# %%
import os
# os.chdir(os.path.dirname(os.path.abspath('examples.py')))
import findpeaks
print(dir(findpeaks))
print(findpeaks.__version__)
# %%
from findpeaks import findpeaks
fp = findpeaks(method="topology", denoise=None, window=3)
# X = fp.import_example("2dpeaks_image")
X = fp.import_example("2dpeaks")
results = fp.fit(X)
fp.plot_preprocessing()
fp.plot()
fp.plot_peristence()
fp.plot_mesh()
fp = findpeaks(method="mask")
X = fp.import_example()
results = fp.fit(X)
fp.plot()
fp.plot_preprocessing()
fp.plot_peristence()
fp.plot_mesh()
# %%
from findpeaks import findpeaks
x_label = "X"
if args.y_label is not None:
y_label = args.y_label
elif "YLabel" in header:
y_label = headers[0]["YLabel"]
else:
y_label = "Y"
# find limits
if args.x_max is None or args.y_max is None:
xmax = list(map(len, x))
ysum = list(map(np.sum, y))
ymax = list(map(np.max, y))
for i in range(len(x)):
peakx = findpeaks(y[i])
peakx = peakx[peakx != 1]
peaky = y[i][peakx]
for j in range(1, xmax[i], int(xmax[i]/1000) + 1):
if np.sum(y[i][:j]) >= ysum[i] * 0.999:
xmax[i] = j
break
ymax[i] = np.max(peaky) * 1.1
xmax = max(xmax)
ymax = max(ymax)
if args.verbose:
print("Automatically detected axis limits:")
print("xmax: ", xmax)
else:
z_label = "Z"
matrix = np.loadtxt(input_file)
input_file.close()
if args.verbose:
print("{:d} by {:d} matrix file loaded.".format(matrix.shape[0],
matrix.shape[1]))
matrix_smooth = ndimage.gaussian_filter(matrix, sigma=2.0, order=0)
if args.x_max is None or args.y_max is None or args.z_max is None:
# find peaks
msum = np.sum(matrix)
xsums = np.sum(matrix, 0)
ysums = np.sum(matrix, 1)
peakx = findpeaks(xsums)
peaky = findpeaks(ysums)
# ignore peaks at 1
peakx = peakx[peakx != 1]
peaky = peaky[peaky != 1]
peakz = matrix[peaky, :][:, peakx]
# peakxv = xsums[peakx]
# print "peakxv: ", peakxv
# xmax = np.max(peakx[peakxv > (msum * 0.0005)]) * 2
# peakyv = ysums[peaky]
# print "peakyv: ", peakyv
# ymax = np.max(peaky[peakyv > (msum * 0.0005)]) * 2
xmax = len(xsums)
ymax = len(ysums)
else:
z_label = "Z"
matrix = np.loadtxt(input_file)
input_file.close()
if args.verbose:
print("{:d} by {:d} matrix file loaded.".format(matrix.shape[0],
matrix.shape[1]))
matrix_smooth = ndimage.gaussian_filter(matrix, sigma=2.0, order=0)
if args.x_max is None or args.y_max is None or args.z_max is None:
# find peaks
msum = np.sum(matrix)
xsums = np.sum(matrix, 0)
ysums = np.sum(matrix, 1)
peakx = findpeaks(xsums)
peaky = findpeaks(ysums)
# ignore peaks at 1
peakx = peakx[peakx != 1]
peaky = peaky[peaky != 1]
peakz = matrix[peaky,:][:,peakx]
# peakxv = xsums[peakx]
# print "peakxv: ", peakxv
# xmax = np.max(peakx[peakxv > (msum * 0.0005)]) * 2
# peakyv = ysums[peaky]
# print "peakyv: ", peakyv
# ymax = np.max(peaky[peakyv > (msum * 0.0005)]) * 2
xmax = len(xsums)
ymax = len(ysums)
x_label = "X"
if args.y_label is not None:
y_label = args.y_label
elif "YLabel" in header:
y_label = headers[0]["YLabel"]
else:
y_label = "Y"
# find limits
if args.x_max is None or args.y_max is None:
xmax = list(map(len, x))
ysum = list(map(np.sum, y))
ymax = list(map(np.max, y))
for i in range(len(x)):
peakx = findpeaks(y[i])
peakx = peakx[peakx != 1]
peaky = y[i][peakx]
for j in range(1, xmax[i], int(xmax[i] / 1000) + 1):
if np.sum(y[i][:j]) >= ysum[i] * 0.999:
xmax[i] = j
break
ymax[i] = np.max(peaky) * 1.1
xmax = max(xmax)
ymax = max(ymax)
if args.verbose:
print("Automatically detected axis limits:")
print("xmax: ", xmax)
xamount = 0.65
else:
xamount = 0.99
# leave only coverage levels we are interested in
last_column = np.transpose(np.matrix(np.sum(matrix[:,(mincov+covbands):], 1)))
matrix = np.concatenate([matrix[:,mincov:(mincov+covbands)], last_column], 1)
# find limits
if args.x_max is None or args.y_max is None:
totals = np.squeeze(np.asarray(np.sum(matrix, 1)))
xmax = len(totals) - 1
ysum = np.sum(totals)
ymax = np.max(totals)
peakx = findpeaks(totals)
peakx = peakx[peakx != 1]
peaky = totals[peakx]
for i in range(1, xmax, int(xmax/100) + 1):
if np.sum(totals[:i]) >= ysum * xamount:
xmax = i
break
ymax = np.max(peaky) * 1.1
if args.verbose:
print("Automatically detected axis limits:")
print("xmax: ", xmax)
print("ymax: ", ymax)
if args.x_max is not None:
xmax = args.x_max
xamount = 0.99
# leave only coverage levels we are interested in
last_column = np.transpose(
np.matrix(np.sum(matrix[:, (mincov + covbands):], 1)))
matrix = np.concatenate([matrix[:, mincov:(mincov + covbands)], last_column],
1)
# find limits
if args.x_max is None or args.y_max is None:
totals = np.squeeze(np.asarray(np.sum(matrix, 1)))
xmax = len(totals) - 1
ysum = np.sum(totals)
ymax = np.max(totals)
peakx = findpeaks(totals)
peakx = peakx[peakx != 1]
peaky = totals[peakx]
for i in range(1, xmax, int(xmax / 100) + 1):
if np.sum(totals[:i]) >= ysum * xamount:
xmax = i
break
ymax = np.max(peaky) * 1.1
if args.verbose:
print("Automatically detected axis limits:")
print("xmax: ", xmax)
print("ymax: ", ymax)
if args.x_max is not None:
xmax = args.x_max
