update rst files for documentations

docs/source/Mask.rst

@@ -42,7 +42,7 @@ Plot the image with the detected peaks:
 .. code:: python
 
     # Import library
-    fp.plot()
+    fp.plot(figure_order='horizontal')
 
 .. _Figure_6:
 

docs/source/Plots.rst

@@ -65,7 +65,7 @@ Depending on the number of user defined pre-processing steps, the plot will add
     # Import library
     from findpeaks import findpeaks
     # Initialize
-    fp = findpeaks(method='topology', scale=True, denoise='fastnl', window=3, togray=True, imsize=(50,150))
+    fp = findpeaks(method='topology', whitelist=['peak'])
     # Import example
     X = fp.import_example("2dpeaks")
     # Detect peaks
@@ -95,7 +95,7 @@ The **plot** function :func:`findpeaks.findpeaks.findpeaks.plot` plots the 3 maj
 .. code:: python
 
     # Plot
-    fp.plot()
+    fp.plot(figure_order='horizontal')
 
 
 .. |figP1| image:: ../figs/plot_example1.png

docs/source/Pre-processing.rst

@@ -134,13 +134,13 @@ Each of these steps can be controlled by setting the input parameters.
     # Import library
     from findpeaks import findpeaks
     # Init
-    fp = findpeaks(method="topology", imsize=(50,100), scale=True, togray=True, denoise=None)
+    fp = findpeaks(method="topology", whitelist=['peak'], imsize=(50,100), scale=True, togray=True, denoise=None)
     # Small dataset
     X = fp.import_example("2dpeaks")
 
     # Interpolate the data using linear by factor 10
     results = fp.fit(X)
-    fp.plot()
+    fp.plot(figure_order='horizontal')
     # fp.plot_persistence()
 
 

docs/source/Topology.rst

@@ -259,7 +259,7 @@ The docstring provide the information regarding the input/output parameters: :fu
     # Import library
     from findpeaks import findpeaks
     # Initialize
-    fp = findpeaks(method='topology')
+    fp = findpeaks(method='topology', whitelist=['peak'])
     # Example 2d image
     X = fp.import_example('2dpeaks')
     # Fit topology method on the 1d-vector
@@ -276,7 +276,7 @@ Plot the image with the detected peaks:
 .. code:: python
 
     # Import library
-    fp.plot()
+    fp.plot(figure_order='horizontal')
 
 .. _Figure_5:
 

docs/source/Use-cases.rst

@@ -108,7 +108,7 @@ Let's load a SAR image, apply denoising techniques and then detect peaks.
     # Fit
     fp.fit(img)
     # Thousands of peaks are detected at this point.
-    fp.plot()
+    fp.plot(figure_order='horizontal')
     fp.plot_mesh()
 
 
@@ -134,7 +134,7 @@ From this point on, we will *pre-process* the image and apply the *topology* met
     # Import image example
     img = fp.import_example('2dpeaks_image')
     # Initializatie
-    fp = findpeaks(scale=True, denoise='fastnl', params={'window': 31}, togray=True, imsize=(300,300))
+    fp = findpeaks(method='topology', limit=160, scale=True, togray=True, imsize=(150, 150), denoise='lee_sigma', params={'window': 17})
     # Fit
     fp.fit(img)
     
@@ -163,7 +163,7 @@ In the next step, we can examine the detected peaks (see below). But these peaks
 .. code:: python
 
     # Plot
-    fp.plot()
+    fp.plot(figure_order='horizontal')
 
 
 .. |figU1| image:: ../figs/sonar_plot.png
@@ -267,7 +267,7 @@ Denoising
 .. code:: python
 
 	from findpeaks import findpeaks
-	fp = findpeaks()
+	fp = findpeaks(limit=160)
 	img = fp.import_example('2dpeaks_image')
 	import findpeaks