Add a tutorial for grdhisteq

examples/tutorials/advanced/grid_equalization.py

@@ -0,0 +1,221 @@
+"""
+Performing grid histogram equalization
+--------------------------------------
+The :meth:`pygmt.grdhisteq.equalize_grid` function creates a grid using
+statistics based on a cumulative distribution function.
+"""
+# sphinx_gallery_thumbnail_number = 3
+
+import pygmt
+
+###############################################################################
+# Load sample data
+# ----------------
+# Load the sample Earth relief data for a region around Yosemite valley
+# and use :meth:`pygmt.grd2xyz` to create a :class:`pandas.Series` with the
+# z values.
+
+grid = pygmt.datasets.load_earth_relief(
+    resolution="03s", region=[-119.825, -119.4, 37.6, 37.825]
+)
+grid_dist = pygmt.grd2xyz(grid=grid, output_type="pandas")["elevation"]
+
+###############################################################################
+# Plot the original digital elevation model and data distribution
+# ---------------------------------------------------------------
+# For comparison, we will create a map of the original digital elevation
+# model and a histogram showing the distribution of elevation data values.
+
+# Create an instance of the Figure class
+fig = pygmt.Figure()
+# Define figure configuration
+pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
+# Define the colormap for the figure
+pygmt.makecpt(series=[500, 3540], cmap="turku")
+# Setup subplots with two panels
+with fig.subplot(
+    nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Digital Elevation Model"
+):
+    # Plot the original digital elevation model in the first panel
+    with fig.set_panel(panel=0):
+        fig.grdimage(grid=grid, projection="M?", frame="WSne", cmap=True)
+    # Plot a histogram showing the z-value distribution in the original digital
+    # elevation model
+    with fig.set_panel(panel=1):
+        fig.histogram(
+            data=grid_dist,
+            projection="X?",
+            region=[500, 3600, 0, 20],
+            series=[500, 3600, 100],
+            frame=["wnSE", "xaf+lElevation (m)", "yaf+lCounts"],
+            cmap=True,
+            histtype=1,
+            pen="1p,black",
+        )
+        fig.colorbar(position="JMR+o1c/0c+w3c/0.3c", frame=True)
+fig.show()
+
+###############################################################################
+# Equalize grid based on a linear distribution
+# --------------------------------------------
+# The :meth:`pygmt.grdhisteq.equalize_grid` method creates a new grid with the
+# z-values representing the position of the original z-values in a given
+# cumulative distribution. By default, it computes the position in a linear
+# distribution. Here, we equalize the grid into nine divisions based on a
+# linear distribution and produce a :class:`pandas.Series` with the z-values
+# for the new grid.
+
+divisions = 9
+linear = pygmt.grdhisteq.equalize_grid(grid=grid, divisions=divisions)
+linear_dist = pygmt.grd2xyz(grid=linear, output_type="pandas")["z"]
+
+###############################################################################
+# Calculate the bins used for data transformation
+# -----------------------------------------------
+# The :meth:`pygmt.grdhisteq.compute_bins` method reports statistics about the
+# grid equalization. Here, we report the bins that would linearly divide the
+# original data into 9 divisions with equal area. In our new grid produced by
+# :meth:`pygmt.grdhisteq.equalize_grid`, all the grid cells with values between
+# ``start`` and ``stop`` of ``bin_id=0`` are assigned the value 0, all grid
+# cells with values between ``start`` and ``stop`` of ``bin_id=1`` are assigned
+# the value 1, and so on.
+
+pygmt.grdhisteq.compute_bins(grid=grid, divisions=divisions)
+
+###############################################################################
+# Plot the equally distributed data
+# ---------------------------------------------------------------
+# Here we create a map showing the grid that has been transformed to
+# have a linear distribution with nine divisions and a histogram of the data
+# values.
+
+# Create an instance of the Figure class
+fig = pygmt.Figure()
+# Define figure configuration
+pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
+# Define the colormap for the figure
+pygmt.makecpt(series=[0, divisions, 1], cmap="lajolla")
+# Setup subplots with two panels
+with fig.subplot(
+    nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Linear distribution"
+):
+    # Plot the grid with a linear distribution in the first panel
+    with fig.set_panel(panel=0):
+        fig.grdimage(grid=linear, projection="M?", frame="WSne", cmap=True)
+    # Plot a histogram showing the linear z-value distribution
+    with fig.set_panel(panel=1):
+        fig.histogram(
+            data=linear_dist,
+            projection="X?",
+            region=[0, divisions, 0, 40],
+            series=[0, divisions, 1],
+            frame="wnSE",
+            cmap=True,
+            histtype=1,
+            pen="1p,black",
+        )
+        fig.colorbar(position="JMR+o1c/0c+w3c/0.3c", frame=True)
+fig.show()
+
+###############################################################################
+# Transform grid based on a normal distribution
+# ---------------------------------------------
+# The ``gaussian`` parameter of :meth:`pygmt.grdhisteq.equalize_grid` can be
+# used to transform the z-values relative to their position in a normal
+# distribution rather than a linear distribution. In this case, the output
+# data are continuous rather than discrete.
+
+normal = pygmt.grdhisteq.equalize_grid(grid=grid, gaussian=True)
+normal_dist = pygmt.grd2xyz(grid=normal, output_type="pandas")["z"]
+
+###############################################################################
+# Plot the normally distributed data
+# ----------------------------------
+# Here we create a map showing the grid that has been transformed to have
+# a normal distribution and a histogram of the data values.
+
+# Create an instance of the Figure class
+fig = pygmt.Figure()
+# Define figure configuration
+pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
+# Define the colormap for the figure
+pygmt.makecpt(series=[-4.5, 4.5], cmap="vik")
+# Setup subplots with two panels
+with fig.subplot(
+    nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Normal distribution"
+):
+    # Plot the grid with a normal distribution in the first panel
+    with fig.set_panel(panel=0):
+        fig.grdimage(grid=normal, projection="M?", frame="WSne", cmap=True)
+    # Plot a histogram showing the normal z-value distribution
+    with fig.set_panel(panel=1):
+        fig.histogram(
+            data=normal_dist,
+            projection="X?",
+            region=[-4.5, 4.5, 0, 20],
+            series=[-4.5, 4.5, 0.2],
+            frame="wnSE",
+            cmap=True,
+            histtype=1,
+            pen="1p,black",
+        )
+        fig.colorbar(position="JMR+o1c/0c+w3c/0.3c", frame=True)
+fig.show()
+
+###############################################################################
+# Equalize grid based on a quadratic distribution
+# -----------------------------------------------
+# The ``quadratic`` parameter of :meth:`pygmt.grdhisteq.equalize_grid` can be
+# used to transform the z-values relative to their position in a quadratic
+# distribution rather than a linear distribution. Here, we equalize the grid
+# into nine divisions based on a quadratic distribution and produce a
+# pandas.Series with the z-values for the new grid.
+
+quadratic = pygmt.grdhisteq.equalize_grid(
+    grid=grid, quadratic=True, divisions=divisions
+)
+quadratic_dist = pygmt.grd2xyz(grid=quadratic, output_type="pandas")["z"]
+
+###############################################################################
+# Calculate the bins used for data transformation
+# -----------------------------------------------
+# We can also use the ``quadratic`` parameter of
+# :meth:`pygmt.grdhisteq.compute_bins` to report the bins used for dividing
+# the grid into 9 divisions based on their position in a quadratic
+# distribution.
+
+pygmt.grdhisteq.compute_bins(grid=grid, divisions=divisions, quadratic=True)
+
+###############################################################################
+# Plot the quadratic distribution of data
+# ---------------------------------------
+# Here we create a map showing the grid that has been transformed to have
+# a quadratic distribution and a histogram of the data values.
+
+# Create an instance of the Figure class
+fig = pygmt.Figure()
+# Define figure configuration
+pygmt.config(FORMAT_GEO_MAP="ddd.x", MAP_FRAME_TYPE="plain")
+# Define the colormap for the figure
+pygmt.makecpt(series=[0, divisions, 1], cmap="lajolla")
+# Setup subplots with two panels
+with fig.subplot(
+    nrows=1, ncols=2, figsize=("13.5c", "4c"), title="Quadratic distribution"
+):
+    # Plot the grid with a quadratic distribution in the first panel
+    with fig.set_panel(panel=0):
+        fig.grdimage(grid=quadratic, projection="M?", frame="WSne", cmap=True)
+    # Plot a histogram showing the quadratic z-value distribution
+    with fig.set_panel(panel=1):
+        fig.histogram(
+            data=quadratic_dist,
+            projection="X?",
+            region=[0, divisions, 0, 40],
+            series=[0, divisions, 1],
+            frame="wnSE",
+            cmap=True,
+            histtype=1,
+            pen="1p,black",
+        )
+        fig.colorbar(position="JMR+o1c/0c+w3c/0.3c", frame=True)
+fig.show()