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"""Functions to calculate mean squared displacements from trajectory data |
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This module includes functions to calculate mean squared displacements and |
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additional measures from input trajectory datasets as calculated by the |
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Trackmate ImageJ plugin. |
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""" |
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import warnings |
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import random as rand |
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import pandas as pd |
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import numpy as np |
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import numpy.ma as ma |
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import scipy.stats as stats |
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from scipy import interpolate |
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import matplotlib.pyplot as plt |
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from matplotlib.pyplot import cm |
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import diff_classifier.aws as aws |
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from scipy.ndimage.morphology import distance_transform_edt as eudist |
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View Code Duplication |
def nth_diff(dataframe, n=1, axis=0): |
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"""Calculates the nth difference between vector elements |
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Returns a new vector of size N - n containing the nth difference between |
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vector elements. |
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Parameters |
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---------- |
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dataframe : pandas.core.series.Series of int or float |
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Input data on which differences are to be calculated. |
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n : int |
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Function calculated xpos(i) - xpos(i - n) for all values in pandas |
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series. |
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axis : {0, 1} |
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Axis along which differences are to be calculated. Default is 0. If 0, |
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input must be a pandas series. If 1, input must be a numpy array. |
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Returns |
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------- |
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diff : pandas.core.series.Series of int or float |
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Pandas series of size N - n, where N is the original size of dataframe. |
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Examples |
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-------- |
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>>> df = np.ones((5, 10)) |
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>>> nth_diff(df) |
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array([[0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0.]]) |
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>>> df = np.ones((5, 10)) |
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>>> nth_diff (df) |
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array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], |
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[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) |
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""" |
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assert isinstance(n, int), "n must be an integer." |
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if dataframe.ndim == 1: |
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length = dataframe.shape[0] |
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if n <= length: |
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test1 = dataframe[:-n].reset_index(drop=True) |
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test2 = dataframe[n:].reset_index(drop=True) |
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diff = test2 - test1 |
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else: |
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diff = np.array([np.nan, np.nan]) |
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else: |
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length = dataframe.shape[0] |
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if n <= length: |
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if axis == 0: |
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test1 = dataframe[:-n, :] |
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test2 = dataframe[n:, :] |
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else: |
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test1 = dataframe[:, :-n] |
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test2 = dataframe[:, n:] |
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diff = test2 - test1 |
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else: |
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diff = np.array([np.nan, np.nan]) |
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return diff |
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View Code Duplication |
def msd_calc(track, length=10): |
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"""Calculates mean squared displacement of input track. |
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Returns numpy array containing MSD data calculated from an individual track. |
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Parameters |
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---------- |
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track : pandas.core.frame.DataFrame |
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Contains, at a minimum a 'Frame', 'X', and 'Y' column |
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Returns |
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------- |
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new_track : pandas.core.frame.DataFrame |
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Similar to input track. All missing frames of individual trajectories |
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are filled in with NaNs, and two new columns, MSDs and Gauss are added: |
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MSDs, calculated mean squared displacements using the formula |
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MSD = <(xpos-x0)**2> |
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Gauss, calculated Gaussianity |
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Examples |
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-------- |
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>>> data1 = {'Frame': [1, 2, 3, 4, 5], |
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... 'X': [5, 6, 7, 8, 9], |
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... 'Y': [6, 7, 8, 9, 10]} |
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>>> df = pd.DataFrame(data=data1) |
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>>> new_track = msd.msd_calc(df, 5) |
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>>> data1 = {'Frame': [1, 2, 3, 4, 5], |
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... 'X': [5, 6, 7, 8, 9], |
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... 'Y': [6, 7, 8, 9, 10]} |
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>>> df = pd.DataFrame(data=data1) |
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>>> new_track = msd.msd_calc(df) |
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""" |
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meansd = np.zeros(length) |
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gauss = np.zeros(length) |
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new_frame = np.linspace(1, length, length) |
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old_frame = track['Frame'] |
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oldxy = [track['X'], track['Y']] |
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fxy = [interpolate.interp1d(old_frame, oldxy[0], bounds_error=False, |
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fill_value=np.nan), |
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interpolate.interp1d(old_frame, oldxy[1], bounds_error=False, |
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fill_value=np.nan)] |
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intxy = [ma.masked_equal(fxy[0](new_frame), np.nan), |
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ma.masked_equal(fxy[1](new_frame), np.nan)] |
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data1 = {'Frame': new_frame, |
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'X': intxy[0], |
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'Y': intxy[1] |
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} |
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new_track = pd.DataFrame(data=data1) |
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for frame in range(0, length-1): |
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xy = [np.square(nth_diff(new_track['X'], n=frame+1)), |
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np.square(nth_diff(new_track['Y'], n=frame+1))] |
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with warnings.catch_warnings(): |
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warnings.simplefilter("ignore", category=RuntimeWarning) |
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meansd[frame+1] = np.nanmean(xy[0] + xy[1]) |
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gauss[frame+1] = np.nanmean(xy[0]**2 + xy[1]**2 |
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)/(2*(meansd[frame+1]**2)) |
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new_track['MSDs'] = pd.Series(meansd, index=new_track.index) |
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new_track['Gauss'] = pd.Series(gauss, index=new_track.index) |
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return new_track |
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View Code Duplication |
def all_msds(data): |
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"""Calculates mean squared displacements of a trajectory dataset |
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Returns numpy array containing MSD data of all tracks in a trajectory |
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pandas dataframe. |
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Parameters |
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---------- |
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data : pandas.core.frame.DataFrame |
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Contains, at a minimum a 'Frame', 'Track_ID', 'X', and |
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'Y' column. Note: it is assumed that frames begins at 1, not 0 with this |
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function. Adjust before feeding into function. |
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Returns |
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------- |
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new_data : pandas.core.frame.DataFrame |
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Similar to input data. All missing frames of individual trajectories |
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are filled in with NaNs, and two new columns, MSDs and Gauss are added: |
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MSDs, calculated mean squared displacements using the formula |
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MSD = <(xpos-x0)**2> |
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Gauss, calculated Gaussianity |
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Examples |
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-------- |
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>>> data1 = {'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], |
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... 'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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... 'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
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... 'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
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>>> df = pd.DataFrame(data=data1) |
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>>> all_msds(df) |
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""" |
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trackids = data.Track_ID.unique() |
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partcount = trackids.shape[0] |
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length = int(max(data['Frame'])) |
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new = {} |
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new['length'] = partcount*length |
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new['frame'] = np.zeros(new['length']) |
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new['ID'] = np.zeros(new['length']) |
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new['xy'] = [np.zeros(new['length']), |
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np.zeros(new['length'])] |
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meansd = np.zeros(new['length']) |
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gauss = np.zeros(new['length']) |
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for particle in range(0, partcount): |
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single_track = data.loc[data['Track_ID'] == |
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trackids[particle] |
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].sort_values(['Track_ID', 'Frame'], |
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ascending=[1, 1] |
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).reset_index(drop=True) |
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if particle == 0: |
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index1 = 0 |
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index2 = length |
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else: |
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index1 = index2 |
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index2 = index2 + length |
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new['single_track'] = msd_calc(single_track, length=length) |
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new['frame'][index1:index2] = np.linspace(1, length, length) |
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new['ID'][index1:index2] = particle+1 |
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new['xy'][0][index1:index2] = new['single_track']['X'] |
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new['xy'][1][index1:index2] = new['single_track']['Y'] |
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meansd[index1:index2] = new['single_track']['MSDs'] |
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gauss[index1:index2] = new['single_track']['Gauss'] |
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data1 = {'Frame': new['frame'], |
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'Track_ID': new['ID'], |
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'X': new['xy'][0], |
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'Y': new['xy'][1], |
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'MSDs': meansd, |
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'Gauss': gauss} |
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new_data = pd.DataFrame(data=data1) |
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return new_data |
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View Code Duplication |
def make_xyarray(data, length=651): |
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"""Rearranges xy position data into 2d arrays |
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Rearranges xy data from input pandas dataframe into 2D numpy array. |
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Parameters |
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---------- |
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data : pd.core.frame.DataFrame |
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Contains, at a minimum a 'Frame', 'Track_ID', 'X', and |
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'Y' column. |
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length : int |
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Desired length or number of frames to which to extend trajectories. |
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Any trajectories shorter than the input length will have the extra space |
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filled in with NaNs. |
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Returns |
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------- |
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xyft : dict of np.ndarray |
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Dictionary containing xy position data, frame data, and trajectory ID |
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data. Contains the following keys: |
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farray, frames data (length x particles) |
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tarray, trajectory ID data (length x particles) |
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xarray, x position data (length x particles) |
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yarray, y position data (length x particles) |
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Examples |
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-------- |
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>>> data1 = {'Frame': [0, 1, 2, 3, 4, 2, 3, 4, 5, 6], |
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... 'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
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... 'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
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... 'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
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>>> df = pd.DataFrame(data=data1) |
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>>> length = max(df['Frame']) + 1 |
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>>> xyft = msd.make_xyarray(df, length=length) |
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{'farray': array([[0., 0.], |
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[1., 1.], |
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[2., 2.], |
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[3., 3.], |
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[4., 4.], |
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[5., 5.], |
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[6., 6.]]), |
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'tarray': array([[1., 2.], |
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[1., 2.], |
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[1., 2.], |
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[1., 2.], |
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[1., 2.], |
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[1., 2.], |
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[1., 2.]]), |
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'xarray': array([[ 5., nan], |
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[ 6., nan], |
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[ 7., 1.], |
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[ 8., 2.], |
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[ 9., 3.], |
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[nan, 4.], |
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'yarray': [nan, 5.]]), |
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array([[ 6., nan], |
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[ 7., nan], |
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[ 8., 2.], |
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[ 9., 3.], |
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[10., 4.], |
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[nan, 5.], |
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[nan, 6.]])} |
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""" |
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# Initial values |
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first_p = int(min(data['Track_ID'])) |
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particles = int(max(data['Track_ID'])) - first_p + 1 |
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xyft = {} |
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xyft['xarray'] = np.zeros((length, particles)) |
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xyft['yarray'] = np.zeros((length, particles)) |
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xyft['farray'] = np.zeros((length, particles)) |
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xyft['tarray'] = np.zeros((length, particles)) |
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xyft['qarray'] = np.zeros((length, particles)) |
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xyft['snarray'] = np.zeros((length, particles)) |
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xyft['iarray'] = np.zeros((length, particles)) |
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310
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track = data[data['Track_ID'] == first_p |
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].sort_values(['Track_ID', 'Frame'], |
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312
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ascending=[1, 1]).reset_index(drop=True) |
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new_frame = np.linspace(0, length-1, length) |
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315
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old_frame = track['Frame'].values.astype(float) |
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oldxy = [track['X'].values, |
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track['Y'].values, |
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track['Quality'].values, |
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track['SN_Ratio'].values, |
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320
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track['Mean_Intensity'].values] |
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fxy = [interpolate.interp1d(old_frame, oldxy[0], bounds_error=False, |
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fill_value=np.nan), |
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interpolate.interp1d(old_frame, oldxy[1], bounds_error=False, |
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fill_value=np.nan), |
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interpolate.interp1d(old_frame, oldxy[2], bounds_error=False, |
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fill_value=np.nan), |
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interpolate.interp1d(old_frame, oldxy[3], bounds_error=False, |
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fill_value=np.nan), |
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interpolate.interp1d(old_frame, oldxy[4], bounds_error=False, |
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fill_value=np.nan)] |
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331
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332
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intxy = [fxy[0](new_frame), fxy[1](new_frame), fxy[2](new_frame), |
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333
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fxy[3](new_frame), fxy[4](new_frame)] |
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335
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# Fill in entire array |
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336
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xyft['xarray'][:, 0] = intxy[0] |
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337
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xyft['yarray'][:, 0] = intxy[1] |
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338
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xyft['farray'][:, 0] = new_frame |
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339
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xyft['tarray'][:, 0] = first_p |
|
340
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xyft['qarray'][:, 0] = intxy[2] |
|
341
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xyft['snarray'][:, 0] = intxy[3] |
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342
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xyft['iarray'][:, 0] = intxy[4] |
|
343
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344
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for part in range(first_p+1, first_p+particles): |
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345
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track = data[data['Track_ID'] == part |
|
346
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].sort_values(['Track_ID', 'Frame'], |
|
347
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|
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ascending=[1, 1]).reset_index(drop=True) |
|
348
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|
349
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|
old_frame = track['Frame'] |
|
350
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oldxy = [track['X'].values, |
|
351
|
|
|
track['Y'].values, |
|
352
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|
track['Quality'].values, |
|
353
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|
track['SN_Ratio'].values, |
|
354
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|
track['Mean_Intensity'].values] |
|
355
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|
356
|
|
|
fxy = [interpolate.interp1d(old_frame, oldxy[0], bounds_error=False, |
|
357
|
|
|
fill_value=np.nan), |
|
358
|
|
|
interpolate.interp1d(old_frame, oldxy[1], bounds_error=False, |
|
359
|
|
|
fill_value=np.nan), |
|
360
|
|
|
interpolate.interp1d(old_frame, oldxy[2], bounds_error=False, |
|
361
|
|
|
fill_value=np.nan), |
|
362
|
|
|
interpolate.interp1d(old_frame, oldxy[3], bounds_error=False, |
|
363
|
|
|
fill_value=np.nan), |
|
364
|
|
|
interpolate.interp1d(old_frame, oldxy[4], bounds_error=False, |
|
365
|
|
|
fill_value=np.nan)] |
|
366
|
|
|
|
|
367
|
|
|
intxy = [fxy[0](new_frame), fxy[1](new_frame), fxy[2](new_frame), |
|
368
|
|
|
fxy[3](new_frame), fxy[4](new_frame)] |
|
369
|
|
|
|
|
370
|
|
|
xyft['xarray'][:, part-first_p] = intxy[0] |
|
371
|
|
|
xyft['yarray'][:, part-first_p] = intxy[1] |
|
372
|
|
|
xyft['farray'][:, part-first_p] = new_frame |
|
373
|
|
|
xyft['tarray'][:, part-first_p] = part |
|
374
|
|
|
xyft['qarray'][:, part-first_p] = intxy[2] |
|
375
|
|
|
xyft['snarray'][:, part-first_p] = intxy[3] |
|
376
|
|
|
xyft['iarray'][:, part-first_p] = intxy[4] |
|
377
|
|
|
|
|
378
|
|
|
return xyft |
|
379
|
|
|
|
|
380
|
|
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|
|
381
|
|
View Code Duplication |
def all_msds2(data, frames=651): |
|
|
|
|
|
|
382
|
|
|
"""Calculates mean squared displacements of input trajectory dataset |
|
383
|
|
|
|
|
384
|
|
|
Returns numpy array containing MSD data of all tracks in a trajectory pandas |
|
385
|
|
|
dataframe. |
|
386
|
|
|
|
|
387
|
|
|
Parameters |
|
388
|
|
|
---------- |
|
389
|
|
|
data : pandas.core.frame.DataFrame |
|
390
|
|
|
Contains, at a minimum a 'Frame', 'Track_ID', 'X', and |
|
391
|
|
|
'Y' column. Note: it is assumed that frames begins at 0. |
|
392
|
|
|
|
|
393
|
|
|
Returns |
|
394
|
|
|
------- |
|
395
|
|
|
new_data : pandas.core.frame.DataFrame |
|
396
|
|
|
Similar to input data. All missing frames of individual trajectories |
|
397
|
|
|
are filled in with NaNs, and two new columns, MSDs and Gauss are added: |
|
398
|
|
|
MSDs, calculated mean squared displacements using the formula |
|
399
|
|
|
MSD = <(xpos-x0)**2> |
|
400
|
|
|
Gauss, calculated Gaussianity |
|
401
|
|
|
|
|
402
|
|
|
Examples |
|
403
|
|
|
-------- |
|
404
|
|
|
>>> data1 = {'Frame': [0, 1, 2, 3, 4, 0, 1, 2, 3, 4], |
|
405
|
|
|
... 'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], |
|
406
|
|
|
... 'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5], |
|
407
|
|
|
... 'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]} |
|
408
|
|
|
>>> df = pd.DataFrame(data=data1) |
|
409
|
|
|
>>> cols = ['Frame', 'Track_ID', 'X', 'Y', 'MSDs', 'Gauss'] |
|
410
|
|
|
>>> om flength = max(df['Frame']) + 1 |
|
411
|
|
|
>>> msd.all_msds2(df, frames=length)[cols] |
|
412
|
|
|
|
|
413
|
|
|
""" |
|
414
|
|
|
if data.shape[0] > 2: |
|
415
|
|
|
try: |
|
416
|
|
|
xyft = make_xyarray(data, length=frames) |
|
417
|
|
|
length = xyft['xarray'].shape[0] |
|
418
|
|
|
particles = xyft['xarray'].shape[1] |
|
419
|
|
|
|
|
420
|
|
|
meansd = np.zeros((length, particles)) |
|
421
|
|
|
gauss = np.zeros((length, particles)) |
|
422
|
|
|
|
|
423
|
|
|
for frame in range(0, length-1): |
|
|
|
|
|
|
424
|
|
|
xpos = np.square(nth_diff(xyft['xarray'], n=frame+1)) |
|
425
|
|
|
ypos = np.square(nth_diff(xyft['yarray'], n=frame+1)) |
|
426
|
|
|
|
|
427
|
|
|
with warnings.catch_warnings(): |
|
428
|
|
|
warnings.simplefilter("ignore", category=RuntimeWarning) |
|
429
|
|
|
meansd[frame+1, :] = np.nanmean(xpos + ypos, axis=0) |
|
430
|
|
|
gauss[frame+1, :] = np.nanmean(xpos**2 + ypos**2, axis=0 |
|
431
|
|
|
)/(2*(meansd[frame+1]**2)) |
|
432
|
|
|
|
|
433
|
|
|
data1 = {'Frame': xyft['farray'].flatten('F'), |
|
434
|
|
|
'Track_ID': xyft['tarray'].flatten('F'), |
|
435
|
|
|
'X': xyft['xarray'].flatten('F'), |
|
436
|
|
|
'Y': xyft['yarray'].flatten('F'), |
|
437
|
|
|
'MSDs': meansd.flatten('F'), |
|
438
|
|
|
'Gauss': gauss.flatten('F'), |
|
439
|
|
|
'Quality': xyft['qarray'].flatten('F'), |
|
440
|
|
|
'SN_Ratio': xyft['snarray'].flatten('F'), |
|
441
|
|
|
'Mean_Intensity': xyft['iarray'].flatten('F')} |
|
442
|
|
|
|
|
443
|
|
|
new_data = pd.DataFrame(data=data1) |
|
444
|
|
|
except ValueError: |
|
445
|
|
|
data1 = {'Frame': [], |
|
446
|
|
|
'Track_ID': [], |
|
447
|
|
|
'X': [], |
|
448
|
|
|
'Y': [], |
|
449
|
|
|
'MSDs': [], |
|
450
|
|
|
'Gauss': [], |
|
451
|
|
|
'Quality': [], |
|
452
|
|
|
'SN_Ratio': [], |
|
453
|
|
|
'Mean_Intensity': []} |
|
454
|
|
|
new_data = pd.DataFrame(data=data1) |
|
455
|
|
|
except IndexError: |
|
456
|
|
|
data1 = {'Frame': [], |
|
457
|
|
|
'Track_ID': [], |
|
458
|
|
|
'X': [], |
|
459
|
|
|
'Y': [], |
|
460
|
|
|
'MSDs': [], |
|
461
|
|
|
'Gauss': [], |
|
462
|
|
|
'Quality': [], |
|
463
|
|
|
'SN_Ratio': [], |
|
464
|
|
|
'Mean_Intensity': []} |
|
465
|
|
|
new_data = pd.DataFrame(data=data1) |
|
466
|
|
|
else: |
|
467
|
|
|
data1 = {'Frame': [], |
|
468
|
|
|
'Track_ID': [], |
|
469
|
|
|
'X': [], |
|
470
|
|
|
'Y': [], |
|
471
|
|
|
'MSDs': [], |
|
472
|
|
|
'Gauss': [], |
|
473
|
|
|
'Quality': [], |
|
474
|
|
|
'SN_Ratio': [], |
|
475
|
|
|
'Mean_Intensity': []} |
|
476
|
|
|
new_data = pd.DataFrame(data=data1) |
|
477
|
|
|
|
|
478
|
|
|
return new_data |
|
479
|
|
|
|
|
480
|
|
|
|
|
481
|
|
View Code Duplication |
def geomean_msdisp(prefix, umppx=0.16, fps=100.02, upload=True, |
|
|
|
|
|
|
482
|
|
|
remote_folder="01_18_Experiment", bucket='ccurtis.data', |
|
483
|
|
|
backup_frames=651): |
|
484
|
|
|
"""Comptes geometric averages of mean squared displacement datasets |
|
485
|
|
|
|
|
486
|
|
|
Calculates geometric averages and stadard errors for MSD datasets. Might |
|
487
|
|
|
error out if not formatted as output from all_msds2. |
|
488
|
|
|
|
|
489
|
|
|
Parameters |
|
490
|
|
|
---------- |
|
491
|
|
|
prefix : string |
|
492
|
|
|
Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
|
493
|
|
|
umppx : float |
|
494
|
|
|
Microns per pixel of original images. |
|
495
|
|
|
fps : float |
|
496
|
|
|
Frames per second of video. |
|
497
|
|
|
upload : bool |
|
498
|
|
|
True if you want to upload to s3. |
|
499
|
|
|
remote_folder : string |
|
500
|
|
|
Folder in S3 bucket to upload to. |
|
501
|
|
|
bucket : string |
|
502
|
|
|
Name of S3 bucket to upload to. |
|
503
|
|
|
|
|
504
|
|
|
Returns |
|
505
|
|
|
------- |
|
506
|
|
|
geo_mean : numpy.ndarray |
|
507
|
|
|
Geometric mean of trajectory MSDs at all time points. |
|
508
|
|
|
geo_stder : numpy.ndarray |
|
509
|
|
|
Geometric standard errot of trajectory MSDs at all time points. |
|
510
|
|
|
|
|
511
|
|
|
""" |
|
512
|
|
|
|
|
513
|
|
|
merged = pd.read_csv('msd_{}.csv'.format(prefix)) |
|
514
|
|
|
try: |
|
515
|
|
|
particles = int(max(merged['Track_ID'])) |
|
516
|
|
|
frames = int(max(merged['Frame'])) |
|
517
|
|
|
ypos = np.zeros((particles+1, frames+1)) |
|
518
|
|
|
|
|
519
|
|
|
for i in range(0, particles+1): |
|
|
|
|
|
|
520
|
|
|
ypos[i, :] = merged.loc[merged.Track_ID == i, 'MSDs']*umppx*umppx |
|
521
|
|
|
xpos = merged.loc[merged.Track_ID == i, 'Frame']/fps |
|
522
|
|
|
|
|
523
|
|
|
geo_mean = np.nanmean(ma.log(ypos), axis=0) |
|
524
|
|
|
geo_stder = ma.masked_equal(stats.sem(ma.log(ypos), axis=0, |
|
525
|
|
|
nan_policy='omit'), 0.0) |
|
526
|
|
|
|
|
527
|
|
|
except ValueError: |
|
528
|
|
|
geo_mean = np.nan*np.ones(backup_frames) |
|
529
|
|
|
geo_stder = np.nan*np.ones(backup_frames) |
|
530
|
|
|
|
|
531
|
|
|
np.savetxt('geomean_{}.csv'.format(prefix), geo_mean, delimiter=",") |
|
532
|
|
|
np.savetxt('geoSEM_{}.csv'.format(prefix), geo_stder, delimiter=",") |
|
533
|
|
|
|
|
534
|
|
|
if upload: |
|
535
|
|
|
aws.upload_s3('geomean_{}.csv'.format(prefix), |
|
536
|
|
|
remote_folder+'/'+'geomean_{}.csv'.format(prefix), |
|
537
|
|
|
bucket_name=bucket) |
|
538
|
|
|
aws.upload_s3('geoSEM_{}.csv'.format(prefix), |
|
539
|
|
|
remote_folder+'/'+'geoSEM_{}.csv'.format(prefix), |
|
540
|
|
|
bucket_name=bucket) |
|
541
|
|
|
|
|
542
|
|
|
return geo_mean, geo_stder |
|
543
|
|
|
|
|
544
|
|
|
|
|
545
|
|
View Code Duplication |
def binning(experiments, wells=4, prefix='test'): |
|
|
|
|
|
|
546
|
|
|
"""Split set of input experiments into groups. |
|
547
|
|
|
|
|
548
|
|
|
Parameters |
|
549
|
|
|
---------- |
|
550
|
|
|
experiments : list of str |
|
551
|
|
|
List of experiment names. |
|
552
|
|
|
wells : int |
|
553
|
|
|
Number of groups to divide experiments into. |
|
554
|
|
|
|
|
555
|
|
|
Returns |
|
556
|
|
|
------- |
|
557
|
|
|
slices : int |
|
558
|
|
|
Number of experiments per group. |
|
559
|
|
|
bins : dict of list of str |
|
560
|
|
|
Dictionary, keys corresponding to group names, and elements containing |
|
561
|
|
|
lists of experiments in each group. |
|
562
|
|
|
bin_names : list of str |
|
563
|
|
|
List of group names |
|
564
|
|
|
|
|
565
|
|
|
""" |
|
566
|
|
|
|
|
567
|
|
|
total_videos = len(experiments) |
|
568
|
|
|
bins = {} |
|
569
|
|
|
slices = int(total_videos/wells) |
|
570
|
|
|
bin_names = [] |
|
571
|
|
|
|
|
572
|
|
|
for num in range(0, wells): |
|
|
|
|
|
|
573
|
|
|
slice1 = num*slices |
|
574
|
|
|
slice2 = (num+1)*(slices) |
|
575
|
|
|
pref = '{}_W{}'.format(prefix, num) |
|
576
|
|
|
bins[pref] = experiments[slice1:slice2] |
|
577
|
|
|
bin_names.append(pref) |
|
578
|
|
|
return slices, bins, bin_names |
|
579
|
|
|
|
|
580
|
|
|
|
|
581
|
|
View Code Duplication |
def precision_weight(group, geo_stder): |
|
|
|
|
|
|
582
|
|
|
"""Calculates precision-based weights from input standard error data |
|
583
|
|
|
|
|
584
|
|
|
Calculates precision weights to be used in precision-averaged MSD |
|
585
|
|
|
calculations. |
|
586
|
|
|
|
|
587
|
|
|
Parameters |
|
588
|
|
|
---------- |
|
589
|
|
|
group : list of str |
|
590
|
|
|
List of experiment names to average. Each element corresponds to a key |
|
591
|
|
|
in geo_stder and geomean. |
|
592
|
|
|
geo_stder : dict of numpy.ndarray |
|
593
|
|
|
Each entry in dictionary corresponds to the standard errors of an MSD |
|
594
|
|
|
profile, the key corresponding to an experiment name. |
|
595
|
|
|
|
|
596
|
|
|
Returns |
|
597
|
|
|
------- |
|
598
|
|
|
weights: numpy.ndarray |
|
599
|
|
|
Precision weights to be used in precision averaging. |
|
600
|
|
|
w_holder : numpy.ndarray |
|
601
|
|
|
Precision values of each video at each time point. |
|
602
|
|
|
|
|
603
|
|
|
""" |
|
604
|
|
|
|
|
605
|
|
|
frames = np.shape(geo_stder[group[0]])[0] |
|
606
|
|
|
slices = len(group) |
|
607
|
|
|
video_counter = 0 |
|
608
|
|
|
w_holder = np.zeros((slices, frames)) |
|
609
|
|
|
for sample in group: |
|
610
|
|
|
w_holder[video_counter, :] = 1/(geo_stder[sample]*geo_stder[sample]) |
|
611
|
|
|
video_counter = video_counter + 1 |
|
612
|
|
|
|
|
613
|
|
|
w_holder = ma.masked_equal(w_holder, 0.0) |
|
614
|
|
|
w_holder = ma.masked_equal(w_holder, 1.0) |
|
615
|
|
|
|
|
616
|
|
|
weights = ma.sum(w_holder, axis=0) |
|
617
|
|
|
|
|
618
|
|
|
return weights, w_holder |
|
619
|
|
|
|
|
620
|
|
|
|
|
621
|
|
View Code Duplication |
def precision_averaging(group, geomean, geo_stder, weights, save=True, |
|
|
|
|
|
|
622
|
|
|
bucket='ccurtis.data', folder='test', |
|
623
|
|
|
experiment='test'): |
|
624
|
|
|
"""Calculates precision-weighted averages of MSD datasets. |
|
625
|
|
|
|
|
626
|
|
|
Parameters |
|
627
|
|
|
---------- |
|
628
|
|
|
group : list of str |
|
629
|
|
|
List of experiment names to average. Each element corresponds to a key |
|
630
|
|
|
in geo_stder and geomean. |
|
631
|
|
|
geomean : dict of numpy.ndarray |
|
632
|
|
|
Each entry in dictionary corresponds to an MSD profiles, they key |
|
633
|
|
|
corresponding to an experiment name. |
|
634
|
|
|
geo_stder : dict of numpy.ndarray |
|
635
|
|
|
Each entry in dictionary corresponds to the standard errors of an MSD |
|
636
|
|
|
profile, the key corresponding to an experiment name. |
|
637
|
|
|
weights : numpy.ndarray |
|
638
|
|
|
Precision weights to be used in precision averaging. |
|
639
|
|
|
|
|
640
|
|
|
Returns |
|
641
|
|
|
------- |
|
642
|
|
|
geo : numpy.ndarray |
|
643
|
|
|
Precision-weighted averaged MSDs from experiments specified in group |
|
644
|
|
|
geo_stder : numpy.ndarray |
|
645
|
|
|
Precision-weighted averaged SEMs from experiments specified in group |
|
646
|
|
|
|
|
647
|
|
|
""" |
|
648
|
|
|
|
|
649
|
|
|
frames = np.shape(geo_stder[group[0]])[0] |
|
650
|
|
|
slices = len(group) |
|
651
|
|
|
|
|
652
|
|
|
video_counter = 0 |
|
653
|
|
|
geo_holder = np.zeros((slices, frames)) |
|
654
|
|
|
gstder_holder = np.zeros((slices, frames)) |
|
655
|
|
|
w_holder = np.zeros((slices, frames)) |
|
656
|
|
|
for sample in group: |
|
657
|
|
|
w_holder[video_counter, :] = (1/(geo_stder[sample]*geo_stder[sample]) |
|
658
|
|
|
)/weights |
|
659
|
|
|
geo_holder[video_counter, :] = w_holder[video_counter, : |
|
660
|
|
|
] * geomean[sample] |
|
661
|
|
|
gstder_holder[video_counter, :] = 1/(geo_stder[sample]*geo_stder[sample] |
|
662
|
|
|
) |
|
663
|
|
|
video_counter = video_counter + 1 |
|
664
|
|
|
|
|
665
|
|
|
w_holder = ma.masked_equal(w_holder, 0.0) |
|
666
|
|
|
w_holder = ma.masked_equal(w_holder, 1.0) |
|
667
|
|
|
geo_holder = ma.masked_equal(geo_holder, 0.0) |
|
668
|
|
|
geo_holder = ma.masked_equal(geo_holder, 1.0) |
|
669
|
|
|
gstder_holder = ma.masked_equal(gstder_holder, 0.0) |
|
670
|
|
|
gstder_holder = ma.masked_equal(gstder_holder, 1.0) |
|
671
|
|
|
|
|
672
|
|
|
geo = ma.sum(geo_holder, axis=0) |
|
673
|
|
|
geo_stder = ma.sqrt((1/ma.sum(gstder_holder, axis=0))) |
|
674
|
|
|
|
|
675
|
|
|
if save: |
|
676
|
|
|
geo_f = 'geomean_{}.csv'.format(experiment) |
|
677
|
|
|
gstder_f = 'geoSEM_{}.csv'.format(experiment) |
|
678
|
|
|
np.savetxt(geo_f, geo, delimiter=',') |
|
679
|
|
|
np.savetxt(gstder_f, geo_stder, delimiter=',') |
|
680
|
|
|
aws.upload_s3(geo_f, '{}/{}'.format(folder, geo_f), bucket_name=bucket) |
|
681
|
|
|
aws.upload_s3(gstder_f, '{}/{}'.format(folder, gstder_f), |
|
682
|
|
|
bucket_name=bucket) |
|
683
|
|
|
|
|
684
|
|
|
geodata = Bunch(geomean=geo, geostd=geo_stder, weighthold=w_holder, |
|
685
|
|
|
geostdhold=gstder_holder) |
|
686
|
|
|
|
|
687
|
|
|
return geodata |
|
688
|
|
|
|
|
689
|
|
|
|
|
690
|
|
View Code Duplication |
def plot_all_experiments(experiments, bucket='ccurtis.data', folder='test', |
|
|
|
|
|
|
691
|
|
|
yrange=(10**-1, 10**1), fps=100.02, |
|
692
|
|
|
xrange=(10**-2, 10**0), upload=True, |
|
693
|
|
|
outfile='test.png', exponential=True, |
|
694
|
|
|
labels=None, log=True): |
|
695
|
|
|
"""Plots precision-weighted averages of MSD datasets. |
|
696
|
|
|
|
|
697
|
|
|
Plots pre-calculated precision-weighted averages of MSD datasets calculated |
|
698
|
|
|
from precision_averaging and stored in an AWS S3 bucket. |
|
699
|
|
|
|
|
700
|
|
|
Parameters |
|
701
|
|
|
---------- |
|
702
|
|
|
group : list of str |
|
703
|
|
|
List of experiment names to plot. Each experiment must have an MSD and |
|
704
|
|
|
SEM file associated with it in s3. |
|
705
|
|
|
bucket : str |
|
706
|
|
|
S3 bucket from which to download data. |
|
707
|
|
|
folder : str |
|
708
|
|
|
Folder in s3 bucket from which to download data. |
|
709
|
|
|
yrange : list of float |
|
710
|
|
|
Y range of plot |
|
711
|
|
|
xrange: list of float |
|
712
|
|
|
X range of plot |
|
713
|
|
|
upload : bool |
|
714
|
|
|
True to upload to S3 |
|
715
|
|
|
outfile : str |
|
716
|
|
|
Filename of output image |
|
717
|
|
|
|
|
718
|
|
|
""" |
|
719
|
|
|
|
|
720
|
|
|
n = len(experiments) |
|
721
|
|
|
|
|
722
|
|
|
if labels==None: |
|
723
|
|
|
labels = experiments |
|
724
|
|
|
|
|
725
|
|
|
color = iter(cm.viridis(np.linspace(0, 0.9, n))) |
|
726
|
|
|
|
|
727
|
|
|
fig = plt.figure(figsize=(8.5, 8.5)) |
|
728
|
|
|
ax = fig.add_subplot(111) |
|
729
|
|
|
plt.xlim(xrange[0], xrange[1]) |
|
730
|
|
|
plt.ylim(yrange[0], yrange[1]) |
|
731
|
|
|
plt.xlabel('Tau (s)', fontsize=25) |
|
732
|
|
|
plt.ylabel(r'Mean Squared Displacement ($\mu$m$^2$)', fontsize=25) |
|
733
|
|
|
|
|
734
|
|
|
geo = {} |
|
735
|
|
|
gstder = {} |
|
736
|
|
|
counter = 0 |
|
737
|
|
|
for experiment in experiments: |
|
738
|
|
|
aws.download_s3('{}/geomean_{}.csv'.format(folder, experiment), |
|
739
|
|
|
'geomean_{}.csv'.format(experiment), bucket_name=bucket) |
|
740
|
|
|
aws.download_s3('{}/geoSEM_{}.csv'.format(folder, experiment), |
|
741
|
|
|
'geoSEM_{}.csv'.format(experiment), bucket_name=bucket) |
|
742
|
|
|
|
|
743
|
|
|
geo[counter] = np.genfromtxt('geomean_{}.csv'.format(experiment)) |
|
744
|
|
|
gstder[counter] = np.genfromtxt('geoSEM_{}.csv'.format(experiment)) |
|
745
|
|
|
geo[counter] = ma.masked_equal(geo[counter], 0.0) |
|
746
|
|
|
gstder[counter] = ma.masked_equal(gstder[counter], 0.0) |
|
747
|
|
|
|
|
748
|
|
|
frames = np.shape(gstder[counter])[0] |
|
749
|
|
|
xpos = np.linspace(0, frames-1, frames)/fps |
|
750
|
|
|
c = next(color) |
|
751
|
|
|
|
|
752
|
|
|
if exponential: |
|
753
|
|
|
ax.plot(xpos, np.exp(geo[counter]), c=c, linewidth=6, |
|
754
|
|
|
label=labels[counter]) |
|
755
|
|
|
ax.fill_between(xpos, np.exp(geo[counter] - 1.96*gstder[counter]), |
|
756
|
|
|
np.exp(geo[counter] + 1.96*gstder[counter]), |
|
757
|
|
|
color=c, alpha=0.4) |
|
758
|
|
|
|
|
759
|
|
|
else: |
|
760
|
|
|
ax.plot(xpos, geo[counter], c=c, linewidth=6, |
|
761
|
|
|
label=labels[counter]) |
|
762
|
|
|
ax.fill_between(xpos, geo[counter] - 1.96*gstder[counter], |
|
763
|
|
|
geo[counter] + 1.96*gstder[counter], color=c, |
|
764
|
|
|
alpha=0.4) |
|
765
|
|
|
|
|
766
|
|
|
counter = counter + 1 |
|
767
|
|
|
|
|
768
|
|
|
if log: |
|
769
|
|
|
ax.set_xscale("log") |
|
770
|
|
|
ax.set_yscale("log") |
|
771
|
|
|
|
|
772
|
|
|
plt.legend(frameon=False, loc=2, prop={'size': 16}) |
|
773
|
|
|
|
|
774
|
|
|
if upload: |
|
775
|
|
|
fig.savefig(outfile, bbox_inches='tight') |
|
776
|
|
|
aws.upload_s3(outfile, folder+'/'+outfile, bucket_name=bucket) |
|
777
|
|
|
|
|
778
|
|
|
|
|
779
|
|
View Code Duplication |
def checkerboard_mask(dims=(512, 512), squares=50, width=25): |
|
|
|
|
|
|
780
|
|
|
"""Creates a 2D Boolean checkerboard mask |
|
781
|
|
|
|
|
782
|
|
|
Creates a Boolean array of evenly spaced squares. |
|
783
|
|
|
Whitespace is set to True. |
|
784
|
|
|
|
|
785
|
|
|
Parameters |
|
786
|
|
|
---------- |
|
787
|
|
|
|
|
788
|
|
|
dims : tuple of int |
|
789
|
|
|
Dimensions of desired Boolean array |
|
790
|
|
|
squares : int |
|
791
|
|
|
Dimensions of in individual square in array |
|
792
|
|
|
width : int |
|
793
|
|
|
Dimension of spacing between squares |
|
794
|
|
|
|
|
795
|
|
|
Returns |
|
796
|
|
|
---------- |
|
797
|
|
|
|
|
798
|
|
|
zeros : numpy.ndarray of bool |
|
799
|
|
|
2D Boolean array of evenly spaced squares |
|
800
|
|
|
|
|
801
|
|
|
""" |
|
802
|
|
|
zeros = np.zeros(dims) == 0 |
|
803
|
|
|
square_d = squares |
|
804
|
|
|
|
|
805
|
|
|
loy = width |
|
806
|
|
|
hiy = loy + square_d |
|
807
|
|
|
|
|
808
|
|
|
for j in range(50): |
|
|
|
|
|
|
809
|
|
|
|
|
810
|
|
|
lox = width |
|
811
|
|
|
hix = lox + square_d |
|
812
|
|
|
for i in range(50): |
|
813
|
|
|
|
|
814
|
|
|
if hix < 512 and hiy < 512: |
|
815
|
|
|
zeros[loy:hiy, lox:hix] = False |
|
816
|
|
|
elif hix < 512: |
|
817
|
|
|
zeros[loy:512-1, lox:hix] = False |
|
818
|
|
|
elif hiy < 512: |
|
819
|
|
|
zeros[loy:hiy, lox:512-1] = False |
|
820
|
|
|
else: |
|
821
|
|
|
zeros[loy:512-1, lox:512-1] = False |
|
822
|
|
|
break |
|
823
|
|
|
|
|
824
|
|
|
lox = hix + width |
|
825
|
|
|
hix = lox + square_d |
|
826
|
|
|
|
|
827
|
|
|
loy = hiy + width |
|
828
|
|
|
hiy = loy + square_d |
|
829
|
|
|
|
|
830
|
|
|
return zeros |
|
831
|
|
|
|
|
832
|
|
|
|
|
833
|
|
View Code Duplication |
def random_walk(nsteps=100, seed=None, start=(0, 0), step=1, mask=None, |
|
|
|
|
|
|
834
|
|
|
stuckprob=0.5): |
|
835
|
|
|
"""Creates 2d random walk trajectory. |
|
836
|
|
|
|
|
837
|
|
|
Parameters |
|
838
|
|
|
---------- |
|
839
|
|
|
nsteps : int |
|
840
|
|
|
Number of steps for trajectory to move. |
|
841
|
|
|
seed : int |
|
842
|
|
|
Seed for pseudo-random number generator for reproducability. |
|
843
|
|
|
start : tuple of int or float |
|
844
|
|
|
Starting xy coordinates at which the random walk begins. |
|
845
|
|
|
step : int or float |
|
846
|
|
|
Magnitude of single step |
|
847
|
|
|
mask : numpy.ndarray of bool |
|
848
|
|
|
Mask of barriers contraining diffusion |
|
849
|
|
|
stuckprop : float |
|
850
|
|
|
Probability of "particle" adhering to barrier when it makes contact |
|
851
|
|
|
|
|
852
|
|
|
Returns |
|
853
|
|
|
------- |
|
854
|
|
|
x : numpy.ndarray |
|
855
|
|
|
Array of x coordinates of random walk. |
|
856
|
|
|
y : numpy.ndarray |
|
857
|
|
|
Array of y coordinates of random walk. |
|
858
|
|
|
|
|
859
|
|
|
""" |
|
860
|
|
|
|
|
861
|
|
|
if type(mask) is np.ndarray: |
|
862
|
|
|
while not mask[start[0], start[1]]: |
|
863
|
|
|
start = (start[0], start[1]+1) |
|
864
|
|
|
eumask = eudist(~mask) |
|
865
|
|
|
|
|
866
|
|
|
np.random.seed(seed=seed) |
|
867
|
|
|
|
|
868
|
|
|
x = np.zeros(nsteps) |
|
869
|
|
|
y = np.zeros(nsteps) |
|
870
|
|
|
x[0] = start[0] |
|
871
|
|
|
y[0] = start[1] |
|
872
|
|
|
|
|
873
|
|
|
# Checks to see if a mask is being used first |
|
874
|
|
|
if not type(mask) is np.ndarray: |
|
875
|
|
|
for i in range(1, nsteps): |
|
|
|
|
|
|
876
|
|
|
val = rand.randint(1, 4) |
|
877
|
|
|
if val == 1: |
|
878
|
|
|
x[i] = x[i - 1] + step |
|
879
|
|
|
y[i] = y[i - 1] |
|
880
|
|
|
elif val == 2: |
|
881
|
|
|
x[i] = x[i - 1] - step |
|
882
|
|
|
y[i] = y[i - 1] |
|
883
|
|
|
elif val == 3: |
|
884
|
|
|
x[i] = x[i - 1] |
|
885
|
|
|
y[i] = y[i - 1] + step |
|
886
|
|
|
else: |
|
887
|
|
|
x[i] = x[i - 1] |
|
888
|
|
|
y[i] = y[i - 1] - step |
|
889
|
|
|
else: |
|
890
|
|
|
# print("Applied mask") |
|
891
|
|
|
for i in range(1, nsteps): |
|
892
|
|
|
val = rand.randint(1, 4) |
|
893
|
|
|
# If mask is being used, checks if entry is in mask or not |
|
894
|
|
|
if mask[int(x[i-1]), int(y[i-1])]: |
|
895
|
|
|
if val == 1: |
|
896
|
|
|
x[i] = x[i - 1] + step |
|
897
|
|
|
y[i] = y[i - 1] |
|
898
|
|
|
elif val == 2: |
|
899
|
|
|
x[i] = x[i - 1] - step |
|
900
|
|
|
y[i] = y[i - 1] |
|
901
|
|
|
elif val == 3: |
|
902
|
|
|
x[i] = x[i - 1] |
|
903
|
|
|
y[i] = y[i - 1] + step |
|
904
|
|
|
else: |
|
905
|
|
|
x[i] = x[i - 1] |
|
906
|
|
|
y[i] = y[i - 1] - step |
|
907
|
|
|
# If it does cross into a False area, probability to be stuck |
|
908
|
|
|
elif np.random.rand() > stuckprob: |
|
909
|
|
|
x[i] = x[i - 1] |
|
910
|
|
|
y[i] = y[i - 1] |
|
911
|
|
|
|
|
912
|
|
|
while eumask[int(x[i]), int(y[i])] > 0: |
|
|
|
|
|
|
913
|
|
|
vals = np.zeros(4) |
|
914
|
|
|
vals[0] = eumask[int(x[i] + step), int(y[i])] |
|
915
|
|
|
vals[1] = eumask[int(x[i] - step), int(y[i])] |
|
916
|
|
|
vals[2] = eumask[int(x[i]), int(y[i] + step)] |
|
917
|
|
|
vals[3] = eumask[int(x[i]), int(y[i] - step)] |
|
918
|
|
|
vali = np.argmin(vals) |
|
919
|
|
|
|
|
920
|
|
|
if vali == 0: |
|
921
|
|
|
x[i] = x[i] + step |
|
922
|
|
|
y[i] = y[i] |
|
923
|
|
|
elif vali == 1: |
|
924
|
|
|
x[i] = x[i] - step |
|
925
|
|
|
y[i] = y[i] |
|
926
|
|
|
elif vali == 2: |
|
927
|
|
|
x[i] = x[i] |
|
928
|
|
|
y[i] = y[i] + step |
|
929
|
|
|
else: |
|
930
|
|
|
x[i] = x[i] |
|
931
|
|
|
y[i] = y[i] - step |
|
932
|
|
|
# Otherwise, particle is stuck on "cell" |
|
933
|
|
|
else: |
|
934
|
|
|
x[i] = x[i - 1] |
|
935
|
|
|
y[i] = y[i - 1] |
|
936
|
|
|
|
|
937
|
|
|
return x, y |
|
938
|
|
|
|
|
939
|
|
|
|
|
940
|
|
|
# def random_walk(nsteps=100, seed=1, start=(0, 0)): |
|
941
|
|
|
# """Creates 2d random walk trajectory. |
|
942
|
|
|
# |
|
943
|
|
|
# Parameters |
|
944
|
|
|
# ---------- |
|
945
|
|
|
# nsteps : int |
|
946
|
|
|
# Number of steps for trajectory to move. |
|
947
|
|
|
# seed : int |
|
948
|
|
|
# Seed for pseudo-random number generator for reproducability. |
|
949
|
|
|
# start : tuple of int or float |
|
950
|
|
|
# Starting xy coordinates at which the random walk begins. |
|
951
|
|
|
# |
|
952
|
|
|
# Returns |
|
953
|
|
|
# ------- |
|
954
|
|
|
# x : numpy.ndarray |
|
955
|
|
|
# Array of x coordinates of random walk. |
|
956
|
|
|
# y : numpy.ndarray |
|
957
|
|
|
# Array of y coordinates of random walk. |
|
958
|
|
|
# |
|
959
|
|
|
# """ |
|
960
|
|
|
# |
|
961
|
|
|
# rand.seed(a=seed) |
|
962
|
|
|
# |
|
963
|
|
|
# x = np.zeros(nsteps) |
|
964
|
|
|
# y = np.zeros(nsteps) |
|
965
|
|
|
# x[0] = start[0] |
|
966
|
|
|
# y[0] = start[1] |
|
967
|
|
|
# |
|
968
|
|
|
# for i in range(1, nsteps): |
|
969
|
|
|
# val = rand.randint(1, 4) |
|
970
|
|
|
# if val == 1: |
|
971
|
|
|
# x[i] = x[i - 1] + 1 |
|
972
|
|
|
# y[i] = y[i - 1] |
|
973
|
|
|
# elif val == 2: |
|
974
|
|
|
# x[i] = x[i - 1] - 1 |
|
975
|
|
|
# y[i] = y[i - 1] |
|
976
|
|
|
# elif val == 3: |
|
977
|
|
|
# x[i] = x[i - 1] |
|
978
|
|
|
# y[i] = y[i - 1] + 1 |
|
979
|
|
|
# else: |
|
980
|
|
|
# x[i] = x[i - 1] |
|
981
|
|
|
# y[i] = y[i - 1] - 1 |
|
982
|
|
|
# |
|
983
|
|
|
# return x, y |
|
984
|
|
|
|
|
985
|
|
|
|
|
986
|
|
View Code Duplication |
def random_traj_dataset(nframes=100, nparts=30, seed=1, fsize=(0, 512), |
|
|
|
|
|
|
987
|
|
|
ndist=(1, 2)): |
|
988
|
|
|
"""Creates a random population of random walks. |
|
989
|
|
|
|
|
990
|
|
|
Parameters |
|
991
|
|
|
---------- |
|
992
|
|
|
nframes : int |
|
993
|
|
|
Number of frames for each random trajectory. |
|
994
|
|
|
nparts : int |
|
995
|
|
|
Number of particles in trajectory dataset. |
|
996
|
|
|
seed : int |
|
997
|
|
|
Seed for pseudo-random number generator for reproducability. |
|
998
|
|
|
fsize : tuple of int or float |
|
999
|
|
|
Scope of points over which particles may start at. |
|
1000
|
|
|
ndist : tuple of int or float |
|
1001
|
|
|
Parameters to generate normal distribution, mu and sigma. |
|
1002
|
|
|
|
|
1003
|
|
|
Returns |
|
1004
|
|
|
------- |
|
1005
|
|
|
dataf : pandas.core.frame.DataFrame |
|
1006
|
|
|
Trajectory data containing a 'Frame', 'Track_ID', 'X', and |
|
1007
|
|
|
'Y' column. |
|
1008
|
|
|
|
|
1009
|
|
|
""" |
|
1010
|
|
|
|
|
1011
|
|
|
frames = [] |
|
1012
|
|
|
trackid = [] |
|
1013
|
|
|
x = [] |
|
1014
|
|
|
y = [] |
|
1015
|
|
|
start = [0, 0] |
|
1016
|
|
|
pseed = seed |
|
1017
|
|
|
|
|
1018
|
|
|
for i in range(nparts): |
|
|
|
|
|
|
1019
|
|
|
rand.seed(a=i+pseed) |
|
1020
|
|
|
start[0] = rand.randint(fsize[0], fsize[1]) |
|
1021
|
|
|
rand.seed(a=i+3+pseed) |
|
1022
|
|
|
start[1] = rand.randint(fsize[0], fsize[1]) |
|
1023
|
|
|
rand.seed(a=i+5+pseed) |
|
1024
|
|
|
weight = rand.normalvariate(mu=ndist[0], sigma=ndist[1]) |
|
1025
|
|
|
|
|
1026
|
|
|
trackid = np.append(trackid, np.array([i]*nframes)) |
|
1027
|
|
|
xi, yi = random_walk(nsteps=nframes, seed=i) |
|
1028
|
|
|
x = np.append(x, weight*xi+start[0]) |
|
1029
|
|
|
y = np.append(y, weight*yi+start[1]) |
|
1030
|
|
|
frames = np.append(frames, np.linspace(0, nframes-1, nframes)) |
|
1031
|
|
|
|
|
1032
|
|
|
datai = {'Frame': frames, |
|
1033
|
|
|
'Track_ID': trackid, |
|
1034
|
|
|
'X': x, |
|
1035
|
|
|
'Y': y, |
|
1036
|
|
|
'Quality': nframes*nparts*[10], |
|
1037
|
|
|
'SN_Ratio': nframes*nparts*[0.1], |
|
1038
|
|
|
'Mean_Intensity': nframes*nparts*[120]} |
|
1039
|
|
|
dataf = pd.DataFrame(data=datai) |
|
1040
|
|
|
|
|
1041
|
|
|
return dataf |
|
1042
|
|
|
|
|
1043
|
|
|
|
|
1044
|
|
|
class Bunch: |
|
1045
|
|
|
def __init__(self, **kwds): |
|
1046
|
|
|
self.__dict__.update(kwds) |
|
1047
|
|
|
|
ccurtis7 /
diff_classifier
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