async def note(ctx, name): name = name.lower().replace(" ", "-") content = Notes(getPath(ctx)).get(name) if content: message = content else: message = f"Note “{name}” does not exist.\nUse `!notes` to get a list of available notes." await ctx.send(message)
async def notes(ctx): # TODO: Make this an embed so commands to read each note can be embedded in # notes names (not sure that's possible) notes = Notes(getPath(ctx)).getAll() if notes: message = "Here are the notes available to read:\n\n" for name in notes.keys(): message += f"* {name}\n" message += "\nUse `!note <name>` to read a note!" else: message = "There are no notes! You can add one with `!writenote <name> <content>`." await ctx.send(message)
async def deletenote(ctx, name): name = name.lower().replace(" ", "-") notes = Notes(getPath(ctx)) if not name.replace("-", "").replace("_", "").isalpha(): await ctx.send("Note name can only contain letters, “-” and “_”.") return if notes.delete(name): message = f"Note “{name}” successfully deleted!" print( f"Deleted note “{name}” on server “{ctx.guild.name}” ({ctx.guild.id})") else: message = f"Note {name} does not exist.\nUse `!notes to get a list of available notes." await ctx.send(message)
async def writenote(ctx, *, args): try: (name, content) = args.split(maxsplit=1) except ValueError: await ctx.send("You must provide a content for the note.\nUsage: `!writenote <name> <content>`") return if not name.replace("-", "").replace("_", "").isalpha(): await ctx.send("Note name can only contain letters, “-” and “_”.") return name = name.lower().replace(" ", "-") content = content.strip() if len(name) > 30: await ctx.send("Note name cannot exceed 30 characters.") return # Write notes to file notes = Notes(getPath(ctx)) notes.write(name, content) print( f"Wrote note “{name}” on server “{ctx.guild.name}” ({ctx.guild.id}): {content}") await ctx.send(f"Successfully wrote note “{name}”, use `!note {name}` to read it!")
from sklearn.model_selection import train_test_split import helpers import argparse from sliding_window import localize_and_classify from PIL import Image import numpy as np import os import cv2 # Find paths and labels dir_dataset = helpers.getPath() filenames = helpers.getFilenames(dir_dataset) labels = helpers.getLabels(dir_dataset) # Flatten the data set print("> ------ SVM classifier ------") print("> Collecting Image data ...") X, y = helpers.flattenImages(filenames, labels) print("> Splitting train and test data ...") X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) print("> Preprocess data ...") X_train_HOG = helpers.SVM_preProcessing(X_train)#[0:5000]) X_test_HOG = helpers.SVM_preProcessing(X_test)#[0:2000]) print("> Creating a model ...") # hog pca = 70, kernel='rbf', C=5, gamma=0.16 leads to good results. svc_clf = helpers.SVM_getModel(X_train_HOG, y_train)#[0:6000]) # Train the classifier
import matplotlib.pyplot as plt import helpers import os import numpy as np # Check to analyze one graph or all graphs in directory method = input("Do you want to analyze 1 graph or all? (0 for all)\n") # For one file if method == '1': # Get path and check if is file path = helpers.getPath("What's the .tData file path?\n", True) title = os.path.basename(path).split('.') fileData = helpers.grabData(path) if(len(fileData) == 0): exit(0) # Store the data groups = len(fileData) tempLabels = [] tempSearches = [] tempTotals = [] for data in fileData: tempSearches.append(data[1][0]) tempTotals.append(data[1][1]) tempLabels.append(data[0]) # Plot data fig, ax = plt.subplots() index = np.arange(groups)