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

# # Logfile parsing
# (back to overview [offline](../Main.ipynb),[online](https://nbviewer.jupyter.org/github/QCoDeS/Qcodes/tree/master/docs/examples/Main.ipynb))
# 
# [read on nbviewer](https://nbviewer.jupyter.org/github/QCoDeS/Qcodes/tree/master/docs/examples/logging/logfile_parsing.ipynb)

# Here, we provide an example concerning how to benefit from QCoDeS logs by simple analysis and visualisation.

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import dateutil
import os

import pandas as pd
import matplotlib.pyplot as plt

import qcodes as qc
from qcodes.logger import logfile_to_dataframe, time_difference, log_to_dataframe
from zipfile import ZipFile


# In[2]:


# put the 30MB into a zip file
filepath = os.path.join(os.getcwd(), 'static', 'pythonlog.zip')
with ZipFile(filepath) as z:
    with z.open('pythonlog.log', 'r') as f:
        my_log = [line.decode() for line in f]


# In[3]:


os.path.exists(filepath)


# In[4]:


logdata = log_to_dataframe(my_log, separator=' - ', columns=['time', 'module', 'function', 'loglevel', 'message'])


# The `logdata` is, now, a nice and tidy `DataFrame` that one can further manipulate to extract more information, if needed.

# In[5]:


logdata


# In[6]:


data = logdata


# ### Get the query time for the SR860
# 
# We know that the log file documents an experiment quering an SR860 for R and amplitude over and over. Let us analyse and visualise query response times.

# In[7]:


qstr_R = 'Querying instrument SR860_120: OUTP\? 2'  # remember to escape
queries_R = data[data.message.str.contains(qstr_R)]
responses_R = data.loc[queries_R.index + 1]

qstr_lvl = 'Querying instrument SR860_120: SLVL\?'  # remember to escape
queries_lvl = data[data.message.str.contains(qstr_lvl)][:-1]
responses_lvl = data.loc[queries_lvl.index + 1]


# ### Find the elapsed times

# In[8]:


elapsed_times_R = time_difference(queries_R.time, responses_R.time)
elapsed_times_lvl =  time_difference(queries_lvl.time, responses_lvl.time)


# ## Visualise!

# In[9]:


from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

fig, ax = plt.subplots(1,1)
ax.plot(queries_R.time.str.replace(',', '.').astype("datetime64[ns]"), elapsed_times_R, '.', label='R')
ax.plot(queries_lvl.time.str.replace(',', '.').astype("datetime64[ns]"), elapsed_times_lvl, '.', label='LVL')
fig.autofmt_xdate()
ax.set_ylabel('Response time (s)')
plt.legend()


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