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

# # Convert pt3 files to Photon-HDF5
# 
# 
# # Prepare the data file
# 
# 
# Before starting, you need to get a data file to be converted to Photon-HDF5.
# You can use one of our example data files available
# [on figshare](https://figshare.com/articles/data_files_for_phconvert/5421565).
# 
# Specify the input data file in the following cell:

# In[ ]:


filename = 'data/DNA_FRET_0.5nM.pt3'


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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[ ]:


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


# ## Load Data

# In[ ]:


d, meta = phc.loader.nsalex_pq(filename,
                               donor = 2,
                               acceptor = 1,
                               alex_period_donor = (0, 2000),
                               alex_period_acceptor = (2000, 3200),
                               excitation_wavelengths = (470e-9, 635e-9),
                               detection_wavelengths = (525e-9, 690e-9),
                               )


# In[ ]:


meta['hardware_name']


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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))


# ## Remove the overflow counts

# In[ ]:


nanotimes = d['photon_data']['nanotimes']
detectors = d['photon_data']['detectors']
timestamps = d['photon_data']['timestamps']

not_overflow = d['photon_data']['nanotimes'] != 0

detectors = detectors[not_overflow]
timestamps = timestamps[not_overflow]
nanotimes = nanotimes[not_overflow]


# In[ ]:


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


# In[ ]:


d['photon_data']['nanotimes'] = nanotimes
d['photon_data']['detectors'] = detectors
d['photon_data']['timestamps'] = timestamps


# In[ ]:


phc.plotter.alternation_hist(d)


# ## Metadata

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author = 'Biswajit'
author_affiliation = 'Leiden University'
description = 'A demonstrative pt3 data file.'
sample_name = 'Copper Azurin in 1mM Ascorbate'
dye_names = 'ATTO655'
buffer_name = 'HEPES pH7 with 100 mM NaCl'


# ### Add meta data

# 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)


# In[ ]:


# Remove some empty groups that may cause errors on saving
_ = meta.pop('dispcurve', None)
_ = meta.pop('imghdr', None)


# In[ ]:


d['user'] = {'picoquant': meta}


# ## Save to Photon-HDF5

# In[ ]:


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


# ## Load Photon-HDF5

# In[ ]:


from pprint import pprint


# In[ ]:


filename = d['_data_file'].filename


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h5data = phc.hdf5.load_photon_hdf5(filename)


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phc.hdf5.dict_from_group(h5data.identity)


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phc.hdf5.dict_from_group(h5data.setup)


# In[ ]:


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


# In[ ]:


h5data._v_file.close()