jacob/FFT
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

finally hopefully

Browse Source
This commit is contained in:
Jacob Holder 2023-04-20 20:58:15 +02:00
parent a015542b03
commit 3c3ff46d21
Signed by: jacob
GPG Key ID: 2194FC747048A7FD
2 changed files with 3 additions and 76 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
clean_python/analysis.py
View File

@ -36,7 +36,6 @@ def merge(files):
plt.plot(out[2, :], "g") plt.plot(out[2, :], "g")
all = sum(merge) all = sum(merge)
summe = np.max(np.sum(all, axis=0)) summe = np.max(np.sum(all, axis=0))
all = all / summe all = all / summe
@ -45,7 +44,6 @@ def merge(files):
plt.plot(all[2, :], "k") plt.plot(all[2, :], "k")
percentage = 1-percentage percentage = 1-percentage
return percentage, all return percentage, all
>>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
def debug(percentage, out): def debug(percentage, out):
@ -87,15 +85,10 @@ def time_scale(p, o):
mono_perc = mono_perc - np.min(mono_perc) mono_perc = mono_perc - np.min(mono_perc)
mono_perc /= np.max(mono_perc) mono_perc /= np.max(mono_perc)
<<<<<<< HEAD
cs_rut = ip.CubicSpline(p[::-1], rut_perc[::-1])
cs_mono = ip.CubicSpline(p[::-1], mono_perc[::-1])
=======
# cs_rut = ip.CubicSpline(p[::-1], rut_perc[::-1]) # cs_rut = ip.CubicSpline(p[::-1], rut_perc[::-1])
# cs_mono = ip.CubicSpline(p[::-1], mono_perc[::-1]) # cs_mono = ip.CubicSpline(p[::-1], mono_perc[::-1])
cs_rut = ip.interp1d(p[::-1], rut_perc[::-1]) cs_rut = ip.interp1d(p[::-1], rut_perc[::-1])
cs_mono = ip.interp1d(p[::-1], mono_perc[::-1]) cs_mono = ip.interp1d(p[::-1], mono_perc[::-1])
>>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
plt.figure() plt.figure()
ph = np.linspace(0.01, 0.99, 100) ph = np.linspace(0.01, 0.99, 100)
@ -118,6 +111,7 @@ def time_scale(p, o):
plt.savefig("timescale.png") plt.savefig("timescale.png")
plt.savefig("timescale.pdf") plt.savefig("timescale.pdf")
def read_file(file): def read_file(file):
files = np.load("./merged.npz") files = np.load("./merged.npz")
p = files["p"] p = files["p"]
@ -127,13 +121,8 @@ def read_file(file):
if __name__ == "__main__": if __name__ == "__main__":
p, o = merge(sys.argv[1:]) p, o = merge(sys.argv[1:])
<<<<<<< HEAD
np.savez("merged.npz", p=p, o=o) np.savez("merged.npz", p=p, o=o)
# eval_data_print(f) # eval_data_print(f)
stacked_plot(p, o) stacked_plot(p, o)
=======
>>>>>>> e1a921c2eb7fb8f51d860e28b81ff3a41af21abc
# debug(p, o)
stacked_plot(p, o)
time_scale(p, o) time_scale(p, o)
plt.show() plt.show()

66
clean_python/extractors.py
View File

@ -79,8 +79,8 @@ class Rect_Evaluator(Evaluator):
new_eval_points = np.arange(len(self.eval_points)) new_eval_points = np.arange(len(self.eval_points))
mask = self.mask.copy() mask = self.mask.copy()
for nc, ev_points in zip(new_eval_points, self.eval_points): for nc, ev_points in zip(new_eval_points, self.eval_points):
maske_low = np.min(ev_points) >= self.mask maske_low = np.min(ev_points) <= self.mask
maske_high = np.max(ev_points) <= self.mask maske_high = np.max(ev_points) >= self.mask
mask[np.logical_and(maske_high, maske_low)] = nc mask[np.logical_and(maske_high, maske_low)] = nc
plt.figure() plt.figure()
@ -103,65 +103,3 @@ class Rect_Evaluator(Evaluator):
count += 1 count += 1
return mask return mask
#
# def main():
# np.random.seed(10)
# points = (np.random.rand(100, 2)-0.5) * 2
# voro = Voronoi_Evaluator(points, [[1],[2]])
# rect = Rect_Evaluator(points, [[1], [2]])
# Z = np.ones((1000, 1000))
# img = Image_Wrapper(Z, -5, .01, -5, .01)
# voro.extract(img)
# rect.extract(img)
#
# plt.scatter(points[[1], 0], points[[1], 1])
# plt.scatter(points[[2], 0], points[[2], 1])
# plt.imshow(img.img, extent=img.ext(), origin="lower")
# #plt.imshow(img.img, origin="lower")
# plt.show()
#
#
# if __name__ == "__main__":
# main()
# class Voronoi_Evaluator(Evaluator):
# def __init__(self, list_points):
# points = np.concatenate(list_points, axis=0)
# self.eval_points = []
# start = 0
# for l in list_points:
# stop = l.shape[0]
# self.eval_points.append(np.arange(start, start + stop))
# start += stop
# self.vor = Voronoi(points)
#
# @persist_to_file("cache_merge_voro")
# def merge_mask_helper(self):
# new_eval_points = np.arange(len(self.eval_points))
# mask = self.mask
# for nc, ev_points in zip(new_eval_points, self.eval_points):
# for num in ev_points:
# mask[self.mask == num] = nc
# return mask
#
# @persist_to_file("cache_voro")
# def gen_mask_helper(self, img: Image_Wrapper):
# mask = np.full_like(img.img, -1)
#
# counter = -1
# region_mask = self.vor.point_region
# for i in np.array(self.vor.regions, dtype=list)[region_mask]:
# counter += 1
# if -1 in i:
# continue
# if len(i) == 0:
# continue
# pts = self.vor.vertices[i]
# pts = np.stack(img.val2pos(
# pts[:, 0], pts[:, 1])).astype(np.int32).T
# if np.any(pts < 0):
# continue
# mask_2 = np.zeros_like(img.img)
# cv2.fillConvexPoly(mask_2, pts, 1)
# mask_2 = mask_2 > 0 # To convert to Boolean
# mask[mask_2] = counter
# return mask