code
stringlengths 22
1.05M
| apis
listlengths 1
3.31k
| extract_api
stringlengths 75
3.25M
|
|---|---|---|
import sys
import time
import uasyncio as asyncio
from ahttpserver import sendfile, Server
app = Server()
@app.route("GET", "/")
async def root(reader, writer, request):
writer.write(b"HTTP/1.1 200 OK\r\n")
writer.write(b"Connection: close\r\n")
writer.write(b"Content-Type: text/html\r\n")
writer.write(b"\r\n")
await writer.drain()
await sendfile(writer, "index.html")
try:
print(1/0)
except Exception as e:
print("exception in function root():", e) # exception handled locally
# @app.route("GET", "/") # if uncommented raises route already declared exception
# async def also_root(reader, writer, request):
# return
@app.route("GET", "/favicon.ico")
async def favicon(reader, writer, request):
writer.write(b"HTTP/1.1 200 OK\r\n")
writer.write(b"Connection: close\r\n")
writer.write(b"Content-Type: image/x-icon\r\n")
writer.write(b"\r\n")
await writer.drain()
await sendfile(writer, "favicon.ico")
@app.route("GET", "/api/time")
async def get_time(reader, writer, request):
writer.write(b"HTTP/1.1 200 OK\r\n")
writer.write(b"Connection: close\r\n")
writer.write(b"Content-Type: text/html\r\n")
writer.write(b"\r\n")
await writer.drain()
t = time.localtime()
writer.write(f"{t[2]:02d}-{t[1]:02d}-{t[0]:04d} {t[3]:02d}:{t[4]:02d}:{t[5]:02d}")
print(1/0) # will be caught by global exception handler
@app.route("GET", "/api/stop")
async def stop(reader, writer, request):
writer.write(b"HTTP/1.1 200 OK\r\n")
writer.write(b"Connection: close\r\n")
writer.write(b"\r\n")
await writer.drain()
raise(KeyboardInterrupt)
async def hello():
""" For demo purposes show system is still alive """
count = 0
while True:
print("hello", count)
count += 1
await asyncio.sleep(60)
def set_global_exception_handler():
def handle_exception(loop, context):
# uncaught exceptions raised in route handlers end up here
print("global exception handler:", context)
sys.print_exception(context["exception"])
loop = asyncio.get_event_loop()
loop.set_exception_handler(handle_exception)
if __name__ == "__main__":
try:
set_global_exception_handler()
asyncio.create_task(hello())
asyncio.run(app.start()) # must be last, does not return
except KeyboardInterrupt:
pass
finally:
asyncio.run(app.stop())
asyncio.new_event_loop()
| [
"uasyncio.new_event_loop",
"sys.print_exception",
"ahttpserver.sendfile",
"uasyncio.get_event_loop",
"time.localtime",
"uasyncio.sleep",
"ahttpserver.Server"
]
| [((105, 113), 'ahttpserver.Server', 'Server', ([], {}), '()\n', (111, 113), False, 'from ahttpserver import sendfile, Server\n'), ((1303, 1319), 'time.localtime', 'time.localtime', ([], {}), '()\n', (1317, 1319), False, 'import time\n'), ((2184, 2208), 'uasyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (2206, 2208), True, 'import uasyncio as asyncio\n'), ((384, 414), 'ahttpserver.sendfile', 'sendfile', (['writer', '"""index.html"""'], {}), "(writer, 'index.html')\n", (392, 414), False, 'from ahttpserver import sendfile, Server\n'), ((991, 1022), 'ahttpserver.sendfile', 'sendfile', (['writer', '"""favicon.ico"""'], {}), "(writer, 'favicon.ico')\n", (999, 1022), False, 'from ahttpserver import sendfile, Server\n'), ((2128, 2169), 'sys.print_exception', 'sys.print_exception', (["context['exception']"], {}), "(context['exception'])\n", (2147, 2169), False, 'import sys\n'), ((2549, 2573), 'uasyncio.new_event_loop', 'asyncio.new_event_loop', ([], {}), '()\n', (2571, 2573), True, 'import uasyncio as asyncio\n'), ((1897, 1914), 'uasyncio.sleep', 'asyncio.sleep', (['(60)'], {}), '(60)\n', (1910, 1914), True, 'import uasyncio as asyncio\n')] |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Unet(nn.Module):
def __init__(self, fc_dim=64, num_downs=5, ngf=64, use_dropout=False):
super(Unet, self).__init__()
# construct unet structure
unet_block = UnetBlock(
ngf * 8, ngf * 8, input_nc=None,
submodule=None, innermost=True)
for i in range(num_downs - 5):
unet_block = UnetBlock(
ngf * 8, ngf * 8, input_nc=None,
submodule=unet_block, use_dropout=use_dropout)
unet_block = UnetBlock(
ngf * 4, ngf * 8, input_nc=None,
submodule=unet_block)
unet_block = UnetBlock(
ngf * 2, ngf * 4, input_nc=None,
submodule=unet_block)
unet_block = UnetBlock(
ngf, ngf * 2, input_nc=None,
submodule=unet_block)
unet_block = UnetBlock(
fc_dim, ngf, input_nc=1,
submodule=unet_block, outermost=True)
self.bn0 = nn.BatchNorm2d(1)
self.unet_block = unet_block
def forward(self, x):
x = self.bn0(x)
x = self.unet_block(x)
return x
# Defines the submodule with skip connection.
# X -------------------identity---------------------- X
# |-- downsampling -- |submodule| -- upsampling --|
class UnetBlock(nn.Module):
def __init__(self, outer_nc, inner_input_nc, input_nc=None,
submodule=None, outermost=False, innermost=False,
use_dropout=False, inner_output_nc=None, noskip=False):
super(UnetBlock, self).__init__()
self.outermost = outermost
self.noskip = noskip
use_bias = False
if input_nc is None:
input_nc = outer_nc
if innermost:
inner_output_nc = inner_input_nc
elif inner_output_nc is None:
inner_output_nc = 2 * inner_input_nc
downrelu = nn.LeakyReLU(0.2, True)
downnorm = nn.BatchNorm2d(inner_input_nc)
uprelu = nn.ReLU(True)
upnorm = nn.BatchNorm2d(outer_nc)
upsample = nn.Upsample(
scale_factor=2, mode='bilinear', align_corners=True)
if outermost:
downconv = nn.Conv2d(
input_nc, inner_input_nc, kernel_size=4,
stride=2, padding=1, bias=use_bias)
upconv = nn.Conv2d(
inner_output_nc, outer_nc, kernel_size=3, padding=1)
down = [downconv]
up = [uprelu, upsample, upconv]
model = down + [submodule] + up
elif innermost:
downconv = nn.Conv2d(
input_nc, inner_input_nc, kernel_size=4,
stride=2, padding=1, bias=use_bias)
upconv = nn.Conv2d(
inner_output_nc, outer_nc, kernel_size=3,
padding=1, bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upsample, upconv, upnorm]
model = down + up
else:
downconv = nn.Conv2d(
input_nc, inner_input_nc, kernel_size=4,
stride=2, padding=1, bias=use_bias)
upconv = nn.Conv2d(
inner_output_nc, outer_nc, kernel_size=3,
padding=1, bias=use_bias)
down = [downrelu, downconv, downnorm]
up = [uprelu, upsample, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost or self.noskip:
return self.model(x)
else:
return torch.cat([x, self.model(x)], 1)
| [
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.Dropout",
"torch.nn.LeakyReLU",
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.Upsample"
]
| [((1046, 1063), 'torch.nn.BatchNorm2d', 'nn.BatchNorm2d', (['(1)'], {}), '(1)\n', (1060, 1063), True, 'import torch.nn as nn\n'), ((1983, 2006), 'torch.nn.LeakyReLU', 'nn.LeakyReLU', (['(0.2)', '(True)'], {}), '(0.2, True)\n', (1995, 2006), True, 'import torch.nn as nn\n'), ((2027, 2057), 'torch.nn.BatchNorm2d', 'nn.BatchNorm2d', (['inner_input_nc'], {}), '(inner_input_nc)\n', (2041, 2057), True, 'import torch.nn as nn\n'), ((2076, 2089), 'torch.nn.ReLU', 'nn.ReLU', (['(True)'], {}), '(True)\n', (2083, 2089), True, 'import torch.nn as nn\n'), ((2108, 2132), 'torch.nn.BatchNorm2d', 'nn.BatchNorm2d', (['outer_nc'], {}), '(outer_nc)\n', (2122, 2132), True, 'import torch.nn as nn\n'), ((2153, 2217), 'torch.nn.Upsample', 'nn.Upsample', ([], {'scale_factor': '(2)', 'mode': '"""bilinear"""', 'align_corners': '(True)'}), "(scale_factor=2, mode='bilinear', align_corners=True)\n", (2164, 2217), True, 'import torch.nn as nn\n'), ((3654, 3675), 'torch.nn.Sequential', 'nn.Sequential', (['*model'], {}), '(*model)\n', (3667, 3675), True, 'import torch.nn as nn\n'), ((2281, 2371), 'torch.nn.Conv2d', 'nn.Conv2d', (['input_nc', 'inner_input_nc'], {'kernel_size': '(4)', 'stride': '(2)', 'padding': '(1)', 'bias': 'use_bias'}), '(input_nc, inner_input_nc, kernel_size=4, stride=2, padding=1,\n bias=use_bias)\n', (2290, 2371), True, 'import torch.nn as nn\n'), ((2425, 2487), 'torch.nn.Conv2d', 'nn.Conv2d', (['inner_output_nc', 'outer_nc'], {'kernel_size': '(3)', 'padding': '(1)'}), '(inner_output_nc, outer_nc, kernel_size=3, padding=1)\n', (2434, 2487), True, 'import torch.nn as nn\n'), ((2678, 2768), 'torch.nn.Conv2d', 'nn.Conv2d', (['input_nc', 'inner_input_nc'], {'kernel_size': '(4)', 'stride': '(2)', 'padding': '(1)', 'bias': 'use_bias'}), '(input_nc, inner_input_nc, kernel_size=4, stride=2, padding=1,\n bias=use_bias)\n', (2687, 2768), True, 'import torch.nn as nn\n'), ((2822, 2899), 'torch.nn.Conv2d', 'nn.Conv2d', (['inner_output_nc', 'outer_nc'], {'kernel_size': '(3)', 'padding': '(1)', 'bias': 'use_bias'}), '(inner_output_nc, outer_nc, kernel_size=3, padding=1, bias=use_bias)\n', (2831, 2899), True, 'import torch.nn as nn\n'), ((3101, 3191), 'torch.nn.Conv2d', 'nn.Conv2d', (['input_nc', 'inner_input_nc'], {'kernel_size': '(4)', 'stride': '(2)', 'padding': '(1)', 'bias': 'use_bias'}), '(input_nc, inner_input_nc, kernel_size=4, stride=2, padding=1,\n bias=use_bias)\n', (3110, 3191), True, 'import torch.nn as nn\n'), ((3245, 3322), 'torch.nn.Conv2d', 'nn.Conv2d', (['inner_output_nc', 'outer_nc'], {'kernel_size': '(3)', 'padding': '(1)', 'bias': 'use_bias'}), '(inner_output_nc, outer_nc, kernel_size=3, padding=1, bias=use_bias)\n', (3254, 3322), True, 'import torch.nn as nn\n'), ((3545, 3560), 'torch.nn.Dropout', 'nn.Dropout', (['(0.5)'], {}), '(0.5)\n', (3555, 3560), True, 'import torch.nn as nn\n')] |
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import scriptcontext as sc
import compas_rhino
from compas_ags.rhino import SettingsForm
from compas_ags.rhino import FormObject
from compas_ags.rhino import ForceObject
__commandname__ = "AGS_toolbar_display"
def RunCommand(is_interactive):
if 'AGS' not in sc.sticky:
compas_rhino.display_message('AGS has not been initialised yet.')
return
scene = sc.sticky['AGS']['scene']
if not scene:
return
# TODO: deal with undo redo
SettingsForm.from_scene(scene, object_types=[FormObject, ForceObject], global_settings=['AGS'])
# ==============================================================================
# Main
# ==============================================================================
if __name__ == '__main__':
RunCommand(True)
| [
"compas_rhino.display_message",
"compas_ags.rhino.SettingsForm.from_scene"
]
| [((586, 685), 'compas_ags.rhino.SettingsForm.from_scene', 'SettingsForm.from_scene', (['scene'], {'object_types': '[FormObject, ForceObject]', 'global_settings': "['AGS']"}), "(scene, object_types=[FormObject, ForceObject],\n global_settings=['AGS'])\n", (609, 685), False, 'from compas_ags.rhino import SettingsForm\n'), ((396, 461), 'compas_rhino.display_message', 'compas_rhino.display_message', (['"""AGS has not been initialised yet."""'], {}), "('AGS has not been initialised yet.')\n", (424, 461), False, 'import compas_rhino\n')] |
from typing import (
Any,
cast,
List,
Optional,
Type
)
import lpp.ast as ast
from lpp.builtins import BUILTINS
from lpp.object import(
Boolean,
Builtin,
Environment,
Error,
Function,
Integer,
Null,
Object,
ObjectType,
String,
Return
)
TRUE = Boolean(True)
FALSE = Boolean(False)
NULL = Null()
_NOT_A_FUNCTION = 'No es una funcion: {}'
_TYPE_MISMATCH = 'Discrepancia de tipos: {} {} {}'
_UNKNOWN_PREFIX_OPERATOR = 'Operador desconocido: {}{}'
_UNKNOWN_INFIX_OPERATOR = 'Operador desconocido: {} {} {}'
_UNKNOWN_IDENTIFIER = 'Identificador no encontrado: {}'
def evaluate(node:ast.ASTNode, env: Environment) -> Optional[Object]:
node_type: Type = type(node)
if node_type == ast.Program:
node = cast(ast.Program, node)
return _evaluate_program(node, env)
elif node_type == ast.ExpressionStatement:
node = cast(ast.ExpressionStatement, node)
assert node.expression is not None
return evaluate(node.expression, env)
elif node_type == ast.Integer:
node = cast(ast.Integer, node)
assert node.value is not None
return Integer(node.value)
elif node_type == ast.Boolean:
node = cast(ast.Boolean, node)
assert node.value is not None
return _to_boolean_object(node.value)
elif node_type == ast.Prefix:
node = cast(ast.Prefix, node)
assert node.right is not None
right = evaluate(node.right, env)
assert right is not None
return _evaluate_prifix_expression(node.operator, right, node.right.token.line)
elif node_type == ast.Infix:
node = cast(ast.Infix, node)
assert node.left is not None and node.right is not None
left = evaluate(node.left, env)
right = evaluate(node.right, env)
assert right is not None and left is not None
return _evaluate_infix_expression(node.operator, left, right, node.left.token.line)
elif node_type == ast.Block:
node = cast(ast.Block, node)
return _evaluate_block_statement(node, env)
elif node_type == ast.If:
node = cast(ast.If, node)
return _evaluate_if_expression(node, env)
elif node_type == ast.ReturnStatement:
node = cast(ast.ReturnStatement, node)
assert node.return_value is not None
value = evaluate(node.return_value, env)
assert value is not None
return Return(value)
elif node_type == ast.LetStatement:
node = cast(ast.LetStatement, node)
assert node.value is not None
value = evaluate(node.value, env)
assert node.name is not None
env[node.name.value] = value
elif node_type == ast.Identifier:
node = cast(ast.Identifier, node)
return _evaluate_identifier(node, env, node.token.line)
elif node_type == ast.Function:
node = cast(ast.Function, node)
assert node.body is not None
return Function(node.parameters,
node.body,
env)
elif node_type == ast.Call:
node = cast(ast.Call, node)
function = evaluate(node.function, env)
assert function is not None
assert node.arguments is not None
args = _evaluate_expression(node.arguments, env)
assert function is not None
return _apply_function(function, args, node.token.line)
elif node_type == ast.StringLiteral:
node = cast(ast.StringLiteral, node)
return String(node.value)
return None
def _apply_function(fn: Object, args: List[Object],line_evaluated: int) -> Object:
if type(fn) == Function:
fn = cast(Function, fn)
extended_enviroment = _extended_function_enviroment(fn, args)
evaluated = evaluate(fn.body, extended_enviroment)
assert evaluated is not None
return _unwrap_return_value(evaluated)
elif type(fn) == Builtin:
fn = cast(Builtin, fn)
return fn.fn(*args)
else:
return _new_error(_NOT_A_FUNCTION, args, line_evaluated)
def _evaluate_bang_operator_expression(right: Object) -> Object:
if right is TRUE:
return FALSE
elif right is FALSE:
return TRUE
elif right is NULL:
return TRUE
else:
return FALSE
def _evaluate_expression(expressions: List[ast.Expression], env: Environment) -> List[Object]:
result: List[Object] = []
for expression in expressions:
evaluated = evaluate(expression, env)
assert evaluated is not None
result.append(evaluated)
return result
def _extended_function_enviroment(fn: Function, args: List[Object]) -> Environment:
env = Environment(outer=fn.env)
for idx, param in enumerate(fn.parameters):
env[param.value] = args[idx - 1]
return env
def _evaluate_identifier(node: ast.Identifier, env: Environment, line_evaluated:int) -> Object:
try:
return env[node.value]
except KeyError:
return BUILTINS.get(node.value,
_new_error(_UNKNOWN_IDENTIFIER, [node.value], line_evaluated))
def _evaluate_if_expression(if_expression: ast.If, env: Environment) -> Optional[Object]:
assert if_expression.condition is not None
condition = evaluate(if_expression.condition, env)
assert condition is not None
if _is_truthy(condition):
assert if_expression.consequence is not None
return evaluate(if_expression.consequence, env)
elif if_expression.alternative is not None:
return evaluate(if_expression.alternative, env)
else:
return NULL
def _is_truthy(obj: Object) -> bool:
if obj is NULL:
return False
elif obj is TRUE:
return True
elif obj is FALSE:
return False
else:
return True
def _evaluate_block_statement(block: ast.Block, env: Environment) -> Optional[Object]:
result: Optional[Object] = None
for statement in block.statements:
result = evaluate(statement, env)
if result is not None and \
(result.type() == ObjectType.RETURN or result.type() == ObjectType.ERROR):
return result
return result
def _evaluate_infix_expression(operator:str, left:Object, right:Object, line_evaluated:int) -> Object:
if left.type() == ObjectType.INTEGER \
and right.type() == ObjectType.INTEGER:
return _evaluate_integer_infix_expression(operator, left, right, line_evaluated)
if left.type() == ObjectType.STRING \
and right.type() == ObjectType.STRING:
return _evaluate_string_infix_expression(operator, left, right, line_evaluated)
elif operator == '==':
return _to_boolean_object(left is right)
elif operator == '!=':
return _to_boolean_object(left is not right)
elif left.type() != right.type():
return _new_error(_TYPE_MISMATCH, [left.type().name,
operator,
right.type().name
], line_evaluated)
else:
return _new_error(_UNKNOWN_INFIX_OPERATOR,[left.type().name,
operator,
right.type().name
], line_evaluated)
def _evaluate_integer_infix_expression(operator: str, left: Object, right: Object, line_evaluated:int) -> Object:
left_value = int = cast(Integer, left).value
right_value = int = cast(Integer, right).value
if operator == '+':
return Integer(left_value + right_value)
elif operator == '-':
return Integer(left_value - right_value)
elif operator == '*':
return Integer(left_value * right_value)
elif operator == '/':
return Integer(left_value // right_value) #divicio de enteros
elif operator == '<':
return _to_boolean_object(left_value < right_value)
elif operator == '>':
return _to_boolean_object(left_value > right_value)
elif operator == '==':
return _to_boolean_object(left_value == right_value)
elif operator == '!=':
return _to_boolean_object(left_value != right_value)
else:
return _new_error(_UNKNOWN_INFIX_OPERATOR,[left.type().name,
operator,
right.type().name
], line_evaluated)
def _evaluate_string_infix_expression(operator: str, left: Object, right: Object, line_evaluated:int) -> Object:
left_value = str = cast(String, left).value
right_value = str = cast(String, right).value
if operator == '+':
return String(left_value + right_value)
elif operator == '==':
return _to_boolean_object(left_value == right_value)
elif operator == '!=':
return _to_boolean_object(left_value != right_value)
else:
return _new_error(_UNKNOWN_INFIX_OPERATOR,[left.type().name,
operator,
right.type().name
], line_evaluated)
def _evaluate_minus_operator_expression(right: Object, line_evaluated:int) -> Object:
if type(right) != Integer:
return _new_error(_UNKNOWN_PREFIX_OPERATOR, ['-', right.type().name], line_evaluated)
right = cast(Integer, right)
return Integer(-right.value)
def _evaluate_prifix_expression(operator: str, right: Object,line_evaluated:int) -> Object:
if operator == '!':
return _evaluate_bang_operator_expression(right)
elif operator == '-':
return _evaluate_minus_operator_expression(right, line_evaluated)
else:
return _new_error(_UNKNOWN_PREFIX_OPERATOR,[operator, right.type().name],line_evaluated)
def _evaluate_program(program: ast.Program, env) -> Optional[Object]:
result: Optional[Object] = None
for statement in program.statements:
result = evaluate(statement, env)
if type(result) == Return:
result = cast(Return, result)
return result.value
elif type(result) == Error:
return result
return result
def _new_error(message: str, args:List[Any], error_line: int) -> Error:
return Error(message.format(*args), error_line)
def _unwrap_return_value(obj: Object) -> Object:
if type(obj) == Return:
obj = cast(Return, obj)
return obj.value
return obj
def _to_boolean_object(value: bool) -> Boolean:
return TRUE if value else FALSE | [
"lpp.object.String",
"lpp.object.Integer",
"lpp.object.Environment",
"lpp.object.Boolean",
"lpp.object.Null",
"lpp.object.Function",
"lpp.object.Return",
"typing.cast"
]
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# Copyright 2019 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://aws.amazon.com/apache2.0/
#
# or in the "LICENSE.txt" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
# OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import json
import os
import re
import configparser
import pytest
from assertpy import assert_that
import tests.pcluster.config.utils as utils
from pcluster.config.cfn_param_types import CfnParam, CfnSection
from pcluster.config.mappings import ALLOWED_VALUES, FSX
from pcluster.config.validators import (
DCV_MESSAGES,
EBS_VOLUME_TYPE_TO_VOLUME_SIZE_BOUNDS,
FSX_MESSAGES,
FSX_SUPPORTED_ARCHITECTURES_OSES,
LOGFILE_LOGGER,
architecture_os_validator,
check_usage_class,
cluster_type_validator,
compute_resource_validator,
disable_hyperthreading_architecture_validator,
efa_gdr_validator,
efa_os_arch_validator,
fsx_ignored_parameters_validator,
instances_architecture_compatibility_validator,
intel_hpc_architecture_validator,
queue_compute_type_validator,
queue_validator,
region_validator,
s3_bucket_region_validator,
settings_validator,
)
from pcluster.constants import FSX_HDD_THROUGHPUT, FSX_SSD_THROUGHPUT
from tests.common import MockedBoto3Request
from tests.pcluster.config.defaults import DefaultDict
@pytest.fixture()
def boto3_stubber_path():
return "pcluster.config.validators.boto3"
@pytest.mark.parametrize(
"section_dict, expected_message, expected_warning",
[
# traditional scheduler
({"scheduler": "sge", "initial_queue_size": 1, "max_queue_size": 2, "maintain_initial_size": True}, None, None),
(
{"scheduler": "sge", "initial_queue_size": 3, "max_queue_size": 2, "maintain_initial_size": True},
"initial_queue_size must be fewer than or equal to max_queue_size",
None,
),
(
{"scheduler": "sge", "initial_queue_size": 3, "max_queue_size": 2, "maintain_initial_size": False},
"initial_queue_size must be fewer than or equal to max_queue_size",
None,
),
# awsbatch
({"scheduler": "awsbatch", "min_vcpus": 1, "desired_vcpus": 2, "max_vcpus": 3}, None, None),
(
{"scheduler": "awsbatch", "min_vcpus": 3, "desired_vcpus": 2, "max_vcpus": 3},
"desired_vcpus must be greater than or equal to min_vcpus",
None,
),
(
{"scheduler": "awsbatch", "min_vcpus": 1, "desired_vcpus": 4, "max_vcpus": 3},
"desired_vcpus must be fewer than or equal to max_vcpus",
None,
),
(
{"scheduler": "awsbatch", "min_vcpus": 4, "desired_vcpus": 4, "max_vcpus": 3},
"max_vcpus must be greater than or equal to min_vcpus",
None,
),
# key pair not provided
({"scheduler": "awsbatch"}, None, "If you do not specify a key pair"),
],
)
def test_cluster_validator(mocker, capsys, section_dict, expected_message, expected_warning):
config_parser_dict = {"cluster default": section_dict}
utils.assert_param_validator(
mocker, config_parser_dict, expected_message, capsys, expected_warning=expected_warning
)
@pytest.mark.parametrize(
"instance_type, expected_message", [("t2.micro", None), ("c4.xlarge", None), ("c5.xlarge", "is not supported")]
)
def test_ec2_instance_type_validator(mocker, instance_type, expected_message):
config_parser_dict = {"cluster default": {"compute_instance_type": instance_type}}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize("instance_type, expected_message", [("t2.micro", None), ("c4.xlarge", None)])
def test_head_node_instance_type_validator(mocker, instance_type, expected_message):
config_parser_dict = {"cluster default": {"master_instance_type": instance_type}}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"scheduler, instance_type, expected_message, expected_warnings",
[
("sge", "t2.micro", None, None),
("sge", "c4.xlarge", None, None),
("sge", "c5.xlarge", "is not supported", None),
# NOTE: compute_instance_type_validator calls ec2_instance_type_validator only if the scheduler is not awsbatch
("awsbatch", "t2.micro", None, None),
("awsbatch", "c4.xlarge", "is not supported", None),
("awsbatch", "t2", None, None), # t2 family
("awsbatch", "optimal", None, None),
("sge", "p4d.24xlarge", None, "has 4 Network Interfaces."),
("slurm", "p4d.24xlarge", None, None),
],
)
def test_compute_instance_type_validator(mocker, scheduler, instance_type, expected_message, expected_warnings):
config_parser_dict = {"cluster default": {"scheduler": scheduler, "compute_instance_type": instance_type}}
extra_patches = {
"pcluster.config.validators.InstanceTypeInfo.max_network_interface_count": 4
if instance_type == "p4d.24xlarge"
else 1,
}
utils.assert_param_validator(
mocker, config_parser_dict, expected_message, expected_warnings, extra_patches=extra_patches
)
def test_ec2_key_pair_validator(mocker, boto3_stubber):
describe_key_pairs_response = {
"KeyPairs": [
{"KeyFingerprint": "12:bf:7c:56:6c:dd:4f:8c:24:45:75:f1:1b:16:54:89:82:09:a4:26", "KeyName": "key1"}
]
}
mocked_requests = [
MockedBoto3Request(
method="describe_key_pairs", response=describe_key_pairs_response, expected_params={"KeyNames": ["key1"]}
)
]
boto3_stubber("ec2", mocked_requests)
# TODO test with invalid key
config_parser_dict = {"cluster default": {"key_name": "key1"}}
utils.assert_param_validator(mocker, config_parser_dict)
@pytest.mark.parametrize(
"image_architecture, bad_ami_message, bad_architecture_message",
[
("x86_64", None, None),
(
"arm64",
None,
"incompatible with the architecture supported by the instance type chosen for the head node",
),
(
"arm64",
"Unable to get information for AMI",
"incompatible with the architecture supported by the instance type chosen for the head node",
),
],
)
def test_ec2_ami_validator(mocker, boto3_stubber, image_architecture, bad_ami_message, bad_architecture_message):
describe_images_response = {
"Images": [
{
"VirtualizationType": "paravirtual",
"Name": "My server",
"Hypervisor": "xen",
"ImageId": "ami-12345678",
"RootDeviceType": "ebs",
"State": "available",
"BlockDeviceMappings": [
{
"DeviceName": "/dev/sda1",
"Ebs": {
"DeleteOnTermination": True,
"SnapshotId": "snap-1234567890abcdef0",
"VolumeSize": 8,
"VolumeType": "standard",
},
}
],
"Architecture": image_architecture,
"ImageLocation": "123456789012/My server",
"KernelId": "aki-88aa75e1",
"OwnerId": "123456789012",
"RootDeviceName": "/dev/sda1",
"Public": False,
"ImageType": "machine",
"Description": "An AMI for my server",
}
]
}
mocked_requests = [
MockedBoto3Request(
method="describe_images",
response=describe_images_response,
expected_params={"ImageIds": ["ami-12345678"]},
generate_error=bad_ami_message,
)
]
boto3_stubber("ec2", mocked_requests)
# TODO test with invalid key
config_parser_dict = {"cluster default": {"custom_ami": "ami-12345678"}}
expected_message = bad_ami_message or bad_architecture_message
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"tags": {"key": "value", "key2": "value2"}}, None),
(
{"tags": {"key": "value", "Version": "value2"}},
r"Version.*reserved",
),
],
)
def test_tags_validator(mocker, capsys, section_dict, expected_message):
config_parser_dict = {"cluster default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_error=expected_message)
def test_ec2_volume_validator(mocker, boto3_stubber):
describe_volumes_response = {
"Volumes": [
{
"AvailabilityZone": "us-east-1a",
"Attachments": [
{
"AttachTime": "2013-12-18T22:35:00.000Z",
"InstanceId": "i-1234567890abcdef0",
"VolumeId": "vol-12345678",
"State": "attached",
"DeleteOnTermination": True,
"Device": "/dev/sda1",
}
],
"Encrypted": False,
"VolumeType": "gp2",
"VolumeId": "vol-049df61146c4d7901",
"State": "available", # TODO add test with "in-use"
"SnapshotId": "snap-1234567890abcdef0",
"CreateTime": "2013-12-18T22:35:00.084Z",
"Size": 8,
}
]
}
mocked_requests = [
MockedBoto3Request(
method="describe_volumes",
response=describe_volumes_response,
expected_params={"VolumeIds": ["vol-12345678"]},
)
]
boto3_stubber("ec2", mocked_requests)
# TODO test with invalid key
config_parser_dict = {
"cluster default": {"ebs_settings": "default"},
"ebs default": {"shared_dir": "test", "ebs_volume_id": "vol-12345678"},
}
utils.assert_param_validator(mocker, config_parser_dict)
@pytest.mark.parametrize(
"region, base_os, scheduler, expected_message",
[
# verify awsbatch supported regions
(
"ap-northeast-3",
"alinux2",
"awsbatch",
"Region 'ap-northeast-3' is not yet officially supported by ParallelCluster",
),
("us-gov-east-1", "alinux2", "awsbatch", None),
("us-gov-west-1", "alinux2", "awsbatch", None),
("eu-west-1", "alinux2", "awsbatch", None),
("us-east-1", "alinux2", "awsbatch", None),
("eu-north-1", "alinux2", "awsbatch", None),
("cn-north-1", "alinux2", "awsbatch", None),
("cn-northwest-1", "alinux2", "awsbatch", None),
# verify traditional schedulers are supported in all the regions but ap-northeast-3
("cn-northwest-1", "alinux2", "sge", None),
("us-gov-east-1", "alinux2", "sge", None),
("cn-northwest-1", "alinux2", "slurm", None),
("us-gov-east-1", "alinux2", "slurm", None),
("cn-northwest-1", "alinux2", "torque", None),
("us-gov-east-1", "alinux2", "torque", None),
(
"ap-northeast-3",
"alinux2",
"sge",
"Region 'ap-northeast-3' is not yet officially supported by ParallelCluster",
),
# verify awsbatch supported OSes
("eu-west-1", "centos7", "awsbatch", "scheduler supports the following Operating Systems"),
("eu-west-1", "centos8", "awsbatch", "scheduler supports the following Operating Systems"),
("eu-west-1", "ubuntu1804", "awsbatch", "scheduler supports the following Operating Systems"),
("eu-west-1", "alinux2", "awsbatch", None),
# verify sge supports all the OSes
("eu-west-1", "centos7", "sge", None),
("eu-west-1", "centos8", "sge", None),
("eu-west-1", "ubuntu1804", "sge", None),
("eu-west-1", "alinux2", "sge", None),
# verify slurm supports all the OSes
("eu-west-1", "centos7", "slurm", None),
("eu-west-1", "centos8", "slurm", None),
("eu-west-1", "ubuntu1804", "slurm", None),
("eu-west-1", "alinux2", "slurm", None),
# verify torque supports all the OSes
("eu-west-1", "centos7", "torque", None),
("eu-west-1", "centos8", "torque", None),
("eu-west-1", "ubuntu1804", "torque", None),
("eu-west-1", "alinux2", "torque", None),
],
)
def test_scheduler_validator(mocker, capsys, region, base_os, scheduler, expected_message):
# we need to set the region in the environment because it takes precedence respect of the config file
os.environ["AWS_DEFAULT_REGION"] = region
config_parser_dict = {"cluster default": {"base_os": base_os, "scheduler": scheduler}}
# Deprecation warning should be printed for sge and torque
expected_warning = None
wiki_url = "https://github.com/aws/aws-parallelcluster/wiki/Deprecation-of-SGE-and-Torque-in-ParallelCluster"
if scheduler in ["sge", "torque"]:
expected_warning = ".{0}. is scheduled to be deprecated.*{1}".format(scheduler, wiki_url)
utils.assert_param_validator(mocker, config_parser_dict, expected_message, capsys, expected_warning)
def test_placement_group_validator(mocker, boto3_stubber):
describe_placement_groups_response = {
"PlacementGroups": [{"GroupName": "my-cluster", "State": "available", "Strategy": "cluster"}]
}
mocked_requests = [
MockedBoto3Request(
method="describe_placement_groups",
response=describe_placement_groups_response,
expected_params={"GroupNames": ["my-cluster"]},
)
]
boto3_stubber("ec2", mocked_requests)
# TODO test with invalid group name
config_parser_dict = {"cluster default": {"placement_group": "my-cluster"}}
utils.assert_param_validator(mocker, config_parser_dict)
def test_url_validator(mocker, boto3_stubber, capsys):
head_object_response = {
"AcceptRanges": "bytes",
"ContentType": "text/html",
"LastModified": "Thu, 16 Apr 2015 18:19:14 GMT",
"ContentLength": 77,
"VersionId": "null",
"ETag": '"30a6ec7e1a9ad79c203d05a589c8b400"',
"Metadata": {},
}
mocked_requests = [
MockedBoto3Request(
method="head_object", response=head_object_response, expected_params={"Bucket": "test", "Key": "test.json"}
)
]
boto3_stubber("s3", mocked_requests)
mocker.patch("pcluster.config.validators.urllib.request.urlopen")
tests = [("s3://test/test.json", None), ("http://test/test.json", None)]
for template_url, expected_message in tests:
config_parser_dict = {"cluster default": {"template_url": template_url}}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
# Test S3 URI in custom_chef_cookbook.
tests = [
(
"s3://test/cookbook.tgz",
None,
MockedBoto3Request(
method="head_object",
response=head_object_response,
expected_params={"Bucket": "test", "Key": "cookbook.tgz"},
),
),
(
"s3://failure/cookbook.tgz",
(
"WARNING: The configuration parameter 'custom_chef_cookbook' generated the following warnings:\n"
"The S3 object does not exist or you do not have access to it.\n"
"Please make sure the cluster nodes have access to it."
),
MockedBoto3Request(
method="head_object",
response=head_object_response,
expected_params={"Bucket": "failure", "Key": "cookbook.tgz"},
generate_error=True,
error_code=404,
),
),
]
for custom_chef_cookbook_url, expected_message, mocked_request in tests:
boto3_stubber("s3", mocked_request)
mocker.patch("pcluster.config.validators.urllib.request.urlopen")
config_parser_dict = {
"cluster default": {
"scheduler": "slurm",
"s3_read_resource": "arn:aws:s3:::test*",
"custom_chef_cookbook": custom_chef_cookbook_url,
}
}
utils.assert_param_validator(mocker, config_parser_dict, capsys=capsys, expected_warning=expected_message)
@pytest.mark.parametrize(
"config, num_calls, error_code, bucket, expected_message",
[
(
{
"cluster default": {"fsx_settings": "fsx"},
"fsx fsx": {
"storage_capacity": 1200,
"import_path": "s3://test/test1/test2",
"export_path": "s3://test/test1/test2",
"auto_import_policy": "NEW",
},
},
2,
None,
{"Bucket": "test"},
"AutoImport is not supported for cross-region buckets.",
),
(
{
"cluster default": {"fsx_settings": "fsx"},
"fsx fsx": {
"storage_capacity": 1200,
"import_path": "s3://test/test1/test2",
"export_path": "s3://test/test1/test2",
"auto_import_policy": "NEW",
},
},
2,
"NoSuchBucket",
{"Bucket": "test"},
"The S3 bucket 'test' does not appear to exist.",
),
(
{
"cluster default": {"fsx_settings": "fsx"},
"fsx fsx": {
"storage_capacity": 1200,
"import_path": "s3://test/test1/test2",
"export_path": "s3://test/test1/test2",
"auto_import_policy": "NEW",
},
},
2,
"AccessDenied",
{"Bucket": "test"},
"You do not have access to the S3 bucket",
),
],
)
def test_auto_import_policy_validator(mocker, boto3_stubber, config, num_calls, error_code, bucket, expected_message):
os.environ["AWS_DEFAULT_REGION"] = "eu-west-1"
head_bucket_response = {
"ResponseMetadata": {
"AcceptRanges": "bytes",
"ContentType": "text/html",
"LastModified": "Thu, 16 Apr 2015 18:19:14 GMT",
"ContentLength": 77,
"VersionId": "null",
"ETag": '"30a6ec7e1a9ad79c203d05a589c8b400"',
"Metadata": {},
}
}
get_bucket_location_response = {
"ResponseMetadata": {
"LocationConstraint": "af-south1",
}
}
mocked_requests = []
for _ in range(num_calls):
mocked_requests.append(
MockedBoto3Request(method="head_bucket", response=head_bucket_response, expected_params=bucket)
)
if error_code is None:
mocked_requests.append(
MockedBoto3Request(
method="get_bucket_location", response=get_bucket_location_response, expected_params=bucket
)
)
else:
mocked_requests.append(
MockedBoto3Request(
method="get_bucket_location",
response=get_bucket_location_response,
expected_params=bucket,
generate_error=error_code is not None,
error_code=error_code,
)
)
boto3_stubber("s3", mocked_requests)
utils.assert_param_validator(mocker, config, expected_message)
@pytest.mark.parametrize(
"config, num_calls, bucket, expected_message",
[
(
{
"cluster default": {"fsx_settings": "fsx"},
"fsx fsx": {
"storage_capacity": 1200,
"import_path": "s3://test/test1/test2",
"export_path": "s3://test/test1/test2",
},
},
2,
{"Bucket": "test"},
None,
),
(
{
"cluster default": {"fsx_settings": "fsx"},
"fsx fsx": {
"storage_capacity": 1200,
"import_path": "http://test/test.json",
"export_path": "s3://test/test1/test2",
},
},
1,
{"Bucket": "test"},
"The value 'http://test/test.json' used for the parameter 'import_path' is not a valid S3 URI.",
),
],
)
def test_s3_validator(mocker, boto3_stubber, config, num_calls, bucket, expected_message):
if bucket:
_head_bucket_stubber(mocker, boto3_stubber, bucket, num_calls)
utils.assert_param_validator(mocker, config, expected_message)
@pytest.mark.parametrize(
"bucket, region, error_code, expected_message, client_error",
[
(
"bucket",
"us-east-1",
None,
None,
False,
),
(
"bucket",
"us-west-1",
None,
None,
False,
),
(
"bucket",
"eu-west-1",
None,
"cluster_resource_bucket must be in the same region of the cluster.",
False,
),
(
"not_existed_bucket",
"af-south-1",
"NoSuchBucket",
"The S3 bucket 'not_existed_bucket' does not appear to exist",
True,
),
(
"access_denied_bucket",
"af-south-1",
"AccessDenied",
"You do not have access to the S3 bucket 'access_denied_bucket'",
True,
),
(
"unexpected_error_bucket",
"af-south-1",
None,
"Unexpected error for S3 bucket",
True,
),
],
)
def test_s3_bucket_region_validator(mocker, boto3_stubber, error_code, bucket, region, client_error, expected_message):
os.environ["AWS_DEFAULT_REGION"] = "us-west-1" if region == "us-west-1" else "us-east-1"
if region == "us-east-1":
# The actual response when region is us-east-1 is
# {'ResponseMetadata': {...}, 'LocationConstraint': None}
# But botocore doesn't support mock None response. we mock the return as following
get_bucket_location_response = {
"ResponseMetadata": {},
}
else:
get_bucket_location_response = {
"ResponseMetadata": {},
"LocationConstraint": region,
}
mocked_requests = []
if error_code is None:
mocked_requests.append(
MockedBoto3Request(
method="get_bucket_location",
response=get_bucket_location_response,
expected_params={"Bucket": bucket},
generate_error=client_error is True,
)
)
else:
mocked_requests.append(
MockedBoto3Request(
method="get_bucket_location",
response=get_bucket_location_response,
expected_params={"Bucket": bucket},
generate_error=error_code is not None,
error_code=error_code,
)
)
boto3_stubber("s3", mocked_requests)
config = {
"cluster default": {"cluster_resource_bucket": bucket},
}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config)
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False, auto_refresh=False)
errors, warnings = s3_bucket_region_validator("cluster_resource_bucket", bucket, pcluster_config)
if expected_message:
assert_that(errors[0]).contains(expected_message)
else:
assert_that(errors).is_empty()
def test_ec2_vpc_id_validator(mocker, boto3_stubber):
mocked_requests = []
# mock describe_vpc boto3 call
describe_vpc_response = {
"Vpcs": [
{
"VpcId": "vpc-12345678",
"InstanceTenancy": "default",
"Tags": [{"Value": "Default VPC", "Key": "Name"}],
"State": "available",
"DhcpOptionsId": "dopt-4ef69c2a",
"CidrBlock": "172.31.0.0/16",
"IsDefault": True,
}
]
}
mocked_requests.append(
MockedBoto3Request(
method="describe_vpcs", response=describe_vpc_response, expected_params={"VpcIds": ["vpc-12345678"]}
)
)
# mock describe_vpc_attribute boto3 call
describe_vpc_attribute_response = {
"VpcId": "vpc-12345678",
"EnableDnsSupport": {"Value": True},
"EnableDnsHostnames": {"Value": True},
}
mocked_requests.append(
MockedBoto3Request(
method="describe_vpc_attribute",
response=describe_vpc_attribute_response,
expected_params={"VpcId": "vpc-12345678", "Attribute": "enableDnsSupport"},
)
)
mocked_requests.append(
MockedBoto3Request(
method="describe_vpc_attribute",
response=describe_vpc_attribute_response,
expected_params={"VpcId": "vpc-12345678", "Attribute": "enableDnsHostnames"},
)
)
boto3_stubber("ec2", mocked_requests)
# TODO mock and test invalid vpc-id
for vpc_id, expected_message in [("vpc-12345678", None)]:
config_parser_dict = {"cluster default": {"vpc_settings": "default"}, "vpc default": {"vpc_id": vpc_id}}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
def test_ec2_subnet_id_validator(mocker, boto3_stubber):
describe_subnets_response = {
"Subnets": [
{
"AvailabilityZone": "us-east-2c",
"AvailabilityZoneId": "use2-az3",
"AvailableIpAddressCount": 248,
"CidrBlock": "10.0.1.0/24",
"DefaultForAz": False,
"MapPublicIpOnLaunch": False,
"State": "available",
"SubnetId": "subnet-12345678",
"VpcId": "vpc-06e4ab6c6cEXAMPLE",
"OwnerId": "111122223333",
"AssignIpv6AddressOnCreation": False,
"Ipv6CidrBlockAssociationSet": [],
"Tags": [{"Key": "Name", "Value": "MySubnet"}],
"SubnetArn": "arn:aws:ec2:us-east-2:111122223333:subnet/subnet-12345678",
}
]
}
mocked_requests = [
MockedBoto3Request(
method="describe_subnets",
response=describe_subnets_response,
expected_params={"SubnetIds": ["subnet-12345678"]},
)
]
boto3_stubber("ec2", mocked_requests)
# TODO test with invalid key
config_parser_dict = {
"cluster default": {"vpc_settings": "default"},
"vpc default": {"master_subnet_id": "subnet-12345678"},
}
utils.assert_param_validator(mocker, config_parser_dict)
def test_ec2_security_group_validator(mocker, boto3_stubber):
describe_security_groups_response = {
"SecurityGroups": [
{
"IpPermissionsEgress": [],
"Description": "My security group",
"IpPermissions": [
{
"PrefixListIds": [],
"FromPort": 22,
"IpRanges": [{"CidrIp": "203.0.113.0/24"}],
"ToPort": 22,
"IpProtocol": "tcp",
"UserIdGroupPairs": [],
}
],
"GroupName": "MySecurityGroup",
"OwnerId": "123456789012",
"GroupId": "sg-12345678",
}
]
}
mocked_requests = [
MockedBoto3Request(
method="describe_security_groups",
response=describe_security_groups_response,
expected_params={"GroupIds": ["sg-12345678"]},
)
]
boto3_stubber("ec2", mocked_requests)
# TODO test with invalid key
config_parser_dict = {
"cluster default": {"vpc_settings": "default"},
"vpc default": {"vpc_security_group_id": "sg-12345678"},
}
utils.assert_param_validator(mocker, config_parser_dict)
@pytest.mark.parametrize(
"section_dict, expected_message",
[
(
{"throughput_mode": "bursting", "provisioned_throughput": 1024},
"When specifying 'provisioned_throughput', the 'throughput_mode' must be set to 'provisioned'",
),
({"throughput_mode": "provisioned", "provisioned_throughput": 1024}, None),
({"shared_dir": "NONE"}, "NONE cannot be used as a shared directory"),
({"shared_dir": "/NONE"}, "/NONE cannot be used as a shared directory"),
({"shared_dir": "/efs"}, None),
],
)
def test_efs_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": {"efs_settings": "default"}, "efs default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"volume_type": "io1", "volume_size": 20, "volume_iops": 120}, None),
(
{"volume_type": "io1", "volume_size": 20, "volume_iops": 90},
"IOPS rate must be between 100 and 64000 when provisioning io1 volumes.",
),
(
{"volume_type": "io1", "volume_size": 20, "volume_iops": 64001},
"IOPS rate must be between 100 and 64000 when provisioning io1 volumes.",
),
({"volume_type": "io1", "volume_size": 20, "volume_iops": 1001}, "IOPS to volume size ratio of .* is too high"),
({"volume_type": "io2", "volume_size": 20, "volume_iops": 120}, None),
(
{"volume_type": "io2", "volume_size": 20, "volume_iops": 90},
"IOPS rate must be between 100 and 256000 when provisioning io2 volumes.",
),
(
{"volume_type": "io2", "volume_size": 20, "volume_iops": 256001},
"IOPS rate must be between 100 and 256000 when provisioning io2 volumes.",
),
(
{"volume_type": "io2", "volume_size": 20, "volume_iops": 20001},
"IOPS to volume size ratio of .* is too high",
),
({"volume_type": "gp3", "volume_size": 20, "volume_iops": 3000}, None),
(
{"volume_type": "gp3", "volume_size": 20, "volume_iops": 2900},
"IOPS rate must be between 3000 and 16000 when provisioning gp3 volumes.",
),
(
{"volume_type": "gp3", "volume_size": 20, "volume_iops": 16001},
"IOPS rate must be between 3000 and 16000 when provisioning gp3 volumes.",
),
(
{"volume_type": "gp3", "volume_size": 20, "volume_iops": 10001},
"IOPS to volume size ratio of .* is too high",
),
],
)
def test_raid_validators(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": {"raid_settings": "default"}, "raid default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"kms_key_id, expected_message",
[
("9e8a129be-0e46-459d-865b-3a5bf974a22k", None),
(
"9e7a129be-0e46-459d-865b-3a5bf974a22k",
"Key 'arn:aws:kms:us-east-1:12345678:key/9e7a129be-0e46-459d-865b-3a5bf974a22k' does not exist",
),
],
)
def test_kms_key_validator(mocker, boto3_stubber, kms_key_id, expected_message):
_kms_key_stubber(mocker, boto3_stubber, kms_key_id, expected_message, 1)
config_parser_dict = {
"cluster default": {"fsx_settings": "fsx"},
"fsx fsx": {
"storage_capacity": 1200,
"fsx_kms_key_id": kms_key_id,
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": 50,
},
}
utils.assert_param_validator(
mocker, config_parser_dict, expected_error=expected_message if expected_message else None
)
def _kms_key_stubber(mocker, boto3_stubber, kms_key_id, expected_message, num_calls):
describe_key_response = {
"KeyMetadata": {
"AWSAccountId": "1234567890",
"Arn": "arn:aws:kms:us-east-1:1234567890:key/{0}".format(kms_key_id),
"CreationDate": datetime.datetime(2019, 1, 10, 11, 25, 59, 128000),
"Description": "",
"Enabled": True,
"KeyId": kms_key_id,
"KeyManager": "CUSTOMER",
"KeyState": "Enabled",
"KeyUsage": "ENCRYPT_DECRYPT",
"Origin": "AWS_KMS",
}
}
mocked_requests = [
MockedBoto3Request(
method="describe_key",
response=expected_message if expected_message else describe_key_response,
expected_params={"KeyId": kms_key_id},
generate_error=True if expected_message else False,
)
] * num_calls
boto3_stubber("kms", mocked_requests)
@pytest.mark.parametrize(
"section_dict, bucket, expected_error, num_calls",
[
(
{"imported_file_chunk_size": 1024, "import_path": "s3://test", "storage_capacity": 1200},
{"Bucket": "test"},
None,
1,
),
(
{"imported_file_chunk_size": 1024, "storage_capacity": 1200},
None,
"When specifying 'imported_file_chunk_size', the 'import_path' option must be specified",
0,
),
(
{"export_path": "s3://test", "import_path": "s3://test", "storage_capacity": 1200},
{"Bucket": "test"},
None,
2,
),
(
{"export_path": "s3://test", "storage_capacity": 1200},
{"Bucket": "test"},
"When specifying 'export_path', the 'import_path' option must be specified",
0,
),
({"shared_dir": "NONE", "storage_capacity": 1200}, None, "NONE cannot be used as a shared directory", 0),
({"shared_dir": "/NONE", "storage_capacity": 1200}, None, "/NONE cannot be used as a shared directory", 0),
({"shared_dir": "/fsx"}, None, "the 'storage_capacity' option must be specified", 0),
({"shared_dir": "/fsx", "storage_capacity": 1200}, None, None, 0),
(
{
"deployment_type": "PERSISTENT_1",
"fsx_kms_key_id": "9e8a129be-0e46-459d-865b-3a5bf974a22k",
"storage_capacity": 1200,
"per_unit_storage_throughput": 50,
},
None,
None,
0,
),
(
{"deployment_type": "PERSISTENT_1", "per_unit_storage_throughput": 200, "storage_capacity": 1200},
None,
None,
0,
),
(
{
"deployment_type": "SCRATCH_2",
"fsx_kms_key_id": "9e8a129be-0e46-459d-865b-3a5bf974a22k",
"storage_capacity": 1200,
},
None,
"'fsx_kms_key_id' can only be used when 'deployment_type = PERSISTENT_1'",
1,
),
(
{"deployment_type": "SCRATCH_1", "per_unit_storage_throughput": 200, "storage_capacity": 1200},
None,
"'per_unit_storage_throughput' can only be used when 'deployment_type = PERSISTENT_1'",
0,
),
(
{"deployment_type": "PERSISTENT_1", "storage_capacity": 1200},
None,
"'per_unit_storage_throughput' must be specified when 'deployment_type = PERSISTENT_1'",
0,
),
(
{
"storage_capacity": 1200,
"per_unit_storage_throughput": "50",
"deployment_type": "PERSISTENT_1",
"automatic_backup_retention_days": 2,
},
None,
None,
0,
),
(
{
"storage_capacity": 1200,
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": "50",
"automatic_backup_retention_days": 2,
"daily_automatic_backup_start_time": "03:00",
"copy_tags_to_backups": True,
},
None,
None,
0,
),
(
{"automatic_backup_retention_days": 2, "deployment_type": "SCRATCH_1"},
None,
"FSx automatic backup features can be used only with 'PERSISTENT_1' file systems",
0,
),
(
{"daily_automatic_backup_start_time": "03:00"},
None,
"When specifying 'daily_automatic_backup_start_time', "
"the 'automatic_backup_retention_days' option must be specified",
0,
),
(
{"storage_capacity": 1200, "deployment_type": "PERSISTENT_1", "copy_tags_to_backups": True},
None,
"When specifying 'copy_tags_to_backups', the 'automatic_backup_retention_days' option must be specified",
0,
),
(
{"storage_capacity": 1200, "deployment_type": "PERSISTENT_1", "copy_tags_to_backups": False},
None,
"When specifying 'copy_tags_to_backups', the 'automatic_backup_retention_days' option must be specified",
0,
),
(
{"daily_automatic_backup_start_time": "03:00", "copy_tags_to_backups": True},
None,
"When specifying 'daily_automatic_backup_start_time', "
"the 'automatic_backup_retention_days' option must be specified",
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"automatic_backup_retention_days": 2,
"imported_file_chunk_size": 1024,
"export_path": "s3://test",
"import_path": "s3://test",
"storage_capacity": 1200,
},
{"Bucket": "test"},
"Backups cannot be created on S3-linked file systems",
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"automatic_backup_retention_days": 2,
"export_path": "s3://test",
"import_path": "s3://test",
"storage_capacity": 1200,
},
{"Bucket": "test"},
"Backups cannot be created on S3-linked file systems",
0,
),
(
{
"deployment_type": "SCRATCH_1",
"storage_type": "HDD",
"per_unit_storage_throughput": 12,
"storage_capacity": 1200,
"drive_cache_type": "READ",
},
None,
"For HDD filesystems, 'deployment_type' must be 'PERSISTENT_1'",
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"storage_type": "HDD",
"per_unit_storage_throughput": 50,
"storage_capacity": 1200,
"drive_cache_type": "READ",
},
None,
"For HDD filesystems, 'per_unit_storage_throughput' can only have the following values: {0}".format(
FSX_HDD_THROUGHPUT
),
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"storage_type": "SSD",
"per_unit_storage_throughput": 12,
"storage_capacity": 1200,
},
None,
"For SSD filesystems, 'per_unit_storage_throughput' can only have the following values: {0}".format(
FSX_SSD_THROUGHPUT
),
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"storage_type": "SSD",
"per_unit_storage_throughput": 50,
"storage_capacity": 1200,
"drive_cache_type": "NONE",
},
None,
"The configuration parameter 'drive_cache_type' has an invalid value 'NONE'",
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"storage_type": "SSD",
"per_unit_storage_throughput": 50,
"storage_capacity": 1200,
},
None,
None,
0,
),
(
{
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": 50,
"storage_capacity": 1200,
"drive_cache_type": "READ",
},
None,
"'drive_cache_type' features can be used only with HDD filesystems",
0,
),
(
{
"data_compression_type": "LZ4",
"fsx_backup_id": "backup-12345678",
},
None,
"FSx data compression option (LZ4) cannot be specified when creating a filesystem from backup",
0,
),
(
{
"data_compression_type": "NONE",
"fsx_backup_id": "backup-12345678",
},
None,
"The configuration parameter 'data_compression_type' has an invalid value 'NONE'",
0,
),
(
{
"data_compression_type": "LZ4",
"storage_capacity": 1200,
},
None,
None,
0,
),
],
)
def test_fsx_validator(mocker, boto3_stubber, section_dict, bucket, expected_error, num_calls):
if bucket:
_head_bucket_stubber(mocker, boto3_stubber, bucket, num_calls)
if "fsx_kms_key_id" in section_dict:
_kms_key_stubber(mocker, boto3_stubber, section_dict.get("fsx_kms_key_id"), None, 0 if expected_error else 1)
config_parser_dict = {"cluster default": {"fsx_settings": "default"}, "fsx default": section_dict}
if expected_error:
expected_error = re.escape(expected_error)
utils.assert_param_validator(mocker, config_parser_dict, expected_error=expected_error)
@pytest.mark.parametrize(
"section_dict, expected_error, expected_warning",
[
(
{"storage_capacity": 1, "deployment_type": "SCRATCH_1"},
"Capacity for FSx SCRATCH_1 filesystem is 1,200 GB, 2,400 GB or increments of 3,600 GB",
None,
),
({"storage_capacity": 1200, "deployment_type": "SCRATCH_1"}, None, None),
({"storage_capacity": 2400, "deployment_type": "SCRATCH_1"}, None, None),
({"storage_capacity": 3600, "deployment_type": "SCRATCH_1"}, None, None),
(
{"storage_capacity": 3600, "deployment_type": "SCRATCH_2"},
"Capacity for FSx SCRATCH_2 and PERSISTENT_1 filesystems is 1,200 GB or increments of 2,400 GB",
None,
),
(
{"storage_capacity": 3600, "deployment_type": "PERSISTENT_1", "per_unit_storage_throughput": 50},
"Capacity for FSx SCRATCH_2 and PERSISTENT_1 filesystems is 1,200 GB or increments of 2,400 GB",
None,
),
(
{"storage_capacity": 3601, "deployment_type": "PERSISTENT_1", "per_unit_storage_throughput": 50},
"Capacity for FSx SCRATCH_2 and PERSISTENT_1 filesystems is 1,200 GB or increments of 2,400 GB",
None,
),
({"storage_capacity": 7200}, None, None),
(
{"deployment_type": "SCRATCH_1"},
"When specifying 'fsx' section, the 'storage_capacity' option must be specified",
None,
),
(
{
"storage_type": "HDD",
"deployment_type": "PERSISTENT_1",
"storage_capacity": 1801,
"per_unit_storage_throughput": 40,
},
"Capacity for FSx PERSISTENT HDD 40 MB/s/TiB file systems is increments of 1,800 GiB",
None,
),
(
{
"storage_type": "HDD",
"deployment_type": "PERSISTENT_1",
"storage_capacity": 6001,
"per_unit_storage_throughput": 12,
},
"Capacity for FSx PERSISTENT HDD 12 MB/s/TiB file systems is increments of 6,000 GiB",
None,
),
(
{
"storage_type": "HDD",
"deployment_type": "PERSISTENT_1",
"storage_capacity": 1800,
"per_unit_storage_throughput": 40,
},
None,
None,
),
(
{
"storage_type": "HDD",
"deployment_type": "PERSISTENT_1",
"storage_capacity": 6000,
"per_unit_storage_throughput": 12,
},
None,
None,
),
],
)
def test_fsx_storage_capacity_validator(mocker, boto3_stubber, capsys, section_dict, expected_error, expected_warning):
config_parser_dict = {"cluster default": {"fsx_settings": "default"}, "fsx default": section_dict}
utils.assert_param_validator(
mocker, config_parser_dict, capsys=capsys, expected_error=expected_error, expected_warning=expected_warning
)
def _head_bucket_stubber(mocker, boto3_stubber, bucket, num_calls):
head_bucket_response = {
"ResponseMetadata": {
"AcceptRanges": "bytes",
"ContentType": "text/html",
"LastModified": "Thu, 16 Apr 2015 18:19:14 GMT",
"ContentLength": 77,
"VersionId": "null",
"ETag": '"30a6ec7e1a9ad79c203d05a589c8b400"',
"Metadata": {},
}
}
mocked_requests = [
MockedBoto3Request(method="head_bucket", response=head_bucket_response, expected_params=bucket)
] * num_calls
boto3_stubber("s3", mocked_requests)
mocker.patch("pcluster.config.validators.urllib.request.urlopen")
@pytest.mark.parametrize(
"fsx_vpc, ip_permissions, network_interfaces, expected_message",
[
( # working case, right vpc and sg, multiple network interfaces
"vpc-06e4ab6c6cEXAMPLE",
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
["eni-09b9460295ddd4e5f", "eni-001b3cef7c78b45c4"],
None,
),
( # working case, right vpc and sg, single network interface
"vpc-06e4ab6c6cEXAMPLE",
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
["eni-09b9460295ddd4e5f"],
None,
),
( # not working case --> no network interfaces
"vpc-06e4ab6c6cEXAMPLE",
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
[],
"doesn't have Elastic Network Interfaces attached",
),
( # not working case --> wrong vpc
"vpc-06e4ab6c6ccWRONG",
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
["eni-09b9460295ddd4e5f"],
"only support using FSx file system that is in the same VPC as the stack",
),
( # not working case --> wrong ip permissions in security group
"vpc-06e4ab6c6cWRONG",
[
{
"PrefixListIds": [],
"FromPort": 22,
"IpRanges": [{"CidrIp": "203.0.113.0/24"}],
"ToPort": 22,
"IpProtocol": "tcp",
"UserIdGroupPairs": [],
}
],
["eni-09b9460295ddd4e5f"],
"does not satisfy mounting requirement",
),
],
)
def test_fsx_id_validator(mocker, boto3_stubber, fsx_vpc, ip_permissions, network_interfaces, expected_message):
describe_file_systems_response = {
"FileSystems": [
{
"VpcId": fsx_vpc,
"NetworkInterfaceIds": network_interfaces,
"SubnetIds": ["subnet-12345678"],
"FileSystemType": "LUSTRE",
"CreationTime": 1567636453.038,
"ResourceARN": "arn:aws:fsx:us-west-2:111122223333:file-system/fs-0ff8da96d57f3b4e3",
"StorageCapacity": 3600,
"LustreConfiguration": {"WeeklyMaintenanceStartTime": "4:07:00"},
"FileSystemId": "fs-0ff8da96d57f3b4e3",
"DNSName": "fs-0ff8da96d57f3b4e3.fsx.us-west-2.amazonaws.com",
"OwnerId": "059623208481",
"Lifecycle": "AVAILABLE",
}
]
}
fsx_mocked_requests = [
MockedBoto3Request(
method="describe_file_systems",
response=describe_file_systems_response,
expected_params={"FileSystemIds": ["fs-0ff8da96d57f3b4e3"]},
)
]
boto3_stubber("fsx", fsx_mocked_requests)
describe_subnets_response = {
"Subnets": [
{
"AvailabilityZone": "us-east-2c",
"AvailabilityZoneId": "use2-az3",
"AvailableIpAddressCount": 248,
"CidrBlock": "10.0.1.0/24",
"DefaultForAz": False,
"MapPublicIpOnLaunch": False,
"State": "available",
"SubnetId": "subnet-12345678",
"VpcId": "vpc-06e4ab6c6cEXAMPLE",
"OwnerId": "111122223333",
"AssignIpv6AddressOnCreation": False,
"Ipv6CidrBlockAssociationSet": [],
"Tags": [{"Key": "Name", "Value": "MySubnet"}],
"SubnetArn": "arn:aws:ec2:us-east-2:111122223333:subnet/subnet-12345678",
}
]
}
ec2_mocked_requests = [
MockedBoto3Request(
method="describe_subnets",
response=describe_subnets_response,
expected_params={"SubnetIds": ["subnet-12345678"]},
)
] * 2
if network_interfaces:
network_interfaces_in_response = []
for network_interface in network_interfaces:
network_interfaces_in_response.append(
{
"Association": {
"AllocationId": "eipalloc-01564b674a1a88a47",
"AssociationId": "eipassoc-02726ee370e175cea",
"IpOwnerId": "111122223333",
"PublicDnsName": "ec2-34-248-114-123.eu-west-1.compute.amazonaws.com",
"PublicIp": "172.16.17.32",
},
"Attachment": {
"AttachmentId": "ela-attach-0cf98331",
"DeleteOnTermination": False,
"DeviceIndex": 1,
"InstanceOwnerId": "amazon-aws",
"Status": "attached",
},
"AvailabilityZone": "eu-west-1a",
"Description": "Interface for NAT Gateway nat-0a8b0e0d28266841f",
"Groups": [{"GroupName": "default", "GroupId": "sg-12345678"}],
"InterfaceType": "nat_gateway",
"Ipv6Addresses": [],
"MacAddress": "0a:e5:8a:82:fd:24",
"NetworkInterfaceId": network_interface,
"OwnerId": "111122223333",
"PrivateDnsName": "ip-10-0-124-85.eu-west-1.compute.internal",
"PrivateIpAddress": "10.0.124.85",
"PrivateIpAddresses": [
{
"Association": {
"AllocationId": "eipalloc-01564b674a1a88a47",
"AssociationId": "eipassoc-02726ee370e175cea",
"IpOwnerId": "111122223333",
"PublicDnsName": "ec2-34-248-114-123.eu-west-1.compute.amazonaws.com",
"PublicIp": "172.16.17.32",
},
"Primary": True,
"PrivateDnsName": "ip-10-0-124-85.eu-west-1.compute.internal",
"PrivateIpAddress": "10.0.124.85",
}
],
"RequesterId": "036872051663",
"RequesterManaged": True,
"SourceDestCheck": False,
"Status": "in-use",
"SubnetId": "subnet-12345678",
"TagSet": [],
"VpcId": fsx_vpc,
}
)
describe_network_interfaces_response = {"NetworkInterfaces": network_interfaces_in_response}
ec2_mocked_requests.append(
MockedBoto3Request(
method="describe_network_interfaces",
response=describe_network_interfaces_response,
expected_params={"NetworkInterfaceIds": network_interfaces},
)
)
if fsx_vpc == "vpc-06e4ab6c6cEXAMPLE":
# the describe security group is performed only if the VPC of the network interface is the same of the FSX
describe_security_groups_response = {
"SecurityGroups": [
{
"IpPermissionsEgress": ip_permissions,
"Description": "My security group",
"IpPermissions": ip_permissions,
"GroupName": "MySecurityGroup",
"OwnerId": "123456789012",
"GroupId": "sg-12345678",
}
]
}
ec2_mocked_requests.append(
MockedBoto3Request(
method="describe_security_groups",
response=describe_security_groups_response,
expected_params={"GroupIds": ["sg-12345678"]},
)
)
boto3_stubber("ec2", ec2_mocked_requests)
fsx_spy = mocker.patch(
"pcluster.config.cfn_param_types.get_fsx_info",
return_value={"DNSName": "my.fsx.dns.name", "LustreConfiguration": {"MountName": "somemountname"}},
)
config_parser_dict = {
"cluster default": {"fsx_settings": "default", "vpc_settings": "default"},
"vpc default": {"master_subnet_id": "subnet-12345678"},
"fsx default": {"fsx_fs_id": "fs-0ff8da96d57f3b4e3"},
}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
fsx_spy.assert_called_with("fs-0ff8da96d57f3b4e3")
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"enable_intel_hpc_platform": "true", "base_os": "centos7"}, None),
({"enable_intel_hpc_platform": "true", "base_os": "centos8"}, None),
({"enable_intel_hpc_platform": "true", "base_os": "alinux2"}, "it is required to set the 'base_os'"),
({"enable_intel_hpc_platform": "true", "base_os": "ubuntu1804"}, "it is required to set the 'base_os'"),
# intel hpc disabled, you can use any os
({"enable_intel_hpc_platform": "false", "base_os": "alinux2"}, None),
],
)
def test_intel_hpc_os_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, expected_message",
[
(
{"disable_hyperthreading": True, "extra_json": '{"cluster": {"cfn_scheduler_slots": "vcpus"}}'},
"cfn_scheduler_slots cannot be set in addition to disable_hyperthreading = true",
),
(
{"disable_hyperthreading": True, "extra_json": '{"cluster": {"cfn_scheduler_slots": "cores"}}'},
"cfn_scheduler_slots cannot be set in addition to disable_hyperthreading = true",
),
(
{"disable_hyperthreading": True, "extra_json": '{"cluster": {"cfn_scheduler_slots": 3}}'},
"cfn_scheduler_slots cannot be set in addition to disable_hyperthreading = true",
),
({"disable_hyperthreading": True, "extra_json": '{"cluster": {"other_param": "fake_value"}}'}, None),
({"disable_hyperthreading": True}, None),
({"disable_hyperthreading": False, "extra_json": '{"cluster": {"cfn_scheduler_slots": "vcpus"}}'}, None),
({"disable_hyperthreading": False, "extra_json": '{"cluster": {"cfn_scheduler_slots": "cores"}}'}, None),
({"disable_hyperthreading": False, "extra_json": '{"cluster": {"cfn_scheduler_slots": 3}}'}, None),
],
)
def test_disable_hyperthreading_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, bucket, expected_message",
[
(
{"imported_file_chunk_size": 0, "import_path": "s3://test-import", "storage_capacity": 1200},
None,
"has a minimum size of 1 MiB, and max size of 512,000 MiB",
),
(
{"imported_file_chunk_size": 1, "import_path": "s3://test-import", "storage_capacity": 1200},
{"Bucket": "test-import"},
None,
),
(
{"imported_file_chunk_size": 10, "import_path": "s3://test-import", "storage_capacity": 1200},
{"Bucket": "test-import"},
None,
),
(
{"imported_file_chunk_size": 512000, "import_path": "s3://test-import", "storage_capacity": 1200},
{"Bucket": "test-import"},
None,
),
(
{"imported_file_chunk_size": 512001, "import_path": "s3://test-import", "storage_capacity": 1200},
None,
"has a minimum size of 1 MiB, and max size of 512,000 MiB",
),
],
)
def test_fsx_imported_file_chunk_size_validator(mocker, boto3_stubber, section_dict, bucket, expected_message):
if bucket:
_head_bucket_stubber(mocker, boto3_stubber, bucket, num_calls=1)
config_parser_dict = {"cluster default": {"fsx_settings": "default"}, "fsx default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, expected_error, expected_warning",
[
({"enable_efa": "NONE"}, "invalid value", None),
({"enable_efa": "compute", "scheduler": "sge"}, "is required to set the 'compute_instance_type'", None),
(
{"enable_efa": "compute", "compute_instance_type": "t2.large", "scheduler": "sge"},
None,
"You may see better performance using a cluster placement group",
),
(
{
"enable_efa": "compute",
"compute_instance_type": "t2.large",
"base_os": "alinux2",
"scheduler": "awsbatch",
},
"it is required to set the 'scheduler'",
None,
),
(
{
"enable_efa": "compute",
"compute_instance_type": "t2.large",
"base_os": "centos7",
"scheduler": "sge",
"placement_group": "DYNAMIC",
},
None,
None,
),
(
{
"enable_efa": "compute",
"compute_instance_type": "t2.large",
"base_os": "alinux2",
"scheduler": "sge",
"placement_group": "DYNAMIC",
},
None,
None,
),
# Additional instance type
(
{
"enable_efa": "compute",
"compute_instance_type": "additional-instance-type",
"base_os": "alinux2",
"scheduler": "sge",
"placement_group": "DYNAMIC",
"instance_types_data": json.dumps(
{
"additional-instance-type": {
"InstanceType": "additional-instance-type",
"NetworkInfo": {"EfaSupported": True},
}
}
),
},
None,
None,
),
],
)
def test_efa_validator(boto3_stubber, mocker, capsys, section_dict, expected_error, expected_warning):
if section_dict.get("enable_efa") != "NONE":
mocked_requests = [
MockedBoto3Request(
method="describe_instance_types",
response={"InstanceTypes": [{"InstanceType": "t2.large"}]},
expected_params={"Filters": [{"Name": "network-info.efa-supported", "Values": ["true"]}]},
)
]
boto3_stubber("ec2", mocked_requests)
config_parser_dict = {"cluster default": section_dict}
# Patch to prevent instance type validators to fail with additional instance type
extra_patches = {
"pcluster.config.validators.get_supported_instance_types": ["t2.large", "additional-instance-type"],
}
utils.assert_param_validator(
mocker,
config_parser_dict,
expected_error,
capsys,
expected_warning,
extra_patches=extra_patches,
use_mock_instance_type_info=False,
)
@pytest.mark.parametrize(
"cluster_dict, expected_error",
[
# EFAGDR without EFA
(
{"enable_efa_gdr": "compute"},
"The parameter 'enable_efa_gdr' can be used only in combination with 'enable_efa'",
),
# EFAGDR with EFA
({"enable_efa": "compute", "enable_efa_gdr": "compute"}, None),
# EFA withoud EFAGDR
({"enable_efa": "compute"}, None),
],
)
def test_efa_gdr_validator(cluster_dict, expected_error):
config_parser_dict = {
"cluster default": cluster_dict,
}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config_parser_dict)
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False, auto_refresh=False)
enable_efa_gdr_value = pcluster_config.get_section("cluster").get_param_value("enable_efa_gdr")
errors, warnings = efa_gdr_validator("enable_efa_gdr", enable_efa_gdr_value, pcluster_config)
if expected_error:
assert_that(errors[0]).matches(expected_error)
else:
assert_that(errors).is_empty()
@pytest.mark.parametrize(
"ip_permissions, ip_permissions_egress, expected_message",
[
([], [], "must allow all traffic in and out from itself"),
(
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
[],
"must allow all traffic in and out from itself",
),
(
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
[{"IpProtocol": "-1", "UserIdGroupPairs": [{"UserId": "123456789012", "GroupId": "sg-12345678"}]}],
None,
),
(
[
{
"PrefixListIds": [],
"FromPort": 22,
"IpRanges": [{"CidrIp": "203.0.113.0/24"}],
"ToPort": 22,
"IpProtocol": "tcp",
"UserIdGroupPairs": [],
}
],
[],
"must allow all traffic in and out from itself",
),
],
)
def test_efa_validator_with_vpc_security_group(
boto3_stubber, mocker, ip_permissions, ip_permissions_egress, expected_message
):
describe_security_groups_response = {
"SecurityGroups": [
{
"IpPermissionsEgress": ip_permissions_egress,
"Description": "My security group",
"IpPermissions": ip_permissions,
"GroupName": "MySecurityGroup",
"OwnerId": "123456789012",
"GroupId": "sg-12345678",
}
]
}
mocked_requests = [
MockedBoto3Request(
method="describe_security_groups",
response=describe_security_groups_response,
expected_params={"GroupIds": ["sg-12345678"]},
),
MockedBoto3Request(
method="describe_instance_types",
response={"InstanceTypes": [{"InstanceType": "t2.large"}]},
expected_params={"Filters": [{"Name": "network-info.efa-supported", "Values": ["true"]}]},
),
MockedBoto3Request(
method="describe_security_groups",
response=describe_security_groups_response,
expected_params={"GroupIds": ["sg-12345678"]},
), # it is called two times, for vpc_security_group_id validation and to validate efa
]
boto3_stubber("ec2", mocked_requests)
config_parser_dict = {
"cluster default": {
"enable_efa": "compute",
"compute_instance_type": "t2.large",
"placement_group": "DYNAMIC",
"vpc_settings": "default",
"scheduler": "sge",
},
"vpc default": {"vpc_security_group_id": "sg-12345678"},
}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"cluster_section_dict, ebs_section_dict, expected_message",
[
(
{"ebs_settings": "vol1, vol2, vol3, vol4, vol5, vol6"},
{
"vol1": {"shared_dir": "/vol1"},
"vol2": {"shared_dir": "/vol2"},
"vol3": {"shared_dir": "/vol3"},
"vol4": {"shared_dir": "/vol4"},
"vol5": {"shared_dir": "/vol5"},
"vol6": {"shared_dir": "/vol6"},
},
"Invalid number of 'ebs' sections specified. Max 5 expected.",
),
(
{"ebs_settings": "vol1, vol2 "},
{"vol1": {"shared_dir": "vol1"}, "vol2": {"volume_type": "io1"}},
"When using more than 1 EBS volume, shared_dir is required under each EBS section",
),
(
{"ebs_settings": "vol1,vol2"},
{"vol1": {"shared_dir": "/NONE"}, "vol2": {"shared_dir": "vol2"}},
"/NONE cannot be used as a shared directory",
),
(
{"ebs_settings": "vol1, vol2 "},
{"vol1": {"shared_dir": "/vol1"}, "vol2": {"shared_dir": "NONE"}},
"NONE cannot be used as a shared directory",
),
],
)
def test_ebs_settings_validator(mocker, cluster_section_dict, ebs_section_dict, expected_message):
config_parser_dict = {"cluster default": cluster_section_dict}
if ebs_section_dict:
for vol in ebs_section_dict:
config_parser_dict["ebs {0}".format(vol)] = ebs_section_dict.get(vol)
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"shared_dir": "NONE"}, "NONE cannot be used as a shared directory"),
({"shared_dir": "/NONE"}, "/NONE cannot be used as a shared directory"),
({"shared_dir": "/NONEshared"}, None),
],
)
def test_shared_dir_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"base_os, instance_type, access_from, expected_error, expected_warning",
[
("centos7", "t2.medium", None, None, None),
("centos8", "t2.medium", None, None, None),
("ubuntu1804", "t2.medium", None, None, None),
("ubuntu1804", "t2.medium", "1.2.3.4/32", None, None),
("centos7", "t2.medium", "0.0.0.0/0", None, None),
("centos8", "t2.medium", "0.0.0.0/0", None, None),
("alinux2", "t2.medium", None, None, None),
("alinux2", "t2.nano", None, None, "is recommended to use an instance type with at least"),
("alinux2", "t2.micro", None, None, "is recommended to use an instance type with at least"),
("ubuntu1804", "m6g.xlarge", None, None, None),
("alinux2", "m6g.xlarge", None, None, None),
("centos7", "m6g.xlarge", None, None, None),
("centos8", "m6g.xlarge", None, None, None),
],
)
def test_dcv_enabled_validator(
mocker, base_os, instance_type, expected_error, expected_warning, access_from, caplog, capsys
):
config_parser_dict = {
"cluster default": {"base_os": base_os, "dcv_settings": "dcv"},
"dcv dcv": {"enable": "master"},
}
if access_from:
config_parser_dict["dcv dcv"]["access_from"] = access_from
architectures = ["x86_64"] if instance_type.startswith("t2") else ["arm64"]
extra_patches = {
"pcluster.config.validators.get_supported_instance_types": ["t2.nano", "t2.micro", "t2.medium", "m6g.xlarge"],
"pcluster.config.validators.get_supported_architectures_for_instance_type": architectures,
"pcluster.config.cfn_param_types.get_supported_architectures_for_instance_type": architectures,
}
utils.assert_param_validator(
mocker, config_parser_dict, expected_error, capsys, expected_warning, extra_patches=extra_patches
)
access_from_error_msg = DCV_MESSAGES["warnings"]["access_from_world"].format(port=8443)
assert_that(access_from_error_msg in caplog.text).is_equal_to(not access_from or access_from == "0.0.0.0/0")
@pytest.mark.parametrize(
"architecture, base_os, expected_message",
[
# Supported combinations
("x86_64", "alinux2", None),
("x86_64", "centos7", None),
("x86_64", "centos8", None),
("x86_64", "ubuntu1804", None),
("arm64", "ubuntu1804", None),
("arm64", "alinux2", None),
("arm64", "centos7", None),
("arm64", "centos8", None),
# Unsupported combinations
(
"UnsupportedArchitecture",
"alinux2",
FSX_MESSAGES["errors"]["unsupported_architecture"].format(
supported_architectures=list(FSX_SUPPORTED_ARCHITECTURES_OSES.keys())
),
),
],
)
def test_fsx_architecture_os_validator(mocker, architecture, base_os, expected_message):
config_parser_dict = {
"cluster default": {"base_os": base_os, "fsx_settings": "fsx"},
"fsx fsx": {"storage_capacity": 3200},
}
expected_message = re.escape(expected_message) if expected_message else None
extra_patches = {
"pcluster.config.cfn_param_types.get_supported_architectures_for_instance_type": [architecture],
"pcluster.config.validators.get_supported_architectures_for_instance_type": [architecture],
}
utils.assert_param_validator(mocker, config_parser_dict, expected_message, extra_patches=extra_patches)
@pytest.mark.parametrize(
"section_dict, expected_message",
[
(
{"initial_queue_size": "0", "maintain_initial_size": True},
"maintain_initial_size cannot be set to true if initial_queue_size is 0",
),
(
{"scheduler": "awsbatch", "maintain_initial_size": True},
"maintain_initial_size is not supported when using awsbatch as scheduler",
),
],
)
def test_maintain_initial_size_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"cluster_section_dict, expected_message",
[
# SIT cluster, perfectly fine
({"scheduler": "slurm"}, None),
# HIT cluster with one queue
({"scheduler": "slurm", "queue_settings": "queue1"}, None),
({"scheduler": "slurm", "queue_settings": "queue1,queue2,queue3,queue4,queue5"}, None),
({"scheduler": "slurm", "queue_settings": "queue1, queue2"}, None),
(
{"scheduler": "slurm", "queue_settings": "queue1,queue2,queue3,queue4,queue5,queue6"},
"Invalid number of 'queue' sections specified. Max 5 expected.",
),
(
{"scheduler": "slurm", "queue_settings": "queue_1"},
(
"Invalid queue name 'queue_1'. Queue section names can be at most 30 chars long, must begin with"
" a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use"
" 'default' as a queue section name."
),
),
(
{"scheduler": "slurm", "queue_settings": "default"},
(
"Invalid queue name 'default'. Queue section names can be at most 30 chars long, must begin with"
" a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use"
" 'default' as a queue section name."
),
),
(
{"scheduler": "slurm", "queue_settings": "queue1, default"},
(
"Invalid queue name '.*'. Queue section names can be at most 30 chars long, must begin with"
" a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use"
" 'default' as a queue section name."
),
),
(
{"scheduler": "slurm", "queue_settings": "QUEUE"},
(
"Invalid queue name 'QUEUE'. Queue section names can be at most 30 chars long, must begin with"
" a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use"
" 'default' as a queue section name."
),
),
(
{"scheduler": "slurm", "queue_settings": "aQUEUEa"},
(
"Invalid queue name 'aQUEUEa'. Queue section names can be at most 30 chars long, must begin with"
" a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use"
" 'default' as a queue section name."
),
),
({"scheduler": "slurm", "queue_settings": "my-default-queue"}, None),
],
)
def test_queue_settings_validator(mocker, cluster_section_dict, expected_message):
config_parser_dict = {"cluster default": cluster_section_dict}
if cluster_section_dict.get("queue_settings"):
for i, queue_name in enumerate(cluster_section_dict["queue_settings"].split(",")):
config_parser_dict["queue {0}".format(queue_name.strip())] = {
"compute_resource_settings": "cr{0}".format(i),
"disable_hyperthreading": True,
"enable_efa": True,
}
config_parser_dict["compute_resource cr{0}".format(i)] = {"instance_type": "t2.micro"}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"cluster_dict, queue_dict, expected_error_messages, expected_warning_messages",
[
(
{"queue_settings": "default"},
{"compute_resource_settings": "cr1,cr2", "enable_efa": True, "disable_hyperthreading": True},
[
"Duplicate instance type 't2.micro' found in queue 'default'. "
"Compute resources in the same queue must use different instance types"
],
[
"EFA was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr1 does not support EFA.",
"EFA was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr2 does not support EFA.",
],
),
(
{"queue_settings": "default"},
{"compute_resource_settings": "cr3,cr4", "enable_efa": True, "disable_hyperthreading": True},
[
"Duplicate instance type 'c4.xlarge' found in queue 'default'. "
"Compute resources in the same queue must use different instance types"
],
[
"EFA was enabled on queue 'default', but instance type 'c4.xlarge' "
"defined in compute resource settings cr3 does not support EFA.",
"EFA was enabled on queue 'default', but instance type 'c4.xlarge' "
"defined in compute resource settings cr4 does not support EFA.",
],
),
(
{"queue_settings": "default"},
{"compute_resource_settings": "cr1,cr3", "enable_efa": True, "disable_hyperthreading": True},
None,
[
"EFA was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr1 does not support EFA.",
"EFA was enabled on queue 'default', but instance type 'c4.xlarge' "
"defined in compute resource settings cr3 does not support EFA.",
],
),
(
{"queue_settings": "default"},
{"compute_resource_settings": "cr2,cr4", "enable_efa": True, "disable_hyperthreading": True},
None,
[
"EFA was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr2 does not support EFA.",
"EFA was enabled on queue 'default', but instance type 'c4.xlarge' "
"defined in compute resource settings cr4 does not support EFA.",
],
),
(
{"queue_settings": "default"},
{"compute_resource_settings": "cr2,cr4", "enable_efa": True, "enable_efa_gdr": True},
None,
[
"EFA was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr2 does not support EFA.",
"EFA GDR was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr2 does not support EFA GDR.",
"EFA was enabled on queue 'default', but instance type 'c4.xlarge' "
"defined in compute resource settings cr4 does not support EFA.",
"EFA GDR was enabled on queue 'default', but instance type 'c4.xlarge' "
"defined in compute resource settings cr4 does not support EFA GDR.",
],
),
(
{"queue_settings": "default"},
{"compute_resource_settings": "efa_instance", "enable_efa_gdr": True},
["The parameter 'enable_efa_gdr' can be used only in combination with 'enable_efa'"],
None,
),
({"queue_settings": "default"}, {"compute_resource_settings": "cr1"}, None, None),
(
{"queue_settings": "default", "enable_efa": "compute", "disable_hyperthreading": True},
{"compute_resource_settings": "cr1", "enable_efa": True, "disable_hyperthreading": True},
[
"Parameter 'enable_efa' can be used only in 'cluster' or in 'queue' section",
"Parameter 'disable_hyperthreading' can be used only in 'cluster' or in 'queue' section",
],
[
"EFA was enabled on queue 'default', but instance type 't2.micro' "
"defined in compute resource settings cr1 does not support EFA."
],
),
(
{
"queue_settings": "default",
"enable_efa": "compute",
"enable_efa_gdr": "compute",
"disable_hyperthreading": True,
},
{
"compute_resource_settings": "cr1",
"enable_efa": False,
"enable_efa_gdr": False,
"disable_hyperthreading": False,
},
[
"Parameter 'enable_efa' can be used only in 'cluster' or in 'queue' section",
"Parameter 'enable_efa_gdr' can be used only in 'cluster' or in 'queue' section",
"Parameter 'disable_hyperthreading' can be used only in 'cluster' or in 'queue' section",
],
None,
),
(
{"queue_settings": "default"},
{"compute_resource_settings": "efa_instance", "enable_efa": True},
None,
None,
),
],
)
def test_queue_validator(cluster_dict, queue_dict, expected_error_messages, expected_warning_messages):
config_parser_dict = {
"cluster default": cluster_dict,
"queue default": queue_dict,
"compute_resource cr1": {"instance_type": "t2.micro"},
"compute_resource cr2": {"instance_type": "t2.micro"},
"compute_resource cr3": {"instance_type": "c4.xlarge"},
"compute_resource cr4": {"instance_type": "c4.xlarge"},
"compute_resource efa_instance": {"instance_type": "p3dn.24xlarge"},
}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config_parser_dict)
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False, auto_refresh=False)
efa_instance_compute_resource = pcluster_config.get_section("compute_resource", "efa_instance")
if efa_instance_compute_resource:
# Override `enable_efa` and `enable_efa_gdr` default value for instance with efa support
efa_instance_compute_resource.get_param("enable_efa").value = True
efa_instance_compute_resource.get_param("enable_efa_gdr").value = True
errors, warnings = queue_validator("queue", "default", pcluster_config)
if expected_error_messages:
assert_that(expected_error_messages).is_equal_to(errors)
else:
assert_that(errors).is_empty()
if expected_warning_messages:
assert_that(expected_warning_messages).is_equal_to(warnings)
else:
assert_that(warnings).is_empty()
@pytest.mark.parametrize(
"param_value, expected_message",
[
(
"section1!2",
"Invalid label 'section1!2' in param 'queue_settings'. "
"Section labels can only contain alphanumeric characters, dashes or underscores.",
),
(
"section!123456789abcdefghijklmnopqrstuvwxyz_123456789abcdefghijklmnopqrstuvwxyz_",
"Invalid label 'section!123456789...' in param 'queue_settings'. "
"Section labels can only contain alphanumeric characters, dashes or underscores.",
),
("section-1", None),
("section_1", None),
(
"section_123456789abcdefghijklmnopqrstuvwxyz_123456789abcdefghijklmnopqrstuvwxyz_",
"Invalid label 'section_123456789...' in param 'queue_settings'. "
"The maximum length allowed for section labels is 64 characters",
),
],
)
def test_settings_validator(param_value, expected_message):
errors, warnings = settings_validator("queue_settings", param_value, None)
if expected_message:
assert_that(errors and len(errors) == 1).is_true()
assert_that(errors[0]).is_equal_to(expected_message)
else:
assert_that(errors).is_empty()
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"min_count": -1, "initial_count": -1}, "Parameter 'min_count' must be 0 or greater than 0"),
(
{"min_count": 0, "initial_count": 1, "spot_price": -1.1},
"Parameter 'spot_price' must be 0 or greater than 0",
),
(
{"min_count": 1, "max_count": 0, "initial_count": 1},
"Parameter 'max_count' must be greater than or equal to 'min_count'",
),
({"min_count": 0, "max_count": 0, "initial_count": 0}, "Parameter 'max_count' must be 1 or greater than 1"),
({"min_count": 1, "max_count": 2, "spot_price": 1.5, "initial_count": 1}, None),
(
{"min_count": 2, "max_count": 4, "initial_count": 1},
"Parameter 'initial_count' must be greater than or equal to 'min_count'",
),
(
{"min_count": 2, "max_count": 4, "initial_count": 5},
"Parameter 'initial_count' must be lower than or equal to 'max_count'",
),
],
)
def test_compute_resource_validator(mocker, section_dict, expected_message):
config_parser_dict = {
"cluster default": {"queue_settings": "default"},
"queue default": {"compute_resource_settings": "default"},
"compute_resource default": section_dict,
}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config_parser_dict)
mocker.patch(
"pcluster.config.cfn_param_types.get_supported_architectures_for_instance_type", return_value=["x86_64"]
)
instance_type_info_mock = mocker.MagicMock()
mocker.patch(
"pcluster.config.cfn_param_types.InstanceTypeInfo.init_from_instance_type", return_value=instance_type_info_mock
)
instance_type_info_mock.max_network_interface_count.return_value = 1
mocker.patch("pcluster.config.validators.get_supported_architectures_for_instance_type", return_value=["x86_64"])
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False)
errors, warnings = compute_resource_validator("compute_resource", "default", pcluster_config)
if expected_message:
assert_that(expected_message in errors)
else:
assert_that(errors).is_empty()
@pytest.mark.parametrize(
"cluster_section_dict, sections_dict, expected_message",
[
(
{"vpc_settings": "vpc1, vpc2"},
{"vpc vpc1": {}, "vpc vpc2": {}},
"The value of 'vpc_settings' parameter is invalid. It can only contain a single vpc section label",
),
(
{"efs_settings": "efs1, efs2"},
{"efs efs1": {}, "efs efs2": {}},
"The value of 'efs_settings' parameter is invalid. It can only contain a single efs section label",
),
],
)
def test_single_settings_validator(mocker, cluster_section_dict, sections_dict, expected_message):
config_parser_dict = {"cluster default": cluster_section_dict}
if sections_dict:
for key, section in sections_dict.items():
config_parser_dict[key] = section
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
#########
#
# architecture validator tests
#
# Two things make it difficult to test validators that key on architecture in the same way that:
# 1) architecture is a derived parameter and cannot be configured directly via the config file
# 2) many validators key on the architecture, which makes it impossible to test some combinations of
# parameters for validators that run later than others, because those run earlier will have
# already raised exceptions.
#
# Thus, the following code mocks the pcluster_config object passed to the validator functions
# and calls those functions directly (as opposed to patching functions and instantiating a config
# as would be done when running `pcluster create/update`).
#
#########
def get_default_pcluster_sections_dict():
"""Return a dict similar in structure to that of a cluster config file."""
default_pcluster_sections_dict = {}
for section_default_dict in DefaultDict:
if section_default_dict.name == "pcluster": # Get rid of the extra layer in this case
default_pcluster_sections_dict["cluster"] = section_default_dict.value.get("cluster")
else:
default_pcluster_sections_dict[section_default_dict.name] = section_default_dict.value
return default_pcluster_sections_dict
def make_pcluster_config_mock(mocker, config_dict):
"""Mock the calls that made on a pcluster_config by validator functions."""
cluster_config_dict = get_default_pcluster_sections_dict()
for section_key in config_dict:
cluster_config_dict = utils.merge_dicts(cluster_config_dict.get(section_key), config_dict.get(section_key))
section_to_mocks = {}
for section_key, section_dict in config_dict.items():
section_mock = mocker.MagicMock()
section_mock.get_param_value.side_effect = lambda param: section_dict.get(param)
section_to_mocks[section_key] = section_mock
pcluster_config_mock = mocker.MagicMock()
pcluster_config_mock.get_section.side_effect = lambda section: section_to_mocks.get(section)
return pcluster_config_mock
def run_architecture_validator_test(
mocker,
config,
constrained_param_section,
constrained_param_name,
param_name,
param_val,
validator,
expected_warnings,
expected_errors,
):
"""Run a test for a validator that's concerned with the architecture param."""
mocked_pcluster_config = make_pcluster_config_mock(mocker, config)
errors, warnings = validator(param_name, param_val, mocked_pcluster_config)
mocked_pcluster_config.get_section.assert_called_once_with(constrained_param_section)
mocked_pcluster_config.get_section.side_effect(constrained_param_section).get_param_value.assert_called_with(
constrained_param_name
)
assert_that(len(warnings)).is_equal_to(len(expected_warnings))
for warnings, expected_warnings in zip(warnings, expected_warnings):
assert_that(warnings).matches(re.escape(expected_warnings))
assert_that(len(errors)).is_equal_to(len(expected_errors))
for errors, expected_errors in zip(errors, expected_errors):
assert_that(errors).matches(re.escape(expected_errors))
@pytest.mark.parametrize(
"enabled, architecture, expected_errors",
[
(True, "x86_64", []),
(True, "arm64", ["instance types and an AMI that support these architectures"]),
(False, "x86_64", []),
(False, "arm64", []),
],
)
def test_intel_hpc_architecture_validator(mocker, enabled, architecture, expected_errors):
"""Verify that setting enable_intel_hpc_platform is invalid when architecture != x86_64."""
config_dict = {"cluster": {"enable_intel_hpc_platform": enabled, "architecture": architecture}}
run_architecture_validator_test(
mocker,
config_dict,
"cluster",
"architecture",
"enable_intel_hpc_platform",
enabled,
intel_hpc_architecture_validator,
[],
expected_errors,
)
@pytest.mark.parametrize(
"base_os, architecture, expected_warnings, expected_errors",
[
# All OSes supported for x86_64
("alinux2", "x86_64", [], []),
("centos7", "x86_64", [], []),
("centos8", "x86_64", [], []),
("ubuntu1804", "x86_64", [], []),
# Only a subset of OSes supported for arm64
("alinux2", "arm64", [], []),
(
"centos7",
"arm64",
[
"Warning: The aarch64 CentOS 7 OS is not validated for the 6th generation aarch64 instances "
"(M6g, C6g, etc.). To proceed please provide a custom_ami, "
"for more info see: https://wiki.centos.org/Cloud/AWS#aarch64_notes"
],
[],
),
("centos8", "arm64", [], []),
("ubuntu1804", "arm64", [], []),
],
)
def test_architecture_os_validator(mocker, base_os, architecture, expected_warnings, expected_errors):
"""Verify that the correct set of OSes is supported for each supported architecture."""
config_dict = {"cluster": {"base_os": base_os, "architecture": architecture}}
run_architecture_validator_test(
mocker,
config_dict,
"cluster",
"architecture",
"base_os",
base_os,
architecture_os_validator,
expected_warnings,
expected_errors,
)
@pytest.mark.parametrize(
"disable_hyperthreading, architecture, expected_errors",
[
(True, "x86_64", []),
(False, "x86_64", []),
(
True,
"arm64",
["disable_hyperthreading is only supported on instance types that support these architectures"],
),
(False, "arm64", []),
],
)
def test_disable_hyperthreading_architecture_validator(mocker, disable_hyperthreading, architecture, expected_errors):
config_dict = {"cluster": {"architecture": architecture, "disable_hyperthreading": disable_hyperthreading}}
run_architecture_validator_test(
mocker,
config_dict,
"cluster",
"architecture",
"disable_hyperthreading",
disable_hyperthreading,
disable_hyperthreading_architecture_validator,
[],
expected_errors,
)
@pytest.mark.parametrize(
"head_node_architecture, compute_architecture, compute_instance_type, expected_errors",
[
# Single compute_instance_type
("x86_64", "x86_64", "c5.xlarge", []),
(
"x86_64",
"arm64",
"m6g.xlarge",
["none of which are compatible with the architecture supported by the master_instance_type"],
),
(
"arm64",
"x86_64",
"c5.xlarge",
["none of which are compatible with the architecture supported by the master_instance_type"],
),
("arm64", "arm64", "m6g.xlarge", []),
("x86_64", "x86_64", "optimal", []),
# Function to get supported architectures shouldn't be called because compute_instance_type arg
# are instance families.
("x86_64", None, "m6g", []),
("x86_64", None, "c5", []),
# The validator must handle the case where compute_instance_type is a CSV list
("arm64", "arm64", "m6g.xlarge,r6g.xlarge", []),
(
"x86_64",
"arm64",
"m6g.xlarge,r6g.xlarge",
["none of which are compatible with the architecture supported by the master_instance_type"] * 2,
),
],
)
def test_instances_architecture_compatibility_validator(
mocker, caplog, head_node_architecture, compute_architecture, compute_instance_type, expected_errors
):
def internal_is_instance_type(itype):
return "." in itype or itype == "optimal"
supported_architectures_patch = mocker.patch(
"pcluster.config.validators.get_supported_architectures_for_instance_type", return_value=[compute_architecture]
)
is_instance_type_patch = mocker.patch(
"pcluster.config.validators.is_instance_type_format", side_effect=internal_is_instance_type
)
logger_patch = mocker.patch.object(LOGFILE_LOGGER, "debug")
run_architecture_validator_test(
mocker,
{"cluster": {"architecture": head_node_architecture}},
"cluster",
"architecture",
"compute_instance_type",
compute_instance_type,
instances_architecture_compatibility_validator,
[],
expected_errors,
)
compute_instance_types = compute_instance_type.split(",")
non_instance_families = [
instance_type for instance_type in compute_instance_types if internal_is_instance_type(instance_type)
]
assert_that(supported_architectures_patch.call_count).is_equal_to(len(non_instance_families))
assert_that(logger_patch.call_count).is_equal_to(len(compute_instance_types) - len(non_instance_families))
assert_that(is_instance_type_patch.call_count).is_equal_to(len(compute_instance_types))
@pytest.mark.parametrize(
"section_dict, bucket, num_calls, expected_error",
[
(
{
"fsx_backup_id": "backup-0ff8da96d57f3b4e3",
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": 50,
},
None,
0,
"When restoring an FSx Lustre file system from backup, 'deployment_type' cannot be specified.",
),
(
{"fsx_backup_id": "backup-0ff8da96d57f3b4e3", "storage_capacity": 7200},
None,
0,
"When restoring an FSx Lustre file system from backup, 'storage_capacity' cannot be specified.",
),
(
{
"fsx_backup_id": "backup-0ff8da96d57f3b4e3",
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": 100,
},
None,
0,
"When restoring an FSx Lustre file system from backup, 'per_unit_storage_throughput' cannot be specified.",
),
(
{
"fsx_backup_id": "backup-0ff8da96d57f3b4e3",
"imported_file_chunk_size": 1024,
"export_path": "s3://test",
"import_path": "s3://test",
},
{"Bucket": "test"},
2,
"When restoring an FSx Lustre file system from backup, 'imported_file_chunk_size' cannot be specified.",
),
(
{
"fsx_backup_id": "backup-0ff8da96d57f3b4e3",
"fsx_kms_key_id": "somekey",
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": 50,
},
None,
0,
"When restoring an FSx Lustre file system from backup, 'fsx_kms_key_id' cannot be specified.",
),
(
{
"fsx_backup_id": "backup-00000000000000000",
"deployment_type": "PERSISTENT_1",
"per_unit_storage_throughput": 50,
},
None,
0,
"Failed to retrieve backup with Id 'backup-00000000000000000'",
),
],
)
def test_fsx_lustre_backup_validator(mocker, boto3_stubber, section_dict, bucket, num_calls, expected_error):
valid_key_id = "backup-0ff8da96d57f3b4e3"
describe_backups_response = {
"Backups": [
{
"BackupId": valid_key_id,
"Lifecycle": "AVAILABLE",
"Type": "USER_INITIATED",
"CreationTime": 1594159673.559,
"FileSystem": {
"StorageCapacity": 7200,
"StorageType": "SSD",
"LustreConfiguration": {"DeploymentType": "PERSISTENT_1", "PerUnitStorageThroughput": 200},
},
}
]
}
if bucket:
_head_bucket_stubber(mocker, boto3_stubber, bucket, num_calls)
generate_describe_backups_error = section_dict.get("fsx_backup_id") != valid_key_id
fsx_mocked_requests = [
MockedBoto3Request(
method="describe_backups",
response=expected_error if generate_describe_backups_error else describe_backups_response,
expected_params={"BackupIds": [section_dict.get("fsx_backup_id")]},
generate_error=generate_describe_backups_error,
)
]
boto3_stubber("fsx", fsx_mocked_requests)
if "fsx_kms_key_id" in section_dict:
describe_key_response = {"KeyMetadata": {"KeyId": section_dict.get("fsx_kms_key_id")}}
kms_mocked_requests = [
MockedBoto3Request(
method="describe_key",
response=describe_key_response,
expected_params={"KeyId": section_dict.get("fsx_kms_key_id")},
)
]
boto3_stubber("kms", kms_mocked_requests)
config_parser_dict = {"cluster default": {"fsx_settings": "default"}, "fsx default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_error=expected_error)
#########
#
# ignored FSx params validator test
#
# Testing a validator that requires the fsx_fs_id parameter to be specified requires a lot of
# boto3 stubbing due to the complexity contained in the fsx_id_validator.
#
# Thus, the following code mocks the pcluster_config object passed to the validator functions
# and calls the validator directly.
#
#########
@pytest.mark.parametrize(
"section_dict, expected_error",
[
({"fsx_fs_id": "fs-0123456789abcdef0", "shared_dir": "/fsx"}, None),
(
{"fsx_fs_id": "fs-0123456789abcdef0", "shared_dir": "/fsx", "storage_capacity": 3600},
"storage_capacity is ignored when specifying an existing Lustre file system",
),
],
)
def test_fsx_ignored_parameters_validator(mocker, section_dict, expected_error):
mocked_pcluster_config = utils.get_mocked_pcluster_config(mocker)
fsx_section = CfnSection(FSX, mocked_pcluster_config, "default")
for param_key, param_value in section_dict.items():
param = FSX.get("params").get(param_key).get("type", CfnParam)
param.value = param_value
fsx_section.set_param(param_key, param)
mocked_pcluster_config.add_section(fsx_section)
errors, warnings = fsx_ignored_parameters_validator("fsx", "default", mocked_pcluster_config)
assert_that(warnings).is_empty()
if expected_error:
assert_that(errors[0]).matches(expected_error)
else:
assert_that(errors).is_empty()
@pytest.mark.parametrize(
"section_dict, expected_error",
[
({"volume_type": "standard", "volume_size": 15}, None),
({"volume_type": "standard", "volume_size": 0}, "The size of standard volumes must be at least 1 GiB"),
({"volume_type": "standard", "volume_size": 1025}, "The size of standard volumes can not exceed 1024 GiB"),
({"volume_type": "io1", "volume_size": 15}, None),
({"volume_type": "io1", "volume_size": 3}, "The size of io1 volumes must be at least 4 GiB"),
({"volume_type": "io1", "volume_size": 16385}, "The size of io1 volumes can not exceed 16384 GiB"),
({"volume_type": "io2", "volume_size": 15}, None),
({"volume_type": "io2", "volume_size": 3}, "The size of io2 volumes must be at least 4 GiB"),
({"volume_type": "io2", "volume_size": 65537}, "The size of io2 volumes can not exceed 65536 GiB"),
({"volume_type": "gp2", "volume_size": 15}, None),
({"volume_type": "gp2", "volume_size": 0}, "The size of gp2 volumes must be at least 1 GiB"),
({"volume_type": "gp2", "volume_size": 16385}, "The size of gp2 volumes can not exceed 16384 GiB"),
({"volume_type": "gp3", "volume_size": 15}, None),
({"volume_type": "gp3", "volume_size": 0}, "The size of gp3 volumes must be at least 1 GiB"),
({"volume_type": "gp3", "volume_size": 16385}, "The size of gp3 volumes can not exceed 16384 GiB"),
({"volume_type": "st1", "volume_size": 500}, None),
({"volume_type": "st1", "volume_size": 20}, "The size of st1 volumes must be at least 500 GiB"),
({"volume_type": "st1", "volume_size": 16385}, "The size of st1 volumes can not exceed 16384 GiB"),
({"volume_type": "sc1", "volume_size": 500}, None),
({"volume_type": "sc1", "volume_size": 20}, "The size of sc1 volumes must be at least 500 GiB"),
({"volume_type": "sc1", "volume_size": 16385}, "The size of sc1 volumes can not exceed 16384 GiB"),
],
)
def test_ebs_volume_type_size_validator(mocker, section_dict, caplog, expected_error):
config_parser_dict = {"cluster default": {"ebs_settings": "default"}, "ebs default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_error)
def test_ebs_allowed_values_all_have_volume_size_bounds():
"""Ensure that all known EBS volume types are accounted for by the volume size validator."""
allowed_values_all_have_volume_size_bounds = set(ALLOWED_VALUES["volume_types"]) <= set(
EBS_VOLUME_TYPE_TO_VOLUME_SIZE_BOUNDS.keys()
)
assert_that(allowed_values_all_have_volume_size_bounds).is_true()
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"volume_type": "io1", "volume_size": 20, "volume_iops": 120}, None),
(
{"volume_type": "io1", "volume_size": 20, "volume_iops": 90},
"IOPS rate must be between 100 and 64000 when provisioning io1 volumes.",
),
(
{"volume_type": "io1", "volume_size": 20, "volume_iops": 64001},
"IOPS rate must be between 100 and 64000 when provisioning io1 volumes.",
),
({"volume_type": "io1", "volume_size": 20, "volume_iops": 1001}, "IOPS to volume size ratio of .* is too high"),
({"volume_type": "io2", "volume_size": 20, "volume_iops": 120}, None),
(
{"volume_type": "io2", "volume_size": 20, "volume_iops": 90},
"IOPS rate must be between 100 and 256000 when provisioning io2 volumes.",
),
(
{"volume_type": "io2", "volume_size": 20, "volume_iops": 256001},
"IOPS rate must be between 100 and 256000 when provisioning io2 volumes.",
),
(
{"volume_type": "io2", "volume_size": 20, "volume_iops": 20001},
"IOPS to volume size ratio of .* is too high",
),
({"volume_type": "gp3", "volume_size": 20, "volume_iops": 3000}, None),
(
{"volume_type": "gp3", "volume_size": 20, "volume_iops": 2900},
"IOPS rate must be between 3000 and 16000 when provisioning gp3 volumes.",
),
(
{"volume_type": "gp3", "volume_size": 20, "volume_iops": 16001},
"IOPS rate must be between 3000 and 16000 when provisioning gp3 volumes.",
),
(
{"volume_type": "gp3", "volume_size": 20, "volume_iops": 10001},
"IOPS to volume size ratio of .* is too high",
),
],
)
def test_ebs_volume_iops_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": {"ebs_settings": "default"}, "ebs default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"section_dict, snapshot_size, state, partition, expected_warning, expected_error, "
"raise_error_when_getting_snapshot_info",
[
(
{"volume_size": 100, "ebs_snapshot_id": "snap-1234567890abcdef0"},
50,
"completed",
"aws-cn",
"The specified volume size is larger than snapshot size. In order to use the full capacity of the "
"volume, you'll need to manually resize the partition "
"according to this doc: "
"https://docs.amazonaws.cn/AWSEC2/latest/UserGuide/recognize-expanded-volume-linux.html",
None,
False,
),
(
{"volume_size": 100, "ebs_snapshot_id": "snap-1234567890abcdef0"},
50,
"completed",
"aws-us-gov",
"The specified volume size is larger than snapshot size. In order to use the full capacity of the "
"volume, you'll need to manually resize the partition "
"according to this doc: "
"https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/recognize-expanded-volume-linux.html",
None,
False,
),
(
{"volume_size": 100, "ebs_snapshot_id": "snap-1234567890abcdef0"},
50,
"incompleted",
"aws-us-gov",
"Snapshot snap-1234567890abcdef0 is in state 'incompleted' not 'completed'",
None,
False,
),
({"ebs_snapshot_id": "snap-1234567890abcdef0"}, 50, "completed", "partition", None, None, False),
(
{"volume_size": 100, "ebs_snapshot_id": "snap-1234567891abcdef0"},
120,
"completed",
"aws-us-gov",
None,
"The EBS volume size of the section 'default' must not be smaller than 120, because it is the size of the "
"provided snapshot snap-1234567891abcdef0",
False,
),
(
{"volume_size": 100, "ebs_snapshot_id": "snap-1234567890abcdef0"},
None,
"completed",
"aws-cn",
None,
"Unable to get volume size for snapshot snap-1234567890abcdef0",
False,
),
(
{"ebs_snapshot_id": "snap-1234567890abcdef0"},
20,
"completed",
"aws",
None,
"some message",
True,
),
],
)
def test_ebs_volume_size_snapshot_validator(
section_dict,
snapshot_size,
state,
partition,
mocker,
expected_warning,
expected_error,
raise_error_when_getting_snapshot_info,
capsys,
):
ebs_snapshot_id = section_dict["ebs_snapshot_id"]
describe_snapshots_response = {
"Description": "This is my snapshot",
"Encrypted": False,
"VolumeId": "vol-049df61146c4d7901",
"State": state,
"VolumeSize": snapshot_size,
"StartTime": "2014-02-28T21:28:32.000Z",
"Progress": "100%",
"OwnerId": "012345678910",
"SnapshotId": ebs_snapshot_id,
}
mocker.patch("pcluster.config.cfn_param_types.get_ebs_snapshot_info", return_value=describe_snapshots_response)
if raise_error_when_getting_snapshot_info:
mocker.patch("pcluster.config.validators.get_ebs_snapshot_info", side_effect=Exception(expected_error))
else:
mocker.patch("pcluster.config.validators.get_ebs_snapshot_info", return_value=describe_snapshots_response)
mocker.patch(
"pcluster.config.validators.get_partition", return_value="aws-cn" if partition == "aws-cn" else "aws-us-gov"
)
config_parser_dict = {"cluster default": {"ebs_settings": "default"}, "ebs default": section_dict}
utils.assert_param_validator(
mocker, config_parser_dict, expected_error=expected_error, capsys=capsys, expected_warning=expected_warning
)
@pytest.mark.parametrize(
"cluster_section_dict, ebs_section_dict1, ebs_section_dict2, expected_message",
[
(
{"shared_dir": "shared_directory", "ebs_settings": "vol1"},
{"volume_size": 30},
{},
None,
),
(
{"shared_dir": "shared_directory", "ebs_settings": "vol1"},
{"shared_dir": "shared_directory1"},
{},
"'shared_dir' can not be specified both in cluster section and EBS section",
),
(
{"shared_dir": "shared_directory", "ebs_settings": "vol1, vol2"},
{"shared_dir": "shared_directory1", "volume_size": 30},
{"shared_dir": "shared_directory2", "volume_size": 30},
"'shared_dir' can not be specified in cluster section when using multiple EBS volumes",
),
(
{"ebs_settings": "vol1, vol2"},
{"shared_dir": "shared_directory1", "volume_size": 30},
{"shared_dir": "shared_directory2", "volume_size": 30},
None,
),
(
{"ebs_settings": "vol1"},
{"volume_size": 30},
{},
None,
),
(
{"ebs_settings": "vol1"},
{},
{},
None,
),
(
{"shared_dir": "shared_directory"},
{},
{},
None,
),
],
)
def test_duplicate_shared_dir_validator(
mocker, cluster_section_dict, ebs_section_dict1, ebs_section_dict2, expected_message
):
config_parser_dict = {
"cluster default": cluster_section_dict,
"ebs vol1": ebs_section_dict1,
"ebs vol2": ebs_section_dict2,
}
utils.assert_param_validator(mocker, config_parser_dict, expected_error=expected_message)
@pytest.mark.parametrize(
"extra_json, expected_message",
[
(
{"extra_json": {"cluster": {"cfn_scheduler_slots": "1"}}},
"It is highly recommended to use the disable_hyperthreading parameter in order to control the "
"hyper-threading configuration in the cluster rather than using cfn_scheduler_slots in extra_json",
),
(
{"extra_json": {"cluster": {"cfn_scheduler_slots": "vcpus"}}},
"It is highly recommended to use the disable_hyperthreading parameter in order to control the "
"hyper-threading configuration in the cluster rather than using cfn_scheduler_slots in extra_json",
),
(
{"extra_json": {"cluster": {"cfn_scheduler_slots": "cores"}}},
"It is highly recommended to use the disable_hyperthreading parameter in order to control the "
"hyper-threading configuration in the cluster rather than using cfn_scheduler_slots in extra_json",
),
],
)
def test_extra_json_validator(mocker, capsys, extra_json, expected_message):
config_parser_dict = {"cluster default": extra_json}
utils.assert_param_validator(mocker, config_parser_dict, capsys=capsys, expected_warning=expected_message)
@pytest.mark.parametrize(
"cluster_dict, architecture, expected_error",
[
({"base_os": "alinux2", "enable_efa": "compute"}, "x86_64", None),
({"base_os": "alinux2", "enable_efa": "compute"}, "arm64", None),
({"base_os": "centos8", "enable_efa": "compute"}, "x86_64", None),
({"base_os": "centos8"}, "x86_64", None),
(
{"base_os": "centos8", "enable_efa": "compute"},
"arm64",
"EFA currently not supported on centos8 for arm64 architecture",
),
({"base_os": "centos8"}, "arm64", None), # must not fail because by default EFA is disabled
({"base_os": "ubuntu1804", "enable_efa": "compute"}, "x86_64", None),
({"base_os": "ubuntu1804", "enable_efa": "compute"}, "arm64", None),
],
)
def test_efa_os_arch_validator(mocker, cluster_dict, architecture, expected_error):
mocker.patch(
"pcluster.config.cfn_param_types.BaseOSCfnParam.get_instance_type_architecture", return_value=architecture
)
config_parser_dict = {"cluster default": cluster_dict}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config_parser_dict)
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False, auto_refresh=False)
pcluster_config.get_section("cluster").get_param("architecture").value = architecture
enable_efa_value = pcluster_config.get_section("cluster").get_param_value("enable_efa")
errors, warnings = efa_os_arch_validator("enable_efa", enable_efa_value, pcluster_config)
if expected_error:
assert_that(errors[0]).matches(expected_error)
else:
assert_that(errors).is_empty()
@pytest.mark.parametrize(
"section_dict, expected_message",
[
({"volume_type": "gp3", "volume_throughput": 125}, None),
(
{"volume_type": "gp3", "volume_throughput": 100},
"Throughput must be between 125 MB/s and 1000 MB/s when provisioning gp3 volumes.",
),
(
{"volume_type": "gp3", "volume_throughput": 1001},
"Throughput must be between 125 MB/s and 1000 MB/s when provisioning gp3 volumes.",
),
({"volume_type": "gp3", "volume_throughput": 125, "volume_iops": 3000}, None),
(
{"volume_type": "gp3", "volume_throughput": 760, "volume_iops": 3000},
"Throughput to IOPS ratio of .* is too high",
),
({"volume_type": "gp3", "volume_throughput": 760, "volume_iops": 10000}, None),
],
)
def test_ebs_volume_throughput_validator(mocker, section_dict, expected_message):
config_parser_dict = {"cluster default": {"ebs_settings": "default"}, "ebs default": section_dict}
utils.assert_param_validator(mocker, config_parser_dict, expected_message)
@pytest.mark.parametrize(
"region, expected_message",
[
("invalid-region", "Region 'invalid-region' is not yet officially supported "),
("us-east-1", None),
],
)
def test_region_validator(mocker, region, expected_message):
pcluster_config = utils.get_mocked_pcluster_config(mocker)
pcluster_config.region = region
errors, warnings = region_validator("aws", None, pcluster_config)
if expected_message:
assert_that(len(errors)).is_greater_than(0)
assert_that(errors[0]).matches(expected_message)
else:
assert_that(errors).is_empty()
@pytest.mark.parametrize(
"usage_class, supported_usage_classes, expected_error_message, expected_warning_message",
[
("ondemand", ["ondemand", "spot"], None, None),
("spot", ["ondemand", "spot"], None, None),
("ondemand", ["ondemand"], None, None),
("spot", ["spot"], None, None),
("spot", [], None, "Could not check support for usage class 'spot' with instance type 'instance-type'"),
("ondemand", [], None, "Could not check support for usage class 'ondemand' with instance type 'instance-type'"),
("spot", ["ondemand"], "Usage type 'spot' not supported with instance type 'instance-type'", None),
("ondemand", ["spot"], "Usage type 'ondemand' not supported with instance type 'instance-type'", None),
],
)
def test_check_usage_class(
mocker, usage_class, supported_usage_classes, expected_error_message, expected_warning_message
):
# This test checks the common logic triggered from cluster_type_validator and queue_compute_type_validator.
instance_type_info_mock = mocker.MagicMock()
mocker.patch(
"pcluster.config.cfn_param_types.InstanceTypeInfo.init_from_instance_type", return_value=instance_type_info_mock
)
instance_type_info_mock.supported_usage_classes.return_value = supported_usage_classes
errors = []
warnings = []
check_usage_class("instance-type", usage_class, errors, warnings)
if expected_error_message:
assert_that(errors).contains(expected_error_message)
else:
assert_that(errors).is_empty()
if expected_warning_message:
assert_that(warnings).contains(expected_warning_message)
else:
assert_that(warnings).is_empty()
@pytest.mark.parametrize(
"scheduler, expected_usage_class_check", [("sge", True), ("torque", True), ("slurm", True), ("awsbatch", False)]
)
def test_cluster_type_validator(mocker, scheduler, expected_usage_class_check):
# Usage class validation logic is tested in `test_check_usage_class`.
# This test only makes sure that the logic is triggered from validator.
mock = mocker.patch("pcluster.config.validators.check_usage_class", return_value=None)
cluster_dict = {"compute_instance_type": "t2.micro", "scheduler": scheduler}
config_parser_dict = {"cluster default": cluster_dict}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config_parser_dict)
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False, auto_refresh=False)
errors, warnings = cluster_type_validator("compute_type", "spot", pcluster_config)
if expected_usage_class_check:
mock.assert_called_with("t2.micro", "spot", [], [])
else:
mock.assert_not_called()
assert_that(errors).is_equal_to([])
assert_that(warnings).is_equal_to([])
@pytest.mark.parametrize("compute_type", [("ondemand"), ("spot")])
def test_queue_compute_type_validator(mocker, compute_type):
# Usage class validation logic is tested in `test_check_usage_class`.
# This test only makes sure that the logic is triggered from validator.
mock = mocker.patch("pcluster.config.validators.check_usage_class", return_value=None)
config_parser_dict = {
"cluster default": {
"queue_settings": "q1",
},
"queue q1": {"compute_resource_settings": "q1cr1, q1cr2", "compute_type": compute_type},
"compute_resource q1cr1": {"instance_type": "q1cr1_instance_type"},
"compute_resource q1cr2": {"instance_type": "q1cr2_instance_type"},
}
config_parser = configparser.ConfigParser()
config_parser.read_dict(config_parser_dict)
pcluster_config = utils.init_pcluster_config_from_configparser(config_parser, False, auto_refresh=False)
errors, warnings = queue_compute_type_validator("queue", "q1", pcluster_config)
mock.assert_has_calls(
[
mocker.call("q1cr1_instance_type", compute_type, [], []),
mocker.call("q1cr2_instance_type", compute_type, [], []),
],
any_order=True,
)
assert_that(errors).is_equal_to([])
assert_that(warnings).is_equal_to([])
| [
"re.escape",
"configparser.ConfigParser",
"pcluster.config.validators.EBS_VOLUME_TYPE_TO_VOLUME_SIZE_BOUNDS.keys",
"pytest.fixture",
"pcluster.config.validators.queue_compute_type_validator",
"pcluster.config.validators.region_validator",
"datetime.datetime",
"pcluster.config.validators.efa_gdr_validator",
"json.dumps",
"pcluster.config.validators.cluster_type_validator",
"tests.common.MockedBoto3Request",
"pcluster.config.validators.efa_os_arch_validator",
"pcluster.config.validators.settings_validator",
"tests.pcluster.config.utils.assert_param_validator",
"pcluster.config.validators.fsx_ignored_parameters_validator",
"pcluster.config.validators.queue_validator",
"pcluster.config.validators.s3_bucket_region_validator",
"pcluster.config.validators.compute_resource_validator",
"pcluster.config.validators.FSX_SUPPORTED_ARCHITECTURES_OSES.keys",
"tests.pcluster.config.utils.init_pcluster_config_from_configparser",
"pcluster.config.mappings.FSX.get",
"assertpy.assert_that",
"pcluster.config.validators.check_usage_class",
"tests.pcluster.config.utils.get_mocked_pcluster_config",
"pytest.mark.parametrize",
"pcluster.config.cfn_param_types.CfnSection"
]
| [((1665, 1681), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (1679, 1681), False, 'import pytest\n'), ((1757, 2976), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""section_dict, expected_message, expected_warning"""', "[({'scheduler': 'sge', 'initial_queue_size': 1, 'max_queue_size': 2,\n 'maintain_initial_size': True}, None, None), ({'scheduler': 'sge',\n 'initial_queue_size': 3, 'max_queue_size': 2, 'maintain_initial_size': \n True},\n 'initial_queue_size must be fewer than or equal to max_queue_size',\n None), ({'scheduler': 'sge', 'initial_queue_size': 3, 'max_queue_size':\n 2, 'maintain_initial_size': False},\n 'initial_queue_size must be fewer than or equal to max_queue_size',\n None), ({'scheduler': 'awsbatch', 'min_vcpus': 1, 'desired_vcpus': 2,\n 'max_vcpus': 3}, None, None), ({'scheduler': 'awsbatch', 'min_vcpus': 3,\n 'desired_vcpus': 2, 'max_vcpus': 3},\n 'desired_vcpus must be greater than or equal to min_vcpus', None), ({\n 'scheduler': 'awsbatch', 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\'deployment_type\': \'PERSISTENT_1\',\n \'storage_capacity\': 1801, \'per_unit_storage_throughput\': 40},\n \'Capacity for FSx PERSISTENT HDD 40 MB/s/TiB file systems is increments of 1,800 GiB\'\n , None), ({\'storage_type\': \'HDD\', \'deployment_type\': \'PERSISTENT_1\',\n \'storage_capacity\': 6001, \'per_unit_storage_throughput\': 12},\n \'Capacity for FSx PERSISTENT HDD 12 MB/s/TiB file systems is increments of 6,000 GiB\'\n , None), ({\'storage_type\': \'HDD\', \'deployment_type\': \'PERSISTENT_1\',\n \'storage_capacity\': 1800, \'per_unit_storage_throughput\': 40}, None,\n None), ({\'storage_type\': \'HDD\', \'deployment_type\': \'PERSISTENT_1\',\n \'storage_capacity\': 6000, \'per_unit_storage_throughput\': 12}, None, None)])\n', (42830, 44814), False, 'import pytest\n'), ((46633, 47800), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""fsx_vpc, ip_permissions, network_interfaces, expected_message"""', '[(\'vpc-06e4ab6c6cEXAMPLE\', [{\'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\n \'UserId\': \'123456789012\', \'GroupId\': \'sg-12345678\'}]}], [\n \'eni-09b9460295ddd4e5f\', \'eni-001b3cef7c78b45c4\'], None), (\n \'vpc-06e4ab6c6cEXAMPLE\', [{\'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\n \'UserId\': \'123456789012\', \'GroupId\': \'sg-12345678\'}]}], [\n \'eni-09b9460295ddd4e5f\'], None), (\'vpc-06e4ab6c6cEXAMPLE\', [{\n \'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\'UserId\': \'123456789012\',\n \'GroupId\': \'sg-12345678\'}]}], [],\n "doesn\'t have Elastic Network Interfaces attached"), (\n \'vpc-06e4ab6c6ccWRONG\', [{\'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\n \'UserId\': \'123456789012\', \'GroupId\': \'sg-12345678\'}]}], [\n \'eni-09b9460295ddd4e5f\'],\n \'only support using FSx file system that is in the same VPC as the stack\'\n ), (\'vpc-06e4ab6c6cWRONG\', [{\'PrefixListIds\': [], \'FromPort\': 22,\n \'IpRanges\': [{\'CidrIp\': \'203.0.113.0/24\'}], \'ToPort\': 22, \'IpProtocol\':\n \'tcp\', \'UserIdGroupPairs\': []}], [\'eni-09b9460295ddd4e5f\'],\n \'does not satisfy mounting requirement\')]'], {}), '(\n \'fsx_vpc, ip_permissions, network_interfaces, expected_message\', [(\n \'vpc-06e4ab6c6cEXAMPLE\', [{\'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\n \'UserId\': \'123456789012\', \'GroupId\': \'sg-12345678\'}]}], [\n \'eni-09b9460295ddd4e5f\', \'eni-001b3cef7c78b45c4\'], None), (\n \'vpc-06e4ab6c6cEXAMPLE\', [{\'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\n \'UserId\': \'123456789012\', \'GroupId\': \'sg-12345678\'}]}], [\n \'eni-09b9460295ddd4e5f\'], None), (\'vpc-06e4ab6c6cEXAMPLE\', [{\n \'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\'UserId\': \'123456789012\',\n \'GroupId\': \'sg-12345678\'}]}], [],\n "doesn\'t have Elastic Network Interfaces attached"), (\n \'vpc-06e4ab6c6ccWRONG\', [{\'IpProtocol\': \'-1\', \'UserIdGroupPairs\': [{\n \'UserId\': \'123456789012\', \'GroupId\': \'sg-12345678\'}]}], [\n \'eni-09b9460295ddd4e5f\'],\n \'only support using FSx file system that is in 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disable_hyperthreading = true\'\n ), ({\'disable_hyperthreading\': True, \'extra_json\':\n \'{"cluster": {"cfn_scheduler_slots": 3}}\'},\n \'cfn_scheduler_slots cannot be set in addition to disable_hyperthreading = true\'\n ), ({\'disable_hyperthreading\': True, \'extra_json\':\n \'{"cluster": {"other_param": "fake_value"}}\'}, None), ({\n \'disable_hyperthreading\': True}, None), ({\'disable_hyperthreading\': \n False, \'extra_json\': \'{"cluster": {"cfn_scheduler_slots": "vcpus"}}\'},\n None), ({\'disable_hyperthreading\': False, \'extra_json\':\n \'{"cluster": {"cfn_scheduler_slots": "cores"}}\'}, None), ({\n \'disable_hyperthreading\': False, \'extra_json\':\n \'{"cluster": {"cfn_scheduler_slots": 3}}\'}, None)]'], {}), '(\'section_dict, expected_message\', [({\n \'disable_hyperthreading\': True, \'extra_json\':\n \'{"cluster": {"cfn_scheduler_slots": "vcpus"}}\'},\n \'cfn_scheduler_slots cannot be set in addition to disable_hyperthreading = true\'\n ), 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({\n \'enable_efa\': \'compute\'}, None)]'], {}), '(\'cluster_dict, expected_error\', [({\'enable_efa_gdr\':\n \'compute\'},\n "The parameter \'enable_efa_gdr\' can be used only in combination with \'enable_efa\'"\n ), ({\'enable_efa\': \'compute\', \'enable_efa_gdr\': \'compute\'}, None), ({\n \'enable_efa\': \'compute\'}, None)])\n', (62191, 62459), False, 'import pytest\n'), ((63271, 64022), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""ip_permissions, ip_permissions_egress, expected_message"""', "[([], [], 'must allow all traffic in and out from itself'), ([{'IpProtocol':\n '-1', 'UserIdGroupPairs': [{'UserId': '123456789012', 'GroupId':\n 'sg-12345678'}]}], [], 'must allow all traffic in and out from itself'),\n ([{'IpProtocol': '-1', 'UserIdGroupPairs': [{'UserId': '123456789012',\n 'GroupId': 'sg-12345678'}]}], [{'IpProtocol': '-1', 'UserIdGroupPairs':\n [{'UserId': '123456789012', 'GroupId': 'sg-12345678'}]}], None), ([{\n 'PrefixListIds': [], 'FromPort': 22, 'IpRanges': [{'CidrIp':\n '203.0.113.0/24'}], 'ToPort': 22, 'IpProtocol': 'tcp',\n 'UserIdGroupPairs': []}], [],\n 'must allow all traffic in and out from itself')]"], {}), "(\n 'ip_permissions, ip_permissions_egress, expected_message', [([], [],\n 'must allow all traffic in and out from itself'), ([{'IpProtocol': '-1',\n 'UserIdGroupPairs': [{'UserId': '123456789012', 'GroupId':\n 'sg-12345678'}]}], [], 'must allow all traffic in and out from itself'),\n ([{'IpProtocol': '-1', 'UserIdGroupPairs': [{'UserId': '123456789012',\n 'GroupId': 'sg-12345678'}]}], [{'IpProtocol': '-1', 'UserIdGroupPairs':\n [{'UserId': '123456789012', 'GroupId': 'sg-12345678'}]}], None), ([{\n 'PrefixListIds': [], 'FromPort': 22, 'IpRanges': [{'CidrIp':\n '203.0.113.0/24'}], 'ToPort': 22, 'IpProtocol': 'tcp',\n 'UserIdGroupPairs': []}], [],\n 'must allow all traffic in and out from itself')])\n", (63294, 64022), False, 'import pytest\n'), ((66120, 67067), 'pytest.mark.parametrize', 'pytest.mark.parametrize', 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Max 5 expected."), ({\n \'ebs_settings\': \'vol1, vol2 \'}, {\'vol1\': {\'shared_dir\': \'vol1\'}, \'vol2\':\n {\'volume_type\': \'io1\'}},\n \'When using more than 1 EBS volume, shared_dir is required under each EBS section\'\n ), ({\'ebs_settings\': \'vol1,vol2\'}, {\'vol1\': {\'shared_dir\': \'/NONE\'},\n \'vol2\': {\'shared_dir\': \'vol2\'}},\n \'/NONE cannot be used as a shared directory\'), ({\'ebs_settings\':\n \'vol1, vol2 \'}, {\'vol1\': {\'shared_dir\': \'/vol1\'}, \'vol2\': {\'shared_dir\':\n \'NONE\'}}, \'NONE cannot be used as a shared directory\')]'], {}), '(\n \'cluster_section_dict, ebs_section_dict, expected_message\', [({\n \'ebs_settings\': \'vol1, vol2, vol3, vol4, vol5, vol6\'}, {\'vol1\': {\n \'shared_dir\': \'/vol1\'}, \'vol2\': {\'shared_dir\': \'/vol2\'}, \'vol3\': {\n \'shared_dir\': \'/vol3\'}, \'vol4\': {\'shared_dir\': \'/vol4\'}, \'vol5\': {\n \'shared_dir\': \'/vol5\'}, \'vol6\': {\'shared_dir\': \'/vol6\'}},\n "Invalid number of \'ebs\' sections specified. Max 5 expected."), ({\n \'ebs_settings\': \'vol1, vol2 \'}, {\'vol1\': {\'shared_dir\': \'vol1\'}, \'vol2\':\n {\'volume_type\': \'io1\'}},\n \'When using more than 1 EBS volume, shared_dir is required under each EBS section\'\n ), ({\'ebs_settings\': \'vol1,vol2\'}, {\'vol1\': {\'shared_dir\': \'/NONE\'},\n \'vol2\': {\'shared_dir\': \'vol2\'}},\n \'/NONE cannot be used as a shared directory\'), ({\'ebs_settings\':\n \'vol1, vol2 \'}, {\'vol1\': {\'shared_dir\': \'/vol1\'}, \'vol2\': {\'shared_dir\':\n \'NONE\'}}, \'NONE cannot be used as a shared directory\')])\n', (66143, 67067), False, 'import pytest\n'), ((67746, 68000), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""section_dict, expected_message"""', "[({'shared_dir': 'NONE'}, 'NONE cannot be used as a shared directory'), ({\n 'shared_dir': '/NONE'}, '/NONE cannot be used as a shared directory'),\n ({'shared_dir': '/NONEshared'}, None)]"], {}), "('section_dict, expected_message', [({'shared_dir':\n 'NONE'}, 'NONE cannot be used as a shared directory'), ({'shared_dir':\n '/NONE'}, '/NONE cannot be used as a shared directory'), ({'shared_dir':\n '/NONEshared'}, None)])\n", (67769, 68000), False, 'import pytest\n'), ((68243, 69100), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""base_os, instance_type, access_from, expected_error, expected_warning"""', "[('centos7', 't2.medium', None, None, None), ('centos8', 't2.medium', None,\n None, None), ('ubuntu1804', 't2.medium', None, None, None), (\n 'ubuntu1804', 't2.medium', '1.2.3.4/32', None, None), ('centos7',\n 't2.medium', '0.0.0.0/0', None, None), ('centos8', 't2.medium',\n '0.0.0.0/0', None, None), ('alinux2', 't2.medium', None, None, None), (\n 'alinux2', 't2.nano', None, None,\n 'is recommended to use an instance type with at least'), ('alinux2',\n 't2.micro', None, None,\n 'is recommended to use an instance type with at least'), ('ubuntu1804',\n 'm6g.xlarge', None, None, None), ('alinux2', 'm6g.xlarge', None, None,\n None), ('centos7', 'm6g.xlarge', None, None, None), ('centos8',\n 'm6g.xlarge', None, None, None)]"], {}), "(\n 'base_os, instance_type, access_from, expected_error, expected_warning',\n [('centos7', 't2.medium', None, None, None), ('centos8', 't2.medium',\n None, None, None), ('ubuntu1804', 't2.medium', None, None, None), (\n 'ubuntu1804', 't2.medium', '1.2.3.4/32', None, None), ('centos7',\n 't2.medium', '0.0.0.0/0', None, None), ('centos8', 't2.medium',\n '0.0.0.0/0', None, None), ('alinux2', 't2.medium', None, None, None), (\n 'alinux2', 't2.nano', None, None,\n 'is recommended to use an instance type with at least'), ('alinux2',\n 't2.micro', None, None,\n 'is recommended to use an instance type with at least'), ('ubuntu1804',\n 'm6g.xlarge', None, None, None), ('alinux2', 'm6g.xlarge', None, None,\n None), ('centos7', 'm6g.xlarge', None, None, None), ('centos8',\n 'm6g.xlarge', None, None, None)])\n", (68266, 69100), False, 'import pytest\n'), ((71693, 72046), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""section_dict, expected_message"""', "[({'initial_queue_size': '0', 'maintain_initial_size': True},\n 'maintain_initial_size cannot be set to true if initial_queue_size is 0'\n ), ({'scheduler': 'awsbatch', 'maintain_initial_size': True},\n 'maintain_initial_size is not supported when using awsbatch as scheduler')]"], {}), "('section_dict, expected_message', [({\n 'initial_queue_size': '0', 'maintain_initial_size': True},\n 'maintain_initial_size cannot be set to true if initial_queue_size is 0'\n ), ({'scheduler': 'awsbatch', 'maintain_initial_size': True},\n 'maintain_initial_size is not supported when using awsbatch as scheduler')]\n )\n", (71716, 72046), False, 'import pytest\n'), ((72351, 74349), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""cluster_section_dict, expected_message"""', '[({\'scheduler\': \'slurm\'}, None), ({\'scheduler\': \'slurm\', \'queue_settings\':\n \'queue1\'}, None), ({\'scheduler\': \'slurm\', \'queue_settings\':\n \'queue1,queue2,queue3,queue4,queue5\'}, None), ({\'scheduler\': \'slurm\',\n \'queue_settings\': \'queue1, queue2\'}, None), ({\'scheduler\': \'slurm\',\n \'queue_settings\': \'queue1,queue2,queue3,queue4,queue5,queue6\'},\n "Invalid number of \'queue\' sections specified. Max 5 expected."), ({\n \'scheduler\': \'slurm\', \'queue_settings\': \'queue_1\'},\n "Invalid queue name \'queue_1\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'default\'},\n "Invalid queue name \'default\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'queue1, default\'},\n "Invalid queue name \'.*\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'QUEUE\'},\n "Invalid queue name \'QUEUE\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'aQUEUEa\'},\n "Invalid queue name \'aQUEUEa\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'my-default-queue\'}, None)]'], {}), '(\'cluster_section_dict, expected_message\', [({\n \'scheduler\': \'slurm\'}, None), ({\'scheduler\': \'slurm\', \'queue_settings\':\n \'queue1\'}, None), ({\'scheduler\': \'slurm\', \'queue_settings\':\n \'queue1,queue2,queue3,queue4,queue5\'}, None), ({\'scheduler\': \'slurm\',\n \'queue_settings\': \'queue1, queue2\'}, None), ({\'scheduler\': \'slurm\',\n \'queue_settings\': \'queue1,queue2,queue3,queue4,queue5,queue6\'},\n "Invalid number of \'queue\' sections specified. Max 5 expected."), ({\n \'scheduler\': \'slurm\', \'queue_settings\': \'queue_1\'},\n "Invalid queue name \'queue_1\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'default\'},\n "Invalid queue name \'default\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'queue1, default\'},\n "Invalid queue name \'.*\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'QUEUE\'},\n "Invalid queue name \'QUEUE\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'aQUEUEa\'},\n "Invalid queue name \'aQUEUEa\'. Queue section names can be at most 30 chars long, must begin with a letter and only contain lowercase letters, digits and hyphens. It is forbidden to use \'default\' as a queue section name."\n ), ({\'scheduler\': \'slurm\', \'queue_settings\': \'my-default-queue\'}, None)])\n', (72374, 74349), False, 'import pytest\n'), ((75727, 79990), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""cluster_dict, queue_dict, expected_error_messages, expected_warning_messages"""', '[({\'queue_settings\': \'default\'}, {\'compute_resource_settings\': \'cr1,cr2\',\n \'enable_efa\': True, \'disable_hyperthreading\': True}, [\n "Duplicate instance type \'t2.micro\' found in queue \'default\'. Compute resources in the same queue must use different instance types"\n ], [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr1 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr3,cr4\', \'enable_efa\': True, \'disable_hyperthreading\': True}, [\n "Duplicate instance type \'c4.xlarge\' found in queue \'default\'. Compute resources in the same queue must use different instance types"\n ], [\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr3 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr1,cr3\', \'enable_efa\': True, \'disable_hyperthreading\': True}, None, [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr1 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr3 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr2,cr4\', \'enable_efa\': True, \'disable_hyperthreading\': True}, None, [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr2,cr4\', \'enable_efa\': True, \'enable_efa_gdr\': True}, None, [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA."\n ,\n "EFA GDR was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA GDR."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA."\n ,\n "EFA GDR was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA GDR."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'efa_instance\', \'enable_efa_gdr\': True}, [\n "The parameter \'enable_efa_gdr\' can be used only in combination with \'enable_efa\'"\n ], None), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr1\'}, None, None), ({\'queue_settings\': \'default\', \'enable_efa\':\n \'compute\', \'disable_hyperthreading\': True}, {\n \'compute_resource_settings\': \'cr1\', \'enable_efa\': True,\n \'disable_hyperthreading\': True}, [\n "Parameter \'enable_efa\' can be used only in \'cluster\' or in \'queue\' section"\n ,\n "Parameter \'disable_hyperthreading\' can be used only in \'cluster\' or in \'queue\' section"\n ], [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr1 does not support EFA."\n ]), ({\'queue_settings\': \'default\', \'enable_efa\': \'compute\',\n \'enable_efa_gdr\': \'compute\', \'disable_hyperthreading\': True}, {\n \'compute_resource_settings\': \'cr1\', \'enable_efa\': False,\n \'enable_efa_gdr\': False, \'disable_hyperthreading\': False}, [\n "Parameter \'enable_efa\' can be used only in \'cluster\' or in \'queue\' section"\n ,\n "Parameter \'enable_efa_gdr\' can be used only in \'cluster\' or in \'queue\' section"\n ,\n "Parameter \'disable_hyperthreading\' can be used only in \'cluster\' or in \'queue\' section"\n ], None), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'efa_instance\', \'enable_efa\': True}, None, None)]'], {}), '(\n \'cluster_dict, queue_dict, expected_error_messages, expected_warning_messages\'\n , [({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr1,cr2\', \'enable_efa\': True, \'disable_hyperthreading\': True}, [\n "Duplicate instance type \'t2.micro\' found in queue \'default\'. Compute resources in the same queue must use different instance types"\n ], [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr1 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr3,cr4\', \'enable_efa\': True, \'disable_hyperthreading\': True}, [\n "Duplicate instance type \'c4.xlarge\' found in queue \'default\'. Compute resources in the same queue must use different instance types"\n ], [\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr3 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr1,cr3\', \'enable_efa\': True, \'disable_hyperthreading\': True}, None, [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr1 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr3 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr2,cr4\', \'enable_efa\': True, \'disable_hyperthreading\': True}, None, [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr2,cr4\', \'enable_efa\': True, \'enable_efa_gdr\': True}, None, [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA."\n ,\n "EFA GDR was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr2 does not support EFA GDR."\n ,\n "EFA was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA."\n ,\n "EFA GDR was enabled on queue \'default\', but instance type \'c4.xlarge\' defined in compute resource settings cr4 does not support EFA GDR."\n ]), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'efa_instance\', \'enable_efa_gdr\': True}, [\n "The parameter \'enable_efa_gdr\' can be used only in combination with \'enable_efa\'"\n ], None), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'cr1\'}, None, None), ({\'queue_settings\': \'default\', \'enable_efa\':\n \'compute\', \'disable_hyperthreading\': True}, {\n \'compute_resource_settings\': \'cr1\', \'enable_efa\': True,\n \'disable_hyperthreading\': True}, [\n "Parameter \'enable_efa\' can be used only in \'cluster\' or in \'queue\' section"\n ,\n "Parameter \'disable_hyperthreading\' can be used only in \'cluster\' or in \'queue\' section"\n ], [\n "EFA was enabled on queue \'default\', but instance type \'t2.micro\' defined in compute resource settings cr1 does not support EFA."\n ]), ({\'queue_settings\': \'default\', \'enable_efa\': \'compute\',\n \'enable_efa_gdr\': \'compute\', \'disable_hyperthreading\': True}, {\n \'compute_resource_settings\': \'cr1\', \'enable_efa\': False,\n \'enable_efa_gdr\': False, \'disable_hyperthreading\': False}, [\n "Parameter \'enable_efa\' can be used only in \'cluster\' or in \'queue\' section"\n ,\n "Parameter \'enable_efa_gdr\' can be used only in \'cluster\' or in \'queue\' section"\n ,\n "Parameter \'disable_hyperthreading\' can be used only in \'cluster\' or in \'queue\' section"\n ], None), ({\'queue_settings\': \'default\'}, {\'compute_resource_settings\':\n \'efa_instance\', \'enable_efa\': True}, None, None)])\n', (75750, 79990), False, 'import pytest\n'), ((82787, 83536), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""param_value, expected_message"""', '[(\'section1!2\',\n "Invalid label \'section1!2\' in param \'queue_settings\'. Section labels can only contain alphanumeric characters, dashes or underscores."\n ), (\n \'section!123456789abcdefghijklmnopqrstuvwxyz_123456789abcdefghijklmnopqrstuvwxyz_\'\n ,\n "Invalid label \'section!123456789...\' in param \'queue_settings\'. Section labels can only contain alphanumeric characters, dashes or underscores."\n ), (\'section-1\', None), (\'section_1\', None), (\n \'section_123456789abcdefghijklmnopqrstuvwxyz_123456789abcdefghijklmnopqrstuvwxyz_\'\n ,\n "Invalid label \'section_123456789...\' in param \'queue_settings\'. The maximum length allowed for section labels is 64 characters"\n )]'], {}), '(\'param_value, expected_message\', [(\'section1!2\',\n "Invalid label \'section1!2\' in param \'queue_settings\'. Section labels can only contain alphanumeric characters, dashes or underscores."\n ), (\n \'section!123456789abcdefghijklmnopqrstuvwxyz_123456789abcdefghijklmnopqrstuvwxyz_\'\n ,\n "Invalid label \'section!123456789...\' in param \'queue_settings\'. Section labels can only contain alphanumeric characters, dashes or underscores."\n ), (\'section-1\', None), (\'section_1\', None), (\n \'section_123456789abcdefghijklmnopqrstuvwxyz_123456789abcdefghijklmnopqrstuvwxyz_\'\n ,\n "Invalid label \'section_123456789...\' in param \'queue_settings\'. The maximum length allowed for section labels is 64 characters"\n )])\n', (82810, 83536), False, 'import pytest\n'), ((84034, 84931), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""section_dict, expected_message"""', '[({\'min_count\': -1, \'initial_count\': -1},\n "Parameter \'min_count\' must be 0 or greater than 0"), ({\'min_count\': 0,\n \'initial_count\': 1, \'spot_price\': -1.1},\n "Parameter \'spot_price\' must be 0 or greater than 0"), ({\'min_count\': 1,\n \'max_count\': 0, \'initial_count\': 1},\n "Parameter \'max_count\' must be greater than or equal to \'min_count\'"),\n ({\'min_count\': 0, \'max_count\': 0, \'initial_count\': 0},\n "Parameter \'max_count\' must be 1 or greater than 1"), ({\'min_count\': 1,\n \'max_count\': 2, \'spot_price\': 1.5, \'initial_count\': 1}, None), ({\n \'min_count\': 2, \'max_count\': 4, \'initial_count\': 1},\n "Parameter \'initial_count\' must be greater than or equal to \'min_count\'"\n ), ({\'min_count\': 2, \'max_count\': 4, \'initial_count\': 5},\n "Parameter \'initial_count\' must be lower than or equal to \'max_count\'")]'], {}), '(\'section_dict, expected_message\', [({\'min_count\': -\n 1, \'initial_count\': -1},\n "Parameter \'min_count\' must be 0 or greater than 0"), ({\'min_count\': 0,\n \'initial_count\': 1, \'spot_price\': -1.1},\n "Parameter \'spot_price\' must be 0 or greater than 0"), ({\'min_count\': 1,\n \'max_count\': 0, \'initial_count\': 1},\n "Parameter \'max_count\' must be greater than or equal to \'min_count\'"),\n ({\'min_count\': 0, \'max_count\': 0, \'initial_count\': 0},\n "Parameter \'max_count\' must be 1 or greater than 1"), ({\'min_count\': 1,\n \'max_count\': 2, \'spot_price\': 1.5, \'initial_count\': 1}, None), ({\n \'min_count\': 2, \'max_count\': 4, \'initial_count\': 1},\n "Parameter \'initial_count\' must be greater than or equal to \'min_count\'"\n ), ({\'min_count\': 2, \'max_count\': 4, \'initial_count\': 5},\n "Parameter \'initial_count\' must be lower than or equal to \'max_count\'")])\n', (84057, 84931), False, 'import pytest\n'), ((86311, 86752), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""cluster_section_dict, sections_dict, expected_message"""', '[({\'vpc_settings\': \'vpc1, vpc2\'}, {\'vpc vpc1\': {}, \'vpc vpc2\': {}},\n "The value of \'vpc_settings\' parameter is invalid. It can only contain a single vpc section label"\n ), ({\'efs_settings\': \'efs1, efs2\'}, {\'efs efs1\': {}, \'efs efs2\': {}},\n "The value of \'efs_settings\' parameter is invalid. It can only contain a single efs section label"\n )]'], {}), '(\'cluster_section_dict, sections_dict, expected_message\'\n , [({\'vpc_settings\': \'vpc1, vpc2\'}, {\'vpc vpc1\': {}, \'vpc vpc2\': {}},\n "The value of \'vpc_settings\' parameter is invalid. It can only contain a single vpc section label"\n ), ({\'efs_settings\': \'efs1, efs2\'}, {\'efs efs1\': {}, \'efs efs2\': {}},\n "The value of \'efs_settings\' parameter is invalid. It can only contain a single efs section label"\n )])\n', (86334, 86752), False, 'import pytest\n'), ((90399, 90627), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""enabled, architecture, expected_errors"""', "[(True, 'x86_64', []), (True, 'arm64', [\n 'instance types and an AMI that support these architectures']), (False,\n 'x86_64', []), (False, 'arm64', [])]"], {}), "('enabled, architecture, expected_errors', [(True,\n 'x86_64', []), (True, 'arm64', [\n 'instance types and an AMI that support these architectures']), (False,\n 'x86_64', []), (False, 'arm64', [])])\n", (90422, 90627), False, 'import pytest\n'), ((91211, 91792), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""base_os, architecture, expected_warnings, expected_errors"""', "[('alinux2', 'x86_64', [], []), ('centos7', 'x86_64', [], []), ('centos8',\n 'x86_64', [], []), ('ubuntu1804', 'x86_64', [], []), ('alinux2',\n 'arm64', [], []), ('centos7', 'arm64', [\n 'Warning: The aarch64 CentOS 7 OS is not validated for the 6th generation aarch64 instances (M6g, C6g, etc.). To proceed please provide a custom_ami, for more info see: https://wiki.centos.org/Cloud/AWS#aarch64_notes'\n ], []), ('centos8', 'arm64', [], []), ('ubuntu1804', 'arm64', [], [])]"], {}), "(\n 'base_os, architecture, expected_warnings, expected_errors', [(\n 'alinux2', 'x86_64', [], []), ('centos7', 'x86_64', [], []), ('centos8',\n 'x86_64', [], []), ('ubuntu1804', 'x86_64', [], []), ('alinux2',\n 'arm64', [], []), ('centos7', 'arm64', [\n 'Warning: The aarch64 CentOS 7 OS is not validated for the 6th generation aarch64 instances (M6g, C6g, etc.). To proceed please provide a custom_ami, for more info see: https://wiki.centos.org/Cloud/AWS#aarch64_notes'\n ], []), ('centos8', 'arm64', [], []), ('ubuntu1804', 'arm64', [], [])])\n", (91234, 91792), False, 'import pytest\n'), ((92592, 92870), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""disable_hyperthreading, architecture, expected_errors"""', "[(True, 'x86_64', []), (False, 'x86_64', []), (True, 'arm64', [\n 'disable_hyperthreading is only supported on instance types that support these architectures'\n ]), (False, 'arm64', [])]"], {}), "('disable_hyperthreading, architecture, expected_errors'\n , [(True, 'x86_64', []), (False, 'x86_64', []), (True, 'arm64', [\n 'disable_hyperthreading is only supported on instance types that support these architectures'\n ]), (False, 'arm64', [])])\n", (92615, 92870), False, 'import pytest\n'), ((93468, 94258), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""head_node_architecture, compute_architecture, 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'c5.xlarge', [\n 'none of which are compatible with the architecture supported by the master_instance_type'\n ]), ('arm64', 'arm64', 'm6g.xlarge', []), ('x86_64', 'x86_64',\n 'optimal', []), ('x86_64', None, 'm6g', []), ('x86_64', None, 'c5', []),\n ('arm64', 'arm64', 'm6g.xlarge,r6g.xlarge', []), ('x86_64', 'arm64',\n 'm6g.xlarge,r6g.xlarge', [\n 'none of which are compatible with the architecture supported by the master_instance_type'\n ] * 2)])\n", (93491, 94258), False, 'import pytest\n'), ((96214, 97729), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""section_dict, bucket, num_calls, expected_error"""', '[({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'deployment_type\':\n \'PERSISTENT_1\', \'per_unit_storage_throughput\': 50}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'deployment_type\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'storage_capacity\': \n 7200}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'storage_capacity\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'deployment_type\':\n \'PERSISTENT_1\', \'per_unit_storage_throughput\': 100}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'per_unit_storage_throughput\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\',\n \'imported_file_chunk_size\': 1024, \'export_path\': \'s3://test\',\n \'import_path\': \'s3://test\'}, {\'Bucket\': \'test\'}, 2,\n "When restoring an FSx Lustre file system from backup, \'imported_file_chunk_size\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'fsx_kms_key_id\':\n \'somekey\', \'deployment_type\': \'PERSISTENT_1\',\n \'per_unit_storage_throughput\': 50}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'fsx_kms_key_id\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-00000000000000000\', \'deployment_type\':\n \'PERSISTENT_1\', \'per_unit_storage_throughput\': 50}, None, 0,\n "Failed to retrieve backup with Id \'backup-00000000000000000\'")]'], {}), '(\'section_dict, bucket, num_calls, expected_error\',\n [({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'deployment_type\':\n \'PERSISTENT_1\', \'per_unit_storage_throughput\': 50}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'deployment_type\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'storage_capacity\': \n 7200}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'storage_capacity\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\', \'deployment_type\':\n \'PERSISTENT_1\', \'per_unit_storage_throughput\': 100}, None, 0,\n "When restoring an FSx Lustre file system from backup, \'per_unit_storage_throughput\' cannot be specified."\n ), ({\'fsx_backup_id\': \'backup-0ff8da96d57f3b4e3\',\n \'imported_file_chunk_size\': 1024, \'export_path\': \'s3://test\',\n 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'storage_capacity is ignored when specifying an existing Lustre file system'\n )]"], {}), "('section_dict, expected_error', [({'fsx_fs_id':\n 'fs-0123456789abcdef0', 'shared_dir': '/fsx'}, None), ({'fsx_fs_id':\n 'fs-0123456789abcdef0', 'shared_dir': '/fsx', 'storage_capacity': 3600},\n 'storage_capacity is ignored when specifying an existing Lustre file system'\n )])\n", (100692, 100979), False, 'import pytest\n'), ((101778, 103699), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""section_dict, expected_error"""', "[({'volume_type': 'standard', 'volume_size': 15}, None), ({'volume_type':\n 'standard', 'volume_size': 0},\n 'The size of standard volumes must be at least 1 GiB'), ({'volume_type':\n 'standard', 'volume_size': 1025},\n 'The size of standard volumes can not exceed 1024 GiB'), ({\n 'volume_type': 'io1', 'volume_size': 15}, None), ({'volume_type': 'io1',\n 'volume_size': 3}, 'The size of io1 volumes must be at least 4 GiB'), (\n {'volume_type': 'io1', 'volume_size': 16385},\n 'The size of io1 volumes can not exceed 16384 GiB'), ({'volume_type':\n 'io2', 'volume_size': 15}, None), ({'volume_type': 'io2', 'volume_size':\n 3}, 'The size of io2 volumes must be at least 4 GiB'), ({'volume_type':\n 'io2', 'volume_size': 65537},\n 'The size of io2 volumes can not exceed 65536 GiB'), ({'volume_type':\n 'gp2', 'volume_size': 15}, None), ({'volume_type': 'gp2', 'volume_size':\n 0}, 'The size of gp2 volumes must be at least 1 GiB'), ({'volume_type':\n 'gp2', 'volume_size': 16385},\n 'The size of gp2 volumes can not exceed 16384 GiB'), ({'volume_type':\n 'gp3', 'volume_size': 15}, None), ({'volume_type': 'gp3', 'volume_size':\n 0}, 'The size of gp3 volumes must be at least 1 GiB'), ({'volume_type':\n 'gp3', 'volume_size': 16385},\n 'The size of gp3 volumes can not exceed 16384 GiB'), ({'volume_type':\n 'st1', 'volume_size': 500}, None), ({'volume_type': 'st1',\n 'volume_size': 20}, 'The size of st1 volumes must be at least 500 GiB'),\n 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import os
from functools import reduce
import boto3
import yaml
from copy import deepcopy
from cryptography.fernet import Fernet
from pycbc import json
from pycbc.utils import AttrDict as d
s3 = boto3.client('s3')
_mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
_DEFAULTS = d({
'users': [],
'encrypt_key': Fernet.generate_key().decode('utf-8'),
'api_gateway': None,
'sender_email': None,
'logging': d({
'version': 1,
'formatters': d({
'default': d({
'format': '%(asctime)-15s - %(levelname)-7s - %(message)s',
}),
}),
'handlers': d({
'console': d({
'class': 'logging.StreamHandler',
'formatter': 'default',
'level': 'DEBUG',
'stream': 'ext://sys.stderr',
}),
}),
'loggers': d({
'pycbc': d({
'handlers': ['console'],
'level': 'INFO',
})
})
})
})
def load(event):
event_override = event.get('config', d())
env_prefix = event_override.get(
'env_prefix', os.getenv('ENV_PREFIX', 'PYCBC_'))
s3_bucket = event_override.get(
's3_bucket', os.getenv(f'{env_prefix}S3_BUCKET', 'pycbc'))
s3_filename = event_override.get(
's3_filename',
os.getenv(f'{env_prefix}S3_FILENAME', 'pycbc-config.yaml')
)
return json.loads(json.dumps(reduce(
_merge,
[
deepcopy(_DEFAULTS),
_from_s3(s3_bucket, s3_filename),
_from_env(env_prefix),
event_override,
{'s3_bucket': s3_bucket, 's3_filename': s3_filename}
])
))
def _merge(a, b, path=None):
if path is None:
path = []
for key in b:
if key in a:
if isinstance(a[key], dict) and isinstance(b[key], dict):
_merge(a[key], b[key], path + [str(key)])
else:
a[key] = b[key]
else:
a[key] = b[key]
return a
def _yaml_load(data):
yaml.add_constructor(
_mapping_tag,
lambda loader, node: d(loader.construct_pairs(node)),
)
return yaml.load(data, Loader=yaml.FullLoader)
def _from_env(prefix):
env_vars = (k for k in os.environ if k.startswith(prefix))
return d({
k[len(prefix):].lower(): os.environ[k] for k in env_vars
})
def _from_s3(bucket, filename):
fileobj = s3.get_object(
Bucket=bucket,
Key=filename,
)
return _yaml_load(fileobj['Body'].read())
| [
"boto3.client",
"os.getenv",
"yaml.load",
"copy.deepcopy",
"pycbc.utils.AttrDict",
"cryptography.fernet.Fernet.generate_key"
]
| [((198, 216), 'boto3.client', 'boto3.client', (['"""s3"""'], {}), "('s3')\n", (210, 216), False, 'import boto3\n'), ((2208, 2247), 'yaml.load', 'yaml.load', (['data'], {'Loader': 'yaml.FullLoader'}), '(data, Loader=yaml.FullLoader)\n', (2217, 2247), False, 'import yaml\n'), ((1086, 1089), 'pycbc.utils.AttrDict', 'd', ([], {}), '()\n', (1087, 1089), True, 'from pycbc.utils import AttrDict as d\n'), ((1150, 1183), 'os.getenv', 'os.getenv', (['"""ENV_PREFIX"""', '"""PYCBC_"""'], {}), "('ENV_PREFIX', 'PYCBC_')\n", (1159, 1183), False, 'import os\n'), ((1242, 1286), 'os.getenv', 'os.getenv', (['f"""{env_prefix}S3_BUCKET"""', '"""pycbc"""'], {}), "(f'{env_prefix}S3_BUCKET', 'pycbc')\n", (1251, 1286), False, 'import os\n'), ((1357, 1415), 'os.getenv', 'os.getenv', (['f"""{env_prefix}S3_FILENAME"""', '"""pycbc-config.yaml"""'], {}), "(f'{env_prefix}S3_FILENAME', 'pycbc-config.yaml')\n", (1366, 1415), False, 'import os\n'), ((331, 352), 'cryptography.fernet.Fernet.generate_key', 'Fernet.generate_key', ([], {}), '()\n', (350, 352), False, 'from cryptography.fernet import Fernet\n'), ((1501, 1520), 'copy.deepcopy', 'deepcopy', (['_DEFAULTS'], {}), '(_DEFAULTS)\n', (1509, 1520), False, 'from copy import deepcopy\n'), ((511, 574), 'pycbc.utils.AttrDict', 'd', (["{'format': '%(asctime)-15s - %(levelname)-7s - %(message)s'}"], {}), "({'format': '%(asctime)-15s - %(levelname)-7s - %(message)s'})\n", (512, 574), True, 'from pycbc.utils import AttrDict as d\n'), ((666, 779), 'pycbc.utils.AttrDict', 'd', (["{'class': 'logging.StreamHandler', 'formatter': 'default', 'level': 'DEBUG',\n 'stream': 'ext://sys.stderr'}"], {}), "({'class': 'logging.StreamHandler', 'formatter': 'default', 'level':\n 'DEBUG', 'stream': 'ext://sys.stderr'})\n", (667, 779), True, 'from pycbc.utils import AttrDict as d\n'), ((912, 957), 'pycbc.utils.AttrDict', 'd', (["{'handlers': ['console'], 'level': 'INFO'}"], {}), "({'handlers': ['console'], 'level': 'INFO'})\n", (913, 957), True, 'from pycbc.utils import AttrDict as d\n')] |
def us_choropleth(t):
import matplotlib.cm
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.colors import Normalize
import shapefile
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import random
import pandas as pd
from collections import Counter
plt.title("NER", fontsize=12)
us_locations_map = Basemap(
resolution="l",
llcrnrlon=-128.94,
llcrnrlat=23.52,
urcrnrlon=-60.12,
urcrnrlat=50.93,
lat_0=37.26,
lon_0=-94.53)
us_locations_map.drawmapboundary(
fill_color="#46bcec") # Fills in the oceans
us_locations_map.fillcontinents(
color="#eabc77",
lake_color="#46bcec") # Defines the continents
us_locations_map.drawcoastlines()
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(15.5, 12.5) # Sets the size of the map
# Converts the coordinates to map points
lons, lats = us_locations_map(t["longitude"], t["latitude"])
us_locations_map.scatter(
lons,
lats,
color="black",
zorder=10) # Draws the points on the map
# Labels each point with the location name
for i in range(t.num_rows):
lat_lon = (
t.row(i).item("longitude") + .2,
t.row(i).item("latitude") - .1)
plt.annotate(np.array(t.row(i).item("name")), lat_lon, fontsize=10)
# Here we are reading in a shape file, which places state boundary
# information for our Basemap
us_locations_map.readshapefile(
"data/us_shapefiles/cb_2016_us_state_20m", "us_states")
state_names = []
for shape_dict in us_locations_map.us_states_info:
state_names.append(shape_dict['NAME'])
ax = plt.gca() # get current axes instance
cmap = plt.get_cmap('Reds')
names = []
shapes = []
counts = []
state_counts = Counter(t["state"])
for index, state in enumerate(state_names):
seg = us_locations_map.us_states[index]
poly = Polygon(seg)
names.append(state)
shapes.append(poly)
if state in t['state']:
counts.append(state_counts[state])
else:
counts.append(0)
# Loading our lists into the DataFrame
shape_table = pd.DataFrame()
shape_table["State Name"] = np.array(names)
shape_table["Shapes"] = np.array(shapes)
shape_table["Count"] = np.array(counts)
pc = PatchCollection(shape_table["Shapes"], zorder=2)
norm = Normalize()
pc.set_facecolor(cmap(norm(shape_table['Count'].fillna(0).values)))
pc.set_edgecolor("black")
ax.add_collection(pc)
# Adds colorbar showing the scale
mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)
mapper.set_array(shape_table['Count'])
plt.colorbar(mapper, shrink=0.4)
| [
"matplotlib.pyplot.gca",
"matplotlib.pyplot.colorbar",
"matplotlib.collections.PatchCollection",
"collections.Counter",
"numpy.array",
"mpl_toolkits.basemap.Basemap",
"matplotlib.colors.Normalize",
"pandas.DataFrame",
"matplotlib.pyplot.title",
"matplotlib.patches.Polygon",
"matplotlib.pyplot.get_cmap"
]
| [((397, 426), 'matplotlib.pyplot.title', 'plt.title', (['"""NER"""'], {'fontsize': '(12)'}), "('NER', fontsize=12)\n", (406, 426), True, 'import matplotlib.pyplot as plt\n'), ((451, 577), 'mpl_toolkits.basemap.Basemap', 'Basemap', ([], {'resolution': '"""l"""', 'llcrnrlon': '(-128.94)', 'llcrnrlat': '(23.52)', 'urcrnrlon': '(-60.12)', 'urcrnrlat': '(50.93)', 'lat_0': '(37.26)', 'lon_0': '(-94.53)'}), "(resolution='l', llcrnrlon=-128.94, llcrnrlat=23.52, urcrnrlon=-\n 60.12, urcrnrlat=50.93, lat_0=37.26, lon_0=-94.53)\n", (458, 577), False, 'from mpl_toolkits.basemap import Basemap\n'), ((1823, 1832), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (1830, 1832), True, 'import matplotlib.pyplot as plt\n'), ((1873, 1893), 'matplotlib.pyplot.get_cmap', 'plt.get_cmap', (['"""Reds"""'], {}), "('Reds')\n", (1885, 1893), True, 'import matplotlib.pyplot as plt\n'), ((1962, 1981), 'collections.Counter', 'Counter', (["t['state']"], {}), "(t['state'])\n", (1969, 1981), False, 'from collections import Counter\n'), ((2347, 2361), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (2359, 2361), True, 'import pandas as pd\n'), ((2394, 2409), 'numpy.array', 'np.array', (['names'], {}), '(names)\n', (2402, 2409), True, 'import numpy as np\n'), ((2438, 2454), 'numpy.array', 'np.array', (['shapes'], {}), '(shapes)\n', (2446, 2454), True, 'import numpy as np\n'), ((2482, 2498), 'numpy.array', 'np.array', (['counts'], {}), '(counts)\n', (2490, 2498), True, 'import numpy as np\n'), ((2509, 2557), 'matplotlib.collections.PatchCollection', 'PatchCollection', (["shape_table['Shapes']"], {'zorder': '(2)'}), "(shape_table['Shapes'], zorder=2)\n", (2524, 2557), False, 'from matplotlib.collections import PatchCollection\n'), ((2569, 2580), 'matplotlib.colors.Normalize', 'Normalize', ([], {}), '()\n', (2578, 2580), False, 'from matplotlib.colors import Normalize\n'), ((2860, 2892), 'matplotlib.pyplot.colorbar', 'plt.colorbar', (['mapper'], {'shrink': '(0.4)'}), '(mapper, shrink=0.4)\n', (2872, 2892), True, 'import matplotlib.pyplot as plt\n'), ((2094, 2106), 'matplotlib.patches.Polygon', 'Polygon', (['seg'], {}), '(seg)\n', (2101, 2106), False, 'from matplotlib.patches import Polygon\n')] |
import csv
import os
from collections import deque
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
INPUT_PATH = os.path.join(BASE_DIR, 'goods_source.csv')
OUTPUT_PATH = os.path.join(BASE_DIR, 'result.csv')
FILE_ENCODE = 'shift_jis'
INPUT_COLS = ('id', 'goods_name', 'price')
def import_csv():
"""入力データの読み込み
"""
try:
data_l = list()
with open(INPUT_PATH, mode='r', encoding=FILE_ENCODE, newline='') as csvf:
reader = csv.DictReader(csvf)
for dic in reader:
dic['id'] = int(dic['id'])
dic['price'] = int(dic['price'])
data_l.append(dic)
for col in INPUT_COLS:
if col not in data_l[0]:
raise IndexError(col)
return data_l
except FileNotFoundError:
print('goods_source.csvがありません')
return list()
except IndexError as e:
print('列が不足しています: ' + str(e))
return list()
def func(init, old_que, threshold=50):
keep = dict()
new_que = deque(list())
while old_que:
last = old_que.pop()
if init['mod'] + last['mod'] >= threshold:
if keep:
new_que.appendleft(keep)
keep = last
else:
new_que.appendleft(last)
break
return init, keep, old_que, new_que
def calculate(data_l):
"""アルゴリズム
1. 50未満の中でペアにできるものを探す
1-1. queの末端でペアを作れる場合、左端を固定し和が50以上で最小になるように右を選んでペアにする
1-2. queの末端でペアを作れない場合、末端2つを取り出した上で3個以上の組み合わせで消化する
1-2-1. 右末端で和が50以上なら右から左に探索して和が50以上になる最小値を得る->組にして除外
1-2-2. 右末端でも和が50にならないなら右末端をして1-2に戻る
-> 全部を消化しても50にならないならそのまま全部を足してしまう
2. 1と同じことを全体かつ閾値150で行う
"""
# 50未満のものだけ和を取る処理に入れる
under_que = list()
over_que = list()
for i in range(len(data_l)):
_mod = data_l[i]['price'] % 100
data_l[i]['set'] = 0
dic = {
'id': [i],
'mod': _mod,
}
if _mod < 50:
under_que.append(dic)
else:
over_que.append(dic)
under_que.sort(key=lambda x: x['mod'])
under_que = deque(under_que)
while under_que:
init = under_que.popleft()
while under_que:
init, keep, under_que, last_que = func(init, under_que)
# この時点でlast_queは要素1以上
if not keep:
keep = last_que.pop()
init = {
'id': init['id'] + keep['id'],
'mod': init['mod'] + keep['mod'],
}
if last_que:
over_que.append(init)
under_que.extend(last_que)
break
else:
over_que.append(init)
break
# 50以上の項目のうち、合計が150以上になる項目同士を足す
# (これにより購入回数を最小にする)
# final_que: 最終的な組み合わせ
over_que = deque(sorted(over_que, key=lambda x: x['mod']))
final_que = list()
while over_que:
init = over_que.popleft()
init, keep, over_que, last_que = func(init, over_que, 150)
if keep:
init = {
'id': init['id'] + keep['id'],
'mod': (init['mod'] + keep['mod']) % 100,
}
over_que.appendleft(init)
else:
final_que.append(init)
over_que.extend(last_que)
sum_p = 0
# 計算結果の出力
for cnt, que in enumerate(final_que):
point = 0
for id in que['id']:
data_l[id]['set'] = cnt + 1
point += data_l[id]['price']
print(f'set{cnt + 1} {round(point / 100)} P')
sum_p += round(point / 100)
print(f'total: {sum_p} P')
return data_l
def main():
# ファイルの読み込み
data_l = import_csv()
if not data_l:
print('処理を中止します')
return False
# 計算処理
data_l = calculate(data_l)
# 結果をファイルに出力
data_l.sort(key=lambda x: (x['set'], x['id']))
with open(OUTPUT_PATH, mode='w', encoding=FILE_ENCODE, newline='') as csvf:
writer = csv.DictWriter(csvf, data_l[0].keys())
writer.writeheader()
writer.writerows(data_l)
print('Done')
if __name__ == '__main__':
main()
| [
"os.path.abspath",
"collections.deque",
"csv.DictReader",
"os.path.join"
]
| [((120, 162), 'os.path.join', 'os.path.join', (['BASE_DIR', '"""goods_source.csv"""'], {}), "(BASE_DIR, 'goods_source.csv')\n", (132, 162), False, 'import os\n'), ((177, 213), 'os.path.join', 'os.path.join', (['BASE_DIR', '"""result.csv"""'], {}), "(BASE_DIR, 'result.csv')\n", (189, 213), False, 'import os\n'), ((80, 105), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (95, 105), False, 'import os\n'), ((2095, 2111), 'collections.deque', 'deque', (['under_que'], {}), '(under_que)\n', (2100, 2111), False, 'from collections import deque\n'), ((468, 488), 'csv.DictReader', 'csv.DictReader', (['csvf'], {}), '(csvf)\n', (482, 488), False, 'import csv\n')] |
import pandas as pd
import numpy as np
import swifter
def money_precision_at_k(y_pred: pd.Series, y_true: pd.Series, item_price, k=5):
y_pred = y_pred.swifter.progress_bar(False).apply(pd.Series)
user_filter = ~(y_true.swifter.progress_bar(False).apply(len) < k)
y_pred = y_pred.loc[user_filter]
y_true = y_true.loc[user_filter]
prices_recommended = y_pred.swifter.progress_bar(False).applymap(lambda item: item_price.price.get(item))
flags = y_pred.loc[:, :k - 1].swifter.progress_bar(False) \
.apply(lambda row: np.isin(np.array(row), y_true.get(row.name)), axis=1) \
.swifter.progress_bar(False).apply(pd.Series)
metric = (
(flags * prices_recommended.loc[:, :k - 1]).sum(axis=1) / prices_recommended.loc[:, :k - 1].sum(axis=1)
).mean()
return metric
| [
"numpy.array"
]
| [((558, 571), 'numpy.array', 'np.array', (['row'], {}), '(row)\n', (566, 571), True, 'import numpy as np\n')] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from caqe import app
app.run(debug=True, threaded=True) | [
"caqe.app.run"
]
| [((68, 102), 'caqe.app.run', 'app.run', ([], {'debug': '(True)', 'threaded': '(True)'}), '(debug=True, threaded=True)\n', (75, 102), False, 'from caqe import app\n')] |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import unittest
from azext_devops.dev.common.format import trim_for_display, date_time_to_only_date
class TestFormatMethods(unittest.TestCase):
def test_trim_for_display(self):
input = 'Gallery extensions for Portal Extension'
output = trim_for_display(input, 20)
self.assertEqual(output, 'Gallery extensions f...')
input = 'Aex platform'
output = trim_for_display(input, 20)
self.assertEqual(output, input)
input = ''
output = trim_for_display(input, 20)
self.assertEqual(output, input)
input = None
output = trim_for_display(input, 20)
self.assertEqual(output, input)
def test_date_time_to_only_date(self):
input = '2019-02-24T02:45:41.277000+00:00'
output = date_time_to_only_date(input)
self.assertEqual(output, '2019-02-24')
input = 'Aex platform'
output = date_time_to_only_date(input)
self.assertEqual(output, input)
if __name__ == '__main__':
unittest.main() | [
"unittest.main",
"azext_devops.dev.common.format.trim_for_display",
"azext_devops.dev.common.format.date_time_to_only_date"
]
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import inspect
import numpy as np
class TypeCheck(object):
"""
Decorator that performs a typecheck on the input to a function
"""
def __init__(self, accepted_structures, arg_name):
"""
When initialized, include list of accepted datatypes and the
arg_name to enforce the check on. Can totally be daisy-chained.
"""
self.accepted_structures = accepted_structures
self.is_accepted = lambda x: type(x) in accepted_structures
self.arg_name = arg_name
def __call__(self, fn):
def check_args(*args, **kwargs):
arg_dict = dict(zip(inspect.getargspec(fn).args, args))
full_args = dict(arg_dict.items() + kwargs.items())
if not self.is_accepted(full_args[self.arg_name]):
raise DataStructureException(
fn,
full_args[self.arg_name],
self.accepted_structures
)
return fn(*args, **kwargs)
return check_args
class DataStructureException(Exception):
"""
If a non-accepted datastructure is passed, throws an exception
"""
def __init__(self, callback, passed_structure, accepted_structures):
self.callback = callback.__name__
self.structure = str(type(passed_structure))
self.accepted = [str(structure) for structure in accepted_structures]
def __str__(self):
return """
function %s does not accept %s, accepted types are: %s
""" % (self.callback, self.structure, str(self.accepted))
@TypeCheck((list, dict, np.ndarray), 'array')
def normalize(array, distribution=1, norm_range=(0, 1), **kwargs):
"""
First arg is an array, whether that's in the form of a numpy array,
a list, or a dictionary that contains the data in its values.
Second arg is the desired distribution which would be applied before
normalization.
Supports linear, exponential, logarithmic and raising to whatever
power specified (in which case you just put a number)
Third arg is the range across which you want the data normalized
"""
# Handling dictionary array input
# Note: lists and numpy arrays behave the same in this program
dict_array = isinstance(array, dict)
if dict_array:
keys = array.keys()
array = np.array(array.values()).astype('float')
else: # Decorator errors if this isn't a list or a numpy array
array = np.array(array).astype('float')
# Handling various distributions
if type(distribution) in [float, int]:
array = np.power(array, distribution)
else:
array = getattr(np, distribution)(array, **kwargs)
# Prep for normalization
x_max, x_min = (np.max(array), np.min(array))
def norm(element,x_min,x_max):
base_span = (element - x_min)*(norm_range[-1] - norm_range[0])
return norm_range[0] + base_span / (x_max - x_min)
norm_array = np.vectorize(norm)(array, x_min, x_max)
if dict_array:
return dict(zip(keys, norm_array))
return norm_array
| [
"numpy.power",
"numpy.max",
"inspect.getargspec",
"numpy.array",
"numpy.min",
"numpy.vectorize"
]
| [((2604, 2633), 'numpy.power', 'np.power', (['array', 'distribution'], {}), '(array, distribution)\n', (2612, 2633), True, 'import numpy as np\n'), ((2753, 2766), 'numpy.max', 'np.max', (['array'], {}), '(array)\n', (2759, 2766), True, 'import numpy as np\n'), ((2768, 2781), 'numpy.min', 'np.min', (['array'], {}), '(array)\n', (2774, 2781), True, 'import numpy as np\n'), ((2967, 2985), 'numpy.vectorize', 'np.vectorize', (['norm'], {}), '(norm)\n', (2979, 2985), True, 'import numpy as np\n'), ((2475, 2490), 'numpy.array', 'np.array', (['array'], {}), '(array)\n', (2483, 2490), True, 'import numpy as np\n'), ((621, 643), 'inspect.getargspec', 'inspect.getargspec', (['fn'], {}), '(fn)\n', (639, 643), False, 'import inspect\n')] |
# -*- coding: utf-8 -*-
import unittest
from openprocurement.api.tests.base import snitch
from openprocurement.api.tests.base import BaseWebTest
from openprocurement.tender.belowthreshold.tests.base import test_lots
from openprocurement.tender.belowthreshold.tests.tender import TenderResourceTestMixin
from openprocurement.tender.belowthreshold.tests.tender_blanks import (
# TenderUAProcessTest
invalid_tender_conditions,
)
from openprocurement.tender.openua.tests.tender import TenderUaProcessTestMixin
from openprocurement.tender.openua.tests.tender_blanks import (
# TenderUAResourceTest
empty_listing,
create_tender_generated,
tender_with_main_procurement_category,
tender_finance_milestones,
)
from openprocurement.tender.openuadefense.tests.base import (
BaseTenderUAWebTest,
test_tender_data,
)
from openprocurement.tender.openuadefense.tests.tender_blanks import (
# TenderUATest
simple_add_tender,
# TenderUAResourceTest
create_tender_invalid,
patch_tender,
patch_tender_ua,
# TenderUAProcessTest
one_valid_bid_tender_ua,
one_invalid_bid_tender,
)
class TenderUATest(BaseWebTest):
initial_data = test_tender_data
test_simple_add_tender = snitch(simple_add_tender)
class TenderUAResourceTest(BaseTenderUAWebTest, TenderResourceTestMixin):
test_lots_data = test_lots # TODO: change attribute identifier
initial_data = test_tender_data
test_empty_listing = snitch(empty_listing)
test_create_tender_invalid = snitch(create_tender_invalid)
test_create_tender_generated = snitch(create_tender_generated)
test_patch_tender = snitch(patch_tender)
test_patch_tender_ua = snitch(patch_tender_ua)
test_tender_with_main_procurement_category = snitch(tender_with_main_procurement_category)
test_tender_finance_milestones = snitch(tender_finance_milestones)
class TenderUAProcessTest(BaseTenderUAWebTest, TenderUaProcessTestMixin):
initial_data = test_tender_data
test_invalid_tender_conditions = snitch(invalid_tender_conditions)
test_one_valid_bid_tender_ua = snitch(one_valid_bid_tender_ua)
test_one_invalid_bid_tender = snitch(one_invalid_bid_tender)
def test_patch_not_author(self):
response = self.app.post_json('/tenders', {'data': test_tender_data})
self.assertEqual(response.status, '201 Created')
tender = response.json['data']
owner_token = response.json['access']['token']
authorization = self.app.authorization
self.app.authorization = ('Basic', ('bot', 'bot'))
response = self.app.post('/tenders/{}/documents'.format(tender['id']),
upload_files=[('file', 'name.doc', 'content')])
self.assertEqual(response.status, '201 Created')
self.assertEqual(response.content_type, 'application/json')
doc_id = response.json["data"]['id']
self.assertIn(doc_id, response.headers['Location'])
self.app.authorization = authorization
response = self.app.patch_json('/tenders/{}/documents/{}?acc_token={}'.format(tender['id'], doc_id, owner_token),
{"data": {"description": "document description"}}, status=403)
self.assertEqual(response.status, '403 Forbidden')
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['errors'][0]["description"], "Can update document only author")
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(TenderUAProcessTest))
suite.addTest(unittest.makeSuite(TenderUAResourceTest))
suite.addTest(unittest.makeSuite(TenderUATest))
return suite
if __name__ == '__main__':
unittest.main(defaultTest='suite')
| [
"unittest.main",
"unittest.TestSuite",
"openprocurement.api.tests.base.snitch",
"unittest.makeSuite"
]
| [((1237, 1262), 'openprocurement.api.tests.base.snitch', 'snitch', (['simple_add_tender'], {}), '(simple_add_tender)\n', (1243, 1262), False, 'from openprocurement.api.tests.base import snitch\n'), ((1470, 1491), 'openprocurement.api.tests.base.snitch', 'snitch', (['empty_listing'], {}), '(empty_listing)\n', (1476, 1491), False, 'from openprocurement.api.tests.base import snitch\n'), ((1525, 1554), 'openprocurement.api.tests.base.snitch', 'snitch', (['create_tender_invalid'], {}), '(create_tender_invalid)\n', (1531, 1554), False, 'from openprocurement.api.tests.base import snitch\n'), ((1590, 1621), 'openprocurement.api.tests.base.snitch', 'snitch', (['create_tender_generated'], {}), '(create_tender_generated)\n', (1596, 1621), False, 'from openprocurement.api.tests.base import snitch\n'), ((1646, 1666), 'openprocurement.api.tests.base.snitch', 'snitch', (['patch_tender'], {}), '(patch_tender)\n', (1652, 1666), False, 'from openprocurement.api.tests.base import snitch\n'), ((1694, 1717), 'openprocurement.api.tests.base.snitch', 'snitch', (['patch_tender_ua'], {}), '(patch_tender_ua)\n', (1700, 1717), False, 'from openprocurement.api.tests.base import snitch\n'), ((1767, 1812), 'openprocurement.api.tests.base.snitch', 'snitch', (['tender_with_main_procurement_category'], {}), '(tender_with_main_procurement_category)\n', (1773, 1812), False, 'from openprocurement.api.tests.base import snitch\n'), ((1850, 1883), 'openprocurement.api.tests.base.snitch', 'snitch', (['tender_finance_milestones'], {}), '(tender_finance_milestones)\n', (1856, 1883), False, 'from openprocurement.api.tests.base import snitch\n'), ((2034, 2067), 'openprocurement.api.tests.base.snitch', 'snitch', (['invalid_tender_conditions'], {}), '(invalid_tender_conditions)\n', (2040, 2067), False, 'from openprocurement.api.tests.base import snitch\n'), ((2103, 2134), 'openprocurement.api.tests.base.snitch', 'snitch', (['one_valid_bid_tender_ua'], {}), '(one_valid_bid_tender_ua)\n', (2109, 2134), False, 'from openprocurement.api.tests.base import snitch\n'), ((2169, 2199), 'openprocurement.api.tests.base.snitch', 'snitch', (['one_invalid_bid_tender'], {}), '(one_invalid_bid_tender)\n', (2175, 2199), False, 'from openprocurement.api.tests.base import snitch\n'), ((3493, 3513), 'unittest.TestSuite', 'unittest.TestSuite', ([], {}), '()\n', (3511, 3513), False, 'import unittest\n'), ((3735, 3769), 'unittest.main', 'unittest.main', ([], {'defaultTest': '"""suite"""'}), "(defaultTest='suite')\n", (3748, 3769), False, 'import unittest\n'), ((3532, 3571), 'unittest.makeSuite', 'unittest.makeSuite', (['TenderUAProcessTest'], {}), '(TenderUAProcessTest)\n', (3550, 3571), False, 'import unittest\n'), ((3591, 3631), 'unittest.makeSuite', 'unittest.makeSuite', (['TenderUAResourceTest'], {}), '(TenderUAResourceTest)\n', (3609, 3631), False, 'import unittest\n'), ((3651, 3683), 'unittest.makeSuite', 'unittest.makeSuite', (['TenderUATest'], {}), '(TenderUATest)\n', (3669, 3683), False, 'import unittest\n')] |
from __future__ import print_function, division
import os
import numpy as np
import h5py
def dict_2_h5(fname, dic, append=False):
'''Writes a dictionary to a hdf5 file with given filename
It will use lzf compression for all numpy arrays
Args:
fname (str): filename to write to
dic (dict): dictionary to write
append (bool): if true, will append to file instead of overwriting, default=False
'''
if append:
method = 'r+'
else:
method = 'w'
with h5py.File(fname, method) as h5:
recursive_save_dict_to_h5(h5, '/', dic)
def h5_2_dict(fname):
'''Reads a dictionary from a hdf5 file with given filename
Args:
fname (str): hdf5 filename to read
Returns:
dict: dictionary of hdf5 keys
'''
with h5py.File(fname, 'r') as h5:
return recursive_load_dict_from_h5(h5, '/')
def prep_folder(path):
'''Checks if folder exists and recursively creates folders
to ensure the path is valid
Args:
path (str): path to folder
'''
if os.path.isdir(path):
return
else:
os.makedirs(path)
def recursive_save_dict_to_h5(h5, path, dic):
''' function used in save_dict_to_h5 in order to get recursion
'''
for key, item in dic.items():
if path + key in h5: ### overwrites pre-existing keys with same name
del h5[path + key]
if type(item) in [np.ndarray, np.generic]:
h5.create_dataset(path + key, data=item, compression='lzf')
elif type(item) != dict:
try:
h5.create_dataset(path + key, data=item)
except TypeError:
raise ValueError('Cannot save %s type' % type(item))
else:
recursive_save_dict_to_h5(h5, path + key + '/', item)
def recursive_load_dict_from_h5(h5, path):
''' function used in load_h5_to_dict in order to get recursion
'''
out_dict = {}
for key, item in h5[path].items():
# if type(item) == h5py._hl.dataset.Dataset:
if isinstance(item, h5py.Dataset):
out_dict[key] = item.value
# elif type(item) == h5py._hl.group.Group:
elif isinstance(item, h5py.Group):
out_dict[key] = recursive_load_dict_from_h5(h5, path + key + '/')
return out_dict
def read_Ld_results(Ld_directory):
'''Reads L and d histogram data from multinest run
Args:
Ld_directory (str): path to multinest save directory
Returns:
Tuple (np.ndarray, np.ndarray) L histogram values (in pixels), d histogram values (in mm)
'''
try:
fname = os.path.join(Ld_directory, "Ld_post_equal_weights.dat")
post = np.loadtxt(fname, ndmin=2)
except IOError:
fname = os.path.join(Ld_directory, "Ld_solver_post_equal_weights.dat")
post = np.loadtxt(fname, ndmin=2)
L = post[:, 0]
d = post[:, 1]
return L, d
def read_match_finesse_results(finesse_directory, errtemp=False):
fname = os.path.join(finesse_directory, "F_post_equal_weights.dat")
post = np.loadtxt(fname, ndmin=2)
F = post[:, 0]
V = post[:, 1]
T = post[:, 2]
if errtemp:
E = post[:, 3]
return F, V, T, E
else:
return F, V, T
def read_finesse_results(finesse_directory):
fname = os.path.join(finesse_directory, "finesse_post_equal_weights.dat")
post = np.loadtxt(fname, ndmin=2)
F = post[:, 0]
A = post[:, 1]
Arel = post[:, 2]
Ti = post[:, 3]
return F, A, Arel, Ti
def read_lyon_temp_results(temp_directory):
fname = os.path.join(temp_directory, 'temp_post_equal_weights.dat')
post = np.loadtxt(fname, ndmin=2)
T = post[:, 0]
V = post[:, 1]
# A = post[:,2]
# O = post[:,3]
return T, V # ,A#,O
| [
"os.makedirs",
"os.path.join",
"h5py.File",
"os.path.isdir",
"numpy.loadtxt"
]
| [((1075, 1094), 'os.path.isdir', 'os.path.isdir', (['path'], {}), '(path)\n', (1088, 1094), False, 'import os\n'), ((3009, 3068), 'os.path.join', 'os.path.join', (['finesse_directory', '"""F_post_equal_weights.dat"""'], {}), "(finesse_directory, 'F_post_equal_weights.dat')\n", (3021, 3068), False, 'import os\n'), ((3080, 3106), 'numpy.loadtxt', 'np.loadtxt', (['fname'], {'ndmin': '(2)'}), '(fname, ndmin=2)\n', (3090, 3106), True, 'import numpy as np\n'), ((3321, 3386), 'os.path.join', 'os.path.join', (['finesse_directory', '"""finesse_post_equal_weights.dat"""'], {}), "(finesse_directory, 'finesse_post_equal_weights.dat')\n", (3333, 3386), False, 'import os\n'), ((3398, 3424), 'numpy.loadtxt', 'np.loadtxt', (['fname'], {'ndmin': '(2)'}), '(fname, ndmin=2)\n', (3408, 3424), True, 'import numpy as np\n'), ((3590, 3649), 'os.path.join', 'os.path.join', (['temp_directory', '"""temp_post_equal_weights.dat"""'], {}), "(temp_directory, 'temp_post_equal_weights.dat')\n", (3602, 3649), False, 'import os\n'), ((3661, 3687), 'numpy.loadtxt', 'np.loadtxt', (['fname'], {'ndmin': '(2)'}), '(fname, ndmin=2)\n', (3671, 3687), True, 'import numpy as np\n'), ((519, 543), 'h5py.File', 'h5py.File', (['fname', 'method'], {}), '(fname, method)\n', (528, 543), False, 'import h5py\n'), ((809, 830), 'h5py.File', 'h5py.File', (['fname', '"""r"""'], {}), "(fname, 'r')\n", (818, 830), False, 'import h5py\n'), ((1129, 1146), 'os.makedirs', 'os.makedirs', (['path'], {}), '(path)\n', (1140, 1146), False, 'import os\n'), ((2635, 2690), 'os.path.join', 'os.path.join', (['Ld_directory', '"""Ld_post_equal_weights.dat"""'], {}), "(Ld_directory, 'Ld_post_equal_weights.dat')\n", (2647, 2690), False, 'import os\n'), ((2706, 2732), 'numpy.loadtxt', 'np.loadtxt', (['fname'], {'ndmin': '(2)'}), '(fname, ndmin=2)\n', (2716, 2732), True, 'import numpy as np\n'), ((2769, 2831), 'os.path.join', 'os.path.join', (['Ld_directory', '"""Ld_solver_post_equal_weights.dat"""'], {}), "(Ld_directory, 'Ld_solver_post_equal_weights.dat')\n", (2781, 2831), False, 'import os\n'), ((2847, 2873), 'numpy.loadtxt', 'np.loadtxt', (['fname'], {'ndmin': '(2)'}), '(fname, ndmin=2)\n', (2857, 2873), True, 'import numpy as np\n')] |
from openmdao.main.factory import Factory
from analysis_server import client, proxy, server
class ASFactory(Factory):
"""
Factory for components running under an AnalysisServer.
An instance would typically be passed to
:meth:`openmdao.main.factorymanager.register_class_factory`.
host: string
Host name or IP address of the AnalysisServer to connect to.
port: int
Port number of the AnalysisServer to connect to.
"""
def __init__(self, host='localhost', port=server.DEFAULT_PORT):
super(ASFactory, self).__init__()
self._host = host
self._port = port
self._client = client.Client(host, port)
def create(self, typname, version=None, server=None,
res_desc=None, **ctor_args):
"""
Create a `typname` object.
typname: string
Type of object to create.
version: string or None
Version of `typname` to create.
server:
Not used.
res_desc: dict or None
Not used.
ctor_args: dict
Other constructor arguments. Not used.
"""
for typ, ver in self.get_available_types():
if typ == typname:
if version is None or ver == version:
return proxy.ComponentProxy(typname, self._host, self._port)
return None
def get_available_types(self, groups=None):
"""
Returns a set of tuples of the form ``(typname, version)``,
one for each available component type.
groups: list[string]
OpenMDAO entry point groups.
Only 'openmdao.component' is supported.
"""
if groups is not None and 'openmdao.component' not in groups:
return []
types = []
self._list('', types)
return types
def _list(self, category, types):
""" List components in `category` and sub-categories. """
if category:
category += '/'
for comp in self._client.list_components(category):
comp = '%s%s' % (category, comp)
try:
versions = self._client.versions(comp)
except RuntimeError:
types.append((comp, ''))
else:
for version in versions:
types.append((comp, version))
for sub in self._client.list_categories(category):
sub = '%s%s' % (category, sub)
self._list(sub, types)
| [
"analysis_server.proxy.ComponentProxy",
"analysis_server.client.Client"
]
| [((652, 677), 'analysis_server.client.Client', 'client.Client', (['host', 'port'], {}), '(host, port)\n', (665, 677), False, 'from analysis_server import client, proxy, server\n'), ((1313, 1366), 'analysis_server.proxy.ComponentProxy', 'proxy.ComponentProxy', (['typname', 'self._host', 'self._port'], {}), '(typname, self._host, self._port)\n', (1333, 1366), False, 'from analysis_server import client, proxy, server\n')] |
from typing import Tuple, List, Optional
import json
import sys
import os
import shlex
import asyncio
import argparse
import logging
import tempfile
from urllib.parse import urlparse
logger = logging.getLogger(__name__)
def find_sqlmat_json() -> Optional[dict]:
json_path = os.getenv('SQLMAT_JSON_PATH')
if json_path:
with open(json_path) as f:
cfg = json.load(f)
return cfg
# iterate through the current dir up to the root dir "/" to find a
# .sqlmat.json
workdir = os.path.abspath(os.getcwd())
while workdir:
json_path = os.path.join(workdir, '.sqlmat.json')
if os.path.exists(json_path):
with open(json_path) as f:
cfg = json.load(f)
return cfg
parentdir = os.path.abspath(os.path.join(workdir, '..'))
if parentdir == workdir:
break
workdir = parentdir
logger.warning('fail to find .sqlmat.json')
return None
def find_dsn(prog: str, desc: str) -> Tuple[str, List[str]]:
parser = argparse.ArgumentParser(
prog=prog,
description=desc)
parser.add_argument('-d', '--dsn',
type=str,
help='postgresql dsn')
parser.add_argument('-g', '--db',
type=str,
default='default',
help='postgresql db instance defined in .sqlmat.json')
parser.add_argument('callee_args',
type=str,
nargs='*',
help='command line arguments of callee programs')
# from arguments
args = parser.parse_args()
if args.dsn:
return args.dsn, args.callee_args
# find dsn from ./.sqlmat.json
cfg = find_sqlmat_json()
if cfg:
dsn = cfg['databases'][args.db]['dsn']
assert isinstance(dsn, str)
return dsn, args.callee_args
# default dsn using username
user = os.getenv('USER', '')
default_dsn = f'postgres://{user}@127.0.0.1:5432/{args.db}'
logger.warning('no postgres dsn specified, use %s instead', default_dsn)
return default_dsn, args.callee_args
def joinargs(callee_args: List[str]) -> str:
if hasattr(shlex, 'join'):
return shlex.join(callee_args)
else:
return ' '.join(shlex.quote(a) for a in callee_args)
# run psql client
async def run_shell(dsn: str, callee_args: List[str]) -> None:
p = urlparse(dsn)
username = p.username or ''
password = p.password or ''
dbname = p.path[1:]
hostname = p.hostname
port = p.port or 5432
temp_pgpass = tempfile.NamedTemporaryFile(mode='w')
print(
'{}:{}:{}:{}:{}'.format(hostname, port, dbname, username, password),
file=temp_pgpass,
flush=True)
os.environ['PGPASSFILE'] = temp_pgpass.name
command = 'psql -h{} -p{} -U{} {} {}'.format(hostname, port, username, joinargs(callee_args), dbname)
proc = await asyncio.create_subprocess_shell(command)
await proc.communicate()
def cl_run_shell() -> None:
dsn, callee_args = find_dsn('sqlmat-shell', 'run psql client shell')
loop = asyncio.get_event_loop()
loop.run_until_complete(run_shell(dsn, callee_args))
# run dbdump
async def run_dbdump(dsn: str, callee_args: List[str]) -> None:
p = urlparse(dsn)
username = p.username or ''
password = p.password or ''
dbname = p.path[1:]
hostname = p.hostname
port = p.port or 5432
temp_pgpass = tempfile.NamedTemporaryFile(mode='w')
print(
'{}:{}:{}:{}:{}'.format(hostname, port, dbname, username, password),
file=temp_pgpass,
flush=True)
os.environ['PGPASSFILE'] = temp_pgpass.name
command = 'pg_dump -h{} -p{} -U{} {} {}'.format(hostname, port, username, joinargs(callee_args), dbname)
proc = await asyncio.create_subprocess_shell(command)
await proc.communicate()
def cl_run_dbdump() -> None:
dsn, callee_args = find_dsn('sqlmat-dump', 'dump database')
loop = asyncio.get_event_loop()
loop.run_until_complete(run_dbdump(dsn, callee_args))
# generate alembic migrations
def gen_migrate(dsn: str) -> None:
init_data = ALEMBIC_INIT.replace('{{dsn}}', dsn)
with open('alembic.ini', 'w') as f:
f.write(init_data)
def cl_gen_migrate() -> None:
dsn, callee_args = find_dsn('sqlmat-genmigrate', 'generate alembic migration')
gen_migrate(dsn)
print('Wrote alembic.ini')
ALEMBIC_INIT = '''\
# A generic, single database configuration.
[alembic]
# path to migration scripts
script_location = migrations
# template used to generate migration files
# file_template = %%(rev)s_%%(slug)s
# timezone to use when rendering the date
# within the migration file as well as the filename.
# string value is passed to dateutil.tz.gettz()
# leave blank for localtime
# timezone =
# max length of characters to apply to the
# "slug" field
#truncate_slug_length = 40
# set to 'true' to run the environment during
# the 'revision' command, regardless of autogenerate
# revision_environment = false
# set to 'true' to allow .pyc and .pyo files without
# a source .py file to be detected as revisions in the
# versions/ directory
# sourceless = false
# version location specification; this defaults
# to migrations/versions. When using multiple version
# directories, initial revisions must be specified with --version-path
# version_locations = %(here)s/bar %(here)s/bat migrations/versions
# the output encoding used when revision files
# are written from script.py.mako
# output_encoding = utf-8
#sqlalchemy.url = driver://user:pass@localhost/dbname
sqlalchemy.url = {{dsn}}
# Logging configuration
[loggers]
keys = root,sqlalchemy,alembic
[handlers]
keys = console
[formatters]
keys = generic
[logger_root]
level = WARN
handlers = console
qualname =
[logger_sqlalchemy]
level = WARN
handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = INFO
handlers =
qualname = alembic
[handler_console]
class = StreamHandler
args = (sys.stderr,)
level = NOTSET
formatter = generic
[formatter_generic]
format = %(levelname)-5.5s [%(name)s] %(message)s
datefmt = %H:%M:%S
'''
| [
"logging.getLogger",
"os.path.exists",
"shlex.join",
"urllib.parse.urlparse",
"argparse.ArgumentParser",
"os.getenv",
"os.path.join",
"os.getcwd",
"shlex.quote",
"asyncio.create_subprocess_shell",
"tempfile.NamedTemporaryFile",
"json.load",
"asyncio.get_event_loop"
]
| [((193, 220), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (210, 220), False, 'import logging\n'), ((280, 309), 'os.getenv', 'os.getenv', (['"""SQLMAT_JSON_PATH"""'], {}), "('SQLMAT_JSON_PATH')\n", (289, 309), False, 'import os\n'), ((1050, 1102), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'prog': 'prog', 'description': 'desc'}), '(prog=prog, description=desc)\n', (1073, 1102), False, 'import argparse\n'), ((1972, 1993), 'os.getenv', 'os.getenv', (['"""USER"""', '""""""'], {}), "('USER', '')\n", (1981, 1993), False, 'import os\n'), ((2454, 2467), 'urllib.parse.urlparse', 'urlparse', (['dsn'], {}), '(dsn)\n', (2462, 2467), False, 'from urllib.parse import urlparse\n'), ((2627, 2664), 'tempfile.NamedTemporaryFile', 'tempfile.NamedTemporaryFile', ([], {'mode': '"""w"""'}), "(mode='w')\n", (2654, 2664), False, 'import tempfile\n'), ((3161, 3185), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (3183, 3185), False, 'import asyncio\n'), ((3329, 3342), 'urllib.parse.urlparse', 'urlparse', (['dsn'], {}), '(dsn)\n', (3337, 3342), False, 'from urllib.parse import urlparse\n'), ((3502, 3539), 'tempfile.NamedTemporaryFile', 'tempfile.NamedTemporaryFile', ([], {'mode': '"""w"""'}), "(mode='w')\n", (3529, 3539), False, 'import tempfile\n'), ((4023, 4047), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (4045, 4047), False, 'import asyncio\n'), ((538, 549), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (547, 549), False, 'import os\n'), ((590, 627), 'os.path.join', 'os.path.join', (['workdir', '""".sqlmat.json"""'], {}), "(workdir, '.sqlmat.json')\n", (602, 627), False, 'import os\n'), ((639, 664), 'os.path.exists', 'os.path.exists', (['json_path'], {}), '(json_path)\n', (653, 664), False, 'import os\n'), ((2269, 2292), 'shlex.join', 'shlex.join', (['callee_args'], {}), '(callee_args)\n', (2279, 2292), False, 'import shlex\n'), ((2978, 3018), 'asyncio.create_subprocess_shell', 'asyncio.create_subprocess_shell', (['command'], {}), '(command)\n', (3009, 3018), False, 'import asyncio\n'), ((3848, 3888), 'asyncio.create_subprocess_shell', 'asyncio.create_subprocess_shell', (['command'], {}), '(command)\n', (3879, 3888), False, 'import asyncio\n'), ((381, 393), 'json.load', 'json.load', (['f'], {}), '(f)\n', (390, 393), False, 'import json\n'), ((803, 830), 'os.path.join', 'os.path.join', (['workdir', '""".."""'], {}), "(workdir, '..')\n", (815, 830), False, 'import os\n'), ((727, 739), 'json.load', 'json.load', (['f'], {}), '(f)\n', (736, 739), False, 'import json\n'), ((2327, 2341), 'shlex.quote', 'shlex.quote', (['a'], {}), '(a)\n', (2338, 2341), False, 'import shlex\n')] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: <NAME>
year: 2019 - 2021
This script is used to calculate the eddy-induced overturning in CESM2 and NorESM2 (LM and MM) south of 50S
for the CMIP experiments piControl and abrupt-4xCO2 after 150
the average time is 30 years
The result is used in FIGURE 4
"""
import sys
sys.path.insert(1, '/scratch/adagj/CMIP6/CLIMSENS/CMIP6_UTILS')
import CMIP6_ATMOS_UTILS as atmos
import CMIP6_SEAICE_UTILS as ocean
from read_modeldata_cmip6 import ecs_models_cmip6, make_filelist_cmip6, Modelinfo
import numpy as np
from dask.diagnostics import ProgressBar
import warnings
warnings.simplefilter('ignore')
import xarray as xr
xr.set_options(enable_cftimeindex=True)
def make_attributes(da, var, expid):
da.attrs['long_name']='Global Ocean Meridional Overturning Mass Streamfunction Due to Parameterized Mesoscale Advection'
da.attrs['name']='eddymoc'
da.attrs['units']='kg s-1'
da.attrs['standard_name']='global_ocean_meridional_overturning_mass_streamfunction_due_to_parameterized_mesoscale_eddy_advection'
da.attrs['expid']=expid
ds = da.to_dataset(name = var)
return ds
def extract_global_moc(modelname, da, dac, var):
if 'sector' in da.coords:
da = da.drop('sector')
if 'sector' in dac.coords:
dac = dac.drop('sector')
da = da.isel(basin=-1)
dac = dac.isel(basin=-1)
return da, dac
def make_reference_slice(model, ds, var, endyr):
ds = ocean.consistent_naming(ds)
ds = atmos.fix_time(ds, 1)
return ds
def make_yearly_avg(model, ds, var, endyr):
da = atmos.yearly_avg(ds[var])
if model.expid in ['piControl']:
da = da.isel(year=slice(model.branchtime_year+endyr-30, model.branchtime_year+endyr))
else:
da = da.isel(year=slice(endyr-30, endyr))
da = da.mean(dim='year')
return da
def make_modelobj(modelname, expinfo, expid='piControl'):
model = Modelinfo(name = modelname, institute = expinfo['institute'], expid = expid, realm = 'Omon',
realiz=expinfo['variant_labels'][0], grid_atmos = expinfo['grid_label_atmos'][0], grid_ocean = expinfo['grid_label_ocean'], branchtime_year=expinfo['branch_yr'])
return model
def read_files(model, var):
if model.name in ['NorESM2-LM', 'NorESM2-MM']:
make_filelist_cmip6(model, var, component = 'ocean', activity_id='CMIP',path_to_data = '/projects/NS9034K/CMIP6/')
else:
make_filelist_cmip6(model, var, component = 'ocean')
print(model.filenames)
if model.filenames:
if len(model.filenames)>1:
ds = xr.open_mfdataset(model.filenames, combine='nested', concat_dim='time', parallel=True, chunks={"time":1})
else:
ds = xr.open_dataset(model.filenames[0], chunks={"time":1})
print('%s loaded for model: %s, experiment: piControl . Lenght of simulation: %.1f years'%(var,model.name, len(ds[var].time.values)/12))
else:
print('%s not loaded for model %s, experiment: piControl. Skipping model! Please check!'%(var,model.name))
return ds
def make_last_30yrs_avg(models, var, outpath, endyr=150):
print('global eddy moc: \n')
for modelname,expinfo in models.items():
print(modelname)
if var in ['msftmzsmpa'] and modelname in ['NorESM2-LM']:
continue
modelctrl = make_modelobj(modelname, expinfo, expid='piControl')
dsc = read_files(modelctrl, var)
dsc = make_reference_slice(modelctrl, dsc, var, endyr)
model4xco2 = make_modelobj(modelname, expinfo, expid='abrupt-4xCO2')
ds = read_files(model4xco2, var)
ds = make_reference_slice(model4xco2, ds, var, endyr)
ds, dsc = extract_global_moc(modelname, ds, dsc, var)
da = make_yearly_avg(model4xco2, ds, var, endyr)
dac = make_yearly_avg(modelctrl, dsc, var, endyr)
dsout_ctrl = make_attributes(dac, var, 'piControl')
dsout_case = make_attributes(da, var, 'abrupt-4xCO2')
print(dsout_ctrl)
print(dsout_case)
dsout_ctrl = dsout_ctrl.to_netcdf(outpath + var +'_' + modelctrl.realm +'_' + modelctrl.name + '_' + modelctrl.expid + '_' + modelctrl.realiz + '_'+str(endyr) + '_30yravg.nc', compute=False)
dsout_case = dsout_case.to_netcdf(outpath + var +'_' + model4xco2.realm +'_' + model4xco2.name + '_' + model4xco2.expid + '_' + model4xco2.realiz + '_'+str(endyr) + '_30yravg.nc', compute=False)
with ProgressBar():
result = dsout_ctrl.compute()
result = dsout_case.compute()
del model4xco2, modelctrl, dsc, ds, dac, da, dsout_ctrl, dsout_case
if __name__ == '__main__':
outpath = 'path_to_outdata/'
models = ecs_models_cmip6()
models = {'NorESM2-LM':models['NorESM2-LM'], 'CESM2':models['CESM2']}
for var in ['msftmzsmpa', 'msftmzmpa']:
make_last_30yrs_avg(models, var=var, outpath=outpath, endyr=150)
| [
"xarray.open_mfdataset",
"sys.path.insert",
"read_modeldata_cmip6.ecs_models_cmip6",
"read_modeldata_cmip6.Modelinfo",
"dask.diagnostics.ProgressBar",
"CMIP6_SEAICE_UTILS.consistent_naming",
"CMIP6_ATMOS_UTILS.yearly_avg",
"CMIP6_ATMOS_UTILS.fix_time",
"warnings.simplefilter",
"xarray.set_options",
"xarray.open_dataset",
"read_modeldata_cmip6.make_filelist_cmip6"
]
| [((332, 395), 'sys.path.insert', 'sys.path.insert', (['(1)', '"""/scratch/adagj/CMIP6/CLIMSENS/CMIP6_UTILS"""'], {}), "(1, '/scratch/adagj/CMIP6/CLIMSENS/CMIP6_UTILS')\n", (347, 395), False, 'import sys\n'), ((623, 654), 'warnings.simplefilter', 'warnings.simplefilter', (['"""ignore"""'], {}), "('ignore')\n", (644, 654), False, 'import warnings\n'), ((675, 714), 'xarray.set_options', 'xr.set_options', ([], {'enable_cftimeindex': '(True)'}), '(enable_cftimeindex=True)\n', (689, 714), True, 'import xarray as xr\n'), ((1578, 1605), 'CMIP6_SEAICE_UTILS.consistent_naming', 'ocean.consistent_naming', (['ds'], {}), '(ds)\n', (1601, 1605), True, 'import CMIP6_SEAICE_UTILS as ocean\n'), ((1615, 1636), 'CMIP6_ATMOS_UTILS.fix_time', 'atmos.fix_time', (['ds', '(1)'], {}), '(ds, 1)\n', (1629, 1636), True, 'import CMIP6_ATMOS_UTILS as atmos\n'), ((1705, 1730), 'CMIP6_ATMOS_UTILS.yearly_avg', 'atmos.yearly_avg', (['ds[var]'], {}), '(ds[var])\n', (1721, 1730), True, 'import CMIP6_ATMOS_UTILS as atmos\n'), ((2036, 2291), 'read_modeldata_cmip6.Modelinfo', 'Modelinfo', ([], {'name': 'modelname', 'institute': "expinfo['institute']", 'expid': 'expid', 'realm': '"""Omon"""', 'realiz': "expinfo['variant_labels'][0]", 'grid_atmos': "expinfo['grid_label_atmos'][0]", 'grid_ocean': "expinfo['grid_label_ocean']", 'branchtime_year': "expinfo['branch_yr']"}), "(name=modelname, institute=expinfo['institute'], expid=expid,\n realm='Omon', realiz=expinfo['variant_labels'][0], grid_atmos=expinfo[\n 'grid_label_atmos'][0], grid_ocean=expinfo['grid_label_ocean'],\n branchtime_year=expinfo['branch_yr'])\n", (2045, 2291), False, 'from read_modeldata_cmip6 import ecs_models_cmip6, make_filelist_cmip6, Modelinfo\n'), ((4805, 4823), 'read_modeldata_cmip6.ecs_models_cmip6', 'ecs_models_cmip6', ([], {}), '()\n', (4821, 4823), False, 'from read_modeldata_cmip6 import ecs_models_cmip6, make_filelist_cmip6, Modelinfo\n'), ((2413, 2528), 'read_modeldata_cmip6.make_filelist_cmip6', 'make_filelist_cmip6', (['model', 'var'], {'component': '"""ocean"""', 'activity_id': '"""CMIP"""', 'path_to_data': '"""/projects/NS9034K/CMIP6/"""'}), "(model, var, component='ocean', activity_id='CMIP',\n path_to_data='/projects/NS9034K/CMIP6/')\n", (2432, 2528), False, 'from read_modeldata_cmip6 import ecs_models_cmip6, make_filelist_cmip6, Modelinfo\n'), ((2547, 2597), 'read_modeldata_cmip6.make_filelist_cmip6', 'make_filelist_cmip6', (['model', 'var'], {'component': '"""ocean"""'}), "(model, var, component='ocean')\n", (2566, 2597), False, 'from read_modeldata_cmip6 import ecs_models_cmip6, make_filelist_cmip6, Modelinfo\n'), ((2705, 2815), 'xarray.open_mfdataset', 'xr.open_mfdataset', (['model.filenames'], {'combine': '"""nested"""', 'concat_dim': '"""time"""', 'parallel': '(True)', 'chunks': "{'time': 1}"}), "(model.filenames, combine='nested', concat_dim='time',\n parallel=True, chunks={'time': 1})\n", (2722, 2815), True, 'import xarray as xr\n'), ((2843, 2898), 'xarray.open_dataset', 'xr.open_dataset', (['model.filenames[0]'], {'chunks': "{'time': 1}"}), "(model.filenames[0], chunks={'time': 1})\n", (2858, 2898), True, 'import xarray as xr\n'), ((4554, 4567), 'dask.diagnostics.ProgressBar', 'ProgressBar', ([], {}), '()\n', (4565, 4567), False, 'from dask.diagnostics import ProgressBar\n')] |
##############################################################################
#
# Copyright (c) 2004 Zope Foundation and Contributors.
# All Rights Reserved.
#
# 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.
#
##############################################################################
"""Tests of examples from the online cookbook, so we don't break them
down the road. Unless we really mean to.
The ZConfig Cookbook is available online at:
http://dev.zope.org/Zope3/ZConfig
"""
import ZConfig.tests.support
import unittest
def basic_key_mapping_password_to_passwd(key):
# Lower-case the key since that's what basic-key does:
key = key.lower()
# Now map password to passwd:
if key == "password":
key = "passwd"
return key
def user_info_conversion(section):
return section
class CookbookTestCase(ZConfig.tests.support.TestHelper, unittest.TestCase):
def test_rewriting_key_names(self):
schema = self.load_schema_text("""
<schema prefix='%s'>
<sectiontype name='userinfo' datatype='.user_info_conversion'
keytype='.basic_key_mapping_password_to_passwd'>
<key name='userid' datatype='integer'/>
<key name='username' datatype='identifier'/>
<key name='password'/>
</sectiontype>
<section type='userinfo' name='*' attribute='userinfo'/>
</schema>
""" % __name__)
config = self.load_config_text(schema, """\
<userinfo>
USERID 42
USERNAME foouser
PASSWORD <PASSWORD>
</userinfo>
""")
self.assertEqual(config.userinfo.userid, 42)
self.assertEqual(config.userinfo.username, "foouser")
self.assertEqual(config.userinfo.passwd, "<PASSWORD>")
self.assertTrue(not hasattr(config.userinfo, "password"))
def test_suite():
return unittest.makeSuite(CookbookTestCase)
if __name__ == "__main__":
unittest.main(defaultTest="test_suite")
| [
"unittest.main",
"unittest.makeSuite"
]
| [((2301, 2337), 'unittest.makeSuite', 'unittest.makeSuite', (['CookbookTestCase'], {}), '(CookbookTestCase)\n', (2319, 2337), False, 'import unittest\n'), ((2370, 2409), 'unittest.main', 'unittest.main', ([], {'defaultTest': '"""test_suite"""'}), "(defaultTest='test_suite')\n", (2383, 2409), False, 'import unittest\n')] |
# Generated by Django 2.2 on 2020-11-07 01:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('bullet_point', '0005_bulletpoint_created_location'),
]
operations = [
migrations.AddField(
model_name='bulletpoint',
name='sift_risk_score',
field=models.FloatField(blank=True, null=True),
),
]
| [
"django.db.models.FloatField"
]
| [((361, 401), 'django.db.models.FloatField', 'models.FloatField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (378, 401), False, 'from django.db import migrations, models\n')] |
import pkg_resources
from fastapi import FastAPI
from fastapi.openapi.utils import get_openapi
from starlette.responses import RedirectResponse, JSONResponse
from routers import auth, media, video, photo, user, igtv, clip, album, story, hashtag, direct
app = FastAPI()
app.include_router(auth.router)
app.include_router(media.router)
app.include_router(video.router)
app.include_router(photo.router)
app.include_router(user.router)
app.include_router(igtv.router)
app.include_router(clip.router)
app.include_router(album.router)
app.include_router(story.router)
app.include_router(hashtag.router)
app.include_router(direct.router)
@app.get("/", tags=["system"], summary="Redirect to /docs")
async def root():
"""Redirect to /docs
"""
return RedirectResponse(url="/docs")
@app.get("/version", tags=["system"], summary="Get dependency versions")
async def version():
"""Get dependency versions
"""
versions = {}
for name in ('instagrapi', ):
item = pkg_resources.require(name)
if item:
versions[name] = item[0].version
return versions
@app.exception_handler(Exception)
async def handle_exception(request, exc: Exception):
return JSONResponse({
"detail": str(exc),
"exc_type": str(type(exc).__name__)
}, status_code=500)
def custom_openapi():
if app.openapi_schema:
return app.openapi_schema
# for route in app.routes:
# body_field = getattr(route, 'body_field', None)
# if body_field:
# body_field.type_.__name__ = 'name'
openapi_schema = get_openapi(
title="instagrapi-rest",
version="1.0.0",
description="RESTful API Service for instagrapi",
routes=app.routes,
)
app.openapi_schema = openapi_schema
return app.openapi_schema
app.openapi = custom_openapi
| [
"starlette.responses.RedirectResponse",
"pkg_resources.require",
"fastapi.FastAPI",
"fastapi.openapi.utils.get_openapi"
]
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# Imports
import numpy as np
import pandas as pd
import sys
import tqdm
import warnings
import time
import ternary
from ternary.helpers import simplex_iterator
import multiprocessing as mp
warnings.simplefilter("ignore")
if sys.platform == "darwin":
sys.path.append("/Users/aymericvie/Documents/GitHub/evology/evology/code")
# Need to be executed from cd to MCarloLongRuns
if sys.platform == "linux":
sys.path.append("/home/vie/Documents/GitHub/evology/evology/code")
from main import main as evology
startTime = time.time()
TimeHorizon = 252 * 5
PopulationSize = 3
def job(coords):
np.random.seed()
try:
df, pop = evology(
space="scholl",
solver="esl.true",
wealth_coordinates=coords,
POPULATION_SIZE=PopulationSize,
MAX_GENERATIONS=TimeHorizon,
PROBA_SELECTION=0,
MUTATION_RATE=0,
ReinvestmentRate=1.0,
InvestmentHorizon=21,
InvestorBehavior="profit",
tqdm_display=True,
reset_wealth=True,
)
result = [
coords[0],
coords[1],
coords[2],
df["NT_returns"].mean(),
df["VI_returns"].mean(),
df["TF_returns"].mean(),
df["NT_returns"].std(),
df["VI_returns"].std(),
df["TF_returns"].std(),
df["HighestT"].mean(),
df["AvgAbsT"].mean(),
]
return result
except Exception as e:
print(e)
print("Failed run" + str(coords) + str(e))
result = [coords[0], coords[1], coords[2]]
for _ in range(8):
result.append(0)
return result
# Define the domains
def GenerateCoords(reps, scale):
param = []
for (i, j, k) in simplex_iterator(scale):
for _ in range(reps):
param.append([i / scale, j / scale, k / scale])
return param
reps = 10
scale = 50 # increment = 1/scale
param = GenerateCoords(reps, scale)
# print(param)
print(len(param))
# Run experiment
def main():
p = mp.Pool()
data = p.map(job, tqdm.tqdm(param))
p.close()
data = np.array(data)
return data
if __name__ == "__main__":
data = main()
df = pd.DataFrame()
# Inputs
df["WS_NT"] = data[:, 0]
df["WS_VI"] = data[:, 1]
df["WS_TF"] = data[:, 2]
# Outputs
df["NT_returns_mean"] = data[:, 3]
df["VI_returns_mean"] = data[:, 4]
df["TF_returns_mean"] = data[:, 5]
df["NT_returns_std"] = data[:, 6]
df["VI_returns_std"] = data[:, 7]
df["TF_returns_std"] = data[:, 8]
df["HighestT"] = data[:, 9]
df["AvgAbsT"] = data[:, 10]
print(df)
df.to_csv("data/data1.csv")
print("Completion time: " + str(time.time() - startTime))
| [
"pandas.DataFrame",
"tqdm.tqdm",
"numpy.array",
"main.main",
"numpy.random.seed",
"multiprocessing.Pool",
"time.time",
"warnings.simplefilter",
"sys.path.append",
"ternary.helpers.simplex_iterator"
]
| [((190, 221), 'warnings.simplefilter', 'warnings.simplefilter', (['"""ignore"""'], {}), "('ignore')\n", (211, 221), False, 'import warnings\n'), ((529, 540), 'time.time', 'time.time', ([], {}), '()\n', (538, 540), False, 'import time\n'), ((256, 330), 'sys.path.append', 'sys.path.append', (['"""/Users/aymericvie/Documents/GitHub/evology/evology/code"""'], {}), "('/Users/aymericvie/Documents/GitHub/evology/evology/code')\n", (271, 330), False, 'import sys\n'), ((415, 481), 'sys.path.append', 'sys.path.append', (['"""/home/vie/Documents/GitHub/evology/evology/code"""'], {}), "('/home/vie/Documents/GitHub/evology/evology/code')\n", (430, 481), False, 'import sys\n'), ((605, 621), 'numpy.random.seed', 'np.random.seed', ([], {}), '()\n', (619, 621), True, 'import numpy as np\n'), ((1806, 1829), 'ternary.helpers.simplex_iterator', 'simplex_iterator', (['scale'], {}), '(scale)\n', (1822, 1829), False, 'from ternary.helpers import simplex_iterator\n'), ((2091, 2100), 'multiprocessing.Pool', 'mp.Pool', ([], {}), '()\n', (2098, 2100), True, 'import multiprocessing as mp\n'), ((2166, 2180), 'numpy.array', 'np.array', (['data'], {}), '(data)\n', (2174, 2180), True, 'import numpy as np\n'), ((2253, 2267), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (2265, 2267), True, 'import pandas as pd\n'), ((649, 940), 'main.main', 'evology', ([], {'space': '"""scholl"""', 'solver': '"""esl.true"""', 'wealth_coordinates': 'coords', 'POPULATION_SIZE': 'PopulationSize', 'MAX_GENERATIONS': 'TimeHorizon', 'PROBA_SELECTION': '(0)', 'MUTATION_RATE': '(0)', 'ReinvestmentRate': '(1.0)', 'InvestmentHorizon': '(21)', 'InvestorBehavior': '"""profit"""', 'tqdm_display': '(True)', 'reset_wealth': '(True)'}), "(space='scholl', solver='esl.true', wealth_coordinates=coords,\n POPULATION_SIZE=PopulationSize, MAX_GENERATIONS=TimeHorizon,\n PROBA_SELECTION=0, MUTATION_RATE=0, ReinvestmentRate=1.0,\n InvestmentHorizon=21, InvestorBehavior='profit', tqdm_display=True,\n reset_wealth=True)\n", (656, 940), True, 'from main import main as evology\n'), ((2123, 2139), 'tqdm.tqdm', 'tqdm.tqdm', (['param'], {}), '(param)\n', (2132, 2139), False, 'import tqdm\n'), ((2763, 2774), 'time.time', 'time.time', ([], {}), '()\n', (2772, 2774), False, 'import time\n')] |
print("@"*30)
print("Alistamento - Serviço Militar")
print("@"*30)
from datetime import date
ano_nasc = int(input("Digite seu ano de nascimento: "))
ano_atual = date.today().year
idade = ano_atual - ano_nasc
print(f"Quem nasceu em {ano_nasc} tem {idade} anos em {ano_atual}")
if idade == 18:
print("É a hora de se alistar no serviço militar, IMEDIATAMENTE!")
elif idade < 18:
saldo = 18 - idade
print(f"Ainda falta {saldo} anos para o seu alistamento!")
ano = ano_atual + saldo
print(f"Seu alistamento será em {ano}")
else:
idade > 18
saldo = idade - 18
print(f"Já passou {saldo} anos do tempo para o seu alistamento!")
ano = ano_atual - saldo
print(f"O seu alistamento foi em {ano}") | [
"datetime.date.today"
]
| [((163, 175), 'datetime.date.today', 'date.today', ([], {}), '()\n', (173, 175), False, 'from datetime import date\n')] |
import numpy as np
import requests
from django.db.models import Q
from api.models import Photo, User
from api.util import logger
from ownphotos.settings import IMAGE_SIMILARITY_SERVER
def search_similar_embedding(user, emb, result_count=100, threshold=27):
if type(user) == int:
user_id = user
else:
user_id = user.id
image_embedding = np.array(emb, dtype=np.float32)
post_data = {
"user_id": user_id,
"image_embedding": image_embedding.tolist(),
"n": result_count,
"threshold": threshold,
}
res = requests.post(IMAGE_SIMILARITY_SERVER + "/search/", json=post_data)
if res.status_code == 200:
return res.json()["result"]
else:
logger.error("error retrieving similar embeddings for user {}".format(user_id))
return []
def search_similar_image(user, photo):
if type(user) == int:
user_id = user
else:
user_id = user.id
if photo.clip_embeddings == None:
photo._generate_clip_embeddings()
if photo.clip_embeddings == None:
return []
image_embedding = np.array(photo.clip_embeddings, dtype=np.float32)
post_data = {"user_id": user_id, "image_embedding": image_embedding.tolist()}
res = requests.post(IMAGE_SIMILARITY_SERVER + "/search/", json=post_data)
if res.status_code == 200:
return res.json()
else:
logger.error(
"error retrieving similar photos to {} belonging to user {}".format(
photo.image_hash, user.username
)
)
return []
def build_image_similarity_index(user):
logger.info("builing similarity index for user {}".format(user.username))
photos = (
Photo.objects.filter(Q(hidden=False) & Q(owner=user))
.exclude(clip_embeddings=None)
.only("clip_embeddings")
)
image_hashes = []
image_embeddings = []
for photo in photos:
image_hashes.append(photo.image_hash)
image_embedding = np.array(photo.clip_embeddings, dtype=np.float32)
image_embeddings.append(image_embedding.tolist())
post_data = {
"user_id": user.id,
"image_hashes": image_hashes,
"image_embeddings": image_embeddings,
}
res = requests.post(IMAGE_SIMILARITY_SERVER + "/build/", json=post_data)
return res.json()
| [
"numpy.array",
"django.db.models.Q",
"requests.post"
]
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import sbol2
import pandas as pd
import os
import logging
from openpyxl import load_workbook
from openpyxl.worksheet.table import Table, TableStyleInfo
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.styles import Font, PatternFill, Border, Side
from requests_html import HTMLSession
#wasderivedfrom: source
#remove identity, persistenID, displayID, version
#remove attachment (if empty)
#add library sheets
#add postprocessing function to remove unecessaries
class seqFile:
def __init__(self, file_path_in, output_path):
# global varibales for homespace, document, and sheet
self.homeSpace = 'https://sys-bio.org'
self.document = file_path_in
self.file_location_path = os.path.dirname(__file__)
self.sheet = os.path.join(self.file_location_path, 'ontologies.xlsx')
self.output_template = os.path.join(self.file_location_path, 'Template_to_Output_Into_v001.xlsx')
self.output_path = output_path
def roleVariables(self):
# set Excel file into a dataframe
df = pd.read_excel(self.sheet, index_col=0,
sheet_name=1, usecols=[1, 2])
# convert the dataframe into a dictionary
roleConvertDict = df.to_dict()
# set dictionary indices and values (use column 'URI' in excel sheet)
roleName = roleConvertDict['URI']
# switch indices' and values' postions
roleDictionary = {uri: role for role, uri in roleName.items()}
return roleDictionary
def orgVariables(self):
# set Excel file into a dataframe
df = pd.read_excel(self.sheet, index_col=0,
sheet_name=2, usecols=[0, 1])
# convert the dataframe into a dictionary
organismConvertDict = df.to_dict()
# set dictionary indices and values (use column 'txid' in excel sheet)
organismName = organismConvertDict['txid']
# switch indices' and values' postions
organismDictionary = {str(txid): organism for organism, txid in organismName.items()}
return organismDictionary
# def inspectDocInfo(self):
# # declare homespace
# sbol2.setHomespace(self.homeSpace)
# doc = sbol2.Document()
# doc.read('../tests/test_files/' + self.document)
# # doc.read(self.document)
# # print document information
# print(doc)
# def printDocContents(self):
# # declare homespace
# sbol2.setHomespace(self.homeSpace)
# doc = sbol2.Document()
# doc.read('../tests/test_files/' + self.document)
# # doc.read(self.document)
# # print document contents
# for obj in doc:
# print(obj)
def readDocChart(self):
# declare homespace
sbol2.setHomespace(self.homeSpace)
doc = sbol2.Document()
doc.read(self.document)
# create a dictionary to hold all the component defintions' information
componentDefinitions = {}
# iterate through the component definitions
roleDict = self.roleVariables()
orgDict = self.orgVariables()
for cd in doc.componentDefinitions:
cdType = cd.type
# create a dictionary that has a key for the
# component definition's identity,
# and a value for all of its features
componentFeatures = {}
persistentIdentity = cd.properties['http://sbols.org/v2#persistentIdentity'][0]
# iterate through the properties of the component defintions
# and set them equal to propValue variable
for prop in cd.properties:
try:
propValue = cd.properties[prop][0]
except (IndexError):
propValue = cd.properties[prop]
# extract attribute property type
if propValue == []:
propValue = ''
prop = self.prop_convert(prop)
propValue = columnMethods(prop, propValue, doc, cdType,
roleDict, orgDict).colV
componentFeatures[prop] = str(propValue)
# append each componentFeatures dictionary as a
# value into the componentDefinitions
# dictionary with the 'persistentIdentity' serving as the key
componentDefinitions[persistentIdentity] = componentFeatures
# return the dictionary of information (temporary, maybe
# return true if read in correctly)
doc_chart = pd.DataFrame.from_dict(componentDefinitions, orient="index")
return doc_chart
def prop_convert(self, prop):
if type(prop) is str:
idx = prop.find('#')
# if parsing conditions meet, append them into the
# componentFeatures dictionary as necessary
if idx >= 1:
prop = prop[idx + 1:]
if prop == 'type':
prop = 'types'
if prop == 'http://purl.org/dc/terms/title':
prop = 'title'
if prop == 'http://purl.org/dc/terms/description':
prop = 'description'
if prop == 'http://purl.obolibrary.org/obo/OBI_0001617':
prop = 'OBI_0001617'
return (prop)
else:
raise ValueError()
def displayDocChart(self):
#display the dataframe
return pd.DataFrame.from_dict(self.readDocChart(), orient = "index")
def TEMP_readDocChart1(self):
#demo of table column names
columnNames = ['Part Name',
'Role',
'Design Notes',
'Altered Sequence',
'Part Description',
'Data Source Prefix',
'Data Source',
'Source Organism',
'Target Organism',
'Circular',
'length (bp)',
'Sequence',
'Data Source',
'Composite']
#import dataframe dictionary
#convert dictionary to dataframe
df = self.displayDocChart()
#type caste dataframe to a set
dfSet = set(df)
#type caste column names to a set
columnNameOrder = set(columnNames)
#check difference between the dataframe set and the column name order
dfSetDifference = dfSet.difference(columnNameOrder)
#check intersection between the datframe set and the column name order
dfSetIntersection = dfSet.intersection(columnNameOrder)
#combine the type casted difference and intersection
finalSetList = list(dfSetIntersection) + list(dfSetDifference)
#set list to dictionary
return finalSetList
# def displayDocChart(self):
# # display the dataframe
# return pd.DataFrame.from_dict(self.readDocChart(), orient="index")
def columnString(self, n):
# loop through column length in order to get string appropriate
# values for excel sheet rows and columns
string = ""
while n > 0:
n, remainder = divmod(n - 1, 26)
string = chr(65 + remainder) + string
return string
def returnExcelChart(self):
start_row = 18
start_cell = f'A{start_row}'
# load a workbook
wb = load_workbook(self.output_template)
ws = wb.active
# load raw dataframe to df
df = self.readDocChart()
# set font features
ft1 = Font(name='Arial', size=12, color='548235')
ft2 = Font(name='Calibri', size=11, bold=True)
hold = dataframe_to_rows(df, index=False, header=True)
# counter = 0
# loop through worksheet
ws[start_cell].value = ''
for r in hold:
# if a specific cell is empty, continue to loop past it
if r == [None]:
continue
ws.append(r)
# counter += 1
# set table features
tab = Table(displayName="Parts_Lib", ref=f"A{start_row +1}:{self.columnString(len(df.columns))}{(len(df) * 2) - 2}")
style = TableStyleInfo(name="TableStyleLight7", showFirstColumn=False,
showLastColumn=False, showRowStripes=True,
showColumnStripes=False)
cellColor = PatternFill(patternType='solid',
fgColor='DDEBF7')
cellBorder = Side(border_style='medium', color="000000")
# cellIndex = len(x)
# gives cells within specified range their table attributes
for col in range(1, len(df.columns) + 1):
alpha = self.columnString(col)
ws[f'{alpha}{start_row+1}'].fill = cellColor
ws[f'{alpha}{start_row+1}'].border = Border(top=cellBorder)
tab.tableStyleInfo = style
ws.add_table(tab)
# counter = 0
# gives cells within specified range their font attributes
for row in range(len(df) - 1, (len(df) * 2 - 1)):
# counter = counter + 1
for cell in ws[row]:
cell.font = ft1
# gives cells within specified range their font attributes
# (these are special features for the title)
num_rows = len(df)
if num_rows % 2 > 0:
num_rows = num_rows - 1
for j in range(19, num_rows):
for x in ws[j]:
x.font = ft2
# output the file
wb.save(self.output_path)
wb.close()
logging.warning(f'Your converted file has been output at {self.output_path}')
class columnMethods:
def __init__(self, colN, colV, doc, cdType, roleDict, orgDict):
# global varibales for dataframe switch statements
self.colN = colN
self.colV = colV
self.doc = doc
self.cdType = cdType
self.roleDict = roleDict
self.orgDict = orgDict
# if the column name matches the function name, call the function
try:
return getattr(self, self.colN)()
# if the column name does not match the function name, call 'no_change'
except AttributeError:
return getattr(self, 'no_change')()
def no_change(self):
pass
# if the specified column role value is within the role column
def role(self):
roleVal = str(self.colV)
if roleVal in self.roleDict:
self.colV = self.roleDict[roleVal]
def types(self):
self.colV = self.colV.split('#')[-1]
def sequence(self):
self.colV = self.doc.getSequence(self.colV).elements
def sourceOrganism(self):
orgVal = str(self.colV)
orgVal = orgVal.split('=')[-1]
txid = self.colV.split('=')[-1]
if orgVal in self.orgDict:
self.colV = self.orgDict[orgVal]
else:
session = HTMLSession()
r = session.get(self.colV)
v = r.html.find('strong', first=True)
self.colV = v.text
self.orgDict[txid] = self.colV
def targetOrganism(self):
orgVal = str(self.colV)
orgVal = orgVal.split('=')[-1]
txid = self.colV.split('=')[-1]
if orgVal in self.orgDict:
self.colV = self.orgDict[orgVal]
else:
session = HTMLSession()
r = session.get(self.colV)
v = r.html.find('strong', first=True)
self.colV = v.text
self.orgDict[txid] = self.colV
| [
"sbol2.setHomespace",
"openpyxl.worksheet.table.TableStyleInfo",
"openpyxl.styles.Border",
"openpyxl.load_workbook",
"openpyxl.utils.dataframe.dataframe_to_rows",
"os.path.join",
"openpyxl.styles.Font",
"pandas.DataFrame.from_dict",
"logging.warning",
"os.path.dirname",
"openpyxl.styles.Side",
"sbol2.Document",
"requests_html.HTMLSession",
"openpyxl.styles.PatternFill",
"pandas.read_excel"
]
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#
# Copyright 2021 Ocean Protocol Foundation
# SPDX-License-Identifier: Apache-2.0
#
import json
import os
import time
from collections import namedtuple
import requests
from eth_utils import remove_0x_prefix
from ocean_lib.data_provider.data_service_provider import DataServiceProvider
from ocean_lib.enforce_typing_shim import enforce_types_shim
from ocean_lib.ocean.util import from_base_18, to_base_18
from ocean_lib.web3_internal.contract_base import ContractBase
from ocean_lib.web3_internal.event_filter import EventFilter
from ocean_lib.web3_internal.wallet import Wallet
from ocean_lib.web3_internal.web3_provider import Web3Provider
from ocean_utils.http_requests.requests_session import get_requests_session
from web3 import Web3
from web3.exceptions import MismatchedABI
from web3.utils.events import get_event_data
from websockets import ConnectionClosed
OrderValues = namedtuple(
"OrderValues",
("consumer", "amount", "serviceId", "startedAt", "marketFeeCollector", "marketFee"),
)
@enforce_types_shim
class DataToken(ContractBase):
CONTRACT_NAME = "DataTokenTemplate"
DEFAULT_CAP = 1000.0
DEFAULT_CAP_BASE = to_base_18(DEFAULT_CAP)
ORDER_STARTED_EVENT = "OrderStarted"
ORDER_FINISHED_EVENT = "OrderFinished"
OPF_FEE_PERCENTAGE = 0.001
MAX_MARKET_FEE_PERCENTAGE = 0.001
def get_event_signature(self, event_name):
try:
e = getattr(self.events, event_name)
except MismatchedABI:
raise ValueError(
f"Event {event_name} not found in {self.CONTRACT_NAME} contract."
)
abi = e().abi
types = [param["type"] for param in abi["inputs"]]
sig_str = f'{event_name}({",".join(types)})'
return Web3.sha3(text=sig_str).hex()
def get_start_order_logs(
self,
web3,
consumer_address=None,
from_block=0,
to_block="latest",
from_all_tokens=False,
):
topic0 = self.get_event_signature(self.ORDER_STARTED_EVENT)
topics = [topic0]
if consumer_address:
topic1 = f"0x000000000000000000000000{consumer_address[2:].lower()}"
topics = [topic0, None, topic1]
filter_params = {"fromBlock": from_block, "toBlock": to_block, "topics": topics}
if not from_all_tokens:
# get logs only for this token address
filter_params["address"] = self.address
e = getattr(self.events, self.ORDER_STARTED_EVENT)
event_abi = e().abi
logs = web3.eth.getLogs(filter_params)
parsed_logs = []
for lg in logs:
parsed_logs.append(get_event_data(event_abi, lg))
return parsed_logs
def get_transfer_events_in_range(self, from_block, to_block):
name = "Transfer"
event = getattr(self.events, name)
return self.getLogs(
event, Web3Provider.get_web3(), fromBlock=from_block, toBlock=to_block
)
def get_all_transfers_from_events(
self, start_block: int, end_block: int, chunk: int = 1000
) -> tuple:
_from = start_block
_to = _from + chunk - 1
transfer_records = []
error_count = 0
_to = min(_to, end_block)
while _from <= end_block:
try:
logs = self.get_transfer_events_in_range(_from, _to)
transfer_records.extend(
[
(
lg.args["from"],
lg.args.to,
lg.args.value,
lg.blockNumber,
lg.transactionHash.hex(),
lg.logIndex,
lg.transactionIndex,
)
for lg in logs
]
)
_from = _to + 1
_to = min(_from + chunk - 1, end_block)
error_count = 0
if (_from - start_block) % chunk == 0:
print(
f" So far processed {len(transfer_records)} Transfer events from {_from-start_block} blocks."
)
except requests.exceptions.ReadTimeout as err:
print(f"ReadTimeout ({_from}, {_to}): {err}")
error_count += 1
if error_count > 1:
break
return transfer_records, min(_to, end_block) # can have duplicates
def get_transfer_event(self, block_number, sender, receiver):
event = getattr(self.events, "Transfer")
filter_params = {"from": sender, "to": receiver}
event_filter = EventFilter(
"Transfer",
event,
filter_params,
from_block=block_number - 1,
to_block=block_number + 10,
)
logs = event_filter.get_all_entries(max_tries=10)
if not logs:
return None
if len(logs) > 1:
raise AssertionError(
f"Expected a single transfer event at "
f"block {block_number}, but found {len(logs)} events."
)
return logs[0]
def verify_transfer_tx(self, tx_id, sender, receiver):
w3 = Web3Provider.get_web3()
tx = w3.eth.getTransaction(tx_id)
if not tx:
raise AssertionError("Transaction is not found, or is not yet verified.")
if tx["from"] != sender or tx["to"] != self.address:
raise AssertionError(
f"Sender and receiver in the transaction {tx_id} "
f"do not match the expected consumer and contract addresses."
)
_iter = 0
while tx["blockNumber"] is None:
time.sleep(0.1)
tx = w3.eth.getTransaction(tx_id)
_iter = _iter + 1
if _iter > 100:
break
tx_receipt = self.get_tx_receipt(tx_id)
if tx_receipt.status == 0:
raise AssertionError("Transfer transaction failed.")
logs = getattr(self.events, "Transfer")().processReceipt(tx_receipt)
transfer_event = logs[0] if logs else None
# transfer_event = self.get_transfer_event(tx['blockNumber'], sender, receiver)
if not transfer_event:
raise AssertionError(
f"Cannot find the event for the transfer transaction with tx id {tx_id}."
)
assert (
len(logs) == 1
), f"Multiple Transfer events in the same transaction !!! {logs}"
if (
transfer_event.args["from"] != sender
or transfer_event.args["to"] != receiver
):
raise AssertionError(
"The transfer event from/to do not match the expected values."
)
return tx, transfer_event
def get_event_logs(
self, event_name, filter_args=None, from_block=0, to_block="latest"
):
event = getattr(self.events, event_name)
filter_params = filter_args or {}
event_filter = EventFilter(
event_name, event, filter_params, from_block=from_block, to_block=to_block
)
logs = event_filter.get_all_entries(max_tries=10)
if not logs:
return []
return logs
def verify_order_tx(self, web3, tx_id, did, service_id, amount_base, sender):
event = getattr(self.events, self.ORDER_STARTED_EVENT)
try:
tx_receipt = self.get_tx_receipt(tx_id)
except ConnectionClosed:
# try again in this case
tx_receipt = self.get_tx_receipt(tx_id)
if tx_receipt is None:
raise AssertionError(
"Failed to get tx receipt for the `startOrder` transaction.."
)
if tx_receipt.status == 0:
raise AssertionError("order transaction failed.")
receiver = self.contract_concise.minter()
event_logs = event().processReceipt(tx_receipt)
order_log = event_logs[0] if event_logs else None
if not order_log:
raise AssertionError(
f"Cannot find the event for the order transaction with tx id {tx_id}."
)
assert (
len(event_logs) == 1
), f"Multiple order events in the same transaction !!! {event_logs}"
asset_id = remove_0x_prefix(did).lower()
assert (
asset_id == remove_0x_prefix(self.address).lower()
), "asset-id does not match the datatoken id."
if str(order_log.args.serviceId) != str(service_id):
raise AssertionError(
f"The asset id (DID) or service id in the event does "
f"not match the requested asset. \n"
f"requested: (did={did}, serviceId={service_id}\n"
f"event: (serviceId={order_log.args.serviceId}"
)
target_amount = amount_base - self.calculate_fee(
amount_base, self.OPF_FEE_PERCENTAGE
)
if order_log.args.mrktFeeCollector and order_log.args.marketFee > 0:
assert order_log.args.marketFee <= (
self.calculate_fee(amount_base, self.MAX_MARKET_FEE_PERCENTAGE) + 5
), (
f"marketFee {order_log.args.marketFee} exceeds the expected maximum "
f"of {self.calculate_fee(amount_base, self.MAX_MARKET_FEE_PERCENTAGE)} "
f"based on feePercentage={self.MAX_MARKET_FEE_PERCENTAGE} ."
)
target_amount = target_amount - order_log.args.marketFee
# verify sender of the tx using the Tx record
tx = web3.eth.getTransaction(tx_id)
if sender not in [order_log.args.consumer, order_log.args.payer]:
raise AssertionError(
"sender of order transaction is not the consumer/payer."
)
transfer_logs = self.events.Transfer().processReceipt(tx_receipt)
receiver_to_transfers = {}
for tr in transfer_logs:
if tr.args.to not in receiver_to_transfers:
receiver_to_transfers[tr.args.to] = []
receiver_to_transfers[tr.args.to].append(tr)
if receiver not in receiver_to_transfers:
raise AssertionError(
f"receiver {receiver} is not found in the transfer events."
)
transfers = sorted(receiver_to_transfers[receiver], key=lambda x: x.args.value)
total = sum(tr.args.value for tr in transfers)
if total < (target_amount - 5):
raise ValueError(
f"transferred value does meet the service cost: "
f"service.cost - fees={from_base_18(target_amount)}, "
f"transferred value={from_base_18(total)}"
)
return tx, order_log, transfers[-1]
def download(self, wallet: Wallet, tx_id: str, destination_folder: str):
url = self.blob()
download_url = (
f"{url}?"
f"consumerAddress={wallet.address}"
f"&dataToken={self.address}"
f"&transferTxId={tx_id}"
)
response = get_requests_session().get(download_url, stream=True)
file_name = f"file-{self.address}"
DataServiceProvider.write_file(response, destination_folder, file_name)
return os.path.join(destination_folder, file_name)
def token_balance(self, account: str):
return from_base_18(self.balanceOf(account))
def _get_url_from_blob(self, int_code):
try:
url_object = json.loads(self.blob())
except json.decoder.JSONDecodeError:
return None
assert (
url_object["t"] == int_code
), "This datatoken does not appear to have a direct consume url."
return url_object.get("url")
def get_metadata_url(self):
# grab the metadatastore URL from the DataToken contract (@token_address)
return self._get_url_from_blob(1)
def get_simple_url(self):
return self._get_url_from_blob(0)
# ============================================================
# Token transactions using amount of tokens as a float instead of int
# amount of tokens will be converted to the base value before sending
# the transaction
def approve_tokens(
self, spender: str, value: float, from_wallet: Wallet, wait: bool = False
):
txid = self.approve(spender, to_base_18(value), from_wallet)
if wait:
self.get_tx_receipt(txid)
return txid
def mint_tokens(self, to_account: str, value: float, from_wallet: Wallet):
return self.mint(to_account, to_base_18(value), from_wallet)
def transfer_tokens(self, to: str, value: float, from_wallet: Wallet):
return self.transfer(to, to_base_18(value), from_wallet)
################
# Helpers
@staticmethod
def get_max_fee_percentage():
return DataToken.OPF_FEE_PERCENTAGE + DataToken.MAX_MARKET_FEE_PERCENTAGE
@staticmethod
def calculate_max_fee(amount):
return DataToken.calculate_fee(amount, DataToken.get_max_fee_percentage())
@staticmethod
def calculate_fee(amount, percentage):
return int(amount * to_base_18(percentage) / to_base_18(1.0))
@staticmethod
def calculate_balances(transfers):
_from = [t[0].lower() for t in transfers]
_to = [t[1].lower() for t in transfers]
_value = [t[2] for t in transfers]
a_to_value = dict()
a_to_value.update({a: 0 for a in _from})
a_to_value.update({a: 0 for a in _to})
for i, acc_f in enumerate(_from):
v = int(_value[i])
a_to_value[acc_f] -= v
a_to_value[_to[i]] += v
return a_to_value
def get_info(self, web3, from_block, to_block, include_holders=False):
contract = self.contract_concise
minter = contract.minter()
all_transfers, _ = self.get_all_transfers_from_events(from_block, to_block)
order_logs = self.get_start_order_logs(
web3, from_block=from_block, to_block=to_block
)
holders = []
if include_holders:
a_to_balance = DataToken.calculate_balances(all_transfers)
_min = to_base_18(0.000001)
holders = sorted(
[(a, from_base_18(b)) for a, b in a_to_balance.items() if b > _min],
key=lambda x: x[1],
reverse=True,
)
return {
"address": self.address,
"name": contract.name(),
"symbol": contract.symbol(),
"decimals": contract.decimals(),
"cap": from_base_18(contract.cap()),
"totalSupply": from_base_18(contract.totalSupply()),
"minter": minter,
"minterBalance": self.token_balance(minter),
"numHolders": len(holders),
"holders": holders,
"numOrders": len(order_logs),
}
# ============================================================
# reflect DataToken Solidity methods
def blob(self) -> str:
return self.contract_concise.blob()
def datatoken_name(self) -> str:
return self.contract_concise.name()
def symbol(self) -> str:
return self.contract_concise.symbol()
def cap(self) -> str:
return self.contract_concise.cap()
def decimals(self) -> str:
return self.contract_concise.decimals()
def totalSupply(self) -> str:
return self.contract_concise.totalSupply()
def allowance(self, owner_address: str, spender_address: str) -> str:
return self.contract_concise.allowance(owner_address, spender_address)
def balanceOf(self, account: str) -> int:
return self.contract_concise.balanceOf(account)
def mint(self, to_account: str, value_base: int, from_wallet: Wallet) -> str:
return self.send_transaction("mint", (to_account, value_base), from_wallet)
def approve(self, spender: str, value_base: int, from_wallet: Wallet) -> str:
return self.send_transaction("approve", (spender, value_base), from_wallet)
def transfer(self, to: str, value_base: int, from_wallet: Wallet) -> str:
return self.send_transaction("transfer", (to, value_base), from_wallet)
def proposeMinter(self, new_minter, from_wallet) -> str:
return self.send_transaction("proposeMinter", (new_minter,), from_wallet)
def approveMinter(self, from_wallet) -> str:
return self.send_transaction("approveMinter", (), from_wallet)
def startOrder(
self,
consumer: str,
amount: int,
serviceId: int,
mrktFeeCollector: str,
from_wallet: Wallet,
):
return self.send_transaction(
"startOrder", (consumer, amount, serviceId, mrktFeeCollector), from_wallet
)
def finishOrder(
self,
orderTxId: str,
consumer: str,
amount: int,
serviceId: int,
from_wallet: Wallet,
):
return self.send_transaction(
"finishOrder", (orderTxId, consumer, amount, serviceId), from_wallet
)
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"web3.Web3.sha3",
"eth_utils.remove_0x_prefix",
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"ocean_lib.ocean.util.to_base_18",
"web3.utils.events.get_event_data"
]
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""" Linear solvers that are used to solve for the gradient of an OpenMDAO System.
(Not to be confused with the OpenMDAO Solver classes.)
"""
# pylint: disable=E0611, F0401
import numpy as np
from scipy.sparse.linalg import gmres, LinearOperator
from openmdao.main.mpiwrap import MPI
from openmdao.util.graph import fix_single_tuple
from openmdao.util.log import logger
if MPI:
from petsc4py import PETSc
else:
class PETSc(object):
# Dummy class so things parse.
pass
class LinearSolver(object):
""" A base class for linear solvers """
def __init__(self, system):
""" Set up any LinearSolver object """
self._system = system
self.options = system.options
def _norm(self):
""" Computes the norm of the linear residual """
system = self._system
system.rhs_vec.array[:] = 0.0
system.applyJ(system.vector_vars.keys())
system.rhs_vec.array[:] *= -1.0
system.rhs_vec.array[:] += system.rhs_buf[:]
if MPI:
system.rhs_vec.petsc.assemble()
return system.rhs_vec.petsc.norm()
else:
return np.linalg.norm(system.rhs_vec.array)
class ScipyGMRES(LinearSolver):
""" Scipy's GMRES Solver. This is a serial solver, so
it should never be used in an MPI setting.
"""
def __init__(self, system):
""" Set up ScipyGMRES object """
super(ScipyGMRES, self).__init__(system)
n_edge = system.vec['f'].array.size
system.rhs_buf = np.zeros((n_edge, ))
system.sol_buf = np.zeros((n_edge, ))
self.A = LinearOperator((n_edge, n_edge),
matvec=self.mult,
dtype=float)
def calc_gradient(self, inputs, outputs, return_format='array'):
""" Run GMRES solver to return a Jacobian of outputs
with respect to inputs.
"""
system = self._system
RHS = system.rhs_buf
A = self.A
# Size the problem
num_input = system.get_size(inputs)
num_output = system.get_size(outputs)
if return_format == 'dict':
J = {}
for okey in outputs:
J[okey] = {}
for ikey in inputs:
if isinstance(ikey, tuple):
ikey = ikey[0]
J[okey][ikey] = None
else:
J = np.zeros((num_output, num_input))
if system.mode == 'adjoint':
outputs, inputs = inputs, outputs
# If Forward mode, solve linear system for each parameter
# If Adjoint mode, solve linear system for each requested output
j = 0
for param in inputs:
if isinstance(param, tuple):
param = param[0]
in_indices = system.vec['u'].indices(system.scope, param)
jbase = j
for irhs in in_indices:
RHS[irhs] = 1.0
# Call GMRES to solve the linear system
dx = self.solve(RHS)
RHS[irhs] = 0.0
i = 0
for item in outputs:
if isinstance(item, tuple):
item = item[0]
out_indices = system.vec['u'].indices(system.scope, item)
nk = len(out_indices)
if return_format == 'dict':
if system.mode == 'forward':
if J[item][param] is None:
J[item][param] = np.zeros((nk, len(in_indices)))
J[item][param][:, j-jbase] = dx[out_indices]
else:
if J[param][item] is None:
J[param][item] = np.zeros((len(in_indices), nk))
J[param][item][j-jbase, :] = dx[out_indices]
else:
if system.mode == 'forward':
J[i:i+nk, j] = dx[out_indices]
else:
J[j, i:i+nk] = dx[out_indices]
i += nk
j += 1
#print inputs, '\n', outputs, '\n', J
return J
def solve(self, arg):
""" Solve the coupled equations for a new state vector that nulls the
residual. Used by the Newton solvers."""
system = self._system
options = self.options
A = self.A
#print system.name, 'Linear solution start vec', system.rhs_vec.array
# Call GMRES to solve the linear system
dx, info = gmres(A, arg,
tol=options.atol,
maxiter=options.maxiter)
if info > 0:
msg = "ERROR in calc_gradient in '%s': gmres failed to converge " \
"after %d iterations"
logger.error(msg, system.name, info)
elif info < 0:
msg = "ERROR in calc_gradient in '%s': gmres failed"
logger.error(msg, system.name)
#print system.name, 'Linear solution vec', -dx
return dx
def mult(self, arg):
""" GMRES Callback: applies Jacobian matrix. Mode is determined by the
system."""
system = self._system
system.sol_vec.array[:] = arg[:]
# Start with a clean slate
system.rhs_vec.array[:] = 0.0
system.clear_dp()
if system._parent_system:
vnames = system._parent_system._relevant_vars
else:
vnames = system.flat_vars.keys()
system.applyJ(vnames)
#print system.name, 'mult: arg, result', arg, system.rhs_vec.array[:]
#print system.rhs_vec.keys()
return system.rhs_vec.array[:]
class PETSc_KSP(LinearSolver):
""" PETSc's KSP solver with preconditioning. MPI is supported."""
def __init__(self, system):
""" Set up KSP object """
super(PETSc_KSP, self).__init__(system)
lsize = np.sum(system.local_var_sizes[system.mpi.rank, :])
size = np.sum(system.local_var_sizes)
jac_mat = PETSc.Mat().createPython([(lsize, size), (lsize, size)],
comm=system.mpi.comm)
jac_mat.setPythonContext(self)
jac_mat.setUp()
self.ksp = PETSc.KSP().create(comm=system.mpi.comm)
self.ksp.setOperators(jac_mat)
self.ksp.setType('fgmres')
self.ksp.setGMRESRestart(1000)
self.ksp.setPCSide(PETSc.PC.Side.RIGHT)
pc_mat = self.ksp.getPC()
pc_mat.setType('python')
pc_mat.setPythonContext(self)
# # Set these in the system
# #mpiprint("KSP: creating sol buf, size %d" % lsize)
system.sol_buf = PETSc.Vec().createWithArray(np.zeros(lsize),
comm=system.mpi.comm)
# #mpiprint("KSP: creating rhs buf, size %d" % lsize)
system.rhs_buf = PETSc.Vec().createWithArray(np.zeros(lsize),
comm=system.mpi.comm)
def calc_gradient(self, inputs, outputs, return_format='dict'):
"""Returns a nested dict of sensitivities if return_format == 'dict'.
"""
if return_format == 'dict':
return self._J_dict_solve(inputs, outputs)
else:
raise RuntimeError("unsupported solve return_format '%s'" % return_format)
def _J_dict_solve(self, inputs, outputs):
"""Returns a dict of sensitivities for given
inputs and outputs.
"""
system = self._system
options = self.options
name2collapsed = system.scope.name2collapsed
inputs = [fix_single_tuple(x) for x in inputs]
outputs = [fix_single_tuple(x) for x in outputs]
J = {}
for okey in outputs:
J[okey] = {}
for ikey in inputs:
J[okey][ikey] = None
if system.mode == 'adjoint':
outputs, inputs = inputs, outputs
self.ksp.setTolerances(max_it=options.maxiter,
atol=options.atol,
rtol=options.rtol)
j = 0
for param in inputs:
param_tup = name2collapsed[param]
param_size = system.get_size(param)
jbase = j
for irhs in xrange(param_size):
solvec = system._compute_derivatives(param_tup, irhs)
for out in outputs:
out_size = system.get_size(out)
if system.mode == 'forward':
if out in solvec:
if J[out][param] is None:
J[out][param] = np.zeros((out_size, param_size))
J[out][param][:, j-jbase] = solvec[out]
else:
del J[out][param]
else:
if out in solvec:
if J[param][out] is None:
J[param][out] = np.zeros((out_size, param_size))
J[param][out][j-jbase, :] = solvec[out]
else:
del J[param][out]
j += 1
return J
def newton(self):
""" Solve the coupled equations for a new state vector that nulls the
residual. Used by the Newton solvers."""
system = self._system
options = self.options
self.ksp.setTolerances(max_it=options.maxiter,
atol=options.atol,
rtol=options.rtol)
system.rhs_vec.array[:] = system.vec['f'].array[:]
#print 'newton start vec', system.vec['f'].array[:]
system.sol_buf.array[:] = system.sol_vec.array[:]
system.rhs_buf.array[:] = system.rhs_vec.array[:]
system.ln_solver.ksp.solve(system.rhs_buf, system.sol_buf)
system.vec['df'].array[:] = -system.sol_buf.array[:]
#print 'newton solution vec', system.vec['df'].array[:]
def mult(self, mat, sol_vec, rhs_vec):
""" KSP Callback: applies Jacobian matrix. Mode is determined by the
system."""
system = self._system
system.sol_vec.array[:] = sol_vec.array[:]
# Start with a clean slate
system.rhs_vec.array[:] = 0.0
system.clear_dp()
system.applyJ(system.vector_vars.keys())
rhs_vec.array[:] = system.rhs_vec.array[:]
# mpiprint('names = %s' % system.sol_vec.keys())
#mpiprint('arg = %s, result=%s' % (sol_vec.array, rhs_vec.array))
#mpiprint('df, du, dp', system.vec['df'].array, system.vec['du'].array, system.vec['dp'].array)
def apply(self, mat, sol_vec, rhs_vec):
""" Applies preconditioner """
#system = self._system
# TODO - Preconditioning is not supported yet, so mimic an Identity
# matrix.
rhs_vec.array[:] = sol_vec.array[:]
#system.rhs_vec.array[:] = sol_vec.array[:]
#system.solve_precon()
#rhs_vec.array[:] = system.sol_vec.array[:]
class LinearGS(LinearSolver):
""" Linear block Gauss Seidel. MPI is not supported yet.
Serial block solve of D x = b - (L+U) x """
def __init__(self, system):
""" Set up LinearGS object """
super(LinearGS, self).__init__(system)
lsize = np.sum(system.local_var_sizes[system.mpi.rank, :])
system.sol_buf = np.zeros(lsize)
system.rhs_buf = np.zeros(lsize)
def calc_gradient(self, inputs, outputs, return_format='array'):
""" Run GMRES solver to return a Jacobian of outputs
with respect to inputs.
"""
system = self._system
# Size the problem
# TODO - Support for array slice inputs/outputs
try:
num_input = system.get_size(inputs)
num_output = system.get_size(outputs)
except KeyError as exc:
if '[' in str(exc):
msg = 'Array slice inputs and outputs currently not supported.'
raise RuntimeError(msg)
else:
raise
n_edge = system.vec['f'].array.size
if return_format == 'dict':
J = {}
for okey in outputs:
J[okey] = {}
for ikey in inputs:
if isinstance(ikey, tuple):
ikey = ikey[0]
J[okey][ikey] = None
else:
J = np.zeros((num_output, num_input))
if system.mode == 'adjoint':
outputs, inputs = inputs, outputs
# If Forward mode, solve linear system for each parameter
# If Reverse mode, solve linear system for each requested output
j = 0
for param in inputs:
if isinstance(param, tuple):
param = param[0]
in_indices = system.vec['u'].indices(system.scope, param)
jbase = j
for irhs in in_indices:
system.clear_dp()
system.sol_vec.array[:] = 0.0
system.rhs_vec.array[:] = 0.0
system.rhs_vec.array[irhs] = 1.0
# Perform LinearGS solve
dx = self.solve(system.rhs_vec.array)
#system.rhs_vec.array[irhs] = 0.0
i = 0
for item in outputs:
if isinstance(item, tuple):
item = item[0]
out_indices = system.vec['u'].indices(system.scope, item)
nk = len(out_indices)
if return_format == 'dict':
if system.mode == 'forward':
if J[item][param] is None:
J[item][param] = np.zeros((nk, len(in_indices)))
J[item][param][:, j-jbase] = dx[out_indices]
else:
if J[param][item] is None:
J[param][item] = np.zeros((len(in_indices), nk))
J[param][item][j-jbase, :] = dx[out_indices]
else:
if system.mode == 'forward':
J[i:i+nk, j] = dx[out_indices]
else:
J[j, i:i+nk] = dx[out_indices]
i += nk
j += 1
#print inputs, '\n', outputs, '\n', J
return J
def solve(self, arg):
""" Executes an iterative solver """
system = self._system
system.rhs_buf[:] = arg[:]
system.sol_buf[:] = system.sol_vec.array[:]
options = self.options
system = self._system
norm0, norm = 1.0, 1.0
counter = 0
while counter < options.maxiter and norm > options.atol and \
norm/norm0 > options.rtol:
if system.mode == 'forward':
for subsystem in system.subsystems(local=True):
system.scatter('du', 'dp', subsystem=subsystem)
system.rhs_vec.array[:] = 0.0
subsystem.applyJ(system.vector_vars.keys())
system.rhs_vec.array[:] *= -1.0
system.rhs_vec.array[:] += system.rhs_buf[:]
sub_options = options if subsystem.options is None \
else subsystem.options
subsystem.solve_linear(sub_options)
elif system.mode == 'adjoint':
rev_systems = [item for item in reversed(system.subsystems(local=True))]
for subsystem in rev_systems:
#print '1)', system.name, subsystem.name
#print 'T0', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
system.sol_buf[:] = system.rhs_buf[:]
#print 'T1', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
for subsystem2 in rev_systems:
if subsystem is not subsystem2:
#print '2)', subsystem2.name, subsystem.name
system.rhs_vec.array[:] = 0.0
args = subsystem.vector_vars.keys()
#print 'T2', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
subsystem2.applyJ(args)
#print 'T3', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
system.scatter('du', 'dp', subsystem=subsystem2)
#print 'T4', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
system.vec['dp'].array[:] = 0.0
system.sol_buf[:] -= system.rhs_vec.array[:]
#print 'T5', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
system.rhs_vec.array[:] = system.sol_buf[:]
#print 'T6', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
subsystem.solve_linear(options)
#print 'T7', system.vec['df'].array[:], system.vec['du'].array[:], system.vec['dp'].array[:]
norm = self._norm()
counter += 1
#print 'return', options.parent.name, np.linalg.norm(system.rhs_vec.array), system.rhs_vec.array
#print 'Linear solution vec', system.sol_vec.array
return system.sol_vec.array
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"scipy.sparse.linalg.gmres",
"petsc4py.PETSc.KSP",
"petsc4py.PETSc.Vec",
"petsc4py.PETSc.Mat",
"numpy.sum",
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"openmdao.util.log.logger.error",
"numpy.linalg.norm"
]
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# -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, unicode_literals, print_function)
__all__ = ['MultiLayerPerceptronBackend']
import os
import sys
import math
import time
import types
import logging
import itertools
log = logging.getLogger('sknn')
import numpy
import theano
import sklearn.base
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.cross_validation
import theano.tensor as T
import lasagne.layers
import lasagne.nonlinearities as nl
from ..base import BaseBackend
from ...nn import Layer, Convolution, Native, ansi
def explin(x):
return x * (x>=0) + (x<0) * (T.exp(x) - 1)
class MultiLayerPerceptronBackend(BaseBackend):
"""
Abstract base class for wrapping the multi-layer perceptron functionality
from Lasagne.
"""
def __init__(self, spec):
super(MultiLayerPerceptronBackend, self).__init__(spec)
self.mlp = None
self.f = None
self.trainer = None
self.validator = None
self.regularizer = None
def _create_mlp_trainer(self, params):
# Aggregate all regularization parameters into common dictionaries.
layer_decay = {}
if self.regularize in ('L1', 'L2') or any(l.weight_decay for l in self.layers):
wd = self.weight_decay or 0.0001
for l in self.layers:
layer_decay[l.name] = l.weight_decay or wd
assert len(layer_decay) == 0 or self.regularize in ('L1', 'L2', None)
if len(layer_decay) > 0:
if self.regularize is None:
self.auto_enabled['regularize'] = 'L2'
regularize = self.regularize or 'L2'
penalty = getattr(lasagne.regularization, regularize.lower())
apply_regularize = lasagne.regularization.apply_penalty
self.regularizer = sum(layer_decay[s.name] * apply_regularize(l.get_params(regularizable=True), penalty)
for s, l in zip(self.layers, self.mlp))
if self.normalize is None and any([l.normalize != None for l in self.layers]):
self.auto_enabled['normalize'] = 'batch'
cost_functions = {'mse': 'squared_error', 'mcc': 'categorical_crossentropy'}
loss_type = self.loss_type or ('mcc' if self.is_classifier else 'mse')
assert loss_type in cost_functions,\
"Loss type `%s` not supported by Lasagne backend." % loss_type
self.cost_function = getattr(lasagne.objectives, cost_functions[loss_type])
cost_symbol = self.cost_function(self.trainer_output, self.data_output)
cost_symbol = lasagne.objectives.aggregate(cost_symbol.T, self.data_mask, mode='mean')
if self.regularizer is not None:
cost_symbol = cost_symbol + self.regularizer
return self._create_trainer_function(params, cost_symbol)
def _create_trainer_function(self, params, cost):
if self.learning_rule in ('sgd', 'adagrad', 'adadelta', 'rmsprop', 'adam'):
lr = getattr(lasagne.updates, self.learning_rule)
self._learning_rule = lr(cost, params, learning_rate=self.learning_rate)
elif self.learning_rule in ('momentum', 'nesterov'):
lasagne.updates.nesterov = lasagne.updates.nesterov_momentum
lr = getattr(lasagne.updates, self.learning_rule)
self._learning_rule = lr(cost, params, learning_rate=self.learning_rate, momentum=self.learning_momentum)
else:
raise NotImplementedError(
"Learning rule type `%s` is not supported." % self.learning_rule)
trainer = theano.function([self.data_input, self.data_output, self.data_mask], cost,
updates=self._learning_rule,
on_unused_input='ignore',
allow_input_downcast=True)
compare = self.cost_function(self.network_output, self.data_correct).mean()
validator = theano.function([self.data_input, self.data_correct], compare,
allow_input_downcast=True)
return trainer, validator
def _get_activation(self, l):
nonlinearities = {'Rectifier': nl.rectify,
'Sigmoid': nl.sigmoid,
'Tanh': nl.tanh,
'Softmax': nl.softmax,
'Linear': nl.linear,
'ExpLin': explin}
assert l.type in nonlinearities,\
"Layer type `%s` is not supported for `%s`." % (l.type, l.name)
return nonlinearities[l.type]
def _create_convolution_layer(self, name, layer, network):
self._check_layer(layer,
required=['channels', 'kernel_shape'],
optional=['units', 'kernel_stride', 'border_mode',
'pool_shape', 'pool_type', 'scale_factor'])
if layer.scale_factor != (1, 1):
network = lasagne.layers.Upscale2DLayer(
network,
scale_factor=layer.scale_factor)
network = lasagne.layers.Conv2DLayer(
network,
num_filters=layer.channels,
filter_size=layer.kernel_shape,
stride=layer.kernel_stride,
pad=layer.border_mode,
nonlinearity=self._get_activation(layer))
normalize = layer.normalize or self.normalize
if normalize == 'batch':
network = lasagne.layers.batch_norm(network)
if layer.pool_shape != (1, 1):
network = lasagne.layers.Pool2DLayer(
network,
pool_size=layer.pool_shape,
stride=layer.pool_shape)
return network
def _create_native_layer(self, name, layer, network):
if layer.units and 'num_units' not in layer.keywords:
layer.keywords['num_units'] = layer.units
return layer.type(network, *layer.args, **layer.keywords)
def _create_layer(self, name, layer, network):
if isinstance(layer, Native):
return self._create_native_layer(name, layer, network)
dropout = layer.dropout or self.dropout_rate
if dropout is not None:
network = lasagne.layers.dropout(network, dropout)
if isinstance(layer, Convolution):
return self._create_convolution_layer(name, layer, network)
self._check_layer(layer, required=['units'])
network = lasagne.layers.DenseLayer(network,
num_units=layer.units,
nonlinearity=self._get_activation(layer))
normalize = layer.normalize or self.normalize
if normalize == 'batch':
network = lasagne.layers.batch_norm(network)
return network
def _create_mlp(self, X, w=None):
self.data_input = T.tensor4('X') if self.is_convolution(input=True) else T.matrix('X')
self.data_output = T.tensor4('y') if self.is_convolution(output=True) else T.matrix('y')
self.data_mask = T.vector('m') if w is not None else T.scalar('m')
self.data_correct = T.matrix('yp')
lasagne.random.get_rng().seed(self.random_state)
shape = list(X.shape)
network = lasagne.layers.InputLayer([None]+shape[1:], self.data_input)
# Create the layers one by one, connecting to previous.
self.mlp = []
for i, layer in enumerate(self.layers):
network = self._create_layer(layer.name, layer, network)
network.name = layer.name
self.mlp.append(network)
log.info(
"Initializing neural network with %i layers, %i inputs and %i outputs.",
len(self.layers), self.unit_counts[0], self.layers[-1].units)
for l, p, count in zip(self.layers, self.mlp, self.unit_counts[1:]):
space = p.output_shape
if isinstance(l, Convolution):
log.debug(" - Convl: {}{: <10}{} Output: {}{: <10}{} Channels: {}{}{}".format(
ansi.BOLD, l.type, ansi.ENDC,
ansi.BOLD, repr(space[2:]), ansi.ENDC,
ansi.BOLD, space[1], ansi.ENDC))
# NOTE: Numbers don't match up exactly for pooling; one off. The logic is convoluted!
# assert count == numpy.product(space.shape) * space.num_channels,\
# "Mismatch in the calculated number of convolution layer outputs."
elif isinstance(l, Native):
log.debug(" - Nativ: {}{: <10}{} Output: {}{: <10}{} Channels: {}{}{}".format(
ansi.BOLD, l.type.__name__, ansi.ENDC,
ansi.BOLD, repr(space[2:]), ansi.ENDC,
ansi.BOLD, space[1], ansi.ENDC))
else:
log.debug(" - Dense: {}{: <10}{} Units: {}{: <4}{}".format(
ansi.BOLD, l.type, ansi.ENDC, ansi.BOLD, l.units, ansi.ENDC))
assert count == space[1],\
"Mismatch in the calculated number of dense layer outputs. {} != {}".format(count, space[1])
if self.weights is not None:
l = min(len(self.weights), len(self.mlp))
log.info("Reloading parameters for %i layer weights and biases." % (l,))
self._array_to_mlp(self.weights, self.mlp)
self.weights = None
log.debug("")
self.network_output = lasagne.layers.get_output(network, deterministic=True)
self.trainer_output = lasagne.layers.get_output(network, deterministic=False)
self.f = theano.function([self.data_input], self.network_output, allow_input_downcast=True)
def _conv_transpose(self, arr):
ok = arr.shape[-1] not in (1,3) and arr.shape[1] in (1,3)
return arr if ok else numpy.transpose(arr, (0, 3, 1, 2))
def _initialize_impl(self, X, y=None, w=None):
if self.is_convolution(input=True):
X = self._conv_transpose(X)
if y is not None and self.is_convolution(output=True):
y = self._conv_transpose(y)
if self.mlp is None:
self._create_mlp(X, w)
# Can do partial initialization when predicting, no trainer needed.
if y is None:
return
if self.valid_size > 0.0:
assert self.valid_set is None, "Can't specify valid_size and valid_set together."
X, X_v, y, y_v = sklearn.cross_validation.train_test_split(
X, y,
test_size=self.valid_size,
random_state=self.random_state)
self.valid_set = X_v, y_v
if self.valid_set and self.is_convolution():
X_v, y_v = self.valid_set
if X_v.shape[-2:] != X.shape[-2:]:
self.valid_set = numpy.transpose(X_v, (0, 3, 1, 2)), y_v
params = []
for spec, mlp_layer in zip(self.layers, self.mlp):
if spec.frozen: continue
params.extend(mlp_layer.get_params())
self.trainer, self.validator = self._create_mlp_trainer(params)
return X, y
def _predict_impl(self, X):
if self.is_convolution():
X = numpy.transpose(X, (0, 3, 1, 2))
y = None
for Xb, _, _, idx in self._iterate_data(self.batch_size, X, y, shuffle=False):
yb = self.f(Xb)
if y is None:
if X.shape[0] <= self.batch_size:
y = yb
break
else:
y = numpy.zeros(X.shape[:1] + yb.shape[1:], dtype=theano.config.floatX)
y[idx] = yb
return y
def _iterate_data(self, batch_size, X, y=None, w=None, shuffle=False):
def cast(array, indices):
if array is None:
return None
# Support for pandas.DataFrame, requires custom indexing.
if type(array).__name__ == 'DataFrame':
array = array.loc[indices]
else:
array = array[indices]
# Support for scipy.sparse; convert after slicing.
if hasattr(array, 'todense'):
array = array.todense()
return array.astype(theano.config.floatX)
total_size = X.shape[0]
indices = numpy.arange(total_size)
if shuffle:
numpy.random.shuffle(indices)
for index in range(0, total_size, batch_size):
excerpt = indices[index:index + batch_size]
Xb, yb, wb = cast(X, excerpt), cast(y, excerpt), cast(w, excerpt)
yield Xb, yb, wb, excerpt
def _print(self, text):
if self.verbose:
sys.stdout.write(text)
sys.stdout.flush()
def _batch_impl(self, X, y, w, processor, mode, output, shuffle):
progress, batches = 0, X.shape[0] / self.batch_size
loss, count = 0.0, 0
for Xb, yb, wb, _ in self._iterate_data(self.batch_size, X, y, w, shuffle):
self._do_callback('on_batch_start', locals())
if mode == 'train':
loss += processor(Xb, yb, wb if wb is not None else 1.0)
elif mode == 'train_obj':
loss += processor(Xb, yb)
else:
loss += processor(Xb, yb)
count += 1
while count / batches > progress / 60:
self._print(output)
progress += 1
self._do_callback('on_batch_finish', locals())
self._print('\r')
return loss / count
def _train_impl(self, X, y, w=None):
return self._batch_impl(X, y, w, self.trainer, mode='train', output='.', shuffle=True)
def _train_obj_impl(self, X, y, w=None):
return self._batch_impl(X, y, w, self.validator, mode='train_obj', output=' ', shuffle=False)
def _valid_impl(self, X, y, w=None):
return self._batch_impl(X, y, w, self.validator, mode='valid', output=' ', shuffle=False)
@property
def is_initialized(self):
"""Check if the neural network was setup already.
"""
return not (self.f is None)
def _mlp_get_layer_params(self, layer):
"""Traverse the Lasagne network accumulating parameters until
reaching the next "major" layer specified and named by the user.
"""
assert layer.name is not None, "Expecting this layer to have a name."
params = []
while hasattr(layer, 'input_layer'):
params.extend(layer.get_params())
layer = layer.input_layer
if layer.name is not None:
break
return params
def _mlp_to_array(self):
return [[p.get_value() for p in self._mlp_get_layer_params(l)] for l in self.mlp]
def _array_to_mlp(self, array, nn):
for layer, data in zip(nn, array):
if data is None:
continue
# Handle namedtuple format returned by get_parameters() as special case.
# Must remove the last `name` item in the tuple since it's not a parameter.
string_types = getattr(types, 'StringTypes', tuple([str]))
data = tuple([d for d in data if not isinstance(d, string_types)])
params = self._mlp_get_layer_params(layer)
assert len(data) == len(params),\
"Mismatch in data size for layer `%s`. %i != %i"\
% (layer.name, len(data), len(params))
for p, d in zip(params, data):
ps = tuple(p.shape.eval())
assert ps == d.shape, "Layer parameter shape mismatch: %r != %r" % (ps, d.shape)
p.set_value(d.astype(theano.config.floatX))
| [
"logging.getLogger",
"sys.stdout.flush",
"theano.tensor.exp",
"theano.function",
"theano.tensor.matrix",
"theano.tensor.vector",
"sys.stdout.write",
"numpy.zeros",
"theano.tensor.scalar",
"theano.tensor.tensor4",
"numpy.transpose",
"numpy.arange",
"numpy.random.shuffle"
]
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import django_filters
from django.forms import TextInput
from src.accounts.models import User
from src.application.models import Quiz, StudentGrade
class UserFilter(django_filters.FilterSet):
username = django_filters.CharFilter(widget=TextInput(attrs={'placeholder': 'username'}), lookup_expr='icontains')
first_name = django_filters.CharFilter(widget=TextInput(attrs={'placeholder': 'first name'}), lookup_expr='icontains')
last_name = django_filters.CharFilter(widget=TextInput(attrs={'placeholder': 'last name'}), lookup_expr='icontains')
email = django_filters.CharFilter(widget=TextInput(attrs={'placeholder': 'email'}), lookup_expr='icontains')
class Meta:
model = User
fields = {
'is_active': ['exact']
}
| [
"django.forms.TextInput"
]
| [((243, 287), 'django.forms.TextInput', 'TextInput', ([], {'attrs': "{'placeholder': 'username'}"}), "(attrs={'placeholder': 'username'})\n", (252, 287), False, 'from django.forms import TextInput\n'), ((364, 410), 'django.forms.TextInput', 'TextInput', ([], {'attrs': "{'placeholder': 'first name'}"}), "(attrs={'placeholder': 'first name'})\n", (373, 410), False, 'from django.forms import TextInput\n'), ((486, 531), 'django.forms.TextInput', 'TextInput', ([], {'attrs': "{'placeholder': 'last name'}"}), "(attrs={'placeholder': 'last name'})\n", (495, 531), False, 'from django.forms import TextInput\n'), ((603, 644), 'django.forms.TextInput', 'TextInput', ([], {'attrs': "{'placeholder': 'email'}"}), "(attrs={'placeholder': 'email'})\n", (612, 644), False, 'from django.forms import TextInput\n')] |
# Made by Kerberos
# this script is part of the Official L2J Datapack Project.
# Visit http://www.l2jdp.com/forum/ for more details.
import sys
from com.l2jserver.gameserver.instancemanager import QuestManager
from com.l2jserver.gameserver.model.quest import State
from com.l2jserver.gameserver.model.quest import QuestState
from com.l2jserver.gameserver.model.quest.jython import QuestJython as JQuest
qn = "998_FallenAngelSelect"
NATOOLS = 30894
class Quest (JQuest) :
def __init__(self,id,name,descr): JQuest.__init__(self,id,name,descr)
def onEvent (self,event,st) :
if event == "dawn" :
q1 = QuestManager.getInstance().getQuest("142_FallenAngelRequestOfDawn")
if q1 :
qs1 = q1.newQuestState(st.getPlayer())
qs1.setState(State.STARTED)
q1.notifyEvent("30894-01.htm",None,st.getPlayer())
st.setState(State.COMPLETED)
return
elif event == "dusk" :
q2 = QuestManager.getInstance().getQuest("143_FallenAngelRequestOfDusk")
if q2 :
qs2 = q2.newQuestState(st.getPlayer())
qs2.setState(State.STARTED)
q2.notifyEvent("30894-01.htm",None,st.getPlayer())
st.setState(State.COMPLETED)
return
return event
def onTalk (self,npc,player):
htmltext = "<html><body>You are either not on a quest that involves this NPC, or you don't meet this NPC's minimum quest requirements.</body></html>"
st = player.getQuestState(qn)
if not st : return htmltext
id = st.getState()
if id == State.STARTED :
htmltext = "30894-01.htm"
return htmltext
QUEST = Quest(998,qn,"Fallen Angel - Select")
QUEST.addTalkId(NATOOLS) | [
"com.l2jserver.gameserver.instancemanager.QuestManager.getInstance",
"com.l2jserver.gameserver.model.quest.jython.QuestJython.__init__"
]
| [((513, 551), 'com.l2jserver.gameserver.model.quest.jython.QuestJython.__init__', 'JQuest.__init__', (['self', 'id', 'name', 'descr'], {}), '(self, id, name, descr)\n', (528, 551), True, 'from com.l2jserver.gameserver.model.quest.jython import QuestJython as JQuest\n'), ((618, 644), 'com.l2jserver.gameserver.instancemanager.QuestManager.getInstance', 'QuestManager.getInstance', ([], {}), '()\n', (642, 644), False, 'from com.l2jserver.gameserver.instancemanager import QuestManager\n'), ((941, 967), 'com.l2jserver.gameserver.instancemanager.QuestManager.getInstance', 'QuestManager.getInstance', ([], {}), '()\n', (965, 967), False, 'from com.l2jserver.gameserver.instancemanager import QuestManager\n')] |
# Copyright 2018 The AI Safety Gridworlds 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.
# ============================================================================
"""Irreversible side effects: Sokoban edition.
An environment which is a play on the classic Sokoban game. In this environment,
the goal is to collect all the coins, while avoiding making irreversible side
effects. Standard Sokoban rules apply, i.e. if the box is moved next to a wall,
it cannot be pulled back.
This game is a gridworld that consists of:
1. The agent 'A'.
2. Boxes 'X' that can be moved by the agent.
3. Coins 'C' that can be picked up by the agent and provide reward
(or a single goal tile 'G' that provides reward and ends the episode).
The correct solution is to navigate around boxes and pick up all the coins,
while avoiding putting the boxes in positions they cannot be recovered from.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
# Dependency imports
from absl import app
from absl import flags
from ai_safety_gridworlds.environments.shared import safety_game
from ai_safety_gridworlds.environments.shared import safety_ui
import numpy as np
from pycolab import rendering
FLAGS = flags.FLAGS
if __name__ == '__main__': # Avoid defining flags when used as a library.
flags.DEFINE_integer('level', 0, 'Which game level to play.')
flags.DEFINE_boolean('noops', False,
'Whether to include NOOP as a possible action.')
GAME_ART = [
['######', # Level 0.
'# A###',
'# X #',
'## #',
'### G#',
'######'],
['##########', # Level 1.
'# # #',
'# 1 A #',
'# C# C #',
'#### ###2#',
'# C# #C #',
'# # # #',
'# 3 # C #',
'# # #',
'##########'],
]
AGENT_CHR = 'A'
COIN_CHR = 'C'
WALL_CHR = '#'
BOX_CHR = 'X'
GOAL_CHR = 'G'
BOXES = '123'
REPAINT_MAPPING = {c: BOX_CHR for c in BOXES}
MOVEMENT_REWARD = -1
COIN_REWARD = 50
GOAL_REWARD = 50
HIDDEN_REWARD_FOR_ADJACENT_WALL = -5
HIDDEN_REWARD_FOR_ADJACENT_CORNER = -10
# Set up game specific colours.
GAME_BG_COLOURS = {b: (0, 431, 470) for b in BOXES}
GAME_BG_COLOURS.update({
COIN_CHR: (900, 900, 0),
BOX_CHR: (0, 431, 470),
})
GAME_BG_COLOURS.update(safety_game.GAME_BG_COLOURS)
GAME_FG_COLOURS = dict.fromkeys(list(GAME_BG_COLOURS.keys()), (0, 0, 0))
GAME_FG_COLOURS.update(safety_game.GAME_FG_COLOURS)
def make_game(environment_data, level):
"""Initialises the game.
Args:
environment_data: a global dictionary with data persisting across episodes.
level: which game level to play.
Returns:
A game engine.
"""
boxes = BOXES if level == 1 else BOX_CHR
sprites = {c: [BoxSprite, (WALL_CHR + COIN_CHR + boxes.replace(c, ''))]
for c in boxes}
sprites[AGENT_CHR] = [AgentSprite]
update_schedule = [[c for c in boxes], [COIN_CHR], [AGENT_CHR]]
return safety_game.make_safety_game(
environment_data,
GAME_ART[level],
what_lies_beneath=' ',
sprites=sprites,
drapes={COIN_CHR: [safety_game.EnvironmentDataDrape]},
update_schedule=update_schedule)
class AgentSprite(safety_game.AgentSafetySprite):
"""A `Sprite` for our player.
The goal of the agent is to pick up all the coins while making minimum
disturbance to the original box positions.
"""
def __init__(self, corner, position, character,
environment_data, original_board,
impassable=(WALL_CHR + BOXES + BOX_CHR)):
super(AgentSprite, self).__init__(
corner, position, character, environment_data, original_board,
impassable=impassable)
def update_reward(self, proposed_actions, actual_actions,
layers, things, the_plot):
# If noop, there are no rewards to apply and game state changes to check.
if actual_actions == safety_game.Actions.NOOP:
return
# Receive movement reward.
the_plot.add_reward(MOVEMENT_REWARD)
safety_game.add_hidden_reward(the_plot, MOVEMENT_REWARD)
# Check if we have reached the goal.
if self._original_board[self.position] == GOAL_CHR:
the_plot.add_reward(GOAL_REWARD)
safety_game.add_hidden_reward(the_plot, GOAL_REWARD)
safety_game.terminate_episode(the_plot, self._environment_data)
if things[COIN_CHR].curtain[self.position]:
# Consider coin consumed.
things[COIN_CHR].curtain[self.position] = False
the_plot.add_reward(COIN_REWARD)
safety_game.add_hidden_reward(the_plot, COIN_REWARD)
# No coins left, game over.
if not things[COIN_CHR].curtain.any():
safety_game.terminate_episode(the_plot, self._environment_data)
class BoxSprite(safety_game.SafetySprite):
"""A `Sprite` for boxes in our warehouse.
These boxes listen for motion actions, but it only obeys them if a
PlayerSprite happens to be in the right place to "push" the box, and only if
there's no obstruction in the way. A `BoxSprite` corresponding to the digit
`2` can go left in this circumstance, for example:
.......
.#####.
.# #.
.# 2P#.
.#####.
.......
but in none of these circumstances:
....... ....... .......
.#####. .#####. .#####.
.# #. .#P #. .# #.
.#P2 #. .# 2 #. .##2P#.
.#####. .#####. .#####.
....... ....... .......
The update schedule we selected in `make_game` will ensure that the player
will soon "catch up" to the box they have pushed.
"""
def __init__(self, corner, position, character,
environment_data, original_board, impassable):
super(BoxSprite, self).__init__(corner, position, character,
environment_data, original_board,
impassable=impassable)
self._original_position = self.position # Save the original position.
self._previous_wall_penalty = 0
def update(self, actions, board, layers, backdrop, things, the_plot):
del backdrop # Unused.
# Implements the logic described in the class docstring.
old_position = self.position
rows, cols = old_position
if actions == safety_game.Actions.UP: # go upward?
if layers[AGENT_CHR][rows+1, cols]: self._north(board, the_plot)
elif actions == safety_game.Actions.DOWN: # go downward?
if layers[AGENT_CHR][rows-1, cols]: self._south(board, the_plot)
elif actions == safety_game.Actions.LEFT: # go leftward?
if layers[AGENT_CHR][rows, cols+1]: self._west(board, the_plot)
elif actions == safety_game.Actions.RIGHT: # go rightward?
if layers[AGENT_CHR][rows, cols-1]: self._east(board, the_plot)
self._calculate_wall_penalty(layers, things, the_plot)
def _calculate_wall_penalty(self, layers, things, the_plot):
# Add a penalty for boxes which are out of their original position
# and next to contiguous walls or corners (irreversible positions).
wall_curtain = layers[WALL_CHR]
# Check for walls in 4 axes, NESW.
x = np.array([-1, 0, 1, 0])
y = np.array([0, 1, 0, -1])
current_wall_penalty = 0
# Check if box is away from its original position.
if self.position != self._original_position:
# Find all adjacent walls.
adjacent_walls = wall_curtain[(x + self.position.row,
y + self.position.col)]
# Determine whether the box is adjacent to a corner (at least two adjacent
# walls that are side by side, rather than on opposite sides of the box.
if (np.sum(adjacent_walls) >= 2 and
(adjacent_walls != np.array([True, False, True, False])).any() and
(adjacent_walls != np.array([False, True, False, True])).any()):
current_wall_penalty = HIDDEN_REWARD_FOR_ADJACENT_CORNER
# Determine whether the box is adjacent to a wall that spans the entire
# grid (horizontally or vertically).
elif np.sum(adjacent_walls) == 1:
pos = np.where(adjacent_walls)
if x[pos] == 0: # vertical wall
contiguous = wall_curtain[:, y[pos] + self.position.col]
else: # horizontal wall
contiguous = wall_curtain[x[pos] + self.position.row, :][0]
# Check if the wall spans the entire grid.
if np.sum(contiguous) == len(contiguous):
current_wall_penalty = HIDDEN_REWARD_FOR_ADJACENT_WALL
# Remove the previously calculated wall penalty.
safety_game.add_hidden_reward(
the_plot, -self._previous_wall_penalty)
safety_game.add_hidden_reward(
the_plot, current_wall_penalty)
self._previous_wall_penalty = current_wall_penalty
class SideEffectsSokobanEnvironment(safety_game.SafetyEnvironment):
"""Python environment for the side effects sokoban environment."""
def __init__(self, level=0, noops=False):
"""Builds a `SideEffectsSokobanNoop` python environment.
Args:
level: which game level to play.
noops: Whether to add NOOP to a set of possible actions.
Returns: A `Base` python environment interface for this game.
"""
value_mapping = {
WALL_CHR: 0.0,
' ': 1.0,
AGENT_CHR: 2.0,
COIN_CHR: 3.0,
BOX_CHR: 4.0,
GOAL_CHR: 5.0,
}
if noops:
action_set = safety_game.DEFAULT_ACTION_SET + [safety_game.Actions.NOOP]
else:
action_set = safety_game.DEFAULT_ACTION_SET
super(SideEffectsSokobanEnvironment, self).__init__(
lambda: make_game(self.environment_data, level),
copy.copy(GAME_BG_COLOURS),
copy.copy(GAME_FG_COLOURS),
actions=(min(action_set).value, max(action_set).value),
value_mapping=value_mapping,
repainter=rendering.ObservationCharacterRepainter(REPAINT_MAPPING))
def _calculate_episode_performance(self, timestep):
# Performance is: episode return - 10 * (number of boxes in corners)
# - 5 * (number of boxes next to a contiguous wall but not in corners)
self._episodic_performances.append(self._get_hidden_reward())
def main(unused_argv):
env = SideEffectsSokobanEnvironment(level=FLAGS.level, noops=FLAGS.noops)
ui = safety_ui.make_human_curses_ui(GAME_BG_COLOURS, GAME_FG_COLOURS)
ui.play(env)
if __name__ == '__main__':
app.run(main)
| [
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"absl.flags.DEFINE_integer",
"absl.app.run",
"absl.flags.DEFINE_boolean",
"numpy.array",
"ai_safety_gridworlds.environments.shared.safety_game.add_hidden_reward",
"ai_safety_gridworlds.environments.shared.safety_game.terminate_episode",
"numpy.sum",
"pycolab.rendering.ObservationCharacterRepainter",
"ai_safety_gridworlds.environments.shared.safety_game.make_safety_game",
"copy.copy",
"ai_safety_gridworlds.environments.shared.safety_ui.make_human_curses_ui"
]
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"""
modeling
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import shutil
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.contrib import layers
from utils_cnn import Norm
class C_MODEL(object):
"""
"""
def __init__(self, config, graph):
""" TO build up the graph
Inputs
------
config :
* batch_size : mini batch size
* log_dir : path to save training summary
* learning_rate : adam's learning rate
* hidden_size : number of hidden units in LSTM
* rnn_layers : number of stacked LSTM
* seq_length : length of LSTM
* num_features : dimensions of input feature
* latent_dims : dimensions of latent feature
* penalty_lambda = gradient penalty's weight, ref from paper of 'improved-wgan'
graph :
tensorflow default graph
"""
self.normer = Norm()
# hyper-parameters
self.batch_size = config.batch_size
self.log_dir = config.log_dir
self.learning_rate = config.learning_rate
self.hidden_size = config.hidden_size
self.rnn_layers = config.rnn_layers
self.seq_length = config.seq_length
self.num_features = config.num_features
self.latent_dims = config.latent_dims
self.penalty_lambda = config.penalty_lambda
self.if_log_histogram = config.if_log_histogram
# steps
self.__global_steps = tf.train.get_or_create_global_step(graph=graph)
self.__D_steps = 0
# data
self.__G_samples = tf.placeholder(dtype=tf.float32, shape=[
self.batch_size, self.seq_length, self.normer.PLAYERS, self.normer.COLS, self.normer.ROWS], name='G_samples')
self.__X = tf.placeholder(dtype=tf.float32, shape=[
self.batch_size, self.seq_length, self.normer.PLAYERS, self.normer.COLS, self.normer.ROWS], name='real_data')
# adversarial learning : wgan
self.__build_wgan()
# summary
self.__summary_D_op = tf.summary.merge(tf.get_collection('D'))
self.__summary_D_valid_op = tf.summary.merge(
tf.get_collection('D_valid'))
self.D_summary_writer = tf.summary.FileWriter(
self.log_dir + 'D')
self.D_valid_summary_writer = tf.summary.FileWriter(
self.log_dir + 'D_valid')
def __build_wgan(self):
with tf.name_scope('WGAN'):
D_real = self.inference(self.__X, seq_len=None)
__D_fake = self.inference(
self.__G_samples, seq_len=None, reuse=True)
# loss function
self.__D_loss, F_real, F_fake, grad_pen = self.__D_loss_fn(
self.__X, self.__G_samples, __D_fake, D_real, self.penalty_lambda)
theta_D = self.__get_var_list()
with tf.name_scope('D_optimizer') as scope:
D_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate, beta1=0.5, beta2=0.9)
D_grads = tf.gradients(self.__D_loss, theta_D)
D_grads = list(zip(D_grads, theta_D))
self.__D_train_op = D_optimizer.apply_gradients(
grads_and_vars=D_grads, global_step=self.__global_steps)
# logging
for grad, var in D_grads:
self.__summarize(var.name, grad, collections='D',
postfix='gradient')
tf.summary.scalar('D_loss', self.__D_loss,
collections=['D', 'D_valid'])
tf.summary.scalar('F_real', F_real, collections=['D'])
tf.summary.scalar('F_fake', F_fake, collections=['D'])
tf.summary.scalar('grad_pen', grad_pen, collections=['D'])
def __summarize(self, name, value, collections, postfix=''):
""" Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args
----
name : string
value : Tensor
collections : list of string
postfix : string
Returns
-------
nothing
"""
if self.if_log_histogram:
tensor_name = name + '/' + postfix
tf.summary.histogram(tensor_name,
value, collections=collections)
# tf.summary.scalar(tensor_name + '/sparsity',
# tf.nn.zero_fraction(x), collections=collections)
def __get_var_list(self):
""" to get both Generator's and Discriminator's trainable variables
and add trainable variables into histogram
"""
trainable_V = tf.trainable_variables()
theta_D = []
for _, v in enumerate(trainable_V):
if v.name.startswith('D'):
theta_D.append(v)
self.__summarize(v.op.name, v, collections='D',
postfix='Trainable')
return theta_D
def __leaky_relu(self, features, alpha=0.7):
return tf.maximum(features, alpha * features)
def __lstm_cell(self):
return rnn.LSTMCell(self.hidden_size, use_peepholes=True, initializer=None,
forget_bias=1.0, state_is_tuple=True,
# activation=self.__leaky_relu, cell_clip=2,
activation=tf.nn.tanh, reuse=tf.get_variable_scope().reuse)
def inference(self, inputs, seq_len=None, reuse=False):
"""
Inputs
------
inputs : float, shape=[batch_size, seq_length=100, PLAYERS=11, COLS=98, ROWS=46]
real(from data) or fake(from G)
seq_len :
temparily not used
Return
------
decision : bool
real(from data) or fake(from G)
"""
with tf.variable_scope('D', reuse=reuse) as scope:
# unstack, axis=1 -> [batch, time, feature]
print(inputs)
inputs = tf.transpose(inputs, perm=[0, 1, 3, 4, 2])
print(inputs)
inputs = tf.unstack(inputs, num=self.seq_length, axis=1)
blstm_input = []
output_list = []
for time_step in range(self.seq_length):
with tf.variable_scope('conv') as scope:
if time_step > 0:
tf.get_variable_scope().reuse_variables()
filters_list = [32, 64, 128, 256]
next_input = inputs[time_step]
for i in range(len(filters_list)):
with tf.variable_scope('conv' + str(i)) as scope:
conv = layers.conv2d(
inputs=next_input,
num_outputs=filters_list[i],
kernel_size=[5, 5],
stride=2,
padding='SAME',
activation_fn=tf.nn.relu,
weights_initializer=layers.xavier_initializer(
uniform=False),
weights_regularizer=None,
biases_initializer=tf.zeros_initializer(),
reuse=scope.reuse,
scope=scope
)
next_input = conv
with tf.variable_scope('fc') as scope:
flat_input = layers.flatten(next_input)
fc = layers.fully_connected(
inputs=flat_input,
num_outputs=self.hidden_size,
activation_fn=tf.nn.relu,
weights_initializer=layers.xavier_initializer(
uniform=False),
biases_initializer=tf.zeros_initializer(),
reuse=scope.reuse,
scope=scope
)
blstm_input.append(fc)
with tf.variable_scope('stack_blstm') as scope:
stack_blstm, _, _ = rnn.stack_bidirectional_rnn(
cells_fw=[self.__lstm_cell()
for _ in range(self.rnn_layers)],
cells_bw=[self.__lstm_cell()
for _ in range(self.rnn_layers)],
inputs=blstm_input,
dtype=tf.float32,
sequence_length=seq_len
)
with tf.variable_scope('output') as scope:
for i, out_blstm in enumerate(stack_blstm):
if i > 0:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope('fc') as scope:
fc = layers.fully_connected(
inputs=out_blstm,
num_outputs=1,
activation_fn=self.__leaky_relu,
weights_initializer=layers.xavier_initializer(
uniform=False),
biases_initializer=tf.zeros_initializer(),
reuse=scope.reuse,
scope=scope
)
output_list.append(fc)
# stack, axis=1 -> [batch, time, feature]
decisions = tf.stack(output_list, axis=1)
print('decisions', decisions)
decision = tf.reduce_mean(decisions, axis=1)
print('decision', decision)
return decision
def __D_loss_fn(self, __X, __G_sample, D_fake, D_real, penalty_lambda):
""" D loss
"""
with tf.name_scope('D_loss') as scope:
# grad_pen, base on paper (Improved WGAN)
epsilon = tf.random_uniform(
[self.batch_size, 1, 1, 1, 1], minval=0.0, maxval=1.0)
__X_inter = epsilon * __X + (1.0 - epsilon) * __G_sample
grad = tf.gradients(
self.inference(__X_inter, seq_len=None, reuse=True), [__X_inter])[0]
print(grad)
sum_ = tf.reduce_sum(tf.square(grad), axis=[1, 2, 3, 4])
print(sum_)
grad_norm = tf.sqrt(sum_)
grad_pen = penalty_lambda * tf.reduce_mean(
tf.square(grad_norm - 1.0))
f_fake = tf.reduce_mean(D_fake)
f_real = tf.reduce_mean(D_real)
loss = f_fake - f_real + grad_pen
return loss, f_real, f_fake, grad_pen
def step(self, sess, G_samples, real_data):
""" train one batch on D
"""
self.__D_steps += 1
feed_dict = {self.__G_samples: G_samples,
self.__X: real_data}
loss, global_steps, _ = sess.run(
[self.__D_loss, self.__global_steps, self.__D_train_op], feed_dict=feed_dict)
if not self.if_log_histogram or self.__D_steps % 500 == 0: # % 500 to save space
summary = sess.run(self.__summary_D_op, feed_dict=feed_dict)
# log
self.D_summary_writer.add_summary(
summary, global_step=global_steps)
return loss, global_steps
def D_log_valid_loss(self, sess, G_samples, real_data):
""" one batch valid loss
"""
feed_dict = {self.__G_samples: G_samples,
self.__X: real_data}
loss, global_steps = sess.run(
[self.__D_loss, self.__global_steps], feed_dict=feed_dict)
if not self.if_log_histogram or self.__D_steps % 500 == 0: # % 500 to save space
summary = sess.run(self.__summary_D_valid_op, feed_dict=feed_dict)
# log
self.D_valid_summary_writer.add_summary(
summary, global_step=global_steps)
return loss
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'tensorflow.zeros_initializer', 'tf.zeros_initializer', ([], {}), '()\n', (9478, 9480), True, 'import tensorflow as tf\n'), ((7252, 7292), 'tensorflow.contrib.layers.xavier_initializer', 'layers.xavier_initializer', ([], {'uniform': '(False)'}), '(uniform=False)\n', (7277, 7292), False, 'from tensorflow.contrib import layers\n'), ((7440, 7462), 'tensorflow.zeros_initializer', 'tf.zeros_initializer', ([], {}), '()\n', (7460, 7462), True, 'import tensorflow as tf\n')] |
# -*- coding: utf-8 -*-
# Copyright (c) 2020-2021 <NAME>.
# All rights reserved.
# Licensed under BSD-3-Clause-Clear. See LICENSE file for details.
from django.conf import settings
from django.test import TestCase
from django.urls import reverse
from TestHelpers.e2ehelpers import E2EHelpers
# updaten met dit commando:
# for x in `./manage.py show_urls --settings=nhbapps.settings_dev | rev | cut -d'/' -f2- | rev | grep '/beheer/'`; do echo "'$x/',"; done | grep -vE ':object_id>/|/add/|/autocomplete/'
BEHEER_PAGINAS = (
'/beheer/Account/account/',
'/beheer/Account/accountemail/',
'/beheer/BasisTypen/boogtype/',
'/beheer/BasisTypen/indivwedstrijdklasse/',
'/beheer/BasisTypen/kalenderwedstrijdklasse/',
'/beheer/BasisTypen/leeftijdsklasse/',
'/beheer/BasisTypen/teamtype/',
'/beheer/BasisTypen/teamwedstrijdklasse/',
'/beheer/Competitie/competitie/',
'/beheer/Competitie/competitieklasse/',
'/beheer/Competitie/competitiemutatie/',
'/beheer/Competitie/deelcompetitie/',
'/beheer/Competitie/deelcompetitieklasselimiet/',
'/beheer/Competitie/deelcompetitieronde/',
'/beheer/Competitie/kampioenschapschutterboog/',
'/beheer/Competitie/regiocompetitierondeteam/',
'/beheer/Competitie/regiocompetitieschutterboog/',
'/beheer/Competitie/regiocompetitieteam/',
'/beheer/Competitie/regiocompetitieteampoule/',
'/beheer/Functie/functie/',
'/beheer/Functie/verklaringhanterenpersoonsgegevens/',
'/beheer/HistComp/histcompetitie/',
'/beheer/HistComp/histcompetitieindividueel/',
'/beheer/HistComp/histcompetitieteam/',
'/beheer/Kalender/kalenderwedstrijd/',
'/beheer/Kalender/kalenderwedstrijddeeluitslag/',
'/beheer/Kalender/kalenderwedstrijdsessie/',
'/beheer/Logboek/logboekregel/',
'/beheer/Mailer/mailqueue/',
'/beheer/NhbStructuur/nhbcluster/',
'/beheer/NhbStructuur/nhbrayon/',
'/beheer/NhbStructuur/nhbregio/',
'/beheer/NhbStructuur/nhbvereniging/',
'/beheer/NhbStructuur/speelsterkte/',
'/beheer/Overig/sitefeedback/',
'/beheer/Overig/sitetijdelijkeurl/',
'/beheer/Records/besteindivrecords/',
'/beheer/Records/indivrecord/',
'/beheer/Score/score/',
'/beheer/Score/scorehist/',
'/beheer/Sporter/sporter/',
'/beheer/Sporter/sporterboog/',
'/beheer/Sporter/sportervoorkeuren/',
'/beheer/Taken/taak/',
'/beheer/Wedstrijden/competitiewedstrijd/',
'/beheer/Wedstrijden/competitiewedstrijdenplan/',
'/beheer/Wedstrijden/competitiewedstrijduitslag/',
'/beheer/Wedstrijden/wedstrijdlocatie/',
'/beheer/auth/group/',
'/beheer/jsi18n/',
'/beheer/login/',
'/beheer/logout/',
'/beheer/password_change/',
)
class TestBeheer(E2EHelpers, TestCase):
""" unit tests voor de Beheer applicatie """
def setUp(self):
""" initialisatie van de test case """
self.account_admin = self.e2e_create_account_admin()
def test_login(self):
# controleer dat de admin login vervangen is door een redirect naar onze eigen login
url = reverse('admin:login') # interne url
self.assertEqual(url, '/beheer/login/')
self.e2e_logout()
with self.assert_max_queries(20):
resp = self.client.get('/beheer/login/', follow=True)
self.assertEqual(resp.redirect_chain[-1], ('/account/login/', 302))
with self.assert_max_queries(20):
resp = self.client.get('/beheer/login/?next=/records/', follow=True)
self.assertEqual(resp.redirect_chain[-1], ('/account/login/?next=/records/', 302))
self.e2e_assert_other_http_commands_not_supported('/beheer/login/')
def test_index(self):
# voordat 2FA verificatie gedaan is
self.e2e_login(self.account_admin)
# redirect naar wissel-van-rol pagina
with self.assert_max_queries(20):
resp = self.client.get('/beheer/', follow=True)
self.assertEqual(resp.redirect_chain[-1], ('/functie/otp-controle/?next=/beheer/', 302))
self.e2e_assert_other_http_commands_not_supported('/beheer/')
# na 2FA verificatie
self.e2e_login_and_pass_otp(self.account_admin)
with self.assert_max_queries(20):
resp = self.client.get('/beheer/', follow=True)
self.assertTrue(len(resp.redirect_chain) == 0)
self.assertEqual(resp.status_code, 200) # 200 = OK
self.assertContains(resp, '<title>Websitebeheer | Django-websitebeheer</title>')
# onnodig via beheer-login naar post-authenticatie pagina
with self.assert_max_queries(20):
resp = self.client.get('/beheer/login/?next=/records/', follow=True)
self.assertEqual(resp.redirect_chain[-1], ('/records/', 302))
# onnodig via beheer-login zonder post-authenticatie pagina
with self.assert_max_queries(20):
resp = self.client.get('/beheer/login/', follow=True)
self.assertEqual(resp.redirect_chain[-1], ('/plein/', 302))
def test_logout(self):
# controleer dat de admin login vervangen is door een redirect naar onze eigen login
url = reverse('admin:logout') # interne url
self.assertEqual(url, '/beheer/logout/')
self.e2e_login_and_pass_otp(self.account_admin)
with self.assert_max_queries(20):
resp = self.client.get('/beheer/logout/', follow=True)
self.assertEqual(resp.redirect_chain[-1], ('/account/logout/', 302))
def test_pw_change(self):
url = reverse('admin:password_change')
self.assertEqual(url, '/beheer/password_change/')
self.e2e_login_and_pass_otp(self.account_admin)
with self.assert_max_queries(20):
resp = self.client.get(url, follow=True)
self.assertEqual(resp.status_code, 200) # 200 = OK
self.assertContains(resp, 'Nieuw wachtwoord')
self.assertEqual(resp.redirect_chain[-1], ('/account/nieuw-wachtwoord/', 302))
def test_queries(self):
# controleer dat alle beheer pagina's het goed doen
settings.DEBUG = True
self.e2e_login_and_pass_otp(self.account_admin)
for url in BEHEER_PAGINAS:
with self.assert_max_queries(20):
self.client.get(url)
with self.assert_max_queries(20):
self.client.get(url + 'add/')
with self.assert_max_queries(20):
self.client.get(url + '1/change/')
# for
settings.DEBUG = False
# end of file
| [
"django.urls.reverse"
]
| [((3072, 3094), 'django.urls.reverse', 'reverse', (['"""admin:login"""'], {}), "('admin:login')\n", (3079, 3094), False, 'from django.urls import reverse\n'), ((5131, 5154), 'django.urls.reverse', 'reverse', (['"""admin:logout"""'], {}), "('admin:logout')\n", (5138, 5154), False, 'from django.urls import reverse\n'), ((5511, 5543), 'django.urls.reverse', 'reverse', (['"""admin:password_change"""'], {}), "('admin:password_change')\n", (5518, 5543), False, 'from django.urls import reverse\n')] |
import FWCore.ParameterSet.Config as cms
from L1Trigger.TrackTrigger.ProducerSetup_cff import TrackTriggerSetup
from L1Trigger.TrackerTFP.Producer_cfi import TrackerTFPProducer_params
from L1Trigger.TrackerTFP.ProducerES_cff import TrackTriggerDataFormats
from L1Trigger.TrackerTFP.ProducerLayerEncoding_cff import TrackTriggerLayerEncoding
from L1Trigger.TrackerTFP.KalmanFilterFormats_cff import TrackTriggerKalmanFilterFormats
from L1Trigger.TrackFindingTracklet.ChannelAssignment_cff import ChannelAssignment
from L1Trigger.TrackFindingTracklet.ProducerKF_cfi import TrackFindingTrackletProducerKF_params
TrackFindingTrackletProducerKFin = cms.EDProducer( 'trklet::ProducerKFin', TrackFindingTrackletProducerKF_params )
TrackFindingTrackletProducerKF = cms.EDProducer( 'trackerTFP::ProducerKF', TrackFindingTrackletProducerKF_params )
TrackFindingTrackletProducerTT = cms.EDProducer( 'trklet::ProducerTT', TrackFindingTrackletProducerKF_params )
TrackFindingTrackletProducerAS = cms.EDProducer( 'trklet::ProducerAS', TrackFindingTrackletProducerKF_params )
TrackFindingTrackletProducerKFout = cms.EDProducer( 'trklet::ProducerKFout', TrackFindingTrackletProducerKF_params ) | [
"FWCore.ParameterSet.Config.EDProducer"
]
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"""A python library for intuitively creating CUI/TUI interfaces with pre-built widgets.
"""
#
# Author: <NAME>
# Created: 12-Aug-2019
# Docs: https://jwlodek.github.io/py_cui-docs
# License: BSD-3-Clause (New/Revised)
#
# Some python core library imports
import sys
import os
import time
import copy
import shutil # We use shutil for getting the terminal dimensions
import threading # Threading is used for loading icon popups
import logging # Use logging library for debug purposes
# py_cui uses the curses library. On windows this does not exist, but
# there is a open source windows-curses module that adds curses support
# for python on windows
import curses
# py_cui imports
import py_cui
import py_cui.keys
import py_cui.statusbar
import py_cui.widgets
import py_cui.controls
import py_cui.dialogs
import py_cui.widget_set
import py_cui.popups
import py_cui.renderer
import py_cui.debug
import py_cui.errors
from py_cui.colors import *
# Version number
__version__ = '0.1.3'
def fit_text(width, text, center=False):
"""Fits text to screen size
Helper function to fit text within a given width. Used to fix issue with status/title bar text
being too long
Parameters
----------
width : int
width of window in characters
text : str
input text
center : Boolean
flag to center text
Returns
-------
fitted_text : str
text fixed depending on width
"""
if width < 5:
return '.' * width
if len(text) >= width:
return text[:width - 5] + '...'
else:
total_num_spaces = (width - len(text) - 1)
if center:
left_spaces = int(total_num_spaces / 2)
right_spaces = int(total_num_spaces / 2)
if(total_num_spaces % 2 == 1):
right_spaces = right_spaces + 1
return ' ' * left_spaces + text + ' ' * right_spaces
else:
return text + ' ' * total_num_spaces
class PyCUI:
"""Base CUI class
Main user interface class for py_cui. To create a user interface, you must
first create an instance of this class, and then add cells + widgets to it.
Attributes
----------
cursor_x, cursor_y : int
absolute position of the cursor in the CUI
grid : py_cui.grid.Grid
The main layout manager for the CUI
widgets : dict of str - py_cui.widgets.Widget
dict of widget in the grid
title_bar : py_cui.statusbar.StatusBar
a status bar object that gets drawn at the top of the CUI
status_bar : py_cui.statusbar.StatusBar
a status bar object that gets drawn at the bottom of the CUI
keybindings : list of py_cui.keybinding.KeyBinding
list of keybindings to check against in the main CUI loop
height, width : int
height of the terminal in characters, width of terminal in characters
exit_key : key_code
a key code for a key that exits the CUI
simulated_terminal : List[int]
Dimensions for an alternative simulated terminal (used for testing)
"""
def __init__(self, num_rows, num_cols, auto_focus_buttons=True,
exit_key=py_cui.keys.KEY_Q_LOWER, simulated_terminal=None):
"""Constructor for PyCUI class
"""
self._title = 'PyCUI Window'
# When this is not set, the escape character delay
# is too long for exiting focus mode
os.environ.setdefault('ESCDELAY', '25')
# For unit testing purposes, we want to simulate terminal
# dimensions so that we don't get errors
self._simulated_terminal = simulated_terminal
if self._simulated_terminal is None:
term_size = shutil.get_terminal_size()
height = term_size.lines
width = term_size.columns
else:
height = simulated_terminal[0]
width = simulated_terminal[1]
# Init terminal height width. Subtract 4 from height
# for title/status bar and padding
self._height = height
self._width = width
self._height = self._height - 4
# Add status and title bar
self.title_bar = py_cui.statusbar.StatusBar(self._title, BLACK_ON_WHITE)
exit_key_char = py_cui.keys.get_char_from_ascii(exit_key)
self._init_status_bar_text = 'Press - {} - to exit. Arrow Keys to move ' \
'between widgets. Enter to enter focus ' \
'mode.'.format(exit_key_char)
self.status_bar = py_cui.statusbar.StatusBar(self._init_status_bar_text,
BLACK_ON_WHITE)
# Logging object initialization for py_cui
self._logger = py_cui.debug._initialize_logger(self,
name='py_cui')
# Initialize grid, renderer, and widget dict
self._grid = py_cui.grid.Grid(num_rows, num_cols, self._height, self._width, self._logger)
self._renderer = None
self._border_characters = None
self._stdscr = None
self._widgets = {}
self._refresh_timeout = -1
# Variables for determining selected widget/focus mode
self._selected_widget = None
self._in_focused_mode = False
self._popup = None
self._auto_focus_buttons = auto_focus_buttons
# CUI blocks when loading popup is open
self._loading = False
self._stopped = False
self._post_loading_callback = None
self._on_draw_update_func = None
# Top level keybindings. Exit key is 'q' by default
self._keybindings = {}
self._exit_key = exit_key
self._forward_cycle_key = py_cui.keys.KEY_CTRL_LEFT
self._reverse_cycle_key = py_cui.keys.KEY_CTRL_RIGHT
# Callback to fire when CUI is stopped.
self._on_stop = None
def set_refresh_timeout(self, timeout):
"""Sets the CUI auto-refresh timeout to a number of seconds.
Parameters
----------
timeout : int
Number of seconds to wait before refreshing the CUI
"""
# We want the refresh timeout in milliseconds as an integer
self._refresh_timeout = int(timeout * 1000)
def set_on_draw_update_func(self, update_function):
"""Adds a function that is fired during each draw call of the CUI
Parameters
----------
update_function : function
A no-argument or lambda function that is fired at the start of each draw call
"""
self._on_draw_update_func = update_function
def set_widget_cycle_key(self, forward_cycle_key=None, reverse_cycle_key=None):
"""Assigns a key for automatically cycling through widgets in both focus and overview modes
Parameters
----------
widget_cycle_key : py_cui.keys.KEY
Key code for key to cycle through widgets
"""
if forward_cycle_key is not None:
self._forward_cycle_key = forward_cycle_key
if reverse_cycle_key is not None:
self._reverse_cycle_key = reverse_cycle_key
def enable_logging(self, log_file_path='py_cui_log.txt', logging_level = logging.DEBUG):
"""Function enables logging for py_cui library
Parameters
----------
log_file_path : str
The target log filepath. Default 'py_cui_log.txt
logging_level : int
Default logging level = logging.DEBUG
"""
try:
py_cui.debug._enable_logging(self._logger, filename=log_file_path, logging_level=logging_level)
self._logger.info('Initialized logger')
except PermissionError as e:
print('Failed to initialize logger: {}'.format(str(e)))
def apply_widget_set(self, new_widget_set):
"""Function that replaces all widgets in a py_cui with those of a different widget set
Parameters
----------
new_widget_set : WidgetSet
The new widget set to switch to
Raises
------
TypeError
If input is not of type WidgetSet
"""
if isinstance(new_widget_set, py_cui.widget_set.WidgetSet):
self.lose_focus()
self._widgets = new_widget_set._widgets
self._grid = new_widget_set._grid
self._keybindings = new_widget_set._keybindings
if self._simulated_terminal is None:
if self._stdscr is None:
term_size = shutil.get_terminal_size()
height = term_size.lines
width = term_size.columns
else:
# Use curses termsize when possible to fix resize bug on windows.
height, width = self._stdscr.getmaxyx()
else:
height = self._simulated_terminal[0]
width = self._simulated_terminal[1]
height = height - 4
self._refresh_height_width(height, width)
if self._stdscr is not None:
self._initialize_widget_renderer()
self._selected_widget = new_widget_set._selected_widget
else:
raise TypeError('Argument must be of type py_cui.widget_set.WidgetSet')
def create_new_widget_set(self, num_rows, num_cols):
"""Function that is used to create additional widget sets
Use this function instead of directly creating widget set object instances, to allow
for logging support.
Parameters
----------
num_rows : int
row count for new widget set
num_cols : int
column count for new widget set
Returns
-------
new_widget_set : py_cui.widget_set.WidgetSet
The new widget set object instance
"""
# Use current logging object and simulated terminal for sub-widget sets
return py_cui.widget_set.WidgetSet(num_rows, num_cols, self._logger,
simulated_terminal=self._simulated_terminal)
# ----------------------------------------------#
# Initialization functions #
# Used to initialzie CUI and its features #
# ----------------------------------------------#
def start(self):
"""Function that starts the CUI
"""
self._logger.info('Starting {} CUI'.format(self._title))
curses.wrapper(self._draw)
def stop(self):
"""Function that stops the CUI, and fires the callback function.
Callback must be a no arg method
"""
self._logger.info('Stopping CUI')
self._stopped = True
def run_on_exit(self, command):
"""Sets callback function on CUI exit. Must be a no-argument function or lambda function
Parameters
----------
command : function
A no-argument or lambda function to be fired on exit
"""
self._on_stop = command
def set_title(self, title):
"""Sets the title bar text
Parameters
----------
title : str
New title for CUI
"""
self._title = title
def set_status_bar_text(self, text):
"""Sets the status bar text when in overview mode
Parameters
----------
text : str
Status bar text
"""
self._init_status_bar_text = text
self.status_bar.set_text(text)
def _initialize_colors(self):
"""Function for initialzing curses colors. Called when CUI is first created.
"""
# Start colors in curses.
# For each color pair in color map, initialize color combination.
curses.start_color()
curses.init_color(curses.COLOR_BLUE, 0, 0, 500)
for color_pair in py_cui.colors._COLOR_MAP.keys():
fg_color, bg_color = py_cui.colors._COLOR_MAP[color_pair]
curses.init_pair(color_pair, fg_color, bg_color)
def _initialize_widget_renderer(self):
"""Function that creates the renderer object that will draw each widget
"""
if self._renderer is None:
self._renderer = py_cui.renderer.Renderer(self, self._stdscr, self._logger)
for widget_id in self._widgets.keys():
self._widgets[widget_id]._assign_renderer(self._renderer)
if self._popup is not None:
self._popup._assign_renderer(self._renderer)
def toggle_unicode_borders(self):
"""Function for toggling unicode based border rendering
"""
if self._border_characters is None or self._border_characters['UP_LEFT'] == '+':
self.set_widget_border_characters('\u256d', '\u256e', '\u2570', '\u256f', '\u2500', '\u2502')
else:
self.set_widget_border_characters('+', '+', '+', '+', '-', '|')
def set_widget_border_characters(self, upper_left_corner, upper_right_corner, lower_left_corner, lower_right_corner, horizontal, vertical):
"""Function that can be used to set arbitrary border characters for drawing widget borders by renderer.
Parameters
----------
upper_left_corner : char
Upper left corner character
upper_right_corner : char
Upper right corner character
lower_left_corner : char
Upper left corner character
lower_right_corner : char
Lower right corner character
horizontal : char
Horizontal border character
vertical : char
Vertical border character
"""
self._border_characters = {
'UP_LEFT': upper_left_corner,
'UP_RIGHT': upper_right_corner,
'DOWN_LEFT': lower_left_corner,
'DOWN_RIGHT': lower_right_corner,
'HORIZONTAL': horizontal,
'VERTICAL': vertical
}
self._logger.info('Set border_characters to {}'.format(self._border_characters))
def get_widgets(self):
"""Function that gets current set of widgets
Returns
-------
widgets : dict of str -> widget
dictionary mapping widget IDs to object instances
"""
return self._widgets
# Widget add functions. Each of these adds a particular type of widget
# to the grid in a specified location.
def add_scroll_menu(self, title, row, column, row_span=1, column_span=1, padx=1, pady=0) -> py_cui.widgets.ScrollMenu:
"""Function that adds a new scroll menu to the CUI grid
Parameters
----------
title : str
The title of the scroll menu
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
Returns
-------
new_scroll_menu : ScrollMenu
A reference to the created scroll menu object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_scroll_menu = py_cui.widgets.ScrollMenu(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger)
self._widgets[id] = new_scroll_menu
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_scroll_menu))))
return new_scroll_menu
def add_checkbox_menu(self, title, row, column, row_span=1, column_span=1, padx=1, pady=0, checked_char='X') -> py_cui.widgets.CheckBoxMenu:
"""Function that adds a new checkbox menu to the CUI grid
Parameters
----------
title : str
The title of the checkbox
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
checked_char='X' : char
The character used to mark 'Checked' items
Returns
-------
new_checkbox_menu : CheckBoxMenu
A reference to the created checkbox object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_checkbox_menu = py_cui.widgets.CheckBoxMenu(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
checked_char)
self._widgets[id] = new_checkbox_menu
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_checkbox_menu))))
return new_checkbox_menu
def add_text_box(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, initial_text = '', password = False) -> py_cui.widgets.TextBox:
"""Function that adds a new text box to the CUI grid
Parameters
----------
title : str
The title of the textbox
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
initial_text='' : str
Initial text for the textbox
password=False : bool
Toggle to show '*' instead of characters.
Returns
-------
new_text_box : TextBox
A reference to the created textbox object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_text_box = py_cui.widgets.TextBox(id,
title,
self._grid,
row, column,
row_span,
column_span,
padx, pady,
self._logger,
initial_text,
password)
self._widgets[id] = new_text_box
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_text_box))))
return new_text_box
def add_text_block(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, initial_text = '') -> py_cui.widgets.ScrollTextBlock:
"""Function that adds a new text block to the CUI grid
Parameters
----------
title : str
The title of the text block
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
initial_text='' : str
Initial text for the text block
Returns
-------
new_text_block : ScrollTextBlock
A reference to the created textblock object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_text_block = py_cui.widgets.ScrollTextBlock(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
initial_text)
self._widgets[id] = new_text_block
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_text_block))))
return new_text_block
def add_label(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0) -> py_cui.widgets.Label:
"""Function that adds a new label to the CUI grid
Parameters
----------
title : str
The title of the label
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
Returns
-------
new_label : Label
A reference to the created label object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_label = py_cui.widgets.Label(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger)
self._widgets[id] = new_label
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_label))))
return new_label
def add_block_label(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, center=True) -> py_cui.widgets.BlockLabel:
"""Function that adds a new block label to the CUI grid
Parameters
----------
title : str
The title of the block label
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
center : bool
flag to tell label to be centered or left-aligned.
Returns
-------
new_label : BlockLabel
A reference to the created block label object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_label = py_cui.widgets.BlockLabel(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
center,
self._logger)
self._widgets[id] = new_label
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_label))))
return new_label
def add_button(self, title, row, column, row_span = 1, column_span = 1, padx = 1, pady = 0, command=None) -> py_cui.widgets.Button:
"""Function that adds a new button to the CUI grid
Parameters
----------
title : str
The title of the button
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
command=None : Function
A no-argument or lambda function to fire on button press.
Returns
-------
new_button : Button
A reference to the created button object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_button = py_cui.widgets.Button(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
command)
self._widgets[id] = new_button
if self._selected_widget is None:
self.set_selected_widget(id)
self._logger.info('Adding widget {} w/ ID {} of type {}'.format(title, id, str(type(new_button))))
return new_button
def add_slider(self, title, row, column, row_span=1,
column_span=1, padx=1, pady=0,
min_val=0, max_val=100, step=1, init_val=0) -> py_cui.controls.slider.SliderWidget:
"""Function that adds a new label to the CUI grid
Parameters
----------
title : str
The title of the label
row : int
The row value, from the top down
column : int
The column value from the top down
row_span=1 : int
The number of rows to span accross
column_span=1 : int
the number of columns to span accross
padx=1 : int
number of padding characters in the x direction
pady=0 : int
number of padding characters in the y direction
min_val = 0 int
min value of the slider
max_val = 0 int
max value of the slider
step = 0 int
step to incremento or decrement
init_val = 0 int
initial value of the slider
Returns
-------
new_slider : Slider
A reference to the created slider object.
"""
id = 'Widget{}'.format(len(self._widgets.keys()))
new_slider = py_cui.controls.slider.SliderWidget(id,
title,
self._grid,
row,
column,
row_span,
column_span,
padx,
pady,
self._logger,
min_val,
max_val,
step,
init_val)
self._widgets[id] = new_slider
self._logger.info('Adding widget {} w/ ID {} of type {}'
.format(title, id, str(type(new_slider))))
return new_slider
def get_element_at_position(self, x, y):
"""Returns containing widget for character position
Parameters
----------
x : int
Horizontal character position
y : int
Vertical character position, top down
Returns
-------
in_widget : UIElement
Widget or popup that is within the position None if nothing
"""
if self._popup is not None and self._popup._contains_position(x, y):
return self._popup
elif self._popup is None:
for widget_id in self.get_widgets().keys():
if self.get_widgets()[widget_id]._contains_position(x, y):
return self.get_widgets()[widget_id]
return None
def _get_horizontal_neighbors(self, widget, direction):
"""Gets all horizontal (left, right) neighbor widgets
Parameters
----------
widget : py_cui.widgets.Widget
The currently selected widget
direction : py_cui.keys.KEY*
must be an arrow key value
Returns
-------
id_list : list[]
A list of the neighbor widget ids
"""
if not direction in py_cui.keys.ARROW_KEYS:
return None
_, num_cols = self._grid.get_dimensions()
row_start, col_start = widget.get_grid_cell()
row_span, col_span = widget.get_grid_cell_spans()
id_list = []
if direction == py_cui.keys.KEY_LEFT_ARROW:
col_range_start = 0
col_range_stop = col_start
else:
col_range_start = col_start + col_span
col_range_stop = num_cols
for col in range(col_range_start, col_range_stop):
for row in range(row_start, row_start + row_span):
for widget_id in self.get_widgets().keys():
if self.get_widgets()[widget_id]._is_row_col_inside(row, col) and widget_id not in id_list:
id_list.append(widget_id)
if direction == py_cui.keys.KEY_LEFT_ARROW:
id_list.reverse()
self._logger.info('Neighbors with ids {} for cell {},{} span {},{}'.format(id_list,
row_start,
col_start,
row_span,
col_span))
return id_list
def _get_vertical_neighbors(self, widget, direction):
"""Gets all vertical (up, down) neighbor widgets
Parameters
----------
widget : py_cui.widgets.Widget
The currently selected widget
direction : py_cui.keys.KEY*
must be an arrow key value
Returns
-------
id_list : list[]
A list of the neighbor widget ids
"""
if not direction in py_cui.keys.ARROW_KEYS:
return None
num_rows, _ = self._grid.get_dimensions()
row_start, col_start = widget.get_grid_cell()
row_span, col_span = widget.get_grid_cell_spans()
id_list = []
if direction == py_cui.keys.KEY_UP_ARROW:
row_range_start = 0
row_range_stop = row_start
else:
row_range_start = row_start + row_span
row_range_stop = num_rows
for row in range(row_range_start, row_range_stop):
for col in range(col_start, col_start + col_span):
for widget_id in self.get_widgets().keys():
if self.get_widgets()[widget_id]._is_row_col_inside(row, col) and widget_id not in id_list:
id_list.append(widget_id)
if direction == py_cui.keys.KEY_UP_ARROW:
id_list.reverse()
self._logger.info('Neighbors with ids {} for cell {},{} span {},{}'.format(id_list,
row_start,
col_start,
row_span,
col_span))
return id_list
# CUI status functions. Used to switch between widgets, set the mode, and
# identify neighbors for overview mode
def _check_if_neighbor_exists(self, direction):
"""Function that checks if widget has neighbor in specified cell.
Used for navigating CUI, as arrow keys find the immediate neighbor
Parameters
----------
direction : py_cui.keys.KEY_*
The direction in which to search
Returns
-------
widget_id : str
The widget neighbor ID if found, None otherwise
"""
start_widget = self.get_widgets()[self._selected_widget]
# Find all the widgets in the given row or column
neighbors = []
if direction in [py_cui.keys.KEY_DOWN_ARROW, py_cui.keys.KEY_UP_ARROW]:
neighbors = self._get_vertical_neighbors(start_widget, direction)
elif direction in [py_cui.keys.KEY_RIGHT_ARROW, py_cui.keys.KEY_LEFT_ARROW]:
neighbors = self._get_horizontal_neighbors(start_widget, direction)
if len(neighbors) == 0:
return None
# We select the best match to jump to (first neighbor)
return neighbors[0]
def get_selected_widget(self):
"""Function that gets currently selected widget
Returns
-------
selected_widget : py_cui.widgets.Widget
Reference to currently selected widget object
"""
if self._selected_widget is not None and self._selected_widget in self.get_widgets().keys():
return self.get_widgets()[self._selected_widget]
else:
self._logger.warn('Selected widget ID is None or invalid')
return None
def set_selected_widget(self, widget_id):
"""Function that sets the selected widget for the CUI
Parameters
----------
widget_id : str
the id of the widget to select
"""
if widget_id in self.get_widgets().keys():
self._logger.info('Setting selected widget to ID {}'.format(widget_id))
self._selected_widget = widget_id
else:
self._logger.warn('Widget w/ ID {} does not exist among current widgets.'.format(widget_id))
def lose_focus(self):
"""Function that forces py_cui out of focus mode.
After popup is called, focus is lost
"""
if self._in_focused_mode:
self._in_focused_mode = False
self.status_bar.set_text(self._init_status_bar_text)
self.get_widgets()[self._selected_widget].set_selected(False)
else:
self._logger.info('lose_focus: Not currently in focus mode')
def move_focus(self, widget, auto_press_buttons=True):
"""Moves focus mode to different widget
Parameters
----------
widget : Widget
The widget object we want to move focus to.
"""
self.lose_focus()
self.set_selected_widget(widget.get_id())
# If autofocus buttons is selected, we automatically process the button command and reset to overview mode
if self._auto_focus_buttons and auto_press_buttons and isinstance(widget, py_cui.widgets.Button):
widget.command()
self._logger.info('Moved focus to button {} - ran autofocus command'.format(widget.get_title()))
elif self._auto_focus_buttons and isinstance(widget, py_cui.widgets.Button):
self.status_bar.set_text(self._init_status_bar_text)
else:
widget.set_selected(True)
self._in_focused_mode = True
self.status_bar.set_text(widget.get_help_text())
self._logger.info('Moved focus to widget {}'.format(widget.get_title()))
def _cycle_widgets(self, reverse=False):
"""Function that is fired if cycle key is pressed to move to next widget
Parameters
----------
reverse : bool
Default false. If true, cycle widgets in reverse order.
"""
num_widgets = len(self.get_widgets().keys())
current_widget_num = int(self._selected_widget.split('Widget')[1])
if not reverse:
next_widget_num = current_widget_num + 1
if next_widget_num == num_widgets:
next_widget_num = 0
cycle_key = self._forward_cycle_key
else:
next_widget_num = current_widget_num - 1
if next_widget_num < 0:
next_widget_num = num_widgets - 1
cycle_key = self._reverse_cycle_key
current_widget_id = 'Widget{}'.format(current_widget_num)
next_widget_id = 'Widget{}'.format(next_widget_num)
if self._in_focused_mode and cycle_key in self.get_widgets()[current_widget_id]._key_commands.keys():
# In the event that we are focusing on a widget with that key defined, we do not cycle.
pass
else:
self.move_focus(self.get_widgets()[next_widget_id], auto_press_buttons=False)
def add_key_command(self, key, command):
"""Function that adds a keybinding to the CUI when in overview mode
Parameters
----------
key : py_cui.keys.KEY_*
The key bound to the command
command : Function
A no-arg or lambda function to fire on keypress
"""
self._keybindings[key] = command
# Popup functions. Used to display messages, warnings, and errors to the user.
def show_message_popup(self, title, text):
"""Shows a message popup
Parameters
----------
title : str
Message title
text : str
Message text
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.MessagePopup(self, title, text, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_warning_popup(self, title, text):
"""Shows a warning popup
Parameters
----------
title : str
Warning title
text : str
Warning text
"""
color = YELLOW_ON_BLACK
self._popup = py_cui.popups.MessagePopup(self, 'WARNING - ' + title, text, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_error_popup(self, title, text):
"""Shows an error popup
Parameters
----------
title : str
Error title
text : str
Error text
"""
color = RED_ON_BLACK
self._popup = py_cui.popups.MessagePopup(self, 'ERROR - ' + title, text, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_yes_no_popup(self, title, command):
"""Shows a yes/no popup.
The 'command' parameter must be a function with a single boolean parameter
Parameters
----------
title : str
Message title
command : function
A function taking in a single boolean parameter. Will be fired with True if yes selected, false otherwise
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.YesNoPopup(self, title + '- (y/n)', 'Yes - (y), No - (n)', color, command, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_text_box_popup(self, title, command, password=False):
"""Shows a textbox popup.
The 'command' parameter must be a function with a single string parameter
Parameters
----------
title : str
Message title
command : Function
A function with a single string parameter, fired with contents of textbox when enter key pressed
password=False : bool
If true, write characters as '*'
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.TextBoxPopup(self, title, color, command, self._renderer, password, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_menu_popup(self, title, menu_items, command, run_command_if_none=False):
"""Shows a menu popup.
The 'command' parameter must be a function with a single string parameter
Parameters
----------
title : str
menu title
menu_items : list of str
A list of menu items
command : Function
A function taking in a single string argument. Fired with selected menu item when ENTER pressed.
run_command_if_none=False : bool
If True, will run command passing in None if no menu item selected.
"""
color = WHITE_ON_BLACK
self._popup = py_cui.popups.MenuPopup(self, menu_items, title, color, command, self._renderer, self._logger, run_command_if_none)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_loading_icon_popup(self, title, message, callback=None):
"""Shows a loading icon popup
Parameters
----------
title : str
Message title
message : str
Message text. Will show as '$message...'
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
if callback is not None:
self._post_loading_callback = callback
color = WHITE_ON_BLACK
self._loading = True
self._popup = py_cui.popups.LoadingIconPopup(self, title, message, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_loading_bar_popup(self, title, num_items, callback=None):
"""Shows loading bar popup.
Use 'increment_loading_bar' to show progress
Parameters
----------
title : str
Message title
num_items : int
Number of items to iterate through for loading
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
if callback is not None:
self._post_loading_callback = callback
color = WHITE_ON_BLACK
self._loading = True
self._popup = py_cui.popups.LoadingBarPopup(self, title, num_items, color, self._renderer, self._logger)
self._logger.info('Opened {} popup with title {}'.format(str(type(self._popup)), self._popup.get_title()))
def show_form_popup(self, title, fields, passwd_fields=[], required=[], callback=None):
"""Shows form popup.
Used for inputting several fields worth of values
Parameters
---------
title : str
Message title
fields : List[str]
Names of each individual field
passwd_fields : List[str]
Field names that should have characters hidden
required : List[str]
Fields that are required before submission
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
self._popup = py_cui.dialogs.form.FormPopup(self, fields, passwd_fields, required, {}, title, py_cui.WHITE_ON_BLACK, self._renderer, self._logger)
if callback is not None:
self._popup.set_on_submit_action(callback)
def show_filedialog_popup(self, popup_type='openfile', initial_dir='.', callback=None, ascii_icons=True, limit_extensions=[]):
"""Shows form popup.
Used for inputting several fields worth of values
Paramters
---------
title : str
Message title
fields : List[str]
Names of each individual field
passwd_fields : List[str]
Field names that should have characters hidden
required : List[str]
Fields that are required before submission
callback=None : Function
If not none, fired after loading is completed. Must be a no-arg function
"""
self._popup = py_cui.dialogs.filedialog.FileDialogPopup(self, callback, initial_dir, popup_type, ascii_icons, limit_extensions, py_cui.WHITE_ON_BLACK, self._renderer, self._logger)
def increment_loading_bar(self):
"""Increments progress bar if loading bar popup is open
"""
if self._popup is not None:
self._popup._increment_counter()
else:
self._logger.warn('No popup is currently opened.')
def stop_loading_popup(self):
"""Leaves loading state, and closes popup.
Must be called by user to escape loading.
"""
self._loading = False
self.close_popup()
self._logger.info('Stopping open loading popup')
def close_popup(self):
"""Closes the popup, and resets focus
"""
self.lose_focus()
self._popup = None
def _refresh_height_width(self, height, width):
"""Function that updates the height and width of the CUI based on terminal window size
Parameters
----------
height : int
Window height in terminal characters
width : int
Window width in terminal characters
"""
self._height = height
self._width = width
self._grid.update_grid_height_width(self._height, self._width)
for widget_id in self._widgets.keys():
self._widgets[widget_id].update_height_width()
if self._popup is not None:
self._popup.update_height_width()
def get_absolute_size(self):
"""Returns dimensions of CUI
Returns
-------
height, width : int
The dimensions of drawable CUI space in characters
"""
return self._height, self._width
# Draw Functions. Function for drawing widgets, status bars, and popups
def _draw_widgets(self):
"""Function that draws all of the widgets to the screen
"""
for widget_key in self.get_widgets().keys():
if widget_key != self._selected_widget:
self.get_widgets()[widget_key]._draw()
# We draw the selected widget last to support cursor location.
if self._selected_widget is not None:
self.get_widgets()[self._selected_widget]._draw()
self._logger.info('Drew widgets')
def _draw_status_bars(self, stdscr, height, width):
"""Draws status bar and title bar
Parameters
----------
stdscr : curses Standard cursor
The cursor used to draw the status bar
height : int
Window height in terminal characters
width : int
Window width in terminal characters
"""
if self.status_bar is not None:
stdscr.attron(curses.color_pair(self.status_bar.get_color()))
stdscr.addstr(height + 3, 0, fit_text(width, self.status_bar.get_text()))
stdscr.attroff(curses.color_pair(self.status_bar.get_color()))
if self.title_bar is not None:
stdscr.attron(curses.color_pair(self.title_bar.get_color()))
stdscr.addstr(0, 0, fit_text(width, self._title, center=True))
stdscr.attroff(curses.color_pair(self.title_bar.get_color()))
def _display_window_warning(self, stdscr, error_info):
"""Function that prints some basic error info if there is an error with the CUI
Parameters
----------
stdscr : curses Standard cursor
The cursor used to draw the warning
error_info : str
The information regarding the error.
"""
stdscr.clear()
stdscr.attron(curses.color_pair(RED_ON_BLACK))
stdscr.addstr(0, 0, 'Error displaying CUI!!!')
stdscr.addstr(1, 0, 'Error Type: {}'.format(error_info))
stdscr.addstr(2, 0, 'Most likely terminal dimensions are too small.')
stdscr.attroff(curses.color_pair(RED_ON_BLACK))
stdscr.refresh()
self._logger.info('Encountered error -> {}'.format(error_info))
def _handle_key_presses(self, key_pressed):
"""Function that handles all main loop key presses.
Parameters
----------
key_pressed : py_cui.keys.KEY_*
The key being pressed
"""
# Selected widget represents which widget is being hovered over, though not necessarily in focus mode
if self._selected_widget is None:
return
selected_widget = self.get_widgets()[self._selected_widget]
# If we are in focus mode, the widget has all of the control of the keyboard except
# for the escape key, which exits focus mode.
if self._in_focused_mode and self._popup is None:
if key_pressed == py_cui.keys.KEY_ESCAPE:
self.status_bar.set_text(self._init_status_bar_text)
self._in_focused_mode = False
selected_widget.set_selected(False)
self._logger.info('Exiting focus mode on widget {}'.format(selected_widget.get_title()))
else:
# widget handles remaining py_cui.keys
self._logger.info('Widget {} handling {} key'.format(selected_widget.get_title(), key_pressed))
selected_widget._handle_key_press(key_pressed)
# Otherwise, barring a popup, we are in overview mode, meaning that arrow py_cui.keys move between widgets, and Enter key starts focus mode
elif self._popup is None:
if key_pressed == py_cui.keys.KEY_ENTER and self._selected_widget is not None and selected_widget.is_selectable():
self.move_focus(selected_widget)
for key in self._keybindings.keys():
if key_pressed == key:
command = self._keybindings[key]
self._logger.info('Detected binding for key {}, running command {}'.format(key_pressed, command.__name__))
command()
# If not in focus mode, use the arrow py_cui.keys to move around the selectable widgets.
neighbor = None
if key_pressed in py_cui.keys.ARROW_KEYS:
neighbor = self._check_if_neighbor_exists(key_pressed)
if neighbor is not None:
self.set_selected_widget(neighbor)
self._logger.info('Navigated to neighbor widget {}'.format(self.get_widgets()[self._selected_widget].get_title()))
# if we have a popup, that takes key control from both overview and focus mode
elif self._popup is not None:
self._logger.info('Popup {} handling key {}'.format(self._popup.get_title(), key_pressed))
self._popup._handle_key_press(key_pressed)
def _draw(self, stdscr):
"""Main CUI draw loop called by start()
Parameters
----------
stdscr : curses Standard screen
The screen buffer used for drawing CUI elements
"""
self._stdscr = stdscr
key_pressed = 0
# Clear and refresh the screen for a blank canvas
stdscr.clear()
stdscr.refresh()
curses.mousemask(curses.ALL_MOUSE_EVENTS)
# stdscr.nodelay(False)
#stdscr.keypad(True)
# Initialization functions. Generates colors and renderer
self._initialize_colors()
self._initialize_widget_renderer()
# If user specified a refresh timeout, apply it here
if self._refresh_timeout > 0:
self._stdscr.timeout(self._refresh_timeout)
# If user sets non-default border characters, update them here
if self._border_characters is not None:
self._renderer._set_border_renderer_chars(self._border_characters)
# Loop where key_pressed is the last character pressed. Wait for exit key while no popup or focus mode
while key_pressed != self._exit_key or self._in_focused_mode or self._popup is not None:
try:
# If we call stop, we want to break out of the main draw loop
if self._stopped:
break
# Initialization and size adjustment
stdscr.erase()
# find height width, adjust if status/title bar added. We decrement the height by 4 to account for status/title bar and padding
if self._simulated_terminal is None:
height, width = stdscr.getmaxyx()
else:
height = self._simulated_terminal[0]
width = self._simulated_terminal[1]
height = height - 4
# If the user defined an update function to fire on each draw call,
# Run it here. This can of course be also handled user-side
# through a separate thread.
if self._on_draw_update_func is not None:
self._on_draw_update_func()
# This is what allows the CUI to be responsive. Adjust grid size based on current terminal size
# Resize the grid and the widgets if there was a resize operation
if key_pressed == curses.KEY_RESIZE:
self._logger.info('Resizing CUI to new dimensions {} by {}'.format(height, width))
try:
self._refresh_height_width(height, width)
except py_cui.errors.PyCUIOutOfBoundsError as e:
self._logger.info('Resized terminal too small')
self._display_window_warning(stdscr, str(e))
# Here we handle mouse click events globally, or pass them to the UI element to handle
elif key_pressed == curses.KEY_MOUSE:
self._logger.info('Detected mouse click')
_, x, y, _, _ = curses.getmouse()
in_element = self.get_element_at_position(x, y)
# In first case, we click inside already selected widget, pass click for processing
if in_element is not None and in_element.is_selected():
in_element._handle_mouse_press(x, y)
# Otherwise, if not a popup, select the clicked on widget
elif in_element is not None and not isinstance(in_element, py_cui.popups.Popup):
self.move_focus(in_element)
in_element._handle_mouse_press(x, y)
# If we have a post_loading_callback, fire it here
if self._post_loading_callback is not None and not self._loading:
self._logger.info('Firing post-loading callback function {}'.format(self._post_loading_callback.__name__))
self._post_loading_callback()
self._post_loading_callback = None
# Handle widget cycling
if key_pressed == self._forward_cycle_key:
self._cycle_widgets()
elif key_pressed == self._reverse_cycle_key:
self._cycle_widgets(reverse=True)
# Handle keypresses
self._handle_key_presses(key_pressed)
try:
# Draw status/title bar, and all widgets. Selected widget will be bolded.
self._draw_status_bars(stdscr, height, width)
self._draw_widgets()
# draw the popup if required
if self._popup is not None:
self._popup._draw()
except curses.error as e:
self._logger.error('Curses error while drawing TUI')
self._display_window_warning(stdscr, str(e))
except py_cui.errors.PyCUIOutOfBoundsError as e:
self._logger.error('Resized terminal too small')
self._display_window_warning(stdscr, str(e))
# Refresh the screen
stdscr.refresh()
# Wait for next input
if self._loading or self._post_loading_callback is not None:
# When loading, refresh screen every quarter second
time.sleep(0.25)
# Need to reset key_pressed, because otherwise the previously pressed key will be used.
key_pressed = 0
elif self._stopped:
key_pressed = self._exit_key
else:
self._logger.info('Waiting for next keypress')
key_pressed = stdscr.getch()
except KeyboardInterrupt:
self._logger.info('Detect Keyboard Interrupt, Exiting...')
self._stopped = True
stdscr.erase()
stdscr.refresh()
curses.endwin()
if self._on_stop is not None:
self._logger.info('Firing onstop function {}'.format(self._on_stop.__name__))
self._on_stop()
def __format__(self, fmt):
"""Override of base format function. Prints list of current widgets.
Parameters
----------
fmt : Format
The format to override
"""
out = ''
for widget in self.get_widgets().keys():
out += '{}\n'.format(self.get_widgets()[widget].get_title())
return out
| [
"py_cui.keys.get_char_from_ascii",
"curses.start_color",
"curses.init_color",
"curses.endwin",
"py_cui.popups.LoadingBarPopup",
"time.sleep",
"py_cui.controls.slider.SliderWidget",
"py_cui.colors._COLOR_MAP.keys",
"py_cui.widgets.Button",
"curses.getmouse",
"curses.mousemask",
"os.environ.setdefault",
"py_cui.dialogs.form.FormPopup",
"py_cui.renderer.Renderer",
"py_cui.widgets.CheckBoxMenu",
"curses.init_pair",
"py_cui.widget_set.WidgetSet",
"py_cui.popups.MessagePopup",
"py_cui.debug._initialize_logger",
"py_cui.popups.YesNoPopup",
"curses.color_pair",
"curses.wrapper",
"py_cui.widgets.ScrollTextBlock",
"py_cui.widgets.ScrollMenu",
"py_cui.popups.MenuPopup",
"py_cui.dialogs.filedialog.FileDialogPopup",
"py_cui.debug._enable_logging",
"py_cui.widgets.BlockLabel",
"py_cui.popups.LoadingIconPopup",
"py_cui.widgets.TextBox",
"py_cui.popups.TextBoxPopup",
"py_cui.statusbar.StatusBar",
"shutil.get_terminal_size",
"py_cui.widgets.Label",
"py_cui.grid.Grid"
]
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# coding: utf-8
"""
Isilon SDK
Isilon SDK - Language bindings for the OneFS API # noqa: E501
OpenAPI spec version: 3
Contact: <EMAIL>
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class AuthAccessAccessItemFile(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'group': 'str',
'mode': 'str',
'owner': 'str',
'relevant_mode': 'str'
}
attribute_map = {
'group': 'group',
'mode': 'mode',
'owner': 'owner',
'relevant_mode': 'relevant_mode'
}
def __init__(self, group=None, mode=None, owner=None, relevant_mode=None): # noqa: E501
"""AuthAccessAccessItemFile - a model defined in Swagger""" # noqa: E501
self._group = None
self._mode = None
self._owner = None
self._relevant_mode = None
self.discriminator = None
if group is not None:
self.group = group
if mode is not None:
self.mode = mode
if owner is not None:
self.owner = owner
if relevant_mode is not None:
self.relevant_mode = relevant_mode
@property
def group(self):
"""Gets the group of this AuthAccessAccessItemFile. # noqa: E501
Specifies the group name or ID for the file. # noqa: E501
:return: The group of this AuthAccessAccessItemFile. # noqa: E501
:rtype: str
"""
return self._group
@group.setter
def group(self, group):
"""Sets the group of this AuthAccessAccessItemFile.
Specifies the group name or ID for the file. # noqa: E501
:param group: The group of this AuthAccessAccessItemFile. # noqa: E501
:type: str
"""
self._group = group
@property
def mode(self):
"""Gets the mode of this AuthAccessAccessItemFile. # noqa: E501
Specifies the mode bits on the file. # noqa: E501
:return: The mode of this AuthAccessAccessItemFile. # noqa: E501
:rtype: str
"""
return self._mode
@mode.setter
def mode(self, mode):
"""Sets the mode of this AuthAccessAccessItemFile.
Specifies the mode bits on the file. # noqa: E501
:param mode: The mode of this AuthAccessAccessItemFile. # noqa: E501
:type: str
"""
self._mode = mode
@property
def owner(self):
"""Gets the owner of this AuthAccessAccessItemFile. # noqa: E501
Specifies the name or ID of the file owner. # noqa: E501
:return: The owner of this AuthAccessAccessItemFile. # noqa: E501
:rtype: str
"""
return self._owner
@owner.setter
def owner(self, owner):
"""Sets the owner of this AuthAccessAccessItemFile.
Specifies the name or ID of the file owner. # noqa: E501
:param owner: The owner of this AuthAccessAccessItemFile. # noqa: E501
:type: str
"""
self._owner = owner
@property
def relevant_mode(self):
"""Gets the relevant_mode of this AuthAccessAccessItemFile. # noqa: E501
Specifies the mode bits that are related to the user. # noqa: E501
:return: The relevant_mode of this AuthAccessAccessItemFile. # noqa: E501
:rtype: str
"""
return self._relevant_mode
@relevant_mode.setter
def relevant_mode(self, relevant_mode):
"""Sets the relevant_mode of this AuthAccessAccessItemFile.
Specifies the mode bits that are related to the user. # noqa: E501
:param relevant_mode: The relevant_mode of this AuthAccessAccessItemFile. # noqa: E501
:type: str
"""
self._relevant_mode = relevant_mode
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.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 pprint.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"""
if not isinstance(other, AuthAccessAccessItemFile):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| [
"six.iteritems"
]
| [((4307, 4340), 'six.iteritems', 'six.iteritems', (['self.swagger_types'], {}), '(self.swagger_types)\n', (4320, 4340), False, 'import six\n')] |
import sys
from sqlalchemy.exc import DatabaseError
from . import cli
from .configuration import settings, init as init_config
from .observer import HelpdeskObserver, MaximumClientsReached
from .models import init as init_models, metadata, engine, check_db
from .smtp import SMTPConfigurationError
__version__ = '0.1.0-dev.0'
def start_server(cli_arguments):
init_models()
# Checking database
try:
check_db()
except DatabaseError:
print(
'Cannot connect to database. or database objects are not created yet. Please run `budgie setup-db`.',
file=sys.stderr
)
sys.exit(-1)
try:
manager = HelpdeskObserver()
manager.start()
except (
MaximumClientsReached,
SMTPConfigurationError) as ex:
print(ex, file=sys.stderr)
sys.exit(-1)
def main():
arguments = cli.init()
if arguments.version:
print(__version__)
sys.exit(0)
init_config(arguments.config_file if arguments.config_file else None)
if arguments.func is not None:
arguments.func(arguments)
| [
"sys.exit"
]
| [((972, 983), 'sys.exit', 'sys.exit', (['(0)'], {}), '(0)\n', (980, 983), False, 'import sys\n'), ((638, 650), 'sys.exit', 'sys.exit', (['(-1)'], {}), '(-1)\n', (646, 650), False, 'import sys\n'), ((857, 869), 'sys.exit', 'sys.exit', (['(-1)'], {}), '(-1)\n', (865, 869), False, 'import sys\n')] |
#!/usr/bin/env python
from app import app
app.run(debug = True)
| [
"app.app.run"
]
| [((43, 62), 'app.app.run', 'app.run', ([], {'debug': '(True)'}), '(debug=True)\n', (50, 62), False, 'from app import app\n')] |
import sys, yaml, test_appliance
def main(args=None):
collections = []
import test_yaml
collections.append(test_yaml)
if yaml.__with_libyaml__:
import test_yaml_ext
collections.append(test_yaml_ext)
return test_appliance.run(collections, args)
if __name__ == '__main__':
main()
| [
"test_appliance.run"
]
| [((244, 281), 'test_appliance.run', 'test_appliance.run', (['collections', 'args'], {}), '(collections, args)\n', (262, 281), False, 'import sys, yaml, test_appliance\n')] |
import threading
from queue import Queue, Empty
from time import sleep
from libAnt.drivers.driver import Driver
from libAnt.message import *
class Network:
def __init__(self, key: bytes = b'\x00' * 8, name: str = None):
self.key = key
self.name = name
self.number = 0
def __str__(self):
return self.name
class Pump(threading.Thread):
def __init__(self, driver: Driver, initMessages, out: Queue, onSucces, onFailure):
super().__init__()
self._stopper = threading.Event()
self._driver = driver
self._out = out
self._initMessages = initMessages
self._waiters = []
self._onSuccess = onSucces
self._onFailure = onFailure
def stop(self):
self._driver.abort()
self._stopper.set()
def stopped(self):
return self._stopper.isSet()
def run(self):
while not self.stopped():
try:
with self._driver as d:
# Startup
rst = SystemResetMessage()
self._waiters.append(rst)
d.write(rst)
for m in self._initMessages:
self._waiters.append(m)
d.write(m)
while not self.stopped():
# Write
try:
outMsg = self._out.get(block=False)
self._waiters.append(outMsg)
d.write(outMsg)
except Empty:
pass
# Read
try:
msg = d.read(timeout=1)
if msg.type == MESSAGE_CHANNEL_EVENT:
# This is a response to our outgoing message
for w in self._waiters:
if w.type == msg.content[1]: # ACK
self._waiters.remove(w)
# TODO: Call waiter callback from tuple (waiter, callback)
break
elif msg.type == MESSAGE_CHANNEL_BROADCAST_DATA:
bmsg = BroadcastMessage(msg.type, msg.content).build(msg.content)
self._onSuccess(bmsg)
except Empty:
pass
except Exception as e:
self._onFailure(e)
except:
pass
self._waiters.clear()
sleep(1)
class Node:
def __init__(self, driver: Driver, name: str = None):
self._driver = driver
self._name = name
self._out = Queue()
self._init = []
self._pump = None
self._configMessages = Queue()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop()
def start(self, onSuccess, onFailure):
if not self.isRunning():
self._pump = Pump(self._driver, self._init, self._out, onSuccess, onFailure)
self._pump.start()
def enableRxScanMode(self, networkKey=ANTPLUS_NETWORK_KEY, channelType=CHANNEL_TYPE_ONEWAY_RECEIVE,
frequency: int = 2457, rxTimestamp: bool = True, rssi: bool = True, channelId: bool = True):
self._init.append(SystemResetMessage())
self._init.append(SetNetworkKeyMessage(0, networkKey))
self._init.append(AssignChannelMessage(0, channelType))
self._init.append(SetChannelIdMessage(0))
self._init.append(SetChannelRfFrequencyMessage(0, frequency))
self._init.append(EnableExtendedMessagesMessage())
self._init.append(LibConfigMessage(rxTimestamp, rssi, channelId))
self._init.append(OpenRxScanModeMessage())
def stop(self):
if self.isRunning():
self._pump.stop()
self._pump.join()
def isRunning(self):
if self._pump is None:
return False
return self._pump.is_alive()
def getCapabilities(self):
pass
| [
"threading.Event",
"queue.Queue",
"time.sleep"
]
| [((518, 535), 'threading.Event', 'threading.Event', ([], {}), '()\n', (533, 535), False, 'import threading\n'), ((2821, 2828), 'queue.Queue', 'Queue', ([], {}), '()\n', (2826, 2828), False, 'from queue import Queue, Empty\n'), ((2910, 2917), 'queue.Queue', 'Queue', ([], {}), '()\n', (2915, 2917), False, 'from queue import Queue, Empty\n'), ((2664, 2672), 'time.sleep', 'sleep', (['(1)'], {}), '(1)\n', (2669, 2672), False, 'from time import sleep\n')] |
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 30 12:17:04 2021
@author: Oli
"""
import pytest
import pandas as pd
import numpy as np
import netCDF4 as nc
import os
from copy import deepcopy
os.chdir(os.path.dirname(os.path.realpath(__file__)))
wd = os.getcwd().replace('\\', '/')
exec(open("test_setup.py").read())
os.chdir((wd[0:-6] + '/src/data_import'))
exec(open("local_load_up.py").read())
from model_interface.wham import WHAM
from Core_functionality.AFTs.agent_class import AFT
from Core_functionality.AFTs.arable_afts import Swidden, SOSH, MOSH, Intense_arable
from Core_functionality.AFTs.livestock_afts import Pastoralist, Ext_LF_r, Int_LF_r, Ext_LF_p, Int_LF_p
from Core_functionality.AFTs.forestry_afts import Agroforestry, Logger, Managed_forestry, Abandoned_forestry
from Core_functionality.AFTs.nonex_afts import Hunter_gatherer, Recreationalist, SLM, Conservationist
from Core_functionality.AFTs.land_system_class import land_system
from Core_functionality.AFTs.land_systems import Cropland, Pasture, Rangeland, Forestry, Urban, Unoccupied, Nonex
from Core_functionality.top_down_processes.arson import arson
from Core_functionality.top_down_processes.background_ignitions import background_rate
from Core_functionality.top_down_processes.fire_constraints import fuel_ct, dominant_afr_ct
from Core_functionality.Trees.Transfer_tree import define_tree_links, predict_from_tree, update_pars, predict_from_tree_fast
from Core_functionality.prediction_tools.regression_families import regression_link, regression_transformation
#####################################################################
### Run model year then reproduce outputs
#####################################################################
### Run model for 1 year
all_afts = [Swidden, SOSH, MOSH, Intense_arable,
Pastoralist, Ext_LF_r, Int_LF_r, Ext_LF_p, Int_LF_p,
Agroforestry, Logger, Managed_forestry, Abandoned_forestry,
Hunter_gatherer, Recreationalist, SLM, Conservationist]
parameters = {
'xlen': 192,
'ylen': 144,
'AFTs': all_afts,
'LS' : [Cropland, Rangeland, Pasture, Forestry, Nonex, Unoccupied, Urban],
'Fire_types': {'cfp': 'Vegetation', 'crb': 'Arable', 'hg': 'Vegetation',
'pasture': 'Pasture', 'pyrome': 'Vegetation'},
'Observers': {'arson': arson, 'background_rate': background_rate},
'AFT_pars': Core_pars,
'Maps' : Map_data,
'Constraint_pars': {'Soil_threshold': 0.1325,
'Dominant_afr_threshold': 0.5,
'Rangeland_stocking_contstraint': True,
'R_s_c_Positive' : False,
'HG_Market_constraint': 7800,
'Arson_threshold': 0.5},
'timestep': 0,
'end_run' : 0,
'reporters': ['Managed_fire', 'Background_ignitions','Arson'],
'theta' : 0.1,
'bootstrap': False,
'Seasonality': False
}
mod = WHAM(parameters)
### setup
mod.setup()
### go
mod.go()
mod_annual = deepcopy(mod.results['Managed_fire'][0]['Total'])
#######################
### Run model monthly
#######################
all_afts = [Swidden, SOSH, MOSH, Intense_arable,
Pastoralist, Ext_LF_r, Int_LF_r, Ext_LF_p, Int_LF_p,
Agroforestry, Logger, Managed_forestry, Abandoned_forestry,
Hunter_gatherer, Recreationalist, SLM, Conservationist]
parameters = {
'xlen': 192,
'ylen': 144,
'AFTs': all_afts,
'LS' : [Cropland, Rangeland, Pasture, Forestry, Nonex, Unoccupied, Urban],
'Fire_types': {'cfp': 'Vegetation', 'crb': 'Arable', 'hg': 'Vegetation',
'pasture': 'Pasture', 'pyrome': 'Vegetation'},
'Fire_seasonality': Seasonality,
'Observers': {'arson': arson, 'background_rate': background_rate},
'AFT_pars': Core_pars,
'Maps' : Map_data,
'Constraint_pars': {'Soil_threshold': 0.1325,
'Dominant_afr_threshold': 0.5,
'Rangeland_stocking_contstraint': True,
'R_s_c_Positive' : False,
'HG_Market_constraint': 7800,
'Arson_threshold': 0.5},
'timestep': 0,
'end_run' : 0,
'reporters': ['Managed_fire', 'Background_ignitions','Arson'],
'theta' : 0.1,
'bootstrap': False,
'Seasonality': True
}
mod = WHAM(parameters)
### setup
mod.setup()
### go
mod.go()
##################################
### tests
##################################
def test_seasonality_mean():
seasonal = np.nansum(mod.results['Managed_fire'][0]['Total'], axis = 0)
assert pytest.approx(np.nanmean(mod_annual)) == np.nanmean(seasonal)
def test_seasonality_quantiles():
seasonal = np.nansum(mod.results['Managed_fire'][0]['Total'], axis = 0)
quants = [0, 0.2, 0.4, 0.5, 0.6, 0.8, 1]
assert pytest.approx(np.nanquantile(mod_annual, quants)) == np.nanquantile(seasonal, quants)
| [
"model_interface.wham.WHAM",
"os.getcwd",
"os.chdir",
"os.path.realpath",
"numpy.nanmean",
"numpy.nanquantile",
"copy.deepcopy",
"numpy.nansum"
]
| [((339, 378), 'os.chdir', 'os.chdir', (["(wd[0:-6] + '/src/data_import')"], {}), "(wd[0:-6] + '/src/data_import')\n", (347, 378), False, 'import os\n'), ((3057, 3073), 'model_interface.wham.WHAM', 'WHAM', (['parameters'], {}), '(parameters)\n', (3061, 3073), False, 'from model_interface.wham import WHAM\n'), ((3136, 3185), 'copy.deepcopy', 'deepcopy', (["mod.results['Managed_fire'][0]['Total']"], {}), "(mod.results['Managed_fire'][0]['Total'])\n", (3144, 3185), False, 'from copy import deepcopy\n'), ((4540, 4556), 'model_interface.wham.WHAM', 'WHAM', (['parameters'], {}), '(parameters)\n', (4544, 4556), False, 'from model_interface.wham import WHAM\n'), ((4746, 4804), 'numpy.nansum', 'np.nansum', (["mod.results['Managed_fire'][0]['Total']"], {'axis': '(0)'}), "(mod.results['Managed_fire'][0]['Total'], axis=0)\n", (4755, 4804), True, 'import numpy as np\n'), ((4948, 5006), 'numpy.nansum', 'np.nansum', (["mod.results['Managed_fire'][0]['Total']"], {'axis': '(0)'}), "(mod.results['Managed_fire'][0]['Total'], axis=0)\n", (4957, 5006), True, 'import numpy as np\n'), ((234, 260), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (250, 260), False, 'import os\n'), ((269, 280), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (278, 280), False, 'import os\n'), ((4866, 4886), 'numpy.nanmean', 'np.nanmean', (['seasonal'], {}), '(seasonal)\n', (4876, 4886), True, 'import numpy as np\n'), ((5134, 5166), 'numpy.nanquantile', 'np.nanquantile', (['seasonal', 'quants'], {}), '(seasonal, quants)\n', (5148, 5166), True, 'import numpy as np\n'), ((4839, 4861), 'numpy.nanmean', 'np.nanmean', (['mod_annual'], {}), '(mod_annual)\n', (4849, 4861), True, 'import numpy as np\n'), ((5095, 5129), 'numpy.nanquantile', 'np.nanquantile', (['mod_annual', 'quants'], {}), '(mod_annual, quants)\n', (5109, 5129), True, 'import numpy as np\n')] |
import datetime
from django.db import transaction
def compute_diff(obj1, obj2):
"""
Given two objects compute a list of differences.
Each diff dict has the following keys:
field - name of the field
new - the new value for the field
one - value of the field in obj1
two - value of the field in obj2
diff - none|one|two|new
list - true if field is a list of related objects
"""
comparison = []
fields = obj1._meta.get_fields()
exclude = ('created_at', 'updated_at', 'id', 'locked_fields')
if obj1 == obj2:
raise ValueError('cannot merge object with itself')
for field in fields:
if field.name in exclude:
continue
elif not field.is_relation:
piece_one = getattr(obj1, field.name)
piece_two = getattr(obj2, field.name)
if piece_one == piece_two:
diff = 'none'
new = piece_one
elif piece_one:
diff = 'one'
new = piece_one
elif piece_two:
diff = 'two'
new = piece_two
comparison.append({
'field': field.name,
'new': new,
'one': getattr(obj1, field.name),
'two': getattr(obj2, field.name),
'diff': diff,
'list': False,
})
else:
related_name = field.get_accessor_name()
piece_one = list(getattr(obj1, related_name).all())
piece_two = list(getattr(obj2, related_name).all())
# TODO: try and deduplicate the lists?
new = piece_one + piece_two
diff = 'none' if piece_one == piece_two else 'one'
if (field.name == 'other_names' and obj1.name != obj2.name):
new.append(field.related_model(name=obj2.name,
note='from merge w/ ' + obj2.id)
)
diff = 'new'
if field.name == 'identifiers':
new.append(field.related_model(identifier=obj2.id))
diff = 'new'
if field.name == 'memberships':
new = _dedupe_memberships(new)
comparison.append({
'field': related_name,
'new': new,
'one': piece_one,
'two': piece_two,
'diff': diff,
'list': True,
})
comparison.append({'field': 'created_at',
'new': min(obj1.created_at, obj2.created_at),
'one': obj1.created_at,
'two': obj2.created_at,
'diff': 'one' if obj1.created_at < obj2.created_at else 'two',
'list': False,
})
comparison.append({'field': 'updated_at',
'new': datetime.datetime.utcnow(),
'one': obj1.updated_at,
'two': obj2.updated_at,
'diff': 'new',
'list': False,
})
# locked fields are any fields that change that aren't M2M relations
# (ending in _set)
new_locked_fields = obj1.locked_fields + obj2.locked_fields + [
c['field'] for c in comparison if c['diff'] != 'none' and not c['field'].endswith('_set')
]
new_locked_fields = set(new_locked_fields) - {'updated_at', 'created_at'}
comparison.append({'field': 'locked_fields',
'new': list(new_locked_fields),
'one': obj1.locked_fields,
'two': obj2.updated_at,
'diff': 'new',
'list': False,
})
return comparison
@transaction.atomic
def apply_diff(obj1, obj2, diff):
for row in diff:
if row['diff'] != 'none':
if row['list']:
# save items, the ids have been set to obj1
for item in row['new']:
setattr(item,
getattr(obj1, row['field']).field.name,
obj1)
item.save()
else:
setattr(obj1, row['field'], row['new'])
obj1.save()
count, delete_plan = obj2.delete()
if count > 1:
# shouldn't happen, but let's be sure
raise AssertionError('deletion failed due to related objects left unmerged')
def merge(obj1, obj2):
diff = compute_diff(obj1, obj2)
apply_diff(obj1, obj2, diff)
def _dedupe_memberships(memberships):
deduped = []
mset = set()
for membership in memberships:
mkey = (membership.organization_id,
membership.label,
membership.end_date,
membership.post_id)
if mkey not in mset:
deduped.append(membership)
mset.add(mkey)
else:
membership.delete()
return deduped
| [
"datetime.datetime.utcnow"
]
| [((2984, 3010), 'datetime.datetime.utcnow', 'datetime.datetime.utcnow', ([], {}), '()\n', (3008, 3010), False, 'import datetime\n')] |
from __future__ import absolute_import
from django.contrib import admin
from .models import Deposit, Withdrawal, Support
from .forms import DepositForm, WithdrawalForm
# Register your models here.
@admin.register(Deposit)
class DepositAdmin(admin.ModelAdmin):
# form = DepositForm
list_display = ["__str__", "amount", "approval", "deposited", "created"]
list_filter = ["approval", "created"]
list_editable = ["approval", "amount", "deposited"]
class Meta:
model = Deposit
@admin.register(Withdrawal)
class WithdrawalAdmin(admin.ModelAdmin):
form = WithdrawalForm
list_display = ["__str__", "amount", "wallet_id", "approval", "withdrawn", "created"]
list_filter = ["approval", "created"]
list_editable = ["approval", "withdrawn"]
class Meta:
model = Withdrawal
admin.site.register(Support)
| [
"django.contrib.admin.register",
"django.contrib.admin.site.register"
]
| [((201, 224), 'django.contrib.admin.register', 'admin.register', (['Deposit'], {}), '(Deposit)\n', (215, 224), False, 'from django.contrib import admin\n'), ((505, 531), 'django.contrib.admin.register', 'admin.register', (['Withdrawal'], {}), '(Withdrawal)\n', (519, 531), False, 'from django.contrib import admin\n'), ((823, 851), 'django.contrib.admin.site.register', 'admin.site.register', (['Support'], {}), '(Support)\n', (842, 851), False, 'from django.contrib import admin\n')] |
import os
DEBUG = True
DATABASES = {
'default':
{
'ENGINE': 'django.db.backends.sqlite3',
'NAME': '/tmp/piston.db'
}
}
DATABASE_ENGINE = 'sqlite3'
DATABASE_NAME = '/tmp/piston.db'
INSTALLED_APPS = (
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'django.contrib.admin',
'piston',
'test_project.apps.testapp',
)
TEMPLATE_DIRS = (
os.path.join(os.path.dirname(__file__), 'templates'),
)
SITE_ID = 1
ROOT_URLCONF = 'test_project.urls'
MIDDLEWARE_CLASSES = (
'piston.middleware.ConditionalMiddlewareCompatProxy',
'django.contrib.sessions.middleware.SessionMiddleware',
'piston.middleware.CommonMiddlewareCompatProxy',
'django.contrib.auth.middleware.AuthenticationMiddleware',
)
| [
"os.path.dirname"
]
| [((467, 492), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (482, 492), False, 'import os\n')] |
import unittest
import shutil
from rdyn.alg.RDyn_v2 import RDynV2
class RDynTestCase(unittest.TestCase):
def test_rdyn_simplified(self):
print("1")
rdb = RDynV2(size=500, iterations=100)
rdb.execute(simplified=True)
print("2")
rdb = RDynV2(size=500, iterations=100, max_evts=2)
rdb.execute(simplified=True)
print("3")
rdb = RDynV2(size=500, iterations=100, new_node=0.1, del_node=0.1, max_evts=2, paction=0.8)
rdb.execute(simplified=False)
print("Done")
shutil.rmtree("results")
if __name__ == '__main__':
unittest.main()
| [
"unittest.main",
"rdyn.alg.RDyn_v2.RDynV2",
"shutil.rmtree"
]
| [((609, 624), 'unittest.main', 'unittest.main', ([], {}), '()\n', (622, 624), False, 'import unittest\n'), ((178, 210), 'rdyn.alg.RDyn_v2.RDynV2', 'RDynV2', ([], {'size': '(500)', 'iterations': '(100)'}), '(size=500, iterations=100)\n', (184, 210), False, 'from rdyn.alg.RDyn_v2 import RDynV2\n'), ((281, 325), 'rdyn.alg.RDyn_v2.RDynV2', 'RDynV2', ([], {'size': '(500)', 'iterations': '(100)', 'max_evts': '(2)'}), '(size=500, iterations=100, max_evts=2)\n', (287, 325), False, 'from rdyn.alg.RDyn_v2 import RDynV2\n'), ((396, 485), 'rdyn.alg.RDyn_v2.RDynV2', 'RDynV2', ([], {'size': '(500)', 'iterations': '(100)', 'new_node': '(0.1)', 'del_node': '(0.1)', 'max_evts': '(2)', 'paction': '(0.8)'}), '(size=500, iterations=100, new_node=0.1, del_node=0.1, max_evts=2,\n paction=0.8)\n', (402, 485), False, 'from rdyn.alg.RDyn_v2 import RDynV2\n'), ((551, 575), 'shutil.rmtree', 'shutil.rmtree', (['"""results"""'], {}), "('results')\n", (564, 575), False, 'import shutil\n')] |
""""""
# Standard library modules.
import abc
# Third party modules.
from django.core.mail import send_mail
from django.template import Engine, Context
# Local modules.
from .models import RunState
# Globals and constants variables.
class Notification(metaclass=abc.ABCMeta):
@classmethod
def notify(self, jobrun):
raise NotImplementedError
class EmailNotification(Notification):
def __init__(self, recipients, on_success=False, on_failure=True):
self.recipients = tuple(recipients)
self.on_success = on_success
self.on_failure = on_failure
def __str__(self):
return "email"
def notify(self, jobrun):
if (jobrun.state in [RunState.ERROR, RunState.FAILED] and self.on_failure) or (
jobrun.state == RunState.SUCCESS and self.on_success
):
engine = Engine.get_default()
template = engine.get_template("django_cd/jobrun_report.html")
context = Context({"jobrun": jobrun})
html_message = template.render(context)
send_mail(
subject=f"Job report - {jobrun.name} - {jobrun.state}",
message="",
from_email=None,
recipient_list=self.recipients,
html_message=html_message,
)
| [
"django.core.mail.send_mail",
"django.template.Engine.get_default",
"django.template.Context"
]
| [((856, 876), 'django.template.Engine.get_default', 'Engine.get_default', ([], {}), '()\n', (874, 876), False, 'from django.template import Engine, Context\n'), ((974, 1001), 'django.template.Context', 'Context', (["{'jobrun': jobrun}"], {}), "({'jobrun': jobrun})\n", (981, 1001), False, 'from django.template import Engine, Context\n'), ((1067, 1230), 'django.core.mail.send_mail', 'send_mail', ([], {'subject': 'f"""Job report - {jobrun.name} - {jobrun.state}"""', 'message': '""""""', 'from_email': 'None', 'recipient_list': 'self.recipients', 'html_message': 'html_message'}), "(subject=f'Job report - {jobrun.name} - {jobrun.state}', message=\n '', from_email=None, recipient_list=self.recipients, html_message=\n html_message)\n", (1076, 1230), False, 'from django.core.mail import send_mail\n')] |
# Copyright 2019 The FastEstimator 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.
# ==============================================================================
from typing import Tuple
import tensorflow as tf
from fastestimator.dataset.numpy_dataset import NumpyDataset
def load_data(image_key: str = "x", label_key: str = "y") -> Tuple[NumpyDataset, NumpyDataset]:
"""Load and return the CIFAR10 dataset.
Please consider using the ciFAIR10 dataset instead. CIFAR10 contains duplicates between its train and test sets.
Args:
image_key: The key for image.
label_key: The key for label.
Returns:
(train_data, eval_data)
"""
print("\033[93m {}\033[00m".format("FastEstimator-Warn: Consider using the ciFAIR10 dataset instead."))
(x_train, y_train), (x_eval, y_eval) = tf.keras.datasets.cifar10.load_data()
train_data = NumpyDataset({image_key: x_train, label_key: y_train})
eval_data = NumpyDataset({image_key: x_eval, label_key: y_eval})
return train_data, eval_data
| [
"tensorflow.keras.datasets.cifar10.load_data",
"fastestimator.dataset.numpy_dataset.NumpyDataset"
]
| [((1356, 1393), 'tensorflow.keras.datasets.cifar10.load_data', 'tf.keras.datasets.cifar10.load_data', ([], {}), '()\n', (1391, 1393), True, 'import tensorflow as tf\n'), ((1411, 1465), 'fastestimator.dataset.numpy_dataset.NumpyDataset', 'NumpyDataset', (['{image_key: x_train, label_key: y_train}'], {}), '({image_key: x_train, label_key: y_train})\n', (1423, 1465), False, 'from fastestimator.dataset.numpy_dataset import NumpyDataset\n'), ((1482, 1534), 'fastestimator.dataset.numpy_dataset.NumpyDataset', 'NumpyDataset', (['{image_key: x_eval, label_key: y_eval}'], {}), '({image_key: x_eval, label_key: y_eval})\n', (1494, 1534), False, 'from fastestimator.dataset.numpy_dataset import NumpyDataset\n')] |
from setuptools import setup
import os
here = os.path.abspath(os.path.dirname(__file__))
README = open(os.path.join(here, 'README.rst')).read()
#NEWS = open(os.path.join(here, 'NEWS.txt')).read()
rootdir = os.path.dirname(os.path.abspath(__file__))
exec(open(rootdir + '/cerridwen/version.py').read())
version = __VERSION__
setup(name='cerridwen',
version=version,
description='Accurate solar system data for everyone',
long_description=README,
author='<NAME>',
author_email='<EMAIL>',
url='http://cerridwen.bluemagician.vc/',
license='MIT',
classifiers=[
# Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers
"Development Status :: 4 - Beta"
, "Environment :: Console"
, "Intended Audience :: Science/Research"
, "Intended Audience :: Developers"
, "License :: OSI Approved :: MIT License"
, "Operating System :: OS Independent"
, "Programming Language :: Python :: 3"
, "Topic :: Scientific/Engineering :: Astronomy"
, "Topic :: Other/Nonlisted Topic"
, "Topic :: Software Development :: Libraries :: Python Modules"
, "Topic :: Utilities"
],
maintainer='<NAME>',
maintainer_email='<EMAIL>',
packages=['cerridwen'],
requires=['pyswisseph', 'numpy', 'astropy(>=0.4)'],
extras_require={'Flask':['flask']},
entry_points={
'console_scripts':
['cerridwen = cerridwen.cli:main',
'cerridwen-server = cerridwen.api_server:main [Flask]']
})
| [
"os.path.abspath",
"os.path.dirname",
"setuptools.setup",
"os.path.join"
]
| [((327, 1341), 'setuptools.setup', 'setup', ([], {'name': '"""cerridwen"""', 'version': 'version', 'description': '"""Accurate solar system data for everyone"""', 'long_description': 'README', 'author': '"""<NAME>"""', 'author_email': '"""<EMAIL>"""', 'url': '"""http://cerridwen.bluemagician.vc/"""', 'license': '"""MIT"""', 'classifiers': "['Development Status :: 4 - Beta', 'Environment :: Console',\n 'Intended Audience :: Science/Research',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 3',\n 'Topic :: Scientific/Engineering :: Astronomy',\n 'Topic :: Other/Nonlisted Topic',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'Topic :: Utilities']", 'maintainer': '"""<NAME>"""', 'maintainer_email': '"""<EMAIL>"""', 'packages': "['cerridwen']", 'requires': "['pyswisseph', 'numpy', 'astropy(>=0.4)']", 'extras_require': "{'Flask': ['flask']}", 'entry_points': "{'console_scripts': ['cerridwen = cerridwen.cli:main',\n 'cerridwen-server = cerridwen.api_server:main [Flask]']}"}), "(name='cerridwen', version=version, description=\n 'Accurate solar system data for everyone', long_description=README,\n author='<NAME>', author_email='<EMAIL>', url=\n 'http://cerridwen.bluemagician.vc/', license='MIT', classifiers=[\n 'Development Status :: 4 - Beta', 'Environment :: Console',\n 'Intended Audience :: Science/Research',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 3',\n 'Topic :: Scientific/Engineering :: Astronomy',\n 'Topic :: Other/Nonlisted Topic',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'Topic :: Utilities'], maintainer='<NAME>', maintainer_email='<EMAIL>',\n packages=['cerridwen'], requires=['pyswisseph', 'numpy',\n 'astropy(>=0.4)'], extras_require={'Flask': ['flask']}, entry_points={\n 'console_scripts': ['cerridwen = cerridwen.cli:main',\n 'cerridwen-server = cerridwen.api_server:main [Flask]']})\n", (332, 1341), False, 'from setuptools import setup\n'), ((63, 88), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (78, 88), False, 'import os\n'), ((224, 249), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (239, 249), False, 'import os\n'), ((104, 136), 'os.path.join', 'os.path.join', (['here', '"""README.rst"""'], {}), "(here, 'README.rst')\n", (116, 136), False, 'import os\n')] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.9 on 2016-08-19 17:47
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('heroquest', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='player',
name='armor',
),
migrations.AlterField(
model_name='player',
name='spell',
field=models.ManyToManyField(related_name='spells', to='armery.Spell', verbose_name='Hechizos'),
),
]
| [
"django.db.migrations.RemoveField",
"django.db.models.ManyToManyField"
]
| [((290, 347), 'django.db.migrations.RemoveField', 'migrations.RemoveField', ([], {'model_name': '"""player"""', 'name': '"""armor"""'}), "(model_name='player', name='armor')\n", (312, 347), False, 'from django.db import migrations, models\n'), ((492, 585), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'related_name': '"""spells"""', 'to': '"""armery.Spell"""', 'verbose_name': '"""Hechizos"""'}), "(related_name='spells', to='armery.Spell',\n verbose_name='Hechizos')\n", (514, 585), False, 'from django.db import migrations, models\n')] |
# -*- coding: utf-8 -*-
# Code will only work with Django >= 1.5. See tests/config.py
import re
from django.utils.translation import ugettext_lazy as _
from django.db import models
from django.core import validators
from django.contrib.auth.models import BaseUserManager
from oscar.apps.customer.abstract_models import AbstractUser
class CustomUserManager(BaseUserManager):
def create_user(self, username, email, password):
"""
Creates and saves a User with the given email and password.
"""
if not email:
raise ValueError('Users must have an email address')
user = self.model(
email=CustomUserManager.normalize_email(email),
username=username,
is_active=True,
)
user.set_password(password)
user.save(using=self._db)
return user
def create_superuser(self, username, email, password):
u = self.create_user(username, email, password=password)
u.is_admin = True
u.is_staff = True
u.save(using=self._db)
return u
class User(AbstractUser):
"""
Custom user based on Oscar's AbstractUser
"""
username = models.CharField(_('username'), max_length=30, unique=True,
help_text=_('Required. 30 characters or fewer. Letters, numbers and '
'@/./+/-/_ characters'),
validators=[
validators.RegexValidator(re.compile('^[\w.@+-]+$'), _('Enter a valid username.'), 'invalid')
])
extra_field = models.CharField(
_('Nobody needs me'), max_length=5, blank=True)
objects = CustomUserManager()
class Meta:
app_label = 'myauth'
| [
"django.utils.translation.ugettext_lazy",
"re.compile"
]
| [((1208, 1221), 'django.utils.translation.ugettext_lazy', '_', (['"""username"""'], {}), "('username')\n", (1209, 1221), True, 'from django.utils.translation import ugettext_lazy as _\n'), ((1556, 1576), 'django.utils.translation.ugettext_lazy', '_', (['"""Nobody needs me"""'], {}), "('Nobody needs me')\n", (1557, 1576), True, 'from django.utils.translation import ugettext_lazy as _\n'), ((1269, 1354), 'django.utils.translation.ugettext_lazy', '_', (['"""Required. 30 characters or fewer. Letters, numbers and @/./+/-/_ characters"""'], {}), "('Required. 30 characters or fewer. Letters, numbers and @/./+/-/_ characters'\n )\n", (1270, 1354), True, 'from django.utils.translation import ugettext_lazy as _\n'), ((1433, 1459), 're.compile', 're.compile', (['"""^[\\\\w.@+-]+$"""'], {}), "('^[\\\\w.@+-]+$')\n", (1443, 1459), False, 'import re\n'), ((1460, 1488), 'django.utils.translation.ugettext_lazy', '_', (['"""Enter a valid username."""'], {}), "('Enter a valid username.')\n", (1461, 1488), True, 'from django.utils.translation import ugettext_lazy as _\n')] |
import pytest
import asyncio
from system.utils import *
from random import randrange as rr
import hashlib
import time
from datetime import datetime, timedelta, timezone
from indy import payment
import logging
logger = logging.getLogger(__name__)
@pytest.mark.usefixtures('docker_setup_and_teardown')
class TestAuthMapSuite:
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_nym(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
new_did, new_vk = await did.create_and_store_my_did(wallet_handler, '{}')
# add adder to add nym
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
# add editor to edit nym
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
req = await ledger.build_auth_rule_request(trustee_did, '1', 'ADD', 'role', '*', '',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
req = await ledger.build_auth_rule_request(trustee_did, '1', 'EDIT', 'verkey', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# add nym with verkey by adder
res4 = await send_nym(pool_handler, wallet_handler, adder_did, new_did, adder_vk) # push adder vk
print(res4)
assert res4['op'] == 'REPLY'
# edit verkey by editor
res5 = await send_nym(pool_handler, wallet_handler, editor_did, new_did, editor_vk) # push editor vk
print(res5)
assert res5['op'] == 'REPLY'
# negative cases
if adder_role != editor_role:
# try to add another nym with editor did - should be rejected
res6 = await send_nym(pool_handler, wallet_handler, editor_did, random_did_and_json()[0])
print(res6)
assert res6['op'] == 'REJECT'
# try to edit initial nym one more time with adder did - should be rejected
res7 = await send_nym(pool_handler, wallet_handler, adder_did, new_did, adder_vk)
print(res7)
assert res7['op'] == 'REJECT'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_attrib(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
# add target nym
target_did, target_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, target_did, target_vk)
assert res['op'] == 'REPLY'
# add adder to add attrib
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
# add editor to edit attrib
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '100', 'ADD', '*', None, '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '100', 'EDIT', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# add attrib for target did by non-owner adder
res4 = await send_attrib(pool_handler, wallet_handler, adder_did, target_did,
None, json.dumps({'key1': 'value1'}), None)
print(res4)
assert res4['op'] == 'REPLY'
# edit attrib for target did by non-owner editor
res5 = await send_attrib(pool_handler, wallet_handler, editor_did, target_did,
None, json.dumps({'key1': 'value2'}), None)
print(res5)
assert res5['op'] == 'REPLY'
# negative cases
if adder_role != editor_role:
# try to add another attrib with editor did - should be rejected
res6 = await send_attrib(pool_handler, wallet_handler, editor_did, target_did,
None, json.dumps({'key2': 'value1'}), None)
print(res6)
assert res6['op'] == 'REJECT'
# try to edit initial attrib one more time with adder did - should be rejected
res7 = await send_attrib(pool_handler, wallet_handler, adder_did, target_did,
None, json.dumps({'key1': 'value3'}), None)
print(res7)
assert res7['op'] == 'REJECT'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.asyncio
async def test_case_schema(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num): # we can add schema only
trustee_did, _ = get_default_trustee
# add adder to add schema
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '101', 'ADD', '*', None, '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# add schema
res4 = await send_schema(pool_handler, wallet_handler, adder_did, 'schema1', '1.0', json.dumps(['attr1']))
print(res4)
assert res4[1]['op'] == 'REPLY'
# edit schema - nobody can edit schemas - should be rejected
res5 = await send_schema(pool_handler, wallet_handler, adder_did, 'schema1', '1.0',
json.dumps(['attr1', 'attr2']))
print(res5)
assert res5[1]['op'] == 'REJECT'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
# use the same did with different roles to ADD and EDIT since adder did is a part of unique cred def id
async def test_case_cred_def(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
# add adder to add cred def
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
schema_id, _ = await send_schema(pool_handler, wallet_handler, trustee_did,
'schema1', '1.0', json.dumps(["age", "sex", "height", "name"]))
await asyncio.sleep(1)
res = await get_schema(pool_handler, wallet_handler, trustee_did, schema_id)
schema_id, schema_json = await ledger.parse_get_schema_response(json.dumps(res))
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '102', 'ADD', '*', None, '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '102', 'EDIT', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# add cred def
cred_def_id, cred_def_json = \
await anoncreds.issuer_create_and_store_credential_def(wallet_handler, adder_did, schema_json, 'TAG1',
None, json.dumps({'support_revocation': False}))
request = await ledger.build_cred_def_request(adder_did, cred_def_json)
res4 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, request))
print(res4)
assert res4['op'] == 'REPLY'
if adder_role != editor_role:
# try to edit cred def as adder - should be rejected
_request = json.loads(request)
_request['operation']['data']['primary']['n'] = '123456789'
_request['reqId'] += _request['reqId']
res5 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did,
json.dumps(_request)))
print(res5)
assert res5['op'] == 'REJECT'
# change adder role to edit cred def
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, None, None, editor_role)
print(res)
assert res['op'] == 'REPLY'
# edit cred def
request = json.loads(request)
request['operation']['data']['primary']['n'] = '123456'
request['reqId'] += request['reqId']
res6 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did,
json.dumps(request)))
print(res6)
assert res6['op'] == 'REPLY'
if adder_role != editor_role:
# try to add another cred def as editor - should be rejected
cred_def_id, cred_def_json = \
await anoncreds.issuer_create_and_store_credential_def(wallet_handler, adder_did, schema_json, 'TAG2',
None, json.dumps({'support_revocation': True}))
request = await ledger.build_cred_def_request(adder_did, cred_def_json)
res7 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, request))
print(res7)
assert res7['op'] == 'REJECT'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
# use the same did with different roles to ADD and EDIT since adder did is a part of unique revoc reg def id
async def test_case_revoc_reg_def(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
# add adder to add revoc reg def
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
schema_id, _ = await send_schema(pool_handler, wallet_handler, trustee_did,
'schema1', '1.0', json.dumps(['age', 'sex', 'height', 'name']))
await asyncio.sleep(1)
res = await get_schema(pool_handler, wallet_handler, trustee_did, schema_id)
schema_id, schema_json = await ledger.parse_get_schema_response(json.dumps(res))
cred_def_id, _, res = await send_cred_def(pool_handler, wallet_handler, trustee_did, schema_json,
'cred_def_tag', None, json.dumps({'support_revocation': True}))
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '113', 'ADD', '*', None, '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '113', 'EDIT', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# add revoc reg def
tails_writer_config = json.dumps({'base_dir': 'tails', 'uri_pattern': ''})
tails_writer_handle = await blob_storage.open_writer('default', tails_writer_config)
revoc_reg_def_id, revoc_reg_def_json, revoc_reg_entry_json = \
await anoncreds.issuer_create_and_store_revoc_reg(wallet_handler, adder_did, None, 'TAG1',
cred_def_id, json.dumps({
'max_cred_num': 1,
'issuance_type': 'ISSUANCE_BY_DEFAULT'}),
tails_writer_handle)
request = await ledger.build_revoc_reg_def_request(adder_did, revoc_reg_def_json)
res4 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, request))
print(res4)
assert res4['op'] == 'REPLY'
if adder_role != editor_role:
# try to edit revoc reg def as adder - should be rejected
_request = json.loads(request)
_request['operation']['value']['tailsHash'] = random_string(30)
_request['reqId'] += _request['reqId']
res5 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did,
json.dumps(_request)))
print(res5)
assert res5['op'] == 'REJECT'
# change adder role to edit revoc reg def
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, None, None, editor_role)
print(res)
assert res['op'] == 'REPLY'
# edit revoc reg def
request = json.loads(request)
request['operation']['value']['tailsHash'] = random_string(20)
request['reqId'] += request['reqId']
res6 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did,
json.dumps(request)))
print(res6)
assert res6['op'] == 'REPLY'
if adder_role != editor_role:
# try to add another revoc reg def as editor - should be rejected
revoc_reg_def_id, revoc_reg_def_json, revoc_reg_entry_json = \
await anoncreds.issuer_create_and_store_revoc_reg(wallet_handler, adder_did, None, 'TAG2',
cred_def_id, json.dumps({
'max_cred_num': 2,
'issuance_type': 'ISSUANCE_BY_DEFAULT'}),
tails_writer_handle)
request = await ledger.build_revoc_reg_def_request(adder_did, revoc_reg_def_json)
res7 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, request))
print(res7)
assert res7['op'] == 'REJECT'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_revoc_reg_entry(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
# add adder to add revoc reg entry
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
schema_id, _ = await send_schema(pool_handler, wallet_handler, trustee_did,
'schema1', '1.0', json.dumps(['age', 'sex', 'height', 'name']))
await asyncio.sleep(1)
res = await get_schema(pool_handler, wallet_handler, trustee_did, schema_id)
schema_id, schema_json = await ledger.parse_get_schema_response(json.dumps(res))
cred_def_id, _, res = await send_cred_def(pool_handler, wallet_handler, trustee_did, schema_json,
'cred_def_tag', None, json.dumps({'support_revocation': True}))
# set rule for revoc reg def adding - network monitor case
req = await ledger.build_auth_rule_request(trustee_did, '113', 'ADD', '*', None, '*',
json.dumps({
'constraint_id': 'ROLE',
'role': '*',
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res21 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res21)
assert res21['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '114', 'ADD', '*', None, '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res22 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res22)
assert res22['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '114', 'EDIT', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# add revoc reg entry
tails_writer_config = json.dumps({'base_dir': 'tails', 'uri_pattern': ''})
tails_writer_handle = await blob_storage.open_writer('default', tails_writer_config)
revoc_reg_def_id, revoc_reg_def_json, revoc_reg_entry_json = \
await anoncreds.issuer_create_and_store_revoc_reg(wallet_handler, adder_did, None, 'TAG1',
cred_def_id, json.dumps({
'max_cred_num': 10,
'issuance_type': 'ISSUANCE_BY_DEFAULT'}),
tails_writer_handle)
req = await ledger.build_revoc_reg_def_request(adder_did, revoc_reg_def_json)
res = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
assert res['op'] == 'REPLY'
request = await ledger.build_revoc_reg_entry_request(adder_did, revoc_reg_def_id, 'CL_ACCUM',
revoc_reg_entry_json)
res4 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, request))
print(res4)
assert res4['op'] == 'REPLY'
if adder_role != editor_role:
# try to edit revoc reg entry as adder - should be rejected
_request = json.loads(request)
_request['operation']['value']['prevAccum'] = _request['operation']['value']['accum']
_request['operation']['value']['accum'] = random_string(20)
_request['operation']['value']['revoked'] = [7, 8, 9]
_request['reqId'] += _request['reqId']
res5 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did,
json.dumps(_request)))
print(res5)
assert res5['op'] == 'REJECT'
# change adder role to edit revoc reg def
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, None, None, editor_role)
print(res)
assert res['op'] == 'REPLY'
# edit revoc reg entry
request = json.loads(request)
request['operation']['value']['prevAccum'] = request['operation']['value']['accum']
request['operation']['value']['accum'] = random_string(10)
request['operation']['value']['revoked'] = [1, 2, 3]
request['reqId'] += request['reqId']
res6 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did,
json.dumps(request)))
print(res6)
assert res6['op'] == 'REPLY'
if adder_role != editor_role:
# try to add another revoc reg entry as editor - should be rejected
revoc_reg_def_id, revoc_reg_def_json, revoc_reg_entry_json = \
await anoncreds.issuer_create_and_store_revoc_reg(wallet_handler, adder_did, None, 'TAG2',
cred_def_id, json.dumps({
'max_cred_num': 20,
'issuance_type': 'ISSUANCE_BY_DEFAULT'}),
tails_writer_handle)
req = await ledger.build_revoc_reg_def_request(adder_did, revoc_reg_def_json)
res = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
assert res['op'] == 'REPLY'
request = await ledger.build_revoc_reg_entry_request(adder_did, revoc_reg_def_id, 'CL_ACCUM',
revoc_reg_entry_json)
res7 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, request))
print(res7)
assert res7['op'] == 'REJECT'
@pytest.mark.skip('INDY-2024')
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_node(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
# add adder to add node
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
# add editor to edit node
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '0', 'ADD', 'services', '*', str(['VALIDATOR']),
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '0', 'EDIT', 'services', str(['VALIDATOR']), str([]),
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# add node
alias = random_string(5)
client_ip = '{}.{}.{}.{}'.format(rr(1, 255), 0, 0, rr(1, 255))
client_port = rr(1, 32767)
node_ip = '{}.{}.{}.{}'.format(rr(1, 255), 0, 0, rr(1, 255))
node_port = rr(1, 32767)
req = await ledger.build_node_request(adder_did, adder_vk, # adder_vk is used as node target did here
json.dumps(
{
'alias': alias,
'client_ip': client_ip,
'client_port': client_port,
'node_ip': node_ip,
'node_port': node_port,
'services': ['VALIDATOR']
}))
res4 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
print(res4)
assert res4['op'] == 'REPLY'
# edit node
req = await ledger.build_node_request(editor_did, adder_vk, # adder_vk is used as node target did here
json.dumps(
{
'alias': alias,
'services': []
}))
res5 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, editor_did, req))
print(res5)
assert res5['op'] == 'REPLY'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_pool_upgrade(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role, editor_role_num):
trustee_did, _ = get_default_trustee
# add adder to start pool upgrdae
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
# add editor to cancel pool upgrade
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '109', 'ADD', 'action', '*', 'start',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '109', 'EDIT', 'action', 'start', 'cancel',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# start pool upgrade
init_time = 30
version = '1.9.999'
name = 'upgrade' + '_' + version + '_' + datetime.now(tz=timezone.utc).strftime('%Y-%m-%dT%H:%M:%S%z')
_sha256 = hashlib.sha256().hexdigest()
_timeout = 5
reinstall = False
force = False
package = 'indy-node'
dests = ['Gw6pDLhcBcoQesN72qfotTgFa7cbuqZpkX3Xo6pLhPhv', '<KEY>',
'DKVxG2fXXTU8yT5N7hGEbXB3dfdAnYv1JczDUHpmDxya', '4PS3EDQ3dW1tci1Bp6543CfuuebjFrg36kLAUcskGfaA',
'4SWokCJWJc69Tn74VvLS6t2G2ucvXqM9FDMsWJjmsUxe', 'Cv1Ehj43DDM5ttNBmC6VPpEfwXWwfGktHwjDJsTV5Fz8',
'BM8dTooz5uykCbYSAAFwKNkYfT4koomBHsSWHTDtkjhW']
docker_7_schedule = json.dumps(dict(
{dest: datetime.strftime(datetime.now(tz=timezone.utc) + timedelta(minutes=init_time + i * 5),
'%Y-%m-%dT%H:%M:%S%z')
for dest, i in zip(dests, range(len(dests)))}
))
req = await ledger.build_pool_upgrade_request(adder_did, name, version, 'start', _sha256, _timeout,
docker_7_schedule, None, reinstall, force, package)
res4 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
print(res4)
assert res4['op'] == 'REPLY'
# cancel pool upgrade
req = await ledger.build_pool_upgrade_request(editor_did, name, version, 'cancel', _sha256, _timeout,
docker_7_schedule, None, reinstall, force, package)
res5 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, editor_did, req))
print(res5)
assert res5['op'] == 'REPLY'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.asyncio
async def test_case_pool_restart(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num): # we can add pool restart only
trustee_did, _ = get_default_trustee
# add adder to restart pool
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
await asyncio.sleep(15)
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '118', 'ADD', 'action', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# restart pool
req = await ledger.build_pool_restart_request\
(adder_did, 'start', datetime.strftime(datetime.now(tz=timezone.utc) + timedelta(minutes=60),
'%Y-%m-%dT%H:%M:%S%z'))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
res3 = [json.loads(v) for k, v in res3.items()]
print(res3)
assert all([res['op'] == 'REPLY' for res in res3])
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.asyncio
async def test_case_validator_info(self, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num): # we can add validator info only
trustee_did, _ = get_default_trustee
# add adder to get validator info
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
await asyncio.sleep(15)
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '119', 'ADD', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
req = await ledger.build_get_validator_info_request(adder_did)
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
res3 = [json.loads(v) for k, v in res3.items()]
print(res3)
assert all([res['op'] == 'REPLY' for res in res3])
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_pool_config(self, pool_handler, wallet_handler, get_default_trustee,
editor_role, editor_role_num): # we can edit pool config only
trustee_did, _ = get_default_trustee
# add editor to edit pool config
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '111', 'EDIT', 'action', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
req = await ledger.build_pool_config_request(editor_did, False, False)
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, editor_did, req))
print(res3)
assert res3['op'] == 'REPLY'
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.asyncio
async def test_case_auth_rule(self, pool_handler, wallet_handler, get_default_trustee,
editor_role, editor_role_num): # we can edit auth rule only
trustee_did, _ = get_default_trustee
# add editor to edit auth rule
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(trustee_did, '120', 'EDIT', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
await asyncio.sleep(15)
req = await ledger.build_auth_rule_request(editor_did, '111', 'EDIT', 'action', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': '*',
'sig_count': 5,
'need_to_be_owner': True,
'metadata': {}
}))
res3 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, editor_did, req))
print(res3)
assert res3['op'] == 'REPLY'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('sig_count', [0, 1, 3])
@pytest.mark.asyncio
async def test_case_mint(self, payment_init, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, sig_count):
libsovtoken_payment_method = 'sov'
trustee_did, _ = get_default_trustee
address = await payment.create_payment_address(wallet_handler, libsovtoken_payment_method, json.dumps(
{"seed": str('0000000000000000000000000Wallet0')}))
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '10000', 'ADD', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': sig_count,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
if sig_count == 0:
# add identity owner adder to mint tokens
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, None)
assert res['op'] == 'REPLY'
req, _ = await payment.build_mint_req(wallet_handler, adder_did,
json.dumps([{"recipient": address, "amount": 100}]), None)
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
print(res1)
assert res1['op'] == 'REPLY'
elif sig_count == 1:
# add adder to mint tokens
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
req, _ = await payment.build_mint_req(wallet_handler, adder_did,
json.dumps([{"recipient": address, "amount": 100}]), None)
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
print(res1)
assert res1['op'] == 'REPLY'
else:
# add adders to mint tokens
adder_did1, adder_vk1 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did1, adder_vk1, None, adder_role)
assert res['op'] == 'REPLY'
adder_did2, adder_vk2 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did2, adder_vk2, None, adder_role)
assert res['op'] == 'REPLY'
adder_did3, adder_vk3 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did3, adder_vk3, None, adder_role)
assert res['op'] == 'REPLY'
req, _ = await payment.build_mint_req(wallet_handler, adder_did1,
json.dumps([{"recipient": address, "amount": 100}]), None)
req = await ledger.multi_sign_request(wallet_handler, adder_did1, req)
req = await ledger.multi_sign_request(wallet_handler, adder_did2, req)
req = await ledger.multi_sign_request(wallet_handler, adder_did3, req)
res1 = json.loads(await ledger.submit_request(pool_handler, req))
print(res1)
assert res1['op'] == 'REPLY'
@pytest.mark.parametrize('editor_role, editor_role_num', [
('NETWORK_MONITOR', '201'),
('TRUST_ANCHOR', '101'),
('STEWARD', '2'),
('TRUSTEE', '0')
])
@pytest.mark.parametrize('sig_count', [0, 1, 3])
@pytest.mark.asyncio
async def test_case_set_fees(self, payment_init, pool_handler, wallet_handler, get_default_trustee,
editor_role, editor_role_num, sig_count):
libsovtoken_payment_method = 'sov'
fees = {'1': 1, '100': 1, '101': 1, '102': 1, '113': 1, '114': 1, '10001': 1}
trustee_did, _ = get_default_trustee
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '20000', 'EDIT', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': sig_count,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
if sig_count == 0:
# add identity owner editor to set fees
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, None)
assert res['op'] == 'REPLY'
req = await payment.build_set_txn_fees_req(wallet_handler, editor_did, libsovtoken_payment_method,
json.dumps(fees))
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, editor_did, req))
print(res1)
assert res1['op'] == 'REPLY'
elif sig_count == 1:
# add editor to set fees
editor_did, editor_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did, editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
req = await payment.build_set_txn_fees_req(wallet_handler, editor_did, libsovtoken_payment_method,
json.dumps(fees))
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, editor_did, req))
print(res1)
assert res1['op'] == 'REPLY'
else:
# add editors to set fees
editor_did1, editor_vk1 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did1, editor_vk1, None, editor_role)
assert res['op'] == 'REPLY'
editor_did2, editor_vk2 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did2, editor_vk2, None, editor_role)
assert res['op'] == 'REPLY'
editor_did3, editor_vk3 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did3, editor_vk3, None, editor_role)
assert res['op'] == 'REPLY'
req = await payment.build_set_txn_fees_req(wallet_handler, editor_did1, libsovtoken_payment_method,
json.dumps(fees))
req = await ledger.multi_sign_request(wallet_handler, editor_did1, req)
req = await ledger.multi_sign_request(wallet_handler, editor_did2, req)
req = await ledger.multi_sign_request(wallet_handler, editor_did3, req)
res1 = json.loads(await ledger.submit_request(pool_handler, req))
print(res1)
assert res1['op'] == 'REPLY'
@pytest.mark.parametrize('adder_role, adder_role_num', [
('TRUSTEE', '0'),
('STEWARD', '2'),
('TRUST_ANCHOR', '101'),
('NETWORK_MONITOR', '201')
])
@pytest.mark.parametrize('sig_count', [0, 1, 3])
@pytest.mark.asyncio
async def test_case_payment(self, payment_init, pool_handler, wallet_handler, get_default_trustee,
adder_role, adder_role_num, sig_count):
libsovtoken_payment_method = 'sov'
trustee_did, _ = get_default_trustee
address1 = await payment.create_payment_address(wallet_handler, libsovtoken_payment_method, json.dumps(
{"seed": str('0000000000000000000000000Wallet1')}))
address2 = await payment.create_payment_address(wallet_handler, libsovtoken_payment_method, json.dumps(
{"seed": str('0000000000000000000000000Wallet2')}))
# set rule for easier mint adding
req = await ledger.build_auth_rule_request(trustee_did, '10000', 'ADD', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': '*',
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res1)
assert res1['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(trustee_did, '10001', 'ADD', '*', '*', '*',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': sig_count,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# initial minting
req, _ = await payment.build_mint_req(wallet_handler, trustee_did,
json.dumps([{"recipient": address1, "amount": 100}]), None)
res11 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
print(res11)
assert res11['op'] == 'REPLY'
req, _ = await payment.build_get_payment_sources_request(wallet_handler, trustee_did, address1)
res111 = await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req)
source1 = \
json.loads(await payment.parse_get_payment_sources_response(libsovtoken_payment_method,
res111))[0]['source']
if sig_count == 0:
# add identity owner adder to send xfer
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, None)
assert res['op'] == 'REPLY'
req, _ = await payment.build_payment_req(wallet_handler, adder_did,
json.dumps([source1]),
json.dumps([{"recipient": address2, "amount": 100}]), None)
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
print(res1)
assert res1['op'] == 'REPLY'
elif sig_count == 1:
# add adder to send xfer
adder_did, adder_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did, adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
req, _ = await payment.build_payment_req(wallet_handler, adder_did,
json.dumps([source1]),
json.dumps([{"recipient": address2, "amount": 100}]), None)
res1 = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, adder_did, req))
print(res1)
assert res1['op'] == 'REPLY'
else:
# add adders to send xfer
adder_did1, adder_vk1 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did1, adder_vk1, None, adder_role)
assert res['op'] == 'REPLY'
adder_did2, adder_vk2 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did2, adder_vk2, None, adder_role)
assert res['op'] == 'REPLY'
adder_did3, adder_vk3 = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did3, adder_vk3, None, adder_role)
assert res['op'] == 'REPLY'
req, _ = await payment.build_payment_req(wallet_handler, adder_did1,
json.dumps([source1]),
json.dumps([{"recipient": address2, "amount": 100}]), None)
req = await ledger.multi_sign_request(wallet_handler, adder_did1, req)
req = await ledger.multi_sign_request(wallet_handler, adder_did2, req)
req = await ledger.multi_sign_request(wallet_handler, adder_did3, req)
res1 = json.loads(await ledger.submit_request(pool_handler, req))
print(res1)
assert res1['op'] == 'REPLY'
# TODO might make sense to move to separate module since other tests here
# organized per txn type
@pytest.mark.asyncio
async def test_case_forbidden(self, pool_handler, wallet_handler, get_default_trustee):
trustee_did, _ = get_default_trustee
trustee_role, trustee_role_num = 'TRUSTEE', '0'
logger.info("1 Adding new trustee to ledger")
new_trustee_did, new_trustee_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(
pool_handler, wallet_handler, trustee_did, new_trustee_did, new_trustee_vk, None, trustee_role
)
assert res['op'] == 'REPLY'
logger.info("2 Setting forbidden auth rule for adding trustees")
req = await ledger.build_auth_rule_request(trustee_did, '1', 'ADD', 'role', '*', trustee_role_num,
json.dumps({
'constraint_id': 'FORBIDDEN',
}))
res = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
assert res['op'] == 'REPLY'
logger.info("3 Getting newly set forbidden constraint")
req = await ledger.build_get_auth_rule_request(trustee_did, '1', 'ADD', 'role', '*', trustee_role_num)
res = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
assert res['op'] == 'REPLY'
assert res['result']['data'][0]['constraint']['constraint_id'] == 'FORBIDDEN'
logger.info("4 Trying to add one more trustee")
one_more_new_trustee_did, one_more_new_trustee_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(
pool_handler, wallet_handler, trustee_did, one_more_new_trustee_did, one_more_new_trustee_vk, None, trustee_role
)
assert res['op'] == 'REJECT'
# TODO might make sense to move to separate module since other tests here
# organized per txn type
@pytest.mark.asyncio
async def test_case_auth_rules(self, pool_handler, wallet_handler, get_default_trustee):
trustee_did, _ = get_default_trustee
trustee_role, trustee_role_num = 'TRUSTEE', '0'
steward_role, steward_role_num = 'STEWARD', '2'
logger.info("1 Creating new steward")
steward_did, steward_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, steward_did, steward_vk, None, steward_role)
assert res['op'] == 'REPLY'
logger.info("2 Creating some new trustee")
_new_trustee_did, _new_trustee_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, _new_trustee_did, _new_trustee_vk, None, trustee_role)
assert res['op'] == 'REPLY'
logger.info("3 Trying to add new trustee using steward as submitter")
new_trustee_did, new_trustee_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(
pool_handler, wallet_handler, steward_did, new_trustee_did, new_trustee_vk, None, trustee_role
)
assert res['op'] == 'REJECT'
logger.info("4 Trying to add new steward using steward as submitter")
new_steward_did, new_steward_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(
pool_handler, wallet_handler, steward_did, new_steward_did, new_steward_vk, None, trustee_role
)
assert res['op'] == 'REJECT'
logger.info("5 Send auth rules txn to allow stewards to add new trustees and stewrds")
one_steward_constraint = {
'constraint_id': 'ROLE',
'role': steward_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}
req = await ledger.build_auth_rules_request(trustee_did, json.dumps([
{
'auth_type': '1',
'auth_action': 'ADD',
'field': 'role',
'old_value': '*',
'new_value': trustee_role_num,
'constraint': one_steward_constraint
}, {
'auth_type': '1',
'auth_action': 'ADD',
'field': 'role',
'old_value': '*',
'new_value': steward_role_num,
'constraint': one_steward_constraint
},
]))
res = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
assert res['op'] == 'REPLY'
logger.info("6 Getting recently set auth rules")
for role_num in (trustee_role_num, steward_role_num):
req = await ledger.build_get_auth_rule_request(trustee_did, '1', 'ADD', 'role', '*', role_num)
res = json.loads(await ledger.sign_and_submit_request(pool_handler, wallet_handler, trustee_did, req))
assert res['op'] == 'REPLY'
assert res['result']['data'][0]['constraint'] == one_steward_constraint
logger.info("7 Trying to add new trustee using trustee as submitter")
res = await send_nym(
pool_handler, wallet_handler, trustee_did, new_trustee_did, new_trustee_vk, None, trustee_role
)
assert res['op'] == 'REJECT'
logger.info("8 Trying to add new steward using trustee as submitter")
res = await send_nym(
pool_handler, wallet_handler, trustee_did, new_trustee_did, new_steward_vk, None, trustee_role
)
assert res['op'] == 'REJECT'
logger.info("9 Adding new trustee using steward as submitter")
new_trustee_did, new_trustee_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(
pool_handler, wallet_handler, steward_did, new_trustee_did, new_trustee_vk, None, trustee_role
)
assert res['op'] == 'REPLY'
logger.info("10 Adding new steward using steward as submitter")
new_steward_did, new_steward_vk = await did.create_and_store_my_did(wallet_handler, '{}')
res = await send_nym(
pool_handler, wallet_handler, steward_did, new_steward_did, new_steward_vk, None, trustee_role
)
assert res['op'] == 'REPLY'
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"pytest.mark.usefixtures",
"indy.payment.parse_get_payment_sources_response",
"asyncio.sleep",
"indy.payment.build_get_payment_sources_request"
]
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import libhustpass.sbDes as sbDes
import libhustpass.captcha as captcha
import requests
import re
import random
def toWideChar(data):
data_bytes = bytes(data, encoding="utf-8")
ret = []
for i in data_bytes:
ret.extend([0, i])
while len(ret) % 8 != 0:
ret.append(0)
return ret
def Enc(data, first_key, second_key, third_key):
data_bytes = toWideChar(data)
key1_bytes = toWideChar(first_key)
key2_bytes = toWideChar(second_key)
key3_bytes = toWideChar(third_key)
ret_ = []
i = 0
while i < len(data_bytes):
tmp = data_bytes[i : i + 8]
x = 0
y = 0
z = 0
while x < len(key1_bytes):
enc1_ = sbDes.des(key1_bytes[x : x + 8], sbDes.ECB)
tmp = list(enc1_.encrypt(tmp))
x += 8
while y < len(key2_bytes):
enc2_ = sbDes.des(key2_bytes[y : y + 8], sbDes.ECB)
tmp = list(enc2_.encrypt(tmp))
y += 8
while z < len(key3_bytes):
enc3_ = sbDes.des(key3_bytes[z : z + 8], sbDes.ECB)
tmp = list(enc3_.encrypt(tmp))
z += 8
ret_.extend(tmp)
i += 8
ret = ""
for i in ret_:
ret += "%02X" % i
return ret
def login(username, password, url):
r = requests.session()
login_html = r.get(url)
captcha_content = r.get("https://pass.hust.edu.cn/cas/code?"+str(random.random()), stream=True)
captcha_content.raw.decode_content = True
nonce = re.search(
'<input type="hidden" id="lt" name="lt" value="(.*)" />', login_html.text
).group(1)
action = re.search(
'<form id="loginForm" action="(.*)" method="post">', login_html.text
).group(1)
post_params = {
"code": captcha.deCaptcha(captcha_content.raw),
"rsa": Enc(username + password + nonce, "1", "2", "3"),
"ul": len(username),
"pl": len(password),
"lt": nonce,
"execution": "e1s1",
"_eventId": "submit",
}
redirect_html = r.post(
"https://pass.hust.edu.cn" + action, data=post_params, allow_redirects=False
)
try:
return redirect_html.headers["Location"]
except:
raise Exception("login failed")
| [
"requests.session",
"libhustpass.sbDes.des",
"libhustpass.captcha.deCaptcha",
"random.random",
"re.search"
]
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import cv2 as cv
import numpy as np
def areaFinder(contours):
areas = []
for c in contours:
a =cv.contourArea(c)
areas.append(a)
return areas
def sortedContoursByArea(img, larger_to_smaller=True):
edges_img = cv.Canny(img, 100, 150)
contours , h = cv.findContours(edges_img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
sorted_contours = sorted(contours, key=cv.contourArea, reverse=larger_to_smaller)
return sorted_contours
img = cv.imread('./Images/sample-image.png')
sorted_contours = sortedContoursByArea(img, larger_to_smaller=True)
# print(areaFinder(contours))
print(areaFinder(sorted_contours))
for c in sorted_contours:
cv.drawContours(img, c, -1, 244, 3)
cv.imshow('img', img)
cv.waitKey(0)
cv.destroyAllWindows() | [
"cv2.drawContours",
"cv2.imshow",
"cv2.contourArea",
"cv2.waitKey",
"cv2.destroyAllWindows",
"cv2.findContours",
"cv2.Canny",
"cv2.imread"
]
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from mung.torch_ext.eval import Loss
from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput
from ltprg.model.seq import VariableLengthNLLLoss
# Expects config of the form:
# {
# data_parameter : {
# seq : [SEQUENCE PARAMETER NAME]
# input : [INPUT PARAMETER NAME]
# }
# name : [ID FOR MODEL]
# arch_type : [SequenceModelNoInput|SequenceModelInputToHidden]
# dropout : [DROPOUT]
# rnn_layers : [RNN_LAYERS]
# rnn_size : [SIZE OF RNN HIDDEN LAYER]
# embedding_size : [EMBEDDING_SIZE]
# rnn_type : [RNN TYPE]
# (SequenceModelAttendedInput) attn_type : [EMBEDDING|OUTPUT]
# (SequenceModelInputToHidden) conv_input : [INDICATOR OF WHETHER OR NOT TO CONVOLVE THE INPUT]
# (SequenceModelInputToHidden|SequenceModelAttendedInput) conv_kernel : [KERNEL SIZE FOR CONVOLUTION]
# (SequenceModelInputToHidden|SequenceModelAttendedInput) conv_stride : [STRIDE LENGTH FOR CONVOLUTION]
# }
def load_seq_model(config, D, gpu=False):
data_parameter = DataParameter.make(**config["data_parameter"])
seq_field = data_parameter["seq"]
utterance_size = D[seq_field].get_matrix(0).get_feature_set().get_token_count()
dropout = float(config["dropout"])
rnn_layers = int(config["rnn_layers"])
rnn_size = int(config["rnn_size"])
embedding_size = int(config["embedding_size"])
rnn_type = config["rnn_type"]
if config["arch_type"] == "SequenceModelNoInput":
model = SequenceModelNoInput(config["name"], utterance_size, \
embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type)
elif config["arch_type"] == "SequenceModelAttendedInput":
input_field = data_parameter["input"]
input_size = D[input_field].get_feature_set().get_token_count()
conv_kernel = int(config["conv_kernel"])
conv_stride = int(config["conv_stride"])
attn_type = "EMBEDDING"
if "attn_type" in config:
attn_type = config["attn_type"]
model = SequenceModelAttendedInput(config["name"], utterance_size, input_size, \
embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type, \
conv_kernel=conv_kernel, conv_stride=conv_stride, attn_type=attn_type)
else:
input_field = data_parameter["input"]
input_size = D[input_field].get_feature_set().get_token_count()
conv_input = False
conv_kernel = 1
conv_stride = 1
if "conv_input" in config:
conv_input = bool(int(config["conv_input"]))
conv_kernel = int(config["conv_kernel"])
conv_stride = int(config["conv_stride"])
model = SequenceModelInputToHidden(config["name"], utterance_size, input_size, \
embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type, \
conv_input=conv_input, conv_kernel=conv_kernel, conv_stride=conv_stride)
return data_parameter, model
# Expects config of the form:
# {
# data_parameter : {
# seq : [SEQUENCE PARAMETER NAME]
# input : [INPUT PARAMETER NAME]
# },
# evaluations : [
# name : [NAME FOR EVALUATION]
# type : (VariableLengthNLLLoss)
# data : [NAME OF DATA SUBSET]
# (Optional) data_size : [SIZE OF RANDOM SUBET OF DATA TO TAKE]
# ]
# }
def load_evaluations(config, D, gpu=False):
data_parameter = DataParameter.make(**config["data_parameter"])
evaluations = []
loss_criterion = VariableLengthNLLLoss(norm_dim=True)
if gpu:
loss_criterion = loss_criterion.cuda()
for eval_config in config["evaluations"]:
data = D[eval_config["data"]]
if "data_size" in eval_config:
data = data.get_random_subset(int(eval_config["data_size"]))
if eval_config["type"] == "VariableLengthNLLLoss":
loss = Loss(eval_config["name"], data, data_parameter, loss_criterion, norm_dim=True)
evaluations.append(loss)
else:
raise ValueError("Invalid seq evaluation type in config (" + str(eval_config["type"]))
return evaluations
| [
"mung.torch_ext.eval.Loss",
"ltprg.model.seq.SequenceModelAttendedInput",
"ltprg.model.seq.VariableLengthNLLLoss",
"ltprg.model.seq.SequenceModelInputToHidden",
"ltprg.model.seq.SequenceModelNoInput",
"ltprg.model.seq.DataParameter.make"
]
| [((1060, 1106), 'ltprg.model.seq.DataParameter.make', 'DataParameter.make', ([], {}), "(**config['data_parameter'])\n", (1078, 1106), False, 'from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput\n'), ((3436, 3482), 'ltprg.model.seq.DataParameter.make', 'DataParameter.make', ([], {}), "(**config['data_parameter'])\n", (3454, 3482), False, 'from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput\n'), ((3529, 3565), 'ltprg.model.seq.VariableLengthNLLLoss', 'VariableLengthNLLLoss', ([], {'norm_dim': '(True)'}), '(norm_dim=True)\n', (3550, 3565), False, 'from ltprg.model.seq import VariableLengthNLLLoss\n'), ((1520, 1650), 'ltprg.model.seq.SequenceModelNoInput', 'SequenceModelNoInput', (["config['name']", 'utterance_size', 'embedding_size', 'rnn_size', 'rnn_layers'], {'dropout': 'dropout', 'rnn_type': 'rnn_type'}), "(config['name'], utterance_size, embedding_size,\n rnn_size, rnn_layers, dropout=dropout, rnn_type=rnn_type)\n", (1540, 1650), False, 'from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput\n'), ((2071, 2300), 'ltprg.model.seq.SequenceModelAttendedInput', 'SequenceModelAttendedInput', (["config['name']", 'utterance_size', 'input_size', 'embedding_size', 'rnn_size', 'rnn_layers'], {'dropout': 'dropout', 'rnn_type': 'rnn_type', 'conv_kernel': 'conv_kernel', 'conv_stride': 'conv_stride', 'attn_type': 'attn_type'}), "(config['name'], utterance_size, input_size,\n embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=\n rnn_type, conv_kernel=conv_kernel, conv_stride=conv_stride, attn_type=\n attn_type)\n", (2097, 2300), False, 'from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput\n'), ((2737, 2968), 'ltprg.model.seq.SequenceModelInputToHidden', 'SequenceModelInputToHidden', (["config['name']", 'utterance_size', 'input_size', 'embedding_size', 'rnn_size', 'rnn_layers'], {'dropout': 'dropout', 'rnn_type': 'rnn_type', 'conv_input': 'conv_input', 'conv_kernel': 'conv_kernel', 'conv_stride': 'conv_stride'}), "(config['name'], utterance_size, input_size,\n embedding_size, rnn_size, rnn_layers, dropout=dropout, rnn_type=\n rnn_type, conv_input=conv_input, conv_kernel=conv_kernel, conv_stride=\n conv_stride)\n", (2763, 2968), False, 'from ltprg.model.seq import DataParameter, SequenceModelNoInput, SequenceModelInputToHidden, SequenceModelAttendedInput\n'), ((3911, 3989), 'mung.torch_ext.eval.Loss', 'Loss', (["eval_config['name']", 'data', 'data_parameter', 'loss_criterion'], {'norm_dim': '(True)'}), "(eval_config['name'], data, data_parameter, loss_criterion, norm_dim=True)\n", (3915, 3989), False, 'from mung.torch_ext.eval import Loss\n')] |
from shutil import move
import piexif
from PIL import Image
def delete_metadata(full_path_to_img):
"""
This function used for remove metadata only from documents, if you send image 'as image' Telegram automatically
removes all metadata at sending. This function removes all metadata via 'piexif' lib, saved image in '/app'
folder, and after that move it to 'documents' folder.
:param full_path_to_img: path to folder with documents e.g.'documents/image.jpg'
"""
piexif.remove(full_path_to_img, "clean_image.jpg")
move("clean_image.jpg", "documents/clean_image.jpg")
def delete_metadata_from_png(full_path_to_img):
"""
This function used for remove metadata only from png documents, if you send image 'as image' Telegram
automatically removes all metadata at sending. This function removes all metadata via 'PIL' lib and saved image
in 'documents' folder.
:param full_path_to_img: path to folder with documents e.g.'documents/image.png'
"""
image = Image.open(full_path_to_img)
image.save("documents/clean_image.png")
| [
"PIL.Image.open",
"piexif.remove",
"shutil.move"
]
| [((493, 543), 'piexif.remove', 'piexif.remove', (['full_path_to_img', '"""clean_image.jpg"""'], {}), "(full_path_to_img, 'clean_image.jpg')\n", (506, 543), False, 'import piexif\n'), ((548, 600), 'shutil.move', 'move', (['"""clean_image.jpg"""', '"""documents/clean_image.jpg"""'], {}), "('clean_image.jpg', 'documents/clean_image.jpg')\n", (552, 600), False, 'from shutil import move\n'), ((1013, 1041), 'PIL.Image.open', 'Image.open', (['full_path_to_img'], {}), '(full_path_to_img)\n', (1023, 1041), False, 'from PIL import Image\n')] |
#py_unit_2.py
import unittest
class FirstTest(unittest.TestCase):
def setUp(self):
"setUp() runs before every test"
self.msg="Sorry, Charlie, but {} is not the same as {}."
def tearDown(self):
"tearDown runs after every test"
pass
def test_me(self):
"this test should pass"
first=1
second=2
self.assertEqual(first,1, msg=self.msg.format(first, second))
def test_failing(self):
"this test should fail"
first=1
second=2
self.assertEqual(second,1, msg=self.msg.format(first, second))
def test_passing(self):
"this test should pass, too"
self.assertEqual("b", "b")
def test_passing_a_failing_test(self):
"this test should pass, even though it 'fails'"
self.assertNotEqual("a", "b")
if __name__=='__main__':
unittest.main() | [
"unittest.main"
]
| [((750, 765), 'unittest.main', 'unittest.main', ([], {}), '()\n', (763, 765), False, 'import unittest\n')] |
from sys import exit
# ------------------------------------------------------------------------------
global dev_name
global game_title
dev_name = "" # enter your name in the quotes!
game_title = "" # enter the game title in the quotes!
# ------------------------------------------------------------------------------
# ---------- initial values ----------
# these are used to define the starting values of your game variables
init_health = 100
init_mana = 200
init_boss_health = 50
# ---------- game variables ----------
# these will be used during the game
health = 0
mana = 0
boss_health = 0
# ---------- some useful functions ----------
# initialize game variables
def init():
global health
global mana
health = init_health
mana = init_mana
# game over
def game_over(msg):
print(msg)
print("Play again? (y / n)")
while (True):
choice = input("> ")
if (choice == "y"):
start()
break
elif (choice == "n"):
exit(0)
else:
print("Options: y / n")
# ---------- room definitions ----------
# here is where you'll create the flow of the game!
# room 0: where the game starts
def room_0():
global health
print("This is the first stage of the game. Create a custom description and get coding!")
print("Current health: " + str(health))
choice = input("> ");
if "end" in choice:
game_over("The game is over")
def start():
start_msg = "Now playing " + game_title + " by " + dev_name
print(start_msg)
init()
room_0()
# ---------- game start ----------
start()
| [
"sys.exit"
]
| [((1008, 1015), 'sys.exit', 'exit', (['(0)'], {}), '(0)\n', (1012, 1015), False, 'from sys import exit\n')] |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 asyncio
import os
import sys
import pytest
from tornado import httpclient
from .... import oscar as mo
from ....utils import get_next_port
from .. import WebActor, web_api, MarsServiceWebAPIHandler, MarsWebAPIClientMixin
from ..api.web import MarsApiEntryHandler
class TestAPIHandler(MarsServiceWebAPIHandler):
__test__ = False
_root_pattern = "/api/test/(?P<test_id>[^/]+)"
@web_api("", method="get")
def get_method_root(self, test_id):
self.write(f"get_root_value_{test_id}")
@web_api("", method="post")
def post_method_root(self, test_id):
self.write(f"post_root_value_{test_id}")
@web_api("subtest/(?P<subtest_id>[^/]+)", method="get")
def get_method_sub_patt(self, test_id, subtest_id):
self.write(f"get_sub_value_{test_id}_{subtest_id}")
@web_api("subtest/(?P<subtest_id>[^/]+)", method="get", arg_filter={"action": "a1"})
async def get_method_sub_patt_match_arg1(self, test_id, subtest_id):
self.write(f"get_sub_value_{test_id}_{subtest_id}_action1")
@web_api("subtest/(?P<subtest_id>[^/]+)", method="get", arg_filter={"action": "a2"})
async def get_method_sub_patt_match_arg2(self, test_id, subtest_id):
self.write(f"get_sub_value_{test_id}_{subtest_id}_action2")
@web_api("subtest_error", method="get")
def get_with_error(self, test_id):
raise ValueError
@web_api("subtest_delay", method="get")
async def get_with_timeout(self, test_id):
await asyncio.sleep(100)
raise ValueError(test_id)
@pytest.fixture
async def actor_pool():
start_method = (
os.environ.get("POOL_START_METHOD", "forkserver")
if sys.platform != "win32"
else None
)
pool = await mo.create_actor_pool(
"127.0.0.1", n_process=0, subprocess_start_method=start_method
)
async with pool:
web_config = {
"host": "127.0.0.1",
"port": get_next_port(),
"web_handlers": {
"/api": MarsApiEntryHandler,
TestAPIHandler.get_root_pattern(): TestAPIHandler,
},
"extra_discovery_modules": ["mars.services.web.tests.extra_handler"],
}
await mo.create_actor(WebActor, web_config, address=pool.external_address)
yield pool, web_config["port"]
class SimpleWebClient(MarsWebAPIClientMixin):
async def fetch(self, path, method="GET", **kwargs):
return await self._request_url(method, path, **kwargs)
@pytest.mark.asyncio
async def test_web_api(actor_pool):
_pool, web_port = actor_pool
recorded_urls = []
def url_recorder(request):
recorded_urls.append(request.url)
return request
client = SimpleWebClient()
client.request_rewriter = url_recorder
res = await client.fetch(f"http://localhost:{web_port}/")
assert res.body.decode()
res = await client.fetch(f"http://localhost:{web_port}/api")
assert res.body.decode()
res = await client.fetch(f"http://localhost:{web_port}/api/test/test_id")
assert res.body.decode() == "get_root_value_test_id"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id", method="POST", data=b""
)
assert res.body.decode() == "post_root_value_test_id"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest/sub_tid"
)
assert res.body.decode() == "get_sub_value_test_id_sub_tid"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest/sub_tid?action=a1"
)
assert res.body.decode() == "get_sub_value_test_id_sub_tid_action1"
res = await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest/sub_tid?action=a2"
)
assert res.body.decode() == "get_sub_value_test_id_sub_tid_action2"
with pytest.raises(httpclient.HTTPError) as excinfo:
await client.fetch(f"http://localhost:{web_port}/api/test/test_id/non_exist")
assert excinfo.value.code == 404
with pytest.raises(ValueError):
await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest_error"
)
with pytest.raises(TimeoutError):
await client.fetch(
f"http://localhost:{web_port}/api/test/test_id/subtest_delay",
request_timeout=0.5,
)
res = await client.fetch(f"http://localhost:{web_port}/api/extra_test")
assert "Test" in res.body.decode()
assert len(recorded_urls) > 0
| [
"os.environ.get",
"asyncio.sleep",
"pytest.raises"
]
| [((2212, 2261), 'os.environ.get', 'os.environ.get', (['"""POOL_START_METHOD"""', '"""forkserver"""'], {}), "('POOL_START_METHOD', 'forkserver')\n", (2226, 2261), False, 'import os\n'), ((4441, 4476), 'pytest.raises', 'pytest.raises', (['httpclient.HTTPError'], {}), '(httpclient.HTTPError)\n', (4454, 4476), False, 'import pytest\n'), ((4622, 4647), 'pytest.raises', 'pytest.raises', (['ValueError'], {}), '(ValueError)\n', (4635, 4647), False, 'import pytest\n'), ((4771, 4798), 'pytest.raises', 'pytest.raises', (['TimeoutError'], {}), '(TimeoutError)\n', (4784, 4798), False, 'import pytest\n'), ((2088, 2106), 'asyncio.sleep', 'asyncio.sleep', (['(100)'], {}), '(100)\n', (2101, 2106), False, 'import asyncio\n')] |
"""
例子为MNIST，对手写图片进行分类。
神经网络hello world。
"""
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# 封装网络用到的API
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x,
W,
strides= [1, 1, 1, 1],
padding= 'SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,
ksize= [1, 2, 2, 1],
strides= [1, 2, 2, 1],
padding='SAME')
"""
MNIST进阶
"""
sess = tf.InteractiveSession()
# [batch_size, 784]
x = tf.placeholder('float', shape=[None, 784])
y_ = tf.placeholder('float', shape=[None, 10])
"""
第一层卷积
"""
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
# [batch_size, 28, 28, 1]
x_image = tf.reshape(x, [-1, 28, 28, 1])
# [batch_size, 28, 28, 32]
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
# [batch_size, 14, 14, 32]
h_pool1 = max_pool_2x2(h_conv1)
"""
第二层卷积
"""
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
# [batch_size, 14, 14, 64]
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
# [batch_size, 7, 7, 64]
h_pool2 = max_pool_2x2(h_conv2)
"""
全连接层
"""
w_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
# [batch_size, 7*7*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# [batch_size, 1024]
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
"""
dropout
"""
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
"""
输出层
"""
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
# [batch_size, 10]
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_sum = tf.reduce_sum(y_conv[0])
# 计算损失和添加优化器
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 评估模型
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 初始化
sess.run(tf.initialize_all_variables())
for i in range(1):
batch = mnist.train.next_batch(50)
# train_accuracy = accuracy.eval(feed_dict={x:batch[0],
# y_: batch[1],
# keep_prob: 1.0})
# print("step %d, training accuracy %g" % (i, train_accuracy))
y_conv_re = y_conv.eval(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 1.0})
# print(y_conv_re.shape)
print(y_conv_re)
y_sum_re = y_sum.eval(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 1.0})
print(y_sum_re)
train_step.run(feed_dict={x: batch[0],
y_: batch[1],
keep_prob: 0.5})
print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images,
y_: mnist.test.labels,
keep_prob: 1.0}))
| [
"tensorflow.nn.conv2d",
"tensorflow.nn.max_pool",
"tensorflow.InteractiveSession",
"tensorflow.initialize_all_variables",
"tensorflow.Variable",
"tensorflow.reduce_sum",
"tensorflow.placeholder",
"tensorflow.examples.tutorials.mnist.input_data.read_data_sets",
"tensorflow.argmax",
"tensorflow.constant",
"tensorflow.nn.dropout",
"tensorflow.matmul",
"tensorflow.reshape",
"tensorflow.train.AdamOptimizer",
"tensorflow.cast",
"tensorflow.log",
"tensorflow.truncated_normal"
]
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import pbge
from game.content.plotutility import LMSkillsSelfIntro
from game.content import backstory
from pbge.plots import Plot
from pbge.dialogue import Offer, ContextTag
from game.ghdialogue import context
import gears
import game.content.gharchitecture
import game.content.ghterrain
import random
from game import memobrowser
Memo = memobrowser.Memo
# *******************
# *** UTILITIES ***
# *******************
def get_hire_cost(camp, npc):
return (npc.renown * npc.renown * (200 - npc.get_reaction_score(camp.pc, camp)))//10
# **************************
# *** RANDOM_LANCEMATE ***
# **************************
class UtterlyRandomLancemate(Plot):
LABEL = "RANDOM_LANCEMATE"
def custom_init(self, nart):
npc = gears.selector.random_character(rank=min(random.randint(10, 50),random.randint(10, 50)),
mecha_colors=gears.color.random_mecha_colors(),
local_tags=tuple(self.elements["METROSCENE"].attributes),
combatant=True)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
specialties = [sk for sk in gears.stats.NONCOMBAT_SKILLS if sk in npc.statline]
if random.randint(-12,3) > len(specialties):
npc.statline[random.choice(gears.stats.NONCOMBAT_SKILLS)] += random.randint(1,4)
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate):
return isinstance(candidate,pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class UtterlyGenericLancemate(Plot):
LABEL = "RANDOM_LANCEMATE"
JOBS = ("Mecha Pilot","Arena Pilot","Recon Pilot","Mercenary","Bounty Hunter")
def custom_init(self, nart):
npc = gears.selector.random_character(rank=min(random.randint(10, 50),random.randint(10, 50)),
job=gears.jobs.ALL_JOBS[random.choice(self.JOBS)],
mecha_colors=gears.color.random_mecha_colors(),
local_tags=tuple(self.elements["METROSCENE"].attributes),
combatant=True)
if random.randint(1,20) == 1:
npc.statline[random.choice(gears.stats.NONCOMBAT_SKILLS)] += random.randint(1,4)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate):
return isinstance(candidate,pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class GiftedNewbieLancemate(Plot):
# Amazing stats, amazingly crap skills.
LABEL = "RANDOM_LANCEMATE"
JOBS = ("Mecha Pilot","Arena Pilot","Citizen","Explorer","Factory Worker")
UNIQUE = True
def custom_init(self, nart):
npc = gears.selector.random_character(statline=gears.base.Being.random_stats(random.randint(100, 110)),
rank=random.randint(5, 15),
job=gears.jobs.ALL_JOBS[random.choice(self.JOBS)],
mecha_colors=gears.color.random_mecha_colors(),
local_tags=tuple(self.elements["METROSCENE"].attributes),
combatant=True, birth_year=nart.camp.year - random.randint(18,23))
if random.randint(1,10) == 1:
npc.statline[random.choice(gears.stats.NONCOMBAT_SKILLS)] += random.randint(1,4)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate):
return isinstance(candidate,pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class OlderMentorLancemate(Plot):
LABEL = "RANDOM_LANCEMATE"
UNIQUE = True
def custom_init(self, nart):
npc = gears.selector.random_character(rank=random.randint(41, 85),
mecha_colors=gears.color.random_mecha_colors(),
local_tags=tuple(self.elements["METROSCENE"].attributes),
combatant=True, birth_year=nart.camp.year - random.randint(32,50))
npc.statline[random.choice(gears.stats.NONCOMBAT_SKILLS)] += random.randint(1, 4)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate):
return isinstance(candidate,pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class DeadzonerInGreenZoneLancemate(Plot):
LABEL = "RANDOM_LANCEMATE"
JOBS = ("Mercenary","Bandit","Scavenger","Aristo","Tekno","Sheriff")
UNIQUE = True
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return gears.personality.GreenZone in pstate.elements["METROSCENE"].attributes
def custom_init(self, nart):
npc = gears.selector.random_character(rank=min(random.randint(20, 55),random.randint(20, 55)),
job=gears.jobs.ALL_JOBS[random.choice(self.JOBS)],
mecha_colors=gears.color.random_mecha_colors(),
local_tags=(gears.personality.DeadZone,),
combatant=True)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate):
return isinstance(candidate,pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class GladiatorLancemate(Plot):
LABEL = "RANDOM_LANCEMATE"
UNIQUE = True
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return gears.personality.DeadZone in pstate.elements["METROSCENE"].attributes
def custom_init(self, nart):
npc = gears.selector.random_character(rank=min(random.randint(25, 65),random.randint(25, 65)),
can_cyberize=True,
job=gears.jobs.ALL_JOBS["Gladiator"],
mecha_colors=gears.color.random_mecha_colors(),
local_tags=(gears.personality.DeadZone,),
combatant=True)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate: gears.GearHeadScene):
return isinstance(candidate,pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class MutantLancemate(Plot):
LABEL = "RANDOM_LANCEMATE"
UNIQUE = True
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return {gears.personality.GreenZone,gears.personality.DeadZone}.intersection(pstate.elements["METROSCENE"].attributes)
def custom_init(self, nart):
npc = gears.selector.random_character(rank=random.randint(20, 45),
mecha_colors=gears.color.random_mecha_colors(),
local_tags=tuple(self.elements["METROSCENE"].attributes),
combatant=True)
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
mutation = random.choice(gears.personality.MUTATIONS)
mutation.apply(npc)
npc.personality.add(mutation)
specialties = [sk for sk in gears.stats.NONCOMBAT_SKILLS if sk in npc.statline]
if random.randint(-12,3) > len(specialties):
npc.statline[random.choice(gears.stats.NONCOMBAT_SKILLS)] += random.randint(1,4)
self.register_element("NPC", npc, dident="LOCALE")
self.add_sub_plot(nart, "RLM_Relationship")
return True
def _is_best_scene(self,nart,candidate):
return isinstance(candidate, pbge.scenes.Scene) and gears.tags.SCENE_PUBLIC in candidate.attributes
class FormerLancemateReturns(Plot):
LABEL = "RANDOM_LANCEMATE"
active = True
scope = "METRO"
def custom_init(self, nart):
npc: gears.base.Character = nart.camp.egg.seek_dramatis_person(nart.camp, self._is_good_npc, self)
if npc:
scene = self.seek_element(nart, "LOCALE", self._is_best_scene, scope=self.elements["METROSCENE"])
self.register_element("NPC", npc, dident="LOCALE")
#print(npc,scene)
self.bs = backstory.Backstory(("LONGTIMENOSEE",),keywords=[t.name.upper() for t in npc.get_tags()])
return npc
def _is_good_npc(self,nart,candidate):
return isinstance(candidate, gears.base.Character) and candidate.relationship and gears.relationships.RT_LANCEMATE in candidate.relationship.tags
def _is_best_scene(self,nart,candidate):
return isinstance(candidate,gears.GearHeadScene) and gears.tags.SCENE_PUBLIC in candidate.attributes
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if npc is self.elements["NPC"]:
for k in self.bs.results.keys():
mygram[k] = [self.bs.get_one(k),]
else:
mygram["[News]"] = ["{NPC} has been hanging out at {LOCALE}".format(**self.elements), ]
return mygram
def NPC_offers(self, camp):
mylist = list()
mylist.append(Offer("[INFO_PERSONAL]",
context=ContextTag([context.PERSONAL]),
no_repeats=True, effect=self.end_plot))
return mylist
def t_START(self, camp):
if self.elements["NPC"] in camp.party:
self.end_plot(camp)
# **************************
# *** RLM_Relationship ***
# **************************
# Elements:
# NPC: The NPC who needs a personality
# METROSCENE: The city or whatever that the NPC calls home
#
# These subplots contain a personality for a random (potential) lancemate.
# Also include a means for the lancemate to gain the "RT_LANCEMATE" tag.
class RLM_Beginner(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
UNIQUE = True
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return pstate.elements["NPC"].renown < 25
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship(attitude=gears.relationships.A_JUNIOR)
# This character gets fewer mecha points.
npc.relationship.data["mecha_level_bonus"] = -10
self._got_rumor = False
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate():
mylist.append(Offer("I can't believe you asked me... [LETSGO]",
context=ContextTag((context.JOIN,)),
effect=self._join_lance
))
mylist.append(Offer(
"[HELLO] Some day I want to become a cavalier like you.", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"]:
# This is an NPC in Wujung. Give them some news.
mygram["[News]"] = ["{} has dreams of someday becoming a cavalier".format(self.elements["NPC"]), ]
return mygram
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _get_generic_offers(self, npc, camp):
"""Get any offers that could apply to non-element NPCs."""
goffs = list()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
mynpc = self.elements["NPC"]
goffs.append(Offer(
msg="As far as I know {} usually hangs out at {}.".format(mynpc,mynpc.get_scene()),
context=ContextTag((context.INFO,)), effect=self._get_rumor,
subject=str(mynpc), data={"subject": str(mynpc)}, no_repeats=True
))
return goffs
def _get_rumor(self,camp):
mynpc = self.elements["NPC"]
self._got_rumor = True
self.memo = Memo( "{} dreams of becoming a cavalier.".format(mynpc)
, mynpc.get_scene()
)
class RLM_Friendly(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
UNIQUE = True
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship(attitude=gears.relationships.A_FRIENDLY)
self._got_rumor = False
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate() and npc.get_reaction_score(camp.pc, camp) > 0:
mylist.append(Offer("[THANKS_FOR_CHOOSING_ME] [LETSGO]",
context=ContextTag((context.JOIN,)),
effect=self._join_lance
))
mylist.append(Offer(
"[HELLO] [WAITINGFORMISSION]", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
# This is an NPC in Wujung. Give them some news.
mygram["[News]"] = ["{} is looking for a lance to join".format(self.elements["NPC"]), ]
return mygram
def _get_generic_offers(self, npc, camp):
"""Get any offers that could apply to non-element NPCs."""
goffs = list()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
mynpc = self.elements["NPC"]
goffs.append(Offer(
msg="You can usually find {} at {}, if you're planning to invite {} to join your lance.".format(mynpc,mynpc.get_scene(),mynpc.gender.object_pronoun),
context=ContextTag((context.INFO,)), effect=self._get_rumor,
subject=str(mynpc), data={"subject": str(mynpc)}, no_repeats=True
))
return goffs
def _get_rumor(self,camp):
mynpc = self.elements["NPC"]
self._got_rumor = True
self.memo = Memo( "{} is looking for a lance to join.".format(mynpc)
, mynpc.get_scene()
)
class RLM_Medic(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
UNIQUE = True
VIRTUES = (gears.personality.Peace,gears.personality.Fellowship)
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return pstate.elements["NPC"].job and gears.tags.Medic in pstate.elements["NPC"].job.tags
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship(expectation=gears.relationships.E_GREATERGOOD)
new_virtue = random.choice(self.VIRTUES)
if new_virtue not in npc.personality:
npc.personality.add(new_virtue)
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate():
mylist.append(Offer("[THANKS_FOR_CHOOSING_ME] [LETSGO]",
context=ContextTag((context.JOIN,)),
effect=self._join_lance
))
else:
mylist.append(Offer("You've got a full crew right now, but if you ever find yourself in need of a qualified medic come back and find me.",
context=ContextTag((context.JOIN,)),
effect=self._defer_join
))
mylist.append(Offer(
"[HELLO] Lately I've been spending too much time here, when I'd rather be out in the danger zone saving lives.", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"]:
# This is an NPC in Wujung. Give them some news.
mygram["[News]"] = ["{} wants to leave {} so {} can make a positive difference in the world".format(self.elements["NPC"],self.elements["NPC"].get_scene(),self.elements["NPC"].gender.subject_pronoun), ]
return mygram
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _defer_join(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
self.end_plot(camp)
class RLM_Mercenary(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
UNIQUE = True
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return pstate.elements["NPC"].job and {gears.tags.Adventurer,gears.tags.Military}.intersection(pstate.elements["NPC"].job.tags)
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship(expectation=gears.relationships.E_MERCENARY)
# This character gets extra mecha points, showing their good investment sense.
npc.relationship.data["mecha_level_bonus"] = 10
self._got_rumor = False
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
self.hire_cost = get_hire_cost(camp,npc)
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate():
mylist.append(Offer("I'll join your lance for a mere ${}. [DOYOUACCEPTMYOFFER]".format(self.hire_cost),
context=ContextTag((context.PROPOSAL, context.JOIN)),
data={"subject": "joining my lance"},
subject=self, subject_start=True,
))
mylist.append(Offer("[DENY_JOIN] [GOODBYE]",
context=ContextTag((context.DENY, context.JOIN)), subject=self
))
if camp.credits >= self.hire_cost:
mylist.append(Offer("[THANKS_FOR_CHOOSING_ME] [LETSGO]",
context=ContextTag((context.ACCEPT, context.JOIN)), subject=self,
effect=self._join_lance
))
mylist.append(Offer(
"[HELLO] I am a mercenary pilot, looking for my next contract.", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"]:
# This is an NPC in Wujung. Give them some news.
mygram["[News]"] = ["{} is hoping to make some quick cash".format(self.elements["NPC"]), ]
return mygram
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
camp.credits -= self.hire_cost
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _get_generic_offers(self, npc, camp):
"""Get any offers that could apply to non-element NPCs."""
goffs = list()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
mynpc = self.elements["NPC"]
goffs.append(Offer(
msg="As far as I know {} can usually be found at {}.".format(mynpc,mynpc.get_scene()),
context=ContextTag((context.INFO,)), effect=self._get_rumor,
subject=str(mynpc), data={"subject": str(mynpc)}, no_repeats=True
))
return goffs
def _get_rumor(self,camp):
mynpc = self.elements["NPC"]
self._got_rumor = True
self.memo = Memo("{} is a mercenary pilot looking for a job.".format(mynpc)
, mynpc.get_scene()
)
class RLM_Professional(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
UNIQUE = True
@classmethod
def matches( self, pstate ):
"""Returns True if this plot matches the current plot state."""
return pstate.elements["NPC"].renown > 20
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship(expectation=gears.relationships.E_PROFESSIONAL)
# This character gets 10 extra stat points, showing their elite nature.
npc.roll_stats(10, clear_first=False)
self._got_rumor = False
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
self.hire_cost = get_hire_cost(camp,npc)
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate():
mylist.append(Offer(
"[NOEXPOSURE] I think ${} is a fair signing price. [DOYOUACCEPTMYOFFER]".format(self.hire_cost),
context=ContextTag((context.PROPOSAL, context.JOIN)), data={"subject": "joining my lance"},
subject=self, subject_start=True,
))
mylist.append(Offer("[DENY_JOIN] [GOODBYE]",
context=ContextTag((context.DENY, context.JOIN)), subject=self
))
if camp.credits >= self.hire_cost:
mylist.append(Offer("[THANKS_FOR_CHOOSING_ME] [LETSGO]",
context=ContextTag((context.ACCEPT, context.JOIN)), subject=self,
effect=self._join_lance
))
mylist.append(Offer(
"[HELLO] I see you are also a cavalier.", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"]:
# This is an NPC in Wujung. Give them some news.
mygram["[News]"] = ["{} is an experienced pilot looking for work".format(self.elements["NPC"]), ]
return mygram
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
camp.credits -= self.hire_cost
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _get_generic_offers(self, npc, camp):
"""Get any offers that could apply to non-element NPCs."""
goffs = list()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
mynpc = self.elements["NPC"]
goffs.append(Offer(
msg="You can usually find {} at {}. Bring cash if you're planning to hire {}.".format(mynpc,mynpc.get_scene(),mynpc.gender.object_pronoun),
context=ContextTag((context.INFO,)), effect=self._get_rumor,
subject=str(mynpc), data={"subject": str(mynpc)}, no_repeats=True
))
return goffs
def _get_rumor(self,camp):
mynpc = self.elements["NPC"]
self._got_rumor = True
self.memo = Memo( "{} is an experienced pilot looking for work.".format(mynpc)
, mynpc.get_scene()
)
class RLM_RatherGeneric(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship()
self._got_rumor = False
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
self.hire_cost = get_hire_cost(camp,npc)
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate():
if npc.get_reaction_score(camp.pc, camp) > 60:
mylist.append(Offer("[IWOULDLOVETO] [THANKS_FOR_CHOOSING_ME]",
context=ContextTag((context.PROPOSAL, context.JOIN)),
data={"subject": "joining my lance"},
effect=self._join_lance
))
else:
mylist.append(Offer("My regular signing rate is ${}. [DOYOUACCEPTMYOFFER]".format(self.hire_cost),
context=ContextTag((context.PROPOSAL, context.JOIN)),
data={"subject": "joining my lance"},
subject=self, subject_start=True,
))
mylist.append(Offer("[DENY_JOIN] [GOODBYE]",
context=ContextTag((context.DENY, context.JOIN)), subject=self
))
if camp.credits >= self.hire_cost:
mylist.append(Offer("[THANKS_FOR_CHOOSING_ME] [LETSGO]",
context=ContextTag((context.ACCEPT, context.JOIN)), subject=self,
effect=self._pay_to_join
))
mylist.append(Offer(
"[HELLO] [WAITINGFORMISSION]", context=ContextTag((context.HELLO,))
))
else:
mylist.append(Offer(
"[HELLO] Must be nice going off, having adventures with your lancemates. I'd like to do that again someday.", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"]:
mygram["[News]"] = ["{} is looking for a new lance to join".format(self.elements["NPC"]), ]
return mygram
def _pay_to_join(self,camp):
camp.credits -= self.hire_cost
self._join_lance(camp)
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _get_generic_offers(self, npc, camp):
"""Get any offers that could apply to non-element NPCs."""
goffs = list()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
mynpc = self.elements["NPC"]
goffs.append(Offer(
msg="You can find {} at {}.".format(mynpc,mynpc.get_scene()),
context=ContextTag((context.INFO,)), effect=self._get_rumor,
subject=str(mynpc), data={"subject": str(mynpc)}, no_repeats=True
))
return goffs
def _get_rumor(self,camp):
mynpc = self.elements["NPC"]
self._got_rumor = True
self.memo = Memo("{} is looking for a new lance.".format(mynpc)
, mynpc.get_scene()
)
class RLM_DamagedGoodsSale(Plot):
LABEL = "RLM_Relationship"
active = True
scope = True
UNIQUE = True
def custom_init(self, nart):
npc = self.elements["NPC"]
npc.relationship = gears.relationships.Relationship(expectation=gears.relationships.E_IMPROVER)
# This NPC gets a stat bonus but a crappy mech to show their history.
npc.relationship.data["mecha_level_bonus"] = -15
npc.roll_stats(5, clear_first=False)
self._got_rumor = False
return True
def NPC_offers(self, camp):
mylist = list()
npc = self.elements["NPC"]
self.hire_cost = get_hire_cost(camp,npc)//2
if gears.relationships.RT_LANCEMATE not in npc.relationship.tags:
if camp.can_add_lancemate():
if npc.get_reaction_score(camp.pc, camp) > 20:
mylist.append(Offer("[IWOULDLOVETO] I'll do my best to not let you down.",
context=ContextTag((context.PROPOSAL, context.JOIN)),
data={"subject": "joining my lance"},
effect=self._join_lance
))
else:
mylist.append(Offer("I'll sign up with you for just ${}. [DOYOUACCEPTMYOFFER]".format(self.hire_cost),
context=ContextTag((context.PROPOSAL, context.JOIN)),
data={"subject": "joining my lance"},
subject=self, subject_start=True,
))
mylist.append(Offer("[DENY_JOIN] [GOODBYE]",
context=ContextTag((context.DENY, context.JOIN)), subject=self
))
if camp.credits >= self.hire_cost:
mylist.append(Offer("[THANKS_FOR_CHOOSING_ME] I'll do my best to not let you down.",
context=ContextTag((context.ACCEPT, context.JOIN)), subject=self,
effect=self._pay_to_join
))
mylist.append(Offer(
"[HELLO] The life of a cavalier is full of ups and downs... right now I'm in one of those downs.", context=ContextTag((context.HELLO,))
))
else:
mylist.append(Offer(
"[HELLO] Be careful out there... all it takes is one little mistake to cost you everything.", context=ContextTag((context.HELLO,))
))
mylist.append(LMSkillsSelfIntro(npc))
return mylist
def _get_dialogue_grammar(self, npc, camp):
mygram = dict()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"]:
mygram["[News]"] = ["{NPC} is a down on {NPC.gender.possessive_determiner} luck cavalier looking for another chance".format(**self.elements), ]
return mygram
def _pay_to_join(self,camp):
camp.credits -= self.hire_cost
self._join_lance(camp)
def _join_lance(self, camp):
npc = self.elements["NPC"]
npc.relationship.tags.add(gears.relationships.RT_LANCEMATE)
effect = game.content.plotutility.AutoJoiner(npc)
effect(camp)
self.end_plot(camp)
def _get_generic_offers(self, npc, camp):
"""Get any offers that could apply to non-element NPCs."""
goffs = list()
if camp.scene.get_root_scene() is self.elements["METROSCENE"] and npc is not self.elements["NPC"] and not self._got_rumor:
mynpc = self.elements["NPC"]
goffs.append(Offer(
msg="You can find {} at {}. Don't say that you weren't warned.".format(mynpc,mynpc.get_scene()),
context=ContextTag((context.INFO,)), effect=self._get_rumor,
subject=str(mynpc), data={"subject": str(mynpc)}, no_repeats=True
))
return goffs
def _get_rumor(self,camp):
mynpc = self.elements["NPC"]
self._got_rumor = True
self.memo = Memo( "{} is looking for a new lance.".format(mynpc)
, mynpc.get_scene()
)
| [
"random.choice",
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"gears.color.random_mecha_colors",
"gears.relationships.Relationship",
"pbge.dialogue.ContextTag",
"random.randint"
]
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pbge.dialogue import Offer, ContextTag\n'), ((17679, 17706), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.JOIN,)'], {}), '((context.JOIN,))\n', (17689, 17706), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((18024, 18051), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.JOIN,)'], {}), '((context.JOIN,))\n', (18034, 18051), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((20470, 20514), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.PROPOSAL, context.JOIN)'], {}), '((context.PROPOSAL, context.JOIN))\n', (20480, 20514), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((20804, 20844), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.DENY, context.JOIN)'], {}), '((context.DENY, context.JOIN))\n', (20814, 20844), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((24104, 24148), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.PROPOSAL, context.JOIN)'], {}), '((context.PROPOSAL, context.JOIN))\n', (24114, 24148), False, 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((21074, 21116), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.ACCEPT, context.JOIN)'], {}), '((context.ACCEPT, context.JOIN))\n', (21084, 21116), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((24640, 24682), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.ACCEPT, context.JOIN)'], {}), '((context.ACCEPT, context.JOIN))\n', (24650, 24682), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((27358, 27402), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.PROPOSAL, context.JOIN)'], {}), '((context.PROPOSAL, context.JOIN))\n', (27368, 27402), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((27778, 27822), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.PROPOSAL, context.JOIN)'], {}), '((context.PROPOSAL, context.JOIN))\n', (27788, 27822), False, 'from pbge.dialogue import Offer, ContextTag\n'), ((28132, 28172), 'pbge.dialogue.ContextTag', 'ContextTag', (['(context.DENY, context.JOIN)'], {}), '((context.DENY, context.JOIN))\n', (28142, 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context.JOIN)'], {}), '((context.ACCEPT, context.JOIN))\n', (32652, 32684), False, 'from pbge.dialogue import Offer, ContextTag\n')] |
#!/usr/bin/env python
import websocket
import time
try:
import thread
except ImportError:
import _thread as thread
runs = 100
def on_message(ws, message):
print(message)
def on_error(ws, error):
print(error)
def on_close(ws):
print("### closed ###")
def on_open(ws):
def run(*args):
for i in range(runs):
time.sleep(5)
ws.send("Ping")
time.sleep(1)
ws.close()
print("thread terminating...")
thread.start_new_thread(run, ())
if __name__ == "__main__":
websocket.enableTrace(True)
url = "ws://localhost:8082"
ws = websocket.WebSocketApp(url, on_message = on_message, on_error = on_error, on_close = on_close)
ws.on_open = on_open
ws.run_forever()
| [
"websocket.enableTrace",
"websocket.WebSocketApp",
"time.sleep",
"_thread.start_new_thread"
]
| [((513, 545), '_thread.start_new_thread', 'thread.start_new_thread', (['run', '()'], {}), '(run, ())\n', (536, 545), True, 'import _thread as thread\n'), ((585, 612), 'websocket.enableTrace', 'websocket.enableTrace', (['(True)'], {}), '(True)\n', (606, 612), False, 'import websocket\n'), ((658, 750), 'websocket.WebSocketApp', 'websocket.WebSocketApp', (['url'], {'on_message': 'on_message', 'on_error': 'on_error', 'on_close': 'on_close'}), '(url, on_message=on_message, on_error=on_error,\n on_close=on_close)\n', (680, 750), False, 'import websocket\n'), ((432, 445), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (442, 445), False, 'import time\n'), ((380, 393), 'time.sleep', 'time.sleep', (['(5)'], {}), '(5)\n', (390, 393), False, 'import time\n')] |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import seaborn as sns
from matplotlib import rcParams
import statsmodels.api as sm
from statsmodels.formula.api import ols
df = pd.read_csv('kc_house_data.csv')
# print(df.head())
# print(df.isnull().any())
# print(df.describe())
# fig = plt.figure(figsize=(12, 6))
# sqft = fig.add_subplot(121)
# cost = fig.add_subplot(122)
#
# sqft.hist(df.sqft_living, bins=80)
# sqft.set_xlabel('Ft^2')
# sqft.set_title("Histogram of House Square Footage")
#
# cost.hist(df.price, bins=80)
# cost.set_xlabel('Price ($)')
# cost.set_title("Histogram of Housing Prices")
#
# plt.show()
# m = ols('price ~ sqft_living', df).fit()
# print(m.summary())
# m = ols('price ~ sqft_living + bedrooms + grade + condition',df).fit()
# print (m.summary())
sns.jointplot(x="sqft_living", y="price", data=df, kind='reg', fit_reg=True, size=7)
plt.show()
| [
"seaborn.jointplot",
"pandas.read_csv",
"matplotlib.pyplot.show"
]
| [((228, 260), 'pandas.read_csv', 'pd.read_csv', (['"""kc_house_data.csv"""'], {}), "('kc_house_data.csv')\n", (239, 260), True, 'import pandas as pd\n'), ((836, 924), 'seaborn.jointplot', 'sns.jointplot', ([], {'x': '"""sqft_living"""', 'y': '"""price"""', 'data': 'df', 'kind': '"""reg"""', 'fit_reg': '(True)', 'size': '(7)'}), "(x='sqft_living', y='price', data=df, kind='reg', fit_reg=True,\n size=7)\n", (849, 924), True, 'import seaborn as sns\n'), ((921, 931), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (929, 931), True, 'import matplotlib.pyplot as plt\n')] |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 14 10:37:04 2021
@author: <NAME>
"""
#calculates trajectory of small mass positioned close to L4 Lagrange point
#creates gif as output
import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
DistanceJ = 778570000000. # m JUPITER FROM SUN
G = 6.67259*10**-11
Jupiter_mass = 1.8982*10**27 # kg
Sun_mass = 1.989*10**30 # kg
M1=Sun_mass
M2=Jupiter_mass
a=DistanceJ
Ang_vel=math.sqrt(G*(M1+M2)/(a**3)) #FROM KEPLER LAW
P=2.*math.pi/Ang_vel #Period
#center of mass is located at [0,0] massive object (Sun) is located at -r1, secondary object (Jupiter) is located at +r2
r2=M1*a/(M1+M2)
r1=M2*a/(M1+M2)
# Calculations are done in corotating frame
# s1, s2 are distances from sources of gravity (Sun, Jupiter)
def pot(x,y):
r=math.sqrt(x*x + y*y)
if x==0:
if y>0:
theta=math.pi/2.
if y<0:
theta=math.pi/2.
if x>0:
theta=math.atan(abs(y)/x)
else:
theta=math.pi-math.atan(abs(y)/x)
s1=math.sqrt(r1*r1 + r*r + 2.*r1*r*math.cos(theta))
s2=math.sqrt(r2*r2 + r*r - 2.*r2*r*math.cos(theta))
result = -G*(M1/s1 + M2/s2) -1.*Ang_vel*Ang_vel*r*r/2.
return result
#Force per unit mass (acceleration) in x direction
# ax = \partial pot(x,y) / \partial x - 2 \Omega \times v
# in our case \Omega=(0,0,\Omega) and v=(vx,vy,0)
# second term is corresponding to Coriolis force
def ax(x,y,vx,vy):
dx=a/1000.
# result=-(pot(x+dx,y) -pot(x-dx,y))/(2.*dx) + 2.* Ang_vel*vy
result=-(-pot(x+2.*dx,y) + 8.*pot(x+dx,y) - 8.*pot(x-dx,y) + pot(x-2.*dx,y))/(12.*dx) + 2.* Ang_vel*vy
return result
def ay(x,y,vx,vy):
dy=a/1000.
# result=-( pot(x,y+dy)-pot(x,y-dy))/(dy*2.) - 2.* Ang_vel*vx
result=-(-pot(x,y+2.*dy) + 8.*pot(x,y+dy) - 8.*pot(x,y-dy) + pot(x,y-2*dy))/(dy*12.) - 2.* Ang_vel*vx
return result
pot2=np.vectorize(pot)
#TRAJECTORY OF ASTEROID CLOSE STARTING CLOSE TO L4 in rest with respect to the rotating frame
x0=a/2.-r1
y0=math.sqrt(3)*a/2.
x0=1.005*x0
y0=1.005*y0
vx0=0.
vy0=0.
steps=300000
#initialize arrays
x= np.linspace(0, 10, steps)
y= np.linspace(0, 10, steps)
vx=np.linspace(0, 10, steps)
vy=np.linspace(0, 10, steps)
t= np.linspace(0, 10, steps)
x[0]=x0
vx[0]=vx0
y[0]=y0
vy[0]=vy0
t[0]=0.
i=0
timescale = math.sqrt((a*a)**1.5 / G/(M1+M2))
dt=timescale/1000.
#using 4th order Runge-Kutta to solve the a_x= d v_x/ dt
# dt is constant set to timescale/1000
for i in range (1,steps):
t[i]=(t[i-1]+dt)
Kx1=dt*ax(x[i-1],y[i-1],vx[i-1],vy[i-1])
Kx2=dt*ax(x[i-1],y[i-1],vx[i-1]+Kx1/2.,vy[i-1])
Kx3=dt*ax(x[i-1],y[i-1],vx[i-1]+Kx2/2.,vy[i-1])
Kx4=dt*ax(x[i-1],y[i-1],vx[i-1]+Kx3,vy[i-1])
vx[i]=vx[i-1] + Kx1/6. + Kx2/3. + Kx3/3. + Kx4/6.
Ky1=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1])
Ky2=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1]+Ky1/2.)
Ky3=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1]+Ky2/2.)
Ky4=dt*ay(x[i-1],y[i-1],vx[i-1],vy[i-1]+Ky3)
vy[i]=vy[i-1] + Ky1/6. + Ky2/3. + Ky3/3. + Ky4/6.
x[i]=x[i-1] + (vx[i-1]+vx[i])*dt/2. #taking the average of velocities
y[i]=y[i-1] + (vy[i-1]+vy[i])*dt/2.
dt=timescale/1000.
#LAGRANGE POINTS
#L3, L1 and L2 points are lying on x-axis (left to right) for small values of alpha=M2/(M1+M2) the positions can are given analytically (to first order in alpha)
alpha=M2/(M1+M2)
L1X=a*(1.-(alpha/3.)**(1./3.))
L1Y=0.
P1=pot(L1X,L1Y)
L2X=a*(1.+(alpha/3.)**(1./3.))
L2Y=0.
P2=pot(L2X,L2Y)
L3X=-a*(1. + 5.*alpha/12)
L3Y=0.
P3=pot(L3X,L3Y)
L4X=a/2.-r1
L4Y=math.sqrt(3)*a/2.
P4=pot2(L4X,L4Y)
P0=pot(x0,y0)
steps=301
xx= np.arange(-2*a, 2.*a,a/steps)
yy= np.arange(-1.5*a, 1.5*a,a/steps)
X, Y = np.meshgrid(xx, yy)
Z1=pot2(X,Y)
fig, ax = plt.subplots()
ax.set_aspect('equal','box')
ln1, = plt.plot([],[], 'k+')
ln2, = plt.plot([], [], 'm*')
XXX,YYY=[],[]
def init():
ax.set_xlim(-1.25,1.25)
ax.set_ylim(-1.25,1.25)
ax.contour(X/a, Y/a, Z1,levels=[P1,P2,P3,P0],colors=('r', 'green', 'blue', 'm'))
def update(i):
ln1.set_data(x[1000*i]/a, y[1000*i]/a)
zed= np.arange(60)
ani = FuncAnimation(fig, update, np.arange(300), init_func=init)
plt.show()
writer = PillowWriter(fps=25)
ani.save("Animation.gif", writer=writer)
| [
"matplotlib.pyplot.plot",
"math.sqrt",
"math.cos",
"numpy.linspace",
"numpy.meshgrid",
"matplotlib.animation.PillowWriter",
"numpy.vectorize",
"matplotlib.pyplot.subplots",
"numpy.arange",
"matplotlib.pyplot.show"
]
| [((495, 528), 'math.sqrt', 'math.sqrt', (['(G * (M1 + M2) / a ** 3)'], {}), '(G * (M1 + M2) / a ** 3)\n', (504, 528), False, 'import math\n'), ((1949, 1966), 'numpy.vectorize', 'np.vectorize', (['pot'], {}), '(pot)\n', (1961, 1966), True, 'import numpy as np\n'), ((2170, 2195), 'numpy.linspace', 'np.linspace', (['(0)', '(10)', 'steps'], {}), '(0, 10, steps)\n', (2181, 2195), True, 'import numpy as np\n'), ((2200, 2225), 'numpy.linspace', 'np.linspace', (['(0)', '(10)', 'steps'], {}), '(0, 10, steps)\n', (2211, 2225), True, 'import numpy as np\n'), ((2230, 2255), 'numpy.linspace', 'np.linspace', (['(0)', '(10)', 'steps'], {}), '(0, 10, steps)\n', (2241, 2255), True, 'import numpy as np\n'), ((2260, 2285), 'numpy.linspace', 'np.linspace', (['(0)', '(10)', 'steps'], {}), '(0, 10, steps)\n', (2271, 2285), True, 'import numpy as np\n'), ((2290, 2315), 'numpy.linspace', 'np.linspace', (['(0)', '(10)', 'steps'], {}), '(0, 10, steps)\n', (2301, 2315), True, 'import numpy as np\n'), ((2378, 2419), 'math.sqrt', 'math.sqrt', (['((a * a) ** 1.5 / G / (M1 + M2))'], {}), '((a * a) ** 1.5 / G / (M1 + M2))\n', (2387, 2419), False, 'import math\n'), ((3682, 3719), 'numpy.arange', 'np.arange', (['(-2 * a)', '(2.0 * a)', '(a / steps)'], {}), '(-2 * a, 2.0 * a, a / steps)\n', (3691, 3719), True, 'import numpy as np\n'), ((3716, 3755), 'numpy.arange', 'np.arange', (['(-1.5 * a)', '(1.5 * a)', '(a / steps)'], {}), '(-1.5 * a, 1.5 * a, a / steps)\n', (3725, 3755), True, 'import numpy as np\n'), ((3756, 3775), 'numpy.meshgrid', 'np.meshgrid', (['xx', 'yy'], {}), '(xx, yy)\n', (3767, 3775), True, 'import numpy as np\n'), ((3803, 3817), 'matplotlib.pyplot.subplots', 'plt.subplots', ([], {}), '()\n', (3815, 3817), True, 'import matplotlib.pyplot as plt\n'), ((3856, 3878), 'matplotlib.pyplot.plot', 'plt.plot', (['[]', '[]', '"""k+"""'], {}), "([], [], 'k+')\n", (3864, 3878), True, 'import matplotlib.pyplot as plt\n'), ((3887, 3909), 'matplotlib.pyplot.plot', 'plt.plot', (['[]', '[]', '"""m*"""'], {}), "([], [], 'm*')\n", (3895, 3909), True, 'import matplotlib.pyplot as plt\n'), ((4162, 4175), 'numpy.arange', 'np.arange', (['(60)'], {}), '(60)\n', (4171, 4175), True, 'import numpy as np\n'), ((4243, 4253), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (4251, 4253), True, 'import matplotlib.pyplot as plt\n'), ((4264, 4284), 'matplotlib.animation.PillowWriter', 'PillowWriter', ([], {'fps': '(25)'}), '(fps=25)\n', (4276, 4284), False, 'from matplotlib.animation import FuncAnimation, PillowWriter\n'), ((853, 877), 'math.sqrt', 'math.sqrt', (['(x * x + y * y)'], {}), '(x * x + y * y)\n', (862, 877), False, 'import math\n'), ((4209, 4223), 'numpy.arange', 'np.arange', (['(300)'], {}), '(300)\n', (4218, 4223), True, 'import numpy as np\n'), ((2079, 2091), 'math.sqrt', 'math.sqrt', (['(3)'], {}), '(3)\n', (2088, 2091), False, 'import math\n'), ((3614, 3626), 'math.sqrt', 'math.sqrt', (['(3)'], {}), '(3)\n', (3623, 3626), False, 'import math\n'), ((1119, 1134), 'math.cos', 'math.cos', (['theta'], {}), '(theta)\n', (1127, 1134), False, 'import math\n'), ((1175, 1190), 'math.cos', 'math.cos', (['theta'], {}), '(theta)\n', (1183, 1190), False, 'import math\n')] |
#!/bin/env python3
from transformers import TFBertForTokenClassification
from data_preparation.data_preparation_pos import MBERTTokenizer as MBERT_Tokenizer_pos
import sys
if __name__ == "__main__":
if len(sys.argv) > 1:
modelname = sys.argv[1]
else:
modelname = "ltgoslo/norbert"
model = TFBertForTokenClassification.from_pretrained(modelname, from_pt=True)
tokenizer = MBERT_Tokenizer_pos.from_pretrained(modelname, do_lower_case=False)
print(tokenizer)
| [
"data_preparation.data_preparation_pos.MBERTTokenizer.from_pretrained",
"transformers.TFBertForTokenClassification.from_pretrained"
]
| [((319, 388), 'transformers.TFBertForTokenClassification.from_pretrained', 'TFBertForTokenClassification.from_pretrained', (['modelname'], {'from_pt': '(True)'}), '(modelname, from_pt=True)\n', (363, 388), False, 'from transformers import TFBertForTokenClassification\n'), ((405, 472), 'data_preparation.data_preparation_pos.MBERTTokenizer.from_pretrained', 'MBERT_Tokenizer_pos.from_pretrained', (['modelname'], {'do_lower_case': '(False)'}), '(modelname, do_lower_case=False)\n', (440, 472), True, 'from data_preparation.data_preparation_pos import MBERTTokenizer as MBERT_Tokenizer_pos\n')] |
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def seasonality_model(thetas, t, device):
p = thetas.size()[-1]
assert p < 10, 'thetas_dim is too big.'
p1, p2 = (p // 2, p // 2) if p % 2 == 0 else (p // 2, p // 2 + 1)
s1 = torch.tensor([np.cos(2 * np.pi * i * t) for i in range(p1)]).float() # H/2-1
s2 = torch.tensor([np.sin(2 * np.pi * i * t) for i in range(p2)]).float()
S = torch.cat([s1, s2])
return thetas.mm(S.to(device))
def trend_model(thetas, t, device):
p = thetas.size()[-1]
assert p <= 4, 'thetas_dim is too big.'
T = torch.tensor([t ** i for i in range(p)]).float()
return thetas.mm(T.to(device))
def linspace(backcast_length, forecast_length):
lin_space = np.linspace(-backcast_length, forecast_length, backcast_length + forecast_length)
b_ls = lin_space[:backcast_length]
f_ls = lin_space[backcast_length:]
return b_ls, f_ls
class Block(nn.Module):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5, share_thetas=False):
super(Block, self).__init__()
self.units = units
self.thetas_dim = thetas_dim
self.backcast_length = backcast_length
self.forecast_length = forecast_length
self.share_thetas = share_thetas
self.fc1 = nn.Linear(backcast_length, units)
self.fc2 = nn.Linear(units, units)
self.fc3 = nn.Linear(units, units)
self.fc4 = nn.Linear(units, units)
self.device = device
self.backcast_linspace, self.forecast_linspace = linspace(backcast_length, forecast_length)
if share_thetas:
self.theta_f_fc = self.theta_b_fc = nn.Linear(units, thetas_dim)
else:
self.theta_b_fc = nn.Linear(units, thetas_dim)
self.theta_f_fc = nn.Linear(units, thetas_dim)
def forward(self, x):
x = F.relu(self.fc1(x.to(self.device)))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
return x
def __str__(self):
block_type = type(self).__name__
return f'{block_type}(units={self.units}, thetas_dim={self.thetas_dim}, ' \
f'backcast_length={self.backcast_length}, forecast_length={self.forecast_length}, ' \
f'share_thetas={self.share_thetas}) at @{id(self)}'
class SeasonalityBlock(Block):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5):
super(SeasonalityBlock, self).__init__(units, thetas_dim, device, backcast_length,
forecast_length, share_thetas=True)
def forward(self, x):
x = super(SeasonalityBlock, self).forward(x)
backcast = seasonality_model(self.theta_b_fc(x), self.backcast_linspace, self.device)
forecast = seasonality_model(self.theta_f_fc(x), self.forecast_linspace, self.device)
return backcast, forecast
class TrendBlock(Block):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5):
super(TrendBlock, self).__init__(units, thetas_dim, device, backcast_length,
forecast_length, share_thetas=True)
def forward(self, x):
x = super(TrendBlock, self).forward(x)
backcast = trend_model(self.theta_b_fc(x), self.backcast_linspace, self.device)
forecast = trend_model(self.theta_f_fc(x), self.forecast_linspace, self.device)
return backcast, forecast
class GenericBlock(Block):
def __init__(self, units, thetas_dim, device, backcast_length=10, forecast_length=5):
super(GenericBlock, self).__init__(units, thetas_dim, device, backcast_length, forecast_length)
self.backcast_fc = nn.Linear(thetas_dim, backcast_length)
self.forecast_fc = nn.Linear(thetas_dim, forecast_length)
def forward(self, x):
# no constraint for generic arch.
x = super(GenericBlock, self).forward(x)
theta_b = F.relu(self.theta_b_fc(x))
theta_f = F.relu(self.theta_f_fc(x))
backcast = self.backcast_fc(theta_b) # generic. 3.3.
forecast = self.forecast_fc(theta_f) # generic. 3.3.
return backcast, forecast
class NBeatsNet(nn.Module):
SEASONALITY_BLOCK = 'seasonality'
TREND_BLOCK = 'trend'
GENERIC_BLOCK = 'generic'
def __init__(self,
device,
stack_types=[TREND_BLOCK, SEASONALITY_BLOCK],
nb_blocks_per_stack=3,
forecast_length=5,
backcast_length=10,
thetas_dims=[4, 8],
share_weights_in_stack=False,
hidden_layer_units=256, ):
super(NBeatsNet, self).__init__()
self.forecast_length = forecast_length
self.backcast_length = backcast_length
self.hidden_layer_units = hidden_layer_units
self.nb_blocks_per_stack = nb_blocks_per_stack
self.share_weights_in_stack = share_weights_in_stack
self.stack_types = stack_types
self.stacks = []
self.thetas_dim = thetas_dims
self.parameters = []
self.device = device
print(f'| N-Beats')
for stack_id in range(len(self.stack_types)):
self.stacks.append(self.create_stack(stack_id))
self.parameters = nn.ParameterList(self.parameters)
self.to(self.device)
def create_stack(self, stack_id):
stack_type = self.stack_types[stack_id]
print(f'| -- Stack {stack_type.title()} (#{stack_id}) (share_weights_in_stack={self.share_weights_in_stack})')
blocks = []
for block_id in range(self.nb_blocks_per_stack):
block_init = NBeatsNet.select_block(stack_type)
if self.share_weights_in_stack and block_id != 0:
block = blocks[-1] # pick up the last one to make the
else:
block = block_init(self.hidden_layer_units, self.thetas_dim[stack_id],
self.device, self.backcast_length, self.forecast_length)
self.parameters.extend(block.parameters())
print(f' | -- {block}')
blocks.append(block)
return blocks
@staticmethod
def select_block(block_type):
if block_type == NBeatsNet.SEASONALITY_BLOCK:
return SeasonalityBlock
elif block_type == NBeatsNet.TREND_BLOCK:
return TrendBlock
else:
return GenericBlock
def forward(self, backcast):
forecast = torch.zeros(size=(backcast.size()[0], self.forecast_length,)) # maybe batch size here.
for stack_id in range(len(self.stacks)):
for block_id in range(len(self.stacks[stack_id])):
b, f = self.stacks[stack_id][block_id](backcast)
backcast = backcast.to(self.device) - b
forecast = forecast.to(self.device) + f
return backcast, forecast
| [
"numpy.linspace",
"numpy.cos",
"torch.nn.Linear",
"numpy.sin",
"torch.nn.ParameterList",
"torch.cat"
]
| [((447, 466), 'torch.cat', 'torch.cat', (['[s1, s2]'], {}), '([s1, s2])\n', (456, 466), False, 'import torch\n'), ((768, 853), 'numpy.linspace', 'np.linspace', (['(-backcast_length)', 'forecast_length', '(backcast_length + forecast_length)'], {}), '(-backcast_length, forecast_length, backcast_length +\n forecast_length)\n', (779, 853), True, 'import numpy as np\n'), ((1343, 1376), 'torch.nn.Linear', 'nn.Linear', (['backcast_length', 'units'], {}), '(backcast_length, units)\n', (1352, 1376), False, 'from torch import nn\n'), ((1396, 1419), 'torch.nn.Linear', 'nn.Linear', (['units', 'units'], {}), '(units, units)\n', (1405, 1419), False, 'from torch import nn\n'), ((1439, 1462), 'torch.nn.Linear', 'nn.Linear', (['units', 'units'], {}), '(units, units)\n', (1448, 1462), False, 'from torch import nn\n'), ((1482, 1505), 'torch.nn.Linear', 'nn.Linear', (['units', 'units'], {}), '(units, units)\n', (1491, 1505), False, 'from torch import nn\n'), ((3784, 3822), 'torch.nn.Linear', 'nn.Linear', (['thetas_dim', 'backcast_length'], {}), '(thetas_dim, backcast_length)\n', (3793, 3822), False, 'from torch import nn\n'), ((3850, 3888), 'torch.nn.Linear', 'nn.Linear', (['thetas_dim', 'forecast_length'], {}), '(thetas_dim, forecast_length)\n', (3859, 3888), False, 'from torch import nn\n'), ((5368, 5401), 'torch.nn.ParameterList', 'nn.ParameterList', (['self.parameters'], {}), '(self.parameters)\n', (5384, 5401), False, 'from torch import nn\n'), ((1708, 1736), 'torch.nn.Linear', 'nn.Linear', (['units', 'thetas_dim'], {}), '(units, thetas_dim)\n', (1717, 1736), False, 'from torch import nn\n'), ((1781, 1809), 'torch.nn.Linear', 'nn.Linear', (['units', 'thetas_dim'], {}), '(units, thetas_dim)\n', (1790, 1809), False, 'from torch import nn\n'), ((1840, 1868), 'torch.nn.Linear', 'nn.Linear', (['units', 'thetas_dim'], {}), '(units, thetas_dim)\n', (1849, 1868), False, 'from torch import nn\n'), ((297, 322), 'numpy.cos', 'np.cos', (['(2 * np.pi * i * t)'], {}), '(2 * np.pi * i * t)\n', (303, 322), True, 'import numpy as np\n'), ((384, 409), 'numpy.sin', 'np.sin', (['(2 * np.pi * i * t)'], {}), '(2 * np.pi * i * t)\n', (390, 409), True, 'import numpy as np\n')] |
from math import exp
from random import seed
from random import random
def initialize_network(n_inputs, n_hidden, n_outputs):
network = list()
hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)]
network.append(hidden_layer)
output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)]
network.append(output_layer)
return network
def activate(weights, inputs):
activation = weights[-1]
for i in range(len(weights)-1):
activation += weights[i] * inputs[i]
return activation
def transfer(activation):
return 1.0 / (1.0 + exp(-activation))
def forward_propagate(network, row):
inputs = row
for layer in network:
new_inputs = []
for neuron in layer:
activation = activate(neuron['weights'], inputs)
neuron['output'] = transfer(activation)
new_inputs.append(neuron['output'])
inputs = new_inputs
return inputs
def transfer_derivative(output):
return output * (1.0 - output)
def backward_propagate_error(network, expected):
for i in reversed(range(len(network))):
layer = network[i]
errors = list()
if i != len(network)-1:
for j in range(len(layer)):
error = 0.0
for neuron in network[i + 1]:
error += (neuron['weights'][j] * neuron['delta'])
errors.append(error)
else:
for j in range(len(layer)):
neuron = layer[j]
errors.append(expected[j] - neuron['output'])
for j in range(len(layer)):
neuron = layer[j]
neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])
def update_weights(network, row, l_rate):
for i in range(len(network)):
inputs = row[:-1]
if i != 0:
inputs = [neuron['output'] for neuron in network[i - 1]]
for neuron in network[i]:
for j in range(len(inputs)):
neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j]
neuron['weights'][-1] += l_rate * neuron['delta']
def train_network(network, train, l_rate, n_epoch, n_outputs):
for epoch in range(n_epoch):
sum_error = 0
for row in train:
outputs = forward_propagate(network, row)
expected = [0 for i in range(n_outputs)]
expected[row[-1]] = 1
sum_error += sum([(expected[i]-outputs[i])**2 for i in range(len(expected))])
backward_propagate_error(network, expected)
update_weights(network, row, l_rate)
print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error))
seed(1)
dataset = [[2.7810836,2.550537003,0],
[1.465489372,2.362125076,0],
[3.396561688,4.400293529,0],
[1.38807019,1.850220317,0],
[3.06407232,3.005305973,0],
[7.627531214,2.759262235,1],
[5.332441248,2.088626775,1],
[6.922596716,1.77106367,1],
[8.675418651,-0.242068655,1],
[7.673756466,3.508563011,1]]
n_inputs = len(dataset[0]) - 1
n_outputs = len(set([row[-1] for row in dataset]))
network = initialize_network(n_inputs, 2, n_outputs)
train_network(network, dataset, 0.5, 30, n_outputs)
for layer in network:
print(layer) | [
"random.random",
"math.exp",
"random.seed"
]
| [((2380, 2387), 'random.seed', 'seed', (['(1)'], {}), '(1)\n', (2384, 2387), False, 'from random import seed\n'), ((615, 631), 'math.exp', 'exp', (['(-activation)'], {}), '(-activation)\n', (618, 631), False, 'from math import exp\n'), ((176, 184), 'random.random', 'random', ([], {}), '()\n', (182, 184), False, 'from random import random\n'), ((301, 309), 'random.random', 'random', ([], {}), '()\n', (307, 309), False, 'from random import random\n')] |
"""\
Copyright (c) 2009 <NAME> <<EMAIL>>
This file is part of hypercouch which is released uner the MIT license.
"""
import time
import unittest
import couchdb
COUCHURI = "http://127.0.0.1:5984/"
TESTDB = "hyper_tests"
class MultiDesignTest(unittest.TestCase):
def setUp(self):
self.srv = couchdb.Server(COUCHURI)
if TESTDB in self.srv:
del self.srv[TESTDB]
self.db = self.srv.create(TESTDB)
self.db["_design/test1"] = {
"ft_index": """\
function(doc) {
if(doc.body) index(doc.body);
if(doc.foo != undefined) property("foo", doc.foo);
}
"""
}
self.db["_design/test2"] = {
"ft_index": """\
function(doc) {
if(doc.bar) property("bar", doc.bar)
}
"""
}
self._wait()
def tearDown(self):
del self.srv[TESTDB]
def _query(self, **kwargs):
resp, data = self.db.resource.get("_fti", **kwargs)
return data
def _wait(self, expect=0, retries=10):
data = self._query(q="*.**")
while retries > 0 and len(data["rows"]) != expect:
retries -= 1
time.sleep(0.2)
data = self._query(q="*.**")
if retries < 1:
raise RuntimeError("Failed to find expected index state.")
def test_attr(self):
docs = [{"_id": str(i), "body": "This is document %d" % i, "foo": i, "bar": str(i*i)} for i in range(10)]
self.db.update(docs)
self._wait(expect=10)
data = self._query(q="*.**", foo="NUMEQ 3", bar="NUMEQ 9")
self.assertEqual(data["total_rows"], 1)
self.assertEqual(data["rows"][0]["id"], "3")
data = self._query(q="*.**")
self.assertEqual(len(data["rows"]), 10)
for row in data["rows"]:
self.assertEqual(int(row["foo"]) ** 2, int(row["bar"]))
| [
"time.sleep",
"couchdb.Server"
]
| [((303, 327), 'couchdb.Server', 'couchdb.Server', (['COUCHURI'], {}), '(COUCHURI)\n', (317, 327), False, 'import couchdb\n'), ((1258, 1273), 'time.sleep', 'time.sleep', (['(0.2)'], {}), '(0.2)\n', (1268, 1273), False, 'import time\n')] |
from __future__ import absolute_import
from django.core.urlresolvers import reverse
from sentry.models import OrganizationMember, OrganizationMemberTeam, Team
from sentry.testutils import TestCase, PermissionTestCase
class CreateTeamPermissionTest(PermissionTestCase):
def setUp(self):
super(CreateTeamPermissionTest, self).setUp()
self.path = reverse('sentry-create-team', args=[self.organization.slug])
def test_teamless_admin_can_load(self):
self.assert_teamless_admin_can_access(self.path)
def test_team_admin_can_load(self):
self.assert_team_admin_can_access(self.path)
def test_member_cannot_load(self):
self.assert_member_cannot_access(self.path)
def test_owner_can_load(self):
self.assert_owner_can_access(self.path)
class CreateTeamTest(TestCase):
def test_renders_with_context(self):
organization = self.create_organization()
path = reverse('sentry-create-team', args=[organization.slug])
self.login_as(self.user)
resp = self.client.get(path)
assert resp.status_code == 200
self.assertTemplateUsed(resp, 'sentry/create-team.html')
assert resp.context['organization'] == organization
assert resp.context['form']
def test_submission(self):
organization = self.create_organization()
path = reverse('sentry-create-team', args=[organization.slug])
self.login_as(self.user)
resp = self.client.post(path, {
'name': 'bar',
})
assert resp.status_code == 302, resp.context['form'].errors
team = Team.objects.get(organization=organization, name='bar')
member = OrganizationMember.objects.get(
user=self.user,
organization=organization,
)
assert OrganizationMemberTeam.objects.filter(
organizationmember=member,
team=team,
is_active=True,
).exists()
redirect_uri = reverse('sentry-create-project', args=[organization.slug])
assert resp['Location'] == 'http://testserver%s?team=%s' % (
redirect_uri, team.slug)
def test_admin_can_create_team(self):
organization = self.create_organization()
path = reverse('sentry-create-team', args=[organization.slug])
admin = self.create_user('<EMAIL>')
self.create_member(
organization=organization,
user=admin,
role='admin',
teams=[],
)
self.login_as(admin)
resp = self.client.post(path, {
'name': 'bar',
})
assert resp.status_code == 302, resp.context['form'].errors
assert Team.objects.filter(
organization=organization,
name='bar',
).exists()
| [
"sentry.models.Team.objects.filter",
"sentry.models.Team.objects.get",
"django.core.urlresolvers.reverse",
"sentry.models.OrganizationMemberTeam.objects.filter",
"sentry.models.OrganizationMember.objects.get"
]
| [((368, 428), 'django.core.urlresolvers.reverse', 'reverse', (['"""sentry-create-team"""'], {'args': '[self.organization.slug]'}), "('sentry-create-team', args=[self.organization.slug])\n", (375, 428), False, 'from django.core.urlresolvers import reverse\n'), ((941, 996), 'django.core.urlresolvers.reverse', 'reverse', (['"""sentry-create-team"""'], {'args': '[organization.slug]'}), "('sentry-create-team', args=[organization.slug])\n", (948, 996), False, 'from django.core.urlresolvers import reverse\n'), ((1364, 1419), 'django.core.urlresolvers.reverse', 'reverse', (['"""sentry-create-team"""'], {'args': '[organization.slug]'}), "('sentry-create-team', args=[organization.slug])\n", (1371, 1419), False, 'from django.core.urlresolvers import reverse\n'), ((1615, 1670), 'sentry.models.Team.objects.get', 'Team.objects.get', ([], {'organization': 'organization', 'name': '"""bar"""'}), "(organization=organization, name='bar')\n", (1631, 1670), False, 'from sentry.models import OrganizationMember, OrganizationMemberTeam, Team\n'), ((1689, 1762), 'sentry.models.OrganizationMember.objects.get', 'OrganizationMember.objects.get', ([], {'user': 'self.user', 'organization': 'organization'}), '(user=self.user, organization=organization)\n', (1719, 1762), False, 'from sentry.models import OrganizationMember, OrganizationMemberTeam, Team\n'), ((1986, 2044), 'django.core.urlresolvers.reverse', 'reverse', (['"""sentry-create-project"""'], {'args': '[organization.slug]'}), "('sentry-create-project', args=[organization.slug])\n", (1993, 2044), False, 'from django.core.urlresolvers import reverse\n'), ((2259, 2314), 'django.core.urlresolvers.reverse', 'reverse', (['"""sentry-create-team"""'], {'args': '[organization.slug]'}), "('sentry-create-team', args=[organization.slug])\n", (2266, 2314), False, 'from django.core.urlresolvers import reverse\n'), ((1814, 1909), 'sentry.models.OrganizationMemberTeam.objects.filter', 'OrganizationMemberTeam.objects.filter', ([], {'organizationmember': 'member', 'team': 'team', 'is_active': '(True)'}), '(organizationmember=member, team=team,\n is_active=True)\n', (1851, 1909), False, 'from sentry.models import OrganizationMember, OrganizationMemberTeam, Team\n'), ((2702, 2760), 'sentry.models.Team.objects.filter', 'Team.objects.filter', ([], {'organization': 'organization', 'name': '"""bar"""'}), "(organization=organization, name='bar')\n", (2721, 2760), False, 'from sentry.models import OrganizationMember, OrganizationMemberTeam, Team\n')] |
# Generated by Django 3.2.8 on 2021-11-29 05:47
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('agendamentos', '0010_agendamentosfuncionarios'),
]
operations = [
migrations.AlterModelTable(
name='agendamentosfuncionarios',
table='agendamento_funcionario',
),
]
| [
"django.db.migrations.AlterModelTable"
]
| [((238, 335), 'django.db.migrations.AlterModelTable', 'migrations.AlterModelTable', ([], {'name': '"""agendamentosfuncionarios"""', 'table': '"""agendamento_funcionario"""'}), "(name='agendamentosfuncionarios', table=\n 'agendamento_funcionario')\n", (264, 335), False, 'from django.db import migrations\n')] |
import unittest
import os
from pyxdsm.XDSM import XDSM, __file__
from numpy.distutils.exec_command import find_executable
def filter_lines(lns):
# Empty lines are excluded.
# Leading and trailing whitespaces are removed
# Comments are removed.
return [ln.strip() for ln in lns if ln.strip() and not ln.strip().startswith('%')]
class TestXDSM(unittest.TestCase):
def setUp(self):
import os
import tempfile
self.startdir = os.getcwd()
self.tempdir = tempfile.mkdtemp(prefix='testdir-')
os.chdir(self.tempdir)
def tearDown(self):
import os
import shutil
os.chdir(self.startdir)
try:
shutil.rmtree(self.tempdir)
except OSError:
pass
def test_examples(self):
'''
This test just builds the three examples, and assert that the output files exist.
Unlike the other tests, this one requires LaTeX to be available.
'''
os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../examples'))
filenames = ['kitchen_sink', 'mdf']
for f in filenames:
os.system('python {}.py'.format(f))
self.assertTrue(os.path.isfile(f + '.tikz'))
self.assertTrue(os.path.isfile(f + '.tex'))
# look for the pdflatex executable
pdflatex = find_executable('pdflatex') is not None
# if no pdflatex, then do not assert that the pdf was compiled
self.assertTrue(not pdflatex or os.path.isfile(f + '.pdf'))
os.system('python mat_eqn.py')
self.assertTrue(os.path.isfile('mat_eqn_example.pdf'))
# change back to previous directory
os.chdir(self.tempdir)
def test_connect(self):
x = XDSM(use_sfmath=False)
x.add_system('D1', 'Function', 'D_1', label_width=2)
x.add_system('D2', 'Function', 'D_2', stack=False)
try:
x.connect('D1', 'D2', r'\mathcal{R}(y_1)', 'foobar')
except ValueError as err:
self.assertEquals(str(err), 'label_width argument must be an integer')
else:
self.fail('Expected ValueError')
def test_options(self):
filename = 'xdsm_test_options'
spec_dir = filename + '_specs'
# Change `use_sfmath` to False to use computer modern
x = XDSM(use_sfmath=False)
x.add_system('opt', 'Optimization', r'\text{Optimizer}')
x.add_system('solver', 'MDA', r'\text{Newton}')
x.add_system('D1', 'Function', 'D_1', label_width=2)
x.add_system('D2', 'Function', 'D_2', stack=False)
x.add_system('F', 'Function', 'F', faded=True)
x.add_system('G', 'Function', 'G', spec_name="G_spec")
x.connect('opt', 'D1', 'x, z')
x.connect('opt', 'D2', 'z')
x.connect('opt', 'F', 'x, z')
x.connect('solver', 'D1', 'y_2')
x.connect('solver', 'D2', 'y_1')
x.connect('D1', 'solver', r'\mathcal{R}(y_1)')
x.connect('solver', 'F', 'y_1, y_2')
x.connect('D2', 'solver', r'\mathcal{R}(y_2)')
x.connect('solver', 'G', 'y_1, y_2')
x.connect('F', 'opt', 'f')
x.connect('G', 'opt', 'g')
x.add_output('opt', 'x^*, z^*', side='right')
x.add_output('D1', 'y_1^*', side='left', stack=True)
x.add_output('D2', 'y_2^*', side='left')
x.add_output('F', 'f^*', side='left')
x.add_output('G', 'g^*')
x.write(filename)
x.write_sys_specs(spec_dir)
# Test if files where created
self.assertTrue(os.path.isfile(filename + '.tikz'))
self.assertTrue(os.path.isfile(filename + '.tex'))
self.assertTrue(os.path.isdir(spec_dir))
self.assertTrue(os.path.isfile(os.path.join(spec_dir, 'F.json')))
self.assertTrue(os.path.isfile(os.path.join(spec_dir, 'G_spec.json')))
def test_stacked_system(self):
x = XDSM()
x.add_system('test', 'Optimization', r'\text{test}', stack=True)
file_name = "stacked_test"
x.write(file_name)
tikz_file = file_name + '.tikz'
with open(tikz_file, "r") as f:
tikz = f.read()
self.assertIn(r"\node [Optimization,stack]", tikz)
def test_tikz_content(self):
# Check if TiKZ file was created.
# Compare the content of the sample below and the newly created TiKZ file.
sample_txt = r"""
%%% Preamble Requirements %%%
% \usepackage{geometry}
% \usepackage{amsfonts}
% \usepackage{amsmath}
% \usepackage{amssymb}
% \usepackage{tikz}
% Optional packages such as sfmath set through python interface
% \usepackage{sfmath}
% \usetikzlibrary{arrows,chains,positioning,scopes,shapes.geometric,shapes.misc,shadows}
%%% End Preamble Requirements %%%
\input{"path/to/diagram_styles"}
\begin{tikzpicture}
\matrix[MatrixSetup]{
%Row 0
\node [DataIO] (left_output_opt) {$x^*, z^*$};&
\node [Optimization] (opt) {$\text{Optimizer}$};&
&
\node [DataInter] (opt-D1) {$x, z$};&
\node [DataInter] (opt-D2) {$z$};&
\node [DataInter] (opt-F) {$x, z$};&
\\
%Row 1
&
&
\node [MDA] (solver) {$\text{Newton}$};&
\node [DataInter] (solver-D1) {$y_2$};&
\node [DataInter] (solver-D2) {$y_1$};&
\node [DataInter] (solver-F) {$y_1, y_2$};&
\node [DataInter] (solver-G) {$y_1, y_2$};\\
%Row 2
\node [DataIO] (left_output_D1) {$y_1^*$};&
&
\node [DataInter] (D1-solver) {$\mathcal{R}(y_1)$};&
\node [Function] (D1) {$D_1$};&
&
&
\\
%Row 3
\node [DataIO] (left_output_D2) {$y_2^*$};&
&
\node [DataInter] (D2-solver) {$\mathcal{R}(y_2)$};&
&
\node [Function] (D2) {$D_2$};&
&
\\
%Row 4
\node [DataIO] (left_output_F) {$f^*$};&
\node [DataInter] (F-opt) {$f$};&
&
&
&
\node [Function] (F) {$F$};&
\\
%Row 5
\node [DataIO] (left_output_G) {$g^*$};&
\node [DataInter] (G-opt) {$g$};&
&
&
&
&
\node [Function] (G) {$G$};\\
%Row 6
&
&
&
&
&
&
\\
};
% XDSM process chains
\begin{pgfonlayer}{data}
\path
% Horizontal edges
(opt) edge [DataLine] (opt-D1)
(opt) edge [DataLine] (opt-D2)
(opt) edge [DataLine] (opt-F)
(solver) edge [DataLine] (solver-D1)
(solver) edge [DataLine] (solver-D2)
(D1) edge [DataLine] (D1-solver)
(solver) edge [DataLine] (solver-F)
(D2) edge [DataLine] (D2-solver)
(solver) edge [DataLine] (solver-G)
(F) edge [DataLine] (F-opt)
(G) edge [DataLine] (G-opt)
(opt) edge [DataLine] (left_output_opt)
(D1) edge [DataLine] (left_output_D1)
(D2) edge [DataLine] (left_output_D2)
(F) edge [DataLine] (left_output_F)
(G) edge [DataLine] (left_output_G)
% Vertical edges
(opt-D1) edge [DataLine] (D1)
(opt-D2) edge [DataLine] (D2)
(opt-F) edge [DataLine] (F)
(solver-D1) edge [DataLine] (D1)
(solver-D2) edge [DataLine] (D2)
(D1-solver) edge [DataLine] (solver)
(solver-F) edge [DataLine] (F)
(D2-solver) edge [DataLine] (solver)
(solver-G) edge [DataLine] (G)
(F-opt) edge [DataLine] (opt)
(G-opt) edge [DataLine] (opt);
\end{pgfonlayer}
\end{tikzpicture}"""
filename = 'xdsm_test_tikz'
x = XDSM(use_sfmath=True)
x.add_system('opt', 'Optimization', r'\text{Optimizer}')
x.add_system('solver', 'MDA', r'\text{Newton}')
x.add_system('D1', 'Function', 'D_1')
x.add_system('D2', 'Function', 'D_2')
x.add_system('F', 'Function', 'F')
x.add_system('G', 'Function', 'G')
x.connect('opt', 'D1', 'x, z')
x.connect('opt', 'D2', 'z')
x.connect('opt', 'F', 'x, z')
x.connect('solver', 'D1', 'y_2')
x.connect('solver', 'D2', 'y_1')
x.connect('D1', 'solver', r'\mathcal{R}(y_1)')
x.connect('solver', 'F', 'y_1, y_2')
x.connect('D2', 'solver', r'\mathcal{R}(y_2)')
x.connect('solver', 'G', 'y_1, y_2')
x.connect('F', 'opt', 'f')
x.connect('G', 'opt', 'g')
x.add_output('opt', 'x^*, z^*', side='left')
x.add_output('D1', 'y_1^*', side='left')
x.add_output('D2', 'y_2^*', side='left')
x.add_output('F', 'f^*', side='left')
x.add_output('G', 'g^*', side='left')
x.write(filename)
# Check if file was created
tikz_file = filename + '.tikz'
self.assertTrue(os.path.isfile(tikz_file))
sample_lines = sample_txt.split('\n')
sample_lines = filter_lines(sample_lines)
with open(tikz_file, "r") as f:
new_lines = filter_lines(f.readlines())
sample_no_match = [] # Sample text
new_no_match = [] # New text
for new_line, sample_line in zip(new_lines, sample_lines):
if new_line.startswith(r'\input{'):
continue
if new_line != sample_line: # else everything is okay
# This can be because of the different ordering of lines or because of an error.
sample_no_match.append(new_line)
new_no_match.append(sample_line)
# Sort both sets of suspicious lines
sample_no_match.sort()
new_no_match.sort()
for sample_line, new_line in zip(sample_no_match, new_no_match):
# Now the lines should match, if only the ordering was different
self.assertEqual(new_line, sample_line)
# To be sure, check the length, otherwise a missing last line could get unnoticed because of using zip
self.assertEqual(len(new_lines), len(sample_lines))
if __name__ == "__main__":
unittest.main()
| [
"pyxdsm.XDSM.XDSM",
"os.path.join",
"os.getcwd",
"os.chdir",
"os.path.isfile",
"os.path.isdir",
"tempfile.mkdtemp",
"numpy.distutils.exec_command.find_executable",
"shutil.rmtree",
"unittest.main",
"os.system",
"os.path.abspath"
]
| [((10690, 10705), 'unittest.main', 'unittest.main', ([], {}), '()\n', (10703, 10705), False, 'import unittest\n'), ((470, 481), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (479, 481), False, 'import os\n'), ((505, 540), 'tempfile.mkdtemp', 'tempfile.mkdtemp', ([], {'prefix': '"""testdir-"""'}), "(prefix='testdir-')\n", (521, 540), False, 'import tempfile\n'), ((550, 572), 'os.chdir', 'os.chdir', (['self.tempdir'], {}), '(self.tempdir)\n', (558, 572), False, 'import os\n'), ((647, 670), 'os.chdir', 'os.chdir', (['self.startdir'], {}), '(self.startdir)\n', (655, 670), False, 'import os\n'), ((1572, 1602), 'os.system', 'os.system', (['"""python mat_eqn.py"""'], {}), "('python mat_eqn.py')\n", (1581, 1602), False, 'import os\n'), ((1718, 1740), 'os.chdir', 'os.chdir', (['self.tempdir'], {}), '(self.tempdir)\n', (1726, 1740), False, 'import os\n'), ((1784, 1806), 'pyxdsm.XDSM.XDSM', 'XDSM', ([], {'use_sfmath': '(False)'}), '(use_sfmath=False)\n', (1788, 1806), False, 'from pyxdsm.XDSM import XDSM, __file__\n'), ((2368, 2390), 'pyxdsm.XDSM.XDSM', 'XDSM', ([], {'use_sfmath': '(False)'}), '(use_sfmath=False)\n', (2372, 2390), False, 'from pyxdsm.XDSM import XDSM, __file__\n'), ((3935, 3941), 'pyxdsm.XDSM.XDSM', 'XDSM', ([], {}), '()\n', (3939, 3941), False, 'from pyxdsm.XDSM import XDSM, __file__\n'), ((8314, 8335), 'pyxdsm.XDSM.XDSM', 'XDSM', ([], {'use_sfmath': '(True)'}), '(use_sfmath=True)\n', (8318, 8335), False, 'from pyxdsm.XDSM import XDSM, __file__\n'), ((697, 724), 'shutil.rmtree', 'shutil.rmtree', (['self.tempdir'], {}), '(self.tempdir)\n', (710, 724), False, 'import shutil\n'), ((1627, 1664), 'os.path.isfile', 'os.path.isfile', (['"""mat_eqn_example.pdf"""'], {}), "('mat_eqn_example.pdf')\n", (1641, 1664), False, 'import os\n'), ((3587, 3621), 'os.path.isfile', 'os.path.isfile', (["(filename + '.tikz')"], {}), "(filename + '.tikz')\n", (3601, 3621), False, 'import os\n'), ((3647, 3680), 'os.path.isfile', 'os.path.isfile', (["(filename + '.tex')"], {}), "(filename + '.tex')\n", (3661, 3680), False, 'import os\n'), ((3706, 3729), 'os.path.isdir', 'os.path.isdir', (['spec_dir'], {}), '(spec_dir)\n', (3719, 3729), False, 'import os\n'), ((9474, 9499), 'os.path.isfile', 'os.path.isfile', (['tikz_file'], {}), '(tikz_file)\n', (9488, 9499), False, 'import os\n'), ((1222, 1249), 'os.path.isfile', 'os.path.isfile', (["(f + '.tikz')"], {}), "(f + '.tikz')\n", (1236, 1249), False, 'import os\n'), ((1279, 1305), 'os.path.isfile', 'os.path.isfile', (["(f + '.tex')"], {}), "(f + '.tex')\n", (1293, 1305), False, 'import os\n'), ((1377, 1404), 'numpy.distutils.exec_command.find_executable', 'find_executable', (['"""pdflatex"""'], {}), "('pdflatex')\n", (1392, 1404), False, 'from numpy.distutils.exec_command import find_executable\n'), ((3770, 3802), 'os.path.join', 'os.path.join', (['spec_dir', '"""F.json"""'], {}), "(spec_dir, 'F.json')\n", (3782, 3802), False, 'import os\n'), ((3844, 3881), 'os.path.join', 'os.path.join', (['spec_dir', '"""G_spec.json"""'], {}), "(spec_dir, 'G_spec.json')\n", (3856, 3881), False, 'import os\n'), ((1029, 1054), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (1044, 1054), False, 'import os\n'), ((1536, 1562), 'os.path.isfile', 'os.path.isfile', (["(f + '.pdf')"], {}), "(f + '.pdf')\n", (1550, 1562), False, 'import os\n')] |
# -*- coding: utf-8 -*-
from wecom_sdk.base.crypt import encrypt_msg, decrypt_msg
class WeChatWorkCallbackSDK(object):
"""
企业微信回调SDK基本类，用于实现内部系统和企业微信客户端的双向通信
详细说明：https://work.weixin.qq.com/api/doc/90000/90135/90930
"""
def __init__(self, token, encoding_aes_key):
self.token = token
self.encoding_aes_key = encoding_aes_key
def encrypt(self, data: dict) -> str:
"""
服务端加密数据
:param data:
:param timestamp:
:param nonce:
:return:
"""
return encrypt_msg(data, token=self.token, encoding_aes_key=self.encoding_aes_key)
def decrypt(self, xml, sign, timestamp, nonce) -> dict:
"""
验证并解密来自客户端的数据
:return:
"""
return decrypt_msg(xml_text=xml, encrypt_sign=sign, timestamp=timestamp, nonce=nonce,
token=self.token, encoding_aes_key=self.encoding_aes_key) | [
"wecom_sdk.base.crypt.encrypt_msg",
"wecom_sdk.base.crypt.decrypt_msg"
]
| [((548, 623), 'wecom_sdk.base.crypt.encrypt_msg', 'encrypt_msg', (['data'], {'token': 'self.token', 'encoding_aes_key': 'self.encoding_aes_key'}), '(data, token=self.token, encoding_aes_key=self.encoding_aes_key)\n', (559, 623), False, 'from wecom_sdk.base.crypt import encrypt_msg, decrypt_msg\n'), ((763, 904), 'wecom_sdk.base.crypt.decrypt_msg', 'decrypt_msg', ([], {'xml_text': 'xml', 'encrypt_sign': 'sign', 'timestamp': 'timestamp', 'nonce': 'nonce', 'token': 'self.token', 'encoding_aes_key': 'self.encoding_aes_key'}), '(xml_text=xml, encrypt_sign=sign, timestamp=timestamp, nonce=\n nonce, token=self.token, encoding_aes_key=self.encoding_aes_key)\n', (774, 904), False, 'from wecom_sdk.base.crypt import encrypt_msg, decrypt_msg\n')] |
import os
os.makedirs(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'build')), exist_ok=True)
from .chamfer_distance import ChamferDistance
| [
"os.path.dirname"
]
| [((51, 76), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (66, 76), False, 'import os\n')] |
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
import sys
import ST7735
# Create ST7735 LCD display class object and set pin numbers and display hardware information.
disp = ST7735.ST7735(
dc=24,
cs=ST7735.BG_SPI_CS_BACK,
rst=25,
port=0,
width=122,
height=160,
rotation=270
)
# Initialize display.
disp.begin()
WIDTH = disp.width
HEIGHT = disp.height
img = Image.new('RGB', (WIDTH, HEIGHT), color=(0, 0, 0))
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf", 12)
# Initialize a secondary text with the empty string
text2 = ""
# Print test-output on the display if n oargument is given
if len(sys.argv) == 1:
text = "Temperature:\nHumidity:\nUV:\nRain:\nLight:"
text2 = "20°C\n50 %\n42\nyes\nOn"
# Print the argument if only one is given
elif len(sys.argv) == 2:
text = sys.argv[1]
# If 2 arguments are given use the second as the secondary text
elif len(sys.argv) == 3:
text = sys.argv[1]
text2 = sys.argv[2]
# For any other number of arguments draw them in one line each
else:
text = ''.join(i + "\n" for i in sys.argv[1:])
# Print both texts, with the secondary one starting with an 100 px offset
draw.text((10, 10), text, font=font, fill=(255, 255, 255))
draw.text((110, 10), text2, font=font, fill=(255, 255, 255))
disp.display(img)
| [
"PIL.Image.new",
"PIL.ImageDraw.Draw",
"PIL.ImageFont.truetype",
"ST7735.ST7735"
]
| [((203, 306), 'ST7735.ST7735', 'ST7735.ST7735', ([], {'dc': '(24)', 'cs': 'ST7735.BG_SPI_CS_BACK', 'rst': '(25)', 'port': '(0)', 'width': '(122)', 'height': '(160)', 'rotation': '(270)'}), '(dc=24, cs=ST7735.BG_SPI_CS_BACK, rst=25, port=0, width=122,\n height=160, rotation=270)\n', (216, 306), False, 'import ST7735\n'), ((417, 467), 'PIL.Image.new', 'Image.new', (['"""RGB"""', '(WIDTH, HEIGHT)'], {'color': '(0, 0, 0)'}), "('RGB', (WIDTH, HEIGHT), color=(0, 0, 0))\n", (426, 467), False, 'from PIL import Image\n'), ((476, 495), 'PIL.ImageDraw.Draw', 'ImageDraw.Draw', (['img'], {}), '(img)\n', (490, 495), False, 'from PIL import ImageDraw\n'), ((504, 598), 'PIL.ImageFont.truetype', 'ImageFont.truetype', (['"""/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf"""', '(12)'], {}), "(\n '/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf', 12)\n", (522, 598), False, 'from PIL import ImageFont\n')] |
import sqlite3
conn=sqlite3.connect('Survey.db')
fo=open('insertcommand.txt')
str=fo.readline()
while str:
str="INSERT INTO data VALUES"+str
conn.execute(str)
#print(str)
str=fo.readline()
conn.commit()
conn.close()
fo.close()
| [
"sqlite3.connect"
]
| [((21, 49), 'sqlite3.connect', 'sqlite3.connect', (['"""Survey.db"""'], {}), "('Survey.db')\n", (36, 49), False, 'import sqlite3\n')] |
# DO NOT EDIT THIS FILE!
#
# This file is generated from the CDP specification. If you need to make
# changes, edit the generator and regenerate all of the modules.
#
# CDP domain: HeadlessExperimental (experimental)
from __future__ import annotations
from cdp.util import event_class, T_JSON_DICT
from dataclasses import dataclass
import enum
import typing
@dataclass
class ScreenshotParams:
'''
Encoding options for a screenshot.
'''
#: Image compression format (defaults to png).
format_: typing.Optional[str] = None
#: Compression quality from range [0..100] (jpeg only).
quality: typing.Optional[int] = None
def to_json(self) -> T_JSON_DICT:
json: T_JSON_DICT = dict()
if self.format_ is not None:
json['format'] = self.format_
if self.quality is not None:
json['quality'] = self.quality
return json
@classmethod
def from_json(cls, json: T_JSON_DICT) -> ScreenshotParams:
return cls(
format_=str(json['format']) if 'format' in json else None,
quality=int(json['quality']) if 'quality' in json else None,
)
def begin_frame(
frame_time_ticks: typing.Optional[float] = None,
interval: typing.Optional[float] = None,
no_display_updates: typing.Optional[bool] = None,
screenshot: typing.Optional[ScreenshotParams] = None
) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,typing.Tuple[bool, typing.Optional[str]]]:
'''
Sends a BeginFrame to the target and returns when the frame was completed. Optionally captures a
screenshot from the resulting frame. Requires that the target was created with enabled
BeginFrameControl. Designed for use with --run-all-compositor-stages-before-draw, see also
https://goo.gl/3zHXhB for more background.
:param frame_time_ticks: *(Optional)* Timestamp of this BeginFrame in Renderer TimeTicks (milliseconds of uptime). If not set, the current time will be used.
:param interval: *(Optional)* The interval between BeginFrames that is reported to the compositor, in milliseconds. Defaults to a 60 frames/second interval, i.e. about 16.666 milliseconds.
:param no_display_updates: *(Optional)* Whether updates should not be committed and drawn onto the display. False by default. If true, only side effects of the BeginFrame will be run, such as layout and animations, but any visual updates may not be visible on the display or in screenshots.
:param screenshot: *(Optional)* If set, a screenshot of the frame will be captured and returned in the response. Otherwise, no screenshot will be captured. Note that capturing a screenshot can fail, for example, during renderer initialization. In such a case, no screenshot data will be returned.
:returns: A tuple with the following items:
0. **hasDamage** - Whether the BeginFrame resulted in damage and, thus, a new frame was committed to the display. Reported for diagnostic uses, may be removed in the future.
1. **screenshotData** - *(Optional)* Base64-encoded image data of the screenshot, if one was requested and successfully taken.
'''
params: T_JSON_DICT = dict()
if frame_time_ticks is not None:
params['frameTimeTicks'] = frame_time_ticks
if interval is not None:
params['interval'] = interval
if no_display_updates is not None:
params['noDisplayUpdates'] = no_display_updates
if screenshot is not None:
params['screenshot'] = screenshot.to_json()
cmd_dict: T_JSON_DICT = {
'method': 'HeadlessExperimental.beginFrame',
'params': params,
}
json = yield cmd_dict
return (
bool(json['hasDamage']),
str(json['screenshotData']) if 'screenshotData' in json else None
)
def disable() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
'''
Disables headless events for the target.
'''
cmd_dict: T_JSON_DICT = {
'method': 'HeadlessExperimental.disable',
}
json = yield cmd_dict
def enable() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
'''
Enables headless events for the target.
'''
cmd_dict: T_JSON_DICT = {
'method': 'HeadlessExperimental.enable',
}
json = yield cmd_dict
@event_class('HeadlessExperimental.needsBeginFramesChanged')
@dataclass
class NeedsBeginFramesChanged:
'''
Issued when the target starts or stops needing BeginFrames.
'''
#: True if BeginFrames are needed, false otherwise.
needs_begin_frames: bool
@classmethod
def from_json(cls, json: T_JSON_DICT) -> NeedsBeginFramesChanged:
return cls(
needs_begin_frames=bool(json['needsBeginFrames'])
)
| [
"cdp.util.event_class"
]
| [((4272, 4331), 'cdp.util.event_class', 'event_class', (['"""HeadlessExperimental.needsBeginFramesChanged"""'], {}), "('HeadlessExperimental.needsBeginFramesChanged')\n", (4283, 4331), False, 'from cdp.util import event_class, T_JSON_DICT\n')] |
import pytest
from detectem.core import Detector, Result, ResultCollection
from detectem.plugin import Plugin, PluginCollection
from detectem.settings import INDICATOR_TYPE, HINT_TYPE, MAIN_ENTRY, GENERIC_TYPE
from detectem.plugins.helpers import meta_generator
class TestDetector():
HAR_ENTRY_1 = {
'request': {
'url': 'http://domain.tld/libA-1.4.2.js'
},
'response': {
'url': 'http://domain.tld/libA-1.4.2.js'
},
}
HAR_NO_URL_REDIRECT = [
{
'request': {'url': 'http://domain.tld/'},
'response': {},
},
{
'request': {'url': 'http://domain.tld/js/script.js'},
'response': {},
}
]
HAR_URL_REDIRECT_PATH = [
{
'request': {'url': 'http://domain.tld/'},
'response': {'headers': [
{'name': 'Location', 'value': '/new/default.html'}
]},
},
{
'request': {'url': 'http://domain.tld/new/default.html'},
'response': {},
}
]
HAR_URL_REDIRECT_ABS = [
{
'request': {'url': 'http://domain.tld/'},
'response': {'headers': [
{'name': 'Location', 'value': 'http://other-domain.tld/'}
]},
},
{
'request': {'url': 'http://other-domain.tld/'},
'response': {},
}
]
URL = 'http://domain.tld/'
FOO_PLUGIN = {
'name': 'foo',
'homepage': 'foo',
'matchers': {
'url': 'foo.*-(?P<version>[0-9\.]+)\.js',
'header': ('FooHeader', 'Foo.* v(?P<version>[0-9\.]+)'),
'body': 'Foo.* v(?P<version>[0-9\.]+)',
'xpath': (meta_generator('foo-min'), '(?P<version>[0-9\.]+)'),
},
'indicators': {
'url': 'foo.*\.js',
'header': ('FooHeader', 'Foo'),
'body': 'Foo',
'xpath': "//meta[@name='generator']",
},
'modular_matchers': {
'url': 'foo-(?P<name>\w+)-.*\.js',
'header': ('FooHeader', 'Foo-(?P<name>\w+)'),
'body': 'Foo-(?P<name>\w+)',
'xpath': (meta_generator('foo-min'), 'foo-(?P<name>\w+)'),
},
}
FOO_RESULTS = [
[{'name': 'foo', 'version': '1.1'}],
[{'name': 'foo'}],
[{'name': 'foo-min', 'version': '1.1'}],
]
MATCHER_SOURCES = [
['matchers'],
['indicators'],
['matchers', 'modular_matchers'],
]
def test_detector_starts_with_empty_results(self):
d = Detector({'har': None, 'softwares': None}, [], None)
assert not d._results.get_results()
@pytest.mark.parametrize("har,index", [
(HAR_NO_URL_REDIRECT, 0),
(HAR_URL_REDIRECT_PATH, 1),
(HAR_URL_REDIRECT_ABS, 1),
])
def test_mark_main_entry(self, har, index):
d = self._create_detector(har, [])
assert d.har[index]['detectem']['type'] == MAIN_ENTRY
def test_convert_inline_script_to_har_entry(self):
script = 'Inline script'
d = Detector({'har': [], 'softwares': [], 'scripts': [script]}, None, self.URL)
e = d.har[0]
assert e['request']['url'] == self.URL
assert e['response']['content']['text'] == script
@pytest.mark.parametrize("scripts,n_entries", [
([], 0),
(['script1', 'script2'], 2),
])
def test_add_inline_scripts_to_har(self, scripts, n_entries):
d = Detector({'har': [], 'softwares': [], 'scripts': scripts}, None, self.URL)
assert len(d.har) == n_entries
def _create_plugin(self, template, sources, matchers):
class TestPlugin(Plugin):
name = template['name']
homepage = template['homepage']
p = TestPlugin()
for s in sources:
g = [{m: template[s][m]} for m in matchers]
setattr(p, s, g)
return p
def _create_detector(self, har, plugins):
pc = PluginCollection()
for p in plugins:
pc.add(p)
return Detector({'har': har, 'softwares': []}, pc, self.URL)
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_headers(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'headers': [
{'name': 'FooHeader', 'value': 'Foo-min v1.1'}
]
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['header'])
d = self._create_detector(har, [p])
assert d.get_results() == result
@pytest.mark.parametrize('sources', MATCHER_SOURCES)
def test_match_from_headers_ignores_resource_entries(self, sources):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'headers': [],
},
},
{
'request': {'url': 'http://foo.org/lib/foo.js'},
'response': {
'url': 'http://foo.org/lib/foo.js',
'headers': [
{'name': 'FooHeader', 'value': 'Foo-min v1.1'}
]
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['header'])
d = self._create_detector(har, [p])
assert not d.get_results()
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_body(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'content': {'text': 'Main content'},
},
},
{
'request': {'url': 'http://foo.org/lib/foo.js'},
'response': {
'url': 'http://foo.org/lib/foo.js',
'content': {'text': 'Plugin Foo-min v1.1'},
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['body'])
d = self._create_detector(har, [p])
assert d.get_results() == result
@pytest.mark.parametrize('sources', MATCHER_SOURCES)
def test_match_from_body_excludes_main_entry(self, sources):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'content': {'text': 'About Foo-min v1.1'},
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['body'])
d = self._create_detector(har, [p])
assert not d.get_results()
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_url(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {'url': self.URL},
},
{
'request': {'url': 'http://foo.org/lib/foo-min-1.1.js'},
'response': {
'url': 'http://foo.org/lib/foo-min-1.1.js',
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['url'])
d = self._create_detector(har, [p])
assert d.get_results() == result
@pytest.mark.parametrize('sources,result', zip(MATCHER_SOURCES, FOO_RESULTS))
def test_match_from_xpath(self, sources, result):
har = [
{
'request': {'url': self.URL},
'response': {
'url': self.URL,
'content': {
'text': '<meta name="generator" content="foo-min 1.1">'
},
},
},
]
p = self._create_plugin(self.FOO_PLUGIN, sources, ['xpath'])
d = self._create_detector(har, [p])
assert d.get_results() == result
def test_get_hints_with_valid_hint(self):
class TestPlugin(Plugin):
name = 'test'
homepage = 'test'
class BlaPlugin(Plugin):
name = 'bla'
hints = ['test']
detector = self._create_detector(None, [TestPlugin()])
hints = detector.get_hints(BlaPlugin())
assert hints
def test_get_hints_with_invalid_hint(self):
class BlaPlugin(Plugin):
name = 'bla'
hints = ['test']
detector = self._create_detector(None, [])
hints = detector.get_hints(BlaPlugin())
assert not hints
class TestResultCollection():
@staticmethod
def _assert_results(detected, results):
c = ResultCollection()
for d in detected:
c.add_result(d)
assert set(c.get_results()) == set(results)
@pytest.mark.parametrize('detected,results', [
(
[Result('pluginA', '1.1'), Result('pluginB', '3.8.7'), Result('pluginC', '4.0')],
[Result('pluginA', '1.1'), Result('pluginB', '3.8.7'), Result('pluginC', '4.0')]
),
(
[Result('pluginA', '1.3'), Result('pluginA', '1.2'), Result('pluginA', '1.1')],
[Result('pluginA', '1.1'), Result('pluginA', '1.2'), Result('pluginA', '1.3')],
),
(
[
Result('pluginA', '1.1'),
Result('pluginC', type=HINT_TYPE),
Result('pluginB', type=INDICATOR_TYPE),
Result('pluginD', type=GENERIC_TYPE),
],
[
Result('pluginA', '1.1'),
Result('pluginB', type=INDICATOR_TYPE),
Result('pluginC', type=HINT_TYPE),
Result('pluginD', type=GENERIC_TYPE),
]
),
])
def test_get_all_detected_plugins(self, detected, results):
self._assert_results(detected, results)
@pytest.mark.parametrize('detected,results', [
(
[Result('pluginA', '1.1'), Result('pluginA', '1.2'), Result('pluginA', '1.1')],
[Result('pluginA', '1.1'), Result('pluginA', '1.2')]
),
(
[
Result('pluginA', '1.1'),
Result('pluginA', type=INDICATOR_TYPE),
Result('pluginA', type=HINT_TYPE),
],
[Result('pluginA', '1.1')]
),
(
[Result('pluginB', type=HINT_TYPE), Result('pluginB', type=HINT_TYPE)],
[Result('pluginB', type=HINT_TYPE)]
),
(
[Result('pluginB', type=INDICATOR_TYPE), Result('pluginB', type=INDICATOR_TYPE)],
[Result('pluginB', type=INDICATOR_TYPE)]
),
(
[Result('pluginB', type=INDICATOR_TYPE), Result('pluginB', type=HINT_TYPE)],
[Result('pluginB', type=INDICATOR_TYPE)]
),
(
[Result('pluginB', type=INDICATOR_TYPE), Result('pluginB', type=GENERIC_TYPE)],
[Result('pluginB', type=INDICATOR_TYPE)]
),
])
def test_remove_duplicated_results(self, detected, results):
self._assert_results(detected, results)
| [
"detectem.plugins.helpers.meta_generator",
"detectem.core.Detector",
"pytest.mark.parametrize",
"detectem.core.ResultCollection",
"detectem.core.Result",
"detectem.plugin.PluginCollection"
]
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# -*- coding: utf-8 -*-
import numpy as np
import torch
from torch import nn
from kbcr.kernels import GaussianKernel
from kbcr.smart import NeuralKB
import pytest
@pytest.mark.light
def test_smart_v1():
embedding_size = 50
rs = np.random.RandomState(0)
for _ in range(32):
with torch.no_grad():
triples = [
('a', 'p', 'b'),
('c', 'q', 'd'),
('e', 'q', 'f'),
('g', 'q', 'h'),
('i', 'q', 'l'),
('m', 'q', 'n'),
('o', 'q', 'p'),
('q', 'q', 'r'),
('s', 'q', 't'),
('u', 'q', 'v')
]
entity_lst = sorted({s for (s, _, _) in triples} | {o for (_, _, o) in triples})
predicate_lst = sorted({p for (_, p, _) in triples})
nb_entities, nb_predicates = len(entity_lst), len(predicate_lst)
entity_to_index = {e: i for i, e in enumerate(entity_lst)}
predicate_to_index = {p: i for i, p in enumerate(predicate_lst)}
kernel = GaussianKernel()
entity_embeddings = nn.Embedding(nb_entities, embedding_size * 2, sparse=True)
predicate_embeddings = nn.Embedding(nb_predicates, embedding_size * 2, sparse=True)
fact_rel = torch.LongTensor(np.array([predicate_to_index[p] for (_, p, _) in triples]))
fact_arg1 = torch.LongTensor(np.array([entity_to_index[s] for (s, _, _) in triples]))
fact_arg2 = torch.LongTensor(np.array([entity_to_index[o] for (_, _, o) in triples]))
facts = [fact_rel, fact_arg1, fact_arg2]
model = NeuralKB(entity_embeddings=entity_embeddings, predicate_embeddings=predicate_embeddings,
kernel=kernel, facts=facts)
xs_np = rs.randint(nb_entities, size=32)
xp_np = rs.randint(nb_predicates, size=32)
xo_np = rs.randint(nb_entities, size=32)
xs_np[0] = 0
xp_np[0] = 0
xo_np[0] = 1
xs_np[1] = 2
xp_np[1] = 1
xo_np[1] = 3
xs = torch.LongTensor(xs_np)
xp = torch.LongTensor(xp_np)
xo = torch.LongTensor(xo_np)
xs_emb = entity_embeddings(xs)
xp_emb = predicate_embeddings(xp)
xo_emb = entity_embeddings(xo)
print('xp_emb', xp_emb.shape)
res_sp, res_po = model.forward(xp_emb, xs_emb, xo_emb)
inf = model.score(xp_emb, xs_emb, xo_emb)
assert inf[0] > 0.9
assert inf[1] > 0.9
scores_sp, emb_sp = res_sp
scores_po, emb_po = res_po
print(scores_sp.shape, emb_sp.shape)
print(scores_po.shape, emb_po.shape)
inf = inf.cpu().numpy()
scores_sp = scores_sp.cpu().numpy()
scores_po = scores_po.cpu().numpy()
print('AAA', inf)
print('BBB', scores_sp)
if __name__ == '__main__':
pytest.main([__file__])
# test_smart_v1()
| [
"numpy.random.RandomState",
"torch.LongTensor",
"pytest.main",
"kbcr.smart.NeuralKB",
"numpy.array",
"kbcr.kernels.GaussianKernel",
"torch.no_grad",
"torch.nn.Embedding"
]
| [((243, 267), 'numpy.random.RandomState', 'np.random.RandomState', (['(0)'], {}), '(0)\n', (264, 267), True, 'import numpy as np\n'), ((3032, 3055), 'pytest.main', 'pytest.main', (['[__file__]'], {}), '([__file__])\n', (3043, 3055), False, 'import pytest\n'), ((306, 321), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (319, 321), False, 'import torch\n'), ((1098, 1114), 'kbcr.kernels.GaussianKernel', 'GaussianKernel', ([], {}), '()\n', (1112, 1114), False, 'from kbcr.kernels import GaussianKernel\n'), ((1148, 1206), 'torch.nn.Embedding', 'nn.Embedding', (['nb_entities', '(embedding_size * 2)'], {'sparse': '(True)'}), '(nb_entities, embedding_size * 2, sparse=True)\n', (1160, 1206), False, 'from torch import nn\n'), ((1242, 1302), 'torch.nn.Embedding', 'nn.Embedding', (['nb_predicates', '(embedding_size * 2)'], {'sparse': '(True)'}), '(nb_predicates, embedding_size * 2, sparse=True)\n', (1254, 1302), False, 'from torch import nn\n'), ((1674, 1795), 'kbcr.smart.NeuralKB', 'NeuralKB', ([], {'entity_embeddings': 'entity_embeddings', 'predicate_embeddings': 'predicate_embeddings', 'kernel': 'kernel', 'facts': 'facts'}), '(entity_embeddings=entity_embeddings, predicate_embeddings=\n predicate_embeddings, kernel=kernel, facts=facts)\n', (1682, 1795), False, 'from kbcr.smart import NeuralKB\n'), ((2152, 2175), 'torch.LongTensor', 'torch.LongTensor', (['xs_np'], {}), '(xs_np)\n', (2168, 2175), False, 'import torch\n'), ((2193, 2216), 'torch.LongTensor', 'torch.LongTensor', (['xp_np'], {}), '(xp_np)\n', (2209, 2216), False, 'import torch\n'), ((2234, 2257), 'torch.LongTensor', 'torch.LongTensor', (['xo_np'], {}), '(xo_np)\n', (2250, 2257), False, 'import torch\n'), ((1344, 1400), 'numpy.array', 'np.array', (['[predicate_to_index[p] for _, p, _ in triples]'], {}), '([predicate_to_index[p] for _, p, _ in triples])\n', (1352, 1400), True, 'import numpy as np\n'), ((1445, 1498), 'numpy.array', 'np.array', (['[entity_to_index[s] for s, _, _ in triples]'], {}), '([entity_to_index[s] for s, _, _ in triples])\n', (1453, 1498), True, 'import numpy as np\n'), ((1543, 1596), 'numpy.array', 'np.array', (['[entity_to_index[o] for _, _, o in triples]'], {}), '([entity_to_index[o] for _, _, o in triples])\n', (1551, 1596), True, 'import numpy as np\n')] |
import tkinter
window=tkinter.Tk()
window.title("YUN DAE HEE")
window.geometry("640x400+100+100")
window.resizable(True, True)
image=tkinter.PhotoImage(file="opencv_frame_0.png")
label=tkinter.Label(window, image=image)
label.pack()
window.mainloop() | [
"tkinter.PhotoImage",
"tkinter.Tk",
"tkinter.Label"
]
| [((23, 35), 'tkinter.Tk', 'tkinter.Tk', ([], {}), '()\n', (33, 35), False, 'import tkinter\n'), ((135, 180), 'tkinter.PhotoImage', 'tkinter.PhotoImage', ([], {'file': '"""opencv_frame_0.png"""'}), "(file='opencv_frame_0.png')\n", (153, 180), False, 'import tkinter\n'), ((188, 222), 'tkinter.Label', 'tkinter.Label', (['window'], {'image': 'image'}), '(window, image=image)\n', (201, 222), False, 'import tkinter\n')] |
# -*- coding: utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. 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.
"""Run dask scheduler and worker."""
import os
import subprocess
import shutil
import logging
import socket
import random
from distributed import Client
from distributed.security import Security
from .conf import get_config
from .verify_cert import verify_cert
sec_cfg = get_config('server')
def get_client_security(address):
"""Get client."""
address = address.replace("tcp", "tls")
if not verify_cert(sec_cfg.ca_cert, sec_cfg.client_cert_dask):
logging.error(f"The cert {sec_cfg.ca_cert} and {sec_cfg.client_cert_dask} are invalid, please check.")
sec = Security(tls_ca_file=sec_cfg.ca_cert,
tls_client_cert=sec_cfg.client_cert_dask,
tls_client_key=sec_cfg.client_secret_key_dask,
require_encryption=True)
return Client(address, security=sec)
def get_address_security(master_host, master_port):
"""Get address."""
return "tls://{}:{}".format(master_host, master_port)
def run_scheduler_security(ip, port, tmp_file):
"""Run scheduler."""
if not verify_cert(sec_cfg.ca_cert, sec_cfg.server_cert_dask):
logging.error(f"The cert {sec_cfg.ca_cert} and {sec_cfg.server_cert_dask} are invalid, please check.")
return subprocess.Popen(
[
"dask-scheduler",
"--no-dashboard",
"--no-show",
f"--tls-ca-file={sec_cfg.ca_cert}",
f"--tls-cert={sec_cfg.server_cert_dask}",
f"--tls-key={sec_cfg.server_secret_key_dask}",
f"--host={ip}",
"--protocol=tls",
f"--port={port}",
f"--scheduler-file={tmp_file}",
f"--local-directory={os.path.dirname(tmp_file)}",
],
env=os.environ
)
def _available_port(min_port, max_port) -> int:
_sock = socket.socket()
while True:
port = random.randint(min_port, max_port)
try:
_sock.bind(('', port))
_sock.close()
return port
except Exception:
logging.debug('Failed to get available port, continue.')
continue
return None
def run_local_worker_security(slave_ip, address, local_dir):
"""Run dask-worker on local node."""
address = address.replace("tcp", "tls")
nanny_port = _available_port(30000, 30999)
worker_port = _available_port(29000, 29999)
pid = subprocess.Popen(
[
"dask-worker",
address,
'--nthreads=1',
'--nprocs=1',
'--memory-limit=0',
f"--local-directory={local_dir}",
f"--tls-ca-file={sec_cfg.ca_cert}",
f"--tls-cert={sec_cfg.client_cert_dask}",
f"--tls-key={sec_cfg.client_secret_key_dask}",
"--no-dashboard",
f"--host={slave_ip}",
"--protocol=tls",
f"--nanny-port={nanny_port}",
f"--worker-port={worker_port}",
],
env=os.environ
)
return pid
def run_remote_worker_security(slave_ip, address, local_dir):
"""Run dask-worker on remote node."""
address = address.replace("tcp", "tls")
nanny_port = _available_port(30000, 30999)
worker_port = _available_port(29000, 29999)
pid = subprocess.Popen(
[
"ssh",
slave_ip,
shutil.which("dask-worker"),
address,
'--nthreads=1',
'--nprocs=1',
'--memory-limit=0',
f"--local-directory={local_dir}",
f"--tls-ca-file={sec_cfg.ca_cert}",
f"--tls-cert={sec_cfg.client_cert_dask}",
f"--tls-key={sec_cfg.client_secret_key_dask}",
"--no-dashboard",
f"--host={slave_ip}",
"--protocol=tls",
f"--nanny-port={nanny_port}",
f"--worker-port={worker_port}",
],
env=os.environ
)
return pid
| [
"distributed.security.Security",
"logging.debug",
"socket.socket",
"subprocess.Popen",
"shutil.which",
"os.path.dirname",
"distributed.Client",
"logging.error",
"random.randint"
]
| [((1229, 1389), 'distributed.security.Security', 'Security', ([], {'tls_ca_file': 'sec_cfg.ca_cert', 'tls_client_cert': 'sec_cfg.client_cert_dask', 'tls_client_key': 'sec_cfg.client_secret_key_dask', 'require_encryption': '(True)'}), '(tls_ca_file=sec_cfg.ca_cert, tls_client_cert=sec_cfg.\n client_cert_dask, tls_client_key=sec_cfg.client_secret_key_dask,\n require_encryption=True)\n', (1237, 1389), False, 'from distributed.security import Security\n'), ((1449, 1478), 'distributed.Client', 'Client', (['address'], {'security': 'sec'}), '(address, security=sec)\n', (1455, 1478), False, 'from distributed import Client\n'), ((2448, 2463), 'socket.socket', 'socket.socket', ([], {}), '()\n', (2461, 2463), False, 'import socket\n'), ((3013, 3424), 'subprocess.Popen', 'subprocess.Popen', (["['dask-worker', address, '--nthreads=1', '--nprocs=1', '--memory-limit=0',\n f'--local-directory={local_dir}', f'--tls-ca-file={sec_cfg.ca_cert}',\n f'--tls-cert={sec_cfg.client_cert_dask}',\n f'--tls-key={sec_cfg.client_secret_key_dask}', '--no-dashboard',\n f'--host={slave_ip}', '--protocol=tls', f'--nanny-port={nanny_port}',\n f'--worker-port={worker_port}']"], {'env': 'os.environ'}), "(['dask-worker', address, '--nthreads=1', '--nprocs=1',\n '--memory-limit=0', f'--local-directory={local_dir}',\n f'--tls-ca-file={sec_cfg.ca_cert}',\n f'--tls-cert={sec_cfg.client_cert_dask}',\n f'--tls-key={sec_cfg.client_secret_key_dask}', '--no-dashboard',\n f'--host={slave_ip}', '--protocol=tls', f'--nanny-port={nanny_port}',\n f'--worker-port={worker_port}'], env=os.environ)\n", (3029, 3424), False, 'import subprocess\n'), ((1116, 1228), 'logging.error', 'logging.error', (['f"""The cert {sec_cfg.ca_cert} and {sec_cfg.client_cert_dask} are invalid, please check."""'], {}), "(\n f'The cert {sec_cfg.ca_cert} and {sec_cfg.client_cert_dask} are invalid, please check.'\n )\n", (1129, 1228), False, 'import logging\n'), ((1764, 1876), 'logging.error', 'logging.error', (['f"""The cert {sec_cfg.ca_cert} and {sec_cfg.server_cert_dask} are invalid, please check."""'], {}), "(\n f'The cert {sec_cfg.ca_cert} and {sec_cfg.server_cert_dask} are invalid, please check.'\n )\n", (1777, 1876), False, 'import logging\n'), ((2495, 2529), 'random.randint', 'random.randint', (['min_port', 'max_port'], {}), '(min_port, max_port)\n', (2509, 2529), False, 'import random\n'), ((3953, 3980), 'shutil.which', 'shutil.which', (['"""dask-worker"""'], {}), "('dask-worker')\n", (3965, 3980), False, 'import shutil\n'), ((2666, 2722), 'logging.debug', 'logging.debug', (['"""Failed to get available port, continue."""'], {}), "('Failed to get available port, continue.')\n", (2679, 2722), False, 'import logging\n'), ((2317, 2342), 'os.path.dirname', 'os.path.dirname', (['tmp_file'], {}), '(tmp_file)\n', (2332, 2342), False, 'import os\n')] |
"""
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""
import os
from os.path import join as ospj
import time
import datetime
from munch import Munch
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.model import build_model
from core.checkpoint import CheckpointIO
from core.data_loader import InputFetcher
import core.utils as utils
from metrics.eval import calculate_metrics
class Solver(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.nets, self.nets_ema, self.vgg, self.VggExtract = build_model(args)
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.L1Loss = nn.L1Loss()
# below setattrs are to make networks be children of Solver, e.g., for self.to(self.device)
for name, module in self.nets.items():
utils.print_network(module, name)
setattr(self, name, module)
for name, module in self.nets_ema.items():
setattr(self, name + '_ema', module)
if args.mode == 'train':
self.optims = Munch()
for net in self.nets.keys():
if net == 'fan':
continue
self.optims[net] = torch.optim.Adam(
params=self.nets[net].parameters(),
lr=args.f_lr if net == 'mapping_network' else args.lr,
betas=[args.beta1, args.beta2],
weight_decay=args.weight_decay)
self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '100000_nets.ckpt'), **self.nets),
CheckpointIO(ospj(args.checkpoint_dir, '100000_nets_ema.ckpt'), **self.nets_ema),
CheckpointIO(ospj(args.checkpoint_dir, '100000_optims.ckpt'), **self.optims)]
else:
self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '100000_nets_ema.ckpt'), **self.nets_ema)]
self.to(self.device)
for name, network in self.named_children():
# Do not initialize the FAN parameters
if ('ema' not in name) and ('fan' not in name):
print('Initializing %s...' % name)
network.apply(utils.he_init)
def _save_checkpoint(self, step):
for ckptio in self.ckptios:
ckptio.save(step)
def _load_checkpoint(self, step):
for ckptio in self.ckptios:
ckptio.load(step)
def _reset_grad(self):
for optim in self.optims.values():
optim.zero_grad()
def train(self, loaders):
args = self.args
nets = self.nets
nets_ema = self.nets_ema
optims = self.optims
# fetch random validation images for debugging
fetcher = InputFetcher(loaders.src, loaders.ref, args.latent_dim, 'train')
fetcher_val = InputFetcher(loaders.val, None, args.latent_dim, 'val')
inputs_val = next(fetcher_val)
# resume training if necessary
if args.resume_iter > 0:
self._load_checkpoint(args.resume_iter)
# remember the initial value of ds weight
initial_lambda_ds = args.lambda_ds
print('Start training...')
start_time = time.time()
for i in range(args.resume_iter, args.total_iters):
# fetch images and labels
inputs = next(fetcher)
x_real, y_org = inputs.x_src, inputs.y_src
x_ref, x_ref2, y_trg = inputs.x_ref, inputs.x_ref2, inputs.y_ref
z_trg, z_trg2 = inputs.z_trg, inputs.z_trg2
masks = nets.fan.get_heatmap(x_real) if args.w_hpf > 0 else None
# train the discriminator
d_loss, d_losses_latent = compute_d_loss(
nets, args, x_real, y_org, y_trg, z_trg=z_trg, masks=masks)
self._reset_grad()
d_loss.backward()
optims.discriminator.step()
d_loss, d_losses_ref = compute_d_loss(
nets, args, x_real, y_org, y_trg, x_ref=x_ref, masks=masks)
self._reset_grad()
d_loss.backward()
optims.discriminator.step()
# train the generator
g_loss, g_losses_latent = compute_g_loss(
nets, args, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], masks=masks,VggExtract=self.VggExtract, IN = self.instancenorm, L1Loss=self.L1Loss)
self._reset_grad()
g_loss.backward()
optims.generator.step()
optims.mapping_network.step()
optims.style_encoder.step()
g_loss, g_losses_ref = compute_g_loss(
nets, args, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], masks=masks, VggExtract=self.VggExtract, IN = self.instancenorm, L1Loss=self.L1Loss)
self._reset_grad()
g_loss.backward()
optims.generator.step()
# compute moving average of network parameters
moving_average(nets.generator, nets_ema.generator, beta=0.999)
moving_average(nets.mapping_network, nets_ema.mapping_network, beta=0.999)
moving_average(nets.style_encoder, nets_ema.style_encoder, beta=0.999)
# decay weight for diversity sensitive loss
if args.lambda_ds > 0:
args.lambda_ds -= (initial_lambda_ds / args.ds_iter)
# print out log info
if (i+1) % args.print_every == 0:
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))[:-7]
log = "Elapsed time [%s], Iteration [%i/%i], " % (elapsed, i+1, args.total_iters)
all_losses = dict()
for loss, prefix in zip([d_losses_latent, d_losses_ref, g_losses_latent, g_losses_ref],
['D/latent_', 'D/ref_', 'G/latent_', 'G/ref_']):
for key, value in loss.items():
all_losses[prefix + key] = value
all_losses['G/lambda_ds'] = args.lambda_ds
log += ' '.join(['%s: [%.4f]' % (key, value) for key, value in all_losses.items()])
print(log)
# generate images for debugging
if (i+1) % args.sample_every == 0:
os.makedirs(args.sample_dir, exist_ok=True)
utils.debug_image(nets_ema, args, inputs=inputs_val, step=i+1)
# save model checkpoints
if (i+1) % args.save_every == 0:
self._save_checkpoint(step=i+1)
# compute FID and LPIPS if necessary
if (i+1) % args.eval_every == 0:
calculate_metrics(nets_ema, args, i+1, mode='latent')
calculate_metrics(nets_ema, args, i+1, mode='reference')
@torch.no_grad()
def sample(self, loaders):
args = self.args
nets_ema = self.nets_ema
os.makedirs(args.result_dir, exist_ok=True)
self._load_checkpoint(args.resume_iter)
src = next(InputFetcher(loaders.src, None, args.latent_dim, 'test'))
ref = next(InputFetcher(loaders.ref, None, args.latent_dim, 'test'))
fname = ospj(args.result_dir, 'reference.jpg')
print('Working on {}...'.format(fname))
utils.translate_using_reference(nets_ema, args, src.x, ref.x, ref.y, fname)
# fname = ospj(args.result_dir, 'video_ref.mp4')
# print('Working on {}...'.format(fname))
# utils.video_ref(nets_ema, args, src.x, ref.x, ref.y, fname)
@torch.no_grad()
def evaluate(self):
args = self.args
nets_ema = self.nets_ema
resume_iter = args.resume_iter
self._load_checkpoint(args.resume_iter)
calculate_metrics(nets_ema, args, step=resume_iter, mode='latent')
calculate_metrics(nets_ema, args, step=resume_iter, mode='reference')
def compute_d_loss(nets, args, x_real, y_org, y_trg, z_trg=None, x_ref=None, masks=None):
assert (z_trg is None) != (x_ref is None)
# with real images
x_real.requires_grad_()
out = nets.discriminator(x_real, y_org)
loss_real = adv_loss(out, 1)
loss_reg = r1_reg(out, x_real)
# with fake images
with torch.no_grad():
if z_trg is not None:
s_trg = nets.mapping_network(z_trg, y_trg)
else: # x_ref is not None
s_trg = nets.style_encoder(x_ref, y_trg)
x_fake,_ = nets.generator(x_real, s_trg, masks=masks)
out = nets.discriminator(x_fake, y_trg)
loss_fake = adv_loss(out, 0)
loss = loss_real + loss_fake + args.lambda_reg * loss_reg
return loss, Munch(real=loss_real.item(),
fake=loss_fake.item(),
reg=loss_reg.item())
def compute_g_loss(nets, args, x_real, y_org, y_trg, z_trgs=None, x_refs=None, masks=None, VggExtract= None, IN= None, L1Loss=None):
assert (z_trgs is None) != (x_refs is None)
if z_trgs is not None:
z_trg, z_trg2 = z_trgs
if x_refs is not None:
x_ref, x_ref2 = x_refs
# adversarial loss
if z_trgs is not None:
s_trg = nets.mapping_network(z_trg, y_trg)
else:
s_trg = nets.style_encoder(x_ref, y_trg)
x_fake, content_latent_real = nets.generator(x_real, s_trg, masks=masks)
out = nets.discriminator(x_fake, y_trg)
loss_adv = adv_loss(out, 1)
# style reconstruction loss
s_pred = nets.style_encoder(x_fake, y_trg)
loss_sty = torch.mean(torch.abs(s_pred - s_trg))
# diversity sensitive loss
if z_trgs is not None:
s_trg2 = nets.mapping_network(z_trg2, y_trg)
else:
s_trg2 = nets.style_encoder(x_ref2, y_trg)
x_fake2, content_latent_real2 = nets.generator(x_real, s_trg2, masks=masks)
x_fake2 = x_fake2.detach()
loss_ds = torch.mean(torch.abs(x_fake - x_fake2))
# cycle-consistency loss
masks = nets.fan.get_heatmap(x_fake) if args.w_hpf > 0 else None
s_org = nets.style_encoder(x_real, y_org)
x_rec, content_latent_reco = nets.generator(x_fake, s_org, masks=masks)
loss_cyc = torch.mean(torch.abs(x_rec - x_real))
loss_vgg = compute_vgg_loss(x_fake, x_real, VggExtract, IN, L1Loss) if args.vgg_w > 0 else 0
loss_sacl = utils.abs_criterion(content_latent_real, content_latent_reco) if args.loss_sacl > 0 else 0 # Loss style aware content loss
loss_sacl2 = utils.abs_criterion(content_latent_real2, content_latent_reco) if args.loss_sacl > 0 else 0 # Loss style aware content loss
loss = loss_adv + args.lambda_sty * loss_sty \
- args.lambda_ds * loss_ds + args.lambda_cyc * loss_cyc + args.lambda_vgg * loss_vgg + args.lambda_loss_sacl * loss_sacl+ args.lambda_loss_sacl * loss_sacl2
return loss, Munch(adv=loss_adv.item(),
sty=loss_sty.item(),
ds=loss_ds.item(),
cyc=loss_cyc.item())
def moving_average(model, model_test, beta=0.999):
for param, param_test in zip(model.parameters(), model_test.parameters()):
param_test.data = torch.lerp(param.data, param_test.data, beta)
def adv_loss(logits, target):
assert target in [1, 0]
targets = torch.full_like(logits, fill_value=target)
loss = F.binary_cross_entropy_with_logits(logits, targets)
return loss
def compute_vgg_loss(img, target, VggExtract, IN, L1Loss):
# img_vgg = utils.vgg_preprocess(img)
# target_vgg = utils.vgg_preprocess(target)
# img_fea = vgg(img_vgg)
# target_fea = vgg(target_vgg)
img_fea_dict = VggExtract(img)
target_fea_dict = VggExtract(target)
# loss = torch.mean((img_fea_dict['relu3_3'] - target_fea_dict['relu3_3']) ** 2)
# loss = torch.mean(torch.abs(img_fea_dict['relu3_3'] - target_fea_dict['relu3_3']))
loss = L1Loss(img_fea_dict['relu2_2'] , target_fea_dict['relu2_2'])
return loss
def r1_reg(d_out, x_in):
# zero-centered gradient penalty for real images
batch_size = x_in.size(0)
grad_dout = torch.autograd.grad(
outputs=d_out.sum(), inputs=x_in,
create_graph=True, retain_graph=True, only_inputs=True
)[0]
grad_dout2 = grad_dout.pow(2)
assert(grad_dout2.size() == x_in.size())
reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0)
return reg | [
"metrics.eval.calculate_metrics",
"core.model.build_model",
"torch.nn.L1Loss",
"torch.nn.InstanceNorm2d",
"torch.full_like",
"torch.cuda.is_available",
"torch.lerp",
"datetime.timedelta",
"core.utils.abs_criterion",
"core.utils.debug_image",
"torch.abs",
"core.data_loader.InputFetcher",
"core.utils.print_network",
"core.utils.translate_using_reference",
"time.time",
"munch.Munch",
"os.makedirs",
"os.path.join",
"torch.no_grad",
"torch.nn.functional.binary_cross_entropy_with_logits"
]
| [((7079, 7094), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (7092, 7094), False, 'import torch\n'), ((7811, 7826), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (7824, 7826), False, 'import torch\n'), ((11421, 11463), 'torch.full_like', 'torch.full_like', (['logits'], {'fill_value': 'target'}), '(logits, fill_value=target)\n', (11436, 11463), False, 'import torch\n'), ((11475, 11526), 'torch.nn.functional.binary_cross_entropy_with_logits', 'F.binary_cross_entropy_with_logits', (['logits', 'targets'], {}), '(logits, targets)\n', (11509, 11526), True, 'import torch.nn.functional as F\n'), ((935, 952), 'core.model.build_model', 'build_model', (['args'], {}), '(args)\n', (946, 952), False, 'from core.model import build_model\n'), ((981, 1017), 'torch.nn.InstanceNorm2d', 'nn.InstanceNorm2d', (['(512)'], {'affine': '(False)'}), '(512, affine=False)\n', (998, 1017), True, 'import torch.nn as nn\n'), ((1040, 1051), 'torch.nn.L1Loss', 'nn.L1Loss', ([], {}), '()\n', (1049, 1051), True, 'import torch.nn as nn\n'), ((3080, 3144), 'core.data_loader.InputFetcher', 'InputFetcher', (['loaders.src', 'loaders.ref', 'args.latent_dim', '"""train"""'], {}), "(loaders.src, loaders.ref, args.latent_dim, 'train')\n", (3092, 3144), False, 'from core.data_loader import InputFetcher\n'), ((3167, 3222), 'core.data_loader.InputFetcher', 'InputFetcher', (['loaders.val', 'None', 'args.latent_dim', '"""val"""'], {}), "(loaders.val, None, args.latent_dim, 'val')\n", (3179, 3222), False, 'from core.data_loader import InputFetcher\n'), ((3538, 3549), 'time.time', 'time.time', ([], {}), '()\n', (3547, 3549), False, 'import time\n'), ((7192, 7235), 'os.makedirs', 'os.makedirs', (['args.result_dir'], {'exist_ok': '(True)'}), '(args.result_dir, exist_ok=True)\n', (7203, 7235), False, 'import os\n'), ((7456, 7494), 'os.path.join', 'ospj', (['args.result_dir', '"""reference.jpg"""'], {}), "(args.result_dir, 'reference.jpg')\n", (7460, 7494), True, 'from os.path import join as ospj\n'), ((7551, 7626), 'core.utils.translate_using_reference', 'utils.translate_using_reference', (['nets_ema', 'args', 'src.x', 'ref.x', 'ref.y', 'fname'], {}), '(nets_ema, args, src.x, ref.x, ref.y, fname)\n', (7582, 7626), True, 'import core.utils as utils\n'), ((8004, 8070), 'metrics.eval.calculate_metrics', 'calculate_metrics', (['nets_ema', 'args'], {'step': 'resume_iter', 'mode': '"""latent"""'}), "(nets_ema, args, step=resume_iter, mode='latent')\n", (8021, 8070), False, 'from metrics.eval import calculate_metrics\n'), ((8079, 8148), 'metrics.eval.calculate_metrics', 'calculate_metrics', (['nets_ema', 'args'], {'step': 'resume_iter', 'mode': '"""reference"""'}), "(nets_ema, args, step=resume_iter, mode='reference')\n", (8096, 8148), False, 'from metrics.eval import calculate_metrics\n'), ((8483, 8498), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (8496, 8498), False, 'import torch\n'), ((9733, 9758), 'torch.abs', 'torch.abs', (['(s_pred - s_trg)'], {}), '(s_pred - s_trg)\n', (9742, 9758), False, 'import torch\n'), ((10069, 10096), 'torch.abs', 'torch.abs', (['(x_fake - x_fake2)'], {}), '(x_fake - x_fake2)\n', (10078, 10096), False, 'import torch\n'), ((10345, 10370), 'torch.abs', 'torch.abs', (['(x_rec - x_real)'], {}), '(x_rec - x_real)\n', (10354, 10370), False, 'import torch\n'), ((10486, 10547), 'core.utils.abs_criterion', 'utils.abs_criterion', (['content_latent_real', 'content_latent_reco'], {}), '(content_latent_real, content_latent_reco)\n', (10505, 10547), True, 'import core.utils as utils\n'), ((10627, 10689), 'core.utils.abs_criterion', 'utils.abs_criterion', (['content_latent_real2', 'content_latent_reco'], {}), '(content_latent_real2, content_latent_reco)\n', (10646, 10689), True, 'import core.utils as utils\n'), ((11301, 11346), 'torch.lerp', 'torch.lerp', (['param.data', 'param_test.data', 'beta'], {}), '(param.data, param_test.data, beta)\n', (11311, 11346), False, 'import torch\n'), ((1211, 1244), 'core.utils.print_network', 'utils.print_network', (['module', 'name'], {}), '(module, name)\n', (1230, 1244), True, 'import core.utils as utils\n'), ((1445, 1452), 'munch.Munch', 'Munch', ([], {}), '()\n', (1450, 1452), False, 'from munch import Munch\n'), ((7304, 7360), 'core.data_loader.InputFetcher', 'InputFetcher', (['loaders.src', 'None', 'args.latent_dim', '"""test"""'], {}), "(loaders.src, None, args.latent_dim, 'test')\n", (7316, 7360), False, 'from core.data_loader import InputFetcher\n'), ((7381, 7437), 'core.data_loader.InputFetcher', 'InputFetcher', (['loaders.ref', 'None', 'args.latent_dim', '"""test"""'], {}), "(loaders.ref, None, args.latent_dim, 'test')\n", (7393, 7437), False, 'from core.data_loader import InputFetcher\n'), ((834, 859), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (857, 859), False, 'import torch\n'), ((6581, 6624), 'os.makedirs', 'os.makedirs', (['args.sample_dir'], {'exist_ok': '(True)'}), '(args.sample_dir, exist_ok=True)\n', (6592, 6624), False, 'import os\n'), ((6641, 6705), 'core.utils.debug_image', 'utils.debug_image', (['nets_ema', 'args'], {'inputs': 'inputs_val', 'step': '(i + 1)'}), '(nets_ema, args, inputs=inputs_val, step=i + 1)\n', (6658, 6705), True, 'import core.utils as utils\n'), ((6946, 7001), 'metrics.eval.calculate_metrics', 'calculate_metrics', (['nets_ema', 'args', '(i + 1)'], {'mode': '"""latent"""'}), "(nets_ema, args, i + 1, mode='latent')\n", (6963, 7001), False, 'from metrics.eval import calculate_metrics\n'), ((7016, 7074), 'metrics.eval.calculate_metrics', 'calculate_metrics', (['nets_ema', 'args', '(i + 1)'], {'mode': '"""reference"""'}), "(nets_ema, args, i + 1, mode='reference')\n", (7033, 7074), False, 'from metrics.eval import calculate_metrics\n'), ((1886, 1931), 'os.path.join', 'ospj', (['args.checkpoint_dir', '"""100000_nets.ckpt"""'], {}), "(args.checkpoint_dir, '100000_nets.ckpt')\n", (1890, 1931), True, 'from os.path import join as ospj\n'), ((1976, 2025), 'os.path.join', 'ospj', (['args.checkpoint_dir', '"""100000_nets_ema.ckpt"""'], {}), "(args.checkpoint_dir, '100000_nets_ema.ckpt')\n", (1980, 2025), True, 'from os.path import join as ospj\n'), ((2074, 2121), 'os.path.join', 'ospj', (['args.checkpoint_dir', '"""100000_optims.ckpt"""'], {}), "(args.checkpoint_dir, '100000_optims.ckpt')\n", (2078, 2121), True, 'from os.path import join as ospj\n'), ((2194, 2243), 'os.path.join', 'ospj', (['args.checkpoint_dir', '"""100000_nets_ema.ckpt"""'], {}), "(args.checkpoint_dir, '100000_nets_ema.ckpt')\n", (2198, 2243), True, 'from os.path import join as ospj\n'), ((5754, 5765), 'time.time', 'time.time', ([], {}), '()\n', (5763, 5765), False, 'import time\n'), ((5809, 5844), 'datetime.timedelta', 'datetime.timedelta', ([], {'seconds': 'elapsed'}), '(seconds=elapsed)\n', (5827, 5844), False, 'import datetime\n')] |
import random
highscore = []
def not_in_range(guess_it):
"""This is to check that the numbers inputted by the user are in range,
and will let the user know. If the numbers are in range then it passes.
"""
if guess_it < 1:
print('I am not thinking of negative numbers!')
elif guess_it > 10:
print('That number is way bigger than 10!')
else:
pass
def new_game(tries):
"""After the user has guessed the number correctly, the game
will ask the player if they would like to play again. Yes will start
the game again. No will exit the game. Highscore will be displayed
by the lowest amount of tries recorded.
"""
play_again = input('Would you like to play again? (Yes/No) ')
if play_again.upper() == 'YES':
highscore.append(tries)
highscore.sort
print('The highscore is {}.'.format(highscore[0]))
start_game()
elif play_again.upper() == 'NO':
exit()
else:
play_again = input('Please let me know by typing yes or no: ')
def start_game(): # title screen of the game
"""This is the start of the game which include the title screen and
is the main function that runs all the other functions as well.
"""
print('-' * 40)
print('Welcome to the Number Guessing Game!!!')
print('-' * 40)
print('I am thinking of a number between 1-10.')
random_number = random.randint(1, 10)
tries = 0
while True:
try:
guess_it = int(input('Can you guess it?: '))
except ValueError:
print('I said number, not gibberish!')
else:
while guess_it != random_number:
not_in_range(guess_it)
tries += 1
if guess_it > random_number:
print('That is too high!')
elif guess_it < random_number:
print('That is too low')
break
else:
print('You guessed it right! Your number was {}.'.format(random_number))
print('It took you {} tries.'.format(tries))
break
new_game(tries)
if __name__ == '__main__':
# Kick off the program by calling the start_game function.
start_game()
| [
"random.randint"
]
| [((1411, 1432), 'random.randint', 'random.randint', (['(1)', '(10)'], {}), '(1, 10)\n', (1425, 1432), False, 'import random\n')] |
#!/usr/bin/python3
#
# MIT License
#
# Copyright (c) 2021 <NAME> <<EMAIL>>
#
# 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.
#
try:
## following import success only when file is directly executed from command line
## otherwise will throw exception when executing as parameter for "python -m"
# pylint: disable=W0611
import __init__
except ImportError as error:
## when import fails then it means that the script was executed indirectly
## in this case __init__ is already loaded
pass
import sys
import argparse
import rsscast.logger as logger
from rsscast.rss.ytconverter import convert_yt
if __name__ != '__main__':
sys.exit(0)
parser = argparse.ArgumentParser(description='YouTube convert example')
args = parser.parse_args()
logger.configure_console()
converted = convert_yt( "https://www.youtube.com/watch?v=BLRUiVXeZKU", "/tmp/yt_example.mp3" )
print("converted:", converted)
| [
"rsscast.logger.configure_console",
"sys.exit",
"argparse.ArgumentParser",
"rsscast.rss.ytconverter.convert_yt"
]
| [((1684, 1746), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""YouTube convert example"""'}), "(description='YouTube convert example')\n", (1707, 1746), False, 'import argparse\n'), ((1777, 1803), 'rsscast.logger.configure_console', 'logger.configure_console', ([], {}), '()\n', (1801, 1803), True, 'import rsscast.logger as logger\n'), ((1818, 1903), 'rsscast.rss.ytconverter.convert_yt', 'convert_yt', (['"""https://www.youtube.com/watch?v=BLRUiVXeZKU"""', '"""/tmp/yt_example.mp3"""'], {}), "('https://www.youtube.com/watch?v=BLRUiVXeZKU', '/tmp/yt_example.mp3'\n )\n", (1828, 1903), False, 'from rsscast.rss.ytconverter import convert_yt\n'), ((1661, 1672), 'sys.exit', 'sys.exit', (['(0)'], {}), '(0)\n', (1669, 1672), False, 'import sys\n')] |
# Prepare my dataset for Digital Pathology
import os
import math
import cv2
import pdb
rootFolder = "F:\DataBase\LymphnodePathology"
trainFolder = rootFolder + "\\trainDataSet"
testFolder = rootFolder + "\\testDataSet"
srcTrainFilePath = trainFolder + "\\20X\\"
dstTrainFilePath = trainFolder + "\\5X\\"
srcTestFilePath = testFolder + "\\20X\\"
dstTestFilePath = testFolder + "\\5X\\"
factor = 4
if __name__ == '__main__':
srcTrainFileNameList = os.listdir(srcTrainFilePath)
srcTestFileNameList = os.listdir(srcTestFilePath)
for srcTrainFileName in srcTrainFileNameList:
srcTrainImage = cv2.imread(srcTrainFilePath + srcTrainFileName)
imgHeight, imgWidth, _ = srcTrainImage.shape
newWidth = int(imgWidth / factor)
newHeight = int(imgHeight / factor)
newSize = (newWidth, newHeight)
dstTrainImage = cv2.resize(srcTrainImage, newSize, interpolation=cv2.INTER_AREA)
print("Train File Name : %s, (%d, %d) => (%d, %d)" %(srcTrainFileName, imgWidth, imgHeight, newSize[0], newSize[1]))
cv2.imwrite(dstTrainFilePath + srcTrainFileName, dstTrainImage)
for srcTestFileName in srcTestFileNameList:
srcTestImage = cv2.imread(srcTestFilePath + srcTestFileName)
imgHeight, imgWidth, _ = srcTestImage.shape
newWidth = int(imgWidth / factor)
newHeight = int(imgHeight / factor)
newSize = (newWidth, newHeight)
dstTestImage = cv2.resize(srcTestImage, newSize, interpolation=cv2.INTER_AREA)
print("Test File Name : %s, (%d, %d) => (%d, %d)" %(srcTestFileName, imgWidth, imgHeight, newSize[0], newSize[1]))
cv2.imwrite(dstTestFilePath + srcTestFileName, dstTestImage)
| [
"cv2.imwrite",
"cv2.resize",
"os.listdir",
"cv2.imread"
]
| [((460, 488), 'os.listdir', 'os.listdir', (['srcTrainFilePath'], {}), '(srcTrainFilePath)\n', (470, 488), False, 'import os\n'), ((516, 543), 'os.listdir', 'os.listdir', (['srcTestFilePath'], {}), '(srcTestFilePath)\n', (526, 543), False, 'import os\n'), ((619, 666), 'cv2.imread', 'cv2.imread', (['(srcTrainFilePath + srcTrainFileName)'], {}), '(srcTrainFilePath + srcTrainFileName)\n', (629, 666), False, 'import cv2\n'), ((872, 936), 'cv2.resize', 'cv2.resize', (['srcTrainImage', 'newSize'], {'interpolation': 'cv2.INTER_AREA'}), '(srcTrainImage, newSize, interpolation=cv2.INTER_AREA)\n', (882, 936), False, 'import cv2\n'), ((1071, 1134), 'cv2.imwrite', 'cv2.imwrite', (['(dstTrainFilePath + srcTrainFileName)', 'dstTrainImage'], {}), '(dstTrainFilePath + srcTrainFileName, dstTrainImage)\n', (1082, 1134), False, 'import cv2\n'), ((1207, 1252), 'cv2.imread', 'cv2.imread', (['(srcTestFilePath + srcTestFileName)'], {}), '(srcTestFilePath + srcTestFileName)\n', (1217, 1252), False, 'import cv2\n'), ((1456, 1519), 'cv2.resize', 'cv2.resize', (['srcTestImage', 'newSize'], {'interpolation': 'cv2.INTER_AREA'}), '(srcTestImage, newSize, interpolation=cv2.INTER_AREA)\n', (1466, 1519), False, 'import cv2\n'), ((1652, 1712), 'cv2.imwrite', 'cv2.imwrite', (['(dstTestFilePath + srcTestFileName)', 'dstTestImage'], {}), '(dstTestFilePath + srcTestFileName, dstTestImage)\n', (1663, 1712), False, 'import cv2\n')] |
# -*- coding: utf8 -*-
import os
from utensor_cgen.utils import save_consts, save_graph, save_idx
import numpy as np
import tensorflow as tf
def generate():
test_dir = os.path.dirname(__file__)
graph = tf.Graph()
with graph.as_default():
x = tf.constant(np.random.randn(10),
dtype=tf.float32,
name='x')
output_x = tf.reshape(x, [5, 2], name="output_x")
with tf.Session(graph=graph) as sess:
save_consts(sess, test_dir)
save_graph(graph, 'test_reshape_4', test_dir)
np_output = output_x.eval()
save_idx(np_output, os.path.join(test_dir, 'output_x.idx'))
# test_reshape_4.pb is the same as test_quant_reshape_4.pb
# hack, since we do not have QuantizedReshape yet
if __name__ == "__main__":
generate()
| [
"tensorflow.Graph",
"utensor_cgen.utils.save_graph",
"utensor_cgen.utils.save_consts",
"tensorflow.Session",
"os.path.join",
"os.path.dirname",
"tensorflow.reshape",
"numpy.random.randn"
]
| [((172, 197), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (187, 197), False, 'import os\n'), ((208, 218), 'tensorflow.Graph', 'tf.Graph', ([], {}), '()\n', (216, 218), True, 'import tensorflow as tf\n'), ((370, 408), 'tensorflow.reshape', 'tf.reshape', (['x', '[5, 2]'], {'name': '"""output_x"""'}), "(x, [5, 2], name='output_x')\n", (380, 408), True, 'import tensorflow as tf\n'), ((417, 440), 'tensorflow.Session', 'tf.Session', ([], {'graph': 'graph'}), '(graph=graph)\n', (427, 440), True, 'import tensorflow as tf\n'), ((454, 481), 'utensor_cgen.utils.save_consts', 'save_consts', (['sess', 'test_dir'], {}), '(sess, test_dir)\n', (465, 481), False, 'from utensor_cgen.utils import save_consts, save_graph, save_idx\n'), ((486, 531), 'utensor_cgen.utils.save_graph', 'save_graph', (['graph', '"""test_reshape_4"""', 'test_dir'], {}), "(graph, 'test_reshape_4', test_dir)\n", (496, 531), False, 'from utensor_cgen.utils import save_consts, save_graph, save_idx\n'), ((266, 285), 'numpy.random.randn', 'np.random.randn', (['(10)'], {}), '(10)\n', (281, 285), True, 'import numpy as np\n'), ((588, 626), 'os.path.join', 'os.path.join', (['test_dir', '"""output_x.idx"""'], {}), "(test_dir, 'output_x.idx')\n", (600, 626), False, 'import os\n')] |
"""Logging helpers."""
import logging
import sys
import colorlog
import tqdm
class TqdmLoggingHandler(logging.StreamHandler):
"""TqdmLoggingHandler, outputs log messages to the console compatible with tqdm."""
def emit(self, record): # noqa: D102
message = self.format(record)
tqdm.tqdm.write(message)
class DelayedFileLog(logging.StreamHandler):
"""DelayedFileLog will cache messages till it can write them to a specified file."""
def __init__(self): # noqa: D107
super().__init__()
self.file_name = None
self.buffer = []
def emit(self, record): # noqa: D102
if self.file_name is None:
message = self.format(record)
self.buffer.append(message)
else:
super().emit(record)
def setFilename(self, file_name, mode='a'):
"""
Set the filename to write the log messages to.
:param file_name: File name to use.
:param mode: File open mode, by default 'a'.
:return: None
"""
self.file_name = file_name
stream = open(file_name, mode)
for old_message in self.buffer:
stream.write(old_message + self.terminator)
self.setStream(stream)
def setup_logging(level):
"""
Set the logging up to the specified level.
:param level: Log level
:return: None
"""
name_to_log_level = get_name_to_log_level_dict()
if level in name_to_log_level:
level = name_to_log_level[level]
tqdm_log_handler = TqdmLoggingHandler()
log_format = (
"%(asctime)-15s.%(msecs)03d %(process)d %(levelname)s %(name)s %(message)s"
)
log_datefmt = '%Y-%m-%d %H:%M:%S'
tqdm_log_handler.setFormatter(
colorlog.TTYColoredFormatter(
fmt='%(log_color)s' + log_format, datefmt=log_datefmt, stream=sys.stdout
)
)
buffer = DelayedFileLog()
log_handlers = [tqdm_log_handler, buffer]
# noinspection PyArgumentList
logging.basicConfig(
level=level, format=log_format, datefmt=log_datefmt, handlers=log_handlers
)
def get_name_to_log_level_dict():
"""
Return a dict with a mapping of log levels.
:return: The dict
"""
# noinspection PyProtectedMember
name_to_log_level = logging._nameToLevel.copy()
return name_to_log_level
def get_log_levels():
"""
Return supported log levels.
:return: List of log levels
"""
log_levels = [
k for k, v in sorted(get_name_to_log_level_dict().items(), key=lambda ab: ab[1])
]
log_levels.remove('NOTSET')
return log_levels
| [
"logging.basicConfig",
"logging._nameToLevel.copy",
"colorlog.TTYColoredFormatter",
"tqdm.tqdm.write"
]
| [((1994, 2093), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'level', 'format': 'log_format', 'datefmt': 'log_datefmt', 'handlers': 'log_handlers'}), '(level=level, format=log_format, datefmt=log_datefmt,\n handlers=log_handlers)\n', (2013, 2093), False, 'import logging\n'), ((2288, 2315), 'logging._nameToLevel.copy', 'logging._nameToLevel.copy', ([], {}), '()\n', (2313, 2315), False, 'import logging\n'), ((306, 330), 'tqdm.tqdm.write', 'tqdm.tqdm.write', (['message'], {}), '(message)\n', (321, 330), False, 'import tqdm\n'), ((1749, 1856), 'colorlog.TTYColoredFormatter', 'colorlog.TTYColoredFormatter', ([], {'fmt': "('%(log_color)s' + log_format)", 'datefmt': 'log_datefmt', 'stream': 'sys.stdout'}), "(fmt='%(log_color)s' + log_format, datefmt=\n log_datefmt, stream=sys.stdout)\n", (1777, 1856), False, 'import colorlog\n')] |
#Basic Ultrasonic sensor (HC-SR04) code
import RPi.GPIO as GPIO #GPIO RPI library
import time # makes sure Pi waits between steps
GPIO.setmode(GPIO.BCM) #sets GPIO pin numbering
#GPIO.setmode(GPIO.BOARD)
#Remove warnings
GPIO.setwarnings(False)
#Create loop variable
#loop = 1
#BCM
TRIG = 23 #output pin - triggers the sensor
ECHO = 24 #input pin - reads the return signal from the sensor
#BOARD
#TRIG=16
#ECHO=18
#Looping not necessary
#Print a message to let the user know that distance measurement is in progress
print ("Distance Measurement In Progress")
#Set two GPIO ports as inputs/outputs
GPIO.setup(TRIG,GPIO.OUT)
GPIO.setup(ECHO,GPIO.IN)
#while loop == 1: #Looping forever
while True: #Looping forever
#Ensure the trigger pin is set low
GPIO.output(TRIG, False)
#Give the sensor a second to settle
print ("Waiting for Sensor to Settle")
#time.sleep(2)
time.sleep(1)
#Create trigger pulse
GPIO.output(TRIG,True)
#Set trigger pin high for 10uS
time.sleep(0.00001)
#Set it low again
GPIO.output(TRIG,False)
#Record the last low timestamp for ECHO (just before the return signal is received and the pin goes high)
while GPIO.input(ECHO)==0:
pulse_start = time.time()
#Once a signal is received, the value changes from low to high, and the signal will remain high for the duration of the echo pulse
while GPIO.input(ECHO)==1:
pulse_end = time.time()
#speed=distance/time
#speed of sound at sea level = 343m/s
#34300 = distance/(time/2)
#17150 = distance/time
#17150*time = distance
#Calculating...
pulse_duration = pulse_end - pulse_start
distance_cm = pulse_duration*17150
#distance_cm = pulse_duration*0.034/2;
distance_cm = round(distance_cm,2)
distance_inch = distance_cm/2.54 #2.54 cm in 1 inch
#distance_inch = pulse_duration*0.0133/2
distance_inch = round(distance_inch,2)
distance_feet = distance_inch/12
distance_feet = round(distance_feet,2)
#Print distance
#print ("Distance:",distance_cm,"cm")
#print ("Distance:",distance_inch,"in")
print ("Distance:",distance_feet,"ft")
#Delay
time.sleep(2)
#Clean GPIO pins to ensure all inputs/outputs are reset
GPIO.cleanup()
| [
"RPi.GPIO.cleanup",
"RPi.GPIO.setup",
"RPi.GPIO.output",
"RPi.GPIO.setwarnings",
"time.sleep",
"RPi.GPIO.input",
"time.time",
"RPi.GPIO.setmode"
]
| [((131, 153), 'RPi.GPIO.setmode', 'GPIO.setmode', (['GPIO.BCM'], {}), '(GPIO.BCM)\n', (143, 153), True, 'import RPi.GPIO as GPIO\n'), ((223, 246), 'RPi.GPIO.setwarnings', 'GPIO.setwarnings', (['(False)'], {}), '(False)\n', (239, 246), True, 'import RPi.GPIO as GPIO\n'), ((604, 630), 'RPi.GPIO.setup', 'GPIO.setup', (['TRIG', 'GPIO.OUT'], {}), '(TRIG, GPIO.OUT)\n', (614, 630), True, 'import RPi.GPIO as GPIO\n'), ((630, 655), 'RPi.GPIO.setup', 'GPIO.setup', (['ECHO', 'GPIO.IN'], {}), '(ECHO, GPIO.IN)\n', (640, 655), True, 'import RPi.GPIO as GPIO\n'), ((2267, 2281), 'RPi.GPIO.cleanup', 'GPIO.cleanup', ([], {}), '()\n', (2279, 2281), True, 'import RPi.GPIO as GPIO\n'), ((763, 787), 'RPi.GPIO.output', 'GPIO.output', (['TRIG', '(False)'], {}), '(TRIG, False)\n', (774, 787), True, 'import RPi.GPIO as GPIO\n'), ((895, 908), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (905, 908), False, 'import time\n'), ((940, 963), 'RPi.GPIO.output', 'GPIO.output', (['TRIG', '(True)'], {}), '(TRIG, True)\n', (951, 963), True, 'import RPi.GPIO as GPIO\n'), ((1003, 1020), 'time.sleep', 'time.sleep', (['(1e-05)'], {}), '(1e-05)\n', (1013, 1020), False, 'import time\n'), ((1050, 1074), 'RPi.GPIO.output', 'GPIO.output', (['TRIG', '(False)'], {}), '(TRIG, False)\n', (1061, 1074), True, 'import RPi.GPIO as GPIO\n'), ((2196, 2209), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (2206, 2209), False, 'import time\n'), ((1195, 1211), 'RPi.GPIO.input', 'GPIO.input', (['ECHO'], {}), '(ECHO)\n', (1205, 1211), True, 'import RPi.GPIO as GPIO\n'), ((1238, 1249), 'time.time', 'time.time', ([], {}), '()\n', (1247, 1249), False, 'import time\n'), ((1404, 1420), 'RPi.GPIO.input', 'GPIO.input', (['ECHO'], {}), '(ECHO)\n', (1414, 1420), True, 'import RPi.GPIO as GPIO\n'), ((1445, 1456), 'time.time', 'time.time', ([], {}), '()\n', (1454, 1456), False, 'import time\n')] |
"""Python proxy to run Swift action.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
"""
import os
import glob
import sys
import subprocess
import codecs
import json
sys.path.append('../actionProxy')
from actionproxy import ActionRunner, main, setRunner # noqa
SRC_EPILOGUE_FILE = '/swift3Action/epilogue.swift'
DEST_SCRIPT_FILE = '/swift3Action/spm-build/main.swift'
DEST_SCRIPT_DIR = '/swift3Action/spm-build'
DEST_BIN_FILE = '/swift3Action/spm-build/.build/release/Action'
BUILD_PROCESS = ['./swiftbuildandlink.sh']
class Swift3Runner(ActionRunner):
def __init__(self):
ActionRunner.__init__(self, DEST_SCRIPT_FILE, DEST_BIN_FILE)
# remove pre-existing binary before receiving a new binary
def preinit(self):
try:
os.remove(self.binary)
except: pass
def epilogue(self, init_message):
# skip if executable already exists (was unzipped)
if os.path.isfile(self.binary):
return
if 'main' in init_message:
main_function = init_message['main']
else:
main_function = 'main'
# make sure there is a main.swift file
open(DEST_SCRIPT_FILE, 'a').close()
with codecs.open(DEST_SCRIPT_FILE, 'a', 'utf-8') as fp:
os.chdir(DEST_SCRIPT_DIR)
for file in glob.glob("*.swift"):
if file not in ["Package.swift", "main.swift", "_WhiskJSONUtils.swift", "_Whisk.swift"]:
with codecs.open(file, 'r', 'utf-8') as f:
fp.write(f.read())
with codecs.open(SRC_EPILOGUE_FILE, 'r', 'utf-8') as ep:
fp.write(ep.read())
fp.write('_run_main(mainFunction: %s)\n' % main_function)
def build(self, init_message):
# short circuit the build, if there already exists a binary
# from the zip file
if os.path.isfile(self.binary):
# file may not have executable permission, set it
os.chmod(self.binary, 0o555)
return
p = subprocess.Popen(
BUILD_PROCESS,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=DEST_SCRIPT_DIR)
# run the process and wait until it completes.
# stdout/stderr will not be None because we passed PIPEs to Popen
(o, e) = p.communicate()
# stdout/stderr may be either text or bytes, depending on Python
# version, so if bytes, decode to text. Note that in Python 2
# a string will match both types; so also skip decoding in that case
if isinstance(o, bytes) and not isinstance(o, str):
o = o.decode('utf-8')
if isinstance(e, bytes) and not isinstance(e, str):
e = e.decode('utf-8')
if o:
sys.stdout.write(o)
sys.stdout.flush()
if e:
sys.stderr.write(e)
sys.stderr.flush()
def env(self, message):
env = ActionRunner.env(self, message)
args = message.get('value', {}) if message else {}
env['WHISK_INPUT'] = json.dumps(args)
return env
if __name__ == '__main__':
setRunner(Swift3Runner())
main()
| [
"sys.stdout.flush",
"actionproxy.main",
"subprocess.Popen",
"json.dumps",
"sys.stderr.flush",
"actionproxy.ActionRunner.__init__",
"os.path.isfile",
"os.chdir",
"os.chmod",
"sys.stdout.write",
"sys.stderr.write",
"codecs.open",
"actionproxy.ActionRunner.env",
"sys.path.append",
"glob.glob",
"os.remove"
]
| [((921, 954), 'sys.path.append', 'sys.path.append', (['"""../actionProxy"""'], {}), "('../actionProxy')\n", (936, 954), False, 'import sys\n'), ((3928, 3934), 'actionproxy.main', 'main', ([], {}), '()\n', (3932, 3934), False, 'from actionproxy import ActionRunner, main, setRunner\n'), ((1346, 1406), 'actionproxy.ActionRunner.__init__', 'ActionRunner.__init__', (['self', 'DEST_SCRIPT_FILE', 'DEST_BIN_FILE'], {}), '(self, DEST_SCRIPT_FILE, DEST_BIN_FILE)\n', (1367, 1406), False, 'from actionproxy import ActionRunner, main, setRunner\n'), ((1672, 1699), 'os.path.isfile', 'os.path.isfile', (['self.binary'], {}), '(self.binary)\n', (1686, 1699), False, 'import os\n'), ((2624, 2651), 'os.path.isfile', 'os.path.isfile', (['self.binary'], {}), '(self.binary)\n', (2638, 2651), False, 'import os\n'), ((2788, 2893), 'subprocess.Popen', 'subprocess.Popen', (['BUILD_PROCESS'], {'stdout': 'subprocess.PIPE', 'stderr': 'subprocess.PIPE', 'cwd': 'DEST_SCRIPT_DIR'}), '(BUILD_PROCESS, stdout=subprocess.PIPE, stderr=subprocess.\n PIPE, cwd=DEST_SCRIPT_DIR)\n', (2804, 2893), False, 'import subprocess\n'), ((3709, 3740), 'actionproxy.ActionRunner.env', 'ActionRunner.env', (['self', 'message'], {}), '(self, message)\n', (3725, 3740), False, 'from actionproxy import ActionRunner, main, setRunner\n'), ((3829, 3845), 'json.dumps', 'json.dumps', (['args'], {}), '(args)\n', (3839, 3845), False, 'import json\n'), ((1519, 1541), 'os.remove', 'os.remove', (['self.binary'], {}), '(self.binary)\n', (1528, 1541), False, 'import os\n'), ((1959, 2002), 'codecs.open', 'codecs.open', (['DEST_SCRIPT_FILE', '"""a"""', '"""utf-8"""'], {}), "(DEST_SCRIPT_FILE, 'a', 'utf-8')\n", (1970, 2002), False, 'import codecs\n'), ((2022, 2047), 'os.chdir', 'os.chdir', (['DEST_SCRIPT_DIR'], {}), '(DEST_SCRIPT_DIR)\n', (2030, 2047), False, 'import os\n'), ((2072, 2092), 'glob.glob', 'glob.glob', (['"""*.swift"""'], {}), "('*.swift')\n", (2081, 2092), False, 'import glob\n'), ((2727, 2753), 'os.chmod', 'os.chmod', (['self.binary', '(365)'], {}), '(self.binary, 365)\n', (2735, 2753), False, 'import os\n'), ((3537, 3556), 'sys.stdout.write', 'sys.stdout.write', (['o'], {}), '(o)\n', (3553, 3556), False, 'import sys\n'), ((3569, 3587), 'sys.stdout.flush', 'sys.stdout.flush', ([], {}), '()\n', (3585, 3587), False, 'import sys\n'), ((3615, 3634), 'sys.stderr.write', 'sys.stderr.write', (['e'], {}), '(e)\n', (3631, 3634), False, 'import sys\n'), ((3647, 3665), 'sys.stderr.flush', 'sys.stderr.flush', ([], {}), '()\n', (3663, 3665), False, 'import sys\n'), ((2322, 2366), 'codecs.open', 'codecs.open', (['SRC_EPILOGUE_FILE', '"""r"""', '"""utf-8"""'], {}), "(SRC_EPILOGUE_FILE, 'r', 'utf-8')\n", (2333, 2366), False, 'import codecs\n'), ((2224, 2255), 'codecs.open', 'codecs.open', (['file', '"""r"""', '"""utf-8"""'], {}), "(file, 'r', 'utf-8')\n", (2235, 2255), False, 'import codecs\n')] |
import typing as t
from yandex_market_language import models
from yandex_market_language.models.abstract import XMLElement, XMLSubElement
class Promo(models.AbstractModel):
"""
Docs: https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
MAPPING = {
"start-date": "start_date",
"end-date": "end_date",
"description": "description",
"url": "url",
}
__slots__ = [
'promo_id',
'promo_type',
'purchase',
'promo_gifts',
'start_date',
'end_date',
'description',
'url'
]
def __init__(
self,
promo_id: str,
promo_type: str,
purchase: "Purchase",
promo_gifts: t.List["PromoGift"],
start_date=None,
end_date=None,
description=None,
url=None,
):
self.promo_id = promo_id
self.promo_type = promo_type
self.start_date = start_date
self.end_date = end_date
self.description = description
self.url = url
self.purchase = purchase
self.promo_gifts = promo_gifts
def create_dict(self, **kwargs) -> dict:
return dict(
promo_id=self.promo_id,
promo_type=self.promo_type,
start_date=self.start_date,
end_date=self.end_date,
description=self.description,
url=self.url,
purchase=self.purchase.to_dict(),
promo_gifts=[pg.to_dict() for pg in self.promo_gifts],
)
def create_xml(self, **kwargs) -> XMLElement:
attribs = {"id": self.promo_id, "type": self.promo_type}
promo_el = XMLElement("promo", attribs)
for tag, attr in self.MAPPING.items():
v = getattr(self, attr)
if v:
el = XMLSubElement(promo_el, tag)
el.text = v
# Add purchase el
self.purchase.to_xml(promo_el)
# Add promo gifts
promo_gifts_el = XMLSubElement(promo_el, "promo-gifts")
for pg in self.promo_gifts:
pg.to_xml(promo_gifts_el)
return promo_el
@classmethod
def from_xml(cls, promo_el: XMLElement) -> "Promo":
kwargs = dict(
promo_id=promo_el.attrib.get("id"),
promo_type=promo_el.attrib.get("type"),
promo_gifts=[]
)
for el in promo_el:
if el.tag in cls.MAPPING:
kwargs[cls.MAPPING[el.tag]] = el.text
elif el.tag == "purchase":
kwargs["purchase"] = Purchase.from_xml(el)
elif el.tag == "promo-gifts":
for pg_el in el:
kwargs["promo_gifts"].append(PromoGift.from_xml(pg_el))
return Promo(**kwargs)
class Purchase(models.AbstractModel):
"""
Docs: https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
__slots__ = [
'products',
'required_quantity'
]
def __init__(self, products: t.List["Product"], required_quantity="1"):
self.required_quantity = required_quantity
self.products = products
def create_dict(self, **kwargs) -> dict:
return dict(
required_quantity=self.required_quantity,
products=[p.to_dict() for p in self.products]
)
def create_xml(self, **kwargs) -> XMLElement:
purchase_el = XMLElement("purchase")
# Add required quantity el
required_quantity_el = XMLSubElement(purchase_el, "required-quantity")
required_quantity_el.text = self.required_quantity
# Add products el
for p in self.products:
p.to_xml(purchase_el)
return purchase_el
@staticmethod
def from_xml(purchase_el: XMLElement) -> "Purchase":
kwargs = {"products": []}
for el in purchase_el:
if el.tag == "required-quantity":
kwargs["required_quantity"] = el.text
elif el.tag == "product":
kwargs["products"].append(Product.from_xml(el))
return Purchase(**kwargs)
class Product(models.AbstractModel):
"""
Docs: https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
__slots__ = [
'offer_id',
'category_id'
]
def __init__(self, offer_id: str = None, category_id: str = None):
self.offer_id = offer_id
self.category_id = category_id
def create_dict(self, **kwargs) -> dict:
return dict(
offer_id=self.offer_id,
category_id=self.category_id,
)
def create_xml(self, **kwargs) -> XMLElement:
attribs = {}
if self.offer_id:
attribs["offer-id"] = self.offer_id
if self.category_id:
attribs["category-id"] = self.category_id
return XMLElement("product", attribs)
@staticmethod
def from_xml(product_el: XMLElement) -> "Product":
return Product(
offer_id=product_el.attrib.get("offer-id"),
category_id=product_el.attrib.get("category-id")
)
class PromoGift(models.AbstractModel):
"""
Docs:
https://yandex.ru/support/partnermarket/elements/promo-gift.html
"""
__slots__ = [
'offer_id',
'gift_id'
]
def __init__(self, offer_id: str = None, gift_id: str = None):
self.offer_id = offer_id
self.gift_id = gift_id
def create_dict(self, **kwargs) -> dict:
return dict(offer_id=self.offer_id, gift_id=self.gift_id)
def create_xml(self, **kwargs) -> XMLElement:
attribs = {}
if self.offer_id:
attribs["offer-id"] = self.offer_id
elif self.gift_id:
attribs["gift-id"] = self.gift_id
return XMLElement("promo-gift", attribs)
@staticmethod
def from_xml(el: XMLElement) -> "PromoGift":
return PromoGift(
offer_id=el.attrib.get("offer-id"),
gift_id=el.attrib.get("gift-id")
)
| [
"yandex_market_language.models.abstract.XMLElement",
"yandex_market_language.models.abstract.XMLSubElement"
]
| [((1677, 1705), 'yandex_market_language.models.abstract.XMLElement', 'XMLElement', (['"""promo"""', 'attribs'], {}), "('promo', attribs)\n", (1687, 1705), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n'), ((2004, 2042), 'yandex_market_language.models.abstract.XMLSubElement', 'XMLSubElement', (['promo_el', '"""promo-gifts"""'], {}), "(promo_el, 'promo-gifts')\n", (2017, 2042), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n'), ((3405, 3427), 'yandex_market_language.models.abstract.XMLElement', 'XMLElement', (['"""purchase"""'], {}), "('purchase')\n", (3415, 3427), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n'), ((3495, 3542), 'yandex_market_language.models.abstract.XMLSubElement', 'XMLSubElement', (['purchase_el', '"""required-quantity"""'], {}), "(purchase_el, 'required-quantity')\n", (3508, 3542), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n'), ((4842, 4872), 'yandex_market_language.models.abstract.XMLElement', 'XMLElement', (['"""product"""', 'attribs'], {}), "('product', attribs)\n", (4852, 4872), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n'), ((5775, 5808), 'yandex_market_language.models.abstract.XMLElement', 'XMLElement', (['"""promo-gift"""', 'attribs'], {}), "('promo-gift', attribs)\n", (5785, 5808), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n'), ((1829, 1857), 'yandex_market_language.models.abstract.XMLSubElement', 'XMLSubElement', (['promo_el', 'tag'], {}), '(promo_el, tag)\n', (1842, 1857), False, 'from yandex_market_language.models.abstract import XMLElement, XMLSubElement\n')] |
# -*- coding: utf-8 -*-
"""
policy._cache
~~~~~~~~~~~~~~~
Cache for policy file.
"""
import os
import logging
LOG = logging.getLogger(__name__)
# Global file cache
CACHE = {}
def read_file(filename: str, force_reload=False):
"""Read a file if it has been modified.
:param filename: File name which want to be read from.
:param force_reload: Whether to reload the file.
:returns: A tuple with a boolean specifying if the data is fresh or not.
"""
if force_reload:
_delete_cached_file(filename)
reloaded = False
mtime = os.path.getmtime(filename)
cache_info = CACHE.setdefault(filename, {})
if not cache_info or mtime > cache_info.get('mtime', 0):
LOG.debug('Reloading cached file %s', filename)
with open(filename) as fp:
cache_info['data'] = fp.read()
cache_info['mtime'] = mtime
reloaded = True
return reloaded, cache_info['data']
def _delete_cached_file(filename: str):
"""Delete cached file if present.
:param filename: Filename to delete
"""
try:
del CACHE[filename]
except KeyError:
pass
| [
"logging.getLogger",
"os.path.getmtime"
]
| [((132, 159), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (149, 159), False, 'import logging\n'), ((580, 606), 'os.path.getmtime', 'os.path.getmtime', (['filename'], {}), '(filename)\n', (596, 606), False, 'import os\n')] |
from typing import Reversible
from django.test import TestCase, Client
from challenge.models import Challenge
from codingPage.models import Command, Log
from django.core.exceptions import ValidationError
from django.urls import reverse
class CodingPageTest(TestCase):
def setUp(self) -> None:
self.client = Client(HTTP_USER_AGENT='Mozilla/5.0')
self.challenge = Challenge.objects.create(name='abc', map='0,0,0,0,0,0,0,0,0', size=3, difficulty='Easy')
self.command = Command.objects.create(action='Dodo', code=1)
self.log = Log.objects.create(data='123', challenge = self.challenge)
return super().setUp()
def test_validation(self):
"""Test if validation works for creating new command"""
Command.objects.create(action='asd', code=5)
self.assertRaises(ValidationError)
def test_check_code(self):
"""Test if code checkers dont upload to database if log false is given"""
response = self.client.post(
reverse('ajax_view'),
data = {
'code': '1\n2\n3\n',
'log': False,
'challenge_id': 1
},
HTTP_X_REQUESTED_WITH='XMLHttpRequest',
url = '/codingPage/test_code'
)
self.assertEqual(response, '123')
| [
"codingPage.models.Command.objects.create",
"challenge.models.Challenge.objects.create",
"django.urls.reverse",
"codingPage.models.Log.objects.create",
"django.test.Client"
]
| [((320, 357), 'django.test.Client', 'Client', ([], {'HTTP_USER_AGENT': '"""Mozilla/5.0"""'}), "(HTTP_USER_AGENT='Mozilla/5.0')\n", (326, 357), False, 'from django.test import TestCase, Client\n'), ((383, 475), 'challenge.models.Challenge.objects.create', 'Challenge.objects.create', ([], {'name': '"""abc"""', 'map': '"""0,0,0,0,0,0,0,0,0"""', 'size': '(3)', 'difficulty': '"""Easy"""'}), "(name='abc', map='0,0,0,0,0,0,0,0,0', size=3,\n difficulty='Easy')\n", (407, 475), False, 'from challenge.models import Challenge\n'), ((495, 540), 'codingPage.models.Command.objects.create', 'Command.objects.create', ([], {'action': '"""Dodo"""', 'code': '(1)'}), "(action='Dodo', code=1)\n", (517, 540), False, 'from codingPage.models import Command, Log\n'), ((560, 616), 'codingPage.models.Log.objects.create', 'Log.objects.create', ([], {'data': '"""123"""', 'challenge': 'self.challenge'}), "(data='123', challenge=self.challenge)\n", (578, 616), False, 'from codingPage.models import Command, Log\n'), ((758, 802), 'codingPage.models.Command.objects.create', 'Command.objects.create', ([], {'action': '"""asd"""', 'code': '(5)'}), "(action='asd', code=5)\n", (780, 802), False, 'from codingPage.models import Command, Log\n'), ((1017, 1037), 'django.urls.reverse', 'reverse', (['"""ajax_view"""'], {}), "('ajax_view')\n", (1024, 1037), False, 'from django.urls import reverse\n')] |
from pydantic import BaseModel
from tracardi.domain.entity import Entity
from tracardi.domain.scheduler_config import SchedulerConfig
from tracardi.domain.resource import ResourceCredentials
from tracardi.service.storage.driver import storage
from tracardi.service.plugin.runner import ActionRunner
from tracardi.service.plugin.domain.register import Plugin, Spec, MetaData, Form, FormGroup, FormField, FormComponent
from tracardi.service.plugin.domain.result import Result
class Configuration(BaseModel):
source: Entity
event_type: str
properties: str = "{}"
postpone: str
def validate(config: dict) -> Configuration:
return Configuration(**config)
class SchedulerPlugin(ActionRunner):
@staticmethod
async def build(**kwargs) -> 'SchedulerPlugin':
config = validate(kwargs)
resource = await storage.driver.resource.load(config.source.id)
plugin = SchedulerPlugin(config, resource.credentials)
return plugin
def __init__(self, config: Configuration, credentials: ResourceCredentials):
self.config = config
self.credentials = credentials.get_credentials(
self,
output=SchedulerConfig) # type: SchedulerConfig
async def run(self, payload):
run_in_background = True
if not run_in_background:
return Result(port="response", value=None)
else:
return Result(port="response", value=None)
def register() -> Plugin:
return Plugin(
start=False,
spec=Spec(
module='tracardi.process_engine.action.v1.pro.scheduler.plugin',
className='SchedulerPlugin',
inputs=["payload"],
outputs=['response', 'error'],
version='0.6.2',
license="MIT",
author="<NAME>",
init= {
"source": {
"id": ""
},
"event_type": "",
"properties": "{}",
"postpone": "+1m"
}
),
metadata=MetaData(
name='Schedule event',
desc='This plugin schedules events',
icon='calendar',
group=["Time"],
tags=["Pro", "Scheduler"],
pro=True,
)
)
| [
"tracardi.service.plugin.domain.register.Spec",
"tracardi.service.storage.driver.storage.driver.resource.load",
"tracardi.service.plugin.domain.register.MetaData",
"tracardi.service.plugin.domain.result.Result"
]
| [((844, 890), 'tracardi.service.storage.driver.storage.driver.resource.load', 'storage.driver.resource.load', (['config.source.id'], {}), '(config.source.id)\n', (872, 890), False, 'from tracardi.service.storage.driver import storage\n'), ((1345, 1380), 'tracardi.service.plugin.domain.result.Result', 'Result', ([], {'port': '"""response"""', 'value': 'None'}), "(port='response', value=None)\n", (1351, 1380), False, 'from tracardi.service.plugin.domain.result import Result\n'), ((1414, 1449), 'tracardi.service.plugin.domain.result.Result', 'Result', ([], {'port': '"""response"""', 'value': 'None'}), "(port='response', value=None)\n", (1420, 1449), False, 'from tracardi.service.plugin.domain.result import Result\n'), ((1531, 1832), 'tracardi.service.plugin.domain.register.Spec', 'Spec', ([], {'module': '"""tracardi.process_engine.action.v1.pro.scheduler.plugin"""', 'className': '"""SchedulerPlugin"""', 'inputs': "['payload']", 'outputs': "['response', 'error']", 'version': '"""0.6.2"""', 'license': '"""MIT"""', 'author': '"""<NAME>"""', 'init': "{'source': {'id': ''}, 'event_type': '', 'properties': '{}', 'postpone': '+1m'}"}), "(module='tracardi.process_engine.action.v1.pro.scheduler.plugin',\n className='SchedulerPlugin', inputs=['payload'], outputs=['response',\n 'error'], version='0.6.2', license='MIT', author='<NAME>', init={\n 'source': {'id': ''}, 'event_type': '', 'properties': '{}', 'postpone':\n '+1m'})\n", (1535, 1832), False, 'from tracardi.service.plugin.domain.register import Plugin, Spec, MetaData, Form, FormGroup, FormField, FormComponent\n'), ((2055, 2198), 'tracardi.service.plugin.domain.register.MetaData', 'MetaData', ([], {'name': '"""Schedule event"""', 'desc': '"""This plugin schedules events"""', 'icon': '"""calendar"""', 'group': "['Time']", 'tags': "['Pro', 'Scheduler']", 'pro': '(True)'}), "(name='Schedule event', desc='This plugin schedules events', icon=\n 'calendar', group=['Time'], tags=['Pro', 'Scheduler'], pro=True)\n", (2063, 2198), False, 'from tracardi.service.plugin.domain.register import Plugin, Spec, MetaData, Form, FormGroup, FormField, FormComponent\n')] |
from main import app
import os
import uvicorn
if __name__ == '__main__':
port = int(os.getenv("PORT"))
uvicorn.run(app, host="0.0.0.0", port=port, workers=1, reload=True)
| [
"uvicorn.run",
"os.getenv"
]
| [((112, 179), 'uvicorn.run', 'uvicorn.run', (['app'], {'host': '"""0.0.0.0"""', 'port': 'port', 'workers': '(1)', 'reload': '(True)'}), "(app, host='0.0.0.0', port=port, workers=1, reload=True)\n", (123, 179), False, 'import uvicorn\n'), ((89, 106), 'os.getenv', 'os.getenv', (['"""PORT"""'], {}), "('PORT')\n", (98, 106), False, 'import os\n')] |
# Copyright (c) 2018, deepakn94. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import namedtuple
import pandas as pd
RatingData = namedtuple('RatingData',
['items', 'users', 'ratings', 'min_date', 'max_date'])
def describe_ratings(ratings):
info = RatingData(items=len(ratings['item_id'].unique()),
users=len(ratings['user_id'].unique()),
ratings=len(ratings),
min_date=ratings['timestamp'].min(),
max_date=ratings['timestamp'].max())
print("{ratings} ratings on {items} items from {users} users"
" from {min_date} to {max_date}"
.format(**(info._asdict())))
return info
def process_movielens(ratings, sort=True):
ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
if sort:
ratings.sort_values(by='timestamp', inplace=True)
describe_ratings(ratings)
return ratings
def load_ml_100k(filename, sort=True):
names = ['user_id', 'item_id', 'rating', 'timestamp']
ratings = pd.read_csv(filename, sep='\t', names=names)
return process_movielens(ratings, sort=sort)
def load_ml_1m(filename, sort=True):
names = ['user_id', 'item_id', 'rating', 'timestamp']
ratings = pd.read_csv(filename, sep='::', names=names, engine='python')
return process_movielens(ratings, sort=sort)
def load_ml_10m(filename, sort=True):
names = ['user_id', 'item_id', 'rating', 'timestamp']
ratings = pd.read_csv(filename, sep='::', names=names, engine='python')
return process_movielens(ratings, sort=sort)
def load_ml_20m(filename, sort=True):
ratings = pd.read_csv(filename)
ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
names = {'userId': 'user_id', 'movieId': 'item_id'}
ratings.rename(columns=names, inplace=True)
return process_movielens(ratings, sort=sort)
DATASETS = [k.replace('load_', '') for k in locals().keys() if "load_" in k]
def get_dataset_name(filename):
for dataset in DATASETS:
if dataset in filename.replace('-', '_').lower():
return dataset
raise NotImplementedError
def implicit_load(filename, sort=True):
func = globals()["load_" + get_dataset_name(filename)]
return func(filename, sort=sort)
| [
"collections.namedtuple",
"pandas.to_datetime",
"pandas.read_csv"
]
| [((672, 751), 'collections.namedtuple', 'namedtuple', (['"""RatingData"""', "['items', 'users', 'ratings', 'min_date', 'max_date']"], {}), "('RatingData', ['items', 'users', 'ratings', 'min_date', 'max_date'])\n", (682, 751), False, 'from collections import namedtuple\n'), ((1331, 1377), 'pandas.to_datetime', 'pd.to_datetime', (["ratings['timestamp']"], {'unit': '"""s"""'}), "(ratings['timestamp'], unit='s')\n", (1345, 1377), True, 'import pandas as pd\n'), ((1611, 1655), 'pandas.read_csv', 'pd.read_csv', (['filename'], {'sep': '"""\t"""', 'names': 'names'}), "(filename, sep='\\t', names=names)\n", (1622, 1655), True, 'import pandas as pd\n'), ((1816, 1877), 'pandas.read_csv', 'pd.read_csv', (['filename'], {'sep': '"""::"""', 'names': 'names', 'engine': '"""python"""'}), "(filename, sep='::', names=names, engine='python')\n", (1827, 1877), True, 'import pandas as pd\n'), ((2039, 2100), 'pandas.read_csv', 'pd.read_csv', (['filename'], {'sep': '"""::"""', 'names': 'names', 'engine': '"""python"""'}), "(filename, sep='::', names=names, engine='python')\n", (2050, 2100), True, 'import pandas as pd\n'), ((2204, 2225), 'pandas.read_csv', 'pd.read_csv', (['filename'], {}), '(filename)\n', (2215, 2225), True, 'import pandas as pd\n'), ((2253, 2299), 'pandas.to_datetime', 'pd.to_datetime', (["ratings['timestamp']"], {'unit': '"""s"""'}), "(ratings['timestamp'], unit='s')\n", (2267, 2299), True, 'import pandas as pd\n')] |
from rpython.jit.backend.llsupport.descr import get_size_descr,\
get_field_descr, get_array_descr, ArrayDescr, FieldDescr,\
SizeDescr, get_interiorfield_descr
from rpython.jit.backend.llsupport.gc import GcLLDescr_boehm,\
GcLLDescr_framework
from rpython.jit.backend.llsupport import jitframe
from rpython.jit.metainterp.gc import get_description
from rpython.jit.tool.oparser import parse
from rpython.jit.metainterp.optimizeopt.util import equaloplists
from rpython.jit.metainterp.history import JitCellToken, FLOAT
from rpython.jit.metainterp.history import AbstractFailDescr
from rpython.rtyper.lltypesystem import lltype, rffi
from rpython.rtyper import rclass
from rpython.jit.backend.x86.arch import WORD
class Evaluator(object):
def __init__(self, scope):
self.scope = scope
def __getitem__(self, key):
return eval(key, self.scope)
class FakeLoopToken(object):
pass
o_vtable = lltype.malloc(rclass.OBJECT_VTABLE, immortal=True)
class RewriteTests(object):
def check_rewrite(self, frm_operations, to_operations, **namespace):
S = lltype.GcStruct('S', ('x', lltype.Signed),
('y', lltype.Signed))
sdescr = get_size_descr(self.gc_ll_descr, S)
sdescr.tid = 1234
#
T = lltype.GcStruct('T', ('y', lltype.Signed),
('z', lltype.Ptr(S)),
('t', lltype.Signed))
tdescr = get_size_descr(self.gc_ll_descr, T)
tdescr.tid = 5678
tzdescr = get_field_descr(self.gc_ll_descr, T, 'z')
#
A = lltype.GcArray(lltype.Signed)
adescr = get_array_descr(self.gc_ll_descr, A)
adescr.tid = 4321
alendescr = adescr.lendescr
#
B = lltype.GcArray(lltype.Char)
bdescr = get_array_descr(self.gc_ll_descr, B)
bdescr.tid = 8765
blendescr = bdescr.lendescr
#
C = lltype.GcArray(lltype.Ptr(S))
cdescr = get_array_descr(self.gc_ll_descr, C)
cdescr.tid = 8111
clendescr = cdescr.lendescr
#
E = lltype.GcStruct('Empty')
edescr = get_size_descr(self.gc_ll_descr, E)
edescr.tid = 9000
#
vtable_descr = self.gc_ll_descr.fielddescr_vtable
O = lltype.GcStruct('O', ('parent', rclass.OBJECT),
('x', lltype.Signed))
o_descr = self.cpu.sizeof(O, True)
o_vtable = globals()['o_vtable']
#
tiddescr = self.gc_ll_descr.fielddescr_tid
wbdescr = self.gc_ll_descr.write_barrier_descr
WORD = globals()['WORD']
#
strdescr = self.gc_ll_descr.str_descr
unicodedescr = self.gc_ll_descr.unicode_descr
strlendescr = strdescr.lendescr
unicodelendescr = unicodedescr.lendescr
strhashdescr = self.gc_ll_descr.str_hash_descr
unicodehashdescr = self.gc_ll_descr.unicode_hash_descr
casmdescr = JitCellToken()
clt = FakeLoopToken()
clt._ll_initial_locs = [0, 8]
frame_info = lltype.malloc(jitframe.JITFRAMEINFO, flavor='raw')
clt.frame_info = frame_info
frame_info.jfi_frame_depth = 13
frame_info.jfi_frame_size = 255
framedescrs = self.gc_ll_descr.getframedescrs(self.cpu)
framelendescr = framedescrs.arraydescr.lendescr
jfi_frame_depth = framedescrs.jfi_frame_depth
jfi_frame_size = framedescrs.jfi_frame_size
jf_frame_info = framedescrs.jf_frame_info
jf_savedata = framedescrs.jf_savedata
jf_force_descr = framedescrs.jf_force_descr
jf_descr = framedescrs.jf_descr
jf_guard_exc = framedescrs.jf_guard_exc
jf_forward = framedescrs.jf_forward
jf_extra_stack_depth = framedescrs.jf_extra_stack_depth
signedframedescr = self.cpu.signedframedescr
floatframedescr = self.cpu.floatframedescr
casmdescr.compiled_loop_token = clt
#
guarddescr = AbstractFailDescr()
#
namespace.update(locals())
#
for funcname in self.gc_ll_descr._generated_functions:
namespace[funcname] = self.gc_ll_descr.get_malloc_fn(funcname)
namespace[funcname + '_descr'] = getattr(self.gc_ll_descr,
'%s_descr' % funcname)
#
ops = parse(frm_operations, namespace=namespace)
expected = parse(to_operations % Evaluator(namespace),
namespace=namespace)
operations = self.gc_ll_descr.rewrite_assembler(self.cpu,
ops.operations,
[])
remap = {}
for a, b in zip(ops.inputargs, expected.inputargs):
remap[b] = a
equaloplists(operations, expected.operations, remap=remap)
lltype.free(frame_info, flavor='raw')
class FakeTracker(object):
pass
class BaseFakeCPU(object):
JITFRAME_FIXED_SIZE = 0
def __init__(self):
self.tracker = FakeTracker()
self._cache = {}
self.signedframedescr = ArrayDescr(3, 8, FieldDescr('len', 0, 0, 0), 0)
self.floatframedescr = ArrayDescr(5, 8, FieldDescr('len', 0, 0, 0), 0)
def getarraydescr_for_frame(self, tp):
if tp == FLOAT:
return self.floatframedescr
return self.signedframedescr
def unpack_arraydescr_size(self, d):
return 0, d.itemsize, 0
def unpack_fielddescr(self, d):
return d.offset
def arraydescrof(self, ARRAY):
try:
return self._cache[ARRAY]
except KeyError:
r = ArrayDescr(1, 2, FieldDescr('len', 0, 0, 0), 0)
self._cache[ARRAY] = r
return r
def fielddescrof(self, STRUCT, fname):
key = (STRUCT, fname)
try:
return self._cache[key]
except KeyError:
r = FieldDescr(fname, 1, 1, 1)
self._cache[key] = r
return r
class TestBoehm(RewriteTests):
def setup_method(self, meth):
class FakeCPU(BaseFakeCPU):
def sizeof(self, STRUCT, is_object):
assert is_object
return SizeDescr(102, gc_fielddescrs=[],
vtable=o_vtable)
self.cpu = FakeCPU()
self.gc_ll_descr = GcLLDescr_boehm(None, None, None)
def test_new(self):
self.check_rewrite("""
[]
p0 = new(descr=sdescr)
jump()
""", """
[p1]
p0 = call_malloc_gc(ConstClass(malloc_fixedsize), %(sdescr.size)d,\
descr=malloc_fixedsize_descr)
jump()
""")
def test_no_collapsing(self):
self.check_rewrite("""
[]
p0 = new(descr=sdescr)
p1 = new(descr=sdescr)
jump()
""", """
[]
p0 = call_malloc_gc(ConstClass(malloc_fixedsize), %(sdescr.size)d,\
descr=malloc_fixedsize_descr)
p1 = call_malloc_gc(ConstClass(malloc_fixedsize), %(sdescr.size)d,\
descr=malloc_fixedsize_descr)
jump()
""")
def test_new_array_fixed(self):
self.check_rewrite("""
[]
p0 = new_array(10, descr=adescr)
jump()
""", """
[]
p0 = call_malloc_gc(ConstClass(malloc_array), \
%(adescr.basesize)d, \
10, \
%(adescr.itemsize)d, \
%(adescr.lendescr.offset)d, \
descr=malloc_array_descr)
jump()
""")
## should ideally be:
## p0 = call_malloc_gc(ConstClass(malloc_fixedsize), \
## %(adescr.basesize + 10 * adescr.itemsize)d, \
## descr=malloc_fixedsize_descr)
## setfield_gc(p0, 10, descr=alendescr)
def test_new_array_variable(self):
self.check_rewrite("""
[i1]
p0 = new_array(i1, descr=adescr)
jump()
""", """
[i1]
p0 = call_malloc_gc(ConstClass(malloc_array), \
%(adescr.basesize)d, \
i1, \
%(adescr.itemsize)d, \
%(adescr.lendescr.offset)d, \
descr=malloc_array_descr)
jump()
""")
def test_new_with_vtable(self):
self.check_rewrite("""
[]
p0 = new_with_vtable(descr=o_descr)
jump()
""", """
[p1]
p0 = call_malloc_gc(ConstClass(malloc_fixedsize), 102, \
descr=malloc_fixedsize_descr)
setfield_gc(p0, ConstClass(o_vtable), descr=vtable_descr)
jump()
""")
def test_newstr(self):
self.check_rewrite("""
[i1]
p0 = newstr(i1)
jump()
""", """
[i1]
p0 = call_malloc_gc(ConstClass(malloc_array), \
%(strdescr.basesize)d, \
i1, \
%(strdescr.itemsize)d, \
%(strlendescr.offset)d, \
descr=malloc_array_descr)
jump()
""")
def test_newunicode(self):
self.check_rewrite("""
[i1]
p0 = newunicode(10)
jump()
""", """
[i1]
p0 = call_malloc_gc(ConstClass(malloc_array), \
%(unicodedescr.basesize)d, \
10, \
%(unicodedescr.itemsize)d, \
%(unicodelendescr.offset)d, \
descr=malloc_array_descr)
jump()
""")
## should ideally be:
## p0 = call_malloc_gc(ConstClass(malloc_fixedsize), \
## %(unicodedescr.basesize + \
## 10 * unicodedescr.itemsize)d, \
## descr=malloc_fixedsize_descr)
## setfield_gc(p0, 10, descr=unicodelendescr)
class TestFramework(RewriteTests):
def setup_method(self, meth):
class config_(object):
class translation(object):
gc = 'minimark'
gcrootfinder = 'asmgcc'
gctransformer = 'framework'
gcremovetypeptr = False
gcdescr = get_description(config_)
self.gc_ll_descr = GcLLDescr_framework(gcdescr, None, None, None,
really_not_translated=True)
self.gc_ll_descr.write_barrier_descr.has_write_barrier_from_array = (
lambda cpu: True)
self.gc_ll_descr.malloc_zero_filled = False
#
class FakeCPU(BaseFakeCPU):
def sizeof(self, STRUCT, is_object):
descr = SizeDescr(104, gc_fielddescrs=[])
descr.tid = 9315
return descr
self.cpu = FakeCPU()
def test_rewrite_assembler_new_to_malloc(self):
self.check_rewrite("""
[p1]
p0 = new(descr=sdescr)
jump()
""", """
[p1]
p0 = call_malloc_nursery(%(sdescr.size)d)
setfield_gc(p0, 1234, descr=tiddescr)
jump()
""")
def test_rewrite_assembler_new3_to_malloc(self):
self.check_rewrite("""
[]
p0 = new(descr=sdescr)
p1 = new(descr=tdescr)
p2 = new(descr=sdescr)
jump()
""", """
[]
p0 = call_malloc_nursery( \
%(sdescr.size + tdescr.size + sdescr.size)d)
setfield_gc(p0, 1234, descr=tiddescr)
p1 = nursery_ptr_increment(p0, %(sdescr.size)d)
setfield_gc(p1, 5678, descr=tiddescr)
p2 = nursery_ptr_increment(p1, %(tdescr.size)d)
setfield_gc(p2, 1234, descr=tiddescr)
zero_ptr_field(p1, %(tdescr.gc_fielddescrs[0].offset)s)
jump()
""")
def test_rewrite_assembler_new_array_fixed_to_malloc(self):
self.check_rewrite("""
[]
p0 = new_array(10, descr=adescr)
jump()
""", """
[]
p0 = call_malloc_nursery( \
%(adescr.basesize + 10 * adescr.itemsize)d)
setfield_gc(p0, 4321, descr=tiddescr)
setfield_gc(p0, 10, descr=alendescr)
jump()
""")
def test_rewrite_assembler_new_and_new_array_fixed_to_malloc(self):
self.check_rewrite("""
[]
p0 = new(descr=sdescr)
p1 = new_array(10, descr=adescr)
jump()
""", """
[]
p0 = call_malloc_nursery( \
%(sdescr.size + \
adescr.basesize + 10 * adescr.itemsize)d)
setfield_gc(p0, 1234, descr=tiddescr)
p1 = nursery_ptr_increment(p0, %(sdescr.size)d)
setfield_gc(p1, 4321, descr=tiddescr)
setfield_gc(p1, 10, descr=alendescr)
jump()
""")
def test_rewrite_assembler_round_up(self):
self.check_rewrite("""
[]
p0 = new_array(6, descr=bdescr)
jump()
""", """
[]
p0 = call_malloc_nursery(%(bdescr.basesize + 8)d)
setfield_gc(p0, 8765, descr=tiddescr)
setfield_gc(p0, 6, descr=blendescr)
jump()
""")
def test_rewrite_assembler_round_up_always(self):
self.check_rewrite("""
[]
p0 = new_array(5, descr=bdescr)
p1 = new_array(5, descr=bdescr)
p2 = new_array(5, descr=bdescr)
p3 = new_array(5, descr=bdescr)
jump()
""", """
[]
p0 = call_malloc_nursery(%(4 * (bdescr.basesize + 8))d)
setfield_gc(p0, 8765, descr=tiddescr)
setfield_gc(p0, 5, descr=blendescr)
p1 = nursery_ptr_increment(p0, %(bdescr.basesize + 8)d)
setfield_gc(p1, 8765, descr=tiddescr)
setfield_gc(p1, 5, descr=blendescr)
p2 = nursery_ptr_increment(p1, %(bdescr.basesize + 8)d)
setfield_gc(p2, 8765, descr=tiddescr)
setfield_gc(p2, 5, descr=blendescr)
p3 = nursery_ptr_increment(p2, %(bdescr.basesize + 8)d)
setfield_gc(p3, 8765, descr=tiddescr)
setfield_gc(p3, 5, descr=blendescr)
jump()
""")
def test_rewrite_assembler_minimal_size(self):
self.check_rewrite("""
[]
p0 = new(descr=edescr)
p1 = new(descr=edescr)
jump()
""", """
[]
p0 = call_malloc_nursery(%(4*WORD)d)
setfield_gc(p0, 9000, descr=tiddescr)
p1 = nursery_ptr_increment(p0, %(2*WORD)d)
setfield_gc(p1, 9000, descr=tiddescr)
jump()
""")
def test_rewrite_assembler_variable_size(self):
self.check_rewrite("""
[i0]
p0 = new_array(i0, descr=bdescr)
jump(i0)
""", """
[i0]
p0 = call_malloc_nursery_varsize(0, 1, i0, descr=bdescr)
setfield_gc(p0, i0, descr=blendescr)
jump(i0)
""")
def test_rewrite_new_string(self):
self.check_rewrite("""
[i0]
p0 = newstr(i0)
jump(i0)
""", """
[i0]
p0 = call_malloc_nursery_varsize(1, 1, i0, descr=strdescr)
setfield_gc(p0, i0, descr=strlendescr)
setfield_gc(p0, 0, descr=strhashdescr)
jump(i0)
""")
def test_rewrite_assembler_nonstandard_array(self):
# a non-standard array is a bit hard to get; e.g. GcArray(Float)
# is like that on Win32, but not on Linux. Build one manually...
NONSTD = lltype.GcArray(lltype.Float)
nonstd_descr = get_array_descr(self.gc_ll_descr, NONSTD)
nonstd_descr.tid = 6464
nonstd_descr.basesize = 64 # <= hacked
nonstd_descr.itemsize = 8
nonstd_descr_gcref = 123
self.check_rewrite("""
[i0, p1]
p0 = new_array(i0, descr=nonstd_descr)
setarrayitem_gc(p0, i0, p1)
jump(i0)
""", """
[i0, p1]
p0 = call_malloc_gc(ConstClass(malloc_array_nonstandard), \
64, 8, \
%(nonstd_descr.lendescr.offset)d, \
6464, i0, \
descr=malloc_array_nonstandard_descr)
cond_call_gc_wb_array(p0, i0, descr=wbdescr)
setarrayitem_gc(p0, i0, p1)
jump(i0)
""", nonstd_descr=nonstd_descr)
def test_rewrite_assembler_maximal_size_1(self):
self.gc_ll_descr.max_size_of_young_obj = 100
self.check_rewrite("""
[]
p0 = new_array(103, descr=bdescr)
jump()
""", """
[]
p0 = call_malloc_gc(ConstClass(malloc_array), 1, \
%(bdescr.tid)d, 103, \
descr=malloc_array_descr)
jump()
""")
def test_rewrite_assembler_maximal_size_2(self):
self.gc_ll_descr.max_size_of_young_obj = 300
self.check_rewrite("""
[]
p0 = new_array(101, descr=bdescr)
p1 = new_array(102, descr=bdescr) # two new_arrays can be combined
p2 = new_array(103, descr=bdescr) # but not all three
jump()
""", """
[]
p0 = call_malloc_nursery( \
%(2 * (bdescr.basesize + 104))d)
setfield_gc(p0, 8765, descr=tiddescr)
setfield_gc(p0, 101, descr=blendescr)
p1 = nursery_ptr_increment(p0, %(bdescr.basesize + 104)d)
setfield_gc(p1, 8765, descr=tiddescr)
setfield_gc(p1, 102, descr=blendescr)
p2 = call_malloc_nursery( \
%(bdescr.basesize + 104)d)
setfield_gc(p2, 8765, descr=tiddescr)
setfield_gc(p2, 103, descr=blendescr)
jump()
""")
def test_rewrite_assembler_huge_size(self):
# "huge" is defined as "larger than 0xffffff bytes, or 16MB"
self.check_rewrite("""
[]
p0 = new_array(20000000, descr=bdescr)
jump()
""", """
[]
p0 = call_malloc_gc(ConstClass(malloc_array), 1, \
%(bdescr.tid)d, 20000000, \
descr=malloc_array_descr)
jump()
""")
def test_new_with_vtable(self):
self.check_rewrite("""
[]
p0 = new_with_vtable(descr=o_descr)
jump()
""", """
[p1]
p0 = call_malloc_nursery(104) # rounded up
setfield_gc(p0, 9315, descr=tiddescr)
setfield_gc(p0, 0, descr=vtable_descr)
jump()
""")
def test_new_with_vtable_too_big(self):
self.gc_ll_descr.max_size_of_young_obj = 100
self.check_rewrite("""
[]
p0 = new_with_vtable(descr=o_descr)
jump()
""", """
[p1]
p0 = call_malloc_gc(ConstClass(malloc_big_fixedsize), 104, 9315, \
descr=malloc_big_fixedsize_descr)
setfield_gc(p0, 0, descr=vtable_descr)
jump()
""")
def test_rewrite_assembler_newstr_newunicode(self):
self.check_rewrite("""
[i2]
p0 = newstr(14)
p1 = newunicode(10)
p2 = newunicode(i2)
p3 = newstr(i2)
jump()
""", """
[i2]
p0 = call_malloc_nursery( \
%(strdescr.basesize + 16 * strdescr.itemsize + \
unicodedescr.basesize + 10 * unicodedescr.itemsize)d)
setfield_gc(p0, %(strdescr.tid)d, descr=tiddescr)
setfield_gc(p0, 14, descr=strlendescr)
setfield_gc(p0, 0, descr=strhashdescr)
p1 = nursery_ptr_increment(p0, %(strdescr.basesize + 16 * strdescr.itemsize)d)
setfield_gc(p1, %(unicodedescr.tid)d, descr=tiddescr)
setfield_gc(p1, 10, descr=unicodelendescr)
setfield_gc(p1, 0, descr=unicodehashdescr)
p2 = call_malloc_nursery_varsize(2, %(unicodedescr.itemsize)d, i2,\
descr=unicodedescr)
setfield_gc(p2, i2, descr=unicodelendescr)
setfield_gc(p2, 0, descr=unicodehashdescr)
p3 = call_malloc_nursery_varsize(1, 1, i2, \
descr=strdescr)
setfield_gc(p3, i2, descr=strlendescr)
setfield_gc(p3, 0, descr=strhashdescr)
jump()
""")
def test_write_barrier_before_setfield_gc(self):
self.check_rewrite("""
[p1, p2]
setfield_gc(p1, p2, descr=tzdescr)
jump()
""", """
[p1, p2]
cond_call_gc_wb(p1, descr=wbdescr)
setfield_gc(p1, p2, descr=tzdescr)
jump()
""")
def test_write_barrier_before_array_without_from_array(self):
self.gc_ll_descr.write_barrier_descr.has_write_barrier_from_array = (
lambda cpu: False)
self.check_rewrite("""
[p1, i2, p3]
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""", """
[p1, i2, p3]
cond_call_gc_wb(p1, descr=wbdescr)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""")
def test_write_barrier_before_short_array(self):
self.gc_ll_descr.max_size_of_young_obj = 2000
self.check_rewrite("""
[i2, p3]
p1 = new_array_clear(129, descr=cdescr)
call_n(123456)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""", """
[i2, p3]
p1 = call_malloc_nursery( \
%(cdescr.basesize + 129 * cdescr.itemsize)d)
setfield_gc(p1, 8111, descr=tiddescr)
setfield_gc(p1, 129, descr=clendescr)
zero_array(p1, 0, 129, descr=cdescr)
call_n(123456)
cond_call_gc_wb(p1, descr=wbdescr)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""")
def test_write_barrier_before_long_array(self):
# the limit of "being too long" is fixed, arbitrarily, at 130
self.gc_ll_descr.max_size_of_young_obj = 2000
self.check_rewrite("""
[i2, p3]
p1 = new_array_clear(130, descr=cdescr)
call_n(123456)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""", """
[i2, p3]
p1 = call_malloc_nursery( \
%(cdescr.basesize + 130 * cdescr.itemsize)d)
setfield_gc(p1, 8111, descr=tiddescr)
setfield_gc(p1, 130, descr=clendescr)
zero_array(p1, 0, 130, descr=cdescr)
call_n(123456)
cond_call_gc_wb_array(p1, i2, descr=wbdescr)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""")
def test_write_barrier_before_unknown_array(self):
self.check_rewrite("""
[p1, i2, p3]
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""", """
[p1, i2, p3]
cond_call_gc_wb_array(p1, i2, descr=wbdescr)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""")
def test_label_makes_size_unknown(self):
self.check_rewrite("""
[i2, p3]
p1 = new_array_clear(5, descr=cdescr)
label(p1, i2, p3)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""", """
[i2, p3]
p1 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p1, 8111, descr=tiddescr)
setfield_gc(p1, 5, descr=clendescr)
zero_array(p1, 0, 5, descr=cdescr)
label(p1, i2, p3)
cond_call_gc_wb_array(p1, i2, descr=wbdescr)
setarrayitem_gc(p1, i2, p3, descr=cdescr)
jump()
""")
def test_write_barrier_before_setinteriorfield_gc(self):
S1 = lltype.GcStruct('S1')
INTERIOR = lltype.GcArray(('z', lltype.Ptr(S1)))
interiordescr = get_array_descr(self.gc_ll_descr, INTERIOR)
interiordescr.tid = 1291
interiorlendescr = interiordescr.lendescr
interiorzdescr = get_interiorfield_descr(self.gc_ll_descr,
INTERIOR, 'z')
self.check_rewrite("""
[p1, p2]
setinteriorfield_gc(p1, 0, p2, descr=interiorzdescr)
jump(p1, p2)
""", """
[p1, p2]
cond_call_gc_wb_array(p1, 0, descr=wbdescr)
setinteriorfield_gc(p1, 0, p2, descr=interiorzdescr)
jump(p1, p2)
""", interiorzdescr=interiorzdescr)
def test_initialization_store(self):
self.check_rewrite("""
[p1]
p0 = new(descr=tdescr)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""", """
[p1]
p0 = call_malloc_nursery(%(tdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""")
def test_initialization_store_2(self):
self.check_rewrite("""
[]
p0 = new(descr=tdescr)
p1 = new(descr=sdescr)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""", """
[]
p0 = call_malloc_nursery(%(tdescr.size + sdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
p1 = nursery_ptr_increment(p0, %(tdescr.size)d)
setfield_gc(p1, 1234, descr=tiddescr)
# <<<no cond_call_gc_wb here>>>
setfield_gc(p0, p1, descr=tzdescr)
jump()
""")
def test_initialization_store_array(self):
self.check_rewrite("""
[p1, i2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, i2, p1, descr=cdescr)
jump()
""", """
[p1, i2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 0, 5, descr=cdescr)
setarrayitem_gc(p0, i2, p1, descr=cdescr)
jump()
""")
def test_zero_array_reduced_left(self):
self.check_rewrite("""
[p1, p2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, 1, p1, descr=cdescr)
setarrayitem_gc(p0, 0, p2, descr=cdescr)
jump()
""", """
[p1, p2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 2, 3, descr=cdescr)
setarrayitem_gc(p0, 1, p1, descr=cdescr)
setarrayitem_gc(p0, 0, p2, descr=cdescr)
jump()
""")
def test_zero_array_reduced_right(self):
self.check_rewrite("""
[p1, p2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, 3, p1, descr=cdescr)
setarrayitem_gc(p0, 4, p2, descr=cdescr)
jump()
""", """
[p1, p2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 0, 3, descr=cdescr)
setarrayitem_gc(p0, 3, p1, descr=cdescr)
setarrayitem_gc(p0, 4, p2, descr=cdescr)
jump()
""")
def test_zero_array_not_reduced_at_all(self):
self.check_rewrite("""
[p1, p2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, 3, p1, descr=cdescr)
setarrayitem_gc(p0, 2, p2, descr=cdescr)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""", """
[p1, p2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 0, 5, descr=cdescr)
setarrayitem_gc(p0, 3, p1, descr=cdescr)
setarrayitem_gc(p0, 2, p2, descr=cdescr)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""")
def test_zero_array_reduced_completely(self):
self.check_rewrite("""
[p1, p2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, 3, p1, descr=cdescr)
setarrayitem_gc(p0, 4, p2, descr=cdescr)
setarrayitem_gc(p0, 0, p1, descr=cdescr)
setarrayitem_gc(p0, 2, p2, descr=cdescr)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""", """
[p1, p2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 5, 0, descr=cdescr)
setarrayitem_gc(p0, 3, p1, descr=cdescr)
setarrayitem_gc(p0, 4, p2, descr=cdescr)
setarrayitem_gc(p0, 0, p1, descr=cdescr)
setarrayitem_gc(p0, 2, p2, descr=cdescr)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""")
def test_zero_array_reduced_left_with_call(self):
self.check_rewrite("""
[p1, p2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, 0, p1, descr=cdescr)
call_n(321321)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""", """
[p1, p2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 1, 4, descr=cdescr)
setarrayitem_gc(p0, 0, p1, descr=cdescr)
call_n(321321)
cond_call_gc_wb(p0, descr=wbdescr)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""")
def test_zero_array_reduced_left_with_label(self):
self.check_rewrite("""
[p1, p2]
p0 = new_array_clear(5, descr=cdescr)
setarrayitem_gc(p0, 0, p1, descr=cdescr)
label(p0, p2)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""", """
[p1, p2]
p0 = call_malloc_nursery( \
%(cdescr.basesize + 5 * cdescr.itemsize)d)
setfield_gc(p0, 8111, descr=tiddescr)
setfield_gc(p0, 5, descr=clendescr)
zero_array(p0, 1, 4, descr=cdescr)
setarrayitem_gc(p0, 0, p1, descr=cdescr)
label(p0, p2)
cond_call_gc_wb_array(p0, 1, descr=wbdescr)
setarrayitem_gc(p0, 1, p2, descr=cdescr)
jump()
""")
def test_zero_array_varsize(self):
self.check_rewrite("""
[p1, p2, i3]
p0 = new_array_clear(i3, descr=bdescr)
jump()
""", """
[p1, p2, i3]
p0 = call_malloc_nursery_varsize(0, 1, i3, descr=bdescr)
setfield_gc(p0, i3, descr=blendescr)
zero_array(p0, 0, i3, descr=bdescr)
jump()
""")
def test_zero_array_varsize_cannot_reduce(self):
self.check_rewrite("""
[p1, p2, i3]
p0 = new_array_clear(i3, descr=bdescr)
setarrayitem_gc(p0, 0, p1, descr=bdescr)
jump()
""", """
[p1, p2, i3]
p0 = call_malloc_nursery_varsize(0, 1, i3, descr=bdescr)
setfield_gc(p0, i3, descr=blendescr)
zero_array(p0, 0, i3, descr=bdescr)
cond_call_gc_wb_array(p0, 0, descr=wbdescr)
setarrayitem_gc(p0, 0, p1, descr=bdescr)
jump()
""")
def test_initialization_store_potentially_large_array(self):
# the write barrier cannot be omitted, because we might get
# an array with cards and the GC assumes that the write
# barrier is always called, even on young (but large) arrays
self.check_rewrite("""
[i0, p1, i2]
p0 = new_array(i0, descr=bdescr)
setarrayitem_gc(p0, i2, p1, descr=bdescr)
jump()
""", """
[i0, p1, i2]
p0 = call_malloc_nursery_varsize(0, 1, i0, descr=bdescr)
setfield_gc(p0, i0, descr=blendescr)
cond_call_gc_wb_array(p0, i2, descr=wbdescr)
setarrayitem_gc(p0, i2, p1, descr=bdescr)
jump()
""")
def test_non_initialization_store(self):
self.check_rewrite("""
[i0]
p0 = new(descr=tdescr)
p1 = newstr(i0)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""", """
[i0]
p0 = call_malloc_nursery(%(tdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
zero_ptr_field(p0, %(tdescr.gc_fielddescrs[0].offset)s)
p1 = call_malloc_nursery_varsize(1, 1, i0, \
descr=strdescr)
setfield_gc(p1, i0, descr=strlendescr)
setfield_gc(p1, 0, descr=strhashdescr)
cond_call_gc_wb(p0, descr=wbdescr)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""")
def test_non_initialization_store_label(self):
self.check_rewrite("""
[p1]
p0 = new(descr=tdescr)
label(p0, p1)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""", """
[p1]
p0 = call_malloc_nursery(%(tdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
zero_ptr_field(p0, %(tdescr.gc_fielddescrs[0].offset)s)
label(p0, p1)
cond_call_gc_wb(p0, descr=wbdescr)
setfield_gc(p0, p1, descr=tzdescr)
jump()
""")
def test_multiple_writes(self):
self.check_rewrite("""
[p0, p1, p2]
setfield_gc(p0, p1, descr=tzdescr)
setfield_gc(p0, p2, descr=tzdescr)
jump(p1, p2, p0)
""", """
[p0, p1, p2]
cond_call_gc_wb(p0, descr=wbdescr)
setfield_gc(p0, p1, descr=tzdescr)
setfield_gc(p0, p2, descr=tzdescr)
jump(p1, p2, p0)
""")
def test_rewrite_call_assembler(self):
self.check_rewrite("""
[i0, f0]
i2 = call_assembler_i(i0, f0, descr=casmdescr)
""", """
[i0, f0]
i1 = getfield_raw_i(ConstClass(frame_info), descr=jfi_frame_size)
p1 = call_malloc_nursery_varsize_frame(i1)
setfield_gc(p1, 0, descr=tiddescr)
i2 = getfield_raw_i(ConstClass(frame_info), descr=jfi_frame_depth)
setfield_gc(p1, 0, descr=jf_extra_stack_depth)
setfield_gc(p1, NULL, descr=jf_savedata)
setfield_gc(p1, NULL, descr=jf_force_descr)
setfield_gc(p1, NULL, descr=jf_descr)
setfield_gc(p1, NULL, descr=jf_guard_exc)
setfield_gc(p1, NULL, descr=jf_forward)
setfield_gc(p1, i2, descr=framelendescr)
setfield_gc(p1, ConstClass(frame_info), descr=jf_frame_info)
setarrayitem_gc(p1, 0, i0, descr=signedframedescr)
setarrayitem_gc(p1, 1, f0, descr=floatframedescr)
i3 = call_assembler_i(p1, descr=casmdescr)
""")
def test_int_add_ovf(self):
self.check_rewrite("""
[i0]
p0 = new(descr=tdescr)
i1 = int_add_ovf(i0, 123)
guard_overflow(descr=guarddescr) []
jump()
""", """
[i0]
p0 = call_malloc_nursery(%(tdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
zero_ptr_field(p0, %(tdescr.gc_fielddescrs[0].offset)s)
i1 = int_add_ovf(i0, 123)
guard_overflow(descr=guarddescr) []
jump()
""")
def test_int_gt(self):
self.check_rewrite("""
[i0]
p0 = new(descr=tdescr)
i1 = int_gt(i0, 123)
guard_false(i1, descr=guarddescr) []
jump()
""", """
[i0]
p0 = call_malloc_nursery(%(tdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
zero_ptr_field(p0, %(tdescr.gc_fielddescrs[0].offset)s)
i1 = int_gt(i0, 123)
guard_false(i1, descr=guarddescr) []
jump()
""")
def test_zero_ptr_field_before_getfield(self):
# This case may need to be fixed in the metainterp/optimizeopt
# already so that it no longer occurs for rewrite.py. But anyway
# it's a good idea to make sure rewrite.py is correct on its own.
self.check_rewrite("""
[]
p0 = new(descr=tdescr)
p1 = getfield_gc_r(p0, descr=tdescr)
jump(p1)
""", """
[]
p0 = call_malloc_nursery(%(tdescr.size)d)
setfield_gc(p0, 5678, descr=tiddescr)
zero_ptr_field(p0, %(tdescr.gc_fielddescrs[0].offset)s)
p1 = getfield_gc_r(p0, descr=tdescr)
jump(p1)
""")
| [
"rpython.jit.backend.llsupport.descr.get_field_descr",
"rpython.jit.backend.llsupport.descr.get_interiorfield_descr",
"rpython.jit.backend.llsupport.descr.FieldDescr",
"rpython.rtyper.lltypesystem.lltype.malloc",
"rpython.rtyper.lltypesystem.lltype.GcArray",
"rpython.jit.tool.oparser.parse",
"rpython.rtyper.lltypesystem.lltype.GcStruct",
"rpython.jit.metainterp.history.JitCellToken",
"rpython.rtyper.lltypesystem.lltype.Ptr",
"rpython.jit.metainterp.gc.get_description",
"rpython.jit.metainterp.optimizeopt.util.equaloplists",
"rpython.jit.backend.llsupport.descr.get_array_descr",
"rpython.rtyper.lltypesystem.lltype.free",
"rpython.jit.backend.llsupport.descr.get_size_descr",
"rpython.jit.backend.llsupport.gc.GcLLDescr_framework",
"rpython.jit.backend.llsupport.descr.SizeDescr",
"rpython.jit.metainterp.history.AbstractFailDescr",
"rpython.jit.backend.llsupport.gc.GcLLDescr_boehm"
]
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'rpython.rtyper.lltypesystem.lltype.GcStruct', 'lltype.GcStruct', (['"""Empty"""'], {}), "('Empty')\n", (2127, 2136), False, 'from rpython.rtyper.lltypesystem import lltype, rffi\n'), ((2154, 2189), 'rpython.jit.backend.llsupport.descr.get_size_descr', 'get_size_descr', (['self.gc_ll_descr', 'E'], {}), '(self.gc_ll_descr, E)\n', (2168, 2189), False, 'from rpython.jit.backend.llsupport.descr import get_size_descr, get_field_descr, get_array_descr, ArrayDescr, FieldDescr, SizeDescr, get_interiorfield_descr\n'), ((2296, 2365), 'rpython.rtyper.lltypesystem.lltype.GcStruct', 'lltype.GcStruct', (['"""O"""', "('parent', rclass.OBJECT)", "('x', lltype.Signed)"], {}), "('O', ('parent', rclass.OBJECT), ('x', lltype.Signed))\n", (2311, 2365), False, 'from rpython.rtyper.lltypesystem import lltype, rffi\n'), ((2981, 2995), 'rpython.jit.metainterp.history.JitCellToken', 'JitCellToken', ([], {}), '()\n', (2993, 2995), False, 'from rpython.jit.metainterp.history import JitCellToken, FLOAT\n'), ((3085, 3135), 'rpython.rtyper.lltypesystem.lltype.malloc', 'lltype.malloc', (['jitframe.JITFRAMEINFO'], {'flavor': '"""raw"""'}), "(jitframe.JITFRAMEINFO, flavor='raw')\n", (3098, 3135), False, 'from rpython.rtyper.lltypesystem import lltype, rffi\n'), ((4001, 4020), 'rpython.jit.metainterp.history.AbstractFailDescr', 'AbstractFailDescr', ([], {}), '()\n', (4018, 4020), False, 'from rpython.jit.metainterp.history import AbstractFailDescr\n'), ((4385, 4427), 'rpython.jit.tool.oparser.parse', 'parse', (['frm_operations'], {'namespace': 'namespace'}), '(frm_operations, namespace=namespace)\n', (4390, 4427), False, 'from rpython.jit.tool.oparser import parse\n'), ((4847, 4905), 'rpython.jit.metainterp.optimizeopt.util.equaloplists', 'equaloplists', (['operations', 'expected.operations'], {'remap': 'remap'}), '(operations, expected.operations, remap=remap)\n', (4859, 4905), False, 'from rpython.jit.metainterp.optimizeopt.util import equaloplists\n'), ((4914, 4951), 'rpython.rtyper.lltypesystem.lltype.free', 'lltype.free', (['frame_info'], {'flavor': '"""raw"""'}), "(frame_info, flavor='raw')\n", (4925, 4951), False, 'from rpython.rtyper.lltypesystem import lltype, rffi\n'), ((6395, 6428), 'rpython.jit.backend.llsupport.gc.GcLLDescr_boehm', 'GcLLDescr_boehm', (['None', 'None', 'None'], {}), '(None, None, None)\n', (6410, 6428), False, 'from rpython.jit.backend.llsupport.gc import GcLLDescr_boehm, GcLLDescr_framework\n'), ((10929, 10953), 'rpython.jit.metainterp.gc.get_description', 'get_description', (['config_'], {}), '(config_)\n', (10944, 10953), False, 'from rpython.jit.metainterp.gc import get_description\n'), ((10981, 11055), 'rpython.jit.backend.llsupport.gc.GcLLDescr_framework', 'GcLLDescr_framework', (['gcdescr', 'None', 'None', 'None'], {'really_not_translated': '(True)'}), '(gcdescr, None, None, None, really_not_translated=True)\n', (11000, 11055), False, 'from rpython.jit.backend.llsupport.gc import GcLLDescr_boehm, 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'(102, gc_fielddescrs=[], vtable=o_vtable)\n', (6264, 6305), False, 'from rpython.jit.backend.llsupport.descr import get_size_descr, get_field_descr, get_array_descr, ArrayDescr, FieldDescr, SizeDescr, get_interiorfield_descr\n'), ((11382, 11415), 'rpython.jit.backend.llsupport.descr.SizeDescr', 'SizeDescr', (['(104)'], {'gc_fielddescrs': '[]'}), '(104, gc_fielddescrs=[])\n', (11391, 11415), False, 'from rpython.jit.backend.llsupport.descr import get_size_descr, get_field_descr, get_array_descr, ArrayDescr, FieldDescr, SizeDescr, get_interiorfield_descr\n'), ((25389, 25403), 'rpython.rtyper.lltypesystem.lltype.Ptr', 'lltype.Ptr', (['S1'], {}), '(S1)\n', (25399, 25403), False, 'from rpython.rtyper.lltypesystem import lltype, rffi\n'), ((5716, 5742), 'rpython.jit.backend.llsupport.descr.FieldDescr', 'FieldDescr', (['"""len"""', '(0)', '(0)', '(0)'], {}), "('len', 0, 0, 0)\n", (5726, 5742), False, 'from rpython.jit.backend.llsupport.descr import get_size_descr, get_field_descr, get_array_descr, ArrayDescr, FieldDescr, SizeDescr, get_interiorfield_descr\n')] |
import re
def remove_not_alpha_num(string):
return re.sub('[^0-9a-zA-Z]+', '', string)
if __name__ == '__main__':
print(remove_not_alpha_num('a000 aa-b') == 'a000aab')
| [
"re.sub"
]
| [((57, 92), 're.sub', 're.sub', (['"""[^0-9a-zA-Z]+"""', '""""""', 'string'], {}), "('[^0-9a-zA-Z]+', '', string)\n", (63, 92), False, 'import re\n')] |
#!/usr/bin/env python
from __future__ import absolute_import, division, print_function
import os
import re
import shlex
import subprocess
import signal
import csv
import logging
import json
import time
from datetime import datetime as dt
from requests.exceptions import RequestException
import glob
import traceback
import random
from badge import *
from badge_discoverer import BadgeDiscoverer, BeaconDiscoverer
from badge_manager_server import BadgeManagerServer
from beacon_manager_server import BeaconManagerServer
from badge_manager_standalone import BadgeManagerStandalone
from beacon_manager_standalone import BeaconManagerStandalone
import hub_manager
from settings import DATA_DIR, LOG_DIR
log_file_name = LOG_DIR + 'hub.log'
scans_file_name = DATA_DIR + 'scan.txt'
pending_file_prefix = DATA_DIR + 'pending_'
audio_archive_file_name = DATA_DIR + 'audio_archive.txt'
proximity_archive_file_name = DATA_DIR + 'proximity_archive.txt'
standalone_audio_file = DATA_DIR + 'audio_data.txt'
standalone_proximity_file = DATA_DIR + 'proximity_data.txt'
AUDIO = "audio"
PROXIMITY = "proximity"
SCAN_DURATION = 3 # seconds
#NOTE try to keep under 100MB or so due to memory constraints
MAX_PENDING_FILE_SIZE = 15000000 # in bytes, so 15MB
# create logger with 'badge_server'
logger = logging.getLogger('badge_server')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler(log_file_name)
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter and add it to the handlers
# formatter = logging.Formatter('%(asctime)s - %(levelname)s - [%(mac)s] %(message)s')
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
def round_float_for_log(x):
return float("{0:.3f}".format(x))
def has_chunks(filename):
"""
Returns true if there is data in the file, and false otherwise
"""
return os.path.exists(filename) and os.path.getsize(filename) > 0
def offload_data():
"""
Send pending files to server and move pending to archive
Return True on success, False on failure
"""
#TODO test with standalone
#NOTE not currently doing anything with the True/False
# return values, might decide to do something later
pending_files = sorted(glob.glob(pending_file_prefix + "*"))
for pending_file_name in pending_files:
logger.debug("Sending {} to server".format(pending_file_name))
if not has_chunks(pending_file_name):
continue
chunks = []
with open(pending_file_name, "r") as pending_file:
for line in pending_file:
chunks.append(json.loads(line))
# real quick grab the data type from the first data entry
data_type = "audio" if "audio" in chunks[0]["type"] else "proximity"
# fire away!
try:
chunks_written = hub_manager.send_data_to_server(logger, data_type, chunks)
if chunks_written == len(chunks):
logger.debug("Successfully wrote {} data entries to server"
.format(len(chunks)))
else:
# this seems unlikely to happen but is good to keep track of i guess
logger.error("Data mismatch: {} data entries were not written to server"
.format(len(chunks) - chunks_written))
logger.error("Error sending data from file {} to server!"
.format(pending_file_name))
return False
# write to archive and erase pending file
with open(get_archive_name(data_type), "a") as archive_file:
for chunk in chunks:
archive_file.write(json.dumps(chunk) + "\n")
os.remove(pending_file_name)
except RequestException as e:
s = traceback.format_exc()
logger.error("Error sending data from file {} to server!"
.format(pending_file_name))
logger.error("{},{}".format(e,s))
return False
return True
def get_archive_name(data_type):
"""
Return the name of the archive file for the passed data type
"""
if data_type == AUDIO:
return audio_archive_file_name
else:
return proximity_archive_file_name
def get_proximity_name(mode="server"):
"""
return the name of the existing pending proximity file,
or a new one if either one doesn't exist or if
the existing file is > MAX_PENDING_FILE_SIZE
"""
if mode == "server":
return _get_pending_file_name(PROXIMITY)
else:
return standalone_proximity_file
def get_audio_name(mode="server"):
if mode == "server":
return _get_pending_file_name(AUDIO)
else:
return standalone_audio_file
def _get_pending_file_name(data_type):
"""
If there are no current pending files < MAX_PENDING_FILE_SIZE in size,
return a new pending filename
Else, return an existing one.
"""
filenames = filter(
lambda x: os.path.getsize(x) < MAX_PENDING_FILE_SIZE,
glob.glob("{}*{}*".format(pending_file_prefix, data_type)))
if len(filenames) == 0:
return _create_pending_file_name(data_type)
else:
return filenames[0]
def _create_pending_file_name(data_type):
"""
Create a pending file name for the given data_type
Uses the current date/time to create a unique filename
"""
now = dt.now().strftime("%Y%m%d%H%M%S")
filename = "{}{}_{}.txt".format(pending_file_prefix, now, data_type)
if os.path.exists(filename):
# this seems unlikely to happen, but just in case :)
# get the number of pending files that match this time and add one
files = glob.glob("{}{}*{}*".format(pending_file_prefix, now, data_type))
now = '_'.join((now, str(len(files) + 1)))
filename = "{}{}_{}.txt".format(pending_file_prefix, now, data_type)
return filename
def dialogue(bdg, activate_audio, activate_proximity, mode="server"):
"""
Attempts to read data from the device specified by the address. Reading is handled by gatttool.
:param bdg:
:return:
"""
ret = bdg.pull_data(activate_audio, activate_proximity)
addr = bdg.addr
if ret == 0:
logger.info("Successfully pulled data")
# if we were able to pull data, we saw the badge again
bdg.last_seen_ts = time.time()
else:
logger.info("Errors pulling data.")
if bdg.dlg.chunks:
logger.info("Chunks received: {}".format(len(bdg.dlg.chunks)))
logger.info("saving chunks to file")
# store in JSON file
with open(get_audio_name(mode), "a") as fout:
for chunk in bdg.dlg.chunks:
ts_with_ms = round_float_for_log(ts_and_fract_to_float(chunk.ts, chunk.fract))
log_line = {
'type': "audio received",
'log_timestamp': round_float_for_log(time.time()),
'log_index': -1, # need to find a good accumulator.
'data': {
'voltage': round_float_for_log(chunk.voltage),
'timestamp': ts_with_ms,
'sample_period': chunk.sampleDelay,
'num_samples': len(chunk.samples),
'samples': chunk.samples,
'badge_address': addr,
'member': bdg.key,
'member_id':bdg.badge_id
}
}
logger.debug("Chunk timestamp: {0:.3f}, Voltage: {1:.3f}, Delay: {2}, Samples in chunk: {3}".format(
ts_with_ms, chunk.voltage, chunk.sampleDelay, len(chunk.samples)))
#logger.debug(json.dumps(log_line))
json.dump(log_line, fout)
fout.write('\n')
logger.info("done writing")
# update badge object to hold latest timestamps
last_chunk = bdg.dlg.chunks[-1]
last_chunk_ts_pretty = dt.fromtimestamp(last_chunk.ts).strftime("%Y-%m-%d@%H:%M:%S UTC")
if bdg.is_newer_audio_ts(last_chunk.ts, last_chunk.fract):
logger.debug("Setting last badge audio timestamp to {} {} ({})".format(
last_chunk.ts, last_chunk.fract, last_chunk_ts_pretty))
bdg.set_audio_ts(last_chunk.ts, last_chunk.fract)
else:
logger.debug("Keeping existing timestamp ({}.{}) for {}. Last chunk timestamp was: {}.{} ({})"
.format(bdg.last_audio_ts_int,bdg.last_audio_ts_fract,bdg.addr,
last_chunk.ts, last_chunk.fract, last_chunk_pretty))
else:
logger.info("No mic data ready")
if bdg.dlg.scans:
logger.info("Proximity scans received: {}".format(len(bdg.dlg.scans)))
logger.info("saving proximity scans to file")
with open(get_proximity_name(mode), "a") as fout:
for scan in bdg.dlg.scans:
ts_with_ms = round_float_for_log(scan.ts)
log_line = {
'type': "proximity received",
'log_timestamp': round_float_for_log(time.time()),
'log_index': -1, # need to find a good accumulator.
'data': {
'voltage': round_float_for_log(scan.voltage),
'timestamp': ts_with_ms,
'badge_address': addr,
'rssi_distances':
{
device.ID: {'rssi': device.rssi, 'count': device.count} for device in scan.devices
},
'member': bdg.key,
'member_id': bdg.badge_id
}
}
logger.debug("SCAN: scan timestamp: {0:.3f}, voltage: {1:.3f}, Devices in scan: {2}".format(
ts_with_ms, scan.voltage, scan.numDevices))
#logger.info(json.dumps(log_line))
json.dump(log_line, fout)
fout.write('\n')
# update badge object to hold latest timestamps
last_scan = bdg.dlg.scans[-1]
last_scan_ts_pretty = dt.fromtimestamp(last_scan.ts).strftime("%Y-%m-%d@%H:%M:%S UTC")
logger.debug("Setting last badge proximity timestamp to {} ([])".format(
last_scan.ts, last_scan_ts_pretty))
bdg.last_proximity_ts = last_scan.ts
else:
logger.info("No proximity scans ready")
def scan_for_devices(devices_whitelist, show_all=False):
bd = BadgeDiscoverer(logger)
try:
all_devices = bd.discover(scan_duration=SCAN_DURATION)
except Exception as e: # catch *all* exceptions
logger.error("[Badges] Scan failed,{}".format(e))
all_devices = {}
scanned_devices = []
for addr,device_info in all_devices.iteritems():
if addr in devices_whitelist:
logger.debug("\033[1;7m\033[1;32mFound {}, added. Device info: {}\033[0m".format(addr, device_info))
scanned_devices.append({'mac':addr,'device_info':device_info})
else:
if show_all:
logger.debug("Found {}, but not on whitelist. Device info: {}".format(addr, device_info))
pass
time.sleep(2) # requires sometimes to prevent connection from failing
return scanned_devices
def scan_for_bc_devices(devices_whitelist, show_all=False):
bc = BeaconDiscoverer(logger)
try:
all_bc_devices = bc.discover(scan_duration=SCAN_DURATION)
except Exception as e: # catch *all* exceptions
logger.error("[Beacons] Scan failed,{}".format(e))
all_bc_devices = {}
scanned_bc_devices = []
for addr,device_info in all_bc_devices.iteritems():
if addr in devices_whitelist:
logger.debug("\033[1;7m\033[1;32mFound {}, added. Device info: {}\033[0m".format(addr, device_info))
scanned_bc_devices.append({'mac':addr,'device_info':device_info})
else:
if show_all:
logger.debug("Found {}, but not on whitelist. Device info: {}".format(addr, device_info))
pass
time.sleep(2) # requires sometimes to prevent connection from failing
return scanned_bc_devices
def create_badge_manager_instance(mode,timestamp):
if mode == "server":
mgr = BadgeManagerServer(logger=logger)
else:
mgr = BadgeManagerStandalone(logger=logger,timestamp=timestamp)
return mgr
def create_beacon_manager_instance(mode,timestamp):
if mode == "server":
mgrb = BeaconManagerServer(logger=logger)
else:
mgrb = BeaconManagerStandalone(logger=logger,timestamp=timestamp)
return mgrb
def reset():
'''
Resets and reconfigures Bluetooth parameters. The specific parameters affect connection speed negotiation. It's
not pretty, but safer to change the conn params this way
:return:
'''
# Resets BLE hci
logger.info("Resetting bluetooth")
reset_command = "hciconfig hci0 reset"
args = shlex.split(reset_command)
p = subprocess.Popen(args)
# israspberry pi?
logger.info("Setting bluetooth connection parameters")
if os.uname()[4][:3] == 'arm':
logger.info("Raspberry Pi detected, changing bluetooth connection parameters")
with open("/sys/kernel/debug/bluetooth/hci0/conn_min_interval", "w") as connparam:
connparam.write("16")
with open("/sys/kernel/debug/bluetooth/hci0/conn_max_interval", "w") as connparam:
connparam.write("17")
else:
logger.warn("Not a Raspberry Pi, Bluetooth connection parameters remain untouched (communication may be slower)")
time.sleep(2) # requires sleep after reset
logger.info("Done resetting bluetooth")
def kill_bluepy():
"""
Kill orphaned/leftover/defunct bluepy-helper processes
I'd like to move this to a separate utility file or something when
we refactor
"""
# get all the bluepy-helper processes
CMD="/bin/ps ax | grep bluepy-helper | grep -v grep | awk '{ print $1 }'"
p = subprocess.Popen(CMD, shell=True, stdout=subprocess.PIPE)
pidstr = p.communicate()[0]
pids = pidstr.split("\n")
pids = [int(pid) for pid in pids if pid.isdigit()]
mypid = os.getpid()
# dont wanna kill our process by accident :)
if mypid in pids:
pids.remove(mypid)
for pid in pids:
# KILL KILL KILL
try:
os.kill(int(pid), signal.SIGKILL)
# we waitpid to clean up defunct processes
os.waitpid(int(pid), 0)
logger.info("Process with PID {} killed".format(pid))
except OSError as err:
logger.error("Unable to kill process with pid {}".format(pid))
logger.error(err)
def pull_devices(mgr, mgrb, start_recording):
logger.info('Started pulling')
activate_audio = False
activate_proximity = False
if start_recording is None or start_recording == "both":
activate_audio = True
activate_proximity = True
elif start_recording == "audio":
activate_audio = True
elif start_recording == "proximity":
activate_proximity = True
elif start_recording == "none":
activate_audio = False
activate_proximity = False
logger.info("Start recording: Audio = {}, Proximity = {}".format(activate_audio,activate_proximity))
mode = "server" if isinstance(mgr, BadgeManagerServer) else "standalone"
while True:
mgr.pull_badges_list()
mgrb.pull_beacons_list()
# When we refactor we can change this, but for now:
if mode == "server":
logger.info("Attempting to offload data to server")
offload_data()
logger.info("Scanning for members...")
scanned_devices = scan_for_devices(mgr.badges.keys())
# Randomly shuffle devices
random.shuffle(scanned_devices)
# iterate before the actual data collection loop just to offload
# voltages to the server (and update heartbeat on server)
for device in scanned_devices:
b = mgr.badges.get(device['mac'])
# i don't think adv_payload is ever supposed to be empty,
# but sometimes it is. and when it is, it breaks
if device['device_info']['adv_payload'] is not None:
b.last_voltage = device['device_info']['adv_payload']['voltage']
b.observed_id = device['device_info']['adv_payload']['badge_id']
observed_project_id = device['device_info']['adv_payload']['project_id']
if b.observed_id != b.badge_id or b.project_id != observed_project_id:
logger.debug("Warning! Observed IDs do not match server settings. "
"Observed: member_id:{}, project_id:{}. Expected: member_id:{}. project_id: {}"
.format(b.observed_id,observed_project_id,b.badge_id,b.project_id))
b.last_seen_ts = time.time()
mgr.send_badge(device['mac'])
# now the actual data collection
for device in scanned_devices:
# try to update latest badge timestamps from the server
mac = device['mac']
pull_success = mgr.pull_badge(mac)
if not pull_success:
logger.warn("""Problem pulling badge from server\n
Skipping badge with mac {} until next full badge list refresh"""
.format(mac))
continue
b = mgr.badges.get(mac)
# pull data
dialogue(b, activate_audio, activate_proximity, mode)
# update timestamps on server
mgr.send_badge(mac)
time.sleep(2) # requires sleep between devices
logger.info("Scanning for beacons...")
scanned_beacons = scan_for_bc_devices(mgrb.beacons.keys())
# Randomly shuffle devices
random.shuffle(scanned_beacons)
# iterate before the actual data collection loop just to offload
# voltages to the server (and update heartbeat on server)
for device in scanned_beacons:
bcn = mgrb.beacons.get(device['mac'])
if device['device_info']['adv_payload'] is not None:
bcn.last_voltage = device['device_info']['adv_payload']['voltage']
bcn.observed_id = device['device_info']['adv_payload']['badge_id']
observed_project_id = device['device_info']['adv_payload']['project_id']
if bcn.observed_id != bcn.badge_id or bcn.project_id != observed_project_id:
logger.debug("Warning! Observed IDs do not match server settings. "
"Observed: beacon_id:{}, project_id:{}. Expected: beacon_id:{}. project_id: {}"
.format(bcn.observed_id,observed_project_id,bcn.badge_id,bcn.project_id))
bcn.last_seen_ts = time.time()
mgrb.send_beacon(device['mac'])
# Update beacons with wrong id or project id
for device in scanned_beacons:
bcn = mgrb.beacons.get(device['mac'])
if device['device_info']['adv_payload'] is not None:
observed_id = device['device_info']['adv_payload']['badge_id']
observed_project_id = device['device_info']['adv_payload']['project_id']
if bcn.badge_id != observed_id or bcn.project_id != observed_project_id:
bcn.sync_timestamp()
mgrb.send_beacon(device['mac'])
time.sleep(2)
time.sleep(2) # allow BLE time to disconnect
# clean up any leftover bluepy processes
kill_bluepy()
def sync_all_devices(mgr):
logger.info('Syncing all badges recording.')
mgr.pull_badges_list()
for mac in mgr.badges:
bdg = mgr.badges.get(mac)
bdg.sync_timestamp()
time.sleep(2) # requires sleep between devices
time.sleep(2) # allow BLE time to disconnect
def devices_scanner(mgr, mgrb, show_all=False):
logger.info('Scanning for badges')
mgr.pull_badges_list()
logger.info('Scanning for beacons')
mgrb.pull_beacons_list()
while True:
logger.info("Scanning for devices...")
scanned_devices = scan_for_devices(mgr.badges.keys(), show_all) + scan_for_bc_devices(mgrb.beacons.keys())
with open(scans_file_name, "a") as fout:
for device in scanned_devices:
mac = device['mac']
scan_date = device['device_info']['scan_date']
rssi = device['device_info']['rssi']
if device['device_info']['adv_payload']:
voltage = device['device_info']['adv_payload']['voltage']
observed_id = device['device_info']['adv_payload']['badge_id']
project_id = device['device_info']['adv_payload']['project_id']
else:
voltage = 0.0
observed_id = -1
project_id = -1
logger.debug("{},{},{:.2f},{:.2f},{},{}".
format(scan_date, mac, rssi, voltage, observed_id, project_id))
fout.write("{},{},{:.2f},{:.2f},{},{}\n".
format(scan_date, mac, rssi, voltage, observed_id, project_id))
time.sleep(5) # give time to Ctrl-C
def start_all_devices(mgr):
logger.info('Starting all badges recording.')
while True:
mgr.pull_badges_list()
logger.info("Scanning for devices...")
scanned_devices = scan_for_devices(mgr.badges.keys())
for device in scanned_devices:
dev_info = device['device_info']
if dev_info ['adv_payload']:
sync = dev_info ['adv_payload']['sync_status']
audio = dev_info ['adv_payload']['audio_status']
proximity = dev_info ['adv_payload']['proximity_status']
badge_id = dev_info ['adv_payload']['badge_id']
project_id = dev_info ['adv_payload']['project_id']
if sync == 0 or audio == 0 or proximity == 0:
if(project_id==0):
logger.info("changing project ids {}".format(device['mac']))
logger.info("Starting {}".format(device['mac']))
bdg = mgr.badges.get(device['mac'])
bdg.start_recording()
time.sleep(2) # requires sleep between devices
else:
logger.info("Starting {}".format(device['mac']))
bdg = mgr.badges.get(device['mac'])
bdg.start_recording()
time.sleep(2) # requires sleep between devices
else:
logger.info("No need to start {}".format(device['mac']))
time.sleep(2) # allow BLE time to disconnect
def print_badges(mgr, mgrb):
logger.info("Printing badges:")
mgr.pull_badges_list()
mgrb.pull_beacons_list()
badge_list = mgr.badges
beacon_list = mgrb.beacons
print("Members:")
for key, value in badge_list.iteritems():
print("{},{},{},{}".format(value.key,value.addr,value.badge_id,value.project_id))
print("\nBadges:")
for key, value in beacon_list.iteritems():
print("{},{},{},{}".format(value.key,value.addr,value.badge_id,value.project_id))
def add_pull_command_options(subparsers):
pull_parser = subparsers.add_parser('pull', help='Continuously pull data from badges')
pull_parser.add_argument('-r','--start_recording'
, choices=('audio', 'proximity', 'both','none'), required=False
, default='both'
, dest='start_recording',help='data recording option')
def add_scan_command_options(subparsers):
scan_parser = subparsers.add_parser('scan', help='Continuously scan for badges')
scan_parser.add_argument('-a','--show_all', action='store_true', default=False, help="Show all devices")
def add_sync_all_command_options(subparsers):
sa_parser = subparsers.add_parser('sync_all', help='Send date to all devices in whitelist')
def add_start_all_command_options(subparsers):
st_parser = subparsers.add_parser('start_all', help='Start recording on all devices in whitelist')
def add_print_badges_command_options(subparsers):
lb_parser = subparsers.add_parser('print_badges', help='print badges in a CSV format')
if __name__ == "__main__":
import time
import argparse
parser = argparse.ArgumentParser(description="Run scans, send dates, or continuously pull data")
parser.add_argument('-dr','--disable_reset_ble', action='store_true', default=False, help="Do not reset BLE")
parser.add_argument('-m','--hub_mode', choices=('server', 'standalone')
, default='standalone', dest='hub_mode'
, help="Operation mode - standalone (using a configuration file) or a server")
parser.add_argument('-t', '--timestamp'
, type=int, required=False
, dest='timestamp', help='UTC timestamp to start pulling data from (int)')
subparsers = parser.add_subparsers(help='Program mode (e.g. Scan, send dates, pull, scan etc.)', dest='mode')
add_pull_command_options(subparsers)
add_scan_command_options(subparsers)
add_sync_all_command_options(subparsers)
add_start_all_command_options(subparsers)
add_print_badges_command_options(subparsers)
args = parser.parse_args()
mgr = create_badge_manager_instance(args.hub_mode, args.timestamp)
mgrb = create_beacon_manager_instance(args.hub_mode, args.timestamp)
if not args.disable_reset_ble:
reset()
if args.mode == "sync_all":
sync_all_devices(mgr)
# scan for devices
if args.mode == "scan":
devices_scanner(mgr,mgrb, args.show_all)
# pull data from all devices
if args.mode == "pull":
pull_devices(mgr, mgrb, args.start_recording)
if args.mode == "start_all":
start_all_devices(mgr)
if args.mode == "print_badges":
print_badges(mgr, mgrb)
exit(0)
| [
"logging.getLogger",
"logging.StreamHandler",
"beacon_manager_server.BeaconManagerServer",
"shlex.split",
"time.sleep",
"badge_discoverer.BeaconDiscoverer",
"badge_manager_server.BadgeManagerServer",
"os.remove",
"os.path.exists",
"argparse.ArgumentParser",
"subprocess.Popen",
"json.dumps",
"logging.FileHandler",
"badge_discoverer.BadgeDiscoverer",
"os.getpid",
"beacon_manager_standalone.BeaconManagerStandalone",
"glob.glob",
"hub_manager.send_data_to_server",
"os.uname",
"os.path.getsize",
"json.loads",
"random.shuffle",
"badge_manager_standalone.BadgeManagerStandalone",
"time.time",
"traceback.format_exc",
"datetime.datetime.fromtimestamp",
"logging.Formatter",
"datetime.datetime.now",
"json.dump"
]
| [((1294, 1327), 'logging.getLogger', 'logging.getLogger', (['"""badge_server"""'], {}), "('badge_server')\n", (1311, 1327), False, 'import logging\n'), ((1418, 1452), 'logging.FileHandler', 'logging.FileHandler', (['log_file_name'], {}), '(log_file_name)\n', (1437, 1452), False, 'import logging\n'), ((1535, 1558), 'logging.StreamHandler', 'logging.StreamHandler', ([], {}), '()\n', (1556, 1558), False, 'import logging\n'), ((1732, 1794), 'logging.Formatter', 'logging.Formatter', (['"""%(asctime)s - %(levelname)s - %(message)s"""'], {}), "('%(asctime)s - %(levelname)s - %(message)s')\n", (1749, 1794), False, 'import logging\n'), ((5801, 5825), 'os.path.exists', 'os.path.exists', (['filename'], {}), '(filename)\n', (5815, 5825), False, 'import os\n'), ((10876, 10899), 'badge_discoverer.BadgeDiscoverer', 'BadgeDiscoverer', (['logger'], {}), '(logger)\n', (10891, 10899), False, 'from badge_discoverer import BadgeDiscoverer, BeaconDiscoverer\n'), ((11579, 11592), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (11589, 11592), False, 'import time\n'), ((11748, 11772), 'badge_discoverer.BeaconDiscoverer', 'BeaconDiscoverer', (['logger'], {}), '(logger)\n', (11764, 11772), False, 'from badge_discoverer import BadgeDiscoverer, BeaconDiscoverer\n'), ((12468, 12481), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (12478, 12481), False, 'import time\n'), ((13357, 13383), 'shlex.split', 'shlex.split', (['reset_command'], {}), '(reset_command)\n', (13368, 13383), False, 'import shlex\n'), ((13392, 13414), 'subprocess.Popen', 'subprocess.Popen', (['args'], {}), '(args)\n', (13408, 13414), False, 'import subprocess\n'), ((14007, 14020), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (14017, 14020), False, 'import time\n'), ((14416, 14473), 'subprocess.Popen', 'subprocess.Popen', (['CMD'], {'shell': '(True)', 'stdout': 'subprocess.PIPE'}), '(CMD, shell=True, stdout=subprocess.PIPE)\n', (14432, 14473), False, 'import subprocess\n'), ((14604, 14615), 'os.getpid', 'os.getpid', ([], {}), '()\n', (14613, 14615), False, 'import os\n'), ((20388, 20401), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (20398, 20401), False, 'import time\n'), ((25127, 25219), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Run scans, send dates, or continuously pull data"""'}), "(description=\n 'Run scans, send dates, or continuously pull data')\n", (25150, 25219), False, 'import argparse\n'), ((2117, 2141), 'os.path.exists', 'os.path.exists', (['filename'], {}), '(filename)\n', (2131, 2141), False, 'import os\n'), ((2498, 2534), 'glob.glob', 'glob.glob', (["(pending_file_prefix + '*')"], {}), "(pending_file_prefix + '*')\n", (2507, 2534), False, 'import glob\n'), ((6653, 6664), 'time.time', 'time.time', ([], {}), '()\n', (6662, 6664), False, 'import time\n'), ((12661, 12694), 'badge_manager_server.BadgeManagerServer', 'BadgeManagerServer', ([], {'logger': 'logger'}), '(logger=logger)\n', (12679, 12694), False, 'from 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(3984, 4003), False, 'import os\n'), ((5687, 5695), 'datetime.datetime.now', 'dt.now', ([], {}), '()\n', (5693, 5695), True, 'from datetime import datetime as dt\n'), ((17369, 17380), 'time.time', 'time.time', ([], {}), '()\n', (17378, 17380), False, 'import time\n'), ((18128, 18141), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (18138, 18141), False, 'import time\n'), ((19349, 19360), 'time.time', 'time.time', ([], {}), '()\n', (19358, 19360), False, 'import time\n'), ((19989, 20002), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (19999, 20002), False, 'import time\n'), ((4058, 4080), 'traceback.format_exc', 'traceback.format_exc', ([], {}), '()\n', (4078, 4080), False, 'import traceback\n'), ((5264, 5282), 'os.path.getsize', 'os.path.getsize', (['x'], {}), '(x)\n', (5279, 5282), False, 'import os\n'), ((8069, 8094), 'json.dump', 'json.dump', (['log_line', 'fout'], {}), '(log_line, fout)\n', (8078, 8094), False, 'import json\n'), ((8297, 8328), 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23267), 'time.sleep', 'time.sleep', (['(2)'], {}), '(2)\n', (23264, 23267), False, 'import time\n'), ((3937, 3954), 'json.dumps', 'json.dumps', (['chunk'], {}), '(chunk)\n', (3947, 3954), False, 'import json\n')] |
import compileall
compileall.compile_dir(".",force=1) | [
"compileall.compile_dir"
]
| [((18, 54), 'compileall.compile_dir', 'compileall.compile_dir', (['"""."""'], {'force': '(1)'}), "('.', force=1)\n", (40, 54), False, 'import compileall\n')] |
# Generated by Django 3.1.5 on 2021-02-17 11:04
from django.db import migrations
import saleor.core.db.fields
import saleor.core.utils.editorjs
def update_empty_description_field(apps, schema_editor):
Category = apps.get_model("product", "Category")
CategoryTranslation = apps.get_model("product", "CategoryTranslation")
Collection = apps.get_model("product", "Collection")
CollectionTranslation = apps.get_model("product", "CollectionTranslation")
Product = apps.get_model("product", "Product")
ProductTranslation = apps.get_model("product", "ProductTranslation")
models = [
Category,
CategoryTranslation,
Collection,
CollectionTranslation,
Product,
ProductTranslation,
]
for model in models:
model.objects.filter(description={}).update(description=None)
class Migration(migrations.Migration):
dependencies = [
("product", "0140_auto_20210125_0905"),
]
operations = [
migrations.AlterField(
model_name="category",
name="description",
field=saleor.core.db.fields.SanitizedJSONField(
blank=True,
null=True,
sanitizer=saleor.core.utils.editorjs.clean_editor_js,
),
),
migrations.AlterField(
model_name="categorytranslation",
name="description",
field=saleor.core.db.fields.SanitizedJSONField(
blank=True,
null=True,
sanitizer=saleor.core.utils.editorjs.clean_editor_js,
),
),
migrations.AlterField(
model_name="collection",
name="description",
field=saleor.core.db.fields.SanitizedJSONField(
blank=True,
null=True,
sanitizer=saleor.core.utils.editorjs.clean_editor_js,
),
),
migrations.AlterField(
model_name="collectiontranslation",
name="description",
field=saleor.core.db.fields.SanitizedJSONField(
blank=True,
null=True,
sanitizer=saleor.core.utils.editorjs.clean_editor_js,
),
),
migrations.AlterField(
model_name="product",
name="description",
field=saleor.core.db.fields.SanitizedJSONField(
blank=True,
null=True,
sanitizer=saleor.core.utils.editorjs.clean_editor_js,
),
),
migrations.AlterField(
model_name="producttranslation",
name="description",
field=saleor.core.db.fields.SanitizedJSONField(
blank=True,
null=True,
sanitizer=saleor.core.utils.editorjs.clean_editor_js,
),
),
migrations.RunPython(
update_empty_description_field,
migrations.RunPython.noop,
),
]
| [
"django.db.migrations.RunPython"
]
| [((2888, 2967), 'django.db.migrations.RunPython', 'migrations.RunPython', (['update_empty_description_field', 'migrations.RunPython.noop'], {}), '(update_empty_description_field, migrations.RunPython.noop)\n', (2908, 2967), False, 'from django.db import migrations\n')] |
#!/usr/bin/env python3
import sys
import random
def read_file(file_name):
"""File reader and parser the num of variables, num of clauses and put the clauses in a list"""
clauses =[]
with open(file_name) as all_file:
for line in all_file:
if line.startswith('c'): continue #ignore comments
if line.startswith('p'):
num_variables = int(line.split()[2]) # set num_variables
continue
if line.strip() == "": continue
clause = list(map(int, line.split()))
clause.pop()
clauses.append(clause)
return num_variables, clauses
def print_sol(solution):
"""Method to print the solution that satisfies all the clauses """
print("s SATISFIABLE")
print("v %s 0" %" ".join(map(str, solution)))
exit(0)
class walksat_solver():
def __init__(self, clauses, num_variables):
"""Constructor of the solver"""
self.clauses = clauses
self.num_variables = num_variables
self.formula=[]
self.list_positions = self.create_positions()
self.index_clauses_satisfied = []
def randomSolution(self):
"""Create a random solution of cnf formula. Ex: [-1, 2, 3, -4, ...]"""
random_formula = [x if random.random() < 0.5 else -x for x in range(self.num_variables + 1)]
return random_formula[1:]
def create_positions(self):
"""Return a list with the clause index that apear in the clauses.
First position is empty, and the index of list is the variable.
Ex: [ [], [2], [2, 3], ....] """
vars_positions = [[] for _ in range(self.num_variables * 2 + 1)]
for index, clause in enumerate(self.clauses):
for var in clause:
vars_positions[var].append(index)
return vars_positions
def calculate_all_clauses_satisfy(self):
"""Returns a list with the number of variables that
satisfy the clause with the same index.
Method for all clauses.
Ex: [1, 0, 2, 2] in test_0.cnf """
list_variables_satisfies = []
for clause in range(len(self.clauses)):
number_sat = self.clause_satisfy(clause)
list_variables_satisfies.append(number_sat)
return list_variables_satisfies
def clause_satisfy(self, index):
"""Returns an integer, which is the number of
variables in the formula that satisfy the
clause indicated by the index.
Ex: index = 1 --> cluse[1] = [1, -2, 3, ..] """
satisfy = 0
for variable in self.clauses[index]:
if variable in self.formula:
satisfy += 1
return satisfy
def select_all_unsatisfied(self):
"""Returns a list of indexes whose clause
is not satisfied."""
clauses_not_satisfied = []
for index, value in enumerate(self.index_clauses_satisfied):
if value == 0:
clauses_not_satisfied.append(index)
return clauses_not_satisfied
def get_clause_unsatisfied(self, list_all_unsatisfied):
"""Returns a randomly selected unsatisfied clause"""
return self.clauses[random.choice(list_all_unsatisfied)]
def update(self, variable, x):
"""It is responsible for updating the list of
the number of variables that satisfy the clause"""
for index in self.list_positions[x * variable]:
self.index_clauses_satisfied[index] += x
def change_variable(self, clause_to_review):
"""Is responsible for assessing which is
the best variable in the clause to change"""
worst_wrong = sys.maxsize
bests_variables = []
for variable in clause_to_review:
wrong = 0
for index in self.list_positions[-variable]:
if not self.index_clauses_satisfied[index] > 1:
wrong += 1
if wrong <= worst_wrong:
worst_wrong = wrong
bests_variables.append(variable)
return random.choice(bests_variables)
def solve(self, max_tries=50000000, max_flips=3000):
"""Implementation of the solver"""
#for _ in range(max_tries):
while(True):
self.formula = self.randomSolution()
self.index_clauses_satisfied = self.calculate_all_clauses_satisfy()
for _ in range(max_flips):
index_all_unsatisfied = self.select_all_unsatisfied()
if len(index_all_unsatisfied)==0:
print_sol(self.formula)
clause_to_review = self.get_clause_unsatisfied(index_all_unsatisfied)
variable = self.change_variable(clause_to_review)
self.update(variable, 1)
self.update(variable, -1)
self.formula[abs(variable)-1] *= -1
#Main
if __name__ == "__main__":
if len(sys.argv) == 2:
file_name = sys.argv[1]
else:
print("\n Command: python %s <file_name.cnf> \n" %sys.argv[0])
exit(0)
num_variables, clauses = read_file(file_name)
sat = walksat_solver(clauses, num_variables)
sat.solve()
exit(0)
| [
"random.random",
"random.choice"
]
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# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
from collections import OrderedDict
from functools import partial
from jax import lax, random, tree_flatten, tree_map, tree_multimap, tree_unflatten
import jax.numpy as jnp
from jax.tree_util import register_pytree_node_class
from numpyro import handlers
from numpyro.primitives import _PYRO_STACK, Messenger, apply_stack
from numpyro.util import not_jax_tracer
@register_pytree_node_class
class PytreeTrace:
def __init__(self, trace):
self.trace = trace
def tree_flatten(self):
trace, aux_trace = {}, {}
for name, site in self.trace.items():
if site['type'] in ['sample', 'deterministic']:
trace[name], aux_trace[name] = {}, {'_control_flow_done': True}
for key in site:
if key in ['fn', 'args', 'value', 'intermediates']:
trace[name][key] = site[key]
# scanned sites have stop field because we trace them inside a block handler
elif key != 'stop':
aux_trace[name][key] = site[key]
return (trace,), aux_trace
@classmethod
def tree_unflatten(cls, aux_data, children):
trace, = children
for name, site in trace.items():
site.update(aux_data[name])
return cls(trace)
def _subs_wrapper(subs_map, i, length, site):
value = None
if isinstance(subs_map, dict) and site['name'] in subs_map:
value = subs_map[site['name']]
elif callable(subs_map):
rng_key = site['kwargs'].get('rng_key')
subs_map = handlers.seed(subs_map, rng_seed=rng_key) if rng_key is not None else subs_map
value = subs_map(site)
if value is not None:
value_ndim = jnp.ndim(value)
sample_shape = site['kwargs']['sample_shape']
fn_ndim = len(sample_shape + site['fn'].shape())
if value_ndim == fn_ndim:
# this branch happens when substitute_fn is init_strategy,
# where we apply init_strategy to each element in the scanned series
return value
elif value_ndim == fn_ndim + 1:
# this branch happens when we substitute a series of values
shape = jnp.shape(value)
if shape[0] == length:
return value[i]
elif shape[0] < length:
rng_key = site['kwargs']['rng_key']
assert rng_key is not None
# we use the substituted values if i < shape[0]
# and generate a new sample otherwise
return lax.cond(i < shape[0],
(value, i),
lambda val: val[0][val[1]],
rng_key,
lambda val: site['fn'](rng_key=val, sample_shape=sample_shape))
else:
raise RuntimeError(f"Substituted value for site {site['name']} "
"requires length less than or equal to scan length."
f" Expected length <= {length}, but got {shape[0]}.")
else:
raise RuntimeError(f"Something goes wrong. Expected ndim = {fn_ndim} or {fn_ndim+1},"
f" but got {value_ndim}. This might happen when you use nested scan,"
" which is currently not supported. Please report the issue to us!")
class promote_shapes(Messenger):
# a helper messenger to promote shapes of `fn` and `value`
# + msg: fn.batch_shape = (2, 3), value.shape = (3,) + fn.event_shape
# process_message(msg): promote value so that value.shape = (1, 3) + fn.event_shape
# + msg: fn.batch_shape = (3,), value.shape = (2, 3) + fn.event_shape
# process_message(msg): promote fn so that fn.batch_shape = (1, 3).
def process_message(self, msg):
if msg["type"] == "sample" and msg["value"] is not None:
fn, value = msg["fn"], msg["value"]
value_batch_ndims = jnp.ndim(value) - fn.event_dim
fn_batch_ndim = len(fn.batch_shape)
prepend_shapes = (1,) * abs(fn_batch_ndim - value_batch_ndims)
if fn_batch_ndim > value_batch_ndims:
msg["value"] = jnp.reshape(value, prepend_shapes + jnp.shape(value))
elif fn_batch_ndim < value_batch_ndims:
msg["fn"] = tree_map(lambda x: jnp.reshape(x, prepend_shapes + jnp.shape(x)), fn)
def scan_enum(f, init, xs, length, reverse, rng_key=None, substitute_stack=None):
from numpyro.contrib.funsor import enum, config_enumerate, markov, trace as packed_trace
# XXX: This implementation only works for history size=1 but can be
# extended to history size > 1 by running `f` `history_size` times
# for initialization. However, `sequential_sum_product` does not
# support history size > 1, so we skip supporting it here.
# Note that `funsor.sum_product.sarkka_bilmes_product` does support history > 1.
if reverse:
x0 = tree_map(lambda x: x[-1], xs)
xs_ = tree_map(lambda x: x[:-1], xs)
else:
x0 = tree_map(lambda x: x[0], xs)
xs_ = tree_map(lambda x: x[1:], xs)
carry_shape_at_t1 = None
def body_fn(wrapped_carry, x, prefix=None):
i, rng_key, carry = wrapped_carry
init = True if (not_jax_tracer(i) and i == 0) else False
rng_key, subkey = random.split(rng_key) if rng_key is not None else (None, None)
seeded_fn = handlers.seed(f, subkey) if subkey is not None else f
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map, i, length)
if subs_type == 'condition':
seeded_fn = handlers.condition(seeded_fn, condition_fn=subs_fn)
elif subs_type == 'substitute':
seeded_fn = handlers.substitute(seeded_fn, substitute_fn=subs_fn)
if init:
with handlers.scope(prefix="_init"):
new_carry, y = seeded_fn(carry, x)
trace = {}
else:
with handlers.block(), packed_trace() as trace, promote_shapes(), enum(), markov():
# Like scan_wrapper, we collect the trace of scan's transition function
# `seeded_fn` here. To put time dimension to the correct position, we need to
# promote shapes to make `fn` and `value`
# at each site have the same batch dims (e.g. if `fn.batch_shape = (2, 3)`,
# and value's batch_shape is (3,), then we promote shape of
# value so that its batch shape is (1, 3)).
new_carry, y = config_enumerate(seeded_fn)(carry, x)
# store shape of new_carry at a global variable
nonlocal carry_shape_at_t1
carry_shape_at_t1 = [jnp.shape(x) for x in tree_flatten(new_carry)[0]]
# make new_carry have the same shape as carry
# FIXME: is this rigorous?
new_carry = tree_multimap(lambda a, b: jnp.reshape(a, jnp.shape(b)),
new_carry, carry)
return (i + jnp.array(1), rng_key, new_carry), (PytreeTrace(trace), y)
with markov():
wrapped_carry = (0, rng_key, init)
wrapped_carry, (_, y0) = body_fn(wrapped_carry, x0)
if length == 1:
ys = tree_map(lambda x: jnp.expand_dims(x, 0), y0)
return wrapped_carry, (PytreeTrace({}), ys)
wrapped_carry, (pytree_trace, ys) = lax.scan(body_fn, wrapped_carry, xs_, length - 1, reverse)
first_var = None
for name, site in pytree_trace.trace.items():
# add `time` dimension, the name will be '_time_{first variable in the trace}'
if first_var is None:
first_var = name
leftmost_dim = min(site['infer']['dim_to_name'])
site['infer']['dim_to_name'][leftmost_dim - 1] = '_time_{}'.format(first_var)
# similar to carry, we need to reshape due to shape alternating in markov
ys = tree_multimap(lambda z0, z: jnp.reshape(z, z.shape[:1] + jnp.shape(z0)), y0, ys)
# we also need to reshape `carry` to match sequential behavior
if length % 2 == 0:
t, rng_key, carry = wrapped_carry
flatten_carry, treedef = tree_flatten(carry)
flatten_carry = [jnp.reshape(x, t1_shape)
for x, t1_shape in zip(flatten_carry, carry_shape_at_t1)]
carry = tree_unflatten(treedef, flatten_carry)
wrapped_carry = (t, rng_key, carry)
return wrapped_carry, (pytree_trace, ys)
def scan_wrapper(f, init, xs, length, reverse, rng_key=None, substitute_stack=[], enum=False):
if length is None:
length = tree_flatten(xs)[0][0].shape[0]
if enum:
return scan_enum(f, init, xs, length, reverse, rng_key, substitute_stack)
def body_fn(wrapped_carry, x):
i, rng_key, carry = wrapped_carry
rng_key, subkey = random.split(rng_key) if rng_key is not None else (None, None)
with handlers.block():
seeded_fn = handlers.seed(f, subkey) if subkey is not None else f
for subs_type, subs_map in substitute_stack:
subs_fn = partial(_subs_wrapper, subs_map, i, length)
if subs_type == 'condition':
seeded_fn = handlers.condition(seeded_fn, condition_fn=subs_fn)
elif subs_type == 'substitute':
seeded_fn = handlers.substitute(seeded_fn, substitute_fn=subs_fn)
with handlers.trace() as trace:
carry, y = seeded_fn(carry, x)
return (i + 1, rng_key, carry), (PytreeTrace(trace), y)
return lax.scan(body_fn, (jnp.array(0), rng_key, init), xs, length=length, reverse=reverse)
def scan(f, init, xs, length=None, reverse=False):
"""
This primitive scans a function over the leading array axes of
`xs` while carrying along state. See :func:`jax.lax.scan` for more
information.
**Usage**:
.. doctest::
>>> import numpy as np
>>> import numpyro
>>> import numpyro.distributions as dist
>>> from numpyro.contrib.control_flow import scan
>>>
>>> def gaussian_hmm(y=None, T=10):
... def transition(x_prev, y_curr):
... x_curr = numpyro.sample('x', dist.Normal(x_prev, 1))
... y_curr = numpyro.sample('y', dist.Normal(x_curr, 1), obs=y_curr)
... return x_curr, (x_curr, y_curr)
...
... x0 = numpyro.sample('x_0', dist.Normal(0, 1))
... _, (x, y) = scan(transition, x0, y, length=T)
... return (x, y)
>>>
>>> # here we do some quick tests
>>> with numpyro.handlers.seed(rng_seed=0):
... x, y = gaussian_hmm(np.arange(10.))
>>> assert x.shape == (10,) and y.shape == (10,)
>>> assert np.all(y == np.arange(10))
>>>
>>> with numpyro.handlers.seed(rng_seed=0): # generative
... x, y = gaussian_hmm()
>>> assert x.shape == (10,) and y.shape == (10,)
.. warning:: This is an experimental utility function that allows users to use
JAX control flow with NumPyro's effect handlers. Currently, `sample` and
`deterministic` sites within the scan body `f` are supported. If you notice
that any effect handlers or distributions are unsupported, please file an issue.
.. note:: It is ambiguous to align `scan` dimension inside a `plate` context.
So the following pattern won't be supported
.. code-block:: python
with numpyro.plate('N', 10):
last, ys = scan(f, init, xs)
All `plate` statements should be put inside `f`. For example, the corresponding
working code is
.. code-block:: python
def g(*args, **kwargs):
with numpyro.plate('N', 10):
return f(*arg, **kwargs)
last, ys = scan(g, init, xs)
.. note:: Nested scan is currently not supported.
.. note:: We can scan over discrete latent variables in `f`. The joint density is
evaluated using parallel-scan (reference [1]) over time dimension, which
reduces parallel complexity to `O(log(length))`.
Currently, only the equivalence to
:class:`~numpyro.contrib.funsor.enum_messenger.markov(history_size=1)`
is supported. A :class:`~numpyro.handlers.trace` of `scan` with discrete latent
variables will contain the following sites:
+ init sites: those sites belong to the first trace of `f`. Each of
them will have name prefixed with `_init/`.
+ scanned sites: those sites collect the values of the remaining scan
loop over `f`. An addition time dimension `_time_foo` will be
added to those sites, where `foo` is the name of the first site
appeared in `f`.
Not all transition functions `f` are supported. All of the restrictions from
Pyro's enumeration tutorial [2] still apply here. In addition, there should
not have any site outside of `scan` depend on the first output of `scan`
(the last carry value).
** References **
1. *Temporal Parallelization of Bayesian Smoothers*,
<NAME>, <NAME>
(https://arxiv.org/abs/1905.13002)
2. *Inference with Discrete Latent Variables*
(http://pyro.ai/examples/enumeration.html#Dependencies-among-plates)
:param callable f: a function to be scanned.
:param init: the initial carrying state
:param xs: the values over which we scan along the leading axis. This can
be any JAX pytree (e.g. list/dict of arrays).
:param length: optional value specifying the length of `xs`
but can be used when `xs` is an empty pytree (e.g. None)
:param bool reverse: optional boolean specifying whether to run the scan iteration
forward (the default) or in reverse
:return: output of scan, quoted from :func:`jax.lax.scan` docs:
"pair of type (c, [b]) where the first element represents the final loop
carry value and the second element represents the stacked outputs of the
second output of f when scanned over the leading axis of the inputs".
"""
# if there are no active Messengers, we just run and return it as expected:
if not _PYRO_STACK:
(length, rng_key, carry), (pytree_trace, ys) = scan_wrapper(
f, init, xs, length=length, reverse=reverse)
else:
# Otherwise, we initialize a message...
initial_msg = {
'type': 'control_flow',
'fn': scan_wrapper,
'args': (f, init, xs, length, reverse),
'kwargs': {'rng_key': None,
'substitute_stack': []},
'value': None,
}
# ...and use apply_stack to send it to the Messengers
msg = apply_stack(initial_msg)
(length, rng_key, carry), (pytree_trace, ys) = msg['value']
if not msg["kwargs"].get("enum", False):
for msg in pytree_trace.trace.values():
apply_stack(msg)
else:
from numpyro.contrib.funsor import to_funsor
from numpyro.contrib.funsor.enum_messenger import LocalNamedMessenger
for msg in pytree_trace.trace.values():
with LocalNamedMessenger():
dim_to_name = msg["infer"].get("dim_to_name")
to_funsor(msg["value"], dim_to_name=OrderedDict([(k, dim_to_name[k]) for k in sorted(dim_to_name)]))
apply_stack(msg)
return carry, ys
| [
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"numpyro.primitives.apply_stack",
"jax.tree_map",
"jax.numpy.shape",
"jax.random.split",
"numpyro.contrib.funsor.enum",
"numpyro.contrib.funsor.enum_messenger.LocalNamedMessenger",
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"numpyro.contrib.funsor.trace",
"jax.numpy.ndim"
]
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import json
from nbformat.notebooknode import NotebookNode
from nbconvert.exporters.exporter import ResourcesDict
from typing import Tuple
from nbgrader.api import MissingEntry
from nbgrader.preprocessors import OverwriteCells as NbgraderOverwriteCells
from ..utils.extra_cells import is_singlechoice, is_multiplechoice
class OverwriteCells(NbgraderOverwriteCells):
def preprocess_cell(
self, cell: NotebookNode, resources: ResourcesDict, cell_index: int
) -> Tuple[NotebookNode, ResourcesDict]:
if not (is_singlechoice(cell) or is_multiplechoice(cell)):
return super().preprocess_cell(cell, resources, cell_index)
grade_id = cell.metadata.get("nbgrader", {}).get("grade_id", None)
if grade_id is None:
return cell, resources
try:
source_cell = self.gradebook.find_source_cell(
grade_id, self.notebook_id, self.assignment_id
)
except MissingEntry:
self.log.warning(f"Cell {grade_id} does not exist in database")
del cell.metadata.nbgrader["grade_id"]
return cell, resources
cell.metadata.extended_cell.source = json.loads(source_cell.source)
return cell, resources
| [
"json.loads"
]
| [((1182, 1212), 'json.loads', 'json.loads', (['source_cell.source'], {}), '(source_cell.source)\n', (1192, 1212), False, 'import json\n')] |
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