import os
import sys
import numpy as np
np.warnings.simplefilter('ignore')
import time
import matplotlib.pyplot as plt
import h5py
import collections
import re

from x3py import x3py
import alignment
import mcutils as mc

cmap = plt.cm.viridis if hasattr(plt.cm,"viridis") else plt.cm.gray
kw_2dplot = dict(
  interpolation = "none",
  aspect        = "auto",
  cmap          = cmap
  )


g_exp   = "mecl3616"
g_exp   = "xppl3716"
g_bml   = g_exp[:3]

x3py.config.updateBeamline(g_bml)

g_folder_init = g_exp+"_init_pars/"
g_folder_out  = g_exp+"_output/"
g_folder_data = "/reg/d/psdm/"+g_bml+"/"+ g_exp +"/hdf5/"

import socket
hostname = socket.gethostname()
if hostname == "x1":
  g_folder_data = "/home/marco/temp"
if hostname == "apcluster0":
  g_folder_data = "/data/marcoc/singleShotXanes/"+ g_exp +"/hdf5/"

# set defaults based on experiment
if g_bml == "xpp":
  g_roi_height = 200
  g_swapx = False
  g_swapy = False
else:
  g_roi_height = 100
  g_swapx = True
  g_swapy = False

print("Working on experiment",g_exp,"(beamline %s)"%g_bml)
print(" folder data      →",g_folder_data)
print(" folder init_pars →",g_folder_init)
print(" folder outout    →",g_folder_out)

#g_folder = "/reg/d/psdm/xpp/xppl3716/ftc/hdf5/"

def readDataset(fnameOrRun=7,
  force=False,
  doBkgSub=False):
  if isinstance(fnameOrRun,str) and (fnameOrRun[-3:]=="npz"):
    d = x3py.toolsVarious.DropObject()
    temp = np.load(fnameOrRun)
    spec1 = temp["spec1"]
    spec2 = temp["spec2"]
    nS    = spec1.shape[0]
    d.spec1 = x3py.toolsDetectors.wrapArray("spec1",spec1,time=np.arange(nS))
    d.spec2 = x3py.toolsDetectors.wrapArray("spec2",spec2,time=np.arange(nS))
  else:
    if isinstance(fnameOrRun,int):
      fnameOrRun=g_folder_data+"/"+g_exp+"-r%04d.h5" % fnameOrRun
    d = x3py.Dataset(fnameOrRun,detectors=["opal0","opal1","fee_spec","opal2","ebeam"])
    if g_bml == "xpp":
      d.spec1 = d.opal0
      d.spec2 = d.opal1
    else:
      d.spec1 = d.fee_spec
      d.spec2 = d.opal2
  if not hasattr(d,"scan"):
    d.scan = x3py.toolsVarious.DropObject()
    d.scan.scanmotor0_values = [0,]
  return d


def getCenter(img,axis=0,threshold=0.05):
  img = img.copy()
  img[img<img.max()*threshold] = 0
  if axis == 1: img=img.T
  p   = img.mean(1)
  x   = np.arange(img.shape[0])
  return int(np.sum(x*p)/np.sum(p))

def showShots(im1,im2):
  nS = im1.shape[0]
  fig,ax = plt.subplots(2,nS,sharex=True,sharey=True)
  if im1.ndim == 3:
    for a,i1,i2 in zip(ax.T,im1,im2):
      a[0].imshow(i1.T,**kw_2dplot)
      a[1].imshow(i2.T,**kw_2dplot)
  else:
    for a,p1,p2 in zip(ax.T,im1,im2):
      a[0].plot(p1)
      a[1].plot(p2)

 
class AnalyzeRun(object):
  def __init__(self,run,initAlign="auto",swapx=g_swapx,swapy=g_swapy):
    """ swapx → swap x axis of first spectrometer
        swapy → swap y axis of first spectrometer
        initAlign: could be:
           1. None if you want default transformation parameters
           2. a dict if you want to overwrite certain parameters of the default ones
           3. an integer (to look for xppl3716_init_pars/run????_transform.npy)
           4. a file name (that has been previosly saved with r.saveTransform(fname)
    """
    self.data = readDataset(run)
    self.scanpos = self.data.scan.scanmotor0_values
    self.nCalib  = self.data.spec1.nCalib
    self.nShotsPerCalib = self.data.spec1.lens
    if isinstance(run,str):
      run = int( re.search("\d{3,4}",run).group() )
    self.run = run
    self.results = collections.OrderedDict()
    self.swap = (swapx,swapy)
    #self.clearCache()

    d = self.data
    self.spec1 = d.spec1 ; # spec1 is the one that is moved
    self.spec2 = d.spec2 ; 

    try:
      self.loadTransform(initAlign)
    except (AttributeError,FileNotFoundError):
      if initAlign is None:
        print("Set to default transform")
        self.initAlign = self.setDefaultTransform()
      else:
        self.initAlign  = initAlign

  def getShot(self,shot=0,calib=None,bkgSub="line",roi=g_roi_height):
    # read data
    im1 = self.spec1.getShots(shot,calib=calib)
    im2 = self.spec2.getShots(shot,calib=calib)
    # subtractBkg bkg
    im1 = alignment.subtractBkg(im1,bkg_type=bkgSub)
    im2 = alignment.subtractBkg(im2,bkg_type=bkgSub)
    # rebin and swap im1 if necessary
    if im1.shape[-1] != 1024:
      im1 = mc.rebin(im1, (im1.shape[0],im1.shape[1],1024) )
    if self.swap[0]:
      im1 = im1[:,:,::-1]
    if self.swap[1]:
      im1 = im1[:,::-1,:]
    if roi is None:
      pass
    elif isinstance(roi,slice):
      im1 = im1[:,roi,:]
      im2 = im2[:,roi,:]
    elif isinstance(roi,int):
      if not hasattr(self,"roi1"): self.roi1 = alignment.findRoi(im1[0],roi)
      if not hasattr(self,"roi2"): self.roi2 = alignment.findRoi(im2[0],roi)
      im1 = im1[:,self.roi1,:]; im2 = im2[:,self.roi2,:]
    return im1,im2

  def guiAlign(self,shot=0,save="auto"):
    im1,im2 = self.getShot(shot)
    gui = alignment.GuiAlignment(im1[0],im2[0])
    input("Enter to start")
    gui.start()
    if save == "auto":
      fname = g_folder_init+"/run%04d_gui_align.npy" % self.run
    else:
      fname = save
    self.initAlign = gui.transform
    gui.save(fname)


  def analyzeScan(self,initpars=None,nShotsPerCalib=20,nC=None,doFit=False,fitEveryCalib=False,nSaveImg=5):
    """ this is a comment """
    if initpars is None: initpars= self.initAlign
    if nC is None: nC = self.nCalib
    out = []
    for i in range(nC):
      if nShotsPerCalib == 'all':
        shots = slice(self.nShotsPerCalib[i])
      else:
        shots = slice(nShotsPerCalib)
      s1,s2 = self.getShot(shots,calib=i)
      if fitEveryCalib is not False:
        ret,bestTransf = alignment.doShots(s1[:fitEveryCalib],s2[:fitEveryCalib],doFit=True,\
                initpars=initpars,nSaveImg=nSaveImg,returnBestTransform=True);
        initpars = bestTransf; self.initAlign=bestTransf
      ret = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,nSaveImg=nSaveImg)
      self.results[i] = ret
      print("Calib cycle %d -> %.3f (best FOM: %.2f)" % (i,self.scanpos[i],np.min(ret.fom)))
      out.append(ret)
    return out

  def doShot(self,shot=0,calib=None,initpars=None,im1=None,im2=None,doFit=True,show=False,showInit=False,save=False,savePlot="auto"):
    if initpars is None: initpars= self.initAlign
    if (im1 is None) or (im2 is None):
      im1,im2 = self.getShot(shot,calib=calib); im1=im1[0]; im2=im2[0]
    r = alignment.doShot(im1,im2,initpars,doFit=doFit,show=showInit)
    im1 = r.im1
    im2 = r.im2
    self.initAlign = r.final_pars
    if show:
      if savePlot == "auto":
        if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
        savePlot = g_folder_out+"/run%04d_calib%s_shot%04d_fit.png" % (self.run,calib,shot)
      alignment.plotShot(im1,im2,res=r,save=savePlot)
    if save: self.saveTransform()
    return r

  def doShots(self,shots=slice(0,50),calib=None,initpars=None,doFit=False,returnBestTransform=False,nSaveImg='all'):
    """
        shots   : slice to define shots to read, use 'all' for all shots in calibcycle
        nSaveImg : save saveImg images in memory (self.results), use 'all' for all
                                 useful for decreasing memory footprint
    """
    if initpars is None: initpars= self.initAlign
    if shots == "all": shots = slice(self.nShotsPerCalib[calib])
    s1,s2 = self.getShot(shots,calib=calib)
    ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
            returnBestTransform=True,nSaveImg=nSaveImg)
    if doFit: self.initAlign = transformForBestFit
    # keep it for later !
    self.results[calib] = ret
    if returnBestTransform:
      return ret,transformForBestFit
    else:
      return ret

  def save(self,fname="auto",overwrite=False):
    if len(self.results) == 0: print("self.results are empty, returning without saving")
    if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
    if fname == "auto":
      fname = g_folder_out+"/run%04d_analysis.h5" % self.run
    if os.path.exists(fname) and not overwrite:
      print("File %s exists, **NOT** saving, use overwrite=True is you want ..."%fname)
      return
    if os.path.exists(fname) and overwrite: os.unlink(fname)
    print("Saving results to %s"%fname)
    h = h5py.File(fname)
    h["roi1"] = (self.roi1.start,self.roi1.stop)
    h["roi2"] = (self.roi2.start,self.roi2.stop)
    h["scanmot0"] = self.data.scan.scanmotor0
    h["scanpos0"] = self.data.scan.scanmotor0_values
    if hasattr(self.data.scan,"scanmotor1"):
      h["scanmot1"] = self.data.scan.scanmotor1
      h["scanpos1"] = self.data.scan.scanmotor1_values
    #h["transform"] = self.initAlign
    for (c,v) in self.results.items():
      cname = "calib%04d/" % c if isinstance(c,int) else "calib%s/" % c
      for p,vv in mc.objToDict(v).items():
        # cannot save in hfd5 certain python objects
        if p == "fit_result" or p.find("final_transform")==0:
          continue
        if isinstance(vv,dict):
          for pname,parray in vv.items():
            name = cname + p + "/" + pname
            h[name] = parray
        else:
          h[cname + p] = vv
    h.close()


  def saveTransform(self,fname="auto",transform=None):
    if transform is None: transform = self.initAlign
    if fname == "auto":
      fname = g_folder_init+"/run%04d_transform.npy" % self.run
    print("Saving roi and transformation parameter to %s"%fname)
    alignment.saveAlignment(fname,self.initAlign,self.roi1,self.roi2)

  def loadTransform(self,fname="auto"):
    if isinstance(fname,dict): raise FileNotFoundError
    if fname == "auto":
      fname = g_folder_init+"/run%04d_transform.npy" % self.run
    elif isinstance(fname,int):
      fname = g_folder_init+"/run%04d_transform.npy" % fname
    if not os.path.exists(fname): print("Asked to read %s, but it does not exist"%fname)
    temp = np.load(fname).item()
    self.initAlign = temp["transform"]
    self.roi1 = temp["roi1"]
    self.roi2 = temp["roi2"]
    print("init transform and ROIs from %s"%fname)


  def clearCache(self):
    del self.roi1 
    del self.roi2
    alignment.clearCache(); # nedded for multiprocessing can leave bad parameters in the cache

  def setDefaultTransform( self ):
    #dict( scalex=0.65,rotation=0.0,transx=90, iblur1=4.3,fix_iblur1=False )
    t = alignment.g_fit_default_kw
    self.initAlign = t
    return t

def quick_mec(run,ref=236,divideByRef=False,returnRes=False):
  """ useful to analyze the runs around 140 (done with the focusing """
  ref_run = 236
  h=h5py.File("mecl3616_output/run%04d_analysis.h5" %ref,"r")
  ref = np.nanmean(h["calibNone"]["ratio"][...],axis=0)
  r = AnalyzeRun(run,initAlign=ref,swapx=True,swapy=False)
  res=r.doShots(slice(5),doFit=False)
  ret = res["ratio"]/ref if divideByRef else res["ratio"]
  if returnRes:
    return ret,res
  else:
    return ret

def quickAndDirty(run,nShots=300,returnAll=True,doFit=False):
  """ useful to analyze the runs around 140 (done with the focusing """
  r = AnalyzeRun(run,swap=True,initAlign=g_folder_init+"/run0144_transform.npy")
  res=r.doShots(slice(nShots),doFit=doFit)
  o = alignment.unravel_results(res)
  ref = np.nanmedian(o["ratio"][:40],0)
  sam = np.nanmedian(o["ratio"][50:],0)
  if returnAll:
    return sam/ref,o["ratio"]/ref
  else:
    return sam/ref