#!/usr/bin/env python
# coding: utf-8

# # Convert ns-ALEX Becker-Hickl SPC/SET files to Photon-HDF5
# 
# ## Summary
# This executable document (it is called a [Jupyter notebook](http://ipython.org/notebook.html)) 
# will guide you through the conversion of a ns-ALEX data file from **SPC/SET**
# to [Photon-HDF5](http://photon-hdf5.org) format.
# 
# If you are reading this notebook online, please refer to 
# [this quick-start guide](http://jupyter-notebook-beginner-guide.readthedocs.org) 
# for instructions on how to install the required software and run the notebook 
# on your machine. 
# 
# If it's the first time you are using a Jupyter notebook, please click on 
# *Help* -> *User Interface Tour* for a quick tour of the interface.
# 
# In this notebook there are "text cells", such as this paragraph, and "code cells",
# containing the code to be executed. To execute a code cell, select it and 
# press SHIFT+ENTER. To edit a code cell it must be selected and with a green 
# frame around it.
# 
# ## Prepare the data files
# Your can run this notebook using example data files available 
# [on figshare](http://dx.doi.org/10.6084/m9.figshare.1455963). If you use these 
# example files, please unzip them and put them in a folder named "data" (lower case) 
# inside the folder containing this notebook.
# 
# Alternatively, you can use your own SPC/SET files. In this case, you need to paste 
# the full path of your SPC file in the following cell, instead of the path between 
# single quotes `'`.
# 
# > **NOTE**: if your path contains the `'` character please use `"` as string delimiter.

# In[ ]:


filename = r'data/dsdna_d7_d17_50_50_1.spc'


# If you also have a SET file it is expected to be in the same folder and 
# have the same name as the SPC file, with extension ".set".
# 
# The next cell will check whether the `filename` location is correct:

# In[ ]:


import os
try: 
    with open(filename): pass
    print('Data file found, you can proceed.')
except IOError:
    print('ATTENTION: Data file not found, please check the filename.\n'
          '           (current value "%s")' % filename)


# In case of file not found, please double check that have you put the example 
# data files in the "data" folder, or that the path you have pasted in `filename`
# is correct. Please re-execute the last two cells until the file is found.

# ## Data file description
# 
# In the next few cells, we specify the additional metadata that will be stored 
# in the Photon-HDF5 file. If you are using the example file you don't need to 
# edit any of these. If are using your own file, please modify these description
# accordingly.

# ### Author
# 
# These fields will go in the [identity](http://photon-hdf5.readthedocs.org/en/latest/phdata.html#identity-group) group:

# In[ ]:


author = 'Eitan Lerner'
author_affiliation = 'UCLA'
creator = 'Antonino Ingargiola'
creator_affiliation = 'UCLA'
license = 'Public Domain'


# ### Sample
# 
# Except for `description`, all these fields will go in the [sample](http://photon-hdf5.readthedocs.org/en/latest/phdata.html#sample-group) group:

# In[ ]:


description = 'A demonstrative smFRET-nsALEX measurement.'
sample_name = '50-50 mixture of two FRET samples'
dye_names = 'ATTO550, ATTO647N'
buffer_name = 'TE50'


# Please edit the previous cells and execute them (SHIFT+ENTER) to make sure 
# there are no errors. Then proceed to the next section.

# ## Load the data
# 
# Before loading the data we need to load a few library functions:

# In[ ]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import numpy as np
import phconvert as phc
print('phconvert version: ' + phc.__version__)


# Next, we can load the input file and assign the measurement parameters 
# ([measurement_specs](http://photon-hdf5.readthedocs.org/en/latest/phdata.html#measurement-specs)), 
# necessary to create a complete Photon-HDF5 file.
# 
# If using your own file, please review all the parameters in the next cell.

# In[ ]:


d, meta = phc.loader.nsalex_bh(filename,
                               donor = 4,
                               acceptor = 6,
                               laser_repetition_rate = 40e6,
                               tcspc_range = 60e-9,
                               timestamps_unit = 60e-9,
                               alex_period_donor = (1800, 3300),
                               alex_period_acceptor = (270, 1500),
                               excitation_wavelengths = (532e-9, 635e-9),
                               detection_wavelengths = (580e-9, 680e-9),
                               allow_missing_set = False)


# The next cell plots a `nanotimes` histogram for the donor and acceptor channel separately.
# The shaded areas marks the donor (*green*) and acceptor (*red*) excitation periods.
# 
# If the histogram looks wrong in some aspects (no photons, wrong detectors
# assignment, wrong period selection) please go back to the previous cell 
# and tweak the relevant parameters until the histogram looks correct.

# In[ ]:


fig, ax = plt.subplots(figsize=(10, 4))
phc.plotter.alternation_hist(d, ax=ax)


# You may also find useful to see how many different detectors are present
# and their number of photons. This information is shown in the next cell.

# In[ ]:


detectors = d['photon_data']['detectors']

print("Detector    Counts")
print("--------   --------")
for det, count in zip(*np.unique(detectors, return_counts=True)):
    print("%8d   %8d" % (det, count))


# # File conversion
# 
# Once you finished editing the the previous sections you can proceed with
# the actual conversion. It is suggested to execute the notebook in
# one step by clicking on the menu *Cells* -> *Run All*.
# 
# After that, you should find a new `.hdf5` file in the same folder of the input
# file. You can check it's content by using [HDFView](https://www.hdfgroup.org/products/java/hdfview/).
# 
# The cells below contain the code to convert the input file to Photon-HDF5.

# ## Add metadata

# In[ ]:


d['description'] = description

d['sample'] = dict(
    sample_name=sample_name,
    dye_names=dye_names,
    buffer_name=buffer_name,
    num_dyes = len(dye_names.split(',')))

d['identity'] = dict(
    author=author,
    author_affiliation=author_affiliation,
    creator=creator,
    creator_affiliation=creator_affiliation)


# ## Add custom (user) data
# 
# Here we save a custom (*user*) group to store all the metadata
# found in the original file (.SET). 
# 
# The important metadata has already been extracted and placed 
# in standard Photon-HDF5 fields.
# However, here we dump the full content of the metadata in 
# the input file to prevent any possible information loss
# during the conversion.

# In[ ]:


# Put `meta` in /user/becker_hickl
d['user'] = dict(becker_hickl=meta)

# Descriptions for custom fields
user_descr = {'/user': 'A custom group.',
              '/user/becker_hickl': 'Full metadata from original .SET file.'}


# ## Save to Photon-HDF5
# 
# This command saves the new file to disk. If the input data does not follows the Photon-HDF5 specification it returns an error (`Invalid_PhotonHDF5`) printing what violates the specs.

# In[ ]:


phc.hdf5.save_photon_hdf5(d, close=False, overwrite=True, user_descr=user_descr)


# In[ ]:


h5file = d['_data_file']


# In[ ]:


#d['_data_file'].close()


# ## Print HDF5 file content
# 
# Finally, we print the file content to see what's inside the newly-created Photon-HDF5. Here we print the content of the root node:

# In[ ]:


phc.hdf5.print_children(h5file.root)


# And here we retrieve some information from the user group:

# In[ ]:


phc.hdf5.dict_from_group(h5file.root.user.becker_hickl.identification)


# In[ ]:


#phc.hdf5.dict_from_group(h5file.root.user.becker_hickl.sys_params)


# In[ ]:


h5file.close()


# ## Load Photon-HDF5
# 
# Finally we try to reload the file to check that there are no errors.

# In[ ]:


from pprint import pprint


# In[ ]:


filename = d['_data_file'].filename


# In[ ]:


h5data = phc.hdf5.load_photon_hdf5(filename)


# In[ ]:


phc.hdf5.dict_from_group(h5data.identity)


# In[ ]:


phc.hdf5.dict_from_group(h5data.setup)


# In[ ]:


pprint(phc.hdf5.dict_from_group(h5data.photon_data))


# In[ ]:


h5data._v_file.close()


# If the next cell output shows "OK" then the execution is terminated.

# In[ ]:


print('OK')