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

# # detect_peaks.py
# 
# A function from [detecta](https://pypi.org/project/detecta/) - Python module to detect events in data.

# One way to detect peaks (local maxima) or valleys (local minima) in data is to use the property that a peak (or valley) must be greater (or smaller) than its immediate neighbors. The function `detect_peaks.py` (code at the end of this notebook) detects peaks (or valleys) based on this feature and other characteristics. The function signature is:
# ```python
# ind = detect_peaks(x, mph=None, mpd=1, threshold=0, edge='rising', kpsh=False, valley=False, show=False, ax=None, title=True)
# ```
# The parameters `mph`, `mpd`, and `threshold` follow the convention of the Matlab function `findpeaks.m`.   
# Let's see how to use `detect_peaks.py`; first let's import the necessary Python libraries and configure the environment:
# 
# <!-- TEASER_END -->

# In[1]:


from detecta import detect_peaks


# In[2]:


import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# Running the function examples:

# In[3]:


from detecta import detect_peaks
x = np.random.randn(100)
x[60:81] = np.nan
# detect all peaks and plot data
ind = detect_peaks(x, show=True)
print(ind)

x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
# set minimum peak height = 0 and minimum peak distance = 20
detect_peaks(x, mph=0, mpd=20, show=True)

x = [0, 1, 0, 2, 0, 3, 0, 2, 0, 1, 0]
# set minimum peak distance = 2
detect_peaks(x, mpd=2, show=True)

x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
# detection of valleys instead of peaks
detect_peaks(x, mph=-1.2, mpd=20, valley=True, show=True)

x = [0, 1, 1, 0, 1, 1, 0]
# detect both edges
detect_peaks(x, edge='both', show=True)

x = [-2, 1, -2, 2, 1, 1, 3, 0]
# set threshold = 2
detect_peaks(x, threshold = 2, show=True)
 
x = [-2, 1, -2, 2, 1, 1, 3, 0]
fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 4))
detect_peaks(x, show=True, ax=axs[0], threshold=0.5, title=False)
detect_peaks(x, show=True, ax=axs[1], threshold=1.5, title=False)


# ## Function performance
# 
# The function `detect_peaks.py` is relatively fast but the parameter minimum peak distance (mpd) slows down the function if the data has several peaks (>1000). Try to decrease the number of peaks by tuning the other parameters or smooth the data before calling this function with several peaks in the data.  
# Here is a simple test of its performance:

# In[4]:


x = np.random.randn(10000)
ind = detect_peaks(x)
print('Data with %d points and %d peaks\n' %(x.size, ind.size))
print('Performance (without the minimum peak distance parameter):')
print('detect_peaks(x)')
get_ipython().run_line_magic('timeit', 'detect_peaks(x)')
print('\nPerformance (using the minimum peak distance parameter):')
print('detect_peaks(x, mpd=10)')
get_ipython().run_line_magic('timeit', 'detect_peaks(x, mpd=10)')


# In[ ]: