Refactor the structure of code

Author: hasanisaeed

LICENSE

@@ -1,6 +1,6 @@
 The Academic License
 
-Copyright (c) [2020] [Saeed Hasani Borzadaran]
+Copyright (c) [2020] [Saeed Hasani]
 
 This project includes academic-research code and documents under development.
 You would be a fool to run any of the code.

README.md

@@ -1,14 +1,45 @@
 # Recurrence Plot
 [Recurrence Plot](https://en.wikipedia.org/wiki/Recurrence_plot) – A recurrence plot (RP) is an advanced technique of **nonlinear** data analysis. It is a visualisation (or a graph) of a square matrix, in which the matrix elements correspond to those times at which a state of a dynamical system recurs (columns and rows correspond then to a certain pair of times).
 
-# Result
+## Result
 ![](results/1D_to_2D.jpg)
 
-# Usage
+## Usage
 **1. Install requirements:**
 
+Ensure you have Python 3 installed. Install the required dependencies using:
+
+
     pip install -r requirements.txt
 
-**2. Run `main.py` script:**
+**2. Run `example.py` script:**
+
+    python3 example.py # or python example.py
+
+This will:
+
+- Generate a random signal.
+- Smooth the signal using a moving average filter.
+- Compute and visualize the recurrence plot.
+- Save the resulting plot as results/1D_to_2D.jpg.
+
+----
+## How to Use the recurrence Package
+
+If you want to use the recurrence package in your own projects:
+
+1) **Import the Required Modules:**
+
+    For recurrence plot functions, import from recurrence.plotting.
+    For signal processing utilities, import from recurrence.convolve.
+
+    Example:
+
+    ```python
+    from recurrence.plotting import setup_plot, save_plot
+    from recurrence.convolve import calculate_convolve
+    ```
 
-    python3 main.py
+2) **Process Your Signal:** 
+   
+   Use `calculate_convolve` to smooth your input signal, then use the `setup_plot` and `save_plot` functions to generate and save recurrence plots.

example.py

@@ -0,0 +1,30 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from recurrence.plotting import setup_plot, save_plot
+from recurrence.convolve import calculate_convolve
+
+if __name__ == "__main__":
+    # Example usage
+    fig = plt.figure(figsize=(8, 2))
+
+    # Configuration
+    row, col = 1, 2
+    size = f"{row}{col}"
+
+    # Generate raw signal
+    raw_signal = np.random.uniform(-1, 1, 50)
+
+    # Process the signal
+    convolved_signal = calculate_convolve(raw_signal)
+
+    # Plot signals and recurrence plot
+    setup_plot(
+        signal=convolved_signal,
+        size=size,
+        cell=1,
+        signal_name='Input Signal',
+        image_name='2D Image of Signal'
+    )
+
+    # Save the plot
+    save_plot(filepath='results/1D_to_2D.jpg')

main.py

@@ -0,0 +1,61 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.spatial.distance import pdist, squareform
+
+# ----------------- Plot config --------------------------------------- #
+SMALL = 8
+MEDIUM = 10
+BIGGER = 11
+
+plt.rc('font', size=SMALL)          # controls default text sizes
+plt.rc('axes', titlesize=SMALL)     # font size of the axes title
+plt.rc('axes', labelsize=MEDIUM)    # font size of the x and y labels
+plt.rc('xtick', labelsize=SMALL)    # font size of the tick labels
+plt.rc('ytick', labelsize=SMALL)    # font size of the tick labels
+plt.rc('legend', fontsize=SMALL)    # legend font size
+plt.rc('figure', titlesize=BIGGER)  # font size of the figure title
+
+plt.rcParams["font.family"] = "Times", "Times New Roman", "serif"
+
+# --------------------------------------------------------------------- #
+
+def save_plot(filepath='results/1D_to_2D.jpg'):
+    """Adjust and save the plot."""
+    left = 0.2
+    right = 0.9
+    bottom = 0.1
+    top = 0.9
+    wspace = 0.2
+    hspace = 0.1
+
+    plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
+    plt.savefig(filepath, bbox_inches='tight')
+
+def recurrence_plot(signal, eps=0.10, steps=3):
+    """Generate a recurrence plot from a 1D signal."""
+    _2d_array = signal[:, None]
+    distance = pdist(_2d_array)
+    distance = np.floor(distance / eps)
+    distance[distance > steps] = steps
+    return squareform(distance)
+
+def subplot(x, size, cell, is_signal=True, title=None, grid=True):
+    """Create a subplot for the signal or image."""
+    ax = plt.subplot(int(f"{size}{cell}"))
+    ax.grid(color='gray', linestyle='dotted', linewidth=0.5)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+
+    if is_signal:
+        plt.plot(x, 'm', linewidth=1)
+    else:
+        plt.imshow(x)
+    
+    plt.title(title)
+    plt.grid(grid)
+
+def setup_plot(signal, size, cell, signal_name='Raw Signal', image_name='2D Image'):
+    """Set up and display the plot for the signal and its recurrence plot."""
+    subplot(signal, size=size, cell=cell, is_signal=True, title=signal_name)
+    rp = recurrence_plot(signal)
+    subplot(rp, size=size, cell=cell + 1, is_signal=False, title=image_name)