""" 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 - Using stair-steps - 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 """ # %% # Import the required packages import numpy as np import pygmt # %% # Generate random data from a normal distribution: rng = np.random.default_rng(seed=100) # Mean of distribution mean = 100 # Standard deviation of distribution stddev = 20 # Create two data sets data01 = rng.normal(loc=mean, scale=stddev, size=42) data02 = rng.normal(loc=mean, scale=stddev * 2, size=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 width, color, and style of the outlines. 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 with vertical bars 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=["WStr", "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 1-point thick solid outline in "darkgray" around the bars pen="1p,darkgray,solid", # Choose counts via the "histtype" parameter histtype=0, ) # Shift plot origin 12 centimeters to the right fig.shift_origin(xshift="12c") # Create histogram for data01 with horizontal bars fig.histogram( region=[0, 200, 0, 0], projection="X10c", frame=["WStr", "xaf10", "ya1f1+lCounts"], data=data01, series=10, fill="red3", pen="1p,darkgray,solid", 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() # %% # Stair-steps # ----------- # # A stair-step diagram can be created by setting ``stairs=True``. Then only # the outer outlines of the bars are drawn, and no internal bars are visible. # 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, # Draw a 1-point thick dotted outline in "red3" pen="1p,red3,dotted", histtype=0, # Draw stair-steps in stead of bars stairs=True, ) # Shift plot origin 12 centimeters to the right fig.shift_origin(xshift="12c") # Create histogram for data02 fig.histogram( region=[0, 200, 0, 0], projection="X10c", frame=["WSne", "xaf10", "ya1f1+lCounts"], data=data02, series=10, # Draw a 1.5-point thick dashed outline in "orange" pen="1.5p,orange,dashed", histtype=0, stairs=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,solid", # 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,solid", # 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,solid", 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,solid", histtype=0, # Show cumulative counts cumulative=True, # Offset (+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 several # 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,solid", 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,solid", histtype=0, label="data02", ) # Add legend fig.legend() fig.show() # %% # Stacked bars # ------------ # # Histograms with stacked bars are not directly supported by PyGMT. Thus, # before plotting, combined data sets have to be created from the single data # sets. Then, stacked bars can be achieved similar to overlaid bars via # plotting two or several histograms on top of each other. # # 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,solid", histtype=0, # The combined data set appears in the final histogram visually # as data set data02 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,solid", 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,solid", 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=f"{binwidth/2}+o-{binwidth/4}", label="data01", ) # Create histogram for data02 fig.histogram( data=data02, series=binwidth, fill="orange", pen="1p,darkgray,solid", histtype=0, barwidth=f"{binwidth/2}+o{binwidth/4}", label="data02", ) # Add legend fig.legend() fig.show() # sphinx_gallery_thumbnail_number = 4