Reset23/the-stack-v2-python · Datasets at Hugging Face

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/bk3d_to_huxing_test.py

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refs/heads/master

2023-03-22T08:36:32.102048

2021-03-08T08:10:25

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import xadmin from .models import Courseinfo, Lession, Video, CourseResource, CourseBanner from organizations.models import Organizationinfo class LessonInline: """添加课程的时候可以顺便添加章节""" model = Lession readonly_fields = ['add_time'] extra = 0 class CourseResourceInline: """添加课程的时候可以顺便添加课程资源""" model = CourseResource readonly_fields = ['add_time'] extra = 0 class CourseinfoAdmin: """课程信息管理""" list_display = ['name', 'teacher', 'course_org', 'desc', 'category', 'degree', 'learn_time', 'students', 'fav_nums', 'click_nums', 'is_banner', 'add_time'] list_filter = ['course_org', 'teacher', 'name', 'image', 'desc', 'degree', 'students', 'learn_time', 'category', 'fav_nums', 'click_nums', 'detail', 'is_banner', 'before_know', 'teacher_tell'] search_fields = ['course_org', 'teacher', 'name', 'image', 'desc', 'degree', 'students', 'learn_time', 'category', 'fav_nums', 'click_nums', 'detail', 'is_banner', 'before_know', 'teacher_tell', 'add_time'] readonly_fields = ['fav_nums', 'click_nums', 'students', 'add_time'] # refresh_times = [3,5] # 设定页面刷新 def queryset(self): """筛选非轮播课程""" qs = super(CourseinfoAdmin,self).queryset() qs = qs.filter(is_banner=False) return qs def save_models(self): """在保存课程时，修改机构的课程总数""" obj = self.new_obj obj.save() if obj.course_org is not None: course_org = obj.course_org course_org.course_nums = Courseinfo.objects.filter(course_org=course_org).count() course_org.save() class CourseBannerAdmin: """课程信息管理-轮播课程""" list_display = ['name', 'teacher', 'course_org', 'desc', 'category', 'degree', 'learn_time', 'students', 'fav_nums', 'click_nums', 'is_banner', 'add_time'] list_filter = ['course_org', 'teacher', 'name', 'image', 'desc', 'degree', 'students', 'learn_time', 'category', 'fav_nums', 'click_nums', 'detail', 'is_banner', 'before_know', 'teacher_tell'] search_fields = ['course_org', 'teacher', 'name', 'image', 'desc', 'degree', 'students', 'learn_time', 'category', 'fav_nums', 'click_nums', 'detail', 'is_banner', 'before_know', 'teacher_tell', 'add_time'] readonly_fields = ['fav_nums', 'click_nums', 'students', 'add_time'] def queryset(self): """筛选轮播课程""" qs = super(CourseBannerAdmin,self).queryset() qs = qs.filter(is_banner=True) return qs def save_models(self): """在保存课程时，修改机构的课程总数""" obj = self.new_obj obj.save() if obj.course_org is not None: course_org = obj.course_org course_org.course_nums = Courseinfo.objects.filter(course_org=course_org).count() course_org.save() class LessionAdmin: """章节管理""" list_display = ['course', 'name', 'add_time'] list_filter = ['course', 'name'] search_fields = ['course', 'name', 'add_time'] readonly_fields = ['add_time'] class VideoAdmin: """课程小节/视频管理""" list_display = ['lession', 'name', 'url', 'learn_time', 'add_time'] list_filter = ['lession', 'name', 'url', 'learn_time'] search_fields = ['lession', 'name', 'url', 'learn_time', 'add_time'] readonly_fields = ['add_time'] class CourseResourceAdmin: """课程资料管理""" list_display = ['lession', 'name', 'download', 'add_time'] list_filter = ['lession', 'name', 'download'] search_fields = ['lession', 'name', 'download', 'add_time'] readonly_fields = ['add_time'] xadmin.site.register(Courseinfo, CourseinfoAdmin) xadmin.site.register(CourseBanner, CourseBannerAdmin) xadmin.site.register(Lession, LessionAdmin) xadmin.site.register(Video, VideoAdmin) xadmin.site.register(CourseResource, CourseResourceAdmin)

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('core', '0004_auto_20150825_1909'), ('institutions', '0004_auto_20150825_1920'), ] operations = [ migrations.CreateModel( name='InstituteAddress', fields=[ ('address_ptr', models.OneToOneField(primary_key=True, to='core.Address', parent_link=True, auto_created=True, serialize=False)), ], bases=('core.address',), ), migrations.RemoveField( model_name='institute', name='address', ), migrations.AddField( model_name='instituteaddress', name='institute_fk', field=models.ForeignKey(to='institutions.Institute'), ), ]

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ablx@posteo.de

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/01/flightscheduler/flights/urls.py

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FernandoDaflon/Django_Rest_Angular_8-

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from django.urls import path, include from . import views from rest_framework import routers from django.conf.urls import url # router = routers.DefaultRouter() # router.register(r'users', views.UserViewSet) urlpatterns = [ # path('', views.index, name='index'), # path('', include(router.urls)) url(r'^$', views.flight_list), url(r'^(?P<pk>[0-9]+)$', views.flight_detail) ]

[ "fernandodaflon@gmail.com" ]

fernandodaflon@gmail.com

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/PyTrain-master/session1/json_work.py

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ragavendranps/Python

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2022-11-05T10:52:34.008508

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import json # some JSON: x = '{ "name":"John", "age":30, "city":"New York"}' # parse x: y = json.loads(x) print(type(x)) print(type(y)) # the result is a Python dictionary: print(y["age"])

[ "67148832+ragavendranps@users.noreply.github.com" ]

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/ML,DL, RL/Machine Learning/ml/m42_xgb_qpu.py

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[]

no_license

brunoleej/study_git

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refs/heads/main

2023-08-19T01:07:42.236110

2021-08-29T16:20:59

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# XGBoost # tree_method = 'gpu_hist' : cPU 대신, 실행을 시켰을 때 전체 GPU는 활동을 안하는데 CUDA만 활동 # predictor='gpu_predictor' : GPU로 예측 수행 # predictor='cpu_predictor' : CPU로 예측 수행 # gpu_id=0 : GPU 선택하여 처리 from xgboost import XGBClassifier, XGBRegressor from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split import numpy as np from sklearn.metrics import r2_score datasets = load_boston() x = datasets.data y = datasets.target x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=66) model = XGBRegressor(n_estimators=100000, learning_rate=0.01, tree_method = 'gpu_hist', # predictor='gpu_predictor' predictor='cpu_predictor', gpu_id=0 ) model.fit(x_train, y_train, verbose=1, eval_metric=['rmse'], eval_set =[(x_train, y_train), (x_test, y_test)], early_stopping_rounds=10000 ) aaa = model.score(x_test, y_test) print("model.score : ",aaa) # model.score : 0.9254888275792001

[ "jk04059@naver.com" ]

jk04059@naver.com

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/lesson03/fib.py

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wangwei96/python_study

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2021-05-06T13:13:46.826038

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#利用递归计算斐波那契数列第lim项的值 def fibnum(a=1,b=0,lim=4,lev=1): if lev==lim: return a return fibnum(a+b,a,lim,lev+1) #输入一个数字，调用求斐波那契数列该项数值的函数 def fib(i): if i>0: print(fibnum(lim=i)) else: print('请输入正确的值！！！') fib()

[ "965158007@qq.com" ]

965158007@qq.com

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/untitled2.py

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[]

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chintugupta07/cabmanagementsystem

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2022-11-15T10:06:09.013704

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from tkinter import * from tkinter import ttk from tkinter import messagebox import sqlite3 import re class login: a=0 b=0 c=0 mobile_no=0 def signup(self): top=Tk() top.title("create form") top.geometry("500x450+425+75") self.var=IntVar() Label(top,text="Register Form",font="Helvetica 12 bold",height="3",width="500",fg="white",bg="black").pack() Label(top,text="Firstname").place(x=100,y=100) self.firstname = Entry(top) self.firstname.place(x=180,y=100) Label(top,text="Lastname").place(x=100,y=150) self.lastname=Entry(top) self.lastname.place(x=180,y=150) Label(top,text="Gender").place(x=100,y=200) R1=Radiobutton(top,text="Male",variable=self.var,value=1,relief=RAISED) R1.place(x=200,y=200) R2=Radiobutton(top,text="Female",variable=self.var,value=0,relief=RAISED) R2.place(x=300,y=200) Label(top,text="Mobile_No.").place(x=100,y=250) self.mobile_no=Entry(top) self.mobile_no.place(x=180,y=250) Label(top,text="Email").place(x=100,y=300) self.email=Entry(top) self.email.place(x=180,y=300) Label(top,text="Password:").place(x=100,y=350) self.password=Entry(top) self.password.place(x=180,y=350) Button(top,text="Submit",command=self.validate).place(x=170,y=400) top.mainloop() def validate(self): temp = self.mobile_no.get() l = len(temp) temp1=self.email.get() temp2=self.firstname.get() temp3=self.lastname.get() if (l!=10 or temp.isdigit()==False) : message=messagebox.showinfo("Warning...!!","INVALID MOBILE NUMBER") pass elif(temp1=='' or re.search('[@]',temp1) is None or re.search('[.]',temp1) is None): message=messagebox.showinfo("Warning...!!","PLEASE ENTER VALID EMAIL") elif(temp2==''): message=messagebox.showinfo("Warning...!!","PLEASE ENTER FIRSTNAME") elif(temp3==''): message=messagebox.showinfo("Warning...!!","PLEASE ENTER LASTNAME") else: self.database() def database(self): name=self.firstname.get() name1=self.lastname.get() temp=self.var.get() abc=int(temp) if abc == 1: g="Male" else: g="Male" mb=self.mobile_no.get() email=self.email.get() passw=self.password.get() conn=sqlite3.connect("untitled2.db") c=conn.cursor() #c.execute("CREATE TABLE form(firstname varchar(50), lastname varchar(50),gender text,mobile_no number,Email text,password text);") c.execute("INSERT INTO form(firstname,lastname,gender,mobile_no,Email,password)values(?,?,?,?,?,?)",(name,name1,g,mb,email,passw)) c.execute("SELECT * from form") for i in c: print("name",i[0]) print("name1",i[1]) print("g",i[2]) print("mb",i[3]) print("email",i[4]) print("password",i[5]) c.close() conn.commit() def login(self): top=Tk() top.title("Login") top.geometry("500x400+425+75") Label(top,text="LOGIN PAGE",font="Helvetica 12 bold",height="3",width="500",fg="white",bg="black").pack() n=Label(top, text="Email").place(x=100,y=100) self.n1=Entry(top) self.n1.place(x=180,y=100) m=Label(top, text="Password").place(x=100,y=150) self.m1=Entry(top) self.m1.place(x=180,y=150) button_1=Button(top,text='SUBMIT',fg='green',command=self.valid).place(x=170,y=200) button_1=Button(top,text='SIGN UP',fg='green',command=self.signup).place(x=270,y=200) top.mainloop() def valid(self): idd=self.n1.get() pas=self.m1.get() if(idd=='' or re.search('[@]',idd) is None or re.search('[.]',idd) is None): message=messagebox.showinfo("Warning...!!","PLEASE ENTER VALID EMAIL") elif(pas==''): message=messagebox.showinfo("Warning...!!","PLEASE ENTER VALID PASSWORD") else: self.check() def check(self): conn=sqlite3.connect("untitled2.db") c=conn.cursor() if(self.n1.get() !='' and self.m1.get()!=''): c.execute("select email,password from form where email=? and password=?",(self.n1.get(),self.m1.get())) check = c.fetchone() print(check) if check is None: message=messagebox.showinfo("Warning...","INVALID EMAIL ID & PASSWORD.") elif check is not None : self.set_trip() def set_trip(self): top=Tk() top.title("Booking Request IN LPU") top.geometry("500x600+425+75") Label(top,text="BOOKING REQUEST",font="Helvetica 12 bold",height="3",width="500",fg="white",bg="black").pack() lb1=Label(top,text="From Block NO.").place(x=100,y=150) self.b=Entry(top,width=12) self.b.place(x=200,y=150) lb2=Label(top,text="To Block No.").place(x=100,y=200) self.a=Entry(top,width=12) self.a.place(x=200,y=200) lb_date=Label(top,text="Date").place(x=100,y=250) Var=IntVar() Var.set(1) spin=Spinbox(top,from_=1,to=31,width=10,textvariable=Var) spin.place(x=200,y=250) lb_month=Label(top,text="Month").place(x=100,y=300) Var1=IntVar() Var1.set(1) spin=Spinbox(top,from_=1,to=12,width=10,textvariable=Var1) spin.place(x=200,y=300) lb_year=Label(top,text="Year").place(x=100,y=350) Var2=IntVar() Var2.set(2018) spin=Spinbox(top,from_=2018,to=2020,width=10,textvariable=Var2) spin.place(x=200,y=350) button_1=Button(top,text='SUBMIT',fg='green',command=self.validd).place(x=150,y=400) top.mainloop() def validd(self): fromm=self.b.get() too=self.a.get() if(fromm==''): message=messagebox.showinfo("Warning...!!","PLEASE ENTER VALID ROUTE") elif(too==''): message=messagebox.showinfo("Warning...!!","PLEASE ENTER VALID ROUTE") else: self.fare() def fare(self): w=int(self.a.get()) q=int(self.b.get()) if w==q: message=messagebox.showinfo("WARNING"," END AND START POINT OF TRIP ARE SAME ") elif w>57 or w<1: message=messagebox.showinfo("WARNING"," ENTER VALID BLOCK NUMBER FROM 1 TO 57 ") elif q>57 or q<1: message=messagebox.showinfo("WARNING"," ENTER VALID BLOCK NUMBER FROM 1 TO 57 ") else: if q>w: self.c=(q-w)*2 else: self.c=(w-q)*2 s="FARE IS :"+str(self.c) message=messagebox.showinfo("THANK YOU",s) def contact(self): top=Tk() top.title("Contact Us") top.geometry("500x300+425+75") Label(top,text="CONTACT US",font="Helvetica 12 bold",height="3",width="500",fg="white",bg="black").pack() o=Label(top,text="KVM CAB DELHI").place(x=100,y=100) o=Label(top,text="Plot No. 356 Near Miler ").place(x=100,y=120) o=Label(top,text="Ganj, New Delhi. 148011").place(x=100,y=140) o=Label(top,text="Email: KVMCAB@gmail.com").place(x=100,y=160) o=Label(top,text="Mob. NO. 9875641235").place(x=100,y=180) o=Label(top,text="Fax NO. 121454545").place(x=100,y=200) top.mainloop() def __init__(self): root=Tk() root.title("CAB MANAGEMENT SYSTEM") root.geometry("500x400+425+125") Label(root,text="WELCOME TO CAB BOOKING PORTAL",font="Helvetica 12 bold",height="3",width="500",fg="white",bg="black").pack() Button(root,text="Login",bg="Yellow",width="15",height="3",command=self.login,relief=RAISED).place(x="210", y="100") Button(root,text="New User",bg="Yellow",width="15",height="3",command=self.signup,relief=RAISED).place(x="210", y="160") Button(root,text="Available Routes",bg="Yellow",width="15",height="3",command=self.set_trip,relief=RAISED).place(x="210", y="220") Button(root,text="Contact Us",bg="Yellow",width="15",height="3",command=self.contact,relief=RAISED).place(x="210", y="280") root.mainloop() ob=login()

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/test/unit/manager/wsgi/ports/test_routes.py

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soulhez/Goperation

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import routes from simpleservice.wsgi import router from goperation.manager.wsgi.port.routers import Routers as port_routes mapper = routes.Mapper() port_route = port_routes() port_route.append_routers(mapper) testing_route = router.ComposingRouter(mapper) for x in mapper.matchlist: print x.name route_dict = mapper._routenames for route_name in route_dict: print route_name, route_dict[route_name].conditions.get('method'), print route_dict[route_name].defaults.get('action'), route_dict[route_name].routepath

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katotetsuro/chainer-maskrcnn

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4,304

py

import chainer import chainer.links as L import chainer.functions as F from chainer.links.model.vision.resnet import ResNet50Layers, BuildingBlock, _global_average_pooling_2d import numpy as np import copy from chainer_maskrcnn.functions.roi_align_2d_yx import _roi_align_2d_yx class FPNRoIKeypointHead(chainer.Chain): mask_size = 56 def __init__(self, n_class, n_keypoints, roi_size_box, roi_size_mask, n_mask_convs=8, loc_initialW=None, score_initialW=None, mask_initialW=None): # n_class includes the background super().__init__() with self.init_scope(): # layers for box prediction path self.conv1 = L.Convolution2D( in_channels=None, out_channels=256, ksize=3, pad=1) self.fc1 = L.Linear(None, 1024) self.fc2 = L.Linear(None, 1024) self.cls_loc = L.Linear(1024, 4, initialW=loc_initialW) self.score = L.Linear(1024, n_class, initialW=score_initialW) # mask prediction path self.mask_convs = chainer.ChainList() for i in range(n_mask_convs): self.mask_convs.add_link( L.Convolution2D(None, 256, ksize=3, pad=1)) self.deconv1 = L.Deconvolution2D( in_channels=None, out_channels=256, ksize=2, stride=2, pad=0, initialW=mask_initialW) self.conv2 = L.Convolution2D( in_channels=None, out_channels=n_keypoints, ksize=1, stride=1, pad=0, initialW=mask_initialW) self.n_class = n_class self.roi_size_box = roi_size_box self.roi_size_mask = roi_size_mask def __call__(self, x, indices_and_rois, levels, spatial_scales): pool_box = list() levels = chainer.cuda.to_cpu(levels).astype(np.int32) if len(np.unique(levels)) == 1: pool_box = _roi_align_2d_yx(x[0], indices_and_rois, self.roi_size_box, self.roi_size_box, spatial_scales[0]) else: for l, i in zip(levels, indices_and_rois): v = _roi_align_2d_yx(x[l], i[None], self.roi_size_box, self.roi_size_box, spatial_scales[l]) pool_box.append(v) pool_box = F.concat(pool_box, axis=0) h = self.conv1(pool_box) h = F.relu(h) h = F.relu(self.fc1(h)) h = F.relu(self.fc2(h)) roi_cls_locs = self.cls_loc(h) roi_scores = self.score(h) # at prediction time, we use two pass method. # at first path, we predict box location and class # at second path, we predict mask with accurate location from first path if chainer.config.train: pool_mask = list() for l, i in zip(levels, indices_and_rois): pool_mask.append(_roi_align_2d_yx(x[l], i[None], self.roi_size_mask, self.roi_size_mask, spatial_scales[l])) mask = F.concat(pool_mask, axis=0) for l in self.mask_convs.children(): mask = F.relu(l(mask)) mask = self.conv2(self.deconv1(mask)) *_, h, w = mask.shape mask = F.resize_images(mask, output_shape=(2 * h, 2 * w)) return roi_cls_locs, roi_scores, mask else: # cache self.x = x return roi_cls_locs, roi_scores def predict_mask(self, levels, indices_and_rois, spatial_scales): pool_mask = list() for l, i in zip(levels, indices_and_rois): pool_mask.append(_roi_align_2d_yx(self.x[l], i[None], self.roi_size_mask, self.roi_size_mask, spatial_scales[l])) mask = F.concat(pool_mask, axis=0) for l in self.mask_convs: mask = F.relu(l(mask)) mask = self.conv2(self.deconv1(mask)) *_, h, w = mask.shape mask = F.resize_images(mask, output_shape=(2 * h, 2 * w)) return mask

[ "nistetsurooy@gmail.com" ]

nistetsurooy@gmail.com

9c0bf6f14b76ace7d83c6afd94b31c12d698f62b

dea486be1f69bc984632ff554e8c00280750bb49

/lib/environ.py

c6af26b607d4e8225e2f1e4870889ba1446cbf7c

[]

no_license

cmkwong/RL_code

b20354a188c08d0a2c0cab78fb39577b7b425fa1

8b930747f4e9e54af6e8bf97b6bf109f7ab39b99

refs/heads/master

2022-04-13T14:46:40.094865

2020-03-14T14:25:15

234,214,393

0

null

UTF-8

Python

false

11,315

py

import gym import gym.spaces from gym.utils import seeding import enum import numpy as np import collections from . import data DEFAULT_BARS_COUNT = 20 DEFAULT_COMMISSION_PERC = 0.1 class Actions(enum.Enum): Skip = 0 Buy = 1 Close = 2 class State: def __init__(self, bars_count, commission_perc, reset_on_close, reward_on_close=True, volumes=True, train_mode=True): assert isinstance(bars_count, int) assert bars_count > 0 assert isinstance(commission_perc, float) assert commission_perc >= 0.0 assert isinstance(reset_on_close, bool) assert isinstance(reward_on_close, bool) self.bars_count = bars_count self.commission_perc = commission_perc self.reset_on_close = reset_on_close self.reward_on_close = reward_on_close self.volumes = volumes self.train_mode = train_mode def reset(self, data, date, extra_set, offset): assert isinstance(data, dict) assert offset >= self.bars_count - 1 self.have_position = False self.open_price = 0.0 self._data = data self._date = date self._extra_set = extra_set # empty if {} self.extra_indicator = False self._offset = offset def normalised_trend_data(self): start = self._offset - self.bars_count + 1 end = self._offset + 1 # normalise the data from an array x = 0 y = 0 target_data = np.ndarray(shape=(self.bars_count, self.extra_trend_size), dtype=np.float64) for indicator in self._extra_set['trend'].values(): y = y + indicator.encoded_size target_data[:, x:y] = indicator.normalise(start, end, self.train_mode) x = y y = x return target_data def normalised_status_data(self): start = self._offset - self.bars_count + 1 end = self._offset + 1 target_data = np.ndarray(shape=(1, self.extra_status_size), dtype=np.float64) # normalise the data from an array x = 0 y = 0 for indicator in self._extra_set['status'].values(): y = y + indicator.encoded_size target_data[0, x:y] = indicator.normalise(start, end, self.train_mode) x = y y = x return target_data @property def shape_data(self): # bars * (h, l, c, bc_o, v) + position_flag + rel_profit (since open) self.extra_trend_size = 0 if len(self._extra_set) is not 0: if len(self._extra_set['trend']) is not 0: for trend_name in list(self._extra_set['trend'].keys()): self.extra_trend_size += self._extra_set['trend'][trend_name].encoded_size if self.volumes: self.base_trend_size = 5 return (self.bars_count, self.base_trend_size + self.extra_trend_size) else: self.base_trend_size = 4 return (self.bars_count, self.base_trend_size + self.extra_trend_size) @property def shape_status(self): self.base_status_size = 2 self.extra_status_size = 0 if len(self._extra_set) is not 0: if len(self._extra_set['status']) is not 0: for status_name in list(self._extra_set['status'].keys()): self.extra_status_size += self._extra_set['status'][status_name].encoded_size return (1, self.base_status_size + self.extra_status_size) def encode(self): # p.336 """ Convert current state into numpy array. """ encoded_data = collections.namedtuple('encoded_data', field_names=['data', 'status']) data = np.ndarray(shape=self.shape_data, dtype=np.float64) status = np.ndarray(shape=self.shape_status, dtype=np.float64) shift_r = 0 # data stacking bese_volume = self._data['volume'][self._offset - self.bars_count + 1] for bar_idx in range(-self.bars_count + 1, 1): shift_c = 0 data[shift_r, shift_c] = (self._data['high'][self._offset + bar_idx] - self._data['open'][self._offset + bar_idx]) / \ self._data['open'][self._offset + bar_idx] shift_c += 1 data[shift_r, shift_c] = (self._data['low'][self._offset + bar_idx] - self._data['open'][self._offset + bar_idx]) / \ self._data['open'][self._offset + bar_idx] shift_c += 1 data[shift_r, shift_c] = (self._data['close'][self._offset + bar_idx] - self._data['open'][self._offset + bar_idx]) / \ self._data['open'][self._offset + bar_idx] shift_c += 1 data[shift_r, shift_c] = (self._data['close'][(self._offset - 1) + bar_idx] - self._data['open'][self._offset + bar_idx]) / \ self._data['open'][self._offset + bar_idx] shift_c += 1 if self.volumes: data[shift_r, shift_c] = self._data['volume'][self._offset + bar_idx] / bese_volume shift_c += 1 shift_r += 1 # status stacking status[0,0] = float(self.have_position) if not self.have_position: status[0,1] = 0.0 else: status[0,1] = (self._data['close'][self._offset] - self.open_price) / self.open_price # extra_data normal_array = np.ndarray(shape=(self.bars_count, self.extra_trend_size), dtype=np.float64) if len(self._extra_set) is not 0: if len(self._extra_set['trend']) is not 0: normal_array = self.normalised_trend_data() data[:, self.base_trend_size:] = normal_array if len(self._extra_set['status']) is not 0: normal_array = self.normalised_status_data() status[0, self.base_status_size:] = normal_array return encoded_data(data=data, status=status) def _cur_close(self): """ Calculate real close price for the current bar """ open = self._data['open'][self._offset] rel_close = self._data['close'][self._offset] return open * (1.0 + rel_close) def step(self, action): """ Perform one step in our price, adjust offset, check for the end of prices and handle position change :param action: :return: reward, done """ assert isinstance(action, Actions) reward = 0.0 done = False # don't need self._cur_close() because it is not relative price close = self._data['close'][self._offset] if action == Actions.Buy and not self.have_position: self.have_position = True self.open_price = close reward -= self.commission_perc elif action == Actions.Close and self.have_position: reward -= self.commission_perc done |= self.reset_on_close # done if reset_on_close if self.reward_on_close: reward += 100.0 * (close - self.open_price) / self.open_price self.have_position = False self.open_price = 0.0 self._offset += 1 prev_close = close close = self._data['close'][self._offset] done |= self._offset >= self._data['close'].shape[0]-1 # done if reached limit if self.have_position and not self.reward_on_close: reward += 100.0 * (close - prev_close) / prev_close # change with respect to last day close-price return reward, done class StocksEnv(gym.Env): metadata = {'render.modes': ['human']} def __init__(self, data, date, extra_set, bars_count=DEFAULT_BARS_COUNT, commission=DEFAULT_COMMISSION_PERC, reset_on_close=True, random_ofs_on_reset=True, reward_on_close=False, volumes=False, train_mode=True): assert isinstance(data, dict) self.universe_data = data self.universe_date = date self.universe_extra_set = extra_set # empty dict if there is no extra data self._state = State(bars_count, commission, reset_on_close, reward_on_close=reward_on_close, volumes=volumes, train_mode=train_mode) self.random_ofs_on_reset = random_ofs_on_reset self.train_mode = train_mode self.seed() # get the shape first for creating the net self.get_data_shape() self.action_space = gym.spaces.Discrete(n=len(Actions)) self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self._state.bars_count, self.data_size), dtype=np.float64) def get_data_shape(self): self.reset() self.price_size = self._state.base_trend_size self.trend_size = self._state.extra_trend_size self.data_size = self.price_size + self.trend_size self.status_size = self._state.base_status_size + self._state.extra_status_size def offset_modify(self, prices, extra_set, train_mode): available_start = 0 if len(extra_set) is not 0: # append the length, cal the min_length invalid_length = [] if len(extra_set['trend']) is not 0: for key in list(extra_set['trend'].keys()): invalid_length.append(extra_set['trend'][key].invalid_len) if len(extra_set['status']) is not 0: for key in list(extra_set['status'].keys()): invalid_length.append(extra_set['status'][key].invalid_len) available_start = np.max(invalid_length) bars = self._state.bars_count if self.random_ofs_on_reset: if train_mode: offset = self.np_random.choice(range(available_start, prices['high'].shape[0] - bars * 10)) + bars else: offset = self.np_random.choice(prices['high'].shape[0] - bars * 10) + bars else: if train_mode: offset = bars + available_start else: offset = bars return offset def reset(self): # make selection of the instrument and it's offset. Then reset the state self._instrument = self.np_random.choice(list(self.universe_data.keys())) data = self.universe_data[self._instrument] date = self.universe_date[self._instrument] extra_set_ = {} if len(self.universe_extra_set) is not 0: extra_set_ = self.universe_extra_set[self._instrument] offset = self.offset_modify(data, extra_set_, self.train_mode) # train_mode=False, random offset is different self._state.reset(data, date, extra_set_, offset) return self._state.encode() def step(self, action_idx): action = Actions(action_idx) reward, done = self._state.step(action) obs = self._state.encode() info = {"instrument": self._instrument, "offset": self._state._offset} return obs, reward, done, info def render(self, mode='human', close=False): pass def close(self): pass def seed(self, seed=None): self.np_random, seed1 = seeding.np_random(seed) seed2 = seeding.hash_seed(seed1 + 1) % 2 ** 31 return [seed1, seed2]

[ "47665725+cmkwong@users.noreply.github.com" ]

47665725+cmkwong@users.noreply.github.com

944220470e54abb868ea7ab6dd1e61634a5a1f51

3e2bb496ee3f18dfad3506f871c69ec2a0d67fe9

/m2m-relations/articles/admin.py

bf4ea0245db8187328cd09190ea7052b0d2c877e

[]

no_license

ns-m/netology_dj_py_Databases_2

0b80c8829e7272e2b65d1de0abdb16a296f29740

abc1aa198f30ac98b63c14cef735729130674f07

refs/heads/master

2023-05-31T11:27:00.368032

2020-05-24T16:26:22

266,418,890

0

null

2021-06-10T22:57:09

2020-05-23T21:01:31

Python

UTF-8

Python

false

1,087

py

from django.contrib import admin from django.core.exceptions import ValidationError from django.forms import BaseInlineFormSet from .models import Article, Section, ArticleSection class ArticleSectionInlineFormset(BaseInlineFormSet): def clean(self): set_common_section = False for form in self.forms: common_section = form.cleaned_data.get('common_section') if common_section: if set_common_section: raise ValidationError('Основным разделом может быть только один!') set_common_section = True if not set_common_section: raise ValidationError('Укажите основной раздел!') return super().clean() class ArticleSectionInline(admin.TabularInline): model = ArticleSection formset = ArticleSectionInlineFormset extra = 1 @admin.register(Article) class ArticleAdmin(admin.ModelAdmin): inlines = [ArticleSectionInline] @admin.register(Section) class SectionAdmin(admin.ModelAdmin): pass

[ "9117479@gmail.com" ]

9117479@gmail.com

3c3ea357ad2d4046b95cdcf5cca0e18967b13045

d3f147d0e7a3a9c10b79f8de00d884c031246013

/numan.py

9766bf3b178d29c35eff36c1a667da62788b652b

[]

no_license

numanrayhan/numan

4509e8e51c373cb9a5608250e3dbab4383fea3d5

6a485d14c52b9753d79aab284046d84431a81274

refs/heads/main

2023-05-07T23:13:13.209815

2021-05-31T18:12:17

372,589,801

0

null

UTF-8

Python

false

18

py

print("my output")

[ "noreply@github.com" ]

numanrayhan.noreply@github.com

dbabe6ca1208d9ea8425eff6d26c6d13353f0b27

9124f0bd5e9c20f7e4bc44cf5b588b5953df195e

/DoutuWeb.py

ac1bad8e8e238dc60328fcce77e3c0be86f29b31

[]

no_license

zhengquantao/crawl

d34d5704b0a1b772b97f63cc3074cdaf3bfdc1cc

dce7bfcf10c0080c2bc4d20465c5aba7687057ca

refs/heads/master

2020-04-10T09:26:15.600629

2018-12-08T12:19:39

160,936,237

1

0

null

UTF-8

Python

false

1,253

py

import requests import re import pymysql db=pymysql.connect(host='127.0.0.1',port=3306,db='pysql',user='root',passwd='12345678',charset='utf8') cursor=db.cursor() cursor.execute("select*from images") # print(cursor.fetchall()) def getHtml(): try: for n in range(125, 151): url = 'http://www.doutula.com/photo/list/?page='+str(n) r=requests.get(url) html=r.text # re=r'data-original="(.*?)".*?alt="(.*?)"' # reg=re.compile(re,re.S) # lists=re.findall(reg,html) geturl=re.findall(r'data-original="(.*?)"',html) getname=re.findall(r'alt="(.*?)"',html) # print(len(getname)) for i in range(len(geturl)): geturls=geturl[i] getnames=getname[i] # print(geturls) # cursor.execute("insert into images(~name~,~imageUrl~) values('{}','{}'".format(getnames,geturls)) cursor.execute("insert into images(name,imageUrl) values(%s,%s)",[getnames,geturls]) # print("正在保存%s"%getnames) print("{:.2f}%".format(i/68*100)) # 提交更新 db.commit() except: return "a" getHtml()

[ "1483906080@qq.com" ]

1483906080@qq.com

f9a929408b32170e178231ad8907c38aa8647599

9cef4ef20efd0eec18846242e78be0b9be144c30

/teacher_cade/day19/14.greenlet.py

e2d537f9ff67e3f0659a5afb381d8128caa9ab71

[]

no_license

Vaild/python-learn

4e6511a62a40b6104b081e0f8fe30f7d829901f5

5d602daf3b4b7e42349b7d9251df1f4dd62c299c

refs/heads/master

2022-11-19T00:47:48.808384

2020-07-20T14:27:49

279,044,379

0

null

UTF-8

Python

false

360

py

#!/usr/bin/python3 # coding=utf-8 from greenlet import greenlet import time def test1(): while True: print("---A--") gr2.switch() time.sleep(0.5) def test2(): while True: print("---B--") gr1.switch() time.sleep(0.5) gr1 = greenlet(test1) gr2 = greenlet(test2) # 切换到gr1中运行 gr1.switch()

[ "cubersongwenbo@gmail.com" ]

cubersongwenbo@gmail.com

47b74b1775ebe7c948754a92b962e1cee4c592e8

4d327de5447519d3c00e6572f74362380783006f

/source/res/scripts/client/gui/Scaleform/daapi/view/lobby/rankedBattles/RankedBattlesCalendarPopover.py

1597d3315544c1d1c8513bc4b036cfea883af256

[]

no_license

XFreyaX/WorldOfTanks-Decompiled

706ac55d919b766aa89f90c97a75672bf2142611

5025466edd0dd3e5e50a6c60feb02ae793f6adac

refs/heads/master

2021-09-21T15:10:32.655452

2018-08-28T07:34:00

null

0

null

UTF-8

Python

false

6,129

py

# Python bytecode 2.7 (decompiled from Python 2.7) # Embedded file name: scripts/client/gui/Scaleform/daapi/view/lobby/rankedBattles/RankedBattlesCalendarPopover.py from datetime import datetime import BigWorld from gui.Scaleform.locale.COMMON import COMMON from gui.Scaleform.daapi.settings.views import VIEW_ALIAS from gui.Scaleform.genConsts.TOOLTIPS_CONSTANTS import TOOLTIPS_CONSTANTS from gui.Scaleform.managers.UtilsManager import UtilsManager from gui.ranked_battles.ranked_models import CYCLE_STATUS from helpers import i18n, dependency from gui.Scaleform.daapi.view.meta.RankedBattlesCalendarPopoverMeta import RankedBattlesCalendarPopoverMeta from gui.Scaleform.locale.RANKED_BATTLES import RANKED_BATTLES from gui.shared.formatters import text_styles from helpers import time_utils from skeletons.gui.game_control import IRankedBattlesController from skeletons.connection_mgr import IConnectionManager ARROW_LEFT = 3 class RankedBattlesCalendarPopover(RankedBattlesCalendarPopoverMeta): rankedController = dependency.descriptor(IRankedBattlesController) connectionMgr = dependency.descriptor(IConnectionManager) arrowDirection = ARROW_LEFT def __init__(self, ctx=None): super(RankedBattlesCalendarPopover, self).__init__() self.__seasonInfo = self.rankedController.getCurrentSeason() self.__currentCycle = self.__seasonInfo.getNumber() self.__selectedDate = time_utils.getCurrentLocalServerTimestamp() self.__weekDays = self._createUtilsManager().getWeekDayNames(full=True, isLower=False, isUpper=False, useRegionSettings=False) data = ctx.get('data', None) if data is not None: self.arrowDirection = data.arrowDirection return def _createUtilsManager(self): return UtilsManager() def _populate(self): super(RankedBattlesCalendarPopover, self)._populate() self.as_setDataS({'rawDate': self.__selectedDate, 'arrowDirection': self.arrowDirection, 'statusText': self.__getCurrnetCycleString(), 'statusTooltip': TOOLTIPS_CONSTANTS.RANKED_CALENDAR_STEPS_INFO}) self.onDaySelect(time_utils.getCurrentTimestamp()) calendar = self.__getCalendar() if calendar is not None: calendar.as_setMinAvailableDateS(self.__seasonInfo.getStartDate()) calendar.as_setMaxAvailableDateS(self.__seasonInfo.getEndDate()) calendar.as_openMonthS(self.__selectedDate) calendar.as_selectDateS(self.__selectedDate) calendar.as_setHighlightedDaysS([self.__seasonInfo.getCycleStartDate(), self.__seasonInfo.getCycleEndDate()]) calendar.as_setDayTooltipTypeS(TOOLTIPS_CONSTANTS.RANKED_CALENDAR_DAY_INFO) return def onDaySelect(self, date): formattedDate = datetime.fromtimestamp(date) selectedDayOfWeek = self.__weekDays[formattedDate.weekday()] self.as_setDayDataS({'primeTimeGroupData': self.__constructPrimeTimes(date), 'dayText': text_styles.superPromoTitle(formattedDate.day), 'dayNameText': text_styles.middleTitle(selectedDayOfWeek)}) def __getCycleListString(self): key = RANKED_BATTLES.RANKEDBATTLEVIEW_STATUSBLOCK_CALENDARPOPOVER_CYCLEITEM cycles = self.__seasonInfo.getAllCycles() result = [] for cycle in sorted(cycles.values()): formatter = text_styles.main if cycle.status == CYCLE_STATUS.CURRENT else text_styles.standard startDate = time_utils.getTimeStructInLocal(cycle.startDate) endDate = time_utils.getTimeStructInLocal(cycle.endDate) result.append(formatter(i18n.makeString(key, cycleNumber=self.__currentCycle, day0='{:02d}'.format(startDate.tm_mday), month0='{:02d}'.format(startDate.tm_mon), day1='{:02d}'.format(endDate.tm_mday), month1='{:02d}'.format(endDate.tm_mon)))) def __constructPrimeTimes(self, selectedTime): items = [] serversPeriodsMapping = self.rankedController.getPrimeTimesForDay(selectedTime, groupIdentical=True) frmt = BigWorld.wg_getShortTimeFormat for serverName in sorted(serversPeriodsMapping.keys()): periodsStr = [] dayPeriods = serversPeriodsMapping[serverName] if dayPeriods: for periodStart, periodEnd in dayPeriods: periodsStr.append(i18n.makeString(RANKED_BATTLES.CALENDARDAY_TIME, start=frmt(periodStart), end=frmt(periodEnd))) else: periodsStr = i18n.makeString(COMMON.COMMON_DASH) if dayPeriods: items.append({'serverNameText': text_styles.highlightText(serverName), 'primeTimeText': '\n'.join(periodsStr)}) return items def __getCurrnetCycleString(self): key = RANKED_BATTLES.RANKEDBATTLEVIEW_STATUSBLOCK_CALENDARPOPOVER_CYCLEITEM cycles = self.__seasonInfo.getAllCycles() for cycle in sorted(cycles.values()): if cycle.status == CYCLE_STATUS.CURRENT: formatter = text_styles.main startDate = time_utils.getTimeStructInLocal(cycle.startDate) endDate = time_utils.getTimeStructInLocal(cycle.endDate) return formatter(i18n.makeString(key, cycleNumber=self.__currentCycle, day0='{:02d}'.format(startDate.tm_mday), month0='{:02d}'.format(startDate.tm_mon), day1='{:02d}'.format(endDate.tm_mday), month1='{:02d}'.format(endDate.tm_mon))) def __getAttentionText(self): key = RANKED_BATTLES.RANKEDBATTLEVIEW_STATUSBLOCK_CALENDARPOPOVER_ATTENTIONTEXT cycleNumber = self.__currentCycle timeDelta = time_utils.getTimeDeltaFromNow(self.__seasonInfo.getCycleEndDate()) endTimeStr = time_utils.getTillTimeString(timeDelta, RANKED_BATTLES.STATUS_TIMELEFT) if timeDelta <= time_utils.ONE_HOUR: formatter = text_styles.alert else: formatter = text_styles.neutral return formatter(i18n.makeString(key, cycleNumber=cycleNumber, timeLeft=endTimeStr)) def __getCalendar(self): return self.components.get(VIEW_ALIAS.CALENDAR)

[ "StranikS_Scan@mail.ru" ]

StranikS_Scan@mail.ru

b94655700bbb6e94cf77d31130c311a69755a8b1

982b49c38e9e4184ef4d7e4fbc45d97ac433738c

/FeatureExtraction/extractExpressionFeatures.py

94cf92f8946934e0d4ed4f2b5a9618342e21ebe9

[]

no_license

biswajitsc/LaSer

35fc236857615059678275a264954b61f0d3c1de

7484bc2a35dc07cd12eae5c1f1cbdb2088c582c7

refs/heads/master

2021-01-10T14:13:53.762777

2015-11-15T09:51:22

40,284,558

3

0

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py

# coding: utf-8 import re import sys import math from convertMathMLExpression import * def extract_MathMLUnigrams(mathML) : unigrams = set() for line in mathML : words = line.split(' ') for word in words : if (len(word) > 0) : unigrams.add(word) print "Unigrams of MathML equations Extracted" numDocs = len(mathML) idf_scores = {} unigrams_postinglist = {} for unigram in unigrams : unigrams_postinglist[unigram] = [] idf_scores[unigram] = 0 i = 0 for line in mathML : i += 1 for unigram in unigrams : string = str(unigram) if string in line : unigrams_postinglist[unigram].append((i, line.count(string))) idf_scores[unigram] += 1 print "Unigram Features Postings List of MathML equations created" return (unigrams, unigrams_postinglist, idf_scores) def main() : input_file = open(sys.argv[1],"r") output_file_unigrams = open("../../Data/UnigramFeatures","w") output_file_bigrams = open("../../Data/BigramFeatures","w") output_file_trigrams = open("../../Data/TrigramFeatures","w") output_file_expressions = open("../../Data/ExtractedExpressions","w") output_file_idfs = open("../../Data/IDF-Scores","w") data = input_file.read() # data = data.replace("\n"," ") # lines = data.split('<m:math') lines = data.split('\n') mathML = [] for line in lines : temp_line = line line = line.replace("<m:","<") line = line.replace("</m:","</") line = line.replace('\n', ' ') symbol = unicode(line, "utf-8") line = symbol.encode('ascii', 'backslashreplace') # if len(line) == 0 : # continue # line = '<math' + line line = line.replace('<?xml version="1.0"?>', "") mathML.append(line) # xmls = line.split('<?xml version="1.0"?>') # for xml in xmls : # xml = re.sub(' +',' ',xml) # xml = xml.replace('\t', ' ') # mathML.append(xml) (unigrams_mathML, unigrams_postinglist, idf_scores) = extract_MathMLUnigrams(mathML) expressions = convertEquation(mathML) print "Num Expressions : ", len(expressions) for expression in expressions : output_file_expressions.write(expression.encode('utf-8') + '\n') unigrams = set() bigrams = set() trigrams = set() for line in expressions : line = line.encode('utf-8') words = line.split(' ') for word in words : if (len(word) > 0) : unigrams.add(word) print "Unigrams of expressions Extracted" for line in expressions : line = line.encode('utf-8') words = line.split(' ') if len(words) >= 2 : i = 0 while (i < (len(words) - 1)) : if (len(words[i]) > 0 and len(words[i + 1]) > 0) : bigrams.add((words[i],words[i + 1])) i += 1 print "Bigrams of expressions Extracted" for line in expressions : line = line.encode('utf-8') words = line.split(' ') if len(words) > 2 : i = 0 while (i < (len(words) - 2)) : if (len(words[i]) > 0 and len(words[i + 1]) > 0 and len(words[i + 2]) > 0) : trigrams.add((words[i],words[i + 1],words[i + 2])) i += 1 print "Trigrams of expressions Extracted" print "Unigrams in MathML : ", len(unigrams_mathML), ", Unigrams in Expression : ", len(unigrams), ", Bigrams in Expression : ", len(bigrams), ", Trigrams in Expression : ", len(trigrams) numDocs = len(mathML) for unigram in unigrams : unigrams_postinglist[unigram] = [] idf_scores[unigram] = 0 bigrams_postinglist = {} for bigram in bigrams : bigrams_postinglist[bigram] = [] idf_scores[bigram] = 0 trigrams_postinglist = {} for trigram in trigrams : trigrams_postinglist[trigram] = [] idf_scores[trigram] = 0 i = 0 for line in expressions : line = line.encode('utf-8') i += 1 for unigram in unigrams : string = str(unigram) if string in line : unigrams_postinglist[unigram].append((i, line.count(string))) idf_scores[unigram] += 1 print "Unigram Features Postings List created" i = 0 for line in expressions : line = line.encode('utf-8') i += 1 if (i % 100 == 0) : print str(i) + "th xml checked for bigrams" for bigram in bigrams : string = (str(bigram[0]) + ' ' + str(bigram[1])) if string in line : bigrams_postinglist[bigram].append((i, line.count(string))) idf_scores[bigram] += 1 print "Bigram Features Postings List created" i = 0 for line in expressions : line = line.encode('utf-8') i += 1 if (i % 100 == 0) : print str(i) + "th xml checked for trigrams" for trigram in trigrams : string = (str(trigram[0]) + ' ' + str(trigram[1]) + ' ' + str(trigram[2])) if string in line : trigrams_postinglist[trigram].append((i, line.count(string))) idf_scores[trigram] += 1 print "Trigram Features Postings List created" i = 0 for unigram in unigrams_postinglist.keys() : if len(unigrams_postinglist[unigram]) <= 5 : unigrams_postinglist.pop(unigram, None) i += 1 print i, " rare Unigram features removed" i = 0 for bigram in bigrams_postinglist.keys() : if len(bigrams_postinglist[bigram]) <= 5 : bigrams_postinglist.pop(bigram, None) i += 1 print i, " rare Bigram features removed" i = 0 for trigram in trigrams_postinglist.keys() : if len(trigrams_postinglist[trigram]) <= 5 : trigrams_postinglist.pop(trigram, None) i += 1 print i, " rare Trigram features removed" output_file_unigrams.write(str(unigrams_postinglist)) output_file_bigrams.write(str(bigrams_postinglist)) output_file_trigrams.write(str(trigrams_postinglist)) for features in idf_scores : idf_scores[features] = (1 + math.log(numDocs/idf_scores[features])) #check error output_file_idfs.write(str(idf_scores)) # i = 0 # weight_matrix = [] # for line in mathML : # values = {} # i += 1 # if (i % 100 == 0) : # print str(i) + "th xml's weights written" # for unigram in unigrams : # for doc_id_weight_pair in unigrams_postinglist[unigram] : # if doc_id_weight_pair[0] == i : # values[unigram] = (idf_scores[unigram] * (1 + math.log(doc_id_weight_pair[1]))) # else : # values[unigram] = idf_scores[unigram] # for bigram in bigrams : # for doc_id_weight_pair in bigrams_postinglist[bigram] : # if doc_id_weight_pair[0] == i : # values[bigram] = (idf_scores[bigram] * (1 + math.log(doc_id_weight_pair[1]))) # else : # values[bigram] = idf_scores[bigram] # for trigram in trigrams : # for doc_id_weight_pair in trigrams_postinglist[trigram] : # if doc_id_weight_pair[0] == i : # values[trigram] = (idf_scores[trigram] * (1 + math.log(doc_id_weight_pair[1]))) # else : # values[trigram] = idf_scores[trigram] # weight_matrix.append(values) # # output_file_weights.write(str(values) + '\n') # return weight_matrix if __name__ == "__main__" : main()

[ "agnivo.saha@gmail.com" ]

agnivo.saha@gmail.com

dc7ba5b213781e5b0e36e68d28b93bc27e52c663

d9503a748d51d6dbef6a76513382e19ad1c3107f

/第８日目/quantumWell_withBarrier_StarkEffect.py

df70897cc286330e945298578d845a0e9d51b218

[]

no_license

quantumshiro/quantumcompute_python

0446fbea0a79b7fba1b26b811763d776fcf8ccdb

1092ceeea2737ddc6ad325ac603ae7618b13b12b

refs/heads/main

2023-02-10T05:41:29.803979

2021-01-05T12:17:07

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############################################################################ #　ポテンシャル障壁ありの無限に深い量子井戸に静電場を加えた電子状態 ############################################################################ import math import cmath import matplotlib.pyplot as plt import matplotlib.animation as animation import scipy.integrate as integrate import numpy as np import numpy.linalg as LA #全体設定 plt.rcParams['font.family'] = 'Times New Roman' #フォント plt.rcParams['font.size'] = 12 #フォントサイズ #複素数 I = 0.0 + 1.0j ###################################### #　物理定数 ###################################### #プランク定数 h = 6.6260896 * 10**-34 hbar = h / (2.0 * math.pi) #電子の質量 me = 9.10938215 * 10**-31 #電子ボルト eV = 1.60217733 * 10**-19 #電気素量 e = 1.60217733 * 10**-19 ###################################### #　物理系の設定 ###################################### #量子井戸の幅 L = 1.0 * 10**-9 #計算区間 x_min = -L / 2.0 x_max = L / 2.0 #状態数 n_max = 30 #行列の要素数 DIM = n_max + 1 #空間分割数 NX = 500 #空間刻み間隔 dx = 1.0 * 10**-9 #壁の厚さ W = L / 5 #壁の高さの最大値 V_max = 30.0 * eV #電場の強さ Ex_max = 1.0 * 10**8 #電場強度の分割数 NEx = 10 #基底状態と励起状態 N = 2 #固有関数 def varphi(n, x): kn = math.pi * (n + 1) / L return math.sqrt(2.0 / L) * math.sin(kn * (x + L / 2.0)) #ポテンシャル項 def V(x, Ex): if(abs(x) <= W / 2.0): return e * Ex * x + V_max else: return e * Ex * x #固有エネルギー def Energy(n): kn = math.pi * (n + 1) / L return hbar * hbar * kn * kn / (2.0 * me) #被積分関数（行列要素計算用） def integral_matrixElement(x, n1, n2, Ex): return varphi(n1 ,x) * V(x, Ex) * varphi(n2, x) / eV #被積分関数（平均計算用） def average_x(x, a): sum = 0 for n in range(n_max + 1): sum += a[n] * varphi(n, x) return x * sum**2 #固有値・固有ベクトルの初期化 eigenvalues = [0] * (NEx + 1) vectors = [0] * (NEx + 1) for nEx in range(NEx + 1): eigenvalues[nEx] = [] vectors[nEx] = [] #存在確率分布グラフ描画用の配列初期化 xs = [] phi = [0] * (NEx + 1) for nEx in range(NEx + 1): phi[nEx] = [0] * N for n in range( len(phi[nEx]) ): phi[nEx][n] = [0] * (NX + 1) #中心の電場依存性グラフ描画用の配列初期化 averageX = [0] * N for n in range(len(averageX)): averageX[n] = [0] * (NEx + 1) #静電場強度ごとに for nEx in range(NEx + 1): print("電場強度：" + str( nEx * 100 / NEx ) + "%") #静電場の強度を設定 Ex = Ex_max / NEx * nEx #エルミート行列（リスト） matrix = [] ###行列要素の計算 for n1 in range(n_max + 1): col=[] for n2 in range(n_max + 1): #ガウス・ルジャンドル積分 result = integrate.quad( integral_matrixElement, #被積分関数 x_min, x_max, #積分区間の下端と上端 args=(n1, n2, Ex) #被積分関数へ渡す引数 ) real = result[0] imag = 0j #無静電場のエネルギー固有値（対角成分） En = Energy(n1)/eV if (n1 == n2) else 0 #行の要素を追加 col.append( En + real ) #行を追加 matrix.append( col ) #リスト → 行列 matrix = np.array( matrix ) ###固有値と固有ベクトルの計算 result = LA.eig( matrix ) eig = result[0] #固有値 vec = result[1] #固有ベクトル #小さい順に並べるためのインデックス（配列） index = np.argsort( eig ) #固有値を小さい順に並び替え eigenvalues[nEx] = eig[ index ] #転置行列 vec = vec.T #固有ベクトルの並び替え vectors[nEx] = vec[ index ] ### 検算：MA-EA=0 ? sum = 0 for i in range(DIM): v = matrix @ vectors[nEx][i] - eigenvalues[nEx][i] * vectors[nEx][i] for j in range(DIM): sum += abs(v[j])**2 print("|MA-EA| =" + str(sum)) ###固有関数の空間分布 for nx in range(NX+1): x = x_min + (x_max - x_min) / NX * nx if(nEx == 0): xs.append( x/dx ) for n in range( len(phi[nEx]) ): for m in range(n_max+1): phi[nEx][n][nx] += vectors[nEx][n][m] * varphi(m, x) #描画用データの整形 phi[nEx][n][nx] = abs(phi[nEx][n][nx])**2 / (1.0 * 10**9) for n in range(len(averageX)): #ガウス・ルジャンドル積分 result = integrate.quad( average_x, #被積分関数 x_min, x_max, #積分区間の下端と上端 args=(vectors[nEx][n]) #被積分関数へ渡す引数 ) #計算結果の取得 averageX[n][nEx] = result[0] * (1.0 * 10**9) #グラフの描画（エネルギー固有値） fig1 = plt.figure(figsize=(10, 6)) plt.title("Energy at Electric field strength") plt.xlabel("Electric field strength[V/m]") plt.ylabel("Energy[eV]") #描画範囲を設定 plt.xlim([0, 10]) #x軸 exs = range( NEx + 1) #y軸 En_0 = [] En_1 = [] for nEx in range(NEx + 1): En_0.append( eigenvalues[nEx][0] ) En_1.append( eigenvalues[nEx][1] ) #print( str(nV) + " " + str( eigenvalues[nV][0] ) + " " + str( eigenvalues[nV][1] )) #基底状態と第１励起状態のグラフを描画 plt.plot(exs, En_0, marker="o", linewidth = 3) plt.plot(exs, En_1, marker="o", linewidth = 3) #グラフの描画（基底状態） fig2 = plt.figure(figsize=(10, 6)) plt.title("Existence probability at Position (n=0)") plt.xlabel("Position[nm]") plt.ylabel("|phi|^2") #描画範囲を設定 plt.xlim([-0.5, 0.5]) plt.ylim([0, 5.0]) #各 for nEx in range(NEx + 1): plt.plot(xs, phi[nEx][0] , linewidth = 3) #グラフの描画（第１励起状態） fig3 = plt.figure(figsize=(10, 6)) plt.title("Existence probability at Position (n=1)") plt.xlabel("Position[nm]") plt.ylabel("|phi|^2") #描画範囲を設定 plt.xlim([-0.5, 0.5]) plt.ylim([0, 5.0]) for nEx in range(NEx + 1): plt.plot(xs, phi[nEx][1] , linewidth = 3) ''' #グラフの描画（期待値） fig4 = plt.figure(figsize=(10, 6)) plt.title("Position at Electric field strength") plt.xlabel("Electric field strength[V/m]") plt.ylabel("Position[nm]") #描画範囲を設定 plt.xlim([0, 10]) #x軸 exs = range( NV + 1) plt.plot(exs, averageX[0], marker="o", linewidth = 3) plt.plot(exs, averageX[1], marker="o", linewidth = 3) #グラフの表示 ''' plt.show()

[ "noreply@github.com" ]

quantumshiro.noreply@github.com

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/Python Programs/pallindromic.py

ce3d2d5e4bc575bd8d2037aba48d7f1ab95407cc

[]

no_license

chetan-mali/Python-Traning

be2274d6ed1e191194aaa12e65c35868eeaab33e

88028bd17b68f1ea1253477b8deda9ece921e4b0

refs/heads/master

2020-04-14T20:43:48.307381

2019-01-05T21:11:05

null

0

null

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py

def check(n): temp=n rev=0 while(n>0): dig=n%10 rev=rev*10+dig n=n//10 if temp==rev: return True flag=0 list1 = [12,61,12,12,14] for i in list1: if i<0: print("False") exit() for i in list1: if check(i)== True: flag=1 if flag==1: print("True") else: print("False")

[ "chetanrox520@gmail.com" ]

chetanrox520@gmail.com

a60ad6b1c07224bf9f068c2ff88e18b2145189e0

fa92165be94abcf7add9e4f57d3b444587ee986d

/app.py

1e33022cce704a8a4f06e3ae4fa00755f40cb9ef

[]

no_license

Diane10/dianefinal

2977ce46b136e377805276acb920ebf827e09f5f

1efa8483dacb4810561920fd8b8915e24a1105f6

refs/heads/main

2023-01-24T03:13:11.100006

2020-11-29T14:35:20

317,114,593

0

null

2020-11-30T04:54:20

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import os import streamlit as st import matplotlib.pyplot as plt import matplotlib matplotlib.use('Agg') import seaborn as sns import streamlit as st from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier from sklearn.ensemble import VotingClassifier from sklearn.ensemble import ExtraTreesClassifier from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Flatten from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score,plot_confusion_matrix,plot_roc_curve,precision_score,recall_score,precision_recall_curve,roc_auc_score,auc from sklearn.preprocessing import LabelEncoder from sklearn.linear_model import LogisticRegression import matplotlib.pyplot as plt import pickle from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold from sklearn.model_selection import LeaveOneOut from sklearn.preprocessing import StandardScaler try: from enum import Enum from io import BytesIO, StringIO from typing import Union import pandas as pd import streamlit as st except Exception as e: print(e) import streamlit.components.v1 as stc """ Common ML Dataset Explorer """ st.title("Machine Learning Tutorial App") st.subheader("Explorer with Streamlit") html_temp = """ <div style="background-color:#000080;"><p style="color:white;font-size:50px;padding:10px">ML is Awesome</p></div> """ st.markdown(html_temp,unsafe_allow_html=True) st.set_option('deprecation.showfileUploaderEncoding', False) st.subheader("Dataset") datasetchoice = st.radio("Do you what to use your own dataset?", ("Yes", "No")) if datasetchoice=='No': def file_selector(folder_path='./datasets'): filenames = os.listdir(folder_path) selected_filename = st.selectbox("Select A file",filenames) return os.path.join(folder_path,selected_filename) filename = file_selector() st.info("You Selected {}".format(filename)) # Read Data df = pd.read_csv(filename) # Show Dataset if st.checkbox("Show Dataset"): st.dataframe(df) # Show Columns if st.button("Column Names"): st.write(df.columns) # Show Shape if st.checkbox("Shape of Dataset"): data_dim = st.radio("Show Dimensions By ",("Rows","Columns")) if data_dim == 'Rows': st.text("Number of Rows") st.write(df.shape[0]) elif data_dim == 'Columns': st.text("Number of Columns") st.write(df.shape[1]) else: st.write(df.shape) # Select Columns if st.checkbox("Select Columns To Show"): all_columns = df.columns.tolist() selected_columns = st.multiselect("Select",all_columns) new_df = df[selected_columns] st.dataframe(new_df) # Show Values if st.button("Value Counts"): st.text("Value Counts By Target/Class") st.write(df.iloc[:,-1].value_counts()) # Show Datatypes if st.button("Data Types"): st.write(df.dtypes) # Show Summary if st.checkbox("Summary"): st.write(df.describe().T) ## Plot and Visualization st.subheader("Data Visualization") # Correlation # Seaborn Plot if st.checkbox("Correlation Plot[Seaborn]"): st.write(sns.heatmap(df.corr(),annot=True)) st.pyplot() # Pie C if st.checkbox("Pie Plot"): all_columns_names = df.columns.tolist() if st.button("Generate Pie Plot"): st.success("Generating A Pie Plot") st.write(df.iloc[:,-1].value_counts().plot.pie(autopct="%1.1f%%")) st.pyplot() # Count Plot if st.checkbox("Plot of Value Counts"): st.text("Value Counts By Target") all_columns_names = df.columns.tolist() primary_col = st.selectbox("Primary Columm to GroupBy",all_columns_names) selected_columns_names = st.multiselect("Select Columns",all_columns_names) if st.button("Plot"): st.text("Generate Plot") if selected_columns_names: vc_plot = df.groupby(primary_col)[selected_columns_names].count() else: vc_plot = df.iloc[:,-1].value_counts() st.write(vc_plot.plot(kind="bar")) st.pyplot() # Customizable Plot st.subheader("Customizable Plot") all_columns_names = df.columns.tolist() type_of_plot = st.selectbox("Select Type of Plot",["area","bar","line","hist","box","kde"]) selected_columns_names = st.multiselect("Select Columns To Plot",all_columns_names) if st.button("Generate Plot"): st.success("Generating Customizable Plot of {} for {}".format(type_of_plot,selected_columns_names)) # Plot By Streamlit if type_of_plot == 'area': cust_data = df[selected_columns_names] st.area_chart(cust_data) elif type_of_plot == 'bar': cust_data = df[selected_columns_names] st.bar_chart(cust_data) elif type_of_plot == 'line': cust_data = df[selected_columns_names] st.line_chart(cust_data) # Custom Plot elif type_of_plot: cust_plot= df[selected_columns_names].plot(kind=type_of_plot) st.write(cust_plot) st.pyplot() if st.button("End of Data Exploration"): st.balloons() st.sidebar.subheader('Choose Classifer') classifier_name = st.sidebar.selectbox( 'Choose classifier', ('KNN', 'SVM', 'Random Forest','Logistic Regression','gradientBoosting','Deep Learning','ADABoost','Unsupervised Learning(K-MEANS)') ) label= LabelEncoder() for col in df.columns: df[col]=label.fit_transform(df[col]) if classifier_name == 'Unsupervised Learning': st.sidebar.subheader('Model Hyperparmeter') n_clusters= st.sidebar.number_input("number of clusters",2,10,step=1,key='clusters') if st.sidebar.button("classify",key='classify'): sc = StandardScaler() X_transformed = sc.fit_transform(df) pca = PCA(n_components=2).fit_transform(X_transformed) # calculation Cov matrix is embeded in PCA kmeans = KMeans(n_clusters) kmeans.fit(pca) st.set_option('deprecation.showPyplotGlobalUse', False) # plt.figure(figsize=(12,10)) plt.scatter(pca[:,0],pca[:,1], c=kmeans.labels_, cmap='rainbow') plt.title('CLustering Projection'); st.pyplot() Y = df.target X = df.drop(columns=['target']) X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.33, random_state=8) from sklearn.preprocessing import StandardScaler sl=StandardScaler() X_trained= sl.fit_transform(X_train) X_tested= sl.fit_transform(X_test) class_name=['yes','no'] st.sidebar.subheader('Advanced Model Hyperparmeter') model_optimizer = st.sidebar.selectbox( 'Choose Optimizer', ('Cross Validation', 'Voting')) if model_optimizer == 'Cross Validation': cv= st.sidebar.radio("cv",("Kfold","LeaveOneOut"),key='cv') n_splits= st.sidebar.slider("maximum number of splits",1,30,key='n_splits') if st.sidebar.button("optimize",key='opt'): if cv=='Kfold': kfold= KFold(n_splits=n_splits) score = cross_val_score(SVC(),X,Y,cv=kfold) st.write("Accuracy:",score.mean()) if cv=='LeaveOneOut': loo = LeaveOneOut() score = cross_val_score(SVC(),X,Y,cv=loo) st.write("Accuracy:",score.mean()) if model_optimizer == 'Voting': voting= st.sidebar.multiselect("What is the algorithms you want to use?",('LogisticRegression','DecisionTreeClassifier','SVC','KNeighborsClassifier','GaussianNB','LinearDiscriminantAnalysis','AdaBoostClassifier','GradientBoostingClassifier','ExtraTreesClassifier')) estimator=[] if 'LogisticRegression' in voting: model1=LogisticRegression() estimator.append(model1) if 'DecisionTreeClassifier' in voting: model2=DecisionTreeClassifier() estimator.append(model2) if 'SVC' in voting: model3=SVC() estimator.append(model3) if 'KNeighborsClassifier' in voting: model4=KNeighborsClassifier() estimator.append(model4) if st.sidebar.button("optimize",key='opt'): ensemble = VotingClassifier(estimator) results = cross_val_score(ensemble, X, Y) st.write(results.mean()) if classifier_name == 'Deep Learning': if st.sidebar.button("classify",key='classify'): model = Sequential() model.add(Flatten()) model.add(Dense(units=25,activation='relu')) model.add(Dense(units=15,activation='softmax')) model.compile(loss='sparse_categorical_crossentropy',optimizer='rmsprop', metrics=['accuracy']) model.fit(X_train, y_train, epochs=10) test_loss, test_acc =model.evaluate(X_test, y_test, verbose=2) st.write('Model accuracy: ',test_acc*100) if classifier_name == 'SVM': st.sidebar.subheader('Model Hyperparmeter') c= st.sidebar.number_input("c(Reguralization)",0.01,10.0,step=0.01,key='c') kernel= st.sidebar.radio("kernel",("linear","rbf"),key='kernel') gamma= st.sidebar.radio("gamma(kernel coefficiency",("scale","auto"),key='gamma') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("SVM result") svcclassifier= SVC(C=c,kernel=kernel,gamma=gamma) svcclassifier.fit(X_trained,y_train) y_pred= svcclassifier.predict(X_tested) acc= accuracy_score(y_test,y_pred) st.write("Accuracy:",acc.round(2)) # st.write("precision_score:",precision_score(y_test,y_pred,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_pred,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(svcclassifier,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(svcclassifier,X_tested,y_test,normalize=False) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(svcclassifier,X_tested,y_test,normalize=False) st.pyplot() if classifier_name == 'Logistic Regression': st.sidebar.subheader('Model Hyperparmeter') c= st.sidebar.number_input("c(Reguralization)",0.01,10.0,step=0.01,key='Logistic') max_iter= st.sidebar.slider("maximum number of iteration",100,500,key='max_item') metrics= st.sidebar.multiselect("Wht is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("Logistic Regression result") Regression= LogisticRegression(C=c,max_iter=max_iter) Regression.fit(X_trained,y_train) y_prediction= Regression.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(Regression,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(Regression,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(Regression,X_tested,y_test) st.pyplot() if classifier_name == 'Random Forest': st.sidebar.subheader('Model Hyperparmeter') n_estimators= st.sidebar.number_input("Number of trees in the forest",100,5000,step=10,key='estimators') max_depth= st.sidebar.number_input("maximum depth of tree",1,20,step=1,key='max_depth') bootstrap= st.sidebar.radio("Boostrap sample when building trees",("True","False"),key='boostrap') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("Random Forest result") model= RandomForestClassifier(n_estimators=n_estimators,max_depth=max_depth,bootstrap=bootstrap) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if classifier_name == 'KNN': st.sidebar.subheader('Model Hyperparmeter') n_neighbors= st.sidebar.number_input("Number of n_neighbors",5,30,step=1,key='neighbors') leaf_size= st.sidebar.slider("leaf size",30,200,key='leaf') weights= st.sidebar.radio("weight function used in prediction",("uniform","distance"),key='weight') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("KNN result") model= KNeighborsClassifier(n_neighbors=n_neighbors,leaf_size=leaf_size,weights=weights) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if classifier_name == 'ADABoost': st.sidebar.subheader('Model Hyperparmeter') n_estimators= st.sidebar.number_input("Number of trees in the forest",100,5000,step=10,key='XGBestimators') seed= st.sidebar.number_input("learning rate",1,150,step=1,key='seed') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("ADABoost result") model=AdaBoostClassifier(n_estimators=n_estimators,learning_rate=seed) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if classifier_name == 'gradientBoosting': st.sidebar.subheader('Model Hyperparmeter') n_estimators= st.sidebar.number_input("Number of trees in the forest",100,5000,step=10,key='XGBestimators') seed= st.sidebar.number_input("learning rate",1,150,step=1,key='seed') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("gradientBoosting result") model=GradientBoostingClassifier(n_estimators=n_estimators,learning_rate=seed) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() elif datasetchoice == 'Yes': data_file = st.file_uploader("Upload CSV",type=['csv']) # if st.button("Process"): # if data_file is not None: # file_details = {"Filename":data_file.name,"FileType":data_file.type,"FileSize":data_file.size} # st.write(file_details) # df = pd.read_csv(data_file) # st.dataframe(df) st.write("Note:if you want to do classification make sure you have target attributes") def file_selector(dataset): if dataset is not None: file_details = {"Filename":dataset.name,"FileType":dataset.type,"FileSize":dataset.size} st.write(file_details) df = pd.read_csv(dataset) return df df = file_selector(data_file) st.dataframe(df) # def file_selector(folder_path='./datasets'): # filenames = os.listdir(folder_path) # selected_filename = st.selectbox("Select A file",filenames) # return os.path.join(folder_path,selected_filename) # filename = file_selector() # st.info("You Selected {}".format(filename)) # # Read Data # df = pd.read_csv(filename) # # Show Dataset if st.checkbox("Show Dataset"): st.dataframe(df) # Show Columns if st.button("Column Names"): st.write(df.columns) # Show Shape if st.checkbox("Shape of Dataset"): data_dim = st.radio("Show Dimensions By ",("Rows","Columns")) if data_dim == 'Rows': st.text("Number of Rows") st.write(df.shape[0]) elif data_dim == 'Columns': st.text("Number of Columns") st.write(df.shape[1]) else: st.write(df.shape) # Select Columns if st.checkbox("Select Columns To Show"): all_columns = df.columns.tolist() selected_columns = st.multiselect("Select",all_columns) new_df = df[selected_columns] st.dataframe(new_df) # Show Values if st.button("Value Counts"): st.text("Value Counts By Target/Class") st.write(df.iloc[:,-1].value_counts()) # Show Datatypes if st.button("Data Types"): st.write(df.dtypes) # Show Summary if st.checkbox("Summary"): st.write(df.describe().T) ## Plot and Visualization st.subheader("Data Visualization") # Correlation # Seaborn Plot if st.checkbox("Correlation Plot[Seaborn]"): st.write(sns.heatmap(df.corr(),annot=True)) st.pyplot() # Pie Chart if st.checkbox("Pie Plot"): all_columns_names = df.columns.tolist() if st.button("Generate Pie Plot"): st.success("Generating A Pie Plot") st.write(df.iloc[:,-1].value_counts().plot.pie(autopct="%1.1f%%")) st.pyplot() # Count Plot if st.checkbox("Plot of Value Counts"): st.text("Value Counts By Target") all_columns_names = df.columns.tolist() primary_col = st.selectbox("Primary Columm to GroupBy",all_columns_names) selected_columns_names = st.multiselect("Select Columns",all_columns_names) if st.button("Plot"): st.text("Generate Plot") if selected_columns_names: vc_plot = df.groupby(primary_col)[selected_columns_names].count() else: vc_plot = df.iloc[:,-1].value_counts() st.write(vc_plot.plot(kind="bar")) st.pyplot() # Customizable Plot st.subheader("Customizable Plot") all_columns_names = df.columns.tolist() type_of_plot = st.selectbox("Select Type of Plot",["area","bar","line","hist","box","kde"]) selected_columns_names = st.multiselect("Select Columns To Plot",all_columns_names) if st.button("Generate Plot"): st.success("Generating Customizable Plot of {} for {}".format(type_of_plot,selected_columns_names)) # Plot By Streamlit if type_of_plot == 'area': cust_data = df[selected_columns_names] st.area_chart(cust_data) elif type_of_plot == 'bar': cust_data = df[selected_columns_names] st.bar_chart(cust_data) elif type_of_plot == 'line': cust_data = df[selected_columns_names] st.line_chart(cust_data) # Custom Plot elif type_of_plot: cust_plot= df[selected_columns_names].plot(kind=type_of_plot) st.write(cust_plot) st.pyplot() if st.button("End of Data Exploration"): st.balloons() st.sidebar.subheader('Choose Classifer') classifier_name = st.sidebar.selectbox( 'Choose classifier', ('KNN', 'SVM', 'Random Forest','Logistic Regression','XGBOOST','Unsupervised Learning') ) label= LabelEncoder() for col in df.columns: df[col]=label.fit_transform(df[col]) if classifier_name == 'Unsupervised Learning': st.sidebar.subheader('Model Hyperparmeter') n_clusters= st.sidebar.number_input("number of clusters",2,10,step=1,key='clusters') if st.sidebar.button("classify",key='classify'): sc = StandardScaler() X_transformed = sc.fit_transform(df) pca = PCA(n_components=2).fit_transform(X_transformed) # calculation Cov matrix is embeded in PCA kmeans = KMeans(n_clusters) kmeans.fit(pca) st.set_option('deprecation.showPyplotGlobalUse', False) # plt.figure(figsize=(12,10)) plt.scatter(pca[:,0],pca[:,1], c=kmeans.labels_, cmap='rainbow') plt.title('CLustering Projection'); st.pyplot() Y = df.target X = df.drop(columns=['target']) X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.33, random_state=8) from sklearn.preprocessing import StandardScaler sl=StandardScaler() X_trained= sl.fit_transform(X_train) X_tested= sl.fit_transform(X_test) class_name=['yes','no'] if classifier_name == 'SVM': st.sidebar.subheader('Model Hyperparmeter') c= st.sidebar.number_input("c(Reguralization)",0.01,10.0,step=0.01,key='c') kernel= st.sidebar.radio("kernel",("linear","rbf"),key='kernel') gamma= st.sidebar.radio("gamma(kernel coefficiency",("scale","auto"),key='gamma') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("SVM result") svcclassifier= SVC(C=c,kernel=kernel,gamma=gamma) svcclassifier.fit(X_trained,y_train) y_pred= svcclassifier.predict(X_tested) acc= accuracy_score(y_test,y_pred) st.write("Accuracy:",acc.round(2)) # st.write("precision_score:",precision_score(y_test,y_pred,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_pred,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(svcclassifier,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(svcclassifier,X_tested,y_test,normalize=False) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(svcclassifier,X_tested,y_test,normalize=False) st.pyplot() if classifier_name == 'Logistic Regression': st.sidebar.subheader('Model Hyperparmeter') c= st.sidebar.number_input("c(Reguralization)",0.01,10.0,step=0.01,key='Logistic') max_iter= st.sidebar.slider("maximum number of iteration",100,500,key='max_item') metrics= st.sidebar.multiselect("Wht is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("Logistic Regression result") Regression= LogisticRegression(C=c,max_iter=max_iter) Regression.fit(X_trained,y_train) y_prediction= Regression.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(Regression,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(Regression,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(Regression,X_tested,y_test) st.pyplot() if classifier_name == 'Random Forest': st.sidebar.subheader('Model Hyperparmeter') n_estimators= st.sidebar.number_input("Number of trees in the forest",100,5000,step=10,key='estimators') max_depth= st.sidebar.number_input("maximum depth of tree",1,20,step=1,key='max_depth') bootstrap= st.sidebar.radio("Boostrap sample when building trees",("True","False"),key='boostrap') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("Random Forest result") model= RandomForestClassifier(n_estimators=n_estimators,max_depth=max_depth,bootstrap=bootstrap) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if classifier_name == 'KNN': st.sidebar.subheader('Model Hyperparmeter') n_neighbors= st.sidebar.number_input("Number of n_neighbors",5,30,step=1,key='neighbors') leaf_size= st.sidebar.slider("leaf size",30,200,key='leaf') weights= st.sidebar.radio("weight function used in prediction",("uniform","distance"),key='weight') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("KNN result") model= KNeighborsClassifier(n_neighbors=n_neighbors,leaf_size=leaf_size,weights=weights) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if classifier_name == 'XGBOOST': st.sidebar.subheader('Model Hyperparmeter') n_estimators= st.sidebar.number_input("Number of trees in the forest",100,5000,step=10,key='XGBestimators') seed= st.sidebar.number_input("number of the seed",1,150,step=1,key='seed') metrics= st.sidebar.multiselect("What is the metrics to plot?",('confusion matrix','roc_curve','precision_recall_curve')) if st.sidebar.button("classify",key='classify'): st.subheader("XGBOOST result") model= xgb.XGBClassifier(n_estimators=n_estimators,seed=seed) model.fit(X_trained,y_train) y_prediction= model.predict(X_tested) acc= accuracy_score(y_test,y_prediction) st.write("Accuracy:",acc.round(2)) st.write("precision_score:",precision_score(y_test,y_prediction,average='micro').round(2)) st.write("recall_score:",recall_score(y_test,y_prediction,average='micro').round(2)) st.write("ROC_AUC_score:",roc_auc_score(y_test,y_prediction,average='micro').round(2)) if 'confusion matrix' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('confusion matrix') plot_confusion_matrix(model,X_tested,y_test) st.pyplot() if 'roc_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('plot_roc_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() if 'precision_recall_curve' in metrics: st.set_option('deprecation.showPyplotGlobalUse', False) st.subheader('precision_recall_curve') plot_roc_curve(model,X_tested,y_test) st.pyplot() # import os # import streamlit as st # # EDA Pkgs # import pandas as pd # # Viz Pkgs # import matplotlib.pyplot as plt # import matplotlib # import io # matplotlib.use('Agg') # import seaborn as sns # def main(): # """ Common ML Dataset Explorer """ # st.title("Machine Learning Tutorial") # st.subheader("Datasets For ML Explorer with Streamlit") # # file = st.file_uploader("please Upload your dataset",type=['.csv']) # st.set_option('deprecation.showfileUploaderEncoding', False) # html_temp = """ # <div style="background-color:tomato;"><p style="color:white;font-size:50px;padding:10px">Streamlit is Awesome</p></div> # """ # import csv # st.markdown(html_temp,unsafe_allow_html=True) # file_buffer = st.file_uploader("Choose a CSV Log File...", type="csv", encoding = None) # dataset = pd.read_csv(file_buffer) # with open(file_buffer,'r') as csv_file: #Opens the file in read mode # csv_reader = csv.reader(csv_file) # if dataset is not None: # df = open(dataset) # st.write(df) # # Show Columns # if st.checkbox("Show Dataset"): # number = st.number_input("Number of Rows to View") # st.dataframe(df.head(number)) # if st.button("Column Names"): # st.write(df.columns) # # Show Shape # if st.checkbox("Shape of Dataset"): # data_dim = st.radio("Show Dimension By ",("Rows","Columns")) # if data_dim == 'Rows': # st.text("Number of Rows") # st.write(df.shape[0]) # elif data_dim == 'Columns': # st.text("Number of Columns") # st.write(df.shape[1]) # else: # st.write(df.shape) # # Select Columns # if st.checkbox("Select Columns To Show"): # all_columns = df.columns.tolist() # selected_columns = st.multiselect("Select",all_columns) # new_df = df[selected_columns] # st.dataframe(new_df) # # Show Values # if st.button("Value Counts"): # st.text("Value Counts By Target/Class") # st.write(df.iloc[:,-1].value_counts()) # # Show Datatypes # if st.button("Data Types"): # st.write(df.dtypes) # # Show Summary # if st.checkbox("Summary"): # st.write(df.describe().T) # ## Plot and Visualization # st.subheader("Data Visualization") # # Correlation # # Seaborn Plot # if st.checkbox("Correlation Plot[Seaborn]"): # st.write(sns.heatmap(df.corr(),annot=True)) # st.pyplot() # # Pie Chart # if st.checkbox("Pie Plot"): # all_columns_names = df.columns.tolist() # if st.button("Generate Pie Plot"): # st.success("Generating A Pie Plot") # st.write(df.iloc[:,-1].value_counts().plot.pie(autopct="%1.1f%%")) # st.pyplot() # # Count Plot # if st.checkbox("Plot of Value Counts"): # st.text("Value Counts By Target") # all_columns_names = df.columns.tolist() # primary_col = st.selectbox("Primary Columm to GroupBy",all_columns_names) # selected_columns_names = st.multiselect("Select Columns",all_columns_names) # if st.button("Plot"): # st.text("Generate Plot") # if selected_columns_names: # vc_plot = df.groupby(primary_col)[selected_columns_names].count() # else: # vc_plot = df.iloc[:,-1].value_counts() # st.write(vc_plot.plot(kind="bar")) # st.pyplot() # # Customizable Plot # st.subheader("Customizable Plot") # all_columns_names = df.columns.tolist() # type_of_plot = st.selectbox("Select Type of Plot",["area","bar","line","hist","box","kde"]) # selected_columns_names = st.multiselect("Select Columns To Plot",all_columns_names) # if st.button("Generate Plot"): # st.success("Generating Customizable Plot of {} for {}".format(type_of_plot,selected_columns_names)) # # Plot By Streamlit # if type_of_plot == 'area': # cust_data = df[selected_columns_names] # st.area_chart(cust_data) # elif type_of_plot == 'bar': # cust_data = df[selected_columns_names] # st.bar_chart(cust_data) # elif type_of_plot == 'line': # cust_data = df[selected_columns_names] # st.line_chart(cust_data) # # Custom Plot # elif type_of_plot: # cust_plot= df[selected_columns_names].plot(kind=type_of_plot) # st.write(cust_plot) # st.pyplot() # if st.button("Thanks"): # st.balloons() # st.sidebar.header("About App") # st.sidebar.info("A Simple EDA App for Exploring Common ML Dataset") # st.sidebar.header("Get Datasets") # st.sidebar.markdown("[Common ML Dataset Repo]("")") # # # # st.sidebar.header("About") # # st.sidebar.info("Jesus Saves@JCharisTech") # # st.sidebar.text("Built with Streamlit") # # st.sidebar.text("Maintained by Jesse JCharis") # if __name__ == '__main__': # main() # import pickle # #pickle.dump(kmeans,open('unsupervisedmodels.pkl','wb')) # import streamlit as st # import pickle # # import numpy as np # # from sklearn.cluster import KMeans # # #kmeans=pickle.load(open('unsupervisedmodels.pkl','rb')) # # from sklearn import datasets # # from sklearn.manifold import TSNE # # import matplotlib.pyplot as plt # # from sklearn.preprocessing import MinMaxScaler # # scaler = MinMaxScaler() # # transformed = scaler.fit_transform(x) # # # Plotting 2d t-Sne # # x_axis = transformed[:,0] # # y_axis = transformed[:,1] # # kmeans = KMeans(n_clusters=4, random_state=42,n_jobs=-1) # # #y_pred =kmeans.fit_predict(transformed) # # # def predict_kmeans(CountryName,StringencyLegacyIndexForDisplay,StringencyIndexForDisplay, StringencyIndex,StringencyLegacyIndex,ContainmentHealthIndexForDisplay,ContainmentHealthIndex,GovernmentResponseIndexForDisplay,ConfirmedCases,ConfirmedDeaths,EconomicSupportIndexForDisplay,E2_Debtcontractrelief,EconomicSupportIndex,C3_Cancelpublicevents,C1_Schoolclosing): # # # input=np.array([[CountryName,StringencyLegacyIndexForDisplay,StringencyIndexForDisplay, StringencyIndex,StringencyLegacyIndex,ContainmentHealthIndexForDisplay,ContainmentHealthIndex,GovernmentResponseIndexForDisplay,ConfirmedCases,ConfirmedDeaths,EconomicSupportIndexForDisplay,E2_Debtcontractrelief,EconomicSupportIndex,C3_Cancelpublicevents,C1_Schoolclosing]]).astype(np.float64) # # # prediction=kmeans.predict(input) # # # return prediction # # st.title("Records of countries classified in the clusters") # # html_temp = """ # # <div style="background-color:#025246 ;padding:12px"> # # <h2 style="color:white;text-align:center;">Unsupervised App </h2> # # </div> # # """ # # st.markdown(html_temp, unsafe_allow_html=True) # # CountryName = st.text_input("CountryName","Type Here",key='0') # # StringencyLegacyIndexForDisplay = st.text_input("StringencyLegacyIndexForDisplay","Type Here",key='1') # # StringencyIndexForDisplay = st.text_input("StringencyIndexForDisplay","Type Here",key='2') # # StringencyIndex = st.text_input("StringencyIndex","Type Here",key='3') # # StringencyLegacyIndex = st.text_input("StringencyLegacyIndex","Type Here",key='4') # # ContainmentHealthIndexForDisplay = st.text_input("ContainmentHealthIndexForDisplay","Type Here",key='5') # # GovernmentResponseIndexForDisplay = st.text_input("GovernmentResponseIndexForDisplay","Type Here",key='6') # # ContainmentHealthIndex = st.text_input("ContainmentHealthIndex","Type Here",key='7') # # ConfirmedCases = st.text_input("ConfirmedCases","Type Here",key='8') # # ConfirmedDeaths = st.text_input("ConfirmedDeaths","Type Here",key='9') # # EconomicSupportIndexForDisplay = st.text_input("EconomicSupportIndexForDisplay","Type Here",key='9') # # E2_Debtcontractrelief = st.text_input("E2_Debtcontractrelief","Type Here",key='10') # # EconomicSupportIndex = st.text_input("EconomicSupportIndex","Type Here",key='11') # # C3_Cancelpublicevents = st.text_input("C3_Cancelpublicevents","Type Here",key='12') # # C1_Schoolclosing = st.text_input("C1_Schoolclosing","Type Here",key='13') # # if st.button("Predict"): # # output=predict_kmeans(CountryName,StringencyLegacyIndexForDisplay,StringencyIndexForDisplay, StringencyIndex,StringencyLegacyIndex,ContainmentHealthIndexForDisplay,ContainmentHealthIndex,GovernmentResponseIndexForDisplay,ConfirmedCases,ConfirmedDeaths,EconomicSupportIndexForDisplay,E2_Debtcontractrelief,EconomicSupportIndex,C3_Cancelpublicevents,C1_Schoolclosing) # # st.success('This country located in this cluster {}'.format(output)) # # -*- coding: utf-8 -*- # """Assignment3.ipynb # """ # import pandas as pd # data= pd.read_csv('https://raw.githubusercontent.com/Diane10/ML/master/assignment3.csv') # # data.info() # # data.isnull().sum() # null_counts = data.isnull().sum().sort_values() # selected = null_counts[null_counts < 8000 ] # percentage = 100 * data.isnull().sum() / len(data) # data_types = data.dtypes # # data_types # missing_values_table = pd.concat([null_counts, percentage, data_types], axis=1) # # missing_values_table # col=['CountryName','Date','StringencyLegacyIndexForDisplay','StringencyIndexForDisplay','ContainmentHealthIndexForDisplay','GovernmentResponseIndexForDisplay', # 'EconomicSupportIndexForDisplay','C8_International travel controls','C1_School closing','C3_Cancel public events','C2_Workplace closing','C4_Restrictions on gatherings', # 'C6_Stay at home requirements','C7_Restrictions on internal movement','H1_Public information campaigns','E1_Income support','C5_Close public transport','E2_Debt/contract relief','StringencyLegacyIndex','H3_Contact tracing','StringencyIndex','ContainmentHealthIndex','E4_International support','EconomicSupportIndex','E3_Fiscal measures','H5_Investment in vaccines','ConfirmedCases','ConfirmedDeaths'] # newdataset=data[col] # newdataset= newdataset.dropna() # from sklearn.preprocessing import LabelEncoder # newdataset['CountryName']=LabelEncoder().fit_transform(newdataset['CountryName']) # # # map features to their absolute correlation values # # corr = newdataset.corr().abs() # # # set equality (self correlation) as zero # # corr[corr == 1] = 0 # # # of each feature, find the max correlation # # # and sort the resulting array in ascending order # # corr_cols = corr.max().sort_values(ascending=False) # # # display the highly correlated features # # display(corr_cols[corr_cols > 0.9]) # # len(newdataset) # X=newdataset[['CountryName','StringencyLegacyIndexForDisplay','StringencyIndexForDisplay', 'StringencyIndex','StringencyLegacyIndex','ContainmentHealthIndexForDisplay','ContainmentHealthIndex','GovernmentResponseIndexForDisplay','ConfirmedCases','ConfirmedDeaths','EconomicSupportIndexForDisplay','E2_Debt/contract relief','EconomicSupportIndex','C3_Cancel public events','C1_School closing']] # # X=newdataset[['CountryName','StringencyLegacyIndexForDisplay','StringencyIndexForDisplay', 'StringencyIndex','StringencyLegacyIndex','ContainmentHealthIndexForDisplay','ContainmentHealthIndex','GovernmentResponseIndexForDisplay','ConfirmedCases','ConfirmedDeaths']] # # df_first_half = X[:1000] # # df_second_half = X[1000:] # # """Feature selector that removes all low-variance features.""" # from sklearn.feature_selection import VarianceThreshold # selector = VarianceThreshold() # x= selector.fit_transform(X) # df_first_half = x[:5000] # df_second_half = x[5000:] # # """Create clusters/classes of similar records using features selected in (1), use an unsupervised learning algorithm of your choice.""" # # Commented out IPython magic to ensure Python compatibility. # from sklearn.cluster import KMeans # from sklearn.decomposition import PCA # import pandas as pd # from sklearn.preprocessing import MinMaxScaler # from matplotlib import pyplot as plt # import streamlit as st # # wcss=[] # # for i in range(1,11): # # kmeans=KMeans(n_clusters=i, init='k-means++',random_state=0) # # kmeans.fit(x) # # wcss.append(kmeans.inertia_) # # st.set_option('deprecation.showPyplotGlobalUse', False) # # plt.plot(range(1,11),wcss) # # plt.title('The Elbow Method') # # plt.xlabel('Number of Clusters') # # plt.ylabel('WCSS') # # plt.show() # # st.pyplot() # model = KMeans(n_clusters = 6) # pca = PCA(n_components=2).fit(x) # pca_2d = pca.transform(x) # model.fit(pca_2d) # labels = model.predict(pca_2d) # # labels # # predicted_label = model.predict([[7.2, 3.5, 0.8, 1.6]]) # # pca = PCA(n_components=2).fit(df_first_half) # # pca_2d = pca.transform(df_first_half) # # pca_2d # xs = pca_2d[:, 0] # ys = pca_2d[:, 1] # plt.scatter(xs, ys, c = labels) # plt.scatter(model.cluster_centers_[:,0],model.cluster_centers_[:,1],color='purple',marker='*',label='centroid') # kmeans = KMeans(n_clusters=10) # kmeans.fit(df_first_half) # plt.scatter(df_first_half[:,0],df_first_half[:,1], c=kmeans.labels_, cmap='rainbow') # range_n_clusters = [2, 3, 4, 5, 6] # # from sklearn.metrics import silhouette_samples, silhouette_score # # import matplotlib.cm as cm # # import numpy as np # # for n_clusters in range_n_clusters: # # # Create a subplot with 1 row and 2 columns # # fig, (ax1, ax2) = plt.subplots(1, 2) # # fig.set_size_inches(18, 7) # # # The 1st subplot is the silhouette plot # # # The silhouette coefficient can range from -1, 1 but in this example all # # # lie within [-0.1, 1] # # ax1.set_xlim([-0.1, 1]) # # # The (n_clusters+1)*10 is for inserting blank space between silhouette # # # plots of individual clusters, to demarcate them clearly. # # ax1.set_ylim([0, len(pca_2d) + (n_clusters + 1) * 10]) # # # Initialize the clusterer with n_clusters value and a random generator # # # seed of 10 for reproducibility. # # clusterer = KMeans(n_clusters=n_clusters, random_state=10) # # cluster_labels = clusterer.fit_predict(pca_2d) # # # The silhouette_score gives the average value for all the samples. # # # This gives a perspective into the density and separation of the formed # # # clusters # # silhouette_avg = silhouette_score(pca_2d, cluster_labels) # # print("For n_clusters =", n_clusters, # # "The average silhouette_score is :", silhouette_avg) # # # Compute the silhouette scores for each sample # # sample_silhouette_values = silhouette_samples(pca_2d, cluster_labels) # # y_lower = 10 # # for i in range(n_clusters): # # # Aggregate the silhouette scores for samples belonging to # # # cluster i, and sort them # # ith_cluster_silhouette_values = \ # # sample_silhouette_values[cluster_labels == i] # # ith_cluster_silhouette_values.sort() # # size_cluster_i = ith_cluster_silhouette_values.shape[0] # # y_upper = y_lower + size_cluster_i # # color = cm.nipy_spectral(float(i) / n_clusters) # # ax1.fill_betweenx(np.arange(y_lower, y_upper), # # 0, ith_cluster_silhouette_values, # # facecolor=color, edgecolor=color, alpha=0.7) # # # Label the silhouette plots with their cluster numbers at the middle # # ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i)) # # # Compute the new y_lower for next plot # # y_lower = y_upper + 10 # 10 for the 0 samples # # ax1.set_title("The silhouette plot for the various clusters.") # # ax1.set_xlabel("The silhouette coefficient values") # # ax1.set_ylabel("Cluster label") # # # The vertical line for average silhouette score of all the values # # ax1.axvline(x=silhouette_avg, color="red", linestyle="--") # # ax1.set_yticks([]) # Clear the yaxis labels / ticks # # ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1]) # # # 2nd Plot showing the actual clusters formed # # colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters) # # ax2.scatter( pca_2d[:, 0], pca_2d[:, 1], marker='.', s=30, lw=0, alpha=0.7, # # c=colors, edgecolor='k') # # # Labeling the clusters # # centers = clusterer.cluster_centers_ # # # Draw white circles at cluster centers # # ax2.scatter(centers[:, 0], centers[:, 1], marker='o', # # c="white", alpha=1, s=200, edgecolor='k') # # for i, c in enumerate(centers): # # ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1, # # s=50, edgecolor='k') # # ax2.set_title("The visualization of the clustered data.") # # ax2.set_xlabel("Feature space for the 1st feature") # # ax2.set_ylabel("Feature space for the 2nd feature") # # plt.suptitle(("Silhouette analysis for KMeans clustering on sample data " # # "with n_clusters = %d" % n_clusters), # # fontsize=14, fontweight='bold') # # plt.show() # #km.cluster_centers_ # from sklearn import datasets # from sklearn.manifold import TSNE # import matplotlib.pyplot as plt # from sklearn.preprocessing import MinMaxScaler # scaler = MinMaxScaler() # transformed = scaler.fit_transform(x) # # Plotting 2d t-Sne # x_axis = transformed[:,0] # y_axis = transformed[:,1] # kmeans = KMeans(n_clusters=4, random_state=42,n_jobs=-1) # y_pred =kmeans.fit_predict(transformed) # predicted_label = kmeans.predict([[7,7.2, 3.5, 0.8, 1.6,7.2, 3.5, 0.8, 1.6,7.2, 3.5, 0.8, 1.67, 7.2, 3.5]]) # predicted_label # # from sklearn.manifold import TSNE # # tsne = TSNE(random_state=17) # # X_tsne = tsne.fit_transform(transformed) # # plt.figure(figsize=(12,10)) # # plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y_pred, # # edgecolor='none', alpha=0.7, s=40, # # cmap=plt.cm.get_cmap('nipy_spectral', 10)) # # plt.colorbar() # # plt.title('cluster. t-SNE projection'); # # pca = PCA(n_components=2) # # X_reduced = pca.fit_transform(transformed) # # print('Projecting %d-dimensional data to 2D' % X.shape[1]) # # plt.figure(figsize=(12,10)) # # plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y_pred, # # edgecolor='none', alpha=0.7, s=40, # # cmap=plt.cm.get_cmap('nipy_spectral', 10)) # # plt.colorbar() # # plt.title('cluster. PCA projection'); # # st.pyplot() # # """https://www.kaggle.com/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering""" # # import seaborn as sns # # import pickle # # pickle.dump(kmeans,open('unsupervisedmodels.pkl','wb')) # # """Create a platform where new records of countries can be classified in the clusters""" # # Commented out IPython magic to ensure Python compatibility. # # %%writefile app.py # import streamlit as st # import pickle # import numpy as np # # kmeans=pickle.load(open('unsupervisedmodels.pkl','rb')) # def predict_kmeans(CountryName,StringencyLegacyIndexForDisplay,StringencyIndexForDisplay, StringencyIndex,StringencyLegacyIndex,ContainmentHealthIndexForDisplay,ContainmentHealthIndex,GovernmentResponseIndexForDisplay,ConfirmedCases,ConfirmedDeaths,EconomicSupportIndexForDisplay,E2_Debtcontractrelief,EconomicSupportIndex,C3_Cancelpublicevents,C1_Schoolclosing): # input=np.array([[CountryName,StringencyLegacyIndexForDisplay,StringencyIndexForDisplay, StringencyIndex,StringencyLegacyIndex,ContainmentHealthIndexForDisplay,ContainmentHealthIndex,GovernmentResponseIndexForDisplay,ConfirmedCases,ConfirmedDeaths,EconomicSupportIndexForDisplay,E2_Debtcontractrelief,EconomicSupportIndex,C3_Cancelpublicevents,C1_Schoolclosing]]).astype(np.float64) # prediction=kmeans.predict(input) # return prediction # def main(): # st.title("Records of countries classified in the clusters") # html_temp = """ # <div style="background-color:#025246 ;padding:10px"> # <h2 style="color:white;text-align:center;">Unsupervised ML App </h2> # </div> # """ # st.markdown(html_temp, unsafe_allow_html=True) # CountryName = st.text_input("CountryName","Type Here",key='0') # StringencyLegacyIndexForDisplay = st.text_input("StringencyLegacyIndexForDisplay","Type Here",key='1') # StringencyIndexForDisplay = st.text_input("StringencyIndexForDisplay","Type Here",key='2') # StringencyIndex = st.text_input("StringencyIndex","Type Here",key='3') # StringencyLegacyIndex = st.text_input("StringencyLegacyIndex","Type Here",key='4') # ContainmentHealthIndexForDisplay = st.text_input("ContainmentHealthIndexForDisplay","Type Here",key='5') # GovernmentResponseIndexForDisplay = st.text_input("GovernmentResponseIndexForDisplay","Type Here",key='6') # ContainmentHealthIndex = st.text_input("ContainmentHealthIndex","Type Here",key='7') # ConfirmedCases = st.text_input("ConfirmedCases","Type Here",key='8') # ConfirmedDeaths = st.text_input("ConfirmedDeaths","Type Here",key='9') # EconomicSupportIndexForDisplay = st.text_input("EconomicSupportIndexForDisplay","Type Here",key='9') # E2_Debtcontractrelief = st.text_input("E2_Debtcontractrelief","Type Here",key='10') # EconomicSupportIndex = st.text_input("EconomicSupportIndex","Type Here",key='11') # C3_Cancelpublicevents = st.text_input("C3_Cancelpublicevents","Type Here",key='12') # C1_Schoolclosing = st.text_input("C1_Schoolclosing","Type Here",key='13') # safe_html=""" # <div style="background-color:#F4D03F;padding:10px > # <h2 style="color:white;text-align:center;"> Your forest is safe</h2> # </div> # """ # danger_html=""" # <div style="background-color:#F08080;padding:10px > # <h2 style="color:black ;text-align:center;"> Your forest is in danger</h2> # </div> # """ # if st.button("Predict"): # output=predict_kmeans(CountryName,StringencyLegacyIndexForDisplay,StringencyIndexForDisplay, StringencyIndex,StringencyLegacyIndex,ContainmentHealthIndexForDisplay,ContainmentHealthIndex,GovernmentResponseIndexForDisplay,ConfirmedCases,ConfirmedDeaths,EconomicSupportIndexForDisplay,E2_Debtcontractrelief,EconomicSupportIndex,C3_Cancelpublicevents,C1_Schoolclosing) # st.success('This country located in this cluster {}'.format(output)) # if __name__=='__main__': # main()

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KiraUnderwood/ParallelProgPython

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#!/usr/bin/env python3 """ Producers serving soup for Consumers to eat """ import queue import threading import time serving_line = queue.Queue(maxsize=5) # producer and consumer use it, queue - for each changing obj bw mult threads def soup_producer(): for i in range(20): # serve 20 bowls of soup serving_line.put_nowait('Bowl #'+str(i)) print('Served Bowl #', str(i), '- remaining capacity:', \ serving_line.maxsize-serving_line.qsize()) time.sleep(0.2) # time to serve a bowl of soup serving_line.put_nowait('no soup for you!') # msg to the consumer for 2 of them since there are 2 serving_line.put_nowait('no soup for you!') def soup_consumer(): while True: bowl = serving_line.get() if bowl == 'no soup for you!': # retrieved from the q (put_nowait) and in this case breaks and terminates the thread break print('Ate', bowl) time.sleep(0.3) # time to eat a bowl of soup if __name__ == '__main__': for i in range(2): threading.Thread(target=soup_consumer).start() threading.Thread(target=soup_producer).start()

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kpodlesnaya@mail.ru

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/upload_python_scripts_and_client/BatchUploadv3.py

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voicebase-support/voicebase-support.github.io

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refs/heads/master

2023-01-27T12:27:42.369113

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# list.csv is a list of file names on local machine # any extra metadata fields under 'extended' need to be indexed on the VB platform before upload # command line example # python BatchUploadv3.py --list list.csv --mediadir ./media --results ./res.csv --token --priority low import argparse import csv import json import os from VoiceBaseClient import VoiceBaseClient # ********* def main *********** def main(): parser = argparse.ArgumentParser( description = "Batch uploader to VoiceBase V3" ) parser.add_argument( '--list', help = "path to csv list of local files (one per line)", required = True ) parser.add_argument( '--mediadir', help = "path to local media files", required = False, default = './' ) parser.add_argument( '--results', help = "path to output csv file of files, media ids, and status", required = True ) parser.add_argument( '--token', help = "Bearer token for V3 API authentication", required = True ) parser.add_argument( '--priority', help = "job priority of the uploads (low, normal, high), default = low", required = False, default = 'low', choices = ['low', 'normal', 'high'] ) args = parser.parse_args() upload(args.list, args.mediadir, args.results, args.token, args.priority) # ********* def upload *********** def upload(list_path, mdir, results_path, token, priority): client = VoiceBaseClient(token = token) media = client.media() counter = 0 with open(list_path, 'r') as list_file: with open(results_path, 'w') as results_file: results_writer = csv.writer( results_file, delimiter = ',', quotechar = '"' ) results_writer.writerow([ 'file', 'mediaId', 'status' ]) # write headers for raw_filename in list_file: filename = raw_filename.rstrip() counter = counter + 1 md = { "externalId": filename, "extended": { "uploadversion": "1" } } m_data = json.dumps(md) pathandfile = os.path.join(mdir, filename) response = upload_one(media, pathandfile, filename,generate_configuration(priority),m_data) media_id = response['mediaId'] status = response['status'] results_writer.writerow([ filename, media_id, status ]); # ********* def generate config json *********** def generate_configuration(priority): return json.dumps({ "transcript": { "formatting" : { "enableNumberFormatting" : False } } }) # ********* def upload one *********** def upload_one(media, filepath, filename, configuration, metadata): with open(filepath, 'r') as media_file: response = media.post( media_file, filename, 'audio/mpeg', configuration = configuration, metadata = metadata ) return response if __name__ == "__main__": main()

[ "lenore.alford@gmail.com" ]

lenore.alford@gmail.com

e8591843cd5828be5194738ec22902074c0117ca

16c20abcf609629b925c9a438c3728800c0eb60d

/opencv/05/opencv-05_01.py

9b21102a8e93d409c5710a978ffcb4281f544813

[]

no_license

AnDeoukKyi/tistory

66aa916daf2a2a4d0051796b9509cac3a03749ef

b98defeade2410723cf30353fd5190c44048c6b0

refs/heads/main

2023-08-13T12:13:18.228278

2021-10-19T13:37:58

385,083,230

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null

UTF-8

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191

py

import cv2 img = cv2.imread("image.png") cv2.imshow("origin", img) #ROI1 ROI = img[100:150,50:150] cv2.imshow("ROI1", ROI) #ROI2 ROI = img[:150,50:] cv2.imshow("ROI2", ROI) cv2.waitKey(0)

[ "abdgf3@naver.com" ]

abdgf3@naver.com

d12df3fe82e2b7cd6f21fa29ba094b5242d3d3bc

d57980cc6f4e1147f6f4fe1bc1c68f8d731bcca5

/train.py

d8d544f2bedfb30954a8a55fb7f42dfce3477a97

[]

no_license

fuding/CAGFace

244813b572953dc218b05b0e38cacc395c19d619

1436d44a089647ee62918b496d85b37a162d8e49

refs/heads/master

2020-11-24T15:11:09.480152

2019-11-25T08:35:40

null

0

null

UTF-8

Python

false

2,114

py

import torch import torch.nn as nn import torch.optim as optim from tqdm import tqdm from model import * from dataloader import Data_Loader from torchvision import transforms from optimizer import Optimizer import matplotlib.pyplot as plt pretrained = True lr_start = 1e-16 #0.0000505 visualize = True dl = Data_Loader('/home/yo0n/바탕화면/Flicker',256,1).loader() if(pretrained): model = torch.load('./checkpoints/cand.pth') """ model = nn.Sequential( model, conv3x3(3,3), nn.ReLU(inplace=True) ) model = model.cuda() """ else: model = CAGFace(1).cuda() print(" --- model loaded --- ") print(model) criteria = nn.SmoothL1Loss() #criteria = nn.L1Loss() ## optimizer #optimizer = optim.SGD(model.parameters(), lr=lr_start) optimizer = optim.Adam(model.parameters(), lr=lr_start) for epoch in range(10): iter = 0 loss_lowest = 9999 loss_avg = [] for im, lb_512, lb_1024 in tqdm(dl): im = im.cuda() out = model(im) loss = criteria(lb_512,out.cpu()) optimizer.zero_grad() loss.backward() optimizer.step() loss_avg.append(loss.item()) iter += 1 if(iter%10 == 0): print("iter : ",iter) l = sum(loss_avg) / len(loss_avg) print("loss : ",l) outImage = out.data.cpu() if visualize: plt.figure(1) plt.subplot(211) plt.imshow(transforms.ToPILImage()(outImage.squeeze())) plt.subplot(212) plt.imshow(transforms.ToPILImage()(lb_512.cpu().squeeze())) plt.pause(1) plt.close("all") if(l < loss_lowest): loss_lowest = l torch.save(model, "./checkpoints/"+str(epoch)+".pth") print("improved!") else: torch.save(model, "./checkpoints/"+str(epoch)+"_update"+".pth") print("epoch : ",epoch," \nloss : ",sum(loss_avg) / len(loss_avg)) torch.save(model, "./checkpoints/"+str(epoch)+"_final"+".pth")

[ "noreply@github.com" ]

fuding.noreply@github.com

896387b71de62c3f33d2bb9ccadb050d69c70b63

b5155a2ece4ee4ca5a1a9e79d104f7b8914de508

/OnlineprogrammingCourse.py

6e34455d59867ea7591de979844e9162af909c18

[]

no_license

DeepakKumarMD/online-course-management

106d5ee3d2bc547e6f077ed26f4fbdc03f336a04

7c120db176a787db06f5e90f28cc3f9ddd706af9

refs/heads/main

2023-03-30T16:39:18.192858

2021-04-02T09:17:26

353,967,706

1

0

null

UTF-8

Python

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29,487

py

from tkinter import * from tkinter import ttk import sqlite3 import tkinter.messagebox from datetime import date from tkinter import filedialog import shutil import os from tkinter import Text, Tk today = date.today() print('software is runing......') firstw = Tk() firstw.title("ONLINE COURSE MANAGEMENT") firstw.geometry("1600x1000+0+0") label = Label(text="ONLINE COURSE MANAGEMENT", font=("times new roman", 35),fg='white', bg="#2A1B3D") label.pack(side=TOP, fill=X) user1 = Label(text="USERNAME", font=("forte", 23), fg="#3B8BEB") user1.place(x=610, y=120) user = Entry(width=17, bd=5, font=("arial", 20)) user.place(x=570, y=200) label.pack(side=TOP, fill=X) user2 = Label(text="PASSWORD", font=("forte", 23),fg="#3B8BEB") user2.place(x=610, y=280) user3 = Entry(width=17, show="*", bd=5, font=("arial", 20)) user3.place(x=570, y=360) firstw.configure(bg='#F9D342') def second(): global secondw secondw = Tk() secondw.title("online course management") secondw.geometry("1600x1000+0+0") def distroy4(): secondw.destroy() root() def student(): student1 = Tk() student1.title("STUDENT DETAILS") def studentid(): rot = Tk() rot.title("VISITORS'S LIST ") rot.geometry("1600x1000+0+0") mainlabel = Label(rot, text="STUDENT DETAILS", font=("times new roman", 35), bg="MediumOrchid2") mainlabel.pack(side=TOP, fill=X) chat1 = ttk.Treeview(rot, height=20, columns=('name', 'sur', 'fee', 'branch'), selectmode="extended") chat1.heading('#0', text='ID', anchor=CENTER) chat1.heading('#1', text=' NAME', anchor=W) chat1.heading('#2', text='FEES', anchor=W) chat1.heading('#3', text='COURSE', anchor=W) chat1.heading('#4', text="LAST NAME", anchor=W) chat1.column('#1', stretch=YES, minwidth=50, width=100) chat1.column('#3', stretch=YES, minwidth=100, width=200) chat1.column('#4', stretch=YES, minwidth=50, width=100) chat1.column('#2', stretch=YES, minwidth=50, width=100) chat1.column('#0', stretch=YES, minwidth=50, width=70) chat1.place(x=470, y=130) ttk.Style().configure("Treeview", background="black", foreground="coral1") ttk.Style().configure("Treeview.Heading", background="blue", foreground="palevioletRed1") rot.configure(background='medium spring green') vsb = ttk.Scrollbar(rot, orient="vertical", command=chat1.yview) vsb.place(x=1027, y=150, height=400 + 20) chat1.configure(yscrollcommand=vsb.set) conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT id ,name, fee , branch,sur FROM kistar ') for row1 in cur.fetchall(): chat1.insert('', 0, text=row1[0], values=(row1[1], row1[2], row1[3], row1[4])) def viewenquiry2(): rt = Tk() rt.title("VISITORS'S LIST") rt.geometry("1600x1000+0+0") mainlabel = Label(rt, text="VISITOR", font=("times new roman", 35), bg="MediumOrchid2") mainlabel.pack(side=TOP, fill=X) chat1 = ttk.Treeview(rt, height=20, columns=('EMAIL', 'ENQUIRY', 'DATE'), selectmode="extended") chat1.heading('#0', text='NAME', anchor=CENTER) chat1.heading('#1', text='EMAIL', anchor=CENTER) chat1.heading('#2', text='ENQUIRY', anchor=CENTER) chat1.heading('#3', text="DATE", anchor=CENTER) chat1.column('#1', stretch=YES, minwidth=50, width=100) chat1.column('#3', stretch=YES, minwidth=50, width=100) chat1.column('#2', stretch=YES, minwidth=50, width=300) chat1.column('#0', stretch=YES, minwidth=50, width=70) vsb = ttk.Scrollbar(rt, orient="vertical", command=chat1.yview) vsb.place(x=955, y=170, height=400 + 20) chat1.configure(yscrollcommand=vsb.set) chat1.place(x=400, y=170) ttk.Style().configure("Treeview", background="red", foreground="coral1") ttk.Style().configure("Treeview.heading", background="blue", foreground="palevioletRed1") rt.configure(background="#3B8BEB") conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT * FROM golu') for row in cur.fetchall(): chat1.insert('', 0, text=row[0], values=(row[1], row[2], row[3])) def distroy5(): secondw.destroy() window() mainlabel = Label(secondw, text="ONLINE COURSE MANAGEMENT", font=("times new roman", 35), fg='white', bg="black") mainlabel.pack(side=TOP, fill=X) button = Button(secondw, width=13,height=5, font=("illuma black", 20), text="COURSE\nREGISTRATION\n FORM", fg="snow", bg="#C3073F", command=distroy4) button.place(x=150, y=350) enquiry = Button(secondw, width=13,height=5, font=("illuma black", 20), text="FEE\nPAYMENT\n PORTAL", fg="snow", bg="#C3073F", command=distroy5) enquiry.place(x=420, y=350) fee_details = Button(secondw, width=13,height=5, font=("illuma black", 20), text="VISITOR'S\n PORTAL", fg="snow", bg="#C3073F", command=enquiry1) fee_details.place(x=700, y=350) viewenquiry = Button(secondw, width=13,height=5, font=("illuma black", 20), text="VIEW ENQUIRY",fg="snow", bg="#C3073F", command=viewenquiry2) viewenquiry.place(x=950, y=350) viewenquiry1 = Button(secondw, width=13,height=5, font=("illuma black", 20), text="COURSE\n&\nSTUDENT\n DETAILS ", fg="snow", bg="#C3073F", command=studentid) viewenquiry1.place(x=1200, y=350) def distroy(): firstw.destroy() def login(): if user.get() == "deeku" and user3.get() == "12345": second() distroy() else: t = tkinter.messagebox.showinfo("INVALID USERNAME OR PASSWORD ", "YOU HAVE ENTERED INVALID USERNAME OR PASSWORD ") user.delete(0, END) user3.delete(0, END) def root(): root = Tk() root.geometry("1600x1000+0+0") root.title("online course management") global entry1 global entry2 global entry3 global entry4 global entry5 global box global name global radio1 global radio2 name = StringVar() global sur sur = StringVar() global gander gander = IntVar() global var1 var1 = IntVar() global var2 var2 = IntVar() global branch branch = StringVar() global rollno rollno = StringVar() global email email = StringVar() global course course = StringVar() global python python = IntVar() global java java = IntVar() global c c = IntVar() global d d = IntVar() global calculate calculate = StringVar() id = IntVar() search = IntVar() NAME = name.get() SUR = sur.get() EMAIL = email.get() BRANCH = branch.get() GANDER = gander.get() PYTHON = python.get() JAVA = java.get() C = c.get() D = d.get() CALCULATE = calculate.get() calculation2 = 2000 label = Label(root, text="REGISTRATION FORM", font=("arial", 25), bg="violetred1") label.pack(side=TOP, fill=X) label1 = Label(root, text="NAME:", font=("arial", 17)) label1.place(x=300, y=150) label2 = Label(root, text="SURNAME:", font=("arial", 17)) label2.place(x=300, y=210) label3 = Label(root, text="EMAIL:", font=("arial", 17)) label3.place(x=300, y=270) label3 = Label(root, text="GENDER:", font=("arial", 17)) label3.place(x=300, y=330) label4 = Label(root, text="BRANCH:", font=("arial", 17)) label4.place(x=300, y=390) label4 = Label(root, text="COURSE", font=("arial", 17)) label4.place(x=300, y=450) label4 = Label(root, text="TOTAL FEE", font=("arial", 17)) label4.place(x=300, y=520) root.configure(background='#116466') # ==============================entryfield======================================== entry5 = Entry(root, textvar=calculate, state="readonly", width=20, font=("arial", 15, "bold"), bd=5) entry5.place(x=500, y=515) entry1 = Entry(root, bd=5, width=20, textvar=name, font=("arial", 15)) entry1.place(x=500, y=150) # entry22=Entry(main,bd=5, width=20,textvar=sam ,font=("arial",15)) # entry22.place(x=500,y=150) entry2 = Entry(root, bd=5, width=20, textvar=sur, font=("arial", 15)) entry2.place(x=500, y=210) entry3 = Entry(root, bd=5, width=20, textvar=email, font=("arial", 15)) entry3.place(x=500, y=270) entry4 = Entry(root, bd=5, text="enter roll no.", width=20, textvar=search, font=("arial", 15)) entry4.place(x=800, y=150) search.set("") entry4 = Entry(root, bd=5, text="enter roll no.", width=20, textvar=search, font=("arial", 15)) entry4.place(x=800, y=150) # ================================radio buttton======================================= radio1 = Radiobutton(root, text="MALE", variable=gander, value=1, font=("arial", 13)) radio1.place(x=510, y=340) radio2 = Radiobutton(root, text="FEMALE", variable=gander, padx=20, value=0, font=("arial", 13)) radio2.place(x=570, y=340) gander.set(3) # ================================droplist====================================== box = ttk.Combobox(root, textvariable=branch, state="readonly", font=("arial", 12, "bold"), width=22) box['values'] = ['SELECT', 'JAVA', 'C++', 'PYTHON', 'C'] box.current(0) box.place(x=503, y=395) # ===============================checkbutton==================================== checkbutton1 = Checkbutton(root, text="beginner", font=("helvetica bold", 10), variable=java) checkbutton1.place(x=502, y=455) checkbutton1 = Checkbutton(root, text="intermediate", font=("helvetica bold", 10), variable=c) checkbutton1.place(x=590, y=455) checkbutton1 = Checkbutton(root, text="advanced", font=("helvetica bold", 10), variable=d) checkbutton1.place(x=700, y=455, ) # checkbutton1 = Checkbutton(root, text="PYTHON", variable=python) # checkbutton1.place(x=650, y=455) python.set(0) java.set(0) c.set(0) d.set(0) def dis(): root.destroy() second() # root.filename=filedialog.askopenfile(initialdir="/",title="select file",filetypes=(("jpeg files","*.jpg"),("all files","*.*"))) # print(root.filename) # os.chdir('c:\\') # shutil.move((root.filename),("C:\\Users\\HP\Desktop\\projectgui\\image")) # =========================buttton========================== button1 = Button(root, text="CALCULATE FEE", width=14, font=("helvetica bold", 15), bg="#FFCB9A", command=calculation) button1.place(x=800, y=510) button12 = Button(root, text="BACK", width=17, font=("arial", 17), bg="#FFCB9A", command=dis) button12.place(x=0, y=0) button2 = Button(root, text="SUBMIT FORM", width=14, font=("helvetica bold", 15), bg="#FFCB9A", command=msg) button2.place(x=600, y=630) button3 = Button(root, text="RESET", width=14, font=("helvetica bold", 15), bg="#FFCB9A", command=golu) button3.place(x=395, y=630) button4 = Button(root, text="SEARCH", width=14, font=("helvetica bold", 15), bg="#FFCB9A", command=all) button4.place(x=1100, y=150) # button7 = Button(root, text="UPLOAD PHOTO", width=14, font=("arial", 10), bg="indianred1",command=file) # button7.place(x=1100, y=210) button4 = Button(root, text="UPDATE", width=14, font=("helvetica bold", 15), bg="#FFCB9A", command=update) button4.place(x=1000, y=630) button5 = Button(root, text="DELETE", width=14, font=("helvetica bold", 15,), bg="#FFCB9A", command=delete) button5.place(x=800, y=630) # button6=Button(root,text="ENQUIRY",width=14,font=("arial",10),bg="indianred1",command=window ) # button6.place(x=300 , y=630) conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute( 'CREATE TABLE IF NOT EXISTS kistar(id INTEGER primary key autoincrement ,name text,sur text,email, branch text,gander text, fee int, python int,java int,c int,d int)') cur.execute("UPDATE SQLITE_SEQUENCE SET seq = 1000 WHERE name = 'kistar'") cur.execute('CREATE TABLE IF NOT EXISTS golu (NAME TEXT, PHONE INT ,PURPOSE TEXT,DATE)') cur.execute( 'CREATE TABLE IF NOT EXISTS FEEINSTALLMENT (id int ,TOTEL FEE INT, REMAIN FEE INT, PAID FEE INT ,INSTALLMENT INT,DATE)') def ka(): NAMEE = entry23.get() PHONE = entry24.get() PURPOSE = box2.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('INSERT INTO golu(NAME,PHONE,PURPOSE,DATE)VALUES(?,?,?,?)', (NAMEE, PHONE, PURPOSE, today)) conn.commit() def r(): j() ka() def enquiry1(): enquiry1 = Tk() enquiry1.title("ENQUIRY") enquiry1.geometry("1600x1000+0+0") purpose = StringVar() global entry23 global entry24 global box2 def enquiry1destroy(): enquiry1.destroy() second() label22 = Label(enquiry1, text="ENQUIRY", font=("arial", 25), bg="violetred1") label22.pack(side=TOP, fill=X) label1 = Label(enquiry1, text="NAME:", font=("arial", 17)) label1.place(x=300, y=150) label2 = Label(enquiry1, text="PHONE NO.:", font=("arial", 17)) label2.place(x=300, y=210) label3 = Label(enquiry1, text="PURPOSE:", font=("arial", 17)) label3.place(x=300, y=270) entry23 = Entry(enquiry1, bd=5, width=20, font=("arial", 15)) entry23.place(x=500, y=150) button = Button(enquiry1, text="submit", width=15, font=("comic sans ms", 20),fg='white',bg="#BA5536", command=r) button.place(x=500, y=320) button1 = Button(enquiry1, text="<< BACK", width=30, bg="violetred1", command=enquiry1destroy) button1.place(x=0, y=0) entry24 = Entry(enquiry1, bd=5, width=20, font=("arial", 15)) entry24.place(x=500, y=210) box2 = ttk.Combobox(enquiry1, textvariable=purpose, state="readonly", font=("arial", 12, "bold"), width=22) box2['values'] = ['SELECT', 'TO LEARN PROGRAMMING', 'TO LEARN MACHINE LEARNING', 'FEE DETAILS'] box2.current(0) box2.place(x=500, y=270) enquiry1.configure(background="#F9ED4E") def cat(): z = IntVar() FE = entry25.get() x = entry26.get() y = entry29.get() FE = entry25.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT fee FROM kistar WHERE id=?', (FE,)) for row24 in cur.fetchall(): entry26.configure(state="normal") entry26.delete(0, END) entry26.insert(0, row24) entry26.configure(state="disable") cur.execute(' SELECT SUM(INSTALLMENT) FROM FEEINSTALLMENT WHERE id=? GROUP BY id ', (FE,)) for row23 in cur.fetchall(): entry27.delete(0, END) entry27.insert(0, row23) ye = entry27.get() z = int(float((entry26.get()))) - int(float((entry27.get()))) # cur.execute('INSERT INTO FEEINSTALLMENT(id , TOTAL,INSTALLMENT,PAID ,REMAIN, DATE)VALUES(?,?,?,?,?,?)',(FE, x, y, ye, z, today,)) entry28.configure(state="normal") entry28.delete(0, END) entry28.insert(0, z) print(row23) entry27.configure(state="disable") entry26.configure(state="disable") entry28.configure(state="disable") conn.commit() print(x) print(FE) print(today) def reset2(): entry26.configure(state="normal") entry25.configure(state="normal") # entry24.configure(state="normal") entry27.configure(state="normal") entry28.configure(state="normal") entry29.configure(state="normal") entry26.delete(0, END) entry25.delete(0, END) entry27.delete(0, END) entry28.delete(0, END) entry29.delete(0, END) # box2.set("SELECT") entry27.configure(state="disable") entry26.configure(state="disable") entry28.configure(state="disable") def fee_add(): z = IntVar() FE = entry25.get() x = entry26.get() y = entry29.get() entry27.configure(state="normal") entry28.configure(state="normal") entry26.configure(state="normal") cur.execute('INSERT INTO FEEINSTALLMENT(id , TOTEL,INSTALLMENT, DATE)VALUES(?,?,?,?)', (FE, x, y, today,)) cur.execute(' SELECT SUM(INSTALLMENT) FROM FEEINSTALLMENT WHERE id=? GROUP BY id ', (FE,)) for row23 in cur.fetchall(): entry27.delete(0, END) entry27.insert(0, row23) ye = entry27.get() z = int(float((entry26.get()))) - int(float((entry27.get()))) cur.execute('UPDATE FEEINSTALLMENT SET PAID=? WHERE id=?', (ye, FE,)) cur.execute('UPDATE FEEINSTALLMENT SET REMAIN=? WHERE id=?', (z, FE,)) entry28.configure(state="normal") entry28.delete(0, END) entry28.insert(0, z) print(row23) entry27.configure(state="disable") entry26.configure(state="disable") entry28.configure(state="disable") conn.commit() print(x) print(FE) print(today) def installment2(): if int(entry29.index("end")) > int(0): fee_add() else: x = tkinter.messagebox.showinfo("NO FEE ADDED", "YOU HAVE NOT ADDED ANY FEE ") def j(): PURPOSE = box2.get() print(PURPOSE) def r(): j() ka() def window(): global main global namee global phone global purpose global entry23 global entry24 global entry25 global entry26 global entry27 global entry28 global box2 global key global fee3 global KEY global ley global sey global ADDFEE global entry29 # entry29=IntVar() # entry26=IntVar() # entry27=IntVar() # key=StringVar() # fee3=StringVar() # ADDFEE=IntVar() main = Tk() main.geometry("1600x1000+0+0") main.title("enqiry") namee = StringVar() phone = IntVar() purpose = StringVar() fe = StringVar() key = IntVar() ley = StringVar() sey = StringVar() # NAMEE=namee.get() # PHONE=phone.get() # PURPOSE=purpose.get() def distroy3(): main.destroy() second() button = Button(main, text="BACK", width=30, bg="#FF1E27", command=distroy3) button.place(x=0, y=0) label3 = Label(main, text="ENTER STUDENT ID", font=("arial", 17)) label3.place(x=100, y=100) label45 = Label(main, text =" FEE AMOUNT :",font=("arial",17)) label45.place(x= 610,y=100) button22 = Button(main, text="ENTER", width=15, font=("comic sans ms", 17), bg="#8BD8BD", command=cat) button22.place(x=170, y=250) button23 = Button(main, text="PAY", width=8, font=("comic sans ms", 20),bg="#8BD8BD", command=installment2) button23.place(x=670, y=250) entry29 = Entry(main, bd=5, width=20, font=("arial", 15)) entry29.place(x=650, y=170) button28 = Button(main, text="RESET", width=26, font=("arial", 10), bg="#FF1E27", command=reset2) button28.place(x=1150, y=0) label31 = Label(main, text="TOTAL FEE", font=("arial", 17)) label31.place(x=900, y=550) label32 = Label(main, text="PAID FEE", font=("arial", 17)) label32.place(x=600, y=550) label33 = Label(main, text="REMAIN FEE", font=("arial", 17)) label33.place(x=300, y=550) entry25 = Entry(main, bd=5, width=20, font=("arial", 15)) entry25.place(x=170, y=170) entry26 = Entry(main, bd=5, width=20, font=("arial", 15)) entry26.place(x=900, y=600) entry27 = Entry(main, bd=5, width=20, font=("arial", 15)) entry27.place(x=600, y=600) entry28 = Entry(main, bd=5, width=20, font=("arial", 15)) entry28.place(x=300, y=600) main.configure(background='#8000FF') # entry27=Entry(main,bd=5,textvariable=fee3, state="readonly", width=20 ,font=("arial",15)) # entry27.place(x=960,y=400) # entry28=Entry(main,bd=5, width=20 ,font=("arial",15)) # entry28.place(x=900,y=400) # =====================================define charecter===================== # ==================================function============================== calculation2 = 2000 def calculation(): NAME = entry1.get() SUR = entry2.get() EMAIL = entry3.get() BOX = box.get() GANDER = gander.get() PYTHON = python.get() JAVA = java.get() C = c.get() D = d.get() print(PYTHON) print(GANDER) CALCULATE = calculate.get() if NAME == ("") and SUR == ("") and EMAIL == ("") and BOX == ("SELECT") and GANDER == (3) and JAVA == ( 0) and PYTHON == (0) and C == (0) and D == (0): kal = tkinter.messagebox.showinfo(" DETAILS INVALID", "FILL ALL THE DETAILS") else: global x if box.get() == "JAVA" and gander.get() == 0: x = (calculation2 - calculation2 * 20 / 100) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") if box.get() == "JAVA" and gander.get() == 1: x = (calculation2 - calculation2 * 10 / 100) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") if box.get() == "PYTHON" and gander.get() == 1: x = (calculation2) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") if box.get() == "PYTHON" and gander.get() == 0: x = (calculation2 - calculation2 * 10 / 100) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") if box.get() == "C++" and gander.get() == 0: x = (calculation2 - calculation2 * 10 / 100) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") if box.get() == "C" and gander.get() == 1: x = (calculation2) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") if box.get() == "C" and gander.get() == 0: x = (calculation2 - calculation2 * 10 / 100) entry5.configure(state="normal") entry5.delete(0, END) entry5.insert(0, x) entry5.configure(state="disable") def msg(): if branch.get() == "SELECT" or gander.get() == 3 or ( python.get() == 0 and java.get == 0 and c.get() == 0 and d.get() == 0): calculate.set("PLESE FILL ALL") if "@" and ".com" not in entry3.get(): kal = tkinter.messagebox.showinfo(" INVALID DETAILS", "ENTER VALID EMAIL ADDRESS") entry3.delete(0, END) else: msg = tkinter.messagebox.askyesno("Form filling confarmation", " WARNING: All data will be erase after 'YES' for new applicant") if msg > 0: NAME = entry1.get() SUR = entry2.get() EMAIL = entry3.get() BRANCH = box.get() GANDER = gander.get() PYTHON = python.get() JAVA = java.get() C = c.get() D = d.get() CALCULATE = calculate.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute( 'INSERT INTO kistar (name,sur, email, branch, gander,fee ,python,java,c,d ) VALUES(?,?,?,?,?,?,?,?,?,?)', (NAME, SUR, EMAIL, BRANCH, GANDER, CALCULATE, PYTHON, JAVA, C, D,)) golu() def golu(): entry1.delete(0, END) entry2.delete(0, END) entry3.delete(0, END) box.set("SELECT") gander.set(3) python.set(0) java.set(0) c.set(0) d.set(0) calculate.set("") entry4.delete(0, END) def search_id(): SEARCH = entry4.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT name FROM kistar WHERE id=?', (SEARCH,)) for row1 in cur.fetchone(): name.set(row1) def search_sur(): SEARCH = entry4.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT sur FROM kistar WHERE id=?', (SEARCH,)) for row2 in cur.fetchone(): sur.set(row2) def search_email(): SEARCH = entry4.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT email FROM kistar WHERE id=?', (SEARCH,)) for row3 in cur.fetchone(): email.set(row3) def search_branch(): SEARCH = entry4.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT branch FROM kistar WHERE id=?', (SEARCH,)) for row4 in cur.fetchone(): branch.set(row4) def search_gander(): SEARCH = entry4.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT gander FROM kistar WHERE id=?', (SEARCH,)) for row5 in cur.fetchone(): gander.set(row5) def search_course(): SEARCH = entry4.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('SELECT python FROM kistar WHERE id=?', (SEARCH,)) for row6 in cur.fetchone(): python.set(row6) cur.execute('SELECT java FROM kistar WHERE id=?', (SEARCH,)) for row7 in cur.fetchone(): java.set(row7) cur.execute('SELECT c FROM kistar WHERE id=?', (SEARCH,)) for row8 in cur.fetchone(): c.set(row8) cur.execute('SELECT d FROM kistar WHERE id=?', (SEARCH,)) for row9 in cur.fetchone(): d.set(row9) cur.execute('SELECT fee FROM kistar WHERE id=?', (SEARCH,)) for row10 in cur.fetchone(): calculate.set(row10) def update(): box1 = tkinter.messagebox.askyesno("CONFARMATION", "if you update you will be unable to see previous data again") if box1 > 0: SEARCH = entry4.get() NAME = entry1.get() SUR = entry2.get() EMAIL = entry3.get() BRANCH = box.get() GENDER = gander.get() FEE = calculate.get() PYTHON = python.get() JAVA = java.get() C = c.get() D = d.get() CALCULATE = entry5.get() conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute('UPDATE kistar SET name=? WHERE id=?', (NAME, SEARCH,)) cur.execute('UPDATE kistar SET sur=? WHERE id=?', (SUR, SEARCH,)) cur.execute('UPDATE kistar SET email=? WHERE id=?', (EMAIL, SEARCH,)) cur.execute('UPDATE kistar SET branch=? WHERE id=?', (BRANCH, SEARCH,)) cur.execute('UPDATE kistar SET gander=? WHERE id=?', (GENDER, SEARCH,)) cur.execute('UPDATE kistar SET fee=? WHERE id=?', (FEE, SEARCH,)) cur.execute('UPDATE kistar SET python=? WHERE id=?', (PYTHON, SEARCH,)) cur.execute('UPDATE kistar SET java=? WHERE id=?', (JAVA, SEARCH,)) cur.execute('UPDATE kistar SET c=? WHERE id=?', (C, SEARCH,)) cur.execute('UPDATE kistar SET d=? WHERE id=?', (D, SEARCH,)) conn.commit() def delete(): box = tkinter.messagebox.askyesno("WARNING", "DATA WILL NOT BE RECOVER AGAIN") if box > 0: SEARCH = search.get() NAME = name.get() SUR = sur.get() EMAIL = email.get() BRANCH = branch.get() GENDER = gander.get() PYTHON = python.get() JAVA = java.get() C = c.get() D = d.get() CALCULATE = calculate.get() else: conn = sqlite3.connect("abcd12345.db") with conn: cur = conn.cursor() cur.execute("DELETE FROM kistar WHERE id=?", (SEARCH,)) conn.commit() golu() def all(): search_id() search_sur() search_email() search_branch() search_gander() search_course() INQUIRY = Button(text="LOGIN", width=17, font=("arial", 20), bg="MediumOrchid2", command=login) INQUIRY.place(x=560, y=480) firstw.mainloop()

[ "noreply@github.com" ]

DeepakKumarMD.noreply@github.com

9bc70906c5a573ba42746d4a2f4efbf81e0e86c1

98f730ec6a43d8be4a34b0f2a44a9d35989d2287

/pynifi_client/models/tenants_entity.py

b4af3df3c70dc03de0e1a0bfb4fb63eb26b9a058

[]

no_license

scottwr98/pynifi-client

9337a4f322536ee466d419a788b8b5948cdc62d7

013ac2ffa591284a0d6cbb9ed552681cc6f91165

refs/heads/master

2020-04-18T08:47:03.680749

2017-11-04T23:59:58

null

0

null

UTF-8

Python

false

4,219

py

# coding: utf-8 """ NiFi Rest Api The Rest Api provides programmatic access to command and control a NiFi instance in real time. Start and stop processors, monitor queues, query provenance data, and more. Each endpoint below includes a description, definitions of the expected input and output, potential response codes, and the authorizations required to invoke each service. # noqa: E501 OpenAPI spec version: 1.4.0 Contact: dev@nifi.apache.org Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from pynifi_client.models.tenant_entity import TenantEntity # noqa: F401,E501 class TenantsEntity(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'users': 'list[TenantEntity]', 'user_groups': 'list[TenantEntity]' } attribute_map = { 'users': 'users', 'user_groups': 'userGroups' } def __init__(self, users=None, user_groups=None): # noqa: E501 """TenantsEntity - a model defined in Swagger""" # noqa: E501 self._users = None self._user_groups = None self.discriminator = None if users is not None: self.users = users if user_groups is not None: self.user_groups = user_groups @property def users(self): """Gets the users of this TenantsEntity. # noqa: E501 :return: The users of this TenantsEntity. # noqa: E501 :rtype: list[TenantEntity] """ return self._users @users.setter def users(self, users): """Sets the users of this TenantsEntity. :param users: The users of this TenantsEntity. # noqa: E501 :type: list[TenantEntity] """ self._users = users @property def user_groups(self): """Gets the user_groups of this TenantsEntity. # noqa: E501 :return: The user_groups of this TenantsEntity. # noqa: E501 :rtype: list[TenantEntity] """ return self._user_groups @user_groups.setter def user_groups(self, user_groups): """Sets the user_groups of this TenantsEntity. :param user_groups: The user_groups of this TenantsEntity. # noqa: E501 :type: list[TenantEntity] """ self._user_groups = user_groups def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, TenantsEntity): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "ajish@rootedinsights.com" ]

ajish@rootedinsights.com

6f2c689cf82b43d48f1d508aa2010818e990e7a7

a80f73c8b5f2b807b4ec6d1c5c1c781ba0bfdc3a

/projecteuler/data_prob61.py

7acbbb2144278544687cee62f4142cd2504e23a9

[]

no_license

Himanshu-Mishr/projecteuler

215d30c1b2742bb2e8f95336db3cdb4799f78680

419be91e480c9f29911f3370c443f0abb528f033

refs/heads/master

2021-01-13T02:30:24.313301

2014-09-07T05:22:39

null

0

null

UTF-8

Python

false

3,339

py

##### triangle number tri = ['1035', '1081', '1128', '1176', '1225', '1275', '1326', '1378', '1431', '1485', '1540', '1596', '1653', '1711', '1770', '1830', '1891', '1953', '2016', '2080', '2145', '2211', '2278', '2346', '2415', '2485', '2556', '2628', '2701', '2775', '2850', '2926', '3003', '3081', '3160', '3240', '3321', '3403', '3486', '3570', '3655', '3741', '3828', '3916', '4005', '4095', '4186', '4278', '4371', '4465', '4560', '4656', '4753', '4851', '4950', '5050', '5151', '5253', '5356', '5460', '5565', '5671', '5778', '5886', '5995', '6105', '6216', '6328', '6441', '6555', '6670', '6786', '6903', '7021', '7140', '7260', '7381', '7503', '7626', '7750', '7875', '8001', '8128', '8256', '8385', '8515', '8646', '8778', '8911', '9045', '9180', '9316', '9453', '9591', '9730', '9870'] ##### square number sqr = ['1024', '1089', '1156', '1225', '1296', '1369', '1444', '1521', '1600', '1681', '1764', '1849', '1936', '2025', '2116', '2209', '2304', '2401', '2500', '2601', '2704', '2809', '2916', '3025', '3136', '3249', '3364', '3481', '3600', '3721', '3844', '3969', '4096', '4225', '4356', '4489', '4624', '4761', '4900', '5041', '5184', '5329', '5476', '5625', '5776', '5929', '6084', '6241', '6400', '6561', '6724', '6889', '7056', '7225', '7396', '7569', '7744', '7921', '8100', '8281', '8464', '8649', '8836', '9025', '9216', '9409', '9604', '9801'] ##### pentagonal number penta = ['1001', '1080', '1162', '1247', '1335', '1426', '1520', '1617', '1717', '1820', '1926', '2035', '2147', '2262', '2380', '2501', '2625', '2752', '2882', '3015', '3151', '3290', '3432', '3577', '3725', '3876', '4030', '4187', '4347', '4510', '4676', '4845', '5017', '5192', '5370', '5551', '5735', '5922', '6112', '6305', '6501', '6700', '6902', '7107', '7315', '7526', '7740', '7957', '8177', '8400', '8626', '8855', '9087', '9322', '9560', '9801'] ##### hexagonal number hexa = ['1035', '1128', '1225', '1326', '1431', '1540', '1653', '1770', '1891', '2016', '2145', '2278', '2415', '2556', '2701', '2850', '3003', '3160', '3321', '3486', '3655', '3828', '4005', '4186', '4371', '4560', '4753', '4950', '5151', '5356', '5565', '5778', '5995', '6216', '6441', '6670', '6903', '7140', '7381', '7626', '7875', '8128', '8385', '8646', '8911', '9180', '9453', '9730'] ##### heptagonal number hepta = ['1071', '1177', '1288', '1404', '1525', '1651', '1782', '1918', '2059', '2205', '2356', '2512', '2673', '2839', '3010', '3186', '3367', '3553', '3744', '3940', '4141', '4347', '4558', '4774', '4995', '5221', '5452', '5688', '5929', '6175', '6426', '6682', '6943', '7209', '7480', '7756', '8037', '8323', '8614', '8910', '9211', '9517', '9828'] ##### octogonal number octa = ['1045', '1160', '1281', '1408', '1541', '1680', '1825', '1976', '2133', '2296', '2465', '2640', '2821', '3008', '3201', '3400', '3605', '3816', '4033', '4256', '4485', '4720', '4961', '5208', '5461', '5720', '5985', '6256', '6533', '6816', '7105', '7400', '7701', '8008', '8321', '8640', '8965', '9296', '9633', '9976']

[ "himanshu.m786@gmail.com" ]

himanshu.m786@gmail.com

f3373b35d609b72af3d72b5f3fa8644a8a46377d

540597e8377f14d73a0e0c8716c67743876fac22

/todotracker/todolist/urls.py

29d2b4d40b41ecff47213b7a6a6f0e165ec9ce03

[]

no_license

sram04/TodoApp

d88be6faa50d9508f7d80aaed8e44e1ba14af052

d60001cd9c4d8b8ed1465ed77439595f2e435901

refs/heads/master

2020-03-19T02:08:50.749507

2018-10-31T17:32:06

135,603,115

0

null

2018-06-20T03:09:02

2018-05-31T15:35:41

HTML

UTF-8

Python

false

327

py

from rest_framework.routers import DefaultRouter from .api import StatusViewSet, TaskItemViewSet, EventViewSet router = DefaultRouter() router.register(r'status', StatusViewSet) router.register(r'taskitems', TaskItemViewSet, 'taskitem-list') router.register(r'events', EventViewSet, 'events-list') urlpatterns = router.urls

[ "sairamendreddy@gmail.com" ]

sairamendreddy@gmail.com

5cae4351928b729521bafe551e04ae158fbbd2f3

d60acaac9e460c5693efe61449667b3c399c53c8

/diffeq/logisticbifurcation.py

392cc43dfa415350c9c23054e6d5784488977d9c

[]

no_license

HussainAther/mathematics

53ea7fb2470c88d674faa924405786ba3b860705

6849cc891bbb9ac69cb20dfb13fe6bb5bd77d8c5

refs/heads/master

2021-07-22T00:07:53.940786

2020-05-07T03:11:17

157,749,226

2

0

null

UTF-8

Python

false

2,159

py

import matplotlib.pyplot as plt import numpy as np """ Logistic map bifurcation """ def logistic(r, x): """ Logistic map function for nonlinear systems """ return r * x * (1 - x) x = np.linspace(0, 1) fig, ax = plt.subplots(1, 1) ax.plot(x, logistic(2, x), "k") def plotsystem(r, x0, n, ax=None): """ Plot the function and the y=x diagonal line. """ t = np.linspace(0, 1) ax.plot(t, logistic(r, t), "k", lw=2) ax.plot([0, 1], [0, 1], "k", lw=2) # Recursively apply y=f(x) and plot two lines: # (x, x) -> (x, y) # (x, y) -> (y, y) x = x0 for i in range(n): y = logistic(r, x) # Plot the two lines. ax.plot([x, x], [x, y], "k", lw=1) ax.plot([x, y], [y, y], "k", lw=1) # Plot the positions with increasing # opacity. ax.plot([x], [y], "ok", ms=10, alpha=(i + 1) / n) x = y ax.set_xlim(0, 1) ax.set_ylim(0, 1) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6), sharey=True) plotsystem(2.5, .1, 10, ax=ax1) plotsystem(3.5, .1, 10, ax=ax2) n = 10000 r = np.linspace(2.5, 4.0, n) iterations = 1000 last = 100 x = 1e-5 * np.ones(n) # lyapunov = np.zeros(n) # fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 9), # sharex=True) for i in range(iterations): x = logistic(r, x) # We compute the partial sum of the # Lyapunov exponent. # lyapunov += np.log(abs(r - 2 * r * x)) # We display the bifurcation diagram. if i >= (iterations - last): ax1.plot(r, x, ",k", alpha=.25) ax1.set_xlim(2.5, 4) ax1.set_title("Bifurcation diagram") # Display the Lyapunov exponent. # Horizontal line. # ax2.axhline(0, color="k", lw=.5, alpha=.5) # Negative Lyapunov exponent. # ax2.plot(r[lyapunov < 0], # lyapunov[lyapunov < 0] / iterations, # ".k", alpha=.5, ms=.5) # Positive Lyapunov exponent. # ax2.plot(r[lyapunov >= 0], # lyapunov[lyapunov >= 0] / iterations, # ".r", alpha=.5, ms=.5) # ax2.set_xlim(2.5, 4) # ax2.set_ylim(-2, 1) # ax2.set_title("Lyapunov exponent") # plt.tight_layout()

[ "shussainather@gmail.com" ]

shussainather@gmail.com

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nota1 = float(input('Digite a primeira nota bimestral: ')) nota2 = float(input('Digite a segunda nota bimestral: ')) nota3 = float(input('Digite a terceira nota bimestral: ')) nota4 = float(input('Digite a quarta nota bimestral: ')) media = (nota1 + nota2 + nota3 + nota4) / 4 mediastr = '' for i in str(media): if i == '.': i = ',' mediastr += i print(f'A média das notas é: {mediastr}.')

[ "aldrinpdscastro@gmail.com" ]

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# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath('./..')) # -- Project information ----------------------------------------------------- project = 'gql 3' copyright = '2020, graphql-python.org' author = 'graphql-python.org' # The full version, including alpha/beta/rc tags from gql import __version__ release = __version__ # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx_rtd_theme' ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Output file base name for HTML help builder. htmlhelp_basename = 'gql-3-doc' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # html_static_path = ['_static'] # -- AutoDoc configuration ------------------------------------------------- # autoclass_content = "both" autodoc_default_options = { 'members': True, 'inherited-members': True, 'special-members': '__init__', 'undoc-members': True, 'show-inheritance': True } autosummary_generate = True

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def extractSleepysmutCom(item): ''' Parser for 'sleepysmut.com' ''' vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title']) if not (chp or vol) or "preview" in item['title'].lower(): return None tagmap = [ ('PRC', 'PRC', 'translated'), ('Loiterous', 'Loiterous', 'oel'), ] for tagname, name, tl_type in tagmap: if tagname in item['tags']: return buildReleaseMessageWithType(item, name, vol, chp, frag=frag, postfix=postfix, tl_type=tl_type) return False

[ "something@fake-url.com" ]

something@fake-url.com

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import requests import ast base_url = "https://developers.zomato.com/api/v2.1/" def initialize_app(config): return Zomato(config) class Zomato: def __init__(self, config): self.user_key = config["user_key"] def get_categories(self): """ Takes no input. Returns a dictionary of IDs and their respective category names. """ headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "categories", headers=headers).content).decode("utf-8") a = ast.literal_eval(r) self.is_key_invalid(a) self.is_rate_exceeded(a) categories = {} for category in a['categories']: categories.update({category['categories']['id'] : category['categories']['name']}) return categories def get_city_ID(self, city_name): """ Takes City Name as input. Returns the ID for the city given as input. """ if city_name.isalpha() == False: raise ValueError('InvalidCityName') city_name = city_name.split(' ') city_name = '%20'.join(city_name) headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "cities?q=" + city_name, headers=headers).content).decode("utf-8") a = ast.literal_eval(r) self.is_key_invalid(a) self.is_rate_exceeded(a) if len(a['location_suggestions']) == 0: raise Exception('invalid_city_name') elif 'name' in a['location_suggestions'][0]: city_name = city_name.replace('%20', ' ') if str(a['location_suggestions'][0]['name']).lower() == str(city_name).lower(): return a['location_suggestions'][0]['id'] else: raise ValueError('InvalidCityId') def get_city_name(self, city_ID): """ Takes City ID as input. Returns the name of the city ID given as input. """ self.is_valid_city_id(city_ID) headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "cities?city_ids=" + str(city_ID), headers=headers).content).decode("utf-8") a = ast.literal_eval(r) self.is_key_invalid(a) self.is_rate_exceeded(a) if a['location_suggestions'][0]['country_name'] == "": raise ValueError('InvalidCityId') else: temp_city_ID = a['location_suggestions'][0]['id'] if temp_city_ID == str(city_ID): return a['location_suggestions'][0]['name'] def get_collections(self, city_ID, limit=None): """ Takes City ID as input. limit parameter is optional. Returns dictionary of Zomato restaurant collections in a city and their respective URLs. """ #self.is_valid_city_id(city_ID) headers = {'Accept': 'application/json', 'user-key': self.user_key} if limit == None: r = (requests.get(base_url + "collections?city_id=" + str(city_ID), headers=headers).content).decode("utf-8") else: if str(limit).isalpha() == True: raise ValueError('LimitNotInteger') else: r = (requests.get(base_url + "collections?city_id=" + str(city_ID) + "&count=" + str(limit), headers=headers).content).decode("utf-8") a = ast.literal_eval(r) self.is_key_invalid(a) self.is_rate_exceeded(a) collections = {} for collection in a['collections']: collections.update({collection['collection']['title'] : collection['collection']['url']}) return collections def get_cuisines(self, city_ID): """ Takes City ID as input. Returns a sorted dictionary of all cuisine IDs and their respective cuisine names. """ self.is_valid_city_id(city_ID) headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "cuisines?city_id=" + str(city_ID), headers=headers).content).decode("utf-8") a = ast.literal_eval(r) self.is_key_invalid(a) self.is_rate_exceeded(a) if len(a['cuisines']) == 0: raise ValueError('InvalidCityId') temp_cuisines = {} cuisines = {} for cuisine in a['cuisines']: temp_cuisines.update({cuisine['cuisine']['cuisine_id'] : cuisine['cuisine']['cuisine_name']}) for cuisine in sorted(temp_cuisines): cuisines.update({cuisine : temp_cuisines[cuisine]}) return cuisines def get_establishment_types(self, city_ID): """ Takes City ID as input. Returns a sorted dictionary of all establishment type IDs and their respective establishment type names. """ self.is_valid_city_id(city_ID) headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "establishments?city_id=" + str(city_ID), headers=headers).content).decode("utf-8") a = ast.literal_eval(r) self.is_key_invalid(a) self.is_rate_exceeded(a) temp_establishment_types = {} establishment_types = {} if 'establishments' in a: for establishment_type in a['establishments']: temp_establishment_types.update({establishment_type['establishment']['id'] : establishment_type['establishment']['name']}) for establishment_type in sorted(temp_establishment_types): establishment_types.update({establishment_type : temp_establishment_types[establishment_type]}) return establishment_types else: raise ValueError('InvalidCityId') def get_nearby_restaurants(self, latitude, longitude): """ Takes the latitude and longitude as inputs. Returns a dictionary of Restaurant IDs and their corresponding Zomato URLs. """ try: float(latitude) float(longitude) except ValueError: raise ValueError('InvalidLatitudeOrLongitude') headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "geocode?lat=" + str(latitude) + "&lon=" + str(longitude), headers=headers).content).decode("utf-8") a = ast.literal_eval(r) nearby_restaurants = {} for nearby_restaurant in a['nearby_restaurants']: nearby_restaurants.update({nearby_restaurant['restaurant']['id'] : nearby_restaurant['restaurant']['url']}) return nearby_restaurants def get_restaurant(self, restaurant_ID): """ Takes Restaurant ID as input. Returns a dictionary of restaurant details. """ self.is_valid_restaurant_id(restaurant_ID) headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "restaurant?res_id=" + str(restaurant_ID), headers=headers).content).decode("utf-8") a = ast.literal_eval(r) if 'code' in a: if a['code'] == 404: raise('InvalidRestaurantId') restaurant_details = {} restaurant_details.update({"name" : a['name']}) restaurant_details.update({"url" : a['url']}) restaurant_details.update({"location" : a['location']['address']}) restaurant_details.update({"city" : a['location']['city']}) restaurant_details.update({"city_ID" : a['location']['city_id']}) restaurant_details.update({"user_rating" : a['user_rating']['aggregate_rating']}) restaurant_details = DotDict(restaurant_details) return restaurant_details def restaurant_search(self, query="", latitude="", longitude="", cuisines="", limit=5): """ Takes either query, latitude and longitude or cuisine as input. Returns a list of Restaurant IDs. """ cuisines = "%2C".join(cuisines.split(",")) if str(limit).isalpha() == True: raise ValueError('LimitNotInteger') headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "search?q=" + str(query) + "&count=" + str(limit) + "&lat=" + str(latitude) + "&lon=" + str(longitude) + "&cuisines=" + str(cuisines + "&sort=rating" + "&order=desc"), headers=headers).content).decode("utf-8") return r#a = ast.literal_eval(r) def get_location(self, query="", limit=5): """ Takes either query, latitude and longitude or cuisine as input. Returns a list of Restaurant IDs. """ if str(limit).isalpha() == True: raise ValueError('LimitNotInteger') headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "locations?query=" + str(query) + "&count=" + str(limit), headers=headers).content).decode("utf-8") return r def restaurant_search_by_keyword(self, query="", cuisines="", limit=5): """ Takes either query, latitude and longitude or cuisine as input. Returns a list of Restaurant IDs. """ cuisines = "%2C".join(cuisines.split(",")) if str(limit).isalpha() == True: raise ValueError('LimitNotInteger') headers = {'Accept': 'application/json', 'user-key': self.user_key} r = (requests.get(base_url + "search?q=" + str(query) + "&count=" + str(limit) + "&cuisines=" + str(cuisines), headers=headers).content).decode("utf-8") return r def is_valid_restaurant_id(self, restaurant_ID): """ Checks if the Restaurant ID is valid or invalid. If invalid, throws a InvalidRestaurantId Exception. """ restaurant_ID = str(restaurant_ID) if restaurant_ID.isnumeric() == False: raise ValueError('InvalidRestaurantId') def is_valid_city_id(self, city_ID): """ Checks if the City ID is valid or invalid. If invalid, throws a InvalidCityId Exception. """ city_ID = str(city_ID) if city_ID.isnumeric() == False: return True# raise ValueError('InvalidCityId') def is_key_invalid(self, a): """ Checks if the API key provided is valid or invalid. If invalid, throws a InvalidKey Exception. """ if 'code' in a: if a['code'] == 403: raise ValueError('InvalidKey') def is_rate_exceeded(self, a): """ Checks if the request limit for the API key is exceeded or not. If exceeded, throws a ApiLimitExceeded Exception. """ if 'code' in a: if a['code'] == 440: raise Exception('ApiLimitExceeded') class DotDict(dict): """ Dot notation access to dictionary attributes """ __getattr__ = dict.get __setattr__ = dict.__setitem__ __delattr__ = dict.__delitem__

[ "samal.rajesh@gmail.com" ]

samal.rajesh@gmail.com

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# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2017-11-28 18:57 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion import wagtail.contrib.table_block.blocks import wagtail.core.blocks import wagtail.core.fields import wagtail.embeds.blocks import wagtail.images.blocks class Migration(migrations.Migration): dependencies = [ ('wagtailcore', '0040_page_draft_title'), ('core', '0006_parentpage'), ] operations = [ migrations.CreateModel( name='AdoptionCentre', fields=[ ('page_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='wagtailcore.Page')), ('menu_title', models.CharField(blank=True, max_length=100, verbose_name='menu title')), ('picture_blocks', wagtail.core.fields.StreamField((('animals_link', wagtail.core.blocks.ListBlock(wagtail.core.blocks.StructBlock((('title', wagtail.core.blocks.CharBlock(max_length=50)), ('image', wagtail.images.blocks.ImageChooserBlock()), ('page', wagtail.core.blocks.PageChooserBlock()))))),))), ], options={ 'verbose_name': 'Adoption Centre ', }, bases=('wagtailcore.page', models.Model), ), migrations.AlterField( model_name='contentpage', name='body', field=wagtail.core.fields.StreamField((('heading_block', wagtail.core.blocks.StructBlock((('heading_text', wagtail.core.blocks.CharBlock(classname='title', required=True)), ('size', wagtail.core.blocks.ChoiceBlock(blank=True, choices=[('', 'Select a header size'), ('h2', 'H2'), ('h3', 'H3'), ('h4', 'H4')], required=False))))), ('paragraph_block', wagtail.core.blocks.RichTextBlock(icon='fa-paragraph', label='Paragraph')), ('image_block', wagtail.core.blocks.StructBlock((('image', wagtail.images.blocks.ImageChooserBlock(required=True)), ('caption', wagtail.core.blocks.CharBlock(required=False)), ('attribution', wagtail.core.blocks.CharBlock(required=False))))), ('block_quote', wagtail.core.blocks.StructBlock((('text', wagtail.core.blocks.TextBlock()), ('attribute_name', wagtail.core.blocks.CharBlock(blank=True, label='e.g. Mary Berry', required=False))))), ('embed_block', wagtail.embeds.blocks.EmbedBlock(help_text='Insert an embed URL e.g https://www.youtube.com/embed/SGJFWirQ3ks', icon='fa-external-link-square', label='Embedded Media', template='core/blocks/embed_block.html')), ('table_block', wagtail.core.blocks.StructBlock((('table', wagtail.contrib.table_block.blocks.TableBlock()), ('caption', wagtail.core.blocks.CharBlock(required=False))))), ('raw_html', wagtail.core.blocks.StructBlock((('html', wagtail.core.blocks.RawHTMLBlock()),)))), blank=True, verbose_name='Page body'), ), ]

[ "nigel@thebluehut.com" ]

nigel@thebluehut.com

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lequan26111991/CIS8005-Data-Programming-for-Analytics

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''' Chapter 3 Excercise 16 ''' from turtle import * pensize(3) penup() goto(-200, -50) pendown() '''Draw a Triangle ''' right(60) circle(40, steps = 3) '''Draw a Square ''' left(15) penup() goto(-100, -50) pendown() begin_fill() color("blue") circle(40, steps = 4) end_fill() '''Draw a pentagon''' left(9) penup() goto(0, -50) pendown() begin_fill() color("green") circle(40, steps = 5) end_fill() '''Draw a hexagon''' left(7) penup() goto(100, -50) pendown() begin_fill() color("yellow") circle(40, steps = 6) end_fill() '''Draw a octagon''' penup() goto(200, -50) pendown() begin_fill() color("purple") '''Draw a circle''' circle(40) end_fill() color("green") ''' Draw Words''' penup() goto(-100, 50) pendown() write("Cool Colorful Shapes", font = ("Times", 18, "bold")) done()

[ "lequan26111991@gmail.com" ]

lequan26111991@gmail.com

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from wpilib.command.command import Command from wpilib.driverstation import DriverStation from selector import Selector class TestSelector(Command): def __init__(self): super().__init__() self.testSelector = Selector([("row1", "op1", "op2", "op3")], (0,)) DriverStation.getInstance() def initialize(self): pass def execute(self): self.testSelector.selectorLogic() """ def isFinished(self): return not DriverStation.isDisabled() """

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/preprocessing_package.py

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knut-henning/MovementClassification_Thesis

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# -*- coding: utf-8 -*- """ Preprocessing package """ import pandas as pd import numpy as np import matplotlib.pyplot as plt from datetime import timedelta plt.style.use('seaborn') __author__ = 'Knut-Henning Kofoed' __email__ = 'knut-henning@hotmail.com' def import_activity(data_path): """ Imports activity data from given path, adds 'Kalv' column with boolean expressions, corrects time and sets datatype. Parameters ---------- data_path : str String with path and name of csv file to import. Returns ------- pd.DataFrame Dataframe object of csv file. """ # Import csv activity_data = pd.read_csv(data_path, header=0, delimiter=';', dtype=str) # Finds instances in "Type dyr" that contains "kalv", sets column value to True activity_data['Kalv'] = activity_data['Type dyr'].map(lambda x: 'kalv' in x) # Removes ' from the datetime string (occurs in Nofence provided activity data) activity_data['Tid'] = activity_data['Tid'].str.rstrip("'") # Convert 'Tid' from 'str' to 'datetime' activity_data['Tid'] = pd.to_datetime(activity_data['Tid']) # Convert "Nofence ID" type from "str" to "int64" activity_data['Nofence ID'] = activity_data['Nofence ID'].astype('int64') return activity_data def correction_activity(activity_data): """ Used to correct the activity classified data provided by Nofence, contains some typos and unwanted spaces that needs correction before usage. Parameters ---------- activity_data : pd.DataFrame Activity DataFrame to do correction on. Returns ------- activity_data : pd.DataFrame Corrected DataFrame. """ # Correct column names columns = ['Nofence ID', 'ID', 'Type dyr', 'Tid', 'Aktivitet', 'Dier kalv', 'Kommentar', 'Kalv'] # Sets the correct column names activity_data.columns = columns # Removes unwanted spaces to the right of the words. activity_data['Aktivitet'] = activity_data['Aktivitet'].str.rstrip() activity_data['Type dyr'] = activity_data['Type dyr'].str.rstrip() # Correct typos in column "Aktivitet" activity_data['Aktivitet'] = activity_data['Aktivitet'].replace({'Beter slutt': 'Beiter slutt'}) # Removes rows that contain the word "Aktivitet" in the column "Aktivitet" activity_data = activity_data[~activity_data['Aktivitet'].str.contains('Aktivitet')] activity_data = activity_data.reset_index(drop=True) return activity_data def offset_time(activity_data, column='Tid', hour=-2, finetune=False, second=0): """ Offset time of datetime column. (mainly used to convert datetime from CEST to UTC) Parameters ---------- activity_data : pd.DataFrame Dataframe to offset datetime on column : str, optional Name of column to do the offset on. The default is 'Tid'. hour : int, optional Number of hours to offset. The default is -2. finetune : boolean, optional Specify if finetuning of seconds is wanted second : int, optional Number of seconds to finetune Returns ------- activity_data : pd.DataFrame DataFrame with offset datetime values. """ if finetune: activity_data[column] = activity_data[column] + pd.DateOffset(hours=hour, seconds=second) else: activity_data[column] = activity_data[column] + pd.DateOffset(hours=hour) return activity_data def start_stop_corr(activity_data): """ Mainly used for activity classification data provided by Nofence. For the later functions to work activity registration has to contain blocks with "VIDEO START" and "VIDEO SLUTT" to work. This function also prints how many rows has missing blocks with these strings. Parameters ---------- activity_data : pd.DataFrame Activity registration data. Returns ------- activity_data : pd.DataFrame Corrected for "VIDEO START" and "VIDEO SLUTT". """ missing = 0 # Variable that stores number of rows that do not contain START/SLUTT in "Aktivitet" column row_iterator = activity_data.iterrows() _, last = next(row_iterator) # First value of row_iterator for i, row in row_iterator: # Saves the index where "VIDEO START" AND "VIDEO SLUTT" is expected in the "Aktivitet" column if (row['Nofence ID'] != last['Nofence ID']) & \ (row['Aktivitet'] != 'VIDEO START') & \ (last['Aktivitet'] != 'VIDEO SLUTT'): df = pd.concat([pd.DataFrame({"Nofence ID": last['Nofence ID'], "ID": last['ID'], "Type dyr": last['Type dyr'], "Tid": last['Tid'], "Aktivitet": 'VIDEO SLUTT', "Kommentar": '', "Kalv": last['Kalv']}, index = [i + missing] ), pd.DataFrame({"Nofence ID": row['Nofence ID'], "ID": row['ID'], "Type dyr": row['Type dyr'], "Tid": row['Tid'], "Aktivitet": 'VIDEO START', "Kommentar": '', "Kalv": row['Kalv']}, index = [i + missing + 1] ) ]) activity_data = pd.concat([activity_data.iloc[:df.index[0]], df, activity_data.iloc[df.index[0]:] ]).reset_index(drop=True) missing += 2 # Saves the index where "VIDEO START" is expected in the "Aktivitet" column elif (row['Nofence ID'] != last['Nofence ID']) & \ (row['Aktivitet'] != 'VIDEO START') & \ (last['Aktivitet'] == 'VIDEO SLUTT'): df = pd.DataFrame({"Nofence ID": row['Nofence ID'], "ID": row['ID'], "Type dyr": row['Type dyr'], "Tid": row['Tid'], "Aktivitet": 'VIDEO START', "Kommentar": '', "Kalv": row['Kalv']}, index = [i + missing] ) activity_data = pd.concat([activity_data.iloc[:df.index[0]], df, activity_data.iloc[df.index[0]:] ]).reset_index(drop=True) missing += 1 # Saves the index where "VIDEO SLUTT" is expected in the "Aktivitet" column elif (row['Nofence ID'] != last['Nofence ID']) & \ (last['Aktivitet'] != 'VIDEO SLUTT') & \ (row['Aktivitet'] == 'VIDEO START'): df = pd.DataFrame({"Nofence ID": last['Nofence ID'], "ID": last['ID'], "Type dyr": last['Type dyr'], "Tid": last['Tid'], "Aktivitet": 'VIDEO SLUTT', "Kommentar": '', "Kalv": last['Kalv']}, index = [i + missing] ) activity_data = pd.concat([activity_data.iloc[:df.index[0]], df, activity_data.iloc[df.index[0]:] ]).reset_index(drop=True) missing += 1 last = row # Checks if the last row contains "VIDEO SLUTT" in the column "Aktivitet" if row['Aktivitet'] != 'VIDEO SLUTT': df = pd.DataFrame({"Nofence ID": row['Nofence ID'], "ID": row['ID'], "Type dyr": row['Type dyr'], "Tid": row['Tid'], "Aktivitet": 'VIDEO SLUTT', "Kommentar": '', "Kalv": row['Kalv']}, index = [i + missing + 1] ) activity_data = pd.concat([activity_data.iloc[:df.index[0]], df, activity_data.iloc[df.index[0]:] ]).reset_index(drop=True) missing += 1 print('Activity dataframe have {} missing rows with "VIDEO START/SLUTT"'.format(missing)) return activity_data def unique_serials(activity_data): """ Creates a list of unique serials that the "Nofence ID" column in dataframe contains. Parameters ---------- activity_data : pd.DataFrame Activity registration data. Returns ------- serials : list List of serials. """ serials = list(activity_data['Nofence ID'].unique()) print('Serials from dataframe: {}'.format(serials)) return serials def activity_time_interval(activity_data): """ Makes a dataframe with all "VIDEO START" "VIDEO SLUTT" intervals Parameters ---------- activity_data : pd.DataFrame Activity registration data. Returns ------- start_stop : pd.DataFrame Rows containing "VIDEO START/SLUTT" """ start_stop = activity_data[(activity_data['Aktivitet'] == 'VIDEO START') | \ (activity_data['Aktivitet'] == 'VIDEO SLUTT')] return start_stop def acc_time_corr(acc_data): """ Gives the data better time resolution since it originally only updated every 32 observation. Parameters ---------- acc_data : pd.DataFrane Accelerometer data. Returns ------- acc_data : pd.DataFrame Accelerometer data with better time resolution. """ times = acc_data[['date', 'header_date']] unique_time = times.drop_duplicates(subset=['header_date']) unique_time.loc[:,'time_delta'] = unique_time.loc[:,'header_date'] - unique_time.loc[:,'header_date'].shift() unique_time = unique_time.append({'time_delta': timedelta(seconds = 3)}, ignore_index=True) time_iterator = unique_time.iterrows() _, last = next(time_iterator) # First value of time_iterator for i, time in time_iterator: dt = time['time_delta'].total_seconds() dt = dt / 32 df_dt = pd.to_timedelta(acc_data['index'].iloc[(i-1)*32:32+((i-1)*32)] * dt, unit='s') acc_data['header_date'].iloc[(i-1)*32:32+((i-1)*32)] = acc_data['header_date'].iloc[(i-1)*32:32+((i-1)*32)] \ + df_dt acc_data.loc[:,'header_date'] = acc_data.header_date.dt.ceil(freq='s') return acc_data def import_aks(serials, start_stop, acc_names='kalvelykke'): """ Sort relevant accelerometerdata based on activity registration data. Parameters ---------- serials : list List of serial numbers you want accelerometerdata from. start_stop : pd.DataFrame Dataframe containing activity registration intervals. acc_names : str, optional String of letters before -ID. The default is "kalvelykke". Returns ------- start_slutt_acc : pd.DataFrame Accelerometer data from the timeintervals and serials expressed in serials and start_stop input. """ # Define column names start_slutt_acc = pd.DataFrame(columns=['serial', 'date', 'header_date', 'index', 'x', 'y', 'z', 'xcal','ycal', 'zcal', 'norm']) for serial in serials: # Import files df_acc = pd.read_csv('accelerometer\\{0}-{1}.csv'.format(acc_names, serial), header=1) # Convert 'date' from str to datetime df_acc['header_date'] = pd.to_datetime(df_acc['header_date'], format='%Y-%m-%dT%H:%M:%S') # Makes a simple dataframe for all "VIDEO START/SLUTT" rows with selected serial start_stop_ID = start_stop[(start_stop["Nofence ID"] == serial)] # Makes simple dataframe for start and stop datetimes and combines to own interval dataframe start_ID = start_stop_ID[(start_stop_ID["Aktivitet"] == 'VIDEO START')] start_ID = start_ID['Tid'].reset_index(drop=True) stop_ID = start_stop_ID[(start_stop_ID["Aktivitet"] == 'VIDEO SLUTT')] stop_ID = stop_ID['Tid'].reset_index(drop=True) intervals = pd.concat([start_ID, stop_ID], axis=1) intervals.columns = ['start', 'stop'] # Combines all intervals to one dataframe with relevant data for i in intervals.index: df_interval = df_acc[(df_acc['header_date'] > intervals['start'][i]) & \ (df_acc['header_date'] <= intervals['stop'][i])] df_interval = acc_time_corr(df_interval) start_slutt_acc = start_slutt_acc.append(df_interval, ignore_index=True) return start_slutt_acc def remove_dier(activity_data): """ Removes the rows i activity registrations that contain "Dier start" or "Dier slutt and that has "Kalv" == False. Parameters ---------- activity_data : pd.DataFrame Activity registration data. Returns ------- activity_data : pd.DataFrame Activity registration data without "Dier start" and "Dier slutt" where "Kalv" == False. """ activity_data = activity_data[(~activity_data['Aktivitet'].str.contains('Dier start')) | \ (~activity_data['Aktivitet'].str.contains('Dier slutt')) & \ (activity_data['Kalv'] == True) ] activity_data = activity_data.reset_index(drop=True) return activity_data def connect_data(activity_data, start_slutt_acc): """ Connects activity registrations and accelerometer data so that the accelerometer observations has lables. Parameters ---------- activity_data : pd.DataFrame Activity registration data. start_slutt_acc : pd.DataFrame Accelerometer data Returns ------- acc_act : pd.DataFrame Accelerometer data with lables. """ # Activities: Resting = 0, Movement = 1, Grazing = 2, Suckle = 3, Ruminate = 4 start_slutt_acc['kalv'] = np.nan start_slutt_acc['aktivitet'] = np.nan # Iterates through list of activity registrations acc = 0 # Start activity row_iterator = activity_data.iterrows() _, last = next(row_iterator) # First Value of row_iterator for i, row in row_iterator: # Makes a mask for relevant timeinterval from accelerometer data that is to be labeled mask = (start_slutt_acc['serial'] == last['Nofence ID']) & \ (start_slutt_acc['header_date'] > last['Tid']) & \ (start_slutt_acc['header_date'] <= row['Tid']) if last['Aktivitet'] == 'VIDEO START': acc = 0 # All cases where the activity registration start the cow/calf is resting start_slutt_acc.loc[mask, 'aktivitet'] = acc elif last['Aktivitet'] == 'VIDEO SLUTT': pass elif last['Aktivitet'] == 'Legger seg': acc = 0 start_slutt_acc.loc[mask, 'aktivitet'] = acc elif last['Aktivitet'] == 'Reiser seg': acc = 0 start_slutt_acc.loc[mask, 'aktivitet'] = acc elif last['Aktivitet'] == 'Dier start': start_slutt_acc.loc[mask, 'aktivitet'] = 3 elif last['Aktivitet'] == 'Dier slutt': start_slutt_acc.loc[mask, 'aktivitet'] = acc elif last['Aktivitet'] == 'Beiter start': start_slutt_acc.loc[mask, 'aktivitet'] = 2 elif last['Aktivitet'] == 'Beiter slutt': start_slutt_acc.loc[mask, 'aktivitet'] = acc elif last['Aktivitet'] == 'Bevegelse start': start_slutt_acc.loc[mask, 'aktivitet'] = 1 elif last['Aktivitet'] == 'Bevegelse slutt': start_slutt_acc.loc[mask, 'aktivitet'] = acc elif last['Aktivitet'] == 'Tygge start': start_slutt_acc.loc[mask, 'aktivitet'] = 4 elif last['Aktivitet'] == 'Tygge slutt': start_slutt_acc.loc[mask, 'aktivitet'] = acc start_slutt_acc.loc[mask, 'kalv'] = last['Kalv'] # Makes a column that informs if data is calf or not last = row # Data has floating point precision errors that need correcting acc_act = start_slutt_acc.round({'xcal': 3, 'ycal': 3, 'zcal': 3}) # Removes rows containing nan and converts the column "aktivitet" from float to int acc_act = acc_act.dropna() acc_act['aktivitet'] = acc_act['aktivitet'].astype('int64') return acc_act def select_serial(df_input, serial): """ Selects data based on serial Parameters ---------- df_input : pd.DataFrame DataFrame to do selection on. serial : TYPE Serial to select. Returns ------- df_output : TYPE Selected data based on serial. """ df_output = df_input[df_input['serial'] == serial] return df_output def save_dataframe(data, path, index=False): """ Saves data to given path as csv. Parameters ---------- data : pd.DataFrame Data to be saved. path : str Location and file name of data to be saved. index : boolean, optional Specifies if index is wanted or not. The default is False. Returns ------- None. """ data.to_csv(path, index=index) if __name__ == '__main__': pass

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def conversion(grade): try: grade = float(grade) if grade >= 18 and grade <= 20: print "A" elif grade >= 16 and grade < 18: print "B" elif grade >= 14 and grade < 16: print "C" elif grade >= 12 and grade < 14: print "D" elif grade >= 0 and grade < 12: print "F" else: print "Note doit etre entre 0 et 20" except: print "Il faut une valeur num\xc3\xa9rique." conversion(raw_input("Encoder la cote sur 20 : "))

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''' @author [Sundar G] @email [sundargopal17@gmail.com] @desc [Huffman Encoding Scheme] ''' #file = open('doc_aede9a09-f36f-421c-ac4f-1c76376016da.docx','r') import docx as d import heapq import sys file = d.Document('HuffmanAssignment.docx') p = file.paragraphs dictionary ={} for i in p: for j in i.text: num = dictionary.keys() k = j.lower() if k in num: dictionary[k] +=1 else: dictionary[k]=1 print(dictionary) sum= dictionary.values() j=0 frequency = {} clean_dict={} alphanum_dict = {key: value for key, value in dictionary.items() if key.isalpha()} num_dict = {key: value for key, value in dictionary.items() if key.isnumeric()} alphanum_dict.update(num_dict) #alphanumerical dictionary print('\n') for i in sum: j = j+i print('Total is :{}'.format(j)) def huffman_encoding(frequency): heap=[[weight,[symbol,'']] for symbol,weight in frequency.items()] heapq.heapify(heap) while len(heap)>1: lo = heapq.heappop(heap) hi = heapq.heappop(heap) for pair in lo[1:]: pair[1] = '0'+ pair[1] for pair in hi[1:]: pair[1] = '1'+ pair[1] heapq.heappush(heap,[lo[0]+hi[0]]+lo[1:]+hi[1:]) return sorted(heapq.heappop(heap)[1:],key=lambda p:(len(p[-1]),p)) coding_scheme=huffman_encoding(dictionary) print(coding_scheme) print('\n') print('\t|Letter/Number |Probability |Huffman Code |') print('\t-----------------------------------------------') for d in coding_scheme: print('\t|{}\t\t|{:.05f}\t|{}'.format(d[0],dictionary[d[0]]/4579.0,d[1])) s=0 for i in coding_scheme: s += dictionary[i[0]]/4579.0 print('Probability sum is:{}'.format(s)) new_dictionary = {} for i in coding_scheme: num = new_dictionary.keys() new_dictionary[i[1]] = i[0] name = list(input('Enter your name to encode:')) roll_number = list(input('Enter your roll number to encode:')) code1 = '' code2 = '' for i in name: for d in coding_scheme: if i==d[0]: code1 +=d[1] for j in roll_number: for d in coding_scheme: if j==d[0]: code2 +=d[1] print('Huffman Code for your name using the given coding scheme is: {}'.format(code1)) print('Huffman code for your roll number using the given coding scheme is: {}'.format(code2)) def huffman_decode(dictionary, text): res = '' while text: for k in dictionary: if text.startswith(k): res += dictionary[k] text = text[len(k):] return res a = huffman_decode(new_dictionary,code1) b = huffman_decode(new_dictionary,code2) print('\nDecoded entity 1 is :{}'.format(a)) print('Decoded entity 2 is :{}'.format(b))

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from flask_wtf import FlaskForm from wtforms import SelectField, BooleanField class SearchForm(FlaskForm): species = SelectField() spot = SelectField() orderBySize = BooleanField() class Meta: csrf = False

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#!/Users/marklastovski/PycharmProjects/supremeaiarena_backend/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install' __requires__ = 'setuptools==40.8.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install')() )

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import os from WMCore.Configuration import Configuration from CRABClient.UserUtilities import config, getUsernameFromCRIC config = Configuration() config.section_("General") config.General.requestName = '2018_ElMu_A' config.General.transferOutputs = True config.General.transferLogs = True config.section_("JobType") config.JobType.allowUndistributedCMSSW = True config.JobType.pluginName = 'Analysis' config.JobType.psetName = 'crab_PSet_2018_ElMu_A.py' config.JobType.maxMemoryMB = 3000 config.JobType.maxJobRuntimeMin = 1315 config.JobType.numCores = 1 config.JobType.scriptExe = 'crab_script_2018_ElMu_A.sh' config.JobType.inputFiles = ['crab_script_2018_ElMu_A.py', os.path.join(os.environ['CMSSW_BASE'],'src/PhysicsTools/NanoAODTools/scripts/haddnano.py'), ] config.JobType.outputFiles = [] #['hist.root'] config.JobType.sendPythonFolder = True config.section_("Data") config.Data.inputDataset = '/MuonEG/Run2018A-02Apr2020-v1/NANOAOD' config.Data.inputDBS = 'global' config.Data.splitting = 'FileBased' if config.Data.splitting == 'FileBased': config.Data.unitsPerJob = 1 # config.Data.totalUnits = $TOTAL_UNITS # config.Data.userInputFiles = [] # config.Data.outLFNDirBase = '/store/user/{user}/NoveCampaign'.format(user=getUsernameFromCRIC()) config.Data.outLFNDirBase = '/store/group/fourtop/NoveCampaign' config.Data.publication = True config.Data.outputDatasetTag = 'NoveCampaign' config.section_("Site") config.Site.storageSite = 'T2_BE_IIHE'

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import sqlite3 db = sqlite3.connect("chinook.db") c = db.cursor() c.execute('select * from customers') row1 = c.fetchone() print(row1) print(row1[3]) db.close()

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""" WSGI config for MessageApp project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "MessageApp.settings") application = get_wsgi_application()

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import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from scipy.optimize import least_squares from scipy.optimize import minimize from scipy.linalg import rq import time def objective_func(x, **kwargs): """ Calculates the difference in image (pixel coordinates) and returns it as a 2*n_points vector Args: - x: numpy array of 11 parameters of P in vector form (remember you will have to fix P_34=1) to estimate the reprojection error - **kwargs: dictionary that contains the 2D and the 3D points. You will have to retrieve these 2D and 3D points and then use them to compute the reprojection error. Returns: - diff: A N_points-d vector (1-D numpy array) of differences betwen projected and actual 2D points """ diff = None ############################## # TODO: Student code goes here P=np.array([[x[0],x[1],x[2],x[3]], [x[4],x[5],x[6],x[7]], [x[8],x[9],x[10],1]]) x_3d= kwargs["pts3d"] x_2d= kwargs["pts2d"] xp= projection(P,x_3d) diff= xp-x_2d diff=diff.flatten() # raise NotImplementedError ############################## return diff def projection(P: np.ndarray, points_3d: np.ndarray) -> np.ndarray: """ Computes projection from [X,Y,Z,1] in homogenous coordinates to (x,y) in non-homogenous image coordinates. Args: - P: 3x4 projection matrix - points_3d : n x 4 array of points [X_i,Y_i,Z_i,1] in homogenouos coordinates or n x 3 array of points [X_i,Y_i,Z_i] Returns: - projected_points_2d : n x 2 array of points in non-homogenous image coordinates """ projected_points_2d = None ############################## # TODO: Student code goes here if(points_3d.shape[1]==3): n_ones= np.ones((points_3d.shape[0],1)) points_3d= np.hstack((points_3d,n_ones)) projected_homo= np.dot(P,points_3d.T) x=projected_homo[0]/projected_homo[2] y=projected_homo[1]/projected_homo[2] projected_points_2d = np.vstack((x,y)).T # raise NotImplementedError ############################## return projected_points_2d def estimate_camera_matrix(pts2d: np.ndarray, pts3d: np.ndarray, initial_guess: np.ndarray) -> np.ndarray: ''' Calls least_squres form scipy.least_squares.optimize and returns an estimate for the camera projection matrix Args: - pts2d: n x 2 array of known points (x_i, y_i) in image coordinates - pts3d: n x 3 array of known points in 3D, (X_i, Y_i, Z_i, 1) - initial_guess: 3x4 projection matrix initial guess Returns: - P: 3x4 estimated projection matrix Note: Because of the requirements of scipy.optimize.least_squares you will have to pass the projection matrix P as a vector. Since we will fix P_34 to 1 you will not need to pass all 12 matrix parameters. You will also have to put pts2d and pts3d into a kwargs dictionary that you will add as an argument to least squares. We recommend that in your call to least_squares you use - method='lm' for Levenberg-Marquardt - verbose=2 (to show optimization output from 'lm') - max_nfev=50000 maximum number of function evaluations - ftol \ - gtol --> convergence criteria - xtol / - kwargs -- dictionary with additional variables for the objective function ''' P = None start_time = time.time() ############################## # TODO: Student code goes here p0=initial_guess.flatten()[:-1] kwargs_dict={"pts2d":pts2d,"pts3d":pts3d} ls= least_squares(objective_func, p0, method='lm', verbose=2, max_nfev=50000, kwargs=kwargs_dict) P=np.array([[ls.x[0],ls.x[1],ls.x[2],ls.x[3]], [ls.x[4],ls.x[5],ls.x[6],ls.x[7]], [ls.x[8],ls.x[9],ls.x[10],1]]) # raise NotImplementedError ############################## print("Time since optimization start", time.time() - start_time) return P def decompose_camera_matrix(P: np.ndarray) -> (np.ndarray, np.ndarray): ''' Decomposes the camera matrix into the K intrinsic and R rotation matrix Args: - P: 3x4 numpy array projection matrix Returns: - K: 3x3 intrinsic matrix (numpy array) - R: 3x3 orthonormal rotation matrix (numpy array) hint: use scipy.linalg.rq() ''' K = None R = None ############################## # TODO: Student code goes here M=P[:,:-1] K, R = rq(M) # raise NotImplementedError ############################## return K, R def calculate_camera_center(P: np.ndarray, K: np.ndarray, R_T: np.ndarray) -> np.ndarray: """ Returns the camera center matrix for a given projection matrix. Args: - P: A numpy array of shape (3, 4) representing the projection matrix Returns: - cc: A numpy array of shape (1, 3) representing the camera center location in world coordinates """ cc = None ############################## # TODO: Student code goes here inv= np.linalg.inv(np.dot(K,R_T)) I_t= np.dot(inv,P) cc=-I_t[:,-1] # raise NotImplementedError ############################## return cc

[ "tpasumarthi3@gatech.edu" ]

tpasumarthi3@gatech.edu

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/tcdev/service/_user.py

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#!/usr/bin/python # -*- coding:utf-8 -*- from django.contrib.auth.models import User, Group import logging logger = logging.getLogger('logger') #=============================================================================== # Model的直接操作方法，get_or_create、get、filter 等。 #=============================================================================== def create_user_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: 元组(obj,boolean) @note: 获取或创建对象 ''' return User.objects.create(**kwargs) def get_or_create_user_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: 元组(obj,boolean) @note: 获取或创建对象 ''' return User.objects.get_or_create(**kwargs) def get_user_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: obj或None @note: 获取对象 ''' try: return User.objects.get(**kwargs) except User.DoesNotExist: logger.error(u"Account对象不存在（%s）" % kwargs) except User.MultipleObjectsReturned: logger.error(u"Account对象存在多条记录（%s）" % kwargs) return None def get_users_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: [obj,] @note: 获取对象列表 ''' return User.objects.filter(**kwargs) #=============================================================================== # Model的直接操作方法，get_or_create、get、filter 等。 #=============================================================================== def create_group_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: 元组(obj,boolean) @note: 获取或创建对象 ''' return Group.objects.create(**kwargs) def get_or_create_group_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: 元组(obj,boolean) @note: 获取或创建对象 ''' return Group.objects.get_or_create(**kwargs) def get_group_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: obj或None @note: 获取对象 ''' try: return Group.objects.get(**kwargs) except Group.DoesNotExist: logger.error(u"Group对象不存在（%s）" % kwargs) except Group.MultipleObjectsReturned: logger.error(u"Group对象存在多条记录（%s）" % kwargs) return None def get_groups_by_params(**kwargs): ''' @param kwargs: key=value 的键值对 @return: [obj,] @note: 获取对象列表 ''' return Group.objects.filter(**kwargs)

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/tuiuiu/tuiuiuimages/migrations/0008_image_created_at_index.py

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caputomarcos/tuiuiu.io

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('tuiuiuimages', '0007_image_file_size'), ] operations = [ migrations.AlterField( model_name='image', name='created_at', field=models.DateTimeField(db_index=True, verbose_name='Created at', auto_now_add=True), ), ]

[ "caputo.marcos@gmail.com" ]

caputo.marcos@gmail.com

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/561L_output_to_BigWig_GFF3.py

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#!usr/bin/python3.6 -w #This program will take a the output from the ZScore_calculation.pl program append # convert it into bigwig tracks. # # Usage: # # $ python3.6 thisScrip.py inputfile # import sys # this will allow for the use of system argument inputs import re import pyBigWig filename = sys.argv[1] # this should be the output of a z-score analysis in tab-delimited format #output = sys.argv[2] gene_coordinate = input('What are the coordinates of your gene (e.g., chr1:2555639..2565382): ') #chromosome = str(input("What chromsome is your gene on? (e.g., chr1): ")) #genomic_start = int(input("What is the starting coordinate of your gene sequence? (e.g., 2555639; without commas): " )) step_size = int(input("What is the step size for your data? (e.g., 1): ")) window_size = int(input("What is the window size for your data? (e.g., 120): ")) strand = input("What strand is your gene on (+ or -)?: ") chromosome_data = re.split('\:', gene_coordinate) chromosome = chromosome_data[0] genomic_coordinates = re.split('\.\.', chromosome_data[1]) genomic_start = genomic_coordinates[0] genomic_end = genomic_coordinates[1] # Create and open output files for writing MFE_wig = pyBigWig.open(filename+'.strand('+strand+')_MFE.bw', 'w') zscore_wig = pyBigWig.open(filename+'.strand('+strand+')_zscore.bw', 'w') pscore_wig = pyBigWig.open(filename+'.strand('+strand+')_pvalue.bw', 'w') ED_wig = pyBigWig.open(filename+'.strand('+strand+')_Ed.bw', 'w') fMFE_wig = pyBigWig.open(filename+'.strand('+strand+')_fMFE.bw', 'w') gff3file = open(filename+'.strand('+strand+').gff3', 'w') #corrected_file = open(filename+'.corrected.txt', 'w') # Write header for corrected file: #corrected_file.write("i\tj\tMFE\trandomMFE\tZscore\tPscore\tED\tfMFE\tSequence\tFold\tCentroid\t#A\t#G\t#C\t#U\n") MFE_wig.addHeader([("chr1",248956422),("chr2",242193529),("chr3",198295559),("chr4",190214555),("chr5",181538259),("chr6",170805979),("chr7",159345973),("chr8",145138636),("chr9",138394717),("chr10",133797422),("chr11",135086622),("chr12",133851895),("chr13",115169878),("chr14",107349540),("chr15",102531392),("chr16",90354753),("chr17",107349540),("chr18",78077248),("chr19",59128983),("chr20",63025520),("chr21",48129895),("chr22",51304566),("chrX",155270560),("chrY",59373566)]) zscore_wig.addHeader([("chr1",248956422),("chr2",242193529),("chr3",198295559),("chr4",190214555),("chr5",181538259),("chr6",170805979),("chr7",159345973),("chr8",145138636),("chr9",138394717),("chr10",133797422),("chr11",135086622),("chr12",133851895),("chr13",115169878),("chr14",107349540),("chr15",102531392),("chr16",90354753),("chr17",107349540),("chr18",78077248),("chr19",59128983),("chr20",63025520),("chr21",48129895),("chr22",51304566),("chrX",155270560),("chrY",59373566)]) pscore_wig.addHeader([("chr1",248956422),("chr2",242193529),("chr3",198295559),("chr4",190214555),("chr5",181538259),("chr6",170805979),("chr7",159345973),("chr8",145138636),("chr9",138394717),("chr10",133797422),("chr11",135086622),("chr12",133851895),("chr13",115169878),("chr14",107349540),("chr15",102531392),("chr16",90354753),("chr17",107349540),("chr18",78077248),("chr19",59128983),("chr20",63025520),("chr21",48129895),("chr22",51304566),("chrX",155270560),("chrY",59373566)]) ED_wig.addHeader([("chr1",248956422),("chr2",242193529),("chr3",198295559),("chr4",190214555),("chr5",181538259),("chr6",170805979),("chr7",159345973),("chr8",145138636),("chr9",138394717),("chr10",133797422),("chr11",135086622),("chr12",133851895),("chr13",115169878),("chr14",107349540),("chr15",102531392),("chr16",90354753),("chr17",107349540),("chr18",78077248),("chr19",59128983),("chr20",63025520),("chr21",48129895),("chr22",51304566),("chrX",155270560),("chrY",59373566)]) fMFE_wig.addHeader([("chr1",248956422),("chr2",242193529),("chr3",198295559),("chr4",190214555),("chr5",181538259),("chr6",170805979),("chr7",159345973),("chr8",145138636),("chr9",138394717),("chr10",133797422),("chr11",135086622),("chr12",133851895),("chr13",115169878),("chr14",107349540),("chr15",102531392),("chr16",90354753),("chr17",107349540),("chr18",78077248),("chr19",59128983),("chr20",63025520),("chr21",48129895),("chr22",51304566),("chrX",155270560),("chrY",59373566)]) MFE_list = [] zscore_list = [] pscore_list = [] ED_list = [] fMFE_list = [] length = (int(genomic_end) - int(genomic_start)) + 1 #print(length) with open(filename, 'r') as g: if strand == "+": # Generating GFF3 file for forward strand glines = g.readlines()[1:] #print(row) for row in glines: if not row.strip(): continue else: gdata = row.split('\t') # this splits each row based on "tab" #print(len(str(gdata[8])), window_size) if ((len(str(gdata[8])) == int(window_size)) and (len(str(gdata[9])) == int(window_size)) and (len(str(gdata[10])) == int(window_size)) and (float(gdata[3]) != 0)) or ((len(str(gdata[9])) == window_size) and (len(str(gdata[10])) == window_size) and (len(str(gdata[11])) == window_size) and (float(gdata[4]) == 0)): #print("Sequence in column 9") #float(gdata[7]) gdata = row.split('\t') # this splits each row based on "tab" if len(gdata) > 15: print("Errors found in file") print("Error in column six:", gdata) gdata.remove(gdata[5]) #corrected_row = ('\t'.join(data)) print("Error removed:", gdata) #print(row) #icoordinate = int((int(data[0])+int(genomic_start)-1)) #jcoordinate = int(int(data[1])+int(genomic_start)) icoordinate = (int(gdata[0])-1)+int(genomic_start) jcoordinate = (int(gdata[1])-1)+int(genomic_start) # if strand == '-1': # icoordinate = int(int(genomic_start)+(int(length)-int(data[1]))) # jcoordinate = int(int(genomic_start)+(int(length)-int(data[0]))) gMFE = float(gdata[2]) grand_MFE = float(gdata[3]) gzscore = gdata[4] if gzscore == "Undef": gzscore = float(00000) else: gzscore = float(gdata[4]) gpvalue = gdata[5] #try: # pvalue = float(gdata[5]) #except ValueError: # print(str(gdata[5])) # pvalue =float(0) gED = float(gdata[6]) gfMFE = float(gdata[7]) gsequence = gdata[8] gfold = gdata[9] gcentroid = gdata[10] gfA = gdata[11] gfG = gdata[12] gfC = gdata[13] gfU = gdata[14] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') else: #print("passed length test") icoordinate = (int(gdata[0])-1)+int(genomic_start) jcoordinate = (int(gdata[1])-1)+int(genomic_start) # if strand == '-1': # icoordinate = int(int(genomic_start)+(int(length)-int(data[1]))) # jcoordinate = int(int(genomic_start)+(int(length)-int(data[0]))) gMFE = float(gdata[2]) grand_MFE = float(gdata[3]) gzscore = gdata[4] if gzscore == "Undef": gzscore = float(00000) else: gzscore = float(gdata[4]) gpvalue = gdata[5] try: gpvalue = float(gdata[5]) except ValueError: print(str(gdata[5])) gpvalue =float(0) gED = float(gdata[6]) gfMFE = float(gdata[7]) gsequence = gdata[8] gfold = gdata[9] gcentroid = gdata[10] gfA = gdata[11] gfG = gdata[12] gfC = gdata[13] gfU = gdata[14] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') else: #print("else") if len(gdata) > 14: print("Errors found in file") print("Error in column five:", gdata) gdata.remove(gdata[4]) #corrected_row = ('\t'.join(data)) print("Error removed:", gdata) #print(row) #icoordinate = int((int(data[0])+int(genomic_start)-1)) #jcoordinate = int(int(data[1])+int(genomic_start)) icoordinate = (int(gdata[0])-1)+int(genomic_start) jcoordinate = (int(gdata[1])-1)+int(genomic_start) # if strand == '-1': # icoordinate = int(int(genomic_start)+(int(length)-int(data[1]))) # jcoordinate = int(int(genomic_start)+(int(length)-int(data[0]))) gMFE = float(gdata[2]) #rand_MFE = float(gdata[3]) gzscore = gdata[3] if gzscore == "Undef": gzscore = float(00000) else: gzscore = float(gdata[3]) gpvalue = gdata[4] #try: # pvalue = float(gdata[5]) #except ValueError: # print(str(gdata[5])) # pvalue =float(0) gED = float(gdata[5]) gfMFE = float(gdata[6]) gsequence = gdata[7] gfold = gdata[8] gcentroid = gdata[9] gfA = gdata[10] gfG = gdata[11] gfC = gdata[12] gfU = gdata[13] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') else: #print(len(gdata)) icoordinate = (int(gdata[0])-1)+int(genomic_start) jcoordinate = (int(gdata[1])-1)+int(genomic_start) # if strand == '-1': # icoordinate = int(int(genomic_start)+(int(length)-int(data[1]))) # jcoordinate = int(int(genomic_start)+(int(length)-int(data[0]))) gMFE = float(gdata[2]) #grand_MFE = float(gdata[3]) gzscore = gdata[3] if gzscore == "Undef": gzscore = float(00000) else: gzscore = float(gdata[3]) gpvalue = gdata[3] try: gpvalue = float(gdata[4]) except ValueError: print(str(gdata[4])) gpvalue =float(0) gED = float(gdata[5]) gfMFE = float(gdata[6]) gsequence = gdata[7] gfold = gdata[8] gcentroid = gdata[9] gfA = gdata[10] gfG = gdata[11] gfC = gdata[12] gfU = gdata[13] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') if strand == "-": #Generating GFF3 file for reverse strand glines = g.readlines()[1:] for row in glines: if not row.strip(): continue else: gdata = row.split('\t') # this splits each row based on "tab" if ((len(str(gdata[8])) == int(window_size)) and (len(str(gdata[9])) == int(window_size)) and (len(str(gdata[10])) == int(window_size)) and (float(gdata[3]) != 0)) or ((len(str(gdata[9])) == window_size) and (len(str(gdata[10])) == window_size) and (len(str(gdata[11])) == window_size) and (float(gdata[4]) == 0)): #float(gdata[7]) if len(gdata) > 15: print("Errors found in file") print("Error in column six:", gdata) gdata.remove(gdata[5]) #corrected_row = ('\t'.join(data)) print("Error removed:", gdata) #print(row) #icoordinate = int((int(data[0])+int(genomic_start)-1)) #jcoordinate = int(int(data[1])+int(genomic_start)) #print(data) #print(gdata) #print(data) icoordinate = int(int(genomic_start)+(int(length)-int(gdata[1]))) jcoordinate = int(int(genomic_start)+(int(length)-int(gdata[0]))) gMFE = gdata[2] g_rand_MFE = gdata[3] gzscore = gdata[4] if gzscore == "Undef": gzscore = 00000 gpvalue = gdata[5] gED = gdata[6] gfMFE = gdata[7] gsequence = gdata[8] gfold = gdata[9] gcentroid = gdata[10] fA = gdata[11] fG = gdata[12] fC = gdata[13] fU = gdata[14] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') else: icoordinate = int(int(genomic_start)+(int(length)-int(gdata[1]))) jcoordinate = int(int(genomic_start)+(int(length)-int(gdata[0]))) gMFE = gdata[2] g_rand_MFE = gdata[3] gzscore = gdata[4] if gzscore == "Undef": gzscore = 00000 gpvalue = gdata[5] gED = gdata[6] gfMFE = gdata[7] gsequence = gdata[8] gfold = gdata[9] gcentroid = gdata[10] fA = gdata[11] fG = gdata[12] fC = gdata[13] fU = gdata[14] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') else: if len(gdata) > 14: print("Errors found in file") print("Error in column five:", gdata) gdata.remove(gdata[4]) #corrected_row = ('\t'.join(data)) print("Error removed:", gdata) #print(row) #icoordinate = int((int(data[0])+int(genomic_start)-1)) #jcoordinate = int(int(data[1])+int(genomic_start)) #print(data) #print(gdata) #print(data) icoordinate = int(int(genomic_start)+(int(length)-int(gdata[1]))) jcoordinate = int(int(genomic_start)+(int(length)-int(gdata[0]))) gMFE = gdata[2] #g_rand_MFE = gdata[3] gzscore = gdata[3] if gzscore == "Undef": gzscore = 00000 gpvalue = gdata[4] gED = gdata[5] gfMFE = gdata[6] gsequence = gdata[7] gfold = gdata[8] gcentroid = gdata[9] fA = gdata[10] fG = gdata[11] fC = gdata[12] fU = gdata[13] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') else: icoordinate = int(int(genomic_start)+(int(length)-int(gdata[1]))) jcoordinate = int(int(genomic_start)+(int(length)-int(gdata[0]))) gMFE = gdata[2] #g_rand_MFE = gdata[3] gzscore = gdata[3] if gzscore == "Undef": gzscore = 00000 gpvalue = gdata[4] gED = gdata[5] gfMFE = gdata[6] gsequence = gdata[7] gfold = gdata[8] gcentroid = gdata[9] fA = gdata[10] fG = gdata[11] fC = gdata[12] fU = gdata[13] #corrected_file.write(gdata[0]+'\t'+gdata[1]+'\t'+gdata[2]+'\t'+gdata[3]+'\t'+gdata[4]+'\t'+gdata[5]+'\t'+gdata[6]+'\t'+gdata[7]+'\t'+gdata[8]+'\t'+gdata[9]+'\t'+gdata[10]+'\t'+gdata[11]+'\t'+gdata[12]+'\t'+gdata[13]+'\t'+gdata[14]) gff3file.write(chromosome+'\t'+'.'+'\t'+'sequence_attribute'+'\t'+str(icoordinate)+'\t'+str(jcoordinate)+'\t'+'.'+'\t'+strand+'\t'+'.\t'+'MFE='+str(gMFE)+';'+'Z-score='+str(gzscore)+';'+'P-value='+str(gpvalue)+';'+'EnsDiv='+str(gED)+';'+'fMFE='+str(gfMFE)+';'+'Sequence='+gsequence+';'+'MFE_Fold='+gfold+';'+'Centroid='+gcentroid+'\n') with open(filename, 'r') as f: if strand == "+": #Generating BW tracks for forward strand. genomic_start = int(genomic_start) lines = f.readlines()[1:] for row in lines: if not row.strip(): continue else: data = row.split('\t') # this splits each row based on "tab" if ((len(str(data[8])) == int(window_size)) and (len(str(data[9])) == int(window_size)) and (len(str(data[10])) == int(window_size)) and (float(data[3]) != 0)) or ((len(str(data[9])) == window_size) and (len(str(data[10])) == window_size) and (len(str(data[11])) == window_size) and (float(data[4]) == 0)): if len(data) > 15: #print("Errors found in file") #print("Error in column six:", data) data.remove(data[5]) #corrected_row = ('\t'.join(data)) #print("Error removed:", data) i = data[0] j = data[1] genomic_end = int(genomic_start)+int(step_size) MFE = float(data[2]) MFE_list.append(MFE) if data[4] == "Undef": zscore = float(00000) else: zscore = float(data[4]) zscore_list.append(zscore) pscore = float(data[5]) pscore_list.append(pscore) ED = float(data[6]) ED_list.append(ED) fMFE = float(data[7]) fMFE_list.append(fMFE) #print(len(data)) else: i = data[0] j = data[1] genomic_end = int(genomic_start)+int(step_size) MFE = float(data[2]) MFE_list.append(MFE) if data[4] == "Undef": zscore = float(00000) else: zscore = float(data[4]) zscore_list.append(zscore) pscore = float(data[5]) pscore_list.append(pscore) ED = float(data[6]) ED_list.append(ED) fMFE = float(data[7]) fMFE_list.append(fMFE) else: if len(data) > 14: #print("Errors found in file") #print("Error in column five:", data) data.remove(data[4]) #corrected_row = ('\t'.join(data)) #print("Error removed:", data) i = data[0] j = data[1] genomic_end = int(genomic_start)+int(step_size) MFE = float(data[2]) MFE_list.append(MFE) if data[3] == "Undef": zscore = float(00000) else: zscore = float(data[3]) zscore_list.append(zscore) pscore = float(data[4]) pscore_list.append(pscore) ED = float(data[5]) ED_list.append(ED) fMFE = float(data[6]) fMFE_list.append(fMFE) #print(len(data)) else: i = data[0] j = data[1] genomic_end = int(genomic_start)+int(step_size) MFE = float(data[2]) MFE_list.append(MFE) if data[3] == "Undef": zscore = float(00000) else: zscore = float(data[3]) zscore_list.append(zscore) pscore = float(data[4]) pscore_list.append(pscore) ED = float(data[5]) ED_list.append(ED) fMFE = float(data[6]) fMFE_list.append(fMFE) if strand == "-": lines = reversed(open(filename).readlines()[1:]) start = genomic_start genomic_start = int(start) + int(window_size) for row in lines: if not row.strip(): continue else: data = row.split('\t') # this splits each row based on "tab" if ((len(str(data[8])) == int(window_size)) and (len(str(data[9])) == int(window_size)) and (len(str(data[10])) == int(window_size)) and (float(data[3]) != 0)) or ((len(str(data[9])) == window_size) and (len(str(data[10])) == window_size) and (len(str(data[11])) == window_size) and (float(data[4]) == 0)): if len(data) > 15: #print("Errors found in file") #print("Error in column six:", data) data.remove(data[5]) #corrected_row = ('\t'.join(data)) #print("Error removed:", data) #print(row) #print(len(data)) #print(row) i = data[0] j = data[1] #genomic_start = int(genomic_start)+int(window_size) MFE = float(data[2]) MFE_list.append(MFE) if data[4] == "Undef": zscore = float(00000) else: zscore = float(data[4]) zscore_list.append(zscore) pscore = float(data[5]) pscore_list.append(pscore) ED = float(data[6]) ED_list.append(ED) fMFE = float(data[7]) fMFE_list.append(fMFE) else: i = data[0] j = data[1] #genomic_start = int(genomic_start)+int(window_size) MFE = float(data[2]) MFE_list.append(MFE) if data[4] == "Undef": zscore = float(00000) else: zscore = float(data[4]) zscore_list.append(zscore) pscore = float(data[5]) pscore_list.append(pscore) ED = float(data[6]) ED_list.append(ED) fMFE = float(data[7]) fMFE_list.append(fMFE) else: if len(data) > 14: #print("Errors found in file") #print("Error in column five:", data) data.remove(data[4]) #corrected_row = ('\t'.join(data)) #print("Error removed:", data) #print(row) #print(len(data)) #print(row) i = data[0] j = data[1] #genomic_start = int(genomic_start)+int(window_size) MFE = float(data[2]) MFE_list.append(MFE) if data[3] == "Undef": zscore = float(00000) else: zscore = float(data[3]) zscore_list.append(zscore) pscore = float(data[4]) pscore_list.append(pscore) ED = float(data[5]) ED_list.append(ED) fMFE = float(data[6]) fMFE_list.append(fMFE) else: i = data[0] j = data[1] #genomic_start = int(genomic_start)+int(window_size) MFE = float(data[2]) MFE_list.append(MFE) if data[3] == "Undef": zscore = float(00000) else: zscore = float(data[3]) zscore_list.append(zscore) pscore = float(data[4]) pscore_list.append(pscore) ED = float(data[5]) ED_list.append(ED) fMFE = float(data[6]) fMFE_list.append(fMFE) #print(MFE_list) #print(chromosome) #print(step_size) MFE_wig.addEntries(chromosome, genomic_start, values=MFE_list, span=step_size, step=step_size) zscore_wig.addEntries(chromosome, genomic_start, values=zscore_list, span=step_size, step=step_size) pscore_wig.addEntries(chromosome, genomic_start, values=pscore_list, span=step_size, step=step_size) ED_wig.addEntries(chromosome, genomic_start, values=ED_list, span=step_size, step=step_size) fMFE_wig.addEntries(chromosome, genomic_start, values=fMFE_list, span=step_size, step=step_size) MFE_wig.close() zscore_wig.close() pscore_wig.close() ED_wig.close() fMFE_wig.close()

[ "randrews@iastate.edu" ]

randrews@iastate.edu

78d13810a1e6bb93bb15484432df7fa156d11023

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/pytorch_fcn/tasks/grid_regression.py

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[]

no_license

erictzeng/ssa-segmentation-release

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import logging import numpy as np import torch import torch.nn as nn from torch.utils.data import DataLoader from torchvision import transforms from pytorch_fcn.data.util import Dispenser from pytorch_fcn.data.util import JointDispenser class GridRegression: def __init__(self, net, source_dataset, target_dataset, source_val_dataset, target_val_dataset, *, batch_size, stride=256, name='gridregression'): self.net = net self.source_dataset = source_dataset self.target_dataset = target_dataset self.source_val_dataset = source_val_dataset self.target_val_dataset = target_val_dataset self.batch_size = batch_size self.stride = stride self.name = name self.loss_fn = nn.MSELoss() self.create_datasets() self.create_head() def create_datasets(self): transform = transforms.Resize(1024) crop_transform = self.net.transform loaders = [] for dataset in [self.source_dataset, self.target_dataset]: grid_dataset = GridRegressionWrapper( dataset, stride=self.stride, transform=transform, crop_transform=crop_transform ) loader = DataLoader( grid_dataset, batch_size=self.batch_size // 2, shuffle=True, num_workers=4 ) loaders.append(loader) val_loaders = [] for dataset in [self.source_val_dataset, self.target_val_dataset]: grid_dataset = GridRegressionWrapper( dataset, stride=self.stride, transform=transform, crop_transform=crop_transform ) loader = DataLoader( grid_dataset, batch_size=1, shuffle=True, num_workers=4 ) val_loaders.append(loader) self.train_dispenser = JointDispenser(*loaders) self.val_loaders = { 'source': val_loaders[0], 'target': val_loaders[1], } def create_head(self): self.head = GridRegressionHead(self.net.out_dim) self.net.attach_head(self.name, self.head) def _predict_batch(self, im): n, g, c, h, w = im.size() im = im.view(n * g, c, h, w).cuda() preds = self.net(im, task=self.name) preds = preds.view(n * g, 2) return preds def step(self): im, label = self.train_dispenser.next_batch() label = label.view(-1, 2).cuda() preds = self._predict_batch(im) loss = self.loss_fn(preds, label) return loss def eval(self): self.net.eval() results = {} for domain, loader in self.val_loaders.items(): correct = 0 total = 0 for im, label in loader: with torch.no_grad(): label = label.view(-1, 2).cuda() preds = self._predict_batch(im) preds = preds.round() correct += preds.eq(label).all(dim=1).sum().item() total += label.size(0) accuracy = correct / total logging.info(f' {self.name}.{domain}: {accuracy}') results[f'{self.name}.{domain}'] = accuracy self.net.train() return results class GridRegressionHead(nn.Module): def __init__(self, ft_dim): super().__init__() self.pool = nn.AdaptiveAvgPool2d((1, 1)) self.rot = nn.Conv2d(ft_dim, 2, 1) def forward(self, x): x = self.pool(x) x = self.rot(x) return x class GridRegressionWrapper: def __init__(self, dataset, stride=256, grid=(4, 2), transform=None, crop_transform=None): self.dataset = dataset self.stride = stride self.grid = grid self.transform = transform self.crop_transform = crop_transform def __len__(self): return len(self.dataset) def __getitem__(self, index): im = self.dataset[index][0] if self.transform is not None: im = self.transform(im) crops = [] targets = [] for x in range(self.grid[0]): for y in range(self.grid[1]): crop = self.crop(im, x, y) if self.crop_transform is not None: crop = self.crop_transform(crop) crops.append(crop) targets.append([float(x), float(y)]) im = torch.stack(crops, dim=0) targets = torch.Tensor(targets) return im, targets def crop(self, im, x, y): left = self.stride * x right = self.stride * (x + 1) up = self.stride * y down = self.stride * (y + 1) im = im.crop((left, up, right, down)) return im

[ "etzeng@eecs.berkeley.edu" ]

etzeng@eecs.berkeley.edu

254c477f7b6696bb460c4e385c44a4647974b388

42f1e442ee88a2bb541de1ce2d30513a183a5c3e

/api/cli/predict.py

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permissive

yastai/YastAI

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def predict(): raise NotImplementedError if __name__ == "__main__": import argparse ap = argparse.ArgumentParser(description="Consume trainend model.") args = vars(ap.parse_args()) predict()

[ "sebastian.borquez.g@gmail.com" ]

sebastian.borquez.g@gmail.com

6ae367f46c4e0630eabdc3e3c950b315dad2b7c9

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/pitstop/encodings/__init__.py

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permissive

darvid/pitstop

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2020-04-09T04:16:21.166646

2018-12-05T02:36:54

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py

"""Provides encoding interfaces for various configuration file formats."""

[ "david.gidwani@gmail.com" ]

david.gidwani@gmail.com

93104fbe2660bf5252f22f0c831b363c1025c19e

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/cfehome/settings.py

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""" Django settings for cfehome project. Generated by 'django-admin startproject' using Django 3.0.4. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = "c(=5(z-ig4(y9e_%j9oo8q1v2yb490nr7s_gsznp^tasg6_sz&" # DEBUG can be True/False or 1/0 DEBUG = int(os.environ.get("DEBUG", default=1)) ALLOWED_HOSTS = [ "http://docker-sample-dev.ap-northeast-2.elasticbeanstalk.com/", "docker-sample-dev.ap-northeast-2.elasticbeanstalk.com", ] # Application definition INSTALLED_APPS = [ "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", ] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "cfehome.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "cfehome.wsgi.application" # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { "default": { "ENGINE": "django.db.backends.sqlite3", "NAME": os.path.join(BASE_DIR, "db.sqlite3"), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, {"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",}, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",}, {"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",}, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = "/static/"

[ "young_ahn@yonsei.ac.kr" ]

young_ahn@yonsei.ac.kr

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# ScrollBar examples. from ocempgui.widgets import * from ocempgui.widgets.Constants import * def create_scrollbar_view (): table = Table (2, 2) table.spacing = 5 table.set_row_align (0, ALIGN_TOP) table.set_row_align (1, ALIGN_TOP) # Simple ScrollBars. frame = HFrame (Label ("Simple ScrollBars")) frame.spacing = 5 hscroll = HScrollBar (100, 400) vscroll = VScrollBar (100, 400) frame.children = hscroll, vscroll table.add_child (0, 0, frame) # Insensitive ScrollBars. frame = HFrame (Label ("Insensitive ScrollBars")) frame.spacing = 5 hscroll = HScrollBar (100, 400) hscroll.sensitive = False vscroll = VScrollBar (100, 400) vscroll.sensitive = False frame.children = hscroll, vscroll table.add_child (0, 1, frame) # ScrollBars with a null range. frame = HFrame (Label ("ScrollBars without a range")) frame.spacing = 5 hscroll = HScrollBar (100, 100) vscroll = VScrollBar (100, 100) frame.children = hscroll, vscroll table.add_child (1, 0, frame) # Small ScrollBars. frame = HFrame (Label ("Tiny ScrollBars")) frame.spacing = 5 hscroll = HScrollBar (10, 100) vscroll = VScrollBar (10, 100) frame.children = hscroll, vscroll table.add_child (1, 1, frame) return table if __name__ == "__main__": # Initialize the drawing window. re = Renderer () re.create_screen (320, 280) re.title = "ScrollBar examples" re.color = (234, 228, 223) re.add_widget (create_scrollbar_view ()) # Start the main rendering loop. re.start ()

[ "andrewpeterson86@gmail.com" ]

andrewpeterson86@gmail.com

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/bcn_rp/migrations/0007_auto_20170325_1713.py

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miquel-corral/bcn_rp

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2020-04-06T04:06:56.615536

2017-03-26T20:32:34

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# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-03-25 17:13 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('bcn_rp', '0006_auto_20170325_1125'), ] operations = [ migrations.AlterField( model_name='assessmentstakeholder', name='name', field=models.CharField(max_length=250), ), ]

[ "miquel.corral@gmail.com" ]

miquel.corral@gmail.com

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/Data Structures/Sorting Algorithms/Selection.Sort.py

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akshat343/Python-Programming

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#Author:Robin Singh #Problem on selection sort algorithm # selection sort:in selection sort we consider two sub array list # 1.)selection sort algorithm divides the input array list into two parts: # 2.)a sorted sublist of items which is built in from left to right at the front (left side) of the array list # 3.)and a sublist of the remaining unsorted items that occupy the rest of the array list # 4.)Initially, the sorted sublist is empty and the unsorted sublist is the entire input array list # 5.)Selection sort iterates all the elements and if the smallest element # 6.)in the list is found then that number is swapped with the first # 7.)and moving the sublist boundaries one element to the right # this sorting technique is similar to insertion sort #Time complexity has also been calculated both in BEST case and WORST case #BEST CASE:O(n^2) #WORST CASE :O(n^2) def selectionsort(a,n): for i in range(0,n-1): min=i for j in range(i+1,n): if a[j]<a[min]: min=j if min !=i: t=a[i] a[i]=a[min] a[min]=t a =[5,3,2,6,89,42,11,75,2,8,9,0] selectionsort(a,len(a)) print(a)

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/vehicle/migrations/0001_initial.py

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asimejaz14/cabrooz-project

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# Generated by Django 3.2.3 on 2021-05-29 08:24 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Vehicle', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('vehicle_name', models.CharField(blank=True, max_length=200, null=True)), ('registration_number', models.CharField(blank=True, max_length=200, null=True, unique=True)), ('vehicle_color', models.CharField(blank=True, max_length=200, null=True)), ('vehicle_model', models.CharField(blank=True, max_length=200, null=True)), ('vehicle_maker', models.CharField(blank=True, max_length=200, null=True)), ], ), ]

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asim.ejaz14@gmail.com

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/数据处理/输入神经元的数据处理/3_GetPopularKeywordData.py

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2022-11-13T07:53:02.455057

2020-07-13T06:15:46

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""" 这个脚本的目的是把数据中出现频率前1534名的关键词找出来由2脚本可以知道前1534名的关键词正好就是出现次数不小于20的公文 """ import json # 读取数据 with open('数据处理\\输入神经元的数据处理\\2_word_weights.json', 'r', encoding='utf-8') as f: _data = json.loads(f.read()) new_data_list = {} # 找到并存储出现次数超过38的关键词 for keyword in _data.keys(): if _data[keyword]['total_doc_num'] >= 20: new_data_list[keyword] = _data[keyword] # 写入数据 with open('数据处理\\输入神经元的数据处理\\3_word_weights.json', 'a+', encoding='utf-8') as f: f.write(json.dumps(new_data_list, ensure_ascii=False))

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/discreet_mathematics/reverse_linked_list.py

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lukorito/ds

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# Input: 1->2->3->4->5->NULL # Output: 5->4->3->2->1->NULL # ITERATIVE # Definition for singly-linked list. class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next class Solution: def reverseList(self, head: ListNode) -> ListNode: prev = None current = head while current is not None: next = current.next current.next = prev prev = current current = next return prev # RECURSIVE class Solution: def reverseList(self, head: ListNode) -> ListNode: if head is None or head.next is None: return head p = self.reverseList(head.next) head.next.next=head head.next = None return p

[ "lukkitt@live.co.uk" ]

lukkitt@live.co.uk

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/examen02.py

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mprotti/CursoPhyton

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#Escribir una clase en python que revierta una cadena de palabras #Entrada: "Mi Diario Python" #Salida: "Python Diario Mi" class revierta(): def __init__(self, string): self.string = string self.invertida = string.split()[::-1] self.invertida = ' '.join(self.invertida) def __str__(self): return ('Frase original: {} \nFrase revertida: {}'.format(self.string, self.invertida)) string = "Mi Diario Python" a = revierta(string) print(a)

[ "m21protti@gmail.com" ]

m21protti@gmail.com

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/Python_LetsKodeIt/Exception-Handling.py

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rajeshvelur/django-deployments-example

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2020-04-01T08:25:17.248545

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# def exceptions_handling(): # try: # a = 30 # b = 0 # c = 20 # # d = (a/b)-c # print(d) # except: # print("Zero Division Exception") # raise Exception("this is an exception") # else: # print("Bcos no exception found") # finally: # print("this will execute always") # # # exceptions_handling() # print(x) def exceptions_handling1(): car = {"make": "BMW", "model": "x3", "Year": 2016} try: print(car["year"]) except: print("exception here") finally: print("this will final statement") exceptions_handling1()

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rajesh.kumar1@spglobal.com

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Aasthaengg/IBMdataset

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2021-05-13T17:27:22

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import sys sys.setrecursionlimit(10**7) readline = sys.stdin.buffer.readline def readstr():return readline().rstrip().decode() def readstrs():return list(readline().decode().split()) def readint():return int(readline()) def readints():return list(map(int,readline().split())) def printrows(x):print('\n'.join(map(str,x))) def printline(x):print(' '.join(map(str,x))) r,c,k = readints() a = [[0]*(c+1) for i in range(r+1)] for i in range(k): R,C,V = readints() a[R][C] = V dp = [0]*(r+1)*(c+1)*4 for x in range((r+1)*(c+1)*4): i = x//((c+1)*4) l = x%((c+1)*4) j = l//4 l %= 4 if i==0 or j==0: continue if l==0: dp[x] = max(dp[i*(c+1)*4 + (j-1)*4], dp[(i-1)*(c+1)*4 + j*4 + 3]) else: dp[x] = max(dp[i*(c+1)*4 + (j-1)*4 + l], dp[(i-1)*(c+1)*4 + j*4 + 3]+a[i][j], dp[i*(c+1)*4 + (j-1)*4 + l-1]+a[i][j]) print(dp[-1])

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66529651+Aastha2104@users.noreply.github.com

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/Contactmanager/Contactmanager/urls.py

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saipraneethkurmelkar/THIRDJANGO

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2019-01-19T10:33:25

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"""Contactmanager URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.conf.urls import include, url from django.contrib import admin from ContactsApp import views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^$', views.show), url(r'^add/', views.add), ]

[ "info@info.com" ]

info@info.com

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/leetcode/213. House Robber II.py

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unclemeth/python_au

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2023-05-08T12:57:09.453617

2021-05-28T21:11:37

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class Solution: def rob(self, nums): if not nums: return 0 if len(nums) < 3: return max(nums) nums1 = nums[:-1] if len(nums1) < 3: return max(nums1) a = nums1[0] b = max(nums1[0], nums1[1]) for i in range(2, len(nums1)): Curr_max = max(a + nums1[i], b) a = b b = Curr_max nums2 = nums[1:] # if not nums2 : return 0 if len(nums2) < 3: return max(nums2) c = nums2[0] d = max(nums2[0], nums2[1]) for i in range(2, len(nums2)): Curr_max = max(c + nums2[i], d) c = d d = Curr_max return max(b, d)

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unclemeth@mail.ru

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/scripts/ansi-to-gitlab-markdown.py

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c-zuo/gcp-turbo

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2023-01-31T16:51:04.439322

2020-12-15T15:12:39

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#!/usr/bin/env python3 from sys import stdin import re addition = re.compile(r"(\x1B\[3\dm)(.+?)(\x1B\[0m)") heading = re.compile(r"(\x1B\[1m)( *# .+)") emphasis = re.compile(r"(\x1B\[1m)(.+?)(\x1B\[0m)") ansi_seqs = re.compile(r"\x1B\[.+?m") print('<pre>') for line in stdin: line = line.rstrip('\n') line = heading.sub("<h4>\\2</h4>", line) line = addition.sub("<b>\\2</b>", line) line = emphasis.sub("<b>\\2</b>", line) line = ansi_seqs.sub("", line) print(line) print('</pre>')

[ "chenzuo@google.com" ]

chenzuo@google.com

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/detector.py

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[]

no_license

fulequn/MTCNN

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2022-04-03T20:22:31.933960

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#!/usr/bin/env python # encoding: utf-8 """ @Author: JianboZhu @Contact: jianbozhu1996@gmail.com @Date: 2019/12/1 @Description: """ import os import glob import numpy as np import cv2 from nets.mtcnn import p_net, o_net, r_net from preprocess.utils import py_nms, process_image, convert_to_square class Detector: def __init__(self, weight_dir, min_face_size=24, threshold=None, # 概率大于threshold的bbox才用 scale_factor=0.65, mode=3, slide_window=False, stride=2): # mode # 1:用p_net 生成r_net的数据 # 2:用p_net r_net 生成 o_net的数据 # 3:用p_net r_net o_net 最后生成结果 assert mode in [1, 2, 3] # 实现图片金字塔 assert scale_factor < 1 # 暂时没有使用 self.slide_window = slide_window self.stride = stride self.mode = mode # 图片金字塔,图片不能小于这个 self.min_face_size = min_face_size # 概率大于threshold的bbox才用 self.threshold = [0.6, 0.7, 0.7] if threshold is None else threshold # 实现图片金字塔,以这个比例缩小图片 self.scale_factor = scale_factor self.p_net = None self.r_net = None self.o_net = None self.init_network(weight_dir) def init_network(self, weight_dir='saved_models'): p_weights, r_weights, o_weights = self._get_weights(weight_dir) print('PNet weight file is: {}'.format(p_weights)) self.p_net = p_net() self.p_net.load_weights(p_weights) if self.mode > 1: self.r_net = r_net() self.r_net.load_weights(r_weights) if self.mode > 2: self.o_net = o_net() self.o_net.load_weights(o_weights) def predict(self, image): im_ = np.array(image) if self.mode == 1: return self.predict_with_p_net(im_) elif self.mode == 2: return self.predict_with_pr_net(im_) elif self.mode == 3: return self.predict_with_pro_net(im_) else: raise NotImplementedError('Not implemented yet') def predict_with_p_net(self, im): return self._detect_with_p_net(im) def predict_with_pr_net(self, im): boxes, boxes_c = self._detect_with_p_net(im) return self._detect_with_r_net(im, boxes_c) def predict_with_pro_net(self, im): boxes, boxes_c = self._detect_with_p_net(im) boxes, boxes_c = self._detect_with_r_net(im, boxes_c) return self._detect_with_o_net(im, boxes_c) def _detect_with_p_net(self, im): # print('p_net_predict---') net_size = 12 current_scale = float(net_size) / self.min_face_size # find initial scale im_resized = process_image(im, current_scale) current_height, current_width, _ = im_resized.shape all_boxes = [] while min(current_height, current_width) > net_size: inputs = np.array([im_resized]) # print('inputs shape: {}'.format(inputs.shape)) labels, bboxes = self.p_net.predict(inputs) # labels = np.squeeze(labels) # bboxes = np.squeeze(bboxes) labels = labels[0] bboxes = bboxes[0] # 概率大于threshold的bbox才用 # print('labels',labels.shape) # print('bboxes',bboxes.shape) boxes = self._generate_bbox(labels[:, :, 1], bboxes, current_scale, self.threshold[0]) # 实现图片金字塔 current_scale *= self.scale_factor im_resized = process_image(im, current_scale) current_height, current_width, _ = im_resized.shape if boxes.size == 0: continue keep = py_nms(boxes[:, :5], 0.7, 'union') boxes = boxes[keep] all_boxes.append(boxes) if len(all_boxes) == 0: return None, None return self._refine_bboxes(all_boxes) def _detect_with_r_net(self, im, dets): h, w, c = im.shape dets = convert_to_square(dets) dets[:, 0:4] = np.round(dets[:, 0:4]) [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self._pad(dets, w, h) num_boxes = dets.shape[0] cropped_ims = np.zeros((num_boxes, 24, 24, 3), dtype=np.float32) for i in range(num_boxes): tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1, x[i]:ex[i] + 1, :] cropped_ims[i, :, :, :] = (cv2.resize(tmp, (24, 24))-127.5) / 128 # cls_scores : num_data*2 # reg: num_data*4 # landmark: num_data*10 cls_scores, reg = self.r_net.predict(cropped_ims) cls_scores = cls_scores[:,1] keep_inds = np.where(cls_scores > self.threshold[1])[0] if len(keep_inds) > 0: boxes = dets[keep_inds] boxes[:, 4] = cls_scores[keep_inds] reg = reg[keep_inds] # landmark = landmark[keep_inds] else: return None, None keep = py_nms(boxes, 0.6) boxes = boxes[keep] boxes_c = self._calibrate_box(boxes, reg[keep]) return boxes, boxes_c def _detect_with_o_net(self, im, dets): h, w, c = im.shape dets = convert_to_square(dets) dets[:, 0:4] = np.round(dets[:, 0:4]) [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self._pad(dets, w, h) num_boxes = dets.shape[0] cropped_ims = np.zeros((num_boxes, 48, 48, 3), dtype=np.float32) for i in range(num_boxes): tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1, x[i]:ex[i] + 1, :] cropped_ims[i, :, :, :] = (cv2.resize(tmp, (48, 48))-127.5) / 128 cls_scores, reg,landmark = self.o_net.predict(cropped_ims) # prob belongs to face cls_scores = cls_scores[:,1] keep_inds = np.where(cls_scores > self.threshold[2])[0] if len(keep_inds) > 0: # pickout filtered box boxes = dets[keep_inds] boxes[:, 4] = cls_scores[keep_inds] reg = reg[keep_inds] landmark = landmark[keep_inds] else: return None, None, None # width w = boxes[:,2] - boxes[:,0] + 1 # height h = boxes[:,3] - boxes[:,1] + 1 landmark[:,0::2] = (np.tile(w,(5,1)) * landmark[:,0::2].T + np.tile(boxes[:,0],(5,1)) - 1).T landmark[:,1::2] = (np.tile(h,(5,1)) * landmark[:,1::2].T + np.tile(boxes[:,1],(5,1)) - 1).T boxes_c = self._calibrate_box(boxes, reg) boxes = boxes[py_nms(boxes, 0.6, "minimum")] keep = py_nms(boxes_c, 0.6, "minimum") boxes_c = boxes_c[keep] landmark = landmark[keep] return boxes, boxes_c, landmark @staticmethod def _refine_bboxes(all_boxes): all_boxes = np.vstack(all_boxes) # merge the detection from first stage keep = py_nms(all_boxes[:, 0:5], 0.5, 'union') all_boxes = all_boxes[keep] boxes = all_boxes[:, :5] bbw = all_boxes[:, 2] - all_boxes[:, 0] + 1 bbh = all_boxes[:, 3] - all_boxes[:, 1] + 1 # refine the boxes boxes_c = np.vstack([all_boxes[:, 0] + all_boxes[:, 5] * bbw, all_boxes[:, 1] + all_boxes[:, 6] * bbh, all_boxes[:, 2] + all_boxes[:, 7] * bbw, all_boxes[:, 3] + all_boxes[:, 8] * bbh, all_boxes[:, 4]]) boxes_c = boxes_c.T return boxes, boxes_c @staticmethod def _calibrate_box(bbox, reg): bbox_c = bbox.copy() w = bbox[:, 2] - bbox[:, 0] + 1 w = np.expand_dims(w, 1) h = bbox[:, 3] - bbox[:, 1] + 1 h = np.expand_dims(h, 1) reg_m = np.hstack([w, h, w, h]) aug = reg_m * reg bbox_c[:, 0:4] = bbox_c[:, 0:4] + aug return bbox_c # @staticmethod # def _convert_to_square(bbox): # # square_bbox = bbox.copy() # # h = bbox[:, 3] - bbox[:, 1] + 1 # w = bbox[:, 2] - bbox[:, 0] + 1 # max_side = np.maximum(h, w) # square_bbox[:, 0] = bbox[:, 0] + w * 0.5 - max_side * 0.5 # square_bbox[:, 1] = bbox[:, 1] + h * 0.5 - max_side * 0.5 # square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1 # square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1 # return square_bbox @staticmethod def _pad(bboxes, w, h): tmpw, tmph = bboxes[:, 2] - bboxes[:, 0] + 1, bboxes[:, 3] - bboxes[:, 1] + 1 num_box = bboxes.shape[0] dx, dy = np.zeros((num_box,)), np.zeros((num_box,)) edx, edy = tmpw.copy() - 1, tmph.copy() - 1 x, y, ex, ey = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3] tmp_index = np.where(ex > w - 1) edx[tmp_index] = tmpw[tmp_index] + w - 2 - ex[tmp_index] ex[tmp_index] = w - 1 tmp_index = np.where(ey > h - 1) edy[tmp_index] = tmph[tmp_index] + h - 2 - ey[tmp_index] ey[tmp_index] = h - 1 tmp_index = np.where(x < 0) dx[tmp_index] = 0 - x[tmp_index] x[tmp_index] = 0 tmp_index = np.where(y < 0) dy[tmp_index] = 0 - y[tmp_index] y[tmp_index] = 0 return_list = [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] return_list = [item.astype(np.int32) for item in return_list] return return_list @staticmethod def _generate_bbox(cls_map, reg, scale, threshold, stride=2, cell_size=12): t_index = np.where(cls_map > threshold) # find nothing if t_index[0].size == 0: return np.array([]) # offset dx1, dy1, dx2, dy2 = [reg[t_index[0], t_index[1], i] for i in range(4)] reg = np.array([dx1, dy1, dx2, dy2]) score = cls_map[t_index[0], t_index[1]] bbox = np.vstack([np.round((stride * t_index[1]) / scale), np.round((stride * t_index[0]) / scale), np.round((stride * t_index[1] + cell_size) / scale), np.round((stride * t_index[0] + cell_size) / scale), score, reg]) return bbox.T @staticmethod def _get_weights(weights_dir): # weights_files = glob.glob('{}/*.h5'.format(weights_dir)) # p_net_weight = None # r_net_weight = None # o_net_weight = None # for wf in weights_files: # if 'pnet' in wf: # p_net_weight = wf # elif 'rnet' in wf: # r_net_weight = wf # elif 'onet' in wf: # o_net_weight = wf # else: # raise ValueError('No valid weights files !') # print(p_net_weight,r_net_weight,o_net_weight) # if p_net_weight is None and r_net_weight is None and o_net_weight is None: # raise ValueError('No valid weights files !') p_net_weight = os.path.join(weights_dir, 'pnet.h5') r_net_weight = os.path.join(weights_dir, 'rnet.h5') o_net_weight = os.path.join(weights_dir, 'onet.h5') return p_net_weight, r_net_weight, o_net_weight

[ "1191353802@qq.com" ]

1191353802@qq.com

3d7b976dfd0d9a0a52763a1c2002dca8ee09d178

4f1f639ab5ca26a5e9efb57902bdf8ff62443446

/t/vektivaSmarWi/smarwiEmulator.py

956c0d9136d5e2429d2db2d90b99e33827ceeba9

[ "BSD-3-Clause" ]

permissive

CESNET/siotgateway

9965ae67d7c86014b6bb799d03b1e0a5063c11c6

f3f4bc59a27760dddb818ae83c7c3371a7c2d757

refs/heads/master

2023-04-15T09:38:47.509990

2019-11-03T20:09:26

133,476,838

1

BSD-3-Clause

2018-11-04T20:51:18

2018-05-15T07:26:52

C++

UTF-8

Python

false

16,112

py

import argparse import http.server import json import logging import paho.mqtt.client as mqtt import re import signal import socketserver import sys import time from threading import Thread, Lock, Event, current_thread DEFAULT_HTTP_SERVER_PORT = 8080 # HTTP server runs on localhost DEFAULT_MQTT_URL = "localhost" DEFAULT_MQTT_PORT = 1883 # Class Thread extended to be able to stop running thread class StoppableThread(Thread): """Thread class with a stop() method. The thread itself has to check regularly for the stopped() condition.""" def __init__(self, *args, **kwargs): super(StoppableThread, self).__init__(*args, **kwargs) self._stopEvent = Event() def stop(self): self._stopEvent.set() def stopped(self): return self._stopEvent.is_set() # Class for handling smarwis class VektivaSmarwiHandler(mqtt.Client): def __init__(self, router, *args): logging.info("Vektiva Smarwi handler instantiated.") self._smarwisData = [] self._threads = {} self._router = router mqtt.Client.__init__(self, *args) def run(self, url, port): self.connect(url, port, 60) self.subscribe("ion/#") self.loop_start() def on_connect(self, mqttc, obj, flags, rc): pass #print("rc: "+str(rc)) def on_message(self, mqttc, obj, msg): topicSplit = msg.topic.split("/") if topicSplit and topicSplit[-1] == "cmd": macAddr = topicSplit[-2][1:] # Second from the end and cut the first character out msg2 = str(msg.payload)[2:-1] msg2 = msg2.replace(";", "/") # in case of open;50 messages route = "/" + macAddr + "/" + msg2 logging.info("MQTT message sending to router (" + route + ")") self._router.route(route) def on_publish(self, mqttc, obj, mid): pass #print("mid: "+str(mid)) def on_subscribe(self, mqttc, obj, mid, granted_qos): pass #print("Subscribed: "+str(mid)+" "+str(granted_qos)) def on_log(self, mqttc, obj, level, string): pass #print(string) def smarwis(self): return self._smarwisData def addSmarwi(self, smarwi): if (isinstance(smarwi, Smarwi) and self.getSmarwi(smarwi.getMAC()) == None): logging.info("Smarwi (" + smarwi.getMAC() + ") added to the local database.") smarwi.on(self) self._smarwisData.append(smarwi) return "OK" return "Err" def removeSmarwi(self, macAddr): for smarwi in self._smarwisData: if macAddr == smarwi.getMAC(): smarwi.eraseMessages(self) self._smarwisData.remove(smarwi) return "OK" return "Err" def getSmarwi(self, macAddr): for smarwi in self._smarwisData: if macAddr == smarwi.getMAC(): return smarwi def route(self, reqType, body, route): macAddr = route[0] smarwi = self.getSmarwi(macAddr) if (hasattr(smarwi, route[1])): # Checks if function for the second part of URL exists method = getattr(smarwi, route[1]) # if exists, proper method is called if (route[1] == "error"): errno = route[2] if len(route) > 2 else "10" thread = StoppableThread(target=smarwi.error, kwargs={'mqttClient':self, 'errno':errno}) else: thread = StoppableThread(target=method, kwargs={'mqttClient':self}) oldThread = self._threads.get(macAddr) if (oldThread == None or not oldThread.isAlive()): # if there is no old thread self._threads.update({macAddr : thread}) else: oldThread.stop() self._threads.update({macAddr: thread}) thread.start() return "OK" return "Err" class Router: def __init__(self, mqttUrl, mqttPort): logging.info("Router instantiated.") self._smarwiHandler = VektivaSmarwiHandler(self) self._smarwiHandler.run(mqttUrl, mqttPort) def serializeSmarwis(self, smarwi): properties = smarwi.getProperties() return properties def route(self, route, reqType = "GET", body = None): # Splits route to list by slash routeParts = route.split('/') # Filters out empty strings e.g. in case of /open routeFiltered = list(filter(None, routeParts)) if (routeFiltered and hasattr(self, routeFiltered[0])): # If list is not empty and checks the first route part method = getattr(self, routeFiltered[0]) # If exists, proper method is called return method(reqType = reqType, body = body, route = routeFiltered[1:]) elif (routeFiltered and re.search("^[a-fA-F0-9]{12}$", routeFiltered[0]) != None) and len(routeFiltered) > 1: return self.control(reqType = reqType, body = body, route = routeFiltered) else: # Else method index is called return self.index(reqType = "GET", body = body, route = routeFiltered) def index(self, reqType, body, route): file = open("index.html", "r") page = file.read() file.close() return page def jquery(self, reqType, body, route): jqueryFileName = "jquery.min.js" try: open(jqueryFileName, "r") except IOError: logging.critical(jqueryFileName + " file does not appear to exist.") return file = open(jqueryFileName, "r") page = file.read() file.close() return page # GET # returns list of currently existing Smarwi devices # POST # creates a new device and adds it to the list # DELETE # deletes a Smarwi device with the MAC address equal to the MAC address specified in the URL def devices(self, reqType, body, route): if (reqType == "GET"): jsonSmarwis = json.dumps(self._smarwiHandler.smarwis(), default=self.serializeSmarwis) return jsonSmarwis elif (reqType == "POST"): logging.debug(body) smarwiJson = json.loads(body) if (smarwiJson.get("macAddr") == None or (re.search("^[a-fA-F0-9]{12}$", smarwiJson.get("macAddr")) == None)): return "Err" macAddr = smarwiJson.get("macAddr").lower() smarwi = Smarwi(macAddr) return self._smarwiHandler.addSmarwi(smarwi) if (reqType == "DELETE"): return self._smarwiHandler.removeSmarwi(route[0].lower()) def control(self, reqType, body, route): route[0] = route[0].lower() return self._smarwiHandler.route(reqType, body, route) class VektivaHTTPHandler(http.server.BaseHTTPRequestHandler): def __init__(self, router, *args): self._router = router http.server.BaseHTTPRequestHandler.__init__(self, *args) def do_GET(self): resp = self._router.route(self.path) self.send_response(200) if (resp == None or "<html>" in resp): self.send_header('content-type', 'text/html') else: self.send_header('content-type', 'application/json') self.send_header('content-type', 'charset="utf-8"') if (resp != None): self.send_header('content-length', len(resp)) self.end_headers() if (resp != None): self.wfile.write(resp.encode()) return VektivaHTTPHandler def do_POST(self): contentLen = int(self.headers['content-length']) postBody = self.rfile.read(contentLen).decode('utf-8') resp = self._router.route(self.path, "POST", postBody) self.send_response(200) if (resp == None or "<html>" in resp): self.send_header('content-type', 'text/html') else: self.send_header('content-type', 'application/json') self.send_header('content-type', 'charset="utf-8"') if (resp != None): self.send_header('content-length', len(resp)) self.end_headers() if (resp != None): self.wfile.write(resp.encode()) return VektivaHTTPHandler def do_DELETE(self): resp = self._router.route(self.path, "DELETE") self.send_response(200) if (resp == None or "<html>" in resp): self.send_header('content-type', 'text/html') else: self.send_header('content-type', 'application/json') self.send_header('content-type', 'charset="utf-8"') if (resp != None): self.send_header('content-length', len(resp)) self.end_headers() if (resp != None): self.wfile.write(resp.encode()) return VektivaHTTPHandler class http_server: def __init__(self, port, router): logging.info("HTTP server instantiated.") signal.signal(signal.SIGINT, self.emulatorShutdown) signal.signal(signal.SIGTERM, self.emulatorShutdown) def handler(*args): VektivaHTTPHandler(router, *args) self._router = router socketserver.TCPServer.allow_reuse_address = True self.httpd = socketserver.TCPServer(("", port), handler) self.httpd.serve_forever() def emulatorShutdown(self, signum, frame): logging.info("Emulator is shutting down.") def serverShutdown(server): server.shutdown() thread = StoppableThread(target=serverShutdown, kwargs={'server':self.httpd}) thread.start() self.httpd.server_close() logging.info("Server was shutted down.") devicesJSONString = self._router.devices("GET", "", "") devices = json.loads(devicesJSONString) logging.debug("Devices: " + devicesJSONString) for device in devices: logging.info("Device " + device["macAddr"] + " is being deleted.") self._router.devices("DELETE", "", [device["macAddr"]]) logging.info("All devices has been deleted.") #Smarwi class representing standalone device class Smarwi: def __init__(self, macAddr): self._macAddr = macAddr self._online = False self._isOpen = False self._lock = Lock() self._errno = "0" self._errorScheduled = False self._fixed = True self._statusno = 250 self._ok = 1 self._ro = 0 def getMAC(self): return self._macAddr def getProperties(self): return { 'macAddr': self._macAddr, 'online': self._online, 'isOpen': self._isOpen, 'errno': self._errno, 'errorScheduled': self._errorScheduled, 'fixed': self._fixed, 'statusno': self._statusno, 'ok': self._ok, 'ro': self._ro } def stop(self, mqttClient): self._lock.acquire() try: if self._online: self._fixed = False self._errno = "0" self._errorScheduled = False self._statusno = 250 self._ok = 1 self._ro = 0 self.status(mqttClient) finally: self._lock.release() def fix(self, mqttClient): self._lock.acquire() try: if self._online: self._statusno = 250 self._ok = 1 self._ro = 0 self._fixed = True self.status(mqttClient) finally: self._lock.release() def status(self, mqttClient): if self._online: mqttClient.publish("ion/dowaroxby/%" + self._macAddr + "/status", 't:swr\n'\ 's:' + str(self._statusno) + '\n'\ 'e:' + ("0" if self._errorScheduled else self._errno) + '\n'\ 'ok:' + ("1" if self._errorScheduled or self._errno == "0" else "0") + '\n'\ 'ro:' + str(self._ro) + '\n'\ 'pos:' + ("o" if self._isOpen else "c") + '\n'\ 'fix:' + ("1" if self._fixed else "0") + '\n'\ 'a:-98\n'\ 'fw:3.4.1-15-g3d0f\n'\ 'mem:25344\n'\ 'up:583521029\n'\ 'ip:268446218\n'\ 'cid:xsismi01\n'\ 'rssi:-56\n'\ 'time:' + str(int(time.time())) + '\n'\ 'wm:1\n'\ 'wp:1\n'\ 'wst:3\n') def error(self, mqttClient, errno = "10"): self._lock.acquire() if (self._online): self._errno = errno self._errorScheduled = True self._lock.release() def on(self, mqttClient): self._lock.acquire() try: if not (self._online): self._online = True mqttClient.publish("ion/dowaroxby/%" + self._macAddr + "/online", "1", retain = True) self.status(mqttClient) finally: self._lock.release() def off(self, mqttClient): self._lock.acquire() try: if (self._online): self._online = False mqttClient.publish("ion/dowaroxby/%" + self._macAddr + "/online", "0", retain = True) self.status(mqttClient) finally: self._lock.release() def eraseMessages(self, mqttClient): self._lock.acquire() try: mqttClient.publish("ion/dowaroxby/%" + self._macAddr + "/online", '''''', retain = True) mqttClient.publish("ion/dowaroxby/%" + self._macAddr + "/status", '''''', retain = True) self._online = False finally: self._lock.release() def open(self, mqttClient): self._lock.acquire() try: # If error happened, SmarWi can not be controlled. It waits until "stop" message if ((not self._errorScheduled) and (self._errno != "0")): return if (self._isOpen): self._statusno = 212 self._fixed = True self._isOpen = True # Sending the first message self.status(mqttClient) #Before the second step, check if error should happen. If so, #error message is published and then method ends. if (self._errorScheduled and self._errno != "0"): self._statusno = 10 self._ok = 0 self._errorScheduled = False self.status(mqttClient) return time.sleep(3) if current_thread().stopped(): return self._statusno = 210 self.status(mqttClient) time.sleep(3) if current_thread().stopped(): return self._statusno = 250 self.status(mqttClient) else: self._statusno = 200 self._fixed = True self._isOpen = True # Sending the first message self.status(mqttClient) #Before the second step, check if error should happen. If so, #error message is published and then method ends. if (self._errorScheduled and self._errno != "0"): self._statusno = 10 self._ok = 0 self._errorScheduled = False self.status(mqttClient) return time.sleep(3) if current_thread().stopped(): return self._statusno = 210 self.status(mqttClient) time.sleep(3) if current_thread().stopped(): return self._statusno = 250 self.status(mqttClient) finally: self._lock.release() def close(self, mqttClient): self._lock.acquire() try: # If error happened, SmarWi can not be controlled. It waits until "stop" if ((not self._errorScheduled) and (self._errno != "0")): return if (self._isOpen): self._statusno = 220 self._isOpen = True self._fixed = True self.status(mqttClient) #Before the second step, check if error should happen. If so, #error message is published and then method ends. if (self._errorScheduled and self._errno != "0"): self._statusno = 10 self._ok = 0 self._errorScheduled = False self.status(mqttClient) return time.sleep(3) if current_thread().stopped(): return self._statusno = 230 self._isOpen = False self.status(mqttClient) time.sleep(3) if current_thread().stopped(): return self._statusno = 250 self.status(mqttClient) else: self._statusno = 232 self._isOpen = False self._fixed = True self.status(mqttClient) #Before the second step, check if error should happen. If so, #error message is published and then method ends. if (self._errorScheduled and self._errno != "0"): self._statusno = 10 self._ok = 0 self._errorScheduled = False self.status(mqttClient) return time.sleep(3) if current_thread().stopped(): return self._statusno = 232 self._isOpen = True self.status(mqttClient) time.sleep(3) if current_thread().stopped(): return self._statusno = 234 self._isOpen = True self.status(mqttClient) time.sleep(3) if current_thread().stopped(): return self._statusno = 230 self._isOpen = False self.status(mqttClient) time.sleep(3) if current_thread().stopped(): return self._statusno = 250 self._isOpen = False self.status(mqttClient) finally: self._lock.release() class main: def __init__(self, httpServerPort, mqttUrl, mqttPort): self.router = Router(mqttUrl, mqttPort) self.server = http_server(httpServerPort, self.router) if __name__ == '__main__': logging.basicConfig(level = logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument("--http-port", metavar="HTTP_SERVER_PORT", type=int, help="The emulator will run on specified port. The default port is 8080.") parser.add_argument("--mqtt-port", metavar="MQTT_BROKER_PORT", type=int, help="The MQTT client will attempt to connect on the specified port. The default port is 1883.") parser.add_argument("--mqtt-url", metavar="MQTT_BROKER_URL", help="The MQTT client will attempt to connect to the specified URL. The default URL is localhost.") args = parser.parse_args() httpServerPort = args.http_port if args.http_port is not None else DEFAULT_HTTP_SERVER_PORT mqttUrl = args.mqtt_url if args.mqtt_url is not None else DEFAULT_MQTT_URL mqttPort = args.mqtt_port if args.mqtt_port is not None else DEFAULT_MQTT_PORT m = main(httpServerPort, mqttUrl, mqttPort)

[ "xbedna62@stud.fit.vutbr.cz" ]

xbedna62@stud.fit.vutbr.cz

ae969e5baa7b48325cb6f6fdbe1d53881a439e63

c248b9e128eaf1fe8b49f5865e2a1e6f6dbf856d

/main_app/migrations/0004_finch_toys.py

b1a60f0048953a80b4e18f7025e7d40296a7956f

[]

no_license

jenny-martin/finchcollector

8826c02689289a115e9c446631b7bc009c7beaf4

b6722d9ea018bf937608a3e0fa8791fa534f79c3

refs/heads/master

2020-06-12T01:09:42.708428

2019-06-27T18:49:29

null

0

null

UTF-8

Python

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py

# Generated by Django 2.2 on 2019-06-27 05:06 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('main_app', '0003_auto_20190627_0504'), ] operations = [ migrations.AddField( model_name='finch', name='toys', field=models.ManyToManyField(to='main_app.Toy'), ), ]

[ "jennymartin@Jennys-MacBook-Pro.local" ]

jennymartin@Jennys-MacBook-Pro.local

240c3495c4ce8e4a18d227a9481367db872addca

dd8db5054f3e9cb3aa434cdf72c68a23bf4ab4cb

/botCode.py

520e4981b7a2402707338ce9a4a9a9b852f2d298

[]

no_license

Skinbow/MathBot

60f10bfdfcb23581a5318c314706899b4b6fedb6

a7917c3002434381db0a30391ae098fe75710e72

refs/heads/master

2020-05-15T18:39:21.012128

2019-04-20T18:05:49

182,435,078

0

null

UTF-8

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false

4,862

py

# -*- coding: utf-8 -*- import config import telebot import random import threading flag = 0 x = 0 y = 0 score = 0 # 0 - игра не началась # 1 - игрок ждет вопрос # 2 - мы ожидаем ответ # 3 - пауза # 10 - начать ли заново? # 11 - ожидать ответа # 12 - начать заново TimeOut = False waitingThread = threading.Thread(target=None) bot = telebot.TeleBot(config.token) def SignalTimeIsOut(): global TimeOut TimeOut = True def WaitForTimeOut(id): global TimeOut while True: if TimeOut == True: TimeIsOut(id) def TimeIsOut(id): global thread1 thread1.join() TimeOut = False bot.send_message(id, "Время вышло!") bot.send_message(id, "Новая задачя:") push(id) def getText(message): checkPlayersAnswer(message) def push(id): global x, y, flag, thread1 x = random.randint(0, 100) y = random.randint(0, 100) keyboard = telebot.types.ReplyKeyboardMarkup(resize_keyboard=True) button1 = telebot.types.KeyboardButton(str(x + y)) r = random.randint(0,200) while r == x + y: r = random.randint(0,200) button2 = telebot.types.KeyboardButton(str(r)) if random.randint(0,2) == 0: keyboard.add(button1) keyboard.add(button2) else: keyboard.add(button2) keyboard.add(button1) ms = bot.send_message(id, str(x) + " + " + str(y), reply_markup=keyboard) bot.register_next_step_handler(ms, getText) flag = 2 thread1 = threading.Timer(4.0, SignalTimeIsOut) thread1.start() '''def deleteScore(id): fname = "/Users/mikhail/Documents/Programming/Python/Bots/HenryBot/bot2/scores_" + str(id) + "_.txt" def file_len(fname): with open(fname) as f: for i, l in enumerate(f): pass return i + 1 if file_len(fname) > 50: file = open(fname, "r") min = int(file[0]) minIndex = 0''' def saveScore(id): global score file = open("/Users/mikhail/Documents/Programming/Python/Bots/HenryBot/bot2/data/scores_" + str(id) + "_.txt", "a") file.write(str(score) + "\n") file.close() def getHighScores(id): file = open("/Users/mikhail/Documents/Programming/Python/Bots/HenryBot/bot2/data/scores_" + str(id) + "_.txt", "r") scores = file.readlines() file.close() n = 0 for i in scores: scores[n] = int(i) n += 1 scores.sort(reverse=True) bot.send_message(id, "Рекорды:") for n in range(5): try: bot.send_message(id, str(scores[n])) except: break def initiateGame(message): global flag global score score = 0 bot.send_message(message.chat.id, "Игра началась :)") flag = 1 bot.send_message(message.chat.id, "Счёт: " + str(score)) waitingThread.start() push(message.chat.id) return def checkPlayersAnswer(message): global flag global score global thread1 try: if int(message.text) == x+y: bot.send_message(message.chat.id, "Молодец!") flag = 1 score += 1 bot.send_message(message.chat.id, "Счёт: " + str(score)) thread1.join() push(message.chat.id) else: bot.send_message(message.chat.id, "Ты проиграл!!!") #deleteScore(message.chat.id) saveScore(message.chat.id) score = 0 flag = 0 except: bot.send_message(message.chat.id, "Ты проиграл!!!") #deleteScore(message.chat.id) saveScore(message.chat.id) score = 0 flag = 0 @bot.message_handler(commands=["start", "stop", "pause", "resume", "get_my_scores"]) def react_to_commands(message): global flag global waitingThread if message.text == "/start": if flag == 0: waitingThread = threading.Thread(target=WaitForTimeOut, args=(message.chat.id,)) initiateGame(message) else: flag = 10 react_to_text(message) elif message.text == "/stop": flag = 0 waitingThread.join() saveScore(message.chat.id) score = 0 elif message.text == "/pause": flag = 3 elif message.text == "/resume": flag = 1 push(message.chat.id) elif message.text == "/get_my_scores": getHighScores(message.chat.id) #flag = 20 #react_to_text(message) else: bot.send_message(message.chat.id, "Неверная команда") @bot.message_handler(content_types=["text"]) def react_to_text(message): global flag #bot.send_message(message.chat.id, "Игра началась :)") if flag == 2: checkPlayersAnswer(message) elif flag == 10: bot.send_message(message.chat.id, "Вы действительно хотите начать заново? (Y/N)") flag = 11 elif flag == 11: if message.text.lower() == 'y': saveScore(message.chat.id) flag = 1 initiateGame(message) elif message.text.lower() == 'n': flag = 2 else: bot.send_message(message.chat.id, 'Введите Y чтобы ответить "да" или введите N чтобы ответить "нет".') #elif flag == 20: # bot.send_message(message.chat.id, "Сколько ") if __name__ == '__main__': bot.polling(none_stop=True)

[ "noreply@github.com" ]

Skinbow.noreply@github.com

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82050bff6e809419a170a992924f33b09bdf26fb

/functions/braceParser.py

b0177cffac00b112bed9b12e2e323ff5c08669f0

[]

no_license

Phoshi/OMGbot

f1ffb9ae4d98bfe04bb68647c48335afdf39b224

402a50dcaa570dad3d17da6b84a201518c85b550

refs/heads/master

2016-09-05T16:34:39.830212

2012-02-29T23:23:24

1,910,305

2

3

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UTF-8

Python

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1,716

py

# -*- coding: utf-8 -*- #BraceParser.py class countError(Exception): def __init__(self, expr, msg): self.expr=expr self.msg=msg def __str__(self): return self.msg def parseBraces(input): matchArray=[] lookingForConditionals=True conditionalArray=[] conditionalSyntax=["(",")"] matchSyntax=["{","}"] conditionalCount=0 conditionalPosition=-1 matchCount=0 matchPosition=-1 for position,letter in enumerate(input): if letter==conditionalSyntax[0] and lookingForConditionals: conditionalCount+=1 if conditionalCount==1: conditionalPosition=position elif letter==conditionalSyntax[1] and lookingForConditionals: conditionalCount-=1 if conditionalCount==0: conditionalArray.append(input[conditionalPosition+1:position]) if letter==matchSyntax[0]: if conditionalCount!=0: continue lookingForConditionals=False matchCount+=1 if matchCount==1: matchPosition=position elif letter==matchSyntax[1]: if conditionalCount!=0: continue matchCount-=1 if matchCount==0: matchArray.append(input[matchPosition+1:position]) if conditionalCount!=0: raise countError(input,"Unbalanced brackets!") elif matchCount!=0: raise countError(input,"Unbalanced parentheses!") return (conditionalArray, matchArray) if __name__=="__main__": input="!if (dicks==true) AND (lasers==awesome) {superdicks;var dicks = false;} else {hyperdicks;var dicks = true;}" print parseBraces(input)

[ "AHPhoshi@gmail.com" ]

AHPhoshi@gmail.com

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8e096d61a991896d12ddcbea45481350610b70f9

/mysite/mysite/settings.py

3b6059f9f0d9af3357feffcb9d854df46efd2620

[]

no_license

belac2014/Distributed-web

73f0317b55676e6f8b36dee082014c2047a8f814

dd73f5ff91d6a19866bf09a15feae3a5041bc999

refs/heads/master

2021-01-18T22:13:56.073045

2016-10-30T15:48:25

72,358,270

0

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3,166

py

""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 1.10. For more information on this file, see https://docs.djangoproject.com/en/1.10/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.10/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.10/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '4$b()gjk^xcrqf4ez8%g@h0j4bvf6o-7fr&2xp7l9*vz!^^d!v' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'polls.apps.PollsConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/1.10/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.10/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.10/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'US/Eastern' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.10/howto/static-files/ STATIC_URL = '/static/'

[ "noreply@github.com" ]

belac2014.noreply@github.com

f759243ca8cb43b3d912b30fca2464aca3d24563

fcbc308e67905631116f7f8bb8723fb615da328b

/week4/linked_list3.py

4e6ca97373fc75c67105e65bb7ace0e59d96bef7

[]

no_license

Sally-A/Python_fundamentals

b08d8f4d838431c4a4bce9772d53e5fa130f5689

14707757b17170c4568635bdcb4f68acda7a7446

refs/heads/main

2023-08-10T11:33:41.516127

2021-09-11T17:17:18

396,183,497

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2

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UTF-8

Python

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py

class Node: def __init__(self, value): self.value = value self.next = None class LinkedList: def __init__(self): self.head = None def append(self, value): new_node = Node(value) if self.head is None: self.head = new_node print("Head Node created:", self.head.value) return node = self.head while node.next is not None: node = node.next node.next = new_node print("Apeended new Node with value:", node.next.value) llist = LinkedList() llist.append("First Node") llist.append("Second Node") llist.append("Third Node")

[ "Sally.Anderson@gmx.com" ]

Sally.Anderson@gmx.com

51bf928b89bd5bd6aae923b7e82be036bc0ee86b

6c646968638c7dc8702059cf9820ff5d8170b97f

/ML_HW3_Maxent.py

8f9da2f787e90ab14da66a24ba730af60a64c91d

[]

no_license

AnushreeDesai35/ML_LogLinearModel

b131d9ed638c612e26ab9e1259993c23aeb19e01

1814f3dc177c80c99b65effddeb261a9ff7f1d89

refs/heads/master

2020-05-07T15:55:01.562397

2019-04-10T20:38:00

180,659,226

0

null

UTF-8

Python

false

4,877

py

#%% Change working directory from the workspace root to the ipynb file location. Turn this addition off with the DataScience.changeDirOnImportExport setting import os try: os.chdir(os.path.join(os.getcwd(), 'ML_HW3_Anushree_Desai_Code')) print(os.getcwd()) except: pass #%% import gzip, pickle import numpy as np from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix import pandas as pd from sklearn.metrics import classification_report import seaborn as sn from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression with gzip.open('mnist_rowmajor.pkl.gz', 'rb') as data_fh: data = pickle.load(data_fh, encoding='latin1') train_images = data['images_train'] train_labels = data['labels_train'] images_train, images_dev, labels_train, labels_dev = train_test_split(train_images, train_labels, test_size=0.20, random_state=4) images_test = data['images_test'] labels_test = data['labels_test'] # print(len(images_train)) # print(len(images_dev)) # print(len(labels_train)) # print(len(labels_dev)) # print(len(images_test)) # print(len(labels_test)) TRAIN_LENGTH = len(images_train) DEV_LENGTH = len(images_dev) TEST_LENGTH = len(images_test) #%% # Feature 1: Signing images feature1_training_set = np.empty((TRAIN_LENGTH, 784)) for idx, i in enumerate(images_train): signed_image_train = list(map((lambda x: 1 if (x > 0) else 0), i)) feature1_training_set[idx] = signed_image_train feature1_dev_set = np.empty((DEV_LENGTH, 784)) for idx, i in enumerate(images_dev): signed_image_dev = list(map((lambda x: 1 if (x > 0) else 0), i)) feature1_dev_set[idx] = signed_image_dev #%% feature1_test_set = np.zeros((TEST_LENGTH, 784)) for idx, i in enumerate(images_test): signed_image_test = list(map((lambda x: 1 if (x > 0) else 0), i)) feature1_test_set[idx] = signed_image_test complete_training = np.zeros((60000, 784)) for idx, i in enumerate(train_images): temp = list(map((lambda x: 1 if (x > 0) else 0), i)) complete_training[idx] = temp #%% # Feature 2: transform if i,j & p,q > 0 def transform(row): arr = np.zeros((783)) for k in range(len(row) - 1): if(row[k] > 0 and row[k + 1] > 0): arr[k] = 1 else: arr[k] = 0 return arr feature2_training_set = np.zeros((TRAIN_LENGTH, 783)) for idx, image in enumerate(images_train): image = transform(image) feature2_training_set[idx] = image feature2_dev_set = np.zeros((DEV_LENGTH, 783)) for idx, image in enumerate(images_dev): image = transform(image) feature2_dev_set[idx] = image #%% def experimentEvaluation(y_correct, y_pred): cm = confusion_matrix(y_correct.flatten(), y_pred.flatten()) df_cm = pd.DataFrame(cm.astype(int), range(10), range(10)) plt.figure(figsize = (10,10)) sn.heatmap(df_cm, annot=True, annot_kws={"size": 16}, fmt="d") plt.xlabel('Predicted label') plt.ylabel('True label') plt.show() accuracy = accuracy_score(y_correct.flatten(), y_pred.flatten()) print('Accuracy: ', accuracy) print(classification_report(y_correct.flatten(), y_pred.flatten())) #%% # Configuration 1: feature = signed images, regularization = l1 logisticRegression = LogisticRegression(penalty = 'l1') logisticRegression.fit(feature1_training_set, labels_train) predictionsConfig1 = logisticRegression.predict(feature1_dev_set) experimentEvaluation(labels_dev, predictionsConfig1) #%% # Configuration 2: feature = signed images, regularization = l2 logisticRegression = LogisticRegression(penalty = 'l2') logisticRegression.fit(feature1_training_set, labels_train) predictionsConfig2 = logisticRegression.predict(feature1_dev_set) experimentEvaluation(labels_dev, predictionsConfig2) #%% # Configuration 3: feature = transformed images, regularization = l1 logisticRegression = LogisticRegression(penalty = 'l1') logisticRegression.fit(feature2_training_set, labels_train) predictionsConfig3 = logisticRegression.predict(feature2_dev_set) experimentEvaluation(labels_dev, predictionsConfig3) #%% # Configuration 4: feature = transformed images, regularization = l2 logisticRegression = LogisticRegression(penalty = 'l2') logisticRegression.fit(feature2_training_set, labels_train) predictionsConfig4 = logisticRegression.predict(feature2_dev_set) experimentEvaluation(labels_dev, predictionsConfig4) #%% # Testing on Test Data training_set = np.concatenate((feature1_training_set, feature1_dev_set), axis=0) # print(training_set.shape) # print(np.concatenate((labels_train, labels_dev), axis=0).shape) logisticRegression = LogisticRegression(penalty = 'l1') logisticRegression.fit(complete_training, train_labels) predictions = logisticRegression.predict(feature1_test_set) experimentEvaluation(labels_test, predictions.reshape((10000, 1)))

[ "anushreerdesai@gmail.com" ]

anushreerdesai@gmail.com

053cc4671f3315c7c3119626f671455faea3e89f

2c04c06f8604a58ac9828aa6ee157593363a4806

/day_16.py

88dc24649a2f7f043f43246829d7a61ca0fd9fa4

[]

no_license

mtskillman/advent_of_code_2015

e3ee16cb26127bb16a0617129ea8a5568c55a6aa

9b3efe1e63a519040dffc5442a65db859fa7475d

refs/heads/master

2020-09-14T23:41:23.163003

2019-12-18T22:08:58

223,296,003

0

null

UTF-8

Python

false

1,911

py

class Sue(object): def __init__(self): self.data = { "Sue": None, 'children': None, 'cats': None, 'samoyeds': None, 'pomeranians': None, 'akitas': None, 'vizslas': None, 'goldfish': None, 'trees': None, 'cars': None, 'perfumes': None } mysue = Sue() mysue.data = { "Sue": None, 'children': 3, 'cats': 7, 'samoyeds': 2, 'pomeranians': 3, 'akitas': 0, 'vizslas': 0, 'goldfish': 5, 'trees': 3, 'cars': 2, 'perfumes': 1 } list_of_sues = [] with open('data.txt', 'r') as shit: mydata = shit.readlines() for line in mydata: newsue = Sue() line = line.split(" ") line[-1] = line[-1].strip("\n") for i, value in enumerate(line): if i % 2 == 1: continue else: value = value.strip(":") newsue.data[value] = int(line[i+1].strip(':,')) list_of_sues.append(newsue) for target in list_of_sues: flag = 1 for k,v in target.data.items(): if v is None or k == "Sue": continue else: dat = mysue.data[k] if dat is None: continue else: if k == "cats" or k == "trees": if v <= dat: flag = 0 elif k == 'goldfish' or k == 'pomeranians': if v >= dat: flag = 0 elif dat != v: flag = 0 if flag: print(target.data['Sue'])

[ "noreply@github.com" ]

mtskillman.noreply@github.com

a02b765eefb1c6726e25ba1b5d0361858f7e85fa

c015cd73e1e8c48b4d4aea3c871835bef02941bd

/testing.py

118b6cd2e58873b1731d4bc1471ae5bb5059df07

[]

no_license

RichaYadav/python

02fa218f9774f4a90df02ac4e9dcf33fc7813612

13fa1a0648a6e24ef7cd791cea3fb1fcbc3c66d2

refs/heads/master

2021-01-23T04:14:46.423229

2017-03-25T19:12:06

86,180,524

0

null

UTF-8

Python

false

300

py

import json from pprint import pprint from db import database class Testit: def parse_insert(self): file = open("test2.json") json_data = json.load(file) pprint(json_data) #for r in json_data['results']: # kind = r['kind'] Testit().parse_insert()

[ "noreply@github.com" ]

RichaYadav.noreply@github.com

264bf2869c0e3418827933e5e74ceb8aca07fb31

467db1b130cf66ed614bf79aaf3d6c8e614ed279

/Quesko/Quesko/asgi.py

dbc1ed039d2a809f3ac90c0eaf2b5470c1633578

[]

no_license

prabhat510/Quesko

c84b2894122ca8896dc910fcf7b7f76a1f2dfe91

39a1c7f4e9835b895b57a90267282ba84fa5756e

refs/heads/master

2023-06-06T22:29:31.102631

2021-06-29T04:36:13

380,930,498

0

null

UTF-8

Python

false

389

py

""" ASGI config for Quesko project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Quesko.settings') application = get_asgi_application()

[ "prabhatbhargava510@gmail.com" ]

prabhatbhargava510@gmail.com

75e88cff3fe29feac841a80d10159553491a9f31

8c11776fc0e57b64eaae57ad858ee1a17bc7e468

/classes/room.py

8c6806ab0319e5da4a5589d0afa80882dcc32578

[]

no_license

sluisdejesus/weekend_02_homework

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refs/heads/main

2023-06-05T16:34:30.715855

2021-06-27T20:42:37

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py

class Room: def __init__(self, name, capacity, entry_fee): self.name = name self.capacity = capacity self.entry_fee = entry_fee self.songlist = [] self.guest_list =[] def guest_list_length(self): return len(self.guest_list) def add_guest(self, guest): self.guest_list.append(guest) def check_guest_in(self, guest): self.capacity -= 1 self.guest_list.append(self) def check_guest_out(self): self.capacity += 1 self.guest_list.remove(self) def add_song(self, song): self.songlist.append(song) def song_list_length(self): return len(self.songlist)

[ "sluisdejesus@gmail.com" ]

sluisdejesus@gmail.com

14c287e40ef9f07fe3dd6944c53d3460a99de7cb

85b7487c00cabf70cbcf180c5015ac4886e78fb1

/test/support/__init__.py

bdbdb8f5f19075bc664a148dbdf532d577c3550c

[]

no_license

mkatsimpris/test_jpeg

7e686f27ac54db4128f4edbeb42b7cd284db0fa4

ee626d87e26a08d5ce80f73a883f00703ff34e70

refs/heads/master

2020-04-06T04:49:58.952565

2016-08-17T21:41:25

49,828,665

3

2

null

2016-07-25T16:50:52

2016-01-17T17:58:21

Verilog

UTF-8

Python

false

254

py

from __future__ import absolute_import from .jpeg_prep_cosim import prep_cosim from .jpeg_v1_intf import JPEGEncV1 from .jpeg_v2_intf import JPEGEncV2 from .jpegenc_v1_top import convert as convertv1 from .utils import set_default_args, get_cli_args

[ "chris.felton@gmail.com" ]

chris.felton@gmail.com

90ca6cd3d511b423fe14fa6ce58530a108c3b1c8

525dbcabc7bc0103c25d31e665234d7288e2b109

/spider/ArticleSpider/ArticleSpider/spiders/fang.py

8555decd5c48703e0d538e3803b5ff019c346ff0

[]

no_license

zihanbobo/spider

8ffce0bc33c2be52f27a3c5ede4953e903c2ae08

bfe14b64d4bc308e3625217ca10b8844628d0777

refs/heads/master

2022-10-29T19:05:39.504154

2020-06-22T07:20:12

null

0

null

UTF-8

Python

false

5,553

py

# -*- coding: utf-8 -*- import scrapy import re import copy from urllib import parse class FangSpider(scrapy.Spider): name = 'fang' allowed_domains = ['fang.com','esf.fang.com'] start_urls = ['https://www.fang.com/SoufunFamily.htm'] def parse(self, response): trs = response.xpath('//div[@class="outCont"]//tr') province = None for tr in trs: tds = tr.xpath('.//td[not(@class)]') province_text = tds[0].xpath('.//text()').extract_first() province_text = re.sub('\s','',province_text) if province_text: province = province_text if province =="其它": continue for a in tds[1].xpath('.//a'): city = a.xpath('.//text()').extract_first() city_link = a.xpath('.//@href').extract_first() url_model = re.split('//',city_link) scheme = url_model[0] domain = url_model[1] city_id = domain.split('.')[0] if 'bj.' in domain: new_house_link = 'https://newhouse.fang.com/house/s/' esf_link = 'https://esf.fang.com/' else : # 构建新房链接 new_house_link = scheme+'//'+city_id+'.newhouse.'+'fang.com/house/s/' # 构建二手房链接 # esf_link = scheme+'//'+'city_id'+domain esf_link = 'https://{}.zu.fang.com/house/a21/'.format(city_id) print(esf_link) # yield scrapy.Request(url=new_house_link,callback=self.parse_newhouse,\ # meta={'info':copy.deepcopy((province,city))}) # yield scrapy.Request(url=esf_link,callback=self.parse_esf,dont_filter=True,meta={'info': copy.deepcopy((province, city))}) break break def parse_newhouse(self,response): # item = {} # province,city = response.meta['info'] # item['provice'] = province # item['city'] = city # li_list = response.xpath('//div[contains(@class,"nl_con")]/ul/li') # for li in li_list: # item['name'] = li.xpath('.//div[@class="nlcd_name"]/a/text()').extract_first() # if item['name']: # item['name'] = item['name'].strip() # item['price'] = li.xpath('.//div[contains(@class,"nhouse_price")]//text()').extract() # if item['price']: # item['price'] = re.sub('\s|\n|\t|广告','',''.join(item['price'])) # else: # item['price'] = '' # if li.xpath('.//div[contains(@class,"house_type")]/a/text()').extract(): # item['rooms'] = ','.join(list(filter(lambda x:x.endswith("居"),li.xpath('.//div[contains(@class,"house_type")]/a/text()').extract()))) # area = li.xpath('.//div[contains(@class,"house_type")]/text()').extract() # if area: # item['area'] = re.sub('\n|\t|\s|/|－', '', ''.join(area)) # else: # item['area'] ='' # item['address'] = li.xpath('.//div[contains(@class,"address")]/a/@title').extract_first() # district = re.sub('\n|\t|\s','',''.join(li.xpath('.//div[contains(@class,"address")]/a//text()').extract())) # item['district'] = re.findall(r'.*\[(.+)\].*',district,re.S) # if item['district']: # item['district'] = item['district'][0] # item['detail_link'] = li.xpath('.//div[contains(@class,"address")]/a/@href') # if item['detail_link']: # item['detail_link'] = 'https:'+item['detail_link'].extract_first() # else: # item['detail_link'] = '' # item['sale'] = li.xpath('.//div[contains(@class,"fangyuan")]/span/text()').extract_first() # yield item # print(item) print(response.url) # next_url = response.xpath('//a[@class="next"]/@href').extract_first() # # if next_url: # # yield scrapy.Request(url=parse.urljoin(response.url,next_url),callback=self.parse_newhouse,meta={'info':copy.deepcopy((province,city))}) def parse_esf(self,response): print(response.url) # item = {} # province, city = response.meta['info'] # item['provice'] = province # item['city'] = city # dls = response.xpath('//div[contains(@class,"shop_list")]/dl') # for dl in dls: # name = dl.xpath('./dd//a/span/text()').extract_first() # if name: # item['name'] = name.strip() # else: # item['name'] = '' # info = ','.join(dl.xpath('.//p[@class="tel_shop"]/text()').extract()) # # if info: # infos = re.sub('\r|\n|\s','',info).split(',') # rooms = None # area = None # toward = None # floor = None # year = None # for inf in infos: # if '厅' in inf: # rooms = inf # elif 'm' in inf: # area = inf # elif '层' in inf: # floor = inf # elif '向' in inf: # toward = inf # else : # year = inf # print(rooms, area, floor, toward, year)

[ "1723742002@qq.com" ]

1723742002@qq.com

d1dd1de24c57b4399179bb5d549a990b01e72380

754a7854a09fb9e3add486e32d0ccb86b02a8847

/14_ng.py

81fa21b37320b13e645093af48fbf3b37a3dda1d

[]

no_license

AmanouToona/atcoder_Intermediate

724824d0aba932b0cbc09b1e05a91741a1435497

61a2f454a8a95a3dd775edaf59ec60ea95766711

refs/heads/master

2023-01-03T19:21:51.710058

2020-10-26T12:16:42

267,562,604

0

null

UTF-8

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1,146

py

# Square869120Contest #4 B - Buildings are Colorful!　 import sys N, K = map(int, sys.stdin.readline().strip().split()) a = list(map(int, sys.stdin.readline().strip().split())) def dfs(height=a[0], cost=0, target=K, can_see=1, i=1): if can_see == target: return cost # 端まで探索したとき if i == len(a): return float('inf') # 建物 i を選ばない場合 if height >= a[i]: cost1 = dfs(height=height, cost=cost, target=K, can_see=can_see, i=i + 1) else: cost1 = dfs(height=a[i], cost=cost, target=K, can_see=can_see + 1, i=i + 1) # print('cost1', cost1, 'can_see', can_see, 'i', i) # 建物　i を選んだ場合 cost += max(0, height + 1 - a[i]) can_see += 1 height = max(a[i], height + 1) if can_see == target: # ここがいけない # print('height', height) # print('can_see', can_see) # print('cost', cost) return cost cost2 = dfs(height=height, cost=cost, target=K, can_see=can_see, i=i + 1) # print('cost2', cost2) return min(cost1, cost2) print(dfs(a[0], cost=0, target=K, can_see=1, i=1))

[ "amanou.toona@gmail.com" ]

amanou.toona@gmail.com

8bd4bf5943f0f605a35ed8fb4e0e76f7c9860468

b781f91398860c1ecfd4d69a4c64b770e40ab602

/time.py

1b419b58c02feb893740ed5702d17d6e80ddc156

[]

no_license

lclarke98/Hue-motion-sensor

90bc53aef710cac9d8156af8585eefcd465dbdbc

3a99e8a608997e713167f27beb8750723e155df3

refs/heads/master

2022-04-08T13:13:09.982197

2020-01-01T22:09:25

196,085,171

0

null

UTF-8

Python

false

632

py

import datetime from astral import Astral city_name = 'London' a = Astral() a.solar_depression = 'civil' city = a[city_name] print('Information for %s/%s\n' % (city_name, city.region)) timezone = city.timezone print('Timezone: %s' % timezone) print('Latitude: %.02f; Longitude: %.02f\n' % \ (city.latitude, city.longitude)) Latitude: 51.60; Longitude: 0.08 sun = city.sun(date=datetime.date(2019, 7, 9), local=True) print('Dawn: %s' % str(sun['dawn'])) print('Sunrise: %s' % str(sun['sunrise'])) print('Noon: %s' % str(sun['noon'])) print('Sunset: %s' % str(sun['sunset'])) print('Dusk: %s' % str(sun['dusk']))

[ "48065423+lclarke98@users.noreply.github.com" ]

48065423+lclarke98@users.noreply.github.com

192bda4fe1e44cd80c1eec10fcbed5a8fa12c812

27648171f6e9675ea1a2716445d34a4346693a86

/rnn_mnist.py

6773ed60e49b792ecba3763595642991632370fd

[]

no_license

kniranjankumar/toyexamples

fc2700baec30f2d2888e3cb4f3f4f7b648402986

f30df3f4265fd4adc9093503abbfd09f65716443

refs/heads/master

2020-03-06T19:35:37.809594

2018-03-30T20:15:38

null

0

null

UTF-8

Python

false

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py

import tensorflow as tf from tensorflow.contrib import rnn import tensorboard #import mnist dataset from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets("/tmp/data/",one_hot=True) #define constants #unrolled through 28 time steps time_steps=28 #hidden LSTM units num_units=128 #rows of 28 pixels n_input=28 #learning rate for adam learning_rate=0.001 #mnist is meant to be classified in 10 classes(0-9). n_classes=10 #size of batch batch_size=128 out_weights=tf.Variable(tf.random_normal([num_units,n_classes])) out_bias=tf.Variable(tf.random_normal([n_classes])) x = tf.placeholder(tf.float32,[None,time_steps,n_input]) y = tf.placeholder(tf.float32,[None,n_classes]) input = tf.unstack(x,time_steps,axis=1) lstm_layer = rnn.BasicLSTMCell(num_units,forget_bias=1) outputs,_ = rnn.static_rnn(lstm_layer,input,dtype=tf.float32) print('out') prediction = tf.layers.dense(inputs=outputs[-1],units=10) # prediction=tf.matmul(outputs[-1],out_weights)+out_bias loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y)) opt = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss) #correct prediction correct_pred = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32)) tf.summary.scalar('loss',loss) merged = tf.summary.merge_all() init = tf.global_variables_initializer() with tf.Session() as sess: train_writer = tf.summary.FileWriter('./train', sess.graph) sess.run(init) for i in range(1000): batch_x,batch_y=mnist.train.next_batch(batch_size=batch_size) batch_x=batch_x.reshape((batch_size,time_steps,n_input)) sess.run(opt, feed_dict={x: batch_x, y: batch_y}) if i %10==0: acc=sess.run(accuracy,feed_dict={x:batch_x,y:batch_y}) los,summary_out=sess.run([loss,merged],feed_dict={x:batch_x,y:batch_y}) train_writer.add_summary(summary_out, i) print("For iter ",iter) print("Accuracy ",acc) print("Loss ",los) print("__________________")

[ "kniranjankumar.eee@gmail.com" ]

kniranjankumar.eee@gmail.com

eab276022ecd0a31712c12691147b17b34c02bb8

8c2fa488008257c9fd69b86b45e9a9842b70fdff

/PS2/Disparity_with_noise/contrast_disparity/disparity_with_contrast.py

cf01e106bb020548f7b4793a7e8a8da4897d456d

[]

no_license

dheeraj141/Computer-Vision-Udacity-810-Problem-Sets

bf002f3c4e6fad274ec3a159f2c44a89b6828123

7b3439083f5706b552ad17cd5ab0d721def80cc9

refs/heads/master

2021-06-24T12:11:55.355716

2021-06-13T00:37:37

208,757,980

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import numpy as np import cv2 as cv import ps12 as ps import sys import math import multiprocessing as mp #main function to calculate SSD def check_img(img): if img is None: return 0 else: return 1 def add_gaussian_noise(mean, sd, left_image): h,w = left_image.shape noise = np.random.normal(mean, sd, (h,w)) left = np.asarray(left_image) left = left.astype('float64') left+=noise left =left.astype('uint8') return left # function to calculate the SSD of the two images # inputs : left_image , right_img direction to calculate SSD # Direction 0 => from left to right and 1 means right to left_image # window size should be odd for symmetry # output : return the disparity map def calculate_SSD(left_image, right_image,direction, window_size,max_disparity): e1 = cv.getTickCount() left = np.asarray(left_image) right = np.asarray(right_image) h,w = left_image.shape window_size_half = int(window_size/2) disparity_left =np.zeros((h,w)) #breakpoint() for i in range(window_size_half,h - window_size_half): #l = [0]*left_image.shape[1] for j in range(window_size_half,w - window_size_half): min_distance = 65535 min_j = 0 for disparity in range(max_disparity): distance = 0 temp =0; for l in range(-window_size_half, window_size_half): for m in range(-window_size_half, window_size_half): if(direction == 0): temp= int(left[i+l, j+m]) - int(right[i+l, (j+m)-disparity]) else: temp= int(right[i+l, j+m]) - int(left[i+l, (j+m+disparity)%w]) distance += temp*temp if (distance <min_distance): min_distance=distance min_j = disparity disparity_left[i,j] = min_j e2 = cv.getTickCount() print("time taken is ", (e2-e1)/cv.getTickFrequency()) return disparity_left def add_contrast(image): new_image = np.zeros(image.shape, image.dtype) alpha = 1.0 # Simple contrast control beta = 0 # Simple brightness control # Initialize values print(' Basic Linear Transforms ') print('-------------------------') try: alpha = float(input('* Enter the alpha value [1.0-3.0]: ')) beta = int(input('* Enter the beta value [0-100]: ')) except ValueError: print('Error, not a number') # Do the operation new_image(i,j) = alpha*image(i,j) + beta # Instead of these 'for' loops we could have used simply: # new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta) # but we wanted to show you how to access the pixels :) for y in range(image.shape[0]): for x in range(image.shape[1]): new_image[y,x] = np.clip(alpha*image[y,x] + beta, 0, 255) return new_image def calculate_SSD_over_range(left_gray, right_gray): left_gray_noise = add_gaussian_noise(0,15, left_gray) right_gray_noise = add_gaussian_noise(0,15, right_gray) for i in range(4,17,4): print("Calculate disparity image with noise for window size {}".format(i)) disparity_left = calculate_SSD(left_gray_noise,right_gray_noise,0,i,40) disparity_right = calculate_SSD(left_gray_noise,right_gray_noise,1,i,40) disparity_left = ps.threshold_image(disparity_left,40) disparity_right = ps.threshold_image(disparity_right,40) #ps.display_image("disparity image", disparity_left) file_name_left = "disparity_image_left" + "window_size" + str(i)+ "noise" file_name_right = "disparity_image_right" + "window_size" + str(i)+"noise" ps.save_image(file_name_left, disparity_left) ps.save_image(file_name_right, disparity_right) def main(argv): if(len(argv) < 1): print("not enough parameters\n") print("usage PS1-1.py <path to image>\n") return -1 left_image = cv.imread(argv[0],cv.IMREAD_COLOR) right_image = cv.imread(argv[1], cv.IMREAD_COLOR) x = check_img(left_image) y = check_img(right_image) if (x == 0 or y == 0): print("Error opening image\n") return -1 left_gray = cv.cvtColor(left_image,cv.COLOR_BGR2GRAY) right_gray = cv.cvtColor(right_image, cv.COLOR_BGR2GRAY) left_contrast_increased = add_contrast(left_gray) disparity_left = calculate_SSD(left_contrast_increased ,right_gray,0,8,40) disparity_right = calculate_SSD(left_contrast_increased ,right_gray,1,8,40) disparity_left = ps.threshold_image(disparity_left,40) disparity_right = ps.threshold_image(disparity_right,40) #ps.display_image("disparity image", disparity_left) file_name_left = "disparity_image_left" + "window_size" + str(12)+ "CONTRAST" file_name_right = "disparity_image_right" + "window_size" + str(12)+"CONTRAST" ps.save_image(file_name_left, disparity_left) ps.save_image(file_name_right, disparity_right) # if someone import this module then this line makes sure that it does not Run #ion its own if __name__ == "__main__": main(sys.argv[1:])

[ "dhiru5040@gmail.com" ]

dhiru5040@gmail.com

b68050786382db1605fb5824277e5debdaa21223

c9829e9c06ac4f3c7de5056816373dd9239b32f9

/FIBD/fibd_nick2.py

e10303797467018ec1c395fe577e813fc289a4a1

[]

no_license

KorfLab/Rosalind

74a6e741c7029adfa9b22d5726290aabbcd8c82a

ac6bcac289d56c88bcff524329b6905d7dc32c8e

refs/heads/master

2020-06-10T11:23:31.438577

2017-04-04T03:08:27

75,968,327

3

null

UTF-8

Python

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889

py

#!/usr/bin/env Python2.7 #fibd_nick2.py #actual recursion which is bad for runtime #adults def rab_a(n,m): if n < 2: return 0 else: return rab_a(n-1,m) + rab_y(n-1,m) - rab_y(n-m,m) #young def rab_y(n,m): if n == 0: return 0 elif n == 1: return 1 else: return rab_a(n-1,m) import argparse if __name__ == "__main__": parser = argparse.ArgumentParser(description = """Determine the number of rabbits given number of months in which they reproduce and a reproductive age of one month and the number of months they live.""") parser.add_argument("n", type=int, help="integer number of months") parser.add_argument("m", type=int, help="integer number of months rabbits live") args = parser.parse_args() n = args.n m = args.m result = rab_a(n,m) + rab_y(n,m) print result

[ "noreply@github.com" ]

KorfLab.noreply@github.com

570990629c0c9b3a5c2630cefadd7527dacb12c3

e9b91d2eb84fefcf9f245249b49c6c7967dc81d2

/udemy-data-science-course/9-data-projects/2-stock-analysis.py

2d0be8a7c87627db58decb47c77c23d43467d77d

[]

no_license

sidchilling/python-data-analysis-learning

ea78b8f3db29f4699835b15df0f63b2a07f7fa6d

674a65f614c897635c4f4a463b7c08facf643b5b

refs/heads/master

2021-01-11T23:02:44.792783

2017-04-20T15:05:40

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null

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py

from __future__ import division import numpy as np import pandas as pd from pandas import DataFrame, Series import matplotlib as mtp mtp.use('TkAgg') import matplotlib.pyplot as plt import seaborn as sns from scipy import stats import time # 1. What was the change in price of the stock over time? # 2. What was the daily return of the stock on average? # 3. What was the moving average of the various stocks? # 4. What was the correlation between different stocks' closing prices? # 5. What was the correlation between different stocks' daily returns? # 6. How much value we put at risk by investing in a particular stock? # 7. How can we attempt to predict future stock behaviour? sns.set_style('whitegrid') # setting the style as white from pandas_datareader.data import DataReader from datetime import datetime import sys def print_dataframes(dfs, num = 5): for stock in dfs.keys(): print '--- DataFrame for {} ---'.format(stock) print dfs[stock].head(n = num) def flatten_axes(axes): axes_array = [] for row in range(0, len(axes)): for col in range(0, len(axes[row])): axes_array.append(axes[row][col]) return axes_array def convert_date(d): # there might be timestamp data if we fetch the data from internet # there will not be timestamp data if we fetch from files if ' ' in d: d = d.split(' ')[0].strip() return datetime.strptime(d, '%Y-%m-%d').strftime("%d %b '%y") tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN'] end = datetime.utcnow() start = datetime(end.year - 1, end.month, end.day) dfs = {} # Map to store the dataframes for each stock for stock in tech_list: fetch_data = False # first check whether this can be read from local file try: dfs[stock] = pd.read_csv('{}.csv'.format(stock)) except: fetch_data = True if fetch_data or dfs[stock].empty: # fetch data from Yahoo print 'Fetching data for: {}'.format(stock) dfs[stock] = DataReader(name = stock, data_source = 'yahoo', start = start, end = end) # save it locally dfs[stock].to_csv('{}.csv'.format(stock)) print_dataframes(dfs) print dfs[tech_list[0]].describe() # Set Date as Index for stock in dfs.keys(): dfs[stock] = dfs[stock].reset_index() dfs[stock]['Date'] = dfs[stock]['Date'].apply(str) dfs[stock]['Date'] = dfs[stock]['Date'].apply(convert_date) dfs[stock] = dfs[stock].set_index(keys = ['Date']) print_dataframes(dfs) def make_subplots(rows = 2, cols = 2): fig, axes = plt.subplots(nrows = rows, ncols = 2) plt.subplots_adjust(wspace = 1, hspace = 1) axes_array = flatten_axes(axes) return (fig, axes_array) # Historical trend of closing prices (fig, axes_array) = make_subplots() index = 0 for stock in tech_list: dfs[stock]['Adj Close'].plot(legend = True, title = '{} Adj Close Trend'.format(stock), ax = axes_array[index], y = 'Date') index = index + 1 plt.show() # Show the Volume trend of AAPL dfs['AAPL']['Volume'].plot(legend = True, figsize = (10, 4), title = 'AAPL Volume Trend') plt.show() # Calculate moving average for all the stocks ma_days = [10, 20, 50, 70, 100] for ma in ma_days: column_name = 'MA for {} days'.format(ma) for stock in dfs.keys(): dfs[stock][column_name] = pd.rolling_mean(arg = dfs[stock]['Adj Close'], window = ma) print_dataframes(dfs, num = 100) def plot_moving_averages(ma_days, close = True): (fig, axes_array) = make_subplots() index = 0 for stock in dfs.keys(): col_names = ['Adj Close'] if close else [] for ma in ma_days: col_names.append('MA for {} days'.format(ma)) dfs[stock][col_names].plot(legend = True, title = '{} MA'.format(stock), ax = axes_array[index]) index = index + 1 plt.show() # Plot the Moving averages for all the stocks for Adj Close, 10, and 20 plot_moving_averages(ma_days = [10, 20]) # Plot the moving averages for all stocks for 50, 70, 100 plot_moving_averages(ma_days = [50, 70, 100], close = False) ## Daily Returns and Risk of the Stock for stock in dfs.keys(): dfs[stock]['Daily Return'] = dfs[stock]['Adj Close'].pct_change() print_dataframes(dfs) (fig, axes_array) = make_subplots() index = 0 for stock in dfs.keys(): dfs[stock]['Daily Return'].plot(legend = True, title = 'Daily Return {}'.format(stock), linestyle = '--', marker = 'o', ax = axes_array[index]) index = index + 1 plt.show() # Show the daily returns on a histogram (fig, axes_array) = make_subplots() index = 0 for stock in dfs.keys(): g = sns.distplot(a = dfs[stock]['Daily Return'].dropna(), bins = 100, hist = True, kde = True, rug = False, ax = axes_array[index]) g.set_title('{}'.format(stock)) index = index + 1 plt.show() ## Make a DataFrame of all the Adj Close prices for each stock closing_dfs = DataFrame() for stock in dfs.keys(): adj_close = dfs[stock]['Adj Close'] adj_close.name = '{}'.format(stock) closing_dfs = pd.concat([closing_dfs, adj_close], axis = 1) print closing_dfs.head() tech_returns = closing_dfs.pct_change() print tech_returns.head() # Show correlation between same stock sns.jointplot(x = 'GOOG', y = 'GOOG', data = tech_returns, kind = 'scatter', color = 'seagreen') plt.show() # Correlation betwenn GOOG and MSFT sns.jointplot(x = 'GOOG', y = 'MSFT', data = tech_returns, kind = 'scatter', color = 'seagreen') plt.show() # Show correlation between all the stocks sns.pairplot(data = tech_returns.dropna()) plt.show() # Show correlation using PairGrid to control the types of graphs returns_fig = sns.PairGrid(data = tech_returns.dropna()) returns_fig.map_upper(plt.scatter, color = 'purple') returns_fig.map_lower(sns.kdeplot, cmap = 'cool_d') returns_fig.map_diag(plt.hist, bins = 30) plt.show() # Correlation between Closing prices returns_fig = sns.PairGrid(data = closing_dfs.dropna()) returns_fig.map_upper(plt.scatter, color = 'purple') returns_fig.map_lower(sns.kdeplot, cmap = 'cool_d') returns_fig.map_diag(plt.hist, bins = 30) plt.show() sns.linearmodels.corrplot(tech_returns.dropna(), annot = True) plt.show() sns.linearmodels.corrplot(closing_dfs.dropna(), annot = True) plt.show() ## Quantify Risk area = np.pi * 20 # so that the points that we draw are visible plt.scatter(x = tech_returns.dropna().mean(), y = tech_returns.dropna().std(), s = area) plt.xlabel('Expected Return') plt.ylabel('Risk') for label, x, y in zip(tech_returns.columns, tech_returns.dropna().mean(), tech_returns.dropna().std()): plt.annotate(label, xy = (x, y), xytext = (50, 50), textcoords = 'offset points', ha = 'right', va = 'bottom', arrowprops = {'arrowstyle' : '-', 'connectionstyle' : 'arc3,rad=-0.3'}) plt.show() ## Value at Risk sns.distplot(a = dfs['AAPL']['Daily Return'].dropna(), bins = 100, hist = True, kde = True, rug = False, color = 'purple') plt.show() ## Bootstap Method # Print the Quantiles print tech_returns['AAPL'].dropna().quantile(0.05) # this means that for 95% times, this will be your worst loss # Do the above for all the stocks for col in tech_returns.columns: stock = '{}'.format(col) print '{}. Risk: {}'.format(stock, tech_returns[stock].dropna().quantile(0.05)) ## Monte-Carlo Method def stock_monte_carlo(start_price, days, mu, sigma, dt): price = np.zeros(days) price[0] = start_price shock = np.zeros(days) drift = np.zeros(days) for x in xrange(1, days): shock[x] = np.random.normal(loc = mu * dt, scale = sigma * np.sqrt(dt)) drift[x] = mu * dt price[x] = price[x - 1] + (price[x - 1] * (drift[x] + shock[x])) return price # Run the Monte carlo method for Google 100 times starting with the # first opening price start_price = dfs['GOOG']['Open'][0] days = 365 dt = 1 / days mu = tech_returns['GOOG'].mean() sigma = tech_returns['GOOG'].std() for run in xrange(100): plt.plot(stock_monte_carlo(start_price = start_price, days = days, mu = mu, sigma = sigma, dt = dt)) plt.xlabel('Days') plt.ylabel('Price') plt.title('Monte Carlo Analysis for Google') plt.show() # Run 10,000 Monte-Carlo Simulations for all stocks for stock in dfs.keys(): runs = 10000 print 'Running {} Monte-Carlo Simulations for {}'.format(runs, stock) simulations = np.zeros(runs) start_price = dfs[stock]['Open'][0] days = 365 dt = 1 / days start_time = time.time() mu = tech_returns[stock].mean() sigma = tech_returns[stock].std() for run in xrange(runs): simulations[run] = stock_monte_carlo(start_price = start_price, days = days, mu = mu, sigma = sigma, dt = dt)[days - 1] # in the previous step we are taking the final day's simulated price q = np.percentile(simulations, 1) plt.hist(simulations, bins = 200) plt.figtext(x = 0.6, y = 0.8, s = 'Start Price: ${}'.format(round(float(start_price), 2))) # Mean ending Price plt.figtext(x = 0.6, y = 0.7, s = 'Mean Final Price: ${}'.format(round(float(simulations.mean()), 2))) # Variance of the price (with 99% confidence interval) plt.figtext(x = 0.6, y = 0.6, s = 'VaR(0.99): ${}'.format(round(float(start_price - q), 2))) # Disply 1% quantile plt.figtext(x = 0.15, y = 0.6, s = 'q(0.99): {}'.format(round(float(q), 2))) # Plot a line at the 1% quantile plt.axvline(x = q, linewidth = 4, color = 'r') # Title plt.title('Final Price Distribution for {} after {} days'.format(stock, days), weight = 'bold') end_time = time.time() print 'Time taken to run {} simulations for {}: {}'.format(runs, stock, (end_time - start_time)) plt.show()

[ "siddharthsaha@Webonises-Macbook-Pro.local" ]

siddharthsaha@Webonises-Macbook-Pro.local

552c2eeb9f0700849e6c8187b82028428a7bf597

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/train.py

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RemusMaz/tensorflow-deeplab-v3

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refs/heads/master

2020-06-22T22:11:55.472815

2019-07-23T11:09:25

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2019-07-23T11:03:10

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"""Train a DeepLab v3 model using tf.estimator API.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys import tensorflow as tf import deeplab_model from utils import preprocessing from tensorflow.python import debug as tf_debug import shutil parser = argparse.ArgumentParser() parser.add_argument('--model_dir', type=str, default='./model', help='Base directory for the model.') parser.add_argument('--clean_model_dir', action='store_true', help='Whether to clean up the model directory if present.') parser.add_argument('--train_epochs', type=int, default=26, help='Number of training epochs: ' 'For 30K iteration with batch size 6, train_epoch = 17.01 (= 30K * 6 / 10,582). ' 'For 30K iteration with batch size 8, train_epoch = 22.68 (= 30K * 8 / 10,582). ' 'For 30K iteration with batch size 10, train_epoch = 25.52 (= 30K * 10 / 10,582). ' 'For 30K iteration with batch size 11, train_epoch = 31.19 (= 30K * 11 / 10,582). ' 'For 30K iteration with batch size 15, train_epoch = 42.53 (= 30K * 15 / 10,582). ' 'For 30K iteration with batch size 16, train_epoch = 45.36 (= 30K * 16 / 10,582).') parser.add_argument('--epochs_per_eval', type=int, default=1, help='The number of training epochs to run between evaluations.') parser.add_argument('--tensorboard_images_max_outputs', type=int, default=6, help='Max number of batch elements to generate for Tensorboard.') parser.add_argument('--batch_size', type=int, default=10, help='Number of examples per batch.') parser.add_argument('--learning_rate_policy', type=str, default='poly', choices=['poly', 'piecewise'], help='Learning rate policy to optimize loss.') parser.add_argument('--max_iter', type=int, default=30000, help='Number of maximum iteration used for "poly" learning rate policy.') parser.add_argument('--data_dir', type=str, default='./dataset/', help='Path to the directory containing the PASCAL VOC data tf record.') parser.add_argument('--base_architecture', type=str, default='resnet_v2_101', choices=['resnet_v2_50', 'resnet_v2_101'], help='The architecture of base Resnet building block.') parser.add_argument('--pre_trained_model', type=str, default='./ini_checkpoints/resnet_v2_101/resnet_v2_101.ckpt', help='Path to the pre-trained model checkpoint.') parser.add_argument('--output_stride', type=int, default=16, choices=[8, 16], help='Output stride for DeepLab v3. Currently 8 or 16 is supported.') parser.add_argument('--freeze_batch_norm', action='store_true', help='Freeze batch normalization parameters during the training.') parser.add_argument('--initial_learning_rate', type=float, default=7e-3, help='Initial learning rate for the optimizer.') parser.add_argument('--end_learning_rate', type=float, default=1e-6, help='End learning rate for the optimizer.') parser.add_argument('--initial_global_step', type=int, default=0, help='Initial global step for controlling learning rate when fine-tuning model.') parser.add_argument('--weight_decay', type=float, default=2e-4, help='The weight decay to use for regularizing the model.') parser.add_argument('--debug', action='store_true', help='Whether to use debugger to track down bad values during training.') _NUM_CLASSES = 21 _HEIGHT = 513 _WIDTH = 513 _DEPTH = 3 _MIN_SCALE = 0.5 _MAX_SCALE = 2.0 _IGNORE_LABEL = 255 _POWER = 0.9 _MOMENTUM = 0.9 _BATCH_NORM_DECAY = 0.9997 _NUM_IMAGES = { 'train': 10582, 'validation': 1449, } def get_filenames(is_training, data_dir): """Return a list of filenames. Args: is_training: A boolean denoting whether the input is for training. data_dir: path to the the directory containing the input data. Returns: A list of file names. """ if is_training: return [os.path.join(data_dir, 'voc_train.record')] else: return [os.path.join(data_dir, 'voc_val.record')] def parse_record(raw_record): """Parse PASCAL image and label from a tf record.""" keys_to_features = { 'image/height': tf.FixedLenFeature((), tf.int64), 'image/width': tf.FixedLenFeature((), tf.int64), 'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''), 'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'), 'label/encoded': tf.FixedLenFeature((), tf.string, default_value=''), 'label/format': tf.FixedLenFeature((), tf.string, default_value='png'), } parsed = tf.parse_single_example(raw_record, keys_to_features) # height = tf.cast(parsed['image/height'], tf.int32) # width = tf.cast(parsed['image/width'], tf.int32) image = tf.image.decode_image( tf.reshape(parsed['image/encoded'], shape=[]), _DEPTH) image = tf.to_float(tf.image.convert_image_dtype(image, dtype=tf.uint8)) image.set_shape([None, None, 3]) label = tf.image.decode_image( tf.reshape(parsed['label/encoded'], shape=[]), 1) label = tf.to_int32(tf.image.convert_image_dtype(label, dtype=tf.uint8)) label.set_shape([None, None, 1]) return image, label def preprocess_image(image, label, is_training): """Preprocess a single image of layout [height, width, depth].""" if is_training: # Randomly scale the image and label. image, label = preprocessing.random_rescale_image_and_label( image, label, _MIN_SCALE, _MAX_SCALE) # Randomly crop or pad a [_HEIGHT, _WIDTH] section of the image and label. image, label = preprocessing.random_crop_or_pad_image_and_label( image, label, _HEIGHT, _WIDTH, _IGNORE_LABEL) # Randomly flip the image and label horizontally. image, label = preprocessing.random_flip_left_right_image_and_label( image, label) image.set_shape([_HEIGHT, _WIDTH, 3]) label.set_shape([_HEIGHT, _WIDTH, 1]) image = preprocessing.mean_image_subtraction(image) return image, label def input_fn(is_training, data_dir, batch_size, num_epochs=1): """Input_fn using the tf.data input pipeline for CIFAR-10 dataset. Args: is_training: A boolean denoting whether the input is for training. data_dir: The directory containing the input data. batch_size: The number of samples per batch. num_epochs: The number of epochs to repeat the dataset. Returns: A tuple of images and labels. """ dataset = tf.data.Dataset.from_tensor_slices(get_filenames(is_training, data_dir)) dataset = dataset.flat_map(tf.data.TFRecordDataset) if is_training: # When choosing shuffle buffer sizes, larger sizes result in better # randomness, while smaller sizes have better performance. # is a relatively small dataset, we choose to shuffle the full epoch. dataset = dataset.shuffle(buffer_size=_NUM_IMAGES['train']) dataset = dataset.map(parse_record) dataset = dataset.map( lambda image, label: preprocess_image(image, label, is_training)) dataset = dataset.prefetch(batch_size) # We call repeat after shuffling, rather than before, to prevent separate # epochs from blending together. dataset = dataset.repeat(num_epochs) dataset = dataset.batch(batch_size) iterator = dataset.make_one_shot_iterator() images, labels = iterator.get_next() return images, labels def main(unused_argv): # Using the Winograd non-fused algorithms provides a small performance boost. os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1' if FLAGS.clean_model_dir: shutil.rmtree(FLAGS.model_dir, ignore_errors=True) # Set up a RunConfig to only save checkpoints once per training cycle. run_config = tf.estimator.RunConfig().replace(save_checkpoints_secs=1e9) model = tf.estimator.Estimator( model_fn=deeplab_model.deeplabv3_model_fn, model_dir=FLAGS.model_dir, config=run_config, params={ 'output_stride': FLAGS.output_stride, 'batch_size': FLAGS.batch_size, 'base_architecture': FLAGS.base_architecture, 'pre_trained_model': FLAGS.pre_trained_model, 'batch_norm_decay': _BATCH_NORM_DECAY, 'num_classes': _NUM_CLASSES, 'tensorboard_images_max_outputs': FLAGS.tensorboard_images_max_outputs, 'weight_decay': FLAGS.weight_decay, 'learning_rate_policy': FLAGS.learning_rate_policy, 'num_train': _NUM_IMAGES['train'], 'initial_learning_rate': FLAGS.initial_learning_rate, 'max_iter': FLAGS.max_iter, 'end_learning_rate': FLAGS.end_learning_rate, 'power': _POWER, 'momentum': _MOMENTUM, 'freeze_batch_norm': FLAGS.freeze_batch_norm, 'initial_global_step': FLAGS.initial_global_step }) for _ in range(FLAGS.train_epochs // FLAGS.epochs_per_eval): tensors_to_log = { 'learning_rate': 'learning_rate', 'cross_entropy': 'cross_entropy', 'train_px_accuracy': 'train_px_accuracy', 'train_mean_iou': 'train_mean_iou', } logging_hook = tf.train.LoggingTensorHook( tensors=tensors_to_log, every_n_iter=10) train_hooks = [logging_hook] eval_hooks = None if FLAGS.debug: debug_hook = tf_debug.LocalCLIDebugHook() train_hooks.append(debug_hook) eval_hooks = [debug_hook] tf.logging.info("Start training.") model.train( input_fn=lambda: input_fn(True, FLAGS.data_dir, FLAGS.batch_size, FLAGS.epochs_per_eval), hooks=train_hooks, # steps=1 # For debug ) tf.logging.info("Start evaluation.") # Evaluate the model and print results eval_results = model.evaluate( # Batch size must be 1 for testing because the images' size differs input_fn=lambda: input_fn(False, FLAGS.data_dir, 1), hooks=eval_hooks, # steps=1 # For debug ) print(eval_results) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

[ "rishizek@gmail.com" ]

rishizek@gmail.com

c973341b4f11c03a21a805bbdb4c380ecfb3f7b0

064370ae71f0c36acafcf3e862623b16768f4f5f

/profiles/admin.py

606cf2db8bf04f7a1236c4d4d00b1c6818bea6ed

[]

no_license

Seishin/thesis-server

c2d6310679c13e870f7563a58583db59377c6164

f50220083805d1249a3403c8c67842258d808ab3

refs/heads/master

2021-01-18T18:16:52.703827

2013-10-14T18:50:54

13,569,870

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Python

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py

from django.contrib import admin from profiles.models import * admin.site.register(Profile) admin.site.register(Student) admin.site.register(Faculty) admin.site.register(Major) admin.site.register(Mark)

[ "seishin90@gmail.com" ]

seishin90@gmail.com

08fd520672df3be648ba10e90ded14bc29eb24ed

41c0259d0717443a641e7e5c883c8ff12d418c30

/week/9/writeup/part1.py

a0c811b57eba66af9b860bded74c28796ad3daf8

[]

no_license

kodirupe/389Rfall18

9cae986e86c81a583ae82ab6c9f3601b1a517f18

d3f4639692c36893e8b5ffa177db592dfa89756c

refs/heads/master

2021-07-25T09:10:50.381596

2018-12-12T02:06:02

147,029,368

0

null

2018-09-01T20:10:59

null

UTF-8

Python

false

846

py

#!/usr/bin/env python #-*- coding:utf-8 -*- # importing a useful library -- feel free to add any others you find necessary import hashlib import string # this will work if you place this script in your writeup folder wordlist = open("../probable-v2-top1575.txt", 'r') # a string equal to 'abcdefghijklmnopqrstuvwxyz'. salts = string.ascii_lowercase hash_fp = open("../hashes",'r') hashes = hash_fp.readlines() stripped_hashes = [] for x in hashes: stripped_hashes.append(x.strip()) password = wordlist.readlines() strip_pass = [] for x in password: strip_pass.append(x.strip()) for salt in salts: for x in strip_pass: salt_password = salt+x h = hashlib.sha512(salt_password) for y in stripped_hashes: if(y == h.hexdigest()): print("Salt: " + salt + "\n" + "Password: " + x)

[ "rupe.kodi@gmail.com" ]

rupe.kodi@gmail.com

19f97b46e444e83a2f72744a9002611efe7ccf0a

69e5676a801c5446ddec5e1cfd8daf527dbb3ab9

/stringcheese/wrangling/get_fficutout.py

917178ace1351e624d1dfa092c3a7e383136a123

[ "MIT" ]

permissive

lgbouma/stringcheese

96d8d48aaa8da9da92744401bba5498399758636

e7f5919335f18d54f331e67f4df1a48e4904526d

refs/heads/master

2020-07-07T10:11:29.697659

2020-03-29T19:05:52

203,321,632

0

null

UTF-8

Python

false

632

py

import requests from astroquery.mast import Tesscut def get_fficutout(c_obj, cutoutdir=None, sector=None): # c_obj (SkyCoord): location of target star print('beginning download tesscut for {}'.format(repr(c_obj))) try: tab = Tesscut.download_cutouts(c_obj, size=20, sector=sector, path=cutoutdir) except (requests.exceptions.HTTPError, requests.exceptions.ConnectionError) as e: print('got {}, try again'.format(repr(e))) tab = Tesscut.download_cutouts(c_obj, size=20, sector=sector, path=cutoutdir)

[ "bouma.luke@gmail.com" ]

bouma.luke@gmail.com

077dd4703b1e8f583618f83d20f53a9b6442c9c0

105c59fdd7e87d0d826177b4eba34ad7f6d22231

/tests/scripts/tree.py

479ec8df167885e5e44bb64b7bd2516a8ee52e1f

[ "MIT" ]

permissive

Jesse-McDonald/EM-MAGIC

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refs/heads/master

2020-03-23T20:53:12.321459

2018-08-02T21:51:26

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0

MIT

2018-08-02T21:51:28

2018-07-23T21:01:02

C++

UTF-8

Python

false

433

py

#Returns the paths of all files in a directory and all sub directories relative to start directory import os def tree(directory,target="f"): paths=[] for currentDir,dirs,files in os.walk(directory): if target=="f": for file in files: paths.append(currentDir+"/"+file) for dir in dirs: paths+=(tree(dir)) if target=="d": paths.append(currentDir) for dir in dirs: paths+=(tree(dir,"d")) return paths

[ "jamcdonald@lcmail.lcsc.edu" ]

jamcdonald@lcmail.lcsc.edu

cf1dec6881d70e2ef54464bca3024d4a54e679c1

8a4cde6d1cf568b168100d561c6d6062f919160f

/stacks.dynamically.py

a118b5b56555ae37144633b50f01e9f79daf8e12

[]

no_license

yash-saini/Python_Programes

0a4146aa687b9f27a1f0490875db832303e41cc7

5312f151c7c04bfac736769bdff77503cb98627b

refs/heads/master

2020-12-19T15:40:43.415349

2020-06-19T16:30:40

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py

class stacks(): def __init__(self,l=[]): self.l=l self.choice() def choice(self): while 1: u=input('1=continue 2=exit') if u==1: n=input('push=1 pop=2') if n==1: data=input('data to be inseeted') self.push(data) elif n==2: self.pop() else: print 'invalid input' else: break def push(self,data=0): self.l.append(data) print self.l def pop(self): self.l.pop() print self.l def main(): c=input('enter the list') s=stacks(c)

[ "noreply@github.com" ]

yash-saini.noreply@github.com

c3f2f9da71a4ddf02fd1bdeb0dd30cc616d53666

d9f5edd91098d9eaed8974c7d4ca015370ec7ca0

/Utility/GetHashtagPopularity.py

cdba172713216b922abfe4e2fecf3887d7e91657

[]

no_license

sxhmilyoyo/Rudetect27

3d249d10f1e20be09b02740ab05398cf16d0d657

33091edfb8f1056c3fdec9d4e1b84bb75e069828

refs/heads/master

2022-12-12T12:09:25.895539

2018-06-05T16:54:09

134,764,510

0

null

2022-12-08T00:59:01

2018-05-24T20:19:10

Python

UTF-8

Python

false

3,841

py

from bs4 import BeautifulSoup import codecs import os import requests import sys # sys.path.append("..") # from Helper import Helper import Utility reload(sys) sys.setdefaultencoding('utf-8') class GetHashtagPopularity(object): """Get the number of results from google search.""" def __init__(self, query, rootpath, folderpath): """Initialize the parameters. Parameters ---------- query : str the query for google search. Returns ------- None """ self.query = query self.rootpath = rootpath self.folderpath = folderpath self.url_base = '' self.parameters = {} self.helper = Utility.Helper(rootpath) def get_page(self, url, para=None): """Get page from google based on url. Parameters ---------- url : str the url of the page. para : dic the parameters of the url. Returns ------- str, str response.url: the response url response.text: the content """ try: response = requests.get(url, params=para) print(response.url) response.encoding = 'utf-8' if response.status_code == 403: print('403 ' + url) sys.exit() return response.text except Exception: print('Error: ' + url) return 'ERROR', 'ERROR' def google_get_num_results(self, content): """Get the number of the results. Parameters ---------- url : str the url of the search. content : str the content of the html. Returns ------- int the number of results. """ page = BeautifulSoup(content, 'lxml') if page.find(id='resultStats'): numStr = page.find(id='resultStats').string.split()[-2] numStr = ''.join(numStr.split(',')) results_cnt = int(numStr) else: print ("{}Error: no resultStats!{}".format('*' * 10, '*' * 10)) return results_cnt def google_crawl(self): """Call the get_page() and google_get_num_results(). Save the {hashtag: num} as the hashtagNum.json. Returns ------- None """ self.url_base = 'http://www.google.com/search?' self.parameters = {} self.parameters['q'] = self.query self.parameters['hl'] = 'en' hashtag = self.query.split()[0] content = self.get_page(self.url_base, self.parameters) folderPath = os.path.join(self.folderpath, 'experiment') fullPath = os.path.join(self.rootpath, folderPath, 'google_test') if os.path.exists(fullPath): with codecs.open(os.path.join(fullPath, hashtag + '.html'), 'wb', 'utf-8') as out: out.write(content) else: os.makedirs(fullPath) with codecs.open(os.path.join(fullPath, hashtag + '.html'), 'wb', 'utf-8') as out: out.write(content) print('crawling Google data...') results_cnt = self.google_get_num_results(content) if os.path.exists(os.path.join(fullPath, 'hashtagNum.json')): hashtagNum = self.helper.loadJson(os.path.join(fullPath, 'hashtagNum.json')) else: hashtagNum = {} hashtagNum[hashtag] = results_cnt self.helper.dumpJson(fullPath, 'hashtagNum.json', hashtagNum) def start_crawl(self): """Start Function. Returns ------- None """ print('Query:' + self.query) self.google_crawl()

[ "xh19920904@icloud.com" ]

xh19920904@icloud.com

a8d257e4514cb20cbd1b1b0e338ee8cdb6d97d59

80ab528c686fb2867fb35f067e0ea42cb29faed9

/playground/playground/urls.py

6268eb7111707afe3dff5070f8a766d6c4d0939e

[]

no_license

HajimeK/playground

1f11698f5967adf53d2d2276457e62dbbccb8d81

586d335210e3588b0b907fc0a087eaf6a62cba84

refs/heads/master

2021-01-10T17:46:08.886233

2016-03-29T14:43:29

54,985,621

0

null

UTF-8

Python

false

568

py

from django.conf.urls import patterns, include, url # Uncomment the next two lines to enable the admin: # from django.contrib import admin # admin.autodiscover() urlpatterns = patterns('', # Examples: # url(r'^$', 'playground.views.home', name='home'), # url(r'^playground/', include('playground.foo.urls')), # Uncomment the admin/doc line below to enable admin documentation: # url(r'^admin/doc/', include('django.contrib.admindocs.urls')), # Uncomment the next line to enable the admin: # url(r'^admin/', include(admin.site.urls)), )

[ "Boz@Hajime-no-MacBook-Air.local" ]

Boz@Hajime-no-MacBook-Air.local

0945e2340abb7961a09bf19356b325727714a0a7

b92b0e9ba2338ab311312dcbbeefcbb7c912fc2e

/build/shogun_lib/examples/undocumented/python_modular/kernel_spherical_modular.py

ef002d63c31f4dc1896ca111b2223acffcd201b9

[]

no_license

behollis/muViewBranch

384f8f97f67723b2a4019294854969d6fc1f53e8

1d80914f57e47b3ad565c4696861f7b3213675e0

refs/heads/master

2021-01-10T13:22:28.580069

2015-10-27T21:43:20

45,059,082

1

0

null

UTF-8

Python

false

919

py

from tools.load import LoadMatrix from numpy import where lm=LoadMatrix() traindat = lm.load_numbers('../data/fm_train_real.dat') testdat = lm.load_numbers('../data/fm_test_real.dat') parameter_list=[[traindat,testdat, 1.0],[traindat,testdat, 5.0]] def kernel_spherical_modular (fm_train_real=traindat,fm_test_real=testdat, sigma=1.0): from shogun.Features import RealFeatures from shogun.Kernel import MultiquadricKernel from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(feats_train, feats_train) kernel=MultiquadricKernel(feats_train, feats_train, sigma, distance) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel if __name__=='__main__': print('Spherical') kernel_spherical_modular(*parameter_list[0])

[ "prosen@305cdda6-5ce1-45b3-a98d-dfc68c8b3305" ]

prosen@305cdda6-5ce1-45b3-a98d-dfc68c8b3305

e5606578ee246cb02c6785fd196d6805f1b96756

5537eec7f43098d216d2b550678c8d10b2a26f09

/venv/tower/lib/python2.7/site-packages/ldif.py

a96b5a03bce37713258634fe0021605318f206ef

[]

no_license

wipro-sdx/Automation

f0ae1512b8d9d491d7bacec94c8906d06d696407

a8c46217d0fbe51a71597b5db87cbe98ed19297a

refs/heads/master

2021-07-08T11:09:05.314435

2018-05-02T07:18:54

131,812,982

0

1

null

2020-07-23T23:22:33

2018-05-02T07:15:28

Python

UTF-8

Python

false

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py

""" ldif - generate and parse LDIF data (see RFC 2849) See http://www.python-ldap.org/ for details. $Id: ldif.py,v 1.101 2016/11/11 14:41:07 stroeder Exp $ Python compability note: Tested with Python 2.0+, but should work with Python 1.5.2+. """ __version__ = '2.4.28' __all__ = [ # constants 'ldif_pattern', # functions 'CreateLDIF','ParseLDIF', # classes 'LDIFWriter', 'LDIFParser', 'LDIFRecordList', 'LDIFCopy', ] import urlparse,urllib,base64,re,types try: from cStringIO import StringIO except ImportError: from StringIO import StringIO attrtype_pattern = r'[\w;.-]+(;[\w_-]+)*' attrvalue_pattern = r'(([^,]|\\,)+|".*?")' attrtypeandvalue_pattern = attrtype_pattern + r'[ ]*=[ ]*' + attrvalue_pattern rdn_pattern = attrtypeandvalue_pattern + r'([ ]*\+[ ]*' + attrtypeandvalue_pattern + r')*[ ]*' dn_pattern = rdn_pattern + r'([ ]*,[ ]*' + rdn_pattern + r')*[ ]*' dn_regex = re.compile('^%s$' % dn_pattern) ldif_pattern = '^((dn(:|::) %(dn_pattern)s)|(%(attrtype_pattern)s(:|::) .*)$)+' % vars() MOD_OP_INTEGER = { 'add' :0, # ldap.MOD_REPLACE 'delete' :1, # ldap.MOD_DELETE 'replace':2, # ldap.MOD_REPLACE } MOD_OP_STR = { 0:'add',1:'delete',2:'replace' } CHANGE_TYPES = ['add','delete','modify','modrdn'] valid_changetype_dict = {} for c in CHANGE_TYPES: valid_changetype_dict[c]=None def is_dn(s): """ returns 1 if s is a LDAP DN """ if s=='': return 1 rm = dn_regex.match(s) return rm!=None and rm.group(0)==s SAFE_STRING_PATTERN = '(^(\000|\n|\r| |:|<)|[\000\n\r\200-\377]+|[ ]+$)' safe_string_re = re.compile(SAFE_STRING_PATTERN) def list_dict(l): """ return a dictionary with all items of l being the keys of the dictionary """ return dict([(i,None) for i in l]) class LDIFWriter: """ Write LDIF entry or change records to file object Copy LDIF input to a file output object containing all data retrieved via URLs """ def __init__(self,output_file,base64_attrs=None,cols=76,line_sep='\n'): """ output_file file object for output base64_attrs list of attribute types to be base64-encoded in any case cols Specifies how many columns a line may have before it's folded into many lines. line_sep String used as line separator """ self._output_file = output_file self._base64_attrs = list_dict([a.lower() for a in (base64_attrs or [])]) self._cols = cols self._last_line_sep = line_sep self.records_written = 0 def _unfold_lines(self,line): """ Write string line as one or more folded lines """ # Check maximum line length line_len = len(line) if line_len<=self._cols: self._output_file.write(line) self._output_file.write(self._last_line_sep) else: # Fold line pos = self._cols self._output_file.write(line[0:min(line_len,self._cols)]) self._output_file.write(self._last_line_sep) while pos<line_len: self._output_file.write(' ') self._output_file.write(line[pos:min(line_len,pos+self._cols-1)]) self._output_file.write(self._last_line_sep) pos = pos+self._cols-1 return # _unfold_lines() def _needs_base64_encoding(self,attr_type,attr_value): """ returns 1 if attr_value has to be base-64 encoded because of special chars or because attr_type is in self._base64_attrs """ return self._base64_attrs.has_key(attr_type.lower()) or \ not safe_string_re.search(attr_value) is None def _unparseAttrTypeandValue(self,attr_type,attr_value): """ Write a single attribute type/value pair attr_type attribute type attr_value attribute value """ if self._needs_base64_encoding(attr_type,attr_value): # Encode with base64 self._unfold_lines(':: '.join([attr_type,base64.encodestring(attr_value).replace('\n','')])) else: self._unfold_lines(': '.join([attr_type,attr_value])) return # _unparseAttrTypeandValue() def _unparseEntryRecord(self,entry): """ entry dictionary holding an entry """ attr_types = entry.keys()[:] attr_types.sort() for attr_type in attr_types: for attr_value in entry[attr_type]: self._unparseAttrTypeandValue(attr_type,attr_value) def _unparseChangeRecord(self,modlist): """ modlist list of additions (2-tuple) or modifications (3-tuple) """ mod_len = len(modlist[0]) if mod_len==2: changetype = 'add' elif mod_len==3: changetype = 'modify' else: raise ValueError("modlist item of wrong length: %d" % (mod_len)) self._unparseAttrTypeandValue('changetype',changetype) for mod in modlist: if mod_len==2: mod_type,mod_vals = mod elif mod_len==3: mod_op,mod_type,mod_vals = mod self._unparseAttrTypeandValue(MOD_OP_STR[mod_op],mod_type) else: raise ValueError("Subsequent modlist item of wrong length") if mod_vals: for mod_val in mod_vals: self._unparseAttrTypeandValue(mod_type,mod_val) if mod_len==3: self._output_file.write('-'+self._last_line_sep) def unparse(self,dn,record): """ dn string-representation of distinguished name record Either a dictionary holding the LDAP entry {attrtype:record} or a list with a modify list like for LDAPObject.modify(). """ # Start with line containing the distinguished name self._unparseAttrTypeandValue('dn',dn) # Dispatch to record type specific writers if isinstance(record,types.DictType): self._unparseEntryRecord(record) elif isinstance(record,types.ListType): self._unparseChangeRecord(record) else: raise ValueError('Argument record must be dictionary or list instead of %s' % (repr(record))) # Write empty line separating the records self._output_file.write(self._last_line_sep) # Count records written self.records_written = self.records_written+1 return # unparse() def CreateLDIF(dn,record,base64_attrs=None,cols=76): """ Create LDIF single formatted record including trailing empty line. This is a compability function. Use is deprecated! dn string-representation of distinguished name record Either a dictionary holding the LDAP entry {attrtype:record} or a list with a modify list like for LDAPObject.modify(). base64_attrs list of attribute types to be base64-encoded in any case cols Specifies how many columns a line may have before it's folded into many lines. """ f = StringIO() ldif_writer = LDIFWriter(f,base64_attrs,cols,'\n') ldif_writer.unparse(dn,record) s = f.getvalue() f.close() return s class LDIFParser: """ Base class for a LDIF parser. Applications should sub-class this class and override method handle() to implement something meaningful. Public class attributes: records_read Counter for records processed so far """ def __init__( self, input_file, ignored_attr_types=None, max_entries=0, process_url_schemes=None, line_sep='\n' ): """ Parameters: input_file File-object to read the LDIF input from ignored_attr_types Attributes with these attribute type names will be ignored. max_entries If non-zero specifies the maximum number of entries to be read from f. process_url_schemes List containing strings with URLs schemes to process with urllib. An empty list turns off all URL processing and the attribute is ignored completely. line_sep String used as line separator """ self._input_file = input_file self._max_entries = max_entries self._process_url_schemes = list_dict([s.lower() for s in (process_url_schemes or [])]) self._ignored_attr_types = list_dict([a.lower() for a in (ignored_attr_types or [])]) self._last_line_sep = line_sep self.version = None # Initialize counters self.line_counter = 0 self.byte_counter = 0 self.records_read = 0 self.changetype_counter = {}.fromkeys(CHANGE_TYPES,0) # Store some symbols for better performance self._base64_decodestring = base64.decodestring # Read very first line try: self._last_line = self._readline() except EOFError: self._last_line = '' def handle(self,dn,entry): """ Process a single content LDIF record. This method should be implemented by applications using LDIFParser. """ pass def _readline(self): s = self._input_file.readline() self.line_counter = self.line_counter + 1 self.byte_counter = self.byte_counter + len(s) if not s: return None elif s[-2:]=='\r\n': return s[:-2] elif s[-1:]=='\n': return s[:-1] else: return s def _unfold_lines(self): """ Unfold several folded lines with trailing space into one line """ if self._last_line is None: raise EOFError('EOF reached after %d lines (%d bytes)' % ( self.line_counter, self.byte_counter, )) unfolded_lines = [ self._last_line ] next_line = self._readline() while next_line and next_line[0]==' ': unfolded_lines.append(next_line[1:]) next_line = self._readline() self._last_line = next_line return ''.join(unfolded_lines) def _next_key_and_value(self): """ Parse a single attribute type and value pair from one or more lines of LDIF data """ # Reading new attribute line unfolded_line = self._unfold_lines() # Ignore comments which can also be folded while unfolded_line and unfolded_line[0]=='#': unfolded_line = self._unfold_lines() if not unfolded_line: return None,None if unfolded_line=='-': return '-',None try: colon_pos = unfolded_line.index(':') except ValueError,e: raise ValueError('no value-spec in %s' % (repr(unfolded_line))) attr_type = unfolded_line[0:colon_pos] # if needed attribute value is BASE64 decoded value_spec = unfolded_line[colon_pos:colon_pos+2] if value_spec==': ': attr_value = unfolded_line[colon_pos+2:].lstrip() elif value_spec=='::': # attribute value needs base64-decoding attr_value = self._base64_decodestring(unfolded_line[colon_pos+2:]) elif value_spec==':<': # fetch attribute value from URL url = unfolded_line[colon_pos+2:].strip() attr_value = None if self._process_url_schemes: u = urlparse.urlparse(url) if self._process_url_schemes.has_key(u[0]): attr_value = urllib.urlopen(url).read() else: attr_value = unfolded_line[colon_pos+1:] return attr_type,attr_value def _consume_empty_lines(self): """ Consume empty lines until first non-empty line. Must only be used between full records! Returns non-empty key-value-tuple. """ # Local symbol for better performance next_key_and_value = self._next_key_and_value # Consume empty lines try: k,v = next_key_and_value() while k==v==None: k,v = next_key_and_value() except EOFError: k,v = None,None return k,v def parse_entry_records(self): """ Continously read and parse LDIF entry records """ # Local symbol for better performance next_key_and_value = self._next_key_and_value try: # Consume empty lines k,v = self._consume_empty_lines() # Consume 'version' line if k=='version': self.version = int(v) k,v = self._consume_empty_lines() except EOFError: return # Loop for processing whole records while k!=None and \ (not self._max_entries or self.records_read<self._max_entries): # Consume first line which must start with "dn: " if k!='dn': raise ValueError('Line %d: First line of record does not start with "dn:": %s' % (self.line_counter,repr(k))) if not is_dn(v): raise ValueError('Line %d: Not a valid string-representation for dn: %s.' % (self.line_counter,repr(v))) dn = v entry = {} # Consume second line of record k,v = next_key_and_value() # Loop for reading the attributes while k!=None: # Add the attribute to the entry if not ignored attribute if not k.lower() in self._ignored_attr_types: try: entry[k].append(v) except KeyError: entry[k]=[v] # Read the next line within the record try: k,v = next_key_and_value() except EOFError: k,v = None,None # handle record self.handle(dn,entry) self.records_read = self.records_read + 1 # Consume empty separator line(s) k,v = self._consume_empty_lines() return # parse_entry_records() def parse(self): """ Invokes LDIFParser.parse_entry_records() for backward compability """ return self.parse_entry_records() # parse() def handle_modify(self,dn,modops,controls=None): """ Process a single LDIF record representing a single modify operation. This method should be implemented by applications using LDIFParser. """ controls = [] or None pass def parse_change_records(self): # Local symbol for better performance next_key_and_value = self._next_key_and_value # Consume empty lines k,v = self._consume_empty_lines() # Consume 'version' line if k=='version': self.version = int(v) k,v = self._consume_empty_lines() # Loop for processing whole records while k!=None and \ (not self._max_entries or self.records_read<self._max_entries): # Consume first line which must start with "dn: " if k!='dn': raise ValueError('Line %d: First line of record does not start with "dn:": %s' % (self.line_counter,repr(k))) if not is_dn(v): raise ValueError('Line %d: Not a valid string-representation for dn: %s.' % (self.line_counter,repr(v))) dn = v # Consume second line of record k,v = next_key_and_value() # Read "control:" lines controls = [] while k!=None and k=='control': try: control_type,criticality,control_value = v.split(' ',2) except ValueError: control_value = None control_type,criticality = v.split(' ',1) controls.append((control_type,criticality,control_value)) k,v = next_key_and_value() # Determine changetype first changetype = None # Consume changetype line of record if k=='changetype': if not v in valid_changetype_dict: raise ValueError('Invalid changetype: %s' % repr(v)) changetype = v k,v = next_key_and_value() if changetype=='modify': # From here we assume a change record is read with changetype: modify modops = [] try: # Loop for reading the list of modifications while k!=None: # Extract attribute mod-operation (add, delete, replace) try: modop = MOD_OP_INTEGER[k] except KeyError: raise ValueError('Line %d: Invalid mod-op string: %s' % (self.line_counter,repr(k))) # we now have the attribute name to be modified modattr = v modvalues = [] try: k,v = next_key_and_value() except EOFError: k,v = None,None while k==modattr: modvalues.append(v) try: k,v = next_key_and_value() except EOFError: k,v = None,None modops.append((modop,modattr,modvalues or None)) k,v = next_key_and_value() if k=='-': # Consume next line k,v = next_key_and_value() except EOFError: k,v = None,None if modops: # append entry to result list self.handle_modify(dn,modops,controls) else: # Consume the unhandled change record while k!=None: k,v = next_key_and_value() # Consume empty separator line(s) k,v = self._consume_empty_lines() # Increment record counters try: self.changetype_counter[changetype] = self.changetype_counter[changetype] + 1 except KeyError: self.changetype_counter[changetype] = 1 self.records_read = self.records_read + 1 return # parse_change_records() class LDIFRecordList(LDIFParser): """ Collect all records of LDIF input into a single list. of 2-tuples (dn,entry). It can be a memory hog! """ def __init__( self, input_file, ignored_attr_types=None,max_entries=0,process_url_schemes=None ): """ See LDIFParser.__init__() Additional Parameters: all_records List instance for storing parsed records """ LDIFParser.__init__(self,input_file,ignored_attr_types,max_entries,process_url_schemes) self.all_records = [] self.all_modify_changes = [] def handle(self,dn,entry): """ Append single record to dictionary of all records. """ self.all_records.append((dn,entry)) def handle_modify(self,dn,modops,controls=None): """ Process a single LDIF record representing a single modify operation. This method should be implemented by applications using LDIFParser. """ controls = [] or None self.all_modify_changes.append((dn,modops,controls)) class LDIFCopy(LDIFParser): """ Copy LDIF input to LDIF output containing all data retrieved via URLs """ def __init__( self, input_file,output_file, ignored_attr_types=None,max_entries=0,process_url_schemes=None, base64_attrs=None,cols=76,line_sep='\n' ): """ See LDIFParser.__init__() and LDIFWriter.__init__() """ LDIFParser.__init__(self,input_file,ignored_attr_types,max_entries,process_url_schemes) self._output_ldif = LDIFWriter(output_file,base64_attrs,cols,line_sep) def handle(self,dn,entry): """ Write single LDIF record to output file. """ self._output_ldif.unparse(dn,entry) def ParseLDIF(f,ignore_attrs=None,maxentries=0): """ Parse LDIF records read from file. This is a compability function. Use is deprecated! """ ldif_parser = LDIFRecordList( f,ignored_attr_types=ignore_attrs,max_entries=maxentries,process_url_schemes=0 ) ldif_parser.parse() return ldif_parser.all_records

[ "admin@example.com" ]

admin@example.com

819f04b29755ef76d76e6ed4a25e45463c984b64

359bdc553338ff1d6a79b97fd2cb944d7dffe9d0

/db_repository/versions/054_migration.py

f7603392694742787cb8c69c6f8cbc0ce40244ff

[]

no_license

HumanInteractionVirtuallyEnhanced/PythonAnywhere

3f8776906d1f41fb89baee95a1729f6ba03f55b6

d9f2fbc7d59d302b38728de5f0dd16830cd72860

refs/heads/master

2021-01-10T17:55:45.561457

2015-10-06T18:07:57

43,767,051

0

null

UTF-8

Python

false

1,111

py

from sqlalchemy import * from migrate import * from migrate.changeset import schema pre_meta = MetaData() post_meta = MetaData() user = Table('user', post_meta, Column('id', Integer, primary_key=True, nullable=False), Column('nickname', String(length=64)), Column('email', String(length=120)), Column('fb_id', Integer), Column('role', SmallInteger, default=ColumnDefault(0)), Column('is_private', Boolean, default=ColumnDefault(True)), Column('recentLoc', String), Column('recentLatLon', String), Column('apsToken', String), Column('fbfriends', String), Column('recTime', DateTime), ) def upgrade(migrate_engine): # Upgrade operations go here. Don't create your own engine; bind # migrate_engine to your metadata pre_meta.bind = migrate_engine post_meta.bind = migrate_engine post_meta.tables['user'].columns['recTime'].create() def downgrade(migrate_engine): # Operations to reverse the above upgrade go here. pre_meta.bind = migrate_engine post_meta.bind = migrate_engine post_meta.tables['user'].columns['recTime'].drop()

[ "rijul.gupta@yale.edu" ]

rijul.gupta@yale.edu

61727ae11a6a47a99626b8922d2250ca78d9f90f

a90871d9fa71859f23e25dfc9eb28981e920d3e3

/myvenv/bin/pip

f9551cf24827ec03a19389bd61e2202cae964331

[]

no_license

josdan/Django-Learning-Library

0eb92548bd8f17980860f9938a448b26f5d97996

9352f90d61fdf25116078e9311fe70a19e35f5e6

refs/heads/master

2021-01-23T07:15:32.432549

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#!/home/jcarmona/Github/Django-Learning-Library/myvenv/bin/python3 # -*- coding: utf-8 -*- import re import sys from pip import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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from PIL import Image, ImageTk from tkinter import filedialog as tk_fd import tkinter as tk import os FILES_IMG = (("Image files", ("*.jpg", "*.png")), ("All files", "*.*")) FILES_STITCH = (("Json file", ("*.json")), ("All files", "*.*")) data_path = None def input_int(prompt, imin=None, imax=None): ''' Prompt the user to input an integer, and don't stop prompting until a valid integer is given. prompt - Prompt given to user imin - Minimum value that will be accepted imax - Maximum value that will be accepted ''' while True: i = input(prompt) try: val = int(i) if imin is not None and val < imin: print("Number should be at least {}".format(imin)) elif imax is not None and val > imax: print("Number should be at most {}".format(imax)) else: return val except ValueError: print("Not a valid integer!") _icon_cache = {} def load_icon(name): if data_path is not None and data_path != "": name = os.path.join(data_path, name) if name in _icon_cache: return _icon_cache[name] img = Image.open(name) imgtk = ImageTk.PhotoImage(img) _icon_cache[name] = imgtk return imgtk def get_in_filename(initialdir, title, filetypes): root = tk.Tk() root.withdraw() value = None # while value is None or value == "" or value == (): value = tk_fd.askopenfilename( initialdir=initialdir, title=title, filetypes=filetypes) root.destroy() return value def get_out_filename(initialdir, title, filetypes): root = tk.Tk() root.withdraw() value = None # while value is None or value == "" or value == (): value = tk_fd.asksaveasfilename( initialdir=initialdir, title=title, filetypes=filetypes) root.destroy() return value def get_directory(initialdir, title): root = tk.Tk() root.withdraw() value = None # while value is None or value == "" or value == (): value = tk_fd.askdirectory( initialdir=initialdir, title=title) root.destroy() return value def get_many_files(initialdir, title, filetypes): root = tk.Tk() root.withdraw() value = None # while value is None or len(value) == 0: value = tk_fd.askopenfilenames( initialdir=initialdir, title=title, filetypes=filetypes) root.destroy() return value

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# -*- coding: utf-8 -*- # Generated by Django 1.11.10 on 2018-06-04 13:24 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('powers', '0012_auto_20180604_0751'), ] operations = [ migrations.AlterField( model_name='bond', name='powers', field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='powers.Powers'), ), migrations.AlterField( model_name='defendant', name='next_court_date', field=models.CharField(max_length=50), ), ]

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""" Sanic """ from setuptools import setup setup( name='Sanic', version="0.1.0", url='http://github.com/channelcat/sanic/', license='BSD', author='Channel Cat', author_email='channelcat@gmail.com', description='A microframework based on uvloop, httptools, and learnings of flask', packages=['sanic'], platforms='any', install_requires=[ 'uvloop>=0.5.3', 'httptools>=0.0.9', 'ujson>=1.35', ], classifiers=[ 'Development Status :: 1 - Alpha', 'Environment :: Web Environment', ], )

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#!/usr/bin/env python2 # Copyright (c) 2015 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. import hashlib import sys import os from random import SystemRandom import base64 import hmac if len(sys.argv) < 2: sys.stderr.write('Please include username as an argument.\n') sys.exit(0) username = sys.argv[1] #This uses os.urandom() underneath cryptogen = SystemRandom() #Create 16 byte hex salt salt_sequence = [cryptogen.randrange(256) for i in range(16)] hexseq = list(map(hex, salt_sequence)) salt = "".join([x[2:] for x in hexseq]) #Create 32 byte b64 password password = base64.urlsafe_b64encode(os.urandom(32)) digestmod = hashlib.sha256 if sys.version_info.major >= 3: password = password.decode('utf-8') digestmod = 'SHA256' m = hmac.new(bytearray(salt, 'utf-8'), bytearray(password, 'utf-8'), digestmod) result = m.hexdigest() print("String to be appended to aurumcoin.conf:") print("rpcauth="+username+":"+salt+"$"+result) print("Your password:\n"+password)

[ "info@aurumcoin-au.com" ]

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