Today's challenge was fun to do and it was not that hard as well.
Solution
import numpy as np data1, data = get_data(day=13) dots = [list(map(int, f.split(","))) for f in data[:data.index("")]] folds = data[data.index("")+1:] folds = [f.split("along ")[1].split("=") for f in folds] folds = [(f[0], int(f[1])) for f in folds] dots = np.array(dots) window = np.zeros(dots.max(axis=0)+3) for c in dots: window[c[0], c[1]] = 1 window = window.T tw = window.copy() # print(tw) for f in folds: print(f) axis,value=f cr,cc = tw.shape print(tw) if axis=="y": #fold y axis chunk = tw[value+1:-2] # print(value-crs,chunk.shape, chunk) crs,ccs = chunk.shape tw[np.abs(value-crs):value] += chunk[::-1] tw = tw[:value] tw = np.append(tw, np.zeros((2, tw.shape[1])), axis=0) #break else: # fold x axis chunk = tw[:, value+1:-2] crs,ccs = chunk.shape print(value-crs,chunk.shape, chunk) tw[:, abs(value-ccs):value] += chunk[:,::-1] tw = tw[:, :value] tw = np.append(tw, np.zeros((tw.shape[0], 2)), axis=1) print(f"Dots: {np.sum(tw>0)}") print(np.array2string(tw>0, separator='', formatter = {'bool':lambda x: ' █'[x]}))
My answer was:
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I write blogs about Basic of Machine Learning and related stuffs, you can find it on q-viper.github.io.
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