synthetic_data.py 7.11 KB
Newer Older
amichaut's avatar
amichaut committed
1
2
3
4
5
6
7
import sys
import os.path as osp
import datetime
import csv
import shutil
import itertools

amichaut's avatar
amichaut committed
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
from pylab import *
import pandas as pd
from skimage import io
from skimage.color import rgb2gray
from skimage.util import img_as_ubyte
import scipy.interpolate as sci
import pickle
import seaborn as sns
from lmfit import Parameters, Model
from scipy.optimize import curve_fit
from scipy import stats
import matplotlib.tri as tri
import napari
import tifffile as tifff


# Plotting parameters
color_list=[c['color'] for c in list(plt.rcParams['axes.prop_cycle'])]+sns.color_palette("Set1", n_colors=9, desat=.5)
plot_param={'figsize':(5,5),'dpi':300,'color_list':color_list,'format':'.png','despine':True,'logx':False,'logy':False,'invert_yaxis':True,'export_data_pts':False}



def make_diff_traj(part_index=0,grid_size=[500,500,500],dim=3,tmax=10,periodic=True,noise_amp=10,x0=[250,250,250],bias=[0,0,0]):
    """Generate a trajectory with a diffusive trajectory, with a bias. bias gives the amplitude at each step along each dimension."""
    #time and index
    t = arange(tmax)
    index = ones(tmax)*part_index
    #displacement
    displacement=pd.DataFrame(np.random.randn(tmax,dim),columns=list('xyz')[0:dim])
    displacement['r2']=0
    for i in range(dim):
        displacement['r2']+=displacement[list('xyz')[i]]**2
    displacement['r']=np.sqrt(displacement['r2'])
    for i in range(dim):
        displacement[list('xyz')[i]]/=displacement['r'] #normalize raw displacement
        displacement[list('xyz')[i]]*=noise_amp #amply amplitude
        displacement[list('xyz')[i]]+=bias[i] #add bias
    displacement=displacement[list('xyz')[0:dim]].values

    #traj
    traj=np.zeros((tmax,dim))
    for i in range(dim):
        traj[:,i]=np.cumsum(displacement[:,i])+x0[i]
        if periodic:
            traj[:,i]=np.remainder(traj[:,i],grid_size[i])
    return pd.DataFrame(np.concatenate([index[:,None],t[:,None],traj],axis=1),columns=['traj','frame']+list('xyz')[0:dim])

def make_spatial_gradient(part_num=100,grid_size=[500,500,500],dim=3,tmax=10,periodic=True,noise_amp=10,bias_basis=[0,0,0],
                         diff_grad={'min':0,'max':10},bias_grad={'min':0,'max':10,'dim':0},grad={'step_num':4,'dim':0}, 
                         x0_range={'x':[0.1,0.9],'y':[0.1,0.9],'z':[0.1,0.9]},dt=1):
    """Make a spatial gradient (number of steps on the gradient given by grad['step_num'})in diffusion or bias, along a specific dimension, given by grad['dim'].
    The gradient can be in diffusion with diff_grad or bias_grad. min and max give the extrema of the gradient, and bias_grad['dim'] give the dimension along the gradient in bias is applied.
    An overall constant bias can be passed by bias_basis. 
    """
    
    df=pd.DataFrame([],columns=['traj','frame']+list('xyz')[0:dim])
    df_param=pd.DataFrame([],columns=['traj','v','D'])
    
    diff_grad_=np.linspace(diff_grad['min'],diff_grad['max'],grad['step_num'])
    bias_grad_=np.linspace(bias_grad['min'],bias_grad['max'],grad['step_num'])
    #spatial boundaries of the regions of particles
    lims=[[x0_range['x'][0]*grid_size[0],x0_range['x'][1]*grid_size[0]],
          [x0_range['y'][0]*grid_size[1],x0_range['y'][1]*grid_size[1]], 
          [x0_range['z'][0]*grid_size[2],x0_range['z'][1]*grid_size[2]]]
    
    part_count=0
    for i in range(grad['step_num']):
        grad_increment=(lims[grad['dim']][1]-lims[grad['dim']][0])/grad['step_num']
        lims_=lims[:]
        lims_[grad['dim']]=[lims_[grad['dim']][0]+i*grad_increment,lims_[grad['dim']][0]+(i+1)*grad_increment]
        noise_amp=diff_grad_[i]
        bias=bias_basis[:]
        bias[bias_grad['dim']]=bias_grad_[i]
        bias_ampl=0
        for k in range(dim):
            bias_ampl+=bias[k]**2
        bias_ampl=np.sqrt(bias_ampl)
        
        for j in range(int(part_num/grad['step_num'])):
            x0=[np.random.uniform(lims_[0][0],lims_[0][1]),
                np.random.uniform(lims_[1][0],lims_[1][1]),
                np.random.uniform(lims_[2][0],lims_[2][1])]
                       
            traj=make_diff_traj(part_index=part_count,noise_amp=noise_amp,x0=x0,bias=bias,tmax=tmax,periodic=periodic,dim=dim)
            df=pd.concat([df,traj])
            v=bias_ampl/dt
            D=noise_amp**2/(2.*dim*dt)
            df_param.loc[part_count,:]=[part_count,v,D]
            
            part_count+=1
    return df,df_param

def make_attraction_node(part_num=100,grid_size=[500,500,500],dim=3,tmax=10,periodic=True,noise_amp=10,bias_basis=[0,0,0], 
                         attraction_ampl=10,node=None,x0_range={'x':[0.1,0.9],'y':[0.1,0.9],'z':[0.1,0.9]},dt=1):
    """Make array of diffusive particles biased toward a node (or away if attraction_ampl is negative)"""
    
    df=pd.DataFrame([],columns=['traj','frame']+list('xyz')[0:dim])
    df_param=pd.DataFrame([],columns=['traj','v','D'])

    if node is None:
        node=[grid_size[d]/2 for d in range(dim)] #by default center

    #spatial boundaries of the regions of particles
    lims=[[x0_range['x'][0]*grid_size[0],x0_range['x'][1]*grid_size[0]],
          [x0_range['y'][0]*grid_size[1],x0_range['y'][1]*grid_size[1]], 
          [x0_range['z'][0]*grid_size[2],x0_range['z'][1]*grid_size[2]]]
    
    for i in range(part_num):
        x0=[np.random.uniform(lims[0][0],lims[0][1]),
            np.random.uniform(lims[1][0],lims[1][1]),
            np.random.uniform(lims[2][0],lims[2][1])]
        
        #unit vector towards node
        node_vec=np.array([node[d]-x0[d] for d in range(dim)])
        sum_=0
        for d in range(dim):
            sum_+=node_vec[d]**2
        node_vec/=np.sqrt(sum_)
        
        bias=node_vec*attraction_ampl
        bias=bias+np.array(bias_basis)
        
        bias_ampl=0
        for k in range(dim):
            bias_ampl+=bias[k]**2
        bias_ampl=np.sqrt(bias_ampl)
        
        traj=make_diff_traj(part_index=i,noise_amp=noise_amp,x0=x0,bias=bias,tmax=tmax,periodic=periodic,dim=dim)
        df=pd.concat([df,traj])
        v=bias_ampl/dt
        D=noise_amp**2/(2.*dim*dt)
        df_param.loc[i,:]=[i,v,D]
        
    return df,df_param

def plot_synthetic_stack(df,outdir,dpi=300,grid_size=[500,500,500],tmax=10):    
    """Plot synthetic data and save it as a grayscaled tiff stack"""
    outdir_temp=osp.join(outdir,'temp')
    safe_mkdir(outdir_temp)

    stack=np.zeros((tmax,grid_size[0],grid_size[1]),'uint8')

    groups=df.groupby('frame')
    #print
    for i in range(tmax):
        group=groups.get_group(i).reset_index(drop=True)
        fig=figure(frameon=False)
        fig.set_size_inches(grid_size[0]/dpi,grid_size[1]/dpi)
        ax = fig.add_axes([0, 0, 1, 1])
        for k in range(group.shape[0]):
            ax.scatter(group.loc[k,'x'],group.loc[k,'y'],s=10)
        ax.set_xlim(0,grid_size[0])
        ax.set_ylim(0,grid_size[1])
        ax.invert_yaxis()
        ax.axis('off')
        fn=osp.join(outdir_temp,'{}.jpg'.format(i))
        fig.savefig(fn,dpi=300)

    #add to stack
    for i in range(tmax):
        fn=osp.join(outdir_temp,'{}.jpg'.format(i))
        im=io.imread(fn,as_gray=True)
        stack[i]=img_as_ubyte(im)

    out_fn=osp.join(outdir,'stack.tiff')
    tifff.imsave(out_fn, stack)
    shutil.rmtree(outdir_temp)