import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from PIL import Image
import preproc as p
import settings as s
paths = s.paths()
rootdir = '/home/anverdie/Documents/Code/Dimmy/to_convert'
for f in os.listdir(rootdir):
if not os.path.isdir(os.path.join(rootdir, f)):
l = np.load(os.path.join(rootdir, f)).reshape(10, 10)
l = np.repeat(l, 30, axis=0)
l = np.repeat(l, 30, axis=1)
pad = np.zeros((90, 300))
l = np.concatenate((pad, l, pad)).T
if not os.path.exists(os.path.join(paths.path2OutputD, 'DLP')):
os.makedirs(os.path.join(paths.path2OutputD, 'DLP'))
np.save(
os.path.join(paths.path2OutputD, 'DLP',
'{}_dlp.npy'.format(f[:-4])), l)
matplotlib.image.imsave(os.path.join(paths.path2OutputD, 'DLP',
'{}_dlp.bmp'.format(f[:-4])),
1 - l,
import os
import math
import scipy
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from skimage.measure import block_reduce
from sklearn.preprocessing import normalize
import settings as sett
paths = sett.paths()
params = sett.parameters()
def fast_fourier(sample, samplerate):
""" Compute a Fast Fourier Transform for visualization purposes
Parameters
----------
sample : array
Array containaing the raw signal
samplerate : int
Number of sample in the signal per unit of time
Returns
-------
array
Fast-Fourier Transform of the signal