blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 3 281 | content_id stringlengths 40 40 | detected_licenses listlengths 0 57 | license_type stringclasses 2 values | repo_name stringlengths 6 116 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 313 values | visit_date timestamp[us] | revision_date timestamp[us] | committer_date timestamp[us] | github_id int64 18.2k 668M ⌀ | star_events_count int64 0 102k | fork_events_count int64 0 38.2k | gha_license_id stringclasses 17 values | gha_event_created_at timestamp[us] | gha_created_at timestamp[us] | gha_language stringclasses 107 values | src_encoding stringclasses 20 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.02M | extension stringclasses 78 values | content stringlengths 2 6.02M | authors listlengths 1 1 | author stringlengths 0 175 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
14894d1e14a3780b504d9142885df48497b80280 | bcc4bbf0e67c9c9d9c8dfc51728e0c71f51650d1 | /node.py | d9d60402408ec6e03a01b08d01105740d78e1f18 | [] | no_license | amirmo76/8-puzzle | 6e267942d6090c145fc1e3db56528e57d129d308 | 8ecd22f56927e3cc8cac91f6c54be8c5c2cb2215 | refs/heads/master | 2020-04-26T01:15:49.922472 | 2019-02-28T22:38:55 | 2019-02-28T22:38:55 | 173,198,734 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 983 | py | class Node:
parent_node = None
node_id = 0
state = [[0 for i in range(3)] for j in range(3)]
depth = 0
priority = 0 # the higher the better
def __init__(self, state, id=0):
self.state = state
self.node_id = id
def diff(self, node) -> list:
return [i for i, j in zip(self.state, node.state) if i == j]
def diff_state(self, state) -> list:
return [i for i, j in zip(self.state, state) if i == j]
def is_equal(self, node) -> bool:
return True if len(self.diff(node)) == len(self.state) else False
def is_equal_state(self, state) -> bool:
return True if len(self.diff_state(state)) == len(self.state) else False
def is_goal(self):
return self.is_equal(Node([[1, 2, 3], [4, 5, 6], [7, 8, 0]]))
def __str__(self):
return "id:" + str(self.node_id) + " ,state:" + self.state.__str__() + " ,depth:" + str(self.depth)
def __repr__(self):
return self.__str__()
| [
"amir.mohseni7697@gmail.com"
] | amir.mohseni7697@gmail.com |
6adc317a4712614473d3f93a01a647e9404d5afd | 9be5e0d2b9a35f098df658f964dd3c00587ec1c8 | /cifradocesar.py | 4065ed529db621aeddb03c999719e8d32c1d395f | [] | no_license | minka-it/minka-sec | 124e99e831b579ccc568fe3c396a6c027168b4d7 | 5e17a0720d9da466375150418c5e7fae44eb3cc8 | refs/heads/master | 2020-05-22T05:45:20.456587 | 2016-09-17T14:23:01 | 2016-09-17T14:23:01 | 63,429,926 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 267 | py | #enconding
#!/usr/bin/python
#-*- coding: utf-8 -*-
mensaje= "VprPGS{jnvg_bar_cyhf_1_vf_3?}"
mensaje2=""
for i in range(0,255):
for x in mensaje:
print ord(x)+i % 255
mensaje2 = mensaje2 + chr(ord(x)+i % 255)
if mensaje2[0]=="I":
print mensaje2
mensaje2="" | [
"yojota@gmail.com"
] | yojota@gmail.com |
3f6522509688cff83958145efe1e2eb5e58e3ba7 | 7b7181b16cfd10773df8bb13dd5c191893abea54 | /BestGrades.py | fdd1c947627747a1bf35134d2fb5d227083e940e | [] | no_license | agbischof/PythonPractice | 721b841f6ae6956300f15ab92c9a96c75b586525 | 66fd64ac26ae0d97152d7034665e6695b80c160c | refs/heads/master | 2020-04-02T00:06:11.219975 | 2014-02-09T20:33:47 | 2014-02-09T20:33:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 351 | py | import string
import math
def main(file):
stu = open(file)
BestGPA = 0
for line in stu:
data = string.split(line)
GPA = float(data[3])/float(data[2])
print data[1], data[0], GPA, " of ", data[3], " credits."
if GPA > BestGPA:
BestGPA = GPA
BestStu = data
print BestStu [1], BestStu[0], BestGPA, " of ", BestStu[3], " credits!" | [
"agbischof@gmail.com"
] | agbischof@gmail.com |
2e74d353ab354e12d997f17a482999f383cf75ab | 68c421b58f43c82ba1f7c5b95138f76094a44f8e | /telemetry_tempest_plugin/scenario/test_gnocchi.py | e283d2f288d9f71f0080f2164111e4f39526039b | [
"Apache-2.0"
] | permissive | NeCTAR-RC/telemetry-tempest-plugin | 37c986541ccad951e2cd7db3394a76a58ea1ce14 | fc990c4ada71ca3c45df2d4733bb1fb4b7f7c9e5 | refs/heads/master | 2021-06-27T03:24:35.519246 | 2019-05-24T06:21:57 | 2019-05-24T06:47:41 | 194,606,282 | 0 | 0 | null | 2019-07-01T05:36:00 | 2019-07-01T05:36:00 | null | UTF-8 | Python | false | false | 1,659 | py | # 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 __future__ import absolute_import
import os
from tempest import config
import tempest.test
from telemetry_tempest_plugin.scenario import utils
CONF = config.CONF
TEST_DIR = os.path.join(os.path.dirname(__file__), 'gnocchi_gabbits')
class GnocchiGabbiTest(tempest.test.BaseTestCase):
credentials = ['admin']
TIMEOUT_SCALING_FACTOR = 5
@classmethod
def skip_checks(cls):
super(GnocchiGabbiTest, cls).skip_checks()
if not CONF.service_available.gnocchi:
raise cls.skipException("Gnocchi support is required")
def _prep_test(self, filename):
token = self.os_admin.auth_provider.get_token()
url = self.os_admin.auth_provider.base_url(
{'service': CONF.metric.catalog_type,
'endpoint_type': CONF.metric.endpoint_type,
'region': CONF.identity.region})
os.environ.update({
"GNOCCHI_SERVICE_URL": url,
"GNOCCHI_SERVICE_TOKEN": token,
"GNOCCHI_AUTHORIZATION": "not used",
})
utils.generate_tests(GnocchiGabbiTest, TEST_DIR)
| [
"sileht@sileht.net"
] | sileht@sileht.net |
bce7cc5f2e71ce4079ecda95b900634320487c56 | f7b84af36babb3a09dea61e9cb46ea3b89267d5f | /src/app/blog/tests/test_post_model.py | 0fc724062e082054d363a5a1d73975aee5adea4c | [
"MIT"
] | permissive | Hammerstad/blog | 600bc1e8bbeb03bd9b566ee8d35170f5e340219a | 3075e59c46321fd5fd5ccafbfb36e6d59b765259 | refs/heads/master | 2020-06-04T01:38:23.997253 | 2015-07-06T11:31:03 | 2015-07-06T11:31:03 | 38,606,389 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 980 | py | from django.contrib.auth.models import User
from django.test import TestCase
from app.blog.models import Post
from app.blog.tests import LONG_BLOG_POST
class PostTestCase(TestCase):
def setUp(self):
user = User.objects.create_user('testuser', 'testuser@test.com', 'testpassword')
Post.objects.create(creator=user, title="Small post", content="some small content")
Post.objects.create(creator=user, title="Long post", content=LONG_BLOG_POST)
def test_small_post_has_same_content_and_summary(self):
small_post = Post.objects.get(title="Small post")
expected = "some small content"
self.assertEqual(expected, small_post.content)
self.assertEqual(expected, small_post.summary)
def test_long_post_has_summary_created_from_content(self):
small_post = Post.objects.get(title="Long post")
expected_summary = LONG_BLOG_POST[:300]
self.assertEqual(expected_summary, small_post.summary) | [
"emhammerstad@gmail.com"
] | emhammerstad@gmail.com |
d6b66c5898c3f0ff071147ca16a962d71c7d3fe2 | f9d564f1aa83eca45872dab7fbaa26dd48210d08 | /huaweicloud-sdk-cae/huaweicloudsdkcae/v1/model/component_snapshot_context.py | 4520d2f8150fd8c09f48b38ca2704f273519d7c8 | [
"Apache-2.0"
] | permissive | huaweicloud/huaweicloud-sdk-python-v3 | cde6d849ce5b1de05ac5ebfd6153f27803837d84 | f69344c1dadb79067746ddf9bfde4bddc18d5ecf | refs/heads/master | 2023-09-01T19:29:43.013318 | 2023-08-31T08:28:59 | 2023-08-31T08:28:59 | 262,207,814 | 103 | 44 | NOASSERTION | 2023-06-22T14:50:48 | 2020-05-08T02:28:43 | Python | UTF-8 | Python | false | false | 20,094 | py | # coding: utf-8
import six
from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization
class ComponentSnapshotContext:
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
sensitive_list = []
openapi_types = {
'app_id': 'str',
'available_replica': 'int',
'build': 'str',
'build_id': 'str',
'build_log_id': 'str',
'env_id': 'str',
'id': 'str',
'image_url': 'str',
'job_id': 'str',
'log_group_id': 'str',
'log_stream_id': 'str',
'name': 'str',
'operation': 'str',
'operation_status': 'str',
'replica': 'int',
'resource_limit': 'str',
'runtime': 'str',
'source': 'str',
'status': 'str',
'version': 'str',
'created_at': 'str',
'updated_at': 'str'
}
attribute_map = {
'app_id': 'app_id',
'available_replica': 'available_replica',
'build': 'build',
'build_id': 'build_id',
'build_log_id': 'build_log_id',
'env_id': 'env_id',
'id': 'id',
'image_url': 'image_url',
'job_id': 'job_id',
'log_group_id': 'log_group_id',
'log_stream_id': 'log_stream_id',
'name': 'name',
'operation': 'operation',
'operation_status': 'operation_status',
'replica': 'replica',
'resource_limit': 'resource_limit',
'runtime': 'runtime',
'source': 'source',
'status': 'status',
'version': 'version',
'created_at': 'created_at',
'updated_at': 'updated_at'
}
def __init__(self, app_id=None, available_replica=None, build=None, build_id=None, build_log_id=None, env_id=None, id=None, image_url=None, job_id=None, log_group_id=None, log_stream_id=None, name=None, operation=None, operation_status=None, replica=None, resource_limit=None, runtime=None, source=None, status=None, version=None, created_at=None, updated_at=None):
"""ComponentSnapshotContext
The model defined in huaweicloud sdk
:param app_id: 应用ID。
:type app_id: str
:param available_replica: 可用实例个数。
:type available_replica: int
:param build: 组件构建信息。
:type build: str
:param build_id: 构建任务ID。
:type build_id: str
:param build_log_id: 构建日志ID。
:type build_log_id: str
:param env_id: 环境ID。
:type env_id: str
:param id: 组件ID。
:type id: str
:param image_url: 镜像地址。
:type image_url: str
:param job_id: 任务ID。
:type job_id: str
:param log_group_id: LTS日志组的ID。
:type log_group_id: str
:param log_stream_id: LTS日志流的ID
:type log_stream_id: str
:param name: 组件名称。
:type name: str
:param operation: 组件操作。
:type operation: str
:param operation_status: 组件操作状态。
:type operation_status: str
:param replica: 实例个数。
:type replica: int
:param resource_limit: 组件规格。
:type resource_limit: str
:param runtime: 语言/运行时。
:type runtime: str
:param source: 组件源信息。
:type source: str
:param status: 组件状态。
:type status: str
:param version: 组件版本。
:type version: str
:param created_at: 创建时间。
:type created_at: str
:param updated_at: 更新时间。
:type updated_at: str
"""
self._app_id = None
self._available_replica = None
self._build = None
self._build_id = None
self._build_log_id = None
self._env_id = None
self._id = None
self._image_url = None
self._job_id = None
self._log_group_id = None
self._log_stream_id = None
self._name = None
self._operation = None
self._operation_status = None
self._replica = None
self._resource_limit = None
self._runtime = None
self._source = None
self._status = None
self._version = None
self._created_at = None
self._updated_at = None
self.discriminator = None
if app_id is not None:
self.app_id = app_id
if available_replica is not None:
self.available_replica = available_replica
if build is not None:
self.build = build
if build_id is not None:
self.build_id = build_id
if build_log_id is not None:
self.build_log_id = build_log_id
if env_id is not None:
self.env_id = env_id
if id is not None:
self.id = id
if image_url is not None:
self.image_url = image_url
if job_id is not None:
self.job_id = job_id
if log_group_id is not None:
self.log_group_id = log_group_id
if log_stream_id is not None:
self.log_stream_id = log_stream_id
if name is not None:
self.name = name
if operation is not None:
self.operation = operation
if operation_status is not None:
self.operation_status = operation_status
if replica is not None:
self.replica = replica
if resource_limit is not None:
self.resource_limit = resource_limit
if runtime is not None:
self.runtime = runtime
if source is not None:
self.source = source
if status is not None:
self.status = status
if version is not None:
self.version = version
if created_at is not None:
self.created_at = created_at
if updated_at is not None:
self.updated_at = updated_at
@property
def app_id(self):
"""Gets the app_id of this ComponentSnapshotContext.
应用ID。
:return: The app_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._app_id
@app_id.setter
def app_id(self, app_id):
"""Sets the app_id of this ComponentSnapshotContext.
应用ID。
:param app_id: The app_id of this ComponentSnapshotContext.
:type app_id: str
"""
self._app_id = app_id
@property
def available_replica(self):
"""Gets the available_replica of this ComponentSnapshotContext.
可用实例个数。
:return: The available_replica of this ComponentSnapshotContext.
:rtype: int
"""
return self._available_replica
@available_replica.setter
def available_replica(self, available_replica):
"""Sets the available_replica of this ComponentSnapshotContext.
可用实例个数。
:param available_replica: The available_replica of this ComponentSnapshotContext.
:type available_replica: int
"""
self._available_replica = available_replica
@property
def build(self):
"""Gets the build of this ComponentSnapshotContext.
组件构建信息。
:return: The build of this ComponentSnapshotContext.
:rtype: str
"""
return self._build
@build.setter
def build(self, build):
"""Sets the build of this ComponentSnapshotContext.
组件构建信息。
:param build: The build of this ComponentSnapshotContext.
:type build: str
"""
self._build = build
@property
def build_id(self):
"""Gets the build_id of this ComponentSnapshotContext.
构建任务ID。
:return: The build_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._build_id
@build_id.setter
def build_id(self, build_id):
"""Sets the build_id of this ComponentSnapshotContext.
构建任务ID。
:param build_id: The build_id of this ComponentSnapshotContext.
:type build_id: str
"""
self._build_id = build_id
@property
def build_log_id(self):
"""Gets the build_log_id of this ComponentSnapshotContext.
构建日志ID。
:return: The build_log_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._build_log_id
@build_log_id.setter
def build_log_id(self, build_log_id):
"""Sets the build_log_id of this ComponentSnapshotContext.
构建日志ID。
:param build_log_id: The build_log_id of this ComponentSnapshotContext.
:type build_log_id: str
"""
self._build_log_id = build_log_id
@property
def env_id(self):
"""Gets the env_id of this ComponentSnapshotContext.
环境ID。
:return: The env_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._env_id
@env_id.setter
def env_id(self, env_id):
"""Sets the env_id of this ComponentSnapshotContext.
环境ID。
:param env_id: The env_id of this ComponentSnapshotContext.
:type env_id: str
"""
self._env_id = env_id
@property
def id(self):
"""Gets the id of this ComponentSnapshotContext.
组件ID。
:return: The id of this ComponentSnapshotContext.
:rtype: str
"""
return self._id
@id.setter
def id(self, id):
"""Sets the id of this ComponentSnapshotContext.
组件ID。
:param id: The id of this ComponentSnapshotContext.
:type id: str
"""
self._id = id
@property
def image_url(self):
"""Gets the image_url of this ComponentSnapshotContext.
镜像地址。
:return: The image_url of this ComponentSnapshotContext.
:rtype: str
"""
return self._image_url
@image_url.setter
def image_url(self, image_url):
"""Sets the image_url of this ComponentSnapshotContext.
镜像地址。
:param image_url: The image_url of this ComponentSnapshotContext.
:type image_url: str
"""
self._image_url = image_url
@property
def job_id(self):
"""Gets the job_id of this ComponentSnapshotContext.
任务ID。
:return: The job_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._job_id
@job_id.setter
def job_id(self, job_id):
"""Sets the job_id of this ComponentSnapshotContext.
任务ID。
:param job_id: The job_id of this ComponentSnapshotContext.
:type job_id: str
"""
self._job_id = job_id
@property
def log_group_id(self):
"""Gets the log_group_id of this ComponentSnapshotContext.
LTS日志组的ID。
:return: The log_group_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._log_group_id
@log_group_id.setter
def log_group_id(self, log_group_id):
"""Sets the log_group_id of this ComponentSnapshotContext.
LTS日志组的ID。
:param log_group_id: The log_group_id of this ComponentSnapshotContext.
:type log_group_id: str
"""
self._log_group_id = log_group_id
@property
def log_stream_id(self):
"""Gets the log_stream_id of this ComponentSnapshotContext.
LTS日志流的ID
:return: The log_stream_id of this ComponentSnapshotContext.
:rtype: str
"""
return self._log_stream_id
@log_stream_id.setter
def log_stream_id(self, log_stream_id):
"""Sets the log_stream_id of this ComponentSnapshotContext.
LTS日志流的ID
:param log_stream_id: The log_stream_id of this ComponentSnapshotContext.
:type log_stream_id: str
"""
self._log_stream_id = log_stream_id
@property
def name(self):
"""Gets the name of this ComponentSnapshotContext.
组件名称。
:return: The name of this ComponentSnapshotContext.
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""Sets the name of this ComponentSnapshotContext.
组件名称。
:param name: The name of this ComponentSnapshotContext.
:type name: str
"""
self._name = name
@property
def operation(self):
"""Gets the operation of this ComponentSnapshotContext.
组件操作。
:return: The operation of this ComponentSnapshotContext.
:rtype: str
"""
return self._operation
@operation.setter
def operation(self, operation):
"""Sets the operation of this ComponentSnapshotContext.
组件操作。
:param operation: The operation of this ComponentSnapshotContext.
:type operation: str
"""
self._operation = operation
@property
def operation_status(self):
"""Gets the operation_status of this ComponentSnapshotContext.
组件操作状态。
:return: The operation_status of this ComponentSnapshotContext.
:rtype: str
"""
return self._operation_status
@operation_status.setter
def operation_status(self, operation_status):
"""Sets the operation_status of this ComponentSnapshotContext.
组件操作状态。
:param operation_status: The operation_status of this ComponentSnapshotContext.
:type operation_status: str
"""
self._operation_status = operation_status
@property
def replica(self):
"""Gets the replica of this ComponentSnapshotContext.
实例个数。
:return: The replica of this ComponentSnapshotContext.
:rtype: int
"""
return self._replica
@replica.setter
def replica(self, replica):
"""Sets the replica of this ComponentSnapshotContext.
实例个数。
:param replica: The replica of this ComponentSnapshotContext.
:type replica: int
"""
self._replica = replica
@property
def resource_limit(self):
"""Gets the resource_limit of this ComponentSnapshotContext.
组件规格。
:return: The resource_limit of this ComponentSnapshotContext.
:rtype: str
"""
return self._resource_limit
@resource_limit.setter
def resource_limit(self, resource_limit):
"""Sets the resource_limit of this ComponentSnapshotContext.
组件规格。
:param resource_limit: The resource_limit of this ComponentSnapshotContext.
:type resource_limit: str
"""
self._resource_limit = resource_limit
@property
def runtime(self):
"""Gets the runtime of this ComponentSnapshotContext.
语言/运行时。
:return: The runtime of this ComponentSnapshotContext.
:rtype: str
"""
return self._runtime
@runtime.setter
def runtime(self, runtime):
"""Sets the runtime of this ComponentSnapshotContext.
语言/运行时。
:param runtime: The runtime of this ComponentSnapshotContext.
:type runtime: str
"""
self._runtime = runtime
@property
def source(self):
"""Gets the source of this ComponentSnapshotContext.
组件源信息。
:return: The source of this ComponentSnapshotContext.
:rtype: str
"""
return self._source
@source.setter
def source(self, source):
"""Sets the source of this ComponentSnapshotContext.
组件源信息。
:param source: The source of this ComponentSnapshotContext.
:type source: str
"""
self._source = source
@property
def status(self):
"""Gets the status of this ComponentSnapshotContext.
组件状态。
:return: The status of this ComponentSnapshotContext.
:rtype: str
"""
return self._status
@status.setter
def status(self, status):
"""Sets the status of this ComponentSnapshotContext.
组件状态。
:param status: The status of this ComponentSnapshotContext.
:type status: str
"""
self._status = status
@property
def version(self):
"""Gets the version of this ComponentSnapshotContext.
组件版本。
:return: The version of this ComponentSnapshotContext.
:rtype: str
"""
return self._version
@version.setter
def version(self, version):
"""Sets the version of this ComponentSnapshotContext.
组件版本。
:param version: The version of this ComponentSnapshotContext.
:type version: str
"""
self._version = version
@property
def created_at(self):
"""Gets the created_at of this ComponentSnapshotContext.
创建时间。
:return: The created_at of this ComponentSnapshotContext.
:rtype: str
"""
return self._created_at
@created_at.setter
def created_at(self, created_at):
"""Sets the created_at of this ComponentSnapshotContext.
创建时间。
:param created_at: The created_at of this ComponentSnapshotContext.
:type created_at: str
"""
self._created_at = created_at
@property
def updated_at(self):
"""Gets the updated_at of this ComponentSnapshotContext.
更新时间。
:return: The updated_at of this ComponentSnapshotContext.
:rtype: str
"""
return self._updated_at
@updated_at.setter
def updated_at(self, updated_at):
"""Sets the updated_at of this ComponentSnapshotContext.
更新时间。
:param updated_at: The updated_at of this ComponentSnapshotContext.
:type updated_at: str
"""
self._updated_at = updated_at
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
if attr in self.sensitive_list:
result[attr] = "****"
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
def __repr__(self):
"""For `print`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ComponentSnapshotContext):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| [
"hwcloudsdk@huawei.com"
] | hwcloudsdk@huawei.com |
0e56bf8ed73462e6d8d4224877b8ef90282c3bfe | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/otherforms/_managing.py | c3d2619519ac3dfd32082a1f69f6bfb2869a39aa | [
"MIT"
] | permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 224 | py |
#calss header
class _MANAGING():
def __init__(self,):
self.name = "MANAGING"
self.definitions = manage
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.basic = ['manage']
| [
"xingwang1991@gmail.com"
] | xingwang1991@gmail.com |
b33f5a76faed95895a6c72548508071977696ad1 | 09160ea617d666f6b3959650a0cbeaee7149ab48 | /basetask.py | 5546846c02d75ce57e159c4c617c23eba1347bdf | [] | no_license | zuoyong8/tasks | bc4414a0fc81ef52f583953dcf6be61cec5bdb63 | 6e88fe2691efc9da0237085ddca9df1b9dccead5 | refs/heads/master | 2022-12-14T17:24:54.042382 | 2018-12-22T02:59:53 | 2018-12-22T02:59:53 | 162,776,748 | 4 | 0 | null | 2022-12-08T01:29:40 | 2018-12-22T02:57:22 | Python | UTF-8 | Python | false | false | 1,212 | py | from redis import Redis
from os import getcwd
from celery import Task
from libs.config import Config
from libs.datas import Datas
class BaseTask(Task):
_redis = None
_config = None
_match_order_data = None
_user_token_data = None
@property
def redis(self):
if self._redis is None:
self._redis = Redis(host='localhost',port=6379,db=1)
return self._redis
@property
def config(self):
if self._config is None:
self._config = Config(getcwd()+'/tasks/app.config')
return self._config
@property
def user_token_data(self):
if self._user_token_data is None:
if self._config is not None:
user_token_url = self._config.get_config_value('urls','URL_USER_LOGIN')
self._user_token_data = Datas(user_token_url)
return self._user_token_data
@property
def match_order_data(self):
if self._match_order_data is None:
if self._config is not None:
match_order_url = self._config.get_config_value('urls','URL_MATCH_ORDER')
self._match_order_data = Datas(match_order_url)
return self._match_order_data
| [
"coder188@outlook.com"
] | coder188@outlook.com |
7b06c684fd2b0a31351ff28413161eaaa81ddb1b | 8bad0b305f5563ea95fb7233e891c23012568094 | /1D-Ultrasonic-Targeting/imager.py | 0f113480b215e242d0f803e1b2519a5c91538c42 | [] | no_license | bkl535/1-day-projects | efb80d9d72f4b16ca0f83517073b7f56abe5e8c6 | a1a390d08bd8d08d0730032c6c7e84d41debe514 | refs/heads/master | 2021-01-10T14:13:16.959345 | 2016-04-10T01:18:53 | 2016-04-10T01:18:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 962 | py | from time import sleep
import xlwt
import serial
import numpy as np
import matplotlib.pyplot as plt
sample_rate=6
n_pos=180
plt.axis([0,180,0,250])
plt.ion()
plt.title('Graph')
plt.xlabel('Angle (deg)')
plt.ylabel('Object Distance (cm)')
plt.show()
dx = []
dtheta = []
for j in range(0,n_pos*4):
dx_in = ser.readline()
dtheta_in = ser.readline()
dx.append(dx_in)
dtheta.append(dtheta)
plt.scatter(dtheta_in,dx_in)
plt.draw()
sleep(1/sample_rate)
plt.ioff()
plt.savefig(graph.png')
plt.close()
f = open("object_finder.txt", "w")
for i in range(len(dx)):
print >>f, dx[i],' ', dtheta[i]
f.close()
wb=xlwt.Workbook()
sheet1=wb.add_sheet('Motion Sensor Data')
sheet1.write(0,0,'Range (cm)')
sheet1.write(0,1, 'Angle (deg)')
for row_index in range(len(dx)):
sheet1.write(row_index+1,0,dx[row_index])
for row_index in range(len(dt)):
sheet1.write(row_index+1,1,dt[row_index])
wb.save('object_finder.xls')
| [
"bkl53@msstate.edu"
] | bkl53@msstate.edu |
3f665983980b3dfbdb68b158d3d5c14afc97defa | bab615fdcd881260ef189c736db62e0297acb8c9 | /geocomp/convexhull/bhatta_sen.py | 43442aaaccf1e51691f9813060dfc711150f642c | [] | no_license | kokosha/MAC0331 | ca6cd1b96f3c6c0bf8ebccd8e78b234d83588845 | 7fac0dfd2ec4fce41ff7ec34062c0f5f1d99401b | refs/heads/master | 2020-04-04T19:51:03.340552 | 2018-12-15T21:43:54 | 2018-12-15T21:43:54 | 156,222,940 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 7,202 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""B. K. Bhattacharya and S. Sen.
On a Simple, Practical, Optimal, Output-Sensitive Randomized
Planar Convex Hull Algorithm.
J. Algorithms, 25:177--193, 1997
http://citeseer.nj.nec.com/206645.html
"""
from geocomp.common import control
from geocomp.common.polygon import Polygon
from geocomp.common.guiprim import *
from geocomp import config
import random
def inside_restricted (a, b, c, p):
"""verifica se p esta dentro do triangulo a,b,c
Admite que left (a, b, c) == left (a, b, p) == TRUE
"""
if not left_on (b, c, p):
return 0
elif not left_on (c, a, p):
return 0
return 1
def bhatta_sen_upper_rec (a, b, S):
"""Constroi a parte superior do fecho convexo"""
# step 1/2/3
again = 1
while again:
if len (S) == 1:
return [ S[0] ]
j = int (random.uniform (0, len (S)//2))
again = 0
p1 = S[2*j+1]
p2 = S[2*j]
p1.hilight ()
p2.hilight ()
if inside_restricted (b, a, S[2*j+1], S[2*j]):
S.remove (S[2*j])
again = 1
elif inside_restricted (b, a, S[2*j], S[2*j+1]):
S.remove (S[2*j+1])
again = 1
p1.unhilight ()
p2.unhilight ()
# step 4
m = 0
if left (S[2*j], S[2*j+1], a):
p1 = S[2*j+1]
p2 = S[2*j]
else:
p1 = S[2*j]
p2 = S[2*j+1]
id = p1.lineto (p2, config.COLOR_ALT4)
area_m = area2 (p1, p2, S[m])
for i in range (1, len(S)):
area_i = area2 (p1, p2, S[i])
if area_i > area_m:
m = i
area_m = area_i
pm = S[m]
control.plot_delete (id)
id = pm.hilight (config.COLOR_ALT1)
control.sleep ()
S1 = []
S2 = []
# step 5
i = j
#map (lambda p: p.hilight (config.COLOR_ALT2), S)
#control.sleep ()
#map (lambda p: p.unhilight (), S)
control.sleep ()
cont = []
for j in range (0, len(S)//2):
cont.extend ([2*j, 2*j+1])
if S[2*j].x < S[2*j+1].x:
p1 = S[2*j]
p2 = S[2*j+1]
else:
p1 = S[2*j+1]
p2 = S[2*j]
p1.hilight ()
p2.hilight (config.COLOR_ALT3)
if p2.x <= pm.x:
if left (pm, p2, p1):
S1.append (p1)
S1.append (p2)
else:
S1.append (p1)
elif pm.x <= p1.x:
if left (pm, p1, p2):
S2.append (p2)
else:
S2.append (p1)
S2.append (p2)
else: # p1.x < pm.x < p2.x
control.sleep ()
S1.append (p1)
S2.append (p2)
p1.unhilight ()
p2.unhilight ()
pm.unhilight (id)
if len (S) % 2 == 1:
S1.append (S[-1])
S2.append (S[-1])
control.sleep ()
list(map (lambda p: p.hilight (), S1))
control.sleep ()
# step 6
for p in S1[:]:
if not left (b, pm, p):
S1.remove (p)
p.unhilight ()
control.sleep ()
list(map (lambda p: p.unhilight (), S1))
control.sleep ()
list(map (lambda p: p.hilight (), S2))
control.sleep ()
for p in S2[:]:
if not left (pm, a, p):
S2.remove (p)
p.unhilight ()
control.sleep ()
list(map (lambda p: p.unhilight (), S2))
# step 7
ret1 = []
ret2 = []
if len (S2) != 0:
id = a.lineto (pm, config.COLOR_ALT4)
ret2 = bhatta_sen_upper_rec (a, pm, S2)
a.remove_lineto (pm, id)
ret2[-1].lineto (pm)
ret2.append (pm)
if len (S1) != 0:
id = pm.lineto (b, config.COLOR_ALT4)
ret1 = bhatta_sen_upper_rec (pm, b, S1)
pm.remove_lineto (b, id)
pm.lineto (ret1[0])
ret2.extend (ret1)
return ret2
#ok, eu fiquei com preguia e simplesmente copiei/colei a funcao
# abaixo, que e' simetrica `a acima
def bhatta_sen_lower_rec (a, b, S):
"""Constroi a parte inferior do fecho convexo"""
# step 1
# step 1/2/3
again = 1
while again:
if len (S) == 1:
return [ S[0] ]
j = int (random.uniform (0, len (S)//2))
again = 0
p1 = S[2*j+1]
p2 = S[2*j]
p1.hilight ()
p2.hilight ()
if inside_restricted (b, a, S[2*j+1], S[2*j]):
S.remove (S[2*j])
again = 1
elif inside_restricted (b, a, S[2*j], S[2*j+1]):
S.remove (S[2*j+1])
again = 1
p1.unhilight ()
p2.unhilight ()
# step 4
m = 0
if left (S[2*j], S[2*j+1], a):
p1 = S[2*j+1]
p2 = S[2*j]
else:
p1 = S[2*j]
p2 = S[2*j+1]
id = p1.lineto (p2, config.COLOR_ALT4)
area_m = area2 (p1, p2, S[m])
for i in range (1, len(S)):
area_i = area2 (p1, p2, S[i])
if area_i > area_m:
m = i
area_m = area_i
pm = S[m]
control.plot_delete (id)
id = pm.hilight (config.COLOR_ALT1)
control.sleep ()
S1 = []
S2 = []
# step 5
i = j
#map (lambda p: p.hilight (config.COLOR_ALT2), S)
#control.sleep ()
#map (lambda p: p.unhilight (), S)
control.sleep ()
cont = []
for j in range (0, len(S)//2):
cont.extend ([2*j, 2*j+1])
if S[2*j].x < S[2*j+1].x:
p1 = S[2*j]
p2 = S[2*j+1]
else:
p1 = S[2*j+1]
p2 = S[2*j]
p1.hilight ()
p2.hilight (config.COLOR_ALT3)
if p2.x <= pm.x:
if right (pm, p2, p1):
S1.append (p1)
S1.append (p2)
else:
S1.append (p1)
elif pm.x <= p1.x:
if right (pm, p1, p2):
S2.append (p2)
else:
S2.append (p1)
S2.append (p2)
else: # p1.x < pm.x < p2.x
control.sleep ()
S1.append (p1)
S2.append (p2)
p1.unhilight ()
p2.unhilight ()
pm.unhilight (id)
if len (S) % 2 == 1:
S1.append (S[-1])
S2.append (S[-1])
control.sleep ()
list(map (lambda p: p.hilight (), S1))
control.sleep ()
# step 6
for p in S1[:]:
if not right (a, pm, p):
S1.remove (p)
p.unhilight ()
control.sleep ()
list(map (lambda p: p.unhilight (), S1))
control.sleep ()
list(map (lambda p: p.hilight (), S2))
control.sleep ()
for p in S2[:]:
if not right (pm, b, p):
S2.remove (p)
p.unhilight ()
control.sleep ()
list(map (lambda p: p.unhilight (), S2))
# step 7
ret1 = []
ret2 = []
if len (S1) != 0:
id = a.lineto (pm, config.COLOR_ALT4)
ret1 = bhatta_sen_lower_rec (a, pm, S1)
a.remove_lineto (pm, id)
ret1[-1].lineto (pm)
ret1.append (pm)
if len (S2) != 0:
id = pm.lineto (b, config.COLOR_ALT4)
ret2 = bhatta_sen_lower_rec (pm, b, S2)
pm.remove_lineto (b, id)
pm.lineto (ret2[0])
ret1.extend (ret2)
return ret1
def Bhatta_Sen (l):
"""Algoritmo otimo proposto por Bhattacharya e Sen para encontrar o fecho convexo de l"""
south = north = east = west = 0
# encontrando o ponto mais baixo
for i in range (1, len(l)):
if l[i].y < l[south].y:
south = i
elif l[i].y == l[south].y:
if l[i].x > l[south].x:
south = i
if l[i].y > l[north].y:
north = i
elif l[i].y == l[north].y:
if l[i].x > l[north].x:
north = i
if l[i].x < l[west].x:
west = i
elif l[i].x == l[west].x:
if l[i].y > l[west].y:
west = i
if l[i].x > l[east].x:
east = i
elif l[i].x == l[east].x:
if l[i].y > l[east].y:
east = i
fecho = []
dirs = [ south, east, north, west ]
for i in range (0, len (dirs)):
j = (i+1) % 4
if dirs[i] == dirs[j]:
continue
fecho.append (l[dirs[i]])
S1 = []
a = l[dirs[i]]
b = l[dirs[j]]
for p in l:
if right (a, b, p):
S1.append (p)
id = a.lineto (b, config.COLOR_ALT4)
aux = []
if len (S1) > 0:
if dirs[i] == south or dirs[i] == west:
aux = bhatta_sen_lower_rec (a, b, S1)
else:
aux = bhatta_sen_upper_rec (a, b, S1)
a.remove_lineto (b, id)
if len (aux) > 0:
a.lineto (aux[0])
aux[-1].lineto (b)
else:
a.lineto (b)
fecho.extend (aux)
if len (l) == 1:
fecho = [ l[0] ]
pol = Polygon (fecho)
pol.plot ()
pol.extra_info = "vertices: %d" %len (fecho)
return pol
| [
"jiang.zhi@usp.br"
] | jiang.zhi@usp.br |
d35548b0e453cd2577815b23e395954965d3dc5b | 6584124fee86f79ce0c9402194d961395583d6c3 | /blog/migrations/0008_auto_20150603_0708.py | 3f31a14ba2dcb128cea981661d996c147b8152a8 | [] | no_license | janusnic/webman | fdcffb7ed2f36d0951fd18bbaa55d0626cd271e1 | 2e5eaadec64314fddc19f27d9313317f7a236b9e | refs/heads/master | 2018-12-28T18:21:00.291717 | 2015-06-05T11:49:00 | 2015-06-05T11:49:00 | 35,676,834 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,550 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import blog.models
class Migration(migrations.Migration):
dependencies = [
('blog', '0007_page'),
]
operations = [
migrations.CreateModel(
name='Slide',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('status', models.CharField(default=b'0', max_length=1, choices=[(b'0', b'Dratf'), (b'1', b'Published'), (b'2', b'Not Published')])),
('title', models.CharField(max_length=32)),
('description', models.TextField(null=True, blank=True)),
('image', models.ImageField(max_length=1024, null=True, upload_to=blog.models.get_blog_file_name, blank=True)),
],
),
migrations.CreateModel(
name='Slider',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('status', models.CharField(default=b'0', max_length=1, choices=[(b'0', b'Dratf'), (b'1', b'Published'), (b'2', b'Not Published')])),
('title', models.CharField(max_length=32)),
('description', models.TextField(null=True, blank=True)),
],
),
migrations.AddField(
model_name='slide',
name='related_slider',
field=models.ForeignKey(to='blog.Slider'),
),
]
| [
"janusnic@gmail.com"
] | janusnic@gmail.com |
472037244a88c43d49ab5670c21260c01b07c702 | a383364706ed999584d1ed949349db23db6f6f65 | /online_inference/maincode/app.py | 95583090de86e630e1e24e380ac06e1bd8468b0a | [] | no_license | made-ml-in-prod-2021/mikhail.gashkov | 0454320b54d5455a984ae1ae10ecc0a6a4b77c47 | c9cf89bda8b2a9de41731a1aed8533b677760120 | refs/heads/main | 2023-06-06T11:10:47.700270 | 2021-06-27T04:12:22 | 2021-06-27T04:12:22 | 353,569,377 | 0 | 0 | null | 2021-06-27T04:12:23 | 2021-04-01T04:10:32 | Python | UTF-8 | Python | false | false | 1,171 | py | import logging
import os
from typing import List
import uvicorn as uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.exceptions import RequestValidationError
from fastapi.responses import PlainTextResponse
from backend import make_predictions
from data_model import HealthModel, HealthResponse
logger = logging.getLogger(__name__)
app = FastAPI()
GREETING_MESSAGE = 'Hello, this is prediction health service. Go to /predict endopoint to predict on your data'
@app.get('/')
def read_root_handler():
return {'text': GREETING_MESSAGE}
@app.exception_handler(HTTPException)
def http_exception_handler(request, exc):
return PlainTextResponse(str(exc.detail), status_code=exc.status_code)
@app.exception_handler(RequestValidationError)
def validation_exception_handler(request, exc):
return PlainTextResponse(str(exc), status_code=400)
@app.get('/predict', response_model=List[HealthResponse])
def predict_handler(request: HealthModel):
logger.info('Making predictions')
return make_predictions(request.data, request.features)
if __name__ == "__main__":
uvicorn.run("app:app", host="0.0.0.0", port=os.getenv("PORT", 8000))
| [
"mihael.gashkov@gmail.com"
] | mihael.gashkov@gmail.com |
b360e3e157190224e81b6e189a55ae789afa61e9 | 34f36fef36150e83601adb1009b4882bd95fc148 | /p.py | af8cb92fa563f86faea52d83e6caf6af44a13b09 | [] | no_license | 3HeadedMonkey/Python | c1a89bc6dff3b55ac13bc03f4c3ff023ce365acd | 06128491e6ef870d5b0e0fda3cad4cc1a49e7213 | refs/heads/master | 2021-09-11T19:56:29.325623 | 2018-03-20T13:02:34 | 2018-03-20T13:02:34 | 126,019,839 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 13 | py | print("%%%")
| [
"3HeadedMonkey@users.noreply.github.com"
] | 3HeadedMonkey@users.noreply.github.com |
7aa31be9cc6026eb4f8b0ce8c4e7e1636d024a8f | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/50/usersdata/134/19167/submittedfiles/contido.py | 0def95c7b5340172307b304f557314328b69cd1f | [] | no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 470 | py | # -*- coding: utf-8 -*-
from __future__ import division
n = input('Quantidade de elementos de a:')
a = []
for i in range(0,n,1):
a.append(input('Digite um valor:'))
m = input('Quantidade de elementos de b:')
b = []
for i in range(0,m,1):
b.append(input('Digite um valor:'))
cont = 0
def lista(a):
a[0]
for i in range (0,len(a),1):
a[i]
return a[i]
cont = 0
if lista(a)==lista(b):
cont = cont + 1
print cont
cont
| [
"rafael.mota@ufca.edu.br"
] | rafael.mota@ufca.edu.br |
ebad41a0a472c097626b041094292085846763a6 | 2b0d94ad66f4e5c625a312333dfcfe1e674a718f | /graph.py | 3059d7a01351b042aa6495a90bb0a6312e6329eb | [] | no_license | boggdan95/HojaDeTrabajo-10 | 853ef90bf9aec743b74892077a59e8703af1eda5 | 320d77967cb4971f1ec372ef83f0fe017f16aec5 | refs/heads/master | 2016-08-11T20:27:40.646654 | 2015-11-09T05:53:13 | 2015-11-09T05:53:13 | 45,612,606 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,343 | py | #!/usr/bin/env python
"""
An example using Graph as a weighted network.
"""
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
try:
import matplotlib.pyplot as plt
except:
raise
import networkx as nx
import numpy
G=nx.Graph()
G.add_edge('a','b',weight=0.6)
G.add_edge('a','c',weight=0.2)
G.add_edge('c','d',weight=0.1)
G.add_edge('c','e',weight=0.7)
G.add_edge('c','f',weight=0.9)
G.add_edge('a','d',weight=0.3)
G.add_edge('a','c',weight=0.3)
G.add_edge('a','e',weight=0.9)
G.add_edge('d','e',weight=0.9)
distanceMatrix = nx.floyd_warshall_numpy(G)
print distanceMatrix[0][0]
elarge=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] >0.5]
esmall=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] <=0.5]
pos=nx.spring_layout(G) # positions for all nodes
# nodes
nx.draw_networkx_nodes(G,pos,node_size=700)
# edges
nx.draw_networkx_edges(G,pos,edgelist=elarge,
width=6)
nx.draw_networkx_edges(G,pos,edgelist=esmall,
width=6,alpha=0.5,edge_color='b',style='dashed')
# labels
nx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif')
edge_labels=dict([((u,v,),d['weight'])
for u,v,d in G.edges(data=True)])
nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)
plt.axis('off')
plt.savefig("weighted_graph.png") # save as png
plt.show() # display | [
"bandabog@gmail.com"
] | bandabog@gmail.com |
b744c07a9686bbcc763c17cbe204208b510095ed | 13525377024c6d91a5eee2f120caf1f837b42683 | /Python/Notebook/Structures_N.py | 528ab8b09f987316f40a183a4eb4157d69c10071 | [] | no_license | BASARANOMO/water-distribution-network-convex-optimization | b6ea0b6c17734ff00f04065ff3a4b216166acffd | 454e87fa668cfbabe3e62991c87b24d679df6c75 | refs/heads/main | 2023-02-25T12:39:50.978120 | 2021-02-03T08:55:07 | 2021-02-03T08:55:07 | 335,556,035 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,942 | py | #!/usr/bin/python
###############################################################################
# #
# STRUCTURES DE DONNEES NECESSAIRES A LA RESOLUTION DES EQUATIONS DU RESEAU #
# #
# Structures_N : matrices normales #
# #
###############################################################################
# Matrices issues de la topologie du reseau
#
# A : matrice d'incidence noeuds-arcs du graphe : M(m,n)
# Ar : sous-matrice de A correspondant aux reservoirs : M(mr,n)
# Ad : sous-matrice complementaire de Ar pour A : M(md,n)
# AdT : plus grande sous-matrice carree inversible de Ad : M(md,md)
# AdI : matrice inverse de AdT : M(md,md)
# AdC : sous-matrice complementaire de AdT pour Ad : M(md,n-md)
# B : matrice d'incidence arcs-cycles du graphe : M(n,n-md)
#
# Debit admissible
#
# q0 : vecteur des debits admissibles des arcs : M(n,1)
from numpy import dot
from numpy import eye
from numpy import zeros
from numpy.linalg import inv
##### Probleme_R : probleme realiste
from Probleme_R import *
##### Matrice d'incidence et sous-matrices associees
# Matrice d'incidence noeuds-arcs du graphe
A = zeros((m, n))
for i in range(m):
A[i, orig == i] = -1
A[i, dest == i] = +1
# Partition de A suivant le type des noeuds
Ar = A[:mr,:]
Ad = A[mr:m,:]
# Sous-matrice de Ad associee a un arbre et inverse
AdT = Ad[:,:md]
AdI = inv(AdT)
# Sous matrice de Ad associee a un coarbre
AdC = Ad[:,md:n]
# Matrice d'incidence arcs-cycles
B = zeros((n, n-md))
B[:md,:] = -dot(AdI, AdC)
B[md:,:] = eye(n-md)
##### Vecteur des debits admissibles
q0 = zeros(n)
q0[:md] = dot(AdI,fd)
| [
"39518094+BASARANOMO@users.noreply.github.com"
] | 39518094+BASARANOMO@users.noreply.github.com |
95d4da6dbcfa202c339c75ac5c91a3666d1ee502 | d6992d0e69f240c631432ec98168e324f7150c1f | /sonun/urls.py | b6afe081276a7aea6f2ca8cf4a6fb80799176836 | [] | no_license | kairatGroupd/sonunfasion | a574e50f463de931f689e0342358430a4b7a4e55 | be5ac690066af2f57c4f877a7a9aeefd76a92947 | refs/heads/master | 2023-04-24T04:20:41.978650 | 2021-05-15T09:16:30 | 2021-05-15T09:16:30 | 362,811,704 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 50 | py | from django.urls import path
urlpatterns = [
]
| [
"isma.kairat101@gmail.com"
] | isma.kairat101@gmail.com |
2bd10e95182d3011ae67968c95b516bf96e7b0ba | 5071f4ff46f7bb92afe86b9032323d73d729e3db | /ch1_p_event_combine.py | 43be556a559dc2fe57310a28c92bc23165278a62 | [] | no_license | yjioni/openCV | 8e2bdbacec6ef82a3fb0c2ca244cc452819904be | 75e655acdc6b6e8d490a171a3509f895c919e8fb | refs/heads/main | 2023-08-17T11:35:26.937612 | 2021-10-11T12:10:49 | 2021-10-11T12:10:49 | 408,723,702 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 877 | py | import cv2
import numpy as np
win_name = 'Track bar'
img = cv2.imread('C:/Users/oing9/Documents/Crawling/img/blank_500.jpg')
cv2.imshow(win_name, img)
color = tuple()
def onChange(x):
global color
print(x)
b = cv2.getTrackbarPos('B', win_name)
g = cv2.getTrackbarPos('G', win_name)
r = cv2.getTrackbarPos('R', win_name)
print(r, g, b)
color = (b, g, r)
return color
cv2.createTrackbar('R', win_name, 255, 255, onChange)
cv2.createTrackbar('G', win_name, 255, 255, onChange)
cv2.createTrackbar('B', win_name, 255, 255, onChange)
def onMouse(event, x, y, flags, param):
global color
if event == cv2.EVENT_LBUTTONDOWN:
cv2.circle(img, (x,y), 10, color, -1)
cv2.imshow(win_name, img)
cv2.setMouseCallback(win_name, onMouse)
while True:
if cv2.waitKey(1) & 0xFF == 27:
break
cv2.destroyAllWindows()
| [
"oing922@gmail.com"
] | oing922@gmail.com |
1bc628b4e98dcf6afdaa50584923cf76595a081b | ca0af7949100176fb5b0aa69f65fc1cc4c16e497 | /study14_TSA_DieboldKilian_predictability_test.py | 529aaa89cec22a30d3f1c6a9bd9767aa66b57069 | [] | no_license | jwackito/ftssimu | 8bda78ad1b42378751a2112f65263d2872f482af | b231943ac7bb77c22ca574556a3ca16d353fac9d | refs/heads/master | 2021-05-25T10:46:34.578584 | 2021-05-03T20:23:17 | 2021-05-03T20:23:17 | 127,146,132 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,566 | py | ''' This module has essential functions supporting
fast and effective computation of permutation entropy and
its different variations.'''
import itertools
import numpy as np
import pandas as pd
from scipy.spatial.distance import euclidean
def s_entropy(freq_list):
''' This function computes the shannon entropy of a given frequency distribution.
USAGE: shannon_entropy(freq_list)
ARGS: freq_list = Numeric vector representing the frequency distribution
OUTPUT: A numeric value representing shannon's entropy'''
freq_list = [element for element in freq_list if element != 0]
sh_entropy = 0.0
for freq in freq_list:
sh_entropy += freq * np.log(freq)
sh_entropy = -sh_entropy
return(sh_entropy)
def ordinal_patterns(ts, embdim, embdelay):
''' This function computes the ordinal patterns of a time series for a given embedding dimension and embedding delay.
USAGE: ordinal_patterns(ts, embdim, embdelay)
ARGS: ts = Numeric vector representing the time series, embdim = embedding dimension (3<=embdim<=7 prefered range), embdelay = embdding delay
OUPTUT: A numeric vector representing frequencies of ordinal patterns'''
time_series = ts
possible_permutations = list(itertools.permutations(range(embdim)))
lst = list()
for i in range(len(time_series) - embdelay * (embdim - 1)):
sorted_index_array = list(np.argsort(time_series[i:(embdim+i)]))
lst.append(sorted_index_array)
lst = np.array(lst)
element, freq = np.unique(lst, return_counts = True, axis = 0)
freq = list(freq)
if len(freq) != len(possible_permutations):
for i in range(len(possible_permutations)-len(freq)):
freq.append(0)
return(freq)
else:
return(freq)
def p_entropy(op):
ordinal_pat = op
max_entropy = np.log(len(ordinal_pat))
p = np.divide(np.array(ordinal_pat), float(sum(ordinal_pat)))
return(s_entropy(p)/max_entropy)
def complexity(op):
''' This function computes the complexity of a time series defined as: Comp_JS = Q_o * JSdivergence * pe
Q_o = Normalizing constant
JSdivergence = Jensen-Shannon divergence
pe = permutation entopry
ARGS: ordinal pattern'''
pe = p_entropy(op)
constant1 = (0.5+((1 - 0.5)/len(op)))* np.log(0.5+((1 - 0.5)/len(op)))
constant2 = ((1 - 0.5)/len(op))*np.log((1 - 0.5)/len(op))*(len(op) - 1)
constant3 = 0.5*np.log(len(op))
Q_o = -1/(constant1+constant2+constant3)
temp_op_prob = np.divide(op, sum(op))
temp_op_prob2 = (0.5*temp_op_prob)+(0.5*(1/len(op)))
JSdivergence = (s_entropy(temp_op_prob2) - 0.5 * s_entropy(temp_op_prob) - 0.5 * np.log(len(op)))
Comp_JS = Q_o * JSdivergence * pe
return(Comp_JS)
def weighted_ordinal_patterns(ts, embdim, embdelay):
time_series = ts
possible_permutations = list(itertools.permutations(range(embdim)))
temp_list = list()
wop = list()
for i in range(len(time_series) - embdelay * (embdim - 1)):
Xi = time_series[i:(embdim+i)]
Xn = time_series[(i+embdim-1): (i+embdim+embdim-1)]
Xi_mean = np.mean(Xi)
Xi_var = (Xi-Xi_mean)**2
weight = np.mean(Xi_var)
sorted_index_array = list(np.argsort(Xi))
temp_list.append([''.join(map(str, sorted_index_array)), weight])
result = pd.DataFrame(temp_list,columns=['pattern','weights'])
freqlst = dict(result['pattern'].value_counts())
for pat in (result['pattern'].unique()):
wop.append(np.sum(result.loc[result['pattern']==pat,'weights'].values))
return(wop)
| [
"jbogado@linti.unlp.edu.ar"
] | jbogado@linti.unlp.edu.ar |
73d56ed3f94761554d905bebd658ee569b78ff51 | 6685ffa043b91daf8ca7a9007f936b09dd2fb752 | /navbar/products/migrations/0001_initial.py | 5929d2d5f573b8af35c501d5c063f137392ea4ee | [] | no_license | TheKalpit/Bootstrap-Navbar-in-Django-Admin | 8113b3e7be8c3a09ffe0291a526be22cbc756099 | a0fdbedbf21c651a7191d9bd00c09c70a04881a9 | refs/heads/master | 2023-01-27T10:59:06.675747 | 2018-09-11T19:51:53 | 2018-09-11T19:51:53 | 147,719,631 | 8 | 8 | null | 2023-01-04T12:09:42 | 2018-09-06T18:56:17 | CSS | UTF-8 | Python | false | false | 1,059 | py | # Generated by Django 2.1.1 on 2018-09-06 17:25
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=127)),
],
),
migrations.CreateModel(
name='Product',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=127)),
],
),
migrations.CreateModel(
name='ProductVariant',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=127)),
],
),
]
| [
"14352316+TheKalpit@users.noreply.github.com"
] | 14352316+TheKalpit@users.noreply.github.com |
6e62b6a94f556f56721f4c83e23b79e90a9d3e9e | 9d01e03d2e1ff388aad8331280892192e02c0f9d | /limix_genetics/test/test_mvnorm.py | 3fe54045b300c7d796064df3b8d5a0c6ef22f528 | [
"MIT"
] | permissive | limix/limix-genetics | a1dd7e18f55ce62265ca3deaeac0b83ff71239d8 | 9c69a59a61e030243a9f865a7f7ae8842859eaee | refs/heads/master | 2021-01-20T11:00:16.974644 | 2017-02-14T13:26:48 | 2017-02-14T13:26:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 364 | py | from limix_genetics import mvn_ecdf, mvn_eicdf
from numpy.testing import assert_allclose
def test_mvnorm():
x = [1, 2]
mean = [1.0, -0.3]
cov = [[1.5, 0.2],
[0.2, 0.7]]
cdf = mvn_ecdf(x, mean, cov)
icdf = mvn_eicdf(cdf, mean, cov)
assert_allclose(cdf, 0.98032128770733662)
assert_allclose(cdf, mvn_ecdf(icdf, mean, cov))
| [
"danilo.horta@gmail.com"
] | danilo.horta@gmail.com |
53dff7f55eafba911413f49df5babf34aabadd83 | d01596ef17d2f19222c9ddc3823c7b9105d60414 | /venv/bin/easy_install | f8bfd259d11a5292b019867dac97932e7d58b1b6 | [] | no_license | DariusBuhai/InfoarenaGODZ-Django | bf41060986e6b3f989104f38b6cb6c21ccf89495 | e3bb875469f2f0c1d090de8f6402e28ba0824391 | refs/heads/master | 2022-03-18T22:36:53.626500 | 2019-11-05T12:19:28 | 2019-11-05T12:19:28 | 219,039,208 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 460 | #!/Users/dariusbuhai/Desktop/Programs/Django/InfoarenaGODZ/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install'
__requires__ = 'setuptools==40.8.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install')()
)
| [
"buhaidarius@gmail.com"
] | buhaidarius@gmail.com | |
432e3093767c4cbbc0c3e171e6b78a3877a04112 | 1fbf79261b27e4f62e2575ec702b1e6ae5820939 | /Python/0131_04_---파이썬---.py | 4c3964919a9ce158ff0be340ed85795975721ba4 | [] | no_license | sunnyhyo/big_data | 9512dafccddf20a9ab91d5fe45598334cfe69d20 | b4e2a3d0cbd690c4b773fcbe28bd5983bfd03d58 | refs/heads/master | 2021-05-02T14:38:39.332507 | 2018-03-03T06:45:37 | 2018-03-03T06:45:37 | 120,723,356 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 232 | py | ss=input('문자열 입력==>')
print('출력 문자역==>', end="") ## end="" 옆으로 출력##
if ss.startswith('---')==False:
print("---", end="")
print(ss, end="")
if ss.endswith('---')==False:
print("---",end='')
| [
"sunnyhyo77@gmail.com"
] | sunnyhyo77@gmail.com |
90d423c92b10cb9fa0de7d72cc09c0403fd34487 | 37f37114f2a8b4ca3f2e335be09ce2ca10d98421 | /venv/bin/pip3.7 | 9a4e0ec702d2ab3c2981b9abc8b0d66a976456ef | [] | no_license | samuraitruong/python-flask-101 | 8d511be6b17bd032816cccef14cfce4c622aa6c5 | 1ded9d75cddae32de383560d5e232e76c1280f92 | refs/heads/master | 2020-05-30T16:26:39.313912 | 2019-06-02T12:45:33 | 2019-06-02T12:45:33 | 189,845,207 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 407 | 7 | #!/Users/imac/PycharmProjects/untitled/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3.7'
__requires__ = 'pip==19.0.3'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==19.0.3', 'console_scripts', 'pip3.7')()
)
| [
"truong.nguyen@team.telstra.com"
] | truong.nguyen@team.telstra.com |
30d8b731ace754c6d55e68b0962b3618613e33f7 | 8092609ad3f42cfb7ca249be35305f2390acc1c6 | /SNOW CLASS.py | bcb5d293130adc1a3af52696fa8c96d6b582dfbe | [] | no_license | BrichtaICS3U/assignment-2-logo-and-action-alex-b15 | a0f83122194dedd68e87a6645bf3b950d2e980f2 | 776671390e0403b2e0893e297e9a924e09467126 | refs/heads/master | 2020-03-11T09:09:49.463330 | 2018-04-20T14:59:16 | 2018-04-20T14:59:16 | 129,903,265 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 703 | py | import pygame
WHITE = (255, 255, 255)
BLACK =(0,0,0)
GREEN =(0, 255, 0)
class Snow(pygame.sprite.Sprite):
def __init__(self, color, width, height, speed):
super().__init__()
self.image = pygame.Surface([width, height])
self.image.fill(GREEN)
self.image.set_colorkey(GREEN)
self.width=width
self.height=height
self.color = color
self.speed = speed
pygame.draw.ellipse(self.image, color, [0, 0, self.width, self.height])
self.rect = self.image.get_rect()
def moveBackward(self, speed):
self.rect.y += self.speed * speed / 20
def changeSpeed(self, speed):
self.speed = speed
| [
"S331394007@OCDSB"
] | S331394007@OCDSB |
066c9e55d84474d25d481229e1e85c28e840dc3d | 8040b4e3549b68d0dd74878cd31b458104644348 | /test/testRoutes.py | 5e848a001e96ef84f8ff4b4d4c805b85b72db3f1 | [
"MIT"
] | permissive | ahaywardtvuk/PyGeodesy | cbf3bf795d5539f09a95492ec967cde5fdce6189 | 3a7c03c42237102af0a9ab23b2d550020a601d98 | refs/heads/master | 2023-08-29T21:02:06.318878 | 2021-09-30T17:17:50 | 2021-09-30T17:17:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 483,502 | py |
# -*- coding: utf-8 -*-
# Test data for the simplify functions.
# <https://GitHub.com/milkbread/Visvalingam-Wyatt> not Whyatt!
# <https://GitHub.com/milkbread/Visvalingam-Wyatt/blob/master/route.json>
# <https://GitHub.com/milkbread/Visvalingam-Wyatt/blob/master/out.json>
# <https://milkbread.GitHub.io/Visvalingam-Wyatt>
from base import TestsBase
from pygeodesy import LatLon_, R_KM, R_M, \
ellipsoidalVincenty, sphericalTrigonometry, \
areaOf, isclockwise, perimeterOf, unstr
try:
from geographiclib.geodesic import Geodesic
except ImportError:
Geodesic = None
__all__ = ('Antarctica', 'Pts', 'PtsFFI', 'RdpFFI',
'PtsJS', 'PtsJS5', 'VwPts')
__version__ = '21.02.11' # '18.10.12'
# <https://GeographicLib.SourceForge.io/html/python/examples.html>
Antarctica = [LatLon_(_lat, _lon) for _lat, _lon in (
(-63.1, -58),
(-72.9, -74),
(-71.9,-102),
(-74.9,-102),
(-74.3,-131),
(-77.5,-163),
(-77.4, 163),
(-71.7, 172),
(-65.9, 140),
(-65.7, 113),
(-66.6, 88),
(-66.9, 59),
(-69.8, 25),
(-70.0, -4),
(-71.0, -14),
(-77.3, -33),
(-77.9, -46),
(-74.7, -61))] # open
# <https://GitHub.com/urschrei/rdp>
PtsFFI = [LatLon_(_lat, _lon) for _lon, _lat in (
(-0.701206, 52.220489), # lon, lat
(-0.701418, 52.220485),
(-0.703903, 52.220596),
(-0.705340, 52.220565),
(-0.705434, 52.220821),
(-0.705471, 52.221374),
(-0.705456, 52.221916),
(-0.705337, 52.222507),
(-0.705456, 52.222827),
(-0.705973, 52.223213),
(-0.707747, 52.224006),
(-0.708401, 52.224445),
(-0.710000, 52.225944),
(-0.710947, 52.226829),
(-0.713120, 52.228862),
(-0.713550, 52.229431),
(-0.713990, 52.231090),
(-0.714404, 52.232280),
(-0.714460, 52.232448),
(-0.714533, 52.232585),
(-0.714608, 52.232688),
(-0.714780, 52.232826),
(-0.714839, 52.232887),
(-0.714862, 52.232940),
(-0.714860, 52.233013),
(-0.714852, 52.233074),
(-0.714808, 52.233158),
(-0.714582, 52.233543),
(-0.714474, 52.233818),
(-0.714422, 52.234100),
(-0.714348, 52.234375),
(-0.714232, 52.234745),
(-0.714111, 52.234989),
(-0.714026, 52.235092),
(-0.713913, 52.235191),
(-0.713690, 52.235431),
(-0.713572, 52.235607),
(-0.713169, 52.236484),
(-0.713114, 52.236579),
(-0.712968, 52.236747),
(-0.712557, 52.237239),
(-0.712412, 52.237400),
(-0.712330, 52.237495),
(-0.711926, 52.238056),
(-0.711604, 52.238529),
(-0.711409, 52.238750),
(-0.711181, 52.238952),
(-0.711001, 52.239093),
(-0.710784, 52.239315),
(-0.710603, 52.239521),
(-0.709919, 52.240230),
(-0.709518, 52.240646),
(-0.709263, 52.240825),
(-0.708887, 52.240669),
(-0.708684, 52.240627),
(-0.708565, 52.240600),
(-0.708298, 52.240573),
(-0.707846, 52.240577),
(-0.707594, 52.240596),
(-0.707289, 52.240673),
(-0.707042, 52.240745),
(-0.706827, 52.240795),
(-0.706592, 52.240829),
(-0.706265, 52.240852),
(-0.705551, 52.240890),
(-0.705282, 52.240890),
(-0.704945, 52.240863),
(-0.704327, 52.240795),
(-0.703726, 52.240756))]
RdpFFI = [LatLon_(_lat, _lon) for _lon, _lat in (
(-0.701206, 52.220489), # lon, lat
(-0.705340, 52.220565),
(-0.705456, 52.222827),
(-0.713120, 52.228862),
(-0.714862, 52.232940),
(-0.709263, 52.240825),
(-0.703726, 52.240756))]
# <https://GitHub.com/mourner/simplify-js/tree/master/test>
PtsJS = [LatLon_(_y, _x) for _x, _y in (
(224.55, 250.15), (226.91, 244.19), (233.31, 241.45), (234.98, 236.06),
(244.21, 232.76), (262.59, 215.31), (267.76, 213.81), (273.57, 201.84),
(273.12, 192.16), (277.62, 189.03), (280.36, 181.41), (286.51, 177.74),
(292.41, 159.37), (296.91, 155.64), (314.95, 151.37), (319.75, 145.16),
(330.33, 137.57), (341.48, 139.96), (369.98, 137.89), (387.39, 142.51),
(391.28, 139.39), (409.52, 141.14), (414.82, 139.75), (427.72, 127.30),
(439.60, 119.74), (474.93, 107.87), (486.51, 106.75), (489.20, 109.45),
(493.79, 108.63), (504.74, 119.66), (512.96, 122.35), (518.63, 120.89),
(524.09, 126.88), (529.57, 127.86), (534.21, 140.93), (539.27, 147.24),
(567.69, 148.91), (575.25, 157.26), (580.62, 158.15), (601.53, 156.85),
(617.74, 159.86), (622.00, 167.04), (629.55, 194.60), (638.90, 195.61),
(641.26, 200.81), (651.77, 204.56), (671.55, 222.55), (683.68, 217.45),
(695.25, 219.15), (700.64, 217.98), (703.12, 214.36), (712.26, 215.87),
(721.49, 212.81), (727.81, 213.36), (729.98, 208.73), (735.32, 208.20),
(739.94, 204.77), (769.98, 208.42), (779.60, 216.87), (784.20, 218.16),
(800.24, 214.62), (810.53, 219.73), (817.19, 226.82), (820.77, 236.17),
(827.23, 236.16), (829.89, 239.89), (851.00, 248.94), (859.88, 255.49),
(865.21, 268.53), (857.95, 280.30), (865.48, 291.45), (866.81, 298.66),
(864.68, 302.71), (867.79, 306.17), (859.87, 311.37), (860.08, 314.35),
(858.29, 314.94), (858.10, 327.60), (854.54, 335.40), (860.92, 343.00),
(856.43, 350.15), (851.42, 352.96), (849.84, 359.59), (854.56, 365.53),
(849.74, 370.38), (844.09, 371.89), (844.75, 380.44), (841.52, 383.67),
(839.57, 390.40), (845.59, 399.05), (848.40, 407.55), (843.71, 411.30),
(844.09, 419.88), (839.51, 432.76), (841.33, 441.04), (847.62, 449.22),
(847.16, 458.44), (851.38, 462.79), (853.97, 471.15), (866.36, 480.77))]
PtsJS5 = [LatLon_(_y, _x) for _x, _y in (
(224.55, 250.15), (267.76, 213.81), (296.91, 155.64), (330.33, 137.57),
(409.52, 141.14), (439.60, 119.74), (486.51, 106.75), (529.57, 127.86),
(539.27, 147.24), (617.74, 159.86), (629.55, 194.60), (671.55, 222.55),
(727.81, 213.36), (739.94, 204.77), (769.98, 208.42), (779.60, 216.87),
(800.24, 214.62), (820.77, 236.17), (859.88, 255.49), (865.21, 268.53),
(857.95, 280.30), (867.79, 306.17), (859.87, 311.37), (854.54, 335.40),
(860.92, 343.00), (849.84, 359.59), (854.56, 365.53), (844.09, 371.89),
(839.57, 390.40), (848.40, 407.55), (839.51, 432.76), (853.97, 471.15),
(866.36, 480.77))]
# Paris-Berlin-Warsaw-Minsk-Moscow, see
# <https://milkbread.GitHub.io/Visvalingam-Wyatt>
Pts = [LatLon_(_lat, _lon) for _lon, _lat in (
(2.329860, 48.860050), # lon, lat
(2.330270, 48.860580),
(2.330650, 48.860691),
(2.331600, 48.860500),
(2.333330, 48.860119),
(2.336570, 48.859371),
(2.336840, 48.859329),
(2.337980, 48.859261),
(2.338480, 48.859180),
(2.339850, 48.858990),
(2.340110, 48.859501),
(2.340350, 48.859970),
(2.340570, 48.860390),
(2.340730, 48.860722),
(2.340820, 48.860901),
(2.340850, 48.860958),
(2.340950, 48.861130),
(2.341210, 48.861691),
(2.341580, 48.862419),
(2.341870, 48.862968),
(2.341910, 48.863049),
(2.341950, 48.863110),
(2.342140, 48.863468),
(2.342200, 48.863590),
(2.342330, 48.863621),
(2.342430, 48.863628),
(2.342530, 48.863628),
(2.342660, 48.863602),
(2.342770, 48.863602),
(2.342840, 48.863560),
(2.343080, 48.863468),
(2.343590, 48.863319),
(2.344070, 48.863201),
(2.344550, 48.863121),
(2.345200, 48.862991),
(2.345810, 48.863060),
(2.346850, 48.862991),
(2.347310, 48.862980),
(2.347620, 48.863010),
(2.347870, 48.863029),
(2.347960, 48.863071),
(2.348150, 48.863468),
(2.348260, 48.863640),
(2.348940, 48.863800),
(2.349060, 48.863861),
(2.349140, 48.863838),
(2.350000, 48.863628),
(2.350930, 48.863411),
(2.351350, 48.864170),
(2.351480, 48.864410),
(2.351940, 48.865238),
(2.352500, 48.866241),
(2.352940, 48.867039),
(2.353170, 48.867451),
(2.353270, 48.867641),
(2.353420, 48.867908),
(2.353580, 48.868191),
(2.353690, 48.868389),
(2.353850, 48.868690),
(2.353990, 48.868950),
(2.354200, 48.869320),
(2.354210, 48.869339),
(2.354940, 48.870609),
(2.355190, 48.871040),
(2.355280, 48.871208),
(2.355570, 48.871719),
(2.355880, 48.872280),
(2.356490, 48.873360),
(2.356750, 48.873829),
(2.357130, 48.874489),
(2.357270, 48.874760),
(2.357330, 48.874851),
(2.357410, 48.874939),
(2.357460, 48.875019),
(2.357690, 48.875019),
(2.358570, 48.875031),
(2.358920, 48.874931),
(2.359020, 48.874901),
(2.359200, 48.875141),
(2.359350, 48.875259),
(2.359560, 48.875340),
(2.360060, 48.875641),
(2.360280, 48.875771),
(2.360550, 48.876091),
(2.360580, 48.876129),
(2.361370, 48.877430),
(2.361600, 48.877838),
(2.362010, 48.878490),
(2.362220, 48.878799),
(2.362250, 48.878860),
(2.362330, 48.878929),
(2.362590, 48.879021),
(2.362900, 48.879250),
(2.364290, 48.880402),
(2.364700, 48.880730),
(2.365150, 48.881100),
(2.365580, 48.881451),
(2.365780, 48.881611),
(2.365990, 48.881779),
(2.366910, 48.882530),
(2.368220, 48.883572),
(2.368570, 48.883850),
(2.368720, 48.883942),
(2.368850, 48.884022),
(2.368990, 48.884171),
(2.369040, 48.884220),
(2.369170, 48.884350),
(2.369340, 48.884480),
(2.370710, 48.885578),
(2.370850, 48.885689),
(2.370970, 48.885780),
(2.371050, 48.885860),
(2.372420, 48.886978),
(2.372700, 48.887211),
(2.372870, 48.887348),
(2.373530, 48.887878),
(2.373950, 48.888241),
(2.374100, 48.888371),
(2.374260, 48.888489),
(2.375960, 48.889839),
(2.376720, 48.890450),
(2.376850, 48.890560),
(2.377010, 48.890690),
(2.377450, 48.891041),
(2.377540, 48.891109),
(2.377680, 48.891220),
(2.378960, 48.892181),
(2.379130, 48.892311),
(2.379280, 48.892429),
(2.379950, 48.892948),
(2.380070, 48.893040),
(2.380170, 48.893120),
(2.381570, 48.894218),
(2.381700, 48.894321),
(2.382190, 48.894680),
(2.382310, 48.894791),
(2.382740, 48.895142),
(2.383250, 48.895561),
(2.383490, 48.895660),
(2.383590, 48.895741),
(2.383650, 48.895779),
(2.383700, 48.895809),
(2.383890, 48.895939),
(2.384010, 48.896049),
(2.384100, 48.896130),
(2.384180, 48.896210),
(2.384260, 48.896278),
(2.384380, 48.896389),
(2.385610, 48.897499),
(2.385690, 48.897579),
(2.385770, 48.897659),
(2.386150, 48.898041),
(2.386240, 48.898140),
(2.386330, 48.898232),
(2.386610, 48.898499),
(2.387750, 48.899559),
(2.387980, 48.899780),
(2.388400, 48.900051),
(2.388640, 48.900181),
(2.388750, 48.900318),
(2.388820, 48.900520),
(2.388890, 48.900719),
(2.389160, 48.900879),
(2.389330, 48.900982),
(2.389460, 48.901089),
(2.390250, 48.901840),
(2.391140, 48.902729),
(2.391520, 48.903069),
(2.392090, 48.903549),
(2.392120, 48.903580),
(2.393980, 48.905231),
(2.394080, 48.905479),
(2.394520, 48.905899),
(2.395340, 48.906681),
(2.395880, 48.907162),
(2.396300, 48.907570),
(2.397030, 48.908249),
(2.398050, 48.909168),
(2.398300, 48.909409),
(2.399990, 48.910980),
(2.400360, 48.911301),
(2.403230, 48.913940),
(2.404030, 48.914711),
(2.404120, 48.914780),
(2.404360, 48.915001),
(2.404860, 48.915482),
(2.405740, 48.916290),
(2.406070, 48.916599),
(2.406820, 48.917290),
(2.407610, 48.918072),
(2.408840, 48.919022),
(2.409140, 48.919281),
(2.409910, 48.920059),
(2.411690, 48.921719),
(2.411850, 48.921848),
(2.412930, 48.922852),
(2.413100, 48.923100),
(2.413260, 48.923210),
(2.413680, 48.923630),
(2.413840, 48.923771),
(2.414280, 48.924171),
(2.415020, 48.924759),
(2.415610, 48.925289),
(2.416950, 48.926701),
(2.417470, 48.927139),
(2.417590, 48.927238),
(2.417970, 48.927460),
(2.418110, 48.927509),
(2.418320, 48.927738),
(2.418650, 48.928082),
(2.419150, 48.928619),
(2.420250, 48.929562),
(2.420320, 48.929630),
(2.420700, 48.930000),
(2.421070, 48.930359),
(2.422530, 48.931641),
(2.423830, 48.932831),
(2.424150, 48.933159),
(2.424780, 48.933659),
(2.425490, 48.934380),
(2.426000, 48.934872),
(2.426320, 48.935200),
(2.426570, 48.935490),
(2.427010, 48.935959),
(2.427340, 48.936291),
(2.427930, 48.936909),
(2.428080, 48.937099),
(2.428420, 48.937450),
(2.428730, 48.937790),
(2.429840, 48.938839),
(2.430530, 48.939720),
(2.431150, 48.940418),
(2.432060, 48.940479),
(2.432480, 48.940559),
(2.433640, 48.940731),
(2.434080, 48.940842),
(2.434770, 48.940929),
(2.435000, 48.940971),
(2.435260, 48.941090),
(2.436180, 48.941540),
(2.437190, 48.942059),
(2.437280, 48.942101),
(2.437840, 48.942329),
(2.438350, 48.942539),
(2.438780, 48.942669),
(2.439050, 48.942730),
(2.439940, 48.942848),
(2.440410, 48.942909),
(2.440930, 48.943031),
(2.441960, 48.943218),
(2.442970, 48.943329),
(2.443940, 48.943481),
(2.444340, 48.943581),
(2.445940, 48.943939),
(2.446270, 48.944019),
(2.448270, 48.944538),
(2.450060, 48.945000),
(2.451370, 48.945351),
(2.452680, 48.945728),
(2.452900, 48.945728),
(2.454310, 48.946060),
(2.455540, 48.946369),
(2.456860, 48.946690),
(2.458690, 48.947109),
(2.458730, 48.947060),
(2.458800, 48.947021),
(2.458930, 48.946991),
(2.459000, 48.946999),
(2.459080, 48.947029),
(2.459150, 48.947079),
(2.459180, 48.947151),
(2.459170, 48.947208),
(2.461370, 48.947720),
(2.461940, 48.948311),
(2.463250, 48.949650),
(2.463490, 48.949940),
(2.463760, 48.949928),
(2.466330, 48.949871),
(2.466370, 48.949799),
(2.466430, 48.949749),
(2.466520, 48.949711),
(2.466580, 48.949699),
(2.466730, 48.949699),
(2.466860, 48.949741),
(2.467400, 48.949692),
(2.467820, 48.949692),
(2.469920, 48.949829),
(2.472930, 48.950180),
(2.473820, 48.950272),
(2.474330, 48.950298),
(2.475240, 48.950352),
(2.477710, 48.950611),
(2.480360, 48.950851),
(2.481900, 48.950981),
(2.483330, 48.951099),
(2.483900, 48.951092),
(2.485350, 48.951141),
(2.485500, 48.951180),
(2.485930, 48.951149),
(2.486160, 48.951180),
(2.486300, 48.951248),
(2.486330, 48.951271),
(2.486410, 48.951241),
(2.486720, 48.951328),
(2.487290, 48.951401),
(2.491060, 48.951740),
(2.492490, 48.951920),
(2.492850, 48.952030),
(2.494550, 48.952229),
(2.496230, 48.952419),
(2.498810, 48.952721),
(2.499010, 48.952740),
(2.502680, 48.953178),
(2.506410, 48.953678),
(2.506450, 48.953690),
(2.508050, 48.953918),
(2.508290, 48.953979),
(2.508580, 48.954079),
(2.508750, 48.954109),
(2.508900, 48.954128),
(2.509050, 48.954121),
(2.509240, 48.954090),
(2.509580, 48.954079),
(2.509730, 48.954102),
(2.509880, 48.954170),
(2.509950, 48.954201),
(2.510010, 48.954300),
(2.510140, 48.954300),
(2.510630, 48.954330),
(2.512180, 48.954639),
(2.513540, 48.954971),
(2.514620, 48.955151),
(2.521420, 48.956329),
(2.525490, 48.956989),
(2.529870, 48.957691),
(2.531110, 48.957771),
(2.532370, 48.958019),
(2.534790, 48.958630),
(2.535170, 48.958752),
(2.535550, 48.958839),
(2.538520, 48.959591),
(2.542460, 48.960732),
(2.545620, 48.961769),
(2.548730, 48.962849),
(2.550420, 48.963409),
(2.555610, 48.965111),
(2.557160, 48.965599),
(2.564680, 48.968071),
(2.566800, 48.968811),
(2.570560, 48.970200),
(2.574050, 48.971600),
(2.578760, 48.973690),
(2.581110, 48.974758),
(2.582220, 48.975220),
(2.588090, 48.977798),
(2.596700, 48.981602),
(2.606930, 48.986118),
(2.609050, 48.986980),
(2.625880, 48.994381),
(2.627740, 48.995270),
(2.628640, 48.995720),
(2.632740, 48.997822),
(2.636360, 48.999821),
(2.639420, 49.001621),
(2.639990, 49.001961),
(2.643460, 49.004150),
(2.644960, 49.005150),
(2.646000, 49.005840),
(2.648860, 49.007881),
(2.652250, 49.010490),
(2.655380, 49.013100),
(2.655860, 49.013611),
(2.656140, 49.013859),
(2.659600, 49.016830),
(2.663360, 49.020771),
(2.665070, 49.022251),
(2.671970, 49.029140),
(2.672740, 49.029900),
(2.677350, 49.034431),
(2.681620, 49.038300),
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(36.979698, 55.112720),
(36.981850, 55.112061),
(36.982861, 55.111900),
(36.983608, 55.111919),
(36.983849, 55.111900),
(36.984032, 55.111889),
(36.984131, 55.111729),
(36.984329, 55.111691),
(36.985050, 55.111481),
(36.986408, 55.111431),
(36.986691, 55.111671),
(36.986691, 55.112030),
(36.986950, 55.112202),
(36.988060, 55.112289),
(36.990520, 55.112129),
(36.995300, 55.112720),
(36.997551, 55.113468),
(37.002251, 55.112461),
(37.002441, 55.112202),
(37.002010, 55.111301),
(37.002331, 55.111259),
(37.002491, 55.111469),
(37.003620, 55.112839),
(37.004219, 55.112991),
(37.004631, 55.113361),
(37.004700, 55.113750),
(37.004871, 55.113708),
(37.005569, 55.112679),
(37.006821, 55.112041),
(37.009731, 55.110920),
(37.011341, 55.110828),
(37.012211, 55.111111),
(37.013489, 55.111740),
(37.015469, 55.112370),
(37.025341, 55.113319),
(37.028591, 55.113392),
(37.033852, 55.113140),
(37.038738, 55.113060),
(37.039631, 55.113041),
(37.040710, 55.113220),
(37.041599, 55.113998),
(37.041950, 55.114510),
(37.042931, 55.114922),
(37.043598, 55.114948),
(37.045071, 55.114681),
(37.045959, 55.114349),
(37.048660, 55.112850),
(37.050270, 55.111179),
(37.055370, 55.108898),
(37.056831, 55.108440),
(37.057072, 55.108139),
(37.057430, 55.107979),
(37.057678, 55.107849),
(37.057838, 55.107262),
(37.058128, 55.107182),
(37.058922, 55.106998),
(37.059620, 55.107159),
(37.060150, 55.107090),
(37.060841, 55.106640),
(37.062290, 55.106491),
(37.063110, 55.106880),
(37.064270, 55.106812),
(37.065941, 55.106300),
(37.066029, 55.105728),
(37.066090, 55.105122),
(37.066662, 55.105049),
(37.070591, 55.105068),
(37.072441, 55.105000),
(37.074471, 55.105499),
(37.076759, 55.106209),
(37.077831, 55.106541),
(37.081692, 55.107811),
(37.082699, 55.108021),
(37.084110, 55.107941),
(37.086071, 55.107460),
(37.090179, 55.107029),
(37.092060, 55.106750),
(37.094391, 55.106251),
(37.096390, 55.105461),
(37.096081, 55.104919),
(37.096249, 55.104839),
(37.096260, 55.104649),
(37.096039, 55.104561),
(37.096230, 55.104519),
(37.096291, 55.104389),
(37.096909, 55.104198),
(37.097580, 55.103859),
(37.098019, 55.103531),
(37.100090, 55.103168),
(37.103882, 55.103390),
(37.107899, 55.102951),
(37.108829, 55.105179),
(37.109989, 55.106289),
(37.114559, 55.108601),
(37.126110, 55.112492),
(37.132870, 55.113869),
(37.135551, 55.114422),
(37.143639, 55.114399),
(37.160591, 55.113689),
(37.167400, 55.112629),
(37.168499, 55.112598),
(37.173981, 55.113960),
(37.177631, 55.115292),
(37.181881, 55.111858),
(37.184792, 55.110481),
(37.186169, 55.109829),
(37.197540, 55.109692),
(37.206772, 55.106010),
(37.211079, 55.105511),
(37.222580, 55.106209),
(37.228321, 55.106129),
(37.233238, 55.105061),
(37.244549, 55.103298),
(37.247639, 55.103149),
(37.251339, 55.102421),
(37.252621, 55.102470),
(37.253731, 55.102798),
(37.256569, 55.103989),
(37.267151, 55.105621),
(37.275108, 55.106789),
(37.276058, 55.107079),
(37.286098, 55.111752),
(37.287560, 55.112240),
(37.291851, 55.112679),
(37.304550, 55.116409),
(37.308929, 55.117290),
(37.312279, 55.117882),
(37.324741, 55.121410),
(37.327599, 55.122120),
(37.335152, 55.123531),
(37.337212, 55.123909),
(37.337811, 55.123749),
(37.338638, 55.123631),
(37.339630, 55.123581),
(37.343979, 55.123459),
(37.345379, 55.123421),
(37.346241, 55.123451),
(37.347469, 55.123619),
(37.348919, 55.123890),
(37.351589, 55.123871),
(37.353069, 55.123852),
(37.354549, 55.123878),
(37.356030, 55.123951),
(37.359230, 55.124241),
(37.365318, 55.124741),
(37.369572, 55.125099),
(37.374989, 55.125599),
(37.376949, 55.125889),
(37.380440, 55.126400),
(37.381500, 55.126640),
(37.384418, 55.127621),
(37.387081, 55.128578),
(37.387680, 55.128719),
(37.388451, 55.128849),
(37.391029, 55.129051),
(37.391918, 55.129059),
(37.392891, 55.128929),
(37.393860, 55.128700),
(37.395061, 55.128460),
(37.396061, 55.128349),
(37.396832, 55.128281),
(37.401058, 55.128029),
(37.404419, 55.127831),
(37.405540, 55.127850),
(37.406551, 55.127918),
(37.407661, 55.128029),
(37.407982, 55.128071),
(37.408821, 55.128250),
(37.410431, 55.128651),
(37.415249, 55.130058),
(37.416939, 55.130619),
(37.418121, 55.131161),
(37.418579, 55.131500),
(37.419781, 55.132660),
(37.421021, 55.133739),
(37.421600, 55.134079),
(37.422401, 55.134399),
(37.423740, 55.134689),
(37.430691, 55.135429),
(37.439819, 55.136429),
(37.444241, 55.137070),
(37.445789, 55.137390),
(37.446259, 55.137711),
(37.446480, 55.137909),
(37.446522, 55.138149),
(37.446522, 55.138840),
(37.446651, 55.139130),
(37.446819, 55.139309),
(37.447330, 55.139400),
(37.448231, 55.139309),
(37.449612, 55.139111),
(37.450611, 55.139000),
(37.450859, 55.139011),
(37.451141, 55.139160),
(37.451370, 55.140060),
(37.451740, 55.141350),
(37.451839, 55.141541),
(37.452782, 55.141369),
(37.453220, 55.142189),
(37.455021, 55.141769),
(37.455151, 55.141739),
(37.456871, 55.141331),
(37.457401, 55.141209),
(37.457920, 55.141090),
(37.458149, 55.141060),
(37.458469, 55.141041),
(37.458759, 55.141090),
(37.460041, 55.141430),
(37.461380, 55.141788),
(37.462379, 55.142052),
(37.462780, 55.142170),
(37.463261, 55.142288),
(37.465839, 55.143002),
(37.467461, 55.143429),
(37.468800, 55.143780),
(37.468929, 55.143810),
(37.470261, 55.144161),
(37.472549, 55.144730),
(37.472809, 55.144791),
(37.472919, 55.144821),
(37.473640, 55.145000),
(37.473930, 55.145069),
(37.474579, 55.145241),
(37.475269, 55.145409),
(37.476589, 55.145729),
(37.477341, 55.145920),
(37.477928, 55.146061),
(37.478760, 55.146271),
(37.480831, 55.146721),
(37.481369, 55.146839),
(37.484119, 55.147449),
(37.484379, 55.147511),
(37.486080, 55.147911),
(37.487350, 55.148220),
(37.487560, 55.148270),
(37.487820, 55.148331),
(37.487961, 55.148350),
(37.488129, 55.148338),
(37.488251, 55.148319),
(37.488350, 55.148300),
(37.488491, 55.148300),
(37.488590, 55.148289),
(37.488670, 55.148258),
(37.488750, 55.148201),
(37.489029, 55.147820),
(37.489441, 55.147301),
(37.489552, 55.147141),
(37.489601, 55.146950),
(37.489651, 55.146370),
(37.489910, 55.143520),
(37.489929, 55.143330),
(37.489910, 55.143139),
(37.489658, 55.142712),
(37.489529, 55.142540),
(37.489479, 55.142349),
(37.489471, 55.142250),
(37.489460, 55.142078),
(37.489571, 55.141670),
(37.490040, 55.140911),
(37.490170, 55.140800),
(37.490341, 55.140751),
(37.490551, 55.140739),
(37.490761, 55.140751),
(37.490978, 55.140789),
(37.491161, 55.140881),
(37.492050, 55.141529),
(37.492809, 55.141972),
(37.493599, 55.142300),
(37.494450, 55.142559),
(37.495399, 55.142799),
(37.499352, 55.143520),
(37.500710, 55.143681),
(37.502541, 55.143810),
(37.503448, 55.143860),
(37.504372, 55.143829),
(37.510361, 55.143398),
(37.512569, 55.143269),
(37.520920, 55.143978),
(37.522591, 55.144081),
(37.523918, 55.144058),
(37.524540, 55.144039),
(37.524780, 55.143970),
(37.525200, 55.143761),
(37.525620, 55.143398),
(37.526211, 55.142811),
(37.528091, 55.140289),
(37.529202, 55.139511),
(37.532959, 55.137581),
(37.537201, 55.134258),
(37.538979, 55.132900),
(37.541039, 55.131809),
(37.542179, 55.131199),
(37.545330, 55.129169),
(37.546650, 55.129459),
(37.546921, 55.129520),
(37.555550, 55.130920),
(37.556320, 55.130970),
(37.557949, 55.131062),
(37.559521, 55.131149),
(37.560650, 55.131229),
(37.567589, 55.128361),
(37.575562, 55.117641),
(37.579189, 55.112530),
(37.580021, 55.111240),
(37.580830, 55.109951),
(37.581760, 55.108349),
(37.582611, 55.106709),
(37.583469, 55.104809),
(37.584179, 55.102982),
(37.584450, 55.101910),
(37.584751, 55.100800),
(37.585098, 55.098652),
(37.585129, 55.096741),
(37.585041, 55.095650),
(37.584900, 55.094589),
(37.584690, 55.093491),
(37.584419, 55.092388),
(37.584171, 55.091629),
(37.583961, 55.091049),
(37.583450, 55.089729),
(37.582878, 55.088551),
(37.582291, 55.087399),
(37.582001, 55.086880),
(37.581619, 55.086182),
(37.581181, 55.085449),
(37.580238, 55.084030),
(37.579189, 55.082489),
(37.578209, 55.081131),
(37.577320, 55.080040),
(37.577030, 55.079651),
(37.576401, 55.078850),
(37.576080, 55.078442))]
# "threshold": "0.0005", ... # lon, lat, area?
VwPts = [LatLon_(_lat, _lon) for _lon, _lat, _a2 in (
(2.32986, 48.86005, 0), # lon, lat
(2.35093, 48.863411, 0.0006764590350000889),
(2.43115, 48.940418, 0.005275181324999963),
(2.54246, 48.960732, 0.0013039877850000996),
(2.63999, 49.001961, 0.010534887319999823),
(2.80339, 49.144669, 0.024102912679999897),
(2.85119, 49.159790, 0.0007342628499998849),
(2.99549, 49.236160, 0.0034791586549993733),
(3.08543, 49.265160, 0.0006159743600001309),
(3.13198, 49.266472, 0.003311627689999829),
(3.21429, 49.302959, 0.0009464529800000195),
(3.28301, 49.356419, 0.0018893187500001478),
(3.32890, 49.358730, 0.09552116839999703),
(3.37508, 49.387329, 0.0005483386499999484),
(3.36840, 49.406940, 0.0008029409999999435),
(3.43405, 49.446411, 0.002959171529999519),
(3.51661, 49.463291, 0.0026900486400000365),
(3.62701, 49.551029, 0.015417812199999662),
(3.65985, 49.594040, 0.0005695053100000875),
(3.71867, 49.705761, 0.00977195401000054),
(3.80852, 49.780540, 0.0014305421600002072),
(3.93060, 49.850300, 0.011768190849826103),
(4.00552, 49.867611, 0.0006279938350000511),
(4.02625, 49.889511, 0.0006053469450000905),
(4.09659, 49.905418, 0.0021937935149999514),
(4.10586, 49.934929, 0.0008882974199991181),
(4.16724, 49.974251, 0.0006570687600002264),
(4.31856, 50.027500, 0.004801179380001489),
(4.37885, 50.030609, 0.0010781928850002172),
(4.46792, 50.070969, 0.03488412753500111),
(4.49368, 50.127609, 0.0008648552000000303),
(4.57809, 50.246059, 0.0010913535349680074),
(4.60485, 50.252739, 0.0005071614200001603),
(4.65955, 50.304298, 0.001078659360000252),
(4.71331, 50.316250, 0.005746164855001361),
(4.86628, 50.422180, 0.0012534538349999722),
(4.85599, 50.445961, 0.0036526170449996402),
(4.94456, 50.503571, 0.0007576398200001946),
(5.00459, 50.525509, 0.0019311189600001934),
(5.06250, 50.582500, 0.01865430001500212),
(5.12338, 50.591831, 0.0014397069050002618),
(5.19067, 50.649441, 0.0005253489150003868),
(5.32410, 50.724720, 0.0013054674849995174),
(5.46203, 50.782970, 0.38402723349999696),
(5.59527, 50.811749, 0.003648713830000012),
(5.70955, 50.859509, 0.0009227223999996644),
(5.78653, 50.907829, 0.0012011808400062926),
(5.83605, 50.912300, 0.0013927635499998384),
(5.96387, 50.980091, 0.0013384183800003785),
(6.14661, 51.039459, 0.0032356071399997893),
(6.21046, 51.077419, 0.0008143443249999117),
(6.33651, 51.126850, 0.04902457900000075),
(6.36855, 51.158169, 0.0007331124400002367),
(6.48883, 51.229980, 0.003027862770000432),
(6.47779, 51.252701, 0.0015974398750002344),
(6.58010, 51.331532, 0.026891873830001863),
(6.75227, 51.371132, 0.007320081329999557),
(6.84989, 51.429039, 0.0016811635150002966),
(6.87801, 51.431438, 0.000512130390000089),
(6.97855, 51.476440, 0.0011294343550004844),
(7.01853, 51.508781, 0.0005533025450000715),
(7.08124, 51.531830, 30.637291359080464),
(7.24011, 51.558880, 0.0005946596100000248),
(7.31798, 51.557789, 0.0031382185750001104),
(7.42589, 51.595341, 0.0014381680599997108),
(7.54105, 51.608761, 0.023957451864999257),
(7.59659, 51.636028, 0.0007998361400001845),
(7.70191, 51.658932, 0.0011498674699997706),
(7.74593, 51.686958, 0.004628844099999091),
(7.92157, 51.708809, 0.004967921119997949),
(8.03324, 51.755070, 0.0006727705950003259),
(8.19468, 51.804260, 0.0005307743000001811),
(8.35782, 51.849621, 0.05281267402498393),
(8.44433, 51.859482, 0.0006462804050000689),
(8.49079, 51.879719, 0.0005782189850000984),
(8.5841000, 51.884579, 0.0009925159049999634),
(8.6627700, 51.909950, 0.0038223860100000872),
(8.7726000, 51.906609, 0.00667983440999894),
(8.9343000, 51.956890, 0.002762111659999994),
(9.0024100, 51.956379, 0.0012302698100002585),
(9.0968700, 51.990940, 0.0009850244750002075),
(9.1550200, 51.991360, 0.00463162946500384),
(9.3353500, 52.079762, 0.00102454309500001),
(9.3618400, 52.104111, 0.016210181225000855),
(9.4800700, 52.106560, 0.0008511323199998173),
(9.5323100, 52.122040, 0.003928847264999778),
(9.6133500, 52.084469, 0.024710846605002817),
(9.7386200, 52.124001, 0.0009839387999998684),
(9.7681500, 52.149029, 0.007084765800000054),
(9.8509100, 52.159939, 0.00086617153000034),
(9.9341700, 52.153461, 0.0005665361000000064),
(10.015680, 52.160728, 0.007558770084999596),
(10.159670, 52.200970, 0.0006649291799999768),
(10.330790, 52.258030, 2.6073560327749523),
(10.514200, 52.262520, 0.001902058545000699),
(10.669100, 52.245571, 0.004948494300001596),
(10.790770, 52.257530, 0.0024091588200003456),
(10.881130, 52.226810, 0.0011020893599978292),
(10.997780, 52.229191, 0.0013778860650000852),
(11.118510, 52.208031, 0.0006157936350002171),
(11.352270, 52.187160, 0.000984766875000195),
(11.436680, 52.188049, 0.18220487750999814),
(11.521640, 52.164879, 0.005529395700000094),
(11.622380, 52.187531, 0.0008918949599999886),
(11.680520, 52.218311, 0.0013726619999999356),
(11.763740, 52.229019, 0.0079287214000009),
(12.056750, 52.232670, 0.0016880092800002455),
(12.214300, 52.252441, 0.0012197837899998105),
(12.343050, 52.264950, 0.0005295360450000073),
(12.442440, 52.279900, 0.003422048374999983),
(12.509530, 52.338360, 0.0008390861550002213),
(12.552950, 52.351181, 0.026837878589999302),
(12.660930, 52.335209, 0.001307028479999809),
(12.807680, 52.337711, 0.005270944050000337),
(12.918790, 52.290451, 0.02498763803498333),
(12.977330, 52.303600, 0.0018784250500008376),
(13.248490, 52.300331, 0.0016729491000007984),
(13.279420, 52.308590, 0.0007041621900002286),
(13.440540, 52.306080, 0.0010678526499998911),
(13.510740, 52.319302, 0.0012690690550003036),
(13.646030, 52.310699, 0.0012363328449999094),
(13.824100, 52.310299, 0.0010152788399996218),
(13.957590, 52.311550, 0.005348844410000293),
(14.063500, 52.334190, 0.0008116855400002243),
(14.180730, 52.343922, 0.20303513344984891),
(14.291050, 52.324291, 0.0019266052749997023),
(14.543540, 52.314289, 0.01409927991499738),
(14.755990, 52.335320, 0.005373102765001079),
(14.842560, 52.328499, 0.0008129688600002133),
(14.955640, 52.338371, 0.001625489265000505),
(15.062210, 52.324680, 0.0007415078349998441),
(15.165720, 52.325298, 0.002478634384999817),
(15.284460, 52.294231, 0.005768687434999004),
(15.394060, 52.318531, 0.0014994112999996828),
(15.542090, 52.323990, 0.014535860675002775),
(15.653580, 52.299480, 0.0005582185399998018),
(15.712540, 52.296532, 0.06615280913498117),
(15.837110, 52.327572, 0.0013612283400006745),
(16.130489, 52.363850, 0.005248244294000376),
(16.448879, 52.390018, 0.08860175424850196),
(16.535200, 52.385269, 0.0012601281000001862),
(16.671410, 52.348579, 0.005768998621500028),
(16.888260, 52.354610, 0.001358722025499731),
(17.004881, 52.345322, 0.006050716657500223),
(17.170919, 52.307411, 0.029430887288497232),
(17.440769, 52.319050, 0.0007205185349998619),
(17.620930, 52.326950, 0.0013525745904998316),
(17.783670, 52.318829, 1.150875008088602),
(17.890829, 52.282341, 0.01088199056599956),
(17.986370, 52.313019, 0.0007916693010001785),
(18.009010, 52.336861, 0.0006768038859996414),
(18.130541, 52.365410, 0.0013594757285001848),
(18.158409, 52.394329, 0.010194835151500213),
(18.418810, 52.389400, 0.0023208751200005082),
(18.527161, 52.386780, 0.0005453816600001758),
(18.615891, 52.398628, 0.0006317860959999628),
(18.681290, 52.393120, 0.003998675989999382),
(18.762569, 52.420631, 0.0012300901194997852),
(18.805080, 52.412800, 0.0005289674395001868),
(18.868090, 52.426079, 0.018334857585000558),
(19.052900, 52.404129, 0.01182622432000123),
(19.164961, 52.405380, 0.0009709318260000118),
(19.248480, 52.423641, 0.0005799199539998452),
(19.305229, 52.413750, 0.0007908931205001335),
(19.482149, 52.444271, 0.0007165993455001046),
(19.611401, 52.465778, 0.13340130416599558),
(19.706430, 52.546162, 0.009254246704500575),
(19.816271, 52.573441, 0.000855054029499975),
(19.885170, 52.606121, 0.0014355817200004946),
(20.056530, 52.647541, 0.21623071443998879),
(20.331970, 52.616940, 0.029833217064998682),
(20.440701, 52.624760, 0.0013162869750000267),
(20.493790, 52.652790, 0.0026567240250003573),
(20.570709, 52.663910, 0.0007827753049998423),
(20.655951, 52.655880, 0.0016156096159978728),
(20.890230, 52.694012, 0.007938527149999652),
(21.043341, 52.678680, 0.005578297333500103),
(21.115641, 52.708408, 0.002397200585999842),
(21.315540, 52.724289, 0.03672012526000031),
(21.398420, 52.698738, 0.0024483483700000817),
(21.633520, 52.685341, 0.6855450359339951),
(21.859060, 52.784401, 0.0043868435999998906),
(22.246490, 52.987999, 0.002620540360000784),
(22.466600, 53.090141, 0.06174189788000266),
(22.924431, 53.164379, 1.3505344985999352),
(22.975700, 53.163528, 0.0008491012775003619),
(23.233709, 53.126122, 0.0050751893489986806),
(23.418150, 53.125820, 0.0017231361000003742),
(23.506680, 53.144360, 0.006176253293999816),
(23.643511, 53.108250, 0.0389964381259985),
(23.796881, 53.108688, 0.001176999713999742),
(23.871290, 53.124249, 0.0024300958499996546),
(24.068720, 53.116779, 0.0062176423539992195),
(24.450899, 53.153690, 0.008191595654608464),
(24.478390, 53.142380, 0.0005606419294997695),
(24.717630, 53.137539, 0.00233563516000034),
(24.851120, 53.146969, 0.00970452865549962),
(25.131519, 53.118011, 0.0023665216855005135),
(25.251310, 53.088760, 0.006705846240499901),
(25.417540, 53.097252, 0.0010895988389995814),
(25.488529, 53.087769, 0.037688359720497694),
(25.598780, 53.107700, 0.0009777281950000749),
(25.715191, 53.146481, 0.007753966616000042),
(25.901340, 53.139820, 0.0005876681744998979),
(25.925850, 53.132629, 15.624207141311611),
(26.067511, 53.181671, 0.0006953675744998947),
(26.120001, 53.209660, 0.0025667765099997154),
(26.330429, 53.266708, 0.015442059139498357),
(26.599449, 53.402821, 0.00306352640349959),
(26.761070, 53.507370, 0.014732591120000332),
(26.813770, 53.512680, 0.0006814578999998298),
(26.966890, 53.553970, 0.05847813158500108),
(27.039070, 53.589420, 0.0018875338250001297),
(27.089849, 53.666660, 0.007829284025000079),
(27.303511, 53.735130, 0.005011199784500702),
(27.391319, 53.803421, 0.0005928050575001501),
(27.432171, 53.817829, 0.0006160187779998591),
(27.494040, 53.869808, 0.0016981824205002005),
(27.585680, 53.916759, 0.9355083148300166),
(27.719620, 53.955311, 0.0009746368959998464),
(27.854549, 54.008701, 0.009018125029501734),
(27.997959, 54.030449, 0.0017747047010011285),
(28.337090, 54.106628, 0.011066727375504988),
(28.481810, 54.202950, 0.0005153089800003474),
(28.512350, 54.237518, 0.0007449054899999776),
(28.571150, 54.255291, 0.027840016371499444),
(28.770069, 54.277180, 0.0006223182559984489),
(29.036091, 54.312710, 0.04917403952351433),
(29.166031, 54.364811, 0.003102402454000118),
(29.304399, 54.372540, 0.0010403242859962842),
(29.807631, 54.477001, 0.005650901765488488),
(29.896370, 54.473911, 0.0017798839699999177),
(30.119080, 54.506271, 0.013423775744500646),
(30.195641, 54.542679, 0.0009894983555001128),
(30.275570, 54.554840, 0.0018340367199999046),
(30.409130, 54.619732, 0.10448855816644895),
(30.591089, 54.642479, 0.001543720579999872),
(30.780769, 54.683159, 0.009890022788000625),
(31.064070, 54.689949, 0.007023433094500995),
(31.201559, 54.703480, 0.0012718874155001928),
(31.372890, 54.738861, 0.0010077760915000265),
(31.481560, 54.749538, 0.08998507272699677),
(31.637960, 54.814232, 0.0011683243500004794),
(31.673710, 54.843960, 0.01575373942200063),
(32.004681, 54.865780, 0.0029648347299996514),
(32.101212, 54.890060, 0.02520276334899922),
(32.147099, 54.925900, 0.0006533915870000944),
(32.299351, 55.016338, 2.5306121627785108),
(32.504452, 55.056099, 0.0014501986585007901),
(32.597309, 55.059959, 0.0024946020060057374),
(32.674419, 55.078171, 0.000604723479999913),
(33.195610, 55.132179, 0.000745868490499901),
(33.346119, 55.152840, 0.007396271786001787),
(33.471249, 55.183289, 0.04676558635650117),
(33.647659, 55.179359, 0.002276000220000347),
(33.897350, 55.199600, 0.0039050867449983546),
(34.061390, 55.187550, 0.06661104649048372),
(34.237122, 55.222080, 0.0033569191450003952),
(34.389042, 55.265400, 0.000708731580000381),
(34.562809, 55.324280, 0.0073479436810012224),
(34.946049, 55.486511, 0.010486538965999144),
(35.120701, 55.505718, 0.17555820487549917),
(35.398941, 55.491329, 0.002017215894500156),
(35.519581, 55.470581, 0.0007216633059995171),
(35.618980, 55.465450, 0.007075233160503159),
(35.913269, 55.465580, 0.0011515429755005692),
(36.064789, 55.457821, 0.27428072454153984),
(36.175480, 55.494419, 0.002097421690499958),
(36.455029, 55.548950, 0.01867971054849846),
(37.198662, 55.626869, 0.0554204532515025),
(37.297470, 55.657600, 0.0015273776339998135),
(37.366482, 55.709980, 0.002582013140000326),
(37.537930, 55.737560, 0.003194690800000177),
(37.569962, 55.779980, 0))]
# <https://GeographicLib.SourceForge.io/html/python/examples.html>
_JFK_LHR1 = [LatLon_(_lat, _lon) for _lat, _lon in (
( 0, 73.8), # equator
(40.6, 73.8), # JFK
(51.6, 0.5), # LHR
( 0, 0.5))]
_JFK_LHR2 = [LatLon_(_lat, _lon) for _lat, _lon in (
(-40.6, 73.8), # double the area
( 40.6, 73.8), # JFK
( 51.6, 0.5), # LHR
(-51.6, 0.5))]
# del _lat, _lon
def _2LL(pts, LL):
# map LatLon_ instances to LL
for p in pts:
yield LL(p.lat, p.lon)
class Tests(TestsBase):
def test7(self, f, xs, fmt='%.3f', LL=None, known=False, **kwds):
n = f.__name__
if n.endswith('Of'):
n = '.'.join(f.__module__.split('.')[-1:] + [n])
g = globals()
for x, p in zip(xs, ('Antarctica',
'PtsFFI', 'RdpFFI', 'Pts', 'VwPts',
'_JFK_LHR1', '_JFK_LHR2')):
pts = g[p]
if LL:
pts = _2LL(pts, LL)
t = unstr(n, p, wrap=True, **kwds)
# wrap since GeographicLib LONG_UNROLL is always set
r = f(pts, wrap=True, **kwds)
self.test(t, r, x, fmt=fmt, known=known)
def testAreas(self):
self.test7(areaOf, (13552524.8,
1.288, 1.241, 131184.240, 140310.144,
4.00413688487425e7, 2*4.00413688487425e7),
known=True, radius=R_KM, adjust=True)
self.test7(areaOf, (13552524.8,
1.288, 1.241, 131184.240, 140310.144,
4.00413688487425e7, 2*4.00413688487425e7),
known=True, radius=R_KM, adjust=False)
# spherical areaOf requires spherical LatLon
self.test7(sphericalTrigonometry.areaOf, (13552524.810, # 13552524.8096748
1.338, 1.289, 125942.444, 118897.757,
40105639.197, 80211278.393), # 2*40105639.197 == .394
LL=sphericalTrigonometry.LatLon, radius=R_KM)
try: # no LatLon restrictions for ellipsoidal areaOf
# XXX keep ellipsoidalVincenty for backward compatibility
self.test7(ellipsoidalVincenty.areaOf, (1.366270e+13, # 13662703680020.1
1.343272e+06, 1.294375e+06, 1.271286e+11, 1.200540e+11,
4.00413688487425e13, 2*4.00413688487425e13),
fmt='%.6e')
self.test7(ellipsoidalVincenty.areaOf, (1.366270e+13, # 13662703680020.1
1.343272e+06, 1.294375e+06, 1.271286e+11, 1.200540e+11,
4.00413688487425e13, 2*4.00413688487425e13),
fmt='%.6e', LL=ellipsoidalVincenty.LatLon)
except (DeprecationWarning, ImportError) as x:
t = ' '.join(str(x).split())
# XXX keep ellipsoidalVincenty for backward compatibility
self.test('ellipsoidalVincenty.areaOf', t, 'DEPRECATED', known=True)
def testClockwise(self):
self.test7(isclockwise, (True, # XXX False if ispolar is True?
True, True, True, True,
False, False),
adjust=False)
def testPerimeters(self):
self.test7(perimeterOf, (16765661.499,
3224.123, 3185.467, 2762313.129, 2672557.850,
15766750.804, 25981742.208),
known=True, radius=R_M, closed=False)
# spherical perimeterOf requires spherical LatLon
self.test7(sphericalTrigonometry.perimeterOf, (15470624.834, # 16765661.499 closed
3224.123, 3185.467, 2762313.116, 2672556.441,
15789078.314, 26041264.665), # 15766750.804, 25981742.208
LL=sphericalTrigonometry.LatLon, radius=R_M, closed=False)
try: # no LatLon restrictions for ellipsoidal perimeterOf
# XXX keep ellipsoidalVincenty for backward compatibility
self.test7(ellipsoidalVincenty.perimeterOf, (15531947.149,
3229.337, 3190.602, 2769709.679, 2679915.858,
15766750.804, 25981742.208), # assumed
LL=ellipsoidalVincenty.LatLon, closed=False)
self.test7(ellipsoidalVincenty.perimeterOf, (16831067.893,
5491.045, 5452.310, 5259077.510, 5171947.931,
23926469.479, 31533501.608), closed=True) # assumed
except (DeprecationWarning, ImportError) as x:
t = ' '.join(str(x).split())
# XXX keep ellipsoidalVincenty for backward compatibility
self.test('ellipsoidalVincenty.perimeterOf', t, 'DEPRECATED', known=True)
def testGeodesic(self):
# <https://GeographicLib.SourceForge.io/html/python/examples.html>
def _s(n, m='(Sphere)'):
return 'geographiclib.' + n + m
def _w(n, m='(WGS84)'):
return 'geographiclib.' + n + m
if Geodesic:
wgs = Geodesic.WGS84
sph = Geodesic(R_M, 0)
w = wgs.Inverse(-41.32, 174.81, 40.96, -5.50, Geodesic.DISTANCE | Geodesic.LONG_UNROLL)
self.test(_w('WNZ-SAL'), w['s12'], 19959679.267, fmt='%.3f')
self.test(_w('WNZ-SAL'), w['lon2'], 354.50, fmt='%.2f')
s = sph.Inverse(-41.32, 174.81, 40.96, -5.50, Geodesic.DISTANCE | Geodesic.LONG_UNROLL)
self.test(_s('WNZ-SAL'), s['s12'], 19967403.498, fmt='%.3f')
self.test(_s('WNZ-SAL'), s['lon2'], 354.50, fmt='%.2f')
w = wgs.Inverse(40.1, 116.6, 37.6, -122.4, Geodesic.DISTANCE | Geodesic.LONG_UNROLL)
self.test(_w('BJS-SFO'), w['s12'], 9513998.0, fmt='%.1f')
self.test(_w('BJS-SFO'), w['lon2'], 237.6, fmt='%.1f')
s = sph.Inverse(40.1, 116.6, 37.6, -122.4, Geodesic.DISTANCE | Geodesic.LONG_UNROLL)
self.test(_s('BJS-SFO'), s['s12'], 9491734.6, fmt='%.1f')
self.test(_s('BJS-SFO'), s['lon2'], 237.6, fmt='%.1f')
w = wgs.Direct(-32.06, 115.74, 225, 20000e3)
self.test(_w('SW-Perth'), w['lat2'], 32.11195529, fmt='%.8f')
self.test(_w('SW-Perth'), w['lon2'], -63.95925278, fmt='%.8f')
s = sph.Direct(-32.06, 115.74, 225, 20000e3)
self.test(_s('SW-Perth'), s['lat2'], 31.96383509, fmt='%.8f')
self.test(_s('SW-Perth'), s['lon2'], -64.14670854, fmt='%.8f')
w = wgs.Inverse(40.6, -73.8, 51.6, -0.5, Geodesic.DISTANCE | Geodesic.AREA | Geodesic.LONG_UNROLL)
self.test(_w('JFK-LHR'), w['S12'], 40041368848742.5, fmt='%.1f')
self.test(_w('JFK-LHR'), w['s12'], 5551759.4, fmt='%.1f')
s = sph.Inverse(40.6, -73.8, 51.6, -0.5, Geodesic.DISTANCE | Geodesic.AREA | Geodesic.LONG_UNROLL)
self.test(_s('JFK-LHR'), s['S12'], 40105639196534.8, fmt='%.1f')
self.test(_s('JFK-LHR'), s['s12'], 5536892.0, fmt='%.1f')
p = wgs.Polygon()
for ll in Antarctica:
p.AddPoint(ll.lat, ll.lon)
_, p, a = p.Compute()
self.test(_w('Antarctica Peri'), p, 16831067.893, fmt='%.3f')
self.test(_w('Antarctica Area'), a, 13662703680020.1, fmt='%.1f')
p = sph.Polygon()
for ll in Antarctica:
p.AddPoint(ll.lat, ll.lon)
_, p, a = p.Compute()
self.test(_s('Antarctica Peri'), p, 16765661.499, fmt='%.3f')
self.test(_s('Antarctica Area'), a, 13552524809674.8, fmt='%.1f')
else:
self.test('no module', _s('Geodesic', ''), _s('Geodesic', ''))
if __name__ == '__main__':
t = Tests(__file__, __version__)
t.testAreas()
t.testPerimeters()
t.testGeodesic()
t.testClockwise()
t.results()
t.exit()
| [
"mrJean1@Gmail.com"
] | mrJean1@Gmail.com |
de0fd47404e19c1931ac4894e375a34b7d739b29 | 5c4e5678ebbdfc35796c7a7437f1a95fd500db96 | /Notebooks/tarea9.py | d50cf77b8ea09b50b66181720d337e3bbb3998c9 | [] | no_license | Gall-oDrone/thesis-po | cdb6c37b3c3abe192127a237fecfc61c83069bcc | 91cc383d6befe1d864c3cb7d13031852db0eb321 | refs/heads/master | 2023-06-08T19:50:13.338470 | 2021-06-27T22:50:57 | 2021-06-27T22:50:57 | 380,850,999 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,005 | py | #Librerias
import tensorflow as tf
import tensorflow
from tensorflow import keras
import os, os.path
from os.path import isfile, join
import numpy as np
import pandas as pd
import seaborn as sns
import random
import matplotlib
import time
import matplotlib.pyplot as plt
from matplotlib import pyplot
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.optimizers import SGD
# Se carga la base de datos CIFAR-10
from keras.datasets import cifar10
(trainX, trainy), (testX, testy) = cifar10.load_data()
# Datos generales de la base de datos
print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))
print('Test: X=%s, y=%s' % (testX.shape, testy.shape))
# Muestra de las primeras 9 imágenes
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(trainX[i])
plt.show()
from tensorflow.keras.utils import to_categorical
from tensorflow import constant
# Se usa la codificación one-hot para
trainy = to_categorical(trainy)
testy = to_categorical(testy)
# convierte los valores de pixeles entre el rango [0-1]
def prep_pixels(train, test):
train_norm = train.astype('float32')
test_norm = test.astype('float32')
train_norm = train_norm / 255.0
test_norm = test_norm / 255.0
return train_norm, test_norm
from keras.models import Sequential
def define_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(10, activation='softmax'))
# compile model
opt = SGD(lr=0.001, momentum=0.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
# plot diagnostic learning curves
def summarize_diagnostics(history):
# plot loss
pyplot.subplot(211)
pyplot.title('Cross Entropy Loss')
pyplot.plot(history.history['loss'], color='blue', label='train')
pyplot.plot(history.history['val_loss'], color='orange', label='test')
# plot accuracy
pyplot.subplot(212)
pyplot.title('Classification Accuracy')
pyplot.plot(history.history['accuracy'], color='blue', label='train')
pyplot.plot(history.history['val_accuracy'], color='orange', label='test')
# save plot to file
filename = sys.argv[0].split('/')[-1]
pyplot.savefig(filename + '_plot.png')
pyplot.close()
# prepare pixel data
trainX, testX = prep_pixels(trainX, testX)
# define model
model = define_model()
# fit model
history = model.fit(trainX, trainy, epochs=20, batch_size=64, validation_data=(testX, testy), verbose=0)
# evaluate model
_, acc = model.evaluate(testX, testy, verbose=0)
print('> %.3f' % (acc * 100.0))
# learning curves
summarize_diagnostics(history) | [
"ubuntu@ip-172-31-60-51.ec2.internal"
] | ubuntu@ip-172-31-60-51.ec2.internal |
152a606f97b30406ce5a75885b83239dd16060fc | 6a1f7e6151ad4afe4b0ce4724973d4533dc64741 | /dentalwebsite/migrations/0002_form_one.py | 152b11f1eca7301258d9b9da7c8060cff55c4673 | [] | no_license | CarlYebes/FirstWebsite | c35d6c5371ff1c86db1fca7f672b6f4ed30afc8c | 805f0b32d929f5621dc651cfa8638edbccb123c0 | refs/heads/master | 2023-08-03T16:50:27.934039 | 2020-07-12T09:54:32 | 2020-07-12T09:54:32 | 273,384,385 | 0 | 0 | null | 2021-09-22T19:25:23 | 2020-06-19T02:17:21 | CSS | UTF-8 | Python | false | false | 623 | py | # Generated by Django 3.0.7 on 2020-07-12 09:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('dentalwebsite', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Form_one',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('number', models.IntegerField()),
('email', models.EmailField(max_length=254)),
],
),
]
| [
"yebes77gmail.com"
] | yebes77gmail.com |
ee25a66b06ddc0dd45433bf9d8379b7c0c4756c8 | b7138c79af422f2c7180c0e7d4ccdabbc09ab599 | /mysite/polls/models.py | 0a07cef39631b94c3db3e49673296127613a9bcb | [] | no_license | hhabgood/first-django-app | 7b8edab35eda16d7ca1e8ac8b64a16f100a96107 | 45f47b390477acf879b5a8f4ae7523b0967c76a2 | refs/heads/master | 2016-09-05T10:00:00.115209 | 2012-12-29T04:31:51 | 2012-12-29T04:31:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 563 | py | import datetime
from django.utils import timezone
from django.db import models
# Create your models here.
class Poll(models.Model):
question = models.CharField(max_length=200)
pub_date = models.DateTimeField('Date Published')
def __unicode__(self):
return self.question
def was_published_recently(self):
return self.pub_date >= timezone.now() - datetime.timedelta(days=1)
class Choice(models.Model):
poll = models.ForeignKey(Poll)
choice = models.CharField(max_length=200)
votes = models.IntegerField()
def __unicode__(self):
return self.choice | [
"henryhabgood@Henrys-MacBook-Pro.local"
] | henryhabgood@Henrys-MacBook-Pro.local |
09db5f8c4dc2f8c5519c4be380ceba9a3514c66f | fbcceb5fecaba387c28210154bf27555dbf4dcaa | /core/views.py | 8403fce0316428b1bad66ec0cb19b03c81b77386 | [] | no_license | saksham1991999/Marine | 01a1735f5888ebc6e8881c1513b1cd78f468c3fa | d5cb5d9f237cfad59a75e1718dcf1981754d3a94 | refs/heads/main | 2023-02-25T19:01:53.174107 | 2021-02-03T08:19:55 | 2021-02-03T08:19:55 | 335,495,683 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,001 | py | from django.shortcuts import render, get_object_or_404
# Create your views here.
from core.models import Location
from core.serializers import LocationSerializer
from rest_framework import viewsets, status
from rest_framework.response import Response
from rest_framework.decorators import action
# ViewSets define the view behavior.
class LocationViewSet(viewsets.ModelViewSet):
queryset = Location.objects.all()
serializer_class = LocationSerializer
def create(self, request, *args, **kwargs):
serializer_context = {
'request': request,
}
try:
serializer = self.get_serializer(data=request.data, context=serializer_context)
serializer.is_valid(raise_exception=True)
self.perform_create(serializer)
headers = self.get_success_headers(serializer.data)
return Response("Marked", status=status.HTTP_201_CREATED, headers=headers)
except:
return Response("Already Marked", status=status.HTTP_400_BAD_REQUEST)
def delete(self, request, pk, format=None):
try:
snippet = self.get_object(pk)
snippet.delete()
return Response("Removed", status=status.HTTP_204_NO_CONTENT)
except:
return Response("Does not exist", status=status.HTTP_204_NO_CONTENT)
@action(detail=False, methods=['post'])
def delete_custom(self, request):
serializer_context = {
'request': request,
}
try:
lat = request.data['lat']
long = request.data['long']
try:
location = get_object_or_404(Location, lat=lat, long=long)
location.delete()
return Response("Deleted", status=status.HTTP_204_NO_CONTENT)
except:
return Response("Does not exist", status=status.HTTP_404_NOT_FOUND)
except:
return Response("Data is not sufficient", status=status.HTTP_400_BAD_REQUEST)
| [
"saksham1991999@users.noreply.github.com"
] | saksham1991999@users.noreply.github.com |
34d9075fcb8f6a7780fc543fbf024cec7ef1ce6c | d05c946e345baa67e7894ee33ca21e24b8d26028 | /general/gmail-api/gmail_api.py | 3ff3265516251943f18ce1f75b9483a7152eb03e | [
"MIT"
] | permissive | x4nth055/pythoncode-tutorials | 327255550812f84149841d56f2d13eaa84efd42e | d6ba5d672f7060ba88384db5910efab1768c7230 | refs/heads/master | 2023-09-01T02:36:58.442748 | 2023-08-19T14:04:34 | 2023-08-19T14:04:34 | 199,449,624 | 1,858 | 2,055 | MIT | 2023-08-25T20:41:56 | 2019-07-29T12:35:40 | Jupyter Notebook | UTF-8 | Python | false | false | 2,348 | py | # for parsing commandline arguments
import argparse
from common import search_messages, gmail_authenticate
from read_emails import read_message
from send_emails import send_message
from delete_emails import delete_messages
from mark_emails import mark_as_read, mark_as_unread
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Send/Search/Delete/Mark messages using gmail's API.")
subparsers = parser.add_subparsers(help='Subcommands')
parser_1 = subparsers.add_parser('send', help='Send an email')
parser_1.add_argument('destination', type=str, help='The destination email address')
parser_1.add_argument('subject', type=str, help='The subject of the email')
parser_1.add_argument('body', type=str, help='The body of the email')
parser_1.add_argument('files', type=str, help='email attachments', nargs='+')
parser_1.set_defaults(action='send')
parser_2 = subparsers.add_parser('delete', help='Delete a set of emails')
parser_2.add_argument('query', type=str, help='a search query that selects emails to delete')
parser_2.set_defaults(action='delete')
parser_3 = subparsers.add_parser('mark', help='Marks a set of emails as read or unread')
parser_3.add_argument('query', type=str, help='a search query that selects emails to mark')
parser_3.add_argument('read_status', type=bool, help='Whether to mark the message as unread, or as read')
parser_3.set_defaults(action='mark')
parser_4 = subparsers.add_parser('search', help='Marks a set of emails as read or unread')
parser_4.add_argument('query', type=str, help='a search query, which messages to display')
parser_4.set_defaults(action='search')
args = parser.parse_args()
service = gmail_authenticate()
if args.action == 'send':
# TODO: add attachements
send_message(service, args.destination, args.subject, args.body, args.files)
elif args.action == 'delete':
delete_messages(service, args.query)
elif args.action == 'mark':
print(args.unread_status)
if args.read_status:
mark_as_read(service, args.query)
else:
mark_as_unread(service, args.query)
elif args.action == 'search':
results = search_messages(service, args.query)
for msg in results:
read_message(service, msg)
| [
"fullclip@protonmail.com"
] | fullclip@protonmail.com |
99f8262b6cec043218a0a2857e49d0730d5c1b2b | 1f64ad2fbd46b5ca779525694224d3c3a0df3621 | /mac/urls.py | 45e3a626e9eff83978ee52b72830c7d09e265cba | [] | no_license | naman267/hostecommerce | 866167216373dee806834c7b93bff20a5d6e7a2c | dd6a79940347373c580a723fc2aaad2844e2a2f5 | refs/heads/main | 2023-01-29T02:03:31.006117 | 2020-11-30T17:16:06 | 2020-11-30T17:16:06 | 317,238,793 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,256 | py | """mac URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.1/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'))
"""
from django.contrib import admin
from django.urls import path, include
from django.conf import settings
from django.conf.urls.static import static
from .import views
from django.views.static import serve
from django.conf.urls import url
urlpatterns = [
path('admin/', admin.site.urls),
path('shop/',include('shop.urls')),
path('blog/',include('blog.urls')),
path('',views.index),
url(r'^media/(?P<path>.*)$', serve,{'document_root':settings.MEDIA_ROOT}),
url(r'^static/(?P<path>.*)$', serve,{'document_root': settings.STATIC_ROOT}),
]+ static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)
| [
"namanb139j@gmail.com"
] | namanb139j@gmail.com |
a5f4ced5d11aa5d3a0ca8c571f25d9806a584c2e | 9a39e38d2e291abf840ee74c625a6e30eb0833bb | /DFS回溯/leetcode_78_排列子集.py | 91056884d7f3037cb0d69557a9760d70aace5cbd | [] | no_license | Gyczero/Leetcode_practice | 62dd261d6d9d51d9f8bec41cfcf3059301cf1b82 | 6708479302cca3ea3d930e6e80264f213ea29c5f | refs/heads/master | 2020-03-25T05:22:13.047777 | 2019-11-10T06:17:42 | 2019-11-10T06:17:42 | 143,443,266 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 883 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/10/18 11:38 上午
# @Author : Frankie
# @File : leetcode_78_排列子集.py
from typing import List
class Solution:
def subsets(self, nums: List[int]) -> List[List[int]]:
"""
思路：DFS
可选数组
choose过程
path记录 res总结结果
终止条件 target
:param nums:
:return:
"""
# 数组排序
nums.sort()
res = []
self._dfs(nums, [], res)
return res
def _dfs(self, nums, path, res):
"""
:return:
"""
# 一定要注意copy()
res.append(path.copy())
if len(nums) == 0:
return
for i in range(len(nums)):
path.append(nums[i])
self._dfs(nums[i+1:], path, res)
path.pop(-1)
| [
"guotaicheng@tuyoogame.com"
] | guotaicheng@tuyoogame.com |
4d8c9e72253b36357ac9da790ac0470411996637 | 64c5341a41e10ea7f19582cbbf3c201d92768b9f | /webInterface/webInterface/aligner_webapp/alignworker/converters/__init__.py | 0e064af14769dfc7fe765fe1bd5b633c0724dfa8 | [] | no_license | CLARIN-PL/yalign | 6b050b5c330b8eaf7e1e2f9ef83ec88a8abe5164 | 6da94fbb74e803bea337e0c171c8abff3b17d7ee | refs/heads/master | 2023-06-10T18:30:42.112215 | 2021-06-24T13:07:17 | 2021-06-24T13:07:17 | 51,368,327 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,508 | py | import os
import logging
import shutil
import subprocess
import tempfile
from alignworker.tmp import get_temp_file
from .tokenizer import tokenize
from .detokenizer import detokenize
from .tmxconverter import to_tmx
from .tsvconverter import to_tsv
log = logging.getLogger(__name__)
def doc_to_plaintext(path):
"""TODO: Docstring for doc_to_plaintext.
:path: TODO
:returns: TODO
"""
out_path = get_temp_file()
if not(path.endswith('.doc') or path.endswith('.docx') or path.endswith('.odt')):
shutil.copyfile(path, out_path)
return out_path
log.info('Converting doc into text file from "%s" to "%s"', path, out_path)
tmp_dir = tempfile.mkdtemp(suffix='_doc_to_text_')
subprocess.call([
'soffice',
'--headless',
'--convert-to',
'txt:Text',
'--outdir',
tmp_dir,
path
])
try:
dirpath, _, filenames = next(os.walk(tmp_dir))
converted_path = os.path.join(dirpath, filenames[0])
shutil.copyfile(converted_path, out_path)
shutil.rmtree(tmp_dir)
except IndexError:
shutil.copyfile(path, out_path)
return out_path
def norm_utf8(path):
"""TODO: Docstring for norm_utf8.
:path: TODO
:returns: TODO
"""
out_path = get_temp_file()
with open(out_path, 'w', encoding='utf-8') as f:
with open(path, encoding='utf-8', errors='replace') as in_f:
for rec in in_f:
f.write(rec)
return out_path
| [
"krzysztof@wolk.pl"
] | krzysztof@wolk.pl |
582cbd1623dd4d7eeb37ca6fb75423a60dfd4e18 | 1bd6d5502534e2450ba45697b7089370095012b9 | /Publications/migrations/0007_auto_20170712_1738.py | 20333a50954933e50982b8bbb1ba6a3b8c3dd34f | [] | no_license | bipindr123/DSCEdjango | bb337b60508de651d306460aba71e88828e6d2bf | e2bf0b73b5834d37c6e189a3e2123b2355db835b | refs/heads/master | 2018-12-08T21:22:29.369776 | 2018-09-16T09:45:48 | 2018-09-16T09:45:48 | 122,481,367 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 544 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.2 on 2017-07-12 12:08
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Publications', '0006_auto_20170712_1735'),
]
operations = [
migrations.AlterField(
model_name='publication',
name='type',
field=models.CharField(choices=[('conference', 'conference'), ('Journal', 'Journal')], max_length=100),
),
]
| [
"bipindr123@gmail.com"
] | bipindr123@gmail.com |
0a9cfcf2ec82d45e6d0ddd8ea55c06257b146864 | d5f3a51e6e7c156965895339cfe3a5914459b579 | /ngflaskcurd/utils/filters.py | 4bf6aa543fa56221c6d320c6312b38b3697f340f | [] | no_license | vaveee/ngflaskcurd | 4b4427dc3aca72a147d43dffa7e5d97b56c51a56 | 85ff9d5df0614c9a53a8ad6ef40e2783e22a9053 | refs/heads/master | 2020-04-26T09:09:02.795216 | 2015-04-27T07:40:36 | 2015-04-27T07:40:36 | 34,651,589 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 957 | py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
from __future__ import absolute_import
import re
import datetime
from jinja2 import evalcontextfilter, Markup, escape
from HTMLParser import HTMLParser
class MLStripper(HTMLParser):
def __init__(self):
self.reset()
self.fed = []
def handle_data(self, d):
self.fed.append(d)
def get_data(self):
return ''.join(self.fed)
class EnvFilters(object):
def __init__(self, app):
if not app:
return
self.app = app
self.addEnvFilters()
def addEnvFilters(self):
self.app.jinja_env.filters['num_to_letter'] = num_to_letter
self.app.jinja_env.filters['sanitize_html'] = sanitize_html
def num_to_letter(value):
return chr(value-1 + ord('A'))
def sanitize_html(html):
s = MLStripper()
s.feed(html)
if len(s.get_data())>110:
return s.get_data()[:100]+"..."
return s.get_data()
| [
"vaveee@qq.com"
] | vaveee@qq.com |
c4676b9a3e52c62f37c50d24fd5b6e05d2b37b6e | 4f04e11e9181b04b8c34138eb226ca113c34985e | /assignments/assignment 2 - traffic B.py | 49df2cccb7afe45ab070884c1efad88a66acd2d4 | [
"CC-BY-4.0",
"CC-BY-3.0",
"MIT"
] | permissive | WRKampi/numerical-mooc | b83f299594d4bf50d4ae217bb6ef78c7d143f0fa | 0a3771289e26f2c91918fd982a85511f79042e03 | refs/heads/master | 2020-12-03T10:40:26.307293 | 2020-11-11T20:10:10 | 2020-11-11T20:10:10 | 26,779,974 | 0 | 0 | NOASSERTION | 2020-11-11T20:10:11 | 2014-11-17T22:16:48 | Jupyter Notebook | UTF-8 | Python | false | false | 1,260 | py | """assignment 3 of the Numerical MOOC.
Traffic flow
1D model solved by forward difference in time and backward difference in space.
Different parameters as in part A
"""
import numpy as np
import matplotlib.pyplot as plt
# problem parameters
V_max = 136. # km/u
L = 11. # km
rho_max = 250. # cars/km
nx = 51
dx = L/(nx-1) # km
dt = 0.001 # hours
nt = int(3./60./dt)
cV = 1./3.6
# initial conditions
x = np.linspace(0,L,nx)
rho0 = np.ones(nx)*20.
rho0[10:20] = 50.
def dFdrho(rho):
return V_max*(1. - 2.*rho/rho_max)
def V(rho):
return V_max*(1. - rho/rho_max)
# time integrations
rho = rho0.copy()
for n in range(1, nt):
rhon = rho.copy()
dFdrhon = dFdrho(rhon)
rho[1:] = rhon[1:] - dFdrhon[1:]*dt/dx*(rhon[1:] - rhon[0:-1])
rho[0] = 20.
if n == 49:
rho_3min = rho.copy()
# questions
print("V0_min", cV*np.min(V(rho0)))
#~ print("v_mean", cV*np.sum(V(rho_3min)*rho_3min)/np.sum(rho_3min))
print("v_mean alt", cV*np.mean(V(rho_3min)))
print("v_min", cV*np.min(V(rho)))
# plot of initial and final solutions
plt.plot(x, rho0, 'b:', label='rho0')
plt.plot(x, rho, 'b', label='rho')
plt.plot(x, V(rho0), 'g:', label='V0')
plt.plot(x, V(rho), 'g', label='V')
plt.legend(loc=0)
plt.ylim(0., V_max + 5.);
plt.show()
| [
"wrk_git@fastmail.fm"
] | wrk_git@fastmail.fm |
c60387fc2f7c95b345959d7f6fe4fb0a6bdf87d6 | ce428689244a1d38c1a2d85ea769b58c8c19378e | /main.py | d50a889d2cf98ef30490a94c7913416835a16d40 | [] | no_license | nandybishal23/Image-to-ASCII-Art | 7be89d6caa639036b311a6395e9ea0333bd5fff1 | 1a43f9bf378cee7355e873d0388330c0945d143a | refs/heads/main | 2023-04-01T20:55:58.229155 | 2021-04-12T03:12:22 | 2021-04-12T03:12:22 | 357,042,067 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 71 | py | import pywhatkit
pywhatkit.image_to_ascii_art('input.jpg','output.png') | [
"59905645+nandybishal23@users.noreply.github.com"
] | 59905645+nandybishal23@users.noreply.github.com |
9cb6fbc14b3a93b651df194e9ff910eb9a3fc3ca | d9c35991fc54af811e329afcf4b1519910394518 | /blob_api/blob_api/asgi.py | 7fb95d49af8e991d15a0c13a88a1acb6eb15aa64 | [] | no_license | Gaurav98700/DevopsCiCd | a6bbae6ba00504322b4021a5c440e77d486c5ef9 | 4328ec4c26167b3657c880617dd6244878613900 | refs/heads/main | 2023-03-20T02:05:07.819916 | 2021-03-15T14:00:14 | 2021-03-15T14:00:14 | 348,227,992 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 393 | py | """
ASGI config for blob_api project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'blob_api.settings')
application = get_asgi_application()
| [
"gaurav.5.gupta@atos.net"
] | gaurav.5.gupta@atos.net |
8edc48e1691172ead4b934b2b58fe6342f292901 | 5b4686ace41ebfcb2c694283b232761010cf31d7 | /libraries/pyserial/serialutil.py | 7d51752ff05e315397c26f9046da44bf76af0ba9 | [
"Apache-2.0"
] | permissive | gepd/Deviot | bbf4d40fbecb8187255a4ab0f3e4dae7e5a7d985 | 150caea06108369b30210eb287a580fcff4904af | refs/heads/develop | 2023-08-18T04:13:56.932126 | 2020-07-13T18:02:23 | 2020-07-13T18:02:23 | 47,856,861 | 335 | 91 | Apache-2.0 | 2023-01-28T02:53:49 | 2015-12-11T23:56:06 | Python | UTF-8 | Python | false | false | 21,692 | py | #! python
#
# Base class and support functions used by various backends.
#
# This file is part of pySerial. https://github.com/pyserial/pyserial
# (C) 2001-2016 Chris Liechti <cliechti@gmx.net>
#
# SPDX-License-Identifier: BSD-3-Clause
import io
import time
# ``memoryview`` was introduced in Python 2.7 and ``bytes(some_memoryview)``
# isn't returning the contents (very unfortunate). Therefore we need special
# cases and test for it. Ensure that there is a ``memoryview`` object for older
# Python versions. This is easier than making every test dependent on its
# existence.
try:
memoryview
except (NameError, AttributeError):
# implementation does not matter as we do not really use it.
# it just must not inherit from something else we might care for.
class memoryview(object): # pylint: disable=redefined-builtin,invalid-name
pass
try:
unicode
except (NameError, AttributeError):
unicode = str # for Python 3, pylint: disable=redefined-builtin,invalid-name
try:
basestring
except (NameError, AttributeError):
basestring = (str,) # for Python 3, pylint: disable=redefined-builtin,invalid-name
# "for byte in data" fails for python3 as it returns ints instead of bytes
def iterbytes(b):
"""Iterate over bytes, returning bytes instead of ints (python3)"""
if isinstance(b, memoryview):
b = b.tobytes()
i = 0
while True:
a = b[i:i + 1]
i += 1
if a:
yield a
else:
break
# all Python versions prior 3.x convert ``str([17])`` to '[17]' instead of '\x11'
# so a simple ``bytes(sequence)`` doesn't work for all versions
def to_bytes(seq):
"""convert a sequence to a bytes type"""
if isinstance(seq, bytes):
return seq
elif isinstance(seq, bytearray):
return bytes(seq)
elif isinstance(seq, memoryview):
return seq.tobytes()
elif isinstance(seq, unicode):
raise TypeError('unicode strings are not supported, please encode to bytes: {!r}'.format(seq))
else:
# handle list of integers and bytes (one or more items) for Python 2 and 3
return bytes(bytearray(seq))
# create control bytes
XON = to_bytes([17])
XOFF = to_bytes([19])
CR = to_bytes([13])
LF = to_bytes([10])
PARITY_NONE, PARITY_EVEN, PARITY_ODD, PARITY_MARK, PARITY_SPACE = 'N', 'E', 'O', 'M', 'S'
STOPBITS_ONE, STOPBITS_ONE_POINT_FIVE, STOPBITS_TWO = (1, 1.5, 2)
FIVEBITS, SIXBITS, SEVENBITS, EIGHTBITS = (5, 6, 7, 8)
PARITY_NAMES = {
PARITY_NONE: 'None',
PARITY_EVEN: 'Even',
PARITY_ODD: 'Odd',
PARITY_MARK: 'Mark',
PARITY_SPACE: 'Space',
}
class SerialException(IOError):
"""Base class for serial port related exceptions."""
class SerialTimeoutException(SerialException):
"""Write timeouts give an exception"""
writeTimeoutError = SerialTimeoutException('Write timeout')
portNotOpenError = SerialException('Attempting to use a port that is not open')
class Timeout(object):
"""\
Abstraction for timeout operations. Using time.monotonic() if available
or time.time() in all other cases.
The class can also be initialized with 0 or None, in order to support
non-blocking and fully blocking I/O operations. The attributes
is_non_blocking and is_infinite are set accordingly.
"""
if hasattr(time, 'monotonic'):
# Timeout implementation with time.monotonic(). This function is only
# supported by Python 3.3 and above. It returns a time in seconds
# (float) just as time.time(), but is not affected by system clock
# adjustments.
TIME = time.monotonic
else:
# Timeout implementation with time.time(). This is compatible with all
# Python versions but has issues if the clock is adjusted while the
# timeout is running.
TIME = time.time
def __init__(self, duration):
"""Initialize a timeout with given duration"""
self.is_infinite = (duration is None)
self.is_non_blocking = (duration == 0)
self.duration = duration
if duration is not None:
self.target_time = self.TIME() + duration
else:
self.target_time = None
def expired(self):
"""Return a boolean, telling if the timeout has expired"""
return self.target_time is not None and self.time_left() <= 0
def time_left(self):
"""Return how many seconds are left until the timeout expires"""
if self.is_non_blocking:
return 0
elif self.is_infinite:
return None
else:
delta = self.target_time - self.TIME()
if delta > self.duration:
# clock jumped, recalculate
self.target_time = self.TIME() + self.duration
return self.duration
else:
return max(0, delta)
def restart(self, duration):
"""\
Restart a timeout, only supported if a timeout was already set up
before.
"""
self.duration = duration
self.target_time = self.TIME() + duration
class SerialBase(io.RawIOBase):
"""\
Serial port base class. Provides __init__ function and properties to
get/set port settings.
"""
# default values, may be overridden in subclasses that do not support all values
BAUDRATES = (50, 75, 110, 134, 150, 200, 300, 600, 1200, 1800, 2400, 4800,
9600, 19200, 38400, 57600, 115200, 230400, 460800, 500000,
576000, 921600, 1000000, 1152000, 1500000, 2000000, 2500000,
3000000, 3500000, 4000000)
BYTESIZES = (FIVEBITS, SIXBITS, SEVENBITS, EIGHTBITS)
PARITIES = (PARITY_NONE, PARITY_EVEN, PARITY_ODD, PARITY_MARK, PARITY_SPACE)
STOPBITS = (STOPBITS_ONE, STOPBITS_ONE_POINT_FIVE, STOPBITS_TWO)
def __init__(self,
port=None,
baudrate=9600,
bytesize=EIGHTBITS,
parity=PARITY_NONE,
stopbits=STOPBITS_ONE,
timeout=None,
xonxoff=False,
rtscts=False,
write_timeout=None,
dsrdtr=False,
inter_byte_timeout=None,
exclusive=None,
**kwargs):
"""\
Initialize comm port object. If a "port" is given, then the port will be
opened immediately. Otherwise a Serial port object in closed state
is returned.
"""
self.is_open = False
self.portstr = None
self.name = None
# correct values are assigned below through properties
self._port = None
self._baudrate = None
self._bytesize = None
self._parity = None
self._stopbits = None
self._timeout = None
self._write_timeout = None
self._xonxoff = None
self._rtscts = None
self._dsrdtr = None
self._inter_byte_timeout = None
self._rs485_mode = None # disabled by default
self._rts_state = True
self._dtr_state = True
self._break_state = False
self._exclusive = None
# assign values using get/set methods using the properties feature
self.port = port
self.baudrate = baudrate
self.bytesize = bytesize
self.parity = parity
self.stopbits = stopbits
self.timeout = timeout
self.write_timeout = write_timeout
self.xonxoff = xonxoff
self.rtscts = rtscts
self.dsrdtr = dsrdtr
self.inter_byte_timeout = inter_byte_timeout
self.exclusive = exclusive
# watch for backward compatible kwargs
if 'writeTimeout' in kwargs:
self.write_timeout = kwargs.pop('writeTimeout')
if 'interCharTimeout' in kwargs:
self.inter_byte_timeout = kwargs.pop('interCharTimeout')
if kwargs:
raise ValueError('unexpected keyword arguments: {!r}'.format(kwargs))
if port is not None:
self.open()
# - - - - - - - - - - - - - - - - - - - - - - - -
# to be implemented by subclasses:
# def open(self):
# def close(self):
# - - - - - - - - - - - - - - - - - - - - - - - -
@property
def port(self):
"""\
Get the current port setting. The value that was passed on init or using
setPort() is passed back.
"""
return self._port
@port.setter
def port(self, port):
"""\
Change the port.
"""
if port is not None and not isinstance(port, basestring):
raise ValueError('"port" must be None or a string, not {}'.format(type(port)))
was_open = self.is_open
if was_open:
self.close()
self.portstr = port
self._port = port
self.name = self.portstr
if was_open:
self.open()
@property
def baudrate(self):
"""Get the current baud rate setting."""
return self._baudrate
@baudrate.setter
def baudrate(self, baudrate):
"""\
Change baud rate. It raises a ValueError if the port is open and the
baud rate is not possible. If the port is closed, then the value is
accepted and the exception is raised when the port is opened.
"""
try:
b = int(baudrate)
except TypeError:
raise ValueError("Not a valid baudrate: {!r}".format(baudrate))
else:
if b < 0:
raise ValueError("Not a valid baudrate: {!r}".format(baudrate))
self._baudrate = b
if self.is_open:
self._reconfigure_port()
@property
def bytesize(self):
"""Get the current byte size setting."""
return self._bytesize
@bytesize.setter
def bytesize(self, bytesize):
"""Change byte size."""
if bytesize not in self.BYTESIZES:
raise ValueError("Not a valid byte size: {!r}".format(bytesize))
self._bytesize = bytesize
if self.is_open:
self._reconfigure_port()
@property
def exclusive(self):
"""Get the current exclusive access setting."""
return self._exclusive
@exclusive.setter
def exclusive(self, exclusive):
"""Change the exclusive access setting."""
self._exclusive = exclusive
if self.is_open:
self._reconfigure_port()
@property
def parity(self):
"""Get the current parity setting."""
return self._parity
@parity.setter
def parity(self, parity):
"""Change parity setting."""
if parity not in self.PARITIES:
raise ValueError("Not a valid parity: {!r}".format(parity))
self._parity = parity
if self.is_open:
self._reconfigure_port()
@property
def stopbits(self):
"""Get the current stop bits setting."""
return self._stopbits
@stopbits.setter
def stopbits(self, stopbits):
"""Change stop bits size."""
if stopbits not in self.STOPBITS:
raise ValueError("Not a valid stop bit size: {!r}".format(stopbits))
self._stopbits = stopbits
if self.is_open:
self._reconfigure_port()
@property
def timeout(self):
"""Get the current timeout setting."""
return self._timeout
@timeout.setter
def timeout(self, timeout):
"""Change timeout setting."""
if timeout is not None:
try:
timeout + 1 # test if it's a number, will throw a TypeError if not...
except TypeError:
raise ValueError("Not a valid timeout: {!r}".format(timeout))
if timeout < 0:
raise ValueError("Not a valid timeout: {!r}".format(timeout))
self._timeout = timeout
if self.is_open:
self._reconfigure_port()
@property
def write_timeout(self):
"""Get the current timeout setting."""
return self._write_timeout
@write_timeout.setter
def write_timeout(self, timeout):
"""Change timeout setting."""
if timeout is not None:
if timeout < 0:
raise ValueError("Not a valid timeout: {!r}".format(timeout))
try:
timeout + 1 # test if it's a number, will throw a TypeError if not...
except TypeError:
raise ValueError("Not a valid timeout: {!r}".format(timeout))
self._write_timeout = timeout
if self.is_open:
self._reconfigure_port()
@property
def inter_byte_timeout(self):
"""Get the current inter-character timeout setting."""
return self._inter_byte_timeout
@inter_byte_timeout.setter
def inter_byte_timeout(self, ic_timeout):
"""Change inter-byte timeout setting."""
if ic_timeout is not None:
if ic_timeout < 0:
raise ValueError("Not a valid timeout: {!r}".format(ic_timeout))
try:
ic_timeout + 1 # test if it's a number, will throw a TypeError if not...
except TypeError:
raise ValueError("Not a valid timeout: {!r}".format(ic_timeout))
self._inter_byte_timeout = ic_timeout
if self.is_open:
self._reconfigure_port()
@property
def xonxoff(self):
"""Get the current XON/XOFF setting."""
return self._xonxoff
@xonxoff.setter
def xonxoff(self, xonxoff):
"""Change XON/XOFF setting."""
self._xonxoff = xonxoff
if self.is_open:
self._reconfigure_port()
@property
def rtscts(self):
"""Get the current RTS/CTS flow control setting."""
return self._rtscts
@rtscts.setter
def rtscts(self, rtscts):
"""Change RTS/CTS flow control setting."""
self._rtscts = rtscts
if self.is_open:
self._reconfigure_port()
@property
def dsrdtr(self):
"""Get the current DSR/DTR flow control setting."""
return self._dsrdtr
@dsrdtr.setter
def dsrdtr(self, dsrdtr=None):
"""Change DsrDtr flow control setting."""
if dsrdtr is None:
# if not set, keep backwards compatibility and follow rtscts setting
self._dsrdtr = self._rtscts
else:
# if defined independently, follow its value
self._dsrdtr = dsrdtr
if self.is_open:
self._reconfigure_port()
@property
def rts(self):
return self._rts_state
@rts.setter
def rts(self, value):
self._rts_state = value
if self.is_open:
self._update_rts_state()
@property
def dtr(self):
return self._dtr_state
@dtr.setter
def dtr(self, value):
self._dtr_state = value
if self.is_open:
self._update_dtr_state()
@property
def break_condition(self):
return self._break_state
@break_condition.setter
def break_condition(self, value):
self._break_state = value
if self.is_open:
self._update_break_state()
# - - - - - - - - - - - - - - - - - - - - - - - -
# functions useful for RS-485 adapters
@property
def rs485_mode(self):
"""\
Enable RS485 mode and apply new settings, set to None to disable.
See serial.rs485.RS485Settings for more info about the value.
"""
return self._rs485_mode
@rs485_mode.setter
def rs485_mode(self, rs485_settings):
self._rs485_mode = rs485_settings
if self.is_open:
self._reconfigure_port()
# - - - - - - - - - - - - - - - - - - - - - - - -
_SAVED_SETTINGS = ('baudrate', 'bytesize', 'parity', 'stopbits', 'xonxoff',
'dsrdtr', 'rtscts', 'timeout', 'write_timeout',
'inter_byte_timeout')
def get_settings(self):
"""\
Get current port settings as a dictionary. For use with
apply_settings().
"""
return dict([(key, getattr(self, '_' + key)) for key in self._SAVED_SETTINGS])
def apply_settings(self, d):
"""\
Apply stored settings from a dictionary returned from
get_settings(). It's allowed to delete keys from the dictionary. These
values will simply left unchanged.
"""
for key in self._SAVED_SETTINGS:
if key in d and d[key] != getattr(self, '_' + key): # check against internal "_" value
setattr(self, key, d[key]) # set non "_" value to use properties write function
# - - - - - - - - - - - - - - - - - - - - - - - -
def __repr__(self):
"""String representation of the current port settings and its state."""
return '{name}<id=0x{id:x}, open={p.is_open}>(port={p.portstr!r}, ' \
'baudrate={p.baudrate!r}, bytesize={p.bytesize!r}, parity={p.parity!r}, ' \
'stopbits={p.stopbits!r}, timeout={p.timeout!r}, xonxoff={p.xonxoff!r}, ' \
'rtscts={p.rtscts!r}, dsrdtr={p.dsrdtr!r})'.format(
name=self.__class__.__name__, id=id(self), p=self)
# - - - - - - - - - - - - - - - - - - - - - - - -
# compatibility with io library
# pylint: disable=invalid-name,missing-docstring
def readable(self):
return True
def writable(self):
return True
def seekable(self):
return False
def readinto(self, b):
data = self.read(len(b))
n = len(data)
try:
b[:n] = data
except TypeError as err:
import array
if not isinstance(b, array.array):
raise err
b[:n] = array.array('b', data)
return n
# - - - - - - - - - - - - - - - - - - - - - - - -
# context manager
def __enter__(self):
if not self.is_open:
self.open()
return self
def __exit__(self, *args, **kwargs):
self.close()
# - - - - - - - - - - - - - - - - - - - - - - - -
def send_break(self, duration=0.25):
"""\
Send break condition. Timed, returns to idle state after given
duration.
"""
if not self.is_open:
raise portNotOpenError
self.break_condition = True
time.sleep(duration)
self.break_condition = False
# - - - - - - - - - - - - - - - - - - - - - - - -
# backwards compatibility / deprecated functions
def flushInput(self):
self.reset_input_buffer()
def flushOutput(self):
self.reset_output_buffer()
def inWaiting(self):
return self.in_waiting
def sendBreak(self, duration=0.25):
self.send_break(duration)
def setRTS(self, value=1):
self.rts = value
def setDTR(self, value=1):
self.dtr = value
def getCTS(self):
return self.cts
def getDSR(self):
return self.dsr
def getRI(self):
return self.ri
def getCD(self):
return self.cd
def setPort(self, port):
self.port = port
@property
def writeTimeout(self):
return self.write_timeout
@writeTimeout.setter
def writeTimeout(self, timeout):
self.write_timeout = timeout
@property
def interCharTimeout(self):
return self.inter_byte_timeout
@interCharTimeout.setter
def interCharTimeout(self, interCharTimeout):
self.inter_byte_timeout = interCharTimeout
def getSettingsDict(self):
return self.get_settings()
def applySettingsDict(self, d):
self.apply_settings(d)
def isOpen(self):
return self.is_open
# - - - - - - - - - - - - - - - - - - - - - - - -
# additional functionality
def read_all(self):
"""\
Read all bytes currently available in the buffer of the OS.
"""
return self.read(self.in_waiting)
def read_until(self, terminator=LF, size=None):
"""\
Read until a termination sequence is found ('\n' by default), the size
is exceeded or until timeout occurs.
"""
lenterm = len(terminator)
line = bytearray()
timeout = Timeout(self._timeout)
while True:
c = self.read(1)
if c:
line += c
if line[-lenterm:] == terminator:
break
if size is not None and len(line) >= size:
break
else:
break
if timeout.expired():
break
return bytes(line)
def iread_until(self, *args, **kwargs):
"""\
Read lines, implemented as generator. It will raise StopIteration on
timeout (empty read).
"""
while True:
line = self.read_until(*args, **kwargs)
if not line:
break
yield line
# - - - - - - - - - - - - - - - - - - - - - - - - -
if __name__ == '__main__':
import sys
s = SerialBase()
sys.stdout.write('port name: {}\n'.format(s.name))
sys.stdout.write('baud rates: {}\n'.format(s.BAUDRATES))
sys.stdout.write('byte sizes: {}\n'.format(s.BYTESIZES))
sys.stdout.write('parities: {}\n'.format(s.PARITIES))
sys.stdout.write('stop bits: {}\n'.format(s.STOPBITS))
sys.stdout.write('{}\n'.format(s))
| [
"guillermoepd@hotmail.com"
] | guillermoepd@hotmail.com |
8f4305a715b7001f035cd96c718cd3ab7f10925d | 05fa3773bb72a267d83b43819440c907a8fc80c3 | /picarus/vision/video_combined_features.py | d22ed8765e0e8ec2a7110cec67f1c3046b98e8a7 | [] | no_license | objects-in-space-and-time/picarus | 91ce2ac87a3acd84603996cf0e2c4f3df0a6e49e | 04745c47396891d97dc7ad3752ebce5c9b79f33a | refs/heads/master | 2020-03-27T06:01:28.562484 | 2012-12-05T07:50:16 | 2012-12-05T07:50:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 685 | py | import video_raw_features
import video_block_features
import sys
import hadoopy
class Mapper(object):
def __init__(self):
self.b = video_block_features.Mapper()
self.r = video_raw_features.Mapper()
def map(self, event_filename, video_data):
hadoopy.counter('CombinedFeatures', 'DontHave')
sys.stderr.write('%s\n' % str(event_filename))
for event_filename, features in self.r.map(event_filename, video_data):
sys.stderr.write('%s\n' % str(event_filename))
for x in self.b.map(event_filename, features):
yield x
if __name__ == '__main__':
hadoopy.run(Mapper, video_block_features.Reducer)
| [
"bwhite@dappervision.com"
] | bwhite@dappervision.com |
0e2ce782e1d999efc7214a20494d52ac43598cab | b7a5f0190086966a06963a01a1e404449a9ad5f5 | /baseline/evaluate_gpu.py | 0148d1db187046762636a21de96191fb232bbebc | [
"MIT"
] | permissive | Chaucergit/deep_reid | 63bbf3a3b9a2cf56b193572a76ecd1e95cd05e38 | 75ab34dd3814485743aa55a1d29a5dd36a68c0d5 | refs/heads/master | 2020-07-05T05:22:55.498753 | 2019-05-01T16:33:34 | 2019-05-01T16:33:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,938 | py | import argparse
import os
from datetime import datetime
import scipy.io
import torch
import numpy as np
import time
#######################################################################
# Evaluate
from utils.file_helper import write
def evaluate(qf,ql,qc,gf,gl,gc):
query = qf.view(-1,1)
# print(query.shape)
score = torch.mm(gf,query)
score = score.squeeze(1).cpu()
score = score.numpy()
# predict index
index = np.argsort(score) #from small to large
index = index[::-1]
score = score[index]
# index = index[0:2000]
# good index
query_index = np.argwhere(gl==ql)
camera_index = np.argwhere(gc==qc)
good_index = np.setdiff1d(query_index, camera_index, assume_unique=True)
junk_index1 = np.argwhere(gl==-1)
junk_index2 = np.intersect1d(query_index, camera_index)
junk_index = np.append(junk_index2, junk_index1) #.flatten())
ap_tmp, CMC_tmp = compute_mAP(index, good_index, junk_index)
return ap_tmp, CMC_tmp, index, score
def compute_mAP(index, good_index, junk_index):
ap = 0
cmc = torch.IntTensor(len(index)).zero_()
if good_index.size==0: # if empty
cmc[0] = -1
return ap,cmc
# remove junk_index
mask = np.in1d(index, junk_index, invert=True)
index = index[mask]
# find good_index index
ngood = len(good_index)
mask = np.in1d(index, good_index)
rows_good = np.argwhere(mask==True)
rows_good = rows_good.flatten()
cmc[rows_good[0]:] = 1
for i in range(ngood):
d_recall = 1.0/ngood
precision = (i+1)*1.0/(rows_good[i]+1)
if rows_good[i]!=0:
old_precision = i*1.0/rows_good[i]
else:
old_precision=1.0
ap = ap + d_recall*(old_precision + precision)/2
return ap, cmc
######################################################################
parser = argparse.ArgumentParser(description='eval')
parser.add_argument('--name', default='market', type=str, help='transfer name')
parser.add_argument('--dohash', action='store_true', help='evaluate on hash or feature')
# parser.add_argument('--name', default='duke_grid-cv-0-test', type=str, help='transfer name')
# parser.add_argument('--name', default='market_duke-r-test', type=str, help='transfer name')
opt = parser.parse_args()
if opt.dohash:
result = scipy.io.loadmat(os.path.join('eval', opt.name,'hash_result.mat'))
else:
result = scipy.io.loadmat(os.path.join('eval', opt.name,'pytorch_result.mat'))
query_feature = torch.FloatTensor(result['query_f'])
query_cam = result['query_cam'][0]
query_label = result['query_label'][0]
gallery_feature = torch.FloatTensor(result['gallery_f'])
gallery_cam = result['gallery_cam'][0]
gallery_label = result['gallery_label'][0]
query_feature = query_feature.cuda()
gallery_feature = gallery_feature.cuda()
# print(query_feature.shape)
CMC = torch.IntTensor(len(gallery_label)).zero_()
ap = 0.0
#print(query_label)
scores, indexs = [], []
for i in range(len(query_label)):
ap_tmp, CMC_tmp, index, score = evaluate(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)
if CMC_tmp[0]==-1:
continue
CMC = CMC + CMC_tmp
ap += ap_tmp
scores.append(score)
indexs.append(index)
# print(i, CMC_tmp[0])
scores = np.array(scores)
indexs = np.array(indexs)
score_path = os.path.join('eval', opt.name, 'score.txt')
pid_path = os.path.join('eval', opt.name, 'pid.txt')
np.savetxt(score_path, scores, fmt='%.4f')
np.savetxt(pid_path, indexs, fmt='%d')
CMC = CMC.float()
CMC = CMC/len(query_label) #average CMC
print(datetime.now().strftime("%Y.%m.%d-%H:%M:%S\t") + 'Rank@1:%f Rank@5:%f Rank@10:%f mAP:%f\n'%(CMC[0],CMC[4],CMC[9],ap/len(query_label)))
write(os.path.join('eval', opt.name, 'acc.txt'),
datetime.now().strftime("%Y.%m.%d-%H:%M:%S\t") + 'Rank@1:%f Rank@5:%f Rank@10:%f mAP:%f\n'%(CMC[0],CMC[4],CMC[9],ap/len(query_label)))
| [
"cweihang@foxmail.com"
] | cweihang@foxmail.com |
d3ad272076ee9759071811293e8a7a681e1901ba | a549c1cb49692a6dab0935b617ed942394629bf9 | /Celery/scheduler/scheduler/settings.py | d5be5b8b062625a69ce8cabb8b26ac68a64b73b5 | [] | no_license | Godwinjthomas/Internship | 18a786ac87127bab6188c4cebeb4814a1efc1ab4 | 830c202b478a2a3521493c64172f97e50d213b74 | refs/heads/main | 2023-06-29T19:55:38.058371 | 2021-07-29T18:17:13 | 2021-07-29T18:17:13 | 390,814,302 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,303 | py | """
Django settings for scheduler project.
Generated by 'django-admin startproject' using Django 3.2.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.2/ref/settings/
"""
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-7l=&ch0vq%dz^bmeli1n_vak#_xil!s46wvz1c@mloffy(j7xb'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django_celery_beat',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'scheduler.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [BASE_DIR / 'templates']
,
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'scheduler.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.2/howto/static-files/
STATIC_URL = '/static/'
# Default primary key field type
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
| [
"87801046+Godwinjthomas@users.noreply.github.com"
] | 87801046+Godwinjthomas@users.noreply.github.com |
ed13abcb1e2058ea6d0fb7f3bf240330c664151f | 2c135601989f7a5a6c591576dcb5646c5ca19d5b | /static_file/loginoutcase | 98e72aa552d8bb98bc3832be5270128abfda32d8 | [] | no_license | zhixuchen/AutoTestPlatform | db077fb9c7ca9524e7fa3781a5397cbff29315a4 | bc539eaa7cbbbcd4a32e911ade41f826f7490d85 | refs/heads/master | 2023-01-09T11:34:28.789596 | 2020-11-16T06:46:15 | 2020-11-16T06:46:15 | 265,992,771 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 579 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2020/11/1 11:17
@Author : chenzhixu
@Email : 1195733026@qq.com
@File : mobile_action.py
@Project : AutoTestPlatform
"""
import unittest
from WebforAutoTestPlatform.static_file.mobile_plugins import MobilePlugin
class LoginOutCase(unittest.TestCase):
@staticmethod
def LoginOutCase():
MobilePlugin(SUITE_ID).mobile_plugins("LoginOut")
def get_case_class(suite_id,suite_params):
global SUITE_PARAMS
SUITE_PARAMS = suite_params
global SUITE_ID
SUITE_ID=suite_id
return LoginOutCase
| [
"noreply@github.com"
] | noreply@github.com | |
7787bd0506a97003a41242177ee3ac24c4b43919 | 1d3d8f023bfb1442a651af62cd00bebde456d354 | /manager/migrations/0010_result_number.py | 858c19df0cf22463ca1e2ad2d22e05b745196598 | [] | no_license | zhanghaoyan/sale | 173cf81827480ade23f400dfec294810618c2d10 | dfb85f3fa8a9a870534e8b4ef2ae1f56f557eb30 | refs/heads/master | 2020-12-30T14:12:49.405924 | 2017-05-22T15:52:49 | 2017-05-22T15:52:49 | 91,289,177 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 445 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10.4 on 2017-05-16 09:49
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('manager', '0009_result_dealtime'),
]
operations = [
migrations.AddField(
model_name='result',
name='number',
field=models.IntegerField(default=1),
),
]
| [
"zhanghaoyan@elex-tech.com"
] | zhanghaoyan@elex-tech.com |
430f17611bdb0d4a7f118f816207e74bb4b21c6c | fb710aec952ba7b9dc6db3c2203df84c1a59fa57 | /MosquittoPuBSub/MQTTSubscriber.py | bdc2a8703e26bb98a94bddb1a475ad81ec4157dd | [] | no_license | dkiiitnr/MosquittoBasic | 9e630bed1b394a27a70bc2c08182e76b95b0fde0 | abe904d1a467bb4cdfc3892eaabc99341819dea0 | refs/heads/master | 2023-03-15T21:04:58.307473 | 2021-03-07T15:10:35 | 2021-03-07T15:10:35 | 345,373,220 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,710 | py | import paho.mqtt.client as mqtt
import time
import pandas as pd
import os
# Taking the variables for the methods
client = mqtt.Client("Data Logger")
topicName = "unbox/frame_to_aggregator"
QOS_val = 2
global counter
i = 0
node_0 = []
node_1 = []
node_2 = []
node_3 = []
new_node_0 = []
new_node_1 = []
new_node_2 = []
new_node_3 = []
save_list_0 = []
save_list_1 = []
save_list_2 = []
save_list_3 = []
def on_connect(pvtClient, userdata, flags, rc):
if rc == 0:
print("Connected to client! Return Code:" + str(rc))
result = client.subscribe(topicName, QOS_val)
elif rc == 5:
print("Authentication Error! Return Code: " + str(rc))
client.disconnect()
def on_message(pvtClient, userdata, msg):
if (msg.payload.decode()[13] == "0"):
dest_id = msg.payload.decode()[0]
pincode = msg.payload.decode()[1]
pincode += msg.payload.decode()[2]
pincode += msg.payload.decode()[3]
pincode += msg.payload.decode()[4]
pincode += msg.payload.decode()[5]
pincode += msg.payload.decode()[6]
dest_mode = msg.payload.decode()[7]
parcel_status = msg.payload.decode()[8]
stop_switch = msg.payload.decode()[9]
emergency_switch = msg.payload.decode()[10]
bag_position = msg.payload.decode()[11]
bag_presence = msg.payload.decode()[12]
node_0 = [int(dest_id), int(pincode), int(dest_mode), int(parcel_status), int(stop_switch),
int(emergency_switch), int(bag_presence), int(bag_position)]
new_node_0.append(node_0)
print('Node 0')
elif (msg.payload.decode()[13] == "1"):
dest_id = msg.payload.decode()[0]
pincode = msg.payload.decode()[1]
pincode += msg.payload.decode()[2]
pincode += msg.payload.decode()[3]
pincode += msg.payload.decode()[4]
pincode += msg.payload.decode()[5]
pincode += msg.payload.decode()[6]
dest_mode = msg.payload.decode()[7]
parcel_status = msg.payload.decode()[8]
stop_switch = msg.payload.decode()[9]
emergency_switch = msg.payload.decode()[10]
bag_position = msg.payload.decode()[11]
bag_presence = msg.payload.decode()[12]
node_1 = [int(dest_id), int(pincode), int(dest_mode), int(parcel_status), int(stop_switch),
int(emergency_switch), int(bag_presence), int(bag_position)]
new_node_1.append(node_1)
print('Node 1')
elif (msg.payload.decode()[13] == "2"):
dest_id = msg.payload.decode()[0]
pincode = msg.payload.decode()[1]
pincode += msg.payload.decode()[2]
pincode += msg.payload.decode()[3]
pincode += msg.payload.decode()[4]
pincode += msg.payload.decode()[5]
pincode += msg.payload.decode()[6]
dest_mode = msg.payload.decode()[7]
parcel_status = msg.payload.decode()[8]
stop_switch = msg.payload.decode()[9]
emergency_switch = msg.payload.decode()[10]
bag_position = msg.payload.decode()[11]
bag_presence = msg.payload.decode()[12]
node_2 = [int(dest_id), int(pincode), int(dest_mode), int(parcel_status), int(stop_switch),
int(emergency_switch), int(bag_presence), int(bag_position)]
new_node_2.append(node_2)
print('Node 2')
elif (msg.payload.decode()[13] == "3"):
global i, list_compare
dest_id = msg.payload.decode()[0]
pincode = msg.payload.decode()[1]
pincode += msg.payload.decode()[2]
pincode += msg.payload.decode()[3]
pincode += msg.payload.decode()[4]
pincode += msg.payload.decode()[5]
pincode += msg.payload.decode()[6]
dest_mode = msg.payload.decode()[7]
parcel_status = msg.payload.decode()[8]
stop_switch = msg.payload.decode()[9]
emergency_switch = msg.payload.decode()[10]
bag_position = msg.payload.decode()[11]
bag_presence = msg.payload.decode()[12]
node_3 = [int(dest_id), int(pincode), int(dest_mode), int(parcel_status), int(stop_switch),
int(emergency_switch), int(bag_presence), int(bag_position)]
new_node_3.append(node_3)
print('dest_id: ', dest_id)
print('pincode: ', pincode)
print('dest_mode: ', dest_mode)
print('parcel_status: ', parcel_status)
print('bag_presence: ', bag_presence)
print('bag_position: ', bag_position)
print('stop_switch: ', stop_switch)
print('emergency_switch', emergency_switch)
if not (new_node_3[i] == new_node_3[i - 1]):
print(new_node_3[i])
save_list_3.append(new_node_3[i])
i += 1
if msg.payload.decode() == "exit(0)":
client.disconnect()
def save_data():
first_node = pd.DataFrame(save_list_0,
columns=["Dest_ID", 'PINCODE', 'Dest_mode', 'Parcel', 'stop_switch', 'emergency_switch',
'bag_presence', 'bag_position'])
first_writer = pd.ExcelWriter('Node 1.xlsx', engine='xlsxwriter')
first_node.to_excel(first_writer, sheet_name="Node_1")
second_node = pd.DataFrame(save_list_1,
columns=["Dest_ID", 'PINCODE', 'Dest_mode', 'Parcel', 'stop_switch', 'emergency_switch',
'bag_presence', 'bag_position'])
second_writer = pd.ExcelWriter('Node 2.xlsx', engine='xlsxwriter')
second_node.to_excel(second_writer, sheet_name="Node_2")
third_node = pd.DataFrame(save_list_2,
columns=["Dest_ID", 'PINCODE', 'Dest_mode', 'Parcel', 'stop_switch', 'emergency_switch',
'bag_presence', 'bag_position'])
third_writer = pd.ExcelWriter('Node 3.xlsx', engine='xlsxwriter')
third_node.to_excel(third_writer, sheet_name="Node_3")
fourth_node = pd.DataFrame(save_list_3,
columns=["Dest_ID", 'PINCODE', 'Dest_mode', 'Parcel', 'stop_switch', 'emergency_switch',
'bag_presence', 'bag_position'])
fourth_writer = pd.ExcelWriter('Node 4.xlsx', engine='xlsxwriter')
fourth_node.to_excel(fourth_writer, sheet_name="Node_4")
def main():
client.on_connect = on_connect
client.on_message = on_message
# client.on_disconnect = on_disconnect
host = "localhost"
port = 1883
keepAlive = 60
client.connect(host, port, keepAlive) # establishing the connection
time.sleep(1) # giving a sleep time for the connection to setup
client.loop_forever()
if __name__ == '__main__':
try:
main()
except:
save_data()
| [
"yadav1996deepak1@gmail.com"
] | yadav1996deepak1@gmail.com |
4d78643d901fd554c695ee14e2f79334177eb94c | 991fc97fa022c0d7f6ae5bc6844b7b1e9e013589 | /domain/achievement/migrations/0001_initial.py | a47b8e7e572b5f3b1f3bdeebf2599d5d03bfa940 | [] | no_license | pseudobabble/ufunction | 436c143801933c23a23404b34525402ffaf8587e | 6cd04061a7dca99aa5a5aba2caec0f778eb98aee | refs/heads/master | 2023-05-28T08:10:32.599993 | 2020-07-01T06:21:02 | 2020-07-01T06:21:02 | 276,285,944 | 0 | 0 | null | 2021-06-10T23:07:02 | 2020-07-01T05:34:15 | Python | UTF-8 | Python | false | false | 9,069 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.21 on 2019-06-08 22:07
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Action',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('action_name', models.CharField(help_text=b'What action is this?', max_length=500, verbose_name='Action')),
('target_metric', models.FloatField(help_text=b'How will you know if you have done this enough to achieve the goal?', verbose_name='Target')),
('target_metric_unit', models.CharField(help_text=b'The units of your target metric', max_length=500, verbose_name='Unit')),
('created_date', models.DateTimeField(auto_now_add=True, verbose_name='Date Created')),
('updated_date', models.DateTimeField(auto_now=True, verbose_name='Date Updated')),
],
),
migrations.CreateModel(
name='Goal',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200, verbose_name='Title')),
('end_state_description', models.TextField(blank=True, help_text=b'Describe what it looks like when you have achieved the goal', verbose_name=b'End State Description')),
('target_date', models.DateField(help_text=b'The date by which you want to have achieved this goal', verbose_name=b'Target Date')),
('complete', models.BooleanField(default=False, verbose_name='Complete?')),
('created_date', models.DateTimeField(auto_now_add=True, verbose_name='Date Created')),
('updated_date', models.DateTimeField(auto_now=True, verbose_name='Date Updated')),
],
options={
'verbose_name': 'Goal',
'verbose_name_plural': 'Goals',
},
),
migrations.CreateModel(
name='Intention',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('intention', models.CharField(help_text=b'What will you do today to bring you closer to your goal?', max_length=1000, verbose_name='Todays Intention')),
('intended_metric', models.FloatField(help_text=b'How much/many times will you perform the action?', verbose_name='Intended Metric')),
('enjoyable_aspects', models.CharField(max_length=1000, verbose_name='What do you enjoy about this activity?')),
('created_date', models.DateTimeField(auto_now_add=True, verbose_name='Date Created')),
('updated_date', models.DateTimeField(auto_now=True, verbose_name='Date Updated')),
('action', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='intentions', to='achievement.Action', verbose_name='Action')),
],
options={
'ordering': ('created_date', 'action'),
'verbose_name': 'Intention',
'verbose_name_plural': 'Intentions',
},
),
migrations.CreateModel(
name='Measurement',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('outcome_metric', models.FloatField(help_text=b'How much/many times did you perform the action?', verbose_name='Outcome Metric')),
('enjoyable_aspects', models.TextField(blank=True, help_text=b'What did you enjoy about doing this today?', verbose_name='Easy Aspects')),
('difficult_aspects', models.TextField(blank=True, help_text=b'What did you find difficult about doing this today?', verbose_name='Difficult Aspects')),
('overcoming_difficult_aspects', models.TextField(blank=True, help_text=b'How did you overcome the difficulties?', verbose_name='Overcome Difficult Aspects')),
('created_date', models.DateTimeField(auto_now_add=True, verbose_name='Date Created')),
('updated_date', models.DateTimeField(auto_now=True, verbose_name='Date Updated')),
('action', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='measurements', to='achievement.Action', verbose_name='Action')),
('intention', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='measurements', to='achievement.Intention', verbose_name='Intention')),
],
options={
'ordering': ('created_date', 'action'),
'verbose_name': 'Measurement',
'verbose_name_plural': 'Measurements',
},
),
migrations.CreateModel(
name='Review',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('review_period', models.CharField(choices=[(b'WEEKLY', b'Weekly'), (b'MONTHLY', b'Monthly'), (b'QUARTERLY', b'Quarterly'), (b'ANNUAL', b'Annual')], max_length=500, verbose_name='Review Period')),
('review_period_start_date', models.DateField(blank=True, verbose_name='Date (Create)')),
('review_period_end_date', models.DateField(blank=True, verbose_name='Date (Update)')),
('enjoyable_aspects', models.TextField(blank=True, help_text=b'What was enjoyable about working towards this goal?', verbose_name='Easy Aspects')),
('difficult_aspects_text', models.TextField(blank=True, help_text=b'What was difficult about working towards this goal?', verbose_name='Difficult Aspects')),
('overcome_difficult_aspects_text', models.TextField(blank=True, help_text=b'What worked to overcome the difficulties', verbose_name='Overcome Difficult Aspects')),
('next_period_focus', models.TextField(blank=True, help_text=b'What do you need to focus on in the next period?', verbose_name='Next Period Focus')),
('created_date', models.DateTimeField(auto_now_add=True, verbose_name='Date Created')),
('updated_date', models.DateTimeField(auto_now=True, verbose_name='Date Updated')),
('goal', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='reviews', to='achievement.Goal', verbose_name='Goal Review')),
],
options={
'ordering': ('goal', 'review_period_end_date'),
'verbose_name': 'Goal Review',
'verbose_name_plural': 'Goal Reviews',
},
),
migrations.CreateModel(
name='Reward',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('reward_title', models.CharField(max_length=1000, verbose_name='Reward Title')),
('reward_description', models.TextField(blank=True, help_text=b'Describe what the reward is - make it sound inviting', verbose_name='Reward Description')),
('achievement_metric', models.FloatField(help_text=b'This is the metric amount you must attain to get the reward', verbose_name='Achievement Metric')),
('obtained', models.BooleanField(default=False, verbose_name='Reward Obtained?')),
('created_date', models.DateTimeField(auto_now_add=True, verbose_name='Date Created')),
('updated_date', models.DateTimeField(auto_now=True, verbose_name='Date Updated')),
('goal', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='rewards', to='achievement.Goal', verbose_name='Reward')),
],
options={
'ordering': ('goal', 'achievement_metric', 'obtained'),
'verbose_name': 'Reward',
'verbose_name_plural': 'Rewards',
},
),
migrations.CreateModel(
name='Theme',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('theme', models.CharField(help_text=b'What is the theme of this goal, eg Health, Wealth, etc', max_length=500, verbose_name=b'Theme')),
('goal', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='themes', to='achievement.Goal', verbose_name='Goal')),
],
),
migrations.AddField(
model_name='action',
name='goal',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='actions', to='achievement.Goal', verbose_name='Goal'),
),
]
| [
"harryjohnson500@gmail.com"
] | harryjohnson500@gmail.com |
4bf48b2ed02568666239a4e90b3c039774f52091 | 14fe3323f06e144ab25a4732df66f0c6a4bdf118 | /pdfmaker.py | 5b80e279bd4a58aba4f9c49f66686ae565df2e6c | [] | no_license | seelengxd/pdfmaker | d981c64c35bbfa6e9d8d356e7ac6c65d6a87a1b4 | 05739387e3bb07f621e07a7d8dd479e4d56b8ee5 | refs/heads/master | 2023-01-21T16:37:21.583534 | 2020-12-01T11:10:03 | 2020-12-01T11:10:03 | 274,309,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,748 | py | #script for classkick
from PIL import Image
import os
import sys
# deal with filepaths so the terminal does its thing in the right place
path, pdfname = sys.argv[1:]
os.chdir(path)
#extract the trash ppm
for pdf in [i for i in os.listdir(path) if i.endswith(".pdf")]:
os.system(f'pdfimages "{path}/{pdf}" image')
os.system(f'mkdir {path}/processed')
for fn in sorted([i for i in os.listdir(path) if i.endswith(".ppm")]):
os.system(f"pnmcrop -black {path}/{fn} > {path}/processed/{fn[:-3]}.png") #take out annoying black thing
os.system(f"rm {path}/*.ppm")
#take the images, process(remove black garbage) and put into pdf
images = [Image.open(f"{path}/processed/{fn}").convert('RGB') for fn in sorted([i for i in os.listdir(path+"/processed") if i.endswith(".png")])][::2]
processed = []
for im in images:
#get A4 or make it a bit bigger so it fits whatever squashed in the side
h = max(1265, im.size[1])
if h != 1265:
h+=50
w = max(903, im.size[0])
a4im = Image.new('RGB', #make A4 page
(w, h), # hopefully size stays the same
(255, 255, 255)) # White
width, height = im.size
pixdata = im.load()
for y in range(im.size[1]): #attempt to fix black thing
for x in range(im.size[0]):
if pixdata[x, y] == (0, 0, 0):
pixdata[x, y] = (255, 255, 255)
a4im.paste(im, ((w-width)//2, (h-height)//2)) #slap image on a4 and center
processed.append(a4im)
processed[0].save(pdfname, save_all=True, append_images=processed[1:])
os.system("rm *.ppm")
os.system("rm -r processed")
#links
#https://stackoverflow.com/questions/27271138/python-pil-pillow-pad-image-to-desired-size-eg-a4?rq=1
#probably more but i can't rmb | [
"seelengxd@gmail.com"
] | seelengxd@gmail.com |
5f0fd310c984a64a57bc59ae1e99311d2051f972 | fe0f6ee607a333b35c3caa68a832dd2610e3f963 | /src/group_operations.py | 97b510d5b7b72a66b3f32a00a9a63052057cbdcd | [] | no_license | chuck1l/pandas_tools | eddf1f9eacf82a10660953c6f6b05d63f69b3913 | 597024639500c002becc673984578ca4dff306bd | refs/heads/main | 2023-06-03T18:59:26.478226 | 2021-06-08T13:14:07 | 2021-06-08T13:14:07 | 373,902,118 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 114 | py | import pandas as pd
import numpy as np
df = pd.read_pickle('../artist_df.pkl')
grouped = df.groupby('artist')
| [
"lawrence.williams@modivcare.com"
] | lawrence.williams@modivcare.com |
70ae3f9668228d23bac6ec72331d7b7bcbcced16 | e2bf28a98b8da0fcb132c37dbd47146a5f6efa20 | /fill-in-the-blanks.py | ec5870dee3cd2dc5c1a87dd909b369d25454ac00 | [] | no_license | udaysolanki4/Python_mini_projects | 3d58612ac0e9675c2aae2283416c500ec16c5dd0 | d9853218a4d7396697a4fd33d18b6928b892bcfd | refs/heads/master | 2020-06-18T20:50:54.641863 | 2019-09-15T10:59:48 | 2019-09-15T10:59:48 | 196,443,421 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,271 | py |
s1=["The bus stand is directly _________ the police station.",'behind']
s2=["As the doctor __________ into the room, the nurse handed him the temperature chart of the patient.The court _______ cognisance of the criminal's words.",'came','took']
s3=["The improvement made by changes in the system was ________ and did not warrant the large expenses.She takes delight _________ teasing boys.",'marginal','in']
def choose(n):
if n==1:
return s1
elif n==2:
return s2
elif n==3:
return s3
print("Enter the difficulty level from follows:\n 1.novice \n 2.Intermediate \n 3.Expert")
n=int(input())
if n>3 or n<1:
print("choose correct level")
exit()
s=choose(n)
if n>1:
print(s[0])
print("fill first blank")
n1=input()
n1.lower()
while s[1]!=n1:
print("Try Again")
n1=input()
n1.lower()
print("Correct answer")
print("fill Second blank")
n1=input()
n1.lower()
while s[2]!=n1:
print("Try Again")
n1=input()
n1.lower()
print("Correct answer")
else:
print(s[0])
print("fill blank")
n1=input()
n1.lower()
while s[1]!=n1:
print("Try Again")
n1=input()
n1.lower()
print("Correct answer")
| [
"noreply@github.com"
] | noreply@github.com |
d324d64a883ad3958f5aa9e0218c1dea7eac664b | 1bed028026b58b4a02e75e3ce751e5525f113a57 | /app/api.py | 7b6b640ec6d2dbac2b8065e41e4cf293c8086536 | [
"MIT"
] | permissive | webclinic017/SFJ-MSR | 203c0d095b25b7957c633e2d7bc9976f992615cd | f87f45df46341ae8d9f2e84484cccbdde26b9baf | refs/heads/main | 2023-08-28T05:58:46.508324 | 2021-10-20T23:23:19 | 2021-10-20T23:23:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,105 | py | # ************************************************************************************************** #
# #
# ::: :::::::: :::::::: ::::::::::: #
# api.py :+:+: :+: :+: :+: :+: :+: :+: #
# +:+ +:+ +:+ +:+ #
# By: ALLALI <hi@allali.me> +#+ +#++: +#++: +#+ #
# +#+ +#+ +#+ +#+ #
# Created: 2021/10/21 00:22:46 by ALLALI #+# #+# #+# #+# #+# #+# #
# Updated: 2021/10/21 00:22:46 by ALLALI ####### ######## ######## ###.ma #
# #
# ************************************************************************************************** #
from fastapi import FastAPI, Body, Depends, WebSocket
from app.model import PostSchema, UserSchema, UserLoginSchema
from app.auth.auth_bearer import sJWTBearer
from app.auth.auth_handler import signJWT
from app.db.mongodb import close, connect, AsyncIOMotorClient, get_database
from app.db.helper import find_user_by_prop, insert_user
from starlette.exceptions import HTTPException
from starlette.status import (
HTTP_200_OK,
HTTP_401_UNAUTHORIZED,
HTTP_204_NO_CONTENT,
HTTP_422_UNPROCESSABLE_ENTITY,
)
posts = [
{
"id": 1,
"title": "Pancake",
"content": "Lorem Ipsum ..."
}
]
users = []
app = FastAPI(title="SFJ-MSR", version="1")
@app.on_event("startup")
async def on_app_start():
"""Anything that needs to be done while app starts
"""
await connect()
print("== MongoDB == ready ================")
@app.on_event("shutdown")
async def on_app_shutdown():
"""Anything that needs to be done while app shutdown
"""
await close()
# helpers
def check_user(data: UserLoginSchema):
for user in users:
if user.email == data.email and user.password == data.password:
return True
return False
# route handlers
@app.get("/", tags=["root"])
async def read_root() -> dict:
return {"message": "Welcome to your blog!."}
@app.get("/posts", tags=["posts"])
async def get_posts(db: AsyncIOMotorClient = Depends(get_database)) -> dict:
db = db.client["SFJ-MSR"]
users = db["users"].find()
print(users)
return {"data": posts}
@app.get("/posts/{id}", tags=["posts"])
async def get_single_post(id: int) -> dict:
if id > len(posts):
return {
"error": "No such post with the supplied ID."
}
for post in posts:
if post["id"] == id:
return {
"data": post
}
@app.post("/posts", dependencies=[Depends(JWTBearer())], tags=["posts"])
async def add_post(post: PostSchema) -> dict:
post.id = len(posts) + 1
posts.append(post.dict())
return {
"data": "post added."
}
@app.post("/user/signup", tags=["user"])
async def create_user(user: UserSchema = Body(...), db: AsyncIOMotorClient = Depends(get_database)):
rowUser = await find_user_by_prop("username", db, user.username)
if rowUser is None:
print("============>")
res = await insert_user(user, db)
raise HTTPException(
status_code=HTTP_200_OK,
detail={'jwtsign': res['_jwtsign'],
'user_id': str(res['dbuser'].inserted_id)}
)
else:
raise HTTPException(
status_code=HTTP_422_UNPROCESSABLE_ENTITY,
detail="User with this username already exists",
)
@app.post("/user/login", tags=["user"])
async def user_login(user: UserLoginSchema = Body(...)):
if check_user(user):
return signJWT(user.email)
return {
"error": "Wrong login details!"
}
| [
"contact@allali.me"
] | contact@allali.me |
ed55f0271ac1d2cd8a0e0037831655e7e69ede08 | 28214a271c00779da8baa208cb155b3a8425d355 | /infrastructure/InchirieriRepo.py | dbce51cb4ecbf0a7e5584705437de9430a9baba7 | [] | no_license | cristimc8/temalab79 | 206e03df6d14a11ee4ca0c345a0b568fd9debc19 | c5bfc68a626e44dc16dcdfd25ce28fefb2944268 | refs/heads/master | 2023-01-22T03:50:58.069379 | 2020-11-25T15:39:30 | 2020-11-25T15:39:30 | 310,410,406 | 0 | 1 | null | 2020-11-25T15:39:31 | 2020-11-05T20:30:48 | Python | UTF-8 | Python | false | false | 1,453 | py | class InchirieriRepo:
def __init__(self) -> None:
self.__listaInchirieri = []
def adauga_inchiriere(self, inchiriere, ui=True):
'''
Functia care adauga o inchiriere noua in lista
in -> client Client, film Film
'''
self.__listaInchirieri.append(inchiriere)
#if ui: UI.display_inchiriere(inchiriere)
def sterge_inchiriere(self, inchiriere):
'''
Functia care sterge inchirierea din lista daca utilizatorul isi sterge contul
'''
self.__listaInchirieri.remove(inchiriere)
def get_inchiriere_client(self, client):
'''
Functia care returneaza lista de inchirieri a unui client
Date de intrare: client Client
'''
listaInchirieri = []
for inchiriere in self.__listaInchirieri:
if inchiriere.getClient() == client:
listaInchirieri.append(inchiriere)
return listaInchirieri
def get_inchirieri_film(self, film):
'''
Functia care returneaza lista inchirierilor a filmului
Date de intrare: film Film
Date de iesire: lista List
'''
listaInchirieri = []
for inchiriere in self.__listaInchirieri:
if inchiriere.getFilm() == film:
listaInchirieri.append(inchiriere)
return listaInchirieri
def get_lista_inchirieri(self):
return list(self.__listaInchirieri)
| [
"cristimc8@gmail.com"
] | cristimc8@gmail.com |
adfbe0b6910b3dfc436ae935c24ba4d89ef0505a | 621fec63ba65d000f8f539bd6b5238202e35644b | /blackandwhite.py | 3fc6f0dbcfad81e78d41f6f3d4d88542996a6f7d | [
"MIT"
] | permissive | DiptoChakrabarty/ImageColrization | f764cffe82183ce951c130aa0d9af0e5c80eca48 | c0cd1ae59ad99bced57b34376ce3191a1c2241a8 | refs/heads/master | 2021-01-01T12:45:54.964214 | 2020-02-24T17:09:19 | 2020-02-24T17:09:19 | 239,286,196 | 0 | 0 | null | 2020-02-09T11:00:26 | 2020-02-09T10:37:02 | Python | UTF-8 | Python | false | false | 489 | py | import cv2
import os
import numpy as np
path="./colored"
upload="./black"
colored=list(os.path.join(path,f) for f in os.listdir(path))
count=0
for images in colored:
count+=1
photo=cv2.imread(images)
resized=cv2.resize(photo,(400,350))
cv2.imwrite(images,resized)
cv2.imshow("lakes",photo)
cv2.waitKey()
cv2.destroyAllWindows()
dest=upload + "/image{}.png".format(count)
black=cv2.cvtColor(resized,cv2.COLOR_BGR2GRAY)
cv2.imwrite(dest,black) | [
"diptochuck123@gmail.com"
] | diptochuck123@gmail.com |
97902651994af5246d31c0ef82be24aaafce3e02 | aa80e6233f4ee38fd5c1efb28df2ef6da4f0b8af | /dictionary.py | f45e889be1c03bdf747395a76781f0937c92f393 | [] | no_license | kafleyj/Python | 444708e965c93a473e2ff2fc97c8fb6803fbfe06 | ba180045c07a8da2445214394c9226abddcc5d0d | refs/heads/main | 2023-05-08T15:21:04.568821 | 2021-05-30T07:03:24 | 2021-05-30T07:03:24 | 372,107,132 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 141 | py | myDict={
"Fast": "In a quick manner",
"Harry":"a coder"
"Marks": [1,2,3,4]
print(myDict['Marks'])
# update and get are als uese
| [
"kafleyj55@gmail.com"
] | kafleyj55@gmail.com |
0d4351f5ba5cbee9cbc4fd70fd0bd187dc02cce1 | deccd45912966718d48ea9594aaf04626491e4d2 | /test/test_poll_api.py | a49bbebea58cc5f22ffe04365f70fcf8ae021b55 | [] | no_license | CS3398-Luna-Sea/SeeBus | 12925872bee84cee25c5b8645a6f7de795767e65 | b685dcbc77a4eea5c482a647475e414074c699c5 | refs/heads/master | 2022-12-15T10:14:31.813506 | 2018-12-06T02:53:34 | 2018-12-06T02:53:34 | 149,679,544 | 0 | 0 | null | 2022-12-08T01:14:10 | 2018-09-20T22:48:47 | Python | UTF-8 | Python | false | false | 759 | py | from unittest import TestCase
import sys
from os import getcwd
sys.path.insert(0, getcwd() + '/backend')
import poll_api as api
class TestPollAPI(TestCase):
def test_get_buses(self):
self.assertTrue(isinstance(api.get_buses(), list),
"poll_api.get_buses() should return a dictionary")
def test_get_buses_on_route(self):
route = 624
buses = api.get_buses_on_route(route)
for bus in buses:
if bus.get_route() != route:
self.fail("Bus route {} does not match queried route {}".format(bus.get_route(), route))
self.assertTrue(isinstance(api.get_buses_on_route(0), list),
"poll_api.get_buses_on_route(int) should return a dictionary")
| [
"zachstence@gmail.com"
] | zachstence@gmail.com |
f1a4d301432e77e5d6f8bb22ff148586cfe5df6b | 0e8665a8611717c34bbffb3f4393554f58145d4b | /Python/signaltools.py | 0f21f3473cc792c41690f6180b6caed6c7f1044a | [] | no_license | OmarJaroudi/Dardish-Code-NLP | f7b8f645bfbf5b4c2904e85707e2d7964868f34e | 5b354cfdb84198e3c19c184f0862c488634f2388 | refs/heads/master | 2020-09-12T10:12:48.335018 | 2020-01-24T07:40:03 | 2020-01-24T07:40:03 | 222,390,937 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 142,222 | py | # Author: Travis Oliphant
# 1999 -- 2002
from __future__ import division, print_function, absolute_import
import operator
import sys
import timeit
from scipy.spatial import cKDTree
from . import sigtools, dlti
from ._upfirdn import upfirdn, _output_len, _upfirdn_modes
from scipy._lib.six import callable
from scipy import linalg, fft as sp_fft
from scipy.fft._helper import _init_nd_shape_and_axes
import numpy as np
import math
from scipy.special import factorial, lambertw
from .windows import get_window
from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
from .filter_design import cheby1, _validate_sos
from .fir_filter_design import firwin
from ._sosfilt import _sosfilt
if sys.version_info >= (3, 5):
from math import gcd
else:
from fractions import gcd
__all__ = ['correlate', 'correlate2d',
'convolve', 'convolve2d', 'fftconvolve', 'oaconvolve',
'order_filter', 'medfilt', 'medfilt2d', 'wiener', 'lfilter',
'lfiltic', 'sosfilt', 'deconvolve', 'hilbert', 'hilbert2',
'cmplx_sort', 'unique_roots', 'invres', 'invresz', 'residue',
'residuez', 'resample', 'resample_poly', 'detrend',
'lfilter_zi', 'sosfilt_zi', 'sosfiltfilt', 'choose_conv_method',
'filtfilt', 'decimate', 'vectorstrength']
_modedict = {'valid': 0, 'same': 1, 'full': 2}
_boundarydict = {'fill': 0, 'pad': 0, 'wrap': 2, 'circular': 2, 'symm': 1,
'symmetric': 1, 'reflect': 4}
def _valfrommode(mode):
try:
return _modedict[mode]
except KeyError:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
def _bvalfromboundary(boundary):
try:
return _boundarydict[boundary] << 2
except KeyError:
raise ValueError("Acceptable boundary flags are 'fill', 'circular' "
"(or 'wrap'), and 'symmetric' (or 'symm').")
def _inputs_swap_needed(mode, shape1, shape2, axes=None):
"""Determine if inputs arrays need to be swapped in `"valid"` mode.
If in `"valid"` mode, returns whether or not the input arrays need to be
swapped depending on whether `shape1` is at least as large as `shape2` in
every calculated dimension.
This is important for some of the correlation and convolution
implementations in this module, where the larger array input needs to come
before the smaller array input when operating in this mode.
Note that if the mode provided is not 'valid', False is immediately
returned.
"""
if mode != 'valid':
return False
if not shape1:
return False
if axes is None:
axes = range(len(shape1))
ok1 = all(shape1[i] >= shape2[i] for i in axes)
ok2 = all(shape2[i] >= shape1[i] for i in axes)
if not (ok1 or ok2):
raise ValueError("For 'valid' mode, one must be at least "
"as large as the other in every dimension")
return not ok1
def correlate(in1, in2, mode='full', method='auto'):
r"""
Cross-correlate two N-dimensional arrays.
Cross-correlate `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the correlation.
``direct``
The correlation is determined directly from sums, the definition of
correlation.
``fft``
The Fast Fourier Transform is used to perform the correlation more
quickly (only available for numerical arrays.)
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See `convolve` Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
correlate : array
An N-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
See Also
--------
choose_conv_method : contains more documentation on `method`.
Notes
-----
The correlation z of two d-dimensional arrays x and y is defined as::
z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])
This way, if x and y are 1-D arrays and ``z = correlate(x, y, 'full')``
then
.. math::
z[k] = (x * y)(k - N + 1)
= \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
where :math:`||x||` is the length of ``x``, :math:`N = \max(||x||,||y||)`,
and :math:`y_m` is 0 when m is outside the range of y.
``method='fft'`` only works for numerical arrays as it relies on
`fftconvolve`. In certain cases (i.e., arrays of objects or when
rounding integers can lose precision), ``method='direct'`` is always used.
Examples
--------
Implement a matched filter using cross-correlation, to recover a signal
that has passed through a noisy channel.
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128
>>> import matplotlib.pyplot as plt
>>> clock = np.arange(64, len(sig), 128)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.plot(clock, sig[clock], 'ro')
>>> ax_orig.set_title('Original signal')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_corr.plot(corr)
>>> ax_corr.plot(clock, corr[clock], 'ro')
>>> ax_corr.axhline(0.5, ls=':')
>>> ax_corr.set_title('Cross-correlated with rectangular pulse')
>>> ax_orig.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0:
return in1 * in2.conj()
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
# Don't use _valfrommode, since correlate should not accept numeric modes
try:
val = _modedict[mode]
except KeyError:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
# this either calls fftconvolve or this function with method=='direct'
if method in ('fft', 'auto'):
return convolve(in1, _reverse_and_conj(in2), mode, method)
elif method == 'direct':
# fastpath to faster numpy.correlate for 1d inputs when possible
if _np_conv_ok(in1, in2, mode):
return np.correlate(in1, in2, mode)
# _correlateND is far slower when in2.size > in1.size, so swap them
# and then undo the effect afterward if mode == 'full'. Also, it fails
# with 'valid' mode if in2 is larger than in1, so swap those, too.
# Don't swap inputs for 'same' mode, since shape of in1 matters.
swapped_inputs = ((mode == 'full') and (in2.size > in1.size) or
_inputs_swap_needed(mode, in1.shape, in2.shape))
if swapped_inputs:
in1, in2 = in2, in1
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = tuple(slice(0, i) for i in in1.shape)
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
if swapped_inputs:
# Reverse and conjugate to undo the effect of swapping inputs
z = _reverse_and_conj(z)
return z
else:
raise ValueError("Acceptable method flags are 'auto',"
" 'direct', or 'fft'.")
def _centered(arr, newshape):
# Return the center newshape portion of the array.
newshape = np.asarray(newshape)
currshape = np.array(arr.shape)
startind = (currshape - newshape) // 2
endind = startind + newshape
myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
return arr[tuple(myslice)]
def _init_freq_conv_axes(in1, in2, mode, axes, sorted_axes=False):
"""Handle the axes argument for frequency-domain convolution.
Returns the inputs and axes in a standard form, eliminating redundant axes,
swapping the inputs if necessary, and checking for various potential
errors.
Parameters
----------
in1 : array
First input.
in2 : array
Second input.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output.
See the documentation `fftconvolve` for more information.
axes : list of ints
Axes over which to compute the FFTs.
sorted_axes : bool, optional
If `True`, sort the axes.
Default is `False`, do not sort.
Returns
-------
in1 : array
The first input, possible swapped with the second input.
in2 : array
The second input, possible swapped with the first input.
axes : list of ints
Axes over which to compute the FFTs.
"""
s1 = in1.shape
s2 = in2.shape
noaxes = axes is None
_, axes = _init_nd_shape_and_axes(in1, shape=None, axes=axes)
if not noaxes and not len(axes):
raise ValueError("when provided, axes cannot be empty")
# Axes of length 1 can rely on broadcasting rules for multipy,
# no fft needed.
axes = [a for a in axes if s1[a] != 1 and s2[a] != 1]
if sorted_axes:
axes.sort()
if not all(s1[a] == s2[a] or s1[a] == 1 or s2[a] == 1
for a in range(in1.ndim) if a not in axes):
raise ValueError("incompatible shapes for in1 and in2:"
" {0} and {1}".format(s1, s2))
# Check that input sizes are compatible with 'valid' mode.
if _inputs_swap_needed(mode, s1, s2, axes=axes):
# Convolution is commutative; order doesn't have any effect on output.
in1, in2 = in2, in1
return in1, in2, axes
def _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=False):
"""Convolve two arrays in the frequency domain.
This function implements only base the FFT-related operations.
Specifically, it converts the signals to the frequency domain, multiplies
them, then converts them back to the time domain. Calculations of axes,
shapes, convolution mode, etc. are implemented in higher level-functions,
such as `fftconvolve` and `oaconvolve`. Those functions should be used
instead of this one.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
axes : array_like of ints
Axes over which to compute the FFTs.
shape : array_like of ints
The sizes of the FFTs.
calc_fast_len : bool, optional
If `True`, set each value of `shape` to the next fast FFT length.
Default is `False`, use `axes` as-is.
Returns
-------
out : array
An N-dimensional array containing the discrete linear convolution of
`in1` with `in2`.
"""
if not len(axes):
return in1 * in2
complex_result = (in1.dtype.kind == 'c' or in2.dtype.kind == 'c')
if calc_fast_len:
# Speed up FFT by padding to optimal size.
fshape = [
sp_fft.next_fast_len(shape[a], not complex_result) for a in axes]
else:
fshape = shape
if not complex_result:
fft, ifft = sp_fft.rfftn, sp_fft.irfftn
else:
fft, ifft = sp_fft.fftn, sp_fft.ifftn
sp1 = fft(in1, fshape, axes=axes)
sp2 = fft(in2, fshape, axes=axes)
ret = ifft(sp1 * sp2, fshape, axes=axes)
if calc_fast_len:
fslice = tuple([slice(sz) for sz in shape])
ret = ret[fslice]
return ret
def _apply_conv_mode(ret, s1, s2, mode, axes):
"""Calculate the convolution result shape based on the `mode` argument.
Returns the result sliced to the correct size for the given mode.
Parameters
----------
ret : array
The result array, with the appropriate shape for the 'full' mode.
s1 : list of int
The shape of the first input.
s2 : list of int
The shape of the second input.
mode : str {'full', 'valid', 'same'}
A string indicating the size of the output.
See the documentation `fftconvolve` for more information.
axes : list of ints
Axes over which to compute the convolution.
Returns
-------
ret : array
A copy of `res`, sliced to the correct size for the given `mode`.
"""
if mode == "full":
return ret.copy()
elif mode == "same":
return _centered(ret, s1).copy()
elif mode == "valid":
shape_valid = [ret.shape[a] if a not in axes else s1[a] - s2[a] + 1
for a in range(ret.ndim)]
return _centered(ret, shape_valid).copy()
else:
raise ValueError("acceptable mode flags are 'valid',"
" 'same', or 'full'")
def fftconvolve(in1, in2, mode="full", axes=None):
"""Convolve two N-dimensional arrays using FFT.
Convolve `in1` and `in2` using the fast Fourier transform method, with
the output size determined by the `mode` argument.
This is generally much faster than `convolve` for large arrays (n > ~500),
but can be slower when only a few output values are needed, and can only
output float arrays (int or object array inputs will be cast to float).
As of v0.19, `convolve` automatically chooses this method or the direct
method based on an estimation of which is faster.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
axes : int or array_like of ints or None, optional
Axes over which to compute the convolution.
The default is over all axes.
Returns
-------
out : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
convolve : Uses the direct convolution or FFT convolution algorithm
depending on which is faster.
oaconvolve : Uses the overlap-add method to do convolution, which is
generally faster when the input arrays are large and
significantly different in size.
Examples
--------
Autocorrelation of white noise is an impulse.
>>> from scipy import signal
>>> sig = np.random.randn(1000)
>>> autocorr = signal.fftconvolve(sig, sig[::-1], mode='full')
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('White noise')
>>> ax_mag.plot(np.arange(-len(sig)+1,len(sig)), autocorr)
>>> ax_mag.set_title('Autocorrelation')
>>> fig.tight_layout()
>>> fig.show()
Gaussian blur implemented using FFT convolution. Notice the dark borders
around the image, due to the zero-padding beyond its boundaries.
The `convolve2d` function allows for other types of image boundaries,
but is far slower.
>>> from scipy import misc
>>> face = misc.face(gray=True)
>>> kernel = np.outer(signal.gaussian(70, 8), signal.gaussian(70, 8))
>>> blurred = signal.fftconvolve(face, kernel, mode='same')
>>> fig, (ax_orig, ax_kernel, ax_blurred) = plt.subplots(3, 1,
... figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_kernel.imshow(kernel, cmap='gray')
>>> ax_kernel.set_title('Gaussian kernel')
>>> ax_kernel.set_axis_off()
>>> ax_blurred.imshow(blurred, cmap='gray')
>>> ax_blurred.set_title('Blurred')
>>> ax_blurred.set_axis_off()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0: # scalar inputs
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
elif in1.size == 0 or in2.size == 0: # empty arrays
return np.array([])
in1, in2, axes = _init_freq_conv_axes(in1, in2, mode, axes,
sorted_axes=False)
s1 = in1.shape
s2 = in2.shape
shape = [max((s1[i], s2[i])) if i not in axes else s1[i] + s2[i] - 1
for i in range(in1.ndim)]
ret = _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=True)
return _apply_conv_mode(ret, s1, s2, mode, axes)
def _calc_oa_lens(s1, s2):
"""Calculate the optimal FFT lengths for overlapp-add convolution.
The calculation is done for a single dimension.
Parameters
----------
s1 : int
Size of the dimension for the first array.
s2 : int
Size of the dimension for the second array.
Returns
-------
block_size : int
The size of the FFT blocks.
overlap : int
The amount of overlap between two blocks.
in1_step : int
The size of each step for the first array.
in2_step : int
The size of each step for the first array.
"""
# Set up the arguments for the conventional FFT approach.
fallback = (s1+s2-1, None, s1, s2)
# Use conventional FFT convolve if sizes are same.
if s1 == s2 or s1 == 1 or s2 == 1:
return fallback
if s2 > s1:
s1, s2 = s2, s1
swapped = True
else:
swapped = False
# There cannot be a useful block size if s2 is more than half of s1.
if s2 >= s1/2:
return fallback
# Derivation of optimal block length
# For original formula see:
# https://en.wikipedia.org/wiki/Overlap-add_method
#
# Formula:
# K = overlap = s2-1
# N = block_size
# C = complexity
# e = exponential, exp(1)
#
# C = (N*(log2(N)+1))/(N-K)
# C = (N*log2(2N))/(N-K)
# C = N/(N-K) * log2(2N)
# C1 = N/(N-K)
# C2 = log2(2N) = ln(2N)/ln(2)
#
# dC1/dN = (1*(N-K)-N)/(N-K)^2 = -K/(N-K)^2
# dC2/dN = 2/(2*N*ln(2)) = 1/(N*ln(2))
#
# dC/dN = dC1/dN*C2 + dC2/dN*C1
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + N/(N*ln(2)*(N-K))
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + 1/(ln(2)*(N-K))
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + (N-K)/(ln(2)*(N-K)^2)
# dC/dN = (-K*ln(2N) + (N-K)/(ln(2)*(N-K)^2)
# dC/dN = (N - K*ln(2N) - K)/(ln(2)*(N-K)^2)
#
# Solve for minimum, where dC/dN = 0
# 0 = (N - K*ln(2N) - K)/(ln(2)*(N-K)^2)
# 0 * ln(2)*(N-K)^2 = N - K*ln(2N) - K
# 0 = N - K*ln(2N) - K
# 0 = N - K*(ln(2N) + 1)
# 0 = N - K*ln(2Ne)
# N = K*ln(2Ne)
# N/K = ln(2Ne)
#
# e^(N/K) = e^ln(2Ne)
# e^(N/K) = 2Ne
# 1/e^(N/K) = 1/(2*N*e)
# e^(N/-K) = 1/(2*N*e)
# e^(N/-K) = K/N*1/(2*K*e)
# N/K*e^(N/-K) = 1/(2*e*K)
# N/-K*e^(N/-K) = -1/(2*e*K)
#
# Using Lambert W function
# https://en.wikipedia.org/wiki/Lambert_W_function
# x = W(y) It is the solution to y = x*e^x
# x = N/-K
# y = -1/(2*e*K)
#
# N/-K = W(-1/(2*e*K))
#
# N = -K*W(-1/(2*e*K))
overlap = s2-1
opt_size = -overlap*lambertw(-1/(2*math.e*overlap), k=-1).real
block_size = sp_fft.next_fast_len(math.ceil(opt_size))
# Use conventional FFT convolve if there is only going to be one block.
if block_size >= s1:
return fallback
if not swapped:
in1_step = block_size-s2+1
in2_step = s2
else:
in1_step = s2
in2_step = block_size-s2+1
return block_size, overlap, in1_step, in2_step
def oaconvolve(in1, in2, mode="full", axes=None):
"""Convolve two N-dimensional arrays using the overlap-add method.
Convolve `in1` and `in2` using the overlap-add method, with
the output size determined by the `mode` argument.
This is generally much faster than `convolve` for large arrays (n > ~500),
and generally much faster than `fftconvolve` when one array is much
larger than the other, but can be slower when only a few output values are
needed or when the arrays are very similar in shape, and can only
output float arrays (int or object array inputs will be cast to float).
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
axes : int or array_like of ints or None, optional
Axes over which to compute the convolution.
The default is over all axes.
Returns
-------
out : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
convolve : Uses the direct convolution or FFT convolution algorithm
depending on which is faster.
fftconvolve : An implementation of convolution using FFT.
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Convolve a 100,000 sample signal with a 512-sample filter.
>>> from scipy import signal
>>> sig = np.random.randn(100000)
>>> filt = signal.firwin(512, 0.01)
>>> fsig = signal.oaconvolve(sig, filt)
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('White noise')
>>> ax_mag.plot(fsig)
>>> ax_mag.set_title('Filtered noise')
>>> fig.tight_layout()
>>> fig.show()
References
----------
.. [1] Wikipedia, "Overlap-add_method".
https://en.wikipedia.org/wiki/Overlap-add_method
.. [2] Richard G. Lyons. Understanding Digital Signal Processing,
Third Edition, 2011. Chapter 13.10.
ISBN 13: 978-0137-02741-5
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0: # scalar inputs
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
elif in1.size == 0 or in2.size == 0: # empty arrays
return np.array([])
elif in1.shape == in2.shape: # Equivalent to fftconvolve
return fftconvolve(in1, in2, mode=mode, axes=axes)
in1, in2, axes = _init_freq_conv_axes(in1, in2, mode, axes,
sorted_axes=True)
if not axes:
return in1*in2
s1 = in1.shape
s2 = in2.shape
# Calculate this now since in1 is changed later
shape_final = [None if i not in axes else
s1[i] + s2[i] - 1 for i in range(in1.ndim)]
# Calculate the block sizes for the output, steps, first and second inputs.
# It is simpler to calculate them all together than doing them in separate
# loops due to all the special cases that need to be handled.
optimal_sizes = ((-1, -1, s1[i], s2[i]) if i not in axes else
_calc_oa_lens(s1[i], s2[i]) for i in range(in1.ndim))
block_size, overlaps, \
in1_step, in2_step = zip(*optimal_sizes)
# Fall back to fftconvolve if there is only one block in every dimension.
if in1_step == s1 and in2_step == s2:
return fftconvolve(in1, in2, mode=mode, axes=axes)
# Figure out the number of steps and padding.
# This would get too complicated in a list comprehension.
nsteps1 = []
nsteps2 = []
pad_size1 = []
pad_size2 = []
for i in range(in1.ndim):
if i not in axes:
pad_size1 += [(0, 0)]
pad_size2 += [(0, 0)]
continue
if s1[i] > in1_step[i]:
curnstep1 = math.ceil((s1[i]+1)/in1_step[i])
if (block_size[i] - overlaps[i])*curnstep1 < shape_final[i]:
curnstep1 += 1
curpad1 = curnstep1*in1_step[i] - s1[i]
else:
curnstep1 = 1
curpad1 = 0
if s2[i] > in2_step[i]:
curnstep2 = math.ceil((s2[i]+1)/in2_step[i])
if (block_size[i] - overlaps[i])*curnstep2 < shape_final[i]:
curnstep2 += 1
curpad2 = curnstep2*in2_step[i] - s2[i]
else:
curnstep2 = 1
curpad2 = 0
nsteps1 += [curnstep1]
nsteps2 += [curnstep2]
pad_size1 += [(0, curpad1)]
pad_size2 += [(0, curpad2)]
# Pad the array to a size that can be reshaped to the desired shape
# if necessary.
if not all(curpad == (0, 0) for curpad in pad_size1):
in1 = np.pad(in1, pad_size1, mode='constant', constant_values=0)
if not all(curpad == (0, 0) for curpad in pad_size2):
in2 = np.pad(in2, pad_size2, mode='constant', constant_values=0)
# Reshape the overlap-add parts to input block sizes.
split_axes = [iax+i for i, iax in enumerate(axes)]
fft_axes = [iax+1 for iax in split_axes]
# We need to put each new dimension before the corresponding dimension
# being reshaped in order to get the data in the right layout at the end.
reshape_size1 = list(in1_step)
reshape_size2 = list(in2_step)
for i, iax in enumerate(split_axes):
reshape_size1.insert(iax, nsteps1[i])
reshape_size2.insert(iax, nsteps2[i])
in1 = in1.reshape(*reshape_size1)
in2 = in2.reshape(*reshape_size2)
# Do the convolution.
fft_shape = [block_size[i] for i in axes]
ret = _freq_domain_conv(in1, in2, fft_axes, fft_shape, calc_fast_len=False)
# Do the overlap-add.
for ax, ax_fft, ax_split in zip(axes, fft_axes, split_axes):
overlap = overlaps[ax]
if overlap is None:
continue
ret, overpart = np.split(ret, [-overlap], ax_fft)
overpart = np.split(overpart, [-1], ax_split)[0]
ret_overpart = np.split(ret, [overlap], ax_fft)[0]
ret_overpart = np.split(ret_overpart, [1], ax_split)[1]
ret_overpart += overpart
# Reshape back to the correct dimensionality.
shape_ret = [ret.shape[i] if i not in fft_axes else
ret.shape[i]*ret.shape[i-1]
for i in range(ret.ndim) if i not in split_axes]
ret = ret.reshape(*shape_ret)
# Slice to the correct size.
slice_final = tuple([slice(islice) for islice in shape_final])
ret = ret[slice_final]
return _apply_conv_mode(ret, s1, s2, mode, axes)
def _numeric_arrays(arrays, kinds='buifc'):
"""
See if a list of arrays are all numeric.
Parameters
----------
ndarrays : array or list of arrays
arrays to check if numeric.
numeric_kinds : string-like
The dtypes of the arrays to be checked. If the dtype.kind of
the ndarrays are not in this string the function returns False and
otherwise returns True.
"""
if type(arrays) == np.ndarray:
return arrays.dtype.kind in kinds
for array_ in arrays:
if array_.dtype.kind not in kinds:
return False
return True
def _prod(iterable):
"""
Product of a list of numbers.
Faster than np.prod for short lists like array shapes.
"""
product = 1
for x in iterable:
product *= x
return product
def _fftconv_faster(x, h, mode):
"""
See if using `fftconvolve` or `_correlateND` is faster. The boolean value
returned depends on the sizes and shapes of the input values.
The big O ratios were found to hold across different machines, which makes
sense as it's the ratio that matters (the effective speed of the computer
is found in both big O constants). Regardless, this had been tuned on an
early 2015 MacBook Pro with 8GB RAM and an Intel i5 processor.
"""
if mode == 'full':
out_shape = [n + k - 1 for n, k in zip(x.shape, h.shape)]
big_O_constant = 10963.92823819 if x.ndim == 1 else 8899.1104874
elif mode == 'same':
out_shape = x.shape
if x.ndim == 1:
if h.size <= x.size:
big_O_constant = 7183.41306773
else:
big_O_constant = 856.78174111
else:
big_O_constant = 34519.21021589
elif mode == 'valid':
out_shape = [n - k + 1 for n, k in zip(x.shape, h.shape)]
big_O_constant = 41954.28006344 if x.ndim == 1 else 66453.24316434
else:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
# see whether the Fourier transform convolution method or the direct
# convolution method is faster (discussed in scikit-image PR #1792)
direct_time = (x.size * h.size * _prod(out_shape))
fft_time = sum(n * math.log(n) for n in (x.shape + h.shape +
tuple(out_shape)))
return big_O_constant * fft_time < direct_time
def _reverse_and_conj(x):
"""
Reverse array `x` in all dimensions and perform the complex conjugate
"""
reverse = (slice(None, None, -1),) * x.ndim
return x[reverse].conj()
def _np_conv_ok(volume, kernel, mode):
"""
See if numpy supports convolution of `volume` and `kernel` (i.e. both are
1D ndarrays and of the appropriate shape). NumPy's 'same' mode uses the
size of the larger input, while SciPy's uses the size of the first input.
Invalid mode strings will return False and be caught by the calling func.
"""
if volume.ndim == kernel.ndim == 1:
if mode in ('full', 'valid'):
return True
elif mode == 'same':
return volume.size >= kernel.size
else:
return False
def _timeit_fast(stmt="pass", setup="pass", repeat=3):
"""
Returns the time the statement/function took, in seconds.
Faster, less precise version of IPython's timeit. `stmt` can be a statement
written as a string or a callable.
Will do only 1 loop (like IPython's timeit) with no repetitions
(unlike IPython) for very slow functions. For fast functions, only does
enough loops to take 5 ms, which seems to produce similar results (on
Windows at least), and avoids doing an extraneous cycle that isn't
measured.
"""
timer = timeit.Timer(stmt, setup)
# determine number of calls per rep so total time for 1 rep >= 5 ms
x = 0
for p in range(0, 10):
number = 10**p
x = timer.timeit(number) # seconds
if x >= 5e-3 / 10: # 5 ms for final test, 1/10th that for this one
break
if x > 1: # second
# If it's macroscopic, don't bother with repetitions
best = x
else:
number *= 10
r = timer.repeat(repeat, number)
best = min(r)
sec = best / number
return sec
def choose_conv_method(in1, in2, mode='full', measure=False):
"""
Find the fastest convolution/correlation method.
This primarily exists to be called during the ``method='auto'`` option in
`convolve` and `correlate`, but can also be used when performing many
convolutions of the same input shapes and dtypes, determining
which method to use for all of them, either to avoid the overhead of the
'auto' option or to use accurate real-world measurements.
Parameters
----------
in1 : array_like
The first argument passed into the convolution function.
in2 : array_like
The second argument passed into the convolution function.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
measure : bool, optional
If True, run and time the convolution of `in1` and `in2` with both
methods and return the fastest. If False (default), predict the fastest
method using precomputed values.
Returns
-------
method : str
A string indicating which convolution method is fastest, either
'direct' or 'fft'
times : dict, optional
A dictionary containing the times (in seconds) needed for each method.
This value is only returned if ``measure=True``.
See Also
--------
convolve
correlate
Notes
-----
For large n, ``measure=False`` is accurate and can quickly determine the
fastest method to perform the convolution. However, this is not as
accurate for small n (when any dimension in the input or output is small).
In practice, we found that this function estimates the faster method up to
a multiplicative factor of 5 (i.e., the estimated method is *at most* 5
times slower than the fastest method). The estimation values were tuned on
an early 2015 MacBook Pro with 8GB RAM but we found that the prediction
held *fairly* accurately across different machines.
If ``measure=True``, time the convolutions. Because this function uses
`fftconvolve`, an error will be thrown if it does not support the inputs.
There are cases when `fftconvolve` supports the inputs but this function
returns `direct` (e.g., to protect against floating point integer
precision).
.. versionadded:: 0.19
Examples
--------
Estimate the fastest method for a given input:
>>> from scipy import signal
>>> a = np.random.randn(1000)
>>> b = np.random.randn(1000000)
>>> method = signal.choose_conv_method(a, b, mode='same')
>>> method
'fft'
This can then be applied to other arrays of the same dtype and shape:
>>> c = np.random.randn(1000)
>>> d = np.random.randn(1000000)
>>> # `method` works with correlate and convolve
>>> corr1 = signal.correlate(a, b, mode='same', method=method)
>>> corr2 = signal.correlate(c, d, mode='same', method=method)
>>> conv1 = signal.convolve(a, b, mode='same', method=method)
>>> conv2 = signal.convolve(c, d, mode='same', method=method)
"""
volume = np.asarray(in1)
kernel = np.asarray(in2)
if measure:
times = {}
for method in ['fft', 'direct']:
times[method] = _timeit_fast(lambda: convolve(volume, kernel,
mode=mode, method=method))
chosen_method = 'fft' if times['fft'] < times['direct'] else 'direct'
return chosen_method, times
# fftconvolve doesn't support complex256
fftconv_unsup = "complex256" if sys.maxsize > 2**32 else "complex192"
if hasattr(np, fftconv_unsup):
if volume.dtype == fftconv_unsup or kernel.dtype == fftconv_unsup:
return 'direct'
# for integer input,
# catch when more precision required than float provides (representing an
# integer as float can lose precision in fftconvolve if larger than 2**52)
if any([_numeric_arrays([x], kinds='ui') for x in [volume, kernel]]):
max_value = int(np.abs(volume).max()) * int(np.abs(kernel).max())
max_value *= int(min(volume.size, kernel.size))
if max_value > 2**np.finfo('float').nmant - 1:
return 'direct'
if _numeric_arrays([volume, kernel], kinds='b'):
return 'direct'
if _numeric_arrays([volume, kernel]):
if _fftconv_faster(volume, kernel, mode):
return 'fft'
return 'direct'
def convolve(in1, in2, mode='full', method='auto'):
"""
Convolve two N-dimensional arrays.
Convolve `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the convolution.
``direct``
The convolution is determined directly from sums, the definition of
convolution.
``fft``
The Fourier Transform is used to perform the convolution by calling
`fftconvolve`.
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
convolve : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
numpy.polymul : performs polynomial multiplication (same operation, but
also accepts poly1d objects)
choose_conv_method : chooses the fastest appropriate convolution method
fftconvolve : Always uses the FFT method.
oaconvolve : Uses the overlap-add method to do convolution, which is
generally faster when the input arrays are large and
significantly different in size.
Notes
-----
By default, `convolve` and `correlate` use ``method='auto'``, which calls
`choose_conv_method` to choose the fastest method using pre-computed
values (`choose_conv_method` can also measure real-world timing with a
keyword argument). Because `fftconvolve` relies on floating point numbers,
there are certain constraints that may force `method=direct` (more detail
in `choose_conv_method` docstring).
Examples
--------
Smooth a square pulse using a Hann window:
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 0.], 100)
>>> win = signal.hann(50)
>>> filtered = signal.convolve(sig, win, mode='same') / sum(win)
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_win, ax_filt) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('Original pulse')
>>> ax_orig.margins(0, 0.1)
>>> ax_win.plot(win)
>>> ax_win.set_title('Filter impulse response')
>>> ax_win.margins(0, 0.1)
>>> ax_filt.plot(filtered)
>>> ax_filt.set_title('Filtered signal')
>>> ax_filt.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
volume = np.asarray(in1)
kernel = np.asarray(in2)
if volume.ndim == kernel.ndim == 0:
return volume * kernel
elif volume.ndim != kernel.ndim:
raise ValueError("volume and kernel should have the same "
"dimensionality")
if _inputs_swap_needed(mode, volume.shape, kernel.shape):
# Convolution is commutative; order doesn't have any effect on output
volume, kernel = kernel, volume
if method == 'auto':
method = choose_conv_method(volume, kernel, mode=mode)
if method == 'fft':
out = fftconvolve(volume, kernel, mode=mode)
result_type = np.result_type(volume, kernel)
if result_type.kind in {'u', 'i'}:
out = np.around(out)
return out.astype(result_type)
elif method == 'direct':
# fastpath to faster numpy.convolve for 1d inputs when possible
if _np_conv_ok(volume, kernel, mode):
return np.convolve(volume, kernel, mode)
return correlate(volume, _reverse_and_conj(kernel), mode, 'direct')
else:
raise ValueError("Acceptable method flags are 'auto',"
" 'direct', or 'fft'.")
def order_filter(a, domain, rank):
"""
Perform an order filter on an N-dimensional array.
Perform an order filter on the array in. The domain argument acts as a
mask centered over each pixel. The non-zero elements of domain are
used to select elements surrounding each input pixel which are placed
in a list. The list is sorted, and the output for that pixel is the
element corresponding to rank in the sorted list.
Parameters
----------
a : ndarray
The N-dimensional input array.
domain : array_like
A mask array with the same number of dimensions as `a`.
Each dimension should have an odd number of elements.
rank : int
A non-negative integer which selects the element from the
sorted list (0 corresponds to the smallest element, 1 is the
next smallest element, etc.).
Returns
-------
out : ndarray
The results of the order filter in an array with the same
shape as `a`.
Examples
--------
>>> from scipy import signal
>>> x = np.arange(25).reshape(5, 5)
>>> domain = np.identity(3)
>>> x
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
>>> signal.order_filter(x, domain, 0)
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 1., 2., 0.],
[ 0., 5., 6., 7., 0.],
[ 0., 10., 11., 12., 0.],
[ 0., 0., 0., 0., 0.]])
>>> signal.order_filter(x, domain, 2)
array([[ 6., 7., 8., 9., 4.],
[ 11., 12., 13., 14., 9.],
[ 16., 17., 18., 19., 14.],
[ 21., 22., 23., 24., 19.],
[ 20., 21., 22., 23., 24.]])
"""
domain = np.asarray(domain)
size = domain.shape
for k in range(len(size)):
if (size[k] % 2) != 1:
raise ValueError("Each dimension of domain argument "
" should have an odd number of elements.")
return sigtools._order_filterND(a, domain, rank)
def medfilt(volume, kernel_size=None):
"""
Perform a median filter on an N-dimensional array.
Apply a median filter to the input array using a local window-size
given by `kernel_size`. The array will automatically be zero-padded.
Parameters
----------
volume : array_like
An N-dimensional input array.
kernel_size : array_like, optional
A scalar or an N-length list giving the size of the median filter
window in each dimension. Elements of `kernel_size` should be odd.
If `kernel_size` is a scalar, then this scalar is used as the size in
each dimension. Default size is 3 for each dimension.
Returns
-------
out : ndarray
An array the same size as input containing the median filtered
result.
See also
--------
scipy.ndimage.median_filter
Notes
-------
The more general function `scipy.ndimage.median_filter` has a more
efficient implementation of a median filter and therefore runs much faster.
"""
volume = np.atleast_1d(volume)
if kernel_size is None:
kernel_size = [3] * volume.ndim
kernel_size = np.asarray(kernel_size)
if kernel_size.shape == ():
kernel_size = np.repeat(kernel_size.item(), volume.ndim)
for k in range(volume.ndim):
if (kernel_size[k] % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
domain = np.ones(kernel_size)
numels = np.prod(kernel_size, axis=0)
order = numels // 2
return sigtools._order_filterND(volume, domain, order)
def wiener(im, mysize=None, noise=None):
"""
Perform a Wiener filter on an N-dimensional array.
Apply a Wiener filter to the N-dimensional array `im`.
Parameters
----------
im : ndarray
An N-dimensional array.
mysize : int or array_like, optional
A scalar or an N-length list giving the size of the Wiener filter
window in each dimension. Elements of mysize should be odd.
If mysize is a scalar, then this scalar is used as the size
in each dimension.
noise : float, optional
The noise-power to use. If None, then noise is estimated as the
average of the local variance of the input.
Returns
-------
out : ndarray
Wiener filtered result with the same shape as `im`.
"""
im = np.asarray(im)
if mysize is None:
mysize = [3] * im.ndim
mysize = np.asarray(mysize)
if mysize.shape == ():
mysize = np.repeat(mysize.item(), im.ndim)
# Estimate the local mean
lMean = correlate(im, np.ones(mysize), 'same') / np.prod(mysize, axis=0)
# Estimate the local variance
lVar = (correlate(im ** 2, np.ones(mysize), 'same') /
np.prod(mysize, axis=0) - lMean ** 2)
# Estimate the noise power if needed.
if noise is None:
noise = np.mean(np.ravel(lVar), axis=0)
res = (im - lMean)
res *= (1 - noise / lVar)
res += lMean
out = np.where(lVar < noise, lMean, res)
return out
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""
Convolve two 2-dimensional arrays.
Convolve `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
A flag indicating how to handle boundaries:
``fill``
pad input arrays with fillvalue. (default)
``wrap``
circular boundary conditions.
``symm``
symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
Examples
--------
Compute the gradient of an image by 2D convolution with a complex Scharr
operator. (Horizontal operator is real, vertical is imaginary.) Use
symmetric boundary condition to avoid creating edges at the image
boundaries.
>>> from scipy import signal
>>> from scipy import misc
>>> ascent = misc.ascent()
>>> scharr = np.array([[ -3-3j, 0-10j, +3 -3j],
... [-10+0j, 0+ 0j, +10 +0j],
... [ -3+3j, 0+10j, +3 +3j]]) # Gx + j*Gy
>>> grad = signal.convolve2d(ascent, scharr, boundary='symm', mode='same')
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(3, 1, figsize=(6, 15))
>>> ax_orig.imshow(ascent, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_mag.imshow(np.absolute(grad), cmap='gray')
>>> ax_mag.set_title('Gradient magnitude')
>>> ax_mag.set_axis_off()
>>> ax_ang.imshow(np.angle(grad), cmap='hsv') # hsv is cyclic, like angles
>>> ax_ang.set_title('Gradient orientation')
>>> ax_ang.set_axis_off()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if not in1.ndim == in2.ndim == 2:
raise ValueError('convolve2d inputs must both be 2D arrays')
if _inputs_swap_needed(mode, in1.shape, in2.shape):
in1, in2 = in2, in1
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
out = sigtools._convolve2d(in1, in2, 1, val, bval, fillvalue)
return out
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""
Cross-correlate two 2-dimensional arrays.
Cross correlate `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
A flag indicating how to handle boundaries:
``fill``
pad input arrays with fillvalue. (default)
``wrap``
circular boundary conditions.
``symm``
symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
correlate2d : ndarray
A 2-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
Examples
--------
Use 2D cross-correlation to find the location of a template in a noisy
image:
>>> from scipy import signal
>>> from scipy import misc
>>> face = misc.face(gray=True) - misc.face(gray=True).mean()
>>> template = np.copy(face[300:365, 670:750]) # right eye
>>> template -= template.mean()
>>> face = face + np.random.randn(*face.shape) * 50 # add noise
>>> corr = signal.correlate2d(face, template, boundary='symm', mode='same')
>>> y, x = np.unravel_index(np.argmax(corr), corr.shape) # find the match
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_template, ax_corr) = plt.subplots(3, 1,
... figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_template.imshow(template, cmap='gray')
>>> ax_template.set_title('Template')
>>> ax_template.set_axis_off()
>>> ax_corr.imshow(corr, cmap='gray')
>>> ax_corr.set_title('Cross-correlation')
>>> ax_corr.set_axis_off()
>>> ax_orig.plot(x, y, 'ro')
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if not in1.ndim == in2.ndim == 2:
raise ValueError('correlate2d inputs must both be 2D arrays')
swapped_inputs = _inputs_swap_needed(mode, in1.shape, in2.shape)
if swapped_inputs:
in1, in2 = in2, in1
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
out = sigtools._convolve2d(in1, in2.conj(), 0, val, bval, fillvalue)
if swapped_inputs:
out = out[::-1, ::-1]
return out
def medfilt2d(input, kernel_size=3):
"""
Median filter a 2-dimensional array.
Apply a median filter to the `input` array using a local window-size
given by `kernel_size` (must be odd). The array is zero-padded
automatically.
Parameters
----------
input : array_like
A 2-dimensional input array.
kernel_size : array_like, optional
A scalar or a list of length 2, giving the size of the
median filter window in each dimension. Elements of
`kernel_size` should be odd. If `kernel_size` is a scalar,
then this scalar is used as the size in each dimension.
Default is a kernel of size (3, 3).
Returns
-------
out : ndarray
An array the same size as input containing the median filtered
result.
See also
--------
scipy.ndimage.median_filter
Notes
-------
The more general function `scipy.ndimage.median_filter` has a more
efficient implementation of a median filter and therefore runs much faster.
"""
image = np.asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = np.asarray(kernel_size)
if kernel_size.shape == ():
kernel_size = np.repeat(kernel_size.item(), 2)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
return sigtools._medfilt2d(image, kernel_size)
def lfilter(b, a, x, axis=-1, zi=None):
"""
Filter data along one-dimension with an IIR or FIR filter.
Filter a data sequence, `x`, using a digital filter. This works for many
fundamental data types (including Object type). The filter is a direct
form II transposed implementation of the standard difference equation
(see Notes).
The function `sosfilt` (and filter design using ``output='sos'``) should be
preferred over `lfilter` for most filtering tasks, as second-order sections
have fewer numerical problems.
Parameters
----------
b : array_like
The numerator coefficient vector in a 1-D sequence.
a : array_like
The denominator coefficient vector in a 1-D sequence. If ``a[0]``
is not 1, then both `a` and `b` are normalized by ``a[0]``.
x : array_like
An N-dimensional input array.
axis : int, optional
The axis of the input data array along which to apply the
linear filter. The filter is applied to each subarray along
this axis. Default is -1.
zi : array_like, optional
Initial conditions for the filter delays. It is a vector
(or array of vectors for an N-dimensional input) of length
``max(len(a), len(b)) - 1``. If `zi` is None or is not given then
initial rest is assumed. See `lfiltic` for more information.
Returns
-------
y : array
The output of the digital filter.
zf : array, optional
If `zi` is None, this is not returned, otherwise, `zf` holds the
final filter delay values.
See Also
--------
lfiltic : Construct initial conditions for `lfilter`.
lfilter_zi : Compute initial state (steady state of step response) for
`lfilter`.
filtfilt : A forward-backward filter, to obtain a filter with linear phase.
savgol_filter : A Savitzky-Golay filter.
sosfilt: Filter data using cascaded second-order sections.
sosfiltfilt: A forward-backward filter using second-order sections.
Notes
-----
The filter function is implemented as a direct II transposed structure.
This means that the filter implements::
a[0]*y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M]
- a[1]*y[n-1] - ... - a[N]*y[n-N]
where `M` is the degree of the numerator, `N` is the degree of the
denominator, and `n` is the sample number. It is implemented using
the following difference equations (assuming M = N)::
a[0]*y[n] = b[0] * x[n] + d[0][n-1]
d[0][n] = b[1] * x[n] - a[1] * y[n] + d[1][n-1]
d[1][n] = b[2] * x[n] - a[2] * y[n] + d[2][n-1]
...
d[N-2][n] = b[N-1]*x[n] - a[N-1]*y[n] + d[N-1][n-1]
d[N-1][n] = b[N] * x[n] - a[N] * y[n]
where `d` are the state variables.
The rational transfer function describing this filter in the
z-transform domain is::
-1 -M
b[0] + b[1]z + ... + b[M] z
Y(z) = -------------------------------- X(z)
-1 -N
a[0] + a[1]z + ... + a[N] z
Examples
--------
Generate a noisy signal to be filtered:
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> t = np.linspace(-1, 1, 201)
>>> x = (np.sin(2*np.pi*0.75*t*(1-t) + 2.1) +
... 0.1*np.sin(2*np.pi*1.25*t + 1) +
... 0.18*np.cos(2*np.pi*3.85*t))
>>> xn = x + np.random.randn(len(t)) * 0.08
Create an order 3 lowpass butterworth filter:
>>> b, a = signal.butter(3, 0.05)
Apply the filter to xn. Use lfilter_zi to choose the initial condition of
the filter:
>>> zi = signal.lfilter_zi(b, a)
>>> z, _ = signal.lfilter(b, a, xn, zi=zi*xn[0])
Apply the filter again, to have a result filtered at an order the same as
filtfilt:
>>> z2, _ = signal.lfilter(b, a, z, zi=zi*z[0])
Use filtfilt to apply the filter:
>>> y = signal.filtfilt(b, a, xn)
Plot the original signal and the various filtered versions:
>>> plt.figure
>>> plt.plot(t, xn, 'b', alpha=0.75)
>>> plt.plot(t, z, 'r--', t, z2, 'r', t, y, 'k')
>>> plt.legend(('noisy signal', 'lfilter, once', 'lfilter, twice',
... 'filtfilt'), loc='best')
>>> plt.grid(True)
>>> plt.show()
"""
a = np.atleast_1d(a)
if len(a) == 1:
# This path only supports types fdgFDGO to mirror _linear_filter below.
# Any of b, a, x, or zi can set the dtype, but there is no default
# casting of other types; instead a NotImplementedError is raised.
b = np.asarray(b)
a = np.asarray(a)
if b.ndim != 1 and a.ndim != 1:
raise ValueError('object of too small depth for desired array')
x = _validate_x(x)
inputs = [b, a, x]
if zi is not None:
# _linear_filter does not broadcast zi, but does do expansion of
# singleton dims.
zi = np.asarray(zi)
if zi.ndim != x.ndim:
raise ValueError('object of too small depth for desired array')
expected_shape = list(x.shape)
expected_shape[axis] = b.shape[0] - 1
expected_shape = tuple(expected_shape)
# check the trivial case where zi is the right shape first
if zi.shape != expected_shape:
strides = zi.ndim * [None]
if axis < 0:
axis += zi.ndim
for k in range(zi.ndim):
if k == axis and zi.shape[k] == expected_shape[k]:
strides[k] = zi.strides[k]
elif k != axis and zi.shape[k] == expected_shape[k]:
strides[k] = zi.strides[k]
elif k != axis and zi.shape[k] == 1:
strides[k] = 0
else:
raise ValueError('Unexpected shape for zi: expected '
'%s, found %s.' %
(expected_shape, zi.shape))
zi = np.lib.stride_tricks.as_strided(zi, expected_shape,
strides)
inputs.append(zi)
dtype = np.result_type(*inputs)
if dtype.char not in 'fdgFDGO':
raise NotImplementedError("input type '%s' not supported" % dtype)
b = np.array(b, dtype=dtype)
a = np.array(a, dtype=dtype, copy=False)
b /= a[0]
x = np.array(x, dtype=dtype, copy=False)
out_full = np.apply_along_axis(lambda y: np.convolve(b, y), axis, x)
ind = out_full.ndim * [slice(None)]
if zi is not None:
ind[axis] = slice(zi.shape[axis])
out_full[tuple(ind)] += zi
ind[axis] = slice(out_full.shape[axis] - len(b) + 1)
out = out_full[tuple(ind)]
if zi is None:
return out
else:
ind[axis] = slice(out_full.shape[axis] - len(b) + 1, None)
zf = out_full[tuple(ind)]
return out, zf
else:
if zi is None:
return sigtools._linear_filter(b, a, x, axis)
else:
return sigtools._linear_filter(b, a, x, axis, zi)
def lfiltic(b, a, y, x=None):
"""
Construct initial conditions for lfilter given input and output vectors.
Given a linear filter (b, a) and initial conditions on the output `y`
and the input `x`, return the initial conditions on the state vector zi
which is used by `lfilter` to generate the output given the input.
Parameters
----------
b : array_like
Linear filter term.
a : array_like
Linear filter term.
y : array_like
Initial conditions.
If ``N = len(a) - 1``, then ``y = {y[-1], y[-2], ..., y[-N]}``.
If `y` is too short, it is padded with zeros.
x : array_like, optional
Initial conditions.
If ``M = len(b) - 1``, then ``x = {x[-1], x[-2], ..., x[-M]}``.
If `x` is not given, its initial conditions are assumed zero.
If `x` is too short, it is padded with zeros.
Returns
-------
zi : ndarray
The state vector ``zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]}``,
where ``K = max(M, N)``.
See Also
--------
lfilter, lfilter_zi
"""
N = np.size(a) - 1
M = np.size(b) - 1
K = max(M, N)
y = np.asarray(y)
if y.dtype.kind in 'bui':
# ensure calculations are floating point
y = y.astype(np.float64)
zi = np.zeros(K, y.dtype)
if x is None:
x = np.zeros(M, y.dtype)
else:
x = np.asarray(x)
L = np.size(x)
if L < M:
x = np.r_[x, np.zeros(M - L)]
L = np.size(y)
if L < N:
y = np.r_[y, np.zeros(N - L)]
for m in range(M):
zi[m] = np.sum(b[m + 1:] * x[:M - m], axis=0)
for m in range(N):
zi[m] -= np.sum(a[m + 1:] * y[:N - m], axis=0)
return zi
def deconvolve(signal, divisor):
"""Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
Returns the quotient and remainder such that
``signal = convolve(divisor, quotient) + remainder``
Parameters
----------
signal : array_like
Signal data, typically a recorded signal
divisor : array_like
Divisor data, typically an impulse response or filter that was
applied to the original signal
Returns
-------
quotient : ndarray
Quotient, typically the recovered original signal
remainder : ndarray
Remainder
Examples
--------
Deconvolve a signal that's been filtered:
>>> from scipy import signal
>>> original = [0, 1, 0, 0, 1, 1, 0, 0]
>>> impulse_response = [2, 1]
>>> recorded = signal.convolve(impulse_response, original)
>>> recorded
array([0, 2, 1, 0, 2, 3, 1, 0, 0])
>>> recovered, remainder = signal.deconvolve(recorded, impulse_response)
>>> recovered
array([ 0., 1., 0., 0., 1., 1., 0., 0.])
See Also
--------
numpy.polydiv : performs polynomial division (same operation, but
also accepts poly1d objects)
"""
num = np.atleast_1d(signal)
den = np.atleast_1d(divisor)
N = len(num)
D = len(den)
if D > N:
quot = []
rem = num
else:
input = np.zeros(N - D + 1, float)
input[0] = 1
quot = lfilter(num, den, input)
rem = num - convolve(den, quot, mode='full')
return quot, rem
def hilbert(x, N=None, axis=-1):
"""
Compute the analytic signal, using the Hilbert transform.
The transformation is done along the last axis by default.
Parameters
----------
x : array_like
Signal data. Must be real.
N : int, optional
Number of Fourier components. Default: ``x.shape[axis]``
axis : int, optional
Axis along which to do the transformation. Default: -1.
Returns
-------
xa : ndarray
Analytic signal of `x`, of each 1-D array along `axis`
Notes
-----
The analytic signal ``x_a(t)`` of signal ``x(t)`` is:
.. math:: x_a = F^{-1}(F(x) 2U) = x + i y
where `F` is the Fourier transform, `U` the unit step function,
and `y` the Hilbert transform of `x`. [1]_
In other words, the negative half of the frequency spectrum is zeroed
out, turning the real-valued signal into a complex signal. The Hilbert
transformed signal can be obtained from ``np.imag(hilbert(x))``, and the
original signal from ``np.real(hilbert(x))``.
Examples
---------
In this example we use the Hilbert transform to determine the amplitude
envelope and instantaneous frequency of an amplitude-modulated signal.
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.signal import hilbert, chirp
>>> duration = 1.0
>>> fs = 400.0
>>> samples = int(fs*duration)
>>> t = np.arange(samples) / fs
We create a chirp of which the frequency increases from 20 Hz to 100 Hz and
apply an amplitude modulation.
>>> signal = chirp(t, 20.0, t[-1], 100.0)
>>> signal *= (1.0 + 0.5 * np.sin(2.0*np.pi*3.0*t) )
The amplitude envelope is given by magnitude of the analytic signal. The
instantaneous frequency can be obtained by differentiating the
instantaneous phase in respect to time. The instantaneous phase corresponds
to the phase angle of the analytic signal.
>>> analytic_signal = hilbert(signal)
>>> amplitude_envelope = np.abs(analytic_signal)
>>> instantaneous_phase = np.unwrap(np.angle(analytic_signal))
>>> instantaneous_frequency = (np.diff(instantaneous_phase) /
... (2.0*np.pi) * fs)
>>> fig = plt.figure()
>>> ax0 = fig.add_subplot(211)
>>> ax0.plot(t, signal, label='signal')
>>> ax0.plot(t, amplitude_envelope, label='envelope')
>>> ax0.set_xlabel("time in seconds")
>>> ax0.legend()
>>> ax1 = fig.add_subplot(212)
>>> ax1.plot(t[1:], instantaneous_frequency)
>>> ax1.set_xlabel("time in seconds")
>>> ax1.set_ylim(0.0, 120.0)
References
----------
.. [1] Wikipedia, "Analytic signal".
https://en.wikipedia.org/wiki/Analytic_signal
.. [2] Leon Cohen, "Time-Frequency Analysis", 1995. Chapter 2.
.. [3] Alan V. Oppenheim, Ronald W. Schafer. Discrete-Time Signal
Processing, Third Edition, 2009. Chapter 12.
ISBN 13: 978-1292-02572-8
"""
x = np.asarray(x)
if np.iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape[axis]
if N <= 0:
raise ValueError("N must be positive.")
Xf = sp_fft.fft(x, N, axis=axis)
h = np.zeros(N)
if N % 2 == 0:
h[0] = h[N // 2] = 1
h[1:N // 2] = 2
else:
h[0] = 1
h[1:(N + 1) // 2] = 2
if x.ndim > 1:
ind = [np.newaxis] * x.ndim
ind[axis] = slice(None)
h = h[tuple(ind)]
x = sp_fft.ifft(Xf * h, axis=axis)
return x
def hilbert2(x, N=None):
"""
Compute the '2-D' analytic signal of `x`
Parameters
----------
x : array_like
2-D signal data.
N : int or tuple of two ints, optional
Number of Fourier components. Default is ``x.shape``
Returns
-------
xa : ndarray
Analytic signal of `x` taken along axes (0,1).
References
----------
.. [1] Wikipedia, "Analytic signal",
https://en.wikipedia.org/wiki/Analytic_signal
"""
x = np.atleast_2d(x)
if x.ndim > 2:
raise ValueError("x must be 2-D.")
if np.iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape
elif isinstance(N, int):
if N <= 0:
raise ValueError("N must be positive.")
N = (N, N)
elif len(N) != 2 or np.any(np.asarray(N) <= 0):
raise ValueError("When given as a tuple, N must hold exactly "
"two positive integers")
Xf = sp_fft.fft2(x, N, axes=(0, 1))
h1 = np.zeros(N[0], 'd')
h2 = np.zeros(N[1], 'd')
for p in range(2):
h = eval("h%d" % (p + 1))
N1 = N[p]
if N1 % 2 == 0:
h[0] = h[N1 // 2] = 1
h[1:N1 // 2] = 2
else:
h[0] = 1
h[1:(N1 + 1) // 2] = 2
exec("h%d = h" % (p + 1), globals(), locals())
h = h1[:, np.newaxis] * h2[np.newaxis, :]
k = x.ndim
while k > 2:
h = h[:, np.newaxis]
k -= 1
x = sp_fft.ifft2(Xf * h, axes=(0, 1))
return x
def cmplx_sort(p):
"""Sort roots based on magnitude.
Parameters
----------
p : array_like
The roots to sort, as a 1-D array.
Returns
-------
p_sorted : ndarray
Sorted roots.
indx : ndarray
Array of indices needed to sort the input `p`.
Examples
--------
>>> from scipy import signal
>>> vals = [1, 4, 1+1.j, 3]
>>> p_sorted, indx = signal.cmplx_sort(vals)
>>> p_sorted
array([1.+0.j, 1.+1.j, 3.+0.j, 4.+0.j])
>>> indx
array([0, 2, 3, 1])
"""
p = np.asarray(p)
indx = np.argsort(abs(p))
return np.take(p, indx, 0), indx
def unique_roots(p, tol=1e-3, rtype='min'):
"""Determine unique roots and their multiplicities from a list of roots.
Parameters
----------
p : array_like
The list of roots.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. Refer to Notes about
the details on roots grouping.
rtype : {'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}, optional
How to determine the returned root if multiple roots are within
`tol` of each other.
- 'max', 'maximum': pick the maximum of those roots
- 'min', 'minimum': pick the minimum of those roots
- 'avg', 'mean': take the average of those roots
When finding minimum or maximum among complex roots they are compared
first by the real part and then by the imaginary part.
Returns
-------
unique : ndarray
The list of unique roots.
multiplicity : ndarray
The multiplicity of each root.
Notes
-----
If we have 3 roots ``a``, ``b`` and ``c``, such that ``a`` is close to
``b`` and ``b`` is close to ``c`` (distance is less than `tol`), then it
doesn't necessarily mean that ``a`` is close to ``c``. It means that roots
grouping is not unique. In this function we use "greedy" grouping going
through the roots in the order they are given in the input `p`.
This utility function is not specific to roots but can be used for any
sequence of values for which uniqueness and multiplicity has to be
determined. For a more general routine, see `numpy.unique`.
Examples
--------
>>> from scipy import signal
>>> vals = [0, 1.3, 1.31, 2.8, 1.25, 2.2, 10.3]
>>> uniq, mult = signal.unique_roots(vals, tol=2e-2, rtype='avg')
Check which roots have multiplicity larger than 1:
>>> uniq[mult > 1]
array([ 1.305])
"""
if rtype in ['max', 'maximum']:
reduce = np.max
elif rtype in ['min', 'minimum']:
reduce = np.min
elif rtype in ['avg', 'mean']:
reduce = np.mean
else:
raise ValueError("`rtype` must be one of "
"{'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}")
p = np.asarray(p)
points = np.empty((len(p), 2))
points[:, 0] = np.real(p)
points[:, 1] = np.imag(p)
tree = cKDTree(points)
p_unique = []
p_multiplicity = []
used = np.zeros(len(p), dtype=bool)
for i in range(len(p)):
if used[i]:
continue
group = tree.query_ball_point(points[i], tol)
group = [x for x in group if not used[x]]
p_unique.append(reduce(p[group]))
p_multiplicity.append(len(group))
used[group] = True
return np.asarray(p_unique), np.asarray(p_multiplicity)
def invres(r, p, k, tol=1e-3, rtype='avg'):
"""
Compute b(s) and a(s) from partial fraction expansion.
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(s) b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
a(s) a[0] s**(N) + a[1] s**(N-1) + ... + a[N]
then the partial-fraction expansion H(s) is defined as::
r[0] r[1] r[-1]
= -------- + -------- + ... + --------- + k(s)
(s-p[0]) (s-p[1]) (s-p[-1])
If there are any repeated roots (closer together than `tol`), then H(s)
has terms like::
r[i] r[i+1] r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i]) (s-p[i])**2 (s-p[i])**n
This function is used for polynomials in positive powers of s or z,
such as analog filters or digital filters in controls engineering. For
negative powers of z (typical for digital filters in DSP), use `invresz`.
Parameters
----------
r : array_like
Residues.
p : array_like
Poles.
k : array_like
Coefficients of the direct polynomial term.
tol : float, optional
The tolerance for two roots to be considered equal. Default is 1e-3.
rtype : {'max', 'min, 'avg'}, optional
How to determine the returned root if multiple roots are within
`tol` of each other.
- 'max': pick the maximum of those roots.
- 'min': pick the minimum of those roots.
- 'avg': take the average of those roots.
Returns
-------
b : ndarray
Numerator polynomial coefficients.
a : ndarray
Denominator polynomial coefficients.
See Also
--------
residue, invresz, unique_roots
"""
extra = k
p, indx = cmplx_sort(p)
r = np.take(r, indx, 0)
pout, mult = unique_roots(p, tol=tol, rtype=rtype)
p = []
for k in range(len(pout)):
p.extend([pout[k]] * mult[k])
a = np.atleast_1d(np.poly(p))
if len(extra) > 0:
b = np.polymul(extra, a)
else:
b = [0]
indx = 0
for k in range(len(pout)):
temp = []
for l in range(len(pout)):
if l != k:
temp.extend([pout[l]] * mult[l])
for m in range(mult[k]):
t2 = temp[:]
t2.extend([pout[k]] * (mult[k] - m - 1))
b = np.polyadd(b, r[indx] * np.atleast_1d(np.poly(t2)))
indx += 1
b = np.real_if_close(b)
while np.allclose(b[0], 0, rtol=1e-14) and (b.shape[-1] > 1):
b = b[1:]
return b, a
def _compute_factors(roots, multiplicity):
"""Compute the total polynomial divided by factors for each root."""
suffixes = []
current = np.array([1])
for pole, mult in zip(roots[-1:0:-1], multiplicity[-1:0:-1]):
monomial = np.array([1, -pole])
for _ in range(mult):
current = np.polymul(current, monomial)
suffixes.append(current)
suffixes = suffixes[::-1]
result = []
current = np.array([1])
for pole, mult, suffix in zip(roots[:-1], multiplicity[:-1], suffixes):
result.append(np.polymul(current, suffix))
monomial = np.array([1, -pole])
for _ in range(mult):
current = np.polymul(current, monomial)
result.append(current)
return result
def _compute_residues(poles, multiplicity, numerator):
denominator_factors = _compute_factors(poles, multiplicity)
numerator = numerator.astype(poles.dtype)
residues = []
for pole, mult, factor in zip(poles, multiplicity,
denominator_factors):
if mult == 1:
residues.append(np.polyval(numerator, pole) /
np.polyval(factor, pole))
else:
numer = numerator.copy()
monomial = np.array([1, -pole])
factor, d = np.polydiv(factor, monomial)
block = []
for _ in range(mult):
numer, n = np.polydiv(numer, monomial)
r = n[0] / d[0]
numer = np.polysub(numer, r * factor)
block.append(r)
residues.extend(reversed(block))
return np.asarray(residues)
def residue(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(s) / a(s).
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(s) b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
a(s) a[0] s**(N) + a[1] s**(N-1) + ... + a[N]
then the partial-fraction expansion H(s) is defined as::
r[0] r[1] r[-1]
= -------- + -------- + ... + --------- + k(s)
(s-p[0]) (s-p[1]) (s-p[-1])
If there are any repeated roots (closer together than `tol`), then H(s)
has terms like::
r[i] r[i+1] r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i]) (s-p[i])**2 (s-p[i])**n
This function is used for polynomials in positive powers of s or z,
such as analog filters or digital filters in controls engineering. For
negative powers of z (typical for digital filters in DSP), use `residuez`.
See Notes for details about the algorithm.
Parameters
----------
b : array_like
Numerator polynomial coefficients.
a : array_like
Denominator polynomial coefficients.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
r : ndarray
Residues corresponding to the poles. For repeated poles, the residues
are ordered to correspond to ascending by power fractions.
p : ndarray
Poles ordered by magnitude in ascending order.
k : ndarray
Coefficients of the direct polynomial term.
See Also
--------
invres, residuez, numpy.poly, unique_roots
Notes
-----
The "deflation through subtraction" algorithm is used for
computations --- method 6 in [1]_.
The form of partial fraction expansion depends on poles multiplicity in
the exact mathematical sense. However there is no way to exactly
determine multiplicity of roots of a polynomial in numerical computing.
Thus you should think of the result of `residue` with given `tol` as
partial fraction expansion computed for the denominator composed of the
computed poles with empirically determined multiplicity. The choice of
`tol` can drastically change the result if there are close poles.
References
----------
.. [1] J. F. Mahoney, B. D. Sivazlian, "Partial fractions expansion: a
review of computational methodology and efficiency", Journal of
Computational and Applied Mathematics, Vol. 9, 1983.
"""
b = np.asarray(b)
a = np.asarray(a)
if (np.issubdtype(b.dtype, np.complexfloating)
or np.issubdtype(a.dtype, np.complexfloating)):
b = b.astype(complex)
a = a.astype(complex)
else:
b = b.astype(float)
a = a.astype(float)
b = np.trim_zeros(np.atleast_1d(b), 'f')
a = np.trim_zeros(np.atleast_1d(a), 'f')
if a.size == 0:
raise ValueError("Denominator `a` is zero.")
poles = np.roots(a)
if b.size == 0:
return np.zeros(poles.shape), cmplx_sort(poles)[0], np.array([])
k, b = np.polydiv(b, a)
unique_poles, multiplicity = unique_roots(poles, tol=tol, rtype=rtype)
unique_poles, order = cmplx_sort(unique_poles)
multiplicity = multiplicity[order]
residues = _compute_residues(unique_poles, multiplicity, b)
index = 0
for pole, mult in zip(unique_poles, multiplicity):
poles[index:index + mult] = pole
index += mult
return residues / a[0], poles, np.trim_zeros(k, 'f')
def residuez(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(z) / a(z).
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(z) b[0] + b[1] z**(-1) + ... + b[M] z**(-M)
H(z) = ------ = ------------------------------------------
a(z) a[0] + a[1] z**(-1) + ... + a[N] z**(-N)
then the partial-fraction expansion H(z) is defined as::
r[0] r[-1]
= --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ...
(1-p[0]z**(-1)) (1-p[-1]z**(-1))
If there are any repeated roots (closer than `tol`), then the partial
fraction expansion has terms like::
r[i] r[i+1] r[i+n-1]
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
This function is used for polynomials in negative powers of z,
such as digital filters in DSP. For positive powers, use `residue`.
See Notes of `residue` for details about the algorithm.
Parameters
----------
b : array_like
Numerator polynomial coefficients.
a : array_like
Denominator polynomial coefficients.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
r : ndarray
Residues corresponding to the poles. For repeated poles, the residues
are ordered to correspond to ascending by power fractions.
p : ndarray
Poles ordered by magnitude in ascending order.
k : ndarray
Coefficients of the direct polynomial term.
See Also
--------
invresz, residue, unique_roots
"""
b = np.asarray(b)
a = np.asarray(a)
if (np.issubdtype(b.dtype, np.complexfloating)
or np.issubdtype(a.dtype, np.complexfloating)):
b = b.astype(complex)
a = a.astype(complex)
else:
b = b.astype(float)
a = a.astype(float)
b = np.trim_zeros(np.atleast_1d(b), 'b')
a = np.trim_zeros(np.atleast_1d(a), 'b')
if a.size == 0:
raise ValueError("Denominator `a` is zero.")
elif a[0] == 0:
raise ValueError("First coefficient of determinant `a` must be "
"non-zero.")
poles = np.roots(a)
if b.size == 0:
return np.zeros(poles.shape), cmplx_sort(poles)[0], np.array([])
b_rev = b[::-1]
a_rev = a[::-1]
k_rev, b_rev = np.polydiv(b_rev, a_rev)
unique_poles, multiplicity = unique_roots(poles, tol=tol, rtype=rtype)
unique_poles, order = cmplx_sort(unique_poles)
multiplicity = multiplicity[order]
residues = _compute_residues(1 / unique_poles, multiplicity, b_rev)
index = 0
powers = np.empty(len(residues), dtype=int)
for pole, mult in zip(unique_poles, multiplicity):
poles[index:index + mult] = pole
powers[index:index + mult] = 1 + np.arange(mult)
index += mult
residues *= (-poles) ** powers / a_rev[0]
return residues, poles, np.trim_zeros(k_rev[::-1], 'b')
def invresz(r, p, k, tol=1e-3, rtype='avg'):
"""
Compute b(z) and a(z) from partial fraction expansion.
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(z) b[0] + b[1] z**(-1) + ... + b[M] z**(-M)
H(z) = ------ = ------------------------------------------
a(z) a[0] + a[1] z**(-1) + ... + a[N] z**(-N)
then the partial-fraction expansion H(z) is defined as::
r[0] r[-1]
= --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ...
(1-p[0]z**(-1)) (1-p[-1]z**(-1))
If there are any repeated roots (closer than `tol`), then the partial
fraction expansion has terms like::
r[i] r[i+1] r[i+n-1]
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
This function is used for polynomials in negative powers of z,
such as digital filters in DSP. For positive powers, use `invres`.
Parameters
----------
r : array_like
Residues.
p : array_like
Poles.
k : array_like
Coefficients of the direct polynomial term.
tol : float, optional
The tolerance for two roots to be considered equal. Default is 1e-3.
rtype : {'max', 'min, 'avg'}, optional
How to determine the returned root if multiple roots are within
`tol` of each other.
- 'max': pick the maximum of those roots.
- 'min': pick the minimum of those roots.
- 'avg': take the average of those roots.
Returns
-------
b : ndarray
Numerator polynomial coefficients.
a : ndarray
Denominator polynomial coefficients.
See Also
--------
residuez, unique_roots, invres
"""
extra = np.asarray(k)
p, indx = cmplx_sort(p)
r = np.take(r, indx, 0)
pout, mult = unique_roots(p, tol=tol, rtype=rtype)
p = []
for k in range(len(pout)):
p.extend([pout[k]] * mult[k])
a = np.atleast_1d(np.poly(p))
if len(extra) > 0:
b = np.polymul(extra, a)
else:
b = [0]
indx = 0
brev = np.asarray(b)[::-1]
for k in range(len(pout)):
temp = []
# Construct polynomial which does not include any of this root
for l in range(len(pout)):
if l != k:
temp.extend([pout[l]] * mult[l])
for m in range(mult[k]):
t2 = temp[:]
t2.extend([pout[k]] * (mult[k] - m - 1))
brev = np.polyadd(brev,
(r[indx] * np.atleast_1d(np.poly(t2)))[::-1])
indx += 1
b = np.real_if_close(brev[::-1])
return b, a
def resample(x, num, t=None, axis=0, window=None):
"""
Resample `x` to `num` samples using Fourier method along the given axis.
The resampled signal starts at the same value as `x` but is sampled
with a spacing of ``len(x) / num * (spacing of x)``. Because a
Fourier method is used, the signal is assumed to be periodic.
Parameters
----------
x : array_like
The data to be resampled.
num : int
The number of samples in the resampled signal.
t : array_like, optional
If `t` is given, it is assumed to be the equally spaced sample
positions associated with the signal data in `x`.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : array_like, callable, string, float, or tuple, optional
Specifies the window applied to the signal in the Fourier
domain. See below for details.
Returns
-------
resampled_x or (resampled_x, resampled_t)
Either the resampled array, or, if `t` was given, a tuple
containing the resampled array and the corresponding resampled
positions.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample_poly : Resample using polyphase filtering and an FIR filter.
Notes
-----
The argument `window` controls a Fourier-domain window that tapers
the Fourier spectrum before zero-padding to alleviate ringing in
the resampled values for sampled signals you didn't intend to be
interpreted as band-limited.
If `window` is a function, then it is called with a vector of inputs
indicating the frequency bins (i.e. fftfreq(x.shape[axis]) ).
If `window` is an array of the same length as `x.shape[axis]` it is
assumed to be the window to be applied directly in the Fourier
domain (with dc and low-frequency first).
For any other type of `window`, the function `scipy.signal.get_window`
is called to generate the window.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * len(x) / num``.
If `t` is not None, then it is used solely to calculate the resampled
positions `resampled_t`
As noted, `resample` uses FFT transformations, which can be very
slow if the number of input or output samples is large and prime;
see `scipy.fft.fft`.
Examples
--------
Note that the end of the resampled data rises to meet the first
sample of the next cycle:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f = signal.resample(y, 100)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'go-', xnew, f, '.-', 10, y[0], 'ro')
>>> plt.legend(['data', 'resampled'], loc='best')
>>> plt.show()
"""
x = np.asarray(x)
X = sp_fft.fft(x, axis=axis)
Nx = x.shape[axis]
# Check if we can use faster real FFT
real_input = np.isrealobj(x)
# Forward transform
if real_input:
X = sp_fft.rfft(x, axis=axis)
else: # Full complex FFT
X = sp_fft.fft(x, axis=axis)
# Apply window to spectrum
if window is not None:
if callable(window):
W = window(sp_fft.fftfreq(Nx))
elif isinstance(window, np.ndarray):
if window.shape != (Nx,):
raise ValueError('window must have the same length as data')
W = window
else:
W = sp_fft.ifftshift(get_window(window, Nx))
newshape_W = [1] * x.ndim
newshape_W[axis] = X.shape[axis]
if real_input:
# Fold the window back on itself to mimic complex behavior
W_real = W.copy()
W_real[1:] += W_real[-1:0:-1]
W_real[1:] *= 0.5
X *= W_real[:newshape_W[axis]].reshape(newshape_W)
else:
X *= W.reshape(newshape_W)
# Copy each half of the original spectrum to the output spectrum, either
# truncating high frequences (downsampling) or zero-padding them
# (upsampling)
# Placeholder array for output spectrum
newshape = list(x.shape)
if real_input:
newshape[axis] = num // 2 + 1
else:
newshape[axis] = num
Y = np.zeros(newshape, X.dtype)
# Copy positive frequency components (and Nyquist, if present)
N = min(num, Nx)
nyq = N // 2 + 1 # Slice index that includes Nyquist if present
sl = [slice(None)] * x.ndim
sl[axis] = slice(0, nyq)
Y[tuple(sl)] = X[tuple(sl)]
if not real_input:
# Copy negative frequency components
if N > 2: # (slice expression doesn't collapse to empty array)
sl[axis] = slice(nyq - N, None)
Y[tuple(sl)] = X[tuple(sl)]
# Split/join Nyquist component(s) if present
# So far we have set Y[+N/2]=X[+N/2]
if N % 2 == 0:
if num < Nx: # downsampling
if real_input:
sl[axis] = slice(N//2, N//2 + 1)
Y[tuple(sl)] *= 2.
else:
# select the component of Y at frequency +N/2,
# add the component of X at -N/2
sl[axis] = slice(-N//2, -N//2 + 1)
Y[tuple(sl)] += X[tuple(sl)]
elif Nx < num: # upsampling
# select the component at frequency +N/2 and halve it
sl[axis] = slice(N//2, N//2 + 1)
Y[tuple(sl)] *= 0.5
if not real_input:
temp = Y[tuple(sl)]
# set the component at -N/2 equal to the component at +N/2
sl[axis] = slice(num-N//2, num-N//2 + 1)
Y[tuple(sl)] = temp
# Inverse transform
if real_input:
y = sp_fft.irfft(Y, num, axis=axis)
else:
y = sp_fft.ifft(Y, axis=axis, overwrite_x=True)
y *= (float(num) / float(Nx))
if t is None:
return y
else:
new_t = np.arange(0, num) * (t[1] - t[0]) * Nx / float(num) + t[0]
return y, new_t
def resample_poly(x, up, down, axis=0, window=('kaiser', 5.0),
padtype='constant', cval=None):
"""
Resample `x` along the given axis using polyphase filtering.
The signal `x` is upsampled by the factor `up`, a zero-phase low-pass
FIR filter is applied, and then it is downsampled by the factor `down`.
The resulting sample rate is ``up / down`` times the original sample
rate. By default, values beyond the boundary of the signal are assumed
to be zero during the filtering step.
Parameters
----------
x : array_like
The data to be resampled.
up : int
The upsampling factor.
down : int
The downsampling factor.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : string, tuple, or array_like, optional
Desired window to use to design the low-pass filter, or the FIR filter
coefficients to employ. See below for details.
padtype : string, optional
`constant`, `line`, `mean`, `median`, `maximum`, `minimum` or any of
the other signal extension modes supported by `scipy.signal.upfirdn`.
Changes assumptions on values beyond the boundary. If `constant`,
assumed to be `cval` (default zero). If `line` assumed to continue a
linear trend defined by the first and last points. `mean`, `median`,
`maximum` and `minimum` work as in `np.pad` and assume that the values
beyond the boundary are the mean, median, maximum or minimum
respectively of the array along the axis.
.. versionadded:: 1.4.0
cval : float, optional
Value to use if `padtype='constant'`. Default is zero.
.. versionadded:: 1.4.0
Returns
-------
resampled_x : array
The resampled array.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample : Resample up or down using the FFT method.
Notes
-----
This polyphase method will likely be faster than the Fourier method
in `scipy.signal.resample` when the number of samples is large and
prime, or when the number of samples is large and `up` and `down`
share a large greatest common denominator. The length of the FIR
filter used will depend on ``max(up, down) // gcd(up, down)``, and
the number of operations during polyphase filtering will depend on
the filter length and `down` (see `scipy.signal.upfirdn` for details).
The argument `window` specifies the FIR low-pass filter design.
If `window` is an array_like it is assumed to be the FIR filter
coefficients. Note that the FIR filter is applied after the upsampling
step, so it should be designed to operate on a signal at a sampling
frequency higher than the original by a factor of `up//gcd(up, down)`.
This function's output will be centered with respect to this array, so it
is best to pass a symmetric filter with an odd number of samples if, as
is usually the case, a zero-phase filter is desired.
For any other type of `window`, the functions `scipy.signal.get_window`
and `scipy.signal.firwin` are called to generate the appropriate filter
coefficients.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * down / float(up)``.
Examples
--------
By default, the end of the resampled data rises to meet the first
sample of the next cycle for the FFT method, and gets closer to zero
for the polyphase method:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f_fft = signal.resample(y, 100)
>>> f_poly = signal.resample_poly(y, 100, 20)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(xnew, f_fft, 'b.-', xnew, f_poly, 'r.-')
>>> plt.plot(x, y, 'ko-')
>>> plt.plot(10, y[0], 'bo', 10, 0., 'ro') # boundaries
>>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best')
>>> plt.show()
This default behaviour can be changed by using the padtype option:
>>> import numpy as np
>>> from scipy import signal
>>> N = 5
>>> x = np.linspace(0, 1, N, endpoint=False)
>>> y = 2 + x**2 - 1.7*np.sin(x) + .2*np.cos(11*x)
>>> y2 = 1 + x**3 + 0.1*np.sin(x) + .1*np.cos(11*x)
>>> Y = np.stack([y, y2], axis=-1)
>>> up = 4
>>> xr = np.linspace(0, 1, N*up, endpoint=False)
>>> y2 = signal.resample_poly(Y, up, 1, padtype='constant')
>>> y3 = signal.resample_poly(Y, up, 1, padtype='mean')
>>> y4 = signal.resample_poly(Y, up, 1, padtype='line')
>>> import matplotlib.pyplot as plt
>>> for i in [0,1]:
... plt.figure()
... plt.plot(xr, y4[:,i], 'g.', label='line')
... plt.plot(xr, y3[:,i], 'y.', label='mean')
... plt.plot(xr, y2[:,i], 'r.', label='constant')
... plt.plot(x, Y[:,i], 'k-')
... plt.legend()
>>> plt.show()
"""
x = np.asarray(x)
if up != int(up):
raise ValueError("up must be an integer")
if down != int(down):
raise ValueError("down must be an integer")
up = int(up)
down = int(down)
if up < 1 or down < 1:
raise ValueError('up and down must be >= 1')
if cval is not None and padtype != 'constant':
raise ValueError('cval has no effect when padtype is ', padtype)
# Determine our up and down factors
# Use a rational approximation to save computation time on really long
# signals
g_ = gcd(up, down)
up //= g_
down //= g_
if up == down == 1:
return x.copy()
n_in = x.shape[axis]
n_out = n_in * up
n_out = n_out // down + bool(n_out % down)
if isinstance(window, (list, np.ndarray)):
window = np.array(window) # use array to force a copy (we modify it)
if window.ndim > 1:
raise ValueError('window must be 1-D')
half_len = (window.size - 1) // 2
h = window
else:
# Design a linear-phase low-pass FIR filter
max_rate = max(up, down)
f_c = 1. / max_rate # cutoff of FIR filter (rel. to Nyquist)
half_len = 10 * max_rate # reasonable cutoff for our sinc-like function
h = firwin(2 * half_len + 1, f_c, window=window)
h *= up
# Zero-pad our filter to put the output samples at the center
n_pre_pad = (down - half_len % down)
n_post_pad = 0
n_pre_remove = (half_len + n_pre_pad) // down
# We should rarely need to do this given our filter lengths...
while _output_len(len(h) + n_pre_pad + n_post_pad, n_in,
up, down) < n_out + n_pre_remove:
n_post_pad += 1
h = np.concatenate((np.zeros(n_pre_pad, dtype=h.dtype), h,
np.zeros(n_post_pad, dtype=h.dtype)))
n_pre_remove_end = n_pre_remove + n_out
# Remove background depending on the padtype option
funcs = {'mean': np.mean, 'median': np.median,
'minimum': np.amin, 'maximum': np.amax}
upfirdn_kwargs = {'mode': 'constant', 'cval': 0}
if padtype in funcs:
background_values = funcs[padtype](x, axis=axis, keepdims=True)
elif padtype in _upfirdn_modes:
upfirdn_kwargs = {'mode': padtype}
if padtype == 'constant':
if cval is None:
cval = 0
upfirdn_kwargs['cval'] = cval
else:
raise ValueError(
'padtype must be one of: maximum, mean, median, minimum, ' +
', '.join(_upfirdn_modes))
if padtype in funcs:
x = x - background_values
# filter then remove excess
y = upfirdn(h, x, up, down, axis=axis, **upfirdn_kwargs)
keep = [slice(None), ]*x.ndim
keep[axis] = slice(n_pre_remove, n_pre_remove_end)
y_keep = y[tuple(keep)]
# Add background back
if padtype in funcs:
y_keep += background_values
return y_keep
def vectorstrength(events, period):
'''
Determine the vector strength of the events corresponding to the given
period.
The vector strength is a measure of phase synchrony, how well the
timing of the events is synchronized to a single period of a periodic
signal.
If multiple periods are used, calculate the vector strength of each.
This is called the "resonating vector strength".
Parameters
----------
events : 1D array_like
An array of time points containing the timing of the events.
period : float or array_like
The period of the signal that the events should synchronize to.
The period is in the same units as `events`. It can also be an array
of periods, in which case the outputs are arrays of the same length.
Returns
-------
strength : float or 1D array
The strength of the synchronization. 1.0 is perfect synchronization
and 0.0 is no synchronization. If `period` is an array, this is also
an array with each element containing the vector strength at the
corresponding period.
phase : float or array
The phase that the events are most strongly synchronized to in radians.
If `period` is an array, this is also an array with each element
containing the phase for the corresponding period.
References
----------
van Hemmen, JL, Longtin, A, and Vollmayr, AN. Testing resonating vector
strength: Auditory system, electric fish, and noise.
Chaos 21, 047508 (2011);
:doi:`10.1063/1.3670512`.
van Hemmen, JL. Vector strength after Goldberg, Brown, and von Mises:
biological and mathematical perspectives. Biol Cybern.
2013 Aug;107(4):385-96. :doi:`10.1007/s00422-013-0561-7`.
van Hemmen, JL and Vollmayr, AN. Resonating vector strength: what happens
when we vary the "probing" frequency while keeping the spike times
fixed. Biol Cybern. 2013 Aug;107(4):491-94.
:doi:`10.1007/s00422-013-0560-8`.
'''
events = np.asarray(events)
period = np.asarray(period)
if events.ndim > 1:
raise ValueError('events cannot have dimensions more than 1')
if period.ndim > 1:
raise ValueError('period cannot have dimensions more than 1')
# we need to know later if period was originally a scalar
scalarperiod = not period.ndim
events = np.atleast_2d(events)
period = np.atleast_2d(period)
if (period <= 0).any():
raise ValueError('periods must be positive')
# this converts the times to vectors
vectors = np.exp(np.dot(2j*np.pi/period.T, events))
# the vector strength is just the magnitude of the mean of the vectors
# the vector phase is the angle of the mean of the vectors
vectormean = np.mean(vectors, axis=1)
strength = abs(vectormean)
phase = np.angle(vectormean)
# if the original period was a scalar, return scalars
if scalarperiod:
strength = strength[0]
phase = phase[0]
return strength, phase
def detrend(data, axis=-1, type='linear', bp=0, overwrite_data=False):
"""
Remove linear trend along axis from data.
Parameters
----------
data : array_like
The input data.
axis : int, optional
The axis along which to detrend the data. By default this is the
last axis (-1).
type : {'linear', 'constant'}, optional
The type of detrending. If ``type == 'linear'`` (default),
the result of a linear least-squares fit to `data` is subtracted
from `data`.
If ``type == 'constant'``, only the mean of `data` is subtracted.
bp : array_like of ints, optional
A sequence of break points. If given, an individual linear fit is
performed for each part of `data` between two break points.
Break points are specified as indices into `data`.
overwrite_data : bool, optional
If True, perform in place detrending and avoid a copy. Default is False
Returns
-------
ret : ndarray
The detrended input data.
Examples
--------
>>> from scipy import signal
>>> randgen = np.random.RandomState(9)
>>> npoints = 1000
>>> noise = randgen.randn(npoints)
>>> x = 3 + 2*np.linspace(0, 1, npoints) + noise
>>> (signal.detrend(x) - noise).max() < 0.01
True
"""
if type not in ['linear', 'l', 'constant', 'c']:
raise ValueError("Trend type must be 'linear' or 'constant'.")
data = np.asarray(data)
dtype = data.dtype.char
if dtype not in 'dfDF':
dtype = 'd'
if type in ['constant', 'c']:
ret = data - np.expand_dims(np.mean(data, axis), axis)
return ret
else:
dshape = data.shape
N = dshape[axis]
bp = np.sort(np.unique(np.r_[0, bp, N]))
if np.any(bp > N):
raise ValueError("Breakpoints must be less than length "
"of data along given axis.")
Nreg = len(bp) - 1
# Restructure data so that axis is along first dimension and
# all other dimensions are collapsed into second dimension
rnk = len(dshape)
if axis < 0:
axis = axis + rnk
newdims = np.r_[axis, 0:axis, axis + 1:rnk]
newdata = np.reshape(np.transpose(data, tuple(newdims)),
(N, _prod(dshape) // N))
if not overwrite_data:
newdata = newdata.copy() # make sure we have a copy
if newdata.dtype.char not in 'dfDF':
newdata = newdata.astype(dtype)
# Find leastsq fit and remove it for each piece
for m in range(Nreg):
Npts = bp[m + 1] - bp[m]
A = np.ones((Npts, 2), dtype)
A[:, 0] = np.cast[dtype](np.arange(1, Npts + 1) * 1.0 / Npts)
sl = slice(bp[m], bp[m + 1])
coef, resids, rank, s = linalg.lstsq(A, newdata[sl])
newdata[sl] = newdata[sl] - np.dot(A, coef)
# Put data back in original shape.
tdshape = np.take(dshape, newdims, 0)
ret = np.reshape(newdata, tuple(tdshape))
vals = list(range(1, rnk))
olddims = vals[:axis] + [0] + vals[axis:]
ret = np.transpose(ret, tuple(olddims))
return ret
def lfilter_zi(b, a):
"""
Construct initial conditions for lfilter for step response steady-state.
Compute an initial state `zi` for the `lfilter` function that corresponds
to the steady state of the step response.
A typical use of this function is to set the initial state so that the
output of the filter starts at the same value as the first element of
the signal to be filtered.
Parameters
----------
b, a : array_like (1-D)
The IIR filter coefficients. See `lfilter` for more
information.
Returns
-------
zi : 1-D ndarray
The initial state for the filter.
See Also
--------
lfilter, lfiltic, filtfilt
Notes
-----
A linear filter with order m has a state space representation (A, B, C, D),
for which the output y of the filter can be expressed as::
z(n+1) = A*z(n) + B*x(n)
y(n) = C*z(n) + D*x(n)
where z(n) is a vector of length m, A has shape (m, m), B has shape
(m, 1), C has shape (1, m) and D has shape (1, 1) (assuming x(n) is
a scalar). lfilter_zi solves::
zi = A*zi + B
In other words, it finds the initial condition for which the response
to an input of all ones is a constant.
Given the filter coefficients `a` and `b`, the state space matrices
for the transposed direct form II implementation of the linear filter,
which is the implementation used by scipy.signal.lfilter, are::
A = scipy.linalg.companion(a).T
B = b[1:] - a[1:]*b[0]
assuming `a[0]` is 1.0; if `a[0]` is not 1, `a` and `b` are first
divided by a[0].
Examples
--------
The following code creates a lowpass Butterworth filter. Then it
applies that filter to an array whose values are all 1.0; the
output is also all 1.0, as expected for a lowpass filter. If the
`zi` argument of `lfilter` had not been given, the output would have
shown the transient signal.
>>> from numpy import array, ones
>>> from scipy.signal import lfilter, lfilter_zi, butter
>>> b, a = butter(5, 0.25)
>>> zi = lfilter_zi(b, a)
>>> y, zo = lfilter(b, a, ones(10), zi=zi)
>>> y
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Another example:
>>> x = array([0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0])
>>> y, zf = lfilter(b, a, x, zi=zi*x[0])
>>> y
array([ 0.5 , 0.5 , 0.5 , 0.49836039, 0.48610528,
0.44399389, 0.35505241])
Note that the `zi` argument to `lfilter` was computed using
`lfilter_zi` and scaled by `x[0]`. Then the output `y` has no
transient until the input drops from 0.5 to 0.0.
"""
# FIXME: Can this function be replaced with an appropriate
# use of lfiltic? For example, when b,a = butter(N,Wn),
# lfiltic(b, a, y=numpy.ones_like(a), x=numpy.ones_like(b)).
#
# We could use scipy.signal.normalize, but it uses warnings in
# cases where a ValueError is more appropriate, and it allows
# b to be 2D.
b = np.atleast_1d(b)
if b.ndim != 1:
raise ValueError("Numerator b must be 1-D.")
a = np.atleast_1d(a)
if a.ndim != 1:
raise ValueError("Denominator a must be 1-D.")
while len(a) > 1 and a[0] == 0.0:
a = a[1:]
if a.size < 1:
raise ValueError("There must be at least one nonzero `a` coefficient.")
if a[0] != 1.0:
# Normalize the coefficients so a[0] == 1.
b = b / a[0]
a = a / a[0]
n = max(len(a), len(b))
# Pad a or b with zeros so they are the same length.
if len(a) < n:
a = np.r_[a, np.zeros(n - len(a))]
elif len(b) < n:
b = np.r_[b, np.zeros(n - len(b))]
IminusA = np.eye(n - 1) - linalg.companion(a).T
B = b[1:] - a[1:] * b[0]
# Solve zi = A*zi + B
zi = np.linalg.solve(IminusA, B)
# For future reference: we could also use the following
# explicit formulas to solve the linear system:
#
# zi = np.zeros(n - 1)
# zi[0] = B.sum() / IminusA[:,0].sum()
# asum = 1.0
# csum = 0.0
# for k in range(1,n-1):
# asum += a[k]
# csum += b[k] - a[k]*b[0]
# zi[k] = asum*zi[0] - csum
return zi
def sosfilt_zi(sos):
"""
Construct initial conditions for sosfilt for step response steady-state.
Compute an initial state `zi` for the `sosfilt` function that corresponds
to the steady state of the step response.
A typical use of this function is to set the initial state so that the
output of the filter starts at the same value as the first element of
the signal to be filtered.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. See `sosfilt` for the SOS filter format
specification.
Returns
-------
zi : ndarray
Initial conditions suitable for use with ``sosfilt``, shape
``(n_sections, 2)``.
See Also
--------
sosfilt, zpk2sos
Notes
-----
.. versionadded:: 0.16.0
Examples
--------
Filter a rectangular pulse that begins at time 0, with and without
the use of the `zi` argument of `scipy.signal.sosfilt`.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> sos = signal.butter(9, 0.125, output='sos')
>>> zi = signal.sosfilt_zi(sos)
>>> x = (np.arange(250) < 100).astype(int)
>>> f1 = signal.sosfilt(sos, x)
>>> f2, zo = signal.sosfilt(sos, x, zi=zi)
>>> plt.plot(x, 'k--', label='x')
>>> plt.plot(f1, 'b', alpha=0.5, linewidth=2, label='filtered')
>>> plt.plot(f2, 'g', alpha=0.25, linewidth=4, label='filtered with zi')
>>> plt.legend(loc='best')
>>> plt.show()
"""
sos = np.asarray(sos)
if sos.ndim != 2 or sos.shape[1] != 6:
raise ValueError('sos must be shape (n_sections, 6)')
n_sections = sos.shape[0]
zi = np.empty((n_sections, 2))
scale = 1.0
for section in range(n_sections):
b = sos[section, :3]
a = sos[section, 3:]
zi[section] = scale * lfilter_zi(b, a)
# If H(z) = B(z)/A(z) is this section's transfer function, then
# b.sum()/a.sum() is H(1), the gain at omega=0. That's the steady
# state value of this section's step response.
scale *= b.sum() / a.sum()
return zi
def _filtfilt_gust(b, a, x, axis=-1, irlen=None):
"""Forward-backward IIR filter that uses Gustafsson's method.
Apply the IIR filter defined by `(b,a)` to `x` twice, first forward
then backward, using Gustafsson's initial conditions [1]_.
Let ``y_fb`` be the result of filtering first forward and then backward,
and let ``y_bf`` be the result of filtering first backward then forward.
Gustafsson's method is to compute initial conditions for the forward
pass and the backward pass such that ``y_fb == y_bf``.
Parameters
----------
b : scalar or 1-D ndarray
Numerator coefficients of the filter.
a : scalar or 1-D ndarray
Denominator coefficients of the filter.
x : ndarray
Data to be filtered.
axis : int, optional
Axis of `x` to be filtered. Default is -1.
irlen : int or None, optional
The length of the nonnegligible part of the impulse response.
If `irlen` is None, or if the length of the signal is less than
``2 * irlen``, then no part of the impulse response is ignored.
Returns
-------
y : ndarray
The filtered data.
x0 : ndarray
Initial condition for the forward filter.
x1 : ndarray
Initial condition for the backward filter.
Notes
-----
Typically the return values `x0` and `x1` are not needed by the
caller. The intended use of these return values is in unit tests.
References
----------
.. [1] F. Gustaffson. Determining the initial states in forward-backward
filtering. Transactions on Signal Processing, 46(4):988-992, 1996.
"""
# In the comments, "Gustafsson's paper" and [1] refer to the
# paper referenced in the docstring.
b = np.atleast_1d(b)
a = np.atleast_1d(a)
order = max(len(b), len(a)) - 1
if order == 0:
# The filter is just scalar multiplication, with no state.
scale = (b[0] / a[0])**2
y = scale * x
return y, np.array([]), np.array([])
if axis != -1 or axis != x.ndim - 1:
# Move the axis containing the data to the end.
x = np.swapaxes(x, axis, x.ndim - 1)
# n is the number of samples in the data to be filtered.
n = x.shape[-1]
if irlen is None or n <= 2*irlen:
m = n
else:
m = irlen
# Create Obs, the observability matrix (called O in the paper).
# This matrix can be interpreted as the operator that propagates
# an arbitrary initial state to the output, assuming the input is
# zero.
# In Gustafsson's paper, the forward and backward filters are not
# necessarily the same, so he has both O_f and O_b. We use the same
# filter in both directions, so we only need O. The same comment
# applies to S below.
Obs = np.zeros((m, order))
zi = np.zeros(order)
zi[0] = 1
Obs[:, 0] = lfilter(b, a, np.zeros(m), zi=zi)[0]
for k in range(1, order):
Obs[k:, k] = Obs[:-k, 0]
# Obsr is O^R (Gustafsson's notation for row-reversed O)
Obsr = Obs[::-1]
# Create S. S is the matrix that applies the filter to the reversed
# propagated initial conditions. That is,
# out = S.dot(zi)
# is the same as
# tmp, _ = lfilter(b, a, zeros(), zi=zi) # Propagate ICs.
# out = lfilter(b, a, tmp[::-1]) # Reverse and filter.
# Equations (5) & (6) of [1]
S = lfilter(b, a, Obs[::-1], axis=0)
# Sr is S^R (row-reversed S)
Sr = S[::-1]
# M is [(S^R - O), (O^R - S)]
if m == n:
M = np.hstack((Sr - Obs, Obsr - S))
else:
# Matrix described in section IV of [1].
M = np.zeros((2*m, 2*order))
M[:m, :order] = Sr - Obs
M[m:, order:] = Obsr - S
# Naive forward-backward and backward-forward filters.
# These have large transients because the filters use zero initial
# conditions.
y_f = lfilter(b, a, x)
y_fb = lfilter(b, a, y_f[..., ::-1])[..., ::-1]
y_b = lfilter(b, a, x[..., ::-1])[..., ::-1]
y_bf = lfilter(b, a, y_b)
delta_y_bf_fb = y_bf - y_fb
if m == n:
delta = delta_y_bf_fb
else:
start_m = delta_y_bf_fb[..., :m]
end_m = delta_y_bf_fb[..., -m:]
delta = np.concatenate((start_m, end_m), axis=-1)
# ic_opt holds the "optimal" initial conditions.
# The following code computes the result shown in the formula
# of the paper between equations (6) and (7).
if delta.ndim == 1:
ic_opt = linalg.lstsq(M, delta)[0]
else:
# Reshape delta so it can be used as an array of multiple
# right-hand-sides in linalg.lstsq.
delta2d = delta.reshape(-1, delta.shape[-1]).T
ic_opt0 = linalg.lstsq(M, delta2d)[0].T
ic_opt = ic_opt0.reshape(delta.shape[:-1] + (M.shape[-1],))
# Now compute the filtered signal using equation (7) of [1].
# First, form [S^R, O^R] and call it W.
if m == n:
W = np.hstack((Sr, Obsr))
else:
W = np.zeros((2*m, 2*order))
W[:m, :order] = Sr
W[m:, order:] = Obsr
# Equation (7) of [1] says
# Y_fb^opt = Y_fb^0 + W * [x_0^opt; x_{N-1}^opt]
# `wic` is (almost) the product on the right.
# W has shape (m, 2*order), and ic_opt has shape (..., 2*order),
# so we can't use W.dot(ic_opt). Instead, we dot ic_opt with W.T,
# so wic has shape (..., m).
wic = ic_opt.dot(W.T)
# `wic` is "almost" the product of W and the optimal ICs in equation
# (7)--if we're using a truncated impulse response (m < n), `wic`
# contains only the adjustments required for the ends of the signal.
# Here we form y_opt, taking this into account if necessary.
y_opt = y_fb
if m == n:
y_opt += wic
else:
y_opt[..., :m] += wic[..., :m]
y_opt[..., -m:] += wic[..., -m:]
x0 = ic_opt[..., :order]
x1 = ic_opt[..., -order:]
if axis != -1 or axis != x.ndim - 1:
# Restore the data axis to its original position.
x0 = np.swapaxes(x0, axis, x.ndim - 1)
x1 = np.swapaxes(x1, axis, x.ndim - 1)
y_opt = np.swapaxes(y_opt, axis, x.ndim - 1)
return y_opt, x0, x1
def filtfilt(b, a, x, axis=-1, padtype='odd', padlen=None, method='pad',
irlen=None):
"""
Apply a digital filter forward and backward to a signal.
This function applies a linear digital filter twice, once forward and
once backwards. The combined filter has zero phase and a filter order
twice that of the original.
The function provides options for handling the edges of the signal.
The function `sosfiltfilt` (and filter design using ``output='sos'``)
should be preferred over `filtfilt` for most filtering tasks, as
second-order sections have fewer numerical problems.
Parameters
----------
b : (N,) array_like
The numerator coefficient vector of the filter.
a : (N,) array_like
The denominator coefficient vector of the filter. If ``a[0]``
is not 1, then both `a` and `b` are normalized by ``a[0]``.
x : array_like
The array of data to be filtered.
axis : int, optional
The axis of `x` to which the filter is applied.
Default is -1.
padtype : str or None, optional
Must be 'odd', 'even', 'constant', or None. This determines the
type of extension to use for the padded signal to which the filter
is applied. If `padtype` is None, no padding is used. The default
is 'odd'.
padlen : int or None, optional
The number of elements by which to extend `x` at both ends of
`axis` before applying the filter. This value must be less than
``x.shape[axis] - 1``. ``padlen=0`` implies no padding.
The default value is ``3 * max(len(a), len(b))``.
method : str, optional
Determines the method for handling the edges of the signal, either
"pad" or "gust". When `method` is "pad", the signal is padded; the
type of padding is determined by `padtype` and `padlen`, and `irlen`
is ignored. When `method` is "gust", Gustafsson's method is used,
and `padtype` and `padlen` are ignored.
irlen : int or None, optional
When `method` is "gust", `irlen` specifies the length of the
impulse response of the filter. If `irlen` is None, no part
of the impulse response is ignored. For a long signal, specifying
`irlen` can significantly improve the performance of the filter.
Returns
-------
y : ndarray
The filtered output with the same shape as `x`.
See Also
--------
sosfiltfilt, lfilter_zi, lfilter, lfiltic, savgol_filter, sosfilt
Notes
-----
When `method` is "pad", the function pads the data along the given axis
in one of three ways: odd, even or constant. The odd and even extensions
have the corresponding symmetry about the end point of the data. The
constant extension extends the data with the values at the end points. On
both the forward and backward passes, the initial condition of the
filter is found by using `lfilter_zi` and scaling it by the end point of
the extended data.
When `method` is "gust", Gustafsson's method [1]_ is used. Initial
conditions are chosen for the forward and backward passes so that the
forward-backward filter gives the same result as the backward-forward
filter.
The option to use Gustaffson's method was added in scipy version 0.16.0.
References
----------
.. [1] F. Gustaffson, "Determining the initial states in forward-backward
filtering", Transactions on Signal Processing, Vol. 46, pp. 988-992,
1996.
Examples
--------
The examples will use several functions from `scipy.signal`.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
First we create a one second signal that is the sum of two pure sine
waves, with frequencies 5 Hz and 250 Hz, sampled at 2000 Hz.
>>> t = np.linspace(0, 1.0, 2001)
>>> xlow = np.sin(2 * np.pi * 5 * t)
>>> xhigh = np.sin(2 * np.pi * 250 * t)
>>> x = xlow + xhigh
Now create a lowpass Butterworth filter with a cutoff of 0.125 times
the Nyquist frequency, or 125 Hz, and apply it to ``x`` with `filtfilt`.
The result should be approximately ``xlow``, with no phase shift.
>>> b, a = signal.butter(8, 0.125)
>>> y = signal.filtfilt(b, a, x, padlen=150)
>>> np.abs(y - xlow).max()
9.1086182074789912e-06
We get a fairly clean result for this artificial example because
the odd extension is exact, and with the moderately long padding,
the filter's transients have dissipated by the time the actual data
is reached. In general, transient effects at the edges are
unavoidable.
The following example demonstrates the option ``method="gust"``.
First, create a filter.
>>> b, a = signal.ellip(4, 0.01, 120, 0.125) # Filter to be applied.
>>> np.random.seed(123456)
`sig` is a random input signal to be filtered.
>>> n = 60
>>> sig = np.random.randn(n)**3 + 3*np.random.randn(n).cumsum()
Apply `filtfilt` to `sig`, once using the Gustafsson method, and
once using padding, and plot the results for comparison.
>>> fgust = signal.filtfilt(b, a, sig, method="gust")
>>> fpad = signal.filtfilt(b, a, sig, padlen=50)
>>> plt.plot(sig, 'k-', label='input')
>>> plt.plot(fgust, 'b-', linewidth=4, label='gust')
>>> plt.plot(fpad, 'c-', linewidth=1.5, label='pad')
>>> plt.legend(loc='best')
>>> plt.show()
The `irlen` argument can be used to improve the performance
of Gustafsson's method.
Estimate the impulse response length of the filter.
>>> z, p, k = signal.tf2zpk(b, a)
>>> eps = 1e-9
>>> r = np.max(np.abs(p))
>>> approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
>>> approx_impulse_len
137
Apply the filter to a longer signal, with and without the `irlen`
argument. The difference between `y1` and `y2` is small. For long
signals, using `irlen` gives a significant performance improvement.
>>> x = np.random.randn(5000)
>>> y1 = signal.filtfilt(b, a, x, method='gust')
>>> y2 = signal.filtfilt(b, a, x, method='gust', irlen=approx_impulse_len)
>>> print(np.max(np.abs(y1 - y2)))
1.80056858312e-10
"""
b = np.atleast_1d(b)
a = np.atleast_1d(a)
x = np.asarray(x)
if method not in ["pad", "gust"]:
raise ValueError("method must be 'pad' or 'gust'.")
if method == "gust":
y, z1, z2 = _filtfilt_gust(b, a, x, axis=axis, irlen=irlen)
return y
# method == "pad"
edge, ext = _validate_pad(padtype, padlen, x, axis,
ntaps=max(len(a), len(b)))
# Get the steady state of the filter's step response.
zi = lfilter_zi(b, a)
# Reshape zi and create x0 so that zi*x0 broadcasts
# to the correct value for the 'zi' keyword argument
# to lfilter.
zi_shape = [1] * x.ndim
zi_shape[axis] = zi.size
zi = np.reshape(zi, zi_shape)
x0 = axis_slice(ext, stop=1, axis=axis)
# Forward filter.
(y, zf) = lfilter(b, a, ext, axis=axis, zi=zi * x0)
# Backward filter.
# Create y0 so zi*y0 broadcasts appropriately.
y0 = axis_slice(y, start=-1, axis=axis)
(y, zf) = lfilter(b, a, axis_reverse(y, axis=axis), axis=axis, zi=zi * y0)
# Reverse y.
y = axis_reverse(y, axis=axis)
if edge > 0:
# Slice the actual signal from the extended signal.
y = axis_slice(y, start=edge, stop=-edge, axis=axis)
return y
def _validate_pad(padtype, padlen, x, axis, ntaps):
"""Helper to validate padding for filtfilt"""
if padtype not in ['even', 'odd', 'constant', None]:
raise ValueError(("Unknown value '%s' given to padtype. padtype "
"must be 'even', 'odd', 'constant', or None.") %
padtype)
if padtype is None:
padlen = 0
if padlen is None:
# Original padding; preserved for backwards compatibility.
edge = ntaps * 3
else:
edge = padlen
# x's 'axis' dimension must be bigger than edge.
if x.shape[axis] <= edge:
raise ValueError("The length of the input vector x must be greater than "
"padlen, which is %d." % edge)
if padtype is not None and edge > 0:
# Make an extension of length `edge` at each
# end of the input array.
if padtype == 'even':
ext = even_ext(x, edge, axis=axis)
elif padtype == 'odd':
ext = odd_ext(x, edge, axis=axis)
else:
ext = const_ext(x, edge, axis=axis)
else:
ext = x
return edge, ext
def _validate_x(x):
x = np.asarray(x)
if x.ndim == 0:
raise ValueError('x must be at least 1D')
return x
def sosfilt(sos, x, axis=-1, zi=None):
"""
Filter data along one dimension using cascaded second-order sections.
Filter a data sequence, `x`, using a digital IIR filter defined by
`sos`.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. Each row corresponds to a second-order
section, with the first three columns providing the numerator
coefficients and the last three providing the denominator
coefficients.
x : array_like
An N-dimensional input array.
axis : int, optional
The axis of the input data array along which to apply the
linear filter. The filter is applied to each subarray along
this axis. Default is -1.
zi : array_like, optional
Initial conditions for the cascaded filter delays. It is a (at
least 2D) vector of shape ``(n_sections, ..., 2, ...)``, where
``..., 2, ...`` denotes the shape of `x`, but with ``x.shape[axis]``
replaced by 2. If `zi` is None or is not given then initial rest
(i.e. all zeros) is assumed.
Note that these initial conditions are *not* the same as the initial
conditions given by `lfiltic` or `lfilter_zi`.
Returns
-------
y : ndarray
The output of the digital filter.
zf : ndarray, optional
If `zi` is None, this is not returned, otherwise, `zf` holds the
final filter delay values.
See Also
--------
zpk2sos, sos2zpk, sosfilt_zi, sosfiltfilt, sosfreqz
Notes
-----
The filter function is implemented as a series of second-order filters
with direct-form II transposed structure. It is designed to minimize
numerical precision errors for high-order filters.
.. versionadded:: 0.16.0
Examples
--------
Plot a 13th-order filter's impulse response using both `lfilter` and
`sosfilt`, showing the instability that results from trying to do a
13th-order filter in a single stage (the numerical error pushes some poles
outside of the unit circle):
>>> import matplotlib.pyplot as plt
>>> from scipy import signal
>>> b, a = signal.ellip(13, 0.009, 80, 0.05, output='ba')
>>> sos = signal.ellip(13, 0.009, 80, 0.05, output='sos')
>>> x = signal.unit_impulse(700)
>>> y_tf = signal.lfilter(b, a, x)
>>> y_sos = signal.sosfilt(sos, x)
>>> plt.plot(y_tf, 'r', label='TF')
>>> plt.plot(y_sos, 'k', label='SOS')
>>> plt.legend(loc='best')
>>> plt.show()
"""
x = _validate_x(x)
sos, n_sections = _validate_sos(sos)
x_zi_shape = list(x.shape)
x_zi_shape[axis] = 2
x_zi_shape = tuple([n_sections] + x_zi_shape)
inputs = [sos, x]
if zi is not None:
inputs.append(np.asarray(zi))
dtype = np.result_type(*inputs)
if dtype.char not in 'fdgFDGO':
raise NotImplementedError("input type '%s' not supported" % dtype)
if zi is not None:
zi = np.array(zi, dtype) # make a copy so that we can operate in place
if zi.shape != x_zi_shape:
raise ValueError('Invalid zi shape. With axis=%r, an input with '
'shape %r, and an sos array with %d sections, zi '
'must have shape %r, got %r.' %
(axis, x.shape, n_sections, x_zi_shape, zi.shape))
return_zi = True
else:
zi = np.zeros(x_zi_shape, dtype=dtype)
return_zi = False
axis = axis % x.ndim # make positive
x = np.moveaxis(x, axis, -1)
zi = np.moveaxis(zi, [0, axis + 1], [-2, -1])
x_shape, zi_shape = x.shape, zi.shape
x = np.reshape(x, (-1, x.shape[-1]))
x = np.array(x, dtype, order='C') # make a copy, can modify in place
zi = np.ascontiguousarray(np.reshape(zi, (-1, n_sections, 2)))
sos = sos.astype(dtype, copy=False)
_sosfilt(sos, x, zi)
x.shape = x_shape
x = np.moveaxis(x, -1, axis)
if return_zi:
zi.shape = zi_shape
zi = np.moveaxis(zi, [-2, -1], [0, axis + 1])
out = (x, zi)
else:
out = x
return out
def sosfiltfilt(sos, x, axis=-1, padtype='odd', padlen=None):
"""
A forward-backward digital filter using cascaded second-order sections.
See `filtfilt` for more complete information about this method.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. Each row corresponds to a second-order
section, with the first three columns providing the numerator
coefficients and the last three providing the denominator
coefficients.
x : array_like
The array of data to be filtered.
axis : int, optional
The axis of `x` to which the filter is applied.
Default is -1.
padtype : str or None, optional
Must be 'odd', 'even', 'constant', or None. This determines the
type of extension to use for the padded signal to which the filter
is applied. If `padtype` is None, no padding is used. The default
is 'odd'.
padlen : int or None, optional
The number of elements by which to extend `x` at both ends of
`axis` before applying the filter. This value must be less than
``x.shape[axis] - 1``. ``padlen=0`` implies no padding.
The default value is::
3 * (2 * len(sos) + 1 - min((sos[:, 2] == 0).sum(),
(sos[:, 5] == 0).sum()))
The extra subtraction at the end attempts to compensate for poles
and zeros at the origin (e.g. for odd-order filters) to yield
equivalent estimates of `padlen` to those of `filtfilt` for
second-order section filters built with `scipy.signal` functions.
Returns
-------
y : ndarray
The filtered output with the same shape as `x`.
See Also
--------
filtfilt, sosfilt, sosfilt_zi, sosfreqz
Notes
-----
.. versionadded:: 0.18.0
Examples
--------
>>> from scipy.signal import sosfiltfilt, butter
>>> import matplotlib.pyplot as plt
Create an interesting signal to filter.
>>> n = 201
>>> t = np.linspace(0, 1, n)
>>> np.random.seed(123)
>>> x = 1 + (t < 0.5) - 0.25*t**2 + 0.05*np.random.randn(n)
Create a lowpass Butterworth filter, and use it to filter `x`.
>>> sos = butter(4, 0.125, output='sos')
>>> y = sosfiltfilt(sos, x)
For comparison, apply an 8th order filter using `sosfilt`. The filter
is initialized using the mean of the first four values of `x`.
>>> from scipy.signal import sosfilt, sosfilt_zi
>>> sos8 = butter(8, 0.125, output='sos')
>>> zi = x[:4].mean() * sosfilt_zi(sos8)
>>> y2, zo = sosfilt(sos8, x, zi=zi)
Plot the results. Note that the phase of `y` matches the input, while
`y2` has a significant phase delay.
>>> plt.plot(t, x, alpha=0.5, label='x(t)')
>>> plt.plot(t, y, label='y(t)')
>>> plt.plot(t, y2, label='y2(t)')
>>> plt.legend(framealpha=1, shadow=True)
>>> plt.grid(alpha=0.25)
>>> plt.xlabel('t')
>>> plt.show()
"""
sos, n_sections = _validate_sos(sos)
x = _validate_x(x)
# `method` is "pad"...
ntaps = 2 * n_sections + 1
ntaps -= min((sos[:, 2] == 0).sum(), (sos[:, 5] == 0).sum())
edge, ext = _validate_pad(padtype, padlen, x, axis,
ntaps=ntaps)
# These steps follow the same form as filtfilt with modifications
zi = sosfilt_zi(sos) # shape (n_sections, 2) --> (n_sections, ..., 2, ...)
zi_shape = [1] * x.ndim
zi_shape[axis] = 2
zi.shape = [n_sections] + zi_shape
x_0 = axis_slice(ext, stop=1, axis=axis)
(y, zf) = sosfilt(sos, ext, axis=axis, zi=zi * x_0)
y_0 = axis_slice(y, start=-1, axis=axis)
(y, zf) = sosfilt(sos, axis_reverse(y, axis=axis), axis=axis, zi=zi * y_0)
y = axis_reverse(y, axis=axis)
if edge > 0:
y = axis_slice(y, start=edge, stop=-edge, axis=axis)
return y
def decimate(x, q, n=None, ftype='iir', axis=-1, zero_phase=True):
"""
Downsample the signal after applying an anti-aliasing filter.
By default, an order 8 Chebyshev type I filter is used. A 30 point FIR
filter with Hamming window is used if `ftype` is 'fir'.
Parameters
----------
x : array_like
The signal to be downsampled, as an N-dimensional array.
q : int
The downsampling factor. When using IIR downsampling, it is recommended
to call `decimate` multiple times for downsampling factors higher than
13.
n : int, optional
The order of the filter (1 less than the length for 'fir'). Defaults to
8 for 'iir' and 20 times the downsampling factor for 'fir'.
ftype : str {'iir', 'fir'} or ``dlti`` instance, optional
If 'iir' or 'fir', specifies the type of lowpass filter. If an instance
of an `dlti` object, uses that object to filter before downsampling.
axis : int, optional
The axis along which to decimate.
zero_phase : bool, optional
Prevent phase shift by filtering with `filtfilt` instead of `lfilter`
when using an IIR filter, and shifting the outputs back by the filter's
group delay when using an FIR filter. The default value of ``True`` is
recommended, since a phase shift is generally not desired.
.. versionadded:: 0.18.0
Returns
-------
y : ndarray
The down-sampled signal.
See Also
--------
resample : Resample up or down using the FFT method.
resample_poly : Resample using polyphase filtering and an FIR filter.
Notes
-----
The ``zero_phase`` keyword was added in 0.18.0.
The possibility to use instances of ``dlti`` as ``ftype`` was added in
0.18.0.
"""
x = np.asarray(x)
q = operator.index(q)
if n is not None:
n = operator.index(n)
if ftype == 'fir':
if n is None:
half_len = 10 * q # reasonable cutoff for our sinc-like function
n = 2 * half_len
b, a = firwin(n+1, 1. / q, window='hamming'), 1.
elif ftype == 'iir':
if n is None:
n = 8
system = dlti(*cheby1(n, 0.05, 0.8 / q))
b, a = system.num, system.den
elif isinstance(ftype, dlti):
system = ftype._as_tf() # Avoids copying if already in TF form
b, a = system.num, system.den
else:
raise ValueError('invalid ftype')
sl = [slice(None)] * x.ndim
a = np.asarray(a)
if a.size == 1: # FIR case
b = b / a
if zero_phase:
y = resample_poly(x, 1, q, axis=axis, window=b)
else:
# upfirdn is generally faster than lfilter by a factor equal to the
# downsampling factor, since it only calculates the needed outputs
n_out = x.shape[axis] // q + bool(x.shape[axis] % q)
y = upfirdn(b, x, up=1, down=q, axis=axis)
sl[axis] = slice(None, n_out, None)
else: # IIR case
if zero_phase:
y = filtfilt(b, a, x, axis=axis)
else:
y = lfilter(b, a, x, axis=axis)
sl[axis] = slice(None, None, q)
return y[tuple(sl)]
| [
"noreply@github.com"
] | noreply@github.com |
044f7c32f493aff71c86622b981ca59b9bd3e71a | 8bd759e487304cdd176524fff8d9b95f1572586f | /backend/api/upbit.py | 0abc7d6c5071b6839312a9ef49d363b0cd5ddf78 | [] | no_license | eodudsla10/DropTheBit_backend | 2732564f1ed93d061a5c14948857aaed0cf2954c | 37f8d0fe5eb522b12faf84ba196e4bf77c2bd11c | refs/heads/master | 2023-04-10T03:03:54.218241 | 2021-04-26T17:31:05 | 2021-04-26T17:31:05 | 360,761,393 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,602 | py | import json
import requests
from pandas.io.json import json_normalize
#url_info = {"upbit" : ["https://api.upbit.com", "/v1/candles/minutes/1" , "market", "KRW-BTC"],
#"binance" : ["https://api.binance.com", "/api/v3/ticker/price", "symbol", "BTCUSDT"],
#"bithum" : ["https://api.bithumb.com", "/public/ticker/BTC", "symbol" , "BTCUSDT" ]}
# url_info = {"upbit" : {"base_url":"https://api.upbit.com", "sub_url":"/v1/candles/minutes/1" , "param_key":"market", "param_value":"KRW-BTC", "symbol":"market", "price":"trade_price"},
# "binance" : {"base_url":"https://api.binance.com", "sub_url":"/api/v3/ticker/price", "param_key":"symbol", "param_value":"BTCUSDT", "symbol":"symbol", "price":"price"},
# }
# #"bithum" : {"base_url":"https://api.bithumb.com", "sub_url":"/public/ticker/BTC", "param_key":"symbol" , "param_value":"BTCUSDT" }}
#코인 이름을 키값으로 설정
url_info = {
"BTC" : [
{"store":"upbit","base_url":"https://api.upbit.com", "sub_url":"/v1/candles/minutes/1" , "param_key":"market", "param_value":"KRW-BTC", "symbol":"market", "price":"trade_price"},
{"store":"binance","base_url":"https://api.binance.com", "sub_url":"/api/v3/ticker/price", "param_key":"symbol", "param_value":"BTCUSDT", "symbol":"symbol", "price":"price", "coin":"bitcoin"}
],
"ETH" : [
{"store":"upbit","base_url":"https://api.upbit.com", "sub_url":"/v1/candles/minutes/1" , "param_key":"market", "param_value":"KRW-ETH", "symbol":"market", "price":"trade_price"},
{"store":"binance","base_url":"https://api.binance.com", "sub_url":"/api/v3/ticker/price", "param_key":"symbol", "param_value":"ETHUSDT", "symbol":"symbol", "price":"price","coin":"ethereim"}
],
}
#"bithum" : {"base_url":"https://api.bithumb.com", "sub_url":"/public/ticker/BTC", "param_key":"symbol" , "param_value":"BTCUSDT" }}
# market = "upbit"
def get_market(market):
dict_res ={}
price = 0.1
symbol = ""
#url = url_info[market][0]
#res = requests.get(url +url_info[market][1], params={url_info[market][2]:url_info[market][3]})
res = requests.get(url_info[market]['base_url'] +url_info[market]['sub_url'], params={url_info[market]['param_key']:url_info[market]['param_value']})
Market_result = res.json()
print(type(Market_result))
if(str(type(Market_result))=="<class 'dict'>"):
print("dict형식 입니다")
print(Market_result)
price = float(Market_result[url_info[market]['price']])
#symbol = Market_result[url_info[market]['symbol']]
# price = float(result['price']) #price str -> float로 형변환
# result['price'] = price #바이낸스 가격 저장
elif(str(type(Market_result))=="<class 'list'>"):
print("list입니다")
# 업비트의 price는 binance와 1000:1 관계
price = Market_result[0][url_info[market]['price']]
#symbol = Market_result[0][url_info[market]['symbol']]
dict_res['market'] = market
dict_res['coin_type'] = "BTC"
dict_res['price'] = price
str_Market_result = json.dumps(dict_res)
json_Market_result = json.loads(str_Market_result) #json의 string 형태의 객체에서 json 형식 내용을 추출할 때 사용한다.
pandas_Market_result = json_normalize(json_Market_result) #json에서 데이터프레임을 쉽게 생성하도록 도움을 준다
return pandas_Market_result
def get_market_all():
list_market =[]
for key in url_info.keys():
# get_market(key)
list_market.append(get_market(key))
return list_market
print(get_market_all()) | [
"eodudsla10@naver.com"
] | eodudsla10@naver.com |
b37de808e75120539eb4dc6c91fd2b891811f745 | 4e8cab639ddfa3e791b5b3a08aa491fb92c1ecaa | /Python_PostgresSQL/Python Refresher/destructuring_variables.py | 070fb9609470e6f1b01a01bdf420cd7ba46e0835 | [] | no_license | LesediSekakatlela/SQL_projects | 49b91bebdf6f9b1176c40c3752232ab8d3d091dd | 9c78fc027dd137ef96446ea0946343293f3be007 | refs/heads/main | 2023-07-13T02:41:41.261558 | 2021-08-20T09:03:23 | 2021-08-20T09:03:23 | 386,646,245 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 440 | py | t = 5, 11
x, y = t
print(x, y)
#2
students_attendance = {"Rolf": 96, "Bob": 80, "Anne": 100}
print(list(students_attendance.items()))
for t in students_attendance.items():
print(t)
#3
people = [("Bob", 42, "Mechanic"), ("James", 24, "Artist"), ("Harry", 32, "Lecturer")]
for name, age, profession in people:
print(f"Name: {name}, Age: {age}, Profession: {profession}")
#4
head, *tail = [1, 2, 3, 4, 5]
print(head)
print(tail) | [
"leseditumelo32@gmail.com"
] | leseditumelo32@gmail.com |
055e41fa46070180a9f651f2d085fa3553b60c43 | ed3d4c3ce2109d96a145a8b4e90cf2763203d542 | /source/lambdas/shared/utility.py | bfd9b81ef665ca9cee835021777acb11513dc95c | [
"Apache-2.0",
"MIT-0"
] | permissive | jhasatis/TabularDocumentDigitization | 939ea104b4c5f43f1e81af20bb1a4636d061d41b | 633e6291eea95a3933d34cae53b68cf6570b9bbb | refs/heads/main | 2023-06-28T01:12:22.621879 | 2021-08-05T17:33:48 | 2021-08-05T17:33:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 394 | py | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
from shared.defines import STAGE, STORE_BUCKET
def GetS3OutputPath(document_id: str, output_filename: str = 'output.json', stage: str = STAGE) -> str:
"""Returns S3 path for output file created by Stage Actor"""
return f's3://{STORE_BUCKET}/{stage}/{document_id}/{output_filename}'
| [
"psnonis@amazon.com"
] | psnonis@amazon.com |
b2226029f546fb15e00557c03454cf6e6907c7a3 | e51213e9379f4fe2eddc5bd65b8aa5bfec4be117 | /voting/wsgi.py | cef305c2ed98dbc9fb58fb5a141eed67b17346b4 | [] | no_license | NatashaSay/voting | 65d49f8f02c062c586eaf2dc1c58491a9c2a8dac | 4674c90382fc7ba67dcbf10d58010efda4e9017a | refs/heads/master | 2022-10-02T03:59:05.255344 | 2020-01-09T20:04:49 | 2020-01-09T20:04:49 | 224,504,007 | 0 | 0 | null | 2022-09-23T22:33:57 | 2019-11-27T19:36:15 | HTML | UTF-8 | Python | false | false | 390 | py | """
WSGI config for voting project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'voting.settings')
application = get_wsgi_application()
| [
"nataliia.saichyshyna@nure.ua"
] | nataliia.saichyshyna@nure.ua |
c805769e7c7870835953e3cc6f75171540adeca0 | ec0b8c74ae4f370e47aa1a19adfa654b2766a19f | /src/run_ganterpreter.py | 33af7402ef8571b199b84fc01cc96fab9861c32e | [
"Apache-2.0"
] | permissive | psc-g/ganterpretation | e8c37ce4a5ddd81c5f933a667ffef0696b56773b | 98a963d098e0cf5a49dced1300840388001eeddf | refs/heads/master | 2021-07-14T02:27:20.704844 | 2020-11-30T21:12:45 | 2020-11-30T21:12:45 | 218,511,004 | 22 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,679 | py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import ganterpreter
flags.DEFINE_string('output_dir', None, 'Directory where to store output.')
flags.DEFINE_string('wav_path', None, 'Path to wav file to use.')
flags.DEFINE_string('video_file_name', None,
'Name of video file, defaults to "video.avi"')
flags.DEFINE_string('model_type', 'biggan-512',
'BigGAN model type to load (biggan-{128, 256, 512})')
flags.DEFINE_list('selected_categories', [],
'Manually specified categories to use for interpolation. '
'Missing categories will be assigned randomly.')
flags.DEFINE_float('inflection_threshold', 0.035,
'Threshold on FFT TotalVariation changes to set '
'inflection points.')
flags.DEFINE_bool('verbose', False, 'Whether to print verbose messages.')
FLAGS = flags.FLAGS
def main(unused_argv):
"""Main method."""
selected_categories = [int(x) for x in FLAGS.selected_categories]
gandy = ganterpreter.GANterpreter(
model_type=FLAGS.model_type,
selected_categories=selected_categories,
verbose=FLAGS.verbose)
gandy.load_wav_file(FLAGS.wav_path, verbose=FLAGS.verbose)
gandy.compute_spectrogram(inflection_threshold=FLAGS.inflection_threshold,
verbose=FLAGS.verbose)
gandy.fill_selected_categories()
gandy.generate_video(FLAGS.output_dir,
video_file_name=FLAGS.video_file_name)
if __name__ == '__main__':
# flags.mark_flag_as_required('base_dir')
app.run(main)
| [
"psc@google.com"
] | psc@google.com |
80c1c37cc41d905504a06271ad41051f849c026c | 30734acba3e5720f697df7db46c404af4529bfc9 | /Agent/Deploy/Source/stembot/stembot/ramdocument.py | 065fe9c000c309406552b62243d6d75f4e09d25c | [
"MIT"
] | permissive | phnomcobra/valarie-content | 0879b055ff41c68377515dcdc62689cb26febd83 | b1f6242605badd2b0b2e53c4320f5d963b5e0b21 | refs/heads/master | 2020-04-14T01:33:45.785396 | 2019-04-21T20:46:40 | 2019-04-21T20:46:40 | 163,563,099 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,763 | py | #!/usr/bin/python
import traceback
from copy import deepcopy
from counters import increment
from threading import Lock
from utils import sucky_uuid
document_lock = Lock()
document = {}
class Document:
def __init__(self):
document_lock.acquire()
if "TBL_JSON_COL" not in document:
document["TBL_JSON_COL"] = {}
if "TBL_JSON_OBJ" not in document:
document["TBL_JSON_OBJ"] = {}
if "TBL_JSON_ATTR" not in document:
document["TBL_JSON_ATTR"] = {}
if "TBL_JSON_IDX" not in document:
document["TBL_JSON_IDX"] = {}
document_lock.release()
def create_object(self, coluuid, objuuid):
try:
document_lock.acquire()
object = {
"objuuid" : objuuid,
"coluuid" : coluuid
}
document["TBL_JSON_OBJ"]["-".join([coluuid, objuuid])] = object
except:
print traceback.format_exc()
finally:
document_lock.release()
def set_object(self, coluuid, objuuid, object):
try:
document_lock.acquire()
object = deepcopy(object)
object["objuuid"] = objuuid
object["coluuid"] = coluuid
document["TBL_JSON_OBJ"]["-".join([coluuid, objuuid])] = object
keys = []
for key, value in document["TBL_JSON_IDX"].iteritems():
if objuuid in key:
keys.append(key)
for key in keys:
del document["TBL_JSON_IDX"][key]
attributes = {}
for key, value in document["TBL_JSON_ATTR"].iteritems():
if value["coluuid"] == coluuid:
attributes[document["TBL_JSON_ATTR"][key]["attribute_name"]] = document["TBL_JSON_ATTR"][key]["path"]
for attribute_name in attributes:
try:
document["TBL_JSON_IDX"]["-".join([coluuid, objuuid, attribute_name])] = {
"coluuid" : coluuid,
"attribute_name" : attribute_name,
"objuuid" : objuuid,
"value" : str(eval("str(self.get_object_no_lock(objuuid)" + attributes[attribute_name] + ")"))
}
except:
continue
except:
print traceback.format_exc()
finally:
document_lock.release()
def get_object_no_lock(self, objuuid):
for key, value in document["TBL_JSON_OBJ"].iteritems():
if objuuid in key:
return deepcopy(value)
raise IndexError, "OBJUUID not found!"
def get_object(self, objuuid):
document_lock.acquire()
for key, value in document["TBL_JSON_OBJ"].iteritems():
if objuuid in key:
document_lock.release()
return deepcopy(value)
document_lock.release()
raise IndexError, "OBJUUID not found!"
def find_objects(self, coluuid, attribute, value):
value = str(value)
document_lock.acquire()
objuuids = []
for key, index in document["TBL_JSON_IDX"].iteritems():
if index["value"] == value and \
index["coluuid"] == coluuid and \
index["attribute_name"] == attribute:
objuuids.append(index["objuuid"])
document_lock.release()
return objuuids
def delete_object(self, objuuid):
try:
document_lock.acquire()
keys = []
for key, value in document["TBL_JSON_OBJ"].iteritems():
if objuuid in key:
keys.append(key)
for key in keys:
del document["TBL_JSON_OBJ"][key]
keys = []
for key, value in document["TBL_JSON_IDX"].iteritems():
if objuuid in key:
keys.append(key)
for key in keys:
del document["TBL_JSON_IDX"][key]
except:
print traceback.format_exc()
finally:
document_lock.release()
def create_attribute(self, coluuid, attribute, path):
try:
document_lock.acquire()
document["TBL_JSON_ATTR"]["-".join([coluuid, attribute])] = {
"path" : path,
"attribute_name" : attribute,
"coluuid" : coluuid
}
for key, value in document["TBL_JSON_IDX"].iteritems():
if value["attribute_name"] == attribute:
del document["TBL_JSON_IDX"][key]
objects = {}
for key, value in document["TBL_JSON_OBJ"].iteritems():
if coluuid in key:
objects[object["objuuid"]] = document["TBL_JSON_OBJ"][key]
for objuuid in objects:
try:
document["TBL_JSON_IDX"]["-".join([coluuid, objuuid, attribute])] = {
"coluuid" : coluuid,
"attribute_name" : attribute,
"objuuid" : objuuid,
"value" : str(eval("str(objects[objuuid]" + path + ")"))
}
except:
traceback.format_exc()
except:
print traceback.format_exc()
finally:
document_lock.release()
def delete_attribute(self, coluuid, attribute):
try:
document_lock.acquire()
keys = []
for key, value in document["TBL_JSON_ATTR"].iteritems():
if value["attribute_name"] == attribute and value["coluuid"] == coluuid:
keys.append(key)
for key in keys:
del document["TBL_JSON_ATTR"][key]
keys = []
for key, value in document["TBL_JSON_IDX"].iteritems():
if value["attribute_name"] == attribute and value["coluuid"] == coluuid:
keys.append(key)
for key in keys:
del document["TBL_JSON_IDX"][key]
except:
print traceback.format_exc()
finally:
document_lock.release()
def list_attributes(self, coluuid):
document_lock.acquire()
attributes = {}
for key, value in document["TBL_JSON_ATTR"].iteritems():
if value["coluuid"] == coluuid:
attributes[document["TBL_JSON_ATTR"][key]["attribute_name"]] = document["TBL_JSON_ATTR"][key]["path"]
document_lock.release()
return attributes
def create_collection(self, uuid = None, name = "New Collection"):
document_lock.acquire()
if not uuid:
uuid = sucky_uuid()
document["TBL_JSON_COL"][uuid] = name
document_lock.release()
return uuid
def delete_collection(self, uuid):
try:
document_lock.acquire()
del document["TBL_JSON_COL"][uuid]
keys = []
for key in document["TBL_JSON_OBJ"]:
if uuid in key:
keys.append(key)
for key in keys:
del document["TBL_JSON_OBJ"][key]
keys = []
for key in document["TBL_JSON_ATTR"]:
if uuid in key:
keys.append(key)
for key in keys:
del document["TBL_JSON_ATTR"][key]
keys = []
for key in document["TBL_JSON_IDX"]:
if uuid in key:
keys.append(key)
for key in keys:
del document["TBL_JSON_IDX"][key]
except:
print traceback.format_exc()
finally:
document_lock.release()
def rename_collection(self, uuid, name):
document_lock.acquire()
document["TBL_JSON_COL"][uuid] = name
document_lock.release()
def list_collections(self):
document_lock.acquire()
collections = {}
for key, value in document["TBL_JSON_COL"].iteritems():
collections[value] = key
document_lock.release()
return collections
def list_collection_objects(self, coluuid):
try:
document_lock.acquire()
objuuids = []
for key, value in document["TBL_JSON_OBJ"].iteritems():
if coluuid in key:
objuuids.append(value["objuuid"])
except:
print traceback.format_exc()
finally:
document_lock.release()
return objuuids
def list_objects(self):
try:
document_lock.acquire()
objuuids = []
for key, value in document["TBL_JSON_OBJ"].iteritems():
objuuids.append(value["objuuid"])
except:
print traceback.format_exc()
finally:
document_lock.release()
return objuuids
class Object(Document):
def __init__(self, coluuid, objuuid):
Document.__init__(self)
self.objuuid = objuuid
self.coluuid = coluuid
self.load()
increment("ram object reads")
def load(self):
try:
self.object = Document.get_object(self, self.objuuid)
except IndexError:
Document.create_object(self, self.coluuid, self.objuuid)
self.object = Document.get_object(self, self.objuuid)
increment("ram object writes")
finally:
increment("ram object reads")
def set(self):
Document.set_object(self, self.coluuid, self.objuuid, self.object)
increment("ram object writes")
def destroy(self):
Document.delete_object(self, self.objuuid)
self.object = None
increment("ram object writes")
class Collection(Document):
def __init__(self, collection_name):
Document.__init__(self)
self.collection_name = collection_name
try:
self.coluuid = Document.list_collections(self)[self.collection_name]
except KeyError:
self.coluuid = Document.create_collection(self, name = self.collection_name)
def destroy(self):
Document.delete_collection(self, self.coluuid)
def rename(self, name):
Document.rename_collection(self, self.coluuid, name)
self.collection_name = name
def create_attribute(self, attribute, path):
Document.create_attribute(self, self.coluuid, attribute, path)
def delete_attribute(self, attribute):
Document.delete_attribute(self, self.coluuid, attribute)
def find(self, **kargs):
objuuid_sets = []
if len(kargs) == 0:
objuuid_sets.append(self.list_objuuids())
for attribute, value in kargs.iteritems():
objuuid_sets.append(Document.find_objects(self, self.coluuid, attribute, value))
intersection = set(objuuid_sets[0])
for objuuids in objuuid_sets[1:]:
intersection = intersection.intersection(set(objuuids))
objects = []
for objuuid in list(intersection):
objects.append(Object(self.coluuid, objuuid))
return objects
def find_objuuids(self, **kargs):
objuuid_sets = []
if len(kargs) == 0:
objuuid_sets.append(self.list_objuuids())
for attribute, value in kargs.iteritems():
objuuid_sets.append(Document.find_objects(self, self.coluuid, attribute, value))
intersection = set(objuuid_sets[0])
for objuuids in objuuid_sets[1:]:
intersection = intersection.intersection(set(objuuids))
objuuids = []
for objuuid in list(intersection):
objuuids.append(objuuid)
return objuuids
def get_object(self, objuuid = None):
if not objuuid:
objuuid = sucky_uuid()
return Object(self.coluuid, objuuid)
def list_objuuids(self):
return Document.list_collection_objects(self, self.coluuid) | [
"root@dev5.phnomlab.net"
] | root@dev5.phnomlab.net |
c30ead0a4a09ff09b00b703aa798719fcdb7d145 | 8f086a9cd492fdb5851eadc7867cb12cf6c1bd78 | /Preprocessing/PreparingSa7e7Moslem.py | c42109030820cff0af60ead89209d36ed5962245 | [] | no_license | MohamedMagdyOmar/MSTRepo | 232f8a8dd3daa0d45e1b812cec1bb309aaf6cd29 | c90974057e31de4b7b18aa21ca9303c2a4066978 | refs/heads/master | 2020-05-20T23:02:19.443493 | 2017-10-07T11:22:12 | 2017-10-07T11:22:12 | 84,539,910 | 0 | 0 | null | 2017-10-07T11:22:13 | 2017-03-10T08:55:30 | Python | UTF-8 | Python | false | false | 657 | py | # -*- coding: utf-8 -*-
import unicodedata
import re
f = open("/home/mohamed/Desktop/test.txt", 'r')
read_data = f.readlines()
f.close()
listOfWords = []
wordCount = 0
for eachSentence in read_data:
wordsInSentence = eachSentence.split()
for word in wordsInSentence:
word = re.sub('[-;}()0123456789/]', '', word)
word = re.sub('[.]', ' .', word)
word = re.sub('["{"]', '', word)
word = re.sub('[:]', ' :', word)
if not (word == ''):
listOfWords.append(word)
f = open("/home/mohamed/Desktop/UpdatedSa7e7Moslem2.txt", 'w')
for word in listOfWords:
f.write(word)
f.write(' ')
f.close() | [
"eng_mohamed_magdy@hotmail.com"
] | eng_mohamed_magdy@hotmail.com |
4d829624041911fed2cd1788ffcb96a7077fce7a | 9eeec8105cec97cd9fd5833c970eb1e8220049e9 | /fuse/utils/config_tools/tests/test_config.py | da719b891ca5a1671c44a7a9afb42339049a2a0c | [
"Apache-2.0"
] | permissive | BiomedSciAI/fuse-med-ml | 9e5891f515c14be968a21f8576e2933716da94d0 | 8f22cd46c836245b9394b73ce2957afc03706bfc | refs/heads/master | 2023-08-29T12:47:28.549164 | 2023-08-28T10:42:52 | 2023-08-28T10:42:52 | 379,590,754 | 45 | 11 | Apache-2.0 | 2023-09-10T11:26:00 | 2021-06-23T12:10:42 | Python | UTF-8 | Python | false | false | 2,061 | py | """
(C) Copyright 2021 IBM Corp.
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.
Created on June 30, 2021
"""
import unittest
from fuse.utils.config_tools import Config
import os
class TestConfig(unittest.TestCase):
def test_config_no_py_extension(self) -> None:
""" """
_curr_dir = os.path.dirname(os.path.abspath(__file__))
_reference_ans = {"test": 240, "banana": 123, "dvivonim": 10}
conf = Config()
z_no_py_ext__internal_include = conf.load(
{"test": 14},
os.path.join(_curr_dir, "some_conf_internal_include"),
{"test": 240},
)
z_with_ext__internal_include = conf.load(
{"test": 14},
os.path.join(_curr_dir, "some_conf_internal_include.py"),
{"test": 240},
)
z_with_ext__external_include = conf.load(
{"test": 14},
os.path.join(_curr_dir, "base_conf_example.py"),
os.path.join(_curr_dir, "some_conf_no_include.py"),
{"test": 240},
)
z_with_ext__no_include = conf.load(
{"test": 14},
os.path.join(_curr_dir, "some_conf_no_include.py"),
{"test": 240},
)
self.assertEqual(z_no_py_ext__internal_include, _reference_ans)
self.assertEqual(z_no_py_ext__internal_include, z_with_ext__internal_include)
self.assertEqual(z_no_py_ext__internal_include, z_with_ext__external_include)
self.assertNotEqual(_reference_ans, z_with_ext__no_include)
if __name__ == "__main__":
unittest.main()
| [
"noreply@github.com"
] | noreply@github.com |
9a104961bcb3bc88f2da05a0f1fde4d7cbf64f5b | 059552af21ad05dc29af6100c4889883538e738d | /utils/utils.py | b9478e1625f3201dfc71a9e240e57866325a35a6 | [] | no_license | Muxindawang/yolov3-pytorch | cd8da416db2cb0b74b6db7590ce5b30e67a3f418 | 7e6e9d763a29638b118e35bda2f4fb127e317791 | refs/heads/master | 2020-09-29T08:41:50.232096 | 2019-12-17T08:24:56 | 2019-12-17T08:24:56 | 227,002,271 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 23,653 | py | # coding:utf8
from __future__ import division
import math
import time
import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
# import matplotlib.pyplot as plt
# import matplotlib.patches as patches
def to_cpu(tensor):
return tensor.detach().cpu()
def load_classes(path):
"""
Loads class labels at 'path'
"""
fp = open(path, "r")
names = fp.read().split("\n")[:-1]
return names
def weights_init_normal(m):
"""包含卷积层BN层的话就初始化数据shape """
classname = m.__class__.__name__ # 获取类名
if classname.find("Conv") != -1: # find函数不包含会返回-1
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
def rescale_boxes(boxes, current_dim, original_shape):
# prediction current_dim = 416 samples
"""
Rescales bounding boxes to the original shape
用于detect.py中 在416*416图片中画完boundingbox之后 将图片大小复原到原尺寸 坐标变化 相对于左上角距离
长 2 正 长边坐标(w长则对应x坐标,l1到左边界限距离) l1*416/max(h,w) 短边坐标 l2*416/max(h,w)+(416-l2*416/max(h,w))/2
"""
orig_h, orig_w = original_shape
# The amount of padding that was added
# max(original_shape)是指扩充的倍数
# 如果h大就扩充x w大就扩充y
pad_x = max(orig_h - orig_w, 0) * (current_dim / max(original_shape))
pad_y = max(orig_w - orig_h, 0) * (current_dim / max(original_shape))
# Image height and width after padding is removed
unpad_w = current_dim - pad_x
unpad_h = current_dim - pad_y
# Rescale bounding boxes to dimension of original image ?????????
boxes[:, 0] = ((boxes[:, 0] - pad_x // 2) / unpad_w) * orig_w
boxes[:, 1] = ((boxes[:, 1] - pad_y // 2) / unpad_h) * orig_h
boxes[:, 2] = ((boxes[:, 2] - pad_x // 2) / unpad_w) * orig_w
boxes[:, 3] = ((boxes[:, 3] - pad_y // 2) / unpad_h) * orig_h
return boxes
def xywh2xyxy(x):
"""x[x,y,w,h]变为x1,y1,x2,y2"""
y = x.new(x.shape)
y[..., 0] = x[..., 0] - x[..., 2] / 2
y[..., 1] = x[..., 1] - x[..., 3] / 2
y[..., 2] = x[..., 0] + x[..., 2] / 2
y[..., 3] = x[..., 1] + x[..., 3] / 2
return y
def ap_per_class(tp, conf, pred_cls, target_cls):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
tp: True positives (list).
conf: Objectness value from 0-1 (list).
pred_cls: Predicted object classes (list).
target_cls: True object classes (list).
# Returns
The average precision as computed in py-faster-rcnn.
precision = tp / (tp + fp) ap是预测对的/所有预测出来的目标
recall = tp / (tp + fn) recall 是预测对的/所有的目标(gt)
ap 是 P-r曲线的面积
"""
# Sort by objectness
i = np.argsort(-conf)
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
# Find unique classes
unique_classes = np.unique(target_cls)
# 找到真实目标中存在的类别
# Create Precision-Recall curve and compute AP for each class
ap, p, r = [], [], []
for c in tqdm.tqdm(unique_classes, desc="Computing AP"):
i = pred_cls == c
n_gt = (target_cls == c).sum() # Number of ground truth objects 真实的目标 recall的分母
n_p = i.sum() # Number of predicted objects 预测出来的目标 ap的分母
if n_p == 0 and n_gt == 0:
continue
elif n_p == 0 or n_gt == 0:
ap.append(0)
r.append(0)
p.append(0)
else:
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum()
tpc = (tp[i]).cumsum()
# Recall
recall_curve = tpc / (n_gt + 1e-16)
r.append(recall_curve[-1])
# Precision
precision_curve = tpc / (tpc + fpc)
p.append(precision_curve[-1])
# AP from recall-precision curve
ap.append(compute_ap(recall_curve, precision_curve))
# Compute F1 score (harmonic mean of precision and recall)
p, r, ap = np.array(p), np.array(r), np.array(ap)
f1 = 2 * p * r / (p + r + 1e-16)
return p, r, ap, f1, unique_classes.astype("int32")
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves.
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
# Arguments
recall: The recall curve (list).
precision: The precision curve (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# correct AP calculation
# first append sentinel values at the end
# rp曲线 recall从0-1
mrec = np.concatenate(([0.0], recall, [1.0])) # [0. 0.0666 0.1333 0.1333 0.4 0.4666 1. ]
mpre = np.concatenate(([0.0], precision, [0.0])) # [0. 1. 0.6666 0.6666 0.4285 0.3043 0. ]
# print(mrec, mpre)
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# 找到各个阶段的最大precision值
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0] # precision前后两个值不一样的点
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
# (recall[i+1] - recall[i])*max(precision when recall>=recall[i+1])
return ap
def get_batch_statistics(outputs, targets, iou_threshold):
"""
Compute true positives, predicted scores and predicted labels per sample
获取测试样本的各项指标
"""
batch_metrics = []
# print "outputs len: {}".format(len(outputs))
# print "targets shape: {}".format(targets.shape)
# outputs: (batch_size, pred_boxes_num, 7) 7 =》x,y,w,h,conf,class_conf,class_pred
# target: (num, 6) 6=>(batch_index, cls, center_x, center_y, widht, height)
# len(outputs) = batch_size
for sample_i in range(len(outputs)):
if outputs[sample_i] is None:
continue
# output: (pred_boxes_num, 7) 7 =》x,y,w,h,conf,class_conf,class_pred
# 每一张图片
output = outputs[sample_i]
# print "output: {}".format(output.shape)
pred_boxes = output[:, :4] # 预测框的x,y,w,h
pred_scores = output[:, 4] # 预测框的置信度
pred_labels = output[:, -1] # 预测框的类别label
# 长度为pred_boxes_num的list，初始化为0，如果预测框和实际框匹配，则设置为1
true_positives = np.zeros(pred_boxes.shape[0])
# 获得真实目标框的类别label
# annotations = targets[targets[:, 0] == sample_i][:, 1:]
annotations = targets[targets[:, 0] == sample_i]
# targets[:,0] 是图片ID 找出targets中 图片索引为sample-i 的targets annotations.shape (num_of_== , 6)
annotations = annotations[:, 1:] if len(annotations) else []
# shape (num_of_== , 5) 类别索引 xywh
target_labels = annotations[:, 0] if len(annotations) else []
# shape (num_of_ ==, 1) 类别索引
if len(annotations): # len(annotations)>0: 表示这张图片有真实的目标框
detected_boxes = []
target_boxes = annotations[:, 1:] # 真实目标框的x,y,w,h
for pred_i, (pred_box, pred_label) in enumerate(zip(pred_boxes, pred_labels)):
# If targets are found break
if len(detected_boxes) == len(annotations):
break
# Ignore if label is not one of the target labels
# 如果该预测框的类别标签不存在与目标框的类别标签集合中，则必定是预测错误
if pred_label not in target_labels:
continue
# 将一个预测框与所有真实目标框做IOU计算，并获取IOU最大的值(iou)，和与之对应的真实目标框的索引号(box_index)
iou, box_index = bbox_iou(pred_box.unsqueeze(0), target_boxes).max(0)
# 如果最大IOU大于阈值，则认为该真实目标框被发现。注意要防止被重复记录
if iou >= iou_threshold and box_index not in detected_boxes:
true_positives[pred_i] = 1 # 对该预测框设置为1
detected_boxes += [box_index] # 记录被发现的实际框索引号，防止预测框重复标记，即一个实际框只能被一个预测框匹配
# 保存当前图片被预测的信息
# true_positives：预测框的正确与否，正确设置为1，错误设置为0
# pred_scores：预测框的x,y,w,h
# pred_labels：预测框的类别标签
batch_metrics.append([true_positives, pred_scores, pred_labels])
return batch_metrics
def bbox_wh_iou(wh1, wh2):
wh2 = wh2.t()
w1, h1 = wh1[0], wh1[1]
w2, h2 = wh2[0], wh2[1]
# print w1, w2, h1, h2
inter_area = torch.min(w1, w2) * torch.min(h1, h2)
union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
# print inter_area, union_area
return inter_area / union_area
def bbox_iou(box1, box2, x1y1x2y2=True):
"""
Returns the IoU of two bounding boxes
"""
if not x1y1x2y2:
# Transform from center and width to exact coordinates
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
else:
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
# get the corrdinates of the intersection rectangle
inter_rect_x1 = torch.max(b1_x1, b2_x1)
inter_rect_y1 = torch.max(b1_y1, b2_y1)
inter_rect_x2 = torch.min(b1_x2, b2_x2)
inter_rect_y2 = torch.min(b1_y2, b2_y2)
# Intersection area
inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(
inter_rect_y2 - inter_rect_y1 + 1, min=0
)
# Union Area
b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
return iou
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
"""
Removes detections with lower object confidence score than 'conf_thres' and performs
Non-Maximum Suppression to further filter detections.
Returns detections with shape:
(x1, y1, x2, y2, object_conf, class_score, class_pred)
"""
# prediction: (batch_size, num_anchors*grid_size*grid_size*3, 85) 85 => (x,y,w,h, conf, cls)
# From (center x, center y, width, height) to (x1, y1, x2, y2)
prediction[..., :4] = xywh2xyxy(prediction[..., :4])
output = [None for _ in range(len(prediction))]
for image_i, image_pred in enumerate(prediction):
# Filter out confidence scores below threshold
# 得到置信预测框：过滤anchor置信度小于阈值的预测框
# print image_pred.shape (num_anchors*grid_size*grid_size*3, 85) 85 => (x,y,w,h, conf, cls)
image_pred = image_pred[image_pred[:, 4] >= conf_thres] # confidence
# print image_pred.shape (more_than_conf_thres_num, 85) 85 => (x,y,w,h, conf, cls)
# 先筛选掉置信度(objecness)小的
# If none are remaining => process next image
# 基于anchor的置信度过滤完后，看看是否还有保留的预测框，如果都被过滤，则认为没有实体目标被检测到
if not image_pred.size(0):
continue
# Object confidence times class confidence
# 计算处理：先选取每个预测框所代表的最大类别值，再将这个值乘以对应的anchor置信度，这样将类别预测精准度和置信度都考虑在内。
# 每个置信预测框都会对应一个score值 类别预测精准度*anchor置信度
score = image_pred[:, 4] * image_pred[:, 5:].max(1)[0]
# Sort by it
# 基于score值，将置信预测框从大到小进行排序
# image_pred = image_pred[(-score).argsort()]
# 置信预测：image_pred ==》(more_than_conf_thres_num, 85) 85 => (x,y,w,h, conf, cls)
image_pred = image_pred[torch.sort(-score, dim=0)[1]]
# image_pred[:, 5:] ==> (more_than_conf_thres_num, cls)
# 该处理是获取每个置信预测框所对应的类别预测分值（class_confs）和类别索引（class_preds）
class_confs, class_preds = image_pred[:, 5:].max(1, keepdim=True)
# 将置信预测框的 x,y,w,h,conf，类别预测分值和类别索引关联到一起
# detections ==》 (more_than_conf_thres_num, 7) 7 =》x,y,w,h,conf,class_conf,class_pred
detections = torch.cat((image_pred[:, :5], class_confs.float(), class_preds.float()), 1)
# Perform non-maximum suppression
keep_boxes = []
while detections.size(0):
# detections[0, :4]是第一个置信预测框，也是当前序列中分值最大的置信预测框
# 计算当前序列的第一个（分值最大）置信预测框与整个序列预测框的IOU，并将IOU大于阈值的设置为1，小于的设置为0。
large_overlap = bbox_iou(detections[0, :4].unsqueeze(0), detections[:, :4]) > nms_thres
# 匹配与当前序列的第一个（分值最大）置信预测框具有相同类别标签的所有预测框（将相同类别标签的预测框标记为1）
label_match = detections[0, -1] == detections[:, -1]
# Indices of boxes with lower confidence scores, large IOUs and matching labels
# 与当前序列的第一个（分值最大）置信预测框IOU大，说明这些预测框与其相交面积大，
# 如果这些预测框的标签与当前序列的第一个（分值最大）置信预测框的相同，则说明是预测的同一个目标，
# 对与当前序列第一个（分值最大）置信预测框预测了同一目标的设置为1（包括当前序列第一个（分值最大）置信预测框本身）。
invalid = large_overlap & label_match
# 取出对应置信预测框的置信度，将置信度作为权重
# invalid边界框 iou大 并且类别相同
weights = detections[invalid, 4:5]
# Merge overlapping bboxes by order of confidence
# 把预测为同一目标的预测框进行合并，合并后认为是最优的预测框。合并方式如下：
detections[0, :4] = (weights * detections[invalid, :4]).sum(0) / weights.sum()
# 保存当前序列中最终识别的预测框
keep_boxes += [detections[0]]
# ~invalid表示取反，将之前的0变为1，即选取剩下的预测框，进行新一轮的计算
detections = detections[~invalid]
if keep_boxes:
# 每张图片的最终预测框有pred_boxes_num个，output[image_i]的shape：
# (pred_boxes_num, 7) 7 =》x,y,w,h,conf,class_conf,class_pred
output[image_i] = torch.stack(keep_boxes)
# (batch_size, pred_boxes_num, 7) 7 =》x,y,w,h,conf,class_conf,class_pred
return output
def build_targets(pred_boxes, pred_cls, target, anchors, ignore_thres):
# pred_boxes => (batch_size, anchor_num, gride, gride, 4)
# pred_cls => (batch_size, anchor_num, gride, gride, 80)
# targets => (num, 6) 6=>(batch_index, cls, center_x, center_y, widht, height) num为数据集中目标的个数
# 真实边界框
# anchors => (3, 2)
# 制造真实边界框
ByteTensor = torch.cuda.ByteTensor if pred_boxes.is_cuda else torch.ByteTensor
FloatTensor = torch.cuda.FloatTensor if pred_boxes.is_cuda else torch.FloatTensor
nB = pred_boxes.size(0) # batch num
nA = pred_boxes.size(1) # anchor num
nC = pred_cls.size(-1) # class num => 80
nG = pred_boxes.size(2) # gride
# Output tensors
obj_mask = ByteTensor(nB, nA, nG, nG).fill_(0) # (batch_size, anchor_num, gride, gride)
noobj_mask = ByteTensor(nB, nA, nG, nG).fill_(1)
class_mask = FloatTensor(nB, nA, nG, nG).fill_(0)
iou_scores = FloatTensor(nB, nA, nG, nG).fill_(0)
tx = FloatTensor(nB, nA, nG, nG).fill_(0)
ty = FloatTensor(nB, nA, nG, nG).fill_(0)
tw = FloatTensor(nB, nA, nG, nG).fill_(0)
th = FloatTensor(nB, nA, nG, nG).fill_(0)
# tx=(gt_X-ax)/aw -faster rcnn
# tx = gtx - ax -yolov3
# gt_x目标真实中心x坐标 ax anchor中心点x坐标 aw anchor的宽度
tcls = FloatTensor(nB, nA, nG, nG, nC).fill_(0) # (batch_size, anchor_num, gride, gride, class_num)
# Convert to position relative to box
# 这一步是将x,y,w,h这四个归一化的变量变为真正的尺寸，因为当前图像的尺寸是nG，所以乘以nG。
# print target[:, 2:6].shape # (num, 4)
target_boxes = target[:, 2:6] * nG # (num, 4) 4=>(center_x, center_y, widht, height)
gxy = target_boxes[:, :2] # (num, 2)
gwh = target_boxes[:, 2:] # (num, 2)
# print target_boxes.shape, gxy.shape, gwh.shape
# Get anchors with best iou
# 这一步是为每一个目标框从三种anchor框中分配一个最优的.
# anchor 是设置的锚框，gwh是真实标记的宽高，这里是比较两者的交集，选出最佳的锚框,因为只是选择哪种锚框，不用考虑中心坐标。
ious = torch.stack([bbox_wh_iou(anchor, gwh) for anchor in anchors]) # (3, num)
# ious（3，num），该处理是为每一个目标框选取一个IOU最大的anchor框，best_ious表示最大IOU的值，best_n表示最大IOU对应anchor的index
best_ious, best_n = ious.max(0) # best_ious 和 best_n 的长度均为 num， best_n是num个目标框对应的anchor索引
# Separate target values
# .t() 表示转置，（num，2） =》（2，num）
# （2，num） 2=>(batch_index, cls） =》 b(num)表示对应num个index, target_labels(num)表示对应num个labels
# long去除小数点
b, target_labels = target[:, :2].long().t()
gx, gy = gxy.t() # gx表示num个x， gy表示num个y
gw, gh = gwh.t()
gi, gj = gxy.long().t() # .long()是把浮点型转为整型（去尾），这样就可以得到目标框中心点所在的网格坐标
# ---------------------------得到目标实体框obj_mask和目标非实体框noobj_mask start----------------------------
# Set masks
# 表示batch中的第b张图片，其网格坐标为(gj, gi)的单元网格存在目标框的中心点，该目标框所匹配的最优anchor索引为best_n
# b, best_n, gj, gi 为索引
obj_mask[b, best_n, gj, gi] = 1
noobj_mask[b, best_n, gj, gi] = 0 # 对目标实体框中心点所在的单元网格，其最优anchor设置为0 （与obj_mask相反）
# Set noobj mask to zero where iou exceeds ignore threshold
# ious.t(): (3, num) => (num, 3)
# 这里不同与上一个策略，上个策略是找到与目标框最优的anchor框，每个目标框对应一个anchor框。
# 这里不考虑最优问题，只要目标框与anchor的IOU大于阈值，就认为是有效anchor框，即noobj_mask对应的位置设置为0
# ious [3,num_iou]
for i, anchor_ious in enumerate(ious.t()):
noobj_mask[b[i], anchor_ious > ignore_thres, gj[i], gi[i]] = 0
# 以上操作得到了目标实体框obj_mask和目标非实体框noobj_mask，目标实体框是与实体一一对应的，一个实体有一个最匹配的目标框；
# 目标非实体框noobj_mask，该框既不是实体最匹配的，而且还要该框与实体IOU小于阈值，这也是为了让正负样例更加明显。
# ---------------------------得到目标实体框obj_mask和目标非实体框noobj_mask end------------------------------
# ---------------------------得到目标实体框的归一化坐标（tx, ty, tw, th） start------------------------------
# Coordinates
# 将x,y,w,h重新归一化，
# 注意：要明白这里为什么要这么做，此处的归一化和传入target的归一化方式不一样，
# 传入target的归一化是实际的x,y,w,h / img_size. 即实际x,y,w,h在img_size中的比例，
# 此处的归一化中，中心坐标x,y是基于单元网络的，w,h是基于anchor框，此处归一化的x,y,w,h，也是模型要拟合的值。
tx[b, best_n, gj, gi] = gx - gx.floor()
# anchor全部放在13*13网格的网格线交叉点处 实际的边界框中心在网格之内 所以 tx表示anchor与实际的距离
ty[b, best_n, gj, gi] = gy - gy.floor()
# Width and height
tw[b, best_n, gj, gi] = torch.log(gw / anchors[best_n][:, 0] + 1e-16)
th[b, best_n, gj, gi] = torch.log(gh / anchors[best_n][:, 1] + 1e-16)
# tw=log(gtw/aw)
# ---------------------------得到目标实体框的归一化坐标（tx, ty, tw, th） end---------------------------------
# One-hot encoding of label
# 表示batch中的第b张图片，其网格坐标为(gj, gi)的单元网格存在目标框的中心点，该目标框所匹配的最优anchor索引为best_n，
# 其类别index为target_labels
tcls[b, best_n, gj, gi, target_labels] = 1
# Compute label correctness and iou at best anchor
# class_mask:将预测正确的标记为1（正确的预测了实体中心点所在的网格坐标，哪个anchor框可以最匹配实体，以及实体的类别）
class_mask[b, best_n, gj, gi] = (pred_cls[b, best_n, gj, gi].argmax(-1) == target_labels).float()
# iou_scores：预测框pred_boxes中的正确框与目标实体框target_boxes的交集IOU，以IOU作为分数，IOU越大，分值越高。
iou_scores[b, best_n, gj, gi] = bbox_iou(pred_boxes[b, best_n, gj, gi], target_boxes, x1y1x2y2=False)
# tconf：正确的目标实体框，其对应anchor框的置信度为1，即置信度的标签，这里转为float，是为了后面和预测的置信度值做loss计算。
tconf = obj_mask.float()
# iou_scores：预测框pred_boxes中的正确框与目标实体框target_boxes的交集IOU，以IOU作为分数，IOU越大，分值越高。
# class_mask：将预测正确的标记为1（正确的预测了实体中心点所在的网格坐标，哪个anchor框可以最匹配实体，以及实体的类别）
# obj_mask：将目标实体框所对应的anchor标记为1，目标实体框所对应的anchor与实体一一对应的
# noobj_mask：将所有与目标实体框IOU小于某一阈值的anchor标记为1
# tx, ty, tw, th： 需要拟合目标实体框的坐标和尺寸
# tcls：目标实体框的所属类别
# tconf：所有anchor的目标置信度
return iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf
if __name__ == '__main__':
recall = np.array([0.0666,0.1333,0.1333,0.4,0.4666])
precision = np.array([1.,0.6666,0.6666,0.4285,0.3043])
ap = compute_ap(recall, precision)
print(ap) | [
"noreply@github.com"
] | noreply@github.com |
ee3737ad9a44751baf07d69836e86be717986f9b | dbaa8c84e7c7f211cf9c86139db37aa77d04615f | /miner/niroj/midstate.py | 9162dbd487d03041a91da023e2b887616f87c892 | [] | no_license | NirojPokhrel/fpga-based-bitcoinminer | 5905e8099f72b7c478955536b439c21077fd8f74 | 258799df96936fc185f1586a71455e6d1c7b4497 | refs/heads/master | 2021-01-13T02:42:22.212691 | 2019-08-16T14:55:21 | 2019-08-16T14:55:21 | 77,180,271 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,547 | py |
import struct
import util
# Some SHA-256 constants...
K = [
0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1,
0x923f82a4, 0xab1c5ed5, 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3,
0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, 0xe49b69c1, 0xefbe4786,
0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da,
0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147,
0x06ca6351, 0x14292967, 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13,
0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, 0xa2bfe8a1, 0xa81a664b,
0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070,
0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a,
0x5b9cca4f, 0x682e6ff3, 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208,
0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2,
]
A0 = 0x6a09e667
B0 = 0xbb67ae85
C0 = 0x3c6ef372
D0 = 0xa54ff53a
E0 = 0x510e527f
F0 = 0x9b05688c
G0 = 0x1f83d9ab
H0 = 0x5be0cd19
def rotateright(i,p):
"""i>>>p"""
p &= 0x1F # p mod 32
return i>>p | ((i<<(32-p)) & 0xFFFFFFFF)
def addu32(*i):
return sum(list(i))&0xFFFFFFFF
def calculateMidstate(data, state=None, rounds=None, debug=False):
"""Given a 512-bit (64-byte) block of (big-endian byteswapped) data,
calculate a Bitcoin-style midstate. (That is, if SHA-256 were big-endian
and only hashed the first block of input.)
"""
if len(data) != 64:
raise ValueError('data must be 64 bytes long')
w = list(struct.unpack('>IIIIIIIIIIIIIIII', data))
if debug:
print w
if state is not None:
if len(state) != 32:
raise ValueError('state must be 32 bytes long')
a,b,c,d,e,f,g,h = struct.unpack('>IIIIIIII', state)
if debug:
print "Second iteration:","a=", a, "b=", b, "c=", c, "d=", d, "e=", e, "f=", f, "g=", g, "h=", h
a_0, b_0, c_0, d_0, e_0, f_0, g_0, h_0 = a,b,c,d,e,f,g,h
else:
a = A0
b = B0
c = C0
d = D0
e = E0
f = F0
g = G0
h = H0
consts = K if rounds is None else K[:rounds]
for k in consts:
s0 = rotateright(a,2) ^ rotateright(a,13) ^ rotateright(a,22)
s1 = rotateright(e,6) ^ rotateright(e,11) ^ rotateright(e,25)
ma = (a&b) ^ (a&c) ^ (b&c)
ch = (e&f) ^ ((~e)&g)
h = addu32(h,w[0],k,ch,s1)
d = addu32(d,h)
h = addu32(h,ma,s0)
a,b,c,d,e,f,g,h = h,a,b,c,d,e,f,g
s0 = rotateright(w[1],7) ^ rotateright(w[1],18) ^ (w[1] >> 3)
s1 = rotateright(w[14],17) ^ rotateright(w[14],19) ^ (w[14] >> 10)
w.append(addu32(w[0], s0, w[9], s1))
w.pop(0)
if debug:
print "Before", "a=", a, "b=", b, "c=", c, "d=", d, "e=", e, "f=", f, "g=", g, "h=", h
if rounds is None:
a = addu32(a, A0)
b = addu32(b, B0)
c = addu32(c, C0)
d = addu32(d, D0)
e = addu32(e, E0)
f = addu32(f, F0)
g = addu32(g, G0)
h = addu32(h, H0)
else:
a = addu32(a, a_0)
b = addu32(b, b_0)
c = addu32(c, c_0)
d = addu32(d, d_0)
e = addu32(e, e_0)
f = addu32(f, f_0)
g = addu32(g, g_0)
h = addu32(h, h_0)
ga = a
gb = b
gc = c
gd = d
ge = e
gf = f
gg = g
gh = h
if debug:
print "a=", a, "b=", b, "c=", c, "d=", d, "e=", e, "f=", f, "g=", g, "h=", h
return struct.pack('>IIIIIIII', a, b, c, d, e, f, g, h)
| [
"nirojpokhrel@gmail.com"
] | nirojpokhrel@gmail.com |
7fa051e8b4c4f78f4441293efc92b534c2a08c5c | c751562ea538476464a13a281f321cbfebf89b76 | /python_stack/Django with Ajax/demos/main/apps/ajax_notes/models.py | c38bde3a9a7e020ce7189fdb85397987dfb674e6 | [] | no_license | brizjose/JBCodingDojo | 7e598419c0e090be4a92f7c3e80323daa9b4bb26 | fc161de86995d285bb5b2c39e28e9adbe04faebc | refs/heads/master | 2020-03-21T09:31:02.402139 | 2019-02-18T03:45:22 | 2019-02-18T03:45:22 | 138,403,753 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 938 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
# Create your models here.
class NoteManager(models.Manager):
def CreateNote(self, context):
messages = []
if context['title'] == "":
messages.append("Title cannot be blank, please insert title")
if context['content'] == "":
messages.append("Note content cannot be blank, please insert content")
if len(messages) == 0:
Note.objects.create(title=context['title'], content=context['content'])
new_note = Note.objects.last().id
return(True, new_note)
else:
return(False, messages)
class Note(models.Model):
title = models.CharField(max_length=255)
content = models.TextField()
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = NoteManager()
| [
"brizjosem@gmail.com"
] | brizjosem@gmail.com |
ff997a1fccf69381da8daaab4c8668bbc4ac74f2 | 564d6a4d305a8ac6a7e01c761831fb2081c02d0f | /sdk/network/azure-mgmt-network/azure/mgmt/network/v2019_12_01/aio/operations/_network_interface_ip_configurations_operations.py | 7e680e53b36d37aa71b6bfced0ce22536e6ef870 | [
"LicenseRef-scancode-generic-cla",
"LGPL-2.1-or-later",
"MIT"
] | permissive | paultaiton/azure-sdk-for-python | 69af4d889bac8012b38f5b7e8108707be679b472 | d435a1a25fd6097454b7fdfbbdefd53e05029160 | refs/heads/master | 2023-01-30T16:15:10.647335 | 2020-11-14T01:09:50 | 2020-11-14T01:09:50 | 283,343,691 | 0 | 0 | MIT | 2020-07-28T22:43:43 | 2020-07-28T22:43:43 | null | UTF-8 | Python | false | false | 8,912 | py | # 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 typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.mgmt.core.exceptions import ARMErrorFormat
from ... import models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class NetworkInterfaceIPConfigurationsOperations:
"""NetworkInterfaceIPConfigurationsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.network.v2019_12_01.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def list(
self,
resource_group_name: str,
network_interface_name: str,
**kwargs
) -> AsyncIterable["models.NetworkInterfaceIPConfigurationListResult"]:
"""Get all ip configurations in a network interface.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either NetworkInterfaceIPConfigurationListResult or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2019_12_01.models.NetworkInterfaceIPConfigurationListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceIPConfigurationListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2019-12-01"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('NetworkInterfaceIPConfigurationListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}/ipConfigurations'} # type: ignore
async def get(
self,
resource_group_name: str,
network_interface_name: str,
ip_configuration_name: str,
**kwargs
) -> "models.NetworkInterfaceIPConfiguration":
"""Gets the specified network interface ip configuration.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_interface_name: The name of the network interface.
:type network_interface_name: str
:param ip_configuration_name: The name of the ip configuration name.
:type ip_configuration_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: NetworkInterfaceIPConfiguration, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2019_12_01.models.NetworkInterfaceIPConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.NetworkInterfaceIPConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2019-12-01"
accept = "application/json"
# Construct URL
url = self.get.metadata['url'] # type: ignore
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'networkInterfaceName': self._serialize.url("network_interface_name", network_interface_name, 'str'),
'ipConfigurationName': self._serialize.url("ip_configuration_name", ip_configuration_name, 'str'),
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('NetworkInterfaceIPConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkInterfaces/{networkInterfaceName}/ipConfigurations/{ipConfigurationName}'} # type: ignore
| [
"noreply@github.com"
] | noreply@github.com |
fcbeb66d771fe65d6f4b915e7b411ead7068a80b | 2f17a24d8b13498a6ada3097ae0be9f1c11fdf6e | /core/migrations/0003_location_rating.py | 2e0e8938dd95877f2413580a07d3897b8b14585a | [] | no_license | neuman/onemonthlandingpage | 1789f4cae051dcb7504930db04dc4d84af900fd7 | 912a3ea1bb12d66772c1140d5539c5f3fa1a51c2 | refs/heads/master | 2021-01-23T03:21:12.125909 | 2015-02-17T06:41:46 | 2015-02-17T06:41:46 | 30,907,744 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 535 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('core', '0002_location_created_at'),
]
operations = [
migrations.AddField(
model_name='location',
name='rating',
field=models.IntegerField(blank=True, null=True, choices=[(b'R', b'Restaurant'), (b'B', b'Bar'), (b'F', b'Fast Food'), (b'V', b'Venue')]),
preserve_default=True,
),
]
| [
"soloptimus@gmail.com"
] | soloptimus@gmail.com |
eb7719667a312580c26095223dd19ffdeaeda145 | 8240633e8b127147750e0f86ce1c9077f4f8a604 | /diceroller.py | e3ce123940eb1c561cdf795b822b50bfc08d2262 | [] | no_license | matvop/codeguild | a91e8f897db748e8d097a734b1a173b3444deb1c | e96fcda5299ed1c3e292360cbb25df8c26c0e130 | refs/heads/master | 2021-01-21T13:43:55.334668 | 2016-05-20T22:57:13 | 2016-05-20T22:57:13 | 55,080,414 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 516 | py | #dice roller program
print('')
print('')
import random
print('How many dice would you like to roll?')
number_of_dice = int(input())
roll_num = 0 #roll number
total = 0 #sum of all die values
while number_of_dice != 0:
rand_num = random.randint(1,6)
roll_num += 1
print('Roll # ' + str(roll_num) + ' came out with ' +str(rand_num))
number_of_dice -= 1
total += rand_num
avg = (total/roll_num) #finding the average die value
print('The average roll was ' + str(avg) + '.')
print('')
print('')
| [
"matvop@gmail.com"
] | matvop@gmail.com |
16302299b7dbf257211b0183d46f92c125d4dd87 | 855773f2d575e2d1e91c1fd6deb7e0581d30b6b4 | /Project-Library Management/LIBRARYMANAGEMENTPROJECT/LIBRARY/MyLibrary/migrations/0006_auto_20200725_2102.py | fe4e4947e2be6d8ee21015e056867480964c0807 | [] | no_license | thekzafar/thekzafar.github.io | e422574b49105e89a2e094b908a997beb473b0e4 | c972a2f2c31793fc76c780e04a3f8ff72f243e00 | refs/heads/master | 2023-03-07T14:56:43.617694 | 2021-02-19T05:38:57 | 2021-02-19T05:38:57 | 340,260,523 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 466 | py | # Generated by Django 3.0.8 on 2020-07-25 15:02
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('MyLibrary', '0005_delete_user'),
]
operations = [
migrations.AlterField(
model_name='borrow',
name='bno',
field=models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='MyLibrary.Book'),
),
]
| [
"karimazafar.yvf@gmail.com"
] | karimazafar.yvf@gmail.com |
391d6b41347eaed37843d533873b24430cff13dc | 724afba6b3534620645b0a2f7f91b1b10297458f | /code/resources/store.py | dc02bf05ba1052dec43559bcdf904348b5670e92 | [] | no_license | Jaco26/flask-with-flasksqlalchemy | 94b9dc091ec1a9cc71aec2171ebff6733c6889d3 | a212408c187cc390874684e7ab5e9ae0e411b617 | refs/heads/master | 2020-04-02T20:15:06.248009 | 2018-10-27T19:59:04 | 2018-10-27T19:59:04 | 154,762,670 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 926 | py | from flask_restful import Resource, reqparse
from models.store import StoreModel
class Store(Resource):
def get(self, name):
store = StoreModel.find_by_name(name)
if store:
return store.json() # default HTTP status = 200
return { 'message': 'The store called {} was not found'.format(name) }, 404
def post(self, name):
if StoreModel.find_by_name(name):
return { 'message': 'The store called {} already exists'.format(name) }, 400
store = StoreModel(name)
try:
store.save_to_db()
except:
return { 'message': 'An error occured while creating the store.' }, 500
return store.json(), 201
def delete(self, name):
store = StoreModel.find_by_name(name)
if store:
store.delete_from_db()
return { 'message': 'Store deleted' }
class StoreList(Resource):
def get(self):
return { 'stores': [store.json() for store in StoreModel.query.all()] } | [
"jacob.albright23@gmail.com"
] | jacob.albright23@gmail.com |
4538004f106fffe79d316a3c4935178f4a9bc725 | 0517d16821ae92719f0d96d8036cf72effb0cc36 | /everscript/__init__.py | df2fb0f35bd217a79b8a7e8a4cfdb9022415082c | [] | no_license | von/everscript | 07ea72157f993e8c2f538c884806e37e9c7521c9 | c61dc9f5fed679cedd9dac80718784d23e798dda | refs/heads/master | 2020-04-12T07:37:46.649065 | 2013-02-13T16:23:23 | 2013-02-13T16:23:23 | 2,486,306 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 191 | py | from constants import *
from EverNote import EverNote, EverNoteException
from Note import Note
from Notes import Notes
from Plugin import Plugin
from ToDo import ToDo
from ToDos import ToDos
| [
"von@vwelch.com"
] | von@vwelch.com |
fa4a06802d3f953cd0c28fba3c8d0c4cb548a7a1 | df8a733eebe79767b4762dc8b5da46149c59174c | /constants.py | ab8a5cd2b141fd13a20b6621b6e7391cf6ae5035 | [
"MIT"
] | permissive | vgkinis/iso_cfm | a113284495c012d538c66d27c05e556ba6beba76 | b3c1620bd01d56e4d057647ad27ee16b72182e87 | refs/heads/master | 2021-06-24T03:07:13.968473 | 2020-12-10T17:50:36 | 2020-12-10T17:50:36 | 152,418,760 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 999 | py | R = 8.314 # gas constant used to calculate Arrhenius's term
S_PER_YEAR = 31557600.0 # number of seconds in a year
RHO_1 = 550.0 # cut off density for the first zone densification (kg/m^3)
RHO_2 = 815.0 # cut off density for the second zone densification (kg/m^3)
RHO_I = 917.0 # density of ice (kg/m^3)
RHO_I_MGM = 0.917 # density of ice (g/m^3)
RHO_1_MGM = 0.550 # cut off density for the first zone densification (g/m^3)
GRAVITY = 9.8 # acceleration due to gravity on Earth
K_TO_C = 273.15 # conversion from Kelvin to Celsius
BDOT_TO_A = S_PER_YEAR * RHO_I_MGM # conversion for accumulation rate
RHO_W_KGM = 1000. # density of water
CP_I = 2.06 # specific heat of ice
LF_I = 334.0 # latent heat of ice | [
"noreply@github.com"
] | noreply@github.com |
07ebd670ecd117507ddf36caa641436cef8c69bb | bad6abecaf33aa2f262681c03e5e3205e6bb8088 | /ntc_project/settings/migrations/0001_initial.py | f3fb47dbdf20cb599663fb87280d3b8061f021f3 | [] | no_license | NotesXI/ntc_repo | 55f064e6e62e24bd0f72be1d8a9c6dcace851e65 | 6517bbb66b520d8b4bfde7434f921dc70bd299ef | refs/heads/master | 2020-03-30T08:21:40.733524 | 2018-11-22T08:27:30 | 2018-11-22T08:27:30 | 151,009,048 | 0 | 4 | null | 2018-11-22T06:36:35 | 2018-09-30T21:33:39 | JavaScript | UTF-8 | Python | false | false | 1,110 | py | # Generated by Django 2.1.2 on 2018-11-22 07:02
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='CreditCards',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('fullName', models.CharField(max_length=60)),
('email', models.CharField(max_length=60)),
('address', models.CharField(max_length=60)),
('city', models.CharField(max_length=60)),
('zip', models.CharField(max_length=60)),
('states', models.CharField(max_length=60)),
('nameOnCard', models.CharField(max_length=60)),
('creditCardNum', models.CharField(max_length=60)),
('expmonth', models.CharField(max_length=60)),
('cvv', models.CharField(max_length=60)),
('expYear', models.CharField(max_length=60)),
],
),
]
| [
"emmanuel.adw@gmail.com"
] | emmanuel.adw@gmail.com |
fcd4080d3f9f605d4b08c466f0ca0c34880aa09c | 01493e3fc3778cd2f2140700a511400abb4c92dc | /example.py | ae95a51c0420009715190286be58a7dd7a9a794d | [] | no_license | Heisenberg-Chef/py_logging | 694f8fe0a681462a953203239be0eaf7f9f1d50d | d5e1d3a09e0e0230bc4d43de8373b4331ad560da | refs/heads/master | 2022-04-15T16:38:24.990840 | 2020-04-10T08:08:48 | 2020-04-10T08:08:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 909 | py | import logging # 引入logging模块
import os.path
import time
# 第一步，创建一个logger
logger = logging.getLogger()
logger.setLevel(logging.INFO) # Log等级总开关
# 第二步，创建一个handler，用于写入日志文件
rq = time.strftime('%Y%m%d%H%M', time.localtime(time.time()))
fh = logging.FileHandler("abc.txt", mode='w')
fh.setLevel(logging.DEBUG) # 输出到file的log等级的开关
# 第三步，定义handler的输出格式
formatter = logging.Formatter("%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s")
fh.setFormatter(formatter)
# 第四步，将logger添加到handler里面
logger.addHandler(fh)
# 日志
logger.debug('this is a logger debug message')
logger.info('this is a logger info message')
logger.warning('this is a logger warning message')
logger.error('this is a logger error message')
logger.critical('this is a logger critical message') | [
"qilei@Heisenberg.local"
] | qilei@Heisenberg.local |
fe002bc5b86c3fcd2422da3a33f253a29d6fbeb6 | 63bbc7ab710f9b5c61fa1cbf9a7ed75912d05e17 | /marine/convergence.py | 7120442c6489b01bcda230584f24472e3f4a34c0 | [] | no_license | jinmang2/Gachon_Research | 7550025c69c914b2a37d41981b3796789b4b3dce | 62c091ecbf1e53235edabf641a1a44253f52d424 | refs/heads/master | 2020-06-04T20:28:47.065122 | 2019-12-04T03:47:45 | 2019-12-04T03:47:45 | 192,180,617 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,141 | py | import numpy as np
import pandas as pd
import math
from scipy import stats
from scipy.stats import norm
class ConvergenceAnalysis(object):
def __init__(self, df, r = 0):
self.df = df
self.t_df = pd.concat([df[i] for i in df.columns if any((df[i].dtype == np.float, df[i].dtype == np.int))], axis=1)
df = self.t_df
print("Version 1.0.0")
print("Welcome. This Class works for ConvergenceAnalysis.")
print("You can access dataframe by these METHODS")
print(" : df, t_df")
self.mean = df.apply(np.mean) # Series
self.var = df.apply(np.var) # Series
self.std = df.apply(np.std) # Series
self.std_s = np.std(df, ddof=1) # Series
self.cv = self.std / self.mean # Series
self.cv_s = self.std_s / self.mean # Series
self.sigma = self.cv / self.cv.values[0] # Series
self.df_rank = df.rank(ascending=False, method="min")
self.rank_var = self.df_rank.apply(np.var) # Series
self.df_gamma = pd.DataFrame(self.df_rank.values + self.df_rank.values.T[0].reshape(-1,1), columns = self.df_rank.columns)
self.gamma_var = self.df_gamma.apply(np.var) # Series
self.gamma = self.gamma_var / self.rank_var.values[0] / 4
print("also, You can access as pd.Series by these METHODS")
print(" : mean, var, std, cv, sigma, std_s, cv_s (_s means, \"sample\")")
print(" rank_var, gamma_var, gamma")
print("and You can access as pd.DataFrame by thes METHODS")
print(" : df_rank, df_gamma")
self.period = int(df.columns[-1]) - int(df.columns[0])
if r:
self.mean_cagr = round((math.pow(self.mean.values[-1] / self.mean.values[0], 1 / self.period)-1), r)
self.sigma_cagr = round((math.pow(self.sigma.values[-1] / self.sigma.values[0], 1 / self.period)-1), r)
self.gamma_cagr = round((math.pow(self.gamma.values[-1] / self.gamma.values[0], 1 / self.period)-1), r)
else:
self.mean_cagr = math.pow(self.mean.values[-1] / self.mean.values[0], 1 / self.period)-1
self.sigma_cagr = math.pow(self.sigma.values[-1] / self.sigma.values[0], 1 / self.period)-1
self.gamma_cagr = math.pow(self.gamma.values[-1] / self.gamma.values[0], 1 / self.period)-1
print("Finally, You can get three CAGR by these METHODS")
print(" : mean_cagr, sigma_cagr, gamma_cagr")
self.dof = len(df)-1
print("\nThis Class has 4 Functions following as")
print(" : t_test(alpha=0.05, r=0), chi_square_test(r=0)")
print(" get_measure_table(name, measure), get_index_table(name)")
def t_test(self, alpha = 0.05, r = 0):
df = self.t_df
self.alpha = alpha
self.z_crit = norm.ppf(1-alpha/2)
self.z = pd.Series()
self.p_value = pd.Series()
self.t_lower = pd.Series()
self.t_upper = pd.Series()
for i in df.columns:
if i == df.columns[0]:
sd = self.cv_s.values[0]
self.z[i] = np.NaN
self.p_value[i] = np.NaN
self.t_lower[i] = np.NaN
self.t_upper[i] = np.NaN
else :
cp = self.cv_s[i]
v_pool = (sd + cp) / 2
new_mean = sd - cp
new_stdev = np.sqrt(2*(v_pool**2)*(v_pool**2+0.5)/self.dof)
self.z[i] = new_mean / new_stdev
self.p_value[i] = 2*(1-norm.cdf(self.z[i]))
d = np.sqrt((sd *(sd+0.5)+cp*(cp+0.5))/self.dof)
self.t_lower[i] = new_mean - self.z_crit * d
self.t_upper[i] = new_mean + self.z_crit * d
if r:
self.z = self.z.map(lambda x:round(x,r))
self.p_value = self.p_value.map(lambda x:round(x,r))
self.t_lower = self.t_lower.map(lambda x:round(x,r))
self.t_upper = self.t_upper.map(lambda x:round(x,r))
print("Do T-Test. Get a P-value.")
print("If you want, You can get statistic values by these METHODS")
print(" : alpha, z_crit, dof(degree of freedom)")
print("And You can get pd.Series by these METHODS")
print(" : z, p_value, t_lower, t_upper")
def chi_square_test(self, r=0):
self.W1 = pd.Series()
self.r = pd.Series()
self.chi_squared = pd.Series()
self.chi_p_value = pd.Series()
self.W2 = pd.Series()
df = self.df_rank
k = len(self.df)
m = 2
for i in df.columns:
if i == df.columns[0]:
sd_yr = df[i]
self.chi_p_value[i] = np.NaN
self.W1[i] = np.NaN
else:
sum_beg_yr = sd_yr + df[i]
DEVSQ = sum((sum_beg_yr - np.mean(sum_beg_yr))**2)
self.W1[i] = 12*DEVSQ/((m**2)*(k**3-k))
self.r[i] = (m * self.W1[i] - 1) / (m-1)
self.chi_squared[i] = m * (k-1) * self.W1[i]
self.chi_p_value[i] = stats.chi2.pdf(self.chi_squared[i], self.dof)
SUMEQ = sum(sum_beg_yr**2)
self.W2[i] = 12*SUMEQ/((m**2)*(k**3-k)) - 3*(k+1)/(k-1)
if r:
self.r = self.r.map(lambda x:round(x,r))
self.chi_squared = self.chi_squared.map(lambda x:round(x,r))
self.chi_p_value = self.chi_p_value.map(lambda x:round(x,r))
print("Do Chi-Square Test. Get a P_value.")
print("You can get pd.Series by these METHODS")
print(" : W1, r, chi_squared, chi_p_value, W2")
def get_measure_table(self, name=False, measure=False):
tu = [["{}".format(name)], ["{}".format(measure)]]
tuples = list(zip(*tu))
index = pd.MultiIndex.from_tuples(tuples)
table = pd.DataFrame(columns=index, index=self.mean.index)
table[("{}".format(name), "{}".format(measure))] = ["%.2f" % i for i in round(self.mean, 2).values]
table.loc["CAGR"] = "%.2f" % (self.mean_cagr*100) + "%"
return table
def get_index_table(self, name=False):
tu = [["{}".format(name),"{}".format(name),"{}".format(name),"{}".format(name)], \
["sigma", "p_value", "gamma", "chi_p_value"]]
tuples = list(zip(*tu))
index = pd.MultiIndex.from_tuples(tuples)
table = pd.DataFrame(columns=index, index=self.sigma.index)
table[("{}".format(name), "sigma")] = ["%.4f" % i for i in round(self.sigma, 4).values]
table[("{}".format(name), "p_value")] = np.where(self.p_value<0.01, "***", \
np.where(self.p_value<0.05, "**", np.where(self.p_value<0.1, "*", "")))
table[("{}".format(name), "gamma")] = ["%.4f" % i for i in round(self.gamma, 4).values]
table[("{}".format(name), "chi_p_value")] = np.where(self.chi_p_value<0.01, "***", \
np.where(self.chi_p_value<0.5, "**", np.where(self.chi_p_value<0.1, "*", "")))
table.loc["CAGR"] = ["%.2f" % (self.sigma_cagr*100) + "%", "", "%.2f" % (self.gamma_cagr*100) + "%", ""]
return table
| [
"noreply@github.com"
] | noreply@github.com |
a9642d6c74ccc57e476b12e9fcc18c36bc7a0d0f | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02913/s870183594.py | 9f40aaa7f32fff9e55ca4ec7a16c32dad0b4c50a | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 479 | py |
M = int(input())
T = input()
ans = 0
for l in range(M):
i, j = 1, 0
S = T[l:]
N = M-l
Z = [0]*N
Z[0] = N
while i < N:
while i+j < N and S[j] == S[i+j]:
j += 1
Z[i] = j
if j == 0:
i += 1
continue
k = 1
while i+k < N and k+Z[k] < j:
Z[i+k] = Z[k]
k += 1
i += k
j -= k
for i in range(N):
ans = max(ans, min(i, Z[i]))
print(ans)
| [
"66529651+Aastha2104@users.noreply.github.com"
] | 66529651+Aastha2104@users.noreply.github.com |
08962005ac1013198fdafc5d96370cc25d722955 | 155d6488c494967bb96eeb231acb27b4a0509e9b | /kNNTest.py | d83b1fda10b39c4532c65b2d5472308bee5f019a | [] | no_license | zerolinux5/machine-learning | 927ea78b1db7fdb05d279d9b73d183a1b22a5d79 | 4102b6b2e82deae95988e6bc94db1d3a03624111 | refs/heads/master | 2021-01-10T19:33:56.967316 | 2014-09-14T04:22:18 | 2014-09-14T04:22:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 591 | py | import kNN
import matplotlib
import matplotlib.pyplot as plt
from numpy import array
group,labels = kNN.createDataSet()
print (group)
print (labels)
print kNN.classify0([0,0], group, labels, 3)
datingDataMat,datingLabels = kNN.fileToMatrix('datingTestSet2.txt')
print datingDataMat
print datingLabels[0:20]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels), 15.0*array(datingLabels))
plt.show()
normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
print normMat
print ranges
print minVals
kNN.datingClassTest() | [
"maganazero5@gmail.com"
] | maganazero5@gmail.com |
b2671507ad3edbd3f78490511834217b722cfa39 | 1f70b745ea7d867cf46226901d42741098837126 | /postimage/migrations/0010_photo_price.py | 00a29b030c7d0f3e3d7cf2da63219f4de95ee45c | [] | no_license | zhan860127/django_sale | 0ce7d94145c93cba805d309919efe9e9028e8412 | ae559d2d92628daaf0b8d9ffcee9c0e70f6b2423 | refs/heads/main | 2023-07-16T19:15:48.769587 | 2021-09-03T13:14:41 | 2021-09-03T13:14:41 | 386,940,778 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 436 | py | # Generated by Django 3.2.5 on 2021-07-20 08:29
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('postimage', '0009_alter_photo_name'),
]
operations = [
migrations.AddField(
model_name='photo',
name='price',
field=models.IntegerField(default=0),
preserve_default=False,
),
]
| [
"zhan860127@gmail.com"
] | zhan860127@gmail.com |
534c61a551e00f0eb519d6ba577102b9ab81f4f2 | 6dc0f0f6c79efed8cd09a77cdf03328f9f1fbadf | /node_modules/fsevents/build/config.gypi | a7f74fad15d8d1eba33d971dd72a082aeb92e157 | [
"MIT"
] | permissive | waynekingcool/loveMH | 465183efb781e324c2ca73372bba08871d04dad0 | 333d78dbd7ea9be69c90f43b28f1f784262962c4 | refs/heads/main | 2023-02-26T14:23:20.821468 | 2021-02-05T10:02:59 | 2021-02-05T10:02:59 | 330,107,233 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,088 | gypi | # Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": []
},
"variables": {
"asan": 0,
"coverage": "false",
"dcheck_always_on": 0,
"debug_nghttp2": "false",
"debug_node": "false",
"enable_lto": "false",
"enable_pgo_generate": "false",
"enable_pgo_use": "false",
"error_on_warn": "false",
"experimental_quic": "false",
"force_dynamic_crt": 0,
"host_arch": "x64",
"icu_data_in": "../../deps/icu-tmp/icudt68l.dat",
"icu_endianness": "l",
"icu_gyp_path": "tools/icu/icu-generic.gyp",
"icu_path": "deps/icu-small",
"icu_small": "false",
"icu_ver_major": "68",
"is_debug": 0,
"llvm_version": "11.0",
"napi_build_version": "7",
"node_byteorder": "little",
"node_debug_lib": "false",
"node_enable_d8": "false",
"node_install_npm": "true",
"node_module_version": 88,
"node_no_browser_globals": "false",
"node_prefix": "/usr/local",
"node_release_urlbase": "https://nodejs.org/download/release/",
"node_shared": "false",
"node_shared_brotli": "false",
"node_shared_cares": "false",
"node_shared_http_parser": "false",
"node_shared_libuv": "false",
"node_shared_nghttp2": "false",
"node_shared_openssl": "false",
"node_shared_zlib": "false",
"node_tag": "",
"node_target_type": "executable",
"node_use_bundled_v8": "true",
"node_use_dtrace": "true",
"node_use_etw": "false",
"node_use_node_code_cache": "true",
"node_use_node_snapshot": "true",
"node_use_openssl": "true",
"node_use_v8_platform": "true",
"node_with_ltcg": "false",
"node_without_node_options": "false",
"openssl_fips": "",
"openssl_is_fips": "false",
"ossfuzz": "false",
"shlib_suffix": "88.dylib",
"target_arch": "x64",
"v8_enable_31bit_smis_on_64bit_arch": 0,
"v8_enable_gdbjit": 0,
"v8_enable_i18n_support": 1,
"v8_enable_inspector": 1,
"v8_enable_lite_mode": 0,
"v8_enable_object_print": 1,
"v8_enable_pointer_compression": 0,
"v8_no_strict_aliasing": 1,
"v8_optimized_debug": 1,
"v8_promise_internal_field_count": 1,
"v8_random_seed": 0,
"v8_trace_maps": 0,
"v8_use_siphash": 1,
"want_separate_host_toolset": 0,
"xcode_version": "11.0",
"nodedir": "/Users/wayneking/Library/Caches/node-gyp/15.4.0",
"standalone_static_library": 1,
"metrics_registry": "https://registry.npm.taobao.org/",
"registry": "https://registry.npm.taobao.org/",
"home": "https://npm.taobao.org",
"globalconfig": "/usr/local/etc/npmrc",
"init.module": "/Users/wayneking/.npm-init.js",
"userconfig": "/Users/wayneking/.npmrc",
"init_module": "/Users/wayneking/.npm-init.js",
"node_gyp": "/usr/local/lib/node_modules/npm/node_modules/node-gyp/bin/node-gyp.js",
"cache": "/Users/wayneking/.npm",
"user_agent": "npm/7.0.15 node/v15.4.0 darwin x64",
"prefix": "/usr/local"
}
}
| [
"2641415119@qq.com"
] | 2641415119@qq.com |
e10440c3b022311e7e3f8255e41e920a81620a1b | c6d84655a8424c8ade139fd79ae1bbb1514f8c88 | /05socket/udp_producer.py | c8f3cd16854e8e3a5104c25dece2780513a69411 | [] | no_license | eryeer/pythonStudy | 64240934ce463e7e9714fc7f0d7a6608cefab837 | 5c6a69ef10c0df38f89aee7e5d5cc9802a2b424b | refs/heads/master | 2020-04-08T15:16:19.579466 | 2019-04-15T06:13:41 | 2019-04-15T06:13:41 | 159,472,137 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 536 | py | import socket
def test(a: str, b: int) -> int:
print(a)
print(b)
return b
def main():
# test("1", 2)
udp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
udp_socket.bind(("", 7890))
# udp_socket.sendto(b"hahaha", ("localhost", 8080))
while True:
send_data = input("input send message")
if send_data == "exit":
udp_socket.close()
return
udp_socket.sendto(send_data.encode("utf-8"), ("localhost", 8080))
if __name__ == '__main__':
main()
| [
"eryeer@163.com"
] | eryeer@163.com |
38355cde10cce1c38b527f4881ae08e583000a80 | 978b3484a069de59033bfa21ae4ea9db033c4b7c | /djpaystack/transaction.py | 2133e2de97046b7200787420e4fb45cf8d2292af | [] | no_license | boiyelove/hookup | 3b83223bf8c9a3b407a0c2f3d4f3496de01fcbee | 59d44ce353d9f4f079e31b904da59e8185a8f401 | refs/heads/master | 2023-03-20T20:38:18.233708 | 2021-03-16T13:08:02 | 2021-03-16T13:08:02 | 140,166,924 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,634 | py | from .base import Base
from .settings import TRANSACTIONURL
class Transactions(Base):
def __init__(self):
super(Transactions, self).__init__()
self.baseurl = TRANSACTIONURL
def initialize(reference='', amount=0, email=''):
'''
PAYSTACK TRANSACTION API
Initialize Transaction
curl https://api.paystack.co/transaction/initialize \
-H "Authorization: Bearer SECRET_KEY" \
-H "Content-Type: application/json" \
-d '{"reference": "7PVGX8MEk85tgeEpVDtD", "amount": 500000, "email": "customer@email.com"}' \
-X POST
'''
raise NotImplementedError('You have not done this yet')
def charge_auth(self, auth_code='', email='', amount=0):
'''
Charge Authorization
curl https://api.paystack.co/transaction/charge_authorization \
-H "Authorization: Bearer SECRET_KEY" \
-H "Content-Type: application/json" \
-d '{"authorization_code": "AUTH_72btv547", "email": "bojack@horsinaround.com", "amount": 500000}' \
-X POST
'''
raise NotImplementedError('You have not done this yet')
def re_auth(self):
'''
Request Reauthorization
curl https://api.paystack.co/transaction/request_reauthorization \
-H "Authorization: Bearer SECRET_KEY" \
-H "Content-Type: application/json" \
-d '{"authorization_code": "AUTH_72btv547", "email": "bojack@horsinaround.com", "amount": 500000}' \
-X POST
'''
return ''
def check_auth(self, auth_code='', email='', amount=0):
'''
Check Authorization
curl https://api.paystack.co/transaction/check_authorization \
-H "Authorization: Bearer SECRET_KEY" \
-H "Content-Type: application/json" \
-d '{"authorization_code": "AUTH_72btv547", "email": "bojack@horsinaround.com", "amount": 500000}' \
-X POST
'''
return ''
def verify(self, reference_code):
'''
Verify transaction
curl https://api.paystack.co/transaction/verify/DG4uishudoq90LD \
-H "Authorization: Bearer SECRET_KEY"
'''
return self.execute(endpoint = ('/verify/' + reference_code))
def verify_by_customer(self, plan_code=None, reference_code=None, email=None, customer_id=None, customer_code=None):
data = self.verify(reference_code)
if customer_id: ver_measure = customer_id
elif customer_code: ver_measure = customer_code
elif email: ver_measure = email
else:
raise TypeError('email or customer_detail cannot be null')
# if (plan_code == data['plan'] ) and (email== data['customer']['email']) : pass
cus = data['customer']
auth = data['authorization']
plan = data['plan']
if ((cus['email'] == email) and (data['plan'] == plan_code)):
return True
return False
def get_auth(self):
return self.payload['authorization']
def get_customer(self):
return self.payload['authorization']
def get_plan(self):
return self.payload['plan']
def fetch(self, id=0):
'''
Fetch Transaction
curl "https://api.paystack.co/transaction/2091" \
-H "Authorization: Bearer SECRET_KEY"
-X GET
'''
return ''
def timeline(self, id=0):
'''
View transaction Timeline
curl https://api.paystack.co/transaction/timeline/21002R319U5139 \
-H "Authorization: Bearer SECRET_KEY"
'''
return ''
def totals(self, user=None):
'''
Transaction Totals
curl "https://api.paystack.co/transaction/totals" \
-H "Authorization: Bearer SECRET_KEY"
-X GET
'''
return ''
def user_totals(self, customer_id):
self.data = {'customer': customer_id, 'status': 'success'}
self.execute()
rmeta = self.payload['meta']
# f = open('data.json', 'w+')
# f.write(str(self.payload))
# f.close()
print(self.payload)
amount = 0
# for item in self.payload['data']:
# amount += item['amount']
return rmeta["total_volume"]
# return amount
def export(self):
'''
Export Transaction
curl "https://api.paystack.co/transaction/export" \
-H "Authorization: Bearer SECRET_KEY"
-X GET
'''
return ''
def verify_transaction(ref_code='', email='', plan_code = ''):
transaction_url = 'https://api.paystack.co/transaction/verify/'
if ref_code == '':
raise ValueError('please provide a referece code')
transaction_url += ref_code
r = self.requests.get(transaction_url, headers=self.header)
r = r.json()
print(r)
if r['status']:
data = r['data']
cus = data['customer']
auth = data['authorization']
plan = data['plan']
print(cus['email'],email,data['plan'], plan_code)
print(type(cus['email']),type(email),type(data['plan']), type(plan_code))
print('True0 True0')
if ((cus['email'] == email) and (data['plan'] == plan_code)):
print('True')
return True
print('False')
return False
| [
"daahrmmieboiye@gmail.com"
] | daahrmmieboiye@gmail.com |
536fba4fdb2a99ebef575064ab074cb45d45a901 | 97dcebb2c2c07e1259c47b52db3c9ff5a9f96d8e | /Tops/asgi.py | 22c5c6892a254320b14e90cdedfcad5d3e0bd201 | [] | no_license | Zemochka/tops | 2ee0ba6ca13ee1a80ef48eafde2908bcb3503fe2 | 61e280c3ad330352ab72825d8aa8af8a146190c2 | refs/heads/master | 2022-09-20T13:53:49.323283 | 2020-05-30T10:50:44 | 2020-05-30T10:50:44 | 266,423,888 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 385 | py | """
ASGI config for Tops project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Tops.settings')
application = get_asgi_application()
| [
"art.kurg2005@gmail.com"
] | art.kurg2005@gmail.com |
9eb8a56e8637de18ee933331a541530023efe9a7 | c4ae0e3f4389459b2bab5e4c591ea6ae14833d09 | /2020/24/code.py | 2c8121657b3f586690aedcb653f8d1814339c028 | [
"MIT"
] | permissive | ahriley/advent-of-code | 145558e2b5f6cc2f9a9c5c2b453f03abd340da75 | 2484ff4e520215b5dfb855761465d683aa2b91eb | refs/heads/main | 2023-02-06T08:14:31.209213 | 2020-12-28T22:52:48 | 2020-12-28T22:52:48 | 159,911,021 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,759 | py | # using cube coordinates: https://www.redblobgames.com/grids/hexagons/
def update(tile, command):
x, y, z = tile
if command == 'w':
x -= 1
y += 1
elif command == 'e':
x += 1
y -= 1
elif command == 'nw':
y += 1
z -= 1
elif command == 'se':
y -= 1
z += 1
elif command == 'ne':
x += 1
z -= 1
elif command == 'sw':
x -= 1
z += 1
assert x + y + z == 0
return [x, y, z]
def neighbors(tile):
directions = ['w', 'e', 'nw', 'ne', 'sw', 'se']
return [update(tile, direction) for direction in directions]
# input
with open('input.txt') as f:
lines = f.readlines()
# part 1
flipped = []
for line in lines:
commands = line.replace('w', 'w,').replace('e', 'e,').split(',')[:-1]
tile = [0, 0, 0]
for command in commands:
tile = update(tile, command)
flipped.remove(tile) if tile in flipped else flipped.append(tile)
ans1 = len(flipped)
# part 2
for _ in range(100):
possible = []
for tile in flipped:
possible += [adj for adj in neighbors(tile) if adj not in possible]
possible += [tile for tile in flipped if tile not in possible]
next_flipped = flipped.copy()
for tile in possible:
num = len([adj for adj in neighbors(tile) if adj in flipped])
if tile in flipped:
if num == 0 or num > 2:
next_flipped.remove(tile)
else:
if num == 2:
next_flipped.append(tile)
flipped = next_flipped
ans2 = len(flipped)
# output
answer = []
answer.append('Part 1: {}'.format(ans1))
answer.append('Part 2: {}'.format(ans2))
with open('solution.txt', 'w') as f:
f.writelines('\n'.join(answer)+'\n')
| [
"30327239+ahriley@users.noreply.github.com"
] | 30327239+ahriley@users.noreply.github.com |
fd2d32708606db8d12113a487e35cbd78ae88004 | 602a8477580edfd1b205696533423a0f196a0491 | /sgi/academico/models/aluno.py | 1c40e5a38c227ab144e1e0c27c22be55c55890a2 | [
"MIT"
] | permissive | jorgevilaca82/SGI | 4d3656a0f5f2ca69a3259f055330754f221a0a2d | c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a | refs/heads/master | 2023-01-24T20:14:23.933544 | 2020-03-10T16:12:55 | 2020-03-10T16:12:55 | 224,872,711 | 0 | 0 | MIT | 2023-01-07T12:18:28 | 2019-11-29T14:49:17 | Python | UTF-8 | Python | false | false | 1,337 | py | from enum import IntEnum, auto
from django.db import models
from django.utils.translation import gettext_lazy as _
from sgi.base import models as bm
class Aluno(bm.PessoaFisica):
"""
A Pessoa Física só se torna um aluno quando está devidamente
associada um curso. Uma Pessoa Física pode ser aluno de mais de um curso,
mas nunca mais que dois e sem conflito de turnos
"""
class Status(IntEnum):
MATRICULADO = auto()
EVADIDO = auto()
TRANCADO = auto()
JUBILADO = auto()
CANCELADO = auto()
EGRESSO = auto()
FORMADO = auto()
AFASTADO = auto()
FALECIDO = auto()
ALUNO_STATUS_CHOICES = (
(Status.MATRICULADO.value, _("Matriculado")),
(Status.EVADIDO.value, _("Evadido")),
(Status.TRANCADO.value, _("Trancado")),
(Status.JUBILADO.value, _("Jubilado")),
(Status.CANCELADO.value, _("Cancelado")),
(Status.EGRESSO.value, _("Egresso")),
(Status.FORMADO.value, _("Formado")),
(Status.AFASTADO.value, _("Afastado")),
(Status.FALECIDO.value, _("Falecido")),
)
status = models.IntegerField(choices=ALUNO_STATUS_CHOICES)
# RA - Registro de Aluno (identificador de matricula)
ra = models.CharField(max_length=20, default="", editable=False, unique=True)
| [
"jorge.vilaca@gmail.com"
] | jorge.vilaca@gmail.com |
e3de3d510be8ef23fc86b2150075c728c03cede3 | 9d6c3de763e25d3f730d04f5e738a31953cf115e | /Python/Desafio_048.py | aa9ef5202fbf28819e2bd3cde88c467137305d31 | [] | no_license | lucasjribeiro/projects | e4b67a51ccbf15a3ba8fcaba600457ffba879d0c | 110d705eee7fd14d342fb8bb5d890fbf04687094 | refs/heads/master | 2021-01-06T21:51:56.236776 | 2020-08-22T07:06:22 | 2020-08-22T07:06:22 | 241,492,240 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 161 | py | soma = 0
contador = 0
for c in range(1, 500, 2):
if c % 3 == 0:
soma = soma + c
contador += + 1
print(c)
print(soma)
print(contador)
| [
"lucasj.ribeiro@hotmail.com"
] | lucasj.ribeiro@hotmail.com |
f845ef1a170fda7ca4deb266135c6e317c07867c | f61ab4deaa5e5bd5171bb28854eaa6eacdc95a4c | /Instuiteapp/apps.py | 7d961d172cbea670f828f552d7a799f9eb80e6de | [] | no_license | Adi19471/Institute_djangomvtadi_DJANGO | dfd67237bc4ca129f84fa62f15bcc17b1d4585ea | f4ce3f1c58a7e152d6011583d287be577f967cce | refs/heads/master | 2023-07-22T17:03:59.781683 | 2021-09-11T07:25:45 | 2021-09-11T07:25:45 | 403,988,727 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 154 | py | from django.apps import AppConfig
class InstuiteappConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'Instuiteapp'
| [
"akumatha@gmail.com"
] | akumatha@gmail.com |
3375544feac75f3d34c1185da121e2cee030f80a | 547a6e76a683990680678750c0d5f673ef0a438e | /trzecie zajecia/student.py | 0dbb87672041b5c1e60d940d94535498d2feb14f | [] | no_license | Ssatyr/programowanie2021 | 3556acb69d83300708da88a340b9581b3142dece | ed154798b40646ed9e36cfd7cdc2ac98396e543d | refs/heads/master | 2023-03-25T17:21:03.767267 | 2021-03-15T21:55:17 | 2021-03-15T21:55:17 | 299,569,099 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,546 | py | import pickle
class Student:
def __init__(self, name, surname, index_nmber, rating_list=[]):
self.__name = name
self.__surname = surname
self.__index_number = index_nmber
self.__rating_list = rating_list[:]
def show(self):
print(f"Student: {self.__name} {self.__surname}")
print(f"Index Number: {self.__index_number}")
print(f"Rating list: {self.__rating_list}")
def set_name(self, name):
self.__name = name
def set_surname(self, surname):
self.__surname = surname
def set_index_nmber(self, index_nmber):
self.__index_number = index_nmber
def avg_rating(self):
return sum(self.__rating_list) / len(self.__rating_list)
def show_rating(self):
print(self.__rating_list)
def change_grades(self):
print("Dodac czy usunac oceny? [d/u]")
x = input()
if x == "d":
print("Jaka ocene dodac?")
d = defense_int_input("", 1, 6)
self.__rating_list.append(d)
if x == "u":
print("Ktora ocene usunac?")
d = defense_int_input("", 1, 6)
self.__rating_list.pop(d-1)
def defense_int_input(text, min_val, max_val):
value = input(text)
while ((int(min_val) > int(value)) or (int(value) > int(max_val))):
print('Wartosc musi byc nie mniejsza niz', min_val,
'i nie wieksza niz', max_val, sep=' ', end='\n')
value = input(text)
return int(value)
def display_menu():
menu_selection = 0
print('\nMenu:')
print('1 - Wyświetlanie listy studentów')
print('2 - Edycja listy studentów')
print('3 - Wyswietlanie wybranych studentów')
print('4 - Odczytywanie listy z pliku')
print('5 - Zapisywanie listy do pliku')
print('6 - Koniec programu')
print('\n')
menu_selection = defense_int_input("Wybierz operacje: ", 1, 6)
return menu_selection
def load():
try:
with open("bin.dat", "rb") as f:
students_list = pickle.load(f)
return students_list
except Exception:
pass
def edycja_menu():
print("Co chcesz zrobic?")
print("1. Dodac ucznia")
print("2. Usunac ucznia")
print("3. Modyfikacja ocen")
odp = int(input())
return odp
def edycja_wybor(wybor):
if wybor == 1:
dodawanie_ucznia()
elif wybor == 2:
usuwanie_ucznia()
elif wybor == 3:
modyfikacja_ocen()
def dodawanie_ucznia():
print("Podaj imie:", end=' ')
imie = input()
print("Podaj nazwisko:", end=' ')
nazwisko = input()
print("Podaj index number:", end=' ')
numer = input()
oceny = []
print("Ile ocen chcesz dodac?")
x = int(input())
for i in range(x):
oceny.append(int(input(f"Podaj {i+1} ocene:")))
nowy = Student(imie, nazwisko, numer, oceny)
students_list.append(nowy)
def usuwanie_ucznia():
print("twoja lista:")
for student in students_list:
student.show()
print()
print("Usunac jednego ucznia czy wiecej? [j/w]")
x = input()
if x == "j":
print("podaj ktory element usunac:", end = " " )
a = int(input())
students_list.pop(a-1)
elif x == "w":
print("podaj zakres uczniow do usuniecia:")
a = int(input("od ktorego:"))
b = int(input("do ktorego:"))
del students_list[a-1:b-1]
def modyfikacja_ocen():
while 1:
print("Oceny którego ucznia chcesz modyfikowac?")
u = int(input())
print ("Oceny ucznia:")
students_list[u-1].show_rating()
students_list[u-1].change_grades()
print ("Czy chcesz kontynuować zmiany ocen? [t/n]")
x = input()
if x == "t":
pass
else:
break
def wyswietlanie_menu():
while 1:
try:
print("Na jakiej podstawie wybrac studentow?")
print("1. Średnia ocen większa niż")
print("2. Średnia ocen mniejsza niż")
print("3. Numer z listy")
wybor = int(input("Podaj opcje:"))
break
except Exception:
print("Mamy problem")
return wybor
def wyswietlanie(wybor):
if wybor == 1:
w_srednia_g()
elif wybor == 2:
w_srednia_d()
elif wybor == 3:
w_numer()
def w_srednia_g():
while 1:
try:
print("Podaj od jakiej sredniej w gore podac:")
x = float(input())
for student in students_list:
if student.avg_rating() >= x:
student.show()
break
except Exception:
print("Mamy problem")
def w_srednia_d():
while 1:
try:
print("Podaj od jakiej sredniej w dol podac:")
x = float(input())
for student in students_list:
if student.avg_rating() <= x:
student.show()
break
except Exception:
print("Mamy problem")
def w_numer():
print("Ilu studentow wyswietlic?")
x = int(input())
if x == 1:
print("Podaj numer z listy:")
a = int(input())
print(students_list[a-1])
elif x > 1:
for i in range (x):
students_list[int(input("Podaj numer:"))-1].show()
def save(students_list):
with open("bin.dat", "wb") as f:
pickle.dump(students_list, f)
print('Lista studentów.')
amount_of_students = 0
students_list = []
#student1 = Student("Rafał", "Nowak", 123456, [1, 2, 3, 4, 5])
#student2 = Student("Jan", "Kowalski", 234567, [2, 3, 4, 5])
#student3 = Student("Hanna", "Szymańska", 345678, [3, 4, 5])
#student4 = Student("Maja", "Jankowska", 456789, [4, 5])
#students_list.append(student1)
#students_list.append(student2)
#students_list.append(student3)
#students_list.append(student4)
#with open("bin.dat", "rb") as f:
# s_l = pickle.load(f)
# print(f"s_l: {s_l}")
# for s in s_l:
# s.show()
menu_selection = display_menu()
while(menu_selection < 6):
if menu_selection == 1:
print('\nLista studentów:\n')
for student in students_list:
student.show()
print()
elif menu_selection == 2:
wybor = edycja_menu()
edycja_wybor(wybor)
elif menu_selection == 3:
wybor = wyswietlanie_menu()
wyswietlanie(wybor)
elif menu_selection == 4:
students_list = load()
elif menu_selection == 5:
save(students_list)
print('Zapisano do pliku.')
menu_selection = display_menu() | [
"witekkardas@gmail.com"
] | witekkardas@gmail.com |
bbdfdc1027ab8f60ae59aaf33634cc9a982ed5b8 | 38f371eb007dd421ecd4dd55ed9e0e089b6d4480 | /FileHandler/HashFile.py | ed8aebe4be3834a88b0e84455f8aa40c516d8a0b | [] | no_license | robtoyota/file_db | f0be9eb1864bca914163c1008537a8a08a31fefd | 384a126e84adb8d565bbd3a007025982cebd223a | refs/heads/develop | 2022-11-28T12:09:52.039644 | 2022-09-06T19:52:46 | 2022-09-06T19:52:46 | 237,338,076 | 0 | 1 | null | 2022-11-22T10:46:31 | 2020-01-31T01:16:21 | Python | UTF-8 | Python | false | false | 1,439 | py | import hashlib
class HashFile:
@staticmethod
def hash_file(file_path: str, hash_types: list) -> dict:
# https://stackoverflow.com/a/22058673/4458445
buffer_size = 128 * 64 # https://stackoverflow.com/a/1131238/4458445
hash_output = {}
# Build the hash objects
if not isinstance(hash_types, list): # Make sure hash_types is an iterable list
hash_types = [hash_types]
# Loop through each of the hash types and build the hashlib objects
hashes = {}
for hash_type in hash_types:
if not isinstance(hash_type, str): # Make sure the hash type is a string
continue
hash_type = hash_type.upper()
if hash_type == 'MD5':
hashes['MD5'] = hashlib.md5()
elif hash_type.upper() == 'SHA1':
hashes['SHA1'] = hashlib.sha1()
try:
# Load the file in to get hashed
with open(file_path, 'rb') as f:
while True:
data = f.read(buffer_size)
if not data:
break
# Loop through each of the different hash types and update the hash with the new chunk
for hash_type, hash in hashes.items():
hash.update(data)
except (PermissionError, OSError):
# TODO: What to do when the hashing fails because the file is inaccessible?
pass
except FileNotFoundError:
# TODO: Delete the file from the DB if it is not found
pass
# Get the hash to be returned
for hash_type, hash in hashes.items():
hash_output[hash_type] = hash.hexdigest()
return hash_output
| [
"49793997+robtoyota@users.noreply.github.com"
] | 49793997+robtoyota@users.noreply.github.com |
29daaaaa525a4d1cd1a8740c918a25fc820f7475 | 7aa207bb4d6f66076bc0b2bb59dffdf6852167bf | /src/niweb/apps/userprofile/migrations/0003_userprofile_avatar.py | 7ccd3b4eaae6031d4f0caff3ebc913028584f3d6 | [
"BSD-2-Clause"
] | permissive | SUNET/ni | d0a9f496ab2980f15083e3527345dc689fdb838c | 56c5611e7fbd882fa56440f38384418f45535cee | refs/heads/master | 2023-08-28T13:39:29.511607 | 2023-06-12T19:05:31 | 2023-06-12T19:05:31 | 169,399,263 | 0 | 1 | NOASSERTION | 2023-06-12T19:05:32 | 2019-02-06T12:00:25 | Python | UTF-8 | Python | false | false | 470 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.26 on 2019-11-11 08:12
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('userprofile', '0002_auto_20191108_1258'),
]
operations = [
migrations.AddField(
model_name='userprofile',
name='avatar',
field=models.ImageField(null=True, upload_to=''),
),
]
| [
"enrique@cazalla.net"
] | enrique@cazalla.net |
c5416e6dd1cf97f29485ef6dcc297e53adb103b7 | a2b6bc9bdd2bdbe5871edb613065dd2397175cb3 | /算法小抄/打家劫舍/打家劫舍2.py | 86f4e208511f26cebd8d468f2cb207d132ffe41b | [] | no_license | Asunqingwen/LeetCode | ed8d2043a31f86e9e256123439388d7d223269be | b7c59c826bcd17cb1333571eb9f13f5c2b89b4ee | refs/heads/master | 2022-09-26T01:46:59.790316 | 2022-09-01T08:20:37 | 2022-09-01T08:20:37 | 95,668,066 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,410 | py | """
你是一个专业的小偷，计划偷窃沿街的房屋，每间房内都藏有一定的现金。这个地方所有的房屋都围成一圈，这意味着第一个房屋和最后一个房屋是紧挨着的。同时，相邻的房屋装有相互连通的防盗系统，如果两间相邻的房屋在同一晚上被小偷闯入，系统会自动报警。
给定一个代表每个房屋存放金额的非负整数数组，计算你在不触动警报装置的情况下，能够偷窃到的最高金额。
示例 1:
输入: [2,3,2]
输出: 3
解释: 你不能先偷窃 1 号房屋（金额 = 2），然后偷窃 3 号房屋（金额 = 2）, 因为他们是相邻的。
示例 2:
输入: [1,2,3,1]
输出: 4
解释: 你可以先偷窃 1 号房屋（金额 = 1），然后偷窃 3 号房屋（金额 = 3）。
偷窃到的最高金额 = 1 + 3 = 4 。
"""
from typing import List
class Solution:
def rob(self, nums: List[int]) -> int:
def robRange(start, end) -> int:
dp1, dp2 = 0, 0
for i in range(end, start - 1, -1):
dp2, dp1 = dp1, max(dp1, dp2 + nums[i])
return dp1
length = len(nums)
if length == 1:
return nums[0]
return max(robRange(0, length - 2), robRange(1, length - 1))
if __name__ == '__main__':
nums = [2, 3, 2]
sol = Solution()
result = sol.rob(nums)
print(result)
| [
"sqw123az@sina.com"
] | sqw123az@sina.com |
b9aaa554d2a6236ae0a218ae7feff738a9870d6b | de492613995ca0e3682d5b04a31f0603d6318f6c | /gletscher/test/test_config.py | b2c4d032cadc12a5d6fd6b78bcf95e07365bb001 | [
"Apache-2.0"
] | permissive | kenzik/gletscher | d0f11791d38a2af546a148e1980acf02ff1dda13 | 564921d6e98d2581c6e911267d77a5c453f17a7a | refs/heads/master | 2021-05-26T17:03:22.944693 | 2013-08-01T04:44:22 | 2013-08-01T04:44:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,376 | py | # Copyright 2013 Patrick Moor <patrick@moor.ws>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from gletscher.config import BackupConfiguration
class TestConfig(unittest.TestCase):
def test_create_new_and_then_read(self):
with tempfile.TemporaryDirectory() as dir:
config = BackupConfiguration.NewEmptyConfiguration(
dir, prompt_command=lambda *a, **b: "42")
self.assertEqual("42", config.aws_access_key())
self.assertEqual(42, config.aws_account_id())
self.assertEqual("42", config.aws_region())
self.assertEqual("42", config.aws_secret_access_key())
self.assertTrue(os.path.isdir(config.catalog_dir_location()))
self.assertTrue(os.path.isdir(config.tmp_dir_location()))
if __name__ == '__main__':
unittest.main()
| [
"patrick@moor.ws"
] | patrick@moor.ws |
99252c919df613b170478deccf524a3f1ddbee2c | 3d62a14eb69baea0737f8c093336cbf6380b30a7 | /dingtalk/api/rest/OapiEduSubDataGetRequest.py | 99501c9ebbb090accf9eaf4d2e957ecccaccf64e | [] | no_license | KangSpace/message_plus_server | 50cbc55c296d5e835a0c6f99f45cf699f6806add | 300954993f44648db3a2124d587533656d970d6c | refs/heads/main | 2023-05-01T15:08:18.195400 | 2021-05-27T04:41:56 | 2021-05-27T04:41:56 | 361,338,925 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 398 | py | '''
Created by auto_sdk on 2021.01.29
'''
from dingtalk.api.base import RestApi
class OapiEduSubDataGetRequest(RestApi):
def __init__(self,url=None):
RestApi.__init__(self,url)
self.orders = None
self.page_num = None
self.page_size = None
self.stat_date = None
def getHttpMethod(self):
return 'POST'
def getapiname(self):
return 'dingtalk.oapi.edu.sub.data.get'
| [
"kango2gler@gmail.com"
] | kango2gler@gmail.com |
6d52f200f92e18da2a2ecf76f522b0ec4c76e78a | 73da55ba59efa99b370ef1ec14ffabe3503a4398 | /acmd/html.py | 81cb011b7b77a2d07565b42f99f9de6923c49c63 | [
"MIT"
] | permissive | tml/aem-cmd | d8e1ead3f022735581ef07eb96eafbfedf48c6cd | 146ea689684cfd1e654234469cba338b38327965 | refs/heads/master | 2021-01-12T09:38:34.584843 | 2016-12-10T03:48:23 | 2016-12-10T03:48:23 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 589 | py | # coding: utf-8
from xml.dom import minidom
def _split(attr):
if '=' in attr:
ret = attr.split('=')
return ret[0], ret[1]
else:
return 'id', attr
def parse_value(src, node_name, attr):
attr_name, attr_val = _split(attr)
doc = minidom.parseString(src)
for elem in doc.getElementsByTagName(node_name):
if elem.attributes.get(attr_name) and \
elem.attributes.get(attr_name).value == attr_val:
return elem.childNodes[0].nodeValue
raise Exception("Failed to locate path in {}".format(attr_name))
| [
"bjorn.skoglund@valtech.com"
] | bjorn.skoglund@valtech.com |
544551ccc864c2395597827a7bce6aa8b7a10158 | 8acd61d043ea65f0697b7a445c704358776011ef | /graf_ec4.py | 00ed23aeb630321a3d0553a6781218a7c651f7c4 | [] | no_license | sj0delacruz/SamirdelaCruz_Ej29 | 6b16119cb5b408aebc0dcc458c87921246118d19 | ae13456dac9a3d40deae976d88b78cbe76d81e2e | refs/heads/master | 2020-05-20T02:25:43.016069 | 2019-05-07T12:19:25 | 2019-05-07T12:19:25 | 185,331,404 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 796 | py | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D, get_test_data
from matplotlib import cm
seleccion = np.loadtxt("dat_ec4.dat")
fig = plt.figure(figsize=(15,7))
ax = fig.add_subplot(1,2,1,projection='3d')
xx, yy = np.mgrid[0:seleccion.shape[0], 0:seleccion.shape[1]]
ax.plot_surface(xx/50, yy/100, seleccion ,rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
plt.ylabel("Posicion [metros]")
plt.xlabel("Tiempo [segundos]")
cueer = np.loadtxt("dat_ec4.dat")
x=np.linspace(0,1,cueer.shape[1])
ax = fig.add_subplot(1,2,2)
plt.plot(x,cueer[0],label="Tiempo inicial")
plt.plot(x,cueer[-1],label="Tiempo final")
plt.legend()
plt.xlabel("Posicion [metros]")
plt.ylabel("Desplazamiento [metros]")
plt.savefig("graf_ec4.png") | [
"noreply@github.com"
] | noreply@github.com |
c0eb26cac3d32b5a0930da88a5dfc825d69eb8b6 | f7a992ae6d178bf3aacd0f80a442ac2b918f6438 | /mystorage/migrations/0002_album_files.py | 46b692aea5e5be4ca8ddfa5db288dc77e6d6481d | [] | no_license | ev-moon/drf_api | 3f3d4baf4384916fd10486c0d2f2ce3bb10c6852 | 1138d00fd0622ffacf2f2d272b0d5872ab6f270d | refs/heads/master | 2020-08-30T06:57:47.901190 | 2019-10-29T13:50:52 | 2019-10-29T13:50:52 | 218,226,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,312 | py | # Generated by Django 2.2.6 on 2019-10-29 13:11
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('mystorage', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Files',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('myfile', models.FileField(upload_to='files')),
('desc', models.TextField(max_length=100)),
('author', models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Album',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(upload_to='images')),
('desc', models.TextField(max_length=100)),
('author', models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"xenos96111@gmail.com"
] | xenos96111@gmail.com |
2b258251be54698c7cf7f09390b9b22df7e3c439 | 53fab060fa262e5d5026e0807d93c75fb81e67b9 | /backup/user_089/ch151_2020_04_13_20_49_15_933281.py | d9c04612c3fb56edfc9d41e6d501ee71b350565e | [] | no_license | gabriellaec/desoft-analise-exercicios | b77c6999424c5ce7e44086a12589a0ad43d6adca | 01940ab0897aa6005764fc220b900e4d6161d36b | refs/heads/main | 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 469 | py | def classifica_lista(x):
resultado = []
if x == [] or len(x) < 2:
return "nenhum"
i = 0
while i < len(x):
if x[i] > x[i+1]:
i += 1
resultado.append('d')
if x[i] < x[i+1]:
i += 1
resultado.append("c")
if "d" and "c" in resultado:
return "nenhum"
if "d" not in resultado:
return "crescente"
if "c" not in resultado:
return "decrescente" | [
"you@example.com"
] | you@example.com |
2faec68eb4efbf907e7b3059435980c03b94e9c2 | 0d2da49c7ca251b70490dbd64e02dc5986c153c0 | /the-type-function.py | 86be258e82565a2dec096ed2c6f8642dd47cf31d | [] | no_license | dwaraka118/Python-in-detail | b1204191605088c71c841a3b4787a89d47d2dca5 | 278de79868baf083e6b5a5c77712409ff50e8162 | refs/heads/master | 2022-11-13T07:25:38.929977 | 2020-07-16T17:10:57 | 2020-07-16T17:10:57 | 272,138,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 446 | py | # part:1
print("This is part : 1 ")
print(type(5))
print(type(10))
print(type(3.8))
print(type(5.0))
print(type("computer"))
print(type("laptop"))
# Part : 2
print(type(5) == type(10))
print(type("computer") == type("laptop"))
print(type(5) == type(5.0))
print(type(5) == type("5"))
print(type(True))
print(type(False))
print(type(True) == type(False))
print(type([1, 2, 3]))
print(type({ "NJ": "Trenton" }))
| [
"noreply@github.com"
] | noreply@github.com |
a2c10fafce2e1c872e2aa04e6002371ee741200c | e519c5dba379c4d66df442dea230162841b75713 | /bubbleZ/tests/__init__.py | 9ffb24b64937999209c5b8df293866df28e74f9c | [
"MIT"
] | permissive | abatten/bubbleZ | af904df823819c548992ff8b4b38cb711891ac53 | 20a895f4510dabd03426c307b40bd12c61edd39a | refs/heads/main | 2023-06-02T06:44:58.911933 | 2021-06-12T05:29:14 | 2021-06-12T05:29:14 | 375,551,234 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 46 | py | from ..pipeline.utils import FileAlreadyExists | [
"adamjbatten@gmail.com"
] | adamjbatten@gmail.com |
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