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5ec4adef5b21520747e106af5d1de30527e70bf7 | a84ebe7120fa09fa70b5f8b99619d9e50bfad4e3 | /tp4/ex2/boutabout.py | de3a32092ff8034b7da7dd889a9fdb10df813366 | [] | no_license | Kevin-Grgd/TP_Outils_Info | e8317fd132914ed736d3ee881fd4f28ca858136b | 9591dcaedb6a9c72efe35c06aee9558ee6023361 | refs/heads/main | 2023-01-19T01:54:23.078363 | 2020-11-27T10:59:27 | 2020-11-27T10:59:27 | 316,474,447 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 281 | py | def bout_a_bout(ch1, ch2):
print("L1 :", len(ch1))
print("L2 :", len(ch2))
l = len(ch1) + len(ch2)
print("Total :", l)
return ch1 + ch2
print("Jamais")
a = "Toto"
b = bout_a_bout(a, "Titi")
c = bout_a_bout(a, b)
print()
print(b)
print(c)
| [
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] | |
31d2e9346836276bd48ea300ebbfd77901710115 | 623d737eae96c7f5081767fb3eec5f796a4746ca | /ToDo_App/ToDos/views.py | d2d7206f8bf6c0414f9f5990d53868524f4b0dac | [] | no_license | Daurigu/ToDo-App-Django | 037e14b14e77296b73c39d67bfa0141fdcc5baf7 | 000e944dbbf18d0743c9c637ca76c91843d8f5f2 | refs/heads/master | 2022-07-15T02:28:49.364088 | 2020-05-19T02:25:21 | 2020-05-19T02:25:21 | 264,742,408 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,246 | py | from django.shortcuts import render, get_object_or_404, redirect
from ToDo_App.form import todo_form
from .models import Todo
# Create your views here.
def view_main_todo(request, *args, **kwargs):
form = todo_form()
if request.method == 'POST':
print(request.POST)
form = todo_form(request.POST)
if form.is_valid():
Todo.objects.create(**form.cleaned_data)
form = todo_form()
context = {
"form": form,
"items": Todo.objects.all().order_by("-date"),
}
return render(request,"index.html", context)
def view_delete_item(request, id):
obj = get_object_or_404(Todo, id=id)
if request.method == 'POST':
obj.delete()
return redirect('../../')
return render(request,"index.html",{})
def view_update_item(request, id):
#form = todo_form()
updateForm = Todo.objects.get(id=id)
if request.method == 'POST':
print(request.POST)
form = todo_form(request.POST)
if form.is_valid():
updateForm.text = request.POST.get('text')
updateForm.save()
return redirect('../../')
context = {
"form": form,
}
return render(request, "edit.html", context) | [
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] | |
d103b3f0053ec9921f4c7434b0e933b2e279f4a1 | 9023153e17338fde4be547340b975b70a75ff8d7 | /secondquestion/preprocess.py | fff628575dc11b6f5c31154a35a9be4fa4839da4 | [] | no_license | tiaotiaosong/CMB_bankrace | 92975b09e6deb4d64ba9867710d32ebe5985bae5 | ed892862e34409787187db75fc0cdf7a2e0ab757 | refs/heads/master | 2020-06-04T06:25:34.429357 | 2019-06-14T08:14:13 | 2019-06-14T08:14:13 | 191,903,718 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 702 | py | import pandas as pd
import matplotlib.pyplot as plt
f = open('b6bce3abb838406daea9af48bf059c633.txt',encoding='UTF-8')
QRTA_NUM=dict()
next(f)
for eachline in f:
Record=eachline.split()
Record[0] = pd.to_datetime(Record[0])
if(Record[1]=='QRTA'and Record[3]=='32'):
if(Record[0] not in QRTA_NUM):
QRTA_NUM[Record[0]] = abs(int(Record[4].split('.')[0]))
else:
QRTA_NUM[Record[0]] += abs(int(Record[4].split('.')[0]))
df1=pd.DataFrame.from_dict(QRTA_NUM,orient='index')
df1.columns=['y']
df1=df1.reset_index()
df1=df1.reset_index(drop=True)
df1.columns=['ds', 'y']
print(df1)
df1.to_csv ("perdayvolume.csv" , index=True,header=True,encoding = "utf-8")
| [
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] | |
30c2f27da31b131a2f06e09ba7e7ee586629ad89 | 33213395f9b7606da83003d9f89966af16a47ed7 | /proyecto/aplicaion/views.py | 8cc778e4ba513cb76491635765690c539a913e97 | [] | no_license | sofiamanana/proyectoAnalisis | e75866d306424e37296c018da9cb7ee34a6450b4 | 3d7914dc2f6ef7813bd4672ada1cd57e01e24e26 | refs/heads/main | 2023-02-10T22:14:28.913022 | 2021-01-09T00:59:37 | 2021-01-09T00:59:37 | 311,785,039 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,454 | py | from django.shortcuts import render
from .models import File, Reportador
from .forms import FileForm
from django.http import HttpResponse
from django.contrib.auth import authenticate
from django.contrib.auth.forms import AuthenticationForm, UserCreationForm
from django.contrib.auth import login as do_login
from django.contrib import auth
from rest_framework import viewsets
from django.contrib.auth.models import User
from .serializers import UserSerializer
# Create your views here.
class UserViewSet(viewsets.ModelViewSet):
queryset = User.objects.all()
serializer_class = UserSerializer
def showfile(request):
lastfile=File.objects.last()
filepath=lastfile.filepath
filename=lastfile.name
form=FileForm(request.POST or None, request.FILES or None)
if form.is_valid():
form.save()
context={'filepath': filepath,
'form': form,
'filename': filename
}
return render(request, 'aplicaion/subir_reporte.html', context)
def login(request):
form = UserCreationForm()
'''
if request.method == 'POST':
form = UserCreationForm(request.POST)
if form.is_valid():
form.save()
user = form.cleaned_data.get('username')
messages.success(request, 'Account was created for ' + user)
return redirect('/')
'''
context = {'form': form}
return render(request, 'aplicaion/index.html',context) | [
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] | |
5e5af9b505acb2bba1a018c991379d63e548cd60 | 411a36e480bae8d7a5f3522be6bd8455d10ad256 | /771. Jewels and Stones/s2.py | 63e4d7926892027160f8fda73aa2373c40590e07 | [] | no_license | rayony/LeetCode | 8f48cf17e2073fe0b2b0e92a4a090a1c937cd128 | c3489131917ae3ef04047d887405bea3967122da | refs/heads/master | 2020-04-15T09:39:01.464909 | 2019-01-15T06:42:19 | 2019-01-15T06:42:19 | 164,559,733 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 555 | py | class Solution:
def numJewelsInStones(self, J, S):
"""
:type J: str
:type S: str
:rtype: int
"""
#init dict using J
dict = {}
for i in range(len(J)):
dict[J[i]]=0
#update count by comparing dict and S
for i in range(len(S)):
if S[i] in dict:
dict[S[i]]+=1
#update count by summing up the dict
count =sum(dict.values())
return count
| [
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] | |
3136f20917b2a34d79434bb479a0d2de152e14b5 | f19de30c28ff2e962dca6bc74fd8f725b29104a7 | /python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py | 9b673bdcd1b958f75508c0457d1874cca6c4c52a | [
"Apache-2.0"
] | permissive | iclementine/Paddle | 9d56d614731dc4042aaffff7c7ad9c0f30f6ae01 | 1cb6d68fd3f32b53eb3ebd8f8a4fa27d502f8a38 | refs/heads/develop | 2023-05-02T09:30:09.964347 | 2020-04-03T04:57:36 | 2020-04-03T04:57:36 | 158,829,753 | 0 | 0 | Apache-2.0 | 2023-03-29T10:40:58 | 2018-11-23T12:26:32 | C++ | UTF-8 | Python | false | false | 6,144 | py | # Copyright (c) 2020 PaddlePaddle 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 __future__ import print_function
import gast
import inspect
import numpy as np
import paddle.fluid as fluid
import unittest
from paddle.fluid.dygraph.jit import dygraph_to_static_func
from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import NameVisitor
SEED = 2020
np.random.seed(SEED)
def while_loop_dyfunc(x):
i = fluid.dygraph.to_variable(x)
while x < 10:
i = i + x
x = x + 1
return i
def while_loop_dyfun_with_conflict_var(x):
i = fluid.dygraph.to_variable(x)
def relu(y):
# 'y' is not visible outside the scope.
return fluid.layers.relu(y)
while x < 10:
# If a tmp variable is created which has same name
# with a argument in function, it should not be
# included in the loop_vars.
add_fn = lambda x, y: x + y
i = add_fn(i, x)
x = x + 1
return i
def while_loop_dyfunc_with_none(x):
i = fluid.dygraph.to_variable(x)\
if x is not None \
else fluid.dygraph.to_variable(x+1)
flag = 1
while x < 10:
i = i + x if flag is not None else x + i
x = x + 1
return i
def for_loop_dyfunc(max_len):
for i in range(max_len):
ret = fluid.layers.zeros(shape=[1], dtype='float32')
fluid.layers.increment(ret, value=2.0, in_place=True)
return ret
def while_loop_bool_op(x):
i = fluid.dygraph.to_variable(x)
while (x >= 0 and x < 10) or x <= -1 or x < -3 or (x < -7 or x < -5):
i = i + x
x = x + 1
return i
def var_create_in_for_loop(max_len):
for i in range(max_len):
ret = fluid.layers.zeros(shape=[3, 4, 5], dtype='float64')
return ret
class TestNameVisitor(unittest.TestCase):
def setUp(self):
self.loop_funcs = [
while_loop_dyfunc, for_loop_dyfunc, while_loop_dyfunc_with_none
]
self.loop_var_names = [
set(["i", "x"]), set(["i", "ret", "max_len"]), set(["i", "x"])
]
self.create_var_names = [set(), set(["ret"]), set()]
def test_loop_vars(self):
for i in range(len(self.loop_funcs)):
func = self.loop_funcs[i]
test_func = inspect.getsource(func)
gast_root = gast.parse(test_func)
name_visitor = NameVisitor(gast_root)
for node in gast.walk(gast_root):
if isinstance(node, (gast.While, gast.For)):
loop_var_names, create_var_names = name_visitor.get_loop_var_names(
node)
self.assertEqual(loop_var_names, self.loop_var_names[i])
self.assertEqual(create_var_names, self.create_var_names[i])
class TestTransformWhileLoop(unittest.TestCase):
def setUp(self):
self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
self.x = np.zeros(shape=(1), dtype=np.int32)
self._init_dyfunc()
def _init_dyfunc(self):
self.dyfunc = while_loop_dyfunc
def _run_static(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
x_var = fluid.layers.assign(self.x)
static_func = dygraph_to_static_func(self.dyfunc)
out = static_func(x_var)
exe = fluid.Executor(self.place)
ret = exe.run(main_program, fetch_list=out)
return ret
def _run_dygraph(self):
with fluid.dygraph.guard(self.place):
ret = self.dyfunc(fluid.dygraph.to_variable(self.x))
return ret.numpy()
def test_ast_to_func(self):
static_numpy = self._run_static()
self.assertTrue(
np.allclose(
np.full(
shape=(1), fill_value=45, dtype=np.int32), static_numpy))
# Enable next lines after Paddle dygraph supports while x < 10
#
# self._run_dygraph()
# self.assertTrue(np.allclose(self._run_dygraph(), self._run_static()))
class TestTransformWhileLoopWithConflicVar(TestTransformWhileLoop):
def _init_dyfunc(self):
self.dyfunc = while_loop_dyfun_with_conflict_var
class TestTransformWhileLoopWithNone(TestTransformWhileLoop):
def _init_dyfunc(self):
self.dyfunc = while_loop_dyfunc_with_none
class TestWhileLoopBoolOp(TestTransformWhileLoop):
def _init_dyfunc(self):
self.dyfunc = while_loop_bool_op
class TestTransformForLoop(unittest.TestCase):
def setUp(self):
self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
self.len = 100
self._init_dyfunc()
def _init_dyfunc(self):
self.dyfunc = for_loop_dyfunc
def _run_static(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
static_func = dygraph_to_static_func(self.dyfunc)
out = static_func(self.len)
exe = fluid.Executor(self.place)
ret = exe.run(main_program, fetch_list=out)
return ret
def _run_dygraph(self):
with fluid.dygraph.guard(self.place):
ret = self.dyfunc(self.len)
return ret.numpy()
def test_ast_to_func(self):
static_numpy = self._run_static()
self._run_dygraph()
self.assertTrue(np.allclose(self._run_dygraph(), self._run_static()))
class TestVarCreateInForLoop(TestTransformForLoop):
def _init_dyfunc(self):
self.dyfunc = var_create_in_for_loop
if __name__ == '__main__':
unittest.main()
| [
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] | |
4c7b6db17e6ab694ca109668ca14e6cdd9f31deb | e43488a799f03823d2eb9c0c6e9ee2727ca8ee5f | /agetoclassIndia.py | 7b2653e39dd3e796f72bb54a2b84d177c8371981 | [] | no_license | lekuid/Practice | efcaa8125782c582f162b3a01c455c775b8cc74b | dd28f2aeeb9bf7dc17a467088fa705c618920bdc | refs/heads/main | 2023-02-25T11:36:05.041165 | 2021-01-19T12:25:01 | 2021-01-19T12:25:01 | 319,907,882 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 992 | py | #consideirng pre school and mursery, this specific code
#is to help me write a blog because I have terrible memory.
import tkinter as tk
def age(n):
if n in range(0,4):
returned['text'] = 'preschool'
elif n in range(5, 17):
returned['text'] = 'class {}'.format(n-4)
elif n in range(17, 21):
returned['text'] = 'college {}'.format(n-16) + 'year'
win= tk.Tk()
win.title('Which Education?')
base = tk.Canvas(win, height=100, width=400, bg='#181818')
base.pack()
main_frame = tk.Frame(base, bg='#181818')
main_frame.place(relwidth=0.5, relheight=1)
n = tk.Entry(main_frame, bg='#222222', fg='#999999', font=40)
n.place(relx=0.2, rely=0.3, relwidth=0.6, relheight=0.25)
submit = tk.Button(main_frame, text='Enlighten Me', bg='#555555', fg='white', command = lambda: age(int(n.get())))
submit.place(relx=0.4, rely=0.6, relheight=0.2, relwidth=0.4)
returned = tk.Label(base, bg='#555555')
returned.place(relx=0.5, relheight=1, relwidth=0.5)
win.mainloop() | [
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] | |
82462b3b84eb3dd1a80c3d879d724b7466866733 | 2e559f86d68b67b1602de4b1f1e358fbc7e688a3 | /django_learn01/django_learn01/urls.py | 2a0aebc699d9b77eac27a9ea94df02a99e291478 | [] | no_license | xinyifuyun/django_learn | 96e0f80c3d85b85846bbde6cedc326f164b32349 | e56528de652ca5f40b8993ea19c3ff6dc52d6ef6 | refs/heads/master | 2021-09-10T08:01:24.943357 | 2018-03-22T14:25:11 | 2018-03-22T14:25:11 | 126,127,264 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 771 | py | """django_learn01 URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url
from django.contrib import admin
urlpatterns = [
url(r'^admin/', admin.site.urls),
]
| [
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] | |
25f5973f8ab2a851097482e039611a07073c7937 | 576f7b951191d6095df8bc2691c8ad7045d55447 | /Basics/ws1.py | 2c093682f5dd9b60db3a8e6edd46257ee45a4641 | [] | no_license | golam-saroar/Python_Learning | f555368420ef65ceef9a80349b9c3bae2c6842b9 | c077a8c2e5738b47cb301f07806af5a4c6714dff | refs/heads/master | 2021-09-22T09:31:35.907800 | 2018-09-07T09:14:25 | 2018-09-07T09:14:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 565 | py | import time
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
browser = webdriver.Chrome()
browser.get('http://www.google.com')
search = browser.find_element_by_name('q')
search.send_keys("google search through python")
search.send_keys(Keys.RETURN) # hit return after you enter search text
time.sleep(60) # sleep for 5 seconds so you can see the results
browser.quit() | [
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] | |
595a03475ccae6898dc86f756ac94ef70b4626a8 | 82b946da326148a3c1c1f687f96c0da165bb2c15 | /sdk/python/pulumi_azure_native/resources/get_tag_at_scope.py | b09a7ed0b50a69f536be91e3e72e2d84977b5b39 | [
"BSD-3-Clause",
"Apache-2.0"
] | permissive | morrell/pulumi-azure-native | 3916e978382366607f3df0a669f24cb16293ff5e | cd3ba4b9cb08c5e1df7674c1c71695b80e443f08 | refs/heads/master | 2023-06-20T19:37:05.414924 | 2021-07-19T20:57:53 | 2021-07-19T20:57:53 | 387,815,163 | 0 | 0 | Apache-2.0 | 2021-07-20T14:18:29 | 2021-07-20T14:18:28 | null | UTF-8 | Python | false | false | 3,078 | py | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
__all__ = [
'GetTagAtScopeResult',
'AwaitableGetTagAtScopeResult',
'get_tag_at_scope',
]
@pulumi.output_type
class GetTagAtScopeResult:
"""
Wrapper resource for tags API requests and responses.
"""
def __init__(__self__, id=None, name=None, properties=None, type=None):
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if properties and not isinstance(properties, dict):
raise TypeError("Expected argument 'properties' to be a dict")
pulumi.set(__self__, "properties", properties)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def id(self) -> str:
"""
The ID of the tags wrapper resource.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the tags wrapper resource.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def properties(self) -> 'outputs.TagsResponse':
"""
The set of tags.
"""
return pulumi.get(self, "properties")
@property
@pulumi.getter
def type(self) -> str:
"""
The type of the tags wrapper resource.
"""
return pulumi.get(self, "type")
class AwaitableGetTagAtScopeResult(GetTagAtScopeResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetTagAtScopeResult(
id=self.id,
name=self.name,
properties=self.properties,
type=self.type)
def get_tag_at_scope(scope: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetTagAtScopeResult:
"""
Wrapper resource for tags API requests and responses.
API Version: 2019-10-01.
:param str scope: The resource scope.
"""
__args__ = dict()
__args__['scope'] = scope
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:resources:getTagAtScope', __args__, opts=opts, typ=GetTagAtScopeResult).value
return AwaitableGetTagAtScopeResult(
id=__ret__.id,
name=__ret__.name,
properties=__ret__.properties,
type=__ret__.type)
| [
"[email protected]"
] | |
bad61a3f30fe9c35194f7b6e7b9709ce2ead722e | e3ff9e938e07be5b8d853d85dc8eccca09de380e | /hw_02-equiprobability/B2-observable-x-position-markov.py | 3683d15a4e0e24fbcbc0f1044bbda277dd569bcb | [] | no_license | pallavsen007/statistical_mechanics | ce0039928fbd050a1e8db0a4c22c0f9360a2c6f6 | bd0377b85628c064ba5dc9cf52ac200864d745e4 | refs/heads/master | 2021-12-12T00:58:53.472651 | 2016-11-30T17:04:27 | 2016-11-30T17:04:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,163 | py | #Problem B1: equiprobability for observable=x-position
import random, pylab
# update disk configuration L using markov chain algorithm
# L = current disk configuration
# delta = step for markov chain algorithm
# sigma = disk radius
def markov_disks_box_update(L,delta,sigma):
a = random.choice(L)
b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)]
min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a)
box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma
if not (box_cond or min_dist < 4.0 * sigma ** 2):
a[:] = b
return L
N = 4
sigma = 0.1197
delta = 0.1
n_runs = 2000000
histo_data = []
L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]
for run in range(n_runs):
L = markov_disks_box_update(L, delta, sigma)
for k in range(N):
histo_data.append(L[k][0])
pylab.hist(histo_data, bins=100, normed=True)
pylab.xlabel('x')
pylab.ylabel('frequency')
pylab.title('Markov Chain algorithm: x coordinate histogram (density eta=0.18)')
pylab.grid()
pylab.savefig('markov_disks_histo.png')
pylab.show()
| [
"[email protected]"
] | |
ab61f660d0a067e4735543d8f22852731a7d8493 | 604ae72a87d4cd5774fb717f60e7ebc6b3466aad | /DecisionTrees/prepareData.py | fd4c1a8bde71ee58f36ca0d2472c4b2f92f54209 | [] | no_license | remalcodex/MachineLearning | ed2e495057b9fdd732eee591c86048cb06667232 | f3f1ea8660bfc3046877c425c3916b266c277778 | refs/heads/master | 2021-09-11T20:38:00.830789 | 2018-04-12T02:56:12 | 2018-04-12T02:56:12 | 105,211,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,859 | py | import numpy as np
import os.path
#Returns the data for features from 1-6 only.
def getData(inFileName):
my_path = os.path.abspath(os.path.dirname(__file__))
path = os.path.join(my_path, "./Updated_Dataset/" + inFileName)
trainingFile = open(path, "r", encoding="utf8")
Y = np.empty((0,1), int)
X = np.empty((0,6),int)
for line in trainingFile:
newLine = line.strip()
if len(newLine) == 0:
continue
if newLine[0] == '+':
Y = np.append(Y, np.array([1]))
elif newLine[0] == '-':
Y = np.append(Y, np.array([0]))
else:
print("Problem with the data")
continue
name = newLine[2:]
nameList = name.split(" ")
if len(nameList) < 2:
print("Data problematic with name")
continue
# Feature 1.
x1 = 0
if len(nameList[0]) >= len(nameList[-1]):
x1 = 1
#Feature 2.
x2 = 0
if len(nameList) >= 3:
x2 = 1
#Feature 3
x3 = 0
firstName = nameList[0]
if firstName[0].lower() == firstName[-1].lower():
x3 = 1
#Feature 4
x4 = 0
if nameList[0] < nameList[-1]:
x4 = 1
#Feature 5
x5 = 0;
firstName = nameList[0]
if len(firstName) >= 2:
if (firstName[1].lower() == "a" or firstName[1].lower() == "e" or firstName[1].lower() == "i" or firstName[1].lower() == "o" or firstName[1].lower() == "u"):
x5 = 1;
#Feature 6
x6 = 0
lastName = nameList[-1]
if len(lastName)%2 == 0:
x6 = 1;
X = np.vstack((X, np.array([x1,x2,x3,x4,x5,x6])))
#print(X)
return X,Y
#Returns data with 20 faetures.
def getMoreData(inFileName):
my_path = os.path.abspath(os.path.dirname(__file__))
path = os.path.join(my_path, "./Updated_Dataset/" + inFileName)
trainingFile = open(path, "r", encoding="utf8")
Y = np.empty((0,1), int)
X = np.empty((0,20),int)
for line in trainingFile:
newLine = line.strip()
if len(newLine) == 0:
continue
if newLine[0] == '+':
Y = np.append(Y, np.array([1]))
elif newLine[0] == '-':
Y = np.append(Y, np.array([0]))
else:
print("Problem with the data")
continue
name = newLine[2:]
nameList = name.split(" ")
if len(nameList) < 2:
print("Data problematic with name")
continue
firstName = nameList[0]
lastName = nameList[-1]
# Feature 1.
x1 = 0
if len(firstName) >= len(lastName):
x1 = 1
#Feature 2.
x2 = 0
if len(nameList) >= 3:
x2 = 1
#Feature 3
x3 = 0
if firstName[0].lower() == firstName[-1].lower():
x3 = 1
#Feature 4
x4 = 0
if nameList[0] < nameList[-1]:
x4 = 1
#Feature 5
x5 = 0;
if len(firstName) >= 2:
if (firstName[1].lower() == "a" or firstName[1].lower() == "e" or firstName[1].lower() == "i" or firstName[1].lower() == "o" or firstName[1].lower() == "u"):
x5 = 1;
#Feature 6
x6 = 0
if len(lastName)%2 == 0:
x6 = 1;
#Feature 7
x7 = 0;
if len(firstName)%2 == 0:
x7 = 1
#Feature 8
x8 = 0
if len(name) > 10:
x8 = 1
#Feature 9
x9 = 0
if firstName[0].lower() == "r":
x9 = 1
#Feature 10
x10 = 0
if firstName[0].lower() == "a":
x10 = 1
#Feature 11
x11 = 1
if len(nameList) > 3:
x11 = 0
#Feature 12
x12 = 0
if firstName[0].lower() == "m":
x12 = 1
#Feature 13
x13 = 1
if len(nameList) >= 3:
if "." in nameList[1]:
x13 = 0
#Feature 14
x14 = 1
if lastName[0].lower() == "r":
x14 = 0
#Feature 15
x15 = 0
if firstName[0].lower() == "d":
x15 = 1
#Feature 16
x16 = 0
if lastName[0].lower() == "t":
x16 = 1
#Feature 17
x17 = 0
if firstName[0].lower() == "p":
x17 = 1
#Feature 18
x18 = 0
if firstName[0].lower() == "j":
x18 = 1
#Feature 19
x19 = 1
if len(nameList) == 3:
x19 = 0
#Feature 20
x20 = 0
if lastName[0].lower() == "W":
x20 = 1
X = np.vstack((X, np.array([x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20])))
#print(X)
return X,Y | [
"[email protected]"
] | |
0b7d0fdaa4a71396493f6b461f3dbe60336569d7 | 03e424616ef4783c28f1ea57a7886aa76aa55edd | /NanoGardener/python/data/VBSjjlnu_vars.py | 5321b2ece5c960492c3afcca12b0d7c32e7d932c | [] | no_license | dbrambilla13/LatinoAnalysis | 8dd7751a6793012f12bbe3db4bfd0ba93bdb2716 | f3a17a095450d200af7d278137f2f089e8f0d8b8 | refs/heads/master | 2021-03-19T03:11:43.998028 | 2020-03-12T14:47:55 | 2020-03-12T14:47:55 | 247,127,553 | 0 | 0 | null | 2020-03-13T17:44:49 | 2020-03-13T17:33:04 | null | UTF-8 | Python | false | false | 12,050 | py | from itertools import chain
from math import cosh, sqrt, cos
from ROOT import TLorentzVector
VBSjjlnu_branches = {
"F": [
"vbs_0_pt", "vbs_0_eta", "vbs_0_phi", "vbs_0_E",
"vbs_1_pt", "vbs_1_eta", "vbs_1_phi", "vbs_1_E",
"vjet_0_pt", "vjet_0_eta", "vjet_0_phi", "vjet_0_E",
"vjet_1_pt", "vjet_1_eta", "vjet_1_phi", "vjet_1_E",
"mjj_vbs", "mjj_vjet",
"deltaeta_vbs", "deltaphi_vbs",
"deltaeta_vjet", "deltaphi_vjet",
"deltaphi_lep_vbs_0", "deltaphi_lep_vbs_1",
"deltaeta_lep_vbs_0", "deltaeta_lep_vbs_1",
"deltaphi_lep_vjet_0", "deltaphi_lep_vjet_1",
"deltaeta_lep_vjet_0", "deltaeta_lep_vjet_1",
"deltaR_lep_vbs", "deltaR_lep_vjet",
"deltaphi_lep_nu", "deltaeta_lep_nu",
"deltaR_lep_nu", "deltaR_vbs", "deltaR_vjet",
"Rvjets_0", "Rvjets_1",
"Zvjets_0", "Zvjets_1", "Zlep",
"Asym_vbs", "Asym_vjet", "Mw_lep", "Mtw_lep", "w_lep_pt",
"Mww", "R_ww", "R_mw", "A_ww",
"Centr_vbs", "Centr_ww", "Lep_proj", "Lep_projw",
"recoMET", "recoMET_pz" ,
],
"I": ["N_jets", "N_jets_forward", "N_jets_central"]
}
VBSjjlnu_vector_branches = [
{
"type": "I",
"len": "N_jets",
"name": "other_jets_index"
}
]
def getDefault():
output = {}
for br in VBSjjlnu_branches["F"]:
output[br] = -999.
for br in VBSjjlnu_branches["I"]:
output[br] = -999
for vec_br in VBSjjlnu_vector_branches:
output[vec_br["name"]] = []
return output
def getVBSkin_resolved(vbsjets, vjets, lepton, met, reco_neutrino, other_jets, other_jets_ind, debug=False):
output = getDefault()
# variables extraction
total_vbs = TLorentzVector(0,0,0,0)
vbs_etas = []
vbs_phis = []
vbs_pts = []
vbs_Es = []
for i, j in enumerate(vbsjets):
total_vbs+= j
vbs_etas.append(j.Eta())
vbs_phis.append(j.Phi())
vbs_pts.append(j.Pt())
vbs_Es.append(j.E())
if debug:
print "VBS pts", vbs_pts
print "VBS etas", vbs_etas
deltaeta_vbs = abs(vbs_etas[0]- vbs_etas[1])
mean_eta_vbs = sum(vbs_etas) / 2
output["vbs_0_pt"] = vbs_pts[0]
output["vbs_1_pt"] = vbs_pts[1]
output["vbs_0_eta"] = abs(vbs_etas[0])
output["vbs_1_eta"] = abs(vbs_etas[1])
output["vbs_0_phi"] = abs(vbs_phis[0])
output["vbs_1_phi"] = abs(vbs_phis[1])
output["vbs_0_E"] = abs(vbs_Es[0])
output["vbs_1_E"] = abs(vbs_Es[1])
output["mjj_vbs"] = total_vbs.M()
output["deltaeta_vbs"] = deltaeta_vbs
output["deltaphi_vbs"] = abs(vbsjets[0].DeltaPhi(vbsjets[1]))
output["deltaR_vbs"] = vbsjets[0].DrEtaPhi(vbsjets[1])
total_vjet = TLorentzVector(0,0,0,0)
vjet_etas = []
vjet_phis = []
vjet_pts = []
vjet_Es = []
for i, j in enumerate(vjets):
total_vjet += j
vjet_etas.append(j.Eta())
vjet_phis.append(j.Phi())
vjet_pts.append(j.Pt())
vjet_Es.append(j.E())
if debug:
print "Vjet pts", vjet_pts
print "Vjet etas", vjet_etas
output["vjet_0_pt"] = vjet_pts[0]
output["vjet_1_pt"] = vjet_pts[1]
output["vjet_0_eta"] = abs(vjet_etas[0])
output["vjet_1_eta"] = abs(vjet_etas[1])
output["vjet_0_phi"] = abs(vjet_phis[0])
output["vjet_1_phi"] = abs(vjet_phis[1])
output["vjet_0_E"] = abs(vjet_Es[0])
output["vjet_1_E"] = abs(vjet_Es[1])
output["mjj_vjet"] = total_vjet.M()
output["deltaphi_vjet"] = abs(vjets[0].DeltaPhi(vjets[1]))
output["deltaeta_vjet"] = abs(vjet_etas[0] - vjet_etas[1])
output["deltaR_vjet"] = vjets[0].DrEtaPhi(vjets[1])
output["recoMET"] = reco_neutrino.Pt()
output["recoMET_pz"] = reco_neutrino.Pz()
output["deltaphi_lep_nu"] = abs(lepton.DeltaPhi(reco_neutrino))
output["deltaeta_lep_nu"] = abs(lepton.Eta() - reco_neutrino.Eta())
output["deltaR_lep_nu"] = lepton.DrEtaPhi(reco_neutrino)
# Delta Phi with lepton
output["deltaphi_lep_vbs_0"] = abs(lepton.DeltaPhi(vbsjets[0]))
output["deltaphi_lep_vbs_1"] = abs(lepton.DeltaPhi(vbsjets[1]))
output["deltaphi_lep_vjet_0"] = abs(lepton.DeltaPhi(vjets[0]))
output["deltaphi_lep_vjet_1"] = abs(lepton.DeltaPhi(vjets[1]))
# Delta Eta with lepton
output["deltaeta_lep_vbs_0"] = abs(lepton.Eta() - vbs_etas[0])
output["deltaeta_lep_vbs_1"] = abs(lepton.Eta() - vbs_etas[1])
output["deltaeta_lep_vjet_0"] = abs(lepton.Eta() - vjet_etas[0])
output["deltaeta_lep_vjet_1"] = abs(lepton.Eta() - vjet_etas[1])
# Look for nearest vbs jet from lepton
output["deltaR_lep_vbs"] = min( [ lepton.DrEtaPhi(vbsjets[0]), lepton.DrEtaPhi(vbsjets[1])])
output["deltaR_lep_vjet"] = min( [ lepton.DrEtaPhi(vjets[0]), lepton.DrEtaPhi(vjets[1])])
# Zeppenfeld variables
if deltaeta_vbs != 0:
output["Zvjets_0"] = (vjet_etas[0] - mean_eta_vbs)/ deltaeta_vbs
output["Zvjets_1"] = (vjet_etas[1] - mean_eta_vbs)/ deltaeta_vbs
output["Zlep"] = (lepton.Eta() - mean_eta_vbs)/ deltaeta_vbs
#R variables
ptvbs01 = vbsjets[0].Pt() * vbsjets[1].Pt()
output["Rvjets_0"] = (lepton.Pt() * vjets[0].Pt()) / ptvbs01
output["Rvjets_1"] = (lepton.Pt() * vjets[1].Pt()) / ptvbs01
#Asymmetry
output["Asym_vbs"] = (vbs_pts[0] - vbs_pts[1]) / sum(vbs_pts)
output["Asym_vjet"] = (vjet_pts[0] - vjet_pts[1]) / sum(vjet_pts)
#WW variables
w_lep = lepton + reco_neutrino
w_had = vjets[0] + vjets[1]
w_lep_t = w_lep.Vect()
w_lep_t.SetZ(0)
w_had_t = w_had.Vect()
w_had_t.SetZ(0)
ww_vec = w_lep + w_had
output["w_lep_pt"] = w_lep.Pt()
output["Mw_lep"] = w_lep.M()
#output["Mtw_lep"] = w_lep_t.M()
output["Mtw_lep"] = sqrt(2 * lepton.Pt() * met.Pt() * (1 - cos( lepton.DeltaPhi(met))));
output["Mww"] = ww_vec.M()
output["R_ww"] = (w_lep.Pt() * w_lep.Pt()) / ptvbs01
output["R_mw"] = ww_vec.M() / ptvbs01
output["A_ww"] = (w_lep_t + w_had_t).Pt() / (w_lep.Pt() + w_had.Pt())
#Centrality
eta_ww = (w_lep.Eta() + w_had.Eta())/2
if deltaeta_vbs != 0.:
output["Centr_vbs"] = abs(vbs_etas[0] - eta_ww - vbs_etas[1]) / deltaeta_vbs
deltaeta_plus = max(vbs_etas) - max([w_lep.Eta(), w_had.Eta()])
deltaeta_minus = min([w_lep.Eta(), w_had.Eta()]) - min(vbs_etas)
output["Centr_ww"] = min([deltaeta_plus, deltaeta_minus])
#Lepton projection
lep_vec_t = lepton.Vect()
lep_vec_t.SetZ(0)
output["Lep_proj"] = (w_lep_t * lep_vec_t) / w_lep.Pt()
output["Lep_projw"] = (w_lep_t * lep_vec_t) / (lepton.Pt() * w_lep.Pt())
# Ht and number of jets with Pt> 20
# using uncut jets
Njets = len(other_jets)
N_jets_forward = 0
N_jets_central = 0
for oj in other_jets:
j_eta, j_pt = oj.Eta(), oj.Pt()
# Looking only to jets != vbs & vjets
if deltaeta_vbs != 0.:
Z = abs((j_eta - mean_eta_vbs)/ deltaeta_vbs)
if Z > 0.5:
N_jets_forward += 1
else:
N_jets_central += 1
output["N_jets"] = Njets
output["N_jets_central"] = N_jets_central
output["N_jets_forward"] = N_jets_forward
output["other_jets_index"] = other_jets_ind
return output
def getVBSkin_boosted(vbsjets, fatjet, lepton, met, reco_neutrino, other_jets, other_jets_ind, debug=False):
output = getDefault()
# variables extraction
total_vbs = TLorentzVector(0,0,0,0)
vbs_etas = []
vbs_phis = []
vbs_pts = []
vbs_Es = []
for i, j in enumerate(vbsjets):
total_vbs+= j
vbs_etas.append(j.Eta())
vbs_phis.append(j.Phi())
vbs_pts.append(j.Pt())
vbs_Es.append(j.E())
if debug:
print "VBS pts", vbs_pts
print "VBS etas", vbs_etas
deltaeta_vbs = abs(vbs_etas[0]- vbs_etas[1])
mean_eta_vbs = sum(vbs_etas) / 2
output["vbs_0_pt"] = vbs_pts[0]
output["vbs_1_pt"] = vbs_pts[1]
output["vbs_0_eta"] = abs(vbs_etas[0])
output["vbs_1_eta"] = abs(vbs_etas[1])
output["vbs_0_phi"] = abs(vbs_phis[0])
output["vbs_1_phi"] = abs(vbs_phis[1])
output["vbs_0_E"] = abs(vbs_Es[0])
output["vbs_1_E"] = abs(vbs_Es[1])
output["mjj_vbs"] = total_vbs.M()
output["deltaeta_vbs"] = deltaeta_vbs
output["deltaphi_vbs"] = abs(vbsjets[0].DeltaPhi(vbsjets[1]))
output["deltaR_vbs"] = vbsjets[0].DrEtaPhi(vbsjets[1])
total_vjet = fatjet
vjet_eta = fatjet.Eta()
vjet_pt = fatjet.Pt()
if debug:
print "Vjet pts", vjet_pts
print "Vjet etas", vjet_etas
output["vjet_0_pt"] = vjet_pt
output["vjet_0_eta"] = vjet_eta
output["vjet_0_phi"] = fatjet.Phi()
output["vjet_0_E"] = fatjet.E()
output["mjj_vjet"] = total_vjet.M()
output["recoMET"] = reco_neutrino.Pt()
output["recoMET_pz"] = reco_neutrino.Pz()
output["deltaphi_lep_nu"] = abs(lepton.DeltaPhi(reco_neutrino))
output["deltaeta_lep_nu"] = abs(lepton.Eta() - reco_neutrino.Eta())
output["deltaR_lep_nu"] = lepton.DrEtaPhi(reco_neutrino)
# Delta Phi with lepton
output["deltaphi_lep_vbs_0"] = abs(lepton.DeltaPhi(vbsjets[0]))
output["deltaphi_lep_vbs_1"] = abs(lepton.DeltaPhi(vbsjets[1]))
output["deltaphi_lep_vjet_0"] = abs(lepton.DeltaPhi(fatjet))
# Delta Eta with lepton
output["deltaeta_lep_vbs_0"] = abs(lepton.Eta() - vbs_etas[0])
output["deltaeta_lep_vbs_1"] = abs(lepton.Eta() - vbs_etas[1])
output["deltaeta_lep_vjet_0"] = abs(lepton.Eta() - vjet_eta)
# Look for nearest vbs jet from lepton
output["deltaR_lep_vbs"] = min( [ lepton.DrEtaPhi(vbsjets[0]), lepton.DrEtaPhi(vbsjets[1])])
output["deltaR_lep_vjet"] = lepton.DrEtaPhi(fatjet)
# Zeppenfeld variables
if deltaeta_vbs != 0.:
output["Zvjets_0"] = (vjet_eta - mean_eta_vbs)/ deltaeta_vbs
output["Zlep"] = (lepton.Eta() - mean_eta_vbs)/ deltaeta_vbs
#R variables
ptvbs01 = vbsjets[0].Pt() * vbsjets[1].Pt()
output["Rvjets_0"] = (lepton.Pt() * vjet_pt) / ptvbs01
#Asymmetry
output["Asym_vbs"] = (vbs_pts[0] - vbs_pts[1]) / sum(vbs_pts)
#WW variables
w_lep = lepton + reco_neutrino
w_had = fatjet
w_lep_t = w_lep.Vect()
w_lep_t.SetZ(0)
w_had_t = w_had.Vect()
w_had_t.SetZ(0)
ww_vec = w_lep + w_had
output["w_lep_pt"] = w_lep.Pt()
output["Mw_lep"] = w_lep.M()
#output["Mtw_lep"] = w_lep_t.M()
output["Mtw_lep"] = sqrt(2 * lepton.Pt() * met.Pt() * (1 - cos( lepton.DeltaPhi(met))));
output["Mww"] = ww_vec.M()
output["R_ww"] = (w_lep.Pt() * w_lep.Pt()) / ptvbs01
output["R_mw"] = ww_vec.M() / ptvbs01
output["A_ww"] = (w_lep_t + w_had_t).Pt() / (w_lep.Pt() + w_had.Pt())
#Centrality
eta_ww = (w_lep.Eta() + w_had.Eta())/2
if deltaeta_vbs != 0.:
output["Centr_vbs"] = abs(vbs_etas[0] - eta_ww - vbs_etas[1]) / deltaeta_vbs
deltaeta_plus = max(vbs_etas) - max([w_lep.Eta(), w_had.Eta()])
deltaeta_minus = min([w_lep.Eta(), w_had.Eta()]) - min(vbs_etas)
output["Centr_ww"] = min([deltaeta_plus, deltaeta_minus])
#Lepton projection
lep_vec_t = lepton.Vect()
lep_vec_t.SetZ(0)
output["Lep_proj"] = (w_lep_t * lep_vec_t) / w_lep.Pt()
output["Lep_projw"] = (w_lep_t * lep_vec_t) / (lepton.Pt() * w_lep.Pt())
# Ht and number of jets with Pt> 20
# using uncut jets
Njets = len(other_jets)
N_jets_forward = 0
N_jets_central = 0
for oj in other_jets:
j_eta, j_pt = oj.Eta(), oj.Pt()
# Looking only to jets != vbs & vjets
if deltaeta_vbs != 0.:
Z = abs((j_eta - mean_eta_vbs)/ deltaeta_vbs)
if Z > 0.5:
N_jets_forward += 1
else:
N_jets_central += 1
output["N_jets"] = Njets
output["N_jets_central"] = N_jets_central
output["N_jets_forward"] = N_jets_forward
output["other_jets_index"] = other_jets_ind
return output | [
"[email protected]"
] | |
0437af2fc9abfade8c541e3cbbf7a3bb016088c4 | 7e1257fd3a05089fdf80fd67e8ec75eed955e711 | /prdnn/tests/test_ddnn.py | 0b926f85d28e063ea1cbc2844139b9507b9f34a7 | [
"MIT"
] | permissive | terminiter/PRDNN | f1ea156f01957bda3d8205c4ca4bcd26882e0c09 | b6ca37ba8fd617c7cf9620faac88484603e5d2fe | refs/heads/master | 2023-06-05T09:18:08.448607 | 2021-06-30T04:01:56 | 2021-06-30T04:01:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,966 | py | """Tests the methods in ddnn.py."""
# pylint: disable=import-error
import numpy as np
import torch
from pysyrenn import ReluLayer, FullyConnectedLayer, ArgMaxLayer
from pysyrenn import HardTanhLayer, MaxPoolLayer, StridedWindowData
try:
from external.bazel_python.pytest_helper import main
IN_BAZEL = True
except ImportError:
IN_BAZEL = False
from prdnn.ddnn import DDNN
def test_compute():
"""Tests that it works for a simple example."""
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
ReluLayer(),
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
ReluLayer(),
]
value_layers = activation_layers[:2] + [
FullyConnectedLayer(3.0 * np.eye(2), np.zeros(shape=(2,))),
ReluLayer(),
]
network = DDNN(activation_layers, value_layers)
assert network.differ_index == 2
output = network.compute([[-2.0, 1.0]])
assert np.allclose(output, [[0.0, 6.0]])
output = network.compute(torch.tensor([[-2.0, 1.0]])).numpy()
assert np.allclose(output, [[0.0, 6.0]])
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
HardTanhLayer(),
]
value_layers = [
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
HardTanhLayer(),
]
network = DDNN(activation_layers, value_layers)
output = network.compute([[0.5, -0.9]])
assert np.allclose(output, [[1.0, -1.8]])
# Test HardTanh
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
HardTanhLayer(),
]
value_layers = [
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
HardTanhLayer(),
]
network = DDNN(activation_layers, value_layers)
output = network.compute([[0.5, -0.9]])
assert np.allclose(output, [[1.0, -1.8]])
# Test MaxPool
width, height, channels = 2, 2, 2
window_data = StridedWindowData((height, width, channels),
(2, 2), (2, 2), (0, 0), channels)
maxpool_layer = MaxPoolLayer(window_data)
activation_layers = [
FullyConnectedLayer(np.eye(8), np.ones(shape=(8,))),
maxpool_layer,
]
value_layers = [
FullyConnectedLayer(-1. * np.eye(8), np.zeros(shape=(8,))),
maxpool_layer,
]
network = DDNN(activation_layers, value_layers)
output = network.compute([[1.0, 2.0, -1.0, -2.5, 0.0, 0.5, 1.5, -3.5]])
# NHWC, so the two channels are: [1, -1, 0, 1.5] and [2, -2.5, 0.5, -3.5]
# So the maxes are 1.5 and 2.0, so the value layer outputs -1.5, -2.0
assert np.allclose(output, [[-1.5, -2.0]])
def test_compute_representatives():
"""Tests that the linear-region endpoints work."""
activation_layers = [
FullyConnectedLayer(np.eye(1), np.zeros(shape=(1,))),
ReluLayer(),
]
value_layers = [
FullyConnectedLayer(np.eye(1), np.ones(shape=(1,))),
ReluLayer(),
]
network = DDNN(activation_layers, value_layers)
assert network.differ_index == 0
points = np.array([[0.0], [0.0]])
representatives = np.array([[1.0], [-1.0]])
output = network.compute(points, representatives=representatives)
assert np.array_equal(output, [[1.], [0.]])
def test_nodiffer():
"""Tests the it works if activation and value layers are identical."""
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
ReluLayer(),
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
ReluLayer(),
]
value_layers = activation_layers
network = DDNN(activation_layers, value_layers)
assert network.differ_index == 4
output = network.compute([[-2.0, 1.0]])
assert np.allclose(output, [[0.0, 4.0]])
def test_bad_layer():
"""Tests that unspported layers after differ_index fail."""
# It should work if it's before the differ_index.
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
ReluLayer(),
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
ArgMaxLayer(),
]
value_layers = activation_layers
network = DDNN(activation_layers, value_layers)
assert network.differ_index == 4
output = network.compute([[-2.0, 1.0]])
assert np.allclose(output, [[1.0]])
# But not after the differ_index.
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
ReluLayer(),
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
ArgMaxLayer(),
]
value_layers = activation_layers[:2] + [
FullyConnectedLayer(3.0 * np.eye(2), np.zeros(shape=(2,))),
ReluLayer(),
]
network = DDNN(activation_layers, value_layers)
assert network.differ_index == 2
try:
output = network.compute([[-2.0, 1.0]])
assert False
except NotImplementedError:
pass
def test_serialization():
"""Tests that it correctly (de)serializes."""
activation_layers = [
FullyConnectedLayer(np.eye(2), np.ones(shape=(2,))),
ReluLayer(),
FullyConnectedLayer(2.0 * np.eye(2), np.zeros(shape=(2,))),
ReluLayer(),
]
value_layers = activation_layers[:2] + [
FullyConnectedLayer(3.0 * np.eye(2), np.zeros(shape=(2,))),
ReluLayer(),
]
network = DDNN(activation_layers, value_layers)
serialized = network.serialize()
assert all(serialized == layer.serialize()
for serialized, layer in zip(serialized.activation_layers,
activation_layers))
assert all(serialized == layer.serialize()
for serialized, layer in zip(serialized.value_layers,
value_layers[2:]))
assert serialized.differ_index == 2
assert DDNN.deserialize(serialized).serialize() == serialized
if IN_BAZEL:
main(__name__, __file__)
| [
"[email protected]"
] | |
0408eac5351346b0ff7ff4b4a372638c9d42f71c | 142696a656d98f2028f6fadd7af2e88ac9627f6e | /setup.py | 08abbc801c6e16e22857dcfa2a8e0bc67156b9b0 | [
"MIT"
] | permissive | akeshavan/mindlogger-build-applet | 2806b32032361dcfa8d0afc41ec41e286ecc6f13 | 02cc39a0f1dad57dae096ef9897c8e2daba90aee | refs/heads/master | 2020-06-10T04:58:19.316646 | 2019-07-01T23:05:21 | 2019-07-01T23:05:21 | 193,588,997 | 1 | 5 | MIT | 2019-08-08T21:06:04 | 2019-06-24T22:22:59 | Python | UTF-8 | Python | false | false | 1,260 | py | import io
import os
import re
from setuptools import find_packages
from setuptools import setup
def read(filename):
filename = os.path.join(os.path.dirname(__file__), filename)
text_type = type(u"")
with io.open(filename, mode="r", encoding='utf-8') as fd:
return re.sub(text_type(r':[a-z]+:`~?(.*?)`'), text_type(r'``\1``'), fd.read())
setup(
name="mindlogger_build_applet",
version="0.1.0",
url="https://github.com/akeshavan/mindlogger-build-applet",
license='MIT',
author="akeshavan",
author_email="[email protected]",
description="build your mindlogger survey in python",
long_description=read("README.rst"),
packages=find_packages(exclude=('tests',)),
install_requires=[],
classifiers=[
'Development Status :: 2 - Pre-Alpha',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
],
)
| [
"[email protected]"
] | |
c61dd8153595d6524f1c0e3b80656d527633d6db | 7e11caaad1281f2310da9ad4f2cd9f4993ffb011 | /shop/wsgi.py | a0c369bbcf815d8088a3478fa65651207e94945f | [] | no_license | pabloparejo/djangoShop | 12c4384868ce17f5f8d4c88e9b9ab01d384b3ea0 | 958cf6203948a5098f0823eec487fae6dbda2765 | refs/heads/master | 2016-09-08T00:23:48.798809 | 2014-09-15T18:21:23 | 2014-09-15T18:21:23 | 18,875,015 | 1 | 2 | null | null | null | null | UTF-8 | Python | false | false | 417 | py | """
WSGI config for shop project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.6/howto/deployment/wsgi/
"""
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "shop.settings")
from django.core.wsgi import get_wsgi_application
from dj_static import Cling
application = Cling(get_wsgi_application())
| [
"[email protected]"
] | |
19cede5124e49deb4814d49a47c9a1de937157cd | 5cfa25aec2161d40df7fb850ed8d405738aaed35 | /ya_disk.py | d13ca54e921bf14f63a64ccd879ca0f4d0aa9b1b | [] | no_license | fatrunner-39/netology-course-work | 08673ef28b8bf18656ee7a34958208480227f967 | 9df34efbb1a3d62bc6d1ca9975918edd60996115 | refs/heads/master | 2023-08-15T15:43:25.373906 | 2021-10-09T09:05:58 | 2021-10-09T09:05:58 | 408,377,198 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,769 | py | import requests
import os
from tqdm import tqdm
# folder = os.chdir(r"C:\Users\alexa_000\PycharmProjects\course_project_python_first\avatars")
# files_list = os.listdir(path=folder)
# print(files_list)
from pprint import pprint
TOKEN = ''
class YaUploader:
def __init__(self, token: str):
self.token = token
def get_headers(self):
return {
'Content-Type': 'application/json',
'Authorization': 'OAuth {}'.format(self.token)
}
def create_folder(self, name):
folder_url = "https://cloud-api.yandex.net/v1/disk/resources"
headers = self.get_headers()
params = {
"path": name
}
folder = requests.put(folder_url, headers=headers, params=params)
return name
def _get_upload_link(self, disk_file_path):
upload_url = "https://cloud-api.yandex.net/v1/disk/resources/upload"
headers = self.get_headers()
params = {"path": disk_file_path, "overwrite": "true"}
response = requests.get(upload_url, headers=headers, params=params)
pprint(response.json())
return response.json()
def upload_file_to_disk(self, disk_file_path, filename):
href = self._get_upload_link(disk_file_path=disk_file_path).get("href", "")
response = requests.put(href, data=open(filename, 'rb'))
response.raise_for_status()
if response.status_code == 201:
print("Success")
if __name__ == '__main__':
ya = YaUploader(token=TOKEN)
folder = os.chdir(os.getcwd() + "\\" + "None")
files_list = os.listdir(path=folder)
print(files_list)
ya.create_folder('None')
for file in tqdm(files_list[-1:-6:-1]):
ya.upload_file_to_disk(f"None/{file}", file)
| [
"[email protected]"
] | |
de0138bc9e16846f031b9be1b5ebb4076f803c7f | a7bd4d4592ce0f6bf7603476f7a401507d8d0b0f | /Recommender/test.py | 8d198beff932e6a99593321c0f87d72c0ec1032b | [] | no_license | shsheep/Data_Science_Study | 2ee1dd9e96924cf8284fc82d6b4b47ba03f22e7c | 4fea310be4e3959654d5515a47036d13cda5e84d | refs/heads/master | 2020-06-11T01:56:37.033674 | 2019-06-26T03:16:55 | 2019-06-26T03:16:55 | 193,820,942 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 177 | py | import numpy as np
normal_array = [[1,2,3], [4,5,6], [7,8,9]]
obj_array = np.array([[1,2,3], [4,5,6], [7,8,9]])
print(normal_array)
print(obj_array)
print(obj_array.shape())
| [
"[email protected]"
] | |
69c51de4be08b8d11da57eac0f5f0acc6f14e3b9 | ef64586d1ffda27abd7b4b1b41ac3264d611b6c5 | /src/panda-3.py | b91d4168afc7ab7b66e4039788f8de81f254f666 | [] | no_license | diegoami/Udacity-data-analisis | bccdcb6e4c913fd61f89a4ebfce444f48ffaccdf | 8547d2c0ea474d637bd09396648f58e66df0d3e4 | refs/heads/master | 2021-01-21T11:30:33.791834 | 2017-06-15T17:36:51 | 2017-06-15T17:36:51 | 91,745,196 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 454 | py | import pandas as pd
s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])
# Try to write code that will add the 2 previous series together,
# but treating missing values from either series as 0. The result
# when printed out should be similar to the following line:
print pd.Series([1, 2, 13, 24, 30, 40], index=['a', 'b', 'c', 'd', 'e', 'f'])
print s1.add(s2, fill_value=0).astype(int)
| [
"[email protected]"
] | |
6be799833292d2cc19957234d12a5cb7b6e2778b | 681f7a4c9d83a02ae5663898649070820d84a2cd | /dream.py | 02852dd8bd37e556db80219b07fc30a95ce03328 | [] | no_license | romanbelaire/DeepDream | 9b04e3fcbaa52c4657ef1235946474bb327f3ffd | 2b334758a84b83fdd06314013a7019bfd64a5221 | refs/heads/master | 2020-04-25T01:02:42.957515 | 2019-03-10T21:06:16 | 2019-03-10T21:06:16 | 172,397,152 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 17,150 | py | #DEEP DREAM PROJECT
#ROMAN BELAIRE
import numpy as np
import scipy
import PIL.Image
import os
import h5py
import argparse
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.applications import inception_v3
from keras import backend as K
#argument parser
#parser = argparse.ArgumentParser()
#parser.add_argument("-r", "--retrain", help="Retrain the model.", action="store_true")
#parser.add_argument("-d", "--dataset_directory", help="Directory containing dataset. Should be sorted into [directory]/train, [directory]/validate, [directory]/test.",
# default="resources/dataset/")
# To fix FailedPreconditionError:
sess = tf.InteractiveSession()
#show connected devices:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print("GPUs: ")
K.tensorflow_backend._get_available_gpus()
######## The following code is based on the keras documentation site
#aimed at making my life easier and creating my retrained inception model without raw tensorflow
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras import Sequential
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
#constant vars
NUM_CLASSES = 6
batchsize = 32
EPOCHS = 30
#from keras import backend as K
def retrain_model():
# create the base pre-trained model
base_model = InceptionV3(weights=None, include_top=False)#weights should be None for new model, 'imagenet' for pre-trained
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(NUM_CLASSES, activation='softmax')(x) #had to make sure the number of classes matched up. fuckin keras doc hard-coded 200 classes
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
layer.trainable = False
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
data_aug = ImageDataGenerator(rotation_range=20, zoom_range=0.15,
width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,
horizontal_flip=True, fill_mode="nearest")
# train the model on the new data for a few epochs
#data generators
train_gen = data_aug.flow_from_directory(directory = 'resources/dataset/train',
target_size = (255, 255), color_mode='rgb',
batch_size=batchsize, class_mode='categorical',
shuffle='True', seed=420) #its important that the seed is an int and not a string lol
val_gen = data_aug.flow_from_directory(directory = 'resources/dataset/validate',
target_size = (255, 255), color_mode='rgb',
batch_size=batchsize, class_mode='categorical',
shuffle='True', seed=69)
print("data generators loaded.")
STEP_SIZE_TRAIN=train_gen.n//train_gen.batch_size
STEP_SIZE_VALID=val_gen.n//val_gen.batch_size
model.fit_generator(generator=train_gen,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=val_gen,
validation_steps=STEP_SIZE_VALID,
epochs=EPOCHS)
# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
print(i, layer.name)
# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')
# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(generator=train_gen,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=val_gen,
validation_steps=STEP_SIZE_VALID,
epochs=EPOCHS)
print("finished generator successfully")
#save our stuff
model_json = model.to_json()
with open("resources/saved_models/sea1/sea_model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("resources/saved_models/sea1/sea_model_weights.h5")
print("weights saved.")
model.save("resources/saved_models/sea1/sea_full_model.h5")
print("full model saved")
return model
########end
def load_full_model(path):
return load_model(path)
# Disable all training specific operations
K.set_learning_phase(0)
# The model will be loaded with pre-trained inceptionv3 weights.
#JK WE USIN MY BRAND NEW SHARK TRAINED MODEL
#model = inception_v3.InceptionV3(weights='resources/output_model.h5', include_top=False)
model = load_full_model("resources/saved_models/model1/full_model.h5")
#model = retrain_model()
dream = model.input
print('Model loaded.')
# You can tweak these setting to obtain new visual effects.
settings = {
'features': {
'mixed2': 3, #wavy layers
'mixed3': 6, #smooth circles
'mixed4': 2, #kind of jagged
'mixed5': 1.5, #wrinkle/fur texture
},
}
# Set a function to load, resize and convert the image.
def preprocess_image(image_path):
# Util function to open, resize and format pictures
# into appropriate tensors.
img = load_img(image_path)
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = inception_v3.preprocess_input(img)
return img
def preprocess_array(arr):
#version of preprocess_image, except for a numpy img_to_array
img = np.expand_dims(arr, axis=0)
img = inception_v3.preprocess_input(img)
return img
# And a function to do the opposite: convert a tensor into an image.
def deprocess_image(x):
# Util function to convert a tensor into a valid image.
if K.image_data_format() == 'channels_first':
print("Deprocess color first")
x = x.reshape((3, x.shape[2], x.shape[3]))
x = x.transpose((1, 2, 0))
else:
x = x.reshape((x.shape[1], x.shape[2], 3))
np.true_divide(x, 2., x, casting='unsafe')
#x /= 2. #swap for line 191?
np.add(x, 0.5, x, casting='unsafe')
#x += 0.5
np.multiply(x, 255., x, casting='unsafe')
#x *= 255.
x = np.clip(x, 0, 255).astype('uint8')
return x
# Set a dictionary that maps the layer name to the layer instance.
# get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers])
# Define the loss. The way this works is first the scalar variable *loss* is set.
# Then the loss will be defined by adding layer contributions to this variable.
loss = K.variable(0.)
for layer_name in settings['features']:
# Add the L2 norm of the features of a layer to the loss.
assert (layer_name in layer_dict.keys(),
'Layer ' + layer_name + ' not found in model.')
coeff = settings['features'][layer_name]
x = layer_dict[layer_name].output
# We avoid border artifacts by only involving non-border pixels in the loss.
scaling = K.prod(K.cast(K.shape(x), 'float32'))
if K.image_data_format() == 'channels_first':
loss += coeff * K.sum(K.square(x[:, :, 2: -2, 2: -2])) / scaling
else:
loss += coeff * K.sum(K.square(x[:, 2: -2, 2: -2, :])) / scaling
# Compute the gradients of the dream wrt the loss.
grads = K.gradients(loss, dream)[0]
# Normalize gradients.
grads /= K.maximum(K.mean(K.abs(grads)), K.epsilon())
# Set up function to retrieve the value of the loss and gradients given an input image.
outputs = [loss, grads]
fetch_loss_and_grads = K.function([dream], outputs)
def eval_loss_and_grads(x):
outs = fetch_loss_and_grads([x])
loss_value = outs[0]
grad_values = outs[1]
return loss_value, grad_values
# Helper funtion to resize
def resize_img(img, size):
img = np.copy(img)
if K.image_data_format() == 'channels_first':
factors = (1, 1,
float(size[0]) / img.shape[2],
float(size[1]) / img.shape[3])
else:
factors = (1,
float(size[0]) / img.shape[1],
float(size[1]) / img.shape[2],
1)
return scipy.ndimage.zoom(img, factors, order=1)
# Define the gradient ascent function over a number of iterations.
def gradient_ascent(x, iterations, step, max_loss=None):
for i in range(iterations):
loss_value, grad_values = eval_loss_and_grads(x)
if max_loss is not None and loss_value > max_loss:
break
print('..Loss value at', i, ':', loss_value)
x += step * grad_values
return x
def rgb2gray(rgb):
return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
def gray2rgb(gray):
gray = gray.transpose((1, 2, 0))
w, h, c = gray.shape
rgb = np.empty((w, h, 3), dtype=np.float32)
rgb[:, :, 2] = rgb[:, :, 1] = rgb[:, :, 0] = gray[:,:,0]
return rgb
def transfer_color(dream_img, original_img):
original_image = np.clip(original_img, 0, 255)
styled_image = np.clip(dream_img, 0, 255)
original_image = original_image[0]
# Luminosity transfer steps:
# 1. Convert stylized RGB->grayscale accoriding to Rec.601 luma (0.299, 0.587, 0.114)
# 2. Convert stylized grayscale into YUV (YCbCr)
# 3. Convert original image into YUV (YCbCr)
# 4. Recombine (stylizedYUV.Y, originalYUV.U, originalYUV.V)
# 5. Convert recombined image from YUV back to RGB
# 1
styled_grayscale = rgb2gray(styled_image)
styled_grayscale_rgb = gray2rgb(styled_grayscale)
# 2
styled_grayscale_yuv = np.array(PIL.Image.fromarray(styled_grayscale_rgb.astype(np.uint8)).convert('YCbCr'))
# 3
original_yuv = np.array(PIL.Image.fromarray(original_image.astype(np.uint8)).convert('YCbCr'))
# 4
w, h, _ = original_yuv.shape
combined_yuv = np.empty((1, w, h, 3), dtype=np.uint8)
#print([styled_grayscale_yuv].shape)
combined_yuv[0, ..., 0] = styled_grayscale_yuv[..., 0]
combined_yuv[0, ..., 1] = original_yuv[..., 1]
combined_yuv[0, ..., 2] = original_yuv[..., 2]
# 5
print("cy" + str(combined_yuv.shape))
img_out = np.array(PIL.Image.fromarray(combined_yuv[0], 'YCbCr').convert('RGB'))
return [img_out]
# Set hyperparameters. The ocatave_scale is the ratio between each successive scale (remember the upscaling mentioned before?).
# Playing with these hyperparameters will also allow you to achieve new effects
step = 0.008 # Gradient ascent step size
num_octave = 5 # Number of scales at which to run gradient ascent
octave_scale = 1.4 # Size ratio between scales
iterations = 20 # Number of ascent steps per scale
max_loss = 10.
base_image_path = "resources/images/orion.jpg"
print('opening ' + base_image_path)
img = PIL.Image.open(base_image_path)
#img
def dream_image(img, save):
img = preprocess_image(base_image_path)
if K.image_data_format() == 'channels_first':
original_shape = img.shape[2:]
else:
original_shape = img.shape[1:3]
successive_shapes = [original_shape]
for i in range(1, num_octave):
shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
successive_shapes.append(shape)
successive_shapes = successive_shapes[::-1]
original_img = np.copy(img)
shrunk_original_img = resize_img(img, successive_shapes[0])
for shape in successive_shapes[:4]: #remove the indexing to have full resolution
print('Processing image shape', shape)
img = resize_img(img, shape)
img = gradient_ascent(img,
iterations=iterations,
step=step,
max_loss=max_loss)
upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)
same_size_original = resize_img(original_img, shape)
lost_detail = same_size_original - upscaled_shrunk_original_img
img += lost_detail
shrunk_original_img = resize_img(original_img, shape)
if save:
save_img('dream.jpg',deprocess_image(np.copy(img)))
print('saved dream')
dreamout = PIL.Image.open('dream.jpg')
return deprocess_image(np.copy(img))
#dreamout
#dream_image(img, True)
def dream_video(frames):
downsampling = 1 #how many resolution ratios down to go
preserve_color = True
new_frames = []
for i, frame in enumerate(frames):
original_frame = frame
print('PROCESSING FRAME ' + str(i) + " of " + str(len(frames)))
if i > 0:
print("averaging two frames")
frame = (np.array(frames[i-1]) + np.array(frame))/2.0
frame = preprocess_array(frame)
if K.image_data_format() == 'channels_first':
original_shape = frame.shape[2:]
else:
original_shape = frame.shape[1:3]
successive_shapes = [original_shape]
for i in range(1, num_octave):
shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
successive_shapes.append(shape)
successive_shapes = successive_shapes[::-1]
max_size = len(successive_shapes) - downsampling
original_img = np.copy(frame)
shrunk_original_img = resize_img(frame, successive_shapes[0])
for shape in successive_shapes[:max_size]:
print('Processing image shape', shape)
print(frame.shape)
frame = resize_img(frame, shape)
frame = gradient_ascent(frame,
iterations=iterations,
step=step,
max_loss=max_loss)
upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)
same_size_original = resize_img(original_img, shape)
lost_detail = same_size_original - upscaled_shrunk_original_img
frame += lost_detail
shrunk_original_img = resize_img(original_img, shape)
if preserve_color:
#following two lines for weird colors
frame = transfer_color(frame, resize_img([original_frame], frame[0,...,0].shape))
#frame = frame.transpose(2, 0, 1)
#frame = [frame]
print(frame.shape)
new_frames.append(deprocess_image(np.copy(frame)))
return new_frames
def dream_video_from_image(img, num_frames):
new_frames = []
new_frames.append(img)
for i in range(0, num_frames):
print('frame ' + str(i))
frame = new_frames[i]
frame = preprocess_array(frame)
if K.image_data_format() == 'channels_first':
original_shape = frame.shape[2:]
else:
original_shape = frame.shape[1:3]
successive_shapes = [original_shape]
for i in range(1, num_octave):
shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
successive_shapes.append(shape)
successive_shapes = successive_shapes[::-1]
original_img = np.copy(frame)
shrunk_original_img = resize_img(frame, successive_shapes[0])
for shape in successive_shapes[:4]:
print('Processing image shape', shape)
frame = resize_img(frame, shape)
frame = gradient_ascent(frame,
iterations=iterations,
step=step,
max_loss=max_loss)
upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)
same_size_original = resize_img(original_img, shape)
lost_detail = same_size_original - upscaled_shrunk_original_img
frame += lost_detail
shrunk_original_img = resize_img(original_img, shape)
new_frames.append(deprocess_image(np.copy(frame)))
return np.delete(new_frames, 0)
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ebd1f22351359902b00f6777b74785355bb7f50f | b867e7996b27f2ba23139baa4f98e3ac87379936 | /Moon.py | 55d5a37af20a742cef8d91f2177b24484a6d07e8 | [] | no_license | msawhney97/Space-Game | 509f855678072e04197f98a4f802393a3eb65001 | c05292e412c0f18be2d5023dadf65ffac202c4d1 | refs/heads/master | 2021-01-25T07:44:31.185069 | 2017-06-07T17:10:12 | 2017-06-07T17:10:12 | 93,659,493 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 505 | py | import pygame
from math import sin, cos
from GameObject import GameObject
class Moon(GameObject):
@staticmethod
def init(angle=90):
Moon.moonImage = pygame.transform.rotate(
pygame.transform.scale(
pygame.image.load('images/cartoon-moon.png').convert_alpha(),
(100, 100)),angle)
def __init__(self,x,y):
super(Moon,self). __init__(x,y,
Moon.moonImage, 20)
def update(self,x,y, r,angle):
super(Moon, self). __init__(x,
y, Moon.moonImage,30)
Moon.init(angle)
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9ba654fc94ab5bd647ac7e9bf67182c4e3b39850 | bb37574bc39e1e90b43b8fe874c96955dc88a814 | /mats/__init__.py | 2401b9e1a7c1c81ebec4afaebfeeec1e75c8fef6 | [
"MIT"
] | permissive | martinbra/mats | a7975cf4323be55b94087d51ba746b247a83eaed | 5fae5cdd405be586bfad821c2335ee980a851f4c | refs/heads/master | 2022-11-21T16:01:33.235316 | 2020-07-29T19:13:37 | 2020-07-29T19:13:37 | 258,060,134 | 0 | 0 | MIT | 2020-07-29T19:13:39 | 2020-04-23T01:14:40 | null | UTF-8 | Python | false | false | 379 | py | import coloredlogs
import logging
from mats.archiving import ArchiveManager
from mats.test import Test
from mats.test_sequence import TestSequence
from mats.tkwidgets import MatsFrame
from mats.version import __version__
__all__ = ['Test', 'TestSequence', 'ArchiveManager', 'MatsFrame', '__version__']
coloredlogs.install(level='DEBUG')
logger = logging.getLogger(__name__)
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22e72933ceb95fe59a488becf877035854228656 | 594349a97cf47ef1e70bac053b1be5762e16e8ac | /project/__init__.py | ccd0818cb24ae4a50b47d105e3eb7155ca14274f | [] | no_license | ctma/flask_tutorial | 9b9b17fde629f79f3b511255450409372754b5a5 | df3e395ba00053886751c1948acc7abd1e4af654 | refs/heads/master | 2021-07-15T22:27:01.004567 | 2017-10-22T20:45:52 | 2017-10-22T20:45:52 | 107,901,257 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 946 | py | import os
import datetime
from flask import Flask, jsonify
from flask_sqlalchemy import SQLAlchemy
# instantiate the app
app = Flask(__name__)
# set config
app_settings = os.getenv('APP_SETTINGS')
app.config.from_object(app_settings)
# instantiate the db
db = SQLAlchemy(app)
# model
class User(db.Model):
__tablename__ = "users"
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
username = db.Column(db.String(128), nullable=False)
email = db.Column(db.String(128), nullable=False)
active = db.Column(db.Boolean(), default=False, nullable=False)
created_at = db.Column(db.DateTime, nullable=False)
def __init__(self, username, email):
self.username = username
self.email = email
self.created_at = datetime.datetime.utcnow()
# routes
@app.route('/ping', methods=['GET'])
def ping_pong():
return jsonify({
'status': 'success',
'message': 'pong!'
})
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55e1835554328c64f71bbf8f81aa177437af3cea | 95a4eede263cd24d8a31ff5e478cb86d79877c3c | /Zgony.py | 28bcdb6518121a0560b2d5ebbdc2157cb4bf35f8 | [] | no_license | lewiis252/baza_covid19_Polska | a91cae0822a8595c8eb12490df4348dc7d554d3f | 875967368e8a3563379e83a34588a3f0359ecccb | refs/heads/main | 2023-08-04T18:18:33.346238 | 2021-09-14T06:22:00 | 2021-09-14T06:22:00 | 406,246,154 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,721 | py | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import date
import Dane
import seaborn as sb
sb.set()
print("...\n")
start_date = date(2020,3,3)
end_date = date.today()
end_date = date(2021,6,3)
dzien = pd.date_range(start_date, end_date)
zgony = Dane.zgony
sr_7_dni = [0,0,0,0,0]
for i in range(5, len(zgony)):
nowa_dana = np.mean(zgony[i-6:i+1])
sr_7_dni.append(nowa_dana)
srednia_7_dniowa_zgonow = np.array(sr_7_dni)
zgony_lacznie = [0]
for i in range(1, len(zgony)):
nowa_suma = zgony[i] + zgony_lacznie[i-1]
zgony_lacznie.append(nowa_suma)
zgony_lacznie = np.array(zgony_lacznie)
print('RAPORT ZGONÓW Z OSTATNICH 8 DNI:')
d = {'data':dzien, 'nowe zgony':zgony, 'średnia tygodniowa zgonów':np.round(srednia_7_dniowa_zgonow,0),
'zgony łącznie':zgony_lacznie}
raport_zgonow = pd.DataFrame(data=d)
raport_zgonow = raport_zgonow.set_index(raport_zgonow.columns[0])
print(raport_zgonow[-8:])
fig, ax = plt.subplots(figsize=(20,8))
plt.bar(dzien, zgony, color='c',label='Liczba zgonów')
plt.ylabel('Zgony', size=15)
ax2 = ax.twinx()
plt.plot(dzien, zgony_lacznie, color='r', label='Zgony łącznie')
plt.ylabel('Zgony łącznie', size=15)
plt.title('Zgony', size=20)
fig.legend(loc='lower center', ncol=3)
plt.savefig("raport\Zgony.svg")
fig, ax = plt.subplots(figsize=(20,8))
plt.bar(dzien, srednia_7_dniowa_zgonow, color='c',label='Liczba zgonów średnia 7-dniowa')
plt.ylabel('Zgony', size=15)
ax2 = ax.twinx()
plt.plot(dzien, zgony_lacznie, color='r', label='Zgony łącznie')
plt.ylabel('Zgony łącznie', size=15)
plt.title('Zgony', size=20)
fig.legend(loc='lower center', ncol=3)
plt.savefig("raport\Zgony średnia 7-dniowa.svg")
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c14444582fc73b4d58d39e69413552ec5593874a | 88994e2e840a70ec702cee09e1a13813aa6f800c | /cg/models/orders/excel_sample.py | b7230139a95a916dd518cb68a5db3854ed5d761e | [] | no_license | Clinical-Genomics/cg | 1e9eb0852f742d555a48e8696914ebe177f7d436 | d2ec6d25b577dd6938bbf92317aeff1d6b3c5b08 | refs/heads/master | 2023-09-01T02:04:04.229120 | 2023-08-31T13:50:31 | 2023-08-31T13:50:31 | 82,567,026 | 19 | 8 | null | 2023-09-14T15:24:13 | 2017-02-20T14:29:43 | Python | UTF-8 | Python | false | false | 4,657 | py | from typing import List, Optional
from cg.models.orders.sample_base import OrderSample
from cg.models.orders.validators.excel_sample_validators import (
convert_sex,
convert_to_date,
convert_to_lower,
convert_to_priority,
numeric_value,
parse_panels,
validate_data_analysis,
validate_parent,
validate_source,
)
from pydantic import AfterValidator, BeforeValidator, Field
from typing_extensions import Annotated
class ExcelSample(OrderSample):
age_at_sampling: str = Field(None, alias="UDF/age_at_sampling")
application: str = Field(..., alias="UDF/Sequencing Analysis")
capture_kit: str = Field(None, alias="UDF/Capture Library version")
cohorts: List[str] = Field(None, alias="UDF/cohorts")
collection_date: Annotated[str, AfterValidator(convert_to_date)] = Field(
None, alias="UDF/Collection Date"
)
comment: str = Field(None, alias="UDF/Comment")
concentration: Annotated[str, AfterValidator(numeric_value)] = Field(
None, alias="UDF/Concentration (nM)"
)
concentration_sample: Annotated[str, AfterValidator(numeric_value)] = Field(
None, alias="UDF/Sample Conc."
)
container: str = Field(None, alias="Container/Type")
container_name: str = Field(None, alias="Container/Name")
control: str = Field(None, alias="UDF/Control")
custom_index: str = Field(None, alias="UDF/Custom index")
customer: str = Field(..., alias="UDF/customer")
data_analysis: Annotated[str, AfterValidator(validate_data_analysis)] = Field(
"MIP DNA", alias="UDF/Data Analysis"
)
data_delivery: Annotated[str, AfterValidator(convert_to_lower)] = Field(
None, alias="UDF/Data Delivery"
)
elution_buffer: str = Field(None, alias="UDF/Sample Buffer")
extraction_method: str = Field(None, alias="UDF/Extraction method")
family_name: str = Field(None, alias="UDF/familyID")
father: Annotated[str, AfterValidator(validate_parent)] = Field(None, alias="UDF/fatherID")
formalin_fixation_time: str = Field(None, alias="UDF/Formalin Fixation Time")
index: str = Field(None, alias="UDF/Index type")
index_number: Annotated[str, AfterValidator(numeric_value)] = Field(
None, alias="UDF/Index number"
)
lab_code: str = Field(None, alias="UDF/Lab Code")
mother: Annotated[str, AfterValidator(validate_parent)] = Field(None, alias="UDF/motherID")
name: str = Field(..., alias="Sample/Name")
organism: str = Field(None, alias="UDF/Strain")
organism_other: str = Field(None, alias="UDF/Other species")
original_lab: str = Field(None, alias="UDF/Original Lab")
original_lab_address: str = Field(None, alias="UDF/Original Lab Address")
panels: Annotated[Optional[List[str]], BeforeValidator(parse_panels)] = Field(
None, alias="UDF/Gene List"
)
pool: str = Field(None, alias="UDF/pool name")
post_formalin_fixation_time: str = Field(None, alias="UDF/Post Formalin Fixation Time")
pre_processing_method: str = Field(None, alias="UDF/Pre Processing Method")
primer: str = Field(None, alias="UDF/Primer")
priority: Annotated[
str,
AfterValidator(convert_to_lower),
AfterValidator(convert_to_priority),
] = Field(None, alias="UDF/priority")
quantity: Annotated[str, AfterValidator(numeric_value)] = Field(None, alias="UDF/Quantity")
reagent_label: str = Field(None, alias="Sample/Reagent Label")
reference_genome: str = Field(None, alias="UDF/Reference Genome Microbial")
region: str = Field(None, alias="UDF/Region")
region_code: str = Field(None, alias="UDF/Region Code")
require_qc_ok: bool = Field(None, alias="UDF/Process only if QC OK")
rml_plate_name: str = Field(None, alias="UDF/RML plate name")
selection_criteria: str = Field(None, alias="UDF/Selection Criteria")
sex: Annotated[str, AfterValidator(convert_sex)] = Field(None, alias="UDF/Gender")
source: Annotated[str, AfterValidator(validate_source)] = Field(None, alias="UDF/Source")
status: Annotated[str, AfterValidator(convert_to_lower)] = Field(None, alias="UDF/Status")
subject_id: str = Field(None, alias="UDF/subjectID")
synopsis: str = Field(None, alias="UDF/synopsis")
tissue_block_size: str = Field(None, alias="UDF/Tissue Block Size")
tumour: bool = Field(None, alias="UDF/tumor")
tumour_purity: str = Field(None, alias="UDF/tumour purity")
volume: Annotated[str, AfterValidator(numeric_value)] = Field(None, alias="UDF/Volume (uL)")
well_position: str = Field(None, alias="Sample/Well Location")
well_position_rml: str = Field(None, alias="UDF/RML well position")
| [
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] | |
68079a55bb86f3c2389cad7e87cb8b8aeaf5d183 | ead3bb5d63ce9106bc34b1f2a883fdcd21c99839 | /blackbox/vae/loss.py | e71b695711f5831b25382751a9f2d154d8978f7f | [] | no_license | AkashGanesan/generic-blackbox | 91fed27d7aa5b21b2f6a99a770c135ebdaa30613 | 62d5840635f4e1a1a5252091bc7f334853acb67f | refs/heads/master | 2020-04-26T05:44:13.408760 | 2019-03-04T05:44:55 | 2019-03-04T05:44:55 | 173,343,235 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,606 | py | import torch
import torch.nn.functional as F
import torch.nn as nn
def bce_loss(input, target):
"""
Numerically stable version of the binary cross-entropy loss function.
As per https://github.com/pytorch/pytorch/issues/751
See the TensorFlow docs for a derivation of this formula:
https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
Input:
- input: PyTorch Tensor of shape (N, ) giving scores.
- target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.
Output:
- A PyTorch Tensor containing the mean BCE loss over the minibatch of
input data.
"""
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def kld_loss(mean,
log_var):
''' KLD loss '''
KLD = - 0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp(), dim=1)
return KLD
def l2_loss(y_pred, y_true, mode='sum'):
"""
Input:
- y_pred: Tensor of shape (seq_len, batch, 2). Predicted trajectory.
- y_true: Tensor of shape (seq_len, batch, 2). Groud truth
predictions.
- loss_mask: Tensor of shape (batch, seq_len)
- mode: Can be one of sum, average, raw
Output:
- loss: l2 loss depending on mode
"""
batch, _, seq_len = y_pred.size()
loss = (y_true - y_pred).norm(dim=1)
# if mode == 'sum':
return torch.sum(loss, dim=1) / seq_len
# elif mode == 'average':
# return torch.sum(loss) / torch.numel(loss_mask.data)
# elif mode == 'raw':
# return loss.sum(dim=2).sum(dim=1)
| [
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f27f76e7fef07ec8bb0ae3de9b75b49542d1e03c | 4a2990a954e9158d09ac8985bec18289fb684a39 | /DigitClassifier.py | b3f6f8bdcafbdb47abccb7d1ecc7da52b06b7781 | [] | no_license | aslakey/DataScience | cb1715fdcafa0afe82c99f389a114aa08315e586 | 881b55489e25751b46ec6e08c218f55ebb16ea03 | refs/heads/master | 2020-04-17T02:25:16.185685 | 2016-08-17T23:11:13 | 2016-08-17T23:11:13 | 45,635,744 | 1 | 1 | null | 2015-11-05T20:27:55 | 2015-11-05T19:56:41 | null | UTF-8 | Python | false | false | 948 | py | from sklearn import svm
from sklearn import datasets
'''
->importing SVM and datasets from sklearn
->classify digits using support vector classification
->manually chose gamma, but could have used grid search
->train data using clf.fit(data,target) method
'''
#load
iris = datasets.load_iris()
digits = datasets.load_digits()
#print(digits.data)
#classify
clf = svm.SVC(gamma=0.001, C=100.) #classifier using support vector classification
#train
clf.fit(digits.data[:-1],digits.target[:-1])
'''
OUT:
SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
gamma=0.001, kernel='rbf', max_iter=-1, probability=False,
random_state=None, shrinking=True, tol=0.001, verbose=False)
**Can store using:
>>> from sklearn.externals import joblib
>>> joblib.dump(clf, 'filename.pkl')
**then later retrive that file:
>>> clf = joblib.load('filename.pkl')
'''
#now classify some digs
clf.predict(digits.data[-1])
#last image looks like an 8!
| [
"[email protected]"
] | |
7457a9afae19893e2d1e10d12355c26f4a7818df | 8e3ca9617020be18b9922757486aca85e22a6b44 | /Tareas/PYTHON_2021-[9] Regiones ricas en AT-2984/Zara Paulina Martínez Sánchez_10047_assignsubmission_file_/regiones_at.py | e4fae38123730a03b1271bdcbb090512098fdaff | [] | no_license | AnaBVA/pythonCCG_2021 | 04609078fdd40bd68684ce5514a78e959d02ff3c | 47677549eec0e1a3460941ef97ace8d7d0bac185 | refs/heads/main | 2023-05-29T02:39:24.780609 | 2021-06-17T06:24:44 | 2021-06-17T06:24:44 | 344,536,557 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,170 | py | """
## NAME
regiones_at.py
## VERSION
[1.0]
## AUTHOR
Zara Paulina Martinez Sanchez < zaram042001 @ gmail.com >
## DATE
[08/06/2021]
## DESCRIPTION
Programa que analiza una secuencia de DNA para buscar regiones ricas en AT
las cuales contengan 5 o mas As y/o Ts. En caso de contener en la secuencia
caracteres diferentes a ATGC se le notifica al usuario
## CATEGORY
Sequence analysis
## USAGE
regiones_at.py no requiere argumentos
## FUNCTIONS
def analizar_sec(seq):
no_bases = re.findall(r"[^ATGC]", seq)
region_at = re.findall(r"[AT]{5,}", seq)
try:
if no_bases:
raise ValueError
except ValueError:
print(f'La secuencia que ingresó cuenta con caracteres no validos: {no_bases}')
else:
if region_at:
print(f'Las regiones ricas en AT son: {region_at}')
else:
print("No existen regiones ricas en AT en su secuencia")
## EXAMPLES
Input:
CTGCATTATATCGTACGAAATTATACGCGCG
Output:
Las regiones ricas en AT son: ['ATTATAT', 'AAATTATA']
## GITHUB LINK
https://github.com/zara-ms/python_class/tree/master/src
"""
# Libreria a usar
import re
def analizar_sec(seq):
no_bases = re.findall(r"[^ATGC]", seq)
region_at = re.findall(r"[AT]{5,}", seq)
# Reconocer caracteres invalidos y marcar error
try:
if no_bases:
raise ValueError
except ValueError:
print(f'La secuencia que ingresó cuenta con caracteres no validos: {no_bases}')
# Buscar secuencias ricas en AT si la secuencia es correcta
else:
if region_at:
print(f'Las regiones ricas en AT son: {region_at}')
else:
print("No existen regiones ricas en AT en su secuencia")
print("Ingrese la secuencia a analizar")
secuencia = input()
secuencia = secuencia.upper()
# Llamar a la funcion
analizar_sec(secuencia)
| [
"[email protected]"
] | |
fd015340592d6a9d46508ab0c1abb1b030137c5d | a90b05c59c119102bb93aa2162585a38b5ae9c84 | /testCaffe.py | 3122805f3561abe06095bf477ec6fa0d1073840d | [] | no_license | quanweikikai/deconv-net | 2fd07b4c7178109eeef21e4258ddb2820bfe3cb9 | 625fce6d34356014ed5da3c74cd7acddb1eac7c1 | refs/heads/master | 2021-01-12T12:25:03.115828 | 2016-11-01T23:03:58 | 2016-11-01T23:03:58 | 72,485,146 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,691 | py | import caffe
import matplotlib.pyplot as plt
import numpy as np
import sys
def plotAll(inputArr, plotShape):
for ii in xrange(plotShape[0]):
startImg = inputArr[0,ii*plotShape[1]+1,...]
for jj in xrange(1,plotShape[1]):
startImg = np.append(startImg,inputArr[0,ii*plotShape[1]+jj,...],axis=0)
if (ii == 0):
lineImg = startImg
else:
lineImg = np.append(lineImg,startImg,axis=1)
return lineImg
net = caffe.Net('../deconvTest/lenet.prototxt','../caffe/examples/mnist/lenet_iter_10000.caffemodel',caffe.TEST)
invNet = caffe.Net('../deconvTest/inverseLenet.prototxt',caffe.TEST)
for layer in invNet.params:
invNet.params[layer][0].data[...] = net.params[layer[2:]][0].data
f, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)
c=np.array( [0.299, 0.58, 0.114] )
a = net.params['conv1'][0].data
img = caffe.io.load_image(sys.argv[1])
img = np.dot(img,c)
img = caffe.io.resize(img,(28,28)) * 255
#forward
net.blobs['test1'].data[0] = img
output = net.forward(['conv1','conv2','SoftmaxOut','ip2'])
#inverse forward
invNet.blobs['input1'].data[0] = output['conv2']
inverseOutput = invNet.forward(['deconv2'])
input2 = inverseOutput['deconv2'][0,...]
input2 = input2/float(np.max(input2))
input2 = caffe.io.resize(input2,(20,24,24))
tmp = np.zeros((1,20,24,24))
tmp[0] = input2
invNet.blobs['input2'].data[0] = tmp
invResult = invNet.forward(['result'])
plotImg1 = plotAll(output['conv2'],(10,5))
plotImg2 = plotAll(output['conv1'],(5,4))
plotImg3 = invResult['result']
ax1.imshow(plotImg1)
ax2.imshow(plotImg2)
ax4.imshow(plotImg3[0,0,...]*255)
ax3.imshow(img)
print output['SoftmaxOut']
print output['ip2']
plt.show()
| [
"[email protected]"
] | |
968c71ff6d547560d6976d04238b5e4ca27acfc8 | b39d9ef9175077ac6f03b66d97b073d85b6bc4d0 | /Cabergoline_CT_2_mg_tablet_SmPC.py | 1fc6492eca70783a261c02b5d7847e96b5dc2672 | [] | no_license | urudaro/data-ue | 2d840fdce8ba7e759b5551cb3ee277d046464fe0 | 176c57533b66754ee05a96a7429c3e610188e4aa | refs/heads/master | 2021-01-22T12:02:16.931087 | 2013-07-16T14:05:41 | 2013-07-16T14:05:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,671 | py | {'_data': [['Unknown',
[['GI',
u'administration site conditions Patients on Adjunct Levodopa Therapy MedDRA Frequency Undesirable Effects System Organ Class Psychiatric disorders Common Confusion, hallucinations Nervous system disorders Common Dizziness, dyskinesia Uncommon Hyperkinesia Cardiac disorders Common Angina Vascular disorders Common Postural hypotension Uncommon Erythromelalgia Respiratory, thoracic and Uncommon Pleural effusion, pulmonary fibrosis mediastinal disorders Gastrointestinal disorders Very common Nausea Common Dyspepsia, gastritis, vomiting General disorders and Common Peripheral edema administration site conditions Investigations Common Decreased hemoglobin, hematocrit, and/or red blood cell (>15% vs baseline) Post-marketing Surveillance MedDRA Frequency Undesirable Effects System Organ Class Immune system disorders Uncommon Hypersensitivity reaction Psychiatric disorders Common Increased libido Uncommon Delusions, psychotic disorder Not Known Aggression, hypersexuality, pathological gambling Nervous system disorders Common Headache, somnolence Not Known Sudden sleep onset, syncope Cardiac disorders Very common Valvulopathy (including regurgitation) and related disorders (pericarditis and pericardial effusion) Vascular disorders Not Known Digital vasospasm Respiratory, thoracic and Common Dyspnea mediastinal disorders Very rare Fibrosis Not Known Respiratory disorder, respiratory failure Hepato-biliary disorders Uncommon Hepatic function abnormal Skin and subcutaneous Uncommon Rash tissue disorders Not Known Alopecia Musculoskeletal and Not Known Leg cramps connective tissue disorders General disorders and Common Asthenia administration site Uncommon Edema, fatigue conditions Investigations Common Liver function tests abnormal Not Known Blood creatinine phosphokinase increased There have been reports of fibrotic and serosal inflammatory conditions, such as pleuritis, pleural effusion, pleural fibrosis, pulmonary fibrosis, pericarditis, pericardial effusion, cardiac valvulopathy and retroperitoneal fibrosis, in patients taking cabergoline (see Secion 4.4). There is limited information available on the reversibility of these reactions. Gastric upset was more frequent in female than in male patients, while CNS events were more frequent in the elderly. A blood pressure decrease of clinical relevance was observed mainly on standing in a minority of patients. The effect was mainly evident in the first weeks of therapy. Neither modification of heart rate nor consistent changes of ECG tracing were observed during cabergoline treatment. Alterations in standard laboratory tests are uncommon during long term therapy with cabergoline. In clinical studies, increases of triglycerides greater than 30% above the upper limit of the laboratory reference range were observed in 6.8% of the cabergoline-treated patients who had values within the normal range at baseline. In most cases the increases were transient. No clear indications of increases over time or significant shifts from normal to abnormal values were observed in the overall group of patients treated with cabergoline. Other: Adverse events have been reported with lower doses of cabergoline (0.25 \u2013 2 mg per week) that are not listed above including: Common (>1/100 to <1/10) Nervous system disorders: Depression, paresthesia. Cardiac disorders: Palpitations Skin and subcutaneous tissue disorders: Facial redness Uncommon (>1/1000 to <1/100) Eye disorders: Hemianopsia Vascular disorders: Nose bleeding Rare (>1/10000 to <1/1000) Vascular disorders: Fainting']]]],
'_pages': [6, 8],
u'_rank': 1,
u'_type': u'LSFU'} | [
"[email protected]"
] | |
44ff9007fe06e2f8d446711a3c996a7d34bb494a | ca5a08c91d070b649be6236b23261e3dbe3d9742 | /Chapter08/08_03_MagicIndex_BruteForce.py | fd9db96cd27eb0be035d53fcddd359ba1b3b5391 | [] | no_license | tdesfont/CtCI-6th-Edition-Python | 3751c9d5137c696661492568fc7fd4e09a64652d | 0464574fdf5591b5c5d25b5777fee0f401c06955 | refs/heads/master | 2020-08-06T12:50:54.586891 | 2019-12-04T16:29:24 | 2019-12-04T16:29:24 | 212,981,529 | 0 | 0 | null | 2019-10-05T10:30:28 | 2019-10-05T10:30:28 | null | UTF-8 | Python | false | false | 202 | py | def magicIndex(A):
for i in range(0, len(A)):
if A[i] == i:
print(i, A[i])
return True
return False
if __name__ == "__main__":
magicIndex([2, 4, 5, 5, 5, 5]) | [
"[email protected]"
] | |
9ce90a6b93e13fbc8d927da0f14756a67b83c503 | e3365bc8fa7da2753c248c2b8a5c5e16aef84d9f | /indices/nntriton.py | c9e4c9e38a2d8a648637823e5cb28fec8650247e | [] | no_license | psdh/WhatsintheVector | e8aabacc054a88b4cb25303548980af9a10c12a8 | a24168d068d9c69dc7a0fd13f606c080ae82e2a6 | refs/heads/master | 2021-01-25T10:34:22.651619 | 2015-09-23T11:54:06 | 2015-09-23T11:54:06 | 42,749,205 | 2 | 3 | null | 2015-09-23T11:54:07 | 2015-09-18T22:06:38 | Python | UTF-8 | Python | false | false | 493 | py | ii = [('CookGHP3.py', 1), ('RogePAV2.py', 4), ('RogePAV.py', 2), ('RennJIT.py', 1), ('LeakWTI2.py', 6), ('AubePRP.py', 2), ('FitzRNS3.py', 1), ('ClarGE2.py', 2), ('GellWPT2.py', 1), ('WilkJMC2.py', 3), ('RoscTTI2.py', 3), ('BuckWGM.py', 1), ('LyelCPG.py', 1), ('KirbWPW2.py', 1), ('BachARE.py', 1), ('BuckWGM2.py', 1), ('MereHHB3.py', 1), ('HogaGMM.py', 1), ('MartHRW.py', 1), ('FitzRNS.py', 2), ('RoscTTI.py', 1), ('RogeSIP.py', 1), ('FitzRNS2.py', 1), ('HogaGMM2.py', 1), ('LyelCPG3.py', 6)] | [
"[email protected]"
] | |
f049797e8ab64f9c8a15f7f1a6ffd77072370038 | 133b2fb99be0d75fcd3543118bf323f927a7624b | /django-for-development/base/tests/test_middleware.py | 201608718c7d0c75659464e6606d49413cfc173d | [
"MIT"
] | permissive | xfenix/django-hmin | 78139035321a94ddfff0767dd66f4fa2b0c42306 | cfea5ebb2c3382ba05fd2af860d2e2d2e421f0f1 | refs/heads/master | 2022-12-13T23:31:29.822759 | 2020-11-15T01:14:17 | 2020-11-15T01:14:17 | 25,469,854 | 12 | 3 | MIT | 2022-12-08T11:02:30 | 2014-10-20T14:22:01 | Python | UTF-8 | Python | false | false | 457 | py | """Test django integration."""
from django.test import Client
from django.http import HttpResponse
def test_middleware_indexpage():
"""Test."""
view_response: HttpResponse = Client().get("/")
assert (
view_response.content
== b'<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Document</title></head><body>This is test</body></html>'
)
| [
"[email protected]"
] | |
57aa130bd1f08c4c19e526d12f189f65810e10e8 | 852b57a1a2a0fa6b0d23bef16c4a989d369936e9 | /playwright/_impl/_local_utils.py | af0683ed2898a6543419183742002991b600fa47 | [
"Apache-2.0"
] | permissive | microsoft/playwright-python | e28badf23e20f948b4063a314e906006dcdff7fa | 42c0bf19d7ae415552172d7c04cdb7afd9dad7fb | refs/heads/main | 2023-08-22T17:49:04.645213 | 2023-08-14T12:52:46 | 2023-08-14T12:52:46 | 276,414,382 | 9,615 | 870 | Apache-2.0 | 2023-09-05T17:07:48 | 2020-07-01T15:28:13 | Python | UTF-8 | Python | false | false | 2,781 | py | # Copyright (c) Microsoft Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
from typing import Dict, List, Optional, cast
from playwright._impl._api_structures import HeadersArray
from playwright._impl._connection import ChannelOwner, StackFrame
from playwright._impl._helper import HarLookupResult, locals_to_params
class LocalUtils(ChannelOwner):
def __init__(
self, parent: ChannelOwner, type: str, guid: str, initializer: Dict
) -> None:
super().__init__(parent, type, guid, initializer)
async def zip(self, params: Dict) -> None:
await self._channel.send("zip", params)
async def har_open(self, file: str) -> None:
params = locals_to_params(locals())
await self._channel.send("harOpen", params)
async def har_lookup(
self,
harId: str,
url: str,
method: str,
headers: HeadersArray,
isNavigationRequest: bool,
postData: Optional[bytes] = None,
) -> HarLookupResult:
params = locals_to_params(locals())
if "postData" in params:
params["postData"] = base64.b64encode(params["postData"]).decode()
return cast(
HarLookupResult,
await self._channel.send_return_as_dict("harLookup", params),
)
async def har_close(self, harId: str) -> None:
params = locals_to_params(locals())
await self._channel.send("harClose", params)
async def har_unzip(self, zipFile: str, harFile: str) -> None:
params = locals_to_params(locals())
await self._channel.send("harUnzip", params)
async def tracing_started(self, tracesDir: Optional[str], traceName: str) -> str:
params = locals_to_params(locals())
return await self._channel.send("tracingStarted", params)
async def trace_discarded(self, stacks_id: str) -> None:
return await self._channel.send("traceDiscarded", {"stacksId": stacks_id})
def add_stack_to_tracing_no_reply(self, id: int, frames: List[StackFrame]) -> None:
self._channel.send_no_reply(
"addStackToTracingNoReply",
{
"callData": {
"stack": frames,
"id": id,
}
},
)
| [
"[email protected]"
] | |
e2d5a3327596d21c570daa59ee7d6e2157b0c335 | f3693916a8b118bf139364604dac3f51235ed613 | /functional/Components/Groups/Groups_PATCH_ID/test_TC_44521_Groups_PATCH_Group_Valid_Provisioning_Policy.py | d6a8a3e05363440a478bb96584c8f3358c700adf | [] | no_license | muktabehera/QE | e7d62284889d8241d22506f6ee20547f1cfe6db1 | 3fedde591568e35f7b80c5bf6cd6732f8eeab4f8 | refs/heads/master | 2021-03-31T02:19:15.369562 | 2018-03-13T02:45:10 | 2018-03-13T02:45:10 | 124,984,177 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,418 | py | # -*- coding: UTF-8 -*-
"""PFE Component Tests - Groups.
* TC-44521 - Groups PATCH:
Verify that user is able to modify group on providing valid values in parameter 'provisioningPolicy' using request PATCH '/groups'.
Equivalent test CURL command:
curl -H "Host: <client_host>" -H "Authorization: Bearer <valid_token>"
-X PATCH -d @<JSON_data_file> -H "Content-Type: application/json"
"<PF_host>://<client_host>/groups/updateGroup"
Same, with test data:
curl -H "Host: <client_host>" -H "Authorization: Bearer <valid_token>"
-X PATCH -d @<JSON_data_file> -H "Content-Type: application/json"
"<PF_host>://<client_host>/groups/updateGroup"
JSON data sent to PathFinder in this test:
{'configAdminCanEdit': True,
'configurations': [],
'deliveryLoadBalancePolicy': 'DNS_NAME',
'dnsName': 'autoQEDVCC1',
'edgeDeviceRoles': ['EDGE', 'ORIGIN', 'DISTRIBUTION'],
'members': [{'id': 'Device_Test_API'}],
'name': 'Updated Group valid Provisioning Policy',
'originLoadBalancePolicy': 'ALL_MEMBERS',
'provisioningPolicy': 'ONE_OR_MORE',
'visibleInAllConfigurations': True}
"""
import pytest
from qe_common import *
logger = init_logger()
@pytest.mark.components
@pytest.allure.story('Groups')
@pytest.allure.feature('PATCH')
class Test_PFE_Components(object):
"""PFE Groups test cases."""
@pytest.allure.link('https://jira.qumu.com/browse/TC-44521')
@pytest.mark.Groups
@pytest.mark.PATCH
def test_TC_44521_PATCH_Groups_Group_Valid_Provisioning_Policy(self, context):
"""TC-44521 - Groups-PATCH
Verify that user is able to modify group on providing valid values in parameter 'provisioningPolicy' using request PATCH '/groups'."""
# Define a test step
with pytest.allure.step("""Test1: Verify that user is able to modify group on providing valid values in parameter 'provisioningPolicy' using request PATCH '/groups'."""):
# Test case configuration
edgeDeviceGroupDetails = context.sc.EdgeDeviceGroupDetails(
configAdminCanEdit=True,
configurations=[],
deliveryLoadBalancePolicy='DNS_NAME',
dnsName='autoQEDVCC1',
edgeDeviceRoles=['EDGE', 'ORIGIN', 'DISTRIBUTION'],
id=None,
members=[{
'id': 'POST_veDevices_AllConfigAdminMulticastTrue'
}],
name='Updated Group valid Provisioning Policy',
originLoadBalancePolicy='ALL_MEMBERS',
provisioningPolicy='ONE_OR_MORE',
proximityDetails=None,
visibleInAllConfigurations=True)
# updateEntity the Groups.
# The `check` call validates return code
# and some of the swagger schema.
# Most schema checks are disabled.
response = check(
context.cl.Groups.updateEntity(
id='GroupforPatch2',
body=edgeDeviceGroupDetails
)
)
# Define a test step
with pytest.allure.step("""Test2: Verify that user is able to modify group on providing valid values in parameter 'provisioningPolicy' using request PATCH '/groups'."""):
# Test case configuration
edgeDeviceGroupDetails = context.sc.EdgeDeviceGroupDetails(
configAdminCanEdit=True,
configurations=[],
deliveryLoadBalancePolicy='DNS_NAME',
dnsName='autoQEDVCC1',
edgeDeviceRoles=['EDGE', 'ORIGIN', 'DISTRIBUTION'],
id=None,
members=[{
'id': 'POST_veDevices_AllConfigAdminMulticastTrue'
}],
name='Updated Group valid Provisioning Policy',
originLoadBalancePolicy='ALL_MEMBERS',
provisioningPolicy='ALL_MEMBERS',
proximityDetails=None,
visibleInAllConfigurations=True)
# updateEntity the Groups.
# The `check` call validates return code
# and some of the swagger schema.
# Most schema checks are disabled.
response = check(
context.cl.Groups.updateEntity(
id='GroupforPatch2',
body=edgeDeviceGroupDetails
)
)
| [
"[email protected]"
] | |
048956f2abf5397292a390d9c48f8da1e6ffdd7a | 6e9ce707772643f1c3c0a6cd35de4d94e78b8a8e | /ann.py | b86b2364fee92e29ffa36e47511f1ca3bff0e41b | [] | no_license | Campos1098/scenario-responses | 764f8e20a97166bb3134822a4913449c5b3e0867 | cb4e306f400f4a72e744c848f422687ac5b5fa35 | refs/heads/main | 2023-09-04T23:12:59.660091 | 2021-11-12T12:35:31 | 2021-11-12T12:35:31 | 427,221,721 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,550 | py | import numpy as np
import train_parser
import test_parser
import math
import warnings
import torch
import torch.nn.functional as F
from torch import nn
from torch import optim
from torch.utils.data import DataLoader, TensorDataset
warnings.filterwarnings ("ignore")
class Tennis_NN(nn.Module):
def __init__(self, lr):
super().__init__()
self.lr = lr
self.lin_stack = nn.Sequential(
nn.Linear(111, lr, True),
nn.BatchNorm1d(lr),
nn.ReLU(),
nn.Linear(lr, 2, True),
nn.Sigmoid()
)
def forward(self, xb):
return self.lin_stack(xb)
# Returns the model and its associated optimiser
def get_model(lr, num):
model = Tennis_NN(num)
return model, optim.SGD(model.parameters(), lr = lr, momentum = 0.9)
# Returns the training and validation data
def get_data(train_ds, bs):
return (
DataLoader(train_ds, batch_size = bs, shuffle = False)
)
# Computes the accuracy of the model
def accuracy(out, yb):
preds = torch.argmax(out, dim=1)
return (preds == yb).float().mean()
def loss_batch(model, loss_func, xb, yb, opt = None):
loss = loss_func(model(xb), yb)
if opt is not None:
loss.backward()
opt.step()
opt.zero_grad()
return loss.item(), len(xb)
def fit(epochs, model, loss_func, opt, train_dl, x_train, y_train, x_valid, y_valid):
train = np.zeros(epochs)
train_loss = np.zeros(epochs)
valid = np.zeros(epochs)
valid_loss = np.zeros(epochs)
inc_count = 0
max_acc = 0
for epoch in range(epochs):
model.train()
for xb, yb in train_dl:
loss_batch(model, loss_func, xb, yb, opt)
model.eval()
train[epoch] = accuracy(model(x_train), y_train)
train_loss[epoch] = loss_func(model(x_train), y_train)
valid[epoch] = accuracy(model(x_valid), y_valid)
valid_loss[epoch] = loss_func(model(x_valid), y_valid)
if valid[epoch] > max_acc:
max_acc = valid[epoch]
inc_count = 0
torch.save(model, "test")
else:
inc_count += 1
if inc_count == 300:
print("EARLY STOPPING: " + str(epoch - 300) + " " + str(valid[epoch - 300]))
inc_count = 0
break
model = torch.load("test")
model.eval()
print("SAVED: " + str(accuracy(model(x_valid), y_valid)))
def train_test(player, opponent, train_offset, valid_offset, test_offset, indices_file):
# Retrieve rally data
parser = train_parser.Parser(player, opponent, "charting-m-points.csv",
"charting-m-matches.csv")
x, y = parser.parse()
# Splitting the data and transofmring them into the appropriate types
x = np.array(x)
y = np.array(y)
x_train = x[train_offset:valid_offset]
y_train = y[train_offset:valid_offset]
with open(indices_file, "r") as f:
indices = f.readline()
indices = indices[1: -1]
indices = indices.split(", ")
indices = [int(index) for index in indices]
f.close()
valid_indices = indices[: math.ceil(len(indices) / 2)]
test_indices = indices[math.ceil(len(indices) / 2) :]
x_valid = x[valid_indices]
x_test = x[test_indices]
y_valid = y[valid_indices]
y_test = y[test_indices]
x_train, x_valid, x_test, y_train, y_valid, y_test = map(torch.tensor, (x_train, x_valid, x_test,
y_train, y_valid, y_test))
x_train = x_train.float()
x_valid = x_valid.float()
x_test = x_test.float()
y_train = y_train.long()
y_valid = y_valid.long()
y_test = y_test.long()
# Setting some model training parameters
lr = 0.0097 # learning rate
epochs = 3000 # how many epochs to train for
bs = 64 # batch size
loss_func = F.cross_entropy
n = len(x_train)
model, opt = get_model(lr, 66)
# Loading the data
train_ds = TensorDataset(x_train, y_train)
train_dl = get_data(train_ds, bs)
# Train the model
model.train()
fit(epochs, model, loss_func, opt, train_dl, x_train, y_train, x_valid, y_valid)
# Reporting post-training model performance
model = torch.load("test")
model.eval()
return accuracy(model(x_test), y_test).item()
def opt(num, x_train, y_train, x_valid, y_valid):
# Setting some model training parameters
lr = num # learning rate
epochs = 3000 # how many epochs to train for
bs = 64 # batch size
loss_func = F.cross_entropy
n = len(x_train)
model, opt = get_model(lr, 217)
# Loading the data
train_ds = TensorDataset(x_train, y_train)
train_dl = get_data(train_ds, bs)
# Train the model
model.train()
fit(epochs, model, loss_func, opt, train_dl, x_train, y_train, x_valid, y_valid)
# Reporting post-training model performance
model = torch.load("test")
model.eval()
out = model(x_valid)
print(accuracy(model(x_valid), y_valid).item())
return accuracy(model(x_valid), y_valid).item()
def self_test(model_path, player, opponent, train_offset, valid_offset, test_offset, indices_file):
model = torch.load(model_path)
# Retrieve rally data
parser = train_parser.Parser(player, opponent, "charting-m-points.csv",
"charting-m-matches.csv")
x, y = parser.parse()
# Splitting the data and transofmring them into the appropriate types
x = np.array(x)
y = np.array(y)
with open(indices_file, "r") as f:
indices = f.readline()
indices = indices[1: -1]
indices = indices.split(", ")
indices = [int(index) for index in indices]
f.close()
test_indices = indices[math.ceil(len(indices) / 2) :]
x_test = x[test_indices]
y_test = y[test_indices]
x_test, y_test = map(torch.tensor, (x_test, y_test))
x_test = x_test.float()
y_test = y_test.long()
# Reporting post-training model performance
model.eval()
acc = accuracy(model(x_test), y_test)
result = " ".join(player.split("_")) + " vs. " + " ".join(opponent.split("_")) + ": " + str(np.round_(acc.item() * 100, 1)) + "%"
return result
def test(model_path, player, opponent, cutoff):
model = torch.load(model_path)
# Retrieve rally data
parser = test_parser.Parser(player, opponent, "charting-m-points.csv",
"charting-m-matches.csv", cutoff)
x, y = parser.parse()
# Splitting the data and transofmring them into the appropriate types
x = np.array(x)
y = np.array(y)
x, y = map(torch.tensor, (x, y))
x = x.float()
y = y.long()
model.eval()
acc = accuracy(model(x), y)
result = " ".join(player.split("_")) + " vs. " + " ".join(opponent.split("_")) + ": " + str(np.round_(acc.item() * 100, 1)) + "%"
return result
# model - the model to generate action probabilities with
# scenario - the scenario to generate action probabilities for
def evaluate_scenario(model_path, scenario):
# Possible stroke and direction actions that can be taken
stroke = ["Forehand groundstroke", "Backhand groundstroke", "Forehand slice", "Backhand slice",
"Forehand volley", "Backhand volley", "Standard overhead/smash", "Backhand overhead/smash",
"Forehand drop shot", "Backhand drop shot", "Forehand lob", "Backhand lob", "Forehand half-volley",
"Backhand half-volley", "Forehand swinging volley", "Backhand swinging volley"]
modifier = ["", "(approach shot), ", "(stop volley), ", "(approach shot, stop volley), "]
direction = ["to the opponents right", "down the middle of the court", "to the opponents left"]
print("The success probabilities for all responses to this scenario are:")
model = torch.load(model_path)
model.eval()
opt_shot = [(0, 0, 0), 0]
# Evluate each possible action that the player can take
for i in range(len(stroke)):
for j in range(len(direction)):
scenario[0][18 + i] = 1
scenario[0][36 + j] = 1
if i in [4, 5, 12, 13, 14, 15]:
for k in range(len(modifier)):
scenario[0][34 + k] = 1
out = model(scenario).detach().numpy()
p = np.max(out, 1) * 100
if p[0] > opt_shot[1]:
opt_shot[0] = (i, k, j)
opt_shot[1] = p[0]
print(stroke[i] + ", " + modifier[k] + direction[j] + ": " + str(np.round(p[0], decimals = 2)) + "%")
scenario[0][21 + i] = 0
scenario[0][35 + j] = 0
scenario[0][34 + k] = 0
else:
for k in range(len(modifier) - 2):
scenario[0][34 + k] = 1
out = model(scenario).detach().numpy()
p = np.max(out, 1) * 100
if p[0] > opt_shot[1]:
opt_shot[0] = (i, k, j)
opt_shot[1] = p[0]
print(stroke[i] + ", " + modifier[k] + direction[j] + ": " + str(np.round(p[0], decimals = 2)) + "%")
scenario[0][21 + i] = 0
scenario[0][35 + j] = 0
scenario[0][34 + k] = 0
print("")
print("The optimal response to this scenario is:")
print(stroke[opt_shot[0][0]] + ", " + modifier[opt_shot[0][1]] +
direction[opt_shot[0][2]] + ": " +
str(np.round(opt_shot[1], decimals = 2)) + "%")
def opt_outer():
# Retrieve rally data
parser = train_parser.Parser("Roger_Federer", "Novak_Djokovic", "charting-m-points.csv",
"charting-m-matches.csv")
x, y = parser.parse()
# Splitting the data and transofmring them into the appropriate types
x = np.array(x)
# x = x[:, :79]
y = np.array(y)
x_train = x[0:2186]
y_train = y[0:2186]
with open("./ELEC4712-3/thesis/indices/rf_nd_indices.txt", "r") as f:
indices = f.readline()
indices = indices[1: -1]
indices = indices.split(", ")
indices = [int(index) for index in indices]
f.close()
valid_indices = indices[: math.ceil(len(indices) / 2)]
test_indices = indices[math.ceil(len(indices) / 2) :]
x_valid = x[valid_indices]
x_test = x[test_indices]
y_valid = y[valid_indices]
y_test = y[test_indices]
x_train, x_valid, x_test, y_train, y_valid, y_test = map(torch.tensor, (x_train, x_valid, x_test,
y_train, y_valid, y_test))
x_train = x_train.float()
x_valid = x_valid.float()
x_test = x_test.float()
y_train = y_train.long()
y_valid = y_valid.long()
y_test = y_test.long()
with open("./ELEC4712-3/thesis/indices/lr_results.txt", "a") as f:
x = np.arange(1e-4, 1e-1, 0.0004)
for i in range(214, len(x)):
f.write(str(x[i]) + " " + str(opt(x[i], x_train, y_train, x_valid, y_valid)) + "\n")
f.close()
print(test("p_o_rf_nd", "Novak_Djokovic", "Roger_Federer", "20151117"))
print(test("p_o_rf_nd", "Novak_Djokovic", "Rafael_Nadal", "20151117"))
print(self_test("p_o_rf_nd", "Roger_Federer", "Novak_Djokovic", 0, 2186, 0, "rf_nd_indices.txt"))
print(test("p_o_rf_nd", "Roger_Federer", "Rafael_Nadal", "20151117"))
print(test("p_o_rf_nd", "Rafael_Nadal", "Roger_Federer", "20151117"))
print(test("p_o_rf_nd", "Rafael_Nadal", "Novak_Djokovic", "20151117"))
# evaluate_scenario("p_o_rf_nd", torch.tensor([[
# 0, 0, 0, 0, 0, 0, 1, 0, 0, # Current player position (0 - 8)
# 1, 0, 0, 0, 0, 0, 0, 0, 0, # Previous player position (9 - 17)
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # Player stroke type (18 - 33)
# 0, 0, # Player shot modifier (34 - 35)
# 0, 0, 0, # Player shot direction (36 - 38)
# 0, 0, 0, 1, 0, 0, 0, 0, 0, # Current opponent position (39 - 47)
# 0, 0, 0, 1, 0, 0, 0, 0, 0, # Previous opponent position (48 - 56)
# 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # Opponent stroke type (57 - 72)
# 0, 0, 0, # Opponent shot modifier (73 - 75)
# 1, 0, 0, # Opponent shot direction (76 - 78)
# -0.16755696984219248, # Rally legnth (79)
# 0, 0, 1, # Court surface (80 - 82)
# 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # Game score (83 - 100)
# 1, 0, 0, 0, 0, 0, 0, 0, 0, # Set score (101 - 109)
# 1 # Best of (110)
# ]]))
| [
"[email protected]"
] | |
59d4bd3560e27c55efa24a0e2641c4c522652d9c | 641f1cc9e827d879489cacd49b5eaeb0dd27d196 | /spaceship.py | 3f802c2a5fdedd2f6b8f24376692f8c73228276f | [] | no_license | Yodaskywall/online_spaceship | 0ee2ef716e5cdd7dd555fbe028806eac439e3873 | e00f59602d066b53d762da831183945e25bef015 | refs/heads/master | 2022-02-25T16:23:52.104749 | 2019-11-03T23:25:08 | 2019-11-03T23:25:08 | 219,359,226 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,635 | py | import pygame
IMAGE_DIR = "images/"
DIM = (150, 135) # Dimensions of the spaceship sprite
BDIM = (10, 30)
class Bullet:
def __init__(self, id, ship_x, ship_y):
self.id = id
self.x = ship_x + DIM[0] / 2 - BDIM[0] / 2
self.y = ship_y - 20
self.speed = 100
def draw(self, win, clientId):
if clientId == self.id:
sprite = f"{IMAGE_DIR}/bullet.png"
else:
sprite = f"{IMAGE_DIR}/bullet2.png"
loaded_sprite = pygame.image.load(sprite)
win.blit(loaded_sprite, (self.x, self.y))
def check_hit(self, game, clientId):
spaceship = game.spaceships[clientId]
if (self.y + BDIM[1] > spaceship.y + DIM[1] // 2 and spaceship.x - BDIM[0] <= self.x <= spaceship.x + DIM[0] and self.id != spaceship.id):
aspaceship = game.spaceships[spaceship.id]
aspaceship.hp -= 1
return aspaceship
def update(self, clientId):
if clientId == self.id:
self.y -= self.speed
else:
self.y += self.speed
class SpaceShip:
def __init__(self, p, clientId):
self.id = clientId
self.width = DIM[0]
self.height = DIM[1]
self.speed = 3
self.hp = 10
self.cooldown = 500
self.last = 0
if p == 0:
self.sprite = f"{IMAGE_DIR}/nave.png"
self.x = round((1200 / 2) - (self.width / 2))
self.y = round(900 * 0.98 - self.height)
else:
self.sprite = f"{IMAGE_DIR}/nave2.png"
self.x = round((1200 / 2) - (self.width / 2))
self.y = round(900 * 0.02)
self.rect = (self.x, self.x + self.width, self.y, self.y + self.width)
def draw(self, win):
loaded_sprite = pygame.image.load(self.sprite)
win.blit(loaded_sprite, (self.x, self.y))
def move(self):
keys = pygame.key.get_pressed()
if self.rect[1] < 1200 and keys[pygame.K_RIGHT]:
self.x += self.speed
self.rect = (self.x, self.x + self.width, self.y, self.y + self.width)
if self.rect[0] > 0 and keys[pygame.K_LEFT]:
self.x -= self.speed
self.rect = (self.x, self.x + self.width, self.y, self.y + self.width)
def shoot(self, bullet_l, n):
keys = pygame.key.get_pressed()
now = pygame.time.get_ticks()
diff = abs(now - self.last)
if keys[pygame.K_SPACE] and diff >= self.cooldown:
bullet_l.append(Bullet(self.id, self.x, self.y))
self.last = now
return n.communicate(Bullet(self.id, self.x, self.y))
| [
"[email protected]"
] | |
6d9ee5db943f3c3f810828b0812b4844581e2d50 | 74ff8919dee51454dbbdf7ca25eefd5081ea6016 | /onodera/py/008_w_random_search.py | 06985201946043aae1569ccdeee89b19d7e8a977 | [
"MIT"
] | permissive | Sprinterzzj/Santa2017 | 1bcf8a84348ad00027cc25ae7e3eb5b5aaf9dfbe | 9d6efe166267a9ea3efe6d0c210a87b4049dbad2 | refs/heads/master | 2020-05-15T21:27:38.664149 | 2018-03-23T03:06:47 | 2018-03-23T03:06:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,148 | py | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
"""
nohup python -u 008_w_random_search.py > log1.txt &
nohup python -u 008_w_random_search.py > log2.txt &
nohup python -u 008_w_random_search.py > log3.txt &
nohup python -u 008_w_random_search.py > log4.txt &
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import math
from collections import Counter
from ortools.graph import pywrapgraph
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
n_children = 1000000 # n children to give
n_gift_type = 1000 # n types of gifts available
n_gift_quantity = 1000 # each type of gifts are limited to this quantity
n_gift_pref = 100 # number of gifts a child ranks
n_child_pref = 1000 # number of children a gift ranks
twins = math.ceil(0.04 * n_children / 2.) * 2 # 4% of all population, rounded to the closest number
triplets = math.ceil(0.005 * n_children / 3.) * 3 # 0.5% of all population, rounded to the closest number
ratio_gift_happiness = 2
ratio_child_happiness = 2
seed = np.random.randint(9999)
print('seed:',seed)
gift_pref = pd.read_csv('../input/child_wishlist_v2.csv.zip',header=None).drop(0, 1).values
child_pref = pd.read_csv('../input/gift_goodkids_v2.csv.zip',header=None).drop(0, 1).values
def lcm(a, b):
"""Compute the lowest common multiple of a and b"""
# in case of large numbers, using floor division
return a * b // math.gcd(a, b)
def avg_normalized_happiness(pred, child_pref, gift_pref):
# check if number of each gift exceeds n_gift_quantity
gift_counts = Counter(elem[1] for elem in pred)
for count in gift_counts.values():
assert count <= n_gift_quantity
# check if triplets have the same gift
for t1 in np.arange(0,triplets,3):
triplet1 = pred[t1]
triplet2 = pred[t1+1]
triplet3 = pred[t1+2]
# print(t1, triplet1, triplet2, triplet3)
assert triplet1[1] == triplet1[1] and triplet2[1] == triplet3[1]
# check if twins have the same gift
for t1 in np.arange(triplets,triplets+twins,2):
twin1 = pred[t1]
twin2 = pred[t1+1]
# print(t1)
assert twin1[1] == twin2[1]
max_child_happiness = n_gift_pref * ratio_child_happiness
max_gift_happiness = n_child_pref * ratio_gift_happiness
total_child_happiness = 0
total_gift_happiness = np.zeros(n_gift_type)
for row in pred:
child_id = row[0]
gift_id = row[1]
# check if child_id and gift_id exist
assert child_id < n_children
assert gift_id < n_gift_type
assert child_id >= 0
assert gift_id >= 0
child_happiness = (n_gift_pref - np.where(gift_pref[child_id]==gift_id)[0]) * ratio_child_happiness
if not child_happiness:
child_happiness = -1
gift_happiness = ( n_child_pref - np.where(child_pref[gift_id]==child_id)[0]) * ratio_gift_happiness
if not gift_happiness:
gift_happiness = -1
total_child_happiness += child_happiness
total_gift_happiness[gift_id] += gift_happiness
print('normalized child happiness=',float(total_child_happiness)/(float(n_children)*float(max_child_happiness)) , \
', normalized gift happiness',np.mean(total_gift_happiness) / float(max_gift_happiness*n_gift_quantity))
# to avoid float rounding error
# find common denominator
# NOTE: I used this code to experiment different parameters, so it was necessary to get the multiplier
# Note: You should hard-code the multipler to speed up, now that the parameters are finalized
denominator1 = n_children*max_child_happiness
denominator2 = n_gift_quantity*max_gift_happiness*n_gift_type
common_denom = lcm(denominator1, denominator2)
multiplier = common_denom / denominator1
# # usually denom1 > demon2
return float(math.pow(total_child_happiness*multiplier,3) + math.pow(np.sum(total_gift_happiness),3)) / float(math.pow(common_denom,3))
# return math.pow(float(total_child_happiness)/(float(n_children)*float(max_child_happiness)),2) + math.pow(np.mean(total_gift_happiness) / float(max_gift_happiness*n_gift_quantity),2)
#random_sub = pd.read_csv('../input/sample_submission_random_v2.csv').values.tolist()
#print(avg_normalized_happiness(random_sub, child_pref, gift_pref))
#gift_pref.shape, child_pref.shape
class Child(object):
def __init__(self, idx, prefer):
self.idx = idx
self.prefer_dict = dict()
for i in range(prefer.shape[0]):
self.prefer_dict[prefer[i]] = [12*(prefer.shape[0] - i), -6]
def add_gifts_prefer(self, giftid, score):
if giftid in self.prefer_dict.keys():
self.prefer_dict[giftid][1] = 6*score
else:
self.prefer_dict[giftid] = [-6, 6*score]
return None
def happiness(self, giftid):
return self.prefer_dict.get(giftid, [-6, -6])
class Child_twin(object):
def __init__(self, idx, prefer1, prefer2):
self.idx = idx
self.prefer_dict = dict()
for p in list(set(list(prefer1) + list(prefer2))):
score = 0
if p in list(prefer1):
score += 2*(100 - list(prefer1).index(p))
else:
score -= 1
if p in list(prefer2):
score += 2*(100 - list(prefer2).index(p))
else:
score -= 1
self.prefer_dict[p] = [3*score, -6]
def add_gifts_prefer(self, giftid, score):
if giftid in self.prefer_dict.keys():
self.prefer_dict[giftid][1] = 3*score
else:
self.prefer_dict[giftid] = [-6, 3*score]
return None
def happiness(self, giftid):
return self.prefer_dict.get(giftid, [-6, -6])
class Child_triplet(object):
def __init__(self, idx, prefer1, prefer2, prefer3):
self.idx = idx
self.prefer_dict = dict()
for p in list(set(list(prefer1) + list(prefer2) + list(prefer3))):
score = 0
if p in list(prefer1):
score += 2*(100 - list(prefer1).index(p))
else:
score -= 1
if p in list(prefer2):
score += 2*(100 - list(prefer2).index(p))
else:
score -= 1
if p in list(prefer3):
score += 2*(100 - list(prefer3).index(p))
else:
score -= 1
self.prefer_dict[p] = [2*score, -6]
def add_gifts_prefer(self, giftid, score):
if giftid in self.prefer_dict.keys():
self.prefer_dict[giftid][1] = 2*score
else:
self.prefer_dict[giftid] = [-6, 2*score]
return None
def happiness(self, giftid):
return self.prefer_dict.get(giftid, [-6, -6])
Children = []
for i in range(0, 5001, 3):
Children.append(Child_triplet(i, gift_pref[i], gift_pref[i+1], gift_pref[i+2]))
Children.append(Child_triplet(i+1, gift_pref[i], gift_pref[i+1], gift_pref[i+2]))
Children.append(Child_triplet(i+2, gift_pref[i], gift_pref[i+1], gift_pref[i+2]))
for i in range(5001, 45001, 2):
Children.append(Child_twin(i, gift_pref[i], gift_pref[i+1]))
Children.append(Child_twin(i+1, gift_pref[i], gift_pref[i+1]))
Children = Children + [Child(i, gift_pref[i]) for i in range(45001, 1000000)]
for j in range(1000):
cf = child_pref[j]
done_list = []
for i in range(cf.shape[0]):
if cf[i] <= 5000 and cf[i] not in done_list:
if cf[i] % 3 == 0:
cid1 = cf[i]
cid2 = cf[i] + 1
cid3 = cf[i] + 2
done_list.append(cid2)
done_list.append(cid3)
elif cf[i] % 3 == 1:
cid1 = cf[i] - 1
cid2 = cf[i]
cid3 = cf[i] + 1
done_list.append(cid1)
done_list.append(cid3)
else:
cid1 = cf[i] - 2
cid2 = cf[i] - 1
cid3 = cf[i]
done_list.append(cid1)
done_list.append(cid2)
if cid1 in list(cf):
score_ = 2*(cf.shape[0] - list(cf).index(cid1))
else:
score_ = -1
if cid2 in list(cf):
score_ += 2*(cf.shape[0] - list(cf).index(cid2))
else:
score_ += -1
if cid3 in list(cf):
score_ += 2*(cf.shape[0] - list(cf).index(cid3))
else:
score_ += -1
Children[cid1].add_gifts_prefer(j, score_)
Children[cid2].add_gifts_prefer(j, score_)
Children[cid3].add_gifts_prefer(j, score_)
elif cf[i] <= 45000 and cf[i] not in done_list:
if cf[i] % 2 == 0:
cid1 = cf[i]
cid2 = cf[i] + 1
done_list.append(cid2)
else:
cid1 = cf[i] - 1
cid2 = cf[i]
done_list.append(cid1)
if cid1 in list(cf):
score_ = 2*(cf.shape[0] - list(cf).index(cid1))
else:
score_ = -1
if cid2 in list(cf):
score_ += 2*(cf.shape[0] - list(cf).index(cid2))
else:
score_ += -1
Children[cid1].add_gifts_prefer(j, score_)
Children[cid2].add_gifts_prefer(j, score_)
elif cf[i] > 45000:
Children[cf[i]].add_gifts_prefer(j, 2*(cf.shape[0] - i))
print("W_CHILD, W_GIFTS, W_CHILD/W_GIFTS, score")
while True:
W_CHILD = np.random.randint(10000, 99999999)
W_GIFTS = int(W_CHILD * np.random.uniform(2/3*0.9, 2/3*1.1))
start_nodes = []
end_nodes = []
capacities = []
unit_costs = []
# triplets
for i in range(0, 5001, 3):
for g in Children[i].prefer_dict.keys():
start_nodes.append(1000000+g)
end_nodes.append(i)
capacities.append(3)
unit_costs.append(-W_CHILD*(Children[i].prefer_dict[g][0] + 6)-W_GIFTS*(Children[i].prefer_dict[g][1] + 6))
# triplets
for i in range(5001, 45001, 2):
for g in Children[i].prefer_dict.keys():
start_nodes.append(1000000+g)
end_nodes.append(i)
capacities.append(2)
unit_costs.append(-W_CHILD*(Children[i].prefer_dict[g][0] + 6)-W_GIFTS*(Children[i].prefer_dict[g][1] + 6))
# other children
for i in range(45001, 1000000):
for g in Children[i].prefer_dict.keys():
start_nodes.append(1000000+g)
end_nodes.append(i)
capacities.append(1)
unit_costs.append(-W_CHILD*(Children[i].prefer_dict[g][0] + 6)-W_GIFTS*(Children[i].prefer_dict[g][1] + 6))
min_cost_flow_1 = pywrapgraph.SimpleMinCostFlow()
# add Arc
# gift -> children
for i in range(len(start_nodes)):
min_cost_flow_1.AddArcWithCapacityAndUnitCost(
int(start_nodes[i]), int(end_nodes[i]), int(capacities[i]), int(unit_costs[i])
)
# children -> 1001000 : collection
for i in range(0, 5001, 3):
min_cost_flow_1.AddArcWithCapacityAndUnitCost(
int(i), int(1001000), int(3), int(0)
)
for i in range(5001, 45001, 2):
min_cost_flow_1.AddArcWithCapacityAndUnitCost(
int(i), int(1001000), int(2), int(0)
)
for i in range(45001, 1000000):
min_cost_flow_1.AddArcWithCapacityAndUnitCost(
int(i), int(1001000), int(1), int(0)
)
# gift -> 1001001 : dust_gift
for i in range(1000):
min_cost_flow_1.AddArcWithCapacityAndUnitCost(
int(1000000+i), int(1001001), int(1000), int(0)
)
# 1001001 -> 1001000 : dust_path
min_cost_flow_1.AddArcWithCapacityAndUnitCost(
int(1001001), int(1001000), int(1000000), int(0)
)
# add Supply
for i in range(1000):
min_cost_flow_1.SetNodeSupply(int(1000000+i), int(1000))
# children
for i in range(0, 5001, 3):
min_cost_flow_1.SetNodeSupply(int(i), int(0))
for i in range(5001, 45001, 2):
min_cost_flow_1.SetNodeSupply(int(i), int(0))
for i in range(45001, 1000000):
min_cost_flow_1.SetNodeSupply(int(i), int(0))
min_cost_flow_1.SetNodeSupply(int(1001001), int(0))
min_cost_flow_1.SetNodeSupply(int(1001000), int(-1000000))
min_cost_flow_1.Solve()
assignment = [-1]*1000000
twins_differ = []
triplets_differ = []
for i in range(min_cost_flow_1.NumArcs()):
if min_cost_flow_1.Flow(i) != 0 and min_cost_flow_1.Head(i) < 1000000:
c = min_cost_flow_1.Head(i)
g = min_cost_flow_1.Tail(i)
f = min_cost_flow_1.Flow(i)
if c >= 45001:
assignment[c] = g - 1000000
elif c >= 5001:
if f == 1:
if assignment[c] == -1:
assignment[c] = g - 1000000
twins_differ.append([c, c+1])
else:
assignment[c+1] = g - 1000000
elif f == 2:
assignment[c] = g - 1000000
assignment[c+1] = g - 1000000
else:
if f == 1:
if assignment[c] == -1:
assignment[c] = g - 1000000
triplets_differ.append([c, c+1, c+2])
elif assignment[c+1] == -1:
assignment[c+1] = g - 1000000
else:
assignment[c+2] = g - 1000000
elif f == 2:
if assignment[c] == -1:
assignment[c] = g - 1000000
assignment[c+1] = g - 1000000
triplets_differ.append([c, c+1, c+2])
else:
assignment[c+1] = g - 1000000
assignment[c+2] = g - 1000000
elif f == 3:
assignment[c] = g - 1000000
assignment[c+1] = g - 1000000
assignment[c+2] = g - 1000000
CHILD_HAPPINESS = sum([Children[i].happiness(assignment[i])[0] for i in range(1000000)])*10
SANTA_HAPPINESS = sum([Children[i].happiness(assignment[i])[1] for i in range(1000000)])
OBJ = CHILD_HAPPINESS**3 + SANTA_HAPPINESS**3
score = OBJ / (12000000000**3)
print('{}, {}, {:.5f}, {}'.format(W_CHILD, W_GIFTS, (W_CHILD/W_GIFTS), score))
# wata: 0.9362730938
| [
"[email protected]"
] | |
91e8aa631768c6b2d163460ed75174c88256162a | fab14fae2b494068aa793901d76464afb965df7e | /benchmarks/f3_wrong_hints/scaling_ltl_timed_transition_system/18-sender_receiver_5.py | df88016e3e38fa248461fa4e0917379e9383acde | [
"MIT"
] | permissive | teodorov/F3 | 673f6f9ccc25acdfdecbfc180f439253474ba250 | c863215c318d7d5f258eb9be38c6962cf6863b52 | refs/heads/master | 2023-08-04T17:37:38.771863 | 2021-09-16T07:38:28 | 2021-09-16T07:38:28 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 19,274 | py | from typing import FrozenSet
from collections import Iterable
from math import log, ceil
from mathsat import msat_term, msat_env
from mathsat import msat_make_constant, msat_declare_function
from mathsat import msat_get_integer_type, msat_get_rational_type, msat_get_bool_type
from mathsat import msat_make_and, msat_make_not, msat_make_or, msat_make_iff
from mathsat import msat_make_leq, msat_make_equal, msat_make_true
from mathsat import msat_make_number, msat_make_plus, msat_make_times
from pysmt.environment import Environment as PysmtEnv
import pysmt.typing as types
from ltl.ltl import TermMap, LTLEncoder
from utils import name_next, symb_to_next
from hint import Hint, Location
delta_name = "delta"
def decl_consts(menv: msat_env, name: str, c_type) -> tuple:
assert not name.startswith("_"), name
s = msat_declare_function(menv, name, c_type)
s = msat_make_constant(menv, s)
x_s = msat_declare_function(menv, name_next(name), c_type)
x_s = msat_make_constant(menv, x_s)
return s, x_s
def make_enum(menv, v_name: str, enum_size: int):
bool_type = msat_get_bool_type(menv)
num_bits = ceil(log(enum_size, 2))
b_vars = []
for idx in range(num_bits):
c_name = "{}{}".format(v_name, idx)
b_vars.append(tuple(decl_consts(menv, c_name, bool_type)))
vals = []
x_vals = []
for enum_val in range(enum_size):
bit_val = format(enum_val, '0{}b'.format(num_bits))
assert len(bit_val) == num_bits
assert all(c in {'0', '1'} for c in bit_val)
assign = [b_vars[idx] if c == '1' else
(msat_make_not(menv, b_vars[idx][0]),
msat_make_not(menv, b_vars[idx][1]))
for idx, c in enumerate(reversed(bit_val))]
pred = assign[0][0]
x_pred = assign[0][1]
for it in assign[1:]:
pred = msat_make_and(menv, pred, it[0])
x_pred = msat_make_and(menv, x_pred, it[1])
vals.append(pred)
x_vals.append(x_pred)
assert len(vals) == enum_size
assert len(x_vals) == enum_size
return b_vars, vals, x_vals
def msat_make_minus(menv: msat_env, arg0: msat_term, arg1: msat_term):
m_one = msat_make_number(menv, "-1")
arg1 = msat_make_times(menv, arg1, m_one)
return msat_make_plus(menv, arg0, arg1)
def msat_make_lt(menv: msat_env, arg0: msat_term, arg1: msat_term):
geq = msat_make_geq(menv, arg0, arg1)
return msat_make_not(menv, geq)
def msat_make_geq(menv: msat_env, arg0: msat_term, arg1: msat_term):
return msat_make_leq(menv, arg1, arg0)
def msat_make_gt(menv: msat_env, arg0: msat_term, arg1: msat_term):
leq = msat_make_leq(menv, arg0, arg1)
return msat_make_not(menv, leq)
def msat_make_impl(menv: msat_env, arg0: msat_term, arg1: msat_term):
n_arg0 = msat_make_not(menv, arg0)
return msat_make_or(menv, n_arg0, arg1)
def diverging_symbs(menv: msat_env) -> frozenset:
real_type = msat_get_rational_type(menv)
delta = msat_declare_function(menv, delta_name, real_type)
delta = msat_make_constant(menv, delta)
return frozenset([delta])
def check_ltl(menv: msat_env, enc: LTLEncoder) -> (Iterable, msat_term,
msat_term, msat_term):
assert menv
assert isinstance(menv, msat_env)
assert enc
assert isinstance(enc, LTLEncoder)
int_type = msat_get_integer_type(menv)
real_type = msat_get_rational_type(menv)
r2s, x_r2s = decl_consts(menv, "r2s", int_type)
s2r, x_s2r = decl_consts(menv, "s2r", int_type)
delta, x_delta = decl_consts(menv, delta_name, real_type)
sender = Sender("s", menv, enc, r2s, x_r2s, s2r, x_s2r, delta)
receiver = Receiver("r", menv, enc, s2r, x_s2r, r2s, x_r2s, delta)
curr2next = {r2s: x_r2s, s2r: x_s2r, delta: x_delta}
for comp in [sender, receiver]:
for s, x_s in comp.symb2next.items():
curr2next[s] = x_s
zero = msat_make_number(menv, "0")
init = msat_make_and(menv, receiver.init, sender.init)
trans = msat_make_and(menv, receiver.trans, sender.trans)
# invar delta >= 0
init = msat_make_and(menv, init,
msat_make_geq(menv, delta, zero))
trans = msat_make_and(menv, trans,
msat_make_geq(menv, x_delta, zero))
# delta > 0 -> (r2s' = r2s & s2r' = s2r)
lhs = msat_make_gt(menv, delta, zero)
rhs = msat_make_and(menv,
msat_make_equal(menv, x_r2s, r2s),
msat_make_equal(menv, x_s2r, s2r))
trans = msat_make_and(menv, trans,
msat_make_impl(menv, lhs, rhs))
# (G F !s.stutter) -> G (s.wait_ack -> F s.send)
lhs = enc.make_G(enc.make_F(msat_make_not(menv, sender.stutter)))
rhs = enc.make_G(msat_make_impl(menv, sender.wait_ack,
enc.make_F(sender.send)))
ltl = msat_make_impl(menv, lhs, rhs)
return TermMap(curr2next), init, trans, ltl
class Module:
def __init__(self, name: str, menv: msat_env, enc: LTLEncoder,
*args, **kwargs):
self.name = name
self.menv = menv
self.enc = enc
self.symb2next = {}
true = msat_make_true(menv)
self.init = true
self.trans = true
def _symb(self, v_name, v_type):
v_name = "{}_{}".format(self.name, v_name)
return decl_consts(self.menv, v_name, v_type)
def _enum(self, v_name: str, enum_size: int):
c_name = "{}_{}".format(self.name, v_name)
return make_enum(self.menv, c_name, enum_size)
class Sender(Module):
def __init__(self, name: str, menv: msat_env, enc: LTLEncoder,
in_c, x_in_c, out_c, x_out_c, delta):
super().__init__(name, menv, enc)
bool_type = msat_get_bool_type(menv)
int_type = msat_get_integer_type(menv)
real_type = msat_get_rational_type(menv)
loc, x_loc = self._symb("l", bool_type)
evt, x_evt = self._symb("evt", bool_type)
msg_id, x_msg_id = self._symb("msg_id", int_type)
timeout, x_timeout = self._symb("timeout", real_type)
c, x_c = self._symb("c", real_type)
self.move = evt
self.stutter = msat_make_not(menv, evt)
self.x_move = x_evt
self.x_stutter = msat_make_not(menv, x_evt)
self.send = loc
self.wait_ack = msat_make_not(menv, loc)
self.x_send = x_loc
self.x_wait_ack = msat_make_not(menv, x_loc)
self.symb2next = {loc: x_loc, evt: x_evt, msg_id: x_msg_id,
timeout: x_timeout, c: x_c}
zero = msat_make_number(menv, "0")
one = msat_make_number(menv, "1")
base_timeout = one
# send & c = 0 & msg_id = 0
self.init = msat_make_and(menv,
msat_make_and(menv, self.send,
msat_make_equal(menv, c,
zero)),
msat_make_equal(menv, msg_id, zero))
# invar: wait_ack -> c <= timeout
self.init = msat_make_and(
menv, self.init,
msat_make_impl(menv, self.wait_ack,
msat_make_leq(menv, c, timeout)))
self.trans = msat_make_impl(menv, self.x_wait_ack,
msat_make_leq(menv, x_c, x_timeout))
# delta > 0 | stutter -> l' = l & msg_id' = msg_id & timeout' = timeout &
# c' = c + delta & out_c' = out_c
lhs = msat_make_or(menv, msat_make_gt(menv, delta, zero), self.stutter)
rhs = msat_make_and(
menv,
msat_make_and(menv,
msat_make_iff(menv, x_loc, loc),
msat_make_equal(menv, x_msg_id, msg_id)),
msat_make_and(menv,
msat_make_equal(menv, x_timeout, timeout),
msat_make_equal(menv, x_c,
msat_make_plus(menv, c, delta))))
rhs = msat_make_and(menv, rhs,
msat_make_equal(menv, x_out_c, out_c))
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
disc_t = msat_make_and(menv, self.move,
msat_make_equal(menv, delta, zero))
# (send & send') ->
# (msg_id' = msg_id & timeout' = base_timeout & c' = 0 & out_c' = out_c)
lhs = msat_make_and(menv, disc_t,
msat_make_and(menv, self.send, self.x_send))
rhs = msat_make_and(
menv,
msat_make_and(menv,
msat_make_equal(menv, x_msg_id, msg_id),
msat_make_equal(menv, x_timeout, base_timeout)),
msat_make_and(menv,
msat_make_equal(menv, x_c, zero),
msat_make_equal(menv, x_out_c, out_c)))
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (send & wait_ack') ->
# (msg_id' = msg_id + 1 & timeout' = base_timeout & c' = 0 & out_c' = out_c)
lhs = msat_make_and(menv, disc_t,
msat_make_and(menv, self.send, self.x_wait_ack))
rhs = msat_make_and(
menv,
msat_make_and(menv,
msat_make_equal(menv, x_msg_id,
msat_make_plus(menv, msg_id, one)),
msat_make_equal(menv, x_timeout, base_timeout)),
msat_make_and(menv,
msat_make_equal(menv, x_c, zero),
msat_make_equal(menv, x_out_c, out_c)))
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (wait_ack) -> (c' = 0 & out_c' = out_c &
# (wait_ack' <-> (in_c != msg_id & c > timeout))
lhs = msat_make_and(menv, disc_t, self.wait_ack)
rhs_iff = msat_make_and(menv,
msat_make_not(menv,
msat_make_equal(menv, in_c,
msg_id)),
msat_make_geq(menv, c, timeout))
rhs_iff = msat_make_iff(menv, self.x_wait_ack, rhs_iff)
rhs = msat_make_and(menv,
msat_make_and(menv,
msat_make_equal(menv, x_c, zero),
msat_make_equal(menv, x_out_c,
out_c)),
rhs_iff)
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (wait_ack & wait_ack') -> (timeout' > timeout)
lhs = msat_make_and(menv, disc_t,
msat_make_and(menv, self.wait_ack,
self.x_wait_ack))
rhs = msat_make_gt(menv, x_timeout, timeout)
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (wait_ack) -> (send' <-> (in_c = msg_id & c < timeout))
lhs = msat_make_and(menv, disc_t, self.wait_ack)
rhs = msat_make_iff(menv, self.x_send,
msat_make_and(menv,
msat_make_equal(menv, in_c, msg_id),
msat_make_lt(menv, c, timeout)))
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (wait_ack & send') -> (timeout' = base_timeout)
lhs = msat_make_and(menv, disc_t,
msat_make_and(menv, self.wait_ack, self.x_send))
rhs = msat_make_equal(menv, x_timeout, base_timeout)
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
class Receiver(Module):
def __init__(self, name: str, menv: msat_env, enc: LTLEncoder,
in_c, x_in_c, out_c, x_out_c, delta):
super().__init__(name, menv, enc)
bool_type = msat_get_bool_type(menv)
loc, x_loc = self._symb("l", bool_type)
self.wait = loc
self.work = msat_make_not(menv, loc)
self.x_wait = x_loc
self.x_work = msat_make_not(menv, x_loc)
self.symb2next = {loc: x_loc}
zero = msat_make_number(menv, "0")
# wait
self.init = self.wait
# delta > 0 -> loc' = loc & out_c' = out_c
lhs = msat_make_gt(menv, delta, zero)
rhs = msat_make_and(menv,
msat_make_iff(menv, x_loc, loc),
msat_make_equal(menv, x_out_c, out_c))
self.trans = msat_make_impl(menv, lhs, rhs)
disc_t = msat_make_equal(menv, delta, zero)
# wait -> (wait' <-> in_c = out_c)
lhs = msat_make_and(menv, disc_t, self.wait)
rhs = msat_make_iff(menv, self.x_wait,
msat_make_equal(menv, in_c, out_c))
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (wait & wait') -> (out_c' = out_c)
lhs = msat_make_and(menv, disc_t,
msat_make_and(menv, self.wait, self.x_wait))
rhs = msat_make_equal(menv, x_out_c, out_c)
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# (wait & work') -> out_c' = in_c
lhs = msat_make_and(menv, disc_t,
msat_make_and(menv, self.wait, self.x_work))
rhs = msat_make_equal(menv, x_out_c, in_c)
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
# work -> out_c' = out_c
lhs = msat_make_and(menv, disc_t, self.work)
rhs = msat_make_equal(menv, x_out_c, out_c)
self.trans = msat_make_and(menv, self.trans,
msat_make_impl(menv, lhs, rhs))
def hints(env: PysmtEnv) -> FrozenSet[Hint]:
assert isinstance(env, PysmtEnv)
mgr = env.formula_manager
delta = mgr.Symbol(delta_name, types.REAL)
r2s = mgr.Symbol("r2s", types.INT)
s2r = mgr.Symbol("r2s", types.INT)
s_l = mgr.Symbol("s_l", types.BOOL)
s_evt = mgr.Symbol("s_evt", types.BOOL)
s_msg_id = mgr.Symbol("s_msg_id", types.INT)
s_timeout = mgr.Symbol("s_timeout", types.REAL)
s_c = mgr.Symbol("s_c", types.REAL)
r_l = mgr.Symbol("r_l", types.BOOL)
symbs = frozenset([delta, r2s, s2r, s_l, s_evt, s_msg_id, s_timeout, s_c,
r_l])
x_delta = symb_to_next(mgr, delta)
x_r2s = symb_to_next(mgr, r2s)
x_s2r = symb_to_next(mgr, s2r)
x_s_l = symb_to_next(mgr, s_l)
x_s_evt = symb_to_next(mgr, s_evt)
x_s_msg_id = symb_to_next(mgr, s_msg_id)
x_s_timeout = symb_to_next(mgr, s_timeout)
x_s_c = symb_to_next(mgr, s_c)
x_r_l = symb_to_next(mgr, r_l)
res = []
r0 = mgr.Real(0)
r1 = mgr.Real(1)
i0 = mgr.Int(0)
i1 = mgr.Int(1)
loc0 = Location(env, mgr.Equals(delta, r0))
loc0.set_progress(0, mgr.Equals(x_delta, r0))
hint = Hint("h_delta0", env, frozenset([delta]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.Equals(s2r, i0))
loc0.set_progress(0, mgr.Equals(x_s2r, i0))
hint = Hint("h_s2r0", env, frozenset([s2r]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.Equals(r2s, i0))
loc0.set_progress(0, mgr.Equals(x_r2s, i0))
hint = Hint("h_r2s0", env, frozenset([r2s]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, s_l)
loc0.set_progress(0, x_s_l)
hint = Hint("h_s_l0", env, frozenset([s_l]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.Equals(s_msg_id, i0))
loc0.set_progress(0, mgr.Equals(x_s_msg_id, i0))
hint = Hint("h_s_msg_id0", env, frozenset([s_msg_id]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.Equals(s_timeout, r0))
loc0.set_progress(0, mgr.Equals(x_s_timeout, r0))
hint = Hint("h_s_timeout0", env, frozenset([s_timeout]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.Equals(s_c, r0))
loc0.set_progress(0, mgr.Equals(x_s_c, r0))
hint = Hint("h_s_c0", env, frozenset([s_c]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, r_l)
loc0.set_progress(0, x_r_l)
hint = Hint("h_r_l0", env, frozenset([r_l]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.GE(delta, r0))
loc0.set_progress(0, mgr.Equals(x_delta, r1))
hint = Hint("h_delta1", env, frozenset([delta]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.GE(s2r, i0))
loc0.set_progress(0, mgr.Equals(x_s2r, i1))
hint = Hint("h_s2r1", env, frozenset([s2r]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.GE(r2s, i0))
loc0.set_progress(0, mgr.Equals(x_r2s, i1))
hint = Hint("h_r2s1", env, frozenset([r2s]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, s_l)
loc0.set_progress(1, mgr.Not(x_s_l))
loc1 = Location(env, mgr.Not(s_l))
loc1.set_progress(0, x_s_l)
hint = Hint("h_s_l1", env, frozenset([s_l]), symbs)
hint.set_locs([loc0, loc1])
res.append(hint)
loc0 = Location(env, s_evt)
loc0.set_progress(1, mgr.Not(x_s_evt))
loc1 = Location(env, mgr.Not(s_evt))
loc1.set_progress(0, x_s_evt)
hint = Hint("h_s_evt1", env, frozenset([s_evt]), symbs)
hint.set_locs([loc0, loc1])
res.append(hint)
loc0 = Location(env, mgr.GE(s_msg_id, i0))
loc0.set_progress(0, mgr.Equals(x_s_msg_id, mgr.Plus(s_msg_id, i1)))
hint = Hint("h_s_msg_id1", env, frozenset([s_msg_id]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.GE(s_timeout, r0))
loc0.set_progress(0, mgr.Equals(x_s_timeout, mgr.Plus(s_timeout, r1)))
hint = Hint("h_s_timeout1", env, frozenset([s_timeout]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, mgr.GE(s_c, r0))
loc0.set_progress(0, mgr.Equals(x_s_c, mgr.Plus(s_c, r1)))
hint = Hint("h_s_c1", env, frozenset([s_c]), symbs)
hint.set_locs([loc0])
res.append(hint)
loc0 = Location(env, r_l)
loc0.set_progress(1, mgr.Not(x_r_l))
loc1 = Location(env, mgr.Not(r_l))
loc1.set_progress(0, x_r_l)
hint = Hint("h_r_l1", env, frozenset([r_l]), symbs)
hint.set_locs([loc0, loc1])
res.append(hint)
loc0 = Location(env, mgr.GE(delta, r0))
loc0.set_progress(0, mgr.Equals(x_delta, mgr.Plus(delta, r1)))
hint = Hint("h_delta2", env, frozenset([delta]), symbs)
hint.set_locs([loc0])
res.append(hint)
return frozenset(res)
| [
"[email protected]"
] | |
dd054ec24319a017c4b3be563688e9b6f157981c | 3c751e5bebd9ee3602b41a41d0fdba968eaadf38 | /08월/08_16/4874.py | 8da111dfd2d6d4e274b27f47fca1a0374f418c72 | [] | no_license | ohsean93/algo | 423b25e52f638540039bd6e57706f45ab71871c8 | 8f4e20a0d955610427db9273d1eb138c7ae1e534 | refs/heads/master | 2020-06-27T01:58:36.484367 | 2019-11-29T00:04:02 | 2019-11-29T00:04:02 | 199,815,006 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 966 | py | import sys
sys.stdin = open("input.txt", "r")
T = int(input())
for test_case in range(T):
num_list = [0] * 129
operator = ('+', '-', '/', '*')
i = -1
for char in input().split():
if char.isdigit():
i += 1
num_list[i] = int(char)
elif char == '.':
continue
elif char in operator:
if i < 1:
ans = 'error'
break
if char == '+':
num_list[i-1] += num_list[i]
i -= 1
elif char == '-':
num_list[i-1] -= num_list[i]
i -= 1
elif char == '/':
num_list[i-1] //= num_list[i]
i -= 1
elif char == '*':
num_list[i-1] *= num_list[i]
i -= 1
else:
if i == 0:
ans = num_list[0]
else:
ans = 'error'
print('#{} {}'.format(test_case+1, ans)) | [
"[email protected]"
] | |
a0c3fb9611547ff89d8af7bcbaed1d9775f86348 | 8e115d2de6e7904d92a7a81bc8232fa3bb4c04f7 | /s_vae_pytorch/examples/mnist.py | 7dc2a5792c50f29482d7afce0c0dc587831b4835 | [
"MIT"
] | permissive | P4ppenheimer/circle_slice_flow_and_variational_determinant_estimator | 9c9ef8fd2cee1175ae33fe91cced7d824645a9be | 6d42c7641e9e060802b69c8c9a89aeb02c46c922 | refs/heads/main | 2023-02-06T03:09:09.548158 | 2020-12-31T12:11:10 | 2020-12-31T12:11:10 | 322,137,213 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,373 | py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from torchvision import datasets, transforms
from collections import defaultdict
from hyperspherical_vae.distributions import VonMisesFisher
from hyperspherical_vae.distributions import HypersphericalUniform
DIM_MNIST = 784
train_loader = torch.utils.data.DataLoader(datasets.MNIST('./data', train=True, download=True,
transform=transforms.ToTensor()), batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.MNIST('./data', train=False, download=True,
transform=transforms.ToTensor()), batch_size=64)
class ModelVAE(torch.nn.Module):
def __init__(self, h_dim, z_dim, activation=F.relu, distribution='normal'):
"""
ModelVAE initializer
:param h_dim: dimension of the hidden layers
:param z_dim: dimension of the latent representation
:param activation: callable activation function
:param distribution: string either `normal` or `vmf`, indicates which distribution to use
"""
super(ModelVAE, self).__init__()
self.z_dim, self.activation, self.distribution = z_dim, activation, distribution
# 2 hidden layers encoder
self.fc_e0 = nn.Linear(784, h_dim * 2)
self.fc_e1 = nn.Linear(h_dim * 2, h_dim)
if self.distribution == 'normal':
# compute mean and std of the normal distribution
self.fc_mean = nn.Linear(h_dim, z_dim)
self.fc_var = nn.Linear(h_dim, z_dim)
elif self.distribution == 'vmf':
# compute mean and concentration of the von Mises-Fisher
self.fc_mean = nn.Linear(h_dim, z_dim)
self.fc_var = nn.Linear(h_dim, 1)
else:
raise NotImplemented
# 2 hidden layers decoder
self.fc_d0 = nn.Linear(z_dim, h_dim)
self.fc_d1 = nn.Linear(h_dim, h_dim * 2)
self.fc_logits = nn.Linear(h_dim * 2, DIM_MNIST)
def encode(self, x):
# 2 hidden layers encoder
x = self.activation(self.fc_e0(x))
x = self.activation(self.fc_e1(x))
if self.distribution == 'normal':
# compute mean and std of the normal distribution
z_mean = self.fc_mean(x)
z_var = F.softplus(self.fc_var(x))
elif self.distribution == 'vmf':
# compute mean and concentration of the von Mises-Fisher
z_mean = self.fc_mean(x)
z_mean = z_mean / z_mean.norm(dim=-1, keepdim=True)
# the `+ 1` prevent collapsing behaviors
z_var = F.softplus(self.fc_var(x)) + 1
else:
raise NotImplemented
return z_mean, z_var
def decode(self, z):
x = self.activation(self.fc_d0(z))
x = self.activation(self.fc_d1(x))
x = self.fc_logits(x)
return x
def reparameterize(self, z_mean, z_var):
if self.distribution == 'normal':
q_z = torch.distributions.normal.Normal(z_mean, z_var)
p_z = torch.distributions.normal.Normal(torch.zeros_like(z_mean), torch.ones_like(z_var))
elif self.distribution == 'vmf':
q_z = VonMisesFisher(z_mean, z_var)
p_z = HypersphericalUniform(self.z_dim - 1)
else:
raise NotImplemented
return q_z, p_z
def forward(self, x):
z_mean, z_var = self.encode(x)
q_z, p_z = self.reparameterize(z_mean, z_var)
z = q_z.rsample()
x_ = self.decode(z)
return (z_mean, z_var), (q_z, p_z), z, x_
def log_likelihood(model, x, n=10):
"""
:param model: model object
:param optimizer: optimizer object
:param n: number of MC samples
:return: MC estimate of log-likelihood
"""
z_mean, z_var = model.encode(x.reshape(-1, 784))
q_z, p_z = model.reparameterize(z_mean, z_var)
z = q_z.rsample(torch.Size([n]))
x_mb_ = model.decode(z)
log_p_z = p_z.log_prob(z)
if model.distribution == 'normal':
log_p_z = log_p_z.sum(-1)
log_p_x_z = -nn.BCEWithLogitsLoss(reduction='none')(x_mb_, x.reshape(-1, 784).repeat((n, 1, 1))).sum(-1)
log_q_z_x = q_z.log_prob(z)
if model.distribution == 'normal':
log_q_z_x = log_q_z_x.sum(-1)
return ((log_p_x_z + log_p_z - log_q_z_x).t().logsumexp(-1) - np.log(n)).mean()
def train(model, optimizer):
for i, (x_mb, y_mb) in enumerate(train_loader):
optimizer.zero_grad()
# dynamic binarization
x_mb = (x_mb > torch.distributions.Uniform(0, 1).sample(x_mb.shape)).float()
_, (q_z, p_z), _, x_mb_ = model(x_mb.reshape(-1, 784))
print('q_z',q_z)
print('p_z',p_z)
print('x_mb',x_mb)
print('x_mb_',x_mb_)
# BCEWithLogits is BCE with sigmoid and
# Loss_BCE(x,y) = y log[sigmoid(x)] + (1 - y) log[1 - sigmoid(x)]
# that means the output of the model, which is the decoder, gets mapped to x, which itself gets mapped to [0,1] via sigmoid
loss_recon = nn.BCEWithLogitsLoss(reduction='none')(x_mb_, x_mb.reshape(-1, 784)).sum(-1).mean()
if model.distribution == 'normal':
loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z).sum(-1).mean()
elif model.distribution == 'vmf':
loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z).mean()
else:
raise NotImplemented
loss = loss_recon + loss_KL
loss.backward()
optimizer.step()
def test(model, optimizer):
print_ = defaultdict(list)
for x_mb, y_mb in test_loader:
# dynamic binarization
x_mb = (x_mb > torch.distributions.Uniform(0, 1).sample(x_mb.shape)).float()
_, (q_z, p_z), _, x_mb_ = model(x_mb.reshape(-1, 784))
print_['recon loss'].append(float(nn.BCEWithLogitsLoss(reduction='none')(x_mb_,
x_mb.reshape(-1, 784)).sum(-1).mean().data))
if model.distribution == 'normal':
print_['KL'].append(float(torch.distributions.kl.kl_divergence(q_z, p_z).sum(-1).mean().data))
elif model.distribution == 'vmf':
print_['KL'].append(float(torch.distributions.kl.kl_divergence(q_z, p_z).mean().data))
else:
raise NotImplemented
print_['ELBO'].append(- print_['recon loss'][-1] - print_['KL'][-1])
print_['LL'].append(float(log_likelihood(model, x_mb).data))
print({k: np.mean(v) for k, v in print_.items()})
# hidden dimension and dimension of latent space
H_DIM = 128
Z_DIM = 5
# normal VAE
modelN = ModelVAE(h_dim=H_DIM, z_dim=Z_DIM, distribution='normal')
optimizerN = optim.Adam(modelN.parameters(), lr=1e-3)
print('##### Normal VAE #####')
# training for 1 epoch
train(modelN, optimizerN)
# test
test(modelN, optimizerN)
print()
# hyper-spherical VAE
modelS = ModelVAE(h_dim=H_DIM, z_dim=Z_DIM + 1, distribution='vmf')
optimizerS = optim.Adam(modelS.parameters(), lr=1e-3)
print('##### Hyper-spherical VAE #####')
# training for 1 epoch
train(modelS, optimizerS)
# test
test(modelS, optimizerS)
| [
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] | |
1d2d063d4047b17f3d2b4865e4865ecf6051c468 | 84c04d74c934cf6e857617745589e974a2d3d733 | /hang man.py | b19161e1c7946d738e6a01ceec7e611ecaafbc33 | [] | no_license | Ryan525600/hangman | f68466517fec7c614a95d04b6a3be83f4ca37c00 | 80b33d7127f4766a49a8543f27982ccdd2f4dfa8 | refs/heads/master | 2023-02-13T06:45:36.395184 | 2021-01-07T14:08:12 | 2021-01-07T14:08:12 | 325,546,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,708 | py | import random
HANGMAN_PICS = ['''
+---+
|
|
|
===''', '''
+---+
O |
|
|
===''', '''
+---+
O |
| |
|
===''', '''
+---+
O |
/| |
|
===''', '''
+---+
O |
/|\ |
|
===''', '''
+---+
O |
/|\ |
/ |
===''', '''
+---+
O |
/|\ |
/ \ |
===''']
words = 'ant baboon badger bat bear beaver camel cat clam cobra cougar coyote crow deer dog donkey duck eagle ferret fox frog goat goose hawk lion lizard llama mole monkey moose mouse mule newt otter owl panda parrot pigeon python rabbit ram rat raven rhino salmon seal shark sheep skunk sloth snake spider stork swan tiger toad trout turkey turtle weasel whale wolf wombat zebra'.split()
#스플릿을 사용하여 단어를 편리하게 입력했다
def getRandomWord(wordlist):
# This function returns a random string from the passed list of strings.
wordIndex = random.randint(0, len(wordList) - 1)
#랜덤으로 수를 하나 뽑았다. 다만, 배열은 0부터 시작하니 1을 빼주어 배열과 수를 일치시켰다. wordList에는 인풋이 들어올거고, 그 인풋에서 1을 빼서 배열과 맞출거다. 페러미터는 곧 인풋.
return wordList[wordIndex]
#wordIndex가 랜덤으로 수를 뽑아 글자수를 리턴해 주었다.
def displayBoard(missedLetters, correctLetters, secretWord):
print(HANGMAN_PICS[len (missedLetters)])
print()
print('Missed letters:', end=' ')
for letter in missedLetters:
print(letter, end=' ')
print()
#this for loop is going to display the missed letters.
| [
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] | |
74e3646c2f02af5d9e071403693416be9eef3e59 | 31d9f7debbc2e1e42df5d1c1dc6ef963ea690165 | /archiv_wgan_GP.py | b199ad62b30a4dde7eb6ffa826cc0692f7639f76 | [] | no_license | im-Kitsch/DLMB | d044fc0b97b73b570ada44b83e9f295c1d31e03b | 6144d673c63dc179b0b0a4603fd5b361c660f6f4 | refs/heads/main | 2023-03-07T11:14:16.581536 | 2021-02-22T19:20:22 | 2021-02-22T19:20:22 | 323,430,276 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 13,026 | py | import argparse
import torchvision
import torch
from torch.utils import data
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
import torchsummary
import util.dataset_util
IF_CUDA = True if torch.cuda.is_available() else False
DEVICE = torch.device('cuda') if IF_CUDA else torch.device('cpu')
TRANS_MEAN = [0.485, 0.456, 0.406]
TRANS_STD = [0.229, 0.224, 0.225]
# src, experimental setting:
# https://github.com/facebookarchive/fb.resnet.torch/blob/master/datasets/imagenet.lua#L69
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
torch.nn.init.normal_(m.weight, 1.0, 0.02)
torch.nn.init.zeros_(m.bias)
class ConvDiscriminator(torch.nn.Module):
def __init__(self, n_ch, img_size):
super(ConvDiscriminator, self).__init__()
self.n_ch = n_ch
self.img_size = img_size
self.main = torch.nn.Sequential(
# input is (n_ch) x 64 x 64
torch.nn.Conv2d(n_ch, img_size, 4, 2, 1, bias=False),
torch.nn.LeakyReLU(0.2, inplace=True),
# state size. (img_size) x 32 x 32
torch.nn.Conv2d(img_size, img_size * 2, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(img_size * 2),
torch.nn.LeakyReLU(0.2, inplace=True),
# state size. (img_size*2) x 16 x 16
torch.nn.Conv2d(img_size * 2, img_size * 4, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(img_size * 4),
torch.nn.LeakyReLU(0.2, inplace=True),
# state size. (img_size*4) x 8 x 8
torch.nn.Conv2d(img_size * 4, img_size * 8, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(img_size * 8),
torch.nn.LeakyReLU(0.2, inplace=True),
# state size. (img_size*8) x 4 x 4
torch.nn.Conv2d(img_size * 8, 1, 4, 1, 0, bias=False),
# torch.nn.Sigmoid()
)
# self.main_activation = torch.nn.Sigmoid()
return
def forward(self, x):
return self.main(x).view(-1, 1)
class ConvGenerator(torch.nn.Module):
def __init__(self, n_ch, img_size, z_dim):
super(ConvGenerator, self).__init__()
self.n_ch = n_ch
self.img_size = img_size
self.z_dim = z_dim
self.main = torch.nn.Sequential(
# input is Z, going into a convolution
torch.nn.ConvTranspose2d(z_dim, img_size * 8, 4, 1, 0, bias=False),
torch.nn.BatchNorm2d(img_size * 8),
torch.nn.ReLU(True),
# state size. (img_size*8) x 4 x 4
torch.nn.ConvTranspose2d(img_size * 8, img_size * 4, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(img_size * 4),
torch.nn.ReLU(True),
# state size. (img_size*4) x 8 x 8
torch.nn.ConvTranspose2d(img_size * 4, img_size * 2, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(img_size * 2),
torch.nn.ReLU(True),
# state size. (img_size*2) x 16 x 16
torch.nn.ConvTranspose2d(img_size * 2, img_size, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(img_size),
torch.nn.ReLU(True),
# state size. (img_size) x 32 x 32
torch.nn.ConvTranspose2d(img_size, n_ch, 4, 2, 1, bias=False),
torch.nn.Tanh()
# state size. (n_ch) x 64 x 64
)
return
def forward(self, noise):
return self.main(noise)
class WGanGP(torch.nn.Module):
def __init__(self, data_name, n_ch, img_size, z_dim, lr_g, lr_d, lr_beta1, lr_beta2, d_step):
super(WGanGP, self).__init__()
self.data_name = data_name
self.img_shape = (n_ch, img_size, img_size)
self.z_dim = z_dim
self.d_step = d_step
self.gp_lambda = 10.
self.conv_gen = ConvGenerator(n_ch=n_ch, img_size=img_size, z_dim=z_dim)
self.conv_dis = ConvDiscriminator(n_ch=n_ch, img_size=img_size)
# TODO not sure if it is needed to use weight init, but seems better than without init
self.conv_gen.main.apply(weights_init) # TODO to find a better method to initialization instead of using main
self.conv_dis.main.apply(weights_init)
if IF_CUDA:
self.conv_gen.cuda()
self.conv_dis.cuda()
self.opt_G = torch.optim.Adam(self.conv_gen.parameters(), lr=lr_g, betas=(lr_beta1, lr_beta2))
self.opt_D = torch.optim.Adam(self.conv_dis.parameters(), lr=lr_d, betas=(lr_beta1, lr_beta2))
self.criterion = torch.nn.BCELoss()
return
def train_net(self, train_loader, n_epoc):
writer = SummaryWriter(comment=f'_WGAN_GP_{self.data_name}') # TODO to add hyper parmeters
test_noise = self.generate_noise(64)
n_sample = len(train_loader.dataset)
for i in range(n_epoc):
epoc_l_d, epoc_l_g, epoc_score_p, epoc_score_f1, epoc_score_f2 = 0., 0., 0., 0., 0.
self.conv_gen.train(), self.conv_dis.train()
with tqdm(total=len(train_loader), desc=f"epoc: {i + 1}") as pbar:
for k, (real_img, _) in enumerate(train_loader):
if IF_CUDA:
real_img = real_img.cuda()
d_loss, p_score, f_score1 = self.train_d_step(real_img)
g_loss, f_score2 = self.train_g_step(real_img.shape[0])
batch_size = real_img.shape[0]
epoc_l_d += d_loss * batch_size
epoc_l_g += g_loss * batch_size
epoc_score_p += p_score * batch_size
epoc_score_f1 += f_score1 * batch_size
epoc_score_f2 += f_score2 * batch_size
pbar.set_postfix({"d_loss": d_loss, "g_loss": g_loss,
"p_score": p_score, "f_score D": f_score1, 'G': f_score2})
pbar.update()
epoc_l_d /= n_sample
epoc_l_g /= n_sample
epoc_score_p /= n_sample
epoc_score_f1 /= n_sample
epoc_score_f2 /= n_sample
pbar.set_postfix({"epoch: d_loss": epoc_l_d, "g_loss": epoc_l_g,
"p_score": epoc_score_p, "f_score D": epoc_score_f1, 'G': epoc_score_f2})
writer.add_scalar('loss/generator', epoc_l_g, i)
writer.add_scalar('loss/discriminator', epoc_l_d, i)
writer.add_scalar('score/real', epoc_score_p, i)
writer.add_scalar('score/fake_D', epoc_score_f1, i)
writer.add_scalar('score/fake_G', epoc_score_f2, i)
self.conv_gen.eval(), self.conv_dis.eval()
test_img = self.conv_gen(test_noise)
test_img = (test_img + 1.0) / 2.0 # Note that this is important to recover the range
test_img = test_img.reshape(64, *self.img_shape)
writer.add_images('img', test_img, i + 1)
writer.close()
return
def train_g_step(self, batch_size):
fake = self.generate_fake(batch_size)
lbl = torch.ones(batch_size, device=DEVICE)
p_f = self.conv_dis(fake)
loss = -p_f.mean()
# loss = self.criterion(p_f.reshape(-1), lbl)
self.opt_G.zero_grad()
loss.backward()
self.opt_G.step()
return loss.item(), p_f.mean().item()
def train_d_step(self, data_real):
d_step = self.d_step
batch_size = data_real.shape[0]
score_real, score_fake, d_loss = 0., 0., 0.
for _d in range(d_step):
data_fake = self.generate_fake(batch_size).detach()
mix_noise = torch.rand(batch_size, 1, 1, 1).cuda()
data_mixed = (1-mix_noise) * data_real + mix_noise * data_fake
data_mixed = data_mixed.detach()
data_mixed.requires_grad_()
p_f = self.conv_dis(data_fake)
p_p = self.conv_dis(data_real)
p_mix = self.conv_dis(data_mixed)
loss_1 = p_f - p_p
# gradient penalty
grad_p_x = torch.autograd.grad(p_mix.sum(), data_mixed, retain_graph=True, create_graph=True)[0]
# p_mix.sum(), trick to cal \par y_i / \parx_i independentl
assert grad_p_x.shape == data_mixed.shape
# print(grad_p_x.shape, data_mixed.shape)
grad_norm = torch.sqrt(grad_p_x.square().sum(axis=(1, 2, 3)) + 1e-14)
loss_2 = self.gp_lambda * torch.square(grad_norm - 1.)
loss = loss_1 + loss_2
loss = loss.mean()
self.opt_D.zero_grad()
loss.backward()
self.opt_D.step()
score_real += p_p.mean().item()
score_fake += p_f.mean().item()
d_loss += loss.item()
return d_loss / d_step, score_real / d_step, score_fake / d_step
# TODO different method to generate noise
def generate_noise(self, batch_size):
return torch.randn(batch_size, self.z_dim, 1, 1, device=DEVICE)
def generate_fake(self, batch_size):
return self.conv_gen(self.generate_noise(batch_size))
def main(args):
if args.data == 'MNIST':
trans = torchvision.transforms.Compose(
[torchvision.transforms.Resize(args.img_size),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.5], [0.5])])
elif args.data == 'CIFAR10':
trans = torchvision.transforms.Compose(
[torchvision.transforms.Resize(args.img_size),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
elif args.data == 'HAM10000':
if args.data_aug is True:
trans = torchvision.transforms.Compose([
torchvision.transforms.RandomResizedCrop(size=(args.img_size, args.img_size), scale=(0.7, 1.0),
ratio=(4 / 5, 5 / 4), interpolation=2),
torchvision.transforms.RandomHorizontalFlip(p=0.5),
torchvision.transforms.RandomVerticalFlip(p=0.5),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(TRANS_MEAN, TRANS_STD)
])
else:
trans = torchvision.transforms.Compose([
torchvision.transforms.Resize((args.img_size, args.img_size)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(TRANS_MEAN, TRANS_STD)
# torchvision.transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
])
else:
raise Exception('dataset not right')
train_data, _, img_shape = util.dataset_util.load_dataset(
dataset_name=args.data, root=args.root, transform=trans, csv_file=args.csv_file)
n_ch, img_size, _ = img_shape
train_loader = data.DataLoader(dataset=train_data, batch_size=args.batch_size, shuffle=True, drop_last=False,
num_workers=4, pin_memory=True)
dc_gan = WGanGP(data_name=args.data, n_ch=n_ch, img_size=img_size,
z_dim=args.z_dim, lr_g=args.lr_g, lr_d=args.lr_d,
lr_beta1=args.lr_beta1, lr_beta2=args.lr_beta2, d_step=args.d_step)
torchsummary.summary(dc_gan.conv_dis, input_size=dc_gan.img_shape, batch_size=-1,
device='cuda' if IF_CUDA else 'cpu')
torchsummary.summary(dc_gan.conv_gen, input_size=(dc_gan.z_dim, 1, 1), batch_size=-1,
device='cuda' if IF_CUDA else 'cpu')
dc_gan.train_net(train_loader=train_loader, n_epoc=args.n_epoc)
return
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="parse args")
parser.add_argument('--data', required=True, help='MNIST|CIFAR10|HAM10000')
parser.add_argument('--root', default='/home/yuan/Documents/datas/', help='root')
parser.add_argument('--csv-file', default='/home/yuan/Documents/datas/HAM10000/HAM10000_metadata.csv')
parser.add_argument('--n-epoc', default=25, type=int)
parser.add_argument('--d-step', default=1, type=int)
parser.add_argument('--batch-size', default=256, type=int)
parser.add_argument('--z-dim', default=64, type=int, help='noise shape')
parser.add_argument('--lr-g', default=3e-4, type=float)
parser.add_argument('--lr-d', default=3e-4, type=float)
parser.add_argument('--lr-beta1', default=0.5, type=float)
parser.add_argument('--lr-beta2', default=0.999, type=float)
# img_size could not be changed here
parser.add_argument('--img-size', default=64, type=int, help='resize the img size')
parser.add_argument('--data-percentage', default=1.0, type=float)
parser.add_argument('--data-aug', action='store_true', help='if use data augmentation or not')
para_args = parser.parse_args()
main(para_args)
# TODO torchsummarpy ; catch ctl-c; recover from last(writer path, model, optimizer, hyperparameter) hyperparameter
| [
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] | |
8b08cdeae9a7ca5cd9efe3869115cdb0b331fcc9 | 7a5a78ede21be8e78a19eb1e48797fa6d6e8642f | /detect_rtsp.py | 49b0ea43532112bdb6045a922fc0e6da1fe24de7 | [
"MIT"
] | permissive | abc873693/yolov3-tf2 | cf8230af5b6683817d6064bc86b7dc98f3e4453f | 24ab3eccf55e8ed108fc83335c1ca12a998ff3a7 | refs/heads/master | 2021-10-26T05:31:55.368987 | 2019-12-03T02:24:47 | 2019-12-03T02:24:47 | 201,893,053 | 0 | 0 | MIT | 2019-12-02T02:17:31 | 2019-08-12T08:52:15 | Python | UTF-8 | Python | false | false | 2,600 | py | import time
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import tensorflow as tf
from yolov3_tf2.models import (
YoloV3, YoloV3Tiny
)
from yolov3_tf2.dataset import transform_images
from yolov3_tf2.utils import draw_outputs
import os
import numpy as np
flags.DEFINE_string('classes', './data/coco.names', 'path to classes file')
flags.DEFINE_string('weights', './checkpoints/yolov3.tf',
'path to weights file')
flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')
flags.DEFINE_integer('url', 'rtsp://192.168.100.10/h264/ch1/main/av_stream', 'rtsp url')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_string('video', './data/video.mp4',
'path to video file or number for webcam)')
def main(_argv):
#%%
if FLAGS.tiny:
yolo = YoloV3Tiny()
else:
yolo = YoloV3()
yolo.load_weights(FLAGS.weights)
logging.info('weights loaded')
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
times = []
cap = cv2.VideoCapture(FLAGS.url)
out = cv2.VideoWriter('appsrc ! videoconvert ! '
'x264enc noise-reduction=10000 speed-preset=ultrafast tune=zerolatency ! '
'rtph264pay config-interval=1 pt=96 !'
'tcpserversink host=140.117.169.194 port=5000 sync=false',
0, 25, (640, 480))
out_path = './out/'
if not os.path.exists(out_path):
os.makedirs(out_path)
#%%
while(cap.isOpened()):
ret, img = cap.read()
if cv2.waitKey(20) & 0xFF == ord('q'):
break
img_in = tf.expand_dims(img, 0)
img_in = transform_images(img_in, FLAGS.size)
t1 = time.time()
boxes, scores, classes, nums = yolo.predict(img_in)
t2 = time.time()
img = draw_outputs(img, (boxes, scores, classes, nums), class_names)
img = cv2.putText(img, "Time: {:.2f}ms".format(sum(times)/len(times)*1000), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# if(nums > 0):
# cv2.imwrite(out_path + 'frame{0}.jpg'.format(index), img)
frameOfWindows = cv2.resize(
img, (800, 600), interpolation=cv2.INTER_CUBIC)
out.write(frameOfWindows)
cv2.imshow('output', frameOfWindows)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass
| [
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] | |
7f7a45fffcfb19dd0b215f1bc5b2c2fa35e9030b | c8e82c528dfe45d5c8beb0bcebd70968ab76fec0 | /ftp/mount.py | a3c29ac28b4ef0b0117f98ff7e638b54db02e2d9 | [] | no_license | BeatifulLife/otatool | 938d26480ada3bc087f7f6d06fdf96c30dbc24cc | 54212e6ea908efb1a8b75584d0d12b22913eafc1 | refs/heads/master | 2021-05-19T05:40:23.087747 | 2020-03-31T09:13:57 | 2020-03-31T09:13:57 | 251,551,629 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 649 | py | from otautil import *
class Mount:
'sudo mount -t cifs -o ro,username=xuzhaoyou,password=mobile#3 //192.168.8.206/data/data server'
def __init__(self,localdir,server,username,password):
self.server=server
self.localdir=localdir
self.username=username
self.password=password
def doMount(self):
assert(self.server is not None)
assert(self.localdir is not None)
assert(self.username is not None)
assert(self.password is not None)
_,recode=runCommand("sudo mount -t cifs -o ro,username="+self.username+",password=" + self.password + " " + self.server + " " + self.localdir)
if recode == 0:
return True
else:
return False | [
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] | |
4d5c6fccf789eecb33e8337992743ceee6b298af | dfc292644081c4a12a8c4ab407cf90a2c2dd9a48 | /travelpro/travelpro/wsgi.py | bb09e6633dd334633017544dbd6c731ef6015940 | [] | no_license | safirmeeyana/safirproject | 90326bcb58f0557dfc7fbbf877bb5d44ecb32201 | 7c393ac7597a3347a456befd94c9b2633d6ae4e7 | refs/heads/master | 2023-02-13T06:31:32.673452 | 2021-01-14T06:22:42 | 2021-01-14T06:22:42 | 329,524,725 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 395 | py | """
WSGI config for travelpro project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'travelpro.settings')
application = get_wsgi_application()
| [
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] | |
745fc542dfa60b44270bb9f54dbb2a5d6b4dbbfa | 7f398550c5676aa917198f01d2ccc1f59fe047a0 | /coffee.py | 1ef729d116dee5758aa9591e4065fe8615793557 | [] | no_license | lsteiner9/python-chapters-1-to-3 | 0ee38e2e44389e67c4e85c0aaa10b19850bb9ee3 | 8b830135d5ee41d9a28915705545a38e710db2af | refs/heads/master | 2023-03-17T20:53:33.779359 | 2021-03-18T02:00:34 | 2021-03-18T02:00:34 | 348,909,438 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 239 | py | # coffee.py
def main():
print("This program calculates the cost of a coffee order.")
pounds = float(input("Enter the number of pounds of coffee ordered: "))
print("The price of this order is:", pounds * 11.36 + 1.50)
main()
| [
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] | |
d62187ceef71a3b7a888fd8d1a7051f01e50144c | d2d4b3e707a483b25c741396069923fcccccb993 | /smartmarket/shops/migrations/0001_initial.py | bc6f3f54e32f679a21d989a2f3a82829ec0a0e9f | [] | no_license | DiegoRinconC/tesis | 7cda2f47190fc31b325825e632134211105d405f | 2daf738e2101f27253c6e90fe1c71d2f8f80cf2d | refs/heads/master | 2020-04-02T03:18:40.700140 | 2018-10-23T03:38:47 | 2018-10-23T03:38:47 | 153,957,609 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,601 | py | # Generated by Django 2.1.2 on 2018-10-23 01:43
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('users', '0002_auto_20181020_1816'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='BrandShop',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('brand_shop', models.CharField(max_length=200)),
('modified_date', models.DateField(auto_now=True)),
('modified_by', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Shop',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('shop', models.CharField(max_length=200)),
('modified_date', models.DateField(auto_now=True)),
('brand_shop', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.DO_NOTHING, to='shops.BrandShop')),
('city', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='users.City')),
('modified_by', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
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] | |
4425abce78cc5ab8f0241155c9e5248cd9b9b861 | 8cacef299fbbedd6e46ec02d274b1baa82433ef8 | /DriverFiles/load_test_session.py | 7c72d2936131ac52e3ab793382bd9ff079eb79f1 | [] | no_license | PotionSell/Buzsaki-Data-Import | 37cdf4ccf440b2e60153d3aa1435611709ea987a | af5f5db900d76bffccfdc5e835425060fb65d69b | refs/heads/master | 2021-01-19T04:25:26.993240 | 2016-07-11T15:44:37 | 2016-07-11T15:44:37 | 60,122,373 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 662 | py | execfile('BuzsakiSession.py')
execfile('write_nwb.py')
#session = Session('ec013.156')
#session.load_LFPdata()
#lfp = session.get_shankLFP(0, True)
#csd = session.get_CSD(4)
write_nwb('ec012ec.356')
write_nwb('ec013.156')
write_nwb('ec013.157')
write_nwb('ec013.756')
write_nwb('ec013.965')
write_nwb('ec014.468')
write_nwb('ec014.639')
write_nwb('ec016.234')
write_nwb('ec016.749')
#os.chdir(cwd)
#execfile('dict_to_arr.py')
#execfile('plot_Signal.py')
#execfile('filter_LFP.py')
#execfile('hilbert.py')
#execfile('dict_to_arr.py')
#t = session.LFP_timestamps
#filt = filter_LFP(t, lfp, session.LFP_rate, 'theta', False)
#phase, amp, hilbData = hilbert(filt)
| [
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] | |
56b674ad741b5ec115b231c05945478a3cee3b59 | 9a104370627671e0549913194c79329920b76342 | /attention_guidance/ag_models/wandb_utils.py | 98591d8111044cd210b4138bff1894e0d52a282b | [
"Apache-2.0",
"MIT"
] | permissive | ameet-1997/AttentionGuidance | 8dcd115ce6be0752de108b68cd798f6200fa62d5 | 8e1e6c3855125fe8f1485fbe57d51285edebfade | refs/heads/main | 2023-02-15T08:08:38.248050 | 2023-02-08T03:07:03 | 2023-02-08T03:07:03 | 300,993,462 | 9 | 1 | null | null | null | null | UTF-8 | Python | false | false | 580 | py | import wandb
import os
def wandb_init_setup(args):
'''
Uses API key and sets initial config and hyperparameters
'''
# Ameet's wandb key
os.environ["WANDB_API_KEY"] = "a8d4de02e5bbee944cdfa143d1dba8f1a7b63fb4"
os.environ["WANDB_WATCH"] = "false"
os.environ["WANDB_PROJECT"] = args.wandb_project
os.environ["WANDB_NAME"] = args.wandb_name
if args.disable_wandb:
os.environ["WANDB_DISABLED"] = 'true'
# # Initialize with hyperparameters and project name
# wandb.init(config=args, name=args.wandb_name, project=args.wandb_project) | [
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] | |
6466a29180d397b35f5306a979bfa235487516c3 | 06a50cfded23b760d5b2a5ae7d5c4761ae2d4dc8 | /auto_upgrade.py | 2b5eb7a276e28b168e17571ac64e9aeaa69017d4 | [
"Apache-2.0"
] | permissive | spencerzhang91/coconuts-on-fire | b0655b3dd2b310b5e62f8cef524c6fddb481e758 | 407d61b3583c472707a4e7b077a9a3ab12743996 | refs/heads/master | 2021-09-21T07:37:07.879409 | 2018-08-22T03:24:36 | 2018-08-22T03:24:36 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 253 | py | #! /usr/local/bin/python3
# can not be used on windows due to line end difference.
import pip
from subprocess import call
for dist in pip.get_installed_distributions():
call("pip3 install --upgrade --no-cache-dir " + dist.project_name, shell=True)
| [
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] | |
e73c0dcd93ba153ddfdbac5cdf8ed995b6b030ab | fbb3a1843b541ee118d4ba686552c063152fb3b2 | /sorting.py | 73a2d82e7c52c0228e4098e54217b6435193a240 | [] | no_license | yusmasv/Quick---Selection-Sort-Visualization | 2bf661ab8ea31b77826a0664ab3bdba995b37a67 | ee82b345d43dcd1105db323bb4ee7a9282fe39ed | refs/heads/main | 2023-01-29T09:21:39.972824 | 2020-12-09T12:25:25 | 2020-12-09T12:25:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,423 | py | import random
import time
import matplotlib.pyplot as plt
import matplotlib.animation as animation
plt.style.use('dark_background')
def swap(A, i, j):
if i != j:
A[i], A[j] = A[j], A[i]
def quicksort(A, start, end):
"""In-place quicksort."""
if start >= end:
return
pivot = A[end]
pivotIdx = start
for i in range(start, end):
if A[i] < pivot:
swap(A, i, pivotIdx)
pivotIdx += 1
yield A
swap(A, end, pivotIdx)
yield A
yield from quicksort(A, start, pivotIdx - 1)
yield from quicksort(A, pivotIdx + 1, end)
def selectionsort(A):
"""In-place selection sort."""
if len(A) == 1:
return
for i in range(len(A)):
# Find minimum unsorted value.
minVal = A[i]
minIdx = i
for j in range(i, len(A)):
if A[j] < minVal:
minVal = A[j]
minIdx = j
yield A
swap(A, i, minIdx)
yield A
if __name__ == "__main__":
# Get user input to determine range of integers (1 to N) and desired
# sorting method (algorithm).
N = int(input("Enter number of integers: "))
method_msg = "Enter sorting method:\n(q)uick\n(s)election\n"
method = input(method_msg)
# Build and randomly shuffle list of integers.
A = [x + 1 for x in range(N)]
random.seed(time.time())
random.shuffle(A)
# Get appropriate generator to supply to matplotlib FuncAnimation method.
if method == "q":
title = "Quicksort"
generator = quicksort(A, 0, N - 1)
else:
title = "Selection sort"
generator = selectionsort(A)
fig, ax = plt.subplots()
ax.set_title(title)
bar_rects = ax.bar(range(len(A)), A, align="edge")
ax.set_xlim(0, N)
ax.set_ylim(0, int(1.07 * N))
text = ax.text(0.02, 0.95, "", transform=ax.transAxes)
iteration = [0]
def update_fig(A, rects, iteration):
for rect, val in zip(rects, A):
rect.set_height(val)
iteration[0] += 1
text.set_text("# of operations: {}".format(iteration[0]))
anim = animation.FuncAnimation(fig, func=update_fig,
fargs=(bar_rects, iteration), frames=generator, interval=1,
repeat=False)
fig.savefig('my_figure.jpg')
plt.show()
| [
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] | |
a75a731523cf01b16cc1565e94dbc9f9a578895a | 9b76741992c13b661dd9c70522d7fe9ad6086cde | /holoviews_test.py | 8de05da2b020393a64ef6d564dda844f85ac53e5 | [] | no_license | rafaelha/py_models | 681078d52a76da20ed29f0a498b77c8a4fb88ae0 | 0b5267bc824567de7495c432255ec88139cdd17a | refs/heads/master | 2023-02-22T03:43:02.617451 | 2018-10-04T18:33:11 | 2018-10-04T18:33:11 | 126,767,708 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 562 | py | import holoviews as hv
import numpy as np
import holoviews.plotting.mpl
#renderer = hv.Store.renderers['matplotlib']
renderer = hv.renderer('matplotlib')#.instance(fig='svg', holomap='gif')
frequencies = [0.5, 0.75, 1.0, 1.25]
def sine_curve(phase, freq):
xvals = [0.1* i for i in range(100)]
return hv.Curve((xvals, [np.sin(phase+freq*x) for x in xvals]))
curve_dict = {f:sine_curve(0,f) for f in frequencies}
hmap = hv.HoloMap(curve_dict, kdims='frequency')
widget = renderer.get_widget(hmap, 'widgets')
#renderer.show(hmap)
renderer.show(widget)
| [
"[email protected]"
] | |
5430d2daacfc6a75623004d72dfaed442200718c | 7b8fd24cc6dbed385173a3857c06f2935724ace6 | /LeetCode/T-46.py | 60d02e43d91078c029f479bbb71b66d9607360df | [] | no_license | Yang-Jianlin/python-learn | eb1cfd731039a8e375827e80b8ef311f9ed75bfb | 048cde2d87e4d06a48bd81678f6a82b02e7c4cb4 | refs/heads/master | 2023-07-12T16:35:13.489422 | 2021-08-23T11:54:10 | 2021-08-23T11:54:10 | 357,464,365 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 681 | py | class Solution:
def __init__(self):
self.res = []
self.temp = []
def permute(self, nums):
n = 1
for i in range(1, len(nums) + 1):
n *= i
self.dfs(nums, 0, n)
return self.res
def dfs(self, nums, position, n):
if position == len(nums):
self.res.append(self.temp[:])
return
else:
for i in nums:
if i not in self.temp:
self.temp.append(i)
self.dfs(nums, position + 1, n)
self.temp.pop()
if __name__ == '__main__':
s = Solution()
nums = [1, 2, 3]
print(s.permute(nums))
| [
"[email protected]"
] | |
7fff043e3f126009e64219c576fed17d3c9b08c1 | f482839a5b2cf75d0ce38755d8aeefff8911e35d | /tictactoe_minimax.py | ee831bc33814df2a17a48f368646f5778e228170 | [] | no_license | gmiller148/TicTacToe_Algos | aa666946a536668e683d36432e47372a140ce402 | ad4f8a2bbad62816b7199eeb80e0574b7101d227 | refs/heads/master | 2020-04-26T16:28:42.055745 | 2019-03-04T05:35:07 | 2019-03-04T05:35:07 | 173,679,933 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,385 | py | class TicTacToe:
def __init__(self, turn=-1):
self.board = [[0,0,0],
[0,0,0],
[0,0,0]]
self.turn = turn
self.state = 'ongoing'
def display(self):
for i in range(3):
row_res = ''
for j in range(3):
if self.board[i][j] == 0:
row_res += ' - '
elif self.board[i][j] == -1:
row_res += ' X '
elif self.board[i][j] == 1:
row_res += ' O '
print(row_res)
print('________')
def check_victory(self):
for row in self.board:
rs = sum(row)
if rs == 3:
self.state = 'over'
return (True, 'O', 1)
elif rs == -3:
self.state = 'over'
return (True, 'X', -1)
for i in range(3):
cs = self.board[0][i] + self.board[1][i] + self.board[2][i]
if cs == 3:
self.state = 'over'
return (True, 'O', 1)
elif cs == -3:
self.state = 'over'
return (True, 'X', -1)
diag1 = sum([self.board[x][x] for x in range(3)])
diag2 = sum([self.board[2-x][x] for x in range(3)])
if diag1 == 3:
self.state = 'over'
return (True, 'O', 1)
elif diag1 == -3:
self.state = 'over'
return (True, 'X', -1)
if diag2 == 3:
self.state = 'over'
return (True, 'O', 1)
elif diag2 == -3:
self.state = 'over'
return (True, 'X', -1)
return (False, '', 0)
def make_move(self, x, y):
if self.board[x][y] == 0:
self.board[x][y] = self.turn
else:
print("Invalid move at x:",x,"y:",y)
return
res = self.check_victory()
if res[0]:
print("Game Over",res[1],"won")
else:
self.turn = -self.turn
class Player:
def __init__(self, symbol):
self.symbol = symbol
def find_moves(self, board):
moves = []
for i in range(3):
for j in range(3):
if board[i][j] == 0:
moves.append((i,j))
return moves
def find_best_move(self,board):
best_move = None
highest_value = -10000
for move in self.find_moves(board):
board[move[0]][move[1]] = self.symbol
value = self.minimax(board)
if value >= highest_value:
highest_value = value
best_move = move
board[move[0]][move[1]] = 0
return best_move
def minimax(self, board, depth=0, is_max_player=False):
status = self.check_victory(board)
if status[0]:
if self.symbol == status[2]:
return 10 - depth
else:
return -10 + depth
if not self.moves_left(board):
return 0
if is_max_player:
best_val = -1000
for move in self.find_moves(board):
board[move[0]][move[1]] = self.symbol
value = self.minimax(board,depth+1,False)
best_val = max(value,best_val)
board[move[0]][move[1]] = 0
return best_val
else:
best_val = 1000
for move in self.find_moves(board):
board[move[0]][move[1]] = -1*self.symbol
value = self.minimax(board,depth+1,True)
best_val = min(value,best_val)
board[move[0]][move[1]] = 0
return best_val
def check_victory(self, board):
for row in board:
rs = sum(row)
if rs == 3:
return (True, 'O', 1)
elif rs == -3:
return (True, 'X', -1)
for i in range(3):
cs = board[0][i] + board[1][i] + board[2][i]
if cs == 3:
return (True, 'O', 1)
elif cs == -3:
return (True, 'X', -1)
diag1 = sum([board[x][x] for x in range(3)])
diag2 = sum([board[2-x][x] for x in range(3)])
if diag1 == 3:
return (True, 'O', 1)
elif diag1 == -3:
return (True, 'X', -1)
if diag2 == 3:
return (True, 'O', 1)
elif diag2 == -3:
return (True, 'X', -1)
return (False, '', 0)
def moves_left(self,board):
for i in range(3):
for j in range(3):
if board[i][j] == 0:
return True
return False
def display(self,board):
for i in range(3):
row_res = ''
for j in range(3):
if board[i][j] == 0:
row_res += ' - '
elif board[i][j] == -1:
row_res += ' X '
elif board[i][j] == 1:
row_res += ' O '
print(row_res)
print('_________')
t = TicTacToe()
t.display()
p = Player(-1)
while t.state == 'ongoing':
move = p.find_best_move(t.board)
t.make_move(move[0],move[1])
t.display()
if t.state != 'ongoing':
break
x = int(input('Row : '))
y = int(input('Col : '))
t.make_move(x,y)
t.display()
| [
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] | |
8c609ecead5fa0b54d67b5af1fe3c7fc57656e93 | f25085778485d49da4fd587a034b037df0ea98f9 | /interview/findSubstrings.py | 614cc8f9c71591756dbc4b3442c8a686cc32688b | [] | no_license | davcs86/codefights | 477d733511a6639668a46fe55ffd47e832aa356c | f6f42c6635c48877ea9904e05bed2c029271c1d9 | refs/heads/master | 2021-01-23T01:17:18.042934 | 2017-04-28T00:05:53 | 2017-04-28T00:05:53 | 85,892,470 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 928 | py | def findSubstrings(words, parts):
parts = sorted(parts, key = len, reverse=True)
for i, w in enumerate(words):
psz = 0
ppos = len(w)
nw = w
for p in parts:
if len(p) >= psz and len(p) <= len(w):
pos = w.find(p)
if (len(p) > psz or pos < ppos) and pos >= 0:
# found
psz = len(p)
ppos = pos
nw = w.replace(p, "["+p+"]", 1)
if len(p) < psz:
break
words[i] = nw
return words
words = ["neuroses",
"myopic",
"sufficient",
"televise",
"coccidiosis",
"gules",
"during",
"construe",
"establish",
"ethyl"]
parts = ["aaaaa",
"Aaaa",
"E",
"z",
"Zzzzz",
"a",
"mel",
"lon",
"el",
"An",
"ise",
"d",
"g",
"wnoVV",
"i",
"IUMc",
"P",
"KQ",
"QfRz",
"Xyj",
"yiHS"]
print parts
print findSubstrings(words, parts) | [
"[email protected]"
] | |
8a453791cd356fd9608e74273bddc8d2c8f8e1f1 | d53f5cabda6350d9cf0b0d7b2ce0d271b21c8b8e | /flamingo/core/templating/__init__.py | 18dd35a059fef1c9737dc1722e512c0c46973db5 | [
"Apache-2.0"
] | permissive | pengutronix/flamingo | 527c82add7373122c243996b35fac28253639743 | e43495366ee73913f2d4565f865c04f90dc95f8d | refs/heads/master | 2023-05-10T17:21:26.998164 | 2023-04-28T09:04:59 | 2023-04-28T09:04:59 | 156,219,977 | 23 | 10 | Apache-2.0 | 2023-04-28T09:05:01 | 2018-11-05T13:12:34 | JavaScript | UTF-8 | Python | false | false | 78 | py | from .base import TemplatingEngine # NOQA
from .jinja2 import Jinja2 # NOQA
| [
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] | |
c383ce7e879faf5ca5db41e4b51971d7be46d695 | 435723c2128a8a125ebc0bd4fdd57b2e438174a0 | /tests/emissionLines/test_fluxes.py | 404ae8362078d91193460af65617c20cd520c088 | [] | no_license | galacticusorg/analysis-python | 824e7a0311329531e42eb06fc99298cf371ec75f | 09e03f8d25ab6711b4e2783454acca1422e7bc59 | refs/heads/master | 2022-03-10T18:39:03.766749 | 2022-03-03T14:49:25 | 2022-03-03T14:49:25 | 203,855,262 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,496 | py | #! /usr/bin/env python
import sys,os
import fnmatch
import numpy as np
import unittest
import warnings
from shutil import copyfile
from galacticus import rcParams
from galacticus.Cloudy import CloudyTable
from galacticus.galaxies import Galaxies
from galacticus.io import GalacticusHDF5
from galacticus.data import GalacticusData
from galacticus.constants import luminositySolar
from galacticus.constants import luminosityAB,erg
from galacticus.constants import mega,centi,parsec
from galacticus.constants import Pi
from galacticus.emissionLines.fluxes import EmissionLineFlux,ergPerSecondPerCentimeterSquared
class TestFluxes(unittest.TestCase):
@classmethod
def setUpClass(self):
DATA = GalacticusData()
self.snapshotFile = DATA.searchDynamic("galacticus.snapshotExample.hdf5")
self.lightconeFile = DATA.searchDynamic("galacticus.lightconeExample.hdf5")
self.removeSnapshotExample = False
self.removeLightconeExample = False
# If the file does not exist, create a copy from the static version.
if self.snapshotFile is None:
self.snapshotFile = DATA.dynamic+"/examples/galacticus.snapshotExample.hdf5"
self.removeSnapshotExample = True
if not os.path.exists(DATA.dynamic+"/examples"):
os.makedirs(DATA.dynamic+"/examples")
copyfile(DATA.static+"/examples/galacticus.snapshotExample.hdf5",self.snapshotFile)
if self.lightconeFile is None:
self.lightconeFile = DATA.dynamic+"/examples/galacticus.lightconeExample.hdf5"
self.removeLightconeExample = True
if not os.path.exists(DATA.dynamic+"/examples"):
os.makedirs(DATA.dynamic+"/examples")
copyfile(DATA.static+"/examples/galacticus.lightconeExample.hdf5",self.lightconeFile)
# Initialize the Totals class.
GH5 = GalacticusHDF5(self.lightconeFile,'r')
GALS = Galaxies(GH5Obj=GH5)
self.LINES = EmissionLineFlux(GALS)
return
@classmethod
def tearDownClass(self):
# Clear memory and close/delete files as necessary.
self.LINES.galaxies.GH5Obj.close()
del self.LINES
if self.removeSnapshotExample:
os.remove(self.snapshotFile)
if self.removeLightconeExample:
os.remove(self.lightconeFile)
return
def test_FluxesMatches(self):
# Tests for correct dataset names
for line in self.LINES.CLOUDY.listAvailableLines():
for component in ["disk","spheroid"]:
name = component+"LineFlux:"+line+":rest:z1.000"
self.assertTrue(self.LINES.matches(name))
name = component+"LineFlux:"+line+":observed:SDSS_r:z1.000"
self.assertTrue(self.LINES.matches(name))
name = component+"LineFlux:"+line+":observed:z1.000:recent"
self.assertTrue(self.LINES.matches(name))
name = component+"LineFlux:"+line+":rest:SDSS_g:z1.000:recent"
self.assertTrue(self.LINES.matches(name))
# Tests for incorrect dataset names
name = "diskLineFlux:notAnEmissionLine:rest:z1.000"
self.assertFalse(self.LINES.matches(name,raiseError=False))
self.assertRaises(RuntimeError,self.LINES.matches,name,raiseError=True)
for name in ["totalLineFlux:balmerAlpha6563:rest:z1.000",
"diskLineFlux:SDSS_r:rest:z1.000",
"diskLineFlux:balmerAlpha6563:obs:z1.000",
"diskLineFlux:balmerAlpha6563:observed:1.000",
"diskLineFlux:balmerAlpha6563:rest:z1.000:dustAtlas",
"diskLineFlux:balmerAlpha6563:z1.000"]:
self.assertFalse(self.LINES.matches(name,raiseError=False))
self.assertRaises(RuntimeError,self.LINES.matches,name,raiseError=True)
return
def test_FluxesGet(self):
# Check bad names
redshift = 1.0
name = "totalLineFlux:balmerAlpha6563:rest:z1.000"
with self.assertRaises(RuntimeError):
DATA = self.LINES.get(name,redshift)
# Check values
zStr = self.LINES.galaxies.GH5Obj.getRedshiftString(redshift)
component = "disk"
for line in self.LINES.CLOUDY.listAvailableLines()[:1]:
fluxName = component+"LineFlux:"+line+":rest:"+zStr
luminosityName = component+"LineLuminosity:"+line+":rest:"+zStr
GALS = self.LINES.galaxies.get(redshift,properties=["redshift",luminosityName])
luminosityDistance = self.LINES.galaxies.GH5Obj.cosmology.luminosity_distance(GALS["redshift"].data)
flux = GALS[luminosityName].data/(4.0*Pi*luminosityDistance**2)
DATA = self.LINES.get(fluxName,redshift)
self.assertEqual(DATA.name,fluxName)
self.assertTrue(np.array_equal(flux,DATA.data))
# Check error raised for snapshot output
return
def test_ergPerSecondPerCentimeterSquared(self):
flux0 = np.random.rand(50)*0.04 + 0.01
# Check conversion
flux = np.log10(np.copy(flux0))
flux += np.log10(luminositySolar)
flux -= np.log10(erg)
flux -= np.log10((mega*parsec/centi)**2)
flux = 10.0**flux
self.assertTrue(np.array_equal(flux,ergPerSecondPerCentimeterSquared(flux0)))
return
if __name__ == "__main__":
unittest.main()
| [
"[email protected]"
] | |
571c97500fcd77b7f891fed895e3e953e3f3cc95 | d7ea218f90ed241255c49db0472eefec0e78f93f | /savanna/plugins/hdp/validator.py | 60980e957eb4280d68f570b4f3f80f241711b31c | [
"Apache-2.0"
] | permissive | simedcn/savanna | 5829c1119930ed02bd09124224962230d0ac71f0 | fc02c010db12c4bdf24c67eb0eb94026252355d0 | refs/heads/master | 2021-01-14T13:06:31.313572 | 2013-08-21T17:44:48 | 2013-08-21T17:44:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,077 | py | # Copyright (c) 2013 Hortonworks, Inc.
#
# 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 inspect
import savanna.exceptions as e
from savanna.plugins.general import exceptions as ex
from savanna.plugins.general import utils
class Validator(object):
def validate(self, cluster):
funcs = inspect.getmembers(Validator, predicate=inspect.ismethod)
for func in funcs:
if func[0].startswith("check_"):
getattr(self, func[0])(cluster)
def check_for_namenode(self, cluster):
count = sum([ng.count for ng
in utils.get_node_groups(cluster, "NAMENODE")])
if count != 1:
raise ex.NotSingleNameNodeException(count)
def check_for_jobtracker_and_tasktracker(self, cluster):
jt_count = sum([ng.count for ng
in utils.get_node_groups(cluster, "JOBTRACKER")])
if jt_count not in [0, 1]:
raise ex.NotSingleJobTrackerException(jt_count)
tt_count = sum([ng.count for ng
in utils.get_node_groups(cluster, "TASKTRACKER")])
if jt_count is 0 and tt_count > 0:
raise ex.TaskTrackersWithoutJobTracker()
def check_for_ambari_server(self, cluster):
count = sum([ng.count for ng
in utils.get_node_groups(cluster, "AMBARI_SERVER")])
if count != 1:
raise NotSingleAmbariServerException(count)
def check_for_ambari_agents(self, cluster):
for ng in cluster.node_groups:
if "AMBARI_AGENT" not in ng.node_processes:
raise AmbariAgentNumberException(ng.name)
class NoNameNodeException(e.SavannaException):
def __init__(self):
message = ("Hadoop cluster should contain at least one namenode")
code = "NO_NAMENODE"
super(NoNameNodeException, self).__init__(message, code)
class NotSingleAmbariServerException(e.SavannaException):
def __init__(self, count):
message = ("Hadoop cluster should contain 1 Ambari Server "
"instance. Actual Ambari server count is %s" % count)
code = "NOT_SINGLE_AMBARI_SERVER"
super(NotSingleAmbariServerException, self).__init__(message, code)
class AmbariAgentNumberException(e.SavannaException):
def __init__(self, count):
message = ("Hadoop cluster should have an ambari agent per "
"node group. Node group %s has no Ambari Agent" % count)
code = "WRONG_NUMBER_AMBARI_AGENTS"
super(AmbariAgentNumberException, self).__init__(message, code)
| [
"[email protected]"
] | |
d24e314a5efa4ce965577b2a2cfb1f67ccebd1d6 | eafed2a5d7de4db7e3c37bfdb2d2f2b1069e80c0 | /api/app/labeller/client.py | 2d2ce90abe135c6892d62370bd29c3a3a67a0b74 | [] | no_license | philipk19238/klarity | 53123aa52abba62bcc62b381599196b13640ba4b | 11335cc74d5433e19e218a9a9b3e43acd669b789 | refs/heads/master | 2023-08-13T08:35:02.485853 | 2021-10-17T19:08:56 | 2021-10-17T19:08:56 | 417,892,692 | 1 | 3 | null | null | null | null | UTF-8 | Python | false | false | 1,598 | py | from collections import defaultdict
from .constants import (
MaterialConstant,
TypeConstant,
ColorConstant,
SizeConstant,
LocationConstant
)
from .trie import Trie
from .tokenizer import Tokenizer
class LabelerClient:
def __init__(self, stop_words):
self.trie = Trie()
self.tokenizer = Tokenizer(stop_words)
self.init_constants(
MaterialConstant,
TypeConstant,
ColorConstant,
SizeConstant,
LocationConstant
)
def init_constants(self, *args):
for constant in args:
self.trie.insert_constant(constant)
def update_model(self, model, labels):
tags = model.tags
for k, v in labels.items():
tags[k] = v
model.tags = tags
return model
def label(self, model):
title = self.tokenizer.clean(model.title)
desc = self.tokenizer.clean(model.description)
title_labels = self.find_labels(title)
desc_labels = self.find_labels(desc)
merged_labels = self.merge_dicts(desc_labels, title_labels)
return self.update_model(model, merged_labels)
def find_labels(self, sentence):
res = defaultdict(set)
pairs = self.trie.search_sentence(sentence)
for key, word in pairs:
res[key].add(word)
return res
def merge_dicts(self, *args):
res = defaultdict(set)
for to_merge in args:
for k, v in to_merge.items():
res[k] = res[k] | v
return res
| [
"[email protected]"
] | |
4d807c601f9a24cfa37be0f007e051f306400386 | 3848612966f853b70167c2e5606e5451dd0ac8f7 | /architecture/make_arch/examples/memcached_path.py | 10a22d2ece5ed4d29a88586284b9ec3373ebc707 | [
"MIT"
] | permissive | delimitrou/uqsim-power-management-beta | 1b99e3c03af812d13dbca573fd712034be75853e | 87f4483a644e6dfc2c3e96497b0920e62b1f2b80 | refs/heads/master | 2022-06-10T17:17:25.345276 | 2022-05-13T17:00:08 | 2022-05-13T17:00:08 | 260,951,471 | 2 | 2 | null | 2022-05-13T17:00:09 | 2020-05-03T14:59:52 | null | UTF-8 | Python | false | false | 745 | py | import sys
import os
import json
import make_arch as march
def main():
node_0 = march.make_serv_path_node(servName="memcached", servDomain="",
codePath=0, startStage=0, endStage=-1, nodeId=0, needSync=False, syncNodeId=None, childs=[1])
node_1 = march.make_serv_path_node(servName = "client", servDomain = "",
codePath = -1, startStage = 0, endStage = -1,
nodeId = 1, needSync = False, syncNodeId = None, childs = [])
nodeList = [node_0, node_1]
memc_read_only_path = march.make_serv_path(pathId=0, entry=0, prob=1.0, nodes=nodeList)
paths = [memc_read_only_path]
with open("/home/zhangyanqi/cornell/SAIL/microSSim/architecture/memcached/path.json", "w+") as f:
json.dump(paths, f, indent=2)
if __name__ == "__main__":
main() | [
"[email protected]"
] | |
4e5a2b20a95130193194dab51ac984aab4b65175 | f24cccd40b8770f3da983e45a7fd3c166331b2fa | /Python_Basics/display_output.py | 73577e849804269937097f1150b4bfef7e944fde | [] | no_license | srajesh636/python_basics | 4b09fb777a626c4fdba467dcde5b80b7804539be | 0b26d85bf61659c9a8c4f8468c7a9a8ee29c7873 | refs/heads/master | 2020-03-16T07:59:38.412131 | 2018-05-08T11:09:29 | 2018-05-08T11:09:29 | 132,588,025 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 67 | py | print("print method is used to display the content on the screen")
| [
"[email protected]"
] | |
97c31e1e4b55d0cb7b5ac2dd08339b8d13a6014d | 0e114f7df2b112511785e21626bb6bdb220b5a6c | /NMS/classes/TkSceneNodeData.py | 633df0ebb7284d5ec9ada0c0b5708fd56cf941c6 | [] | no_license | monkeyman192/NMSDK | 020c580bc7b0517bdef5b28d167924fde51dfa7f | c94bb9071e576fd16650f0b26fc5d681181976af | refs/heads/master | 2023-08-09T09:08:40.453170 | 2023-07-26T23:28:53 | 2023-07-26T23:28:53 | 73,231,820 | 25 | 6 | null | 2023-07-26T23:10:29 | 2016-11-08T22:13:48 | Python | UTF-8 | Python | false | false | 744 | py | # TkSceneNodeData struct
from .Struct import Struct
from .String import String
from .TkTransformData import TkTransformData
from .List import List
class TkSceneNodeData(Struct):
def __init__(self, **kwargs):
super(TkSceneNodeData, self).__init__()
""" Contents of the struct """
self.data['Name'] = String(kwargs.get('Name', ""), 0x80)
self.data['NameHash'] = kwargs.get('NameHash', 0)
self.data['Type'] = String(kwargs.get('Type', 'MODEL'), 0x10)
self.data['Transform'] = kwargs.get('Transform', TkTransformData())
self.data['Attributes'] = kwargs.get('Attributes', List())
self.data['Children'] = kwargs.get('Children', List())
""" End of the struct contents"""
| [
"[email protected]"
] | |
992e47a305d7797ce8662af91191b183c4dc5d44 | 5516f874c85b7b2a194fee536f10eff22636925e | /OOP/first_class.py | 404db6dc2e32a8005c85d728cf876ca71050e07d | [] | no_license | vokborok/lutz | b58140f8420500de8d47bd358cacda4db5972ea5 | e6b5fe636cbccce5ec76ed0716d33eeee90f10df | refs/heads/main | 2023-07-12T17:40:54.272404 | 2021-08-16T22:09:46 | 2021-08-16T22:09:46 | 365,612,376 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 122 | py | class FirstClass:
def setdata(self, value):
self.data = value
def display(self):
print(self.data)
| [
"[email protected]"
] | |
6eb1c5eec9aff34ec78b04b73f38d2d8ea238cc0 | a4da1f7c9a8726bface6e20fe77bc96e94627d62 | /classwork/modules/varscope.py | 2cb803bdbe90e718d8325f5846084de36abc7a62 | [] | no_license | KrackedJack/dbda-feb2019-python | 97d8b8e7428e735d589c36111706723070abad49 | 9ae82552f50ff9f0d340d0ae97c9233cd4df19d7 | refs/heads/master | 2020-11-24T14:07:57.997410 | 2019-12-15T12:41:35 | 2019-12-15T12:41:35 | 228,185,218 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 176 | py | x=20
def func():
global x
x = 30
print("x:",x)
def infunc():
#nonlocal x
global x
x = 45
print("x:",x)
print("calling infunc()")
infunc()
func()
print("x: ",x) | [
"[email protected]"
] | |
045b797fe7eb6cce795c14a6615378305af53da0 | 711756b796d68035dc6a39060515200d1d37a274 | /output_cog/optimized_31572.py | a27c7cb0e13923113a3cd85c080912670b03b57f | [] | no_license | batxes/exocyst_scripts | 8b109c279c93dd68c1d55ed64ad3cca93e3c95ca | a6c487d5053b9b67db22c59865e4ef2417e53030 | refs/heads/master | 2020-06-16T20:16:24.840725 | 2016-11-30T16:23:16 | 2016-11-30T16:23:16 | 75,075,164 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,840 | py | import _surface
import chimera
try:
import chimera.runCommand
except:
pass
from VolumePath import markerset as ms
try:
from VolumePath import Marker_Set, Link
new_marker_set=Marker_Set
except:
from VolumePath import volume_path_dialog
d= volume_path_dialog(True)
new_marker_set= d.new_marker_set
marker_sets={}
surf_sets={}
if "Cog2_GFPN" not in marker_sets:
s=new_marker_set('Cog2_GFPN')
marker_sets["Cog2_GFPN"]=s
s= marker_sets["Cog2_GFPN"]
mark=s.place_marker((478.695, 491.618, 534.974), (0.89, 0.1, 0.1), 18.4716)
if "Cog2_0" not in marker_sets:
s=new_marker_set('Cog2_0')
marker_sets["Cog2_0"]=s
s= marker_sets["Cog2_0"]
mark=s.place_marker((451.617, 440.284, 572.499), (0.89, 0.1, 0.1), 17.1475)
if "Cog2_1" not in marker_sets:
s=new_marker_set('Cog2_1')
marker_sets["Cog2_1"]=s
s= marker_sets["Cog2_1"]
mark=s.place_marker((421.251, 373.074, 608.156), (0.89, 0.1, 0.1), 17.1475)
if "Cog2_GFPC" not in marker_sets:
s=new_marker_set('Cog2_GFPC')
marker_sets["Cog2_GFPC"]=s
s= marker_sets["Cog2_GFPC"]
mark=s.place_marker((499.981, 362.186, 493.433), (0.89, 0.1, 0.1), 18.4716)
if "Cog2_Anch" not in marker_sets:
s=new_marker_set('Cog2_Anch')
marker_sets["Cog2_Anch"]=s
s= marker_sets["Cog2_Anch"]
mark=s.place_marker((338.529, 244.809, 727.359), (0.89, 0.1, 0.1), 18.4716)
if "Cog3_GFPN" not in marker_sets:
s=new_marker_set('Cog3_GFPN')
marker_sets["Cog3_GFPN"]=s
s= marker_sets["Cog3_GFPN"]
mark=s.place_marker((453.438, 458.283, 555.029), (1, 1, 0), 18.4716)
if "Cog3_0" not in marker_sets:
s=new_marker_set('Cog3_0')
marker_sets["Cog3_0"]=s
s= marker_sets["Cog3_0"]
mark=s.place_marker((453.114, 459.5, 554.125), (1, 1, 0.2), 17.1475)
if "Cog3_1" not in marker_sets:
s=new_marker_set('Cog3_1')
marker_sets["Cog3_1"]=s
s= marker_sets["Cog3_1"]
mark=s.place_marker((454.706, 473.954, 530.234), (1, 1, 0.2), 17.1475)
if "Cog3_2" not in marker_sets:
s=new_marker_set('Cog3_2')
marker_sets["Cog3_2"]=s
s= marker_sets["Cog3_2"]
mark=s.place_marker((445.868, 467.792, 504.458), (1, 1, 0.2), 17.1475)
if "Cog3_3" not in marker_sets:
s=new_marker_set('Cog3_3')
marker_sets["Cog3_3"]=s
s= marker_sets["Cog3_3"]
mark=s.place_marker((419.783, 471.665, 494.786), (1, 1, 0.2), 17.1475)
if "Cog3_4" not in marker_sets:
s=new_marker_set('Cog3_4')
marker_sets["Cog3_4"]=s
s= marker_sets["Cog3_4"]
mark=s.place_marker((401.639, 456.549, 510.057), (1, 1, 0.2), 17.1475)
if "Cog3_5" not in marker_sets:
s=new_marker_set('Cog3_5')
marker_sets["Cog3_5"]=s
s= marker_sets["Cog3_5"]
mark=s.place_marker((383.687, 469.433, 493.196), (1, 1, 0.2), 17.1475)
if "Cog3_GFPC" not in marker_sets:
s=new_marker_set('Cog3_GFPC')
marker_sets["Cog3_GFPC"]=s
s= marker_sets["Cog3_GFPC"]
mark=s.place_marker((469.191, 481.848, 558.095), (1, 1, 0.4), 18.4716)
if "Cog3_Anch" not in marker_sets:
s=new_marker_set('Cog3_Anch')
marker_sets["Cog3_Anch"]=s
s= marker_sets["Cog3_Anch"]
mark=s.place_marker((298.112, 462.255, 432.399), (1, 1, 0.4), 18.4716)
if "Cog4_GFPN" not in marker_sets:
s=new_marker_set('Cog4_GFPN')
marker_sets["Cog4_GFPN"]=s
s= marker_sets["Cog4_GFPN"]
mark=s.place_marker((259.758, 343.869, 591.668), (0, 0, 0.8), 18.4716)
if "Cog4_0" not in marker_sets:
s=new_marker_set('Cog4_0')
marker_sets["Cog4_0"]=s
s= marker_sets["Cog4_0"]
mark=s.place_marker((259.758, 343.869, 591.668), (0, 0, 0.8), 17.1475)
if "Cog4_1" not in marker_sets:
s=new_marker_set('Cog4_1')
marker_sets["Cog4_1"]=s
s= marker_sets["Cog4_1"]
mark=s.place_marker((287.288, 348.283, 581.84), (0, 0, 0.8), 17.1475)
if "Cog4_2" not in marker_sets:
s=new_marker_set('Cog4_2')
marker_sets["Cog4_2"]=s
s= marker_sets["Cog4_2"]
mark=s.place_marker((314.635, 353.765, 572.544), (0, 0, 0.8), 17.1475)
if "Cog4_3" not in marker_sets:
s=new_marker_set('Cog4_3')
marker_sets["Cog4_3"]=s
s= marker_sets["Cog4_3"]
mark=s.place_marker((341.364, 364.2, 567.353), (0, 0, 0.8), 17.1475)
if "Cog4_4" not in marker_sets:
s=new_marker_set('Cog4_4')
marker_sets["Cog4_4"]=s
s= marker_sets["Cog4_4"]
mark=s.place_marker((365.858, 379.968, 567.214), (0, 0, 0.8), 17.1475)
if "Cog4_5" not in marker_sets:
s=new_marker_set('Cog4_5')
marker_sets["Cog4_5"]=s
s= marker_sets["Cog4_5"]
mark=s.place_marker((387.236, 399.269, 572.405), (0, 0, 0.8), 17.1475)
if "Cog4_6" not in marker_sets:
s=new_marker_set('Cog4_6')
marker_sets["Cog4_6"]=s
s= marker_sets["Cog4_6"]
mark=s.place_marker((405.032, 421.155, 581.55), (0, 0, 0.8), 17.1475)
if "Cog4_GFPC" not in marker_sets:
s=new_marker_set('Cog4_GFPC')
marker_sets["Cog4_GFPC"]=s
s= marker_sets["Cog4_GFPC"]
mark=s.place_marker((205.981, 333.036, 445.315), (0, 0, 0.8), 18.4716)
if "Cog4_Anch" not in marker_sets:
s=new_marker_set('Cog4_Anch')
marker_sets["Cog4_Anch"]=s
s= marker_sets["Cog4_Anch"]
mark=s.place_marker((598.405, 534.572, 716.502), (0, 0, 0.8), 18.4716)
if "Cog5_GFPN" not in marker_sets:
s=new_marker_set('Cog5_GFPN')
marker_sets["Cog5_GFPN"]=s
s= marker_sets["Cog5_GFPN"]
mark=s.place_marker((403.357, 414.699, 621.968), (0.3, 0.3, 0.3), 18.4716)
if "Cog5_0" not in marker_sets:
s=new_marker_set('Cog5_0')
marker_sets["Cog5_0"]=s
s= marker_sets["Cog5_0"]
mark=s.place_marker((403.357, 414.699, 621.968), (0.3, 0.3, 0.3), 17.1475)
if "Cog5_1" not in marker_sets:
s=new_marker_set('Cog5_1')
marker_sets["Cog5_1"]=s
s= marker_sets["Cog5_1"]
mark=s.place_marker((429.663, 403.467, 620.712), (0.3, 0.3, 0.3), 17.1475)
if "Cog5_2" not in marker_sets:
s=new_marker_set('Cog5_2')
marker_sets["Cog5_2"]=s
s= marker_sets["Cog5_2"]
mark=s.place_marker((450.853, 385.335, 613.4), (0.3, 0.3, 0.3), 17.1475)
if "Cog5_3" not in marker_sets:
s=new_marker_set('Cog5_3')
marker_sets["Cog5_3"]=s
s= marker_sets["Cog5_3"]
mark=s.place_marker((450.131, 365.283, 592.147), (0.3, 0.3, 0.3), 17.1475)
if "Cog5_GFPC" not in marker_sets:
s=new_marker_set('Cog5_GFPC')
marker_sets["Cog5_GFPC"]=s
s= marker_sets["Cog5_GFPC"]
mark=s.place_marker((513.337, 446.467, 521.88), (0.3, 0.3, 0.3), 18.4716)
if "Cog5_Anch" not in marker_sets:
s=new_marker_set('Cog5_Anch')
marker_sets["Cog5_Anch"]=s
s= marker_sets["Cog5_Anch"]
mark=s.place_marker((386.052, 276.451, 653.213), (0.3, 0.3, 0.3), 18.4716)
if "Cog6_GFPN" not in marker_sets:
s=new_marker_set('Cog6_GFPN')
marker_sets["Cog6_GFPN"]=s
s= marker_sets["Cog6_GFPN"]
mark=s.place_marker((473.752, 433.46, 555.492), (0.21, 0.49, 0.72), 18.4716)
if "Cog6_0" not in marker_sets:
s=new_marker_set('Cog6_0')
marker_sets["Cog6_0"]=s
s= marker_sets["Cog6_0"]
mark=s.place_marker((473.764, 433.457, 555.488), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_1" not in marker_sets:
s=new_marker_set('Cog6_1')
marker_sets["Cog6_1"]=s
s= marker_sets["Cog6_1"]
mark=s.place_marker((483.159, 457.741, 568.226), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_2" not in marker_sets:
s=new_marker_set('Cog6_2')
marker_sets["Cog6_2"]=s
s= marker_sets["Cog6_2"]
mark=s.place_marker((468.537, 481.85, 573.984), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_3" not in marker_sets:
s=new_marker_set('Cog6_3')
marker_sets["Cog6_3"]=s
s= marker_sets["Cog6_3"]
mark=s.place_marker((442.525, 491.541, 567.233), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_4" not in marker_sets:
s=new_marker_set('Cog6_4')
marker_sets["Cog6_4"]=s
s= marker_sets["Cog6_4"]
mark=s.place_marker((423.292, 501.056, 548.324), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_5" not in marker_sets:
s=new_marker_set('Cog6_5')
marker_sets["Cog6_5"]=s
s= marker_sets["Cog6_5"]
mark=s.place_marker((400.77, 499.392, 531.192), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_6" not in marker_sets:
s=new_marker_set('Cog6_6')
marker_sets["Cog6_6"]=s
s= marker_sets["Cog6_6"]
mark=s.place_marker((377.516, 489.405, 518.664), (0.21, 0.49, 0.72), 17.1475)
if "Cog6_GFPC" not in marker_sets:
s=new_marker_set('Cog6_GFPC')
marker_sets["Cog6_GFPC"]=s
s= marker_sets["Cog6_GFPC"]
mark=s.place_marker((404.183, 491.253, 600.111), (0.21, 0.49, 0.72), 18.4716)
if "Cog6_Anch" not in marker_sets:
s=new_marker_set('Cog6_Anch')
marker_sets["Cog6_Anch"]=s
s= marker_sets["Cog6_Anch"]
mark=s.place_marker((353.742, 484.147, 434.656), (0.21, 0.49, 0.72), 18.4716)
if "Cog7_GFPN" not in marker_sets:
s=new_marker_set('Cog7_GFPN')
marker_sets["Cog7_GFPN"]=s
s= marker_sets["Cog7_GFPN"]
mark=s.place_marker((435.58, 469.772, 626.287), (0.7, 0.7, 0.7), 18.4716)
if "Cog7_0" not in marker_sets:
s=new_marker_set('Cog7_0')
marker_sets["Cog7_0"]=s
s= marker_sets["Cog7_0"]
mark=s.place_marker((442.07, 447.811, 613.584), (0.7, 0.7, 0.7), 17.1475)
if "Cog7_1" not in marker_sets:
s=new_marker_set('Cog7_1')
marker_sets["Cog7_1"]=s
s= marker_sets["Cog7_1"]
mark=s.place_marker((458.254, 400.796, 584.742), (0.7, 0.7, 0.7), 17.1475)
if "Cog7_2" not in marker_sets:
s=new_marker_set('Cog7_2')
marker_sets["Cog7_2"]=s
s= marker_sets["Cog7_2"]
mark=s.place_marker((475.76, 353.371, 557.837), (0.7, 0.7, 0.7), 17.1475)
if "Cog7_GFPC" not in marker_sets:
s=new_marker_set('Cog7_GFPC')
marker_sets["Cog7_GFPC"]=s
s= marker_sets["Cog7_GFPC"]
mark=s.place_marker((544.667, 395.707, 545.915), (0.7, 0.7, 0.7), 18.4716)
if "Cog7_Anch" not in marker_sets:
s=new_marker_set('Cog7_Anch')
marker_sets["Cog7_Anch"]=s
s= marker_sets["Cog7_Anch"]
mark=s.place_marker((449.222, 255.85, 531.598), (0.7, 0.7, 0.7), 18.4716)
if "Cog8_0" not in marker_sets:
s=new_marker_set('Cog8_0')
marker_sets["Cog8_0"]=s
s= marker_sets["Cog8_0"]
mark=s.place_marker((519.447, 415.745, 547.547), (1, 0.5, 0), 17.1475)
if "Cog8_1" not in marker_sets:
s=new_marker_set('Cog8_1')
marker_sets["Cog8_1"]=s
s= marker_sets["Cog8_1"]
mark=s.place_marker((511.16, 425.322, 572.61), (1, 0.5, 0), 17.1475)
if "Cog8_2" not in marker_sets:
s=new_marker_set('Cog8_2')
marker_sets["Cog8_2"]=s
s= marker_sets["Cog8_2"]
mark=s.place_marker((487.173, 418.149, 585.288), (1, 0.5, 0), 17.1475)
if "Cog8_3" not in marker_sets:
s=new_marker_set('Cog8_3')
marker_sets["Cog8_3"]=s
s= marker_sets["Cog8_3"]
mark=s.place_marker((485.574, 401.29, 608.518), (1, 0.5, 0), 17.1475)
if "Cog8_4" not in marker_sets:
s=new_marker_set('Cog8_4')
marker_sets["Cog8_4"]=s
s= marker_sets["Cog8_4"]
mark=s.place_marker((479.216, 385.439, 631.641), (1, 0.5, 0), 17.1475)
if "Cog8_5" not in marker_sets:
s=new_marker_set('Cog8_5')
marker_sets["Cog8_5"]=s
s= marker_sets["Cog8_5"]
mark=s.place_marker((460.226, 371.123, 647.809), (1, 0.5, 0), 17.1475)
if "Cog8_GFPC" not in marker_sets:
s=new_marker_set('Cog8_GFPC')
marker_sets["Cog8_GFPC"]=s
s= marker_sets["Cog8_GFPC"]
mark=s.place_marker((462.48, 436.145, 600.066), (1, 0.6, 0.1), 18.4716)
if "Cog8_Anch" not in marker_sets:
s=new_marker_set('Cog8_Anch')
marker_sets["Cog8_Anch"]=s
s= marker_sets["Cog8_Anch"]
mark=s.place_marker((454.734, 305.751, 697.003), (1, 0.6, 0.1), 18.4716)
for k in surf_sets.keys():
chimera.openModels.add([surf_sets[k]])
| [
"[email protected]"
] | |
f5738ebf2b316d78bcf67e9b2c3851c42892334e | 0c4d4d199da126ff7d5d8317aaaf31fa6182d43e | /Shuffle.py | 461c6951fd94b3110048c2ed93f2dc7cdef104a6 | [] | no_license | riquellopes/challenges | c067101171d2716e3ddb8a928f332c4fe0c5bfb2 | cedfba39d6866bd4ff1ec40d0f3641e07f805a16 | refs/heads/master | 2020-09-13T17:33:28.457695 | 2018-08-22T21:56:16 | 2018-08-22T21:56:16 | 94,462,509 | 0 | 0 | null | 2018-10-28T12:43:17 | 2017-06-15T17:21:01 | Python | UTF-8 | Python | false | false | 605 | py | # you can write to stdout for debugging purposes, e.g.
# print("this is a debug message")
"""
>>> solution(123456)
162534
>>> solution(162534)
146325
"""
def solution(A):
# write your code in Python 3.6
numbers = list(str(A))
size = len(numbers)
to_remove = 0
digit = []
while True:
if len(digit) == size:
break
if to_remove == 0:
num = numbers.pop(to_remove)
to_remove = -1
else:
num = numbers.pop()
to_remove = 0
digit.append(num)
return int("".join(digit))
| [
"[email protected]"
] | |
5d3ef43a52c9d3f468feeb9ed9bdd8f5ff9dfba6 | 4f7742df83849517c5675513a1d111b01fc1deb3 | /examples/precision_landing.py | a39daefaa1bd9c71eb0bac4f698b1f829ac703d5 | [
"BSD-3-Clause"
] | permissive | mcorner/dji-asdk-to-python | 319025ee1b5ebba5b26b8cdd144eec5dec243f0f | 59464f36dc046a0b96b1544fff31e0e40f1322a1 | refs/heads/master | 2023-01-06T16:31:24.757081 | 2020-10-29T18:57:10 | 2020-10-29T18:57:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,386 | py | from dji_asdk_to_python.products.aircraft import Aircraft
import numpy as np
from dji_asdk_to_python.precision_landing.aproximation import ArucoAproximation
from dji_asdk_to_python.precision_landing.landing import ArucoLanding
from time import sleep
import os
from dji_asdk_to_python.errors import CustomError
APP_IP = "192.168.50.158"
aircraft = Aircraft(APP_IP)
camera_distortion = np.loadtxt("/home/luis/Documentos/psbposas/dji-asdk-to-python/examples/calibration/camera_distortion.txt", delimiter=",")
camera_matrix = np.loadtxt("/home/luis/Documentos/psbposas/dji-asdk-to-python/examples/calibration/camera_matrix.txt", delimiter=",")
stage1 = ArucoAproximation(drone_ip=APP_IP,camera_distortion=camera_distortion, camera_matrix=camera_matrix, marker_id=17, marker_size_cm=70)
stage2 = ArucoLanding(drone_ip=APP_IP,camera_distortion=camera_distortion, camera_matrix=camera_matrix, marker_id=62, marker_size_cm=12)
streaming_manager = aircraft.getLiveStreamManager()
rtp_manager = streaming_manager.getRTPManager()
rtp_manager.setWidth(1280)
rtp_manager.setHeigth(720)
result = rtp_manager.startStream()
print("result startStream %s" % result)
if isinstance(result, CustomError):
raise Exception("%s" % result)
stage1.start(rtp_manager)
input("PRESS A KEY TO ENTER STAGE 2") #DBest notification of top platform deployment should be awaited here
stage2.start(rtp_manager)
| [
"[email protected]"
] | |
d79d232b2c92ccaf4f09f8887399945e4d279992 | b05761d771bb5a85d39d370c649567c1ff3eb089 | /venv/lib/python3.10/site-packages/jedi/third_party/typeshed/third_party/2and3/google/protobuf/internal/python_message.pyi | 739b65ed584976d74587def3ecc0bcf58b01737f | [] | no_license | JawshyJ/Coding_Practice | 88c49cab955eab04609ec1003b6b8c20f103fc06 | eb6b229d41aa49b1545af2120e6bee8e982adb41 | refs/heads/master | 2023-02-19T10:18:04.818542 | 2023-02-06T21:22:58 | 2023-02-06T21:22:58 | 247,788,631 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 96 | pyi | /home/runner/.cache/pip/pool/20/44/ab/d4e8c0643f62760d4e816ccc7de5764ad6b4f11d2e1cb01bc1e9634c3e | [
"[email protected]"
] | |
d1dcdef8f4dc3fe9d977de9f8c810384be8f24d1 | 6426ca723494c69f7a18d6378458dad0b7abf99a | /HW3_Cocktail,ExchangeSort/cocktailshakeSort.py | dee7a95c24df85bcc21fcc02485679640a03ea53 | [] | no_license | NoirNorie/Algorithm_Python | 3364f8ac8a530ede3857dc46f224df2c8c7eee80 | a137fb30f1c44373cc22d3110eecd51d1e057540 | refs/heads/master | 2022-12-24T14:45:08.122396 | 2020-10-03T15:49:45 | 2020-10-03T15:49:45 | 298,333,862 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,146 | py | import random, time, sys
def checkSort(a,n):
isSorted = True
for i in range(1,n):
if a[i] > a[i+1]:
isSorted = False
if (not isSorted):
break
if (isSorted):
print("정렬 완료")
else:
print("정렬 오류 발생")
# cocktailshakeSort가 너무 길어서 csSort로 줄여서 작성
def csSort(a,n):
i, j = N, 1
while(i>j):
if (i+j != N): # 배열의 앞에서 뒤로 진행
for k in range(j,i,1):
if (a[k] > a[k+1]):
a[k], a[k+1] = a[k+1], a[k]
i -= 1
else : # 배열의 뒤에서 앞으로 진행
for k in range(i,j,-1):
if (a[k-1] > a[k]):
a[k-1], a[k] = a[k], a[k-1]
j += 1
N = 5000
b = []
b.append(-1)
for i in range(N):
b.append(random.randint(1,N))
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('임의로 값이 삽입된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
b = []
b.append(-1)
for i in range(N):
b.append(i)
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('정렬된 값이 삽입된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
b = []
b.append(-1)
for i in range(N-1,-1,-1):
b.append(i)
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('역순으로 정렬된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
print()
N = 10000
b = []
b.append(-1)
for i in range(N):
b.append(random.randint(1,N))
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('임의로 값이 삽입된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
b = []
b.append(-1)
for i in range(N):
b.append(i)
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('정렬된 값이 삽입된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
b = []
b.append(-1)
for i in range(N-1,-1,-1):
b.append(i)
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('역순으로 정렬된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
N = 15000
b = []
b.append(-1)
for i in range(N):
b.append(random.randint(1,N))
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('임의로 값이 삽입된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
b = []
b.append(-1)
for i in range(N):
b.append(i)
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('정렬된 값이 삽입된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time))
b = []
b.append(-1)
for i in range(N-1,-1,-1):
b.append(i)
start_time = time.time()
csSort(b,N)
end_time = time.time() - start_time
print('역순으로 정렬된 배열의 칵테일쉐이커 정렬의 실행 시간 (N = %d) : %0.3f'%(N, end_time)) | [
"[email protected]"
] | |
3b18713036101f6e001dab4bead2f1f625494818 | ebbd58c88dc3ea5c3ff5b7c63cde731c063bd6cc | /sigma/gods-unchained-packs/tests/tests/test_bundle_open.py | d9c24e9aa2f78b8b3f358b4ec26e9c3ec59ce713 | [] | no_license | the-mog/resources | eb65efebc47fe75cefe85049d162d9032b6cd958 | 58b446d3ba6e16acda163869b798e191077631ac | refs/heads/main | 2023-01-21T08:36:47.822319 | 2020-11-23T15:21:18 | 2020-11-23T15:21:18 | 315,348,311 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,948 | py | import pytest
import random
from web3.contract import ConciseContract
##########################
# VALID OPERATIONS
##########################
def test_bundle_open(
accounts,
assert_tx_failed,
get_logs_for_event,
instantiate,
pack_deploy,
pack_prices,
pack_types,
w3,
):
# Deploy a PackFive and required supporting contracts (processor, referral, cards, vault)
(pack, processor, referrals, cards, vault, pack_r, processor_r, referrals_r, cards_r, vault_r) = pack_deploy()
# Set packs for purchase
bundle_size = 3 # 3 packs of 5 cards in a bundle
tx_hash = pack.functions.setPack(pack_types['Rare'], pack_prices['Rare'], "Rare bundle", "RB", bundle_size, 0).transact({'from': accounts[0]})
rare_bundle = instantiate(get_logs_for_event(pack.events.PackAdded, tx_hash)[0]['args']['bundle'], abi=None, contract="Bundle")
processor.functions.setCanSell(rare_bundle.address, True).transact({'from': accounts[0]})
rare_bundle.functions.purchase(1, accounts[2]).transact({'from': accounts[1], 'value': pack_prices['Rare'] * bundle_size})
tx = rare_bundle.functions.open(1).transact({'from': accounts[1]})
logs = get_logs_for_event(pack.events.BundlesOpened, tx)
assert logs[0]['args']['id'] == 0, "Bundle open id"
assert logs[0]['args']['packType'] == 0, "Bundle open pack type"
assert logs[0]['args']['user'] == accounts[1], "Bundle open user"
assert logs[0]['args']['count'] == 1, "Bundle open count"
assert logs[0]['args']['packCount'] == 3, "Bundle open packCount"
logs = get_logs_for_event(pack.events.PurchaseRecorded, tx)
assert logs[0]['args']['id'] == 0, "purchaseRecorded ID"
assert logs[0]['args']['packType'] == pack_types['Rare'], "purchaseRecorded packType"
assert logs[0]['args']['user'] == accounts[1], "purchaseRecorded user"
assert logs[0]['args']['count'] == 3, "purchaseRecorded count"
assert logs[0]['args']['lockup'] == 0, "purchaseRecorded lockup"
logs = get_logs_for_event(rare_bundle.events.Transfer, tx)
assert logs[0]['args']['from'] == accounts[1], "Burn from user"
assert logs[0]['args']['to'] == '0x' + '00' * 20, "Burn to 0"
assert logs[0]['args']['value'] == 1, "Burn x tokens"
def test_open_max_count(
accounts,
assert_tx_failed,
get_logs_for_event,
instantiate,
pack_deploy,
pack_prices,
pack_types,
w3,
):
# Deploy a PackFive and required supporting contracts (processor, referral, cards, vault)
(pack, processor, referrals, cards, vault, pack_r, processor_r, referrals_r, cards_r, vault_r) = pack_deploy()
# Set packs for purchase
bundle_size = 1
num_bundles = 2**15 # 2 packs of 5 cards in a bundle
tx_hash = pack.functions.setPack(pack_types['Rare'], pack_prices['Rare'], "Rare bundle", "RB", bundle_size, 0).transact({'from': accounts[0]})
rare_bundle = instantiate(get_logs_for_event(pack.events.PackAdded, tx_hash)[0]['args']['bundle'], abi=None, contract="Bundle")
processor.functions.setCanSell(rare_bundle.address, True).transact({'from': accounts[0]})
rare_bundle.functions.purchase(num_bundles, accounts[2]).transact({'from': accounts[1], 'value': pack_prices['Rare'] * bundle_size * num_bundles})
rare_bundle.functions.open(num_bundles).transact({'from': accounts[1]})
# This fails now because at least one bundle must be opened.
@pytest.mark.xfail
def test_open_zero(
accounts,
assert_tx_failed,
get_logs_for_event,
instantiate,
pack_deploy,
pack_prices,
pack_types,
w3,
):
# Deploy a PackFive and required supporting contracts (processor, referral, cards, vault)
(pack, processor, referrals, cards, vault, pack_r, processor_r, referrals_r, cards_r, vault_r) = pack_deploy()
# Set packs for purchase
bundle_size = 3 # 3 packs of 5 cards in a bundle
tx_hash = pack.functions.setPack(pack_types['Rare'], pack_prices['Rare'], "Rare bundle", "RB", bundle_size, 0).transact({'from': accounts[0]})
rare_bundle = instantiate(get_logs_for_event(pack.events.PackAdded, tx_hash)[0]['args']['bundle'], abi=None, contract="Bundle")
processor.functions.setCanSell(rare_bundle.address, True).transact({'from': accounts[0]})
rare_bundle.functions.purchase(1, accounts[2]).transact({'from': accounts[1], 'value': pack_prices['Rare'] * bundle_size})
# Should this fail cause it creates logs
rare_bundle.functions.open(0).transact({'from': accounts[1]})
##########################
# INVALID OPERATIONS
##########################
def test_open_too_many(
accounts,
assert_tx_failed,
get_logs_for_event,
instantiate,
pack_deploy,
pack_prices,
pack_types,
w3,
):
# Deploy a PackFive and required supporting contracts (processor, referral, cards, vault)
(pack, processor, referrals, cards, vault, pack_r, processor_r, referrals_r, cards_r, vault_r) = pack_deploy()
# Set packs for purchase
bundle_size = 3 # 3 packs of 5 cards in a bundle
num_bundles = 5
tx_hash = pack.functions.setPack(pack_types['Rare'], pack_prices['Rare'], "Rare bundle", "RB", bundle_size, 0).transact({'from': accounts[0]})
rare_bundle = instantiate(get_logs_for_event(pack.events.PackAdded, tx_hash)[0]['args']['bundle'], abi=None, contract="Bundle")
processor.functions.setCanSell(rare_bundle.address, True).transact({'from': accounts[0]})
rare_bundle.functions.purchase(num_bundles, accounts[2]).transact({'from': accounts[1], 'value': pack_prices['Rare'] * bundle_size * num_bundles})
assert_tx_failed(rare_bundle.functions.open(num_bundles + 1), {'from': accounts[1]})
assert_tx_failed(rare_bundle.functions.open(2**256 - 1), {'from': accounts[1]})
assert_tx_failed(rare_bundle.functions.open(2**16 - 1), {'from': accounts[1]})
| [
"[email protected]"
] | |
55be114a4a3c24440decb8cc9e79341dec924dba | 058f6cf55de8b72a7cdd6e592d40243a91431bde | /tests/llvm/static/test.py | 5819067099605c281f435a3dad4f6c284961eb24 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | LLNL/FPChecker | 85e8ebf1d321b3208acee7ddfda2d8878a238535 | e665ef0f050316f6bc4dfc64c1f17355403e771b | refs/heads/master | 2023-08-30T23:24:43.749418 | 2022-04-14T19:57:44 | 2022-04-14T19:57:44 | 177,033,795 | 24 | 6 | Apache-2.0 | 2022-09-19T00:09:50 | 2019-03-21T22:34:14 | Python | UTF-8 | Python | false | false | 3,694 | py | #!/usr/bin/env python
import test_config
import subprocess
import os
import sys
def main():
print "* Static Tests *"
###########################################################################
t = "Test: find instrumentation functions"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_find_inst_functions/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
###########################################################################
t = "Test: num. fp operations"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_number_fp_operations/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
###########################################################################
t = "Test: a device function is found"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_device_func_found/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
###########################################################################
t = "Test: a global function is found"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_global_func_found/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
###########################################################################
t = "Test: main() is found"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_main_is_found/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
###########################################################################
t = "Test: global array instrumentation"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_global_array_instrumentation/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
###########################################################################
t = "Test: correct func are found and instrumented"
testTarget = test_config.textWidth.format(t)
sys.stdout.write(testTarget)
os.chdir(test_config.path + "/test_correct_inst_functions_found/")
cmd = ["./test.py"]
cmdOutput = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
sys.stdout.write(cmdOutput)
os.chdir("../")
###########################################################################
main()
| [
"[email protected]"
] | |
ac03c81ca91034b84d892212f2c3f714f8fb0a32 | 2185c16a9f6564183e86a49942a2ae861bce534c | /IIHT/holland_house_scraper/scraper.py | 2af00020f9bea906a35b99bc22677eeb35023511 | [] | no_license | patricksile/code_folder | a9c3ebe32f6eed122fb877b955643d8944d8453a | b8f5cce6ea07ed567621e848c7a61ab457f66670 | refs/heads/master | 2023-01-22T14:06:33.768186 | 2018-07-25T12:05:44 | 2018-07-25T12:05:44 | 142,292,596 | 0 | 0 | null | 2023-01-11T22:20:31 | 2018-07-25T11:47:20 | JavaScript | UTF-8 | Python | false | false | 2,278 | py | # # /usr/bin/env python3.5
# from urllib.parse import urlparse # module to clean links (built-in)
# import urllib.parse
# from time import sleep
# import webbrowser # Open links with a web browser(built-in)
# import requests # Downloads files and web pages (external)
# import bs4 # Parses HTML (external)
# import selenium # Launches and controls a web browser (external)
# import time # To insert some delay on actions
# import smtplib # To send files through smtp server
# import email.mime.text # To send data to an email
# # open file with websites raw links
# web_file = open('websites.txt','r')
# # read object web_file and save in links_file
# links_file = web_file.read().split('\n') #adfsdfsdfdsf
# # clean_links = [] # object clean_links of the class array
# clean_links = [urlparse(line).netloc for line in links_file] # object clean_links of the class array
# cities = ['haarlem', 'amsterdam'] # list of cities for test purpose with www.huurwoningen.nl which is the first link in the object list clean_links
# # page_download = requests.get("http://%s/in/%s/?min_price=%d&max_price=%d"%("www.huurwoningen.nl", "haarlem", 300, 600)).text
# # webbrowser.open("http://www.huurwoningen.nl/in/haarlem/?min_price=300&max_price=600")
# # sleep(10)
# # page_download = requests.get("https://jobs.jumia.cm/en/jobs-douala/?by=digital+marketer")
# # page_download_bs4 = bs4.BeautifulSoup(page_download.text,"lxml")
# for link in clean_links[0]:
# for city in cities: # https://www.huurwoningen.nl/in/haarlem/?min_price=100&max_price=300
# for max_price in range(500, 601,100):
# #
# time.sleep(5) # 5seconds sleep or delay
# webbrowser.open("https://%s/in/%s/?min_price=%d&max_price=%d"%(link, city, 300, max_price)) # opening each pages in a new tab
# # page_download = requests.get("http://%s/in/%s/?min_price=%d&max_price=%d"%(link, city, 300, max_price))# downloading the page in the page_download
# # print(page_downlaod)
# # page_download.raise_for_status()# http error checking
# soup = bs4.BeautifulSoup(page_download.text)# creating a bs4 object for further html parsing
# price_link = soup.select('div span .linsting_price')# price link elements to select of object list
# for i in range(len(price_link))
| [
"[email protected]"
] | |
4c005a778fac7b075557916fb12e526b31ac3231 | 3a31504d63065a2bacc4afa473a1a9662534aa7d | /re_sys/views.py | 099f774af69209f8512b3fb822f991d39f18e64d | [] | no_license | wuweiwuyanzu/Personalized-recommend | 09ca9218e6405e3e71d78cf42cd54ff9398e9040 | c48bb53be6623beab8bcecef85354228637dc8ef | refs/heads/master | 2020-06-27T04:20:09.560639 | 2019-05-03T15:43:58 | 2019-05-03T15:43:58 | 199,842,889 | 1 | 0 | null | 2019-07-31T11:29:10 | 2019-07-31T11:29:09 | null | UTF-8 | Python | false | false | 2,310 | py | #!/usr/bin/env python
#coding=utf-8
from django.shortcuts import render
from re_sys.recommend import re_model
from re_sys.recommend import utils
import time
print('----初始化加载模型----')
global_model = re_model.Model()
global_loaded_graph, global_sess = global_model.loead_sess()
# Create your views here.
def index(request):
return render(request,'index.html')
def recommend(request):
movie_id = request.GET.get('movie_id')
try:
if((int(movie_id)<0) | (int(movie_id)>3953)):
return render(request,'index.html')
except ValueError:
return render(request, 'index.html')
global global_model
model=global_model
print('-------正在推荐--------',movie_id)
global_loaded_graph, global_sess
choice_movie_name,list_same_movies_names,list_pepole_like_movies_names,list_same_movies_ids,list_pepole_like_movies_ids =model.recommend_by_movie(int(movie_id))
print('选择电影：',choice_movie_name)
print('相似的电影：',list_same_movies_names)
print('喜欢这个电影的人还喜欢：',list_pepole_like_movies_names)
list_dict_choice=[]
for i in choice_movie_name:
# time.sleep(0.2) # 爬虫速度
list_dict_choice.append(utils.movie_dic(i))
list_dict_choice[0]['movie_id']=movie_id
# list_dict_choice[0]['title']=choice_movie_name
list_dict_same = []
for i in list_same_movies_names[:4]:
# time.sleep(0.2)
list_dict_same.append(utils.movie_dic(i))
for i in range(len(list_dict_same)):
list_dict_same[i]['movie_id']=list_same_movies_ids[i]
list_dict_otherlike = []
for i in list_pepole_like_movies_names[:4]:
# time.sleep(0.2)
list_dict_otherlike.append(utils.movie_dic(i))
for i in range(len(list_dict_otherlike)):
list_dict_otherlike[i]['movie_id'] = list_pepole_like_movies_ids[i]
#list_dict_otherlike[i]['title'] = list_dict_otherlike[i]
print('返回结果')
print(list_dict_choice)
print(len(list_dict_same))
# print(len(list_dict_otherlike))
context = {}
context['list_dict_choice'] = list_dict_choice[:4]
context['list_dict_same'] = list_dict_same
context['list_dict_otherlike'] = list_dict_otherlike
return render(request,'index.html',context)
| [
"[email protected]"
] | |
95e8f2292640c638b8debe67833e96aaa4b6f3e4 | c41c0a760d11d384ba2ece1040875d23ee9088a2 | /python_stack/django_projects/belt_reviewer/apps/login_registration/views.py | d202873f333c1d28af3a9a841121f16fe717817a | [] | no_license | frednava67/dojo | 6a0d6250c992fc6910a539518891f237fcc62f4a | d342bbb474606c0bc019247aeb0212cc4704cd23 | refs/heads/master | 2018-12-20T02:05:47.798107 | 2018-11-13T05:34:49 | 2018-11-13T05:34:49 | 149,045,555 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,732 | py |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.shortcuts import render, HttpResponse, redirect
from django.contrib import messages
import re, bcrypt
from .models import User
# the index function is called when root is visited
def index(request):
print("login_registration/index()")
if "user_id" not in request.session:
context = {
"first_name": "",
"last_name": "",
"email": ""
}
if "reg_attempt_failed" in request.session:
context = {
"first_name": request.session["first_name"],
"last_name": request.session["last_name"],
"email": request.session["email"]
}
return render(request, "index.html", context)
def process_registration(request):
print("login_registration/process_registration()")
bFlashMessage = False
if request.method == "POST":
bFlashMessage = User.objects.basic_validator(request)
request.session["first_name"] = request.POST['first_name']
request.session["last_name"] = request.POST['last_name']
request.session["email"] = request.POST['email']
f_name = request.POST['first_name']
l_name = request.POST['last_name']
email = request.POST['email']
pwd = request.POST['password']
request.session["first_name"] = f_name
request.session["last_name"] = l_name
request.session["email"] = email
if bFlashMessage:
request.session["reg_attempt_failed"] = True
return redirect("/login_registration")
else:
request.session.clear()
pwhash = bcrypt.hashpw(pwd.encode(), bcrypt.gensalt())
new_user = User.objects.create(first_name=f_name, last_name=l_name, email=email, pwhashval=pwhash.decode())
request.session["user_id"] = new_user.id
request.session["first_name"] = f_name
return redirect("/")
def process_login(request):
print("login_registration/process_login")
if request.method == "POST":
loginemail = request.POST['loginemail']
loginpassword = request.POST['loginpassword']
print(loginemail)
print(User.objects.all().values())
obj = User.objects.filter(email=loginemail)
print("count", obj.count())
i = obj.count()
if (i > 0):
tempHash = obj[0].pwhashval
bPasswordCheck = bcrypt.checkpw(loginpassword.encode(), tempHash.encode())
print("bPasswordCheck", bPasswordCheck)
request.session.clear()
if (i == 0 or bPasswordCheck != True):
request.session["loginemail"] = loginemail
messages.error(request, u"You were not able to login.", 'login')
return redirect('/')
else:
request.session["first_name"] = obj[0].first_name
request.session["user_id"] = obj[0].id
print(request.session["user_id"])
else:
request.session["loginemail"] = loginemail
messages.error(request, u"You were not able to login.", 'login')
return redirect('/')
return redirect('/')
def reset(request):
print("reset()")
request.session.clear()
return redirect('/')
# def runonce(request):
# print("runonce()")
# #password
# badpassword1 = "password"
# hash1 = bcrypt.hashpw(badpassword1.encode(), bcrypt.gensalt())
# print(User.objects.create(first_name="Foghorn", last_name="Leghorn", email="[email protected]", pwhashval=hash1.decode()))
# response = "Hello, I ran your RUNONCE request!"
# return HttpResponse(response)
| [
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] | |
d5260e5c6f8e6b776dd7948859e56fac6f69d8c5 | e63a895b941207285d1ee1e36c5a2bf6bf3ed5bc | /progress.py | 1a6229bbba8d161e43f0610f710d6b5cd09e2238 | [
"MIT"
] | permissive | SSaeedHoseini/dockerscriptpy | dcea26ee8a743286e849262a03e447b096113c93 | f601937d0143bac0124d5b769ff3ea10625a24ab | refs/heads/master | 2020-12-09T04:40:40.304676 | 2020-01-13T08:18:46 | 2020-01-13T08:18:46 | 233,195,809 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,296 | py | import shutil
def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', autosize = False):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : positive number of decimals in percent complete (Int)
length - Optional : character length of bar (Int)
fill - Optional : bar fill character (Str)
autosize - Optional : automatically resize the length of the progress bar to the terminal window (Bool)
"""
percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
styling = '%s |%s| %s%% %s' % (prefix, fill, percent, suffix)
if autosize:
cols, _ = shutil.get_terminal_size(fallback = (length, 1))
length = cols - len(styling)
filledLength = int(length * iteration // total)
bar = fill * filledLength + '-' * (length - filledLength)
print('\r%s' % styling.replace(fill, bar), end = '\r')
# Print New Line on Complete
if iteration == total:
print()
| [
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] | |
bb313215f567104597c8c9dfc261320fd344893a | 7e90ba580736a1cf03fbeb8461b5b746599f2008 | /core/config.py | 418f320e38639a791466c7335a7406d02e1fca01 | [] | no_license | ppaydd/problem_count | 7b455b51707cc0a7dac6a7e10043ae9874ba2e80 | cd85d1ca0f877213d505962c627f5f241827ea8c | refs/heads/master | 2021-01-12T19:20:02.536722 | 2016-04-12T06:39:38 | 2016-04-12T06:39:38 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,056 | py | import os
headers = {
'User-Agent': '''Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5)
AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.87
Safari/537.36''',
'Connection': 'keep-alive',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
}
# Error Code
get_data_failed = -1
match_user_failed = -2
# Maximum Process Number
# depend on the number of core
MPN = os.cpu_count()
# Spide Time Limits
TIME = 10
# Each OJ's URL
codeforces_url = 'http://codeforces.com/api/user.status?handle={0}&from=1&count=1000000000'
hdu_url = 'http://acm.hdu.edu.cn/search.php?field=author&key='
fzu_url = 'http://acm.fzu.edu.cn/user.php?uname='
poj_url = 'http://poj.org/searchuser?key={0}&B1=Search'
noj_url = 'https://ac.2333.moe/User/user_list.xhtml?page='
spoj_url = 'http://www.spoj.com/ranks/users/start='
lightoj_login_url = 'http://lightoj.com/login_check.php'
lightoj_userlist_url = 'http://lightoj.com/volume_ranklist.php?rank_start='
bzoj_url = 'http://www.lydsy.com/JudgeOnline/userinfo.php?user='
sgu_url = 'http://acm.sgu.ru/teaminfo.php?id='
ural_url = 'http://acm.timus.ru/search.aspx?Str='
zoj_url = 'http://www.icpc.moe/onlinejudge/showRuns.do?contestId=1&search=true&firstId=-1&lastId=-1&problemCode=&handle={0}&idStart=&idEnd='
acdream_url = 'http://acdream.info/user/'
nyist_url = 'http://acm.nyist.edu.cn/JudgeOnline/profile.php?userid='
# Corresponding regular expression pattern.
# hdu
hdu_table_pattern = '<table width="80%" border="0" align="center" cellspacing="2" class=\'TABLE_TEXT\'>([\s\S]*?)</table>'
hdu_td_pattern = '<td>([\s\S]*?)</td>'
hdu_username_pattern = '<A href="[\s\S]*?">([\s\S]*?)</A>'
hdu_ac_number_pattern = '<A href="[\s\S]*?">([\s\S]*?)</A>'
hdu_submit_number_pattern = '<A href="[\s\S]*?">([\s\S]*?)</A>'
# lightoj
lightoj_page_count_pattern = '<a class="user_link" style="color: #c75f3e;" href="[\s\S]*?">([\s\S]*?)</a>'
lightoj_tr_data_one_pattern = '<tr class="newone">([\s\S]*?)</tr>'
lightoj_tr_data_two_pattern = '<tr class="newtwo">([\s\S]*?)</tr>'
lightoj_user_data_two_pattern = '<a class="user_link_newtwo" href="[\s\S]*?">([\s\S]*?)</a>'
lightoj_user_data_one_pattern = '<a class="user_link_newone" href="[\s\S]*?">([\s\S]*?)</a>'
lightoj_td_data_one_pattern = '<td class="newone" >([\s\S]*?)</td>'
lightoj_td_data_two_pattern = '<td class="newtwo" >([\s\S]*?)</td>'
# noj
noj_page_count_pattern = '<a title="尾页" href="([\s\S]*?)" class="page_a">'
noj_td_pattern = '<td style="text-align: center;">([\s\S]*?)</td>'
noj_username_pattern = '<a target="_blank" href="[\s\S]*?">([\s\S]*?)</a>'
# poj
poj_table_pattern = '<table border=1 width=80%>([\s\S]*?)</table>'
poj_td_pattern = '<td>([\s\S]*?)</td>'
poj_username_pattern = '<a href=[\s\S]*?>([\s\S]*?)</a>'
# sgu
sgu_table_pattern = '<table width=90% align=center>([\s\S]*?)</table>'
sgu_tr_pattern = '<td>([\s\S]*?)</td>'
sgu_ac_number_pattern = 'Accepted: ([0-9]*)'
#bzoj
bzoj_ac_pattern = '<a href=\'[\s\S]*?jresult=4\'>([\s\S]*?)</a>'
# zoj
zoj_user_pattern = '<td class="runUserName"><a href="([\s\S]*?)"><font color="db6d00">[\s\S]*?</font></a></td>'
zoj_div_pattern = '<div id="content_body">([\s\S]*?)</div>'
zoj_ac_pattern = '<font color="red" size="4">([\s\S]*?)</font>'
# acdream
acdream_ul_pattern = '<ul class="user-info">([\s\S]*?)</ul>'
acdream_ac_number_pattern = '<a href="[\s\S]*?">([\s\S]*?)</a>'
# fzu
fzu_ac_number_pattern = '<td>([\d]*?)</td>'
# ural
ural_table_pattern = '<TABLE WIDTH="100%" CLASS="ranklist">([\s\S]*?)</TABLE>'
ural_tr_pattern = '<TR CLASS="content">([\s\S]*?)</TR>'
ural_user_pattern = '<A HREF=[\s\S]*>([\s\S]*?)</A>'
ural_ac_number_pattern = '<TD>([\d]*)</TD>'
# database configuration
database = {
'host': 'localhost',
'db': 'problem_count',
'user': 'root',
'password': 'zc87496604',
'charset': 'utf8',
}
# sql
find_data = 'select id, ac_number from {0} where username=\"{1}\"'
insert_data = 'insert into {0} (username, ac_number) values (\"{1}\", {2})'
update_data = 'update {0} set username=\"{1}\",ac_number={2} where id={3}'
| [
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] | |
88a8301be9b8c59278ac84736ea6dc73a1306874 | 260856baeb517cf64a386341f0cfff30c628a987 | /5.1.py | 808d95fe4de9391f12fd2dce6f10a0dd7849c6e3 | [] | no_license | awstnx/vtip | a702d58614128d693fc9f342dc5ce8a4d3b5ecc2 | 31377b4610004bc8a7813aeca76960f46af8d442 | refs/heads/master | 2022-12-21T21:49:57.050309 | 2020-09-29T15:57:57 | 2020-09-29T15:57:57 | 295,413,097 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,062 | py | def bmi(mass, height):
"""Вычисляет индекс массы тела по введенным массе и росту человека"""
return (mass/((height/100)**2))
person_mass, person_height = map(float, input('Введите свою массу в килограммах и рост в сантиметрах через пробел: ').split( ))
BodyMassIndex = bmi(person_mass, person_height)
if BodyMassIndex < 16:
print('Выраженный дефицит массы тела')
elif 16 <= BodyMassIndex <18.5:
print('Недостаточная масса тела')
elif 18.5 <= BodyMassIndex < 25:
print('Нормальная масса тела')
elif 25 <= BodeMassIndex < 30:
print('Избыточная масса тела')
elif 30 <= BodyMassIndex < 35:
print('Ожирение первой степени')
elif 35 <= BodyMassIndex < 40:
print('Ожирение второй степени')
elif BodyMassIndex >= 40:
print('Ожирение третьей степени')
| [
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] | |
71353370837cd21408b171136866a05a30ffa482 | 0d4ec25fb2819de88a801452f176500ccc269724 | /search_sorted_matrx.py | e1e912f49ceb5959ab470dfeaec97c8d36ef1e22 | [] | no_license | zopepy/leetcode | 7f4213764a6a079f58402892bd0ede0514e06fcf | 3bfee704adb1d94efc8e531b732cf06c4f8aef0f | refs/heads/master | 2022-01-09T16:13:09.399620 | 2019-05-29T20:00:11 | 2019-05-29T20:00:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,079 | py | class Solution:
def searchMatrix(self, matrix, target):
"""
:type matrix: List[List[int]]
:type target: int
:rtype: bool
"""
if matrix == [] or matrix == [[]]:
return False
l = len(matrix)
b = len(matrix[0])
if matrix[0][0] > target or matrix[l-1][b-1] < target:
return False
left, right = 0, l-1
while left <= right:
mid = (left + right) >> 1
if matrix[mid][0] == target:
return True
elif matrix[mid][0] < target:
left = mid + 1
else:
right = mid - 1
left -= 1
start, end = 0, b-1
while start <= end:
mid = (start + end) >> 1
if matrix[left][mid] == target:
return True
elif matrix[left][mid] < target:
start = mid+1
else:
end = mid-1
return False
inp = [[1,3,5,7],[10,11,16,20],[23,30,34,50]]
print(Solution().searchMatrix(inp, 24)) | [
"[email protected]"
] | |
01b54643ccf3a75120100a24b778a1accb4fb555 | bbd603fcd9541ed8168c765ee7c84fc379c6b692 | /scripts/e087_bert_question_adamw.py | a39ce51eb9ad183a96f109022c4df0854080f73a | [] | no_license | yoichi-yamakawa/kaggle-google-quest | 2513a41889ffdb68e8f3bc3fb55a41a5dd873d0f | decffc69d5657f5114970eb2ea226df8ec8cfaf6 | refs/heads/master | 2021-01-04T16:58:27.894352 | 2020-02-11T00:46:04 | 2020-02-11T00:46:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,732 | py | import itertools
import os
import random
from logging import getLogger
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import GroupKFold
from torch import optim
from torch.nn import BCEWithLogitsLoss, DataParallel
from torch.utils.data import DataLoader
from torch.utils.data.sampler import RandomSampler
from tqdm import tqdm
from transformers import BertModel, AdamW
from refactor.datasets import QUESTDataset
from refactor.models import BertModelForBinaryMultiLabelClassifier
from refactor.utils import compute_spearmanr, test, train_one_epoch
from utils import (load_checkpoint, logInit, parse_args,
save_and_clean_for_prediction, save_checkpoint, sel_log,
send_line_notification)
EXP_ID = os.path.basename(__file__).split('_')[0]
MNT_DIR = './mnt'
DEVICE = 'cuda'
MODEL_PRETRAIN = 'bert-base-uncased'
MODEL_CONFIG_PATH = './mnt/datasets/model_configs/bert-model-uncased-config.pkl'
TOKENIZER_TYPE = 'bert'
TOKENIZER_PRETRAIN = 'bert-base-uncased'
BATCH_SIZE = 8
MAX_EPOCH = 6
MAX_SEQ_LEN = 512
T_MAX_LEN = 30
Q_MAX_LEN = 239 * 2
A_MAX_LEN = 239 * 0
DO_LOWER_CASE = True if MODEL_PRETRAIN == 'bert-base-uncased' else False
LABEL_COL = [
'question_asker_intent_understanding',
'question_body_critical',
'question_conversational',
'question_expect_short_answer',
'question_fact_seeking',
'question_has_commonly_accepted_answer',
'question_interestingness_others',
'question_interestingness_self',
'question_multi_intent',
'question_not_really_a_question',
'question_opinion_seeking',
'question_type_choice',
'question_type_compare',
'question_type_consequence',
'question_type_definition',
'question_type_entity',
'question_type_instructions',
'question_type_procedure',
'question_type_reason_explanation',
'question_type_spelling',
'question_well_written',
# 'answer_helpful',
# 'answer_level_of_information',
# 'answer_plausible',
# 'answer_relevance',
# 'answer_satisfaction',
# 'answer_type_instructions',
# 'answer_type_procedure',
# 'answer_type_reason_explanation',
# 'answer_well_written'
]
def seed_everything(seed=71):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything()
def main(args, logger):
# trn_df = pd.read_csv(f'{MNT_DIR}/inputs/origin/train.csv')
trn_df = pd.read_pickle(f'{MNT_DIR}/inputs/nes_info/trn_df.pkl')
trn_df['is_original'] = 1
gkf = GroupKFold(
n_splits=5).split(
X=trn_df.question_body,
groups=trn_df.question_body_le,
)
histories = {
'trn_loss': {},
'val_loss': {},
'val_metric': {},
'val_metric_raws': {},
}
loaded_fold = -1
loaded_epoch = -1
if args.checkpoint:
histories, loaded_fold, loaded_epoch = load_checkpoint(args.checkpoint)
fold_best_metrics = []
fold_best_metrics_raws = []
for fold, (trn_idx, val_idx) in enumerate(gkf):
if fold < loaded_fold:
fold_best_metrics.append(np.max(histories["val_metric"][fold]))
fold_best_metrics_raws.append(
histories["val_metric_raws"][fold][np.argmax(histories["val_metric"][fold])])
continue
sel_log(
f' --------------------------- start fold {fold} --------------------------- ', logger)
fold_trn_df = trn_df.iloc[trn_idx] # .query('is_original == 1')
fold_trn_df = fold_trn_df.drop(
['is_original', 'question_body_le'], axis=1)
# use only original row
fold_val_df = trn_df.iloc[val_idx].query('is_original == 1')
fold_val_df = fold_val_df.drop(
['is_original', 'question_body_le'], axis=1)
if args.debug:
fold_trn_df = fold_trn_df.sample(100, random_state=71)
fold_val_df = fold_val_df.sample(100, random_state=71)
temp = pd.Series(list(itertools.chain.from_iterable(
fold_trn_df.question_title.apply(lambda x: x.split(' ')) +
fold_trn_df.question_body.apply(lambda x: x.split(' ')) +
fold_trn_df.answer.apply(lambda x: x.split(' '))
))).value_counts()
tokens = temp[temp >= 10].index.tolist()
# tokens = []
tokens = [
'CAT_TECHNOLOGY'.casefold(),
'CAT_STACKOVERFLOW'.casefold(),
'CAT_CULTURE'.casefold(),
'CAT_SCIENCE'.casefold(),
'CAT_LIFE_ARTS'.casefold(),
]
trn_dataset = QUESTDataset(
df=fold_trn_df,
mode='train',
tokens=tokens,
augment=[],
tokenizer_type=TOKENIZER_TYPE,
pretrained_model_name_or_path=TOKENIZER_PRETRAIN,
do_lower_case=DO_LOWER_CASE,
LABEL_COL=LABEL_COL,
t_max_len=T_MAX_LEN,
q_max_len=Q_MAX_LEN,
a_max_len=A_MAX_LEN,
tqa_mode='tq_a',
TBSEP='[TBSEP]',
pos_id_type='arange',
MAX_SEQUENCE_LENGTH=MAX_SEQ_LEN,
)
# update token
trn_sampler = RandomSampler(data_source=trn_dataset)
trn_loader = DataLoader(trn_dataset,
batch_size=BATCH_SIZE,
sampler=trn_sampler,
num_workers=os.cpu_count(),
worker_init_fn=lambda x: np.random.seed(),
drop_last=True,
pin_memory=True)
val_dataset = QUESTDataset(
df=fold_val_df,
mode='valid',
tokens=tokens,
augment=[],
tokenizer_type=TOKENIZER_TYPE,
pretrained_model_name_or_path=TOKENIZER_PRETRAIN,
do_lower_case=DO_LOWER_CASE,
LABEL_COL=LABEL_COL,
t_max_len=T_MAX_LEN,
q_max_len=Q_MAX_LEN,
a_max_len=A_MAX_LEN,
tqa_mode='tq_a',
TBSEP='[TBSEP]',
pos_id_type='arange',
MAX_SEQUENCE_LENGTH=MAX_SEQ_LEN,
)
val_sampler = RandomSampler(data_source=val_dataset)
val_loader = DataLoader(val_dataset,
batch_size=BATCH_SIZE,
sampler=val_sampler,
num_workers=os.cpu_count(),
worker_init_fn=lambda x: np.random.seed(),
drop_last=False,
pin_memory=True)
fobj = BCEWithLogitsLoss()
state_dict = BertModel.from_pretrained(MODEL_PRETRAIN).state_dict()
model = BertModelForBinaryMultiLabelClassifier(num_labels=len(LABEL_COL),
config_path=MODEL_CONFIG_PATH,
state_dict=state_dict,
token_size=len(
trn_dataset.tokenizer),
MAX_SEQUENCE_LENGTH=MAX_SEQ_LEN,
)
# optimizer = optim.Adam(model.parameters(), lr=3e-5)
# optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, nesterov=True)
optimizer = AdamW(model.parameters(), lr=3e-5, correct_bias=False, eps=1e-7)
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=MAX_EPOCH, eta_min=1e-5)
# load checkpoint model, optim, scheduler
if args.checkpoint and fold == loaded_fold:
load_checkpoint(args.checkpoint, model, optimizer, scheduler)
for epoch in tqdm(list(range(MAX_EPOCH))):
if fold <= loaded_fold and epoch <= loaded_epoch:
continue
if epoch < 1:
model.freeze_unfreeze_bert(freeze=True, logger=logger)
else:
model.freeze_unfreeze_bert(freeze=False, logger=logger)
model = DataParallel(model)
model = model.to(DEVICE)
trn_loss = train_one_epoch(model, fobj, optimizer, trn_loader, DEVICE)
val_loss, val_metric, val_metric_raws, val_y_preds, val_y_trues, val_qa_ids = test(
model, fobj, val_loader, DEVICE, mode='valid')
scheduler.step()
if fold in histories['trn_loss']:
histories['trn_loss'][fold].append(trn_loss)
else:
histories['trn_loss'][fold] = [trn_loss, ]
if fold in histories['val_loss']:
histories['val_loss'][fold].append(val_loss)
else:
histories['val_loss'][fold] = [val_loss, ]
if fold in histories['val_metric']:
histories['val_metric'][fold].append(val_metric)
else:
histories['val_metric'][fold] = [val_metric, ]
if fold in histories['val_metric_raws']:
histories['val_metric_raws'][fold].append(val_metric_raws)
else:
histories['val_metric_raws'][fold] = [val_metric_raws, ]
logging_val_metric_raws = ''
for val_metric_raw in val_metric_raws:
logging_val_metric_raws += f'{float(val_metric_raw):.4f}, '
sel_log(
f'fold : {fold} -- epoch : {epoch} -- '
f'trn_loss : {float(trn_loss.detach().to("cpu").numpy()):.4f} -- '
f'val_loss : {float(val_loss.detach().to("cpu").numpy()):.4f} -- '
f'val_metric : {float(val_metric):.4f} -- '
f'val_metric_raws : {logging_val_metric_raws}',
logger)
model = model.to('cpu')
model = model.module
save_checkpoint(
f'{MNT_DIR}/checkpoints/{EXP_ID}/{fold}',
model,
optimizer,
scheduler,
histories,
val_y_preds,
val_y_trues,
val_qa_ids,
fold,
epoch,
val_loss,
val_metric)
fold_best_metrics.append(np.max(histories["val_metric"][fold]))
fold_best_metrics_raws.append(
histories["val_metric_raws"][fold][np.argmax(histories["val_metric"][fold])])
save_and_clean_for_prediction(
f'{MNT_DIR}/checkpoints/{EXP_ID}/{fold}',
trn_dataset.tokenizer,
clean=False)
del model
# calc training stats
fold_best_metric_mean = np.mean(fold_best_metrics)
fold_best_metric_std = np.std(fold_best_metrics)
fold_stats = f'{EXP_ID} : {fold_best_metric_mean:.4f} +- {fold_best_metric_std:.4f}'
sel_log(fold_stats, logger)
send_line_notification(fold_stats)
fold_best_metrics_raws_mean = np.mean(fold_best_metrics_raws, axis=0)
fold_raw_stats = ''
for metric_stats_raw in fold_best_metrics_raws_mean:
fold_raw_stats += f'{float(metric_stats_raw):.4f},'
sel_log(fold_raw_stats, logger)
send_line_notification(fold_raw_stats)
sel_log('now saving best checkpoints...', logger)
if __name__ == '__main__':
args = parse_args(None)
log_file = f'{EXP_ID}.log'
logger = getLogger(__name__)
logger = logInit(logger, f'{MNT_DIR}/logs/', log_file)
sel_log(f'args: {sorted(vars(args).items())}', logger)
# send_line_notification(f' ------------- start {EXP_ID} ------------- ')
main(args, logger)
| [
"[email protected]"
] | |
c0568f477ed273def386782acf8cb794c81ac227 | 1bab2b06c7cc813c0eff23e63783859ae60c7e73 | /ex9.py | d20d92d79f6a5f414212ea1548a5b228af2365b3 | [] | no_license | asimkaleem/LPTHW | a19681e9927817034a8fd18c9f3e037766f59648 | f565e2b1581d0453f83d3e40a0e0bb65d2bb000b | refs/heads/master | 2021-09-10T15:01:27.357864 | 2018-03-28T06:52:54 | 2018-03-28T06:52:54 | 126,018,322 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 378 | py | # Here's some new strange stuff, remember type it exactly.
days = "Mon Tue Wed Thu Fri Sat Sun"
months = "Jan\nFeb\nMar\nApr\nMay\nJun\nJul\nAug"
print "Here are the days: ", days
print "Here are the months: ", months
print """
There's something going on here.
With the three double- quotes.
We'll be able to type as much as we like.
Even 4 lines if we want, or 5, or 6.
"""
| [
"[email protected]"
] | |
7eb4211874de5cb57b04a4c673199d8f475ebd62 | 77b4bb15ed2cd7d4db07f0098ff1a6638790c3d8 | /tests/test_mongo_controller_auto_increment.py | 1db6cc8280a3b5577927397de3c49a908b11b4fa | [
"MIT"
] | permissive | Simon-Le/layabase | d10276d1dc37a4d36b39aad89b0bc00d818ae2d7 | 4670a3d0849785e22b80a88af634c69220cf0113 | refs/heads/develop | 2020-12-03T15:03:08.352875 | 2020-04-13T23:59:33 | 2020-04-13T23:59:33 | 231,363,516 | 0 | 0 | MIT | 2020-04-13T20:04:17 | 2020-01-02T10:58:46 | null | UTF-8 | Python | false | false | 10,614 | py | import enum
import flask
import flask_restplus
import pytest
from layaberr import ValidationFailed
import layabase
import layabase.mongo
class EnumTest(enum.Enum):
Value1 = 1
Value2 = 2
@pytest.fixture
def controller():
class TestCollection:
__collection_name__ = "test"
key = layabase.mongo.Column(
int, is_primary_key=True, should_auto_increment=True
)
enum_field = layabase.mongo.Column(
EnumTest, is_nullable=False, description="Test Documentation"
)
optional_with_default = layabase.mongo.Column(str, default_value="Test value")
controller = layabase.CRUDController(TestCollection)
layabase.load("mongomock", [controller])
return controller
@pytest.fixture
def app(controller):
application = flask.Flask(__name__)
application.testing = True
api = flask_restplus.Api(application)
namespace = api.namespace("Test", path="/")
controller.namespace(namespace)
@namespace.route("/test")
class TestResource(flask_restplus.Resource):
@namespace.expect(controller.query_get_parser)
@namespace.marshal_with(controller.get_response_model)
def get(self):
return []
@namespace.expect(controller.json_post_model)
def post(self):
return []
@namespace.expect(controller.json_put_model)
def put(self):
return []
@namespace.expect(controller.query_delete_parser)
def delete(self):
return []
return application
def test_post_with_specified_incremented_field_is_ignored_and_valid(client, controller):
assert controller.post({"key": "my_key", "enum_field": "Value1"}) == {
"optional_with_default": "Test value",
"key": 1,
"enum_field": "Value1",
}
def test_post_with_enum_is_valid(client, controller):
assert controller.post({"key": "my_key", "enum_field": EnumTest.Value1}) == {
"optional_with_default": "Test value",
"key": 1,
"enum_field": "Value1",
}
def test_post_with_invalid_enum_choice_is_invalid(client, controller):
with pytest.raises(ValidationFailed) as exception_info:
controller.post({"key": "my_key", "enum_field": "InvalidValue"})
assert exception_info.value.errors == {
"enum_field": ["Value \"InvalidValue\" is not within ['Value1', 'Value2']."]
}
assert exception_info.value.received_data == {"enum_field": "InvalidValue"}
def test_post_many_with_specified_incremented_field_is_ignored_and_valid(
client, controller
):
assert controller.post_many(
[
{"key": "my_key", "enum_field": "Value1"},
{"key": "my_key", "enum_field": "Value2"},
]
) == [
{"optional_with_default": "Test value", "enum_field": "Value1", "key": 1},
{"optional_with_default": "Test value", "enum_field": "Value2", "key": 2},
]
def test_open_api_definition(client):
response = client.get("/swagger.json")
assert response.json == {
"swagger": "2.0",
"basePath": "/",
"paths": {
"/test": {
"post": {
"responses": {"200": {"description": "Success"}},
"operationId": "post_test_resource",
"parameters": [
{
"name": "payload",
"required": True,
"in": "body",
"schema": {
"$ref": "#/definitions/TestCollection_PostRequestModel"
},
}
],
"tags": ["Test"],
},
"put": {
"responses": {"200": {"description": "Success"}},
"operationId": "put_test_resource",
"parameters": [
{
"name": "payload",
"required": True,
"in": "body",
"schema": {
"$ref": "#/definitions/TestCollection_PutRequestModel"
},
}
],
"tags": ["Test"],
},
"delete": {
"responses": {"200": {"description": "Success"}},
"operationId": "delete_test_resource",
"parameters": [
{
"name": "key",
"in": "query",
"type": "array",
"items": {"type": "integer"},
"collectionFormat": "multi",
},
{
"name": "enum_field",
"in": "query",
"type": "array",
"items": {"type": "string"},
"collectionFormat": "multi",
},
{
"name": "optional_with_default",
"in": "query",
"type": "array",
"items": {"type": "string"},
"collectionFormat": "multi",
},
],
"tags": ["Test"],
},
"get": {
"responses": {
"200": {
"description": "Success",
"schema": {
"$ref": "#/definitions/TestCollection_GetResponseModel"
},
}
},
"operationId": "get_test_resource",
"parameters": [
{
"name": "key",
"in": "query",
"type": "array",
"items": {"type": "integer"},
"collectionFormat": "multi",
},
{
"name": "enum_field",
"in": "query",
"type": "array",
"items": {"type": "string"},
"collectionFormat": "multi",
},
{
"name": "optional_with_default",
"in": "query",
"type": "array",
"items": {"type": "string"},
"collectionFormat": "multi",
},
{
"name": "limit",
"in": "query",
"type": "integer",
"minimum": 0,
"exclusiveMinimum": True,
},
{
"name": "offset",
"in": "query",
"type": "integer",
"minimum": 0,
},
{
"name": "X-Fields",
"in": "header",
"type": "string",
"format": "mask",
"description": "An optional fields mask",
},
],
"tags": ["Test"],
},
}
},
"info": {"title": "API", "version": "1.0"},
"produces": ["application/json"],
"consumes": ["application/json"],
"tags": [{"name": "Test"}],
"definitions": {
"TestCollection_PostRequestModel": {
"properties": {
"enum_field": {
"type": "string",
"description": "Test Documentation",
"readOnly": False,
"example": "Value1",
"enum": ["Value1", "Value2"],
},
"key": {"type": "integer", "readOnly": True, "example": 1},
"optional_with_default": {
"type": "string",
"readOnly": False,
"default": "Test value",
"example": "Test value",
},
},
"type": "object",
},
"TestCollection_PutRequestModel": {
"properties": {
"enum_field": {
"type": "string",
"description": "Test Documentation",
"readOnly": False,
"example": "Value1",
"enum": ["Value1", "Value2"],
},
"key": {"type": "integer", "readOnly": True, "example": 1},
"optional_with_default": {
"type": "string",
"readOnly": False,
"default": "Test value",
"example": "Test value",
},
},
"type": "object",
},
"TestCollection_GetResponseModel": {
"properties": {
"enum_field": {
"type": "string",
"description": "Test Documentation",
"readOnly": False,
"example": "Value1",
"enum": ["Value1", "Value2"],
},
"key": {"type": "integer", "readOnly": True, "example": 1},
"optional_with_default": {
"type": "string",
"readOnly": False,
"default": "Test value",
"example": "Test value",
},
},
"type": "object",
},
},
"responses": {
"ParseError": {"description": "When a mask can't be parsed"},
"MaskError": {"description": "When any error occurs on mask"},
},
}
| [
"[email protected]"
] | |
08d8980c21d8013ca60ccf34189734c1caf085e7 | efa5d0866a8a0aa9dd9dde27f0d9d9c1c9f551c1 | /setup.py | 64e766a5d40a628bcba3f62dc0b975d579775bed | [
"Apache-2.0"
] | permissive | miketwo/pyschedule | 7fef3dfb77259ae34a46535292bc6b40cbbfb1c8 | 792305faae9d0413ed22e7c57d5e9610fded7751 | refs/heads/master | 2021-01-10T22:22:09.056650 | 2016-06-04T23:54:25 | 2016-06-04T23:54:25 | 60,433,964 | 0 | 0 | null | 2016-06-04T23:02:41 | 2016-06-04T23:02:37 | Python | UTF-8 | Python | false | false | 596 | py | from setuptools import setup, find_packages
setup(name='pyschedule',
version='0.2.13',
description='A python package to formulate and solve resource-constrained scheduling problems: flow- and job-shop, travelling salesman, vehicle routing and all kind of combinations',
url='https://github.com/timnon/pyschedule',
author='Tim Nonner',
author_email='[email protected]',
license='Apache 2.0',
packages=['pyschedule','pyschedule.solvers','pyschedule.plotters'],
package_dir={'':'src'},
include_package_data=True,
install_requires=['pulp'])
| [
"[email protected]"
] | |
bc481205615d5df87eba3418a666917f5ec8ce66 | 157c7325539a713b35bb418913303d5a9036ac56 | /vision_cv_google.py | cb616651c4d4c26a66226f97ac3f40c01f4bc0f1 | [
"MIT"
] | permissive | konsan1101/pycv3 | f06ee37a636a6499463a286a64f9b1ccf47310c5 | 12688afb54a133f8706df2da9c7d3e34d1e70590 | refs/heads/master | 2020-06-03T11:44:49.664856 | 2019-06-12T13:36:17 | 2019-06-12T13:36:17 | 191,554,751 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,506 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import numpy as np
import cv2
import base64
from requests import Request, Session
#from bs4 import BeautifulSoup
import json
# Google
#GOOGLE_VISION_KEY = 'xx'
GOOGLE_VISION_KEY = 'xx'
def google_vision(image_path):
global GOOGLE_VISION_KEY
bin_image = open(image_path, 'rb').read()
#enc_image = base64.b64encode(bin_image)
enc_image = base64.b64encode(bin_image).decode("utf-8")
str_url = "https://vision.googleapis.com/v1/images:annotate?key="
str_headers = {'Content-Type': 'application/json'}
str_json_data = {
'requests': [
{
'image': {
'content': enc_image
},
'features': [
{
'type': "LABEL_DETECTION",
'maxResults': 10
},
{
'type': "TEXT_DETECTION",
'maxResults': 10
}
]
}
]
}
#print("begin request")
obj_session = Session()
obj_request = Request("POST",
str_url + GOOGLE_VISION_KEY,
data=json.dumps(str_json_data),
headers=str_headers
)
obj_prepped = obj_session.prepare_request(obj_request)
obj_response = obj_session.send(obj_prepped,
verify=True,
timeout=60
)
#print("end request")
if obj_response.status_code == 200:
print (obj_response.text)
#with open('data.json', 'w') as outfile:
# json.dump(obj_response.text, outfile)
return obj_response.text
else:
print (obj_response.text)
return "error"
if __name__ == '__main__':
print("main init")
img = "CaptureImage.jpg"
txt = "CaptureText.txt"
lng = "ja"
if len(sys.argv)>=2:
img = sys.argv[1]
if len(sys.argv)>=3:
txt = sys.argv[2]
if len(sys.argv)>=4:
lng = sys.argv[3]
print("main image proc")
image_img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
if len(image_img.shape) == 3:
image_height, image_width, image_channels = image_img.shape[:3]
else:
image_height, image_width = image_img.shape[:2]
image_channels = 1
#if (img=='Test_Image_1.jpg' or img=='CaptureName.jpg') and image_channels == 3:
# temp_img = np.zeros((image_height*2,image_width*2,3), np.uint8)
# cv2.rectangle(temp_img,(0,0),(image_width*2,image_height*2),(255,255,255),-1)
# temp_img[0+image_height/2:image_height/2+image_height, 0+image_width/2:image_width/2+image_width] = image_img.copy()
# image_img = cv2.resize(temp_img, (image_width, image_height))
if image_channels != 1:
gray_img = cv2.cvtColor(image_img, cv2.COLOR_BGR2GRAY)
else:
gray_img = image_img.copy()
#hist_img = cv2.equalizeHist(gray_img)
#blur_img = cv2.blur(gray_img, (3,3), 0)
_, thresh_img = cv2.threshold(gray_img, 140, 255, cv2.THRESH_BINARY)
temp_img = image_img.copy()
#temp_img = gray_img.copy()
#temp_img = thresh_img.copy()
cv2.imwrite("temp/@" + img, temp_img)
#cv2.imshow("Base", temp_img)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
#jpg_parm = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
#_, img_data = cv2.imencode('.jpg', temp_img, jpg_parm)
#img_data64 = base64.b64encode(img_data)
img_data = open("temp/@" + img, 'rb')
print("main Google AI")
res = google_vision("temp/@" + img)
print( "" )
print( res )
print( "" )
js = json.loads(res)
data = js["responses"]
#print(data)
#print( json.dumps( data, sort_keys = True, indent = 4) )
try:
print( "" )
s = "[ LABEL_DETECTION ]"
f = open(txt, 'w')
print( s )
f.writelines( s )
for t in data:
for d in t["labelAnnotations"]:
print( str(d["description"]) )
f.writelines( str(d["description"]) )
except:
pass
finally:
f.close()
print( "" )
print("main Bye!")
print( "" )
| [
"[email protected]"
] | |
bd3182dba60a1531c8779781ab6bbc9f1099b12c | fd7a2c2265363dc06c9e23c1ce3182bb99be6b70 | /ingest/ingest_dx_tomo.py | 20d4fce4584a768b2270fa93933601f5ba776895 | [] | no_license | als-computing/scicatlive-modifications | 093d03e0027c30173c1140139b6dd708dbfc1fee | 5186511fc1554a1c6d349e14ddd72cf41d1ac1b4 | refs/heads/main | 2023-03-05T05:51:03.878037 | 2021-02-22T20:03:07 | 2021-02-22T20:03:07 | 338,426,336 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,781 | py | import h5py
import json
import sys
import datetime
import hashlib
import urllib
import base64
import logging
import json # for easy parsing
from pathlib import Path
from pprint import pprint
import numpy as np
import requests # for HTTP requests
from splash_ingest.ingestors import MappedHD5Ingestor
from splash_ingest.model import Mapping
import h5py
class ScicatTomo(object):
# settables
host = "noether.lbl.gov" #
baseurl = "http://" + host + "/api/v3/"
# timeouts = (4, 8) # we are hitting a transmission timeout...
timeouts = None # we are hitting a transmission timeout...
sslVerify = False # do not check certificate
username="ingestor" # default username
password="aman" # default password
# You should see a nice, but abbreviated table here with the logbook contents.
token = None # store token here
settables = ['host', 'baseurl', 'timeouts', 'sslVerify', 'username', 'password', 'token']
pid = None # gets set if you search for something
entries = None # gets set if you search for something
datasetType = "RawDatasets"
datasetTypes = ["RawDatasets", "DerivedDatasets", "Proposals"]
def __init__(self, **kwargs):
# nothing to do
for key, value in kwargs.items():
assert key in self.settables, f"key {key} is not a valid input argument"
setattr(self, key, value)
# get token
self.token = self.get_token(username=self.username, password=self.password)
def get_token(self, username=None, password=None):
if username is None: username = self.username
if password is None: password = self.password
"""logs in using the provided username / password combination and receives token for further communication use"""
logging.info("Getting new token ...")
response = requests.post(
self.baseurl + "Users/login",
json={"username": username, "password": password},
timeout=self.timeouts,
stream=False,
verify=self.sslVerify,
)
if not response.ok:
logging.error(f'** Error received: {response}')
err = response.json()["error"]
logging.error(f'{err["name"]}, {err["statusCode"]}: {err["message"]}')
sys.exit(1) # does not make sense to continue here
data = response.json()
logging.error(f"Response: {data}")
data = response.json()
# print("Response:", data)
token = data["id"] # not sure if semantically correct
logging.info(f"token: {token}")
self.token = token # store new token
return token
def send_to_scicat(self, url, dataDict = None, cmd="post"):
""" sends a command to the SciCat API server using url and token, returns the response JSON
Get token with the getToken method"""
if cmd == "post":
response = requests.post(
url,
params={"access_token": self.token},
json=dataDict,
timeout=self.timeouts,
stream=False,
verify=self.sslVerify,
)
elif cmd == "delete":
response = requests.delete(
url, params={"access_token": self.token},
timeout=self.timeouts,
stream=False,
verify=self.sslVerify,
)
elif cmd == "get":
response = requests.get(
url,
params={"access_token": self.token},
json=dataDict,
timeout=self.timeouts,
stream=False,
verify=self.sslVerify,
)
elif cmd == "patch":
response = requests.patch(
url,
params={"access_token": self.token},
json=dataDict,
timeout=self.timeouts,
stream=False,
verify=self.sslVerify,
)
rdata = response.json()
if not response.ok:
err = response.json()["error"]
logging.error(f'{err["name"]}, {err["statusCode"]}: {err["message"]}')
logging.error("returning...")
rdata = response.json()
logging.error(f"Response: {json.dumps(rdata, indent=4)}")
return rdata
def get_file_size_from_path_obj(self, pathobj):
filesize = pathobj.lstat().st_size
return filesize
def getFileChecksumFromPathObj(self, pathobj):
with open(pathobj) as file_to_check:
# pipe contents of the file through
return hashlib.md5(file_to_check.read()).hexdigest()
def clear_previous_attachments(self, datasetId, datasetType):
# remove previous entries to avoid tons of attachments to a particular dataset.
# todo: needs appropriate permissions!
self.get_entries(url = self.baseurl + "Attachments", whereDict = {"datasetId": str(datasetId)})
for entry in self.entries:
url = self.baseurl + f"Attachments/{urllib.parse.quote_plus(entry['id'])}"
self.send_to_scicat(url, {}, cmd="delete")
def add_data_block(self, datasetId = None, filename = None, datasetType="RawDatasets", clearPrevious = False):
if clearPrevious:
self.clear_previous_attachments(datasetId, datasetType)
dataBlock = {
# "id": pid,
"size": self.get_file_size_from_path_obj(filename),
"dataFileList": [
{
"path": str(filename.absolute()),
"size": self.get_file_size_from_path_obj(filename),
"time": self.getFileModTimeFromPathObj(filename),
"chk": "", # do not do remote: getFileChecksumFromPathObj(filename)
"uid": str(
filename.stat().st_uid
), # not implemented on windows: filename.owner(),
"gid": str(filename.stat().st_gid),
"perm": str(filename.stat().st_mode),
}
],
"ownerGroup": "BAM 6.5",
"accessGroups": ["BAM", "BAM 6.5"],
"createdBy": "datasetUpload",
"updatedBy": "datasetUpload",
"datasetId": datasetId,
"updatedAt": datetime.datetime.isoformat(datetime.datetime.utcnow()) + "Z",
"createdAt": datetime.datetime.isoformat(datetime.datetime.utcnow()) + "Z",
# "createdAt": "",
# "updatedAt": ""
}
url = self.baseurl + f"{datasetType}/{urllib.parse.quote_plus(datasetId)}/origdatablocks"
logging.debug(url)
resp = self.send_to_scicat(url, dataBlock)
return resp
def get_entries(self, url, whereDict = {}):
# gets the complete response when searching for a particular entry based on a dictionary of keyword-value pairs
resp = self.send_to_scicat(url, {"filter": {"where": whereDict}}, cmd="get")
self.entries = resp
return resp
def get_pid(self, url, whereDict = {}, returnIfNone=0, returnField = 'pid'):
# returns only the (first matching) pid (or proposalId in case of proposals) matching a given search request
resp = self.get_entries(url, whereDict)
if resp == []:
# no raw dataset available
pid = returnIfNone
else:
pid = resp[0][returnField]
self.pid = pid
return pid
def add_thumbnail(self, datasetId = None, filename = None, datasetType="RawDatasets", clearPrevious = False):
if clearPrevious:
self.clear_previous_attachments(datasetId, datasetType)
def encodeImageToThumbnail(filename, imType = 'jpg'):
header = "data:image/{imType};base64,".format(imType=imType)
with open(filename, 'rb') as f:
data = f.read()
dataBytes = base64.b64encode(data)
dataStr = dataBytes.decode('UTF-8')
return header + dataStr
dataBlock = {
"caption": filename.stem,
"thumbnail" : encodeImageToThumbnail(filename),
"datasetId": datasetId,
"ownerGroup": "BAM 6.5",
}
url = self.baseurl + f"{datasetType}/{urllib.parse.quote_plus(datasetId)}/attachments"
logging.debug(url)
resp = requests.post(
url,
params={"access_token": self.token},
timeout=self.timeouts,
stream=False,
json = dataBlock,
verify=self.sslVerify,
)
return resp
def doRaw(self, scm: ScicatTomo, file_name, run_start, thumbnail=None):
# scb = self.scb # for convenience
# year, datasetName, lbEntry = self.getLbEntryFromFileName(filename)
# # this sets scs.year, scs.datasetName, scs.lbEntry
# logging.info(f" working on {filename}")
# sciMeta = scb.h5GetDict(filename, sciMetadataKeyDict)
# if str(lbEntry.sampleid).startswith("{}".format(year)):
# sampleId = str(lbEntry.sampleid)
# else:
# sampleId = scb.h5Get(filename, "/entry1/sample/name")
# # see if entry exists:
# pid = scb.getPid( # changed from "RawDatasets" to "datasets" which should be agnostic
# scb.baseurl + "datasets", {"datasetName": datasetName}, returnIfNone=0
# )
# if (pid != 0) and self.deleteExisting:
# # delete offending item
# url = scb.baseurl + "RawDatasets/{id}".format(id=urllib.parse.quote_plus(pid))
# scb.sendToSciCat(url, {}, cmd="delete")
# pid = 0
data = { # model for the raw datasets as defined in the RawDatasets
"owner": None,
"contactEmail": "[email protected]",
"createdBy": self.username,
"updatedBy": self.username,
"creationLocation": "SAXS002",
"creationTime": None,
"updatedAt": datetime.datetime.isoformat(datetime.datetime.utcnow()) + "Z",
# "createdAt": datetime.datetime.isoformat(datetime.datetime.utcnow()) + "Z",
# "creationTime": h5Get(filename, "/processed/process/date"),
"datasetName": datasetName,
"type": "raw",
"instrumentId": "SAXS002",
"ownerGroup": "BAM 6.5",
"accessGroups": ["BAM", "BAM 6.5"],
"proposalId": str(lbEntry.proposal),
"dataFormat": "NeXus",
"principalInvestigator": scb.h5Get(filename, "/entry1/sample/sampleowner"),
"pid": pid,
"size": 0,
"sourceFolder": filename.parent.as_posix(),
"size": scb.getFileSizeFromPathObj(filename),
"scientificMetadata": sciMeta,
"sampleId": str(sampleId),
}
urlAdd = "RawDatasets"
# determine thumbnail:
# upload
if thumbnail.exists():
npid = self.uploadBit(pid = pid, urlAdd = urlAdd, data = data, attachFile = thumbnail)
logging.info("* * * * adding datablock")
self.scb.addDataBlock(npid, file_name, datasetType='datasets', clearPrevious=False)
def gen_ev_docs(scm: ScicatTomo):
with open('/home/dylan/work/als-computing/splash-ingest/.scratch/832Mapping.json', 'r') as json_file:
data = json.load(json_file)
map = Mapping(**data)
with h5py.File('/home/dylan/data/beamlines/als832/20210204_172932_ddd.h5', 'r') as h5_file:
ingestor = MappedHD5Ingestor(
map,
h5_file,
'root',
'/home/dylan/data/beamlines/als832/thumbs')
for name, doc in ingestor.generate_docstream():
if 'start' in name:
doRaw(doc, scm)
if 'descriptor' in name:
pprint(doc)
scm = ScicatTomo()
gen_ev_docs(scm) | [
"[email protected]"
] | |
cb376017989fd0dd30b31b43274c50aed7951e85 | e36c42157b6eb5c5e951d5a56b717ce2edf682fc | /session_server/common.py | d0628b325e47a3fe69c25f95dde05f3f944e113b | [] | no_license | parkchansoo/pamisol_temporary | 253cfd9cc972c3b26463207f765aa4f9004cd5e3 | 6fd0f92fd6988dc6a005435ef6548c02397dcf96 | refs/heads/master | 2022-12-13T12:23:33.975379 | 2018-02-15T02:40:02 | 2018-02-15T02:40:02 | 121,584,090 | 0 | 0 | null | 2022-12-08T00:51:43 | 2018-02-15T02:28:01 | Python | UTF-8 | Python | false | false | 1,316 | py | status_code ={
"REGISTER_SUCCESS": {
"code": 1000,
"msg": "Register Success",
},
"REGISTER_FAILURE": {
"code": 1001,
"msg": "Register Failure",
},
"LOGIN_SUCCESS": {
"code": 1010,
"msg": "Login Success",
},
"LOGIN_FAILURE": {
"code": 1011,
"msg": "Login Failure",
},
"LOGOUT_SUCCESS": {
"code": 1020,
"msg": "Logout Success",
},
"LOGOUT_FAILURE": {
"code": 1021,
"msg": "Logout Failure",
},
"SAVE_TOKEN_SUCCESS":{
"code": 1030,
"msg": "Token save Success"
},
"SAVE_TOKEN_FAILURE":{
"code": 1031,
"msg": "Token save Failure"
},
"VERIFY_TOKEN_SUCCESS":{
"code": 1040,
"msg": "Token Verification Success"
},
"VERIFY_TOKEN_FAILURE":{
"code": 1041,
"msg": "Token Verification Failure"
},
"EXPIRE_TOKEN_SUCCESS":{
"code": 1050,
"msg": "Token Expiration Success"
},
"EXPIRE_TOKEN_FAILURE":{
"code": 1051,
"msg": "Token Expiration Failure"
},
"AUTH_SUCCESS":{
"code": 1060,
"msg": "Authentication Success"
},
"AUTH_FAILURE":{
"code": 1061,
"msg": "Authentication Failure"
}
} | [
"[email protected]"
] | |
20e4455b062aaabbef8f56ad00ac2e90b32e6512 | e6f0ee76b3b98407fae4ac736100d50dff94c3f1 | /SimpleERP/ERP/migrations/0014_auto_20180422_1314.py | 9861ae49fa0d04baeb80a4f848e1d0966426b72a | [] | no_license | skth5199/SimpleERP | f5e4835f578fdb82f0f50fdce1e2198f00b062ab | 9ca5192d2c88e897474ca5fa326897eba4ef1e2f | refs/heads/master | 2022-05-18T00:14:38.341656 | 2022-04-25T19:31:57 | 2022-04-25T19:31:57 | 134,972,882 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,169 | py | # Generated by Django 2.0 on 2018-04-22 13:14
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('ERP', '0013_auto_20180422_1253'),
]
operations = [
migrations.RenameField(
model_name='price',
old_name='tax_amount',
new_name='buying_tax_amount',
),
migrations.RenameField(
model_name='pricelog',
old_name='tax_amount',
new_name='buying_tax_amount',
),
migrations.RemoveField(
model_name='price',
name='tax_group',
),
migrations.RemoveField(
model_name='pricelog',
name='tax_group',
),
migrations.AddField(
model_name='price',
name='buying_tax_group',
field=models.ForeignKey(default=None, on_delete=django.db.models.deletion.CASCADE, related_name='Price_buying_tax_group', to='ERP.TaxGroup'),
),
migrations.AddField(
model_name='price',
name='selling_tax_amount',
field=models.FloatField(default=0),
),
migrations.AddField(
model_name='price',
name='selling_tax_group',
field=models.ForeignKey(default=None, on_delete=django.db.models.deletion.CASCADE, related_name='Price_selling_tax_group', to='ERP.TaxGroup'),
),
migrations.AddField(
model_name='pricelog',
name='buying_tax_group',
field=models.ForeignKey(default=None, on_delete=django.db.models.deletion.CASCADE, related_name='PriceLog_buying_tax_group', to='ERP.TaxGroup'),
),
migrations.AddField(
model_name='pricelog',
name='selling_tax_amount',
field=models.FloatField(default=0),
),
migrations.AddField(
model_name='pricelog',
name='selling_tax_group',
field=models.ForeignKey(default=None, on_delete=django.db.models.deletion.CASCADE, related_name='PriceLog_selling_tax_group', to='ERP.TaxGroup'),
),
]
| [
"[email protected]"
] | |
177938fe0a876bb495853e00039504169e457847 | 726ce8dddbb12af1662e002633bfe538ddf77708 | /BCPy2000-33960-py2.5.egg/BCPy2000/VisionEggRenderer.py | 996063aed6e9ce75dc545a4a40ab94a7daccdace | [] | no_license | bopopescu/BCPy2000-1 | f9264bb020ba734be0bcc8e8173d2746b0f17eeb | 0f877075a846d17e7593222628e9fe49ab863039 | refs/heads/master | 2022-11-26T07:58:03.493727 | 2019-06-02T20:25:58 | 2019-06-02T20:25:58 | 282,195,357 | 0 | 0 | null | 2020-07-24T10:52:24 | 2020-07-24T10:52:24 | null | UTF-8 | Python | false | false | 13,833 | py | # -*- coding: utf-8 -*-
#
# $Id: VisionEggRenderer.py 3328 2011-06-18 02:17:13Z jhill $
#
# This file is part of the BCPy2000 framework, a Python framework for
# implementing modules that run on top of the BCI2000 <http://bci2000.org/>
# platform, for the purpose of realtime biosignal processing.
#
# Copyright (C) 2007-11 Jeremy Hill, Thomas Schreiner,
# Christian Puzicha, Jason Farquhar
#
# [email protected]
#
# The BCPy2000 framework is free software: you can redistribute it
# and/or modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
__all__ = ['Text', 'Block', 'Disc', 'ImageStimulus']
import os,sys
import pygame #; pygame.init()
import logging
import VisionEgg.Core
import VisionEgg.Text
import VisionEgg.WrappedText
try: from BCI2000PythonApplication import BciGenericRenderer,BciStimulus # development copy
except: from BCPy2000.GenericApplication import BciGenericRenderer,BciStimulus # installed copy
#################################################################
#################################################################
def delegate_getattr(self, v, key):
p = getattr(v, 'parameters', None)
if p != None and hasattr(p, key): return True,getattr(p, key)
if v != None and hasattr(v, key): return True,getattr(v, key)
return False,None
#################################################################
def delegate_setattr(self, v, key, value, restrict_to=None):
p = getattr(v, 'parameters', None)
if p != None and hasattr(p, key):
if restrict_to != None and not 'parameters.'+key in restrict_to: raise AttributeError, "the '%s' attribute is read-only" % key
setattr(p, key, value)
return True
if v != None and hasattr(v, key):
if restrict_to != None and not key in restrict_to: raise AttributeError, "the '%s' attribute is read-only" % key
setattr(p, key, value)
return True
return False
#################################################################
class VisionEggRenderer(BciGenericRenderer):
"""
This is a subclass of BciGenericRenderer that renders stimuli via the
VisionEgg package (which is based on pygame and PyOpenGL) and polls for
mouse and keyboard events via pygame. The VisionEggRenderer is our
default implementation, but you can implement other renderers (see
the documentation for the BciGenericRenderer class).
The object wraps a number of VisionEgg instances including a Screen
and a Viewport, but it behaves most like a Screen --- indeed any
attributes of the underlying VisionEgg.Core.Screen instance, and its
.parameters, are also accessible directly as if they were attributes
of this wrapper object.
In particular, the following attributes (only accessible after the
window has opened) are most useful:
.size (read-only) contains the window's (width,height) in pixels.
.bgcolor is used to get and set the background colour of the window.
.color is an alias for bgcolor.
"""###
#############################################################
def __init__(self):
self.__dict__['_frame_timer'] = None
self.__dict__['_viewport'] = None
self.__dict__['_screen'] = None
self.__dict__['monofont'] = self.findfont(('lucida console', 'monaco', 'monospace', 'courier new', 'courier'))
# default config settings (can be changed in self.Preflight):
VisionEgg.config.VISIONEGG_MAX_PRIORITY = 0
VisionEgg.config.VISIONEGG_HIDE_MOUSE = 0
VisionEgg.config.VISIONEGG_GUI_INIT = 0
VisionEgg.config.VISIONEGG_FULLSCREEN = 0
VisionEgg.config.VISIONEGG_FRAMELESS_WINDOW = 1
VisionEgg.config.VISIONEGG_LOG_FILE = None
#VisionEgg.start_default_logging()
#############################################################
def use_frame_timer(self, setting=True, renew=False):
if setting:
if renew or not self._frame_timer: self._frame_timer = VisionEgg.Core.FrameTimer()
else:
self._frame_timer = None
#############################################################
def findfont(self, fontnames):
"""
Tries to find a system font file corresponding to one of the
supplied list of names. Returns None if no match is found.
"""###
def matchfont(fontname):
bold = italic = False
for i in range(0,1):
if fontname.lower().endswith(' italic'): italic = True; fontname = fontname[:-len(' italic')]
if fontname.lower().endswith(' bold'): bold = True; fontname = fontname[:-len(' bold')]
return pygame.font.match_font(fontname, bold=int(bold), italic=int(italic))
if not isinstance(fontnames, (list,tuple)): fontnames = [fontnames]
fontnames = [f for f in fontnames if f != None]
f = (filter(None, map(matchfont, fontnames)) + [None])[0]
if f == None and sys.platform == 'darwin': # pygame on OSX doesn't seem even to try to find fonts...
f = (filter(os.path.isfile, map(lambda x:os.path.realpath('/Library/Fonts/%s.ttf'%x),fontnames)) + [None])[0]
return f
#############################################################
def setup(self, left=None,top=None,width=None,height=None,changemode=None,framerate=None,bitdepth=None, **kwargs):
"""
Call this to set certain commonly-defined parameters for the screen
during BciApplication.Preflight(). The renderer object will read
these parameters in order to initialize the stimulus window, before
BciApplication.Initialize() is called.
"""###
BciGenericRenderer.setup(self, left=left,top=top,width=width,height=height,changemode=changemode,framerate=framerate,bitdepth=bitdepth,**kwargs)
pos = os.environ.get('SDL_VIDEO_WINDOW_POS','').split(',')
if len(pos)==2: prevleft,prevtop = int(pos[0]),int(pos[1])
else: prevleft,prevtop = 160,120
if left != None and top == None: top = prevtop
if top != None and left == None: left = prevleft
if left != None and top != None:
if sys.platform != 'darwin': # yup, yet another thing that's broken in pygame under OSX
os.environ['SDL_VIDEO_WINDOW_POS'] = '%d,%d' % (int(left), int(top))
if width != None: VisionEgg.config.VISIONEGG_SCREEN_W = int(width)
if height != None: VisionEgg.config.VISIONEGG_SCREEN_H = int(height)
if changemode != None: VisionEgg.config.VISIONEGG_FULLSCREEN = int(changemode)
if framerate != None: VisionEgg.config.VISIONEGG_MONITOR_REFRESH_HZ = float(framerate)
if bitdepth != None: VisionEgg.config.VISIONEGG_PREFERRED_BPP = int(bitdepth)
for k,v in kwargs.items():
kk = (k, k.upper(), 'VISIONEGG_'+k.upper())
for k in kk:
if hasattr(VisionEgg.config, k):
setattr(VisionEgg.config, k, v)
#print "VisionEgg.config.%s = %s" % (k, repr(v))
break
else:
raise AttributeError, "VisionEgg.config has no attribute '%s'" % kk[0]
#############################################################
def GetDefaultFont(self):
d = VisionEgg.Text.Text.constant_parameters_and_defaults
return d['font_name'][0], d['font_size'][0]
#############################################################
def SetDefaultFont(self, name=None, size=None):
"""
Set the name and/or size of the font that will be used
by default for Text stimuli. Returns True if the named font
can be found, False if not.
"""###
dd = [
VisionEgg.Text.Text.constant_parameters_and_defaults,
VisionEgg.WrappedText.WrappedText.constant_parameters_and_defaults,
]
if name != None:
if os.path.isabs(name) and os.path.isfile(name):
font = name
else:
font = self.findfont(name)
if font == None: return False
for d in dd: d['font_name'] = (font,) + d['font_name'][1:]
if size != None:
for d in dd: d['font_size'] = (size,) + d['font_size'][1:]
return True
#############################################################
def Initialize(self, bci):
self.__dict__['_bci'] = bci # this is a mutual reference loop, but what the hell: self and bci only die when the process dies
logging.raiseExceptions = 0 # suppresses the "No handlers could be found" chatter
pygame.quit(); pygame.init()
self._screen = VisionEgg.Core.get_default_screen()
self._viewport = VisionEgg.Core.Viewport(screen=self._screen)
self.use_frame_timer(self._frame_timer != None, renew=True)
#############################################################
def GetFrameRate(self):
if sys.platform == 'darwin':
import platform
if platform.architecture()[0].startswith('64'):
print "query_refresh_rate is broken under darwin on 64bit architectures"
return float(VisionEgg.config.VISIONEGG_MONITOR_REFRESH_HZ)
try: return float(self._screen.query_refresh_rate())
except:
print "VisionEgg failed to query refresh rate"
return float(VisionEgg.config.VISIONEGG_MONITOR_REFRESH_HZ)
#############################################################
def RaiseWindow(self):
try:
import ctypes # !! Windows-specific code.
stimwin = ctypes.windll.user32.FindWindowA(0, "Vision Egg")
self._bci._raise_window(stimwin)
except:
pass
#############################################################
def GetEvents(self):
return pygame.event.get()
#############################################################
def DefaultEventHandler(self, event):
return (event.type == pygame.locals.QUIT) or (event.type == pygame.locals.KEYDOWN and event.key == pygame.locals.K_ESCAPE)
#############################################################
def StartFrame(self, objlist):
if self._bci: self._bci.ftdb(label='screen.clear') #--------------------
self._screen.clear()
if self._bci: self._bci.ftdb(label='viewport.draw') #--------------------
self._viewport.parameters.stimuli = objlist
self._viewport.draw()
#############################################################
def FinishFrame(self):
if self._bci: self._bci.ftdb(label='swap_buffers') #--------------------
VisionEgg.Core.swap_buffers()
if self._bci: self._bci.ftdb(label='glFlush') #--------------------
VisionEgg.GL.glFlush()
if self._frame_timer: self._frame_timer.tick()
#############################################################
def Cleanup(self):
if self._frame_timer:
self._frame_timer.log_histogram()
self._frame_timer = True
self._viewport = None
self._screen.close()
self._screen = None
VisionEgg.Text._font_objects = {}
# VisionEgg 1.1 allowed these cached pygame.font.Font objects to persist even
# after pygame quits or is reloaded: this causes a crash the second time around.
# VisionEgg 1.0 didn't cache, so we never ran across the problem under Python 2.4.
# Andrew fixed it in VE 1.1.1.
pygame.quit()
#############################################################
def __getattr__(self, key):
if key == 'color': key = 'bgcolor'
v = self.__dict__.get('_screen')
if v == None: raise AttributeError, "a Screen object has not yet been instantiated inside this object"
gotit,value = self.__delegate_getattr__(v, key)
if not gotit: raise AttributeError, "'%s' object has no attribute or parameter '%s'" % (self.__class__.__name__, key)
return value
#############################################################
def __setattr__(self, key, value):
if key in self.__dict__:
self.__dict__[key] = value
else:
if key == 'color': key = 'bgcolor'
v = self.__dict__.get('_screen')
if v == None: raise AttributeError, "a Screen object has not yet been instantiated inside this object"
if not self.__delegate_setattr__(v, key, value, restrict_to=['parameters.bgcolor']):
raise AttributeError, "'%s' object has no attribute or parameter '%s'"%(self.__class__.__name__, key)
#############################################################
def _getAttributeNames(self):
v = self.__dict__.get('_screen')
if v == None: return ()
return ('color', 'bgcolor', 'size', 'parameters')
#############################################################
__delegate_setattr__ = delegate_setattr
__delegate_getattr__ = delegate_getattr
#################################################################
#################################################################
def GetVEParameterNames(self):
p = getattr(self.__dict__.get('obj'), 'parameters', None)
if p == None: return ()
return p.__dict__.keys()
BciStimulus._getAttributeNames = GetVEParameterNames
BciStimulus.__delegate_setattr__ = delegate_setattr
BciStimulus.__delegate_getattr__ = delegate_getattr
import VisionEgg.Textures, VisionEgg.GL
class ImageStimulus(VisionEgg.Textures.TextureStimulus):
"""
A subclass of VisionEgg.Textures.TextureStimulus
"""###
def __init__(self, **kwargs):
if 'texture' in kwargs and not isinstance(kwargs['texture'], VisionEgg.Textures.Texture):
kwargs['texture'] = VisionEgg.Textures.Texture(kwargs['texture'])
kwargs['mipmaps_enabled'] = kwargs.get('mipmaps_enabled', 0)
kwargs['internal_format'] = kwargs.get('internal_format', VisionEgg.GL.GL_RGBA)
kwargs['texture_min_filter'] = kwargs.get('texture_min_filter', VisionEgg.GL.GL_LINEAR)
VisionEgg.Textures.TextureStimulus.__init__(self, **kwargs)
from VisionEgg.Text import Text
from VisionEgg.MoreStimuli import Target2D as Block
from VisionEgg.MoreStimuli import FilledCircle as Disc
BciGenericRenderer.subclass = VisionEggRenderer
#################################################################
#################################################################
| [
"[email protected]"
] | |
4ccf4f0a65743ccb7d85345fa763d577d726f775 | 2b2de1801e45582e0ba67fbd451df84c298dd0fe | /Basic Python/14. dictionary.py | fd65d0f6e670ddd8043de709d3277a5b43fba9be | [] | no_license | Geca981020/Undergraduate-Study | 99c92e439058fed88658bf6b0535c0009a0bc762 | 78d82a453e05a3695fc42d34c95eae2fc16123ce | refs/heads/main | 2023-05-25T07:17:26.025753 | 2021-06-05T07:36:12 | 2021-06-05T07:36:12 | 337,929,295 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 275 | py | # dictionary Declaration&insert
people = {'name': 'hong gil dong'}
people['phone'] = '010-1234-5678'
people['birthday'] = 1122
# dictionary Searching
print(people['phone'])
# dictionary remove
del people['phone']
print(people)
people.clear()
print(people)
| [
"[email protected]"
] | |
0aff2ea3edae0934d10f6d423ca02ad299969547 | 596b852bb8428a6db4dc1153f68f6d3e0da6efec | /findNextLargestString.py | 88ebc60cfeca04d01f820e661809dfe014416ce5 | [] | no_license | bsofcs/interviewPrep | 3da14bf509bb169b0adb185bd3e1f08c371f6d72 | ace8ac2d3b47652efeb7052f93557e753796a677 | refs/heads/master | 2022-11-16T15:33:44.780238 | 2020-07-03T19:35:43 | 2020-07-03T19:35:43 | 272,368,723 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 379 | py | def findNextLargestString(arr,val):
if arr is None or val is None:
return None
if val>=arr[-1] or val<arr[0]:
return arr[0]
low,high=0,len(arr)-1
while low<high:
mid=low+(high-low)//2
if arr[mid]<=val and arr[mid+1]>val:
return(arr[mid+1])
if arr[mid]>val:
high=mid-1
else:
low=mid+1
arr=['b','c','g','h']
val='g'
print(findNextLargestString(arr,val)) | [
"[email protected]"
] | |
fab9a78af75beca94afbca19f6f69d7997087601 | 02ee91a60e80629fcce24d511ada9204d1f360a1 | /constrained_network.py | 97a4afba2ba9abd3f19e15e1c06595f3db34f6a7 | [] | no_license | tjwldnjss13/ANN-PNU | 7b73b93971cac5e671dbeaf1172d8cd25b6ab442 | 380596eb39a1834dce5c6b1a0a1942ff2246b2a4 | refs/heads/master | 2022-11-11T20:06:55.547303 | 2020-06-22T02:23:03 | 2020-06-22T02:23:03 | 265,425,814 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,059 | py | import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import os
from filters import *
class ConstrainedNet:
epoch_list = []
train_loss_list = []
train_acc_list = []
valid_loss_list = []
valid_acc_list = []
test_loss = 0
test_acc = 0
lr_list = []
def __init__(self, lr=.001, lr_decay=None, lr_decay_term=None, epochs=10):
self.lr = lr
self.lr_decay = lr_decay
self.lr_decay_term = lr_decay_term
self.lr_b = lr * 2
self.epochs = epochs
self.W_ItoH1 = 2 * np.random.random((3, 3)) - 1
self.W_H1toH2 = 2 * np.random.random((2, 5, 5)) - 1
self.W_H2toO = 2 * np.random.random((16, 10)) - 1
def exec_all(self, fp, filter=None):
images, labels = self.dataset(fp, filter)
train_images, train_labels = images[:300], labels[:300]
valid_images, valid_labels = images[300:400], labels[300:400]
test_images, test_labels = images[400:], labels[400:]
self.train([train_images, train_labels], [valid_images, valid_labels])
self.test([test_images, test_labels])
self.visualize()
def dataset(self, fp, filter):
images = []
labels = []
for n in range(50):
for label in range(10):
fn = str(label) + '.' + str(n) + '.png'
image_path = os.path.join(fp, fn)
image = Image.open(image_path).convert('L')
image = self.preprocessed_image(image, filter)
images.append(image)
labels.append(label)
images = np.array(images)
labels = np.array(labels)
labels_one_hot = []
for i in range(len(labels)):
arr = np.zeros(10)
arr[labels[i]] = 1
labels_one_hot.append(arr)
labels = np.array(labels_one_hot)
return images, labels
@staticmethod
def preprocessed_image(image_file, filter):
image = np.array(image_file)
if filter is None:
image_padded = np.zeros((17, 17))
image = image.astype('float32') / 255
image_padded[:16, :16] = image
return image_padded
else:
f_size = len(filter)
# if_size = 16 - f_size + 1
image_filtered = np.zeros((17, 17))
image_padded = np.zeros((16 + f_size, 16 + f_size))
for i in range(16 + f_size):
for j in range(16 + f_size):
image_padded[i, j] = 255
image_padded[int(f_size / 2):int(f_size / 2) + 16, int(f_size / 2): int(f_size / 2) + 16] = image
for i in range(17):
for j in range(17):
image_filtered[i, j] = np.sum(image_padded[i:i + f_size, j:j + f_size] * filter)
image_filtered = image_filtered.astype('float32') / 255
# image_padded[1:1 + if_size, 1:1 + if_size] = image_filtered
return image_filtered
# @staticmethod
# def preprocessed_image(image_file, filter):
# image = np.array(image_file)
# image_padded = np.zeros((17, 17))
# if filter is None:
# image = image.astype('float32') / 255
# image_padded[:17, :17] = image
# else:
# f_size = len(filter)
# if_size = 16 - f_size + 1
# image_filtered = np.zeros((if_size, if_size))
# for i in range(if_size):
# for j in range(if_size):
# image_filtered[i, j] = np.sum(image[i:i + f_size, j:j + f_size] * filter)
# image_filtered = image_filtered.astype('float32') / 255
# image_padded[1:1 + if_size, 1:1 + if_size] = image_filtered
#
# return image_padded
def feedforward(self, image):
pre_H1 = np.zeros((2, 8, 8))
for k in range(2):
for i in range(8):
for j in range(8):
pre_H1[k, i, j] = np.sum(image[2 * i:2 * i + 3, 2 * j:2 * j + 3] * self.W_ItoH1)
post_H1 = ConstrainedNet.relu(pre_H1)
pre_H2 = np.zeros((4, 4))
for k in range(2):
for i in range(4):
for j in range(4):
pre_H2[i, j] += np.sum(post_H1[k, i:i + 5, j:j + 5] * self.W_H1toH2[k])
post_H2 = ConstrainedNet.relu(pre_H2)
post_H2_flattened = np.reshape(post_H2, 16)
pre_O = np.matmul(post_H2_flattened, self.W_H2toO)
post_O = ConstrainedNet.softmax(pre_O)
return post_O
def train(self, train_dataset, valid_dataset):
train_images, train_labels = train_dataset[0], train_dataset[1]
valid_images, valid_labels = valid_dataset[0], valid_dataset[1]
for epoch in range(self.epochs):
if self.lr_decay is not None and self.lr_decay_term is not None:
if epoch != 0 and epoch % self.lr_decay_term == 0:
self.lr *= self.lr_decay
self.epoch_list.append(epoch)
self.lr_list.append(self.lr)
print('[{}/{} epoch]'.format(epoch + 1, self.epochs), end=' ')
train_acc = 0
train_loss = 0
# Feedforward
for train_idx in range(len(train_images)):
image = train_images[train_idx]
label = train_labels[train_idx]
pre_H1 = np.zeros((2, 8, 8))
for k in range(2):
for i in range(8):
for j in range(8):
pre_H1[k, i, j] = np.sum(image[2 * i:2 * i + 3, 2 * j:2 * j + 3] * self.W_ItoH1)
post_H1 = ConstrainedNet.relu(pre_H1)
pre_H2 = np.zeros((4, 4))
for k in range(2):
for i in range(4):
for j in range(4):
pre_H2[i, j] += np.sum(post_H1[k, i:i + 5, j:j + 5] * self.W_H1toH2[k])
post_H2 = ConstrainedNet.relu(pre_H2)
post_H2_flattened = np.reshape(post_H2, 16)
pre_O = np.matmul(post_H2_flattened, self.W_H2toO)
post_O = ConstrainedNet.softmax(pre_O)
softmax_ce = ConstrainedNet.softmax_cross_entropy(label, post_O)
if np.argmax(post_O) == np.argmax(label):
train_acc += 1
train_loss += softmax_ce
# Backpropagate
# O
D_post_O = post_O - label
softmax_derv_m = ConstrainedNet.softmax_derv(pre_O)
D_pre_O = np.zeros(10)
for i in range(10):
for j in range(10):
D_pre_O[i] += D_post_O[j] * softmax_derv_m[i, j]
# Weight (H2 -- O)
W_H2toO_old = self.W_H2toO
for i in range(16):
for j in range(10):
self.W_H2toO[i, j] -= self.lr * D_pre_O[j] * post_H2_flattened[i]
# H2
D_post_H2_flattened = np.zeros(16)
for i in range(16):
for j in range(10):
D_post_H2_flattened[i] += W_H2toO_old[i, j] * D_pre_O[j]
D_post_H2 = np.reshape(D_post_H2_flattened, (4, 4))
D_pre_H2 = np.zeros((4, 4))
for i in range(4):
for j in range(4):
D_pre_H2[i, j] = D_post_H2[i, j] * ConstrainedNet.relu_derv(pre_H2[i, j])
# Weight (H1 -- H2)
W_H1toH2_old = self.W_H1toH2
for k in range(2):
for i in range(5):
for j in range(5):
self.W_H1toH2[k, i, j] -= self.lr * np.sum(post_H1[k, i:i + 4, j:j + 4] * D_pre_H2)
# H1
W_H1toH2_old_inv = []
for k in range(2):
W_H1toH2_old_inv.append(np.flip(W_H1toH2_old[k]))
W_H1toH2_old_inv = np.array(W_H1toH2_old_inv)
D_pre_H2_padded = np.zeros((12, 12))
D_pre_H2_padded[4:8, 4:8] = D_pre_H2
# for i in range(4, 8):
# for j in range(4, 8):
# D_pre_H2_padded[i,j] = D_pre_H2[i-4, j-4]
D_post_H1 = np.zeros((2, 8, 8))
D_pre_H1 = np.zeros((2, 8, 8))
for k in range(2):
for i in range(8):
for j in range(8):
D_post_H1[k, i, j] = np.sum(D_pre_H2_padded[i:i + 5, j:j + 5] * W_H1toH2_old_inv[k])
D_pre_H1[k, i, j] = D_post_H1[k, i, j] * ConstrainedNet.relu_derv(pre_H1[k, i, j])
# Weight (I -- H1)
for k in range(2):
for i in range(3):
for j in range(3):
self.W_ItoH1[i, j] -= self.lr * np.sum(image[i:i + 15:2, j:j + 15:2] * D_pre_H1[k])
train_acc /= len(train_images)
train_loss /= len(train_images)
print('(Train) Accuracy : {:.4f}, Loss : {:.5f}'.format(train_acc, train_loss), end=' ')
self.train_loss_list.append(train_loss)
self.train_acc_list.append(train_acc)
# Validation
valid_acc = 0
valid_loss = 0
for valid_idx in range(len(valid_images)):
image = valid_images[valid_idx]
label = valid_labels[valid_idx]
valid_O = self.feedforward(image)
if np.argmax(valid_O) == np.argmax(label):
valid_acc += 1
valid_loss += ConstrainedNet.softmax_cross_entropy(label, valid_O)
valid_acc /= len(valid_images)
valid_loss /= len(valid_images)
print('(Valid) Accuracy : {:.4f}, Loss : {:.5f}'.format(valid_acc, valid_loss))
self.valid_loss_list.append(valid_loss)
self.valid_acc_list.append(valid_acc)
def test(self, test_dataset):
test_images, test_labels = test_dataset[0], test_dataset[1]
test_acc = 0
test_loss = 0
for test_idx in range(len(test_images)):
image, label = test_images[test_idx], test_labels[test_idx]
test_O = self.feedforward(image)
if np.argmax(test_O) == np.argmax(label):
test_acc += 1
test_loss += ConstrainedNet.softmax_cross_entropy(label, test_O)
test_acc /= len(test_images)
test_loss /= len(test_images)
self.test_acc = test_acc
self.test_loss = test_loss
print(' (Test) Accuracy : {:.4f}, Loss : {:.5f}'.format(test_acc, test_loss))
def visualize(self):
epochs = np.array(self.epoch_list)
train_losses = np.array(self.train_loss_list)
valid_losses = np.array(self.valid_loss_list)
train_accs = np.array(self.train_acc_list)
valid_accs = np.array(self.valid_acc_list)
lrs = np.array(self.lr_list)
plt.figure(0)
plt.plot(epochs, train_losses, 'r-', label='Train loss')
plt.plot(epochs, valid_losses, 'b:', label='Valid loss')
plt.title('Train/Validation Loss')
plt.legend()
plt.figure(1)
plt.plot(epochs, train_accs, 'r-', label='Train acc')
plt.plot(epochs, valid_accs, 'b:', label='Valid acc')
plt.title('Train/Validation Acc')
plt.legend()
if self.lr_decay is not None:
plt.figure(2)
plt.plot(epochs, lrs, 'g-', label='Learning curve')
plt.title('Learning Curve')
plt.show()
@staticmethod
def bit_l2_cost_function(target, output):
return .5 * np.square(target - output)
@staticmethod
def l2_cost_function(target, output):
return np.mean(ConstrainedNet.bit_l2_cost_function(target, output))
@staticmethod
def bit_cost_function(target, output):
return -target * np.log(output) - (1 - target) * np.log(1 - output)
@staticmethod
def cost_function(target, output):
return np.mean(ConstrainedNet.bit_cost_function(target, output))
@staticmethod
def softmax_cross_entropy(target, softmax_class):
losses = 0
for i in range(len(target)):
if softmax_class[i] == 0:
losses += target[i] * np.log(.00001)
else:
losses += target[i] * np.log(softmax_class[i])
return -losses
@staticmethod
def sigmoid(x):
return 1 / (1 + np.exp(-x))
@staticmethod
def sigmoid_derv(x):
return ConstrainedNet.sigmoid(x) * (1 - ConstrainedNet.sigmoid(x))
@staticmethod
def softmax(x):
sum = np.sum(np.exp(x))
return np.exp(x) / sum
@staticmethod
def softmax_derv(x):
softmax_x = ConstrainedNet.softmax(x)
jacobian_m = np.diag(softmax_x)
for i in range(len(jacobian_m)):
for j in range(len(jacobian_m)):
if i == j:
jacobian_m[i, j] = softmax_x[i] * (1 - softmax_x[i])
else:
jacobian_m[i, j] = -softmax_x[i] * softmax_x[j]
return jacobian_m
@staticmethod
def leaky_relu(x):
return np.maximum(.01 * x, x)
@staticmethod
def leaky_relu_derv(x):
if x > 0:
return 1
else:
return .01
@staticmethod
def relu(x):
return np.maximum(0, x)
@staticmethod
def relu_derv(x):
if x is 0:
return 1
else:
return 0
@staticmethod
def softmax(x):
exps = np.exp(x - np.max(x))
return exps / np.sum(exps)
if __name__ == '__main__':
# SOBEL_X : .0047985 (.55)
# SOBEL_Y : .00425 (.50)
# PREWITT_X : .004855 (.55)
# PREWITT_Y : .00425 (.51)
# LAPLACIAN : .00708 (.45)
# LOG : .007 (.33)
cn1 = ConstrainedNet(lr=.006, epochs=500)
cn1.exec_all('./digit data', LOG)
| [
"[email protected]"
] | |
4a5cebd758b9f2a3c1588a39c9bb4cbcd4a2080f | 0b7008ebe62448d929f5159f1fde7af11ef278ee | /2016/06-1.py | 70459eef1c2870cf9e35a6458cbd24554d8c03db | [] | no_license | TheMiles/aoc | 6db7cd1fbafbde0de0e532cc60f1da45020367d2 | 51d4940c53bcef266a93de5593db071e08285031 | refs/heads/master | 2020-06-12T18:28:02.180745 | 2019-12-19T18:18:15 | 2019-12-19T18:18:15 | 75,777,935 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 710 | py | #!/usr/bin/env python3
import argparse
import hashlib
parser = argparse.ArgumentParser()
parser.add_argument('file', type=argparse.FileType('r'),
help='the input file')
args = parser.parse_args()
number_of_lines = 0
histogram = []
for l in [ x.strip() for x in args.file]:
number_of_lines += 1
if len(histogram) < len(l):
histogram.extend([{} for _ in range(len(l)-len(histogram))])
for i, c in enumerate(l):
d = histogram[i]
d[c] = d.get(c,0) + 1
cleartext = ''
for d in histogram:
min_number = number_of_lines;
min_char = '-'
for key, value in d.items():
if value < min_number:
min_number = value
min_char = key
cleartext += min_char
print(cleartext)
| [
"[email protected]"
] | |
f138b1a144eedf0c0d0871e5d677b9735bb782e0 | 7665b25d1f5ec432b976537a50c5bc73858be6c6 | /stack.py | 23544462324610e87e7a313a23b23dff4cb76a98 | [] | no_license | arindam7development/Python-Factory | 14b2ea3a073eb40dda23a36f52dd7a07df4f87a0 | 7cd517e3f6f0ed178ea6c959bf4b73a8600ea6dc | refs/heads/master | 2021-01-22T01:33:49.212537 | 2015-04-21T05:57:20 | 2015-04-21T05:57:20 | 33,924,534 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 427 | py | # The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”).
#To add an item to the top of the stack, use append().
#To retrieve an item from the top of the stack, use pop() without an explicit index.
stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack.append(10)
print (stack)
stack.pop(1)
print (stack)
stack.pop(2)
print (stack)
| [
"[email protected]"
] | |
ff5b5983c22a847685d889c11aa61868c0103063 | e5ed648d069cca47531c178ca4f7fc6447f09dfa | /micropython/nucleo-f767zi/tcp_htget_u.py | 161e48c389f7358ec3bd554472c560717baef7c4 | [
"MIT"
] | permissive | bokunimowakaru/iot | fef89df949121be46494dcfc604085edb22ca756 | 07c42bdcd273812e54465638f74acc641bf346b9 | refs/heads/master | 2023-07-19T10:18:33.286104 | 2023-07-10T13:39:39 | 2023-07-10T13:39:39 | 163,511,968 | 7 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,802 | py | # coding: utf-8
# IoT連携の基本 HTTP GETμ for MicroPython (よりメモリの節約が可能なusocket使用)
# Copyright (c) 2019 Wataru KUNINO
import network # ネットワーク通信ライブラリ
import usocket # ソケット通信ライブラリ
import ujson # JSON変換ライブラリを組み込む
from sys import exit # ライブラリsysからexitを組み込む
host_s = 'bokunimo.net' # アクセス先のホスト名
path_s = '/iot/cq/test.json' # アクセスするファイルパス
pyb.LED(1).on() # LED(緑色)を点灯
eth = network.Ethernet() # Ethernetのインスタンスethを生成
try: # 例外処理の監視を開始
eth.active(True) # Ethernetを起動
eth.ifconfig('dhcp') # DHCPクライアントを設定
except Exception as e: # 例外処理発生時
print(e) # エラー内容を表示
exit()
try: # 例外処理の監視を開始
addr = usocket.getaddrinfo(host_s, 80)[0][-1]
sock = usocket.socket()
sock.connect(addr)
req = 'GET ' + path_s + ' HTTP/1.0\r\n'
req += 'Host: ' + host_s + '\r\n\r\n'
sock.send(bytes(req,'UTF-8'))
except Exception as e: # 例外処理発生時
print(e) # エラー内容を表示
sock.close()
exit()
body = '<head>'
while True:
res = str(sock.readline(), 'UTF-8')
print(res.strip())
if len(res) <= 0:
break
if res == '\n' or res == '\r\n':
body = '<body>'
break
if body != '<body>':
print('no body data')
sock.close()
exit()
body = ''
while True:
res = str(sock.readline(), 'UTF-8').strip()
if len(res) <= 0:
break
body += res
print(body)
res_dict = ujson.loads(body) # 受信データを変数res_dictへ代入
print('--------------------------------------') # -----------------------------
print('title :', res_dict.get('title')) # 項目'title'の内容を取得・表示
print('descr :', res_dict.get('descr')) # 項目'descr'の内容を取得・表示
print('state :', res_dict.get('state')) # 項目'state'の内容を取得・表示
print('url :', res_dict.get('url')) # 項目'url'内容を取得・表示
print('date :', res_dict.get('date')) # 項目'date'内容を取得・表示
sock.close() # ソケットの終了
pyb.LED(1).off() # LED(緑色)を消灯
| [
"[email protected]"
] | |
de4f669765dab0e5633ca48adaee9cd29c083726 | f281f7ca2843fa51f87e87e7a9cb721ef0a938e7 | /src/svs/inacademia_server.py | 5c51e7315be97932f95cca5102cec4fdacd9dab4 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | SvHu/svs | c45407decdc215fd5f5782f65184d26d8f031bf1 | 6a62fc11373ced691ce4f6bbe75ef7790dee0ef2 | refs/heads/master | 2020-12-25T20:42:51.697729 | 2016-07-10T08:37:48 | 2016-07-10T08:37:48 | 63,401,231 | 0 | 0 | null | 2016-07-15T07:29:33 | 2016-07-15T07:29:32 | Python | UTF-8 | Python | false | false | 14,754 | py | import json
import logging.config
import os
import urllib
import cherrypy
from oic.utils.keyio import KeyBundle
from oic.utils.webfinger import WebFinger, OIC_ISSUER
from saml2.response import DecryptionFailed
from saml2 import BINDING_HTTP_POST, BINDING_HTTP_REDIRECT
from svs.cherrypy_util import PathDispatcher, response_to_cherrypy
from svs.client_db import ClientDB
from svs.message_utils import abort_with_client_error, abort_with_enduser_error, \
negative_transaction_response
from svs.oidc import InAcademiaOpenIDConnectFrontend
from svs.saml import InAcademiaSAMLBackend
from svs.user_interaction import ConsentPage, EndUserErrorResponse
from svs.i18n_tool import ugettext as _
from svs.log_utils import log_transaction_start, log_internal
from svs.utils import deconstruct_state, construct_state
logger = logging.getLogger(__name__)
def setup_logging(config_dict=None, env_key="LOG_CFG", config_file="conf/logging_conf.json",
level=logging.INFO):
"""Setup logging configuration.
The configuration is fetched in order from:
1. Supplied configuration dictionary
2. Configuration file specified in environment variable 'LOG_CFG'
3. Configuration file specified as parameter
4. Basic config, configured with log level 'INFO'
"""
if config_dict is not None:
logging.config.dictConfig(config_dict)
else:
env_conf = os.getenv(env_key, None)
if env_conf:
config_file = env_conf
if os.path.exists(config_file):
with open(config_file, 'r') as f:
config = json.load(f)
logging.config.dictConfig(config)
else:
logging.basicConfig(level=level)
def main():
import argparse
import pkg_resources
parser = argparse.ArgumentParser()
parser.add_argument("--mdx", dest="mdx", required=True, type=str,
help="base url to the MDX server")
parser.add_argument("--cdb", dest="cdb", required=True, type=str,
help="path to the client metadata file")
parser.add_argument("--disco", dest="disco_url", type=str,
help="base url to the discovery server")
parser.add_argument("-b", dest="base", required=True, type=str, help="base url for the service")
parser.add_argument("-H", dest="host", default="0.0.0.0", type=str, help="host for the service")
parser.add_argument("-p", dest="port", default=8087, type=int,
help="port for the service to listen on")
args = parser.parse_args()
# Force base url to end with '/'
base_url = args.base
setup_logging()
# add directory to PATH environment variable to find xmlsec
os.environ["PATH"] += os.pathsep + '/usr/local/bin'
# ============== SAML ===============
SP = InAcademiaSAMLBackend(base_url, args.mdx, args.disco_url)
# ============== OIDC ===============
client_db = ClientDB(args.cdb)
client_db.update()
OP = InAcademiaOpenIDConnectFrontend(base_url, client_db)
# ============== Web server ===============
inacademia = InAcademiaMediator(base_url, OP, SP)
cherrypy.config.update({
# "request.error_response": _send_418,
"tools.I18nTool.on": True,
"tools.I18nTool.default": "en",
"tools.I18nTool.mo_dir": pkg_resources.resource_filename("svs", "data/i18n/locales"),
"tools.I18nTool.domain": "messages",
})
cherrypy.config.update({'engine.autoreload.on': False})
cherrypy.server.socket_host = args.host
cherrypy.server.socket_port = args.port
cherrypy.tree.mount(inacademia, "/", config={
"/static": {
"tools.staticdir.on": True,
"tools.staticdir.dir": os.path.join(os.getcwd(), "static"),
},
"/robots.txt": {
"tools.staticfile.on": True,
"tools.staticfile.filename": pkg_resources.resource_filename("svs",
"site/static/robots.txt"),
},
"/webroot": {
"tools.staticdir.on": True,
"tools.staticdir.dir": pkg_resources.resource_filename("svs", "site/static/")
}
})
cherrypy.tree.mount(None, "/.well-known", config={
"/": {
"request.dispatch": PathDispatcher({
"/webfinger": inacademia.webfinger,
"/openid-configuration": inacademia.openid_configuration,
})
}
})
cherrypy.tree.mount(None, "/acs", config={
"/": {
"request.dispatch": PathDispatcher({
"/post": inacademia.acs_post,
"/redirect": inacademia.acs_redirect,
})
}
})
cherrypy.tree.mount(None, "/consent", config={
"/": {
"request.dispatch": PathDispatcher({
"/": inacademia.consent_index,
"/allow": inacademia.consent_allow,
"/deny": inacademia.consent_deny
})
}
})
cherrypy.engine.signal_handler.set_handler("SIGTERM", cherrypy.engine.signal_handler.bus.exit)
cherrypy.engine.signal_handler.set_handler("SIGUSR1", client_db.update)
cherrypy.engine.start()
cherrypy.engine.block()
class InAcademiaMediator(object):
"""The main CherryPy application, with all exposed endpoints.
This app mediates between a OpenIDConnect provider front-end, which uses SAML as the back-end for authenticating
users.
"""
def __init__(self, base_url, op, sp):
self.base_url = base_url
self.op = op
self.sp = sp
# Setup key for encrypting/decrypting the state (passed in the SAML RelayState).
source = "file://symkey.json"
self.key_bundle = KeyBundle(source=source, fileformat="jwk")
for key in self.key_bundle.keys():
key.deserialize()
@cherrypy.expose
def index(self):
raise cherrypy.HTTPRedirect("http://www.inacademia.org")
@cherrypy.expose
def status(self):
return
@cherrypy.expose
def authorization(self, *args, **kwargs):
"""Where the OP Authentication Request arrives.
"""
transaction_session = self.op.verify_authn_request(cherrypy.request.query_string)
state = self._encode_state(transaction_session)
log_transaction_start(logger, cherrypy.request, state, transaction_session["client_id"],
transaction_session["scope"],
transaction_session["redirect_uri"])
return self.sp.redirect_to_auth(state, transaction_session["scope"])
@cherrypy.expose
def disco(self, state=None, entityID=None, **kwargs):
"""Where the SAML Discovery Service response arrives.
"""
if state is None:
raise cherrypy.HTTPError(404, _('Page not found.'))
transaction_session = self._decode_state(state)
if "error" in kwargs:
abort_with_client_error(state, transaction_session, cherrypy.request, logger,
"Discovery service error: '{}'.".format(kwargs["error"]))
elif entityID is None or entityID == "":
abort_with_client_error(state, transaction_session, cherrypy.request, logger,
"No entity id returned from discovery server.")
return self.sp.disco(entityID, state, transaction_session)
@cherrypy.expose
def error(self, lang=None, error=None):
"""Where the i18n of the error page is handled.
"""
if error is None:
raise cherrypy.HTTPError(404, _("Page not found."))
self._set_language(lang)
error = json.loads(urllib.unquote_plus(error))
raise EndUserErrorResponse(**error)
def webfinger(self, rel=None, resource=None):
"""Where the WebFinger request arrives.
This function is mapped explicitly using PathDiscpatcher.
"""
try:
assert rel == OIC_ISSUER
assert resource is not None
except AssertionError as e:
raise cherrypy.HTTPError(400, "Missing or incorrect parameter in webfinger request.")
cherrypy.response.headers["Content-Type"] = "application/jrd+json"
return WebFinger().response(resource, self.op.OP.baseurl)
def openid_configuration(self):
"""Where the OP configuration request arrives.
This function is mapped explicitly using PathDispatcher.
"""
cherrypy.response.headers["Content-Type"] = "application/json"
cherrypy.response.headers["Cache-Control"] = "no-store"
return self.op.OP.capabilities.to_json()
def consent_allow(self, state=None, released_claims=None):
"""Where the approved consent arrives.
This function is mapped explicitly using PathDispatcher.
"""
if state is None or released_claims is None:
raise cherrypy.HTTPError(404, _("Page not found."))
state = json.loads(urllib.unquote_plus(state))
released_claims = json.loads(urllib.unquote_plus(released_claims))
transaction_session = self._decode_state(state["state"])
log_internal(logger, "consented claims: {}".format(json.dumps(released_claims)),
cherrypy.request, state["state"], transaction_session["client_id"])
return self.op.id_token(released_claims, state["idp_entity_id"], state["state"],
transaction_session)
def consent_deny(self, state=None, released_claims=None):
"""Where the denied consent arrives.
This function is mapped explicitly using PathDispatcher.
"""
if state is None:
raise cherrypy.HTTPError(404, _("Page not found."))
state = json.loads(urllib.unquote_plus(state))
transaction_session = self._decode_state(state["state"])
negative_transaction_response(state["state"], transaction_session, cherrypy.request, logger,
"User did not give consent.", state["idp_entity_id"])
def consent_index(self, lang=None, state=None, released_claims=None):
"""Where the i18n of the consent page arrives.
This function is mapped explicitly using PathDispatcher.
"""
if state is None or released_claims is None:
raise cherrypy.HTTPError(404, _("Page not found."))
self._set_language(lang)
state = json.loads(urllib.unquote_plus(state))
rp_client_id = self._decode_state(state["state"])["client_id"]
released_claims = json.loads(urllib.unquote_plus(released_claims))
client_name = self._get_client_name(rp_client_id)
return ConsentPage.render(client_name, state["idp_entity_id"], released_claims,
state["state"])
def acs_post(self, SAMLResponse=None, RelayState=None, **kwargs):
"""Where the SAML Authentication Response arrives.
This function is mapped explicitly using PathDiscpatcher.
"""
return self._acs(SAMLResponse, RelayState, BINDING_HTTP_POST)
def acs_redirect(self, SAMLResponse=None, RelayState=None):
"""Where the SAML Authentication Response arrives.
"""
return self._acs(SAMLResponse, RelayState, BINDING_HTTP_REDIRECT)
def _acs(self, SAMLResponse, RelayState, binding):
"""Handle the SAMLResponse from the IdP and produce the consent page.
:return: HTML of the OP consent page.
"""
transaction_session = self._decode_state(RelayState)
user_id, affiliation, identity, auth_time, idp_entity_id = self.sp.acs(SAMLResponse,
binding, RelayState,
transaction_session)
# if we have passed all checks, ask the user for consent before finalizing
released_claims = self.op.get_claims_to_release(user_id, affiliation, identity, auth_time,
idp_entity_id,
self.sp.metadata, transaction_session)
log_internal(logger, "claims to consent: {}".format(json.dumps(released_claims)),
cherrypy.request, RelayState, transaction_session["client_id"])
client_name = self._get_client_name(transaction_session["client_id"])
return ConsentPage.render(client_name, idp_entity_id, released_claims, RelayState)
def _set_language(self, lang):
"""Set the language.
"""
if lang is None:
lang = "en"
# Modify the Accept-Language header and use the CherryPy i18n tool for translation
cherrypy.request.headers["Accept-Language"] = lang
i18n_args = {
"default": cherrypy.config["tools.I18nTool.default"],
"mo_dir": cherrypy.config["tools.I18nTool.mo_dir"],
"domain": cherrypy.config["tools.I18nTool.domain"]
}
cherrypy.tools.I18nTool.callable(**i18n_args)
def _decode_state(self, state):
"""Decode the transaction data.
If the state can not be decoded, the transaction will fail with error page for the user. We can't
notify the client since the transaction state now is unknown.
"""
try:
return deconstruct_state(state, self.key_bundle.keys())
except DecryptionFailed as e:
abort_with_enduser_error(state, "-", cherrypy.request, logger,
_(
"We could not complete your validation because an error occurred while handling "
"your request. Please return to the service which initiated the validation "
"request and try again."),
"Transaction state missing or broken in incoming response.")
def _encode_state(self, payload):
"""Encode the transaction data.
"""
_kids = self.key_bundle.kids()
_kids.sort()
return construct_state(payload, self.key_bundle.get_key_with_kid(_kids[-1]))
def _get_client_name(self, client_id):
"""Get the display name for the client.
:return: the clients display name, or client_id if no display name is known.
"""
try:
client_info = self.op.OP.cdb[client_id]
return client_info.get("display_name", client_id)
except KeyError as e:
return client_id
if __name__ == '__main__':
main()
| [
"[email protected]"
] | |
ad082d6a706f4aaef8bf00a35b031d026601dbd4 | e8487b1670fc06852af90fd5f00dd4d45e51c8a2 | /TexGenerator/year2014/sem1_39_kr2.py | f1fa0085e50c4eac9d1e96d3d4153f3f3cf5fc49 | [] | no_license | AntipovDen/Matan | f235e4f2ac8f2effeb42170a3d3c265a53bc860a | 1823a4c605227103cd838fc98be8cf0e223239fc | refs/heads/master | 2021-09-08T19:53:05.372844 | 2021-08-27T13:33:11 | 2021-08-27T13:33:11 | 68,631,208 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,679 | py | __author__ = 'Den'
from random import randint
p1 = ['Продифференцируйте f(x):\\tabularnewline\r\n$f(x) = x^\\frac{2}{\\ln x} - 2x^{\\log_x e} e^{1+\\ln x} + e^{1+\\frac{2}{\\log_x e}}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n',
'Продифференцируйте f(x) 50 раз:\\tabularnewline\r\n$f(x) = (x^2 - 1)(4 \\sin^3 x + \\sin 3x)$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n']
p2 = ['Посчитайте предел, пользуясь правилом Лопиталя:\\tabularnewline\r\n$\\lim\\limits_{x \\to 0} \\sin x \\ln \\cot x$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n',
'Посчитайте предел, пользуясь правилом Лопиталя:\\tabularnewline\r\n$\\lim\\limits_{x \\to +\\infty} (\\pi - 2\\arctan\\sqrt{x})\\sqrt{x}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n']
p3 = ['Разложите по формуле Тейлора с остатком $o((x-1)^{2n+1})$:\\tabularnewline\r\n$f(x) = (3x^2 - 6x + 4)e^{2x^2-4x+5}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n',
'Разложите по формуле Тейлора с остатком $o((x-1)^{2n})$:\\tabularnewline\r\n$f(x) = \\frac{x^2-2x+1}{\\sqrt[3]{x(2 - x)}}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n',
'Разложите по формуле Тейлора с остатком $o((x - 1)^n)$:\\tabularnewline\r\n$f(x) = \\ln \\sqrt[4]{\\frac{x - 2}{5 - x}}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n']
p4 =['Посчитайте предел, пользуясь формулой Тейлора:\\tabularnewline\r\n$\\lim\\limits_{x \\to 0} (\\sqrt{1 + 2 \\tan x} + \\ln(1 - x))^\\frac{1}{x^2}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n',
'Посчитайте предел, пользуясь формулой Тейлора:\\tabularnewline\r\n$\\lim\\limits_{x \\to 0} \\left(\\frac{x \\sin x}{2 \\cosh - 2}\\right)^\\frac{1}{\\sin^2 x}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n',
'Посчитайте предел, пользуясь формулой Тейлора:\\tabularnewline\r\n$\\lim\\limits_{x \\to 0} \\frac{\\ln (1 + x) + \\frac{1}{2}\\sinh (x^2) - x}{\\sqrt{1 + \\tan x} - \\sqrt{1 + \\sin x}}$\\tabularnewline\r\n\\noalign{\\vskip4mm}\r\n']
varNames = ['Rick Grimes',
'Carl Grimes',
'Lori Grimes',
'Shane',
'Glenn',
'Carol',
'Daryl',
'Merle',
'Andrea',
'Meggie',
'Beth',
'Hershel',
'Michonne',
'The Governor',
'Tyreese',
'Sasha',
'Bob',
'Tara']
varNames.sort();
varNumber = 0;
def genVariant():
return [randint(0, 1), randint(0, 1), randint(0, 2), randint(0, 2)]
def printVariant():
global varNumber
v = genVariant()
print('\\begin{tabular}{l}')
print('Вариант', varNames[varNumber], '\\tabularnewline')
varNumber += 1
print(p1[v[0]])
print(p2[v[1]])
print(p3[v[2]])
print(p4[v[3]])
print('\\end{tabular}', end='')
print('\\begin{tabular}{cc}')
for i in range(4):
printVariant()
print('& %')
printVariant()
print('\\tabularnewline')
print('\\noalign{\\vskip4mm}')
print('\\end{tabular}')
print('\\begin{tabular}{cc}')
for i in range(4):
printVariant()
print('& %')
printVariant()
print('\\tabularnewline')
print('\\noalign{\\vskip4mm}')
print('\\end{tabular}')
print('\\begin{tabular}{cc}')
for i in range(1):
printVariant()
print('& %')
printVariant()
print('\\tabularnewline')
print('\\noalign{\\vskip4mm}')
print('\\end{tabular}') | [
"[email protected]"
] | |
594a6854aa036bb05a7b70e39cf7494fa72f71a0 | 395f0f1faa1ba05b1dcd026b5c4c8ae6f49a931e | /states.py | 4a4a9399414bca3d40096145970df92b68a655e4 | [] | no_license | ddthj/Rogue-Neurons | f9b6c8b1a49f8acc0f730111e9f1fe7256f0bfc9 | da849b9614cc20adf8121c84a4ca80b1b6dae9d6 | refs/heads/master | 2020-05-16T22:31:44.384901 | 2019-04-25T02:23:32 | 2019-04-25T02:23:32 | 183,338,907 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,377 | py | import math
from objects import *
from util import *
'''
states.py - contains every state in the form of classes
plus the controller, which takes state output and converts it to something we can return to the framework
'''
class state: #all states inherit from this
def __init__(self):
self.expired = False
def reset(self):
self.expired = False
def execute(self,agent):
pass
class atba(state): #always towards ball agent
def __init__(self):
super().__init__()
def execute(self,agent):
#all states produce a target and target speed
target = agent.ball.location
speed = 2300
return control(agent,target,speed)
class shoot(state):#takes shot on opponent goal
def __init__(self):
super().__init__()
def execute(self,agent):
goal = Vector3(0,5100*-side(agent.team),0)
distance = (agent.ball.location - agent.me.location).magnitude() / 2.5
goal_to_ball = (agent.ball.location- goal).normalize()
target = agent.ball.location + distance * goal_to_ball
perp = goal_to_ball.cross(Vector3(0,0,1))
adjustment = perp * cap(perp.dot(agent.ball.velocity), -distance/2.3, distance/2.3)
target += adjustment
speed = 2300
if distance > 2050:
target, retarget = retarget_boost(agent,target) #it's bad to call this at close distances
return control(agent,Target(target),speed,False)
class contest(state): #hits the ball asap, dodges into it.
def __init__(self):
super().__init__()
def execute(self,agent):
target,retarget = retarget_boost(agent,agent.ball.location)
speed = 2300
return control(agent,Target(target, agent.ball.velocity),speed,not retarget)
class clear(state): #hits ball to side of field
def __init__(self):
super().__init__()
def execute(self,agent):
distance = (agent.me.location - agent.ball.location).flatten().magnitude()
goal_vector = Vector3(-sign(agent.ball.location[0],False),0,0)
target = agent.ball.location + (goal_vector*(40+(distance/5)))
target += Vector3(0,25*side(agent.team),0)
speed = 2300 * cap(distance / (-180 + agent.ball.location[2]*2), 0.1, 1)
return control(agent, Target(target), speed, False)
class retreat(state): #returns to goal and stops
def __init__(self):
super().__init__()
def execute(self,agent):
goal = Vector3(0,5100*side(agent.team),70)
if (agent.me.location - goal).magnitude() < 500:
speed = 30
target = agent.ball.location
else:
speed = 1800
target,retarget = retarget_boost(agent,goal)
return control(agent, Target(target), speed, False)
class recover(state): #tries to land facing in the direction it's moving
def __init__(self):
super().__init__()
def execute(self,agent):
target = agent.me.location + agent.me.velocity.flatten()
speed = 30
return control(agent,Target(target), speed, False)
def control(agent,target, target_speed, f = False): #turns targets and speeds into controller outputes
c = agent.refresh()
local_target = agent.me.matrix.dot(target.location - agent.me.location)
local_velocity = agent.me.matrix.dot(agent.me.velocity)[0]
turn_radius = radius(local_velocity)
turn_center = Vector3(0,sign(local_target[1])*(turn_radius + 70),0)
slowdown = (turn_center - local_target.flatten()).magnitude() / cap(turn_radius * 1.5, 1, 1200)
target_speed = cap(target_speed * slowdown, -abs(target_speed),abs(target_speed))
c.handbrake = True if slowdown < 0.44 else False
c.steer,c.yaw,c.pitch,c.roll,angle_to_target = defaultPD(agent, local_target, True)
c.throttle,c.boost = throttle(target_speed, local_velocity, 1)
if agent.me.airborn and (angle_to_target > 0.2 or (agent.me.location - target.location).magnitude() > 800):
c.boost = False
closing_vel = cap((target.location - agent.me.location).normalize().dot(agent.me.velocity-target.velocity),0.01, 2300)
if agent.sinceJump < 1.5 or (f == True and (target.location - agent.me.location).magnitude() / closing_vel < 0.38 and abs(angle_to_target) < 0.21):
flip(agent,c,local_target,angle_to_target)
return c
| [
"[email protected]"
] | |
37dcd51d3214a03cb4d9bd5ef3121c0b9a56103a | a85d1d6a54c8c143d0d64f02b80c54aba78b3a84 | /1116/클라이언트 타임.py | 078160e9ce1284f47a7a314e32060291650920da | [] | no_license | w51w/python | 30007548ba19076285954099125f42bc63a3d204 | bc556a520ad0a9d99b5445fc92113c4afa83b4c2 | refs/heads/master | 2023-01-28T17:37:15.344106 | 2020-12-06T14:56:47 | 2020-12-06T15:42:40 | 308,628,844 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 176 | py | #client_time
import socket
sock = socket.socket() #인수 생략 가능
address = ('localhost', 4444)
sock.connect((address))
print("현재 시간: ", sock.recv(1024).decode()) | [
"[email protected]"
] | |
554a381d585961861f2b683247f2d7acdb9d391e | 602bdbd1d8ef4d36ccfdcae5756bc8e448d30584 | /share/ecommerce/voucher/basic.py | 5f14cce3af33ec80ad9e80dd70fb9a3ead072d59 | [] | no_license | timparkin/timparkingallery | 1136027bf9cfbad31319958f20771a6fdc9f5fc4 | 6e6c02684a701817a2efae27e21b77765daa2c33 | refs/heads/master | 2016-09-06T00:28:16.965416 | 2008-11-25T21:15:45 | 2008-11-25T21:15:45 | 12,716 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,182 | py | import re
import formal
from ecommerce.voucher import base
class Voucher(object):
def getCreator(self):
return BasicVoucherDefinitionCreator()
def getEditor(self):
return BasicVoucherDefinitionEditor()
def getType(self):
return BasicVoucherDefinition
def getUpdateSQL(self):
sql = """
update %(table)s
set start_date=%%(start_date)s, end_date=%%(end_date)s, amount=%%(amount)s
where
voucher_definition_id = %%(voucher_definition_id)s"""
return sql
class BasicVoucherDefinition(base.BaseVoucherDefinition):
_attrs = (
'voucher_definition_id',
'code',
'count',
'multiuse',
'start_date',
'end_date',
'amount'
)
AMOUNT_RE = re.compile( '^\d+(\.\d+)?%?$' )
def addFields(form, forCreate = False):
if forCreate:
codeField = formal.String(required=True, strip=True)
countField = formal.Integer()
multiuseField = formal.Boolean()
else:
codeField = formal.String(immutable=True)
countField = formal.Integer(immutable=True)
multiuseField = formal.Boolean(immutable=True)
form.add( formal.Field('code', codeField) )
form.add( formal.Field('count', countField) )
form.add( formal.Field('multiuse', multiuseField) )
form.add( formal.Field('start_date', formal.Date()) )
form.add( formal.Field('end_date', formal.Date()) )
form.add( formal.Field('amount', formal.String(required=True, strip=True), description="Either an amount or a '%'") )
return form
class BasicVoucherDefinitionCreator(object):
def addFields(self, form):
addFields(form, forCreate = True)
def create(self, ctx, form, data):
if not data['multiuse'] and not data['count']:
raise formal.FormError( "One of 'multiuse' and 'count' must be specified" )
if data['multiuse'] and data['count']:
raise formal.FormError( "Only one of 'multiuse' and 'count' must be specified" )
if not AMOUNT_RE.match(data['amount']):
raise formal.FieldError( "Unrecognised format", 'amount' )
voucherDefinition = BasicVoucherDefinition(**data)
if data['multiuse']:
voucher = base.Voucher(code=data['code'])
voucherDefinition.addVoucher(voucher)
else:
codes = base.generateCodes(data['code'], data['count'])
for code in codes:
voucher = base.Voucher(code=code)
voucherDefinition.addVoucher(voucher)
return voucherDefinition
class BasicVoucherDefinitionEditor(object):
def addFieldsAndData(self, form, voucherDefinition):
addFields(form)
form.data = voucherDefinition.getDataDict()
def update(self, voucherDefinition, data):
if not AMOUNT_RE.match(data['amount']):
raise formal.FieldError( "Unrecognised format", 'amount' )
voucherDefinition.start_date = data['start_date']
voucherDefinition.end_date = data['end_date']
voucherDefinition.amount = data['amount']
| [
"[email protected]"
] | |
d9410fc95bb27e72892fdb3ed8a98dd71d4aad6d | db45f73a9b2a2cd8e867c577e68e9b3e6f0244f9 | /ScanWatch/storage/ScanDataBase.py | 2e91241f31ea76a964bbdd9b536be9e3d898ea69 | [
"MIT"
] | permissive | qihangwang/ScanWatch | 753d6c431df1e9e0468fd0195ac8fc8aedea0b6b | 97f60cd3ad394dc0bfb50e846bbfaa1eeb9cc197 | refs/heads/master | 2023-06-30T16:57:56.414960 | 2021-07-17T10:14:53 | 2021-07-17T10:14:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,003 | py | from typing import Dict, List
from ScanWatch.storage.DataBase import DataBase
from ScanWatch.storage.tables import get_transaction_table
from ScanWatch.utils.enums import TRANSACTION, NETWORK
class ScanDataBase(DataBase):
"""
Handles the recording of the address transactions in a local database
"""
def __init__(self, name: str = 'scan_db'):
"""
Initialise a Scan database instance
:param name: name of the database
:type name: str
"""
super().__init__(name)
def add_transactions(self, address: str, nt_type: NETWORK, tr_type: TRANSACTION, transactions: List[Dict]):
"""
Add a list of transactions to the database
:param address: address involved in the transaction
:type address: str
:param nt_type: type of network
:type nt_type: NETWORK
:param tr_type: type of the transaction to record
:type tr_type: TRANSACTION
:param transactions: list of the transaction to record
:type transactions: List[Dict]
:return: None
:rtype: None
"""
table = get_transaction_table(address, nt_type, tr_type)
for transaction in transactions:
row = table.dict_to_tuple(transaction)
self.add_row(table, row, auto_commit=False)
self.commit()
def get_transactions(self, address: str, nt_type: NETWORK, tr_type: TRANSACTION) -> List[Dict]:
"""
Return the List of the transactions recorded in the database
:param address: address involved in the transactions
:type address: str
:param nt_type: type of network
:type nt_type: NETWORK
:param tr_type: type of the transaction to fetch
:type tr_type: TRANSACTION
:return: list of the transaction recorded
:rtype: List[Dict]
"""
table = get_transaction_table(address, nt_type, tr_type)
rows = self.get_all_rows(table)
return [table.tuple_to_dict(row) for row in rows]
def get_last_block_number(self, address: str, nt_type: NETWORK, tr_type: TRANSACTION) -> int:
"""
Return the last block number seen in recorded transactions (per address, type of transaction and network)
If None are found, return 0
:param address: address involved in the transactions
:type address: str
:param nt_type: type of network
:type nt_type: NETWORK
:param tr_type: type of the transaction to fetch
:type tr_type: TRANSACTION
:return: last block number
:rtype: int
"""
table = get_transaction_table(address, nt_type, tr_type)
selection = f"MAX({table.blockNumber})"
result = self.get_conditions_rows(table, selection=selection)
default = 0
try:
result = result[0][0]
except IndexError:
return default
if result is None:
return default
return int(result)
| [
"[email protected]"
] |
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