Focusing on Focal Mechanisms with Python

To better understand the faulting motions that create earthquakes, it can be useful to graphically display focal mechanisms. [A focal mechanism is the orientation and type of slip of a fault, and it is represented as a “beach-ball” symbol](https://www.usgs.gov/natural-hazards/earthquake-hazards/science/focal-mechanisms-or-beachballs?qt-science_center_objects=0#qt-science_center_objects). \ ![Image is sourced from the United States Geological Survey earthquake hazards webpage on focal mechanisms](https://cdn.hackernoon.com/images/tYPL7XK4s8dvOR0UDJr1w47GG2y2-7m15x28ft.png) Within a “beach-ball,” the dark quarters represent compressional forces and the white quarters represent tensional forces. A “beach-ball” has two possible orientations of fault movement, each along with one of the planes dividing the “beach-ball” into fourths, and determining the correct orientation requires additional information. When “beach-balls” present four equally sized quadrants in a “top-down” view like the one in the above diagram, strike-slip faulting is represented. Rotated “beach-balls” with a white quarter in the center represent normal faulting, while “beach-balls” with a dark quarter in the center represent thrust faulting. \ ## The Demonstration In this demo, focal mechanism data will be retrieved from the [Southern California Earthquake Data Center (SCEDC)](https://scedc.caltech.edu/index.html) and then plotted as focal mechanism “beach-balls” on a map with the [geospatial Python package PyGMT](https://www.pygmt.org/latest/index.html). \ Please keep in mind that with the exception of the folder creation script, all code blocks shown in this demo are intended to be run as part of a complete script, which is included at the bottom of the page. \ ## Getting Started **Create Project Folders** To follow this demo as written, project folders need to be created on the Desktop. A diagram of the desired directory tree for Windows 10 is shown below: ![Created with Lucidchart](https://cdn.hackernoon.com/images/tYPL7XK4s8dvOR0UDJr1w47GG2y2-ad5hr28mh.png) These folders can be created either by running the script provided below from anywhere after changing the *main_dir* file path to reflect your username or by manually creating a *Focal_Mechanism_Demo* folder and within it *Data*, *Methods*, and *Results* folders. \ ```python ''' 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) ``` \ Data files used and created by the demo script will be located in the *Data* folder, and the focal mechanism map will save to the *Results* folder. The *Methods* folder should contain the demo scripts. \ ## Retrieve Focal Mechanism Data Focal mechanism data will be acquired by downloading it from the [SCEDC focal mechanism catalog](https://service.scedc.caltech.edu/eq-catalogs/FMsearch.php). This demo uses the following search parameters: \ **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 \ ![Example of entering the focal mechanism parameters into the Southern California Earthquake Data Center's focal mechanism catalog search](https://cdn.hackernoon.com/images/tYPL7XK4s8dvOR0UDJr1w47GG2y2-y46mg28wm.png) After clicking the search button, the results are presented as a text page. Select the data and column headers as shown below, and copy them into a text file. Then save the text file to the *Data* folder and name it *focal_mechanism_data*. \ ![Example of selecting and copying the focal mechanism data and column headers](https://cdn.hackernoon.com/images/tYPL7XK4s8dvOR0UDJr1w47GG2y2-2qm528mj.png) # Creating The Project Files ## Conditioning The Focal Mechanism Data The focal mechanism data as retrieved from the SCEDC does not have a uniform delimiter separating the columns and has an empty line between the column header and the column values. In order to read the data into a data frame, it needs to be conditioned so that the aforementioned issues are resolved. \ The following demo script function removes the empty line and standardizes the delimiter to be tabs, then saves the conditioned data as a CSV file: \ ```python # 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) ``` \ ## Filtering Focal Mechanism Data Into The “Aki” Format After conditioning the focal mechanism data, it needs to be filtered so that the resulting file can be read by the PyGMT *[meca](https://www.pygmt.org/dev/api/generated/pygmt.Figure.meca.html)*[ function](https://www.pygmt.org/dev/api/generated/pygmt.Figure.meca.html), which plots focal mechanism “beach-balls.” As the data provided by the SCEDC is in terms of strike, drip, rake, and magnitude, the focal mechanism “beach-balls” will be determined by way of the Aki and Richards (1980) convention. In this demo, earthquakes greater than or equal to M7.0 will be offset to showcase how offsetting is accomplished. The *meca* function requires a file to be passed directly to the *spec* parameter when offsetting, so a CSV file with specific columns and column order pertaining to the convention needs to be created. Additionally, the column headers must be removed, so that header names don’t plot as text. \ The following demo script function splits the focal mechanism data into two data frames at M7.0 earthquakes, then converts the magnitudes so that they are exponentially scaled; this is how the “beach-balls” will be sized when they are plotted. Next, it adds offset coordinates and text label columns to the data frame of M7.0 and greater earthquakes so they will be offset from their point of origin when plotted. Finally, the two data frames are saved as CSV files: \ ```python # 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)] # 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 ``` \ ## Creating The Legend When the focal mechanisms “beach-balls” are eventually plotted on a map, the size differences will need to be given context with a legend. For this kind of legend, the PyGMT *[legend](https://www.pygmt.org/dev/api/generated/pygmt.Figure.legend.html)*[ function](https://www.pygmt.org/dev/api/generated/pygmt.Figure.legend.html) needs to be passed a postscript file. \ The following demo script function creates a postscript file for the legend: \ ```python # 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 \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 ``` \ ## Building The Map Now that the focal mechanism data and legend have been properly prepared, it’s time to plot everything on a map. \ The following demo script function plots focal mechanism “beach-balls” over an [automatically downloaded 3 arc second Digital Elevation Model from the Shuttle Radar Topography Mission](https://gmt.soest.hawaii.edu/doc/latest/datasets.html). The “beach-balls” are scaled exponentially in size by the magnitude and colored by depth, with magnitude, and depth legends plotted along the map axes. An inset map displaying the study region relative to the greater region around it is plotted in the upper right corner of the map: \ ```python # 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 region = [-118.0, -117.15, 35.5, 36.1] # map projection (Mercator): projection = 'M6i' # frame annotations: [ , , ] frame = ['SWne', 'xa', 'ya'] # map scale - displays a fancy (f) distance scale (lScale) in kilometers (u). Format of / / + + 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: . Optionally add 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: + cbar_frame = 'af+l"Depth (km)"' ### LEGENDS ----------------------------------------------------------------------------- # path to the legend file legend = os.path.join(self.main_dir, 'Data', 'focal_mechanism_legend.txt') # + + / legend_position = 'JMR+w1.05i+o0.15i/-0.02i' # + #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 . 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 . 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. i_xshift = '12.5c' # shifts the inset map in the y-direction. Positive numbers shift up, negative numbers shifts down. 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 , as opposed the default that limits by significan digits 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!') ``` \ # Result ![](https://cdn.hackernoon.com/images/tYPL7XK4s8dvOR0UDJr1w47GG2y2-kt602818.png) \ I hope that this has been helpful for anyone looking to plot and offset focal mechanisms with PyGMT. \ # Complete Code ```python ''' 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)] # 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 \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 region = [-118.0, -117.15, 35.5, 36.1] # map projection (Mercator): projection = 'M6i' # frame annotations: [ , , ] frame = ['SWne', 'xa', 'ya'] # map scale - displays a fancy (f) distance scale (lScale) in kilometers (u). Format of / / + + 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: . Optionally add 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: + cbar_frame = 'af+l"Depth (km)"' ### LEGENDS ----------------------------------------------------------------------------- # path to the legend file legend = os.path.join(self.main_dir, 'Data', 'focal_mechanism_legend.txt') # + + / legend_position = 'JMR+w1.05i+o0.15i/-0.02i' # + #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 . 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 . 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. i_xshift = '12.5c' # shifts the inset map in the y-direction. Positive numbers shift up, negative numbers shifts down. 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 , as opposed the default that limits by significan digits 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) ``` \

Focusing on Focal Mechanisms with Python

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