debug cross co example in doc of cardio respi synchro and improve doc of other examples (remove typos)

examples/example_01_getting_started.py

@@ -42,14 +42,13 @@
 
 ##############################################################################
 # 
-# Analyse respiration
+# Analyze respiration
 # -------------------
 # 
 # :py:func:`~physio.compute_respiration` is an easy function to:
 #
-#    * preprocess the respiration signal
-#    * compute cycle
-#    * compute cycle features
+#    * preprocess the respiratory signal (resp)
+#    * compute cycle features (resp_cycles)
 
 
 resp, resp_cycles = physio.compute_respiration(raw_resp, srate)
@@ -61,11 +60,11 @@
 
 ##############################################################################
 # 
-# repiration cycles and features
+# Respiration cycles and features
 # ------------------------------
 #  
-# resp_cycles is a dataframe containing all respiration cycles as rows and columns.
-# It contains features like duration, amplitudes, durations
+# resp_cycles is a dataframe containing all respiratory cycles as rows.
+# Columns contain features like duration, amplitudes, volumes.
 # 
 
 print(resp_cycles.shape)
@@ -98,8 +97,8 @@
 #  
 # :py:func:`~physio.compute_ecg` is an easy function to:
 #
-#     * Preprocess the ECG signal output, which is normalized by default
-#     * Detect R peaks
+#     * Preprocess the ECG signal output, which is normalized by default (ecg)
+#     * Detect R peaks (ecg_peaks)
 
 
 ecg, ecg_peaks = physio.compute_ecg(raw_ecg, srate)
@@ -122,10 +121,10 @@
 # ECG metrics
 # -----------
 # 
-# :py:func:`~physio.compute_ecg_metrics` is a simple function to compute temporal based metrics around ECG
+# :py:func:`~physio.compute_ecg_metrics` is a simple function to compute time-domain Heart Rate Variability (HRV) metrics.
 # 
 #
-# We can visualize theses metrics and the RR interval distribution.
+# We can visualize these metrics and the RR interval distribution.
 
 
 ecg_metrics = physio.compute_ecg_metrics(ecg_peaks)
@@ -150,21 +149,21 @@
 # to a cycle template by stretching with linear resampling to a fixed number of
 # points per cycle.
 #
-# This is helpfull to check if a signal is driven by a cyclic event like respiration.
+# This is helpful to explore if features of a signal are driven by a cyclic phenomenon like respiration.
 # 
-# Here, we deform the signal trace by "itself" : the respiration cycle.
-# This leads to an average respiration template.
+# Here, we deform the signal trace by "itself" : the respiratory cycle.
+# This leads to an average respiratory template.
 #
 # Importantly, this can be done using one or several segment inside the cycle.
 
-# here we have 3 time per cycle so 2 segments
+# here we have 3 times per cycle so 2 segments
 cycle_times = resp_cycles[['inspi_time', 'expi_time', 'next_inspi_time']].values
 deformed_resp_1seg = physio.deform_traces_to_cycle_template(resp, times, cycle_times,
                                                 points_per_cycle=40, segment_ratios=0.4,
                                                 output_mode='stacked')
 print(deformed_resp_1seg.shape, cycle_times.shape)
 
-# here we have 2 time per cycle so 1 segment
+# here we have 2 times per cycle so 1 segment
 cycle_times = resp_cycles[['inspi_time', 'next_inspi_time']].values
 deformed_resp_2seg = physio.deform_traces_to_cycle_template(resp, times, cycle_times,
                                                 points_per_cycle=40, segment_ratios=None,
@@ -183,7 +182,7 @@
 # Cyclic deformation on ECG
 # -------------------------
 # 
-# Lets use the same for ECG trace
+# Let's use the same for ECG trace
 # 
 # We can also use a simple vector in this case it is converted a a 1 segment case.
 
@@ -196,7 +195,7 @@
 
 fig, ax = plt.subplots()
 physio.plot_cyclic_deformation(deformed_ecg, two_cycles=True, ax=ax)
-ax.set_title('two ECG cycle avaerage')
+ax.set_title('Two ECG cycle averaged')
 
 ##############################################################################
 # 

examples/example_02_respiration.py

@@ -20,7 +20,7 @@
 # ------------------------------------------
 #
 # The fastest way is to use compute_respiration() using a predefined
-# parameter presetset. Here is a simple example.
+# parameter preset. Here is a simple example.
 
 
 # read data
@@ -46,22 +46,31 @@
 
 ax.set_xlim(110, 170)
 
+##############################################################################
+# 
+# Cycle detection: Default parameters
+# -----------------------------------
+# 
+# Here are the default parameters : those aiming to process a human airflow signal. 
+# It is possible to get default parameters dictionnary by calling it with the :py:func:`~physio.get_respiration_parameters` function.
+
+# this is a nested dict of parameters of every processing step
+parameters = physio.get_respiration_parameters('human_airflow')
+pprint(parameters)
 
 ##############################################################################
 # 
 # Cycle detection: Parameters tuning
 # -----------------------------------
 # 
-# Here is a simple recipe to change some predefined parameters.
-# We change here the length of the smoothing parameter.
+# If necessary, it is possible to change the predefined parameters.
+# For example, we change here the length of the smoothing parameter.
 
-# get paramseters set
-# this is a nested dict of parameter of every step
-parameters = physio.get_respiration_parameters('human_airflow')
-# lets change on parameter in the structure
+# let's change on parameter in the structure ...
 parameters['smooth']['sigma_ms'] = 100.
 pprint(parameters)
 
+# ... and use them by providing it to "parameters"
 resp, resp_cycles = physio.compute_respiration(raw_resp, srate, parameters=parameters)
 
 

examples/example_03_ecg.py

@@ -47,8 +47,8 @@
 # Detect ECG R peaks: Parameters tuning
 # -------------------------------------
 # 
-# Here is a simple recipe to change some predefined parameters.
-# We change here some filtering parameters.
+# Here is a simple recipe to change the default parameters.
+# We change here some filtering parameters (frequency band, filter type, order of the filter).
 
 # get paramseters predefined set for 'human_ecg'
 # this is a nested dict of parameter of every step
@@ -74,7 +74,7 @@
 
 ##############################################################################
 # 
-# ECG: compute metrics
+# ECG: compute time-domain heart rate variability (HRV) metrics
 # --------------------
 #
 
@@ -87,13 +87,13 @@
 # ECG : compute instantaneous rate
 # --------------------------------
 #
-# The RR-interval (aka rri) time series is a common tool to analyse the heart rate variability (hrv).
-# This is equivalent to computing the instantaneous heart rate.
+# The RR-interval (aka rri) time series is a common tool to analyse the heart rate variability (HRV).
+# This is equivalent to compute the instantaneous heart rate.
 # Heart rate [bpm] = 1 / rri * 60
 #
-# Most people use rri in ms, we feel that use heart rate in bpm is more intuitive. 
-# With bpm an increase in the curve = heart acceleration. 
-# With ms an increase in the curve = heart decceleration. 
+# Most people use rri in ms, we feel that the use of heart rate in bpm is more intuitive. 
+# With bpm unit, an increase in the curve means heart rate acceleration. 
+# With ms unit, an increase in the curve means heart rate deceleration. 
 #
 # Feel free to use the units you prefer (bpm or ms)
 
@@ -125,7 +125,7 @@
 
 ##############################################################################
 # 
-# ECG: compute hrv spectrum
+# ECG: compute frequency-domain heart rate variability (HRV) metrics
 # -------------------------
 #
 # 

examples/example_05_rsa.py

@@ -61,7 +61,7 @@
 # This is done with one unique function that returns:
 #   
 #   * One dataframe with all RSA features
-#   * The cyclic deformed cardiac rate
+#   * The cyclically deformed cardiac rate
 
 points_per_cycle = 50
 

examples/example_06_cardio_respiratory_synchronization.py

@@ -155,6 +155,4 @@
 ax.legend()
 
 
-plt.show()
-
-ax.set_xlim(-0.01, 1.01)
+plt.show()