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

# # FRETBursts - ns-ALEX example
# 
# *This notebook is part of a [tutorial series](https://github.com/tritemio/FRETBursts_notebooks) for the [FRETBursts](http://tritemio.github.io/FRETBursts/) burst analysis software.*

# > For a step-by-step introduction to FRETBursts usage please refer to 
# > [us-ALEX smFRET burst analysis](FRETBursts - us-ALEX smFRET burst analysis.ipynb).
# >
# > In this notebook we present a typical [FRETBursts](http://tritemio.github.io/FRETBursts/)
# > workflow for **ns-ALEX smFRET burst analysis**. 
# 
# *While FRETBursts does not specifically includes functions for fitting 
# TCSPC fluorescence decays, a fitting with exponential decays and 
# IRF deconvolution can be easily performed using standard python libraries. 
# For an example and a brief discussion see the notebook 
# [Lifetime decay fit](http://nbviewer.ipython.org/github/tritemio/notebooks/blob/master/Lifetime_decay_fit.ipynb).*

# ## Loading FRETBursts

# In[1]:


from fretbursts import *


# In[2]:


sns = init_notebook()


# # Downloading the sample data file
# 
# The full list of smFRET measurements used in the [FRETBursts tutorials](https://github.com/tritemio/FRETBursts_notebooks) 
# can be found on [Figshare](http://dx.doi.org/10.6084/m9.figshare.1456362).
# 
# Here we download the ns-ALEX data-file and put it in a folder 
# named `data`, inside the notebook folder. 
# For this purpose we use the `download_file` function provided 
# by FRETBursts:

# In[3]:


url = 'http://files.figshare.com/2182602/dsdna_d7_d17_50_50_1.hdf5'


# In[4]:


download_file(url, save_dir='./data')


# In[5]:


filename = './data/dsdna_d7_d17_50_50_1.hdf5'
filename


# ## Selecting a data file
# 
# Alternatively you can use an open-file dialog in order to select a data file:

# In[6]:


# filename = OpenFileDialog()
# filename


# In[7]:


import os
if os.path.isfile(filename):
    print("Perfect, I found the file!")
else:
    print("Sorry, I can't find the file:\n%s" % filename)


# ## Load the selected file
# 
# Here we load the file and we set the alternation parameters:

# In[8]:


d = loader.photon_hdf5(filename)
#d = loader.nsalex(fname)


# In[9]:


d.time_max


# In[10]:


d.det_t


# In[11]:


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


# In[12]:


#d.add(A_ON=(200, 1500), D_ON=(1750, 3200), det_donor_accept=(4, 6))


# In[13]:


d.nanotimes_t


# In[14]:


bpl.plot_alternation_hist(d)


# Execute the previous 2 cells until you get a satisfying 
# selection for the excitation periods. Then run the following 
# to apply the parameters:

# In[15]:


loader.alex_apply_period(d)


# ## Burst search and selection

# In[16]:


d.calc_bg(fun=bg.exp_fit, time_s=30, tail_min_us='auto', F_bg=1.7)


# In[17]:


dplot(d, timetrace_bg)


# In[18]:


dplot(d, timetrace)
xlim(1, 2)
ylim(-50, 50)


# In[19]:


d.burst_search()


# In[20]:


ds = d.select_bursts(select_bursts.size, th1=30)


# In[21]:


alex_jointplot(ds)


# In[22]:


ds.leakage = 0.05


# In[23]:


alex_jointplot(ds)


# In[24]:


dplot(ds, hist_fret, show_kde=True)


# ## Nanotimes
# 
# The nanotimes for the measurement is saved in the `.nanotimes` 
# attribute of the `Data()` object (here either `d` or `ds`).
# 
# As an example here we get the array of nanotimes for all photons, 
# donor emission and acceptor emission:

# In[25]:


d.nanotimes


# In[26]:


nanotimes = d.nanotimes[0]
nanotimes_d = nanotimes[d.get_D_em()]
nanotimes_a = nanotimes[d.get_A_em()]


# We can plot the histogram for this 3 nanotimes:

# In[27]:


hist_params = dict(bins=range(4096), histtype='step', alpha=0.6, lw=1.5)
hist(nanotimes, color='k', label='Total ph.', **hist_params)
hist(nanotimes_d, color='g', label='D. em. ph.', **hist_params)
hist(nanotimes_a, color='r', label='A. em. ph.', **hist_params)
plt.legend()
plt.yscale('log')


# We can also select only nanotimes of photons inside bursts. 
# Here, as an example, we will use the `ds` variable that 
# contains a selection of bursts.
# 
# First we compute a selection mask (a boolean array) for 
# photons inside bursts:

# In[28]:


ph_in_bursts_mask = d.ph_in_bursts_mask_ich()


# Then we apply this selection to the nanotimes array. 
# To get the donor- and acceptor-emission nanotimes we combine 
# the in-bursts selection mask (`ph_in_bursts_mask`) with 
# the donor or acceptor emission mask (that we get with
# `.get_D_em()` and `.get_D_em()`):

# In[29]:


bursts_nanotimes_t = nanotimes[ph_in_bursts_mask]
bursts_nanotimes_d = nanotimes[ph_in_bursts_mask * d.get_D_em()]
bursts_nanotimes_a = nanotimes[ph_in_bursts_mask * d.get_A_em()]


# And, as before, we can histogram the nanotimes:

# In[30]:


hist_params = dict(bins=range(4096), histtype='step', alpha=0.6, lw=1.5)
hist(bursts_nanotimes_t, color='k', label='Total ph.', **hist_params)
hist(bursts_nanotimes_d, color='g', label='D. em. ph.', **hist_params)
hist(bursts_nanotimes_a, color='r', label='A. em. ph.', **hist_params)
plt.legend()
plt.yscale('log')


# ## Saving to a file
# 
# Saving some of all these arrays to file is straightforward.

# ### Save array to txt comma-separed-values
# 
# To save a single array to a file we can use the [`.tofile`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.tofile.html) method:

# In[31]:


nanotimes.tofile('nanotimes_t.csv', sep=',\n')  # save in CSV txt format


# ### Save to legacy MATLAB format
# 
# To save a set of arrays in MATLAB format  we can use the 
# [`scipy.io.savemat`](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.io.savemat.html) function.
# 
# Here we save 3 arrays `bursts_nanotimes_t`, `bursts_nanotimes_d` 
# and `bursts_nanotimes_a` to a file called *bursts_nanotimes.mat*:

# In[32]:


from scipy.io import savemat


# In[33]:


savemat('bursts_nanotimes.mat', 
        dict(bn_t=bursts_nanotimes_t, 
             bn_d=bursts_nanotimes_d, 
             bn_a=bursts_nanotimes_a,))


# When loaded in MATLAB the arrays will be named `bn_t`, `bn_d` and `bn_a`.

# In[34]:





# In[34]:





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# ---
# **Executed:** Tue Jul 11 21:53:19 2017
# 
# **Duration:** 22 seconds.
# 
# **Autogenerated from:** [FRETBursts - ns-ALEX example.ipynb](out/FRETBursts - ns-ALEX example.ipynb)