Single Plot Charts with Chartly Examples
========================================

Line Plot
~~~~~~~~~~~~

A line plot can be created by using the `line_plot` keyword. Users can pass both x and y data or just y data to the line plot.

The following customization options are available for the Line Plot:

- `color` (str): The color of the line.
- `linestyle` (str): The style of the line.
- `marker` (str): The style of the markers.
- `markersize` (int): The size of the markers.
- `markercolor` (str): The color of the markers.
- `markeredgecolor` (str): The edge color of the markers.

.. code-block:: python

    import chartly
    import numpy as np

    # Only y data
    data = np.random.randn(300)

    # customize the plot
    customs = {"color": "red", "linestyle": "--"}

    # label the plot
    super_axes_labels = {"super_ylabel": "Y", "super_title": "Generic Plot Example"}

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # Set the arguments for the plot
    plot_payload = {
        "plot": "line_plot",
        "data": data,
        "customs": customs,
    }

    # Create a subplot and plot the data
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/gen_plot_only_y.jpg
    :alt: GenericPlotOnlyY
    :align: center
    :height: 500px


.. code-block:: python

    import chartly
    import numpy as np

    # x and y data
    x = np.linspace(0, 10, 100)
    y = np.sin(x)

    # define plot labels
    super_axes_labels = {
        "super_ylabel": "Y",
        "super_xlabel": "X", 
        "super_title": "Generic Plot Example with X & Y",
    }

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # customize the plot
    customs = {"color": "mediumpurple", "linestyle": "--"}

    # set the arguments
    plot_payload = {"plot": "line_plot", "data": [x, y], "customs": customs}

    # Create a subplot and plot the data
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/gen_plot_x_y.jpg
    :alt: GenericPlotXY
    :align: center
    :height: 500px


.. code-block:: python

    import chartly
    import numpy as np

    # define data
    data = np.random.randn(100)

    # customize the plot
    customs = {"color": "navy", "linestyle": "--", "marker": "*", "markercolor": "red", "markersize": 15}

    # label the plot
    super_axes_labels = {"super_ylabel": "Y", "super_title": "Line Plot with Markers Example"}

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # set the arguments
    plot_payload = {
        "plot": "line_plot",
        "data": data,
        "customs": customs,
    }

    plot.new_subplot(plot_payload)

    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/line_plot_with_scatter.jpg
    :alt: LinePlotWithScatter
    :align: center
    :height: 500px


Scatter Plot
~~~~~~~~~~~~

A scatter plot can be created using the `scatter` keyword. The following customization options are available for the scatter plot:

- `color` (str): The color of the markers.
- `marker` (str): The style of the markers.
- `size` (int): The size of the markers.
- `alpha` (float): The transparency of the markers.
- `label` (str): The label of the markers.

.. code-block:: python

    import chartly
    import numpy as np

    x = np.random.randn(100)
    y = np.random.randn(100)

    # label the plot
    super_axes_labels = {
        "super_ylabel": "Y",
        "super_xlabel": "X",
        "super_title": "Scatter Plot Example",
    }

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # customize the plot
    customs = {"color": "blue", "marker": "o", "size": 50, "alpha": 0.5, "label": "data"}

    # set the arguments
    plot_payload = {"plot": "scatter", "data": [x, y], "customs": customs}

    # Create a subplot and plot the data
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/scatter_eg.jpg

    :alt: ScatterExample
    :align: center
    :height: 500px


Histogram
~~~~~~~~~

A histogram plot can be created using the `histogram` keyword. The histogram plot requires a 1D array of data to be passed to the plot. The following customization options are available for the histogram plot:

- `color` (str): The color of the histogram.
- `num_bins` (int): The number of bins in the histogram.
- `ran` (tuple): The range of the histogram.

.. code-block:: python

    import chartly
    import numpy as np

    data = np.random.randn(1000)

    # label the plot
    super_axes_labels = {
        "super_ylabel": "Frequency",
        "super_xlabel": "Value",
        "super_title": "Histogram Plot Example",
    }

    plot = chartly.Chart(super_axes_labels)

    # customize the plot
    customs = {"color": "salmon", "num_bins": 30}

    # set the arguments
    plot_payload = {"plot": "histogram", "data": data, "customs": customs}

    # Create a subplot and plot the data
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/hist_eg.jpg
    :alt: HistogramExample
    :align: center
    :height: 500px


Contour Plot
~~~~~~~~~~~~

Contour plots can be created using the `contour` keyword. The contour plot requires 2D arrays of X, Y and Z data to be passed to the plot. The following customization options are available for the contour plot:

- `inline?` (bool): Whether the contour labels are inline or not.
- `color` (str): The color of the contour plot. Ensure that the `cmap` is set to None.
- `levels` (int): The number of contour levels.
- `colormap` (str): The colormap of the contour plot.
- `filled?` (bool): Whether the contour plot is filled or not.
- `fontsize` (int): The font size of the contour labels.
- `hatch?` (bool): Whether the contour plot has a hatched area or not.
- `hatch_customs` (dict): The hatch customization options.

.. code-block:: python

    import chartly
    import numpy as np

    x = np.linspace(-3.0, 3.0, 100)
    y = np.linspace(-3.0, 3.0, 100)
    X, Y = np.meshgrid(x, y)
    Z = np.cos(X/3) * np.sin(Y/3)

    # label the plot
    super_axes_labels = {
        "super_xlabel": "X",
        "super_ylabel": "Y",
        "super_title": "Contour Plot Example",
   }

    # Create a charts instance
    plot = chartly.Chart(super_axes_labels)

    # customize the plot
    customs = {"colormap": "magma", "fontsize": 14, "filled?": True}

    # set the arguments
    plot_payload = {"plot": "contour", "data": [X, Y, Z], "customs": customs}

    # Create a subplot and plot the data
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()


.. image:: https://chartly.s3.amazonaws.com/static/img/contour_eg.jpg
    :alt: ContourExample
    :align: center
    :height: 500px


Normal Probability Plot
~~~~~~~~~~~~~~~~~~~~~~~

The normal probability plot is used to determine if a dataset is approximately normally distributed. A normal probability plot can be created using the `probability_plot` keyword. The normal probability plot requires a 1D array of data to be passed to the plot. The following customization options are available for the normal probability plot:

- `color` (str): The color of the markers of the plot.

.. code-block:: python

    import chartly
    import numpy as np

    data = np.random.randn(150)

    # label the plot
    super_axes_labels = {"super_title": "Normal Probability Plot Example"}

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # customize the plot
    customs = {"color": "firebrick"}

    # set the arguments
    args = {"plot": "probability_plot", "data": data, "customs": customs}

    # Create a subplot and plot the data
    plot.new_subplot(args)

    # build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/norm_prob_eg.jpg
    :alt: NormalProbabilityExample
    :align: center
    :height: 500px


Cumulative Distribution Function Plot
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The CDF plot of a dataset can be created using the `cdf` keyword. The CDF plot requires a 1D array of data to be passed to the plot. The following customization options are available for the CDF plot:

- `color` (str): The color of the CDF plot.


.. code-block:: python

    import chartly
    import numpy as np

    data = np.random.exponential(scale=1.0, size=500)

    # label the main figure
    super_axes_labels = {
        "super_title": "Cumulative Distribution Function Plot Example",
        "super_ylabel": "Probability",
    }

    # Create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # customize the plot
    axes_labels = {"linelabel": "CDF"}
    args = {"plot":"cdf", "data": data, "axes_labels": axes_labels}

    # Create a subplot and plot the data
    plot.new_subplot(args)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/cdf_eg.jpg
    :alt: CDFExample
    :align: center
    :height: 500px


Normal Cumulative Distribution Function Plot
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The CDF of a dataset can be compared to the CDF of a normal distribution using the normal CDF plot. The normal CDF plot can be created using the `normal_cdf` keyword. Users can pass multiple datasets to the plot.


.. code-block:: python

    import chartly
    import numpy as np

    dataset_one = np.random.exponential(scale=1.0, size=500)
    dataset_two = np.random.normal(loc=2, scale=1, size=500)
    dataset_three = np.random.gamma(2, 2, 500)
    data = [dataset_one, dataset_two, dataset_three]

    # label the main figure
    super_axes_labels = {"super_title": "Normal Cumulative Distribution Function Plot Example"}

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # set the arguments
    args = {"plot": "normal_cdf", "data": data}

    # Create a subplot and plot the data
    plot.new_subplot(args)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/norm_cdf_eg.jpg
    :alt: NormalCDFExample
    :align: center
    :height: 500px


Density Plot
~~~~~~~~~~~~

The density function of a distribution can be created using the `density` keyword. The density plot requires a 1D array of data to be passed to the plot. The following customization options are available for the density plot:


- `color` (str): The color of the density plot.
- `fill` (bool): Whether the density plot is filled or not.


.. code-block:: python

    import chartly
    import numpy as np

    data = np.random.exponential(scale=1.0, size=500)

    # label the plot
    super_axes_labels = {"super_title": "Density Plot Example"}

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # define the customs
    customs = {"fill": True, "color": "mediumvioletred", "label": "density"}

    # set the arguments
    plot_payload = {"plot": "density", "data": data, "customs": customs}

    # Create the subplot and plot density plot
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/density_eg.jpg
    :alt: DensityExample
    :align: center
    :height: 500px


Box Plot
~~~~~~~~~~~~

A boxplot of one or more datasets can be plotted using the `BoxPlot` class. A dataset list or a list of dataset lists can be passed to the `BoxPlot` plot. The following customization options are available for the `BoxPlot` plot:


- `showfliers` (bool): Whether to show the outliers in the boxplot.
- `boxlabels` (list): The labels of the boxplots.


.. code-block:: python

    import chartly
    import numpy as np

    dataset_one = np.random.exponential(scale=1.0, size=500)
    dataset_two = np.random.normal(loc=2, scale=1, size=500)
    dataset_three = np.random.gamma(2, 2, 500)
    data = [dataset_one, dataset_two, dataset_three]

    # label the main figure
    super_axes_labels = {"super_title": "BoxPlot Example"}

    # create a chart instance
    plot = chartly.Chart(super_axes_labels)

    # define the customs
    customs = {"showfliers": False}

    # set the arguments
    plot_payload = {"plot": "boxplot", "data": data, "customs": customs}

    # Create new subplot and plot the boxplot
    plot.new_subplot(plot_payload)

    # Build and display the main figure
    plot()

.. image:: https://chartly.s3.amazonaws.com/static/img/boxplot_eg.jpg
    :alt: BoxPlotExample
    :align: center
    :height: 500px