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# -*- coding: utf-8 -*-
"""
Created on Sat Jul 3126 17:50:00 20167

pyNVscan Advanced Tools by Scipy for hot spot

@author: listen
"""

import matplotlib
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

import skimage.morphology as morph
import skimage.exposure as skie

PZT_LIMIT_X = 100.0
PZT_LIMIT_Y = 100.0

def nothing(x):
    pass


def read_CSV_Head(csv_head):
    x0 = csv_head[0]
    y0 = csv_head[1]
    x1 = csv_head[2]
    y1 = csv_head[3]
    step_move = csv_head[4]    
    return x0, y0, x1, y1, step_move
    

#由区域扫描结果Data.csv，创建head
def creat_CSV_Head_File(my_matrix):
    x0 = np.min(my_matrix[:,0])
    x1 = np.max(my_matrix[:,0])
    y0 = np.min(my_matrix[:,1])
    y1 = np.max(my_matrix[:,1])
    step_move = my_matrix[1,0] - my_matrix[0,0]
    dx = x1 - x0
    dy = y1 - y0
    lx = np.size(my_matrix[:,0])
    ldata = np.size(my_matrix[:,2])
    if abs(lx-(dx/step_move+1.0)*(dy/step_move+1.0)) >= 0.01:
        print "Error: creat large_scan_head.csv"
        sys.exit(255)
    elif x0<0 or y0<0 or x1>PZT_LIMIT_X or y1>PZT_LIMIT_Y: 
        print "Error: PZT XY LIMITs"
        sys.exit(255)
    elif x0>=x1 or y0>=y1:
        print "Error: creat Large Scan"
        sys.exit(255)
    head = [x0, y0, x1, y1, step_move, lx, ldata]
    
    return head

        
        
#最大公约数
def gcd(a, b):
    if a < b:
        a, b = b, a

    while b != 0:
        temp = a % b
        a = b
        b = temp

    return a
    
    
#由csv绘制纯图像,返回比例尺值
def csv_to_PNG(my_matrix, csv_head, mode, contour_i):
#   print csv_head
    x0,y0,x1,y1,step_move = read_CSV_Head(csv_head)
    # Becouse of numpy arange()
    x2 = x1 + step_move * 0.5
    y2 = y1 + step_move * 0.5
    size_xy = int(csv_head[5])
    progress_len = int(csv_head[6])
    wave_data = my_matrix[:,2]

    w_data = np.zeros(size_xy) # ready for one-photon_count
    #progress_len
    w_data[0:progress_len] = wave_data[0:progress_len] # Warning: NOT progress_len-1
    X = np.arange(x0, x2, step_move)
    Y = np.arange(y0, y2, step_move)
    len_X = np.size(X)
    len_Y = np.size(Y)

    Z0 = w_data.reshape(-1,len_X)
    Z0[1::2,:] = Z0[1::2,::-1]
    
    #创建浮点灰度图
    img = Z0 / Z0.max()
    #圈定坐标和筛选
    lm = morph.local_maxima(img)
    xc1, yc1 = np.where(lm.T == True)
    v = img[(yc1, xc1)]
    lim = 0.5
    xc2, yc2 = xc1[v > lim], yc1[v > lim]

    extent = np.array([x0, x1, y0, y1]) + np.array([-step_move,step_move,-step_move,step_move])*0.5
    #自适应去除matplotlib figure tight_layout PAD,放大到6*100像素的宽
    fd = gcd(len_X,len_Y)
    fx = len_X / fd
    fy = len_Y / fd
    if fx>(fy*0.5) and fx<=3:
        fd = 6/fx
        fx = 6
        fy = fd*fy
        
    fig = plt.figure(figsize=(fx,fy))
    axes = plt.subplot(111)
    axes.set_xlim(0, img.shape[1])
    axes.set_ylim(0, img.shape[0])
    axes.scatter(xc2, yc2, s=80,  facecolor='none', edgecolor='#FF7400')
#   axes.imshow(Z0, extent=extent, origin="lower",interpolation='nearest')
    #   imshow, contour mode, 
    #0    +        -
    #1    +        +
    #2    -        +
    if mode < 2 :
        axes.imshow(img , cmap='gray',origin="lower")
    if mode > 0 :
        axes.contour(X, Y, img , contour_i, cmap='cool')
    #for OpenCV ,NO AXEX,NO PAD
    axes.set_xticks([])
    axes.set_yticks([])
    axes.spines['right'].set_color('none')
    axes.spines['top'].set_color('none')
    axes.spines['bottom'].set_color('none')
    axes.spines['left'].set_color('none')
    plt.tight_layout(pad=0)
    
    #png_ruler = (x1 - x0 + step_move) / (fx * 100.0)    # 保存图像的比例尺：1个像素代表的微米    
    fig.show()
    

#图片像素坐标[左上角(0,0)],变换到PZT坐标(微米)[左下角(0,0)] ，(dx,dy)偏移比例 
def pngXY_to_pztXY(csv_head, gray, png_x, png_y):
    x0,y0,x1,y1,step_move = read_CSV_Head(csv_head)
    
    Lx,Ly = gray.shape
    
#   pzt_x = png_x / Lx * (x1 - x0 + step_move) + x0 - step_move*0.5
#   pzt_y = (Ly - png_y) / Ly * (y1 - y0 + step_move) + y0 - step_move*0.5
    
    pzt_x = png_x / (Lx - 1.0) * (x1 - x0 + step_move) + x0 - step_move*0.5
    pzt_y = (Ly - png_y) / (Ly - 1.0) * (y1 - y0 + step_move) + y0 - step_move*0.5
    
    if pzt_x<0:
        pzt_x = 0;
    elif pzt_x>PZT_LIMIT_X:
        pzt_x =PZT_LIMIT_X
    elif pzt_y<0:
        pzt_y = 0;
    elif pzt_y>PZT_LIMIT_Y:
        pzt_y = PZT_LIMIT_Y
        
    return pzt_x, pzt_y
    
    
#PZT坐标(微米)变换到large_scan.png图片像素坐标
def pztXY_to_pngXY(csv_head, gray, pzt_x, pzt_y):
    x0,y0,x1,y1,step_move = read_CSV_Head(csv_head)
    
    Lx,Ly = gray.shape
    
#   png_x = (pzt_x - x0 + step_move*0.5) / (x1 - x0 + step_move) * Lx
#   png_y = Ly - (pzt_y - y0 + step_move*0.5) / (y1 - y0 + step_move) * Ly
    
    png_x = (pzt_x - x0 + step_move*0.5) / (x1 - x0 + step_move) * (Lx - 1.0)
    png_y = Ly - (pzt_y - y0 + step_move*0.5) / (y1 - y0 + step_move) * (Ly - 1.0)
    
#   png_x = np.uint16(round(png_x))    
#   png_y = np.uint16(round(png_y))
    return png_x, png_y