fixing broken links

_sources/auto_examples/wavelet_analysis/plot_sp_wavelet_filtering_example.rst.txt

@@ -65,8 +65,7 @@ since they reprepresent the outliers. However, some of the (weired) articles use
 A simple block-diagram shown below is the procedure of wavelet filtering.
 
 
-
-.. figure:: https://raw.githubusercontent.com/spkit/images/master/extra/images/wavelet_filtering_block_dia_1.png
+.. figure:: https://raw.githubusercontent.com/spkit/images/master/extra/wavelet_filtering_block_dia_1.png
    :align: center
    :scale: 80%
 
@@ -86,9 +85,9 @@ if you want to zero out coefficient below the threshold or above by setting *fil
 However, default setting is *threshold='optimal'* and *filter_out_below=True*.
 
 There are a few more options to tune threshold and mode of filtering. 
-For more details see doc section or run :func:`sp.wavelet_filtering`
+For more details see doc section or run :func:`spkit.wavelet_filtering`
 
-.. GENERATED FROM PYTHON SOURCE LINES 72-86
+.. GENERATED FROM PYTHON SOURCE LINES 71-85
 
 .. code-block:: Python
 
@@ -113,12 +112,12 @@ For more details see doc section or run :func:`sp.wavelet_filtering`
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 87-89
+.. GENERATED FROM PYTHON SOURCE LINES 86-88
 
 Wavelet filtering with optimal threshold ans db3
 --------------------------------------------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 89-93
+.. GENERATED FROM PYTHON SOURCE LINES 88-92
 
 .. code-block:: Python
 
@@ -144,12 +143,12 @@ Wavelet filtering with optimal threshold ans db3
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 94-96
+.. GENERATED FROM PYTHON SOURCE LINES 93-95
 
 With SD threshold
 -------------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 96-100
+.. GENERATED FROM PYTHON SOURCE LINES 95-99
 
 .. code-block:: Python
 
@@ -175,12 +174,12 @@ With SD threshold
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 101-103
+.. GENERATED FROM PYTHON SOURCE LINES 100-102
 
 With IQR
 ----------
 
-.. GENERATED FROM PYTHON SOURCE LINES 103-107
+.. GENERATED FROM PYTHON SOURCE LINES 102-106
 
 .. code-block:: Python
 
@@ -206,12 +205,12 @@ With IQR
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 108-110
+.. GENERATED FROM PYTHON SOURCE LINES 107-109
 
 Limit the number of levels for decomposition to 2
 ---------------------------------------------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 110-114
+.. GENERATED FROM PYTHON SOURCE LINES 109-113
 
 .. code-block:: Python
 
@@ -237,12 +236,12 @@ Limit the number of levels for decomposition to 2
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 115-117
+.. GENERATED FROM PYTHON SOURCE LINES 114-116
 
 With *db12*
 ------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 117-121
+.. GENERATED FROM PYTHON SOURCE LINES 116-120
 
 .. code-block:: Python
 
@@ -268,12 +267,12 @@ With *db12*
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 122-124
+.. GENERATED FROM PYTHON SOURCE LINES 121-123
 
 With Symlet - *sym4*
 ---------------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 124-128
+.. GENERATED FROM PYTHON SOURCE LINES 123-127
 
 .. code-block:: Python
 
@@ -299,12 +298,12 @@ With Symlet - *sym4*
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 129-131
+.. GENERATED FROM PYTHON SOURCE LINES 128-130
 
 With Coiflet - *coif4*
 -----------------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 131-133
+.. GENERATED FROM PYTHON SOURCE LINES 130-132
 
 .. code-block:: Python
 
@@ -331,7 +330,7 @@ With Coiflet - *coif4*
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.800 seconds)
+   **Total running time of the script:** (0 minutes 0.744 seconds)
 
 
 .. _sphx_glr_download_auto_examples_wavelet_analysis_plot_sp_wavelet_filtering_example.py:

_sources/auto_examples/wavelet_analysis/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:00.800** total execution time for 2 files **from auto_examples/wavelet_analysis**:
+**00:00.744** total execution time for 2 files **from auto_examples/wavelet_analysis**:
 
 .. container::
 
@@ -33,7 +33,7 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_wavelet_analysis_plot_sp_wavelet_filtering_example.py` (``plot_sp_wavelet_filtering_example.py``)
-     - 00:00.800
+     - 00:00.744
      - 0.0
    * - :ref:`sphx_glr_auto_examples_wavelet_analysis_plot_sp_scalogram_cwt_example.py` (``plot_sp_scalogram_cwt_example.py``)
      - 00:00.000

_sources/modules/sp_cwt.rst.txt

@@ -295,29 +295,33 @@ Admiddibility const :math:`C_{\psi} =\frac{1}{n}` and  :math:`w = 2\pi f`
 
 .. math::
 
-   \psi(t) &= \frac{1}{\pi} \frac{1-t^2}{(1+t^2)^2}\\
-   \psi(t) &= p(t) + \frac{d}{dt}p(t)\\
+   \psi(t) &= \frac{1}{\pi} \frac{1-t^2}{(1+t^2)^2}
+
+   \psi(t) &= p(t) + \frac{d}{dt}p(t)
+
    \psi(w) &= |w|e^{-|w|}
  
  
 **where**
-.
 
-.. math::
+  .. math::
 
-   p(t) &=\frac{1}{\pi}\frac{1}{1+t^2}\\
-   w &= 2\pi f
+    p(t) &=\frac{1}{\pi}\frac{1}{1+t^2}
+
+    w &= 2\pi f
    
 
- >>> XW,S = sp.cwt.ScalogramCWT(x,t,fs=fs,,wType='Poisson',method = 2,PlotPSD=True)
+
+>>> XW,S = sp.cwt.ScalogramCWT(x,t,fs=fs,,wType='Poisson',method = 2,PlotPSD=True)
 
 
 #Type 3 (n)
 ~~~~~~~~~~
 
 .. math::
 
-   \psi(t) &= \frac{1}{2\pi}(1-jt)^{-(n+1)}\\
+   \psi(t) &= \frac{1}{2\pi}(1-jt)^{-(n+1)}
+
    \psi(w) &= \frac{1}{\Gamma{n+1}}w^{n}e^{-w}u(w)
  
 
@@ -326,13 +330,15 @@ Admiddibility const :math:`C_{\psi} =\frac{1}{n}` and  :math:`w = 2\pi f`
 
 .. math::
 
-   \text{unit step function }\quad u(w) &=1 \quad \text{ if  $w>=0$ }\quad \text{else  } 0\\
+   
+   \text{unit step function }\quad u(w) &=1 \quad \text{ if  $w>=0$ }\quad \text{else  } 0
+
    w &= 2\pi f
 
 
 
- >>> XW,S = sp.cwt.ScalogramCWT(x,t,fs=fs,wType='Poisson',method = 3,PlotPSD=True)
-  
+
+>>> XW,S = sp.cwt.ScalogramCWT(x,t,fs=fs,wType='Poisson',method = 3,PlotPSD=True)
 
 #TODO   
 

_sources/modules/sp_dwt.rst.txt

@@ -13,7 +13,7 @@ Wavelet Filtering & Shrinkage
 Other than classical spectral filtering, Wavelet filtering is one of common techniques used in signal processing. 
 It allows to filter out short-time duration patterns captured by used wavelet. The patterns to be filtered out depends 
 on the wavelet family (e.g. *db3*) used and number of level of decomposition.
-Algorithmically, it is very straightforward. Decompose a signal :math:`x(n)`, into wavelet coefficients :math:`X(k):math:`, 
+Algorithmically, it is very straightforward. Decompose a signal :math:`x(n)`, into wavelet coefficients :math:`X(k)`, 
 where each coefficient represents the strength of wavelet pattern at particular time. With some threshold, 
 remove the coefficients by zeroing out and reconstruct the signal back.
 
@@ -37,8 +37,10 @@ where :math:`\tilde{\sigma}` is estimation of noise variance and :math:`N` lengt
 and :math:`X(k)` are wavelet coeffients of :math:`x(n)`
 
 There are other methods to choose threshold too. One can choose a 
-* :math:`\theta =1.5\times SD(X(k))` or 
-* :math:`\theta =IQR(X(k))` 
+
+    * :math:`\theta =1.5\times SD(X(k))` or 
+    * :math:`\theta =IQR(X(k))` 
+
 as to select the outliers, by standard deviation and interquartile range, respectively.
 
 According to the theory, the **optimal threshold** should be applied by zeroing out the coefficients 
@@ -55,21 +57,22 @@ A simple block-diagram shown below is the procedure of wavelet filtering.
 
 
 **References:**
-    * [1] D.L. Donoho, J.M. Johnstone, **Ideal spatial adaptation by wavelet shrinkage** Biometrika, 81 (1994), pp. 425-455
+
+    * [1] D.L. Donoho, J.M. Johnstone, *Ideal spatial adaptation by wavelet shrinkage* Biometrika, 81 (1994), pp. 425-455
 
 
 **API**
 
-    * **spkit.wavelet_filtering(...)**
-    * **spkit.wavelet_filtering_win(...)**
+    * :func:`spkit.wavelet_filtering`
+    * :func:`spkit.wavelet_filtering_win`
 
 In ***spkit***, we have implemented all three methods for threshold computing, can be chosen by *threshold = 'optimal', 
 'sd' or 'iqr'* or can be passed as a float value for a fixed threshold, e.g. *threshold = 0.5*. It also support to choose,
 if you want to zero out coefficient below the threshold or above by setting *filter_out_below* True or False. 
 However, default setting is *threshold='optimal'* and *filter_out_below=True*.
 
 There are a few more options to tune threshold and mode of filtering. 
-For more details see doc section or run :func:`sp.wavelet_filtering`
+For more details see doc section or run :func:`spkit.wavelet_filtering`
 
 
 
@@ -112,10 +115,12 @@ Wavelet decomposed signals
     plt.show()
 
 
-`Check Example here<../modules/generated/spkit.wavelet_decomposed_signals.html>`_
+.. raw:: html
 
+    <a href = "../modules/generated/spkit.wavelet_decomposed_signals.html" target="_blank">Check Example here</a>
 
 
+Check Example here :func:`spkit.wavelet_decomposed_signals`
 
 Temporal analysis
 ==================

_sources/modules/sp_filtering_drift.rst.txt

@@ -8,7 +8,7 @@
 Drift Removing
 ==================
 
-.. currentmodule:: spkit.core.processing
+.. currentmodule:: spkit
 
 
 While recording signal, it is very common to observe the baseline wondering
@@ -30,7 +30,7 @@ Filter out DC component - Remving drift using Recursive (IIR type) filter
 
           .. math::
               
-             y[n] = \frac{(alpha-1)}{alpha} * ( x[n] - x[n-1] -y[n-1])
+             y[n] = \frac{(\alpha-1)}{\alpha} * ( x[n] - x[n-1] -y[n-1])
 
           where :math:`y[-1] = x[0]`, :math:`x[-1] = x[0]`
           resulting :math:`y[0] = 0`
@@ -49,6 +49,7 @@ Filter out DC component - Remving drift using Recursive (IIR type) filter
   xf = sp.filterDC(x,alpha=256,return_background=False)
 
 
+check : :func:`filterDC`
 
 Spectral filter - IIR Filters
 =============================
@@ -64,6 +65,9 @@ A common approach to remove drift is also to use spectral filtering such as butt
   xf = sp.filter_X(x.copy(),fs=128.0, band=[0.5], btype='highpass',ftype='SOS')
 
 
+check : :func:`filter_X`
+
+
 Savitzky-Golay filter
 =====================
 
@@ -86,3 +90,4 @@ Filter out DC component using Savitzky-Golay filter
   xf = sp.filterDC_sGolay(x,window_length=127, polyorder=3)
 
 
+check : :func:`filterDC_sGolay`

_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:00.800** total execution time for 34 files **from all galleries**:
+**00:00.744** total execution time for 34 files **from all galleries**:
 
 .. container::
 
@@ -33,7 +33,7 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_wavelet_analysis_plot_sp_wavelet_filtering_example.py` (``../examples/wavelet_analysis/plot_sp_wavelet_filtering_example.py``)
-     - 00:00.800
+     - 00:00.744
      - 0.0
    * - :ref:`sphx_glr_auto_examples_electroencephalogram_plot_sp_ATAR_algorithm_tuning.py` (``../examples/electroencephalogram/plot_sp_ATAR_algorithm_tuning.py``)
      - 00:00.000

_sources/user_guide.rst.txt

@@ -12,8 +12,8 @@ User Guide
 
 .. seealso:: User Guide - API
 
-    **User Guide is in continues development. It is not complete yet, we are keep updating the contents.**
-    Please check API: :ref:`api_ref`, which is the most complete documentation of spkit.
+    **User Guide is in a continues development. It is not a complete guide yet, we are keep updating the contents.**
+    For now, please check the API: :ref:`api_ref`, which is the most complete documentation of all the functions and modules.
 
 
 

auto_examples/wavelet_analysis/plot_sp_wavelet_filtering_example.html

@@ -15,7 +15,7 @@
 <meta property="og:url" content="https://spkit.github.io/auto_examples/wavelet_analysis/plot_sp_wavelet_filtering_example.html" />
 <meta property="og:site_name" content="spkit" />
 <meta property="og:description" content="Background Other than classical spectral filtering, Wavelet filtering is one of common techniques used in signal processing. It allows to filter out short-time duration patterns captured by used wa..." />
-<meta property="og:image" content="https://raw.githubusercontent.com/spkit/images/master/extra/images/wavelet_filtering_block_dia_1.png" />
+<meta property="og:image" content="https://raw.githubusercontent.com/spkit/images/master/extra/wavelet_filtering_block_dia_1.png" />
 <meta property="og:image:alt" content="spkit" />
 <meta name="description" content="Background Other than classical spectral filtering, Wavelet filtering is one of common techniques used in signal processing. It allows to filter out short-time duration patterns captured by used wa..." />
 
@@ -295,7 +295,7 @@ <h4><i>Simple and easy to use for signal analysis and predictive analysis</i></h
 since they reprepresent the outliers. However, some of the (weired) articles use these thresholds in other-way round.</p>
 <p>A simple block-diagram shown below is the procedure of wavelet filtering.</p>
 <figure class="align-center">
-<a class="reference internal image-reference" href="https://raw.githubusercontent.com/spkit/images/master/extra/images/wavelet_filtering_block_dia_1.png"><img alt="https://raw.githubusercontent.com/spkit/images/master/extra/images/wavelet_filtering_block_dia_1.png" src="https://raw.githubusercontent.com/spkit/images/master/extra/images/wavelet_filtering_block_dia_1.png" /></a>
+<a class="reference internal image-reference" href="https://raw.githubusercontent.com/spkit/images/master/extra/wavelet_filtering_block_dia_1.png"><img alt="https://raw.githubusercontent.com/spkit/images/master/extra/wavelet_filtering_block_dia_1.png" src="https://raw.githubusercontent.com/spkit/images/master/extra/wavelet_filtering_block_dia_1.png" /></a>
 </figure>
 <dl class="simple">
 <dt><strong>References:</strong></dt><dd><ul class="simple">
@@ -315,7 +315,7 @@ <h4><i>Simple and easy to use for signal analysis and predictive analysis</i></h
 if you want to zero out coefficient below the threshold or above by setting <em>filter_out_below</em> True or False.
 However, default setting is <em>threshold=’optimal’</em> and <em>filter_out_below=True</em>.</p>
 <p>There are a few more options to tune threshold and mode of filtering.
-For more details see doc section or run <code class="xref py py-func docutils literal notranslate"><span class="pre">sp.wavelet_filtering</span></code></p>
+For more details see doc section or run <a class="reference internal" href="../../modules/generated/spkit.wavelet_filtering.html#spkit.wavelet_filtering" title="spkit.wavelet_filtering"><code class="xref py py-func docutils literal notranslate"><span class="pre">spkit.wavelet_filtering</span></code></a></p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">spkit</span> <span class="k">as</span> <span class="nn">sp</span>
@@ -390,7 +390,7 @@ <h2>With Coiflet - <em>coif4</em><a class="headerlink" href="#with-coiflet-coif4
 <img src="../../_images/sphx_glr_plot_sp_wavelet_filtering_example_007.png" srcset="../../_images/sphx_glr_plot_sp_wavelet_filtering_example_007.png" alt="plot sp wavelet filtering example" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>WPD: False  wv: coif4  threshold: optimal  k: 1.5  mode: elim  filter_out_below?: True
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.800 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.744 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-wavelet-analysis-plot-sp-wavelet-filtering-example-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/spkit/spkit/0.9.X?urlpath=lab/tree/notebooks/auto_examples/wavelet_analysis/plot_sp_wavelet_filtering_example.ipynb"><img alt="Launch binder" src="../../_images/binder_badge_logo7.svg" width="150px" /></a>