UsingSSBDapi.py

# coding: utf-8

# # Using Python SSBDapi to access SSBD data for visualization and analysis

# In[1]:

get_ipython().magic(u"config InlineBackend.figure_formats=['png']")


# In[2]:

from IPython.display import display


# In[3]:

import matplotlib.pyplot as plt


# In[4]:

import numpy as np
import pandas as pd


# In[5]:

from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d


# In[6]:

get_ipython().magic(u'matplotlib inline')


# ## Loading ivisual library allows iPython to use VPython to display 3D graphics
# 
# * Installing ivisual can be found here https://github.com/mwcraig/ivisual-notebook-info

# In[7]:

from ivisual import *


# ## Loading json library allows iPython to deal with JSON formatted data

# In[8]:

import json


# # Loading SSBDapi Library

# In[9]:

import SSBDapi as ssbdapi


# ## Using ssbdapi function 'meta_data' to search for simulation 'sim' in the field 'basedon'

# In[10]:

q=ssbdapi.ssbd()
q.set_display('on')
resultdata = q.meta_data('basedon', 'sim')


# ## Using ssbdapi function 'meta_data' to search for 'osaka' in the field 'address'

# In[11]:

resultdata = q.meta_data('address', 'osaka')


# ## Using ssbdapi function 'meta_data' to search for 'osaka' in the field 'address'

# In[12]:

resultdata = q.meta_data(field='contributors', search='keller')


# ## Using ssbdapi function 'data' to search for string '505' in the field 'localid'

# In[13]:

resultdata = q.data(field='localid', search='505')


# ## Return data is in JSON format

# In[14]:

print(resultdata)


# ## Retrieve function to get all the coordinates at a specific time point

# In[15]:

def retrieve_coord_tp(bdmlID, timept, display='on'):
    q=ssbdapi.ssbd()
    q.set_display(display)
    tmp_result = q.coordXYZ(bdmlID, timept)
    total = tmp_result['meta']['total_count']
    #print "total=", total
    result=tmp_result
    tmp_offset=0
    while total>0:
        tmp_offset=tmp_offset+100
        tmp_result=q.coordXYZ(bdmlID, timept, offset=tmp_offset)
        result['objects']=result['objects']+tmp_result['objects']
        total=total-100
    return(result)


# ## Call retrieve coordinates function to get the coordinates of BDMLID that contains string 'd15115' at time point 15

# In[16]:

resultdata=retrieve_coord_tp('d15115', 15)


# ## Using the ssbdapi function 'scale' and search for bdmlID using field 'bdml__bdml_ID' 

# In[17]:

s=q.scale(field='bdml__bdml_ID', search='d15115') 


# ## Setup a canvas to display 3D graphics

# In[21]:

canvas(title="Displaying 3D graphics", background=(0.8,0.8,0.8) )
c = color.red
r = 0.1
x = 0
y = 0
Z = 0


# ## Visualization - displaying the coordinates as sphere in 3D

# In[22]:

for j in s['objects']:
    sx = j['xScale']
    sy = j['yScale']
    sz = j['zScale']
    sr = j['xScale']
    st = j['tScale']
    tu = j['tUnit']


# In[23]:

for i in resultdata['objects']:
        sphere(pos=(i['x']*sx, i['y']*sy, i['z']*sz), color=c, radius=i['radius'])


# ## Analysis - plotting the proliferation curve

# In[24]:

no_of_nucleus = []
timept = []
tp = 1
resultdata = retrieve_coord_tp('d15115', tp, display='off')
nn =resultdata['meta']['total_count']
while nn > 0:
    no_of_nucleus.append(nn)
    timept.append(tp)
    # print "tp="+str(tp)+" nn="+str(nn)
    tp=tp+1
    resultdata = retrieve_coord_tp('d15115', tp, display='off')
    nn =resultdata['meta']['total_count']
    
    


# ## Get the title, organism and contact name of the dataset

# In[25]:

q.set_display('off')
resultmetadata = q.data(field='bdmlUUID', search='d15115')
for i in resultmetadata['objects']:
    title= i['meta_data']['title']
    name= i['meta_data']['name']
    pmid = i['meta_data']['PMID']
    organism = i['meta_data']['organism']


# ## Plotting the curve

# In[26]:

fig=plt.figure()
plt.plot(timept, no_of_nucleus, 'r')
ax = fig.add_subplot(1,1,1)
ax.set_ylabel('cellstage')
ax.set_xlabel('timepoint')
plottitle = title+'  ['+organism+']  ('+name+')  PMID: '+str(pmid)+' Cell divsion over time '
plt.title(plottitle);


# ### Plotting using actual time instead of time point

# In[27]:

tmp=np.array(timept)
time=tmp*st
fig=plt.figure()
plt.plot(time, no_of_nucleus, 'r')
ax = fig.add_subplot(1,1,1)
ax.set_ylabel('cellstage')
xlabel = 'time ( '+tu+' )'
ax.set_xlabel(xlabel)
plottitle = title+'  ('+name+')  PMID: '+str(pmid)+' Cell divsion over time '
plt.title(plottitle);


# In[ ]:




# In[ ]: