Deploy v0.6.0 from a697116

fatiando · Feb 7, 2024 · f342689 · f342689
1 parent 7d92ec5
commit f342689
Show file tree

Hide file tree

Showing 290 changed files with 46,349 additions and 1 deletion.
diff --git a/latest b/latest
@@ -1 +1 @@
-v0.5.0
+v0.6.0
diff --git a/v0.6.0/.buildinfo b/v0.6.0/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 8d406fb63a1221c5bc7b5c2cda3edecd
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/v0.6.0/_downloads/0194e96d990d7f56b81ee71b0c6e5d06/osborne-magnetic.ipynb b/v0.6.0/_downloads/0194e96d990d7f56b81ee71b0c6e5d06/osborne-magnetic.ipynb
@@ -0,0 +1,104 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Magnetic airborne survey of the Osborne Mine, Australia\n\nThis is a section of a survey acquired in 1990 by the Queensland\nGovernment, Australia. The line data have approximately 80 m terrain\nclearance and 200 m line spacing. The section contains\nthe total field magnetic anomalies associated with the Osborne Mine,\nLightning Creek sill complex, and the Brumby prospect.\n\n**Original source:**\n[Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa\nInlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia,\nCanberra](http://pid.geoscience.gov.au/dataset/ga/142419)_\n\n**Pre-processing:** [Source code for preparation of the original dataset for\nredistribution in Ensaio](https://github.com/fatiando-data/osborne-magnetic)_\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\nimport pygmt\n\nimport ensaio"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Download and cache the data and return the path to it on disk\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fname = ensaio.fetch_osborne_magnetic(version=1)\nprint(fname)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Load the CSV formatted data with pandas\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "data = pd.read_csv(fname)\ndata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Make a PyGMT map with the data points colored by the total field magnetic\nanomaly.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = pygmt.Figure()\nfig.basemap(\n    projection=\"M15c\",\n    region=[\n        data.longitude.min(),\n        data.longitude.max(),\n        data.latitude.min(),\n        data.latitude.max(),\n    ],\n    frame=\"af\",\n)\nscale = 1500\npygmt.makecpt(cmap=\"polar+h\", series=[-scale, scale], background=True)\nfig.plot(\n    x=data.longitude,\n    y=data.latitude,\n    fill=data.total_field_anomaly_nt,\n    style=\"c0.075c\",\n    cmap=True,\n)\nfig.colorbar(\n    frame='af+l\"total field magnetic anomaly [nT]\"',\n    position=\"JBC+h+o0/1c+e\",\n)\nfig.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The anomaly at the bottom left is the Osborne Mine. The ones on the top right\nare the Lightning Creek sill complex (the largest) and the Brumby prospect\n(one of the smaller anomalies).\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/v0.6.0/_downloads/09e80c17b55b388d28c3a4d43e7c3f6c/alps-gps-velocity.py b/v0.6.0/_downloads/09e80c17b55b388d28c3a4d43e7c3f6c/alps-gps-velocity.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2021 The Ensaio Developers.
+# Distributed under the terms of the BSD 3-Clause License.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# This code is part of the Fatiando a Terra project (https://www.fatiando.org)
+#
+"""
+GPS velocities (3-component) for the Alps
+-----------------------------------------
+
+This is a compilation of 3D GPS velocities for the Alps. The horizontal
+velocities are reference to the Eurasian frame. All velocity components and
+even the position have error estimates, which is very useful and rare to find
+in a lot of datasets.
+
+**Original source:**
+`Sánchez et al. (2018) <https://doi.org/10.1594/PANGAEA.886889>`__
+
+**Pre-processing:** `Source code for preparation of the original dataset for
+redistribution in Ensaio
+<https://github.com/fatiando-data/alps-gps-velocity>`__
+
+"""
+import numpy as np
+import pandas as pd
+import pygmt
+
+import ensaio
+
+###############################################################################
+# Download and cache the data and return the path to it on disk
+fname = ensaio.fetch_alps_gps(version=1)
+print(fname)
+
+###############################################################################
+# Load the CSV formatted data with pandas
+data = pd.read_csv(fname)
+data
+
+###############################################################################
+# To plot the vectors with PyGMT, we need to convert the horizontal components
+# into angle (azimuth) and length.
+angle = np.degrees(np.arctan2(data.velocity_north_mmyr, data.velocity_east_mmyr))
+length = np.hypot(data.velocity_north_mmyr, data.velocity_east_mmyr)
+
+###############################################################################
+# Now we can make a PyGMT map with the horizontal velocity vectors and vertical
+# velocities encoded as colored points.
+
+# West, East, South, North boundaries of the map
+region = [-5, 20, 40, 55]
+
+fig = pygmt.Figure()
+with fig.subplot(
+    nrows=1,
+    ncols=2,
+    figsize=("35c", "15c"),
+    sharey="l",  # shared y-axis on the left side
+    frame="WSrt",
+):
+    with fig.set_panel(0):
+        fig.basemap(region=region, projection="M?", frame="af")
+        fig.coast(area_thresh=1e4, land="#eeeeee")
+        scale_factor = 2 / length.max()
+        fig.plot(
+            x=data.longitude,
+            y=data.latitude,
+            direction=[angle, length * scale_factor],
+            style="v0.15c+e",
+            fill="blue",
+            pen="1p,blue",
+        )
+        # Plot a quiver caption
+        fig.plot(
+            x=-4,
+            y=42,
+            direction=[[0], [1 * scale_factor]],
+            style="v0.15c+e",
+            fill="blue",
+            pen="1p,blue",
+        )
+        fig.text(
+            x=-4,
+            y=42.2,
+            text="1 mm/yr",
+            justify="BL",
+            font="10p,Helvetica,blue",
+        )
+    with fig.set_panel(1):
+        fig.basemap(region=region, projection="M?", frame="af")
+        fig.coast(area_thresh=1e4, land="#eeeeee")
+        pygmt.makecpt(
+            cmap="polar",
+            series=[data.velocity_up_mmyr.min(), data.velocity_up_mmyr.max()],
+        )
+        fig.plot(
+            x=data.longitude,
+            y=data.latitude,
+            fill=data.velocity_up_mmyr,
+            style="c0.2c",
+            cmap=True,
+            pen="0.5p,black",
+        )
+        fig.colorbar(
+            frame='af+l"vertical velocity [mm/yr]"',
+            position="jTL+w7c/0.3c+h+o1/1",
+        )
+fig.show()
diff --git a/v0.6.0/_downloads/1cb5ba9b8bc1bcb2c6b55713025dfce2/osborne-magnetic.py b/v0.6.0/_downloads/1cb5ba9b8bc1bcb2c6b55713025dfce2/osborne-magnetic.py
@@ -0,0 +1,72 @@
+# Copyright (c) 2021 The Ensaio Developers.
+# Distributed under the terms of the BSD 3-Clause License.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# This code is part of the Fatiando a Terra project (https://www.fatiando.org)
+#
+"""
+Magnetic airborne survey of the Osborne Mine, Australia
+-------------------------------------------------------
+
+This is a section of a survey acquired in 1990 by the Queensland
+Government, Australia. The line data have approximately 80 m terrain
+clearance and 200 m line spacing. The section contains
+the total field magnetic anomalies associated with the Osborne Mine,
+Lightning Creek sill complex, and the Brumby prospect.
+
+**Original source:**
+`Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa
+Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia,
+Canberra <http://pid.geoscience.gov.au/dataset/ga/142419>`__
+
+**Pre-processing:** `Source code for preparation of the original dataset for
+redistribution in Ensaio <https://github.com/fatiando-data/osborne-magnetic>`__
+"""
+import pandas as pd
+import pygmt
+
+import ensaio
+
+###############################################################################
+# Download and cache the data and return the path to it on disk
+fname = ensaio.fetch_osborne_magnetic(version=1)
+print(fname)
+
+###############################################################################
+# Load the CSV formatted data with pandas
+data = pd.read_csv(fname)
+data
+
+###############################################################################
+# Make a PyGMT map with the data points colored by the total field magnetic
+# anomaly.
+fig = pygmt.Figure()
+fig.basemap(
+    projection="M15c",
+    region=[
+        data.longitude.min(),
+        data.longitude.max(),
+        data.latitude.min(),
+        data.latitude.max(),
+    ],
+    frame="af",
+)
+scale = 1500
+pygmt.makecpt(cmap="polar+h", series=[-scale, scale], background=True)
+fig.plot(
+    x=data.longitude,
+    y=data.latitude,
+    fill=data.total_field_anomaly_nt,
+    style="c0.075c",
+    cmap=True,
+)
+fig.colorbar(
+    frame='af+l"total field magnetic anomaly [nT]"',
+    position="JBC+h+o0/1c+e",
+)
+fig.show()
+
+###############################################################################
+# The anomaly at the bottom left is the Osborne Mine. The ones on the top right
+# are the Lightning Creek sill complex (the largest) and the Brumby prospect
+# (one of the smaller anomalies).
diff --git a/v0.6.0/_downloads/20eb49f47556798b88df3fd28d788bba/earth-gravity.ipynb b/v0.6.0/_downloads/20eb49f47556798b88df3fd28d788bba/earth-gravity.ipynb
@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Earth gravity grid at 10 arc-minute resolution\n\nThe grid is grid-node registered and stored in netCDF with CF-compliant\nmetadata. The gravity values are derived from the EIGEN-6C4 spherical harmonic\nmodel. Here \"gravity\" refers to the combined gravitational and centrifugal\naccelerations.\n\nThe data are calculated uniformly at 10 km above the WGS84 ellipsoid.\n\n**Original source:** [EIGEN-6C4 model](https://doi.org/10.5880/icgem.2015.1)_\n\n**Pre-processing:** [Source code for preparation of the original dataset for\nredistribution in Ensaio](https://github.com/fatiando-data/earth-gravity-10arcmin)_\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pygmt\nimport xarray as xr\n\nimport ensaio"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Download and cache the data and return the path to it on disk.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fname = ensaio.fetch_earth_gravity(version=1)\nprint(fname)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Load the netCDF grid with xarray.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "data = xr.load_dataarray(fname)\ndata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Make a PyGMT pseudo-color map of the grid in a Mollweide projection.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = pygmt.Figure()\nfig.basemap(\n    region=\"g\",\n    projection=\"W15c\",\n    frame=True,\n)\nfig.grdimage(data, cmap=\"viridis\", shading=\"+nt0.5\")\nfig.colorbar(frame='af+l\"gravity [mGal]\"')\nfig.coast(shorelines=True, resolution=\"c\", area_thresh=1e4)\nfig.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}