'''

Created on Nov 1, 2015

Data analysis of Lightsheet acquisition

Paper: Long-term engraftment of primary bone marrow stroma promotes hematopoietic reconstitution after transplantation

Author: Jean-Paul Abbuehl

@author: Jean-Paul Abbuehl

'''

# Load depedencies

import pandas as pd

import numpy as np

import seaborn as sns

import os

import operator

from xml.etree import ElementTree as ET

from itertools import cycle

import matplotlib.mlab as mlab

import matplotlib.image as mpimg

import matplotlib.pyplot as plt

import matplotlib.ticker as ticker

from pylab import *

from pylab import savefig as savefig

from mpl_toolkits.axes_grid1 import make_axes_locatable

from pandas.tools.plotting import parallel_coordinates

from shapely.affinity import scale

from shapely.geometry import shape, Polygon

from shapely.geometry import Point as PT

from shapely.ops import cascaded_union

from bokeh.plotting import figure, show, output_file

import scipy.spatial as spatial

from scipy.spatial import cKDTree

from scipy.stats import spearmanr

from PIL import Image

from PIL.FontFile import WIDTH

from sklearn import mixture, metrics

from sklearn.cluster import DBSCAN

import h5py

import re

import collections as coll

import itertools as it

import javabridge as jv

import bioformats as bf

# Load second file

from registration import *

def run():

ROI_crop(coordinates, diameter)

def suffix(gating, logic):

return output

def ROI_define():

return poly, scaleX, scaleY

def DUPLICATE_remove(data, distance_threshold, graph=False):

return output

def CELL_plotting(data, channel, markersize, limit=True, top_title='title'):

plt.show()

def ROI_filtering(data, poly, graph):

return data

def convertXYforPlot(data, pixelSize, scaleX, scaleY):

return data

# Must take into account registration

def coordinate_conversion(data, width, height):

return geom2, scaleX, scaleY

class Canvas(object):

return np.array(self.vert)

def DATA_loading(light_correction, registration):

return data

def CELL_classifier(data, gating, logic, plotting, map_plotting):

return remaining

def DISTANCE_frequency(data1, data2, length):

return result

def distance_correction(data):

return data

# Calculate Nearest distance from each point in data1 to any point in data2

def unique_rows(a):

a = np.ascontiguousarray(a)

unique_a = np.unique(a.view([('', a.dtype)] * a.shape[1]))

return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))

def DISTANCE_spatial(data1, data2, neighbors=1):

return distance, index

def DATA_colIndex(parameter, channel):

to_find = 'CH' + str(channel) + '_' + parameter

return keys.index(to_find)

# Get views and compare MFIs of positive nucleus population

def illumination_correction(data, percentile):

raise

def ROI_plot(data, poly, title, pltsize, size, xlim, ylim):

plt.show()

def format_class(Cclass, min_index, max_index, logic):

return output

def index_min(values):

return min(xrange(len(values)), key=values.__getitem__)

def fit_mixture(data, ncomp, title, doplot, logic):

return ms, ws, classes

def read_views(registration):

return output

def read_view(view):

return None

def XML_process(infile):

return (Xview_nb, Yview_nb, PixFormat)

def snake_generator(Ncol, Nrow):

return (Xsnake, Ysnake)

def rgb2gray(rgb):

return np.dot(rgb[..., :3], [0.299, 0.587, 0.144])

def simple_merge():

return Xoutput, Youtput

def overlapping_grid(nrow, ncol, overlap, format):

return grid

def GRID_plotting(grid):

plt.show()

def estimate_param_DBScan(data, eps, min_samples):

print('Best minPTS: %d' % best_min_samples)

def cDBScan(data, title, eps, graph_cluster, min_samples, pltsize, msize):