A comprehensive tutorial for plotting focal mechanism "beach-balls" using the PyGMT package for Python. The walk-through covers downloading focal mechanism data from the SCEDC, conditioning the data, creating a postscript legend, and plotting everything on a map.

Highlights include: - Offsetting "beach-balls" from their point of origin - Scaling "beach-balls" by magnitude - Coloring "beach-balls" by depth - Plotting label text above each "beach-ball" - Controlling the size of the label text - Automatically downloading an SRTM DEM - Creating a postscript legend file

Image is sourced from the United States Geological Survey earthquake hazards webpage on focal mechanisms

Created with Lucidchart

'''
Decription:
This script creates folders for the focal_mechanism_demo.py script if they don't already exist
'''

import os

# main directory path
main_dir = r'C:\Users\USER\Desktop\Focal_Mechanism_Demo'

# path to the directory holding the project data files
data_folder = os.path.join(main_dir, 'Data')

# path to the directory holding the project Python scripts
methods_folder = os.path.join(main_dir, 'Methods')

# path to the directory holding the map generated by the scripts
results_folder = os.path.join(main_dir, 'Results')

directories = [main_dir, data_folder, methods_folder, results_folder]

# Iterates through the list of directories and creates them if they don't already exist
for directory in directories:
    os.makedirs(directory, exist_ok = True)

Example of entering the focal mechanism parameters into the Southern California Earthquake Data Center's focal mechanism catalog search

Example of selecting and copying the focal mechanism data and column headers

    # Conditions focal mechanism files downloaded from the Southern California Earthquake Data Center so that they can be used
    def Condition_Focal_Mechanism_Data(self):
        import os
        import re

        print('Conditioning focal mechanisms...')


        input_file = os.path.join(self.main_dir, 'Data', 'focal_mechanism_data.txt')
        # reads in the file by line rather than the default of by character
        with open(input_file, 'r') as f:
            lines = f.readlines()

        # holds the conditioned lines, namely the removal of empty lines
        conditioned_lines = []
        # iterates over each line and adds them to a list as long as they are not empty
        for line in lines:
            if line == '\n':
                continue
            conditioned_lines.append(line)
        
        # empty placeholder for the conditioned lines list to be rejoined into a string
        conditioned_data = ''
        # rejoins the conditioned lines into a string so that it can be further conditioned
        conditioned_data = conditioned_data.join(conditioned_lines)

        # regular expression pattern for finding 1 or more white spaces (key) and the replacment value for matches (value)
        replacements = {' +':'\t'}
        # subsitutes text using the patterns in "replacements"
        for key, value in replacements.items():
            data = re.sub(key, value, conditioned_data)

        output_file = os.path.join(self.main_dir, 'Data', 'focal_mechanism_data.csv')
        with open(output_file, 'w') as f:
            f.write(data)

    # Creates a focal mechanism dataframe in the "aki" format from the focal mechanism data
    def Filter_AKI_Format_Focal_Mechanism_Data(self):
        import os
        import pandas as pd

        print('Writing focal mechanisms to CSV...')


        focal_mechanisms =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data.csv')
        df_focal_mechanisms = pd.read_csv(focal_mechanisms , sep='\t')
        
        # saves the unified depth column as a pandas series so that it can be recalled later for coloring the focal mechanisms by depth
        fm_depths = df_focal_mechanisms['DEPTH']
        # saves the unified depth magnitude column as a pandas series so that it can be recalled later for creating the legend
        fm_mags = df_focal_mechanisms['MAG']

        # magnitude of earthquakes used to filter out the focal mechanisms that will be offset
        magnitude_filter = 7

        # focal mechanisms with earthquake magnitude less than the magnitude filter
        df_mag_filtered_focal_mecha = df_focal_mechanisms[(df_focal_mechanisms.MAG < magnitude_filter)]
        # focal mechanisms with earthquake magnitude greater than or equal to the magnitude filter
        df_offset_mag_filtered_focal_mecha =df_focal_mechanisms[(df_focal_mechanisms.MAG >= magnitude_filter)]

        # reads the unmodifed magnitude of the focal mechanisms that will be offset into a list so it can be later used to label them
        fm_label = []
        for i in df_offset_mag_filtered_focal_mecha.MAG:
            fm_label.append(i)

        fm_dataframes = [df_mag_filtered_focal_mecha, df_offset_mag_filtered_focal_mecha]

        # iterates through each focal mechanism dataframe and scales them expoentially so that they will be plotted as exponentially larger sizes, and resets the index in place while not including the old index as a column which is done for tidyness 
        for dataframe in fm_dataframes:
            dataframe.reset_index(inplace=True, drop=True)
            dataframe['MAG'] = 0.1 * (2 ** dataframe.MAG)

        # new dataframes to hold only the columns used for the "aki" focal mechanism format
        fm_aki_format = pd.DataFrame()
        fm_aki_format_offset = pd.DataFrame()
        aki_dataframes = [fm_aki_format, fm_aki_format_offset]
        # column names of the aki format, with keys being columns from the focal mechanism dataframes and values being the new column names to be used
        aki_format_attributes = {'LON':'longitude', 'LAT':'latitude', 'DEPTH':'depth', 'STRIKE':'strike', 'DIP':'dip', 'RAKE':'rake', 'MAG':'magnitude'}

        # add the desired columns for the 'aki' focal mechanism format to the new dataframes in the desired order
        for aki_data, fm_data in zip(aki_dataframes, fm_dataframes):
            for key, value in aki_format_attributes.items():
                aki_data[value] = fm_data[key]

        # offset coordinates (lon:lat) used for offsetting the selected focal mechanisms
        offset_coordinates = {-117.70:35.78}
        fm_aki_format_offset['offset_longitude'] = offset_coordinates.keys()
        fm_aki_format_offset['offset_latitude'] = offset_coordinates.values()

        # adds label data for plotting above each focal mechanism beachball
        fm_aki_format_offset['label_mag'] = fm_label

        # path to the "small" EQ focal mechanisms 
        focal_mechanisms_lesser_output =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_less_than_M{}.csv').format(magnitude_filter)
        # path to the "large" EQ focal mechanisms 
        focal_mechanisms_greater_output =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_greater_than_or_equal_to_M{}.csv').format(magnitude_filter)
        focal_mechanism_outputs = [focal_mechanisms_lesser_output, focal_mechanisms_greater_output]

        # writes the aki format dataframes to csv files, leaving out the header. This is important because if the header is included, fig.meca will plot the header text
        for dataframe, path in zip(aki_dataframes, focal_mechanism_outputs):
            dataframe.to_csv(path, sep='\t', index=False, header=None)

        return fm_depths, fm_mags, magnitude_filter

# Creates a postscript legend file
def Create_Legend(self, fm_mags):
    import os
    import math

    # File path to postscript legend file to be created
    focal_mechanism_legend = os.path.join(self.main_dir, 'Data', 'focal_mechanism_legend.txt')

    # Creates a postscript file and writes some explainer, legend header text, and the column format
    with open(focal_mechanism_legend, 'w') as f:
        f.write(
            '# G is vertical gap, V is vertical line, N sets # of columns,\n'
            '# D draws horizontal line, H is header, L is column header,\n'
            '# S is symbol <symbol size><symbol color><symbol border thickness><text position><text>\n'
            '\n'
            'H 12p,Helvetica Magnitude\n'
            'D 0.1i 1p\n'
            'G 0.05i\n'
            'N 1\n'
            '\n'
        )

    # sets the index to the minimum magnitude of the unified dataset. This is used to set the minimum size for the circles used to represent magnitude within the legend, as well as label them
    i = math.floor(fm_mags.min())
    # sets the vertical space between each magnitude circle/text line
    v_spacer = 0.08

    # creteas a legend entry for each integer magnitude within the dataset and appends it to the legend file
    with open(focal_mechanism_legend, 'a') as f:
        while i <= round(fm_mags.max(), 0):\
            # replicates the exponential scaling of the magnitide and linear scaling of the fig.meca function so that the magnitude circles in the legend are the same size as the plotted focal mechanisms
            size = 0.1*(0.1*(2**i))
            size = round(size, 2)
            
            # creates a legend of transparent circles that are labeld with the associated magnitude
            f.write(
                'S 0.20i c {} white@100 0.5p 0.6i M{}\n'
                'G {}i\n'
                .format(size, i, v_spacer)
            )

            # exponentially scales the vertical spacing between each legend entry so that they are evenly spaced in this particular demo
            v_spacer = v_spacer**0.5
            i += 1

    # Plots the map    
    def Plot_Map(self, fm_depths, magnitude_filter):
        import os
        import pygmt

        print('Building map...')

        ### BASEMAP -----------------------------------------------------------------------------
        # sets the region to be displayed in the map <min lon><max lon><min lat><max lat>
        region = [-118.0, -117.15, 35.5, 36.1]  
        # map projection (Mercator): <type><size><units>
        projection = 'M6i' 
        # frame  annotations: [<frame sides with/without ticks>, <x-axis tick rate>, <y-axis tick rate>]
        frame = ['SWne', 'xa', 'ya']
        # map scale - displays a fancy (f) distance scale (lScale) in kilometers (u). Format of <type><longitude><latitude>/<latitude of km symbol>/<scale length in km>+<unit>+<label><label text>
        map_scale = 'f-118.0/32.15/20/100+u+lScale:'
        


        ### GRID -----------------------------------------------------------------------------
        # sets the color palette table for the digital elevation map
        grid_cmap = 'grayC'



        ### MECA -----------------------------------------------------------------------------
        # focal mechanism data of earthquakes less than the magnitude filter
        focal_mechanisms_less_than_magnitude_filter = os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_less_than_M{}.csv').format(magnitude_filter)
        # focal mechanism data of earthquakes greater than or equal to the magnitude filter
        focal_mechanisms_greater_than_magnitude_filter = os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_greater_than_or_equal_to_M{}.csv').format(magnitude_filter)
        focal_mechanisms = [focal_mechanisms_less_than_magnitude_filter, focal_mechanisms_greater_than_magnitude_filter]
        # column format: <longitiude><latitude><strike><dip><rake><magnitude>. Optionally add <offset longitude><offset latitude><label> to the end
        convention = 'aki'
        # scale defines the size for magnitude = 5 (0.5). Also optionally plots labels above the focal mechanisms (+f15p,Helvetica,black). 
        fm_scale = '0.5+f15p,Helvetica,black'
        # thickness of the lines drawn from the offset focal mechanisms back to their point of origin
        fm_offset = '1p'
        # colors by which to color focal mechanisms
        depth_color = 'yellow,red,purple'



        ### COLORBAR -----------------------------------------------------------------------------
        # automaticly determined fancy frame with label. Format of: <type>+<label><label text>
        cbar_frame = 'af+l"Depth (km)"'



        ### LEGENDS -----------------------------------------------------------------------------
        # path to the legend file
        legend = os.path.join(self.main_dir, 'Data', 'focal_mechanism_legend.txt')
        # <position>+<width>+<offset x-dir>/<offset y-dir>
        legend_position = 'JMR+w1.05i+o0.15i/-0.02i'
        # <background color>+<line thickness>
        #legend_box = '+gwhite+p1p'



        ### INSET MAP -----------------------------------------------------------------------------
        # extent of the inset map
        i_region = [-130, -105, 27, 45]
        i_projection = 'M1.75i'
        # sets the border types, line thickness, and color <border type><line thickness><color>. In this case 1 = national boundaries, 2 = state boundaries within America
        i_borders = ['1/0.8p,gray40', '2/0.8p,gray40']
        # sets the color of the land
        i_land = 'gray'
        # sets the shorline type, line thickness, and color <shoreline type><line thickness><line color>. In this case, 1 = coastline
        i_shorelines = '1/0.1p,gray40'
        i_pen = '1p,black'
        # sets the water color
        i_water = 'azure2'
        # shifts the inset map in the x-direction. Positive numbers shift up, negative numbers shifts down. <amount><units>
        i_xshift = '12.5c'
        # shifts the inset map in the y-direction. Positive numbers shift up, negative numbers shifts down. <amount><units>
        i_yshift ='11.0c'
        # first two list elements of 'rectangle' are the longitude and latitude of the bottom left corner of the rectangle, and the last two elements are the longitude and latitude of the uppper right corner. 
        rectangle = [[region[0], region[2], region[1], region[3]]]
        # sets style of plot as rectagle (r) with first two list items as the longitude and latitude of the bottom left corner of the rectangle, and the last two list items as the longitude and latitude of the uppper right corner (+s)
        i_style = 'r+s'
        # sets an auto-detrmined frame with ticks in intervals of 10 (a10), then displays the frame with ticks on the top and right sides of the map (NE), but doesn't show ticks for the left and bottoms sides (sw)
        i_frame = ['a10', 'swNE']
        


        ### Save File -----------------------------------------------------------------------------
        # map save name
        save_name = os.path.join(self.main_dir, 'Results', 'Focal_Mechanism_Demo.png')
        # sets the transparency of the white space around the map    
        save_transparency = False



        # Establishes a figure to hold map layers
        fig = pygmt.Figure()

        # Forces Lat/Lon to display as degree decimal
        pygmt.config(FORMAT_GEO_MAP = 'D')
        # Forces Lat/Lon to display with two decimal places <f>, as opposed the default that limits by significan digits <g>
        pygmt.config(FORMAT_FLOAT_OUT = '%.2f')
        # Forces the map frame annotation to be smaller
        pygmt.config(FONT_ANNOT_PRIMARY = '10p,Helvetica,black')
        # Forces the scale font to be smaller
        pygmt.config(FONT_LABEL = '10p,Helvetica,black')


        #  Basemap layer
        fig.basemap(
            region = region,
            projection = projection,
            frame = frame
        )

        
        # topography layer
        fig.grdimage(
            # downloads a 3 arc second shuttle radar topography mission digital elevation model of the region extent
            '@srtm_relief_03s',
            # applies shading to the grid 
            shading = True, 
            cmap = grid_cmap,
        )

        
        depth_series = [fm_depths.min(), fm_depths.max()]

        # makes a color palette table to color the events by depth
        pygmt.makecpt(
            cmap = depth_color,
            series = depth_series
        )

        for focal_mechanism in focal_mechanisms:

            fig.meca(
                # requires a file to be provided directly to the spec parameter if offseting the focal mechanisms is desired
                spec = focal_mechanism,
                # sets the focal mechanism convention
                convention = convention,
                # linear scaling factor with respect to M5 earthquakes
                scale = fm_scale,
                # sets the focal mechanisms to be colored by the previously created color palette table
                C = True,
                # controls whether to offset the focal mechanims
                offset = fm_offset,
            )


        # Plots the color bar for depth
        fig.colorbar(
            frame = cbar_frame
        )

        
        # plots the legend
        fig.legend(
            spec = legend,
            position = legend_position,
        )
        

        # plots the map scale
        fig.basemap(
            map_scale = map_scale
            )


        
        # Plots a mini coast map as an offset inset map
        fig.coast(
            region = i_region,
            projection = i_projection,
            frame = i_frame,
            land = i_land,
            borders = i_borders,
            shorelines = i_shorelines,
            water = i_water,
            xshift = i_xshift,
            yshift = i_yshift
        )

        # Plots a rectangle of the study area in the inset map
        fig.plot(
            data = rectangle,  
            style = i_style, 
            pen = i_pen,
            projection = i_projection
        )


        # Saves a copy of the generated figure
        fig.savefig(save_name, transparent = save_transparency)

        print('Map Saved!')

'''
PyGMT v0.4.1

Decription:
This script creates a map of focal mechanisms that are scaled in size according to magnitude and colored according to depth. 
It offsets selected focal mechanisms from their point of origin and draws a line to them.

Instructions:
1) Run the create_folder.py script to create the directory paths
2) Perform a focal mechanism search of the Souther California Earthquake Data Center focal mechanism catalog using the provided parameters
3) Copy the focal mechanism data and headers into a text file saved to the Data folder of this demo
4) Run this script

Focal Mechanism data source: https://service.scedc.caltech.edu/eq-catalogs/FMsearch.php

Date range: 2019/01/01 to 2020/01/01
Magnitude range: 5 to 10
Latitude range: 35.5 to 36.1
Longitude range: -118 to -117.15
Depth range: -5 to 100
'''

class Map_Builder():

    # Path to main project folder
    main_dir = r'C:\Users\USER\Desktop\Focal_Mechanism_Demo'


    # Conditions focal mechanism files downloaded from the Southern California Earthquake Data Center so that they can be used
    def Condition_Focal_Mechanism_Data(self):
        import os
        import re

        print('Conditioning focal mechanisms...')


        input_file = os.path.join(self.main_dir, 'Data', 'focal_mechanism_data.txt')
        # reads in the file by line rather than the default of by character
        with open(input_file, 'r') as f:
            lines = f.readlines()

        # holds the conditioned lines, namely the removal of empty lines
        conditioned_lines = []
        # iterates over each line and adds them to a list as long as they are not empty
        for line in lines:
            if line == '\n':
                continue
            conditioned_lines.append(line)
        
        # empty placeholder for the conditioned lines list to be rejoined into a string
        conditioned_data = ''
        # rejoins the conditioned lines into a string so that it can be further conditioned
        conditioned_data = conditioned_data.join(conditioned_lines)

        # regular expression pattern for finding 1 or more white spaces (key) and the replacment value for matches (value)
        replacements = {' +':'\t'}
        # subsitutes text using the patterns in "replacements"
        for key, value in replacements.items():
            data = re.sub(key, value, conditioned_data)

        output_file = os.path.join(self.main_dir, 'Data', 'focal_mechanism_data.csv')
        with open(output_file, 'w') as f:
            f.write(data)



    # Creates a focal mechanism dataframe in the "aki" format from the focal mechanism data
    def Filter_AKI_Format_Focal_Mechanism_Data(self):
        import os
        import pandas as pd

        print('Writing focal mechanisms to CSV...')


        focal_mechanisms =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data.csv')
        df_focal_mechanisms = pd.read_csv(focal_mechanisms , sep='\t')
        
        # saves the unified depth column as a pandas series so that it can be recalled later for coloring the focal mechanisms by depth
        fm_depths = df_focal_mechanisms['DEPTH']
        # saves the unified depth magnitude column as a pandas series so that it can be recalled later for creating the legend
        fm_mags = df_focal_mechanisms['MAG']

        # magnitude of earthquakes used to filter out the focal mechanisms that will be offset
        magnitude_filter = 7

        # focal mechanisms with earthquake magnitude less than the magnitude filter
        df_mag_filtered_focal_mecha = df_focal_mechanisms[(df_focal_mechanisms.MAG < magnitude_filter)]
        # focal mechanisms with earthquake magnitude greater than or equal to the magnitude filter
        df_offset_mag_filtered_focal_mecha =df_focal_mechanisms[(df_focal_mechanisms.MAG >= magnitude_filter)]

        # reads the unmodifed magnitude of the focal mechanisms that will be offset into a list so it can be later used to label them
        fm_label = []
        for i in df_offset_mag_filtered_focal_mecha.MAG:
            fm_label.append(i)

        fm_dataframes = [df_mag_filtered_focal_mecha, df_offset_mag_filtered_focal_mecha]

        # iterates through each focal mechanism dataframe and scales them expoentially so that they will be plotted as exponentially larger sizes, and resets the index in place while not including the old index as a column which is done for tidyness 
        for dataframe in fm_dataframes:
            dataframe.reset_index(inplace=True, drop=True)
            dataframe['MAG'] = 0.1 * (2 ** dataframe.MAG)

        # new dataframes to hold only the columns used for the "aki" focal mechanism format
        fm_aki_format = pd.DataFrame()
        fm_aki_format_offset = pd.DataFrame()
        aki_dataframes = [fm_aki_format, fm_aki_format_offset]
        # column names of the aki format, with keys being columns from the focal mechanism dataframes and values being the new column names to be used
        aki_format_attributes = {'LON':'longitude', 'LAT':'latitude', 'DEPTH':'depth', 'STRIKE':'strike', 'DIP':'dip', 'RAKE':'rake', 'MAG':'magnitude'}

        # add the desired columns for the 'aki' focal mechanism format to the new dataframes in the desired order
        for aki_data, fm_data in zip(aki_dataframes, fm_dataframes):
            for key, value in aki_format_attributes.items():
                aki_data[value] = fm_data[key]

        # offset coordinates (lon:lat) used for offsetting the selected focal mechanisms
        offset_coordinates = {-117.70:35.78}
        fm_aki_format_offset['offset_longitude'] = offset_coordinates.keys()
        fm_aki_format_offset['offset_latitude'] = offset_coordinates.values()

        # adds label data for plotting above each focal mechanism beachball
        fm_aki_format_offset['label_mag'] = fm_label

        # path to the "small" EQ focal mechanisms 
        focal_mechanisms_lesser_output =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_less_than_M{}.csv').format(magnitude_filter)
        # path to the "large" EQ focal mechanisms 
        focal_mechanisms_greater_output =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_greater_than_or_equal_to_M{}.csv').format(magnitude_filter)
        focal_mechanism_outputs = [focal_mechanisms_lesser_output, focal_mechanisms_greater_output]

        # writes the aki format dataframes to csv files, leaving out the header. This is important because if the header is included, fig.meca will plot the header text
        for dataframe, path in zip(aki_dataframes, focal_mechanism_outputs):
            dataframe.to_csv(path, sep='\t', index=False, header=None)

        return fm_depths, fm_mags, magnitude_filter



    # Creates a postscript legend file
    def Create_Legend(self, fm_mags):
        import os
        import math

        # File path to postscript legend file to be created
        focal_mechanism_legend = os.path.join(self.main_dir, 'Data', 'focal_mechanism_legend.txt')

        # Creates a postscript file and writes some explainer, legend header text, and the column format
        with open(focal_mechanism_legend, 'w') as f:
            f.write(
                '# G is vertical gap, V is vertical line, N sets # of columns,\n'
                '# D draws horizontal line, H is header, L is column header,\n'
                '# S is symbol <symbol size><symbol color><symbol border thickness><text position><text>\n'
                '\n'
                'H 12p,Helvetica Magnitude\n'
                'D 0.1i 1p\n'
                'G 0.05i\n'
                'N 1\n'
                '\n'
            )

        # sets the index to the minimum magnitude of the unified dataset. This is used to set the minimum size for the circles used to represent magnitude within the legend, as well as label them
        i = math.floor(fm_mags.min())
        # sets the vertical space between each magnitude circle/text line
        v_spacer = 0.08

        # creteas a legend entry for each integer magnitude within the dataset and appends it to the legend file
        with open(focal_mechanism_legend, 'a') as f:
            while i <= round(fm_mags.max(), 0):\
                # replicates the exponential scaling of the magnitide and linear scaling of the fig.meca function so that the magnitude circles in the legend are the same size as the plotted focal mechanisms
                size = 0.1*(0.1*(2**i))
                size = round(size, 2)
                
                # creates a legend of transparent circles that are labeld with the associated magnitude
                f.write(
                    'S 0.20i c {} white@100 0.5p 0.6i M{}\n'
                    'G {}i\n'
                    .format(size, i, v_spacer)
                )

                # exponentially scales the vertical spacing between each legend entry so that they are evenly spaced in this particular demo
                v_spacer = v_spacer**0.5
                i += 1
                

    # Plots the map    
    def Plot_Map(self, fm_depths, magnitude_filter):
        import os
        import pygmt

        print('Building map...')

        ### BASEMAP -----------------------------------------------------------------------------
        # sets the region to be displayed in the map <min lon><max lon><min lat><max lat>
        region = [-118.0, -117.15, 35.5, 36.1]  
        # map projection (Mercator): <type><size><units>
        projection = 'M6i' 
        # frame  annotations: [<frame sides with/without ticks>, <x-axis tick rate>, <y-axis tick rate>]
        frame = ['SWne', 'xa', 'ya']
        # map scale - displays a fancy (f) distance scale (lScale) in kilometers (u). Format of <type><longitude><latitude>/<latitude of km symbol>/<scale length in km>+<unit>+<label><label text>
        map_scale = 'f-118.0/32.15/20/100+u+lScale:'
        


        ### GRID -----------------------------------------------------------------------------
        # sets the color palette table for the digital elevation map
        grid_cmap = 'grayC'



        ### MECA -----------------------------------------------------------------------------
        # focal mechanism data of earthquakes less than the magnitude filter
        focal_mechanisms_less_than_magnitude_filter =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_less_than_M{}.csv').format(magnitude_filter)
        # focal mechanism data of earthquakes greater than or equal to the magnitude filter
        focal_mechanisms_greater_than_magnitude_filter =  os.path.join(self.main_dir, 'Data', 'focal_mechanism_data_greater_than_or_equal_to_M{}.csv').format(magnitude_filter)
        focal_mechanisms = [focal_mechanisms_less_than_magnitude_filter, focal_mechanisms_greater_than_magnitude_filter]
        # column format: <longitiude><latitude><strike><dip><rake><magnitude>. Optionally add <offset longitude><offset latitude><label> to the end
        convention = 'aki'
        # scale defines the size for magnitude = 5 (0.5). Also optionally plots labels above the focal mechanisms (+f15p,Helvetica,black). 
        fm_scale = '0.5+f15p,Helvetica,black'
        # thickness of the lines drawn from the offset focal mechanisms back to their point of origin
        fm_offset = '1p'
        # colors by which to color focal mechanisms
        depth_color = 'yellow,red,purple'



        ### COLORBAR -----------------------------------------------------------------------------
        # automaticly determined fancy frame with label. Format of: <type>+<label><label text>
        cbar_frame = 'af+l"Depth (km)"'



        ### LEGENDS -----------------------------------------------------------------------------
        # path to the legend file
        legend = os.path.join(self.main_dir, 'Data', 'focal_mechanism_legend.txt')
        # <position>+<width>+<offset x-dir>/<offset y-dir>
        legend_position = 'JMR+w1.05i+o0.15i/-0.02i'
        # <background color>+<line thickness>
        #legend_box = '+gwhite+p1p'



        ### INSET MAP -----------------------------------------------------------------------------
        # extent of the inset map
        i_region = [-130, -105, 27, 45]
        i_projection = 'M1.75i'
        # sets the border types, line thickness, and color <border type><line thickness><color>. In this case 1 = national boundaries, 2 = state boundaries within America
        i_borders = ['1/0.8p,gray40', '2/0.8p,gray40']
        # sets the color of the land
        i_land = 'gray'
        # sets the shorline type, line thickness, and color <shoreline type><line thickness><line color>. In this case, 1 = coastline
        i_shorelines = '1/0.1p,gray40'
        i_pen = '1p,black'
        # sets the water color
        i_water = 'azure2'
        # shifts the inset map in the x-direction. Positive numbers shift up, negative numbers shifts down. <amount><units>
        i_xshift = '12.5c'
        # shifts the inset map in the y-direction. Positive numbers shift up, negative numbers shifts down. <amount><units>
        i_yshift ='11.0c'
        # first two list elements of 'rectangle' are the longitude and latitude of the bottom left corner of the rectangle, and the last two elements are the longitude and latitude of the uppper right corner. 
        rectangle = [[region[0], region[2], region[1], region[3]]]
        # sets style of plot as rectagle (r) with first two list items as the longitude and latitude of the bottom left corner of the rectangle, and the last two list items as the longitude and latitude of the uppper right corner (+s)
        i_style = 'r+s'
        # sets an auto-detrmined frame with ticks in intervals of 10 (a10), then displays the frame with ticks on the top and right sides of the map (NE), but doesn't show ticks for the left and bottoms sides (sw)
        i_frame = ['a10', 'swNE']
        


        ### Save File -----------------------------------------------------------------------------
        # map save name
        save_name = os.path.join(self.main_dir, 'Results', 'Focal_Mechanism_Demo.png')
        # sets the transparency of the white space around the map    
        save_transparency = False



        # Establishes a figure to hold map layers
        fig = pygmt.Figure()

        # Forces Lat/Lon to display as degree decimal
        pygmt.config(FORMAT_GEO_MAP = 'D')
        # Forces Lat/Lon to display with two decimal places <f>, as opposed the default that limits by significan digits <g>
        pygmt.config(FORMAT_FLOAT_OUT = '%.2f')
        # Forces the map frame annotation to be smaller
        pygmt.config(FONT_ANNOT_PRIMARY = '10p,Helvetica,black')
        # Forces the scale font to be smaller
        pygmt.config(FONT_LABEL = '10p,Helvetica,black')


        #  Basemap layer
        fig.basemap(
            region = region,
            projection = projection,
            frame = frame
        )

        
        # topography layer
        fig.grdimage(
            # downloads a 3 arc second shuttle radar topography mission digital elevation model of the region extent
            '@srtm_relief_03s',
            # applies shading to the grid 
            shading = True, 
            cmap = grid_cmap,
        )

        
        depth_series = [fm_depths.min(), fm_depths.max()]

        # makes a color palette table to color the events by depth
        pygmt.makecpt(
            cmap = depth_color,
            series = depth_series
        )

        for focal_mechanism in focal_mechanisms:

            fig.meca(
                # requires a file to be provided directly to the spec parameter if offseting the focal mechanisms is desired
                spec = focal_mechanism,
                # sets the focal mechanism convention
                convention = convention,
                # linear scaling factor with respect to M5 earthquakes
                scale = fm_scale,
                # sets the focal mechanisms to be colored by the previously created color palette table
                C = True,
                # controls whether to offset the focal mechanims
                offset = fm_offset,
            )


        # Plots the color bar for depth
        fig.colorbar(
            frame = cbar_frame
        )

        
        # plots the legend
        fig.legend(
            spec = legend,
            position = legend_position,
        )
        

        # plots the map scale
        fig.basemap(
            map_scale = map_scale
            )


        
        # Plots a mini coast map as an offset inset map
        fig.coast(
            region = i_region,
            projection = i_projection,
            frame = i_frame,
            land = i_land,
            borders = i_borders,
            shorelines = i_shorelines,
            water = i_water,
            xshift = i_xshift,
            yshift = i_yshift
        )

        # Plots a rectangle of the study area in the inset map
        fig.plot(
            data = rectangle,  
            style = i_style, 
            pen = i_pen,
            projection = i_projection
        )


        # Saves a copy of the generated figure
        fig.savefig(save_name, transparent = save_transparency)

        print('Map Saved!')



data = Map_Builder()
data.Condition_Focal_Mechanism_Data()
fm_depths, fm_mags, magnitude_filter = data.Filter_AKI_Format_Focal_Mechanism_Data()
data.Create_Legend(fm_mags)
data.Plot_Map(fm_depths, magnitude_filter)