peak-finding tutorial

Author: emmanuelle

unconverted/python/peak-finding.md

@@ -8,9 +8,19 @@ jupyter:
       format_version: '1.1'
       jupytext_version: 1.1.1
   kernelspec:
-    display_name: Python 2
+    display_name: Python 3
     language: python
-    name: python2
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
+      version: 3
+    file_extension: .py
+    mimetype: text/x-python
+    name: python
+    nbconvert_exporter: python
+    pygments_lexer: ipython3
+    version: 3.6.7
   plotly:
     description: Learn how to find peaks and valleys on datasets in Python
     display_as: peak-analysis
@@ -26,131 +36,100 @@ jupyter:
     title: Peak Finding in Python | plotly
 ---
 
-#### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
-<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
-<br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
-
-
 #### Imports
-The tutorial below imports [NumPy](http://www.numpy.org/), [Pandas](https://plot.ly/pandas/intro-to-pandas-tutorial/), [SciPy](https://www.scipy.org/) and [PeakUtils](http://pythonhosted.org/PeakUtils/).
+The tutorial below imports [Pandas](https://plot.ly/pandas/intro-to-pandas-tutorial/), and [SciPy](https://www.scipy.org/).
 
 ```python
-import plotly.plotly as py
-import plotly.graph_objs as go
-from plotly.tools import FigureFactory as FF
-
-import numpy as np
 import pandas as pd
-import scipy
-import peakutils
+from scipy.signal import find_peaks
 ```
 
 #### Import Data
 To start detecting peaks, we will import some data on milk production by month:
 
 ```python
+import plotly.graph_objects as go
+import pandas as pd
+
 milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
 time_series = milk_data['Monthly milk production (pounds per cow)']
-time_series = time_series.tolist()
 
-df = milk_data[0:15]
-
-table = FF.create_table(df)
-py.iplot(table, filename='milk-production-dataframe')
-```
-
-#### Original Plot
-
-```python
-trace = go.Scatter(
-    x = [j for j in range(len(time_series))],
+fig = go.Figure(data=go.Scatter(
     y = time_series,
     mode = 'lines'
-)
+))
 
-data = [trace]
-py.iplot(data, filename='milk-production-plot')
+fig.show()
 ```
 
-#### With Peak Detection
+#### Peak Detection
+
 We need to find the x-axis indices for the peaks in order to determine where the peaks are located.
 
 ```python
-cb = np.array(time_series)
-indices = peakutils.indexes(cb, thres=0.02/max(cb), min_dist=0.1)
+import plotly.graph_objects as go
+import pandas as pd
+from scipy.signal import find_peaks
+
+milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
+time_series = milk_data['Monthly milk production (pounds per cow)']
+
+indices = find_peaks(time_series)[0]
 
-trace = go.Scatter(
-    x=[j for j in range(len(time_series))],
+fig = go.Figure()
+fig.add_trace(go.Scatter(
     y=time_series,
-    mode='lines',
+    mode='lines+markers',
     name='Original Plot'
-)
+))
 
-trace2 = go.Scatter(
+fig.add_trace(go.Scatter(
     x=indices,
     y=[time_series[j] for j in indices],
     mode='markers',
     marker=dict(
         size=8,
-        color='rgb(255,0,0)',
+        color='red',
         symbol='cross'
     ),
     name='Detected Peaks'
-)
+))
 
-data = [trace, trace2]
-py.iplot(data, filename='milk-production-plot-with-peaks')
+fig.show()
 ```
 
 #### Only Highest Peaks
 We can attempt to set our threshold so that we identify as many of the _highest peaks_ that we can.
 
 ```python
-cb = np.array(time_series)
-indices = peakutils.indexes(cb, thres=0.678, min_dist=0.1)
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+from scipy.signal import find_peaks
+
+milk_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/monthly-milk-production-pounds.csv')
+time_series = milk_data['Monthly milk production (pounds per cow)']
+
+indices = find_peaks(time_series, threshold=20)[0]
 
-trace = go.Scatter(
-    x=[j for j in range(len(time_series))],
+fig = go.Figure()
+fig.add_trace(go.Scatter(
     y=time_series,
-    mode='lines',
+    mode='lines+markers',
     name='Original Plot'
-)
+))
 
-trace2 = go.Scatter(
+fig.add_trace(go.Scatter(
     x=indices,
     y=[time_series[j] for j in indices],
     mode='markers',
     marker=dict(
         size=8,
-        color='rgb(255,0,0)',
+        color='red',
         symbol='cross'
     ),
     name='Detected Peaks'
-)
-
-data = [trace, trace2]
-py.iplot(data, filename='milk-production-plot-with-higher-peaks')
-```
-
-```python
-from IPython.display import display, HTML
-
-display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
-display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))
-
-! pip install git+https://github.com/plotly/publisher.git --upgrade
-import publisher
-publisher.publish(
-    'python-Peak-Finding.ipynb', 'python/peak-finding/', 'Peak Finding | plotly',
-    'Learn how to find peaks and valleys on datasets in Python',
-    title='Peak Finding in Python | plotly',
-    name='Peak Finding',
-    language='python',
-    page_type='example_index', has_thumbnail='false', display_as='peak-analysis', order=3,
-    ipynb= '~notebook_demo/120')
-```
-
-```python
+))
 
+fig.show()
 ```