def plot_clust(h5_main, labels, mean_resp, x_pts, data_avg=None, tidx_off=0):
try:
_labels = np.array(labels.value).T[0]
except:
_labels = np.array(labels)
labels_unique = np.unique(_labels)
parms_dict = hdf_utils.get_params(h5_main)
clust_tfp = []
# color defaults are blue, orange, green, red, purple...
colors = [
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
]
fig, ax = plt.subplots(nrows=1, figsize=(12, 6))
ax.set_xlabel('Distance to Nearest Boundary (um)')
ax.set_ylabel('tfp (us)')
for i in labels_unique:
labels_tfp = []
if not data_avg.any():
for sig in h5_main[_labels == labels_unique[i], :]:
pix = pixel.Pixel(sig, parms_dict)
pix.inst_freq = sig
pix.fit_freq_product()
labels_tfp.append(pix.tfp)
labels_tfp = np.array(labels_tfp)
else:
labels_tfp = data_avg[_labels == labels_unique[i]]
ax.plot(x_pts[_labels == labels_unique[i]] * 1e6,
labels_tfp * 1e6,
c=colors[i],
linestyle='None',
marker='.')
parms_dict['trigger'] += tidx_off / parms_dict['sampling_rate']
pix = pixel.Pixel(mean_resp[i], parms_dict)
pix.inst_freq = mean_resp[i]
pix.fit_freq_product()
clust_tfp.append(pix.tfp)
parms_dict['trigger'] -= tidx_off / parms_dict['sampling_rate']
ax.plot(np.mean(x_pts[_labels == labels_unique[i]] * 1e6),
pix.tfp * 1e6,
marker='o',
markerfacecolor=colors[i],
markersize=8,
markeredgecolor='k')
print('tfp of clusters: ', clust_tfp)
return ax, fig
def test_pixel(h5_file,
param_changes={},
pxls=1,
showplots=True,
verbose=True,
clear_filter=False):
"""
Takes a random pixel and does standard processing.
:param h5_file: H5 file to process
:type h5_file: h5Py File, path, Dataset
:param param_changes:
:type param_changes: dict, optional
:param pxls: Number of random pixels to survey
:type pxls: int, optional
:param showplots: Whether to create a new plot or not.
:type showplots: bool, optional
:param verbose: To print to command line. Currently for future-proofing
:type verbose : bool , optional
:param clear_filter: Whether to do filtering (FIR) or not
:type clear_filter: bool, optional
"""
# get_pixel can work on Datasets or the H5_File
if any(param_changes):
hdf_utils.change_params(h5_file, new_vals=param_changes)
parameters = hdf_utils.get_params(h5_file)
cols = parameters['num_cols']
rows = parameters['num_rows']
# Creates random pixels to sample
pixels = []
if pxls == 1:
pixels.append([0, 0])
if pxls > 1:
from numpy.random import randint
for i in range(pxls):
pixels.append([randint(0, rows), randint(0, cols)])
# Analyzes all pixels
for rc in pixels:
h5_px = hdf_utils.get_pixel(h5_file, rc=rc)
if clear_filter:
h5_px.clear_filter_flags()
h5_px.analyze()
print(rc, h5_px.tfp)
if showplots == True:
plt.plot(h5_px.best_fit, 'r--')
plt.plot(h5_px.cut, 'g-')
return