- Remove coverage logs

- Update changelogs.rst

source/changelog.rst

@@ -6,13 +6,29 @@ What's new
 .. contents:: Table of Contents
    :depth: 2
 
+v0.1.2 (September 2020)
+--------------------
+
+ **New functions**
+
+a. Add :py:func:`systole.utils.to_rr()`. for peaks or index vectors convertion to RR intervals
+b. Add :py:func:`systole.recording.BrainVisionExG()`, a class to read physio recording from BrainVision ExG products via TCP/IP connection.
+c. Add :py:func:`systole.recording.findOximeter()`, find the USB port where Nonin Oximeter is plugged by looping through the USB port and checking the input.
+d. Add :py:func:`systole.detection.ecg_peaks()`. A wrapper around py-ecg-detectors for basic ECG peaks detection.
+
+**Enhancements**
+a. Improved documentation and examples.
+b. Simplification of PPG example data import.
+c. Improved interactive plotting functions.
+
+
 v0.1.1 (June 2020)
 ------------------
 
 **New functions**
 
 a. Add the **plotly** sub-module, a set of Plotly functions comprising :py:func:`systole.plotly.plot_raw`, :py:func:`systole.plotly.plot_subspaces`, :py:func:`systole.plotly.plot_ectopic`, :py:func:`systole.plotly.plot_shortLong`, :py:func:`systole.plotly.plot_frequency`, :py:func:`systole.plotly.plot_nonlinear`, :py:func:`systole.plotly.plot_timedomain`.
-b. Add :py:func:`plotly.utils.simulate_rr()`, for random RR interval simulation with different kind of artefacts. Can also return peak vector.
+b. Add :py:func:`systole.utils.simulate_rr()`, for random RR interval simulation with different kind of artefacts. Can also return peak vector.
 c. The **correction** sub-module has been largely rewritten and now include :py:func:`systole.correction.correct_extra`, :py:func:`systole.correction.correct_missed`, :py:func:`systole.correction.interpolate_bads`, :py:func:`systole.correction.correct_rr`, :py:func:`systole.correction.correct_peaks`, :py:func:`systole.correction.correct_missed_peaks`, :py:func:`systole.correction.correct_extra_peaks`. These function can correct artefacts either using peaks addition/removal or by interpolation of the RR time series.
 
 **Enhancements**

systole/datasets/__init__.py

@@ -15,7 +15,7 @@ class serialSim():
     sampling rate (75 Hz).
     """
 
-    def __init__(self, id='1'):
+    def __init__(self):
         self.sfreq = 75
         self.ppg = import_ppg().ppg.to_numpy()
         self.start = time.time()
@@ -29,7 +29,7 @@ def inWaiting(self):
 
         return lenInWating
 
-    def read(self, length):
+    def read(self, lenght):
 
         if len(self.ppg) == 0:
             self.ppg = import_ppg().ppg.to_numpy()