Repository: malcolmw/pykonal
Branch: master
Commit: d91d914d5066
Files: 47
Total size: 38.1 MB
Directory structure:
gitextract_79682ixy/
├── .gitignore
├── LICENSE
├── MANIFEST.in
├── README.md
├── jupyter/
│ ├── Locator2.ipynb
│ ├── figure_3.ipynb
│ ├── figure_4.ipynb
│ ├── figure_5.ipynb
│ ├── figure_6.ipynb
│ ├── figure_7.ipynb
│ └── figure_8.ipynb
├── pykonal/
│ ├── __init__.py
│ ├── __version__.py
│ ├── constants.pxd
│ ├── constants.pyx
│ ├── data/
│ │ ├── ak135.npz
│ │ ├── marmousi2/
│ │ │ ├── LICENSE
│ │ │ ├── README
│ │ │ └── marmousi2.npz
│ │ └── mitp2008.npz
│ ├── fields.pxd
│ ├── fields.pyx
│ ├── heapq.pxd
│ ├── heapq.pyx
│ ├── inventory.py
│ ├── locate.pxd
│ ├── locate.pyx
│ ├── solver.pxd
│ ├── solver.pyx
│ ├── stats.pxd
│ ├── stats.pyx
│ ├── tests/
│ │ ├── data/
│ │ │ └── test_EikonalSolver_uniform_velocity_cartesian.npz
│ │ ├── test_EikonalSolver.py
│ │ ├── test_GridND.py
│ │ └── test_LinearInterpolator3D.py
│ └── transformations.py
├── pyproject.toml
├── scripts/
│ ├── eq_loc/
│ │ ├── eq_loc.cfg
│ │ └── eq_loc.py
│ └── mcmc/
│ ├── 315_loc.cfg
│ └── 315_locate_serial_mcmc.py
├── setup.py
└── tutorials/
├── tomography/
│ ├── cartesian_tomography_2D.ipynb
│ └── vmodel.txt
└── tracing_PKiKP/
├── .ipynb_checkpoints/
│ └── tracing_PKiKP_rays-checkpoint.ipynb
├── IASP91.csv
└── tracing_PKiKP_rays.ipynb
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*.DS_Store
jupyter/.ipynb_checkpoints
scripts/eq_loc/test_data
*.log
*.cpp
*.c
build
dist
pykonal.egg-info
pykonal/__pycache__
================================================
FILE: LICENSE
================================================
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reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
{one line to give the program's name and a brief idea of what it does.}
Copyright (C) {year} {name of author}
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
{project} Copyright (C) {year} {fullname}
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
.
================================================
FILE: MANIFEST.in
================================================
include pykonal/*.pyx
include pykonal/*.pxd
include pykonal/*.cpp
include pykonal/data
include README.md
================================================
FILE: README.md
================================================
# Welcome to the *pykonal* repository!
This code implements the Fast Marching Method (FMM; Sethian *et al.*, 1996) for solving the eikonal equation in Cartesian or spherical coordinates in 2 or 3 dimensions. The method implements mixed first- and second-order finite differences.
PyKonal offers two features that are absent from the comparable [scikit-fmm](https://pythonhosted.org/scikit-fmm/ "sckit-fmm documentation") package: (a) an implementation in spherical coordinates, and (b) functionality to compute shortest-traveltime paths.
## Documentation
Documentation is available [here](https://malcolmw.github.io/pykonal-docs/ "PyKonal documentation").
## Citation
If you make use of this code in published work, please cite White *et al.* (2020).
## Installation
### PIP—recommended
```bash
sh$> pip install pykonal
```
### From source code
Download and unzip the [latest release](https://github.com/malcolmw/pykonal/releases "Releases").
```bash
sh$> cd path/to/pykonal
sh$> pip install .
```
## Bugs
Please report bugs, feature requests, and questions through the [Issues](https://github.com/malcolmw/pykonal/issues "PyKonal Issues tracker") tracker.
## References
1. Sethian, J. A. (1996). A fast marching level set method for monotonically advancing fronts. *Proceedings of the National Academy of Sciences, 93*(4), 1591–1595. https://doi.org/10.1073/pnas.93.4.1591
2. White, M. C. A., Fang, H., Nakata, N., & Ben-Zion, Y. (2020). PyKonal: A Python Package for Solving the Eikonal Equation in Spherical and Cartesian Coordinates Using the Fast Marching Method. *Seismological Research Letters, 91*(4), 2378-2389. https://doi.org/10.1785/0220190318
================================================
FILE: jupyter/Locator2.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext Cython\n",
"import matplotlib.pyplot as plt\n",
"import multiprocessing as mp\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pykonal\n",
"import seispy\n",
"\n",
"DTYPE_REAL = np.float64\n",
"EARTH_RADIUS = 6371.\n",
"\n",
"def geo2sph(arr) -> np.ndarray:\n",
" \"\"\"\n",
" Map Geographical coordinates to spherical coordinates.\n",
" \"\"\"\n",
" geo = np.array(arr, dtype=DTYPE_REAL)\n",
" sph = np.empty_like(geo)\n",
" sph[..., 0] = EARTH_RADIUS - geo[..., 2]\n",
" sph[..., 1] = np.pi / 2 - np.radians(geo[..., 0])\n",
" sph[..., 2] = np.radians(geo[..., 1])\n",
" return (sph)\n",
"\n",
"\n",
"def sph2geo(arr) -> np.ndarray:\n",
" \"\"\"\n",
" Map spherical coordinates to geographic coordinates.\n",
" \"\"\"\n",
" sph = np.array(arr, dtype=DTYPE_REAL)\n",
" geo = np.empty_like(sph)\n",
" geo[..., 0] = np.degrees(np.pi / 2 - sph[..., 1])\n",
" geo[..., 1] = np.degrees(sph[..., 2])\n",
" geo[..., 2] = EARTH_RADIUS - sph[..., 0]\n",
" return (geo)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"# Load velocity model\n",
"grid = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\n",
"with np.load(\"/home/malcolmw/google_drive/malcolm.white@usc.edu/data/velocity/White_et_al_2019a/White_et_al_2019a.regular.npz\") as npz:\n",
" grid.min_coords = npz[\"grid_parameters\"][:3]\n",
" grid.node_intervals = npz[\"grid_parameters\"][3:6]\n",
" grid.npts = npz[\"grid_parameters\"][6:] + [1, 0, 0]\n",
" pwave_velocity = np.append(npz[\"vp\"], npz[\"vp\"][[-1]], axis=0)\n",
" swave_velocity = np.append(npz[\"vs\"], npz[\"vs\"][[-1]], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"db = seispy.pandas.catalog.Catalog(\n",
" \"/home/malcolmw/google_drive/malcolm.white@usc.edu/data/events/malcolmw/SJFZ_catalog_2008-2016.h5\",\n",
" fmt=\"hdf5\"\n",
")\n",
"\n",
"stations_dataframe = db[\"site\"][\n",
" [\"sta\", \"lat\", \"lon\", \"elev\"]\n",
"].drop_duplicates(\n",
" \"sta\"\n",
").merge(\n",
" db[\"snetsta\"][\n",
" [\"snet\", \"sta\"]\n",
" ].drop_duplicates(\n",
" \"sta\"\n",
" ),\n",
" on=\"sta\"\n",
")\n",
"stations_dataframe[\"station_id\"] = stations_dataframe[\"snet\"] + \".\" + stations_dataframe[\"sta\"]\n",
"\n",
"arrivals_dataframe = db[\"arrival\"][\n",
" [\"sta\", \"time\", \"arid\"]\n",
"].merge(\n",
" db[\"assoc\"][\n",
" [\"arid\", \"orid\", \"phase\"]\n",
" ],\n",
" on=\"arid\"\n",
").merge(\n",
" stations_dataframe[[\"sta\", \"station_id\"]],\n",
" on=\"sta\"\n",
").merge(\n",
" db[\"origin\"][[\"orid\", \"evid\"]],\n",
" on=\"orid\"\n",
").sort_values(\n",
" \"evid\"\n",
").set_index(\n",
" \"evid\"\n",
")[\n",
" [\"station_id\", \"phase\", \"time\", \"arid\"]\n",
"]\n",
"\n",
"stations_dataframe[\"depth\"] = -stations_dataframe[\"elev\"]\n",
"stations_dataframe = stations_dataframe[\n",
" [\"station_id\", \"lat\", \"lon\", \"depth\"]\n",
"].set_index(\n",
" \"station_id\"\n",
")\n",
"stations_dataframe = stations_dataframe[\n",
" stations_dataframe.index.isin(arrivals_dataframe[\"station_id\"].unique())\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"stations_dict = dict(zip(stations_dataframe.index.values, geo2sph(stations_dataframe)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"# Only events inside the focus region of White et al. 2019 with 128 or more arrivals\n",
"df = arrivals_dataframe.reset_index().groupby(\"evid\").count()\n",
"arrivals_dataframe = arrivals_dataframe[\n",
" (arrivals_dataframe.index.isin(df[df[\"station_id\"] >= 128].index))\n",
" &(arrivals_dataframe.index.isin(\n",
" db[\"origin\"][seispy.coords.as_geographic(db[\"origin\"][[\"lat\", \"lon\", \"depth\"]]).in_rectangle()][\"evid\"]\n",
" )\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def generate_arrivals(arrivals_dataframe):\n",
" for event_id in arrivals_dataframe.index.unique()[:]:\n",
" yield (event_id, arrivals_dataframe.loc[event_id].set_index([\"station_id\", \"phase\"]).to_dict()[\"time\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"locator = pykonal.locate.EQLocator(stations_dict, tt_dir=\"/home/malcolmw/scratch/traveltimes\")\n",
"locator.grid.min_coords = grid.min_coords\n",
"locator.grid.node_intervals = grid.node_intervals\n",
"locator.grid.npts = grid.npts\n",
"locator.pwave_velocity = pwave_velocity\n",
"locator.swave_velocity = swave_velocity\n",
"# locator.compute_all_traveltime_lookup_tables(\"P\")\n",
"# locator.compute_all_traveltime_lookup_tables(\"S\")\n",
"for event_id, arrivals in generate_arrivals(arrivals_dataframe):\n",
" locator.clear_arrivals()\n",
" locator.add_arrivals(arrivals)\n",
" locator.load_traveltimes()\n",
" %time loc0 = locator.grid_search()\n",
" %time loc1 = locator.locate()\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"locator.counter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# [locator.arrivals[key] for key in sorted(locator.arrivals)], \n",
"[locator.traveltimes[key] for key in sorted(locator.arrivals)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"locator.grid.max_coords, locator.grid.min_coords + locator.grid.node_intervals * (locator.grid.npts - 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sph2geo(loc0[:3]), sph2geo(loc1[:3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"locator.cost(loc0), locator.cost(loc1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%cython\n",
"\n",
"import numpy as np\n",
"import scipy.optimize\n",
"cimport numpy as np\n",
"\n",
"\n",
"ctypedef np.float64_t _REAL_t\n",
"\n",
"EARTH_RADIUS = 6371.\n",
"DTYPE_REAL = np.float64\n",
"\n",
"def geo2sph(arr):\n",
" \"\"\"\n",
" Map Geographical coordinates to spherical coordinates.\n",
" \"\"\"\n",
" geo = np.array(arr, dtype=DTYPE_REAL)\n",
" sph = np.empty_like(geo)\n",
" sph[..., 0] = EARTH_RADIUS - geo[..., 2]\n",
" sph[..., 1] = np.pi / 2 - np.radians(geo[..., 0])\n",
" sph[..., 2] = np.radians(geo[..., 1])\n",
" return (sph)\n",
"\n",
"\n",
"def sph2geo(arr):\n",
" \"\"\"\n",
" Map spherical coordinates to geographic coordinates.\n",
" \"\"\"\n",
" sph = np.array(arr, dtype=DTYPE_REAL)\n",
" geo = np.empty_like(sph)\n",
" geo[..., 0] = np.degrees(np.pi / 2 - sph[..., 1])\n",
" geo[..., 1] = np.degrees(sph[..., 2])\n",
" geo[..., 2] = EARTH_RADIUS - sph[..., 0]\n",
" return (geo)\n",
"\n",
"\n",
"cdef class EQLocator(object):\n",
" cdef dict _arrivals\n",
" cdef dict _tt_calculators\n",
" cdef _REAL_t[:,:,:,:] _grid\n",
" cdef tuple _bounds\n",
" cdef dict _priors\n",
" \n",
" def __init__(self, arrivals, tt_calculators, grid):\n",
" self.arrivals = {key: arrivals[key] for key in tt_calculators}\n",
" self.tt_calculators = tt_calculators\n",
" self.grid = grid\n",
" \n",
" @property\n",
" def arrivals(self):\n",
" return (self._arrivals)\n",
" \n",
" @arrivals.setter\n",
" def arrivals(self, value):\n",
" self._arrivals = value\n",
" \n",
" @property\n",
" def grid(self):\n",
" return (np.asarray(self._grid))\n",
" \n",
" @grid.setter\n",
" def grid(self, value):\n",
" self._grid = value\n",
" \n",
" @property\n",
" def tt_calculators(self):\n",
" return (self._tt_calculators)\n",
" \n",
" @tt_calculators.setter\n",
" def tt_calculators(self, value):\n",
" self._tt_calculators = value\n",
" \n",
" cpdef initial_guess(self):\n",
" values = [self.arrivals[key]-self.tt_calculators[key].values for key in self.tt_calculators]\n",
" values = np.ma.masked_invalid(np.stack(values))\n",
" std = values.std(axis=0)\n",
" arg_min = np.argmin(std)\n",
" idx_min = np.unravel_index(arg_min, std.shape)\n",
" geo = sph2geo(self.grid[idx_min])\n",
" time = values.mean(axis=0)[idx_min]\n",
" return (np.array([*geo, time]))\n",
"\n",
" cpdef cost(self, _REAL_t[:] hypocenter):\n",
" cdef tuple key\n",
" cdef _REAL_t csum = 0\n",
" cdef _REAL_t lat, lon, depth, time\n",
" lat = hypocenter[0]\n",
" lon = hypocenter[1]\n",
" depth = hypocenter[2]\n",
" time = hypocenter[3]\n",
" sph_coords = geo2sph((lat, lon, depth))\n",
" if not np.all(sph_coords > np.asarray(self.grid).min(axis=(0,1,2))) or not np.all(sph_coords < np.asarray(self.grid).max(axis=(0,1,2))):\n",
" return (np.inf)\n",
" for key in self.arrivals:\n",
" tt_calculator = self.tt_calculators[key].value\n",
" csum += np.square(self.arrivals[key]-(time+tt_calculator(sph_coords)))\n",
" return (np.sqrt(csum/len(self.arrivals)))\n",
" \n",
" cpdef locate(self):\n",
" cdef _REAL_t[4] h0\n",
" h0 = self.initial_guess()\n",
" soln = scipy.optimize.differential_evolution(\n",
" self.cost,\n",
" ((h0[0]-0.1, h0[0]+0.1), (h0[1]-0.1, h0[1]+0.1), (0, 30), (h0[3]-5, h0[3]+5))\n",
" )\n",
" return (soln.x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load a test data set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db = seispy.pandas.catalog.Catalog(\n",
" \"/home/malcolmw/google_drive/malcolm.white@usc.edu/data/events/malcolmw/SJFZ_catalog_2008-2016.h5\",\n",
" fmt=\"hdf5\"\n",
")\n",
"\n",
"gc_events_dataframe = pd.read_csv(\n",
" \"/home/malcolmw/scratch/events.grow\", \n",
" delim_whitespace=True, \n",
" header=None,\n",
" names=[\"year\", \"month\", \"day\", \"hour\", \"minute\", \"second\", \"lat\", \"lon\", \"depth\", \"null_0\", \"null_1\", \"null_2\", \"null_3\", \"event_id\"]\n",
")\n",
"gc_events_dataframe[\"time\"] = seispy.pandas.time.to_epoch(seispy.pandas.time.ymd_to_dt(gc_events_dataframe))*1e-9\n",
"gc_events_dataframe[\"event_id\"] -= int(2e9)\n",
"db[\"origin\"] = db[\"origin\"][[\"orid\", \"evid\"]].merge(\n",
" gc_events_dataframe[[\"lat\", \"lon\", \"depth\", \"time\", \"event_id\"]],\n",
" left_on=\"evid\",\n",
" right_on=\"event_id\"\n",
").drop(columns=[\"evid\"])\n",
"\n",
"stations_dataframe = db[\"site\"][\n",
" [\"sta\", \"lat\", \"lon\", \"elev\"]\n",
"].drop_duplicates(\n",
" \"sta\"\n",
").merge(\n",
" db[\"snetsta\"][\n",
" [\"snet\", \"sta\"]\n",
" ].drop_duplicates(\n",
" \"sta\"\n",
" ),\n",
" on=\"sta\"\n",
")\n",
"stations_dataframe[\"station_id\"] = stations_dataframe[\"snet\"] + \".\" + stations_dataframe[\"sta\"]\n",
"\n",
"arrivals_dataframe = db[\"arrival\"][\n",
" [\"sta\", \"time\", \"arid\"]\n",
"].merge(\n",
" db[\"assoc\"][\n",
" [\"arid\", \"orid\", \"phase\"]\n",
" ],\n",
" on=\"arid\"\n",
").merge(\n",
" stations_dataframe[[\"sta\", \"station_id\"]],\n",
" on=\"sta\"\n",
").merge(\n",
" db[\"origin\"][[\"orid\", \"event_id\"]],\n",
" on=\"orid\"\n",
").sort_values(\n",
" \"event_id\"\n",
").set_index(\n",
" \"event_id\"\n",
")[\n",
" [\"station_id\", \"phase\", \"time\", \"arid\"]\n",
"]\n",
"\n",
"stations_dataframe[\"depth\"] = -stations_dataframe[\"elev\"]\n",
"stations_dataframe = stations_dataframe[\n",
" [\"station_id\", \"lat\", \"lon\", \"depth\"]\n",
"].set_index(\n",
" \"station_id\"\n",
")\n",
"stations_dataframe = stations_dataframe[\n",
" stations_dataframe.index.isin(arrivals_dataframe[\"station_id\"].unique())\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read and resample the velocity model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with np.load(\"/home/malcolmw/google_drive/malcolm.white@usc.edu/data/velocity/White_et_al_2019a/White_et_al_2019a.regular.npz\") as npz:\n",
" vp = pykonal.field.ScalarField3D(coord_sys=\"spherical\")\n",
" vs = pykonal.field.ScalarField3D(coord_sys=\"spherical\")\n",
" vp.min_coords = vs.min_coords = npz[\"grid_parameters\"][:3]\n",
" vp.node_intervals = vs.node_intervals = npz[\"grid_parameters\"][3:6]\n",
" vp.npts = vs.npts = npz[\"grid_parameters\"][6:] + [1, 0, 0]\n",
" vp.values = np.append(npz[\"vp\"], npz[\"vp\"][[-1]], axis=0)\n",
" vs.values = np.append(npz[\"vs\"], npz[\"vs\"][[-1]], axis=0)\n",
" \n",
"# Resample the velocity model (18, 32, 32) --> (64, 128, 128)\n",
"rho_min, theta_min, phi_min = vp.min_coords\n",
"rho_max, theta_max, phi_max = vp.max_coords\n",
"nrho, ntheta, nphi = 64, 128, 128\n",
"\n",
"drho = (rho_max - rho_min) / (nrho - 1)\n",
"rho = np.linspace(rho_min, rho_max, nrho)\n",
"\n",
"dtheta = (theta_max - theta_min) / (ntheta - 1)\n",
"theta = np.linspace(theta_min, theta_max, ntheta)\n",
"\n",
"dphi = (phi_max - phi_min) / (nphi - 1)\n",
"phi = np.linspace(phi_min, phi_max, nphi)\n",
"\n",
"rtp = np.moveaxis(\n",
" np.stack(np.meshgrid(rho, theta, phi, indexing=\"ij\")),\n",
" 0, \n",
" -1\n",
")\n",
"vp_new = vp.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\n",
"vs_new = vs.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\n",
"\n",
"vp = pykonal.field.ScalarField3D(coord_sys=\"spherical\")\n",
"vs = pykonal.field.ScalarField3D(coord_sys=\"spherical\")\n",
"vp.min_coords = vs.min_coords = rho_min, theta_min, phi_min\n",
"vp.node_intervals = vs.node_intervals = drho, dtheta, dphi\n",
"vp.npts = vs.npts = nrho, ntheta, nphi\n",
"vp.values = vp_new\n",
"vs.values = vs_new"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute traveltime-lookup tables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def target(args, vp=vp, vs=vs):\n",
" station_id, coords = args[0], geo2sph(args[1].values)\n",
" print(station_id)\n",
" solver = compute_traveltime_lookup_table(coords, vp)\n",
" solver.tt.savez(f\"/home/malcolmw/scratch/traveltimes/{station_id}.P.npz\")\n",
" solver = compute_traveltime_lookup_table(coords, vs)\n",
" solver.tt.savez(f\"/home/malcolmw/scratch/traveltimes/{station_id}.S.npz\")\n",
" \n",
"with mp.Pool() as pool:\n",
" pool.map(target, stations_dataframe.iterrows())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Locate events"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def generate_arrivals(arrivals_dataframe):\n",
" for event_id in arrivals_dataframe.index.unique()[:]:\n",
" yield (event_id, arrivals_dataframe.loc[event_id].set_index([\"station_id\", \"phase\"]).to_dict()[\"time\"])\n",
" \n",
"def target(args, nodes=vp.nodes):\n",
" event_id, arrivals = args\n",
" print(f\"Locating event #{event_id}\")\n",
" tt_calculators = {\n",
" key: pykonal.field.load(f\"/home/malcolmw/scratch/traveltimes/{key[0]}.{key[1]}.npz\") for key in arrivals\n",
" }\n",
" locator = EQLocator(\n",
" arrivals=arrivals,\n",
" tt_calculators=tt_calculators,\n",
" grid=nodes\n",
" )\n",
" return (event_id, locator.locate())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with mp.Pool(4) as pool:\n",
" locations = pool.map(target, generate_arrivals(arrivals_dataframe))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"relocated_events_dataframe = pd.DataFrame([[loc[0], *loc[1]] for loc in locations], columns=[\"event_id\", \"lat\", \"lon\", \"depth\", \"time\"]).set_index(\"event_id\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"original_events_dataframe = db[\"origin\"][db[\"origin\"][\"event_id\"].isin(arrivals_dataframe.index.unique())].set_index(\"event_id\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.close(\"all\")\n",
"fig = plt.figure()\n",
"ax00 = fig.add_subplot(2, 2, 1)\n",
"ax01 = fig.add_subplot(2, 2, 2)\n",
"ax10 = fig.add_subplot(2, 2, 3)\n",
"ax11 = fig.add_subplot(2, 2, 4)\n",
"for ax, key in ((ax00, \"lat\"), (ax01, \"lon\"), (ax10, \"depth\"), (ax11, \"time\")):\n",
" ax.hist(original_events_dataframe[key] - relocated_events_dataframe[key], bins=32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(12, 12))\n",
"ax1 = fig.add_subplot(1, 3, 1, aspect=1)\n",
"ax2 = fig.add_subplot(1, 3, 2, aspect=1)\n",
"ax3 = fig.add_subplot(1, 3, 3, aspect=1)\n",
"ax1.scatter(\n",
" original_events_dataframe[\"lon\"], \n",
" original_events_dataframe[\"lat\"],\n",
" c=original_events_dataframe[\"depth\"],\n",
" vmin=0,\n",
" vmax=25\n",
")\n",
"ax2.scatter(\n",
" relocated_events_dataframe[\"lon\"],\n",
" relocated_events_dataframe[\"lat\"],\n",
" c=original_events_dataframe[\"depth\"],\n",
" vmin=0,\n",
" vmax=25\n",
")\n",
"ax3.quiver(\n",
" original_events_dataframe[\"lon\"], \n",
" original_events_dataframe[\"lat\"],\n",
" relocated_events_dataframe[\"lon\"] - original_events_dataframe[\"lon\"],\n",
" relocated_events_dataframe[\"lat\"] - original_events_dataframe[\"lat\"],\n",
")\n",
"for ax in (ax1, ax2, ax3):\n",
" ax.set_xlim(-117, -116)\n",
" ax.set_ylim(33, 34)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(12, 12))\n",
"ax = fig.add_subplot(1, 1, 1, aspect=1)\n",
"ax.quiver(\n",
" original_events_dataframe[\"lon\"], \n",
" original_events_dataframe[\"lat\"],\n",
" relocated_events_dataframe[\"lon\"] - original_events_dataframe[\"lon\"],\n",
" relocated_events_dataframe[\"lat\"] - original_events_dataframe[\"lat\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for event_id, origin in relocated_events_dataframe.iterrows():\n",
" arrivals = arrivals_dataframe.loc[event_id].set_index([\"station_id\", \"phase\"]).to_dict()[\"time\"]\n",
" tt_calculators = {\n",
" key: pykonal.field.load(f\"/home/malcolmw/scratch/traveltimes/{key[0]}.{key[1]}.npz\") for key in arrivals\n",
" }\n",
" locator = EQLocator(\n",
" arrivals=arrivals,\n",
" tt_calculators=tt_calculators,\n",
" grid=vp.nodes\n",
" )\n",
" print(\n",
" event_id, \n",
" locator.cost(origin[[\"lat\", \"lon\", \"depth\", \"time\"]].values), \n",
" locator.cost(original_events_dataframe.loc[event_id, [\"lat\", \"lon\", \"depth\", \"time\"]].values)\n",
" )"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: jupyter/figure_3.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 3\n",
"Compatible with PyKonal Version 0.2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.gridspec as gs\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pykonal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a solver in Cartesian coordinates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"solver_c = pykonal.EikonalSolver(coord_sys='cartesian')\n",
"solver_c.velocity.min_coords = -25, -25, 0\n",
"solver_c.velocity.node_intervals = 1, 1, 1\n",
"solver_c.velocity.npts = 51, 51, 1\n",
"solver_c.velocity.values = np.ones(solver_c.velocity.npts) # Uniform velocity model\n",
"\n",
"# Add a point source at the origin of the grid.\n",
"src_idx = (25, 25, 0)\n",
"solver_c.traveltime.values[src_idx] = 0\n",
"solver_c.unknown[src_idx] = False\n",
"solver_c.trial.push(*src_idx)\n",
"\n",
"# Solve the system.\n",
"%time solver_c.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a solver in Spherical coordinates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"solver_s = pykonal.EikonalSolver(coord_sys='spherical')\n",
"solver_s.velocity.min_coords = 0.01, np.pi/2, 0\n",
"solver_s.velocity.node_intervals = 1, 1, np.pi/10\n",
"solver_s.velocity.npts = 25, 1, 21\n",
"solver_s.velocity.values = np.ones(solver_s.velocity.npts) # Uniform velocity model\n",
"\n",
"# Add a point source at the origin of the grid.\n",
"for it in range(solver_s.velocity.npts[1]):\n",
" for ip in range(solver_s.velocity.npts[2]):\n",
" src_idx = (0, it, ip)\n",
" vv = solver_s.velocity.values[src_idx]\n",
" solver_s.traveltime.values[src_idx] = solver_s.velocity.nodes[src_idx + (0,)] / vv\n",
" solver_s.unknown[src_idx] = False\n",
" solver_s.trial.push(*src_idx)\n",
" \n",
"# Solve the system\n",
"%time solver_s.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iz, it = 0, 0\n",
"plot_kwargs = dict(\n",
" shading='gouraud'\n",
")\n",
"plt.close('all')\n",
"\n",
"nodes = solver_s.traveltime.nodes\n",
"xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
"yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
"\n",
"fig = plt.figure(figsize=(4.5, 4))\n",
"\n",
"gridspec = gs.GridSpec(nrows=2, ncols=4, width_ratios=[0.08, 1, 1, 0.08])\n",
"ax1 = fig.add_subplot(gridspec[0, 1], aspect=1)\n",
"ax2 = fig.add_subplot(gridspec[0, 2], aspect=1)\n",
"ax3 = fig.add_subplot(gridspec[1, 1], aspect=1)\n",
"ax4 = fig.add_subplot(gridspec[1, 2], aspect=1)\n",
"cax1 = fig.add_subplot(gridspec[0, 3])\n",
"cax2 = fig.add_subplot(gridspec[1, 3])\n",
"ax1.set_title('Cartesian')\n",
"ax2.set_title('Spherical')\n",
"qmesh = ax1.pcolormesh(\n",
" solver_c.traveltime.nodes[:,:,iz,0],\n",
" solver_c.traveltime.nodes[:,:,iz,1],\n",
" solver_c.traveltime.values[:,:,iz],\n",
" cmap=plt.get_cmap('jet_r'),\n",
" **plot_kwargs\n",
")\n",
"qmesh = ax2.pcolormesh(\n",
" xx[:,it,:],\n",
" yy[:,it,:],\n",
" solver_s.traveltime.values[:,it,:],\n",
" vmin=qmesh.get_array().min(),\n",
" vmax=qmesh.get_array().max(),\n",
" cmap=plt.get_cmap('jet_r'),\n",
" **plot_kwargs\n",
")\n",
"cbar1 = fig.colorbar(qmesh, cax=cax1)\n",
"cbar1.set_label('Travel time [s]')\n",
"qmesh = ax3.pcolormesh(\n",
" solver_c.traveltime.nodes[:,:,iz,0],\n",
" solver_c.traveltime.nodes[:,:,iz,1],\n",
" solver_c.traveltime.values[:,:,iz]-np.sqrt(np.sum(np.square(solver_c.traveltime.nodes[:,:,iz]), axis=-1)),\n",
" vmin=0.0,\n",
" vmax=0.35,\n",
" cmap=plt.get_cmap('bone_r'),\n",
" **plot_kwargs\n",
")\n",
"qmesh = ax4.pcolormesh(\n",
" xx[:,it,:],\n",
" yy[:,it,:],\n",
" solver_s.traveltime.values[:,it,:]-np.sqrt(xx[:,it,:]**2 + yy[:,it,:]**2),\n",
" vmin=0.0,\n",
" vmax=0.35,\n",
" cmap=plt.get_cmap('bone_r'),\n",
" **plot_kwargs\n",
")\n",
"cbar2 = fig.colorbar(qmesh, cax=cax2)\n",
"cbar2.set_label('Absolute error [s]')\n",
"\n",
"for ax in (ax1, ax2):\n",
" ax.set_xticklabels([])\n",
" \n",
"for ax in (ax2, ax4):\n",
" ax.set_yticklabels([])\n",
" \n",
"panel = ord(\"a\")\n",
"for ax in (ax1, ax2, ax3, ax4):\n",
" ax.text(\n",
" 0, 1.025, f\"({chr(panel)})\",\n",
" ha=\"right\",\n",
" va=\"bottom\",\n",
" transform=ax.transAxes\n",
" )\n",
" panel += 1\n",
"\n",
"fig.text(0.5, 0, \"x [km]\", ha=\"center\", va=\"bottom\")\n",
"fig.text(0.05, 0.5, \"y [km]\", ha=\"left\", va=\"center\", rotation=90)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: jupyter/figure_4.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 4\n",
"Compatible with PyKonal Version 0.2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.gridspec as gs\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pykonal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a solver in Cartesian coordinates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"solver_c = pykonal.EikonalSolver(coord_sys='cartesian')\n",
"solver_c.velocity.min_coords = -25, -25, 0\n",
"solver_c.velocity.node_intervals = 1, 1, 1\n",
"solver_c.velocity.npts = 51, 51, 1\n",
"solver_c.velocity.values = np.ones(solver_c.velocity.npts) # Uniform velocity model\n",
"\n",
"# Add a line source at y = 0.\n",
"for ix in range(solver_c.velocity.npts[0]):\n",
" src_idx = (ix, 25, 0)\n",
" solver_c.traveltime.values[src_idx] = 0\n",
" solver_c.unknown[src_idx] = False\n",
" solver_c.trial.push(*src_idx)\n",
"\n",
"# Solve the system\n",
"%time solver_c.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a solver in spherical coordinates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"solver_s = pykonal.EikonalSolver(coord_sys='spherical')\n",
"solver_s.velocity.min_coords = 0.01, np.pi/2, 0\n",
"solver_s.velocity.node_intervals = 1, 1, np.pi/10\n",
"solver_s.velocity.npts = 26, 1, 21\n",
"solver_s.velocity.values = np.ones(solver_s.velocity.npts) # Uniform velocity model\n",
"\n",
"# Add a line source at y = 0.\n",
"for ir in range(solver_s.velocity.npts[0]):\n",
" for it in range(solver_s.velocity.npts[1]):\n",
" for ip in np.argwhere(solver_s.traveltime.nodes[0,0,:,2] % np.pi == 0).flatten():\n",
" src_idx = (ir, it, ip)\n",
" solver_s.traveltime.values[src_idx] = 0\n",
" solver_s.unknown[src_idx] = False\n",
" solver_s.trial.push(*src_idx)\n",
"\n",
"# Solve the system\n",
"%time solver_s.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iz, it = 0, 0\n",
"plot_kwargs = dict(\n",
" shading='gouraud'\n",
")\n",
"plt.close('all')\n",
"\n",
"nodes = solver_s.traveltime.nodes\n",
"xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
"yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
"\n",
"fig = plt.figure(figsize=(4.5, 4))\n",
"\n",
"gridspec = gs.GridSpec(nrows=2, ncols=4, width_ratios=[0.08, 1, 1, 0.08])\n",
"ax1 = fig.add_subplot(gridspec[0, 1], aspect=1)\n",
"ax2 = fig.add_subplot(gridspec[0, 2], aspect=1)\n",
"ax3 = fig.add_subplot(gridspec[1, 1], aspect=1)\n",
"ax4 = fig.add_subplot(gridspec[1, 2], aspect=1)\n",
"cax1 = fig.add_subplot(gridspec[0, 3])\n",
"cax2 = fig.add_subplot(gridspec[1, 3])\n",
"ax1.set_title('Cartesian')\n",
"ax2.set_title('Spherical')\n",
"qmesh = ax1.pcolormesh(\n",
" solver_c.traveltime.nodes[:,:,iz,0],\n",
" solver_c.traveltime.nodes[:,:,iz,1],\n",
" solver_c.traveltime.values[:,:,iz],\n",
" cmap=plt.get_cmap('jet_r'),\n",
" **plot_kwargs\n",
")\n",
"qmesh = ax2.pcolormesh(\n",
" xx[:,it,:],\n",
" yy[:,it,:],\n",
" solver_s.traveltime.values[:,it,:],\n",
" vmin=qmesh.get_array().min(),\n",
" vmax=qmesh.get_array().max(),\n",
" cmap=plt.get_cmap('jet_r'),\n",
" **plot_kwargs\n",
")\n",
"cbar1 = fig.colorbar(qmesh, cax=cax1)\n",
"cbar1.set_label('Travel time [s]')\n",
"qmesh = ax3.pcolormesh(\n",
" solver_c.traveltime.nodes[:,:,iz,0],\n",
" solver_c.traveltime.nodes[:,:,iz,1],\n",
" solver_c.traveltime.values[:,:,iz]-np.abs(solver_c.traveltime.nodes[:,:,iz,1]),\n",
" vmin=0.0,\n",
" vmax=0.35,\n",
" cmap=plt.get_cmap('bone_r'),\n",
" **plot_kwargs\n",
")\n",
"qmesh = ax4.pcolormesh(\n",
" xx[:,it,:],\n",
" yy[:,it,:],\n",
" np.abs(solver_s.traveltime.values[:,it,:]-np.abs(yy[:,it,:])),\n",
" vmin=0.0,\n",
" vmax=0.35,\n",
" cmap=plt.get_cmap('bone_r'),\n",
" **plot_kwargs\n",
")\n",
"cbar2 = fig.colorbar(qmesh, cax=cax2)\n",
"cbar2.set_label('Absolute error [s]')\n",
"\n",
"for ax in (ax1, ax2):\n",
" ax.set_xticklabels([])\n",
" \n",
"for ax in (ax2, ax4):\n",
" ax.set_yticklabels([])\n",
" \n",
"panel = ord(\"a\")\n",
"for ax in (ax1, ax2, ax3, ax4):\n",
" ax.text(\n",
" 0, 1.025, f\"({chr(panel)})\",\n",
" ha=\"right\",\n",
" va=\"bottom\",\n",
" transform=ax.transAxes\n",
" )\n",
" panel += 1\n",
"\n",
"fig.text(0.5, 0, \"x [km]\", ha=\"center\", va=\"bottom\")\n",
"fig.text(0.05, 0.5, \"y [km]\", ha=\"left\", va=\"center\", rotation=90)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: jupyter/figure_5.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 5\n",
"Compatible with PyKonal Version 0.2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"import matplotlib\n",
"import matplotlib.gridspec as gs\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import pkg_resources\n",
"import pykonal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Marmousi 2D velocity model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fname = pkg_resources.resource_filename(\n",
" 'pykonal',\n",
" os.path.join('data', \"marmousi2\", 'marmousi2.npz')\n",
")\n",
"with np.load(fname) as infile:\n",
" vv = infile['vv']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize the EikonalSolver"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"velocity = pykonal.fields.ScalarField3D(coord_sys=\"cartesian\")\n",
"velocity.min_coords = 0, 0, 0\n",
"velocity.node_intervals = 0.004, 0.004, 1\n",
"velocity.npts = vv.shape\n",
"velocity.values = vv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"traveltime_fields = dict()\n",
"for decimation_factor in range(7, -1, -1):\n",
" decimation_factor = 2**decimation_factor\n",
" \n",
" vv = velocity.values[::decimation_factor, ::decimation_factor]\n",
"\n",
" solver = pykonal.EikonalSolver(coord_sys=\"cartesian\")\n",
"\n",
" solver.velocity.min_coords = 0, 0, 0\n",
" solver.velocity.node_intervals = velocity.node_intervals * decimation_factor\n",
" solver.velocity.npts = vv.shape\n",
" solver.velocity.values = vv\n",
"\n",
" src_idx = 0, 0, 0\n",
" solver.traveltime.values[src_idx] = 0\n",
" solver.unknown[src_idx] = False\n",
" solver.trial.push(*src_idx)\n",
"\n",
" %time solver.solve()\n",
" traveltime_fields[decimation_factor] = solver.traveltime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.close('all')\n",
"fig = plt.figure(figsize=(5, 4.25))\n",
"ax = fig.add_subplot(1, 1, 1, frameon=False)\n",
"plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
"ax.set_xlabel(\"Horizontal offset [km]\")\n",
"ax.set_ylabel(\"Depth [km]\")\n",
"\n",
"gridspec = gs.GridSpec(nrows=8, ncols=2, height_ratios=[0.08, 1, 1, 1, 1, 0.2, 0.08, 0.72], width_ratios=[1, 1])\n",
"cax00 = fig.add_subplot(gridspec[0, 0])\n",
"cax01 = fig.add_subplot(gridspec[0, 1])\n",
"cax51 = fig.add_subplot(gridspec[6, 1])\n",
"ax10 = fig.add_subplot(gridspec[1, 0])\n",
"ax11 = fig.add_subplot(gridspec[1, 1])\n",
"ax20 = fig.add_subplot(gridspec[2, 0])\n",
"ax21 = fig.add_subplot(gridspec[2, 1])\n",
"ax30 = fig.add_subplot(gridspec[3, 0])\n",
"ax31 = fig.add_subplot(gridspec[3, 1])\n",
"ax40 = fig.add_subplot(gridspec[4, 0])\n",
"ax41 = fig.add_subplot(gridspec[4, 1])\n",
"ax50 = fig.add_subplot(gridspec[5:, 0])\n",
"\n",
"panel = ord(\"a\")\n",
"for ax in (ax10, ax11, ax20, ax21, ax30, ax31, ax40, ax41, ax50):\n",
" ax.text(-0.05, 0.8, f\"({chr(panel)})\", ha=\"right\", va=\"top\", transform=ax.transAxes)\n",
" panel += 1\n",
"\n",
"qmesh = ax10.pcolormesh(\n",
" velocity.nodes[:,:,0,0],\n",
" velocity.nodes[:,:,0,1],\n",
" velocity.values[:,:,0],\n",
" cmap=plt.get_cmap(\"hot\")\n",
")\n",
"cbar = fig.colorbar(qmesh, cax=cax00, orientation=\"horizontal\")\n",
"cbar.set_label(\"Velocity [km/s]\")\n",
"cbar.ax.xaxis.tick_top()\n",
"cbar.ax.xaxis.set_label_position(\"top\")\n",
"\n",
"tt0 = traveltime_fields[1]\n",
"qmesh = ax11.pcolormesh(\n",
" tt0.nodes[:,:,0,0],\n",
" tt0.nodes[:,:,0,1],\n",
" tt0.values[:,:,0],\n",
" cmap=plt.get_cmap(\"jet\"),\n",
")\n",
"ax11.contour(\n",
" tt0.nodes[:,:,0,0],\n",
" tt0.nodes[:,:,0,1],\n",
" tt0.values[:,:,0],\n",
" colors=\"k\",\n",
" levels=np.arange(0, tt0.values.max(), 0.25),\n",
" linewidths=0.5,\n",
" linestyles=\"--\"\n",
")\n",
"cbar = fig.colorbar(qmesh, cax=cax01, orientation=\"horizontal\")\n",
"cbar.set_label(\"Traveltime [s]\")\n",
"cbar.ax.xaxis.tick_top()\n",
"cbar.ax.xaxis.set_label_position(\"top\")\n",
"\n",
"for ax, decimation_factor in ((ax20, 128), (ax21, 64), (ax30, 32), (ax31, 16), (ax40, 8), (ax41, 4), (ax50, 2)):\n",
" tt = traveltime_fields[decimation_factor]\n",
" qmesh = ax.pcolormesh(\n",
" tt.nodes[:,:,0,0],\n",
" tt.nodes[:,:,0,1],\n",
" np.abs(tt.values[:,:,0] - tt0.values[::decimation_factor, ::decimation_factor, 0]),\n",
" cmap=plt.get_cmap(\"bone_r\"),\n",
" vmin=0,\n",
" vmax=0.62\n",
" )\n",
" ax.text(\n",
" 0.05, 0.95,\n",
" f\"$d={decimation_factor}$\",\n",
" ha=\"left\",\n",
" va=\"top\",\n",
" transform=ax.transAxes\n",
" )\n",
"cbar = fig.colorbar(qmesh, cax=cax51, orientation=\"horizontal\")\n",
"cbar.set_label(\"$\\Delta t$ [s]\")\n",
"\n",
"for ax in (ax10, ax11, ax20, ax21, ax30, ax31, ax40):\n",
" ax.set_xticklabels([])\n",
"for ax in (ax11, ax21, ax31, ax41):\n",
" ax.yaxis.tick_right()\n",
"for ax in (ax10, ax11, ax20, ax21, ax30, ax31, ax40, ax41, ax50):\n",
" ax.set_yticks([0, 4])\n",
" ax.set_xticks([0, 8, 16, 24])\n",
" ax.set_xlim(0, 27.136)\n",
" ax.set_ylim(0, 5.12)\n",
" ax.invert_yaxis()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: jupyter/figure_6.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 6\n",
"Compatible with PyKonal Version 0.2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.gridspec as gs\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pykonal\n",
"\n",
"from matplotlib import markers\n",
"from matplotlib.path import Path\n",
"from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def align_marker(marker, halign='center', valign='middle',):\n",
" \"\"\"\n",
" create markers with specified alignment.\n",
"\n",
" Parameters\n",
" ----------\n",
"\n",
" marker : a valid marker specification.\n",
" See mpl.markers\n",
"\n",
" halign : string, float {'left', 'center', 'right'}\n",
" Specifies the horizontal alignment of the marker. *float* values\n",
" specify the alignment in units of the markersize/2 (0 is 'center',\n",
" -1 is 'right', 1 is 'left').\n",
"\n",
" valign : string, float {'top', 'middle', 'bottom'}\n",
" Specifies the vertical alignment of the marker. *float* values\n",
" specify the alignment in units of the markersize/2 (0 is 'middle',\n",
" -1 is 'top', 1 is 'bottom').\n",
"\n",
" Returns\n",
" -------\n",
"\n",
" marker_array : numpy.ndarray\n",
" A Nx2 array that specifies the marker path relative to the\n",
" plot target point at (0, 0).\n",
"\n",
" Notes\n",
" -----\n",
" The mark_array can be passed directly to ax.plot and ax.scatter, e.g.::\n",
"\n",
" ax.plot(1, 1, marker=align_marker('>', 'left'))\n",
"\n",
" \"\"\"\n",
"\n",
" halign = {'right': -1.,\n",
" 'middle': 0.,\n",
" 'center': 0.,\n",
" 'left': 1.,\n",
" }[halign]\n",
"\n",
" valign = {'top': -1.,\n",
" 'middle': 0.,\n",
" 'center': 0.,\n",
" 'bottom': 1.,\n",
" }[valign]\n",
"\n",
" # Define the base marker\n",
" bm = markers.MarkerStyle(marker)\n",
"\n",
" # Get the marker path and apply the marker transform to get the\n",
" # actual marker vertices (they should all be in a unit-square\n",
" # centered at (0, 0))\n",
" m_arr = bm.get_path().transformed(bm.get_transform()).vertices\n",
"\n",
" # Shift the marker vertices for the specified alignment.\n",
" m_arr[:, 0] += halign / 2\n",
" m_arr[:, 1] += valign / 2\n",
"\n",
" return Path(m_arr, bm.get_path().codes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define the velocity model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set the velocity gradient in 1/s\n",
"velocity_gradient = 0.25\n",
"\n",
"velocity = pykonal.fields.ScalarField3D(coord_sys=\"cartesian\")\n",
"velocity.min_coords = 0, 0, 0\n",
"velocity.npts = 4096, 1024, 1\n",
"velocity.node_intervals = [40, 10, 1] / velocity.npts\n",
"velocity.values = np.full(velocity.npts, fill_value=4.5)\n",
"\n",
"for iy in range(velocity.npts[1]):\n",
" velocity.values[:,iy] += velocity_gradient * velocity.nodes[0,iy,0,1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Determine the analytical solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set the take-off angle in radians.\n",
"takeoff_angle = np.radians(50)\n",
"\n",
"# Set the index of the node corresponding to the source location.\n",
"src_idx = np.array([128, 128, 0])\n",
"\n",
"# Get the source coordinates.\n",
"src_coords = velocity.nodes[tuple(src_idx)]\n",
"\n",
"# Compute the radius of the circle defining the raypath.\n",
"radius = velocity.value(src_coords) / (velocity_gradient * np.sin(takeoff_angle))\n",
"\n",
"# Compute the coordinates of the center of the circle.\n",
"center_coords = src_coords + radius*np.array([np.cos(takeoff_angle), np.sin(takeoff_angle), 0])\n",
"\n",
"# Define function mapping horizontal coordinate to vertical\n",
"def vertical_coords(horizontal_coord, src_coords=src_coords, radius=radius, takeoff_angle=takeoff_angle):\n",
" sqrt = np.sqrt(radius**2 - (horizontal_coord - src_coords[0] - radius*np.cos(takeoff_angle))**2)\n",
" first_two_terms = src_coords[1] - radius * np.sin(takeoff_angle)\n",
" return (first_two_terms + sqrt, first_two_terms - sqrt)\n",
"\n",
"# Define function mapping vertical coordinate to horizontal\n",
"def horizontal_coords(vertical_coord, src_coords=src_coords, radius=radius, takeoff_angle=takeoff_angle):\n",
" sqrt = np.sqrt(radius**2 - (src_coords[1] - vertical_coord - radius*np.sin(takeoff_angle))**2)\n",
" first_two_terms = src_coords[0] + radius * np.cos(takeoff_angle)\n",
" return (first_two_terms + sqrt, first_two_terms - sqrt)\n",
"\n",
"rec_coords = [horizontal_coords(0)[0], 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute numerical solutions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rays = dict()\n",
"\n",
"for decimation_factor in range(6, 1, -1):\n",
" decimation_factor = 2**decimation_factor\n",
" \n",
" vv = velocity.values[::decimation_factor, ::decimation_factor]\n",
"\n",
" solver = pykonal.EikonalSolver(coord_sys=\"cartesian\")\n",
"\n",
" solver.velocity.min_coords = 0, 0, 0\n",
" solver.velocity.node_intervals = velocity.node_intervals * decimation_factor\n",
" solver.velocity.npts = vv.shape\n",
" solver.velocity.values = vv\n",
"\n",
" idx = tuple((src_idx / decimation_factor).astype(np.int) - [1, 1, 0])\n",
" solver.traveltime.values[idx] = 0\n",
" solver.unknown[idx] = False\n",
" solver.trial.push(*idx)\n",
"\n",
" %time solver.solve()\n",
" rays[decimation_factor] = solver.trace_ray(np.array([rec_coords[0], 0, 0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"zoom = 22\n",
"dx = 0.5\n",
"dy = 0.5\n",
"anchor_y = 1.1\n",
"\n",
"\n",
"plt.close(\"all\")\n",
"fig = plt.figure(figsize=(6, 3))\n",
"\n",
"# Set up the main Axes.\n",
"ax0 = fig.add_subplot(2, 1, 2, aspect=1)\n",
"ax0.set_xlabel(\"Horizontal offset [km]\")\n",
"ax0.set_ylabel(\"Depth [km]\")\n",
"ax0.set_xlim(0, rec_coords[0]+src_coords[0])\n",
"ax0.set_ylim(-2, 10)\n",
"ax0.invert_yaxis()\n",
"\n",
"# Set up the first inset Axes.\n",
"axins1 = zoomed_inset_axes(\n",
" ax0,\n",
" zoom=zoom,\n",
" loc=\"lower left\",\n",
" bbox_to_anchor=(0, anchor_y),\n",
" bbox_transform=ax0.transAxes\n",
")\n",
"axins1.set_xlim(src_coords[0]-dx/2, src_coords[0]+dx/2)\n",
"axins1.set_ylim(src_coords[1]+dy/2, src_coords[1]-dy/2)\n",
"\n",
"# Set up the second inset Axes.\n",
"axins2 = zoomed_inset_axes(\n",
" ax0,\n",
" zoom=zoom,\n",
" loc=\"lower center\",\n",
" bbox_to_anchor=(0.5, anchor_y),\n",
" bbox_transform=ax0.transAxes\n",
")\n",
"turning_point = (src_coords[0] + rec_coords[0]) / 2\n",
"axins2.set_xlim(turning_point-dx/2, turning_point+dx/2)\n",
"axins2.set_ylim(vertical_coords(turning_point)[0]+dy/2, vertical_coords(turning_point)[0]-dy/2)\n",
"\n",
"# Set up the third inset Axes.\n",
"axins3 = zoomed_inset_axes(\n",
" ax0,\n",
" zoom=zoom,\n",
" loc=\"lower right\",\n",
" bbox_to_anchor=(1, anchor_y),\n",
" bbox_transform=ax0.transAxes\n",
")\n",
"axins3.set_xlim(horizontal_coords(0)[0]-3*dx/4, horizontal_coords(0)[0]+dx/4)\n",
"axins3.set_ylim(3*dy/4, -dy/4)\n",
"\n",
"for ax in (ax0, axins1):\n",
" # Plot the source location.\n",
" ax.scatter(\n",
" src_coords[0], src_coords[1], \n",
" marker=\"*\",\n",
" s=250,\n",
" facecolor=\"w\",\n",
" edgecolor=\"k\"\n",
" )\n",
"\n",
"for ax in (ax0, axins3):\n",
" # Plot the receiver location.\n",
" ax.scatter(\n",
" rec_coords[0], rec_coords[1], \n",
" marker=align_marker(\"v\", valign=\"bottom\"),\n",
" s=250,\n",
" facecolor=\"w\",\n",
" edgecolor=\"k\"\n",
" )\n",
" \n",
"xx = np.linspace(src_coords[0], rec_coords[0])\n",
"yy = vertical_coords(xx)[0]\n",
"label = True\n",
"for ax in (ax0, axins1, axins2, axins3):\n",
" # Plot the synthetic raypaths.\n",
" for decimation_factor in rays:\n",
" ax.plot(\n",
" rays[decimation_factor][:,0], \n",
" rays[decimation_factor][:,1],\n",
" linewidth=1,\n",
" label=f\"d={decimation_factor}\" if label is True else None\n",
" )\n",
" # Plot the analytic raypath.\n",
" ax.plot(xx, yy, \"k--\", label=\"Analytical\" if label is True else None)\n",
" label = False\n",
" \n",
"for ax in (axins1, axins2, axins3):\n",
" mark_inset(ax0, ax, loc1=3, loc2=4, fc=\"none\", ec=\"0.5\", linestyle=\"-.\")\n",
" ax.text(0.5, 0.95, f\"{zoom}x\", ha=\"center\", va=\"top\", transform=ax.transAxes)\n",
" ax.set_xticks(np.arange(*ax.get_xlim(), 0.2))\n",
" ax.set_yticks(np.arange(*ax.get_ylim(), -0.2))\n",
" ax.set_xticklabels([])\n",
" ax.set_yticklabels([])\n",
" ax.tick_params(direction=\"in\")\n",
"\n",
"label = ord(\"a\")\n",
"for ax in (axins1, axins2, axins3, ax0):\n",
" ax.text(\n",
" 0, 1.05, f\"{chr(label)})\",\n",
" ha=\"center\",\n",
" va=\"bottom\",\n",
" transform=ax.transAxes\n",
" )\n",
" label += 1\n",
"fig.legend(loc=\"center right\")\n",
"fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: jupyter/figure_7.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 7\n",
"Compatible with PyKonal Version 0.2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.gridspec as gs\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import pkg_resources\n",
"import pykonal"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fname = pkg_resources.resource_filename(\n",
" \"pykonal\",\n",
" os.path.join(\"data\", \"mitp2008.npz\")\n",
")\n",
"\n",
"with np.load(fname) as npz:\n",
" vv = pykonal.fields.ScalarField3D(coord_sys='spherical')\n",
" dv = pykonal.fields.ScalarField3D(coord_sys='spherical')\n",
" vv.min_coords = dv.min_coords = npz['min_coords_2d'] + [0, 0, np.pi/4]\n",
" vv.node_intervals = dv.node_intervals = npz['node_intervals_2d']\n",
" vv.npts = dv.npts = npz['npts_2d'][0], npz['npts_2d'][1], npz['npts_2d'][2]//4\n",
" vv.values = npz['vv_2d'][:,:,128:256]\n",
" dv.values = npz[\"dv_2d\"][:,:,128:256]\n",
"\n",
"# Resample the velocity model (64, 1, 128) --> (1024, 1, 2048)\n",
"rho_min, theta_min, phi_min = vv.min_coords\n",
"rho_max, theta_max, phi_max = vv.max_coords\n",
"nrho, ntheta, nphi = 1024, 1, 2048\n",
"\n",
"drho = (rho_max - rho_min) / (nrho - 1)\n",
"rho = np.linspace(rho_min, rho_max, nrho)\n",
"\n",
"# dtheta = (theta_max - theta_min) / (ntheta - 1)\n",
"dtheta = 1\n",
"theta = np.linspace(theta_min, theta_max, ntheta)\n",
"\n",
"dphi = (phi_max - phi_min) / (nphi - 1)\n",
"phi = np.linspace(phi_min, phi_max, nphi)\n",
"\n",
"rtp = np.moveaxis(\n",
" np.stack(np.meshgrid(rho, theta, phi, indexing=\"ij\")),\n",
" 0, \n",
" -1\n",
")\n",
"vv_new = vv.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\n",
"dv_new = dv.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\n",
"\n",
"vv = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\n",
"dv = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\n",
"vv.min_coords = dv.min_coords = rho_min, theta_min, phi_min\n",
"vv.node_intervals = dv.node_intervals = drho, dtheta, dphi\n",
"vv.npts = dv.npts = nrho, ntheta, nphi\n",
"vv.values = vv_new\n",
"dv.values = dv_new\n",
"\n",
"velocity = vv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SRC_IDX = np.array([512, 0, 1024])\n",
"\n",
"traveltime_fields = dict()\n",
"for decimation_factor in range(7, -1, -1):\n",
" decimation_factor = 2**decimation_factor\n",
" \n",
" vv = velocity.values[::decimation_factor, :, ::decimation_factor]\n",
"\n",
" solver = pykonal.EikonalSolver(coord_sys=\"spherical\")\n",
"\n",
" solver.vv.min_coords = velocity.min_coords\n",
" solver.vv.node_intervals = velocity.node_intervals * decimation_factor\n",
" solver.vv.npts = vv.shape\n",
" solver.vv.values = vv\n",
"\n",
" src_idx = tuple(SRC_IDX // decimation_factor - [1, 0, 1])\n",
" print(src_idx)\n",
" solver.traveltime.values[src_idx] = 0\n",
" solver.unknown[src_idx] = False\n",
" solver.trial.push(*src_idx)\n",
"\n",
" %time solver.solve()\n",
" traveltime_fields[decimation_factor] = solver.traveltime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.close('all')\n",
"fig = plt.figure(figsize=(4.5, 6.5))\n",
"\n",
"fig.text(0, 0.5, \"$y$ [km]\", ha=\"left\", va=\"center\", rotation=90)\n",
"fig.text(0.5, 0.05, \"$x$ [km]\", ha=\"center\", va=\"bottom\")\n",
"\n",
"gridspec = gs.GridSpec(nrows=6, ncols=2, height_ratios=[0.08, 1, 1, 1, 1, 1], width_ratios=[1, 1])\n",
"cax00 = fig.add_subplot(gridspec[0, 0])\n",
"cax01 = fig.add_subplot(gridspec[0, 1])\n",
"ax10 = fig.add_subplot(gridspec[1, 0], aspect=1)\n",
"ax11 = fig.add_subplot(gridspec[1, 1], aspect=1)\n",
"ax20 = fig.add_subplot(gridspec[2, 0], aspect=1)\n",
"ax21 = fig.add_subplot(gridspec[2, 1], aspect=1)\n",
"ax30 = fig.add_subplot(gridspec[3, 0], aspect=1)\n",
"ax31 = fig.add_subplot(gridspec[3, 1], aspect=1)\n",
"ax40 = fig.add_subplot(gridspec[4, 0], aspect=1)\n",
"ax41 = fig.add_subplot(gridspec[4, 1], aspect=1)\n",
"ax50 = fig.add_subplot(gridspec[5, 0], aspect=1)\n",
"\n",
"gridspec = gs.GridSpec(nrows=8, ncols=2, height_ratios=[0.08, 1, 1, 1, 1, 0.2, 0.08, 0.72], width_ratios=[1, 1])\n",
"cax51 = fig.add_subplot(gridspec[6, 1])\n",
"\n",
"panel = ord(\"a\")\n",
"for ax in (ax10, ax11, ax20, ax21, ax30, ax31, ax40, ax41, ax50):\n",
" ax.text(-0.05, 1.1, f\"({chr(panel)})\", ha=\"right\", va=\"top\", transform=ax.transAxes)\n",
" panel += 1\n",
"\n",
"nodes = dv.nodes\n",
"xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
"yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
"qmesh = ax10.pcolormesh(\n",
" xx[:,0],\n",
" yy[:,0],\n",
" dv.values[:,0],\n",
" cmap=plt.get_cmap(\"seismic_r\"),\n",
" vmin=-1.25,\n",
" vmax=1.25,\n",
" shading=\"gouraud\"\n",
")\n",
"cbar = fig.colorbar(qmesh, cax=cax00, orientation=\"horizontal\")\n",
"cbar.set_label(\"$dV_P/V_P$ [\\%]\")\n",
"cbar.ax.xaxis.tick_top()\n",
"cbar.ax.xaxis.set_label_position(\"top\")\n",
"\n",
"tt0 = traveltime_fields[1]\n",
"nodes = tt0.nodes\n",
"xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
"yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
"qmesh = ax11.pcolormesh(\n",
" xx[:,0],\n",
" yy[:,0],\n",
" tt0.values[:,0],\n",
" cmap=plt.get_cmap(\"jet\"),\n",
" shading=\"gouraud\"\n",
")\n",
"ax11.contour(\n",
" xx[:,0],\n",
" yy[:,0],\n",
" tt0.values[:,0],\n",
" colors=\"k\",\n",
" levels=np.arange(0, tt0.values.max(), 20),\n",
" linewidths=0.5,\n",
" linestyles=\"--\"\n",
")\n",
"ax11.scatter(\n",
" xx[511, 0, 1023], yy[511, 0, 1023],\n",
" marker=\"*\",\n",
" s=250,\n",
" facecolor=\"w\",\n",
" edgecolor=\"k\"\n",
")\n",
"cbar = fig.colorbar(qmesh, cax=cax01, orientation=\"horizontal\")\n",
"cbar.set_label(\"Traveltime [s]\")\n",
"cbar.ax.xaxis.tick_top()\n",
"cbar.ax.xaxis.set_label_position(\"top\")\n",
"\n",
"for ax, decimation_factor in ((ax20, 128), (ax21, 64), (ax30, 32), (ax31, 16), (ax40, 8), (ax41, 4), (ax50, 2)):\n",
" tt = traveltime_fields[decimation_factor]\n",
" nodes = tt.nodes\n",
" xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
" yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
" qmesh = ax.pcolormesh(\n",
" xx[:,0],\n",
" yy[:,0],\n",
" np.abs(tt.values[:,0] - tt0.values[::decimation_factor, 0, ::decimation_factor]),\n",
" cmap=plt.get_cmap(\"bone_r\"),\n",
" vmax=50,\n",
" shading=\"gouraud\"\n",
" )\n",
" ax.text(\n",
" 0.05, 0.95,\n",
" f\"$d={decimation_factor}$\",\n",
" ha=\"left\",\n",
" va=\"top\",\n",
" transform=ax.transAxes\n",
" )\n",
"cbar = fig.colorbar(qmesh, cax=cax51, orientation=\"horizontal\")\n",
"cbar.set_label(\"$\\Delta t$ [s]\")\n",
"for ax in (ax10, ax11, ax20, ax21, ax30, ax31, ax40):\n",
" ax.set_xticklabels([])\n",
"for ax in (ax11, ax21, ax31, ax41):\n",
" ax.yaxis.tick_right()\n",
"for ax in (ax10, ax11, ax20, ax21, ax30, ax31, ax40, ax41, ax50):\n",
" ax.set_yticks([3500, 5500])\n",
" ax.set_xlim(-4500, 4500)\n",
" ax.set_ylim(2500, 6500)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: jupyter/figure_8.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 8\n",
"Compatible with PyKonal Version 0.2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import pkg_resources\n",
"import pykonal\n",
"import scipy.ndimage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"velocity = pykonal.fields.ScalarField3D(coord_sys=\"cartesian\")\n",
"velocity.min_coords = 0, 0, 0\n",
"velocity.node_intervals = 0.1, 0.1, 0.1\n",
"velocity.npts = 512, 128, 1\n",
"velocity.values = scipy.ndimage.gaussian_filter(20. * np.random.randn(*velocity.npts) + 6., 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"solver_dg = pykonal.EikonalSolver(coord_sys=\"cartesian\")\n",
"solver_dg.vv.min_coords = velocity.min_coords\n",
"solver_dg.vv.node_intervals = velocity.node_intervals\n",
"solver_dg.vv.npts = velocity.npts\n",
"solver_dg.vv.values = velocity.values\n",
"\n",
"src_idx = (127, 32, 0)\n",
"solver_dg.tt.values[src_idx] = 0\n",
"solver_dg.unknown[src_idx] = False\n",
"solver_dg.trial.push(*src_idx)\n",
"solver_dg.solve()\n",
"\n",
"\n",
"solver_ug = pykonal.EikonalSolver(coord_sys=\"cartesian\")\n",
"solver_ug.vv.min_coords = solver_dg.vv.min_coords\n",
"solver_ug.vv.node_intervals = solver_dg.vv.node_intervals\n",
"solver_ug.vv.npts = solver_dg.vv.npts\n",
"solver_ug.vv.values = solver_dg.vv.values\n",
"\n",
"for ix in range(solver_ug.tt.npts[0]):\n",
" idx = (ix, solver_ug.tt.npts[1]-1, 0)\n",
" solver_ug.tt.values[idx] = solver_dg.tt.values[idx]\n",
" solver_ug.unknown[idx] = False\n",
" solver_ug.trial.push(*idx)\n",
"solver_ug.solve()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.close(\"all\")\n",
"fig = plt.figure(figsize=(6, 2.5))\n",
"\n",
"ax1 = fig.add_subplot(2, 1, 1)\n",
"ax2 = fig.add_subplot(2, 1, 2)\n",
"\n",
"ax = fig.add_subplot(1, 1, 1, frameon=False)\n",
"plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
"ax.set_ylabel(\"Depth [km]\")\n",
"ax2.set_xlabel(\"Horizontal offset [km]\")\n",
"ax1.set_xticklabels([])\n",
"\n",
"for solver, ax, panel in (\n",
" (solver_dg, ax1, f\"(a)\"), \n",
" (solver_ug, ax2, f\"(b)\")\n",
"):\n",
" ax.text(-0.025, 1.05, panel, va=\"bottom\", ha=\"right\", transform=ax.transAxes)\n",
" qmesh = ax.pcolormesh(\n",
" solver.vv.nodes[:,:,0,0], \n",
" solver.vv.nodes[:,:,0,1], \n",
" solver.vv.values[:,:,0],\n",
" cmap=plt.get_cmap(\"hot\")\n",
" )\n",
" ax.contour(\n",
" solver.tt.nodes[:,:,0,0], \n",
" solver.tt.nodes[:,:,0,1], \n",
" solver.tt.values[:,:,0],\n",
" colors=\"k\",\n",
" linestyles=\"--\",\n",
" linewidths=1,\n",
" levels=np.arange(0, solver.tt.values.max(), 0.25)\n",
" )\n",
" ax.scatter(\n",
" solver.vv.nodes[src_idx + (0,)],\n",
" solver.vv.nodes[src_idx + (1,)],\n",
" marker=\"*\",\n",
" facecolor=\"w\",\n",
" edgecolor=\"k\",\n",
" s=256\n",
" )\n",
" ax.invert_yaxis()\n",
"cbar = fig.colorbar(qmesh, ax=(ax1, ax2))\n",
"cbar.set_label(\"Velocity [km/s]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py37]",
"language": "python",
"name": "conda-env-py37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: pykonal/__init__.py
================================================
from .__version__ import (
__major_version__,
__minor_version__,
__patch__,
__release__,
__version_tuple__,
__version__
)
from .solver import EikonalSolver
from . import constants
from . import fields
from . import heapq
from . import inventory
from . import locate
from . import solver
from . import stats
================================================
FILE: pykonal/__version__.py
================================================
__major_version__ = 0
__minor_version__ = 4
__patch__ = 1
__release__ = ''
__version_tuple__ = (
__major_version__,
__minor_version__,
__patch__
)
__version_number__ = '.'.join([str(v) for v in __version_tuple__])
__version__ = f'{__version_number__}{__release__}'
================================================
FILE: pykonal/constants.pxd
================================================
cimport numpy as np
ctypedef np.float64_t REAL_t
ctypedef np.uint16_t UINT_t
ctypedef np.npy_bool BOOL_t
================================================
FILE: pykonal/constants.pyx
================================================
"""
Package-level constants.
"""
import numpy as np
DTYPE_REAL = np.float64
DTYPE_UINT = np.uint32
DTYPE_INT = np.int32
DTYPE_BOOL = np.bool_
EARTH_RADIUS = 6371.
================================================
FILE: pykonal/data/marmousi2/LICENSE
================================================
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Domain Dedication. Except for the limited purpose of indicating that
material is shared under a Creative Commons public license or as
otherwise permitted by the Creative Commons policies published at
creativecommons.org/policies, Creative Commons does not authorize the
use of the trademark "Creative Commons" or any other trademark or logo
of Creative Commons without its prior written consent including,
without limitation, in connection with any unauthorized modifications
to any of its public licenses or any other arrangements,
understandings, or agreements concerning use of licensed material. For
the avoidance of doubt, this paragraph does not form part of the
public licenses.
Creative Commons may be contacted at creativecommons.org.
================================================
FILE: pykonal/data/marmousi2/README
================================================
AGL Elastic Marmousi
# Statement
The AGL Elastic Marmousi model was created at the University of Houston,
and was released by its authors Gary S. Martin, Robert Wiley and Kurt J.
Marfurt with the permission of the program director of the Allied
Geophysical Labs consortium in 2019.
# Description
The AGL elastic Marmousi model, also commonly referred to as the Marmousi2
model, is an elastic extension of the original Marmousi model created by
Gary S. Martin, Robert Wiley and Kurt J. Marfurt (2004, 2006). The model
has a lateral extension of 17 km and a depth of 3.5 km and includes a
total of 199 layers geological layers, as well as an extended water layer
of 450 m depth to simulate a deep water setting. The layers were derived
from the same horizon picks that were used for the original Marmousi model
and were supplied by Aline Bourgeois from the Institut Francais du Petrole
(IFP). The simulated data were computed on a supercomputer system at the
University of Houston (UH), supported by Sun Microsystems. The data set
includes subsurface models for density, P-wave and S-wave velocity, as well
as a series of processed data sets (NMO stacks, pre- and poststack time and
depth migrated sections).
# Copyright notice
Copyright 2004 Allied Geophysical Laboratory, University of Houston, TX, USA
# License
This model is licensed under the Creative Commons Attribution 4.0
International License. To view a copy of the license, visit
https://creativecommons.org/licenses/by/4.0 or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
The license permits any user to freely copy and redistribute the material
in any medium or format. Users are free to remix, transform, and build
upon the material for any purpose, including commercially. Refer to
LICENSE.txt for all terms and conditions, as well as the disclaimer of
warranties.
Please DO NOT separate the model and data from this license and ALWAYS
include the above statement and link with the data.
# Acknowledgments
This model was created by Gary S. Martin (ION), Robert Wiley (University
of Houston) and Kurt J. Marfurt (University of Oklahoma), with support
from Aline Bourgeois (formerly IFP), Fred Hilterman (formerly University
of Houston), Don Larson and Shawn Larson (Lawrence Livermore National
Laboratory, formerly Los Alamos National Laboratory).
Funding for this work was in part provided by the United States Department
of Energy's Next Generation Modeling and Imaging project.
This model was added to the SEG Open Data collection with help from Joseph
Dellinger (BP), Robert Stewart (University of Houston) and Karl Schleicher
(University of Texas at Austin).
# References
Martin, G. S., 2004, The Marmousi2 model, elastic synthetic data, and an
analysis of imaging and AVO in a structurally complex environment:
Master’s thesis. University of Houston. Retrieved from
www.agl.uh.edu/pdf/theses/martin.pdf
Martin, G. S., Marfurt, K. J., and Larsen, S., 2005, Marmousi‐2: An
updated model for the investigation of AVO in structurally complex areas:
SEG Technical Program Expanded Abstracts 2002, 1979–1982.
doi:10.1190/1.1817083
Martin, G. S., Wiley, R., and Marfurt, K. J., 2006, Marmousi2: An elastic
upgrade for Marmousi: The Leading Edge, 25, 156–166.
doi:10.1190/1.2172306
================================================
FILE: pykonal/data/marmousi2/marmousi2.npz
================================================
[File too large to display: 36.3 MB]
================================================
FILE: pykonal/fields.pxd
================================================
cimport numpy as np
from . cimport constants
cdef class Field3D(object):
cdef str cy_coord_sys
cdef str cy_field_type
cdef constants.BOOL_t[3] cy_iax_isnull
cdef constants.BOOL_t[3] cy_iax_isperiodic
cdef constants.REAL_t[3] cy_max_coords
cdef constants.REAL_t[3] cy_min_coords
cdef constants.REAL_t[:,:,:,:] cy_norm
cdef constants.UINT_t[3] cy_npts
cdef constants.REAL_t[3] cy_node_intervals
cdef constants.BOOL_t _update_max_coords(Field3D self)
cdef constants.BOOL_t _update_iax_isnull(Field3D self)
cdef constants.BOOL_t _update_iax_isperiodic(Field3D self)
cpdef constants.BOOL_t to_hdf(
Field3D self,
str path,
str key=*,
constants.BOOL_t overwrite=*
)
cdef class ScalarField3D(Field3D):
cdef constants.REAL_t[:,:,:] cy_values
cpdef np.ndarray[constants.REAL_t, ndim=1] resample(
ScalarField3D self,
constants.REAL_t[:,:] points,
constants.REAL_t null=*
)
cpdef np.ndarray[constants.REAL_t, ndim=2] trace_ray(
ScalarField3D self,
constants.REAL_t[:] end
)
cpdef constants.REAL_t value(
ScalarField3D self,
constants.REAL_t[:] point,
constants.REAL_t null=*
)
cpdef VectorField3D _gradient_of_cartesian(ScalarField3D self)
cpdef VectorField3D _gradient_of_spherical(ScalarField3D self)
cdef class VectorField3D(Field3D):
cdef constants.REAL_t[:,:,:,:] cy_values
cpdef np.ndarray[constants.REAL_t, ndim=1] value(
VectorField3D self,
constants.REAL_t[:] point
)
cpdef Field3D load(str path)
================================================
FILE: pykonal/fields.pyx
================================================
# distutils: language=c++
"""
This module provides three classes (:class:`Field3D `,
:class:`ScalarField3D `, and
:class:`VectorField3D `).
:class:`Field3D ` is a base class intended to
be used only as an abstraction. :class:`ScalarField3D `
and :class:`VectorField3D ` both inherit
from :class:`Field3D ` and add important
functionality that make the classes useful. They are primarily intended
for storing and interpolating data, and, in the case of scalar fields,
computing the gradient.
This module also provides an I/O function
(:func:`read_hdf `) to read data that was saved using
the :func:`to_hdf ` method of the above
classes.
.. autofunction:: pykonal.fields.load(path)
.. autofunction:: pykonal.fields.read_hdf(path)
"""
import warnings
# Third-party imports
import h5py
import numpy as np
# Local imports
from . import constants
from . import transformations
# Cython built-in imports.
from libc.math cimport sqrt, sin
from libcpp.vector cimport vector as cpp_vector
# Third-party Cython imports
cimport numpy as np
# Local Cython imports
from . cimport constants
cdef class Field3D(object):
"""
Base class for representing generic 3D fields.
"""
def __init__(self, coord_sys="cartesian"):
self.cy_coord_sys = coord_sys
@property
def coord_sys(self):
"""
[*Read only*, :class:`str`] Coordinate system of grid on which
field data is represented {"Cartesian", "spherical"}.
"""
return (self.cy_coord_sys)
@property
def field_type(self):
return (self.cy_field_type)
@property
def iax_isnull(self):
"""
[*Read only*, :class:`numpy.ndarray`\ (shape=(3,), dtype=numpy.bool)]
Array of booleans indicating whether each axis is null. The
axis with only one layer of nodes in 2D problems will be null.
"""
arr = np.array(
[self.cy_iax_isnull[iax] for iax in range(3)],
dtype=constants.DTYPE_BOOL
)
return (arr)
@property
def iax_isperiodic(self):
"""
[*Read only*, :class:`numpy.ndarray`\ (shape=(3,), dtype=numpy.bool)]
Array of booleans indicating whether each axis is periodic. In
practice, only the azimuthal (:math:`\phi`) axis in spherical
coordinates will ever be periodic.
"""
arr = np.array(
[self.cy_iax_isperiodic[iax] for iax in range(3)],
dtype=constants.DTYPE_BOOL
)
return (arr)
@property
def min_coords(self):
"""
[*Read/Write*, :class:`numpy.ndarray`\ (shape=(3,), dtype=numpy.float)]
Array specifying the lower bound of each axis. This attribute
must be initialized by the user.
:math:`\phi` coordinate must be in [-:math:`\pi`, :math:`\pi`) or
[0, 2:math:`\pi`] for spherical coordinates.
"""
return (np.asarray(self.cy_min_coords))
@min_coords.setter
def min_coords(self, value):
if self.coord_sys == "spherical" and value[0] == 0:
raise (ValueError("ρ must be > 0 for spherical coordinates."))
if self.coord_sys == "spherical" and value[2] < -np.pi:
raise (ValueError("φ must be in [-π,π) or [0,2π) for spherical coordinates."))
self.cy_min_coords = np.asarray(value, dtype=constants.DTYPE_REAL)
self._update_max_coords()
self._update_iax_isperiodic()
@property
def max_coords(self):
"""
[*Read only*, :class:`numpy.ndarray`\ (shape=(3,), dtype=numpy.float)]
Array specifying the upper bound of each axis.
"""
return (np.asarray(self.cy_max_coords))
@property
def node_intervals(self):
"""
[*Read/Write*, :class:`numpy.ndarray`\ (shape=(3,), dtype=numpy.float)]
Array of node intervals along each axis. This attribute must be
initialized by the user.
"""
return (np.asarray(self.cy_node_intervals))
@node_intervals.setter
def node_intervals(self, value):
value = np.asarray(value, dtype=constants.DTYPE_REAL)
if np.any(value <= 0):
raise (ValueError("All node intervals must be > 0"))
self.cy_node_intervals = value
self._update_max_coords()
self._update_iax_isperiodic()
@property
def nodes(self):
"""
[*Read only*, :class:`numpy.ndarray`\ (shape=(N0,N1,N2,3), dtype=numpy.float)]
Array specifying the grid-node coordinates.
"""
nodes = [
np.linspace(
self.min_coords[idx],
self.max_coords[idx],
self.npts[idx],
dtype=constants.DTYPE_REAL
)
for idx in range(3)
]
nodes = np.meshgrid(*nodes, indexing="ij")
nodes = np.stack(nodes)
nodes = np.moveaxis(nodes, 0, -1)
return (nodes)
@property
def norm(self):
"""
[*Read-only*, numpy.ndarray(shape=(N0,N1,N2,3), dtype=numpy.float)] 4D array of scaling
factors for gradient operator.
"""
try:
return (np.asarray(self.cy_norm))
except AttributeError:
norm = np.tile(
self.node_intervals,
np.append(self.npts, 1)
)
if self.coord_sys == "spherical":
norm[..., 1] *= self.nodes[..., 0]
norm[..., 2] *= self.nodes[..., 0]
norm[..., 2] *= np.sin(self.nodes[..., 1])
self.cy_norm = norm
return (np.asarray(self.cy_norm))
@property
def npts(self):
"""
[*Read/Write*, :class:`numpy.ndarray`\ (shape=(3,), dtype=numpy.int)]
Array specifying the number of nodes along each axis. This
attribute must be initialized by the user.
"""
return (np.asarray(self.cy_npts))
@npts.setter
def npts(self, value):
self.cy_npts = np.asarray(value, dtype=constants.DTYPE_UINT)
self._update_max_coords()
self._update_iax_isnull()
self._update_iax_isperiodic()
@property
def step_size(self):
"""
[*Read only*, :class:`float`] Step size used for ray tracing.
"""
norm = self.norm[..., ~self.iax_isnull]
return (norm[~np.isclose(norm, 0)].min() / 4)
cdef constants.BOOL_t _update_iax_isperiodic(Field3D self):
if self.cy_coord_sys == "spherical":
self.cy_iax_isperiodic[2] = np.isclose(
self.cy_max_coords[2]+self.cy_node_intervals[2]-self.cy_min_coords[2],
2 * np.pi
)
return (True)
cdef constants.BOOL_t _update_iax_isnull(Field3D self):
cdef Py_ssize_t iax
for iax in range(3):
self.cy_iax_isnull[iax] = True if self.cy_npts[iax] == 1 else False
return (True)
cdef constants.BOOL_t _update_max_coords(Field3D self):
cdef Py_ssize_t iax
cdef constants.REAL_t dx
for iax in range(3):
dx = self.cy_node_intervals[iax] * (self.cy_npts[iax] - 1)
self.cy_max_coords[iax] = self.cy_min_coords[iax] + dx
if self.cy_coord_sys == "spherical":
if self.cy_min_coords[2] >= 0 and self.cy_max_coords[2] > 2*np.pi:
raise(ValueError("Phi coordinates must be in [-π, π) or [0, 2π)."))
elif self.cy_min_coords[2] < 0 and self.cy_max_coords[2] > np.pi:
raise(ValueError("Phi coordinates must be in [-π, π) or [0, 2π)."))
return (True)
def savez(self, path):
"""
savez(self, path)
Save the field to disk using numpy.savez.
:param path: Path to output file.
:type path: str
:return: Returns True upon successful execution.
:rtype: bool
"""
warning_message = "The savez() method is deprecated and will be removed"\
" from future versions of PyKonal. Use pykonal.fields.Field3D.to_hdf()"\
" instead."
warnings.warn(warning_message, DeprecationWarning)
np.savez_compressed(
path,
min_coords=self.min_coords,
node_intervals=self.node_intervals,
npts=self.npts,
values=self.values,
coord_sys=[self.coord_sys]
)
return (True)
cpdef constants.BOOL_t to_hdf(Field3D self, str path, str key=None, constants.BOOL_t overwrite=False):
with h5py.File(path, mode="a") as f5:
if key is not None:
if key in f5 and overwrite is True:
del (f5[key])
group = f5.create_group(key)
else:
group = f5["/"]
group.attrs["coord_sys"] = self.coord_sys
group.attrs["field_type"] = self.field_type
for attr in ("min_coords", "node_intervals", "npts", "values"):
if attr in group and overwrite is True:
del (group[attr])
group.create_dataset(attr, data=getattr(self, attr))
return (True)
def transform_coordinates(self, coord_sys, origin, force_phi_positive=False):
"""
transform_coordinates(self, coord_sys, origin)
Transform node coordinates to a new frame of reference.
:param coord_sys: Coordinate system to transform to
("*spherical*", or "*Cartesian*")
:type coord_sys: str
:param origin: Coordinates of the origin of the new frame with
respect to the old frame of reference.
:type origin: tuple(float, float, float)
:param force_phi_positive: Force :math:`\phi` to be in [0, 2:math:`pi`)
for output spherical coordinates.
:type force_phi_positive: bool
:return: Node coordinates in new frame of reference.
:rtype: numpy.ndarray(shape=(N0,N1,N2,3), dtype=numpy.float)
"""
if self.coord_sys == "spherical" and coord_sys.lower() == "spherical":
return (
transformations.sph2sph(
self.nodes,
origin,
force_phi_positive=force_phi_positive
)
)
elif self.coord_sys == "cartesian" and coord_sys.lower() == "spherical":
return (transformations.xyz2sph(self.nodes, origin))
elif self.coord_sys == "spherical" and coord_sys.lower() == "cartesian":
return (transformations.sph2xyz(self.nodes, origin))
elif self.coord_sys == "cartesian" and coord_sys.lower() == "cartesian":
return (transformations.xyz2xyz(self.nodes, origin))
else:
raise (NotImplementedError())
cdef class ScalarField3D(Field3D):
"""
Class for representing 3D scalar fields.
"""
def __init__(self, coord_sys="cartesian"):
super(ScalarField3D, self).__init__(coord_sys=coord_sys)
self.cy_field_type = "scalar"
@property
def gradient(self):
"""
[*Read only*, numpy.ndarray(shape=(N0,N1,N2,3), dtype=numpy.float)]
Gradient of the field.
"""
if self.coord_sys == "cartesian":
return (self._gradient_of_cartesian())
elif self.coord_sys == "spherical":
return (self._gradient_of_spherical())
@property
def values(self):
"""
[*Read/Write*, numpy.ndarray(shape=(N0,N1,N2), dtype=numpy.float)]
Value of the field at each grid node.
"""
try:
return (np.asarray(self.cy_values))
except AttributeError:
self.cy_values = np.full(self.npts, fill_value=np.nan)
return (np.asarray(self.cy_values))
@values.setter
def values(self, value):
values = np.asarray(value, dtype=constants.DTYPE_REAL)
if not np.all(values.shape == self.npts):
raise (ValueError("Shape of values does not match npts attribute."))
self.cy_values = values
cpdef np.ndarray[constants.REAL_t, ndim=1] resample(ScalarField3D self, constants.REAL_t[:,:] points, constants.REAL_t null=np.nan):
"""
resample(self, points, null=numpy.nan)
Resample the field at an arbitrary set of points using
trilinear interpolation.
:param points: Points at which to resample the field.
:type points: numpy.ndarray(shape=(N,3), dtype=numpy.float)
:param null: Default (null) value to return for points lying
outside the interpolation domain.
:type null: float
:return: Resampled field values.
:rtype: numpy.ndarray(shape=(N,), dtype=numpy.float)
"""
cdef Py_ssize_t idx
cdef np.ndarray[constants.REAL_t, ndim=1] resampled # Using a MemoryViewBuffer might make this faster.
resampled = np.empty(points.shape[0], dtype=constants.DTYPE_REAL)
for idx in range(points.shape[0]):
resampled[idx] = self.value(points[idx], null=null)
return (resampled)
cpdef np.ndarray[constants.REAL_t, ndim=2] trace_ray(
ScalarField3D self,
constants.REAL_t[:] end
):
"""
trace_ray(self, end)
Trace the ray ending at *end* (given in the
same coordinate system as self.coord_sys.)
This method traces the ray that ends at *end* in reverse
direction by taking small steps along the path of steepest
descent. The resulting ray is reversed before being returned,
so it is in the normal forward-time orientation.
:param end: Coordinates of the ray's end point.
:type end: numpy.ndarray(shape=(3,), dtype=numpy.float)
:return: The ray path ending at *end*.
:rtype: numpy.ndarray(shape=(N,3), dtype=numpy.float)
"""
cdef cpp_vector[constants.REAL_t] ray
cdef constants.REAL_t norm, step_size, value, value_1back
cdef constants.REAL_t[3] gg
cdef constants.REAL_t[:] point
cdef Py_ssize_t idx
cdef str coord_sys
cdef np.ndarray[constants.REAL_t, ndim=1] ray_np
cdef VectorField3D grad
coord_sys = self.coord_sys
step_size = self.step_size
grad = self.gradient
for idx in range(3):
ray.push_back(end[idx])
value = np.infty
while True:
point = &ray[ray.size()-3]
try:
value_1back = value
value = self.value(point)
if value >= value_1back or np.isnan(value):
ray.resize(ray.size()-3) # drop bad pt
break
except ValueError:
return (None)
gg = grad.value(point)
norm = sqrt(gg[0]**2 + gg[1]**2 + gg[2]**2)
if np.isnan(norm):
raise (ValueError("Encountered NaN gradient."))
for idx in range(3):
gg[idx] /= norm
if coord_sys == "spherical":
gg[1] /= point[0]
gg[2] /= point[0] * sin(point[1])
for idx in range(3):
ray.push_back(point[idx] - step_size * gg[idx])
ray_np = np.empty(ray.size(), dtype=constants.DTYPE_REAL)
for idx in range(ray_np.size):
ray_np[idx] = ray[idx]
return (np.flipud(ray_np.reshape(-1,3)))
cpdef constants.REAL_t value(
ScalarField3D self,
constants.REAL_t[:] point,
constants.REAL_t null=np.nan
):
"""
value(self, point, null=numpy.nan)
Interpolate the field at *point* using trilinear interpolation.
:param point: Coordinates of the point at which to interpolate
the field.
:type point: numpy.ndarray(shape=(3,), dtype=numpy.float)
:param null: Default (null) value to return if point lies
outside the interpolation domain.
:type null: float
:return: Value of the field at *point*.
:rtype: float
"""
cdef constants.REAL_t[3] delta, idx
cdef constants.REAL_t f000, f100, f110, f101, f111, f010, f011, f001
cdef constants.REAL_t f00, f10, f01, f11
cdef constants.REAL_t f0, f1
cdef constants.REAL_t f
cdef Py_ssize_t[3][2] ii
cdef Py_ssize_t i1, i2, i3, iax, di1, di2, di3
for iax in range(3):
if (
(
point[iax] < self.cy_min_coords[iax]
or point[iax] > self.cy_max_coords[iax]
)
and not self.cy_iax_isperiodic[iax]
and not self.cy_iax_isnull[iax]
):
return (null)
idx[iax] = (point[iax] - self.cy_min_coords[iax]) / self.cy_node_intervals[iax]
if self.cy_iax_isnull[iax]:
ii[iax][0] = 0
ii[iax][1] = 0
else:
ii[iax][0] = idx[iax]
ii[iax][1] = (ii[iax][0]+1) % self.npts[iax]
delta[iax] = idx[iax] % 1
f000 = self.cy_values[ii[0][0], ii[1][0], ii[2][0]]
f100 = self.cy_values[ii[0][1], ii[1][0], ii[2][0]]
f110 = self.cy_values[ii[0][1], ii[1][1], ii[2][0]]
f101 = self.cy_values[ii[0][1], ii[1][0], ii[2][1]]
f111 = self.cy_values[ii[0][1], ii[1][1], ii[2][1]]
f010 = self.cy_values[ii[0][0], ii[1][1], ii[2][0]]
f011 = self.cy_values[ii[0][0], ii[1][1], ii[2][1]]
f001 = self.cy_values[ii[0][0], ii[1][0], ii[2][1]]
f00 = f000 + (f100 - f000) * delta[0]
f10 = f010 + (f110 - f010) * delta[0]
f01 = f001 + (f101 - f001) * delta[0]
f11 = f011 + (f111 - f011) * delta[0]
f0 = f00 + (f10 - f00) * delta[1]
f1 = f01 + (f11 - f01) * delta[1]
f = f0 + (f1 - f0) * delta[2]
return (f)
cpdef VectorField3D _gradient_of_cartesian(ScalarField3D self):
"""
The gradient of a field represented on a Cartesian grid.
"""
gg = np.gradient(
self.values,
*[
self.node_intervals[iax]
for iax in range(3) if not self.iax_isnull[iax]
],
axis=[
iax
for iax in range(3)
if not self.iax_isnull[iax]
]
)
gg = np.moveaxis(np.stack(gg), 0, -1)
for iax in range(3):
if self.iax_isnull[iax]:
gg = np.insert(gg, iax, np.zeros(self.npts), axis=-1)
grad = VectorField3D(coord_sys=self.coord_sys)
grad.min_coords = self.min_coords
grad.node_intervals = self.node_intervals
grad.npts = self.npts
grad.values = gg
return (grad)
cpdef VectorField3D _gradient_of_spherical(ScalarField3D self):
"""
The gradient of a field represented on a spherical grid.
"""
grid = self.nodes
d0, d1, d2 = self.node_intervals
n0, n1, n2 = self.npts
if not self.iax_isnull[0]:
# Second-order forward difference evaluated along the lower edge
g0_lower = (
(
self.values[2]
- 4*self.values[1]
+ 3*self.values[0]
) / (2*d0)
).reshape(1, n1, n2)
# Second order central difference evaluated in the interior
g0_interior = (self.values[2:] - self.values[:-2]) / (2*d0)
# Second order backward difference evaluated along the upper edge
g0_upper = (
(
self.values[-3]
- 4*self.values[-2]
+ 3*self.values[-1]
) / (2*d0)
).reshape(1, n1, n2)
g0 = np.concatenate([g0_lower, g0_interior, g0_upper], axis=0)
else:
g0 = np.zeros((n0, n1, n2))
if not self.iax_isnull[1]:
# Second-order forward difference evaluated along the lower edge
g1_lower = (
(
self.values[:,2]
- 4*self.values[:,1]
+ 3*self.values[:,0]
) / (2*grid[:,0,:,0]*d1)
).reshape(n0, 1, n2)
# Second order central difference evaluated in the interior
g1_interior = (
self.values[:,2:]
- self.values[:,:-2]
) / (2*grid[:,1:-1,:,0]*d1)
# Second order backward difference evaluated along the upper edge
g1_upper = (
(
self.values[:,-3]
- 4*self.values[:,-2]
+ 3*self.values[:,-1]
) / (2*grid[:,-1,:,0]*d1)
).reshape(n0, 1, n2)
g1 = np.concatenate([g1_lower, g1_interior, g1_upper], axis=1)
else:
g1 = np.zeros((n0, n1, n2))
if not self.iax_isnull[2]:
# Second-order forward difference evaluated along the lower edge
g2_lower = (
(
self.values[:,:,2]
- 4*self.values[:,:,1]
+ 3*self.values[:,:,0]
) / (2*grid[:,:,0,0]*np.sin(grid[:,:,0,1])*d2)
).reshape(n0, n1, 1)
# Second order central difference evaluated in the interior
g2_interior = (
self.values[:,:,2:]
- self.values[:,:,:-2]
) / (2*grid[:,:,1:-1,0]*np.sin(grid[:,:,1:-1,1])*d2)
# Second order backward difference evaluated along the upper edge
g2_upper = (
(
self.values[:,:,-3]
- 4*self.values[:,:,-2]
+ 3*self.values[:,:,-1]
) / (2*grid[:,:,-1,0]*np.sin(grid[:,:,-1,1])*d2)
).reshape(n0, n1, 1)
g2 = np.concatenate([g2_lower, g2_interior, g2_upper], axis=2)
else:
g2 = np.zeros((n0, n1, n2))
gg = np.moveaxis(np.stack([g0, g1, g2]), 0, -1)
grad = VectorField3D(coord_sys=self.coord_sys)
grad.min_coords = self.min_coords
grad.node_intervals = self.node_intervals
grad.npts = self.npts
grad.values = gg
return (grad)
cdef class VectorField3D(Field3D):
"""
Class for representing 3D vector fields.
"""
def __init__(self, coord_sys="cartesian"):
super(VectorField3D, self).__init__(coord_sys=coord_sys)
self.cy_field_type = "vector"
@property
def values(self):
"""
[*Read/Write*, numpy.ndarray(shape=(N0,N1,N2,3), dtype=numpy.float)]
Value of the field at each grid node.
"""
try:
return (np.asarray(self.cy_values))
except AttributeError:
self.cy_values = np.full(self.npts, fill_value=np.nan)
return (np.asarray(self.cy_values))
@values.setter
def values(self, value):
values = np.asarray(value)
if not np.all(values.shape[:3] == self.npts):
raise (ValueError("Shape of values does not match npts attribute."))
self.cy_values = np.asarray(value)
cpdef np.ndarray[constants.REAL_t, ndim=1] value(VectorField3D self, constants.REAL_t[:] point):
"""
value(self, point)
Interpolate the field at *point* using trilinear interpolation.
:param point: Coordinates of the point at which to interpolate
the field.
:type point: numpy.ndarray(shape=(3,), dtype=numpy.float)
:return: Value of the field at *point*.
:rtype: numpy.ndarray(shape=(3,), dtype=numpy.float)
"""
cdef constants.REAL_t[3] ff
cdef constants.REAL_t[3] delta, idx
cdef constants.REAL_t f000, f100, f110, f101, f111, f010, f011, f001
cdef constants.REAL_t f00, f10, f01, f11
cdef constants.REAL_t f0, f1
cdef constants.REAL_t f
cdef Py_ssize_t[3][2] ii
cdef Py_ssize_t i1, i2, i3, iax, di1, di2, di3
for iax in range(3):
#if (
# (
# point[iax] < self.min_coords[iax]
# or point[iax] > self.max_coords[iax]
# )
# and not self.is_periodic[iax]
# and not self._iax_isnull[iax]
#):
# raise(
# OutOfBoundsError(
# f"Point outside of interpolation domain requested: ({point[0]}, {point[1]}, {point[2]})"
# )
# )
idx[iax] = (point[iax] - self.min_coords[iax]) / self.node_intervals[iax]
if self.iax_isnull[iax]:
ii[iax][0] = 0
ii[iax][1] = 0
else:
ii[iax][0] = idx[iax]
ii[iax][1] = (ii[iax][0]+1) % self.npts[iax]
delta[iax] = idx[iax] % 1
for iax in range(3):
f000 = self.cy_values[ii[0][0], ii[1][0], ii[2][0], iax]
f100 = self.cy_values[ii[0][1], ii[1][0], ii[2][0], iax]
f110 = self.cy_values[ii[0][1], ii[1][1], ii[2][0], iax]
f101 = self.cy_values[ii[0][1], ii[1][0], ii[2][1], iax]
f111 = self.cy_values[ii[0][1], ii[1][1], ii[2][1], iax]
f010 = self.cy_values[ii[0][0], ii[1][1], ii[2][0], iax]
f011 = self.cy_values[ii[0][0], ii[1][1], ii[2][1], iax]
f001 = self.cy_values[ii[0][0], ii[1][0], ii[2][1], iax]
f00 = f000 + (f100 - f000) * delta[0]
f10 = f010 + (f110 - f010) * delta[0]
f01 = f001 + (f101 - f001) * delta[0]
f11 = f011 + (f111 - f011) * delta[0]
f0 = f00 + (f10 - f00) * delta[1]
f1 = f01 + (f11 - f01) * delta[1]
f = f0 + (f1 - f0) * delta[2]
ff[iax] = f
return (np.asarray(ff))
cpdef Field3D load(str path):
"""
.. deprecated:: 0.3.2
Use :func:`read_hdf` instead.
Load field data from disk.
:param path: Path to input file.
:type path: str
:return: A Field3D-derivative class initialized with data in *path*.
:rtype: ScalarField3D or VectorField3D
"""
warning_message = "The load() function is deprecated and will be removed"\
" from future versions of PyKonal. Use pykonal.fields.read_hdf()"\
" instead."
warnings.warn(warning_message, DeprecationWarning)
with np.load(path) as npz:
coord_sys = str(npz["coord_sys"][0])
if len(npz["values"].shape) == 4:
field = VectorField3D(coord_sys=coord_sys)
else:
field = ScalarField3D(coord_sys=coord_sys)
field.min_coords = npz["min_coords"]
field.node_intervals = npz["node_intervals"]
field.npts = npz["npts"]
field.values = npz["values"]
return (field)
def read_hdf(path, min_coords=None, max_coords=None):
"""
Load field data from HDF5 file on disk.
:param path: Path to input file.
:type path: str
:param min_coords: Minimum bounding coordinates to read. This is for
reading a limited portion of the file and should
be given in the same coordinates as self.coord_sys.
:type min_coords: numpy.ndarray(shape=(3,), dtype=numpy.float), optional
:param max_coords: Maximum bounding coordinates to read. This is for
reading a limited portion of the file and should
be given in the same coordinates as self.coord_sys.
:type max_coords: numpy.ndarray(shape=(3,), dtype=numpy.float), optional
:return: A Field3D-derivative class initialized with data in *path*.
:rtype: ScalarField3D or VectorField3D
"""
with h5py.File(path, mode="r") as f5:
_coord_sys = f5.attrs["coord_sys"]
_field_type = f5.attrs["field_type"]
_min_coords = f5["min_coords"][:]
_node_intervals = f5["node_intervals"][:]
_npts = f5["npts"][:]
if min_coords is not None:
min_coords = np.array(min_coords)
if max_coords is not None:
max_coords = np.array(max_coords)
if min_coords is not None and max_coords is not None:
if np.any(min_coords >= max_coords):
raise(ValueError("All values of min_coords must satisfy min_coords < max_coords."))
if min_coords is not None:
idx_start = (min_coords - _min_coords) / _node_intervals
idx_start = np.floor(idx_start)
idx_start = idx_start.astype(np.int32)
idx_start = np.clip(idx_start, 0, _npts - 1)
else:
idx_start = np.array([0, 0, 0])
if max_coords is not None:
idx_end = (max_coords - _min_coords) / _node_intervals
idx_end = np.ceil(idx_end) + 1
idx_end = idx_end.astype(np.int32)
idx_end = np.clip(idx_end, idx_start + 1, _npts)
else:
idx_end = _npts
if _field_type == "scalar":
field = ScalarField3D(coord_sys=_coord_sys)
elif _field_type == "vector":
field = VectorField3D(coord_sys=_coord_sys)
else:
raise (ValueError(f"Unrecognized field type: {_field_type}"))
field.min_coords = _min_coords + idx_start * _node_intervals
field.node_intervals = _node_intervals
field.npts = idx_end - idx_start
idxs = tuple(slice(idx_start[idx], idx_end[idx]) for idx in range(3))
field.values = f5["values"][idxs]
return (field)
================================================
FILE: pykonal/heapq.pxd
================================================
# distutils: language=c++
from libcpp.vector cimport vector as cpp_vector
from . cimport constants
cdef struct Index3D:
Py_ssize_t i1, i2, i3
cdef class Heap(object):
cdef cpp_vector[Index3D] cy_keys
cdef constants.REAL_t[:,:,:] cy_values
cdef Py_ssize_t[:,:,:] cy_heap_index
cpdef (Py_ssize_t, Py_ssize_t, Py_ssize_t) pop(Heap self)
cpdef constants.BOOL_t push(Heap self, Py_ssize_t i1, Py_ssize_t i2, Py_ssize_t i3)
cpdef constants.BOOL_t sift_down(Heap self, Py_ssize_t j_start, Py_ssize_t j)
cpdef constants.BOOL_t sift_up(Heap self, Py_ssize_t j_start)
================================================
FILE: pykonal/heapq.pyx
================================================
"""
A module providing heap-sort functionality.
This module provides a single class (:class:`pykonal.heapq.Heap`) which
implements a binary min-heap structure whose elements are indices
(each having three components) sorted by an auxiliary value. Indices can
be pushed onto and popped from the Heap and the auxiliary sort values
can be updated on the fly, although care must be taken to resort the
Heap so as to maintain the heap invariant when updating the underlying
sort values.
"""
# Imports.
import numpy as np
from . import constants
# C Imports.
cimport numpy as np
from . cimport constants
cdef struct Index3D:
Py_ssize_t i1, i2, i3
cdef class Heap(object):
"""
Binary Min-Heap structure for storing indices sorted by auxiliary
values.
"""
def __init__(self, values):
self.cy_values = values
self.cy_heap_index = np.full(values.shape, fill_value=-1)
@property
def heap_index(self):
"""
[*Read only*, numpy.ndarray(shape=(N0,N1,N2), dtype=numpy.int)]
Array of indices indicating the heap position of each node.
Index -1 indicates that a node is not on the heap.
"""
return (np.asarray(self.cy_heap_index, dtype=constants.DTYPE_INT))
@property
def keys(self):
"""
[*Read only*, list] Sorted list of node
indices currently on the heap.
"""
cdef Index3D idx
output = []
for i in range(self.cy_keys.size()):
idx = self.cy_keys[i]
output.append((idx.i1, idx.i2, idx.i3))
return (output)
@property
def size(self):
"""
[*Read only*, int] Number of node indices on the heap.
"""
return (self.cy_keys.size())
@property
def values(self):
"""
[*Read/Write*, numpy.ndarray(shape=(N0,N1,N2), dtype=numpy.float)]
Auxiliary values to sort by.
"""
return (np.asarray(self.cy_values))
@values.setter
def values(self, values):
self.cy_values = values
cpdef (Py_ssize_t, Py_ssize_t, Py_ssize_t) pop(Heap self):
"""
pop(self)
Pop the index of the item with the smallest sort value from the
heap, maintaining the heap invariant.
:return: Index of node on the heap with smallest sort value.
:rtype: tuple(int, int, int)
"""
cdef Index3D last, idx_return
last = self.cy_keys.back()
self.cy_keys.pop_back()
self.cy_heap_index[last.i1, last.i2, last.i3] = -1
if self.cy_keys.size() > 0:
idx_return = self.cy_keys[0]
self.cy_heap_index[idx_return.i1, idx_return.i2, idx_return.i3] = -1
self.cy_keys[0] = last
self.cy_heap_index[last.i1, last.i2, last.i3] = 0
self.sift_up(0)
return ((idx_return.i1, idx_return.i2, idx_return.i3))
return ((last.i1, last.i2, last.i3))
cpdef constants.BOOL_t push(Heap self, Py_ssize_t i1, Py_ssize_t i2, Py_ssize_t i3):
"""
push(self, i1, i2, i3)
Push index onto the heap, maintaining the heap invariant.
:param i1: First component of index.
:type i1: int
:param i2: Second component of index.
:type i2: int
:param i3: Third component of index.
:type i3: int
:return: True upon successful completion.
:rtype: bool
.. todo:: Check that index is in range.
"""
cdef Index3D idx
idx.i1, idx.i2, idx.i3 = i1, i2, i3
self.cy_keys.push_back(idx)
self.cy_heap_index[idx.i1, idx.i2, idx.i3] = self.cy_keys.size()-1
self.sift_down(0, self.cy_keys.size()-1)
return (True)
cpdef constants.BOOL_t sift_down(Heap self, Py_ssize_t j_start, Py_ssize_t j):
"""
sift_down(self, j_start, j)
Sift the heap element at *j* down the heap (towards the root),
without going past *j_start*, until finding a place that it
fits.
:param j_start: Heap index creating a barrier beyond which heap
element *j* cannot be sifted.
:type j_start: int
:param j: Heap index of element to sift towards root.
:type j: int
:return: Returns True upon successful execution.
:rtype: bool
"""
cdef Py_ssize_t j_parent
cdef Index3D idx_new, idx_parent
idx_new = self.cy_keys[j]
# Follow the path to the root, moving parents down until finding a place
# new item fits.
while j > j_start:
j_parent = (j - 1) >> 1
idx_parent = self.cy_keys[j_parent]
if self.cy_values[idx_new.i1, idx_new.i2, idx_new.i3] < self.cy_values[idx_parent.i1, idx_parent.i2, idx_parent.i3]:
self.cy_keys[j] = idx_parent
self.cy_heap_index[idx_parent.i1, idx_parent.i2, idx_parent.i3] = j
j = j_parent
continue
break
self.cy_keys[j] = idx_new
self.cy_heap_index[idx_new.i1, idx_new.i2, idx_new.i3] = j
return (True)
cpdef constants.BOOL_t sift_up(Heap self, Py_ssize_t j_start):
"""
sift_up(self, j_start)
Sift the heap element at *j_start* up the heap (away from the
root), until finding a place that it fits.
:param j_start: Heap index of element to sift away from root.
:type j_start: int
:return: Returns True upon successful execution.
:rtype: bool
"""
cdef Py_ssize_t j, j_child, j_end, j_right
cdef Index3D idx_child, idx_right, idx_new
j_end = self.cy_keys.size()
j = j_start
idx_new = self.cy_keys[j_start]
# Bubble up the smaller child until hitting a leaf.
j_child = 2 * j_start + 1 # leftmost child position
while j_child < j_end:
# Set childpos to index of smaller child.
j_right = j_child + 1
idx_child, idx_right = self.cy_keys[j_child], self.cy_keys[j_right]
if j_right < j_end and not self.cy_values[idx_child.i1, idx_child.i2, idx_child.i3] < self.cy_values[idx_right.i1, idx_right.i2, idx_right.i3]:
j_child = j_right
# Move the smaller child up.
self.cy_keys[j] = self.cy_keys[j_child]
self.cy_heap_index[self.cy_keys[j_child].i1, self.cy_keys[j_child].i2, self.cy_keys[j_child].i3] = j
j = j_child
j_child = 2 * j + 1
# The leaf at pos is empty now. Put newitem there, and bubble it up
# to its final resting place (by sifting its parents down).
self.cy_keys[j] = idx_new
self.cy_heap_index[idx_new.i1, idx_new.i2, idx_new.i3] = j
self.sift_down(j_start, j)
return (True)
================================================
FILE: pykonal/inventory.py
================================================
import h5py
import numpy as np
import os
from . import fields
class TraveltimeInventory(object):
def __init__(self, path, mode="r"):
self._mode = mode
self._path = path
self._f5 = h5py.File(path, mode=mode)
def __del__(self):
self.f5.close()
def __enter__(self):
return (self)
def __exit__(self, exc_type, exc_value, exc_traceback):
self.__del__()
@property
def f5(self):
return (self._f5)
@property
def mode(self):
return (self._mode)
@mode.setter
def mode(self, value):
self._mode = value
self.f5.close()
self._f5 = h5py.File(self.path, mode=value)
@property
def path(self):
return (self._path)
def add(self, field, key):
group = self.f5.create_group(key)
group.attrs["coord_sys"] = field.coord_sys
group.attrs["field_type"] = field.field_type
for attr in ("min_coords", "node_intervals", "npts", "values"):
group.create_dataset(attr, data=getattr(field, attr))
return (True)
def merge(self, paths):
for path in paths:
_, filename = os.path.split(path)
filename, file_ext = os.path.splitext(filename)
network, station, phase = filename.split(".")
field = fields.read_hdf(path)
self.add(field, "/".join([network, station, phase]))
return (True)
def read(self, key, min_coords=None, max_coords=None):
group = self.f5[key]
_coord_sys = group.attrs["coord_sys"]
_field_type = group.attrs["field_type"]
_min_coords = group["min_coords"][:]
_node_intervals = group["node_intervals"][:]
_npts = group["npts"][:]
if min_coords is not None:
min_coords = np.array(min_coords)
if max_coords is not None:
max_coords = np.array(max_coords)
if min_coords is not None and max_coords is not None:
if np.any(min_coords >= max_coords):
raise(ValueError("All values of min_coords must satisfy min_coords < max_coords."))
if min_coords is not None:
idx_start = (min_coords - _min_coords) / _node_intervals
idx_start = np.floor(idx_start)
idx_start = idx_start.astype(np.int32)
idx_start = np.clip(idx_start, 0, _npts - 1)
else:
idx_start = np.array([0, 0, 0])
if max_coords is not None:
idx_end = (max_coords - _min_coords) / _node_intervals
idx_end = np.ceil(idx_end) + 1
idx_end = idx_end.astype(np.int32)
idx_end = np.clip(idx_end, idx_start + 1, _npts)
else:
idx_end = _npts
if _field_type == "scalar":
field = fields.ScalarField3D(coord_sys=_coord_sys)
elif _field_type == "vector":
field = fields.VectorField3D(coord_sys=_coord_sys)
else:
raise (ValueError(f"Unrecognized field type: {_field_type}"))
field.min_coords = _min_coords + idx_start * _node_intervals
field.node_intervals = _node_intervals
field.npts = idx_end - idx_start
idxs = tuple(slice(idx_start[idx], idx_end[idx]) for idx in range(3))
field.values = group["values"][idxs]
return (field)
================================================
FILE: pykonal/locate.pxd
================================================
cimport numpy as np
from . cimport constants
from . cimport fields
cdef class EQLocator(object):
cdef str cy_coord_sys
cdef dict cy_stations
cdef object cy_traveltime_inventory
cdef fields.ScalarField3D cy_grid
cdef fields.ScalarField3D cy_pwave_velocity
cdef fields.ScalarField3D cy_swave_velocity
cdef dict cy_arrivals
cdef dict cy_traveltimes
cdef dict cy_residual_rvs
cpdef constants.BOOL_t add_arrivals(EQLocator self, dict arrivals)
cpdef constants.BOOL_t add_residual_rvs(EQLocator self, dict residua_rvs)
cpdef constants.BOOL_t clear_arrivals(EQLocator self)
cpdef constants.BOOL_t clear_residual_rvs(EQLocator self)
cpdef constants.BOOL_t read_traveltimes(
EQLocator self,
constants.REAL_t[:] min_coords=*,
constants.REAL_t[:] max_coords=*
)
cpdef constants.REAL_t log_likelihood(
EQLocator self,
constants.REAL_t[:] model
)
#cpdef np.ndarray[constants.REAL_t, ndim=1] grid_search(EQLocator self)
cpdef constants.REAL_t rms(EQLocator self, constants.REAL_t[:] hypocenter)
cpdef np.ndarray[constants.REAL_t, ndim=1] locate(
EQLocator self,
np.ndarray[constants.REAL_t, ndim=1] initial,
np.ndarray[constants.REAL_t, ndim=1] delta
)
================================================
FILE: pykonal/locate.pyx
================================================
# Cython compiler directives.
# distutils: language=c++
# cython: profile=True
import numpy as np
import os
import pykonal
import scipy.optimize
import tempfile
from . import constants as _constants
from . import inventory as _inventory
from . import solver as _solver
from . import transformations as _transformations
cimport numpy as np
from libc.math cimport sqrt
from . cimport fields
from . cimport constants
inf = np.inf
cdef class EQLocator(object):
"""
EQLocator(stations, tt_inv, coord_sys='spherical')
A class to locate earthquakes.
"""
def __init__(
self,
traveltime_inventory: str,
coord_sys: str="spherical"
):
self.cy_arrivals = {}
self.cy_traveltimes = {}
self.cy_coord_sys = coord_sys
inventory = _inventory.TraveltimeInventory(
traveltime_inventory,
mode="r"
)
self.cy_traveltime_inventory = inventory
def __del__(self):
self.traveltime_inventory.f5.close()
def __enter__(self):
return (self)
def __exit__(self, exc_type, exc_value, exc_traceback):
self.__del__()
cpdef constants.BOOL_t add_arrivals(EQLocator self, dict arrivals):
self.cy_arrivals = {**self.cy_arrivals, **arrivals}
return (True)
cpdef constants.BOOL_t add_residual_rvs(EQLocator self, dict residual_rvs):
self.cy_residual_rvs = {**self.cy_residual_rvs, **residual_rvs}
return (True)
cpdef constants.BOOL_t clear_arrivals(EQLocator self):
self.cy_arrivals = {}
return (True)
cpdef constants.BOOL_t clear_residual_rvs(EQLocator self):
self.cy_residual_rvs = {}
return (True)
@property
def arrivals(self) -> dict:
return (self.cy_arrivals)
@arrivals.setter
def arrivals(self, value: dict):
self.cy_arrivals = value
@property
def coord_sys(self) -> str:
return (self.cy_coord_sys)
@property
def grid(self) -> object:
if self.cy_grid is None:
self.cy_grid = fields.ScalarField3D(coord_sys=self.cy_coord_sys)
return (self.cy_grid)
@property
def traveltime_inventory(self) -> object:
return (self.cy_traveltime_inventory)
@property
def pwave_velocity(self) -> object:
if self.cy_pwave_velocity is None:
self.cy_pwave_velocity = fields.ScalarField3D(
coord_sys=self.cy_coord_sys
)
self.cy_pwave_velocity.min_coords = self.cy_grid.min_coords
self.cy_pwave_velocity.node_intervals = self.cy_grid.node_intervals
self.cy_pwave_velocity.npts = self.cy_grid.npts
return (self.cy_pwave_velocity)
@pwave_velocity.setter
def pwave_velocity(self, value: np.ndarray):
if self.cy_pwave_velocity is None:
self.pwave_velocity
self.cy_pwave_velocity.values = value
@property
def vp(self) -> object:
return (self.pwave_velocity)
@vp.setter
def vp(self, value: np.ndarray):
self.pwave_velocity = value
@property
def residual_rvs(self) -> dict:
return (self.cy_residual_rvs)
@residual_rvs.setter
def residual_rvs(self, value: dict):
self.cy_residual_rvs = value
@property
def swave_velocity(self) -> object:
if self.cy_swave_velocity is None:
self.cy_swave_velocity = fields.ScalarField3D(
coord_sys=self.cy_coord_sys
)
self.cy_swave_velocity.min_coords = self.cy_grid.min_coords
self.cy_swave_velocity.node_intervals = self.cy_grid.node_intervals
self.cy_swave_velocity.npts = self.cy_grid.npts
return (self.cy_swave_velocity)
@swave_velocity.setter
def swave_velocity(self, value: np.ndarray):
if self.cy_swave_velocity is None:
self.swave_velocity
self.cy_swave_velocity.values = value
@property
def traveltimes(self) -> dict:
return (self.cy_traveltimes)
@traveltimes.setter
def traveltimes(self, value: dict):
self.cy_traveltimes = value
@property
def vs(self) -> object:
return (self.swave_velocity)
@vs.setter
def vs(self, value: np.ndarray):
self.swave_velocity = value
cpdef constants.BOOL_t read_traveltimes(
EQLocator self,
constants.REAL_t[:] min_coords=None,
constants.REAL_t[:] max_coords=None
):
inventory = self.cy_traveltime_inventory
self.cy_traveltimes = {
index: inventory.read(
"/".join(index),
min_coords=min_coords,
max_coords=max_coords
) for index in self.cy_arrivals
}
return True
#cpdef np.ndarray[constants.REAL_t, ndim=1] grid_search(EQLocator self):
# values = [self.cy_arrivals[key]-np.ma.masked_invalid(self.cy_traveltimes[key].values) for key in self.cy_traveltimes]
# values = np.stack(values)
# std = values.std(axis=0)
# arg_min = np.argmin(std)
# idx_min = np.unravel_index(arg_min, std.shape)
# coords = self.cy_grid.nodes[idx_min]
# time = values.mean(axis=0)[idx_min]
# return (np.array([*coords, time], dtype=_constants.DTYPE_REAL))
#
cpdef constants.REAL_t rms(EQLocator self, constants.REAL_t[:] hypocenter):
cdef tuple key
cdef dict arrivals
cdef dict traveltimes
cdef constants.REAL_t csum = 0
cdef constants.REAL_t num
cdef constants.REAL_t t0
arrivals = self.cy_arrivals
traveltimes = self.cy_traveltimes
t0 = hypocenter[3]
for key in arrivals:
num = arrivals[key] - t0
num -= traveltimes[key].value(hypocenter[:3], null=np.inf)
csum += num * num
return (sqrt(csum/len(arrivals)))
cpdef np.ndarray[constants.REAL_t, ndim=1] locate(
EQLocator self,
np.ndarray[constants.REAL_t, ndim=1] initial,
np.ndarray[constants.REAL_t, ndim=1] delta
):
"""
Locate event using a grid search and Differential Evolution
Optimization to minimize the residual RMS.
"""
min_coords = initial - delta
max_coords = initial + delta
bounds = np.stack([min_coords, max_coords]).T
self.read_traveltimes(
min_coords=min_coords[:3],
max_coords=max_coords[:3]
)
soln = scipy.optimize.differential_evolution(self.rms, bounds)
return (soln.x)
cpdef constants.REAL_t log_likelihood(
EQLocator self,
constants.REAL_t[:] model
):
cdef constants.REAL_t t_pred, residual
cdef constants.REAL_t log_likelihood = 0.0
cdef tuple key
cdef EQLocator[:] junk
for key in self.cy_arrivals:
t_pred = model[3] + self.cy_traveltimes[key].value(model[:3])
residual = self.cy_arrivals[key] - t_pred
log_likelihood = log_likelihood + self.cy_residual_rvs[key].logpdf(residual)
return (log_likelihood)
================================================
FILE: pykonal/solver.pxd
================================================
cimport numpy as np
from libcpp cimport bool as bool_t
from . cimport constants
from . cimport fields
from . cimport heapq
cdef class EikonalSolver(object):
cdef str cy_coord_sys
cdef fields.ScalarField3D cy_velocity
cdef fields.ScalarField3D cy_traveltime
cdef heapq.Heap cy_trial
cdef constants.BOOL_t[:,:,:] cy_known
cdef constants.BOOL_t[:,:,:] cy_unknown
cdef constants.REAL_t[:,:,:,:] cy_norm
cdef constants.UINT_t[3] cy_is_periodic
cpdef constants.BOOL_t solve(EikonalSolver self)
cpdef np.ndarray[constants.REAL_t, ndim=2] trace_ray(
EikonalSolver self,
constants.REAL_t[:] end
)
================================================
FILE: pykonal/solver.pyx
================================================
# Cython compiler directives.
# distutils: language=c++
"""
The core module of PyKonal for solving the Eikonal equation.
This module provides the core class (:class:`pykonal.solver.EikonalSolver`),
which gets imported into the root-level namespace and can thus be
instantiated as below:
.. code-block:: python
import pykonal
solver = pykonal.EikonalSolver(coord_sys="cartesian")
An additional convenience class (:class:`pykonal.solver.PointSourceSolver`)
is made available in this module.
"""
import warnings
# Python thrid-party imports.
import numpy as np
# Local imports.
from . import constants
# Cython built-in imports.
cimport cython
from libc.math cimport sqrt, sin
from libcpp.vector cimport vector as cpp_vector
from libc.stdlib cimport malloc, free
# Cython third-party imports.
cimport numpy as np
# Cython local imports.
from . cimport constants
from . cimport fields
from . cimport heapq
cdef class EikonalSolver(object):
"""
The core class of PyKonal for solving the Eikonal equation.
.. code-block:: python
import numpy as np
import pykonal
# Instantiate EikonalSolver object.
solver = pykonal.solver.EikonalSolver(coord_sys="cartesian")
# Initialize EikonalSolver object's velocity attribute.
solver.velocity.min_coords = 0, 0, 0
solver.velocity.node_intervals = 1, 1, 1
solver.velocity.npts = 16, 16, 16
solver.velocity.values = np.ones(solver.velocity.npts)
# Initialize the traveltime field with a source at node with
# index (0, 0, 0).
src_idx = (0, 0, 0)
# Remove source node from *Unknown*
solver.unknown[src_idx] = False
# Add source node to *Trial*.
solver.trial.push(*src_idx)
# Solve the system.
solver.solve()
# Extract the traveltime values.
tt = solver.traveltime.values
"""
def __init__(self, coord_sys="cartesian"):
self.cy_coord_sys = coord_sys
self.cy_velocity = fields.ScalarField3D(coord_sys=self.coord_sys)
@property
def trial(self):
"""
[*Read/Write*, :class:`pykonal.heapq.Heap`] Heap of node
indices in *Trial*.
"""
if self.cy_trial is None:
self.cy_trial = heapq.Heap(self.traveltime.values)
return (self.cy_trial)
@property
def coord_sys(self):
"""
[*Read only*, str] Coordinate system of solver
{"Cartesian", "spherical"}.
"""
return (self.cy_coord_sys)
@property
def known(self):
"""
[*Read/Write*, numpy.ndarray(shape=(N0,N1,N2), dtype=numpy.bool)] 3D array of booleans
indicating whether nodes are in *Known*.
"""
try:
return (np.asarray(self.cy_known))
except AttributeError:
self.cy_known = np.zeros(self.tt.npts, dtype=constants.DTYPE_BOOL)
return (np.asarray(self.cy_known))
@property
def unknown(self):
"""
[*Read/Write*, numpy.ndarray(shape=(N0,N1,N2), dtype=numpy.bool)] 3D array of booleans
indicating whether nodes are in *Unknown*.
"""
try:
return (np.asarray(self.cy_unknown))
except AttributeError:
self.cy_unknown = np.ones(self.tt.npts, dtype=constants.DTYPE_BOOL)
return (np.asarray(self.cy_unknown))
@property
def norm(self):
"""
.. deprecated:: 0.3.2
Use self.velocity.norm or self.traveltime.velocity instead.
[*Read-only*, numpy.ndarray(shape=(N0,N1,N2,3), dtype=numpy.float)] 4D array of scaling
factors for gradient operator.
"""
warning_message = "This attribute is deprecated and will go away in a "\
"future version of PyKonal. Use pykonal.solver.EikonalSolver"\
".traveltime.norm instead."
warnings.warn(warning_message, DeprecationWarning)
try:
return (np.asarray(self.cy_norm))
except AttributeError:
norm = np.tile(
self.traveltime.node_intervals,
np.append(self.traveltime.npts, 1)
)
if self.coord_sys == 'spherical':
norm[..., 1] *= self.traveltime.nodes[..., 0]
norm[..., 2] *= self.traveltime.nodes[..., 0]
norm[..., 2] *= np.sin(self.traveltime.nodes[..., 1])
self.cy_norm = norm
return (np.asarray(self.cy_norm))
@property
def step_size(self):
"""
.. deprecated:: 0.3.2
Use self.velocity.step_size of self.traveltime.step_size instead.
[*Read only*, float] Step size used for ray tracing.
"""
warning_message = "This attribute is deprecated and will go away in a "\
"future version of PyKonal. Use pykonal.solver.EikonalSolver"\
".traveltime.step_size instead."
warnings.warn(warning_message, DeprecationWarning)
return (self.norm[~np.isclose(self.norm, 0)].min() / 4)
@property
def traveltime(self):
"""
[*Read/Write*, :class:`pykonal.fields.ScalarField3D`] 3D array
of traveltime values.
"""
if self.cy_traveltime is None:
self.cy_traveltime = fields.ScalarField3D(coord_sys=self.coord_sys)
self.cy_traveltime.min_coords = self.velocity.min_coords
self.cy_traveltime.node_intervals = self.velocity.node_intervals
self.cy_traveltime.npts = self.velocity.npts
self.cy_traveltime.values = np.full_like(self.velocity.values, fill_value=np.inf)
return (self.cy_traveltime)
@property
def tt(self):
"""
[*Read/Write*, :class:`pykonal.fields.ScalarField3D`] Alias for
self.traveltime.
"""
return (self.traveltime)
@property
def velocity(self):
"""
[*Read/Write*, :class:`pykonal.fields.ScalarField3D`] 3D array
of velocity values.
"""
return (self.cy_velocity)
@property
def vv(self):
"""
[*Read/Write*, :class:`pykonal.fields.ScalarField3D`] Alias for
self.velocity.
"""
return (self.velocity)
@cython.initializedcheck(False)
cpdef constants.BOOL_t solve(EikonalSolver self):
"""
solve(self)
Solve the Eikonal equation using the FMM.
:return: Returns True upon successful execution.
:rtype: bool
"""
cdef Py_ssize_t i, iax, idrxn, iheap
cdef Py_ssize_t[6][3] nbrs
cdef Py_ssize_t[3] nbr, switch, max_idx, active_idx
cdef Py_ssize_t[2] drxns = [-1, 1]
cdef Py_ssize_t[:,:,:] heap_index
cdef (Py_ssize_t, Py_ssize_t, Py_ssize_t) idx
cdef int count_a = 0
cdef int count_b = 0
cdef int inbr
cdef int[2] order
cdef constants.REAL_t a, b, c, bfd, ffd, new
cdef constants.REAL_t[2] fdu
cdef constants.REAL_t[3] aa, bb, cc
cdef constants.REAL_t[:,:,:] tt, vv
cdef constants.REAL_t[:,:,:,:] norm
cdef constants.BOOL_t[3] iax_isperiodic,
cdef constants.BOOL_t[:,:,:] known, unknown
cdef heapq.Heap trial
for iax in range(3):
max_idx[iax] = self.cy_traveltime.cy_npts[iax]
iax_isperiodic[iax] = self.cy_traveltime.cy_iax_isperiodic[iax]
tt = self.traveltime.values
vv = self.velocity.values
norm = self.velocity.norm
known = self.known
unknown = self.unknown
trial = self.trial
heap_index = trial.cy_heap_index
while trial.cy_keys.size() > 0:
# Let Active be the point in Trial with the smallest
# traveltime value.
idx = trial.pop()
active_idx = [idx[0], idx[1], idx[2]]
known[active_idx[0], active_idx[1], active_idx[2]] = True
# Determine the indices of neighbouring nodes.
inbr = 0
for iax in range(3):
switch = [0, 0, 0]
for idrxn in range(2):
switch[iax] = drxns[idrxn]
for jax in range(3):
if iax_isperiodic[jax]:
nbrs[inbr][jax] = (
active_idx[jax]
+ switch[jax]
+ max_idx[jax]
)\
% max_idx[jax]
else:
nbrs[inbr][jax] = active_idx[jax] + switch[jax]
inbr += 1
# Recompute the traveltime values at all Trial neighbours
# of Active by solving the piecewise quadratic equation.
for i in range(6):
nbr = nbrs[i]
if not stencil(nbr[0], nbr[1], nbr[2], max_idx[0], max_idx[1], max_idx[2]) or known[nbr[0], nbr[1], nbr[2]]:
continue
if vv[nbr[0], nbr[1], nbr[2]] > 0:
for iax in range(3):
switch = [0, 0, 0]
idrxn = 0
if norm[nbr[0], nbr[1], nbr[2], iax] == 0:
aa[iax], bb[iax], cc[iax] = 0, 0, 0
continue
for idrxn in range(2):
switch[iax] = drxns[idrxn]
nbr1_i1 = (nbr[0]+switch[0]+max_idx[0]) % max_idx[0]\
if iax_isperiodic[0] else nbr[0]+switch[0]
nbr1_i2 = (nbr[1]+switch[1]+max_idx[1]) % max_idx[1]\
if iax_isperiodic[1] else nbr[1]+switch[1]
nbr1_i3 = (nbr[2]+switch[2]+max_idx[2]) % max_idx[2]\
if iax_isperiodic[2] else nbr[2]+switch[2]
nbr2_i1 = (nbr[0]+2*switch[0]+max_idx[0]) % max_idx[0]\
if iax_isperiodic[0] else nbr[0]+2*switch[0]
nbr2_i2 = (nbr[1]+2*switch[1]+max_idx[1]) % max_idx[1]\
if iax_isperiodic[1] else nbr[1]+2*switch[1]
nbr2_i3 = (nbr[2]+2*switch[2]+max_idx[2]) % max_idx[2]\
if iax_isperiodic[2] else nbr[2]+2*switch[2]
if (
(
drxns[idrxn] == -1
and (nbr[iax] > 1 or iax_isperiodic[iax])
)
or
(
drxns[idrxn] == 1
and (nbr[iax] < max_idx[iax] - 2 or iax_isperiodic[iax])
)
)\
and known[nbr2_i1, nbr2_i2, nbr2_i3]\
and known[nbr1_i1, nbr1_i2, nbr1_i3]\
and tt[nbr2_i1, nbr2_i2, nbr2_i3] \
<= tt[nbr1_i1, nbr1_i2, nbr1_i3]\
:
order[idrxn] = 2
fdu[idrxn] = drxns[idrxn] * (
- 3 * tt[nbr[0], nbr[1], nbr[2]]
+ 4 * tt[nbr1_i1, nbr1_i2, nbr1_i3]
- tt[nbr2_i1, nbr2_i2, nbr2_i3]
) / (2 * norm[nbr[0], nbr[1], nbr[2], iax])
elif (
(
drxns[idrxn] == -1
and (nbr[iax] > 0 or iax_isperiodic[iax])
)
or (
drxns[idrxn] == 1
and (nbr[iax] < max_idx[iax] - 1 or iax_isperiodic[iax])
)
)\
and known[nbr1_i1, nbr1_i2, nbr1_i3]\
:
order[idrxn] = 1
fdu[idrxn] = drxns[idrxn] * (
tt[nbr1_i1, nbr1_i2, nbr1_i3]
- tt[nbr[0], nbr[1], nbr[2]]
) / norm[nbr[0], nbr[1], nbr[2], iax]
else:
order[idrxn], fdu[idrxn] = 0, 0
if fdu[0] > -fdu[1]:
# Do the update using the backward operator
idrxn, switch[iax] = 0, -1
else:
# Do the update using the forward operator
idrxn, switch[iax] = 1, 1
nbr1_i1 = (nbr[0]+switch[0]+max_idx[0]) % max_idx[0]\
if iax_isperiodic[0] else nbr[0]+switch[0]
nbr1_i2 = (nbr[1]+switch[1]+max_idx[1]) % max_idx[1]\
if iax_isperiodic[1] else nbr[1]+switch[1]
nbr1_i3 = (nbr[2]+switch[2]+max_idx[2]) % max_idx[2]\
if iax_isperiodic[2] else nbr[2]+switch[2]
nbr2_i1 = (nbr[0]+2*switch[0]+max_idx[0]) % max_idx[0]\
if iax_isperiodic[0] else nbr[0]+2*switch[0]
nbr2_i2 = (nbr[1]+2*switch[1]+max_idx[1]) % max_idx[1]\
if iax_isperiodic[1] else nbr[1]+2*switch[1]
nbr2_i3 = (nbr[2]+2*switch[2]+max_idx[2]) % max_idx[2]\
if iax_isperiodic[2] else nbr[2]+2*switch[2]
if order[idrxn] == 2:
aa[iax] = 9 / (4*norm[nbr[0], nbr[1], nbr[2], iax] ** 2)
bb[iax] = (
6 * tt[nbr2_i1, nbr2_i2, nbr2_i3]
- 24 * tt[nbr1_i1, nbr1_i2, nbr1_i3]
) / (4 * norm[nbr[0], nbr[1], nbr[2], iax]**2)
cc[iax] = (
tt[nbr2_i1, nbr2_i2, nbr2_i3]**2
- 8 * tt[nbr2_i1, nbr2_i2, nbr2_i3]
* tt[nbr1_i1, nbr1_i2, nbr1_i3]
+ 16 * tt[nbr1_i1, nbr1_i2, nbr1_i3]**2
) / (4 * norm[nbr[0], nbr[1], nbr[2], iax]**2)
elif order[idrxn] == 1:
aa[iax] = 1 / norm[nbr[0], nbr[1], nbr[2], iax]**2
bb[iax] = -2 * tt[nbr1_i1, nbr1_i2, nbr1_i3]\
/ norm[nbr[0], nbr[1], nbr[2], iax] ** 2
cc[iax] = tt[nbr1_i1, nbr1_i2, nbr1_i3]**2\
/ norm[nbr[0], nbr[1], nbr[2], iax]**2
elif order[idrxn] == 0:
aa[iax], bb[iax], cc[iax] = 0, 0, 0
a = aa[0] + aa[1] + aa[2]
if a == 0:
count_a += 1
continue
b = bb[0] + bb[1] + bb[2]
c = cc[0] + cc[1] + cc[2] - 1/vv[nbr[0], nbr[1], nbr[2]]**2
if b**2 < 4*a*c:
# This is a hack to solve the quadratic equation
# when the discrimnant is negative. This hack
# simply sets the discriminant to zero.
new = -b / (2*a)
count_b += 1
else:
new = (-b + sqrt(b**2 - 4*a*c)) / (2*a)
if new < tt[nbr[0], nbr[1], nbr[2]]:
tt[nbr[0], nbr[1], nbr[2]] = new
# Tag as Trial all neighbours of Active that are not
# Alive. If the neighbour is in Far, remove it from
# that list and add it to Trial.
if unknown[nbr[0], nbr[1], nbr[2]]:
trial.push(nbr[0], nbr[1], nbr[2])
unknown[nbr[0], nbr[1], nbr[2]] = False
else:
trial.sift_down(0, heap_index[nbr[0], nbr[1], nbr[2]])
return (True)
cpdef np.ndarray[constants.REAL_t, ndim=2] trace_ray(
EikonalSolver self,
constants.REAL_t[:] end
):
"""
trace_ray(self, end)
An alias to self.traveltime.trace_ray().
"""
return (self.traveltime.trace_ray(end))
class PointSourceSolver(EikonalSolver):
"""
A convenience class to improve precision when solving the common
problem of a point source.
This class implements a pair of complementary computational grids
to improve precision. A refined spherical grid centered on the
source is used in the near-source region. The user can specify
whether a Cartesian or spherical grid is used in the far field.
The refinement of the near-field grid will be handled automatically
using some reasonable values if the user does not manually configure
it.
"""
def __init__(self, coord_sys="cartesian"):
super(PointSourceSolver, self).__init__(coord_sys=coord_sys)
@property
def dphi(self):
"""
[*Read/Write*, float] Node interval (in radians) along the
azimuthal axis of the refined near-field grid.
"""
if not hasattr(self, "_dphi"):
self._dphi = (2 * np.pi) / self.nphi
return (self._dphi)
@dphi.setter
def dphi(self, value):
self._dphi = value
@property
def drho(self):
"""
[*Read/Write*, float] Node interval (in arbitrary distance units)
along the radial axis of the refined near-field grid.
"""
if not hasattr(self, "_drho"):
if self.coord_sys == "cartesian":
self._drho = self.vv.node_intervals.min() / 8
else:
self._drho = self.vv.node_intervals[0] / 8
return (self._drho)
@drho.setter
def drho(self, value):
self._drho = value
@property
def dtheta(self):
"""
[*Read/Write*, float] Node interval (in radians) along the
polar axis of the refined near-field grid.
"""
if not hasattr(self, "_dtheta"):
self._dtheta = np.pi / (self.ntheta - 1)
return (self._dtheta)
@dtheta.setter
def dtheta(self, value):
self._dtheta = value
@property
def nphi(self):
"""
[*Read*, int] Number of grid nodes along the azimuthal axis
of the refined near-field grid.
"""
if not hasattr(self, "_nphi"):
self._nphi = 64
return (self._nphi)
@property
def nrho(self):
"""
[*Read/Write*, int] Number of grid nodes along the radial axis
of the refined near-field grid.
"""
if not hasattr(self, "_nrho"):
self._nrho = 64
return (self._nrho)
@nrho.setter
def nrho(self, value):
self._nrho = value
@property
def ntheta(self):
"""
[*Read*, int] Number of grid nodes along the polar axis
of the refined near-field grid.
"""
if not hasattr(self, "_ntheta"):
self._ntheta = 33
return (self._ntheta)
@property
def near_field(self):
"""
[*Read/Write*, :class:`EikonalSolver`] Solver for the Eikonal
equation in the near-field region.
"""
if not hasattr(self, "_near_field"):
self._near_field = EikonalSolver(coord_sys="spherical")
return (self._near_field)
@property
def src_loc(self):
"""
[*Read/Write*, (float, float, float)] Location of the point
source in the coordinates of the far-field grid.
"""
return (self._src_loc)
@src_loc.setter
def src_loc(self, value):
self._src_loc = value
def initialize_near_field_grid(self) -> bool:
"""
Initialize the near-field grid.
:return: Returns True upon successful completion.
:rtype: bool
"""
# TODO:: rho0 should be be the smallest non-zero value between
# The distance to the closest node and drho.
self.near_field.vv.min_coords = self.drho, 0, 0
self.near_field.vv.node_intervals = self.drho, self.dtheta, self.dphi
self.near_field.vv.npts = self.nrho, self.ntheta, self.nphi
return (True)
def initialize_near_field_narrow_band(self) -> bool:
"""
Initialize the narrow band of the near-field grid using the
layer of nodes closest to the source.
:return: Returns True upon successful completion.
:rtype: bool
"""
for it in range(self.ntheta):
for ip in range(self.nphi):
idx = (0, it, ip)
vv = self.near_field.vv.values[idx]
if not np.isnan(vv):
self.near_field.tt.values[idx] = self.drho / vv
self.near_field.unknown[idx] = False
self.near_field.trial.push(*idx)
def initialize_far_field_narrow_band(self) -> bool:
"""
Initialize the narrow band of the far-field grid using all the
nodes with finite traveltimes.
:return: Returns True upon successful completion.
:rtype: bool
"""
# Update the FMM state variables.
for idx in np.argwhere(~np.isinf(self.tt.values)):
idx = tuple(idx)
self.unknown[idx] = False
self.trial.push(*idx)
def interpolate_near_field_traveltime_onto_far_field(self) -> bool:
"""
Interpolate the near-field traveltime values onto the far-field
grid.
:return: Returns True upon successful completion.
:rtype: bool
"""
# Transform the coordinates of the far-field grid nodes to the
# near-field coordinate system.
nodes = self.vv.transform_coordinates("spherical", self.src_loc)
# Find the indices of the far-field grid nodes that are inside
# the near-field grid.
bool_idx = True
for iax in range(3):
bool_idx = bool_idx &(
(self.near_field.vv.iax_isnull[iax])
|(self.near_field.vv.iax_isperiodic[iax])
|(
(nodes[...,iax] >= self.near_field.vv.min_coords[iax])
&(nodes[...,iax] <= self.near_field.vv.max_coords[iax])
)
)
idxs = np.nonzero(bool_idx)
# Sample the near-field taveltime field on the far-field nodes.
# Make sure to filter out any NaN values.
tt = self.near_field.tt.resample(nodes[idxs].reshape(-1, 3))
idxs = np.swapaxes(np.stack(idxs), 0, 1)[~np.isnan(tt)]
self.tt.values[(idxs[:,0], idxs[:,1], idxs[:,2])] = tt[~np.isnan(tt)]
def interpolate_far_field_velocity_onto_near_field(self) -> bool:
"""
Interpolate the far-field velocity model onto the near-field
grid.
:return: Returns True upon successful completion.
:rtype: bool
"""
# Compute the coordinates of the origin of the far-field grid
# with respect to the near-field coordinate system.
if self.coord_sys == "cartesian":
r0 = np.sqrt(np.sum(np.square(self.src_loc)))
t0 = np.pi - np.arccos(self.src_loc[2]/ r0)
p0 = (np.arctan2(self.src_loc[1], self.src_loc[0]) + np.pi) % (2 * np.pi)
else:
r0 = self.src_loc[0]
t0 = np.pi - self.src_loc[1]
p0 = (self.src_loc[2] + np.pi) % (2 * np.pi)
origin = (r0, t0, p0)
# Transform the coordinates of the near-field grid nodes to the
# far-field coordinate system.
nodes = self.near_field.vv.transform_coordinates(
self.coord_sys,
origin
)
# Find the indices of the near-field grid nodes that are inside
# the far-field grid.
bool_idx = True
for iax in range(3):
bool_idx = bool_idx &(
(self.vv.iax_isnull[iax])
|(self.vv.iax_isperiodic[iax])
|(
(nodes[...,iax] >= self.vv.min_coords[iax])
&(nodes[...,iax] <= self.vv.max_coords[iax])
)
)
idxs = np.nonzero(bool_idx)
# Sample the far-field velocity model on the near-field nodes.
self.near_field.vv.values[idxs] = self.vv.resample(nodes[idxs].reshape(-1, 3))
return (True)
def solve(self):
"""
Solve the Eikonal equation on the far-field grid using the
refined source grid in the near-field region.
:return: Returns True upon successful completion.
:rtype: bool
"""
# Initialize the near-field grid.
self.initialize_near_field_grid()
# Interpolate far-field velocity model onto near-field nodes.
self.interpolate_far_field_velocity_onto_near_field()
# Initialize the narrow band of the near-field grid.
self.initialize_near_field_narrow_band()
# Propagate the wavefront through the near field.
self.near_field.solve()
# Interpolate the near-field traveltime values onto the far-field
# grid.
self.interpolate_near_field_traveltime_onto_far_field()
# Initialize the narrow band of the far-field grid.
self.initialize_far_field_narrow_band()
# Propagate the wavefront through the far field.
super(PointSourceSolver, self).solve()
return (True)
cdef inline bint stencil(
Py_ssize_t idx0, Py_ssize_t idx1, Py_ssize_t idx2,
Py_ssize_t max_idx0, Py_ssize_t max_idx1, Py_ssize_t max_idx2
):
return (
(idx0 >= 0)
and (idx0 < max_idx0)
and (idx1 >= 0)
and (idx1 < max_idx1)
and (idx2 >= 0)
and (idx2 < max_idx2)
)
================================================
FILE: pykonal/stats.pxd
================================================
from . cimport constants
cdef class NormalDistribution(object):
cdef constants.REAL_t cy_mu
cdef constants.REAL_t cy_sigma
cpdef constants.REAL_t logpdf(NormalDistribution self, constants.REAL_t x)
================================================
FILE: pykonal/stats.pyx
================================================
cimport numpy as np
from . cimport constants
from libc.math cimport log, pi, sqrt
cdef class NormalDistribution(object):
def __init__(self, mu: constants.REAL_t=0, sigma: constants.REAL_t=1):
self.cy_mu = mu
self.cy_sigma = sigma
@property
def mu(self):
return (self.cy_mu)
@property
def sigma(self):
return (self.cy_sigma)
cpdef constants.REAL_t logpdf(NormalDistribution self, constants.REAL_t x):
return (
log(1 / (self.cy_sigma * sqrt(2 * pi)))
- (((x - self.cy_mu) / self.cy_sigma) ** 2) / 2
)
================================================
FILE: pykonal/tests/test_EikonalSolver.py
================================================
import nose
import numpy as np
import os
import pkg_resources
import pykonal
import unittest
class EikonalSolverTestCase(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_uniform_velocity_cartesian(self):
fname = pkg_resources.resource_filename(
'pykonal',
os.path.join(
'pykonal',
'tests',
'data',
'test_EikonalSolver_uniform_velocity_cartesian.npz'
)
)
solver = pykonal.EikonalSolver()
with np.load(fname) as inf:
uu = inf['uu']
solver.vgrid.min_coords = inf['vgrid_min_coords']
solver.vgrid.node_intervals = inf['vgrid_node_intervals']
solver.vgrid.npts = inf['vgrid_npts']
solver.vv = inf['vv']
solver.pgrid.min_coords = inf['pgrid_min_coords']
solver.pgrid.node_intervals = inf['pgrid_node_intervals']
solver.pgrid.npts = inf['pgrid_npts']
solver.add_source(inf['src'])
solver.solve()
np.testing.assert_array_almost_equal(uu, solver.uu)
if __name__ == '__main__':
nose.main()
================================================
FILE: pykonal/tests/test_GridND.py
================================================
import nose
import numpy as np
import pykonal
import unittest
def random_grid():
grid = pykonal.GridND(ndim=3)
grid.min_coords = 100 \
* np.sign(np.random.randint(-1, 1, 3))\
* np.random.rand(grid.ndim)
grid.node_intervals = np.random.rand(3) * 100
grid.npts = np.random.randint(1, 100, 3)
return (grid)
class GridNDTestCase(unittest.TestCase):
def setUp(self):
self.grid = pykonal.GridND(ndim=3)
self.grid.min_coords = -10, 0, 10
self.grid.node_intervals = 0.1, 1, 10.
self.grid.npts = 100, 1, 10
def tearDown(self):
pass
def test_iax_null(self):
self.assertNotIn(0, self.grid.iax_null)
self.assertIn(1, self.grid.iax_null)
self.assertNotIn(2, self.grid.iax_null)
def test_mesh(self):
grid = random_grid()
self.assertTrue(
np.all(
np.min(grid[...], axis=(0, 1, 2)) == grid.min_coords
)
)
self.assertTrue(
np.all(
np.max(grid[...], axis=(0, 1, 2)) == grid.max_coords
)
)
def test_min_coords(self):
for i in range(10):
grid = random_grid()
self.assertEqual(grid.min_coords.dtype, pykonal.DTYPE_REAL)
def test_max_coords(self):
for i in range(10):
grid = random_grid()
self.assertEqual(grid.max_coords.dtype, pykonal.DTYPE_REAL)
self.assertTrue(
np.all(grid.max_coords >= grid.min_coords)
)
def test_node_intervals(self):
for i in range(10):
grid = random_grid()
self.assertEqual(grid.node_intervals.dtype, pykonal.DTYPE_REAL)
def test_npts(self):
for i in range(10):
grid = random_grid()
with self.assertRaises(TypeError):
grid.npts = grid.npts[0]
with self.assertRaises(ValueError):
grid.npts = grid.npts[:2]
self.assertEqual(grid.npts.dtype, pykonal.DTYPE_UINT)
if __name__ == '__main__':
nose.main()
================================================
FILE: pykonal/tests/test_LinearInterpolator3D.py
================================================
import nose
import numpy as np
import pykonal
import unittest
def random_grid():
grid = pykonal.Grid3D()
grid.min_coords = 100 \
* np.sign(np.random.randint(-1, 1, 3))\
* np.random.rand(3)
grid.node_intervals = np.random.rand(3) * 100
grid.npts = np.random.randint(1, 100, 3)
while True:
vv = np.random.randint(1, 1e4) * np.random.rand(*grid.npts)
vv = vv.astype(pykonal.DTYPE_REAL)
if vv.min() > 0:
break
return (grid, vv)
class LinearInterpolator3DTestCase(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_interpolate_2D(self):
grid = pykonal.Grid3D(coord_sys='cartesian')
grid.min_coords = 0, 0, 0
grid.node_intervals = 1, 1, 1
grid.npts = 10, 10, 1
vv = np.random.rand(10, 10, 1)
interpolator = pykonal.LinearInterpolator3D(grid, vv)
interpolator([5, 5, 5])
def test_interpolate(self):
for i in range(10):
grid, vv = random_grid()
vmin, vmax = vv.min(), vv.max()
delta = grid.max_coords - grid.min_coords
ii = pykonal.LinearInterpolator3D(grid, vv)
for j in range(10):
xyz = (grid.min_coords + np.random.rand(3) * delta).astype(pykonal.DTYPE_REAL)
v1, v2 = ii.interpolate(xyz), ii(xyz)
self.assertEqual(v1, v2)
self.assertGreater(v1, vmin)
self.assertLess(v1, vmax)
def test_interpolate_OutOfBoundsError(self):
for i in range(10):
grid, vv = random_grid()
delta = grid.max_coords - grid.min_coords
ii = pykonal.LinearInterpolator3D(grid, vv)
with self.assertRaises(pykonal.OutOfBoundsError):
ii.interpolate(grid.max_coords + 2 * delta)
with self.assertRaises(pykonal.OutOfBoundsError):
ii(grid.max_coords + 2 * delta)
with self.assertRaises(pykonal.OutOfBoundsError):
ii.interpolate(grid.min_coords - 2 * delta)
with self.assertRaises(pykonal.OutOfBoundsError):
ii(grid.min_coords - 2 * delta)
def test_interpolate_error_float(self):
for i in range(10):
grid, vv = random_grid()
delta = grid.max_coords - grid.min_coords
ii = pykonal.LinearInterpolator3D(
grid,
np.full(
vv.shape,
fill_value=pykonal.ERROR_REAL,
dtype=pykonal.DTYPE_REAL
)
)
for j in range(10):
xyz = (grid.min_coords + np.random.rand(3) * delta).astype(pykonal.DTYPE_REAL)
v1, v2 = ii.interpolate(xyz), ii(xyz)
self.assertEqual(v1, v2)
self.assertEqual(v1, pykonal.ERROR_REAL)
def test_interpolate_edge_case_lower(self):
for i in range(10):
grid, vv = random_grid()
ii = pykonal.LinearInterpolator3D(grid, vv)
v1, v2 = ii.interpolate(grid.min_coords), ii(grid.min_coords)
self.assertEqual(v1, v2)
self.assertEqual(v1, vv[0, 0, 0])
def test_interpolate_edge_case_upper(self):
for i in range(10):
grid, vv = random_grid()
ii = pykonal.LinearInterpolator3D(grid, vv)
v1, v2 = ii.interpolate(grid.max_coords), ii(grid.max_coords)
self.assertEqual(v1, v2)
self.assertEqual(v1, vv[-1, -1, -1])
def test_periodic(self):
grid = pykonal.Grid3D(coord_sys='spherical')
grid.min_coords = 1, 0, 0
grid.node_intervals = 1, np.pi/20, np.pi/20
grid.npts = 2, 21, 40
vv = np.cos(grid.nodes[...,2])
interpolator = pykonal.LinearInterpolator3D(grid, vv)
interpolator([1.5, np.pi/2, 39.5*np.pi/20])
if __name__ == '__main__':
nose.main()
================================================
FILE: pykonal/transformations.py
================================================
"""
A module to facilitate coordinate-system transformations.
.. autofunction:: geo2sph(nodes)
.. autofunction:: sph2sph(nodes, origin=(0,0,0))
.. autofunction:: xyz2sph(nodes, origin=(0,0,0))
.. autofunction:: sph2geo(nodes, origin=(0,0,0))
.. autofunction:: sph2xyz(nodes, origin=(0,0,0))
.. autofunction:: xyz2xyz(nodes, origin=(0,0,0))
"""
import numpy as np
from . import constants
def geo2sph(nodes):
"""
Transform geographical coordinates to new spherical coordinates system.
"""
geo = np.array(nodes, dtype=constants.DTYPE_REAL)
sph = np.empty_like(geo)
sph[..., 0] = constants.EARTH_RADIUS - geo[..., 2]
sph[..., 1] = np.pi / 2 - np.radians(geo[..., 0])
sph[..., 2] = np.radians(geo[..., 1])
return (sph)
def sph2sph(nodes, origin=(0,0,0), force_phi_positive=False):
"""
Transform spherical coordinates to new spherical coordinate system.
:param nodes: Coordinates (spherical) to transform.
:type nodes: numpy.ndarray(shape=(...,3), dtype=numpy.float)
:param origin: Coordinates (spherical) of the origin of the new
coordinate system with respect to the old coordinate
system.
:type origin: tuple(float, float, float)
:return: Coordinates in new (spherical) coordinate system.
:rtype: numpy.ndarray(shape=(...,3), dtype=numpy.float)
"""
xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])
yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])
zz = nodes[...,0] * np.cos(nodes[...,1])
x0 = origin[0] * np.sin(origin[1]) * np.cos(origin[2])
y0 = origin[0] * np.sin(origin[1]) * np.sin(origin[2])
z0 = origin[0] * np.cos(origin[1])
xx -= x0
yy -= y0
zz -= z0
xyz = np.moveaxis(np.stack([xx, yy, zz]), 0, -1)
rr = np.sqrt(np.sum(np.square(xyz), axis=-1))
old = np.seterr(divide='ignore', invalid='ignore')
tt = np.arccos(xyz[...,2] / rr)
np.seterr(**old)
pp = np.arctan2(xyz[...,1], xyz[...,0])
pp = np.mod(pp, 2*np.pi)
if force_phi_positive is False:
np.mod(pp, -np.pi, pp, where=pp>np.pi)
rtp = np.moveaxis(np.stack([rr, tt, pp]), 0, -1)
return (rtp)
def xyz2sph(nodes, origin=(0,0,0), force_phi_positive=False):
"""
Transform Cartesian coordinates to new spherical coordinate system.
:param nodes: Coordinates (Cartesian) to transform.
:type nodes: numpy.ndarray(shape=(...,3), dtype=numpy.float)
:param origin: Coordinates (Cartesian) of the origin of the new
coordinate system with respect to the old coordinate
system.
:type origin: tuple(float, float, float)
:return: Coordinates in new (spherical) coordinate system.
:rtype: numpy.ndarray(shape=(...,3), dtype=numpy.float)
"""
xyz = nodes - origin
rr = np.sqrt(np.sum(np.square(xyz), axis=-1))
old = np.seterr(divide='ignore', invalid='ignore')
tt = np.arccos(xyz[...,2] / rr)
np.seterr(**old)
pp = np.arctan2(xyz[...,1], xyz[...,0])
if force_phi_positive:
pp = np.mod(pp, 2*np.pi)
rtp = np.moveaxis(np.stack([rr, tt, pp]), 0, -1)
return (rtp)
def sph2geo(nodes):
"""
Transform spherical coordinates to new geographical coordinate system.
"""
sph = np.array(nodes, dtype=constants.DTYPE_REAL)
geo = np.empty_like(sph)
geo[..., 0] = np.degrees(np.pi / 2 - sph[..., 1])
geo[..., 1] = np.degrees(sph[..., 2])
geo[..., 2] = constants.EARTH_RADIUS - sph[..., 0]
return (geo)
def sph2xyz(nodes, origin=(0,0,0)):
"""
Transform spherical coordinates to new Cartesian coordinate system.
:param nodes: Coordinates (spherical) to transform.
:type nodes: numpy.ndarray(shape=(...,3), dtype=numpy.float)
:param origin: Coordinates (spherical) of the origin of the new
coordinate system with respect to the old coordinate
system.
:type origin: tuple(float, float, float)
:return: Coordinates in new (Cartesian) coordinate system.
:rtype: numpy.ndarray(shape=(...,3), dtype=numpy.float)
"""
xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])
yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])
zz = nodes[...,0] * np.cos(nodes[...,1])
origin = [
origin[0] * np.sin(origin[1]) * np.cos(origin[2]),
origin[0] * np.sin(origin[1]) * np.sin(origin[2]),
origin[0] * np.cos(origin[1])
]
xx -= origin[0]
yy -= origin[1]
zz -= origin[2]
xyz = np.moveaxis(np.stack([xx, yy, zz]), 0, -1)
return (xyz)
def xyz2xyz(nodes, origin=(0,0,0)):
"""
Transform Cartesian coordinates to new Cartesian coordinate system.
:param nodes: Coordinates (Cartesian) to transform.
:type nodes: numpy.ndarray(shape=(...,3), dtype=numpy.float)
:param origin: Coordinates (Cartesian) of the origin of the new
coordinate system with respect to the old coordinate
system.
:type origin: tuple(float, float, float)
:return: Coordinates in new (Cartesian) coordinate system.
:rtype: numpy.ndarray(shape=(...,3), dtype=numpy.float)
"""
return (nodes - origin)
def rotation_matrix(alpha, beta, gamma):
"""
Rotation matrix used to rotate a Set of cartesian coordinates.
The rotation matrix is defined such that coordinates are rotated by
alpha radians about the z-axis, then beta radians about the y'-axis
and then gamma radians about the z''-axis.
:param alpha: Angle to rotate about the z-axis.
:type alpha: float
:param beta: Angle to rotate about the y'-axis.
:type beta: float
:param gamma: Angle to rotate about the z''-axis.
:type gamma: float
:return: Rotation matrix.
:rtype: numpy.ndarray(shape=(3,3), dtype=numpy.float)
"""
aa = np.array(
[
[np.cos(alpha), -np.sin(alpha), 0 ],
[np.sin(alpha), np.cos(alpha), 0 ],
[0, 0, 1 ]
]
)
bb = np.array(
[
[ np.cos(beta), 0, np.sin(beta)],
[ 0, 1, 0 ],
[-np.sin(beta), 0, np.cos(beta)]
]
)
cc = np.array(
[
[np.cos(gamma), -np.sin(gamma), 0 ],
[np.sin(gamma), np.cos(gamma), 0 ],
[0, 0, 1 ]
]
)
return (aa.dot(bb).dot(cc))
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ['setuptools==68.2.2', 'wheel', 'Cython>=0.29.14', 'numpy']
build-backend = 'setuptools.build_meta'
[project]
name = 'pykonal'
version = '0.4.1'
authors = [
{name='Malcolm C. A. White', email='malcolmw@mit.edu'}
]
description = 'Solve the Eikonal equation in 3D Cartesian or spherical coordinates.'
readme = 'README.md'
requires-python = '>= 3.6'
classifiers = [
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Operating System :: MacOS :: MacOS X',
'Operating System :: POSIX :: Linux',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Topic :: Scientific/Engineering :: Physics'
]
[project.urls]
'Homepage' = 'https://github.com/malcolmw/pykonal'
'Bug Tracker' = 'https://github.com/malcolmw/pykonal/issues'
================================================
FILE: scripts/eq_loc/eq_loc.cfg
================================================
[DEFAULT]
traveltime_directory = /home/malcolmw/src/pykonal/scripts/eq_loc/test_data/traveltimes
overwrite_traveltimes = False
pwave_velocity_model = /home/malcolmw/src/pykonal/scripts/eq_loc/test_data/scec_cvmh_3d_p.npz
swave_velocity_model = /home/malcolmw/src/pykonal/scripts/eq_loc/test_data/scec_cvmh_3d_s.npz
[Differential Evolution]
dlat_search = 0.1
dlon_search = 0.1
dz_search = 10
dt_search = 10
================================================
FILE: scripts/eq_loc/eq_loc.py
================================================
"""
An MPI script to locate earthquakes.
.. author: Malcolm C. A. White
"""
import argparse
import configparser
import logging
import mpi4py.MPI as MPI
import numpy as np
import pandas as pd
import pykonal
import signal
PROCESSOR_NAME = MPI.Get_processor_name()
COMM = MPI.COMM_WORLD
WORLD_SIZE = COMM.Get_size()
RANK = COMM.Get_rank()
ROOT_RANK = 0
ID_REQUEST_TAG = 100
ID_TRANSMISSION_TAG = 101
DTYPES = {
"latitude": np.float64,
"longitude": np.float64,
"depth": np.float64,
"time": np.float64,
"residual": np.float64,
"event_id": np.int64
}
geo2sph = pykonal.transformations.geo2sph
sph2geo = pykonal.transformations.sph2geo
def parse_args():
"""
Parse and return command line arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"arrivals_file",
type=str,
help="Input file containing phase arrival data."
)
parser.add_argument(
"network_geometry",
type=str,
help="Input file containing network geometry (station locations)."
)
parser.add_argument(
"output_file",
type=str,
help="Output file containing event locations."
)
parser.add_argument(
"-l",
"--log_file",
type=str,
default="eq_loc.log",
help="Log file."
)
parser.add_argument(
"-c",
"--configuration_file",
type=str,
default="eq_loc.cfg",
help="Configuration file."
)
parser.add_argument(
"-v",
"--verbose",
action="store_true",
help="Be verbose."
)
return (parser.parse_args())
def parse_cfg(configuration_file):
"""
Parse and return contents of the configuration file.
"""
cfg = dict()
parser = configparser.ConfigParser()
parser.read(configuration_file)
cfg["traveltime_directory"] = parser.get(
"DEFAULT",
"traveltime_directory",
fallback=None
)
cfg["overwrite_traveltimes"] = parser.getboolean(
"DEFAULT",
"overwrite_traveltimes",
fallback=False
)
cfg["pwave_velocity_model"] = parser.get(
"DEFAULT",
"pwave_velocity_model",
fallback=None
)
cfg["swave_velocity_model"] = parser.get(
"DEFAULT",
"swave_velocity_model",
fallback=None
)
cfg["dlat_search"] = parser.getfloat(
"Differential Evolution",
"dlat_search",
fallback=0.1
)
cfg["dlon_search"] = parser.getfloat(
"Differential Evolution",
"dlon_search",
fallback=0.1
)
cfg["dz_search"] = parser.getfloat(
"Differential Evolution",
"dz_search",
fallback=10
)
cfg["dt_search"] = parser.getfloat(
"Differential Evolution",
"dt_search",
fallback=10
)
for key in cfg:
if cfg[key] == "None":
cfg[key] = None
return (cfg)
def main(argc, cfg):
"""
This is the main control loop.
"""
logger.debug("Starting thread.")
# Enter Control/Worker loops.
if RANK == ROOT_RANK:
id_distribution_loop(argc, cfg)
events = None
else:
events = event_location_loop(argc, cfg)
# Gather events from all threads.
events = COMM.gather(events, root=ROOT_RANK)
# Write event locations.
if RANK == ROOT_RANK:
events = pd.concat(events, ignore_index=True)
write_events(events, argc.output_file)
logger.info("Thread terminated without error.")
return (True)
def configure_logging(verbose, logfile):
"""
A utility function to configure logging.
"""
if verbose is True:
level = logging.DEBUG
else:
level = logging.INFO
for name in (__name__,):
logger = logging.getLogger(name)
logger.setLevel(level)
if level == logging.DEBUG:
formatter = logging.Formatter(
fmt="%(asctime)s::%(levelname)s::%(funcName)s()::%(lineno)d::"\
"{:s}::{:04d}:: %(message)s".format(PROCESSOR_NAME, RANK),
datefmt="%Y%jT%H:%M:%S"
)
else:
formatter = logging.Formatter(
fmt="%(asctime)s::%(levelname)s::{:s}::{:04d}:: "\
"%(message)s".format(PROCESSOR_NAME, RANK),
datefmt="%Y%jT%H:%M:%S"
)
if logfile is not None:
file_handler = logging.FileHandler(logfile)
file_handler.setLevel(level)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(level)
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
return (True)
def log_errors(func):
"""
A decorator to for error logging.
"""
def wrapped_func(*args, **kwargs):
try:
return (func(*args, **kwargs))
except Exception as exc:
logger.error(
f"{func.__name__}() raised {type(exc)}: {exc}"
)
raise (exc)
return (wrapped_func)
@log_errors
def signal_handler(sig, frame):
"""
A utility function to to handle interrupting signals.
"""
raise (SystemError("Interrupting signal received... aborting"))
@log_errors
def event_location_loop(argc, cfg):
"""
A worker loop to locate events.
"""
# Define columns to output.
columns = ["latitude", "longitude", "depth", "time", "residual", "event_id"]
# Load network geometry from disk.
stations = load_stations(argc.network_geometry)
# Load velocity models.
pwave_velocity = pykonal.fields.load(cfg["pwave_velocity_model"])
swave_velocity = pykonal.fields.load(cfg["swave_velocity_model"])
# Initialize EQLocator object.
locator = pykonal.locate.EQLocator(
station_dict(stations),
tt_dir=cfg["traveltime_directory"]
)
locator.grid.min_coords = pwave_velocity.min_coords
locator.grid.node_intervals = pwave_velocity.node_intervals
locator.grid.npts = pwave_velocity.npts
locator.pwave_velocity = pwave_velocity.values
locator.swave_velocity = swave_velocity.values
# Load arrival data from disk.
arrivals = load_arrivals(argc.arrivals_file)
# Initialize an empty dataframe to hold event locations.
events = pd.DataFrame()
# Give aliases for a few configuration parameters.
dlat = cfg["dlat_search"]
dlon = cfg["dlon_search"]
dz = cfg["dz_search"]
dt = cfg["dt_search"]
while True:
# Request an event
COMM.send(RANK, dest=ROOT_RANK, tag=ID_REQUEST_TAG)
event_id = COMM.recv(source=ROOT_RANK, tag=ID_TRANSMISSION_TAG)
if event_id is None:
logger.debug("Received sentinel.")
return (events)
logger.debug(f"Received ID #{event_id}.")
# Clear arrivals from previous event.
locator.clear_arrivals()
locator.add_arrivals(arrival_dict(arrivals, event_id))
locator.load_traveltimes()
loc = locator.locate(dlat=dlat, dlon=dlon, dz=dz, dt=dt)
# Get residual RMS, reformat result, and append to events
# DataFrame.
rms = locator.rms(loc)
loc[:3] = sph2geo(loc[:3])
event = pd.DataFrame(
[np.concatenate((loc, [rms, event_id]))],
columns=columns
)
events = events.append(event, ignore_index=True)
return (False)
@log_errors
def id_distribution_loop(argc, cfg):
"""
A loop to distribute event IDs to hungry workers.
"""
arrivals = load_arrivals(argc.arrivals_file)
event_ids = arrivals["event_id"].unique()
# Distribute event IDs.
for idx in range(len(event_ids)):
event_id = event_ids[idx]
requesting_rank = COMM.recv(source=MPI.ANY_SOURCE, tag=ID_REQUEST_TAG)
COMM.send(event_id, dest=requesting_rank, tag=ID_TRANSMISSION_TAG)
# Distribute sentinel.
for irank in range(WORLD_SIZE - 1):
requesting_rank = COMM.recv(source=MPI.ANY_SOURCE, tag=ID_REQUEST_TAG)
COMM.send(None, dest=requesting_rank, tag=ID_TRANSMISSION_TAG)
return (True)
@log_errors
def load_arrivals(input_file):
"""
Load and return arrival data from input file.
Returns: pandas.DataFrame object with "network", "station", "phase",
"time", and "event_id" fields.
"""
with pd.HDFStore(input_file, mode="r") as store:
arrivals = store["arrivals"]
arrivals = arrivals[["network", "station", "phase", "time", "event_id"]]
return (arrivals)
@log_errors
def load_stations(input_file):
"""
Load and return network geometry from input file.
Input file must be HDF5 file created using pandas.HDFStore with a
"stations" table that contains "network", "station", "latitude",
"longitude", and "elevation" fields. Units of degrees are assumed
for "latitude" and "longitude", and units of kilometers are assumed
for "elevation".
Returns: pandas.DataFrame object with "network", "station",
"latitude", "longitude", and "depth" fields. Units of "depth" are
kilometers.
"""
with pd.HDFStore(input_file, mode="r") as store:
stations = store["stations"]
stations["depth"] = -stations["elevation"]
stations = stations[
["network", "station", "latitude", "longitude", "depth"]
]
return (stations)
@log_errors
def arrival_dict(dataframe, event_id):
"""
Return a dictionary with phase-arrival data suitable for passing to
the EQLocator.add_arrivals() method.
Returned dictionary has ("station_id", "phase") keys, where
"station_id" = f"{network}.{station}", and values are
phase-arrival timestamps.
"""
dataframe = dataframe.set_index("event_id")
fields = ["network", "station", "phase", "time"]
dataframe = dataframe.loc[event_id, fields]
_arrival_dict = {
(f"{network}.{station}", phase): timestamp
for network, station, phase, timestamp in dataframe.values
}
return (_arrival_dict)
@log_errors
def station_dict(dataframe):
"""
Return a dictionary with network geometry suitable for passing to
the EQLocator constructor.
Returned dictionary has "station_id" keys, where "station_id" =
f"{network}.{station}", and values are spherical coordinates of
station locations.
"""
if np.any(dataframe[["network", "station"]].duplicated()):
raise (IOError("Multiple coordinates supplied for single station(s)"))
dataframe = dataframe.set_index(["network", "station"])
_station_dict = {
f"{network}.{station}": geo2sph(
dataframe.loc[
(network, station),
["latitude", "longitude", "depth"]
].values
) for network, station in dataframe.index
}
return (_station_dict)
@log_errors
def write_events(dataframe, output_file):
"""
Write event locations to HDF5 file via pandas.HDFStore.
"""
logger.debug("Saving event locations to disk.")
# Convert dtypes before saving event locations.
for field in DTYPES:
dataframe[field] = dataframe[field].astype(DTYPES[field])
with pd.HDFStore(output_file, mode="w") as store:
store["events"] = dataframe
return (True)
if __name__ == "__main__":
# Add some signal handlers to abort all threads.
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGCONT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
# Parse command line arguments.
argc = parse_args()
# Load parameters for parameter file.
cfg = parse_cfg(argc.configuration_file)
# Configure logging.
configure_logging(argc.verbose, argc.log_file)
logger = logging.getLogger(__name__)
# Start the main loop.
main(argc, cfg)
================================================
FILE: scripts/mcmc/315_loc.cfg
================================================
[DEFAULT]
mu_P = 0.031
sigma_P = 0.195
mu_S = 0.079
sigma_S = 0.291
confidence_level = 95.
nwalkers = 32
nsamples = 4096
max_samples = 32768
================================================
FILE: scripts/mcmc/315_locate_serial_mcmc.py
================================================
"""
An MPI script to locate earthquakes.
.. author: Malcolm C. A. White
"""
import argparse
import configparser
import emcee
import logging
import multiprocessing as mp
import numpy as np
import pandas as pd
import pathlib
import pykonal
import scipy.special
import signal
import warnings
warnings.filterwarnings("ignore")
DTYPES = {
"latitude": np.float32,
"longitude": np.float32,
"depth": np.float32,
"time": np.float64,
"residual": np.float32,
"event_id": np.int32,
"latitude_mad": np.float32,
"longitude_mad": np.float32,
"depth_mad": np.float32,
"time_mad": np.float64,
}
ARRIVAL_FIELDS_INPUT = [
"network",
"station",
"phase",
"time",
"event_id",
"weight"
]
ARRIVAL_FIELDS_OUTPUT = [*ARRIVAL_FIELDS_INPUT, "residual"]
EVENT_FIELDS_INPUT = ["latitude", "longitude", "depth", "time", "event_id"]
EVENT_FIELDS_OUTPUT = [
*EVENT_FIELDS_INPUT,
"residual",
"latitude_mad",
"longitude_mad",
"depth_mad",
"time_mad",
]
STATION_FIELDS_INPUT = [
"network",
"station",
"latitude",
"longitude",
"elevation"
]
geo2sph = pykonal.transformations.geo2sph
sph2geo = pykonal.transformations.sph2geo
def parse_args():
"""
Parse and return command line arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"catalog",
type=str,
help="Input catalog."
)
parser.add_argument(
"network_geometry",
type=str,
help="Input file containing network geometry (station locations)."
)
parser.add_argument(
"pick_errors",
type=str,
help="Input file containing pick errors."
)
parser.add_argument(
"traveltime_inventory",
type=str,
help="Input traveltime inventory."
)
parser.add_argument(
"output_file",
type=str,
help="Output file containing event locations."
)
parser.add_argument(
"-l",
"--log_file",
type=str,
default="eq_loc.log",
help="Log file."
)
parser.add_argument(
"-c",
"--configuration_file",
type=str,
default="eq_loc.cfg",
help="Configuration file."
)
parser.add_argument(
"-e",
"--enddate",
type=str,
help="End date."
)
parser.add_argument(
"-n",
"--nproc",
type=int,
default=mp.cpu_count(),
help="Number of processors to use."
)
parser.add_argument(
"-s",
"--startdate",
type=str,
help="Start date."
)
parser.add_argument(
"-v",
"--verbose",
action="store_true",
help="Be verbose."
)
argc = parser.parse_args()
if argc.startdate is not None:
argc.startdate = pd.to_datetime(argc.startdate, format="%Y%j")
if argc.enddate is not None:
argc.enddate = pd.to_datetime(argc.enddate, format="%Y%j")
return (argc)
def parse_cfg(configuration_file):
"""
Parse and return contents of the configuration file.
"""
cfg = dict()
parser = configparser.ConfigParser()
parser.read(configuration_file)
cfg["mu_P"] = parser.getfloat(
"DEFAULT",
"mu_P"
)
cfg["sigma_P"] = parser.getfloat(
"DEFAULT",
"sigma_P"
)
cfg["mu_S"] = parser.getfloat(
"DEFAULT",
"mu_S"
)
cfg["sigma_S"] = parser.getfloat(
"DEFAULT",
"sigma_S"
)
cfg["confidence_level"] = parser.getfloat(
"DEFAULT",
"confidence_level",
fallback=90.
)
cfg["nwalkers"] = parser.getint(
"DEFAULT",
"nwalkers",
fallback=32
)
cfg["nsamples"] = parser.getint(
"DEFAULT",
"nsamples",
fallback=1024
)
cfg["max_samples"] = parser.getint(
"DEFAULT",
"max_samples",
fallback=32768
)
for key in cfg:
if cfg[key] == "None":
cfg[key] = None
return (cfg)
def main(argc, cfg):
"""
This is the main control loop.
"""
logger.debug("Starting main().")
# Load catalog from disk.
catalog = load_catalog(
argc.catalog,
starttime=argc.startdate,
endtime=argc.enddate
)
# Load network geometry from disk.
stations = load_stations(argc.network_geometry)
# Load pick errors from disk.
pick_errors = load_pick_errors(argc.pick_errors)
# Locate events
events = locate_events(catalog, stations, pick_errors, argc, cfg)
# Write event locations.
write_events(events, argc.output_file)
logger.info("Finished relocating events.")
return (True)
class VoigtProfile(object):
def __init__(self, mu=0, sigma=1, gamma=1):
self.mu = mu
self.sigma = sigma
self.gamma = gamma
def logpdf(self, x):
return (np.log(scipy.special.voigt_profile(x - self.mu, self.sigma, self.gamma)))
def configure_logging(verbose, logfile):
"""
A utility function to configure logging.
"""
if verbose is True:
level = logging.DEBUG
else:
level = logging.INFO
for name in (__name__,):
logger = logging.getLogger(name)
logger.setLevel(level)
if level == logging.DEBUG:
formatter = logging.Formatter(
fmt="%(asctime)s::%(levelname)s::%(funcName)s()::%(lineno)d::"\
" %(message)s",
datefmt="%Y%jT%H:%M:%S"
)
else:
formatter = logging.Formatter(
fmt="%(asctime)s::%(levelname)s::%(message)s",
datefmt="%Y%jT%H:%M:%S"
)
if logfile is not None:
file_handler = logging.FileHandler(logfile)
file_handler.setLevel(level)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(level)
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
return (True)
def log_errors(func):
"""
A decorator to for error logging.
"""
def wrapped_func(*args, **kwargs):
try:
return (func(*args, **kwargs))
except Exception as exc:
logger.error(
f"{func.__name__}() raised {type(exc)}: {exc}"
)
raise (exc)
return (wrapped_func)
def log_probability(args):
global gl_LOCATOR
rho, theta, phi, time = args
log_likelihood = gl_LOCATOR.log_likelihood(args)
if np.isnan(log_likelihood):
return (-np.inf)
else:
log_prior = np.log(rho**2 + np.sin(theta))
return (log_likelihood + log_prior)
@log_errors
def locate_events(catalog, stations, pick_errors, argc, cfg):
"""
A worker loop to locate events.
"""
global gl_LOCATOR
pick_errors = pick_errors.set_index(["network", "station", "phase"])
residual_rvs = dict()
for network, station, phase in pick_errors.index:
mu, gamma = pick_errors.loc[(network, station, phase), ["loc", "scale"]]
mu = (cfg[f"mu_{phase}"] + mu) / 2
residual_rvs[(network, station, phase)] = VoigtProfile(
mu=mu,
sigma=cfg[f"sigma_{phase}"],
gamma=gamma
)
# Load network geometry from disk.
stations = load_stations(argc.network_geometry)
with pykonal.locate.EQLocator(
station_dict(stations),
argc.traveltime_inventory
) as gl_LOCATOR:
arrivals = catalog["arrivals"]
events = catalog["events"]
events = events.set_index("event_id")
# Initialize an empty list to hold updated event locations.
relocated_events = list()
for event_id, event in events.iterrows():
logger.info(f"Relocating event ID #{event_id}.")
# Clear arrivals from previous event.
gl_LOCATOR.clear_arrivals()
gl_LOCATOR.add_arrivals(arrival_dict(arrivals, event_id))
gl_LOCATOR.residual_rvs = {
(network, station, phase):
residual_rvs[(network, station, phase)]
if (network, station, phase) in residual_rvs
else residual_rvs[("XX", "XX", phase)]
for network, station, phase in gl_LOCATOR.arrivals
}
model0 = geo2sph(event[["latitude", "longitude", "depth"]])
model0 = np.append(model0, event["time"])
pos = model0 + 1e-4 * np.random.randn(cfg["nwalkers"], 4)
nwalkers, ndim = pos.shape
gl_LOCATOR.read_traveltimes()
with mp.Pool(argc.nproc) as pool:
nsamples = cfg["nsamples"]
while True:
sampler = emcee.EnsembleSampler(
nwalkers,
ndim,
log_probability,
pool=pool
)
sampler.run_mcmc(pos, nsamples, progress=True)
try:
discard = int(5 * np.mean(sampler.get_autocorr_time()))
except (emcee.autocorr.AutocorrError, ValueError) as exc:
nsamples *= 2
if nsamples > cfg["max_samples"]:
success = False
break
continue
success = True
break
if success is False:
logger.info(f"Failed to relocate event ID #{event_id}.")
continue
log_prob = sampler.get_log_prob()
chain = sampler.get_chain().copy()
loc = chain[np.unravel_index(np.argmax(log_prob), log_prob.shape)]
rms = gl_LOCATOR.rms(loc)
loc[:3] = sph2geo(loc[:3])
flat_samples = sampler.get_chain(discard=discard, flat=True).copy()
flat_samples[:, :3] = sph2geo(flat_samples[:, :3])
mad = np.mean(np.abs(flat_samples - loc), axis=0)
for i, label in enumerate(("latitude", "longitude", "depth", "time")):
logger.debug(f"{label} = {loc[i]:.3f} +/- {mad[i]:.3f}")
relocated_events.append(
np.concatenate(
(loc, [event_id, rms], mad)
)
)
events = pd.DataFrame(relocated_events, columns=EVENT_FIELDS_OUTPUT)
return (events)
@log_errors
def load_catalog(input_file, starttime=None, endtime=None):
"""
Load and return arrival data from input file.
Returns: pandas.DataFrame object with "network", "station", "phase",
"time", and "event_id" fields.
"""
input_file = pathlib.Path(input_file)
if input_file.suffix in (".h5", ".hdf5"):
catalog = _load_catalog_hdf5(input_file)
else:
raise (NotImplementedError())
# Subset catalog for the time range of interest.
if starttime is not None:
catalog["events"] = catalog["events"][
pd.to_datetime(catalog["events"]["time"]*1e9) > starttime
]
if endtime is not None:
catalog["events"] = catalog["events"][
pd.to_datetime(catalog["events"]["time"]*1e9) < endtime
]
catalog["arrivals"] = catalog["arrivals"][
catalog["arrivals"]["event_id"].isin(
catalog["events"]["event_id"]
)
]
return (catalog)
@log_errors
def _load_catalog_hdf5(input_file):
arrivals = pd.read_hdf(input_file, key="arrivals")
events = pd.read_hdf(input_file, key="events")
arrivals = arrivals[ARRIVAL_FIELDS_INPUT]
events = events[EVENT_FIELDS_INPUT]
catalog = dict(arrivals=arrivals, events=events)
return (catalog)
@log_errors
def load_pick_errors(input_file):
"""
Load and return pick errors from input file.
"""
pick_errors = pd.read_hdf(input_file)
return (pick_errors)
@log_errors
def load_stations(input_file):
"""
Load and return network geometry from input file.
Input file must be HDF5 file created using pandas.HDFStore with a
"stations" table that contains "network", "station", "latitude",
"longitude", and "elevation" fields. Units of degrees are assumed
for "latitude" and "longitude", and units of kilometers are assumed
for "elevation".
Returns: pandas.DataFrame object with "network", "station",
"latitude", "longitude", and "depth" fields. Units of "depth" are
kilometers.
"""
input_file = pathlib.Path(input_file)
if input_file.suffix in (".h5", ".hdf5"):
return (_load_stations_hdf5(input_file))
elif input_file.suffix in (".txt", ".dat"):
return (_load_stations_txt(input_file))
@log_errors
def _load_stations_hdf5(input_file):
with pd.HDFStore(input_file, mode="r") as store:
stations = store["stations"]
stations = stations.drop_duplicates(["network", "station"])
stations = stations[STATION_FIELDS_INPUT]
stations["depth"] = -stations["elevation"]
return (stations)
@log_errors
def _load_stations_txt(input_file):
stations = pd.read_csv(input_file, delim_whitespace=True)
stations = stations[STATION_FIELDS_INPUT]
stations["depth"] = -stations["elevation"]
return (stations)
@log_errors
def arrival_dict(dataframe, event_id):
"""
Return a dictionary with phase-arrival data suitable for passing to
the EQLocator.add_arrivals() method.
Returned dictionary has ("station_id", "phase") keys, where
"station_id" = f"{network}.{station}", and values are
phase-arrival timestamps.
"""
dataframe = dataframe.set_index("event_id")
fields = ["network", "station", "phase", "time"]
dataframe = dataframe.loc[event_id, fields]
_arrival_dict = {
(network, station, phase): timestamp
for network, station, phase, timestamp in dataframe.values
}
return (_arrival_dict)
@log_errors
def station_dict(dataframe):
"""
Return a dictionary with network geometry suitable for passing to
the EQLocator constructor.
Returned dictionary has "station_id" keys, where "station_id" =
f"{network}.{station}", and values are spherical coordinates of
station locations.
"""
if np.any(dataframe[["network", "station"]].duplicated()):
raise (IOError("Multiple coordinates supplied for single station(s)"))
dataframe = dataframe.set_index(["network", "station"])
_station_dict = {
(network, station): geo2sph(
dataframe.loc[
(network, station),
["latitude", "longitude", "depth"]
].values
) for network, station in dataframe.index
}
return (_station_dict)
@log_errors
def write_events(dataframe, output_file):
"""
Write event locations to HDF5 file via pandas.HDFStore.
"""
logger.debug("Saving event locations to disk.")
# Convert dtypes before saving event locations.
for field in DTYPES:
dataframe[field] = dataframe[field].astype(DTYPES[field])
with pd.HDFStore(output_file, mode="w") as store:
store["events"] = dataframe
return (True)
if __name__ == "__main__":
# Parse command line arguments.
argc = parse_args()
# Load parameters for parameter file.
cfg = parse_cfg(argc.configuration_file)
# Configure logging.
configure_logging(argc.verbose, argc.log_file)
logger = logging.getLogger(__name__)
# Start the main loop.
main(argc, cfg)
================================================
FILE: setup.py
================================================
"""
setup.py adapted from https://github.com/kennethreitz/setup.py
"""
import io
import numpy as np
import os
#from distutils.core import setup
from setuptools import setup
from Cython.Build import cythonize
# Package meta-data.
name = "pykonal"
description = "Solve the Eikonal equation in 3D Cartesian or spherical coordinates."
url = "https://github.com/malcolmw/pykonal"
email = "malcolmw@mit.edu"
author = "Malcolm C. A. White"
requires_python = ">=3"
packages = ["pykonal"]
package_data = {
"pykonal": ["data/*", "data/marmousi2/*", "tests/data/*"],
}
required = ["cython>=0.29.14", "h5py", "numpy", "scipy"]
extras = {"tests": ["nose"]}
ext_modules = cythonize(
[
"pykonal/constants.pyx",
"pykonal/fields.pyx",
"pykonal/heapq.pyx",
"pykonal/locate.pyx",
"pykonal/solver.pyx",
"pykonal/stats.pyx"
],
compiler_directives={
"language_level": 3,
"boundscheck": False,
"cdivision": True
}
)
include_dirs = [np.get_include()]
license = "GNU GPLv3"
here = os.path.abspath(os.path.dirname(__file__))
# Import the README and use it as the long-description.
# Note: this will only work if "README.md" is present in your MANIFEST.in file!
try:
raise FileNotFoundError
with io.open(os.path.join(here, "README.md"), encoding="utf-8") as f:
long_description = "\n" + f.read()
except FileNotFoundError:
long_description = description
# Load the package's __version__.py module as a dictionary.
about = {}
project_slug = name.lower().replace("-", "_").replace(" ", "_")
with open(os.path.join(here, project_slug, "__version__.py")) as f:
exec(f.read(), about)
# Where the magic happens:
setup(
name=name,
version=about["__version__"],
description=description,
long_description=long_description,
#long_description_content_type="text/markdown",
author=author,
author_email=email,
python_requires=requires_python,
url=url,
packages=packages,
package_data=package_data,
ext_modules=ext_modules,
include_dirs=include_dirs,
install_requires=required,
extras_require=extras,
license=license,
classifiers=[
'Development Status :: 4 - Beta',
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Operating System :: MacOS :: MacOS X",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: Implementation :: CPython",
"Topic :: Scientific/Engineering :: Physics"
]
)
================================================
FILE: tutorials/tomography/cartesian_tomography_2D.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "11651601-56f2-4015-9984-3a64deb1d0b3",
"metadata": {},
"outputs": [],
"source": [
"import multiprocessing as mp\n",
"import pykonal\n",
"import scipy.spatial\n",
"import tqdm.notebook as tqdm"
]
},
{
"cell_type": "markdown",
"id": "fbf76d92-c03b-4a29-b410-fac113f22c97",
"metadata": {},
"source": [
"# 1. Build synthetic data\n",
"## 1.1. Build true velocity model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a8b543fc-31f7-4a9c-a31b-05875d9a0076",
"metadata": {},
"outputs": [],
"source": [
"vmodel_true = pykonal.fields.ScalarField3D(coord_sys=\"cartesian\")\n",
"vmodel_true.min_coords = 0, -10, 0\n",
"vmodel_true.node_intervals = 5, 5, 1\n",
"vmodel_true.npts = 461, 203, 1\n",
"\n",
"\n",
"vv = pd.read_csv(\"vmodel.txt\")\n",
"vmodel_true.values = np.interp(vmodel_true.nodes[..., 1], vv[\"depth\"], vv[\"vs\"]*1.9)\n",
"# depth = vmodel.nodes[..., 1]\n",
"# vmodel.values = 3700 + (depth-50)\n",
"# # Set v=1500 m/s for the top 50 m\n",
"# vmodel.values[vmodel.nodes[..., 1] <= 50] = 1500"
]
},
{
"cell_type": "markdown",
"id": "dac7bfaf-7f19-4316-a1f5-b0092008cd16",
"metadata": {},
"source": [
"## 1.2. Define source/receiver geometry"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f3757cca-0249-4499-ab00-d22b49282f45",
"metadata": {},
"outputs": [],
"source": [
"nrx = 76 # Number of receivers\n",
"nsx = 36 # Number of sources\n",
"dx = vmodel_true.max_coords[0] - vmodel_true.min_coords[0]\n",
"drx = dx / nrx\n",
"dsx = dx / nsx\n",
"rxs = np.arange(drx/2, vmodel_true.max_coords[0], drx) # Receiver offsets\n",
"sxs = np.arange(dsx/2, vmodel_true.max_coords[0], dsx) # Source offests"
]
},
{
"cell_type": "markdown",
"id": "6b7829b9-7d7b-46e0-9e01-66fc207e1ab0",
"metadata": {},
"source": [
"### Plot geometry"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "968bec5f-de5a-476c-b34c-8e1e967951fd",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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JklSChJ2RSP5PwOeBUds4/kHgqmJ/PJUgpEtbUbap2N+yXOozezokSZJK0jmADRgTEQu6bad2f+6IeBvwZGbe1dNrR8SXgHbgiq6iHk7LXsqlPrOnQ5IkqT49lZmH93L8SOAdEfFWYAQwOiJ+mJknRcTJwNuAY4ohU1DpwZjYrf4E4LGifEIP5VKf2dMhSZJUghxAEnlfEskz88zMnJCZk6gkiP+iCDjmAmcA78jMF7pVuR44MSKGR8RkKgnjd2bmKmBdRMwpZq36AHDdIH8canD2dEiSJJUhoaOcQUrfAYYDNxcz396RmR/JzEURMQ9YTGXY1emZ2VHUOQ24FBgJ3FhsUp9FuhKmJEnSTnfwIUPzup+O6Xf9Ayc+ftd2hldJNcOeDkmSpFIEHT3maEuNx5wOSZIkSVVlT4ckSVIJEuh0lLuahEGHJElSSRxepWZh0CFJklSCxKBDzcOcDkmSJElVZU+HJElSSTrTng41B4MOSZKkEji8Ss3EoEOSJKkESdDhSHc1CYMOSZKkkji8Ss3C8FqSJElSVdnTIUmSVAJzOtRMDDokSZJKEXSkg07UHAw6JEmSSpBApyPd1SQMOiRJkkri8Co1C8NrSZIkSVVlT4ckSVIJMs3pUPMw6JAkSSpJp8Or1CQMOiRJkkpQmTLXng41B+90SZIkSVVlT4ckSVIpzOlQ8zDokCRJKoHrdKiZGHRIkiSVpCNNJFdzMOiQJEkqQRImkqtpeKdLkiRJqqqa7emIIcPe2zJsxEk9HcuO9v2iZcj4zcrIlRHxmMc8tiPHYOv7qbdjtfo+POaxso+B3yWP1fYxetC5cf0Psn3jlT0d21k6TSRXk4jMLLsNPYpo+XDrrntcuOfRHx665bH1f7ifF3+/gD3f+MGt6nnMYztyrNba4zGP1euxWmuPxzy2PX/8xSWbOp5fe1pm5/e2e3KVHHDwrnn2tbP6Xf/Pp9x5V2YePohNkqpmp/d0RMRc4J+BVuCSzDy35zPz8s6NL57TOnrMPiMmzNzsyC7TXsfK//sRWkePwWMeG8ixWmuPxzxWr8dqrT0e81hP92iX9W2L6Nz44h8hL9/mSTtBEiaSq2ns1J6OiGgFHgaOBdqA3wLvzczFPZ/f8uFh+039v+Pe/82tjv3x11fQ+cBPGTZiBLH3ZIbN+XOG7TMZgHX33cQLS/6bfU88e6t66+67iefu+zmj9tiTXLNss7rVqvfCkv9mz6M/xMY7rtis7oZVD2+33r4nns3GJ5dtVnfTXpPZ2LZku/WAzep2DN2VFzuHMu4DW3+ePb2HrrodqxbT3hmMevfZL33GvdXrqvvCz78Na9sY8vJXbXZ9qlEP+n9PvFT3/v9g2Mhdd9r9VI/3Rb3dT1DO74qyrm3Zvyu66j6/KRn/F/+yQ3Xr5b5opvup+/Wpl+/89up1WfWDz+TGxx7+yzJ7OQAmH7xbfv0n/e/p+MDU+X3q6Sj+/7UAWJmZb4uIvYCrgEnAcuA9mfnH4twzgQ8BHcDHM/Omovww4FJgJPBT4BNZq8NlVJN29kDCI4Clmfn7zNwIXAkct+3T8/L21X9gfduizUo3PrmM9ruv5WuvWc8txz/HFybey7NXfo6NTy4DYLdZR7NpzYqt6gEM2/cAWp96mDMn3rtV3WrU223W0Wx8chnP/OgzW9Udtu8BvdbbtGYF6x64hWev/NxmddvvvpaNTy7rtd76tkVsfHLZZnW/PPMxWp96mHUP3NJrva7PuKvuL97TwVdfs55nfvSZlz7jbdXrXvcrMx/jtj8futX1Gex6XXX7e09sfHIZ7Xddw9fmbNpp91M93hf1dj911d3ZvyvKura18Luiq+7Qp5c15H3RTPdTPX7n+1IPKr0cm1Yv7yi7l6NLJy393nbAJ4Al3R5/Abg1M6cAtxaPiYgZwInATGAucGERsABcBJwKTCm2uQN532o+OzvoGA+s6Pa4rSjrUWZuHLqphWdv2/z3wsY7ruDrR8FnXjecV45r5bOvHcZXXw+b5l8BQLQOpaV1OM/cdtlWz7npzqv42zcM4zOvHbZV3WrUi9ahjGQDXz8qtqrb/tureq3X0jqcjb/6HmcdxWZ1v/Z62IUNvdZ75rbL2HjHFVvVPesNw9j031v/YWfL97BV3dcN52tHxUuf8bbq9Vh3i+sz2PW66vb3nqjUjZ16P9XjfVFv91NX3Z39u6Ksa1szvyteO4yz3ji8Ie+LZrqf6vE735d6AM/edjm5acPKzNy01RvZyTKhI1v6vfVFREwA/gy4pFvxcUDXh3cZcHy38iszc0NmLgOWAkdExDhgdGbeXvRuXN6tjtQnOzvo6Gng4lZdcxFxakQsiIgFkHSubtvsrxm5ZhlHT2rdrM6bJrfS+VTlrxnr2xYRzz9HblEPoPOJhzh68pAe61aj3vq2RQzp3Nhj3Y4nHu61Xjz/HK2bnu/xvbZ2buy1Xq5uq7R5q7pDaNn4fK/11rct2sZnPISOJx7utR5s//oMdr2+1O3vtR1I3Ua7L+rtfupL3Ua6trX2u6IR74tmup/q8Tvf13qdq9sA1lATgs4BbH30T8Dngc5uZftm5iqA4uc+Rfm2/jg8vtjfslzqs50ddLQBE7s9ngBsNY1dZl6cmYdn5uHDGMEBHVM36+2IvSfzi2Xtm9W5ZVkHLWMq4zafve1yDuycxgEd07bqJdm0sX2bdatR79nbLmdE5+ge67Zv3NRrvQM7p22jbjsjOnfvtd4BHdMqbV7e0WPd3uo9e9vllc+4h7rtGzf1Wg/YZt2u6zPY9V6qW4V7YiB1G+2+qLf76aW6TXJta+l3xa3LOhryvmim+6kev/N9rXdAx1To4Q+eZUgG3NMxpuuPtMV2avfnj4i3AU9m5l19bNK2/jjcpz8aS73Z2UHHb4EpETE5IoZRGTd4/fYq7ReTNuvtyMlz+MptG/nH32zknlUdnPebDXztvzsZ+po/f+mvGPvFpK3qrW9bxIb1yddu6+hWdyNf+zV0TppTlXqdq9vYv+PVfO22Sjtfqvvfnax/MXutt19MYv+OV/OV2zZyXrfX/eptG9m/4/Be6+0Xk9iwPvmbX3V2q7uBr9/Wwf4dh/dar3N1Gzl5Dl/9b16q+4+/2cjXb+tg/YvZa731bYsYNufPi9fd8FLdr962kc5Jc6pSb/DvierfT/V4X9Tb/VTW74qyrm0t/a4467b2hrwvmul+qsfv/I7UayBPdf2Rttgu3uL4kcA7ImI5lTzaoyPih8ATxZApip9PFudv64/DbcX+luVSn+30dToi4q1Uuvpage9n5jm9nT869srXxDGszGUs3289+7z/Gzz5gzN42WOddA59hvUtz0DHUNbuuTcv++A/8+QPzmDSYyMYH5W/bGxZb9JjIxjNnjw+dDHPtaxh47Bh7Pb//R1rb/pu1eqNj8msy7UsH7KA9cNeYOikV7Fu9WoOWL3HdusB/C4X8dSoNoaO3IVNL77AmHUTODBmbrfeylzG78euZdTYsWxafjcjNu7CpPbDGRV7bLfe8v3Ws8ebP8JzV3+RYRs3slvn3rxs0wye5Y/brbfP+7/B49//BHv8cQ20bmJE5+60bNqdx/drqVq9wbondub9VI/3Rb3dT2X9rijr2tbS74pGvS+a6X6qx+98X+vdklfXxPoW+88alWdc0/9mnD79l31+HxHxBuCzxexV5wFrMvPciPgCsFdmfj4iZgL/TmXin/2oJJlPycyOiPgt8NfAfCqzV/1LZv60341X09nZPR1k5k8zc2pmHri9gKO7cexP5+oVPHPH1XSubmMyBzGl/XUcvPEtzGw/GtaufunYOPbfZr1x7M+o2IMp7a9j9oY/Y+P64MXf31XVegCjYg9mth/N+k0jaB87HdY+1ad6AJM5iI3rg86D3sLG9S1M5qA+1RvH/rB2Ne1jp7N+00hmth/NqNijT/U6V6/gxd/fxcb1Lcze8GdMaX8do2KPPtV75o6rYe1TzGw/moM3voUp7a9jMgdVtd5g3RM7836qx/ui3u6nsn5XlHVta+l3RaPeF810P9Xjd35H6tWCJOjM/m8DcC5wbEQ8QmUZg3MBMnMRMA9YDPwMOD0zu8asnUYlGX0p8DvgxoE0QM2nZlck77J7y945Z1hlVra2jt+xuH0+M4a8hgmtB252nsc8NljHaq09HvNYvR6rtfZ4zGNdx36+4d9roqfj5bNG52euPqLf9T950K018T6kvtjpK5LvsNZWWvbYHYAJOZsNz3cwYdfZtLw0bTQe89igHqu19njMY/V6rNba4zGPvXTsCWpCAp19nPpWqne139Mxcly+9oAPlt0MSZLUIG5a/Hc10UMwcdbo/PSP5/S7/qdn3FwT70Pqi5rv6cghLWwau2vZzZAkSRpkQUff19uQ6lrNBx2SJEmNyOFVaiY1H3R0Dmlhw97Dym6GJEnSoLOnQ82i5oOOHALr9/SvAJIkSVK9qvmgo7MFNoz2rwCSJKmxZIbDq9Q0aj7oyFbYtHvZrZAkSRp8HQYdahL1EXSMqu1pfSVJknZUAp3mdKhJ1HzQQQt07NJZdiskSZIGWdjToabhnS5JkiSpquqgpyNhZEfZrZAkSRpUlXU6HF6l5lD7QUckrcMNOiRJUuPpcNCJmkTNBx0R0DrUoEOSJDWWJOzpUNOog6AjGdJq0CFJkhpPpz0dahI1H3QAtLY4Za4kSZJUr2o+6IiAlhanzJUkSY0lEzocXqUmUfNBhyRJUqMyp0PNoi6CjvD7KEmSGkwlkdycDjWHOgg6kpYwp0OSJEmqV3UQdEiSJDWmDhzOoeZg0CFJklQCVyRXMzHokCRJKoU5HWoedRB0uFqnJElqTJ0Or1KTMLyWJEmSVFV10NMhSZLUeFwcUM2kLoKOdMZcSZLUgMzpULOoi6BDkiSp0aR5q2oidRF0uCK5JElqRCaSq1nYpydJktSAImJERNwZEfdFxKKIOKsoPzQi7oiIeyNiQUQc0a3OmRGxNCIeiog3dys/LCIeKI6dH+GfhLVj6qCnI2kJkzokSVJj2QmLA24Ajs7M5yJiKPDriLgR+DpwVmbeGBFvBf4BeENEzABOBGYC+wG3RMTUzOwALgJOBe4AfgrMBW6sZuPVWOzpkCRJKklntvR7256seK54OLTYsthGF+W7A48V+8cBV2bmhsxcBiwFjoiIccDozLw9MxO4HDh+sD4DNYc66OmQJElqQDngRPIxEbGg2+OLM/Pi7idERCtwF/AK4ILMnB8RnwRuioh/pPIH6NcVp4+n0pPRpa0o21Tsb1ku9VnNBx0BhMOrJEmStvRUZh7e2wnF0KhDI2IP4NqImEVlmNSnMvOaiHgP8D3gTdBjVnv2Ui71Wc0HHQAt3teSJKnBJDtv9qrMXBsRv6SSi3Ey8Ini0I+BS4r9NmBit2oTqAy9aiv2tyyX+qz2g45wylxJktSYqplIHhFjgU1FwDGSSm/GN6gEDH8C/BI4GnikqHI98O8R8S0qieRTgDszsyMi1kXEHGA+8AHgX6rWcDWkmg86Apy9SpIkNZydMHvVOOCyIq+jBZiXmTdExFrgnyNiCLCeynArMnNRRMwDFgPtwOnF8CyA04BLgZFUZq1y5irtkH4HHRExkcrsBS8DOqkkL/1zROwFXAVMApYD78nMPxZ1zgQ+BHQAH8/Mm7b/SklrS2d/mylJklSzqhl0ZOb9wCt7KP81cNg26pwDnNND+QJg1mC3Uc1jID0d7cBnMvPuiBgF3BURNwOnALdm5rkR8QXgC8AZ25n7uVcmkkuSJEn1q9/rdGTmqsy8u9hfByyhMn3accBlxWmX8b/zOPc493N/X1+SJKmeJZUpc/u7SfVkUHI6ImISle67+cC+mbkKKoFJROxTnLatuZ97er5TKcYXDt9nlDkdkiSpIe2s2auksg046IiI3YBrgE9m5rOx7amm+jzHc7GwzcUAo6a9LFvDnA5JktRgsuqJ5FLNGFDQERFDqQQcV2TmT4riJyJiXNHLMQ54sijf1tzPvb8GyRATySVJUoPZCbNXSTVjILNXBZUVLJdk5re6HbqeyqIz5xY/r+tWvtXcz9t9HZwyV5IkSapnA+npOBJ4P/BARNxblH2RSrAxLyI+BDwKnADbnft52wKGOLxKkiQ1IHs61Cz6HXQUczxv65tyzDbq9Dj3c28cXiVJkhpR1+xVUjOo+RXJJUmSGlUadKhJ1HzQYU6HJEmSVN/qIugwp0OSJDUi1+lQs6j5oINIhrRsP99ckiSpnqTrdKiJ1HzQYU+HJElqVOZ0qFnUQdCR5nRIkqQG5OxVah61H3QEDHV4lSRJklS3aj7okCRJalQOr1KzqIugowWHV0mSpMaSmEiu5lHzQUdlRXKHV0mSpAaTlRmspGZQF0HH0DDokCRJjcd1OtQsaj7oAFcklyRJkupZzQcdEdjTIUmSGk5iIrmaR+0HHQ6vkiRJDcl1OtQ86iPoMJFckiQ1IBPJ1Sxaym6AJEmSpMZWBz0drtMhSZIakzkdaha1H3REMrSlvexmSJIkDapMgw41j9oPOnD2KkmS1JhMJFezqIOgw9mrJElSYzKRXM3CoEOSJKkBRcQI4FfAcCr/57s6M79aHPtr4GNAO/Cfmfn5ovxM4ENAB/DxzLypKD8MuBQYCfwU+ESmIZP6rg6CDhgW5nRIkqTGU+Wcjg3A0Zn5XEQMBX4dETdSCRyOAw7JzA0RsQ9ARMwATgRmAvsBt0TE1MzsAC4CTgXuoBJ0zAVurGbj1VicMleSJKkESZDZ/227z1/xXPFwaLElcBpwbmZuKM57sjjnOODKzNyQmcuApcARETEOGJ2Ztxe9G5cDxw/mZ6HGV/s9HeHwKkmS1JiqPT4pIlqBu4BXABdk5vyImAocFRHnAOuBz2bmb4HxVHoyurQVZZuK/S3LpT6r/aDDnA5JktSIBj5l7piIWNDt8cWZefFmL1EZGnVoROwBXBsRs6j8/29PYA7wamBeRBxAZVR7D63cZrnUZ3UQdMBQczokSZK29FRmHt6XEzNzbUT8kkouRhvwk2Ko1J0R0QmMKcondqs2AXisKJ/QQ7nUZzUfdLSQjIhNZTdDkiRp8FWxvyAixgKbioBjJPAm4BvAc8DRwC+LoVbDgKeA64F/j4hvUUkknwLcmZkdEbEuIuYA84EPAP9SvZarEdV80BGks1dJkqSGVOXZq8YBlxV5HS3AvMy8ISKGAd+PiIXARuDkotdjUUTMAxZTmUr39GJ4FlSSzy+lMvPVjThzlXZQ7QcdkYww6JAkSQ2omitdZOb9wCt7KN8InLSNOucA5/RQvgCYNdhtVPNwylxJkiRJVVX7PR2kieSSJKnhJFUfXiXVjJoPOiqJ5AYdkiSpwSRg0KEmUfNBR2XK3M6ymyFJkjToqpnTIdWSmg86WoARBh2SJKkRGXSoSdR80BEkQ/1GSpIkSXWr9oOOCEaE4x0lSVKjCRPJ1TRqPuiQJElqWA7mUJMYcNBRrHK5AFiZmW+LiL2Aq4BJwHLgPZn5x+LcM4EPAR3AxzPzpu0+PzA0XE5EkiQ1mHTKXDWPwejp+ASwBBhdPP4CcGtmnhsRXygenxERM4ATgZnAfsAtETE1Mzt6e/IWghHROgjNlCRJklSGAQUdETEB+DPgHODTRfFxwBuK/cuAXwJnFOVXZuYGYFlELAWOAG7v9TWAIRh0SJKkBuTwKjWJgfZ0/BPweWBUt7J9M3MVQGauioh9ivLxwB3dzmsrynoVBEPD1BNJktSIHF6l5tDvZImIeBvwZGbe1dcqPZT1GN9HxKkRsSAiFqxe0+voK0mSpPqVA9ikOjKQLoQjgXdExFuBEcDoiPgh8EREjCt6OcYBTxbntwETu9WfADzW0xNn5sXAxQCHzx7h10qSJDUm/5ejJtHvno7MPDMzJ2TmJCoJ4r/IzJOA64GTi9NOBq4r9q8HToyI4RExGZgC3NnvlkuSJEmqC9VIljgXmBcRHwIeBU4AyMxFETEPWAy0A6dvb+YqSZKkhpWAU+aqSQxK0JGZv6QySxWZuQY4ZhvnnUNlpitJkqSmlw0+vCoidgXW+4dmueqeJElSWRoskTwiWiLifRHxnxHxJPAgsCoiFkXEeRExpew2qhwGHZIkSWXJ6P9Wm24DDgTOBF6WmRMzcx/gKCpLJ5wbESeV2UCVwwUwJEmSNFjelJmbtizMzKeBa4BrImLozm+WymZPhyRJUkki+7/Voq6AIyJOiIhRxf5XIuInEfGq7ueouRh0SJIklWEg+Rw1GnR085XMXBcRrwf+FLgMuKjkNqlEBh2SJEmlGEA+R+3mdHTpmq3qz4CLMvM6YFiJ7VHJDDokSZI02FZGxL8C7wF+GhHD8f+dTc2LL0mSVJYGG14VEa+NiKASbNwEzM3MtcBewOfKbJvK5exVkiRJZanR4GEATgYuAB4GfgasA8jMVcCqEtulkhl0SJIklaXBgo7M/AhAREwH3gJcGhG7U1m/42fA/7g6eXMy6JAkSSpDUg8J4f2SmQ9SWY382xExEngjcALwLeDwMtumchh0SJIkqWoy80Xgp8WmJmUiuSRJUkmquThgRIyIiDsj4r6IWBQRZ21x/LMRkRExplvZmRGxNCIeiog3dys/LCIeKI6dXySL9/bah0fEtRFxd0Tc37Xt+CekRmFPhyRJUlmqm9OxATg6M5+LiKHAryPixsy8IyImAscCj3adHBEzgBOBmcB+wC0RMbXIwbgIOBW4g0qPxVzgxl5e+woqs1U9AHQO/ltTvbGnQ5IkqQFlxXPFw6HF1hXmfBv4PJuHPccBV2bmhsxcBiwFjoiIccDozLw9MxO4HDh+Oy+/OjOvz8xlmfmHrm2Q3prqkD0dkiRJJenLMKlejImIBd0eX5yZF2/2/BGtwF3AK4ALMnN+RLwDWJmZ920xSmo8lZ6MLm1F2aZif8vy3nw1Ii4BbqXS4wJAZv6kT+9MDcegQ5IkqT49lZm9zgRVDI06NCL2AK6NiEOALwF/2sPpPeVpZC/lvfkLYDqV3pWu4VUJGHQ0KYMOSZKksuykKXMzc21E/JLKEKrJQFcvxwTg7og4gkoPxsRu1SYAjxXlE3oo783szDx4cFqvRmBOhyRJUhlygNt2RMTYooeDYq2MNwH3ZOY+mTkpMydRCShelZmPA9cDJ0bE8IiYDEwB7ixWE18XEXOKWas+AFy3nZe/o0hMlwB7OiRJkspT3dmrxgGXFXkdLcC8zLxhm03JXBQR84DFQDtwerfVw08DLgVGUpm1qreZqwBeD5wcEcuo5HRE5SXykAG8H9Uxgw5JkqQGlJn3A6/czjmTtnh8DnBOD+ctAGbtwMvP3YFz1QQMOiRJkkoywNmrak5ERDFV7zanx+06Z2e2S+Uzp0OSJKksVczpKMltEfHXEfHy7oURMSwijo6Iy4CTS2qbSmRPhyRJUllqN3jor7nAB4EfFcnoa4ERQCvwc+DbmXlvaa1TaQw6JEmSShDZeMOrMnM9cCFwYUQMBcYAL2bm2lIbptIZdEiSJGnQZeYmYFXZ7VBtMOiQJEkqy05aHFAqm0GHJElSWRpseJW0LQYdkiRJJWm0nI4tRcRuwJ6ZuaLstqhcBh2SJEllafCgA7gO+H1E/CYz/63sxqg8rtMhSZKkankwM/+SHVvNXA3IoEOSJKkM+b/T5vZnqxOvjojzgKkRMSUizJxvUg6vkiRJKkv9BA87JCJ+A3wpM4+IiAnAYcD7gYOAE0ptnEphT4ckSVJZcgBbbTsV+FhE3ApMzMzrMvNvMtOAo0nZ0yFJkqRBlZkLgXdHxKuArxejqr6cmfeW2jCVxp4OSZKkkjRBTsdS4G+BNmBByW1RiezpkCRJ0qCKiF8AU4D1wOJiO6XMNqlcBh2SJEllqZ8eix31WWBJZr5YdkNUGwY0vCoi9oiIqyPiwYhYEhGvjYi9IuLmiHik+Llnt/PPjIilEfFQRLx54M2XJEmqUw08ZW5m3m3Aoe4GmtPxz8DPMnM6MBtYAnwBuDUzpwC3Fo+JiBnAicBMYC5wYUS0DvD1JUmSJNW4fgcdETEa+D/A9wAyc2NmrgWOAy4rTrsMOL7YPw64MjM3ZOYyKolFR/T39SVJkupe406ZK21mID0dBwCrgX+LiHsi4pKI2BXYNzNXARQ/9ynOHw+s6Fa/rSjbSkScGhELImLB6jUdA2iiJElSDTPoUJMYSNAxBHgVcFFmvhJ4nmIo1Tb0tOx9j1+ZzLw4Mw/PzMPH7u0ILEmS1HiCxs3pkLY0kKCjDWjLzPnF46upBCFPRMQ4gOLnk93On9it/gTgsQG8viRJUn2zp0NNot9BR2Y+DqyIiGlF0TFU5mC+Hji5KDsZuK7Yvx44MSKGR8RkKnM339nf15ckSZJUHwa6TsdfA1dExDDg98BfUAlk5kXEh4BHgRMAMnNRRMyjEpi0A6dnpgkbkiSpOTlMSk1kQEFHZt4LHN7DoWO2cf45wDkDeU1JkqSGYdChJuGK5JIkSWUx6FCTGOjigJIkSZLUK4MOSZKkklRzytyIGBERd0bEfRGxKCLOKsrPi4gHI+L+iLg2IvboVufMiFgaEQ9FxJu7lR8WEQ8Ux86PiJ6WQpC2yaBDkiSpLNWdMncDcHRmzgYOBeZGxBzgZmBWZh4CPAycCRARM4ATgZnAXODCiOhaMO0i4FQqs49OKY5LfWbQIUmSVIaBBBx9CDqy4rni4dBiy8z8eWa2F+V3UFk7DeA44MrM3JCZy4ClwBHFumujM/P2zEzgcuD4/r9xNSODDkmSpJJUe0XyiGiNiHupLNZ8c7dFnbt8ELix2B8PrOh2rK0oG1/sb1ku9ZlBhyRJUn0aExELum2nbnlCZnZk5qFUejOOiIhZXcci4ktU1k67oquoh9fIXsqlPnPKXEmSpLIM7L/uT2VmT+ulbf0ymWsj4pdUcjEWRsTJwNuAY4ohU1DpwZjYrdoE4LGifEIP5VKf2dMhSZJUkirPXjW2a2aqiBgJvAl4MCLmAmcA78jMF7pVuR44MSKGR8RkKgnjd2bmKmBdRMwpZq36AHDdYH4Oanz2dEiSJJWluoOUxgGXFTNQtQDzMvOGiFgKDAduLma+vSMzP5KZiyJiHrCYyrCr0zOzo3iu04BLgZFUckBuRNoBBh2SJEll6PvUt/17+sz7gVf2UP6KXuqcA5zTQ/kCYNbWNaS+cXiVJEmSpKqyp0OSJKkEQc/TQkmNyKBDkiSpLE48qyZh0CFJklSSvi7yJ9U7czokSZIkVZU9HZIkSWWxp0NNwqBDkiSpLAYdahIGHZIkSWXo48riUiMw6JAkSSqLQYeahInkkiRJkqrKng5JkqSSOLxKzcKgQ5IkqSwGHWoSBh2SJEklsadDzcKcDkmSJElVZU+HJElSGRKHV6lpGHRIkiSVxaBDTcKgQ5IkqQSBOR1qHgYdkiRJZTHoUJMwkVySJElSVdnTIUmSVJJIuzrUHAw6JEmSyuDsVWoiBh2SJEklMZFczcKgQ5IkqSwGHWoSJpJLkiRJqip7OiRJkkri8Co1C4MOSZKkshh0qEkMaHhVRHwqIhZFxMKI+FFEjIiIvSLi5oh4pPi5Z7fzz4yIpRHxUES8eeDNlyRJqlNZ6eno7ybVk34HHRExHvg4cHhmzgJagROBLwC3ZuYU4NbiMRExozg+E5gLXBgRrQNrviRJkqRaN9BE8iHAyIgYAuwCPAYcB1xWHL8MOL7YPw64MjM3ZOYyYClwxABfX5IkqX7lALbtKEag3BkR9xUjU84qynd4VEpEHBYRDxTHzo+IGJwPQM2i30FHZq4E/hF4FFgFPJOZPwf2zcxVxTmrgH2KKuOBFd2eoq0okyRJajpB1YdXbQCOzszZwKHA3IiYQ/9GpVwEnApMKba5g/QxqEkMZHjVnlR6LyYD+wG7RsRJvVXpoazHr0xEnBoRCyJiweo1Hf1toiRJUm3L7P+23afOzMzniodDiy3ZwVEpETEOGJ2Zt2dmApd3qyP1yUCGV70JWJaZqzNzE/AT4HXAE8XNSfHzyeL8NmBit/oTqAzH2kpmXpyZh2fm4WP3Nu1DkiQ1pmonkkdEa0TcS+X/Yzdn5nx2fFTK+GJ/y3KpzwYSdDwKzImIXYpxfccAS4DrgZOLc04Griv2rwdOjIjhETGZStfcnQN4fUmSpGY2pmtkSLGduuUJmdmRmYdS+WPvERExq5fn29aolD6PVpG2pd/rdGTm/Ii4GrgbaAfuAS4GdgPmRcSHqAQmJxTnL4qIecDi4vzTM9OxU5IkqTn1MSG8F09l5uF9eqnMtRHxSyq5GE9ExLjMXNXHUSltxf6W5VKfDWj2qsz8amZOz8xZmfn+Ygzgmsw8JjOnFD+f7nb+OZl5YGZOy8wbB958SZKk+hWd/d+2+9wRYyNij2J/JJWh8Q+yg6NSiiFY6yJiTjG65QPd6kh94orkkiRJZanuIKVxwGXFDFQtwLzMvCEibmfHR6WcBlwKjARuLDapzww6JEmSSlLNlcUz837glT2Ur6GSi9tTnXOAc3ooXwD0lg8i9WqgiwNKkiRJUq/s6ZAkSSpD0qf1NqRGYNAhSZJUkmoOr5JqiUGHJElSWQw61CTM6ZAkSZJUVfZ0SJIklSBweJWah0GHJElSGTJNJFfTMOiQJEkqiT0dahYGHZIkSWUx6FCTMJFckiRJUlXZ0yFJklQSh1epWRh0SJIklSGBTqMONQeDDkmSpLIYc6hJGHRIkiSVxOFVahYmkkuSJEmqKns6JEmSyuLigGoSBh2SJEklcXiVmoVBhyRJUhkSE8nVNMzpkCRJklRV9nRIkiSVIIAwp0NNwqBDkiSpLJ1lN0DaOQw6JEmSSmJPh5qFQYckSVIZTCRXEzGRXJIkSVJV2dMhSZJUinRxQDUNgw5JkqSSuDigmoVBhyRJUlns6VCTMOiQJEkqQ0I4Za6ahInkkiRJkqrKoEOSJKksmf3ftiMiJkbEbRGxJCIWRcQnivJDI+KOiLg3IhZExBHd6pwZEUsj4qGIeHO38sMi4oHi2PkREVX5PNSwDDokSZLKkgPYtq8d+ExmHgTMAU6PiBnAPwBnZeahwN8UjymOnQjMBOYCF0ZEa/FcFwGnAlOKbW7/37SakUGHJElSSSKz39v2ZOaqzLy72F8HLAHGUwlZRhen7Q48VuwfB1yZmRsycxmwFDgiIsYBozPz9sxM4HLg+EH8GNQETCSXJEmqT2MiYkG3xxdn5sU9nRgRk4BXAvOBTwI3RcQ/UvkD9OuK08YDd3Sr1laUbSr2tyyX+sygQ5IkqSwDmzL3qcw8fHsnRcRuwDXAJzPz2Yg4G/hUZl4TEe8Bvge8CegpTyN7KZf6zOFVkiRJZUigcwBbH0TEUCoBxxWZ+ZOi+GSga//HQFcieRswsVv1CVSGXrUV+1uWS3223aAjIr4fEU9GxMJuZXtFxM0R8Ujxc89ux5z1QJIkaTuC/udz9CWno/i/1veAJZn5rW6HHgP+pNg/Gnik2L8eODEihkfEZCoJ43dm5ipgXUTMKZ7zA8B1g/MpqFn0pafjUraeoeALwK2ZOQW4tXjsrAeSJEk7oopT5gJHAu8Hji6mx703It4K/CXwzYi4D/g7Kv8/IzMXAfOAxcDPgNMzs6N4rtOAS6gkl/8OuHEQPwU1ge3mdGTmr4rko+6OA95Q7F8G/BI4g26zHgDLIqJr1oPlFLMeAERE16wH3rCSJElVkJm/pud8DIDDtlHnHOCcHsoXALMGr3VqNv1NJN+36GojM1dFxD5FubMeSJIk9dXAEsmlujHYs1cNyqwHEXEqRVffy8c7wZYkSWpAXYnkUhPo7//on4iIcUUvxzjgyaJ8UGY9KOaYvhggIta1jnvkoX62U7VnDPBU2Y3QoPKaNhavZ2PxevZs/7Ib0KUvCeFSI+hv0HE9lenWzi1+Xtet/N8j4lvAfvzvrAcdEbEuIuZQWZTmA8C/9PG1HurLHNSqDxGxwOvZWLymjcXr2Vi8nnXAoENNYrtBR0T8iErS+JiIaAO+SiXYmBcRHwIeBU6AyqwHEdE160E7W896cCkwkkoCuUnkkiRJUhPoy+xV793GoWO2cb6zHkiSJG1Xn6e+lepePWRpX1x2AzSovJ6Nx2vaWLyejcXrWcsSgw41jZoPOoqkcjUIr2fj8Zo2Fq9nY/F61gFnr1KT6MuK5JIkSZLUbzUddETE3Ih4KCKWRsQXym6P+iYilkfEAxFxb0QsKMr2ioibI+KR4uee3c4/s7jGD0XEm8truQAi4vsR8WRELOxWtsPXLyIOK+6DpRFxfkRsa1VcVdE2rufXImJl8R29NyLe2u2Y17OGRcTEiLgtIpZExKKI+ERR7ne0TkVmvzepntRs0BERrcAFwFuAGcB7I2JGua3SDnhjZh7abarGLwC3ZuYU4NbiMcU1PRGYCcwFLiyuvcpzKZVr0V1/rt9FVBb5nFJsWz6ndo5L6fmz/3bxHT00M38KXs860Q58JjMPAuYApxfXze9ovcrs/ybVkZoNOoAjgKWZ+fvM3AhcCRxXcpvUf8cBlxX7lwHHdyu/MjM3ZOYyYCmVa6+SZOavgKe3KN6h61csGjo6M2/PzAQu71ZHO9E2rue2eD1rXGauysy7i/11wBJgPH5H61MCndn/TaojtRx0jAdWdHvcVpSp9iXw84i4KyJOLcr2zcxVUPlHE9inKPc614cdvX7ji/0ty1U7PhYR9xfDr7qG4ng960hETAJeSWXRXb+jdWkAvRz2dKjO1HLQ0dPYUr9h9eHIzHwVlaFxp0fE/+nlXK9zfdvW9fO61raLgAOBQ4FVwDeLcq9nnYiI3YBrgE9m5rO9ndpDmddU0k5Xy0FHGzCx2+MJwGMltUU7IDMfK34+CVxLZbjUE0V3PsXPJ4vTvc71YUevX1uxv2W5akBmPpGZHZnZCfxf/ndIo9ezDkTEUCoBxxWZ+ZOi2O9ovbKnQ02iloOO3wJTImJyRAyjkgh3fclt0nZExK4RMaprH/hTYCGVa3dycdrJwHXF/vXAiRExPCImU0lmvHPntlp9sEPXrxjesS4i5hQz4nygWx2VrOs/p4V3UvmOgtez5hWf//eAJZn5rW6H/I7WK4MONYmaXRwwM9sj4mPATUAr8P3MXFRys7R9+wLXFjMvDgH+PTN/FhG/BeZFxIeAR4ETADJzUUTMAxZTmZXl9MzsKKfpAoiIHwFvAMZERBvwVeBcdvz6nUZl5qSRwI3Fpp1sG9fzDRFxKJXhNMuBvwKvZ504Eng/8EBE3FuUfRG/o/WpK5FcagKRRsqSJEk73e7D983X7ffn/a7/s+Xfvqvb1PRSTavl4VWSJEmSGkDNDq+SJElqeI44UZMw6JAkSSqDOR1qIgYdkiRJZbGnQ03CnA5JkiRJVWVPhyRJUlns6VCTMOiQJEkqhYv8qXkYdEiSJJUhgc7Oslsh7RQGHZIkSWWxp0NNwkRySZIkSVVl0CFJklSWzP5v2xEREyPitohYEhGLIuIT3Y79dUQ8VJT/Q7fyMyNiaXHszd3KD4uIB4pj50dEDPpnoYbm8CpJkqRSZLUXB2wHPpOZd0fEKOCuiLgZ2Bc4DjgkMzdExD4AETEDOBGYCewH3BIRUzOzA7gIOBW4A/gpMBe4sZqNV2Mx6JAkSSpDQmb1EskzcxWwqthfFxFLgPHAXwLnZuaG4tiTRZXjgCuL8mURsRQ4IiKWA6Mz83aAiLgcOB6DDu0Ah1dJkiQ1uIiYBLwSmA9MBY6KiPkR8V8R8eritPHAim7V2oqy8cX+luVSn9nTIUmSVJaBDa8aExELuj2+ODMv3vKkiNgNuAb4ZGY+GxFDgD2BOcCrgXkRcQDQU55G9lIu9ZlBhyRJUlkGNmXuU5l5eG8nRMRQKgHHFZn5k6K4DfhJZiZwZ0R0AmOK8ondqk8AHivKJ/RQLvWZw6skSZLKkFlZHLC/23YUM0x9D1iSmd/qduj/AUcX50wFhgFPAdcDJ0bE8IiYDEwB7ixyQ9ZFxJziOT8AXDeIn4SagD0dkiRJZanu4oBHAu8HHoiIe4uyLwLfB74fEQuBjcDJRa/HooiYByymMvPV6cXMVQCnAZcCI6kkkJtErh0S6UqYkiRJO93urWPytbu+vd/1b1p36V3bG14l1Qp7OiRJkkqSfRgmJTUCgw5JkqRS9G1lcakRGHRIkiSVIan2iuRSzTDokCRJKksVVySXaolT5kqSJEmqKns6JEmSSpBAOrxKTcKgQ5IkqQyZDq9S0zDokCRJKok9HWoW5nRIkiRJqip7OiRJksri8Co1iUgXpZEkSdrpIuJnwJgBPMVTmTl3sNojVZNBhyRJkqSqMqdDkiRJUlUZdEiSJEmqKoMOSZIkSVVl0CFJkiSpqgw6JEmSJFXV/w9/OekPwhu+qwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.close(\"all\")\n",
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"qmesh = ax.pcolormesh(\n",
" vmodel_true.nodes[:, :, 0, 0],\n",
" vmodel_true.nodes[:, :, 0, 1],\n",
" vmodel_true.values[:, :, 0]\n",
")\n",
"ax.scatter(rxs, np.zeros_like(rxs), marker=\"v\", clip_on=False, s=128, edgecolor=\"k\", zorder=100, label=\"Receivers\")\n",
"ax.scatter(sxs, np.zeros_like(sxs), marker=\"o\", clip_on=False, s=32, edgecolor=\"k\", zorder=100, label=\"Sources\")\n",
"ax.set_aspect(1)\n",
"ax.invert_yaxis()\n",
"fig.legend()\n",
"\n",
"cbar = fig.colorbar(qmesh, ax=ax)\n",
"cbar.set_label(\"$v_P$ (m/s)\")"
]
},
{
"cell_type": "markdown",
"id": "6a3a4a80-94ac-4c84-a92d-aca28cca58b0",
"metadata": {},
"source": [
"# Define some convenience functions for wavefront propagation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "26646bd0-6b9e-49cd-8d5b-0202426cd032",
"metadata": {},
"outputs": [],
"source": [
"def sph2xyz(rtp, x0=0, y0=0):\n",
" x = x0 + rtp[..., 0] * np.cos(rtp[..., 2])\n",
" y = y0 + rtp[..., 0] * np.sin(rtp[..., 2])\n",
" xyz = np.stack([x, y, np.zeros_like(x)], axis=-1)\n",
" return (xyz)\n",
"\n",
"def xyz2sph(xyz, x0=0, y0=0):\n",
" dx = xyz[..., 0] - x0\n",
" dy = xyz[..., 1] - y0\n",
" r = np.sqrt(np.square(dx) + np.square(dy))\n",
" p = np.arctan2(dy, dx)\n",
" rtp = np.stack([r, np.pi/2 * np.ones_like(r), p], axis=-1)\n",
" return (rtp)\n",
"\n",
"def propagate_wavefront(src, vmodel):\n",
" # Define the far-field grid\n",
" ff = pykonal.EikonalSolver(coord_sys=\"cartesian\")\n",
" ff.vv.min_coords = vmodel.min_coords\n",
" ff.vv.node_intervals = vmodel.node_intervals\n",
" ff.vv.npts = vmodel.npts\n",
" ff.vv.values = vmodel.values\n",
"\n",
" # Define a near-field grid centered on the source location\n",
" nf = pykonal.EikonalSolver(coord_sys=\"spherical\")\n",
" nf.vv.min_coords = 1, np.pi/2, 0\n",
" nf.vv.node_intervals = 10, 1, np.pi/31\n",
" nf.vv.npts = 50, 1, 32\n",
"\n",
" # Interpolate velocity from the far-field grid to the near-field grid\n",
" xyz = sph2xyz(nf.vv.nodes, x0=src[0], y0=src[1])\n",
" nf.vv.values = ff.vv.resample(xyz.reshape(-1, 3)).reshape(xyz.shape[:-1])\n",
"\n",
" # Initialize the near-field narrow-band\n",
" for ip in range(nf.tt.npts[2]):\n",
" idx = (0, 0, ip)\n",
" vv = nf.vv.values[idx]\n",
" if not np.isnan(vv):\n",
" nf.tt.values[idx] = nf.tt.node_intervals[0] / vv\n",
" nf.unknown[idx] = False\n",
" nf.trial.push(*idx)\n",
"\n",
" # Propagate the wavefront across the near-field grid\n",
" nf.solve()\n",
"\n",
" # Map the traveltime values from the near-field grid onto the far-field grid\n",
" rtp = xyz2sph(ff.tt.nodes, x0=src[0], y0=src[1])\n",
" rtp = rtp.reshape(-1, 3)\n",
" tt = nf.tt.resample(rtp)\n",
" tt = tt.reshape(ff.tt.npts)\n",
"\n",
" idxs = np.nonzero(np.isnan(tt))\n",
" tt[idxs] = np.inf\n",
"\n",
" ff.tt.values = tt\n",
"\n",
" # Initialize far-field narrow band\n",
" for idx in np.argwhere(~np.isinf(ff.tt.values)):\n",
" idx = tuple(idx)\n",
" ff.unknown[idx] = False\n",
" ff.trial.push(*idx)\n",
"\n",
" # Propagate the wavefront across the remainder of the far field.\n",
" ff.solve()\n",
" \n",
" return (ff)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1b901286-8e17-4139-87bc-9662756a7841",
"metadata": {},
"outputs": [],
"source": [
"dataf_true = pd.DataFrame(columns=[\"sxid\", \"rxid\", \"tt\"])\n",
"\n",
"for sxid, sx in enumerate(sxs):\n",
" src = (sx, 0, 0)\n",
" ff = propagate_wavefront(src, vmodel_true)\n",
" tt = ff.tt.resample(np.stack([rxs, np.zeros_like(rxs), np.zeros_like(rxs)], axis=1))\n",
" dataf_true = pd.concat(\n",
" [\n",
" dataf_true,\n",
" pd.DataFrame(\n",
" dict(\n",
" sxid=sxid+1,\n",
" rxid=np.arange(1, nrx+1),\n",
" tt=tt\n",
" )\n",
" ),\n",
" ],\n",
" ignore_index=True\n",
" )\n",
"\n",
"dataf_true = dataf_true.astype(dict(sxid=int, rxid=int, tt=float))"
]
},
{
"cell_type": "markdown",
"id": "79f2552b-22ff-4e7a-8eb0-8adfd3210846",
"metadata": {},
"source": [
"# Demonstrate ray tracing"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "2c46da36-74b9-4487-aeb8-5d27e35d4212",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.close(\"all\")\n",
"fig, ax = plt.subplots(figsize=(12, 4))\n",
"ax.pcolormesh(ff.tt.nodes[:, :, 0, 0], ff.tt.nodes[:, :, 0, 1], ff.tt.values[:, :, 0])\n",
"for sx in sxs[::12]:\n",
" src = (sx, 0, 0)\n",
" for rx in rxs:\n",
" ff = propagate_wavefront(src, vmodel_true)\n",
" ray = ff.trace_ray(np.array([rx, 0, 0]))\n",
" ax.plot(ray[:, 0], ray[:, 1], color=\"k\", linewidth=1)\n",
"ax.invert_yaxis()\n",
"ax.set_aspect(1)"
]
},
{
"cell_type": "markdown",
"id": "cd77747c-393d-425d-942e-93b4f2dcb291",
"metadata": {},
"source": [
"# Invert for velocity structure\n",
"## Create initial model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f786318e-3618-455b-9ee8-b03f486efc53",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.7435115629752391"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(vmodel_true.values.max() - vmodel_true.values.min()) / 1000"
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "9e7e12c8-e0b1-425f-b0b9-9feaf853e581",
"metadata": {},
"outputs": [],
"source": [
"vmodel = pykonal.fields.ScalarField3D(coord_sys=\"cartesian\")\n",
"vmodel.min_coords = 0, -10, 0\n",
"vmodel.node_intervals = 5, 5, 1\n",
"vmodel.npts = 461, 203, 1\n",
"\n",
"\n",
"vv = pd.read_csv(\"vmodel.txt\")\n",
"vmodel.values = 2500 + vmodel.nodes[..., 1] * 1.75"
]
},
{
"cell_type": "markdown",
"id": "6497689e-b568-459e-b562-044e42e95405",
"metadata": {},
"source": [
"## Plot initial model"
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "1eadcb45-dca6-4e76-a2b6-f7d6ef35d8aa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.close(\"all\")\n",
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"qmesh = ax.pcolormesh(\n",
" vmodel.nodes[:, :, 0, 0],\n",
" vmodel.nodes[:, :, 0, 1],\n",
" vmodel.values[:, :, 0]\n",
")\n",
"ax.set_aspect(1)\n",
"ax.invert_yaxis()\n",
"fig.legend()\n",
"\n",
"cbar = fig.colorbar(qmesh, ax=ax)\n",
"cbar.set_label(\"$v_P$ (m/s)\")"
]
},
{
"cell_type": "markdown",
"id": "064b9db8-e3c7-4ad4-943f-359d687ffe65",
"metadata": {},
"source": [
"## Generate \"observed\" data by adding noise to true data"
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "5e5d53c0-f24d-4ec4-a807-13f914e20695",
"metadata": {},
"outputs": [],
"source": [
"dataf_obs = dataf_true\n",
"dataf_obs[\"tt\"] += np.random.choice((-1, 1), size=len(dataf_obs)) \\\n",
" * np.random.rand(len(dataf_obs)) \\\n",
" * dataf_obs[\"tt\"] \\\n",
" * 0.05"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "215065d7-26fe-45f1-b2f9-73eb32d76dfd",
"metadata": {},
"outputs": [],
"source": [
"def random_realization(args):\n",
" \n",
" nvoronoi, frac, dataf_obs, seed = args\n",
" np.random.seed(seed)\n",
"\n",
" ########################\n",
" # Randomly sample data #\n",
" ########################\n",
" dataf = dataf_obs.sample(frac=frac, random_state=seed)\n",
" dataf = dataf.sort_values([\"sxid\", \"rxid\"])\n",
" \n",
" ##########################\n",
" # Generate Voronoi cells #\n",
" ##########################\n",
" delta = vmodel.max_coords - vmodel.min_coords\n",
" vcells = vmodel.min_coords + np.random.rand(nvoronoi, 3) * delta\n",
" tree = scipy.spatial.cKDTree(vcells[:, :2])\n",
" \n",
" ###############################\n",
" # Compute projection matrix P #\n",
" ###############################\n",
" nnodes = np.prod(vmodel.nodes.shape[:-1])\n",
" row_idxs = np.arange(nnodes)\n",
" _, column_idxs = tree.query(vmodel.nodes[:, :, 0, :2].reshape(-1, 2))\n",
"\n",
" values = np.ones(nnodes,)\n",
" P = scipy.sparse.coo_matrix(\n",
" (values, (row_idxs, column_idxs)),\n",
" shape=(nnodes, tree.n)\n",
" )\n",
" \n",
" ################################\n",
" # Compute sensitivity matrix G #\n",
" ################################\n",
" column_idxs = np.array([], dtype=np.int32)\n",
" nsegments = np.array([], dtype=np.int32)\n",
" nonzero_values = np.array([], dtype=np.float64)\n",
" residuals = np.array([], dtype=np.float64)\n",
"\n",
" for sxid in tqdm.tqdm(dataf[\"sxid\"].unique()):\n",
" sx = sxs[sxid-1]\n",
" ff = propagate_wavefront((sx, 0, 0), vmodel)\n",
" for _, (rxid, tt) in dataf.set_index(\"sxid\").loc[sxid].iterrows():\n",
" rx = rxs[int(rxid)-1]\n",
" residual = tt - ff.tt.resample(np.array([[rx, 0, 0]]))[0] # observed - synthetic\n",
" ray = ff.trace_ray(np.array([rx, 0, 0]))\n",
" _, _column_idxs = tree.query(ray[:, :2])\n",
" _column_idxs, counts = np.unique(_column_idxs, return_counts=True)\n",
" column_idxs = np.append(column_idxs, _column_idxs)\n",
" nsegments = np.append(nsegments, len(_column_idxs))\n",
" nonzero_values = np.append(nonzero_values, counts * ff.tt.step_size)\n",
" residuals = np.append(residuals, residual)\n",
"\n",
" row_idxs = np.array([\n",
" i for i in range(len(nsegments)) for j in range(nsegments[i])\n",
" ])\n",
"\n",
" G = scipy.sparse.coo_matrix(\n",
" (nonzero_values, (row_idxs, column_idxs)),\n",
" shape=(len(nsegments), nvoronoi)\n",
" )\n",
" \n",
" \n",
" ##########################\n",
" # Solve for model update #\n",
" ##########################\n",
" \n",
" # Parameters of LSMR solver \n",
" damp = 1\n",
" atol = 1e-3\n",
" btol = 1e-4\n",
" conlim = 50\n",
" maxiter = 100\n",
"\n",
" x, istop, itn, normr, normar, norma, conda, normx = scipy.sparse.linalg.lsmr(\n",
" G,\n",
" residuals,\n",
" damp,\n",
" atol,\n",
" btol,\n",
" conlim,\n",
" maxiter,\n",
" show=False\n",
" )\n",
"\n",
" delta_slowness = P * x\n",
" delta_slowness = delta_slowness.reshape(vmodel.npts)\n",
" slowness = np.power(vmodel.values, -1) + delta_slowness\n",
" velocity = np.power(slowness, -1)\n",
" \n",
" return (velocity)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"id": "56c7d6d0-ee8b-4542-a26e-840bc85772b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n",
"10\n",
"11\n",
"12\n",
"13\n",
"14\n",
"15\n"
]
}
],
"source": [
"nvoronoi = 128\n",
"frac = 0.5\n",
"nreal = 128\n",
"niter = 16\n",
"\n",
"models = [vmodel.values.copy()]\n",
"\n",
"for iiter in range(niter):\n",
" print(iiter)\n",
" with mp.Pool() as pool:\n",
" vvs = pool.map(\n",
" random_realization,\n",
" ((nvoronoi, frac, dataf_obs, seed) for seed in np.random.randint(0, 2147483647, size=nreal))\n",
" )\n",
" vmodel.values = np.median(np.stack(vvs), axis=0)\n",
" models.append(vmodel.values.copy())"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "996a614c-a705-4738-b19c-122796f9c858",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.close(\"all\")\n",
"fig, axes = plt.subplots(figsize=(36, 12), nrows=4, ncols=12)\n",
"\n",
"for vv, ax in zip(vvs, axes.flatten()):\n",
" qmesh = ax.pcolormesh(\n",
" vmodel.nodes[:, :, 0, 0], \n",
" vmodel.nodes[:, :, 0, 1], \n",
" vv[:, :, 0], \n",
" vmin=vmodel_true.values.min(), \n",
" vmax=vmodel_true.values.max()\n",
" )\n",
" ax.invert_yaxis()\n",
"\n",
"fig.colorbar(qmesh, ax=axes.flatten())"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "be7896e7-e0b5-4282-b986-ada307347064",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "ad307091-ab30-4257-9c00-579bf81ee6fb",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9abed9c22a8647ec93b43db153464f93",
"version_major": 2,
"version_minor": 0
},
"image/png": 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",
"text/html": [
"\n",
"
\n",
"
\n",
" Figure\n",
"
\n",
" \n",
"
\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vv = np.median(np.stack(vvs), axis=0)\n",
"\n",
"plt.close(\"all\")\n",
"fig, axes = plt.subplots(figsize=(8, 8), nrows=4, ncols=4)\n",
"\n",
"for model, ax in zip(models, axes.flatten()):\n",
" qmesh = ax.pcolormesh(\n",
" vmodel.nodes[:, :, 0, 0], \n",
" vmodel.nodes[:, :, 0, 1], \n",
" model[:, :, 0], \n",
" vmin=vmodel_true.values.min(), \n",
" vmax=vmodel_true.values.max()\n",
" )\n",
" ax.invert_yaxis()\n",
"fig.colorbar(qmesh, ax=axes.flatten())\n",
"\n",
"for ax in axes[: -1].flatten():\n",
" ax.set_xticklabels([])\n",
" \n",
"for ax in axes[:, 1:].flatten():\n",
" ax.set_yticklabels([])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d561d480-e51d-4aa2-bfc4-1899f725471e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py39]",
"language": "python",
"name": "conda-env-py39-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: tutorials/tomography/vmodel.txt
================================================
depth,vs
0,1334.69487742103
2,1334.69487742103
4,1334.69487742103
6,1334.69487742103
8,1334.69487742103
10,1334.69487742103
12,1332.29433050332
14,1329.45673681531
16,1326.9732036462
18,1325.64041625994
20,1326.45731768436
22,1329.97288465531
24,1337.30730552666
26,1348.36583716096
28,1362.83593147935
30,1380.51186288842
32,1400.70316857282
34,1423.07338107039
36,1446.74588704226
38,1471.1683724478
40,1495.85325122273
42,1520.53063829778
44,1544.82057545451
46,1568.51260685213
48,1591.7102771564
50,1614.35197337622
52,1636.5812055039
54,1658.41251324984
56,1680.01882706403
58,1701.22693777067
60,1722.10704457978
62,1742.41024895609
64,1761.80955100369
66,1780.14546446349
68,1797.24068466805
70,1812.98714513521
72,1827.37810268174
74,1840.23768510164
76,1851.7861285539
78,1862.1802872305
80,1871.43387323893
82,1879.7156824358
84,1886.99357112322
86,1893.45891681468
88,1899.19338518209
90,1904.23431556529
92,1908.67551744301
94,1912.47752400494
96,1915.77073751207
98,1918.58627808635
100,1920.95545273112
102,1922.92372654388
104,1924.44489745931
106,1925.62155421818
108,1926.47717704629
110,1927.03906938443
112,1927.34566319028
114,1927.40419222145
116,1927.2804628115
118,1926.99990231718
120,1926.58623186738
122,1926.06487732359
124,1925.46522895993
126,1924.82704583983
128,1924.1782587212
130,1923.54350397612
132,1922.95053615843
134,1922.44202970359
136,1922.01859603237
138,1921.69005997624
140,1921.46438022392
142,1921.34686929578
144,1921.35344186557
146,1921.46238878409
148,1921.6745793402
150,1921.99161362305
152,1922.41183577292
154,1922.94479685114
156,1923.56973724424
158,1924.28915100547
160,1925.10172643214
162,1926.00521267298
164,1927.00685309248
166,1928.08974016998
168,1929.25713200942
170,1930.50314147053
172,1931.82753726673
174,1933.23676026725
176,1934.71550060661
178,1936.26692654533
180,1937.8820679905
182,1939.56439478889
184,1941.3162086758
186,1943.12450641092
188,1944.99212561605
190,1946.90965142989
192,1948.88074601225
194,1950.90596935405
196,1952.97504214494
198,1955.0900322283
200,1957.24235750422
202,1959.43481163598
204,1961.66833806644
206,1963.93331365535
208,1966.23248428331
210,1968.55833195416
212,1970.91420697782
214,1973.30194141882
216,1975.71295625688
218,1978.15175475832
220,1980.61099919371
222,1983.09395822339
224,1985.59770326547
226,1988.11371364632
228,1990.63830143652
230,1993.16692745052
232,1995.69081420425
234,1998.20321356055
236,2000.70361425962
238,2003.17319688187
240,2005.62382527262
242,2008.043141187
244,2010.43170571409
246,2012.80040981031
248,2015.13528125639
250,2017.45493132435
252,2019.7580192727
254,2022.05275650521
256,2024.34539793647
258,2026.63676993845
260,2028.93219926555
262,2031.23645848078
264,2033.55236959972
266,2035.87801690305
268,2038.21887035739
270,2040.5709011839
272,2042.93930170332
274,2045.32514312697
276,2047.72415911892
278,2050.14354833293
280,2052.57660314999
282,2055.02801742812
284,2057.49808716409
286,2059.98136864876
288,2062.48419527018
290,2064.99961334346
292,2067.53186291389
294,2070.08201837473
296,2072.6456566318
298,2075.22958290142
300,2077.82671124608
302,2080.44228120216
304,2083.0781941417
306,2085.72937898593
308,2088.40263210135
310,2091.09200909683
312,2093.80492712835
314,2096.54933777266
316,2099.32459439248
318,2102.14751731371
320,2105.01327362801
322,2107.94501827274
324,2110.96673019341
326,2114.05730011457
328,2117.24151639013
330,2120.48939216654
332,2123.81554121057
334,2127.21256836669
336,2130.64546004862
338,2134.11764120993
340,2137.60867947116
342,2141.11756185616
344,2144.63747054254
346,2148.16221549656
348,2151.69169776983
350,2155.22401104491
352,2158.759239826
354,2162.29750845176
356,2165.837923743
358,2169.38083163257
360,2172.92572158671
362,2176.47322123665
364,2180.02321190805
366,2183.57538681959
368,2187.13005672577
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================================================
FILE: tutorials/tracing_PKiKP/.ipynb_checkpoints/tracing_PKiKP_rays-checkpoint.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Teleseismic ray tracing: PKiKP\n",
"\n",
"## Malcolm C. A. White\n",
"### 17 December 2020\n",
"\n",
"This notebook presents a brief tutorial on how to trace raypaths for teleseismic PKiKP phases. The examples shown are 2D examples to simplify plotting and improve efficiency, but the basic procedure is identical for the 3D case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Preliminary setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.1. Imports and constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pykonal\n",
"\n",
"EARTH_RADIUS = 6371 # Earth radius in km.\n",
"CMB = 3480 # Core-mantle boundary radius in km.\n",
"ICB = 1220 # Inner-core outer-core boundary radius in km."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.2. Define some convenient plotting functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_field(field, ax, irho=slice(None), itheta=slice(None), iphi=slice(None), cmap=plt.get_cmap(\"hot\"), boundaries=[ICB, CMB, EARTH_RADIUS], vmin=None, vmax=None):\n",
" nodes = field.nodes[irho, itheta, iphi]\n",
" xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
" yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
"\n",
" qmesh = ax.pcolormesh(\n",
" xx, \n",
" yy, \n",
" field.values[irho, itheta, iphi], \n",
" cmap=cmap,\n",
" vmin=vmin,\n",
" vmax=vmax,\n",
" shading=\"gouraud\"\n",
" )\n",
"\n",
" theta = np.linspace(field.min_coords[2], field.max_coords[2], 64)\n",
" for boundary in boundaries:\n",
" ax.plot(\n",
" boundary * np.cos(theta),\n",
" boundary * np.sin(theta),\n",
" color=\"k\",\n",
" linewidth=1\n",
" )\n",
"\n",
" return (qmesh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.3. Load the IASP91 velocity model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iasp91 = pd.read_csv(\n",
" \"IASP91.csv\",\n",
" header=None,\n",
" names=[\"depth\", \"radius\", \"Vp\", \"Vs\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Tracing PKiKP reflections\n",
"\n",
"## 2.1. Propagating the wavefront\n",
"This section consists of a loop to propagate the wavefront along different segments of the propagation path. First, the wavefront propagates from the source to the inner-core boundary. Then, the wavefront is propagated from the inner-core boundary back to the surface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define how many nodes to use in the radial and azimuthal directions.\n",
"nrho, nphi = 256, 256\n",
"\n",
"# An empty list to hold solvers for different segments of the propagation.\n",
"solvers = []\n",
"\n",
"# Define the source location.\n",
"src_loc = 5971, np.pi/2, np.pi/2\n",
"\n",
"# This parameter defines segments of the propagation path. The wavefronts go\n",
"# from the source to the inner core boundary, then from the inner-core boundary\n",
"# to the Earth's surface.\n",
"path = (\n",
" (EARTH_RADIUS, ICB), # This defines the first segment of the path.\n",
" (ICB, EARTH_RADIUS) # This defines the second segment.\n",
")\n",
"\n",
"plt.close(\"all\")\n",
"\n",
"for ipath in range(len(path)):\n",
" path_seg = path[ipath]\n",
"\n",
" if ipath == 0:\n",
" # If this is the first segment of the propagation path, then use a\n",
" # PointSourceSolver.\n",
" solver = pykonal.solver.PointSourceSolver(coord_sys=\"spherical\") \n",
" else:\n",
" # Otherwise use an ordinary EikonalSolver.\n",
" solver = pykonal.EikonalSolver(coord_sys=\"spherical\")\n",
"\n",
" solvers.append(solver)\n",
"\n",
" # Define the computational domain.\n",
" solver.vv.min_coords = min(path_seg), np.pi / 2, 0 \n",
" solver.vv.node_intervals = (\n",
" (max(path_seg) - min(path_seg)) / (nrho - 1), \n",
" 1, \n",
" np.pi / (nphi - 1)\n",
" )\n",
" solver.vv.npts = nrho, 1, nphi\n",
"\n",
" # Interpolate IASP91 onto the computational grid.\n",
" solver.vv.values = np.interp(\n",
" solver.vv.nodes[..., 0],\n",
" iasp91[\"radius\"].values[-1::-1],\n",
" iasp91[\"Vp\"].values[-1::-1]\n",
" )\n",
"\n",
" if ipath == 0:\n",
" # If this is the first segment of the propagation path, set the source\n",
" # location.\n",
" solver.src_loc = src_loc\n",
" else:\n",
" # Otherwise interpolate the traveltime field of the previous segment of\n",
" # of the propagation path onto the boundary of the current segment.\n",
" if path_seg[0] < path_seg[1]:\n",
" # If this is a upgoing wavefront, then interpolate onto the lower\n",
" # boundary.\n",
" irho = 0\n",
" else:\n",
" # Otherwise interpolate onto the upper boundary.\n",
" irho = nrho - 1\n",
" # Set the traveltime at each node along the boundary of the current\n",
" # segment of the propagation path equal to the value at that position\n",
" # from the previous segment.\n",
" for iphi in range(nphi):\n",
" idx = (irho, 0, iphi)\n",
" node = solver.tt.nodes[idx]\n",
" solver.tt.values[idx] = solvers[ipath-1].tt.resample(node.reshape(1, 3))\n",
" solver.unknown[idx] = False\n",
" solver.trial.push(*idx)\n",
"\n",
" # Finally, solve the eikonal equation for the traveltime field.\n",
" print(\"Solving the eikonal equation...\")\n",
" %time solver.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.2. Plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.close(\"all\")\n",
"fig, axes = plt.subplots(figsize=(12, 6), nrows=1, ncols=3)\n",
"for ax in axes:\n",
" ax.set_aspect(1)\n",
" \n",
"axes[0].set_title(\"Velocity\")\n",
"qmesh = plot_field(solvers[0].vv, axes[0], itheta=0)\n",
"cbar = fig.colorbar(qmesh, ax=axes[0], orientation=\"horizontal\")\n",
"cbar.set_label(\"$v_P$ (km s$^{-1}$)\")\n",
"xx = src_loc[0] * np.cos(src_loc[2])\n",
"yy = src_loc[0] * np.sin(src_loc[2])\n",
"axes[0].scatter(xx, yy, marker=\"*\", facecolor=\"w\", edgecolor=\"k\", linewidth=1, s=256, zorder=100)\n",
"\n",
"axes[1].set_title(\"Downgoing wavefront\")\n",
"qmesh = plot_field(solvers[0].tt, axes[1], itheta=0, cmap=plt.get_cmap(\"nipy_spectral_r\"), vmin=0, vmax=solvers[-1].tt.values.max())\n",
"cbar = fig.colorbar(qmesh, ax=axes[1], orientation=\"horizontal\")\n",
"cbar.set_label(\"Traveltime (s)\")\n",
"axes[1].set_yticklabels([])\n",
"\n",
"axes[2].set_title(\"Upgoing (reflected) wavefront\")\n",
"qmesh = plot_field(solvers[1].tt, axes[2], itheta=0, cmap=plt.get_cmap(\"nipy_spectral_r\"), vmin=0, vmax=solvers[-1].tt.values.max())\n",
"cbar = fig.colorbar(qmesh, ax=axes[2], orientation=\"horizontal\")\n",
"cbar.set_label(\"Traveltime (s)\")\n",
"axes[2].yaxis.tick_right()\n",
"\n",
"for ax in axes:\n",
" ax.tick_params(axis=\"y\", left=True, right=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.3. Trace some reflected rays"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rx_loc = (6371, np.pi/2, src_loc[2] - np.pi/4)\n",
"\n",
"for rx_phi in src_loc[2] + np.array([7, 5, 3, 1, -2, -4, -6]) * np.pi/16:\n",
" # Define a receiver location.\n",
" rx_loc = (6371, np.pi/2, rx_phi)\n",
"\n",
" # Create an empty raypath.\n",
" ray = np.empty((0, 3))\n",
"\n",
" # Trace the ray from the receiver to the inner-core-boundary.\n",
" ipath = len(solvers) - 1 # Start with the last segment of the propagation path.\n",
" path_seg = path[ipath]\n",
" solver = solvers[ipath]\n",
" ray_seg = solver.trace_ray(np.array(rx_loc))\n",
" \n",
" # The ray refracts along the lower (i.e., inner-core) boundary, so this is\n",
" # a hack to drop the refracted part of the raypath.\n",
" idx = np.where( (ray_seg[...,0] - path_seg[0]) < solver.tt.step_size * 3 )[0][-1]\n",
" ray = np.vstack([ray, ray_seg[idx:]])\n",
"\n",
" # Trace the ray from the inner-core boundary to the source.\n",
" ipath -= 1\n",
" solver = solvers[ipath]\n",
" ray_seg = solver.trace_ray(ray[0])\n",
" ray = np.vstack([ray_seg, ray])\n",
" \n",
" \n",
" # Plot the raypath on the velocity model.\n",
" xx = ray[..., 0] * np.cos(ray[..., 2])\n",
" yy = ray[..., 0] * np.sin(ray[..., 2])\n",
" axes[0].plot(xx, yy, color=\"k\", linewidth=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py38]",
"language": "python",
"name": "conda-env-py38-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: tutorials/tracing_PKiKP/IASP91.csv
================================================
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================================================
FILE: tutorials/tracing_PKiKP/tracing_PKiKP_rays.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Teleseismic ray tracing: PKiKP\n",
"\n",
"## Malcolm C. A. White\n",
"### 17 December 2020\n",
"\n",
"This notebook presents a brief tutorial on how to trace raypaths for teleseismic PKiKP phases. The examples shown are 2D examples to simplify plotting and improve efficiency, but the basic procedure is identical for the 3D case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Preliminary setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.1. Imports and constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib ipympl\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pykonal\n",
"\n",
"EARTH_RADIUS = 6371 # Earth radius in km.\n",
"CMB = 3480 # Core-mantle boundary radius in km.\n",
"ICB = 1220 # Inner-core outer-core boundary radius in km."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.2. Define some convenient plotting functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_field(field, ax, irho=slice(None), itheta=slice(None), iphi=slice(None), cmap=plt.get_cmap(\"hot\"), boundaries=[ICB, CMB, EARTH_RADIUS], vmin=None, vmax=None):\n",
" nodes = field.nodes[irho, itheta, iphi]\n",
" xx = nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\n",
" yy = nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])\n",
"\n",
" qmesh = ax.pcolormesh(\n",
" xx, \n",
" yy, \n",
" field.values[irho, itheta, iphi], \n",
" cmap=cmap,\n",
" vmin=vmin,\n",
" vmax=vmax,\n",
" shading=\"gouraud\"\n",
" )\n",
"\n",
" theta = np.linspace(field.min_coords[2], field.max_coords[2], 64)\n",
" for boundary in boundaries:\n",
" ax.plot(\n",
" boundary * np.cos(theta),\n",
" boundary * np.sin(theta),\n",
" color=\"k\",\n",
" linewidth=1\n",
" )\n",
"\n",
" return (qmesh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.3. Load the IASP91 velocity model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iasp91 = pd.read_csv(\n",
" \"IASP91.csv\",\n",
" header=None,\n",
" names=[\"depth\", \"radius\", \"Vp\", \"Vs\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Tracing PKiKP reflections\n",
"\n",
"## 2.1. Propagating the wavefront\n",
"This section consists of a loop to propagate the wavefront along different segments of the propagation path. First, the wavefront propagates from the source to the inner-core boundary. Then, the wavefront is propagated from the inner-core boundary back to the surface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define how many nodes to use in the radial and azimuthal directions.\n",
"nrho, nphi = 256, 256\n",
"\n",
"# An empty list to hold solvers for different segments of the propagation.\n",
"solvers = []\n",
"\n",
"# Define the source location.\n",
"src_loc = 5971, np.pi/2, np.pi/2\n",
"\n",
"# This parameter defines segments of the propagation path. The wavefronts go\n",
"# from the source to the inner core boundary, then from the inner-core boundary\n",
"# to the Earth's surface.\n",
"path = (\n",
" (EARTH_RADIUS, ICB), # This defines the first segment of the path.\n",
" (ICB, EARTH_RADIUS) # This defines the second segment.\n",
")\n",
"\n",
"plt.close(\"all\")\n",
"\n",
"for ipath in range(len(path)):\n",
" path_seg = path[ipath]\n",
"\n",
" if ipath == 0:\n",
" # If this is the first segment of the propagation path, then use a\n",
" # PointSourceSolver.\n",
" solver = pykonal.solver.PointSourceSolver(coord_sys=\"spherical\") \n",
" else:\n",
" # Otherwise use an ordinary EikonalSolver.\n",
" solver = pykonal.EikonalSolver(coord_sys=\"spherical\")\n",
"\n",
" solvers.append(solver)\n",
"\n",
" # Define the computational domain.\n",
" solver.vv.min_coords = min(path_seg), np.pi / 2, 0 \n",
" solver.vv.node_intervals = (\n",
" (max(path_seg) - min(path_seg)) / (nrho - 1), \n",
" 1, \n",
" np.pi / (nphi - 1)\n",
" )\n",
" solver.vv.npts = nrho, 1, nphi\n",
"\n",
" # Interpolate IASP91 onto the computational grid.\n",
" solver.vv.values = np.interp(\n",
" solver.vv.nodes[..., 0],\n",
" iasp91[\"radius\"].values[-1::-1],\n",
" iasp91[\"Vp\"].values[-1::-1]\n",
" )\n",
"\n",
" if ipath == 0:\n",
" # If this is the first segment of the propagation path, set the source\n",
" # location.\n",
" solver.src_loc = src_loc\n",
" else:\n",
" # Otherwise interpolate the traveltime field of the previous segment of\n",
" # of the propagation path onto the boundary of the current segment.\n",
" if path_seg[0] < path_seg[1]:\n",
" # If this is a upgoing wavefront, then interpolate onto the lower\n",
" # boundary.\n",
" irho = 0\n",
" else:\n",
" # Otherwise interpolate onto the upper boundary.\n",
" irho = nrho - 1\n",
" # Set the traveltime at each node along the boundary of the current\n",
" # segment of the propagation path equal to the value at that position\n",
" # from the previous segment.\n",
" for iphi in range(nphi):\n",
" idx = (irho, 0, iphi)\n",
" node = solver.tt.nodes[idx]\n",
" solver.tt.values[idx] = solvers[ipath-1].tt.resample(node.reshape(1, 3))\n",
" solver.unknown[idx] = False\n",
" solver.trial.push(*idx)\n",
"\n",
" # Finally, solve the eikonal equation for the traveltime field.\n",
" print(\"Solving the eikonal equation...\")\n",
" %time solver.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.2. Plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.close(\"all\")\n",
"fig, axes = plt.subplots(figsize=(12, 6), nrows=1, ncols=3)\n",
"for ax in axes:\n",
" ax.set_aspect(1)\n",
" \n",
"axes[0].set_title(\"Velocity\")\n",
"qmesh = plot_field(solvers[0].vv, axes[0], itheta=0)\n",
"cbar = fig.colorbar(qmesh, ax=axes[0], orientation=\"horizontal\")\n",
"cbar.set_label(\"$v_P$ (km s$^{-1}$)\")\n",
"xx = src_loc[0] * np.cos(src_loc[2])\n",
"yy = src_loc[0] * np.sin(src_loc[2])\n",
"axes[0].scatter(xx, yy, marker=\"*\", facecolor=\"w\", edgecolor=\"k\", linewidth=1, s=256, zorder=100)\n",
"\n",
"axes[1].set_title(\"Downgoing wavefront\")\n",
"qmesh = plot_field(solvers[0].tt, axes[1], itheta=0, cmap=plt.get_cmap(\"nipy_spectral_r\"), vmin=0, vmax=solvers[-1].tt.values.max())\n",
"cbar = fig.colorbar(qmesh, ax=axes[1], orientation=\"horizontal\")\n",
"cbar.set_label(\"Traveltime (s)\")\n",
"axes[1].set_yticklabels([])\n",
"\n",
"axes[2].set_title(\"Upgoing (reflected) wavefront\")\n",
"qmesh = plot_field(solvers[1].tt, axes[2], itheta=0, cmap=plt.get_cmap(\"nipy_spectral_r\"), vmin=0, vmax=solvers[-1].tt.values.max())\n",
"cbar = fig.colorbar(qmesh, ax=axes[2], orientation=\"horizontal\")\n",
"cbar.set_label(\"Traveltime (s)\")\n",
"axes[2].yaxis.tick_right()\n",
"\n",
"for ax in axes:\n",
" ax.tick_params(axis=\"y\", left=True, right=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.3. Trace some reflected rays"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rx_loc = (6371, np.pi/2, src_loc[2] - np.pi/4)\n",
"\n",
"for rx_phi in src_loc[2] + np.array([7, 5, 3, 1, -2, -4, -6]) * np.pi/16:\n",
" # Define a receiver location.\n",
" rx_loc = (6371, np.pi/2, rx_phi)\n",
"\n",
" # Create an empty raypath.\n",
" ray = np.empty((0, 3))\n",
"\n",
" # Trace the ray from the receiver to the inner-core-boundary.\n",
" ipath = len(solvers) - 1 # Start with the last segment of the propagation path.\n",
" path_seg = path[ipath]\n",
" solver = solvers[ipath]\n",
" ray_seg = solver.trace_ray(np.array(rx_loc))\n",
" \n",
" # The ray refracts along the lower (i.e., inner-core) boundary, so this is\n",
" # a hack to drop the refracted part of the raypath.\n",
" idx = np.where( (ray_seg[...,0] - path_seg[0]) < solver.tt.step_size * 3 )[0][-1]\n",
" ray = np.vstack([ray, ray_seg[idx:]])\n",
"\n",
" # Trace the ray from the inner-core boundary to the source.\n",
" ipath -= 1\n",
" solver = solvers[ipath]\n",
" ray_seg = solver.trace_ray(ray[0])\n",
" ray = np.vstack([ray_seg, ray])\n",
" \n",
" \n",
" # Plot the raypath on the velocity model.\n",
" xx = ray[..., 0] * np.cos(ray[..., 2])\n",
" yy = ray[..., 0] * np.sin(ray[..., 2])\n",
" axes[0].plot(xx, yy, color=\"k\", linewidth=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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\n",
"