Add tutorial for cartesian histograms

examples/tutorials/advanced/cartesian_histograms.py

@@ -0,0 +1,338 @@
+r"""
+Cartesian histograms
+====================
+
+Cartesian histograms can be generated using the :meth:`pygmt.Figure.histogram`
+method. In this tutorial, different histogram related aspects are addressed:
+
+- Using vertical and horizontal bars
+- Showing counts and frequency percent
+- Adding annotations to the bars
+- Showing cumulative values
+- Using color and pattern as fill for the bars
+- Using overlaid, stacked, and grouped bars
+"""
+
+# sphinx_gallery_thumbnail_number = 4
+
+
+# Import the required packages
+import numpy as np
+import pygmt
+
+###############################################################################
+# Generate random data from a normal distribution:
+
+np.random.seed(100)
+
+# Mean of distribution
+mean = 100
+# Standard deviation of distribution
+stddev = 20
+
+# Create two data sets
+data01 = np.random.normal(mean, stddev, 42)
+data02 = np.random.normal(mean, stddev * 2, 42)
+
+
+###############################################################################
+# Vertical and horizontal bars
+# ----------------------------
+# To define the width of the bins, the ``series`` parameter has to be
+# specified. The bars can be filled via the ``fill`` parameter with either a
+# color or a pattern (see later in this tutorial). Use the ``pen`` parameter
+# to adjust the outline of the bars.
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data01
+fig.histogram(
+    # Define the plot range as a list of xmin, xmax, ymin, ymax
+    # Let ymin and ymax determined automatically by setting both to the same
+    # value
+    region=[0, 200, 0, 0],
+    projection="X10c",  # Cartesian projection with a width of 10 centimeters
+    # Add frame, annotations (a), ticks (f), and y-axis label (+l) "Counts"
+    # The numbers give the steps of annotations and ticks
+    frame=["WSne", "xaf10", "ya1f1+lCounts"],
+    data=data01,
+    # Set the bin width via the "series" parameter
+    series=10,
+    # Fill the bars with color "red3"
+    fill="red3",
+    # Draw a darkgray 1-point thick outline around the bars via the "pen"
+    # parameter
+    pen="1p,darkgray",
+    # Choose counts via the "histtype" parameter
+    histtype=0,
+)
+
+fig.show()
+
+###############################################################################
+# By default, a histogram with vertical bars is created. Horizontal bars can
+# be achieved via ``horizontal=True``.
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data01
+fig.histogram(
+    region=[0, 200, 0, 0],
+    projection="X10c",
+    frame=["WSne", "xaf10", "ya1f1+lCounts"],
+    data=data01,
+    series=10,
+    fill="red3",
+    pen="1p,darkgray",
+    histtype=0,
+    # Use horizontal bars
+    # Please note the flip of the x and y axes regarding annotations, ticks,
+    # gridlines, and axis labels
+    horizontal=True,
+)
+
+fig.show()
+
+
+###############################################################################
+# Counts and frequency percent
+# ----------------------------
+# By default, a histogram showing the counts in each bin is created
+# (``histtype=0``). To show the frequency percent set the ``histtpye``
+# parameter to ``1``. For further options please have a look at the
+# documentation of :meth:`pygmt.Figure.histogram`.
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data02 showing counts
+fig.histogram(
+    region=[0, 200, 0, 0],
+    projection="X10c",
+    frame=["WSnr", "xaf10", "ya1f1+lCounts"],
+    data=data02,
+    series=10,
+    fill="orange",
+    pen="1p,darkgray",
+    # Choose counts via the "histtype" parameter
+    histtype=0,
+)
+
+# Shift plot origin 11 centimeters to the right
+fig.shift_origin(xshift="11c")
+
+# Create histogram for data02 showing frequency percent
+fig.histogram(
+    region=[0, 200, 0, 0],
+    projection="X10c",
+    # Add suffix % (+u)
+    frame=["lSnE", "xaf10", "ya2f1+u%+lFrequency percent"],
+    data=data02,
+    series=10,
+    fill="orange",
+    pen="1p,darkgray",
+    # Choose frequency percent via the "histtype" parameter
+    histtype=1,
+)
+
+fig.show()
+
+
+###############################################################################
+# Cumulative values
+# -----------------
+# To create a histogram showing the cumulative values set ``cumulative=True``.
+# Here, the bars of the cumulative histogram are filled with a pattern via
+# the ``fill`` parameter. Annotate each bar with the counts it represents
+# using the ``annotate`` parameter.
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data01 showing the counts per bin
+fig.histogram(
+    region=[0, 200, 0, len(data01) + 1],
+    projection="X10c",
+    frame=["WSne", "xaf10", "ya5f1+lCounts"],
+    data=data01,
+    series=10,
+    fill="red3",
+    pen="1p,darkgray",
+    histtype=0,
+    # Annotate each bar with the counts it represents
+    annotate=True,
+)
+
+# Shift plot origin 11 centimeters to the right
+fig.shift_origin(xshift="11c")
+
+# Create histogram for data01 showing the cumulative counts
+fig.histogram(
+    region=[0, 200, 0, len(data01) + 1],
+    projection="X10c",
+    frame=["wSnE", "xaf10", "ya5f1+lCumulative counts"],
+    data=data01,
+    series=10,
+    # Use pattern (p) number 8 as fill for the bars
+    # Set the background (+b) to white [Default]
+    # Set the foreground (+f) to black [Default]
+    fill="p8+bwhite+fblack",
+    pen="1p,darkgray",
+    histtype=0,
+    # Show cumulative counts
+    cumulative=True,
+    # Offest (+o) the label by 10 points in negative y-direction
+    annotate="+o-10p",
+)
+
+fig.show()
+
+
+###############################################################################
+# Overlaid bars
+# -------------
+# Overlaid or overlapping bars can be achieved by plotting two or serveral
+# histograms, each for one data set, on top of each other. The legend entry
+# can be specified via the ``label`` parameter.
+#
+# Limitations of histograms with overlaid bars are:
+#
+# - Mixing of colors or/and patterns
+# - Visually more colors or/and patterns than data sets
+# - Visually a "third histogram" (or more in case of more than two data sets)
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data01
+fig.histogram(
+    region=[0, 200, 0, 0],
+    projection="X10c",
+    frame=["WSne", "xaf10", "ya1f1+lCounts"],
+    data=data01,
+    series=10,
+    fill="red3",
+    pen="1p,darkgray",
+    histtype=0,
+    # Set legend entry
+    label="data01",
+)
+
+# Create histogram for data02
+# It is plotted on top of the histogram for data01
+fig.histogram(
+    data=data02,
+    series=10,
+    # Fill bars with color "orange", use a transparency of 50% ("@50")
+    fill="orange@50",
+    pen="1p,darkgray",
+    histtype=0,
+    label="data02",
+)
+
+# Add legend
+fig.legend()
+
+fig.show()
+
+
+###############################################################################
+# Stacked bars
+# ------------
+# Stacked bars can be achieved similar to overlaid bars via plotting two or
+# several histograms on top of each other. However, before plotting, combined
+# data sets have to be created from the singel data sets.
+#
+# Limitations of histograms with stacked bars are:
+#
+# - No common baseline
+# - Partly not directly clear whether overlaid or stacked bars
+
+# Combine the two data sets to one data set
+data_merge = np.concatenate((data01, data02), axis=None)
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data02 by using the combined data set
+fig.histogram(
+    region=[0, 200, 0, 0],
+    projection="X10c",
+    frame=["WSne", "xaf10", "ya1f1+lCounts"],
+    data=data_merge,
+    series=10,
+    fill="orange",
+    pen="1p,darkgray",
+    histtype=0,
+    label="data02",
+)
+
+# Create histogram for data01
+# It is plotted on top of the histogram for data02
+fig.histogram(
+    data=data01,
+    series=10,
+    fill="red3",
+    pen="1p,darkgray",
+    histtype=0,
+    label="data01",
+)
+
+# Add legend
+fig.legend()
+
+fig.show()
+
+
+###############################################################################
+# Grouped bars
+# ------------
+# By setting the ``barwidth`` parameter in respect to the values passed to the
+# ``series`` parameter histograms with grouped bars can be created.
+#
+# Limitations of histograms with grouped bars are:
+#
+# - Careful setting width and position of the bars in respect to the bin width
+# - Difficult to see the variations of the single data sets
+
+# Width used for binning the data
+binwidth = 10
+
+# Create new figure instance
+fig = pygmt.Figure()
+
+# Create histogram for data01
+fig.histogram(
+    region=[0, 200, 0, 0],
+    projection="X10c",
+    frame=["WSne", "xaf10g10", "ya1f1+lCounts"],
+    data=data01,
+    series=binwidth,
+    fill="red3",
+    pen="1p,darkgray",
+    histtype=0,
+    # Calculate the bar width in respect to the bin width, here for two
+    # data sets half of the bin width
+    # Offset (+o) the bars to align each bar with the left limit of the
+    # corresponding bin
+    barwidth=str(binwidth / 2) + "+o-" + str(binwidth / 4),
+    label="data01",
+)
+
+# Create histogram for data02
+fig.histogram(
+    data=data02,
+    series=binwidth,
+    fill="orange",
+    pen="1p,darkgray",
+    histtype=0,
+    barwidth=str(binwidth / 2) + "+o" + str(binwidth / 4),
+    label="data02",
+)
+
+# Add legend
+fig.legend()
+
+fig.show()