# coding: utf-8

import sys

sys.path.append("./src/")

from pypointcloud import *

from spline_tools import ParametricSpline

import scipy as S

results = None

with open("./D_mel_wt__atlas_r2.vpc") as infile:

results = read_vpc(infile)

d = results[1]

x = d[:,0]

y = d[:,1]

z = d[:,2]

ap_line = None

x_min = x.min()

x_max = x.max()

z_min = z.min()

z_max = z.max()

import matplotlib.pyplot as plt

import matplotlib.patches as patches

from matplotlib.widgets import Button

N_SPLINES = 9

points_per_spline = 5

from scipy.interpolate import griddata

mesh_x = S.loadtxt("mesh_x.txt")

mesh_y = S.loadtxt("mesh_y.txt")

the_splines = list()

for i in range(mesh_x.shape[0]):

the_splines.append(ParametricSpline(mesh_x[i],mesh_y[i]))

SAMPLE_NUMBER = 100

ts = S.linspace(0.0,1.0,SAMPLE_NUMBER)

old_xy = S.vstack([aspline(ts) for aspline in the_splines])

new_xy = S.vstack([S.hstack([i*S.ones((SAMPLE_NUMBER,1)), ts.reshape(-1,1)]) for i in range(len(the_splines))])

new_xs = griddata(old_xy, new_xy[:,0], (x, z), method='linear')

new_ys = griddata(old_xy, new_xy[:,1], (x, z), method='linear')

disp_genes = ["kni__3","D__3","hbP__3","bcdP__3","KrP__3","gt__3","eve__3","odd__3","rho__3","sna__3"]

#disp_genes = ["eve__3"]

for one_gene_name in disp_genes:

colnum = results[0]["column"].index(one_gene_name)-1

colors = S.vstack([d[:,colnum],S.zeros(d.shape[0]),S.zeros(d.shape[0])]).T

colors -= colors.min()

colors*=S.power(colors.max(),-1.0)

fig = plt.figure(figsize=(4,2))

ax = fig.add_subplot(1,1,1)

ax.set_title(one_gene_name)

ax.scatter(new_xs,new_ys,s=45.0,c=colors,alpha=0.75)

for i in range(8):

ax.axvline(float(i),c="g")

fig.tight_layout()

plt.show()