visuals
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Si11ium
@ -1,19 +1,17 @@
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import os
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import re
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from collections import defaultdict
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from tqdm import tqdm
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from argparse import ArgumentParser
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from distutils.util import strtobool
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from argparse import ArgumentParser
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import numpy as np
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import tensorflow as tf
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import plotly as pl
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from plotly import tools
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import plotly.graph_objs as go
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import colorlover as cl
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import dill
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from sklearn.manifold.t_sne import TSNE
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def build_args():
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arg_parser = ArgumentParser()
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@ -22,95 +20,174 @@ def build_args():
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return arg_parser.parse_args()
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def numberFromStrings(string) -> list:
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numberfromstring = [int(x) for x in re.findall('\d+', string)]
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return numberfromstring
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def plot_latent_trajectories(data_dict, filename='latent_trajectory_plot'):
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# TODO Fist and Last Position Markers
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def visulize_as_tiled_subplot(plotting_tuple, filename='plot'):
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def norm(val, a=0, b=0.25):
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return (val - a) / (b - a)
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data = np.asarray(plotting_tuple)
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bupu = cl.scales['9']['seq']['BuPu']
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scale = cl.interp(bupu, len(data_dict)) # Map color scale to N bins
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fig = tools.make_subplots(rows=1, cols=3,
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subplot_titles=('Layers: 1', 'Layers: 2', 'Layers: 3'),
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horizontal_spacing=0.05)
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# Fit the mebedding space
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transformer = TSNE()
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for trajectory in data_dict:
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transformer.fit(trajectory)
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for x in range(1, 4):
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# Only select Plots with x Layers
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scatter_slice = data[np.where(data[:, 2] == x)]
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# Only Select Plots with x Cells
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scatter_slice = scatter_slice[np.where(scatter_slice[:, 1] <= 10)]
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# Normalize colors
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colors = scatter_slice[:, 4]
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# colors = np.apply_along_axis(norm, 0, scatter_slice[:, 4])
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scatter = go.Scatter(x=scatter_slice[:, 3],
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y=scatter_slice[:, 1],
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hoverinfo='text',
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text=['Absolute Loss:<br>{}'.format(val) for val in colors],
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mode='markers',
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showlegend=False,
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marker=dict(size=10, color=colors, colorscale='Jet',
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# Only plot the colorscale once, use one for all
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showscale=True if x == 1 else False,
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cmax=0.25, cmin=0,
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colorbar=dict(y=0.5, x=1, tickmode='array', ticks='outside',
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tickvals=[0, 0.05, 0.10, 0.15, 0.20, 0.25],
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ticktext=["0.00", "0.05", "0.10", "0.15", "0.20", "0.25"]
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)
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)
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)
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fig.append_trace(scatter, 1, x,)
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# TODO: Layout Loop
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if x == 1:
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fig['layout']['yaxis{}'.format(x)].update(tickwidth=1, title='Number of Cells')
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if x == 2:
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fig['layout']['xaxis{}'.format(x)].update(tickwidth=1, title='Position -X')
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# Transform data accordingly and plot it
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data = []
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for t_id, trajectory in enumerate(data_dict):
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transformed = transformer.fit(trajectory)
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line_trace = go.Scatter(
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x=transformed[:, 0],
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y=transformed[:, 1],
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text='Hovertext goes here'.format(),
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line=dict(color=scale[t_id]),
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# legendgroup='Position -{}'.format(pos),
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# name='Position -{}'.format(pos),
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showlegend=False,
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# hoverinfo='text',
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mode='lines')
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line_start = go.Scatter(mode='markers', x=transformed[0, 0], y=transformed[0, 1],
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marker=dict(
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color='rgb(255, 0, 0)',
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size=2
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),
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showlegend=False
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)
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line_end = go.Scatter(mode='markers', x=transformed[-1, 0], y=transformed[-1, 1],
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marker=dict(
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color='rgb(0, 0, 0)',
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size=2
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),
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showlegend=False
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)
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data.extend([line_trace, line_start, line_end])
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fig['layout'].update(title='{} - Mean Absolute Loss'.format(os.path.split('DESTINATION_OR_EXPERIMENT_NAME')[-1].upper()),
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height=300, width=800, margin=dict(l=50, r=0, t=60, b=50))
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layout = dict(title='{} - Latent Trajectory Movement'.format('Penis'),
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height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
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# import plotly.io as pio
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# pio.write_image(fig, filename)
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pl.offline.plot(fig, filename=filename)
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def visulize_as_splatter3d(plotting_tuple, filename='plot'):
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# timesteps, cells, layers, positions, val
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_ , cells, layers, position, val = zip(*plotting_tuple)
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text = ['Cells: {}<br>Layers: {}<br>Position: {}<br>Mean(Min()): {}'.format(cells, layers, position, val)
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for _, cells, layers, position, val in plotting_tuple]
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def plot_latent_trajectories_3D(param_dict, filename='plot'):
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def norm(val, a=0, b=0.25):
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return (val - a) / (b - a)
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bupu = cl.scales['9']['seq']['BuPu']
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scale = cl.interp(bupu, len(param_dict.get('trajectories', []))) # Map color scale to N bins
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max_len = max([len(trajectory) for trajectory in param_dict.get('trajectories', [])])
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# Fit the mebedding space
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transformer = TSNE()
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for trajectory in param_dict.get('trajectories', []):
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transformer.fit(trajectory)
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# Transform data accordingly and plot it
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data = []
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for t_id, trajectory in enumerate(param_dict.get('trajectories', [])):
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transformed = transformer.fit(trajectory)
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trace = go.Scatter3d(
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x=transformed[:, 0],
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y=transformed[:, 1],
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z=np.arange(max(max_len)),
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text='Hovertext goes here'.format(),
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line=dict(color=scale[t_id]),
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# legendgroup='Position -{}'.format(pos),
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# name='Position -{}'.format(pos),
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showlegend=False,
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# hoverinfo='text',
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mode='lines')
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data.append(trace)
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data = [go.Scatter3d(x=cells, y=layers, z=position, text=text, hoverinfo='text', mode='markers',
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marker=dict(color=val, colorscale='Jet', opacity=0.8,
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colorbar=dict(y=0.5, x=0.9, title="Mean(Min(Seeds))"))
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)]
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layout = go.Layout(scene=dict(aspectratio=dict(x=2, y=2, z=1),
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xaxis=dict(tickwidth=1, title='Number of Cells'),
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yaxis=dict(tickwidth=1, title='Number of Layers'),
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zaxis=dict(tickwidth=1, title='Position -pX')),
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xaxis=dict(tickwidth=1, title='Number of Cells'),
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yaxis=dict(tickwidth=1, title='Number of Layers'),
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zaxis=dict(tickwidth=1, title='Position -pX')),
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title='{} - Latent Trajectory Movement'.format('Penis'),
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width=800, height=800,
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margin=dict(l=0, r=0, b=0, t=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename) # filename='3d-scatter_plot'
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def compile_run_name(path: str) -> dict:
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"""
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Retrieve all names, extract index positions and group by seeds.
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def plot_histogram(bars_dict_list, filename='histogram_plot'):
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# catagorical
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ryb = cl.scales['10']['div']['RdYlBu']
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:param path: Path to the current TB folder of a sinle NN configuration
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:return: List of foldernames to filter for.
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"""
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config_keys = ['run_seed', 'timesteps', 'index_position', 'cell_count', 'layers', 'cell_type']
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found_configurations = defaultdict(list)
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for dname in os.listdir(path):
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if os.path.isdir(os.path.join(path, dname)):
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this_config = {key: value for key, value in zip(config_keys, dname.split("_"))}
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found_configurations[this_config['index_position']].append(dname)
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data = []
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for bar_id, bars_dict in bars_dict_list:
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hist = go.Histogram(
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histfunc="count",
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y=bars_dict.get('value', 14),
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x=bars_dict.get('name', 'gimme a name'),
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showlegend=False,
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marker=dict(
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color=ryb[bar_id]
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),
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)
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data.append(hist)
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return found_configurations
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layout=dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
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height=400, width=400, margin=dict(l=0, r=0, t=0, b=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def line_plot(line_dict_list, filename='lineplot'):
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# lines with standard deviation
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# Transform data accordingly and plot it
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data = []
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rdylgn = cl.scales['10']['div']['RdYlGn']
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rdylgn_background = [scale + (0.4,) for scale in cl.to_numeric(rdylgn)]
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for line_id, line_dict in enumerate(line_dict_list):
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name = line_dict.get('name', 'gimme a name')
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upper_bound = go.Scatter(
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name='Upper Bound',
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x=line_dict['x'],
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y=line_dict['upper_y'],
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mode='lines',
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marker=dict(color="#444"),
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line=dict(width=0),
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fillcolor=rdylgn_background[line_id],
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)
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trace = go.Scatter(
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x=line_dict['x'],
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y=line_dict['main_y'],
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mode='lines',
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name=name,
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line=dict(color=line_id),
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fillcolor=rdylgn_background[line_id],
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fill='tonexty')
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lower_bound = go.Scatter(
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name='Lower Bound',
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x=line_dict['x'],
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y=line_dict['lower_y'],
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marker=dict(color="#444"),
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line=dict(width=0),
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mode='lines')
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data.extend([upper_bound, trace, lower_bound])
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layout=dict(title='{} Line Plot'.format('Experiment Name Penis'),
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height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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if __name__ == '__main__':
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@ -118,7 +195,4 @@ if __name__ == '__main__':
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in_file = args.in_file[0]
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out_file = args.out_file
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with open(in_file, 'rb') as dill_file:
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experiment = dill.load(dill_file)
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print('hi')
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print('aha')
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