#!/usr/bin/env python
# DoseAccumulate.py
#   Copyright (C) 2007 STFC, Graeme Winter
#
#   This code is distributed under the BSD license, a copy of which is
#   included in the root directory of this package.
#
# A module to determine the accumulated dose as a function of exposure epoch
# for a given set of images, assuming:
#
# (i)  the set is complete
# (ii) the set come from a single (logical) crystal
#
# This is to work with fortran program "doser" and also to help fix
# Bug # 2798.
#

import os, sys

if not os.environ.has_key('XIA2_ROOT'):
  raise RuntimeError, 'XIA2_ROOT undefined'

if not os.environ['XIA2_ROOT'] is sys.path:
  sys.path.append(os.environ['XIA2_ROOT'])

from Wrappers.XIA.Diffdump import Diffdump
from Handlers.Streams import Debug

def epocher(images):
  '''Get a list of epochs for each image in this list, returning as
  a list of tuples.'''

  result = []
  for i in images:
    dd = Diffdump()
    dd.set_image(i)
    header = dd.readheader()

    e = header['epoch']
    result.append((i, e))

  return result

def accumulate(images):
  '''Accumulate dose as a function of image epoch.'''

  dose = { }
  integrated_dose = { }

  for i in images:
    dd = Diffdump()
    dd.set_image(i)
    header = dd.readheader()

    d = header['exposure_time']
    e = header['epoch']

    dose[e] = d

  keys = dose.keys()

  keys.sort()

  accum = 0.0

  for k in keys:
    integrated_dose[k] = accum + 0.5 * dose[k]
    accum += dose[k]

  # ok, now check that the value for the maximum dose is < 1e6 - elsewise
  # chef may explode. or write out ****** as the values are too large for
  # an f8.1 format statement.

  max_dose = max([integrated_dose[k] for k in keys])

  factor = 1.0

  while max_dose / factor > 1.0e6:
    factor *= 10.0

  Debug.write('Doses scaled by %5.2e' % factor)

  # now divide all of those doses by factor

  for k in keys:
    integrated_dose[k] /= factor

  return integrated_dose, factor

if __name__ == '__main__':
  if len(sys.argv) < 2:
    raise RuntimeError, '%s /path/to/image' % sys.argv[0]

  # FIXME with this I would like to be able to give a first batch
  # for a template, then print out the batch numbers computed from
  # the image number and an offset, to enable offline adding
  # of data with doser to test out development versions of chef.

  # don't worry, be static!

  offsets = {
      '9172_1_E1_###.img':0,
      '9172_1_E2_###.img':1000,
      '9172_2_###.img':2000,
      '9172_102_###.img':3000,
      '12287_1_E1_###.img':0,
      '12287_1_E2_###.img':100,
      '12287_2_###.img':200,
      '13185_2_E1_###.img':0,
      '13185_2_E2_###.img':1000,
      '13185_3_###.img':2000,
      'insulin_1_###.img':0,
      'thau_hires_1_###.mccd':0,
      'thau_lores_1_###.mccd':1000,
      '27032_1_E1_###.mccd':0,
      '27032_1_E2_###.mccd':100,
      '27032_2_###.mccd':200
      }

  batches = { }

  from Experts.FindImages import find_matching_images, \
       template_directory_number2image, image2template_directory
  from Handlers.Flags import Flags

  # just cos we can...
  Flags.set_trust_timestamps(True)

  image_names = []
  for image in sys.argv[1:]:

    template, directory = image2template_directory(image)
    images = find_matching_images(template, directory)

    batch_images = []

    for i in images:
      image = template_directory_number2image(
          template, directory, i)
      image_names.append(image)
      batch_images.append(image)

    epochs = epocher(batch_images)
    for j in range(len(epochs)):
      batch = images[j] + offsets[template]
      batches[epochs[j][1]] = batch

  dose = accumulate(image_names)
  epochs = dose.keys()
  epochs.sort()

  e0 = min(epochs)

  for e in epochs:
    print 'batch %d time %f dose %f' % \
          (batches[e], e - e0, dose[e])