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

# # Convert ns-ALEX HT3 files to Photon-HDF5
# 
# ## Summary
# This executable document (is called a [Jupyter notebook](http://ipython.org/notebook.html)) will guide you through the conversion of a ns-ALEX data file from **HT3** to [Photon-HDF5](http://photon-hdf5.org) format.
# 
# 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, like this paragraph, and code cells, containing the code to be executed.
# To execute a selected code cell hit SHIFT+ENTER. To edit a code cell it must be selected and with a green frame around it.
# 
# 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 a Jupyter notebook on your machine. 
# 
# ## 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 on your own HT3 file. In this case you need to paste 
# the full path of your HT3 file in the following cell, replacing value between single quotes `'`.

# In[ ]:


filename = r'data/Pre.ht3'


# The next cell will check if 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 you put the example 
# data files in the "data" folder, or that the path you put in `filename`
# is correct. Please re-execute the last two cells until the file is found.

# ## Data file description
# 
# Here 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 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'


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

# In[ ]:


description = 'A demostrative smFRET-nsALEX measurement.'
sample_name = 'Doubly-labeled ssDNA partially hybridized to a complementary strand.'
dye_names = ['ATTO488', 'ATTO647N']
buffer_name = 'Tris20 mM Ph 7.8'


# 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_ht3(filename,
                                donor = 0,
                                acceptor = 1,
                                alex_period_donor = (150, 1500),
                                alex_period_acceptor = (1540, 3050),
                                excitation_wavelengths = (470e-9, 635e-9),
                                detection_wavelengths = (525e-9, 690e-9),
                                time_reversed = 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=[n.encode() for n in dye_names],
    buffer_name=buffer_name,
    num_dyes = len(dye_names))

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


# ## Validate the Photon-HDF5 structure
# 
# Before writing to disk, we assure the file structure follows the Photon-HDF5 format:

# In[ ]:


phc.hdf5.assert_valid_photon_hdf5(d)


# ## Save to Photon-HDF5
# 
# This command saves the new file to disk.

# In[ ]:


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


# In[ ]:


#d['_data_file'].close()


# ## Save HT3 metadata
# 
# Here we save a custom (*user*) group where we put all the metadata
# found in the input .HT3 file. The important metadata from the .HT3
# file is already saved in the standard Photon-HDF5 fields.
# 
# Here we save the full original metadata in order to make sure that
# no information is lost during the conversion.

# In[ ]:


h5file = d['_data_file']
h5file.create_group('/', 'user')
pq_group = h5file.create_group('/user', 'picoquant')
for key in meta: 
    if np.size(meta[key]) > 0:
        h5file.create_table(pq_group, key, obj=meta[key])


# In[ ]:


#h5file.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 roor node:

# In[ ]:


phc.hdf5.print_children(h5file.root)


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

# In[ ]:


phc.hdf5.print_children(h5file.root.user.picoquant)


# 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))


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

# In[ ]:


print('OK')