def plot_distribution(filename, source_folder_positions, method = 'fit_ellipse'):
data, info = load_time_trace(filename, source_folder_positions=source_folder_positions, verbose=False)
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
for i, mode in enumerate('xy'):
x = data[method.lower().split('_')[-1] + ' ' + mode]
print(mode + ' standard dev: ', np.std(x))
if isinstance(min(x), float):
bins = 100
else:
bins = range(int(min(x) - 1), int(max(x) + 1))
axes[i].hist(x, bins=bins)
axes[i].set_xlabel('position ' + mode)
axes[i].set_title('std dev {:0.2f} px'.format(np.std(x)))
x, y = data[method.lower().split('_')[-1] + ' x'], data[method.lower().split('_')[-1] + ' y']
if isinstance(min(x), float):
bins_x, bins_y = 100, 100
else:
bins_x = range(int(min(x) - 1), int(max(x) + 1))
bins_y = range(int(min(y) - 1), int(max(y) + 1))
ret = axes[2].hist2d(x, y, bins=[bins_x, bins_y])
axes[2].set_xlabel('position x (px)')
axes[2].set_ylabel('position y (px)')
return fig
# get all the files and sort them by the run number
# position_file_names = get_position_file_names(source_folder_positions, method=method, runs=list(range(60, 180)))
# position_file_names = get_position_file_names(source_folder_positions, method=method, runs=list(range(83, 85)))
position_file_names = get_position_file_names(source_folder_positions,
method=method,
tag=tag,
runs=list(range(50, 54)))
################################################################################
#### run the script
################################################################################
for i, filename in enumerate(position_file_names):
print(filename)
data, info = load_time_trace(
filename,
source_folder_positions=source_folder_positions,
verbose=False)
return_dict = get_calibration_factor(data,
particle_diameter=particle_diameter,
verbose=False)
print(return_dict)
update_info(filename,
key='calibration factor: (um/px)',
value=return_dict['calibration factor: (um/px)'],
folder_positions=source_folder_positions,
dataset='ellipse',
verbose=True)
def spectra_2D_map(position_file_names,source_folder_positions=None, modes='xy', navrg=10,
frequency_range=None, tag='_Sample_6_Bead_1_', nmax=None,
y_axis = '',
method = 'fit_ellipse', verbose=False):
"""
calculated the psds and plots them as a waterfall plot
Args:
position_file_names:
modes:
navrg:
y_axis: determines what to use for the y-axis, ['run', 'time (h)', ''] if '' y is equilly spaced
xlim:
tag:
nmax:
verbose:
Returns:
"""
fig, ax = plt.subplots(len(modes), 1, sharex=True, figsize=(18, 5 * len(modes)))
if len(modes)==1:
ax = [ax]
psd_data = {'y_axis':[]}
for i, filename in enumerate(position_file_names):
run = int(filename.split(tag)[1].split('-')[0].strip('_'))
if verbose:
print(filename, run)
data, info = load_time_trace(filename, source_folder_positions=source_folder_positions, verbose=False)
dt = 1./info['info']['FrameRate']
if verbose:
print('FrameRate', info['info']['FrameRate'])
if y_axis == '':
psd_data['y_axis'].append(i)
elif y_axis == 'run':
psd_data['y_axis'].append(run)
elif y_axis == 'time (h)':
time = info['info']['File_Modified_Date_Local']
time = datetime.strptime(time.split('.')[0], '%Y-%m-%d %H:%M:%S')
if i==0:
start = time
time = time-start
psd_data['y_axis'].append(time.seconds/(60 * 60) + time.days * 24)
# calculate the psd
for mode in modes:
if method == 'fit_ellipse':
if mode == 'r':
x = data['ellipse angle']
elif mode == 'z':
x = data['ellipse a'] * data['ellipse b']
else:
x = data['ellipse ' + mode]
elif method.lower() == 'bright px':
x = data['bright px ' + mode]
x -= np.mean(x)
if nmax is not None:
x = x[0:nmax]
f, p = power_spectral_density(x, time_step=dt, frequency_range=frequency_range)
p = avrg(p, navrg)
if mode in psd_data:
psd_data[mode] = np.vstack([psd_data[mode],p])
else:
psd_data[mode] = p
psd_data['f'] = avrg(f, navrg)
for a, mode in enumerate(modes):
z = np.log10(psd_data[mode]) # get data
y = psd_data['y_axis']
x = psd_data['f']
ax[a].pcolormesh(x, y, z) # plot
ax[a].set_title(mode + ' data')
ax[a].set_xlabel('frequency (Hz)')
ax[a].set_ylabel(y_axis)
return fig
def waterfall(position_file_names,source_folder_positions=None, modes='xy', navrg=10, off_set_factor=-3,
frequency_range=None, tag='_Sample_6_Bead_1_', nmax=None,
method = 'fit_ellipse', verbose=False):
"""
calculated the psds and plots them as a waterfall plot
Args:
position_file_names:
modes:
navrg:
off_set_factor:
xlim:
tag:
nmax:
verbose:
Returns:
"""
fig, ax = plt.subplots(len(modes), 1, sharex=True, figsize=(18, 5 * len(modes)))
if len(modes)==1:
ax = [ax]
for i, filename in enumerate(position_file_names):
run = int(filename.split(tag)[1].split('-')[0].strip('_'))
if verbose:
print(filename, run)
data, info = load_time_trace(filename, source_folder_positions=source_folder_positions, verbose=False)
dt = 1./info['info']['FrameRate']
# calculate the psd
psd_data = {}
for mode in modes:
if method == 'fit_ellipse':
if mode == 'r':
x = data['ellipse angle']
elif mode == 'z':
x = data['ellipse a'] * data['ellipse b']
else:
x = data['ellipse ' + mode]
elif method.lower() == 'bright px':
x = data['bright px ' + mode]
x -= np.mean(x)
if nmax is not None:
x = x[0:nmax]
f, p = power_spectral_density(x, time_step=dt, frequency_range=frequency_range)
psd_data[mode] = p
psd_data['f'] = f
f = psd_data['f'][1:] # get rid of first point (DC)
f = avrg(f, navrg) # refold (assume that signal is aliasied)
# f = np.mean(df_modes['FrameRate'])-avrg(f, navrg) # refold (assume that signal is aliasied)
for a, mode in enumerate(modes):
d = psd_data[mode] # get data
d = avrg(d[1:] * 10 ** (off_set_factor * i), navrg) # shift on a log scale to get the waterfall effect
ax[a].semilogy(f, d, label=str(run), alpha=0.5) # plot
# if xlim is not None:
# assert len(xlim) == 2
# ax[0].set_xlim(xlim)
for a, mode in enumerate(modes):
ax[a].set_title(mode + ' data')
ax[a].set_xlabel('frequency (Hz)')
plt.legend(loc=(1, 0.0))
return fig