
import matplotlib.pyplot as plt
from matplotlib import collections
from scipy.ndimage.morphology import distance_transform_edt as bwdist
from math import *
import random
from impedance_modeles import *
import time
from progress.bar import FillingCirclesBar
from tasks import *
from threading import Thread
from multiprocessing import Process
import os
import liblitmus
def poly_area(x,y):
return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))
def meters2grid(pose_m, nrows=500, ncols=500):
pose_on_grid = np.array(pose_m)*100 + np.array([ncols/2, nrows/2])
return np.array( pose_on_grid, dtype=int)
def grid2meters(pose_grid, nrows=500, ncols=500):
pose_meters = ( np.array(pose_grid) - np.array([ncols/2, nrows/2]) ) / 100.0
return pose_meters
def gradient_planner(f, current_point, ncols=500, nrows=500, movement_rate=0.06):
[gy, gx] = np.gradient(-f);
iy, ix = np.array( meters2grid(current_point), dtype=int )
w = 30
vx = np.mean(gx[ix-int(w/2) : ix+int(w/2), iy-int(w/2) : iy+int(w/2)])
vy = np.mean(gy[ix-int(w/2) : ix+int(w/2), iy-int(w/2) : iy+int(w/2)])
V = np.array([vx, vy])
dt = 0.06 / norm(V);
next_point = current_point + dt*V;
return next_point, V
def combined_potential(obstacles_poses, R_obstacles, goal, nrows=500, ncols=500):
obstacles_map = map(obstacles_poses, R_obstacles)
goal = meters2grid(goal)
d = bwdist(obstacles_map==0);
d2 = (d/100.) + 1;
d0 = 2;
nu = 200;
repulsive = nu*((1./d2 - 1./d0)**2);
repulsive [d2 > d0] = 0;
[x, y] = np.meshgrid(np.arange(ncols), np.arange(nrows))
xi = 1/700.;
attractive = xi * ( (x - goal[0])**2 + (y - goal[1])**2 );
f = attractive + repulsive;
return f
def map(obstacles_poses, R_obstacles, nrows=500, ncols=500):
obstacles_map = np.zeros((nrows, ncols));
[x, y] = np.meshgrid(np.arange(ncols), np.arange(nrows))
for pose in obstacles_poses:
pose = meters2grid(pose)
x0 = pose[0]; y0 = pose[1]
t = ((x - x0)**2 + (y - y0)**2) < (100*R_obstacles)**2
obstacles_map[t] = 1;
obstacles_map[400:, 130:150] = 1;
obstacles_map[130:150, :200] = 1;
obstacles_map[330:380, 300:] = 1;
return obstacles_map
def move_obstacles(obstacles_poses, obstacles_goal_poses):
for p in range(len(obstacles_poses)):
pose = obstacles_poses[p]; goal = obstacles_goal_poses[p]
dx, dy = (goal - pose) / norm(goal-pose) * 0.05
pose[0] += dx; pose[1] += dy;
return obstacles_poses
def formation(num_robots, leader_des, v, R_swarm):
if num_robots<=1: return []
u = np.array([-v[1], v[0]])
des4 = leader_des - v*R_swarm*sqrt(3)
if num_robots==2: return [des4]
des2 = leader_des - v*R_swarm*sqrt(3)/2 + u*R_swarm/2
des3 = leader_des - v*R_swarm*sqrt(3)/2 - u*R_swarm/2
if num_robots==3: return [des2, des3]
return [des2, des3, des4]
def gradient_interactive():
animate = 1
random_obstacles = 1
num_random_obstacles = 8
num_robots = 4
moving_obstacles = 1
impedance = 0
formation_gradient = 1 # followers are attracting to their formation position and repelling from obstacles
draw_gradients = 1 # 1-gradients plot, 0-grid
postprocessing = 0 # show processed data figures after the flight
interactive = 0 # 1-human guided swarm (requires MoCap system), 0-potential fields as a planner to goal pose
human_name = 'palm' # vicon mocap object
pos_coef = 3.0 # scale of the leader's movement relatively to the human operator
initialized = False
max_its = 500 if interactive else 120
VISUALIZE = False
if VISUALIZE:
progress_bar = FillingCirclesBar('Number of Iterations', max=max_its)
should_write_movie = 0; movie_file_name = os.getcwd()+'/videos/output.avi'
movie_writer = get_movie_writer(should_write_movie, 'Simulation Potential Fields', movie_fps=10., plot_pause_len=0.01)
R_obstacles = 0.05
R_swarm = 0.3
start = np.array([-1.8, 1.8]); goal = np.array([1.8, -1.8])
V0 = (goal - start) / norm(goal-start)
U0 = np.array([-V0[1], V0[0]]) / norm(V0)
imp_pose_prev = np.array([0, 0])
imp_vel_prev = np.array([0, 0])
imp_time_prev = time.time()
if random_obstacles:
obstacles_poses = np.random.uniform(low=-2.5, high=2.5, size=(num_random_obstacles,2))
obstacles_goal_poses = np.random.uniform(low=-1.3, high=1.3, size=(num_random_obstacles,2))
else:
obstacles_poses = np.array([[-2, 1], [1.5, 0.5], [-1.0, 1.5], [0.1, 0.1], [1, -2], [-1.8, -1.8]])
obstacles_goal_poses = np.array([[-0, 0], [0.0, 0.0], [ 0.0, 0.0], [0.0, 0.0], [0, 0], [ 0.0, 0.0]])
route1 = start
current_point1 = start
robots_poses = [start] + formation(num_robots, start, V0, R_swarm)
routes = [route1] + robots_poses[1:]
centroid_route = [ sum([p[0] for p in robots_poses])/len(robots_poses), sum([p[1] for p in robots_poses])/len(robots_poses) ]
des_poses = robots_poses
vels = []
for r in range(num_robots): vels.append([])
norm_vels = []
for r in range(num_robots): norm_vels.append([])
area_array = []
start_time = time.time()
fig = plt.figure(figsize=(10, 10))
with get_dummy_context_mgr():
for i in range(max_its):
if moving_obstacles: obstacles_poses = move_obstacles(obstacles_poses, obstacles_goal_poses)
f1 = combined_potential(obstacles_poses, R_obstacles, goal)
des_poses[0], vels[0] = gradient_planner(f1, current_point1)
direction = ( goal - des_poses[0] ) / norm(goal - des_poses[0])
norm_vels[0].append(norm(vels[0]))
des_poses[1:] = formation(num_robots, des_poses[0], direction, R_swarm)
v = direction; u = np.array([-v[1], v[0]])
if formation_gradient:
for p in range(1, num_robots):
robots_obstacles = [x for i,x in enumerate(robots_poses) if i!=p]
f = combined_potential(obstacles_poses, R_obstacles, des_poses[p])
des_poses[p], vels[p] = gradient_planner(f, des_poses[p])
norm_vels[p].append(norm(vels[p]))
for r in range(num_robots):
routes[r] = np.vstack([routes[r], des_poses[r]])
current_point1 = des_poses[0]
pp = des_poses
centroid = [ sum([p[0] for p in pp])/len(pp), sum([p[1] for p in pp])/len(pp) ]
centroid_route = np.vstack([centroid_route, centroid])
dist_to_goal = norm(centroid - goal)
if dist_to_goal < 1.5*R_swarm:
print('\nReached the goal')
break
if VISUALIZE:
progress_bar.next()
plt.cla()
draw_map(start, goal, obstacles_poses, R_obstacles, f1, draw_gradients=draw_gradients)
draw_robots(current_point1, routes, num_robots, robots_poses, centroid, vels[0])
if animate:
plt.draw()
plt.pause(0.01)
global jobs
jobs += 1
liblitmus.call_sleep_next_period()
if VISUALIZE:
print('\nDone')
progress_bar.finish()
plt.show()
end_time = time.time()
print('Simulation execution time: ', round(end_time-start_time,2))
if __name__ == "__main__":
wcet = 150
period = 200
deadline = 200
phase = 0
early = False
numReps = 35
jobs = 0
liblitmus.call_set_rt_task_param(wcet, period, deadline, phase, early)
print("\nFinished setting rt params.\n")
liblitmus.call_init_litmus()
print("\nCalled init_litmus.\n")
liblitmus.set_task_mode_litmusrt()
print("\nNow a real-time task.\n")
print("\nAbout to wait for synchronous release.\n")
liblitmus.call_wait_for_ts_release()
for i in range(numReps):
gradient_interactive()
liblitmus.set_task_mode_background()
print("\nNow a background task again.\n")
print("Number of jobs:", jobs)
| true
| true
|
1c442f8e6a0dc8f5fe2a81b2f44f17d32075be5c
| 2,598
|
py
|
Python
|
openarticlegauge/plugins/hindawi.py
|
CottageLabs/OpenArticleGauge
|
58d29b4209a7b59041d61326ffe1cf03f98f3cff
|
[
"BSD-3-Clause"
] | 1
|
2016-04-07T18:29:27.000Z
|
2016-04-07T18:29:27.000Z
|
openarticlegauge/plugins/hindawi.py
|
CottageLabs/OpenArticleGauge
|
58d29b4209a7b59041d61326ffe1cf03f98f3cff
|
[
"BSD-3-Clause"
] | 11
|
2015-01-06T15:53:09.000Z
|
2022-03-01T01:46:14.000Z
|
openarticlegauge/plugins/hindawi.py
|
CottageLabs/OpenArticleGauge
|
58d29b4209a7b59041d61326ffe1cf03f98f3cff
|
[
"BSD-3-Clause"
] | null | null | null |
"""
This plugin handles Hindawi articles.
Hindawi publish from a single domain and use a consistent format for licenses
so this one should be relatively straightforward.
"""
from openarticlegauge import plugin
class HindawiPlugin(plugin.Plugin):
_short_name = __name__.split('.')[-1]
__version__='0.1' # consider incrementing or at least adding a minor version
# e.g. "0.1.1" if you change this plugin
__desc__ = "Obtains licenses from articles published by Hindawi"
# The domains that this plugin will say it can support.
# Specified without the schema (protocol - e.g. "http://") part.
_base_urls = ["www.hindawi.com"]
# so if the http://www.hindawi.com/journals/ecam/2013/429706/ URL comes in,
# it should be supported.
_license_mappings = [
{'This is an open access article distributed under the <a rel="license" href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution License</a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.':
{'type': 'cc-by', # license type, see the licenses module for available ones
'version':'3.0', # version of the license if specified, can be blank
# also declare some properties which override info about this license in the licenses list (see licenses module)
'url': 'http://creativecommons.org/licenses/by/3.0'}
}
]
def capabilities(self):
return {
"type_detect_verify" : False,
"canonicalise" : [],
"detect_provider" : [],
"license_detect" : True
}
def supports(self, provider):
"""
Does this plugin support this provider
"""
return self.supports_by_base_url(provider)
def license_detect(self, record):
"""
To respond to the provider identifier: http://www.hindawi.com
This should determine the licence conditions of the Hindawi article and populate
the record['bibjson']['license'] (note the US spelling) field.
"""
lic_statements = self._license_mappings
# For all URL-s associated with this resource...
for url in record.provider_urls:
# ... run the dumb string matcher if the URL is supported.
if self.supports_base_url(url):
self.simple_extract(lic_statements, record, url)
return (self._short_name, self.__version__)
| 41.238095
| 302
| 0.635104
|
from openarticlegauge import plugin
class HindawiPlugin(plugin.Plugin):
_short_name = __name__.split('.')[-1]
__version__='0.1'
__desc__ = "Obtains licenses from articles published by Hindawi"
_base_urls = ["www.hindawi.com"]
_license_mappings = [
{'This is an open access article distributed under the <a rel="license" href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution License</a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.':
{'type': 'cc-by',
'version':'3.0',
'url': 'http://creativecommons.org/licenses/by/3.0'}
}
]
def capabilities(self):
return {
"type_detect_verify" : False,
"canonicalise" : [],
"detect_provider" : [],
"license_detect" : True
}
def supports(self, provider):
return self.supports_by_base_url(provider)
def license_detect(self, record):
lic_statements = self._license_mappings
for url in record.provider_urls:
if self.supports_base_url(url):
self.simple_extract(lic_statements, record, url)
return (self._short_name, self.__version__)
| true
| true
|
1c443024fe6467b58121c2e7ba26b7dca51aeb4e
| 1,070
|
py
|
Python
|
algorithms/backtrack/subsets.py
|
nisaruj/algorithms
|
1e03cd259c2d7ada113eb99843dcada9f20adf54
|
[
"MIT"
] | 6
|
2018-12-12T09:14:05.000Z
|
2019-04-29T22:07:28.000Z
|
algorithms/backtrack/subsets.py
|
nisaruj/algorithms
|
1e03cd259c2d7ada113eb99843dcada9f20adf54
|
[
"MIT"
] | null | null | null |
algorithms/backtrack/subsets.py
|
nisaruj/algorithms
|
1e03cd259c2d7ada113eb99843dcada9f20adf54
|
[
"MIT"
] | 7
|
2019-03-21T10:18:22.000Z
|
2021-09-22T07:34:10.000Z
|
"""
Given a set of distinct integers, nums, return all possible subsets.
Note: The solution set must not contain duplicate subsets.
For example,
If nums = [1,2,3], a solution is:
[
[3],
[1],
[2],
[1,2,3],
[1,3],
[2,3],
[1,2],
[]
]
"""
def subsets(nums):
"""
O(2**n)
"""
def backtrack(res, nums, stack, pos):
if pos == len(nums):
res.append(list(stack))
else:
# take nums[pos]
stack.append(nums[pos])
backtrack(res, nums, stack, pos+1)
stack.pop()
# dont take nums[pos]
backtrack(res, nums, stack, pos+1)
res = []
backtrack(res, nums, [], 0)
return res
"""
simplified backtrack
def backtrack(res, nums, cur, pos):
if pos >= len(nums):
res.append(cur)
else:
backtrack(res, nums, cur+[nums[pos]], pos+1)
backtrack(res, nums, cur, pos+1)
"""
# Iteratively
def subsets_v2(self, nums):
res = [[]]
for num in sorted(nums):
res += [item+[num] for item in res]
return res
| 17.833333
| 68
| 0.526168
|
def subsets(nums):
def backtrack(res, nums, stack, pos):
if pos == len(nums):
res.append(list(stack))
else:
stack.append(nums[pos])
backtrack(res, nums, stack, pos+1)
stack.pop()
backtrack(res, nums, stack, pos+1)
res = []
backtrack(res, nums, [], 0)
return res
def subsets_v2(self, nums):
res = [[]]
for num in sorted(nums):
res += [item+[num] for item in res]
return res
| true
| true
|
1c4430552bfe4f27d5a07dbbf8a7fb3bc4ff2e65
| 1,659
|
py
|
Python
|
tests/optimization/test_genetic_algorithm.py
|
iamchetry/DataChallenge-Fall2021
|
fa7748c9ea2f3c0f6bde8d0b094fc75463e28f33
|
[
"BSD-3-Clause"
] | 108
|
2018-03-23T20:06:03.000Z
|
2022-01-06T19:32:46.000Z
|
tests/optimization/test_genetic_algorithm.py
|
hachmannlab/ChemML
|
42b152579872a57c834884596f700c76b9320280
|
[
"BSD-3-Clause"
] | 18
|
2019-08-09T21:16:14.000Z
|
2022-02-14T21:52:06.000Z
|
tests/optimization/test_genetic_algorithm.py
|
hachmannlab/ChemML
|
42b152579872a57c834884596f700c76b9320280
|
[
"BSD-3-Clause"
] | 28
|
2018-04-28T17:07:33.000Z
|
2022-02-28T07:22:56.000Z
|
import pytest
from chemml.optimization import GeneticAlgorithm
space = ({'alpha': {'uniform': [-20, 0],
'mutation': [0, 2]}},
{'neurons': {'int': [0,10]}},
{'act': {'choice':range(0,100,5)}})
def evaluate(individual):
return sum(individual)
def test_algorithms():
al = [3]
for i in al:
ga_search = GeneticAlgorithm(
evaluate,
space=space,
pop_size=10,
mutation_size=4,
crossover_size=4,
algorithm=i)
_, best_individual = ga_search.search(n_generations=4)
assert sum([best_individual[i] for i in best_individual]) <= 200
def test_sequential_min():
ga_search = GeneticAlgorithm(evaluate,
fitness=("min", ),
space=space,
pop_size=10,
mutation_size=5,
crossover_size=5,
algorithm=3)
for _ in range(4):
_, best_individual = ga_search.search(n_generations=1)
assert sum([best_individual[i] for i in best_individual]) <= 200
def test_crossovers():
co = ['SinglePoint', 'DoublePoint', 'Blend']
for c in co:
ga_search = GeneticAlgorithm(
evaluate,
space=space,
crossover_type=c,
pop_size=10,
mutation_size=4,
crossover_size=4,
algorithm=3)
_, best_individual = ga_search.search(n_generations=4)
assert sum([best_individual[i] for i in best_individual]) <= 200
| 30.163636
| 72
| 0.517782
|
import pytest
from chemml.optimization import GeneticAlgorithm
space = ({'alpha': {'uniform': [-20, 0],
'mutation': [0, 2]}},
{'neurons': {'int': [0,10]}},
{'act': {'choice':range(0,100,5)}})
def evaluate(individual):
return sum(individual)
def test_algorithms():
al = [3]
for i in al:
ga_search = GeneticAlgorithm(
evaluate,
space=space,
pop_size=10,
mutation_size=4,
crossover_size=4,
algorithm=i)
_, best_individual = ga_search.search(n_generations=4)
assert sum([best_individual[i] for i in best_individual]) <= 200
def test_sequential_min():
ga_search = GeneticAlgorithm(evaluate,
fitness=("min", ),
space=space,
pop_size=10,
mutation_size=5,
crossover_size=5,
algorithm=3)
for _ in range(4):
_, best_individual = ga_search.search(n_generations=1)
assert sum([best_individual[i] for i in best_individual]) <= 200
def test_crossovers():
co = ['SinglePoint', 'DoublePoint', 'Blend']
for c in co:
ga_search = GeneticAlgorithm(
evaluate,
space=space,
crossover_type=c,
pop_size=10,
mutation_size=4,
crossover_size=4,
algorithm=3)
_, best_individual = ga_search.search(n_generations=4)
assert sum([best_individual[i] for i in best_individual]) <= 200
| true
| true
|
1c44335e84dfb5cd043b5622e45e9e7089d0a86c
| 1,232
|
py
|
Python
|
2016/day_13.py
|
viddrobnic/adventofcode
|
8f06f4ad3ed6744d20d222b050a15b8ff0ff9c82
|
[
"MIT"
] | null | null | null |
2016/day_13.py
|
viddrobnic/adventofcode
|
8f06f4ad3ed6744d20d222b050a15b8ff0ff9c82
|
[
"MIT"
] | null | null | null |
2016/day_13.py
|
viddrobnic/adventofcode
|
8f06f4ad3ed6744d20d222b050a15b8ff0ff9c82
|
[
"MIT"
] | 1
|
2020-12-01T16:49:12.000Z
|
2020-12-01T16:49:12.000Z
|
from queue import Queue
seed = 1362
seen = set()
def is_empty(x, y):
n = x*x + 3*x + 2*x*y + y + y*y + seed
return bin(n).count('1') % 2 == 0
def valid_moves(x, y):
result = []
actions = [-1, 1]
for action in actions:
new_x = x + action
if x > 0 and is_empty(new_x, y) and (new_x, y) not in seen:
result.append((new_x, y))
new_y = y + action
if y > 0 and is_empty(x, new_y) and (x, new_y) not in seen:
result.append((x, new_y))
return result
state = {
'coords': (1, 1),
'moves': 0
}
que = Queue()
que.put(state)
locations = 0
solved_1 = False
solved_2 = False
while not solved_1 or not solved_2:
current_state = que.get()
moves = current_state['moves']
if current_state['coords'] in seen:
continue
seen.add(current_state['coords'])
if current_state['coords'] == (31, 39):
solved_1 = True
print('#1:', moves)
possible_moves = valid_moves(*current_state['coords'])
for move in possible_moves:
new_state = {'coords': move, 'moves': moves + 1}
que.put(new_state)
if moves <= 50:
locations += 1
else:
solved_2 = True
print('#2:', locations)
| 20.196721
| 67
| 0.564935
|
from queue import Queue
seed = 1362
seen = set()
def is_empty(x, y):
n = x*x + 3*x + 2*x*y + y + y*y + seed
return bin(n).count('1') % 2 == 0
def valid_moves(x, y):
result = []
actions = [-1, 1]
for action in actions:
new_x = x + action
if x > 0 and is_empty(new_x, y) and (new_x, y) not in seen:
result.append((new_x, y))
new_y = y + action
if y > 0 and is_empty(x, new_y) and (x, new_y) not in seen:
result.append((x, new_y))
return result
state = {
'coords': (1, 1),
'moves': 0
}
que = Queue()
que.put(state)
locations = 0
solved_1 = False
solved_2 = False
while not solved_1 or not solved_2:
current_state = que.get()
moves = current_state['moves']
if current_state['coords'] in seen:
continue
seen.add(current_state['coords'])
if current_state['coords'] == (31, 39):
solved_1 = True
print('#1:', moves)
possible_moves = valid_moves(*current_state['coords'])
for move in possible_moves:
new_state = {'coords': move, 'moves': moves + 1}
que.put(new_state)
if moves <= 50:
locations += 1
else:
solved_2 = True
print('#2:', locations)
| true
| true
|
1c4433f0812e9b10a3f57fc23795522eae70a302
| 1,207
|
py
|
Python
|
tempest/services/volume/json/admin/volume_services_client.py
|
midokura/tempest
|
b0ec1d280f057d5d9c2eda081bcbda7e381ecb3b
|
[
"Apache-2.0"
] | null | null | null |
tempest/services/volume/json/admin/volume_services_client.py
|
midokura/tempest
|
b0ec1d280f057d5d9c2eda081bcbda7e381ecb3b
|
[
"Apache-2.0"
] | null | null | null |
tempest/services/volume/json/admin/volume_services_client.py
|
midokura/tempest
|
b0ec1d280f057d5d9c2eda081bcbda7e381ecb3b
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2014 NEC Corporation
# 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.
import json
import urllib
from tempest.common import service_client
class BaseVolumesServicesClientJSON(service_client.ServiceClient):
def list_services(self, params=None):
url = 'os-services'
if params:
url += '?%s' % urllib.urlencode(params)
resp, body = self.get(url)
body = json.loads(body)
self.expected_success(200, resp.status)
return service_client.ResponseBodyList(resp, body['services'])
class VolumesServicesClientJSON(BaseVolumesServicesClientJSON):
"""Volume V1 volume services client"""
| 32.621622
| 78
| 0.714996
|
import json
import urllib
from tempest.common import service_client
class BaseVolumesServicesClientJSON(service_client.ServiceClient):
def list_services(self, params=None):
url = 'os-services'
if params:
url += '?%s' % urllib.urlencode(params)
resp, body = self.get(url)
body = json.loads(body)
self.expected_success(200, resp.status)
return service_client.ResponseBodyList(resp, body['services'])
class VolumesServicesClientJSON(BaseVolumesServicesClientJSON):
| true
| true
|
1c4434aaeea67d038888d9e299fd5181e631f135
| 137
|
py
|
Python
|
tests/test_placeholder.py
|
bveeramani/sysx
|
ca67995d4fafb5280d16ccb3825cdd6c2a7c7e48
|
[
"Apache-2.0"
] | null | null | null |
tests/test_placeholder.py
|
bveeramani/sysx
|
ca67995d4fafb5280d16ccb3825cdd6c2a7c7e48
|
[
"Apache-2.0"
] | null | null | null |
tests/test_placeholder.py
|
bveeramani/sysx
|
ca67995d4fafb5280d16ccb3825cdd6c2a7c7e48
|
[
"Apache-2.0"
] | null | null | null |
"""A placeholder test to prevent pytest from erroring."""
def test_placeholder():
"""Test that 1 + 1 = 2."""
assert 1 + 1 == 2
| 19.571429
| 57
| 0.59854
|
def test_placeholder():
assert 1 + 1 == 2
| true
| true
|
1c443523f65f5f8c973e9eb58b9cf057505dc784
| 630
|
py
|
Python
|
tests/unit/db/test_users.py
|
jaimecruz21/lifeloopweb
|
ba0ffe1ea94ba3323a4e9c66c9506a338cae3212
|
[
"MIT"
] | null | null | null |
tests/unit/db/test_users.py
|
jaimecruz21/lifeloopweb
|
ba0ffe1ea94ba3323a4e9c66c9506a338cae3212
|
[
"MIT"
] | null | null | null |
tests/unit/db/test_users.py
|
jaimecruz21/lifeloopweb
|
ba0ffe1ea94ba3323a4e9c66c9506a338cae3212
|
[
"MIT"
] | null | null | null |
import pytest
from lifeloopweb.db.models import User
from lifeloopweb import exception
import tests
class TestUser(tests.TestBase):
def test_get_email_from_full_name_and_email(self):
full_name_and_email = "Jason Meridth (jason@meridth.io)"
result = User.get_email_from_full_name_and_email(
full_name_and_email)
assert result == 'jason@meridth.io'
def test_get_email_from_full_name_and_email_with_invalid_email(self):
full_name_and_email = "invalid"
with pytest.raises(exception.InvalidEmail):
User.get_email_from_full_name_and_email(full_name_and_email)
| 33.157895
| 73
| 0.757143
|
import pytest
from lifeloopweb.db.models import User
from lifeloopweb import exception
import tests
class TestUser(tests.TestBase):
def test_get_email_from_full_name_and_email(self):
full_name_and_email = "Jason Meridth (jason@meridth.io)"
result = User.get_email_from_full_name_and_email(
full_name_and_email)
assert result == 'jason@meridth.io'
def test_get_email_from_full_name_and_email_with_invalid_email(self):
full_name_and_email = "invalid"
with pytest.raises(exception.InvalidEmail):
User.get_email_from_full_name_and_email(full_name_and_email)
| true
| true
|
1c443672071b863adb6d9fc4151f01406c3f4e08
| 1,165
|
py
|
Python
|
funcstructs/prototypes/necklace_groups.py
|
caleblevy/endofunction-structures
|
084ddeab8d12307dd95b8727190c589a1bf659df
|
[
"MIT"
] | 5
|
2015-05-06T05:08:26.000Z
|
2017-04-21T03:32:13.000Z
|
funcstructs/prototypes/necklace_groups.py
|
caleblevy/endofunction-structures
|
084ddeab8d12307dd95b8727190c589a1bf659df
|
[
"MIT"
] | null | null | null |
funcstructs/prototypes/necklace_groups.py
|
caleblevy/endofunction-structures
|
084ddeab8d12307dd95b8727190c589a1bf659df
|
[
"MIT"
] | null | null | null |
"""Caleb Levy, 2015."""
from funcstructs.structures import necklaces
from funcstructs import combinat
from . import polynomials, integer_partitions
def count_by_period(beads):
return necklaces.FixedContentNecklaces(beads).count_by_period()
def period_combos(beads, reps):
"""All possible combinations of periods from given counts by period"""
necklace_counts = count_by_period(beads)
periods = [i for i, val in enumerate(necklace_counts) if val]
for part in integer_partitions.max_length_partitions(reps, len(periods)):
for combo in polynomials.multisets_with_multiplicities(periods, part):
yield combo
def period_combo_count(necklace_counts, combo):
"""Number of necklaces from a combination of periods"""
val = 1
for period, mult in combo.items():
val *= combinat.nCWRk(necklace_counts[period], mult)
return val
def necklace_groups_by_period_combo(beads, reps):
"""Return generator yield pairs of period groups and their counts"""
necklace_counts = count_by_period(beads)
for combo in period_combos(beads, reps):
yield combo, period_combo_count(necklace_counts, combo)
| 33.285714
| 78
| 0.744206
|
from funcstructs.structures import necklaces
from funcstructs import combinat
from . import polynomials, integer_partitions
def count_by_period(beads):
return necklaces.FixedContentNecklaces(beads).count_by_period()
def period_combos(beads, reps):
necklace_counts = count_by_period(beads)
periods = [i for i, val in enumerate(necklace_counts) if val]
for part in integer_partitions.max_length_partitions(reps, len(periods)):
for combo in polynomials.multisets_with_multiplicities(periods, part):
yield combo
def period_combo_count(necklace_counts, combo):
val = 1
for period, mult in combo.items():
val *= combinat.nCWRk(necklace_counts[period], mult)
return val
def necklace_groups_by_period_combo(beads, reps):
necklace_counts = count_by_period(beads)
for combo in period_combos(beads, reps):
yield combo, period_combo_count(necklace_counts, combo)
| true
| true
|
1c443772534ffc1c373c281238456b449eecb7ea
| 309
|
py
|
Python
|
samples/frontend/manage.py
|
liuyu81/datagator-contrib
|
813529e211f680732bd1dc9568f5b4f2bdcacdcc
|
[
"Apache-2.0"
] | 2
|
2015-02-20T02:50:07.000Z
|
2017-05-02T19:26:42.000Z
|
samples/frontend/manage.py
|
liuyu81/datagator-contrib
|
813529e211f680732bd1dc9568f5b4f2bdcacdcc
|
[
"Apache-2.0"
] | null | null | null |
samples/frontend/manage.py
|
liuyu81/datagator-contrib
|
813529e211f680732bd1dc9568f5b4f2bdcacdcc
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DATAGATOR_DEVELOP", "1")
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "datagator.wsgi.settings")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
| 25.75
| 78
| 0.763754
|
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DATAGATOR_DEVELOP", "1")
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "datagator.wsgi.settings")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
| true
| true
|
1c4437932e33a6e182efd278f5f9d445858bdcb6
| 1,021
|
py
|
Python
|
main.py
|
SamAlhabash/fast-api-with-mongodb
|
20f5b05f37fda088ffcd6479d79847234ffc1370
|
[
"MIT"
] | null | null | null |
main.py
|
SamAlhabash/fast-api-with-mongodb
|
20f5b05f37fda088ffcd6479d79847234ffc1370
|
[
"MIT"
] | null | null | null |
main.py
|
SamAlhabash/fast-api-with-mongodb
|
20f5b05f37fda088ffcd6479d79847234ffc1370
|
[
"MIT"
] | null | null | null |
from fastapi import FastAPI
from config.config import settings
from starlette.middleware.cors import CORSMiddleware
from api.api_v1.api import api_router
from api.api_v1.services.database import connect_db, close_db
import uvicorn
app = FastAPI(
title=settings.PROJECT_NAME,
description=settings.PROJECT_DESC,
version=settings.PROJECT_VERSION,
openapi_url="/open-api.json",
docs_url="/swagger",
redoc_url="/redoc"
)
if settings.BACKEND_CORS_ORIGINS:
app.add_middleware(
CORSMiddleware,
allow_origins=[str(origin)
for origin in settings.BACKEND_CORS_ORIGINS],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.add_event_handler("startup", connect_db)
app.add_event_handler("shutdown", close_db)
app.include_router(api_router, prefix=settings.API_V1_STR)
if __name__ == '__main__':
uvicorn.run(app='main:app',
host="0.0.0.0",
port=8000,
reload=True)
| 28.361111
| 68
| 0.688541
|
from fastapi import FastAPI
from config.config import settings
from starlette.middleware.cors import CORSMiddleware
from api.api_v1.api import api_router
from api.api_v1.services.database import connect_db, close_db
import uvicorn
app = FastAPI(
title=settings.PROJECT_NAME,
description=settings.PROJECT_DESC,
version=settings.PROJECT_VERSION,
openapi_url="/open-api.json",
docs_url="/swagger",
redoc_url="/redoc"
)
if settings.BACKEND_CORS_ORIGINS:
app.add_middleware(
CORSMiddleware,
allow_origins=[str(origin)
for origin in settings.BACKEND_CORS_ORIGINS],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.add_event_handler("startup", connect_db)
app.add_event_handler("shutdown", close_db)
app.include_router(api_router, prefix=settings.API_V1_STR)
if __name__ == '__main__':
uvicorn.run(app='main:app',
host="0.0.0.0",
port=8000,
reload=True)
| true
| true
|
1c4437a501f34a54f0bce2a6c39e661634304ed4
| 415
|
py
|
Python
|
api/urls.py
|
GomaGoma676/ScrumTaskApi_Backend
|
f977f6fae514ee92f3f37b94c052d953b8dcc693
|
[
"MIT"
] | 1
|
2020-11-03T10:17:48.000Z
|
2020-11-03T10:17:48.000Z
|
api/urls.py
|
GomaGoma676/ScrumTaskApi_Backend
|
f977f6fae514ee92f3f37b94c052d953b8dcc693
|
[
"MIT"
] | null | null | null |
api/urls.py
|
GomaGoma676/ScrumTaskApi_Backend
|
f977f6fae514ee92f3f37b94c052d953b8dcc693
|
[
"MIT"
] | 1
|
2021-03-20T15:24:42.000Z
|
2021-03-20T15:24:42.000Z
|
from django.urls import path
from django.conf.urls import include
from rest_framework import routers
from .views import TaskViewSet, UserViewSet, SprintViewSet, TagViewSet
router = routers.DefaultRouter()
router.register('users', UserViewSet)
router.register('tasks', TaskViewSet)
router.register('sprints', SprintViewSet)
router.register('tags', TagViewSet)
urlpatterns = [
path('', include(router.urls)),
]
| 27.666667
| 70
| 0.785542
|
from django.urls import path
from django.conf.urls import include
from rest_framework import routers
from .views import TaskViewSet, UserViewSet, SprintViewSet, TagViewSet
router = routers.DefaultRouter()
router.register('users', UserViewSet)
router.register('tasks', TaskViewSet)
router.register('sprints', SprintViewSet)
router.register('tags', TagViewSet)
urlpatterns = [
path('', include(router.urls)),
]
| true
| true
|
1c44380bc3440a460421d22b63007e3644b1af03
| 587
|
py
|
Python
|
python_script.py
|
benjiyo/computer_usage_statistics
|
e53cd5facdbec34062b092eabeb0121f1727e36f
|
[
"MIT"
] | 1
|
2016-12-08T14:10:06.000Z
|
2016-12-08T14:10:06.000Z
|
python_script.py
|
benjiyo/computer_usage_statistics
|
e53cd5facdbec34062b092eabeb0121f1727e36f
|
[
"MIT"
] | null | null | null |
python_script.py
|
benjiyo/computer_usage_statistics
|
e53cd5facdbec34062b092eabeb0121f1727e36f
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# Call this script with "hour" as argument
# Use the file "bash_script" to call this script
import sys
hour = int(sys.argv[1])
with open('PATH_TO_REPOSITORY/histogram.txt', 'r') as myfile:
data = myfile.read()
newdata = data.splitlines()
newdata[hour] = int(newdata[hour]) + 1
tmpstr = str(newdata)
tmpstr = tmpstr.replace("'","")
tmpstr = tmpstr.replace(" ","")
tmpstr = tmpstr.replace(",","\n")
tmpstr = tmpstr.replace("[","");
tmpstr = tmpstr.replace("]","");
with open('PATH_TO_REPOSITORY/histogram.txt', 'w') as myfile:
myfile.write(tmpstr)
| 23.48
| 61
| 0.667802
|
import sys
hour = int(sys.argv[1])
with open('PATH_TO_REPOSITORY/histogram.txt', 'r') as myfile:
data = myfile.read()
newdata = data.splitlines()
newdata[hour] = int(newdata[hour]) + 1
tmpstr = str(newdata)
tmpstr = tmpstr.replace("'","")
tmpstr = tmpstr.replace(" ","")
tmpstr = tmpstr.replace(",","\n")
tmpstr = tmpstr.replace("[","");
tmpstr = tmpstr.replace("]","");
with open('PATH_TO_REPOSITORY/histogram.txt', 'w') as myfile:
myfile.write(tmpstr)
| true
| true
|
1c4438564c78ea33e922ec1b15a0905f72d7a386
| 3,669
|
py
|
Python
|
Tests/test_weakset_stdlib.py
|
pxl9588/ironpython3
|
3417b2d29f4116b8f44af31defb9a098686cd566
|
[
"Apache-2.0"
] | 1
|
2019-06-27T13:04:33.000Z
|
2019-06-27T13:04:33.000Z
|
Tests/test_weakset_stdlib.py
|
pxl9588/ironpython3
|
3417b2d29f4116b8f44af31defb9a098686cd566
|
[
"Apache-2.0"
] | null | null | null |
Tests/test_weakset_stdlib.py
|
pxl9588/ironpython3
|
3417b2d29f4116b8f44af31defb9a098686cd566
|
[
"Apache-2.0"
] | null | null | null |
# Licensed to the .NET Foundation under one or more agreements.
# The .NET Foundation licenses this file to you under the Apache 2.0 License.
# See the LICENSE file in the project root for more information.
##
## Run selected tests from test_weakset from StdLib
##
import unittest
import sys
from iptest import run_test
import test.test_weakset
def load_tests(loader, standard_tests, pattern):
if sys.implementation.name == 'ironpython':
suite = unittest.TestSuite()
suite.addTest(test.test_weakset.TestWeakSet('test_add'))
suite.addTest(test.test_weakset.TestWeakSet('test_and'))
suite.addTest(test.test_weakset.TestWeakSet('test_clear'))
suite.addTest(test.test_weakset.TestWeakSet('test_constructor_identity'))
suite.addTest(test.test_weakset.TestWeakSet('test_contains'))
suite.addTest(test.test_weakset.TestWeakSet('test_copy'))
suite.addTest(test.test_weakset.TestWeakSet('test_difference'))
suite.addTest(test.test_weakset.TestWeakSet('test_difference_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_discard'))
suite.addTest(test.test_weakset.TestWeakSet('test_eq'))
suite.addTest(test.test_weakset.TestWeakSet('test_gc'))
suite.addTest(test.test_weakset.TestWeakSet('test_gt'))
suite.addTest(test.test_weakset.TestWeakSet('test_hash'))
suite.addTest(test.test_weakset.TestWeakSet('test_iand'))
suite.addTest(test.test_weakset.TestWeakSet('test_init'))
suite.addTest(test.test_weakset.TestWeakSet('test_inplace_on_self'))
suite.addTest(test.test_weakset.TestWeakSet('test_intersection'))
suite.addTest(test.test_weakset.TestWeakSet('test_intersection_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_ior'))
suite.addTest(test.test_weakset.TestWeakSet('test_isdisjoint'))
suite.addTest(test.test_weakset.TestWeakSet('test_isub'))
suite.addTest(test.test_weakset.TestWeakSet('test_ixor'))
suite.addTest(test.test_weakset.TestWeakSet('test_len'))
#suite.addTest(test.test_weakset.TestWeakSet('test_len_cycles'))
suite.addTest(test.test_weakset.TestWeakSet('test_len_race'))
suite.addTest(test.test_weakset.TestWeakSet('test_lt'))
suite.addTest(test.test_weakset.TestWeakSet('test_methods'))
suite.addTest(test.test_weakset.TestWeakSet('test_ne'))
suite.addTest(test.test_weakset.TestWeakSet('test_new_or_init'))
suite.addTest(test.test_weakset.TestWeakSet('test_or'))
suite.addTest(test.test_weakset.TestWeakSet('test_pop'))
suite.addTest(test.test_weakset.TestWeakSet('test_remove'))
suite.addTest(test.test_weakset.TestWeakSet('test_sub'))
suite.addTest(test.test_weakset.TestWeakSet('test_sub_and_super'))
suite.addTest(test.test_weakset.TestWeakSet('test_subclass_with_custom_hash'))
suite.addTest(test.test_weakset.TestWeakSet('test_symmetric_difference'))
suite.addTest(test.test_weakset.TestWeakSet('test_symmetric_difference_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_union'))
suite.addTest(test.test_weakset.TestWeakSet('test_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_update_set'))
#suite.addTest(test.test_weakset.TestWeakSet('test_weak_destroy_and_mutate_while_iterating'))
suite.addTest(test.test_weakset.TestWeakSet('test_weak_destroy_while_iterating'))
suite.addTest(test.test_weakset.TestWeakSet('test_xor'))
return suite
else:
return loader.loadTestsFromModule(test.test_weakset, pattern)
run_test(__name__)
| 53.955882
| 101
| 0.746525
|
un_test
import test.test_weakset
def load_tests(loader, standard_tests, pattern):
if sys.implementation.name == 'ironpython':
suite = unittest.TestSuite()
suite.addTest(test.test_weakset.TestWeakSet('test_add'))
suite.addTest(test.test_weakset.TestWeakSet('test_and'))
suite.addTest(test.test_weakset.TestWeakSet('test_clear'))
suite.addTest(test.test_weakset.TestWeakSet('test_constructor_identity'))
suite.addTest(test.test_weakset.TestWeakSet('test_contains'))
suite.addTest(test.test_weakset.TestWeakSet('test_copy'))
suite.addTest(test.test_weakset.TestWeakSet('test_difference'))
suite.addTest(test.test_weakset.TestWeakSet('test_difference_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_discard'))
suite.addTest(test.test_weakset.TestWeakSet('test_eq'))
suite.addTest(test.test_weakset.TestWeakSet('test_gc'))
suite.addTest(test.test_weakset.TestWeakSet('test_gt'))
suite.addTest(test.test_weakset.TestWeakSet('test_hash'))
suite.addTest(test.test_weakset.TestWeakSet('test_iand'))
suite.addTest(test.test_weakset.TestWeakSet('test_init'))
suite.addTest(test.test_weakset.TestWeakSet('test_inplace_on_self'))
suite.addTest(test.test_weakset.TestWeakSet('test_intersection'))
suite.addTest(test.test_weakset.TestWeakSet('test_intersection_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_ior'))
suite.addTest(test.test_weakset.TestWeakSet('test_isdisjoint'))
suite.addTest(test.test_weakset.TestWeakSet('test_isub'))
suite.addTest(test.test_weakset.TestWeakSet('test_ixor'))
suite.addTest(test.test_weakset.TestWeakSet('test_len'))
suite.addTest(test.test_weakset.TestWeakSet('test_len_race'))
suite.addTest(test.test_weakset.TestWeakSet('test_lt'))
suite.addTest(test.test_weakset.TestWeakSet('test_methods'))
suite.addTest(test.test_weakset.TestWeakSet('test_ne'))
suite.addTest(test.test_weakset.TestWeakSet('test_new_or_init'))
suite.addTest(test.test_weakset.TestWeakSet('test_or'))
suite.addTest(test.test_weakset.TestWeakSet('test_pop'))
suite.addTest(test.test_weakset.TestWeakSet('test_remove'))
suite.addTest(test.test_weakset.TestWeakSet('test_sub'))
suite.addTest(test.test_weakset.TestWeakSet('test_sub_and_super'))
suite.addTest(test.test_weakset.TestWeakSet('test_subclass_with_custom_hash'))
suite.addTest(test.test_weakset.TestWeakSet('test_symmetric_difference'))
suite.addTest(test.test_weakset.TestWeakSet('test_symmetric_difference_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_union'))
suite.addTest(test.test_weakset.TestWeakSet('test_update'))
suite.addTest(test.test_weakset.TestWeakSet('test_update_set'))
suite.addTest(test.test_weakset.TestWeakSet('test_weak_destroy_while_iterating'))
suite.addTest(test.test_weakset.TestWeakSet('test_xor'))
return suite
else:
return loader.loadTestsFromModule(test.test_weakset, pattern)
run_test(__name__)
| true
| true
|
1c44391f7131c3bfff778a039706cd9754d9e372
| 890
|
py
|
Python
|
examples/pybullet/examples/switchConstraintSolver.py
|
stolk/bullet3
|
41a0d72759a47ef2df986b0bfe56a03e22516123
|
[
"Zlib"
] | 158
|
2016-11-17T19:37:51.000Z
|
2022-03-21T19:57:55.000Z
|
examples/pybullet/examples/switchConstraintSolver.py
|
stolk/bullet3
|
41a0d72759a47ef2df986b0bfe56a03e22516123
|
[
"Zlib"
] | 94
|
2016-11-18T09:55:57.000Z
|
2021-01-14T08:50:40.000Z
|
examples/pybullet/examples/switchConstraintSolver.py
|
stolk/bullet3
|
41a0d72759a47ef2df986b0bfe56a03e22516123
|
[
"Zlib"
] | 51
|
2017-05-24T10:20:25.000Z
|
2022-03-17T15:07:02.000Z
|
import pybullet as p
import time
p.connect(p.GUI)
#p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_PGS, globalCFM = 0.0001)
p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_DANTZIG,
globalCFM=0.000001)
#p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_PGS, globalCFM = 0.0001)
p.loadURDF("plane.urdf")
radius = 0.025
distance = 1.86
yaw = 135
pitch = -11
targetPos = [0, 0, 0]
p.setPhysicsEngineParameter(solverResidualThreshold=0.001, numSolverIterations=200)
p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos)
objectId = -1
for i in range(10):
objectId = p.loadURDF("cube_small.urdf", [1, 1, radius + i * 2 * radius])
p.changeDynamics(objectId, -1, 100)
timeStep = 1. / 240.
p.setGravity(0, 0, -10)
while (p.isConnected()):
p.stepSimulation()
time.sleep(timeStep)
| 28.709677
| 98
| 0.748315
|
import pybullet as p
import time
p.connect(p.GUI)
p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_DANTZIG,
globalCFM=0.000001)
p.loadURDF("plane.urdf")
radius = 0.025
distance = 1.86
yaw = 135
pitch = -11
targetPos = [0, 0, 0]
p.setPhysicsEngineParameter(solverResidualThreshold=0.001, numSolverIterations=200)
p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos)
objectId = -1
for i in range(10):
objectId = p.loadURDF("cube_small.urdf", [1, 1, radius + i * 2 * radius])
p.changeDynamics(objectId, -1, 100)
timeStep = 1. / 240.
p.setGravity(0, 0, -10)
while (p.isConnected()):
p.stepSimulation()
time.sleep(timeStep)
| true
| true
|
1c4439485e85dc067e2ce19d19190057cd711a5a
| 13,225
|
py
|
Python
|
project/Python Code/implementations.py
|
parkjan4/HiggsBoson
|
1e31f9bd2c6cb03c6acc8caed573046bbc0d2c08
|
[
"MIT"
] | null | null | null |
project/Python Code/implementations.py
|
parkjan4/HiggsBoson
|
1e31f9bd2c6cb03c6acc8caed573046bbc0d2c08
|
[
"MIT"
] | null | null | null |
project/Python Code/implementations.py
|
parkjan4/HiggsBoson
|
1e31f9bd2c6cb03c6acc8caed573046bbc0d2c08
|
[
"MIT"
] | null | null | null |
from proj1_helpers import *
import numpy as np
import random
import matplotlib.pyplot as plt
######################### Loss Functions #########################
# Compute loss with Mean Squared Error
def compute_loss(y, tx, w):
e = y.reshape((len(y),1)) - tx.dot(w).reshape((len(y),1))
return 1/2*np.mean(e**2)
# Compute gradient for gradient descent
def compute_gradient(y, tx, w):
e = y.reshape((len(y),1)) - tx.dot(w).reshape((len(y),1))
grad = -tx.T.dot(e) / len(e)
return grad
def sigmoid(x):
return 1.0 / (1 + np.exp(-x))
def compute_loss_logistic(y, tx, w):
# loss formula works only for y = {0,1}
y[y == -1] = 0
y = y.reshape((len(y),1))
sigma = sigmoid(tx.dot(w)).reshape((len(y),1))
loss = y.T.dot(np.log(sigma)) + (1 - y).T.dot(np.log(1 - sigma))
return np.squeeze(- loss)
def compute_gradient_logistic(y, tx, w):
sigma = sigmoid(tx.dot(w)).reshape((len(y),1))
y = y.reshape((len(sigma),1))
grad = tx.T.dot(sigma - y)
return grad
######################### Methods Implementation #########################
# Gradient Descent
def least_squares_GD(y, tx, initial_w, max_iters, gamma):
ws = [initial_w]
losses = []
w = initial_w
for n_iter in range(max_iters):
gradient = compute_gradient(y, tx, w)
loss = compute_loss(y, tx, w)
w = w - gamma * gradient
ws.append(w)
losses.append(loss)
return ws[-1], losses[-1]
# Stochastic Gradient Descent
def least_squares_SGD(y, tx, initial_w, max_iters, gamma):
ws = [initial_w]
losses = []
w = initial_w
n_iter = 0
batch_size = 1
for batch_y, batch_tx in batch_iter(y, tx, batch_size, max_iters):
grad = compute_gradient(batch_y, batch_tx, w)
loss = compute_loss(batch_y, batch_tx, w)
w = w - gamma * grad
ws.append(w)
losses.append(loss)
n_iter += 1
return ws[-1], losses[-1]
def least_squares(y, tx):
w = np.linalg.solve(tx.T.dot(tx), tx.T.dot(y))
loss = compute_loss(y, tx ,w)
return w, loss
def ridge_regression(y, tx, lambda_):
w = np.linalg.solve(tx.T.dot(tx) + lambda_ * np.eye(tx.shape[1]), tx.T.dot(y))
loss = compute_loss(y, tx, w)
return w, loss
def logistic_regression(y, tx, w, max_iters, gamma):
for n_iter in range(max_iters):
loss = compute_loss_logistic(y, tx, w)
grad = compute_gradient_logistic(y, tx, w)
w -= gamma * grad
return w, loss
def reg_logistic_regression(y, tx, lambda_, w, max_iters, gamma):
for n_iter in range(max_iters):
loss = compute_loss_logistic(y, tx, w) + lambda_ * np.squeeze(w.T.dot(w))
grad = compute_gradient_logistic(y, tx, w) + 2 * lambda_ * w
w -= gamma * grad
return w, loss
######################### Improvements #########################
def RR_optimal_lambda_finder(y, tx, learning_algo):
k_folds = 10
lambdas = np.logspace(-4,0,30)
seeds = range(10)
# define an empty matrix to store cross validation errors
CV_errors = np.empty((len(seeds), len(lambdas)), dtype=float)
for i, seed in enumerate(seeds):
for j, lambda_ in enumerate(lambdas):
errors = cross_validation(y, tx, k_folds, learning_algo, lambda_, seed)
CV_error = np.mean(errors)
CV_errors[i, j] = CV_error
best_accuracy = max(np.mean(CV_errors, axis=0))
opt_lambda = lambdas[np.argmax(np.mean(CV_errors, axis=0))]
return opt_lambda, best_accuracy
def build_poly(x, degree):
"""polynomial basis functions for input data x, for j=0 up to j=degree."""
poly = np.ones((len(x), 1))
for deg in range(1, degree+1):
poly = np.c_[poly, np.power(x, deg)]
return poly[:,1:]
def interaction_forward_selection(y, tx):
'''For every possible 2nd order interaction term, add to the original \
feature set iff its inclusion leads to higher accuracy based on 5-fold CV'''
# define reference accuracy (with NO interaction terms)
reference = np.mean(cross_validation(y, tx, 5, least_squares, 0, 1))
# define list to store feature indices whose interaction is useful
interaction_terms = []
counter = 0
num_features = 30 # original number of features
for col1 in range(num_features):
for col2 in range(num_features):
if col1 >= col2: continue
temp_tx = np.c_[tx, tx[:,col1] * tx[:,col2]]
accuracy = np.mean(cross_validation(y, temp_tx, 5, least_squares, 0, 1))
# if new accuracy is higher, add the term
if accuracy > reference:
reference = accuracy
tx = temp_tx
interaction_terms.append((col1, col2))
counter += 1
print("{p:.2f}% complete, best accuracy: {a:.9f}".format(p=100* counter / 435, a=reference))
return tx, interaction_terms
def third_interaction_forward_selection(y, tx):
'''For every possible 3rd order interaction term, add to the original \
feature set iff its inclusion leads to higher accuracy based on 5-fold CV'''
# define reference accuracy (with NO interaction terms)
reference = np.mean(cross_validation(y, tx, 5, least_squares, 0, 1))
# define list to store feature indices whose interaction is useful
third_interaction_terms = []
counter = 0 # delete this line
num_features = 30 # original number of features
for col1 in range(num_features):
for col2 in range(num_features):
if col1 >= col2: continue
for col3 in range(num_features):
if col2 >= col3: continue
temp_tx = np.c_[tx, tx[:,col1] * tx[:,col2] * tx[:,col3]]
accuracy = np.mean(cross_validation(y, temp_tx, 5, least_squares, 0, 1))
# if new accuracy is higher, add the term
if accuracy > reference:
reference = accuracy
tx = temp_tx
third_interaction_terms.append((col1, col2, col3))
counter += 1 # delete this line
print("{p:.2f}% complete, best accuracy: {a:.9f}".format(p=100* counter / 4060, a=reference))
return tx, third_interaction_terms
def build_k_indices(y, k_fold, seed):
"""build k indices for k-fold."""
num_row = y.shape[0]
interval = int(num_row / k_fold)
np.random.seed(seed)
indices = np.random.permutation(num_row)
k_indices = [indices[k * interval: (k + 1) * interval]
for k in range(k_fold)]
return np.array(k_indices)
def cross_validation(y, tx, k_folds, learning_algo, lambda_, seed):
# build k_folds instances of indices
k_indices = build_k_indices(y, k_folds, seed)
# define list to store cross validation error
errors = []
for k in range(k_folds):
tx_valid = tx[k_indices[k,:]]
y_valid = y[k_indices[k,:]]
tx_train = tx[k_indices[list(set(range(k_indices.shape[0])) - set([k])),:].reshape((k_indices.shape[0]-1)*k_indices.shape[1]),:]
y_train = y[k_indices[list(set(range(k_indices.shape[0])) - set([k])),:].reshape((k_indices.shape[0]-1)*k_indices.shape[1])]
# least squares using normal equations
if learning_algo == least_squares:
w, loss_tr = learning_algo(y_train, tx_train)
# ridge regression using normal equations
elif learning_algo == ridge_regression:
w, loss_tr = learning_algo(y_train, tx_train, lambda_)
# least squares gradient descent
elif learning_algo == least_squares_GD:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 1000
gamma = 0.0000001
w, loss_tr = learning_algo(y_train, tx_train, initial_w, max_iters, gamma)
# least squares stochastic gradient descent
elif learning_algo == least_squares_SGD:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 1000
gamma = 0.0000001
w, loss_tr = learning_algo(y_train, tx_train, initial_w, max_iters, gamma)
# logistic regression gradient descent
elif learning_algo == logistic_regression:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 500
gamma = 0.000000000000001
w, loss_tr = learning_algo(y_train, tx_train, initial_w, max_iters, gamma)
# regularized logistic regression gradient descent
elif learning_algo == reg_logistic_regression:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 500
gamma = 0.000000000000001
w, loss_tr = learning_algo(y_train, tx_train, lambda_, initial_w, max_iters, gamma)
y_hat = predict_labels(w, tx_valid)
errors.append(sum(y_valid.reshape((len(y_valid),1))==y_hat.reshape((len(y_hat),1))) / len(y_valid))
# return the average error rate across the folds
return errors
def data_segmentation(y, tx):
'''
PRI_jet_num is a feature which only takes a value of 0, 1, 2, or 3.
Many features become undefined (-999) based on which value it takes.
The purpose of this function is to split the data based on the four values.
Source: http://opendata.cern.ch/record/328
Input:
y: reponse
tx: data matrix
Returns:
four sets of response and data matrices segmented based on PRI_jet_num
'''
# data segmentation
temp_matrix = np.c_[y, tx]
indices_0 = temp_matrix[:,23]==0
temp_matrix_0 = temp_matrix[indices_0,:]
y_0 = temp_matrix_0[:,0]
tx_0 = temp_matrix_0[:,1:]
indices_1 = temp_matrix[:,23]==1
temp_matrix_1 = temp_matrix[indices_1,:]
y_1 = temp_matrix_1[:,0]
tx_1 = temp_matrix_1[:,1:]
indices_2 = temp_matrix[:,23]==2
temp_matrix_2 = temp_matrix[indices_2,:]
y_2 = temp_matrix_2[:,0]
tx_2 = temp_matrix_2[:,1:]
indices_3 = temp_matrix[:,23]==3
temp_matrix_3 = temp_matrix[indices_3,:]
y_3 = temp_matrix_3[:,0]
tx_3 = temp_matrix_3[:,1:]
# when PRI_jet_num is 0, the following features are undefined and thus removed
tx_0 = np.delete(tx_0, np.s_[4,5,6,12,22,23,24,25,26,27,28,29], axis=1)
# when PRI_jet_num is 1, the following features are undefined and thus removed
tx_1 = np.delete(tx_1, np.s_[4,5,6,12,22,26,27,28], axis=1)
# at least, PRI_jet_num itself is removed
tx_2 = np.delete(tx_2, np.s_[22], axis=1)
tx_3 = np.delete(tx_3, np.s_[22], axis=1)
# replace any remaining -999 values with the mean of that feature
tx_0 = replace_with_mean(tx_0)
tx_1 = replace_with_mean(tx_1)
tx_2 = replace_with_mean(tx_2)
tx_3 = replace_with_mean(tx_3)
return y_0, tx_0, y_1, tx_1, y_2, tx_2, y_3, tx_3, indices_0, indices_1, indices_2, indices_3
def backward_selection(y, tx):
'''Performs backward feature selection using least squares algorithm
Input:
y: response
tx: data matrix
Output:
new data matrix with (potentially) fewer features'''
cols_removed = []
temp_tx, col_removed = backward_selection_algorithm(y, tx)
while tx.shape[1] != temp_tx.shape[1]: # means a feature was removed
tx = temp_tx
cols_removed.append(col_removed)
temp_tx, col_removed = backward_selection_algorithm(y, temp_tx)
return tx, cols_removed
def backward_selection_algorithm(y, tx):
k_folds = 10
seed = 1
index_to_remove = []
reference = np.mean(cross_validation(y, tx, k_folds, least_squares, 0.0001, seed))
for c in range(tx.shape[1]):
temp_tx = tx[:,list(set(range(tx.shape[1])) - set([c]))]
CV_accuracy = np.mean(cross_validation(y, temp_tx, k_folds, least_squares, 0.0001, seed))
if CV_accuracy > reference:
reference = CV_accuracy
index_to_remove.append(c)
if len(index_to_remove) == 0: # means no features were removed
return tx, -1
return tx[:,list(set(range(tx.shape[1])) - set([index_to_remove[-1]]))], index_to_remove[-1]
def replace_with_mean(tx):
'''replace all -999 values with mean value of each column'''
for col in range(tx.shape[1]):
# find indices for which the value is -999
indices = tx[:,col]==-999
# replace with mean value
tx[indices,col] = np.mean(tx[~indices,col])
return tx
######################### Helpers #########################
# Creates batches for stochastic gradient descent
def batch_iter(y, tx, batch_size, num_batches=1, shuffle=True):
data_size = len(y)
if shuffle:
shuffle_indices = np.random.permutation(np.arange(data_size))
shuffled_y = y[shuffle_indices]
shuffled_tx = tx[shuffle_indices]
else:
shuffled_y = y
shuffled_tx = tx
for batch_num in range(num_batches):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size)
if start_index != end_index:
yield shuffled_y[start_index:end_index], shuffled_tx[start_index:end_index]
| 36.035422
| 136
| 0.61603
|
from proj1_helpers import *
import numpy as np
import random
import matplotlib.pyplot as plt
ion_terms
def build_k_indices(y, k_fold, seed):
num_row = y.shape[0]
interval = int(num_row / k_fold)
np.random.seed(seed)
indices = np.random.permutation(num_row)
k_indices = [indices[k * interval: (k + 1) * interval]
for k in range(k_fold)]
return np.array(k_indices)
def cross_validation(y, tx, k_folds, learning_algo, lambda_, seed):
k_indices = build_k_indices(y, k_folds, seed)
errors = []
for k in range(k_folds):
tx_valid = tx[k_indices[k,:]]
y_valid = y[k_indices[k,:]]
tx_train = tx[k_indices[list(set(range(k_indices.shape[0])) - set([k])),:].reshape((k_indices.shape[0]-1)*k_indices.shape[1]),:]
y_train = y[k_indices[list(set(range(k_indices.shape[0])) - set([k])),:].reshape((k_indices.shape[0]-1)*k_indices.shape[1])]
if learning_algo == least_squares:
w, loss_tr = learning_algo(y_train, tx_train)
elif learning_algo == ridge_regression:
w, loss_tr = learning_algo(y_train, tx_train, lambda_)
elif learning_algo == least_squares_GD:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 1000
gamma = 0.0000001
w, loss_tr = learning_algo(y_train, tx_train, initial_w, max_iters, gamma)
elif learning_algo == least_squares_SGD:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 1000
gamma = 0.0000001
w, loss_tr = learning_algo(y_train, tx_train, initial_w, max_iters, gamma)
elif learning_algo == logistic_regression:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 500
gamma = 0.000000000000001
w, loss_tr = learning_algo(y_train, tx_train, initial_w, max_iters, gamma)
elif learning_algo == reg_logistic_regression:
initial_w = np.zeros((tx.shape[1],1))
max_iters = 500
gamma = 0.000000000000001
w, loss_tr = learning_algo(y_train, tx_train, lambda_, initial_w, max_iters, gamma)
y_hat = predict_labels(w, tx_valid)
errors.append(sum(y_valid.reshape((len(y_valid),1))==y_hat.reshape((len(y_hat),1))) / len(y_valid))
return errors
def data_segmentation(y, tx):
temp_matrix = np.c_[y, tx]
indices_0 = temp_matrix[:,23]==0
temp_matrix_0 = temp_matrix[indices_0,:]
y_0 = temp_matrix_0[:,0]
tx_0 = temp_matrix_0[:,1:]
indices_1 = temp_matrix[:,23]==1
temp_matrix_1 = temp_matrix[indices_1,:]
y_1 = temp_matrix_1[:,0]
tx_1 = temp_matrix_1[:,1:]
indices_2 = temp_matrix[:,23]==2
temp_matrix_2 = temp_matrix[indices_2,:]
y_2 = temp_matrix_2[:,0]
tx_2 = temp_matrix_2[:,1:]
indices_3 = temp_matrix[:,23]==3
temp_matrix_3 = temp_matrix[indices_3,:]
y_3 = temp_matrix_3[:,0]
tx_3 = temp_matrix_3[:,1:]
tx_0 = np.delete(tx_0, np.s_[4,5,6,12,22,23,24,25,26,27,28,29], axis=1)
tx_1 = np.delete(tx_1, np.s_[4,5,6,12,22,26,27,28], axis=1)
tx_2 = np.delete(tx_2, np.s_[22], axis=1)
tx_3 = np.delete(tx_3, np.s_[22], axis=1)
tx_0 = replace_with_mean(tx_0)
tx_1 = replace_with_mean(tx_1)
tx_2 = replace_with_mean(tx_2)
tx_3 = replace_with_mean(tx_3)
return y_0, tx_0, y_1, tx_1, y_2, tx_2, y_3, tx_3, indices_0, indices_1, indices_2, indices_3
def backward_selection(y, tx):
cols_removed = []
temp_tx, col_removed = backward_selection_algorithm(y, tx)
while tx.shape[1] != temp_tx.shape[1]:
tx = temp_tx
cols_removed.append(col_removed)
temp_tx, col_removed = backward_selection_algorithm(y, temp_tx)
return tx, cols_removed
def backward_selection_algorithm(y, tx):
k_folds = 10
seed = 1
index_to_remove = []
reference = np.mean(cross_validation(y, tx, k_folds, least_squares, 0.0001, seed))
for c in range(tx.shape[1]):
temp_tx = tx[:,list(set(range(tx.shape[1])) - set([c]))]
CV_accuracy = np.mean(cross_validation(y, temp_tx, k_folds, least_squares, 0.0001, seed))
if CV_accuracy > reference:
reference = CV_accuracy
index_to_remove.append(c)
if len(index_to_remove) == 0:
return tx, -1
return tx[:,list(set(range(tx.shape[1])) - set([index_to_remove[-1]]))], index_to_remove[-1]
def replace_with_mean(tx):
for col in range(tx.shape[1]):
indices = tx[:,col]==-999
tx[indices,col] = np.mean(tx[~indices,col])
return tx
| true
| true
|
1c443992ade1ebf92009bd9acefe5a6589fee159
| 652
|
py
|
Python
|
Egzersiz/efecan/FonksiyonEgzersiz.py
|
ibrahimediz/ornekproje
|
c5ebeafc43a9c6d2aa639d0d95eedbce65991576
|
[
"Apache-2.0"
] | null | null | null |
Egzersiz/efecan/FonksiyonEgzersiz.py
|
ibrahimediz/ornekproje
|
c5ebeafc43a9c6d2aa639d0d95eedbce65991576
|
[
"Apache-2.0"
] | null | null | null |
Egzersiz/efecan/FonksiyonEgzersiz.py
|
ibrahimediz/ornekproje
|
c5ebeafc43a9c6d2aa639d0d95eedbce65991576
|
[
"Apache-2.0"
] | null | null | null |
from string import ascii_lowercase,ascii_uppercase,punctuation,digits
import random as rnd
def passwordfunc ():
pwd=''
liste = [ascii_lowercase,ascii_uppercase,punctuation,digits]
opt=input("uzunluk belirtmek ister misiniz? (yes or no)")
if opt =='Yes':
lent=input("Şifre uzunluğu ne kadar olsun: ")
for _ in range(len(lent)):
pwd+=rnd.choice(rnd.choice(liste))
else:
for _ in range(15):
pwd+=rnd.choice(rnd.choice(liste))
if pwd not in ascii_lowercase and ascii_uppercase and punctuation and digits:
return pwd
else:
passwordfunc ()
print(passwordfunc ())
| 29.636364
| 81
| 0.659509
|
from string import ascii_lowercase,ascii_uppercase,punctuation,digits
import random as rnd
def passwordfunc ():
pwd=''
liste = [ascii_lowercase,ascii_uppercase,punctuation,digits]
opt=input("uzunluk belirtmek ister misiniz? (yes or no)")
if opt =='Yes':
lent=input("Şifre uzunluğu ne kadar olsun: ")
for _ in range(len(lent)):
pwd+=rnd.choice(rnd.choice(liste))
else:
for _ in range(15):
pwd+=rnd.choice(rnd.choice(liste))
if pwd not in ascii_lowercase and ascii_uppercase and punctuation and digits:
return pwd
else:
passwordfunc ()
print(passwordfunc ())
| true
| true
|
1c443a79ec656385843cf4a30bde8b1696a07a76
| 3,116
|
py
|
Python
|
setup.py
|
cigroup-ol/metaopt
|
6dfd5105d3c6eaf00f96670175cae16021069514
|
[
"BSD-3-Clause"
] | 8
|
2015-02-02T21:42:23.000Z
|
2019-06-30T18:12:43.000Z
|
setup.py
|
cigroup-ol/metaopt
|
6dfd5105d3c6eaf00f96670175cae16021069514
|
[
"BSD-3-Clause"
] | 4
|
2015-09-24T14:12:38.000Z
|
2021-12-08T22:42:52.000Z
|
setup.py
|
cigroup-ol/metaopt
|
6dfd5105d3c6eaf00f96670175cae16021069514
|
[
"BSD-3-Clause"
] | 6
|
2015-02-27T12:35:33.000Z
|
2020-10-15T21:04:02.000Z
|
# -*- coding: utf-8 -*-
"""setup.py script for MetaOpt."""
from __future__ import division, print_function, with_statement
import os
import sys
try:
from setuptools import setup, find_packages
except ImportError:
import ez_setup
ez_setup.use_setuptools()
from setuptools import setup, find_packages
from pip.req import parse_requirements
import metaopt
def extract_package_name(requirement):
return str(requirement.req).replace('-', '_').split('==')[0]
def find_requirements(req_file='requirements.txt'):
return [extract_package_name(r) for r in parse_requirements(req_file)]
DESCRIPTION = 'MetaOpt is a library that optimizes black-box functions using ' + \
'a limited amount of time and utilizing multiple processors. ' + \
'The main focus of MetaOpt is the parameter tuning for machine ' + \
'learning and heuristic optimization.'
if os.path.isfile('README.rst'):
LONG_DESCRIPTION = "\n\n".join([open('README.rst').read(),
open('CHANGELOG.rst').read()])
else:
LONG_DESCRIPTION = DESCRIPTION
setup(
author=metaopt.__author__,
author_email=metaopt.__author_email__,
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Console',
'Environment :: Plugins',
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Information Technology',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python :: 2.5',
'Programming Language :: Python :: 2.6',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.0',
'Programming Language :: Python :: 3.1',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: Software Development :: Libraries :: Python Modules',
'Topic :: System :: Distributed Computing'
],
data_files=[("", ["README.rst", "LICENSE.rst", "requirements_examples.txt",
"requirements_lint.txt", "requirements_test.txt"])],
description=DESCRIPTION,
ext_modules=[],
install_requires=[],
license=metaopt.__license__,
long_description=LONG_DESCRIPTION,
name='metaopt',
packages=find_packages(exclude=('examples', 'docs', 'tests')),
package_data={'': ['LICENSE.rst', 'README.rst', 'requirements*.txt']},
setup_requires=[],
tests_require=find_requirements('requirements_test.txt'),
test_suite='metaopt.tests',
url=metaopt.__url__,
use_2to3=(sys.version_info >= (3,)),
version=metaopt.__version__,
)
| 38
| 82
| 0.643774
|
from __future__ import division, print_function, with_statement
import os
import sys
try:
from setuptools import setup, find_packages
except ImportError:
import ez_setup
ez_setup.use_setuptools()
from setuptools import setup, find_packages
from pip.req import parse_requirements
import metaopt
def extract_package_name(requirement):
return str(requirement.req).replace('-', '_').split('==')[0]
def find_requirements(req_file='requirements.txt'):
return [extract_package_name(r) for r in parse_requirements(req_file)]
DESCRIPTION = 'MetaOpt is a library that optimizes black-box functions using ' + \
'a limited amount of time and utilizing multiple processors. ' + \
'The main focus of MetaOpt is the parameter tuning for machine ' + \
'learning and heuristic optimization.'
if os.path.isfile('README.rst'):
LONG_DESCRIPTION = "\n\n".join([open('README.rst').read(),
open('CHANGELOG.rst').read()])
else:
LONG_DESCRIPTION = DESCRIPTION
setup(
author=metaopt.__author__,
author_email=metaopt.__author_email__,
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Console',
'Environment :: Plugins',
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Information Technology',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python :: 2.5',
'Programming Language :: Python :: 2.6',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.0',
'Programming Language :: Python :: 3.1',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: Software Development :: Libraries :: Python Modules',
'Topic :: System :: Distributed Computing'
],
data_files=[("", ["README.rst", "LICENSE.rst", "requirements_examples.txt",
"requirements_lint.txt", "requirements_test.txt"])],
description=DESCRIPTION,
ext_modules=[],
install_requires=[],
license=metaopt.__license__,
long_description=LONG_DESCRIPTION,
name='metaopt',
packages=find_packages(exclude=('examples', 'docs', 'tests')),
package_data={'': ['LICENSE.rst', 'README.rst', 'requirements*.txt']},
setup_requires=[],
tests_require=find_requirements('requirements_test.txt'),
test_suite='metaopt.tests',
url=metaopt.__url__,
use_2to3=(sys.version_info >= (3,)),
version=metaopt.__version__,
)
| true
| true
|
1c443b008425d87a2b13f8d0a5cd54540e1eb168
| 4,058
|
py
|
Python
|
setup.py
|
jemilc/shap
|
ed284b6278813c5292d83dc2a22976a0fdedd4ec
|
[
"MIT"
] | 1
|
2020-05-28T18:31:41.000Z
|
2020-05-28T18:31:41.000Z
|
setup.py
|
jemilc/shap
|
ed284b6278813c5292d83dc2a22976a0fdedd4ec
|
[
"MIT"
] | null | null | null |
setup.py
|
jemilc/shap
|
ed284b6278813c5292d83dc2a22976a0fdedd4ec
|
[
"MIT"
] | 2
|
2021-12-13T19:34:37.000Z
|
2021-12-13T23:45:36.000Z
|
from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext as _build_ext
import os
import re
import codecs
# to publish use:
# > python setup.py sdist bdist_wheel upload
# which depends on ~/.pypirc
here = os.path.abspath(os.path.dirname(__file__))
def read(*parts):
with codecs.open(os.path.join(here, *parts), 'r') as fp:
return fp.read()
def find_version(*file_paths):
version_file = read(*file_paths)
version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find version string.")
# Extend the default build_ext class to bootstrap numpy installation
# that are needed to build C extensions.
# see https://stackoverflow.com/questions/19919905/how-to-bootstrap-numpy-installation-in-setup-py
class build_ext(_build_ext):
def finalize_options(self):
_build_ext.finalize_options(self)
if isinstance(__builtins__, dict):
__builtins__["__NUMPY_SETUP__"] = False
else:
setattr(__builtins__, "__NUMPY_SETUP__", False)
import numpy
print("numpy.get_include()", numpy.get_include())
self.include_dirs.append(numpy.get_include())
def run_setup(with_binary=True, test_xgboost=True, test_lightgbm=True):
ext_modules = []
if with_binary:
ext_modules.append(
Extension('shap._cext', sources=['shap/_cext.cc'])
)
if test_xgboost and test_lightgbm:
tests_require = ['nose', 'xgboost', 'lightgbm']
elif test_xgboost:
tests_require = ['nose', 'xgboost']
elif test_lightgbm:
tests_require = ['nose', 'lightgbm']
else:
tests_require = ['nose']
setup(
name='shap',
version=find_version("shap", "__init__.py"),
description='A unified approach to explain the output of any machine learning model.',
long_description="SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of " + \
"any machine learning model. SHAP connects game theory with local explanations, uniting " + \
"several previous methods and representing the only possible consistent and locally accurate " + \
"additive feature attribution method based on expectations.",
long_description_content_type="text/markdown",
url='http://github.com/slundberg/shap',
author='Scott Lundberg',
author_email='slund1@cs.washington.edu',
license='MIT',
packages=['shap', 'shap.explainers', 'shap.explainers.other', 'shap.plots', 'shap.benchmark'],
package_data={'shap': ['plots/resources/*']},
cmdclass={'build_ext': build_ext},
setup_requires=['numpy'],
install_requires=['numpy', 'scipy', 'scikit-learn', 'matplotlib', 'pandas', 'tqdm', 'ipython'],
test_suite='nose.collector',
tests_require=tests_require,
ext_modules=ext_modules,
zip_safe=False
)
def try_run_setup(**kwargs):
""" Fails gracefully when various install steps don't work.
"""
try:
run_setup(**kwargs)
except Exception as e:
print(str(e))
if "xgboost" in str(e).lower():
kwargs["test_xgboost"] = False
print("Couldn't install XGBoost for testing!")
try_run_setup(**kwargs)
elif "lightgbm" in str(e).lower():
kwargs["test_lightgbm"] = False
print("Couldn't install LightGBM for testing!")
try_run_setup(**kwargs)
elif kwargs["with_binary"]:
kwargs["with_binary"] = False
print("WARNING: The C extension could not be compiled, sklearn tree models not supported.")
try_run_setup(**kwargs)
else:
print("ERROR: Failed to build!")
# we seem to need this import guard for appveyor
if __name__ == "__main__":
try_run_setup(with_binary=True, test_xgboost=True, test_lightgbm=True)
| 38.283019
| 123
| 0.646624
|
from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext as _build_ext
import os
import re
import codecs
here = os.path.abspath(os.path.dirname(__file__))
def read(*parts):
with codecs.open(os.path.join(here, *parts), 'r') as fp:
return fp.read()
def find_version(*file_paths):
version_file = read(*file_paths)
version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find version string.")
# Extend the default build_ext class to bootstrap numpy installation
# that are needed to build C extensions.
# see https://stackoverflow.com/questions/19919905/how-to-bootstrap-numpy-installation-in-setup-py
class build_ext(_build_ext):
def finalize_options(self):
_build_ext.finalize_options(self)
if isinstance(__builtins__, dict):
__builtins__["__NUMPY_SETUP__"] = False
else:
setattr(__builtins__, "__NUMPY_SETUP__", False)
import numpy
print("numpy.get_include()", numpy.get_include())
self.include_dirs.append(numpy.get_include())
def run_setup(with_binary=True, test_xgboost=True, test_lightgbm=True):
ext_modules = []
if with_binary:
ext_modules.append(
Extension('shap._cext', sources=['shap/_cext.cc'])
)
if test_xgboost and test_lightgbm:
tests_require = ['nose', 'xgboost', 'lightgbm']
elif test_xgboost:
tests_require = ['nose', 'xgboost']
elif test_lightgbm:
tests_require = ['nose', 'lightgbm']
else:
tests_require = ['nose']
setup(
name='shap',
version=find_version("shap", "__init__.py"),
description='A unified approach to explain the output of any machine learning model.',
long_description="SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of " + \
"any machine learning model. SHAP connects game theory with local explanations, uniting " + \
"several previous methods and representing the only possible consistent and locally accurate " + \
"additive feature attribution method based on expectations.",
long_description_content_type="text/markdown",
url='http://github.com/slundberg/shap',
author='Scott Lundberg',
author_email='slund1@cs.washington.edu',
license='MIT',
packages=['shap', 'shap.explainers', 'shap.explainers.other', 'shap.plots', 'shap.benchmark'],
package_data={'shap': ['plots/resources/*']},
cmdclass={'build_ext': build_ext},
setup_requires=['numpy'],
install_requires=['numpy', 'scipy', 'scikit-learn', 'matplotlib', 'pandas', 'tqdm', 'ipython'],
test_suite='nose.collector',
tests_require=tests_require,
ext_modules=ext_modules,
zip_safe=False
)
def try_run_setup(**kwargs):
try:
run_setup(**kwargs)
except Exception as e:
print(str(e))
if "xgboost" in str(e).lower():
kwargs["test_xgboost"] = False
print("Couldn't install XGBoost for testing!")
try_run_setup(**kwargs)
elif "lightgbm" in str(e).lower():
kwargs["test_lightgbm"] = False
print("Couldn't install LightGBM for testing!")
try_run_setup(**kwargs)
elif kwargs["with_binary"]:
kwargs["with_binary"] = False
print("WARNING: The C extension could not be compiled, sklearn tree models not supported.")
try_run_setup(**kwargs)
else:
print("ERROR: Failed to build!")
# we seem to need this import guard for appveyor
if __name__ == "__main__":
try_run_setup(with_binary=True, test_xgboost=True, test_lightgbm=True)
| true
| true
|
1c443bbd588fb502514c1dc2517c751f29430408
| 478
|
py
|
Python
|
server/functionsfake.py
|
Yavonix/011-Battlebot-App
|
143e1990548837d81c9fcbf805e5c727e2038850
|
[
"MIT"
] | 1
|
2021-07-29T03:26:29.000Z
|
2021-07-29T03:26:29.000Z
|
server/functionsfake.py
|
Yavonix/011-Battlebot-App
|
143e1990548837d81c9fcbf805e5c727e2038850
|
[
"MIT"
] | null | null | null |
server/functionsfake.py
|
Yavonix/011-Battlebot-App
|
143e1990548837d81c9fcbf805e5c727e2038850
|
[
"MIT"
] | null | null | null |
# Dummy program for development
def jointMode(ID):
print("JointModeEvent")
# Move a dynamixel that has been set up as a joint.
def moveJoint(ID, position, speed):
print("MoveJointEvent")
# === WHEEL FUNCTIONS === #
# Set up a dynamixel so that it behaves like wheel.
def wheelMode(ID):
pass
#print("WheelModeEvent")
# Move a dynamixel that has been set up as a wheel.
def moveWheel(ID, speed):
pass
#print("WheelSpeedEvent")
| 22.761905
| 51
| 0.656904
|
def jointMode(ID):
print("JointModeEvent")
def moveJoint(ID, position, speed):
print("MoveJointEvent")
def wheelMode(ID):
pass
def moveWheel(ID, speed):
pass
| true
| true
|
1c443dbe02aa3601a4b584eb0b5bc976480a3a4d
| 4,034
|
py
|
Python
|
devine.py
|
noutcha/devine
|
3a7d6767f9032d1b988efa4104adb3a5eca97c39
|
[
"MIT"
] | null | null | null |
devine.py
|
noutcha/devine
|
3a7d6767f9032d1b988efa4104adb3a5eca97c39
|
[
"MIT"
] | null | null | null |
devine.py
|
noutcha/devine
|
3a7d6767f9032d1b988efa4104adb3a5eca97c39
|
[
"MIT"
] | null | null | null |
"""
Prémière version du jeu Deviner.
On crée une fenêtre simple qui demande à l'utilisateur de deviner le nombre secret.
Avec une possibilité de guide en indiquant si le nombre choisi est plus grand
ou plus petit que le nombre secret
source d'inspiration: https://pythonfaqfr.readthedocs.io/en/latest/prog_even_tkinter.html#
"""
# coding: utf-8
# On importe randint pour geberer les nombres aléatoire
from random import randint
# On importe Tkinter pour la creation de l'interface graphique
import tkinter as tk #ici importation avec renommage, #from tkinter import * à éviter
from tkinter.messagebox import *
# On defini notre fonction de traitement
def user_recup_choix(event):
"fonction de rappel ou fonction de post-traitement quand le joueur a entré un nombre."
nbre_choisi = int(reponse.get()) # on recupère la reponse de l'user
reponse.delete(0, tk.END) # on vide le champ de saisie
proposition["text"] = nbre_choisi #On recupère le choix de l'user
if nombre_secret > nbre_choisi:
result["text"] = "Le nombre est plus grand" # On affiche le texte pour aider l'user
elif nombre_secret < nbre_choisi:
result["text"] = "Le nombre est plus petit"
else:
# On enlève les éléments dont on n'a plus besoin
lbl_reponse.destroy()
reponse.destroy()
# On replace les Labels `proposition` et `resultat` dans la ligne
# en dessous du titre
proposition.grid_forget()
proposition.grid(row=1, column=0)
result.grid_forget()
result.grid(row=1, column=1)
# On configure le label avec le texte voulu, dans le font voulu et
# dans la couleur désirée.
result.config(text="Tu as trouvé le nombre. Bravo!",
font=("", 12),
fg="green")
app = tk.Tk() # creation de la fenêtre
app.title("Mon premier jeu Avec Tkinter") # titre de ma fenetre
# fonction qutter
def Quitter():
if askyesno('Confirmation', 'Êtes-vous sûr de vouloir quitter le jeu ?'):
app.quit()
else:
showinfo('Confirmation', 'Continuer!')
# fonction a propos
def Apropos():
showinfo('A propos !', 'Devine (V1.0.0) est juste un jeu banal. \n le principe est simple, j\'ai un nombre et tu dois deviner ce nombre !')
# Menu de l'application
menubar = tk.Menu(app)
menu1 = tk.Menu(menubar, tearoff=0)
menu1.add_command(label="Créer")
menu1.add_command(label="Editer")
menu1.add_separator()
menu1.add_command(label="Quitter", command=app.quit)
menubar.add_cascade(label="Fichier", menu=menu1)
menu2 = tk.Menu(menubar, tearoff=0)
menu2.add_command(label="Couper")
menu2.add_command(label="Copier")
menu2.add_command(label="Coller")
menubar.add_cascade(label="Editer", menu=menu2)
menu3 = tk.Menu(menubar, tearoff=0)
menu3.add_command(label="A propos", command=Apropos)
menubar.add_cascade(label="Aide", menu=menu3)
app.config(menu=menubar)
# fin menu
titre = tk.Label(app, text="Devine le nombre auquel je pense", font=("", 16))
titre.grid(row=0, columnspan=2, pady=8)
#Génération du nombre secret
nombre_secret = randint(0, 100) + 1
lbl_reponse = tk.Label(app, text="Choisi un nombre entre 1 et 100 inclus:")
lbl_reponse.grid(row=1, column=0, pady=5, padx=5)
reponse = tk.Entry(app) # On demande ici la saisie dans le champ
reponse.grid(row=1, column=1, pady=5, padx=5)
reponse.bind("<Return>", user_recup_choix) # On affecte le resultat de la saisi dans la variable
#Création d'un Rejouer
bouton_rejouer = tk.Button(app, text="Rejouer")
bouton_rejouer.grid(row=3, column=1, pady=10, padx=10)
#Création d'un bouton Quitter
bouton_quitter = tk.Button(app, text="Quitter", command=Quitter)
bouton_quitter.grid(row=3, column=2, pady=15, padx=15)
proposition = tk.Label(app, text="")
proposition.grid(row=2, column=0, pady=5, padx=5)
result = tk.Label(app, text="")
result.grid(row=2, column=1, pady=5, padx=5)
app.mainloop()
| 33.616667
| 143
| 0.683689
|
from random import randint
import tkinter as tk #ici importation avec renommage, #from tkinter import * à éviter
from tkinter.messagebox import *
# On defini notre fonction de traitement
def user_recup_choix(event):
nbre_choisi = int(reponse.get()) # on recupère la reponse de l'user
reponse.delete(0, tk.END)
proposition["text"] = nbre_choisi
if nombre_secret > nbre_choisi:
result["text"] = "Le nombre est plus grand" # On affiche le texte pour aider l'user
elif nombre_secret < nbre_choisi:
result["text"] = "Le nombre est plus petit"
else:
lbl_reponse.destroy()
reponse.destroy()
# On replace les Labels `proposition` et `resultat` dans la ligne
# en dessous du titre
proposition.grid_forget()
proposition.grid(row=1, column=0)
result.grid_forget()
result.grid(row=1, column=1)
# On configure le label avec le texte voulu, dans le font voulu et
# dans la couleur désirée.
result.config(text="Tu as trouvé le nombre. Bravo!",
font=("", 12),
fg="green")
app = tk.Tk() # creation de la fenêtre
app.title("Mon premier jeu Avec Tkinter") # titre de ma fenetre
# fonction qutter
def Quitter():
if askyesno('Confirmation', 'Êtes-vous sûr de vouloir quitter le jeu ?'):
app.quit()
else:
showinfo('Confirmation', 'Continuer!')
# fonction a propos
def Apropos():
showinfo('A propos !', 'Devine (V1.0.0) est juste un jeu banal. \n le principe est simple, j\'ai un nombre et tu dois deviner ce nombre !')
menubar = tk.Menu(app)
menu1 = tk.Menu(menubar, tearoff=0)
menu1.add_command(label="Créer")
menu1.add_command(label="Editer")
menu1.add_separator()
menu1.add_command(label="Quitter", command=app.quit)
menubar.add_cascade(label="Fichier", menu=menu1)
menu2 = tk.Menu(menubar, tearoff=0)
menu2.add_command(label="Couper")
menu2.add_command(label="Copier")
menu2.add_command(label="Coller")
menubar.add_cascade(label="Editer", menu=menu2)
menu3 = tk.Menu(menubar, tearoff=0)
menu3.add_command(label="A propos", command=Apropos)
menubar.add_cascade(label="Aide", menu=menu3)
app.config(menu=menubar)
# fin menu
titre = tk.Label(app, text="Devine le nombre auquel je pense", font=("", 16))
titre.grid(row=0, columnspan=2, pady=8)
#Génération du nombre secret
nombre_secret = randint(0, 100) + 1
lbl_reponse = tk.Label(app, text="Choisi un nombre entre 1 et 100 inclus:")
lbl_reponse.grid(row=1, column=0, pady=5, padx=5)
reponse = tk.Entry(app) # On demande ici la saisie dans le champ
reponse.grid(row=1, column=1, pady=5, padx=5)
reponse.bind("<Return>", user_recup_choix) # On affecte le resultat de la saisi dans la variable
#Création d'un Rejouer
bouton_rejouer = tk.Button(app, text="Rejouer")
bouton_rejouer.grid(row=3, column=1, pady=10, padx=10)
bouton_quitter = tk.Button(app, text="Quitter", command=Quitter)
bouton_quitter.grid(row=3, column=2, pady=15, padx=15)
proposition = tk.Label(app, text="")
proposition.grid(row=2, column=0, pady=5, padx=5)
result = tk.Label(app, text="")
result.grid(row=2, column=1, pady=5, padx=5)
app.mainloop()
| true
| true
|
1c443dc6a22ecc7fee90d76acd68cbc1bacd1fb9
| 19,705
|
py
|
Python
|
includes/BOLTS/freecad/extrusions/profiles.py
|
codysandahl/3dprinting
|
98d588864e5ba5826c7ed16959aa7b1040a760b3
|
[
"MIT"
] | null | null | null |
includes/BOLTS/freecad/extrusions/profiles.py
|
codysandahl/3dprinting
|
98d588864e5ba5826c7ed16959aa7b1040a760b3
|
[
"MIT"
] | null | null | null |
includes/BOLTS/freecad/extrusions/profiles.py
|
codysandahl/3dprinting
|
98d588864e5ba5826c7ed16959aa7b1040a760b3
|
[
"MIT"
] | null | null | null |
# **************************************************************************************
# * *
# * BOLTS - Open Library of Technical Specifications *
# * *
# * Copyright (C) 2014 Johannes Reinhardt <jreinhardt@ist-dein-freund.de> *
# * *
# * This library is free software; you can redistribute it and/or *
# * modify it under the terms of the GNU Lesser General Public *
# * License as published by the Free Software Foundation; either *
# * version 2.1 of the License, or any later version. *
# * *
# * This library 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 *
# * Lesser General Public License for more details. *
# * *
# * You should have received a copy of the GNU Lesser General Public *
# * License along with this library; if not, write to the Free Software *
# * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA *
# * *
# **************************************************************************************
import math
import Part
from FreeCAD import Vector
from DraftGeomUtils import fillet as draft_fillet
# ************************************************************************************************
def vslot20x20(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 8 * [vslot_outline]
fillets = [5, 17, 29, 41]
corner_offset = 0
circle_offsets = [0]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# color
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
# ************************************************************************************************
def vslot20x40(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(-w, 0, True, True, False, False),
(-w, 0, False, True, True, False),
(-w, 0, True, False, True, False),
(-w, 0, False, False, True, True),
(-w, 0, True, True, True, True),
(-w, 0, False, True, False, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 12 * [vslot_outline]
fillets = [5, 29, 41, 65]
corner_offset = -1 * w
circle_offsets = [0, -w]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# color
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
# ************************************************************************************************
def vslot20x60(params, document):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(-w, 0, True, True, False, False),
(-w, 0, False, True, True, False),
(-2 * w, 0, True, True, False, False),
(-2 * w, 0, False, True, True, False),
(-2 * w, 0, True, False, True, False),
(-2 * w, 0, False, False, True, True),
(-2 * w, 0, True, True, True, True),
(-2 * w, 0, False, True, False, True),
(-w, 0, True, True, True, True),
(-w, 0, False, True, False, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 16 * [vslot_outline]
# add fillets in reverse order, as this inserts additional edges
fillets = [5, 41, 53, 89]
corner_offset = -2 * w
circle_offsets = [0, -w, -2 * w]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# color
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
# ************************************************************************************************
def vslot20x80(params, document):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(-w, 0, True, True, False, False),
(-w, 0, False, True, True, False),
(-2 * w, 0, True, True, False, False),
(-2 * w, 0, False, True, True, False),
(-3 * w, 0, True, True, False, False),
(-3 * w, 0, False, True, True, False),
(-3 * w, 0, True, False, True, False),
(-3 * w, 0, False, False, True, True),
(-3 * w, 0, True, True, True, True),
(-3 * w, 0, False, True, False, True),
(-2 * w, 0, True, True, True, True),
(-2 * w, 0, False, True, False, True),
(-w, 0, True, True, True, True),
(-w, 0, False, True, False, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 20 * [vslot_outline]
# add fillets in reverse order, as this inserts additional edges
fillets = [5, 53, 65, 113]
corner_offset = -3 * w
circle_offsets = [0, -w, -2 * w, -3 * w]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# color
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
# ************************************************************************************************
def tslot20x20(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 8 * [tslot_outline]
fillets = [5, 17, 29, 41]
corner_offset = 0
circle_offsets = [0]
face = tslot(symmetry, vertices, fillets, [], [], corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# ************************************************************************************************
def tslot20x20_three_slot(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = [tslot_outline] + 2 * [tslot_closed] + 5 * [tslot_outline]
fillets = [5, 7, 19, 31]
closed_symmetry = [
(0, 0, False, True, False, False),
]
closed_vertices = [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets,
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# ************************************************************************************************
def tslot20x20_two_slot(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = [tslot_outline] + 4 * [tslot_closed] + 3 * [tslot_outline]
fillets = [5, 7, 9, 21]
closed_symmetry = [
(0, 0, False, True, False, False),
(0, 0, False, False, True, False),
]
closed_vertices = 2 * [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets,
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# ************************************************************************************************
def tslot20x20_two_slot_opp(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = (
[tslot_outline]
+ 2 * [tslot_closed]
+ 2 * [tslot_outline]
+ 2 * [tslot_closed]
+ [tslot_outline]
)
fillets = [5, 7, 19, 21]
closed_symmetry = [
(0, 0, False, True, False, False),
(0, 0, False, True, False, True),
]
closed_vertices = 2 * [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets,
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# ************************************************************************************************
def tslot20x20_one_slot(
params,
document
):
name = params["name"]
le = params["l"]
# due to symmetry this can be nicely decomposed
# x offset, y offset, reverse, switch, mir_x, mir_y
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = [tslot_outline] + 6 * [tslot_closed] + [tslot_outline]
fillets = [5, 7, 9, 11]
closed_symmetry = [
(0, 0, False, True, False, False),
(0, 0, False, False, True, False),
(0, 0, False, True, False, True),
]
closed_vertices = 3 * [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
# ************************************************************************************************
# helper
def fillet(
lines,
indices,
radius
):
"""
fillets the corner between the segments and their successors in lines indicated by indices
"""
lines = lines[:]
# sort them in descending order, as filleting inserts additional edges
indices.sort()
indices.reverse()
for i in indices:
lines[slice(i, i + 2)] = draft_fillet(lines[slice(i, i + 2)], radius)
return lines
def assemble(
symmetry,
vertices,
offset_global=(0, 0)
):
"""
Assemble a wire from a list of symmetry information and a list of list of vertices
symmetry information is a tuple of
offset x, offset y, bool reverse, bool switch_comp, bool mirror_x, bool mirror_y
"""
offset = Vector(offset_global[0], offset_global[1], 0)
lines = []
vlast = None
vcur = None
for sym, verts in zip(symmetry, vertices):
o_x, o_y, reverse, switch, mir_x, mir_y = sym
mir_x = -1 if mir_x else 1
mir_y = -1 if mir_y else 1
if reverse:
verts = verts[::-1]
if vcur is None:
vcur = Vector(verts[0])
if switch:
vcur[0], vcur[1] = vcur[1], vcur[0]
vcur[0] = mir_x * vcur[0] + o_x + offset[0]
vcur[1] = mir_y * vcur[1] + o_y + offset[1]
for v in verts[1:]:
vlast = vcur
vcur = Vector(v)
if switch:
vcur[0], vcur[1] = vcur[1], vcur[0]
vcur[0] = mir_x * vcur[0] + o_x + offset[0]
vcur[1] = mir_y * vcur[1] + o_y + offset[1]
lines.append(Part.makeLine(vlast, vcur))
return lines
# ************************************************************************************************
# profile size
w = 20
# ************************************************************************************************
# Vslot profile:
# the size of the inner square
d = 5.68 + 3 / math.sqrt(2)
# one eight of the outline
vslot_outline = [
(0.5 * d, 0, 0),
(0.5 * d, 0.5 * 5.68, 0),
(0.5 * w - 1.8 - 1.64, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2), 0),
(0.5 * w - 1.8, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2), 0),
(0.5 * w - 1.8, 0.5 * 5.68, 0),
(0.5 * w, 0.5 * 5.68 + 1.8, 0),
(0.5 * w, 0.5 * w, 0)
]
space_symmetry = [
(0, 0, False, False, True, False),
(-w, 0, True, False, False, False),
(-w, 0, False, False, False, True),
(0, 0, True, False, True, True)
]
# big spaces
vslot_space = [
(0.5 * d, 0, 0),
(0.5 * d, 0.5 * 5.68, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8 - 1.96, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8, 0),
(0.5 * w, 0.5 * w - 1.8, 0),
]
# corner holes
vslot_cornerhole = [
(0.5 * w - 1.8, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2) + 1.07, 0),
(0.5 * w - 1.8, 0.5 * w - 1.8, 0),
(0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2) + 1.07, 0.5 * w - 1.8, 0),
(0.5 * w - 1.8, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2) + 1.07, 0)
]
def vslot(
symmetry,
vertices,
fillets,
corner_offset,
circle_offsets
):
outline = assemble(symmetry, vertices)
outline = fillet(outline, fillets, 1.5)
outline = Part.Wire(outline)
holes = []
# corners
# x offset, y offset, reverse, switch, mir_x, mir_y
corner_symmetry = [
(0, 0, False, False, False, False),
(corner_offset, 0, False, False, True, False),
(corner_offset, 0, False, False, True, True),
(0, 0, False, False, False, True),
]
for sym in corner_symmetry:
holes.append(Part.Wire(assemble([sym], [vslot_cornerhole])))
if sym[4] == sym[5]:
holes[-1].reverse()
# circular holes
for offset in circle_offsets:
holes.append(Part.Wire(Part.makeCircle(2.1, Vector(offset, 0, 0))))
holes[-1].reverse()
# big spaces
print("Space")
for offset in circle_offsets[:-1]:
print(space_symmetry, vslot_space)
holes.append(Part.Wire(assemble(space_symmetry, 4 * [vslot_space], (offset, 0))))
holes[-1].reverse()
print("Space")
# put everything together
return Part.Face([outline] + holes)
# ************************************************************************************************
# T slot profile:
# outline
tslot_outline = [
(5.0, 0, 0),
(5.0, 3.5, 0),
(7.5, 6.0, 0),
(9.0, 6.0, 0),
(9.0, 3.0, 0),
(10.0, 3.0, 0),
(10.0, 10.0, 0),
]
# closed slots ouline
tslot_closed = [
(10.0, 0.0, 0),
(10.0, 10.0, 0),
]
# closed slots spaces
tslot_closed_space = [
(5.0, 0, 0),
(5.0, 3.5, 0),
(7.5, 6.0, 0),
(9.0, 6.0, 0),
(9.0, -6.0, 0),
(7.5, -6.0, 0),
(5.0, -3.5, 0),
(5.0, 0, 0),
]
# big spaces
tslot_space = [
(0.5 * d, 0, 0),
(0.5 * d, 0.5 * 5.68, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8 - 1.96, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8, 0),
(0.5 * w, 0.5 * w - 1.8, 0),
]
def tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets
):
outline = assemble(symmetry, vertices)
outline = fillet(outline, fillets, 1.5)
outline = Part.Wire(outline)
holes = []
# closed holes
for sym, vert in zip(closed_symmetry, closed_vertices):
holes.append(Part.Wire(assemble([sym], [vert])))
if not sym[5]:
holes[-1].reverse()
# circular holes
for offset in circle_offsets:
holes.append(Part.Wire(Part.makeCircle(2.25, Vector(offset, 0, 0))))
holes[-1].reverse()
# put everything together
return Part.Face([outline] + holes)
| 29.279346
| 98
| 0.489267
|
import math
import Part
from FreeCAD import Vector
from DraftGeomUtils import fillet as draft_fillet
def vslot20x20(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 8 * [vslot_outline]
fillets = [5, 17, 29, 41]
corner_offset = 0
circle_offsets = [0]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
def vslot20x40(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(-w, 0, True, True, False, False),
(-w, 0, False, True, True, False),
(-w, 0, True, False, True, False),
(-w, 0, False, False, True, True),
(-w, 0, True, True, True, True),
(-w, 0, False, True, False, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 12 * [vslot_outline]
fillets = [5, 29, 41, 65]
corner_offset = -1 * w
circle_offsets = [0, -w]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
def vslot20x60(params, document):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(-w, 0, True, True, False, False),
(-w, 0, False, True, True, False),
(-2 * w, 0, True, True, False, False),
(-2 * w, 0, False, True, True, False),
(-2 * w, 0, True, False, True, False),
(-2 * w, 0, False, False, True, True),
(-2 * w, 0, True, True, True, True),
(-2 * w, 0, False, True, False, True),
(-w, 0, True, True, True, True),
(-w, 0, False, True, False, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 16 * [vslot_outline]
fillets = [5, 41, 53, 89]
corner_offset = -2 * w
circle_offsets = [0, -w, -2 * w]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
def vslot20x80(params, document):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(-w, 0, True, True, False, False),
(-w, 0, False, True, True, False),
(-2 * w, 0, True, True, False, False),
(-2 * w, 0, False, True, True, False),
(-3 * w, 0, True, True, False, False),
(-3 * w, 0, False, True, True, False),
(-3 * w, 0, True, False, True, False),
(-3 * w, 0, False, False, True, True),
(-3 * w, 0, True, True, True, True),
(-3 * w, 0, False, True, False, True),
(-2 * w, 0, True, True, True, True),
(-2 * w, 0, False, True, False, True),
(-w, 0, True, True, True, True),
(-w, 0, False, True, False, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 20 * [vslot_outline]
fillets = [5, 53, 65, 113]
corner_offset = -3 * w
circle_offsets = [0, -w, -2 * w, -3 * w]
face = vslot(symmetry, vertices, fillets, corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
if params["finish"] == "Black":
part.ViewObject.DiffuseColor = (0.1, 0.1, 0.1)
def tslot20x20(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = 8 * [tslot_outline]
fillets = [5, 17, 29, 41]
corner_offset = 0
circle_offsets = [0]
face = tslot(symmetry, vertices, fillets, [], [], corner_offset, circle_offsets)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
def tslot20x20_three_slot(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = [tslot_outline] + 2 * [tslot_closed] + 5 * [tslot_outline]
fillets = [5, 7, 19, 31]
closed_symmetry = [
(0, 0, False, True, False, False),
]
closed_vertices = [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets,
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
def tslot20x20_two_slot(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = [tslot_outline] + 4 * [tslot_closed] + 3 * [tslot_outline]
fillets = [5, 7, 9, 21]
closed_symmetry = [
(0, 0, False, True, False, False),
(0, 0, False, False, True, False),
]
closed_vertices = 2 * [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets,
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
def tslot20x20_two_slot_opp(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = (
[tslot_outline]
+ 2 * [tslot_closed]
+ 2 * [tslot_outline]
+ 2 * [tslot_closed]
+ [tslot_outline]
)
fillets = [5, 7, 19, 21]
closed_symmetry = [
(0, 0, False, True, False, False),
(0, 0, False, True, False, True),
]
closed_vertices = 2 * [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets,
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
def tslot20x20_one_slot(
params,
document
):
name = params["name"]
le = params["l"]
symmetry = [
(0, 0, False, False, False, False),
(0, 0, True, True, False, False),
(0, 0, False, True, True, False),
(0, 0, True, False, True, False),
(0, 0, False, False, True, True),
(0, 0, True, True, True, True),
(0, 0, False, True, False, True),
(0, 0, True, False, False, True),
]
vertices = [tslot_outline] + 6 * [tslot_closed] + [tslot_outline]
fillets = [5, 7, 9, 11]
closed_symmetry = [
(0, 0, False, True, False, False),
(0, 0, False, False, True, False),
(0, 0, False, True, False, True),
]
closed_vertices = 3 * [tslot_closed_space]
corner_offset = 0
circle_offsets = [0]
face = tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets
)
part = document.addObject("Part::Feature", "BOLTS_part")
part.Label = name
part.Shape = face.extrude(Vector(0, 0, le)).removeSplitter()
def fillet(
lines,
indices,
radius
):
lines = lines[:]
indices.sort()
indices.reverse()
for i in indices:
lines[slice(i, i + 2)] = draft_fillet(lines[slice(i, i + 2)], radius)
return lines
def assemble(
symmetry,
vertices,
offset_global=(0, 0)
):
offset = Vector(offset_global[0], offset_global[1], 0)
lines = []
vlast = None
vcur = None
for sym, verts in zip(symmetry, vertices):
o_x, o_y, reverse, switch, mir_x, mir_y = sym
mir_x = -1 if mir_x else 1
mir_y = -1 if mir_y else 1
if reverse:
verts = verts[::-1]
if vcur is None:
vcur = Vector(verts[0])
if switch:
vcur[0], vcur[1] = vcur[1], vcur[0]
vcur[0] = mir_x * vcur[0] + o_x + offset[0]
vcur[1] = mir_y * vcur[1] + o_y + offset[1]
for v in verts[1:]:
vlast = vcur
vcur = Vector(v)
if switch:
vcur[0], vcur[1] = vcur[1], vcur[0]
vcur[0] = mir_x * vcur[0] + o_x + offset[0]
vcur[1] = mir_y * vcur[1] + o_y + offset[1]
lines.append(Part.makeLine(vlast, vcur))
return lines
w = 20
d = 5.68 + 3 / math.sqrt(2)
vslot_outline = [
(0.5 * d, 0, 0),
(0.5 * d, 0.5 * 5.68, 0),
(0.5 * w - 1.8 - 1.64, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2), 0),
(0.5 * w - 1.8, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2), 0),
(0.5 * w - 1.8, 0.5 * 5.68, 0),
(0.5 * w, 0.5 * 5.68 + 1.8, 0),
(0.5 * w, 0.5 * w, 0)
]
space_symmetry = [
(0, 0, False, False, True, False),
(-w, 0, True, False, False, False),
(-w, 0, False, False, False, True),
(0, 0, True, False, True, True)
]
vslot_space = [
(0.5 * d, 0, 0),
(0.5 * d, 0.5 * 5.68, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8 - 1.96, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8, 0),
(0.5 * w, 0.5 * w - 1.8, 0),
]
vslot_cornerhole = [
(0.5 * w - 1.8, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2) + 1.07, 0),
(0.5 * w - 1.8, 0.5 * w - 1.8, 0),
(0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2) + 1.07, 0.5 * w - 1.8, 0),
(0.5 * w - 1.8, 0.5 * w - 1.8 - 1.64 - 1.5 / math.sqrt(2) + 1.07, 0)
]
def vslot(
symmetry,
vertices,
fillets,
corner_offset,
circle_offsets
):
outline = assemble(symmetry, vertices)
outline = fillet(outline, fillets, 1.5)
outline = Part.Wire(outline)
holes = []
corner_symmetry = [
(0, 0, False, False, False, False),
(corner_offset, 0, False, False, True, False),
(corner_offset, 0, False, False, True, True),
(0, 0, False, False, False, True),
]
for sym in corner_symmetry:
holes.append(Part.Wire(assemble([sym], [vslot_cornerhole])))
if sym[4] == sym[5]:
holes[-1].reverse()
for offset in circle_offsets:
holes.append(Part.Wire(Part.makeCircle(2.1, Vector(offset, 0, 0))))
holes[-1].reverse()
print("Space")
for offset in circle_offsets[:-1]:
print(space_symmetry, vslot_space)
holes.append(Part.Wire(assemble(space_symmetry, 4 * [vslot_space], (offset, 0))))
holes[-1].reverse()
print("Space")
return Part.Face([outline] + holes)
tslot_outline = [
(5.0, 0, 0),
(5.0, 3.5, 0),
(7.5, 6.0, 0),
(9.0, 6.0, 0),
(9.0, 3.0, 0),
(10.0, 3.0, 0),
(10.0, 10.0, 0),
]
tslot_closed = [
(10.0, 0.0, 0),
(10.0, 10.0, 0),
]
tslot_closed_space = [
(5.0, 0, 0),
(5.0, 3.5, 0),
(7.5, 6.0, 0),
(9.0, 6.0, 0),
(9.0, -6.0, 0),
(7.5, -6.0, 0),
(5.0, -3.5, 0),
(5.0, 0, 0),
]
tslot_space = [
(0.5 * d, 0, 0),
(0.5 * d, 0.5 * 5.68, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8 - 1.96, 0),
(0.5 * w - 2.7, 0.5 * w - 1.8, 0),
(0.5 * w, 0.5 * w - 1.8, 0),
]
def tslot(
symmetry,
vertices,
fillets,
closed_symmetry,
closed_vertices,
corner_offset,
circle_offsets
):
outline = assemble(symmetry, vertices)
outline = fillet(outline, fillets, 1.5)
outline = Part.Wire(outline)
holes = []
for sym, vert in zip(closed_symmetry, closed_vertices):
holes.append(Part.Wire(assemble([sym], [vert])))
if not sym[5]:
holes[-1].reverse()
for offset in circle_offsets:
holes.append(Part.Wire(Part.makeCircle(2.25, Vector(offset, 0, 0))))
holes[-1].reverse()
return Part.Face([outline] + holes)
| true
| true
|
1c4441ebc66c0b0b71ab468deeaecf28874f330c
| 6,448
|
py
|
Python
|
tools/repl_test.py
|
zoosky/deno
|
020898762fa081113608504ab5012b2b27c70668
|
[
"MIT"
] | 3
|
2020-07-08T11:32:22.000Z
|
2020-07-10T11:34:25.000Z
|
tools/repl_test.py
|
zoosky/deno
|
020898762fa081113608504ab5012b2b27c70668
|
[
"MIT"
] | null | null | null |
tools/repl_test.py
|
zoosky/deno
|
020898762fa081113608504ab5012b2b27c70668
|
[
"MIT"
] | null | null | null |
# Copyright 2018-2020 the Deno authors. All rights reserved. MIT license.
import os
import shutil
from subprocess import CalledProcessError, PIPE, Popen
import sys
import time
from test_util import DenoTestCase, run_tests
class TestRepl(DenoTestCase):
def input(self, *lines, **kwargs):
exit_ = kwargs.pop("exit", True)
sleep_ = kwargs.pop("sleep", 0)
env_ = kwargs.pop("env", None)
p = Popen([self.deno_exe],
stdout=PIPE,
stderr=PIPE,
stdin=PIPE,
env=env_)
try:
# Note: The repl takes a >100ms until it's ready.
time.sleep(sleep_)
for line in lines:
p.stdin.write(line.encode("utf-8") + b'\n')
p.stdin.flush()
time.sleep(sleep_)
if exit_:
p.stdin.write(b'Deno.exit(0)\n')
else:
time.sleep(1) # wait to be killed by js
out, err = p.communicate()
except CalledProcessError as e:
p.kill()
p.wait()
raise e
retcode = p.poll()
# Ignore Windows CRLF (\r\n).
return out.replace('\r\n', '\n'), err.replace('\r\n', '\n'), retcode
def test_console_log(self):
out, err, code = self.input("console.log('hello')", "'world'")
self.assertEqual(out, 'hello\nundefined\nworld\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_eof(self):
out, err, code = self.input("1 + 2", exit=False)
self.assertEqual(out, '3\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_exit_command(self):
out, err, code = self.input("exit", "'ignored'", exit=False)
self.assertEqual(out, '')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_help_command(self):
out, err, code = self.input("help")
expectedOut = '\n'.join([
"_ Get last evaluation result",
"_error Get last thrown error",
"exit Exit the REPL",
"help Print this help message",
"",
])
self.assertEqual(out, expectedOut)
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_function(self):
out, err, code = self.input("Deno.writeFileSync")
self.assertEqual(out, '[Function: writeFileSync]\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_multiline(self):
out, err, code = self.input("(\n1 + 2\n)")
self.assertEqual(out, '3\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
# This should print error instead of wait for input
def test_eval_unterminated(self):
out, err, code = self.input("eval('{')")
self.assertEqual(out, '')
assert "Unexpected end of input" in err
self.assertEqual(code, 0)
def test_reference_error(self):
out, err, code = self.input("not_a_variable")
self.assertEqual(out, '')
assert "not_a_variable is not defined" in err
self.assertEqual(code, 0)
# def test_set_timeout(self):
# out, err, code = self.input(
# "setTimeout(() => { console.log('b'); Deno.exit(0); }, 1)",
# "'a'",
# exit=False)
# self.assertEqual(out, '1\na\nb\n')
# self.assertEqual(err, '')
# self.assertEqual(code, 0)
# def test_set_timeout_interlaced(self):
# out, err, code = self.input(
# "setTimeout(() => console.log('a'), 1)",
# "setTimeout(() => console.log('b'), 6)",
# sleep=0.8)
# self.assertEqual(out, '1\n2\na\nb\n')
# self.assertEqual(err, '')
# self.assertEqual(code, 0)
# def test_async_op(self):
# out, err, code = self.input(
# "fetch('http://localhost:4545/tests/001_hello.js')" +
# ".then(res => res.text()).then(console.log)",
# sleep=1)
# self.assertEqual(out, 'Promise {}\nconsole.log("Hello World");\n\n')
# self.assertEqual(err, '')
# self.assertEqual(code, 0)
def test_syntax_error(self):
out, err, code = self.input("syntax error")
self.assertEqual(out, '')
assert "Unexpected identifier" in err
self.assertEqual(code, 0)
def test_type_error(self):
out, err, code = self.input("console()")
self.assertEqual(out, '')
assert "console is not a function" in err
self.assertEqual(code, 0)
def test_variable(self):
out, err, code = self.input("var a = 123;", "a")
self.assertEqual(out, 'undefined\n123\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_lexical_scoped_variable(self):
out, err, code = self.input("let a = 123;", "a")
self.assertEqual(out, 'undefined\n123\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_missing_deno_dir(self):
deno_dir = "nonexistent"
new_env = os.environ.copy()
new_env["DENO_DIR"] = deno_dir
out, err, code = self.input("1", exit=False, env=new_env)
self.assertTrue(os.path.isdir(deno_dir))
shutil.rmtree(deno_dir)
self.assertEqual(out, "1\n")
self.assertEqual(err, "")
self.assertEqual(code, 0)
def test_save_last_eval(self):
out, err, code = self.input("1", "_")
self.assertEqual(out, '1\n1\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_save_last_thrown(self):
out, err, code = self.input("throw 1", "_error")
self.assertEqual(out, '1\n')
self.assertEqual(err, 'Thrown: 1\n')
self.assertEqual(code, 0)
def test_assign_underscore(self):
out, err, code = self.input("_ = 1", "2", "_")
self.assertEqual(
out, 'Last evaluation result is no longer saved to _.\n1\n2\n1\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_assign_underscore_error(self):
out, err, code = self.input("_error = 1", "throw 2", "_error")
self.assertEqual(
out, 'Last thrown error is no longer saved to _error.\n1\n1\n')
self.assertEqual(err, 'Thrown: 2\n')
self.assertEqual(code, 0)
if __name__ == "__main__":
run_tests()
| 34.297872
| 78
| 0.559088
|
import os
import shutil
from subprocess import CalledProcessError, PIPE, Popen
import sys
import time
from test_util import DenoTestCase, run_tests
class TestRepl(DenoTestCase):
def input(self, *lines, **kwargs):
exit_ = kwargs.pop("exit", True)
sleep_ = kwargs.pop("sleep", 0)
env_ = kwargs.pop("env", None)
p = Popen([self.deno_exe],
stdout=PIPE,
stderr=PIPE,
stdin=PIPE,
env=env_)
try:
time.sleep(sleep_)
for line in lines:
p.stdin.write(line.encode("utf-8") + b'\n')
p.stdin.flush()
time.sleep(sleep_)
if exit_:
p.stdin.write(b'Deno.exit(0)\n')
else:
time.sleep(1) # wait to be killed by js
out, err = p.communicate()
except CalledProcessError as e:
p.kill()
p.wait()
raise e
retcode = p.poll()
# Ignore Windows CRLF (\r\n).
return out.replace('\r\n', '\n'), err.replace('\r\n', '\n'), retcode
def test_console_log(self):
out, err, code = self.input("console.log('hello')", "'world'")
self.assertEqual(out, 'hello\nundefined\nworld\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_eof(self):
out, err, code = self.input("1 + 2", exit=False)
self.assertEqual(out, '3\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_exit_command(self):
out, err, code = self.input("exit", "'ignored'", exit=False)
self.assertEqual(out, '')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_help_command(self):
out, err, code = self.input("help")
expectedOut = '\n'.join([
"_ Get last evaluation result",
"_error Get last thrown error",
"exit Exit the REPL",
"help Print this help message",
"",
])
self.assertEqual(out, expectedOut)
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_function(self):
out, err, code = self.input("Deno.writeFileSync")
self.assertEqual(out, '[Function: writeFileSync]\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_multiline(self):
out, err, code = self.input("(\n1 + 2\n)")
self.assertEqual(out, '3\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
# This should print error instead of wait for input
def test_eval_unterminated(self):
out, err, code = self.input("eval('{')")
self.assertEqual(out, '')
assert "Unexpected end of input" in err
self.assertEqual(code, 0)
def test_reference_error(self):
out, err, code = self.input("not_a_variable")
self.assertEqual(out, '')
assert "not_a_variable is not defined" in err
self.assertEqual(code, 0)
# def test_set_timeout(self):
# out, err, code = self.input(
# "setTimeout(() => { console.log('b'); Deno.exit(0); }, 1)",
# "'a'",
# exit=False)
# self.assertEqual(out, '1\na\nb\n')
# self.assertEqual(err, '')
# self.assertEqual(code, 0)
# def test_set_timeout_interlaced(self):
# out, err, code = self.input(
# "setTimeout(() => console.log('a'), 1)",
# "setTimeout(() => console.log('b'), 6)",
# sleep=0.8)
# self.assertEqual(out, '1\n2\na\nb\n')
# self.assertEqual(err, '')
# self.assertEqual(code, 0)
# def test_async_op(self):
# out, err, code = self.input(
# "fetch('http://localhost:4545/tests/001_hello.js')" +
# ".then(res => res.text()).then(console.log)",
# sleep=1)
# self.assertEqual(out, 'Promise {}\nconsole.log("Hello World");\n\n')
# self.assertEqual(err, '')
# self.assertEqual(code, 0)
def test_syntax_error(self):
out, err, code = self.input("syntax error")
self.assertEqual(out, '')
assert "Unexpected identifier" in err
self.assertEqual(code, 0)
def test_type_error(self):
out, err, code = self.input("console()")
self.assertEqual(out, '')
assert "console is not a function" in err
self.assertEqual(code, 0)
def test_variable(self):
out, err, code = self.input("var a = 123;", "a")
self.assertEqual(out, 'undefined\n123\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_lexical_scoped_variable(self):
out, err, code = self.input("let a = 123;", "a")
self.assertEqual(out, 'undefined\n123\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_missing_deno_dir(self):
deno_dir = "nonexistent"
new_env = os.environ.copy()
new_env["DENO_DIR"] = deno_dir
out, err, code = self.input("1", exit=False, env=new_env)
self.assertTrue(os.path.isdir(deno_dir))
shutil.rmtree(deno_dir)
self.assertEqual(out, "1\n")
self.assertEqual(err, "")
self.assertEqual(code, 0)
def test_save_last_eval(self):
out, err, code = self.input("1", "_")
self.assertEqual(out, '1\n1\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_save_last_thrown(self):
out, err, code = self.input("throw 1", "_error")
self.assertEqual(out, '1\n')
self.assertEqual(err, 'Thrown: 1\n')
self.assertEqual(code, 0)
def test_assign_underscore(self):
out, err, code = self.input("_ = 1", "2", "_")
self.assertEqual(
out, 'Last evaluation result is no longer saved to _.\n1\n2\n1\n')
self.assertEqual(err, '')
self.assertEqual(code, 0)
def test_assign_underscore_error(self):
out, err, code = self.input("_error = 1", "throw 2", "_error")
self.assertEqual(
out, 'Last thrown error is no longer saved to _error.\n1\n1\n')
self.assertEqual(err, 'Thrown: 2\n')
self.assertEqual(code, 0)
if __name__ == "__main__":
run_tests()
| true
| true
|
1c4441f790e3934fdb9d9e916372990dbd0cc4c5
| 1,505
|
py
|
Python
|
src/ToolBox/SOS/tests/t_cmd_bpmd_module_function_iloffset.py
|
elinor-fung/coreclr
|
c1801e85024add717f518feb6a9caed60d54500f
|
[
"MIT"
] | 8
|
2020-01-15T11:09:15.000Z
|
2021-08-25T08:54:55.000Z
|
src/ToolBox/SOS/tests/t_cmd_bpmd_module_function_iloffset.py
|
elinor-fung/coreclr
|
c1801e85024add717f518feb6a9caed60d54500f
|
[
"MIT"
] | 3
|
2018-01-03T00:57:25.000Z
|
2018-10-05T16:17:52.000Z
|
src/ToolBox/SOS/tests/t_cmd_bpmd_module_function_iloffset.py
|
elinor-fung/coreclr
|
c1801e85024add717f518feb6a9caed60d54500f
|
[
"MIT"
] | 1
|
2020-11-17T14:55:53.000Z
|
2020-11-17T14:55:53.000Z
|
# Licensed to the .NET Foundation under one or more agreements.
# The .NET Foundation licenses this file to you under the MIT license.
# See the LICENSE file in the project root for more information.
import lldb
import re
import testutils as test
# bpmd <module name> <managed function name> [<il offset>]
def runScenario(assembly, debugger, target):
process = target.GetProcess()
res = lldb.SBCommandReturnObject()
ci = debugger.GetCommandInterpreter()
# Run debugger, wait until libcoreclr is loaded,
# set breakpoint at Test.Main and stop there
test.stop_in_main(debugger, assembly)
ci.HandleCommand("bpmd " + assembly + " Test.UnlikelyInlined 66", res)
out_msg = res.GetOutput()
err_msg = res.GetError()
print(out_msg)
print(err_msg)
# Interpreter must have this command and able to run it
test.assertTrue(res.Succeeded())
# Output is not empty
# Should be at least 'Adding pending breakpoints...'
test.assertTrue(len(out_msg) > 0)
# Error message is empty
test.assertTrue(len(err_msg) == 0)
process.Continue()
# Process must be stopped at UnlinkelyInlined
test.assertEqual(process.GetState(), lldb.eStateStopped)
# The reason of this stop must be a breakpoint
test.assertEqual(process.GetSelectedThread().GetStopReason(),
lldb.eStopReasonBreakpoint)
#
# Delete all breakpoints, continue current process and checks its exit code
test.exit_lldb(debugger, assembly)
| 31.354167
| 79
| 0.710299
|
import lldb
import re
import testutils as test
def runScenario(assembly, debugger, target):
process = target.GetProcess()
res = lldb.SBCommandReturnObject()
ci = debugger.GetCommandInterpreter()
test.stop_in_main(debugger, assembly)
ci.HandleCommand("bpmd " + assembly + " Test.UnlikelyInlined 66", res)
out_msg = res.GetOutput()
err_msg = res.GetError()
print(out_msg)
print(err_msg)
test.assertTrue(res.Succeeded())
test.assertTrue(len(out_msg) > 0)
test.assertTrue(len(err_msg) == 0)
process.Continue()
test.assertEqual(process.GetState(), lldb.eStateStopped)
test.assertEqual(process.GetSelectedThread().GetStopReason(),
lldb.eStopReasonBreakpoint)
test.exit_lldb(debugger, assembly)
| true
| true
|
1c44426edb5f00a1194a178579284f2e8d2aa873
| 1,626
|
py
|
Python
|
scripts/iclr_2018/rl_size.py
|
alcinos/dps
|
5467db1216e9f9089376d2c71f524ced2382e4f6
|
[
"Apache-2.0"
] | null | null | null |
scripts/iclr_2018/rl_size.py
|
alcinos/dps
|
5467db1216e9f9089376d2c71f524ced2382e4f6
|
[
"Apache-2.0"
] | null | null | null |
scripts/iclr_2018/rl_size.py
|
alcinos/dps
|
5467db1216e9f9089376d2c71f524ced2382e4f6
|
[
"Apache-2.0"
] | null | null | null |
import numpy as np
import os
import clify
import argparse
from config import rl_config as config
config.update(
image_shape_grid=(3, 3),
reductions="sum",
)
grid = [dict(n_train=1, do_train=False)] + [dict(n_train=x) for x in 2**np.arange(0, 18, 2)]
parser = argparse.ArgumentParser()
parser.add_argument("--task", choices="A B C D E F 0".split(), default='')
args, _ = parser.parse_known_args()
stage_1 = dict()
stage_2 = dict(min_digits=4, max_digits=4)
stage_3 = dict(min_digits=5, max_digits=5)
if args.task == "0":
grid = dict(n_train=2**np.arange(14, 18, 2))
config.update(image_shape_grid=(2, 2))
elif args.task == "A":
zero_dir = "/home/e2crawfo/rl_size_0/"
config.load_path = [
os.path.join(zero_dir, d, 'weights/best_of_stage_0') for d in os.listdir(zero_dir)
]
config.update(stage_1)
elif args.task == "B":
A_dir = "/home/e2crawfo/rl_size_A/"
config.load_path = [
os.path.join(A_dir, d, 'weights/best_of_stage_0') for d in os.listdir(A_dir)
]
config.update(stage_2)
elif args.task == "C":
B_dir = "/home/e2crawfo/rl_size_B/"
config.load_path = [
os.path.join(B_dir, d, 'weights/best_of_stage_0') for d in os.listdir(B_dir)
]
config.update(stage_3)
elif args.task == "D":
config.update(stage_1)
elif args.task == "E":
config.update(stage_2)
elif args.task == "F":
config.update(stage_3)
else:
raise Exception()
from dps.hyper import build_and_submit, default_host_pool
clify.wrap_function(build_and_submit)(
config=config, distributions=grid, n_param_settings=None, host_pool=default_host_pool)
| 26.225806
| 92
| 0.682657
|
import numpy as np
import os
import clify
import argparse
from config import rl_config as config
config.update(
image_shape_grid=(3, 3),
reductions="sum",
)
grid = [dict(n_train=1, do_train=False)] + [dict(n_train=x) for x in 2**np.arange(0, 18, 2)]
parser = argparse.ArgumentParser()
parser.add_argument("--task", choices="A B C D E F 0".split(), default='')
args, _ = parser.parse_known_args()
stage_1 = dict()
stage_2 = dict(min_digits=4, max_digits=4)
stage_3 = dict(min_digits=5, max_digits=5)
if args.task == "0":
grid = dict(n_train=2**np.arange(14, 18, 2))
config.update(image_shape_grid=(2, 2))
elif args.task == "A":
zero_dir = "/home/e2crawfo/rl_size_0/"
config.load_path = [
os.path.join(zero_dir, d, 'weights/best_of_stage_0') for d in os.listdir(zero_dir)
]
config.update(stage_1)
elif args.task == "B":
A_dir = "/home/e2crawfo/rl_size_A/"
config.load_path = [
os.path.join(A_dir, d, 'weights/best_of_stage_0') for d in os.listdir(A_dir)
]
config.update(stage_2)
elif args.task == "C":
B_dir = "/home/e2crawfo/rl_size_B/"
config.load_path = [
os.path.join(B_dir, d, 'weights/best_of_stage_0') for d in os.listdir(B_dir)
]
config.update(stage_3)
elif args.task == "D":
config.update(stage_1)
elif args.task == "E":
config.update(stage_2)
elif args.task == "F":
config.update(stage_3)
else:
raise Exception()
from dps.hyper import build_and_submit, default_host_pool
clify.wrap_function(build_and_submit)(
config=config, distributions=grid, n_param_settings=None, host_pool=default_host_pool)
| true
| true
|
1c444325429659bd2cf13dac06eb07c96a0b5de1
| 2,456
|
py
|
Python
|
pcdet/models/model_utils/model_nms_utils.py
|
collector-m/ST3D
|
720e04aa3dc4bb95ac336171b240b6c3130144e5
|
[
"Apache-2.0"
] | null | null | null |
pcdet/models/model_utils/model_nms_utils.py
|
collector-m/ST3D
|
720e04aa3dc4bb95ac336171b240b6c3130144e5
|
[
"Apache-2.0"
] | null | null | null |
pcdet/models/model_utils/model_nms_utils.py
|
collector-m/ST3D
|
720e04aa3dc4bb95ac336171b240b6c3130144e5
|
[
"Apache-2.0"
] | null | null | null |
import torch
from ...ops.iou3d_nms import iou3d_nms_utils
def class_agnostic_nms(box_scores, box_preds, nms_config, score_thresh=None):
src_box_scores = box_scores
if score_thresh is not None:
scores_mask = (box_scores >= score_thresh)
box_scores = box_scores[scores_mask]
box_preds = box_preds[scores_mask]
selected = []
if box_scores.shape[0] > 0:
box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0]))
boxes_for_nms = box_preds[indices]
keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)(
boxes_for_nms[:, 0:7], box_scores_nms, nms_config.NMS_THRESH, **nms_config
)
selected = indices[keep_idx[:nms_config.NMS_POST_MAXSIZE]]
if score_thresh is not None:
original_idxs = scores_mask.nonzero().view(-1)
selected = original_idxs[selected]
return selected, src_box_scores[selected]
def multi_classes_nms(cls_scores, box_preds, nms_config, score_thresh=None):
"""
Args:
cls_scores: (N, num_class)
box_preds: (N, 7 + C)
nms_config:
score_thresh:
Returns:
"""
pred_scores, pred_labels, pred_boxes = [], [], []
for k in range(cls_scores.shape[1]):
if score_thresh is not None:
scores_mask = (cls_scores[:, k] >= score_thresh)
box_scores = cls_scores[scores_mask, k]
cur_box_preds = box_preds[scores_mask]
else:
box_scores = cls_scores[:, k]
cur_box_preds = box_preds
selected = []
if box_scores.shape[0] > 0:
box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0]))
boxes_for_nms = cur_box_preds[indices]
keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)(
boxes_for_nms[:, 0:7], box_scores_nms, nms_config.NMS_THRESH, **nms_config
)
selected = indices[keep_idx[:nms_config.NMS_POST_MAXSIZE]]
pred_scores.append(box_scores[selected])
pred_labels.append(box_scores.new_ones(len(selected)).long() * k)
pred_boxes.append(cur_box_preds[selected])
pred_scores = torch.cat(pred_scores, dim=0)
pred_labels = torch.cat(pred_labels, dim=0)
pred_boxes = torch.cat(pred_boxes, dim=0)
return pred_scores, pred_labels, pred_boxes
| 37.212121
| 116
| 0.661645
|
import torch
from ...ops.iou3d_nms import iou3d_nms_utils
def class_agnostic_nms(box_scores, box_preds, nms_config, score_thresh=None):
src_box_scores = box_scores
if score_thresh is not None:
scores_mask = (box_scores >= score_thresh)
box_scores = box_scores[scores_mask]
box_preds = box_preds[scores_mask]
selected = []
if box_scores.shape[0] > 0:
box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0]))
boxes_for_nms = box_preds[indices]
keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)(
boxes_for_nms[:, 0:7], box_scores_nms, nms_config.NMS_THRESH, **nms_config
)
selected = indices[keep_idx[:nms_config.NMS_POST_MAXSIZE]]
if score_thresh is not None:
original_idxs = scores_mask.nonzero().view(-1)
selected = original_idxs[selected]
return selected, src_box_scores[selected]
def multi_classes_nms(cls_scores, box_preds, nms_config, score_thresh=None):
pred_scores, pred_labels, pred_boxes = [], [], []
for k in range(cls_scores.shape[1]):
if score_thresh is not None:
scores_mask = (cls_scores[:, k] >= score_thresh)
box_scores = cls_scores[scores_mask, k]
cur_box_preds = box_preds[scores_mask]
else:
box_scores = cls_scores[:, k]
cur_box_preds = box_preds
selected = []
if box_scores.shape[0] > 0:
box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0]))
boxes_for_nms = cur_box_preds[indices]
keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)(
boxes_for_nms[:, 0:7], box_scores_nms, nms_config.NMS_THRESH, **nms_config
)
selected = indices[keep_idx[:nms_config.NMS_POST_MAXSIZE]]
pred_scores.append(box_scores[selected])
pred_labels.append(box_scores.new_ones(len(selected)).long() * k)
pred_boxes.append(cur_box_preds[selected])
pred_scores = torch.cat(pred_scores, dim=0)
pred_labels = torch.cat(pred_labels, dim=0)
pred_boxes = torch.cat(pred_boxes, dim=0)
return pred_scores, pred_labels, pred_boxes
| true
| true
|
1c4445883af2eafcd549de66e969b087215c7666
| 2,335
|
py
|
Python
|
_matplotlibsettings.py
|
WillemWybo/Electrical_compartmentalization_in_neurons
|
1ff297be97412ff40042485479b78148fba11c27
|
[
"MIT"
] | null | null | null |
_matplotlibsettings.py
|
WillemWybo/Electrical_compartmentalization_in_neurons
|
1ff297be97412ff40042485479b78148fba11c27
|
[
"MIT"
] | null | null | null |
_matplotlibsettings.py
|
WillemWybo/Electrical_compartmentalization_in_neurons
|
1ff297be97412ff40042485479b78148fba11c27
|
[
"MIT"
] | null | null | null |
import matplotlib
# matplotlib.use("Agg")
import matplotlib.pyplot as pl
import matplotlib.animation as manimation
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Rectangle
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import rc, rcParams
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
from matplotlib.offsetbox import AnchoredText
# colours = ['DeepPink', 'Purple', 'MediumSlateBlue', 'Blue', 'Teal',
# 'ForestGreen', 'DarkOliveGreen', 'DarkGoldenRod',
# 'DarkOrange', 'Coral', 'Red', 'Sienna', 'Black', 'DarkGrey']
colours = list(pl.rcParams['axes.prop_cycle'].by_key()['color'])
# matplotlib settings
legendsize = 10
labelsize = 15
ticksize = 15
lwidth = 1.5
markersize = 6.
fontsize = 16
lettersize = 20.
#~ font = {'family' : 'serif',
#~ 'weight' : 'normal',
#~ 'size' : fontsize}
#'sans-serif':'Helvetica'}
#'family':'serif','serif':['Palatino']}
#~ rc('font', **font)
rc('font',**{'family':'serif','serif':['Palatino'], 'size': 15.0})
rc('mathtext',**{'fontset': 'stixsans'})
# rc('text', usetex=True)
# rcParams['text.latex.preamble'].append(r"\usepackage{amsmath}\usepackage{xfrac}")
rc('legend',**{'fontsize': 'medium'})
rc('xtick',**{'labelsize': 'small'})
rc('ytick',**{'labelsize': 'small'})
rc('axes',**{'labelsize': 'large', 'labelweight': 'normal'})
cs = ['r', 'b', 'g', 'c', 'y']
mfs = ['D', 'o', 'v', '^', 's', 'p']
mls = ['+', '*', 'x', '1', '2']
lss = ['-', '--', '-.', ':']
cmap = pl.get_cmap('jet')
def myAx(ax):
# customize the ax
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
return ax
def myLegend(ax, add_frame=True, **kwarg):
leg = ax.legend(**kwarg)
if add_frame:
frame = leg.get_frame()
frame.set_color('white')
frame.set_alpha(0.8)
return leg
def myColorbar(ax, im, **kwargs):
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
return pl.colorbar(im, cax=cax, **kwargs)
| 31.133333
| 83
| 0.65182
|
import matplotlib
import matplotlib.pyplot as pl
import matplotlib.animation as manimation
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Rectangle
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import rc, rcParams
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
from matplotlib.offsetbox import AnchoredText
colours = list(pl.rcParams['axes.prop_cycle'].by_key()['color'])
legendsize = 10
labelsize = 15
ticksize = 15
lwidth = 1.5
markersize = 6.
fontsize = 16
lettersize = 20.
rc('font',**{'family':'serif','serif':['Palatino'], 'size': 15.0})
rc('mathtext',**{'fontset': 'stixsans'})
rc('legend',**{'fontsize': 'medium'})
rc('xtick',**{'labelsize': 'small'})
rc('ytick',**{'labelsize': 'small'})
rc('axes',**{'labelsize': 'large', 'labelweight': 'normal'})
cs = ['r', 'b', 'g', 'c', 'y']
mfs = ['D', 'o', 'v', '^', 's', 'p']
mls = ['+', '*', 'x', '1', '2']
lss = ['-', '--', '-.', ':']
cmap = pl.get_cmap('jet')
def myAx(ax):
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
return ax
def myLegend(ax, add_frame=True, **kwarg):
leg = ax.legend(**kwarg)
if add_frame:
frame = leg.get_frame()
frame.set_color('white')
frame.set_alpha(0.8)
return leg
def myColorbar(ax, im, **kwargs):
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
return pl.colorbar(im, cax=cax, **kwargs)
| true
| true
|
1c4445b06c65961d851d7965795b569e737f3f68
| 256
|
py
|
Python
|
image_resize.py
|
cristianoc20/Rebar_Detection
|
8a1cf22bc82c5998376cc034c9a7317401d5b2e3
|
[
"MIT"
] | 58
|
2020-03-07T16:40:20.000Z
|
2022-03-31T08:57:05.000Z
|
image_resize.py
|
JackySnake/Rebar_Detection
|
8a1cf22bc82c5998376cc034c9a7317401d5b2e3
|
[
"MIT"
] | 13
|
2020-03-08T11:00:58.000Z
|
2022-03-12T00:17:59.000Z
|
image_resize.py
|
JackySnake/Rebar_Detection
|
8a1cf22bc82c5998376cc034c9a7317401d5b2e3
|
[
"MIT"
] | 17
|
2020-03-09T09:22:33.000Z
|
2022-02-23T09:20:18.000Z
|
import glob as gb #导入glob模块
import cv2
import os
# 返回该路径下所有的 jpg 文件的路径
img_path = gb.glob("./data/test_dataset/*.jpg")
for path in img_path:
(filepath, tempfilename) = os.path.split(path)
(filename, extension) = os.path.splitext(tempfilename)
| 23.272727
| 58
| 0.71875
|
import glob as gb
import cv2
import os
img_path = gb.glob("./data/test_dataset/*.jpg")
for path in img_path:
(filepath, tempfilename) = os.path.split(path)
(filename, extension) = os.path.splitext(tempfilename)
| true
| true
|
1c44481706e54082a191a60d87d3f384cd42fec6
| 10,802
|
py
|
Python
|
vn/generator.py
|
Jemesson/nlp-conceptual-model
|
26ad7249be4ccb3863c0852738eaa8b6ddcf63f3
|
[
"MIT"
] | null | null | null |
vn/generator.py
|
Jemesson/nlp-conceptual-model
|
26ad7249be4ccb3863c0852738eaa8b6ddcf63f3
|
[
"MIT"
] | null | null | null |
vn/generator.py
|
Jemesson/nlp-conceptual-model
|
26ad7249be4ccb3863c0852738eaa8b6ddcf63f3
|
[
"MIT"
] | 1
|
2021-11-02T03:41:17.000Z
|
2021-11-02T03:41:17.000Z
|
import os
from lang.owlprefix import PREFIX_DICT
from vn.utility import t, is_i, tab, is_comment, occurence_list, is_us
from jinja2 import FileSystemLoader, Environment
class Generator:
def __init__(self, classes, relationships, onto=True, is_long=None):
self.classes = classes
self.relationships = relationships
self.long = is_long
self.onto = onto
def prt(self, onto):
for c in self.classes:
c.stories.sort()
if not self.onto:
return self.gen_prolog_from_onto()
if self.long is None:
li = self.gen_ontology(onto)
else:
li = self.gen_ontology(onto)
return li
def gen_ontology(self, onto):
ontologytext = ''
rellist = []
clist = []
ontologytext += onto.gen_head(onto.get_prefixes()).prt() + "\n"
if self.relationships:
ontologytext += onto.gh.comment("Relationships")
unique_rels = self.make_unique_relationships()
for r in unique_rels:
ontologytext += r.prt() + "\n"
if self.classes:
ontologytext += onto.gh.comment("Classes")
for c in self.classes:
ontologytext += c.prt() + "\n"
return ontologytext
def make_unique_relationships(self):
rel_names = set([r.name for r in self.relationships])
new_rels = []
for rn in rel_names:
rels_of_name = []
pairs = []
cnt = 1
for r in self.relationships:
if r.name == rn:
if [r.domain, r.range] not in pairs:
rels_of_name.append(r)
pairs.append([r.domain, r.range])
if len(rels_of_name) > 1:
for ron in rels_of_name:
new_relationship = OntProperty(ron.ontobj, "Object", ron.name + str(cnt), ron.domain, ron.range)
new_relationship.stories = ron.stories
new_rels.append(new_relationship)
cnt += 1
else:
for r in self.relationships:
if r.name == rn:
new_rels.append(r)
return new_rels
def gen_prolog_from_onto(self):
prologtext = []
concept = ""
for c in self.classes:
concept = self.get_concept(c.name)
prologtext.append(concept)
for s in c.stories:
if self.get_found(concept, s):
prologtext.append(self.get_found(concept, s))
for r in self.relationships:
d_concept = self.get_concept(r.domain)
r_concept = self.get_concept(r.range)
rel = ""
linkrel = ['role', 'means', 'ends']
diffrel = linkrel + ['isa']
if str.lower(r.name) in diffrel:
if str.lower(r.name) in linkrel:
prologtext.append(str.lower(r.name) + "(" + d_concept + ",'" + r.range + "')")
else:
prologtext.append(str.lower(r.name) + "(" + d_concept + "," + r_concept + ")")
else:
rel = "rel(" + d_concept + ",'" + r.name + "'," + r_concept + ")"
prologtext.append(rel)
for s in r.stories:
if self.get_found(rel, s):
prologtext.append(self.get_found(rel, s))
prologtext.sort()
return '.\n'.join(prologtext)
def get_concept(self, text):
return "concept('" + str(text) + "')"
def get_found(self, text, story):
if story >= 0:
return "found(" + text + ",'US" + str(story) + "')"
return False
@staticmethod
def gen_report(path, report_template):
"""Generates a report using Jinja2
:param report_dict: Dictionary containing all variables used in the report
:returns: HTML page
"""
loader = FileSystemLoader(searchpath=path + "/templates/")
env = Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)
env.globals['text'] = t
env.globals['is_i'] = is_i
env.globals['apply_tab'] = tab
env.globals['is_comment'] = is_comment
env.globals['occurence_list'] = occurence_list
env.tests['is_us'] = is_us
template = env.get_template("report.html")
return template.render(report_template)
class GenHelp:
def __init__(self, ontology, option=None):
self.ontology = ontology
self.option = option
def make_prefix(self, indicator, link):
return "Prefix: " + indicator + ": <" + link + ">\n"
def make_obj(self, name, prefix='', isname=None):
if not self.option:
return prefix + ":" + name + "\n"
else:
if prefix is '':
prefix = self.ontology
else:
prefix = PREFIX_DICT[prefix]
return "<" + prefix + name + ">\n"
def make_part(self, left, right):
return "\t" + left + ": " + right
def space(self):
return ""
def comment(self, com):
return "# " + com + "\n"
class Ontology:
def __init__(self, sysname, stories, option=None):
self.sys_name = sysname
self.ontology = "http://fakesite.org/" + "_".join(str(sysname).lower().split()) + ".owl#"
self.ontology_name = "onto"
self.option = option
self.gh = GenHelp(self.ontology, option)
self.stories = stories
self.classes = []
self.relationships = []
def gen_head(self, parts):
return Header(self, parts)
def get_prefixes(self):
return [str(c.prefix) for c in self.classes]
def make_class(self, name, parent="Thing", prefix=''):
return OntClass(self, name, parent, prefix)
def make_relationship(self, name, domain, range):
new_property = OntProperty(self, "Object", name, domain, range)
return new_property
def get_class_by_name(self, story, name, parent='', is_role=False):
if self.is_empty(name):
return False
c_stories = []
if self.classes:
for c in self.classes:
if str.lower(name) == str.lower(c.name) and (str.lower(parent) == str.lower(c.parent) or (
self.is_empty(parent) and self.is_empty(c.parent))):
if is_role:
c.is_role = True
c.stories.append(story)
return c
if str.lower(name) == str.lower(c.name) and not self.is_empty(c.parent) and self.is_empty(parent):
if is_role:
c.is_role = True
c.stories.append(story)
return c
if str.lower(name) == str.lower(c.name) and not self.is_empty(parent):
c_stories = c.stories
self.classes.remove(c)
new_class = self.make_class(name, parent)
if is_role:
new_class.is_role = True
new_class.stories = c_stories
new_class.stories.append(story)
self.classes.append(new_class)
if not self.is_empty(parent):
parent_class = self.get_class_by_name(-1, parent, '')
return new_class
def is_empty(self, word):
if word.isspace() or word == '':
return True
return False
def new_relationship(self, story, pre, rel, post):
if self.relationships:
for r in self.relationships:
if r.domain == pre and r.name == rel and r.range == post:
r.stories.append(story)
return r
new_rel = self.make_relationship(rel, pre, post)
new_rel.stories.append(story)
self.relationships.append(new_rel)
return new_rel
class OntClass(object):
def __init__(self, ontology, name, parent, prefix=''):
self.ontobj = ontology
self.name = name
self.parent = parent
self.prefix = prefix
self.stories = []
self.is_role = False
def prt(self):
name = ''.join(self.name.split())
parent = ''.join(self.parent.split())
returnstr = ""
returnstr += "Class: " + self.ontobj.gh.make_obj(name)
if self.parent == "Thing" or self.parent == '':
pass
else:
returnstr += self.ontobj.gh.make_part("SubClassOf", self.ontobj.gh.make_obj(parent, self.prefix))
if self.name != name or self.is_role:
returnstr += "\tAnnotations:"
if self.name != name:
returnstr += "\n\t\trdfs:label \"%s\"" % (self.name)
if self.name != name and self.is_role:
returnstr += ","
if self.is_role:
returnstr += "\n\t\trdfs:comment \"Functional Role\""
returnstr += "\n"
return returnstr
def set_role(self):
self.is_role = True
class OntProperty(object):
def __init__(self, ontology, type, name, domain, range):
self.ontobj = ontology
self.type = type
self.name = name
self.domain = domain
self.range = range
self.stories = []
def prt(self):
name = ''.join(self.name.split())
domain = ''.join(self.domain.split())
range = ''.join(self.range.split())
returnstr = ""
returnstr += self.type + "Property: " + self.ontobj.gh.make_obj(name)
returnstr += self.ontobj.gh.make_part("Domain", self.ontobj.gh.make_obj(domain))
returnstr += self.ontobj.gh.make_part("Range", self.ontobj.gh.make_obj(range))
return returnstr
class Header:
def __init__(self, ontology, used_prefixes):
self.ontobj = ontology
self.standard_prefixes = ['owl', 'rdf', 'rdfs', 'xsd', 'dc']
self.used_prefixes = self.standard_prefixes + used_prefixes
def prt(self):
returnstr = ""
returnstr += self.ontobj.gh.comment("Generated with Visual Narrator")
returnstr += self.ontobj.gh.make_prefix('', self.ontobj.ontology)
for prefix in self.used_prefixes:
if prefix is not '':
link = str(PREFIX_DICT[prefix])
returnstr += self.ontobj.gh.make_prefix(prefix, link)
returnstr += self.ontobj.gh.make_prefix(self.ontobj.ontology_name, self.ontobj.ontology)
returnstr += "\nOntology: <:>\n\n"
returnstr += "Annotations:\n\tdc:title \"" + str(
self.ontobj.sys_name) + "\",\n\tdc:creator \"Visual Narrator\",\n\trdfs:comment \"Generated with Visual Narrator\"\n\n"
returnstr += "AnnotationProperty: dc:creator\n\n"
returnstr += "AnnotationProperty: dc:title\n\n"
return returnstr
| 33.339506
| 131
| 0.552861
|
import os
from lang.owlprefix import PREFIX_DICT
from vn.utility import t, is_i, tab, is_comment, occurence_list, is_us
from jinja2 import FileSystemLoader, Environment
class Generator:
def __init__(self, classes, relationships, onto=True, is_long=None):
self.classes = classes
self.relationships = relationships
self.long = is_long
self.onto = onto
def prt(self, onto):
for c in self.classes:
c.stories.sort()
if not self.onto:
return self.gen_prolog_from_onto()
if self.long is None:
li = self.gen_ontology(onto)
else:
li = self.gen_ontology(onto)
return li
def gen_ontology(self, onto):
ontologytext = ''
rellist = []
clist = []
ontologytext += onto.gen_head(onto.get_prefixes()).prt() + "\n"
if self.relationships:
ontologytext += onto.gh.comment("Relationships")
unique_rels = self.make_unique_relationships()
for r in unique_rels:
ontologytext += r.prt() + "\n"
if self.classes:
ontologytext += onto.gh.comment("Classes")
for c in self.classes:
ontologytext += c.prt() + "\n"
return ontologytext
def make_unique_relationships(self):
rel_names = set([r.name for r in self.relationships])
new_rels = []
for rn in rel_names:
rels_of_name = []
pairs = []
cnt = 1
for r in self.relationships:
if r.name == rn:
if [r.domain, r.range] not in pairs:
rels_of_name.append(r)
pairs.append([r.domain, r.range])
if len(rels_of_name) > 1:
for ron in rels_of_name:
new_relationship = OntProperty(ron.ontobj, "Object", ron.name + str(cnt), ron.domain, ron.range)
new_relationship.stories = ron.stories
new_rels.append(new_relationship)
cnt += 1
else:
for r in self.relationships:
if r.name == rn:
new_rels.append(r)
return new_rels
def gen_prolog_from_onto(self):
prologtext = []
concept = ""
for c in self.classes:
concept = self.get_concept(c.name)
prologtext.append(concept)
for s in c.stories:
if self.get_found(concept, s):
prologtext.append(self.get_found(concept, s))
for r in self.relationships:
d_concept = self.get_concept(r.domain)
r_concept = self.get_concept(r.range)
rel = ""
linkrel = ['role', 'means', 'ends']
diffrel = linkrel + ['isa']
if str.lower(r.name) in diffrel:
if str.lower(r.name) in linkrel:
prologtext.append(str.lower(r.name) + "(" + d_concept + ",'" + r.range + "')")
else:
prologtext.append(str.lower(r.name) + "(" + d_concept + "," + r_concept + ")")
else:
rel = "rel(" + d_concept + ",'" + r.name + "'," + r_concept + ")"
prologtext.append(rel)
for s in r.stories:
if self.get_found(rel, s):
prologtext.append(self.get_found(rel, s))
prologtext.sort()
return '.\n'.join(prologtext)
def get_concept(self, text):
return "concept('" + str(text) + "')"
def get_found(self, text, story):
if story >= 0:
return "found(" + text + ",'US" + str(story) + "')"
return False
@staticmethod
def gen_report(path, report_template):
loader = FileSystemLoader(searchpath=path + "/templates/")
env = Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)
env.globals['text'] = t
env.globals['is_i'] = is_i
env.globals['apply_tab'] = tab
env.globals['is_comment'] = is_comment
env.globals['occurence_list'] = occurence_list
env.tests['is_us'] = is_us
template = env.get_template("report.html")
return template.render(report_template)
class GenHelp:
def __init__(self, ontology, option=None):
self.ontology = ontology
self.option = option
def make_prefix(self, indicator, link):
return "Prefix: " + indicator + ": <" + link + ">\n"
def make_obj(self, name, prefix='', isname=None):
if not self.option:
return prefix + ":" + name + "\n"
else:
if prefix is '':
prefix = self.ontology
else:
prefix = PREFIX_DICT[prefix]
return "<" + prefix + name + ">\n"
def make_part(self, left, right):
return "\t" + left + ": " + right
def space(self):
return ""
def comment(self, com):
return "# " + com + "\n"
class Ontology:
def __init__(self, sysname, stories, option=None):
self.sys_name = sysname
self.ontology = "http://fakesite.org/" + "_".join(str(sysname).lower().split()) + ".owl#"
self.ontology_name = "onto"
self.option = option
self.gh = GenHelp(self.ontology, option)
self.stories = stories
self.classes = []
self.relationships = []
def gen_head(self, parts):
return Header(self, parts)
def get_prefixes(self):
return [str(c.prefix) for c in self.classes]
def make_class(self, name, parent="Thing", prefix=''):
return OntClass(self, name, parent, prefix)
def make_relationship(self, name, domain, range):
new_property = OntProperty(self, "Object", name, domain, range)
return new_property
def get_class_by_name(self, story, name, parent='', is_role=False):
if self.is_empty(name):
return False
c_stories = []
if self.classes:
for c in self.classes:
if str.lower(name) == str.lower(c.name) and (str.lower(parent) == str.lower(c.parent) or (
self.is_empty(parent) and self.is_empty(c.parent))):
if is_role:
c.is_role = True
c.stories.append(story)
return c
if str.lower(name) == str.lower(c.name) and not self.is_empty(c.parent) and self.is_empty(parent):
if is_role:
c.is_role = True
c.stories.append(story)
return c
if str.lower(name) == str.lower(c.name) and not self.is_empty(parent):
c_stories = c.stories
self.classes.remove(c)
new_class = self.make_class(name, parent)
if is_role:
new_class.is_role = True
new_class.stories = c_stories
new_class.stories.append(story)
self.classes.append(new_class)
if not self.is_empty(parent):
parent_class = self.get_class_by_name(-1, parent, '')
return new_class
def is_empty(self, word):
if word.isspace() or word == '':
return True
return False
def new_relationship(self, story, pre, rel, post):
if self.relationships:
for r in self.relationships:
if r.domain == pre and r.name == rel and r.range == post:
r.stories.append(story)
return r
new_rel = self.make_relationship(rel, pre, post)
new_rel.stories.append(story)
self.relationships.append(new_rel)
return new_rel
class OntClass(object):
def __init__(self, ontology, name, parent, prefix=''):
self.ontobj = ontology
self.name = name
self.parent = parent
self.prefix = prefix
self.stories = []
self.is_role = False
def prt(self):
name = ''.join(self.name.split())
parent = ''.join(self.parent.split())
returnstr = ""
returnstr += "Class: " + self.ontobj.gh.make_obj(name)
if self.parent == "Thing" or self.parent == '':
pass
else:
returnstr += self.ontobj.gh.make_part("SubClassOf", self.ontobj.gh.make_obj(parent, self.prefix))
if self.name != name or self.is_role:
returnstr += "\tAnnotations:"
if self.name != name:
returnstr += "\n\t\trdfs:label \"%s\"" % (self.name)
if self.name != name and self.is_role:
returnstr += ","
if self.is_role:
returnstr += "\n\t\trdfs:comment \"Functional Role\""
returnstr += "\n"
return returnstr
def set_role(self):
self.is_role = True
class OntProperty(object):
def __init__(self, ontology, type, name, domain, range):
self.ontobj = ontology
self.type = type
self.name = name
self.domain = domain
self.range = range
self.stories = []
def prt(self):
name = ''.join(self.name.split())
domain = ''.join(self.domain.split())
range = ''.join(self.range.split())
returnstr = ""
returnstr += self.type + "Property: " + self.ontobj.gh.make_obj(name)
returnstr += self.ontobj.gh.make_part("Domain", self.ontobj.gh.make_obj(domain))
returnstr += self.ontobj.gh.make_part("Range", self.ontobj.gh.make_obj(range))
return returnstr
class Header:
def __init__(self, ontology, used_prefixes):
self.ontobj = ontology
self.standard_prefixes = ['owl', 'rdf', 'rdfs', 'xsd', 'dc']
self.used_prefixes = self.standard_prefixes + used_prefixes
def prt(self):
returnstr = ""
returnstr += self.ontobj.gh.comment("Generated with Visual Narrator")
returnstr += self.ontobj.gh.make_prefix('', self.ontobj.ontology)
for prefix in self.used_prefixes:
if prefix is not '':
link = str(PREFIX_DICT[prefix])
returnstr += self.ontobj.gh.make_prefix(prefix, link)
returnstr += self.ontobj.gh.make_prefix(self.ontobj.ontology_name, self.ontobj.ontology)
returnstr += "\nOntology: <:>\n\n"
returnstr += "Annotations:\n\tdc:title \"" + str(
self.ontobj.sys_name) + "\",\n\tdc:creator \"Visual Narrator\",\n\trdfs:comment \"Generated with Visual Narrator\"\n\n"
returnstr += "AnnotationProperty: dc:creator\n\n"
returnstr += "AnnotationProperty: dc:title\n\n"
return returnstr
| true
| true
|
1c4449b854f1469faa313a012269b7964208ff55
| 1,128
|
py
|
Python
|
scrapy/utils/console.py
|
emschorsch/scrapy
|
acb7bad1ff4037b4a613ac94e2d3357bf92bdb8f
|
[
"BSD-3-Clause"
] | 1
|
2015-04-01T20:02:08.000Z
|
2015-04-01T20:02:08.000Z
|
scrapy/utils/console.py
|
emschorsch/scrapy
|
acb7bad1ff4037b4a613ac94e2d3357bf92bdb8f
|
[
"BSD-3-Clause"
] | 2
|
2021-12-13T20:51:32.000Z
|
2022-02-11T03:47:35.000Z
|
scrapy/utils/console.py
|
emschorsch/scrapy
|
acb7bad1ff4037b4a613ac94e2d3357bf92bdb8f
|
[
"BSD-3-Clause"
] | 1
|
2017-11-09T20:33:59.000Z
|
2017-11-09T20:33:59.000Z
|
def start_python_console(namespace=None, noipython=False):
"""Start Python console binded to the given namespace. If IPython is
available, an IPython console will be started instead, unless `noipython`
is True. Also, tab completion will be used on Unix systems.
"""
if namespace is None:
namespace = {}
try:
try: # use IPython if available
if noipython:
raise ImportError
import IPython
try:
IPython.embed(user_ns=namespace)
except AttributeError:
shell = IPython.Shell.IPShellEmbed(argv=[], user_ns=namespace)
shell()
except ImportError:
import code
try: # readline module is only available on unix systems
import readline
except ImportError:
pass
else:
import rlcompleter
readline.parse_and_bind("tab:complete")
code.interact(banner='', local=namespace)
except SystemExit: # raised when using exit() in python code.interact
pass
| 36.387097
| 78
| 0.585106
|
def start_python_console(namespace=None, noipython=False):
if namespace is None:
namespace = {}
try:
try:
if noipython:
raise ImportError
import IPython
try:
IPython.embed(user_ns=namespace)
except AttributeError:
shell = IPython.Shell.IPShellEmbed(argv=[], user_ns=namespace)
shell()
except ImportError:
import code
try:
import readline
except ImportError:
pass
else:
import rlcompleter
readline.parse_and_bind("tab:complete")
code.interact(banner='', local=namespace)
except SystemExit:
pass
| true
| true
|
1c4449dad8f315ea5b5f73b491c6a3fecb29e786
| 2,948
|
py
|
Python
|
datasets/generate_synthia_label_info.py
|
JDAI-CV/FADA
|
a1c6403963184a3427eda68cc94b03ff6143368a
|
[
"Apache-2.0"
] | 120
|
2020-07-20T02:23:02.000Z
|
2022-03-31T02:21:31.000Z
|
datasets/generate_synthia_label_info.py
|
JDAI-CV/FADA
|
a1c6403963184a3427eda68cc94b03ff6143368a
|
[
"Apache-2.0"
] | 27
|
2020-07-29T02:55:52.000Z
|
2022-03-12T08:03:42.000Z
|
datasets/generate_synthia_label_info.py
|
JDAI-CV/FADA
|
a1c6403963184a3427eda68cc94b03ff6143368a
|
[
"Apache-2.0"
] | 25
|
2020-08-01T06:11:08.000Z
|
2022-02-19T07:26:57.000Z
|
import argparse
import os
import math
import numpy as np
import pickle
from PIL import Image
from tqdm import tqdm
import imageio
from multiprocessing import Pool
parser = argparse.ArgumentParser(description="Generate label stat info")
parser.add_argument("-d",
"--datadir",
default="",
help="path to load data",
type=str,
)
parser.add_argument("-n",
"--nprocs",
default=16,
help="Number of processes",
type=int,
)
parser.add_argument("-o",
"--output_dir",
default="",
help="path to save label info",
type=str,
)
args = parser.parse_args()
imgdir = os.path.join(args.datadir, 'RAND_CITYSCAPES', 'RGB')
labdir = os.path.join(args.datadir, 'RAND_CITYSCAPES', 'GT', 'LABELS')
labfiles = os.listdir(labdir)
nprocs = args.nprocs
savedir = args.output_dir
ignore_label = 255
id_to_trainid = {
3: 0,
4: 1,
2: 2,
21: 3,
5: 4,
7: 5,
15: 6,
9: 7,
6: 8,
1: 9,
10: 10,
17: 11,
8: 12,
19: 13,
12: 14,
11: 15,}
def generate_label_info():
label_to_file = [[] for _ in range(len(id_to_trainid.keys()))]
file_to_label = {e:[] for e in os.listdir(imgdir)}
for labfile in tqdm(labfiles):
label = np.unique(np.asarray(imageio.imread(os.path.join(labdir, labfile), format='PNG-FI'))[:,:,0])
for lab in label:
if lab in id_to_trainid.keys():
l = id_to_trainid[lab]
label_to_file[l].append(labfile)
file_to_label[labfile].append(l)
return label_to_file, file_to_label
def _foo(i):
label_to_file = [[] for _ in range(len(id_to_trainid.keys()))]
file_to_label = dict()
labfile = labfiles[i]
file_to_label[labfile] = []
label = np.unique(np.array(Image.open(os.path.join(labdir, labfile)), dtype=np.float32))
for lab in label:
if lab in id_to_trainid.keys():
l = id_to_trainid[lab]
label_to_file[l].append(labfile)
file_to_label[labfile].append(l)
return label_to_file, file_to_label
def main():
label_to_file = [[] for _ in range(len(id_to_trainid.keys()))]
file_to_label = {e:[] for e in os.listdir(imgdir)}
if nprocs==1:
label_to_file, file_to_label = generate_label_info()
else:
with Pool(nprocs) as p:
r = list(tqdm(p.imap(_foo, range(len(labfiles))), total=len(labfiles)))
for l2f, f2l in r:
for lab in range(len(l2f)):
label_to_file[lab].extend(l2f[lab])
for fname in f2l.keys():
file_to_label[fname].extend(f2l[fname])
with open(os.path.join(savedir, 'synthia_label_info.p'), 'wb') as f:
pickle.dump((label_to_file, file_to_label), f)
if __name__ == "__main__":
main()
| 27.811321
| 108
| 0.578019
|
import argparse
import os
import math
import numpy as np
import pickle
from PIL import Image
from tqdm import tqdm
import imageio
from multiprocessing import Pool
parser = argparse.ArgumentParser(description="Generate label stat info")
parser.add_argument("-d",
"--datadir",
default="",
help="path to load data",
type=str,
)
parser.add_argument("-n",
"--nprocs",
default=16,
help="Number of processes",
type=int,
)
parser.add_argument("-o",
"--output_dir",
default="",
help="path to save label info",
type=str,
)
args = parser.parse_args()
imgdir = os.path.join(args.datadir, 'RAND_CITYSCAPES', 'RGB')
labdir = os.path.join(args.datadir, 'RAND_CITYSCAPES', 'GT', 'LABELS')
labfiles = os.listdir(labdir)
nprocs = args.nprocs
savedir = args.output_dir
ignore_label = 255
id_to_trainid = {
3: 0,
4: 1,
2: 2,
21: 3,
5: 4,
7: 5,
15: 6,
9: 7,
6: 8,
1: 9,
10: 10,
17: 11,
8: 12,
19: 13,
12: 14,
11: 15,}
def generate_label_info():
label_to_file = [[] for _ in range(len(id_to_trainid.keys()))]
file_to_label = {e:[] for e in os.listdir(imgdir)}
for labfile in tqdm(labfiles):
label = np.unique(np.asarray(imageio.imread(os.path.join(labdir, labfile), format='PNG-FI'))[:,:,0])
for lab in label:
if lab in id_to_trainid.keys():
l = id_to_trainid[lab]
label_to_file[l].append(labfile)
file_to_label[labfile].append(l)
return label_to_file, file_to_label
def _foo(i):
label_to_file = [[] for _ in range(len(id_to_trainid.keys()))]
file_to_label = dict()
labfile = labfiles[i]
file_to_label[labfile] = []
label = np.unique(np.array(Image.open(os.path.join(labdir, labfile)), dtype=np.float32))
for lab in label:
if lab in id_to_trainid.keys():
l = id_to_trainid[lab]
label_to_file[l].append(labfile)
file_to_label[labfile].append(l)
return label_to_file, file_to_label
def main():
label_to_file = [[] for _ in range(len(id_to_trainid.keys()))]
file_to_label = {e:[] for e in os.listdir(imgdir)}
if nprocs==1:
label_to_file, file_to_label = generate_label_info()
else:
with Pool(nprocs) as p:
r = list(tqdm(p.imap(_foo, range(len(labfiles))), total=len(labfiles)))
for l2f, f2l in r:
for lab in range(len(l2f)):
label_to_file[lab].extend(l2f[lab])
for fname in f2l.keys():
file_to_label[fname].extend(f2l[fname])
with open(os.path.join(savedir, 'synthia_label_info.p'), 'wb') as f:
pickle.dump((label_to_file, file_to_label), f)
if __name__ == "__main__":
main()
| true
| true
|
1c444a39822bf470342e8467d1ba5fb4a3c7f873
| 1,765
|
py
|
Python
|
conversations/migrations/0001_initial.py
|
alstn2468/Django_Airbnb_Clone
|
eeb61e4a36320a0b269d96f47cc6755dbc4c40f8
|
[
"MIT"
] | 5
|
2019-11-26T00:34:24.000Z
|
2021-01-04T06:04:48.000Z
|
conversations/migrations/0001_initial.py
|
alstn2468/Django_Airbnb_Clone
|
eeb61e4a36320a0b269d96f47cc6755dbc4c40f8
|
[
"MIT"
] | 3
|
2021-06-09T19:05:40.000Z
|
2021-09-08T01:49:01.000Z
|
conversations/migrations/0001_initial.py
|
alstn2468/Django_Airbnb_Clone
|
eeb61e4a36320a0b269d96f47cc6755dbc4c40f8
|
[
"MIT"
] | 6
|
2019-11-24T11:47:09.000Z
|
2021-08-16T20:21:35.000Z
|
# Generated by Django 2.2.5 on 2019-12-22 12:48
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = []
operations = [
migrations.CreateModel(
name="Conversation",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("created_at", models.DateTimeField(auto_now_add=True)),
("updated_at", models.DateTimeField(auto_now=True)),
],
options={"abstract": False,},
),
migrations.CreateModel(
name="Message",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("created_at", models.DateTimeField(auto_now_add=True)),
("updated_at", models.DateTimeField(auto_now=True)),
("message", models.TextField()),
(
"conversation",
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name="messages",
to="conversations.Conversation",
),
),
],
options={"abstract": False,},
),
]
| 30.431034
| 72
| 0.420397
|
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = []
operations = [
migrations.CreateModel(
name="Conversation",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("created_at", models.DateTimeField(auto_now_add=True)),
("updated_at", models.DateTimeField(auto_now=True)),
],
options={"abstract": False,},
),
migrations.CreateModel(
name="Message",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("created_at", models.DateTimeField(auto_now_add=True)),
("updated_at", models.DateTimeField(auto_now=True)),
("message", models.TextField()),
(
"conversation",
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name="messages",
to="conversations.Conversation",
),
),
],
options={"abstract": False,},
),
]
| true
| true
|
1c444b23a8f36b78e2011034fb20d666cb5720f3
| 2,548
|
py
|
Python
|
perfil/models.py
|
KleuberJacob/Ecommerce-utilizando-Python-Django-e-Bootstrap4
|
acf2239d408f26eb223a1fe1c03046fcf62d6733
|
[
"MIT"
] | 41
|
2019-08-20T06:55:51.000Z
|
2022-03-23T09:59:27.000Z
|
perfil/models.py
|
KleuberJacob/Ecommerce-utilizando-Python-Django-e-Bootstrap4
|
acf2239d408f26eb223a1fe1c03046fcf62d6733
|
[
"MIT"
] | 8
|
2021-03-18T21:29:25.000Z
|
2022-01-13T01:32:24.000Z
|
perfil/models.py
|
KleuberJacob/Ecommerce-utilizando-Python-Django-e-Bootstrap4
|
acf2239d408f26eb223a1fe1c03046fcf62d6733
|
[
"MIT"
] | 31
|
2020-03-12T00:01:48.000Z
|
2022-03-24T23:47:10.000Z
|
from django.db import models
from django.contrib.auth.models import User
from django.forms import ValidationError
import re
from utils.validacpf import valida_cpf
class Perfil(models.Model):
usuario = models.OneToOneField(User, on_delete=models.CASCADE,
verbose_name='Usuário')
idade = models.PositiveIntegerField()
data_nascimento = models.DateField()
cpf = models.CharField(max_length=11)
endereco = models.CharField(max_length=50)
numero = models.CharField(max_length=5)
complemento = models.CharField(max_length=30)
bairro = models.CharField(max_length=30)
cep = models.CharField(max_length=8)
cidade = models.CharField(max_length=30)
estado = models.CharField(
max_length=2,
default='SP',
choices=(
('AC', 'Acre'),
('AL', 'Alagoas'),
('AP', 'Amapá'),
('AM', 'Amazonas'),
('BA', 'Bahia'),
('CE', 'Ceará'),
('DF', 'Distrito Federal'),
('ES', 'Espírito Santo'),
('GO', 'Goiás'),
('MA', 'Maranhão'),
('MT', 'Mato Grosso'),
('MS', 'Mato Grosso do Sul'),
('MG', 'Minas Gerais'),
('PA', 'Pará'),
('PB', 'Paraíba'),
('PR', 'Paraná'),
('PE', 'Pernambuco'),
('PI', 'Piauí'),
('RJ', 'Rio de Janeiro'),
('RN', 'Rio Grande do Norte'),
('RS', 'Rio Grande do Sul'),
('RO', 'Rondônia'),
('RR', 'Roraima'),
('SC', 'Santa Catarina'),
('SP', 'São Paulo'),
('SE', 'Sergipe'),
('TO', 'Tocantins'),
)
)
def __str__(self):
return f'{self.usuario}'
def clean(self):
error_messages = {}
cpf_enviado = self.cpf or None
cpf_salvo = None
perfil = Perfil.objects.filter(cpf=cpf_enviado).first()
if perfil:
cpf_salvo = perfil.cpf
if cpf_salvo is not None and self.pk != perfil.pk:
error_messages['cpf'] = 'CPF já existe.'
if not valida_cpf(self.cpf):
error_messages['cpf'] = 'Digite um CPF válido'
if re.search(r'[^0-9]', self.cep) or len(self.cep) < 8:
error_messages['cep'] = 'CEP inválido, digite os 8 digitos do CEP.'
if error_messages:
raise ValidationError(error_messages)
class Meta:
verbose_name = 'Perfil'
verbose_name_plural = 'Perfis'
| 30.698795
| 79
| 0.522763
|
from django.db import models
from django.contrib.auth.models import User
from django.forms import ValidationError
import re
from utils.validacpf import valida_cpf
class Perfil(models.Model):
usuario = models.OneToOneField(User, on_delete=models.CASCADE,
verbose_name='Usuário')
idade = models.PositiveIntegerField()
data_nascimento = models.DateField()
cpf = models.CharField(max_length=11)
endereco = models.CharField(max_length=50)
numero = models.CharField(max_length=5)
complemento = models.CharField(max_length=30)
bairro = models.CharField(max_length=30)
cep = models.CharField(max_length=8)
cidade = models.CharField(max_length=30)
estado = models.CharField(
max_length=2,
default='SP',
choices=(
('AC', 'Acre'),
('AL', 'Alagoas'),
('AP', 'Amapá'),
('AM', 'Amazonas'),
('BA', 'Bahia'),
('CE', 'Ceará'),
('DF', 'Distrito Federal'),
('ES', 'Espírito Santo'),
('GO', 'Goiás'),
('MA', 'Maranhão'),
('MT', 'Mato Grosso'),
('MS', 'Mato Grosso do Sul'),
('MG', 'Minas Gerais'),
('PA', 'Pará'),
('PB', 'Paraíba'),
('PR', 'Paraná'),
('PE', 'Pernambuco'),
('PI', 'Piauí'),
('RJ', 'Rio de Janeiro'),
('RN', 'Rio Grande do Norte'),
('RS', 'Rio Grande do Sul'),
('RO', 'Rondônia'),
('RR', 'Roraima'),
('SC', 'Santa Catarina'),
('SP', 'São Paulo'),
('SE', 'Sergipe'),
('TO', 'Tocantins'),
)
)
def __str__(self):
return f'{self.usuario}'
def clean(self):
error_messages = {}
cpf_enviado = self.cpf or None
cpf_salvo = None
perfil = Perfil.objects.filter(cpf=cpf_enviado).first()
if perfil:
cpf_salvo = perfil.cpf
if cpf_salvo is not None and self.pk != perfil.pk:
error_messages['cpf'] = 'CPF já existe.'
if not valida_cpf(self.cpf):
error_messages['cpf'] = 'Digite um CPF válido'
if re.search(r'[^0-9]', self.cep) or len(self.cep) < 8:
error_messages['cep'] = 'CEP inválido, digite os 8 digitos do CEP.'
if error_messages:
raise ValidationError(error_messages)
class Meta:
verbose_name = 'Perfil'
verbose_name_plural = 'Perfis'
| true
| true
|
1c444b8125c72d4ac5a956840cbcad32199c8ff1
| 1,592
|
py
|
Python
|
Notes/apps/users/models.py
|
Mi-As/Lists-RestApi
|
eea238f9fc5fda4b992f33dd2c7a4725a74849bb
|
[
"MIT"
] | 1
|
2020-10-31T20:15:21.000Z
|
2020-10-31T20:15:21.000Z
|
Notes/apps/users/models.py
|
Mi-As/Note-RestApi
|
eea238f9fc5fda4b992f33dd2c7a4725a74849bb
|
[
"MIT"
] | null | null | null |
Notes/apps/users/models.py
|
Mi-As/Note-RestApi
|
eea238f9fc5fda4b992f33dd2c7a4725a74849bb
|
[
"MIT"
] | null | null | null |
from werkzeug.security import check_password_hash, generate_password_hash
import uuid
from ... import db
# import like this bc: https://stackoverflow.com/questions/43576422/sqlalchemy-flask-class-is-not-defined
from ..notes import models as notes
from ...authentication import models as authenticaton
from . import services
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
public_id = db.Column(db.String(50), nullable=False, unique=True)
name = db.Column(db.String, nullable=False)
email = db.Column(db.String, nullable=False, unique=True)
password = db.Column(db.String(255), nullable=False)
# Relationships
# One-to-Many
role_name = db.Column(db.String, db.ForeignKey('role.name'))
# Many-to-One
notes = db.relationship('Note', passive_deletes='all')
tokens = db.relationship('Token', passive_deletes='all')
def __init__(self, name, email, password, role_name='user'):
self.public_id = str(uuid.uuid4())
self.name = name
self.email = email
self.set_password(password)
self.set_role_name(role_name)
def set_role_name(self, role_name):
role = services.get_role({'name':role_name})
assert role, "no such user_role!"
self.role_name = role_name
def set_password(self, secret):
self.password = generate_password_hash(secret)
def check_password(self, secret):
return check_password_hash(self.password, secret)
class Role(db.Model): # ['Admin', 'User']
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String, unique=True, nullable=False)
has_full_access = db.Column(db.Boolean, nullable=False, default=False)
| 31.84
| 105
| 0.748744
|
from werkzeug.security import check_password_hash, generate_password_hash
import uuid
from ... import db
from ..notes import models as notes
from ...authentication import models as authenticaton
from . import services
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
public_id = db.Column(db.String(50), nullable=False, unique=True)
name = db.Column(db.String, nullable=False)
email = db.Column(db.String, nullable=False, unique=True)
password = db.Column(db.String(255), nullable=False)
role_name = db.Column(db.String, db.ForeignKey('role.name'))
notes = db.relationship('Note', passive_deletes='all')
tokens = db.relationship('Token', passive_deletes='all')
def __init__(self, name, email, password, role_name='user'):
self.public_id = str(uuid.uuid4())
self.name = name
self.email = email
self.set_password(password)
self.set_role_name(role_name)
def set_role_name(self, role_name):
role = services.get_role({'name':role_name})
assert role, "no such user_role!"
self.role_name = role_name
def set_password(self, secret):
self.password = generate_password_hash(secret)
def check_password(self, secret):
return check_password_hash(self.password, secret)
class Role(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String, unique=True, nullable=False)
has_full_access = db.Column(db.Boolean, nullable=False, default=False)
| true
| true
|
1c444cea01536e9894e87342726ed2edcb74d0f2
| 6,369
|
py
|
Python
|
fitapp/south_migrations/0002_auto__add_timeseriesdatatype__add_unique_timeseriesdatatype_category_r.py
|
evansnj/django-fitbit-old2
|
87f4aa389ab9ea2f63fa41aa4d3bbb3f55cd78ac
|
[
"Apache-2.0"
] | null | null | null |
fitapp/south_migrations/0002_auto__add_timeseriesdatatype__add_unique_timeseriesdatatype_category_r.py
|
evansnj/django-fitbit-old2
|
87f4aa389ab9ea2f63fa41aa4d3bbb3f55cd78ac
|
[
"Apache-2.0"
] | null | null | null |
fitapp/south_migrations/0002_auto__add_timeseriesdatatype__add_unique_timeseriesdatatype_category_r.py
|
evansnj/django-fitbit-old2
|
87f4aa389ab9ea2f63fa41aa4d3bbb3f55cd78ac
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
# Safe User import for Django < 1.5
try:
from django.contrib.auth import get_user_model
except ImportError:
from django.contrib.auth.models import User
else:
User = get_user_model()
# With the default User model these will be 'auth.User' and 'auth.user'
# so instead of using orm['auth.User'] we can use orm[user_orm_label]
user_orm_label = '%s.%s' % (User._meta.app_label, User._meta.object_name)
user_model_label = '%s.%s' % (User._meta.app_label, User._meta.module_name)
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'TimeSeriesDataType'
db.create_table('fitapp_timeseriesdatatype', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('category', self.gf('django.db.models.fields.IntegerField')()),
('resource', self.gf('django.db.models.fields.CharField')(max_length=128)),
))
db.send_create_signal('fitapp', ['TimeSeriesDataType'])
# Adding unique constraint on 'TimeSeriesDataType', fields ['category', 'resource']
db.create_unique('fitapp_timeseriesdatatype', ['category', 'resource'])
# Adding model 'TimeSeriesData'
db.create_table('fitapp_timeseriesdata', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('user', self.gf('django.db.models.fields.related.ForeignKey')(to=orm[user_orm_label])),
('resource_type', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['fitapp.TimeSeriesDataType'])),
('date', self.gf('django.db.models.fields.DateField')()),
('value', self.gf('django.db.models.fields.CharField')(default=None, max_length=32, null=True)),
))
db.send_create_signal('fitapp', ['TimeSeriesData'])
# Adding unique constraint on 'TimeSeriesData', fields ['user', 'resource_type', 'date']
db.create_unique('fitapp_timeseriesdata', ['user_id', 'resource_type_id', 'date'])
def backwards(self, orm):
# Removing unique constraint on 'TimeSeriesData', fields ['user', 'resource_type', 'date']
db.delete_unique('fitapp_timeseriesdata', ['user_id', 'resource_type_id', 'date'])
# Removing unique constraint on 'TimeSeriesDataType', fields ['category', 'resource']
db.delete_unique('fitapp_timeseriesdatatype', ['category', 'resource'])
# Deleting model 'TimeSeriesDataType'
db.delete_table('fitapp_timeseriesdatatype')
# Deleting model 'TimeSeriesData'
db.delete_table('fitapp_timeseriesdata')
models = {
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
'auth.permission': {
'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
user_model_label: {
'Meta': {
'object_name': User.__name__,
'db_table': "'%s'" % User._meta.db_table
},
User._meta.pk.attname: (
'django.db.models.fields.AutoField', [],
{'primary_key': 'True',
'db_column': "'%s'" % User._meta.pk.column}
),
},
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'fitapp.timeseriesdata': {
'Meta': {'unique_together': "(('user', 'resource_type', 'date'),)", 'object_name': 'TimeSeriesData'},
'date': ('django.db.models.fields.DateField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'resource_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['fitapp.TimeSeriesDataType']"}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['%s']" % user_orm_label}),
'value': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '32', 'null': 'True'})
},
'fitapp.timeseriesdatatype': {
'Meta': {'unique_together': "(('category', 'resource'),)", 'object_name': 'TimeSeriesDataType'},
'category': ('django.db.models.fields.IntegerField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'resource': ('django.db.models.fields.CharField', [], {'max_length': '128'})
},
'fitapp.userfitbit': {
'Meta': {'object_name': 'UserFitbit'},
'auth_secret': ('django.db.models.fields.TextField', [], {}),
'auth_token': ('django.db.models.fields.TextField', [], {}),
'fitbit_user': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'user': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['%s']" % user_orm_label, 'unique': 'True'})
}
}
complete_apps = ['fitapp']
| 52.636364
| 182
| 0.594756
|
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
try:
from django.contrib.auth import get_user_model
except ImportError:
from django.contrib.auth.models import User
else:
User = get_user_model()
user_orm_label = '%s.%s' % (User._meta.app_label, User._meta.object_name)
user_model_label = '%s.%s' % (User._meta.app_label, User._meta.module_name)
class Migration(SchemaMigration):
def forwards(self, orm):
db.create_table('fitapp_timeseriesdatatype', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('category', self.gf('django.db.models.fields.IntegerField')()),
('resource', self.gf('django.db.models.fields.CharField')(max_length=128)),
))
db.send_create_signal('fitapp', ['TimeSeriesDataType'])
db.create_unique('fitapp_timeseriesdatatype', ['category', 'resource'])
db.create_table('fitapp_timeseriesdata', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('user', self.gf('django.db.models.fields.related.ForeignKey')(to=orm[user_orm_label])),
('resource_type', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['fitapp.TimeSeriesDataType'])),
('date', self.gf('django.db.models.fields.DateField')()),
('value', self.gf('django.db.models.fields.CharField')(default=None, max_length=32, null=True)),
))
db.send_create_signal('fitapp', ['TimeSeriesData'])
db.create_unique('fitapp_timeseriesdata', ['user_id', 'resource_type_id', 'date'])
def backwards(self, orm):
db.delete_unique('fitapp_timeseriesdata', ['user_id', 'resource_type_id', 'date'])
db.delete_unique('fitapp_timeseriesdatatype', ['category', 'resource'])
db.delete_table('fitapp_timeseriesdatatype')
db.delete_table('fitapp_timeseriesdata')
models = {
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
'auth.permission': {
'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
user_model_label: {
'Meta': {
'object_name': User.__name__,
'db_table': "'%s'" % User._meta.db_table
},
User._meta.pk.attname: (
'django.db.models.fields.AutoField', [],
{'primary_key': 'True',
'db_column': "'%s'" % User._meta.pk.column}
),
},
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'fitapp.timeseriesdata': {
'Meta': {'unique_together': "(('user', 'resource_type', 'date'),)", 'object_name': 'TimeSeriesData'},
'date': ('django.db.models.fields.DateField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'resource_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['fitapp.TimeSeriesDataType']"}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['%s']" % user_orm_label}),
'value': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '32', 'null': 'True'})
},
'fitapp.timeseriesdatatype': {
'Meta': {'unique_together': "(('category', 'resource'),)", 'object_name': 'TimeSeriesDataType'},
'category': ('django.db.models.fields.IntegerField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'resource': ('django.db.models.fields.CharField', [], {'max_length': '128'})
},
'fitapp.userfitbit': {
'Meta': {'object_name': 'UserFitbit'},
'auth_secret': ('django.db.models.fields.TextField', [], {}),
'auth_token': ('django.db.models.fields.TextField', [], {}),
'fitbit_user': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'user': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['%s']" % user_orm_label, 'unique': 'True'})
}
}
complete_apps = ['fitapp']
| true
| true
|
1c444d318a36178d6e5513e846e17f7a823e3ccb
| 931
|
py
|
Python
|
esphome/components/binary_sensor/homeassistant.py
|
pi4homez/esphome
|
697e9b0c28bb690719fa1d16ca8198ce5fd1d2be
|
[
"MIT"
] | 5
|
2019-04-14T09:43:29.000Z
|
2021-07-17T06:36:44.000Z
|
esphome/components/binary_sensor/homeassistant.py
|
pi4homez/esphome
|
697e9b0c28bb690719fa1d16ca8198ce5fd1d2be
|
[
"MIT"
] | null | null | null |
esphome/components/binary_sensor/homeassistant.py
|
pi4homez/esphome
|
697e9b0c28bb690719fa1d16ca8198ce5fd1d2be
|
[
"MIT"
] | 4
|
2019-07-08T08:58:44.000Z
|
2021-12-18T21:56:22.000Z
|
import voluptuous as vol
from esphome.components import binary_sensor
import esphome.config_validation as cv
from esphome.const import CONF_ENTITY_ID, CONF_ID, CONF_NAME
from esphome.cpp_generator import Pvariable
from esphome.cpp_types import App
DEPENDENCIES = ['api']
HomeassistantBinarySensor = binary_sensor.binary_sensor_ns.class_('HomeassistantBinarySensor',
binary_sensor.BinarySensor)
PLATFORM_SCHEMA = cv.nameable(binary_sensor.BINARY_SENSOR_PLATFORM_SCHEMA.extend({
cv.GenerateID(): cv.declare_variable_id(HomeassistantBinarySensor),
vol.Required(CONF_ENTITY_ID): cv.entity_id,
}))
def to_code(config):
rhs = App.make_homeassistant_binary_sensor(config[CONF_NAME], config[CONF_ENTITY_ID])
subs = Pvariable(config[CONF_ID], rhs)
binary_sensor.setup_binary_sensor(subs, config)
BUILD_FLAGS = '-DUSE_HOMEASSISTANT_BINARY_SENSOR'
| 34.481481
| 94
| 0.762621
|
import voluptuous as vol
from esphome.components import binary_sensor
import esphome.config_validation as cv
from esphome.const import CONF_ENTITY_ID, CONF_ID, CONF_NAME
from esphome.cpp_generator import Pvariable
from esphome.cpp_types import App
DEPENDENCIES = ['api']
HomeassistantBinarySensor = binary_sensor.binary_sensor_ns.class_('HomeassistantBinarySensor',
binary_sensor.BinarySensor)
PLATFORM_SCHEMA = cv.nameable(binary_sensor.BINARY_SENSOR_PLATFORM_SCHEMA.extend({
cv.GenerateID(): cv.declare_variable_id(HomeassistantBinarySensor),
vol.Required(CONF_ENTITY_ID): cv.entity_id,
}))
def to_code(config):
rhs = App.make_homeassistant_binary_sensor(config[CONF_NAME], config[CONF_ENTITY_ID])
subs = Pvariable(config[CONF_ID], rhs)
binary_sensor.setup_binary_sensor(subs, config)
BUILD_FLAGS = '-DUSE_HOMEASSISTANT_BINARY_SENSOR'
| true
| true
|
1c444d5e1dda6613ae5959af1930b90d1660a982
| 4,000
|
py
|
Python
|
notebooks/parameter_tuning_ex_02.py
|
castorfou/scikit-learn-mooc
|
235748eff57409eb17d8355024579c6df44c0563
|
[
"CC-BY-4.0"
] | 1
|
2021-05-25T07:29:44.000Z
|
2021-05-25T07:29:44.000Z
|
notebooks/parameter_tuning_ex_02.py
|
castorfou/scikit-learn-mooc
|
235748eff57409eb17d8355024579c6df44c0563
|
[
"CC-BY-4.0"
] | null | null | null |
notebooks/parameter_tuning_ex_02.py
|
castorfou/scikit-learn-mooc
|
235748eff57409eb17d8355024579c6df44c0563
|
[
"CC-BY-4.0"
] | null | null | null |
#!/usr/bin/env python
# coding: utf-8
# # 📝 Exercise M3.01
#
# The goal is to write an exhaustive search to find the best parameters
# combination maximizing the model statistical performance.
#
# Here we use a small subset of the Adult Census dataset to make to code
# fast to execute. Once your code works on the small subset, try to
# change `train_size` to a larger value (e.g. 0.8 for 80% instead of
# 20%).
# In[1]:
import pandas as pd
from sklearn.model_selection import train_test_split
adult_census = pd.read_csv("../datasets/adult-census.csv")
target_name = "class"
target = adult_census[target_name]
data = adult_census.drop(columns=[target_name, "education-num"])
data_train, data_test, target_train, target_test = train_test_split(
data, target, train_size=0.2, random_state=42)
# In[2]:
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector
from sklearn.preprocessing import OrdinalEncoder
categorical_preprocessor = OrdinalEncoder(handle_unknown="use_encoded_value",
unknown_value=-1)
preprocessor = ColumnTransformer(
[('cat-preprocessor', categorical_preprocessor,
selector(dtype_include=object))],
remainder='passthrough', sparse_threshold=0)
# This line is currently required to import HistGradientBoostingClassifier
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import Pipeline
model = Pipeline([
("preprocessor", preprocessor),
("classifier", HistGradientBoostingClassifier(random_state=42))
])
#
# Use the previously defined model (called `model`) and using two nested `for`
# loops, make a search of the best combinations of the `learning_rate` and
# `max_leaf_nodes` parameters. In this regard, you will need to train and test
# the model by setting the parameters. The evaluation of the model should be
# performed using `cross_val_score`. We will use the following parameters
# search:
# - `learning_rate` for the values 0.01, 0.1, 1 and 10. This parameter controls
# the ability of a new tree to correct the error of the previous sequence of
# trees
# - `max_leaf_nodes` for the values 3, 10, 30. This parameter controls the
# depth of each tree.
# In[3]:
for parameter in model.get_params():
print(parameter)
# In[5]:
# Write your code here.
from sklearn.model_selection import cross_val_score
learning_rate_range = [0.01, 0.1, 1, 10]
max_leaf_nodes_range = [3, 10, 30]
for learning_rate in learning_rate_range:
for max_leaf_nodes in max_leaf_nodes_range:
model.set_params(classifier__learning_rate=learning_rate)
model.set_params(classifier__max_leaf_nodes=max_leaf_nodes)
scores = cross_val_score(model, data_test, target_test)
print(f"Accuracy score via cross-validation using learning_rate:{learning_rate}, max_leaf_nodes:{max_leaf_nodes}:\n"
f"{scores.mean():.3f} +/- {scores.std():.3f}")
# # correction
# In[6]:
from sklearn.model_selection import cross_val_score
learning_rate = [0.01, 0.1, 1, 10]
max_leaf_nodes = [3, 10, 30]
best_score = 0
best_params = {}
for lr in learning_rate:
for mln in max_leaf_nodes:
print(f"Evaluating model with learning rate {lr:.3f}"
f" and max leaf nodes {mln}... ", end="")
model.set_params(
classifier__learning_rate=lr,
classifier__max_leaf_nodes=mln
)
scores = cross_val_score(model, data_train, target_train, cv=2)
mean_score = scores.mean()
print(f"score: {mean_score:.3f}")
if mean_score > best_score:
best_score = mean_score
best_params = {'learning-rate': lr, 'max leaf nodes': mln}
print(f"Found new best model with score {best_score:.3f}!")
print(f"The best accuracy obtained is {best_score:.3f}")
print(f"The best parameters found are:\n {best_params}")
# In[ ]:
| 30.30303
| 124
| 0.72
|
t pandas as pd
from sklearn.model_selection import train_test_split
adult_census = pd.read_csv("../datasets/adult-census.csv")
target_name = "class"
target = adult_census[target_name]
data = adult_census.drop(columns=[target_name, "education-num"])
data_train, data_test, target_train, target_test = train_test_split(
data, target, train_size=0.2, random_state=42)
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector
from sklearn.preprocessing import OrdinalEncoder
categorical_preprocessor = OrdinalEncoder(handle_unknown="use_encoded_value",
unknown_value=-1)
preprocessor = ColumnTransformer(
[('cat-preprocessor', categorical_preprocessor,
selector(dtype_include=object))],
remainder='passthrough', sparse_threshold=0)
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import Pipeline
model = Pipeline([
("preprocessor", preprocessor),
("classifier", HistGradientBoostingClassifier(random_state=42))
])
for parameter in model.get_params():
print(parameter)
from sklearn.model_selection import cross_val_score
learning_rate_range = [0.01, 0.1, 1, 10]
max_leaf_nodes_range = [3, 10, 30]
for learning_rate in learning_rate_range:
for max_leaf_nodes in max_leaf_nodes_range:
model.set_params(classifier__learning_rate=learning_rate)
model.set_params(classifier__max_leaf_nodes=max_leaf_nodes)
scores = cross_val_score(model, data_test, target_test)
print(f"Accuracy score via cross-validation using learning_rate:{learning_rate}, max_leaf_nodes:{max_leaf_nodes}:\n"
f"{scores.mean():.3f} +/- {scores.std():.3f}")
learn.model_selection import cross_val_score
learning_rate = [0.01, 0.1, 1, 10]
max_leaf_nodes = [3, 10, 30]
best_score = 0
best_params = {}
for lr in learning_rate:
for mln in max_leaf_nodes:
print(f"Evaluating model with learning rate {lr:.3f}"
f" and max leaf nodes {mln}... ", end="")
model.set_params(
classifier__learning_rate=lr,
classifier__max_leaf_nodes=mln
)
scores = cross_val_score(model, data_train, target_train, cv=2)
mean_score = scores.mean()
print(f"score: {mean_score:.3f}")
if mean_score > best_score:
best_score = mean_score
best_params = {'learning-rate': lr, 'max leaf nodes': mln}
print(f"Found new best model with score {best_score:.3f}!")
print(f"The best accuracy obtained is {best_score:.3f}")
print(f"The best parameters found are:\n {best_params}")
| true
| true
|
1c444d99a421ed03ab656d5c6c2d5a7c3d4e9f61
| 1,979
|
py
|
Python
|
exercises/en/test_02_07a.py
|
UBC-MDS/exploratory-data-viz
|
83b704ce10d1ff5e10bfd4cdfa872ac52993fd54
|
[
"CC-BY-4.0"
] | null | null | null |
exercises/en/test_02_07a.py
|
UBC-MDS/exploratory-data-viz
|
83b704ce10d1ff5e10bfd4cdfa872ac52993fd54
|
[
"CC-BY-4.0"
] | 88
|
2020-12-04T06:56:51.000Z
|
2021-05-10T22:02:45.000Z
|
exercises/en/test_02_07a.py
|
UBC-MDS/exploratory-data-viz
|
83b704ce10d1ff5e10bfd4cdfa872ac52993fd54
|
[
"CC-BY-4.0"
] | 4
|
2021-01-13T09:30:57.000Z
|
2021-08-03T20:49:31.000Z
|
def test():
# Here we can either check objects created in the solution code, or the
# string value of the solution, available as __solution__. A helper for
# printing formatted messages is available as __msg__. See the testTemplate
# in the meta.json for details.
# If an assertion fails, the message will be displayed
assert not fuel_efficiency is None, "Your answer does not exist. Have you passed in the correct variable?"
assert type(fuel_efficiency) == type(alt.Chart()), "Your answer is not an Altair Chart object. Check to make sure that you have assigned an alt.Chart object to fuel_efficiency."
assert fuel_efficiency.data.equals(data.cars()), "Make sure you are using cars() dataset from vega_datasets."
assert fuel_efficiency.mark == 'area', "Make sure you are using the area mark type."
assert (fuel_efficiency.encoding.x.field in {'Year', 'Year:temporal', 'Year:T'} or
fuel_efficiency.encoding.x.shorthand in {'Year', 'Year:temporal', 'Year:T'}), "Make sure you are using 'Year' as the x-axis encoding."
assert (fuel_efficiency.encoding.y.field in {'Miles_per_Gallon', 'Miles_per_Gallon:quantitative', 'Miles_per_Gallon:Q'} or
"Miles_per_Gallon" in fuel_efficiency.encoding.y.shorthand), "Make sure you are using mean of the 'Miles_per_Gallon' as the y-axis encoding."
assert ((fuel_efficiency.encoding.y.field in {'Miles_per_Gallon', 'Miles_per_Gallon:quantitative', 'Miles_per_Gallon:Q'} and fuel_efficiency.encoding.y.aggregate == 'mean') or
fuel_efficiency.encoding.y.shorthand in {'mean(Miles_per_Gallon)', 'mean(Miles_per_Gallon):quantitative', 'mean(Miles_per_Gallon):Q'}), "You're very close. Make sure that you are using the mean aggregate for the y-axis encoding."
assert type(fuel_efficiency.title) == str and len(fuel_efficiency.title) >= 5, "Make sure you specify a descriptive title for the fuel_efficiency plot."
__msg__.good("You're correct, well done!")
| 98.95
| 241
| 0.738757
|
def test():
assert not fuel_efficiency is None, "Your answer does not exist. Have you passed in the correct variable?"
assert type(fuel_efficiency) == type(alt.Chart()), "Your answer is not an Altair Chart object. Check to make sure that you have assigned an alt.Chart object to fuel_efficiency."
assert fuel_efficiency.data.equals(data.cars()), "Make sure you are using cars() dataset from vega_datasets."
assert fuel_efficiency.mark == 'area', "Make sure you are using the area mark type."
assert (fuel_efficiency.encoding.x.field in {'Year', 'Year:temporal', 'Year:T'} or
fuel_efficiency.encoding.x.shorthand in {'Year', 'Year:temporal', 'Year:T'}), "Make sure you are using 'Year' as the x-axis encoding."
assert (fuel_efficiency.encoding.y.field in {'Miles_per_Gallon', 'Miles_per_Gallon:quantitative', 'Miles_per_Gallon:Q'} or
"Miles_per_Gallon" in fuel_efficiency.encoding.y.shorthand), "Make sure you are using mean of the 'Miles_per_Gallon' as the y-axis encoding."
assert ((fuel_efficiency.encoding.y.field in {'Miles_per_Gallon', 'Miles_per_Gallon:quantitative', 'Miles_per_Gallon:Q'} and fuel_efficiency.encoding.y.aggregate == 'mean') or
fuel_efficiency.encoding.y.shorthand in {'mean(Miles_per_Gallon)', 'mean(Miles_per_Gallon):quantitative', 'mean(Miles_per_Gallon):Q'}), "You're very close. Make sure that you are using the mean aggregate for the y-axis encoding."
assert type(fuel_efficiency.title) == str and len(fuel_efficiency.title) >= 5, "Make sure you specify a descriptive title for the fuel_efficiency plot."
__msg__.good("You're correct, well done!")
| true
| true
|
1c444da6a8b80e2466802ae61d8ef26911df5b78
| 3,644
|
py
|
Python
|
caffe2/python/operator_test/fc_operator_test.py
|
DavidKo3/mctorch
|
53ffe61763059677978b4592c8b2153b0c15428f
|
[
"BSD-3-Clause"
] | 1
|
2019-07-21T02:13:22.000Z
|
2019-07-21T02:13:22.000Z
|
caffe2/python/operator_test/fc_operator_test.py
|
DavidKo3/mctorch
|
53ffe61763059677978b4592c8b2153b0c15428f
|
[
"BSD-3-Clause"
] | null | null | null |
caffe2/python/operator_test/fc_operator_test.py
|
DavidKo3/mctorch
|
53ffe61763059677978b4592c8b2153b0c15428f
|
[
"BSD-3-Clause"
] | null | null | null |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from hypothesis import assume, given, settings, HealthCheck
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestFcOperator(hu.HypothesisTestCase):
def _run_test(self, n, m, k, transposed, multi_dim, dtype, engine, gc, dc):
if dtype == np.float16:
# fp16 only supported with CUDA
assume(gc.device_type == caffe2_pb2.CUDA)
dc = [d for d in dc if d.device_type == caffe2_pb2.CUDA]
if engine == 'TENSORCORE':
# TensorCore only makes sense with CUDA
assume(gc.device_type == caffe2_pb2.CUDA)
# ensures TensorCore kernels can be called
m *= 8
k *= 8
n *= 8
X = np.random.rand(m, k).astype(dtype) - 0.5
if multi_dim:
if transposed:
W = np.random.rand(k, n, 1, 1).astype(dtype) - 0.5
else:
W = np.random.rand(n, k, 1, 1).astype(dtype) - 0.5
else:
if transposed:
W = np.random.rand(k, n).astype(dtype) - 0.5
else:
W = np.random.rand(n, k).astype(dtype) - 0.5
b = np.random.rand(n).astype(dtype) - 0.5
def fc_op(X, W, b):
return [np.dot(X, W.reshape(n, k).transpose()) + b.reshape(n)]
def fc_tranposed_op(X, W, b):
return [np.dot(X, W.reshape(k, n)) + b.reshape(n)]
op = core.CreateOperator(
'FCTransposed' if transposed else 'FC',
['X', 'W', 'b'],
'out',
engine=engine,
)
if dtype == np.float16 and gc.device_type == caffe2_pb2.CUDA:
a = caffe2_pb2.Argument()
a.i = 1
a.name = "float16_compute"
op.arg.extend([a])
# Check against numpy reference
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X, W, b],
reference=fc_tranposed_op if transposed else fc_op,
)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [X, W, b], [0])
# Gradient checks
threshold = 0.5 if dtype == np.float16 else 0.005
stepsize = 0.5 if dtype == np.float16 else 0.05
for i in range(3):
self.assertGradientChecks(gc, op, [X, W, b], i, [0],
threshold=threshold, stepsize=stepsize)
@settings(max_examples=50, suppress_health_check=[HealthCheck.filter_too_much])
@given(n=st.integers(1, 5),
m=st.integers(0, 5),
k=st.integers(1, 5),
multi_dim=st.sampled_from([True, False]),
dtype=st.sampled_from([np.float32, np.float16]),
engine=st.sampled_from(['', 'TENSORCORE']),
**hu.gcs)
def test_fc(self, **kwargs):
self._run_test(transposed=False, **kwargs)
@settings(max_examples=50, suppress_health_check=[HealthCheck.filter_too_much])
@given(n=st.integers(1, 5),
m=st.integers(0, 5),
k=st.integers(1, 5),
multi_dim=st.sampled_from([True, False]),
dtype=st.sampled_from([np.float32, np.float16]),
engine=st.sampled_from(['', 'TENSORCORE']),
**hu.gcs)
def test_fc_transposed(self, **kwargs):
self._run_test(transposed=True, **kwargs)
if __name__ == "__main__":
import unittest
unittest.main()
| 35.038462
| 83
| 0.570801
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from hypothesis import assume, given, settings, HealthCheck
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestFcOperator(hu.HypothesisTestCase):
def _run_test(self, n, m, k, transposed, multi_dim, dtype, engine, gc, dc):
if dtype == np.float16:
assume(gc.device_type == caffe2_pb2.CUDA)
dc = [d for d in dc if d.device_type == caffe2_pb2.CUDA]
if engine == 'TENSORCORE':
assume(gc.device_type == caffe2_pb2.CUDA)
m *= 8
k *= 8
n *= 8
X = np.random.rand(m, k).astype(dtype) - 0.5
if multi_dim:
if transposed:
W = np.random.rand(k, n, 1, 1).astype(dtype) - 0.5
else:
W = np.random.rand(n, k, 1, 1).astype(dtype) - 0.5
else:
if transposed:
W = np.random.rand(k, n).astype(dtype) - 0.5
else:
W = np.random.rand(n, k).astype(dtype) - 0.5
b = np.random.rand(n).astype(dtype) - 0.5
def fc_op(X, W, b):
return [np.dot(X, W.reshape(n, k).transpose()) + b.reshape(n)]
def fc_tranposed_op(X, W, b):
return [np.dot(X, W.reshape(k, n)) + b.reshape(n)]
op = core.CreateOperator(
'FCTransposed' if transposed else 'FC',
['X', 'W', 'b'],
'out',
engine=engine,
)
if dtype == np.float16 and gc.device_type == caffe2_pb2.CUDA:
a = caffe2_pb2.Argument()
a.i = 1
a.name = "float16_compute"
op.arg.extend([a])
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X, W, b],
reference=fc_tranposed_op if transposed else fc_op,
)
self.assertDeviceChecks(dc, op, [X, W, b], [0])
threshold = 0.5 if dtype == np.float16 else 0.005
stepsize = 0.5 if dtype == np.float16 else 0.05
for i in range(3):
self.assertGradientChecks(gc, op, [X, W, b], i, [0],
threshold=threshold, stepsize=stepsize)
@settings(max_examples=50, suppress_health_check=[HealthCheck.filter_too_much])
@given(n=st.integers(1, 5),
m=st.integers(0, 5),
k=st.integers(1, 5),
multi_dim=st.sampled_from([True, False]),
dtype=st.sampled_from([np.float32, np.float16]),
engine=st.sampled_from(['', 'TENSORCORE']),
**hu.gcs)
def test_fc(self, **kwargs):
self._run_test(transposed=False, **kwargs)
@settings(max_examples=50, suppress_health_check=[HealthCheck.filter_too_much])
@given(n=st.integers(1, 5),
m=st.integers(0, 5),
k=st.integers(1, 5),
multi_dim=st.sampled_from([True, False]),
dtype=st.sampled_from([np.float32, np.float16]),
engine=st.sampled_from(['', 'TENSORCORE']),
**hu.gcs)
def test_fc_transposed(self, **kwargs):
self._run_test(transposed=True, **kwargs)
if __name__ == "__main__":
import unittest
unittest.main()
| true
| true
|
1c444db0233ca5c275f3a1c2a5a0e92f86e9e40a
| 6,119
|
py
|
Python
|
toolkitui.py
|
thebirdsbeak/morse_trainer
|
2cc5277de066703218bc2277532a7a9b985a5b92
|
[
"MIT"
] | null | null | null |
toolkitui.py
|
thebirdsbeak/morse_trainer
|
2cc5277de066703218bc2277532a7a9b985a5b92
|
[
"MIT"
] | null | null | null |
toolkitui.py
|
thebirdsbeak/morse_trainer
|
2cc5277de066703218bc2277532a7a9b985a5b92
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'toolkitui.ui'
#
# Created by: PyQt5 UI code generator 5.15.1
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(800, 600)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.gridLayout_2 = QtWidgets.QGridLayout(self.centralwidget)
self.gridLayout_2.setObjectName("gridLayout_2")
self.gridLayout = QtWidgets.QGridLayout()
self.gridLayout.setObjectName("gridLayout")
self.toolkitBrowser = QtWidgets.QTextBrowser(self.centralwidget)
self.toolkitBrowser.setObjectName("toolkitBrowser")
self.gridLayout.addWidget(self.toolkitBrowser, 0, 0, 1, 1)
self.gridLayout_2.addLayout(self.gridLayout, 0, 0, 1, 1)
self.horizontalLayout_2 = QtWidgets.QHBoxLayout()
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.kochButton = QtWidgets.QPushButton(self.centralwidget)
self.kochButton.setObjectName("kochButton")
self.horizontalLayout_2.addWidget(self.kochButton)
self.practiceButton = QtWidgets.QPushButton(self.centralwidget)
self.practiceButton.setObjectName("practiceButton")
self.horizontalLayout_2.addWidget(self.practiceButton)
self.simulatedButton = QtWidgets.QPushButton(self.centralwidget)
self.simulatedButton.setObjectName("simulatedButton")
self.horizontalLayout_2.addWidget(self.simulatedButton)
self.quizButton = QtWidgets.QPushButton(self.centralwidget)
self.quizButton.setObjectName("quizButton")
self.horizontalLayout_2.addWidget(self.quizButton)
self.gridLayout_2.addLayout(self.horizontalLayout_2, 1, 0, 1, 1)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 29))
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", "Morse Toolkit"))
self.toolkitBrowser.setHtml(_translate("MainWindow", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:\'Cantarell\'; font-size:11pt; font-weight:400; font-style:normal;\">\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:14pt; text-decoration: underline;\">Welcome to the Morse Toolkit</span></p>\n"
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:\'Sans Serif\'; font-size:16pt; text-decoration: underline;\"><br /></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">Select one of the modes from the buttons below.</span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\"> </span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">1. Koch Trainer: A classic Koch training tool for learning letters by their sounds.</span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">2. Practice: Practice listening for words, callsign or individual letters. </span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">3. Simulated QSO: Practice formal QSO exchanges. </span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">4. Quiz Mode: Answer questions from excerpts of morse QSOs</span></p>\n"
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:\'Sans Serif\'; font-size:12pt;\"><br /></p>\n"
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:\'Sans Serif\'; font-size:16pt; text-decoration: underline;\"><br /></p></body></html>"))
self.kochButton.setText(_translate("MainWindow", "Koch Trainer"))
self.practiceButton.setText(_translate("MainWindow", "Practice"))
self.simulatedButton.setText(_translate("MainWindow", "Simulated QSO"))
self.quizButton.setText(_translate("MainWindow", "Quiz Mode"))
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_())
| 71.151163
| 280
| 0.708776
|
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(800, 600)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.gridLayout_2 = QtWidgets.QGridLayout(self.centralwidget)
self.gridLayout_2.setObjectName("gridLayout_2")
self.gridLayout = QtWidgets.QGridLayout()
self.gridLayout.setObjectName("gridLayout")
self.toolkitBrowser = QtWidgets.QTextBrowser(self.centralwidget)
self.toolkitBrowser.setObjectName("toolkitBrowser")
self.gridLayout.addWidget(self.toolkitBrowser, 0, 0, 1, 1)
self.gridLayout_2.addLayout(self.gridLayout, 0, 0, 1, 1)
self.horizontalLayout_2 = QtWidgets.QHBoxLayout()
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.kochButton = QtWidgets.QPushButton(self.centralwidget)
self.kochButton.setObjectName("kochButton")
self.horizontalLayout_2.addWidget(self.kochButton)
self.practiceButton = QtWidgets.QPushButton(self.centralwidget)
self.practiceButton.setObjectName("practiceButton")
self.horizontalLayout_2.addWidget(self.practiceButton)
self.simulatedButton = QtWidgets.QPushButton(self.centralwidget)
self.simulatedButton.setObjectName("simulatedButton")
self.horizontalLayout_2.addWidget(self.simulatedButton)
self.quizButton = QtWidgets.QPushButton(self.centralwidget)
self.quizButton.setObjectName("quizButton")
self.horizontalLayout_2.addWidget(self.quizButton)
self.gridLayout_2.addLayout(self.horizontalLayout_2, 1, 0, 1, 1)
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 29))
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", "Morse Toolkit"))
self.toolkitBrowser.setHtml(_translate("MainWindow", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:\'Cantarell\'; font-size:11pt; font-weight:400; font-style:normal;\">\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:14pt; text-decoration: underline;\">Welcome to the Morse Toolkit</span></p>\n"
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:\'Sans Serif\'; font-size:16pt; text-decoration: underline;\"><br /></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">Select one of the modes from the buttons below.</span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\"> </span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">1. Koch Trainer: A classic Koch training tool for learning letters by their sounds.</span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">2. Practice: Practice listening for words, callsign or individual letters. </span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">3. Simulated QSO: Practice formal QSO exchanges. </span></p>\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'Sans Serif\'; font-size:10pt;\">4. Quiz Mode: Answer questions from excerpts of morse QSOs</span></p>\n"
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:\'Sans Serif\'; font-size:12pt;\"><br /></p>\n"
"<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:\'Sans Serif\'; font-size:16pt; text-decoration: underline;\"><br /></p></body></html>"))
self.kochButton.setText(_translate("MainWindow", "Koch Trainer"))
self.practiceButton.setText(_translate("MainWindow", "Practice"))
self.simulatedButton.setText(_translate("MainWindow", "Simulated QSO"))
self.quizButton.setText(_translate("MainWindow", "Quiz Mode"))
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_())
| true
| true
|
1c444de510c835bf1a3b57ad6b627da2b0e5f8d3
| 18,760
|
py
|
Python
|
tensorflow/python/ops/gradients_test.py
|
topsun888/tensorflow
|
bad7c50b9dc9789ad7dd0a62daca40b7269841ed
|
[
"Apache-2.0"
] | 2
|
2017-10-14T09:13:27.000Z
|
2017-10-26T18:34:28.000Z
|
tensorflow/python/ops/gradients_test.py
|
kiliczsh/tensorflow
|
f49aca4532c155597c669cf2189f211cafbebf96
|
[
"Apache-2.0"
] | 1
|
2021-04-12T03:51:59.000Z
|
2021-04-12T03:51:59.000Z
|
tensorflow/python/ops/gradients_test.py
|
kiliczsh/tensorflow
|
f49aca4532c155597c669cf2189f211cafbebf96
|
[
"Apache-2.0"
] | 5
|
2018-02-27T00:34:23.000Z
|
2022-02-28T16:38:08.000Z
|
# Copyright 2015 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 tensorflow.ops.gradients."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import warnings
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework.constant_op import constant
from tensorflow.python.ops import array_grad # pylint: disable=unused-import
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_grad # pylint: disable=unused-import
from tensorflow.python.ops import data_flow_ops # pylint: disable=unused-import
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_grad # pylint: disable=unused-import
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_grad # pylint: disable=unused-import
from tensorflow.python.ops import state_grad # pylint: disable=unused-import
from tensorflow.python.ops import functional_ops # pylint: disable=unused-import
from tensorflow.python.ops.nn_ops import bias_add
from tensorflow.python.platform import googletest
def _OpsBetween(graph, to_ops, from_ops):
"""Build the list of operations between two lists of Operations.
Args:
graph: a Graph.
to_ops: list of Operations.
from_ops: list of Operations.
Returns:
The list of operations between "from_ops" and "to_ops", sorted by
decreasing operation id. This list contains all elements of to_ops.
TODO(touts): Think about returning an empty list if from_ops are not
reachable from to_ops. Presently it returns to_ops in that case.
"""
# List of booleans, indexed by operation id, indicating if
# an op is reached from the output of "input_ops".
reached_ops = [False] * (graph._last_id + 1)
# We only care to reach up to "output_ops" so we mark the
# output ops as reached to avoid recursing past them.
for op in to_ops:
reached_ops[op._id] = True
gradients._MarkReachedOps(from_ops, reached_ops)
between_ops = gradients._GatherInputs(to_ops, reached_ops)
between_ops.sort(key=lambda x: -x._id)
return between_ops
class GradientsTest(test_util.TensorFlowTestCase):
def _OpNames(self, op_list):
return ["%s/%d" % (str(op.name), op._id) for op in op_list]
def _assertOpListEqual(self, ops1, ops2):
self.assertEquals(self._OpNames(ops1), self._OpNames(ops2))
def testOpsBetweenSimple(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
t3 = array_ops.pack([t1, t2])
# Full graph
self._assertOpListEqual([t3.op, t2.op, t1.op],
_OpsBetween(g, [t3.op], [t1.op, t2.op]))
# Only t1, t3.
self._assertOpListEqual([t3.op, t1.op],
_OpsBetween(g, [t3.op], [t1.op]))
def testOpsBetweenUnreachable(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
_ = array_ops.pack([t1, t2])
t4 = constant(1.0)
t5 = constant(2.0)
t6 = array_ops.pack([t4, t5])
# Elements of to_ops are always listed.
self._assertOpListEqual([t6.op], _OpsBetween(g, [t6.op], [t1.op]))
def testOpsBetweenCut(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
t3 = array_ops.pack([t1, t2])
t4 = constant([1.0])
t5 = array_ops.concat(0, [t4, t3])
t6 = constant([2.0])
t7 = array_ops.concat(0, [t5, t6])
self._assertOpListEqual([t7.op, t5.op, t4.op],
_OpsBetween(g, [t7.op], [t4.op]))
def testOpsBetweenCycle(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
t3 = array_ops.pack([t1, t2])
t4 = array_ops.concat(0, [t3, t3, t3])
t5 = constant([1.0])
t6 = array_ops.concat(0, [t4, t5])
t7 = array_ops.concat(0, [t6, t3])
self._assertOpListEqual([t6.op, t4.op, t3.op],
_OpsBetween(g, [t6.op], [t3.op]))
self._assertOpListEqual([t7.op, t6.op, t5.op, t4.op, t3.op, t1.op],
_OpsBetween(g, [t7.op], [t1.op, t5.op]))
self._assertOpListEqual([t6.op, t5.op, t4.op, t3.op, t2.op],
_OpsBetween(g, [t6.op], [t2.op, t5.op]))
def testGradients(self):
with ops.Graph().as_default():
inp = constant(1.0, shape=[32, 100], name="in")
w = constant(1.0, shape=[100, 10], name="w")
b = constant(1.0, shape=[10], name="b")
xw = math_ops.matmul(inp, w, name="xw")
h = bias_add(xw, b, name="h")
w_grad = gradients.gradients(h, w)[0]
self.assertEquals("MatMul", w_grad.op.type)
self.assertEquals(w_grad.op._original_op, xw.op)
self.assertTrue(w_grad.op.get_attr("transpose_a"))
self.assertFalse(w_grad.op.get_attr("transpose_b"))
def testUnusedOutput(self):
with ops.Graph().as_default():
w = constant(1.0, shape=[2, 2])
x = constant(1.0, shape=[2, 2])
wx = math_ops.matmul(w, x)
split_wx = array_ops.split(0, 2, wx)
c = math_ops.reduce_sum(split_wx[1])
gw = gradients.gradients(c, [w])[0]
self.assertEquals("MatMul", gw.op.type)
def testColocateGradients(self):
with ops.Graph().as_default() as g:
w = constant(1.0, shape=[1, 1])
x = constant(1.0, shape=[1, 2])
with g.device("/gpu:0"):
wx = math_ops.matmul(w, x)
gw = gradients.gradients(wx, [w], colocate_gradients_with_ops=True)[0]
self.assertEqual(gw.op.colocation_groups(), wx.op.colocation_groups())
def testColocateGradientsWithAggregation(self):
with ops.Graph().as_default() as g:
with g.device("/gpu:1"):
w = constant(1.0, shape=[1, 1])
x = constant(1.0, shape=[1, 2])
y = constant(1.0, shape=[1, 2])
wx = math_ops.matmul(w, x)
wy = math_ops.matmul(w, y)
with g.device("/gpu:0"):
z = wx + wy
gw1 = gradients.gradients(z, [w], colocate_gradients_with_ops=True)[0]
self.assertEqual(gw1.op.colocation_groups(), wx.op.colocation_groups())
gw2 = gradients.gradients(z, [w], colocate_gradients_with_ops=False)[0]
self.assertTrue(wx.op.colocation_groups() != gw2.op.colocation_groups())
def testColocateGradientsWithAggregationInMultipleDevices(self):
with ops.Graph().as_default() as g:
with g.device("/gpu:1"):
w = constant(1.0, shape=[1, 1])
x = constant(1.0, shape=[1, 2])
y = constant(1.0, shape=[1, 2])
with g.device("/task:1"):
wx = math_ops.matmul(w, x)
with g.device("/task:2"):
wy = math_ops.matmul(w, y)
with g.device("/gpu:0"):
z = wx + wy
gw1 = gradients.gradients(z, [w], colocate_gradients_with_ops=True)[0]
self.assertEqual(gw1.op.colocation_groups(), w.op.colocation_groups())
gw2 = gradients.gradients(z, [w], colocate_gradients_with_ops=False)[0]
self.assertTrue(w.op.colocation_groups() != gw2.op.colocation_groups())
def testBoundaryStop(self):
# Test that we don't differentiate 'x'. The gradient function for 'x' is
# set explicitly to None so we will get an exception if the gradient code
# tries to differentiate 'x'.
with ops.Graph().as_default() as g:
c = constant(1.0)
x = array_ops.identity(c)
y = x + 1.0
z = y + 1
grads = gradients.gradients(z, [x])
self.assertTrue(all(x is not None for x in grads))
def testBoundaryContinue(self):
# Test that we differentiate both 'x' and 'y' correctly when x is a
# predecessor of y.
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y * 3.0
grads = gradients.gradients(z, [x, y])
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(6.0, grads[0].eval())
def testAggregationMethodAccumulateN(self):
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y + y + y + y + y + y + y + y + y + y
grads = gradients.gradients(
z,
[x, y],
aggregation_method=
gradients.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(20.0, grads[0].eval())
self.assertEqual(10.0, grads[1].eval())
def testAggregationMethodAddN(self):
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y + y + y + y + y + y + y + y + y + y
grads = gradients.gradients(
z,
[x, y],
aggregation_method=gradients.AggregationMethod.ADD_N)
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(20.0, grads[0].eval())
self.assertEqual(10.0, grads[1].eval())
def testAggregationMethodTree(self):
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y + y + y + y + y + y + y + y + y + y
grads = gradients.gradients(
z,
[x, y],
aggregation_method=gradients.AggregationMethod.EXPERIMENTAL_TREE)
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(20.0, grads[0].eval())
self.assertEqual(10.0, grads[1].eval())
def testNoGradientForStringOutputs(self):
with ops.Graph().as_default() as g:
@ops.RegisterGradient("TestOp")
def _TestOpGrad(op, float_grad, string_grad):
"""Gradient function for TestOp."""
self.assertEquals(float_grad.dtype, dtypes.float32)
self.assertFalse(string_grad)
return float_grad
ops.RegisterShape("TestOp")(None)
c = constant(1.0)
x, y = g.create_op("TestOp", [c], [dtypes.float32, dtypes.string]).outputs
z = x * 2.0
w = z * 3.0
grads = gradients.gradients(z, [c])
self.assertTrue(isinstance(grads[0], ops.Tensor))
def testSingletonIndexedSlices(self):
with ops.Graph().as_default():
x = tf.placeholder(tf.float32)
y = tf.identity(x)
dy = tf.IndexedSlices(tf.placeholder(tf.float32),
tf.placeholder(tf.int32))
dx, = gradients.gradients(y, x, grad_ys=dy)
# The gradient of tf.identity should pass the value through unchanged.
# A previous version of the code did this only for tf.Tensor, not
# tf.IndexedSlices.
self.assertEqual(dx, dy)
class FunctionGradientsTest(test_util.TensorFlowTestCase):
@classmethod
def XSquarePlusB(cls, x, b):
return x * x + b
@classmethod
def XSquarePlusBGradient(cls, x, b, g):
# Perturb gradients (multiply by 2), so we can test that this was called.
g *= 2.0
return g * 2.0 * x, g
@classmethod
def _PythonGradient(cls, op, grad):
# Perturb gradients (multiply by 3), so we can test that this was called.
grad *= 3.0
return grad * op.inputs[0] * 2.0, grad
@classmethod
def _GetFunc(cls, **kwargs):
return function.Defun(tf.float32, tf.float32, **kwargs)(
cls.XSquarePlusB)
def _GetFuncGradients(self, f, x_value, b_value):
x = tf.constant(x_value, name="x")
b = tf.constant(b_value, name="b")
y = f(x, b)
grads = gradients.gradients(y, [x, b])
with self.test_session() as sess:
return sess.run(grads)
def testFunctionGradientsBasic(self):
g = ops.Graph()
with g.as_default():
f = self._GetFunc()
# Get gradients (should add SymbolicGradient node for function).
grads = self._GetFuncGradients(f, [2.0], [1.0])
self.assertAllEqual([4.0], grads[0])
self.assertAllEqual([1.0], grads[1])
def testFunctionGradientsComposition(self):
with ops.Graph().as_default():
f = self._GetFunc()
x = tf.constant([2.0], name="x")
b1 = tf.constant([1.0], name="b1")
b2 = tf.constant([1.0], name="b2")
y = f(f(x, b1), b2)
# Build gradient graph (should add SymbolicGradient node for function).
grads = gradients.gradients(y, [x, b1])
with self.test_session() as sess:
self.assertAllEqual([40.0], sess.run(grads)[0])
self.assertAllEqual([10.0], sess.run(grads)[1])
def testFunctionGradientsWithGradFunc(self):
g = ops.Graph()
with g.as_default():
grad_func = function.Defun(tf.float32, tf.float32, tf.float32)(
self.XSquarePlusBGradient)
f = self._GetFunc(grad_func=grad_func)
# Get gradients (should add SymbolicGradient node for function, which
# uses the grad_func above, which multiplies all gradients by 2).
grads = self._GetFuncGradients(f, [2.0], [1.0])
self.assertAllEqual([4.0 * 2], grads[0])
self.assertAllEqual([1.0 * 2], grads[1])
def testFunctionGradientWithRegistration(self):
g = ops.Graph()
with g.as_default():
f = self._GetFunc(python_grad_func=self._PythonGradient)
# Get gradients, using the python gradient function. It multiplies the
# gradients by 3.
grads = self._GetFuncGradients(f, [2.0], [1.0])
self.assertAllEqual([4.0 * 3], grads[0])
self.assertAllEqual([1.0 * 3], grads[1])
def testFunctionGradientWithGradFuncAndRegistration(self):
g = ops.Graph()
with g.as_default():
grad_func = function.Defun(tf.float32, tf.float32, tf.float32)(
self.XSquarePlusBGradient)
with self.assertRaisesRegexp(ValueError, "Gradient defined twice"):
f = self._GetFunc(grad_func=grad_func,
python_grad_func=self._PythonGradient)
f.add_to_graph(tf.Graph())
class StopGradientTest(test_util.TensorFlowTestCase):
def testStopGradient(self):
with ops.Graph().as_default():
inp = constant(1.0, shape=[100, 32], name="in")
out = array_ops.stop_gradient(inp)
igrad = gradients.gradients(out, inp)[0]
assert igrad is None
class HessianVectorProductTest(test_util.TensorFlowTestCase):
def testHessianVectorProduct(self):
# Manually compute the Hessian explicitly for a low-dimensional problem
# and check that HessianVectorProduct matches multiplication by the
# explicit Hessian.
# Specifically, the Hessian of f(x) = x^T A x is
# H = A + A^T.
# We expect HessianVectorProduct(f(x), x, v) to be H v.
m = 4
rng = np.random.RandomState([1, 2, 3])
mat_value = rng.randn(m, m).astype("float32")
v_value = rng.randn(m, 1).astype("float32")
x_value = rng.randn(m, 1).astype("float32")
hess_value = mat_value + mat_value.T
hess_v_value = np.dot(hess_value, v_value)
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
mat = constant_op.constant(mat_value)
v = constant_op.constant(v_value)
x = constant_op.constant(x_value)
mat_x = math_ops.matmul(mat, x, name="Ax")
x_mat_x = math_ops.matmul(array_ops.transpose(x), mat_x, name="xAx")
hess_v = gradients._hessian_vector_product(x_mat_x, [x], [v])[0]
hess_v_actual = hess_v.eval()
self.assertAllClose(hess_v_value, hess_v_actual)
class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase):
def testIndexedSlicesToTensor(self):
with self.test_session():
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
self.assertAllEqual(np_val.shape, c_sparse.dense_shape.eval())
c_dense = math_ops.mul(c_sparse, 1.0)
self.assertAllClose(np_val, c_dense.eval())
def testIndexedSlicesToTensorList(self):
with self.test_session():
numpy_list = []
dense_list = []
sparse_list = []
for _ in range(3):
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
numpy_list.append(np_val)
dense_list.append(c)
sparse_list.append(c_sparse)
packed_dense = array_ops.pack(dense_list)
packed_sparse = array_ops.pack(sparse_list)
self.assertAllClose(packed_dense.eval(), packed_sparse.eval())
def testInt64Indices(self):
with self.test_session():
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
c_sparse = ops.IndexedSlices(
c_sparse.values, math_ops.cast(c_sparse.indices, dtypes.int64),
c_sparse.dense_shape)
self.assertAllEqual(np_val.shape, c_sparse.dense_shape.eval())
c_dense = math_ops.mul(c_sparse, 1.0)
self.assertAllClose(np_val, c_dense.eval())
def testWarnings(self):
# Smaller than the threshold: no warning.
c_sparse = ops.IndexedSlices(array_ops.placeholder(dtypes.float32),
array_ops.placeholder(dtypes.int32),
constant([4, 4, 4, 4]))
with warnings.catch_warnings(record=True) as w:
math_ops.mul(c_sparse, 1.0)
self.assertEqual(0, len(w))
# Greater than or equal to the threshold: warning.
c_sparse = ops.IndexedSlices(array_ops.placeholder(dtypes.float32),
array_ops.placeholder(dtypes.int32),
constant([100, 100, 100, 100]))
with warnings.catch_warnings(record=True) as w:
math_ops.mul(c_sparse, 1.0)
self.assertEqual(1, len(w))
self.assertTrue(
"with 100000000 elements. This may consume a large amount of memory."
in str(w[0].message))
# Unknown dense shape: warning.
c_sparse = ops.IndexedSlices(array_ops.placeholder(dtypes.float32),
array_ops.placeholder(dtypes.int32),
array_ops.placeholder(dtypes.int32))
with warnings.catch_warnings(record=True) as w:
math_ops.mul(c_sparse, 1.0)
self.assertEqual(1, len(w))
self.assertTrue(
"of unknown shape. This may consume a large amount of memory."
in str(w[0].message))
if __name__ == "__main__":
googletest.main()
| 37.89899
| 81
| 0.646375
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import warnings
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework.constant_op import constant
from tensorflow.python.ops import array_grad
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_grad
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_grad
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_grad
from tensorflow.python.ops import state_grad
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops.nn_ops import bias_add
from tensorflow.python.platform import googletest
def _OpsBetween(graph, to_ops, from_ops):
reached_ops = [False] * (graph._last_id + 1)
for op in to_ops:
reached_ops[op._id] = True
gradients._MarkReachedOps(from_ops, reached_ops)
between_ops = gradients._GatherInputs(to_ops, reached_ops)
between_ops.sort(key=lambda x: -x._id)
return between_ops
class GradientsTest(test_util.TensorFlowTestCase):
def _OpNames(self, op_list):
return ["%s/%d" % (str(op.name), op._id) for op in op_list]
def _assertOpListEqual(self, ops1, ops2):
self.assertEquals(self._OpNames(ops1), self._OpNames(ops2))
def testOpsBetweenSimple(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
t3 = array_ops.pack([t1, t2])
self._assertOpListEqual([t3.op, t2.op, t1.op],
_OpsBetween(g, [t3.op], [t1.op, t2.op]))
self._assertOpListEqual([t3.op, t1.op],
_OpsBetween(g, [t3.op], [t1.op]))
def testOpsBetweenUnreachable(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
_ = array_ops.pack([t1, t2])
t4 = constant(1.0)
t5 = constant(2.0)
t6 = array_ops.pack([t4, t5])
self._assertOpListEqual([t6.op], _OpsBetween(g, [t6.op], [t1.op]))
def testOpsBetweenCut(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
t3 = array_ops.pack([t1, t2])
t4 = constant([1.0])
t5 = array_ops.concat(0, [t4, t3])
t6 = constant([2.0])
t7 = array_ops.concat(0, [t5, t6])
self._assertOpListEqual([t7.op, t5.op, t4.op],
_OpsBetween(g, [t7.op], [t4.op]))
def testOpsBetweenCycle(self):
with ops.Graph().as_default() as g:
t1 = constant(1.0)
t2 = constant(2.0)
t3 = array_ops.pack([t1, t2])
t4 = array_ops.concat(0, [t3, t3, t3])
t5 = constant([1.0])
t6 = array_ops.concat(0, [t4, t5])
t7 = array_ops.concat(0, [t6, t3])
self._assertOpListEqual([t6.op, t4.op, t3.op],
_OpsBetween(g, [t6.op], [t3.op]))
self._assertOpListEqual([t7.op, t6.op, t5.op, t4.op, t3.op, t1.op],
_OpsBetween(g, [t7.op], [t1.op, t5.op]))
self._assertOpListEqual([t6.op, t5.op, t4.op, t3.op, t2.op],
_OpsBetween(g, [t6.op], [t2.op, t5.op]))
def testGradients(self):
with ops.Graph().as_default():
inp = constant(1.0, shape=[32, 100], name="in")
w = constant(1.0, shape=[100, 10], name="w")
b = constant(1.0, shape=[10], name="b")
xw = math_ops.matmul(inp, w, name="xw")
h = bias_add(xw, b, name="h")
w_grad = gradients.gradients(h, w)[0]
self.assertEquals("MatMul", w_grad.op.type)
self.assertEquals(w_grad.op._original_op, xw.op)
self.assertTrue(w_grad.op.get_attr("transpose_a"))
self.assertFalse(w_grad.op.get_attr("transpose_b"))
def testUnusedOutput(self):
with ops.Graph().as_default():
w = constant(1.0, shape=[2, 2])
x = constant(1.0, shape=[2, 2])
wx = math_ops.matmul(w, x)
split_wx = array_ops.split(0, 2, wx)
c = math_ops.reduce_sum(split_wx[1])
gw = gradients.gradients(c, [w])[0]
self.assertEquals("MatMul", gw.op.type)
def testColocateGradients(self):
with ops.Graph().as_default() as g:
w = constant(1.0, shape=[1, 1])
x = constant(1.0, shape=[1, 2])
with g.device("/gpu:0"):
wx = math_ops.matmul(w, x)
gw = gradients.gradients(wx, [w], colocate_gradients_with_ops=True)[0]
self.assertEqual(gw.op.colocation_groups(), wx.op.colocation_groups())
def testColocateGradientsWithAggregation(self):
with ops.Graph().as_default() as g:
with g.device("/gpu:1"):
w = constant(1.0, shape=[1, 1])
x = constant(1.0, shape=[1, 2])
y = constant(1.0, shape=[1, 2])
wx = math_ops.matmul(w, x)
wy = math_ops.matmul(w, y)
with g.device("/gpu:0"):
z = wx + wy
gw1 = gradients.gradients(z, [w], colocate_gradients_with_ops=True)[0]
self.assertEqual(gw1.op.colocation_groups(), wx.op.colocation_groups())
gw2 = gradients.gradients(z, [w], colocate_gradients_with_ops=False)[0]
self.assertTrue(wx.op.colocation_groups() != gw2.op.colocation_groups())
def testColocateGradientsWithAggregationInMultipleDevices(self):
with ops.Graph().as_default() as g:
with g.device("/gpu:1"):
w = constant(1.0, shape=[1, 1])
x = constant(1.0, shape=[1, 2])
y = constant(1.0, shape=[1, 2])
with g.device("/task:1"):
wx = math_ops.matmul(w, x)
with g.device("/task:2"):
wy = math_ops.matmul(w, y)
with g.device("/gpu:0"):
z = wx + wy
gw1 = gradients.gradients(z, [w], colocate_gradients_with_ops=True)[0]
self.assertEqual(gw1.op.colocation_groups(), w.op.colocation_groups())
gw2 = gradients.gradients(z, [w], colocate_gradients_with_ops=False)[0]
self.assertTrue(w.op.colocation_groups() != gw2.op.colocation_groups())
def testBoundaryStop(self):
# set explicitly to None so we will get an exception if the gradient code
# tries to differentiate 'x'.
with ops.Graph().as_default() as g:
c = constant(1.0)
x = array_ops.identity(c)
y = x + 1.0
z = y + 1
grads = gradients.gradients(z, [x])
self.assertTrue(all(x is not None for x in grads))
def testBoundaryContinue(self):
# Test that we differentiate both 'x' and 'y' correctly when x is a
# predecessor of y.
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y * 3.0
grads = gradients.gradients(z, [x, y])
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(6.0, grads[0].eval())
def testAggregationMethodAccumulateN(self):
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y + y + y + y + y + y + y + y + y + y
grads = gradients.gradients(
z,
[x, y],
aggregation_method=
gradients.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(20.0, grads[0].eval())
self.assertEqual(10.0, grads[1].eval())
def testAggregationMethodAddN(self):
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y + y + y + y + y + y + y + y + y + y
grads = gradients.gradients(
z,
[x, y],
aggregation_method=gradients.AggregationMethod.ADD_N)
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(20.0, grads[0].eval())
self.assertEqual(10.0, grads[1].eval())
def testAggregationMethodTree(self):
with self.test_session():
x = constant(1.0)
y = x * 2.0
z = y + y + y + y + y + y + y + y + y + y
grads = gradients.gradients(
z,
[x, y],
aggregation_method=gradients.AggregationMethod.EXPERIMENTAL_TREE)
self.assertTrue(all(x is not None for x in grads))
self.assertEqual(20.0, grads[0].eval())
self.assertEqual(10.0, grads[1].eval())
def testNoGradientForStringOutputs(self):
with ops.Graph().as_default() as g:
@ops.RegisterGradient("TestOp")
def _TestOpGrad(op, float_grad, string_grad):
self.assertEquals(float_grad.dtype, dtypes.float32)
self.assertFalse(string_grad)
return float_grad
ops.RegisterShape("TestOp")(None)
c = constant(1.0)
x, y = g.create_op("TestOp", [c], [dtypes.float32, dtypes.string]).outputs
z = x * 2.0
w = z * 3.0
grads = gradients.gradients(z, [c])
self.assertTrue(isinstance(grads[0], ops.Tensor))
def testSingletonIndexedSlices(self):
with ops.Graph().as_default():
x = tf.placeholder(tf.float32)
y = tf.identity(x)
dy = tf.IndexedSlices(tf.placeholder(tf.float32),
tf.placeholder(tf.int32))
dx, = gradients.gradients(y, x, grad_ys=dy)
# The gradient of tf.identity should pass the value through unchanged.
# A previous version of the code did this only for tf.Tensor, not
# tf.IndexedSlices.
self.assertEqual(dx, dy)
class FunctionGradientsTest(test_util.TensorFlowTestCase):
@classmethod
def XSquarePlusB(cls, x, b):
return x * x + b
@classmethod
def XSquarePlusBGradient(cls, x, b, g):
# Perturb gradients (multiply by 2), so we can test that this was called.
g *= 2.0
return g * 2.0 * x, g
@classmethod
def _PythonGradient(cls, op, grad):
# Perturb gradients (multiply by 3), so we can test that this was called.
grad *= 3.0
return grad * op.inputs[0] * 2.0, grad
@classmethod
def _GetFunc(cls, **kwargs):
return function.Defun(tf.float32, tf.float32, **kwargs)(
cls.XSquarePlusB)
def _GetFuncGradients(self, f, x_value, b_value):
x = tf.constant(x_value, name="x")
b = tf.constant(b_value, name="b")
y = f(x, b)
grads = gradients.gradients(y, [x, b])
with self.test_session() as sess:
return sess.run(grads)
def testFunctionGradientsBasic(self):
g = ops.Graph()
with g.as_default():
f = self._GetFunc()
# Get gradients (should add SymbolicGradient node for function).
grads = self._GetFuncGradients(f, [2.0], [1.0])
self.assertAllEqual([4.0], grads[0])
self.assertAllEqual([1.0], grads[1])
def testFunctionGradientsComposition(self):
with ops.Graph().as_default():
f = self._GetFunc()
x = tf.constant([2.0], name="x")
b1 = tf.constant([1.0], name="b1")
b2 = tf.constant([1.0], name="b2")
y = f(f(x, b1), b2)
# Build gradient graph (should add SymbolicGradient node for function).
grads = gradients.gradients(y, [x, b1])
with self.test_session() as sess:
self.assertAllEqual([40.0], sess.run(grads)[0])
self.assertAllEqual([10.0], sess.run(grads)[1])
def testFunctionGradientsWithGradFunc(self):
g = ops.Graph()
with g.as_default():
grad_func = function.Defun(tf.float32, tf.float32, tf.float32)(
self.XSquarePlusBGradient)
f = self._GetFunc(grad_func=grad_func)
# Get gradients (should add SymbolicGradient node for function, which
# uses the grad_func above, which multiplies all gradients by 2).
grads = self._GetFuncGradients(f, [2.0], [1.0])
self.assertAllEqual([4.0 * 2], grads[0])
self.assertAllEqual([1.0 * 2], grads[1])
def testFunctionGradientWithRegistration(self):
g = ops.Graph()
with g.as_default():
f = self._GetFunc(python_grad_func=self._PythonGradient)
# Get gradients, using the python gradient function. It multiplies the
# gradients by 3.
grads = self._GetFuncGradients(f, [2.0], [1.0])
self.assertAllEqual([4.0 * 3], grads[0])
self.assertAllEqual([1.0 * 3], grads[1])
def testFunctionGradientWithGradFuncAndRegistration(self):
g = ops.Graph()
with g.as_default():
grad_func = function.Defun(tf.float32, tf.float32, tf.float32)(
self.XSquarePlusBGradient)
with self.assertRaisesRegexp(ValueError, "Gradient defined twice"):
f = self._GetFunc(grad_func=grad_func,
python_grad_func=self._PythonGradient)
f.add_to_graph(tf.Graph())
class StopGradientTest(test_util.TensorFlowTestCase):
def testStopGradient(self):
with ops.Graph().as_default():
inp = constant(1.0, shape=[100, 32], name="in")
out = array_ops.stop_gradient(inp)
igrad = gradients.gradients(out, inp)[0]
assert igrad is None
class HessianVectorProductTest(test_util.TensorFlowTestCase):
def testHessianVectorProduct(self):
# Manually compute the Hessian explicitly for a low-dimensional problem
# and check that HessianVectorProduct matches multiplication by the
# explicit Hessian.
# Specifically, the Hessian of f(x) = x^T A x is
# H = A + A^T.
# We expect HessianVectorProduct(f(x), x, v) to be H v.
m = 4
rng = np.random.RandomState([1, 2, 3])
mat_value = rng.randn(m, m).astype("float32")
v_value = rng.randn(m, 1).astype("float32")
x_value = rng.randn(m, 1).astype("float32")
hess_value = mat_value + mat_value.T
hess_v_value = np.dot(hess_value, v_value)
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
mat = constant_op.constant(mat_value)
v = constant_op.constant(v_value)
x = constant_op.constant(x_value)
mat_x = math_ops.matmul(mat, x, name="Ax")
x_mat_x = math_ops.matmul(array_ops.transpose(x), mat_x, name="xAx")
hess_v = gradients._hessian_vector_product(x_mat_x, [x], [v])[0]
hess_v_actual = hess_v.eval()
self.assertAllClose(hess_v_value, hess_v_actual)
class IndexedSlicesToTensorTest(test_util.TensorFlowTestCase):
def testIndexedSlicesToTensor(self):
with self.test_session():
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
self.assertAllEqual(np_val.shape, c_sparse.dense_shape.eval())
c_dense = math_ops.mul(c_sparse, 1.0)
self.assertAllClose(np_val, c_dense.eval())
def testIndexedSlicesToTensorList(self):
with self.test_session():
numpy_list = []
dense_list = []
sparse_list = []
for _ in range(3):
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
numpy_list.append(np_val)
dense_list.append(c)
sparse_list.append(c_sparse)
packed_dense = array_ops.pack(dense_list)
packed_sparse = array_ops.pack(sparse_list)
self.assertAllClose(packed_dense.eval(), packed_sparse.eval())
def testInt64Indices(self):
with self.test_session():
np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
c = constant_op.constant(np_val)
c_sparse = math_ops._as_indexed_slices(c)
c_sparse = ops.IndexedSlices(
c_sparse.values, math_ops.cast(c_sparse.indices, dtypes.int64),
c_sparse.dense_shape)
self.assertAllEqual(np_val.shape, c_sparse.dense_shape.eval())
c_dense = math_ops.mul(c_sparse, 1.0)
self.assertAllClose(np_val, c_dense.eval())
def testWarnings(self):
# Smaller than the threshold: no warning.
c_sparse = ops.IndexedSlices(array_ops.placeholder(dtypes.float32),
array_ops.placeholder(dtypes.int32),
constant([4, 4, 4, 4]))
with warnings.catch_warnings(record=True) as w:
math_ops.mul(c_sparse, 1.0)
self.assertEqual(0, len(w))
# Greater than or equal to the threshold: warning.
c_sparse = ops.IndexedSlices(array_ops.placeholder(dtypes.float32),
array_ops.placeholder(dtypes.int32),
constant([100, 100, 100, 100]))
with warnings.catch_warnings(record=True) as w:
math_ops.mul(c_sparse, 1.0)
self.assertEqual(1, len(w))
self.assertTrue(
"with 100000000 elements. This may consume a large amount of memory."
in str(w[0].message))
# Unknown dense shape: warning.
c_sparse = ops.IndexedSlices(array_ops.placeholder(dtypes.float32),
array_ops.placeholder(dtypes.int32),
array_ops.placeholder(dtypes.int32))
with warnings.catch_warnings(record=True) as w:
math_ops.mul(c_sparse, 1.0)
self.assertEqual(1, len(w))
self.assertTrue(
"of unknown shape. This may consume a large amount of memory."
in str(w[0].message))
if __name__ == "__main__":
googletest.main()
| true
| true
|
1c444e381a5528baf355eff89d4e5396f0231430
| 42,894
|
py
|
Python
|
Lib/test/test_time.py
|
gdebirka/cpython
|
b18fd54f8c27e4b2aac222e75ac58aa85e5a7988
|
[
"0BSD"
] | 1
|
2022-01-22T22:34:08.000Z
|
2022-01-22T22:34:08.000Z
|
Lib/test/test_time.py
|
gdebirka/cpython
|
b18fd54f8c27e4b2aac222e75ac58aa85e5a7988
|
[
"0BSD"
] | 2
|
2022-02-06T01:15:13.000Z
|
2022-03-01T10:04:24.000Z
|
Lib/test/test_time.py
|
gdebirka/cpython
|
b18fd54f8c27e4b2aac222e75ac58aa85e5a7988
|
[
"0BSD"
] | 2
|
2021-09-07T13:43:49.000Z
|
2021-12-19T08:34:56.000Z
|
from test import support
from test.support import warnings_helper
import decimal
import enum
import locale
import math
import platform
import sys
import sysconfig
import time
import threading
import unittest
try:
import _testcapi
except ImportError:
_testcapi = None
from test.support import skip_if_buggy_ucrt_strfptime
# Max year is only limited by the size of C int.
SIZEOF_INT = sysconfig.get_config_var('SIZEOF_INT') or 4
TIME_MAXYEAR = (1 << 8 * SIZEOF_INT - 1) - 1
TIME_MINYEAR = -TIME_MAXYEAR - 1 + 1900
SEC_TO_US = 10 ** 6
US_TO_NS = 10 ** 3
MS_TO_NS = 10 ** 6
SEC_TO_NS = 10 ** 9
NS_TO_SEC = 10 ** 9
class _PyTime(enum.IntEnum):
# Round towards minus infinity (-inf)
ROUND_FLOOR = 0
# Round towards infinity (+inf)
ROUND_CEILING = 1
# Round to nearest with ties going to nearest even integer
ROUND_HALF_EVEN = 2
# Round away from zero
ROUND_UP = 3
# _PyTime_t is int64_t
_PyTime_MIN = -2 ** 63
_PyTime_MAX = 2 ** 63 - 1
# Rounding modes supported by PyTime
ROUNDING_MODES = (
# (PyTime rounding method, decimal rounding method)
(_PyTime.ROUND_FLOOR, decimal.ROUND_FLOOR),
(_PyTime.ROUND_CEILING, decimal.ROUND_CEILING),
(_PyTime.ROUND_HALF_EVEN, decimal.ROUND_HALF_EVEN),
(_PyTime.ROUND_UP, decimal.ROUND_UP),
)
class TimeTestCase(unittest.TestCase):
def setUp(self):
self.t = time.time()
def test_data_attributes(self):
time.altzone
time.daylight
time.timezone
time.tzname
def test_time(self):
time.time()
info = time.get_clock_info('time')
self.assertFalse(info.monotonic)
self.assertTrue(info.adjustable)
def test_time_ns_type(self):
def check_ns(sec, ns):
self.assertIsInstance(ns, int)
sec_ns = int(sec * 1e9)
# tolerate a difference of 50 ms
self.assertLess((sec_ns - ns), 50 ** 6, (sec, ns))
check_ns(time.time(),
time.time_ns())
check_ns(time.monotonic(),
time.monotonic_ns())
check_ns(time.perf_counter(),
time.perf_counter_ns())
check_ns(time.process_time(),
time.process_time_ns())
if hasattr(time, 'thread_time'):
check_ns(time.thread_time(),
time.thread_time_ns())
if hasattr(time, 'clock_gettime'):
check_ns(time.clock_gettime(time.CLOCK_REALTIME),
time.clock_gettime_ns(time.CLOCK_REALTIME))
@unittest.skipUnless(hasattr(time, 'clock_gettime'),
'need time.clock_gettime()')
def test_clock_realtime(self):
t = time.clock_gettime(time.CLOCK_REALTIME)
self.assertIsInstance(t, float)
@unittest.skipUnless(hasattr(time, 'clock_gettime'),
'need time.clock_gettime()')
@unittest.skipUnless(hasattr(time, 'CLOCK_MONOTONIC'),
'need time.CLOCK_MONOTONIC')
def test_clock_monotonic(self):
a = time.clock_gettime(time.CLOCK_MONOTONIC)
b = time.clock_gettime(time.CLOCK_MONOTONIC)
self.assertLessEqual(a, b)
@unittest.skipUnless(hasattr(time, 'pthread_getcpuclockid'),
'need time.pthread_getcpuclockid()')
@unittest.skipUnless(hasattr(time, 'clock_gettime'),
'need time.clock_gettime()')
def test_pthread_getcpuclockid(self):
clk_id = time.pthread_getcpuclockid(threading.get_ident())
self.assertTrue(type(clk_id) is int)
# when in 32-bit mode AIX only returns the predefined constant
if platform.system() == "AIX" and (sys.maxsize.bit_length() <= 32):
self.assertEqual(clk_id, time.CLOCK_THREAD_CPUTIME_ID)
# Solaris returns CLOCK_THREAD_CPUTIME_ID when current thread is given
elif sys.platform.startswith("sunos"):
self.assertEqual(clk_id, time.CLOCK_THREAD_CPUTIME_ID)
else:
self.assertNotEqual(clk_id, time.CLOCK_THREAD_CPUTIME_ID)
t1 = time.clock_gettime(clk_id)
t2 = time.clock_gettime(clk_id)
self.assertLessEqual(t1, t2)
@unittest.skipUnless(hasattr(time, 'clock_getres'),
'need time.clock_getres()')
def test_clock_getres(self):
res = time.clock_getres(time.CLOCK_REALTIME)
self.assertGreater(res, 0.0)
self.assertLessEqual(res, 1.0)
@unittest.skipUnless(hasattr(time, 'clock_settime'),
'need time.clock_settime()')
def test_clock_settime(self):
t = time.clock_gettime(time.CLOCK_REALTIME)
try:
time.clock_settime(time.CLOCK_REALTIME, t)
except PermissionError:
pass
if hasattr(time, 'CLOCK_MONOTONIC'):
self.assertRaises(OSError,
time.clock_settime, time.CLOCK_MONOTONIC, 0)
def test_conversions(self):
self.assertEqual(time.ctime(self.t),
time.asctime(time.localtime(self.t)))
self.assertEqual(int(time.mktime(time.localtime(self.t))),
int(self.t))
def test_sleep(self):
self.assertRaises(ValueError, time.sleep, -2)
self.assertRaises(ValueError, time.sleep, -1)
time.sleep(1.2)
def test_epoch(self):
# bpo-43869: Make sure that Python use the same Epoch on all platforms:
# January 1, 1970, 00:00:00 (UTC).
epoch = time.gmtime(0)
# Only test the date and time, ignore other gmtime() members
self.assertEqual(tuple(epoch)[:6], (1970, 1, 1, 0, 0, 0), epoch)
def test_strftime(self):
tt = time.gmtime(self.t)
for directive in ('a', 'A', 'b', 'B', 'c', 'd', 'H', 'I',
'j', 'm', 'M', 'p', 'S',
'U', 'w', 'W', 'x', 'X', 'y', 'Y', 'Z', '%'):
format = ' %' + directive
try:
time.strftime(format, tt)
except ValueError:
self.fail('conversion specifier: %r failed.' % format)
self.assertRaises(TypeError, time.strftime, b'%S', tt)
# embedded null character
self.assertRaises(ValueError, time.strftime, '%S\0', tt)
def _bounds_checking(self, func):
# Make sure that strftime() checks the bounds of the various parts
# of the time tuple (0 is valid for *all* values).
# The year field is tested by other test cases above
# Check month [1, 12] + zero support
func((1900, 0, 1, 0, 0, 0, 0, 1, -1))
func((1900, 12, 1, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, -1, 1, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 13, 1, 0, 0, 0, 0, 1, -1))
# Check day of month [1, 31] + zero support
func((1900, 1, 0, 0, 0, 0, 0, 1, -1))
func((1900, 1, 31, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, -1, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 32, 0, 0, 0, 0, 1, -1))
# Check hour [0, 23]
func((1900, 1, 1, 23, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, -1, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 24, 0, 0, 0, 1, -1))
# Check minute [0, 59]
func((1900, 1, 1, 0, 59, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, -1, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 60, 0, 0, 1, -1))
# Check second [0, 61]
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, -1, 0, 1, -1))
# C99 only requires allowing for one leap second, but Python's docs say
# allow two leap seconds (0..61)
func((1900, 1, 1, 0, 0, 60, 0, 1, -1))
func((1900, 1, 1, 0, 0, 61, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 62, 0, 1, -1))
# No check for upper-bound day of week;
# value forced into range by a ``% 7`` calculation.
# Start check at -2 since gettmarg() increments value before taking
# modulo.
self.assertEqual(func((1900, 1, 1, 0, 0, 0, -1, 1, -1)),
func((1900, 1, 1, 0, 0, 0, +6, 1, -1)))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 0, -2, 1, -1))
# Check day of the year [1, 366] + zero support
func((1900, 1, 1, 0, 0, 0, 0, 0, -1))
func((1900, 1, 1, 0, 0, 0, 0, 366, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 0, 0, -1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 0, 0, 367, -1))
def test_strftime_bounding_check(self):
self._bounds_checking(lambda tup: time.strftime('', tup))
def test_strftime_format_check(self):
# Test that strftime does not crash on invalid format strings
# that may trigger a buffer overread. When not triggered,
# strftime may succeed or raise ValueError depending on
# the platform.
for x in [ '', 'A', '%A', '%AA' ]:
for y in range(0x0, 0x10):
for z in [ '%', 'A%', 'AA%', '%A%', 'A%A%', '%#' ]:
try:
time.strftime(x * y + z)
except ValueError:
pass
def test_default_values_for_zero(self):
# Make sure that using all zeros uses the proper default
# values. No test for daylight savings since strftime() does
# not change output based on its value and no test for year
# because systems vary in their support for year 0.
expected = "2000 01 01 00 00 00 1 001"
with warnings_helper.check_warnings():
result = time.strftime("%Y %m %d %H %M %S %w %j", (2000,)+(0,)*8)
self.assertEqual(expected, result)
@skip_if_buggy_ucrt_strfptime
def test_strptime(self):
# Should be able to go round-trip from strftime to strptime without
# raising an exception.
tt = time.gmtime(self.t)
for directive in ('a', 'A', 'b', 'B', 'c', 'd', 'H', 'I',
'j', 'm', 'M', 'p', 'S',
'U', 'w', 'W', 'x', 'X', 'y', 'Y', 'Z', '%'):
format = '%' + directive
strf_output = time.strftime(format, tt)
try:
time.strptime(strf_output, format)
except ValueError:
self.fail("conversion specifier %r failed with '%s' input." %
(format, strf_output))
def test_strptime_bytes(self):
# Make sure only strings are accepted as arguments to strptime.
self.assertRaises(TypeError, time.strptime, b'2009', "%Y")
self.assertRaises(TypeError, time.strptime, '2009', b'%Y')
def test_strptime_exception_context(self):
# check that this doesn't chain exceptions needlessly (see #17572)
with self.assertRaises(ValueError) as e:
time.strptime('', '%D')
self.assertIs(e.exception.__suppress_context__, True)
# additional check for IndexError branch (issue #19545)
with self.assertRaises(ValueError) as e:
time.strptime('19', '%Y %')
self.assertIs(e.exception.__suppress_context__, True)
def test_asctime(self):
time.asctime(time.gmtime(self.t))
# Max year is only limited by the size of C int.
for bigyear in TIME_MAXYEAR, TIME_MINYEAR:
asc = time.asctime((bigyear, 6, 1) + (0,) * 6)
self.assertEqual(asc[-len(str(bigyear)):], str(bigyear))
self.assertRaises(OverflowError, time.asctime,
(TIME_MAXYEAR + 1,) + (0,) * 8)
self.assertRaises(OverflowError, time.asctime,
(TIME_MINYEAR - 1,) + (0,) * 8)
self.assertRaises(TypeError, time.asctime, 0)
self.assertRaises(TypeError, time.asctime, ())
self.assertRaises(TypeError, time.asctime, (0,) * 10)
def test_asctime_bounding_check(self):
self._bounds_checking(time.asctime)
def test_ctime(self):
t = time.mktime((1973, 9, 16, 1, 3, 52, 0, 0, -1))
self.assertEqual(time.ctime(t), 'Sun Sep 16 01:03:52 1973')
t = time.mktime((2000, 1, 1, 0, 0, 0, 0, 0, -1))
self.assertEqual(time.ctime(t), 'Sat Jan 1 00:00:00 2000')
for year in [-100, 100, 1000, 2000, 2050, 10000]:
try:
testval = time.mktime((year, 1, 10) + (0,)*6)
except (ValueError, OverflowError):
# If mktime fails, ctime will fail too. This may happen
# on some platforms.
pass
else:
self.assertEqual(time.ctime(testval)[20:], str(year))
@unittest.skipUnless(hasattr(time, "tzset"),
"time module has no attribute tzset")
def test_tzset(self):
from os import environ
# Epoch time of midnight Dec 25th 2002. Never DST in northern
# hemisphere.
xmas2002 = 1040774400.0
# These formats are correct for 2002, and possibly future years
# This format is the 'standard' as documented at:
# http://www.opengroup.org/onlinepubs/007904975/basedefs/xbd_chap08.html
# They are also documented in the tzset(3) man page on most Unix
# systems.
eastern = 'EST+05EDT,M4.1.0,M10.5.0'
victoria = 'AEST-10AEDT-11,M10.5.0,M3.5.0'
utc='UTC+0'
org_TZ = environ.get('TZ',None)
try:
# Make sure we can switch to UTC time and results are correct
# Note that unknown timezones default to UTC.
# Note that altzone is undefined in UTC, as there is no DST
environ['TZ'] = eastern
time.tzset()
environ['TZ'] = utc
time.tzset()
self.assertEqual(
time.gmtime(xmas2002), time.localtime(xmas2002)
)
self.assertEqual(time.daylight, 0)
self.assertEqual(time.timezone, 0)
self.assertEqual(time.localtime(xmas2002).tm_isdst, 0)
# Make sure we can switch to US/Eastern
environ['TZ'] = eastern
time.tzset()
self.assertNotEqual(time.gmtime(xmas2002), time.localtime(xmas2002))
self.assertEqual(time.tzname, ('EST', 'EDT'))
self.assertEqual(len(time.tzname), 2)
self.assertEqual(time.daylight, 1)
self.assertEqual(time.timezone, 18000)
self.assertEqual(time.altzone, 14400)
self.assertEqual(time.localtime(xmas2002).tm_isdst, 0)
self.assertEqual(len(time.tzname), 2)
# Now go to the southern hemisphere.
environ['TZ'] = victoria
time.tzset()
self.assertNotEqual(time.gmtime(xmas2002), time.localtime(xmas2002))
# Issue #11886: Australian Eastern Standard Time (UTC+10) is called
# "EST" (as Eastern Standard Time, UTC-5) instead of "AEST"
# (non-DST timezone), and "EDT" instead of "AEDT" (DST timezone),
# on some operating systems (e.g. FreeBSD), which is wrong. See for
# example this bug:
# http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=93810
self.assertIn(time.tzname[0], ('AEST' 'EST'), time.tzname[0])
self.assertTrue(time.tzname[1] in ('AEDT', 'EDT'), str(time.tzname[1]))
self.assertEqual(len(time.tzname), 2)
self.assertEqual(time.daylight, 1)
self.assertEqual(time.timezone, -36000)
self.assertEqual(time.altzone, -39600)
self.assertEqual(time.localtime(xmas2002).tm_isdst, 1)
finally:
# Repair TZ environment variable in case any other tests
# rely on it.
if org_TZ is not None:
environ['TZ'] = org_TZ
elif 'TZ' in environ:
del environ['TZ']
time.tzset()
def test_insane_timestamps(self):
# It's possible that some platform maps time_t to double,
# and that this test will fail there. This test should
# exempt such platforms (provided they return reasonable
# results!).
for func in time.ctime, time.gmtime, time.localtime:
for unreasonable in -1e200, 1e200:
self.assertRaises(OverflowError, func, unreasonable)
def test_ctime_without_arg(self):
# Not sure how to check the values, since the clock could tick
# at any time. Make sure these are at least accepted and
# don't raise errors.
time.ctime()
time.ctime(None)
def test_gmtime_without_arg(self):
gt0 = time.gmtime()
gt1 = time.gmtime(None)
t0 = time.mktime(gt0)
t1 = time.mktime(gt1)
self.assertAlmostEqual(t1, t0, delta=0.2)
def test_localtime_without_arg(self):
lt0 = time.localtime()
lt1 = time.localtime(None)
t0 = time.mktime(lt0)
t1 = time.mktime(lt1)
self.assertAlmostEqual(t1, t0, delta=0.2)
def test_mktime(self):
# Issue #1726687
for t in (-2, -1, 0, 1):
try:
tt = time.localtime(t)
except (OverflowError, OSError):
pass
else:
self.assertEqual(time.mktime(tt), t)
# Issue #13309: passing extreme values to mktime() or localtime()
# borks the glibc's internal timezone data.
@unittest.skipUnless(platform.libc_ver()[0] != 'glibc',
"disabled because of a bug in glibc. Issue #13309")
def test_mktime_error(self):
# It may not be possible to reliably make mktime return an error
# on all platforms. This will make sure that no other exception
# than OverflowError is raised for an extreme value.
tt = time.gmtime(self.t)
tzname = time.strftime('%Z', tt)
self.assertNotEqual(tzname, 'LMT')
try:
time.mktime((-1, 1, 1, 0, 0, 0, -1, -1, -1))
except OverflowError:
pass
self.assertEqual(time.strftime('%Z', tt), tzname)
def test_monotonic(self):
# monotonic() should not go backward
times = [time.monotonic() for n in range(100)]
t1 = times[0]
for t2 in times[1:]:
self.assertGreaterEqual(t2, t1, "times=%s" % times)
t1 = t2
# monotonic() includes time elapsed during a sleep
t1 = time.monotonic()
time.sleep(0.5)
t2 = time.monotonic()
dt = t2 - t1
self.assertGreater(t2, t1)
# bpo-20101: tolerate a difference of 50 ms because of bad timer
# resolution on Windows
self.assertTrue(0.450 <= dt)
# monotonic() is a monotonic but non adjustable clock
info = time.get_clock_info('monotonic')
self.assertTrue(info.monotonic)
self.assertFalse(info.adjustable)
def test_perf_counter(self):
time.perf_counter()
def test_process_time(self):
# process_time() should not include time spend during a sleep
start = time.process_time()
time.sleep(0.100)
stop = time.process_time()
# use 20 ms because process_time() has usually a resolution of 15 ms
# on Windows
self.assertLess(stop - start, 0.020)
info = time.get_clock_info('process_time')
self.assertTrue(info.monotonic)
self.assertFalse(info.adjustable)
def test_thread_time(self):
if not hasattr(time, 'thread_time'):
if sys.platform.startswith(('linux', 'win')):
self.fail("time.thread_time() should be available on %r"
% (sys.platform,))
else:
self.skipTest("need time.thread_time")
# thread_time() should not include time spend during a sleep
start = time.thread_time()
time.sleep(0.100)
stop = time.thread_time()
# use 20 ms because thread_time() has usually a resolution of 15 ms
# on Windows
self.assertLess(stop - start, 0.020)
info = time.get_clock_info('thread_time')
self.assertTrue(info.monotonic)
self.assertFalse(info.adjustable)
@unittest.skipUnless(hasattr(time, 'clock_settime'),
'need time.clock_settime')
def test_monotonic_settime(self):
t1 = time.monotonic()
realtime = time.clock_gettime(time.CLOCK_REALTIME)
# jump backward with an offset of 1 hour
try:
time.clock_settime(time.CLOCK_REALTIME, realtime - 3600)
except PermissionError as err:
self.skipTest(err)
t2 = time.monotonic()
time.clock_settime(time.CLOCK_REALTIME, realtime)
# monotonic must not be affected by system clock updates
self.assertGreaterEqual(t2, t1)
def test_localtime_failure(self):
# Issue #13847: check for localtime() failure
invalid_time_t = None
for time_t in (-1, 2**30, 2**33, 2**60):
try:
time.localtime(time_t)
except OverflowError:
self.skipTest("need 64-bit time_t")
except OSError:
invalid_time_t = time_t
break
if invalid_time_t is None:
self.skipTest("unable to find an invalid time_t value")
self.assertRaises(OSError, time.localtime, invalid_time_t)
self.assertRaises(OSError, time.ctime, invalid_time_t)
# Issue #26669: check for localtime() failure
self.assertRaises(ValueError, time.localtime, float("nan"))
self.assertRaises(ValueError, time.ctime, float("nan"))
def test_get_clock_info(self):
clocks = ['monotonic', 'perf_counter', 'process_time', 'time']
for name in clocks:
info = time.get_clock_info(name)
#self.assertIsInstance(info, dict)
self.assertIsInstance(info.implementation, str)
self.assertNotEqual(info.implementation, '')
self.assertIsInstance(info.monotonic, bool)
self.assertIsInstance(info.resolution, float)
# 0.0 < resolution <= 1.0
self.assertGreater(info.resolution, 0.0)
self.assertLessEqual(info.resolution, 1.0)
self.assertIsInstance(info.adjustable, bool)
self.assertRaises(ValueError, time.get_clock_info, 'xxx')
class TestLocale(unittest.TestCase):
def setUp(self):
self.oldloc = locale.setlocale(locale.LC_ALL)
def tearDown(self):
locale.setlocale(locale.LC_ALL, self.oldloc)
def test_bug_3061(self):
try:
tmp = locale.setlocale(locale.LC_ALL, "fr_FR")
except locale.Error:
self.skipTest('could not set locale.LC_ALL to fr_FR')
# This should not cause an exception
time.strftime("%B", (2009,2,1,0,0,0,0,0,0))
class _TestAsctimeYear:
_format = '%d'
def yearstr(self, y):
return time.asctime((y,) + (0,) * 8).split()[-1]
def test_large_year(self):
# Check that it doesn't crash for year > 9999
self.assertEqual(self.yearstr(12345), '12345')
self.assertEqual(self.yearstr(123456789), '123456789')
class _TestStrftimeYear:
# Issue 13305: For years < 1000, the value is not always
# padded to 4 digits across platforms. The C standard
# assumes year >= 1900, so it does not specify the number
# of digits.
if time.strftime('%Y', (1,) + (0,) * 8) == '0001':
_format = '%04d'
else:
_format = '%d'
def yearstr(self, y):
return time.strftime('%Y', (y,) + (0,) * 8)
def test_4dyear(self):
# Check that we can return the zero padded value.
if self._format == '%04d':
self.test_year('%04d')
else:
def year4d(y):
return time.strftime('%4Y', (y,) + (0,) * 8)
self.test_year('%04d', func=year4d)
def skip_if_not_supported(y):
msg = "strftime() is limited to [1; 9999] with Visual Studio"
# Check that it doesn't crash for year > 9999
try:
time.strftime('%Y', (y,) + (0,) * 8)
except ValueError:
cond = False
else:
cond = True
return unittest.skipUnless(cond, msg)
@skip_if_not_supported(10000)
def test_large_year(self):
return super().test_large_year()
@skip_if_not_supported(0)
def test_negative(self):
return super().test_negative()
del skip_if_not_supported
class _Test4dYear:
_format = '%d'
def test_year(self, fmt=None, func=None):
fmt = fmt or self._format
func = func or self.yearstr
self.assertEqual(func(1), fmt % 1)
self.assertEqual(func(68), fmt % 68)
self.assertEqual(func(69), fmt % 69)
self.assertEqual(func(99), fmt % 99)
self.assertEqual(func(999), fmt % 999)
self.assertEqual(func(9999), fmt % 9999)
def test_large_year(self):
self.assertEqual(self.yearstr(12345).lstrip('+'), '12345')
self.assertEqual(self.yearstr(123456789).lstrip('+'), '123456789')
self.assertEqual(self.yearstr(TIME_MAXYEAR).lstrip('+'), str(TIME_MAXYEAR))
self.assertRaises(OverflowError, self.yearstr, TIME_MAXYEAR + 1)
def test_negative(self):
self.assertEqual(self.yearstr(-1), self._format % -1)
self.assertEqual(self.yearstr(-1234), '-1234')
self.assertEqual(self.yearstr(-123456), '-123456')
self.assertEqual(self.yearstr(-123456789), str(-123456789))
self.assertEqual(self.yearstr(-1234567890), str(-1234567890))
self.assertEqual(self.yearstr(TIME_MINYEAR), str(TIME_MINYEAR))
# Modules/timemodule.c checks for underflow
self.assertRaises(OverflowError, self.yearstr, TIME_MINYEAR - 1)
with self.assertRaises(OverflowError):
self.yearstr(-TIME_MAXYEAR - 1)
class TestAsctime4dyear(_TestAsctimeYear, _Test4dYear, unittest.TestCase):
pass
class TestStrftime4dyear(_TestStrftimeYear, _Test4dYear, unittest.TestCase):
pass
class TestPytime(unittest.TestCase):
@skip_if_buggy_ucrt_strfptime
@unittest.skipUnless(time._STRUCT_TM_ITEMS == 11, "needs tm_zone support")
def test_localtime_timezone(self):
# Get the localtime and examine it for the offset and zone.
lt = time.localtime()
self.assertTrue(hasattr(lt, "tm_gmtoff"))
self.assertTrue(hasattr(lt, "tm_zone"))
# See if the offset and zone are similar to the module
# attributes.
if lt.tm_gmtoff is None:
self.assertTrue(not hasattr(time, "timezone"))
else:
self.assertEqual(lt.tm_gmtoff, -[time.timezone, time.altzone][lt.tm_isdst])
if lt.tm_zone is None:
self.assertTrue(not hasattr(time, "tzname"))
else:
self.assertEqual(lt.tm_zone, time.tzname[lt.tm_isdst])
# Try and make UNIX times from the localtime and a 9-tuple
# created from the localtime. Test to see that the times are
# the same.
t = time.mktime(lt); t9 = time.mktime(lt[:9])
self.assertEqual(t, t9)
# Make localtimes from the UNIX times and compare them to
# the original localtime, thus making a round trip.
new_lt = time.localtime(t); new_lt9 = time.localtime(t9)
self.assertEqual(new_lt, lt)
self.assertEqual(new_lt.tm_gmtoff, lt.tm_gmtoff)
self.assertEqual(new_lt.tm_zone, lt.tm_zone)
self.assertEqual(new_lt9, lt)
self.assertEqual(new_lt.tm_gmtoff, lt.tm_gmtoff)
self.assertEqual(new_lt9.tm_zone, lt.tm_zone)
@unittest.skipUnless(time._STRUCT_TM_ITEMS == 11, "needs tm_zone support")
def test_strptime_timezone(self):
t = time.strptime("UTC", "%Z")
self.assertEqual(t.tm_zone, 'UTC')
t = time.strptime("+0500", "%z")
self.assertEqual(t.tm_gmtoff, 5 * 3600)
@unittest.skipUnless(time._STRUCT_TM_ITEMS == 11, "needs tm_zone support")
def test_short_times(self):
import pickle
# Load a short time structure using pickle.
st = b"ctime\nstruct_time\np0\n((I2007\nI8\nI11\nI1\nI24\nI49\nI5\nI223\nI1\ntp1\n(dp2\ntp3\nRp4\n."
lt = pickle.loads(st)
self.assertIs(lt.tm_gmtoff, None)
self.assertIs(lt.tm_zone, None)
@unittest.skipIf(_testcapi is None, 'need the _testcapi module')
class CPyTimeTestCase:
"""
Base class to test the C _PyTime_t API.
"""
OVERFLOW_SECONDS = None
def setUp(self):
from _testcapi import SIZEOF_TIME_T
bits = SIZEOF_TIME_T * 8 - 1
self.time_t_min = -2 ** bits
self.time_t_max = 2 ** bits - 1
def time_t_filter(self, seconds):
return (self.time_t_min <= seconds <= self.time_t_max)
def _rounding_values(self, use_float):
"Build timestamps used to test rounding."
units = [1, US_TO_NS, MS_TO_NS, SEC_TO_NS]
if use_float:
# picoseconds are only tested to pytime_converter accepting floats
units.append(1e-3)
values = (
# small values
1, 2, 5, 7, 123, 456, 1234,
# 10^k - 1
9,
99,
999,
9999,
99999,
999999,
# test half even rounding near 0.5, 1.5, 2.5, 3.5, 4.5
499, 500, 501,
1499, 1500, 1501,
2500,
3500,
4500,
)
ns_timestamps = [0]
for unit in units:
for value in values:
ns = value * unit
ns_timestamps.extend((-ns, ns))
for pow2 in (0, 5, 10, 15, 22, 23, 24, 30, 33):
ns = (2 ** pow2) * SEC_TO_NS
ns_timestamps.extend((
-ns-1, -ns, -ns+1,
ns-1, ns, ns+1
))
for seconds in (_testcapi.INT_MIN, _testcapi.INT_MAX):
ns_timestamps.append(seconds * SEC_TO_NS)
if use_float:
# numbers with an exact representation in IEEE 754 (base 2)
for pow2 in (3, 7, 10, 15):
ns = 2.0 ** (-pow2)
ns_timestamps.extend((-ns, ns))
# seconds close to _PyTime_t type limit
ns = (2 ** 63 // SEC_TO_NS) * SEC_TO_NS
ns_timestamps.extend((-ns, ns))
return ns_timestamps
def _check_rounding(self, pytime_converter, expected_func,
use_float, unit_to_sec, value_filter=None):
def convert_values(ns_timestamps):
if use_float:
unit_to_ns = SEC_TO_NS / float(unit_to_sec)
values = [ns / unit_to_ns for ns in ns_timestamps]
else:
unit_to_ns = SEC_TO_NS // unit_to_sec
values = [ns // unit_to_ns for ns in ns_timestamps]
if value_filter:
values = filter(value_filter, values)
# remove duplicates and sort
return sorted(set(values))
# test rounding
ns_timestamps = self._rounding_values(use_float)
valid_values = convert_values(ns_timestamps)
for time_rnd, decimal_rnd in ROUNDING_MODES :
with decimal.localcontext() as context:
context.rounding = decimal_rnd
for value in valid_values:
debug_info = {'value': value, 'rounding': decimal_rnd}
try:
result = pytime_converter(value, time_rnd)
expected = expected_func(value)
except Exception:
self.fail("Error on timestamp conversion: %s" % debug_info)
self.assertEqual(result,
expected,
debug_info)
# test overflow
ns = self.OVERFLOW_SECONDS * SEC_TO_NS
ns_timestamps = (-ns, ns)
overflow_values = convert_values(ns_timestamps)
for time_rnd, _ in ROUNDING_MODES :
for value in overflow_values:
debug_info = {'value': value, 'rounding': time_rnd}
with self.assertRaises(OverflowError, msg=debug_info):
pytime_converter(value, time_rnd)
def check_int_rounding(self, pytime_converter, expected_func,
unit_to_sec=1, value_filter=None):
self._check_rounding(pytime_converter, expected_func,
False, unit_to_sec, value_filter)
def check_float_rounding(self, pytime_converter, expected_func,
unit_to_sec=1, value_filter=None):
self._check_rounding(pytime_converter, expected_func,
True, unit_to_sec, value_filter)
def decimal_round(self, x):
d = decimal.Decimal(x)
d = d.quantize(1)
return int(d)
class TestCPyTime(CPyTimeTestCase, unittest.TestCase):
"""
Test the C _PyTime_t API.
"""
# _PyTime_t is a 64-bit signed integer
OVERFLOW_SECONDS = math.ceil((2**63 + 1) / SEC_TO_NS)
def test_FromSeconds(self):
from _testcapi import PyTime_FromSeconds
# PyTime_FromSeconds() expects a C int, reject values out of range
def c_int_filter(secs):
return (_testcapi.INT_MIN <= secs <= _testcapi.INT_MAX)
self.check_int_rounding(lambda secs, rnd: PyTime_FromSeconds(secs),
lambda secs: secs * SEC_TO_NS,
value_filter=c_int_filter)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(TypeError):
PyTime_FromSeconds(float('nan'))
def test_FromSecondsObject(self):
from _testcapi import PyTime_FromSecondsObject
self.check_int_rounding(
PyTime_FromSecondsObject,
lambda secs: secs * SEC_TO_NS)
self.check_float_rounding(
PyTime_FromSecondsObject,
lambda ns: self.decimal_round(ns * SEC_TO_NS))
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(ValueError):
PyTime_FromSecondsObject(float('nan'), time_rnd)
def test_AsSecondsDouble(self):
from _testcapi import PyTime_AsSecondsDouble
def float_converter(ns):
if abs(ns) % SEC_TO_NS == 0:
return float(ns // SEC_TO_NS)
else:
return float(ns) / SEC_TO_NS
self.check_int_rounding(lambda ns, rnd: PyTime_AsSecondsDouble(ns),
float_converter,
NS_TO_SEC)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(TypeError):
PyTime_AsSecondsDouble(float('nan'))
def create_decimal_converter(self, denominator):
denom = decimal.Decimal(denominator)
def converter(value):
d = decimal.Decimal(value) / denom
return self.decimal_round(d)
return converter
def test_AsTimeval(self):
from _testcapi import PyTime_AsTimeval
us_converter = self.create_decimal_converter(US_TO_NS)
def timeval_converter(ns):
us = us_converter(ns)
return divmod(us, SEC_TO_US)
if sys.platform == 'win32':
from _testcapi import LONG_MIN, LONG_MAX
# On Windows, timeval.tv_sec type is a C long
def seconds_filter(secs):
return LONG_MIN <= secs <= LONG_MAX
else:
seconds_filter = self.time_t_filter
self.check_int_rounding(PyTime_AsTimeval,
timeval_converter,
NS_TO_SEC,
value_filter=seconds_filter)
@unittest.skipUnless(hasattr(_testcapi, 'PyTime_AsTimespec'),
'need _testcapi.PyTime_AsTimespec')
def test_AsTimespec(self):
from _testcapi import PyTime_AsTimespec
def timespec_converter(ns):
return divmod(ns, SEC_TO_NS)
self.check_int_rounding(lambda ns, rnd: PyTime_AsTimespec(ns),
timespec_converter,
NS_TO_SEC,
value_filter=self.time_t_filter)
@unittest.skipUnless(hasattr(_testcapi, 'PyTime_AsTimeval_clamp'),
'need _testcapi.PyTime_AsTimeval_clamp')
def test_AsTimeval_clamp(self):
from _testcapi import PyTime_AsTimeval_clamp
if sys.platform == 'win32':
from _testcapi import LONG_MIN, LONG_MAX
tv_sec_max = LONG_MAX
tv_sec_min = LONG_MIN
else:
tv_sec_max = self.time_t_max
tv_sec_min = self.time_t_min
for t in (_PyTime_MIN, _PyTime_MAX):
ts = PyTime_AsTimeval_clamp(t, _PyTime.ROUND_CEILING)
with decimal.localcontext() as context:
context.rounding = decimal.ROUND_CEILING
us = self.decimal_round(decimal.Decimal(t) / US_TO_NS)
tv_sec, tv_usec = divmod(us, SEC_TO_US)
if tv_sec_max < tv_sec:
tv_sec = tv_sec_max
tv_usec = 0
elif tv_sec < tv_sec_min:
tv_sec = tv_sec_min
tv_usec = 0
self.assertEqual(ts, (tv_sec, tv_usec))
@unittest.skipUnless(hasattr(_testcapi, 'PyTime_AsTimespec_clamp'),
'need _testcapi.PyTime_AsTimespec_clamp')
def test_AsTimespec_clamp(self):
from _testcapi import PyTime_AsTimespec_clamp
for t in (_PyTime_MIN, _PyTime_MAX):
ts = PyTime_AsTimespec_clamp(t)
tv_sec, tv_nsec = divmod(t, NS_TO_SEC)
if self.time_t_max < tv_sec:
tv_sec = self.time_t_max
tv_nsec = 0
elif tv_sec < self.time_t_min:
tv_sec = self.time_t_min
tv_nsec = 0
self.assertEqual(ts, (tv_sec, tv_nsec))
def test_AsMilliseconds(self):
from _testcapi import PyTime_AsMilliseconds
self.check_int_rounding(PyTime_AsMilliseconds,
self.create_decimal_converter(MS_TO_NS),
NS_TO_SEC)
def test_AsMicroseconds(self):
from _testcapi import PyTime_AsMicroseconds
self.check_int_rounding(PyTime_AsMicroseconds,
self.create_decimal_converter(US_TO_NS),
NS_TO_SEC)
class TestOldPyTime(CPyTimeTestCase, unittest.TestCase):
"""
Test the old C _PyTime_t API: _PyTime_ObjectToXXX() functions.
"""
# time_t is a 32-bit or 64-bit signed integer
OVERFLOW_SECONDS = 2 ** 64
def test_object_to_time_t(self):
from _testcapi import pytime_object_to_time_t
self.check_int_rounding(pytime_object_to_time_t,
lambda secs: secs,
value_filter=self.time_t_filter)
self.check_float_rounding(pytime_object_to_time_t,
self.decimal_round,
value_filter=self.time_t_filter)
def create_converter(self, sec_to_unit):
def converter(secs):
floatpart, intpart = math.modf(secs)
intpart = int(intpart)
floatpart *= sec_to_unit
floatpart = self.decimal_round(floatpart)
if floatpart < 0:
floatpart += sec_to_unit
intpart -= 1
elif floatpart >= sec_to_unit:
floatpart -= sec_to_unit
intpart += 1
return (intpart, floatpart)
return converter
def test_object_to_timeval(self):
from _testcapi import pytime_object_to_timeval
self.check_int_rounding(pytime_object_to_timeval,
lambda secs: (secs, 0),
value_filter=self.time_t_filter)
self.check_float_rounding(pytime_object_to_timeval,
self.create_converter(SEC_TO_US),
value_filter=self.time_t_filter)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(ValueError):
pytime_object_to_timeval(float('nan'), time_rnd)
def test_object_to_timespec(self):
from _testcapi import pytime_object_to_timespec
self.check_int_rounding(pytime_object_to_timespec,
lambda secs: (secs, 0),
value_filter=self.time_t_filter)
self.check_float_rounding(pytime_object_to_timespec,
self.create_converter(SEC_TO_NS),
value_filter=self.time_t_filter)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(ValueError):
pytime_object_to_timespec(float('nan'), time_rnd)
@unittest.skipUnless(sys.platform == "darwin", "test weak linking on macOS")
class TestTimeWeaklinking(unittest.TestCase):
# These test cases verify that weak linking support on macOS works
# as expected. These cases only test new behaviour introduced by weak linking,
# regular behaviour is tested by the normal test cases.
#
# See the section on Weak Linking in Mac/README.txt for more information.
def test_clock_functions(self):
import sysconfig
import platform
config_vars = sysconfig.get_config_vars()
var_name = "HAVE_CLOCK_GETTIME"
if var_name not in config_vars or not config_vars[var_name]:
raise unittest.SkipTest(f"{var_name} is not available")
mac_ver = tuple(int(x) for x in platform.mac_ver()[0].split("."))
clock_names = [
"CLOCK_MONOTONIC", "clock_gettime", "clock_gettime_ns", "clock_settime",
"clock_settime_ns", "clock_getres"]
if mac_ver >= (10, 12):
for name in clock_names:
self.assertTrue(hasattr(time, name), f"time.{name} is not available")
else:
for name in clock_names:
self.assertFalse(hasattr(time, name), f"time.{name} is available")
if __name__ == "__main__":
unittest.main()
| 37.892226
| 108
| 0.582739
|
from test import support
from test.support import warnings_helper
import decimal
import enum
import locale
import math
import platform
import sys
import sysconfig
import time
import threading
import unittest
try:
import _testcapi
except ImportError:
_testcapi = None
from test.support import skip_if_buggy_ucrt_strfptime
SIZEOF_INT = sysconfig.get_config_var('SIZEOF_INT') or 4
TIME_MAXYEAR = (1 << 8 * SIZEOF_INT - 1) - 1
TIME_MINYEAR = -TIME_MAXYEAR - 1 + 1900
SEC_TO_US = 10 ** 6
US_TO_NS = 10 ** 3
MS_TO_NS = 10 ** 6
SEC_TO_NS = 10 ** 9
NS_TO_SEC = 10 ** 9
class _PyTime(enum.IntEnum):
ROUND_FLOOR = 0
ROUND_CEILING = 1
ROUND_HALF_EVEN = 2
ROUND_UP = 3
_PyTime_MIN = -2 ** 63
_PyTime_MAX = 2 ** 63 - 1
ROUNDING_MODES = (
(_PyTime.ROUND_FLOOR, decimal.ROUND_FLOOR),
(_PyTime.ROUND_CEILING, decimal.ROUND_CEILING),
(_PyTime.ROUND_HALF_EVEN, decimal.ROUND_HALF_EVEN),
(_PyTime.ROUND_UP, decimal.ROUND_UP),
)
class TimeTestCase(unittest.TestCase):
def setUp(self):
self.t = time.time()
def test_data_attributes(self):
time.altzone
time.daylight
time.timezone
time.tzname
def test_time(self):
time.time()
info = time.get_clock_info('time')
self.assertFalse(info.monotonic)
self.assertTrue(info.adjustable)
def test_time_ns_type(self):
def check_ns(sec, ns):
self.assertIsInstance(ns, int)
sec_ns = int(sec * 1e9)
self.assertLess((sec_ns - ns), 50 ** 6, (sec, ns))
check_ns(time.time(),
time.time_ns())
check_ns(time.monotonic(),
time.monotonic_ns())
check_ns(time.perf_counter(),
time.perf_counter_ns())
check_ns(time.process_time(),
time.process_time_ns())
if hasattr(time, 'thread_time'):
check_ns(time.thread_time(),
time.thread_time_ns())
if hasattr(time, 'clock_gettime'):
check_ns(time.clock_gettime(time.CLOCK_REALTIME),
time.clock_gettime_ns(time.CLOCK_REALTIME))
@unittest.skipUnless(hasattr(time, 'clock_gettime'),
'need time.clock_gettime()')
def test_clock_realtime(self):
t = time.clock_gettime(time.CLOCK_REALTIME)
self.assertIsInstance(t, float)
@unittest.skipUnless(hasattr(time, 'clock_gettime'),
'need time.clock_gettime()')
@unittest.skipUnless(hasattr(time, 'CLOCK_MONOTONIC'),
'need time.CLOCK_MONOTONIC')
def test_clock_monotonic(self):
a = time.clock_gettime(time.CLOCK_MONOTONIC)
b = time.clock_gettime(time.CLOCK_MONOTONIC)
self.assertLessEqual(a, b)
@unittest.skipUnless(hasattr(time, 'pthread_getcpuclockid'),
'need time.pthread_getcpuclockid()')
@unittest.skipUnless(hasattr(time, 'clock_gettime'),
'need time.clock_gettime()')
def test_pthread_getcpuclockid(self):
clk_id = time.pthread_getcpuclockid(threading.get_ident())
self.assertTrue(type(clk_id) is int)
if platform.system() == "AIX" and (sys.maxsize.bit_length() <= 32):
self.assertEqual(clk_id, time.CLOCK_THREAD_CPUTIME_ID)
elif sys.platform.startswith("sunos"):
self.assertEqual(clk_id, time.CLOCK_THREAD_CPUTIME_ID)
else:
self.assertNotEqual(clk_id, time.CLOCK_THREAD_CPUTIME_ID)
t1 = time.clock_gettime(clk_id)
t2 = time.clock_gettime(clk_id)
self.assertLessEqual(t1, t2)
@unittest.skipUnless(hasattr(time, 'clock_getres'),
'need time.clock_getres()')
def test_clock_getres(self):
res = time.clock_getres(time.CLOCK_REALTIME)
self.assertGreater(res, 0.0)
self.assertLessEqual(res, 1.0)
@unittest.skipUnless(hasattr(time, 'clock_settime'),
'need time.clock_settime()')
def test_clock_settime(self):
t = time.clock_gettime(time.CLOCK_REALTIME)
try:
time.clock_settime(time.CLOCK_REALTIME, t)
except PermissionError:
pass
if hasattr(time, 'CLOCK_MONOTONIC'):
self.assertRaises(OSError,
time.clock_settime, time.CLOCK_MONOTONIC, 0)
def test_conversions(self):
self.assertEqual(time.ctime(self.t),
time.asctime(time.localtime(self.t)))
self.assertEqual(int(time.mktime(time.localtime(self.t))),
int(self.t))
def test_sleep(self):
self.assertRaises(ValueError, time.sleep, -2)
self.assertRaises(ValueError, time.sleep, -1)
time.sleep(1.2)
def test_epoch(self):
epoch = time.gmtime(0)
self.assertEqual(tuple(epoch)[:6], (1970, 1, 1, 0, 0, 0), epoch)
def test_strftime(self):
tt = time.gmtime(self.t)
for directive in ('a', 'A', 'b', 'B', 'c', 'd', 'H', 'I',
'j', 'm', 'M', 'p', 'S',
'U', 'w', 'W', 'x', 'X', 'y', 'Y', 'Z', '%'):
format = ' %' + directive
try:
time.strftime(format, tt)
except ValueError:
self.fail('conversion specifier: %r failed.' % format)
self.assertRaises(TypeError, time.strftime, b'%S', tt)
self.assertRaises(ValueError, time.strftime, '%S\0', tt)
def _bounds_checking(self, func):
func((1900, 0, 1, 0, 0, 0, 0, 1, -1))
func((1900, 12, 1, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, -1, 1, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 13, 1, 0, 0, 0, 0, 1, -1))
func((1900, 1, 0, 0, 0, 0, 0, 1, -1))
func((1900, 1, 31, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, -1, 0, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 32, 0, 0, 0, 0, 1, -1))
func((1900, 1, 1, 23, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, -1, 0, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 24, 0, 0, 0, 1, -1))
func((1900, 1, 1, 0, 59, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, -1, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 60, 0, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, -1, 0, 1, -1))
# allow two leap seconds (0..61)
func((1900, 1, 1, 0, 0, 60, 0, 1, -1))
func((1900, 1, 1, 0, 0, 61, 0, 1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 62, 0, 1, -1))
# No check for upper-bound day of week;
# value forced into range by a ``% 7`` calculation.
# Start check at -2 since gettmarg() increments value before taking
# modulo.
self.assertEqual(func((1900, 1, 1, 0, 0, 0, -1, 1, -1)),
func((1900, 1, 1, 0, 0, 0, +6, 1, -1)))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 0, -2, 1, -1))
# Check day of the year [1, 366] + zero support
func((1900, 1, 1, 0, 0, 0, 0, 0, -1))
func((1900, 1, 1, 0, 0, 0, 0, 366, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 0, 0, -1, -1))
self.assertRaises(ValueError, func,
(1900, 1, 1, 0, 0, 0, 0, 367, -1))
def test_strftime_bounding_check(self):
self._bounds_checking(lambda tup: time.strftime('', tup))
def test_strftime_format_check(self):
# Test that strftime does not crash on invalid format strings
# that may trigger a buffer overread. When not triggered,
# strftime may succeed or raise ValueError depending on
# the platform.
for x in [ '', 'A', '%A', '%AA' ]:
for y in range(0x0, 0x10):
for z in [ '%', 'A%', 'AA%', '%A%', 'A%A%', '%
try:
time.strftime(x * y + z)
except ValueError:
pass
def test_default_values_for_zero(self):
# Make sure that using all zeros uses the proper default
# values. No test for daylight savings since strftime() does
# not change output based on its value and no test for year
# because systems vary in their support for year 0.
expected = "2000 01 01 00 00 00 1 001"
with warnings_helper.check_warnings():
result = time.strftime("%Y %m %d %H %M %S %w %j", (2000,)+(0,)*8)
self.assertEqual(expected, result)
@skip_if_buggy_ucrt_strfptime
def test_strptime(self):
# Should be able to go round-trip from strftime to strptime without
# raising an exception.
tt = time.gmtime(self.t)
for directive in ('a', 'A', 'b', 'B', 'c', 'd', 'H', 'I',
'j', 'm', 'M', 'p', 'S',
'U', 'w', 'W', 'x', 'X', 'y', 'Y', 'Z', '%'):
format = '%' + directive
strf_output = time.strftime(format, tt)
try:
time.strptime(strf_output, format)
except ValueError:
self.fail("conversion specifier %r failed with '%s' input." %
(format, strf_output))
def test_strptime_bytes(self):
# Make sure only strings are accepted as arguments to strptime.
self.assertRaises(TypeError, time.strptime, b'2009', "%Y")
self.assertRaises(TypeError, time.strptime, '2009', b'%Y')
def test_strptime_exception_context(self):
# check that this doesn't chain exceptions needlessly (see
with self.assertRaises(ValueError) as e:
time.strptime('', '%D')
self.assertIs(e.exception.__suppress_context__, True)
with self.assertRaises(ValueError) as e:
time.strptime('19', '%Y %')
self.assertIs(e.exception.__suppress_context__, True)
def test_asctime(self):
time.asctime(time.gmtime(self.t))
for bigyear in TIME_MAXYEAR, TIME_MINYEAR:
asc = time.asctime((bigyear, 6, 1) + (0,) * 6)
self.assertEqual(asc[-len(str(bigyear)):], str(bigyear))
self.assertRaises(OverflowError, time.asctime,
(TIME_MAXYEAR + 1,) + (0,) * 8)
self.assertRaises(OverflowError, time.asctime,
(TIME_MINYEAR - 1,) + (0,) * 8)
self.assertRaises(TypeError, time.asctime, 0)
self.assertRaises(TypeError, time.asctime, ())
self.assertRaises(TypeError, time.asctime, (0,) * 10)
def test_asctime_bounding_check(self):
self._bounds_checking(time.asctime)
def test_ctime(self):
t = time.mktime((1973, 9, 16, 1, 3, 52, 0, 0, -1))
self.assertEqual(time.ctime(t), 'Sun Sep 16 01:03:52 1973')
t = time.mktime((2000, 1, 1, 0, 0, 0, 0, 0, -1))
self.assertEqual(time.ctime(t), 'Sat Jan 1 00:00:00 2000')
for year in [-100, 100, 1000, 2000, 2050, 10000]:
try:
testval = time.mktime((year, 1, 10) + (0,)*6)
except (ValueError, OverflowError):
pass
else:
self.assertEqual(time.ctime(testval)[20:], str(year))
@unittest.skipUnless(hasattr(time, "tzset"),
"time module has no attribute tzset")
def test_tzset(self):
from os import environ
xmas2002 = 1040774400.0
eastern = 'EST+05EDT,M4.1.0,M10.5.0'
victoria = 'AEST-10AEDT-11,M10.5.0,M3.5.0'
utc='UTC+0'
org_TZ = environ.get('TZ',None)
try:
environ['TZ'] = eastern
time.tzset()
environ['TZ'] = utc
time.tzset()
self.assertEqual(
time.gmtime(xmas2002), time.localtime(xmas2002)
)
self.assertEqual(time.daylight, 0)
self.assertEqual(time.timezone, 0)
self.assertEqual(time.localtime(xmas2002).tm_isdst, 0)
environ['TZ'] = eastern
time.tzset()
self.assertNotEqual(time.gmtime(xmas2002), time.localtime(xmas2002))
self.assertEqual(time.tzname, ('EST', 'EDT'))
self.assertEqual(len(time.tzname), 2)
self.assertEqual(time.daylight, 1)
self.assertEqual(time.timezone, 18000)
self.assertEqual(time.altzone, 14400)
self.assertEqual(time.localtime(xmas2002).tm_isdst, 0)
self.assertEqual(len(time.tzname), 2)
environ['TZ'] = victoria
time.tzset()
self.assertNotEqual(time.gmtime(xmas2002), time.localtime(xmas2002))
self.assertIn(time.tzname[0], ('AEST' 'EST'), time.tzname[0])
self.assertTrue(time.tzname[1] in ('AEDT', 'EDT'), str(time.tzname[1]))
self.assertEqual(len(time.tzname), 2)
self.assertEqual(time.daylight, 1)
self.assertEqual(time.timezone, -36000)
self.assertEqual(time.altzone, -39600)
self.assertEqual(time.localtime(xmas2002).tm_isdst, 1)
finally:
if org_TZ is not None:
environ['TZ'] = org_TZ
elif 'TZ' in environ:
del environ['TZ']
time.tzset()
def test_insane_timestamps(self):
# and that this test will fail there. This test should
# exempt such platforms (provided they return reasonable
# results!).
for func in time.ctime, time.gmtime, time.localtime:
for unreasonable in -1e200, 1e200:
self.assertRaises(OverflowError, func, unreasonable)
def test_ctime_without_arg(self):
# Not sure how to check the values, since the clock could tick
# at any time. Make sure these are at least accepted and
# don't raise errors.
time.ctime()
time.ctime(None)
def test_gmtime_without_arg(self):
gt0 = time.gmtime()
gt1 = time.gmtime(None)
t0 = time.mktime(gt0)
t1 = time.mktime(gt1)
self.assertAlmostEqual(t1, t0, delta=0.2)
def test_localtime_without_arg(self):
lt0 = time.localtime()
lt1 = time.localtime(None)
t0 = time.mktime(lt0)
t1 = time.mktime(lt1)
self.assertAlmostEqual(t1, t0, delta=0.2)
def test_mktime(self):
for t in (-2, -1, 0, 1):
try:
tt = time.localtime(t)
except (OverflowError, OSError):
pass
else:
self.assertEqual(time.mktime(tt), t)
'glibc',
"disabled because of a bug in glibc. Issue #13309")
def test_mktime_error(self):
# It may not be possible to reliably make mktime return an error
# on all platforms. This will make sure that no other exception
# than OverflowError is raised for an extreme value.
tt = time.gmtime(self.t)
tzname = time.strftime('%Z', tt)
self.assertNotEqual(tzname, 'LMT')
try:
time.mktime((-1, 1, 1, 0, 0, 0, -1, -1, -1))
except OverflowError:
pass
self.assertEqual(time.strftime('%Z', tt), tzname)
def test_monotonic(self):
# monotonic() should not go backward
times = [time.monotonic() for n in range(100)]
t1 = times[0]
for t2 in times[1:]:
self.assertGreaterEqual(t2, t1, "times=%s" % times)
t1 = t2
# monotonic() includes time elapsed during a sleep
t1 = time.monotonic()
time.sleep(0.5)
t2 = time.monotonic()
dt = t2 - t1
self.assertGreater(t2, t1)
# bpo-20101: tolerate a difference of 50 ms because of bad timer
# resolution on Windows
self.assertTrue(0.450 <= dt)
# monotonic() is a monotonic but non adjustable clock
info = time.get_clock_info('monotonic')
self.assertTrue(info.monotonic)
self.assertFalse(info.adjustable)
def test_perf_counter(self):
time.perf_counter()
def test_process_time(self):
# process_time() should not include time spend during a sleep
start = time.process_time()
time.sleep(0.100)
stop = time.process_time()
# use 20 ms because process_time() has usually a resolution of 15 ms
# on Windows
self.assertLess(stop - start, 0.020)
info = time.get_clock_info('process_time')
self.assertTrue(info.monotonic)
self.assertFalse(info.adjustable)
def test_thread_time(self):
if not hasattr(time, 'thread_time'):
if sys.platform.startswith(('linux', 'win')):
self.fail("time.thread_time() should be available on %r"
% (sys.platform,))
else:
self.skipTest("need time.thread_time")
# thread_time() should not include time spend during a sleep
start = time.thread_time()
time.sleep(0.100)
stop = time.thread_time()
# use 20 ms because thread_time() has usually a resolution of 15 ms
# on Windows
self.assertLess(stop - start, 0.020)
info = time.get_clock_info('thread_time')
self.assertTrue(info.monotonic)
self.assertFalse(info.adjustable)
@unittest.skipUnless(hasattr(time, 'clock_settime'),
'need time.clock_settime')
def test_monotonic_settime(self):
t1 = time.monotonic()
realtime = time.clock_gettime(time.CLOCK_REALTIME)
# jump backward with an offset of 1 hour
try:
time.clock_settime(time.CLOCK_REALTIME, realtime - 3600)
except PermissionError as err:
self.skipTest(err)
t2 = time.monotonic()
time.clock_settime(time.CLOCK_REALTIME, realtime)
# monotonic must not be affected by system clock updates
self.assertGreaterEqual(t2, t1)
def test_localtime_failure(self):
# Issue #13847: check for localtime() failure
invalid_time_t = None
for time_t in (-1, 2**30, 2**33, 2**60):
try:
time.localtime(time_t)
except OverflowError:
self.skipTest("need 64-bit time_t")
except OSError:
invalid_time_t = time_t
break
if invalid_time_t is None:
self.skipTest("unable to find an invalid time_t value")
self.assertRaises(OSError, time.localtime, invalid_time_t)
self.assertRaises(OSError, time.ctime, invalid_time_t)
# Issue #26669: check for localtime() failure
self.assertRaises(ValueError, time.localtime, float("nan"))
self.assertRaises(ValueError, time.ctime, float("nan"))
def test_get_clock_info(self):
clocks = ['monotonic', 'perf_counter', 'process_time', 'time']
for name in clocks:
info = time.get_clock_info(name)
#self.assertIsInstance(info, dict)
self.assertIsInstance(info.implementation, str)
self.assertNotEqual(info.implementation, '')
self.assertIsInstance(info.monotonic, bool)
self.assertIsInstance(info.resolution, float)
# 0.0 < resolution <= 1.0
self.assertGreater(info.resolution, 0.0)
self.assertLessEqual(info.resolution, 1.0)
self.assertIsInstance(info.adjustable, bool)
self.assertRaises(ValueError, time.get_clock_info, 'xxx')
class TestLocale(unittest.TestCase):
def setUp(self):
self.oldloc = locale.setlocale(locale.LC_ALL)
def tearDown(self):
locale.setlocale(locale.LC_ALL, self.oldloc)
def test_bug_3061(self):
try:
tmp = locale.setlocale(locale.LC_ALL, "fr_FR")
except locale.Error:
self.skipTest('could not set locale.LC_ALL to fr_FR')
# This should not cause an exception
time.strftime("%B", (2009,2,1,0,0,0,0,0,0))
class _TestAsctimeYear:
_format = '%d'
def yearstr(self, y):
return time.asctime((y,) + (0,) * 8).split()[-1]
def test_large_year(self):
# Check that it doesn't crash for year > 9999
self.assertEqual(self.yearstr(12345), '12345')
self.assertEqual(self.yearstr(123456789), '123456789')
class _TestStrftimeYear:
if time.strftime('%Y', (1,) + (0,) * 8) == '0001':
_format = '%04d'
else:
_format = '%d'
def yearstr(self, y):
return time.strftime('%Y', (y,) + (0,) * 8)
def test_4dyear(self):
if self._format == '%04d':
self.test_year('%04d')
else:
def year4d(y):
return time.strftime('%4Y', (y,) + (0,) * 8)
self.test_year('%04d', func=year4d)
def skip_if_not_supported(y):
msg = "strftime() is limited to [1; 9999] with Visual Studio"
try:
time.strftime('%Y', (y,) + (0,) * 8)
except ValueError:
cond = False
else:
cond = True
return unittest.skipUnless(cond, msg)
@skip_if_not_supported(10000)
def test_large_year(self):
return super().test_large_year()
@skip_if_not_supported(0)
def test_negative(self):
return super().test_negative()
del skip_if_not_supported
class _Test4dYear:
_format = '%d'
def test_year(self, fmt=None, func=None):
fmt = fmt or self._format
func = func or self.yearstr
self.assertEqual(func(1), fmt % 1)
self.assertEqual(func(68), fmt % 68)
self.assertEqual(func(69), fmt % 69)
self.assertEqual(func(99), fmt % 99)
self.assertEqual(func(999), fmt % 999)
self.assertEqual(func(9999), fmt % 9999)
def test_large_year(self):
self.assertEqual(self.yearstr(12345).lstrip('+'), '12345')
self.assertEqual(self.yearstr(123456789).lstrip('+'), '123456789')
self.assertEqual(self.yearstr(TIME_MAXYEAR).lstrip('+'), str(TIME_MAXYEAR))
self.assertRaises(OverflowError, self.yearstr, TIME_MAXYEAR + 1)
def test_negative(self):
self.assertEqual(self.yearstr(-1), self._format % -1)
self.assertEqual(self.yearstr(-1234), '-1234')
self.assertEqual(self.yearstr(-123456), '-123456')
self.assertEqual(self.yearstr(-123456789), str(-123456789))
self.assertEqual(self.yearstr(-1234567890), str(-1234567890))
self.assertEqual(self.yearstr(TIME_MINYEAR), str(TIME_MINYEAR))
# Modules/timemodule.c checks for underflow
self.assertRaises(OverflowError, self.yearstr, TIME_MINYEAR - 1)
with self.assertRaises(OverflowError):
self.yearstr(-TIME_MAXYEAR - 1)
class TestAsctime4dyear(_TestAsctimeYear, _Test4dYear, unittest.TestCase):
pass
class TestStrftime4dyear(_TestStrftimeYear, _Test4dYear, unittest.TestCase):
pass
class TestPytime(unittest.TestCase):
@skip_if_buggy_ucrt_strfptime
@unittest.skipUnless(time._STRUCT_TM_ITEMS == 11, "needs tm_zone support")
def test_localtime_timezone(self):
# Get the localtime and examine it for the offset and zone.
lt = time.localtime()
self.assertTrue(hasattr(lt, "tm_gmtoff"))
self.assertTrue(hasattr(lt, "tm_zone"))
# See if the offset and zone are similar to the module
# attributes.
if lt.tm_gmtoff is None:
self.assertTrue(not hasattr(time, "timezone"))
else:
self.assertEqual(lt.tm_gmtoff, -[time.timezone, time.altzone][lt.tm_isdst])
if lt.tm_zone is None:
self.assertTrue(not hasattr(time, "tzname"))
else:
self.assertEqual(lt.tm_zone, time.tzname[lt.tm_isdst])
# Try and make UNIX times from the localtime and a 9-tuple
# created from the localtime. Test to see that the times are
# the same.
t = time.mktime(lt); t9 = time.mktime(lt[:9])
self.assertEqual(t, t9)
# Make localtimes from the UNIX times and compare them to
# the original localtime, thus making a round trip.
new_lt = time.localtime(t); new_lt9 = time.localtime(t9)
self.assertEqual(new_lt, lt)
self.assertEqual(new_lt.tm_gmtoff, lt.tm_gmtoff)
self.assertEqual(new_lt.tm_zone, lt.tm_zone)
self.assertEqual(new_lt9, lt)
self.assertEqual(new_lt.tm_gmtoff, lt.tm_gmtoff)
self.assertEqual(new_lt9.tm_zone, lt.tm_zone)
@unittest.skipUnless(time._STRUCT_TM_ITEMS == 11, "needs tm_zone support")
def test_strptime_timezone(self):
t = time.strptime("UTC", "%Z")
self.assertEqual(t.tm_zone, 'UTC')
t = time.strptime("+0500", "%z")
self.assertEqual(t.tm_gmtoff, 5 * 3600)
@unittest.skipUnless(time._STRUCT_TM_ITEMS == 11, "needs tm_zone support")
def test_short_times(self):
import pickle
# Load a short time structure using pickle.
st = b"ctime\nstruct_time\np0\n((I2007\nI8\nI11\nI1\nI24\nI49\nI5\nI223\nI1\ntp1\n(dp2\ntp3\nRp4\n."
lt = pickle.loads(st)
self.assertIs(lt.tm_gmtoff, None)
self.assertIs(lt.tm_zone, None)
@unittest.skipIf(_testcapi is None, 'need the _testcapi module')
class CPyTimeTestCase:
OVERFLOW_SECONDS = None
def setUp(self):
from _testcapi import SIZEOF_TIME_T
bits = SIZEOF_TIME_T * 8 - 1
self.time_t_min = -2 ** bits
self.time_t_max = 2 ** bits - 1
def time_t_filter(self, seconds):
return (self.time_t_min <= seconds <= self.time_t_max)
def _rounding_values(self, use_float):
units = [1, US_TO_NS, MS_TO_NS, SEC_TO_NS]
if use_float:
# picoseconds are only tested to pytime_converter accepting floats
units.append(1e-3)
values = (
# small values
1, 2, 5, 7, 123, 456, 1234,
# 10^k - 1
9,
99,
999,
9999,
99999,
999999,
# test half even rounding near 0.5, 1.5, 2.5, 3.5, 4.5
499, 500, 501,
1499, 1500, 1501,
2500,
3500,
4500,
)
ns_timestamps = [0]
for unit in units:
for value in values:
ns = value * unit
ns_timestamps.extend((-ns, ns))
for pow2 in (0, 5, 10, 15, 22, 23, 24, 30, 33):
ns = (2 ** pow2) * SEC_TO_NS
ns_timestamps.extend((
-ns-1, -ns, -ns+1,
ns-1, ns, ns+1
))
for seconds in (_testcapi.INT_MIN, _testcapi.INT_MAX):
ns_timestamps.append(seconds * SEC_TO_NS)
if use_float:
# numbers with an exact representation in IEEE 754 (base 2)
for pow2 in (3, 7, 10, 15):
ns = 2.0 ** (-pow2)
ns_timestamps.extend((-ns, ns))
# seconds close to _PyTime_t type limit
ns = (2 ** 63 // SEC_TO_NS) * SEC_TO_NS
ns_timestamps.extend((-ns, ns))
return ns_timestamps
def _check_rounding(self, pytime_converter, expected_func,
use_float, unit_to_sec, value_filter=None):
def convert_values(ns_timestamps):
if use_float:
unit_to_ns = SEC_TO_NS / float(unit_to_sec)
values = [ns / unit_to_ns for ns in ns_timestamps]
else:
unit_to_ns = SEC_TO_NS // unit_to_sec
values = [ns // unit_to_ns for ns in ns_timestamps]
if value_filter:
values = filter(value_filter, values)
# remove duplicates and sort
return sorted(set(values))
# test rounding
ns_timestamps = self._rounding_values(use_float)
valid_values = convert_values(ns_timestamps)
for time_rnd, decimal_rnd in ROUNDING_MODES :
with decimal.localcontext() as context:
context.rounding = decimal_rnd
for value in valid_values:
debug_info = {'value': value, 'rounding': decimal_rnd}
try:
result = pytime_converter(value, time_rnd)
expected = expected_func(value)
except Exception:
self.fail("Error on timestamp conversion: %s" % debug_info)
self.assertEqual(result,
expected,
debug_info)
# test overflow
ns = self.OVERFLOW_SECONDS * SEC_TO_NS
ns_timestamps = (-ns, ns)
overflow_values = convert_values(ns_timestamps)
for time_rnd, _ in ROUNDING_MODES :
for value in overflow_values:
debug_info = {'value': value, 'rounding': time_rnd}
with self.assertRaises(OverflowError, msg=debug_info):
pytime_converter(value, time_rnd)
def check_int_rounding(self, pytime_converter, expected_func,
unit_to_sec=1, value_filter=None):
self._check_rounding(pytime_converter, expected_func,
False, unit_to_sec, value_filter)
def check_float_rounding(self, pytime_converter, expected_func,
unit_to_sec=1, value_filter=None):
self._check_rounding(pytime_converter, expected_func,
True, unit_to_sec, value_filter)
def decimal_round(self, x):
d = decimal.Decimal(x)
d = d.quantize(1)
return int(d)
class TestCPyTime(CPyTimeTestCase, unittest.TestCase):
# _PyTime_t is a 64-bit signed integer
OVERFLOW_SECONDS = math.ceil((2**63 + 1) / SEC_TO_NS)
def test_FromSeconds(self):
from _testcapi import PyTime_FromSeconds
# PyTime_FromSeconds() expects a C int, reject values out of range
def c_int_filter(secs):
return (_testcapi.INT_MIN <= secs <= _testcapi.INT_MAX)
self.check_int_rounding(lambda secs, rnd: PyTime_FromSeconds(secs),
lambda secs: secs * SEC_TO_NS,
value_filter=c_int_filter)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(TypeError):
PyTime_FromSeconds(float('nan'))
def test_FromSecondsObject(self):
from _testcapi import PyTime_FromSecondsObject
self.check_int_rounding(
PyTime_FromSecondsObject,
lambda secs: secs * SEC_TO_NS)
self.check_float_rounding(
PyTime_FromSecondsObject,
lambda ns: self.decimal_round(ns * SEC_TO_NS))
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(ValueError):
PyTime_FromSecondsObject(float('nan'), time_rnd)
def test_AsSecondsDouble(self):
from _testcapi import PyTime_AsSecondsDouble
def float_converter(ns):
if abs(ns) % SEC_TO_NS == 0:
return float(ns // SEC_TO_NS)
else:
return float(ns) / SEC_TO_NS
self.check_int_rounding(lambda ns, rnd: PyTime_AsSecondsDouble(ns),
float_converter,
NS_TO_SEC)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(TypeError):
PyTime_AsSecondsDouble(float('nan'))
def create_decimal_converter(self, denominator):
denom = decimal.Decimal(denominator)
def converter(value):
d = decimal.Decimal(value) / denom
return self.decimal_round(d)
return converter
def test_AsTimeval(self):
from _testcapi import PyTime_AsTimeval
us_converter = self.create_decimal_converter(US_TO_NS)
def timeval_converter(ns):
us = us_converter(ns)
return divmod(us, SEC_TO_US)
if sys.platform == 'win32':
from _testcapi import LONG_MIN, LONG_MAX
# On Windows, timeval.tv_sec type is a C long
def seconds_filter(secs):
return LONG_MIN <= secs <= LONG_MAX
else:
seconds_filter = self.time_t_filter
self.check_int_rounding(PyTime_AsTimeval,
timeval_converter,
NS_TO_SEC,
value_filter=seconds_filter)
@unittest.skipUnless(hasattr(_testcapi, 'PyTime_AsTimespec'),
'need _testcapi.PyTime_AsTimespec')
def test_AsTimespec(self):
from _testcapi import PyTime_AsTimespec
def timespec_converter(ns):
return divmod(ns, SEC_TO_NS)
self.check_int_rounding(lambda ns, rnd: PyTime_AsTimespec(ns),
timespec_converter,
NS_TO_SEC,
value_filter=self.time_t_filter)
@unittest.skipUnless(hasattr(_testcapi, 'PyTime_AsTimeval_clamp'),
'need _testcapi.PyTime_AsTimeval_clamp')
def test_AsTimeval_clamp(self):
from _testcapi import PyTime_AsTimeval_clamp
if sys.platform == 'win32':
from _testcapi import LONG_MIN, LONG_MAX
tv_sec_max = LONG_MAX
tv_sec_min = LONG_MIN
else:
tv_sec_max = self.time_t_max
tv_sec_min = self.time_t_min
for t in (_PyTime_MIN, _PyTime_MAX):
ts = PyTime_AsTimeval_clamp(t, _PyTime.ROUND_CEILING)
with decimal.localcontext() as context:
context.rounding = decimal.ROUND_CEILING
us = self.decimal_round(decimal.Decimal(t) / US_TO_NS)
tv_sec, tv_usec = divmod(us, SEC_TO_US)
if tv_sec_max < tv_sec:
tv_sec = tv_sec_max
tv_usec = 0
elif tv_sec < tv_sec_min:
tv_sec = tv_sec_min
tv_usec = 0
self.assertEqual(ts, (tv_sec, tv_usec))
@unittest.skipUnless(hasattr(_testcapi, 'PyTime_AsTimespec_clamp'),
'need _testcapi.PyTime_AsTimespec_clamp')
def test_AsTimespec_clamp(self):
from _testcapi import PyTime_AsTimespec_clamp
for t in (_PyTime_MIN, _PyTime_MAX):
ts = PyTime_AsTimespec_clamp(t)
tv_sec, tv_nsec = divmod(t, NS_TO_SEC)
if self.time_t_max < tv_sec:
tv_sec = self.time_t_max
tv_nsec = 0
elif tv_sec < self.time_t_min:
tv_sec = self.time_t_min
tv_nsec = 0
self.assertEqual(ts, (tv_sec, tv_nsec))
def test_AsMilliseconds(self):
from _testcapi import PyTime_AsMilliseconds
self.check_int_rounding(PyTime_AsMilliseconds,
self.create_decimal_converter(MS_TO_NS),
NS_TO_SEC)
def test_AsMicroseconds(self):
from _testcapi import PyTime_AsMicroseconds
self.check_int_rounding(PyTime_AsMicroseconds,
self.create_decimal_converter(US_TO_NS),
NS_TO_SEC)
class TestOldPyTime(CPyTimeTestCase, unittest.TestCase):
# time_t is a 32-bit or 64-bit signed integer
OVERFLOW_SECONDS = 2 ** 64
def test_object_to_time_t(self):
from _testcapi import pytime_object_to_time_t
self.check_int_rounding(pytime_object_to_time_t,
lambda secs: secs,
value_filter=self.time_t_filter)
self.check_float_rounding(pytime_object_to_time_t,
self.decimal_round,
value_filter=self.time_t_filter)
def create_converter(self, sec_to_unit):
def converter(secs):
floatpart, intpart = math.modf(secs)
intpart = int(intpart)
floatpart *= sec_to_unit
floatpart = self.decimal_round(floatpart)
if floatpart < 0:
floatpart += sec_to_unit
intpart -= 1
elif floatpart >= sec_to_unit:
floatpart -= sec_to_unit
intpart += 1
return (intpart, floatpart)
return converter
def test_object_to_timeval(self):
from _testcapi import pytime_object_to_timeval
self.check_int_rounding(pytime_object_to_timeval,
lambda secs: (secs, 0),
value_filter=self.time_t_filter)
self.check_float_rounding(pytime_object_to_timeval,
self.create_converter(SEC_TO_US),
value_filter=self.time_t_filter)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(ValueError):
pytime_object_to_timeval(float('nan'), time_rnd)
def test_object_to_timespec(self):
from _testcapi import pytime_object_to_timespec
self.check_int_rounding(pytime_object_to_timespec,
lambda secs: (secs, 0),
value_filter=self.time_t_filter)
self.check_float_rounding(pytime_object_to_timespec,
self.create_converter(SEC_TO_NS),
value_filter=self.time_t_filter)
# test nan
for time_rnd, _ in ROUNDING_MODES:
with self.assertRaises(ValueError):
pytime_object_to_timespec(float('nan'), time_rnd)
@unittest.skipUnless(sys.platform == "darwin", "test weak linking on macOS")
class TestTimeWeaklinking(unittest.TestCase):
# These test cases verify that weak linking support on macOS works
# as expected. These cases only test new behaviour introduced by weak linking,
# regular behaviour is tested by the normal test cases.
#
# See the section on Weak Linking in Mac/README.txt for more information.
def test_clock_functions(self):
import sysconfig
import platform
config_vars = sysconfig.get_config_vars()
var_name = "HAVE_CLOCK_GETTIME"
if var_name not in config_vars or not config_vars[var_name]:
raise unittest.SkipTest(f"{var_name} is not available")
mac_ver = tuple(int(x) for x in platform.mac_ver()[0].split("."))
clock_names = [
"CLOCK_MONOTONIC", "clock_gettime", "clock_gettime_ns", "clock_settime",
"clock_settime_ns", "clock_getres"]
if mac_ver >= (10, 12):
for name in clock_names:
self.assertTrue(hasattr(time, name), f"time.{name} is not available")
else:
for name in clock_names:
self.assertFalse(hasattr(time, name), f"time.{name} is available")
if __name__ == "__main__":
unittest.main()
| true
| true
|
1c444ef4cd29e04ad3311978e8ac577fb7bda338
| 629
|
py
|
Python
|
extras/makeDepPopulation.py
|
augustodn/COVID-19_Ar
|
4911b52cd979ab346eaf9e716883500d392bfb01
|
[
"MIT"
] | null | null | null |
extras/makeDepPopulation.py
|
augustodn/COVID-19_Ar
|
4911b52cd979ab346eaf9e716883500d392bfb01
|
[
"MIT"
] | null | null | null |
extras/makeDepPopulation.py
|
augustodn/COVID-19_Ar
|
4911b52cd979ab346eaf9e716883500d392bfb01
|
[
"MIT"
] | 1
|
2020-09-29T11:58:12.000Z
|
2020-09-29T11:58:12.000Z
|
from openpyxl import load_workbook
filename = 'poblacion_dpto.csv'
csvFN = open(filename, 'w')
wb = load_workbook('poblacion_dpto.xlsx')
ws = wb['Sheet1']
csvFN.write('in1,nombre,pob_total\n')
for row in ws.values:
try:
if 'AREA' in row[0]:
indecCode = row[0][7:]
name = row[1]
newAreaFound = True
if ' Total' == row[0] and newAreaFound:
totPopulation = int(row[3])
print(indecCode, name, totPopulation)
csvFN.write(f'{indecCode:5},{name},{totPopulation}\n')
newAreaFound = False
except:
pass
csvFN.close()
| 23.296296
| 66
| 0.585056
|
from openpyxl import load_workbook
filename = 'poblacion_dpto.csv'
csvFN = open(filename, 'w')
wb = load_workbook('poblacion_dpto.xlsx')
ws = wb['Sheet1']
csvFN.write('in1,nombre,pob_total\n')
for row in ws.values:
try:
if 'AREA' in row[0]:
indecCode = row[0][7:]
name = row[1]
newAreaFound = True
if ' Total' == row[0] and newAreaFound:
totPopulation = int(row[3])
print(indecCode, name, totPopulation)
csvFN.write(f'{indecCode:5},{name},{totPopulation}\n')
newAreaFound = False
except:
pass
csvFN.close()
| true
| true
|
1c444fd9aef9620eaa7ffa85c445148e8fcc02e6
| 15,493
|
py
|
Python
|
scripts/.ipynb_checkpoints/gdal_functions-checkpoint.py
|
Thomas-Brd/3D_landslide_detection
|
95ec6fc4dd013ecc7c3c8cb22dbbbc8712467830
|
[
"CC-BY-3.0"
] | 1
|
2022-02-15T09:56:34.000Z
|
2022-02-15T09:56:34.000Z
|
scripts/.ipynb_checkpoints/gdal_functions-checkpoint.py
|
Thomas-Brd/3D_landslide_detection
|
95ec6fc4dd013ecc7c3c8cb22dbbbc8712467830
|
[
"CC-BY-3.0"
] | null | null | null |
scripts/.ipynb_checkpoints/gdal_functions-checkpoint.py
|
Thomas-Brd/3D_landslide_detection
|
95ec6fc4dd013ecc7c3c8cb22dbbbc8712467830
|
[
"CC-BY-3.0"
] | 3
|
2020-09-17T13:22:07.000Z
|
2021-11-06T14:14:54.000Z
|
# coding: utf-8
# Thomas Bernard
# fonctions utilisant gdal
from osgeo import gdal
import ogr, osr
import numpy as np
#import rasterio
#from rasterio.plot import show
import subprocess
import os
import matplotlib.pyplot as plt
#import scripts.eros_function as eros
def read_tif_file(path_to_file):
ds = gdal.Open(path_to_file)
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
band= ds.GetRasterBand(1)
mask = band.GetNoDataValue()
array = band.ReadAsArray()
return array, gt, proj, mask
def ReadRasterfile(dataset):
"""
This function open a raster file, transform it into
a numpy array and get information from it
"""
for x in range(1, dataset.RasterCount + 1):
band = dataset.GetRasterBand(x)
# Projection
projection = dataset.GetProjection()
# Raster extent
upx, xres, xskew, upy, yskew, yres = dataset.GetGeoTransform()
coordinates = [upx, xres, xskew, upy, yskew, yres]
# Dimensions
sizeX = dataset.RasterXSize
sizeY = dataset.RasterYSize
# Data as a numpy array
array = band.ReadAsArray()
# Get nodata value from the GDAL band object
nodata = band.GetNoDataValue()
#Create a masked array for making calculations without nodata values
array = np.ma.masked_equal(array, nodata)
type(array)
return array, sizeX, sizeY, projection, band, coordinates
del array, sizeX, sizeY, projection, band, coordinates
# converts coordinates to index
def bbox2ix(bbox,gt):
xo = int(round((bbox[0] - gt[0])/gt[1]))
yo = int(round((gt[3] - bbox[3])/gt[1]))
xd = int(round((bbox[1] - bbox[0])/gt[1]))
yd = int(round((bbox[3] - bbox[2])/gt[1]))
return(xo,yo,xd,yd)
def rasclip(ras,shp):
ds = gdal.Open(ras)
gt = ds.GetGeoTransform()
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSource = driver.Open(shp, 0)
layer = dataSource.GetLayer()
for feature in layer:
xo,yo,xd,yd = bbox2ix(feature.GetGeometryRef().GetEnvelope(),gt)
arr = ds.ReadAsArray(xo,yo,xd,yd)
yield arr
layer.ResetReading()
ds = None
dataSource = None
return arr
def WriteGeoTIF(Tiffname, nb_xpixels, nb_ypixels, size_pixels, y_position, x_position, epsg, array):
from osgeo import gdal, osr
drv = gdal.GetDriverByName('GTiff')
ds = drv.Create(Tiffname, nb_xpixels, nb_ypixels, 1, gdal.GDT_Float32)
gt = [x_position, size_pixels, 0, y_position, 0,-size_pixels ]
ds.SetGeoTransform(gt)
srs = osr.SpatialReference()
srs.ImportFromEPSG(epsg)
ds.SetProjection(srs.ExportToWkt())
ds.GetRasterBand(1).WriteArray(array)
ds.GetRasterBand(1).SetNoDataValue(-9999.0)
return
def RastertoTXTfile(path,file, output_filename):
ds = gdal.Open(path+file)
translate_options = gdal.TranslateOptions(format='XYZ')
ds = gdal.Translate(destName = path+output_filename, srcDS = ds, options = translate_options)
return
def create_masks(path, filename, path_masks):
"""
This function create a mask from a tif file
What you need:
A path location of the Watersheds
A file name of the watersheds tif file
A path to write the resulted masks
"""
# Remove the previous folder if any
if os.path.isdir(path_masks) == True:
contenu=os.listdir(path_masks)
for x in contenu:
os.remove(path_masks+x)#on supprime tous les fichier dans le dossier
os.rmdir(path_masks)#puis on supprime le dossier
# Create topo folder
dataset = gdal.Open(path +filename + '.tif', gdal.GA_ReadOnly)
array,sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
array[array<0]=-9999
# Get origin coordinates
xyzlohi = [coordinates[0],coordinates[3],coordinates[3]-sizeY,coordinates[0]+sizeX]
# get id of watersheds
watershed_id = np.unique(array)
watershed_id = watershed_id[1:]
# Create masks
masks={}
os.mkdir(path_masks)
watersheds_filename = {}
count=1
for i in watershed_id:
array_copy = np.copy(array)
array_copy[array!=i] = -9999
eros.write(array_copy,sizeX,sizeY,coordinates[1],xyzlohi,path_masks+'SBV'+str(i)+'_mask.alt')
watersheds_filename['FN{0}'.format(count)] = 'SBV'+str(i)
count=count+1
del dataset
return watersheds_filename
def get_outlets_coordinates(path, filename, outlet_coordinates):
"""
This functions get the outlets coordinates for each watershed in the tif file "*_watershed.tif"
What you need:
A path location of the Watersheds
A file name of the watersheds tif file
A panda dataframe of the coordinates of all points of the river network ('*_coord.txt')
"""
# Origine of the grid in the appropriate coordinate system
dataset = gdal.Open(path +filename + '.tif', gdal.GA_ReadOnly)
array, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
outlet_coordinates['Y_origin'] = coordinates[3]
outlet_coordinates['X_origin'] = coordinates[0]
# Get outlet coordinates in Eros format
outlet_coordinates['Xgrid_position'] = outlet_coordinates['X_coordinates'] - outlet_coordinates['X_origin']
outlet_coordinates['Ygrid_position'] = outlet_coordinates['Y_origin'] - outlet_coordinates['Y_coordinates']
outlet_coordinates['Ygrid_position']=outlet_coordinates['Ygrid_position'].astype(int)
outlet_coordinates['Xgrid_position']=outlet_coordinates['Xgrid_position'].astype(int)
# The coordinates are sorted by descending order of the drainage area
outlet_coordinates.sort_values(by=['Contributing area'],ascending=False)
outlet_coordinates.reset_index(drop=True,inplace=True)
del dataset
return outlet_coordinates
def filename_by_Strahler_order(path, filename):
"""
This functions return filename in function of Strahler order
What you need:
A path location of the Strahler order grid and watersheds
A file name of the watersheds tif file and Strahler order (same one)
"""
# Import Strahler grid
dataset = gdal.Open(path +filename + '_ord.tif', gdal.GA_ReadOnly)
array_Strahler, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
# Import Watersheds grid
watersheds = gdal.Open(path +filename + '_watersheds.tif', gdal.GA_ReadOnly)
array_watersheds, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(watersheds)
# Where Strahler grid is 1 get id watersheds into list 1
first_order_array = array_watersheds[array_Strahler==1]
# get id of watersheds
first_order_watershed_id = np.unique(first_order_array)
# ohterwise get id watersheds into list 2
bigger_order_array = array_watersheds[np.logical_and(array_Strahler!=1,array_Strahler>0)]
bigger_order_watershed_id = np.unique(bigger_order_array)
# save filename into dictionnary
first_order_Watersheds = {}
high_order_Watersheds = {}
# Save first order watersheds
count = 1
for i in first_order_watershed_id:
first_order_Watersheds['FN{0}'.format(count)] = 'SBV' + str(i)
count = count + 1
# Save higher order watersheds
count = 1
for ii in bigger_order_watershed_id[1:]:
high_order_Watersheds['FN{0}'.format(count)] = 'SBV' + str(ii)
count = count+1
# Save last watersheds
last_watershed = 'SBV2'
return first_order_Watersheds, high_order_Watersheds, last_watershed
def define_inputs_and_outlets(path,filename,Input_outlet_distance,outlet_coordinates,plot_option):
"""
This function define the inputs coordinates and the outlet coordinates located upstream and downstream
What you need:
Input_outlet_distance: Distance in meter where to locate the inputs from the detected outlets
"""
# Open watershed tif file for ploting option
dataset = gdal.Open(path+filename + '_watersheds.tif', gdal.GA_ReadOnly)
array, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
river_network = gdal.Open(path+filename+ '_ord.tif', gdal.GA_ReadOnly)
river_array, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(river_network)
Y_origin = coordinates[3]
X_origin = coordinates[0]
# Open _coord txt file
import pandas as pd
header = ['X_coordinates','Y_coordinates','Distance to the downstream end of a terminal link','Elevation','Contributing area']
tab_coord=pd.read_csv(path + filename+ '_coord.txt',sep='\t',names = header,index_col=False,usecols=[1, 2, 3,4, 5],na_values='-9999')
# Add a line at the end of the txt file for the last outlet
tab_coord =tab_coord.append({'X_coordinates' : 9999 , 'Y_coordinates' : 9999,'Distance to the downstream end of a terminal link':tab_coord.loc[len(tab_coord)-1,'Distance to the downstream end of a terminal link']+100},ignore_index=True)
# chercher pour chaque Xgrid_position et Ygrid_positon si valeur après est inf alors prendre coordonnées K lignes après
outlet_watershed_dico = {}
input_watershed_dico = {}
input_area_dico = {}
count = 1
for i in outlet_coordinates['Distance to the downstream end of a terminal link']:
index_list = tab_coord.index[np.around(tab_coord['Distance to the downstream end of a terminal link'],3)== np.around(i,3)].tolist()
# Lists
input_coord_list =[]
input_area_list=[]
for ii in index_list:
# manage the last line of the text file
if len(tab_coord) - ii > Input_outlet_distance:
# Take coord k lines after if the +1 line is smaller
if np.logical_and(tab_coord.loc[ii+1,'Distance to the downstream end of a terminal link'] < tab_coord.loc[ii,'Distance to the downstream end of a terminal link'],np.abs(tab_coord['Distance to the downstream end of a terminal link'][ii+1]-tab_coord['Distance to the downstream end of a terminal link'][ii])<10) :
outlet_watershed_dico['Outlet{0}'.format(count)] = [tab_coord.loc[ii+Input_outlet_distance,'X_coordinates'],tab_coord.loc[ii+Input_outlet_distance,'Y_coordinates']]
# get the input coordinates
if np.logical_or(tab_coord.loc[ii+1,'Distance to the downstream end of a terminal link'] > tab_coord.loc[ii,'Distance to the downstream end of a terminal link'],np.abs(tab_coord['Distance to the downstream end of a terminal link'][ii+1]-tab_coord['Distance to the downstream end of a terminal link'][ii])>10):
input_coord_list.append([tab_coord.loc[ii-Input_outlet_distance,'X_coordinates'],tab_coord.loc[ii-Input_outlet_distance,'Y_coordinates']])
input_area_list.append(tab_coord.loc[ii-Input_outlet_distance,'Contributing area'])
input_watershed_dico['Input{0}'.format(count)] = input_coord_list
input_area_dico['Area{0}'.format(count)] = input_area_list
count= count + 1
# transform coordinates in grid format in each dictionnary
for j in outlet_watershed_dico:
outlet_watershed_dico[str(j)] = [outlet_watershed_dico[str(j)][0]-X_origin, Y_origin - outlet_watershed_dico[str(j)][1]]
outlet_watershed_dico[str(j)][0] = outlet_watershed_dico[str(j)][0].astype(int)
outlet_watershed_dico[str(j)][1] = outlet_watershed_dico[str(j)][1].astype(int)
for g in range(1,len(input_watershed_dico)+1):
for gg in range(0,len(input_watershed_dico['Input{0}'.format(g)])):
input_watershed_dico['Input{0}'.format(g)][gg] = [input_watershed_dico['Input{0}'.format(g)][gg][0]-X_origin, Y_origin - input_watershed_dico['Input{0}'.format(g)][gg][1]]
input_watershed_dico['Input{0}'.format(g)][gg][0] = input_watershed_dico['Input{0}'.format(g)][gg][0].astype(int)
input_watershed_dico['Input{0}'.format(g)][gg][1] = input_watershed_dico['Input{0}'.format(g)][gg][1].astype(int)
# plot coordinates
if plot_option == 1:
fig, ax = plt.subplots(1, figsize=(20, 20))
plt.imshow(array)
masked_river = np.ma.masked_where(river_array < 1, river_array)
plt.imshow(masked_river,cmap=plt.cm.gray)
# plot all input and outlet points
for y in outlet_watershed_dico:
plt.plot(outlet_watershed_dico[str(y)][0],outlet_watershed_dico[str(y)][1],'k.',markersize=10)
for p in range(1,len(input_watershed_dico)+1):
for pp in range(0,len(input_watershed_dico['Input{0}'.format(p)])):
plt.plot(input_watershed_dico['Input{0}'.format(p)][pp][0],input_watershed_dico['Input{0}'.format(p)][pp][1],'r.',markersize=10)
del dataset
return outlet_watershed_dico, input_watershed_dico, input_area_dico
def sort_watersheds(path_masks,Watersheds_filename, outlet_watershed_dico,last_watershed):
"""
This function classify the watersheds by contributing area order
"""
list_watersheds = []
list_position = []
for i in range(1,len(Watersheds_filename)+1):
grd_mask, sizeX, sizeY, cs, xyzlohi = eros.open_file(path_masks+Watersheds_filename['FN{0}'.format(i)]+'_mask.alt')
if np.size(grd_mask) - np.size(grd_mask[grd_mask==-9999]) < 50:
pass
else:
for ii in range(1,len(outlet_watershed_dico)+1):
if grd_mask[outlet_watershed_dico['Outlet{0}'.format(ii)][1],outlet_watershed_dico['Outlet{0}'.format(ii)][0]] == np.max(grd_mask):
list_watersheds.append(Watersheds_filename['FN{0}'.format(i)])
list_position.append(ii)
watershed_classified = [x for _,x in sorted(zip(list_position,list_watersheds ))]
watershed_classified_dico={}
watershed_classified_dico['FN1'] = last_watershed
for j in range(0,len(watershed_classified)):
watershed_classified_dico['FN{0}'.format(j+2)] = watershed_classified[j]
return watershed_classified_dico
def merge_results(path_topo,path_simulations,path_masks,path_tif_foleder,Watershed_name,all_watersheds_filename,extension_dico, results_folders,epsg,y_position,x_position):
"""
This function allows to merge all the eros file results into one
"""
array = {}
masks ={}
# Open array topo
array['Ar0'], sizeX, sizeY, cs, xyzlohi = eros.open_file(path+Watershed_name+'.alt')
for i in range(1, len(results_extension)+1):
count=1
for ii in results_folders:
# Open simulation result
array['Ar{0}'.format(count)], sizeX, sizeY, cs, xyzlohi = eros.open_file(path_simulations+ii+'/'+Watershed_name+'.10.'+results_extension['Ext{0}'.format(i)])
# Open simulation result Open corresponding mask
masks['masks{0}'.format(count)], sizeX, sizeY, cs, xyzlohi = eros.open_file(path_masks +all_watersheds_filename['FN{0}'.format(count)]+'_mask.alt')
#
array['Ar0'][masks['masks{0}'.format(count)]>=0] = array['Ar{0}'.format(count)][masks['masks{0}'.format(count)]>=0]
count = count + 1
eros.write(array['Ar0'],sizeX, sizeY, cs, xyzlohi,path_floodos_folder+Watershed_name+'.10.'+results_extension['Ext{0}'.format(i)])
gdalf.WriteGeoTIF(path_tif_folder+Watershed_name+'_'+results_extension['Ext{0}'.format(i)]+'.tif', sizeX, sizeY, cs, y_position, x_position, epsg, array['Ar1'])
| 46.525526
| 328
| 0.684825
|
from osgeo import gdal
import ogr, osr
import numpy as np
import subprocess
import os
import matplotlib.pyplot as plt
def read_tif_file(path_to_file):
ds = gdal.Open(path_to_file)
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
band= ds.GetRasterBand(1)
mask = band.GetNoDataValue()
array = band.ReadAsArray()
return array, gt, proj, mask
def ReadRasterfile(dataset):
for x in range(1, dataset.RasterCount + 1):
band = dataset.GetRasterBand(x)
projection = dataset.GetProjection()
upx, xres, xskew, upy, yskew, yres = dataset.GetGeoTransform()
coordinates = [upx, xres, xskew, upy, yskew, yres]
sizeX = dataset.RasterXSize
sizeY = dataset.RasterYSize
array = band.ReadAsArray()
nodata = band.GetNoDataValue()
array = np.ma.masked_equal(array, nodata)
type(array)
return array, sizeX, sizeY, projection, band, coordinates
del array, sizeX, sizeY, projection, band, coordinates
def bbox2ix(bbox,gt):
xo = int(round((bbox[0] - gt[0])/gt[1]))
yo = int(round((gt[3] - bbox[3])/gt[1]))
xd = int(round((bbox[1] - bbox[0])/gt[1]))
yd = int(round((bbox[3] - bbox[2])/gt[1]))
return(xo,yo,xd,yd)
def rasclip(ras,shp):
ds = gdal.Open(ras)
gt = ds.GetGeoTransform()
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSource = driver.Open(shp, 0)
layer = dataSource.GetLayer()
for feature in layer:
xo,yo,xd,yd = bbox2ix(feature.GetGeometryRef().GetEnvelope(),gt)
arr = ds.ReadAsArray(xo,yo,xd,yd)
yield arr
layer.ResetReading()
ds = None
dataSource = None
return arr
def WriteGeoTIF(Tiffname, nb_xpixels, nb_ypixels, size_pixels, y_position, x_position, epsg, array):
from osgeo import gdal, osr
drv = gdal.GetDriverByName('GTiff')
ds = drv.Create(Tiffname, nb_xpixels, nb_ypixels, 1, gdal.GDT_Float32)
gt = [x_position, size_pixels, 0, y_position, 0,-size_pixels ]
ds.SetGeoTransform(gt)
srs = osr.SpatialReference()
srs.ImportFromEPSG(epsg)
ds.SetProjection(srs.ExportToWkt())
ds.GetRasterBand(1).WriteArray(array)
ds.GetRasterBand(1).SetNoDataValue(-9999.0)
return
def RastertoTXTfile(path,file, output_filename):
ds = gdal.Open(path+file)
translate_options = gdal.TranslateOptions(format='XYZ')
ds = gdal.Translate(destName = path+output_filename, srcDS = ds, options = translate_options)
return
def create_masks(path, filename, path_masks):
if os.path.isdir(path_masks) == True:
contenu=os.listdir(path_masks)
for x in contenu:
os.remove(path_masks+x)
os.rmdir(path_masks)
dataset = gdal.Open(path +filename + '.tif', gdal.GA_ReadOnly)
array,sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
array[array<0]=-9999
xyzlohi = [coordinates[0],coordinates[3],coordinates[3]-sizeY,coordinates[0]+sizeX]
watershed_id = np.unique(array)
watershed_id = watershed_id[1:]
masks={}
os.mkdir(path_masks)
watersheds_filename = {}
count=1
for i in watershed_id:
array_copy = np.copy(array)
array_copy[array!=i] = -9999
eros.write(array_copy,sizeX,sizeY,coordinates[1],xyzlohi,path_masks+'SBV'+str(i)+'_mask.alt')
watersheds_filename['FN{0}'.format(count)] = 'SBV'+str(i)
count=count+1
del dataset
return watersheds_filename
def get_outlets_coordinates(path, filename, outlet_coordinates):
dataset = gdal.Open(path +filename + '.tif', gdal.GA_ReadOnly)
array, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
outlet_coordinates['Y_origin'] = coordinates[3]
outlet_coordinates['X_origin'] = coordinates[0]
outlet_coordinates['Xgrid_position'] = outlet_coordinates['X_coordinates'] - outlet_coordinates['X_origin']
outlet_coordinates['Ygrid_position'] = outlet_coordinates['Y_origin'] - outlet_coordinates['Y_coordinates']
outlet_coordinates['Ygrid_position']=outlet_coordinates['Ygrid_position'].astype(int)
outlet_coordinates['Xgrid_position']=outlet_coordinates['Xgrid_position'].astype(int)
outlet_coordinates.sort_values(by=['Contributing area'],ascending=False)
outlet_coordinates.reset_index(drop=True,inplace=True)
del dataset
return outlet_coordinates
def filename_by_Strahler_order(path, filename):
dataset = gdal.Open(path +filename + '_ord.tif', gdal.GA_ReadOnly)
array_Strahler, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
watersheds = gdal.Open(path +filename + '_watersheds.tif', gdal.GA_ReadOnly)
array_watersheds, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(watersheds)
first_order_array = array_watersheds[array_Strahler==1]
first_order_watershed_id = np.unique(first_order_array)
bigger_order_array = array_watersheds[np.logical_and(array_Strahler!=1,array_Strahler>0)]
bigger_order_watershed_id = np.unique(bigger_order_array)
first_order_Watersheds = {}
high_order_Watersheds = {}
count = 1
for i in first_order_watershed_id:
first_order_Watersheds['FN{0}'.format(count)] = 'SBV' + str(i)
count = count + 1
count = 1
for ii in bigger_order_watershed_id[1:]:
high_order_Watersheds['FN{0}'.format(count)] = 'SBV' + str(ii)
count = count+1
last_watershed = 'SBV2'
return first_order_Watersheds, high_order_Watersheds, last_watershed
def define_inputs_and_outlets(path,filename,Input_outlet_distance,outlet_coordinates,plot_option):
dataset = gdal.Open(path+filename + '_watersheds.tif', gdal.GA_ReadOnly)
array, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(dataset)
river_network = gdal.Open(path+filename+ '_ord.tif', gdal.GA_ReadOnly)
river_array, sizeX, sizeY, projection, band, coordinates = ReadRasterfile(river_network)
Y_origin = coordinates[3]
X_origin = coordinates[0]
import pandas as pd
header = ['X_coordinates','Y_coordinates','Distance to the downstream end of a terminal link','Elevation','Contributing area']
tab_coord=pd.read_csv(path + filename+ '_coord.txt',sep='\t',names = header,index_col=False,usecols=[1, 2, 3,4, 5],na_values='-9999')
tab_coord =tab_coord.append({'X_coordinates' : 9999 , 'Y_coordinates' : 9999,'Distance to the downstream end of a terminal link':tab_coord.loc[len(tab_coord)-1,'Distance to the downstream end of a terminal link']+100},ignore_index=True)
outlet_watershed_dico = {}
input_watershed_dico = {}
input_area_dico = {}
count = 1
for i in outlet_coordinates['Distance to the downstream end of a terminal link']:
index_list = tab_coord.index[np.around(tab_coord['Distance to the downstream end of a terminal link'],3)== np.around(i,3)].tolist()
input_coord_list =[]
input_area_list=[]
for ii in index_list:
if len(tab_coord) - ii > Input_outlet_distance:
if np.logical_and(tab_coord.loc[ii+1,'Distance to the downstream end of a terminal link'] < tab_coord.loc[ii,'Distance to the downstream end of a terminal link'],np.abs(tab_coord['Distance to the downstream end of a terminal link'][ii+1]-tab_coord['Distance to the downstream end of a terminal link'][ii])<10) :
outlet_watershed_dico['Outlet{0}'.format(count)] = [tab_coord.loc[ii+Input_outlet_distance,'X_coordinates'],tab_coord.loc[ii+Input_outlet_distance,'Y_coordinates']]
if np.logical_or(tab_coord.loc[ii+1,'Distance to the downstream end of a terminal link'] > tab_coord.loc[ii,'Distance to the downstream end of a terminal link'],np.abs(tab_coord['Distance to the downstream end of a terminal link'][ii+1]-tab_coord['Distance to the downstream end of a terminal link'][ii])>10):
input_coord_list.append([tab_coord.loc[ii-Input_outlet_distance,'X_coordinates'],tab_coord.loc[ii-Input_outlet_distance,'Y_coordinates']])
input_area_list.append(tab_coord.loc[ii-Input_outlet_distance,'Contributing area'])
input_watershed_dico['Input{0}'.format(count)] = input_coord_list
input_area_dico['Area{0}'.format(count)] = input_area_list
count= count + 1
for j in outlet_watershed_dico:
outlet_watershed_dico[str(j)] = [outlet_watershed_dico[str(j)][0]-X_origin, Y_origin - outlet_watershed_dico[str(j)][1]]
outlet_watershed_dico[str(j)][0] = outlet_watershed_dico[str(j)][0].astype(int)
outlet_watershed_dico[str(j)][1] = outlet_watershed_dico[str(j)][1].astype(int)
for g in range(1,len(input_watershed_dico)+1):
for gg in range(0,len(input_watershed_dico['Input{0}'.format(g)])):
input_watershed_dico['Input{0}'.format(g)][gg] = [input_watershed_dico['Input{0}'.format(g)][gg][0]-X_origin, Y_origin - input_watershed_dico['Input{0}'.format(g)][gg][1]]
input_watershed_dico['Input{0}'.format(g)][gg][0] = input_watershed_dico['Input{0}'.format(g)][gg][0].astype(int)
input_watershed_dico['Input{0}'.format(g)][gg][1] = input_watershed_dico['Input{0}'.format(g)][gg][1].astype(int)
if plot_option == 1:
fig, ax = plt.subplots(1, figsize=(20, 20))
plt.imshow(array)
masked_river = np.ma.masked_where(river_array < 1, river_array)
plt.imshow(masked_river,cmap=plt.cm.gray)
for y in outlet_watershed_dico:
plt.plot(outlet_watershed_dico[str(y)][0],outlet_watershed_dico[str(y)][1],'k.',markersize=10)
for p in range(1,len(input_watershed_dico)+1):
for pp in range(0,len(input_watershed_dico['Input{0}'.format(p)])):
plt.plot(input_watershed_dico['Input{0}'.format(p)][pp][0],input_watershed_dico['Input{0}'.format(p)][pp][1],'r.',markersize=10)
del dataset
return outlet_watershed_dico, input_watershed_dico, input_area_dico
def sort_watersheds(path_masks,Watersheds_filename, outlet_watershed_dico,last_watershed):
list_watersheds = []
list_position = []
for i in range(1,len(Watersheds_filename)+1):
grd_mask, sizeX, sizeY, cs, xyzlohi = eros.open_file(path_masks+Watersheds_filename['FN{0}'.format(i)]+'_mask.alt')
if np.size(grd_mask) - np.size(grd_mask[grd_mask==-9999]) < 50:
pass
else:
for ii in range(1,len(outlet_watershed_dico)+1):
if grd_mask[outlet_watershed_dico['Outlet{0}'.format(ii)][1],outlet_watershed_dico['Outlet{0}'.format(ii)][0]] == np.max(grd_mask):
list_watersheds.append(Watersheds_filename['FN{0}'.format(i)])
list_position.append(ii)
watershed_classified = [x for _,x in sorted(zip(list_position,list_watersheds ))]
watershed_classified_dico={}
watershed_classified_dico['FN1'] = last_watershed
for j in range(0,len(watershed_classified)):
watershed_classified_dico['FN{0}'.format(j+2)] = watershed_classified[j]
return watershed_classified_dico
def merge_results(path_topo,path_simulations,path_masks,path_tif_foleder,Watershed_name,all_watersheds_filename,extension_dico, results_folders,epsg,y_position,x_position):
array = {}
masks ={}
array['Ar0'], sizeX, sizeY, cs, xyzlohi = eros.open_file(path+Watershed_name+'.alt')
for i in range(1, len(results_extension)+1):
count=1
for ii in results_folders:
array['Ar{0}'.format(count)], sizeX, sizeY, cs, xyzlohi = eros.open_file(path_simulations+ii+'/'+Watershed_name+'.10.'+results_extension['Ext{0}'.format(i)])
masks['masks{0}'.format(count)], sizeX, sizeY, cs, xyzlohi = eros.open_file(path_masks +all_watersheds_filename['FN{0}'.format(count)]+'_mask.alt')
array['Ar0'][masks['masks{0}'.format(count)]>=0] = array['Ar{0}'.format(count)][masks['masks{0}'.format(count)]>=0]
count = count + 1
eros.write(array['Ar0'],sizeX, sizeY, cs, xyzlohi,path_floodos_folder+Watershed_name+'.10.'+results_extension['Ext{0}'.format(i)])
gdalf.WriteGeoTIF(path_tif_folder+Watershed_name+'_'+results_extension['Ext{0}'.format(i)]+'.tif', sizeX, sizeY, cs, y_position, x_position, epsg, array['Ar1'])
| true
| true
|
1c444fedb8ca59daf57a90006b988852221217fd
| 14,700
|
py
|
Python
|
third_party_package/RDKit_2015_03_1/rdkit/ML/Descriptors/CompoundDescriptors.py
|
Ivy286/cluster_basedfps
|
7fc216537f570436f008ea567c137d03ba2b6d81
|
[
"WTFPL"
] | 9
|
2019-04-23T01:46:12.000Z
|
2021-08-16T07:07:12.000Z
|
third_party_package/RDKit_2015_03_1/rdkit/ML/Descriptors/CompoundDescriptors.py
|
Ivy286/cluster_basedfps
|
7fc216537f570436f008ea567c137d03ba2b6d81
|
[
"WTFPL"
] | null | null | null |
third_party_package/RDKit_2015_03_1/rdkit/ML/Descriptors/CompoundDescriptors.py
|
Ivy286/cluster_basedfps
|
7fc216537f570436f008ea567c137d03ba2b6d81
|
[
"WTFPL"
] | 5
|
2016-09-21T03:47:48.000Z
|
2019-07-30T22:17:35.000Z
|
#
# Copyright (C) 2001,2002 greg Landrum and Rational Discovery LLC
#
""" descriptor calculator for compounds defined by a composition alone
(only the composition is required)
"""
from __future__ import print_function
from rdkit import RDConfig
from rdkit.utils import chemutils
import os
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.ML.Descriptors import Parser,Descriptors
from rdkit.six.moves import xrange
# the list of possible ways to count valence electrons that we know
countOptions = [('NVAL','total number of valence electrons'),
('NVAL_NO_FULL_F','number of valence electrons neglecting filled f shells'),
('NVAL_NO_FULL_D','number of valence electrons neglecting filled d shells'),
('NVAL_NO_FULL','number of valence electrons neglecting filled f and d shells')]
def GetAllDescriptorNames(db,tbl1,tbl2,user='sysdba',password='masterkey'):
""" gets possible descriptor names from a database
**Arguments**
- db: the name of the database to use
- tbl1: the name of the table to be used for reading descriptor values
- tbl2: the name of the table to be used for reading notes about the
descriptors (*descriptions of the descriptors if you like*)
- user: the user name for DB access
- password: the password for DB access
**Returns**
a 2-tuple containing:
1) a list of column names
2) a list of column descriptors
**Notes**
- this uses _Dbase.DbInfo_ and Dfunctionality for querying the database
- it is assumed that tbl2 includes 'property' and 'notes' columns
"""
conn = DbConnect(db,user=user,password=password)
colNames = conn.GetColumnNames(table=tbl1)
colDesc = map(lambda x:(x[0].upper(),x[1]),
conn.GetColumns('property,notes',table=tbl2))
for name,desc in countOptions:
colNames.append(name)
colDesc.append((name,desc))
return colNames,colDesc
class CompoundDescriptorCalculator(Descriptors.DescriptorCalculator):
""" used for calculating descriptors
This is the central point for descriptor calculation
**Notes**
- There are two kinds of descriptors this cares about:
1) *Simple Descriptors* can be calculated solely using atomic descriptor
values and the composition of the compound. The full list of possible
simple descriptors is determined by the types of *Calculator Methods*
(see below) and the contents of an atomic database.
Simple Descriptors can be marked as *nonZeroDescriptors*. These are used
to winnow out atom types where particular atomic descriptors are zero
(usually indicating that the value is unknown)
Simple Descriptors are maintained locally in the _simpleList_
2) *Compound Descriptors* may rely upon more complicated computation schemes
and descriptors for the compound as a whole (e.g. structural variables, etc.).
The full list of compound descriptors is limitless. They are calculated using
the _ML.Descriptors.Parser_ module.
Compound Descriptors are maintained locally in the _compoundList_
- This class has a some special methods which are labelled as *Calculator Method*
These are used internally to take atomic descriptors and reduce them to a single
simple descriptor value for a composition. They are primarily intended for internal use.
- a *composition vector* is a list of 2-tuples: '[(atom1name,atom1Num),...]'
where atom1Num is the contribution of the atom to the stoichiometry of the
compound. No assumption is made about the stoichiometries (i.e. they don't
have to be either integral or all sum to one).
"""
#------------
# methods used to calculate descriptors
#------------
def SUM(self,desc,compos):
""" *Calculator Method*
sums the descriptor values across the composition
**Arguments**
- desc: the name of the descriptor
- compos: the composition vector
**Returns**
a float
"""
res = 0.0
for atom,num in compos:
res = res + self.atomDict[atom][desc]*num
return res
def MEAN(self,desc,compos):
""" *Calculator Method*
averages the descriptor values across the composition
**Arguments**
- desc: the name of the descriptor
- compos: the composition vector
**Returns**
a float
"""
res = 0.0
nSoFar = 0.0
for atom,num in compos:
res = res + self.atomDict[atom][desc]*num
nSoFar = nSoFar + num
return res/nSoFar
def DEV(self,desc,compos):
""" *Calculator Method*
average deviation of the descriptor values across the composition
**Arguments**
- desc: the name of the descriptor
- compos: the composition vector
**Returns**
a float
"""
mean = self.MEAN(desc,compos)
res = 0.0
nSoFar = 0.0
for atom,num in compos:
res = res + abs(self.atomDict[atom][desc]-mean)*num
nSoFar = nSoFar + num
return res/nSoFar
def MIN(self,desc,compos):
""" *Calculator Method*
minimum of the descriptor values across the composition
**Arguments**
- desc: the name of the descriptor
- compos: the composition vector
**Returns**
a float
"""
return min(map(lambda x,y=desc,z=self:z.atomDict[x[0]][y],compos))
def MAX(self,desc,compos):
""" *Calculator Method*
maximum of the descriptor values across the composition
**Arguments**
- desc: the name of the descriptor
- compos: the composition vector
**Returns**
a float
"""
return max(map(lambda x,y=desc,z=self:z.atomDict[x[0]][y],compos))
#------------
# Other methods
#------------
def ProcessSimpleList(self):
""" Handles the list of simple descriptors
This constructs the list of _nonZeroDescriptors_ and _requiredDescriptors_.
There's some other magic going on that I can't decipher at the moment.
"""
global countOptions
self.nonZeroDescriptors = []
lCopy = self.simpleList[:]
tList = map(lambda x:x[0],countOptions)
for i in xrange(len(lCopy)):
entry = lCopy[i]
if 'NONZERO' in entry[1]:
if entry[0] not in tList:
self.nonZeroDescriptors.append('%s != 0'%entry[0])
if len(entry[1]) == 1:
self.simpleList.remove(entry)
else:
self.simpleList[self.simpleList.index(entry)][1].remove('NONZERO')
self.requiredDescriptors = map(lambda x:x[0],self.simpleList)
for entry in tList:
if entry in self.requiredDescriptors:
self.requiredDescriptors.remove(entry)
def ProcessCompoundList(self):
""" Adds entries from the _compoundList_ to the list of _requiredDescriptors_
Each compound descriptor is surveyed. Any atomic descriptors it requires
are added to the list of _requiredDescriptors_ to be pulled from the database.
"""
# add in the atomic descriptors we will need
for entry in self.compoundList:
for atomicDesc in entry[1]:
if atomicDesc != '' and atomicDesc not in self.requiredDescriptors:
self.requiredDescriptors.append(atomicDesc)
def BuildAtomDict(self):
""" builds the local atomic dict
We don't want to keep around all descriptor values for all atoms, so this
method takes care of only pulling out the descriptors in which we are
interested.
**Notes**
- this uses _chemutils.GetAtomicData_ to actually pull the data
"""
self.ProcessSimpleList()
self.ProcessCompoundList()
self.atomDict = {}
whereString = ' and '.join(self.nonZeroDescriptors)
if whereString != '':
whereString = 'where ' + whereString
chemutils.GetAtomicData(self.atomDict,self.requiredDescriptors,self.dbName,self.dbTable,
whereString,self.dbUser,self.dbPassword,
includeElCounts=1)
def CalcSimpleDescriptorsForComposition(self,compos='',composList=None):
""" calculates all simple descriptors for a given composition
**Arguments**
- compos: a string representation of the composition
- composList: a *composVect*
The client must provide either _compos_ or _composList_. If both are
provided, _composList_ takes priority.
**Returns**
the list of descriptor values
**Notes**
- when _compos_ is provided, this uses _chemutils.SplitComposition_
to split the composition into its individual pieces
- if problems are encountered because of either an unknown descriptor or
atom type, a _KeyError_ will be raised.
"""
if composList is None:
composList = chemutils.SplitComposition(compos)
try:
res = []
for i in xrange(len(self.simpleList)):
descName,targets = self.simpleList[i]
for target in targets:
try:
method = getattr(self,target)
except AttributeError:
print('Method %s does not exist'%(target))
else:
res.append(method(descName,composList))
except KeyError as msg:
print('composition %s caused problems'%composList)
raise KeyError(msg)
return res
def CalcCompoundDescriptorsForComposition(self,compos='',composList=None,
propDict={}):
""" calculates all simple descriptors for a given composition
**Arguments**
- compos: a string representation of the composition
- composList: a *composVect*
- propDict: a dictionary containing the properties of the composition
as a whole (e.g. structural variables, etc.)
The client must provide either _compos_ or _composList_. If both are
provided, _composList_ takes priority.
**Returns**
the list of descriptor values
**Notes**
- when _compos_ is provided, this uses _chemutils.SplitComposition_
to split the composition into its individual pieces
"""
if composList is None:
composList = chemutils.SplitComposition(compos)
res = []
for i in xrange(len(self.compoundList)):
val = Parser.CalcSingleCompoundDescriptor(composList,self.compoundList[i][1:],
self.atomDict,propDict)
res.append(val)
return res
def CalcDescriptorsForComposition(self,composVect,propDict):
""" calculates all descriptors for a given composition
**Arguments**
- compos: a string representation of the composition
- propDict: a dictionary containing the properties of the composition
as a whole (e.g. structural variables, etc.). These are used to
generate Compound Descriptors
**Returns**
the list of all descriptor values
**Notes**
- this uses _chemutils.SplitComposition_
to split the composition into its individual pieces
"""
composList = chemutils.SplitComposition(composVect[0])
try:
r1 = self.CalcSimpleDescriptorsForComposition(composList=composList)
except KeyError as msg:
res = []
else:
r2 = self.CalcCompoundDescriptorsForComposition(composList=composList,
propDict=propDict)
res = r1+r2
return tuple(res)
CalcDescriptors = CalcDescriptorsForComposition
def GetDescriptorNames(self):
""" returns a list of the names of the descriptors this calculator generates
"""
if self.descriptorNames is not None:
return self.descriptorNames
else:
res = []
for i in xrange(len(self.simpleList)):
descName,targets = self.simpleList[i]
for target in targets:
try:
method = getattr(self,target)
except AttributeError:
print('Method %s does not exist'%(target))
else:
res.append('%s_%s'%(target,descName))
for entry in self.compoundList:
res.append(entry[0])
self.descriptorNames = res[:]
return tuple(res)
def __init__(self,simpleList,compoundList=None,
dbName=None,
dbTable='atomic_data',dbUser='sysdba',dbPassword='masterkey'):
""" Constructor
**Arguments**
- simpleList: list of simple descriptors to be calculated
(see below for format)
- compoundList: list of compound descriptors to be calculated
(see below for format)
- dbName: name of the atomic database to be used
- dbTable: name the table in _dbName_ which has atomic data
- dbUser: user name for DB access
- dbPassword: password for DB access
**Note**
- format of simpleList:
a list of 2-tuples containing:
1) name of the atomic descriptor
2) a list of operations on that descriptor (e.g. NonZero, Max, etc.)
These must correspond to the *Calculator Method* names above.
- format of compoundList:
a list of 2-tuples containing:
1) name of the descriptor to be calculated
2) list of selected atomic descriptor names (define $1, $2, etc.)
3) list of selected compound descriptor names (define $a, $b, etc.)
4) text formula defining the calculation (see _Parser_)
"""
if dbName is None:
dbName = RDConfig.RDDataDatabase
Descriptors.DescriptorCalculator.__init__(self)
#self.simpleList = map(lambda x:(string.upper(x[0]),map(string.upper,x[1])),
# simpleList)
self.simpleList = [(x[0].upper(), [y.upper() for y in x[1]])
for x in simpleList]
self.descriptorNames = None
self.compoundList = compoundList
if self.compoundList is None:
self.compoundList = []
self.dbName = dbName
self.dbTable = dbTable
self.dbUser = dbUser
self.dbPassword = dbPassword
if __name__ == '__main__':
d = [('DED',['NonZero','Mean','Dev']),
('M_B_electroneg',['NonZero']),
('Cov_rad',['Max','Min'])]
o = DescriptorCalculator(d)
o.BuildAtomDict()
print('len:',len(o.atomDict.keys()))
for key in o.atomDict.keys()[-4:-1]:
print(key,o.atomDict[key])
print('descriptors:',o.GetDescriptorNames())
composList = ['Nb','Nb3','NbPt','Nb2Pt']
for compos in composList:
descs = o.CalcSimpleDescriptorsForComposition(compos)
print(compos,descs)
| 30.625
| 96
| 0.644014
|
from __future__ import print_function
from rdkit import RDConfig
from rdkit.utils import chemutils
import os
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.ML.Descriptors import Parser,Descriptors
from rdkit.six.moves import xrange
countOptions = [('NVAL','total number of valence electrons'),
('NVAL_NO_FULL_F','number of valence electrons neglecting filled f shells'),
('NVAL_NO_FULL_D','number of valence electrons neglecting filled d shells'),
('NVAL_NO_FULL','number of valence electrons neglecting filled f and d shells')]
def GetAllDescriptorNames(db,tbl1,tbl2,user='sysdba',password='masterkey'):
conn = DbConnect(db,user=user,password=password)
colNames = conn.GetColumnNames(table=tbl1)
colDesc = map(lambda x:(x[0].upper(),x[1]),
conn.GetColumns('property,notes',table=tbl2))
for name,desc in countOptions:
colNames.append(name)
colDesc.append((name,desc))
return colNames,colDesc
class CompoundDescriptorCalculator(Descriptors.DescriptorCalculator):
def SUM(self,desc,compos):
res = 0.0
for atom,num in compos:
res = res + self.atomDict[atom][desc]*num
return res
def MEAN(self,desc,compos):
res = 0.0
nSoFar = 0.0
for atom,num in compos:
res = res + self.atomDict[atom][desc]*num
nSoFar = nSoFar + num
return res/nSoFar
def DEV(self,desc,compos):
mean = self.MEAN(desc,compos)
res = 0.0
nSoFar = 0.0
for atom,num in compos:
res = res + abs(self.atomDict[atom][desc]-mean)*num
nSoFar = nSoFar + num
return res/nSoFar
def MIN(self,desc,compos):
return min(map(lambda x,y=desc,z=self:z.atomDict[x[0]][y],compos))
def MAX(self,desc,compos):
return max(map(lambda x,y=desc,z=self:z.atomDict[x[0]][y],compos))
def ProcessSimpleList(self):
global countOptions
self.nonZeroDescriptors = []
lCopy = self.simpleList[:]
tList = map(lambda x:x[0],countOptions)
for i in xrange(len(lCopy)):
entry = lCopy[i]
if 'NONZERO' in entry[1]:
if entry[0] not in tList:
self.nonZeroDescriptors.append('%s != 0'%entry[0])
if len(entry[1]) == 1:
self.simpleList.remove(entry)
else:
self.simpleList[self.simpleList.index(entry)][1].remove('NONZERO')
self.requiredDescriptors = map(lambda x:x[0],self.simpleList)
for entry in tList:
if entry in self.requiredDescriptors:
self.requiredDescriptors.remove(entry)
def ProcessCompoundList(self):
for entry in self.compoundList:
for atomicDesc in entry[1]:
if atomicDesc != '' and atomicDesc not in self.requiredDescriptors:
self.requiredDescriptors.append(atomicDesc)
def BuildAtomDict(self):
self.ProcessSimpleList()
self.ProcessCompoundList()
self.atomDict = {}
whereString = ' and '.join(self.nonZeroDescriptors)
if whereString != '':
whereString = 'where ' + whereString
chemutils.GetAtomicData(self.atomDict,self.requiredDescriptors,self.dbName,self.dbTable,
whereString,self.dbUser,self.dbPassword,
includeElCounts=1)
def CalcSimpleDescriptorsForComposition(self,compos='',composList=None):
if composList is None:
composList = chemutils.SplitComposition(compos)
try:
res = []
for i in xrange(len(self.simpleList)):
descName,targets = self.simpleList[i]
for target in targets:
try:
method = getattr(self,target)
except AttributeError:
print('Method %s does not exist'%(target))
else:
res.append(method(descName,composList))
except KeyError as msg:
print('composition %s caused problems'%composList)
raise KeyError(msg)
return res
def CalcCompoundDescriptorsForComposition(self,compos='',composList=None,
propDict={}):
if composList is None:
composList = chemutils.SplitComposition(compos)
res = []
for i in xrange(len(self.compoundList)):
val = Parser.CalcSingleCompoundDescriptor(composList,self.compoundList[i][1:],
self.atomDict,propDict)
res.append(val)
return res
def CalcDescriptorsForComposition(self,composVect,propDict):
composList = chemutils.SplitComposition(composVect[0])
try:
r1 = self.CalcSimpleDescriptorsForComposition(composList=composList)
except KeyError as msg:
res = []
else:
r2 = self.CalcCompoundDescriptorsForComposition(composList=composList,
propDict=propDict)
res = r1+r2
return tuple(res)
CalcDescriptors = CalcDescriptorsForComposition
def GetDescriptorNames(self):
if self.descriptorNames is not None:
return self.descriptorNames
else:
res = []
for i in xrange(len(self.simpleList)):
descName,targets = self.simpleList[i]
for target in targets:
try:
method = getattr(self,target)
except AttributeError:
print('Method %s does not exist'%(target))
else:
res.append('%s_%s'%(target,descName))
for entry in self.compoundList:
res.append(entry[0])
self.descriptorNames = res[:]
return tuple(res)
def __init__(self,simpleList,compoundList=None,
dbName=None,
dbTable='atomic_data',dbUser='sysdba',dbPassword='masterkey'):
if dbName is None:
dbName = RDConfig.RDDataDatabase
Descriptors.DescriptorCalculator.__init__(self)
self.simpleList = [(x[0].upper(), [y.upper() for y in x[1]])
for x in simpleList]
self.descriptorNames = None
self.compoundList = compoundList
if self.compoundList is None:
self.compoundList = []
self.dbName = dbName
self.dbTable = dbTable
self.dbUser = dbUser
self.dbPassword = dbPassword
if __name__ == '__main__':
d = [('DED',['NonZero','Mean','Dev']),
('M_B_electroneg',['NonZero']),
('Cov_rad',['Max','Min'])]
o = DescriptorCalculator(d)
o.BuildAtomDict()
print('len:',len(o.atomDict.keys()))
for key in o.atomDict.keys()[-4:-1]:
print(key,o.atomDict[key])
print('descriptors:',o.GetDescriptorNames())
composList = ['Nb','Nb3','NbPt','Nb2Pt']
for compos in composList:
descs = o.CalcSimpleDescriptorsForComposition(compos)
print(compos,descs)
| true
| true
|
1c44505dd302e64a42b635bedc36746e9c6b4531
| 3,825
|
py
|
Python
|
dali/test/python/test_RN50_external_source_parallel_data.py
|
awolant/DALI
|
ace3e0bee44b7b10cdf7255ec02e143646c68ce1
|
[
"ECL-2.0",
"Apache-2.0"
] | 1
|
2019-05-31T14:00:58.000Z
|
2019-05-31T14:00:58.000Z
|
dali/test/python/test_RN50_external_source_parallel_data.py
|
Shruti-Raj-Vansh-Singh/DALI
|
e7df0b255c59a118843bfe3ecaf317d7ee1ed6bb
|
[
"ECL-2.0",
"Apache-2.0"
] | 2
|
2021-06-11T17:05:37.000Z
|
2021-06-23T03:45:04.000Z
|
dali/test/python/test_RN50_external_source_parallel_data.py
|
Shruti-Raj-Vansh-Singh/DALI
|
e7df0b255c59a118843bfe3ecaf317d7ee1ed6bb
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python3
# Copyright (c) 2020, NVIDIA CORPORATION. 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.
from test_utils import AverageMeter
import os
import argparse
import time
import cv2
import numpy as np
from nvidia.dali.plugin.base_iterator import LastBatchPolicy
from test_RN50_external_source_parallel_utils import (
parse_test_arguments, external_source_parallel_pipeline, external_source_pipeline,
file_reader_pipeline, get_pipe_factories)
# This test requires significant amount of shared memory to be able to pass
# the batches between worker processes and the main process. If running in docker
# make sure that -shm-size is big enough.
def iteration_test(args):
test_pipe_factories = get_pipe_factories(
args.test_pipes, external_source_parallel_pipeline, file_reader_pipeline,
external_source_pipeline)
for pipe_factory in test_pipe_factories:
# TODO(klecki): We don't handle sharding in this test yet, would need to do it manually
# for External Source pipelines
pipes = [pipe_factory(
batch_size=args.batch_size,
num_threads=args.workers,
device_id=gpu,
data_path=args.data_path,
prefetch=args.prefetch,
reader_queue_depth=args.reader_queue_depth,
py_start_method=args.worker_init,
py_num_workers=args.py_workers
) for gpu in range(args.gpus)]
# First start the Python workers, so we fork without CUDA context.
for pipe in pipes:
pipe.start_py_workers()
for pipe in pipes:
pipe.build()
samples_no = pipes[0].epoch_size("Reader")
if args.benchmark_iters is None:
expected_iters = samples_no // args.batch_size + (samples_no % args.batch_size != 0)
else:
expected_iters = args.benchmark_iters
print("RUN {}".format(pipe_factory.__name__))
for i in range(args.epochs):
if i == 0:
print("Warm up")
else:
print("Test run " + str(i))
data_time = AverageMeter()
end = time.time()
frequency = 50
for j in range(expected_iters):
stop_iter = False
for pipe in pipes:
try:
pipe.run()
except StopIteration:
assert j == expected_iters - 1
stop_iter = True
if stop_iter:
break
if j % frequency == 0 and j != 0:
data_time.update((time.time() - end) / frequency)
end = time.time()
print("{} {}/ {}, avg time: {} [s], worst time: {} [s], speed: {} [img/s]".format(
pipe_factory.__name__,
j,
expected_iters,
data_time.avg,
data_time.max_val,
args.batch_size * args.gpus / data_time.avg,
))
for pipe in pipes:
pipe.reset()
print("OK {}".format(pipe_factory.__name__))
if __name__ == "__main__":
args = parse_test_arguments(False)
iteration_test(args)
| 36.778846
| 102
| 0.603399
|
from test_utils import AverageMeter
import os
import argparse
import time
import cv2
import numpy as np
from nvidia.dali.plugin.base_iterator import LastBatchPolicy
from test_RN50_external_source_parallel_utils import (
parse_test_arguments, external_source_parallel_pipeline, external_source_pipeline,
file_reader_pipeline, get_pipe_factories)
def iteration_test(args):
test_pipe_factories = get_pipe_factories(
args.test_pipes, external_source_parallel_pipeline, file_reader_pipeline,
external_source_pipeline)
for pipe_factory in test_pipe_factories:
# for External Source pipelines
pipes = [pipe_factory(
batch_size=args.batch_size,
num_threads=args.workers,
device_id=gpu,
data_path=args.data_path,
prefetch=args.prefetch,
reader_queue_depth=args.reader_queue_depth,
py_start_method=args.worker_init,
py_num_workers=args.py_workers
) for gpu in range(args.gpus)]
# First start the Python workers, so we fork without CUDA context.
for pipe in pipes:
pipe.start_py_workers()
for pipe in pipes:
pipe.build()
samples_no = pipes[0].epoch_size("Reader")
if args.benchmark_iters is None:
expected_iters = samples_no // args.batch_size + (samples_no % args.batch_size != 0)
else:
expected_iters = args.benchmark_iters
print("RUN {}".format(pipe_factory.__name__))
for i in range(args.epochs):
if i == 0:
print("Warm up")
else:
print("Test run " + str(i))
data_time = AverageMeter()
end = time.time()
frequency = 50
for j in range(expected_iters):
stop_iter = False
for pipe in pipes:
try:
pipe.run()
except StopIteration:
assert j == expected_iters - 1
stop_iter = True
if stop_iter:
break
if j % frequency == 0 and j != 0:
data_time.update((time.time() - end) / frequency)
end = time.time()
print("{} {}/ {}, avg time: {} [s], worst time: {} [s], speed: {} [img/s]".format(
pipe_factory.__name__,
j,
expected_iters,
data_time.avg,
data_time.max_val,
args.batch_size * args.gpus / data_time.avg,
))
for pipe in pipes:
pipe.reset()
print("OK {}".format(pipe_factory.__name__))
if __name__ == "__main__":
args = parse_test_arguments(False)
iteration_test(args)
| true
| true
|
1c44516c6c2cb62d2ea46dd638986291034ed6e1
| 367
|
py
|
Python
|
payeezy_python/example/Dependancy/requests-master/requests/packages/urllib3/util/response.py
|
Dylfin/payeezy_direct_API
|
d0ec010dd265421ddc002e665221312178e2b0fe
|
[
"MIT"
] | 76
|
2015-04-28T18:50:16.000Z
|
2022-03-21T18:52:44.000Z
|
payeezy_python/example/Dependancy/requests-master/requests/packages/urllib3/util/response.py
|
Dylfin/payeezy_direct_API
|
d0ec010dd265421ddc002e665221312178e2b0fe
|
[
"MIT"
] | 17
|
2015-07-15T17:46:41.000Z
|
2022-03-01T19:14:48.000Z
|
payeezy_python/example/Dependancy/requests-master/requests/packages/urllib3/util/response.py
|
Dylfin/payeezy_direct_API
|
d0ec010dd265421ddc002e665221312178e2b0fe
|
[
"MIT"
] | 149
|
2015-04-13T04:51:09.000Z
|
2021-07-06T14:16:20.000Z
|
def is_fp_closed(obj):
"""
Checks whether a given file-like object is closed.
:param obj:
The file-like object to check.
"""
if hasattr(obj, 'fp'):
# Object is a container for another file-like object that gets released
# on exhaustion (e.g. HTTPResponse)
return obj.fp is None
return obj.closed
| 26.214286
| 80
| 0.60218
|
def is_fp_closed(obj):
if hasattr(obj, 'fp'):
return obj.fp is None
return obj.closed
| true
| true
|
1c4452faf589fe4cf63d1a0d78c548ac6bdf060e
| 11,373
|
py
|
Python
|
train_plain_bert_dot4_con.py
|
playing-code/fairseq2
|
ac97b18c0aecca9eb36146492a1e95e521cb345a
|
[
"MIT"
] | null | null | null |
train_plain_bert_dot4_con.py
|
playing-code/fairseq2
|
ac97b18c0aecca9eb36146492a1e95e521cb345a
|
[
"MIT"
] | null | null | null |
train_plain_bert_dot4_con.py
|
playing-code/fairseq2
|
ac97b18c0aecca9eb36146492a1e95e521cb345a
|
[
"MIT"
] | null | null | null |
import json
import pickle
import numpy as np
import random
# from fairseq.data import Dictionary
import sys
import torch
import argparse
import os
from model_plain_bert_dot4 import Plain_bert
from fairseq.models.roberta import RobertaModel
from utils_sample import NewsIterator
from utils_sample import cal_metric
import utils_sample as utils
# import dgl
# import dgl.function as fn
#from gpu_mem_track import MemTracker
#import inspect
#from multiprocessing import Pool
import torch.nn as nn
import math
from fairseq.data import (
data_utils,
Dictionary,
IdDataset,
MaskTokensDataset,
NestedDictionaryDataset,
NumelDataset,
NumSamplesDataset,
PadDataset,
PrependTokenDataset,
SortDataset,
TokenBlockDataset,
)
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
import apex
random.seed(1)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
cudaid=0
metrics=['group_auc','mean_mrr','ndcg@5;10']
lr=1e-4
T_warm=5000
all_iteration=33040
# def init_process(rank,local_rank,args,minutes=720):
# """ Initialize the distributed environment. """
# # os.environ['MASTER_ADDR'] = '127.0.0.1'
# # os.environ['MASTER_PORT'] = '1234'
# # torch.distributed.init_process_group(backend, rank=rank, world_size=size)
# dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
# master_ip='localhost', master_port='12345')
# dist.init_process_group(backend='nccl',
# init_method=dist_init_method,
# # If you have a larger dataset, you will need to increase it.
# timeout=timedelta(minutes=minutes),
# world_size=args.size,
# rank=rank)
# num_gpus = torch.cuda.device_count()
# torch.cuda.set_device(local_rank)
# assert torch.distributed.is_initialized()
def parse_args():
parser = argparse.ArgumentParser("Transformer-XH")
parser.add_argument("--data_dir",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--save_dir",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--data_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--test_data_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--feature_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--test_feature_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--size",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--gpu_size",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--gpu_size_test",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--batch_size",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--log_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--field",
type=str,
help="local_rank for distributed training on gpus")
return parser.parse_args()
def adjust_learning_rate(optimizer,iteration,lr=lr, T_warm=T_warm, all_iteration=all_iteration ):#得看一些一共有多少个iteration再确定
if iteration<=T_warm:
lr=lr*float(iteration)/T_warm
elif iteration<all_iteration:
lr = lr * (1 - (iteration - T_warm) / (all_iteration - T_warm))
else:
lr=0
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def group_labels_func(labels, preds, group_keys):
"""Devide labels and preds into several group according to values in group keys.
Args:
labels (list): ground truth label list.
preds (list): prediction score list.
group_keys (list): group key list.
Returns:
all_labels: labels after group.
all_preds: preds after group.
"""
all_keys = list(set(group_keys))
group_labels = {k: [] for k in all_keys}
group_preds = {k: [] for k in all_keys}
for l, p, k in zip(labels, preds, group_keys):
group_labels[k].append(l)
group_preds[k].append(p)
all_labels = []
all_preds = []
for k in all_keys:
all_labels.append(group_labels[k])
all_preds.append(group_preds[k])
return all_labels, all_preds
def test(model,args):
preds = []
labels = []
imp_indexes = []
metrics=['group_auc']
test_file=os.path.join(args.data_dir, args.test_data_file)
preds = []
labels = []
imp_indexes = []
feature_file=os.path.join(args.data_dir,args.feature_file)
iterator=NewsIterator(batch_size=args.gpu_size_test, npratio=-1,feature_file=feature_file,field=args.field)
print('test...')
with torch.no_grad():
data_batch=iterator.load_data_from_file(test_file)
batch_t=0
for imp_index , user_index, his_id, candidate_id , label in data_batch:
batch_t+=len(candidate_id)
his_id=his_id.cuda(cudaid)
candidate_id= candidate_id.cuda(cudaid)
logit=model(his_id,candidate_id,None,mode='validation')
# print('???',label_t,label)
# assert 1==0
logit=list(np.reshape(np.array(logit.cpu()), -1))
label=list(np.reshape(np.array(label), -1))
imp_index=list(np.reshape(np.array(imp_index), -1))
assert len(logit)==len(label)
assert len(logit)==len(imp_index)
labels.extend(label)
preds.extend(logit)
imp_indexes.extend(imp_index)
print('all data: ',len(labels))
group_labels, group_preds = group_labels_func(labels, preds, imp_indexes)
res = cal_metric(group_labels, group_preds, metrics)
return res['group_auc']
def train(model,optimizer, args):
print('params: '," T_warm: ",T_warm," all_iteration: ",all_iteration," lr: ",lr)
cuda_list=range(args.size)
accumulation_steps=int(args.batch_size/args.size/args.gpu_size)
#model = nn.DataParallel(model, device_ids=cuda_list)
# torch.cuda.set_device(cudaid)
# torch.distributed.init_process_group(backend='nccl', init_method='tcp://localhost:23456', rank=0, world_size=1)
# model=torch.nn.parallel.DistributedDataParallel(model, device_ids=cuda_list,output_device=0,find_unused_parameters=True)
model = torch.nn.DataParallel(model,device_ids=cuda_list)
accum_batch_loss=0
iterator=NewsIterator(batch_size=args.gpu_size*args.size, npratio=4,feature_file=os.path.join(args.data_dir,args.feature_file),field=args.field)
train_file=os.path.join(args.data_dir, args.data_file)
#for epoch in range(0,100):
batch_t=0
iteration=0
print('train...',cuda_list)
#w=open(os.path.join(args.data_dir,args.log_file),'w')
writer = SummaryWriter(os.path.join(args.data_dir, args.log_file) )
epoch=0
model.train()
# batch_t=52880-1
# iteration=3305-1
batch_t=0
iteration=0
step=0
best_score=-1
#w=open(os.path.join(args.data_dir,args.log_file),'w')
# model.eval()
# auc=test(model,args)
# model.eval()
# auc=test(model,args)
# print(auc)
for epoch in range(0,10):
#while True:
all_loss=0
all_batch=0
data_batch=iterator.load_data_from_file(train_file)
for imp_index , user_index, his_id, candidate_id , label in data_batch:
batch_t+=1
assert candidate_id.shape[1]==2
his_id=his_id.cuda(cudaid)
candidate_id= candidate_id.cuda(cudaid)
label = label.cuda(cudaid)
loss=model(his_id,candidate_id, label)
sample_size=candidate_id.shape[0]
loss=loss.sum()/sample_size/math.log(2)
accum_batch_loss+=float(loss)
all_loss+=float(loss)
all_batch+=1
loss = loss/accumulation_steps
loss.backward()
if (batch_t)%accumulation_steps==0:
iteration+=1
adjust_learning_rate(optimizer,iteration)
optimizer.step()
optimizer.zero_grad()
print(' batch_t: ',batch_t, ' iteration: ', iteration, ' epoch: ',epoch,' accum_batch_loss: ',accum_batch_loss/accumulation_steps,' lr: ', optimizer.param_groups[0]['lr'])
writer.add_scalar('Loss/train', accum_batch_loss/accumulation_steps, iteration)
writer.add_scalar('Ltr/train', optimizer.param_groups[0]['lr'], iteration)
accum_batch_loss=0
if iteration%2==0:
torch.cuda.empty_cache()
model.eval()
auc=test(model,args)
print(auc)
writer.add_scalar('auc/valid', auc, step)
step+=1
if auc>best_score:
torch.save(model.state_dict(), os.path.join(args.save_dir,'Plain_robert_dot_best.pkl'))
best_score=auc
print('best score: ',best_score)
torch.cuda.empty_cache()
model.train()
torch.save(model.state_dict(), os.path.join(args.save_dir,'Plain_robert_dot'+str(epoch)+'.pkl'))
#w.close()
if __name__ == '__main__':
# cuda_num=int(sys.argv[1])
random.seed(1)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
#main()
args = parse_args()
model=Plain_bert(args)
#optimizer = torch.optim.Adam(model.parameters(), lr=lr,betas=(0.9,0.98),eps=1e-6,weight_decay=0.0)
optimizer = apex.optimizers.FusedLAMB(model.parameters(), lr=lr,betas=(0.9,0.98),eps=1e-6,weight_decay=0.0,max_grad_norm=1.0)
# for name, param in model.named_parameters():
# print(name,param.shape,param.requires_grad)
roberta = RobertaModel.from_pretrained(os.path.join(args.data_dir,'roberta.base'), checkpoint_file='checkpoint_best.pt')
#roberta = RobertaModel.from_pretrained(args.save_dir, checkpoint_file='checkpoint_best.pt')
# for name, param in roberta.named_parameters():
# print(name,param.shape,param.requires_grad)
model_dict = model.state_dict()
pretrained_dict={}
for name,parameters in roberta.named_parameters():
if 'lm_head' not in name:
pretrained_dict['encoder.'+name[31:]]=parameters
print(pretrained_dict.keys())
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
# for item in model.parameters():
# print(item.requires_grad)
model.cuda(cudaid)
train(model,optimizer,args)
| 32.401709
| 187
| 0.62578
|
import json
import pickle
import numpy as np
import random
import sys
import torch
import argparse
import os
from model_plain_bert_dot4 import Plain_bert
from fairseq.models.roberta import RobertaModel
from utils_sample import NewsIterator
from utils_sample import cal_metric
import utils_sample as utils
import torch.nn as nn
import math
from fairseq.data import (
data_utils,
Dictionary,
IdDataset,
MaskTokensDataset,
NestedDictionaryDataset,
NumelDataset,
NumSamplesDataset,
PadDataset,
PrependTokenDataset,
SortDataset,
TokenBlockDataset,
)
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
import apex
random.seed(1)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
cudaid=0
metrics=['group_auc','mean_mrr','ndcg@5;10']
lr=1e-4
T_warm=5000
all_iteration=33040
ining on gpus")
parser.add_argument("--save_dir",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--data_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--test_data_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--feature_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--test_feature_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--size",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--gpu_size",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--gpu_size_test",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--batch_size",
type=int,
default=1,
help="local_rank for distributed training on gpus")
parser.add_argument("--log_file",
type=str,
help="local_rank for distributed training on gpus")
parser.add_argument("--field",
type=str,
help="local_rank for distributed training on gpus")
return parser.parse_args()
def adjust_learning_rate(optimizer,iteration,lr=lr, T_warm=T_warm, all_iteration=all_iteration ):
if iteration<=T_warm:
lr=lr*float(iteration)/T_warm
elif iteration<all_iteration:
lr = lr * (1 - (iteration - T_warm) / (all_iteration - T_warm))
else:
lr=0
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def group_labels_func(labels, preds, group_keys):
all_keys = list(set(group_keys))
group_labels = {k: [] for k in all_keys}
group_preds = {k: [] for k in all_keys}
for l, p, k in zip(labels, preds, group_keys):
group_labels[k].append(l)
group_preds[k].append(p)
all_labels = []
all_preds = []
for k in all_keys:
all_labels.append(group_labels[k])
all_preds.append(group_preds[k])
return all_labels, all_preds
def test(model,args):
preds = []
labels = []
imp_indexes = []
metrics=['group_auc']
test_file=os.path.join(args.data_dir, args.test_data_file)
preds = []
labels = []
imp_indexes = []
feature_file=os.path.join(args.data_dir,args.feature_file)
iterator=NewsIterator(batch_size=args.gpu_size_test, npratio=-1,feature_file=feature_file,field=args.field)
print('test...')
with torch.no_grad():
data_batch=iterator.load_data_from_file(test_file)
batch_t=0
for imp_index , user_index, his_id, candidate_id , label in data_batch:
batch_t+=len(candidate_id)
his_id=his_id.cuda(cudaid)
candidate_id= candidate_id.cuda(cudaid)
logit=model(his_id,candidate_id,None,mode='validation')
logit=list(np.reshape(np.array(logit.cpu()), -1))
label=list(np.reshape(np.array(label), -1))
imp_index=list(np.reshape(np.array(imp_index), -1))
assert len(logit)==len(label)
assert len(logit)==len(imp_index)
labels.extend(label)
preds.extend(logit)
imp_indexes.extend(imp_index)
print('all data: ',len(labels))
group_labels, group_preds = group_labels_func(labels, preds, imp_indexes)
res = cal_metric(group_labels, group_preds, metrics)
return res['group_auc']
def train(model,optimizer, args):
print('params: '," T_warm: ",T_warm," all_iteration: ",all_iteration," lr: ",lr)
cuda_list=range(args.size)
accumulation_steps=int(args.batch_size/args.size/args.gpu_size)
model = torch.nn.DataParallel(model,device_ids=cuda_list)
accum_batch_loss=0
iterator=NewsIterator(batch_size=args.gpu_size*args.size, npratio=4,feature_file=os.path.join(args.data_dir,args.feature_file),field=args.field)
train_file=os.path.join(args.data_dir, args.data_file)
batch_t=0
iteration=0
print('train...',cuda_list)
writer = SummaryWriter(os.path.join(args.data_dir, args.log_file) )
epoch=0
model.train()
batch_t=0
iteration=0
step=0
best_score=-1
for epoch in range(0,10):
all_loss=0
all_batch=0
data_batch=iterator.load_data_from_file(train_file)
for imp_index , user_index, his_id, candidate_id , label in data_batch:
batch_t+=1
assert candidate_id.shape[1]==2
his_id=his_id.cuda(cudaid)
candidate_id= candidate_id.cuda(cudaid)
label = label.cuda(cudaid)
loss=model(his_id,candidate_id, label)
sample_size=candidate_id.shape[0]
loss=loss.sum()/sample_size/math.log(2)
accum_batch_loss+=float(loss)
all_loss+=float(loss)
all_batch+=1
loss = loss/accumulation_steps
loss.backward()
if (batch_t)%accumulation_steps==0:
iteration+=1
adjust_learning_rate(optimizer,iteration)
optimizer.step()
optimizer.zero_grad()
print(' batch_t: ',batch_t, ' iteration: ', iteration, ' epoch: ',epoch,' accum_batch_loss: ',accum_batch_loss/accumulation_steps,' lr: ', optimizer.param_groups[0]['lr'])
writer.add_scalar('Loss/train', accum_batch_loss/accumulation_steps, iteration)
writer.add_scalar('Ltr/train', optimizer.param_groups[0]['lr'], iteration)
accum_batch_loss=0
if iteration%2==0:
torch.cuda.empty_cache()
model.eval()
auc=test(model,args)
print(auc)
writer.add_scalar('auc/valid', auc, step)
step+=1
if auc>best_score:
torch.save(model.state_dict(), os.path.join(args.save_dir,'Plain_robert_dot_best.pkl'))
best_score=auc
print('best score: ',best_score)
torch.cuda.empty_cache()
model.train()
torch.save(model.state_dict(), os.path.join(args.save_dir,'Plain_robert_dot'+str(epoch)+'.pkl'))
if __name__ == '__main__':
random.seed(1)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
args = parse_args()
model=Plain_bert(args)
optimizer = apex.optimizers.FusedLAMB(model.parameters(), lr=lr,betas=(0.9,0.98),eps=1e-6,weight_decay=0.0,max_grad_norm=1.0)
roberta = RobertaModel.from_pretrained(os.path.join(args.data_dir,'roberta.base'), checkpoint_file='checkpoint_best.pt')
model_dict = model.state_dict()
pretrained_dict={}
for name,parameters in roberta.named_parameters():
if 'lm_head' not in name:
pretrained_dict['encoder.'+name[31:]]=parameters
print(pretrained_dict.keys())
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model.cuda(cudaid)
train(model,optimizer,args)
| true
| true
|
1c44534ae52d49a685a8b666effb3e998f3d5f6a
| 667
|
py
|
Python
|
survey_api/reports/serializers/__init__.py
|
OpenStackweb/openstack-survey-api
|
6ede5d0521d9055cff0adb939db2d11a216336f5
|
[
"Apache-2.0"
] | null | null | null |
survey_api/reports/serializers/__init__.py
|
OpenStackweb/openstack-survey-api
|
6ede5d0521d9055cff0adb939db2d11a216336f5
|
[
"Apache-2.0"
] | 3
|
2020-02-11T23:49:55.000Z
|
2021-06-10T21:13:14.000Z
|
survey_api/reports/serializers/__init__.py
|
OpenStackweb/openstack-survey-api
|
6ede5d0521d9055cff0adb939db2d11a216336f5
|
[
"Apache-2.0"
] | null | null | null |
"""
* Copyright 2019 OpenStack Foundation
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
* http://www.apache.org/licenses/LICENSE-2.0
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
"""
from .survey_serializer import SurveySerializer, SurveyTemplateSerializer
| 44.466667
| 75
| 0.772114
|
from .survey_serializer import SurveySerializer, SurveyTemplateSerializer
| true
| true
|
1c445417358840223a835234f4a381e0ad420f0e
| 1,217
|
py
|
Python
|
facebook_business/adobjects/serverside/http_service_interface.py
|
MyrikLD/facebook-python-business-sdk
|
a53c8ba0e8f7d0b41b385c60089f6ba00fa5c814
|
[
"CNRI-Python"
] | 576
|
2018-05-01T19:09:32.000Z
|
2022-03-31T11:45:11.000Z
|
facebook_business/adobjects/serverside/http_service_interface.py
|
MyrikLD/facebook-python-business-sdk
|
a53c8ba0e8f7d0b41b385c60089f6ba00fa5c814
|
[
"CNRI-Python"
] | 217
|
2018-05-03T07:31:59.000Z
|
2022-03-29T14:19:52.000Z
|
facebook_business/adobjects/serverside/http_service_interface.py
|
MyrikLD/facebook-python-business-sdk
|
a53c8ba0e8f7d0b41b385c60089f6ba00fa5c814
|
[
"CNRI-Python"
] | 323
|
2018-05-01T20:32:26.000Z
|
2022-03-29T07:05:12.000Z
|
# Copyright 2014 Facebook, Inc.
# You are hereby granted a non-exclusive, worldwide, royalty-free license to
# use, copy, modify, and distribute this software in source code or binary
# form for use in connection with the web services and APIs provided by
# Facebook.
# As with any software that integrates with the Facebook platform, your use
# of this software is subject to the Facebook Developer Principles and
# Policies [http://developers.facebook.com/policy/]. This copyright 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.
from abc import ABC, abstractmethod
class HttpServiceInterface(ABC):
@abstractmethod
def execute(self, url, method, request_options, headers, params):
pass
| 45.074074
| 76
| 0.775678
|
from abc import ABC, abstractmethod
class HttpServiceInterface(ABC):
@abstractmethod
def execute(self, url, method, request_options, headers, params):
pass
| true
| true
|
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