examples/Example_2/Run_Elastic_stresses_py.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a991914",
   "metadata": {},
   "source": [
    "# Running an ESPy calculation\n",
    "In this tutorial, we will run several calculations using the ```Elastic_stresses_py``` library:\n",
    "1. We will run a calculation using the command line and configuration/inputs through text files. \n",
    "2. We will run a calculation using the Python API. \n",
    "3. We will run a calculation modifying the inputs and outputs using the Python API, which simulates how you would use ESPy in a loop or over multiple calculations at once. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a506e2",
   "metadata": {},
   "source": [
    "# Part 1: Command line API\n",
    "The basic usage of ```Elastic_stresses_py``` is through the command line.  We could first create an empty config file in the current directory (```.```) with: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "909663aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Write a default config file for elastic_stresses_py. \n",
      "Writing file ./my_config.txt \n"
     ]
    }
   ],
   "source": [
    "!elastic_stresses_config_writer ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5185464",
   "metadata": {},
   "source": [
    "You can check out the contents of this default config file. Remember that in a general case, the values and filepaths in this config file will need to be changed for your specific application. This file can be edited in a text editor.  For now, the values are good and we can move on to running the example calculation: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dbb24b9",
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "\n",
      "Welcome to a simple forward modeling tool for calculating elastic displacements and coulomb stresses. \n",
      "Configuring with the following Params:\n",
      "  -Config file: my_config.txt \n",
      "  -Input file: M6.8_2014.intxt \n",
      "  -Disp_points_file: CA_GPS_ll.txt \n",
      "  -Outdir: Outputs/my_experiment/ \n",
      "  -Elastic moduli: 30000000000.000000, 30000000000.000000 \n",
      "  -Poisson's ratio and Alpha: 0.250000, 0.666667\n",
      "Reading source and receiver fault information from file M6.8_2014.intxt \n",
      "Reading displacement points from file CA_GPS_ll.txt \n",
      "--> Read 105 displacement points \n",
      "Number of total sources: 1\n",
      "Number of rectangular sources: 1\n",
      "Moment Magnitude from Rectangular Fault Patches (assuming G=30.0GPa): 6.800000\n",
      "Beginning stress calculation.\n",
      "Number of sources: 1 \n",
      "Number of receivers: 1 \n",
      "Coefficient of friction: 0.400000\n",
      "Splitting 1 receiver faults into 100 subfaults each.\n",
      "Computing synthetic grid of displacements\n",
      "/Users/kama8443/miniconda3/envs/elastic_py/lib/python3.11/site-packages/cutde/coordinators.py:77: UserWarning: The obs_pts input array has Fortran ordering. Converting to C ordering. This may be expensive.\n",
      "  warnings.warn(\n",
      "Resolving stresses on receiver fault(s).\n",
      "Resolving stresses on a horizontal profile.\n",
      "Writing file Outputs/my_experiment/used_config.txt \n",
      "Writing file Outputs/my_experiment/used_inputs.txt \n",
      "Writing Outputs/my_experiment/ll_disps.txt \n",
      "Min, Max disp_points east: -0.000301, 0.012441 m\n",
      "Min, Max disp_points north: -0.002757, 0.000744 m\n",
      "Writing Outputs/my_experiment/receiver_traces.txt\n",
      "Writing Outputs/my_experiment/source_traces.txt\n",
      "Writing outfile Outputs/my_experiment/subfaulted.inp \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Writing file Outputs/my_experiment/stresses_full.txt \n",
      "Making plot of Shear stress on receiver fault patches: Outputs/my_experiment/Stresses_Shear.png. \n",
      "Making plot of Normal stress on receiver fault patches: Outputs/my_experiment/Stresses_Normal.png. \n",
      "Making plot of Coulomb stress on receiver fault patches: Outputs/my_experiment/Stresses_Coulomb.png. \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/my_experiment/Coulomb_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/my_experiment/Normal_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/my_experiment/Shear_map.png \n",
      "Making plot of Coulomb stress on receiver fault patches: Outputs/my_experiment/Stresses_cross_section_Coulomb.png. \n",
      "Writing file Outputs/my_experiment/coulomb_xsection.txt\n",
      "Making plot of Normal stress on receiver fault patches: Outputs/my_experiment/Stresses_cross_section_Normal.png. \n",
      "Making plot of Shear stress on receiver fault patches: Outputs/my_experiment/Stresses_cross_section_Shear.png. \n",
      "Writing synthetic grid of displacements in Outputs/my_experiment/disps_model_grid.txt \n",
      "Making plot of predicted displacement throughout model domain.\n",
      "Max vertical displacement is 0.032031 m\n",
      "Writing synthetic grid into files Outputs/my_experiment/ etc.xy_east.txt\n",
      "Printing vert.grd\n",
      "Printing east.grd\n",
      "Printing north.grd\n",
      "Mapping vertical deformation in Outputs/my_experiment/vertical_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Writing Outputs/my_experiment/stresses_horiz_profile.txt \n",
      "Saving figure Outputs/my_experiment/horizontal_profile_stresses.png \n"
     ]
    }
   ],
   "source": [
    "!elastic_stresses_driver my_config.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7914502d",
   "metadata": {},
   "source": [
    "Now you can check out the results. You calcualted a set of displacements and stresses from a hypothetical M6.8 earthquake at 16.4 km depth offshore northern California. The input format within ```M6.8_2014.intxt``` was the **Source_WC** format, which means a rectangular dislocation with dimensions calculated from Wells and Coppersmith (1994). \n",
    "\n",
    "Graphics resolution (DPI) on saved png's might be better when running this code from a script or a command line, rather than from a Jupyter notebook - I'm not sure why. However, Jupyter notebooks graphics will be good enough for exploration. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552d96e4",
   "metadata": {},
   "source": [
    "# Part 2: Python API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c21dda",
   "metadata": {},
   "source": [
    "Now, let's do the same thing through the Python API that ```Elastic_stresses_py``` is actually using on the inside. \n",
    "\n",
    "We always calculate in 4 steps: \n",
    "1. Configure\n",
    "2. Input\n",
    "3. Compute\n",
    "4. Output\n",
    "\n",
    "If you open the ```elastic_stresses_driver.py``` [script here](https://github.com/kmaterna/Elastic_stresses_py/blob/master/PyCoulomb/bin/elastic_stresses_driver.py), you'll see them expressed in this way: \n",
    "```\n",
    "params = PyCoulomb.configure_calc.configure_stress_calculation(config_file);\n",
    "[inputs, obs_disp_points, obs_strain_points] = PyCoulomb.input_values.read_inputs(params);\n",
    "out_object = PyCoulomb.run_dc3d.do_stress_computation(params, inputs, obs_disp_points, obs_strain_points);\n",
    "PyCoulomb.output_manager.produce_outputs(params, inputs, obs_disp_points, obs_strain_points, out_object);\n",
    "```\n",
    "\n",
    "In this API example, we're just running the steps one-by-one instead of all at once.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7a2a2e",
   "metadata": {},
   "source": [
    "### Step 1: Configure\n",
    "We begin by executing the \"Configure\" step: reading a config file into an object type called ```Params``` (definition [here](https://github.com/kmaterna/Elastic_stresses_py/blob/master/PyCoulomb/coulomb_collections.py#L7)). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "624373f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Configuring with the following Params:\n",
      "  -Config file: my_config.txt \n",
      "  -Input file: M6.8_2014.intxt \n",
      "  -Disp_points_file: CA_GPS_ll.txt \n",
      "  -Outdir: Outputs/my_experiment/ \n",
      "  -Elastic moduli: 30000000000.000000, 30000000000.000000 \n",
      "  -Poisson's ratio and Alpha: 0.250000, 0.666667\n"
     ]
    }
   ],
   "source": [
    "from elastic_stresses_py import PyCoulomb\n",
    "\n",
    "# Create deafult Params object from the configuration file\n",
    "default_params = PyCoulomb.configure_calc.configure_stress_calculation(\"my_config.txt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc0e009",
   "metadata": {},
   "source": [
    "### Step 2: Input\n",
    "We then execute the input phase, reading an input file (```M6.8_2014.intxt```) into an object type called ```Input_object``` (definition [here](https://github.com/kmaterna/Elastic_stresses_py/blob/master/PyCoulomb/coulomb_collections.py#L29)). Optional objects for gps displacement points (```obs_disp_points```, definition [here](https://github.com/kmaterna/Elastic_stresses_py/blob/master/PyCoulomb/coulomb_collections.py#L55)) and/or strain calculation points (not used in this example) are also created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebab2081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading source and receiver fault information from file M6.8_2014.intxt \n",
      "Reading displacement points from file CA_GPS_ll.txt \n",
      "--> Read 105 displacement points \n",
      "Number of total sources: 1\n",
      "Number of rectangular sources: 1\n",
      "Moment Magnitude from Rectangular Fault Patches (assuming G=30.0GPa): 6.800000\n"
     ]
    }
   ],
   "source": [
    "# Read inputs.\n",
    "[default_inputs, obs_disp_points, _] = PyCoulomb.input_values.read_inputs(default_params);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b681d46d",
   "metadata": {},
   "source": [
    "### Step 3 and Step 4: Compute, Output\n",
    "Now we are ready to execute the calculation like normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5bf119b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning stress calculation.\n",
      "Number of sources: 1 \n",
      "Number of receivers: 1 \n",
      "Coefficient of friction: 0.400000\n",
      "Splitting 1 receiver faults into 100 subfaults each.\n",
      "Computing synthetic grid of displacements\n",
      "Resolving stresses on receiver fault(s).\n",
      "Resolving stresses on a horizontal profile.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kama8443/miniconda3/envs/elastic_py/lib/python3.11/site-packages/cutde/coordinators.py:77: UserWarning: The obs_pts input array has Fortran ordering. Converting to C ordering. This may be expensive.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing file Outputs/my_experiment/used_config.txt \n",
      "Writing file Outputs/my_experiment/used_inputs.txt \n",
      "Writing Outputs/my_experiment/ll_disps.txt \n",
      "Min, Max disp_points east: -0.000301, 0.012441 m\n",
      "Min, Max disp_points north: -0.002757, 0.000744 m\n",
      "Writing Outputs/my_experiment/receiver_traces.txt\n",
      "Writing Outputs/my_experiment/source_traces.txt\n",
      "Writing outfile Outputs/my_experiment/subfaulted.inp \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Writing file Outputs/my_experiment/stresses_full.txt \n",
      "Making plot of Shear stress on receiver fault patches: Outputs/my_experiment/Stresses_Shear.png. \n",
      "Making plot of Normal stress on receiver fault patches: Outputs/my_experiment/Stresses_Normal.png. \n",
      "Making plot of Coulomb stress on receiver fault patches: Outputs/my_experiment/Stresses_Coulomb.png. \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/my_experiment/Coulomb_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/my_experiment/Normal_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/my_experiment/Shear_map.png \n",
      "Making plot of Coulomb stress on receiver fault patches: Outputs/my_experiment/Stresses_cross_section_Coulomb.png. \n",
      "Writing file Outputs/my_experiment/coulomb_xsection.txt\n",
      "Making plot of Normal stress on receiver fault patches: Outputs/my_experiment/Stresses_cross_section_Normal.png. \n",
      "Making plot of Shear stress on receiver fault patches: Outputs/my_experiment/Stresses_cross_section_Shear.png. \n",
      "Writing synthetic grid of displacements in Outputs/my_experiment/disps_model_grid.txt \n",
      "Making plot of predicted displacement throughout model domain.\n",
      "Max vertical displacement is 0.032031 m\n",
      "Writing synthetic grid into files Outputs/my_experiment/ etc.xy_east.txt\n",
      "Printing vert.grd\n",
      "Printing east.grd\n",
      "Printing north.grd\n",
      "Mapping vertical deformation in Outputs/my_experiment/vertical_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Writing Outputs/my_experiment/stresses_horiz_profile.txt \n",
      "Saving figure Outputs/my_experiment/horizontal_profile_stresses.png \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5100x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_object = PyCoulomb.run_dc3d.do_stress_computation(default_params, default_inputs, obs_disp_points, ());  # compute\n",
    "PyCoulomb.output_manager.produce_outputs(default_params, default_inputs, obs_disp_points, (), out_object);  # output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e665d7",
   "metadata": {},
   "source": [
    "# Part 3: Modifying Inputs and Configuration Parameters with Python API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b652e8",
   "metadata": {},
   "source": [
    "Now, let's say you wanted to create a different earthquake source: let's say you wanted to change the strike from 228° to 200° and the depth from 16 km to 1 km.  Let's also say you wanted to change the output directory so you don't overwrite the original experiment results. \n",
    "\n",
    "\n",
    "As a reminder, the steps of ```elastic_stresses_driver.py``` are: \n",
    "```\n",
    "params = PyCoulomb.configure_calc.configure_stress_calculation(config_file);\n",
    "[inputs, obs_disp_points, obs_strain_points] = PyCoulomb.input_values.read_inputs(params);\n",
    "out_object = PyCoulomb.run_dc3d.do_stress_computation(params, inputs, obs_disp_points, obs_strain_points);\n",
    "PyCoulomb.output_manager.produce_outputs(params, inputs, obs_disp_points, obs_strain_points, out_object);\n",
    "```\n",
    "\n",
    "In this API example, we will modify the \"Configure\" and \"Input\" steps.  \"Compute\" and \"Output\" will remain the same as usual. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994828ca",
   "metadata": {},
   "source": [
    "### Step 1: Configure\n",
    "We first execute the \"Configure\" step like normal: reading a config file into an object type called ```Params```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "faa26df3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Configuring with the following Params:\n",
      "  -Config file: my_config.txt \n",
      "  -Input file: M6.8_2014.intxt \n",
      "  -Disp_points_file: CA_GPS_ll.txt \n",
      "  -Outdir: Outputs/my_experiment/ \n",
      "  -Elastic moduli: 30000000000.000000, 30000000000.000000 \n",
      "  -Poisson's ratio and Alpha: 0.250000, 0.666667\n"
     ]
    }
   ],
   "source": [
    "from elastic_stresses_py import PyCoulomb\n",
    "\n",
    "# Create deafult Params object from the configuration file\n",
    "default_params = PyCoulomb.configure_calc.configure_stress_calculation(\"my_config.txt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a30cbc3",
   "metadata": {},
   "source": [
    "We next modify the Params object to direct our outputs into a different output folder, one that represents the modified geometry.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5df6c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old experiment outdir: Outputs/my_experiment/\n",
      "New experiment outdir: Outputs/strike_200_shallow_depth/\n"
     ]
    }
   ],
   "source": [
    "# Modify the parameter object: Replace params.outdir with new outdir\n",
    "\n",
    "print(\"Old experiment outdir:\", default_params.outdir);\n",
    "experiment_params = default_params.modify_params_object(outdir=\"Outputs/strike_200_shallow_depth/\");\n",
    "print(\"New experiment outdir:\", experiment_params.outdir);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687d302d",
   "metadata": {},
   "source": [
    "### Step 2: Input\n",
    "We then execute the normal input phase, reading an input file (```M6.8_2014.intxt```) into an ```Input_object``` and reading the GPS points into ```obs_disp_points```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d262f018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading source and receiver fault information from file M6.8_2014.intxt \n",
      "Reading displacement points from file CA_GPS_ll.txt \n",
      "--> Read 105 displacement points \n",
      "Number of total sources: 1\n",
      "Number of rectangular sources: 1\n",
      "Moment Magnitude from Rectangular Fault Patches (assuming G=30.0GPa): 6.800000\n"
     ]
    }
   ],
   "source": [
    "# Read inputs like normal.  default_inputs = \"Input_object\"\n",
    "\n",
    "[default_inputs, obs_disp_points, _] = PyCoulomb.input_values.read_inputs(experiment_params);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac39c237",
   "metadata": {},
   "source": [
    "Then we modify the source fault within the input object.  **NOTE: THIS IS USUALLY BEST DONE THROUGH MODIFYING A FAULT_SLIP_OBJECT** instead of the native pycoulomb fault object. The fault_slip_object is an internal class for rectangular faults, defined [here](https://github.com/kmaterna/Elastic_stresses_py/blob/master/PyCoulomb/fault_slip_object/fault_slip_object.py#L21). It can be converted back and forth into the pycoulomb fault object used in the stress calculation,  defined [here](https://github.com/kmaterna/Elastic_stresses_py/blob/master/PyCoulomb/coulomb_collections.py#L17). If you modify the pycoulomb fault object directly, it may be possible to create unrealistic scenarios (e.g., independently change the amounts of rtlat/reverse slip of a source fault but not change the rake; change the top of a source fault but not the bottom, change the strike of a source fault but not the xstart/xfinish). Please use the fault_slip_object for manipulation in most cases.  Directly modifying the pycoulomb fault object is only recommended for point sources, where these issues of finite length/width/slip don't apply.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "faa49fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the single source in this object\n",
    "# The safest way is through the internal fault_slip_object\n",
    "\n",
    "internal_source = PyCoulomb.fault_slip_object.fault_slip_object.coulomb_fault_to_fault_object(default_inputs.source_object)\n",
    "internal_source[0].strike = 200;   # modify the strike\n",
    "internal_source[0].depth = 4;  # modify the depth\n",
    "modified_source = PyCoulomb.fault_slip_object.fault_slip_object.fault_object_to_coulomb_fault(internal_source, \n",
    "                                                                            zerolon_system=default_inputs.zerolon, \n",
    "                                                                            zerolat_system=default_inputs.zerolat);\n",
    "inputs = default_inputs.modify_inputs_object(source_object=modified_source);  # with new source fault\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd164d0",
   "metadata": {},
   "source": [
    "### Step 3 and Step 4: Compute, Output\n",
    "Now we are ready to execute the calculation like normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8dcd50c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning stress calculation.\n",
      "Number of sources: 1 \n",
      "Number of receivers: 1 \n",
      "Coefficient of friction: 0.400000\n",
      "Splitting 1 receiver faults into 100 subfaults each.\n",
      "Computing synthetic grid of displacements\n",
      "Resolving stresses on receiver fault(s).\n",
      "Resolving stresses on a horizontal profile.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kama8443/miniconda3/envs/elastic_py/lib/python3.11/site-packages/cutde/coordinators.py:77: UserWarning: The obs_pts input array has Fortran ordering. Converting to C ordering. This may be expensive.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing file Outputs/strike_200_shallow_depth/used_config.txt \n",
      "Writing file Outputs/strike_200_shallow_depth/used_inputs.txt \n",
      "Writing Outputs/strike_200_shallow_depth/ll_disps.txt \n",
      "Min, Max disp_points east: -0.001322, 0.011076 m\n",
      "Min, Max disp_points north: 0.000311, 0.006013 m\n",
      "Writing Outputs/strike_200_shallow_depth/receiver_traces.txt\n",
      "Writing Outputs/strike_200_shallow_depth/source_traces.txt\n",
      "Writing outfile Outputs/strike_200_shallow_depth/subfaulted.inp \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Writing file Outputs/strike_200_shallow_depth/stresses_full.txt \n",
      "Making plot of Shear stress on receiver fault patches: Outputs/strike_200_shallow_depth/Stresses_Shear.png. \n",
      "Making plot of Normal stress on receiver fault patches: Outputs/strike_200_shallow_depth/Stresses_Normal.png. \n",
      "Making plot of Coulomb stress on receiver fault patches: Outputs/strike_200_shallow_depth/Stresses_Coulomb.png. \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/strike_200_shallow_depth/Coulomb_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/strike_200_shallow_depth/Normal_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Mapping stress in Outputs/strike_200_shallow_depth/Shear_map.png \n",
      "Making plot of Coulomb stress on receiver fault patches: Outputs/strike_200_shallow_depth/Stresses_cross_section_Coulomb.png. \n",
      "Writing file Outputs/strike_200_shallow_depth/coulomb_xsection.txt\n",
      "Making plot of Normal stress on receiver fault patches: Outputs/strike_200_shallow_depth/Stresses_cross_section_Normal.png. \n",
      "Making plot of Shear stress on receiver fault patches: Outputs/strike_200_shallow_depth/Stresses_cross_section_Shear.png. \n",
      "Writing synthetic grid of displacements in Outputs/strike_200_shallow_depth/disps_model_grid.txt \n",
      "Making plot of predicted displacement throughout model domain.\n",
      "Max vertical displacement is 0.078117 m\n",
      "Writing synthetic grid into files Outputs/strike_200_shallow_depth/ etc.xy_east.txt\n",
      "Printing vert.grd\n",
      "Printing east.grd\n",
      "Printing north.grd\n",
      "Mapping vertical deformation in Outputs/strike_200_shallow_depth/vertical_map.png \n",
      "Reading aftershocks from file CA_aftershocks_2014.txt \n",
      "Writing Outputs/strike_200_shallow_depth/stresses_horiz_profile.txt \n",
      "Saving figure Outputs/strike_200_shallow_depth/horizontal_profile_stresses.png \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5100x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_object = PyCoulomb.run_dc3d.do_stress_computation(experiment_params, inputs, obs_disp_points, strain_points=());\n",
    "PyCoulomb.output_manager.produce_outputs(experiment_params, inputs, obs_disp_points, obs_strain_points=(), out_object=out_object);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d69ccaba",
   "metadata": {},
   "source": [
    "Notice the differences between the plots in the first example and the second example.  The stresses on this profile are much higher at a depth of 1 km because the source is extremely close. If the depths are very close to the depth of the dislocation, you can see singularities at the fault corners. There are also larger displacements in both horizontal and vertical due to the reduced depth of the source. Some of the stresses on the receiver faults change sign due to the change in source depth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d62a9eff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}