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4a076eacb579e6f24f57706c9a29188eacb31618
| 2,028
|
py
|
Python
|
aliyun-python-sdk-oos/aliyunsdkoos/request/v20190601/ListInstanceStateReportsRequest.py
|
ankitdobhal/aliyun-openapi-python-sdk
|
991b1c2d91adc468480defc23ba790d4369cce7b
|
[
"Apache-2.0"
] | null | null | null |
aliyun-python-sdk-oos/aliyunsdkoos/request/v20190601/ListInstanceStateReportsRequest.py
|
ankitdobhal/aliyun-openapi-python-sdk
|
991b1c2d91adc468480defc23ba790d4369cce7b
|
[
"Apache-2.0"
] | null | null | null |
aliyun-python-sdk-oos/aliyunsdkoos/request/v20190601/ListInstanceStateReportsRequest.py
|
ankitdobhal/aliyun-openapi-python-sdk
|
991b1c2d91adc468480defc23ba790d4369cce7b
|
[
"Apache-2.0"
] | null | null | null |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
from aliyunsdkoos.endpoint import endpoint_data
class ListInstanceStateReportsRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'oos', '2019-06-01', 'ListInstanceStateReports','oos')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_InstanceId(self):
return self.get_query_params().get('InstanceId')
def set_InstanceId(self,InstanceId):
self.add_query_param('InstanceId',InstanceId)
def get_NextToken(self):
return self.get_query_params().get('NextToken')
def set_NextToken(self,NextToken):
self.add_query_param('NextToken',NextToken)
def get_MaxResults(self):
return self.get_query_params().get('MaxResults')
def set_MaxResults(self,MaxResults):
self.add_query_param('MaxResults',MaxResults)
def get_StateConfigurationId(self):
return self.get_query_params().get('StateConfigurationId')
def set_StateConfigurationId(self,StateConfigurationId):
self.add_query_param('StateConfigurationId',StateConfigurationId)
| 36.214286
| 83
| 0.773176
|
4a076f72fecc430b2c78fcb990b9f901fce49ddb
| 5,517
|
py
|
Python
|
conans/util/progress_bar.py
|
matthiasng/conan
|
634eadc319da928084633a344d42785edccb8d6c
|
[
"MIT"
] | 6,205
|
2015-12-01T13:40:05.000Z
|
2022-03-31T07:30:25.000Z
|
conans/util/progress_bar.py
|
matthiasng/conan
|
634eadc319da928084633a344d42785edccb8d6c
|
[
"MIT"
] | 8,747
|
2015-12-01T16:28:48.000Z
|
2022-03-31T23:34:53.000Z
|
conans/util/progress_bar.py
|
Mattlk13/conan
|
005fc53485557b0a570bb71670f2ca9c66082165
|
[
"MIT"
] | 961
|
2015-12-01T16:56:43.000Z
|
2022-03-31T13:50:52.000Z
|
import os
from contextlib import contextmanager
import time
from tqdm import tqdm
from conans.client.output import ConanOutput
TIMEOUT_BEAT_SECONDS = 30
TIMEOUT_BEAT_CHARACTER = '.'
LEFT_JUSTIFY_DESC = 28
LEFT_JUSTIFY_MESSAGE = 90
def left_justify_message(msg):
return msg.ljust(LEFT_JUSTIFY_MESSAGE)
def left_justify_description(msg):
return msg.ljust(LEFT_JUSTIFY_DESC)
class ProgressOutput(ConanOutput):
def __init__(self, output):
super(ProgressOutput, self).__init__(output._stream, output._stream_err, output._color)
def _write(self, data, newline=False):
end = "\n" if newline else ""
tqdm.write(str(data), file=self._stream, end=end)
def _write_err(self, data, newline=False):
end = "\n" if newline else ""
tqdm.write(str(data), file=self._stream_err, end=end)
class Progress(object):
def __init__(self, length, output, description, post_description=None):
self._tqdm_bar = None
self._total_length = length
self._output = output
self._processed_size = 0
self._description = description
self._post_description = "{} completed".format(
self._description) if not post_description else post_description
self._last_time = time.time()
if self._output and self._output.is_terminal and self._description:
self._tqdm_bar = tqdm(total=self._total_length,
desc=left_justify_description(self._description),
file=self._output, unit="B", leave=False, dynamic_ncols=False,
ascii=True, unit_scale=True, unit_divisor=1024)
def initial_value(self, value):
self._processed_size = value
self._pb_update(value)
def _pb_update(self, chunk_size):
if self._tqdm_bar is not None:
self._tqdm_bar.update(chunk_size)
elif self._output and time.time() - self._last_time > TIMEOUT_BEAT_SECONDS:
self._last_time = time.time()
self._output.write(TIMEOUT_BEAT_CHARACTER)
def update(self, chunks):
for chunk in chunks:
yield chunk
data_size = len(chunk)
self._processed_size += data_size
self._pb_update(data_size)
if self._total_length > self._processed_size:
self._pb_update(self._total_length - self._processed_size)
self.pb_close()
def pb_close(self):
if self._tqdm_bar is not None:
self._tqdm_bar.close()
msg = "\r{} [{:1.2f}k]".format(self._post_description, self._processed_size / 1024.0)
tqdm.write(left_justify_message(msg), file=self._output, end="\n")
class FileWrapper(Progress):
def __init__(self, fileobj, output, description, post_description=None):
self._fileobj = fileobj
self.seek(0, os.SEEK_END)
super(FileWrapper, self).__init__(self.tell(), output, description, post_description)
self.seek(0)
def seekable(self):
return self._fileobj.seekable()
def seek(self, *args, **kwargs):
return self._fileobj.seek(*args, **kwargs)
def tell(self):
return self._fileobj.tell()
def read(self, size):
prev = self.tell()
ret = self._fileobj.read(size)
self._pb_update(self.tell() - prev)
return ret
class ListWrapper(object):
def __init__(self, files_list, output, description, post_description=None):
self._files_list = files_list
self._total_length = len(self._files_list)
self._iterator = iter(self._files_list)
self._last_progress = None
self._i_file = 0
self._output = output
self._description = description
self._post_description = "{} completed".format(
self._description) if not post_description else post_description
self._last_time = time.time()
if self._output and self._output.is_terminal:
self._tqdm_bar = tqdm(total=len(files_list),
desc=left_justify_description(self._description),
file=self._output, unit="files ", leave=False, dynamic_ncols=False,
ascii=True)
def update(self):
self._i_file = self._i_file + 1
if self._output and self._output.is_terminal:
self._tqdm_bar.update()
elif self._output and time.time() - self._last_time > TIMEOUT_BEAT_SECONDS:
self._last_time = time.time()
self._output.write(TIMEOUT_BEAT_CHARACTER)
def pb_close(self):
if self._output and self._output.is_terminal:
self._tqdm_bar.close()
msg = "\r{} [{} files]".format(self._post_description, self._total_length)
tqdm.write(left_justify_message(msg), file=self._output, end="\n")
def __iter__(self):
return self
def __next__(self):
val = next(self._iterator)
self.update()
return val
def next(self):
return self.__next__()
@contextmanager
def open_binary(path, output, description):
with open(path, mode='rb') as file_handler:
file_wrapped = FileWrapper(file_handler, output, description)
yield file_wrapped
file_wrapped.pb_close()
@contextmanager
def iterate_list_with_progress(files_list, output, description):
list_wrapped = ListWrapper(files_list, output, description)
yield list_wrapped
list_wrapped.pb_close()
| 34.055556
| 101
| 0.646185
|
4a0770b2d1cd1a1a587f80e26d9c659bc4a20bad
| 5,022
|
py
|
Python
|
srv6_sdn_control_plane/southbound/netconf/sb_netconf_client.py
|
everywan-io/srv6-sdn-control-plane
|
afb7ce82571c852f784b763b8dec766b75f350fd
|
[
"Apache-2.0"
] | null | null | null |
srv6_sdn_control_plane/southbound/netconf/sb_netconf_client.py
|
everywan-io/srv6-sdn-control-plane
|
afb7ce82571c852f784b763b8dec766b75f350fd
|
[
"Apache-2.0"
] | null | null | null |
srv6_sdn_control_plane/southbound/netconf/sb_netconf_client.py
|
everywan-io/srv6-sdn-control-plane
|
afb7ce82571c852f784b763b8dec766b75f350fd
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python
import time
from netconf.client import NetconfSSHSession
try:
from lxml import etree
except ImportError:
from xml.etree import ElementTree as etree
# Utility to close Netconf sessions
def close_netconf_session(session):
# Let's take the reference of the transport
transport = session.pkt_stream.stream
# Let's close the Netconf session
session.close()
# This is a workaround for RST_ACK
time.sleep(0.05)
# Close the transport
transport.close()
# Flush the cache
transport.cache.flush()
# Let's create a NetConf session
session = NetconfSSHSession("127.0.0.1", 830, "srv6", "srv6")
# From the hello, we got the capabilities
for capability in session.capabilities:
print(capability)
config = """
<edit-config>
<target>
<running/>
</target>
<default-operation>none</default-operation>
<test-option>test-then-set</test-option>
<error-option>rollback-on-error</error-option>
<config xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<srv6-explicit-path operation="create" xmlns="urn:ietf:params:xml:ns:yang:srv6-explicit-path">
<path>
<destination>1111:4::2/128</destination>
<sr-path>
<srv6-segment>1111:3::2</srv6-segment>
</sr-path>
<encapmode>inline</encapmode>
<device>eth0</device>
</path>
</srv6-explicit-path>
</config>
</edit-config>
"""
# Single add
result = session.send_rpc(config)
print(format(etree.tostring(result[0], pretty_print=True)))
config = """
<edit-config>
<target>
<running/>
</target>
<default-operation>none</default-operation>
<test-option>test-then-set</test-option>
<error-option>rollback-on-error</error-option>
<config xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<srv6-explicit-path operation="create" xmlns="urn:ietf:params:xml:ns:yang:srv6-explicit-path">
<path>
<destination>2222:4::2/128</destination>
<sr-path>
<srv6-segment>2222:3::2</srv6-segment>
</sr-path>
<encapmode>inline</encapmode>
<device>eth0</device>
</path>
<path>
<destination>3333:4::2/128</destination>
<sr-path>
<srv6-segment>3333:3::2</srv6-segment>
<srv6-segment>3333:2::2</srv6-segment>
<srv6-segment>3333:1::2</srv6-segment>
</sr-path>
<encapmode>encap</encapmode>
<device>eth0</device>
</path>
</srv6-explicit-path>
</config>
</edit-config>
"""
# Bulk add
result = session.send_rpc(config)
print(format(etree.tostring(result[0], pretty_print=True)))
# Close the session
close_netconf_session(session)
# Delete all the routes created before
configs = [
"""
<edit-config>
<target>
<running/>
</target>
<default-operation>none</default-operation>
<test-option>test-then-set</test-option>
<error-option>rollback-on-error</error-option>
<config xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<srv6-explicit-path operation="remove" xmlns="urn:ietf:params:xml:ns:yang:srv6-explicit-path">
<path>
<destination>1111:4::2/128</destination>
<sr-path>
<srv6-segment>1111:3::2</srv6-segment>
</sr-path>
<encapmode>inline</encapmode>
<device>eth0</device>
</path>
</srv6-explicit-path>
</config>
</edit-config>
""",
"""
<edit-config>
<target>
<running/>
</target>
<default-operation>none</default-operation>
<test-option>test-then-set</test-option>
<error-option>rollback-on-error</error-option>
<config xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<srv6-explicit-path operation="remove" xmlns="urn:ietf:params:xml:ns:yang:srv6-explicit-path">
<path>
<destination>2222:4::2/128</destination>
<sr-path>
<srv6-segment>2222:3::2</srv6-segment>
</sr-path>
<encapmode>inline</encapmode>
<device>eth0</device>
</path>
</srv6-explicit-path>
</config>
</edit-config>
""",
"""
<edit-config>
<target>
<running/>
</target>
<default-operation>none</default-operation>
<test-option>test-then-set</test-option>
<error-option>rollback-on-error</error-option>
<config xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<srv6-explicit-path operation="remove" xmlns="urn:ietf:params:xml:ns:yang:srv6-explicit-path">
<path>
<destination>3333:4::2/128</destination>
<sr-path>
<srv6-segment>3333:3::2</srv6-segment>
<srv6-segment>3333:2::2</srv6-segment>
<srv6-segment>3333:1::2</srv6-segment>
</sr-path>
<encapmode>encap</encapmode>
<device>eth0</device>
</path>
</srv6-explicit-path>
</config>
</edit-config>
""",
]
# Iterate over the array and delete one by one all the paths
for config in configs:
# Each time we create a new session
session = NetconfSSHSession("127.0.0.1", 830, "srv6", "srv6")
result = session.send_rpc(config)
print(format(etree.tostring(result[0], pretty_print=True)))
close_netconf_session(session)
| 28.697143
| 96
| 0.651334
|
4a07716323b1294a7acf972df50274b9ae162e07
| 4,449
|
py
|
Python
|
src/spider/spider/plugins/ouest_france_immo.py
|
asteroide/immo_spider
|
864828c389173f6d6417392983bc8d39b5fd4ea2
|
[
"Apache-2.0"
] | null | null | null |
src/spider/spider/plugins/ouest_france_immo.py
|
asteroide/immo_spider
|
864828c389173f6d6417392983bc8d39b5fd4ea2
|
[
"Apache-2.0"
] | null | null | null |
src/spider/spider/plugins/ouest_france_immo.py
|
asteroide/immo_spider
|
864828c389173f6d6417392983bc8d39b5fd4ea2
|
[
"Apache-2.0"
] | null | null | null |
from lxml import html # nosec
from io import StringIO
import requests
import logging
import hashlib
logger = logging.getLogger("spider.ofi")
__url__ = "https://www.ouestfrance-immo.com/"
__urls__ = [
"https://www.ouestfrance-immo.com/acheter/maison/?lieux=24303&rayon=30&prix=0_200000",
"https://www.ouestfrance-immo.com/acheter/maison/?lieux=24163&rayon=30&prix=0_200000",
"https://www.ouestfrance-immo.com/acheter/maison/dinard-35-35800/?prix=0_200000",
"https://www.ouestfrance-immo.com/acheter/maison/lamballe-22-22400/?prix=0_200000",
"https://www.ouestfrance-immo.com/acheter/maison/guerande-44-44350/?prix=0_200000"
]
# https://www.ouestfrance-immo.com/acheter/maison/vannes-56-56000/?prix=50000_80000&surface=60_0&chambres=3_0
# return_exemple = [
# {
# 'address': "",
# "description": "",
# "price": "",
# "date": "",
# "size": "",
# "groundsurface": "",
# "extra": {}
# }
# ]
class ofi(object):
# data_template = {
# "address": "",
# "description": "",
# "price": "",
# "date": "",
# "surface": "",
# "groundsurface": "",
# "url": [],
# "photos": [],
# "extra": {},
# }
def compute_ad(self, url):
url = "https://www.ouestfrance-immo.com" + url
xml_str = StringIO(requests.get(url, verify=True).text)
tree = html.parse(xml_str)
description = " ".join(tree.xpath('/html/body/div/section/div/div/div[@class=\'txtAnn\']/text()'))
_id = hashlib.sha1(description.encode('utf-8')).hexdigest()
price = " ".join(tree.xpath('/html/body/div/section/div/div/strong[@itemprop="price"]/text()'))
price = price.replace('€', "").strip().replace(" ", "")
price = int(price)
address = "".join(tree.xpath('/html/body/div/section/div/div/h2[@id="caractDetail"]/text()')).replace("Vente maison", "").strip()
ground_surface = " ".join(tree.xpath('/html/body/div/section/div/div/div/ul/li[text()="Surf. terrain : "]/strong/text()')).replace(" ", "")
try:
ground_surface = int(ground_surface.replace("m²", ""))
except ValueError:
ground_surface = 0
options = " ".join(tree.xpath('/html/body/div/section/div/div/div/ul/li[@class="options"]/text()'))
surface = " ".join(tree.xpath('/html/body/div/section/div/div/div/ul/li[text()="Surf. habitable : "]/strong/text()')).replace(" ", "")
try:
surface = int(surface.replace("m²", ""))
except ValueError:
surface = 0
date = " ".join(tree.xpath('/html/body/div/section/div/h2/em/text()')).replace(" ", "").split("-")[-1].strip()
img_urls = map(lambda x: x.get("src"), tree.xpath('//ul/li/img'))
img_urls = list(filter(lambda x: "photo" in x, img_urls))
return {
'id': _id,
'address': address,
"description": description,
"price": price,
"date": date,
"surface": surface,
"groundsurface": ground_surface,
"url": url,
"img_urls": img_urls,
"show": True,
"extra": {
"options": options
},
}
def compute(self):
ads = []
for url in __urls__:
xml_str = StringIO(requests.get(url, verify=True).text)
tree = html.parse(xml_str)
for _a in tree.xpath('/html/body/div/section/div/div/ul/li/div/a[@class="txt lienDetail"]'):
# logger.debug(_a.get("href"))
_ad = self.compute_ad(_a.get("href"))
ads.append(_ad)
# logger.debug("ad = {}".format(_ad))
# addresses = tree.xpath('/html/body//h2/a/span/text()')
#
# for _address in addresses:
# _dict = {
# 'address': _address.encode("utf-8"),
# "description": "",
# "price": "",
# "date": "",
# "surface": "",
# "groundsurface": "",
# "url": url,
# "extra": {},
# }
# logger.info("addresses={}".format(_address.encode("utf-8")))
# ads.append(_dict)
return ads
__driver__ = ofi()
| 37.70339
| 147
| 0.5118
|
4a07717ec896a0d570840cab9c21ff6a53ef7923
| 2,508
|
py
|
Python
|
tests/performance/runs/taurus/__init__.py
|
dhanainme/multi-model-server
|
cd5a693032b1bec4c46b0f7a9844df496a62c1a8
|
[
"Apache-2.0"
] | 527
|
2017-12-04T20:58:19.000Z
|
2019-11-14T03:15:39.000Z
|
tests/performance/runs/taurus/__init__.py
|
dhanainme/multi-model-server
|
cd5a693032b1bec4c46b0f7a9844df496a62c1a8
|
[
"Apache-2.0"
] | 303
|
2017-12-05T06:14:08.000Z
|
2019-11-16T01:35:15.000Z
|
tests/performance/runs/taurus/__init__.py
|
dhanainme/multi-model-server
|
cd5a693032b1bec4c46b0f7a9844df496a62c1a8
|
[
"Apache-2.0"
] | 144
|
2017-12-05T19:27:39.000Z
|
2019-11-15T22:15:50.000Z
|
#!/usr/bin/env python
# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
# http://www.apache.org/licenses/LICENSE-2.0
# or in the "license" file accompanying this file. This file 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.
"""
Convert the Taurus Test suite XML to Junit XML
"""
# pylint: disable=redefined-builtin
import glob
import shutil
import os
from .reader import get_mon_metrics_list
def get_taurus_options(artifacts_dir, jmeter_path=None):
"""The options for Taurus BZT command"""
options = []
if jmeter_path:
options.append('-o modules.jmeter.path={}'.format(jmeter_path))
options.append('-o settings.artifacts-dir={}'.format(artifacts_dir))
options.append('-o modules.console.disable=true')
options.append('-o settings.env.BASEDIR={}'.format(artifacts_dir))
options_str = ' '.join(options)
return options_str
def update_taurus_metric_files(suite_artifacts_dir, test_file):
"""
It renames the server and local metric monitoring log files to metrics.csv.
The order of the columns in header of server metric monitoring SALogs file generated by taurus
is not inline with data. So as a work around this function rewrites the header based on order
defined in the test yaml.
"""
metrics_new_file = os.path.join(suite_artifacts_dir, "metrics.csv")
server_metric_file_pattern = os.path.join(suite_artifacts_dir, "SAlogs_*")
metrics_log_file = glob.glob(server_metric_file_pattern)
if metrics_log_file:
metrics = get_mon_metrics_list(test_file)
if metrics:
with open(metrics_log_file[0]) as from_file:
line = from_file.readline()
with open(metrics_log_file[0], mode="w") as to_file:
to_file.write(','.join(line.split(',')[0:1] + metrics) + "\n")
shutil.copyfileobj(from_file, to_file)
os.rename(metrics_log_file[0], metrics_new_file)
else:
metrics_log_file = os.path.join(suite_artifacts_dir, "local_monitoring_logs.csv")
if os.path.exists(metrics_log_file):
os.rename(metrics_log_file, metrics_new_file)
| 39.1875
| 98
| 0.710128
|
4a0774885da3e013efa2d9bf2e2c55be546c741a
| 40,353
|
py
|
Python
|
DCLS/construct/modules/Dcls.py
|
K-H-Ismail/Dilated-Convolution-with-Learnable-Spacings-PyTorch
|
4714eddbd007f36930938ee53a172abcd46febfb
|
[
"MIT"
] | 13
|
2021-12-09T01:24:56.000Z
|
2022-03-21T10:31:33.000Z
|
DCLS/construct/modules/Dcls.py
|
K-H-Ismail/Dilated-Convolution-with-Learnable-Spacings-PyTorch
|
4714eddbd007f36930938ee53a172abcd46febfb
|
[
"MIT"
] | null | null | null |
DCLS/construct/modules/Dcls.py
|
K-H-Ismail/Dilated-Convolution-with-Learnable-Spacings-PyTorch
|
4714eddbd007f36930938ee53a172abcd46febfb
|
[
"MIT"
] | 1
|
2022-02-12T06:26:57.000Z
|
2022-02-12T06:26:57.000Z
|
# coding=utf-8
import math
import warnings
import torch
from torch import Tensor
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import DCLS
import DCLS.construct.functions.dcls_functionnal as SD
#import DCLS.construct.functions.swc_functionnal as SW
from torch.nn import init
from torch.nn.modules import Module
from torch.nn.modules.utils import _single, _pair, _triple, _reverse_repeat_tuple
from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
from typing import Optional, List, Tuple
import operator
import functools
try:
from depthwise_conv2d_implicit_gemm import _DepthWiseConv2dImplicitGEMMFP32, _DepthWiseConv2dImplicitGEMMFP16
except ImportError as error:
# Output expected ImportErrors.
Logging.log_exception(error)
# Include the name and path attributes in output.
Logging.log(f'error.name: {error.name}')
Logging.log(f'error.path: {error.path}')
Logging.log('switching to native conv2d')
except Exception as exception:
# Output unexpected Exceptions.
Logging.log_exception(exception, False)
Logging.log('switching to native conv2d')
convolution_notes = \
{"groups_note": r"""* :attr:`groups` controls the connections between inputs and outputs.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. For example,
* At groups=1, all inputs are convolved to all outputs.
* At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels
and producing half the output channels, and both subsequently
concatenated.
* At groups= :attr:`in_channels`, each input channel is convolved with
its own set of filters (of size
:math:`\frac{\text{out\_channels}}{\text{in\_channels}}`).""",
"depthwise_separable_note": r"""When `groups == in_channels` and `out_channels == K * in_channels`,
where `K` is a positive integer, this operation is also known as a "depthwise convolution".
In other words, for an input of size :math:`(N, C_{in}, L_{in})`,
a depthwise convolution with a depthwise multiplier `K` can be performed with the arguments
:math:`(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in})`."""} # noqa: B950
class _DclsNd(Module):
__constants__ = ['stride', 'padding', 'dilated_kernel_size', 'groups',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_count', 'scaling']
__annotations__ = {'bias': Optional[torch.Tensor]}
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
...
_in_channels: int
out_channels: int
kernel_count: int
stride: Tuple[int, ...]
padding: Tuple[int, ...]
dilated_kernel_size: Tuple[int, ...]
transposed: bool
output_padding: Tuple[int, ...]
groups: int
padding_mode: str
weight: Tensor
bias: Optional[Tensor]
scaling: float
def __init__(self,
in_channels: int,
out_channels: int,
kernel_count: int,
stride: Tuple[int, ...],
padding: Tuple[int, ...],
dilated_kernel_size: Tuple[int, ...],
transposed: bool,
output_padding: Tuple[int, ...],
groups: int,
bias: bool,
padding_mode: str,
scaling: float) -> None:
super(_DclsNd, self).__init__()
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
valid_padding_modes = {'zeros', 'reflect', 'replicate', 'circular'}
if padding_mode not in valid_padding_modes:
raise ValueError("padding_mode must be one of {}, but got padding_mode='{}'".format(
valid_padding_modes, padding_mode))
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_count = kernel_count
self.stride = stride
self.padding = padding
self.dilated_kernel_size = dilated_kernel_size
self.transposed = transposed
self.output_padding = output_padding
self.groups = groups
self.scaling = scaling
self.padding_mode = padding_mode
# `_reversed_padding_repeated_twice` is the padding to be passed to
# `F.pad` if needed (e.g., for non-zero padding types that are
# implemented as two ops: padding + conv). `F.pad` accepts paddings in
# reverse order than the dimension.
self._reversed_padding_repeated_twice = _reverse_repeat_tuple(self.padding, 2)
if transposed:
self.weight = Parameter(torch.Tensor(
in_channels, out_channels // groups, kernel_count))
else:
self.weight = Parameter(torch.Tensor(
out_channels, in_channels // groups, kernel_count))
if bias:
self.bias = Parameter(torch.empty(out_channels))
else:
self.register_parameter('bias', None)
self.P = Parameter(torch.Tensor(len(dilated_kernel_size), out_channels, in_channels // groups, kernel_count))
self.reset_parameters()
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
for i in range(len(self.dilated_kernel_size)):
lim = self.dilated_kernel_size[i] // 2
with torch.no_grad():
init.normal_(self.P.select(0,i), 0, 0.5).clamp(-lim, lim).div_(self.scaling)
def clamp_parameters(self) -> None:
for i in range(len(self.dilated_kernel_size)):
with torch.no_grad():
lim = self.dilated_kernel_size[i] // 2
self.P.select(0,i).clamp_(-lim, lim)
def extra_repr(self):
s = ('{in_channels}, {out_channels}, kernel_count={kernel_count} (previous kernel_size)'
', stride={stride}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilated_kernel_size != (1,) * len(self.dilated_kernel_size):
s += ', dilated_kernel_size={dilated_kernel_size} (learnable)'
if self.scaling != 1.0:
s += ', scaling={scaling} (applied scaling)'
if self.output_padding != (0,) * len(self.output_padding):
s += ', output_padding={output_padding}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
if self.padding_mode != 'zeros':
s += ', padding_mode={padding_mode}'
if (self.in_channels == self.out_channels == self.groups
and self.padding[0] == self.dilated_kernel_size[0] // 2):
s += ', (using DepthWiseConv2dImplicitGEMMFP32)'
return s.format(**self.__dict__)
def __setstate__(self, state):
super(_DclsNd, self).__setstate__(state)
if not hasattr(self, 'padding_mode'):
self.padding_mode = 'zeros'
class _DclsN_Md(Module):
__constants__ = ['dim_dilation', 'stride', 'padding', 'dilated_kernel_size', 'groups',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_count', 'scaling']
__annotations__ = {'bias': Optional[torch.Tensor]}
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
...
_in_channels: int
out_channels: int
kernel_count: int
stride: Tuple[int, ...]
padding: Tuple[int, ...]
dilated_kernel_size: Tuple[int, ...]
dim_dilation: Tuple[int, ...]
transposed: bool
output_padding: Tuple[int, ...]
groups: int
padding_mode: str
weight: Tensor
bias: Optional[Tensor]
scaling: float
def __init__(self,
in_channels: int,
out_channels: int,
kernel_count: int,
stride: Tuple[int, ...],
padding: Tuple[int, ...],
dilated_kernel_size: Tuple[int, ...],
dim_dilation: Tuple[int, ...],
transposed: bool,
output_padding: Tuple[int, ...],
groups: int,
bias: bool,
padding_mode: str,
scaling: float) -> None:
super(_DclsN_Md, self).__init__()
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
valid_padding_modes = {'zeros', 'reflect', 'replicate', 'circular'}
if padding_mode not in valid_padding_modes:
raise ValueError("padding_mode must be one of {}, but got padding_mode='{}'".format(
valid_padding_modes, padding_mode))
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_count = kernel_count
self.stride = stride
self.padding = padding
self.dilated_kernel_size = dilated_kernel_size
self.dim_dilation = dim_dilation
self.transposed = transposed
self.output_padding = output_padding
self.groups = groups
self.scaling = scaling
self.padding_mode = padding_mode
# `_reversed_padding_repeated_twice` is the padding to be passed to
# `F.pad` if needed (e.g., for non-zero padding types that are
# implemented as two ops: padding + conv). `F.pad` accepts paddings in
# reverse order than the dimension.
self._reversed_padding_repeated_twice = _reverse_repeat_tuple(self.padding, 2)
if transposed:
self.weight = Parameter(torch.Tensor(
in_channels, out_channels // groups, *kernel_size))
else:
self.weight = Parameter(torch.Tensor(
out_channels, in_channels // groups, *kernel_size))
if bias:
self.bias = Parameter(torch.empty(out_channels))
else:
self.register_parameter('bias', None)
self.P = Parameter(torch.Tensor(len(dim_dilation), out_channels, in_channels // groups, *kernel_size))
self.reset_parameters()
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
for i in range(len(self.dim_dilation)):
lim = self.kernel_size[i] // 2
with torch.no_grad():
init.normal_(self.P.select(0,i), 0, 0.5).clamp(-lim, lim).div_(scaling)
def extra_repr(self):
s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1,) * len(self.dilation):
s += ', dilation_max={dilation} (learnable along {dim_dilation})'
if self.gain != 1.0:
s += ', gain={gain} (an extra multiplicative factor is applied to scaling)'
if self.output_padding != (0,) * len(self.output_padding):
s += ', output_padding={output_padding}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
if self.padding_mode != 'zeros':
s += ', padding_mode={padding_mode}'
return s.format(**self.__dict__)
def __setstate__(self, state):
super(_DclsN_Md, self).__setstate__(state)
if not hasattr(self, 'padding_mode'):
self.padding_mode = 'zeros'
class Dcls1d(_DclsNd):
__doc__ = r"""Applies a 1D convolution over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size
:math:`(N, C_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be
precisely described as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k)
\star \text{input}(N_i, k)
where :math:`\star` is the valid `cross-correlation`_ operator,
:math:`N` is a batch size, :math:`C` denotes a number of channels,
:math:`L` is a length of signal sequence.
""" + r"""
This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
* :attr:`stride` controls the stride for the cross-correlation, a single
number or a one-element tuple.
* :attr:`padding` controls the amount of implicit padding on both sides
for :attr:`padding` number of points.
* :attr:`dilation` controls the spacing between the kernel points; also
known as the à trous algorithm. It is harder to describe, but this `link`_
has a nice visualization of what :attr:`dilation` does.
{groups_note}
Note:
{depthwise_separable_note}
Note:
{cudnn_reproducibility_note}
Args:
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the convolving kernel
stride (int or tuple, optional): Stride of the convolution. Default: 1
padding (int or tuple, optional): Zero-padding added to both sides of
the input. Default: 0
padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
``'replicate'`` or ``'circular'``. Default: ``'zeros'``
dilation (int or tuple, optional): Spacing between kernel
elements. Default: 1
groups (int, optional): Number of blocked connections from input
channels to output channels. Default: 1
bias (bool, optional): If ``True``, adds a learnable bias to the
output. Default: ``True``
""" + r"""
Shape:
- Input: :math:`(N, C_{in}, L_{in})`
- Output: :math:`(N, C_{out}, L_{out})` where
.. math::
L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation}
\times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels},
\frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`
bias (Tensor): the learnable bias of the module of shape
(out_channels). If :attr:`bias` is ``True``, then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`
Examples::
>>> m = nn.Conv1d(16, 33, 3, stride=2)
>>> input = torch.randn(20, 16, 50)
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_count: int,
stride: _size_1_t = 1,
padding: _size_1_t = 0,
dilated_kernel_size: _size_1_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
scaling: float = 1.0
):
# we create new variables below to make mypy happy since kernel_size has
# type Union[int, Tuple[int]] and kernel_size_ has type Tuple[int]
stride_ = _single(stride)
padding_ = _single(padding)
dilated_kernel_size_ = _single(dilated_kernel_size)
super(Dcls1d, self).__init__(
in_channels, out_channels, kernel_count, stride_, padding_, dilated_kernel_size_,
False, _single(0), groups, bias, padding_mode, scaling)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor], P: Tensor):
if self.padding_mode != 'zeros':
return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
SD.ConstructKernel1d.apply(weight, P, self.dilated_kernel_size, self.scaling), bias, self.stride,
_single(0), _single(1), self.groups)
return F.conv1d(input, SD.ConstructKernel1d.apply(weight, P, self.dilated_kernel_size, self.scaling), bias, self.stride,
self.padding, _single(1), self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias, self.P.select(0,0))
class Dcls2d(_DclsNd):
__doc__ = r"""Applies a 2D convolution over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size
:math:`(N, C_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})`
can be precisely described as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)
where :math:`\star` is the valid 2D `cross-correlation`_ operator,
:math:`N` is a batch size, :math:`C` denotes a number of channels,
:math:`H` is a height of input planes in pixels, and :math:`W` is
width in pixels.
""" + r"""
This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
* :attr:`stride` controls the stride for the cross-correlation, a single
number or a tuple.
* :attr:`padding` controls the amount of implicit padding on both
sides for :attr:`padding` number of points for each dimension.
* :attr:`dilation` controls the spacing between the kernel points; also
known as the à trous algorithm. It is harder to describe, but this `link`_
has a nice visualization of what :attr:`dilation` does.
{groups_note}
The parameters :attr:`kernel_count`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:
- a single ``int`` -- in which case the same value is used for the height and width dimension
- a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,
and the second `int` for the width dimension
Note:
{depthwise_separable_note}
Note:
{cudnn_reproducibility_note}
Args:
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_count (int): Number of elements in the convolving kernel
stride (int or tuple, optional): Stride of the convolution. Default: 1
padding (int or tuple, optional): Zero-padding added to both sides of
the input. Default: 0
padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
``'replicate'`` or ``'circular'``. Default: ``'zeros'``
dilated_kernel_size (int or tuple, optional): Size of dilated kernel. Default: 1
groups (int, optional): Number of blocked connections from input
channels to output channels. Default: 1
bias (bool, optional): If ``True``, adds a learnable bias to the
output. Default: ``True``
""" + r"""
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape
(out_channels). If :attr:`bias` is ``True``,
then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
Examples:
>>> # With square kernels and equal stride
>>> m = nn.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_count: int,
stride: _size_2_t = 1,
padding: _size_2_t = 0,
dilated_kernel_size: _size_2_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
scaling: float = 1.0
):
stride_ = _pair(stride)
padding_ = _pair(padding)
dilated_kernel_size_ = _pair(dilated_kernel_size)
super(Dcls2d, self).__init__(
in_channels, out_channels, kernel_count, stride_, padding_, dilated_kernel_size_,
False, _pair(0), groups, bias, padding_mode, scaling)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor], P1: Tensor, P2: Tensor):
if (self.in_channels == self.out_channels == self.groups
and self.padding[0] == self.dilated_kernel_size[0] // 2):
if input.dtype == torch.float32:
x = _DepthWiseConv2dImplicitGEMMFP32.apply(
input, SD.ConstructKernel2d.apply(weight, P1, P2, self.dilated_kernel_size, self.scaling))
elif x.dtype == torch.float16:
x = _DepthWiseConv2dImplicitGEMMFP16.apply(
input, SD.ConstructKernel2d.apply(weight, P1, P2, self.dilated_kernel_size, self.scaling))
else:
raise TypeError("Only support fp32 and fp16, get {}".format(x.dtype))
if self.bias is not None:
x = x + self.bias.to(x).view(1, -1, 1, 1)
return x
else:
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
SD.ConstructKernel2d.apply(weight, P1, P2, self.dilated_kernel_size, self.scaling), bias,
self.stride, _pair(0), _pair(1), self.groups)
return F.conv2d(input, SD.ConstructKernel2d.apply(weight, P1, P2, self.dilated_kernel_size, self.scaling), bias,
self.stride, self.padding, _pair(1), self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias, self.P.select(0,0), self.P.select(0,1));
class Dcls3d(_DclsNd):
__doc__ = r"""Applies a 3D convolution over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size :math:`(N, C_{in}, D, H, W)`
and output :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` can be precisely described as:
.. math::
out(N_i, C_{out_j}) = bias(C_{out_j}) +
\sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)
where :math:`\star` is the valid 3D `cross-correlation`_ operator
""" + r"""
This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
* :attr:`stride` controls the stride for the cross-correlation.
* :attr:`padding` controls the amount of implicit padding on both
sides for :attr:`padding` number of points for each dimension.
* :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm.
It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does.
{groups_note}
The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:
- a single ``int`` -- in which case the same value is used for the depth, height and width dimension
- a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
the second `int` for the height dimension and the third `int` for the width dimension
Note:
{depthwise_separable_note}
Note:
{cudnn_reproducibility_note}
Args:
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the convolving kernel
stride (int or tuple, optional): Stride of the convolution. Default: 1
padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0
padding_mode (string, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
""" + r"""
Shape:
- Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where
.. math::
D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2]
\times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``,
then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
Examples::
>>> # With square kernels and equal stride
>>> m = nn.Conv3d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(4, 2, 0))
>>> input = torch.randn(20, 16, 10, 50, 100)
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_count: int,
stride: _size_3_t = 1,
padding: _size_3_t = 0,
dilated_kernel_size: _size_3_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
scaling: float = 1.0
):
stride_ = _triple(stride)
padding_ = _triple(padding)
dilated_kernel_size_ = _triple(dilated_kernel_size)
super(Dcls3d, self).__init__(
in_channels, out_channels, kernel_count, stride_, padding_, dilated_kernel_size_,
False, _triple(0), groups, bias, padding_mode, scaling)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor], P1: Tensor, P2: Tensor, P3: Tensor):
if self.padding_mode != 'zeros':
return F.conv3d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
SD.ConstructKernel3d.apply(weight, P1, P2, P3, self.dilated_kernel_size, self.scaling), bias,
self.stride, _triple(0), _triple(1), self.groups)
return F.conv3d(input, SD.ConstructKernel3d.apply(weight, P1, P2, P3, self.dilated_kernel_size, self.scaling),
bias, self.stride, self.padding, _triple(1), self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias, self.P.select(0,0), self.P.select(0,1), self.P.select(0,2))
class Dcls2_1d(_DclsN_Md):
__doc__ = r"""Applies a 2D convolution over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size
:math:`(N, C_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})`
can be precisely described as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)
where :math:`\star` is the valid 2D `cross-correlation`_ operator,
:math:`N` is a batch size, :math:`C` denotes a number of channels,
:math:`H` is a height of input planes in pixels, and :math:`W` is
width in pixels.
""" + r"""
This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
* :attr:`stride` controls the stride for the cross-correlation, a single
number or a tuple.
* :attr:`padding` controls the amount of implicit padding on both
sides for :attr:`padding` number of points for each dimension.
* :attr:`dilation` controls the spacing between the kernel points; also
known as the à trous algorithm. It is harder to describe, but this `link`_
has a nice visualization of what :attr:`dilation` does.
{groups_note}
The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:
- a single ``int`` -- in which case the same value is used for the height and width dimension
- a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,
and the second `int` for the width dimension
Note:
{depthwise_separable_note}
Note:
{cudnn_reproducibility_note}
Args:
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the convolving kernel
stride (int or tuple, optional): Stride of the convolution. Default: 1
padding (int or tuple, optional): Zero-padding added to both sides of
the input. Default: 0
padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
``'replicate'`` or ``'circular'``. Default: ``'zeros'``
dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
groups (int, optional): Number of blocked connections from input
channels to output channels. Default: 1
bias (bool, optional): If ``True``, adds a learnable bias to the
output. Default: ``True``
""" + r"""
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape
(out_channels). If :attr:`bias` is ``True``,
then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
Examples:
>>> # With square kernels and equal stride
>>> m = nn.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_2_t,
stride: _size_2_t = 1,
padding: _size_2_t = 0,
dilation: _size_2_t = 1,
dim_dilation: _size_1_t = 0,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros' # TODO: refine this type
):
def _adjust_padding(padding, dilation):
if (type(padding) == tuple):
return (padding[0] + dilation[0] // 2, padding[1])
else:
return _pair(padding + dilation // 2)
kernel_size_ = _pair(kernel_size)
stride_ = _pair(stride)
padding_ = _adjust_padding(padding, dilation)
dilation_ = _pair(dilation)
super(Dcls2_1d, self).__init__(
in_channels, out_channels, kernel_size_, stride_, padding_, dilation_, dim_dilation,
False, _pair(0), groups, bias, padding_mode)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor], P1: Tensor):
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
SD.ConstructKernel2_1d.apply(weight, P1, self.dilation), bias, self.stride,
_pair(0), _pair(1), self.groups)
return F.conv2d(input, SD.ConstructKernel2_1d.apply(weight, P1, self.dilation), bias, self.stride,
self.padding, _pair(1), self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias, self.P.select(0,0))
class Dcls3_1d(_DclsN_Md):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_3_t,
stride: _size_3_t = 1,
padding: _size_3_t = 0,
dilation: _size_3_t = 1,
dim_dilation: _size_1_t = 0,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
gain: float = 1.0
):
kernel_size_ = _triple(kernel_size)
stride_ = _triple(stride)
padding_ = _triple(padding)
dilation_ = _triple(dilation)
dim_dilation_ = _triple(dim_dilation)
super(Dcls3_1d, self).__init__(
in_channels, out_channels, kernel_size_, stride_, padding_, dilation_, dim_dilation_,
False, _triple(0), groups, bias, padding_mode, gain)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor], P: Tensor):
if self.padding_mode != 'zeros':
return F.conv3d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
SD.ConstructKernel3_1d.apply(weight, P, self.dilation), bias, self.stride,
_triple(0), _triple(1), self.groups)
return F.conv3d(input, SD.ConstructKernel3_1d.apply(weight, P, self.dilation), bias, self.stride,
self.padding, _triple(1), self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias, self.P.select(0,0))
class Dcls3_2d(_DclsN_Md):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_3_t,
stride: _size_3_t = 1,
padding: _size_3_t = 0,
dilation: _size_3_t = 1,
dim_dilation: _size_2_t = (0,1),
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros'
):
def _adjust_padding(padding, dilation):
if (type(padding) == tuple):
return (padding[0] + dilation[0] // 2, padding[1] + dilation[1] // 2, padding[2])
else:
return _triple(padding + dilation // 2)
kernel_size_ = _triple(kernel_size)
stride_ = _triple(stride)
padding_ = _adjust_padding()
dilation_ = _triple(dilation)
super(Dcls3_1d, self).__init__(
in_channels, out_channels, kernel_size_, stride_, padding_, dilation_, dim_dilation,
False, _triple(0), groups, bias, padding_mode)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor], P1: Tensor, P2: Tensor):
if self.padding_mode != 'zeros':
return F.conv3d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
SD.ConstructKernel3_2d.apply(weight, P1, P2, self.dilation), bias, self.stride,
_triple(0), _triple(1), self.groups)
return F.conv3d(input, SD.ConstructKernel3_2d.apply(weight, P1, P2, self.dilation), bias, self.stride,
self.padding, _triple(1), self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias, self.P.select(0,0), self.P.select(0,1))
| 43.719393
| 134
| 0.603152
|
4a0774c0389b128a618b1cebc893c38a906c38d5
| 6,052
|
py
|
Python
|
nlptoolkit/classification/models/BERT/train_funcs.py
|
jackashore/NLP_Toolkit
|
e5bd8bcfad87f4906c45e66351adf93bd5c2727f
|
[
"Apache-2.0"
] | null | null | null |
nlptoolkit/classification/models/BERT/train_funcs.py
|
jackashore/NLP_Toolkit
|
e5bd8bcfad87f4906c45e66351adf93bd5c2727f
|
[
"Apache-2.0"
] | null | null | null |
nlptoolkit/classification/models/BERT/train_funcs.py
|
jackashore/NLP_Toolkit
|
e5bd8bcfad87f4906c45e66351adf93bd5c2727f
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 1 17:44:38 2019
@author: WT
"""
import os
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from .preprocessing_funcs import preprocess, load_pickle
import logging
from tqdm import tqdm
logging.basicConfig(format='%(asctime)s [%(levelname)s]: %(message)s', \
datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.INFO)
logger = logging.getLogger(__file__)
def load_dataloaders(args):
train_path = "./data/train_processed.pkl"
test_path = "./data/infer_processed.pkl"
if os.path.isfile(train_path) and os.path.isfile(test_path):
df_train = pd.read_pickle(train_path)
df_test = pd.read_pickle(test_path)
logger.info("Loaded preprocessed data.")
else:
logger.info("Preprocessing...")
preprocess(args)
df_train = pd.read_pickle(train_path)
df_test = pd.read_pickle(test_path)
train_set = sentiments(df_train, tokens_length=args.tokens_length, labels=True)
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=0, pin_memory=False)
if args.train_test_split == 1:
test_set = sentiments(df_test, tokens_length=args.tokens_length, labels=True)
else:
test_set = sentiments(df_test, tokens_length=args.tokens_length, labels=False)
test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=0, pin_memory=False)
del df_train, df_test
return train_loader, test_loader, len(train_set)
class sentiments(Dataset):
def __init__(self, df, tokens_length=300, labels=True):
self.X = torch.tensor(df["text"],requires_grad=False)
self.labels = labels
if self.labels == True:
self.y = torch.tensor(df["label"],requires_grad=False)
self.type = torch.zeros([len(df["text"]), tokens_length], requires_grad=False).long()
s = torch.ones([len(df["text"]), tokens_length],requires_grad=False).long()
for i in range(len(s)):
if df["fills"].loc[i] != 0:
s[i, -df["fills"].loc[i]:] = 0
self.mask = s
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
if self.labels == True:
return self.X[idx], self.type[idx], self.mask[idx], self.y[idx]
else:
return self.X[idx], self.type[idx], self.mask[idx], 0
def load_state(net, optimizer, scheduler, args, load_best=False):
""" Loads saved model and optimizer states if exists """
base_path = "./data/"
checkpoint_path = os.path.join(base_path,"test_checkpoint_%d.pth.tar" % args.model_no)
best_path = os.path.join(base_path,"test_model_best_%d.pth.tar" % args.model_no)
start_epoch, best_pred, checkpoint = 0, 0, None
if (load_best == True) and os.path.isfile(best_path):
checkpoint = torch.load(best_path)
logger.info("Loaded best model.")
elif os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
logger.info("Loaded checkpoint model.")
if checkpoint != None:
start_epoch = checkpoint['epoch']
best_pred = checkpoint['best_acc']
net.load_state_dict(checkpoint['state_dict'])
if optimizer is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
if scheduler is not None:
scheduler.load_state_dict(checkpoint['scheduler'])
logger.info("Loaded model and optimizer.")
return start_epoch, best_pred
def load_results(args):
""" Loads saved results if exists """
losses_path = "./data/test_losses_per_epoch_%d.pkl" % args.model_no
accuracy_path = "./data/test_accuracy_per_epoch_%d.pkl" % args.model_no
if os.path.isfile(losses_path) and os.path.isfile(accuracy_path):
losses_per_epoch = load_pickle("test_losses_per_epoch_%d.pkl" % args.model_no)
accuracy_per_epoch = load_pickle("test_accuracy_per_epoch_%d.pkl" % args.model_no)
logger.info("Loaded results buffer")
else:
losses_per_epoch, accuracy_per_epoch = [], []
return losses_per_epoch, accuracy_per_epoch
def model_eval(net, test_loader, cuda=None):
correct = 0
total = 0
print("Evaluating...")
with torch.no_grad():
net.eval()
for data in tqdm(test_loader):
images, token_type, mask, labels = data
if cuda:
images, token_type, mask, labels = images.cuda(), token_type.cuda(), mask.cuda(), labels.cuda()
images = images.long(); labels = labels.long()
outputs = net(images, token_type_ids=token_type, attention_mask=mask)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print("Accuracy of the network on the %d test data points: %d %%" % (total,\
100*correct/total))
return 100*correct/total
def infer(infer_loader, net):
logger.info("Evaluating on inference data...")
cuda = next(net.parameters()).is_cuda
net.eval()
preds = []
with torch.no_grad():
for i, data in tqdm(enumerate(infer_loader, 0), total = len(infer_loader)):
inputs, token_type, mask, _ = data
if cuda:
inputs, token_type, mask = inputs.cuda(), token_type.cuda(), mask.cuda()
inputs = inputs.long()
outputs = net(inputs, token_type_ids=token_type, attention_mask=mask)
_, predicted = torch.max(outputs.data, 1)
predicted = list(predicted.cpu().numpy()) if cuda else list(predicted.numpy())
preds.extend(predicted)
df_results = pd.DataFrame(columns=["index", "predicted_label"])
df_results.loc[:, "index"] = [i for i in range(len(preds))]
df_results.loc[:, "predicted_label"] = preds
df_results.to_csv("./data/results.csv", columns=df_results.columns, index=False)
return df_results
| 43.855072
| 115
| 0.645406
|
4a07752c2cb82a06c340f465bc5c580896aa370a
| 4,545
|
py
|
Python
|
strings/strip_url_params.py
|
n33t1/Algorithms
|
80a59c2fd3860a35a20919f59160d1408c69b29a
|
[
"MIT"
] | null | null | null |
strings/strip_url_params.py
|
n33t1/Algorithms
|
80a59c2fd3860a35a20919f59160d1408c69b29a
|
[
"MIT"
] | null | null | null |
strings/strip_url_params.py
|
n33t1/Algorithms
|
80a59c2fd3860a35a20919f59160d1408c69b29a
|
[
"MIT"
] | null | null | null |
"""
Write a function that does the following:
Removes any duplicate query string parameters from the url
Removes any query string parameters specified within the 2nd argument (optional array)
An example:
www.saadbenn.com?a=1&b=2&a=2') // returns 'www.saadbenn.com?a=1&b=2'
"""
import unittest
from collections import defaultdict
import urllib
import urllib.parse
# Here is a very non-pythonic grotesque solution
def strip_url_params1(url, params_to_strip=None):
if not params_to_strip:
params_to_strip = []
if url:
result = '' # final result to be returned
tokens = url.split('?')
domain = tokens[0]
query_string = tokens[-1]
result += domain
# add the '?' to our result if it is in the url
if len(tokens) > 1:
result += '?'
if not query_string:
return url
else:
# logic for removing duplicate query strings
# build up the list by splitting the query_string using digits
key_value_string = []
string = ''
for char in query_string:
if char.isdigit():
key_value_string.append(string + char)
string = ''
else:
string += char
dict = defaultdict(int)
# logic for checking whether we should add the string to our result
for i in key_value_string:
_token = i.split('=')
if _token[0]:
length = len(_token[0])
if length == 1:
if _token and (not(_token[0] in dict)):
if params_to_strip:
if _token[0] != params_to_strip[0]:
dict[_token[0]] = _token[1]
result = result + _token[0] + '=' + _token[1]
else:
if not _token[0] in dict:
dict[_token[0]] = _token[1]
result = result + _token[0] + '=' + _token[1]
else:
check = _token[0]
letter = check[1]
if _token and (not(letter in dict)):
if params_to_strip:
if letter != params_to_strip[0]:
dict[letter] = _token[1]
result = result + _token[0] + '=' + _token[1]
else:
if not letter in dict:
dict[letter] = _token[1]
result = result + _token[0] + '=' + _token[1]
return result
# A very friendly pythonic solution (easy to follow)
def strip_url_params2(url, param_to_strip=[]):
if '?' not in url:
return url
queries = (url.split('?')[1]).split('&')
queries_obj = [query[0] for query in queries]
for i in range(len(queries_obj) - 1, 0, -1):
if queries_obj[i] in param_to_strip or queries_obj[i] in queries_obj[0:i]:
queries.pop(i)
return url.split('?')[0] + '?' + '&'.join(queries)
# Here is my friend's solution using python's builtin libraries
def strip_url_params3(url, strip=None):
if not strip: strip = []
parse = urllib.parse.urlparse(url)
query = urllib.parse.parse_qs(parse.query)
query = {k: v[0] for k, v in query.items() if k not in strip}
query = urllib.parse.urlencode(query)
new = parse._replace(query=query)
return new.geturl()
class TestSuite(unittest.TestCase):
def test_strip_url_params1(self):
self.assertEqual(strip_url_params1("www.saadbenn.com?a=1&b=2&a=2"), "www.saadbenn.com?a=1&b=2")
self.assertEqual(strip_url_params1("www.saadbenn.com?a=1&b=2", ['b']), "www.saadbenn.com?a=1")
def test_strip_url_params2(self):
self.assertEqual(strip_url_params2("www.saadbenn.com?a=1&b=2&a=2"), "www.saadbenn.com?a=1&b=2")
self.assertEqual(strip_url_params2("www.saadbenn.com?a=1&b=2", ['b']), "www.saadbenn.com?a=1")
def test_strip_url_params3(self):
self.assertEqual(strip_url_params3("www.saadbenn.com?a=1&b=2&a=2"), "www.saadbenn.com?a=1&b=2")
self.assertEqual(strip_url_params3("www.saadbenn.com?a=1&b=2", ['b']), "www.saadbenn.com?a=1")
if __name__ == "__main__":
unittest.main()
| 38.193277
| 103
| 0.528933
|
4a0775fbd16977b09df48441431cec3a4721a28f
| 707
|
py
|
Python
|
Python-Unit-Testing/employee.py
|
HenkICT/Python_Unit_Testing_3
|
13b05a0a1b3928ad3235d7d5ed5971cf124aafee
|
[
"MIT"
] | null | null | null |
Python-Unit-Testing/employee.py
|
HenkICT/Python_Unit_Testing_3
|
13b05a0a1b3928ad3235d7d5ed5971cf124aafee
|
[
"MIT"
] | null | null | null |
Python-Unit-Testing/employee.py
|
HenkICT/Python_Unit_Testing_3
|
13b05a0a1b3928ad3235d7d5ed5971cf124aafee
|
[
"MIT"
] | null | null | null |
import requests
class Employee3:
"""A sample SampP y Employee class"""
raise_amt = 1.05
def __init__(self, first, last, pay):
self.first = first
self.last = last
self.pay = pay
@property
def email(self):
return "{}.{}@email.com".format(self.first, self.last)
@property
def fullname(self):
return "{} {}".format(self.first, self.last)
def apply_raise(self):
self.pay = int(self.pay * self.raise_amt)
def monthly_schedule(self, month):
response = requests.get(f"http://company.com/{self.last}/{month}")
if response.ok:
return response.text
else:
return "Bad Response!"
| 22.806452
| 74
| 0.582744
|
4a0777b4c989b328d258c91f97eeb5ae59991d4c
| 715
|
py
|
Python
|
ssvos/utils/dist_utils.py
|
wuyongfa-genius/SSVOS_mindspore
|
9f5f6bb29d9fc78d5dbb4e4b163b597887b03c47
|
[
"MIT"
] | 1
|
2021-12-30T08:54:43.000Z
|
2021-12-30T08:54:43.000Z
|
ssvos/utils/dist_utils.py
|
wuyongfa-genius/SSVOS_mindspore
|
9f5f6bb29d9fc78d5dbb4e4b163b597887b03c47
|
[
"MIT"
] | null | null | null |
ssvos/utils/dist_utils.py
|
wuyongfa-genius/SSVOS_mindspore
|
9f5f6bb29d9fc78d5dbb4e4b163b597887b03c47
|
[
"MIT"
] | null | null | null |
"""Some distribute training utils."""
import os
from mindspore import context
from mindspore.context import ParallelMode
from mindspore import communication as dist
def init_dist(parallel_mode=ParallelMode.DATA_PARALLEL):
device_id = int(os.getenv('DEVICE_ID'))
rank_size = int(os.getenv('RANK_SIZE'))
context.set_context(device_id=device_id)
context.set_auto_parallel_context(parallel_mode=parallel_mode,
gradients_mean=True,
device_num=rank_size)
dist.init()
return dist.get_rank(), dist.get_group_size()
# if __name__=="__main__":
# rank, group_size = init_dist()
# print(rank)
# print(group_size)
| 28.6
| 66
| 0.678322
|
4a0777d8eda7d56b5e81ec3e8d961fe557a92c4e
| 251
|
py
|
Python
|
read.py
|
dezounet/google_hash_code
|
48aa82b8b07eb257c91beeb4201d5c39d103e338
|
[
"MIT"
] | null | null | null |
read.py
|
dezounet/google_hash_code
|
48aa82b8b07eb257c91beeb4201d5c39d103e338
|
[
"MIT"
] | null | null | null |
read.py
|
dezounet/google_hash_code
|
48aa82b8b07eb257c91beeb4201d5c39d103e338
|
[
"MIT"
] | null | null | null |
def read(filename):
header = None
lines = []
with open(filename) as f:
for line in f:
if header is None:
header = line
else:
lines.append(line)
# TODO
return None
| 15.6875
| 34
| 0.462151
|
4a07783bcae2184410744c1f1fba3afc943e4f21
| 29,418
|
py
|
Python
|
pypower/mesh.py
|
adematti/pypower
|
e037ccc96b9c8d917e2918ddeba2fc30c6d65067
|
[
"BSD-3-Clause"
] | 10
|
2021-11-09T01:59:36.000Z
|
2022-01-20T08:47:17.000Z
|
pypower/mesh.py
|
adematti/pypower
|
e037ccc96b9c8d917e2918ddeba2fc30c6d65067
|
[
"BSD-3-Clause"
] | 1
|
2021-12-09T08:51:59.000Z
|
2022-01-11T22:20:08.000Z
|
pypower/mesh.py
|
adematti/pypower
|
e037ccc96b9c8d917e2918ddeba2fc30c6d65067
|
[
"BSD-3-Clause"
] | null | null | null |
"""Implementation of methods to paint a catalog on mesh; workhorse is :class:`CatalogMesh`."""
import numpy as np
from mpi4py import MPI
from pmesh.pm import ParticleMesh
from pmesh.window import FindResampler, ResampleWindow
from .utils import BaseClass, _make_array, _get_box
from .direct_power import _format_positions, _format_weights
from . import mpi
def _get_real_dtype(dtype):
# Return real-dtype equivalent
return np.empty(0, dtype=dtype).real.dtype
def _get_resampler(resampler):
# Return :class:`ResampleWindow` from string or :class:`ResampleWindow` instance
if isinstance(resampler, ResampleWindow):
return resampler
conversions = {'ngp': 'nnb', 'cic': 'cic', 'tsc': 'tsc', 'pcs': 'pcs'}
if resampler not in conversions:
raise ValueError('Unknown resampler {}, choices are {}'.format(resampler, list(conversions.keys())))
resampler = conversions[resampler]
return FindResampler(resampler)
def _get_resampler_name(resampler):
# Translate input :class:`ResampleWindow` instance to string
conversions = {'nearest': 'ngp', 'tunednnb': 'ngp', 'tunedcic': 'cic', 'tunedtsc': 'tsc', 'tunedpcs': 'pcs'}
return conversions[resampler.kind]
def _get_compensation_window(resampler='cic', shotnoise=False):
r"""
Return the compensation function, which corrects for the particle-mesh assignment (resampler) kernel.
Taken from https://github.com/bccp/nbodykit/blob/master/nbodykit/source/mesh/catalog.py,
following https://arxiv.org/abs/astro-ph/0409240.
("shotnoise" formula for pcs has been checked with WolframAlpha).
Parameters
----------
resampler : string, default='cic'
Resampler used to assign particles to the mesh.
Choices are ['ngp', 'cic', 'tcs', 'pcs'].
shotnoise : bool, default=False
If ``False``, return expression for eq. 18 in https://arxiv.org/abs/astro-ph/0409240.
This the correct choice when applying interlacing, as aliased images (:math:`\mathbf{n} \neq (0,0,0)`) are suppressed in eq. 17.
If ``True``, return expression for eq. 19.
Returns
-------
window : callable
Window function, taking as input :math:`\pi k_{i} / k_{N} = k / c`
where :math:`k_{N}` is the Nyquist wavenumber and :math:`c` is the cell size,
for each :math:`x`, :math:`y`, :math:`z`, axis.
"""
resampler = resampler.lower()
if shotnoise:
if resampler == 'ngp':
def window(*x):
return 1.
elif resampler == 'cic':
def window(*x):
toret = 1.
for xi in x:
toret = toret * (1 - 2. / 3 * np.sin(0.5 * xi) ** 2) ** 0.5
return toret
elif resampler == 'tsc':
def window(*x):
toret = 1.
for xi in x:
s = np.sin(0.5 * xi)**2
toret = toret * (1 - s + 2. / 15 * s**2) ** 0.5
return toret
elif resampler == 'pcs':
def window(*x):
toret = 1.
for xi in x:
s = np.sin(0.5 * xi)**2
toret = toret * (1 - 4. / 3. * s + 2. / 5. * s**2 - 4. / 315. * s**3) ** 0.5
return toret
else:
p = {'ngp': 1, 'cic': 2, 'tsc': 3, 'pcs': 4}[resampler]
def window(*x):
toret = 1.
for xi in x:
toret = toret * np.sinc(0.5 / np.pi * xi) ** p
return toret
return window
def _wrap_positions(array, boxsize, offset=0.):
return (array - offset) % boxsize + offset
def _get_mesh_attrs(nmesh=None, boxsize=None, boxcenter=None, cellsize=None, positions=None, boxpad=2., check=True, mpicomm=mpi.COMM_WORLD):
"""
Compute enclosing box.
Parameters
----------
nmesh : array, int, default=None
Mesh size, i.e. number of mesh nodes along each axis.
If not provided, see ``value``.
boxsize : float, default=None
Physical size of the box.
If not provided, see ``positions``.
boxcenter : array, float, default=None
Box center.
If not provided, see ``positions``.
cellsize : array, float, default=None
Physical size of mesh cells.
If not ``None``, ``boxsize`` is ``None`` and mesh size ``nmesh`` is not ``None``, used to set ``boxsize`` to ``nmesh * cellsize``.
If ``nmesh`` is ``None``, it is set to (the nearest integer(s) to) ``boxsize / cellsize`` if ``boxsize`` is provided,
else to the nearest even integer to ``boxsize / cellsize``, and ``boxsize`` is then reset to ``nmesh * cellsize``.
positions : (list of) (N, 3) arrays, default=None
If ``boxsize`` and / or ``boxcenter`` is ``None``, use this (list of) position arrays
to determine ``boxsize`` and / or ``boxcenter``.
boxpad : float, default=2.
When ``boxsize`` is determined from ``positions``, take ``boxpad`` times the smallest box enclosing ``positions`` as ``boxsize``.
check : bool, default=True
If ``True``, and input ``positions`` (if provided) are not contained in the box, raise a :class:`ValueError`.
mpicomm : MPI communicator, default=MPI.COMM_WORLD
The MPI communicator.
Returns
-------
nmesh : array of shape (3,)
Mesh size, i.e. number of mesh nodes along each axis.
boxsize : array
Physical size of the box.
boxcenter : array
Box center.
"""
provided_boxsize = boxsize is not None
if not provided_boxsize or boxcenter is None or check:
if positions is None:
raise ValueError('positions must be provided if boxsize and boxcenter are not specified, or check is True')
if not isinstance(positions, (tuple, list)):
positions = [positions]
# Find bounding coordinates
pos_min, pos_max = _get_box(*positions)
pos_min, pos_max = np.min(mpicomm.allgather(pos_min), axis=0), np.max(mpicomm.allgather(pos_max), axis=0)
delta = np.abs(pos_max - pos_min)
if boxcenter is None: boxcenter = 0.5 * (pos_min + pos_max)
if boxsize is None:
if cellsize is not None and nmesh is not None:
boxsize = nmesh * cellsize
else:
boxsize = delta.max() * boxpad
if check and (boxsize < delta).any():
raise ValueError('boxsize {} too small to contain all data (max {})'.format(boxsize, delta))
if nmesh is None:
if cellsize is not None:
nmesh = boxsize / cellsize
if provided_boxsize:
nmesh = np.rint(nmesh).astype('i8')
else:
nmesh = np.ceil(nmesh).astype('i8')
nmesh += nmesh % 2 # to make it even
boxsize = nmesh * cellsize # enforce exact cellsize
else:
raise ValueError('nmesh (or cellsize) must be specified')
nmesh = _make_array(nmesh, 3, dtype='i4')
boxsize = _make_array(boxsize, 3, dtype='f8')
boxcenter = _make_array(boxcenter, 3, dtype='f8')
return nmesh, boxsize, boxcenter
def ArrayMesh(array, boxsize, nmesh=None, mpiroot=0, mpicomm=MPI.COMM_WORLD):
"""
Turn numpy array into :class:`pmesh.pm.RealField`.
Parameters
----------
array : array
Mesh numpy array gathered on ``mpiroot``.
boxsize : array, float, default=None
Physical size of the box along each axis.
nmesh : array, int, default=None
If ``mpiroot`` is ``None``, mesh size, i.e. number of mesh nodes along each axis.
mpiroot : int, default=0
MPI rank where input array is gathered.
If input array is scattered accross all ranks in C ordering, pass ``mpiroot = None`` and specify ``nmesh``.
mpicomm : MPI communicator, default=MPI.COMM_WORLD
The MPI communicator.
Returns
-------
mesh : pmesh.pm.RealField
"""
if mpiroot is None:
dtype = array.dtype
if nmesh is None:
raise ValueError('In case input mesh is scattered accross all ranks, provide its shape (nmesh)')
shape = _make_array(nmesh, 3, dtype='i8')
else:
if mpicomm.rank == mpiroot:
dtype, shape = array.dtype, array.shape
else:
dtype, shape, array = None, None, None
dtype = mpicomm.bcast(dtype, root=mpiroot)
shape = mpicomm.bcast(shape, root=mpiroot)
boxsize = _make_array(boxsize, 3, dtype='f8')
pm = ParticleMesh(BoxSize=boxsize, Nmesh=shape, dtype=dtype, comm=mpicomm)
mesh = pm.create(type='real')
if mpiroot is None or mpicomm.rank == mpiroot:
array = np.ravel(array) # ignore data from other ranks
else:
array = np.empty((0,), dtype=dtype)
mesh.unravel(array)
return mesh
class CatalogMesh(BaseClass):
"""Class to paint catalog of positions and weights to mesh."""
_slab_npoints_max = int(1024 * 1024 * 4)
def __init__(self, data_positions, data_weights=None, randoms_positions=None, randoms_weights=None,
shifted_positions=None, shifted_weights=None,
nmesh=None, boxsize=None, boxcenter=None, cellsize=None, boxpad=2., wrap=False, dtype='f8',
resampler='tsc', interlacing=2, position_type='xyz', copy=False, mpiroot=None, mpicomm=MPI.COMM_WORLD):
"""
Initialize :class:`CatalogMesh`.
Note
----
When running with MPI, input positions and weights are assumed to be scatted on all MPI ranks of ``mpicomm``.
If this is not the case, use :func:`mpi.scatter_array`.
Parameters
----------
data_positions : list, array
Positions in the data catalog. Typically of shape (3, N) or (N, 3).
data_weights : array of shape (N,), default=None
Optionally, data weights.
randoms_positions : list, array
Positions in the randoms catalog. Typically of shape (3, N) or (N, 3).
randoms_weights : array of shape (N,), default=None
Randoms weights.
shifted_positions : array, default=None
Optionally, in case of BAO reconstruction, positions of the shifted catalog.
shifted_weights : array, default=None
Optionally, in case of BAO reconstruction, weigths of the shifted catalog.
nmesh : array, int, default=None
Mesh size, i.e. number of mesh nodes along each axis.
boxsize : array, float, default=None
Physical size of the box along each axis, defaults to maximum extent taken by all input positions, times ``boxpad``.
boxcenter : array, float, default=None
Box center, defaults to center of the Cartesian box enclosing all input positions.
cellsize : array, float, default=None
Physical size of mesh cells.
If not ``None``, and mesh size ``nmesh`` is not ``None``, used to set ``boxsize`` as ``nmesh * cellsize``.
If ``nmesh`` is ``None``, it is set as (the nearest integer(s) to) ``boxsize / cellsize``.
wrap : bool, default=False
Whether to wrap input positions?
If ``False`` and input positions do not fit in the the box size, raise a :class:`ValueError`.
boxpad : float, default=2.
When ``boxsize`` is determined from ``positions``, take ``boxpad`` times the smallest box enclosing ``positions`` as ``boxsize``.
dtype : string, dtype, default='f8'
The data type to use for the mesh.
Input ``positions`` and ``weights`` are cast to the corresponding (real) precision.
resampler : string, ResampleWindow, default='tsc'
Resampler used to assign particles to the mesh.
Choices are ['ngp', 'cic', 'tcs', 'pcs'].
interlacing : bool, int, default=2
Whether to use interlacing to reduce aliasing when painting the particles on the mesh.
If positive int, the interlacing order (minimum: 2).
position_type : string, default='xyz'
Type of input positions, one of:
- "pos": Cartesian positions of shape (N, 3)
- "xyz": Cartesian positions of shape (3, N)
- "rdd": RA/Dec in degree, distance of shape (3, N)
copy : bool, default=False
If ``False``, avoids copy of positions and weights if they are of (real) type ``dtype``, ``mpiroot`` is ``None``,
and ``position_type`` is "pos" (for positions).
Setting to ``True`` is only useful if one wants to modify positions or weights that have been passed as input
while keeping those attached to the current mesh instance the same.
mpiroot : int, default=None
If ``None``, input positions and weights are assumed to be scatted across all ranks.
Else the MPI rank where input positions and weights are gathered.
mpicomm : MPI communicator, default=MPI.COMM_WORLD
The MPI communicator.
"""
self.mpicomm = mpicomm
self.dtype = np.dtype(dtype)
self.rdtype = _get_real_dtype(self.dtype)
self._set_positions(data_positions=data_positions, randoms_positions=randoms_positions, shifted_positions=shifted_positions, position_type=position_type, copy=copy, mpiroot=mpiroot)
self._set_weights(data_weights=data_weights, randoms_weights=randoms_weights, shifted_weights=shifted_weights, copy=copy, mpiroot=mpiroot)
self._set_box(boxsize=boxsize, cellsize=cellsize, nmesh=nmesh, boxcenter=boxcenter, boxpad=boxpad, wrap=wrap)
self._set_resampler(resampler)
self._set_interlacing(interlacing)
def __repr__(self):
"""String representation of current mesh."""
info = ['{}={}'.format(name, getattr(self, name)) for name in ['nmesh', 'boxsize', 'boxcenter', 'dtype']]
return '{}({})'.format(self.__class__.__name__, ', '.join(info))
@property
def compensation(self):
"""Return dictionary specifying compensation scheme for particle-mesh resampling."""
return {'resampler': _get_resampler_name(self.resampler), 'shotnoise': not bool(self.interlacing)}
def clone(self, data_positions=None, data_weights=None, randoms_positions=None, randoms_weights=None,
shifted_positions=None, shifted_weights=None,
boxsize=None, cellsize=None, nmesh=None, boxcenter=None, dtype=None,
resampler=None, interlacing=None, position_type='xyz', mpicomm=None):
"""
Clone current instance, i.e. copy and set new positions and weights.
Arguments 'boxsize', 'nmesh', 'boxcenter', 'dtype', 'resampler', 'interlacing', 'mpicomm', if ``None``,
are overriden by those of the current instance.
"""
new = self.__class__.__new__(self.__class__)
kwargs = {}
loc = locals()
for name in ['boxsize', 'nmesh', 'boxcenter', 'dtype', 'resampler', 'interlacing', 'mpicomm']:
kwargs[name] = loc[name] if loc[name] is not None else getattr(self, name)
if cellsize is not None: # if cellsize is provided, remove default nmesh or boxsize value from current instance.
kwargs['cellsize'] = cellsize
if nmesh is None: kwargs.pop('nmesh')
elif boxsize is None: kwargs.pop('boxsize')
new.__init__(data_positions=data_positions, data_weights=data_weights, randoms_positions=randoms_positions, randoms_weights=randoms_weights,
shifted_positions=shifted_positions, shifted_weights=shifted_weights, position_type=position_type, **kwargs)
return new
def _set_interlacing(self, interlacing):
self.interlacing = int(interlacing)
if self.interlacing != interlacing:
raise ValueError('Interlacing must be either bool (False, 0) or an integer >= 2')
if self.interlacing == 1:
if self.mpicomm.rank == 0:
self.log_warning('Provided interlacing is {}; setting it to 2.'.format(interlacing))
self.interlacing = 2
def _set_box(self, nmesh=None, boxsize=None, cellsize=None, boxcenter=None, boxpad=2., wrap=False):
# Set :attr:`nmesh`, :attr:`boxsize` and :attr:`boxcenter`
positions = [self.data_positions]
if self.with_randoms: positions += [self.randoms_positions]
if self.with_shifted: positions += [self.shifted_positions]
self.nmesh, self.boxsize, self.boxcenter = _get_mesh_attrs(nmesh=nmesh, boxsize=boxsize, cellsize=cellsize, boxcenter=boxcenter,
positions=positions, boxpad=boxpad, check=not wrap, mpicomm=self.mpicomm)
if wrap:
for position in positions:
_wrap_positions(position, self.boxsize, self.boxcenter - self.boxsize / 2.)
def _set_positions(self, data_positions, randoms_positions=None, shifted_positions=None, position_type='xyz', copy=False, mpiroot=None):
# Set data and optionally shifted and randoms positions, scattering on all ranks if not already
if position_type is not None: position_type = position_type.lower()
self.position_type = position_type
for name in ['data', 'randoms', 'shifted']:
positions_name = '{}_positions'.format(name)
positions = locals()[positions_name]
positions = _format_positions(positions, position_type=self.position_type, dtype=self.rdtype, copy=copy, mpicomm=self.mpicomm, mpiroot=mpiroot)
setattr(self, positions_name, positions)
if name == 'data' and positions is None:
raise ValueError('Provide at least an array of data positions')
size = 0 if positions is None else self.mpicomm.allreduce(len(positions))
setattr(self, '{}_size'.format(name), size)
def _set_weights(self, data_weights, randoms_weights=None, shifted_weights=None, copy=False, mpiroot=None):
# Set data and optionally shifted and randoms weights and their sum, scattering on all ranks if not already
for name in ['data', 'randoms', 'shifted']:
positions_name = '{}_positions'.format(name)
positions = getattr(self, positions_name, None)
weights_name = '{}_weights'.format(name)
weights = locals()[weights_name]
size = len(positions) if positions is not None else None
weights = _format_weights(weights, weight_type='product_individual', dtype=self.rdtype, size=size, copy=copy, mpicomm=self.mpicomm, mpiroot=mpiroot)[0]
weights = weights[0] if weights else None
if size is None and weights is not None:
raise ValueError('{} are provided, but not {}'.format(weights_name, positions_name))
setattr(self, weights_name, weights)
if weights is None:
if size is None: sum_weights = 0.
else: sum_weights = self.mpicomm.allreduce(size)
else:
sum_weights = self.mpicomm.allreduce(sum(weights))
setattr(self, 'sum_{}'.format(weights_name), sum_weights)
@property
def with_randoms(self):
"""Whether randoms positions have been provided."""
return self.randoms_positions is not None
@property
def with_shifted(self):
"""Whether "shifted" positions have been provided (e.g. for reconstruction)."""
return self.shifted_positions is not None
def _set_resampler(self, resampler='cic'):
# Set :attr:`resampler`
self.resampler = _get_resampler(resampler=resampler)
def to_mesh(self, field=None, dtype=None, compensate=False):
"""
Paint positions/weights to mesh.
Parameters
----------
field : string, default=None
Field to paint to mesh, one of:
- "data": data positions and weights
- "shifted": shifted positions and weights (available only if shifted positions are provided)
- "randoms": randoms positions and weights
- "data-normalized_shifted": shifted positions and weights, renormalized (by alpha)
such that their sum is same as data weights
- "data-normalized_randoms": randoms positions and weights, renormalized (by alpha)
such that their sum is same as data weights
- "fkp": FKP field, i.e. data - alpha * (shifted if provided else randoms)
- ``None``: defaults to "data" if no shifted/randoms, else "fkp"
dtype : string, dtype, default='f8'
The data type of the mesh when painting, to override current :attr:`dtype`.
compensate : bool, default=False
Wether to apply compensation for particle-mesh assignment scheme.
Returns
-------
out : RealField
Mesh, with values in "weights" units (not *normalized* as density).
"""
if dtype is None: dtype = self.dtype
if field is None:
field = 'fkp' if (self.with_randoms or self.with_shifted) else 'data'
field = field.lower()
allowed_fields = set(['data', 'normalized_data'])
if self.with_shifted: allowed_fields |= set(['shifted', 'data-normalized_shifted', 'fkp'])
if self.with_randoms: allowed_fields |= set(['randoms', 'data-normalized_randoms', 'fkp'])
if field not in allowed_fields:
raise ValueError('Unknown field {}. Choices are {}'.format(field, allowed_fields))
positions, weights = [], []
if field in ['data', 'fkp']:
positions += [self.data_positions]
weights += [(self.data_weights, None)]
if field in ['normalized_data']:
positions += [self.data_positions]
weights += [(self.data_weights, self.nmesh.prod(dtype='f8') / self.sum_data_weights)] # mean mesh is 1
if field in ['fkp']:
if self.with_shifted:
positions += [self.shifted_positions]
weights += [(self.shifted_weights, -self.sum_data_weights / self.sum_shifted_weights)]
else:
positions += [self.randoms_positions]
weights += [(self.randoms_weights, -self.sum_data_weights / self.sum_randoms_weights)]
if field in ['shifted', 'data-normalized_shifted']:
positions += [self.shifted_positions]
if field == 'data-normalized_shifted':
weights += [(self.shifted_weights, self.sum_data_weights / self.sum_shifted_weights)]
else:
weights += [(self.shifted_weights, None)]
if field in ['randoms', 'data-normalized_randoms']:
positions += [self.randoms_positions]
if field == 'data-normalized_randoms':
weights += [(self.randoms_weights, self.sum_data_weights / self.sum_randoms_weights)]
else:
weights += [(self.randoms_weights, None)]
pm = ParticleMesh(BoxSize=self.boxsize, Nmesh=self.nmesh, dtype=dtype, comm=self.mpicomm)
offset = self.boxcenter - self.boxsize / 2.
# offset = self.boxcenter
# offset = 0.
def paint(positions, weights, scaling, out, transform=None):
positions = positions - offset
factor = bool(self.interlacing) + 0.5
scalar_weights = weights is None
if scaling is not None:
if scalar_weights: weights = scaling
else: weights = weights * scaling
# We work by slab to limit memory footprint
# Merely copy-pasted from https://github.com/bccp/nbodykit/blob/4aec168f176939be43f5f751c90363b39ec6cf3a/nbodykit/source/mesh/catalog.py#L300
def paint_slab(sl):
# Decompose positions such that they live in the same region as the mesh in the current process
p = positions[sl]
size = len(p)
layout = pm.decompose(p, smoothing=factor * self.resampler.support)
# If we are receiving too many particles, abort and retry with a smaller chunksize
recvlengths = pm.comm.allgather(layout.recvlength)
if any(recvlength > 2 * self._slab_npoints_max for recvlength in recvlengths):
if pm.comm.rank == 0:
self.log_info('Throttling slab size as some ranks will receive too many particles. ({:d} > {:d})'.format(max(recvlengths), self._slab_npoints_max * 2))
raise StopIteration
p = layout.exchange(p)
w = weights if scalar_weights else layout.exchange(weights[sl])
# hold = True means no zeroing of out
pm.paint(p, mass=w, resampler=self.resampler, transform=transform, hold=True, out=out)
return size
islab = 0
slab_npoints = self._slab_npoints_max
sizes = pm.comm.allgather(len(positions))
csize = sum(sizes)
local_size_max = max(sizes)
painted_size = 0
import gc
while islab < local_size_max:
sl = slice(islab, islab + slab_npoints)
if pm.comm.rank == 0:
self.log_info('Slab {:d} ~ {:d} / {:d}.'.format(islab, islab + slab_npoints, local_size_max))
try:
painted_size_slab = paint_slab(sl)
except StopIteration:
slab_npoints = slab_npoints // 2
if slab_npoints < 1:
raise RuntimeError('Cannot find a slab size that fits into memory.')
continue
finally:
# collect unfreed items
gc.collect()
painted_size += pm.comm.allreduce(painted_size_slab)
if pm.comm.rank == 0:
self.log_info('Painted {:d} out of {:d} objects to mesh.'.format(painted_size, csize))
islab += slab_npoints
slab_npoints = min(self._slab_npoints_max, int(slab_npoints * 1.2))
out = pm.create(type='real', value=0.)
for p, w in zip(positions, weights): paint(p, *w, out)
if self.interlacing:
if self.mpicomm.rank == 0:
self.log_info('Running interlacing at order {:d}.'.format(self.interlacing))
cellsize = self.boxsize / self.nmesh
shifts = np.arange(self.interlacing) * 1. / self.interlacing
# remove 0 shift, already computed
shifts = shifts[1:]
out = out.r2c()
for shift in shifts:
transform = pm.affine.shift(shift) # this shifts particle positions by ``shift`` before painting to mesh
# paint to two shifted meshes
mesh_shifted = pm.create(type='real', value=0.)
for p, w in zip(positions, weights): paint(p, *w, mesh_shifted, transform=transform)
mesh_shifted = mesh_shifted.r2c()
for k, s1, s2 in zip(out.slabs.x, out.slabs, mesh_shifted.slabs):
kc = sum(k[i] * cellsize[i] for i in range(3))
# pmesh convention is F(k) = 1/N^3 \sum_{r} e^{-ikr} F(r)
# shifting by "shift * cellsize" we compute F(k) = 1/N^3 \sum_{r} e^{-ikr} F(r - shift * cellsize)
# i.e. F(k) = e^{- i shift * kc} 1/N^3 e^{-ikr} F(r)
# Hence compensation below
s1[...] = s1[...] + s2[...] * np.exp(shift * 1j * kc)
if compensate:
self._compensate(out)
out = out.c2r()
out[:] /= self.interlacing
elif compensate:
out = out.r2c()
self._compensate(out)
out = out.c2r()
return out
def _compensate(self, cfield):
if self.mpicomm.rank == 0:
self.log_info('Applying compensation {}.'.format(self.compensation))
# Apply compensation window for particle-assignment scheme
window = _get_compensation_window(**self.compensation)
cellsize = self.boxsize / self.nmesh
for k, slab in zip(cfield.slabs.x, cfield.slabs):
kc = tuple(ki * ci for ki, ci in zip(k, cellsize))
slab[...] /= window(*kc)
def unnormalized_shotnoise(self):
r"""
Return unnormalized shotnoise, as:
.. math::
\sum_{i=1}^{N_{g}} w_{i,g}^{2} + \alpha^{2} \sum_{i=1}^{N_{r}} w_{i,r}^{2}
Where the sum runs over data (and optionally) shifted/randoms weights.
"""
def sum_weights2(positions, weights=None):
if weights is None:
return self.mpicomm.allreduce(len(positions))
return self.mpicomm.allreduce(sum(weights**2))
shotnoise = sum_weights2(self.data_positions, self.data_weights)
if self.with_shifted:
alpha = self.sum_data_weights / self.sum_shifted_weights
shotnoise += alpha**2 * sum_weights2(self.shifted_positions, self.shifted_weights)
elif self.with_randoms:
alpha = self.sum_data_weights / self.sum_randoms_weights
shotnoise += alpha**2 * sum_weights2(self.randoms_positions, self.randoms_weights)
return shotnoise
| 45.18894
| 189
| 0.607077
|
4a0778efdbc5ecbeb28e7c9b562c922612ce607a
| 3,730
|
py
|
Python
|
examples/mcmc/gibbs_linreg.py
|
Bhumbra/probayes
|
e5ac193076e4188b9b38c0e18466223ab4d041f7
|
[
"BSD-3-Clause"
] | null | null | null |
examples/mcmc/gibbs_linreg.py
|
Bhumbra/probayes
|
e5ac193076e4188b9b38c0e18466223ab4d041f7
|
[
"BSD-3-Clause"
] | null | null | null |
examples/mcmc/gibbs_linreg.py
|
Bhumbra/probayes
|
e5ac193076e4188b9b38c0e18466223ab4d041f7
|
[
"BSD-3-Clause"
] | null | null | null |
"""
Example of linear regression using Gibbs taken from Radford Neil's slides at:
http://www.cs.toronto.edu/~radford/csc2541.S11/week3.pdf
p(y|x, beta_0, beta_1, y_sigma) = N(beta_1*x + beta_0, y_sigma)
p(beta_0) = N(beta_0_mu, beta_0_sigma)
p(beta_1) = N(beta_1_mu, beta_1_sigma)
p(1/y_sigma^2) = Gamma(y_sigma_alpha, 1/y_sigma_beta)
"""
import numpy as np
import scipy.stats
import probayes as pb
from pylab import *; ion()
from mpl_toolkits.mplot3d import Axes3D # import needed for 3D projection
n_steps = 1000
# Simulate data
rand_size = 60
x_range = [-3, 3]
slope = 1.5
intercept = -1.
y_noise = 0.5
x_obs = np.random.normal(0, 1, size=rand_size)
y_obs = np.random.normal(slope*x_obs + intercept, y_noise)
# Set up RVs, RFs, and SP
x = pb.RV('x', vtype=float, vset=x_range)
y = pb.RV('y', vtype=float, vset=[-np.inf, np.inf])
beta_0 = pb.RV('beta_0', vtype=float, vset=[-6., 6.])
beta_1 = pb.RV('beta_1', vtype=float, vset=[-6., 6.])
y_sigma = pb.RV('y_sigma', vtype=float, vset=[(0.001), 10.])
# Define likelihood and conditional functions
def norm_reg(x, y, beta_0, beta_1, y_sigma):
return scipy.stats.norm.logpdf(y, loc=beta_0 + beta_1*x, scale=y_sigma)
def cond_reg(x, y, beta_0, beta_1, y_sigma, unknown,
beta_0_mu=0, beta_0_sigma=1, beta_1_mu=0, beta_1_sigma=1.,
y_sigma_alpha=1., y_sigma_beta=1.):
if unknown == 'y_sigma':
cond_alpha = y_sigma_alpha + 0.5*rand_size
cond_beta = y_sigma_beta + 0.5*np.sum((y - beta_0 - beta_1*x)**2)
y_sigma = 1 / np.sqrt(np.random.gamma(cond_alpha, 1/cond_beta))
return y_sigma
y_prec = 1 / (y_sigma**2)
if unknown == 'beta_0':
beta_0_prec = 1/(beta_0_sigma**2)
cond_var = 1 / (beta_0_prec + rand_size*y_prec)
cond_mu = (beta_0_prec*beta_0_mu + y_prec*np.sum(y - beta_1*x)) * cond_var
cond_sigma = np.sqrt(cond_var)
beta_0 = np.random.normal(cond_mu, cond_sigma)
return beta_0
if unknown == 'beta_1':
beta_1_prec = 1/(beta_1_sigma**2)
cond_var = 1 / (beta_1_prec + y_prec*np.sum(x**2))
cond_mu = (beta_1_prec*beta_1_mu + y_prec*np.sum(x*(y - beta_0))) * cond_var
cond_sigma = np.sqrt(cond_var)
beta_1 = np.random.normal(cond_mu, cond_sigma)
return beta_1
raise ValueError("Unknown unknown: {}".format(unknown))
# Setup up RFs and SP
stats = x & y
paras = beta_0 & beta_1 & y_sigma
paras.set_tfun(cond_reg, tsteps=1, x=x_obs, y=y_obs)
process = pb.SP(stats, paras)
process.set_tfun(paras)
process.set_prob(norm_reg, pscale='log')
process.set_scores('gibbs')
lr = scipy.stats.linregress(x_obs, y_obs)
init_state = {'beta_0': lr.intercept, 'beta_1': lr.slope, 'y_sigma': np.sqrt(lr.stderr)}
sampler = process.sampler(init_state, {'x,y': [x_obs,y_obs]}, stop=n_steps, iid=True, joint=True)
samples = [sample for sample in sampler]
summary = process(samples)
n_accept = summary.u.count(True)
inference = summary.v.rescaled()
b0, b1, ys, post = inference['beta_0'], inference['beta_1'], \
inference['y_sigma'], inference.prob
hat_beta_0 = np.median(b0)
hat_beta_1 = np.median(b1)
hat_y_sigma = np.median(ys)
hat_beta_0_str = '{:.2f}'.format(hat_beta_0)
hat_beta_1_str = '{:.2f}'.format(hat_beta_1)
hat_y_sigma_str = '{:.2f}'.format(hat_y_sigma)
# PLOT DATA
fig = figure()
ax = fig.add_subplot(111, projection='3d')
c_norm = Normalize(vmin=np.min(post), vmax=np.max(post))
c_map = cm.jet(c_norm(post))
ax.plot(b0, b1, ys, '-', color=(0.7, 0.7, 0.7, 0.3))
ax.scatter(b0, b1, ys, color=c_map, marker='.', alpha=1.)
ax.set_xlabel(r'$\beta_0$')
ax.set_ylabel(r'$\beta_1$')
ax.set_zlabel(r'$\sigma_y$')
ax.set_title(r'$\hat{\beta_0}=' + hat_beta_0_str + r',\hat{\beta_1}=' + hat_beta_1_str + \
r',\hat{\sigma_y}=' + hat_y_sigma_str + r'$')
| 36.213592
| 97
| 0.681233
|
4a077912972a3dc8a863c463135f31a27a36677d
| 1,332
|
py
|
Python
|
codeFilesPackage/nameDayViewer.py
|
karadalex/PythonOrganizerAppProject
|
37c7d10b240e9d883d4a8a50c4e94cf5315275d9
|
[
"MIT"
] | 3
|
2015-12-16T01:54:09.000Z
|
2016-01-31T00:55:37.000Z
|
codeFilesPackage/nameDayViewer.py
|
karadalex/PythonOrganizerAppProject
|
37c7d10b240e9d883d4a8a50c4e94cf5315275d9
|
[
"MIT"
] | null | null | null |
codeFilesPackage/nameDayViewer.py
|
karadalex/PythonOrganizerAppProject
|
37c7d10b240e9d883d4a8a50c4e94cf5315275d9
|
[
"MIT"
] | null | null | null |
import gotoMainFolderDirectory
import textFileOperations
import greeklish
def nameDayDictionaryCreation():
gotoMainFolderDirectory.go()
dataString = textFileOperations.textFileToString("mediaFilesPackage/eortes.dat")
dataList = dataString.split('\n\n')
nameDayDictionary = {}
for day in dataList:
dayList = day.split("\n")
date = dayList[0]
date = date.split(" ")
date = date[0]
dayList.pop(0)
dayString = ""
for name in dayList:
name = name.strip()
name = name.replace("(", "")
name = name.replace(")", "")
namesList = name.split(",")
name = ""
if len(namesList) > 1:
name += "("
for i in range(len(namesList)):
namesList[i] = greeklish.greekStringToGreeklishString(namesList[i])
name += namesList[i]+","
if len(namesList) > 1:
name += ")"
dayString += name+"\n"
# put date and names in dictionary
# dictionary key: date, format: "day/month"
# dictionary value: names, type:String
nameDayDictionary.update({date:dayString})
return nameDayDictionary
# Uncomment to check function nameDayDictionaryCreation()
#print nameDayDictionaryCreation()
| 32.487805
| 84
| 0.57958
|
4a077a1fe02b056d0eeafd1a46bb6cfadef4746b
| 8,727
|
py
|
Python
|
setup.py
|
AlanDecode/detectron2
|
94af461322feba0c3cadde886367445d62bc45a7
|
[
"Apache-2.0"
] | 1
|
2021-09-27T17:14:13.000Z
|
2021-09-27T17:14:13.000Z
|
setup.py
|
AlanDecode/detectron2
|
94af461322feba0c3cadde886367445d62bc45a7
|
[
"Apache-2.0"
] | null | null | null |
setup.py
|
AlanDecode/detectron2
|
94af461322feba0c3cadde886367445d62bc45a7
|
[
"Apache-2.0"
] | 2
|
2020-12-10T12:58:12.000Z
|
2022-03-25T02:27:46.000Z
|
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
from torch.utils.hipify import hipify_python
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
assert torch_ver >= [1, 6], "Requires PyTorch >= 1.6"
def get_version():
init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py")
init_py = open(init_py_path, "r").readlines()
version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
version = version_line.split("=")[-1].strip().strip("'\"")
# The following is used to build release packages.
# Users should never use it.
suffix = os.getenv("D2_VERSION_SUFFIX", "")
version = version + suffix
if os.getenv("BUILD_NIGHTLY", "0") == "1":
from datetime import datetime
date_str = datetime.today().strftime("%y%m%d")
version = version + ".dev" + date_str
new_init_py = [l for l in init_py if not l.startswith("__version__")]
new_init_py.append('__version__ = "{}"\n'.format(version))
with open(init_py_path, "w") as f:
f.write("".join(new_init_py))
return version
def get_extensions():
this_dir = path.dirname(path.abspath(__file__))
extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc")
main_source = path.join(extensions_dir, "vision.cpp")
sources = glob.glob(path.join(extensions_dir, "**", "*.cpp"))
from torch.utils.cpp_extension import ROCM_HOME
is_rocm_pytorch = (
True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False
)
hipify_ver = (
[int(x) for x in torch.utils.hipify.__version__.split(".")]
if hasattr(torch.utils.hipify, "__version__")
else [0, 0, 0]
)
if is_rocm_pytorch and hipify_ver < [1, 0, 0]: # TODO not needed since pt1.8
# Earlier versions of hipification and extension modules were not
# transparent, i.e. would require an explicit call to hipify, and the
# hipification would introduce "hip" subdirectories, possibly changing
# the relationship between source and header files.
# This path is maintained for backwards compatibility.
hipify_python.hipify(
project_directory=this_dir,
output_directory=this_dir,
includes="/detectron2/layers/csrc/*",
show_detailed=True,
is_pytorch_extension=True,
)
source_cuda = glob.glob(path.join(extensions_dir, "**", "hip", "*.hip")) + glob.glob(
path.join(extensions_dir, "hip", "*.hip")
)
shutil.copy(
"detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h",
"detectron2/layers/csrc/box_iou_rotated/hip/box_iou_rotated_utils.h",
)
shutil.copy(
"detectron2/layers/csrc/deformable/deform_conv.h",
"detectron2/layers/csrc/deformable/hip/deform_conv.h",
)
sources = [main_source] + sources
sources = [
s
for s in sources
if not is_rocm_pytorch or torch_ver < [1, 7] or not s.endswith("hip/vision.cpp")
]
else:
# common code between cuda and rocm platforms,
# for hipify version [1,0,0] and later.
source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob(
path.join(extensions_dir, "*.cu")
)
sources = [main_source] + sources
extension = CppExtension
extra_compile_args = {"cxx": []}
define_macros = []
if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) or os.getenv(
"FORCE_CUDA", "0"
) == "1":
extension = CUDAExtension
sources += source_cuda
if not is_rocm_pytorch:
define_macros += [("WITH_CUDA", None)]
extra_compile_args["nvcc"] = [
"-O3",
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
]
else:
define_macros += [("WITH_HIP", None)]
extra_compile_args["nvcc"] = []
if torch_ver < [1, 7]:
# supported by https://github.com/pytorch/pytorch/pull/43931
CC = os.environ.get("CC", None)
if CC is not None:
extra_compile_args["nvcc"].append("-ccbin={}".format(CC))
include_dirs = [extensions_dir]
ext_modules = [
extension(
"detectron2._C",
sources,
include_dirs=include_dirs,
define_macros=define_macros,
extra_compile_args=extra_compile_args,
)
]
return ext_modules
def get_model_zoo_configs() -> List[str]:
"""
Return a list of configs to include in package for model zoo. Copy over these configs inside
detectron2/model_zoo.
"""
# Use absolute paths while symlinking.
source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs")
destination = path.join(
path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs"
)
# Symlink the config directory inside package to have a cleaner pip install.
# Remove stale symlink/directory from a previous build.
if path.exists(source_configs_dir):
if path.islink(destination):
os.unlink(destination)
elif path.isdir(destination):
shutil.rmtree(destination)
if not path.exists(destination):
try:
os.symlink(source_configs_dir, destination)
except OSError:
# Fall back to copying if symlink fails: ex. on Windows.
shutil.copytree(source_configs_dir, destination)
config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob(
"configs/**/*.py", recursive=True
)
return config_paths
# For projects that are relative small and provide features that are very close
# to detectron2's core functionalities, we install them under detectron2.projects
PROJECTS = {
"detectron2.projects.point_rend": "projects/PointRend/point_rend",
"detectron2.projects.deeplab": "projects/DeepLab/deeplab",
"detectron2.projects.panoptic_deeplab": "projects/Panoptic-DeepLab/panoptic_deeplab",
}
setup(
name="detectron2",
version=get_version(),
author="FAIR",
url="https://github.com/facebookresearch/detectron2",
description="Detectron2 is FAIR's next-generation research "
"platform for object detection and segmentation.",
packages=find_packages(exclude=("configs", "tests*")) + list(PROJECTS.keys()),
package_dir=PROJECTS,
package_data={"detectron2.model_zoo": get_model_zoo_configs()},
python_requires=">=3.6",
install_requires=[
# Do not add opencv here. Just like pytorch, user should install
# opencv themselves, preferrably by OS's package manager, or by
# choosing the proper pypi package name at https://github.com/skvark/opencv-python
"termcolor>=1.1",
"Pillow>=7.1", # or use pillow-simd for better performance
"yacs>=0.1.6",
"tabulate",
"cloudpickle",
"matplotlib",
"tqdm>4.29.0",
"tensorboard",
# Lock version of fvcore/iopath because they may have breaking changes
# NOTE: when updating fvcore/iopath version, make sure fvcore depends
# on the same version of iopath.
"fvcore>=0.1.5,<0.1.6", # required like this to make it pip installable
"iopath>=0.1.7,<0.1.9",
"pycocotools>=2.0.2", # corresponds to https://github.com/ppwwyyxx/cocoapi
"future", # used by caffe2
"pydot", # used to save caffe2 SVGs
"dataclasses; python_version<'3.7'",
"omegaconf==2.1.0.dev22",
# When adding to the list, may need to update docs/requirements.txt
# or add mock in docs/conf.py
],
extras_require={
"all": [
"shapely",
"pygments>=2.2",
"psutil",
"hydra-core",
"panopticapi @ https://github.com/cocodataset/panopticapi/archive/master.zip",
],
"dev": [
"flake8==3.8.1",
"isort==4.3.21",
"black==20.8b1",
"flake8-bugbear",
"flake8-comprehensions",
],
},
ext_modules=get_extensions(),
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
)
| 35.189516
| 97
| 0.623009
|
4a077a90153d5babe5212b0cac4c615c93028192
| 1,526
|
py
|
Python
|
bindings/python/debug_script.py
|
Keithcat1/synthizer
|
242a06855a36b9a9049d5fb00630800cda4a2984
|
[
"Unlicense"
] | 2
|
2022-01-02T14:41:45.000Z
|
2022-01-12T16:38:59.000Z
|
bindings/python/debug_script.py
|
Keithcat1/synthizer
|
242a06855a36b9a9049d5fb00630800cda4a2984
|
[
"Unlicense"
] | 9
|
2021-11-04T00:26:52.000Z
|
2022-03-23T02:12:16.000Z
|
bindings/python/debug_script.py
|
Keithcat1/synthizer
|
242a06855a36b9a9049d5fb00630800cda4a2984
|
[
"Unlicense"
] | 2
|
2022-03-02T21:34:57.000Z
|
2022-03-14T12:44:43.000Z
|
# Used so that I can get a quick python -i up for today's debugging session. You don't want to learn from this code; it may not even work.
import synthizer
from synthizer import EchoTapConfig
import time
import random
import sys
import math
import atexit
# Normally you want to use the synthizer.initialized context manager, but I'm using this example
# as a script that sets up a Python shell for debugging, and I
# forgot to shut this down and had to kill via task manager one too many times.
#
# You always need to shut Synthizer down, but I'll be improving things so that failing to do so
# doesn't freeze things so badly that you have to kill it via task manager.
atexit.register(synthizer.shutdown)
synthizer.initialize(
log_level=synthizer.LogLevel.DEBUG, logging_backend=synthizer.LoggingBackend.STDERR
)
ctx = synthizer.Context(enable_events=True)
buffer = synthizer.Buffer.from_stream_params("file", sys.argv[1])
gen = synthizer.BufferGenerator(ctx)
gen2 = synthizer.BufferGenerator(ctx)
#gen = synthizer.StreamingGenerator.from_file(ctx, sys.argv[1])
#gen2 = synthizer.StreamingGenerator.from_file(ctx, sys.argv[1])
gen.buffer = buffer
gen2.buffer=buffer
# ctx.panner_strategy = synthizer.PannerStrategy.HRTF
src = synthizer.PannedSource(ctx)
src.add_generator(gen)
src.add_generator(gen2)
gen.config_delete_behavior(linger=True)
gen2.config_delete_behavior(linger=True)
src.config_delete_behavior(linger=True)
gen.dec_ref()
gen2.dec_ref()
src.dec_ref()
#src.panner_strategy = synthizer.PannerStrategy.HRTF
| 36.333333
| 138
| 0.79882
|
4a077af565d2a2e05c68c0693ce65f77b375e392
| 5,544
|
py
|
Python
|
datastore/ndb/transactions/main.py
|
xiaopeng163/python-docs-samples
|
b2bbfe15c27798d012f4a6e1fde33ae292a1e62a
|
[
"Apache-2.0"
] | null | null | null |
datastore/ndb/transactions/main.py
|
xiaopeng163/python-docs-samples
|
b2bbfe15c27798d012f4a6e1fde33ae292a1e62a
|
[
"Apache-2.0"
] | null | null | null |
datastore/ndb/transactions/main.py
|
xiaopeng163/python-docs-samples
|
b2bbfe15c27798d012f4a6e1fde33ae292a1e62a
|
[
"Apache-2.0"
] | 1
|
2018-05-13T05:31:10.000Z
|
2018-05-13T05:31:10.000Z
|
# Copyright 2015 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cgi
import random
import urllib
import flask
# [START taskq-imp]
from google.appengine.api import taskqueue
from google.appengine.ext import ndb
# [END taskq-imp]
class Note(ndb.Model):
"""Models an individual Note entry with content."""
content = ndb.StringProperty()
def parent_key(page_name):
return ndb.Key("Parent", page_name)
app = flask.Flask(__name__)
@app.route('/')
def main_page():
page_name = flask.request.args.get('page_name', 'default')
response = """
<html><body>
<h2>Permenant note page: %s</h2>""" % cgi.escape(page_name)
parent = parent_key(page_name)
notes = Note.query(ancestor=parent).fetch(20)
for note in notes:
response += '<h3>%s</h3>' % cgi.escape(note.key.id())
response += '<blockquote>%s</blockquote>' % cgi.escape(note.content)
response += (
"""<hr>
<form action="/add?%s" method="post">
Submit Note: <input value="Title" name="note_title"><br>
<textarea value="Note" name="note_text" rows="4" cols="60">
</textarea>
<input type="submit" value="Etch in stone"></form>"""
% urllib.urlencode({'page_name': page_name}))
response += """
<hr>
<form>Switch page: <input value="%s" name="page_name">
<input type="submit" value="Switch"></form>
</body>
</html>""" % cgi.escape(page_name, quote=True)
return response
# [START standard]
@ndb.transactional
def insert_if_absent(note_key, note):
fetch = note_key.get()
if fetch is None:
note.put()
return True
return False
# [END standard]
# [START two-tries]
@ndb.transactional(retries=1)
def insert_if_absent_2_retries(note_key, note):
# do insert
# [END two-tries]
fetch = note_key.get()
if fetch is None:
note.put()
return True
return False
# [START cross-group]
@ndb.transactional(xg=True)
def insert_if_absent_xg(note_key, note):
# do insert
# [END cross-group]
fetch = note_key.get()
if fetch is None:
note.put()
return True
return False
# [START sometimes]
def insert_if_absent_sometimes(note_key, note):
# do insert
# [END sometimes]
fetch = note_key.get()
if fetch is None:
note.put()
return True
return False
# [START indep]
@ndb.transactional(propagation=ndb.TransactionOptions.INDEPENDENT)
def insert_if_absent_indep(note_key, note):
# do insert
# [END indep]
fetch = note_key.get()
if fetch is None:
note.put()
return True
return False
# [START taskq]
@ndb.transactional
def insert_if_absent_taskq(note_key, note):
taskqueue.add(url=flask.url_for('taskq_worker'), transactional=True)
# do insert
# [END taskq]
fetch = note_key.get()
if fetch is None:
note.put()
return True
return False
@app.route('/worker')
def taskq_worker():
pass
def pick_random_insert(note_key, note):
choice = random.randint(0, 5)
if choice == 0:
# [START calling2]
inserted = insert_if_absent(note_key, note)
# [END calling2]
elif choice == 1:
inserted = insert_if_absent_2_retries(note_key, note)
elif choice == 2:
inserted = insert_if_absent_xg(note_key, note)
elif choice == 3:
# [START sometimes-call]
inserted = ndb.transaction(lambda:
insert_if_absent_sometimes(note_key, note))
# [END sometimes-call]
elif choice == 4:
inserted = insert_if_absent_indep(note_key, note)
elif choice == 5:
inserted = insert_if_absent_taskq(note_key, note)
return inserted
@app.route('/add', methods=['POST'])
def add_note():
page_name = flask.request.args.get('page_name', 'default')
note_title = flask.request.form['note_title']
note_text = flask.request.form['note_text']
parent = parent_key(page_name)
choice = random.randint(0, 1)
if choice == 0:
# Use transactional function
# [START calling]
note_key = ndb.Key(Note, note_title, parent=parent)
note = Note(key=note_key, content=note_text)
# [END calling]
if pick_random_insert(note_key, note) is False:
return ('Already there<br><a href="%s">Return</a>'
% flask.url_for('main_page', page_name=page_name))
return flask.redirect(flask.url_for('main_page', page_name=page_name))
elif choice == 1:
# Use get_or_insert, which is transactional
note = Note.get_or_insert(note_title, parent=parent, content=note_text)
if note.content != note_text:
return ('Already there<br><a href="%s">Return</a>'
% flask.url_for('main_page', page_name=page_name))
return flask.redirect(flask.url_for('main_page', page_name=page_name))
if __name__ == '__main__':
app.run()
| 28
| 79
| 0.637626
|
4a077b04986156dc74cbb7220f0ae0fa98fae8f0
| 598
|
py
|
Python
|
management/commands/generate_backup_key.py
|
audacious-software/Simple-Backup-Django
|
bdacadc916da93e68f19696b2167fc71ee4dd919
|
[
"Apache-2.0"
] | null | null | null |
management/commands/generate_backup_key.py
|
audacious-software/Simple-Backup-Django
|
bdacadc916da93e68f19696b2167fc71ee4dd919
|
[
"Apache-2.0"
] | null | null | null |
management/commands/generate_backup_key.py
|
audacious-software/Simple-Backup-Django
|
bdacadc916da93e68f19696b2167fc71ee4dd919
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# pylint: disable=no-member,line-too-long
from __future__ import print_function
import base64
import nacl.secret
import nacl.utils
from django.core.management.base import BaseCommand
class Command(BaseCommand):
help = 'Generates a SecretBox key to use for backups.'
def add_arguments(self, parser):
pass
def handle(self, *args, **options): # pylint: disable=too-many-locals,too-many-branches,too-many-statements
key = nacl.utils.random(nacl.secret.SecretBox.KEY_SIZE)
print('BACKUP KEY: ' + base64.b64encode(key).decode('utf-8'))
| 26
| 111
| 0.714047
|
4a077b44639308bf77a35575fa964eebb5224c3c
| 5,293
|
py
|
Python
|
kubernetes/client/models/v1beta1_subject_access_review.py
|
SEJeff/client-python
|
baba523c28a684b3f537502977d600dedd1f17c5
|
[
"Apache-2.0"
] | null | null | null |
kubernetes/client/models/v1beta1_subject_access_review.py
|
SEJeff/client-python
|
baba523c28a684b3f537502977d600dedd1f17c5
|
[
"Apache-2.0"
] | null | null | null |
kubernetes/client/models/v1beta1_subject_access_review.py
|
SEJeff/client-python
|
baba523c28a684b3f537502977d600dedd1f17c5
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.5.0-beta.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
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 pprint import pformat
from six import iteritems
import re
class V1beta1SubjectAccessReview(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, metadata=None, spec=None, status=None):
"""
V1beta1SubjectAccessReview - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'metadata': 'V1ObjectMeta',
'spec': 'V1beta1SubjectAccessReviewSpec',
'status': 'V1beta1SubjectAccessReviewStatus'
}
self.attribute_map = {
'metadata': 'metadata',
'spec': 'spec',
'status': 'status'
}
self._metadata = metadata
self._spec = spec
self._status = status
@property
def metadata(self):
"""
Gets the metadata of this V1beta1SubjectAccessReview.
:return: The metadata of this V1beta1SubjectAccessReview.
:rtype: V1ObjectMeta
"""
return self._metadata
@metadata.setter
def metadata(self, metadata):
"""
Sets the metadata of this V1beta1SubjectAccessReview.
:param metadata: The metadata of this V1beta1SubjectAccessReview.
:type: V1ObjectMeta
"""
self._metadata = metadata
@property
def spec(self):
"""
Gets the spec of this V1beta1SubjectAccessReview.
Spec holds information about the request being evaluated
:return: The spec of this V1beta1SubjectAccessReview.
:rtype: V1beta1SubjectAccessReviewSpec
"""
return self._spec
@spec.setter
def spec(self, spec):
"""
Sets the spec of this V1beta1SubjectAccessReview.
Spec holds information about the request being evaluated
:param spec: The spec of this V1beta1SubjectAccessReview.
:type: V1beta1SubjectAccessReviewSpec
"""
if spec is None:
raise ValueError("Invalid value for `spec`, must not be `None`")
self._spec = spec
@property
def status(self):
"""
Gets the status of this V1beta1SubjectAccessReview.
Status is filled in by the server and indicates whether the request is allowed or not
:return: The status of this V1beta1SubjectAccessReview.
:rtype: V1beta1SubjectAccessReviewStatus
"""
return self._status
@status.setter
def status(self, status):
"""
Sets the status of this V1beta1SubjectAccessReview.
Status is filled in by the server and indicates whether the request is allowed or not
:param status: The status of this V1beta1SubjectAccessReview.
:type: V1beta1SubjectAccessReviewStatus
"""
self._status = status
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_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:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
| 29.243094
| 105
| 0.598148
|
4a077ba99a12727fbc0145b3a2f7a79f103d0b4d
| 2,395
|
py
|
Python
|
watertap3/watertap3/utils/cost_curves.py
|
NREL/WaterTAP3
|
74b83dbd189784ccfddac4bc5d27002190473619
|
[
"BSD-3-Clause"
] | null | null | null |
watertap3/watertap3/utils/cost_curves.py
|
NREL/WaterTAP3
|
74b83dbd189784ccfddac4bc5d27002190473619
|
[
"BSD-3-Clause"
] | 34
|
2021-06-25T17:54:12.000Z
|
2021-06-25T17:54:27.000Z
|
watertap3/watertap3/utils/cost_curves.py
|
NREL/WaterTAP3
|
74b83dbd189784ccfddac4bc5d27002190473619
|
[
"BSD-3-Clause"
] | 4
|
2021-06-25T18:32:31.000Z
|
2022-03-24T20:24:18.000Z
|
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
__all__ = ['epa_cost_curve',
'basic_unit']
def epa_cost_curve(unit_process, **kwargs):
df = pd.read_csv('data/epa_cost_curves.csv', index_col='unit_process')
df = df.loc[unit_process]
params = ['flow_in', 'cap_total', 'electricity_intensity', 'tds_in', 'num_stage', 'radon_rem', 'ebct']
def power(x, a, b):
return a * x ** b
if kwargs:
temp = list(dict(**kwargs).items())[0]
k, v = temp[0], temp[1]
if k == 'tds_in':
if unit_process == 'cation_exchange':
if v >= 1000:
df = df[df.tds_in == 1000]
elif v < 1000 and v >= 600:
df = df[df.tds_in == 600]
else:
df = df[df.tds_in == 200]
elif unit_process == 'anion_exchange':
if v >= 150:
df = df[df.tds_in == 150]
elif v < 150 and v >= 100:
df = df[df.tds_in == 100]
else:
df = df[df.tds_in == 50]
if k == 'radon_rem':
if v >= 0.9:
df = df[df.radon_rem == 0.99]
else:
df = df[df.radon_rem == 0.9]
if k == 'ebct':
if v > 30:
df = df[df.ebct == 60]
else:
df = df[df.ebct == 30]
df.dropna(axis=1, inplace=True)
cols = df.columns
mats_name = [c for c in cols if c not in params]
mats_cost = {}
for mat in mats_name:
mats_cost[mat] = np.mean(df[mat])
x = df.flow_in.to_list()
y_cost = df.cap_total.to_list()
y_elect = df.electricity_intensity.to_list()
cost, _ = curve_fit(power, x, y_cost)
elect, _ = curve_fit(power, x, y_elect)
return cost, elect, mats_name, mats_cost, df
def basic_unit(unit_process, case_specific=None):
if case_specific == 'solaire':
df = pd.read_csv('data/basic_units_solaire.csv', index_col='unit_process')
else:
df = pd.read_csv('data/basic_unit.csv', index_col='unit_process')
df = df.loc[unit_process]
flow_basis = df.flow_basis
cap_basis = df.cap_basis
cap_exp = df.cap_exp
elect = df.electricity_intensity
year = df.year
kind = df.kind
return flow_basis, cap_basis, cap_exp, elect, year, kind
| 29.567901
| 106
| 0.533612
|
4a077bb1b0d9c182cc5237880e95d98cac0dce3b
| 9,920
|
py
|
Python
|
sensor-2.py
|
kinivi/end_to_end_example
|
71df6fa847155f4c42dc091f2c20f9a2cf001483
|
[
"MIT"
] | null | null | null |
sensor-2.py
|
kinivi/end_to_end_example
|
71df6fa847155f4c42dc091f2c20f9a2cf001483
|
[
"MIT"
] | null | null | null |
sensor-2.py
|
kinivi/end_to_end_example
|
71df6fa847155f4c42dc091f2c20f9a2cf001483
|
[
"MIT"
] | null | null | null |
# Copyright 2017 Google Inc. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Sample device that consumes configuration from Google Cloud IoT.
This example represents a simple device with a temperature sensor and a fan
(simulated with software). When the device's fan is turned on, its temperature
decreases by one degree per second, and when the device's fan is turned off,
its temperature increases by one degree per second.
Every second, the device publishes its temperature reading to Google Cloud IoT
Core. The server meanwhile receives these temperature readings, and decides
whether to re-configure the device to turn its fan on or off. The server will
instruct the device to turn the fan on when the device's temperature exceeds 10
degrees, and to turn it off when the device's temperature is less than 0
degrees. In a real system, one could use the cloud to compute the optimal
thresholds for turning on and off the fan, but for illustrative purposes we use
a simple threshold model.
To connect the device you must have downloaded Google's CA root certificates,
and a copy of your private key file. See cloud.google.com/iot for instructions
on how to do this. Run this script with the corresponding algorithm flag.
$ python cloudiot_pubsub_example_mqtt_device.py \
--project_id=my-project-id \
--registry_id=example-my-registry-id \
--device_id=my-device-id \
--private_key_file=rsa_private.pem \
--algorithm=RS256
With a single server, you can run multiple instances of the device with
different device ids, and the server will distinguish them. Try creating a few
devices and running them all at the same time.
"""
import argparse
import datetime
import json
import os
import random
import ssl
import time
import jwt
import paho.mqtt.client as mqtt
def create_jwt(project_id, private_key_file, algorithm):
"""Create a JWT (https://jwt.io) to establish an MQTT connection."""
token = {
'iat': datetime.datetime.utcnow(),
'exp': datetime.datetime.utcnow() + datetime.timedelta(minutes=60),
'aud': project_id
}
with open(private_key_file, 'r') as f:
private_key = f.read()
print('Creating JWT using {} from private key file {}'.format(
algorithm, private_key_file))
return jwt.encode(token, private_key, algorithm=algorithm)
def error_str(rc):
"""Convert a Paho error to a human readable string."""
return '{}: {}'.format(rc, mqtt.error_string(rc))
class Device(object):
"""Represents the state of a single device."""
def __init__(self):
self.humidity = 0
self.attributes = {"id": "sensor-2", "location": [41.80555, 20.10730], "time": str(datetime.datetime.utcnow())}
self.fan_on = False
self.connected = False
def update_sensor_data(self):
"""Pretend to read the device's sensor data.
If the fan is on, assume the temperature decreased one degree,
otherwise assume that it increased one degree.
"""
if self.fan_on:
self.humidity = random.randint(0, 100)
else:
self.humidity = random.randint(0, 100)
def wait_for_connection(self, timeout):
"""Wait for the device to become connected."""
total_time = 0
while not self.connected and total_time < timeout:
time.sleep(1)
total_time += 1
if not self.connected:
raise RuntimeError('Could not connect to MQTT bridge.')
def on_connect(self, unused_client, unused_userdata, unused_flags, rc):
"""Callback for when a device connects."""
print('Connection Result:', error_str(rc))
self.connected = True
def on_disconnect(self, unused_client, unused_userdata, rc):
"""Callback for when a device disconnects."""
print('Disconnected:', error_str(rc))
self.connected = False
def on_publish(self, unused_client, unused_userdata, unused_mid):
"""Callback when the device receives a PUBACK from the MQTT bridge."""
print('Published message acked.')
def on_subscribe(self, unused_client, unused_userdata, unused_mid,
granted_qos):
"""Callback when the device receives a SUBACK from the MQTT bridge."""
print('Subscribed: ', granted_qos)
if granted_qos[0] == 128:
print('Subscription failed.')
def on_message(self, unused_client, unused_userdata, message):
"""Callback when the device receives a message on a subscription."""
payload = message.payload.decode('utf-8')
print('Received message \'{}\' on topic \'{}\' with Qos {}'.format(
payload, message.topic, str(message.qos)))
# The device will receive its latest config when it subscribes to the
# config topic. If there is no configuration for the device, the device
# will receive a config with an empty payload.
if not payload:
return
# The config is passed in the payload of the message. In this example,
# the server sends a serialized JSON string.
data = json.loads(payload)
if data['fan_on'] != self.fan_on:
# If changing the state of the fan, print a message and
# update the internal state.
self.fan_on = data['fan_on']
if self.fan_on:
print('Fan turned on.')
else:
print('Fan turned off.')
def parse_command_line_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description='Example Google Cloud IoT MQTT device connection code.')
parser.add_argument(
'--project_id',
default=os.environ.get("GOOGLE_CLOUD_PROJECT"),
required=True,
help='GCP cloud project name.')
parser.add_argument(
'--registry_id', required=True, help='Cloud IoT registry id')
parser.add_argument(
'--device_id',
required=True,
help='Cloud IoT device id')
parser.add_argument(
'--private_key_file', required=True, help='Path to private key file.')
parser.add_argument(
'--algorithm',
choices=('RS256', 'ES256'),
required=True,
help='Which encryption algorithm to use to generate the JWT.')
parser.add_argument(
'--cloud_region', default='us-central1', help='GCP cloud region')
parser.add_argument(
'--ca_certs',
default='roots.pem',
help='CA root certificate. Get from https://pki.google.com/roots.pem')
parser.add_argument(
'--num_messages',
type=int,
default=100,
help='Number of messages to publish.')
parser.add_argument(
'--mqtt_bridge_hostname',
default='mqtt.googleapis.com',
help='MQTT bridge hostname.')
parser.add_argument(
'--mqtt_bridge_port', type=int, default=8883, help='MQTT bridge port.')
parser.add_argument(
'--message_type', choices=('event', 'state'),
default='event',
help=('Indicates whether the message to be published is a '
'telemetry event or a device state message.'))
return parser.parse_args()
def main():
args = parse_command_line_args()
# Create the MQTT client and connect to Cloud IoT.
client = mqtt.Client(
client_id='projects/{}/locations/{}/registries/{}/devices/{}'.format(
args.project_id,
args.cloud_region,
args.registry_id,
args.device_id))
client.username_pw_set(
username='unused',
password=create_jwt(
args.project_id,
args.private_key_file,
args.algorithm))
client.tls_set(ca_certs=args.ca_certs, tls_version=ssl.PROTOCOL_TLSv1_2)
device = Device()
client.on_connect = device.on_connect
client.on_publish = device.on_publish
client.on_disconnect = device.on_disconnect
client.on_subscribe = device.on_subscribe
client.on_message = device.on_message
client.connect(args.mqtt_bridge_hostname, args.mqtt_bridge_port)
client.loop_start()
# This is the topic that the device will publish telemetry events
# (temperature data) to.
mqtt_telemetry_topic = '/devices/{}/events'.format(args.device_id)
# This is the topic thaat the device will receive configuration updates on.
mqtt_config_topic = '/devices/{}/config'.format(args.device_id)
# Wait up to 5 seconds for the device to connect.
device.wait_for_connection(5)
# Subscribe to the config topic.
client.subscribe(mqtt_config_topic, qos=1)
# Update and publish temperature readings at a rate of one per second.
for _ in range(args.num_messages):
# In an actual device, this would read the device's sensors. Here,
# you update the temperature based on whether the fan is on.
device.update_sensor_data()
# Report the device's temperature to the server by serializing it
# as a JSON string.
payload = json.dumps({'humidity': device.humidity, 'attributes': device.attributes})
print('Publishing payload', payload)
client.publish(mqtt_telemetry_topic, payload, qos=1)
# Send events every second.
time.sleep(0.5)
client.disconnect()
client.loop_stop()
print('Finished loop successfully. Goodbye!')
if __name__ == '__main__':
main()
| 37.718631
| 119
| 0.671573
|
4a077cc4ca11038ad0bd7f7b78446c7f1efa3b69
| 51,067
|
py
|
Python
|
Lib/test/test_tempfile.py
|
pelotoncycle/cpython-fork
|
1ab99a0e912aac9c3f16555f23284d7e381f2f69
|
[
"PSF-2.0"
] | 332
|
2015-08-22T12:43:56.000Z
|
2022-03-17T01:05:43.000Z
|
Lib/test/test_tempfile.py
|
sky-skynet/Python3
|
b816507f56ee14b730b7ab52a61eb17f9eb9d815
|
[
"PSF-2.0"
] | 36
|
2015-05-30T08:39:19.000Z
|
2022-03-04T20:42:33.000Z
|
Lib/test/test_tempfile.py
|
sky-skynet/Python3
|
b816507f56ee14b730b7ab52a61eb17f9eb9d815
|
[
"PSF-2.0"
] | 74
|
2015-05-29T17:18:53.000Z
|
2022-01-15T14:06:44.000Z
|
# tempfile.py unit tests.
import tempfile
import errno
import io
import os
import signal
import sys
import re
import warnings
import contextlib
import weakref
from unittest import mock
import unittest
from test import support
from test.support import script_helper
if hasattr(os, 'stat'):
import stat
has_stat = 1
else:
has_stat = 0
has_textmode = (tempfile._text_openflags != tempfile._bin_openflags)
has_spawnl = hasattr(os, 'spawnl')
# TEST_FILES may need to be tweaked for systems depending on the maximum
# number of files that can be opened at one time (see ulimit -n)
if sys.platform.startswith('openbsd'):
TEST_FILES = 48
else:
TEST_FILES = 100
# This is organized as one test for each chunk of code in tempfile.py,
# in order of their appearance in the file. Testing which requires
# threads is not done here.
class TestLowLevelInternals(unittest.TestCase):
def test_infer_return_type_singles(self):
self.assertIs(str, tempfile._infer_return_type(''))
self.assertIs(bytes, tempfile._infer_return_type(b''))
self.assertIs(str, tempfile._infer_return_type(None))
def test_infer_return_type_multiples(self):
self.assertIs(str, tempfile._infer_return_type('', ''))
self.assertIs(bytes, tempfile._infer_return_type(b'', b''))
with self.assertRaises(TypeError):
tempfile._infer_return_type('', b'')
with self.assertRaises(TypeError):
tempfile._infer_return_type(b'', '')
def test_infer_return_type_multiples_and_none(self):
self.assertIs(str, tempfile._infer_return_type(None, ''))
self.assertIs(str, tempfile._infer_return_type('', None))
self.assertIs(str, tempfile._infer_return_type(None, None))
self.assertIs(bytes, tempfile._infer_return_type(b'', None))
self.assertIs(bytes, tempfile._infer_return_type(None, b''))
with self.assertRaises(TypeError):
tempfile._infer_return_type('', None, b'')
with self.assertRaises(TypeError):
tempfile._infer_return_type(b'', None, '')
# Common functionality.
class BaseTestCase(unittest.TestCase):
str_check = re.compile(r"^[a-z0-9_-]{8}$")
b_check = re.compile(br"^[a-z0-9_-]{8}$")
def setUp(self):
self._warnings_manager = support.check_warnings()
self._warnings_manager.__enter__()
warnings.filterwarnings("ignore", category=RuntimeWarning,
message="mktemp", module=__name__)
def tearDown(self):
self._warnings_manager.__exit__(None, None, None)
def nameCheck(self, name, dir, pre, suf):
(ndir, nbase) = os.path.split(name)
npre = nbase[:len(pre)]
nsuf = nbase[len(nbase)-len(suf):]
if dir is not None:
self.assertIs(type(name), str if type(dir) is str else bytes,
"unexpected return type")
if pre is not None:
self.assertIs(type(name), str if type(pre) is str else bytes,
"unexpected return type")
if suf is not None:
self.assertIs(type(name), str if type(suf) is str else bytes,
"unexpected return type")
if (dir, pre, suf) == (None, None, None):
self.assertIs(type(name), str, "default return type must be str")
# check for equality of the absolute paths!
self.assertEqual(os.path.abspath(ndir), os.path.abspath(dir),
"file %r not in directory %r" % (name, dir))
self.assertEqual(npre, pre,
"file %r does not begin with %r" % (nbase, pre))
self.assertEqual(nsuf, suf,
"file %r does not end with %r" % (nbase, suf))
nbase = nbase[len(pre):len(nbase)-len(suf)]
check = self.str_check if isinstance(nbase, str) else self.b_check
self.assertTrue(check.match(nbase),
"random characters %r do not match %r"
% (nbase, check.pattern))
class TestExports(BaseTestCase):
def test_exports(self):
# There are no surprising symbols in the tempfile module
dict = tempfile.__dict__
expected = {
"NamedTemporaryFile" : 1,
"TemporaryFile" : 1,
"mkstemp" : 1,
"mkdtemp" : 1,
"mktemp" : 1,
"TMP_MAX" : 1,
"gettempprefix" : 1,
"gettempprefixb" : 1,
"gettempdir" : 1,
"gettempdirb" : 1,
"tempdir" : 1,
"template" : 1,
"SpooledTemporaryFile" : 1,
"TemporaryDirectory" : 1,
}
unexp = []
for key in dict:
if key[0] != '_' and key not in expected:
unexp.append(key)
self.assertTrue(len(unexp) == 0,
"unexpected keys: %s" % unexp)
class TestRandomNameSequence(BaseTestCase):
"""Test the internal iterator object _RandomNameSequence."""
def setUp(self):
self.r = tempfile._RandomNameSequence()
super().setUp()
def test_get_six_char_str(self):
# _RandomNameSequence returns a six-character string
s = next(self.r)
self.nameCheck(s, '', '', '')
def test_many(self):
# _RandomNameSequence returns no duplicate strings (stochastic)
dict = {}
r = self.r
for i in range(TEST_FILES):
s = next(r)
self.nameCheck(s, '', '', '')
self.assertNotIn(s, dict)
dict[s] = 1
def supports_iter(self):
# _RandomNameSequence supports the iterator protocol
i = 0
r = self.r
for s in r:
i += 1
if i == 20:
break
@unittest.skipUnless(hasattr(os, 'fork'),
"os.fork is required for this test")
def test_process_awareness(self):
# ensure that the random source differs between
# child and parent.
read_fd, write_fd = os.pipe()
pid = None
try:
pid = os.fork()
if not pid:
os.close(read_fd)
os.write(write_fd, next(self.r).encode("ascii"))
os.close(write_fd)
# bypass the normal exit handlers- leave those to
# the parent.
os._exit(0)
parent_value = next(self.r)
child_value = os.read(read_fd, len(parent_value)).decode("ascii")
finally:
if pid:
# best effort to ensure the process can't bleed out
# via any bugs above
try:
os.kill(pid, signal.SIGKILL)
except OSError:
pass
os.close(read_fd)
os.close(write_fd)
self.assertNotEqual(child_value, parent_value)
class TestCandidateTempdirList(BaseTestCase):
"""Test the internal function _candidate_tempdir_list."""
def test_nonempty_list(self):
# _candidate_tempdir_list returns a nonempty list of strings
cand = tempfile._candidate_tempdir_list()
self.assertFalse(len(cand) == 0)
for c in cand:
self.assertIsInstance(c, str)
def test_wanted_dirs(self):
# _candidate_tempdir_list contains the expected directories
# Make sure the interesting environment variables are all set.
with support.EnvironmentVarGuard() as env:
for envname in 'TMPDIR', 'TEMP', 'TMP':
dirname = os.getenv(envname)
if not dirname:
env[envname] = os.path.abspath(envname)
cand = tempfile._candidate_tempdir_list()
for envname in 'TMPDIR', 'TEMP', 'TMP':
dirname = os.getenv(envname)
if not dirname: raise ValueError
self.assertIn(dirname, cand)
try:
dirname = os.getcwd()
except (AttributeError, OSError):
dirname = os.curdir
self.assertIn(dirname, cand)
# Not practical to try to verify the presence of OS-specific
# paths in this list.
# We test _get_default_tempdir some more by testing gettempdir.
class TestGetDefaultTempdir(BaseTestCase):
"""Test _get_default_tempdir()."""
def test_no_files_left_behind(self):
# use a private empty directory
with tempfile.TemporaryDirectory() as our_temp_directory:
# force _get_default_tempdir() to consider our empty directory
def our_candidate_list():
return [our_temp_directory]
with support.swap_attr(tempfile, "_candidate_tempdir_list",
our_candidate_list):
# verify our directory is empty after _get_default_tempdir()
tempfile._get_default_tempdir()
self.assertEqual(os.listdir(our_temp_directory), [])
def raise_OSError(*args, **kwargs):
raise OSError()
with support.swap_attr(io, "open", raise_OSError):
# test again with failing io.open()
with self.assertRaises(FileNotFoundError):
tempfile._get_default_tempdir()
self.assertEqual(os.listdir(our_temp_directory), [])
open = io.open
def bad_writer(*args, **kwargs):
fp = open(*args, **kwargs)
fp.write = raise_OSError
return fp
with support.swap_attr(io, "open", bad_writer):
# test again with failing write()
with self.assertRaises(FileNotFoundError):
tempfile._get_default_tempdir()
self.assertEqual(os.listdir(our_temp_directory), [])
class TestGetCandidateNames(BaseTestCase):
"""Test the internal function _get_candidate_names."""
def test_retval(self):
# _get_candidate_names returns a _RandomNameSequence object
obj = tempfile._get_candidate_names()
self.assertIsInstance(obj, tempfile._RandomNameSequence)
def test_same_thing(self):
# _get_candidate_names always returns the same object
a = tempfile._get_candidate_names()
b = tempfile._get_candidate_names()
self.assertTrue(a is b)
@contextlib.contextmanager
def _inside_empty_temp_dir():
dir = tempfile.mkdtemp()
try:
with support.swap_attr(tempfile, 'tempdir', dir):
yield
finally:
support.rmtree(dir)
def _mock_candidate_names(*names):
return support.swap_attr(tempfile,
'_get_candidate_names',
lambda: iter(names))
class TestBadTempdir:
def test_read_only_directory(self):
with _inside_empty_temp_dir():
oldmode = mode = os.stat(tempfile.tempdir).st_mode
mode &= ~(stat.S_IWUSR | stat.S_IWGRP | stat.S_IWOTH)
os.chmod(tempfile.tempdir, mode)
try:
if os.access(tempfile.tempdir, os.W_OK):
self.skipTest("can't set the directory read-only")
with self.assertRaises(PermissionError):
self.make_temp()
self.assertEqual(os.listdir(tempfile.tempdir), [])
finally:
os.chmod(tempfile.tempdir, oldmode)
def test_nonexisting_directory(self):
with _inside_empty_temp_dir():
tempdir = os.path.join(tempfile.tempdir, 'nonexistent')
with support.swap_attr(tempfile, 'tempdir', tempdir):
with self.assertRaises(FileNotFoundError):
self.make_temp()
def test_non_directory(self):
with _inside_empty_temp_dir():
tempdir = os.path.join(tempfile.tempdir, 'file')
open(tempdir, 'wb').close()
with support.swap_attr(tempfile, 'tempdir', tempdir):
with self.assertRaises((NotADirectoryError, FileNotFoundError)):
self.make_temp()
class TestMkstempInner(TestBadTempdir, BaseTestCase):
"""Test the internal function _mkstemp_inner."""
class mkstemped:
_bflags = tempfile._bin_openflags
_tflags = tempfile._text_openflags
_close = os.close
_unlink = os.unlink
def __init__(self, dir, pre, suf, bin):
if bin: flags = self._bflags
else: flags = self._tflags
output_type = tempfile._infer_return_type(dir, pre, suf)
(self.fd, self.name) = tempfile._mkstemp_inner(dir, pre, suf, flags, output_type)
def write(self, str):
os.write(self.fd, str)
def __del__(self):
self._close(self.fd)
self._unlink(self.name)
def do_create(self, dir=None, pre=None, suf=None, bin=1):
output_type = tempfile._infer_return_type(dir, pre, suf)
if dir is None:
if output_type is str:
dir = tempfile.gettempdir()
else:
dir = tempfile.gettempdirb()
if pre is None:
pre = output_type()
if suf is None:
suf = output_type()
file = self.mkstemped(dir, pre, suf, bin)
self.nameCheck(file.name, dir, pre, suf)
return file
def test_basic(self):
# _mkstemp_inner can create files
self.do_create().write(b"blat")
self.do_create(pre="a").write(b"blat")
self.do_create(suf="b").write(b"blat")
self.do_create(pre="a", suf="b").write(b"blat")
self.do_create(pre="aa", suf=".txt").write(b"blat")
def test_basic_with_bytes_names(self):
# _mkstemp_inner can create files when given name parts all
# specified as bytes.
dir_b = tempfile.gettempdirb()
self.do_create(dir=dir_b, suf=b"").write(b"blat")
self.do_create(dir=dir_b, pre=b"a").write(b"blat")
self.do_create(dir=dir_b, suf=b"b").write(b"blat")
self.do_create(dir=dir_b, pre=b"a", suf=b"b").write(b"blat")
self.do_create(dir=dir_b, pre=b"aa", suf=b".txt").write(b"blat")
# Can't mix str & binary types in the args.
with self.assertRaises(TypeError):
self.do_create(dir="", suf=b"").write(b"blat")
with self.assertRaises(TypeError):
self.do_create(dir=dir_b, pre="").write(b"blat")
with self.assertRaises(TypeError):
self.do_create(dir=dir_b, pre=b"", suf="").write(b"blat")
def test_basic_many(self):
# _mkstemp_inner can create many files (stochastic)
extant = list(range(TEST_FILES))
for i in extant:
extant[i] = self.do_create(pre="aa")
def test_choose_directory(self):
# _mkstemp_inner can create files in a user-selected directory
dir = tempfile.mkdtemp()
try:
self.do_create(dir=dir).write(b"blat")
finally:
os.rmdir(dir)
@unittest.skipUnless(has_stat, 'os.stat not available')
def test_file_mode(self):
# _mkstemp_inner creates files with the proper mode
file = self.do_create()
mode = stat.S_IMODE(os.stat(file.name).st_mode)
expected = 0o600
if sys.platform == 'win32':
# There's no distinction among 'user', 'group' and 'world';
# replicate the 'user' bits.
user = expected >> 6
expected = user * (1 + 8 + 64)
self.assertEqual(mode, expected)
@unittest.skipUnless(has_spawnl, 'os.spawnl not available')
def test_noinherit(self):
# _mkstemp_inner file handles are not inherited by child processes
if support.verbose:
v="v"
else:
v="q"
file = self.do_create()
self.assertEqual(os.get_inheritable(file.fd), False)
fd = "%d" % file.fd
try:
me = __file__
except NameError:
me = sys.argv[0]
# We have to exec something, so that FD_CLOEXEC will take
# effect. The core of this test is therefore in
# tf_inherit_check.py, which see.
tester = os.path.join(os.path.dirname(os.path.abspath(me)),
"tf_inherit_check.py")
# On Windows a spawn* /path/ with embedded spaces shouldn't be quoted,
# but an arg with embedded spaces should be decorated with double
# quotes on each end
if sys.platform == 'win32':
decorated = '"%s"' % sys.executable
tester = '"%s"' % tester
else:
decorated = sys.executable
retval = os.spawnl(os.P_WAIT, sys.executable, decorated, tester, v, fd)
self.assertFalse(retval < 0,
"child process caught fatal signal %d" % -retval)
self.assertFalse(retval > 0, "child process reports failure %d"%retval)
@unittest.skipUnless(has_textmode, "text mode not available")
def test_textmode(self):
# _mkstemp_inner can create files in text mode
# A text file is truncated at the first Ctrl+Z byte
f = self.do_create(bin=0)
f.write(b"blat\x1a")
f.write(b"extra\n")
os.lseek(f.fd, 0, os.SEEK_SET)
self.assertEqual(os.read(f.fd, 20), b"blat")
def make_temp(self):
return tempfile._mkstemp_inner(tempfile.gettempdir(),
tempfile.gettempprefix(),
'',
tempfile._bin_openflags,
str)
def test_collision_with_existing_file(self):
# _mkstemp_inner tries another name when a file with
# the chosen name already exists
with _inside_empty_temp_dir(), \
_mock_candidate_names('aaa', 'aaa', 'bbb'):
(fd1, name1) = self.make_temp()
os.close(fd1)
self.assertTrue(name1.endswith('aaa'))
(fd2, name2) = self.make_temp()
os.close(fd2)
self.assertTrue(name2.endswith('bbb'))
def test_collision_with_existing_directory(self):
# _mkstemp_inner tries another name when a directory with
# the chosen name already exists
with _inside_empty_temp_dir(), \
_mock_candidate_names('aaa', 'aaa', 'bbb'):
dir = tempfile.mkdtemp()
self.assertTrue(dir.endswith('aaa'))
(fd, name) = self.make_temp()
os.close(fd)
self.assertTrue(name.endswith('bbb'))
class TestGetTempPrefix(BaseTestCase):
"""Test gettempprefix()."""
def test_sane_template(self):
# gettempprefix returns a nonempty prefix string
p = tempfile.gettempprefix()
self.assertIsInstance(p, str)
self.assertGreater(len(p), 0)
pb = tempfile.gettempprefixb()
self.assertIsInstance(pb, bytes)
self.assertGreater(len(pb), 0)
def test_usable_template(self):
# gettempprefix returns a usable prefix string
# Create a temp directory, avoiding use of the prefix.
# Then attempt to create a file whose name is
# prefix + 'xxxxxx.xxx' in that directory.
p = tempfile.gettempprefix() + "xxxxxx.xxx"
d = tempfile.mkdtemp(prefix="")
try:
p = os.path.join(d, p)
fd = os.open(p, os.O_RDWR | os.O_CREAT)
os.close(fd)
os.unlink(p)
finally:
os.rmdir(d)
class TestGetTempDir(BaseTestCase):
"""Test gettempdir()."""
def test_directory_exists(self):
# gettempdir returns a directory which exists
for d in (tempfile.gettempdir(), tempfile.gettempdirb()):
self.assertTrue(os.path.isabs(d) or d == os.curdir,
"%r is not an absolute path" % d)
self.assertTrue(os.path.isdir(d),
"%r is not a directory" % d)
def test_directory_writable(self):
# gettempdir returns a directory writable by the user
# sneaky: just instantiate a NamedTemporaryFile, which
# defaults to writing into the directory returned by
# gettempdir.
file = tempfile.NamedTemporaryFile()
file.write(b"blat")
file.close()
def test_same_thing(self):
# gettempdir always returns the same object
a = tempfile.gettempdir()
b = tempfile.gettempdir()
c = tempfile.gettempdirb()
self.assertTrue(a is b)
self.assertNotEqual(type(a), type(c))
self.assertEqual(a, os.fsdecode(c))
def test_case_sensitive(self):
# gettempdir should not flatten its case
# even on a case-insensitive file system
case_sensitive_tempdir = tempfile.mkdtemp("-Temp")
_tempdir, tempfile.tempdir = tempfile.tempdir, None
try:
with support.EnvironmentVarGuard() as env:
# Fake the first env var which is checked as a candidate
env["TMPDIR"] = case_sensitive_tempdir
self.assertEqual(tempfile.gettempdir(), case_sensitive_tempdir)
finally:
tempfile.tempdir = _tempdir
support.rmdir(case_sensitive_tempdir)
class TestMkstemp(BaseTestCase):
"""Test mkstemp()."""
def do_create(self, dir=None, pre=None, suf=None):
output_type = tempfile._infer_return_type(dir, pre, suf)
if dir is None:
if output_type is str:
dir = tempfile.gettempdir()
else:
dir = tempfile.gettempdirb()
if pre is None:
pre = output_type()
if suf is None:
suf = output_type()
(fd, name) = tempfile.mkstemp(dir=dir, prefix=pre, suffix=suf)
(ndir, nbase) = os.path.split(name)
adir = os.path.abspath(dir)
self.assertEqual(adir, ndir,
"Directory '%s' incorrectly returned as '%s'" % (adir, ndir))
try:
self.nameCheck(name, dir, pre, suf)
finally:
os.close(fd)
os.unlink(name)
def test_basic(self):
# mkstemp can create files
self.do_create()
self.do_create(pre="a")
self.do_create(suf="b")
self.do_create(pre="a", suf="b")
self.do_create(pre="aa", suf=".txt")
self.do_create(dir=".")
def test_basic_with_bytes_names(self):
# mkstemp can create files when given name parts all
# specified as bytes.
d = tempfile.gettempdirb()
self.do_create(dir=d, suf=b"")
self.do_create(dir=d, pre=b"a")
self.do_create(dir=d, suf=b"b")
self.do_create(dir=d, pre=b"a", suf=b"b")
self.do_create(dir=d, pre=b"aa", suf=b".txt")
self.do_create(dir=b".")
with self.assertRaises(TypeError):
self.do_create(dir=".", pre=b"aa", suf=b".txt")
with self.assertRaises(TypeError):
self.do_create(dir=b".", pre="aa", suf=b".txt")
with self.assertRaises(TypeError):
self.do_create(dir=b".", pre=b"aa", suf=".txt")
def test_choose_directory(self):
# mkstemp can create directories in a user-selected directory
dir = tempfile.mkdtemp()
try:
self.do_create(dir=dir)
finally:
os.rmdir(dir)
class TestMkdtemp(TestBadTempdir, BaseTestCase):
"""Test mkdtemp()."""
def make_temp(self):
return tempfile.mkdtemp()
def do_create(self, dir=None, pre=None, suf=None):
output_type = tempfile._infer_return_type(dir, pre, suf)
if dir is None:
if output_type is str:
dir = tempfile.gettempdir()
else:
dir = tempfile.gettempdirb()
if pre is None:
pre = output_type()
if suf is None:
suf = output_type()
name = tempfile.mkdtemp(dir=dir, prefix=pre, suffix=suf)
try:
self.nameCheck(name, dir, pre, suf)
return name
except:
os.rmdir(name)
raise
def test_basic(self):
# mkdtemp can create directories
os.rmdir(self.do_create())
os.rmdir(self.do_create(pre="a"))
os.rmdir(self.do_create(suf="b"))
os.rmdir(self.do_create(pre="a", suf="b"))
os.rmdir(self.do_create(pre="aa", suf=".txt"))
def test_basic_with_bytes_names(self):
# mkdtemp can create directories when given all binary parts
d = tempfile.gettempdirb()
os.rmdir(self.do_create(dir=d))
os.rmdir(self.do_create(dir=d, pre=b"a"))
os.rmdir(self.do_create(dir=d, suf=b"b"))
os.rmdir(self.do_create(dir=d, pre=b"a", suf=b"b"))
os.rmdir(self.do_create(dir=d, pre=b"aa", suf=b".txt"))
with self.assertRaises(TypeError):
os.rmdir(self.do_create(dir=d, pre="aa", suf=b".txt"))
with self.assertRaises(TypeError):
os.rmdir(self.do_create(dir=d, pre=b"aa", suf=".txt"))
with self.assertRaises(TypeError):
os.rmdir(self.do_create(dir="", pre=b"aa", suf=b".txt"))
def test_basic_many(self):
# mkdtemp can create many directories (stochastic)
extant = list(range(TEST_FILES))
try:
for i in extant:
extant[i] = self.do_create(pre="aa")
finally:
for i in extant:
if(isinstance(i, str)):
os.rmdir(i)
def test_choose_directory(self):
# mkdtemp can create directories in a user-selected directory
dir = tempfile.mkdtemp()
try:
os.rmdir(self.do_create(dir=dir))
finally:
os.rmdir(dir)
@unittest.skipUnless(has_stat, 'os.stat not available')
def test_mode(self):
# mkdtemp creates directories with the proper mode
dir = self.do_create()
try:
mode = stat.S_IMODE(os.stat(dir).st_mode)
mode &= 0o777 # Mask off sticky bits inherited from /tmp
expected = 0o700
if sys.platform == 'win32':
# There's no distinction among 'user', 'group' and 'world';
# replicate the 'user' bits.
user = expected >> 6
expected = user * (1 + 8 + 64)
self.assertEqual(mode, expected)
finally:
os.rmdir(dir)
def test_collision_with_existing_file(self):
# mkdtemp tries another name when a file with
# the chosen name already exists
with _inside_empty_temp_dir(), \
_mock_candidate_names('aaa', 'aaa', 'bbb'):
file = tempfile.NamedTemporaryFile(delete=False)
file.close()
self.assertTrue(file.name.endswith('aaa'))
dir = tempfile.mkdtemp()
self.assertTrue(dir.endswith('bbb'))
def test_collision_with_existing_directory(self):
# mkdtemp tries another name when a directory with
# the chosen name already exists
with _inside_empty_temp_dir(), \
_mock_candidate_names('aaa', 'aaa', 'bbb'):
dir1 = tempfile.mkdtemp()
self.assertTrue(dir1.endswith('aaa'))
dir2 = tempfile.mkdtemp()
self.assertTrue(dir2.endswith('bbb'))
class TestMktemp(BaseTestCase):
"""Test mktemp()."""
# For safety, all use of mktemp must occur in a private directory.
# We must also suppress the RuntimeWarning it generates.
def setUp(self):
self.dir = tempfile.mkdtemp()
super().setUp()
def tearDown(self):
if self.dir:
os.rmdir(self.dir)
self.dir = None
super().tearDown()
class mktemped:
_unlink = os.unlink
_bflags = tempfile._bin_openflags
def __init__(self, dir, pre, suf):
self.name = tempfile.mktemp(dir=dir, prefix=pre, suffix=suf)
# Create the file. This will raise an exception if it's
# mysteriously appeared in the meanwhile.
os.close(os.open(self.name, self._bflags, 0o600))
def __del__(self):
self._unlink(self.name)
def do_create(self, pre="", suf=""):
file = self.mktemped(self.dir, pre, suf)
self.nameCheck(file.name, self.dir, pre, suf)
return file
def test_basic(self):
# mktemp can choose usable file names
self.do_create()
self.do_create(pre="a")
self.do_create(suf="b")
self.do_create(pre="a", suf="b")
self.do_create(pre="aa", suf=".txt")
def test_many(self):
# mktemp can choose many usable file names (stochastic)
extant = list(range(TEST_FILES))
for i in extant:
extant[i] = self.do_create(pre="aa")
## def test_warning(self):
## # mktemp issues a warning when used
## warnings.filterwarnings("error",
## category=RuntimeWarning,
## message="mktemp")
## self.assertRaises(RuntimeWarning,
## tempfile.mktemp, dir=self.dir)
# We test _TemporaryFileWrapper by testing NamedTemporaryFile.
class TestNamedTemporaryFile(BaseTestCase):
"""Test NamedTemporaryFile()."""
def do_create(self, dir=None, pre="", suf="", delete=True):
if dir is None:
dir = tempfile.gettempdir()
file = tempfile.NamedTemporaryFile(dir=dir, prefix=pre, suffix=suf,
delete=delete)
self.nameCheck(file.name, dir, pre, suf)
return file
def test_basic(self):
# NamedTemporaryFile can create files
self.do_create()
self.do_create(pre="a")
self.do_create(suf="b")
self.do_create(pre="a", suf="b")
self.do_create(pre="aa", suf=".txt")
def test_method_lookup(self):
# Issue #18879: Looking up a temporary file method should keep it
# alive long enough.
f = self.do_create()
wr = weakref.ref(f)
write = f.write
write2 = f.write
del f
write(b'foo')
del write
write2(b'bar')
del write2
if support.check_impl_detail(cpython=True):
# No reference cycle was created.
self.assertIsNone(wr())
def test_iter(self):
# Issue #23700: getting iterator from a temporary file should keep
# it alive as long as it's being iterated over
lines = [b'spam\n', b'eggs\n', b'beans\n']
def make_file():
f = tempfile.NamedTemporaryFile(mode='w+b')
f.write(b''.join(lines))
f.seek(0)
return f
for i, l in enumerate(make_file()):
self.assertEqual(l, lines[i])
self.assertEqual(i, len(lines) - 1)
def test_creates_named(self):
# NamedTemporaryFile creates files with names
f = tempfile.NamedTemporaryFile()
self.assertTrue(os.path.exists(f.name),
"NamedTemporaryFile %s does not exist" % f.name)
def test_del_on_close(self):
# A NamedTemporaryFile is deleted when closed
dir = tempfile.mkdtemp()
try:
f = tempfile.NamedTemporaryFile(dir=dir)
f.write(b'blat')
f.close()
self.assertFalse(os.path.exists(f.name),
"NamedTemporaryFile %s exists after close" % f.name)
finally:
os.rmdir(dir)
def test_dis_del_on_close(self):
# Tests that delete-on-close can be disabled
dir = tempfile.mkdtemp()
tmp = None
try:
f = tempfile.NamedTemporaryFile(dir=dir, delete=False)
tmp = f.name
f.write(b'blat')
f.close()
self.assertTrue(os.path.exists(f.name),
"NamedTemporaryFile %s missing after close" % f.name)
finally:
if tmp is not None:
os.unlink(tmp)
os.rmdir(dir)
def test_multiple_close(self):
# A NamedTemporaryFile can be closed many times without error
f = tempfile.NamedTemporaryFile()
f.write(b'abc\n')
f.close()
f.close()
f.close()
def test_context_manager(self):
# A NamedTemporaryFile can be used as a context manager
with tempfile.NamedTemporaryFile() as f:
self.assertTrue(os.path.exists(f.name))
self.assertFalse(os.path.exists(f.name))
def use_closed():
with f:
pass
self.assertRaises(ValueError, use_closed)
def test_no_leak_fd(self):
# Issue #21058: don't leak file descriptor when io.open() fails
closed = []
os_close = os.close
def close(fd):
closed.append(fd)
os_close(fd)
with mock.patch('os.close', side_effect=close):
with mock.patch('io.open', side_effect=ValueError):
self.assertRaises(ValueError, tempfile.NamedTemporaryFile)
self.assertEqual(len(closed), 1)
# How to test the mode and bufsize parameters?
class TestSpooledTemporaryFile(BaseTestCase):
"""Test SpooledTemporaryFile()."""
def do_create(self, max_size=0, dir=None, pre="", suf=""):
if dir is None:
dir = tempfile.gettempdir()
file = tempfile.SpooledTemporaryFile(max_size=max_size, dir=dir, prefix=pre, suffix=suf)
return file
def test_basic(self):
# SpooledTemporaryFile can create files
f = self.do_create()
self.assertFalse(f._rolled)
f = self.do_create(max_size=100, pre="a", suf=".txt")
self.assertFalse(f._rolled)
def test_del_on_close(self):
# A SpooledTemporaryFile is deleted when closed
dir = tempfile.mkdtemp()
try:
f = tempfile.SpooledTemporaryFile(max_size=10, dir=dir)
self.assertFalse(f._rolled)
f.write(b'blat ' * 5)
self.assertTrue(f._rolled)
filename = f.name
f.close()
self.assertFalse(isinstance(filename, str) and os.path.exists(filename),
"SpooledTemporaryFile %s exists after close" % filename)
finally:
os.rmdir(dir)
def test_rewrite_small(self):
# A SpooledTemporaryFile can be written to multiple within the max_size
f = self.do_create(max_size=30)
self.assertFalse(f._rolled)
for i in range(5):
f.seek(0, 0)
f.write(b'x' * 20)
self.assertFalse(f._rolled)
def test_write_sequential(self):
# A SpooledTemporaryFile should hold exactly max_size bytes, and roll
# over afterward
f = self.do_create(max_size=30)
self.assertFalse(f._rolled)
f.write(b'x' * 20)
self.assertFalse(f._rolled)
f.write(b'x' * 10)
self.assertFalse(f._rolled)
f.write(b'x')
self.assertTrue(f._rolled)
def test_writelines(self):
# Verify writelines with a SpooledTemporaryFile
f = self.do_create()
f.writelines((b'x', b'y', b'z'))
f.seek(0)
buf = f.read()
self.assertEqual(buf, b'xyz')
def test_writelines_sequential(self):
# A SpooledTemporaryFile should hold exactly max_size bytes, and roll
# over afterward
f = self.do_create(max_size=35)
f.writelines((b'x' * 20, b'x' * 10, b'x' * 5))
self.assertFalse(f._rolled)
f.write(b'x')
self.assertTrue(f._rolled)
def test_sparse(self):
# A SpooledTemporaryFile that is written late in the file will extend
# when that occurs
f = self.do_create(max_size=30)
self.assertFalse(f._rolled)
f.seek(100, 0)
self.assertFalse(f._rolled)
f.write(b'x')
self.assertTrue(f._rolled)
def test_fileno(self):
# A SpooledTemporaryFile should roll over to a real file on fileno()
f = self.do_create(max_size=30)
self.assertFalse(f._rolled)
self.assertTrue(f.fileno() > 0)
self.assertTrue(f._rolled)
def test_multiple_close_before_rollover(self):
# A SpooledTemporaryFile can be closed many times without error
f = tempfile.SpooledTemporaryFile()
f.write(b'abc\n')
self.assertFalse(f._rolled)
f.close()
f.close()
f.close()
def test_multiple_close_after_rollover(self):
# A SpooledTemporaryFile can be closed many times without error
f = tempfile.SpooledTemporaryFile(max_size=1)
f.write(b'abc\n')
self.assertTrue(f._rolled)
f.close()
f.close()
f.close()
def test_bound_methods(self):
# It should be OK to steal a bound method from a SpooledTemporaryFile
# and use it independently; when the file rolls over, those bound
# methods should continue to function
f = self.do_create(max_size=30)
read = f.read
write = f.write
seek = f.seek
write(b"a" * 35)
write(b"b" * 35)
seek(0, 0)
self.assertEqual(read(70), b'a'*35 + b'b'*35)
def test_properties(self):
f = tempfile.SpooledTemporaryFile(max_size=10)
f.write(b'x' * 10)
self.assertFalse(f._rolled)
self.assertEqual(f.mode, 'w+b')
self.assertIsNone(f.name)
with self.assertRaises(AttributeError):
f.newlines
with self.assertRaises(AttributeError):
f.encoding
f.write(b'x')
self.assertTrue(f._rolled)
self.assertEqual(f.mode, 'rb+')
self.assertIsNotNone(f.name)
with self.assertRaises(AttributeError):
f.newlines
with self.assertRaises(AttributeError):
f.encoding
def test_text_mode(self):
# Creating a SpooledTemporaryFile with a text mode should produce
# a file object reading and writing (Unicode) text strings.
f = tempfile.SpooledTemporaryFile(mode='w+', max_size=10)
f.write("abc\n")
f.seek(0)
self.assertEqual(f.read(), "abc\n")
f.write("def\n")
f.seek(0)
self.assertEqual(f.read(), "abc\ndef\n")
self.assertFalse(f._rolled)
self.assertEqual(f.mode, 'w+')
self.assertIsNone(f.name)
self.assertIsNone(f.newlines)
self.assertIsNone(f.encoding)
f.write("xyzzy\n")
f.seek(0)
self.assertEqual(f.read(), "abc\ndef\nxyzzy\n")
# Check that Ctrl+Z doesn't truncate the file
f.write("foo\x1abar\n")
f.seek(0)
self.assertEqual(f.read(), "abc\ndef\nxyzzy\nfoo\x1abar\n")
self.assertTrue(f._rolled)
self.assertEqual(f.mode, 'w+')
self.assertIsNotNone(f.name)
self.assertEqual(f.newlines, os.linesep)
self.assertIsNotNone(f.encoding)
def test_text_newline_and_encoding(self):
f = tempfile.SpooledTemporaryFile(mode='w+', max_size=10,
newline='', encoding='utf-8')
f.write("\u039B\r\n")
f.seek(0)
self.assertEqual(f.read(), "\u039B\r\n")
self.assertFalse(f._rolled)
self.assertEqual(f.mode, 'w+')
self.assertIsNone(f.name)
self.assertIsNone(f.newlines)
self.assertIsNone(f.encoding)
f.write("\u039B" * 20 + "\r\n")
f.seek(0)
self.assertEqual(f.read(), "\u039B\r\n" + ("\u039B" * 20) + "\r\n")
self.assertTrue(f._rolled)
self.assertEqual(f.mode, 'w+')
self.assertIsNotNone(f.name)
self.assertIsNotNone(f.newlines)
self.assertEqual(f.encoding, 'utf-8')
def test_context_manager_before_rollover(self):
# A SpooledTemporaryFile can be used as a context manager
with tempfile.SpooledTemporaryFile(max_size=1) as f:
self.assertFalse(f._rolled)
self.assertFalse(f.closed)
self.assertTrue(f.closed)
def use_closed():
with f:
pass
self.assertRaises(ValueError, use_closed)
def test_context_manager_during_rollover(self):
# A SpooledTemporaryFile can be used as a context manager
with tempfile.SpooledTemporaryFile(max_size=1) as f:
self.assertFalse(f._rolled)
f.write(b'abc\n')
f.flush()
self.assertTrue(f._rolled)
self.assertFalse(f.closed)
self.assertTrue(f.closed)
def use_closed():
with f:
pass
self.assertRaises(ValueError, use_closed)
def test_context_manager_after_rollover(self):
# A SpooledTemporaryFile can be used as a context manager
f = tempfile.SpooledTemporaryFile(max_size=1)
f.write(b'abc\n')
f.flush()
self.assertTrue(f._rolled)
with f:
self.assertFalse(f.closed)
self.assertTrue(f.closed)
def use_closed():
with f:
pass
self.assertRaises(ValueError, use_closed)
def test_truncate_with_size_parameter(self):
# A SpooledTemporaryFile can be truncated to zero size
f = tempfile.SpooledTemporaryFile(max_size=10)
f.write(b'abcdefg\n')
f.seek(0)
f.truncate()
self.assertFalse(f._rolled)
self.assertEqual(f._file.getvalue(), b'')
# A SpooledTemporaryFile can be truncated to a specific size
f = tempfile.SpooledTemporaryFile(max_size=10)
f.write(b'abcdefg\n')
f.truncate(4)
self.assertFalse(f._rolled)
self.assertEqual(f._file.getvalue(), b'abcd')
# A SpooledTemporaryFile rolls over if truncated to large size
f = tempfile.SpooledTemporaryFile(max_size=10)
f.write(b'abcdefg\n')
f.truncate(20)
self.assertTrue(f._rolled)
if has_stat:
self.assertEqual(os.fstat(f.fileno()).st_size, 20)
if tempfile.NamedTemporaryFile is not tempfile.TemporaryFile:
class TestTemporaryFile(BaseTestCase):
"""Test TemporaryFile()."""
def test_basic(self):
# TemporaryFile can create files
# No point in testing the name params - the file has no name.
tempfile.TemporaryFile()
def test_has_no_name(self):
# TemporaryFile creates files with no names (on this system)
dir = tempfile.mkdtemp()
f = tempfile.TemporaryFile(dir=dir)
f.write(b'blat')
# Sneaky: because this file has no name, it should not prevent
# us from removing the directory it was created in.
try:
os.rmdir(dir)
except:
# cleanup
f.close()
os.rmdir(dir)
raise
def test_multiple_close(self):
# A TemporaryFile can be closed many times without error
f = tempfile.TemporaryFile()
f.write(b'abc\n')
f.close()
f.close()
f.close()
# How to test the mode and bufsize parameters?
def test_mode_and_encoding(self):
def roundtrip(input, *args, **kwargs):
with tempfile.TemporaryFile(*args, **kwargs) as fileobj:
fileobj.write(input)
fileobj.seek(0)
self.assertEqual(input, fileobj.read())
roundtrip(b"1234", "w+b")
roundtrip("abdc\n", "w+")
roundtrip("\u039B", "w+", encoding="utf-16")
roundtrip("foo\r\n", "w+", newline="")
def test_no_leak_fd(self):
# Issue #21058: don't leak file descriptor when io.open() fails
closed = []
os_close = os.close
def close(fd):
closed.append(fd)
os_close(fd)
with mock.patch('os.close', side_effect=close):
with mock.patch('io.open', side_effect=ValueError):
self.assertRaises(ValueError, tempfile.TemporaryFile)
self.assertEqual(len(closed), 1)
# Helper for test_del_on_shutdown
class NulledModules:
def __init__(self, *modules):
self.refs = [mod.__dict__ for mod in modules]
self.contents = [ref.copy() for ref in self.refs]
def __enter__(self):
for d in self.refs:
for key in d:
d[key] = None
def __exit__(self, *exc_info):
for d, c in zip(self.refs, self.contents):
d.clear()
d.update(c)
class TestTemporaryDirectory(BaseTestCase):
"""Test TemporaryDirectory()."""
def do_create(self, dir=None, pre="", suf="", recurse=1):
if dir is None:
dir = tempfile.gettempdir()
tmp = tempfile.TemporaryDirectory(dir=dir, prefix=pre, suffix=suf)
self.nameCheck(tmp.name, dir, pre, suf)
# Create a subdirectory and some files
if recurse:
d1 = self.do_create(tmp.name, pre, suf, recurse-1)
d1.name = None
with open(os.path.join(tmp.name, "test.txt"), "wb") as f:
f.write(b"Hello world!")
return tmp
def test_mkdtemp_failure(self):
# Check no additional exception if mkdtemp fails
# Previously would raise AttributeError instead
# (noted as part of Issue #10188)
with tempfile.TemporaryDirectory() as nonexistent:
pass
with self.assertRaises(FileNotFoundError) as cm:
tempfile.TemporaryDirectory(dir=nonexistent)
self.assertEqual(cm.exception.errno, errno.ENOENT)
def test_explicit_cleanup(self):
# A TemporaryDirectory is deleted when cleaned up
dir = tempfile.mkdtemp()
try:
d = self.do_create(dir=dir)
self.assertTrue(os.path.exists(d.name),
"TemporaryDirectory %s does not exist" % d.name)
d.cleanup()
self.assertFalse(os.path.exists(d.name),
"TemporaryDirectory %s exists after cleanup" % d.name)
finally:
os.rmdir(dir)
@support.skip_unless_symlink
def test_cleanup_with_symlink_to_a_directory(self):
# cleanup() should not follow symlinks to directories (issue #12464)
d1 = self.do_create()
d2 = self.do_create(recurse=0)
# Symlink d1/foo -> d2
os.symlink(d2.name, os.path.join(d1.name, "foo"))
# This call to cleanup() should not follow the "foo" symlink
d1.cleanup()
self.assertFalse(os.path.exists(d1.name),
"TemporaryDirectory %s exists after cleanup" % d1.name)
self.assertTrue(os.path.exists(d2.name),
"Directory pointed to by a symlink was deleted")
self.assertEqual(os.listdir(d2.name), ['test.txt'],
"Contents of the directory pointed to by a symlink "
"were deleted")
d2.cleanup()
@support.cpython_only
def test_del_on_collection(self):
# A TemporaryDirectory is deleted when garbage collected
dir = tempfile.mkdtemp()
try:
d = self.do_create(dir=dir)
name = d.name
del d # Rely on refcounting to invoke __del__
self.assertFalse(os.path.exists(name),
"TemporaryDirectory %s exists after __del__" % name)
finally:
os.rmdir(dir)
def test_del_on_shutdown(self):
# A TemporaryDirectory may be cleaned up during shutdown
with self.do_create() as dir:
for mod in ('builtins', 'os', 'shutil', 'sys', 'tempfile', 'warnings'):
code = """if True:
import builtins
import os
import shutil
import sys
import tempfile
import warnings
tmp = tempfile.TemporaryDirectory(dir={dir!r})
sys.stdout.buffer.write(tmp.name.encode())
tmp2 = os.path.join(tmp.name, 'test_dir')
os.mkdir(tmp2)
with open(os.path.join(tmp2, "test.txt"), "w") as f:
f.write("Hello world!")
{mod}.tmp = tmp
warnings.filterwarnings("always", category=ResourceWarning)
""".format(dir=dir, mod=mod)
rc, out, err = script_helper.assert_python_ok("-c", code)
tmp_name = out.decode().strip()
self.assertFalse(os.path.exists(tmp_name),
"TemporaryDirectory %s exists after cleanup" % tmp_name)
err = err.decode('utf-8', 'backslashreplace')
self.assertNotIn("Exception ", err)
self.assertIn("ResourceWarning: Implicitly cleaning up", err)
def test_exit_on_shutdown(self):
# Issue #22427
with self.do_create() as dir:
code = """if True:
import sys
import tempfile
import warnings
def generator():
with tempfile.TemporaryDirectory(dir={dir!r}) as tmp:
yield tmp
g = generator()
sys.stdout.buffer.write(next(g).encode())
warnings.filterwarnings("always", category=ResourceWarning)
""".format(dir=dir)
rc, out, err = script_helper.assert_python_ok("-c", code)
tmp_name = out.decode().strip()
self.assertFalse(os.path.exists(tmp_name),
"TemporaryDirectory %s exists after cleanup" % tmp_name)
err = err.decode('utf-8', 'backslashreplace')
self.assertNotIn("Exception ", err)
self.assertIn("ResourceWarning: Implicitly cleaning up", err)
def test_warnings_on_cleanup(self):
# ResourceWarning will be triggered by __del__
with self.do_create() as dir:
d = self.do_create(dir=dir, recurse=3)
name = d.name
# Check for the resource warning
with support.check_warnings(('Implicitly', ResourceWarning), quiet=False):
warnings.filterwarnings("always", category=ResourceWarning)
del d
support.gc_collect()
self.assertFalse(os.path.exists(name),
"TemporaryDirectory %s exists after __del__" % name)
def test_multiple_close(self):
# Can be cleaned-up many times without error
d = self.do_create()
d.cleanup()
d.cleanup()
d.cleanup()
def test_context_manager(self):
# Can be used as a context manager
d = self.do_create()
with d as name:
self.assertTrue(os.path.exists(name))
self.assertEqual(name, d.name)
self.assertFalse(os.path.exists(name))
if __name__ == "__main__":
unittest.main()
| 35.316044
| 96
| 0.579024
|
4a077d23d9e9d462b2c63a4c8ba4b7ecd387391d
| 3,556
|
py
|
Python
|
consensus/poet/cli/sawtooth_poet_cli/main.py
|
suparnadhar/SuparnaGit
|
bec2704d8b6bc1802523ec26dcb902f59a747a4d
|
[
"Apache-2.0"
] | 1
|
2017-08-04T10:31:00.000Z
|
2017-08-04T10:31:00.000Z
|
consensus/poet/cli/sawtooth_poet_cli/main.py
|
suparnadhar/SuparnaGit
|
bec2704d8b6bc1802523ec26dcb902f59a747a4d
|
[
"Apache-2.0"
] | null | null | null |
consensus/poet/cli/sawtooth_poet_cli/main.py
|
suparnadhar/SuparnaGit
|
bec2704d8b6bc1802523ec26dcb902f59a747a4d
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017 Intel Corporation
#
# 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 argparse
import logging
import os
import sys
import traceback
from colorlog import ColoredFormatter
from sawtooth_poet_cli.exceptions import CliException
from sawtooth_poet_cli.genesis import add_genesis_parser
from sawtooth_poet_cli.genesis import do_genesis
from sawtooth_poet_cli.enclave import add_enclave_parser
from sawtooth_poet_cli.enclave import do_enclave
def create_console_handler(verbose_level):
clog = logging.StreamHandler()
formatter = ColoredFormatter(
"%(log_color)s[%(asctime)s %(levelname)-8s%(module)s]%(reset)s "
"%(white)s%(message)s",
datefmt="%H:%M:%S",
reset=True,
log_colors={
'DEBUG': 'cyan',
'INFO': 'green',
'WARNING': 'yellow',
'ERROR': 'red',
'CRITICAL': 'red',
})
clog.setFormatter(formatter)
if verbose_level == 0:
clog.setLevel(logging.WARN)
elif verbose_level == 1:
clog.setLevel(logging.INFO)
else:
clog.setLevel(logging.DEBUG)
return clog
def setup_loggers(verbose_level):
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
logger.addHandler(create_console_handler(verbose_level))
def create_parent_parser(prog_name):
parent_parser = argparse.ArgumentParser(prog=prog_name, add_help=False)
parent_parser.add_argument(
'-v', '--verbose',
action='count',
help='enable more verbose output')
return parent_parser
def create_parser(prog_name):
parent_parser = create_parent_parser(prog_name)
parser = argparse.ArgumentParser(
parents=[parent_parser],
formatter_class=argparse.RawDescriptionHelpFormatter)
subparsers = parser.add_subparsers(title='subcommand', dest='command')
subparsers.required = True
add_genesis_parser(subparsers, parent_parser)
add_enclave_parser(subparsers, parent_parser)
return parser
def main(prog_name=os.path.basename(sys.argv[0]), args=None,
with_loggers=True):
if args is None:
args = sys.argv[1:]
parser = create_parser(prog_name)
args = parser.parse_args(args)
if with_loggers is True:
if args.verbose is None:
verbose_level = 0
else:
verbose_level = args.verbose
setup_loggers(verbose_level=verbose_level)
if args.command == 'genesis':
do_genesis(args)
elif args.command == 'enclave':
do_enclave(args)
else:
raise AssertionError('invalid command: {}'.format(args.command))
def main_wrapper():
# pylint: disable=bare-except
try:
main()
except CliException as e:
print("Error: {}".format(e), file=sys.stderr)
sys.exit(1)
except KeyboardInterrupt:
pass
except SystemExit as e:
raise e
except:
traceback.print_exc(file=sys.stderr)
sys.exit(1)
| 28.222222
| 80
| 0.667885
|
4a077d610bff944659b940eb306946fcfc8768f5
| 72,433
|
py
|
Python
|
sqlova/utils/utils_wikisql.py
|
ds-keshev/sqlova
|
8523af748520cfa78025c6ba28f6b3ed5df8de62
|
[
"Apache-2.0"
] | null | null | null |
sqlova/utils/utils_wikisql.py
|
ds-keshev/sqlova
|
8523af748520cfa78025c6ba28f6b3ed5df8de62
|
[
"Apache-2.0"
] | null | null | null |
sqlova/utils/utils_wikisql.py
|
ds-keshev/sqlova
|
8523af748520cfa78025c6ba28f6b3ed5df8de62
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2019-present NAVER Corp.
# Apache License v2.0
# Wonseok Hwang
import os, json
import random as rd
from copy import deepcopy
from matplotlib.pylab import *
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from .utils import generate_perm_inv
from .utils import json_default_type_checker
from .wikisql_formatter import get_squad_style_ans
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load data -----------------------------------------------------------------------------------------------
def load_wikisql(path_wikisql, toy_model, toy_size, bert=False, no_w2i=False, no_hs_tok=False, aug=False):
# Get data
train_data, train_table = load_wikisql_data(path_wikisql, mode='train', toy_model=toy_model, toy_size=toy_size, no_hs_tok=no_hs_tok, aug=aug)
dev_data, dev_table = load_wikisql_data(path_wikisql, mode='dev', toy_model=toy_model, toy_size=toy_size, no_hs_tok=no_hs_tok)
# Get word vector
if no_w2i:
w2i, wemb = None, None
else:
w2i, wemb = load_w2i_wemb(path_wikisql, bert)
return train_data, train_table, dev_data, dev_table, w2i, wemb
def load_wikisql_data(path_wikisql, mode='train', toy_model=False, toy_size=10, no_hs_tok=False, aug=False):
""" Load training sets
"""
if aug:
mode = f"aug.{mode}"
print('Augmented data is loaded!')
path_sql = os.path.join(path_wikisql, mode+'_tok.jsonl')
if no_hs_tok:
path_table = os.path.join(path_wikisql, mode + '.tables.jsonl')
else:
path_table = os.path.join(path_wikisql, mode+'_tok.tables.jsonl')
data = []
table = {}
with open(path_sql) as f:
for idx, line in enumerate(f):
if toy_model and idx >= toy_size:
break
t1 = json.loads(line.strip())
data.append(t1)
with open(path_table) as f:
for idx, line in enumerate(f):
if toy_model and idx > toy_size:
break
t1 = json.loads(line.strip())
table[t1['id']] = t1
return data, table
def load_w2i_wemb(path_wikisql, bert=False):
""" Load pre-made subset of TAPI.
"""
if bert:
with open(os.path.join(path_wikisql, 'w2i_bert.json'), 'r') as f_w2i:
w2i = json.load(f_w2i)
wemb = load(os.path.join(path_wikisql, 'wemb_bert.npy'), )
else:
with open(os.path.join(path_wikisql, 'w2i.json'), 'r') as f_w2i:
w2i = json.load(f_w2i)
wemb = load(os.path.join(path_wikisql, 'wemb.npy'), )
return w2i, wemb
def get_loader_wikisql(data_train, data_dev, bS, shuffle_train=True, shuffle_dev=False):
train_loader = torch.utils.data.DataLoader(
batch_size=bS,
dataset=data_train,
shuffle=shuffle_train,
num_workers=4,
collate_fn=lambda x: x # now dictionary values are not merged!
)
dev_loader = torch.utils.data.DataLoader(
batch_size=bS,
dataset=data_dev,
shuffle=shuffle_dev,
num_workers=4,
collate_fn=lambda x: x # now dictionary values are not merged!
)
return train_loader, dev_loader
def get_fields_1(t1, tables, no_hs_t=False, no_sql_t=False):
nlu1 = t1['question']
nlu_t1 = t1['question_tok']
tid1 = t1['table_id']
sql_i1 = t1['sql']
sql_q1 = t1['query']
if no_sql_t:
sql_t1 = None
else:
sql_t1 = t1['query_tok']
tb1 = tables[tid1]
if not no_hs_t:
hs_t1 = tb1['header_tok']
else:
hs_t1 = []
hs1 = tb1['header']
return nlu1, nlu_t1, tid1, sql_i1, sql_q1, sql_t1, tb1, hs_t1, hs1
def get_fields(t1s, tables, no_hs_t=False, no_sql_t=False):
nlu, nlu_t, tid, sql_i, sql_q, sql_t, tb, hs_t, hs = [], [], [], [], [], [], [], [], []
for t1 in t1s:
if no_hs_t:
nlu1, nlu_t1, tid1, sql_i1, sql_q1, sql_t1, tb1, hs_t1, hs1 = get_fields_1(t1, tables, no_hs_t, no_sql_t)
else:
nlu1, nlu_t1, tid1, sql_i1, sql_q1, sql_t1, tb1, hs_t1, hs1 = get_fields_1(t1, tables, no_hs_t, no_sql_t)
nlu.append(nlu1)
nlu_t.append(nlu_t1)
tid.append(tid1)
sql_i.append(sql_i1)
sql_q.append(sql_q1)
sql_t.append(sql_t1)
tb.append(tb1)
hs_t.append(hs_t1)
hs.append(hs1)
return nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hs
# Embedding -------------------------------------------------------------------------
def word_to_idx1(words1, w2i, no_BE):
w2i_l1 = []
l1 = len(words1) # +2 because of <BEG>, <END>
for w in words1:
idx = w2i.get(w, 0)
w2i_l1.append(idx)
if not no_BE:
l1 += 2
w2i_l1 = [1] + w2i_l1 + [2]
return w2i_l1, l1
def words_to_idx(words, w2i, no_BE=False):
"""
Input: [ ['I', 'am', 'hero'],
['You', 'are 'geneus'] ]
output:
w2i = [ B x max_seq_len, 1]
wemb = [B x max_seq_len, dim]
- Zero-padded when word is not available (teated as <UNK>)
"""
bS = len(words)
l = torch.zeros(bS, dtype=torch.long).to(device) # length of the seq. of words.
w2i_l_list = [] # shall be replaced to arr
# wemb_NLq_batch = []
for i, words1 in enumerate(words):
w2i_l1, l1 = word_to_idx1(words1, w2i, no_BE)
w2i_l_list.append(w2i_l1)
l[i] = l1
# Prepare tensor of wemb
# overwrite w2i_l
w2i_l = torch.zeros([bS, int(max(l))], dtype=torch.long).to(device)
for b in range(bS):
w2i_l[b, :l[b]] = torch.LongTensor(w2i_l_list[b]).to(device)
return w2i_l, l
def hs_to_idx(hs_t, w2i, no_BE=False):
""" Zero-padded when word is not available (teated as <UNK>)
Treat each "header tokens" as if they are NL-utterance tokens.
"""
bS = len(hs_t) # now, B = B_NLq
hpu_t = [] # header pseudo-utterance
l_hs = []
for hs_t1 in hs_t:
hpu_t += hs_t1
l_hs1 = len(hs_t1)
l_hs.append(l_hs1)
w2i_hpu, l_hpu = words_to_idx(hpu_t, w2i, no_BE=no_BE)
return w2i_hpu, l_hpu, l_hs
# Encoding ---------------------------------------------------------------------
def encode(lstm, wemb_l, l, return_hidden=False, hc0=None, last_only=False):
""" [batch_size, max token length, dim_emb]
"""
bS, mL, eS = wemb_l.shape
# sort before packking
l = array(l)
perm_idx = argsort(-l)
perm_idx_inv = generate_perm_inv(perm_idx)
# pack sequence
packed_wemb_l = nn.utils.rnn.pack_padded_sequence(wemb_l[perm_idx, :, :],
l[perm_idx],
batch_first=True)
# Time to encode
if hc0 is not None:
hc0 = (hc0[0][:, perm_idx], hc0[1][:, perm_idx])
# ipdb.set_trace()
packed_wemb_l = packed_wemb_l.float() # I don't know why..
packed_wenc, hc_out = lstm(packed_wemb_l, hc0)
hout, cout = hc_out
# unpack
wenc, _l = nn.utils.rnn.pad_packed_sequence(packed_wenc, batch_first=True)
if last_only:
# Take only final outputs for each columns.
wenc = wenc[tuple(range(bS)), l[perm_idx] - 1] # [batch_size, dim_emb]
wenc.unsqueeze_(1) # [batch_size, 1, dim_emb]
wenc = wenc[perm_idx_inv]
if return_hidden:
# hout.shape = [number_of_directoin * num_of_layer, seq_len(=batch size), dim * number_of_direction ] w/ batch_first.. w/o batch_first? I need to see.
hout = hout[:, perm_idx_inv].to(device)
cout = cout[:, perm_idx_inv].to(device) # Is this correct operation?
return wenc, hout, cout
else:
return wenc
def encode_hpu(lstm, wemb_hpu, l_hpu, l_hs):
wenc_hpu, hout, cout = encode( lstm,
wemb_hpu,
l_hpu,
return_hidden=True,
hc0=None,
last_only=True )
wenc_hpu = wenc_hpu.squeeze(1)
bS_hpu, mL_hpu, eS = wemb_hpu.shape
hS = wenc_hpu.size(-1)
wenc_hs = wenc_hpu.new_zeros(len(l_hs), max(l_hs), hS)
wenc_hs = wenc_hs.to(device)
# Re-pack according to batch.
# ret = [B_NLq, max_len_headers_all, dim_lstm]
st = 0
for i, l_hs1 in enumerate(l_hs):
wenc_hs[i, :l_hs1] = wenc_hpu[st:(st + l_hs1)]
st += l_hs1
return wenc_hs
# Statistics -------------------------------------------------------------------------------------------------------------------
def get_wc1(conds):
"""
[ [wc, wo, wv],
[wc, wo, wv], ...
]
"""
wc1 = []
for cond in conds:
wc1.append(cond[0])
return wc1
def get_wo1(conds):
"""
[ [wc, wo, wv],
[wc, wo, wv], ...
]
"""
wo1 = []
for cond in conds:
wo1.append(cond[1])
return wo1
def get_wv1(conds):
"""
[ [wc, wo, wv],
[wc, wo, wv], ...
]
"""
wv1 = []
for cond in conds:
wv1.append(cond[2])
return wv1
def get_g(sql_i):
""" for backward compatibility, separated with get_g"""
g_sc = []
g_sa = []
g_wn = []
g_wc = []
g_wo = []
g_wv = []
for b, psql_i1 in enumerate(sql_i):
g_sc.append( psql_i1["sel"] )
g_sa.append( psql_i1["agg"])
conds = psql_i1['conds']
if not psql_i1["agg"] < 0:
g_wn.append( len( conds ) )
g_wc.append( get_wc1(conds) )
g_wo.append( get_wo1(conds) )
g_wv.append( get_wv1(conds) )
else:
raise EnvironmentError
return g_sc, g_sa, g_wn, g_wc, g_wo, g_wv
def get_g_wvi_corenlp(t):
g_wvi_corenlp = []
for t1 in t:
g_wvi_corenlp.append( t1['wvi_corenlp'] )
return g_wvi_corenlp
def update_w2i_wemb(word, wv, idx_w2i, n_total, w2i, wemb):
""" Follow same approach from SQLNet author's code.
Used inside of generaet_w2i_wemb.
"""
# global idx_w2i, w2i, wemb # idx, word2vec, word to idx dictionary, list of embedding vec, n_total: total number of words
if (word in wv) and (word not in w2i):
idx_w2i += 1
w2i[word] = idx_w2i
wemb.append(wv[word])
n_total += 1
return idx_w2i, n_total
def make_w2i_wemb(args, path_save_w2i_wemb, wv, data_train, data_dev, data_test, table_train, table_dev, table_test):
w2i = {'<UNK>': 0, '<BEG>': 1, '<END>': 2} # to use it when embeds NL query.
idx_w2i = 2
n_total = 3
wemb = [np.zeros(300, dtype=np.float32) for _ in range(3)] # 128 is of TAPI vector.
idx_w2i, n_total = generate_w2i_wemb(data_train, wv, idx_w2i, n_total, w2i, wemb)
idx_w2i, n_total = generate_w2i_wemb_table(table_train, wv, idx_w2i, n_total, w2i, wemb)
idx_w2i, n_total = generate_w2i_wemb(data_dev, wv, idx_w2i, n_total, w2i, wemb)
idx_w2i, n_total = generate_w2i_wemb_table(table_dev, wv, idx_w2i, n_total, w2i, wemb)
idx_w2i, n_total = generate_w2i_wemb(data_test, wv, idx_w2i, n_total, w2i, wemb)
idx_w2i, n_total = generate_w2i_wemb_table(table_test, wv, idx_w2i, n_total, w2i, wemb)
path_w2i = os.path.join(path_save_w2i_wemb, 'w2i.json')
path_wemb = os.path.join(path_save_w2i_wemb, 'wemb.npy')
wemb = np.stack(wemb, axis=0)
with open(path_w2i, 'w') as f_w2i:
json.dump(w2i, f_w2i)
np.save(path_wemb, wemb)
return w2i, wemb
def generate_w2i_wemb_table(tables, wv, idx_w2i, n_total, w2i, wemb):
""" Generate subset of GloVe
update_w2i_wemb. It uses wv, w2i, wemb, idx_w2i as global variables.
To do
1. What should we do with the numeric?
"""
# word_set from NL query
for table_id, table_contents in tables.items():
# NLq = t1['question']
# word_tokens = NLq.rstrip().replace('?', '').split(' ')
headers = table_contents['header_tok'] # [ ['state/terriotry'], ['current', 'slogan'], [],
for header_tokens in headers:
for token in header_tokens:
idx_w2i, n_total = update_w2i_wemb(token, wv, idx_w2i, n_total, w2i, wemb)
# WikiSQL generaets unbelivable query... using state/territory in the NLq. Unnatural.. but as is
# when there is slash, unlike original SQLNet which treats them as single token, we use
# both tokens. e.g. 'state/terriotry' -> 'state'
# token_spl = token.split('/')
# for token_spl1 in token_spl:
# idx_w2i, n_total = update_w2i_wemb(token_spl1, wv, idx_w2i, n_total, w2i, wemb)
return idx_w2i, n_total
def generate_w2i_wemb(train_data, wv, idx_w2i, n_total, w2i, wemb):
""" Generate subset of GloVe
update_w2i_wemb. It uses wv, w2i, wemb, idx_w2i as global variables.
To do
1. What should we do with the numeric?
"""
# word_set from NL query
for i, t1 in enumerate(train_data):
# NLq = t1['question']
# word_tokens = NLq.rstrip().replace('?', '').split(' ')
word_tokens = t1['question_tok']
# Currently, TAPI does not use "?". So, it is removed.
for word in word_tokens:
idx_w2i, n_total = update_w2i_wemb(word, wv, idx_w2i, n_total, w2i, wemb)
n_total += 1
return idx_w2i, n_total
def generate_w2i_wemb_e2k_headers(e2k_dicts, wv, idx_w2i, n_total, w2i, wemb):
""" Generate subset of TAPI from english-to-korean dict of table headers etc..
update_w2i_wemb. It uses wv, w2i, wemb, idx_w2i as global variables.
To do
1. What should we do with the numeric?
Current version do not treat them specially. But this would be modified later so that we can use tags.
"""
# word_set from NL query
for table_name, e2k_dict in e2k_dicts.items():
word_tokens_list = list(e2k_dict.values())
# Currently, TAPI does not use "?". So, it is removed.
for word_tokens in word_tokens_list:
for word in word_tokens:
idx_w2i, n_total = update_w2i_wemb(word, wv, idx_w2i, n_total, w2i, wemb)
n_total += 1
return idx_w2i, n_total
# BERT =================================================================================================================
def tokenize_nlu1(tokenizer, nlu1):
nlu1_tok = tokenizer.tokenize(nlu1)
return nlu1_tok
def tokenize_hds1(tokenizer, hds1):
hds_all_tok = []
for hds11 in hds1:
sub_tok = tokenizer.tokenize(hds11)
hds_all_tok.append(sub_tok)
def generate_inputs(tokenizer, nlu1_tok, hds1):
tokens = []
segment_ids = []
tokens.append("[CLS]")
i_st_nlu = len(tokens) # to use it later
segment_ids.append(0)
for token in nlu1_tok:
tokens.append(token)
segment_ids.append(0)
i_ed_nlu = len(tokens)
tokens.append("[SEP]")
segment_ids.append(0)
i_hds = []
# for doc
for i, hds11 in enumerate(hds1):
i_st_hd = len(tokens)
sub_tok = tokenizer.tokenize(hds11)
tokens += sub_tok
i_ed_hd = len(tokens)
i_hds.append((i_st_hd, i_ed_hd))
segment_ids += [1] * len(sub_tok)
if i < len(hds1)-1:
tokens.append("[SEP]")
segment_ids.append(0)
elif i == len(hds1)-1:
tokens.append("[SEP]")
segment_ids.append(1)
else:
raise EnvironmentError
i_nlu = (i_st_nlu, i_ed_nlu)
return tokens, segment_ids, i_nlu, i_hds
def gen_l_hpu(i_hds):
"""
# Treat columns as if it is a batch of natural language utterance with batch-size = # of columns * # of batch_size
i_hds = [(17, 18), (19, 21), (22, 23), (24, 25), (26, 29), (30, 34)])
"""
l_hpu = []
for i_hds1 in i_hds:
for i_hds11 in i_hds1:
l_hpu.append(i_hds11[1] - i_hds11[0])
return l_hpu
def get_bert_output_s2s(model_bert, tokenizer, nlu_t, hds, sql_vocab, max_seq_length):
"""
s2s version. Treat SQL-tokens as pseudo-headers
sql_vocab = ("sql select", "sql where", "sql and", "sql equal", "sql greater than", "sql less than")
e.g.)
Q: What is the name of the player with score greater than 15?
H: Name of the player, score
Input: [CLS], what, is, ...,
[SEP], name, of, the, player, [SEP], score,
[SEP] sql, select, [SEP], sql, where, [SEP], sql, and, [SEP], ...
Here, input is tokenized further by WordPiece (WP) tokenizer and fed into BERT.
INPUT
:param model_bert:
:param tokenizer: WordPiece toknizer
:param nlu: Question
:param nlu_t: CoreNLP tokenized nlu.
:param hds: Headers
:param hs_t: None or 1st-level tokenized headers
:param max_seq_length: max input token length
OUTPUT
tokens: BERT input tokens
nlu_tt: WP-tokenized input natural language questions
orig_to_tok_index: map the index of 1st-level-token to the index of 2nd-level-token
tok_to_orig_index: inverse map.
"""
l_n = []
l_hs = [] # The length of columns for each batch
l_input = []
input_ids = []
tokens = []
segment_ids = []
input_mask = []
i_nlu = [] # index to retreive the position of contextual vector later.
i_hds = []
i_sql_vocab = []
doc_tokens = []
nlu_tt = []
t_to_tt_idx = []
tt_to_t_idx = []
for b, nlu_t1 in enumerate(nlu_t):
hds1 = hds[b]
l_hs.append(len(hds1))
# 1. 2nd tokenization using WordPiece
tt_to_t_idx1 = [] # number indicates where sub-token belongs to in 1st-level-tokens (here, CoreNLP).
t_to_tt_idx1 = [] # orig_to_tok_idx[i] = start index of i-th-1st-level-token in all_tokens.
nlu_tt1 = [] # all_doc_tokens[ orig_to_tok_idx[i] ] returns first sub-token segement of i-th-1st-level-token
for (i, token) in enumerate(nlu_t1):
t_to_tt_idx1.append(
len(nlu_tt1)) # all_doc_tokens[ indicate the start position of original 'white-space' tokens.
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tt_to_t_idx1.append(i)
nlu_tt1.append(sub_token) # all_doc_tokens are further tokenized using WordPiece tokenizer
nlu_tt.append(nlu_tt1)
tt_to_t_idx.append(tt_to_t_idx1)
t_to_tt_idx.append(t_to_tt_idx1)
l_n.append(len(nlu_tt1))
# hds1_all_tok = tokenize_hds1(tokenizer, hds1)
# [CLS] nlu [SEP] col1 [SEP] col2 [SEP] ...col-n [SEP]
# 2. Generate BERT inputs & indices.
# Combine hds1 and sql_vocab
tokens1, segment_ids1, i_sql_vocab1, i_nlu1, i_hds1 = generate_inputs_s2s(tokenizer, nlu_tt1, hds1, sql_vocab)
# i_hds1
input_ids1 = tokenizer.convert_tokens_to_ids(tokens1)
# Input masks
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask1 = [1] * len(input_ids1)
# 3. Zero-pad up to the sequence length.
l_input.append( len(input_ids1) )
while len(input_ids1) < max_seq_length:
input_ids1.append(0)
input_mask1.append(0)
segment_ids1.append(0)
assert len(input_ids1) == max_seq_length
assert len(input_mask1) == max_seq_length
assert len(segment_ids1) == max_seq_length
input_ids.append(input_ids1)
tokens.append(tokens1)
segment_ids.append(segment_ids1)
input_mask.append(input_mask1)
i_nlu.append(i_nlu1)
i_hds.append(i_hds1)
i_sql_vocab.append(i_sql_vocab1)
# Convert to tensor
all_input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)
all_input_mask = torch.tensor(input_mask, dtype=torch.long).to(device)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long).to(device)
# 4. Generate BERT output.
all_encoder_layer, pooled_output = model_bert(all_input_ids, all_segment_ids, all_input_mask)
# 5. generate l_hpu from i_hds
l_hpu = gen_l_hpu(i_hds)
return all_encoder_layer, pooled_output, tokens, i_nlu, i_hds, i_sql_vocab, \
l_n, l_hpu, l_hs, l_input, \
nlu_tt, t_to_tt_idx, tt_to_t_idx
def get_bert_output(model_bert, tokenizer, nlu_t, hds, max_seq_length):
"""
Here, input is toknized further by WordPiece (WP) tokenizer and fed into BERT.
INPUT
:param model_bert:
:param tokenizer: WordPiece toknizer
:param nlu: Question
:param nlu_t: CoreNLP tokenized nlu.
:param hds: Headers
:param hs_t: None or 1st-level tokenized headers
:param max_seq_length: max input token length
OUTPUT
tokens: BERT input tokens
nlu_tt: WP-tokenized input natural language questions
orig_to_tok_index: map the index of 1st-level-token to the index of 2nd-level-token
tok_to_orig_index: inverse map.
"""
l_n = []
l_hs = [] # The length of columns for each batch
input_ids = []
tokens = []
segment_ids = []
input_mask = []
i_nlu = [] # index to retreive the position of contextual vector later.
i_hds = []
doc_tokens = []
nlu_tt = []
t_to_tt_idx = []
tt_to_t_idx = []
for b, nlu_t1 in enumerate(nlu_t):
hds1 = hds[b]
l_hs.append(len(hds1))
# 1. 2nd tokenization using WordPiece
tt_to_t_idx1 = [] # number indicates where sub-token belongs to in 1st-level-tokens (here, CoreNLP).
t_to_tt_idx1 = [] # orig_to_tok_idx[i] = start index of i-th-1st-level-token in all_tokens.
nlu_tt1 = [] # all_doc_tokens[ orig_to_tok_idx[i] ] returns first sub-token segement of i-th-1st-level-token
for (i, token) in enumerate(nlu_t1):
t_to_tt_idx1.append(
len(nlu_tt1)) # all_doc_tokens[ indicate the start position of original 'white-space' tokens.
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tt_to_t_idx1.append(i)
nlu_tt1.append(sub_token) # all_doc_tokens are further tokenized using WordPiece tokenizer
nlu_tt.append(nlu_tt1)
tt_to_t_idx.append(tt_to_t_idx1)
t_to_tt_idx.append(t_to_tt_idx1)
l_n.append(len(nlu_tt1))
# hds1_all_tok = tokenize_hds1(tokenizer, hds1)
# [CLS] nlu [SEP] col1 [SEP] col2 [SEP] ...col-n [SEP]
# 2. Generate BERT inputs & indices.
tokens1, segment_ids1, i_nlu1, i_hds1 = generate_inputs(tokenizer, nlu_tt1, hds1)
input_ids1 = tokenizer.convert_tokens_to_ids(tokens1)
# Input masks
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask1 = [1] * len(input_ids1)
# 3. Zero-pad up to the sequence length.
while len(input_ids1) < max_seq_length:
input_ids1.append(0)
input_mask1.append(0)
segment_ids1.append(0)
assert len(input_ids1) == max_seq_length
assert len(input_mask1) == max_seq_length
assert len(segment_ids1) == max_seq_length
input_ids.append(input_ids1)
tokens.append(tokens1)
segment_ids.append(segment_ids1)
input_mask.append(input_mask1)
i_nlu.append(i_nlu1)
i_hds.append(i_hds1)
# Convert to tensor
all_input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)
all_input_mask = torch.tensor(input_mask, dtype=torch.long).to(device)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long).to(device)
# 4. Generate BERT output.
all_encoder_layer, pooled_output = model_bert(all_input_ids, all_segment_ids, all_input_mask)
# 5. generate l_hpu from i_hds
l_hpu = gen_l_hpu(i_hds)
return all_encoder_layer, pooled_output, tokens, i_nlu, i_hds, \
l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx
def get_wemb_n(i_nlu, l_n, hS, num_hidden_layers, all_encoder_layer, num_out_layers_n):
"""
Get the representation of each tokens.
"""
bS = len(l_n)
l_n_max = max(l_n)
wemb_n = torch.zeros([bS, l_n_max, hS * num_out_layers_n]).to(device)
#print(all_encoder_layer)
#print(wemb_n.shape)
for b in range(bS):
# [B, max_len, dim]
# Fill zero for non-exist part.
l_n1 = l_n[b]
i_nlu1 = i_nlu[b]
for i_noln in range(num_out_layers_n):
i_layer = num_hidden_layers - 1 - i_noln
st = i_noln * hS
ed = (i_noln + 1) * hS
wemb_n[b, 0:(i_nlu1[1] - i_nlu1[0]), st:ed] = all_encoder_layer[i_layer][b, i_nlu1[0]:i_nlu1[1], :]
return wemb_n
#
def get_wemb_h(i_hds, l_hpu, l_hs, hS, num_hidden_layers, all_encoder_layer, num_out_layers_h):
"""
As if
[ [table-1-col-1-tok1, t1-c1-t2, ...],
[t1-c2-t1, t1-c2-t2, ...].
...
[t2-c1-t1, ...,]
]
"""
bS = len(l_hs)
l_hpu_max = max(l_hpu)
num_of_all_hds = sum(l_hs)
wemb_h = torch.zeros([num_of_all_hds, l_hpu_max, hS * num_out_layers_h]).to(device)
b_pu = -1
for b, i_hds1 in enumerate(i_hds):
for b1, i_hds11 in enumerate(i_hds1):
b_pu += 1
for i_nolh in range(num_out_layers_h):
i_layer = num_hidden_layers - 1 - i_nolh
st = i_nolh * hS
ed = (i_nolh + 1) * hS
wemb_h[b_pu, 0:(i_hds11[1] - i_hds11[0]), st:ed] \
= all_encoder_layer[i_layer][b, i_hds11[0]:i_hds11[1],:]
return wemb_h
def get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length, num_out_layers_n=1, num_out_layers_h=1):
# get contextual output of all tokens from bert
all_encoder_layer, pooled_output, tokens, i_nlu, i_hds,\
l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx = get_bert_output(model_bert, tokenizer, nlu_t, hds, max_seq_length)
# all_encoder_layer: BERT outputs from all layers.
# pooled_output: output of [CLS] vec.
# tokens: BERT intput tokens
# i_nlu: start and end indices of question in tokens
# i_hds: start and end indices of headers
# get the wemb
wemb_n = get_wemb_n(i_nlu, l_n, bert_config.hidden_size, bert_config.num_hidden_layers, all_encoder_layer,
num_out_layers_n)
wemb_h = get_wemb_h(i_hds, l_hpu, l_hs, bert_config.hidden_size, bert_config.num_hidden_layers, all_encoder_layer,
num_out_layers_h)
return wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx
def gen_pnt_n(g_wvi, mL_w, mL_nt):
"""
Generate one-hot idx indicating vectors with their lenghts.
:param g_wvi: e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]]
where_val idx in nlu_t. 0 = <BEG>, -1 = <END>.
:param mL_w: 4
:param mL_nt: 200
:return:
"""
bS = len(g_wvi)
for g_wvi1 in g_wvi:
for g_wvi11 in g_wvi1:
l11 = len(g_wvi11)
mL_g_wvi = max([max([0] + [len(tok) for tok in gwsi]) for gwsi in g_wvi]) - 1
# zero because of '' case.
# -1 because we already have <BEG>
if mL_g_wvi < 1:
mL_g_wvi = 1
# NLq_token_pos = torch.zeros(bS, 5 - 1, mL_g_wvi, self.max_NLq_token_num)
# l_g_wvi = torch.zeros(bS, 5 - 1)
pnt_n = torch.zeros(bS, mL_w, mL_g_wvi, mL_nt).to(device) # one hot
l_g_wvi = torch.zeros(bS, mL_w).to(device)
for b, g_wvi1 in enumerate(g_wvi):
i_wn = 0 # To prevent error from zero number of condition.
for i_wn, g_wvi11 in enumerate(g_wvi1):
# g_wvi11: [0, where_conds pos in NLq, end]
g_wvi11_n1 = g_wvi11[:-1] # doesn't count <END> idx.
l_g_wvi[b, i_wn] = len(g_wvi11_n1)
for t, idx in enumerate(g_wvi11_n1):
pnt_n[b, i_wn, t, idx] = 1
# Pad
if i_wn < (mL_w - 1): # maximum number of conidtions is 4
pnt_n[b, i_wn + 1:, 0, 1] = 1 # # cannot understand... [<BEG>, <END>]??
l_g_wvi[b, i_wn + 1:] = 1 # it means there is only <BEG>.
return pnt_n, l_g_wvi
def pred_sc(s_sc):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
"""
# get g_num
pr_sc = []
for s_sc1 in s_sc:
pr_sc.append(s_sc1.argmax().item())
return pr_sc
def pred_sc_beam(s_sc, beam_size):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
"""
# get g_num
pr_sc_beam = []
for s_sc1 in s_sc:
val, idxes = s_sc1.topk(k=beam_size)
pr_sc_beam.append(idxes.tolist())
return pr_sc_beam
def pred_sa(s_sa):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
"""
# get g_num
pr_sa = []
for s_sa1 in s_sa:
pr_sa.append(s_sa1.argmax().item())
return pr_sa
def pred_wn(s_wn):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
"""
# get g_num
pr_wn = []
for s_wn1 in s_wn:
pr_wn.append(s_wn1.argmax().item())
# print(pr_wn, s_wn1)
# if s_wn1.argmax().item() == 3:
# input('')
return pr_wn
def pred_wc_old(sql_i, s_wc):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
"""
# get g_num
pr_wc = []
for b, sql_i1 in enumerate(sql_i):
wn = len(sql_i1['conds'])
s_wc1 = s_wc[b]
pr_wc1 = argsort(-s_wc1.data.cpu().numpy())[:wn]
pr_wc1.sort()
pr_wc.append(list(pr_wc1))
return pr_wc
def pred_wc(wn, s_wc):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
! Returned index is sorted!
"""
# get g_num
pr_wc = []
for b, wn1 in enumerate(wn):
s_wc1 = s_wc[b]
pr_wc1 = argsort(-s_wc1.data.cpu().numpy())[:wn1]
pr_wc1.sort()
pr_wc.append(list(pr_wc1))
return pr_wc
def pred_wc_sorted_by_prob(s_wc):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
! Returned index is sorted by prob.
All colume-indexes are returned here.
"""
# get g_num
bS = len(s_wc)
pr_wc = []
for b in range(bS):
s_wc1 = s_wc[b]
pr_wc1 = argsort(-s_wc1.data.cpu().numpy())
pr_wc.append(list(pr_wc1))
return pr_wc
def pred_wo(wn, s_wo):
"""
return: [ pr_wc1_i, pr_wc2_i, ...]
"""
# s_wo = [B, 4, n_op]
pr_wo_a = s_wo.argmax(dim=2) # [B, 4]
# get g_num
pr_wo = []
for b, pr_wo_a1 in enumerate(pr_wo_a):
wn1 = wn[b]
pr_wo.append(list(pr_wo_a1.data.cpu().numpy()[:wn1]))
return pr_wo
def pred_wvi_se(wn, s_wv):
"""
s_wv: [B, 4, mL, 2]
- predict best st-idx & ed-idx
"""
s_wv_st, s_wv_ed = s_wv.split(1, dim=3) # [B, 4, mL, 2] -> [B, 4, mL, 1], [B, 4, mL, 1]
s_wv_st = s_wv_st.squeeze(3) # [B, 4, mL, 1] -> [B, 4, mL]
s_wv_ed = s_wv_ed.squeeze(3)
pr_wvi_st_idx = s_wv_st.argmax(dim=2) # [B, 4, mL] -> [B, 4, 1]
pr_wvi_ed_idx = s_wv_ed.argmax(dim=2)
pr_wvi = []
for b, wn1 in enumerate(wn):
pr_wvi1 = []
for i_wn in range(wn1):
pr_wvi_st_idx11 = pr_wvi_st_idx[b][i_wn]
pr_wvi_ed_idx11 = pr_wvi_ed_idx[b][i_wn]
pr_wvi1.append([pr_wvi_st_idx11.item(), pr_wvi_ed_idx11.item()])
pr_wvi.append(pr_wvi1)
return pr_wvi
def pred_wvi_se_beam(max_wn, s_wv, beam_size):
"""
s_wv: [B, 4, mL, 2]
- predict best st-idx & ed-idx
output:
pr_wvi_beam = [B, max_wn, n_pairs, 2]. 2 means [st, ed].
prob_wvi_beam = [B, max_wn, n_pairs]
"""
bS = s_wv.shape[0]
s_wv_st, s_wv_ed = s_wv.split(1, dim=3) # [B, 4, mL, 2] -> [B, 4, mL, 1], [B, 4, mL, 1]
s_wv_st = s_wv_st.squeeze(3) # [B, 4, mL, 1] -> [B, 4, mL]
s_wv_ed = s_wv_ed.squeeze(3)
prob_wv_st = F.softmax(s_wv_st, dim=-1).detach().to('cpu').numpy()
prob_wv_ed = F.softmax(s_wv_ed, dim=-1).detach().to('cpu').numpy()
k_logit = int(ceil(sqrt(beam_size)))
n_pairs = k_logit**2
assert n_pairs >= beam_size
values_st, idxs_st = s_wv_st.topk(k_logit) # [B, 4, mL] -> [B, 4, k_logit]
values_ed, idxs_ed = s_wv_ed.topk(k_logit) # [B, 4, mL] -> [B, 4, k_logit]
# idxs = [B, k_logit, 2]
# Generate all possible combination of st, ed indices & prob
pr_wvi_beam = [] # [B, max_wn, k_logit**2 [st, ed] paris]
prob_wvi_beam = zeros([bS, max_wn, n_pairs])
for b in range(bS):
pr_wvi_beam1 = []
idxs_st1 = idxs_st[b]
idxs_ed1 = idxs_ed[b]
for i_wn in range(max_wn):
idxs_st11 = idxs_st1[i_wn]
idxs_ed11 = idxs_ed1[i_wn]
pr_wvi_beam11 = []
pair_idx = -1
for i_k in range(k_logit):
for j_k in range(k_logit):
pair_idx += 1
st = idxs_st11[i_k].item()
ed = idxs_ed11[j_k].item()
pr_wvi_beam11.append([st, ed])
p1 = prob_wv_st[b, i_wn, st]
p2 = prob_wv_ed[b, i_wn, ed]
prob_wvi_beam[b, i_wn, pair_idx] = p1*p2
pr_wvi_beam1.append(pr_wvi_beam11)
pr_wvi_beam.append(pr_wvi_beam1)
# prob
return pr_wvi_beam, prob_wvi_beam
def is_whitespace_g_wvi(c):
# if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
if c == " ":
return True
return False
def convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_wp_t, wp_to_wh_index, nlu):
"""
- Convert to the string in whilte-space-separated tokens
- Add-hoc addition.
"""
pr_wv_str_wp = [] # word-piece version
pr_wv_str = []
for b, pr_wvi1 in enumerate(pr_wvi):
pr_wv_str_wp1 = []
pr_wv_str1 = []
wp_to_wh_index1 = wp_to_wh_index[b]
nlu_wp_t1 = nlu_wp_t[b]
nlu_t1 = nlu_t[b]
for i_wn, pr_wvi11 in enumerate(pr_wvi1):
st_idx, ed_idx = pr_wvi11
# Ad-hoc modification of ed_idx to deal with wp-tokenization effect.
# e.g.) to convert "butler cc (" ->"butler cc (ks)" (dev set 1st question).
pr_wv_str_wp11 = nlu_wp_t1[st_idx:ed_idx+1]
pr_wv_str_wp1.append(pr_wv_str_wp11)
st_wh_idx = wp_to_wh_index1[st_idx]
ed_wh_idx = wp_to_wh_index1[ed_idx]
pr_wv_str11 = nlu_t1[st_wh_idx:ed_wh_idx+1]
pr_wv_str1.append(pr_wv_str11)
pr_wv_str_wp.append(pr_wv_str_wp1)
pr_wv_str.append(pr_wv_str1)
return pr_wv_str, pr_wv_str_wp
def pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv):
pr_sc = pred_sc(s_sc)
pr_sa = pred_sa(s_sa)
pr_wn = pred_wn(s_wn)
pr_wc = pred_wc(pr_wn, s_wc)
pr_wo = pred_wo(pr_wn, s_wo)
pr_wvi = pred_wvi_se(pr_wn, s_wv)
return pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi
def merge_wv_t1_eng(where_str_tokens, NLq):
"""
Almost copied of SQLNet.
The main purpose is pad blank line while combining tokens.
"""
nlq = NLq.lower()
where_str_tokens = [tok.lower() for tok in where_str_tokens]
alphabet = 'abcdefghijklmnopqrstuvwxyz0123456789$'
special = {'-LRB-': '(',
'-RRB-': ')',
'-LSB-': '[',
'-RSB-': ']',
'``': '"',
'\'\'': '"',
}
# '--': '\u2013'} # this generate error for test 5661 case.
ret = ''
double_quote_appear = 0
for raw_w_token in where_str_tokens:
# if '' (empty string) of None, continue
if not raw_w_token:
continue
# Change the special characters
w_token = special.get(raw_w_token, raw_w_token) # maybe necessary for some case?
# check the double quote
if w_token == '"':
double_quote_appear = 1 - double_quote_appear
# Check whether ret is empty. ret is selected where condition.
if len(ret) == 0:
pass
# Check blank character.
elif len(ret) > 0 and ret + ' ' + w_token in nlq:
# Pad ' ' if ret + ' ' is part of nlq.
ret = ret + ' '
elif len(ret) > 0 and ret + w_token in nlq:
pass # already in good form. Later, ret + w_token will performed.
# Below for unnatural question I guess. Is it likely to appear?
elif w_token == '"':
if double_quote_appear:
ret = ret + ' ' # pad blank line between next token when " because in this case, it is of closing apperas
# for the case of opening, no blank line.
elif w_token[0] not in alphabet:
pass # non alphabet one does not pad blank line.
# when previous character is the special case.
elif (ret[-1] not in ['(', '/', '\u2013', '#', '$', '&']) and (ret[-1] != '"' or not double_quote_appear):
ret = ret + ' '
ret = ret + w_token
return ret.strip()
def find_sql_where_op(gt_sql_tokens_part):
"""
gt_sql_tokens_part: Between 'WHERE' and 'AND'(if exists).
"""
# sql_where_op = ['=', 'EQL', '<', 'LT', '>', 'GT']
sql_where_op = ['EQL','LT','GT'] # wv sometimes contains =, < or >.
for sql_where_op in sql_where_op:
if sql_where_op in gt_sql_tokens_part:
found_sql_where_op = sql_where_op
break
return found_sql_where_op
def find_sub_list(sl, l):
# from stack overflow.
results = []
sll = len(sl)
for ind in (i for i, e in enumerate(l) if e == sl[0]):
if l[ind:ind + sll] == sl:
results.append((ind, ind + sll - 1))
return results
def get_g_wvi_bert(nlu, nlu_t, wh_to_wp_index, sql_i, sql_t, tokenizer, nlu_wp_t):
"""
Generate SQuAD style start and end index of wv in nlu. Index is for of after WordPiece tokenization.
Assumption: where_str always presents in the nlu.
"""
g_wvi = []
for b, sql_i1 in enumerate(sql_i):
nlu1 = nlu[b]
nlu_t1 = nlu_t[b]
nlu_wp_t1 = nlu_wp_t[b]
sql_t1 = sql_t[b]
wh_to_wp_index1 = wh_to_wp_index[b]
st = sql_t1.index('WHERE') + 1 if 'WHERE' in sql_t1 else len(sql_t1)
g_wvi1 = []
while st < len(sql_t1):
if 'AND' not in sql_t1[st:]:
ed = len(sql_t1)
else:
ed = sql_t1[st:].index('AND') + st
sql_wop = find_sql_where_op(sql_t1[st:ed]) # sql where operator
st_wop = st + sql_t1[st:ed].index(sql_wop)
wv_str11_t = sql_t1[st_wop + 1:ed]
results = find_sub_list(wv_str11_t, nlu_t1)
st_idx, ed_idx = results[0]
st_wp_idx = wh_to_wp_index1[st_idx]
ed_wp_idx = wh_to_wp_index1[ed_idx]
g_wvi11 = [st_wp_idx, ed_wp_idx]
g_wvi1.append(g_wvi11)
st = ed + 1
g_wvi.append(g_wvi1)
return g_wvi
def get_g_wvi_bert_from_g_wvi_corenlp(wh_to_wp_index, g_wvi_corenlp):
"""
Generate SQuAD style start and end index of wv in nlu. Index is for of after WordPiece tokenization.
Assumption: where_str always presents in the nlu.
"""
g_wvi = []
for b, g_wvi_corenlp1 in enumerate(g_wvi_corenlp):
wh_to_wp_index1 = wh_to_wp_index[b]
g_wvi1 = []
for i_wn, g_wvi_corenlp11 in enumerate(g_wvi_corenlp1):
st_idx, ed_idx = g_wvi_corenlp11
st_wp_idx = wh_to_wp_index1[st_idx]
ed_wp_idx = wh_to_wp_index1[ed_idx]
g_wvi11 = [st_wp_idx, ed_wp_idx]
g_wvi1.append(g_wvi11)
g_wvi.append(g_wvi1)
return g_wvi
def get_g_wvi_bert_from_sql_i(nlu, nlu_t, wh_to_wp_index, sql_i, sql_t, tokenizer, nlu_wp_t):
"""
Generate SQuAD style start and end index of wv in nlu. Index is for of after WordPiece tokenization.
Assumption: where_str always presents in the nlu.
"""
g_wvi = []
for b, sql_i1 in enumerate(sql_i):
nlu1 = nlu[b]
nlu_t1 = nlu_t[b]
nlu_wp_t1 = nlu_wp_t[b]
sql_t1 = sql_t[b]
wh_to_wp_index1 = wh_to_wp_index[b]
st = sql_t1.index('WHERE') + 1 if 'WHERE' in sql_t1 else len(sql_t1)
g_wvi1 = []
while st < len(sql_t1):
if 'AND' not in sql_t1[st:]:
ed = len(sql_t1)
else:
ed = sql_t1[st:].index('AND') + st
sql_wop = find_sql_where_op(sql_t1[st:ed]) # sql where operator
st_wop = st + sql_t1[st:ed].index(sql_wop)
wv_str11_t = sql_t1[st_wop + 1:ed]
results = find_sub_list(wv_str11_t, nlu_t1)
st_idx, ed_idx = results[0]
st_wp_idx = wh_to_wp_index1[st_idx]
ed_wp_idx = wh_to_wp_index1[ed_idx]
g_wvi11 = [st_wp_idx, ed_wp_idx]
g_wvi1.append(g_wvi11)
st = ed + 1
g_wvi.append(g_wvi1)
return g_wvi
def get_cnt_sc(g_sc, pr_sc):
cnt = 0
for b, g_sc1 in enumerate(g_sc):
pr_sc1 = pr_sc[b]
if pr_sc1 == g_sc1:
cnt += 1
return cnt
def get_cnt_sc_list(g_sc, pr_sc):
cnt_list = []
for b, g_sc1 in enumerate(g_sc):
pr_sc1 = pr_sc[b]
if pr_sc1 == g_sc1:
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_cnt_sa(g_sa, pr_sa):
cnt = 0
for b, g_sa1 in enumerate(g_sa):
pr_sa1 = pr_sa[b]
if pr_sa1 == g_sa1:
cnt += 1
return cnt
def get_cnt_wn(g_wn, pr_wn):
cnt = 0
for b, g_wn1 in enumerate(g_wn):
pr_wn1 = pr_wn[b]
if pr_wn1 == g_wn1:
cnt += 1
return cnt
def get_cnt_wc(g_wc, pr_wc):
cnt = 0
for b, g_wc1 in enumerate(g_wc):
pr_wc1 = pr_wc[b]
pr_wn1 = len(pr_wc1)
g_wn1 = len(g_wc1)
if pr_wn1 != g_wn1:
continue
else:
wc1 = array(g_wc1)
wc1.sort()
if array_equal(pr_wc1, wc1):
cnt += 1
return cnt
def get_cnt_wc_list(g_wc, pr_wc):
cnt_list= []
for b, g_wc1 in enumerate(g_wc):
pr_wc1 = pr_wc[b]
pr_wn1 = len(pr_wc1)
g_wn1 = len(g_wc1)
if pr_wn1 != g_wn1:
cnt_list.append(0)
continue
else:
wc1 = array(g_wc1)
wc1.sort()
if array_equal(pr_wc1, wc1):
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_cnt_wo(g_wn, g_wc, g_wo, pr_wc, pr_wo, mode):
""" pr's are all sorted as pr_wc are sorted in increasing order (in column idx)
However, g's are not sorted.
Sort g's in increasing order (in column idx)
"""
cnt = 0
for b, g_wo1 in enumerate(g_wo):
g_wc1 = g_wc[b]
pr_wc1 = pr_wc[b]
pr_wo1 = pr_wo[b]
pr_wn1 = len(pr_wo1)
g_wn1 = g_wn[b]
if g_wn1 != pr_wn1:
continue
else:
# Sort based on wc sequence.
if mode == 'test':
idx = argsort(array(g_wc1))
g_wo1_s = array(g_wo1)[idx]
g_wo1_s = list(g_wo1_s)
elif mode == 'train':
# due to teacher forcing, no need to sort.
g_wo1_s = g_wo1
else:
raise ValueError
if type(pr_wo1) != list:
raise TypeError
if g_wo1_s == pr_wo1:
cnt += 1
return cnt
def get_cnt_wo_list(g_wn, g_wc, g_wo, pr_wc, pr_wo, mode):
""" pr's are all sorted as pr_wc are sorted in increasing order (in column idx)
However, g's are not sorted.
Sort g's in increasing order (in column idx)
"""
cnt_list=[]
for b, g_wo1 in enumerate(g_wo):
g_wc1 = g_wc[b]
pr_wc1 = pr_wc[b]
pr_wo1 = pr_wo[b]
pr_wn1 = len(pr_wo1)
g_wn1 = g_wn[b]
if g_wn1 != pr_wn1:
cnt_list.append(0)
continue
else:
# Sort based wc sequence.
if mode == 'test':
idx = argsort(array(g_wc1))
g_wo1_s = array(g_wo1)[idx]
g_wo1_s = list(g_wo1_s)
elif mode == 'train':
# due to tearch forcing, no need to sort.
g_wo1_s = g_wo1
else:
raise ValueError
if type(pr_wo1) != list:
raise TypeError
if g_wo1_s == pr_wo1:
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_cnt_wv(g_wn, g_wc, g_wvi, pr_wvi, mode):
""" usalbe only when g_wc was used to find pr_wv
g_wvi
"""
cnt = 0
for b, g_wvi1 in enumerate(g_wvi):
pr_wvi1 = pr_wvi[b]
g_wc1 = g_wc[b]
pr_wn1 = len(pr_wvi1)
g_wn1 = g_wn[b]
# Now sorting.
# Sort based wc sequence.
if mode == 'test':
idx1 = argsort(array(g_wc1))
elif mode == 'train':
idx1 = list( range( g_wn1) )
else:
raise ValueError
if g_wn1 != pr_wn1:
continue
else:
flag = True
for i_wn, idx11 in enumerate(idx1):
g_wvi11 = g_wvi1[idx11]
pr_wvi11 = pr_wvi1[i_wn]
if g_wvi11 != pr_wvi11:
flag = False
# print(g_wv1, g_wv11)
# print(pr_wv1, pr_wv11)
# input('')
break
if flag:
cnt += 1
return cnt
def get_cnt_wvi_list(g_wn, g_wc, g_wvi, pr_wvi, mode):
""" usalbe only when g_wc was used to find pr_wv
"""
cnt_list =[]
for b, g_wvi1 in enumerate(g_wvi):
g_wc1 = g_wc[b]
pr_wvi1 = pr_wvi[b]
pr_wn1 = len(pr_wvi1)
g_wn1 = g_wn[b]
# Now sorting.
# Sort based wc sequence.
if mode == 'test':
idx1 = argsort(array(g_wc1))
elif mode == 'train':
idx1 = list( range( g_wn1) )
else:
raise ValueError
if g_wn1 != pr_wn1:
cnt_list.append(0)
continue
else:
flag = True
for i_wn, idx11 in enumerate(idx1):
g_wvi11 = g_wvi1[idx11]
pr_wvi11 = pr_wvi1[i_wn]
if g_wvi11 != pr_wvi11:
flag = False
# print(g_wv1, g_wv11)
# print(pr_wv1, pr_wv11)
# input('')
break
if flag:
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_cnt_wv_list(g_wn, g_wc, g_sql_i, pr_sql_i, mode):
""" usalbe only when g_wc was used to find pr_wv
"""
cnt_list =[]
for b, g_wc1 in enumerate(g_wc):
pr_wn1 = len(pr_sql_i[b]["conds"])
g_wn1 = g_wn[b]
# Now sorting.
# Sort based wc sequence.
if mode == 'test':
idx1 = argsort(array(g_wc1))
elif mode == 'train':
idx1 = list( range( g_wn1) )
else:
raise ValueError
if g_wn1 != pr_wn1:
cnt_list.append(0)
continue
else:
flag = True
for i_wn, idx11 in enumerate(idx1):
g_wvi_str11 = str(g_sql_i[b]["conds"][idx11][2]).lower()
pr_wvi_str11 = str(pr_sql_i[b]["conds"][i_wn][2]).lower()
# print(g_wvi_str11)
# print(pr_wvi_str11)
# print(g_wvi_str11==pr_wvi_str11)
if g_wvi_str11 != pr_wvi_str11:
flag = False
# print(g_wv1, g_wv11)
# print(pr_wv1, pr_wv11)
# input('')
break
if flag:
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_cnt_sw(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, mode):
""" usalbe only when g_wc was used to find pr_wv
"""
cnt_sc = get_cnt_sc(g_sc, pr_sc)
cnt_sa = get_cnt_sa(g_sa, pr_sa)
cnt_wn = get_cnt_wn(g_wn, pr_wn)
cnt_wc = get_cnt_wc(g_wc, pr_wc)
cnt_wo = get_cnt_wo(g_wn, g_wc, g_wo, pr_wc, pr_wo, mode)
cnt_wv = get_cnt_wv(g_wn, g_wc, g_wvi, pr_wvi, mode)
return cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv
def get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
g_sql_i, pr_sql_i,
mode):
""" usalbe only when g_wc was used to find pr_wv
"""
cnt_sc = get_cnt_sc_list(g_sc, pr_sc)
cnt_sa = get_cnt_sc_list(g_sa, pr_sa)
cnt_wn = get_cnt_sc_list(g_wn, pr_wn)
cnt_wc = get_cnt_wc_list(g_wc, pr_wc)
cnt_wo = get_cnt_wo_list(g_wn, g_wc, g_wo, pr_wc, pr_wo, mode)
if pr_wvi:
cnt_wvi = get_cnt_wvi_list(g_wn, g_wc, g_wvi, pr_wvi, mode)
else:
cnt_wvi = [0]*len(cnt_sc)
cnt_wv = get_cnt_wv_list(g_wn, g_wc, g_sql_i, pr_sql_i, mode) # compare using wv-str which presented in original data.
return cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wvi, cnt_wv
def get_cnt_lx_list(cnt_sc1, cnt_sa1, cnt_wn1, cnt_wc1, cnt_wo1, cnt_wv1):
# all cnt are list here.
cnt_list = []
cnt_lx = 0
for csc, csa, cwn, cwc, cwo, cwv in zip(cnt_sc1, cnt_sa1, cnt_wn1, cnt_wc1, cnt_wo1, cnt_wv1):
if csc and csa and cwn and cwc and cwo and cwv:
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_cnt_x_list(engine, tb, g_sc, g_sa, g_sql_i, pr_sc, pr_sa, pr_sql_i):
cnt_x1_list = []
g_ans = []
pr_ans = []
for b in range(len(g_sc)):
g_ans1 = engine.execute(tb[b]['id'], g_sc[b], g_sa[b], g_sql_i[b]['conds'])
# print(f'cnt: {cnt}')
# print(f"pr_sql_i: {pr_sql_i[b]['conds']}")
try:
pr_ans1 = engine.execute(tb[b]['id'], pr_sc[b], pr_sa[b], pr_sql_i[b]['conds'])
if bool(pr_ans1): # not empty due to lack of the data from incorretly generated sql
if g_ans1 == pr_ans1:
cnt_x1 = 1
else:
cnt_x1 = 0
else:
cnt_x1 = 0
except:
# type error etc... Execution-guided decoding may be used here.
pr_ans1 = None
cnt_x1 = 0
cnt_x1_list.append(cnt_x1)
g_ans.append(g_ans1)
pr_ans.append(pr_ans1)
return cnt_x1_list, g_ans, pr_ans
def get_mean_grad(named_parameters):
"""
Get list of mean, std of grad of each parameters
Code based on web searched result..
"""
mu_list = []
sig_list = []
for name, param in named_parameters:
if param.requires_grad: # and ("bias" not in name) :
# bias makes std = nan as it is of single parameters
magnitude = param.grad.abs()
mu_list.append(magnitude.mean())
if len(magnitude) == 1:
# why nan for single param? Anyway to avoid that..
sig_list.append(torch.tensor(0))
else:
sig_list.append(magnitude.std())
# if "svp_se"
return mu_list, sig_list
def generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, nlu):
pr_sql_i = []
for b, nlu1 in enumerate(nlu):
conds = []
for i_wn in range(pr_wn[b]):
conds1 = []
conds1.append(pr_wc[b][i_wn])
conds1.append(pr_wo[b][i_wn])
merged_wv11 = merge_wv_t1_eng(pr_wv_str[b][i_wn], nlu[b])
conds1.append(merged_wv11)
conds.append(conds1)
pr_sql_i1 = {'agg': pr_sa[b], 'sel': pr_sc[b], 'conds': conds}
pr_sql_i.append(pr_sql_i1)
return pr_sql_i
def save_for_evaluation(path_save, results, dset_name, ):
path_save_file = os.path.join(path_save, f'results_{dset_name}.jsonl')
with open(path_save_file, 'w', encoding='utf-8') as f:
for i, r1 in enumerate(results):
json_str = json.dumps(r1, ensure_ascii=False, default=json_default_type_checker)
json_str += '\n'
f.writelines(json_str)
def save_for_evaluation_aux(path_save, results, dset_name, ):
path_save_file = os.path.join(path_save, f'results_aux_{dset_name}.jsonl')
with open(path_save_file, 'w', encoding='utf-8') as f:
for i, r1 in enumerate(results):
json_str = json.dumps(r1, ensure_ascii=False, default=json_default_type_checker)
json_str += '\n'
f.writelines(json_str)
def check_sc_sa_pairs(tb, pr_sc, pr_sa, ):
"""
Check whether pr_sc, pr_sa are allowed pairs or not.
agg_ops = ['', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG']
"""
bS = len(pr_sc)
check = [False] * bS
for b, pr_sc1 in enumerate(pr_sc):
pr_sa1 = pr_sa[b]
hd_types1 = tb[b]['types']
hd_types11 = hd_types1[pr_sc1]
if hd_types11 == 'text':
if pr_sa1 == 0 or pr_sa1 == 3: # ''
check[b] = True
else:
check[b] = False
elif hd_types11 == 'real':
check[b] = True
else:
raise Exception("New TYPE!!")
return check
def remap_sc_idx(idxs, pr_sc_beam):
for b, idxs1 in enumerate(idxs):
for i_beam, idxs11 in enumerate(idxs1):
sc_beam_idx = idxs[b][i_beam][0]
sc_idx = pr_sc_beam[b][sc_beam_idx]
idxs[b][i_beam][0] = sc_idx
return idxs
def sort_and_generate_pr_w(pr_sql_i):
pr_wc = []
pr_wo = []
pr_wv = []
for b, pr_sql_i1 in enumerate(pr_sql_i):
conds1 = pr_sql_i1["conds"]
pr_wc1 = []
pr_wo1 = []
pr_wv1 = []
# Generate
for i_wn, conds11 in enumerate(conds1):
pr_wc1.append( conds11[0])
pr_wo1.append( conds11[1])
pr_wv1.append( conds11[2])
# sort based on pr_wc1
idx = argsort(pr_wc1)
pr_wc1 = array(pr_wc1)[idx].tolist()
pr_wo1 = array(pr_wo1)[idx].tolist()
pr_wv1 = array(pr_wv1)[idx].tolist()
conds1_sorted = []
for i, idx1 in enumerate(idx):
conds1_sorted.append( conds1[idx1] )
pr_wc.append(pr_wc1)
pr_wo.append(pr_wo1)
pr_wv.append(pr_wv1)
pr_sql_i1['conds'] = conds1_sorted
return pr_wc, pr_wo, pr_wv, pr_sql_i
def generate_sql_q(sql_i, tb):
sql_q = []
for b, sql_i1 in enumerate(sql_i):
tb1 = tb[b]
sql_q1 = generate_sql_q1(sql_i1, tb1)
sql_q.append(sql_q1)
return sql_q
def generate_sql_q1(sql_i1, tb1):
"""
sql = {'sel': 5, 'agg': 4, 'conds': [[3, 0, '59']]}
agg_ops = ['', 'max', 'min', 'count', 'sum', 'avg']
cond_ops = ['=', '>', '<', 'OP']
Temporal as it can show only one-time conditioned case.
sql_query: real sql_query
sql_plus_query: More redable sql_query
"PLUS" indicates, it deals with the some of db specific facts like PCODE <-> NAME
"""
agg_ops = ['', 'max', 'min', 'count', 'sum', 'avg']
cond_ops = ['=', '>', '<', 'OP']
headers = tb1["header"]
# select_header = headers[sql['sel']].lower()
# try:
# select_table = tb1["name"]
# except:
# print(f"No table name while headers are {headers}")
select_table = tb1["id"]
select_agg = agg_ops[sql_i1['agg']]
select_header = headers[sql_i1['sel']]
sql_query_part1 = f'SELECT {select_agg}({select_header}) '
where_num = len(sql_i1['conds'])
if where_num == 0:
sql_query_part2 = f'FROM {select_table}'
# sql_plus_query_part2 = f'FROM {select_table}'
else:
sql_query_part2 = f'FROM {select_table} WHERE'
# sql_plus_query_part2 = f'FROM {select_table_refined} WHERE'
# ----------------------------------------------------------------------------------------------------------
for i in range(where_num):
# check 'OR'
# number_of_sub_conds = len(sql['conds'][i])
where_header_idx, where_op_idx, where_str = sql_i1['conds'][i]
where_header = headers[where_header_idx]
where_op = cond_ops[where_op_idx]
if i > 0:
sql_query_part2 += ' AND'
# sql_plus_query_part2 += ' AND'
sql_query_part2 += f" {where_header} {where_op} {where_str}"
sql_query = sql_query_part1 + sql_query_part2
# sql_plus_query = sql_plus_query_part1 + sql_plus_query_part2
return sql_query
def get_pnt_idx1(col_pool_type, st_ed):
st, ed = st_ed
if col_pool_type == 'start_tok':
pnt_idx1 = st
elif col_pool_type == 'end_tok':
pnt_idx1 = ed
elif col_pool_type == 'avg':
pnt_idx1 = arange(st, ed, 1)
return pnt_idx1
def gen_g_pnt_idx(g_wvi, sql_i, i_hds, i_sql_vocab, col_pool_type):
"""
sql_vocab = (
0.. "sql none", "sql max", "sql min", "sql count", "sql sum", "sql average", ..5
6.. "sql select", "sql where", "sql and", .. 8
9.. "sql equal", "sql greater than", "sql less than", .. 11
12.. "sql start", "sql end" .. 13
)
"""
g_pnt_idxs = []
for b, sql_i1 in enumerate(sql_i):
i_sql_vocab1 = i_sql_vocab[b]
i_hds1 = i_hds[b]
g_pnt_idxs1 = []
# start token
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[-2])
g_pnt_idxs1.append(pnt_idx1)
# select token
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[6])
g_pnt_idxs1.append(pnt_idx1)
# select agg
idx_agg = sql_i1["agg"]
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[idx_agg])
g_pnt_idxs1.append(pnt_idx1)
# select column
idx_sc = sql_i1["sel"]
pnt_idx1 = get_pnt_idx1(col_pool_type, i_hds1[idx_sc])
g_pnt_idxs1.append(pnt_idx1)
conds = sql_i1["conds"]
wn = len(conds)
if wn <= 0:
pass
else:
# select where
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[7])
g_pnt_idxs1.append(pnt_idx1)
for i_wn, conds1 in enumerate(conds):
# where column
idx_wc = conds1[0]
pnt_idx1 = get_pnt_idx1(col_pool_type, i_hds1[idx_wc])
g_pnt_idxs1.append(pnt_idx1)
# where op
idx_wo = conds1[1]
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[idx_wo + 9])
g_pnt_idxs1.append(pnt_idx1)
# where val
st, ed = g_wvi[b][i_wn]
end_pos_of_sql_vocab = i_sql_vocab1[-1][-1]
g_pnt_idxs1.append(st + 1 + end_pos_of_sql_vocab) # due to inital [CLS] token in BERT-input vector
g_pnt_idxs1.append(ed + 1 + end_pos_of_sql_vocab) # due to inital [CLS] token in BERT-input vector
# and token
if i_wn < wn - 1:
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[8])
g_pnt_idxs1.append(pnt_idx1)
# end token
pnt_idx1 = get_pnt_idx1(col_pool_type, i_sql_vocab1[-1])
g_pnt_idxs1.append(pnt_idx1)
g_pnt_idxs.append(g_pnt_idxs1)
return g_pnt_idxs
def pred_pnt_idxs(score, pnt_start_tok, pnt_end_tok):
pr_pnt_idxs = []
for b, score1 in enumerate(score):
# score1 = [T, max_seq_length]
pr_pnt_idxs1 = [pnt_start_tok]
for t, score11 in enumerate(score1):
pnt = score11.argmax().item()
pr_pnt_idxs1.append(pnt)
if pnt == pnt_end_tok:
break
pr_pnt_idxs.append(pr_pnt_idxs1)
return pr_pnt_idxs
def generate_sql_q_s2s(pnt_idxs, tokens, tb):
sql_q = []
for b, pnt_idxs1 in enumerate(pnt_idxs):
tb1 = tb[b]
sql_q1 = generate_sql_q1_s2s(pnt_idxs1, tokens[b], tb1)
sql_q.append(sql_q1)
return sql_q
def generate_sql_q1_s2s(pnt_idxs1, tokens1, tb1):
"""
agg_ops = ['', 'max', 'min', 'count', 'sum', 'avg']
cond_ops = ['=', '>', '<', 'OP']
Temporal as it can show only one-time conditioned case.
sql_query: real sql_query
sql_plus_query: More redable sql_query
"PLUS" indicates, it deals with the some of db specific facts like PCODE <-> NAME
"""
sql_query = ""
for t, pnt_idxs11 in enumerate(pnt_idxs1):
tok = tokens1[pnt_idxs11]
sql_query += tok
if t < len(pnt_idxs1)-1:
sql_query += " "
return sql_query
# Generate sql_i from pnt_idxs
def find_where_pnt_belong(pnt, vg):
idx_sub = -1
for i, st_ed in enumerate(vg):
st, ed = st_ed
if pnt < ed and pnt >= st:
idx_sub = i
return idx_sub
def gen_pnt_i_from_pnt(pnt, i_sql_vocab1, i_nlu1, i_hds1):
# Find where it belong
vg_list = [i_sql_vocab1, [i_nlu1], i_hds1] # as i_nlu has only single st and ed
i_vg = -1
i_vg_sub = -1
for i, vg in enumerate(vg_list):
idx_sub = find_where_pnt_belong(pnt, vg)
if idx_sub > -1:
i_vg = i
i_vg_sub = idx_sub
break
return i_vg, i_vg_sub
def gen_i_vg_from_pnt_idxs(pnt_idxs, i_sql_vocab, i_nlu, i_hds):
i_vg_list = []
i_vg_sub_list = []
for b, pnt_idxs1 in enumerate(pnt_idxs):
# if properly generated,
sql_q1_list = []
i_vg_list1 = [] # index of (sql_vocab, nlu, hds)
i_vg_sub_list1 = [] # index inside of each vocab group
for t, pnt in enumerate(pnt_idxs1):
i_vg, i_vg_sub = gen_pnt_i_from_pnt(pnt, i_sql_vocab[b], i_nlu[b], i_hds[b])
i_vg_list1.append(i_vg)
i_vg_sub_list1.append(i_vg_sub)
# sql_q1 = sql_q1.join(' ')
# sql_q.append(sql_q1)
i_vg_list.append(i_vg_list1)
i_vg_sub_list.append(i_vg_sub_list1)
return i_vg_list, i_vg_sub_list
def gen_sql_q_from_i_vg(tokens, nlu, nlu_t, hds, tt_to_t_idx, pnt_start_tok, pnt_end_tok, pnt_idxs, i_vg_list, i_vg_sub_list):
"""
(
"none", "max", "min", "count", "sum", "average",
"select", "where", "and",
"equal", "greater than", "less than",
"start", "end"
),
"""
sql_q = []
sql_i = []
for b, nlu_t1 in enumerate(nlu_t):
sql_q1_list = []
sql_i1 = {}
tt_to_t_idx1 = tt_to_t_idx[b]
nlu_st_observed = False
agg_observed = False
wc_obs = False
wo_obs = False
conds = []
for t, i_vg in enumerate(i_vg_list[b]):
i_vg_sub = i_vg_sub_list[b][t]
pnt = pnt_idxs[b][t]
if i_vg == 0:
# sql_vocab
if pnt == pnt_start_tok or pnt == pnt_end_tok:
pass
else:
tok = tokens[b][pnt]
if tok in ["none", "max", "min", "count", "sum", "average"]:
agg_observed = True
if tok == "none":
pass
sql_i1["agg"] = ["none", "max", "min", "count", "sum", "average"].index(tok)
else:
if tok in ["greater", "less", "equal"]:
if tok == 'greater':
tok = '>'
elif tok == 'less':
tok = '<'
elif tok == 'equal':
tok = '='
# gen conds1
if wc_obs:
conds1.append( ['=','>','<'].index(tok) )
wo_obs = True
sql_q1_list.append(tok)
elif i_vg == 1:
# nlu case
if not nlu_st_observed:
idx_nlu_st = pnt
nlu_st_observed = True
else:
# now to wrap up
idx_nlu_ed = pnt
st_wh_idx = tt_to_t_idx1[idx_nlu_st - pnt_end_tok - 2]
ed_wh_idx = tt_to_t_idx1[idx_nlu_ed - pnt_end_tok - 2]
pr_wv_str11 = nlu_t1[st_wh_idx:ed_wh_idx + 1]
merged_wv11 = merge_wv_t1_eng(pr_wv_str11, nlu[b])
sql_q1_list.append(merged_wv11)
nlu_st_observed = False
if wc_obs and wo_obs:
conds1.append(merged_wv11)
conds.append(conds1)
wc_obs = False
wo_obs = False
elif i_vg == 2:
# headers
tok = hds[b][i_vg_sub]
if agg_observed:
sql_q1_list.append(f"({tok})")
sql_i1["sel"] = i_vg_sub
agg_observed = False
else:
wc_obs = True
conds1 = [i_vg_sub]
sql_q1_list.append(tok)
# insert table name between.
sql_i1["conds"] = conds
sql_i.append(sql_i1)
sql_q1 = ' '.join(sql_q1_list)
sql_q.append(sql_q1)
return sql_q, sql_i
def get_cnt_lx_list_s2s(g_pnt_idxs, pr_pnt_idxs):
# all cnt are list here.
cnt_list = []
for b, g_pnt_idxs1 in enumerate(g_pnt_idxs):
pr_pnt_idxs1 = pr_pnt_idxs[b]
if g_pnt_idxs1 == pr_pnt_idxs1:
cnt_list.append(1)
else:
cnt_list.append(0)
return cnt_list
def get_wemb_h_FT_Scalar_1(i_hds, l_hs, hS, all_encoder_layer, col_pool_type='start_tok'):
"""
As if
[ [table-1-col-1-tok1, t1-c1-t2, ...],
[t1-c2-t1, t1-c2-t2, ...].
...
[t2-c1-t1, ...,]
]
# i_hds = [ [ Batch 1 ] [ Batch 2 ] ]
# [Batch 1] = [ (col1_st_idx, col1_ed_idx), (col2_st_idx, col2_ed_idx), ...]
# i_hds = [[(11, 14), (15, 19), (20, 21), (22, 24), (25, 27), (28, 29)],
# [(16, 19), (20, 24), (25, 26), (27, 29), (30, 32), (33, 34)]]
pool_type = 'start_tok', 'end_tok', 'avg'
"""
bS = len(l_hs)
l_hs_max = max(l_hs)
wemb_h = torch.zeros([bS, l_hs_max, hS]).to(device)
for b, i_hds1 in enumerate(i_hds):
for i_hd, st_ed_pair in enumerate(i_hds1):
st, ed = st_ed_pair
if col_pool_type == 'start_tok':
vec = all_encoder_layer[-1][b, st,:]
elif col_pool_type == 'end_tok':
vec = all_encoder_layer[-1][b, ed, :]
elif col_pool_type == 'avg':
vecs = all_encoder_layer[-1][b, st:ed,:]
vec = vecs.mean(dim=1, keepdim=True)
else:
raise ValueError
wemb_h[b, i_hd, :] = vec
return wemb_h
def cal_prob(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi):
"""
:param s_sc: [B, l_h]
:param s_sa: [B, l_a] # 16
:param s_wn: [B, 5]
:param s_wc: [B, l_h]
:param s_wo: [B, 4, l_o] #
:param s_wv: [B, 4, 22]
:return:
"""
# First get selected index
#
# Predict prob
p_sc = cal_prob_sc(s_sc, pr_sc)
p_sa = cal_prob_sa(s_sa, pr_sa)
p_wn = cal_prob_wn(s_wn, pr_wn)
p_wc = cal_prob_wc(s_wc, pr_wc)
p_wo = cal_prob_wo(s_wo, pr_wo)
p_wvi = cal_prob_wvi_se(s_wv, pr_wvi)
# calculate select-clause probability
p_select = cal_prob_select(p_sc, p_sa)
# calculate where-clause probability
p_where = cal_prob_where(p_wn, p_wc, p_wo, p_wvi)
# calculate total probability
p_tot = cal_prob_tot(p_select, p_where)
return p_tot, p_select, p_where, p_sc, p_sa, p_wn, p_wc, p_wo, p_wvi
def cal_prob_tot(p_select, p_where):
p_tot = []
for b, p_select1 in enumerate(p_select):
p_where1 = p_where[b]
p_tot.append( p_select1 * p_where1 )
return p_tot
def cal_prob_select(p_sc, p_sa):
p_select = []
for b, p_sc1 in enumerate(p_sc):
p1 = 1.0
p1 *= p_sc1
p1 *= p_sa[b]
p_select.append(p1)
return p_select
def cal_prob_where(p_wn, p_wc, p_wo, p_wvi):
p_where = []
for b, p_wn1 in enumerate(p_wn):
p1 = 1.0
p1 *= p_wn1
p_wc1 = p_wc[b]
for i_wn, p_wc11 in enumerate(p_wc1):
p_wo11 = p_wo[b][i_wn]
p_wv11_st, p_wv11_ed = p_wvi[b][i_wn]
p1 *= p_wc11
p1 *= p_wo11
p1 *= p_wv11_st
p1 *= p_wv11_ed
p_where.append(p1)
return p_where
def cal_prob_sc(s_sc, pr_sc):
ps = F.softmax(s_sc, dim=1)
p = []
for b, ps1 in enumerate(ps):
pr_sc1 = pr_sc[b]
p1 = ps1[pr_sc1]
p.append(p1.item())
return p
def cal_prob_sa(s_sa, pr_sa):
ps = F.softmax(s_sa, dim=1)
p = []
for b, ps1 in enumerate(ps):
pr_sa1 = pr_sa[b]
p1 = ps1[pr_sa1]
p.append(p1.item())
return p
def cal_prob_wn(s_wn, pr_wn):
ps = F.softmax(s_wn, dim=1)
p = []
for b, ps1 in enumerate(ps):
pr_wn1 = pr_wn[b]
p1 = ps1[pr_wn1]
p.append(p1.item())
return p
def cal_prob_wc(s_wc, pr_wc):
ps = torch.sigmoid(s_wc)
ps_out = []
for b, pr_wc1 in enumerate(pr_wc):
ps1 = array(ps[b].cpu())
ps_out1 = ps1[pr_wc1]
ps_out.append(list(ps_out1))
return ps_out
def cal_prob_wo(s_wo, pr_wo):
# assume there is always at least single condition.
ps = F.softmax(s_wo, dim=2)
ps_out = []
for b, pr_wo1 in enumerate(pr_wo):
ps_out1 = []
for n, pr_wo11 in enumerate(pr_wo1):
ps11 = ps[b][n]
ps_out1.append( ps11[pr_wo11].item() )
ps_out.append(ps_out1)
return ps_out
def cal_prob_wvi_se(s_wv, pr_wvi):
prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy()
p_wv = []
for b, pr_wvi1 in enumerate(pr_wvi):
p_wv1 = []
for i_wn, pr_wvi11 in enumerate(pr_wvi1):
st, ed = pr_wvi11
p_st = prob_wv[b, i_wn, st, 0]
p_ed = prob_wv[b, i_wn, ed, 1]
p_wv1.append([p_st, p_ed])
p_wv.append(p_wv1)
return p_wv
def generate_inputs_s2s(tokenizer, nlu1_tt, hds1, sql_vocab1):
"""
[CLS] sql_vocab [SEP] question [SEP] headers
To make sql_vocab in a fixed position.
"""
tokens = []
segment_ids = []
tokens.append("[CLS]")
# sql_vocab
i_sql_vocab = []
# for doc
for i, sql_vocab11 in enumerate(sql_vocab1):
i_st_sql = len(tokens)
sub_tok = tokenizer.tokenize(sql_vocab11)
tokens += sub_tok
i_ed_sql = len(tokens)
i_sql_vocab.append((i_st_sql, i_ed_sql))
segment_ids += [1] * len(sub_tok)
if i < len(sql_vocab1) - 1:
tokens.append("[SEP]")
segment_ids.append(0)
elif i == len(sql_vocab1) - 1:
tokens.append("[SEP]")
segment_ids.append(1)
else:
raise EnvironmentError
# question
i_st_nlu = len(tokens) # to use it later
segment_ids.append(0)
for token in nlu1_tt:
tokens.append(token)
segment_ids.append(0)
i_ed_nlu = len(tokens)
tokens.append("[SEP]")
segment_ids.append(0)
i_nlu = (i_st_nlu, i_ed_nlu)
# headers
i_hds = []
# for doc
for i, hds11 in enumerate(hds1):
i_st_hd = len(tokens)
sub_tok = tokenizer.tokenize(hds11)
tokens += sub_tok
i_ed_hd = len(tokens)
i_hds.append((i_st_hd, i_ed_hd))
segment_ids += [1] * len(sub_tok)
if i < len(hds1)-1:
tokens.append("[SEP]")
segment_ids.append(0)
elif i == len(hds1)-1:
tokens.append("[SEP]")
segment_ids.append(1)
else:
raise EnvironmentError
return tokens, segment_ids, i_sql_vocab, i_nlu, i_hds
def sort_pr_wc(pr_wc, g_wc):
"""
Input: list
pr_wc = [B, n_conds]
g_wc = [B, n_conds]
Return: list
pr_wc_sorted = [B, n_conds]
"""
pr_wc_sorted = []
for b, pr_wc1 in enumerate(pr_wc):
g_wc1 = g_wc[b]
pr_wc1_sorted = []
if set(g_wc1) == set(pr_wc1):
pr_wc1_sorted = deepcopy(g_wc1)
else:
# no sorting when g_wc1 and pr_wc1 are different.
pr_wc1_sorted = deepcopy(pr_wc1)
pr_wc_sorted.append(pr_wc1_sorted)
return pr_wc_sorted
| 29.697827
| 158
| 0.565999
|
4a077d7d6c688a6504f86da37ba4242c33b3b17b
| 3,065
|
py
|
Python
|
tools/perf/benchmarks/rasterize_and_record_micro.py
|
metux/chromium-deb
|
3c08e9b89a1b6f95f103a61ff4f528dbcd57fc42
|
[
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | null | null | null |
tools/perf/benchmarks/rasterize_and_record_micro.py
|
metux/chromium-deb
|
3c08e9b89a1b6f95f103a61ff4f528dbcd57fc42
|
[
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | null | null | null |
tools/perf/benchmarks/rasterize_and_record_micro.py
|
metux/chromium-deb
|
3c08e9b89a1b6f95f103a61ff4f528dbcd57fc42
|
[
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | null | null | null |
# Copyright 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from core import perf_benchmark
from measurements import rasterize_and_record_micro
import page_sets
from telemetry import benchmark
class _RasterizeAndRecordMicro(perf_benchmark.PerfBenchmark):
@classmethod
def AddBenchmarkCommandLineArgs(cls, parser):
parser.add_option('--start-wait-time', type='float',
default=2,
help='Wait time before the benchmark is started '
'(must be long enough to load all content)')
parser.add_option('--rasterize-repeat', type='int',
default=100,
help='Repeat each raster this many times. Increase '
'this value to reduce variance.')
parser.add_option('--record-repeat', type='int',
default=100,
help='Repeat each record this many times. Increase '
'this value to reduce variance.')
parser.add_option('--timeout', type='int',
default=120,
help='The length of time to wait for the micro '
'benchmark to finish, expressed in seconds.')
parser.add_option('--report-detailed-results',
action='store_true',
help='Whether to report additional detailed results.')
@classmethod
def Name(cls):
return 'rasterize_and_record_micro'
def CreatePageTest(self, options):
return rasterize_and_record_micro.RasterizeAndRecordMicro(
options.start_wait_time, options.rasterize_repeat,
options.record_repeat, options.timeout, options.report_detailed_results)
# RasterizeAndRecord disabled on mac because of crbug.com/350684.
# RasterizeAndRecord disabled on windows because of crbug.com/338057.
@benchmark.Disabled('mac', 'win',
'android') # http://crbug.com/610018
class RasterizeAndRecordMicroTop25(_RasterizeAndRecordMicro):
"""Measures rasterize and record performance on the top 25 web pages.
http://www.chromium.org/developers/design-documents/rendering-benchmarks"""
page_set = page_sets.Top25PageSet
@classmethod
def Name(cls):
return 'rasterize_and_record_micro.top_25'
def GetExpectations(self):
return page_sets.Top25StoryExpectations()
# New benchmark only enabled on Linux until we've observed behavior for a
# reasonable period of time.
@benchmark.Disabled('mac', 'win', 'android')
class RasterizeAndRecordMicroPartialInvalidation(_RasterizeAndRecordMicro):
"""Measures rasterize and record performance for partial inval. on big pages.
http://www.chromium.org/developers/design-documents/rendering-benchmarks"""
page_set = page_sets.PartialInvalidationCasesPageSet
@classmethod
def Name(cls):
return 'rasterize_and_record_micro.partial_invalidation'
def GetExpectations(self):
return page_sets.PartialInvalidationCasesStoryExpectations()
| 38.797468
| 80
| 0.695269
|
4a077d7d6d23ce7bc45caf87d0dbd3d8ef5551e9
| 135
|
py
|
Python
|
falcon/base/file.py
|
lorne-luo/falcon
|
a56ee8e121d70086721292ae33f1070a1f7e1f7b
|
[
"BSD-3-Clause"
] | null | null | null |
falcon/base/file.py
|
lorne-luo/falcon
|
a56ee8e121d70086721292ae33f1070a1f7e1f7b
|
[
"BSD-3-Clause"
] | 7
|
2020-02-11T23:56:08.000Z
|
2022-02-10T07:35:07.000Z
|
falcon/base/file.py
|
lorne-luo/falcon
|
a56ee8e121d70086721292ae33f1070a1f7e1f7b
|
[
"BSD-3-Clause"
] | 1
|
2021-05-11T09:57:38.000Z
|
2021-05-11T09:57:38.000Z
|
import os
def create_folder(path):
folder = os.path.dirname(path)
if not os.path.exists(folder):
os.makedirs(folder)
| 16.875
| 34
| 0.666667
|
4a077e46b35c57120a8245209f74be572227f840
| 2,634
|
py
|
Python
|
test_proj/apps/app1/tests/test_overloads.py
|
andrewbird2/django-data-validation
|
8c3a8e14440f0299a5f4b188dfaa7864b53b5123
|
[
"MIT"
] | 1
|
2020-08-05T16:42:48.000Z
|
2020-08-05T16:42:48.000Z
|
test_proj/apps/app1/tests/test_overloads.py
|
andrewbird2/django-data-validation
|
8c3a8e14440f0299a5f4b188dfaa7864b53b5123
|
[
"MIT"
] | 1
|
2020-11-04T07:06:37.000Z
|
2020-11-04T07:06:37.000Z
|
test_proj/apps/app1/tests/test_overloads.py
|
andrewbird2/django-data-validation
|
8c3a8e14440f0299a5f4b188dfaa7864b53b5123
|
[
"MIT"
] | 1
|
2020-11-04T02:16:05.000Z
|
2020-11-04T02:16:05.000Z
|
import pytest
from datavalidation import data_validator
from datavalidation.runners import ModelValidationRunner, ObjectValidationRunner
from app1.models import Overloaded
def test_bad_instancemethod_overloading():
""" test that overloading an instance method with an instance method fails """
try:
class _Test:
@data_validator
def foo(self):
pass
@foo.overload
def foo(self):
pass
assert False, "expected exception"
except RuntimeError:
pass
def test_bad_classmethod_overloading():
""" test that overloading a class method with a class method fails """
try:
class _Test:
@data_validator
@classmethod
def foo(cls):
pass
@foo.overload
@classmethod
def foo(cls):
pass
assert False, "expected exception"
except RuntimeError:
pass
def test_bad_naming():
""" test that overloading a method with a different name fails """
try:
class _Test:
@data_validator
def foo(self):
pass
@foo.overload
@classmethod
def bar(cls):
pass
assert False, "expected exception"
except ValueError:
pass
@pytest.mark.django_db
def test_model_runner(caplog):
""" test that the model validation runner uses the class methods from
overloaded validators
"""
summaries = ModelValidationRunner(Overloaded).run()
assert len(summaries) == 3 # 2 overloaded + 1 from BaseModel
messages = [
message for name, level, message in caplog.record_tuples
if name == "app1.models.overloads"
]
assert len(messages) == 2 # the two overloaded function
# the model validation runner should use the class methods where available
assert all("class method" in msg for msg in messages)
@pytest.mark.django_db
def test_object_runner(caplog):
""" test that the object validation runner uses the instance methods
from overloaded validators
"""
obj = Overloaded.objects.first()
result = ObjectValidationRunner(obj).run(class_methods=True)
assert result == (3, 0, 0)
messages = [
message for name, level, message in caplog.record_tuples
if name == "app1.models.overloads"
]
assert len(messages) == 2 # the two overloaded function
# the object validation runner should use the instance methods where available
assert all("instance method" in msg for msg in messages)
| 28.322581
| 82
| 0.630979
|
4a077eadf68999876ef3b23fe81822ee0cb0862e
| 7,533
|
bzl
|
Python
|
intellij_platform_sdk/build_defs.bzl
|
d-haxton/intellij
|
3acafac0566ed0b314afb4d0873b289790e9a37d
|
[
"Apache-2.0"
] | null | null | null |
intellij_platform_sdk/build_defs.bzl
|
d-haxton/intellij
|
3acafac0566ed0b314afb4d0873b289790e9a37d
|
[
"Apache-2.0"
] | null | null | null |
intellij_platform_sdk/build_defs.bzl
|
d-haxton/intellij
|
3acafac0566ed0b314afb4d0873b289790e9a37d
|
[
"Apache-2.0"
] | null | null | null |
"""Convenience methods for plugin_api."""
# The current indirect ij_product mapping (eg. "intellij-latest")
INDIRECT_IJ_PRODUCTS = {
"intellij-latest": "intellij-2019.2",
"intellij-latest-mac": "intellij-2019.2-mac",
"intellij-beta": "intellij-2019.3",
"intellij-canary": "intellij-2020.1",
"intellij-ue-latest": "intellij-ue-2019.2",
"intellij-ue-latest-mac": "intellij-ue-2019.2-mac",
"intellij-ue-beta": "intellij-ue-2019.3",
"intellij-ue-canary": "intellij-ue-2020.1",
"android-studio-latest": "android-studio-3.6",
"android-studio-beta": "android-studio-3.6",
"android-studio-beta-mac": "android-studio-3.6-mac",
"android-studio-canary": "android-studio-4.0",
"clion-latest": "clion-2019.2",
"clion-beta": "clion-2019.3",
}
DIRECT_IJ_PRODUCTS = {
"intellij-2019.2": struct(
ide = "intellij",
directory = "intellij_ce_2019_2",
),
"intellij-2019.2-mac": struct(
ide = "intellij",
directory = "intellij_ce_2019_2",
),
"intellij-ue-2019.2": struct(
ide = "intellij-ue",
directory = "intellij_ue_2019_2",
),
"intellij-ue-2019.2-mac": struct(
ide = "intellij-ue",
directory = "intellij_ue_2019_2",
),
"intellij-2019.3": struct(
ide = "intellij",
directory = "intellij_ce_2019_3",
),
"intellij-2019.3-mac": struct(
ide = "intellij",
directory = "intellij_ce_2019_3",
),
"intellij-ue-2019.3": struct(
ide = "intellij-ue",
directory = "intellij_ue_2019_3",
),
"intellij-ue-2019.3-mac": struct(
ide = "intellij-ue",
directory = "intellij_ue_2019_3",
),
"intellij-2020.1": struct(
ide = "intellij",
directory = "intellij_ce_2020_1",
),
"intellij-2020.1-mac": struct(
ide = "intellij",
directory = "intellij_ce_2020_1",
),
"intellij-ue-2020.1": struct(
ide = "intellij-ue",
directory = "intellij_ue_2020_1",
),
"intellij-ue-2020.1-mac": struct(
ide = "intellij-ue",
directory = "intellij_ue_2020_1",
),
"android-studio-3.6": struct(
ide = "android-studio",
directory = "android_studio_3_6",
),
"android-studio-3.6-mac": struct(
ide = "android-studio",
directory = "android_studio_3_6",
),
"android-studio-4.0": struct(
ide = "android-studio",
directory = "android_studio_4_0",
),
"clion-2019.2": struct(
ide = "clion",
directory = "clion_2019_2",
),
"clion-2019.3": struct(
ide = "clion",
directory = "clion_2019_3",
),
}
def select_for_plugin_api(params):
"""Selects for a plugin_api.
Args:
params: A dict with ij_product -> value.
You may only include direct ij_products here,
not indirects (eg. intellij-latest).
Returns:
A select statement on all plugin_apis. Unless you include a "default",
a non-matched plugin_api will result in an error.
Example:
java_library(
name = "foo",
srcs = select_for_plugin_api({
"intellij-2016.3.1": [...my intellij 2016.3 sources ....],
"intellij-2012.2.4": [...my intellij 2016.2 sources ...],
}),
)
"""
for indirect_ij_product in INDIRECT_IJ_PRODUCTS:
if indirect_ij_product in params:
error_message = "".join([
"Do not select on indirect ij_product %s. " % indirect_ij_product,
"Instead, select on an exact ij_product.",
])
fail(error_message)
return _do_select_for_plugin_api(params)
def _do_select_for_plugin_api(params):
"""A version of select_for_plugin_api which accepts indirect products."""
if not params:
fail("Empty select_for_plugin_api")
expanded_params = dict(**params)
# Expand all indirect plugin_apis to point to their
# corresponding direct plugin_api.
#
# {"intellij-2016.3.1": "foo"} ->
# {"intellij-2016.3.1": "foo", "intellij-latest": "foo"}
fallback_value = None
for indirect_ij_product, resolved_plugin_api in INDIRECT_IJ_PRODUCTS.items():
if resolved_plugin_api in params:
expanded_params[indirect_ij_product] = params[resolved_plugin_api]
if not fallback_value:
fallback_value = params[resolved_plugin_api]
if indirect_ij_product in params:
expanded_params[resolved_plugin_api] = params[indirect_ij_product]
# Map the shorthand ij_products to full config_setting targets.
# This makes it more convenient so the user doesn't have to
# fully specify the path to the plugin_apis
select_params = dict()
for ij_product, value in expanded_params.items():
if ij_product == "default":
select_params["//conditions:default"] = value
else:
select_params["//intellij_platform_sdk:" + ij_product] = value
return select(
select_params,
no_match_error = "define an intellij product version, e.g. --define=ij_product=intellij-latest",
)
def select_for_ide(intellij = None, intellij_ue = None, android_studio = None, clion = None, default = []):
"""Selects for the supported IDEs.
Args:
intellij: Files to use for IntelliJ. If None, will use default.
intellij_ue: Files to use for IntelliJ UE. If None, will use value chosen for 'intellij'.
android_studio: Files to use for Android Studio. If None will use default.
clion: Files to use for CLion. If None will use default.
default: Files to use for any IDEs not passed.
Returns:
A select statement on all plugin_apis to lists of files, sorted into IDEs.
Example:
java_library(
name = "foo",
srcs = select_for_ide(
clion = [":cpp_only_sources"],
default = [":java_only_sources"],
),
)
"""
intellij = intellij if intellij != None else default
intellij_ue = intellij_ue if intellij_ue != None else intellij
android_studio = android_studio if android_studio != None else default
clion = clion if clion != None else default
ide_to_value = {
"intellij": intellij,
"intellij-ue": intellij_ue,
"android-studio": android_studio,
"clion": clion,
}
# Map (direct ij_product) -> corresponding ide value
params = dict()
for ij_product, value in DIRECT_IJ_PRODUCTS.items():
params[ij_product] = ide_to_value[value.ide]
params["default"] = default
return select_for_plugin_api(params)
def _plugin_api_directory(value):
return "@" + value.directory + "//"
def select_from_plugin_api_directory(intellij, android_studio, clion, intellij_ue = None):
"""Internal convenience method to generate select statement from the IDE's plugin_api directories."""
ide_to_value = {
"intellij": intellij,
"intellij-ue": intellij_ue if intellij_ue else intellij,
"android-studio": android_studio,
"clion": clion,
}
# Map (direct ij_product) -> corresponding product directory
params = dict()
for ij_product, value in DIRECT_IJ_PRODUCTS.items():
params[ij_product] = [_plugin_api_directory(value) + item for item in ide_to_value[value.ide]]
# No ij_product == intellij-latest
params["default"] = params[INDIRECT_IJ_PRODUCTS["intellij-latest"]]
return select_for_plugin_api(params)
| 34.240909
| 107
| 0.628169
|
4a077f3fbedc8a5776aa5a74591d5e2d5034d470
| 7,469
|
py
|
Python
|
deploy.py
|
varun-raghavendra/fl_faas_fabric
|
36310d24805c5bd7258f2e432997ac9b91aee61a
|
[
"MIT"
] | 6
|
2021-05-19T20:36:55.000Z
|
2022-03-20T05:56:21.000Z
|
deploy.py
|
varun-raghavendra/fl_faas_fabric
|
36310d24805c5bd7258f2e432997ac9b91aee61a
|
[
"MIT"
] | null | null | null |
deploy.py
|
varun-raghavendra/fl_faas_fabric
|
36310d24805c5bd7258f2e432997ac9b91aee61a
|
[
"MIT"
] | 2
|
2022-03-16T08:59:15.000Z
|
2022-03-20T11:47:55.000Z
|
#!/usr/bin/env python
import yaml
import sys, getopt
from typing import List
import traceback
import asyncio
import json
import logging
from Clusters import BaseDeployment
from Clusters import OpenWhiskDeployment
from Clusters import GoogleDeployment
functions_meta = []
from commons.Logger import ScriptLogger
logging.basicConfig(level=logging.DEBUG)
logger = ScriptLogger(__name__, 'SWI.log')
logger.setLevel(logging.DEBUG)
logging.captureWarnings(True)
async def deploy_to_clusters(configfile: str, provider: str, scenario_name: str, functions_list: list,
cluster_obj: BaseDeployment = None,
providers_list: list = None, all_clusters: bool = False):
with open(configfile, 'r') as stream:
try:
data = yaml.safe_load(stream)
if all_clusters:
for cluster in data['providers'][provider]:
curr_cluster = data['providers'][provider][cluster]
scenario = data['scenarios'][scenario_name]
for function in functions_list:
function_object = scenario['functions'][function][provider]
await cluster_obj.deploy(curr_cluster, function, function_object)
else:
for cluster_name in providers_list:
for cluster in data['providers'][provider]:
curr_cluster = data['providers'][provider][cluster]
scenario = data['scenarios'][scenario_name]
if cluster_name == cluster:
for function in functions_list:
function_object = scenario['functions'][function][provider]
await cluster_obj.deploy(curr_cluster, function, function_object)
break
except yaml.YAMLError as exc:
print(exc)
async def remove_from_clusters(configfile: str, provider: str, scenario_name: str,
functions_list: list, cluster_obj: BaseDeployment = None,
providers_list: list = None, all_clusters: bool = False):
with open(configfile, 'r') as stream:
try:
data = yaml.safe_load(stream)
if all_clusters:
for cluster in data['providers'][provider]:
curr_cluster = data['providers'][provider][cluster]
scenario = data['scenarios'][scenario_name]
for function in functions_list:
function_object = scenario['functions'][function][provider]
await cluster_obj.delete(curr_cluster, function, function_object)
else:
for cluster_name in providers_list:
for cluster in data['providers'][provider]:
curr_cluster = data['providers'][provider][cluster]
scenario = data['scenarios'][scenario_name]
if cluster_name == cluster:
for function in functions_list:
function_object = scenario['functions'][function][provider]
await cluster_obj.delete(curr_cluster, function, function_object)
break
except yaml.YAMLError as exc:
print(exc)
async def main(argv):
openwhisk_obj = OpenWhiskDeployment()
google_obj = GoogleDeployment()
configfile = ''
all_providers = False
ow_providers_list = []
gcf_providers_list = []
functions_list = []
scenario_name = ""
deployment = False
remove = False
meta = False
try:
arguments, values = getopt.getopt(argv, "hc:ao:g:s:f:drm", ["help", "configfile=", "all_providers",
"ow_providers_list=", "gcf_providers_list=",
"scenario_name=", "functions_list=",
"deploy", "remove", "get_meta_data"])
except getopt.GetoptError:
print('main.py -c <configfile path> -a <for all providers> '
'-o <OW provider_list separated by comma> -g <GCF provider_list separated by comma> '
'-s <scenario_name> -f <functions list separated by comma> '
'-m <for saving functions meta data in a file>'
'-d <for deploying> -r <for removing>')
sys.exit(2)
for current_argument, current_value in arguments:
if current_argument in ("-h", "--help"):
print('python3 deploy.py \n -c <configfile path> \n -a <for all providers> '
'\n -o <OW provider_list separated by comma> \n -g <GCF provider_list separated by comma>'
'\n -s <scenario_name> '
'\n -f <functions separated by comma> \n -m <for saving functions meta data in a file> '
'\n -d <for deploying> \n -r <for removing>')
elif current_argument in ("-c", "--configfile"):
configfile = current_value
elif current_argument in ("-a", "--all_providers"):
all_providers = True
elif current_argument in ("-d", "--deploy"):
deployment = True
elif current_argument in ("-r", "--remove"):
remove = True
elif current_argument in ("-o", "--ow_providers_list"):
all_arguments = current_value.split(',')
ow_providers_list = all_arguments
elif current_argument in ("-g", "--gcf_providers_list"):
all_arguments = current_value.split(',')
gcf_providers_list = all_arguments
elif current_argument in ("-s", "--scenario_name"):
scenario_name = current_value
elif current_argument in ("-f", "--functions_list"):
all_arguments = current_value.split(',')
functions_list = all_arguments
tasks: List[asyncio.Task] = []
if deployment:
tasks.append(
asyncio.create_task(
deploy_to_clusters(configfile, 'openwhisk', scenario_name,
functions_list, openwhisk_obj, ow_providers_list, all_providers)
)
)
tasks.append(
asyncio.create_task(
deploy_to_clusters(configfile, 'google', scenario_name,
functions_list, google_obj, gcf_providers_list, all_providers)
)
)
elif remove:
tasks.append(
asyncio.create_task(
remove_from_clusters(configfile, 'openwhisk', scenario_name,
functions_list, openwhisk_obj, ow_providers_list, all_providers)
)
)
tasks.append(
asyncio.create_task(
remove_from_clusters(configfile, 'google', scenario_name,
functions_list, google_obj, gcf_providers_list, all_providers)
)
)
# wait for all workers
if len(tasks):
try:
await asyncio.wait(tasks)
except Exception as e:
print("Exception in main worker loop")
print(e)
traceback.print_exc()
print("All deployment/removal finished")
if __name__ == "__main__":
asyncio.run(main(sys.argv[1:]))
| 41.960674
| 109
| 0.560985
|
4a077fe5a0441d17f0620091c48549194d1a0098
| 1,544
|
py
|
Python
|
openfe/tests/setup/test_lomap_atommapper.py
|
mikemhenry/openfe
|
d4c78af62a7ae05b99eb95d173661ac134b7e7b9
|
[
"MIT"
] | null | null | null |
openfe/tests/setup/test_lomap_atommapper.py
|
mikemhenry/openfe
|
d4c78af62a7ae05b99eb95d173661ac134b7e7b9
|
[
"MIT"
] | null | null | null |
openfe/tests/setup/test_lomap_atommapper.py
|
mikemhenry/openfe
|
d4c78af62a7ae05b99eb95d173661ac134b7e7b9
|
[
"MIT"
] | null | null | null |
# This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import pytest
from rdkit import Chem
import openfe
from openfe.setup import LomapAtomMapper, LigandMolecule
def test_simple(lomap_basic_test_files):
# basic sanity check on the LigandAtomMapper
mol1 = lomap_basic_test_files['methylcyclohexane']
mol2 = lomap_basic_test_files['toluene']
mapper = LomapAtomMapper()
mapping_gen = mapper.suggest_mappings(mol1, mol2)
mapping = next(mapping_gen)
assert isinstance(mapping, openfe.setup.LigandAtomMapping)
# methylcyclohexane to toluene is a 1:1 mapping between all atoms
# so 7 values should be present
assert len(mapping.mol1_to_mol2) == 7
def test_generator_length(lomap_basic_test_files):
# check that we get one mapping back from Lomap LigandAtomMapper then the
# generator stops correctly
mol1 = lomap_basic_test_files['methylcyclohexane']
mol2 = lomap_basic_test_files['toluene']
mapper = LomapAtomMapper()
mapping_gen = mapper.suggest_mappings(mol1, mol2)
_ = next(mapping_gen)
with pytest.raises(StopIteration):
next(mapping_gen)
def test_bad_mapping(lomap_basic_test_files):
toluene = lomap_basic_test_files['toluene']
NigelTheNitrogen = LigandMolecule(Chem.MolFromSmiles('N'), name='Nigel')
mapper = LomapAtomMapper()
mapping_gen = mapper.suggest_mappings(toluene, NigelTheNitrogen)
with pytest.raises(StopIteration):
next(mapping_gen)
| 30.27451
| 77
| 0.754534
|
4a07815e84acb8e0e44ebecaecc3982f1555eca9
| 168,831
|
py
|
Python
|
numpy/lib/function_base.py
|
chatcannon/numpy
|
f1b3f00f7abdd97d59dc5b1c0bb922a692452736
|
[
"BSD-3-Clause"
] | 1
|
2022-02-16T05:32:38.000Z
|
2022-02-16T05:32:38.000Z
|
numpy/lib/function_base.py
|
chatcannon/numpy
|
f1b3f00f7abdd97d59dc5b1c0bb922a692452736
|
[
"BSD-3-Clause"
] | null | null | null |
numpy/lib/function_base.py
|
chatcannon/numpy
|
f1b3f00f7abdd97d59dc5b1c0bb922a692452736
|
[
"BSD-3-Clause"
] | 1
|
2018-11-15T19:41:09.000Z
|
2018-11-15T19:41:09.000Z
|
from __future__ import division, absolute_import, print_function
import collections
import operator
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d, transpose
from numpy.core.numeric import (
ones, zeros, arange, concatenate, array, asarray, asanyarray, empty,
empty_like, ndarray, around, floor, ceil, take, dot, where, intp,
integer, isscalar, absolute, AxisError
)
from numpy.core.umath import (
pi, multiply, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin,
mod, exp, log10
)
from numpy.core.fromnumeric import (
ravel, nonzero, sort, partition, mean, any, sum
)
from numpy.core.numerictypes import typecodes, number
from numpy.lib.twodim_base import diag
from .utils import deprecate
from numpy.core.multiarray import (
_insert, add_docstring, digitize, bincount, normalize_axis_index,
interp as compiled_interp, interp_complex as compiled_interp_complex
)
from numpy.core.umath import _add_newdoc_ufunc as add_newdoc_ufunc
from numpy.compat import long
from numpy.compat.py3k import basestring
if sys.version_info[0] < 3:
# Force range to be a generator, for np.delete's usage.
range = xrange
import __builtin__ as builtins
else:
import builtins
__all__ = [
'select', 'piecewise', 'trim_zeros', 'copy', 'iterable', 'percentile',
'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'disp', 'flip',
'rot90', 'extract', 'place', 'vectorize', 'asarray_chkfinite', 'average',
'histogram', 'histogramdd', 'bincount', 'digitize', 'cov', 'corrcoef',
'msort', 'median', 'sinc', 'hamming', 'hanning', 'bartlett',
'blackman', 'kaiser', 'trapz', 'i0', 'add_newdoc', 'add_docstring',
'meshgrid', 'delete', 'insert', 'append', 'interp', 'add_newdoc_ufunc'
]
def rot90(m, k=1, axes=(0,1)):
"""
Rotate an array by 90 degrees in the plane specified by axes.
Rotation direction is from the first towards the second axis.
.. versionadded:: 1.12.0
Parameters
----------
m : array_like
Array of two or more dimensions.
k : integer
Number of times the array is rotated by 90 degrees.
axes: (2,) array_like
The array is rotated in the plane defined by the axes.
Axes must be different.
Returns
-------
y : ndarray
A rotated view of `m`.
See Also
--------
flip : Reverse the order of elements in an array along the given axis.
fliplr : Flip an array horizontally.
flipud : Flip an array vertically.
Notes
-----
rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1))
rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1))
Examples
--------
>>> m = np.array([[1,2],[3,4]], int)
>>> m
array([[1, 2],
[3, 4]])
>>> np.rot90(m)
array([[2, 4],
[1, 3]])
>>> np.rot90(m, 2)
array([[4, 3],
[2, 1]])
>>> m = np.arange(8).reshape((2,2,2))
>>> np.rot90(m, 1, (1,2))
array([[[1, 3],
[0, 2]],
[[5, 7],
[4, 6]]])
"""
axes = tuple(axes)
if len(axes) != 2:
raise ValueError("len(axes) must be 2.")
m = asanyarray(m)
if axes[0] == axes[1] or absolute(axes[0] - axes[1]) == m.ndim:
raise ValueError("Axes must be different.")
if (axes[0] >= m.ndim or axes[0] < -m.ndim
or axes[1] >= m.ndim or axes[1] < -m.ndim):
raise ValueError("Axes={} out of range for array of ndim={}."
.format(axes, m.ndim))
k %= 4
if k == 0:
return m[:]
if k == 2:
return flip(flip(m, axes[0]), axes[1])
axes_list = arange(0, m.ndim)
(axes_list[axes[0]], axes_list[axes[1]]) = (axes_list[axes[1]],
axes_list[axes[0]])
if k == 1:
return transpose(flip(m,axes[1]), axes_list)
else:
# k == 3
return flip(transpose(m, axes_list), axes[1])
def flip(m, axis):
"""
Reverse the order of elements in an array along the given axis.
The shape of the array is preserved, but the elements are reordered.
.. versionadded:: 1.12.0
Parameters
----------
m : array_like
Input array.
axis : integer
Axis in array, which entries are reversed.
Returns
-------
out : array_like
A view of `m` with the entries of axis reversed. Since a view is
returned, this operation is done in constant time.
See Also
--------
flipud : Flip an array vertically (axis=0).
fliplr : Flip an array horizontally (axis=1).
Notes
-----
flip(m, 0) is equivalent to flipud(m).
flip(m, 1) is equivalent to fliplr(m).
flip(m, n) corresponds to ``m[...,::-1,...]`` with ``::-1`` at position n.
Examples
--------
>>> A = np.arange(8).reshape((2,2,2))
>>> A
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> flip(A, 0)
array([[[4, 5],
[6, 7]],
[[0, 1],
[2, 3]]])
>>> flip(A, 1)
array([[[2, 3],
[0, 1]],
[[6, 7],
[4, 5]]])
>>> A = np.random.randn(3,4,5)
>>> np.all(flip(A,2) == A[:,:,::-1,...])
True
"""
if not hasattr(m, 'ndim'):
m = asarray(m)
indexer = [slice(None)] * m.ndim
try:
indexer[axis] = slice(None, None, -1)
except IndexError:
raise ValueError("axis=%i is invalid for the %i-dimensional input array"
% (axis, m.ndim))
return m[tuple(indexer)]
def iterable(y):
"""
Check whether or not an object can be iterated over.
Parameters
----------
y : object
Input object.
Returns
-------
b : bool
Return ``True`` if the object has an iterator method or is a
sequence and ``False`` otherwise.
Examples
--------
>>> np.iterable([1, 2, 3])
True
>>> np.iterable(2)
False
"""
try:
iter(y)
except TypeError:
return False
return True
def _hist_bin_sqrt(x):
"""
Square root histogram bin estimator.
Bin width is inversely proportional to the data size. Used by many
programs for its simplicity.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
return x.ptp() / np.sqrt(x.size)
def _hist_bin_sturges(x):
"""
Sturges histogram bin estimator.
A very simplistic estimator based on the assumption of normality of
the data. This estimator has poor performance for non-normal data,
which becomes especially obvious for large data sets. The estimate
depends only on size of the data.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
return x.ptp() / (np.log2(x.size) + 1.0)
def _hist_bin_rice(x):
"""
Rice histogram bin estimator.
Another simple estimator with no normality assumption. It has better
performance for large data than Sturges, but tends to overestimate
the number of bins. The number of bins is proportional to the cube
root of data size (asymptotically optimal). The estimate depends
only on size of the data.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
return x.ptp() / (2.0 * x.size ** (1.0 / 3))
def _hist_bin_scott(x):
"""
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
def _hist_bin_doane(x):
"""
Doane's histogram bin estimator.
Improved version of Sturges' formula which works better for
non-normal data. See
stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
if x.size > 2:
sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3)))
sigma = np.std(x)
if sigma > 0.0:
# These three operations add up to
# g1 = np.mean(((x - np.mean(x)) / sigma)**3)
# but use only one temp array instead of three
temp = x - np.mean(x)
np.true_divide(temp, sigma, temp)
np.power(temp, 3, temp)
g1 = np.mean(temp)
return x.ptp() / (1.0 + np.log2(x.size) +
np.log2(1.0 + np.absolute(g1) / sg1))
return 0.0
def _hist_bin_fd(x):
"""
The Freedman-Diaconis histogram bin estimator.
The Freedman-Diaconis rule uses interquartile range (IQR) to
estimate binwidth. It is considered a variation of the Scott rule
with more robustness as the IQR is less affected by outliers than
the standard deviation. However, the IQR depends on fewer points
than the standard deviation, so it is less accurate, especially for
long tailed distributions.
If the IQR is 0, this function returns 1 for the number of bins.
Binwidth is inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
iqr = np.subtract(*np.percentile(x, [75, 25]))
return 2.0 * iqr * x.size ** (-1.0 / 3.0)
def _hist_bin_auto(x):
"""
Histogram bin estimator that uses the minimum width of the
Freedman-Diaconis and Sturges estimators.
The FD estimator is usually the most robust method, but its width
estimate tends to be too large for small `x`. The Sturges estimator
is quite good for small (<1000) datasets and is the default in the R
language. This method gives good off the shelf behaviour.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
See Also
--------
_hist_bin_fd, _hist_bin_sturges
"""
# There is no need to check for zero here. If ptp is, so is IQR and
# vice versa. Either both are zero or neither one is.
return min(_hist_bin_fd(x), _hist_bin_sturges(x))
# Private dict initialized at module load time
_hist_bin_selectors = {'auto': _hist_bin_auto,
'doane': _hist_bin_doane,
'fd': _hist_bin_fd,
'rice': _hist_bin_rice,
'scott': _hist_bin_scott,
'sqrt': _hist_bin_sqrt,
'sturges': _hist_bin_sturges}
def histogram(a, bins=10, range=None, normed=False, weights=None,
density=None):
r"""
Compute the histogram of a set of data.
Parameters
----------
a : array_like
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optional
If `bins` is an int, it defines the number of equal-width
bins in the given range (10, by default). If `bins` is a
sequence, it defines the bin edges, including the rightmost
edge, allowing for non-uniform bin widths.
.. versionadded:: 1.11.0
If `bins` is a string from the list below, `histogram` will use
the method chosen to calculate the optimal bin width and
consequently the number of bins (see `Notes` for more detail on
the estimators) from the data that falls within the requested
range. While the bin width will be optimal for the actual data
in the range, the number of bins will be computed to fill the
entire range, including the empty portions. For visualisation,
using the 'auto' option is suggested. Weighted data is not
supported for automated bin size selection.
'auto'
Maximum of the 'sturges' and 'fd' estimators. Provides good
all around performance.
'fd' (Freedman Diaconis Estimator)
Robust (resilient to outliers) estimator that takes into
account data variability and data size.
'doane'
An improved version of Sturges' estimator that works better
with non-normal datasets.
'scott'
Less robust estimator that that takes into account data
variability and data size.
'rice'
Estimator does not take variability into account, only data
size. Commonly overestimates number of bins required.
'sturges'
R's default method, only accounts for data size. Only
optimal for gaussian data and underestimates number of bins
for large non-gaussian datasets.
'sqrt'
Square root (of data size) estimator, used by Excel and
other programs for its speed and simplicity.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range
is simply ``(a.min(), a.max())``. Values outside the range are
ignored. The first element of the range must be less than or
equal to the second. `range` affects the automatic bin
computation as well. While bin width is computed to be optimal
based on the actual data within `range`, the bin count will fill
the entire range including portions containing no data.
normed : bool, optional
This keyword is deprecated in NumPy 1.6.0 due to confusing/buggy
behavior. It will be removed in NumPy 2.0.0. Use the ``density``
keyword instead. If ``False``, the result will contain the
number of samples in each bin. If ``True``, the result is the
value of the probability *density* function at the bin,
normalized such that the *integral* over the range is 1. Note
that this latter behavior is known to be buggy with unequal bin
widths; use ``density`` instead.
weights : array_like, optional
An array of weights, of the same shape as `a`. Each value in
`a` only contributes its associated weight towards the bin count
(instead of 1). If `density` is True, the weights are
normalized, so that the integral of the density over the range
remains 1.
density : bool, optional
If ``False``, the result will contain the number of samples in
each bin. If ``True``, the result is the value of the
probability *density* function at the bin, normalized such that
the *integral* over the range is 1. Note that the sum of the
histogram values will not be equal to 1 unless bins of unity
width are chosen; it is not a probability *mass* function.
Overrides the ``normed`` keyword if given.
Returns
-------
hist : array
The values of the histogram. See `density` and `weights` for a
description of the possible semantics.
bin_edges : array of dtype float
Return the bin edges ``(length(hist)+1)``.
See Also
--------
histogramdd, bincount, searchsorted, digitize
Notes
-----
All but the last (righthand-most) bin is half-open. In other words,
if `bins` is::
[1, 2, 3, 4]
then the first bin is ``[1, 2)`` (including 1, but excluding 2) and
the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which
*includes* 4.
.. versionadded:: 1.11.0
The methods to estimate the optimal number of bins are well founded
in literature, and are inspired by the choices R provides for
histogram visualisation. Note that having the number of bins
proportional to :math:`n^{1/3}` is asymptotically optimal, which is
why it appears in most estimators. These are simply plug-in methods
that give good starting points for number of bins. In the equations
below, :math:`h` is the binwidth and :math:`n_h` is the number of
bins. All estimators that compute bin counts are recast to bin width
using the `ptp` of the data. The final bin count is obtained from
``np.round(np.ceil(range / h))`.
'Auto' (maximum of the 'Sturges' and 'FD' estimators)
A compromise to get a good value. For small datasets the Sturges
value will usually be chosen, while larger datasets will usually
default to FD. Avoids the overly conservative behaviour of FD
and Sturges for small and large datasets respectively.
Switchover point is usually :math:`a.size \approx 1000`.
'FD' (Freedman Diaconis Estimator)
.. math:: h = 2 \frac{IQR}{n^{1/3}}
The binwidth is proportional to the interquartile range (IQR)
and inversely proportional to cube root of a.size. Can be too
conservative for small datasets, but is quite good for large
datasets. The IQR is very robust to outliers.
'Scott'
.. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}}
The binwidth is proportional to the standard deviation of the
data and inversely proportional to cube root of ``x.size``. Can
be too conservative for small datasets, but is quite good for
large datasets. The standard deviation is not very robust to
outliers. Values are very similar to the Freedman-Diaconis
estimator in the absence of outliers.
'Rice'
.. math:: n_h = 2n^{1/3}
The number of bins is only proportional to cube root of
``a.size``. It tends to overestimate the number of bins and it
does not take into account data variability.
'Sturges'
.. math:: n_h = \log _{2}n+1
The number of bins is the base 2 log of ``a.size``. This
estimator assumes normality of data and is too conservative for
larger, non-normal datasets. This is the default method in R's
``hist`` method.
'Doane'
.. math:: n_h = 1 + \log_{2}(n) +
\log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}})
g_1 = mean[(\frac{x - \mu}{\sigma})^3]
\sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}}
An improved version of Sturges' formula that produces better
estimates for non-normal datasets. This estimator attempts to
account for the skew of the data.
'Sqrt'
.. math:: n_h = \sqrt n
The simplest and fastest estimator. Only takes into account the
data size.
Examples
--------
>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
(array([ 0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))
>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, density=True)
>>> hist
array([ 0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
>>> hist.sum()
2.4999999999999996
>>> np.sum(hist*np.diff(bin_edges))
1.0
.. versionadded:: 1.11.0
Automated Bin Selection Methods example, using 2 peak random data
with 2000 points:
>>> import matplotlib.pyplot as plt
>>> rng = np.random.RandomState(10) # deterministic random data
>>> a = np.hstack((rng.normal(size=1000),
... rng.normal(loc=5, scale=2, size=1000)))
>>> plt.hist(a, bins='auto') # arguments are passed to np.histogram
>>> plt.title("Histogram with 'auto' bins")
>>> plt.show()
"""
a = asarray(a)
if weights is not None:
weights = asarray(weights)
if np.any(weights.shape != a.shape):
raise ValueError(
'weights should have the same shape as a.')
weights = weights.ravel()
a = a.ravel()
# Do not modify the original value of range so we can check for `None`
if range is None:
if a.size == 0:
# handle empty arrays. Can't determine range, so use 0-1.
mn, mx = 0.0, 1.0
else:
mn, mx = a.min() + 0.0, a.max() + 0.0
else:
mn, mx = [mi + 0.0 for mi in range]
if mn > mx:
raise ValueError(
'max must be larger than min in range parameter.')
if not np.all(np.isfinite([mn, mx])):
raise ValueError(
'range parameter must be finite.')
if mn == mx:
mn -= 0.5
mx += 0.5
if isinstance(bins, basestring):
# if `bins` is a string for an automatic method,
# this will replace it with the number of bins calculated
if bins not in _hist_bin_selectors:
raise ValueError("{0} not a valid estimator for bins".format(bins))
if weights is not None:
raise TypeError("Automated estimation of the number of "
"bins is not supported for weighted data")
# Make a reference to `a`
b = a
# Update the reference if the range needs truncation
if range is not None:
keep = (a >= mn)
keep &= (a <= mx)
if not np.logical_and.reduce(keep):
b = a[keep]
if b.size == 0:
bins = 1
else:
# Do not call selectors on empty arrays
width = _hist_bin_selectors[bins](b)
if width:
bins = int(np.ceil((mx - mn) / width))
else:
# Width can be zero for some estimators, e.g. FD when
# the IQR of the data is zero.
bins = 1
# Histogram is an integer or a float array depending on the weights.
if weights is None:
ntype = np.dtype(np.intp)
else:
ntype = weights.dtype
# We set a block size, as this allows us to iterate over chunks when
# computing histograms, to minimize memory usage.
BLOCK = 65536
if not iterable(bins):
if np.isscalar(bins) and bins < 1:
raise ValueError(
'`bins` should be a positive integer.')
# At this point, if the weights are not integer, floating point, or
# complex, we have to use the slow algorithm.
if weights is not None and not (np.can_cast(weights.dtype, np.double) or
np.can_cast(weights.dtype, np.complex)):
bins = linspace(mn, mx, bins + 1, endpoint=True)
if not iterable(bins):
# We now convert values of a to bin indices, under the assumption of
# equal bin widths (which is valid here).
# Initialize empty histogram
n = np.zeros(bins, ntype)
# Pre-compute histogram scaling factor
norm = bins / (mx - mn)
# Compute the bin edges for potential correction.
bin_edges = linspace(mn, mx, bins + 1, endpoint=True)
# We iterate over blocks here for two reasons: the first is that for
# large arrays, it is actually faster (for example for a 10^8 array it
# is 2x as fast) and it results in a memory footprint 3x lower in the
# limit of large arrays.
for i in arange(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
if weights is None:
tmp_w = None
else:
tmp_w = weights[i:i + BLOCK]
# Only include values in the right range
keep = (tmp_a >= mn)
keep &= (tmp_a <= mx)
if not np.logical_and.reduce(keep):
tmp_a = tmp_a[keep]
if tmp_w is not None:
tmp_w = tmp_w[keep]
tmp_a_data = tmp_a.astype(float)
tmp_a = tmp_a_data - mn
tmp_a *= norm
# Compute the bin indices, and for values that lie exactly on mx we
# need to subtract one
indices = tmp_a.astype(np.intp)
indices[indices == bins] -= 1
# The index computation is not guaranteed to give exactly
# consistent results within ~1 ULP of the bin edges.
decrement = tmp_a_data < bin_edges[indices]
indices[decrement] -= 1
# The last bin includes the right edge. The other bins do not.
increment = ((tmp_a_data >= bin_edges[indices + 1])
& (indices != bins - 1))
indices[increment] += 1
# We now compute the histogram using bincount
if ntype.kind == 'c':
n.real += np.bincount(indices, weights=tmp_w.real,
minlength=bins)
n.imag += np.bincount(indices, weights=tmp_w.imag,
minlength=bins)
else:
n += np.bincount(indices, weights=tmp_w,
minlength=bins).astype(ntype)
# Rename the bin edges for return.
bins = bin_edges
else:
bins = asarray(bins)
if (np.diff(bins) < 0).any():
raise ValueError(
'bins must increase monotonically.')
# Initialize empty histogram
n = np.zeros(bins.shape, ntype)
if weights is None:
for i in arange(0, len(a), BLOCK):
sa = sort(a[i:i+BLOCK])
n += np.r_[sa.searchsorted(bins[:-1], 'left'),
sa.searchsorted(bins[-1], 'right')]
else:
zero = array(0, dtype=ntype)
for i in arange(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
tmp_w = weights[i:i+BLOCK]
sorting_index = np.argsort(tmp_a)
sa = tmp_a[sorting_index]
sw = tmp_w[sorting_index]
cw = np.concatenate(([zero, ], sw.cumsum()))
bin_index = np.r_[sa.searchsorted(bins[:-1], 'left'),
sa.searchsorted(bins[-1], 'right')]
n += cw[bin_index]
n = np.diff(n)
if density is not None:
if density:
db = array(np.diff(bins), float)
return n/db/n.sum(), bins
else:
return n, bins
else:
# deprecated, buggy behavior. Remove for NumPy 2.0.0
if normed:
db = array(np.diff(bins), float)
return n/(n*db).sum(), bins
else:
return n, bins
def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
"""
Compute the multidimensional histogram of some data.
Parameters
----------
sample : array_like
The data to be histogrammed. It must be an (N,D) array or data
that can be converted to such. The rows of the resulting array
are the coordinates of points in a D dimensional polytope.
bins : sequence or int, optional
The bin specification:
* A sequence of arrays describing the bin edges along each dimension.
* The number of bins for each dimension (nx, ny, ... =bins)
* The number of bins for all dimensions (nx=ny=...=bins).
range : sequence, optional
A sequence of lower and upper bin edges to be used if the edges are
not given explicitly in `bins`. Defaults to the minimum and maximum
values along each dimension.
normed : bool, optional
If False, returns the number of samples in each bin. If True,
returns the bin density ``bin_count / sample_count / bin_volume``.
weights : (N,) array_like, optional
An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
Weights are normalized to 1 if normed is True. If normed is False,
the values of the returned histogram are equal to the sum of the
weights belonging to the samples falling into each bin.
Returns
-------
H : ndarray
The multidimensional histogram of sample x. See normed and weights
for the different possible semantics.
edges : list
A list of D arrays describing the bin edges for each dimension.
See Also
--------
histogram: 1-D histogram
histogram2d: 2-D histogram
Examples
--------
>>> r = np.random.randn(100,3)
>>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
>>> H.shape, edges[0].size, edges[1].size, edges[2].size
((5, 8, 4), 6, 9, 5)
"""
try:
# Sample is an ND-array.
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = atleast_2d(sample).T
N, D = sample.shape
nbin = empty(D, int)
edges = D*[None]
dedges = D*[None]
if weights is not None:
weights = asarray(weights)
try:
M = len(bins)
if M != D:
raise ValueError(
'The dimension of bins must be equal to the dimension of the '
' sample x.')
except TypeError:
# bins is an integer
bins = D*[bins]
# Select range for each dimension
# Used only if number of bins is given.
if range is None:
# Handle empty input. Range can't be determined in that case, use 0-1.
if N == 0:
smin = zeros(D)
smax = ones(D)
else:
smin = atleast_1d(array(sample.min(0), float))
smax = atleast_1d(array(sample.max(0), float))
else:
if not np.all(np.isfinite(range)):
raise ValueError(
'range parameter must be finite.')
smin = zeros(D)
smax = zeros(D)
for i in arange(D):
smin[i], smax[i] = range[i]
# Make sure the bins have a finite width.
for i in arange(len(smin)):
if smin[i] == smax[i]:
smin[i] = smin[i] - .5
smax[i] = smax[i] + .5
# avoid rounding issues for comparisons when dealing with inexact types
if np.issubdtype(sample.dtype, np.inexact):
edge_dt = sample.dtype
else:
edge_dt = float
# Create edge arrays
for i in arange(D):
if isscalar(bins[i]):
if bins[i] < 1:
raise ValueError(
"Element at index %s in `bins` should be a positive "
"integer." % i)
nbin[i] = bins[i] + 2 # +2 for outlier bins
edges[i] = linspace(smin[i], smax[i], nbin[i]-1, dtype=edge_dt)
else:
edges[i] = asarray(bins[i], edge_dt)
nbin[i] = len(edges[i]) + 1 # +1 for outlier bins
dedges[i] = diff(edges[i])
if np.any(np.asarray(dedges[i]) <= 0):
raise ValueError(
"Found bin edge of size <= 0. Did you specify `bins` with"
"non-monotonic sequence?")
nbin = asarray(nbin)
# Handle empty input.
if N == 0:
return np.zeros(nbin-2), edges
# Compute the bin number each sample falls into.
Ncount = {}
for i in arange(D):
Ncount[i] = digitize(sample[:, i], edges[i])
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
# counted in the last bin, and not as an outlier.
for i in arange(D):
# Rounding precision
mindiff = dedges[i].min()
if not np.isinf(mindiff):
decimal = int(-log10(mindiff)) + 6
# Find which points are on the rightmost edge.
not_smaller_than_edge = (sample[:, i] >= edges[i][-1])
on_edge = (around(sample[:, i], decimal) ==
around(edges[i][-1], decimal))
# Shift these points one bin to the left.
Ncount[i][where(on_edge & not_smaller_than_edge)[0]] -= 1
# Flattened histogram matrix (1D)
# Reshape is used so that overlarge arrays
# will raise an error.
hist = zeros(nbin, float).reshape(-1)
# Compute the sample indices in the flattened histogram matrix.
ni = nbin.argsort()
xy = zeros(N, int)
for i in arange(0, D-1):
xy += Ncount[ni[i]] * nbin[ni[i+1:]].prod()
xy += Ncount[ni[-1]]
# Compute the number of repetitions in xy and assign it to the
# flattened histmat.
if len(xy) == 0:
return zeros(nbin-2, int), edges
flatcount = bincount(xy, weights)
a = arange(len(flatcount))
hist[a] = flatcount
# Shape into a proper matrix
hist = hist.reshape(sort(nbin))
for i in arange(nbin.size):
j = ni.argsort()[i]
hist = hist.swapaxes(i, j)
ni[i], ni[j] = ni[j], ni[i]
# Remove outliers (indices 0 and -1 for each dimension).
core = D*[slice(1, -1)]
hist = hist[core]
# Normalize if normed is True
if normed:
s = hist.sum()
for i in arange(D):
shape = ones(D, int)
shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
if (hist.shape != nbin - 2).any():
raise RuntimeError(
"Internal Shape Error")
return hist, edges
def average(a, axis=None, weights=None, returned=False):
"""
Compute the weighted average along the specified axis.
Parameters
----------
a : array_like
Array containing data to be averaged. If `a` is not an array, a
conversion is attempted.
axis : None or int or tuple of ints, optional
Axis or axes along which to average `a`. The default,
axis=None, will average over all of the elements of the input array.
If axis is negative it counts from the last to the first axis.
.. versionadded:: 1.7.0
If axis is a tuple of ints, averaging is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
weights : array_like, optional
An array of weights associated with the values in `a`. Each value in
`a` contributes to the average according to its associated weight.
The weights array can either be 1-D (in which case its length must be
the size of `a` along the given axis) or of the same shape as `a`.
If `weights=None`, then all data in `a` are assumed to have a
weight equal to one.
returned : bool, optional
Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`)
is returned, otherwise only the average is returned.
If `weights=None`, `sum_of_weights` is equivalent to the number of
elements over which the average is taken.
Returns
-------
average, [sum_of_weights] : array_type or double
Return the average along the specified axis. When returned is `True`,
return a tuple with the average as the first element and the sum
of the weights as the second element. The return type is `Float`
if `a` is of integer type, otherwise it is of the same type as `a`.
`sum_of_weights` is of the same type as `average`.
Raises
------
ZeroDivisionError
When all weights along axis are zero. See `numpy.ma.average` for a
version robust to this type of error.
TypeError
When the length of 1D `weights` is not the same as the shape of `a`
along axis.
See Also
--------
mean
ma.average : average for masked arrays -- useful if your data contains
"missing" values
Examples
--------
>>> data = range(1,5)
>>> data
[1, 2, 3, 4]
>>> np.average(data)
2.5
>>> np.average(range(1,11), weights=range(10,0,-1))
4.0
>>> data = np.arange(6).reshape((3,2))
>>> data
array([[0, 1],
[2, 3],
[4, 5]])
>>> np.average(data, axis=1, weights=[1./4, 3./4])
array([ 0.75, 2.75, 4.75])
>>> np.average(data, weights=[1./4, 3./4])
Traceback (most recent call last):
...
TypeError: Axis must be specified when shapes of a and weights differ.
"""
a = np.asanyarray(a)
if weights is None:
avg = a.mean(axis)
scl = avg.dtype.type(a.size/avg.size)
else:
wgt = np.asanyarray(weights)
if issubclass(a.dtype.type, (np.integer, np.bool_)):
result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8')
else:
result_dtype = np.result_type(a.dtype, wgt.dtype)
# Sanity checks
if a.shape != wgt.shape:
if axis is None:
raise TypeError(
"Axis must be specified when shapes of a and weights "
"differ.")
if wgt.ndim != 1:
raise TypeError(
"1D weights expected when shapes of a and weights differ.")
if wgt.shape[0] != a.shape[axis]:
raise ValueError(
"Length of weights not compatible with specified axis.")
# setup wgt to broadcast along axis
wgt = np.broadcast_to(wgt, (a.ndim-1)*(1,) + wgt.shape)
wgt = wgt.swapaxes(-1, axis)
scl = wgt.sum(axis=axis, dtype=result_dtype)
if np.any(scl == 0.0):
raise ZeroDivisionError(
"Weights sum to zero, can't be normalized")
avg = np.multiply(a, wgt, dtype=result_dtype).sum(axis)/scl
if returned:
if scl.shape != avg.shape:
scl = np.broadcast_to(scl, avg.shape).copy()
return avg, scl
else:
return avg
def asarray_chkfinite(a, dtype=None, order=None):
"""Convert the input to an array, checking for NaNs or Infs.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes lists, lists of tuples, tuples, tuples of tuples, tuples
of lists and ndarrays. Success requires no NaNs or Infs.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
Whether to use row-major (C-style) or
column-major (Fortran-style) memory representation.
Defaults to 'C'.
Returns
-------
out : ndarray
Array interpretation of `a`. No copy is performed if the input
is already an ndarray. If `a` is a subclass of ndarray, a base
class ndarray is returned.
Raises
------
ValueError
Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).
See Also
--------
asarray : Create and array.
asanyarray : Similar function which passes through subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array. If all elements are finite
``asarray_chkfinite`` is identical to ``asarray``.
>>> a = [1, 2]
>>> np.asarray_chkfinite(a, dtype=float)
array([1., 2.])
Raises ValueError if array_like contains Nans or Infs.
>>> a = [1, 2, np.inf]
>>> try:
... np.asarray_chkfinite(a)
... except ValueError:
... print('ValueError')
...
ValueError
"""
a = asarray(a, dtype=dtype, order=order)
if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
raise ValueError(
"array must not contain infs or NaNs")
return a
def piecewise(x, condlist, funclist, *args, **kw):
"""
Evaluate a piecewise-defined function.
Given a set of conditions and corresponding functions, evaluate each
function on the input data wherever its condition is true.
Parameters
----------
x : ndarray or scalar
The input domain.
condlist : list of bool arrays or bool scalars
Each boolean array corresponds to a function in `funclist`. Wherever
`condlist[i]` is True, `funclist[i](x)` is used as the output value.
Each boolean array in `condlist` selects a piece of `x`,
and should therefore be of the same shape as `x`.
The length of `condlist` must correspond to that of `funclist`.
If one extra function is given, i.e. if
``len(funclist) - len(condlist) == 1``, then that extra function
is the default value, used wherever all conditions are false.
funclist : list of callables, f(x,*args,**kw), or scalars
Each function is evaluated over `x` wherever its corresponding
condition is True. It should take an array as input and give an array
or a scalar value as output. If, instead of a callable,
a scalar is provided then a constant function (``lambda x: scalar``) is
assumed.
args : tuple, optional
Any further arguments given to `piecewise` are passed to the functions
upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then
each function is called as ``f(x, 1, 'a')``.
kw : dict, optional
Keyword arguments used in calling `piecewise` are passed to the
functions upon execution, i.e., if called
``piecewise(..., ..., alpha=1)``, then each function is called as
``f(x, alpha=1)``.
Returns
-------
out : ndarray
The output is the same shape and type as x and is found by
calling the functions in `funclist` on the appropriate portions of `x`,
as defined by the boolean arrays in `condlist`. Portions not covered
by any condition have a default value of 0.
See Also
--------
choose, select, where
Notes
-----
This is similar to choose or select, except that functions are
evaluated on elements of `x` that satisfy the corresponding condition from
`condlist`.
The result is::
|--
|funclist[0](x[condlist[0]])
out = |funclist[1](x[condlist[1]])
|...
|funclist[n2](x[condlist[n2]])
|--
Examples
--------
Define the sigma function, which is -1 for ``x < 0`` and +1 for ``x >= 0``.
>>> x = np.linspace(-2.5, 2.5, 6)
>>> np.piecewise(x, [x < 0, x >= 0], [-1, 1])
array([-1., -1., -1., 1., 1., 1.])
Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for
``x >= 0``.
>>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])
array([ 2.5, 1.5, 0.5, 0.5, 1.5, 2.5])
Apply the same function to a scalar value.
>>> y = -2
>>> np.piecewise(y, [y < 0, y >= 0], [lambda x: -x, lambda x: x])
array(2)
"""
x = asanyarray(x)
n2 = len(funclist)
if (isscalar(condlist) or not (isinstance(condlist[0], list) or
isinstance(condlist[0], ndarray))):
if not isscalar(condlist) and x.size == 1 and x.ndim == 0:
condlist = [[c] for c in condlist]
else:
condlist = [condlist]
condlist = array(condlist, dtype=bool)
n = len(condlist)
# This is a hack to work around problems with NumPy's
# handling of 0-d arrays and boolean indexing with
# numpy.bool_ scalars
zerod = False
if x.ndim == 0:
x = x[None]
zerod = True
if n == n2 - 1: # compute the "otherwise" condition.
totlist = np.logical_or.reduce(condlist, axis=0)
# Only able to stack vertically if the array is 1d or less
if x.ndim <= 1:
condlist = np.vstack([condlist, ~totlist])
else:
condlist = [asarray(c, dtype=bool) for c in condlist]
totlist = condlist[0]
for k in range(1, n):
totlist |= condlist[k]
condlist.append(~totlist)
n += 1
y = zeros(x.shape, x.dtype)
for k in range(n):
item = funclist[k]
if not isinstance(item, collections.Callable):
y[condlist[k]] = item
else:
vals = x[condlist[k]]
if vals.size > 0:
y[condlist[k]] = item(vals, *args, **kw)
if zerod:
y = y.squeeze()
return y
def select(condlist, choicelist, default=0):
"""
Return an array drawn from elements in choicelist, depending on conditions.
Parameters
----------
condlist : list of bool ndarrays
The list of conditions which determine from which array in `choicelist`
the output elements are taken. When multiple conditions are satisfied,
the first one encountered in `condlist` is used.
choicelist : list of ndarrays
The list of arrays from which the output elements are taken. It has
to be of the same length as `condlist`.
default : scalar, optional
The element inserted in `output` when all conditions evaluate to False.
Returns
-------
output : ndarray
The output at position m is the m-th element of the array in
`choicelist` where the m-th element of the corresponding array in
`condlist` is True.
See Also
--------
where : Return elements from one of two arrays depending on condition.
take, choose, compress, diag, diagonal
Examples
--------
>>> x = np.arange(10)
>>> condlist = [x<3, x>5]
>>> choicelist = [x, x**2]
>>> np.select(condlist, choicelist)
array([ 0, 1, 2, 0, 0, 0, 36, 49, 64, 81])
"""
# Check the size of condlist and choicelist are the same, or abort.
if len(condlist) != len(choicelist):
raise ValueError(
'list of cases must be same length as list of conditions')
# Now that the dtype is known, handle the deprecated select([], []) case
if len(condlist) == 0:
# 2014-02-24, 1.9
warnings.warn("select with an empty condition list is not possible"
"and will be deprecated",
DeprecationWarning, stacklevel=2)
return np.asarray(default)[()]
choicelist = [np.asarray(choice) for choice in choicelist]
choicelist.append(np.asarray(default))
# need to get the result type before broadcasting for correct scalar
# behaviour
dtype = np.result_type(*choicelist)
# Convert conditions to arrays and broadcast conditions and choices
# as the shape is needed for the result. Doing it separately optimizes
# for example when all choices are scalars.
condlist = np.broadcast_arrays(*condlist)
choicelist = np.broadcast_arrays(*choicelist)
# If cond array is not an ndarray in boolean format or scalar bool, abort.
deprecated_ints = False
for i in range(len(condlist)):
cond = condlist[i]
if cond.dtype.type is not np.bool_:
if np.issubdtype(cond.dtype, np.integer):
# A previous implementation accepted int ndarrays accidentally.
# Supported here deliberately, but deprecated.
condlist[i] = condlist[i].astype(bool)
deprecated_ints = True
else:
raise ValueError(
'invalid entry in choicelist: should be boolean ndarray')
if deprecated_ints:
# 2014-02-24, 1.9
msg = "select condlists containing integer ndarrays is deprecated " \
"and will be removed in the future. Use `.astype(bool)` to " \
"convert to bools."
warnings.warn(msg, DeprecationWarning, stacklevel=2)
if choicelist[0].ndim == 0:
# This may be common, so avoid the call.
result_shape = condlist[0].shape
else:
result_shape = np.broadcast_arrays(condlist[0], choicelist[0])[0].shape
result = np.full(result_shape, choicelist[-1], dtype)
# Use np.copyto to burn each choicelist array onto result, using the
# corresponding condlist as a boolean mask. This is done in reverse
# order since the first choice should take precedence.
choicelist = choicelist[-2::-1]
condlist = condlist[::-1]
for choice, cond in zip(choicelist, condlist):
np.copyto(result, choice, where=cond)
return result
def copy(a, order='K'):
"""
Return an array copy of the given object.
Parameters
----------
a : array_like
Input data.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible. (Note that this function and :meth:`ndarray.copy` are very
similar, but have different default values for their order=
arguments.)
Returns
-------
arr : ndarray
Array interpretation of `a`.
Notes
-----
This is equivalent to:
>>> np.array(a, copy=True) #doctest: +SKIP
Examples
--------
Create an array x, with a reference y and a copy z:
>>> x = np.array([1, 2, 3])
>>> y = x
>>> z = np.copy(x)
Note that, when we modify x, y changes, but not z:
>>> x[0] = 10
>>> x[0] == y[0]
True
>>> x[0] == z[0]
False
"""
return array(a, order=order, copy=True)
# Basic operations
def gradient(f, *varargs, **kwargs):
"""
Return the gradient of an N-dimensional array.
The gradient is computed using second order accurate central differences
in the interior points and either first or second order accurate one-sides
(forward or backwards) differences at the boundaries.
The returned gradient hence has the same shape as the input array.
Parameters
----------
f : array_like
An N-dimensional array containing samples of a scalar function.
varargs : list of scalar or array, optional
Spacing between f values. Default unitary spacing for all dimensions.
Spacing can be specified using:
1. single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension.
i.e. `dx`, `dy`, `dz`, ...
3. N arrays to specify the coordinates of the values along each
dimension of F. The length of the array must match the size of
the corresponding dimension
4. Any combination of N scalars/arrays with the meaning of 2. and 3.
If `axis` is given, the number of varargs must equal the number of axes.
Default: 1.
edge_order : {1, 2}, optional
Gradient is calculated using N-th order accurate differences
at the boundaries. Default: 1.
.. versionadded:: 1.9.1
axis : None or int or tuple of ints, optional
Gradient is calculated only along the given axis or axes
The default (axis = None) is to calculate the gradient for all the axes
of the input array. axis may be negative, in which case it counts from
the last to the first axis.
.. versionadded:: 1.11.0
Returns
-------
gradient : ndarray or list of ndarray
A set of ndarrays (or a single ndarray if there is only one dimension)
corresponding to the derivatives of f with respect to each dimension.
Each derivative has the same shape as f.
Examples
--------
>>> f = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
>>> np.gradient(f)
array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])
>>> np.gradient(f, 2)
array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])
Spacing can be also specified with an array that represents the coordinates
of the values F along the dimensions.
For instance a uniform spacing:
>>> x = np.arange(f.size)
>>> np.gradient(f, x)
array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])
Or a non uniform one:
>>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=np.float)
>>> np.gradient(f, x)
array([ 1. , 3. , 3.5, 6.7, 6.9, 2.5])
For two dimensional arrays, the return will be two arrays ordered by
axis. In this example the first array stands for the gradient in
rows and the second one in columns direction:
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
[array([[ 2., 2., -1.],
[ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ],
[ 1. , 1. , 1. ]])]
In this example the spacing is also specified:
uniform for axis=0 and non uniform for axis=1
>>> dx = 2.
>>> y = [1., 1.5, 3.5]
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), dx, y)
[array([[ 1. , 1. , -0.5],
[ 1. , 1. , -0.5]]), array([[ 2. , 2. , 2. ],
[ 2. , 1.7, 0.5]])]
It is possible to specify how boundaries are treated using `edge_order`
>>> x = np.array([0, 1, 2, 3, 4])
>>> f = x**2
>>> np.gradient(f, edge_order=1)
array([ 1., 2., 4., 6., 7.])
>>> np.gradient(f, edge_order=2)
array([-0., 2., 4., 6., 8.])
The `axis` keyword can be used to specify a subset of axes of which the
gradient is calculated
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), axis=0)
array([[ 2., 2., -1.],
[ 2., 2., -1.]])
Notes
-----
Assuming that :math:`f\\in C^{3}` (i.e., :math:`f` has at least 3 continous
derivatives) and let be :math:`h_{*}` a non homogeneous stepsize, the
spacing the finite difference coefficients are computed by minimising
the consistency error :math:`\\eta_{i}`:
.. math::
\\eta_{i} = f_{i}^{\\left(1\\right)} -
\\left[ \\alpha f\\left(x_{i}\\right) +
\\beta f\\left(x_{i} + h_{d}\\right) +
\\gamma f\\left(x_{i}-h_{s}\\right)
\\right]
By substituting :math:`f(x_{i} + h_{d})` and :math:`f(x_{i} - h_{s})`
with their Taylor series expansion, this translates into solving
the following the linear system:
.. math::
\\left\\{
\\begin{array}{r}
\\alpha+\\beta+\\gamma=0 \\\\
-\\beta h_{d}+\\gamma h_{s}=1 \\\\
\\beta h_{d}^{2}+\\gamma h_{s}^{2}=0
\\end{array}
\\right.
The resulting approximation of :math:`f_{i}^{(1)}` is the following:
.. math::
\\hat f_{i}^{(1)} =
\\frac{
h_{s}^{2}f\\left(x_{i} + h_{d}\\right)
+ \\left(h_{d}^{2} - h_{s}^{2}\\right)f\\left(x_{i}\\right)
- h_{d}^{2}f\\left(x_{i}-h_{s}\\right)}
{ h_{s}h_{d}\\left(h_{d} + h_{s}\\right)}
+ \\mathcal{O}\\left(\\frac{h_{d}h_{s}^{2}
+ h_{s}h_{d}^{2}}{h_{d}
+ h_{s}}\\right)
It is worth noting that if :math:`h_{s}=h_{d}`
(i.e., data are evenly spaced)
we find the standard second order approximation:
.. math::
\\hat f_{i}^{(1)}=
\\frac{f\\left(x_{i+1}\\right) - f\\left(x_{i-1}\\right)}{2h}
+ \\mathcal{O}\\left(h^{2}\\right)
With a similar procedure the forward/backward approximations used for
boundaries can be derived.
References
----------
.. [1] Quarteroni A., Sacco R., Saleri F. (2007) Numerical Mathematics
(Texts in Applied Mathematics). New York: Springer.
.. [2] Durran D. R. (1999) Numerical Methods for Wave Equations
in Geophysical Fluid Dynamics. New York: Springer.
.. [3] Fornberg B. (1988) Generation of Finite Difference Formulas on
Arbitrarily Spaced Grids,
Mathematics of Computation 51, no. 184 : 699-706.
`PDF <http://www.ams.org/journals/mcom/1988-51-184/
S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_.
"""
f = np.asanyarray(f)
N = f.ndim # number of dimensions
axes = kwargs.pop('axis', None)
if axes is None:
axes = tuple(range(N))
else:
axes = _nx.normalize_axis_tuple(axes, N)
len_axes = len(axes)
n = len(varargs)
if n == 0:
dx = [1.0] * len_axes
elif n == len_axes or (n == 1 and np.isscalar(varargs[0])):
dx = list(varargs)
for i, distances in enumerate(dx):
if np.isscalar(distances):
continue
if len(distances) != f.shape[axes[i]]:
raise ValueError("distances must be either scalars or match "
"the length of the corresponding dimension")
diffx = np.diff(dx[i])
# if distances are constant reduce to the scalar case
# since it brings a consistent speedup
if (diffx == diffx[0]).all():
diffx = diffx[0]
dx[i] = diffx
if len(dx) == 1:
dx *= len_axes
else:
raise TypeError("invalid number of arguments")
edge_order = kwargs.pop('edge_order', 1)
if kwargs:
raise TypeError('"{}" are not valid keyword arguments.'.format(
'", "'.join(kwargs.keys())))
if edge_order > 2:
raise ValueError("'edge_order' greater than 2 not supported")
# use central differences on interior and one-sided differences on the
# endpoints. This preserves second order-accuracy over the full domain.
outvals = []
# create slice objects --- initially all are [:, :, ..., :]
slice1 = [slice(None)]*N
slice2 = [slice(None)]*N
slice3 = [slice(None)]*N
slice4 = [slice(None)]*N
otype = f.dtype.char
if otype not in ['f', 'd', 'F', 'D', 'm', 'M']:
otype = 'd'
# Difference of datetime64 elements results in timedelta64
if otype == 'M':
# Need to use the full dtype name because it contains unit information
otype = f.dtype.name.replace('datetime', 'timedelta')
elif otype == 'm':
# Needs to keep the specific units, can't be a general unit
otype = f.dtype
# Convert datetime64 data into ints. Make dummy variable `y`
# that is a view of ints if the data is datetime64, otherwise
# just set y equal to the array `f`.
if f.dtype.char in ["M", "m"]:
y = f.view('int64')
else:
y = f
for i, axis in enumerate(axes):
if y.shape[axis] < edge_order + 1:
raise ValueError(
"Shape of array too small to calculate a numerical gradient, "
"at least (edge_order + 1) elements are required.")
# result allocation
out = np.empty_like(y, dtype=otype)
uniform_spacing = np.isscalar(dx[i])
# Numerical differentiation: 2nd order interior
slice1[axis] = slice(1, -1)
slice2[axis] = slice(None, -2)
slice3[axis] = slice(1, -1)
slice4[axis] = slice(2, None)
if uniform_spacing:
out[slice1] = (f[slice4] - f[slice2]) / (2. * dx[i])
else:
dx1 = dx[i][0:-1]
dx2 = dx[i][1:]
a = -(dx2)/(dx1 * (dx1 + dx2))
b = (dx2 - dx1) / (dx1 * dx2)
c = dx1 / (dx2 * (dx1 + dx2))
# fix the shape for broadcasting
shape = np.ones(N, dtype=int)
shape[axis] = -1
a.shape = b.shape = c.shape = shape
# 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:]
out[slice1] = a * f[slice2] + b * f[slice3] + c * f[slice4]
# Numerical differentiation: 1st order edges
if edge_order == 1:
slice1[axis] = 0
slice2[axis] = 1
slice3[axis] = 0
dx_0 = dx[i] if uniform_spacing else dx[i][0]
# 1D equivalent -- out[0] = (y[1] - y[0]) / (x[1] - x[0])
out[slice1] = (y[slice2] - y[slice3]) / dx_0
slice1[axis] = -1
slice2[axis] = -1
slice3[axis] = -2
dx_n = dx[i] if uniform_spacing else dx[i][-1]
# 1D equivalent -- out[-1] = (y[-1] - y[-2]) / (x[-1] - x[-2])
out[slice1] = (y[slice2] - y[slice3]) / dx_n
# Numerical differentiation: 2nd order edges
else:
slice1[axis] = 0
slice2[axis] = 0
slice3[axis] = 1
slice4[axis] = 2
if uniform_spacing:
a = -1.5 / dx[i]
b = 2. / dx[i]
c = -0.5 / dx[i]
else:
dx1 = dx[i][0]
dx2 = dx[i][1]
a = -(2. * dx1 + dx2)/(dx1 * (dx1 + dx2))
b = (dx1 + dx2) / (dx1 * dx2)
c = - dx1 / (dx2 * (dx1 + dx2))
# 1D equivalent -- out[0] = a * y[0] + b * y[1] + c * y[2]
out[slice1] = a * y[slice2] + b * y[slice3] + c * y[slice4]
slice1[axis] = -1
slice2[axis] = -3
slice3[axis] = -2
slice4[axis] = -1
if uniform_spacing:
a = 0.5 / dx[i]
b = -2. / dx[i]
c = 1.5 / dx[i]
else:
dx1 = dx[i][-2]
dx2 = dx[i][-1]
a = (dx2) / (dx1 * (dx1 + dx2))
b = - (dx2 + dx1) / (dx1 * dx2)
c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2))
# 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1]
out[slice1] = a * y[slice2] + b * y[slice3] + c * y[slice4]
outvals.append(out)
# reset the slice object in this dimension to ":"
slice1[axis] = slice(None)
slice2[axis] = slice(None)
slice3[axis] = slice(None)
slice4[axis] = slice(None)
if len_axes == 1:
return outvals[0]
else:
return outvals
def diff(a, n=1, axis=-1):
"""
Calculate the n-th discrete difference along given axis.
The first difference is given by ``out[n] = a[n+1] - a[n]`` along
the given axis, higher differences are calculated by using `diff`
recursively.
Parameters
----------
a : array_like
Input array
n : int, optional
The number of times values are differenced.
axis : int, optional
The axis along which the difference is taken, default is the last axis.
Returns
-------
diff : ndarray
The n-th differences. The shape of the output is the same as `a`
except along `axis` where the dimension is smaller by `n`. The
type of the output is the same as that of the input.
See Also
--------
gradient, ediff1d, cumsum
Notes
-----
For boolean arrays, the preservation of type means that the result
will contain `False` when consecutive elements are the same and
`True` when they differ.
Examples
--------
>>> x = np.array([1, 2, 4, 7, 0])
>>> np.diff(x)
array([ 1, 2, 3, -7])
>>> np.diff(x, n=2)
array([ 1, 1, -10])
>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
>>> np.diff(x)
array([[2, 3, 4],
[5, 1, 2]])
>>> np.diff(x, axis=0)
array([[-1, 2, 0, -2]])
"""
if n == 0:
return a
if n < 0:
raise ValueError(
"order must be non-negative but got " + repr(n))
a = asanyarray(a)
nd = a.ndim
slice1 = [slice(None)]*nd
slice2 = [slice(None)]*nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
slice1 = tuple(slice1)
slice2 = tuple(slice2)
if n > 1:
return diff(a[slice1]-a[slice2], n-1, axis=axis)
else:
return a[slice1]-a[slice2]
def interp(x, xp, fp, left=None, right=None, period=None):
"""
One-dimensional linear interpolation.
Returns the one-dimensional piecewise linear interpolant to a function
with given values at discrete data-points.
Parameters
----------
x : array_like
The x-coordinates of the interpolated values.
xp : 1-D sequence of floats
The x-coordinates of the data points, must be increasing if argument
`period` is not specified. Otherwise, `xp` is internally sorted after
normalizing the periodic boundaries with ``xp = xp % period``.
fp : 1-D sequence of float or complex
The y-coordinates of the data points, same length as `xp`.
left : optional float or complex corresponding to fp
Value to return for `x < xp[0]`, default is `fp[0]`.
right : optional float or complex corresponding to fp
Value to return for `x > xp[-1]`, default is `fp[-1]`.
period : None or float, optional
A period for the x-coordinates. This parameter allows the proper
interpolation of angular x-coordinates. Parameters `left` and `right`
are ignored if `period` is specified.
.. versionadded:: 1.10.0
Returns
-------
y : float or complex (corresponding to fp) or ndarray
The interpolated values, same shape as `x`.
Raises
------
ValueError
If `xp` and `fp` have different length
If `xp` or `fp` are not 1-D sequences
If `period == 0`
Notes
-----
Does not check that the x-coordinate sequence `xp` is increasing.
If `xp` is not increasing, the results are nonsense.
A simple check for increasing is::
np.all(np.diff(xp) > 0)
Examples
--------
>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> np.interp(2.5, xp, fp)
1.0
>>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
array([ 3. , 3. , 2.5 , 0.56, 0. ])
>>> UNDEF = -99.0
>>> np.interp(3.14, xp, fp, right=UNDEF)
-99.0
Plot an interpolant to the sine function:
>>> x = np.linspace(0, 2*np.pi, 10)
>>> y = np.sin(x)
>>> xvals = np.linspace(0, 2*np.pi, 50)
>>> yinterp = np.interp(xvals, x, y)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(xvals, yinterp, '-x')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.show()
Interpolation with periodic x-coordinates:
>>> x = [-180, -170, -185, 185, -10, -5, 0, 365]
>>> xp = [190, -190, 350, -350]
>>> fp = [5, 10, 3, 4]
>>> np.interp(x, xp, fp, period=360)
array([7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75])
Complex interpolation
>>> x = [1.5, 4.0]
>>> xp = [2,3,5]
>>> fp = [1.0j, 0, 2+3j]
>>> np.interp(x, xp, fp)
array([ 0.+1.j , 1.+1.5j])
"""
fp = np.asarray(fp)
if np.iscomplexobj(fp):
interp_func = compiled_interp_complex
input_dtype = np.complex128
else:
interp_func = compiled_interp
input_dtype = np.float64
if period is None:
if isinstance(x, (float, int, number)):
return interp_func([x], xp, fp, left, right).item()
elif isinstance(x, np.ndarray) and x.ndim == 0:
return interp_func([x], xp, fp, left, right).item()
else:
return interp_func(x, xp, fp, left, right)
else:
if period == 0:
raise ValueError("period must be a non-zero value")
period = abs(period)
left = None
right = None
return_array = True
if isinstance(x, (float, int, number)):
return_array = False
x = [x]
x = np.asarray(x, dtype=np.float64)
xp = np.asarray(xp, dtype=np.float64)
fp = np.asarray(fp, dtype=input_dtype)
if xp.ndim != 1 or fp.ndim != 1:
raise ValueError("Data points must be 1-D sequences")
if xp.shape[0] != fp.shape[0]:
raise ValueError("fp and xp are not of the same length")
# normalizing periodic boundaries
x = x % period
xp = xp % period
asort_xp = np.argsort(xp)
xp = xp[asort_xp]
fp = fp[asort_xp]
xp = np.concatenate((xp[-1:]-period, xp, xp[0:1]+period))
fp = np.concatenate((fp[-1:], fp, fp[0:1]))
if return_array:
return interp_func(x, xp, fp, left, right)
else:
return interp_func(x, xp, fp, left, right).item()
def angle(z, deg=0):
"""
Return the angle of the complex argument.
Parameters
----------
z : array_like
A complex number or sequence of complex numbers.
deg : bool, optional
Return angle in degrees if True, radians if False (default).
Returns
-------
angle : ndarray or scalar
The counterclockwise angle from the positive real axis on
the complex plane, with dtype as numpy.float64.
See Also
--------
arctan2
absolute
Examples
--------
>>> np.angle([1.0, 1.0j, 1+1j]) # in radians
array([ 0. , 1.57079633, 0.78539816])
>>> np.angle(1+1j, deg=True) # in degrees
45.0
"""
if deg:
fact = 180/pi
else:
fact = 1.0
z = asarray(z)
if (issubclass(z.dtype.type, _nx.complexfloating)):
zimag = z.imag
zreal = z.real
else:
zimag = 0
zreal = z
return arctan2(zimag, zreal) * fact
def unwrap(p, discont=pi, axis=-1):
"""
Unwrap by changing deltas between values to 2*pi complement.
Unwrap radian phase `p` by changing absolute jumps greater than
`discont` to their 2*pi complement along the given axis.
Parameters
----------
p : array_like
Input array.
discont : float, optional
Maximum discontinuity between values, default is ``pi``.
axis : int, optional
Axis along which unwrap will operate, default is the last axis.
Returns
-------
out : ndarray
Output array.
See Also
--------
rad2deg, deg2rad
Notes
-----
If the discontinuity in `p` is smaller than ``pi``, but larger than
`discont`, no unwrapping is done because taking the 2*pi complement
would only make the discontinuity larger.
Examples
--------
>>> phase = np.linspace(0, np.pi, num=5)
>>> phase[3:] += np.pi
>>> phase
array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531])
>>> np.unwrap(phase)
array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ])
"""
p = asarray(p)
nd = p.ndim
dd = diff(p, axis=axis)
slice1 = [slice(None, None)]*nd # full slices
slice1[axis] = slice(1, None)
ddmod = mod(dd + pi, 2*pi) - pi
_nx.copyto(ddmod, pi, where=(ddmod == -pi) & (dd > 0))
ph_correct = ddmod - dd
_nx.copyto(ph_correct, 0, where=abs(dd) < discont)
up = array(p, copy=True, dtype='d')
up[slice1] = p[slice1] + ph_correct.cumsum(axis)
return up
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
def trim_zeros(filt, trim='fb'):
"""
Trim the leading and/or trailing zeros from a 1-D array or sequence.
Parameters
----------
filt : 1-D array or sequence
Input array.
trim : str, optional
A string with 'f' representing trim from front and 'b' to trim from
back. Default is 'fb', trim zeros from both front and back of the
array.
Returns
-------
trimmed : 1-D array or sequence
The result of trimming the input. The input data type is preserved.
Examples
--------
>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
array([0, 0, 0, 1, 2, 3, 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
>>> np.trim_zeros([0, 1, 2, 0])
[1, 2]
"""
first = 0
trim = trim.upper()
if 'F' in trim:
for i in filt:
if i != 0.:
break
else:
first = first + 1
last = len(filt)
if 'B' in trim:
for i in filt[::-1]:
if i != 0.:
break
else:
last = last - 1
return filt[first:last]
@deprecate
def unique(x):
"""
This function is deprecated. Use numpy.lib.arraysetops.unique()
instead.
"""
try:
tmp = x.flatten()
if tmp.size == 0:
return tmp
tmp.sort()
idx = concatenate(([True], tmp[1:] != tmp[:-1]))
return tmp[idx]
except AttributeError:
items = sorted(set(x))
return asarray(items)
def extract(condition, arr):
"""
Return the elements of an array that satisfy some condition.
This is equivalent to ``np.compress(ravel(condition), ravel(arr))``. If
`condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.
Note that `place` does the exact opposite of `extract`.
Parameters
----------
condition : array_like
An array whose nonzero or True entries indicate the elements of `arr`
to extract.
arr : array_like
Input array of the same size as `condition`.
Returns
-------
extract : ndarray
Rank 1 array of values from `arr` where `condition` is True.
See Also
--------
take, put, copyto, compress, place
Examples
--------
>>> arr = np.arange(12).reshape((3, 4))
>>> arr
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> condition = np.mod(arr, 3)==0
>>> condition
array([[ True, False, False, True],
[False, False, True, False],
[False, True, False, False]], dtype=bool)
>>> np.extract(condition, arr)
array([0, 3, 6, 9])
If `condition` is boolean:
>>> arr[condition]
array([0, 3, 6, 9])
"""
return _nx.take(ravel(arr), nonzero(ravel(condition))[0])
def place(arr, mask, vals):
"""
Change elements of an array based on conditional and input values.
Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
`place` uses the first N elements of `vals`, where N is the number of
True values in `mask`, while `copyto` uses the elements where `mask`
is True.
Note that `extract` does the exact opposite of `place`.
Parameters
----------
arr : ndarray
Array to put data into.
mask : array_like
Boolean mask array. Must have the same size as `a`.
vals : 1-D sequence
Values to put into `a`. Only the first N elements are used, where
N is the number of True values in `mask`. If `vals` is smaller
than N, it will be repeated, and if elements of `a` are to be masked,
this sequence must be non-empty.
See Also
--------
copyto, put, take, extract
Examples
--------
>>> arr = np.arange(6).reshape(2, 3)
>>> np.place(arr, arr>2, [44, 55])
>>> arr
array([[ 0, 1, 2],
[44, 55, 44]])
"""
if not isinstance(arr, np.ndarray):
raise TypeError("argument 1 must be numpy.ndarray, "
"not {name}".format(name=type(arr).__name__))
return _insert(arr, mask, vals)
def disp(mesg, device=None, linefeed=True):
"""
Display a message on a device.
Parameters
----------
mesg : str
Message to display.
device : object
Device to write message. If None, defaults to ``sys.stdout`` which is
very similar to ``print``. `device` needs to have ``write()`` and
``flush()`` methods.
linefeed : bool, optional
Option whether to print a line feed or not. Defaults to True.
Raises
------
AttributeError
If `device` does not have a ``write()`` or ``flush()`` method.
Examples
--------
Besides ``sys.stdout``, a file-like object can also be used as it has
both required methods:
>>> from StringIO import StringIO
>>> buf = StringIO()
>>> np.disp('"Display" in a file', device=buf)
>>> buf.getvalue()
'"Display" in a file\\n'
"""
if device is None:
device = sys.stdout
if linefeed:
device.write('%s\n' % mesg)
else:
device.write('%s' % mesg)
device.flush()
return
# See http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html
_DIMENSION_NAME = r'\w+'
_CORE_DIMENSION_LIST = '(?:{0:}(?:,{0:})*)?'.format(_DIMENSION_NAME)
_ARGUMENT = r'\({}\)'.format(_CORE_DIMENSION_LIST)
_ARGUMENT_LIST = '{0:}(?:,{0:})*'.format(_ARGUMENT)
_SIGNATURE = '^{0:}->{0:}$'.format(_ARGUMENT_LIST)
def _parse_gufunc_signature(signature):
"""
Parse string signatures for a generalized universal function.
Arguments
---------
signature : string
Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)``
for ``np.matmul``.
Returns
-------
Tuple of input and output core dimensions parsed from the signature, each
of the form List[Tuple[str, ...]].
"""
if not re.match(_SIGNATURE, signature):
raise ValueError(
'not a valid gufunc signature: {}'.format(signature))
return tuple([tuple(re.findall(_DIMENSION_NAME, arg))
for arg in re.findall(_ARGUMENT, arg_list)]
for arg_list in signature.split('->'))
def _update_dim_sizes(dim_sizes, arg, core_dims):
"""
Incrementally check and update core dimension sizes for a single argument.
Arguments
---------
dim_sizes : Dict[str, int]
Sizes of existing core dimensions. Will be updated in-place.
arg : ndarray
Argument to examine.
core_dims : Tuple[str, ...]
Core dimensions for this argument.
"""
if not core_dims:
return
num_core_dims = len(core_dims)
if arg.ndim < num_core_dims:
raise ValueError(
'%d-dimensional argument does not have enough '
'dimensions for all core dimensions %r'
% (arg.ndim, core_dims))
core_shape = arg.shape[-num_core_dims:]
for dim, size in zip(core_dims, core_shape):
if dim in dim_sizes:
if size != dim_sizes[dim]:
raise ValueError(
'inconsistent size for core dimension %r: %r vs %r'
% (dim, size, dim_sizes[dim]))
else:
dim_sizes[dim] = size
def _parse_input_dimensions(args, input_core_dims):
"""
Parse broadcast and core dimensions for vectorize with a signature.
Arguments
---------
args : Tuple[ndarray, ...]
Tuple of input arguments to examine.
input_core_dims : List[Tuple[str, ...]]
List of core dimensions corresponding to each input.
Returns
-------
broadcast_shape : Tuple[int, ...]
Common shape to broadcast all non-core dimensions to.
dim_sizes : Dict[str, int]
Common sizes for named core dimensions.
"""
broadcast_args = []
dim_sizes = {}
for arg, core_dims in zip(args, input_core_dims):
_update_dim_sizes(dim_sizes, arg, core_dims)
ndim = arg.ndim - len(core_dims)
dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim])
broadcast_args.append(dummy_array)
broadcast_shape = np.lib.stride_tricks._broadcast_shape(*broadcast_args)
return broadcast_shape, dim_sizes
def _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims):
"""Helper for calculating broadcast shapes with core dimensions."""
return [broadcast_shape + tuple(dim_sizes[dim] for dim in core_dims)
for core_dims in list_of_core_dims]
def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes):
"""Helper for creating output arrays in vectorize."""
shapes = _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims)
arrays = tuple(np.empty(shape, dtype=dtype)
for shape, dtype in zip(shapes, dtypes))
return arrays
class vectorize(object):
"""
vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False,
signature=None)
Generalized function class.
Define a vectorized function which takes a nested sequence of objects or
numpy arrays as inputs and returns an single or tuple of numpy array as
output. The vectorized function evaluates `pyfunc` over successive tuples
of the input arrays like the python map function, except it uses the
broadcasting rules of numpy.
The data type of the output of `vectorized` is determined by calling
the function with the first element of the input. This can be avoided
by specifying the `otypes` argument.
Parameters
----------
pyfunc : callable
A python function or method.
otypes : str or list of dtypes, optional
The output data type. It must be specified as either a string of
typecode characters or a list of data type specifiers. There should
be one data type specifier for each output.
doc : str, optional
The docstring for the function. If `None`, the docstring will be the
``pyfunc.__doc__``.
excluded : set, optional
Set of strings or integers representing the positional or keyword
arguments for which the function will not be vectorized. These will be
passed directly to `pyfunc` unmodified.
.. versionadded:: 1.7.0
cache : bool, optional
If `True`, then cache the first function call that determines the number
of outputs if `otypes` is not provided.
.. versionadded:: 1.7.0
signature : string, optional
Generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for
vectorized matrix-vector multiplication. If provided, ``pyfunc`` will
be called with (and expected to return) arrays with shapes given by the
size of corresponding core dimensions. By default, ``pyfunc`` is
assumed to take scalars as input and output.
.. versionadded:: 1.12.0
Returns
-------
vectorized : callable
Vectorized function.
Examples
--------
>>> def myfunc(a, b):
... "Return a-b if a>b, otherwise return a+b"
... if a > b:
... return a - b
... else:
... return a + b
>>> vfunc = np.vectorize(myfunc)
>>> vfunc([1, 2, 3, 4], 2)
array([3, 4, 1, 2])
The docstring is taken from the input function to `vectorize` unless it
is specified:
>>> vfunc.__doc__
'Return a-b if a>b, otherwise return a+b'
>>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')
>>> vfunc.__doc__
'Vectorized `myfunc`'
The output type is determined by evaluating the first element of the input,
unless it is specified:
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<type 'numpy.int32'>
>>> vfunc = np.vectorize(myfunc, otypes=[np.float])
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<type 'numpy.float64'>
The `excluded` argument can be used to prevent vectorizing over certain
arguments. This can be useful for array-like arguments of a fixed length
such as the coefficients for a polynomial as in `polyval`:
>>> def mypolyval(p, x):
... _p = list(p)
... res = _p.pop(0)
... while _p:
... res = res*x + _p.pop(0)
... return res
>>> vpolyval = np.vectorize(mypolyval, excluded=['p'])
>>> vpolyval(p=[1, 2, 3], x=[0, 1])
array([3, 6])
Positional arguments may also be excluded by specifying their position:
>>> vpolyval.excluded.add(0)
>>> vpolyval([1, 2, 3], x=[0, 1])
array([3, 6])
The `signature` argument allows for vectorizing functions that act on
non-scalar arrays of fixed length. For example, you can use it for a
vectorized calculation of Pearson correlation coefficient and its p-value:
>>> import scipy.stats
>>> pearsonr = np.vectorize(scipy.stats.pearsonr,
... signature='(n),(n)->(),()')
>>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
(array([ 1., -1.]), array([ 0., 0.]))
Or for a vectorized convolution:
>>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
>>> convolve(np.eye(4), [1, 2, 1])
array([[ 1., 2., 1., 0., 0., 0.],
[ 0., 1., 2., 1., 0., 0.],
[ 0., 0., 1., 2., 1., 0.],
[ 0., 0., 0., 1., 2., 1.]])
See Also
--------
frompyfunc : Takes an arbitrary Python function and returns a ufunc
Notes
-----
The `vectorize` function is provided primarily for convenience, not for
performance. The implementation is essentially a for loop.
If `otypes` is not specified, then a call to the function with the
first argument will be used to determine the number of outputs. The
results of this call will be cached if `cache` is `True` to prevent
calling the function twice. However, to implement the cache, the
original function must be wrapped which will slow down subsequent
calls, so only do this if your function is expensive.
The new keyword argument interface and `excluded` argument support
further degrades performance.
References
----------
.. [1] NumPy Reference, section `Generalized Universal Function API
<http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>`_.
"""
def __init__(self, pyfunc, otypes=None, doc=None, excluded=None,
cache=False, signature=None):
self.pyfunc = pyfunc
self.cache = cache
self.signature = signature
self._ufunc = None # Caching to improve default performance
if doc is None:
self.__doc__ = pyfunc.__doc__
else:
self.__doc__ = doc
if isinstance(otypes, str):
for char in otypes:
if char not in typecodes['All']:
raise ValueError("Invalid otype specified: %s" % (char,))
elif iterable(otypes):
otypes = ''.join([_nx.dtype(x).char for x in otypes])
elif otypes is not None:
raise ValueError("Invalid otype specification")
self.otypes = otypes
# Excluded variable support
if excluded is None:
excluded = set()
self.excluded = set(excluded)
if signature is not None:
self._in_and_out_core_dims = _parse_gufunc_signature(signature)
else:
self._in_and_out_core_dims = None
def __call__(self, *args, **kwargs):
"""
Return arrays with the results of `pyfunc` broadcast (vectorized) over
`args` and `kwargs` not in `excluded`.
"""
excluded = self.excluded
if not kwargs and not excluded:
func = self.pyfunc
vargs = args
else:
# The wrapper accepts only positional arguments: we use `names` and
# `inds` to mutate `the_args` and `kwargs` to pass to the original
# function.
nargs = len(args)
names = [_n for _n in kwargs if _n not in excluded]
inds = [_i for _i in range(nargs) if _i not in excluded]
the_args = list(args)
def func(*vargs):
for _n, _i in enumerate(inds):
the_args[_i] = vargs[_n]
kwargs.update(zip(names, vargs[len(inds):]))
return self.pyfunc(*the_args, **kwargs)
vargs = [args[_i] for _i in inds]
vargs.extend([kwargs[_n] for _n in names])
return self._vectorize_call(func=func, args=vargs)
def _get_ufunc_and_otypes(self, func, args):
"""Return (ufunc, otypes)."""
# frompyfunc will fail if args is empty
if not args:
raise ValueError('args can not be empty')
if self.otypes is not None:
otypes = self.otypes
nout = len(otypes)
# Note logic here: We only *use* self._ufunc if func is self.pyfunc
# even though we set self._ufunc regardless.
if func is self.pyfunc and self._ufunc is not None:
ufunc = self._ufunc
else:
ufunc = self._ufunc = frompyfunc(func, len(args), nout)
else:
# Get number of outputs and output types by calling the function on
# the first entries of args. We also cache the result to prevent
# the subsequent call when the ufunc is evaluated.
# Assumes that ufunc first evaluates the 0th elements in the input
# arrays (the input values are not checked to ensure this)
args = [asarray(arg) for arg in args]
if builtins.any(arg.size == 0 for arg in args):
raise ValueError('cannot call `vectorize` on size 0 inputs '
'unless `otypes` is set')
inputs = [arg.flat[0] for arg in args]
outputs = func(*inputs)
# Performance note: profiling indicates that -- for simple
# functions at least -- this wrapping can almost double the
# execution time.
# Hence we make it optional.
if self.cache:
_cache = [outputs]
def _func(*vargs):
if _cache:
return _cache.pop()
else:
return func(*vargs)
else:
_func = func
if isinstance(outputs, tuple):
nout = len(outputs)
else:
nout = 1
outputs = (outputs,)
otypes = ''.join([asarray(outputs[_k]).dtype.char
for _k in range(nout)])
# Performance note: profiling indicates that creating the ufunc is
# not a significant cost compared with wrapping so it seems not
# worth trying to cache this.
ufunc = frompyfunc(_func, len(args), nout)
return ufunc, otypes
def _vectorize_call(self, func, args):
"""Vectorized call to `func` over positional `args`."""
if self.signature is not None:
res = self._vectorize_call_with_signature(func, args)
elif not args:
res = func()
else:
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
# Convert args to object arrays first
inputs = [array(a, copy=False, subok=True, dtype=object)
for a in args]
outputs = ufunc(*inputs)
if ufunc.nout == 1:
res = array(outputs, copy=False, subok=True, dtype=otypes[0])
else:
res = tuple([array(x, copy=False, subok=True, dtype=t)
for x, t in zip(outputs, otypes)])
return res
def _vectorize_call_with_signature(self, func, args):
"""Vectorized call over positional arguments with a signature."""
input_core_dims, output_core_dims = self._in_and_out_core_dims
if len(args) != len(input_core_dims):
raise TypeError('wrong number of positional arguments: '
'expected %r, got %r'
% (len(input_core_dims), len(args)))
args = tuple(asanyarray(arg) for arg in args)
broadcast_shape, dim_sizes = _parse_input_dimensions(
args, input_core_dims)
input_shapes = _calculate_shapes(broadcast_shape, dim_sizes,
input_core_dims)
args = [np.broadcast_to(arg, shape, subok=True)
for arg, shape in zip(args, input_shapes)]
outputs = None
otypes = self.otypes
nout = len(output_core_dims)
for index in np.ndindex(*broadcast_shape):
results = func(*(arg[index] for arg in args))
n_results = len(results) if isinstance(results, tuple) else 1
if nout != n_results:
raise ValueError(
'wrong number of outputs from pyfunc: expected %r, got %r'
% (nout, n_results))
if nout == 1:
results = (results,)
if outputs is None:
for result, core_dims in zip(results, output_core_dims):
_update_dim_sizes(dim_sizes, result, core_dims)
if otypes is None:
otypes = [asarray(result).dtype for result in results]
outputs = _create_arrays(broadcast_shape, dim_sizes,
output_core_dims, otypes)
for output, result in zip(outputs, results):
output[index] = result
if outputs is None:
# did not call the function even once
if otypes is None:
raise ValueError('cannot call `vectorize` on size 0 inputs '
'unless `otypes` is set')
if builtins.any(dim not in dim_sizes
for dims in output_core_dims
for dim in dims):
raise ValueError('cannot call `vectorize` with a signature '
'including new output dimensions on size 0 '
'inputs')
outputs = _create_arrays(broadcast_shape, dim_sizes,
output_core_dims, otypes)
return outputs[0] if nout == 1 else outputs
def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
aweights=None):
"""
Estimate a covariance matrix, given data and weights.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
then the covariance matrix element :math:`C_{ij}` is the covariance of
:math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
of :math:`x_i`.
See the notes for an outline of the algorithm.
Parameters
----------
m : array_like
A 1-D or 2-D array containing multiple variables and observations.
Each row of `m` represents a variable, and each column a single
observation of all those variables. Also see `rowvar` below.
y : array_like, optional
An additional set of variables and observations. `y` has the same form
as that of `m`.
rowvar : bool, optional
If `rowvar` is True (default), then each row represents a
variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows
contain observations.
bias : bool, optional
Default normalization (False) is by ``(N - 1)``, where ``N`` is the
number of observations given (unbiased estimate). If `bias` is True,
then normalization is by ``N``. These values can be overridden by using
the keyword ``ddof`` in numpy versions >= 1.5.
ddof : int, optional
If not ``None`` the default value implied by `bias` is overridden.
Note that ``ddof=1`` will return the unbiased estimate, even if both
`fweights` and `aweights` are specified, and ``ddof=0`` will return
the simple average. See the notes for the details. The default value
is ``None``.
.. versionadded:: 1.5
fweights : array_like, int, optional
1-D array of integer freguency weights; the number of times each
observation vector should be repeated.
.. versionadded:: 1.10
aweights : array_like, optional
1-D array of observation vector weights. These relative weights are
typically large for observations considered "important" and smaller for
observations considered less "important". If ``ddof=0`` the array of
weights can be used to assign probabilities to observation vectors.
.. versionadded:: 1.10
Returns
-------
out : ndarray
The covariance matrix of the variables.
See Also
--------
corrcoef : Normalized covariance matrix
Notes
-----
Assume that the observations are in the columns of the observation
array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The
steps to compute the weighted covariance are as follows::
>>> w = f * a
>>> v1 = np.sum(w)
>>> v2 = np.sum(w * a)
>>> m -= np.sum(m * w, axis=1, keepdims=True) / v1
>>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2)
Note that when ``a == 1``, the normalization factor
``v1 / (v1**2 - ddof * v2)`` goes over to ``1 / (np.sum(f) - ddof)``
as it should.
Examples
--------
Consider two variables, :math:`x_0` and :math:`x_1`, which
correlate perfectly, but in opposite directions:
>>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
>>> x
array([[0, 1, 2],
[2, 1, 0]])
Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance
matrix shows this clearly:
>>> np.cov(x)
array([[ 1., -1.],
[-1., 1.]])
Note that element :math:`C_{0,1}`, which shows the correlation between
:math:`x_0` and :math:`x_1`, is negative.
Further, note how `x` and `y` are combined:
>>> x = [-2.1, -1, 4.3]
>>> y = [3, 1.1, 0.12]
>>> X = np.vstack((x,y))
>>> print(np.cov(X))
[[ 11.71 -4.286 ]
[ -4.286 2.14413333]]
>>> print(np.cov(x, y))
[[ 11.71 -4.286 ]
[ -4.286 2.14413333]]
>>> print(np.cov(x))
11.71
"""
# Check inputs
if ddof is not None and ddof != int(ddof):
raise ValueError(
"ddof must be integer")
# Handles complex arrays too
m = np.asarray(m)
if m.ndim > 2:
raise ValueError("m has more than 2 dimensions")
if y is None:
dtype = np.result_type(m, np.float64)
else:
y = np.asarray(y)
if y.ndim > 2:
raise ValueError("y has more than 2 dimensions")
dtype = np.result_type(m, y, np.float64)
X = array(m, ndmin=2, dtype=dtype)
if not rowvar and X.shape[0] != 1:
X = X.T
if X.shape[0] == 0:
return np.array([]).reshape(0, 0)
if y is not None:
y = array(y, copy=False, ndmin=2, dtype=dtype)
if not rowvar and y.shape[0] != 1:
y = y.T
X = np.vstack((X, y))
if ddof is None:
if bias == 0:
ddof = 1
else:
ddof = 0
# Get the product of frequencies and weights
w = None
if fweights is not None:
fweights = np.asarray(fweights, dtype=np.float)
if not np.all(fweights == np.around(fweights)):
raise TypeError(
"fweights must be integer")
if fweights.ndim > 1:
raise RuntimeError(
"cannot handle multidimensional fweights")
if fweights.shape[0] != X.shape[1]:
raise RuntimeError(
"incompatible numbers of samples and fweights")
if any(fweights < 0):
raise ValueError(
"fweights cannot be negative")
w = fweights
if aweights is not None:
aweights = np.asarray(aweights, dtype=np.float)
if aweights.ndim > 1:
raise RuntimeError(
"cannot handle multidimensional aweights")
if aweights.shape[0] != X.shape[1]:
raise RuntimeError(
"incompatible numbers of samples and aweights")
if any(aweights < 0):
raise ValueError(
"aweights cannot be negative")
if w is None:
w = aweights
else:
w *= aweights
avg, w_sum = average(X, axis=1, weights=w, returned=True)
w_sum = w_sum[0]
# Determine the normalization
if w is None:
fact = X.shape[1] - ddof
elif ddof == 0:
fact = w_sum
elif aweights is None:
fact = w_sum - ddof
else:
fact = w_sum - ddof*sum(w*aweights)/w_sum
if fact <= 0:
warnings.warn("Degrees of freedom <= 0 for slice",
RuntimeWarning, stacklevel=2)
fact = 0.0
X -= avg[:, None]
if w is None:
X_T = X.T
else:
X_T = (X*w).T
c = dot(X, X_T.conj())
c *= 1. / np.float64(fact)
return c.squeeze()
def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue):
"""
Return Pearson product-moment correlation coefficients.
Please refer to the documentation for `cov` for more detail. The
relationship between the correlation coefficient matrix, `R`, and the
covariance matrix, `C`, is
.. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }
The values of `R` are between -1 and 1, inclusive.
Parameters
----------
x : array_like
A 1-D or 2-D array containing multiple variables and observations.
Each row of `x` represents a variable, and each column a single
observation of all those variables. Also see `rowvar` below.
y : array_like, optional
An additional set of variables and observations. `y` has the same
shape as `x`.
rowvar : bool, optional
If `rowvar` is True (default), then each row represents a
variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows
contain observations.
bias : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.10.0
ddof : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.10.0
Returns
-------
R : ndarray
The correlation coefficient matrix of the variables.
See Also
--------
cov : Covariance matrix
Notes
-----
Due to floating point rounding the resulting array may not be Hermitian,
the diagonal elements may not be 1, and the elements may not satisfy the
inequality abs(a) <= 1. The real and imaginary parts are clipped to the
interval [-1, 1] in an attempt to improve on that situation but is not
much help in the complex case.
This function accepts but discards arguments `bias` and `ddof`. This is
for backwards compatibility with previous versions of this function. These
arguments had no effect on the return values of the function and can be
safely ignored in this and previous versions of numpy.
"""
if bias is not np._NoValue or ddof is not np._NoValue:
# 2015-03-15, 1.10
warnings.warn('bias and ddof have no effect and are deprecated',
DeprecationWarning, stacklevel=2)
c = cov(x, y, rowvar)
try:
d = diag(c)
except ValueError:
# scalar covariance
# nan if incorrect value (nan, inf, 0), 1 otherwise
return c / c
stddev = sqrt(d.real)
c /= stddev[:, None]
c /= stddev[None, :]
# Clip real and imaginary parts to [-1, 1]. This does not guarantee
# abs(a[i,j]) <= 1 for complex arrays, but is the best we can do without
# excessive work.
np.clip(c.real, -1, 1, out=c.real)
if np.iscomplexobj(c):
np.clip(c.imag, -1, 1, out=c.imag)
return c
def blackman(M):
"""
Return the Blackman window.
The Blackman window is a taper formed by using the first three
terms of a summation of cosines. It was designed to have close to the
minimal leakage possible. It is close to optimal, only slightly worse
than a Kaiser window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
Returns
-------
out : ndarray
The window, with the maximum value normalized to one (the value one
appears only if the number of samples is odd).
See Also
--------
bartlett, hamming, hanning, kaiser
Notes
-----
The Blackman window is defined as
.. math:: w(n) = 0.42 - 0.5 \\cos(2\\pi n/M) + 0.08 \\cos(4\\pi n/M)
Most references to the Blackman window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function. It is known as a
"near optimal" tapering function, almost as good (by some measures)
as the kaiser window.
References
----------
Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
Dover Publications, New York.
Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
Examples
--------
>>> np.blackman(12)
array([ -1.38777878e-17, 3.26064346e-02, 1.59903635e-01,
4.14397981e-01, 7.36045180e-01, 9.67046769e-01,
9.67046769e-01, 7.36045180e-01, 4.14397981e-01,
1.59903635e-01, 3.26064346e-02, -1.38777878e-17])
Plot the window and the frequency response:
>>> from numpy.fft import fft, fftshift
>>> window = np.blackman(51)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Blackman window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
<matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
<matplotlib.figure.Figure object at 0x...>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Blackman window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
<matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
(-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
if M < 1:
return array([])
if M == 1:
return ones(1, float)
n = arange(0, M)
return 0.42 - 0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1))
def bartlett(M):
"""
Return the Bartlett window.
The Bartlett window is very similar to a triangular window, except
that the end points are at zero. It is often used in signal
processing for tapering a signal, without generating too much
ripple in the frequency domain.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
Returns
-------
out : array
The triangular window, with the maximum value normalized to one
(the value one appears only if the number of samples is odd), with
the first and last samples equal to zero.
See Also
--------
blackman, hamming, hanning, kaiser
Notes
-----
The Bartlett window is defined as
.. math:: w(n) = \\frac{2}{M-1} \\left(
\\frac{M-1}{2} - \\left|n - \\frac{M-1}{2}\\right|
\\right)
Most references to the Bartlett window come from the signal
processing literature, where it is used as one of many windowing
functions for smoothing values. Note that convolution with this
window produces linear interpolation. It is also known as an
apodization (which means"removing the foot", i.e. smoothing
discontinuities at the beginning and end of the sampled signal) or
tapering function. The fourier transform of the Bartlett is the product
of two sinc functions.
Note the excellent discussion in Kanasewich.
References
----------
.. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
Biometrika 37, 1-16, 1950.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
The University of Alberta Press, 1975, pp. 109-110.
.. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal
Processing", Prentice-Hall, 1999, pp. 468-471.
.. [4] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
.. [5] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 429.
Examples
--------
>>> np.bartlett(12)
array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273,
0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636,
0.18181818, 0. ])
Plot the window and its frequency response (requires SciPy and matplotlib):
>>> from numpy.fft import fft, fftshift
>>> window = np.bartlett(51)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Bartlett window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
<matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
<matplotlib.figure.Figure object at 0x...>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Bartlett window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
<matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
(-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
if M < 1:
return array([])
if M == 1:
return ones(1, float)
n = arange(0, M)
return where(less_equal(n, (M-1)/2.0), 2.0*n/(M-1), 2.0 - 2.0*n/(M-1))
def hanning(M):
"""
Return the Hanning window.
The Hanning window is a taper formed by using a weighted cosine.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
Returns
-------
out : ndarray, shape(M,)
The window, with the maximum value normalized to one (the value
one appears only if `M` is odd).
See Also
--------
bartlett, blackman, hamming, kaiser
Notes
-----
The Hanning window is defined as
.. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
The Hanning was named for Julius von Hann, an Austrian meteorologist.
It is also known as the Cosine Bell. Some authors prefer that it be
called a Hann window, to help avoid confusion with the very similar
Hamming window.
Most references to the Hanning window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
The University of Alberta Press, 1975, pp. 106-108.
.. [3] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
.. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 425.
Examples
--------
>>> np.hanning(12)
array([ 0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037,
0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,
0.07937323, 0. ])
Plot the window and its frequency response:
>>> from numpy.fft import fft, fftshift
>>> window = np.hanning(51)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Hann window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
<matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
<matplotlib.figure.Figure object at 0x...>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of the Hann window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
<matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
(-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
if M < 1:
return array([])
if M == 1:
return ones(1, float)
n = arange(0, M)
return 0.5 - 0.5*cos(2.0*pi*n/(M-1))
def hamming(M):
"""
Return the Hamming window.
The Hamming window is a taper formed by using a weighted cosine.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
Returns
-------
out : ndarray
The window, with the maximum value normalized to one (the value
one appears only if the number of samples is odd).
See Also
--------
bartlett, blackman, hanning, kaiser
Notes
-----
The Hamming window is defined as
.. math:: w(n) = 0.54 - 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
and is described in Blackman and Tukey. It was recommended for
smoothing the truncated autocovariance function in the time domain.
Most references to the Hamming window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
University of Alberta Press, 1975, pp. 109-110.
.. [3] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
.. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 425.
Examples
--------
>>> np.hamming(12)
array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594,
0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909,
0.15302337, 0.08 ])
Plot the window and the frequency response:
>>> from numpy.fft import fft, fftshift
>>> window = np.hamming(51)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Hamming window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
<matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
<matplotlib.figure.Figure object at 0x...>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Hamming window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
<matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
(-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
if M < 1:
return array([])
if M == 1:
return ones(1, float)
n = arange(0, M)
return 0.54 - 0.46*cos(2.0*pi*n/(M-1))
## Code from cephes for i0
_i0A = [
-4.41534164647933937950E-18,
3.33079451882223809783E-17,
-2.43127984654795469359E-16,
1.71539128555513303061E-15,
-1.16853328779934516808E-14,
7.67618549860493561688E-14,
-4.85644678311192946090E-13,
2.95505266312963983461E-12,
-1.72682629144155570723E-11,
9.67580903537323691224E-11,
-5.18979560163526290666E-10,
2.65982372468238665035E-9,
-1.30002500998624804212E-8,
6.04699502254191894932E-8,
-2.67079385394061173391E-7,
1.11738753912010371815E-6,
-4.41673835845875056359E-6,
1.64484480707288970893E-5,
-5.75419501008210370398E-5,
1.88502885095841655729E-4,
-5.76375574538582365885E-4,
1.63947561694133579842E-3,
-4.32430999505057594430E-3,
1.05464603945949983183E-2,
-2.37374148058994688156E-2,
4.93052842396707084878E-2,
-9.49010970480476444210E-2,
1.71620901522208775349E-1,
-3.04682672343198398683E-1,
6.76795274409476084995E-1
]
_i0B = [
-7.23318048787475395456E-18,
-4.83050448594418207126E-18,
4.46562142029675999901E-17,
3.46122286769746109310E-17,
-2.82762398051658348494E-16,
-3.42548561967721913462E-16,
1.77256013305652638360E-15,
3.81168066935262242075E-15,
-9.55484669882830764870E-15,
-4.15056934728722208663E-14,
1.54008621752140982691E-14,
3.85277838274214270114E-13,
7.18012445138366623367E-13,
-1.79417853150680611778E-12,
-1.32158118404477131188E-11,
-3.14991652796324136454E-11,
1.18891471078464383424E-11,
4.94060238822496958910E-10,
3.39623202570838634515E-9,
2.26666899049817806459E-8,
2.04891858946906374183E-7,
2.89137052083475648297E-6,
6.88975834691682398426E-5,
3.36911647825569408990E-3,
8.04490411014108831608E-1
]
def _chbevl(x, vals):
b0 = vals[0]
b1 = 0.0
for i in range(1, len(vals)):
b2 = b1
b1 = b0
b0 = x*b1 - b2 + vals[i]
return 0.5*(b0 - b2)
def _i0_1(x):
return exp(x) * _chbevl(x/2.0-2, _i0A)
def _i0_2(x):
return exp(x) * _chbevl(32.0/x - 2.0, _i0B) / sqrt(x)
def i0(x):
"""
Modified Bessel function of the first kind, order 0.
Usually denoted :math:`I_0`. This function does broadcast, but will *not*
"up-cast" int dtype arguments unless accompanied by at least one float or
complex dtype argument (see Raises below).
Parameters
----------
x : array_like, dtype float or complex
Argument of the Bessel function.
Returns
-------
out : ndarray, shape = x.shape, dtype = x.dtype
The modified Bessel function evaluated at each of the elements of `x`.
Raises
------
TypeError: array cannot be safely cast to required type
If argument consists exclusively of int dtypes.
See Also
--------
scipy.special.iv, scipy.special.ive
Notes
-----
We use the algorithm published by Clenshaw [1]_ and referenced by
Abramowitz and Stegun [2]_, for which the function domain is
partitioned into the two intervals [0,8] and (8,inf), and Chebyshev
polynomial expansions are employed in each interval. Relative error on
the domain [0,30] using IEEE arithmetic is documented [3]_ as having a
peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000).
References
----------
.. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in
*National Physical Laboratory Mathematical Tables*, vol. 5, London:
Her Majesty's Stationery Office, 1962.
.. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical
Functions*, 10th printing, New York: Dover, 1964, pp. 379.
http://www.math.sfu.ca/~cbm/aands/page_379.htm
.. [3] http://kobesearch.cpan.org/htdocs/Math-Cephes/Math/Cephes.html
Examples
--------
>>> np.i0([0.])
array(1.0)
>>> np.i0([0., 1. + 2j])
array([ 1.00000000+0.j , 0.18785373+0.64616944j])
"""
x = atleast_1d(x).copy()
y = empty_like(x)
ind = (x < 0)
x[ind] = -x[ind]
ind = (x <= 8.0)
y[ind] = _i0_1(x[ind])
ind2 = ~ind
y[ind2] = _i0_2(x[ind2])
return y.squeeze()
## End of cephes code for i0
def kaiser(M, beta):
"""
Return the Kaiser window.
The Kaiser window is a taper formed by using a Bessel function.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
beta : float
Shape parameter for window.
Returns
-------
out : array
The window, with the maximum value normalized to one (the value
one appears only if the number of samples is odd).
See Also
--------
bartlett, blackman, hamming, hanning
Notes
-----
The Kaiser window is defined as
.. math:: w(n) = I_0\\left( \\beta \\sqrt{1-\\frac{4n^2}{(M-1)^2}}
\\right)/I_0(\\beta)
with
.. math:: \\quad -\\frac{M-1}{2} \\leq n \\leq \\frac{M-1}{2},
where :math:`I_0` is the modified zeroth-order Bessel function.
The Kaiser was named for Jim Kaiser, who discovered a simple
approximation to the DPSS window based on Bessel functions. The Kaiser
window is a very good approximation to the Digital Prolate Spheroidal
Sequence, or Slepian window, which is the transform which maximizes the
energy in the main lobe of the window relative to total energy.
The Kaiser can approximate many other windows by varying the beta
parameter.
==== =======================
beta Window shape
==== =======================
0 Rectangular
5 Similar to a Hamming
6 Similar to a Hanning
8.6 Similar to a Blackman
==== =======================
A beta value of 14 is probably a good starting point. Note that as beta
gets large, the window narrows, and so the number of samples needs to be
large enough to sample the increasingly narrow spike, otherwise NaNs will
get returned.
Most references to the Kaiser window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by
digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285.
John Wiley and Sons, New York, (1966).
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
University of Alberta Press, 1975, pp. 177-178.
.. [3] Wikipedia, "Window function",
http://en.wikipedia.org/wiki/Window_function
Examples
--------
>>> np.kaiser(12, 14)
array([ 7.72686684e-06, 3.46009194e-03, 4.65200189e-02,
2.29737120e-01, 5.99885316e-01, 9.45674898e-01,
9.45674898e-01, 5.99885316e-01, 2.29737120e-01,
4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
Plot the window and the frequency response:
>>> from numpy.fft import fft, fftshift
>>> window = np.kaiser(51, 14)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Kaiser window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
<matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
<matplotlib.figure.Figure object at 0x...>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Kaiser window")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
<matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
(-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
from numpy.dual import i0
if M == 1:
return np.array([1.])
n = arange(0, M)
alpha = (M-1)/2.0
return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(float(beta))
def sinc(x):
"""
Return the sinc function.
The sinc function is :math:`\\sin(\\pi x)/(\\pi x)`.
Parameters
----------
x : ndarray
Array (possibly multi-dimensional) of values for which to to
calculate ``sinc(x)``.
Returns
-------
out : ndarray
``sinc(x)``, which has the same shape as the input.
Notes
-----
``sinc(0)`` is the limit value 1.
The name sinc is short for "sine cardinal" or "sinus cardinalis".
The sinc function is used in various signal processing applications,
including in anti-aliasing, in the construction of a Lanczos resampling
filter, and in interpolation.
For bandlimited interpolation of discrete-time signals, the ideal
interpolation kernel is proportional to the sinc function.
References
----------
.. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web
Resource. http://mathworld.wolfram.com/SincFunction.html
.. [2] Wikipedia, "Sinc function",
http://en.wikipedia.org/wiki/Sinc_function
Examples
--------
>>> x = np.linspace(-4, 4, 41)
>>> np.sinc(x)
array([ -3.89804309e-17, -4.92362781e-02, -8.40918587e-02,
-8.90384387e-02, -5.84680802e-02, 3.89804309e-17,
6.68206631e-02, 1.16434881e-01, 1.26137788e-01,
8.50444803e-02, -3.89804309e-17, -1.03943254e-01,
-1.89206682e-01, -2.16236208e-01, -1.55914881e-01,
3.89804309e-17, 2.33872321e-01, 5.04551152e-01,
7.56826729e-01, 9.35489284e-01, 1.00000000e+00,
9.35489284e-01, 7.56826729e-01, 5.04551152e-01,
2.33872321e-01, 3.89804309e-17, -1.55914881e-01,
-2.16236208e-01, -1.89206682e-01, -1.03943254e-01,
-3.89804309e-17, 8.50444803e-02, 1.26137788e-01,
1.16434881e-01, 6.68206631e-02, 3.89804309e-17,
-5.84680802e-02, -8.90384387e-02, -8.40918587e-02,
-4.92362781e-02, -3.89804309e-17])
>>> plt.plot(x, np.sinc(x))
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Sinc Function")
<matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
<matplotlib.text.Text object at 0x...>
>>> plt.xlabel("X")
<matplotlib.text.Text object at 0x...>
>>> plt.show()
It works in 2-D as well:
>>> x = np.linspace(-4, 4, 401)
>>> xx = np.outer(x, x)
>>> plt.imshow(np.sinc(xx))
<matplotlib.image.AxesImage object at 0x...>
"""
x = np.asanyarray(x)
y = pi * where(x == 0, 1.0e-20, x)
return sin(y)/y
def msort(a):
"""
Return a copy of an array sorted along the first axis.
Parameters
----------
a : array_like
Array to be sorted.
Returns
-------
sorted_array : ndarray
Array of the same type and shape as `a`.
See Also
--------
sort
Notes
-----
``np.msort(a)`` is equivalent to ``np.sort(a, axis=0)``.
"""
b = array(a, subok=True, copy=True)
b.sort(0)
return b
def _ureduce(a, func, **kwargs):
"""
Internal Function.
Call `func` with `a` as first argument swapping the axes to use extended
axis on functions that don't support it natively.
Returns result and a.shape with axis dims set to 1.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
func : callable
Reduction function capable of receiving a single axis argument.
It is is called with `a` as first argument followed by `kwargs`.
kwargs : keyword arguments
additional keyword arguments to pass to `func`.
Returns
-------
result : tuple
Result of func(a, **kwargs) and a.shape with axis dims set to 1
which can be used to reshape the result to the same shape a ufunc with
keepdims=True would produce.
"""
a = np.asanyarray(a)
axis = kwargs.get('axis', None)
if axis is not None:
keepdim = list(a.shape)
nd = a.ndim
axis = _nx.normalize_axis_tuple(axis, nd)
for ax in axis:
keepdim[ax] = 1
if len(axis) == 1:
kwargs['axis'] = axis[0]
else:
keep = set(range(nd)) - set(axis)
nkeep = len(keep)
# swap axis that should not be reduced to front
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
# merge reduced axis
a = a.reshape(a.shape[:nkeep] + (-1,))
kwargs['axis'] = -1
else:
keepdim = [1] * a.ndim
r = func(a, **kwargs)
return r, keepdim
def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
"""
Compute the median along the specified axis.
Returns the median of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : {int, sequence of int, None}, optional
Axis or axes along which the medians are computed. The default
is to compute the median along a flattened version of the array.
A sequence of axes is supported since version 1.9.0.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow use of memory of input array `a` for
calculations. The input array will be modified by the call to
`median`. This will save memory when you do not need to preserve
the contents of the input array. Treat the input as undefined,
but it will probably be fully or partially sorted. Default is
False. If `overwrite_input` is ``True`` and `a` is not already an
`ndarray`, an error will be raised.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
.. versionadded:: 1.9.0
Returns
-------
median : ndarray
A new array holding the result. If the input contains integers
or floats smaller than ``float64``, then the output data-type is
``np.float64``. Otherwise, the data-type of the output is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean, percentile
Notes
-----
Given a vector ``V`` of length ``N``, the median of ``V`` is the
middle value of a sorted copy of ``V``, ``V_sorted`` - i
e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the
two middle values of ``V_sorted`` when ``N`` is even.
Examples
--------
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10, 7, 4],
[ 3, 2, 1]])
>>> np.median(a)
3.5
>>> np.median(a, axis=0)
array([ 6.5, 4.5, 2.5])
>>> np.median(a, axis=1)
array([ 7., 2.])
>>> m = np.median(a, axis=0)
>>> out = np.zeros_like(m)
>>> np.median(a, axis=0, out=m)
array([ 6.5, 4.5, 2.5])
>>> m
array([ 6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.median(b, axis=1, overwrite_input=True)
array([ 7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.median(b, axis=None, overwrite_input=True)
3.5
>>> assert not np.all(a==b)
"""
r, k = _ureduce(a, func=_median, axis=axis, out=out,
overwrite_input=overwrite_input)
if keepdims:
return r.reshape(k)
else:
return r
def _median(a, axis=None, out=None, overwrite_input=False):
# can't be reasonably be implemented in terms of percentile as we have to
# call mean to not break astropy
a = np.asanyarray(a)
# Set the partition indexes
if axis is None:
sz = a.size
else:
sz = a.shape[axis]
if sz % 2 == 0:
szh = sz // 2
kth = [szh - 1, szh]
else:
kth = [(sz - 1) // 2]
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
kth.append(-1)
if overwrite_input:
if axis is None:
part = a.ravel()
part.partition(kth)
else:
a.partition(kth, axis=axis)
part = a
else:
part = partition(a, kth, axis=axis)
if part.shape == ():
# make 0-D arrays work
return part.item()
if axis is None:
axis = 0
indexer = [slice(None)] * part.ndim
index = part.shape[axis] // 2
if part.shape[axis] % 2 == 1:
# index with slice to allow mean (below) to work
indexer[axis] = slice(index, index+1)
else:
indexer[axis] = slice(index-1, index+1)
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact) and sz > 0:
# warn and return nans like mean would
rout = mean(part[indexer], axis=axis, out=out)
return np.lib.utils._median_nancheck(part, rout, axis, out)
else:
# if there are no nans
# Use mean in odd and even case to coerce data type
# and check, use out array.
return mean(part[indexer], axis=axis, out=out)
def percentile(a, q, axis=None, out=None,
overwrite_input=False, interpolation='linear', keepdims=False):
"""
Compute the qth percentile of the data along the specified axis.
Returns the qth percentile(s) of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
q : float in range of [0,100] (or sequence of floats)
Percentile to compute, which must be between 0 and 100 inclusive.
axis : {int, sequence of int, None}, optional
Axis or axes along which the percentiles are computed. The
default is to compute the percentile(s) along a flattened
version of the array. A sequence of axes is supported since
version 1.9.0.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow use of memory of input array `a`
calculations. The input array will be modified by the call to
`percentile`. This will save memory when you do not need to
preserve the contents of the input array. In this case you
should not make any assumptions about the contents of the input
`a` after this function completes -- treat it as undefined.
Default is False. If `a` is not already an array, this parameter
will have no effect as `a` will be converted to an array
internally regardless of the value of this parameter.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
This optional parameter specifies the interpolation method to
use when the desired quantile lies between two data points
``i < j``:
* linear: ``i + (j - i) * fraction``, where ``fraction``
is the fractional part of the index surrounded by ``i``
and ``j``.
* lower: ``i``.
* higher: ``j``.
* nearest: ``i`` or ``j``, whichever is nearest.
* midpoint: ``(i + j) / 2``.
.. versionadded:: 1.9.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in
the result as dimensions with size one. With this option, the
result will broadcast correctly against the original array `a`.
.. versionadded:: 1.9.0
Returns
-------
percentile : scalar or ndarray
If `q` is a single percentile and `axis=None`, then the result
is a scalar. If multiple percentiles are given, first axis of
the result corresponds to the percentiles. The other axes are
the axes that remain after the reduction of `a`. If the input
contains integers or floats smaller than ``float64``, the output
data-type is ``float64``. Otherwise, the output data-type is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean, median, nanpercentile
Notes
-----
Given a vector ``V`` of length ``N``, the ``q``-th percentile of
``V`` is the value ``q/100`` of the way from the minimum to the
maximum in a sorted copy of ``V``. The values and distances of
the two nearest neighbors as well as the `interpolation` parameter
will determine the percentile if the normalized ranking does not
match the location of ``q`` exactly. This function is the same as
the median if ``q=50``, the same as the minimum if ``q=0`` and the
same as the maximum if ``q=100``.
Examples
--------
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10, 7, 4],
[ 3, 2, 1]])
>>> np.percentile(a, 50)
3.5
>>> np.percentile(a, 50, axis=0)
array([[ 6.5, 4.5, 2.5]])
>>> np.percentile(a, 50, axis=1)
array([ 7., 2.])
>>> np.percentile(a, 50, axis=1, keepdims=True)
array([[ 7.],
[ 2.]])
>>> m = np.percentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, 50, axis=0, out=out)
array([[ 6.5, 4.5, 2.5]])
>>> m
array([[ 6.5, 4.5, 2.5]])
>>> b = a.copy()
>>> np.percentile(b, 50, axis=1, overwrite_input=True)
array([ 7., 2.])
>>> assert not np.all(a == b)
"""
q = array(q, dtype=np.float64, copy=True)
r, k = _ureduce(a, func=_percentile, q=q, axis=axis, out=out,
overwrite_input=overwrite_input,
interpolation=interpolation)
if keepdims:
if q.ndim == 0:
return r.reshape(k)
else:
return r.reshape([len(q)] + k)
else:
return r
def _percentile(a, q, axis=None, out=None,
overwrite_input=False, interpolation='linear', keepdims=False):
a = asarray(a)
if q.ndim == 0:
# Do not allow 0-d arrays because following code fails for scalar
zerod = True
q = q[None]
else:
zerod = False
# avoid expensive reductions, relevant for arrays with < O(1000) elements
if q.size < 10:
for i in range(q.size):
if q[i] < 0. or q[i] > 100.:
raise ValueError("Percentiles must be in the range [0,100]")
q[i] /= 100.
else:
# faster than any()
if np.count_nonzero(q < 0.) or np.count_nonzero(q > 100.):
raise ValueError("Percentiles must be in the range [0,100]")
q /= 100.
# prepare a for partioning
if overwrite_input:
if axis is None:
ap = a.ravel()
else:
ap = a
else:
if axis is None:
ap = a.flatten()
else:
ap = a.copy()
if axis is None:
axis = 0
Nx = ap.shape[axis]
indices = q * (Nx - 1)
# round fractional indices according to interpolation method
if interpolation == 'lower':
indices = floor(indices).astype(intp)
elif interpolation == 'higher':
indices = ceil(indices).astype(intp)
elif interpolation == 'midpoint':
indices = 0.5 * (floor(indices) + ceil(indices))
elif interpolation == 'nearest':
indices = around(indices).astype(intp)
elif interpolation == 'linear':
pass # keep index as fraction and interpolate
else:
raise ValueError(
"interpolation can only be 'linear', 'lower' 'higher', "
"'midpoint', or 'nearest'")
n = np.array(False, dtype=bool) # check for nan's flag
if indices.dtype == intp: # take the points along axis
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices = concatenate((indices, [-1]))
ap.partition(indices, axis=axis)
# ensure axis with qth is first
ap = np.rollaxis(ap, axis, 0)
axis = 0
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices = indices[:-1]
n = np.isnan(ap[-1:, ...])
if zerod:
indices = indices[0]
r = take(ap, indices, axis=axis, out=out)
else: # weight the points above and below the indices
indices_below = floor(indices).astype(intp)
indices_above = indices_below + 1
indices_above[indices_above > Nx - 1] = Nx - 1
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices_above = concatenate((indices_above, [-1]))
weights_above = indices - indices_below
weights_below = 1.0 - weights_above
weights_shape = [1, ] * ap.ndim
weights_shape[axis] = len(indices)
weights_below.shape = weights_shape
weights_above.shape = weights_shape
ap.partition(concatenate((indices_below, indices_above)), axis=axis)
# ensure axis with qth is first
ap = np.rollaxis(ap, axis, 0)
weights_below = np.rollaxis(weights_below, axis, 0)
weights_above = np.rollaxis(weights_above, axis, 0)
axis = 0
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices_above = indices_above[:-1]
n = np.isnan(ap[-1:, ...])
x1 = take(ap, indices_below, axis=axis) * weights_below
x2 = take(ap, indices_above, axis=axis) * weights_above
# ensure axis with qth is first
x1 = np.rollaxis(x1, axis, 0)
x2 = np.rollaxis(x2, axis, 0)
if zerod:
x1 = x1.squeeze(0)
x2 = x2.squeeze(0)
if out is not None:
r = add(x1, x2, out=out)
else:
r = add(x1, x2)
if np.any(n):
warnings.warn("Invalid value encountered in percentile",
RuntimeWarning, stacklevel=3)
if zerod:
if ap.ndim == 1:
if out is not None:
out[...] = a.dtype.type(np.nan)
r = out
else:
r = a.dtype.type(np.nan)
else:
r[..., n.squeeze(0)] = a.dtype.type(np.nan)
else:
if r.ndim == 1:
r[:] = a.dtype.type(np.nan)
else:
r[..., n.repeat(q.size, 0)] = a.dtype.type(np.nan)
return r
def trapz(y, x=None, dx=1.0, axis=-1):
"""
Integrate along the given axis using the composite trapezoidal rule.
Integrate `y` (`x`) along given axis.
Parameters
----------
y : array_like
Input array to integrate.
x : array_like, optional
The sample points corresponding to the `y` values. If `x` is None,
the sample points are assumed to be evenly spaced `dx` apart. The
default is None.
dx : scalar, optional
The spacing between sample points when `x` is None. The default is 1.
axis : int, optional
The axis along which to integrate.
Returns
-------
trapz : float
Definite integral as approximated by trapezoidal rule.
See Also
--------
sum, cumsum
Notes
-----
Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
will be taken from `y` array, by default x-axis distances between
points will be 1.0, alternatively they can be provided with `x` array
or with `dx` scalar. Return value will be equal to combined area under
the red lines.
References
----------
.. [1] Wikipedia page: http://en.wikipedia.org/wiki/Trapezoidal_rule
.. [2] Illustration image:
http://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
Examples
--------
>>> np.trapz([1,2,3])
4.0
>>> np.trapz([1,2,3], x=[4,6,8])
8.0
>>> np.trapz([1,2,3], dx=2)
8.0
>>> a = np.arange(6).reshape(2, 3)
>>> a
array([[0, 1, 2],
[3, 4, 5]])
>>> np.trapz(a, axis=0)
array([ 1.5, 2.5, 3.5])
>>> np.trapz(a, axis=1)
array([ 2., 8.])
"""
y = asanyarray(y)
if x is None:
d = dx
else:
x = asanyarray(x)
if x.ndim == 1:
d = diff(x)
# reshape to correct shape
shape = [1]*y.ndim
shape[axis] = d.shape[0]
d = d.reshape(shape)
else:
d = diff(x, axis=axis)
nd = y.ndim
slice1 = [slice(None)]*nd
slice2 = [slice(None)]*nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
try:
ret = (d * (y[slice1] + y[slice2]) / 2.0).sum(axis)
except ValueError:
# Operations didn't work, cast to ndarray
d = np.asarray(d)
y = np.asarray(y)
ret = add.reduce(d * (y[slice1]+y[slice2])/2.0, axis)
return ret
#always succeed
def add_newdoc(place, obj, doc):
"""
Adds documentation to obj which is in module place.
If doc is a string add it to obj as a docstring
If doc is a tuple, then the first element is interpreted as
an attribute of obj and the second as the docstring
(method, docstring)
If doc is a list, then each element of the list should be a
sequence of length two --> [(method1, docstring1),
(method2, docstring2), ...]
This routine never raises an error.
This routine cannot modify read-only docstrings, as appear
in new-style classes or built-in functions. Because this
routine never raises an error the caller must check manually
that the docstrings were changed.
"""
try:
new = getattr(__import__(place, globals(), {}, [obj]), obj)
if isinstance(doc, str):
add_docstring(new, doc.strip())
elif isinstance(doc, tuple):
add_docstring(getattr(new, doc[0]), doc[1].strip())
elif isinstance(doc, list):
for val in doc:
add_docstring(getattr(new, val[0]), val[1].strip())
except:
pass
# Based on scitools meshgrid
def meshgrid(*xi, **kwargs):
"""
Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
one-dimensional coordinate arrays x1, x2,..., xn.
.. versionchanged:: 1.9
1-D and 0-D cases are allowed.
Parameters
----------
x1, x2,..., xn : array_like
1-D arrays representing the coordinates of a grid.
indexing : {'xy', 'ij'}, optional
Cartesian ('xy', default) or matrix ('ij') indexing of output.
See Notes for more details.
.. versionadded:: 1.7.0
sparse : bool, optional
If True a sparse grid is returned in order to conserve memory.
Default is False.
.. versionadded:: 1.7.0
copy : bool, optional
If False, a view into the original arrays are returned in order to
conserve memory. Default is True. Please note that
``sparse=False, copy=False`` will likely return non-contiguous
arrays. Furthermore, more than one element of a broadcast array
may refer to a single memory location. If you need to write to the
arrays, make copies first.
.. versionadded:: 1.7.0
Returns
-------
X1, X2,..., XN : ndarray
For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,
return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'
or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'
with the elements of `xi` repeated to fill the matrix along
the first dimension for `x1`, the second for `x2` and so on.
Notes
-----
This function supports both indexing conventions through the indexing
keyword argument. Giving the string 'ij' returns a meshgrid with
matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.
In the 2-D case with inputs of length M and N, the outputs are of shape
(N, M) for 'xy' indexing and (M, N) for 'ij' indexing. In the 3-D case
with inputs of length M, N and P, outputs are of shape (N, M, P) for
'xy' indexing and (M, N, P) for 'ij' indexing. The difference is
illustrated by the following code snippet::
xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
for i in range(nx):
for j in range(ny):
# treat xv[i,j], yv[i,j]
xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')
for i in range(nx):
for j in range(ny):
# treat xv[j,i], yv[j,i]
In the 1-D and 0-D case, the indexing and sparse keywords have no effect.
See Also
--------
index_tricks.mgrid : Construct a multi-dimensional "meshgrid"
using indexing notation.
index_tricks.ogrid : Construct an open multi-dimensional "meshgrid"
using indexing notation.
Examples
--------
>>> nx, ny = (3, 2)
>>> x = np.linspace(0, 1, nx)
>>> y = np.linspace(0, 1, ny)
>>> xv, yv = np.meshgrid(x, y)
>>> xv
array([[ 0. , 0.5, 1. ],
[ 0. , 0.5, 1. ]])
>>> yv
array([[ 0., 0., 0.],
[ 1., 1., 1.]])
>>> xv, yv = np.meshgrid(x, y, sparse=True) # make sparse output arrays
>>> xv
array([[ 0. , 0.5, 1. ]])
>>> yv
array([[ 0.],
[ 1.]])
`meshgrid` is very useful to evaluate functions on a grid.
>>> x = np.arange(-5, 5, 0.1)
>>> y = np.arange(-5, 5, 0.1)
>>> xx, yy = np.meshgrid(x, y, sparse=True)
>>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
>>> h = plt.contourf(x,y,z)
"""
ndim = len(xi)
copy_ = kwargs.pop('copy', True)
sparse = kwargs.pop('sparse', False)
indexing = kwargs.pop('indexing', 'xy')
if kwargs:
raise TypeError("meshgrid() got an unexpected keyword argument '%s'"
% (list(kwargs)[0],))
if indexing not in ['xy', 'ij']:
raise ValueError(
"Valid values for `indexing` are 'xy' and 'ij'.")
s0 = (1,) * ndim
output = [np.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1:])
for i, x in enumerate(xi)]
if indexing == 'xy' and ndim > 1:
# switch first and second axis
output[0].shape = (1, -1) + s0[2:]
output[1].shape = (-1, 1) + s0[2:]
if not sparse:
# Return the full N-D matrix (not only the 1-D vector)
output = np.broadcast_arrays(*output, subok=True)
if copy_:
output = [x.copy() for x in output]
return output
def delete(arr, obj, axis=None):
"""
Return a new array with sub-arrays along an axis deleted. For a one
dimensional array, this returns those entries not returned by
`arr[obj]`.
Parameters
----------
arr : array_like
Input array.
obj : slice, int or array of ints
Indicate which sub-arrays to remove.
axis : int, optional
The axis along which to delete the subarray defined by `obj`.
If `axis` is None, `obj` is applied to the flattened array.
Returns
-------
out : ndarray
A copy of `arr` with the elements specified by `obj` removed. Note
that `delete` does not occur in-place. If `axis` is None, `out` is
a flattened array.
See Also
--------
insert : Insert elements into an array.
append : Append elements at the end of an array.
Notes
-----
Often it is preferable to use a boolean mask. For example:
>>> mask = np.ones(len(arr), dtype=bool)
>>> mask[[0,2,4]] = False
>>> result = arr[mask,...]
Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further
use of `mask`.
Examples
--------
>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> arr
array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12]])
>>> np.delete(arr, 1, 0)
array([[ 1, 2, 3, 4],
[ 9, 10, 11, 12]])
>>> np.delete(arr, np.s_[::2], 1)
array([[ 2, 4],
[ 6, 8],
[10, 12]])
>>> np.delete(arr, [1,3,5], None)
array([ 1, 3, 5, 7, 8, 9, 10, 11, 12])
"""
wrap = None
if type(arr) is not ndarray:
try:
wrap = arr.__array_wrap__
except AttributeError:
pass
arr = asarray(arr)
ndim = arr.ndim
arrorder = 'F' if arr.flags.fnc else 'C'
if axis is None:
if ndim != 1:
arr = arr.ravel()
ndim = arr.ndim
axis = -1
if ndim == 0:
# 2013-09-24, 1.9
warnings.warn(
"in the future the special handling of scalars will be removed "
"from delete and raise an error", DeprecationWarning, stacklevel=2)
if wrap:
return wrap(arr)
else:
return arr.copy(order=arrorder)
axis = normalize_axis_index(axis, ndim)
slobj = [slice(None)]*ndim
N = arr.shape[axis]
newshape = list(arr.shape)
if isinstance(obj, slice):
start, stop, step = obj.indices(N)
xr = range(start, stop, step)
numtodel = len(xr)
if numtodel <= 0:
if wrap:
return wrap(arr.copy(order=arrorder))
else:
return arr.copy(order=arrorder)
# Invert if step is negative:
if step < 0:
step = -step
start = xr[-1]
stop = xr[0] + 1
newshape[axis] -= numtodel
new = empty(newshape, arr.dtype, arrorder)
# copy initial chunk
if start == 0:
pass
else:
slobj[axis] = slice(None, start)
new[slobj] = arr[slobj]
# copy end chunck
if stop == N:
pass
else:
slobj[axis] = slice(stop-numtodel, None)
slobj2 = [slice(None)]*ndim
slobj2[axis] = slice(stop, None)
new[slobj] = arr[slobj2]
# copy middle pieces
if step == 1:
pass
else: # use array indexing.
keep = ones(stop-start, dtype=bool)
keep[:stop-start:step] = False
slobj[axis] = slice(start, stop-numtodel)
slobj2 = [slice(None)]*ndim
slobj2[axis] = slice(start, stop)
arr = arr[slobj2]
slobj2[axis] = keep
new[slobj] = arr[slobj2]
if wrap:
return wrap(new)
else:
return new
_obj = obj
obj = np.asarray(obj)
# After removing the special handling of booleans and out of
# bounds values, the conversion to the array can be removed.
if obj.dtype == bool:
warnings.warn("in the future insert will treat boolean arrays and "
"array-likes as boolean index instead of casting it "
"to integer", FutureWarning, stacklevel=2)
obj = obj.astype(intp)
if isinstance(_obj, (int, long, integer)):
# optimization for a single value
obj = obj.item()
if (obj < -N or obj >= N):
raise IndexError(
"index %i is out of bounds for axis %i with "
"size %i" % (obj, axis, N))
if (obj < 0):
obj += N
newshape[axis] -= 1
new = empty(newshape, arr.dtype, arrorder)
slobj[axis] = slice(None, obj)
new[slobj] = arr[slobj]
slobj[axis] = slice(obj, None)
slobj2 = [slice(None)]*ndim
slobj2[axis] = slice(obj+1, None)
new[slobj] = arr[slobj2]
else:
if obj.size == 0 and not isinstance(_obj, np.ndarray):
obj = obj.astype(intp)
if not np.can_cast(obj, intp, 'same_kind'):
# obj.size = 1 special case always failed and would just
# give superfluous warnings.
# 2013-09-24, 1.9
warnings.warn(
"using a non-integer array as obj in delete will result in an "
"error in the future", DeprecationWarning, stacklevel=2)
obj = obj.astype(intp)
keep = ones(N, dtype=bool)
# Test if there are out of bound indices, this is deprecated
inside_bounds = (obj < N) & (obj >= -N)
if not inside_bounds.all():
# 2013-09-24, 1.9
warnings.warn(
"in the future out of bounds indices will raise an error "
"instead of being ignored by `numpy.delete`.",
DeprecationWarning, stacklevel=2)
obj = obj[inside_bounds]
positive_indices = obj >= 0
if not positive_indices.all():
warnings.warn(
"in the future negative indices will not be ignored by "
"`numpy.delete`.", FutureWarning, stacklevel=2)
obj = obj[positive_indices]
keep[obj, ] = False
slobj[axis] = keep
new = arr[slobj]
if wrap:
return wrap(new)
else:
return new
def insert(arr, obj, values, axis=None):
"""
Insert values along the given axis before the given indices.
Parameters
----------
arr : array_like
Input array.
obj : int, slice or sequence of ints
Object that defines the index or indices before which `values` is
inserted.
.. versionadded:: 1.8.0
Support for multiple insertions when `obj` is a single scalar or a
sequence with one element (similar to calling insert multiple
times).
values : array_like
Values to insert into `arr`. If the type of `values` is different
from that of `arr`, `values` is converted to the type of `arr`.
`values` should be shaped so that ``arr[...,obj,...] = values``
is legal.
axis : int, optional
Axis along which to insert `values`. If `axis` is None then `arr`
is flattened first.
Returns
-------
out : ndarray
A copy of `arr` with `values` inserted. Note that `insert`
does not occur in-place: a new array is returned. If
`axis` is None, `out` is a flattened array.
See Also
--------
append : Append elements at the end of an array.
concatenate : Join a sequence of arrays along an existing axis.
delete : Delete elements from an array.
Notes
-----
Note that for higher dimensional inserts `obj=0` behaves very different
from `obj=[0]` just like `arr[:,0,:] = values` is different from
`arr[:,[0],:] = values`.
Examples
--------
>>> a = np.array([[1, 1], [2, 2], [3, 3]])
>>> a
array([[1, 1],
[2, 2],
[3, 3]])
>>> np.insert(a, 1, 5)
array([1, 5, 1, 2, 2, 3, 3])
>>> np.insert(a, 1, 5, axis=1)
array([[1, 5, 1],
[2, 5, 2],
[3, 5, 3]])
Difference between sequence and scalars:
>>> np.insert(a, [1], [[1],[2],[3]], axis=1)
array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])
>>> np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1),
... np.insert(a, [1], [[1],[2],[3]], axis=1))
True
>>> b = a.flatten()
>>> b
array([1, 1, 2, 2, 3, 3])
>>> np.insert(b, [2, 2], [5, 6])
array([1, 1, 5, 6, 2, 2, 3, 3])
>>> np.insert(b, slice(2, 4), [5, 6])
array([1, 1, 5, 2, 6, 2, 3, 3])
>>> np.insert(b, [2, 2], [7.13, False]) # type casting
array([1, 1, 7, 0, 2, 2, 3, 3])
>>> x = np.arange(8).reshape(2, 4)
>>> idx = (1, 3)
>>> np.insert(x, idx, 999, axis=1)
array([[ 0, 999, 1, 2, 999, 3],
[ 4, 999, 5, 6, 999, 7]])
"""
wrap = None
if type(arr) is not ndarray:
try:
wrap = arr.__array_wrap__
except AttributeError:
pass
arr = asarray(arr)
ndim = arr.ndim
arrorder = 'F' if arr.flags.fnc else 'C'
if axis is None:
if ndim != 1:
arr = arr.ravel()
ndim = arr.ndim
axis = ndim - 1
elif ndim == 0:
# 2013-09-24, 1.9
warnings.warn(
"in the future the special handling of scalars will be removed "
"from insert and raise an error", DeprecationWarning, stacklevel=2)
arr = arr.copy(order=arrorder)
arr[...] = values
if wrap:
return wrap(arr)
else:
return arr
else:
axis = normalize_axis_index(axis, ndim)
slobj = [slice(None)]*ndim
N = arr.shape[axis]
newshape = list(arr.shape)
if isinstance(obj, slice):
# turn it into a range object
indices = arange(*obj.indices(N), **{'dtype': intp})
else:
# need to copy obj, because indices will be changed in-place
indices = np.array(obj)
if indices.dtype == bool:
# See also delete
warnings.warn(
"in the future insert will treat boolean arrays and "
"array-likes as a boolean index instead of casting it to "
"integer", FutureWarning, stacklevel=2)
indices = indices.astype(intp)
# Code after warning period:
#if obj.ndim != 1:
# raise ValueError('boolean array argument obj to insert '
# 'must be one dimensional')
#indices = np.flatnonzero(obj)
elif indices.ndim > 1:
raise ValueError(
"index array argument obj to insert must be one dimensional "
"or scalar")
if indices.size == 1:
index = indices.item()
if index < -N or index > N:
raise IndexError(
"index %i is out of bounds for axis %i with "
"size %i" % (obj, axis, N))
if (index < 0):
index += N
# There are some object array corner cases here, but we cannot avoid
# that:
values = array(values, copy=False, ndmin=arr.ndim, dtype=arr.dtype)
if indices.ndim == 0:
# broadcasting is very different here, since a[:,0,:] = ... behaves
# very different from a[:,[0],:] = ...! This changes values so that
# it works likes the second case. (here a[:,0:1,:])
values = np.rollaxis(values, 0, (axis % values.ndim) + 1)
numnew = values.shape[axis]
newshape[axis] += numnew
new = empty(newshape, arr.dtype, arrorder)
slobj[axis] = slice(None, index)
new[slobj] = arr[slobj]
slobj[axis] = slice(index, index+numnew)
new[slobj] = values
slobj[axis] = slice(index+numnew, None)
slobj2 = [slice(None)] * ndim
slobj2[axis] = slice(index, None)
new[slobj] = arr[slobj2]
if wrap:
return wrap(new)
return new
elif indices.size == 0 and not isinstance(obj, np.ndarray):
# Can safely cast the empty list to intp
indices = indices.astype(intp)
if not np.can_cast(indices, intp, 'same_kind'):
# 2013-09-24, 1.9
warnings.warn(
"using a non-integer array as obj in insert will result in an "
"error in the future", DeprecationWarning, stacklevel=2)
indices = indices.astype(intp)
indices[indices < 0] += N
numnew = len(indices)
order = indices.argsort(kind='mergesort') # stable sort
indices[order] += np.arange(numnew)
newshape[axis] += numnew
old_mask = ones(newshape[axis], dtype=bool)
old_mask[indices] = False
new = empty(newshape, arr.dtype, arrorder)
slobj2 = [slice(None)]*ndim
slobj[axis] = indices
slobj2[axis] = old_mask
new[slobj] = values
new[slobj2] = arr
if wrap:
return wrap(new)
return new
def append(arr, values, axis=None):
"""
Append values to the end of an array.
Parameters
----------
arr : array_like
Values are appended to a copy of this array.
values : array_like
These values are appended to a copy of `arr`. It must be of the
correct shape (the same shape as `arr`, excluding `axis`). If
`axis` is not specified, `values` can be any shape and will be
flattened before use.
axis : int, optional
The axis along which `values` are appended. If `axis` is not
given, both `arr` and `values` are flattened before use.
Returns
-------
append : ndarray
A copy of `arr` with `values` appended to `axis`. Note that
`append` does not occur in-place: a new array is allocated and
filled. If `axis` is None, `out` is a flattened array.
See Also
--------
insert : Insert elements into an array.
delete : Delete elements from an array.
Examples
--------
>>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
When `axis` is specified, `values` must have the correct shape.
>>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
Traceback (most recent call last):
...
ValueError: arrays must have same number of dimensions
"""
arr = asanyarray(arr)
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
axis = arr.ndim-1
return concatenate((arr, values), axis=axis)
| 32.904112
| 88
| 0.573793
|
4a07817c41c6d5349f6381a0ae823dd662de84e9
| 28,681
|
py
|
Python
|
utils_hausdorff.py
|
EIU-GIScience-Center/Polyline_Hausdorff
|
af8c8f7f138cd4201d60ad6067feebefee74711c
|
[
"MIT"
] | null | null | null |
utils_hausdorff.py
|
EIU-GIScience-Center/Polyline_Hausdorff
|
af8c8f7f138cd4201d60ad6067feebefee74711c
|
[
"MIT"
] | null | null | null |
utils_hausdorff.py
|
EIU-GIScience-Center/Polyline_Hausdorff
|
af8c8f7f138cd4201d60ad6067feebefee74711c
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Wed May 26 13:29:02 2021
@author: bjkronenfeld
"""
import math as m
import utils_geom as g
def componentDistance (dr,k,len_a):
"""
Calculates the distance from a location on segment a on polyline A to
a component of polyline B.
Parameters
----------
A : list of (x,y) tuples
First polyline.
dr : distance representation
distance representation of component of polyline B.
k : float
k-value of location along segment a.
a : int
ID of segment on polyline A.
Returns
-------
Float
Distance from location k on segment a to dr component.
# LJ and MT
"""
if dr[0] == True:
answer = segDistance(dr,k,len_a)
else:
answer = vertDistance(dr,k,len_a)
return answer
def component_label(comp):
"""for debugging"""
if comp == None:
return "None"
else:
if comp[0] == True:
return"seg {}".format(comp[1])
else:
return "vert {}".format(comp[1])
def compValid(n_vert,comp):
"""
Determines if the input component is valid on the input polyline.
Args:
polyline : list of tuple
The input polyline.
comp : (bool, float)
The input component
Returns:
Bool
# BK
"""
if comp[1] < 0:
return False
if comp[0]: # component is a segment
if comp[1] > n_vert-2:
return False
else: # component is a vertex
if comp[1] > n_vert-1:
return False
return True
def distanceRepresentation (A,B,a,bcomp):
"""
Computes the distance representation of a component of B with respect to segment a
Args:
A,B - the two polylines
a (int) - segment of A
bcomp - componet of B
Returns:
distance representation
# LJ and TJ
"""
# if bcomp is a vertex
if bcomp[0] == False:
answer = vertDistRep(A, B, a, bcomp[1])
return answer
#if bcomp is a segment
else:
answer = segDistRep(A, B, a, bcomp[1])
return answer
def effectiveInterval(A,B,a,bcomp):
"""
Determines the range along segment a that is closer to the component bcomp
than to either adjacent component.
Parameters
----------
A : [(x,y),(x,y)...]
The main polyline.
B : [(x,y),(x,y)...]
The other polyline
a : Integer
Segment on A
bcomp : Component
component of B
Returns
-------
tuple of two k-values
@author: Folaitan & TJones
"""
# if statement to determine if bcomp is a segment or vertex
if bcomp[0] == True:
sint = segEffectiveInterval(A, B, a, bcomp[1])
return sint
elif bcomp[0] == False:
vint = vertEffectiveInterval(A, B, a, bcomp[1])
return vint
def segDistance(dr,k,len_a):
"""
Computes the distance from dr to k on a segment a.
Parameters
----------
dr : (is_seg,sk or None,sin_theta or q-value)
dist rep of segament B.
k: k-value along segment a.
len_a: length of segment a.
Returns
----------
float
The distance from dr to k on segment a.
@author: megshithakur and Farouk
"""
# check if segments are parallel
if dr[1] == None:
return dr[2] * len_a
else: # normal case
# segDistance formula
segDist=abs((k-dr[1])*len_a*dr[2])
return segDist
def segDistRep (A,B,a,b):
"""
Constructs the component distance representation for segment b with respect to segment a.
Parameters
----------
A : list of (x,y) tuples
The first polyline.
B : list of (x,y) tuples
The second polyline.
a : int
Index of segment on A
b : int
Index of segment on B
Returns
----------
tuple (distance representation):
isSeg : boolean
True (always True by definition)
k : float
k-value of intersection between two segments, or None if segments
are parallel
sin_theta : float
sine of angle between lines through segments, or q-distance between
lines if they are parallel.
# LJ and TJ
"""
# initiate list for results
dist_rep = []
# Create a boolean statement saying that this is indeed a segment
dist_rep.append(True)
# run intersection tool to find the point of intersection between two infinite lines
x = g.intersection(A[a], A[a+1], B[b], B[b+1])
# if we've found an intersection, determine sin theta
if x[0] != None:
# If there is an intersection, where is it on the x-axis, and then append it
K = g.kvalue(x, A[a], A[a+1]) # convert to a k-value
dist_rep.append(K)
# Find the angle created by the two lines
s_rad = g.angle([A[a],A[a+1]],[B[b],B[b+1]])
# Calculate the sine of the angle
s = m.sin(s_rad)
#Append the sine of the angle
dist_rep.append(s)
# Handle case of no intersection
# If sin theta is zero, there really isn't an intersection (but this
# might not be caught above due to floating point precision errors)
if x[0] == None or dist_rep[2] == 0:
# reset
dist_rep=[]
dist_rep.append(True)
# Append a no result (no intersection) into the final list
dist_rep.append(None)
# Find the distance between the two lines
q = g.distance_to_line(B[b],A[a],A[a+1])
# normalize by length of a
q=q/g.distance(A[a], A[a+1])
# Append the distance
dist_rep.append(q)
dist_rept = tuple(dist_rep)
return dist_rept
def withinUnitInterval(a,b):
"""
Computes the portion of the input interval that is within the interval [0,1]
Parameters
----------
interval : (float,float)
An effective interval, not necessarily in sequence.
Returns
-------
The portion of the input interval within [0,1], in sequence from low to high,
or (-inf,-inf) if the input interval does not overlap the unit interval
"""
kmin = min(min(a,b),1)
kmax = max(max(a,b),0)
if kmin==1 or kmax==0:
return (float('-inf'),float('-inf'))
else:
return (kmin,kmax)
def segEffectiveInterval(A,B,a,b, tolerance=0.000001):
"""
Computes the effective interval of segment b on segment a, that is the interval
on a for which the interior of segment b is closer than either endpoint
Parameters
----------
A : list of (x,y) tuples
The first polyline.
B : list of (x,y) tuples
The second polyline.
a : int
Index of segment on A.
b : int
Index of segment on B.
tolerance : float
If seg b's k-values on seg a are within this tolerance,
the segments will be treated as perpendicular
Returns:
tuple
k1 : float
First k-value of effective interval.
k2 : float
Second k-value of effective interval.
@author: megshithakur and tannerjones
"""
a1 = A[a]
a2 = A[a+1]
b1 = B[b]
b2 = B[b+1]
# check for perpendicular segments
prjout1 = g.project_pt_to_line(b1, a1, a2)
prjout2 = g.project_pt_to_line(b2, a1, a2)
k1 = g.kvalue(prjout1,a1,a2)
k2 = g.kvalue(prjout2,a1,a2)
if abs(k1-k2) <= tolerance:
# segment is perpendicular, so effective interval is entire segment
return [0,1]
else:
# project out from each b vertex to segment a
k1 = g.project_out(a1,a2,b1,b2)
k2 = g.project_out(a1,a2,b2,b1)
# return them in sequence, bound to range [0,1]
return withinUnitInterval(k1,k2)
def switchPoint (dr1, dr2):
"""
Determines point along a where the nearest point on B switches from the
component represented by dr1 to the component represented by dr2.
Parameters
----------
dr1 : dr1
Distance representation 1
dr2 : dr2
Distance representation 2
Returns
-------
[float] list of k-values
"""
# input: segment, segment - return Seg Seg switch point
if dr1[0] == True and dr2[0] == True:
segseg = segSegSwitchPoint(dr1, dr2)
return segseg
# input: vertex, segment - return vert seg switch point
elif dr1[0] == False and dr2[0] == True:
vertseg = vertSegSwitchPoint(dr1, dr2)
return vertseg
# input: segment, vertex - return vert seg switch point
elif dr1[0] == True and dr2[0] == False:
vertseg = vertSegSwitchPoint(dr2, dr1)
return vertseg
# input: vertex, vertex - return vert vert switch point
elif dr1[0] == False and dr2[0] == False:
vertvert = vertVertSwitchPoint(dr1, dr2)
return vertvert
def vertDistance(dr, k, len_a):
"""
Computes the distance from the vertex represented by dr to the location k
on the segment a that the distance representation was constructed from.
Parameters
----------
dr : (is_seg, vk,q)
distance representation of a vertex of B, being a tuple of three values
k : the k-value along segment a
len_a : the length of segment a
Returns
-------
float
the distance from point k on segment a to the vertex of B represented
by dr
@author: Folaitan & @Ljansen
"""
d = ((k-dr[1])**2) + (dr[2]**2)
d = m.sqrt(d)
d = len_a * d
return d
def vertDistRep(A,B,a,b):
"""
Constructs the Distance Representation for vertex b with respect to segment a.
Parameters
----------
A : list of (x,y) tuples
The first polyline.
B : list of (x,y) tuples
The second polyline.
a : int
Index of segment on A
b : int
Index of vertex on B
Returns
----------
tuple (distance representation):
is_seg : bool
False(always False by definition).
k : float
k-value of the location of the perpendicular projection of b onto the line through a.
q : float
q-distance from b to the line through a.
@author: MT and TJ
"""
# initiate list for results
FinalList = []
#Create a boolean statement saying that this is not a segment
FinalList.append(False)
#run project to line tool to find perpendicular intersection point between point b and segment a
p = g.project_pt_to_line(B[b], A[a], A[a+1])
k = g.kvalue(p, A[a], A[a+1])
#append result to results list
FinalList.append(k)
#run distance tool to find distance between the new point (k) and b
q = g.distance(p, B[b])
q = q/g.distance(A[a],A[a+1])
#append the result
FinalList.append(q)
FinalList = tuple(FinalList)
return FinalList
def vertEffectiveInterval(A,B,a,b,tolerance=0.000001):
"""
Computes the effective interval of vertex b on segment a, that is the interval
on a for which vertex b is closer than either adjacent segment
Parameters
----------
A : list of (x,y) tuples
The first polyline.
B : list of (x,y) tuples
The second polyline.
a : int
Index of segment on A
b : int
Index of vertex on B
tolerance : float
If b segs' k-values on seg a are within this tolerance,
the segments will be treated as perpendicular
Returns:
----------
tuple of two floats, with (-inf,-inf) representing no effective
interval.
@author: megshithakur and tannerJones
"""
# get coordinates
a1, a2 = A[a],A[a+1]
# handle cases where b is an end of the polyline
if b==0 or b==len(B)-1:
# get neighbor segment id
if b==0:
nb = 1
else:
nb = len(B)-2
# get coordinates of b vertex and neighbor
bc,bnb = B[b],B[nb]
# determine projections onto segment a
prjc = g.project_pt_to_line(bc,a1,a2)
prjnb = g.project_pt_to_line(bnb,a1,a2)
# determine k-values
kc = g.kvalue(prjc, a1, a2)
knb = g.kvalue(prjnb, a1, a2)
# check for perpendicularity
if abs(kc-knb) <= tolerance:
# calculate distances from each B vertex to segment a
distc = g.distance(bc,prjc)
distnb = g.distance(bnb,prjnb)
# is vertex b on segment a?
if distc < tolerance:
return (0,1)
# is the neighboring vertex on segment a?
elif distnb < tolerance:
return (float('-inf'),float('-inf'))
# are the two vertices on opposite sides of segment a?
else:
# calculate areas
areac = g.area([a1,a2,bc,a1])
areanb = g.area([a1,a2,bnb,a1])
# are the two vertices on opposite sides of segment a?
if (areac > 0) != (areanb > 0):
return (float('-inf'),float('-inf'))
# is the center vertex closer to segment b than the neighbor
elif distc < distnb:
return [0,1]
else:
return (float('-inf'),float('-inf'))
else:
# project points out from b to segment a
kbout = g.project_out(a1, a2, bc, bnb)
# return interval from kbout to end of segment a opposite neighbor
if knb < kc:
return withinUnitInterval(kbout,1)
else:
return withinUnitInterval(0,kbout)
else: # vertex b has two neighbors
# get coordinates of B vertices
bp,bc,bn = B[b-1],B[b],B[b+1] # prev, current, next vertices on B
# project each vertex of B onto segment A
prjp = g.project_pt_to_line(bp,a1,a2)
prjc = g.project_pt_to_line(bc,a1,a2)
prjn = g.project_pt_to_line(bn,a1,a2)
# get k-values of projections of B vertices onto a
kp = g.kvalue(prjp, a1, a2)
kc = g.kvalue(prjc, a1, a2)
kn = g.kvalue(prjn, a1, a2)
# check for perpendicular segments
prev_perp = abs(kc-kp) <= tolerance
next_perp = abs(kn-kp) <= tolerance
if prev_perp and next_perp: # both perpendicular
# get distances and areas to all vertices
dcur = g.distance(bc,prjc)
dprev = g.distance(bp,prjp)
dnext = g.distance(bp,prjn)
areacur = g.area([a1,a2,bc,a1])
areaprev = g.area([a1,a2,bp,a1])
areanext = g.area([a1,a2,bn,a1])
# vertex has effective interval only if it is closest to segment a
# and all vertices are on same side of segment a
if (areaprev > 0) == (areanext > 0) and (areacur > 0) == (areanext > 0) and dcur < dprev and dcur < dnext:
return (0,-1)
else:
return (float('-inf'),float('-inf'))
elif prev_perp or next_perp: # one perpendicular
# get coordinates and k-values of vertex on perpendicular segment, other vertex
if prev_perp:
bperp = bp
bother = bn
kother = kn
prjperp = prjp
else:
bperp = bn
bother = bp
kother = kp
prjperp = prjn
# get distances and areas of each vertex on perpendicular segment
dperp = g.distance(bperp,prjperp)
dcur = g.distance(bc,prjc)
areaperp = g.area([a1,a2,bperp,a1])
areacur = g.area([a1,a2,bc,a1])
# Is perpendicular segment on one side of A and current vertex is closer?
if (areaperp > 0) == (areacur > 0) and dcur < dperp:
# project out from other segment
otherprjoutk = g.project_out(a1,a2,bc,bother)
# interval is from end of line opposite other to k-value of other
if kother > kc:
return (0,otherprjoutk)
else:
return (otherprjoutk,1)
else:
return (float('-inf'),float('-inf'))
else: # neither perpendicular
# get k-values of projections of vertex out from each B segment onto segment a
kcpout = g.project_out(a1, a2, bc, bp)
kcnout = g.project_out(a1, a2, bc, bn)
# check sides of neighboring vertices with respect to b
if kp < kc and kn < kc: # both neighbors left of b
maxk = max(kcpout,kcnout)
return withinUnitInterval(maxk,1)
elif kp > kc and kn > kc: # both neighbors right of b
mink = min(kcpout,kcnout)
return withinUnitInterval(0,mink)
else: # nieghbors on either side of b
# determine min and max k-values of b based on positions of
# previous and next vertices
if kp < kc: # previous neighbor left
mink = kcpout
maxk = kcnout
else: # previous neighbor right
mink = kcnout
maxk = kcpout
if mink <= maxk:
return withinUnitInterval(mink,maxk)
else:
return (float('-inf'),float('-inf'))
def segSegSwitchPoint(dr1, dr2):
"""
Determines the k-values of the two locations along segment a that are equidistant
to the two segments b1 and b2 represented by dr1 and dr2, i.e. the location at
which the nearest component of a “switches” from b1 to b2.
Parameters
----------
dr1 : seg_p1
distance representation of a segment on b
dr2 : seg_p2
distance representation of another segment on b
Returns
-------
List of 1 or 2 floats representing k-value(s) of the switch points
@author: Folaitan & Ljansen
"""
k = []
if dr1[1] == None and dr2[1] == None: # both segments parallel to a
pass
elif dr1[1] == None: # first segment parallel to a
k_out_1 = dr2[1] + (dr1[2]/dr2[2])
k_out_2 = dr2[1] - (dr1[2]/dr2[2])
k.append(k_out_1)
k.append(k_out_2)
elif dr2[1] == None: # second segment parallel to a
k_out_1 = dr1[1] + ((dr2[2])/dr1[2])
k_out_2 = dr1[1] - ((dr2[2])/dr1[2])
k.append(k_out_1)
k.append(k_out_2)
else: # neither segment parallel to a
# create more readable variables
k1 = dr1[1]
k2 = dr2[1]
# calculate alpha parameter
a = dr2[2]/dr1[2]
if a == 1: # first solution is not valid; return second solution
k_out_2 = (k1+a*k2)/(1+a)
k.append(k_out_2)
elif a == -1: # second solution is not valid; return first solution
k_out_1 = (k1-a*k2)/(1-a)
k.append(k_out_1)
else: # return both solutions
k_out_1 = (k1-a*k2)/(1-a)
k.append(k_out_1)
k_out_2 = (k1+a*k2)/(1+a)
k.append(k_out_2)
# return values in ascending order, for consistency
return sorted(k)
def vertSegSwitchPoint(vdr, sdr):
"""
Determines the k-value of the location along segment a that is equidistant
to the input vertex and input segment, i.e. the location at which the nearest
component “switches” from the first component to the second.
Parameters
----------
vdr : ver_rep : (False, k, q)
distance representation of a vertex on B
sdr : seg_rep : (True, k, sin_theta) or (True, none, q)
distance representation of a segment on B
Returns
-------
[float,float]
List containing two floats representing the k-values of the switch points
@author: Folaitan & Ljansen
"""
r_list = []
# get distance representation values into more readable variables
k_vert = vdr[1]
q_vert = vdr[2]
k_seg = sdr[1]
sin_theta = sdr[2]
# three cases
if sin_theta== 1: # b segment is perpendicular to A
numerator = ((k_seg**2)-(k_vert**2)-(q_vert**2))
denominator = (2*k_seg) - (2*k_vert)
if denominator == 0:
# point and line have same k-value, so this is the switch point
return [k_seg]
vertSegPoint = numerator/denominator
r_list.append(vertSegPoint)
else:
if sdr[1] == None: # b segment is parallel to a
q_seg = sin_theta # distance representation value is q not sin_theta
a = 1
b = -2*k_vert
c = k_vert**2 + q_vert**2 - q_seg**2
else: # normal case
a = (sin_theta**2)-1
b = (2*k_vert)-((2*k_seg)*(sin_theta**2))
c = ((k_seg**2)*(sin_theta**2))-(k_vert**2)-(q_vert**2)
inside_root = (b**2)-(4*a*c)
# catch floating point precision issues:
# better to show a switch point when there is none than to miss one
if -0.0000000000001 < inside_root < 0:
inside_root = 0
# if inside_root is less than zero, quadratic formula has no
# solution and there is no switch point to return
# if it equals zero, there is one switch point
if inside_root >= 0:
r_list.append((-b + (m.sqrt(inside_root)))/(2*a))
if inside_root > 0:
r_list.append((-b - (m.sqrt(inside_root)))/(2*a))
# let's put these in ascending order, just to be safe
return sorted(r_list)
def vertVertSwitchPoint (dr1,dr2):
"""
Determines the k-value of the location along segment a that is equidistant
to the two vertices b1 and b2 represented by dr1 and dr2, i.e. the location
at which the nearest component of a “switches” from b1 to b2.
Parameters
----------
dr1 : tuple, distance representation of a vertex of B
dr2 : tuple, distance representation of a different vertex of B
Returns : [k] - list containing one float representing the k-value of the switch point
# MT and LJ
"""
#checks to see if k values for dr1 and dr2 are not equal
if dr1[1] != dr2[1]:
#calculates k value for switch point
k = [((dr2[2]**2 - dr1[2]**2) + (dr2[1]**2 - dr1[1]**2)) / (2 * (dr2[1]-dr1[1]))]
else:
#k is an empty list
k = []
return k
def candidateComponents(A,B,a):
"""
Identifies all components of B that could be the target of the Hausdorff
distance from segment a on A.
Parameters
----------
A : [(x,y),...] list of tuples
The coordinates of the main polyline.
B : [(x,y),...] list of tuples
The coordinates of the other polyline.
a : int
The index of a segment on polyline A.
Returns
----------
[(bool, int)]
A list of candidate components on B.
"""
# for now, simply return a list of all components of B
result = []
# get vertex components
for i in range(len(B)):
result.append((False,i))
for i in range(len(B)-1):
result.append((True,i))
return result
def nearSegment(A,B,a):
"""
Identifies the segment of of B nearest to vertex a on A.
Parameters
----------
A : [(x,y),...] list of tuples
The coordinates of the main polyline.
B : [(x,y),...] list of tuples
The coordinates of the other polyline.
a : int
The index of a vertex on polyline A.
Returns
----------
int
The index of the nearest segment on B.
"""
# for now, this will be coded with a "brute force" method, checking the
# distance from every segment of B to vertex a of A
# later this should be updated to use an indexing structure such as
# an r-tree for computational efficiency
# initialize to first segment
min_index = 0
min_d = g.distance_to_segment(A[a], B[0], B[1])
# check other segments
for b in range(1,len(B)-1):
d = g.distance_to_segment(A[a],B[b],B[b+1])
if d < min_d:
min_d = d
min_index = b
return min_index
def nearComponent(A,B,a,b):
"""
Among segment b, vertex b and vertex b+1, determines which component is nearest to vertex a.
Parameters
----------
A : [(x,y),...] list of tuples
The coordinates of the main polyline.
B : [(x,y),...] list of tuples
The coordinates of the other polyline.
a : int
The index of a vertex on polyline A.
b : int
The index of a segment on polyline B.
Returns
----------
(component, float)
The nearest component of B along with its distance from vertex a.
"""
# # get distances from each component
# d_seg = g.distance_to_segment(A[a],B[b],B[b+1])
# d_vert1 = g.distance(A[a],B[b])
# d_vert2 = g.distance(A[a], B[b+1])
# # if distance to d_seg is strictly the minimum, return the segment
# if d_seg == min(d_seg,d_vert1,d_vert2):
# return ((True,b),d_seg)
# elif d_vert1 < d_vert2:
# return((False,b),d_vert1)
# else:
# return ((False,b+1),d_vert2)
# calculate k-value of projection of vertex a onto segment b
prj = g.project_pt_to_line(A[a], B[b], B[b+1])
k = g.kvalue(prj, B[b], B[b+1])
if k <= 0: # return vertex b
d = g.distance(A[a],B[b])
return ((False,b),d)
elif k >= 1: # return vertex b+1
d = g.distance(A[a],B[b+1])
return ((False,b+1),d)
else: # return segment b
d = g.distance(A[a],prj)
return ((True,b),d)
def nearLoc(srcloc,trgline,trgcomp):
"""
Finds the location on the target component nearest to the source
location.
Parameters
----------
srcloc : (float,float)
The coordinates of the source location.
trgline : [(x,y),...] list of tuples
The coordinates of the target polyline.
trgcomp : (bool,int)
The target component.
Returns
----------
(float,float)
The nearest location on the target component to the source location.
"""
if trgcomp[0] == False: # target is a vertex
return trgline[trgcomp[0]]
else: # target is a segment
trgstart = trgline[trgcomp[1]]
trgend = trgline[trgcomp[1]+1]
srcprj = g.project_pt_to_line(srcloc, trgstart,trgend)
k = g.kvalue(srcprj,trgstart,trgend)
if k <= 0:
return trgstart
elif k >=1:
return trgend
else:
x = trgstart[0] + k * (trgend[0]-trgstart[0])
y = trgstart[1] + k * (trgend[1]-trgstart[1])
return (x,y)
def checkSegment(c1,c2):
"""
Determines whether or not it is necessary to further process a segment
given that its endpoints have been processed already.
Parameters
----------
c1 : component
The component of B closest to the first vertex of a segment of A.
c2 : component
The component of B closest to the second vertex of a segment of A.
Returns
----------
(component, float)
The nearest component of B along with its distance from vertex a.
"""
# No need to check a segment if either:
# c1 and c2 are the same component, or
# c1 and c2 are consective vertices
if c1==c2:
return False
elif c1[0] == False and c2[0] == False and abs(c1[1]-c2[1])==1:
return False
else:
return True
| 33.782097
| 119
| 0.547331
|
4a0781e6a425de72554cf6eb161a0c6a29d86ad1
| 840
|
py
|
Python
|
DeeProtein/sense.py
|
juzb/DeeProtein
|
487694a24abdb4656499111c8a8904dfcb1d98ab
|
[
"MIT"
] | 12
|
2019-02-21T14:09:13.000Z
|
2021-03-05T02:02:21.000Z
|
DeeProtein/sense.py
|
juzb/DeeProtein
|
487694a24abdb4656499111c8a8904dfcb1d98ab
|
[
"MIT"
] | null | null | null |
DeeProtein/sense.py
|
juzb/DeeProtein
|
487694a24abdb4656499111c8a8904dfcb1d98ab
|
[
"MIT"
] | 5
|
2019-05-15T05:37:41.000Z
|
2021-09-29T12:20:00.000Z
|
import subprocess
while True:
#name = input('Please enter four letter name for this run: ')
name = "AAAA"
sequence = input('Please enter the sequence to analyze: ')
gos = input('Please enter the GO terms to analyze sperarated by commas: ')
with open('/results/tmp/masked_dataset.txt', 'w') as ofile:
ofile.write('{};{};{};{};{};{}'.format(name,
'A',
gos,
sequence,
'.' * len(sequence),
'_' * len(sequence)))
subprocess.call(['bash', '/code/analyze_sensitivity.sh', gos])
print('Performed sensitivity analysis. '
'Please find the results in /results\n\n')
| 40
| 78
| 0.458333
|
4a0782340d892fe8904921f79672f6a1effbb022
| 8,050
|
py
|
Python
|
frappe/core/doctype/doctype/test_doctype.py
|
ramen123/frappe
|
ede92ef61ad640036bdd98bffdf2ea593de0a5ef
|
[
"MIT"
] | null | null | null |
frappe/core/doctype/doctype/test_doctype.py
|
ramen123/frappe
|
ede92ef61ad640036bdd98bffdf2ea593de0a5ef
|
[
"MIT"
] | null | null | null |
frappe/core/doctype/doctype/test_doctype.py
|
ramen123/frappe
|
ede92ef61ad640036bdd98bffdf2ea593de0a5ef
|
[
"MIT"
] | 1
|
2021-11-19T18:46:53.000Z
|
2021-11-19T18:46:53.000Z
|
# -*- coding: utf-8 -*-
# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors
# See license.txt
from __future__ import unicode_literals
import frappe
import unittest
# test_records = frappe.get_test_records('DocType')
class TestDocType(unittest.TestCase):
def new_doctype(self, name, unique=0, depends_on=''):
return frappe.get_doc({
"doctype": "DocType",
"module": "Core",
"custom": 1,
"fields": [{
"label": "Some Field",
"fieldname": "some_fieldname",
"fieldtype": "Data",
"unique": unique,
"depends_on": depends_on,
}],
"permissions": [{
"role": "System Manager",
"read": 1
}],
"name": name
})
def test_validate_name(self):
self.assertRaises(frappe.NameError, self.new_doctype("_Some DocType").insert)
self.assertRaises(frappe.NameError, self.new_doctype("8Some DocType").insert)
self.assertRaises(frappe.NameError, self.new_doctype("Some (DocType)").insert)
for name in ("Some DocType", "Some_DocType"):
if frappe.db.exists("DocType", name):
frappe.delete_doc("DocType", name)
doc = self.new_doctype(name).insert()
doc.delete()
def test_doctype_unique_constraint_dropped(self):
if frappe.db.exists("DocType", "With_Unique"):
frappe.delete_doc("DocType", "With_Unique")
dt = self.new_doctype("With_Unique", unique=1)
dt.insert()
doc1 = frappe.new_doc("With_Unique")
doc2 = frappe.new_doc("With_Unique")
doc1.some_fieldname = "Something"
doc1.name = "one"
doc2.some_fieldname = "Something"
doc2.name = "two"
doc1.insert()
self.assertRaises(frappe.UniqueValidationError, doc2.insert)
dt.fields[0].unique = 0
dt.save()
doc2.insert()
doc1.delete()
doc2.delete()
def test_validate_search_fields(self):
doc = self.new_doctype("Test Search Fields")
doc.search_fields = "some_fieldname"
doc.insert()
self.assertEqual(doc.name, "Test Search Fields")
# check if invalid fieldname is allowed or not
doc.search_fields = "some_fieldname_1"
self.assertRaises(frappe.ValidationError, doc.save)
# check if no value fields are allowed in search fields
field = doc.append("fields", {})
field.fieldname = "some_html_field"
field.fieldtype = "HTML"
field.label = "Some HTML Field"
doc.search_fields = "some_fieldname,some_html_field"
self.assertRaises(frappe.ValidationError, doc.save)
def test_depends_on_fields(self):
doc = self.new_doctype("Test Depends On", depends_on="eval:doc.__islocal == 0")
doc.insert()
# check if the assignment operation is allowed in depends_on
field = doc.fields[0]
field.depends_on = "eval:doc.__islocal = 0"
self.assertRaises(frappe.ValidationError, doc.save)
def test_all_depends_on_fields_conditions(self):
import re
docfields = frappe.get_all("DocField", or_filters={
"ifnull(depends_on, '')": ("!=", ''),
"ifnull(collapsible_depends_on, '')": ("!=", '')
}, fields=["parent", "depends_on", "collapsible_depends_on", "fieldname", "fieldtype"])
pattern = """[\w\.:_]+\s*={1}\s*[\w\.@'"]+"""
for field in docfields:
for depends_on in ["depends_on", "collapsible_depends_on"]:
condition = field.get(depends_on)
if condition:
self.assertFalse(re.match(pattern, condition))
def test_sync_field_order(self):
from frappe.modules.import_file import get_file_path
import os
# create test doctype
test_doctype = frappe.get_doc({
"doctype": "DocType",
"module": "Core",
"fields": [
{
"label": "Field 1",
"fieldname": "field_1",
"fieldtype": "Data"
},
{
"label": "Field 2",
"fieldname": "field_2",
"fieldtype": "Data"
},
{
"label": "Field 3",
"fieldname": "field_3",
"fieldtype": "Data"
},
{
"label": "Field 4",
"fieldname": "field_4",
"fieldtype": "Data"
}
],
"permissions": [{
"role": "System Manager",
"read": 1
}],
"name": "Test Field Order DocType",
"__islocal": 1
})
path = get_file_path(test_doctype.module, test_doctype.doctype, test_doctype.name)
initial_fields_order = ['field_1', 'field_2', 'field_3', 'field_4']
frappe.delete_doc_if_exists("DocType", "Test Field Order DocType")
if os.path.isfile(path):
os.remove(path)
try:
frappe.flags.allow_doctype_export = 1
test_doctype.save()
# assert that field_order list is being created with the default order
test_doctype_json = frappe.get_file_json(path)
self.assertTrue(test_doctype_json.get("field_order"))
self.assertEqual(len(test_doctype_json['fields']), len(test_doctype_json['field_order']))
self.assertListEqual([f['fieldname'] for f in test_doctype_json['fields']], test_doctype_json['field_order'])
self.assertListEqual([f['fieldname'] for f in test_doctype_json['fields']], initial_fields_order)
self.assertListEqual(test_doctype_json['field_order'], initial_fields_order)
# remove field_order to test reload_doc/sync/migrate is backwards compatible without field_order
del test_doctype_json['field_order']
with open(path, 'w+') as txtfile:
txtfile.write(frappe.as_json(test_doctype_json))
# assert that field_order is actually removed from the json file
test_doctype_json = frappe.get_file_json(path)
self.assertFalse(test_doctype_json.get("field_order"))
# make sure that migrate/sync is backwards compatible without field_order
frappe.reload_doctype(test_doctype.name, force=True)
test_doctype.reload()
# assert that field_order list is being created with the default order again
test_doctype.save()
test_doctype_json = frappe.get_file_json(path)
self.assertTrue(test_doctype_json.get("field_order"))
self.assertEqual(len(test_doctype_json['fields']), len(test_doctype_json['field_order']))
self.assertListEqual([f['fieldname'] for f in test_doctype_json['fields']], test_doctype_json['field_order'])
self.assertListEqual([f['fieldname'] for f in test_doctype_json['fields']], initial_fields_order)
self.assertListEqual(test_doctype_json['field_order'], initial_fields_order)
# reorder fields: swap row 1 and 3
test_doctype.fields[0], test_doctype.fields[2] = test_doctype.fields[2], test_doctype.fields[0]
for i, f in enumerate(test_doctype.fields):
f.idx = i + 1
# assert that reordering fields only affects `field_order` rather than `fields` attr
test_doctype.save()
test_doctype_json = frappe.get_file_json(path)
self.assertListEqual([f['fieldname'] for f in test_doctype_json['fields']], initial_fields_order)
self.assertListEqual(test_doctype_json['field_order'], ['field_3', 'field_2', 'field_1', 'field_4'])
# reorder `field_order` in the json file: swap row 2 and 4
test_doctype_json['field_order'][1], test_doctype_json['field_order'][3] = test_doctype_json['field_order'][3], test_doctype_json['field_order'][1]
with open(path, 'w+') as txtfile:
txtfile.write(frappe.as_json(test_doctype_json))
# assert that reordering `field_order` from json file is reflected in DocType upon migrate/sync
frappe.reload_doctype(test_doctype.name, force=True)
test_doctype.reload()
self.assertListEqual([f.fieldname for f in test_doctype.fields], ['field_3', 'field_4', 'field_1', 'field_2'])
# insert row in the middle and remove first row (field 3)
test_doctype.append("fields", {
"label": "Field 5",
"fieldname": "field_5",
"fieldtype": "Data"
})
test_doctype.fields[4], test_doctype.fields[3] = test_doctype.fields[3], test_doctype.fields[4]
test_doctype.fields[3], test_doctype.fields[2] = test_doctype.fields[2], test_doctype.fields[3]
test_doctype.remove(test_doctype.fields[0])
for i, f in enumerate(test_doctype.fields):
f.idx = i + 1
test_doctype.save()
test_doctype_json = frappe.get_file_json(path)
self.assertListEqual([f['fieldname'] for f in test_doctype_json['fields']], ['field_1', 'field_2', 'field_4', 'field_5'])
self.assertListEqual(test_doctype_json['field_order'], ['field_4', 'field_5', 'field_1', 'field_2'])
except:
raise
finally:
frappe.flags.allow_doctype_export = 0
| 35.152838
| 150
| 0.70795
|
4a0782e15f60970af79e4a97e02ab5d733d2ade5
| 3,434
|
py
|
Python
|
utils/preprocessing.py
|
HUFS-VLab/tf-SSS-AE
|
f693f1df2199a15623fb4c717c87dcd39461a6d5
|
[
"MIT"
] | null | null | null |
utils/preprocessing.py
|
HUFS-VLab/tf-SSS-AE
|
f693f1df2199a15623fb4c717c87dcd39461a6d5
|
[
"MIT"
] | null | null | null |
utils/preprocessing.py
|
HUFS-VLab/tf-SSS-AE
|
f693f1df2199a15623fb4c717c87dcd39461a6d5
|
[
"MIT"
] | null | null | null |
import os
import sys
import glob
import json
import librosa
import argparse
import numpy as np
def min_max_scaling(x):
"""
Args:
S: np.array, Spectrogram. Shape=(f, t)
Returns:
S: np.array, scalied Spectrogram. Shape=(f, t)
"""
_max = np.max(x)
_min = np.min(x)
x = (x - _min + 1e-7) / (_max - _min)
return x
def time_average(S):
""" Summation or Average by time
Args:
S : np.array, Spectrogram. Shape=(n_mfcc, time) or (frame_bins, time)
Returns:
spect : np.array, spectrum. Shape=(n_mfcc) or (frame_bins)
"""
spectrum = np.mean(S, axis=-1)
return spectrum
def preprocess(data_list, args):
example = data_list[0]
item_name = example['item']
item_type = example['type']
print(f">> target : {item_name}_{item_type}")
save_dir_path = os.path.join(args.main_dir, f'seqlen_{args.seq_len}_mels_{args.n_mels}', args.dataset_name)
save_path = os.path.join(save_dir_path, item_name)
os.makedirs(save_path, exist_ok=True)
for data in data_list:
# Original
wav_name = os.path.basename(data['wav'])
wav_path = os.path.join(args.dataset_path, data['wav']+'.wav')
sr = data['sr']
wav = librosa.load(wav_path, sr=sr)[0]
n_fft = args.n_fft
hop_length = args.hop_length
S = librosa.feature.melspectrogram(y=wav, sr=sr,
n_fft=n_fft,hop_length=hop_length,
n_mels=args.n_mels)
S_len = S.shape[1]
"""
Temporal Adaptive Average pooling
"""
q = int(S_len / args.seq_len)
r = S_len % args.seq_len
if q != 0:
margin = (q + 1) * args.seq_len - S_len
padded_S = np.zeros((S.shape[0], S.shape[1]+margin)).astype(np.float32)
padded_S[:,:S_len] = S
S = padded_S
S_len += margin
kernel_size = int(S_len / args.seq_len)
spectrum_list = []
for i in range(args.seq_len):
kernel_start = i * kernel_size
kernel_end = kernel_start + kernel_size
local_S = S[:,kernel_start:kernel_end]
spectrum = time_average(local_S)
spectrum_list.append(spectrum)
sequence = np.stack(spectrum_list, 0) # Shape = (sequence_length, n_dims)
sequence = min_max_scaling(sequence)
np.save(f"{save_path}/{wav_name}.npy", sequence)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--main-dir', type=str, default='', help='-')
parser.add_argument('--dataset-name', type=str, default='', help='-')
parser.add_argument('--dataset-path', type=str, default='', help='-')
parser.add_argument('--target-manifest', type=str, default='', help='-')
parser.add_argument('--seq-len', type=int, default=32, help='-')
parser.add_argument('--n-mels', type=int, default=80, help='-')
parser.add_argument('--n-fft', type=int, default=2048, help='-')
args, unknown = parser.parse_known_args()
args.win_length = args.n_fft
args.hop_length = int(args.n_fft / 4)
with open(args.target_manifest, 'r') as f:
data_list = json.load(f)
print(">> Preproecssing..")
preprocess(data_list, args)
print(">> Done")
| 30.660714
| 112
| 0.576878
|
4a07836f56c7e56b27499cec911a172c6ee79b50
| 947
|
py
|
Python
|
tests/filters/test_value_blocklist_check.py
|
cuspymd/CredSweeper
|
376e7faff41d8b58f0d9e2a82955ad0929ee8290
|
[
"MIT"
] | 1
|
2022-03-03T18:11:59.000Z
|
2022-03-03T18:11:59.000Z
|
tests/filters/test_value_blocklist_check.py
|
shadowscatcher/CredSweeper
|
0387ed76aca4a12154e15c49db8dc0901a014275
|
[
"MIT"
] | null | null | null |
tests/filters/test_value_blocklist_check.py
|
shadowscatcher/CredSweeper
|
0387ed76aca4a12154e15c49db8dc0901a014275
|
[
"MIT"
] | null | null | null |
import pytest
from credsweeper.filters import ValueBlocklistCheck
from tests.test_utils.dummy_line_data import get_line_data
class TestValueBlocklistCheck:
def test_value_blocklist_p(self, file_path: pytest.fixture, success_line: pytest.fixture) -> None:
line_data = get_line_data(file_path, line=success_line, pattern=r"(?P<value>.*$)")
assert ValueBlocklistCheck().run(line_data) is False
@pytest.mark.parametrize("line", [
"string12",
])
def test_value_blocklist_n(self, file_path: pytest.fixture, line: str) -> None:
line_data = get_line_data(file_path, line=line, pattern=r"(?P<value>.*$)")
assert ValueBlocklistCheck().run(line_data) is True
def test_value_blocklist_none_value_n(self, file_path: pytest.fixture, success_line: pytest.fixture) -> None:
line_data = get_line_data(file_path, line=success_line)
assert ValueBlocklistCheck().run(line_data) is True
| 43.045455
| 113
| 0.736008
|
4a0784f207726a72ff8b7cc9d99774694c2b4222
| 5,303
|
py
|
Python
|
app.py
|
Saket-Upadhyay/FlagCheckDiscordChal
|
a73f15bee5bcaa36b610253a06646e955c50b420
|
[
"MIT"
] | null | null | null |
app.py
|
Saket-Upadhyay/FlagCheckDiscordChal
|
a73f15bee5bcaa36b610253a06646e955c50b420
|
[
"MIT"
] | null | null | null |
app.py
|
Saket-Upadhyay/FlagCheckDiscordChal
|
a73f15bee5bcaa36b610253a06646e955c50b420
|
[
"MIT"
] | null | null | null |
from flask import Flask
from flask import request
import hashlib as hl
app = Flask(__name__)
@app.route('/')
def ma():
return """
<html>
<head>
<title>
FrigidSec DPC Flag Check</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="keywords" content="FrigidSec">
<meta name="description" content="FrigidSec Discord Challenge Check">
<style>
body {background-color:#ffffff;background-repeat:no-repeat;background-position:top left;background-attachment:fixed;}
h1{text-align:center;font-family:Impact, sans-serif;color:#000000;background-color:#ffffff;}
p {text-align:center;font-family:Georgia, serif;font-size:14px;font-style:normal;font-weight:bold;color:#000000;background-color:#ffffff;}
</style>
</head>
<h1>FrigidSec DPC Flag Checker API</h1>
<center><h2>Enter SHA256 hash of your flag below and click submit.</h2> <br> <form action="/whatisthisbehaviourmona" method="POST">
<input name="check">
<input type="submit">
</form></center>
"""
@app.route('/whatisthisbehaviourmona',methods=['POST','GET'])
def hello_world():
FlagList=[]
with open("flaglist.dat",'r') as ff:
FlagList=ff.readlines()
if request.method == "POST":
REQ_DAT=request.values.get("check")
print(REQ_DAT)
print(FlagList)
if str(REQ_DAT) == "" or str(REQ_DAT) == None or len(REQ_DAT) < 10:
return """
<!DOCTYPE html>
<html>
<head>
<title>
FrigidSec DPC Flag Check</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="keywords" content="FrigidSec">
<meta name="description" content="FrigidSec Discord Challenge Check">
<style>
body {background-color:#ffffff;background-repeat:no-repeat;background-position:top left;background-attachment:fixed;}
h1{text-align:center;font-family:Impact, sans-serif;color:#000000;background-color:#ffffff;}
p {text-align:center;font-family:Georgia, serif;font-size:14px;font-style:normal;font-weight:bold;color:#000000;background-color:#ffffff;}
</style>
</head>
<body>
<h1>FrigidSec DPC Flag Checker API</h1>
<br>
<h3>Are you sure you provided <a style=\"color:red;\">SHA-256</a> hash ?? Check again mate, it doesn't looks like one.</h3>
</body>
</html>
"""
elif str(REQ_DAT) in FlagList or str(str(REQ_DAT)+"\n") in FlagList:
return """
<!DOCTYPE html>
<html>
<head>
<title>
FrigidSec DPC Flag Check</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="keywords" content="FrigidSec">
<meta name="description" content="FrigidSec Discord Challenge Check">
<style>
body {background-color:#ffffff;background-repeat:no-repeat;background-position:top left;background-attachment:fixed;}
h1{text-align:center;font-family:Impact, sans-serif;color:#000000;background-color:#ffffff;}
p {text-align:center;font-family:Georgia, serif;font-size:14px;font-style:normal;font-weight:bold;color:#000000;background-color:#ffffff;}
</style>
</head>
<h1>FrigidSec DPC Flag Checker API</h1>
<center><h2>You got a <br> <a style=\"color:green;\">VALID</a> <br> flag! <br>Nice Job. Just don't get rusty over time!</h2></center>
"""
else:
return """
<html>
<head>
<title>
FrigidSec DPC Flag Check</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="keywords" content="FrigidSec">
<meta name="description" content="FrigidSec Discord Challenge Check">
<style>
body {background-color:#ffffff;background-repeat:no-repeat;background-position:top left;background-attachment:fixed;}
h1{text-align:center;font-family:Impact, sans-serif;color:#000000;background-color:#ffffff;}
p {text-align:center;font-family:Georgia, serif;font-size:14px;font-style:normal;font-weight:bold;color:#000000;background-color:#ffffff;}
</style>
</head>
<h1>FrigidSec DPC Flag Checker API</h1>
<center><h2>You got a <br><a style=\"color:red;\">INVALID :(</a> <br>flag, but don't give up mate!</h2></center>
"""
else:
return """
<html>
<head>
<title>
FrigidSec DPC Flag Check</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="keywords" content="FrigidSec">
<meta name="description" content="FrigidSec Discord Challenge Check">
<style>
body {background-color:#ffffff;background-repeat:no-repeat;background-position:top left;background-attachment:fixed;}
h1{text-align:center;font-family:Impact, sans-serif;color:#000000;background-color:#ffffff;}
p {text-align:center;font-family:Georgia, serif;font-size:14px;font-style:normal;font-weight:bold;color:#000000;background-color:#ffffff;}
</style>
</head>
<h1>FrigidSec DPC Flag Checker API</h1>
<center>
<p>This API checks flag when you give SHA256 dump of your flag in ?check= parameter via POST</p>
<p></p>
<p>For example: </p>
<p>https://frigidsec-dpc-flagcheck.herokuapp.com/whatisthisbehaviourmona?check=e525dd0a29c3b8e9b223d7cc79d1393dd2b8c92ca9761968233d944242939605</p>
<br>
<h3>But what are you doing here when we have provided a SIMPLE input field <a href="/">AT THIS PLACE</a>? Not everything is a CTF, sometimes it's just simple software. Is that too much to ask?</h3>
</center>
"""
if __name__ == '__main__':
app.run("0.0.0.0",8080)
| 37.878571
| 197
| 0.708844
|
4a07851f0b4b4ea80e7f0e2749d728a55b1131fd
| 1,248
|
py
|
Python
|
google/devtools/testing/v1/devtools-testing-v1-py/google/devtools/testing_v1/services/test_execution_service/transports/__init__.py
|
googleapis/googleapis-gen
|
d84824c78563d59b0e58d5664bfaa430e9ad7e7a
|
[
"Apache-2.0"
] | 7
|
2021-02-21T10:39:41.000Z
|
2021-12-07T07:31:28.000Z
|
google/devtools/testing/v1/devtools-testing-v1-py/google/devtools/testing_v1/services/test_execution_service/transports/__init__.py
|
googleapis/googleapis-gen
|
d84824c78563d59b0e58d5664bfaa430e9ad7e7a
|
[
"Apache-2.0"
] | 6
|
2021-02-02T23:46:11.000Z
|
2021-11-15T01:46:02.000Z
|
google/devtools/testing/v1/devtools-testing-v1-py/google/devtools/testing_v1/services/test_execution_service/transports/__init__.py
|
googleapis/googleapis-gen
|
d84824c78563d59b0e58d5664bfaa430e9ad7e7a
|
[
"Apache-2.0"
] | 4
|
2021-01-28T23:25:45.000Z
|
2021-08-30T01:55:16.000Z
|
# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
from typing import Dict, Type
from .base import TestExecutionServiceTransport
from .grpc import TestExecutionServiceGrpcTransport
from .grpc_asyncio import TestExecutionServiceGrpcAsyncIOTransport
# Compile a registry of transports.
_transport_registry = OrderedDict() # type: Dict[str, Type[TestExecutionServiceTransport]]
_transport_registry['grpc'] = TestExecutionServiceGrpcTransport
_transport_registry['grpc_asyncio'] = TestExecutionServiceGrpcAsyncIOTransport
__all__ = (
'TestExecutionServiceTransport',
'TestExecutionServiceGrpcTransport',
'TestExecutionServiceGrpcAsyncIOTransport',
)
| 36.705882
| 91
| 0.796474
|
4a07857eadc6db1db6a058fb9d14e9943a9dd7e6
| 5,873
|
py
|
Python
|
models/ssl.py
|
martinmanuel9/extreme_verification_latency
|
16f5ba2b1a37f6d60ed2089d6cab7331e688b0cc
|
[
"MIT"
] | null | null | null |
models/ssl.py
|
martinmanuel9/extreme_verification_latency
|
16f5ba2b1a37f6d60ed2089d6cab7331e688b0cc
|
[
"MIT"
] | null | null | null |
models/ssl.py
|
martinmanuel9/extreme_verification_latency
|
16f5ba2b1a37f6d60ed2089d6cab7331e688b0cc
|
[
"MIT"
] | 1
|
2022-02-25T20:37:09.000Z
|
2022-02-25T20:37:09.000Z
|
#!/usr/bin/env python
"""
Application: COMPOSE Framework
File name: ssl.py
Author: Martin Manuel Lopez
Advisor: Dr. Gregory Ditzler
Creation: 08/05/2021
COMPOSE Origin: Muhammad Umer and Robi Polikar
The University of Arizona
Department of Electrical and Computer Engineering
College of Engineering
"""
# MIT License
#
# Copyright (c) 2021
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from sys import getsizeof
from numpy.lib.type_check import real
import pandas as pd
class ssl():
"""
ssl is a class of semi-supervise learning classifiers that may be used in stationary and non-stationary
environments. Depending on the classifier chosen a variety of class balancing techniques are available to
reduce SSL problem of assigning all data to one class.
"""
_verbose = 2 # controls output of screen which plots when possible and renders command line operations
# 0 : Suppress all output
# 1 : Give text updates to command window
# 2 : Plot data when dimensionality allows and give text updates to command window
_data =[] # N instances x D dimensions : Features of data with labeled data grouped at top of matrix
_labels = []
_classifier = [] # Type of SSL classifier to use
_classifierOpts = [] # Options that correspond with SSL Classifier selected - see individual methods for options
_balance = [] # Type of class balancing to use
_balanceOpts = [] # Options that correspond with Balance Function selected - see individual methods for options
n_features=[] # Number of features in data (i.e. dimensionality of data)
n_classes=[] # Number of classes different class labels
n_instances=[] # Number of instances in data
n_labeled=[] # Number of labeled instances in data
n_unlabeled=[] # Number of unlabeled instances in data
input_label_format=[] # Format of labels passed by user - 'integer' OR 'vector'
input_label_ids=[] # Records the class identifiers of the labels passed by user
label_format=[] # Current format of label
# The cells below contain text strings that match the SSL
# classifiers and class balance methods available in this object
# If if other classifiers or balancing methods are added to this
# class these cells must be modified to include those methods
valid_classifier = ['s3vm', 'label_prop','label_spread', 'cluster_n_label', 'cluster_n_label_v2', 'label_prop_bal']
valid_balance = ['none','mass','bid'] #,'reg'} # may need to delete the reg as idk what it means here
def set_ssl(self, verbose, *args):
"""
Sets COMPOSE dataset and information processing options
Check if the input parameters are not empty for compose
This checks if the dataset is empty and checks what option of feedback you want
Gets dataset and verbose (the command to display options as COMPOSE processes)
Verbose: 0 : no info is displayed
1 : Command Line progress updates
2 : Plots when possible and Command Line progress updates
"""
self._verbose = verbose
# need to limit arguements to 2 for dataset and verbose
max_args = 2
try:
len(*args) <= max_args
except ValueError:
print("Number of input parameters must be a min of two. Input valid dataset and valid option to display information")
# set object displayed info setting
if self._verbose >= 0 and self._verbose <=2:
self._verbose = verbose
else:
print("Only 3 options to display information: 0 - No Info ; 1 - Command Line Progress Updates; 2 - Plots when possilbe and Command Line Progress")
return verbose
def set_data(self, data, labels, *args):
"""
Load data and labels in ssl
"""
# check to see if the size of the data matches the size of the labels
if getsizeof(data) == getsizeof(labels):
self._data = data
self._labels = labels
# Obtain size information of data
sizeData = getsizeof(data) # Obtain size info from data
df_unlabeled = pd.DataFrame.sum(self.n_unlabeled, axis=1) # sum across each row
unlabeled = df_unlabeled['0'].valuecounts() # count the instances that have zero which are the unlabeled
self.n_labeled = self.n_instances - self.n_unlabeled # The remaining instances must be labeled
| 45.527132
| 158
| 0.657245
|
4a0785dcfdfdce586850930ef07c97a418a12d63
| 9,552
|
py
|
Python
|
nanotune/model/utils.py
|
microsoft/nanotune
|
68be8f5b74a52d57b74ccac228e120d9ab48e3e4
|
[
"MIT"
] | 5
|
2021-02-24T14:32:37.000Z
|
2022-01-05T16:37:26.000Z
|
nanotune/model/utils.py
|
microsoft/nanotune
|
68be8f5b74a52d57b74ccac228e120d9ab48e3e4
|
[
"MIT"
] | 149
|
2021-03-23T14:44:39.000Z
|
2022-03-31T06:09:07.000Z
|
nanotune/model/utils.py
|
LaudateCorpus1/nanotune
|
0ada354597b16f6dbb17ca7be01ab7668b6d5049
|
[
"MIT"
] | 10
|
2021-03-29T13:36:38.000Z
|
2022-02-16T23:06:35.000Z
|
import os
from typing import List, Optional
import numpy.typing as npt
import matplotlib.pyplot as plt
import numpy as np
import scipy.fftpack as fp
import scipy.signal as sg
from scipy.ndimage import gaussian_filter, generic_gradient_magnitude, sobel
from skimage.transform import resize
import nanotune as nt
from nanotune.data.dataset import default_coord_names
N_2D = nt.config["core"]["standard_shapes"]["2"]
def generate_one_f_noise(
how_many: int = 20000,
save_to_file: bool = True,
filename: Optional[str] = None,
) -> npt.NDArray[np.float64]:
""" """
fx_1d = fp.frequenciesshift(fp.frequenciesfreq(1000, d=0.02))
condensed_data_all = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 0, np.prod(N_2D)]
)
for niter in range(how_many):
condensed_data = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 1, np.prod(N_2D)]
)
fx, fy = np.meshgrid(fx_1d, fx_1d, indexing="ij")
f = np.sqrt(fx ** 2 + fy ** 2)
f[f > 0] = np.divide(1, f[f > 0])
# if low_pass_cutoff is not None:
# f[f > low_pass_cutoff] = 0
# if high_pass_cutoff is not None:
# f[f < high_pass_cutoff] = 0
exponents = np.random.uniform(low=0, high=2 * np.pi, size=f.shape)
power_spect = np.multiply(f, np.exp(1j * exponents))
noise = np.abs(fp.ifrequencies2(power_spect))
noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise))
grad = generic_gradient_magnitude(noise, sobel)
noise = resize(noise, N_2D, anti_aliasing=True, mode="constant").flatten()
grad = resize(grad, N_2D, anti_aliasing=True, mode="constant").flatten()
power_spect = resize(
np.abs(power_spect), N_2D, anti_aliasing=True, mode="constant"
).flatten()
index = nt.config["core"]["data_types"]["signal"]
condensed_data[index, 0, :] = noise
index = nt.config["core"]["data_types"]["frequencies"]
condensed_data[index, 0, :] = power_spect
index = nt.config["core"]["data_types"]["gradient"]
condensed_data[index, 0, :] = grad
condensed_data_all = np.concatenate(
(condensed_data_all, condensed_data), axis=1
)
if save_to_file:
if filename is None:
filename = "one_over_f_noise.npy"
path = os.path.join(nt.config["db_folder"], filename)
np.save(path, condensed_data_all)
return condensed_data_all
def generate_white_noise(
how_many: int = 20000,
save_to_file: bool = True,
filename: Optional[str] = None,
) -> npt.NDArray[np.float64]:
""" """
condensed_data_all = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 0, np.prod(N_2D)]
)
for niter in range(how_many):
condensed_data = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 1, np.prod(N_2D)]
)
coeff = np.random.normal(0, 1, N_2D)
noise = np.abs(fp.ifrequencies2(coeff))
grad = generic_gradient_magnitude(noise, sobel)
index = nt.config["core"]["data_types"]["signal"]
condensed_data[index, 0, :] = noise.flatten()
index = nt.config["core"]["data_types"]["frequencies"]
condensed_data[index, 0, :] = coeff.flatten()
index = nt.config["core"]["data_types"]["gradient"]
condensed_data[index, 0, :] = grad.flatten()
condensed_data_all = np.concatenate(
(condensed_data_all, condensed_data), axis=1
)
if save_to_file:
if filename is None:
filename = "white_noise.npy"
path = os.path.join(nt.config["db_folder"], filename)
np.save(path, condensed_data_all)
return condensed_data_all
def generate_current_drop(
how_many: int = 20000,
save_to_file: bool = True,
filename: Optional[str] = None,
) -> npt.NDArray[np.float64]:
""" """
condensed_data_all = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 0, np.prod(N_2D)]
)
for niter in range(how_many):
condensed_data = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 1, np.prod(N_2D)]
)
xm, ym = np.meshgrid(np.linspace(0, 50, 50), np.linspace(0, 50, 50))
drop = np.sqrt((xm + ym) ** 2)
drop = (drop - np.min(drop)) / (np.max(drop) - np.min(drop))
amp = np.random.uniform(0, 10, 1)
offset = np.random.uniform(-5, 5, 1)
drop = np.tanh(amp * drop + offset)
drop = (drop - np.min(drop)) / (np.max(drop) - np.min(drop))
drop_freq = fp.frequencies2(drop)
drop_freq = fp.frequenciesshift(drop_freq)
drop_freq = np.abs(drop_freq)
grad = generic_gradient_magnitude(drop, sobel)
index = nt.config["core"]["data_types"]["signal"]
condensed_data[index, 0, :] = drop.flatten()
index = nt.config["core"]["data_types"]["frequencies"]
condensed_data[index, 0, :] = drop_freq.flatten()
index = nt.config["core"]["data_types"]["gradient"]
condensed_data[index, 0, :] = grad.flatten()
condensed_data_all = np.concatenate(
(condensed_data_all, condensed_data), axis=1
)
if save_to_file:
if filename is None:
filename = "current_drop.npy"
path = os.path.join(nt.config["db_folder"], filename)
np.save(path, condensed_data_all)
return condensed_data_all
def generate_random_telegraph_noise(
how_many: int = 20000,
save_to_file: bool = True,
filename: Optional[str] = None,
) -> npt.NDArray[np.float64]:
""" """
condensed_data_all = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 0, np.prod(N_2D)]
)
for niter in range(how_many):
condensed_data = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 1, np.prod(N_2D)]
)
x = np.ones(N_2D)
s = 1
# for n_switches in range(0, 1):
lam = np.random.uniform(0, 0.2, 1)
trnsp = np.random.randint(2, size=1)
poisson = np.random.poisson(lam=lam, size=N_2D)
poisson[poisson > 1] = 1
for ix in range(N_2D[0]):
for iy in range(N_2D[0]):
if poisson[ix, iy] == 1:
s *= -1
x[ix, iy] *= s
if trnsp:
x = x.T
x = (x + 1) / 2
noise_spect = fp.frequencies2(x)
noise_spect = fp.frequenciesshift(noise_spect)
noise_spect = np.abs(noise_spect)
grad = generic_gradient_magnitude(x, sobel)
index = nt.config["core"]["data_types"]["signal"]
condensed_data[index, 0, :] = x.flatten()
index = nt.config["core"]["data_types"]["frequencies"]
condensed_data[index, 0, :] = noise_spect.flatten()
index = nt.config["core"]["data_types"]["gradient"]
condensed_data[index, 0, :] = grad.flatten()
condensed_data_all = np.concatenate(
(condensed_data_all, condensed_data), axis=1
)
if save_to_file:
if filename is None:
filename = "random_telegraph_noise.npy"
path = os.path.join(nt.config["db_folder"], filename)
np.save(path, condensed_data_all)
return condensed_data_all
# define normalized 2D gaussian
def gauss2d(x=0, y=0, mx=0, my=0, sx=1, sy=1):
norm = 1.0 / (2.0 * np.pi * sx * sy)
norm = norm * np.exp(
-((x - mx) ** 2.0 / (2.0 * sx ** 2.0) + (y - my) ** 2.0 / (2.0 * sy ** 2.0))
)
return norm
def generate_random_blobs(
how_many: int = 20000,
save_to_file: bool = True,
filename: Optional[str] = None,
n_blobs: int = 15,
stdx: Optional[List[float]] = None,
stdy: Optional[List[float]] = None,
) -> npt.NDArray[np.float64]:
""" """
if stdx is None:
stdx = [0.3, 0.8]
if stdy is None:
stdy = [0.3, 0.8]
condensed_data_all = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 0, np.prod(N_2D)]
)
for niter in range(how_many):
condensed_data = np.empty(
[len(nt.config["core"]["data_types"]) - 1, 1, np.prod(N_2D)]
)
x = np.linspace(-1, 1)
y = np.linspace(-1, 1)
x, y = np.meshgrid(x, y)
z = np.zeros(N_2D)
for n_blob in range(n_blobs):
z += gauss2d(
x,
y,
mx=np.random.uniform(-1, 1, 1),
my=np.random.uniform(-1, 1, 1),
sx=np.random.uniform(*stdx, 1), # type: ignore
sy=np.random.uniform(*stdy, 1), # type: ignore
)
z = (z - np.min(z)) / (np.max(z) - np.min(z))
noise_spect = fp.frequencies2(z)
noise_spect = fp.frequenciesshift(noise_spect)
noise_spect = np.abs(noise_spect)
grad = generic_gradient_magnitude(z, sobel)
index = nt.config["core"]["data_types"]["signal"]
condensed_data[index, 0, :] = z.flatten()
index = nt.config["core"]["data_types"]["frequencies"]
condensed_data[index, 0, :] = noise_spect.flatten()
index = nt.config["core"]["data_types"]["gradient"]
condensed_data[index, 0, :] = grad.flatten()
condensed_data_all = np.concatenate(
(condensed_data_all, condensed_data), axis=1
)
if save_to_file:
if filename is None:
filename = "random_blobs.npy"
path = os.path.join(nt.config["db_folder"], filename)
np.save(path, condensed_data_all)
return condensed_data_all
| 30.912621
| 84
| 0.57925
|
4a0785e2e68cf21e6d83218b634e0c8958251912
| 853
|
py
|
Python
|
forms.py
|
amesamoyers/PlanetaryGeologicMappers
|
a2d0afc1539790462119ea66bb670514fe3b7da5
|
[
"Unlicense"
] | null | null | null |
forms.py
|
amesamoyers/PlanetaryGeologicMappers
|
a2d0afc1539790462119ea66bb670514fe3b7da5
|
[
"Unlicense"
] | null | null | null |
forms.py
|
amesamoyers/PlanetaryGeologicMappers
|
a2d0afc1539790462119ea66bb670514fe3b7da5
|
[
"Unlicense"
] | null | null | null |
from wtforms import TextField, StringField, Form, PasswordField
from wtforms.validators import AnyOf, DataRequired, required
from wtforms.widgets import TextArea
class PageForm(Form):
page_title = StringField(u"Title",
[DataRequired(message = "No webpage title given.")],
widget = TextArea())
page_name = StringField(u"Name",
[DataRequired(message = "No webpage name given.")],
widget = TextArea())
page_content = StringField(u"Content",
[DataRequired(message = "No webpage content given.")],
widget = TextArea())
class LoginForm(Form):
admin_name = TextField(u'Admin Name', [required()])
admin_password = PasswordField(u'Admin Password', [required()])
| 40.619048
| 85
| 0.584994
|
4a07868fed30b57fe608bb5b36db75e2d1a29744
| 4,526
|
py
|
Python
|
backyard_flyer_solution.py
|
SagarmathaTech/jad-fcnd-term1-p2-motion-planning
|
281bfb87ee671094caa5f22861ab41f9884b7ca1
|
[
"MIT"
] | 22
|
2018-05-31T22:54:15.000Z
|
2022-03-03T12:57:48.000Z
|
backyard_flyer_solution.py
|
SagarmathaTech/jad-fcnd-term1-p2-motion-planning
|
281bfb87ee671094caa5f22861ab41f9884b7ca1
|
[
"MIT"
] | 3
|
2018-08-07T10:43:04.000Z
|
2022-03-10T06:52:27.000Z
|
backyard_flyer_solution.py
|
SagarmathaTech/jad-fcnd-term1-p2-motion-planning
|
281bfb87ee671094caa5f22861ab41f9884b7ca1
|
[
"MIT"
] | 28
|
2018-03-26T17:19:57.000Z
|
2022-02-28T04:29:01.000Z
|
# -*- coding: utf-8 -*-
"""
Solution to the Backyard Flyer Project.
"""
import time
from enum import Enum
import numpy as np
from udacidrone import Drone
from udacidrone.connection import MavlinkConnection, WebSocketConnection # noqa: F401
from udacidrone.messaging import MsgID
class States(Enum):
MANUAL = 0
ARMING = 1
TAKEOFF = 2
WAYPOINT = 3
LANDING = 4
DISARMING = 5
class BackyardFlyer(Drone):
def __init__(self, connection):
super().__init__(connection)
self.target_position = np.array([0.0, 0.0, 0.0])
self.all_waypoints = []
self.in_mission = True
self.check_state = {}
# initial state
self.flight_state = States.MANUAL
# register all your callbacks here
self.register_callback(MsgID.LOCAL_POSITION, self.local_position_callback)
self.register_callback(MsgID.LOCAL_VELOCITY, self.velocity_callback)
self.register_callback(MsgID.STATE, self.state_callback)
def local_position_callback(self):
if self.flight_state == States.TAKEOFF:
if -1.0 * self.local_position[2] > 0.95 * self.target_position[2]:
self.all_waypoints = self.calculate_box()
self.waypoint_transition()
elif self.flight_state == States.WAYPOINT:
if np.linalg.norm(self.target_position[0:2] - self.local_position[0:2]) < 1.0:
if len(self.all_waypoints) > 0:
self.waypoint_transition()
else:
if np.linalg.norm(self.local_velocity[0:2]) < 1.0:
self.landing_transition()
def velocity_callback(self):
if self.flight_state == States.LANDING:
if self.global_position[2] - self.global_home[2] < 0.1:
if abs(self.local_position[2]) < 0.01:
self.disarming_transition()
def state_callback(self):
if self.in_mission:
if self.flight_state == States.MANUAL:
self.arming_transition()
elif self.flight_state == States.ARMING:
if self.armed:
self.takeoff_transition()
elif self.flight_state == States.DISARMING:
if ~self.armed & ~self.guided:
self.manual_transition()
def calculate_box(self):
print("Setting Home")
local_waypoints = [[10.0, 0.0, 3.0], [10.0, 10.0, 3.0], [0.0, 10.0, 3.0], [0.0, 0.0, 3.0]]
return local_waypoints
def arming_transition(self):
print("arming transition")
self.take_control()
self.arm()
self.set_home_position(self.global_position[0], self.global_position[1],
self.global_position[2]) # set the current location to be the home position
self.flight_state = States.ARMING
def takeoff_transition(self):
print("takeoff transition")
# self.global_home = np.copy(self.global_position) # can't write to this variable!
target_altitude = 3.0
self.target_position[2] = target_altitude
self.takeoff(target_altitude)
self.flight_state = States.TAKEOFF
def waypoint_transition(self):
print("waypoint transition")
self.target_position = self.all_waypoints.pop(0)
print('target position', self.target_position)
self.cmd_position(self.target_position[0], self.target_position[1], self.target_position[2], 0.0)
self.flight_state = States.WAYPOINT
def landing_transition(self):
print("landing transition")
self.land()
self.flight_state = States.LANDING
def disarming_transition(self):
print("disarm transition")
self.disarm()
self.release_control()
self.flight_state = States.DISARMING
def manual_transition(self):
print("manual transition")
self.stop()
self.in_mission = False
self.flight_state = States.MANUAL
def start(self):
self.start_log("Logs", "NavLog.txt")
# self.connect()
print("starting connection")
# self.connection.start()
super().start()
# Only required if they do threaded
# while self.in_mission:
# pass
self.stop_log()
if __name__ == "__main__":
conn = MavlinkConnection('tcp:127.0.0.1:5760', threaded=False, PX4=False)
#conn = WebSocketConnection('ws://127.0.0.1:5760')
drone = BackyardFlyer(conn)
time.sleep(2)
drone.start()
| 32.328571
| 107
| 0.617543
|
4a078699f572a5189ecac85685b523705cccf793
| 3,133
|
py
|
Python
|
_2michaeltaylor/settings.py
|
mjt145/2michaeltaylor
|
ab2d4fde1d614842ab367f95bad262d1c7ea2878
|
[
"MIT"
] | null | null | null |
_2michaeltaylor/settings.py
|
mjt145/2michaeltaylor
|
ab2d4fde1d614842ab367f95bad262d1c7ea2878
|
[
"MIT"
] | null | null | null |
_2michaeltaylor/settings.py
|
mjt145/2michaeltaylor
|
ab2d4fde1d614842ab367f95bad262d1c7ea2878
|
[
"MIT"
] | null | null | null |
"""
Django settings for _2michaeltaylor project.
Generated by 'django-admin startproject' using Django 1.8.3.
For more information on this file, see
https://docs.djangoproject.com/en/1.8/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.8/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MAIN_DIR = os.path.dirname(os.path.dirname(__file__))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'ie06ntlaluelb7lh5@4-qyksf6+_3pkle^jh0kco!5slnlabm0'
# 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',
'core',
)
MIDDLEWARE_CLASSES = (
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'django.middleware.security.SecurityMiddleware',
)
ROOT_URLCONF = '_2michaeltaylor.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(MAIN_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 = '_2michaeltaylor.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.8/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Parse database configuration from $DATABASE_URL
import dj_database_url
DATABASES['default'] = dj_database_url.config()
# Honor the 'X-Forwarded-Proto' header for request.is_secure()
SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
# Allow all host headers
ALLOWED_HOSTS = ['*']
# Internationalization
# https://docs.djangoproject.com/en/1.8/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/1.8/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = (
os.path.join(MAIN_DIR, 'static'),
)
STATIC_ROOT = 'staticfiles'
| 26.108333
| 71
| 0.713693
|
4a07876666a6d2387a3098c36153378ee88e83a9
| 123
|
py
|
Python
|
Lab_Dash/Route.py
|
SimonSchubotz/Electronic-Laboratory-Notebook
|
a5dc3daa76b07370c1ee5b7e74fb6c780c3d3c97
|
[
"Apache-2.0"
] | null | null | null |
Lab_Dash/Route.py
|
SimonSchubotz/Electronic-Laboratory-Notebook
|
a5dc3daa76b07370c1ee5b7e74fb6c780c3d3c97
|
[
"Apache-2.0"
] | null | null | null |
Lab_Dash/Route.py
|
SimonSchubotz/Electronic-Laboratory-Notebook
|
a5dc3daa76b07370c1ee5b7e74fb6c780c3d3c97
|
[
"Apache-2.0"
] | null | null | null |
from channels.routing import ProtocolTypeRouter
application = ProtocolTypeRouter({
})
""" Routing the dash application
"""
| 20.5
| 47
| 0.788618
|
4a078941483f4012cc20c4e55bcd43625789dd15
| 1,786
|
py
|
Python
|
tests/test_tasks/test_task_methods.py
|
hp2500/openml-python
|
62cc534cd18e6e011a88a83816fec95a90399a9b
|
[
"BSD-3-Clause"
] | 1
|
2019-09-02T00:28:26.000Z
|
2019-09-02T00:28:26.000Z
|
tests/test_tasks/test_task_methods.py
|
hp2500/openml-python
|
62cc534cd18e6e011a88a83816fec95a90399a9b
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_tasks/test_task_methods.py
|
hp2500/openml-python
|
62cc534cd18e6e011a88a83816fec95a90399a9b
|
[
"BSD-3-Clause"
] | 1
|
2019-09-02T00:29:32.000Z
|
2019-09-02T00:29:32.000Z
|
from time import time
import openml
from openml.testing import TestBase
# Common methods between tasks
class OpenMLTaskMethodsTest(TestBase):
def setUp(self):
super(OpenMLTaskMethodsTest, self).setUp()
def tearDown(self):
super(OpenMLTaskMethodsTest, self).tearDown()
def test_tagging(self):
task = openml.tasks.get_task(1)
tag = "testing_tag_{}_{}".format(self.id(), time())
task_list = openml.tasks.list_tasks(tag=tag)
self.assertEqual(len(task_list), 0)
task.push_tag(tag)
task_list = openml.tasks.list_tasks(tag=tag)
self.assertEqual(len(task_list), 1)
self.assertIn(1, task_list)
task.remove_tag(tag)
task_list = openml.tasks.list_tasks(tag=tag)
self.assertEqual(len(task_list), 0)
def test_get_train_and_test_split_indices(self):
openml.config.cache_directory = self.static_cache_dir
task = openml.tasks.get_task(1882)
train_indices, test_indices = task.get_train_test_split_indices(0, 0)
self.assertEqual(16, train_indices[0])
self.assertEqual(395, train_indices[-1])
self.assertEqual(412, test_indices[0])
self.assertEqual(364, test_indices[-1])
train_indices, test_indices = task.get_train_test_split_indices(2, 2)
self.assertEqual(237, train_indices[0])
self.assertEqual(681, train_indices[-1])
self.assertEqual(583, test_indices[0])
self.assertEqual(24, test_indices[-1])
self.assertRaisesRegexp(ValueError, "Fold 10 not known",
task.get_train_test_split_indices, 10, 0)
self.assertRaisesRegexp(ValueError, "Repeat 10 not known",
task.get_train_test_split_indices, 0, 10)
| 38.826087
| 77
| 0.666853
|
4a078a6a2555fea3fbe7de9c61245a148170bce8
| 3,060
|
py
|
Python
|
mobilenet.py
|
xingmimfl/pytorch_Mobilenet
|
aaeacd2b21d1cf1c70f3e9f4a080aad5b06f3345
|
[
"MIT"
] | null | null | null |
mobilenet.py
|
xingmimfl/pytorch_Mobilenet
|
aaeacd2b21d1cf1c70f3e9f4a080aad5b06f3345
|
[
"MIT"
] | 1
|
2019-12-19T03:28:32.000Z
|
2019-12-19T03:28:32.000Z
|
mobilenet.py
|
xingmimfl/pytorch_Mobilenet
|
aaeacd2b21d1cf1c70f3e9f4a080aad5b06f3345
|
[
"MIT"
] | null | null | null |
import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
relu=True, bn=True, same_padding=False, bias=False):
super(Conv2d, self).__init__()
padding = int((kernel_size - 1) / 2) if same_padding else 0
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding, bias=bias)
self.bn = nn.BatchNorm2d(out_channels) if bn else None
self.relu = nn.ReLU(inplace=True) if relu else None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class DepthwiseSepConv2d(nn.Module):
def __init__(self, in_channels, out_channels, strides):
super(DepthwiseSepConv2d, self).__init__()
self.depthwise_conv = nn.Conv2d(in_channels=in_channels, out_channels=in_channels,
kernel_size=3, stride=strides, groups=in_channels,
padding=1, bias=False)
self.pointwise_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=1, stride=1, bias=False)
self.depthwise_bn = nn.BatchNorm2d(in_channels)
self.pointwise_bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.depthwise_conv(x)
x = self.depthwise_bn(x)
x = self.relu(x)
x = self.pointwise_conv(x)
x = self.pointwise_bn(x)
x = self.relu(x)
return x
class MobileNet(nn.Module):
def __init__(self, num_classes=1000, width_multiplier=1, Training=False):
"""
num_classes: number of predicted classes.
Training: whether or not the model is being trained.
"""
super(MobileNet, self).__init__()
self.features = nn.Sequential(
Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=2, same_padding=True),
DepthwiseSepConv2d(32, 64, 1),
DepthwiseSepConv2d(64, 128, 2),
DepthwiseSepConv2d(128, 128, 1),
DepthwiseSepConv2d(128, 256, 2),
DepthwiseSepConv2d(256, 256, 1),
DepthwiseSepConv2d(256, 512, 2),
DepthwiseSepConv2d(512, 512, 1),
DepthwiseSepConv2d(512, 512, 1),
DepthwiseSepConv2d(512, 512, 1),
DepthwiseSepConv2d(512, 512, 1),
DepthwiseSepConv2d(512, 512, 1),
DepthwiseSepConv2d(512, 1024, 2),
DepthwiseSepConv2d(1024, 1024, 1),
nn.AvgPool2d(kernel_size=7)
)
self.fc = nn.Linear(in_features=1024, out_features=num_classes)
def forward(self,x):
x = self.features(x)
#print "x.size():\t", x.size()
x = x.view(-1, 1024)
x = self.fc(x)
return x
if __name__=="__main__":
net = MobileNet()
print net
| 37.317073
| 105
| 0.589542
|
4a078bac82355d56bd81fbf1d57909ebdca9ecbb
| 10,191
|
py
|
Python
|
components/micropython/modules/sha2017_backup/woezel.py
|
badgeteam/Firmware
|
6192b2902c70beb7a298a256d9087274d045fbc0
|
[
"Apache-2.0"
] | 7
|
2019-02-11T10:02:14.000Z
|
2019-08-02T00:08:45.000Z
|
components/micropython/modules/sha2017_backup/woezel.py
|
badgeteam/Firmware
|
6192b2902c70beb7a298a256d9087274d045fbc0
|
[
"Apache-2.0"
] | 17
|
2019-01-05T18:02:11.000Z
|
2019-03-09T21:46:43.000Z
|
components/micropython/modules/sha2017_backup/woezel.py
|
badgeteam/Firmware
|
6192b2902c70beb7a298a256d9087274d045fbc0
|
[
"Apache-2.0"
] | 4
|
2019-02-15T16:03:20.000Z
|
2019-06-27T22:23:24.000Z
|
import sys
import gc
import uos as os
import uerrno as errno
import ujson as json
import uzlib
import upip_utarfile as tarfile
gc.collect()
debug = False
install_path = None
cleanup_files = []
gzdict_sz = 16 + 15
file_buf = bytearray(512)
class NotFoundError(Exception):
pass
class LatestInstalledError(Exception):
pass
def op_split(path):
if path == "":
return ("", "")
r = path.rsplit("/", 1)
if len(r) == 1:
return ("", path)
head = r[0]
if not head:
head = "/"
return (head, r[1])
def op_basename(path):
return op_split(path)[1]
# Expects *file* name
def _makedirs(name, mode=0o777):
ret = False
s = ""
comps = name.rstrip("/").split("/")[:-1]
if comps[0] == "":
s = "/"
for c in comps:
if s and s[-1] != "/":
s += "/"
s += c
try:
os.mkdir(s)
ret = True
except OSError as e:
if e.args[0] != errno.EEXIST and e.args[0] != errno.EISDIR:
raise
ret = False
return ret
def save_file(fname, subf):
global file_buf
with open(fname, "wb") as outf:
while True:
sz = subf.readinto(file_buf)
if not sz:
break
outf.write(file_buf, sz)
def install_tar(f, prefix):
meta = {}
for info in f:
#print(info)
fname = info.name
try:
fname = fname[fname.index("/") + 1:]
except ValueError:
fname = ""
save = True
for p in ("setup.", "PKG-INFO", "README"):
#print(fname, p)
if fname.startswith(p) or ".egg-info" in fname:
if fname.endswith("/requires.txt"):
meta["deps"] = f.extractfile(info).read()
save = False
if debug:
print("Skipping", fname)
break
if save:
outfname = prefix + fname
if info.type != tarfile.DIRTYPE:
if debug:
print("Extracting " + outfname)
_makedirs(outfname)
subf = f.extractfile(info)
save_file(outfname, subf)
return meta
def expandhome(s):
if "~/" in s:
h = os.getenv("HOME")
s = s.replace("~/", h + "/")
return s
import ussl
import usocket
def url_open(url):
if debug:
print(url)
proto, _, host, urlpath = url.split('/', 3)
try:
ai = usocket.getaddrinfo(host, 443)
except OSError as e:
fatal("Unable to resolve %s (no Internet?)" % host, e)
#print("Address infos:", ai)
if len(ai) == 0:
fatal("Unable to resolve %s (no Internet?)" % host, errno.EHOSTUNREACH)
addr = ai[0][4]
s = usocket.socket(ai[0][0])
try:
#print("Connect address:", addr)
s.connect(addr)
if proto == "https:":
s = ussl.wrap_socket(s, server_hostname=host)
# MicroPython rawsocket module supports file interface directly
s.write("GET /%s HTTP/1.0\r\nHost: %s\r\n\r\n" % (urlpath, host))
l = s.readline()
protover, status, msg = l.split(None, 2)
if status != b"200":
if status == b"404" or status == b"301":
raise NotFoundError("Package not found")
raise ValueError(status)
while 1:
l = s.readline()
if not l:
raise ValueError("Unexpected EOF in HTTP headers")
if l == b'\r\n':
break
except Exception as e:
s.close()
raise e
return s
def get_pkg_metadata(name):
f = url_open("https://badge.team/eggs/get/%s/json" % name)
try:
return json.load(f)
finally:
f.close()
def get_pkg_list():
f = url_open("https://badge.team/eggs/list/json")
try:
return json.load(f)
finally:
f.close()
def search_pkg_list(query):
f = url_open("https://badge.team/eggs/search/%s/json" % query)
try:
return json.load(f)
finally:
f.close()
def fatal(msg, exc=None):
print("Error:", msg)
if exc and debug:
raise exc
sys.exit(1)
def install_pkg(pkg_spec, install_path, force_reinstall):
data = get_pkg_metadata(pkg_spec)
already_installed = False
try:
os.stat("%s%s/" % (install_path, pkg_spec))
except OSError as e:
if e.args[0] == errno.EINVAL:
print("Package %s already installed" % (pkg_spec))
already_installed = True
else:
print("Package %s not yet installed" % (pkg_spec))
else:
# fallback for unix version
print("Package %s already installed" % (pkg_spec))
already_installed = True
latest_ver = data["info"]["version"]
verf = "%s%s/version" % (install_path, pkg_spec)
if already_installed:
try:
with open(verf, "r") as fver:
old_ver = fver.read()
except:
print("No version file found")
else:
if old_ver == latest_ver:
if not force_reinstall:
raise LatestInstalledError("Latest version installed")
else:
print("Removing previous rev. %s" % old_ver)
for rm_file in os.listdir("%s%s" % (install_path, pkg_spec)):
os.remove("%s%s/%s" % (install_path, pkg_spec, rm_file))
packages = data["releases"][latest_ver]
del data
gc.collect()
assert len(packages) == 1
package_url = packages[0]["url"]
print("Installing %s rev. %s from %s" % (pkg_spec, latest_ver, package_url))
package_fname = op_basename(package_url)
f1 = url_open(package_url)
try:
f2 = uzlib.DecompIO(f1, gzdict_sz)
f3 = tarfile.TarFile(fileobj=f2)
meta = install_tar(f3, "%s%s/" % (install_path, pkg_spec))
finally:
f1.close()
del f3
del f2
with open(verf, "w") as fver:
fver.write(latest_ver)
del fver
gc.collect()
return meta
def install(to_install, install_path=None, force_reinstall=False):
# Calculate gzip dictionary size to use
global gzdict_sz
sz = gc.mem_free() + gc.mem_alloc()
if sz <= 65536:
# this will probably give errors with some packages, but we
# just don't have enough memory.
gzdict_sz = 16 + 13
if install_path is None:
install_path = get_install_path()
if install_path[-1] != "/":
install_path += "/"
if not isinstance(to_install, list):
to_install = [to_install]
print("Installing to: " + install_path)
# sets would be perfect here, but don't depend on them
installed = []
try:
while to_install:
if debug:
print("Queue:", to_install)
pkg_spec = to_install.pop(0)
if pkg_spec in installed:
continue
meta = install_pkg(pkg_spec, install_path, force_reinstall)
installed.append(pkg_spec)
if debug:
print(meta)
deps = meta.get("deps", "").rstrip(" \t\n\r\v\f\x00")
if deps:
deps = deps.decode("utf-8").split("\n")
to_install.extend(deps)
except Exception as e:
print("Error installing '{}': {}, packages may be partially installed".format(
pkg_spec, e),
file=sys.stderr)
raise e
def display_pkg(packages):
for package in packages:
print(package["name"])
print(" Slug: " + package["slug"])
print(" Version: " + package["revision"])
print(" Description: " + package["description"])
def search(query="*"):
if query == "*":
packages = get_pkg_list()
else:
packages = search_pkg_list(query)
display_pkg(packages)
def get_install_path():
global install_path
if install_path is None:
# sys.path[0] is current module's path
install_path = sys.path[1]
install_path = expandhome(install_path)
return install_path
def cleanup():
for fname in cleanup_files:
try:
os.unlink(fname)
except OSError:
print("Warning: Cannot delete " + fname)
def help():
print("""\
woezel - Clone of the Simple PyPI package manager for MicroPython
Usage: micropython -m woezel install [-p <path>] <package>... | -r <requirements.txt>
import woezel
woezel.install(package_or_list, [<path>])
woezel.search([query])
If <path> is not given, packages will be installed into sys.path[1]
(can be set from MICROPYPATH environment variable, if current system
supports that).""")
print("Current value of sys.path[1]:", sys.path[1])
print("""\
Note: only MicroPython packages are supported for installation,
woezel, like upip does not support arbitrary code in setup.py.
""")
def main():
global debug
global install_path
install_path = None
if len(sys.argv) < 2 or sys.argv[1] == "-h" or sys.argv[1] == "--help":
help()
return
if sys.argv[1] != "install":
fatal("Only 'install' command supported")
to_install = []
i = 2
while i < len(sys.argv) and sys.argv[i][0] == "-":
opt = sys.argv[i]
i += 1
if opt == "-h" or opt == "--help":
help()
return
elif opt == "-p":
install_path = sys.argv[i]
i += 1
elif opt == "-r":
list_file = sys.argv[i]
i += 1
with open(list_file) as f:
while True:
l = f.readline()
if not l:
break
if l[0] == "#":
continue
to_install.append(l.rstrip(" \t\n\r\v\f\x00"))
elif opt == "--debug":
debug = True
else:
fatal("Unknown/unsupported option: " + opt)
to_install.extend(sys.argv[i:])
if not to_install:
help()
return
install(to_install)
if not debug:
cleanup()
if __name__ == "__main__":
main()
| 27.469003
| 86
| 0.540281
|
4a078c75b2e45c2072b55df47666e43db044972b
| 348
|
py
|
Python
|
lazythumbs/tests/__init__.py
|
caktus/lazythumbs
|
006ac42f9f4ac600d4c85d0929f4e2c755d4f853
|
[
"MIT"
] | 1
|
2017-07-24T22:06:25.000Z
|
2017-07-24T22:06:25.000Z
|
lazythumbs/tests/__init__.py
|
caktus/lazythumbs
|
006ac42f9f4ac600d4c85d0929f4e2c755d4f853
|
[
"MIT"
] | null | null | null |
lazythumbs/tests/__init__.py
|
caktus/lazythumbs
|
006ac42f9f4ac600d4c85d0929f4e2c755d4f853
|
[
"MIT"
] | null | null | null |
from lazythumbs.tests.test_server import RenderTest, GetViewTest
from lazythumbs.tests.test_templatetag import LazythumbSyntaxTest, LazythumbGeometryCompileTest, LazythumbRenderTest
from lazythumbs.tests.test_templatetag import ImgAttrsRenderTest
from lazythumbs.tests.test_util import TestGeometry, TestComputeIMG, TestGetImgAttrs, TestGetFormat
| 69.6
| 116
| 0.893678
|
4a078d17125636f32bdc3c0268b56986ecfb7ef8
| 3,505
|
py
|
Python
|
setup.py
|
FelixdenBreejen/PySCIPOpt
|
a6dbfdfd565d29da705d147fddfc732c8bc5ca93
|
[
"MIT"
] | null | null | null |
setup.py
|
FelixdenBreejen/PySCIPOpt
|
a6dbfdfd565d29da705d147fddfc732c8bc5ca93
|
[
"MIT"
] | null | null | null |
setup.py
|
FelixdenBreejen/PySCIPOpt
|
a6dbfdfd565d29da705d147fddfc732c8bc5ca93
|
[
"MIT"
] | null | null | null |
from setuptools import setup, Extension
import os, platform, sys, re
import numpy as np
# look for environment variable that specifies path to SCIP
scipoptdir = os.environ.get('SCIPOPTDIR', '').strip('"')
extra_compile_args = []
extra_link_args = []
# determine include directory
if os.path.exists(os.path.join(scipoptdir, 'src')):
# SCIP seems to be installed in place
includedir = os.path.abspath(os.path.join(scipoptdir, 'src'))
else:
# assume that SCIP is installed on the system
includedir = os.path.abspath(os.path.join(scipoptdir, 'include'))
print('Using include path <%s>.' % includedir)
# determine library
if os.path.exists(os.path.join(scipoptdir, 'lib/shared/libscipsolver.so')):
# SCIP seems to be created with make
libdir = os.path.abspath(os.path.join(scipoptdir, 'lib/shared'))
libname = 'scipsolver'
extra_compile_args.append('-DNO_CONFIG_HEADER')
else:
# assume that SCIP is installed on the system
libdir = os.path.abspath(os.path.join(scipoptdir, 'lib'))
libname = 'scip'
if platform.system() in ['Windows']:
libname = 'libscip'
print('Using SCIP library <%s> at <%s>.' % (libname,libdir))
# set runtime libraries
if platform.system() in ['Linux', 'Darwin']:
extra_link_args.append('-Wl,-rpath,'+libdir)
# enable debug mode if requested
if "--debug" in sys.argv:
extra_compile_args.append('-UNDEBUG')
sys.argv.remove("--debug")
use_cython = True
packagedir = os.path.join('src', 'pyscipopt')
with open(os.path.join(packagedir, '__init__.py'), 'r') as initfile:
version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]',
initfile.read(), re.MULTILINE).group(1)
try:
from Cython.Build import cythonize
except ImportError:
if not os.path.exists(os.path.join(packagedir, 'scip.c')):
print('Cython is required')
quit(1)
use_cython = False
if not os.path.exists(os.path.join(packagedir, 'scip.pyx')):
use_cython = False
ext = '.pyx' if use_cython else '.c'
extensions = [Extension('pyscipopt.scip', [os.path.join(packagedir, 'scip'+ext)],
include_dirs=[includedir],
library_dirs=[libdir],
libraries=[libname],
extra_compile_args = extra_compile_args,
extra_link_args=extra_link_args
)]
if use_cython:
extensions = cythonize(extensions, compiler_directives={'language_level': 3})
with open('README.md') as f:
long_description = f.read()
setup(
name='PySCIPOpt',
version=version,
description='Python interface and modeling environment for SCIP',
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/SCIP-Interfaces/PySCIPOpt',
author='Zuse Institute Berlin',
author_email='scip@zib.de',
license='MIT',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'Intended Audience :: Education',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Cython',
'Topic :: Scientific/Engineering :: Mathematics'],
ext_modules=extensions,
install_requires=['wheel'],
packages=['pyscipopt'],
package_dir={'pyscipopt': packagedir},
package_data={'pyscipopt': ['scip.pyx', 'scip.pxd', '*.pxi']},
include_dirs = [np.get_include()]
)
| 33.380952
| 81
| 0.649358
|
4a078d4fe3e7eeba7438846e65e635e630f94abb
| 755
|
py
|
Python
|
src/ZPublisher/Publish.py
|
Mattlk13/Zope
|
b26ba322565f640f1c62b4a8d6b407cf5df5fdcd
|
[
"ZPL-2.1"
] | null | null | null |
src/ZPublisher/Publish.py
|
Mattlk13/Zope
|
b26ba322565f640f1c62b4a8d6b407cf5df5fdcd
|
[
"ZPL-2.1"
] | 1
|
2020-11-11T07:11:31.000Z
|
2020-11-11T07:11:31.000Z
|
src/ZPublisher/Publish.py
|
Mattlk13/Zope
|
b26ba322565f640f1c62b4a8d6b407cf5df5fdcd
|
[
"ZPL-2.1"
] | null | null | null |
##############################################################################
#
# Copyright (c) 2002 Zope Foundation and Contributors.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE
#
##############################################################################
from zope.deferredimport import deprecated
# BBB Zope 5.0
deprecated(
'Please import from ZPublisher.',
Retry='ZPublisher:Retry',
)
| 34.318182
| 78
| 0.6
|
4a078da5a233ca5c6490ecb64daa4f88a1b5b136
| 19,947
|
py
|
Python
|
author_tests.py
|
PhysicsUofRAUI/lifeLongLearning
|
36e098d4319d3500509861454fa3e27a67416802
|
[
"MIT"
] | null | null | null |
author_tests.py
|
PhysicsUofRAUI/lifeLongLearning
|
36e098d4319d3500509861454fa3e27a67416802
|
[
"MIT"
] | 38
|
2020-06-09T00:07:09.000Z
|
2021-02-06T17:18:20.000Z
|
author_tests.py
|
PhysicsUofRAUI/lifeLongLearning
|
36e098d4319d3500509861454fa3e27a67416802
|
[
"MIT"
] | null | null | null |
import unittest
from flask_testing import TestCase
from config import TestConfiguration
from app import create_app as c_app
import os
from flask import session, url_for, template_rendered
from app.models import Author, Worksheet, WorksheetCategory
from app.database import db
from contextlib import contextmanager
from werkzeug.security import generate_password_hash, check_password_hash
def login_author(client, email, password):
return client.post('/author_login', data=dict(
email=email,
password=password
), follow_redirects=True)
def logout_author(client):
return client.get('/author_logout', follow_redirects=True)
def login(client, username, password):
return client.post('/login', data=dict(
username=username,
password=password
), follow_redirects=True)
def logout(client):
return client.get('/logout', follow_redirects=True)
@contextmanager
def captured_templates(app):
recorded = []
def record(sender, template, context, **extra):
recorded.append((template, context))
template_rendered.connect(record, app)
try:
yield recorded
finally:
template_rendered.disconnect(record, app)
class BasicTests(TestCase):
############################
#### setup and teardown ####
############################
def create_app(self):
app = c_app(TestConfiguration)
return app
# executed prior to each test
def setUp(self):
pass
# executed after each test
def tearDown(self):
pass
#########################################
####### Tests For Pages Admin Uses ######
#########################################
def test_add_author_page(self):
response = self.client.get('/add_author', follow_redirects=True)
self.assertEqual(response.status_code, 200)
def test_edit_author_page(self):
response = self.client.get('/edit_author/1', follow_redirects=True)
self.assertEqual(response.status_code, 200)
def test_delete_author_page(self):
response = self.client.get('/delete_author/1', follow_redirects=True)
self.assertEqual(response.status_code, 200)
# checking if the user gets kicked out of protected views when not logged in
# will check this to see if the request results in a redirect
def test_add_author_page_r(self):
response = self.client.get('/add_author', follow_redirects=False)
self.assertEqual(response.status_code, 302)
def test_edit_author_page_r(self):
response = self.client.get('/edit_author/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
def test_delete_author_page_r(self):
response = self.client.get('/delete_author/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
#########################################
###### Tests For Pages Author Uses ######
#########################################
def test_author_change_email_nl(self):
response = self.client.get('/author_change_email/1', follow_redirects=True)
self.assertEqual(response.status_code, 200)
response = self.client.get('/author_change_email/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
def test_author_change_about_nl(self):
response = self.client.get('/author_change_about/1', follow_redirects=True)
self.assertEqual(response.status_code, 200)
response = self.client.get('/author_change_about/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
def test_author_change_password_nl(self):
response = self.client.get('/author_change_password/1', follow_redirects=True)
self.assertEqual(response.status_code, 200)
response = self.client.get('/author_change_password/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
def test_author_change_screenname_nl(self):
response = self.client.get('/author_change_screenname/1', follow_redirects=True)
self.assertEqual(response.status_code, 200)
response = self.client.get('/author_change_screenname/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
def test_author_dashboard_nl(self):
response = self.client.get('/author_dashboard', follow_redirects=True)
self.assertEqual(response.status_code, 200)
response = self.client.get('/author_dashboard', follow_redirects=False)
self.assertEqual(response.status_code, 302)
class DatabaseEditTests(TestCase):
def create_app(self):
app = c_app(TestConfiguration)
return app
# executed prior to each test
def setUp(self):
self.app_context = self.app.app_context()
self.app_context.push()
db.create_all()
login(self.client, os.getenv('LOGIN_USERNAME'), os.getenv('LOGIN_PASSWORD'))
# executed after each test
def tearDown(self):
logout(self.client)
db.session.remove()
db.drop_all()
self.app_context.pop()
#########################################
####### Tests For Pages Admin Uses ######
#########################################
def test_add_author_page_li(self):
response = self.client.get('/add_author', follow_redirects=False)
self.assertEqual(response.status_code, 200)
response_1 = self.client.post('/add_author', follow_redirects=True, data=dict(name='Kody', email='kodyrogers21@gmail.com',
about='I am a hacker', screenname='blah', password='password'))
auth = Author.query.filter_by(name='Kody').first()
self.assertEqual(response_1.status_code, 200)
self.assertNotEqual(auth, None)
self.assertEqual(auth.name, 'Kody')
self.assertEqual(auth.screenname, None)
self.assertEqual(auth.about, None)
self.assertEqual(auth.email, 'kodyrogers21@gmail.com')
self.assertEqual(check_password_hash(auth.password, 'password'), True)
response_1 = self.client.post('/add_author', follow_redirects=True, data=dict(name='Kody1', email='kodyrogers@gmail.com', password='honkog'))
auth_1 = Author.query.filter_by(name='Kody1').first()
self.assertEqual(response_1.status_code, 200)
self.assertNotEqual(auth_1, None)
self.assertEqual(auth_1.name, 'Kody1')
self.assertEqual(auth_1.screenname, None)
self.assertEqual(auth_1.about, None)
self.assertEqual(check_password_hash(auth_1.password, 'honkog'), True)
def test_edit_author_page_li(self):
author = Author(name='KJsa', password='password', email='kodya@hotmail.com')
db.session.add(author)
db.session.commit()
response = self.client.get('/edit_author/1', follow_redirects=False)
self.assertEqual(response.status_code, 200)
response_1 = self.client.post('/edit_author/1', follow_redirects=True, data=dict(password='RockOn'))
self.assertEqual(response_1.status_code, 200)
edited_author = Author.query.filter_by(name='KJsa').first()
self.assertNotEqual(edited_author, None)
self.assertNotEqual(edited_author.password, generate_password_hash('RockOn'))
self.assertEqual(edited_author.email, 'kodya@hotmail.com')
response_2 = self.client.post('/edit_author/1', follow_redirects=True, data=dict(password='RockOn', about='hey hey',
screenname='yoh', name='Kody', email='kody15@nhl.com'))
self.assertEqual(response_2.status_code, 200)
edited_author_1 = Author.query.filter_by(name='Kody').first()
self.assertNotEqual(edited_author_1, None)
self.assertNotEqual(edited_author_1.password, generate_password_hash('RockOn'))
self.assertEqual(edited_author_1.email, 'kody15@nhl.com')
self.assertEqual(edited_author_1.screenname, None)
self.assertEqual(edited_author_1.about, None)
def test_delete_author_page_li(self):
auth_1 = Author(name='Kidkaid', email='kodyrogers21@gmail.com', password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(auth_1)
db.session.commit()
response = self.client.get('/delete_author/1', follow_redirects=False)
self.assertEqual(response.status_code, 302)
auth_1 = Author.query.filter_by(name='kidkaid').first()
self.assertEqual(auth_1, None)
#########################################
###### Tests For Pages Author Uses ######
#########################################
def test_author_change_about(self) :
auth_1 = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod', about='What up?',
password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(auth_1)
db.session.commit()
login_author(self.client, email='kodyrogers21@gmail.com', password='RockOn')
response = self.client.get(url_for('author.author_change_about', id=auth_1.id), follow_redirects=False)
self.assertEqual(response.status_code, 200)
response_1 = self.client.post('/author_change_about/1',
data=dict(about='I love rock music'), follow_redirects=True)
auth = Author.query.filter_by(name='KJsa').first()
self.assertEqual(response_1.status_code, 200)
self.assertEqual(response_1.status_code, 200)
self.assertEqual(auth.email, 'kodyrogers21@gmail.com')
self.assertEqual(auth.about, 'I love rock music')
logout_author(self.client)
def test_author_change_password(self) :
auth_1 = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod', about='What up?',
password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(auth_1)
db.session.commit()
login_author(self.client, email='kodyrogers21@gmail.com', password='RockOn')
response = self.client.get(url_for('author.author_change_password', id=auth_1.id), follow_redirects=False)
self.assertEqual(response.status_code, 200)
response_1 = self.client.post('/author_change_password/1',
data=dict(password='weeeehooo'), follow_redirects=True)
auth = Author.query.filter_by(name='KJsa').first()
self.assertEqual(response_1.status_code, 200)
self.assertEqual(response_1.status_code, 200)
self.assertEqual(auth.email, 'kodyrogers21@gmail.com')
self.assertEqual(check_password_hash(auth.password, 'weeeehooo'), True)
logout_author(self.client)
def test_author_change_screenname(self) :
auth_1 = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod', about='What up?',
password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(auth_1)
db.session.commit()
login_author(self.client, email='kodyrogers21@gmail.com', password='RockOn')
response = self.client.get(url_for('author.author_change_screenname', id=auth_1.id), follow_redirects=False)
self.assertEqual(response.status_code, 200)
response_1 = self.client.post('/author_change_screenname/1',
data=dict(screenname='logical'), follow_redirects=True)
auth = Author.query.filter_by(name='KJsa').first()
self.assertEqual(response_1.status_code, 200)
self.assertEqual(response_1.status_code, 200)
self.assertEqual(auth.email, 'kodyrogers21@gmail.com')
self.assertEqual(auth.password, 'pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
self.assertEqual(auth.screenname, 'logical')
logout_author(self.client)
def test_author_change_email(self) :
auth_1 = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod', about='What up?',
password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(auth_1)
db.session.commit()
login_author(self.client, email='kodyrogers21@gmail.com', password='RockOn')
response = self.client.get(url_for('author.author_change_email', id=auth_1.id), follow_redirects=False)
self.assertEqual(response.status_code, 200)
response_1 = self.client.post('/author_change_email/1',
data=dict(email='kody15@hotmail.com'), follow_redirects=True)
auth = Author.query.filter_by(name='KJsa').first()
self.assertEqual(response_1.status_code, 200)
self.assertEqual(response_1.status_code, 200)
self.assertEqual(auth.email, 'kody15@hotmail.com')
self.assertEqual(auth.password, 'pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
self.assertEqual(auth.screenname, 'kod')
logout_author(self.client)
def test_author_dashboard(self):
w_cat = WorksheetCategory(name='dundk')
db.session.add(w_cat)
auth_1 = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod', about='What up?',
password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(auth_1)
db.session.commit()
worksheet = Worksheet(pdf_url='tudolsoos.pdf', name='tudoloods', author_id=1, author=auth_1, category_id=1, category=w_cat)
db.session.add(worksheet)
db.session.commit()
worksheet = Worksheet.query.filter_by(name='tudoloods').first()
w_cat = WorksheetCategory.query.filter_by(name='dundk').first()
w_cat_1 = WorksheetCategory(name='dund32k')
db.session.add(w_cat_1)
w_cat_2 = WorksheetCategory(name='dundfsdk')
db.session.add(w_cat_2)
db.session.commit()
auth_2 = Author(name='Kidkafdidf', email='kodyrogers29@gmail.com', password='pbkdf2:sha256:150000$JbvZOh4x$40097777eeefb55bc6987f4e6983d3401dca4d863a9a8971b36548d41af927dd')
db.session.add(auth_2)
auth_3 = Author(name='Kif', email='kodyrogers22@gmail.com', password='pbkdf2:sha256:150000$JbvZOh4x$40097777eeefb55bc6987f4e6983d3401dca4d863a9a8971b36548d41af927dd')
db.session.add(auth_3)
db.session.commit()
worksheet_1 = Worksheet(pdf_url='tudolsoo.pdf', name='tloods', author_id=1, author=auth_1, category_id=1, category=w_cat)
worksheet_2 = Worksheet(pdf_url='tudolsos.pdf', name='tudoldaghoods', author_id=2, author=auth_2, category_id=2, category=w_cat_1)
worksheet_3 = Worksheet(pdf_url='tudolos.pdf', name='tudol', author_id=3, author=auth_3, category_id=3, category=w_cat_2)
worksheet_4 = Worksheet(pdf_url='tudsoos.pdf', name='tudolsagdgsshjoods', author_id=2, author=auth_2, category_id=2, category=w_cat_1)
worksheet_5 = Worksheet(pdf_url='tolsoos.pdf', name='tudoldfag', author_id=1, author=auth_1, category_id=1, category=w_cat)
worksheet_6 = Worksheet(pdf_url='lsoos.pdf', name='tudosdag', author_id=2, author=auth_2, category_id=2, category=w_cat_1)
worksheet_7 = Worksheet(pdf_url='tch.pdf', name='tudosgsggs', author_id=3, author=auth_3, category_id=3, category=w_cat_2)
worksheet_8 = Worksheet(pdf_url='tudsfgos.pdf', name='montreal', author_id=2, author=auth_2, category_id=2, category=w_cat_1)
worksheet_9 = Worksheet(pdf_url='tersoos.pdf', name='toronto', author_id=3, author=auth_3, category_id=3, category=w_cat_2)
worksheet_10 = Worksheet(pdf_url='tudosgagos.pdf', name='ottowa', author_id=2, author=auth_2, category_id=2, category=w_cat_1)
worksheet_11 = Worksheet(pdf_url='tusgsgos.pdf', name='saskatoon', author_id=1, author=auth_1, category_id=1, category=w_cat)
worksheet_12 = Worksheet(pdf_url='tusgsssoos.pdf', name='winnipeg', author_id=2, author=auth_2, category_id=2, category=w_cat_1)
db.session.add(worksheet_1)
db.session.add(worksheet_2)
db.session.add(worksheet_3)
db.session.add(worksheet_4)
db.session.add(worksheet_5)
db.session.add(worksheet_6)
db.session.add(worksheet_7)
db.session.add(worksheet_8)
db.session.add(worksheet_9)
db.session.add(worksheet_10)
db.session.add(worksheet_11)
db.session.add(worksheet_12)
db.session.commit()
with self.app.test_client() as c:
with captured_templates(self.app) as templates:
c.post('/author_login', data=dict(
email='kodyrogers21@gmail.com',
password='RockOn'
), follow_redirects=True)
r = c.get(url_for('author.author_dashboard', id=1))
self.assertEqual(r.status_code, 200)
template, context = templates[0]
self.assertEqual(context['worksheets'], [worksheet_11, worksheet_5, worksheet_1, worksheet])
c.get('/author_logout', follow_redirects=True)
class UserLoginLogout(TestCase):
############################
#### setup and teardown ####
############################
def create_app(self):
app = c_app(TestConfiguration)
return app
# executed prior to each test
def setUp(self):
self.app_context = self.app.app_context()
self.app_context.push()
db.create_all()
# executed after each test
def tearDown(self):
db.session.remove()
db.drop_all()
self.app_context.pop()
def test_author_login(self):
author = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod', about='What up?',
password='pbkdf2:sha256:150000$73fMtgAp$1a1d8be4973cb2676c5f17275c43dc08583c8e450c94a282f9c443d34f72464c')
db.session.add(author)
db.session.commit()
with self.app.test_client() as c:
response = c.post('/author_login', data=dict(
email='kodyrogers21@gmail.com',
password='RockOn'
), follow_redirects=True)
self.assertEqual(response.status_code, 200)
self.assertEqual(session['author_logged_in'], True)
self.assertEqual(session['author_name'], 'KJsa')
response_1 = c.get('/author_logout', follow_redirects=True)
self.assertEqual(response_1.status_code, 200)
self.assertEqual(session['author_logged_in'], False)
class DatabaseModelsTests(TestCase):
############################
#### setup and teardown ####
############################
def create_app(self):
app = c_app(TestConfiguration)
return app
# executed prior to each test
def setUp(self):
self.app_context = self.app.app_context()
self.app_context.push()
db.create_all()
# executed after each test
def tearDown(self):
db.session.remove()
db.drop_all()
self.app_context.pop()
def test_author_model(self) :
author = Author(name='KJsa', email='kodyrogers21@gmail.com', screenname='kod',
about='What up?', password='pbkdf2:sha256:150000$CgCWVBC6$4090facdcd3e093c7b458362daddbaa7b53387c6042ad46b5970dc7b6d00183c')
db.session.add(author)
db.session.commit()
assert author in db.session
if __name__ == "__main__":
unittest.main()
| 37.706994
| 181
| 0.658244
|
4a078e0da1e1ec8cced001df1c5d6e294240e586
| 177
|
py
|
Python
|
docs/source/_filters/names.py
|
t-elisee/sepal-doc
|
6ef93090e18584037f1663bc36d9d1736aceb64b
|
[
"MIT"
] | 2
|
2021-06-15T19:48:14.000Z
|
2022-03-19T03:24:55.000Z
|
docs/source/_filters/names.py
|
apuzzi/sepal-doc
|
ed76e626a544ce62034b734873e646396ed766a2
|
[
"MIT"
] | 91
|
2021-03-11T10:41:43.000Z
|
2022-03-30T15:58:07.000Z
|
docs/source/_filters/names.py
|
apuzzi/sepal-doc
|
ed76e626a544ce62034b734873e646396ed766a2
|
[
"MIT"
] | 15
|
2021-03-12T11:58:58.000Z
|
2022-03-01T10:24:41.000Z
|
from enchant.tokenize import Filter
class Names(Filter):
"""If a word start with a Capital letter ignore it"""
def _skip(self, word):
return word[0].isupper()
| 22.125
| 57
| 0.672316
|
4a078f0d7e524b7bc696dea421f664cbfdac01bd
| 1,053
|
py
|
Python
|
configs/_base_/models/upernet_van.py
|
MenghaoGuo/VAN-Segmentation
|
e0053db0ca88a164bc868c08cb9d2e27d614ee2a
|
[
"Apache-2.0"
] | 2
|
2022-02-25T03:05:35.000Z
|
2022-02-26T08:31:59.000Z
|
configs/_base_/models/upernet_van.py
|
MenghaoGuo/VAN-Segmentation
|
e0053db0ca88a164bc868c08cb9d2e27d614ee2a
|
[
"Apache-2.0"
] | null | null | null |
configs/_base_/models/upernet_van.py
|
MenghaoGuo/VAN-Segmentation
|
e0053db0ca88a164bc868c08cb9d2e27d614ee2a
|
[
"Apache-2.0"
] | null | null | null |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='van_tiny',
style='pytorch'),
decode_head=dict(
type='UPerHead',
in_channels=[32, 64, 160, 256],
in_index=[0, 1, 2, 3],
pool_scales=(1, 2, 3, 6),
channels=512,
dropout_ratio=0.1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=dict(
type='FCNHead',
in_channels=160,
in_index=2,
channels=256,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))
| 29.25
| 74
| 0.592593
|
4a078f7fdc9a4db6e6d57059517bb05f81d6153f
| 3,846
|
py
|
Python
|
services/api-server/tests/unit/fakes/solvers_faker.py
|
colinRawlings/osparc-simcore
|
bf2f18d5bc1e574d5f4c238d08ad15156184c310
|
[
"MIT"
] | 25
|
2018-04-13T12:44:12.000Z
|
2022-03-12T15:01:17.000Z
|
services/api-server/tests/unit/fakes/solvers_faker.py
|
colinRawlings/osparc-simcore
|
bf2f18d5bc1e574d5f4c238d08ad15156184c310
|
[
"MIT"
] | 2,553
|
2018-01-18T17:11:55.000Z
|
2022-03-31T16:26:40.000Z
|
services/api-server/tests/unit/fakes/solvers_faker.py
|
mrnicegyu11/osparc-simcore
|
b6fa6c245dbfbc18cc74a387111a52de9b05d1f4
|
[
"MIT"
] | 20
|
2018-01-18T19:45:33.000Z
|
2022-03-29T07:08:47.000Z
|
from dataclasses import dataclass
from pathlib import Path
from typing import Callable, Dict, Iterator, Tuple
import packaging.version
import yaml
from fastapi import HTTPException, status
from importlib_resources import files
from models_library.services import ServiceDockerData
from simcore_service_api_server.models.schemas.solvers import (
LATEST_VERSION,
Solver,
SolverKeyId,
VersionStr,
)
SKey = Tuple[SolverKeyId, VersionStr]
@dataclass
class SolversFaker:
solvers: Dict[SKey, Solver]
def get(self, key, *, url=None) -> Solver:
return self.solvers[key].copy(update={"url": url})
def values(self, url_resolver: Callable) -> Iterator[Solver]:
for s in self.solvers.values():
yield s.copy(update={"url": url_resolver(s)})
def get_by_name_and_version(
self, name: str, version: str, url_resolver: Callable
) -> Solver:
try:
return next(
s.copy(update={"url": url_resolver(s.id)})
for s in self.solvers.values()
if s.id.endswith(name) and s.version == version
)
except StopIteration as err:
raise KeyError() from err
def get_latest(self, name: str, url_resolver: Callable) -> Solver:
_all = list(s for s in self.solvers.values() if s.id.endswith(name))
latest = sorted(_all, key=lambda s: packaging.version.parse(s.version))[-1]
return latest.copy(update={"url": url_resolver(latest.id)})
@classmethod
def load_images(cls) -> Iterator[ServiceDockerData]:
mocks_dir: Path = files("simcore_service_api_server").joinpath("mocks")
for filepath in mocks_dir.glob("*.y*ml"):
image = yaml.safe_load(filepath.read_text())
yield ServiceDockerData.parse_obj(image)
@classmethod
def solver_items(cls) -> Iterator[Tuple[SKey, Solver]]:
for image in cls.load_images():
solver = Solver.create_from_image(image)
yield (solver.id, solver.version), solver
@classmethod
def create_from_mocks(cls) -> "SolversFaker":
return cls(solvers=dict(cls.solver_items()))
the_fake_impl = SolversFaker.create_from_mocks()
# /files API fake implementations
# GET /solvers
async def list_solvers(
url_for: Callable,
):
def _url_resolver(solver: Solver):
return url_for(
"get_solver_release", solver_key=solver.id, version=solver.version
)
return list(the_fake_impl.values(_url_resolver))
async def get_solver_by_name_and_version(
solver_name: SolverKeyId,
version: VersionStr,
url_for: Callable,
):
try:
print(f"/{solver_name}/{version}", flush=True)
def _url_resolver(solver: Solver):
return url_for(
"get_solver_release", solver_key=solver.id, version=solver.version
)
if version == LATEST_VERSION:
solver = the_fake_impl.get_latest(solver_name, _url_resolver)
else:
solver = the_fake_impl.get_by_name_and_version(
solver_name, version, _url_resolver
)
return solver
except KeyError as err:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Solver {solver_name}:{version} not found",
) from err
async def get_solver(
solver_name: SolverKeyId,
version: VersionStr,
url_for: Callable,
):
try:
solver = the_fake_impl.get(
(solver_name, version),
url=url_for("get_solver_release", solver_key=solver_name, version=version),
)
return solver
except KeyError as err:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Solver {solver_name}:{version} not found",
) from err
| 29.358779
| 87
| 0.650026
|
4a078fc71d9c5eebf3dc38055b15e4bc3e5bde21
| 38,255
|
py
|
Python
|
src/sage/interfaces/phc.py
|
sheerluck/sage
|
b5e572b7d231f70c139d9978d68add80c4ef353d
|
[
"BSL-1.0"
] | 1,742
|
2015-01-04T07:06:13.000Z
|
2022-03-30T11:32:52.000Z
|
src/sage/interfaces/phc.py
|
sheerluck/sage
|
b5e572b7d231f70c139d9978d68add80c4ef353d
|
[
"BSL-1.0"
] | 66
|
2015-03-19T19:17:24.000Z
|
2022-03-16T11:59:30.000Z
|
src/sage/interfaces/phc.py
|
sheerluck/sage
|
b5e572b7d231f70c139d9978d68add80c4ef353d
|
[
"BSL-1.0"
] | 495
|
2015-01-10T10:23:18.000Z
|
2022-03-24T22:06:11.000Z
|
r"""
Interface to PHC.
PHC computes numerical information about systems of polynomials over
the complex numbers.
PHC implements polynomial homotopy methods to exploit structure in
order to better approximate all isolated solutions. The package also
includes extra tools to handle positive dimensional solution
components.
AUTHORS:
- PHC was written by J. Verschelde, R. Cools, and many others (?)
- William Stein and Kelly ?? -- first version of interface to PHC
- Marshall Hampton -- second version of interface to PHC
- Marshall Hampton and Alex Jokela -- third version, path tracking
"""
# ****************************************************************************
# Copyright (C) 2006 William Stein <wstein@gmail.com>
# Copyright (C) 2008 Marshall Hampton <hamptonio@gmail.com>
#
# Distributed under the terms of the GNU General Public License (GPL)
# as published by the Free Software Foundation; either version 2 of
# the License, or (at your option) any later version.
# https://www.gnu.org/licenses/
# ****************************************************************************
import os
import re
import pexpect
import random
from sage.misc.all import tmp_filename
from sage.rings.real_mpfr import RR
from sage.rings.cc import CC
from sage.rings.integer import Integer
from sage.plot.line import line
from sage.plot.point import point
def get_solution_dicts(output_file_contents, input_ring, get_failures = True):
"""
Return a list of dictionaries of variable:value (key:value)
pairs. Only used internally; see the solution_dict function in
the PHC_Object class definition for details.
INPUT:
- output_file_contents -- phc solution output as a string
- input_ring -- a PolynomialRing that variable names can be coerced into
OUTPUT:
a list of dictionaries of solutions
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x1,x2> = PolynomialRing(QQ,2)
sage: test_sys = [(x1-1)^5-x2, (x2-1)^5-1]
sage: sol = phc.blackbox(test_sys, R2) # optional -- phc
sage: test = get_solution_dicts(sol.output_file_contents,R2) # optional -- phc
sage: str(sum([q[x1].real() for q in test]))[0:4] # optional -- phc
'25.0'
"""
output_list = output_file_contents.splitlines()
solution_dicts = []
for solution_line in range(len(output_list)-1,-1,-1):
if output_list[solution_line].find('THE SOLUTIONS') == 0:
break
try:
var_number = int(output_list[solution_line+2].split(' ')[1])
# sol_number = int(output_list[solution_line+2].split(' ')[0])
except IndexError:
var_number = int(output_list[solution_line+1].split(' ')[1])
# sol_number = int(output_list[solution_line+1].split(' ')[0])
for i in range(solution_line + 1,len(output_list)):
if output_list[i].count('the solution for t') == 1:
if output_list[i-3].count('success') > 0 or get_failures:
temp_dict = {}
for j in range(1,var_number+1):
rawsplit = output_list[i+j].split(': ')[1].split(' ')
for extras in range(rawsplit.count('')):
rawsplit.remove('')
temp_var = output_list[i+j].split(': ')[0].replace(' ','')
temp_dict[input_ring(temp_var)] = CC(rawsplit[0],rawsplit[1])
solution_dicts.append(temp_dict)
return solution_dicts
def get_classified_solution_dicts(output_file_contents, input_ring, get_failures = True):
"""
Return a dictionary of lists of dictionaries of variable:value (key:value)
pairs. Only used internally; see the classified_solution_dict function in
the PHC_Object class definition for details.
INPUT:
- output_file_contents -- phc solution output as a string
- input_ring -- a PolynomialRing that variable names can be coerced into
OUTPUT:
- a dictionary of lists if dictionaries of solutions, classifies by type
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x1,x2> = PolynomialRing(QQ,2)
sage: test_sys = [(x1-2)^5-x2, (x2-1)^5-1]
sage: sol = phc.blackbox(test_sys, R2) # optional -- phc
sage: sol_classes = get_classified_solution_dicts(sol.output_file_contents,R2) # optional -- phc
sage: len(sol_classes['real']) # optional -- phc
1
"""
output_list = output_file_contents.splitlines()
solution_dicts = {}
solution_types = ['complex', 'real','failure']
for sol_type in solution_types:
solution_dicts[sol_type] = []
for solution_line in range(len(output_list)-1,-1,-1):
if output_list[solution_line].find('THE SOLUTIONS') == 0:
break
var_number = int(output_list[solution_line+2].split(' ')[1])
# sol_number = int(output_list[solution_line+2].split(' ')[0])
for i in range(solution_line + 1,len(output_list)):
if output_list[i].count('the solution for t') == 1:
phc_type = output_list[i+var_number+1].split(' = ')[-1]
if phc_type.find('complex') != -1:
phc_type = 'complex'
elif phc_type.find('real') != -1:
phc_type = 'real'
else:
phc_type = 'failure'
temp_dict = {}
for j in range(1,var_number+1):
rawsplit = output_list[i+j].split(': ')[1].split(' ')
for extras in range(rawsplit.count('')):
rawsplit.remove('')
temp_var = output_list[i+j].split(': ')[0].replace(' ','')
if phc_type == 'real':
temp_dict[input_ring(temp_var)] = RR(rawsplit[0])
else:
temp_dict[input_ring(temp_var)] = CC(rawsplit[0],rawsplit[1])
solution_dicts[phc_type].append(temp_dict)
return solution_dicts
def get_variable_list(output_file_contents):
"""
Return the variables, as strings, in the order in which PHCpack has processed them.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x1,x2> = PolynomialRing(QQ,2)
sage: test_sys = [(x1-2)^5-x2, (x2-1)^5-1]
sage: sol = phc.blackbox(test_sys, R2) # optional -- phc
sage: get_variable_list(sol.output_file_contents) # optional -- phc
['x1', 'x2']
"""
output_list = output_file_contents.splitlines()
for solution_line in range(len(output_list)-1,-1,-1):
if output_list[solution_line].find('THE SOLUTIONS') == 0:
break
var_number = int(output_list[solution_line+2].split(' ')[1])
varlist = []
for var_ind in range(var_number):
var = output_list[solution_line + 8 + var_ind].split(' ')[1]
varlist.append(var)
return varlist
class PHC_Object:
def __init__(self, output_file_contents, input_ring):
"""
A container for data from the PHCpack program - lists of float
solutions, etc. Currently the file contents are kept as a string;
for really large outputs this would be bad.
INPUT:
- output_file_contents: the string output of PHCpack
- input_ring: for coercion of the variables into the desired ring.
EXAMPLES::
sage: from sage.interfaces.phc import phc
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [(x-1)^2+(y-1)-1, x^2+y^2-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: str(sum([x[0] for x in sol.solutions()]).real())[0:3] # optional -- phc
'2.0'
"""
self.output_file_contents = output_file_contents
self.input_ring = input_ring
def save_as_start(self, start_filename = None, sol_filter = ''):
"""
Saves a solution as a phcpack start file. The usual output is
just as a string, but it can be saved to a file as well. Even
if saved to a file, it still returns the output string.
EXAMPLES::
sage: from sage.interfaces.phc import phc
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^3-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: start_save = sol.save_as_start() # optional -- phc
sage: end_sys = [x^7-2,y^5-x^2] # optional -- phc
sage: sol = phc.start_from(start_save, end_sys, R2) # optional -- phc
sage: len(sol.solutions()) # optional -- phc
15
"""
start_data = ''
output_list = self.output_file_contents.splitlines()
for a_line in output_list:
if a_line.find('ROOT COUNTS') != -1 or a_line.find('START SOLUTIONS') != -1:
break
else:
start_data += a_line + '\n'
for index in range(len(output_list)-1,0,-1):
a_line = output_list[index]
if a_line.find('THE SOLUTIONS') != -1:
found_solutions = index
break
start_data += output_list[found_solutions] + '\n\n'
try:
var_number = int(output_list[found_solutions+1].split(' ')[1])
except Exception:
# bad error handling
var_number = int(output_list[found_solutions+2].split(' ')[1])
sol_count = 0
sol_data = ''
for i in range(found_solutions + 2, len(output_list)):
if output_list[i].count('the solution for t') == 1 and output_list[i+1+var_number].find(sol_filter) != -1:
phc_type = output_list[i+var_number+1].split(' = ')[-1]
if phc_type.find('no solution') == -1:
sol_count += 1
for ind2 in range(i-3,i+var_number+2):
sol_data += output_list[ind2] + '\n'
jan_bar = '===========================================================================\n'
sol_data += jan_bar
start_data += str(sol_count) + ' ' + str(var_number) + '\n'
start_data += jan_bar + sol_data
if start_filename is not None:
with open(start_filename, 'w') as start_file:
start_file.write(start_data)
return start_data
def classified_solution_dicts(self):
"""
Return a dictionary of lists of dictionaries of solutions.
Its not as crazy as it sounds; the keys are the types of solutions as
classified by phcpack: regular vs. singular, complex vs. real
INPUT:
- None
OUTPUT:
- A dictionary of lists of dictionaries of solutions
EXAMPLES::
sage: from sage.interfaces.phc import phc
sage: R.<x,y> = PolynomialRing(CC,2)
sage: p_sys = [x^10-y,y^2-1]
sage: sol = phc.blackbox(p_sys,R) # optional -- phc
sage: classifieds = sol.classified_solution_dicts() # optional -- phc
sage: str(sum([q[y] for q in classifieds['real']]))[0:3] # optional -- phc
'2.0'
"""
try:
return self.__classified_sols
except AttributeError:
pass
classified_sols = get_classified_solution_dicts(self.output_file_contents, self.input_ring)
self.__classified_sols = classified_sols
return classified_sols
def solution_dicts(self, get_failures = False):
"""
Return a list of solutions in dictionary form: variable:value.
INPUT:
- self -- for access to self_out_file_contents, the string
of raw PHCpack output.
- get_failures (optional) -- a boolean. The default (False)
is to not process failed homotopies. These either lie on
positive-dimensional components or at infinity.
OUTPUT:
- solution_dicts: a list of dictionaries. Each dictionary
element is of the form variable:value, where the variable
is an element of the input_ring, and the value is in
ComplexField.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R.<x,y,z> = PolynomialRing(QQ,3)
sage: fs = [x^2-1,y^2-x,z^2-y]
sage: sol = phc.blackbox(fs,R) # optional -- phc
sage: s_list = sol.solution_dicts() # optional -- phc
sage: s_list.sort() # optional -- phc
sage: s_list[0] # optional -- phc
{y: 1.00000000000000, z: -1.00000000000000, x: 1.00000000000000}
"""
try:
return self.__solution_dicts
except AttributeError:
pass
solution_dicts = get_solution_dicts(self.output_file_contents, self.input_ring, get_failures = get_failures)
self.__solution_dicts = solution_dicts
return solution_dicts
def solutions(self, get_failures = False):
"""
Return a list of solutions in the ComplexField.
Use the variable_list function to get the order of variables used by
PHCpack, which is usually different than the term order of the
input_ring.
INPUT:
- self -- for access to self_out_file_contents, the string
of raw PHCpack output.
- get_failures (optional) -- a boolean. The default (False)
is to not process failed homotopies. These either lie on
positive-dimensional components or at infinity.
OUTPUT:
- solutions: a list of lists of ComplexField-valued solutions.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x1,x2> = PolynomialRing(QQ,2)
sage: test_sys = [x1^5-x1*x2^2-1, x2^5-x1*x2-1]
sage: sol = phc.blackbox(test_sys, R2) # optional -- phc
sage: len(sol.solutions()) # optional -- phc
25
"""
try:
return self.__solutions
except AttributeError:
pass
solution_dicts = get_solution_dicts(self.output_file_contents, self.input_ring, get_failures = get_failures)
self.__solution_dicts = solution_dicts
solutions = [sol_dict.values() for sol_dict in solution_dicts]
self.__solutions = solutions
return solutions
def variable_list(self):
"""
Return the variables, as strings, in the order in which
PHCpack has processed them.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x1,x2> = PolynomialRing(QQ,2)
sage: test_sys = [x1^5-x1*x2^2-1, x2^5-x1*x2-1]
sage: sol = phc.blackbox(test_sys, R2) # optional -- phc
sage: sol.variable_list() # optional -- phc
['x1', 'x2']
"""
try:
return self.__var_list
except AttributeError:
pass
var_list = get_variable_list(self.output_file_contents)
self.__var_list = var_list
return var_list
class PHC:
"""
A class to interface with PHCpack, for computing numerical
homotopies and root counts.
EXAMPLES::
sage: from sage.interfaces.phc import phc
sage: R.<x,y> = PolynomialRing(CDF,2)
sage: testsys = [x^2 + 1, x*y - 1]
sage: phc.mixed_volume(testsys) # optional -- phc
2
sage: v = phc.blackbox(testsys, R) # optional -- phc
sage: sols = v.solutions() # optional -- phc
sage: sols.sort() # optional -- phc
sage: sols # optional -- phc
[[-1.00000000000000*I, 1.00000000000000*I], [1.00000000000000*I, -1.00000000000000*I]]
sage: sol_dict = v.solution_dicts() # optional -- phc
sage: x_sols_from_dict = [d[x] for d in sol_dict] # optional -- phc
sage: x_sols_from_dict.sort(); x_sols_from_dict # optional -- phc
[-1.00000000000000*I, 1.00000000000000*I]
sage: residuals = [[test_equation.change_ring(CDF).subs(sol) for test_equation in testsys] for sol in v.solution_dicts()] # optional -- phc
sage: residuals # optional -- phc
[[0, 0], [0, 0]]
"""
def _output_from_command_list(self, command_list, polys, verbose = False):
"""
A pexpect interface to phcpack, given a command list for
interactive dialogs. The input file is supplied from the
polynomial list, output file is also supplied. This is
only used as a building block for the interface.
INPUT:
- command_list -- a list of commands to phc
- polys -- a polynomial system as a list of polynomials
OUTPUT:
- an output string from phc
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [(x-1)^2+(y-1)-1, x^2+y^2-1] # optional -- phc
sage: a = phc._output_from_command_list(['phc -m','4','n','n','n'], start_sys) # optional -- phc
"""
# Get temporary file names (these will be in SAGE_HOME/.sage/tmp/pid)
input_filename = tmp_filename()
output_filename = tmp_filename()
# Get the input polynomial text
input = self._input_file(polys)
if verbose:
print("Writing the input file to %s" % input_filename)
with open(input_filename, 'w') as file:
file.write(input)
if verbose:
print("The following file will be the input polynomial file to phc.")
print(input)
# Create a phc process
child_phc = pexpect.spawn(command_list[0])
# feed it the commands
child_phc.sendline('y')
child_phc.sendline(input_filename)
child_phc.sendline(output_filename)
for command_string in command_list[1:]:
if verbose:
print(command_string)
child_phc.sendline(command_string)
child_phc.expect('results')
read_stuff = child_phc.read()
if verbose:
print(read_stuff)
child_phc.close()
if not os.path.exists(output_filename):
raise RuntimeError("The output file does not exist; something went wrong running phc.")
# Delete the input file
os.unlink(input_filename)
# Return the output filename
return output_filename
def _input_file(self, polys):
"""
This is used internally to implement the PHC interface.
INPUT:
- polys -- a list of polynomials in a Sage polynomial ring
over a field that embeds into the complex
numbers.
OUTPUT:
- a PHC input file (as a text string) that describes these -
polynomials.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [(x-1)^2+(y-1)-1, x^2+y^2-1]
sage: phc._input_file(start_sys) # optional -- phc
'2\nx^2 - 2*x + y - 1;\nx^2 + y^2 - 1;\n'
"""
if not isinstance(polys, (list, tuple)):
raise TypeError('polys must be a list or tuple')
s = '%s\n'%len(polys)
for f in polys:
s += f._repr_() + ';\n' # note the semicolon *terminators*
return s
def _parse_path_file(self, input_filename, verbose = False):
"""
Takes a phpack output file containing path tracking information
and parses it into a list of lists of dictionaries - i.e. a
list of solutions paths, where each solution path is a list of
dictionaries of variable and homotopy parameter values.
INPUT:
- input_filename -- file must have path-tracking information
OUTPUT:
- a list of lists of dictionaries, described above
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^5-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: start_save = sol.save_as_start() # optional -- phc
sage: end_sys = [x^5-2,y^5-x^2] # optional -- phc
sage: path_track_filename = phc._path_track_file(start_save, end_sys, R2, c_skew = .001) # optional -- phc
sage: sol_paths = phc._parse_path_file(path_track_filename) # optional -- phc
sage: len(sol_paths) # optional -- phc
25
"""
if not os.path.exists(input_filename):
raise RuntimeError("The file containing output from phc (" + input_filename + ") cannot be found")
fh = open(input_filename)
line_idx = 0
begin = 0
count = 0
solutions_dicts = []
steps_dicts = []
# regular expressions for matching certain output types
var_cnt_regex = re.compile('^ +([0-9]+)')
output_regex = re.compile('^OUTPUT INFORMATION DURING')
t_regex = re.compile(r'(^t +: +(-{0,1}[0-9]+\.[0-9]+E[-+][0-9]+) +(-{0,1}[0-9]+\.[0-9]+E[-+][0-9]+)$)', re.IGNORECASE)
sols_regex = re.compile(r'(^ *(([a-z]|[0-9])+) +: +(-?[0-9]+\.[0-9]+E[-+][0-9]+) +(-?[0-9]+\.[0-9]+E[-+][0-9]+)$)', re.IGNORECASE)
complete_regex= re.compile('^TIMING INFORMATION')
breakfast = False
a_line = fh.readline()
end_test = ''
while a_line:
# processing....
a_line = a_line.replace("\n", '')
if line_idx == 0:
m = var_cnt_regex.match(a_line)
if m:
count = Integer(m.group(1))
if count > 0:
m = output_regex.match(a_line)
if m:
begin = 1
if begin:
m = t_regex.match(a_line)
if m:
# put the t-values into a dict
# m.group(2) contains the real val
# m.group(3) contains the imaginary val
# fh_w.write( "T=> G1(" + m.group(2) + '),G2(' + m.group(3) + ")\n")
# read off two lines - this should be 'm' and 'the solution for t :'
a_line = fh.readline()
end_test = a_line # store this to check for end of solution
a_line = fh.readline()
t_val = CC(m.group(2), m.group(3))
temp_dict = {}
temp_dict["t"] = t_val
for i in range(0, count):
a_line = fh.readline()
m = sols_regex.match(a_line)
if m:
# m.group(2) contains our var name
# m.group(4) contains our real val
# m.group(5) contains our imaginary val
temp_dict[m.group(2)] = CC(m.group(4),m.group(5))
steps_dicts.append(temp_dict)
# check if its the end of a solution
if end_test.find('Length of path') != -1:
if verbose:
print("recording sol")
if steps_dicts != []:
solutions_dicts.append(steps_dicts)
steps_dicts = []
m = complete_regex.match(a_line)
if m:
breakfast = True
if breakfast:
break
line_idx += 1
a_line = fh.readline()
fh.close()
return solutions_dicts
def _path_track_file(self, start_filename_or_string, polys, input_ring, c_skew = 0.001, verbose = False):
"""
Return the filename which contains path tracking output.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^6-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: start_save = sol.save_as_start() # optional -- phc
sage: end_sys = [x^7-2,y^5-x^2] # optional -- phc
sage: path_track_filename = phc._path_track_file(start_save, end_sys, R2, c_skew = .001) # optional -- phc
sage: sol_paths = phc._parse_path_file(path_track_filename) # optional -- phc
sage: len(sol_paths) # optional -- phc
30
"""
# Probably unnecessarily redundant from the start_from function
if start_filename_or_string.find('THE SOLUTIONS') != -1:
start_filename = tmp_filename()
with open(start_filename, 'w') as start_file:
start_file.write(start_filename_or_string)
elif os.path.exists(start_filename_or_string):
start_filename = start_filename_or_string
else:
raise RuntimeError("There is something wrong with your start string or filename")
return self._output_from_command_list(['phc','0','0','A',start_filename, 'y','1','0','n','k','2','a','1',str(c_skew),'0','0','2'], polys, verbose = verbose)
def path_track(self, start_sys, end_sys, input_ring, c_skew = .001, saved_start = None):
"""
This function computes homotopy paths between the solutions of start_sys and end_sys.
INPUT:
- start_sys -- a square polynomial system, given as a list of polynomials
- end_sys -- same type as start_sys
- input_ring -- for coercion of the variables into the desired ring.
- c_skew -- optional. the imaginary part of homotopy multiplier; nonzero values
are often necessary to avoid intermediate path collisions
- saved_start -- optional. A phc output file. If not given, start system solutions
are computed via the phc.blackbox function.
OUTPUT:
- a list of paths as dictionaries, with the keys variables and t-values on the path.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^6-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: start_save = sol.save_as_start() # optional -- phc
sage: end_sys = [x^7-2,y^5-x^2] # optional -- phc
sage: sol_paths = phc.path_track(start_sys, end_sys, R2, saved_start = start_save) # optional -- phc
sage: len(sol_paths) # optional -- phc
30
"""
if not saved_start:
sol = phc.blackbox(start_sys, input_ring)
saved_start = sol.save_as_start()
path_track_filename = phc._path_track_file(saved_start, end_sys, input_ring = input_ring, c_skew = c_skew)
sol_paths = phc._parse_path_file(path_track_filename)
os.unlink(path_track_filename)
return sol_paths
def plot_paths_2d(self, start_sys, end_sys, input_ring, c_skew = .001, endpoints = True, saved_start = None, rand_colors = False):
"""
This returns a graphics object of solution paths in the complex plane.
INPUT:
- start_sys -- a square polynomial system, given as a list of polynomials
- end_sys -- same type as start_sys
- input_ring -- for coercion of the variables into the desired ring.
- c_skew -- optional. the imaginary part of homotopy multiplier; nonzero values
are often necessary to avoid intermediate path collisions
- endpoints -- optional. Whether to draw in the ends of paths as points.
- saved_start -- optional. A phc output file. If not given, start system solutions
are computed via the phc.blackbox function.
OUTPUT:
- lines and points of solution paths
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: from sage.structure.sage_object import SageObject
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^5-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: start_save = sol.save_as_start() # optional -- phc
sage: end_sys = [x^5-25,y^5-x^2] # optional -- phc
sage: testing = phc.plot_paths_2d(start_sys, end_sys, R2) # optional -- phc
sage: type(testing) # optional -- phc (normally use plot here)
<class 'sage.plot.graphics.Graphics'>
"""
paths = phc.path_track(start_sys, end_sys, input_ring, c_skew = c_skew, saved_start = saved_start)
path_lines = []
sol_pts = []
if rand_colors:
r_color = {}
for a_var in input_ring.gens():
var_name = str(a_var)
r_color[var_name] = (random(),random(),random())
for a_sol in paths:
for a_var in input_ring.gens():
var_name = str(a_var)
temp_line = []
for data in a_sol:
temp_line.append([data[var_name].real(), data[var_name].imag()])
if rand_colors:
path_lines.append(line(temp_line, rgbcolor = r_color[var_name]))
else:
path_lines.append(line(temp_line))
if endpoints:
sol_pts = []
for a_sol in paths:
for a_var in input_ring.gens():
var_name = str(a_var)
sol_pts.append(point([a_sol[0][var_name].real(), a_sol[0][var_name].imag()]))
sol_pts.append(point([a_sol[-1][var_name].real(), a_sol[-1][var_name].imag()]))
return sum(sol_pts) + sum(path_lines)
else:
return sum(path_lines)
def mixed_volume(self, polys, verbose=False):
"""
Computes the mixed volume of the polynomial system given by the input polys.
INPUT:
- polys -- a list of multivariate polynomials (elements of a multivariate
polynomial ring).
- verbose -- print lots of verbose information about what this function does.
OUTPUT:
- The mixed volume.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y,z> = PolynomialRing(QQ,3)
sage: test_sys = [(x+y+z)^2-1,x^2-x,y^2-1]
sage: phc.mixed_volume(test_sys) # optional -- phc
4
"""
output_filename = self._output_from_command_list(['phc -m','4','n','n','n'], polys, verbose = verbose)
with open(output_filename) as out:
out.read()
# All done
out_lines = out.split('\n')
for a_line in out_lines:
# the two conditions below are necessary because of changes in output format
if a_line.find('The mixed volume equals :') == 0 or a_line.find('common mixed volume :') == 0:
if verbose:
print('found line: ' + a_line)
mixed_vol = Integer(a_line.split(':')[1])
break
try:
return mixed_vol
except NameError:
raise RuntimeError("Mixed volume not found in output; something went wrong running phc.")
def start_from(self, start_filename_or_string, polys, input_ring, path_track_file = None, verbose = False):
"""
This computes solutions starting from a phcpack solution file.
INPUT:
- start_filename_or_string -- the filename for a phcpack start system,
or the contents of such a file as a string. Variable names must match
the inputring variables. The value of the homotopy variable t should
be 1, not 0.
- polys -- a list of multivariate polynomials (elements of a multivariate
polynomial ring).
- input_ring: for coercion of the variables into the desired ring.
- path_track_file: whether to save path-tracking information
- verbose -- print lots of verbose information about what this function does.
OUTPUT:
- A solution in the form of a PHCObject.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^6-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: start_save = sol.save_as_start() # optional -- phc
sage: end_sys = [x^7-2,y^5-x^2] # optional -- phc
sage: sol = phc.start_from(start_save, end_sys, R2) # optional -- phc
sage: len(sol.solutions()) # optional -- phc
30
"""
input_filename = tmp_filename()
output_filename = tmp_filename()
if start_filename_or_string.find('THE SOLUTIONS') != -1:
start_filename = tmp_filename()
with open(start_filename, 'w') as start_file:
start_file.write(start_filename_or_string)
elif os.path.exists(start_filename_or_string):
start_filename = start_filename_or_string
else:
raise RuntimeError("There is something wrong with your start string or filename")
# Get the input polynomial text
input = self._input_file(polys)
if verbose:
print("Writing the input file to %s" % input_filename)
with open(input_filename, 'w') as f:
f.write(input)
if verbose:
print("The following file will be the input polynomial file to phc.")
print(input)
# Create a phc process
child_phc = pexpect.spawn('phc')
child_phc.sendline('y')
child_phc.sendline(input_filename)
child_phc.sendline(output_filename)
child_phc.sendline('0')
child_phc.sendline('0')
child_phc.expect('Nonlinear Reduction')
child_phc.sendline('A')
child_phc.sendline(start_filename)
child_phc.sendline('y')
child_phc.sendline('1')
child_phc.sendline('0')
if verbose:
phc_dialog = child_phc.read(size = 40)
print(phc_dialog)
child_phc.sendline('n')
child_phc.sendline('0')
if verbose:
child_phc.expect('CURRENT CONTINUATION')
phc_dialog = child_phc.read(size = 40)
print(phc_dialog)
child_phc.sendline('0')
if path_track_file is None:
child_phc.sendline('0')
else:
child_phc.sendline('2')
child_phc.expect('results')
dots = child_phc.read()
if verbose:
print("should be . : " + dots)
#close down the process:
child_phc.close()
if not os.path.exists(output_filename):
raise RuntimeError("The output file does not exist; something went wrong running phc.")
# Read the output produced by PHC
with open(output_filename) as f:
out = f.read()
# Delete the temporary files
os.unlink(output_filename)
os.unlink(input_filename)
# All done
return PHC_Object(out, input_ring)
def blackbox(self, polys, input_ring, verbose = False):
"""
Return as a string the result of running PHC with the given polynomials
under blackbox mode (the '-b' option).
INPUT:
- polys -- a list of multivariate polynomials (elements of a multivariate
polynomial ring).
- input_ring -- for coercion of the variables into the desired ring.
- verbose -- print lots of verbose information about what this function does.
OUTPUT:
- a PHC_Object object containing the phcpack output string.
EXAMPLES::
sage: from sage.interfaces.phc import *
sage: R2.<x,y> = PolynomialRing(QQ,2)
sage: start_sys = [x^6-y^2,y^5-1]
sage: sol = phc.blackbox(start_sys, R2) # optional -- phc
sage: len(sol.solutions()) # optional -- phc
30
"""
# Get three temporary file names (these will be in SAGE_HOME/.sage/tmp/pid)
input_filename = tmp_filename()
output_filename = input_filename + ".phc"
log_filename = tmp_filename()
# Get the input polynomial text
input = self._input_file(polys)
if verbose:
print("Writing the input file to %s" % input_filename)
with open(input_filename, 'w') as f:
f.write(input)
if verbose:
print("The following file will be the input polynomial file to phc.")
print(input)
# Create the phc command line>
cmd = 'phc -b %s %s'%(input_filename, output_filename)
if verbose:
print("The phc command line is:")
print(cmd)
# Do it -- make the system call.
e = os.system(cmd)
# Was there an error?
if e:
from sage.misc.sage_ostools import have_program
if not have_program('phc'):
print(str(os.system('which phc')) + ' PHC needs to be installed and in your path')
raise RuntimeError
# todo -- why? etc.
with open(log_filename) as f:
msg = f.read()
raise RuntimeError(msg + "\nError running phc.")
if not os.path.exists(output_filename):
raise RuntimeError("The output file does not exist; something went wrong running phc.")
# Read the output produced by PHC
with open(output_filename) as f:
out = f.read()
# All done
return PHC_Object(out, input_ring)
################################
# The unique phc interface instance.
phc = PHC()
| 39.848958
| 164
| 0.570174
|
4a079032a1da226c2dd508a2e2094461ee70a958
| 282
|
py
|
Python
|
backend/models/constants.py
|
DanielAguirre/metrics-mvp
|
a438fb6f1765fd40a61bf6bc2f8f147936c42d75
|
[
"MIT"
] | null | null | null |
backend/models/constants.py
|
DanielAguirre/metrics-mvp
|
a438fb6f1765fd40a61bf6bc2f8f147936c42d75
|
[
"MIT"
] | null | null | null |
backend/models/constants.py
|
DanielAguirre/metrics-mvp
|
a438fb6f1765fd40a61bf6bc2f8f147936c42d75
|
[
"MIT"
] | null | null | null |
import pytz
DEFAULT_TIME_STR_INTERVALS = [
('03:00','07:00'),
('07:00','10:00'),
('10:00','16:00'),
('16:00','19:00'),
('19:00','03:00+1'),
]
PACIFIC_TIMEZONE = pytz.timezone('US/Pacific')
AGENCY = 'sf-muni'
DEFAULT_STAT_KEYS = ['count', 'avg', 'min', 'median', 'max']
| 18.8
| 60
| 0.578014
|
4a07905668b54fa624cbc29413390b7e067ef08c
| 2,145
|
py
|
Python
|
salt/sdb/etcd_db.py
|
yuriks/salt
|
d2a5bd8adddb98ec1718d79384aa13b4f37e8028
|
[
"Apache-2.0",
"MIT"
] | 1
|
2020-03-31T22:51:16.000Z
|
2020-03-31T22:51:16.000Z
|
salt/sdb/etcd_db.py
|
yuriks/salt
|
d2a5bd8adddb98ec1718d79384aa13b4f37e8028
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
salt/sdb/etcd_db.py
|
yuriks/salt
|
d2a5bd8adddb98ec1718d79384aa13b4f37e8028
|
[
"Apache-2.0",
"MIT"
] | 1
|
2021-09-30T07:00:01.000Z
|
2021-09-30T07:00:01.000Z
|
# -*- coding: utf-8 -*-
'''
etcd Database Module
:maintainer: SaltStack
:maturity: New
:depends: python-etcd
:platform: all
.. versionadded:: 2015.5.0
This module allows access to the etcd database using an ``sdb://`` URI. This
package is located at ``https://pypi.python.org/pypi/python-etcd``.
Like all sdb modules, the etcd module requires a configuration profile to
be configured in either the minion or master configuration file. This profile
requires very little. In the example:
.. code-block:: yaml
myetcd:
driver: etcd
etcd.host: 127.0.0.1
etcd.port: 2379
The ``driver`` refers to the etcd module, ``etcd.host`` refers to the host that
is hosting the etcd database and ``etcd.port`` refers to the port on that host.
.. code-block:: yaml
password: sdb://myetcd/mypassword
'''
# import python libs
from __future__ import absolute_import, print_function, unicode_literals
import logging
try:
import salt.utils.etcd_util
HAS_LIBS = True
except ImportError:
HAS_LIBS = False
log = logging.getLogger(__name__)
__func_alias__ = {
'set_': 'set'
}
__virtualname__ = 'etcd'
def __virtual__():
'''
Only load the module if keyring is installed
'''
if HAS_LIBS:
return __virtualname__
return False
def set_(key, value, service=None, profile=None): # pylint: disable=W0613
'''
Set a key/value pair in the etcd service
'''
client = _get_conn(profile)
client.set(key, value)
return get(key, service, profile)
def get(key, service=None, profile=None): # pylint: disable=W0613
'''
Get a value from the etcd service
'''
client = _get_conn(profile)
result = client.get(key)
return result.value
def delete(key, service=None, profile=None): # pylint: disable=W0613
'''
Get a value from the etcd service
'''
client = _get_conn(profile)
try:
client.delete(key)
return True
except Exception: # pylint: disable=broad-except
return False
def _get_conn(profile):
'''
Get a connection
'''
return salt.utils.etcd_util.get_conn(profile)
| 21.887755
| 79
| 0.66993
|
4a0792bdb2be056d05608db13489b45edaf14f5a
| 8,336
|
py
|
Python
|
tests/test_dgilib_interface_communication.py
|
martinabr/pydgilib
|
9e27b11e74518375ae78959a71f896e92a51cdb1
|
[
"BSD-3-Clause"
] | 2
|
2019-04-05T13:27:54.000Z
|
2020-10-09T22:56:22.000Z
|
tests/test_dgilib_interface_communication.py
|
martinabr/pydgilib
|
9e27b11e74518375ae78959a71f896e92a51cdb1
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_dgilib_interface_communication.py
|
martinabr/pydgilib
|
9e27b11e74518375ae78959a71f896e92a51cdb1
|
[
"BSD-3-Clause"
] | 1
|
2019-09-11T07:48:45.000Z
|
2019-09-11T07:48:45.000Z
|
"""This module holds the automated tests for DGILib Interface Communication."""
from pydgilib.dgilib import DGILib
from pydgilib.dgilib_config import (
NUM_INTERFACES, INTERFACE_TIMESTAMP, INTERFACE_SPI, INTERFACE_USART,
INTERFACE_I2C, INTERFACE_GPIO, INTERFACE_POWER_DATA, INTERFACE_POWER_SYNC,
INTERFACE_RESERVED)
from time import sleep
import pytest
verbosity = (0, 99)
# Number of seconds to log data for in read and clear tests
polling_duration = 1
INTERFACES = [INTERFACE_TIMESTAMP,
INTERFACE_SPI,
INTERFACE_USART,
INTERFACE_I2C,
INTERFACE_GPIO,
INTERFACE_POWER_DATA,
INTERFACE_POWER_SYNC,
80, # Not in documentation
INTERFACE_RESERVED]
INTERFACES_ENABLE = [INTERFACE_SPI,
INTERFACE_USART,
INTERFACE_I2C,
INTERFACE_GPIO,
INTERFACE_POWER_SYNC,
80, # Not in documentation
INTERFACE_RESERVED]
INTERFACES_SET_CONFIG = [INTERFACE_TIMESTAMP,
INTERFACE_SPI,
INTERFACE_USART,
INTERFACE_I2C,
INTERFACE_GPIO,
INTERFACE_POWER_SYNC,
80, # Not in documentation
INTERFACE_RESERVED]
INTERFACES_WRITE = [INTERFACE_USART,
INTERFACE_I2C,
INTERFACE_GPIO,
INTERFACE_RESERVED]
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_list(verbose):
"""test_interface_list.
DGILibInterfaceCommunication.interface_list
"""
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
assert isinstance(interfaces, list)
assert len(interfaces) < NUM_INTERFACES
for interface in interfaces:
assert interface in INTERFACES
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_enable(verbose):
"""test_interface_enable.
DGILibInterfaceCommunication.interface_enable
"""
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_ENABLE:
if interface_id in interfaces:
assert dgilib.interface_enable(interface_id) is None
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_disable(verbose):
"""test_interface_disable.
DGILibInterfaceCommunication.interface_disable
"""
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES:
if interface_id in interfaces:
assert dgilib.interface_disable(interface_id) is None
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_get_configuration(verbose):
"""test_interface_get_configuration.
DGILibInterfaceCommunication.interface_get_configuration
"""
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES:
if interface_id in interfaces:
config = dgilib.interface_get_configuration(interface_id)
assert isinstance(config, tuple)
assert len(config) == 2
assert isinstance(config[0], list)
assert isinstance(config[1], list)
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_set_configuration(verbose):
"""test_interface_set_configuration.
DGILibInterfaceCommunication.interface_set_configuration
Gets the configuration and sets it to the same values.
"""
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_SET_CONFIG:
if interface_id in interfaces:
config = dgilib.interface_get_configuration(interface_id)
assert dgilib.interface_set_configuration(
interface_id, *config) is None
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_clear_buffer(verbose):
"""test_interface_clear_buffer.
DGILibInterfaceCommunication.interface_clear_buffer
"""
# When not enabled
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES:
if interface_id in interfaces:
assert dgilib.interface_clear_buffer(interface_id) is None
# When enabled
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_ENABLE:
if interface_id in interfaces:
dgilib.interface_enable(interface_id)
assert dgilib.interface_clear_buffer(interface_id) is None
dgilib.interface_disable(interface_id)
# When enabled and polling
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_ENABLE:
if interface_id in interfaces:
dgilib.interface_enable(interface_id)
dgilib.start_polling()
sleep(polling_duration)
assert dgilib.interface_clear_buffer(interface_id) is None
dgilib.stop_polling()
dgilib.interface_disable(interface_id)
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_read_data(verbose):
"""test_interface_read_data.
DGILibInterfaceCommunication.interface_read_data
"""
# When not enabled
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_ENABLE:
if interface_id in interfaces:
data = dgilib.interface_read_data(interface_id)
assert isinstance(data, tuple)
assert len(data) == 2
assert isinstance(data[0], list)
assert isinstance(data[1], list)
assert len(data[0]) == len(data[1])
# When enabled
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_ENABLE:
if interface_id in interfaces:
dgilib.interface_enable(interface_id)
data = dgilib.interface_read_data(interface_id)
assert isinstance(data, tuple)
assert len(data) == 2
assert isinstance(data[0], list)
assert isinstance(data[1], list)
assert len(data[0]) == len(data[1])
dgilib.interface_disable(interface_id)
# When enabled and polling
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_ENABLE:
if interface_id in interfaces:
dgilib.interface_enable(interface_id)
dgilib.start_polling()
sleep(polling_duration)
data = dgilib.interface_read_data(interface_id)
assert isinstance(data, tuple)
assert len(data) == 2
assert isinstance(data[0], list)
assert isinstance(data[1], list)
assert len(data[0]) == len(data[1])
dgilib.stop_polling()
dgilib.interface_disable(interface_id)
@pytest.mark.parametrize("verbose", verbosity)
def test_interface_write_data(verbose):
"""test_interface_write_data.
DGILibInterfaceCommunication.interface_write_data
"""
# When not enabled
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_WRITE:
if interface_id in interfaces:
assert dgilib.interface_write_data(interface_id, [0]) is None
# When enabled
with DGILib(verbose=verbose) as dgilib:
interfaces = dgilib.interface_list()
for interface_id in INTERFACES_WRITE:
if interface_id in interfaces:
dgilib.interface_enable(interface_id)
assert dgilib.interface_write_data(interface_id, [0]) is None
dgilib.interface_disable(interface_id)
| 36.884956
| 79
| 0.644794
|
4a079322b499512cd37f392ca760ca6d5a26fa79
| 10,217
|
py
|
Python
|
tezpool.py
|
vogelito/tezpool
|
db480340f2f6d7d2dbe76406a3864fd09e61784a
|
[
"MIT"
] | null | null | null |
tezpool.py
|
vogelito/tezpool
|
db480340f2f6d7d2dbe76406a3864fd09e61784a
|
[
"MIT"
] | null | null | null |
tezpool.py
|
vogelito/tezpool
|
db480340f2f6d7d2dbe76406a3864fd09e61784a
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
#http://doc.tzalpha.net/api/rpc_proposal.html?highlight=june
#http://doc.tzalpha.net/api/rpc.html#usage
import json
import requests
import time
import argparse
import math
import sys
# Constants
PRESERVED_CYCLES = 5
BLOCK_REWARD = 16 * 1000000.
ENDORSMENT_REWARD = 2 * 1000000.
BLOCKS_PER_CYCLE = 4096
# Force python3
if sys.version_info[0] < 3:
print ('python2 not supported, please use python3')
sys.exit (0)
# Parse command line args
parser = argparse.ArgumentParser(description='Tezos delegate redistribution script')
parser.add_argument('-c', '--config', metavar='config.json', dest='cfile', action='store',
default='config.json',
help='set a config file (default: config.json)')
parser.add_argument('action', metavar='action', action='store',
type=str, choices=['updatependings', 'paypendings', 'updatedocs'],
help='action to perform (updatependings, paypendings, updatedocs)')
args = parser.parse_args ()
# Load the config file
try:
conf = json.load (open (args.cfile, 'r'))
except:
print ('Unable to load config file.')
sys.exit ()
def try_get(uri, try_n=5):
try:
return requests.get (conf['host'] + uri)
except:
if try_n > 0:
print ('Get failed, retrying %d' % try_n)
return try_get(uri, try_n - 1)
else:
raise Exception('Reached max retries for get request: ' + uri)
def formatBalance (bal):
return str (int (bal) / 1000000)
def getCurrentCycle ():
return try_get ('/chains/main/blocks/head/helpers/current_level').json()['cycle']
def getBlockHashByIndex (idx):
head = try_get ('/chains/main/blocks/head/header').json()
head_level = head['level']
head_hash = head['hash']
return try_get ('/chains/main/blocks/' + head_hash + '~' + str (head_level - idx) + '/header').json()['hash']
def getFrozenBalance (cycle = None):
if cycle == None:
block = 'head'
else:
ccycle = getCurrentCycle ()
clevel = try_get ('/chains/main/blocks/head/helpers/levels_in_current_cycle?offset=-'+str(ccycle - cycle)).json()
block = getBlockHashByIndex (clevel['last'])
r = try_get ('/chains/main/blocks/' + block + '/context/delegates/' + conf['pkh'] + '/frozen_balance_by_cycle').json()
if cycle != None:
return list (filter (lambda y: y['cycle'] == cycle, r))[0]
else:
return r
def getCycleSnapshot (cycle):
#snapshot_block_offset = try_get ('/chains/main/blocks/head/context/raw/json/rolls/owner/snapshot/' + str(cycle)).json()[0]
# Then multiply the result with 256 and sum the cycle index, we get the block of the snapshot
#snapshot_block_index = ((cycle-PRESERVED_CYCLES-2)*4096)+((snapshot_block_offset+1)*256)
snapshot_block_index = ((cycle-PRESERVED_CYCLES-2)*4096)+4095
# Get the delegate information for the given snapshot
block_hash = getBlockHashByIndex (snapshot_block_index)
delegate_info = try_get ("/chains/main/blocks/" + block_hash + "/context/delegates/" + conf['pkh']).json()
delegated = []
# Get the delegated balance of each contract
for x in delegate_info['delegated_contracts']:
contract_info = try_get ("/chains/main/blocks/" + block_hash + "/context/contracts/" + x).json()
contract_info2 = {
"balance": contract_info['balance'],
"manager": contract_info['manager'],
"address": x,
"alias": conf['deleguees'][x] if (x in conf['deleguees']) else None,
"percentage": (int (10000. * 100. * float (contract_info['balance']) / float (delegate_info['staking_balance']))) / 10000.
}
delegated.append(contract_info2)
# Append the delegate as contractor
delegated.append({
"balance": delegate_info['balance'],
"manager": conf['pkh'],
"address": conf['pkh'],
"alias": conf['name'],
"percentage": (int (10000. * 100. * float (delegate_info['balance']) / float (delegate_info['staking_balance']))) / 10000.
})
return {
"cycle": cycle,
"staking_balance": delegate_info['staking_balance'],
"delegated": delegated
}
def getBakingAndEndorsmentRights (cycle, curcycle):
nhead = curcycle * 4096 - cycle * 4096
if nhead < 0:
nhead = ""
else:
nhead = "~" + str(nhead)
bak = try_get ("/chains/main/blocks/head" + nhead + "/helpers/baking_rights?delegate=" + conf['pkh'] + '&cycle=' + str(cycle)).json()
endors = try_get ("/chains/main/blocks/head" + nhead + "/helpers/endorsing_rights?delegate=" + conf['pkh'] + '&cycle=' + str(cycle)).json()
b = list(filter(lambda x: x['priority'] == 0, bak))
e = endors
return {
'blocks': b,
'endorsment': e,
'estimated_reward': len(b) * BLOCK_REWARD + len(e) * ENDORSMENT_REWARD
}
def getRewardForPastCycle (cycle):
return getFrozenBalance (cycle)
if args.action == 'updatedocs':
curcycle = getCurrentCycle()
# Load the old docs if any
try:
f = open ('docs/data.json', 'r')
data = json.loads (f.read())
f.close ()
lastcycle = max(list(map(lambda y: y['cycle'], data['cycles']))) + 1
data['cycles'] = list (filter (lambda y: y['cycle'] <= lastcycle, data['cycles']))
except:
data = {
"cycles": []
}
lastcycle = int (conf['startcycle'])
print ('Starting from cycle', lastcycle)
for cycle in range (lastcycle, getCurrentCycle() + PRESERVED_CYCLES + 1):
print ('Updating docs data for cycle', cycle)
snap = getCycleSnapshot(cycle)
brights = getBakingAndEndorsmentRights(cycle, curcycle)
data['cycles'].append ({
"cycle": cycle,
"snapshot": snap,
"rights": brights
})
data['pkh'] = conf['pkh']
data['name'] = conf['name']
data['deleguees'] = conf['deleguees']
data['percentage'] = conf['percentage']
data['currentcycle'] = curcycle
f = open ('docs/data.json', 'w')
f.write (json.dumps(data, separators=(',',':'), indent=4))
f.close ()
print ('Up to date')
elif args.action == 'updatependings':
try:
f = open ('paylog.json', 'r')
data = json.loads (f.read())
f.close ()
except:
data = { 'cycle': int (conf['startcycle']) - 1, 'frozen': 0, 'frozenminusfee': 0, 'pendingminusfee': 0, 'pending': 0, 'paid': 0, 'deleguees': {}, 'cycles': {} }
curcycle = getCurrentCycle()
data['frozen'] = 0
data['frozenminusfee'] = 0
for x in data['deleguees']:
data['deleguees'][x]['frozen'] = 0
for cycle in range (data['cycle'] + 1, curcycle):
print ('Updating for cycle', cycle)
frozen = (curcycle - cycle - 1) < PRESERVED_CYCLES
try:
rew = getRewardForPastCycle (cycle)
except:
print ('Cant get reward for cycle', cycle)
continue
rewsubfee = int (int (rew['rewards']) - int (rew['rewards']) * (100 - conf['percentage']) / 100.)
if not frozen:
data['cycle'] = cycle
data['pending'] += int (rew['rewards'])
data['pendingminusfee'] += int (rewsubfee)
else:
data['frozen'] += int (rew['rewards'])
data['frozenminusfee'] += int (rewsubfee)
data['cycles'][str(cycle)] = {
'frozenminusfee': rewsubfee if frozen else 0,
'frozen': int (rew['rewards']) if frozen else 0,
'rewardminusfee': rewsubfee if not frozen else 0,
'reward': int (rew['rewards']) if not frozen else 0,
}
snap = getCycleSnapshot (cycle)
for d in snap['delegated']:
drew = int (rewsubfee * d['percentage'] / 100.)
if not (d['address'] in data['deleguees']) and ((conf['private'] and d['alias'] != None) or (not conf['private'])):
data['deleguees'][d['address']] = {
'address': d['address'],
'frozen': drew if frozen else 0,
'pending': drew if not frozen else 0,
'paid': 0,
'alias': d['alias'],
'cycles': { }
}
data['deleguees'][d['address']]['cycles'][str(cycle)] = { 'cycle': cycle, 'percentage': d['percentage'], 'balance': d['balance'], 'frozen': drew if frozen else 0, 'reward': drew if not frozen else 0 }
elif (d['address'] in data['deleguees']) and ((conf['private'] and d['alias'] != None) or (not conf['private'])):
data['deleguees'][d['address']]['frozen'] += drew if frozen else 0
data['deleguees'][d['address']]['pending'] += drew if not frozen else 0
data['deleguees'][d['address']]['cycles'][str(cycle)] = { 'cycle': cycle, 'percentage': d['percentage'], 'balance': d['balance'], 'frozen': drew if frozen else 0, 'reward': drew if not frozen else 0 }
# Save the paylog
f = open ('paylog.json', 'w')
f.write (json.dumps (data, separators=(',',':'), indent=4))
f.close ()
f = open ('docs/paylog.json', 'w')
f.write (json.dumps (data, separators=(',',':'), indent=4))
f.close ()
elif args.action == 'paypendings':
f = open ('paylog.json', 'r')
data = json.loads (f.read())
f.close ()
if data['pendingminusfee'] == 0:
print ('No pending payments available')
sys.exit(0)
print ('There are', formatBalance(data['pendingminusfee']), 'XTZ pending in the pool')
paydata = ""
paiddeleguees = 0
for x in data['deleguees']:
v = data['deleguees'][x]
if float (formatBalance(v['pending'])) < float(conf['payout']['minpayout']):
continue
if conf['payout']['method'] == 'tezos-client':
if x != conf['pkh']:
print ('Sending', formatBalance(v['pending']), 'XTZ to', x)
paydata += 'echo Sending ' + str (formatBalance(v['pending'])) + ' XTZ to ' + x + '\n'
paydata += conf['payout']['tezos_client'] + ' transfer ' + str (formatBalance(v['pending'])) + ' from "' + conf['payout']['from_account'] + '" to "' + x + '"\n'
paydata += 'sleep 1\n\n'
else:
print ('Not sending', formatBalance(v['pending']), 'XTZ to', x, 'because it\' the pool address')
data['deleguees'][x]['paid'] += data['deleguees'][x]['pending']
data['paid'] += data['deleguees'][x]['pending']
data['pendingminusfee'] -= data['deleguees'][x]['pending']
data['pending'] -= data['deleguees'][x]['pending']
data['deleguees'][x]['pending'] = 0
paiddeleguees += 1
else:
print('Payout method', conf['payout']['method'], 'is not available')
sys.exit (0)
if paiddeleguees == 0:
print ('No payments to do, exiting')
sys.exit (0)
if conf['payout']['method'] == 'tezos-client':
f = open ('payouts.sh', 'w')
f.write (paydata)
f.close ()
print ('payouts.sh written; exec the bash command inside to send the transactions.')
f = open ('paylog.json', 'w')
f.write (json.dumps (data, separators=(',',':'), indent=4))
f.close ()
f = open ('docs/paylog.json', 'w')
f.write (json.dumps (data, separators=(',',':'), indent=4))
f.close ()
print ('paylog.json updated')
| 31.436923
| 204
| 0.645982
|
4a0793933df876a4631d39a2dd3d969543e39ab1
| 22,320
|
py
|
Python
|
nippy/nippy.py
|
UEF-BBC/nippy
|
05d0eb44e40b6c8f0c7cbabdc828410c2fad8b0c
|
[
"MIT"
] | 38
|
2018-11-13T06:46:11.000Z
|
2022-03-15T08:26:43.000Z
|
nippy/nippy.py
|
UEF-BBC/nippy
|
05d0eb44e40b6c8f0c7cbabdc828410c2fad8b0c
|
[
"MIT"
] | 1
|
2019-11-24T08:19:36.000Z
|
2019-11-24T08:19:36.000Z
|
nippy/nippy.py
|
UEF-BBC/nippy
|
05d0eb44e40b6c8f0c7cbabdc828410c2fad8b0c
|
[
"MIT"
] | 16
|
2019-11-03T22:36:28.000Z
|
2022-03-06T10:46:32.000Z
|
# Semi-automatic preprocessing script for NIR data. This script contains the preprocessing functions and some utility
# functions (like data export).
#
# jtorniainen, ioafara // Department of Applied Physics, University of Eastern Finland
# 2020, MIT License
import scipy.signal
import scipy.io as io
import scipy.ndimage as nd
import numpy as np
from sklearn.preprocessing import normalize, scale
from . import handler
import pickle
import os
from scipy import sparse
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.utils.validation import FLOAT_DTYPES
class SavitzkyGolay(TransformerMixin, BaseEstimator):
def __init__(self, *, filter_win=11, poly_order=3, deriv_order=0, delta=1.0, copy=True):
self.copy = copy
self.filter_win = filter_win
self.poly_order = poly_order
self.deriv_order = deriv_order
self.delta = delta
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
# Make sure filter window length is odd
filter_win = self.filter_win
if self.filter_win % 2 == 0:
filter_win += 1
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = savgol(X.T, filter_win=filter_win, poly_order=self.poly_order, deriv_order=self.deriv_order, delta=self.delta).T
return X
def _more_tags(self):
return {'allow_nan': True}
class LocalStandardNormalVariate(TransformerMixin, BaseEstimator):
def __init__(self, *, num_windows=3, copy=True):
self.copy = copy
self.num_windows = num_windows
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = lsnv(X.T, num_windows=self.num_windows).T
return X
def _more_tags(self):
return {'allow_nan': True}
class Normalize(TransformerMixin, BaseEstimator):
def __init__(self, *, imin=0, imax=1, copy=True):
self.copy = copy
self.imin = imin
self.imax = imax
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = norml(X.T, imin=self.imin, imax=self.imax).T
return X
def _more_tags(self):
return {'allow_nan': True}
class NoPreprocessing(TransformerMixin, BaseEstimator):
def __init__(self, *, copy=True):
self.copy = copy
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
return X
class Detrend(TransformerMixin, BaseEstimator):
def __init__(self, *, bp=0, copy=True):
self.copy = copy
self.bp = bp
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = detrend(X.T, bp=self.bp).T
return X
def _more_tags(self):
return {'allow_nan': True}
class MultipleScatterCorrection(TransformerMixin, BaseEstimator):
def __init__(self, *, copy=True):
self.copy = copy
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = msc(X.T).T
return X
def _more_tags(self):
return {'allow_nan': True}
class RobustNormalVariate(TransformerMixin, BaseEstimator):
def __init__(self, *, iqr1=75, iqr2=25, copy=True):
self.copy = copy
self.iqr1 = iqr1
self.iqr2 = iqr2
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = rnv(X.T, iqr=[self.iqr1, self.iqr2]).T
return X
def _more_tags(self):
return {'allow_nan': True}
class Baseline(TransformerMixin, BaseEstimator):
def __init__(self, *, copy=True):
self.copy = copy
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = baseline(X.T).T
return X
def _more_tags(self):
return {'allow_nan': True}
class StandardNormalVariate(TransformerMixin, BaseEstimator):
def __init__(self, *, copy=True):
self.copy = copy
def fit(self, X, y=None):
if sparse.issparse(X):
raise ValueError('Sparse matrices not supported!"')
return self
def transform(self, X, copy=None):
copy = copy if copy is not None else self.copy
X = self._validate_data(X, reset=True, accept_sparse='csr', copy=copy, estimator=self, dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
X = snv(X.T).T
return X
def _more_tags(self):
return {'allow_nan': True}
class Preprocessor(object):
""" Preprocessor object can be used to run nippy as an iterator (see documentation for examples). """
def __init__(self, wavelength, spectra, configuration_file):
"""
Args:
wavelength <numpy.ndarray>: Vector of wavelengths.
spectra <numpy.ndarray>: NIRS data matrix.
configuration_file <str>: A path to the configuration file.
"""
self.wavelength = wavelength
self.spectra = spectra
self.configuration = handler.read_configuration(configuration_file)
self.current_pipe_idx = 0
def __iter__(self):
return self
def __next__(self):
""" Returns the next preprocessed dataset and a summary of preprocessing operations. """
if self.current_pipe_idx >= len(self.configuration):
raise StopIteration
else:
this_idx = self.current_pipe_idx
wavelength_, spectra_ = run_pipeline(self.wavelength.copy(),
self.spectra.copy(),
self.configuration[this_idx])
self.current_pipe_idx += 1
return wavelength_, spectra_, self.configuration[this_idx]
# PREPROCESSING FUNCTIONS
def baseline(spectra):
""" Removes baseline (mean) from each spectrum.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
Returns:
spectra <numpy.ndarray>: Mean-centered NIRS data matrix
"""
return spectra - np.mean(spectra, axis=0)
def snv(spectra):
""" Perform scatter correction using the standard normal variate.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
Returns:
spectra <numpy.ndarray>: NIRS data with (S/R)NV applied.
"""
return (spectra - np.mean(spectra, axis=0)) / np.std(spectra, axis=0)
def rnv(spectra, iqr=[75, 25]):
""" Perform scatter correction using robust normal variate.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
iqr <list>: IQR ranges [lower, upper] for robust normal variate.
Returns:
spectra <numpy.ndarray>: NIRS data with (S/R)NV applied.
"""
return (spectra - np.median(spectra, axis=0)) / np.subtract(*np.percentile(spectra, iqr, axis=0))
def lsnv(spectra, num_windows=10):
""" Perform local scatter correction using the standard normal variate.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
num_windows <int>: number of equispaced windows to use (window size (in points) is length / num_windows)
Returns:
spectra <numpy.ndarray>: NIRS data with local SNV applied.
"""
parts = np.array_split(spectra, num_windows, axis=0)
for idx, part in enumerate(parts):
parts[idx] = snv(part)
return np.concatenate(parts, axis=0)
def savgol(spectra, filter_win=11, poly_order=3, deriv_order=0, delta=1.0):
""" Perform Savitzky–Golay filtering on the data (also calculates derivatives). This function is a wrapper for
scipy.signal.savgol_filter.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
filter_win <int>: Size of the filter window in samples (default 11).
poly_order <int>: Order of the polynomial estimation (default 3).
deriv_order <int>: Order of the derivation (default 0).
Returns:
spectra <numpy.ndarray>: NIRS data smoothed with Savitzky-Golay filtering
"""
return scipy.signal.savgol_filter(spectra, filter_win, poly_order, deriv_order, delta=delta, axis=0)
def trim(wavelength, spectra, bins):
""" Trim spectra to a specified wavelength bin (or bins).
Args:
wavelength <numpy.ndarray>: Vector of wavelengths.
spectra <numpy.ndarray>: NIRS data matrix.
bins <list>: A bin or a list of bins defining the trim operation.
Returns:
spectra <numpy.ndarray>: NIRS data smoothed with Savitzky-Golay filtering
"""
if type(bins[0]) != list:
bins = [bins]
spectra_trim = np.array([]).reshape(0, spectra.shape[1])
wavelength_trim = np.array([])
for wave_range in bins:
mask = np.bitwise_and(wavelength >= wave_range[0], wavelength <= wave_range[1])
spectra_trim = np.vstack((spectra_trim, spectra[mask, :]))
wavelength_trim = np.hstack((wavelength_trim, wavelength[mask]))
return wavelength_trim, spectra_trim
def resample(wavelength, spectra, resampling_ratio):
""" Resample spectra according to the resampling ratio.
Args:
wavelength <numpy.ndarray>: Vector of wavelengths.
spectra <numpy.ndarray>: NIRS data matrix.
resampling_ratio <float>: new length with respect to original length
Returns:
wavelength_ <numpy.ndarray>: Resampled wavelengths.
spectra_ <numpy.ndarray>: Resampled NIR spectra
"""
new_length = int(np.round(wavelength.size * resampling_ratio))
spectra_, wavelength_ = scipy.signal.resample(spectra, new_length, wavelength)
return wavelength_, spectra_
def norml(spectra, udefined=True, imin=0, imax=1):
""" Perform spectral normalisation with user-defined limits.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
udefined <bool>: use user defined limits
imin <float>: user defined minimum
imax <float>: user defined maximum
Returns:
spectra <numpy.ndarray>: Normalized NIR spectra
"""
if udefined:
f = (imax - imin)/(np.max(spectra) - np.min(spectra))
n = spectra.shape
arr = np.empty((0, n[0]), dtype=float) #create empty array for spectra
for i in range(0, n[1]):
d = spectra[:,i]
dnorm = imin + f*d
arr = np.append(arr, [dnorm], axis=0)
return np.transpose(arr)
else:
return spectra / np.linalg.norm(spectra, axis=0)
def detrend(spectra, bp=0):
""" Perform spectral detrending to remove linear trend from data.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
bp <list>: 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.
Returns:
spectra <numpy.ndarray>: Detrended NIR spectra
"""
return scipy.signal.detrend(spectra, bp=bp)
def msc(spectra):
""" Performs multiplicative scatter correction to the mean.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
Returns:
spectra <numpy.ndarray>: Scatter corrected NIR spectra.
"""
spectra = scale(spectra, with_std=False, axis=0) # Demean
reference = np.mean(spectra, axis=1)
for col in range(spectra.shape[1]):
a, b = np.polyfit(reference, spectra[:, col], deg=1)
spectra[:, col] = (spectra[:, col] - b) / a
return spectra
def emsc(wave, spectra, remove_mean=False):
""" Performs (basic) extended multiplicative scatter correction to the mean.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
Returns:
spectra <numpy.ndarray>: Scatter corrected NIR spectra.
"""
if remove_mean:
spectra = scale(spectra, with_std=False, axis=0)
p1 = .5 * (wave[0] + wave[-1])
p2 = 2 / (wave[0] - wave[-1])
# Compute model terms
model = np.ones((wave.size, 4))
model[:, 1] = p2 * (wave[0] - wave) - 1
model[:, 2] = (p2 ** 2) * ((wave - p1) ** 2)
model[:, 3] = np.mean(spectra, axis=1)
# Solve correction parameters
params = np.linalg.lstsq(model, spectra)[0].T
# Apply correction
spectra = spectra - np.dot(params[:, :-1], model[:, :-1].T).T
spectra = np.multiply(spectra, 1 / np.repeat(params[:, -1].reshape(1, -1), spectra.shape[0], axis=0))
return spectra
def clip(wavelength, spectra, threshold, substitute=None):
""" Removes or substitutes values above the given threshold.
Args:
wavelength <numpy.ndarray>: Vector of wavelengths.
spectra <numpy.ndarray>: NIRS data matrix.
threshold <float>: threshold value for rejection
substitute <float>: substitute value for rejected values (None removes values from the spectra)
Returns:
wavelength <numpy.ndarray>: Vector of wavelengths.
spectra <numpy.ndarray>: NIR spectra with threshold exceeding values removed.
"""
if substitute == None: # remove threshold violations
mask = np.any(spectra > threshold, axis=1)
spectra = spectra[~mask, :]
wavelength = wavelength[~mask]
else: # substitute threshold violations with a value
spectra[spectra > threshold] = substitute
return wavelength, spectra
return wavelength, spectra
def smooth(spectra, filter_win, window_type='flat', mode='reflect'):
""" Smooths the spectra using convolution.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
filter_win <float>: length of the filter window in samples.
window_type <str>: filtering window to use for convolution (see scipy.signal.windows)
mode <str>: convolution mode
Returns:
spectra <numpy.ndarray>: Smoothed NIR spectra.
"""
if window_type == 'flat':
window = np.ones(filter_win)
else:
window = scipy.signal.windows.get_window(window_type, filter_win)
window = window / np.sum(window)
for column in range(spectra.shape[1]):
spectra[:, column] = nd.convolve(spectra[:, column], window, mode=mode)
return spectra
def derivate(spectra, order=1, delta=1):
""" Computes Nth order derivates with the desired spacing using numpy.gradient.
Args:
spectra <numpy.ndarray>: NIRS data matrix.
order <float>: Order of the derivation.
delta <int>: Delta of the derivate (in samples).
Returns:
spectra <numpy.ndarray>: Derivated NIR spectra.
"""
for n in range(order):
spectra = np.gradient(spectra, delta, axis=0)
return spectra
# UTILITY FUNCTIONS
def export_pipelines_to_csv(output_path, datasets, pipelines, mkdir=False):
""" Exports all datasets and the related pipelines to csv files.
Args:
filename <str> output directory.
datasets <list> list of datasets processed by nippy.
pipelines <list> list of nippy pipelines.
mkdir <bool> create output directory if it does not exist.
"""
if mkdir and not os.path.isdir(output_path):
os.mkdir(output_path)
for idx, dataset in enumerate(datasets):
filename = os.path.join(output_path, '{}.csv'.format(idx + 1))
np.savetxt(filename, np.hstack((dataset[0].reshape(-1, 1), dataset[1])), delimiter=',')
with open(os.path.join(output_path, 'pipelines.log'), 'w') as f:
for idx, pipe in enumerate(pipelines):
f.write('{};{}\n'.format(idx + 1, str(pipe)))
def export_pipelines_to_mat(output_path, datasets, pipelines, mkdir=False):
""" Exports all datasets and the related pipelines to csv files.
Args:
filename <str> output directory.
datasets <list> list of datasets processed by nippy.
pipelines <list> list of nippy pipelines.
mkdir <bool> create output directory if it does not exist.
"""
if mkdir and not os.path.isdir(output_path):
os.mkdir(output_path)
new_datasets = []
for idx, data, pipe in zip(range(len(datasets)), datasets, pipelines):
dataset = {'data': data[1], 'wave': data[0], 'params': str(pipe)}
io.savemat(os.path.join(output_path, '{}.mat'.format(idx + 1)), dataset)
with open(os.path.join(output_path, 'pipelines.log'), 'w') as f:
for idx, pipe in enumerate(pipelines):
f.write('{};{}\n'.format(idx + 1, str(pipe)))
def export_pipelines_to_pickle(filename, datasets, pipelines):
""" Exports all datasets and the related pipelines to a pickle file.
Args:
filename <str> output filepath.
datasets <list> list of datasets processed by nippy.
pipelines <list> list of nippy pipelines.
"""
data = {'datasets': datasets, 'pipelines': pipelines}
pickle.dump(data, open(filename, 'wb'))
def run_pipeline(wavelength_, spectra_, pipeline):
if 'CLIP' in pipeline.keys() and pipeline['CLIP'] != None:
wavelength_, spectra_ = clip(wavelength_, spectra_, **pipeline['CLIP'])
if 'BASELINE' in pipeline.keys() and pipeline['BASELINE'] != None:
spectra_ = baseline(spectra_, **pipeline['BASELINE'])
if 'SNV' in pipeline.keys() and pipeline['SNV'] != None:
spectra_ = snv(spectra_, **pipeline['SNV'])
if 'RNV' in pipeline.keys() and pipeline['RNV'] != None:
spectra_ = rnv(spectra_, **pipeline['RNV'])
if 'LSNV' in pipeline.keys() and pipeline['LSNV'] != None:
spectra_ = lsnv(spectra_, **pipeline['LSNV'])
if 'MSC' in pipeline.keys() and pipeline['MSC'] != None:
spectra_ = msc(spectra_)
if 'EMSC' in pipeline.keys() and pipeline['EMSC'] != None:
spectra_ = emsc(wavelength_, spectra_)
if 'NORML' in pipeline.keys() and pipeline['NORML'] != None:
spectra_ = norml(spectra_, **pipeline['NORML'])
if 'SAVGOL' in pipeline.keys() and pipeline['SAVGOL'] != None:
spectra_ = savgol(spectra_, **pipeline['SAVGOL'])
if 'SMOOTH' in pipeline.keys() and pipeline['SMOOTH'] != None:
spectra_ = smooth(spectra_, **pipeline['SMOOTH'])
if 'DERIVATE' in pipeline.keys() and pipeline['DERIVATE'] != None:
spectra_ = derivate(spectra_, **pipeline['DERIVATE'])
if 'DETREND' in pipeline.keys() and pipeline['DETREND'] != None:
spectra_ = detrend(spectra_, **pipeline['DETREND'])
if 'RESAMPLE' in pipeline.keys() and pipeline['RESAMPLE'] != None:
wavelength_, spectra_ = resample(wavelength_, spectra_, **pipeline['RESAMPLE'])
if 'TRIM' in pipeline.keys() and pipeline['TRIM'] != None:
wavelength_, spectra_ = trim(wavelength_, spectra_, **pipeline['TRIM'])
return wavelength_, spectra_
def nippy(wavelength, spectra, pipelines):
""" Main processing script of nippy. Applies operations specified in the 'pipelines' parameter to the given spectra.
Args:
wavelength <numpy.ndarray>: Vector of wavelengths.
spectra <numpy.ndarray>: NIRS data matrix.
pipelines <list>: list of nippy pipelines.
Returns:
datasets <list>: a list containing different preprocessed versions of the original spectra and wavelength.
"""
datasets = []
for idx, pipeline in enumerate(pipelines):
wavelength_, spectra_ = run_pipeline(wavelength.copy(), spectra.copy(), pipeline)
print('Running pipe {}:\n{}\n'.format(idx + 1, pipeline))
datasets.append((wavelength_, spectra_))
return datasets
| 33.972603
| 144
| 0.643772
|
4a079467bead3863947a5088abba36624d3e5616
| 186
|
py
|
Python
|
examples/article/Code4.py
|
UnixJunkie/mordred
|
d65d3fa451aca3f32adf4124a83532978ae57e46
|
[
"BSD-3-Clause"
] | 199
|
2017-04-26T07:40:32.000Z
|
2022-03-29T10:52:19.000Z
|
examples/article/Code4.py
|
UnixJunkie/mordred
|
d65d3fa451aca3f32adf4124a83532978ae57e46
|
[
"BSD-3-Clause"
] | 87
|
2016-01-15T09:02:20.000Z
|
2022-03-21T23:18:08.000Z
|
examples/article/Code4.py
|
UnixJunkie/mordred
|
d65d3fa451aca3f32adf4124a83532978ae57e46
|
[
"BSD-3-Clause"
] | 64
|
2018-03-07T13:21:47.000Z
|
2022-03-16T00:56:11.000Z
|
from distutils.version import StrictVersion
from mordred.RingCount import RingCount
# Start Code 4
presets = list(RingCount.preset(version=StrictVersion("1.0.0")))
print(len(presets))
| 23.25
| 64
| 0.795699
|
4a0794a6468fc0c8a4ce2d50bc189152025a84d4
| 2,133
|
py
|
Python
|
tests/display_module/test_conductor.py
|
MetaGenScope/metagenscope-server
|
609cd57c626c857c8efde8237a1f22f4d1e6065d
|
[
"MIT"
] | null | null | null |
tests/display_module/test_conductor.py
|
MetaGenScope/metagenscope-server
|
609cd57c626c857c8efde8237a1f22f4d1e6065d
|
[
"MIT"
] | null | null | null |
tests/display_module/test_conductor.py
|
MetaGenScope/metagenscope-server
|
609cd57c626c857c8efde8237a1f22f4d1e6065d
|
[
"MIT"
] | null | null | null |
"""Test suite for DisplayModuleConductors."""
from uuid import uuid4
from app.display_modules.conductor import DisplayModuleConductor, SampleConductor
from app.display_modules.sample_similarity import SampleSimilarityDisplayModule
from app.tool_results.kraken import KrakenResultModule
from app.tool_results.krakenhll import KrakenHLLResultModule
from app.tool_results.metaphlan2 import Metaphlan2ResultModule
from tests.base import BaseTestCase
KRAKEN_NAME = KrakenResultModule.name()
KRAKENHLL_NAME = KrakenHLLResultModule.name()
METAPHLAN2_NAME = Metaphlan2ResultModule.name()
class TestDisplayModuleConductor(BaseTestCase):
"""Test suite for display module Conductor."""
def test_downstream_modules(self):
"""Ensure downstream_modules is computed correctly."""
downstream_modules = DisplayModuleConductor.downstream_modules(KrakenResultModule)
self.assertIn(SampleSimilarityDisplayModule, downstream_modules)
class TestSampleConductor(BaseTestCase):
"""Test suite for display module Conductor."""
def test_get_valid_modules(self):
"""Ensure valid_modules is computed correctly."""
tools_present = set([KRAKEN_NAME, KRAKENHLL_NAME, METAPHLAN2_NAME])
downstream_modules = SampleConductor.downstream_modules(KrakenResultModule)
sample_id = str(uuid4())
conductor = SampleConductor(sample_id, downstream_modules)
valid_modules = conductor.get_valid_modules(tools_present)
self.assertIn(SampleSimilarityDisplayModule, valid_modules)
def test_partial_valid_modules(self):
"""Ensure valid_modules is computed correctly if tools are missing."""
tools_present = set([KRAKEN_NAME])
downstream_modules = SampleConductor.downstream_modules(KrakenResultModule)
sample_id = str(uuid4())
conductor = SampleConductor(sample_id, downstream_modules)
valid_modules = conductor.get_valid_modules(tools_present)
self.assertTrue(SampleSimilarityDisplayModule not in valid_modules)
class TestGroupConductor(BaseTestCase):
"""Test suite for display module Conductor."""
pass
| 40.245283
| 90
| 0.78106
|
4a0795da12bea7fcb0a4656aaafe8d424d3d63b5
| 3,382
|
py
|
Python
|
server/djangobackend/settings.py
|
valencialejo/agfzb-CloudAppDevelopment_Capstone
|
247a29cc28de5abb6f903b36396f6315a9ae4f7e
|
[
"Apache-2.0"
] | null | null | null |
server/djangobackend/settings.py
|
valencialejo/agfzb-CloudAppDevelopment_Capstone
|
247a29cc28de5abb6f903b36396f6315a9ae4f7e
|
[
"Apache-2.0"
] | null | null | null |
server/djangobackend/settings.py
|
valencialejo/agfzb-CloudAppDevelopment_Capstone
|
247a29cc28de5abb6f903b36396f6315a9ae4f7e
|
[
"Apache-2.0"
] | null | null | null |
"""
Django settings for djangobackend project.
Generated by 'django-admin startproject' using Django 3.1.3.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
import os
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/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'ao5z(o(z@cvzodm99d32jkxa5e8a1!q_4sqss5-a%n6tg$#h$+'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
APPEND_SLASH = True
ALLOWED_HOSTS = ['localhost','valencialejo.us-south.cf.appdomain.cloud']
# Application definition
INSTALLED_APPS = [
'djangoapp.apps.DjangoappConfig',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
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 = 'djangobackend.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.template.context_processors.media',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'djangobackend.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/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/3.1/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/3.1/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
MEDIA_ROOT = os.path.join(STATIC_ROOT, 'media')
MEDIA_URL = '/media/'
| 26.421875
| 91
| 0.701656
|
4a0796563c70f9c21e5d266cbd94a3fcc30777af
| 1,630
|
py
|
Python
|
contest_manager/emails.py
|
zapme/contest-manager
|
462c1022faf869295d55c3676fdaf2c6917b0c4c
|
[
"BSD-4-Clause"
] | 1
|
2021-05-05T05:31:32.000Z
|
2021-05-05T05:31:32.000Z
|
contest_manager/emails.py
|
zapme/contest-manager
|
462c1022faf869295d55c3676fdaf2c6917b0c4c
|
[
"BSD-4-Clause"
] | 1
|
2021-03-23T16:22:50.000Z
|
2021-03-23T16:22:50.000Z
|
contest_manager/emails.py
|
zapme/contest-manager
|
462c1022faf869295d55c3676fdaf2c6917b0c4c
|
[
"BSD-4-Clause"
] | null | null | null |
"""Contains e-mail templates for log submission receipts."""
OK_TEMPLATE = """We're happy to confirm that we have received your logs for
{name}. Your receipt number for this particular submission is {receipt}.
Please save this e-mail, which contains your receipt number, at least
until the scores have been released for this contest. It contains
important confirmation that you have submitted logs through our system in
the unlikely event of a sudden loss of data.
If you have made a mistake in submitting your log, please feel free to
resubmit your logs using the same submission form. The prior entry will be
replaced and only the last submission will be candidate for scoring. As a
security consideration, we will send a notification to the email address
left in the prior submission to let them know that their submission has
been replaced.
On behalf of the contest organizers, the Contest Manager thanks you for
participating in the contest and hopes that you had great fun.
73,
The WY4RC Contest Manager
{time}
"""
DUP_TEMPLATE = """
We are letting you know that your log submission for {name} has been
replaced by a new one. If this replacement is made by you, there is no need
to take further action, and we thank you for providing corrected
information.
If you did not resubmit logs, someone else might have submitted logs on
behalf of you without authorization. We encourage you submit your log
immediately before the log due date, and provide the following receipt
number to the site administrator *immediately* in order for them to take
corrective action:
{receipt}.
73
The WY4RC Contest Manager
{time}
"""
| 36.222222
| 75
| 0.790798
|
4a079706ec90b71698a6b567c4484d02b6289f15
| 4,900
|
py
|
Python
|
kylinpy/sqla_dialect.py
|
liuyonghengheng/kylinpy
|
61b92b96619f5c6d9f0f92ec08cb4d5cfa272d10
|
[
"MIT"
] | null | null | null |
kylinpy/sqla_dialect.py
|
liuyonghengheng/kylinpy
|
61b92b96619f5c6d9f0f92ec08cb4d5cfa272d10
|
[
"MIT"
] | null | null | null |
kylinpy/sqla_dialect.py
|
liuyonghengheng/kylinpy
|
61b92b96619f5c6d9f0f92ec08cb4d5cfa272d10
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import itertools
import sqlalchemy.exc
from sqlalchemy import pool
from sqlalchemy.engine import default
from sqlalchemy.sql import compiler
from kylinpy.exceptions import NoSuchTableError
from kylinpy.kylindb import Connection
from kylinpy.utils.keywords import CALCITE_KEYWORDS
from kylinpy.utils.sqla_types import kylin_to_sqla
SUPERSET_KEYWORDS = set([
'__timestamp',
])
class KylinIdentifierPreparer(compiler.IdentifierPreparer):
compiler.IdentifierPreparer.reserved_words = \
set(itertools.chain(*[[e.lower(), e] for e in CALCITE_KEYWORDS]))
compiler.IdentifierPreparer.reserved_words.update(SUPERSET_KEYWORDS)
def __init__(self, dialect, initial_quote='"',
final_quote=None, escape_quote='"', omit_schema=True):
super(KylinIdentifierPreparer, self).__init__(
dialect, initial_quote, final_quote, escape_quote, omit_schema,
)
def format_label(self, label, name=None):
return self.quote(name or label.name)
class KylinSQLCompiler(compiler.SQLCompiler):
_cached_metadata = set()
def __init__(self, *args, **kwargs):
super(KylinSQLCompiler, self).__init__(*args, **kwargs)
def _compose_select_body(self, text, select, inner_columns, froms, byfrom, kwargs):
text = super(KylinSQLCompiler, self)._compose_select_body(
text, select, inner_columns, froms, byfrom, kwargs)
return text
def visit_column(self, *args, **kwargs):
result = super(KylinSQLCompiler, self).visit_column(*args, **kwargs)
return result
def visit_label(self, *args, **kwargs):
self.__class__._cached_metadata.add([c.name for c in args][0])
result = super(KylinSQLCompiler, self).visit_label(*args, **kwargs)
return result
class KylinDialect(default.DefaultDialect):
def get_primary_keys(self, connection, table_name, schema=None, **kw):
pass
name = 'kylin'
driver = 'kylin'
statement_compiler = KylinSQLCompiler
preparer = KylinIdentifierPreparer
preexecute_pk_sequences = True
supports_pk_autoincrement = True
supports_sequences = True
sequences_optional = True
supports_native_decimal = True
supports_default_values = True
supports_native_boolean = True
poolclass = pool.SingletonThreadPool
supports_unicode_statements = True
default_paramstyle = 'pyformat'
def __init__(self, *args, **kwargs):
super(KylinDialect, self).__init__(*args, **kwargs)
@classmethod
def dbapi(cls):
return Connection
def initialize(self, connection):
self.server_version_info = None
self.default_schema_name = None
self.default_isolation_level = None
self.returns_unicode_strings = True
def create_connect_args(self, url):
kwargs = {
'host': url.host,
'port': url.port or 7070,
'username': url.username,
'password': url.password,
'project': url.database or 'default',
}
kwargs.update(url.query)
return [[], kwargs]
def do_execute(self, cursor, statement, parameters, context=None):
super(KylinDialect, self).do_execute(cursor, statement, parameters, context)
def get_table_names(self, connection, schema=None, **kw):
conn = connection.connect()
tables = conn.connection.connection.get_all_tables(schema)
return tables
def get_schema_names(self, connection, schema=None, **kw):
conn = connection.connect()
schemas = conn.connection.connection.get_all_schemas()
return schemas
def has_table(self, connection, table_name, schema=None):
# disable check table exists
return False
def has_sequence(self, connection, sequence_name, schema=None):
return False
def get_columns(self, connection, table_name, schema=None, **kw):
conn = connection.connect()
try:
columns = conn.connection.connection.get_table_source(table_name, schema).columns
return [{
'name': col.name,
'type': kylin_to_sqla(col.datatype),
} for col in columns]
except NoSuchTableError:
raise sqlalchemy.exc.NoSuchTableError
def get_foreign_keys(self, connection, table_name, schema=None, **kw):
return []
def get_indexes(self, connection, table_name, schema=None, **kw):
return []
def get_view_names(self, connection, schema=None, **kw):
return []
def get_pk_constraint(self, conn, table_name, schema=None, **kw):
return {}
def get_unique_constraints(self, connection, table_name, schema=None, **kw):
return []
| 32.450331
| 93
| 0.682449
|
4a0797e578b5e4bbe6553cb65f685755a28a99a6
| 9,768
|
py
|
Python
|
src/tucuxi/sqs.py
|
unj-inovacao/tucuxi
|
104b1f178b38dcc625bd64643c0986a1cfee8f53
|
[
"MIT"
] | null | null | null |
src/tucuxi/sqs.py
|
unj-inovacao/tucuxi
|
104b1f178b38dcc625bd64643c0986a1cfee8f53
|
[
"MIT"
] | 7
|
2020-05-28T19:10:01.000Z
|
2020-08-14T17:34:13.000Z
|
src/tucuxi/sqs.py
|
unj-inovacao/tucuxi
|
104b1f178b38dcc625bd64643c0986a1cfee8f53
|
[
"MIT"
] | null | null | null |
"""Some useful high-level methods to interact with AWS S3."""
import json
import logging
import re
import time
from functools import reduce
from typing import Any
from typing import Callable
from typing import Dict
from typing import Generator
from typing import List
from typing import Optional
from typing import Tuple
from boltons.iterutils import chunked_iter
from .session import Session
logger = logging.getLogger(__name__)
class Sqs:
"""SQS Client."""
def __init__(
self,
queue_url: str,
region: str = "us-east-1",
session: Optional[Session] = None,
) -> None:
"""[summary]
Args:
queue_url (str): [description]
region (str): [description]. Defaults to "us-east-1".
session (Optional[Session]): [description]. Defaults to None.
"""
if not region:
region = re.search(r"https://sqs\.(.*)\.a", queue_url).group( # type: ignore
1
)
if not session:
session = Session()
sess = session.get_session()
self.client = sess.client("sqs", region_name=region)
self.queue_url = queue_url
def _batch(
self,
entries: Any,
key: str,
operation: Callable[..., Dict[str, str]],
raise_on_error: bool = False,
apply: Callable[..., Any] = lambda x: x,
) -> Dict[str, List[bool]]:
"""[summary]
Args:
entries (Any): [description]
key (str): [description]
operation (Callable[..., Dict[str, str]]): [description]
raise_on_error (bool): [description]. Defaults to False.
apply (Callable[..., Any]): [description]. Defaults to lambdax:x.
Returns:
Dict[str, List[bool]]: [description]
Raises:
Exception
"""
res_list = []
for i_chunk, chunk in enumerate(chunked_iter(entries, 10)):
payload = [
{"Id": str(i_chunk * 10 + i), key: apply(m)}
for i, m in enumerate(chunk)
]
res = operation(QueueUrl=self.queue_url, Entries=payload)
print(res)
if raise_on_error and res.get("Failed"):
raise (Exception)
res_list.append(res)
return reduce(
lambda c, r: {
key: c.get(key, []) + r.get(key, []) for key in ["Successful", "Failed"]
},
res_list, # type: ignore
)
def send_message(self, message: Any, delay: int = 10) -> Any:
"""[summary]
Args:
message (Any): [description]
delay (int): [description]. Defaults to 10.
Returns:
Any: [description]
"""
logger.debug(f"Sending message to {self.queue_url}")
return self.client.send_message(
QueueUrl=self.queue_url,
DelaySeconds=delay,
MessageBody=json.dumps(message),
)
def send_message_batch(
self, messages: List[Any], raise_on_error: bool = False
) -> Dict[str, List[bool]]:
"""[summary]
Args:
messages (List[Any]): [description]
raise_on_error (bool): [description]. Defaults to False.
Returns:
Dict[str, List[bool]]: [description]
"""
return self._batch(
messages,
"MessageBody",
self.client.send_message_batch,
raise_on_error,
json.dumps,
)
def listen(
self,
wait_time: int = 0,
max_number_of_messages: int = 1,
poll_interval: int = 30,
auto_delete: bool = True,
) -> Generator[Tuple[str, Any], None, None]:
"""[summary]
Args:
wait_time (int): [description]. Defaults to 0.
max_number_of_messages (int): [description]. Defaults to 1.
poll_interval (int): [description]. Defaults to 30.
auto_delete (bool): [description]. Defaults to True.
Yields:
Generator[tuple]: [description]
"""
# TODO Look for other packages to have ideas. Example, auto sending to error queue.
logger.info(f"Starting to listen to {self.queue_url}")
while True:
# calling with WaitTimeSecconds of zero show the same behavior as
# not specifiying a wait time, ie: short polling
messages = self.client.receive_message(
QueueUrl=self.queue_url,
WaitTimeSeconds=wait_time,
MaxNumberOfMessages=max_number_of_messages,
)
if "Messages" in messages:
logger.info("{} messages received".format(len(messages["Messages"])))
for m in messages["Messages"]:
receipt_handle = m["ReceiptHandle"]
m_body = m["Body"]
# TODO Better exception handling
try:
params_dict = json.loads(m_body)
except BaseException:
logger.warning(
"Unable to parse message - JSON is not formatted properly"
)
continue
logger.debug(f"Yielding message {receipt_handle}")
if auto_delete:
self.delete_message(receipt_handle)
yield receipt_handle, params_dict
else:
if poll_interval:
time.sleep(poll_interval)
else:
break
def delete_message(self, receipt_handle: str) -> Any:
"""[summary]
Args:
receipt_handle (str): [description]
Returns:
Any: [description]
"""
logger.debug(f"Deleting message {receipt_handle} from {self.queue_url}")
return self.client.delete_message(
QueueUrl=self.queue_url, ReceiptHandle=receipt_handle
)
def delete_message_batch(
self, receipts: List[str], raise_on_error: bool = False
) -> Dict[str, List[bool]]:
"""[summary]
Args:
receipts (List[str]): [description]
raise_on_error (bool): [description]. Defaults to False.
Returns:
Dict[str, List[bool]]: [description]
"""
return self._batch(
receipts, "ReceiptHandle", self.client.delete_message_batch, raise_on_error
)
# # TODO: Maybe remove original listen?
# def listen_queue(
# *args, sqs_session: Sqs, wait_time: int = 0, max_number_of_messages: int = 1, batch_size: int = 1, poll_interval: int = 30,
# s3_session: Optional[Any] = None, error_queue: Optional[Sqs] = None, auto_delete: bool = True, destination_bucket: Optional[S3] = None, **kwargs):
# """[summary]
# Args:
# sqs_session (Sqs): [description]
# wait_time (int): [description]. Defaults to 0.
# max_number_of_messages (int): [description]. Defaults to 1.
# batch_size (int): [description]. Defaults to 1.
# poll_interval (int): [description]. Defaults to 30.
# s3_session (Optional[Any]): [description]. Defaults to None.
# error_queue (Optional[Sqs]): [description]. Defaults to None.
# auto_delete (bool): [description]. Defaults to True.
# destination_bucket (Optional[S3]): [description]. Defaults to None.
# """
# def func(f):
# def w_f(*args, **kwargs):
# while True:
# messages = list()
# for _ in range(batch_size):
# message = sqs_session.sess.receive_message(
# QueueUrl=sqs_session.queue_url,
# WaitTimeSeconds=wait_time,
# MaxNumberOfMessages=max_number_of_messages
# )
# if "Messages" in message:
# logger.info("{} messages received".format(len(messages["Messages"])))
# for m in messages["Messages"]:
# receipt_handle = m["ReceiptHandle"]
# m_body = m["Body"]
# try:
# if s3_session is not None:
# message_content = s3_session.get_object(m_body)
# else:
# message_content = json.loads(m_body)
# except Exception:
# logger.warning(
# "Unable to handle message",
# stack_info=True
# )
# continue
# if auto_delete:
# sqs_session.delete_message(
# receipt_handle=receipt_handle
# )
# messages.append((receipt_handle, message_content))
# else:
# if poll_interval:
# time.sleep(poll_interval)
# else:
# break
# try:
# f(*args, sqs_messages=messages, **kwargs)
# except Exception as e:
# logger.error(f"Exception {e} occurred", stack_info=True)
# if error_queue is not None:
# error_queue.send_message(
# message=message,
# )
# return w_f
# return func
| 35.78022
| 156
| 0.507985
|
4a0798176672e89b1c53474567ecfd32fc237c5e
| 755
|
py
|
Python
|
data structures/linkedlist/2.6 palindrome.py
|
iFun/Algo
|
e9e2d42c72c595e0cd138dcb0150b6a1bdc7c073
|
[
"MIT"
] | null | null | null |
data structures/linkedlist/2.6 palindrome.py
|
iFun/Algo
|
e9e2d42c72c595e0cd138dcb0150b6a1bdc7c073
|
[
"MIT"
] | null | null | null |
data structures/linkedlist/2.6 palindrome.py
|
iFun/Algo
|
e9e2d42c72c595e0cd138dcb0150b6a1bdc7c073
|
[
"MIT"
] | null | null | null |
# implement a function to check if a linked list is a palindrome
from linkedlist import *
def main():
ll = linked_list()
ll.add_node(1)
ll.add_node(2)
ll.add_node(3)
ll.add_node(3)
ll.add_node(2)
ll.add_node(1)
slow_node = ll.head
fast_node = ll.head
stack = []
while fast_node is not None and fast_node.next is not None:
stack.append(slow_node.data)
slow_node = slow_node.next
fast_node = fast_node.next.next
#deal with when linkedlist is odd num
if fast_node is not None:
slow_node = slow_node.next
while slow_node is not None:
if(stack.pop() != slow_node.data):
print("not palindrome")
return False
slow_node = slow_node.next
print("palindrome")
return True
main()
| 18.875
| 65
| 0.675497
|
4a079934f0030a2dfafb41d9ea059a56e42918f2
| 246,407
|
py
|
Python
|
openbmctool.py
|
bluerise/testmaster
|
87682d7f6e3aced7c68ed73f39a19cdd325a9aa6
|
[
"Apache-2.0"
] | 3
|
2020-12-05T11:45:30.000Z
|
2021-11-28T03:15:02.000Z
|
openbmctool.py
|
bluerise/testmaster
|
87682d7f6e3aced7c68ed73f39a19cdd325a9aa6
|
[
"Apache-2.0"
] | 4
|
2019-03-13T09:23:28.000Z
|
2022-02-28T10:06:16.000Z
|
openbmctool.py
|
bluerise/testmaster
|
87682d7f6e3aced7c68ed73f39a19cdd325a9aa6
|
[
"Apache-2.0"
] | 4
|
2019-02-05T23:53:07.000Z
|
2020-12-05T11:45:33.000Z
|
#!/usr/bin/env python3
"""
Copyright 2017,2019 IBM Corporation
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 argparse
import requests
import getpass
import json
import os
import urllib3
import time, datetime
import binascii
import subprocess
import platform
import zipfile
import tarfile
import tempfile
import hashlib
import re
import uuid
import ssl
import socket
import select
import http.client
from subprocess import check_output
import traceback
MAX_NBD_PACKET_SIZE = 131088
jsonHeader = {'Content-Type' : 'application/json'}
xAuthHeader = {}
baseTimeout = 60
serverTypeMap = {
'ActiveDirectory' : 'active_directory',
'OpenLDAP' : 'openldap'
}
class NBDPipe:
def openHTTPSocket(self,args):
try:
_create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
# Legacy Python that doesn't verify HTTPS certificates by default
pass
else:
# Handle target environment that doesn't support HTTPS verification
ssl._create_default_https_context = _create_unverified_https_context
token = gettoken(args)
self.conn = http.client.HTTPSConnection(args.host,port=443)
uri = "/redfish/v1/Systems/system/LogServices/Dump/attachment/"+args.dumpNum
self.conn.request("GET",uri, headers={"X-Auth-Token":token})
def openTCPSocket(self):
# Create a TCP/IP socket
self.tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Connect the socket to the port where the server is listening
server_address = ('localhost', 1043)
self.tcp.connect(server_address)
def waitformessage(self):
inputs = [self.conn.sock,self.tcp]
outputs = []
message_queues = {}
while True:
readable, writable, exceptional = select.select(
inputs, outputs, inputs)
for s in readable:
if s is self.conn.sock:
data = self.conn.sock.recv(MAX_NBD_PACKET_SIZE)
print("<<HTTP")
if data:
self.tcp.send(data)
else:
print ("BMC Closed the connection")
self.conn.close()
self.tcp.close()
sys.exit(1)
elif s is self.tcp:
data = self.tcp.recv(MAX_NBD_PACKET_SIZE)
print(">>TCP")
if data:
self.conn.sock.send(data)
else:
print("NBD server closed the connection")
self.conn.sock.close()
self.tcp.close()
sys.exit(1)
for s in exceptional:
inputs.remove(s)
print("Exceptional closing the socket")
s.close()
def getsize(host,args,session):
url = "https://"+host+"/redfish/v1/Systems/system/LogServices/Dump/Entries/"+str(args.dumpNum)
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
if resp.status_code==200:
size = resp.json()['AdditionalDataSizeBytes']
return size
else:
return "Failed get Size"
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def gettoken(args):
mysess = requests.session()
resp = mysess.post('https://'+args.host+'/login', headers=jsonHeader,json={"data":[args.user,args.PW]},verify=False)
if resp.status_code == 200:
cookie = resp.headers['Set-Cookie']
match = re.search('SESSION=(\w+);', cookie)
return match.group(1)
def get_pid(name):
try:
pid = map(int, check_output(["pidof", "-s",name]))
except Exception:
pid = 0
return pid
def findThisProcess( process_name ):
ps = subprocess.Popen("ps -eaf | grep "+process_name, shell=True, stdout=subprocess.PIPE)
output = ps.stdout.read()
ps.stdout.close()
ps.wait()
pid = get_pid(process_name)
return output
def isThisProcessRunning( process_name ):
pid = get_pid(process_name)
if (pid == 0 ):
return False
else:
return True
def NBDSetup(host,args,session):
user=os.getenv("SUDO_USER")
if user is None:
path = os.getcwd()
nbdServerPath = path + "/nbd-server"
if not os.path.exists(nbdServerPath):
print("Error: this program did not run as sudo!\nplease copy nbd-server to current directory and run script again")
exit()
if isThisProcessRunning('nbd-server') == True:
print("nbd-server already Running! killing the nbd-server")
os.system('killall nbd-server')
if (args.dumpSaveLoc is not None):
if(os.path.exists(args.dumpSaveLoc)):
print("Error: File already exists.")
exit()
fp= open(args.dumpSaveLoc,"w")
sizeInBytes = getsize(host,args,session)
#Round off size to mutiples of 1024
size = int(sizeInBytes)
mod = size % 1024
if mod :
roundoff = 1024 - mod
size = size + roundoff
cmd = 'chmod 777 ' + args.dumpSaveLoc
os.system(cmd)
#Run truncate to create file with given size
cmd = 'truncate -s ' + str(size) + ' '+ args.dumpSaveLoc
os.system(cmd)
if user is None:
cmd = './nbd-server 1043 '+ args.dumpSaveLoc
else:
cmd = 'nbd-server 1043 '+ args.dumpSaveLoc
os.system(cmd)
def hilight(textToColor, color, bold):
"""
Used to add highlights to various text for displaying in a terminal
@param textToColor: string, the text to be colored
@param color: string, used to color the text red or green
@param bold: boolean, used to bold the textToColor
@return: Buffered reader containing the modified string.
"""
if(sys.platform.__contains__("win")):
if(color == "red"):
os.system('color 04')
elif(color == "green"):
os.system('color 02')
else:
os.system('color') #reset to default
return textToColor
else:
attr = []
if(color == "red"):
attr.append('31')
elif(color == "green"):
attr.append('32')
else:
attr.append('0')
if bold:
attr.append('1')
else:
attr.append('0')
return '\x1b[%sm%s\x1b[0m' % (';'.join(attr),textToColor)
def connectionErrHandler(jsonFormat, errorStr, err):
"""
Error handler various connection errors to bmcs
@param jsonFormat: boolean, used to output in json format with an error code.
@param errorStr: string, used to color the text red or green
@param err: string, the text from the exception
"""
if errorStr == "Timeout":
if not jsonFormat:
return("FQPSPIN0000M: Connection timed out. Ensure you have network connectivity to the bmc")
else:
conerror = {}
conerror['CommonEventID'] = 'FQPSPIN0000M'
conerror['sensor']="N/A"
conerror['state']="N/A"
conerror['additionalDetails'] = "N/A"
conerror['Message']="Connection timed out. Ensure you have network connectivity to the BMC"
conerror['LengthyDescription'] = "While trying to establish a connection with the specified BMC, the BMC failed to respond in adequate time. Verify the BMC is functioning properly, and the network connectivity to the BMC is stable."
conerror['Serviceable']="Yes"
conerror['CallHomeCandidate']= "No"
conerror['Severity'] = "Critical"
conerror['EventType'] = "Communication Failure/Timeout"
conerror['VMMigrationFlag'] = "Yes"
conerror["AffectedSubsystem"] = "Interconnect (Networking)"
conerror["timestamp"] = str(int(time.time()))
conerror["UserAction"] = "Verify network connectivity between the two systems and the bmc is functional."
eventdict = {}
eventdict['event0'] = conerror
eventdict['numAlerts'] = '1'
errorMessageStr = errorMessageStr = json.dumps(eventdict, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False)
return(errorMessageStr)
elif errorStr == "ConnectionError":
if not jsonFormat:
return("FQPSPIN0001M: " + str(err))
else:
conerror = {}
conerror['CommonEventID'] = 'FQPSPIN0001M'
conerror['sensor']="N/A"
conerror['state']="N/A"
conerror['additionalDetails'] = str(err)
conerror['Message']="Connection Error. View additional details for more information"
conerror['LengthyDescription'] = "A connection error to the specified BMC occurred and additional details are provided. Review these details to resolve the issue."
conerror['Serviceable']="Yes"
conerror['CallHomeCandidate']= "No"
conerror['Severity'] = "Critical"
conerror['EventType'] = "Communication Failure/Timeout"
conerror['VMMigrationFlag'] = "Yes"
conerror["AffectedSubsystem"] = "Interconnect (Networking)"
conerror["timestamp"] = str(int(time.time()))
conerror["UserAction"] = "Correct the issue highlighted in additional details and try again"
eventdict = {}
eventdict['event0'] = conerror
eventdict['numAlerts'] = '1'
errorMessageStr = json.dumps(eventdict, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False)
return(errorMessageStr)
else:
return("Unknown Error: "+ str(err))
def setColWidth(keylist, numCols, dictForOutput, colNames):
"""
Sets the output width of the columns to display
@param keylist: list, list of strings representing the keys for the dictForOutput
@param numcols: the total number of columns in the final output
@param dictForOutput: dictionary, contains the information to print to the screen
@param colNames: list, The strings to use for the column headings, in order of the keylist
@return: A list of the column widths for each respective column.
"""
colWidths = []
for x in range(0, numCols):
colWidths.append(0)
for key in dictForOutput:
for x in range(0, numCols):
colWidths[x] = max(colWidths[x], len(str(dictForOutput[key][keylist[x]])))
for x in range(0, numCols):
colWidths[x] = max(colWidths[x], len(colNames[x])) +2
return colWidths
def loadPolicyTable(pathToPolicyTable):
"""
loads a json based policy table into a dictionary
@param value: boolean, the value to convert
@return: A string of "Yes" or "No"
"""
policyTable = {}
if(os.path.exists(pathToPolicyTable)):
with open(pathToPolicyTable, 'r') as stream:
try:
contents =json.load(stream)
policyTable = contents['events']
except Exception as err:
print(err)
return policyTable
def boolToString(value):
"""
converts a boolean value to a human readable string value
@param value: boolean, the value to convert
@return: A string of "Yes" or "No"
"""
if(value):
return "Yes"
else:
return "No"
def stringToInt(text):
"""
returns an integer if the string can be converted, otherwise returns the string
@param text: the string to try to convert to an integer
"""
if text.isdigit():
return int(text)
else:
return text
def naturalSort(text):
"""
provides a way to naturally sort a list
@param text: the key to convert for sorting
@return list containing the broken up string parts by integers and strings
"""
stringPartList = []
for c in re.split('(\d+)', text):
stringPartList.append(stringToInt(c))
return stringPartList
def tableDisplay(keylist, colNames, output):
"""
Logs into the BMC and creates a session
@param keylist: list, keys for the output dictionary, ordered by colNames
@param colNames: Names for the Table of the columns
@param output: The dictionary of data to display
@return: Session object
"""
colWidth = setColWidth(keylist, len(colNames), output, colNames)
row = ""
outputText = ""
for i in range(len(colNames)):
if (i != 0): row = row + "| "
row = row + colNames[i].ljust(colWidth[i])
outputText += row + "\n"
output_keys = list(output.keys())
output_keys.sort(key=naturalSort)
for key in output_keys:
row = ""
for i in range(len(keylist)):
if (i != 0): row = row + "| "
row = row + output[key][keylist[i]].ljust(colWidth[i])
outputText += row + "\n"
return outputText
def checkFWactivation(host, args, session):
"""
Checks the software inventory for an image that is being activated.
@return: True if an image is being activated, false is no activations are happening
"""
url="https://"+host+"/xyz/openbmc_project/software/enumerate"
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
print(connectionErrHandler(args.json, "Timeout", None))
return(True)
except(requests.exceptions.ConnectionError) as err:
print( connectionErrHandler(args.json, "ConnectionError", err))
return True
fwInfo = resp.json()['data']
for key in fwInfo:
if 'Activation' in fwInfo[key]:
if 'Activating' in fwInfo[key]['Activation'] or 'Activating' in fwInfo[key]['RequestedActivation']:
return True
return False
def login(host, username, pw,jsonFormat, allowExpiredPassword):
"""
Logs into the BMC and creates a session
@param host: string, the hostname or IP address of the bmc to log into
@param username: The user name for the bmc to log into
@param pw: The password for the BMC to log into
@param jsonFormat: boolean, flag that will only allow relevant data from user command to be display. This function becomes silent when set to true.
@param allowExpiredPassword: true, if the requested operation should
be allowed when the password is expired
@return: Session object
"""
if(jsonFormat==False):
print("Attempting login...")
mysess = requests.session()
try:
r = mysess.post('https://'+host+'/login', headers=jsonHeader, json = {"data": [username, pw]}, verify=False, timeout=baseTimeout)
if r.status_code == 200:
cookie = r.headers['Set-Cookie']
match = re.search('SESSION=(\w+);', cookie)
if match:
xAuthHeader['X-Auth-Token'] = match.group(1)
jsonHeader.update(xAuthHeader)
loginMessage = json.loads(r.text)
if (loginMessage['status'] != "ok"):
print(loginMessage["data"]["description"].encode('utf-8'))
sys.exit(1)
if (('extendedMessage' in r.json()) and
('The password for this account must be changed' in r.json()['extendedMessage'])):
if not allowExpiredPassword:
print("The password for this system has expired and must be changed"+
"\nsee openbmctool.py set_password --help")
logout(host, username, pw, mysess, jsonFormat)
sys.exit(1)
# if(sys.version_info < (3,0)):
# urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
# if sys.version_info >= (3,0):
# requests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)
return mysess
else:
return None
except(requests.exceptions.Timeout):
return (connectionErrHandler(jsonFormat, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return (connectionErrHandler(jsonFormat, "ConnectionError", err))
def logout(host, username, pw, session, jsonFormat):
"""
Logs out of the bmc and terminates the session
@param host: string, the hostname or IP address of the bmc to log out of
@param username: The user name for the bmc to log out of
@param pw: The password for the BMC to log out of
@param session: the active session to use
@param jsonFormat: boolean, flag that will only allow relevant data from user command to be display. This function becomes silent when set to true.
"""
try:
r = session.post('https://'+host+'/logout', headers=jsonHeader,json = {"data": [username, pw]}, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
print(connectionErrHandler(jsonFormat, "Timeout", None))
if(jsonFormat==False):
if r.status_code == 200:
print('User ' +username + ' has been logged out')
def fru(host, args, session):
"""
prints out the system inventory. deprecated see fruPrint and fruList
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
#url="https://"+host+"/org/openbmc/inventory/system/chassis/enumerate"
#print(url)
#res = session.get(url, headers=httpHeader, verify=False)
#print(res.text)
#sample = res.text
#inv_list = json.loads(sample)["data"]
url="https://"+host+"/xyz/openbmc_project/inventory/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
sample = res.text
# inv_list.update(json.loads(sample)["data"])
#
# #determine column width's
# colNames = ["FRU Name", "FRU Type", "Has Fault", "Is FRU", "Present", "Version"]
# colWidths = setColWidth(["FRU Name", "fru_type", "fault", "is_fru", "present", "version"], 6, inv_list, colNames)
#
# print("FRU Name".ljust(colWidths[0])+ "FRU Type".ljust(colWidths[1]) + "Has Fault".ljust(colWidths[2]) + "Is FRU".ljust(colWidths[3])+
# "Present".ljust(colWidths[4]) + "Version".ljust(colWidths[5]))
# format the output
# for key in sorted(inv_list.keys()):
# keyParts = key.split("/")
# isFRU = "True" if (inv_list[key]["is_fru"]==1) else "False"
#
# fruEntry = (keyParts[len(keyParts) - 1].ljust(colWidths[0]) + inv_list[key]["fru_type"].ljust(colWidths[1])+
# inv_list[key]["fault"].ljust(colWidths[2])+isFRU.ljust(colWidths[3])+
# inv_list[key]["present"].ljust(colWidths[4])+ inv_list[key]["version"].ljust(colWidths[5]))
# if(isTTY):
# if(inv_list[key]["is_fru"] == 1):
# color = "green"
# bold = True
# else:
# color='black'
# bold = False
# fruEntry = hilight(fruEntry, color, bold)
# print (fruEntry)
return sample
def fruPrint(host, args, session):
"""
prints out all inventory
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@return returns the total fru list.
"""
url="https://"+host+"/xyz/openbmc_project/inventory/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
frulist={}
# print(res.text)
if res.status_code==200:
frulist['Hardware'] = res.json()['data']
else:
if not args.json:
return "Error retrieving the system inventory. BMC message: {msg}".format(msg=res.json()['message'])
else:
return res.json()
url="https://"+host+"/xyz/openbmc_project/software/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
# print(res.text)
if res.status_code==200:
frulist['Software'] = res.json()['data']
else:
if not args.json():
return "Error retrieving the system inventory. BMC message: {msg}".format(msg=res.json()['message'])
else:
return res.json()
return frulist
def fruList(host, args, session):
"""
prints out all inventory or only a specific specified item
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if(args.items==True):
return fruPrint(host, args, session)
else:
return fruPrint(host, args, session)
def fruStatus(host, args, session):
"""
prints out the status of all FRUs
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/inventory/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
# print(res.text)
frulist = res.json()['data']
frus = {}
for key in frulist:
component = frulist[key]
isFru = False
present = False
func = False
hasSels = False
keyPieces = key.split('/')
fruName = keyPieces[-1]
if 'core' in fruName: #associate cores to cpus
fruName = keyPieces[-2] + '-' + keyPieces[-1]
if 'Functional' in component:
if('Present' in component):
if 'FieldReplaceable' in component:
if component['FieldReplaceable'] == 1:
isFru = True
if "fan" in fruName:
isFru = True;
if component['Present'] == 1:
present = True
if component['Functional'] == 1:
func = True
if ((key + "/fault") in frulist):
hasSels = True;
if args.verbose:
if hasSels:
loglist = []
faults = frulist[key+"/fault"]['endpoints']
for item in faults:
loglist.append(item.split('/')[-1])
frus[fruName] = {"compName": fruName, "Functional": boolToString(func), "Present":boolToString(present), "IsFru": boolToString(isFru), "selList": ', '.join(loglist).strip() }
else:
frus[fruName] = {"compName": fruName, "Functional": boolToString(func), "Present":boolToString(present), "IsFru": boolToString(isFru), "selList": "None" }
else:
frus[fruName] = {"compName": fruName, "Functional": boolToString(func), "Present":boolToString(present), "IsFru": boolToString(isFru), "hasSEL": boolToString(hasSels) }
elif "power_supply" in fruName or "powersupply" in fruName:
if component['Present'] ==1:
present = True
isFru = True
if ((key + "/fault") in frulist):
hasSels = True;
if args.verbose:
if hasSels:
loglist = []
faults = frulist[key+"/fault"]['endpoints']
for item in faults:
loglist.append(item.split('/')[-1])
frus[fruName] = {"compName": fruName, "Functional": "No", "Present":boolToString(present), "IsFru": boolToString(isFru), "selList": ', '.join(loglist).strip() }
else:
frus[fruName] = {"compName": fruName, "Functional": "Yes", "Present":boolToString(present), "IsFru": boolToString(isFru), "selList": "None" }
else:
frus[fruName] = {"compName": fruName, "Functional": boolToString(not hasSels), "Present":boolToString(present), "IsFru": boolToString(isFru), "hasSEL": boolToString(hasSels) }
if not args.json:
if not args.verbose:
colNames = ["Component", "Is a FRU", "Present", "Functional", "Has Logs"]
keylist = ["compName", "IsFru", "Present", "Functional", "hasSEL"]
else:
colNames = ["Component", "Is a FRU", "Present", "Functional", "Assoc. Log Number(s)"]
keylist = ["compName", "IsFru", "Present", "Functional", "selList"]
return tableDisplay(keylist, colNames, frus)
else:
return str(json.dumps(frus, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False))
def sensor(host, args, session):
"""
prints out all sensors
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the sensor sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/sensors/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
#Get OCC status
url="https://"+host+"/org/open_power/control/enumerate"
try:
occres = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
if not args.json:
colNames = ['sensor', 'type', 'units', 'value', 'target']
sensors = res.json()["data"]
output = {}
for key in sensors:
senDict = {}
keyparts = key.split("/")
# Associations like the following also show up here:
# /xyz/openbmc_project/sensors/<type>/<name>/<assoc-name>
# Skip them.
# Note: keyparts[0] = '' which is why there are 7 segments.
if len(keyparts) > 6:
continue
senDict['sensorName'] = keyparts[-1]
senDict['type'] = keyparts[-2]
try:
senDict['units'] = sensors[key]['Unit'].split('.')[-1]
except KeyError:
senDict['units'] = "N/A"
if('Scale' in sensors[key]):
scale = 10 ** sensors[key]['Scale']
else:
scale = 1
try:
senDict['value'] = str(sensors[key]['Value'] * scale)
except KeyError:
if 'value' in sensors[key]:
senDict['value'] = sensors[key]['value']
else:
senDict['value'] = "N/A"
if 'Target' in sensors[key]:
senDict['target'] = str(sensors[key]['Target'])
else:
senDict['target'] = 'N/A'
output[senDict['sensorName']] = senDict
occstatus = occres.json()["data"]
if '/org/open_power/control/occ0' in occstatus:
occ0 = occstatus["/org/open_power/control/occ0"]['OccActive']
if occ0 == 1:
occ0 = 'Active'
else:
occ0 = 'Inactive'
output['OCC0'] = {'sensorName':'OCC0', 'type': 'Discrete', 'units': 'N/A', 'value': occ0, 'target': 'Active'}
occ1 = occstatus["/org/open_power/control/occ1"]['OccActive']
if occ1 == 1:
occ1 = 'Active'
else:
occ1 = 'Inactive'
output['OCC1'] = {'sensorName':'OCC1', 'type': 'Discrete', 'units': 'N/A', 'value': occ0, 'target': 'Active'}
else:
output['OCC0'] = {'sensorName':'OCC0', 'type': 'Discrete', 'units': 'N/A', 'value': 'Inactive', 'target': 'Inactive'}
output['OCC1'] = {'sensorName':'OCC1', 'type': 'Discrete', 'units': 'N/A', 'value': 'Inactive', 'target': 'Inactive'}
keylist = ['sensorName', 'type', 'units', 'value', 'target']
return tableDisplay(keylist, colNames, output)
else:
return res.text + occres.text
def sel(host, args, session):
"""
prints out the bmc alerts
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the sel sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/logging/entry/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
def parseESEL(args, eselRAW):
"""
parses the esel data and gets predetermined search terms
@param eselRAW: string, the raw esel string from the bmc
@return: A dictionary containing the quick snapshot data unless args.fullEsel is listed then a full PEL log is returned
"""
eselParts = {}
esel_bin = binascii.unhexlify(''.join(eselRAW.split()[16:]))
#search terms contains the search term as the key and the return dictionary key as it's value
searchTerms = { 'Signature Description':'signatureDescription', 'devdesc':'devdesc',
'Callout type': 'calloutType', 'Procedure':'procedure', 'Sensor Type': 'sensorType'}
uniqueID = str(uuid.uuid4())
eselBinPath = tempfile.gettempdir() + os.sep + uniqueID + 'esel.bin'
with open(eselBinPath, 'wb') as f:
f.write(esel_bin)
errlPath = ""
#use the right errl file for the machine architecture
arch = platform.machine()
if(arch =='x86_64' or arch =='AMD64'):
if os.path.exists('/opt/ibm/ras/bin/x86_64/errl'):
errlPath = '/opt/ibm/ras/bin/x86_64/errl'
elif os.path.exists('errl/x86_64/errl'):
errlPath = 'errl/x86_64/errl'
else:
errlPath = 'x86_64/errl'
elif (platform.machine()=='ppc64le'):
if os.path.exists('/opt/ibm/ras/bin/ppc64le/errl'):
errlPath = '/opt/ibm/ras/bin/ppc64le/errl'
elif os.path.exists('errl/ppc64le/errl'):
errlPath = 'errl/ppc64le/errl'
else:
errlPath = 'ppc64le/errl'
else:
print("machine architecture not supported for parsing eSELs")
return eselParts
if(os.path.exists(errlPath)):
output= subprocess.check_output([errlPath, '-d', '--file='+eselBinPath]).decode('utf-8')
# output = proc.communicate()[0]
lines = output.split('\n')
if(hasattr(args, 'fullEsel')):
return output
for i in range(0, len(lines)):
lineParts = lines[i].split(':')
if(len(lineParts)>1): #ignore multi lines, output formatting lines, and other information
for term in searchTerms:
if(term in lineParts[0]):
temp = lines[i][lines[i].find(':')+1:].strip()[:-1].strip()
if lines[i+1].find(':') != -1:
if (len(lines[i+1].split(':')[0][1:].strip())==0):
while(len(lines[i][:lines[i].find(':')].strip())>2):
#has multiple lines, process and update line counter
if((i+1) <= len(lines)):
i+=1
else:
i=i-1
break
#Append the content from the next line removing the pretty display characters
#Finds the first colon then starts 2 characters after, then removes all whitespace
temp = temp + lines[i][lines[i].find(':')+2:].strip()[:-1].strip()[:-1].strip()
if(searchTerms[term] in eselParts):
eselParts[searchTerms[term]] = eselParts[searchTerms[term]] + ", " + temp
else:
eselParts[searchTerms[term]] = temp
os.remove(eselBinPath)
else:
print("errl file cannot be found")
return eselParts
def getESELSeverity(esel):
"""
Finds the severity type in an eSEL from the User Header section.
@param esel - the eSEL data
@return severity - e.g. 'Critical'
"""
# everything but 1 and 2 are Critical
# '1': 'recovered',
# '2': 'predictive',
# '4': 'unrecoverable',
# '5': 'critical',
# '6': 'diagnostic',
# '7': 'symptom'
severities = {
'1': 'Informational',
'2': 'Warning'
}
try:
headerPosition = esel.index('55 48') # 'UH'
# The severity is the last byte in the 8 byte section (a byte is ' bb')
severity = esel[headerPosition:headerPosition+32].split(' ')[-1]
type = severity[0]
except ValueError:
print("Could not find severity value in UH section in eSEL")
type = 'x';
return severities.get(type, 'Critical')
def sortSELs(events):
"""
sorts the sels by timestamp, then log entry number
@param events: Dictionary containing events
@return: list containing a list of the ordered log entries, and dictionary of keys
"""
logNumList = []
timestampList = []
eventKeyDict = {}
eventsWithTimestamp = {}
logNum2events = {}
for key in events:
if key == 'numAlerts': continue
if 'callout' in key: continue
timestamp = (events[key]['timestamp'])
if timestamp not in timestampList:
eventsWithTimestamp[timestamp] = [events[key]['logNum']]
else:
eventsWithTimestamp[timestamp].append(events[key]['logNum'])
#map logNumbers to the event dictionary keys
eventKeyDict[str(events[key]['logNum'])] = key
timestampList = list(eventsWithTimestamp.keys())
timestampList.sort()
for ts in timestampList:
if len(eventsWithTimestamp[ts]) > 1:
tmplist = eventsWithTimestamp[ts]
tmplist.sort()
logNumList = logNumList + tmplist
else:
logNumList = logNumList + eventsWithTimestamp[ts]
return [logNumList, eventKeyDict]
def parseAlerts(policyTable, selEntries, args):
"""
parses alerts in the IBM CER format, using an IBM policy Table
@param policyTable: dictionary, the policy table entries
@param selEntries: dictionary, the alerts retrieved from the bmc
@return: A dictionary of the parsed entries, in chronological order
"""
eventDict = {}
eventNum =""
count = 0
esel = ""
eselParts = {}
i2cdevice= ""
eselSeverity = None
'prepare and sort the event entries'
sels = {}
for key in selEntries:
if '/xyz/openbmc_project/logging/entry/' not in key: continue
if 'callout' not in key:
sels[key] = selEntries[key]
sels[key]['logNum'] = key.split('/')[-1]
sels[key]['timestamp'] = selEntries[key]['Timestamp']
sortedEntries = sortSELs(sels)
logNumList = sortedEntries[0]
eventKeyDict = sortedEntries[1]
for logNum in logNumList:
key = eventKeyDict[logNum]
hasEsel=False
i2creadFail = False
if 'callout' in key:
continue
else:
messageID = str(selEntries[key]['Message'])
addDataPiece = selEntries[key]['AdditionalData']
calloutIndex = 0
calloutFound = False
for i in range(len(addDataPiece)):
if("CALLOUT_INVENTORY_PATH" in addDataPiece[i]):
calloutIndex = i
calloutFound = True
fruCallout = str(addDataPiece[calloutIndex]).split('=')[1]
if("CALLOUT_DEVICE_PATH" in addDataPiece[i]):
i2creadFail = True
fruCallout = str(addDataPiece[calloutIndex]).split('=')[1]
# Fall back to "I2C"/"FSI" if dev path isn't in policy table
if (messageID + '||' + fruCallout) not in policyTable:
i2cdevice = str(addDataPiece[i]).strip().split('=')[1]
i2cdevice = '/'.join(i2cdevice.split('/')[-4:])
if 'fsi' in str(addDataPiece[calloutIndex]).split('=')[1]:
fruCallout = 'FSI'
else:
fruCallout = 'I2C'
calloutFound = True
if("CALLOUT_GPIO_NUM" in addDataPiece[i]):
if not calloutFound:
fruCallout = 'GPIO'
calloutFound = True
if("CALLOUT_IIC_BUS" in addDataPiece[i]):
if not calloutFound:
fruCallout = "I2C"
calloutFound = True
if("CALLOUT_IPMI_SENSOR_NUM" in addDataPiece[i]):
if not calloutFound:
fruCallout = "IPMI"
calloutFound = True
if("ESEL" in addDataPiece[i]):
esel = str(addDataPiece[i]).strip().split('=')[1]
eselSeverity = getESELSeverity(esel)
if args.devdebug:
eselParts = parseESEL(args, esel)
hasEsel=True
if("GPU" in addDataPiece[i]):
fruCallout = '/xyz/openbmc_project/inventory/system/chassis/motherboard/gpu' + str(addDataPiece[i]).strip()[-1]
calloutFound = True
if("PROCEDURE" in addDataPiece[i]):
fruCallout = str(hex(int(str(addDataPiece[i]).split('=')[1])))[2:]
calloutFound = True
if("RAIL_NAME" in addDataPiece[i]):
calloutFound=True
fruCallout = str(addDataPiece[i]).split('=')[1].strip()
if("INPUT_NAME" in addDataPiece[i]):
calloutFound=True
fruCallout = str(addDataPiece[i]).split('=')[1].strip()
if("SENSOR_TYPE" in addDataPiece[i]):
calloutFound=True
fruCallout = str(addDataPiece[i]).split('=')[1].strip()
if(calloutFound):
if fruCallout.strip() != "":
policyKey = messageID +"||" + fruCallout
# Also use the severity for hostboot errors
if eselSeverity and messageID == 'org.open_power.Host.Error.Event':
policyKey += '||' + eselSeverity
# if not in the table, fall back to the original key
if policyKey not in policyTable:
policyKey = policyKey.replace('||'+eselSeverity, '')
if policyKey not in policyTable:
policyKey = messageID
else:
policyKey = messageID
else:
policyKey = messageID
event = {}
eventNum = str(count)
if policyKey in policyTable:
for pkey in policyTable[policyKey]:
if(type(policyTable[policyKey][pkey])== bool):
event[pkey] = boolToString(policyTable[policyKey][pkey])
else:
if (i2creadFail and pkey == 'Message'):
event[pkey] = policyTable[policyKey][pkey] + ' ' +i2cdevice
else:
event[pkey] = policyTable[policyKey][pkey]
event['timestamp'] = selEntries[key]['Timestamp']
event['resolved'] = bool(selEntries[key]['Resolved'])
if(hasEsel):
if args.devdebug:
event['eselParts'] = eselParts
event['raweSEL'] = esel
event['logNum'] = key.split('/')[-1]
eventDict['event' + eventNum] = event
else:
severity = str(selEntries[key]['Severity']).split('.')[-1]
if severity == 'Error':
severity = 'Critical'
eventDict['event'+eventNum] = {}
eventDict['event' + eventNum]['error'] = "error: Not found in policy table: " + policyKey
eventDict['event' + eventNum]['timestamp'] = selEntries[key]['Timestamp']
eventDict['event' + eventNum]['Severity'] = severity
if(hasEsel):
if args.devdebug:
eventDict['event' +eventNum]['eselParts'] = eselParts
eventDict['event' +eventNum]['raweSEL'] = esel
eventDict['event' +eventNum]['logNum'] = key.split('/')[-1]
eventDict['event' +eventNum]['resolved'] = bool(selEntries[key]['Resolved'])
count += 1
return eventDict
def selDisplay(events, args):
"""
displays alerts in human readable format
@param events: Dictionary containing events
@return:
"""
activeAlerts = []
historyAlerts = []
sortedEntries = sortSELs(events)
logNumList = sortedEntries[0]
eventKeyDict = sortedEntries[1]
keylist = ['Entry', 'ID', 'Timestamp', 'Serviceable', 'Severity','Message']
if(args.devdebug):
colNames = ['Entry', 'ID', 'Timestamp', 'Serviceable', 'Severity','Message', 'eSEL contents']
keylist.append('eSEL')
else:
colNames = ['Entry', 'ID', 'Timestamp', 'Serviceable', 'Severity', 'Message']
for log in logNumList:
selDict = {}
alert = events[eventKeyDict[str(log)]]
if('error' in alert):
selDict['Entry'] = alert['logNum']
selDict['ID'] = 'Unknown'
selDict['Timestamp'] = datetime.datetime.fromtimestamp(int(alert['timestamp']/1000)).strftime("%Y-%m-%d %H:%M:%S")
msg = alert['error']
polMsg = msg.split("policy table:")[0]
msg = msg.split("policy table:")[1]
msgPieces = msg.split("||")
err = msgPieces[0]
if(err.find("org.open_power.")!=-1):
err = err.split("org.open_power.")[1]
elif(err.find("xyz.openbmc_project.")!=-1):
err = err.split("xyz.openbmc_project.")[1]
else:
err = msgPieces[0]
callout = ""
if len(msgPieces) >1:
callout = msgPieces[1]
if(callout.find("/org/open_power/")!=-1):
callout = callout.split("/org/open_power/")[1]
elif(callout.find("/xyz/openbmc_project/")!=-1):
callout = callout.split("/xyz/openbmc_project/")[1]
else:
callout = msgPieces[1]
selDict['Message'] = polMsg +"policy table: "+ err + "||" + callout
selDict['Serviceable'] = 'Unknown'
selDict['Severity'] = alert['Severity']
else:
selDict['Entry'] = alert['logNum']
selDict['ID'] = alert['CommonEventID']
selDict['Timestamp'] = datetime.datetime.fromtimestamp(int(alert['timestamp']/1000)).strftime("%Y-%m-%d %H:%M:%S")
selDict['Message'] = alert['Message']
selDict['Serviceable'] = alert['Serviceable']
selDict['Severity'] = alert['Severity']
eselOrder = ['refCode','signatureDescription', 'eselType', 'devdesc', 'calloutType', 'procedure']
if ('eselParts' in alert and args.devdebug):
eselOutput = ""
for item in eselOrder:
if item in alert['eselParts']:
eselOutput = eselOutput + item + ": " + alert['eselParts'][item] + " | "
selDict['eSEL'] = eselOutput
else:
if args.devdebug:
selDict['eSEL'] = "None"
if not alert['resolved']:
activeAlerts.append(selDict)
else:
historyAlerts.append(selDict)
mergedOutput = activeAlerts + historyAlerts
colWidth = setColWidth(keylist, len(colNames), dict(enumerate(mergedOutput)), colNames)
output = ""
if(len(activeAlerts)>0):
row = ""
output +="----Active Alerts----\n"
for i in range(0, len(colNames)):
if i!=0: row =row + "| "
row = row + colNames[i].ljust(colWidth[i])
output += row + "\n"
for i in range(0,len(activeAlerts)):
row = ""
for j in range(len(activeAlerts[i])):
if (j != 0): row = row + "| "
row = row + activeAlerts[i][keylist[j]].ljust(colWidth[j])
output += row + "\n"
if(len(historyAlerts)>0):
row = ""
output+= "----Historical Alerts----\n"
for i in range(len(colNames)):
if i!=0: row =row + "| "
row = row + colNames[i].ljust(colWidth[i])
output += row + "\n"
for i in range(0, len(historyAlerts)):
row = ""
for j in range(len(historyAlerts[i])):
if (j != 0): row = row + "| "
row = row + historyAlerts[i][keylist[j]].ljust(colWidth[j])
output += row + "\n"
# print(events[eventKeyDict[str(log)]])
return output
def selPrint(host, args, session):
"""
prints out all bmc alerts
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if(args.policyTableLoc is None):
if os.path.exists('policyTable.json'):
ptableLoc = "policyTable.json"
elif os.path.exists('/opt/ibm/ras/lib/policyTable.json'):
ptableLoc = '/opt/ibm/ras/lib/policyTable.json'
else:
ptableLoc = 'lib/policyTable.json'
else:
ptableLoc = args.policyTableLoc
policyTable = loadPolicyTable(ptableLoc)
rawselEntries = ""
if(hasattr(args, 'fileloc') and args.fileloc is not None):
if os.path.exists(args.fileloc):
with open(args.fileloc, 'r') as selFile:
selLines = selFile.readlines()
rawselEntries = ''.join(selLines)
else:
print("Error: File not found")
sys.exit(1)
else:
rawselEntries = sel(host, args, session)
loadFailed = False
try:
selEntries = json.loads(rawselEntries)
except ValueError:
loadFailed = True
if loadFailed:
cleanSels = json.dumps(rawselEntries).replace('\\n', '')
#need to load json twice as original content was string escaped a second time
selEntries = json.loads(json.loads(cleanSels))
selEntries = selEntries['data']
if 'description' in selEntries:
if(args.json):
return("{\n\t\"numAlerts\": 0\n}")
else:
return("No log entries found")
else:
if(len(policyTable)>0):
events = parseAlerts(policyTable, selEntries, args)
if(args.json):
events["numAlerts"] = len(events)
retValue = str(json.dumps(events, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False))
return retValue
elif(hasattr(args, 'fullSel')):
return events
else:
#get log numbers to order event entries sequentially
return selDisplay(events, args)
else:
if(args.json):
return selEntries
else:
print("error: Policy Table not found.")
return selEntries
def selList(host, args, session):
"""
prints out all all bmc alerts, or only prints out the specified alerts
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
return(sel(host, args, session))
def selClear(host, args, session):
"""
clears all alerts
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/logging/action/DeleteAll"
data = "{\"data\": [] }"
try:
res = session.post(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
if res.status_code == 200:
return "The Alert Log has been cleared. Please allow a few minutes for the action to complete."
else:
print("Unable to clear the logs, trying to clear 1 at a time")
sels = json.loads(sel(host, args, session))['data']
for key in sels:
if 'callout' not in key:
logNum = key.split('/')[-1]
url = "https://"+ host+ "/xyz/openbmc_project/logging/entry/"+logNum+"/action/Delete"
try:
session.post(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
sys.exit(1)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
sys.exit(1)
return ('Sel clearing complete')
def selSetResolved(host, args, session):
"""
sets a sel entry to resolved
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/logging/entry/" + str(args.selNum) + "/attr/Resolved"
data = "{\"data\": 1 }"
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
if res.status_code == 200:
return "Sel entry "+ str(args.selNum) +" is now set to resolved"
else:
return "Unable to set the alert to resolved"
def selResolveAll(host, args, session):
"""
sets a sel entry to resolved
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
rawselEntries = sel(host, args, session)
loadFailed = False
try:
selEntries = json.loads(rawselEntries)
except ValueError:
loadFailed = True
if loadFailed:
cleanSels = json.dumps(rawselEntries).replace('\\n', '')
#need to load json twice as original content was string escaped a second time
selEntries = json.loads(json.loads(cleanSels))
selEntries = selEntries['data']
if 'description' in selEntries:
if(args.json):
return("{\n\t\"selsResolved\": 0\n}")
else:
return("No log entries found")
else:
d = vars(args)
successlist = []
failedlist = []
for key in selEntries:
if 'callout' not in key:
d['selNum'] = key.split('/')[-1]
resolved = selSetResolved(host,args,session)
if 'Sel entry' in resolved:
successlist.append(d['selNum'])
else:
failedlist.append(d['selNum'])
output = ""
successlist.sort()
failedlist.sort()
if len(successlist)>0:
output = "Successfully resolved: " +', '.join(successlist) +"\n"
if len(failedlist)>0:
output += "Failed to resolve: " + ', '.join(failedlist) + "\n"
return output
def chassisPower(host, args, session):
"""
called by the chassis function. Controls the power state of the chassis, or gets the status
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if(args.powcmd == 'on'):
if checkFWactivation(host, args, session):
return ("Chassis Power control disabled during firmware activation")
print("Attempting to Power on...:")
url="https://"+host+"/xyz/openbmc_project/state/host0/attr/RequestedHostTransition"
data = '{"data":"xyz.openbmc_project.State.Host.Transition.On"}'
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
elif(args.powcmd == 'softoff'):
if checkFWactivation(host, args, session):
return ("Chassis Power control disabled during firmware activation")
print("Attempting to Power off gracefully...:")
url="https://"+host+"/xyz/openbmc_project/state/host0/attr/RequestedHostTransition"
data = '{"data":"xyz.openbmc_project.State.Host.Transition.Off"}'
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
elif(args.powcmd == 'hardoff'):
if checkFWactivation(host, args, session):
return ("Chassis Power control disabled during firmware activation")
print("Attempting to Power off immediately...:")
url="https://"+host+"/xyz/openbmc_project/state/chassis0/attr/RequestedPowerTransition"
data = '{"data":"xyz.openbmc_project.State.Chassis.Transition.Off"}'
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
elif(args.powcmd == 'status'):
url="https://"+host+"/xyz/openbmc_project/state/chassis0/attr/CurrentPowerState"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
chassisState = json.loads(res.text)['data'].split('.')[-1]
url="https://"+host+"/xyz/openbmc_project/state/host0/attr/CurrentHostState"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
hostState = json.loads(res.text)['data'].split('.')[-1]
url="https://"+host+"/xyz/openbmc_project/state/bmc0/attr/CurrentBMCState"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
bmcState = json.loads(res.text)['data'].split('.')[-1]
if(args.json):
outDict = {"Chassis Power State" : chassisState, "Host Power State" : hostState, "BMC Power State":bmcState}
return json.dumps(outDict, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False)
else:
return "Chassis Power State: " +chassisState + "\nHost Power State: " + hostState + "\nBMC Power State: " + bmcState
else:
return "Invalid chassis power command"
def chassisIdent(host, args, session):
"""
called by the chassis function. Controls the identify led of the chassis. Sets or gets the state
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if(args.identcmd == 'on'):
print("Attempting to turn identify light on...:")
url="https://"+host+"/xyz/openbmc_project/led/groups/enclosure_identify/attr/Asserted"
data = '{"data":true}'
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
elif(args.identcmd == 'off'):
print("Attempting to turn identify light off...:")
url="https://"+host+"/xyz/openbmc_project/led/groups/enclosure_identify/attr/Asserted"
data = '{"data":false}'
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
elif(args.identcmd == 'status'):
url="https://"+host+"/xyz/openbmc_project/led/groups/enclosure_identify"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
status = json.loads(res.text)['data']
if(args.json):
return status
else:
if status['Asserted'] == 0:
return "Identify light is off"
else:
return "Identify light is blinking"
else:
return "Invalid chassis identify command"
def chassis(host, args, session):
"""
controls the different chassis commands
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fru sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if(hasattr(args, 'powcmd')):
result = chassisPower(host,args,session)
elif(hasattr(args, 'identcmd')):
result = chassisIdent(host, args, session)
else:
return "This feature is not yet implemented"
return result
def getTask(host, args, session):
"""
Get operation on the Task Monitor URI
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the task sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if args.taskURI is not None:
url ='https://'+host+str(args.taskURI)
try:
r = session.post(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
if (r.status_code == 200 and not args.json):
return r.text
elif (r.status_code == 200 and args.json):
return r.json()
else:
return ('Failed to retrieve the data on Task Monitor URI')
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
else:
return 'You must specify the Task Monitor URI'
def dumpRetrieve(host, args, session):
"""
Downloads dump of given dump type
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
dumpType = args.dumpType
if (args.dumpType=="SystemDump"):
dumpResp=systemDumpRetrieve(host,args,session)
elif(args.dumpType=="bmc"):
dumpResp=bmcDumpRetrieve(host,args,session)
return dumpResp
def dumpList(host, args, session):
"""
Lists dump of the given dump type
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if (args.dumpType=="SystemDump"):
dumpResp=systemDumpList(host,args,session)
elif(args.dumpType=="bmc"):
dumpResp=bmcDumpList(host,args,session)
return dumpResp
def dumpDelete(host, args, session):
"""
Deletes dump of the given dump type
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if (args.dumpType=="SystemDump"):
dumpResp=systemDumpDelete(host,args,session)
elif(args.dumpType=="bmc"):
dumpResp=bmcDumpDelete(host,args,session)
return dumpResp
def dumpDeleteAll(host, args, session):
"""
Deletes all dumps of the given dump type
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if (args.dumpType=="SystemDump"):
dumpResp=systemDumpDeleteAll(host,args,session)
elif(args.dumpType=="bmc"):
dumpResp=bmcDumpDeleteAll(host,args,session)
return dumpResp
def dumpCreate(host, args, session):
"""
Creates dump for the given dump type
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if (args.dumpType=="SystemDump"):
dumpResp=systemDumpCreate(host,args,session)
elif(args.dumpType=="bmc"):
dumpResp=bmcDumpCreate(host,args,session)
return dumpResp
def bmcDumpRetrieve(host, args, session):
"""
Downloads a dump file from the bmc
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
dumpNum = args.dumpNum
if (args.dumpSaveLoc is not None):
saveLoc = args.dumpSaveLoc
else:
saveLoc = tempfile.gettempdir()
url ='https://'+host+'/download/dump/' + str(dumpNum)
try:
r = session.get(url, headers=jsonHeader, stream=True, verify=False, timeout=baseTimeout)
if (args.dumpSaveLoc is not None):
if os.path.exists(saveLoc):
if saveLoc[-1] != os.path.sep:
saveLoc = saveLoc + os.path.sep
filename = saveLoc + host+'-dump' + str(dumpNum) + '.tar.xz'
else:
return 'Invalid save location specified'
else:
filename = tempfile.gettempdir()+os.sep + host+'-dump' + str(dumpNum) + '.tar.xz'
with open(filename, 'wb') as f:
for chunk in r.iter_content(chunk_size =1024):
if chunk:
f.write(chunk)
return 'Saved as ' + filename
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def bmcDumpList(host, args, session):
"""
Lists the number of dump files on the bmc
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url ='https://'+host+'/xyz/openbmc_project/dump/list'
try:
r = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
dumpList = r.json()
formattedList = []
#remove items that aren't dump entries 'entry, internal, manager endpoints'
if 'data' in dumpList:
for entry in dumpList['data']:
if 'entry' in entry:
if entry.split('/')[-1].isnumeric():
formattedList.append(entry)
dumpList['data']= formattedList
return dumpList
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def bmcDumpDelete(host, args, session):
"""
Deletes BMC dump files from the bmc
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
dumpList = []
successList = []
failedList = []
if args.dumpNum is not None:
if isinstance(args.dumpNum, list):
dumpList = args.dumpNum
else:
dumpList.append(args.dumpNum)
for dumpNum in dumpList:
url ='https://'+host+'/xyz/openbmc_project/dump/entry/'+str(dumpNum)+'/action/Delete'
try:
r = session.post(url, headers=jsonHeader, json = {"data": []}, verify=False, timeout=baseTimeout)
if r.status_code == 200:
successList.append(str(dumpNum))
else:
failedList.append(str(dumpNum))
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
output = "Successfully deleted dumps: " + ', '.join(successList)
if(len(failedList)>0):
output+= '\nFailed to delete dumps: ' + ', '.join(failedList)
return output
else:
return 'You must specify an entry number to delete'
def bmcDumpDeleteAll(host, args, session):
"""
Deletes All BMC dump files from the bmc
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
dumpResp = bmcDumpList(host, args, session)
if 'FQPSPIN0000M' in dumpResp or 'FQPSPIN0001M'in dumpResp:
return dumpResp
dumpList = dumpResp['data']
d = vars(args)
dumpNums = []
for dump in dumpList:
dumpNum = dump.strip().split('/')[-1]
if dumpNum.isdigit():
dumpNums.append(int(dumpNum))
d['dumpNum'] = dumpNums
return bmcDumpDelete(host, args, session)
def bmcDumpCreate(host, args, session):
"""
Creates a bmc dump file
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url = 'https://'+host+'/xyz/openbmc_project/dump/action/CreateDump'
try:
r = session.post(url, headers=jsonHeader, json = {"data": []}, verify=False, timeout=baseTimeout)
info = r.json()
if(r.status_code == 200 and not args.json):
return ('Dump successfully created')
elif(args.json):
return info
elif 'data' in info:
if 'QuotaExceeded' in info['data']['description']:
return 'BMC dump space is full. Please delete at least one existing dump entry and try again.'
else:
return "Failed to create a BMC dump. BMC Response:\n {resp}".format(resp=info)
else:
return "Failed to create a BMC dump. BMC Response:\n {resp}".format(resp=info)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def systemDumpRetrieve(host, args, session):
"""
Downloads system dump
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
NBDSetup(host,args,session)
pipe = NBDPipe()
pipe.openHTTPSocket(args)
pipe.openTCPSocket()
pipe.waitformessage()
def systemDumpList(host, args, session):
"""
Lists system dumps
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url = "https://"+host+"/redfish/v1/Systems/system/LogServices/Dump/Entries"
try:
r = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
dumpList = r.json()
return dumpList
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def systemDumpDelete(host, args, session):
"""
Deletes system dump
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
dumpList = []
successList = []
failedList = []
if args.dumpNum is not None:
if isinstance(args.dumpNum, list):
dumpList = args.dumpNum
else:
dumpList.append(args.dumpNum)
for dumpNum in dumpList:
url = 'https://'+host+'/redfish/v1/Systems/system/LogServices/Dump/Entries/'+ str(dumpNum)
try:
r = session.delete(url, headers=jsonHeader, json = {"data": []}, verify=False, timeout=baseTimeout)
if r.status_code == 200:
successList.append(str(dumpNum))
else:
failedList.append(str(dumpNum))
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
output = "Successfully deleted dumps: " + ', '.join(successList)
if(len(failedList)>0):
output+= '\nFailed to delete dumps: ' + ', '.join(failedList)
return output
else:
return 'You must specify an entry number to delete'
def systemDumpDeleteAll(host, args, session):
"""
Deletes All system dumps
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url = 'https://'+host+'/redfish/v1/Systems/system/LogServices/Dump/Actions/LogService.ClearLog'
try:
r = session.post(url, headers=jsonHeader, json = {"data": []}, verify=False, timeout=baseTimeout)
if(r.status_code == 200 and not args.json):
return ('Dumps successfully cleared')
elif(args.json):
return r.json()
else:
return ('Failed to clear dumps')
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def systemDumpCreate(host, args, session):
"""
Creates a system dump
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url = 'https://'+host+'/redfish/v1/Systems/system/LogServices/Dump/Actions/LogService.CollectDiagnosticData'
params = {'DiagnosticDataType':'OEM', 'OEMDiagnosticDataType':'System'}
try:
r = session.post(url, headers=jsonHeader, params=params, data = json.dumps(params), verify=False, timeout=baseTimeout)
if(r.status_code == 200):
return r.json()
else:
return ('Failed to create dump')
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def csdDumpInitiate(host, args, session):
"""
Starts the process of getting the current list of dumps then initiates the creation of one.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
errorInfo = ""
dumpcount = 0
try:
d = vars(args)
d['json'] = True
except Exception as e:
errorInfo += "Failed to set the json flag to True \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
try:
for i in range(3):
dumpInfo = bmcDumpList(host, args, session)
if 'data' in dumpInfo:
dumpcount = len(dumpInfo['data'])
break
else:
errorInfo+= "Dump List Message returned: " + json.dumps(dumpInfo,indent=0, separators=(',', ':')).replace('\n','') +"\n"
except Exception as e:
errorInfo+= "Failed to collect the list of dumps.\nException: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
#Create a user initiated dump
dumpFailure = True
try:
for i in range(3):
dumpcreated = bmcDumpCreate(host, args, session)
if 'message' in dumpcreated:
if 'ok' in dumpcreated['message'].lower():
dumpFailure = False
break
elif 'data' in dumpcreated:
if 'QuotaExceeded' in dumpcreated['data']['description']:
print('Not enough dump space on the BMC to create a new dump. Please delete the oldest entry (lowest number) and rerun the collect_service_data command.')
errorInfo+='Dump Space is full. No new dump was created with this collection'
break
else:
errorInfo+= "Dump create message returned: " + json.dumps(dumpcreated,indent=0, separators=(',', ':')).replace('\n','') +"\n"
else:
errorInfo+= "Dump create message returned: " + json.dumps(dumpcreated,indent=0, separators=(',', ':')).replace('\n','') +"\n"
else:
errorInfo+= "Dump create message returned: " + json.dumps(dumpcreated,indent=0, separators=(',', ':')).replace('\n','') +"\n"
except Exception as e:
errorInfo+= "Dump create exception encountered: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output = {}
output['errors'] = errorInfo
output['dumpcount'] = dumpcount
if dumpFailure: output['dumpFailure'] = True
return output
def csdInventory(host, args,session, fileDir):
"""
Collects the BMC inventory, retrying if necessary
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========Inventory =============\n"
output={}
inventoryCollected = False
try:
for i in range(3):
frulist = fruPrint(host, args, session)
if 'Hardware' in frulist:
inventoryCollected = True
break
else:
errorInfo += json.dumps(frulist, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n'
except Exception as e:
errorInfo += "Inventory collection exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if inventoryCollected:
try:
with open(fileDir +os.sep+'inventory.txt', 'w') as f:
f.write(json.dumps(frulist, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n')
print("Inventory collected and stored in " + fileDir + os.sep + "inventory.txt")
output['fileLoc'] = fileDir+os.sep+'inventory.txt'
except Exception as e:
print("Failed to write inventory to file.")
errorInfo += "Error writing inventory to the file. Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def csdSensors(host, args,session, fileDir):
"""
Collects the BMC sensor readings, retrying if necessary
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========Sensors =============\n"
sensorsCollected = False
output={}
try:
d = vars(args)
d['json'] = False
except Exception as e:
errorInfo += "Failed to set the json flag to False \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
try:
for i in range(3):
sensorReadings = sensor(host, args, session)
if 'OCC0' in sensorReadings:
sensorsCollected = True
break
else:
errorInfo += sensorReadings
except Exception as e:
errorInfo += "Sensor reading collection exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if sensorsCollected:
try:
with open(fileDir +os.sep+'sensorReadings.txt', 'w') as f:
f.write(sensorReadings)
print("Sensor readings collected and stored in " + fileDir + os.sep+ "sensorReadings.txt")
output['fileLoc'] = fileDir+os.sep+'sensorReadings.txt'
except Exception as e:
print("Failed to write sensor readings to file system.")
errorInfo += "Error writing sensor readings to the file. Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def csdLEDs(host,args, session, fileDir):
"""
Collects the BMC LED status, retrying if necessary
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========LEDs =============\n"
ledsCollected = False
output={}
try:
d = vars(args)
d['json'] = True
except Exception as e:
errorInfo += "Failed to set the json flag to False \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
try:
url="https://"+host+"/xyz/openbmc_project/led/enumerate"
httpHeader = {'Content-Type':'application/json'}
for i in range(3):
try:
ledRes = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
if ledRes.status_code == 200:
ledsCollected = True
leds = ledRes.json()['data']
break
else:
errorInfo += ledRes.text
except(requests.exceptions.Timeout):
errorInfo+=json.dumps( connectionErrHandler(args.json, "Timeout", None), sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n'
except(requests.exceptions.ConnectionError) as err:
errorInfo += json.dumps(connectionErrHandler(args.json, "ConnectionError", err), sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n'
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
except Exception as e:
errorInfo += "LED status collection exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if ledsCollected:
try:
with open(fileDir +os.sep+'ledStatus.txt', 'w') as f:
f.write(json.dumps(leds, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n')
print("LED status collected and stored in " + fileDir + os.sep+ "ledStatus.txt")
output['fileLoc'] = fileDir+os.sep+'ledStatus.txt'
except Exception as e:
print("Failed to write LED status to file system.")
errorInfo += "Error writing LED status to the file. Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def csdSelShortList(host, args, session, fileDir):
"""
Collects the BMC log entries, retrying if necessary
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========SEL Short List =============\n"
selsCollected = False
output={}
try:
d = vars(args)
d['json'] = False
except Exception as e:
errorInfo += "Failed to set the json flag to False \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
try:
for i in range(3):
sels = selPrint(host,args,session)
if '----Active Alerts----' in sels or 'No log entries found' in sels or '----Historical Alerts----' in sels:
selsCollected = True
break
else:
errorInfo += sels + '\n'
except Exception as e:
errorInfo += "SEL short list collection exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if selsCollected:
try:
with open(fileDir +os.sep+'SELshortlist.txt', 'w') as f:
f.write(sels)
print("SEL short list collected and stored in " + fileDir + os.sep+ "SELshortlist.txt")
output['fileLoc'] = fileDir+os.sep+'SELshortlist.txt'
except Exception as e:
print("Failed to write SEL short list to file system.")
errorInfo += "Error writing SEL short list to the file. Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def csdParsedSels(host, args, session, fileDir):
"""
Collects the BMC log entries, retrying if necessary
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========SEL Parsed List =============\n"
selsCollected = False
output={}
try:
d = vars(args)
d['json'] = True
d['fullEsel'] = True
except Exception as e:
errorInfo += "Failed to set the json flag to True \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
try:
for i in range(3):
parsedfullsels = json.loads(selPrint(host,args,session))
if 'numAlerts' in parsedfullsels:
selsCollected = True
break
else:
errorInfo += parsedfullsels + '\n'
except Exception as e:
errorInfo += "Parsed full SELs collection exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if selsCollected:
try:
sortedSELs = sortSELs(parsedfullsels)
with open(fileDir +os.sep+'parsedSELs.txt', 'w') as f:
for log in sortedSELs[0]:
esel = ""
parsedfullsels[sortedSELs[1][str(log)]]['timestamp'] = datetime.datetime.fromtimestamp(int(parsedfullsels[sortedSELs[1][str(log)]]['timestamp']/1000)).strftime("%Y-%m-%d %H:%M:%S")
if ('raweSEL' in parsedfullsels[sortedSELs[1][str(log)]] and args.devdebug):
esel = parsedfullsels[sortedSELs[1][str(log)]]['raweSEL']
del parsedfullsels[sortedSELs[1][str(log)]]['raweSEL']
f.write(json.dumps(parsedfullsels[sortedSELs[1][str(log)]],sort_keys=True, indent=4, separators=(',', ': ')))
if(args.devdebug and esel != ""):
f.write(parseESEL(args, esel))
print("Parsed SELs collected and stored in " + fileDir + os.sep+ "parsedSELs.txt")
output['fileLoc'] = fileDir+os.sep+'parsedSELs.txt'
except Exception as e:
print("Failed to write fully parsed SELs to file system.")
errorInfo += "Error writing fully parsed SELs to the file. Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def csdFullEnumeration(host, args, session, fileDir):
"""
Collects a full enumeration of /xyz/openbmc_project/, retrying if necessary
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========BMC Full Enumeration =============\n"
bmcFullCollected = False
output={}
try:
d = vars(args)
d['json'] = True
except Exception as e:
errorInfo += "Failed to set the json flag to False \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
try:
print("Attempting to get a full BMC enumeration")
url="https://"+host+"/xyz/openbmc_project/enumerate"
httpHeader = {'Content-Type':'application/json'}
for i in range(3):
try:
bmcRes = session.get(url, headers=jsonHeader, verify=False, timeout=180)
if bmcRes.status_code == 200:
bmcFullCollected = True
fullEnumeration = bmcRes.json()
break
else:
errorInfo += bmcRes.text
except(requests.exceptions.Timeout):
errorInfo+=json.dumps( connectionErrHandler(args.json, "Timeout", None), sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n'
except(requests.exceptions.ConnectionError) as err:
errorInfo += json.dumps(connectionErrHandler(args.json, "ConnectionError", err), sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n'
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
except Exception as e:
errorInfo += "RAW BMC data collection exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if bmcFullCollected:
try:
with open(fileDir +os.sep+'bmcFullRaw.txt', 'w') as f:
f.write(json.dumps(fullEnumeration, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False) + '\n')
print("RAW BMC data collected and saved into " + fileDir + os.sep+ "bmcFullRaw.txt")
output['fileLoc'] = fileDir+os.sep+'bmcFullRaw.txt'
except Exception as e:
print("Failed to write RAW BMC data to file system.")
errorInfo += "Error writing RAW BMC data collection to the file. Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def csdCollectAllDumps(host, args, session, fileDir):
"""
Collects all of the bmc dump files and stores them in fileDir
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
@param fileDir: string representation of the path to use for putting files created
"""
errorInfo = "===========BMC Dump Collection =============\n"
dumpListCollected = False
output={}
dumpList = {}
try:
d = vars(args)
d['json'] = True
d['dumpSaveLoc'] = fileDir
except Exception as e:
errorInfo += "Failed to set the json flag to True, or failed to set the dumpSave Location \n Exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
print('Collecting bmc dump files')
try:
for i in range(3):
dumpResp = bmcDumpList(host, args, session)
if 'message' in dumpResp:
if 'ok' in dumpResp['message'].lower():
dumpList = dumpResp['data']
dumpListCollected = True
break
else:
errorInfo += "Status was not OK when retrieving the list of dumps available. \n Response: \n{resp}\n".format(resp=dumpResp)
else:
errorInfo += "Invalid response received from the BMC while retrieving the list of dumps available.\n {resp}\n".format(resp=dumpResp)
except Exception as e:
errorInfo += "BMC dump list exception: {eInfo}\n".format(eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
if dumpListCollected:
output['fileList'] = []
for dump in dumpList:
try:
if '/xyz/openbmc_project/dump/internal/manager' not in dump:
d['dumpNum'] = int(dump.strip().split('/')[-1])
print('retrieving dump file ' + str(d['dumpNum']))
filename = bmcDumpRetrieve(host, args, session).split('Saved as ')[-1]
output['fileList'].append(filename)
except Exception as e:
print("Unable to collect dump: {dumpInfo}".format(dumpInfo=dump))
errorInfo += "Exception collecting a bmc dump {dumpInfo}\n {eInfo}\n".format(dumpInfo=dump, eInfo=e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
output['errors'] = errorInfo
return output
def collectServiceData(host, args, session):
"""
Collects all data needed for service from the BMC
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the collectServiceData sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
global toolVersion
filelist = []
errorInfo = ""
#get current number of bmc dumps and create a new bmc dump
dumpInitdata = csdDumpInitiate(host, args, session)
if 'dumpFailure' in dumpInitdata:
return 'Collect service data is stopping due to not being able to create a new dump. No service data was collected.'
dumpcount = dumpInitdata['dumpcount']
errorInfo += dumpInitdata['errors']
#create the directory to put files
try:
args.silent = True
myDir = tempfile.gettempdir()+os.sep + host + "--" + datetime.datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
os.makedirs(myDir)
except Exception as e:
print('Unable to create the temporary directory for data collection. Ensure sufficient privileges to create temporary directory. Aborting.')
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
errorInfo += "Exception: Error: {err}, Details: {etype}, {fname}, {lineno}\n".format(err=e, etype=exc_type, fname=fname, lineno=exc_tb.tb_lineno)
errorInfo += traceback.format_exc()
return("Python exception: {eInfo}".format(eInfo = e))
#Collect Inventory
inventoryData = csdInventory(host, args, session, myDir)
if 'fileLoc' in inventoryData:
filelist.append(inventoryData['fileLoc'])
errorInfo += inventoryData['errors']
#Read all the sensor and OCC status
sensorData = csdSensors(host,args,session,myDir)
if 'fileLoc' in sensorData:
filelist.append(sensorData['fileLoc'])
errorInfo += sensorData['errors']
#Collect all of the LEDs status
ledStatus = csdLEDs(host, args, session, myDir)
if 'fileLoc' in ledStatus:
filelist.append(ledStatus['fileLoc'])
errorInfo += ledStatus['errors']
#Collect the bmc logs
selShort = csdSelShortList(host, args, session, myDir)
if 'fileLoc' in selShort:
filelist.append(selShort['fileLoc'])
errorInfo += selShort['errors']
parsedSELs = csdParsedSels(host, args, session, myDir)
if 'fileLoc' in parsedSELs:
filelist.append(parsedSELs['fileLoc'])
errorInfo += parsedSELs['errors']
#collect RAW bmc enumeration
bmcRaw = csdFullEnumeration(host, args, session, myDir)
if 'fileLoc' in bmcRaw:
filelist.append(bmcRaw['fileLoc'])
errorInfo += bmcRaw['errors']
#wait for new dump to finish being created
waitingForNewDump = True
count = 0;
print("Waiting for new BMC dump to finish being created. Wait time could be up to 5 minutes")
while(waitingForNewDump):
dumpList = bmcDumpList(host, args, session)['data']
if len(dumpList) > dumpcount:
waitingForNewDump = False
break;
elif(count>150):
print("Timed out waiting for bmc to make a new dump file. Continuing without it.")
break;
else:
time.sleep(2)
count += 1
#collect all of the dump files
getBMCDumps = csdCollectAllDumps(host, args, session, myDir)
if 'fileList' in getBMCDumps:
filelist+= getBMCDumps['fileList']
errorInfo += getBMCDumps['errors']
#write the runtime errors to a file
try:
with open(myDir +os.sep+'openbmctoolRuntimeErrors.txt', 'w') as f:
f.write(errorInfo)
print("OpenBMC tool runtime errors collected and stored in " + myDir + os.sep+ "openbmctoolRuntimeErrors.txt")
filelist.append(myDir+os.sep+'openbmctoolRuntimeErrors.txt')
except Exception as e:
print("Failed to write OpenBMC tool runtime errors to file system.")
#create the zip file
try:
filename = myDir.split(tempfile.gettempdir()+os.sep)[-1] + "_" + toolVersion + '_openbmc.zip'
zf = zipfile.ZipFile(myDir+os.sep + filename, 'w')
for myfile in filelist:
zf.write(myfile, os.path.basename(myfile))
zf.close()
print("Zip file with all collected data created and stored in: {fileInfo}".format(fileInfo=myDir+os.sep+filename))
except Exception as e:
print("Failed to create zip file with collected information")
return "data collection finished"
def healthCheck(host, args, session):
"""
runs a health check on the platform
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the bmc sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
#check fru status and get as json to easily work through
d = vars(args)
useJson = d['json']
d['json'] = True
d['verbose']= False
frus = json.loads(fruStatus(host, args, session))
hwStatus= "OK"
performanceStatus = "OK"
for key in frus:
if frus[key]["Functional"] == "No" and frus[key]["Present"] == "Yes":
hwStatus= "Degraded"
if("power_supply" in key or "powersupply" in key):
gpuCount =0
for comp in frus:
if "gv100card" in comp:
gpuCount +=1
if gpuCount > 4:
hwStatus = "Critical"
performanceStatus="Degraded"
break;
elif("fan" in key):
hwStatus = "Degraded"
else:
performanceStatus = "Degraded"
if useJson:
output = {"Hardware Status": hwStatus, "Performance": performanceStatus}
output = json.dumps(output, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False)
else:
output = ("Hardware Status: " + hwStatus +
"\nPerformance: " +performanceStatus )
#SW407886: Clear the duplicate entries
#collect the dups
d['devdebug'] = False
sels = json.loads(selPrint(host, args, session))
logNums2Clr = []
oldestLogNum={"logNum": "bogus" ,"key" : ""}
count = 0
if sels['numAlerts'] > 0:
for key in sels:
if "numAlerts" in key:
continue
try:
if "slave@00:00/00:00:00:06/sbefifo1-dev0/occ1-dev0" in sels[key]['Message']:
count += 1
if count > 1:
#preserve first occurrence
if sels[key]['timestamp'] < sels[oldestLogNum['key']]['timestamp']:
oldestLogNum['key']=key
oldestLogNum['logNum'] = sels[key]['logNum']
else:
oldestLogNum['key']=key
oldestLogNum['logNum'] = sels[key]['logNum']
logNums2Clr.append(sels[key]['logNum'])
except KeyError:
continue
if(count >0):
logNums2Clr.remove(oldestLogNum['logNum'])
#delete the dups
if count >1:
data = "{\"data\": [] }"
for logNum in logNums2Clr:
url = "https://"+ host+ "/xyz/openbmc_project/logging/entry/"+logNum+"/action/Delete"
try:
session.post(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
deleteFailed = True
except(requests.exceptions.ConnectionError) as err:
deleteFailed = True
#End of defect resolve code
d['json'] = useJson
return output
def bmc(host, args, session):
"""
handles various bmc level commands, currently bmc rebooting
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the bmc sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if(args.type is not None):
return bmcReset(host, args, session)
if(args.info):
return "Not implemented at this time"
def bmcReset(host, args, session):
"""
controls resetting the bmc. warm reset reboots the bmc, cold reset removes the configuration and reboots.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the bmcReset sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
if checkFWactivation(host, args, session):
return ("BMC reset control disabled during firmware activation")
if(args.type == "warm"):
print("\nAttempting to reboot the BMC...:")
url="https://"+host+"/xyz/openbmc_project/state/bmc0/attr/RequestedBMCTransition"
data = '{"data":"xyz.openbmc_project.State.BMC.Transition.Reboot"}'
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
return res.text
elif(args.type =="cold"):
print("\nAttempting to reboot the BMC...:")
url="https://"+host+"/xyz/openbmc_project/state/bmc0/attr/RequestedBMCTransition"
data = '{"data":"xyz.openbmc_project.State.BMC.Transition.Reboot"}'
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
return res.text
else:
return "invalid command"
def gardClear(host, args, session):
"""
clears the gard records from the bmc
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the gardClear sub command
@param session: the active session to use
"""
url="https://"+host+"/org/open_power/control/gard/action/Reset"
data = '{"data":[]}'
try:
res = session.post(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
if res.status_code == 404:
return "Command not supported by this firmware version"
else:
return res.text
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
def activateFWImage(host, args, session):
"""
activates a firmware image on the bmc
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fwflash sub command
@param session: the active session to use
@param fwID: the unique ID of the fw image to activate
"""
fwID = args.imageID
#determine the existing versions
url="https://"+host+"/xyz/openbmc_project/software/enumerate"
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
existingSoftware = json.loads(resp.text)['data']
altVersionID = ''
versionType = ''
imageKey = '/xyz/openbmc_project/software/'+fwID
if imageKey in existingSoftware:
versionType = existingSoftware[imageKey]['Purpose']
for key in existingSoftware:
if imageKey == key:
continue
if 'Purpose' in existingSoftware[key]:
if versionType == existingSoftware[key]['Purpose']:
altVersionID = key.split('/')[-1]
url="https://"+host+"/xyz/openbmc_project/software/"+ fwID + "/attr/Priority"
url1="https://"+host+"/xyz/openbmc_project/software/"+ altVersionID + "/attr/Priority"
data = "{\"data\": 0}"
data1 = "{\"data\": 1 }"
try:
resp = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
resp1 = session.put(url1, headers=jsonHeader, data=data1, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if(not args.json):
if resp.status_code == 200 and resp1.status_code == 200:
return 'Firmware flash and activation completed. Please reboot the bmc and then boot the host OS for the changes to take effect. '
else:
return "Firmware activation failed."
else:
return resp.text + resp1.text
def activateStatus(host, args, session):
if checkFWactivation(host, args, session):
return("Firmware is currently being activated. Do not reboot the BMC or start the Host OS")
else:
return("No firmware activations are pending")
def extractFWimage(path, imageType):
"""
extracts the bmc image and returns information about the package
@param path: the path and file name of the firmware image
@param imageType: The type of image the user is trying to flash. Host or BMC
@return: the image id associated with the package. returns an empty string on error.
"""
f = tempfile.TemporaryFile()
tmpDir = tempfile.gettempdir()
newImageID = ""
if os.path.exists(path):
try:
imageFile = tarfile.open(path,'r')
contents = imageFile.getmembers()
for tf in contents:
if 'MANIFEST' in tf.name:
imageFile.extract(tf.name, path=tmpDir)
with open(tempfile.gettempdir() +os.sep+ tf.name, 'r') as imageInfo:
for line in imageInfo:
if 'purpose' in line:
purpose = line.split('=')[1]
if imageType not in purpose.split('.')[-1]:
print('The specified image is not for ' + imageType)
print('Please try again with the image for ' + imageType)
return ""
if 'version' == line.split('=')[0]:
version = line.split('=')[1].strip().encode('utf-8')
m = hashlib.sha512()
m.update(version)
newImageID = m.hexdigest()[:8]
break
try:
os.remove(tempfile.gettempdir() +os.sep+ tf.name)
except OSError:
pass
return newImageID
except tarfile.ExtractError as e:
print('Unable to extract information from the firmware file.')
print('Ensure you have write access to the directory: ' + tmpDir)
return newImageID
except tarfile.TarError as e:
print('This is not a valid firmware file.')
return newImageID
print("This is not a valid firmware file.")
return newImageID
else:
print('The filename and path provided are not valid.')
return newImageID
def getAllFWImageIDs(fwInvDict):
"""
gets a list of all the firmware image IDs
@param fwInvDict: the dictionary to search for FW image IDs
@return: list containing string representation of the found image ids
"""
idList = []
for key in fwInvDict:
if 'Version' in fwInvDict[key]:
idList.append(key.split('/')[-1])
return idList
def fwFlash(host, args, session):
"""
updates the bmc firmware and pnor firmware
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the fwflash sub command
@param session: the active session to use
"""
d = vars(args)
if(args.type == 'bmc'):
purp = 'BMC'
else:
purp = 'Host'
#check power state of the machine. No concurrent FW updates allowed
d['powcmd'] = 'status'
powerstate = chassisPower(host, args, session)
if 'Chassis Power State: On' in powerstate:
return("Aborting firmware update. Host is powered on. Please turn off the host and try again.")
#determine the existing images on the bmc
url="https://"+host+"/xyz/openbmc_project/software/enumerate"
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
oldsoftware = json.loads(resp.text)['data']
#Extract the tar and get information from the manifest file
newversionID = extractFWimage(args.fileloc, purp)
if newversionID == "":
return "Unable to verify FW image."
#check if the new image is already on the bmc
if newversionID not in getAllFWImageIDs(oldsoftware):
#upload the file
httpHeader = {'Content-Type':'application/octet-stream'}
httpHeader.update(xAuthHeader)
url="https://"+host+"/upload/image"
data=open(args.fileloc,'rb').read()
print("Uploading file to BMC")
try:
resp = session.post(url, headers=httpHeader, data=data, verify=False)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
return "Failed to upload the file to the bmc"
else:
print("Upload complete.")
#verify bmc processed the image
software ={}
for i in range(0, 5):
url="https://"+host+"/xyz/openbmc_project/software/enumerate"
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
software = json.loads(resp.text)['data']
#check if bmc is done processing the new image
if (newversionID in getAllFWImageIDs(software)):
break
else:
time.sleep(15)
#activate the new image
print("Activating new image: "+newversionID)
url="https://"+host+"/xyz/openbmc_project/software/"+ newversionID + "/attr/RequestedActivation"
data = '{"data":"xyz.openbmc_project.Software.Activation.RequestedActivations.Active"}'
try:
resp = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
#wait for the activation to complete, timeout after ~1 hour
i=0
while i < 360:
url="https://"+host+"/xyz/openbmc_project/software/"+ newversionID
data = '{"data":"xyz.openbmc_project.Software.Activation.RequestedActivations.Active"}'
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
fwInfo = json.loads(resp.text)['data']
if 'Activating' not in fwInfo['Activation'] and 'Activating' not in fwInfo['RequestedActivation']:
print('')
break
else:
sys.stdout.write('.')
sys.stdout.flush()
time.sleep(10) #check every 10 seconds
return "Firmware flash and activation completed. Please reboot the bmc and then boot the host OS for the changes to take effect. "
else:
print("This image has been found on the bmc. Activating image: " + newversionID)
d['imageID'] = newversionID
return activateFWImage(host, args, session)
def getFWInventoryAttributes(rawFWInvItem, ID):
"""
gets and lists all of the firmware in the system.
@return: returns a dictionary containing the image attributes
"""
reqActivation = rawFWInvItem["RequestedActivation"].split('.')[-1]
pendingActivation = ""
if reqActivation == "None":
pendingActivation = "No"
else:
pendingActivation = "Yes"
firmwareAttr = {ID: {
"Purpose": rawFWInvItem["Purpose"].split('.')[-1],
"Version": rawFWInvItem["Version"],
"RequestedActivation": pendingActivation,
"ID": ID}}
if "ExtendedVersion" in rawFWInvItem:
firmwareAttr[ID]['ExtendedVersion'] = rawFWInvItem['ExtendedVersion'].split(',')
else:
firmwareAttr[ID]['ExtendedVersion'] = ""
return firmwareAttr
def parseFWdata(firmwareDict):
"""
creates a dictionary with parsed firmware data
@return: returns a dictionary containing the image attributes
"""
firmwareInfoDict = {"Functional": {}, "Activated":{}, "NeedsActivated":{}}
for key in firmwareDict['data']:
#check for valid endpoint
if "Purpose" in firmwareDict['data'][key]:
id = key.split('/')[-1]
if firmwareDict['data'][key]['Activation'].split('.')[-1] == "Active":
fwActivated = True
else:
fwActivated = False
if 'Priority' in firmwareDict['data'][key]:
if firmwareDict['data'][key]['Priority'] == 0:
firmwareInfoDict['Functional'].update(getFWInventoryAttributes(firmwareDict['data'][key], id))
elif firmwareDict['data'][key]['Priority'] >= 0 and fwActivated:
firmwareInfoDict['Activated'].update(getFWInventoryAttributes(firmwareDict['data'][key], id))
else:
firmwareInfoDict['NeedsActivated'].update(getFWInventoryAttributes(firmwareDict['data'][key], id))
else:
firmwareInfoDict['NeedsActivated'].update(getFWInventoryAttributes(firmwareDict['data'][key], id))
emptySections = []
for key in firmwareInfoDict:
if len(firmwareInfoDict[key])<=0:
emptySections.append(key)
for key in emptySections:
del firmwareInfoDict[key]
return firmwareInfoDict
def displayFWInvenory(firmwareInfoDict, args):
"""
gets and lists all of the firmware in the system.
@return: returns a string containing all of the firmware information
"""
output = ""
if not args.json:
for key in firmwareInfoDict:
for subkey in firmwareInfoDict[key]:
firmwareInfoDict[key][subkey]['ExtendedVersion'] = str(firmwareInfoDict[key][subkey]['ExtendedVersion'])
if not args.verbose:
output = "---Running Images---\n"
colNames = ["Purpose", "Version", "ID"]
keylist = ["Purpose", "Version", "ID"]
output += tableDisplay(keylist, colNames, firmwareInfoDict["Functional"])
if "Activated" in firmwareInfoDict:
output += "\n---Available Images---\n"
output += tableDisplay(keylist, colNames, firmwareInfoDict["Activated"])
if "NeedsActivated" in firmwareInfoDict:
output += "\n---Needs Activated Images---\n"
output += tableDisplay(keylist, colNames, firmwareInfoDict["NeedsActivated"])
else:
output = "---Running Images---\n"
colNames = ["Purpose", "Version", "ID", "Pending Activation", "Extended Version"]
keylist = ["Purpose", "Version", "ID", "RequestedActivation", "ExtendedVersion"]
output += tableDisplay(keylist, colNames, firmwareInfoDict["Functional"])
if "Activated" in firmwareInfoDict:
output += "\n---Available Images---\n"
output += tableDisplay(keylist, colNames, firmwareInfoDict["Activated"])
if "NeedsActivated" in firmwareInfoDict:
output += "\n---Needs Activated Images---\n"
output += tableDisplay(keylist, colNames, firmwareInfoDict["NeedsActivated"])
return output
else:
return str(json.dumps(firmwareInfoDict, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False))
def firmwareList(host, args, session):
"""
gets and lists all of the firmware in the system.
@return: returns a string containing all of the firmware information
"""
url="https://{hostname}/xyz/openbmc_project/software/enumerate".format(hostname=host)
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
firmwareDict = json.loads(res.text)
#sort the received information
firmwareInfoDict = parseFWdata(firmwareDict)
#display the information
return displayFWInvenory(firmwareInfoDict, args)
def deleteFWVersion(host, args, session):
"""
deletes a firmware version on the BMC
@param host: string, the hostname or IP address of the BMC
@param args: contains additional arguments used by the fwflash sub command
@param session: the active session to use
@param fwID: the unique ID of the fw version to delete
"""
fwID = args.versionID
print("Deleting version: "+fwID)
url="https://"+host+"/xyz/openbmc_project/software/"+ fwID + "/action/Delete"
data = "{\"data\": [] }"
try:
res = session.post(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
if res.status_code == 200:
return ('The firmware version has been deleted')
else:
return ('Unable to delete the specified firmware version')
def deleteFWAll(host, args, session):
"""
deletes ALL contents for firmware software catalog
@param host: string, the hostname or IP address of the BMC
@param args: contains additional arguments used by the fwflash sub command
@param session: the active session to use
"""
print("Deleting ALL firmware versions")
url="https://"+host+"/xyz/openbmc_project/software/action/DeleteAll"
data = "{\"data\": [] }"
try:
res = session.post(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
if res.status_code == 200:
return ('All firmware versions were deleted')
else:
return ('Uspecified error while deleting All firmware versions')
def restLogging(host, args, session):
"""
Called by the logging function. Turns REST API logging on/off.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the logging sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/logging/rest_api_logs/attr/Enabled"
if(args.rest_logging == 'on'):
data = '{"data": 1}'
elif(args.rest_logging == 'off'):
data = '{"data": 0}'
else:
return "Invalid logging rest_api command"
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
def remoteLogging(host, args, session):
"""
Called by the logging function. View config information for/disable remote logging (rsyslog).
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the logging sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/logging/config/remote"
try:
if(args.remote_logging == 'view'):
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
elif(args.remote_logging == 'disable'):
res = session.put(url + '/attr/Port', headers=jsonHeader, json = {"data": 0}, verify=False, timeout=baseTimeout)
res = session.put(url + '/attr/Address', headers=jsonHeader, json = {"data": ""}, verify=False, timeout=baseTimeout)
else:
return "Invalid logging remote_logging command"
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
def remoteLoggingConfig(host, args, session):
"""
Called by the logging function. Configures remote logging (rsyslog).
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the logging sub command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will be provided in json format for programmatic consumption
"""
url="https://"+host+"/xyz/openbmc_project/logging/config/remote"
try:
res = session.put(url + '/attr/Port', headers=jsonHeader, json = {"data": args.port}, verify=False, timeout=baseTimeout)
res = session.put(url + '/attr/Address', headers=jsonHeader, json = {"data": args.address}, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
return res.text
def redfishSupportPresent(host, session):
url = "https://" + host + "/redfish/v1"
try:
resp = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return False
except(requests.exceptions.ConnectionError) as err:
return False
if resp.status_code != 200:
return False
else:
return True
def certificateUpdate(host, args, session):
"""
Called by certificate management function. update server/client/authority certificates
Example:
certificate update server https -f cert.pem
certificate update authority ldap -f Root-CA.pem
certificate update client ldap -f cert.pem
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the certificate update sub command
@param session: the active session to use
"""
httpHeader = {'Content-Type': 'application/octet-stream'}
httpHeader.update(xAuthHeader)
data = open(args.fileloc, 'r').read()
try:
if redfishSupportPresent(host, session):
if(args.type.lower() == 'server' and args.service.lower() != "https"):
return "Invalid service type"
if(args.type.lower() == 'client' and args.service.lower() != "ldap"):
return "Invalid service type"
if(args.type.lower() == 'authority' and args.service.lower() != "ldap"):
return "Invalid service type"
url = "";
if(args.type.lower() == 'server'):
url = "https://" + host + \
"/redfish/v1/Managers/bmc/NetworkProtocol/HTTPS/Certificates"
elif(args.type.lower() == 'client'):
url = "https://" + host + \
"/redfish/v1/AccountService/LDAP/Certificates"
elif(args.type.lower() == 'authority'):
url = "https://" + host + \
"/redfish/v1/Managers/bmc/Truststore/Certificates"
else:
return "Unsupported certificate type"
resp = session.post(url, headers=httpHeader, data=data,
verify=False)
else:
url = "https://" + host + "/xyz/openbmc_project/certs/" + \
args.type.lower() + "/" + args.service.lower()
resp = session.put(url, headers=httpHeader, data=data, verify=False)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
print(resp.text)
return "Failed to update the certificate"
else:
print("Update complete.")
def certificateDelete(host, args, session):
"""
Called by certificate management function to delete certificate
Example:
certificate delete server https
certificate delete authority ldap
certificate delete client ldap
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the certificate delete sub command
@param session: the active session to use
"""
if redfishSupportPresent(host, session):
return "Not supported, please use certificate replace instead";
httpHeader = {'Content-Type': 'multipart/form-data'}
httpHeader.update(xAuthHeader)
url = "https://" + host + "/xyz/openbmc_project/certs/" + args.type.lower() + "/" + args.service.lower()
print("Deleting certificate url=" + url)
try:
resp = session.delete(url, headers=httpHeader)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
print(resp.text)
return "Failed to delete the certificate"
else:
print("Delete complete.")
def certificateReplace(host, args, session):
"""
Called by certificate management function. replace server/client/
authority certificates
Example:
certificate replace server https -f cert.pem
certificate replace authority ldap -f Root-CA.pem
certificate replace client ldap -f cert.pem
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the certificate
replace sub command
@param session: the active session to use
"""
cert = open(args.fileloc, 'r').read()
try:
if redfishSupportPresent(host, session):
httpHeader = {'Content-Type': 'application/json'}
httpHeader.update(xAuthHeader)
url = "";
if(args.type.lower() == 'server' and args.service.lower() != "https"):
return "Invalid service type"
if(args.type.lower() == 'client' and args.service.lower() != "ldap"):
return "Invalid service type"
if(args.type.lower() == 'authority' and args.service.lower() != "ldap"):
return "Invalid service type"
if(args.type.lower() == 'server'):
url = "/redfish/v1/Managers/bmc/NetworkProtocol/HTTPS/Certificates/1"
elif(args.type.lower() == 'client'):
url = "/redfish/v1/AccountService/LDAP/Certificates/1"
elif(args.type.lower() == 'authority'):
url = "/redfish/v1/Managers/bmc/Truststore/Certificates/1"
replaceUrl = "https://" + host + \
"/redfish/v1/CertificateService/Actions/CertificateService.ReplaceCertificate"
data ={"CertificateUri":{"@odata.id":url}, "CertificateType":"PEM",
"CertificateString":cert}
resp = session.post(replaceUrl, headers=httpHeader, json=data, verify=False)
else:
httpHeader = {'Content-Type': 'application/octet-stream'}
httpHeader.update(xAuthHeader)
url = "https://" + host + "/xyz/openbmc_project/certs/" + \
args.type.lower() + "/" + args.service.lower()
resp = session.delete(url, headers=httpHeader)
resp = session.put(url, headers=httpHeader, data=cert, verify=False)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
print(resp.text)
return "Failed to replace the certificate"
else:
print("Replace complete.")
return resp.text
def certificateDisplay(host, args, session):
"""
Called by certificate management function. display server/client/
authority certificates
Example:
certificate display server
certificate display authority
certificate display client
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the certificate
display sub command
@param session: the active session to use
"""
if not redfishSupportPresent(host, session):
return "Not supported";
httpHeader = {'Content-Type': 'application/octet-stream'}
httpHeader.update(xAuthHeader)
if(args.type.lower() == 'server'):
url = "https://" + host + \
"/redfish/v1/Managers/bmc/NetworkProtocol/HTTPS/Certificates/1"
elif(args.type.lower() == 'client'):
url = "https://" + host + \
"/redfish/v1/AccountService/LDAP/Certificates/1"
elif(args.type.lower() == 'authority'):
url = "https://" + host + \
"/redfish/v1/Managers/bmc/Truststore/Certificates/1"
try:
resp = session.get(url, headers=httpHeader, verify=False)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
print(resp.text)
return "Failed to display the certificate"
else:
print("Display complete.")
return resp.text
def certificateList(host, args, session):
"""
Called by certificate management function.
Example:
certificate list
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the certificate
list sub command
@param session: the active session to use
"""
if not redfishSupportPresent(host, session):
return "Not supported";
httpHeader = {'Content-Type': 'application/octet-stream'}
httpHeader.update(xAuthHeader)
url = "https://" + host + \
"/redfish/v1/CertificateService/CertificateLocations/"
try:
resp = session.get(url, headers=httpHeader, verify=False)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
print(resp.text)
return "Failed to list certificates"
else:
print("List certificates complete.")
return resp.text
def certificateGenerateCSR(host, args, session):
"""
Called by certificate management function. Generate CSR for server/
client certificates
Example:
certificate generatecsr server NJ w3.ibm.com US IBM IBM-UNIT NY EC prime256v1 cp abc.com an.com,bm.com gn sn un in
certificate generatecsr client NJ w3.ibm.com US IBM IBM-UNIT NY EC prime256v1 cp abc.com an.com,bm.com gn sn un in
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the certificate replace sub command
@param session: the active session to use
"""
if not redfishSupportPresent(host, session):
return "Not supported";
httpHeader = {'Content-Type': 'application/octet-stream'}
httpHeader.update(xAuthHeader)
url = "";
if(args.type.lower() == 'server'):
url = "/redfish/v1/Managers/bmc/NetworkProtocol/HTTPS/Certificates/"
usage_list = ["ServerAuthentication"]
elif(args.type.lower() == 'client'):
url = "/redfish/v1/AccountService/LDAP/Certificates/"
usage_list = ["ClientAuthentication"]
elif(args.type.lower() == 'authority'):
url = "/redfish/v1/Managers/bmc/Truststore/Certificates/"
print("Generating CSR url=" + url)
generateCSRUrl = "https://" + host + \
"/redfish/v1/CertificateService/Actions/CertificateService.GenerateCSR"
try:
alt_name_list = args.alternativeNames.split(",")
data ={"CertificateCollection":{"@odata.id":url},
"CommonName":args.commonName, "City":args.city,
"Country":args.country, "Organization":args.organization,
"OrganizationalUnit":args.organizationUnit, "State":args.state,
"KeyPairAlgorithm":args.keyPairAlgorithm, "KeyCurveId":args.keyCurveId,
"AlternativeNames":alt_name_list, "ContactPerson":args.contactPerson,
"Email":args.email, "GivenName":args.givenname, "Initials":args.initials,
"KeyUsage":usage_list, "Surname":args.surname,
"UnstructuredName":args.unstructuredname}
resp = session.post(generateCSRUrl, headers=httpHeader,
json=data, verify=False)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if resp.status_code != 200:
print(resp.text)
return "Failed to generate CSR"
else:
print("GenerateCSR complete.")
return resp.text
def enableLDAPConfig(host, args, session):
"""
Called by the ldap function. Configures LDAP.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will
be provided in json format for programmatic consumption
"""
if(isRedfishSupport):
return enableLDAP(host, args, session)
else:
return enableLegacyLDAP(host, args, session)
def enableLegacyLDAP(host, args, session):
"""
Called by the ldap function. Configures LDAP on Lagecy systems.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will
be provided in json format for programmatic consumption
"""
url='https://'+host+'/xyz/openbmc_project/user/ldap/action/CreateConfig'
scope = {
'sub' : 'xyz.openbmc_project.User.Ldap.Create.SearchScope.sub',
'one' : 'xyz.openbmc_project.User.Ldap.Create.SearchScope.one',
'base': 'xyz.openbmc_project.User.Ldap.Create.SearchScope.base'
}
serverType = {
'ActiveDirectory' : 'xyz.openbmc_project.User.Ldap.Create.Type.ActiveDirectory',
'OpenLDAP' : 'xyz.openbmc_project.User.Ldap.Create.Type.OpenLdap'
}
data = {"data": [args.uri, args.bindDN, args.baseDN, args.bindPassword, scope[args.scope], serverType[args.serverType]]}
try:
res = session.post(url, headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def enableLDAP(host, args, session):
"""
Called by the ldap function. Configures LDAP for systems with latest user-manager design changes
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output will
be provided in json format for programmatic consumption
"""
scope = {
'sub' : 'xyz.openbmc_project.User.Ldap.Config.SearchScope.sub',
'one' : 'xyz.openbmc_project.User.Ldap.Config.SearchScope.one',
'base': 'xyz.openbmc_project.User.Ldap.Config.SearchScope.base'
}
serverType = {
'ActiveDirectory' : 'xyz.openbmc_project.User.Ldap.Config.Type.ActiveDirectory',
'OpenLDAP' : 'xyz.openbmc_project.User.Ldap.Config.Type.OpenLdap'
}
url = "https://"+host+"/xyz/openbmc_project/user/ldap/"
serverTypeEnabled = getLDAPTypeEnabled(host,session)
serverTypeToBeEnabled = args.serverType
#If the given LDAP type is already enabled, then return
if (serverTypeToBeEnabled == serverTypeEnabled):
return("Server type " + serverTypeToBeEnabled + " is already enabled...")
try:
# Copy the role map from the currently enabled LDAP server type
# to the newly enabled server type
# Disable the currently enabled LDAP server type. Unless
# it is disabled, we cannot enable a new LDAP server type
if (serverTypeEnabled is not None):
if (serverTypeToBeEnabled != serverTypeEnabled):
res = syncRoleMap(host,args,session,serverTypeEnabled,serverTypeToBeEnabled)
data = "{\"data\": 0 }"
res = session.put(url + serverTypeMap[serverTypeEnabled] + '/attr/Enabled', headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
data = {"data": args.baseDN}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/LDAPBaseDN', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property LDAPBaseDN failed...")
return(res.text)
data = {"data": args.bindDN}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/LDAPBindDN', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property LDAPBindDN failed...")
return(res.text)
data = {"data": args.bindPassword}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/LDAPBindDNPassword', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property LDAPBindDNPassword failed...")
return(res.text)
data = {"data": scope[args.scope]}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/LDAPSearchScope', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property LDAPSearchScope failed...")
return(res.text)
data = {"data": args.uri}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/LDAPServerURI', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property LDAPServerURI failed...")
return(res.text)
data = {"data": args.groupAttrName}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/GroupNameAttribute', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property GroupNameAttribute failed...")
return(res.text)
data = {"data": args.userAttrName}
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/UserNameAttribute', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
if (res.status_code != requests.codes.ok):
print("Updates to the property UserNameAttribute failed...")
return(res.text)
#After updating the properties, enable the new server type
data = "{\"data\": 1 }"
res = session.put(url + serverTypeMap[serverTypeToBeEnabled] + '/attr/Enabled', headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def disableLDAP(host, args, session):
"""
Called by the ldap function. Deletes the LDAP Configuration.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
"""
try:
if (isRedfishSupport) :
url = "https://"+host+"/xyz/openbmc_project/user/ldap/"
serverTypeEnabled = getLDAPTypeEnabled(host,session)
if (serverTypeEnabled is not None):
#To keep the role map in sync,
#If the server type being disabled has role map, then
# - copy the role map to the other server type(s)
for serverType in serverTypeMap.keys():
if (serverType != serverTypeEnabled):
res = syncRoleMap(host,args,session,serverTypeEnabled,serverType)
#Disable the currently enabled LDAP server type
data = "{\"data\": 0 }"
res = session.put(url + serverTypeMap[serverTypeEnabled] + '/attr/Enabled', headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
else:
return("LDAP server has not been enabled...")
else :
url='https://'+host+'/xyz/openbmc_project/user/ldap/config/action/delete'
data = {"data": []}
res = session.post(url, headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def enableDHCP(host, args, session):
"""
Called by the network function. Enables DHCP.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/"+args.Interface+\
"/attr/DHCPEnabled"
data = "{\"data\": 1 }"
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "The specified Interface"+"("+args.Interface+")"+\
" doesn't exist"
return res.text
def disableDHCP(host, args, session):
"""
Called by the network function. Disables DHCP.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/"+args.Interface+\
"/attr/DHCPEnabled"
data = "{\"data\": 0 }"
try:
res = session.put(url, headers=jsonHeader, data=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "The specified Interface"+"("+args.Interface+")"+\
" doesn't exist"
return res.text
def getHostname(host, args, session):
"""
Called by the network function. Prints out the Hostname.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/config/attr/HostName"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def setHostname(host, args, session):
"""
Called by the network function. Sets the Hostname.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/config/attr/HostName"
data = {"data": args.HostName}
try:
res = session.put(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def getDomainName(host, args, session):
"""
Called by the network function. Prints out the DomainName.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/"+args.Interface+\
"/attr/DomainName"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The DomainName is not configured on Interface"+"("+args.Interface+")"
return res.text
def setDomainName(host, args, session):
"""
Called by the network function. Sets the DomainName.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/"+args.Interface+\
"/attr/DomainName"
data = {"data": args.DomainName.split(",")}
try:
res = session.put(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "Failed to set Domain Name"
return res.text
def getMACAddress(host, args, session):
"""
Called by the network function. Prints out the MACAddress.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/"+args.Interface+\
"/attr/MACAddress"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "Failed to get MACAddress"
return res.text
def setMACAddress(host, args, session):
"""
Called by the network function. Sets the MACAddress.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/"+args.Interface+\
"/attr/MACAddress"
data = {"data": args.MACAddress}
try:
res = session.put(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "Failed to set MACAddress"
return res.text
def getDefaultGateway(host, args, session):
"""
Called by the network function. Prints out the DefaultGateway.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/config/attr/DefaultGateway"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "Failed to get Default Gateway info"
return res.text
def setDefaultGateway(host, args, session):
"""
Called by the network function. Sets the DefaultGateway.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/config/attr/DefaultGateway"
data = {"data": args.DefaultGW}
try:
res = session.put(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "Failed to set Default Gateway"
return res.text
def viewNWConfig(host, args, session):
"""
Called by the ldap function. Prints out network configured properties
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
@return returns LDAP's configured properties.
"""
url = "https://"+host+"/xyz/openbmc_project/network/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
except(requests.exceptions.RequestException) as err:
return connectionErrHandler(args.json, "RequestException", err)
if res.status_code == 404:
return "LDAP server config has not been created"
return res.text
def getDNS(host, args, session):
"""
Called by the network function. Prints out DNS servers on the interface
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host + "/xyz/openbmc_project/network/" + args.Interface\
+ "/attr/Nameservers"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The NameServer is not configured on Interface"+"("+args.Interface+")"
return res.text
def setDNS(host, args, session):
"""
Called by the network function. Sets DNS servers on the interface.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host + "/xyz/openbmc_project/network/" + args.Interface\
+ "/attr/Nameservers"
data = {"data": args.DNSServers.split(",")}
try:
res = session.put(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "Failed to set DNS"
return res.text
def getNTP(host, args, session):
"""
Called by the network function. Prints out NTP servers on the interface
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host + "/xyz/openbmc_project/network/" + args.Interface\
+ "/attr/NTPServers"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The NTPServer is not configured on Interface"+"("+args.Interface+")"
return res.text
def setNTP(host, args, session):
"""
Called by the network function. Sets NTP servers on the interface.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host + "/xyz/openbmc_project/network/" + args.Interface\
+ "/attr/NTPServers"
data = {"data": args.NTPServers.split(",")}
try:
res = session.put(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 403:
return "Failed to set NTP"
return res.text
def addIP(host, args, session):
"""
Called by the network function. Configures IP address on given interface
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host + "/xyz/openbmc_project/network/" + args.Interface\
+ "/action/IP"
protocol = {
'ipv4': 'xyz.openbmc_project.Network.IP.Protocol.IPv4',
'ipv6': 'xyz.openbmc_project.Network.IP.Protocol.IPv6'
}
data = {"data": [protocol[args.type], args.address, int(args.prefixLength),
args.gateway]}
try:
res = session.post(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The specified Interface" + "(" + args.Interface + ")" +\
" doesn't exist"
return res.text
def getIP(host, args, session):
"""
Called by the network function. Prints out IP address of given interface
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host+"/xyz/openbmc_project/network/" + args.Interface +\
"/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The specified Interface" + "(" + args.Interface + ")" +\
" doesn't exist"
return res.text
def deleteIP(host, args, session):
"""
Called by the network function. Deletes the IP address from given Interface
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
"""
url = "https://"+host+"/xyz/openbmc_project/network/" + args.Interface+\
"/enumerate"
data = {"data": []}
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The specified Interface" + "(" + args.Interface + ")" +\
" doesn't exist"
objDict = json.loads(res.text)
if not objDict['data']:
return "No object found for given address on given Interface"
for obj in objDict['data']:
try:
if args.address in objDict['data'][obj]['Address']:
url = "https://"+host+obj+"/action/Delete"
try:
res = session.post(url, headers=jsonHeader, json=data,
verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
else:
continue
except KeyError:
continue
return "No object found for address " + args.address + \
" on Interface(" + args.Interface + ")"
def addVLAN(host, args, session):
"""
Called by the network function. Creates VLAN on given interface.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host+"/xyz/openbmc_project/network/action/VLAN"
data = {"data": [args.Interface,int(args.Identifier)]}
try:
res = session.post(url, headers=jsonHeader, json=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 400:
return "Adding VLAN to interface" + "(" + args.Interface + ")" +\
" failed"
return res.text
def deleteVLAN(host, args, session):
"""
Called by the network function. Creates VLAN on given interface.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://" + host+"/xyz/openbmc_project/network/"+args.Interface+"/action/Delete"
data = {"data": []}
try:
res = session.post(url, headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "The specified VLAN"+"("+args.Interface+")" +" doesn't exist"
return res.text
def viewDHCPConfig(host, args, session):
"""
Called by the network function. Shows DHCP configured Properties.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url="https://"+host+"/xyz/openbmc_project/network/config/dhcp"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def configureDHCP(host, args, session):
"""
Called by the network function. Configures/updates DHCP Properties.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
try:
url="https://"+host+"/xyz/openbmc_project/network/config/dhcp"
if(args.DNSEnabled == True):
data = '{"data": 1}'
else:
data = '{"data": 0}'
res = session.put(url + '/attr/DNSEnabled', headers=jsonHeader,
data=data, verify=False, timeout=baseTimeout)
if(args.HostNameEnabled == True):
data = '{"data": 1}'
else:
data = '{"data": 0}'
res = session.put(url + '/attr/HostNameEnabled', headers=jsonHeader,
data=data, verify=False, timeout=baseTimeout)
if(args.NTPEnabled == True):
data = '{"data": 1}'
else:
data = '{"data": 0}'
res = session.put(url + '/attr/NTPEnabled', headers=jsonHeader,
data=data, verify=False, timeout=baseTimeout)
if(args.SendHostNameEnabled == True):
data = '{"data": 1}'
else:
data = '{"data": 0}'
res = session.put(url + '/attr/SendHostNameEnabled', headers=jsonHeader,
data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def nwReset(host, args, session):
"""
Called by the network function. Resets networks setting to factory defaults.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
"""
url = "https://"+host+"/xyz/openbmc_project/network/action/Reset"
data = '{"data":[] }'
try:
res = session.post(url, headers=jsonHeader, data=data, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def getLDAPTypeEnabled(host,session):
"""
Called by LDAP related functions to find the LDAP server type that has been enabled.
Returns None if LDAP has not been configured.
@param host: string, the hostname or IP address of the bmc
@param session: the active session to use
"""
enabled = False
url = 'https://'+host+'/xyz/openbmc_project/user/ldap/'
for key,value in serverTypeMap.items():
data = {"data": []}
try:
res = session.get(url + value + '/attr/Enabled', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
print(connectionErrHandler(args.json, "Timeout", None))
return
except(requests.exceptions.ConnectionError) as err:
print(connectionErrHandler(args.json, "ConnectionError", err))
return
enabled = res.json()['data']
if (enabled):
return key
def syncRoleMap(host,args,session,fromServerType,toServerType):
"""
Called by LDAP related functions to sync the role maps
Returns False if LDAP has not been configured.
@param host: string, the hostname or IP address of the bmc
@param session: the active session to use
@param fromServerType : Server type whose role map has to be copied
@param toServerType : Server type to which role map has to be copied
"""
url = "https://"+host+"/xyz/openbmc_project/user/ldap/"
try:
#Note: If the fromServerType has no role map, then
#the toServerType will not have any role map.
#delete the privilege mapping from the toServerType and
#then copy the privilege mapping from fromServerType to
#toServerType.
args.serverType = toServerType
res = deleteAllPrivilegeMapping(host, args, session)
data = {"data": []}
res = session.get(url + serverTypeMap[fromServerType] + '/role_map/enumerate', headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
#Previously enabled server type has no role map
if (res.status_code != requests.codes.ok):
#fromServerType has no role map; So, no need to copy
#role map to toServerType.
return
objDict = json.loads(res.text)
dataDict = objDict['data']
for key,value in dataDict.items():
data = {"data": [value["GroupName"], value["Privilege"]]}
res = session.post(url + serverTypeMap[toServerType] + '/action/Create', headers=jsonHeader, json = data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def createPrivilegeMapping(host, args, session):
"""
Called by the ldap function. Creates the group and the privilege mapping.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
"""
try:
if (isRedfishSupport):
url = 'https://'+host+'/xyz/openbmc_project/user/ldap/'
#To maintain the interface compatibility between op930 and op940, the server type has been made
#optional. If the server type is not specified, then create the role-mapper for the currently
#enabled server type.
serverType = args.serverType
if (serverType is None):
serverType = getLDAPTypeEnabled(host,session)
if (serverType is None):
return("LDAP server has not been enabled. Please specify LDAP serverType to proceed further...")
data = {"data": [args.groupName,args.privilege]}
res = session.post(url + serverTypeMap[serverType] + '/action/Create', headers=jsonHeader, json = data, verify=False, timeout=baseTimeout)
else:
url = 'https://'+host+'/xyz/openbmc_project/user/ldap/action/Create'
data = {"data": [args.groupName,args.privilege]}
res = session.post(url, headers=jsonHeader, json = data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def listPrivilegeMapping(host, args, session):
"""
Called by the ldap function. Lists the group and the privilege mapping.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
"""
if (isRedfishSupport):
serverType = args.serverType
if (serverType is None):
serverType = getLDAPTypeEnabled(host,session)
if (serverType is None):
return("LDAP has not been enabled. Please specify LDAP serverType to proceed further...")
url = 'https://'+host+'/xyz/openbmc_project/user/ldap/'+serverTypeMap[serverType]+'/role_map/enumerate'
else:
url = 'https://'+host+'/xyz/openbmc_project/user/ldap/enumerate'
data = {"data": []}
try:
res = session.get(url, headers=jsonHeader, json = data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def deletePrivilegeMapping(host, args, session):
"""
Called by the ldap function. Deletes the mapping associated with the group.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
"""
ldapNameSpaceObjects = listPrivilegeMapping(host, args, session)
ldapNameSpaceObjects = json.loads(ldapNameSpaceObjects)["data"]
path = ''
data = {"data": []}
if (isRedfishSupport):
if (args.serverType is None):
serverType = getLDAPTypeEnabled(host,session)
if (serverType is None):
return("LDAP has not been enabled. Please specify LDAP serverType to proceed further...")
# search for the object having the mapping for the given group
for key,value in ldapNameSpaceObjects.items():
if value['GroupName'] == args.groupName:
path = key
break
if path == '':
return "No privilege mapping found for this group."
# delete the object
url = 'https://'+host+path+'/action/Delete'
else:
# not interested in the config objet
ldapNameSpaceObjects.pop('/xyz/openbmc_project/user/ldap/config', None)
# search for the object having the mapping for the given group
for key,value in ldapNameSpaceObjects.items():
if value['GroupName'] == args.groupName:
path = key
break
if path == '':
return "No privilege mapping found for this group."
# delete the object
url = 'https://'+host+path+'/action/delete'
try:
res = session.post(url, headers=jsonHeader, json = data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def deleteAllPrivilegeMapping(host, args, session):
"""
Called by the ldap function. Deletes all the privilege mapping and group defined.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
"""
ldapNameSpaceObjects = listPrivilegeMapping(host, args, session)
ldapNameSpaceObjects = json.loads(ldapNameSpaceObjects)["data"]
path = ''
data = {"data": []}
if (isRedfishSupport):
if (args.serverType is None):
serverType = getLDAPTypeEnabled(host,session)
if (serverType is None):
return("LDAP has not been enabled. Please specify LDAP serverType to proceed further...")
else:
# Remove the config object.
ldapNameSpaceObjects.pop('/xyz/openbmc_project/user/ldap/config', None)
try:
# search for GroupName property and delete if it is available.
for path in ldapNameSpaceObjects.keys():
# delete the object
url = 'https://'+host+path+'/action/Delete'
res = session.post(url, headers=jsonHeader, json = data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
return res.text
def viewLDAPConfig(host, args, session):
"""
Called by the ldap function. Prints out active LDAP configuration properties
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@param session: the active session to use
@return returns LDAP's configured properties.
"""
try:
if (isRedfishSupport):
url = "https://"+host+"/xyz/openbmc_project/user/ldap/"
serverTypeEnabled = getLDAPTypeEnabled(host,session)
if (serverTypeEnabled is not None):
data = {"data": []}
res = session.get(url + serverTypeMap[serverTypeEnabled], headers=jsonHeader, json=data, verify=False, timeout=baseTimeout)
else:
return("LDAP server has not been enabled...")
else :
url = "https://"+host+"/xyz/openbmc_project/user/ldap/config"
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
if res.status_code == 404:
return "LDAP server config has not been created"
return res.text
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def localUsers(host, args, session):
"""
Enables and disables local BMC users.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the logging sub command
@param session: the active session to use
"""
url="https://{hostname}/xyz/openbmc_project/user/enumerate".format(hostname=host)
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
usersDict = json.loads(res.text)
if not usersDict['data']:
return "No users found"
output = ""
for user in usersDict['data']:
# Skip LDAP and another non-local users
if 'UserEnabled' not in usersDict['data'][user]:
continue
name = user.split('/')[-1]
url = "https://{hostname}{user}/attr/UserEnabled".format(hostname=host, user=user)
if args.local_users == "queryenabled":
try:
res = session.get(url, headers=jsonHeader,verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
result = json.loads(res.text)
output += ("User: {name} Enabled: {result}\n").format(name=name, result=result['data'])
elif args.local_users in ["enableall", "disableall"]:
action = ""
if args.local_users == "enableall":
data = '{"data": true}'
action = "Enabling"
else:
data = '{"data": false}'
action = "Disabling"
output += "{action} {name}\n".format(action=action, name=name)
try:
resp = session.put(url, headers=jsonHeader, data=data, verify=False, timeout=baseTimeout)
except(requests.exceptions.Timeout):
return connectionErrHandler(args.json, "Timeout", None)
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
else:
return "Invalid local users argument"
return output
def setPassword(host, args, session):
"""
Set local user password
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the logging sub
command
@param session: the active session to use
@param args.json: boolean, if this flag is set to true, the output
will be provided in json format for programmatic consumption
@return: Session object
"""
try:
if(isRedfishSupport):
url = "https://" + host + "/redfish/v1/AccountService/Accounts/"+ \
args.user
data = {"Password":args.password}
res = session.patch(url, headers=jsonHeader, json=data,
verify=False, timeout=baseTimeout)
else:
url = "https://" + host + "/xyz/openbmc_project/user/" + args.user + \
"/action/SetPassword"
res = session.post(url, headers=jsonHeader,
json={"data": [args.password]}, verify=False,
timeout=baseTimeout)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
except(requests.exceptions.RequestException) as err:
return connectionErrHandler(args.json, "RequestException", err)
return res.status_code
def getThermalZones(host, args, session):
"""
Get the available thermal control zones
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used to get the thermal
control zones
@param session: the active session to use
@return: Session object
"""
url = "https://" + host + "/xyz/openbmc_project/control/thermal/enumerate"
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=30)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
except(requests.exceptions.RequestException) as err:
return connectionErrHandler(args.json, "RequestException", err)
if (res.status_code == 404):
return "No thermal control zones found"
zonesDict = json.loads(res.text)
if not zonesDict['data']:
return "No thermal control zones found"
for zone in zonesDict['data']:
z = ",".join(str(zone.split('/')[-1]) for zone in zonesDict['data'])
return "Zones: [ " + z + " ]"
def getThermalMode(host, args, session):
"""
Get thermal control mode
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used to get the thermal
control mode
@param session: the active session to use
@param args.zone: the zone to get the mode on
@return: Session object
"""
url = "https://" + host + "/xyz/openbmc_project/control/thermal/" + \
args.zone
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=30)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
except(requests.exceptions.RequestException) as err:
return connectionErrHandler(args.json, "RequestException", err)
if (res.status_code == 404):
return "Thermal control zone(" + args.zone + ") not found"
propsDict = json.loads(res.text)
if not propsDict['data']:
return "No thermal control properties found on zone(" + args.zone + ")"
curMode = "Current"
supModes = "Supported"
result = "\n"
for prop in propsDict['data']:
if (prop.casefold() == curMode.casefold()):
result += curMode + " Mode: " + propsDict['data'][curMode] + "\n"
if (prop.casefold() == supModes.casefold()):
s = ", ".join(str(sup) for sup in propsDict['data'][supModes])
result += supModes + " Modes: [ " + s + " ]\n"
return result
def setThermalMode(host, args, session):
"""
Set thermal control mode
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used for setting the thermal
control mode
@param session: the active session to use
@param args.zone: the zone to set the mode on
@param args.mode: the mode to enable
@return: Session object
"""
url = "https://" + host + "/xyz/openbmc_project/control/thermal/" + \
args.zone + "/attr/Current"
# Check args.mode against supported modes using `getThermalMode` output
modes = getThermalMode(host, args, session)
modes = os.linesep.join([m for m in modes.splitlines() if m])
modes = modes.replace("\n", ";").strip()
modesDict = dict(m.split(': ') for m in modes.split(';'))
sModes = ''.join(s for s in modesDict['Supported Modes'] if s not in '[ ]')
if args.mode.casefold() not in \
(m.casefold() for m in sModes.split(',')) or not args.mode:
result = ("Unsupported mode('" + args.mode + "') given, " +
"select a supported mode: \n" +
getThermalMode(host, args, session))
return result
data = '{"data":"' + args.mode + '"}'
try:
res = session.get(url, headers=jsonHeader, verify=False, timeout=30)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
except(requests.exceptions.RequestException) as err:
return connectionErrHandler(args.json, "RequestException", err)
if (data and res.status_code != 404):
try:
res = session.put(url, headers=jsonHeader,
data=data, verify=False,
timeout=30)
except(requests.exceptions.Timeout):
return(connectionErrHandler(args.json, "Timeout", None))
except(requests.exceptions.ConnectionError) as err:
return connectionErrHandler(args.json, "ConnectionError", err)
except(requests.exceptions.RequestException) as err:
return connectionErrHandler(args.json, "RequestException", err)
if res.status_code == 403:
return "The specified thermal control zone(" + args.zone + ")" + \
" does not exist"
return res.text
else:
return "Setting thermal control mode(" + args.mode + ")" + \
" not supported or operation not available"
def createCommandParser():
"""
creates the parser for the command line along with help for each command and subcommand
@return: returns the parser for the command line
"""
parser = argparse.ArgumentParser(description='Process arguments')
parser.add_argument("-H", "--host", help='A hostname or IP for the BMC')
parser.add_argument("-U", "--user", help='The username to login with')
group = parser.add_mutually_exclusive_group()
group.add_argument("-A", "--askpw", action='store_true', help='prompt for password')
group.add_argument("-P", "--PW", help='Provide the password in-line')
group.add_argument("-E", "--PWenvvar", action='store_true', help='Get password from envvar OPENBMCTOOL_PASSWORD')
parser.add_argument('-j', '--json', action='store_true', help='output json data only')
parser.add_argument('-t', '--policyTableLoc', help='The location of the policy table to parse alerts')
parser.add_argument('-c', '--CerFormat', action='store_true', help=argparse.SUPPRESS)
parser.add_argument('-T', '--procTime', action='store_true', help= argparse.SUPPRESS)
parser.add_argument('-V', '--version', action='store_true', help='Display the version number of the openbmctool')
subparsers = parser.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
#fru command
parser_inv = subparsers.add_parser("fru", help='Work with platform inventory')
inv_subparser = parser_inv.add_subparsers(title='subcommands', description='valid inventory actions', help="valid inventory actions", dest='command')
inv_subparser.required = True
#fru print
inv_print = inv_subparser.add_parser("print", help="prints out a list of all FRUs")
inv_print.set_defaults(func=fruPrint)
#fru list [0....n]
inv_list = inv_subparser.add_parser("list", help="print out details on selected FRUs. Specifying no items will list the entire inventory")
inv_list.add_argument('items', nargs='?', help="print out details on selected FRUs. Specifying no items will list the entire inventory")
inv_list.set_defaults(func=fruList)
#fru status
inv_status = inv_subparser.add_parser("status", help="prints out the status of all FRUs")
inv_status.add_argument('-v', '--verbose', action='store_true', help='Verbose output')
inv_status.set_defaults(func=fruStatus)
#sensors command
parser_sens = subparsers.add_parser("sensors", help="Work with platform sensors")
sens_subparser=parser_sens.add_subparsers(title='subcommands', description='valid sensor actions', help='valid sensor actions', dest='command')
sens_subparser.required = True
#sensor print
sens_print= sens_subparser.add_parser('print', help="prints out a list of all Sensors.")
sens_print.set_defaults(func=sensor)
#sensor list[0...n]
sens_list=sens_subparser.add_parser("list", help="Lists all Sensors in the platform. Specify a sensor for full details. ")
sens_list.add_argument("sensNum", nargs='?', help="The Sensor number to get full details on" )
sens_list.set_defaults(func=sensor)
#thermal control commands
parser_therm = subparsers.add_parser("thermal", help="Work with thermal control parameters")
therm_subparser=parser_therm.add_subparsers(title='subcommands', description='Thermal control actions to work with', help='Valid thermal control actions to work with', dest='command')
#thermal control zones
parser_thermZones = therm_subparser.add_parser("zones", help="Get a list of available thermal control zones")
parser_thermZones.set_defaults(func=getThermalZones)
#thermal control modes
parser_thermMode = therm_subparser.add_parser("modes", help="Work with thermal control modes")
thermMode_sub = parser_thermMode.add_subparsers(title='subactions', description='Work with thermal control modes', help="Work with thermal control modes")
#get thermal control mode
parser_getThermMode = thermMode_sub.add_parser("get", help="Get current and supported thermal control modes")
parser_getThermMode.add_argument('-z', '--zone', required=True, help='Thermal zone to work with')
parser_getThermMode.set_defaults(func=getThermalMode)
#set thermal control mode
parser_setThermMode = thermMode_sub.add_parser("set", help="Set the thermal control mode")
parser_setThermMode.add_argument('-z', '--zone', required=True, help='Thermal zone to work with')
parser_setThermMode.add_argument('-m', '--mode', required=True, help='The supported thermal control mode')
parser_setThermMode.set_defaults(func=setThermalMode)
#sel command
parser_sel = subparsers.add_parser("sel", help="Work with platform alerts")
sel_subparser = parser_sel.add_subparsers(title='subcommands', description='valid SEL actions', help = 'valid SEL actions', dest='command')
sel_subparser.required = True
#sel print
sel_print = sel_subparser.add_parser("print", help="prints out a list of all sels in a condensed list")
sel_print.add_argument('-d', '--devdebug', action='store_true', help=argparse.SUPPRESS)
sel_print.add_argument('-v', '--verbose', action='store_true', help="Changes the output to being very verbose")
sel_print.add_argument('-f', '--fileloc', help='Parse a file instead of the BMC output')
sel_print.set_defaults(func=selPrint)
#sel list
sel_list = sel_subparser.add_parser("list", help="Lists all SELs in the platform. Specifying a specific number will pull all the details for that individual SEL")
sel_list.add_argument("selNum", nargs='?', type=int, help="The SEL entry to get details on")
sel_list.set_defaults(func=selList)
sel_get = sel_subparser.add_parser("get", help="Gets the verbose details of a specified SEL entry")
sel_get.add_argument('selNum', type=int, help="the number of the SEL entry to get")
sel_get.set_defaults(func=selList)
sel_clear = sel_subparser.add_parser("clear", help="Clears all entries from the SEL")
sel_clear.set_defaults(func=selClear)
sel_setResolved = sel_subparser.add_parser("resolve", help="Sets the sel entry to resolved")
sel_setResolved.add_argument('-n', '--selNum', type=int, help="the number of the SEL entry to resolve")
sel_ResolveAll_sub = sel_setResolved.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
sel_ResolveAll = sel_ResolveAll_sub.add_parser('all', help='Resolve all SEL entries')
sel_ResolveAll.set_defaults(func=selResolveAll)
sel_setResolved.set_defaults(func=selSetResolved)
parser_chassis = subparsers.add_parser("chassis", help="Work with chassis power and status")
chas_sub = parser_chassis.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
parser_chassis.add_argument('status', action='store_true', help='Returns the current status of the platform')
parser_chassis.set_defaults(func=chassis)
parser_chasPower = chas_sub.add_parser("power", help="Turn the chassis on or off, check the power state")
parser_chasPower.add_argument('powcmd', choices=['on','softoff', 'hardoff', 'status'], help='The value for the power command. on, off, or status')
parser_chasPower.set_defaults(func=chassisPower)
#control the chassis identify led
parser_chasIdent = chas_sub.add_parser("identify", help="Control the chassis identify led")
parser_chasIdent.add_argument('identcmd', choices=['on', 'off', 'status'], help='The control option for the led: on, off, blink, status')
parser_chasIdent.set_defaults(func=chassisIdent)
#collect service data
parser_servData = subparsers.add_parser("collect_service_data", help="Collect all bmc data needed for service")
parser_servData.add_argument('-d', '--devdebug', action='store_true', help=argparse.SUPPRESS)
parser_servData.set_defaults(func=collectServiceData)
#system quick health check
parser_healthChk = subparsers.add_parser("health_check", help="Work with platform sensors")
parser_healthChk.set_defaults(func=healthCheck)
#tasks
parser_tasks = subparsers.add_parser("task", help="Work with tasks")
tasks_sub = parser_tasks.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
tasks_sub.required = True
get_Task = tasks_sub.add_parser('get', help="Get on Task Monitor URL")
get_Task.add_argument("-u", "--taskURI", help="Task Monitor URI")
get_Task.set_defaults(func=getTask)
#work with dumps
parser_bmcdump = subparsers.add_parser("dump", help="Work with dumps")
parser_bmcdump.add_argument("-t", "--dumpType", default='bmc', choices=['bmc','SystemDump'],help="Type of dump")
bmcDump_sub = parser_bmcdump.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
bmcDump_sub.required = True
dump_Create = bmcDump_sub.add_parser('create', help="Create a dump of given type")
dump_Create.set_defaults(func=dumpCreate)
dump_list = bmcDump_sub.add_parser('list', help="list all dumps")
dump_list.set_defaults(func=dumpList)
parserdumpdelete = bmcDump_sub.add_parser('delete', help="Delete dump")
parserdumpdelete.add_argument("-n", "--dumpNum", nargs='*', type=int, help="The Dump entry to delete")
parserdumpdelete.set_defaults(func=dumpDelete)
bmcDumpDelsub = parserdumpdelete.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
deleteAllDumps = bmcDumpDelsub.add_parser('all', help='Delete all dumps')
deleteAllDumps.set_defaults(func=dumpDeleteAll)
parser_dumpretrieve = bmcDump_sub.add_parser('retrieve', help='Retrieve a dump file')
parser_dumpretrieve.add_argument("-n,", "--dumpNum", help="The Dump entry to retrieve")
parser_dumpretrieve.add_argument("-s", "--dumpSaveLoc", help="The location to save the bmc dump file or file path for system dump")
parser_dumpretrieve.set_defaults(func=dumpRetrieve)
#bmc command for reseting the bmc
parser_bmc = subparsers.add_parser('bmc', help="Work with the bmc")
bmc_sub = parser_bmc.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
parser_BMCReset = bmc_sub.add_parser('reset', help='Reset the bmc' )
parser_BMCReset.add_argument('type', choices=['warm','cold'], help="Warm: Reboot the BMC, Cold: CLEAR config and reboot bmc")
parser_bmc.add_argument('info', action='store_true', help="Displays information about the BMC hardware, including device revision, firmware revision, IPMI version supported, manufacturer ID, and information on additional device support.")
parser_bmc.set_defaults(func=bmc)
#add alias to the bmc command
parser_mc = subparsers.add_parser('mc', help="Work with the management controller")
mc_sub = parser_mc.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
parser_MCReset = mc_sub.add_parser('reset', help='Reset the bmc' )
parser_MCReset.add_argument('type', choices=['warm','cold'], help="Reboot the BMC")
#parser_MCReset.add_argument('cold', action='store_true', help="Reboot the BMC and CLEAR the configuration")
parser_mc.add_argument('info', action='store_true', help="Displays information about the BMC hardware, including device revision, firmware revision, IPMI version supported, manufacturer ID, and information on additional device support.")
parser_MCReset.set_defaults(func=bmcReset)
parser_mc.set_defaults(func=bmc)
#gard clear
parser_gc = subparsers.add_parser("gardclear", help="Used to clear gard records")
parser_gc.set_defaults(func=gardClear)
#firmware_flash
parser_fw = subparsers.add_parser("firmware", help="Work with the system firmware")
fwflash_subproc = parser_fw.add_subparsers(title='subcommands', description='valid firmware commands', help='sub-command help', dest='command')
fwflash_subproc.required = True
fwflash = fwflash_subproc.add_parser('flash', help="Flash the system firmware")
fwflash.add_argument('type', choices=['bmc', 'pnor'], help="image type to flash")
fwflash.add_argument('-f', '--fileloc', required=True, help="The absolute path to the firmware image")
fwflash.set_defaults(func=fwFlash)
fwActivate = fwflash_subproc.add_parser('activate', help="Activate existing image on the bmc")
fwActivate.add_argument('imageID', help="The image ID to activate from the firmware list. Ex: 63c95399")
fwActivate.set_defaults(func=activateFWImage)
fwActivateStatus = fwflash_subproc.add_parser('activation_status', help="Check Status of activations")
fwActivateStatus.set_defaults(func=activateStatus)
fwList = fwflash_subproc.add_parser('list', help="List all of the installed firmware")
fwList.add_argument('-v', '--verbose', action='store_true', help='Verbose output')
fwList.set_defaults(func=firmwareList)
fwprint = fwflash_subproc.add_parser('print', help="List all of the installed firmware")
fwprint.add_argument('-v', '--verbose', action='store_true', help='Verbose output')
fwprint.set_defaults(func=firmwareList)
fwDelete = fwflash_subproc.add_parser('delete', help="Delete an existing firmware version")
fwDelete.add_argument('versionID', help="The version ID to delete from the firmware list. Ex: 63c95399")
fwDelete.set_defaults(func=deleteFWVersion)
fwDeleteAll = fwflash_subproc.add_parser('deleteAll', help="Delete ALL firmware versions")
fwDeleteAll.set_defaults(func=deleteFWAll)
#logging
parser_logging = subparsers.add_parser("logging", help="logging controls")
logging_sub = parser_logging.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
#turn rest api logging on/off
parser_rest_logging = logging_sub.add_parser("rest_api", help="turn rest api logging on/off")
parser_rest_logging.add_argument('rest_logging', choices=['on', 'off'], help='The control option for rest logging: on, off')
parser_rest_logging.set_defaults(func=restLogging)
#remote logging
parser_remote_logging = logging_sub.add_parser("remote_logging", help="Remote logging (rsyslog) commands")
parser_remote_logging.add_argument('remote_logging', choices=['view', 'disable'], help='Remote logging (rsyslog) commands')
parser_remote_logging.set_defaults(func=remoteLogging)
#configure remote logging
parser_remote_logging_config = logging_sub.add_parser("remote_logging_config", help="Configure remote logging (rsyslog)")
parser_remote_logging_config.add_argument("-a", "--address", required=True, help="Set IP address of rsyslog server")
parser_remote_logging_config.add_argument("-p", "--port", required=True, type=int, help="Set Port of rsyslog server")
parser_remote_logging_config.set_defaults(func=remoteLoggingConfig)
#certificate management
parser_cert = subparsers.add_parser("certificate", help="Certificate management")
certMgmt_subproc = parser_cert.add_subparsers(title='subcommands', description='valid certificate commands', help='sub-command help', dest='command')
certUpdate = certMgmt_subproc.add_parser('update', help="Update the certificate")
certUpdate.add_argument('type', choices=['server', 'client', 'authority'], help="certificate type to update")
certUpdate.add_argument('service', choices=['https', 'ldap'], help="Service to update")
certUpdate.add_argument('-f', '--fileloc', required=True, help="The absolute path to the certificate file")
certUpdate.set_defaults(func=certificateUpdate)
certDelete = certMgmt_subproc.add_parser('delete', help="Delete the certificate")
certDelete.add_argument('type', choices=['server', 'client', 'authority'], help="certificate type to delete")
certDelete.add_argument('service', choices=['https', 'ldap'], help="Service to delete the certificate")
certDelete.set_defaults(func=certificateDelete)
certReplace = certMgmt_subproc.add_parser('replace',
help="Replace the certificate")
certReplace.add_argument('type', choices=['server', 'client', 'authority'],
help="certificate type to replace")
certReplace.add_argument('service', choices=['https', 'ldap'],
help="Service to replace the certificate")
certReplace.add_argument('-f', '--fileloc', required=True,
help="The absolute path to the certificate file")
certReplace.set_defaults(func=certificateReplace)
certDisplay = certMgmt_subproc.add_parser('display',
help="Print the certificate")
certDisplay.add_argument('type', choices=['server', 'client', 'authority'],
help="certificate type to display")
certDisplay.set_defaults(func=certificateDisplay)
certList = certMgmt_subproc.add_parser('list',
help="Certificate list")
certList.set_defaults(func=certificateList)
certGenerateCSR = certMgmt_subproc.add_parser('generatecsr', help="Generate CSR")
certGenerateCSR.add_argument('type', choices=['server', 'client', 'authority'],
help="Generate CSR")
certGenerateCSR.add_argument('city',
help="The city or locality of the organization making the request")
certGenerateCSR.add_argument('commonName',
help="The fully qualified domain name of the component that is being secured.")
certGenerateCSR.add_argument('country',
help="The country of the organization making the request")
certGenerateCSR.add_argument('organization',
help="The name of the organization making the request.")
certGenerateCSR.add_argument('organizationUnit',
help="The name of the unit or division of the organization making the request.")
certGenerateCSR.add_argument('state',
help="The state, province, or region of the organization making the request.")
certGenerateCSR.add_argument('keyPairAlgorithm', choices=['RSA', 'EC'],
help="The type of key pair for use with signing algorithms.")
certGenerateCSR.add_argument('keyCurveId',
help="The curve ID to be used with the key, if needed based on the value of the 'KeyPairAlgorithm' parameter.")
certGenerateCSR.add_argument('contactPerson',
help="The name of the user making the request")
certGenerateCSR.add_argument('email',
help="The email address of the contact within the organization")
certGenerateCSR.add_argument('alternativeNames',
help="Additional hostnames of the component that is being secured")
certGenerateCSR.add_argument('givenname',
help="The given name of the user making the request")
certGenerateCSR.add_argument('surname',
help="The surname of the user making the request")
certGenerateCSR.add_argument('unstructuredname',
help="he unstructured name of the subject")
certGenerateCSR.add_argument('initials',
help="The initials of the user making the request")
certGenerateCSR.set_defaults(func=certificateGenerateCSR)
# local users
parser_users = subparsers.add_parser("local_users", help="Work with local users")
parser_users.add_argument('local_users', choices=['disableall','enableall', 'queryenabled'], help="Disable, enable or query local user accounts")
parser_users.add_argument('-v', '--verbose', action='store_true', help='Verbose output')
parser_users.set_defaults(func=localUsers)
#LDAP
parser_ldap = subparsers.add_parser("ldap", help="LDAP controls")
ldap_sub = parser_ldap.add_subparsers(title='subcommands', description='valid subcommands',help="sub-command help", dest='command')
#configure and enable LDAP
parser_ldap_config = ldap_sub.add_parser("enable", help="Configure and enables the LDAP")
parser_ldap_config.add_argument("-a", "--uri", required=True, help="Set LDAP server URI")
parser_ldap_config.add_argument("-B", "--bindDN", required=True, help="Set the bind DN of the LDAP server")
parser_ldap_config.add_argument("-b", "--baseDN", required=True, help="Set the base DN of the LDAP server")
parser_ldap_config.add_argument("-p", "--bindPassword", required=True, help="Set the bind password of the LDAP server")
parser_ldap_config.add_argument("-S", "--scope", choices=['sub','one', 'base'],
help='Specifies the search scope:subtree, one level or base object.')
parser_ldap_config.add_argument("-t", "--serverType", required=True, choices=['ActiveDirectory','OpenLDAP'],
help='Specifies the configured server is ActiveDirectory(AD) or OpenLdap')
parser_ldap_config.add_argument("-g","--groupAttrName", required=False, default='', help="Group Attribute Name")
parser_ldap_config.add_argument("-u","--userAttrName", required=False, default='', help="User Attribute Name")
parser_ldap_config.set_defaults(func=enableLDAPConfig)
# disable LDAP
parser_disable_ldap = ldap_sub.add_parser("disable", help="disables the LDAP")
parser_disable_ldap.set_defaults(func=disableLDAP)
# view-config
parser_ldap_config = \
ldap_sub.add_parser("view-config", help="prints out a list of all \
LDAPS's configured properties")
parser_ldap_config.set_defaults(func=viewLDAPConfig)
#create group privilege mapping
parser_ldap_mapper = ldap_sub.add_parser("privilege-mapper", help="LDAP group privilege controls")
parser_ldap_mapper_sub = parser_ldap_mapper.add_subparsers(title='subcommands', description='valid subcommands',
help="sub-command help", dest='command')
parser_ldap_mapper_create = parser_ldap_mapper_sub.add_parser("create", help="Create mapping of ldap group and privilege")
parser_ldap_mapper_create.add_argument("-t", "--serverType", choices=['ActiveDirectory','OpenLDAP'],
help='Specifies the configured server is ActiveDirectory(AD) or OpenLdap')
parser_ldap_mapper_create.add_argument("-g","--groupName",required=True,help="Group Name")
parser_ldap_mapper_create.add_argument("-p","--privilege",choices=['priv-admin','priv-operator','priv-user','priv-callback'],required=True,help="Privilege")
parser_ldap_mapper_create.set_defaults(func=createPrivilegeMapping)
#list group privilege mapping
parser_ldap_mapper_list = parser_ldap_mapper_sub.add_parser("list",help="List privilege mapping")
parser_ldap_mapper_list.add_argument("-t", "--serverType", choices=['ActiveDirectory','OpenLDAP'],
help='Specifies the configured server is ActiveDirectory(AD) or OpenLdap')
parser_ldap_mapper_list.set_defaults(func=listPrivilegeMapping)
#delete group privilege mapping
parser_ldap_mapper_delete = parser_ldap_mapper_sub.add_parser("delete",help="Delete privilege mapping")
parser_ldap_mapper_delete.add_argument("-t", "--serverType", choices=['ActiveDirectory','OpenLDAP'],
help='Specifies the configured server is ActiveDirectory(AD) or OpenLdap')
parser_ldap_mapper_delete.add_argument("-g","--groupName",required=True,help="Group Name")
parser_ldap_mapper_delete.set_defaults(func=deletePrivilegeMapping)
#deleteAll group privilege mapping
parser_ldap_mapper_delete = parser_ldap_mapper_sub.add_parser("purge",help="Delete All privilege mapping")
parser_ldap_mapper_delete.add_argument("-t", "--serverType", choices=['ActiveDirectory','OpenLDAP'],
help='Specifies the configured server is ActiveDirectory(AD) or OpenLdap')
parser_ldap_mapper_delete.set_defaults(func=deleteAllPrivilegeMapping)
# set local user password
parser_set_password = subparsers.add_parser("set_password",
help="Set password of local user")
parser_set_password.add_argument( "-p", "--password", required=True,
help="Password of local user")
parser_set_password.set_defaults(func=setPassword)
# network
parser_nw = subparsers.add_parser("network", help="network controls")
nw_sub = parser_nw.add_subparsers(title='subcommands',
description='valid subcommands',
help="sub-command help",
dest='command')
# enable DHCP
parser_enable_dhcp = nw_sub.add_parser("enableDHCP",
help="enables the DHCP on given "
"Interface")
parser_enable_dhcp.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_enable_dhcp.set_defaults(func=enableDHCP)
# disable DHCP
parser_disable_dhcp = nw_sub.add_parser("disableDHCP",
help="disables the DHCP on given "
"Interface")
parser_disable_dhcp.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_disable_dhcp.set_defaults(func=disableDHCP)
# get HostName
parser_gethostname = nw_sub.add_parser("getHostName",
help="prints out HostName")
parser_gethostname.set_defaults(func=getHostname)
# set HostName
parser_sethostname = nw_sub.add_parser("setHostName", help="sets HostName")
parser_sethostname.add_argument("-H", "--HostName", required=True,
help="A HostName for the BMC")
parser_sethostname.set_defaults(func=setHostname)
# get domainname
parser_getdomainname = nw_sub.add_parser("getDomainName",
help="prints out DomainName of "
"given Interface")
parser_getdomainname.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it "
"can be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_getdomainname.set_defaults(func=getDomainName)
# set domainname
parser_setdomainname = nw_sub.add_parser("setDomainName",
help="sets DomainName of given "
"Interface")
parser_setdomainname.add_argument("-D", "--DomainName", required=True,
help="Ex: DomainName=Domain1,Domain2,...")
parser_setdomainname.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it "
"can be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_setdomainname.set_defaults(func=setDomainName)
# get MACAddress
parser_getmacaddress = nw_sub.add_parser("getMACAddress",
help="prints out MACAddress the "
"given Interface")
parser_getmacaddress.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it "
"can be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_getmacaddress.set_defaults(func=getMACAddress)
# set MACAddress
parser_setmacaddress = nw_sub.add_parser("setMACAddress",
help="sets MACAddress")
parser_setmacaddress.add_argument("-MA", "--MACAddress", required=True,
help="A MACAddress for the given "
"Interface")
parser_setmacaddress.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_setmacaddress.set_defaults(func=setMACAddress)
# get DefaultGW
parser_getdefaultgw = nw_sub.add_parser("getDefaultGW",
help="prints out DefaultGateway "
"the BMC")
parser_getdefaultgw.set_defaults(func=getDefaultGateway)
# set DefaultGW
parser_setdefaultgw = nw_sub.add_parser("setDefaultGW",
help="sets DefaultGW")
parser_setdefaultgw.add_argument("-GW", "--DefaultGW", required=True,
help="A DefaultGateway for the BMC")
parser_setdefaultgw.set_defaults(func=setDefaultGateway)
# view network Config
parser_ldap_config = nw_sub.add_parser("view-config", help="prints out a "
"list of all network's configured "
"properties")
parser_ldap_config.set_defaults(func=viewNWConfig)
# get DNS
parser_getDNS = nw_sub.add_parser("getDNS",
help="prints out DNS servers on the "
"given interface")
parser_getDNS.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_getDNS.set_defaults(func=getDNS)
# set DNS
parser_setDNS = nw_sub.add_parser("setDNS",
help="sets DNS servers on the given "
"interface")
parser_setDNS.add_argument("-d", "--DNSServers", required=True,
help="Ex: DNSSERVERS=DNS1,DNS2,...")
parser_setDNS.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_setDNS.set_defaults(func=setDNS)
# get NTP
parser_getNTP = nw_sub.add_parser("getNTP",
help="prints out NTP servers on the "
"given interface")
parser_getNTP.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_getNTP.set_defaults(func=getNTP)
# set NTP
parser_setNTP = nw_sub.add_parser("setNTP",
help="sets NTP servers on the given "
"interface")
parser_setNTP.add_argument("-N", "--NTPServers", required=True,
help="Ex: NTPSERVERS=NTP1,NTP2,...")
parser_setNTP.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_setNTP.set_defaults(func=setNTP)
# configure IP
parser_ip_config = nw_sub.add_parser("addIP", help="Sets IP address to"
"given interface")
parser_ip_config.add_argument("-a", "--address", required=True,
help="IP address of given interface")
parser_ip_config.add_argument("-gw", "--gateway", required=False, default='',
help="The gateway for given interface")
parser_ip_config.add_argument("-l", "--prefixLength", required=True,
help="The prefixLength of IP address")
parser_ip_config.add_argument("-p", "--type", required=True,
choices=['ipv4', 'ipv6'],
help="The protocol type of the given"
"IP address")
parser_ip_config.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_ip_config.set_defaults(func=addIP)
# getIP
parser_getIP = nw_sub.add_parser("getIP", help="prints out IP address"
"of given interface")
parser_getIP.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_getIP.set_defaults(func=getIP)
# rmIP
parser_rmIP = nw_sub.add_parser("rmIP", help="deletes IP address"
"of given interface")
parser_rmIP.add_argument("-a", "--address", required=True,
help="IP address to remove form given Interface")
parser_rmIP.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_rmIP.set_defaults(func=deleteIP)
# add VLAN
parser_create_vlan = nw_sub.add_parser("addVLAN", help="enables VLAN "
"on given interface with given "
"VLAN Identifier")
parser_create_vlan.add_argument("-I", "--Interface", required=True,
choices=['eth0', 'eth1'],
help="Name of the ethernet interface")
parser_create_vlan.add_argument("-n", "--Identifier", required=True,
help="VLAN Identifier")
parser_create_vlan.set_defaults(func=addVLAN)
# delete VLAN
parser_delete_vlan = nw_sub.add_parser("deleteVLAN", help="disables VLAN "
"on given interface with given "
"VLAN Identifier")
parser_delete_vlan.add_argument("-I", "--Interface", required=True,
help="Name of the ethernet interface(it can"
"be obtained by the "
"command:network view-config)"
"Ex: eth0 or eth1 or VLAN(VLAN=eth0_50 etc)")
parser_delete_vlan.set_defaults(func=deleteVLAN)
# viewDHCPConfig
parser_viewDHCPConfig = nw_sub.add_parser("viewDHCPConfig",
help="Shows DHCP configured "
"Properties")
parser_viewDHCPConfig.set_defaults(func=viewDHCPConfig)
# configureDHCP
parser_configDHCP = nw_sub.add_parser("configureDHCP",
help="Configures/updates DHCP "
"Properties")
parser_configDHCP.add_argument("-d", "--DNSEnabled", type=str2bool,
required=True, help="Sets DNSEnabled property")
parser_configDHCP.add_argument("-n", "--HostNameEnabled", type=str2bool,
required=True,
help="Sets HostNameEnabled property")
parser_configDHCP.add_argument("-t", "--NTPEnabled", type=str2bool,
required=True,
help="Sets NTPEnabled property")
parser_configDHCP.add_argument("-s", "--SendHostNameEnabled", type=str2bool,
required=True,
help="Sets SendHostNameEnabled property")
parser_configDHCP.set_defaults(func=configureDHCP)
# network factory reset
parser_nw_reset = nw_sub.add_parser("nwReset",
help="Resets networks setting to "
"factory defaults. "
"note:Reset settings will be applied "
"after BMC reboot")
parser_nw_reset.set_defaults(func=nwReset)
return parser
def main(argv=None):
"""
main function for running the command line utility as a sub application
"""
global toolVersion
toolVersion = "1.19"
global isRedfishSupport
parser = createCommandParser()
args = parser.parse_args(argv)
totTimeStart = int(round(time.time()*1000))
if(sys.version_info < (3,0)):
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
if sys.version_info >= (3,0):
requests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)
if (args.version):
print("Version: "+ toolVersion)
sys.exit(0)
if (hasattr(args, 'fileloc') and args.fileloc is not None and 'print' in args.command):
mysess = None
print(selPrint('N/A', args, mysess))
else:
if(hasattr(args, 'host') and hasattr(args,'user')):
if (args.askpw):
pw = getpass.getpass()
elif(args.PW is not None):
pw = args.PW
elif(args.PWenvvar):
pw = os.environ['OPENBMCTOOL_PASSWORD']
else:
print("You must specify a password")
sys.exit()
logintimeStart = int(round(time.time()*1000))
mysess = login(args.host, args.user, pw, args.json,
args.command == 'set_password')
if(mysess == None):
print("Login Failed!")
sys.exit()
if(sys.version_info < (3,0)):
if isinstance(mysess, basestring):
print(mysess)
sys.exit(1)
elif sys.version_info >= (3,0):
if isinstance(mysess, str):
print(mysess)
sys.exit(1)
logintimeStop = int(round(time.time()*1000))
isRedfishSupport = redfishSupportPresent(args.host,mysess)
commandTimeStart = int(round(time.time()*1000))
output = args.func(args.host, args, mysess)
commandTimeStop = int(round(time.time()*1000))
if isinstance(output, dict):
print(json.dumps(output, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False))
else:
print(output)
if (mysess is not None):
logout(args.host, args.user, pw, mysess, args.json)
if(args.procTime):
print("Total time: " + str(int(round(time.time()*1000))- totTimeStart))
print("loginTime: " + str(logintimeStop - logintimeStart))
print("command Time: " + str(commandTimeStop - commandTimeStart))
else:
print("usage:\n"
" OPENBMCTOOL_PASSWORD=secret # if using -E\n"
" openbmctool.py [-h] -H HOST -U USER {-A | -P PW | -E} [-j]\n" +
"\t[-t POLICYTABLELOC] [-V]\n" +
"\t{fru,sensors,sel,chassis,collect_service_data, \
health_check,dump,bmc,mc,gardclear,firmware,logging}\n" +
"\t...\n" +
"openbmctool.py: error: the following arguments are required: -H/--host, -U/--user")
sys.exit()
if __name__ == '__main__':
"""
main function when called from the command line
"""
import sys
isTTY = sys.stdout.isatty()
assert sys.version_info >= (2,7)
main()
| 45.555001
| 244
| 0.623148
|
4a0799a82dfbc8f8d048a6622c159e0ca6ee1f96
| 1,471
|
py
|
Python
|
microhttp_restful/tests/helpers/testcases.py
|
meyt/microhttp-restful
|
68be4cd882fb0f5eabc18a9b5b9b3aaf3239d8a7
|
[
"MIT"
] | 1
|
2018-09-26T08:56:13.000Z
|
2018-09-26T08:56:13.000Z
|
microhttp_restful/tests/helpers/testcases.py
|
meyt/microhttp-restful
|
68be4cd882fb0f5eabc18a9b5b9b3aaf3239d8a7
|
[
"MIT"
] | 13
|
2017-11-08T14:05:56.000Z
|
2019-01-31T12:11:31.000Z
|
microhttp_restful/tests/helpers/testcases.py
|
meyt/microhttp-restful
|
68be4cd882fb0f5eabc18a9b5b9b3aaf3239d8a7
|
[
"MIT"
] | null | null | null |
from unittest import TestCase
from webtest import TestApp
from microhttp.ext import db
from microhttp_restful.tests.helpers import MockApplication, DeclarativeBase
class WebTestMetaDataMixin:
def metadata(self, url, params='', headers=None, extra_environ=None,
status=None, upload_files=None, expect_errors=False,
content_type=None):
# noinspection PyUnresolvedReferences
return self._gen_request('METADATA', url, params=params, headers=headers,
extra_environ=extra_environ, status=status,
upload_files=upload_files,
expect_errors=expect_errors,
content_type=content_type)
class WebTestApp(TestApp, WebTestMetaDataMixin):
pass
class WebAppTestCase(TestCase):
application = None
session = None
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.application = MockApplication()
cls.application.configure(force=True)
cls.wsgi_app = WebTestApp(cls.application, lint=False)
cls.session = db.get_session()
DeclarativeBase.metadata.create_all(bind=cls.session.get_bind())
@classmethod
def tearDownClass(cls):
super().tearDownClass()
cls.session.close()
cls.session.get_bind().dispose()
with db.get_database_manager() as manager:
manager.drop_database()
| 31.978261
| 81
| 0.647179
|
4a0799c1f3d951add5436af44bd338174e1eeb4b
| 1,593
|
py
|
Python
|
bayesnet/math/product.py
|
ctgk/bayes
|
96eab9305eaeecc5a5b032cdf92a8285de4f60bf
|
[
"MIT"
] | 21
|
2019-01-08T05:58:41.000Z
|
2021-11-26T14:24:11.000Z
|
bayesnet/math/product.py
|
ctgk/bayes
|
96eab9305eaeecc5a5b032cdf92a8285de4f60bf
|
[
"MIT"
] | null | null | null |
bayesnet/math/product.py
|
ctgk/bayes
|
96eab9305eaeecc5a5b032cdf92a8285de4f60bf
|
[
"MIT"
] | 11
|
2019-05-04T13:44:19.000Z
|
2021-08-05T04:26:19.000Z
|
import numpy as np
from bayesnet.tensor.constant import Constant
from bayesnet.tensor.tensor import Tensor
from bayesnet.function import Function
class Product(Function):
def __init__(self, axis=None, keepdims=False):
if isinstance(axis, int):
axis = (axis,)
elif isinstance(axis, tuple):
axis = tuple(sorted(axis))
self.axis = axis
self.keepdims = keepdims
def forward(self, x):
x = self._convert2tensor(x)
self.x = x
self.output = np.prod(self.x.value, axis=self.axis, keepdims=True)
if not self.keepdims:
output = np.squeeze(self.output)
if output.size == 1:
output = output.item()
else:
output = self.output
if isinstance(self.x, Constant):
return Constant(output)
return Tensor(output, function=self)
def backward(self, delta):
if not self.keepdims and self.axis is not None:
for ax in self.axis:
delta = np.expand_dims(delta, ax)
dx = delta * self.output / self.x.value
self.x.backward(dx)
def prod(x, axis=None, keepdims=False):
"""
product of all element in the array
Parameters
----------
x : tensor_like
input array
axis : int, tuple of ints
axis or axes along which a product is performed
keepdims : bool
keep dimensionality or not
Returns
-------
product : tensor_like
product of all element
"""
return Product(axis=axis, keepdims=keepdims).forward(x)
| 27.465517
| 74
| 0.596359
|
4a079a4908d51557fe9413ce63f9c0dbfea53926
| 3,691
|
py
|
Python
|
tensorlayer/lazy_imports.py
|
Howdy-Personally/tensorlayer-master
|
bb92e4e187419d5e7ded8331d5c7cbf5615ee744
|
[
"Apache-2.0"
] | 4,484
|
2017-12-27T03:28:35.000Z
|
2021-12-02T14:42:58.000Z
|
tensorlayer/lazy_imports.py
|
Mesica/tensorlayer
|
c5def14c4d66d150863f975d9001a5e1891d003f
|
[
"Apache-2.0"
] | 549
|
2017-12-28T07:19:52.000Z
|
2021-11-05T02:34:20.000Z
|
tensorlayer/lazy_imports.py
|
Mesica/tensorlayer
|
c5def14c4d66d150863f975d9001a5e1891d003f
|
[
"Apache-2.0"
] | 1,076
|
2017-12-27T12:25:46.000Z
|
2021-11-24T09:12:36.000Z
|
#! /usr/bin/python
# -*- coding: utf-8 -*-
"""This module provides lazy import functionality to improve the import
performance of nitime. For example, some parts of nitime leverage and import
matplotlib, which is quite a big package, yet most of the nitime code does not
depend on matplotlib. By lazily-loading a module, we defer the overhead of
importing it until the first time it is actually used, thereby speeding up
nitime imports.
A generic :class:`LazyImport` class is implemented which takes the module name
as a parameter, and acts as a proxy for that module, importing it only when
the module is used, but effectively acting as the module in every other way
(including inside IPython with respect to introspection and tab completion)
with the *exception* of reload() - reloading a :class:`LazyImport` raises an
:class:`ImportError`.
Commonly used nitime lazy imports are also defined in :mod:`nitime.lazy`, so
they can be reused throughout nitime.
"""
import os
import sys
import types
class LazyImport(types.ModuleType):
"""
This class takes the module name as a parameter, and acts as a proxy for
that module, importing it only when the module is used, but effectively
acting as the module in every other way (including inside IPython with
respect to introspection and tab completion) with the *exception* of
reload()- reloading a :class:`LazyImport` raises an :class:`ImportError`.
>>> mlab = LazyImport('matplotlib.mlab')
No import happens on the above line, until we do something like call an
``mlab`` method or try to do tab completion or introspection on ``mlab``
in IPython.
>>> mlab
<module 'matplotlib.mlab' will be lazily loaded>
Now the :class:`LazyImport` will do an actual import, and call the dist
function of the imported module.
>>> mlab.dist(1969,2011)
42.0
"""
def __getattribute__(self, x):
# This method will be called only once, since we'll change
# self.__class__ to LoadedLazyImport, and __getattribute__ will point
# to module.__getattribute__
name = object.__getattribute__(self, '__name__')
__import__(name)
# if name above is 'package.foo.bar', package is returned, the docs
# recommend that in order to get back the full thing, that we import
# and then lookup the full name is sys.modules, see:
# http://docs.python.org/library/functions.html#__import__
module = sys.modules[name]
# Now that we've done the import, cutout the middleman and make self
# act as the imported module
class LoadedLazyImport(types.ModuleType):
__getattribute__ = module.__getattribute__
__repr__ = module.__repr__
object.__setattr__(self, '__class__', LoadedLazyImport)
# The next line will make "reload(l)" a silent no-op
return module.__getattribute__(x)
def __repr__(self):
return "<module '%s' will be lazily loaded>" % object.__getattribute__(self, '__name__')
if 'READTHEDOCS' in os.environ:
lazy_doc = """
WARNING: To get Sphinx documentation to build we disable
LazyImports, which makes Sphinx incorrectly report this
class as having a base class of object. In reality,
:class:`LazyImport`'s base class is
:class:`types.ModuleType`.
"""
lazy_doc += LazyImport.__doc__
class LazyImport(object):
__doc__ = lazy_doc
def __init__(self, x):
__import__(x)
self.module = sys.modules[x]
def __getattr__(self, x):
return self.module.__getattribute__(x)
| 36.91
| 96
| 0.686806
|
4a079a506f19e19b6a637e6a3c37c667ec774665
| 2,493
|
py
|
Python
|
code-files/frosch2010_CC_language.py
|
Frosch2010/discord-color-cards
|
0669e4aa9c73f8db9e148c88dad85c44889b3216
|
[
"MIT"
] | 1
|
2021-04-02T19:24:09.000Z
|
2021-04-02T19:24:09.000Z
|
code-files/frosch2010_CC_language.py
|
Frosch2010/discord-color-cards
|
0669e4aa9c73f8db9e148c88dad85c44889b3216
|
[
"MIT"
] | 1
|
2021-04-03T12:50:12.000Z
|
2021-04-05T21:47:15.000Z
|
code-files/frosch2010_CC_language.py
|
Frosch2010/discord-color-cards
|
0669e4aa9c73f8db9e148c88dad85c44889b3216
|
[
"MIT"
] | null | null | null |
class cc_language:
#General
cc_wrong_arguments = ""
cc_wrong_game_command = ""
cc_shutdown_bot = ""
#Game
cc_game_already_running = ""
cc_cards_per_player_set_to = ""
cc_no_game_running = ""
cc_user_already_joined = ""
cc_user_joined_game = ""
cc_more_players_needed = ""
cc_user_started_game = ""
cc_user_not_part = ""
cc_player_won = ""
cc_user_leave_no_part = ""
cc_game_end_because_user_left = ""
cc_user_left = ""
cc_user_cant_leave_his_turn = ""
cc_user_no_turn = ""
cc_card_not_exist = ""
cc_user_cant_lay_card = ""
cc_user_your_turn = ""
cc_wish_without_color = ""
cc_wish_unknown_color = ""
cc_input_only_numbers = ""
cc_input_no_number_arg = ""
cc_game_stopped_by = ""
cc_game_cant_stopped = ""
cc_game_player_has_cc = ""
cc_game_player_can_lay = ""
cc_game_player_cant_lay = ""
cc_please_choose_wish_color_react = ""
cc_please_choose_card_color_react = ""
cc_please_choose_card_num_react = ""
cc_false_choose_color_react = ""
cc_false_choose_number_react = ""
cc_no_kick_user = ""
cc_kick_user_isnt_player = ""
cc_cant_kick_current_player = ""
cc_user_kicked = ""
cc_suspend_player_cant_lay_direct_chat = ""
cc_suspend_player_cant_lay = ""
cc_suspend_player_false_card = ""
cc_suspend_player_must_counter = ""
cc_suspend_player_counter_cant_get_new_cards = ""
cc_suspend_player_cant_get_new_cards = ""
cc_suspend_player_want_sit_out = ""
cc_suspend_player_cant_sit_out = ""
cc_suspend_player_cant_skip = ""
cc_plus_card_player_can_lay = ""
cc_plus_card_player_cant_lay = ""
cc_plus_card_player_lay_false_card = ""
cc_plus_card_player_cant_lay_false_card = ""
cc_plus_card_player_cant_take = ""
cc_plus_card_player_take = ""
cc_plus_card_player_cant_skip = ""
cc_plus_card_player_cant_get_new_cards = ""
cc_plus_card_player_counter_cant_get_new_cards = ""
#Generate card-str
cc_timer_action_sit_out = ""
cc_timer_action_take_plus_cards = ""
cc_your_cards = ""
cc_current_mid_card = ""
cc_player_sequence = ""
cc_players_turn = ""
cc_player_laid_card = ""
cc_player_picked_up_card = ""
#Voice
cc_voice_players_turn = ""
cc_voice_player_won = ""
cc_voice_player_sit_out = ""
| 25.438776
| 56
| 0.669073
|
4a079b233a3899a6940acf43a1dc2786fa6318c0
| 377
|
py
|
Python
|
Simulation_Settings.py
|
alpertucanberk/2D_Genetic_Algorithm_Agents
|
786aae62618d5f0291e89ea825919d4e1bbab694
|
[
"MIT"
] | null | null | null |
Simulation_Settings.py
|
alpertucanberk/2D_Genetic_Algorithm_Agents
|
786aae62618d5f0291e89ea825919d4e1bbab694
|
[
"MIT"
] | null | null | null |
Simulation_Settings.py
|
alpertucanberk/2D_Genetic_Algorithm_Agents
|
786aae62618d5f0291e89ea825919d4e1bbab694
|
[
"MIT"
] | null | null | null |
from Neural_Network import forward_propagation
NUM_STEPS_PER_GAME = 200
SCREEN_WIDTH = 250
SCREEN_HEIGHT = 250
WALL_THICKNESS = 5
AGENT_SIZE = 2
FOOD_SIZE = 1
FOOD_DENSITY = 12
MIN_AGENT_DIST = 5
LAYER_SIZES = [4]
NEURAL_NETWORK = forward_propagation
MUTATION_RATE = 3
NUM_GENES_MUTATED = 5
num_individuals_per_pop = 20
num_generations = 200
num_parents_mating = 10
| 13.464286
| 46
| 0.795756
|
4a079e19531e3ad878e98335cea8cd0e0d6ef857
| 1,948
|
py
|
Python
|
gpvdm_gui/gui/inp_util.py
|
roderickmackenzie/gpvdm
|
914fd2ee93e7202339853acaec1d61d59b789987
|
[
"BSD-3-Clause"
] | 12
|
2016-09-13T08:58:13.000Z
|
2022-01-17T07:04:52.000Z
|
gpvdm_gui/gui/inp_util.py
|
roderickmackenzie/gpvdm
|
914fd2ee93e7202339853acaec1d61d59b789987
|
[
"BSD-3-Clause"
] | 3
|
2017-11-11T12:33:02.000Z
|
2019-03-08T00:48:08.000Z
|
gpvdm_gui/gui/inp_util.py
|
roderickmackenzie/gpvdm
|
914fd2ee93e7202339853acaec1d61d59b789987
|
[
"BSD-3-Clause"
] | 6
|
2019-01-03T06:17:12.000Z
|
2022-01-01T15:59:00.000Z
|
#
# General-purpose Photovoltaic Device Model - a drift diffusion base/Shockley-Read-Hall
# model for 1st, 2nd and 3rd generation solar cells.
# Copyright (C) 2008-2022 Roderick C. I. MacKenzie r.c.i.mackenzie at googlemail.com
#
# https://www.gpvdm.com
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License v2.0, as published by
# the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
## @package inp_util
# utility functions for inp these functions should not touch the disk.
#
def inp_file_to_list(lines):
sub_items=[]
items=[]
for l in lines:
if l.startswith("#") and len(sub_items)!=0:
items.append(sub_items)
sub_items=[]
if l=="#end" or l=="#ver":
break
sub_items.append(l)
return items
def inp_get_all_tokens(lines):
ret=[]
for l in lines:
if l.startswith("#"):
if l!="#end" and l!="#ver":
ret.append(l)
return ret
def inp_search_token_value_multiline(lines, token):
ret=[]
for i in range(0, len(lines)):
if lines[i]==token:
pos=i+1
while (lines[pos][0]!="#"):
ret.append(lines[pos])
pos=pos+1
return ret
return False
def inp_check_ver(file_path, ver):
"""Check ver of file"""
lines=inp_load_file(file_path)
if lines==False:
return False
for i in range(0, len(lines)):
if lines[i]=="#ver":
if len(lines)>i+2:
if lines[i+1]==ver:
if lines[i+2]=="#end":
return True
return False
return False
| 24.658228
| 89
| 0.680698
|
4a079e5634b5dfa1d0f96060237febbd32a708e3
| 1,764
|
py
|
Python
|
Code/gov/spiders/gov_spider.py
|
GurKirat21/Profile-Exposer
|
ecbf517d4dd8932829f21fb516e81acea3e9daf9
|
[
"Apache-2.0"
] | 8
|
2020-09-30T20:03:09.000Z
|
2020-10-25T10:23:54.000Z
|
Code/gov/spiders/gov_spider.py
|
GurKirat21/Profile-Exposer
|
ecbf517d4dd8932829f21fb516e81acea3e9daf9
|
[
"Apache-2.0"
] | 1
|
2020-10-04T11:27:29.000Z
|
2020-10-04T11:27:29.000Z
|
Code/gov/spiders/gov_spider.py
|
GurKirat21/Profile-Exposer
|
ecbf517d4dd8932829f21fb516e81acea3e9daf9
|
[
"Apache-2.0"
] | 89
|
2020-09-30T20:03:23.000Z
|
2021-05-01T08:01:26.000Z
|
import scrapy
from bs4 import BeautifulSoup
import re
from urllib.parse import urlparse
from functions import pred
import scrape
class govSpider(scrapy.Spider):
name = "mygovscraper"
allowed_domains = []
start_urls = []
def __init__(self, filename="starter_sites.txt", *args, **kwargs):
super(govSpider, self).__init__(*args, **kwargs)
if(filename):
with open(filename,'r') as f:
for u in f:
u = u.strip()
self.start_urls.append(u)
self.allowed_domains.append(urlparse(u).netloc)
print(f"Crawler has started crawling with {len(self.start_urls)} inital site(s). Please wait for timeout or press ctrl+c repeatedly to force stop.")
def start_requests(self):
for url in self.start_urls:
yield scrapy.Request(url=url,callback = self.parse)
def parse(self,response):
# self.logger.info("Scraped %s", response.url)
f = open('log.txt', 'a')
f.write("Scraped {}\n".format(response.url))
f.close()
soup = BeautifulSoup(response.text, 'html.parser')
scrape.parse_soup(response.url,soup.body)
for href in soup.find_all('a'):
try:
raw = href["href"]
tag = href.text
except:
continue
if(raw[0]=='h' or raw[0]=='/'):
if(pred(tag)):
# print(tag)
# f2 = open("tags.txt", 'a')
# f2.write(tag)
# f2.write("\n")
# f2.close()
new = response.urljoin(raw)
yield scrapy.Request(new, self.parse)
| 32.666667
| 156
| 0.52381
|
4a079ebf3487f49f7161f5d8a42940528b927c63
| 1,435
|
py
|
Python
|
src/abaqus/Sketcher/ConstrainedSketchParameter/Parameter.py
|
Haiiliin/PyAbaqus
|
f20db6ebea19b73059fe875a53be370253381078
|
[
"MIT"
] | 7
|
2022-01-21T09:15:45.000Z
|
2022-02-15T09:31:58.000Z
|
src/abaqus/Sketcher/ConstrainedSketchParameter/Parameter.py
|
Haiiliin/PyAbaqus
|
f20db6ebea19b73059fe875a53be370253381078
|
[
"MIT"
] | null | null | null |
src/abaqus/Sketcher/ConstrainedSketchParameter/Parameter.py
|
Haiiliin/PyAbaqus
|
f20db6ebea19b73059fe875a53be370253381078
|
[
"MIT"
] | null | null | null |
from .ConstrainedSketchParameter import ConstrainedSketchParameter
class Parameter(ConstrainedSketchParameter):
def __init__(self, name: str, path: str = '', expression: str = '', previous: str = ''):
"""This method creates a parameter and optionally associates a dimension with this
parameter.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].sketches[name].Parameter
----------
Parameters
----------
name
A String specifying the name of the ConstrainedSketch object. No two parameters
in the same ConstrainedSketch can have the same name.
path
A String specifying the ConstrainedSketchDimension object with which this parameter is
associated.
expression
A String specifying the expression or value associated with the
ConstrainedSketch.
previous
A String specifying the name of the previous ConstrainedSketch, if it exists.
The *previous* argument implies an order among the parameters. No two
parameters can reference the same parameter as the previous parameter.
Returns
-------
sketch: ConstrainedSketch
A ConstrainedSketch object.
"""
pass
| 33.372093
| 99
| 0.595819
|
4a079f022f8bcc8c3bd0c103b18d731cf3c9a9db
| 2,836
|
py
|
Python
|
python/commonil.py
|
mattrepl/binaryninja-api
|
ac8bb0fe99c87b27bf20feb5a405480ae7286755
|
[
"MIT"
] | null | null | null |
python/commonil.py
|
mattrepl/binaryninja-api
|
ac8bb0fe99c87b27bf20feb5a405480ae7286755
|
[
"MIT"
] | null | null | null |
python/commonil.py
|
mattrepl/binaryninja-api
|
ac8bb0fe99c87b27bf20feb5a405480ae7286755
|
[
"MIT"
] | null | null | null |
# Copyright (c) 2019-2021 Vector 35 Inc
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
from dataclasses import dataclass
# This file contains a list of top level abstract classes for implementing BNIL instructions
@dataclass(frozen=True, repr=False)
class Constant:
pass
@dataclass(frozen=True, repr=False)
class BinaryOperation:
pass
@dataclass(frozen=True, repr=False)
class UnaryOperation:
pass
@dataclass(frozen=True, repr=False)
class Comparison(BinaryOperation):
pass
@dataclass(frozen=True, repr=False)
class SSA:
pass
@dataclass(frozen=True, repr=False)
class Phi(SSA):
pass
@dataclass(frozen=True, repr=False)
class FloatingPoint:
pass
@dataclass(frozen=True, repr=False)
class ControlFlow:
pass
@dataclass(frozen=True, repr=False)
class Terminal(ControlFlow):
pass
@dataclass(frozen=True, repr=False)
class Loop(ControlFlow):
pass
@dataclass(frozen=True, repr=False)
class Call(ControlFlow):
pass
@dataclass(frozen=True, repr=False)
class Syscall(Call):
pass
@dataclass(frozen=True, repr=False)
class Tailcall(Call):
pass
@dataclass(frozen=True, repr=False)
class Return(Terminal):
pass
@dataclass(frozen=True, repr=False)
class Signed:
pass
@dataclass(frozen=True, repr=False)
class Arithmetic:
pass
@dataclass(frozen=True, repr=False)
class Carry(Arithmetic):
pass
@dataclass(frozen=True, repr=False)
class DoublePrecision(Arithmetic):
pass
@dataclass(frozen=True, repr=False)
class Memory:
pass
@dataclass(frozen=True, repr=False)
class Load:
pass
@dataclass(frozen=True, repr=False)
class Store:
pass
@dataclass(frozen=True, repr=False)
class RegisterStack:
pass
@dataclass(frozen=True, repr=False)
class SetVar:
pass
@dataclass(frozen=True, repr=False)
class StackOperation:
pass
@dataclass(frozen=True, repr=False)
class SetReg:
pass
| 19.162162
| 92
| 0.767983
|
4a079f460b4277e47313d9bf9f4d9ede68f33f74
| 18,510
|
py
|
Python
|
window/lp_generator.py
|
rkoco/lp-mapf
|
8ffa93bd33feb244ac2db7230ea3b9ff2deb7038
|
[
"MIT"
] | null | null | null |
window/lp_generator.py
|
rkoco/lp-mapf
|
8ffa93bd33feb244ac2db7230ea3b9ff2deb7038
|
[
"MIT"
] | null | null | null |
window/lp_generator.py
|
rkoco/lp-mapf
|
8ffa93bd33feb244ac2db7230ea3b9ff2deb7038
|
[
"MIT"
] | null | null | null |
import os
import clingo
import asp_solver
import json
class Problem:
def __init__(self, window_bound):
self.obstacles = []
self.map = []
self.height = 0
self.width = 0
self.num_agents = 0
self.agents_pos = []
self.max_distance = -1
self.heuristic = []
self.heuristic_initial = []
self.best_dirs = []
self.dirX = [1,0,-1,0]
self.dirY = [0,1,0,-1]
# Directions name for each (they are swapped because the search is done backwards)
self.dir_name = ['left', 'down', 'right', 'up']
self.instance_number = ''
self.opt_sumtime = 0
self.opt_timestep = -1
self.sol = []
self.max_time = -1
self.total_cost = 0
self.agent_cost = []
self.min_sum = 0
self.solved = False
self.window_bound = window_bound
def read_instance(self, inp):
with open(inp, 'r') as in_file:
self.instance_number = in_file.readline().strip()
in_file.readline()
line = in_file.readline().strip().split(',')
self.height = int(line[0])
self.width = int(line[1])
for y in range(self.height):
line = in_file.readline().strip()
row = []
x = 0
for cell in line:
if cell != '.':
row.append(1)
self.obstacles.append((y,x))
else:
row.append(0)
x+=1
self.map.append(row)
in_file.readline()
self.num_agents = int(in_file.readline())
for a in range(self.num_agents):
agent_map = []
agent_map_init = []
agent_dirs = []
for y in range(self.height):
agent_row = []
agent_row_init = []
row_dirs = []
for x in range(self.width):
agent_row.append(-1) #infty heuristic (not defined)
agent_row_init.append(-1)
row_dirs.append([])
agent_map.append(agent_row)
agent_map_init.append(agent_row_init)
agent_dirs.append(row_dirs)
self.heuristic.append(agent_map)
self.heuristic_initial.append(agent_map_init)
self.best_dirs.append(agent_dirs)
line = in_file.readline().split(',')
pos = (int(line[3]),int(line[4]),int(line[1]),int(line[2]))
self.agents_pos.append(pos)
def read_map(self, inp):
self.obstacles = []
self.map = []
with open(inp, 'r') as in_file:
line = in_file.readline().split(',')
self.height = int(line[0])
self.width = int(line[1])
y = 0
for l in in_file.readlines():
line = l.split(',')
row = []
x = 0
for cell in line:
row.append(int(cell))
if int(cell) == 1:
obs = (y, x)
self.obstacles.append(obs)
x += 1
y += 1
self.map.append(row)
def read_agents(self, inp):
self.agents_pos = []
with open(inp, 'r') as in_file:
line = in_file.readline()
self.num_agents = int(line)
#Put heuristics (and best dirs) for each agent for each cell
for i in range(self.num_agents):
agent_map = []
agent_map_init = []
agent_dirs = []
for y in range(self.height):
agent_row = []
agent_row_init = []
row_dirs = []
for x in range(self.width):
agent_row.append(-1) #infty heuristic (not defined)
agent_row_init.append(-1)
row_dirs.append([])
agent_map.append(agent_row)
agent_map_init.append(agent_row_init)
agent_dirs.append(row_dirs)
self.heuristic.append(agent_map)
self.heuristic_initial.append(agent_map_init)
self.best_dirs.append(agent_dirs)
for l in in_file.readlines():
line = l.split(',')
pos = (int(line[0]),int(line[1]),int(line[2]),int(line[3]))
self.agents_pos.append(pos)
def dijkstra_init(self, ag_id):
posY = self.agents_pos[ag_id][0]
posX = self.agents_pos[ag_id][1]
obj = (posY, posX)
open_list = []
open_list.append(obj)
self.heuristic_initial[ag_id][posY][posX] = 0
while True:
if not open_list:
break
u = open_list.pop(0)
#print(u)
ux = u[1]
uy = u[0]
#Succesors
for i in range(4):
vx = ux + self.dirX[i]
vy = uy + self.dirY[i]
#Check if pos is valid:
if vx < self.width and vx >= 0 and vy < self.height and vy >= 0 and self.map[vy][vx] != 1:
v_cost = self.heuristic_initial[ag_id][uy][ux] + 1
gv = self.heuristic_initial[ag_id][vy][vx]
if gv == -1 or v_cost < gv:
self.heuristic_initial[ag_id][vy][vx] = v_cost
#reset the list, there is a better path
open_list.append((vy,vx))
def solve_agent(self, ag_id):
posY = self.agents_pos[ag_id][2]
posX = self.agents_pos[ag_id][3]
obj = (posY, posX)
open_list = []
open_list.append(obj)
self.heuristic[ag_id][posY][posX] = 0
self.best_dirs[ag_id][posY][posX] = ['wait']
while True:
if not open_list:
break
u = open_list.pop(0)
#print(u)
ux = u[1]
uy = u[0]
#Succesors
for i in range(4):
vx = ux + self.dirX[i]
vy = uy + self.dirY[i]
#Check if pos is valid:
if vx < self.width and vx >= 0 and vy < self.height and vy >= 0 and self.map[vy][vx] != 1:
v_cost = self.heuristic[ag_id][uy][ux] + 1
gv = self.heuristic[ag_id][vy][vx]
if gv == -1 or v_cost < gv:
self.heuristic[ag_id][vy][vx] = v_cost
#reset the list, there is a better path
self.best_dirs[ag_id][vy][vx] = []
self.best_dirs[ag_id][vy][vx].append(self.dir_name[i])
open_list.append((vy,vx))
if v_cost == gv:
#If the cost is the same, the new path is equivalent and also is a best move
self.best_dirs[ag_id][vy][vx].append(self.dir_name[i])
def calc_time(self):
self.opt_sumtime = 0
self.opt_timestep = -1
for ag in range(self.num_agents):
posX = self.agents_pos[ag][0]
posY = self.agents_pos[ag][1]
best_time = self.heuristic[ag][posX][posY]
if (self.opt_timestep == -1 or best_time > self.opt_timestep):
self.opt_timestep = best_time
self.opt_sumtime += best_time
print(self.opt_timestep)
def gen_solution(self):
self.sol = []
self.total_cost = 0
self.agent_cost = []
self.max_time = 0
self.min_sum = 0
for ag in range(self.num_agents):
print('solving for ag', ag)
self.solve_agent(ag)
self.dijkstra_init(ag)
self.agent_cost.append(0)
#for ag in range(self.num_agents):
#self.dijkstra_init(ag)
for ag in range(self.num_agents):
posY = self.agents_pos[ag][0]
posX = self.agents_pos[ag][1]
ag_sol = [(posX,posY)]
t = 0
while True:
best_dir = self.best_dirs[ag][posY][posX]
if len(best_dir) > 0:
best_dir = best_dir[0]
else:
print('????')
print(ag,posY,posX,best_dir)
if best_dir == 'left':
posX -= 1
elif best_dir == 'down':
posY -= 1
elif best_dir == 'right':
posX += 1
elif best_dir == 'up':
posY +=1
elif best_dir == 'wait':
if self.max_time < t:
self.max_time = t
break
self.total_cost += 1
self.agent_cost[ag] += 1
#print((posX,posY))
ag_sol.append((posX,posY))
t+=1
self.sol.append(ag_sol)
self.min_sum = self.total_cost - self.max_time
#print(self.sol)
#print('----')
#print(self.max_time)
self.solved = True
def write_to_lp_window(self, outp, positions, penalty):
with open('{0}{1}.lp'.format(outp, ''), 'w') as out_file:
print(os.path.abspath(out_file.name))
out_file.write('#const window_bound = {0}.\n'.format(self.window_bound))
out_file.write('window_time(1..window_bound).\n\n')
#write the map
out_file.write('rangeX(0..{0}).\n'.format(self.width-1))
out_file.write('rangeY(0..{0}).\n\n'.format(self.height-1))
out_file.write('%% Obstacles in map: \n')
for obs in self.obstacles:
out_file.write('obstacle({0},{1}).\n'.format(obs[1], obs[0]))
out_file.write('\n')
#goal positions:
out_file.write('%% Goal positions: \n')
for ag in range(self.num_agents):
out_file.write('goal({0},{1},{2}).\n'.format(ag, self.agents_pos[ag][3], self.agents_pos[ag][2]))
out_file.write('\n')
#dijkstra values
'''
out_file.write('%% Dijkstra values: \n')
for ag in range(self.num_agents):
h_val = self.heuristic[ag][self.agents_pos[ag][0]][self.agents_pos[ag][1]]
out_file.write('dijkstra({0},{1}).\n'.format(ag, h_val))
out_file.write('\n')
'''
#write the agents:
out_file.write('%% Agents: \n')
for ag in range(self.num_agents):
out_file.write('robot({0}).\n'.format(ag))
out_file.write('\n')
out_file.write('%% Initial positions: \n')
for ag in range(self.num_agents):
out_file.write('on({0},{1},{2},0).\n'.format(ag, positions[ag][1], positions[ag][0]))
out_file.write('\n')
#min cost
for ag in range(self.num_agents):
posX = positions[ag][1]
posY = positions[ag][0]
obj = (posY, posX,0)
open_list = []
open_list.append(obj)
in_range = False
printed_pos = set([(posX,posY)])
h = self.heuristic[ag][posY][posX]
out_file.write('cost_to_go({0},{1},{2},{3}).\n'.format(ag, posX, posY, h))
if h == 0:
in_range = True
out_file.write('exit_penalty({0},{1}).\n'.format(ag,penalty[ag]))
while True:
if not open_list:
break
u = open_list.pop(0)
#print(u)
ux = u[1]
uy = u[0]
l = u[2]
if u[2] == self.window_bound:
break
#Succesors
for i in range(4):
vx = ux + self.dirX[i]
vy = uy + self.dirY[i]
#Check if pos is valid:
if vx < self.width and vx >= 0 and vy < self.height and vy >= 0 and self.map[vy][vx] != 1 and (vx,vy) not in printed_pos:
h1 = self.heuristic[ag][vy][vx]
out_file.write('cost_to_go({0},{1},{2},{3}).\n'.format(ag, vx, vy, h1))
printed_pos.add((vx,vy))
open_list.append((vy,vx,l+1))
if h1 == 0:
out_file.write('cost_to_go({0},{1},{2},{3}).\n'.format(ag, self.agents_pos[ag][3], self.agents_pos[ag][2], 0))
in_range = True
if in_range:
out_file.write('in_range({0}).\n'.format(ag))
#print(ag)
#print(printed_pos)
out_file.write('\n\n')
def read_sol(self, inp):
self.ag_sol = []
for ag in range(self.num_agents):
self.ag_sol.append([])
with open(inp, 'r') as in_file:
preds = in_file.readline().split()
for p in preds:
if 'en' in p:
info=p.replace("on(","")
info=info.replace(")","")
tup = info.split(",")
tup = [int(tup[0][1:])-1,int(tup[1]),int(tup[2]),int(tup[3])]
self.ag_sol[tup[0]].append((tup[1],tup[2], tup[3]))
for ag in range(self.num_agents):
self.ag_sol[ag].sort(key=lambda tup: tup[2])
print(self.ag_sol[ag])
sol_cost = 0
for ag in range(self.num_agents):
#print(self.ag_sol[ag])
final_pos = (self.agents_pos[ag][3], self.agents_pos[ag][2])
for pos in self.ag_sol[ag]:
if pos[0] == final_pos[0] and pos[1] == final_pos[1]:
break
sol_cost += 1
print(solv.sol_cost)
def check_solved(self, positions):
solved_agents = []
for ag in range(self.num_agents):
posX = positions[ag][1]
posY = positions[ag][0]
if self.agents_pos[ag][3] != positions[ag][1]:
solved_agents.append(False)
elif self.agents_pos[ag][2] != positions[ag][0]:
solved_agents.append(False)
else:
solved_agents.append(True)
return solved_agents
def clingo_solve(self, inp):
print('solving with clingo...')
num = self.max_time
while True:
solv = asp_solver.IncrementalSolver(inp, num, self.num_agents, self.min_sum, self.total_cost, 4, True)
clingo.clingo_main(solv, [inp, 'bases/baseH.lp','--opt-strat=usc,disjoint' ,'--outf=3' , '--time-limit=300', '-c','bound={0}'.format(num)])
if solv.sol_cost > 0:
ms = int(solv.theoric_makespan)
break
if ms > num:
num = ms
solv = asp_solver.IncrementalSolver(inp, num, self.num_agents, self.min_sum, self.total_cost, 4, True)
clingo.clingo_main(solv, [inp, 'bases/baseH.lp','--opt-strat=usc,disjoint' ,'--outf=3' , '--time-limit=300', '-c','bound={0}'.format(num)])
break
num += 1
self.sol = solv.resp
self.check_makespan()
print('-----------------')
print('Estadisticas Clingo:')
print(json.dumps(solv.stats, sort_keys=True, indent=4, separators=(',', ': ')))
print('Encontrada Solucion')
print('\tCosto total: {0}'.format(solv.sol_cost))
print('\tMakespan: {0}'.format(self.sol_time))
print('-----------------')
def check_makespan(self):
makespan = -1
for ag in self.sol:
last_x = -1
last_y = -1
step = 0
wait_on_goal = 0
for pos in ag:
if last_x == pos[0] and last_y == pos[1]:
wait_on_goal += 1
else:
wait_on_goal = 0
last_x = pos[0]
last_y = pos[1]
step+=1
ag_makespan = step - wait_on_goal
if ag_makespan > makespan:
makespan = ag_makespan
#print(makespan)
self.sol_time = makespan - 1
'''
posX = positions[ag][1]
posY = positions[ag][0]
in_range = False
#print('holi')
#print(self.agents_pos[ag][3], self.agents_pos[ag][2])
if posX == self.agents_pos[ag][3] and posY == self.agents_pos[ag][2]:
out_file.write('cost_to_go({0},{1},{2},{3}).\n'.format(ag, self.agents_pos[ag][3], self.agents_pos[ag][2], 0))
in_range = True
printed_pos = set([(-1,-1)])
for i in range(self.window_bound+1):
for j in range(self.window_bound+1):
for d in range(1):
x = posX + i * self.dirX[d]
y = posY + j * self.dirY[d]
if ag == 0:
print(x,y)
if x < 0 or y < 0 or x >= self.width or y >= self.height or (i + j > self.window_bound+1) or (x,y) in printed_pos:
continue
h1 = self.heuristic[ag][y][x]
if h1 != -1:
out_file.write('cost_to_go({0},{1},{2},{3}).\n'.format(ag, x, y, h1))
printed_pos.add((x,y))
if x == self.agents_pos[ag][3] and y == self.agents_pos[ag][2]:
out_file.write('cost_to_go({0},{1},{2},{3}).\n'.format(ag, self.agents_pos[ag][3], self.agents_pos[ag][2], 0))
in_range = True
'''
#??
| 36.081871
| 159
| 0.449271
|
4a07a052dd52049314cc531762643b9fca10f4fc
| 87
|
py
|
Python
|
tests/test_beanie.py
|
mikeckennedy/beanie
|
3a3b52f7c4fcb07d51f5afb6b88f56161526c963
|
[
"Apache-2.0"
] | null | null | null |
tests/test_beanie.py
|
mikeckennedy/beanie
|
3a3b52f7c4fcb07d51f5afb6b88f56161526c963
|
[
"Apache-2.0"
] | null | null | null |
tests/test_beanie.py
|
mikeckennedy/beanie
|
3a3b52f7c4fcb07d51f5afb6b88f56161526c963
|
[
"Apache-2.0"
] | null | null | null |
from beanie import __version__
def test_version():
assert __version__ == "1.8.2"
| 14.5
| 33
| 0.712644
|
4a07a08b36014037a1a51d2dc8f62805742c2fbd
| 870
|
py
|
Python
|
tests/test_model_parser.py
|
bcaitech1/p4-mod-model_diet
|
36d8a747e12c375b07d132ed4d08f9fc77126a8b
|
[
"MIT"
] | 1
|
2021-11-30T12:01:55.000Z
|
2021-11-30T12:01:55.000Z
|
tests/test_model_parser.py
|
bcaitech1/p4-mod-model_diet
|
36d8a747e12c375b07d132ed4d08f9fc77126a8b
|
[
"MIT"
] | null | null | null |
tests/test_model_parser.py
|
bcaitech1/p4-mod-model_diet
|
36d8a747e12c375b07d132ed4d08f9fc77126a8b
|
[
"MIT"
] | null | null | null |
"""Model parse test.
- Author: Jongkuk Lim
- Contact: lim.jeikei@gmail.com
"""
import os
import torch
from src.model import Model
class TestModelParser:
"""Test model parser."""
# pylint: disable=no-self-use
INPUT = torch.rand(1, 3, 32, 32)
def test_show_case(self):
"""Test show case model."""
model = Model(os.path.join("model_configs", "show_case.yaml"))
assert model(TestModelParser.INPUT).shape == torch.Size([1, 10])
def test_vgg(self):
"""Test vgg model."""
model = Model(os.path.join("model_configs", "vgg.yaml"))
assert model(TestModelParser.INPUT).shape == torch.Size([1, 10])
def test_example(self):
"""Test example model."""
model = Model(os.path.join("model_configs", "example.yaml"))
assert model(TestModelParser.INPUT).shape == torch.Size([1, 10])
| 24.857143
| 72
| 0.628736
|
4a07a0b559147597e70c9cf3ef586d755358221f
| 1,763
|
py
|
Python
|
algorithm/neural-network/tfXOR.py
|
mk43/machine-learning
|
1ca1baf797fe6f593a88ad4e0d7ac7e5c24ce139
|
[
"Apache-2.0"
] | 6
|
2018-02-22T00:27:44.000Z
|
2019-11-21T18:12:48.000Z
|
algorithm/neural-network/tfXOR.py
|
mk43/machine-learning
|
1ca1baf797fe6f593a88ad4e0d7ac7e5c24ce139
|
[
"Apache-2.0"
] | null | null | null |
algorithm/neural-network/tfXOR.py
|
mk43/machine-learning
|
1ca1baf797fe6f593a88ad4e0d7ac7e5c24ce139
|
[
"Apache-2.0"
] | 4
|
2018-02-19T05:59:23.000Z
|
2020-04-08T08:53:02.000Z
|
# coding: utf-8
import numpy as np
import tensorflow as tf
def sigmoid(x):
return 1 / (1 + np.power(np.e, -2 * (x)))
def add_layer(inputs, in_size, out_size, activation_function=None, ):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
if __name__ == "__main__":
x1 = np.asarray([0, 0, 1, 1])
x2 = np.asarray([0, 1, 0, 1])
X = np.row_stack((x1, x2))
y = np.asarray([0, 1, 1, 0]).reshape(1, 4)
data_X = tf.placeholder(tf.float32, [None, 2])
data_y = tf.placeholder(tf.float32, [None, 1])
layer_one = add_layer(data_X, 2, 2, activation_function=sigmoid)
prediction = add_layer(layer_one, 2, 1, activation_function=sigmoid)
# layer_one = add_layer(data_X, 2, 2, activation_function=tf.nn.sigmoid)
# prediction = add_layer(layer_one, 2, 1, activation_function=tf.nn.sigmoid)
loss = tf.reduce_mean(tf.reduce_sum(- data_y * tf.log(prediction) - (1 - data_y) * tf.log(1 - prediction)))
train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(4000):
sess.run(train, feed_dict={data_X: X.T, data_y: y.T})
print(sess.run(prediction, feed_dict={data_X: X.T, data_y: y.T}))
# output:
# [[0.00200064]
# [0.9985947 ]
# [0.9985983 ]
# [0.00144795]]
# --------------
# [[0.01765717]
# [0.98598236]
# [0.98598194]
# [0.0207849 ]]
# --------------
# [[0.00104381]
# [0.9991435 ]
# [0.49951136]
# [0.5003463 ]]
| 29.383333
| 111
| 0.628474
|
4a07a0dd83ae4cb05580d85663a00fc7582bdeaa
| 10,505
|
py
|
Python
|
utils/cmpcodesize/cmpcodesize/compare.py
|
francisvm/swift
|
15e209ea2fde679ee78438d4ba949144acb7fee4
|
[
"Apache-2.0"
] | 2
|
2016-03-05T00:19:14.000Z
|
2018-09-07T19:34:56.000Z
|
utils/cmpcodesize/cmpcodesize/compare.py
|
francisvm/swift
|
15e209ea2fde679ee78438d4ba949144acb7fee4
|
[
"Apache-2.0"
] | 153
|
2018-01-21T15:24:47.000Z
|
2018-09-13T12:46:16.000Z
|
utils/cmpcodesize/cmpcodesize/compare.py
|
francisvm/swift
|
15e209ea2fde679ee78438d4ba949144acb7fee4
|
[
"Apache-2.0"
] | 11
|
2017-12-13T08:08:15.000Z
|
2019-06-18T14:27:32.000Z
|
# ====--- compare.py - Compare built products' sizes -*- coding: utf-8 -*-===//
#
# This source file is part of the Swift.org open source project
#
# Copyright (c) 2014 - 2017 Apple Inc. and the Swift project authors
# Licensed under Apache License v2.0 with Runtime Library Exception
#
# See https://swift.org/LICENSE.txt for license information
# See https://swift.org/CONTRIBUTORS.txt for the list of Swift project authors
from __future__ import print_function
import collections
import os
import re
import subprocess
from operator import itemgetter
categories = [
# Cpp
["CPP", re.compile('^(__Z|_+swift)')],
# Objective-C
["ObjC", re.compile('^[+-]\[')],
# Swift
["Partial Apply", re.compile('^__(TPA|T0.*T[aA]$)')],
["Protocol Witness", re.compile('^__(TTW|T0.*TW$)')],
["Value Witness", re.compile('^__(Tw|T0.*w..$)')],
["Type Metadata", re.compile('^__(TM|T0.*(N|M.)$)')],
# Function signature specialization of a generic specialization.
["FuncSigGen Spec", re.compile(
'^__(TTSf.*__TTSg|T0.*T[gGpP]q?[0-9].*Tfq?[0-9])')],
["Generic Spec", re.compile('^__(TTSg|T0.*T[gG]q?[0-9])')],
["Partial Spec", re.compile('^__(T0.*T[pP]q?[0-9])')],
["FuncSig Spec", re.compile('^__(TTSf|T0.*Tfq?[0-9])')],
["Generic Function", re.compile(
'__(T[^0].*q(x|d?[0-9]*_)|T0.*q(z|d?[0-9]*_))')],
["Static Func", re.compile('^__(TZF|T0.*FZ)')],
["Swift @objc Func", re.compile('^__(TTo|T0.*To$)')],
["Accessor", re.compile('^__(TW[atTlI]|T0.*W[atTlI]$)')],
["Getter/Setter", re.compile('^__(T[Fvi][gsmwWl]|T0.*f[gGsmwWal]$)')],
["Swift Function", re.compile('^__(TF|T0.*(F|f.|f[AuU][0-9]*_)$)')],
["Unknown", re.compile('')]
]
def add_function(sizes, function, start_addr, end_addr, group_by_prefix):
if not function or start_addr is None or end_addr is None:
return
size = end_addr - start_addr
if group_by_prefix:
if function.endswith('_merged'):
function = function[:-7]
for cat in categories:
cat_name = cat[0]
pattern = cat[1]
if pattern.match(function):
sizes[cat_name] += size
return
assert False, "function name not matching any pattern"
else:
sizes[function] += size
def flatten(*args):
for x in args:
if hasattr(x, '__iter__'):
for y in flatten(*x):
yield y
else:
yield x
def read_sizes(sizes, file_name, function_details, group_by_prefix):
# Check if multiple architectures are supported by the object file.
# Prefer arm64 if available.
architectures = subprocess.check_output(
["otool", "-V", "-f", file_name]).split("\n")
arch = None
arch_pattern = re.compile('architecture ([\S]+)')
for architecture in architectures:
arch_match = arch_pattern.match(architecture)
if arch_match:
if arch is None:
arch = arch_match.group(1)
if "arm64" in arch:
arch = "arm64"
if arch is not None:
arch_params = ["-arch", arch]
else:
arch_params = []
if function_details:
content = subprocess.check_output(
flatten([
"otool",
arch_params,
"-l",
"-v",
"-t",
file_name]
)).split("\n")
content += subprocess.check_output(flatten(
["otool", arch_params, "-v", "-s", "__TEXT", "__textcoal_nt",
file_name])).split("\n")
else:
content = subprocess.check_output(
flatten(["otool", arch_params, "-l", file_name])).split("\n")
sect_name = None
curr_func = None
start_addr = None
end_addr = None
section_pattern = re.compile(' +sectname ([\S]+)')
size_pattern = re.compile(' +size ([\da-fx]+)')
asmline_pattern = re.compile('^([0-9a-fA-F]+)\s')
label_pattern = re.compile('^((\-*\[[^\]]*\])|[^\/\s]+):$')
for line in content:
asmline_match = asmline_pattern.match(line)
if asmline_match:
addr = int(asmline_match.group(1), 16)
if start_addr is None:
start_addr = addr
end_addr = addr
elif line == "Section":
sect_name = None
else:
label_match = label_pattern.match(line)
size_match = size_pattern.match(line)
section_match = section_pattern.match(line)
if label_match:
func_name = label_match.group(1)
add_function(sizes, curr_func, start_addr,
end_addr, group_by_prefix)
curr_func = func_name
start_addr = None
end_addr = None
elif size_match and sect_name and group_by_prefix:
size = int(size_match.group(1), 16)
sizes[sect_name] += size
elif section_match:
sect_name = section_match.group(1)
if sect_name == "__textcoal_nt":
sect_name = "__text"
add_function(sizes, curr_func, start_addr, end_addr, group_by_prefix)
def compare_sizes(old_sizes, new_sizes, name_key, title, total_size_key=""):
old_size = old_sizes[name_key]
new_size = new_sizes[name_key]
if total_size_key:
old_total_size = old_sizes[total_size_key]
new_total_size = new_sizes[total_size_key]
if old_size is not None and new_size is not None:
if old_size != 0:
perc = "%.1f%%" % (
(1.0 - float(new_size) / float(old_size)) * 100.0)
else:
perc = "- "
if total_size_key:
print("%-26s%16s: %8d (%2d%%) %8d (%2d%%) %7s" %
(title, name_key, old_size,
old_size * 100.0 / old_total_size,
new_size, new_size * 100.0 / new_total_size, perc))
else:
print("%-26s%16s: %14d %14d %7s" %
(title, name_key, old_size, new_size, perc))
def compare_sizes_of_file(old_files, new_files, all_sections, list_categories):
old_sizes = collections.defaultdict(int)
new_sizes = collections.defaultdict(int)
for old_file in old_files:
read_sizes(old_sizes, old_file, list_categories, True)
for new_file in new_files:
read_sizes(new_sizes, new_file, list_categories, True)
if len(old_files) == 1 and len(new_files) == 1:
old_base = os.path.basename(old_files[0])
new_base = os.path.basename(new_files[0])
title = old_base
if old_base != new_base:
title += "-" + new_base
else:
title = "old-new"
compare_sizes(old_sizes, new_sizes, "__text", title, "")
if list_categories:
for cat in categories:
cat_name = cat[0]
compare_sizes(old_sizes, new_sizes, cat_name, "", "__text")
if all_sections:
section_title = " section"
compare_sizes(old_sizes, new_sizes, "__textcoal_nt", section_title)
compare_sizes(old_sizes, new_sizes, "__stubs", section_title)
compare_sizes(old_sizes, new_sizes, "__const", section_title)
compare_sizes(old_sizes, new_sizes, "__cstring", section_title)
compare_sizes(old_sizes, new_sizes, "__objc_methname", section_title)
compare_sizes(old_sizes, new_sizes, "__const", section_title)
compare_sizes(old_sizes, new_sizes, "__objc_const", section_title)
compare_sizes(old_sizes, new_sizes, "__data", section_title)
compare_sizes(old_sizes, new_sizes, "__swift1_proto", section_title)
compare_sizes(old_sizes, new_sizes, "__common", section_title)
compare_sizes(old_sizes, new_sizes, "__bss", section_title)
def list_function_sizes(size_array):
for pair in sorted(size_array, key=itemgetter(1)):
name = pair[0]
size = pair[1]
yield "%8d %s" % (size, name)
def compare_function_sizes(old_files, new_files):
old_sizes = collections.defaultdict(int)
new_sizes = collections.defaultdict(int)
for name in old_files:
read_sizes(old_sizes, name, True, False)
for name in new_files:
read_sizes(new_sizes, name, True, False)
only_in_file1 = []
only_in_file2 = []
in_both = []
only_in_file1size = 0
only_in_file2size = 0
in_both_size = 0
for func, old_size in old_sizes.items():
new_size = new_sizes[func]
if new_size != 0:
in_both.append((func, old_size, new_size))
else:
only_in_file1.append((func, old_size))
only_in_file1size += old_size
for func, new_size in new_sizes.items():
old_size = old_sizes[func]
if old_size == 0:
only_in_file2.append((func, new_size))
only_in_file2size += new_size
if only_in_file1:
print("Only in old file(s)")
print(os.linesep.join(list_function_sizes(only_in_file1)))
print("Total size of functions only in old file: {}".format(
only_in_file1size))
print()
if only_in_file2:
print("Only in new files(s)")
print(os.linesep.join(list_function_sizes(only_in_file2)))
print("Total size of functions only in new file: {}".format(
only_in_file2size))
print()
if in_both:
size_increase = 0
size_decrease = 0
print("%8s %8s %8s" % ("old", "new", "diff"))
for triple in sorted(
in_both,
key=lambda tup: (tup[2] - tup[1], tup[1])):
func = triple[0]
old_size = triple[1]
new_size = triple[2]
diff = new_size - old_size
if diff > 0:
size_increase += diff
else:
size_decrease -= diff
if diff == 0:
in_both_size += new_size
print("%8d %8d %8d %s" %
(old_size, new_size, new_size - old_size, func))
print("Total size of functions " +
"with the same size in both files: {}".format(in_both_size))
print("Total size of functions " +
"that got smaller: {}".format(size_decrease))
print("Total size of functions " +
"that got bigger: {}".format(size_increase))
print("Total size change of functions present " +
"in both files: {}".format(size_increase - size_decrease))
| 35.853242
| 79
| 0.582389
|
4a07a13a0ae2871d96f4550ac24061e5687c86f3
| 11,243
|
py
|
Python
|
teal/teal.py
|
bustawin/teal
|
0c128fce0a1d9992199626bd8447532cce476c18
|
[
"BSD-3-Clause"
] | null | null | null |
teal/teal.py
|
bustawin/teal
|
0c128fce0a1d9992199626bd8447532cce476c18
|
[
"BSD-3-Clause"
] | null | null | null |
teal/teal.py
|
bustawin/teal
|
0c128fce0a1d9992199626bd8447532cce476c18
|
[
"BSD-3-Clause"
] | null | null | null |
import inspect
from typing import Dict, Type
import click_spinner
import ereuse_utils
import flask_cors
from anytree import Node
from apispec import APISpec
from click import option
from ereuse_utils import ensure_utf8
from flask import Flask, jsonify
from flask.globals import _app_ctx_stack
from flask_sqlalchemy import SQLAlchemy
from marshmallow import ValidationError
from werkzeug.exceptions import HTTPException, UnprocessableEntity
from teal.auth import Auth
from teal.cli import TealCliRunner
from teal.client import Client
from teal.config import Config as ConfigClass
from teal.db import SchemaSQLAlchemy
from teal.json_util import TealJSONEncoder
from teal.request import Request
from teal.resource import Converters, LowerStrConverter, Resource
class Teal(Flask):
"""
An opinionated REST and JSON first server built on Flask using
MongoDB and Marshmallow.
"""
test_client_class = Client
request_class = Request
json_encoder = TealJSONEncoder
cli_context_settings = {'help_option_names': ('-h', '--help')}
test_cli_runner_class = TealCliRunner
def __init__(self,
config: ConfigClass,
db: SQLAlchemy,
schema: str = None,
import_name=__name__.split('.')[0],
static_url_path=None,
static_folder='static',
static_host=None,
host_matching=False,
subdomain_matching=False,
template_folder='templates',
instance_path=None,
instance_relative_config=False,
root_path=None,
use_init_db=True,
Auth: Type[Auth] = Auth):
"""
:param config:
:param db:
:param schema: A string describing the main PostgreSQL's schema.
``None`` disables this functionality.
If you use a factory of apps (for example by using
:func:`teal.teal.prefixed_database_factory`) and then set this
value differently per each app (as each app has a separate config)
you effectively create a `multi-tenant app <https://
news.ycombinator.com/item?id=4268792>`_.
Your models by default will be created in this ``SCHEMA``,
unless you set something like::
class User(db.Model):
__table_args__ = {'schema': 'users'}
In which case this will be created in the ``users`` schema.
Schemas are interesting over having multiple databases (i.e. using
flask-sqlalchemy's data binding) because you can have relationships
between them.
Note that this only works with PostgreSQL.
:param import_name:
:param static_url_path:
:param static_folder:
:param static_host:
:param host_matching:
:param subdomain_matching:
:param template_folder:
:param instance_path:
:param instance_relative_config:
:param root_path:
:param Auth:
"""
self.schema = schema
ensure_utf8(self.__class__.__name__)
super().__init__(import_name, static_url_path, static_folder, static_host, host_matching,
subdomain_matching, template_folder, instance_path,
instance_relative_config, root_path)
self.config.from_object(config)
flask_cors.CORS(self)
# Load databases
self.auth = Auth()
self.url_map.converters[Converters.lower.name] = LowerStrConverter
self.load_resources()
self.register_error_handler(HTTPException, self._handle_standard_error)
self.register_error_handler(ValidationError, self._handle_validation_error)
self.db = db
db.init_app(self)
if use_init_db:
self.cli.command('init-db', context_settings=self.cli_context_settings)(self.init_db)
self.spec = None # type: APISpec
self.apidocs()
# noinspection PyAttributeOutsideInit
def load_resources(self):
self.resources = {} # type: Dict[str, Resource]
"""
The resources definitions loaded on this App, referenced by their
type name.
"""
self.tree = {} # type: Dict[str, Node]
"""
A tree representing the hierarchy of the instances of
ResourceDefinitions. ResourceDefinitions use these nodes to
traverse their hierarchy.
Do not use the normal python class hierarchy as it is global,
thus unreliable if you run different apps with different
schemas (for example, an extension that is only added on the
third app adds a new type of user).
"""
for ResourceDef in self.config['RESOURCE_DEFINITIONS']:
resource_def = ResourceDef(self) # type: Resource
self.register_blueprint(resource_def)
if resource_def.cli_commands:
@self.cli.group(resource_def.cli_name,
context_settings=self.cli_context_settings,
short_help='{} management.'.format(resource_def.type))
def dummy_group():
pass
for cli_command, *args in resource_def.cli_commands: # Register CLI commands
# todo cli commands with multiple arguments end-up reversed
# when teal has been executed multiple times (ex. testing)
# see _param_memo func in click package
dummy_group.command(*args)(cli_command)
# todo should we use resource_def.name instead of type?
# are we going to have collisions? (2 resource_def -> 1 schema)
self.resources[resource_def.type] = resource_def
self.tree[resource_def.type] = Node(resource_def.type)
# Link tree nodes between them
for _type, node in self.tree.items():
resource_def = self.resources[_type]
_, Parent, *superclasses = inspect.getmro(resource_def.__class__)
if Parent is not Resource:
node.parent = self.tree[Parent.type]
@staticmethod
def _handle_standard_error(e: HTTPException):
"""
Handles HTTPExceptions by transforming them to JSON.
"""
try:
response = jsonify(e)
response.status_code = e.code
except (AttributeError, TypeError) as e:
code = getattr(e, 'code', 500)
response = jsonify({
'message': str(e),
'code': code,
'type': e.__class__.__name__
})
response.status_code = code
return response
@staticmethod
def _handle_validation_error(e: ValidationError):
data = {
'message': e.messages,
'code': UnprocessableEntity.code,
'type': e.__class__.__name__
}
response = jsonify(data)
response.status_code = UnprocessableEntity.code
return response
@option('--erase/--no-erase',
default=False,
help='Delete all contents from the database (including common schemas)?')
@option('--exclude-schema',
default=None,
help='Schema to exclude creation (and deletion if --erase is set). '
'Required the SchemaSQLAlchemy.')
def init_db(self, erase: bool = False, exclude_schema=None):
"""
Initializes a database from scratch,
creating tables and needed resources.
Note that this does not create the database per se.
If executing this directly, remember to use an app_context.
Resources can hook functions that will be called when this
method executes, by subclassing :meth:`teal.resource.
Resource.load_resource`.
"""
assert _app_ctx_stack.top, 'Use an app context.'
print('Initializing database...'.ljust(30), end='')
with click_spinner.spinner():
if erase:
if exclude_schema: # Using then a schema teal sqlalchemy
assert isinstance(self.db, SchemaSQLAlchemy)
self.db.drop_schema()
else: # using regular flask sqlalchemy
self.db.drop_all()
self._init_db(exclude_schema)
self._init_resources()
self.db.session.commit()
print('done.')
def _init_db(self, exclude_schema=None) -> bool:
"""Where the database is initialized. You can override this.
:return: A flag stating if the database has been created (can
be False in case check is True and the schema already
exists).
"""
if exclude_schema: # Using then a schema teal sqlalchemy
assert isinstance(self.db, SchemaSQLAlchemy)
self.db.create_all(exclude_schema=exclude_schema)
else: # using regular flask sqlalchemy
self.db.create_all()
return True
def _init_resources(self, **kw):
for resource in self.resources.values():
resource.init_db(self.db, **kw)
def apidocs(self):
"""Apidocs configuration and generation."""
self.spec = APISpec(
plugins=(
'apispec.ext.flask',
'apispec.ext.marshmallow',
),
**self.config.get_namespace('API_DOC_CONFIG_')
)
for name, resource in self.resources.items():
if resource.SCHEMA:
self.spec.definition(name,
schema=resource.SCHEMA,
extra_fields=self.config.get_namespace('API_DOC_CLASS_'))
self.add_url_rule('/apidocs', view_func=self.apidocs_endpoint)
def apidocs_endpoint(self):
"""An endpoint that prints a JSON OpenApi 2.0 specification."""
if not getattr(self, '_apidocs', None):
# We are forced to to this under a request context
for path, view_func in self.view_functions.items():
if path != 'static':
self.spec.add_path(view=view_func)
self._apidocs = self.spec.to_dict()
return jsonify(self._apidocs)
class DumpeableHTTPException(ereuse_utils.Dumpeable):
"""Exceptions that inherit this class will be able to dump
to dicts and JSONs.
"""
def dump(self):
# todo this is heavily ad-hoc and should be more generic
value = super().dump()
value['type'] = self.__class__.__name__
value['code'] = self.code
value.pop('exc', None)
value.pop('response', None)
if 'data' in value:
value['fields'] = value['data']['messages']
del value['data']
if 'message' not in value:
value['message'] = value.pop('description', str(self))
return value
# Add dump capacity to Werkzeug's HTTPExceptions
HTTPException.__bases__ = HTTPException.__bases__ + (DumpeableHTTPException,)
| 39.449123
| 97
| 0.60491
|
4a07a162ff79e4b57d834f09cafad42f8bf238d0
| 174
|
py
|
Python
|
guiauto/gui/base_test.py
|
saasaa831/guidesktop
|
68abe5e896c4d29cf12898abd3b27c60553a3948
|
[
"Apache-2.0"
] | null | null | null |
guiauto/gui/base_test.py
|
saasaa831/guidesktop
|
68abe5e896c4d29cf12898abd3b27c60553a3948
|
[
"Apache-2.0"
] | null | null | null |
guiauto/gui/base_test.py
|
saasaa831/guidesktop
|
68abe5e896c4d29cf12898abd3b27c60553a3948
|
[
"Apache-2.0"
] | null | null | null |
class BaseTest:
driver = None
general = None
def __init__(self, driver, parent_handle):
self.driver = driver
self.parent_handle = parent_handle
| 19.333333
| 46
| 0.655172
|
4a07a183e39459e6c943a88ee76ed6bd5dc8a53a
| 26,879
|
py
|
Python
|
deploy_config_generator/output/__init__.py
|
ApplauseOSS/deploy-config-generator
|
04674ba02f5a797e25c682aa9ff755989741a0c1
|
[
"MIT"
] | 3
|
2019-04-05T14:16:17.000Z
|
2021-06-25T20:53:03.000Z
|
deploy_config_generator/output/__init__.py
|
ApplauseOSS/deploy-config-generator
|
04674ba02f5a797e25c682aa9ff755989741a0c1
|
[
"MIT"
] | 6
|
2019-04-04T20:20:16.000Z
|
2021-09-27T21:04:39.000Z
|
deploy_config_generator/output/__init__.py
|
ApplauseOSS/deploy-config-generator
|
04674ba02f5a797e25c682aa9ff755989741a0c1
|
[
"MIT"
] | null | null | null |
import copy
import inspect
import os.path
import re
import six
from deploy_config_generator.site_config import SiteConfig
from deploy_config_generator.display import Display
from deploy_config_generator.template import Template
from deploy_config_generator.errors import DeployConfigGenerationError, DeployConfigError, ConfigError
from deploy_config_generator.utils import show_traceback
class OutputPluginBase(object):
'''
Base class for output plugins
'''
_vars = None
_output_dir = None
_display = None
_section = None
_plugin_config = None
_fields = None
_config_version = None
COMMON_DEFAULT_CONFIG = dict(
enabled=True,
)
PRIORITY = 1
def __init__(self, varset, output_dir, config_version):
self._vars = varset
self._output_dir = output_dir
self._display = Display()
self._template = Template()
self._site_config = SiteConfig()
self._config_version = config_version
self.build_config()
# Comparison functions for sorting plugins
# Sort first by priority and then by name (for consistency)
def __lt__(self, other):
return (self.PRIORITY < other.PRIORITY or (self.PRIORITY == other.PRIORITY and self.NAME < other.NAME))
def __gt__(self, other):
return (self.PRIORITY > other.PRIORITY or (self.PRIORITY == other.PRIORITY and self.NAME > other.NAME))
def __le__(self, other):
return (self.PRIORITY <= other.PRIORITY or (self.PRIORITY == other.PRIORITY and self.NAME <= other.NAME))
def __ge__(self, other):
return (self.PRIORITY >= other.PRIORITY or (self.PRIORITY == other.PRIORITY and self.NAME >= other.NAME))
def __eq__(self, other):
return (self.PRIORITY == other.PRIORITY or (self.PRIORITY == other.PRIORITY and self.NAME == other.NAME))
def __ne__(self, other):
return (self.PRIORITY != other.PRIORITY or (self.PRIORITY == other.PRIORITY and self.NAME != other.NAME))
def build_config(self):
'''
Build the plugin config
'''
self._plugin_config = self.COMMON_DEFAULT_CONFIG.copy()
self._plugin_config.update(self.DEFAULT_CONFIG)
# Helper var to tidy up the code
self._fields = copy.deepcopy(self._plugin_config['fields'])
# Convert field definitions into PluginField objects
for section in self._fields:
section_fields = self._fields[section]
for k, v in section_fields.items():
section_fields[k] = PluginField(k, v, self._config_version, self._template)
self.build_config_site()
def build_config_site(self):
'''
Merge in plugin config values from site config
This will also do a deep merge of deeply nested field definitions
'''
if self.NAME in self._site_config.plugins:
for k, v in self._site_config['plugins'][self.NAME].items():
if k == 'fields':
for section in v:
for field_name, field in v[section].items():
# Create section if it doesn't exist
if section not in self._fields:
self._fields[section] = {}
# Update existing field config or create new
if field_name in self._fields[section]:
self._fields[section][field_name].update_config(field)
else:
self._fields[section][field_name] = PluginField(field_name, field, self._config_version, self._template)
else:
if k in self._plugin_config:
if isinstance(v, dict):
self._plugin_config[k] = v.copy()
elif isinstance(v, list):
self._plugin_config[k] = v[:]
else:
self._plugin_config[k] = v
else:
raise ConfigError('unrecognized config option: %s' % k)
def set_section(self, section):
'''
Sets the active section of the deploy config
This is used to figure out which set of fields to process
'''
self._section = section
def has_field(self, field):
'''
Check if a field exists in the current section for this plugin
'''
if self._section in self._fields and field in self._fields[self._section]:
if self._fields[self._section][field].is_valid_for_config_version():
return True
return False
def get_required_fields(self):
'''
Return a list of fields in the current section with required=True
'''
ret = []
if self._section in self._fields:
for k, v in self._fields[self._section].items():
if v.required and v.default is None and v.is_valid_for_config_version():
ret.append(k)
return ret
def is_field_locked(self, field):
'''
Check if a field has been marked as 'locked' (cannot be overridden by user)
'''
if self._section in self._fields and field in self._fields[self._section]:
if self._fields[self._section][field].locked:
return True
return False
def is_needed(self, app):
'''
Determine whether this plugin is needed based on the provided deploy config
'''
# We aren't needed if we're marked as disabled (enabled: False)
if self._plugin_config.get('enabled', True) is False:
return False
# We aren't needed if we have no fields for the current section
if self._section not in self._fields:
return False
# We are needed if we're the configured default plugin
if self._site_config.default_output == self.NAME:
return True
# Check if any of our required top-level fields are provided
for field in self.get_required_fields():
if field in app:
return True
# If nothing above matched, then we're probably not needed
return False
def merge_with_field_defaults(self, app):
'''
Merge user-provided values with configured field defaults
'''
ret = {}
# Apply defaults/transforms
for field, value in self._fields[self._section].items():
ret[field] = value.apply_default(app.get(field, None))
ret[field] = value.apply_transform(ret.get(field, None))
return ret
def validate_fields(self, app):
'''
Validate the provided app config against plugin field definitions
'''
# Check that all required top-level fields are provided
req_fields = self.get_required_fields()
for field in req_fields:
if field not in app:
raise DeployConfigError("required field '%s' not defined" % field)
# Check field/subfield types, required, and if field is locked
unmatched = []
for field, value in app.items():
if self.has_field(field):
if self.is_field_locked(field):
raise DeployConfigError("the field '%s' has been locked by the plugin config and cannot be overridden" % field)
field_unmatched = self._fields[self._section][field].validate(value)
unmatched.extend(field_unmatched)
else:
unmatched.append(field)
return unmatched
def build_app_vars(self, index, app, path=''):
# Build vars for template
app_vars = {
'PLUGIN_NAME': self.NAME,
'APP_INDEX': index,
# App config
'APP': self.merge_with_field_defaults(app),
# Parsed vars
'VARS': dict(self._vars),
}
return app_vars
def pre_process(self, config):
pass
def generate(self, config):
'''
Write out the generated config to disk
'''
try:
self.pre_process(config)
for section in config:
if section in self._fields:
self.set_section(section)
for idx, app in enumerate(config[section]):
# We want a 1-based index for the output files
index = idx + 1
if self.is_needed(app):
# Build vars for template
app_vars = self.build_app_vars(index, app)
# Check conditionals
for field, value in self._fields[self._section].items():
app_vars['APP'][field] = value.check_conditionals(app_vars['APP'].get(field, None), app_vars)
# Generate output
output = self.generate_output(app_vars)
path_suffix = None
if isinstance(output, (tuple, list)):
output, path_suffix = output
if output is None:
continue
path = os.path.join(self._output_dir, '%s-%03d%s%s' % (self.NAME, index, ('-%s' % path_suffix if path_suffix else ''), self.FILE_EXT))
self._display.v('Writing output file %s' % path)
with open(path, 'w') as f:
f.write(output)
except Exception as e:
show_traceback(self._display.get_verbosity())
raise DeployConfigGenerationError(str(e))
def generate_output(self, app_vars):
'''
Generate output content
By default, this renders the Jinja template defined in the 'TEMPLATE'
class var. However, it can be overridden by an output plugin to provide
a custom method for generating the output.
'''
output = self._template.render_template(inspect.cleandoc(self.TEMPLATE), app_vars)
return output
class PluginField(object):
'''
Class representing a field from a deploy config that's supported by an output
plugin
'''
_name = None
_config = None
_parent = None
_config_version = None
BASE_CONFIG = {
# Whether field is required
'required': False,
# Default value
'default': None,
# Whether field is locked (value cannot be provided by user)
'locked': False,
# Expected type for field
'type': None,
# Transformation (for strings)
# This should be a dict containing one of the following keys:
# * prefix - prefix to add to value
# * suffix - suffix to add to value
'transform': None,
# Expected type for sub-items (for lists and free-form dicts)
'subtype': None,
# How to combine defaults
# * None - no combining, user value replaces default
# * 'append' - default value is included at end of list
# * 'prepend' - default value is included at beginning of list
# * 'merge' - user value is merged with default value (for lists/dicts)
'default_action': None,
# Key to use for merging (for lists of dicts)
'merge_key': None,
# Minimum/maximum config version that field is valid for
'min_version': None,
'max_version': None,
# Field definitions (for dicts)
'fields': None,
# Whether the field supports a conditional (for dicts)
'conditional': False,
# Field name to use for conditional
'conditional_key': 'condition',
# Loop var (for use in conditionals)
'loop_var': 'item',
# Validation regex pattern (for strings)
'validation_pattern': None,
}
def __init__(self, name, config, config_version, template, parent=None):
self._name = name
self._parent = parent
self._config_version = config_version
self._template = template
self._config = self.BASE_CONFIG.copy()
if config is not None:
self._config.update(copy.deepcopy(config))
self.convert_fields()
def __getattr__(self, key, default=None):
return self._config.get(key, default)
__getitem__ = __getattr__
get = __getattr__
def __setattr__(self, key, value):
if self._config is not None and key in self._config:
self._config[key] = value
else:
super(PluginField, self).__setattr__(key, value)
def __contains__(self, key):
return (key in self._config)
def __str__(self):
return '<PluginField name=%s config=%s>' % (self._name, self._config)
__repr__ = __str__
def is_valid_for_config_version(self):
'''
Compare min/max version for field to config version
'''
if self._config_version is None:
return True
if self._config['min_version'] is not None:
if float(self._config_version) < float(self._config['min_version']):
return False
if self._config['max_version'] is not None:
if float(self._config_version) > float(self._config['max_version']):
return False
return True
def convert_fields(self):
'''
Replace items in 'fields' dict with PluginField objects
'''
if self._config['fields'] is not None:
for k, v in self._config['fields'].items():
self._config['fields'][k] = PluginField(k, v, self._config_version, self._template, parent=self)
def update_config(self, config):
'''
Deep merge field attributes from site config with current config
'''
for k, v in config.items():
if k == 'fields':
for field_name, field in v.items():
# Update existing field config or create new
if self.fields is not None and field_name in self.fields:
self.fields[field_name].update_config(field)
else:
if self.fields is None:
self.fields = {}
self.fields[field_name] = PluginField(field_name, field, self._config_version, self._template, parent=self)
else:
if isinstance(v, dict):
if self._config[k] is None:
self._config[k] = {}
self._config[k].update(v)
elif isinstance(v, list):
self._config[k] = v[:]
else:
self._config[k] = v
def get_full_name(self):
'''
Construct full name of field from parent(s)
This is used when generating exceptions
'''
field_name = self._name
parent = self._parent
while parent is not None:
field_name = '%s.%s' % (parent._name, field_name)
parent = parent._parent
return field_name
def convert_bool(self, value):
if value in ('true', 'True', 'yes', 'on'):
return True
if value in ('false', 'False', 'no', 'off'):
return False
return None
def validate_check_type(self, value, expected_type=None):
'''
Determine the type of the passed value
'''
if isinstance(value, list):
return 'list'
if isinstance(value, dict):
return 'dict'
if isinstance(value, bool):
return 'bool'
if isinstance(value, six.integer_types):
return 'int'
if isinstance(value, float):
return 'float'
if isinstance(value, six.string_types):
# Values from variables always come in as a string, so we need special
# logic to determine their actual type based on the field type
try:
if expected_type == 'float' and float(value) is not None:
return 'float'
if expected_type == 'int' and int(value) is not None:
return 'int'
if expected_type == 'bool' and self.convert_bool(value) is not None:
return 'bool'
except Exception:
pass
return 'str'
raise DeployConfigError('unsupported type: %s' % type(value))
def validate(self, value, use_subtype=False):
'''
Validate passed value against field config
'''
unmatched = []
if value is None:
return unmatched
field_type = self.type
if use_subtype:
# Use the field subtype
field_type = self.subtype
# Nothing to validate if no field type is specified
if field_type is None:
return unmatched
value_type = self.validate_check_type(value, field_type)
if value_type != field_type:
# TODO: replace this with the ability to specify multiple types for a field
# Hack to allow an int value to satisfy a float
if field_type == 'float' and value_type == 'int':
pass
else:
raise DeployConfigError("value for field '%s' is wrong type, expected '%s' and got: %s" % (self.get_full_name(), field_type, value_type))
if field_type == 'list' and self.subtype is not None:
# Validate each list item separately if a field subtype is specified
for value_item in value:
# Use field's subtype for list items
item_unmatched = self.validate(value_item, use_subtype=True)
unmatched.extend(item_unmatched)
elif field_type == 'dict':
# Recursively validate sub-field values
if self.fields is not None:
for k, v in value.items():
if k not in self.fields or not self.fields[k].is_valid_for_config_version():
unmatched.append('%s.%s' % (self.get_full_name(), k))
continue
field_unmatched = self.fields[k].validate(v)
unmatched.extend(field_unmatched)
# Check for required and locked sub-fields
for tmp_field_name, tmp_field in self.fields.items():
if tmp_field.required and value.get(tmp_field_name, None) is None and tmp_field.default is None:
raise DeployConfigError("field '%s' is required, but no value provided" % tmp_field.get_full_name())
if tmp_field.locked and value.get(tmp_field_name, None) is not None:
raise DeployConfigError("field '%s' is locked, but a value was provided" % tmp_field.get_full_name())
# Validate free-form value type
elif self.subtype is not None and not use_subtype:
for value_item in value.values():
item_unmatched = self.validate(value_item, use_subtype=True)
unmatched.extend(item_unmatched)
elif field_type == 'str':
if self.validation_pattern is not None:
if not re.match(self.validation_pattern, value):
raise DeployConfigError("value for field '%s' did not match validation pattern: %s" % (self.get_full_name(), self.validation_pattern))
return unmatched
def apply_transform(self, value, use_subtype=False):
'''
Apply transformations to string values
'''
if value is None:
return value
field_type = self.type
if use_subtype:
field_type = self.subtype
value_type = self.validate_check_type(value)
ret = None
if value_type == 'list':
# Apply transformations to all items in the list
ret = []
for value_item in value:
ret.append(self.apply_transform(value_item, use_subtype=True))
elif value_type == 'dict':
ret = {}
if self.fields is not None:
# Recursively apply transformations to sub-fields
for field in self.fields:
if field in value:
ret[field] = self.fields[field].apply_transform(value[field])
else:
ret = value
elif value_type == 'str':
# Convert types for values that came in from a variable (which always
# produces a string)
if field_type == 'bool':
# Convert values to boolean if they're expected to be boolean
ret = self.convert_bool(value)
elif field_type == 'float':
ret = float(value)
elif field_type == 'int':
ret = int(value)
elif isinstance(self.transform, dict):
if 'prefix' in self.transform:
ret = self.transform['prefix'] + value
elif 'suffix' in self.transform:
ret = value + self.transform['suffix']
else:
ret = value
else:
if field_type == 'float' and value_type == 'int':
# An int can satisfy a 'float' field, but we want to make sure
# that it's a float for output
ret = float(value)
else:
ret = value
return ret
def apply_default_list(self, value, field_type):
'''
Apply default values for a list (helper function)
'''
ret = []
if self.subtype is not None:
if value:
for value_item in value:
new_val = self.apply_default(value_item, use_subtype=True)
if new_val is not None:
ret.append(new_val)
else:
if value:
ret = value[:]
if self.default is not None:
def_val = self.default
if not isinstance(def_val, list):
def_val = [def_val]
# User values are merged with default values
if self.default_action == 'merge':
# Create a copy of the default values, since we'll be modifying it
def_val = def_val[:]
# Iterate over user values and compare against default values
for tmp_value in ret:
for idx, tmp_def_val in enumerate(def_val):
if self.subtype == 'dict' and self.merge_key is not None:
# Delete default value if the merge key value matches the current value
if tmp_value.get(self.merge_key, "MERGE_KEY_USER") == tmp_def_val.get(self.merge_key, "MERGE_KEY_DEFAULT"):
del def_val[idx]
break
else:
# Delete default value if it matches the current value
if tmp_value == tmp_def_val:
del def_val[idx]
break
# Prepend remaining defaults to user values
ret = def_val + ret
# User values go after default value
elif self.default_action == 'prepend':
ret = def_val + ret
# User values go before default value
elif self.default_action == 'append':
ret = ret + def_val
elif not ret:
ret = self.default
return ret
def apply_default(self, value, use_subtype=False):
'''
Apply default values from the field config
'''
ret = None
field_type = self.type
if use_subtype:
# Use the field subtype
field_type = self.subtype
if field_type == 'list':
ret = self.apply_default_list(value, field_type)
elif field_type == 'dict':
# Recursively apply defaults for sub-fields
ret = {}
if self.fields is not None:
if value is None:
value = {}
for field in self.fields:
ret[field] = self.fields[field].apply_default(value.get(field, None))
else:
# Don't apply defaults for subtype
if use_subtype:
ret = value
else:
if value is None:
ret = self.default
else:
if self.default_action == 'merge':
ret = self.default.copy()
ret.update(value)
else:
ret = value.copy()
else:
# Use default if no value was provided
if value is None:
ret = self.default
else:
ret = value
return ret
def check_conditionals(self, value, app_vars, use_subtype=False):
'''
Check conditionals and filter value
'''
ret = None
field_type = self.type
if use_subtype:
# Use the field subtype
field_type = self.subtype
if field_type == 'list':
ret = []
for idx, item in enumerate(value):
if self.loop_var:
# Add loop item and index vars
app_vars = app_vars.copy()
app_vars.update({self.loop_var: item, ('%s_index' % self.loop_var): idx})
tmp_value = self.check_conditionals(item, app_vars, use_subtype=True)
# Don't add item to returned data if its condition evaluated to False
if tmp_value is not None:
ret.append(tmp_value)
elif field_type == 'dict':
ret = {}
if self.fields is not None:
if value is None:
value = {}
for field in self.fields:
ret[field] = self.fields[field].check_conditionals(value.get(field, None), app_vars)
else:
ret = value
if self.conditional and self.conditional_key in ret:
if ret[self.conditional_key] is not None:
if not self._template.evaluate_condition(ret[self.conditional_key], app_vars):
return None
# Remove the conditional key from the returned data
del ret[self.conditional_key]
else:
ret = value
return ret
| 40.602719
| 162
| 0.55322
|
4a07a201150c9c6db78a02308f6259bc3d918d6a
| 23,738
|
py
|
Python
|
official/vision/beta/projects/movinet/modeling/movinet_layers_test.py
|
hjkim-haga/TF-OD-API
|
22ac477ff4dfb93fe7a32c94b5f0b1e74330902b
|
[
"Apache-2.0"
] | null | null | null |
official/vision/beta/projects/movinet/modeling/movinet_layers_test.py
|
hjkim-haga/TF-OD-API
|
22ac477ff4dfb93fe7a32c94b5f0b1e74330902b
|
[
"Apache-2.0"
] | null | null | null |
official/vision/beta/projects/movinet/modeling/movinet_layers_test.py
|
hjkim-haga/TF-OD-API
|
22ac477ff4dfb93fe7a32c94b5f0b1e74330902b
|
[
"Apache-2.0"
] | null | null | null |
<<<<<<< HEAD
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Tests for movinet_layers.py."""
from absl.testing import parameterized
import tensorflow as tf
from official.vision.beta.modeling.layers import nn_layers
from official.vision.beta.projects.movinet.modeling import movinet_layers
class MovinetLayersTest(parameterized.TestCase, tf.test.TestCase):
def test_squeeze3d(self):
squeeze = movinet_layers.Squeeze3D()
inputs = tf.ones([5, 1, 1, 1, 3])
predicted = squeeze(inputs)
expected = tf.ones([5, 3])
self.assertEqual(predicted.shape, expected.shape)
self.assertAllEqual(predicted, expected)
def test_mobile_conv2d(self):
conv2d = movinet_layers.MobileConv2D(
filters=3,
kernel_size=(3, 3),
strides=(1, 1),
padding='same',
kernel_initializer='ones',
use_bias=False,
use_depthwise=False,
use_temporal=False,
use_buffered_input=True,
)
inputs = tf.ones([1, 2, 2, 2, 3])
predicted = conv2d(inputs)
expected = tf.constant(
[[[[[12., 12., 12.],
[12., 12., 12.]],
[[12., 12., 12.],
[12., 12., 12.]]],
[[[12., 12., 12.],
[12., 12., 12.]],
[[12., 12., 12.],
[12., 12., 12.]]]]])
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
def test_mobile_conv2d_temporal(self):
conv2d = movinet_layers.MobileConv2D(
filters=3,
kernel_size=(3, 1),
strides=(1, 1),
padding='causal',
kernel_initializer='ones',
use_bias=False,
use_depthwise=True,
use_temporal=True,
use_buffered_input=True,
)
inputs = tf.ones([1, 2, 2, 1, 3])
paddings = [[0, 0], [2, 0], [0, 0], [0, 0], [0, 0]]
padded_inputs = tf.pad(inputs, paddings)
predicted = conv2d(padded_inputs)
expected = tf.constant(
[[[[[1., 1., 1.]],
[[1., 1., 1.]]],
[[[2., 2., 2.]],
[[2., 2., 2.]]]]])
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
def test_stream_buffer(self):
conv3d_stream = nn_layers.Conv3D(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
padding='causal',
kernel_initializer='ones',
use_bias=False,
use_buffered_input=True,
)
buffer = movinet_layers.StreamBuffer(buffer_size=2)
conv3d = nn_layers.Conv3D(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
padding='causal',
kernel_initializer='ones',
use_bias=False,
use_buffered_input=False,
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv3d(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = buffer(frame, states=states)
x = conv3d_stream(x)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[12., 12., 12.]]],
[[[24., 24., 24.]]],
[[[36., 36., 36.]]],
[[[36., 36., 36.]]]]])
def test_stream_conv_block_2plus1d(self):
conv_block = movinet_layers.ConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='2plus1d',
)
stream_conv_block = movinet_layers.StreamConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='2plus1d',
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv_block(inputs)
predicted_disabled, _ = stream_conv_block(inputs)
self.assertEqual(predicted_disabled.shape, expected.shape)
self.assertAllClose(predicted_disabled, expected)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = stream_conv_block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[35.9640400, 35.9640400, 35.9640400]]],
[[[71.9280700, 71.9280700, 71.9280700]]],
[[[107.892105, 107.892105, 107.892105]]],
[[[107.892105, 107.892105, 107.892105]]]]])
def test_stream_conv_block_3d_2plus1d(self):
conv_block = movinet_layers.ConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='3d_2plus1d',
)
stream_conv_block = movinet_layers.StreamConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='3d_2plus1d',
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv_block(inputs)
predicted_disabled, _ = stream_conv_block(inputs)
self.assertEqual(predicted_disabled.shape, expected.shape)
self.assertAllClose(predicted_disabled, expected)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = stream_conv_block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[35.9640400, 35.9640400, 35.9640400]]],
[[[71.9280700, 71.9280700, 71.9280700]]],
[[[107.892105, 107.892105, 107.892105]]],
[[[107.892105, 107.892105, 107.892105]]]]])
def test_stream_conv_block(self):
conv_block = movinet_layers.ConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
)
stream_conv_block = movinet_layers.StreamConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv_block(inputs)
predicted_disabled, _ = stream_conv_block(inputs)
self.assertEqual(predicted_disabled.shape, expected.shape)
self.assertAllClose(predicted_disabled, expected)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = stream_conv_block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[11.994005, 11.994005, 11.994005]]],
[[[23.988010, 23.988010, 23.988010]]],
[[[35.982014, 35.982014, 35.982014]]],
[[[35.982014, 35.982014, 35.982014]]]]])
def test_stream_squeeze_excitation(self):
se = movinet_layers.StreamSqueezeExcitation(
3, causal=True, kernel_initializer='ones')
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
expected, _ = se(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = se(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected, 1e-5, 1e-5)
self.assertAllClose(
predicted,
[[[[[0.9998109, 0.9998109, 0.9998109]],
[[0.9998109, 0.9998109, 0.9998109]]],
[[[1.9999969, 1.9999969, 1.9999969]],
[[1.9999969, 1.9999969, 1.9999969]]],
[[[3., 3., 3.]],
[[3., 3., 3.]]],
[[[4., 4., 4.]],
[[4., 4., 4.]]]]],
1e-5, 1e-5)
def test_stream_movinet_block(self):
block = movinet_layers.MovinetBlock(
out_filters=3,
expand_filters=6,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
)
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
expected, _ = block(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
def test_stream_classifier_head(self):
head = movinet_layers.Head(project_filters=5)
classifier_head = movinet_layers.ClassifierHead(
head_filters=10, num_classes=4)
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
x, _ = head(inputs)
expected = classifier_head(x)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
for frame in frames:
x, states = head(frame, states=states)
predicted = classifier_head(x)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
if __name__ == '__main__':
tf.test.main()
=======
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Tests for movinet_layers.py."""
from absl.testing import parameterized
import tensorflow as tf
from official.vision.beta.modeling.layers import nn_layers
from official.vision.beta.projects.movinet.modeling import movinet_layers
class MovinetLayersTest(parameterized.TestCase, tf.test.TestCase):
def test_squeeze3d(self):
squeeze = movinet_layers.Squeeze3D()
inputs = tf.ones([5, 1, 1, 1, 3])
predicted = squeeze(inputs)
expected = tf.ones([5, 3])
self.assertEqual(predicted.shape, expected.shape)
self.assertAllEqual(predicted, expected)
def test_mobile_conv2d(self):
conv2d = movinet_layers.MobileConv2D(
filters=3,
kernel_size=(3, 3),
strides=(1, 1),
padding='same',
kernel_initializer='ones',
use_bias=False,
use_depthwise=False,
use_temporal=False,
use_buffered_input=True,
)
inputs = tf.ones([1, 2, 2, 2, 3])
predicted = conv2d(inputs)
expected = tf.constant(
[[[[[12., 12., 12.],
[12., 12., 12.]],
[[12., 12., 12.],
[12., 12., 12.]]],
[[[12., 12., 12.],
[12., 12., 12.]],
[[12., 12., 12.],
[12., 12., 12.]]]]])
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
def test_mobile_conv2d_temporal(self):
conv2d = movinet_layers.MobileConv2D(
filters=3,
kernel_size=(3, 1),
strides=(1, 1),
padding='causal',
kernel_initializer='ones',
use_bias=False,
use_depthwise=True,
use_temporal=True,
use_buffered_input=True,
)
inputs = tf.ones([1, 2, 2, 1, 3])
paddings = [[0, 0], [2, 0], [0, 0], [0, 0], [0, 0]]
padded_inputs = tf.pad(inputs, paddings)
predicted = conv2d(padded_inputs)
expected = tf.constant(
[[[[[1., 1., 1.]],
[[1., 1., 1.]]],
[[[2., 2., 2.]],
[[2., 2., 2.]]]]])
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
def test_stream_buffer(self):
conv3d_stream = nn_layers.Conv3D(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
padding='causal',
kernel_initializer='ones',
use_bias=False,
use_buffered_input=True,
)
buffer = movinet_layers.StreamBuffer(buffer_size=2)
conv3d = nn_layers.Conv3D(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
padding='causal',
kernel_initializer='ones',
use_bias=False,
use_buffered_input=False,
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv3d(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = buffer(frame, states=states)
x = conv3d_stream(x)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[12., 12., 12.]]],
[[[24., 24., 24.]]],
[[[36., 36., 36.]]],
[[[36., 36., 36.]]]]])
def test_stream_conv_block_2plus1d(self):
conv_block = movinet_layers.ConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='2plus1d',
)
stream_conv_block = movinet_layers.StreamConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='2plus1d',
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv_block(inputs)
predicted_disabled, _ = stream_conv_block(inputs)
self.assertEqual(predicted_disabled.shape, expected.shape)
self.assertAllClose(predicted_disabled, expected)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = stream_conv_block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[35.9640400, 35.9640400, 35.9640400]]],
[[[71.9280700, 71.9280700, 71.9280700]]],
[[[107.892105, 107.892105, 107.892105]]],
[[[107.892105, 107.892105, 107.892105]]]]])
def test_stream_conv_block_3d_2plus1d(self):
conv_block = movinet_layers.ConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='3d_2plus1d',
)
stream_conv_block = movinet_layers.StreamConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
conv_type='3d_2plus1d',
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv_block(inputs)
predicted_disabled, _ = stream_conv_block(inputs)
self.assertEqual(predicted_disabled.shape, expected.shape)
self.assertAllClose(predicted_disabled, expected)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = stream_conv_block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[35.9640400, 35.9640400, 35.9640400]]],
[[[71.9280700, 71.9280700, 71.9280700]]],
[[[107.892105, 107.892105, 107.892105]]],
[[[107.892105, 107.892105, 107.892105]]]]])
def test_stream_conv_block(self):
conv_block = movinet_layers.ConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
)
stream_conv_block = movinet_layers.StreamConvBlock(
filters=3,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
kernel_initializer='ones',
use_bias=False,
activation='relu',
)
inputs = tf.ones([1, 4, 2, 2, 3])
expected = conv_block(inputs)
predicted_disabled, _ = stream_conv_block(inputs)
self.assertEqual(predicted_disabled.shape, expected.shape)
self.assertAllClose(predicted_disabled, expected)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = stream_conv_block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[11.994005, 11.994005, 11.994005]]],
[[[23.988010, 23.988010, 23.988010]]],
[[[35.982014, 35.982014, 35.982014]]],
[[[35.982014, 35.982014, 35.982014]]]]])
def test_stream_squeeze_excitation(self):
se = movinet_layers.StreamSqueezeExcitation(
3, causal=True, kernel_initializer='ones')
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
expected, _ = se(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = se(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected, 1e-5, 1e-5)
self.assertAllClose(
predicted,
[[[[[0.9998109, 0.9998109, 0.9998109]],
[[0.9998109, 0.9998109, 0.9998109]]],
[[[1.9999969, 1.9999969, 1.9999969]],
[[1.9999969, 1.9999969, 1.9999969]]],
[[[3., 3., 3.]],
[[3., 3., 3.]]],
[[[4., 4., 4.]],
[[4., 4., 4.]]]]],
1e-5, 1e-5)
def test_stream_squeeze_excitation_2plus3d(self):
se = movinet_layers.StreamSqueezeExcitation(
3,
se_type='2plus3d',
causal=True,
activation='hard_swish',
gating_activation='hard_sigmoid',
kernel_initializer='ones')
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
expected, _ = se(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = se(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
self.assertAllClose(
predicted,
[[[[[1., 1., 1.]],
[[1., 1., 1.]]],
[[[2., 2., 2.]],
[[2., 2., 2.]]],
[[[3., 3., 3.]],
[[3., 3., 3.]]],
[[[4., 4., 4.]],
[[4., 4., 4.]]]]])
def test_stream_movinet_block(self):
block = movinet_layers.MovinetBlock(
out_filters=3,
expand_filters=6,
kernel_size=(3, 3, 3),
strides=(1, 2, 2),
causal=True,
)
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
expected, _ = block(inputs)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
predicted = []
for frame in frames:
x, states = block(frame, states=states)
predicted.append(x)
predicted = tf.concat(predicted, axis=1)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
def test_stream_classifier_head(self):
head = movinet_layers.Head(project_filters=5)
classifier_head = movinet_layers.ClassifierHead(
head_filters=10, num_classes=4)
inputs = tf.range(4, dtype=tf.float32) + 1.
inputs = tf.reshape(inputs, [1, 4, 1, 1, 1])
inputs = tf.tile(inputs, [1, 1, 2, 1, 3])
x, _ = head(inputs)
expected = classifier_head(x)
for num_splits in [1, 2, 4]:
frames = tf.split(inputs, inputs.shape[1] // num_splits, axis=1)
states = {}
for frame in frames:
x, states = head(frame, states=states)
predicted = classifier_head(x)
self.assertEqual(predicted.shape, expected.shape)
self.assertAllClose(predicted, expected)
if __name__ == '__main__':
tf.test.main()
>>>>>>> 0650ea24129892fb026a27b37028b500fb9383fa
| 30.708926
| 75
| 0.58122
|
4a07a21ad1b43836427de7e97f96a4df4c5c6e19
| 174
|
py
|
Python
|
webapp/app/logs/__init__.py
|
alan-turing-institute/CROP
|
467956ba8e273daa6afbfafd89bd2c3462a8156e
|
[
"MIT"
] | 9
|
2020-02-11T17:57:47.000Z
|
2022-03-22T14:24:55.000Z
|
webapp/app/logs/__init__.py
|
alan-turing-institute/CROP
|
467956ba8e273daa6afbfafd89bd2c3462a8156e
|
[
"MIT"
] | 64
|
2020-02-11T17:35:36.000Z
|
2022-03-31T13:19:08.000Z
|
webapp/app/logs/__init__.py
|
alan-turing-institute/CROP
|
467956ba8e273daa6afbfafd89bd2c3462a8156e
|
[
"MIT"
] | 2
|
2020-08-16T06:10:24.000Z
|
2021-04-15T10:11:51.000Z
|
from flask import Blueprint
blueprint = Blueprint(
'logs_blueprint',
__name__,
url_prefix='/logs',
template_folder='templates',
static_folder='static'
)
| 17.4
| 32
| 0.695402
|
4a07a258000adccc5889a116274e96c9df102886
| 714
|
py
|
Python
|
coin_dectector/web.py
|
jorisroovers/opencv-playground
|
4a5d179be422ea58f05ad1b050724e27b5a75820
|
[
"Apache-2.0"
] | null | null | null |
coin_dectector/web.py
|
jorisroovers/opencv-playground
|
4a5d179be422ea58f05ad1b050724e27b5a75820
|
[
"Apache-2.0"
] | null | null | null |
coin_dectector/web.py
|
jorisroovers/opencv-playground
|
4a5d179be422ea58f05ad1b050724e27b5a75820
|
[
"Apache-2.0"
] | null | null | null |
from coins import detector
from flask import Flask
from flask import request, jsonify, render_template, send_from_directory
app = Flask(__name__)
@app.route("/")
def index():
return render_template("index.html", name="joris")
@app.route('/assets/<path:path>')
def assets(path):
return send_from_directory('assets', path)
@app.route('/generated/<path:path>')
def generated(path):
return send_from_directory('generated', path)
@app.route("/detect", methods=['POST'])
def detect():
data = request.json
dst_path = detector.detect('assets/coins.png', float(data['param1']), float(data['param2']))
return jsonify(**{"url": dst_path})
if __name__ == "__main__":
app.run(debug=True)
| 22.3125
| 96
| 0.697479
|
4a07a3474c184c19daedaba6ca3716aea54cec3f
| 821
|
py
|
Python
|
config_example.py
|
stevemason/mqtt-audio-alert
|
439e0b34ec7bfa14144a42496c72d82cc36ebc04
|
[
"Apache-2.0"
] | null | null | null |
config_example.py
|
stevemason/mqtt-audio-alert
|
439e0b34ec7bfa14144a42496c72d82cc36ebc04
|
[
"Apache-2.0"
] | null | null | null |
config_example.py
|
stevemason/mqtt-audio-alert
|
439e0b34ec7bfa14144a42496c72d82cc36ebc04
|
[
"Apache-2.0"
] | null | null | null |
"""Private config items for mqtt-audio-alert."""
sounds = {
# 'NAMEOFSOUND1': '/PATH/TO/AUDIOFILE.mp3',
# 'NAMEOFSOUND2': '/PATH/TO/AUDIOFILE.mp3'
}
# Time ranged where sounds are permitted to play.
# Multiple time ranges are allowed.
active_times = [
#['07:00', '12:00'],
#['13:15', '14:15'],
['00:00', '23:59']
]
mpg123 = '/usr/bin/mpg123'
#audiodevice = 'hw:1,0'
audiodevice = '' # leave blank for default device
topic = 'mqtt-audio-alert' # which MQTT topic to subscribe to
client_id = 'mqtt-audio-alert1' # leave blank for default client_id
mqtt_host = '' # address of MQTT broker
mqtt_port = 1883
log_file = './mqtt-audio-alert.log'
username = '' # leave blank for no username
password = '' # leave blank for no password
#cert = "./root-ca.crt"
cert = '' # leave blank for no TLS
| 25.65625
| 68
| 0.652862
|
4a07a3b31e1c17469f33e7fa3f0eee660d333d8b
| 11,265
|
py
|
Python
|
ssasse_platform/ActiveScanningEngine/custom_scans/dnp3_read_analog_inputs.py
|
aashok3/ssass-e
|
77da9a4c1cef7006fe4a9c6a64f46a0eaade87ca
|
[
"BSD-3-Clause"
] | 4
|
2021-02-16T17:27:37.000Z
|
2022-01-25T09:29:30.000Z
|
ssasse_platform/ActiveScanningEngine/custom_scans/dnp3_read_analog_inputs.py
|
aashok3/ssass-e
|
77da9a4c1cef7006fe4a9c6a64f46a0eaade87ca
|
[
"BSD-3-Clause"
] | 3
|
2021-05-05T16:38:54.000Z
|
2021-06-04T20:05:28.000Z
|
ssasse_platform/ActiveScanningEngine/custom_scans/dnp3_read_analog_inputs.py
|
aashok3/ssass-e
|
77da9a4c1cef7006fe4a9c6a64f46a0eaade87ca
|
[
"BSD-3-Clause"
] | 5
|
2021-04-16T21:50:57.000Z
|
2021-05-25T16:36:26.000Z
|
# -*- coding: utf-8 -*- {{{
# vim: set fenc=utf-8 ft=python sw=4 ts=4 sts=4 et:
#
# Copyright (2021) Battelle Memorial Institute
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# }}}
import binascii
import os
import socket
import sys
import crcmod
import time
import itertools
import struct
import json
import subprocess
import shlex
import pyshark
import re
import xml.etree.ElementTree as ET
from xml.dom import minidom
import logging
_log = logging.getLogger(__name__)
from multiprocessing import Process, Queue
CRC_Fun = crcmod.predefined.mkPredefinedCrcFun("crc-16-dnp")
pkt_count = 1
config_path = os.path.join(os.getcwd(), "ssasse_platform", "ActiveScanningEngine", "config.json")
#config_path = "../config.json"
print(config_path)
fr = open(config_path, "r")
CONFIG = json.loads(fr.read())
fr.close()
def my_function(q, cap_filter):
global pkt_count
capture = pyshark.LiveCapture(CONFIG['scanning_interface'], display_filter=cap_filter, use_xml=True)
try:
capture.apply_on_packets(parse_packet, packet_count=2)
q.put(pkt_count)
except Exception as exc:
_log.error(exc)
def parse_packet(packet):
global pkt_count
doc = minidom.parseString(str(packet, 'utf-8'))
f = open("packet" + str(pkt_count) + ".xml", "w")
doc.writexml(f)
f.close()
pkt_count += 1
def check_crc(buff, count):
count -= 2
tmp_buff = buff[:-2]
crc = CRC_Fun(bytes(tmp_buff))
count += 2
if hex(buff[count-2]) != hex(crc & 0xff) or hex(buff[count-1]) != hex(crc >> 8):
return 1
else:
return 0
def isNthBitSet(integer, n):
if integer & (1 << (n - 1)):
return True
else:
return False
def mygrouper(n, iterable):
args = [iter(iterable)] * n
return ([e for e in t if e != None] for t in itertools.izip_longest(*args))
def dnp3_request_link_status(master, slave, ip, port):
DNP_COMS = False
SOCK_ERR_FLAG = False
dnp3_data_link_header = [0x05, 0x64, 0x05, 0xc9]
ip_address = ip
dnp3_slave = slave
dnp3_master = master
dnp3_data_link_header.append(dnp3_slave & 0xff)
dnp3_data_link_header.append(dnp3_slave >> 8)
dnp3_data_link_header.append(dnp3_master & 0xff)
dnp3_data_link_header.append(dnp3_master >> 8)
req_info = bytearray(struct.pack('B B B B B B B B', *dnp3_data_link_header))
dnp3_data_link_checksum = CRC_Fun(bytes(req_info))
req_info.append(dnp3_data_link_checksum & 0xff)
req_info.append(dnp3_data_link_checksum >> 8)
dnp_port = port
#Open connection
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_address = (ip_address, dnp_port)
sock.settimeout(10)
#Send packet and receive response
#print("GOT HERE")
try:
#print('sending {!r}'.format(binascii.hexlify(req_info)))
sock.connect(server_address)
#print "GOT HERE1"
sock.sendall(req_info)
res = sock.recv(1024)
is_Status = 0
crc_check = 0
tmp_dnp_data = [0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00]
tmp_dnp_data_counter = 0
if (res):
length_offset = 2
DL_control_offset = 3
for i in range(len(res)):
if res[i] == 0x05 and res[i+1] == 0x64:
if res[i+DL_control_offset] == 0x0b:
is_Status = 1
for j in range(i+int(res[i+length_offset]) + 5):
tmp_dnp_data[tmp_dnp_data_counter] = res[j]
tmp_dnp_data_counter += 1
tmp_dnp_data = bytearray(tmp_dnp_data)
else:
is_Status = 0
if is_Status == 1:
crc_check = check_crc(tmp_dnp_data, tmp_dnp_data_counter)
if crc_check == 0:
DNP_COMS = True
except socket.error as error:
_log.error("Not able to establish connection on port {} with {}: Socket Error: {}".format(port, ip, error))
if str(error) != "[Errno 104] Connection reset by peer":
SOCK_ERR_FLAG = True
finally:
# print('closing socket')
sock.close()
return (DNP_COMS, SOCK_ERR_FLAG)
def dnp3_read_analog_inputs(ip_address, dnp3_port, dnp3_master, dnp3_slave):
global pkt_count
#print("dnp3_read_device_attributes: {}".format(kwargs))
#print "Got to dnp3_read_device_attributes"
dnp3_data_link_header = [0x05, 0x64, 0x0b, 0xc4]
dnp3_data_link_header.append(dnp3_slave & 0xff)
dnp3_data_link_header.append(dnp3_slave >> 8)
dnp3_data_link_header.append(dnp3_master & 0xff)
dnp3_data_link_header.append(dnp3_master >> 8)
dnp3_data = [0xc0, 0xc0, 0x01, 0x1e, 0x00, 0x06]
#---------------MAIN--------------------
#Calculate Checksums
packed_dnp3_data_link_header = bytearray(struct.pack('B B B B B B B B', *dnp3_data_link_header))
dnp3_data_link_checksum = CRC_Fun(bytes(packed_dnp3_data_link_header))
packed_dnp3_data_link_header.append(dnp3_data_link_checksum & 0xff)
packed_dnp3_data_link_header.append(dnp3_data_link_checksum >> 8)
packed_dnp3_application_data = bytearray(struct.pack('B B B B B B', *dnp3_data))
dnp3_data_checksum = CRC_Fun(bytes(packed_dnp3_application_data))
packed_dnp3_application_data.append(dnp3_data_checksum & 0xff)
packed_dnp3_application_data.append(dnp3_data_checksum >> 8)
#Build Packet Data
req_info = packed_dnp3_data_link_header + packed_dnp3_application_data
#print("Before Request Link Status")
retry_count = 2
while retry_count > 0:
time.sleep(10)
DNP3_COMS, SOCK_ERR_FLAG = dnp3_request_link_status(dnp3_master, dnp3_slave, ip_address, dnp3_port)
if DNP3_COMS == True or SOCK_ERR_FLAG == True:
break
retry_count -= 1
results = dict.fromkeys(['TARGET_IPADDR', 'SCAN_NAME', 'DNP3_COMMS', 'MULTIPLE_ANINP_OBJ', 'DEFAULT_ANINP_VAR', 'SCAN_RESULT', 'SCAN_RESULT_DESC'])
results['TARGET_IPADDR'] = ip_address
results['SCAN_NAME'] = 'dnp3_read_analog_inputs'
#print("After Request Link Status")
#Sleep to provide time for connection to close properly
if SOCK_ERR_FLAG == True:
results['SCAN_RESULT'] = -1
results['SCAN_RESULT_DESC'] = 'Socket error connecting to {0}:{1}'.format(ip_address, dnp3_port)
return results
if DNP3_COMS:
#print('dnp3_coms == true')
time.sleep(3)
#Open connection
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_address = (ip_address, dnp3_port)
sock.settimeout(10)
#Send packet and receive response
#print "before results"
objvar_list = []
objvar_counter = 0
#Check if DNP3 Communication is even possible
if not DNP3_COMS:
results['DNP3_COMMS'] = 0
results['SCAN_RESULT'] = 0
results['SCAN_RESULT_DESC'] = 'No Link Status returned from DNP3 slave at {0}. It is possible that the slave device does not accept the scanner as a master'.format(ip_address)
return results
else:
results['DNP3_COMMS'] = 1
try:
cap_filter = "dnp3 and ip.addr == " + str(ip_address)
queue = Queue()
p = Process(target=my_function, args=(queue, cap_filter))
p.start()
#Give tshark a second to start
time.sleep(3)
sock.connect(server_address)
sock.sendall(req_info)
res = sock.recv(1024)
p.join() #this blocks until the process terminate
pkt_count = queue.get()
for i in range(1, pkt_count):
tree = ET.parse('packet' + str(i) + '.xml')
root = tree.getroot()
#ET.dump(root)
for child in root:
if child.get('name') == 'dnp3':
for child2 in child:
if 'Application Layer: ' in child2.get('show'):
for child3 in child2:
if child3.get('show') == 'RESPONSE Data Objects':
for child4 in child3:
if child4.get('name') == 'dnp3.al.obj':
pattern = re.compile("\(Obj:[0-9]+, Var:[0-9]+\)")
objvar = pattern.findall(child4.get('showname'))[0]
if objvar != None:
objvar_counter += 1
results['DEFAULT_ANINP_VAR'] = objvar.split(':')[2][0:-1]
os.remove('packet' + str(i) + '.xml')
if objvar_counter > 1:
results['MULTIPLE_ANINP_OBJ'] = 1
results['DEFAULT_ANINP_VAR'] = None
except socket.error as error:
_log.error("Not able to establish connection on port {} with {}: Socket Error: {}".format(dnp3_port, ip_address, error))
SOCK_ERR_FLAG = True
finally:
sock.close()
if not SOCK_ERR_FLAG:
results['SCAN_RESULT'] = 1
results['SCAN_RESULT_DESC'] = 'Success'
else:
results['SCAN_RESULT'] = -1
results['SCAN_RESULT_DESC'] = 'Socket error connecting to {0} on port {1}'.format(ip_address, dnp3_port)
return results
def main():
results = dnp3_read_analog_inputs(sys.argv[1],int(sys.argv[2]),int(sys.argv[3]),int(sys.argv[4]))
results_dict = json.dumps(results)
print(results_dict)
if __name__ == '__main__':
main()
| 35.536278
| 183
| 0.624767
|
4a07a41e70d99b09385e334fcefe414287d03a03
| 3,809
|
py
|
Python
|
tensorflow/python/keras/utils/dataset_creator_test.py
|
koreybea/tensorflow
|
e252fffb16f2706688604dc91c426bae367ae5e8
|
[
"Apache-2.0"
] | 6
|
2021-03-30T07:42:04.000Z
|
2022-03-23T02:42:36.000Z
|
tensorflow/python/keras/utils/dataset_creator_test.py
|
koreybea/tensorflow
|
e252fffb16f2706688604dc91c426bae367ae5e8
|
[
"Apache-2.0"
] | 7
|
2021-02-21T21:05:59.000Z
|
2022-02-10T01:39:06.000Z
|
tensorflow/python/keras/utils/dataset_creator_test.py
|
koreybea/tensorflow
|
e252fffb16f2706688604dc91c426bae367ae5e8
|
[
"Apache-2.0"
] | 4
|
2019-06-15T01:13:28.000Z
|
2020-12-16T02:28:45.000Z
|
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for dataset_creator."""
from tensorflow.python.compat import v2_compat
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.distribute import multi_worker_test_base
from tensorflow.python.distribute import parameter_server_strategy_v2
from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver
from tensorflow.python.keras.engine import sequential
from tensorflow.python.keras.layers import core as core_layers
from tensorflow.python.keras.optimizer_v2 import gradient_descent
from tensorflow.python.keras.utils import dataset_creator
from tensorflow.python.platform import test
from tensorflow.python.training.server_lib import ClusterSpec
class DatasetCreatorTest(test.TestCase):
def test_dataset_creator(self):
with self.assertRaisesRegex(
TypeError, "`dataset_fn` for `DatasetCreator` must be a `callable`."):
dataset_creator.DatasetCreator(2)
dataset_fn = lambda: 3
with self.assertRaisesRegex(
TypeError, "The `callable` provided to `DatasetCreator` must return "
"a Dataset."):
dataset_creator.DatasetCreator(dataset_fn)()
dataset_fn = lambda: dataset_ops.DatasetV2.from_tensor_slices([1, 1])
got = dataset_creator.DatasetCreator(dataset_fn)()
self.assertEqual(
next(iter(got)),
next(iter(dataset_ops.DatasetV2.from_tensor_slices([1, 1]))))
def _get_dataset_fn(self):
def dataset_fn(input_context):
global_batch_size = 64
batch_size = input_context.get_per_replica_batch_size(global_batch_size)
dataset = dataset_ops.DatasetV2.from_tensors(([1.], [1.])).repeat()
dataset = dataset.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(2)
return dataset
return dataset_fn
def test_dataset_creator_model_fit_without_strategy(self):
model = sequential.Sequential([core_layers.Dense(10)])
model.compile(gradient_descent.SGD(), loss="mse")
history = model.fit(
dataset_creator.DatasetCreator(self._get_dataset_fn()),
epochs=10,
steps_per_epoch=10,
verbose=0)
self.assertLen(history.history["loss"], 10)
def test_dataset_creator_usage_in_parameter_server_model_fit(self):
cluster_def = multi_worker_test_base.create_in_process_cluster(
num_workers=2, num_ps=1, rpc_layer="grpc")
cluster_def["chief"] = [
"localhost:%d" % multi_worker_test_base.pick_unused_port()
]
strategy = parameter_server_strategy_v2.ParameterServerStrategyV2(
SimpleClusterResolver(ClusterSpec(cluster_def), rpc_layer="grpc"))
with strategy.scope():
model = sequential.Sequential([core_layers.Dense(10)])
model.compile(gradient_descent.SGD(), loss="mse")
history = model.fit(
dataset_creator.DatasetCreator(self._get_dataset_fn()),
epochs=10,
steps_per_epoch=10,
verbose=0)
self.assertLen(history.history["loss"], 10)
if __name__ == "__main__":
v2_compat.enable_v2_behavior()
test.main()
| 39.268041
| 80
| 0.7288
|
4a07a545e98a3229144a79d968cbf7bfb9fb0a18
| 1,988
|
py
|
Python
|
test/functional/p2p_invalid_locator.py
|
xiaolin1579/vektorcoin
|
6e33506d8fba8883f401a89af0b7a76d44fb8bed
|
[
"MIT"
] | 1
|
2021-02-16T10:45:46.000Z
|
2021-02-16T10:45:46.000Z
|
test/functional/p2p_invalid_locator.py
|
xiaolin1579/vektorcoin
|
6e33506d8fba8883f401a89af0b7a76d44fb8bed
|
[
"MIT"
] | null | null | null |
test/functional/p2p_invalid_locator.py
|
xiaolin1579/vektorcoin
|
6e33506d8fba8883f401a89af0b7a76d44fb8bed
|
[
"MIT"
] | 1
|
2021-02-09T14:29:27.000Z
|
2021-02-09T14:29:27.000Z
|
#!/usr/bin/env python3
# Copyright (c) 2015-2017 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test node responses to invalid locators.
"""
from test_framework.messages import msg_getheaders, msg_getblocks, MAX_LOCATOR_SZ
from test_framework.mininode import P2PInterface
from test_framework.test_framework import VEKTORCOINTestFramework
class InvalidLocatorTest(VEKTORCOINTestFramework):
def set_test_params(self):
self.num_nodes = 1
self.setup_clean_chain = False
def skip_test_if_missing_module(self):
self.skip_if_no_wallet()
def run_test(self):
node = self.nodes[0] # convenience reference to the node
node.generate(1) # Get node out of IBD
self.log.info('Test max locator size')
block_count = node.getblockcount()
for msg in [msg_getheaders(), msg_getblocks()]:
self.log.info('Wait for disconnect when sending {} hashes in locator'.format(MAX_LOCATOR_SZ + 1))
node.add_p2p_connection(P2PInterface())
msg.locator.vHave = [int(node.getblockhash(i - 1), 16) for i in range(block_count, block_count - (MAX_LOCATOR_SZ + 1), -1)]
node.p2p.send_message(msg)
node.p2p.wait_for_disconnect()
node.disconnect_p2ps()
self.log.info('Wait for response when sending {} hashes in locator'.format(MAX_LOCATOR_SZ))
node.add_p2p_connection(P2PInterface())
msg.locator.vHave = [int(node.getblockhash(i - 1), 16) for i in range(block_count, block_count - (MAX_LOCATOR_SZ), -1)]
node.p2p.send_message(msg)
if type(msg) == msg_getheaders:
node.p2p.wait_for_header(int(node.getbestblockhash(), 16))
else:
node.p2p.wait_for_block(int(node.getbestblockhash(), 16))
if __name__ == '__main__':
InvalidLocatorTest().main()
| 42.297872
| 135
| 0.682596
|
4a07a76b663c9d137900b84dac9edf12caff5dd1
| 556
|
py
|
Python
|
04_introduccion-al-computo-con-python/modulo_II/clase03-busqueda-binaria.py
|
Aibique-Forks/articicial-inteligence-and-data-science
|
fbdc866e4e46060cbde5b887806bdfeac645838e
|
[
"MIT"
] | 30
|
2020-06-19T16:21:04.000Z
|
2022-02-19T01:48:39.000Z
|
04_introduccion-al-computo-con-python/modulo_II/clase03-busqueda-binaria.py
|
Aibique-Forks/articicial-inteligence-and-data-science
|
fbdc866e4e46060cbde5b887806bdfeac645838e
|
[
"MIT"
] | 87
|
2021-02-12T04:42:13.000Z
|
2021-09-20T04:25:29.000Z
|
04_introduccion-al-computo-con-python/modulo_II/clase03-busqueda-binaria.py
|
Aibique-Forks/articicial-inteligence-and-data-science
|
fbdc866e4e46060cbde5b887806bdfeac645838e
|
[
"MIT"
] | 11
|
2020-08-13T04:04:01.000Z
|
2022-01-20T20:10:43.000Z
|
"""
Tema: Busqueda Binaria.
Curso: Pensamiento computacional.
Plataforma: Platzi.
Profesor: David Aroesti.
Alumno: @edinsonrequena.
"""
objetivo = int(input('Type a number: '))
epsilon = 0.001
bajo = 0.0
alto = max(1.0, objetivo)
respuesta = (alto + bajo) / 2
while abs(respuesta**2 - objetivo) >= epsilon:
print(f'bajo={bajo}, alto={alto}, respuesta={respuesta}')
if respuesta**2 < objetivo:
bajo = respuesta
else:
alto = respuesta
respuesta = (alto + bajo) / 2
print(f'La raiz cuadrada de {objetivo} es {respuesta}')
| 21.384615
| 61
| 0.656475
|
4a07a7fe0d4105cab2f625b7153e4a7c8aa4bcb3
| 4,323
|
py
|
Python
|
ytpld.py
|
D54/youtube-pldump
|
be388f2963c40f9306fa45b6f4746f6c00cbdfbf
|
[
"MIT"
] | null | null | null |
ytpld.py
|
D54/youtube-pldump
|
be388f2963c40f9306fa45b6f4746f6c00cbdfbf
|
[
"MIT"
] | null | null | null |
ytpld.py
|
D54/youtube-pldump
|
be388f2963c40f9306fa45b6f4746f6c00cbdfbf
|
[
"MIT"
] | 1
|
2018-03-04T12:04:24.000Z
|
2018-03-04T12:04:24.000Z
|
from json import load, dump
from requests import post, get
from urllib.parse import urlparse, parse_qs, urlencode, urlunparse
from webbrowser import open as webopen
from http.server import BaseHTTPRequestHandler, HTTPServer
from sys import exit
from datetime import datetime
from yaml import dump as ydump
with open('client_secret.json') as f:
cs = load(f)['installed']
def show_auth_page():
u = list(urlparse(cs['auth_uri']))
u[4] = urlencode({
'client_id': cs['client_id'],
'redirect_uri': 'http://localhost:10000',
'response_type': 'code',
'scope': 'https://www.googleapis.com/auth/youtube.readonly'
})
webopen(urlunparse(u))
def listen_for_code():
finished = False
re = None
class S(BaseHTTPRequestHandler):
def do_GET(self):
nonlocal finished, re
url = urlparse(self.path)
if url.path == '/':
re = parse_qs(url.query)
self.send_response(200)
self.send_header('Content-type', 'text/html')
self.end_headers()
self.wfile.write("<html><head><title>OAuth</title></head><body><h1>Now you can close this window/tab.</h1></body></html>".encode('utf-8'))
finished = True
else:
self.send_response(204)
self.end_headers()
httpd = HTTPServer(('localhost', 10000), S)
while not finished:
httpd.handle_request()
return {k: ', '.join(v) for k, v in re.items()}
def request_token(code):
r = post(cs['token_uri'], data={
'code': code,
'client_id': cs['client_id'],
'client_secret': cs['client_secret'],
'redirect_uri': 'http://localhost:10000',
'grant_type': 'authorization_code'
})
d = int(datetime.strptime(r.headers['Date'], '%a, %d %b %Y %H:%M:%S %Z').timestamp())
r = r.json()
r.pop('token_type')
e = r.pop('expires_in')
r['expires_at'] = d + e
return r
def refresh_token(refresh_token):
r = post(cs['token_uri'], data={
'refresh_token': refresh_token,
'client_id': cs['client_id'],
'client_secret': cs['client_secret'],
'grant_type': 'refresh_token'
})
d = int(datetime.strptime(r.headers['Date'], '%a, %d %b %Y %H:%M:%S %Z').timestamp())
r = r.json()
r.pop('token_type')
e = r.pop('expires_in')
r['expires_at'] = d + e
return r
def auth():
global cred
show_auth_page()
code = listen_for_code()
if 'error' in code:
print('An error occured during the authentication process:')
print(code['error'])
exit(1)
cred = request_token(code['code'])
with open('credentials.json', 'w') as f:
dump(cred, f)
def refresh():
new_cred = refresh_token(cred['refresh_token'])
cred.update(new_cred)
with open('credentials.json', 'w') as f:
dump(cred, f)
def apireq(path, params={}):
_params = {'part': 'snippet'}
_params.update(params)
baseURL = 'https://www.googleapis.com/youtube/v3'
r = get(baseURL + path, params=_params, headers={'Authorization': 'Bearer %s' % cred['access_token']})
if r.status_code == 401:
refresh()
return apireq(path, params)
return r.json()
def apireqlist(path, params={}):
_params = {'maxResults': 50}
_params.update(params)
r = apireq(path, _params)
re = r['items']
if 'nextPageToken' in r:
_params['pageToken'] = r['nextPageToken']
re += apireqlist(path, _params)
return re
try:
with open('credentials.json') as f:
cred = load(f)
except FileNotFoundError as e:
auth()
playlists = apireqlist('/playlists', {'mine': 'true'})
out = [{'id': x['id'], 'title': x['snippet']['title']} for x in playlists]
out = sorted(out, key=lambda x: x['title'])
for pl in out:
print('Downloading [%s] ' % pl['title'], end='')
items = apireqlist('/playlistItems', {'playlistId': pl['id']})
pl['items'] = [{'id': x['snippet']['resourceId']['videoId'], 'title': x['snippet']['title']} for x in items]
print(' Done')
with open('dump.yaml', 'w') as f:
ydump(out, f, width=250)
| 30.020833
| 155
| 0.573213
|
4a07a80706a19809cd2a12cc691382456220c900
| 214
|
py
|
Python
|
8kyu/are_you_playing_banjo.py
|
nhsz/codewars
|
82703959e910254d6feff4162f78c6dbd7a1c3ed
|
[
"MIT"
] | 1
|
2018-12-02T23:04:38.000Z
|
2018-12-02T23:04:38.000Z
|
8kyu/are_you_playing_banjo.py
|
nhsz/codewars
|
82703959e910254d6feff4162f78c6dbd7a1c3ed
|
[
"MIT"
] | null | null | null |
8kyu/are_you_playing_banjo.py
|
nhsz/codewars
|
82703959e910254d6feff4162f78c6dbd7a1c3ed
|
[
"MIT"
] | null | null | null |
# http://www.codewars.com/kata/53af2b8861023f1d88000832/
def are_you_playing_banjo(name):
if name[0].lower() == "r":
return name + " plays banjo"
else:
return name + " does not play banjo"
| 26.75
| 56
| 0.640187
|
4a07a8200357cc116b3986c9008cd09c32e90ef1
| 7,509
|
py
|
Python
|
tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.py
|
mpangrazzi/haystack
|
eb514a6167b84a4b6923dfc397c7a40ab3da2e44
|
[
"Apache-2.0"
] | null | null | null |
tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.py
|
mpangrazzi/haystack
|
eb514a6167b84a4b6923dfc397c7a40ab3da2e44
|
[
"Apache-2.0"
] | null | null | null |
tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.py
|
mpangrazzi/haystack
|
eb514a6167b84a4b6923dfc397c7a40ab3da2e44
|
[
"Apache-2.0"
] | null | null | null |
# ## Task: Build a Question Answering pipeline without Elasticsearch
#
# Haystack provides alternatives to Elasticsearch for developing quick prototypes.
#
# You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
#
# If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
from haystack.document_stores import InMemoryDocumentStore, SQLDocumentStore
from haystack.nodes import FARMReader, TransformersReader, TfidfRetriever
from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http, print_answers
def tutorial3_basic_qa_pipeline_without_elasticsearch():
# In-Memory Document Store
document_store = InMemoryDocumentStore()
# or, alternatively, SQLite Document Store
# document_store = SQLDocumentStore(url="sqlite:///qa.db")
# ## Preprocessing of documents
#
# Haystack provides a customizable pipeline for:
# - converting files into texts
# - cleaning texts
# - splitting texts
# - writing them to a Document Store
# In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index
# them in Elasticsearch.
# Let's first get some documents that we want to query
# Here: 517 Wikipedia articles for Game of Thrones
doc_dir = "data/tutorial3"
s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip"
fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
# convert files to dicts containing documents that can be indexed to our datastore
docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
# It must take a str as input, and return a str.
# Now, let's write the docs to our DB.
document_store.write_documents(docs)
# ## Initalize Retriever, Reader & Pipeline
#
# ### Retriever
#
# Retrievers help narrowing down the scope for the Reader to smaller units of text where
# a given question could be answered.
#
# With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more
# retrievers, please refer to the tutorial-1.
# An in-memory TfidfRetriever based on Pandas dataframes
retriever = TfidfRetriever(document_store=document_store)
# ### Reader
#
# A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
# on powerful, but slower deep learning models.
#
# Haystack currently supports Readers based on the frameworks FARM and Transformers.
# With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
# **Here:** a medium sized RoBERTa QA model using a Reader based on
# FARM (https://huggingface.co/deepset/roberta-base-squad2)
# **Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
# **Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or
# "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
# **Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost.
# Higher values mean the model prefers "no answer possible".
# #### FARMReader
#
# Load a local model or any of the QA models on
# Hugging Face's model hub (https://huggingface.co/models)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
# #### TransformersReader
# Alternative:
# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
# ### Pipeline
#
# With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
# Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
# To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
# You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
from haystack.pipelines import ExtractiveQAPipeline
pipe = ExtractiveQAPipeline(reader, retriever)
## Voilà! Ask a question!
prediction = pipe.run(
query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
)
# prediction = pipe.run(query="Who created the Dothraki vocabulary?", params={"Reader": {"top_k": 5}})
# prediction = pipe.run(query="Who is the sister of Sansa?", params={"Reader": {"top_k": 5}})
# Now you can either print the object directly
print("\n\nRaw object:\n")
from pprint import pprint
pprint(prediction)
# Sample output:
# {
# 'answers': [ <Answer: answer='Eddard', type='extractive', score=0.9919578731060028, offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}], document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
# <Answer: answer='Ned', type='extractive', score=0.9767240881919861, offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}], document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
# ...
# ]
# 'documents': [ <Document: content_type='text', score=0.8034909798951382, meta={'name': '332_Sansa_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe', content='\n===\'\'A Game of Thrones\'\'===\nSansa Stark begins the novel by being betrothed to Crown ...'>,
# <Document: content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2', 'content='\n===Season 2===\nGendry travels North with Yoren and other Night's Watch recruits, including Arya ...'>,
# ...
# ],
# 'no_ans_gap': 11.688868522644043,
# 'node_id': 'Reader',
# 'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},
# 'query': 'Who is the father of Arya Stark?',
# 'root_node': 'Query'
# }
# Note that the documents contained in the above object are the documents filtered by the Retriever from
# the document store. Although the answers were extracted from these documents, it's possible that many
# answers were taken from a single one of them, and that some of the documents were not source of any answer.
# Or use a util to simplify the output
# Change `minimum` to `medium` or `all` to raise the level of detail
print("\n\nSimplified output:\n")
print_answers(prediction, details="minimum")
if __name__ == "__main__":
tutorial3_basic_qa_pipeline_without_elasticsearch()
# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/
| 53.255319
| 307
| 0.691836
|
4a07a8a2e1798487e0a37311eeb08f9598b5a420
| 1,163
|
py
|
Python
|
lab0/algebra_utils.py
|
rdugue/MIT_AI_LABS
|
97d30195aa842f8edf0fb863ceae2599fe4f669e
|
[
"MIT"
] | 1
|
2017-05-01T10:07:02.000Z
|
2017-05-01T10:07:02.000Z
|
lab0/algebra_utils.py
|
rdugue/MIT_AI_LABS
|
97d30195aa842f8edf0fb863ceae2599fe4f669e
|
[
"MIT"
] | null | null | null |
lab0/algebra_utils.py
|
rdugue/MIT_AI_LABS
|
97d30195aa842f8edf0fb863ceae2599fe4f669e
|
[
"MIT"
] | 1
|
2018-02-20T17:24:34.000Z
|
2018-02-20T17:24:34.000Z
|
"""
These are functions for transferring algebra.py's test cases over the
Internet. You shouldn't need to mess with these.
"""
from algebra import simplify_if_possible, Sum, Product, Expression
def distribution(val):
if isinstance(val, Expression):
raise ValueError("expression has already been decoded")
return encode_sumprod(simplify_if_possible(decode_sumprod(val)))
def encode_sumprod(lst):
retVal = []
if isinstance(lst, Sum):
retVal.append('Sum')
elif isinstance(lst, Product):
retVal.append('Product')
for elt in lst:
if isinstance(elt, (Sum, Product)):
retVal.append( encode_sumprod(elt) )
else:
retVal.append(elt)
return retVal
def decode_sumprod(lst):
retVal = []
for elt in lst[1:]:
if isinstance(elt, (list, tuple)):
retVal.append(decode_sumprod(elt))
else:
retVal.append(elt)
if lst[0] == 'Sum':
retVal = Sum(retVal)
elif lst[0] == 'Product':
retVal = Product(retVal)
else:
raise Exception, "Error: List was not an encoded Sum or Product!"
return retVal
| 24.229167
| 73
| 0.628547
|
4a07aa88c15e5ad0c0de9b2cd591a664c87b0ecd
| 478
|
py
|
Python
|
myproject/cookie_app/migrations/0009_auto_20141211_0631.py
|
nathanielbecker/business-contacter-django-app
|
369270f46087b7b593f5b4cff6bddd89707cdc62
|
[
"Apache-2.0"
] | null | null | null |
myproject/cookie_app/migrations/0009_auto_20141211_0631.py
|
nathanielbecker/business-contacter-django-app
|
369270f46087b7b593f5b4cff6bddd89707cdc62
|
[
"Apache-2.0"
] | null | null | null |
myproject/cookie_app/migrations/0009_auto_20141211_0631.py
|
nathanielbecker/business-contacter-django-app
|
369270f46087b7b593f5b4cff6bddd89707cdc62
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('cookie_app', '0008_auto_20141120_0807'),
]
operations = [
migrations.AlterField(
model_name='barebones_crud',
name='FollowUp',
field=models.BooleanField(default=False, verbose_name='pizzafff'),
preserve_default=True,
),
]
| 22.761905
| 78
| 0.625523
|
4a07ab32ac709f82a80af9324ca7877f04086ec5
| 14,276
|
py
|
Python
|
google/cloud/forseti/common/gcp_type/iam_policy.py
|
johnrevans6/forseti-security
|
d4b907a076ef4caaea9d3232c8fd0ad5822cd2d6
|
[
"Apache-2.0"
] | null | null | null |
google/cloud/forseti/common/gcp_type/iam_policy.py
|
johnrevans6/forseti-security
|
d4b907a076ef4caaea9d3232c8fd0ad5822cd2d6
|
[
"Apache-2.0"
] | null | null | null |
google/cloud/forseti/common/gcp_type/iam_policy.py
|
johnrevans6/forseti-security
|
d4b907a076ef4caaea9d3232c8fd0ad5822cd2d6
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017 The Forseti Security Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GCP IAM Policy.
See: https://cloud.google.com/iam/reference/rest/v1/Policy
"""
import re
from google.cloud.forseti.common.gcp_type import errors
from google.cloud.forseti.common.util import logger
from google.cloud.forseti.common.util.regular_exp import escape_and_globify
LOGGER = logger.get_logger(__name__)
def _get_iam_members(members):
"""Get a list of this binding's members as IamPolicyMembers.
Args:
members (list): A list of members (strings).
Returns:
list: A list of IamPolicyMembers.
"""
return [IamPolicyMember.create_from(m) for m in members]
class IamPolicy(object):
"""GCP IAM Policy."""
def __init__(self):
"""Initialize."""
self.audit_configs = None
self.bindings = []
@classmethod
def create_from(cls, policy_json):
"""Create an IamPolicy object from json representation.
Args:
policy_json (dict): The json representing the IAM policy.
Returns:
IamPolicy: An IamPolicy.
"""
policy = cls()
if not policy_json:
raise errors.InvalidIamPolicyError(
'Invalid policy {}'.format(policy_json))
policy.bindings = [IamPolicyBinding.create_from(b)
for b in policy_json.get('bindings', [])]
if 'auditConfigs' in policy_json:
policy.audit_configs = IamAuditConfig.create_from(
policy_json.get('auditConfigs'))
return policy
def __eq__(self, other):
"""Tests equality of IamPolicy.
Args:
other (object): Object to compare.
Returns:
bool: True if equals, False otherwise.
"""
if not isinstance(other, type(self)):
return NotImplemented
return (self.bindings == other.bindings and
self.audit_configs == other.audit_configs)
def __ne__(self, other):
"""Tests inequality of IamPolicy.
Args:
other (object): Object to compare.
Returns:
bool: True if not equals, False otherwise.
"""
return not self == other
def __repr__(self):
"""String representation of IamPolicy.
Returns:
str: Representation of IamPolicy
"""
if self.audit_configs:
return 'IamPolicy: <bindings={}, audit_configs={}>'.format(
self.bindings, self.audit_configs)
return 'IamPolicy: <bindings={}>'.format(self.bindings)
def is_empty(self):
"""Tests whether this policy's bindings are empty.
Returns:
bool: True if bindings are empty; False otherwise.
"""
return not bool(self.bindings)
class IamPolicyBinding(object):
"""IAM Policy Binding."""
def __init__(self, role_name, members=None):
"""Initialize.
Args:
role_name (str): The string name of the role.
members (list): The role members of the policy binding.
"""
if not role_name or not members:
raise errors.InvalidIamPolicyBindingError(
('Invalid IAM policy binding: '
'role_name={}, members={}'.format(role_name, members)))
self.role_name = role_name
self.members = _get_iam_members(members)
self.role_pattern = re.compile(escape_and_globify(role_name),
flags=re.IGNORECASE)
def __eq__(self, other):
"""Tests equality of IamPolicyBinding.
Args:
other (object): Object to compare.
Returns:
bool: Whether objects are equal.
"""
if not isinstance(other, type(self)):
return NotImplemented
return (self.role_name == other.role_name and
self.members == other.members)
def __ne__(self, other):
"""Tests inequality of IamPolicyBinding.
Args:
other (object): Object to compare.
Returns:
bool: Whether objects are not equal.
"""
return not self == other
def __repr__(self):
"""String representation of IamPolicyBinding.
Returns:
str: The representation of IamPolicyBinding.
"""
return 'IamBinding: <role_name={}, members={}>'.format(
self.role_name, self.members)
@classmethod
def create_from(cls, binding):
"""Create an IamPolicyBinding from a binding dict.
Args:
binding (dict): The binding (role mapped to members).
Returns:
IamPolicyBinding: A new IamPolicyBinding created with the
role and members.
"""
if isinstance(binding, type(cls)):
return binding
try:
return cls(binding.get('role'), binding.get('members'))
except errors.InvalidIamPolicyMemberError:
LOGGER.debug(
'Invalid IAM policy member: %s.', binding.get('members'))
return None
def merge_members(self, other):
"""Add `other` members to mine if the role names are the same.
Use case: merging members from ancestor bindings with the same role
name.
Args:
other (IamPolicyBinding): the other IAM policy binding
"""
if not isinstance(other, type(self)):
raise errors.InvalidIamPolicyBindingError(
'Cannot merge, other is not of type \'IamPolicyBinding\'')
if other.role_name != self.role_name:
return
for member in other.members:
if member not in self.members:
self.members.append(member)
class IamPolicyMember(object):
"""IAM Policy Member.
See https://cloud.google.com/iam/reference/rest/v1/Policy#Binding.
Parse an identity from a policy binding.
"""
ALL_USERS = 'allUsers'
ALL_AUTH_USERS = 'allAuthenticatedUsers'
member_types = {ALL_USERS, ALL_AUTH_USERS, 'user', 'group',
'serviceAccount', 'domain'}
def __init__(self, member_type, member_name=None):
"""Initialize.
Args:
member_type (str): The string member type (see `member_types`).
member_name (str): The string member name.
"""
if not member_type or not self._member_type_exists(member_type):
raise errors.InvalidIamPolicyMemberError(
'Invalid policy member: {}'.format(member_type))
self.type = member_type
self.name = member_name
self.name_pattern = None
if member_name:
self.name_pattern = re.compile(escape_and_globify(self.name),
flags=re.IGNORECASE)
def __eq__(self, other):
"""Tests equality of IamPolicyMember.
Args:
other (object): The object to compare.
Returns:
bool: Whether the objects are equal.
"""
if not isinstance(other, type(self)):
return NotImplemented
return (self.type == other.type and
self.name == other.name)
def __ne__(self, other):
"""Tests inequality of IamPolicyMember.
Args:
other (object): The object to compare.
Returns:
bool: Whether the objects are not equal.
"""
return not self == other
def __hash__(self):
"""Hash function for IamPolicyMember.
Returns:
hash: The hashed object.
"""
return hash((self.type, self.name))
def __repr__(self):
"""String representation of IamPolicyMember.
Returns:
str: The representation of IamPolicyMember.
"""
return '%s:%s' % (self.type, self.name)
def _member_type_exists(self, member_type):
"""Determine if the member type exists in valid member types.
Args:
member_type (str): Member type.
Returns:
bool: If member type is valid.
"""
return member_type in self.member_types
@classmethod
def create_from(cls, member):
"""Create an IamPolicyMember from the member identity string.
Args:
member (str): The IAM policy binding member.
Returns:
IamPolicyMember: Created from the member string.
"""
identity_parts = member.split(':')
member_name = None
if len(identity_parts) > 1:
member_name = identity_parts[1]
return cls(identity_parts[0], member_name=member_name)
def _is_matching_domain(self, other):
"""Determine whether IAM policy member belongs to domain.
This applies to a situation where a rule has a `domain` style `members`
specification and the policy to check specifies users.
Args:
other (IamPolicyMember): The policy binding member to check.
Returns:
bool: True if `other` is a member of the domain, False otherwise.
"""
if self.type != 'domain' or other.type != 'user':
return False
try:
_, domain = other.name.rsplit('@', 1)
except ValueError:
return False
return self.name == domain
def matches(self, other):
"""Determine if another member matches.
Args:
other (str): The policy binding member name.
Returns:
bool: True if the member matches this member, otherwise False.
"""
other_member = None
if isinstance(other, type(self)):
other_member = other
else:
other_member = IamPolicyMember.create_from(other)
# Bucket IAM supports a special "allUsers" member, whose value is simply
# "allUsers", without a colon separator and a second fragment.
if (self.type == self.ALL_USERS and
other_member.type == self.ALL_USERS):
return True
# Match if:
# {member_type}:{member_name} regex-matches self's
# {member_type}:{member_name} .
if (self.type == other_member.type and
self.name_pattern.match(other_member.name)):
return True
if self._is_matching_domain(other_member):
return True
return False
class IamAuditConfig(object):
"""IAM Audit Config.
Captures the mapping from service to log type to exempted members for a
project, folder or organization.
"""
ALL_SERVICES = 'allServices'
VALID_LOG_TYPES = frozenset(['AUDIT_READ', 'DATA_READ', 'DATA_WRITE'])
def __init__(self, service_configs):
"""Initialize.
Args:
service_configs (dict): A dictionary mapping service names to
dictionaries mapping log types to sets of exempeted members.
"""
self.service_configs = service_configs
def __eq__(self, other):
"""Tests equality of IamAuditConfig.
Args:
other (object): Object to compare.
Returns:
bool: Whether objects are equal.
"""
if not isinstance(other, type(self)):
return NotImplemented
return self.service_configs == other.service_configs
def __ne__(self, other):
"""Tests inequality of IamAuditConfig.
Args:
other (object): Object to compare.
Returns:
bool: Whether objects are not equal.
"""
return not self == other
def __repr__(self):
"""String representation of IamAuditConfig.
Returns:
str: The representation of IamAuditConfig.
"""
return 'IamAuditConfig: <service_configs={}>'.format(
self.service_configs)
@classmethod
def create_from(cls, audit_configs_list):
"""Creates an IamAuditConfig from a list of auditConfig dicts.
Args:
audit_configs_list (list): A list of auditConfigs for each service.
Returns:
IamAuditConfig: A new IamAuditConfig created with the service audit
configs.
"""
service_configs = {}
for audit_config in audit_configs_list:
service_name = audit_config.get('service')
log_configs = {}
for log_config in audit_config.get('auditLogConfigs'):
log_configs[log_config.get('logType')] = set(
log_config.get('exemptedMembers', []))
if not service_name or not log_configs or None in log_configs:
raise errors.InvalidIamAuditConfigError(
'Invalid IAM audit config: {}'.format(audit_config))
service_configs[service_name] = log_configs
return cls(service_configs)
def merge_configs(self, other):
"""Adds `other` audit configs to mine, combining exempted member.
Use case: merging audit configs from ancestor IAM policies.
Args:
other (IamAuditConfig): the other IAM audit configs
"""
if not isinstance(other, type(self)):
raise errors.InvalidIamAuditConfigError(
'Cannot merge, other is not of type \'IamAuditConfig\'')
for service_name, log_configs in other.service_configs.iteritems():
if service_name not in self.service_configs:
self.service_configs[service_name] = {}
service_config = self.service_configs[service_name]
for log_type, exemptions in log_configs.iteritems():
service_config[log_type] = exemptions.union(service_config.get(
log_type, set()))
| 31.238512
| 80
| 0.601639
|
4a07abe5cccad3f96b7e7b5a16e2547f1944405f
| 157
|
py
|
Python
|
tests/model_control/detailed/transf_Logit/model_control_one_enabled_Logit_PolyTrend_Seasonal_Hour_SVR.py
|
jmabry/pyaf
|
afbc15a851a2445a7824bf255af612dc429265af
|
[
"BSD-3-Clause"
] | null | null | null |
tests/model_control/detailed/transf_Logit/model_control_one_enabled_Logit_PolyTrend_Seasonal_Hour_SVR.py
|
jmabry/pyaf
|
afbc15a851a2445a7824bf255af612dc429265af
|
[
"BSD-3-Clause"
] | 1
|
2019-11-30T23:39:38.000Z
|
2019-12-01T04:34:35.000Z
|
tests/model_control/detailed/transf_Logit/model_control_one_enabled_Logit_PolyTrend_Seasonal_Hour_SVR.py
|
jmabry/pyaf
|
afbc15a851a2445a7824bf255af612dc429265af
|
[
"BSD-3-Clause"
] | null | null | null |
import pyaf.tests.model_control.test_ozone_custom_models_enabled as testmod
testmod.build_model( ['Logit'] , ['PolyTrend'] , ['Seasonal_Hour'] , ['SVR'] );
| 39.25
| 79
| 0.745223
|
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