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import matplotlib as mpl |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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from scipy.spatial import Voronoi |
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import scipy.stats as stats |
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import os |
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import os.path as op |
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from shapely.geometry import Point |
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from shapely.geometry.polygon import Polygon |
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import numpy.ma as ma |
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import matplotlib.cm as cm |
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import diff_classifier.aws as aws |
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View Code Duplication |
def voronoi_finite_polygons_2d(vor, radius=None): |
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""" |
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Reconstruct infinite voronoi regions in a 2D diagram to finite |
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regions. |
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Parameters |
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---------- |
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vor : Voronoi |
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Input diagram |
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radius : float, optional |
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Distance to 'points at infinity'. |
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Returns |
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------- |
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regions : list of tuples |
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Indices of vertices in each revised Voronoi regions. |
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vertices : list of tuples |
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Coordinates for revised Voronoi vertices. Same as coordinates |
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of input vertices, with 'points at infinity' appended to the |
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end. |
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""" |
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if vor.points.shape[1] != 2: |
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raise ValueError("Requires 2D input") |
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new_regions = [] |
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new_vertices = vor.vertices.tolist() |
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center = vor.points.mean(axis=0) |
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if radius is None: |
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radius = vor.points.ptp().max() |
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# Construct a map containing all ridges for a given point |
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all_ridges = {} |
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for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices): |
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all_ridges.setdefault(p1, []).append((p2, v1, v2)) |
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all_ridges.setdefault(p2, []).append((p1, v1, v2)) |
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counter = 0 |
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for p1, region in enumerate(vor.point_region): |
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try: |
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vertices = vor.regions[region] |
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if all(v >= 0 for v in vertices): |
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# finite region |
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new_regions.append(vertices) |
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continue |
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# reconstruct a non-finite region |
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ridges = all_ridges[p1] |
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new_region = [v for v in vertices if v >= 0] |
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for p2, v1, v2 in ridges: |
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if v2 < 0: |
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v1, v2 = v2, v1 |
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if v1 >= 0: |
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# finite ridge: already in the region |
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continue |
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# Compute the missing endpoint of an infinite ridge |
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t = vor.points[p2] - vor.points[p1] # tangent |
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t /= np.linalg.norm(t) |
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n = np.array([-t[1], t[0]]) # normal |
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midpoint = vor.points[[p1, p2]].mean(axis=0) |
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direction = np.sign(np.dot(midpoint - center, n)) * n |
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far_point = vor.vertices[v2] + direction * radius |
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new_region.append(len(new_vertices)) |
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new_vertices.append(far_point.tolist()) |
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# sort region counterclockwise |
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vs = np.asarray([new_vertices[v] for v in new_region]) |
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c = vs.mean(axis=0) |
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angles = np.arctan2(vs[:, 1] - c[1], vs[:, 0] - c[0]) |
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new_region = np.array(new_region)[np.argsort(angles)] |
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# finish |
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new_regions.append(new_region.tolist()) |
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except KeyError: |
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counter = counter + 1 |
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# print('Oops {}'.format(counter)) |
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return new_regions, np.asarray(new_vertices) |
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View Code Duplication |
def plot_heatmap(prefix, feature='asymmetry1', vmin=0, vmax=1, resolution=512, rows=4, cols=4, |
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upload=True, dpi=None, figsize=(12, 10), remote_folder = "01_18_Experiment", |
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bucket='ccurtis.data'): |
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""" |
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Plot heatmap of trajectories in video with colors corresponding to features. |
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Parameters |
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---------- |
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prefix: string |
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Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
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feature: string |
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Feature to be plotted. See features_analysis.py |
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vmin: float64 |
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Lower intensity bound for heatmap. |
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vmax: float64 |
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Upper intensity bound for heatmap. |
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resolution: int |
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Resolution of base image. Only needed to calculate bounds of image. |
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rows: int |
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Rows of base images used to build tiled image. |
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cols: int |
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Columns of base images used to build tiled images. |
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upload: boolean |
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True if you want to upload to s3. |
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dpi: int |
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Desired dpi of output image. |
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figsize: list |
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Desired dimensions of output image. |
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Returns |
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------- |
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""" |
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# Inputs |
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# ---------- |
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merged_ft = pd.read_csv('features_{}.csv'.format(prefix)) |
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string = feature |
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leveler = merged_ft[string] |
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t_min = vmin |
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t_max = vmax |
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ires = resolution |
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# Building points and color schemes |
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# ---------- |
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zs = ma.masked_invalid(merged_ft[string]) |
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zs = ma.masked_where(zs <= t_min, zs) |
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zs = ma.masked_where(zs >= t_max, zs) |
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to_mask = ma.getmask(zs) |
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zs = ma.compressed(zs) |
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xs = ma.compressed(ma.masked_where(to_mask, merged_ft['X'].astype(int))) |
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ys = ma.compressed(ma.masked_where(to_mask, merged_ft['Y'].astype(int))) |
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points = np.zeros((xs.shape[0], 2)) |
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points[:, 0] = xs |
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points[:, 1] = ys |
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vor = Voronoi(points) |
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# Plot |
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# ---------- |
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fig = plt.figure(figsize=figsize, dpi=dpi) |
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regions, vertices = voronoi_finite_polygons_2d(vor) |
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my_map = cm.get_cmap('viridis') |
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norm = mpl.colors.Normalize(t_min, t_max, clip=True) |
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mapper = cm.ScalarMappable(norm=norm, cmap=cm.viridis) |
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test = 0 |
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p2 = 0 |
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counter = 0 |
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for i in range(0, points.shape[0]-1): |
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try: |
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polygon = vertices[regions[p2]] |
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point1 = Point(points[test, :]) |
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poly1 = Polygon(polygon) |
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check = poly1.contains(point1) |
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if check: |
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plt.fill(*zip(*polygon), color=my_map(norm(zs[test])), alpha=0.7) |
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p2 = p2 + 1 |
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test = test + 1 |
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else: |
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p2 = p2 |
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test = test + 1 |
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except IndexError: |
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print('Index mismatch possible.') |
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mapper.set_array(10) |
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plt.colorbar(mapper) |
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plt.xlim(0, ires*cols) |
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plt.ylim(0, ires*rows) |
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plt.axis('off') |
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print('Plotted {} heatmap successfully.'.format(prefix)) |
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outfile = 'hm_{}_{}.png'.format(feature, prefix) |
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fig.savefig(outfile, bbox_inches='tight') |
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if upload == True: |
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aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket) |
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View Code Duplication |
def plot_scatterplot(prefix, feature='asymmetry1', vmin=0, vmax=1, resolution=512, rows=4, cols=4, |
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upload=True, remote_folder = "01_18_Experiment", |
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bucket='ccurtis.data'): |
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""" |
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Plot scatterplot of trajectories in video with colors corresponding to features. |
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Parameters |
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---------- |
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prefix: string |
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Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
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feature: string |
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Feature to be plotted. See features_analysis.py |
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vmin: float64 |
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Lower intensity bound for heatmap. |
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vmax: float64 |
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Upper intensity bound for heatmap. |
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resolution: int |
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Resolution of base image. Only needed to calculate bounds of image. |
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rows: int |
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Rows of base images used to build tiled image. |
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cols: int |
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Columns of base images used to build tiled images. |
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upload: boolean |
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True if you want to upload to s3. |
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""" |
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# Inputs |
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# ---------- |
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merged_ft = pd.read_csv('features_{}.csv'.format(prefix)) |
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string = feature |
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leveler = merged_ft[string] |
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t_min = vmin |
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t_max = vmax |
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ires = resolution |
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norm = mpl.colors.Normalize(t_min, t_max, clip=True) |
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mapper = cm.ScalarMappable(norm=norm, cmap=cm.viridis) |
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zs = ma.masked_invalid(merged_ft[string]) |
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zs = ma.masked_where(zs <= t_min, zs) |
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zs = ma.masked_where(zs >= t_max, zs) |
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to_mask = ma.getmask(zs) |
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zs = ma.compressed(zs) |
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xs = ma.compressed(ma.masked_where(to_mask, merged_ft['X'].astype(int))) |
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ys = ma.compressed(ma.masked_where(to_mask, merged_ft['Y'].astype(int))) |
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fig = plt.figure(figsize=(12, 10)) |
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plt.scatter(xs, ys, c=zs, s=10) |
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mapper.set_array(10) |
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plt.colorbar(mapper) |
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plt.xlim(0, ires*cols) |
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plt.ylim(0, ires*rows) |
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plt.axis('off') |
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print('Plotted {} scatterplot successfully.'.format(prefix)) |
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outfile = 'scatter_{}.png'.format(prefix) |
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fig.savefig(outfile, bbox_inches='tight') |
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if upload == True: |
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aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket) |
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View Code Duplication |
def plot_trajectories(prefix, resolution=512, rows=4, cols=4, upload=True, |
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remote_folder = "01_18_Experiment", bucket='ccurtis.data'): |
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""" |
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Plot trajectories in video. |
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Parameters |
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---------- |
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prefix: string |
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Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
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resolution: int |
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Resolution of base image. Only needed to calculate bounds of image. |
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rows: int |
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Rows of base images used to build tiled image. |
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cols: int |
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Columns of base images used to build tiled images. |
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upload: boolean |
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True if you want to upload to s3. |
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280
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""" |
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merged = pd.read_csv('msd_{}.csv'.format(prefix)) |
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particles = int(max(merged['Track_ID'])) |
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ires = resolution |
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fig = plt.figure(figsize=(12, 12)) |
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for part in range(0, particles): |
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x = merged[merged['Track_ID'] == part]['X'] |
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y = merged[merged['Track_ID'] == part]['Y'] |
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plt.plot(x, y, color='k', alpha=0.7) |
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plt.xlim(0, ires*cols) |
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plt.ylim(0, ires*rows) |
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plt.axis('off') |
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print('Plotted {} trajectories successfully.'.format(prefix)) |
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outfile = 'traj_{}.png'.format(prefix) |
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fig.savefig(outfile, bbox_inches='tight') |
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if upload == True: |
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aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket) |
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View Code Duplication |
def plot_histogram(prefix, xlabel='Log Diffusion Coefficient Dist', ylabel='Trajectory Count', |
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303
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fps=100.02, umppx=0.16, frames=651, y_range=100, frame_interval=20, frame_range=100, |
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analysis='log', theta='D', upload=True, remote_folder = "01_18_Experiment", |
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bucket='ccurtis.data'): |
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""" |
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Plot heatmap of trajectories in video with colors corresponding to features. |
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Parameters |
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---------- |
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prefix: string |
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Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
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xlabel: string |
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X axis label. |
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ylabel: string |
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Y axis label. |
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fps: float64 |
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Frames per second of video. |
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umppx: float64 |
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Resolution of video in microns per pixel. |
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frames: int |
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Number of frames in video. |
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y_range: float64 or int |
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Desire y range of graph. |
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frame_interval: int |
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Desired spacing between MSDs/Deffs to be plotted. |
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analysis: string |
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Desired output format. If log, will plot log(MSDs/Deffs) |
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theta: string |
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Desired output. D for diffusion coefficients. Anything else, MSDs. |
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upload: boolean |
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True if you want to upload to s3. |
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334
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""" |
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merged = pd.read_csv('msd_{}.csv'.format(prefix)) |
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data = merged |
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frame_range = range(frame_interval, frame_range+frame_interval, frame_interval) |
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339
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# load data |
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341
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# generate keys for legend |
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bar = {} |
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keys = [] |
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entries = [] |
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for i in range(0, len(list(frame_range))): |
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keys.append(i) |
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entries.append(str(10*frame_interval*(i+1)) + 'ms') |
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349
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set_x_limit = False |
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set_y_limit = True |
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colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] |
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fig = plt.figure(figsize=(16, 6)) |
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354
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counter = 0 |
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for i in frame_range: |
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toi = i/fps |
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if theta == "MSD": |
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factor = 1 |
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359
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else: |
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factor = 4*toi |
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361
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362
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if analysis == 'log': |
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dist = np.log(umppx*umppx*merged.loc[merged.Frame == i, 'MSDs'].dropna()/factor) |
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364
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test_bins = np.linspace(-5, 5, 76) |
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else: |
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dist = umppx*umppx*merged.loc[merged.Frame == i, 'MSDs'].dropna()/factor |
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367
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test_bins = np.linspace(0, 20, 76) |
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368
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369
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histogram, test_bins = np.histogram(dist, bins=test_bins) |
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370
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371
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# Plot_general_histogram_code |
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avg = np.mean(dist) |
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373
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374
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plt.rc('axes', linewidth=2) |
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375
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plot = histogram |
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376
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bins = test_bins |
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377
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width = 0.7 * (bins[1] - bins[0]) |
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378
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center = (bins[:-1] + bins[1:])/2 |
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379
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bar[keys[counter]] = plt.bar(center, plot, align='center', width=width, color=colors[counter], label=entries[counter]) |
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380
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plt.axvline(avg, color=colors[counter]) |
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381
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plt.xlabel(xlabel, fontsize=30) |
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382
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plt.ylabel(ylabel, fontsize=30) |
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383
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plt.tick_params(axis='both', which='major', labelsize=20) |
|
384
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385
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counter = counter + 1 |
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386
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if set_y_limit: |
|
387
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plt.gca().set_ylim([0, y_range]) |
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388
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389
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if set_x_limit: |
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390
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plt.gca().set_xlim([0, x_range]) |
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|
391
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392
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plt.legend(fontsize=20, frameon=False) |
|
393
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|
outfile = 'hist_{}.png'.format(prefix) |
|
394
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fig.savefig(outfile, bbox_inches='tight') |
|
395
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if upload==True: |
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|
396
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aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket) |
|
397
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398
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399
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|
View Code Duplication |
def plot_particles_in_frame(prefix, x_range=600, y_range=2000, upload=True, |
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400
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remote_folder = "01_18_Experiment", bucket='ccurtis.data'): |
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|
401
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|
""" |
|
402
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|
|
Plot number of particles per frame as a function of time. |
|
403
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|
404
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|
Parameters |
|
405
|
|
|
---------- |
|
406
|
|
|
prefix: string |
|
407
|
|
|
Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
|
408
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|
|
x_range: float64 or int |
|
409
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|
|
Desire x range of graph. |
|
410
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|
|
y_range: float64 or int |
|
411
|
|
|
Desire y range of graph. |
|
412
|
|
|
upload: boolean |
|
413
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|
|
True if you want to upload to s3. |
|
414
|
|
|
|
|
415
|
|
|
""" |
|
416
|
|
|
merged = pd.read_csv('msd_{}.csv'.format(prefix)) |
|
417
|
|
|
frames = int(max(merged['Frame'])) |
|
418
|
|
|
framespace = np.linspace(0, frames, frames) |
|
419
|
|
|
particles = np.zeros((framespace.shape[0])) |
|
420
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|
|
for i in range(0, frames): |
|
421
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|
|
particles[i] = merged.loc[merged.Frame == i, 'MSDs'].dropna().shape[0] |
|
422
|
|
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|
423
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|
|
fig = plt.figure(figsize=(5, 5)) |
|
424
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|
|
plt.plot(framespace, particles, linewidth=4) |
|
425
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|
|
plt.xlim(0, x_range) |
|
426
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|
plt.ylim(0, y_range) |
|
427
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|
|
plt.xlabel('Frames', fontsize=20) |
|
428
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|
|
plt.ylabel('Particles', fontsize=20) |
|
429
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|
430
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|
|
outfile = 'in_frame_{}.png'.format(prefix) |
|
431
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|
|
fig.savefig(outfile, bbox_inches='tight') |
|
432
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|
|
if upload == True: |
|
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|
|
433
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|
|
aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket) |
|
434
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|
435
|
|
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|
436
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|
View Code Duplication |
def plot_individual_msds(prefix, x_range=100, y_range=20, umppx=0.16, fps=100.02, alpha=0.01, folder='.', upload=True, |
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|
|
437
|
|
|
remote_folder="01_18_Experiment", bucket='ccurtis.data'): |
|
438
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|
|
""" |
|
439
|
|
|
Plot MSDs of trajectories and the geometric average. |
|
440
|
|
|
|
|
441
|
|
|
Parameters |
|
442
|
|
|
---------- |
|
443
|
|
|
prefix: string |
|
444
|
|
|
Prefix of file name to be plotted e.g. features_P1.csv prefix is P1. |
|
445
|
|
|
x_range: float64 or int |
|
446
|
|
|
Desire x range of graph. |
|
447
|
|
|
y_range: float64 or int |
|
448
|
|
|
Desire y range of graph. |
|
449
|
|
|
fps: float64 |
|
450
|
|
|
Frames per second of video. |
|
451
|
|
|
umppx: float64 |
|
452
|
|
|
Resolution of video in microns per pixel. |
|
453
|
|
|
alpha: float64 |
|
454
|
|
|
Transparency factor. Between 0 and 1. |
|
455
|
|
|
upload: boolean |
|
456
|
|
|
True if you want to upload to s3. |
|
457
|
|
|
|
|
458
|
|
|
Returns |
|
459
|
|
|
------- |
|
460
|
|
|
geo_mean: numpy array |
|
461
|
|
|
Geometric mean of trajectory MSDs at all time points. |
|
462
|
|
|
geo_SEM: numpy array |
|
463
|
|
|
Geometric standard errot of trajectory MSDs at all time points. |
|
464
|
|
|
|
|
465
|
|
|
""" |
|
466
|
|
|
|
|
467
|
|
|
merged = pd.read_csv('{}/msd_{}.csv'.format(folder, prefix)) |
|
468
|
|
|
|
|
469
|
|
|
fig = plt.figure(figsize=(10, 10)) |
|
470
|
|
|
particles = int(max(merged['Track_ID'])) |
|
471
|
|
|
frames = int(max(merged['Frame'])) |
|
472
|
|
|
y = np.zeros((particles+1, frames+1)) |
|
|
|
|
|
|
473
|
|
|
for i in range(0, particles+1): |
|
474
|
|
|
y[i, :] = merged.loc[merged.Track_ID == i, 'MSDs']*umppx*umppx |
|
475
|
|
|
x = merged.loc[merged.Track_ID == i, 'Frame']/fps |
|
|
|
|
|
|
476
|
|
|
plt.plot(x, y[i, :], 'k', alpha=alpha) |
|
477
|
|
|
|
|
478
|
|
|
geo_mean = np.nanmean(ma.log(y), axis=0) |
|
479
|
|
|
geo_SEM = stats.sem(ma.log(y), axis=0, nan_policy='omit') |
|
|
|
|
|
|
480
|
|
|
plt.plot(x, np.exp(geo_mean), 'k', linewidth=4) |
|
|
|
|
|
|
481
|
|
|
plt.plot(x, np.exp(geo_mean-geo_SEM), 'k--', linewidth=2) |
|
482
|
|
|
plt.plot(x, np.exp(geo_mean+geo_SEM), 'k--', linewidth=2) |
|
483
|
|
|
plt.xlim(0, x_range) |
|
484
|
|
|
plt.ylim(0, y_range) |
|
485
|
|
|
plt.xlabel('Tau (s)', fontsize=25) |
|
486
|
|
|
plt.ylabel(r'Mean Squared Displacement ($\mu$m$^2$/s)', fontsize=25) |
|
487
|
|
|
|
|
488
|
|
|
outfile = '{}/msds_{}.png'.format(folder, prefix) |
|
489
|
|
|
outfile2 = '{}/geomean_{}.csv'.format(folder, prefix) |
|
490
|
|
|
outfile3 = '{}/geoSEM_{}.csv'.format(folder, prefix) |
|
491
|
|
|
fig.savefig(outfile, bbox_inches='tight') |
|
492
|
|
|
np.savetxt(outfile2, geo_mean, delimiter=",") |
|
493
|
|
|
np.savetxt(outfile3, geo_SEM, delimiter=",") |
|
494
|
|
|
if upload==True: |
|
|
|
|
|
|
495
|
|
|
aws.upload_s3(outfile, remote_folder+'/'+outfile, bucket_name=bucket) |
|
496
|
|
|
aws.upload_s3(outfile2, remote_folder+'/'+outfile2, bucket_name=bucket) |
|
497
|
|
|
aws.upload_s3(outfile3, remote_folder+'/'+outfile3, bucket_name=bucket) |
|
498
|
|
|
return geo_mean, geo_SEM |
|
499
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|
ccurtis7 /
diff_classifier
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The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods:
If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions.