From f8a3540ee4d49ba402491d73cb6c9eadf5d4f957 Mon Sep 17 00:00:00 2001 From: maxrjones Date: Thu, 5 Dec 2024 19:15:01 +0000 Subject: [PATCH] deploy: 68b9c78e4095bace19ca1768ee48a87690d9f810 --- _sources/intro.md | 34 ++++----- _sources/tut01_firstfigure.ipynb | 119 +++++++++++++++---------------- _sources/tut02_spe_pd_gpd.ipynb | 48 +++++++------ _sources/tut04_geophysics.ipynb | 42 +++++------ searchindex.js | 2 +- tut01_firstfigure.html | 119 +++++++++++++++---------------- tut02_spe_pd_gpd.html | 50 +++++++------ tut03_spe_xarray.html | 20 +++--- tut04_geophysics.html | 56 +++++++-------- tut05_topography.html | 28 ++++---- 10 files changed, 261 insertions(+), 257 deletions(-) diff --git a/_sources/intro.md b/_sources/intro.md index 52b384d..a4f34c6 100644 --- a/_sources/intro.md +++ b/_sources/intro.md @@ -8,13 +8,12 @@ tutorials for making maps 🗺️ and animations 🎦 :gutter: 1 :padding: 1 - :::::{grid-item} ::::{grid} 1 1 1 1 :gutter: 1 :::{grid-item-card} Tutorial 1 - First figure 🚀 and Subplots / layout -:img-top: _images/fd70248f75b8b37ee54b3135f77705f98c6d8489eb18b6910184b447d0f0638d.png +:img-top: \_images/fd70248f75b8b37ee54b3135f77705f98c6d8489eb18b6910184b447d0f0638d.png :link: ./tut01_firstfigure.html by [Jing-Hui Tong](https://orcid.org/0009-0002-7195-3071) +++ @@ -29,7 +28,7 @@ by [Jing-Hui Tong](https://orcid.org/0009-0002-7195-3071) ::: :::{grid-item-card} Tutorial 2 - Integration with the scientific Python ecosystem 🐍: pandas and GeoPandas (tabular data 🗒️) -:img-top: _images/7f18327908c8dd210197cc51845e45a933f356b9fd12bd029a4a8cbda080eb2b.png +:img-top: \_images/7f18327908c8dd210197cc51845e45a933f356b9fd12bd029a4a8cbda080eb2b.png :link: ./tut02_spe_pd_gpd.html by [Yvonne Fröhlich](https://orcid.org/0000-0002-8566-0619) +++ @@ -47,13 +46,12 @@ by [Yvonne Fröhlich](https://orcid.org/0000-0002-8566-0619) :::: ::::: - :::::{grid-item} ::::{grid} 1 1 1 1 :gutter: 1 :::{grid-item-card} Tutorial 3 - Integration with the scientific Python ecosystem 🐍: Xarray (gridded data 🌐) -:img-top: _images/a18fcb026fb0d0c83360f2b8382a360e552dd50db6aa2db5311259bf5223d3a2.png +:img-top: \_images/a18fcb026fb0d0c83360f2b8382a360e552dd50db6aa2db5311259bf5223d3a2.png :link: ./tut03_spe_xarray.html by [Max Jones](https://orcid.org/0000-0003-0180-8928) +++ @@ -89,13 +87,12 @@ and [Yvonne Fröhlich](https://orcid.org/0000-0002-8566-0619) :::: ::::: - :::::{grid-item} ::::{grid} 1 1 1 1 :gutter: 1 :::{grid-item-card} Tutorial 5 - 3-D Topography (Planetary / Antarctic maps) -:img-top: _images/0a6fc097b1fcaa99eff8632dab714787757564ed60e07f20825101c6a8e045ae.png +:img-top: \_images/0a6fc097b1fcaa99eff8632dab714787757564ed60e07f20825101c6a8e045ae.png :link: ./tut05_topography.html by [Wei Ji Leong](https://orcid.org/0000-0003-2354-1988) and [André Belém](https://orcid.org/0000-0002-8865-6180) @@ -109,7 +106,7 @@ and [André Belém](https://orcid.org/0000-0002-8865-6180) ::: :::{grid-item-card} Tutorial 6 - Animations with GMT -:img-top: _images/5847818951ca8fbc9b86a6f2c67389b6.png +:img-top: \_images/5847818951ca8fbc9b86a6f2c67389b6.png :link: ./tut06_animation.html by [Federico Esteban](https://orcid.org/0000-0002-0641-7371) +++ @@ -124,15 +121,12 @@ by [Federico Esteban](https://orcid.org/0000-0002-0641-7371) :::: ::::: - :::::: - Each tutorial is rendered on this website for easy viewing 👀, but they are all Jupyter notebooks designed to be ran interactively 💫. See the instructions below on how you can start running the tutorials in no time! 🚀 - # 🌠 Quickstart To run these notebooks in an interactive Jupyter session online, 🖱️ click on the button @@ -181,18 +175,18 @@ Here's the instructions to install the `agu24workshop` environment: 4. Run the following commands on the 🧑‍💻 command-line to create the virtual environment - ```bash - cd /path/to/agu24workshop - conda env create --name agu24workshop --file environment.yml - ``` + ```bash + cd /path/to/agu24workshop + conda env create --name agu24workshop --file environment.yml + ``` 5. Once the installation is completed 🏁, launch [Jupyter Lab](https://jupyterlab.readthedocs.io) as follows: - ```bash - conda activate agu24workshop - jupyter lab - ``` + ```bash + conda activate agu24workshop + jupyter lab + ``` This should open up a page in your default browser. If not, you can click and open the 🔗 link that says `http://localhost:8888/lab?token=...` in your command-line @@ -202,7 +196,7 @@ Here's the instructions to install the `agu24workshop` environment: https://www.generic-mapping-tools.org/agu24workshop/tut01_firstfigure.html) using either the download button on the ↗️ top right (select '.ipynb') or from GitHub at https://github.com/GenericMappingTools/agu24workshop/tree/main/book. Make sure to put - the *.ipynb file(s) inside of the 'agu24workshop' folder. + the \*.ipynb file(s) inside of the 'agu24workshop' folder. 7. Open the Jupyter notebook in the left-pane file browser, e.g. by 🖱️ double-clicking on `first-figure.ipynb`. You are now ready to run through the course materials 🎉! diff --git a/_sources/tut01_firstfigure.ipynb b/_sources/tut01_firstfigure.ipynb index f25ebb8..bc3f2cd 100644 --- a/_sources/tut01_firstfigure.ipynb +++ b/_sources/tut01_firstfigure.ipynb @@ -121,7 +121,7 @@ "metadata": {}, "outputs": [], "source": [ - "fig=pygmt.Figure()" + "fig = pygmt.Figure()" ] }, { @@ -155,14 +155,12 @@ "metadata": {}, "outputs": [], "source": [ - "region=[-180, -150, 50, 65]\n", + "region = [-180, -150, 50, 65]\n", "\n", - "fig.coast(region=region, \n", - " projection=\"M10c\",\n", - " shorelines=True)\n", + "fig.coast(region=region, projection=\"M10c\", shorelines=True)\n", "\n", "# To see the figure looks like\n", - "fig.show() " + "fig.show()" ] }, { @@ -212,9 +210,7 @@ "metadata": {}, "outputs": [], "source": [ - "fig.coast(water=\"lightblue\", \n", - " land=\"grey\",\n", - " frame=[\"WSne\",\"a10f5g5\"]) \n", + "fig.coast(water=\"lightblue\", land=\"grey\", frame=[\"WSne\", \"a10f5g5\"])\n", "fig.show()" ] }, @@ -249,16 +245,15 @@ "metadata": {}, "outputs": [], "source": [ - "grid=pygmt.datasets.load_earth_relief(resolution=\"03m\", region=region)\n", + "grid = pygmt.datasets.load_earth_relief(resolution=\"03m\", region=region)\n", "\n", "# You also can access then cut the grid\n", - "# and this way can save grid (outgrid) as a file in your folder. \n", + "# and this way can save grid (outgrid) as a file in your folder.\n", "# https://www.generic-mapping-tools.org/remote-datasets/\n", "\n", - "# pygmt.grdcut(grid=\"@earth_relief_03m\", \n", - "# region=region,\n", - "# outgrid=\"Alaska.grd\", \n", - "# ) " + "# pygmt.grdcut(\n", + "# grid=\"@earth_relief_03m\", region=region, outgrid=\"Alaska.grd\"\n", + "# )" ] }, { @@ -300,14 +295,11 @@ "metadata": {}, "outputs": [], "source": [ - "fig=pygmt.Figure()\n", + "fig = pygmt.Figure()\n", "\n", - "fig.grdimage(grid=grid,\n", - " region=region, \n", - " projection=\"M10c\")\n", + "fig.grdimage(grid=grid, region=region, projection=\"M10c\")\n", "\n", - "fig.coast(shorelines=\"1p,black\",\n", - " frame=[\"WSne\", \"a5f2.5\"]) \n", + "fig.coast(shorelines=\"1p,black\", frame=[\"WSne\", \"a5f2.5\"])\n", "\n", "fig.show()" ] @@ -334,12 +326,13 @@ "metadata": {}, "outputs": [], "source": [ - "pygmt.makecpt(cmap=\"geo\", \n", - " series=[-8000, 7000, 500],\n", - " continuous=True,\n", - " # The file name with extension .cpt to store the generated CPT file\n", - " output=\"eleva.cpt\") \n", - " " + "pygmt.makecpt(\n", + " cmap=\"geo\",\n", + " series=[-8000, 7000, 500],\n", + " continuous=True,\n", + " # The file name with extension .cpt to store the generated CPT file\n", + " output=\"eleva.cpt\",\n", + ")" ] }, { @@ -381,19 +374,17 @@ "metadata": {}, "outputs": [], "source": [ - "fig=pygmt.Figure()\n", + "fig = pygmt.Figure()\n", "\n", - "fig.grdimage(grid=grid,\n", - " region=region, \n", - " projection=\"M10c\",\n", - " cmap=\"eleva.cpt\") \n", + "fig.grdimage(grid=grid, region=region, projection=\"M10c\", cmap=\"eleva.cpt\")\n", "\n", - "fig.coast(shorelines=\"1p,black\",\n", - " frame=[\"WSne\", \"a5f2.5\"]) \n", + "fig.coast(shorelines=\"1p,black\", frame=[\"WSne\", \"a5f2.5\"])\n", "\n", - "fig.colorbar(cmap=\"eleva.cpt\", \n", - " frame=\"a2000f500+lElevation (m)\",\n", - " position=\"JBC+w10c/0.4c+mc+h\",) \n", + "fig.colorbar(\n", + " cmap=\"eleva.cpt\",\n", + " frame=\"a2000f500+lElevation (m)\",\n", + " position=\"JBC+w10c/0.4c+mc+h\",\n", + ")\n", "\n", "fig.show()" ] @@ -425,39 +416,45 @@ "metadata": {}, "outputs": [], "source": [ - "fig=pygmt.Figure()\n", + "fig = pygmt.Figure()\n", "\n", - "grid_crust_age=pygmt.datasets.load_earth_age(resolution=\"30m\", region=[-210, -150, 35, 65])\n", + "grid_crust_age = pygmt.datasets.load_earth_age(\n", + " resolution=\"30m\", region=[-210, -150, 35, 65]\n", + ")\n", "\n", - "with fig.subplot(\n", - " nrows=1, ncols=2, figsize=[\"18c\",\"12c\"]\n", - "):\n", + "with fig.subplot(nrows=1, ncols=2, figsize=[\"18c\", \"12c\"]):\n", "\n", " with fig.set_panel(panel=0):\n", "\n", - " fig.grdimage(grid=grid,\n", - " region=region, \n", - " # \"?\" means that map width automatically determined from the subplot width.\n", - " projection=\"M?\", \n", - " cmap=\"eleva.cpt\") \n", - " fig.coast(shorelines=\"1p,black\",\n", - " frame=[\"WSne\", \"a5f2.5\"]) \n", - " fig.colorbar(cmap=\"eleva.cpt\", \n", - " position=\"JBC+w8c/0.3c+mc+h\", \n", - " frame=\"a2000f500+lElevation (m)\") \n", + " fig.grdimage(\n", + " grid=grid,\n", + " region=region,\n", + " # \"?\" means that map width automatically determined from the subplot width.\n", + " projection=\"M?\",\n", + " cmap=\"eleva.cpt\",\n", + " )\n", + " fig.coast(shorelines=\"1p,black\", frame=[\"WSne\", \"a5f2.5\"])\n", + " fig.colorbar(\n", + " cmap=\"eleva.cpt\",\n", + " position=\"JBC+w8c/0.3c+mc+h\",\n", + " frame=\"a2000f500+lElevation (m)\",\n", + " )\n", "\n", " with fig.set_panel(panel=1):\n", "\n", " pygmt.makecpt(cmap=\"@earth_age.cpt\", series=[0, 340])\n", - " fig.grdimage(grid=grid_crust_age,\n", - " region=[-210, -150, 35, 65], \n", - " # changing projection method\n", - " projection=\"L-180/55/35/65/?\",\n", - " frame=[\"a5f2.5\",\"SEnw\"],\n", - " cmap=True) \n", - " \n", - " fig.colorbar(position=\"JBC+w6c/0.3c+mc+h\", \n", - " frame=\"a40f20+lSeafloor crustal asge (Ma)\") \n", + " fig.grdimage(\n", + " grid=grid_crust_age,\n", + " region=[-210, -150, 35, 65],\n", + " # changing projection method\n", + " projection=\"L-180/55/35/65/?\",\n", + " frame=[\"a5f2.5\", \"SEnw\"],\n", + " cmap=True,\n", + " )\n", + "\n", + " fig.colorbar(\n", + " position=\"JBC+w6c/0.3c+mc+h\", frame=\"a40f20+lSeafloor crustal asge (Ma)\"\n", + " )\n", "\n", "fig.show()" ] diff --git a/_sources/tut02_spe_pd_gpd.ipynb b/_sources/tut02_spe_pd_gpd.ipynb index 0291e71..3bf96da 100644 --- a/_sources/tut02_spe_pd_gpd.ipynb +++ b/_sources/tut02_spe_pd_gpd.ipynb @@ -59,10 +59,8 @@ "metadata": {}, "outputs": [], "source": [ - "import pygmt\n", - "\n", - "import pandas as pd\n", "import geopandas as gpd\n", + "import pygmt\n", "\n", "# Use a resolution of only 150 dpi for the images within the Jupyter notebook, to keep the file small\n", "img_dpi = 150" @@ -114,8 +112,10 @@ "metadata": {}, "outputs": [], "source": [ - "mag_min = df_jp_eqs[\"magnitude\"].min(); print(mag_min)\n", - "mag_max = df_jp_eqs[\"magnitude\"].max(); print(mag_max)" + "mag_min = df_jp_eqs[\"magnitude\"].min()\n", + "print(mag_min)\n", + "mag_max = df_jp_eqs[\"magnitude\"].max()\n", + "print(mag_max)" ] }, { @@ -250,9 +250,9 @@ "outputs": [], "source": [ "gpd_rivers_org = gpd.read_file(\n", - " \"https://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes/\" + \\\n", - " \"zipped-shapefile-with-wise-large-rivers-vector-line/zipped-shapefile-with-wise-large-rivers-vector-line/\" + \\\n", - " \"at_download/file/wise_large_rivers.zip\"\n", + " \"https://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes/\"\n", + " + \"zipped-shapefile-with-wise-large-rivers-vector-line/zipped-shapefile-with-wise-large-rivers-vector-line/\"\n", + " + \"at_download/file/wise_large_rivers.zip\"\n", ")\n", "gpd_rivers_org.head()" ] @@ -273,7 +273,7 @@ "outputs": [], "source": [ "gpd_rivers_org.crs\n", - "gpd_rivers = gpd_rivers_org.to_crs('EPSG:4326')\n", + "gpd_rivers = gpd_rivers_org.to_crs(\"EPSG:4326\")\n", "gpd_rivers.head()" ] }, @@ -297,7 +297,7 @@ "fig = pygmt.Figure()\n", "\n", "fig.coast(\n", - " projection=\"M10c\", \n", + " projection=\"M10c\",\n", " region=[-10, 30, 35, 57],\n", " land=\"gray99\",\n", " shorelines=\"1/0.1p,gray50\",\n", @@ -338,7 +338,7 @@ "fig = pygmt.Figure()\n", "\n", "fig.coast(\n", - " projection=\"M10c\", \n", + " projection=\"M10c\",\n", " region=[-10, 35, 35, 58],\n", " land=\"gray99\",\n", " shorelines=\"1/0.1p,gray50\",\n", @@ -346,7 +346,11 @@ ")\n", "\n", "# Plot the subsets differently and specify the text for the legend entries\n", - "fig.plot(data=gpd_rivers_short, pen=\"0.5p,orange\", label=f\"shorter {len_limit} m+Hriver length+f9p\")\n", + "fig.plot(\n", + " data=gpd_rivers_short,\n", + " pen=\"0.5p,orange\",\n", + " label=f\"shorter {len_limit} m+Hriver length+f9p\",\n", + ")\n", "fig.plot(data=gpd_rivers_long, pen=\"0.5p,darkred\", label=f\"longer {len_limit} m\")\n", "\n", "# Place the legend at the Top Left corner with an offset of 0.1 centimeters from the map frame\n", @@ -425,8 +429,10 @@ "metadata": {}, "outputs": [], "source": [ - "popul_min = gdf_airbnb[\"population\"].min(); print(popul_min)\n", - "popul_max = gdf_airbnb[\"population\"].max(); print(popul_max)" + "popul_min = gdf_airbnb[\"population\"].min()\n", + "print(popul_min)\n", + "popul_max = gdf_airbnb[\"population\"].max()\n", + "print(popul_max)" ] }, { @@ -450,9 +456,9 @@ "\n", "# Plot the polygons with color-coding for the population\n", "fig.plot(\n", - " data=gdf_airbnb, \n", - " pen=\"0.2p,gray10\", \n", - " fill=\"+z\", \n", + " data=gdf_airbnb,\n", + " pen=\"0.2p,gray10\",\n", + " fill=\"+z\",\n", " cmap=True,\n", " aspatial=\"Z=population\",\n", ")\n", @@ -496,19 +502,21 @@ "fig = pygmt.Figure()\n", "\n", "fig.coast(\n", - " projection=\"M10c\", \n", + " projection=\"M10c\",\n", " region=[-10, 35, 35, 58],\n", " land=\"gray99\",\n", " shorelines=\"1/0.1p,gray50\",\n", " frame=True,\n", ")\n", "\n", - "pygmt.makecpt(cmap=\"SCM/oslo\",series=[gpd_rivers.Shape_Leng.min(), 1500000], reverse=True)\n", + "pygmt.makecpt(\n", + " cmap=\"SCM/oslo\", series=[gpd_rivers.Shape_Leng.min(), 1500000], reverse=True\n", + ")\n", "fig.colorbar(frame=[\"x+lriver length\", \"y+lm\"], position=\"+ef0.2c\")\n", "\n", "for i_river in range(len(gpd_rivers)):\n", " fig.plot(\n", - " data=gpd_rivers[gpd_rivers.index==i_river],\n", + " data=gpd_rivers[gpd_rivers.index == i_river],\n", " zvalue=gpd_rivers.loc[i_river, \"Shape_Leng\"],\n", " pen=\"0.5p\",\n", " cmap=True,\n", diff --git a/_sources/tut04_geophysics.ipynb b/_sources/tut04_geophysics.ipynb index da1c574..66d71d4 100644 --- a/_sources/tut04_geophysics.ipynb +++ b/_sources/tut04_geophysics.ipynb @@ -71,9 +71,9 @@ "metadata": {}, "outputs": [], "source": [ - "import pygmt\n", "import numpy as np\n", - "import pandas as pd \n", + "import pandas as pd\n", + "import pygmt\n", "\n", "# Use a resolution of only 150 dpi for the images within the Jupyter notebook, to keep the file small\n", "img_dpi = 150" @@ -128,7 +128,7 @@ "metadata": {}, "outputs": [], "source": [ - "region = [-90, -60, -40, 0] \n", + "region = [-90, -60, -40, 0]\n", "\n", "grid = pygmt.xyz2grd(data=data, spacing=\"5m\", region=region)" ] @@ -215,11 +215,11 @@ "# fig.grdimage(grid=grid, cmap=True, transparency=25, shading=\"+a\")\n", "\n", "fig.grdcontour(\n", - " grid=grid, \n", - " annotation=\"1000+fgray20\", \n", - " levels=1000, \n", - " limit=[-4000, 0], \n", - " pen=\"0.5p,white\"\n", + " grid=grid,\n", + " annotation=\"1000+fgray20\",\n", + " levels=1000,\n", + " limit=[-4000, 0],\n", + " pen=\"0.5p,white\",\n", ")\n", "\n", "fig.show(dpi=img_dpi)" @@ -254,17 +254,17 @@ "start_x = -85 # Longitude in degrees East\n", "start_y = -20 # Latitude in degrees North\n", "end_x = -62\n", - "end_y= -20\n", + "end_y = -20\n", "\n", "# Plot the survey line\n", "fig.plot(x=[start_x, end_x], y=[start_y, end_y], pen=\"1p,red\")\n", "\n", "# Mark start and end points with labels\n", "fig.text(\n", - " x=[start_x, end_x], \n", + " x=[start_x, end_x],\n", " y=[start_y, end_y],\n", " text=[\"A\", \"A'\"],\n", - " offset=\"0c/0.3c\", \n", + " offset=\"0c/0.3c\",\n", " font=\"10p,Helvetica-Bold,white\",\n", " fill=\"red\",\n", ")\n", @@ -294,10 +294,10 @@ "outputs": [], "source": [ "track_df = pygmt.project(\n", - " center=[start_x, start_y], \n", - " endpoint=[end_x, end_y], \n", + " center=[start_x, start_y],\n", + " endpoint=[end_x, end_y],\n", " # Output data in steps of 1 km with setting unit=True\n", - " generate=\"1\", \n", + " generate=\"1\",\n", " unit=True,\n", ")\n", "track_df.head()" @@ -524,7 +524,7 @@ " # Add a connection line between the plotting and event locations\n", " offset=\"+p0.1p,orange+s0.2c\",\n", ")\n", - " \n", + "\n", "fig.show(dpi=img_dpi)" ] }, @@ -662,20 +662,20 @@ "pygmt.makecpt(cmap=\"SCM/acton\", series=[0, 500], reverse=True)\n", "\n", "fig.plot(\n", - " x=df_low_mag[\"lon\"], \n", - " y=df_low_mag[\"lat\"], \n", + " x=df_low_mag[\"lon\"],\n", + " y=df_low_mag[\"lat\"],\n", " style=\"cc\", # Plot circles (fist \"c\") in centimeters (second \"c\")\n", - " size=np.log10(df_low_mag[\"mag\"])/4, # size varies based on magnitude\n", + " size=np.log10(df_low_mag[\"mag\"]) / 4, # size varies based on magnitude\n", " fill=df_low_mag[\"depth\"],\n", " cmap=True,\n", " pen=\"0.1p,black\",\n", ")\n", "\n", "fig.plot(\n", - " x=df_high_mag[\"lon\"], \n", - " y=df_high_mag[\"lat\"], \n", + " x=df_high_mag[\"lon\"],\n", + " y=df_high_mag[\"lat\"],\n", " style=\"ac\", # Plot stars (\"a\") in centimeters (\"c\")\n", - " size=np.log10(df_high_mag[\"mag\"]), \n", + " size=np.log10(df_high_mag[\"mag\"]),\n", " fill=df_high_mag[\"depth\"],\n", " cmap=True,\n", " pen=\"0.5p,black\",\n", diff --git a/searchindex.js b/searchindex.js index 9c9c736..fa6e5e7 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"0.1 2-D map view": [[5, "d-map-view"]], "0.2 Zoomed in view": [[5, "zoomed-in-view"]], "0\ufe0f\u20e3 General stuff": [[2, "general-stuff"], [4, "general-stuff"]], "0\ufe0f\u20e3 Importing": [[1, "importing"]], "0\ufe0f\u20e3 Mars relief data": [[5, "mars-relief-data"]], "0\ufe0f\u20e3 What is Xarray?": [[3, "what-is-xarray"]], "1. Introduction": [[6, "introduction"]], "1.1 Drawing coastline \u2013 pygmt.Figure.coast": [[1, "drawing-coastline-pygmt-figure-coast"]], "1.1 Tabular data - pandas.DataFrame": [[2, "tabular-data-pandas-dataframe"]], "1.1. What is an Animation?": [[6, "what-is-an-animation"]], "1.2 Coloring and add map element (frame and ticks)": [[1, "coloring-and-add-map-element-frame-and-ticks"]], "1.2 Create a Cartesian histogram": [[2, "create-a-cartesian-histogram"]], "1.2. How to Make an Animation": [[6, "how-to-make-an-animation"]], "1.3 Create a geographical map showing the epicenters (scatter plot)": [[2, "create-a-geographical-map-showing-the-epicenters-scatter-plot"]], "1.3 Stacking approach of GMT / PyGMT": [[1, "stacking-approach-of-gmt-pygmt"]], "1.3. Why use GMT for animations?": [[6, "why-use-gmt-for-animations"]], "1.4. Types of animations in GMT": [[6, "types-of-animations-in-gmt"]], "1.5. Prerequisites": [[6, "prerequisites"]], "1\ufe0f\u20e3 Prepare gridded data: pygmt.xyz2grd": [[4, "prepare-gridded-data-pygmt-xyz2grd"]], "1\ufe0f\u20e3 Starting your first figure \u2013 pygmt.Figure": [[1, "starting-your-first-figure-pygmt-figure"]], "1\ufe0f\u20e3 Using grdview for 3-D Visualization": [[5, "using-grdview-for-3-d-visualization"]], "1\ufe0f\u20e3 Why use Xarray with PyGMT": [[3, "why-use-xarray-with-pygmt"]], "1\ufe0f\u20e3 pandas": [[2, "pandas"]], "2. Tutorial 1. Earth spinning": [[6, "tutorial-1-earth-spinning"]], "2.1 Getting a DEM mosaic": [[5, "getting-a-dem-mosaic"]], "2.1 Line geometry": [[2, "line-geometry"]], "2.1. Goals of the Tutorial": [[6, "goals-of-the-tutorial"]], "2.1.1 Spatial Data - geopandas.GeoDataFrame with line geometry": [[2, "spatial-data-geopandas-geodataframe-with-line-geometry"]], "2.1.2 Create a geographical map of the rivers": [[2, "create-a-geographical-map-of-the-rivers"]], "2.1.3 Plot subsets of the rivers differently": [[2, "plot-subsets-of-the-rivers-differently"]], "2.1.4 Plot the rivers with color-coding for the river length": [[2, "plot-the-rivers-with-color-coding-for-the-river-length"]], "2.2 Getting RGB imagery": [[5, "getting-rgb-imagery"]], "2.2 Polygon geometry": [[2, "polygon-geometry"]], "2.2. Make first image": [[6, "make-first-image"]], "2.2.1 Spatial Data - geopandas.GeoDataFrame with polygon geometry": [[2, "spatial-data-geopandas-geodataframe-with-polygon-geometry"]], "2.2.2 Create a choropleth map": [[2, "create-a-choropleth-map"]], "2.3. Make the Master Frame": [[6, "make-the-master-frame"]], "2.3.1. 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Introduction": [[6, "introduction"]], "1.1 Drawing coastline \u2013 pygmt.Figure.coast": [[1, "drawing-coastline-pygmt-figure-coast"]], "1.1 Tabular data - pandas.DataFrame": [[2, "tabular-data-pandas-dataframe"]], "1.1. What is an Animation?": [[6, "what-is-an-animation"]], "1.2 Coloring and add map element (frame and ticks)": [[1, "coloring-and-add-map-element-frame-and-ticks"]], "1.2 Create a Cartesian histogram": [[2, "create-a-cartesian-histogram"]], "1.2. How to Make an Animation": [[6, "how-to-make-an-animation"]], "1.3 Create a geographical map showing the epicenters (scatter plot)": [[2, "create-a-geographical-map-showing-the-epicenters-scatter-plot"]], "1.3 Stacking approach of GMT / PyGMT": [[1, "stacking-approach-of-gmt-pygmt"]], "1.3. Why use GMT for animations?": [[6, "why-use-gmt-for-animations"]], "1.4. Types of animations in GMT": [[6, "types-of-animations-in-gmt"]], "1.5. Prerequisites": [[6, "prerequisites"]], "1\ufe0f\u20e3 Prepare gridded data: pygmt.xyz2grd": [[4, "prepare-gridded-data-pygmt-xyz2grd"]], "1\ufe0f\u20e3 Starting your first figure \u2013 pygmt.Figure": [[1, "starting-your-first-figure-pygmt-figure"]], "1\ufe0f\u20e3 Using grdview for 3-D Visualization": [[5, "using-grdview-for-3-d-visualization"]], "1\ufe0f\u20e3 Why use Xarray with PyGMT": [[3, "why-use-xarray-with-pygmt"]], "1\ufe0f\u20e3 pandas": [[2, "pandas"]], "2. Tutorial 1. Earth spinning": [[6, "tutorial-1-earth-spinning"]], "2.1 Getting a DEM mosaic": [[5, "getting-a-dem-mosaic"]], "2.1 Line geometry": [[2, "line-geometry"]], "2.1. Goals of the Tutorial": [[6, "goals-of-the-tutorial"]], "2.1.1 Spatial Data - geopandas.GeoDataFrame with line geometry": [[2, "spatial-data-geopandas-geodataframe-with-line-geometry"]], "2.1.2 Create a geographical map of the rivers": [[2, "create-a-geographical-map-of-the-rivers"]], "2.1.3 Plot subsets of the rivers differently": [[2, "plot-subsets-of-the-rivers-differently"]], "2.1.4 Plot the rivers with color-coding for the river length": [[2, "plot-the-rivers-with-color-coding-for-the-river-length"]], "2.2 Getting RGB imagery": [[5, "getting-rgb-imagery"]], "2.2 Polygon geometry": [[2, "polygon-geometry"]], "2.2. Make first image": [[6, "make-first-image"]], "2.2.1 Spatial Data - geopandas.GeoDataFrame with polygon geometry": [[2, "spatial-data-geopandas-geodataframe-with-polygon-geometry"]], "2.2.2 Create a choropleth map": [[2, "create-a-choropleth-map"]], "2.3. Make the Master Frame": [[6, "make-the-master-frame"]], "2.3.1. What is GMT movie?": [[6, "what-is-gmt-movie"]], "2.3.2. First Attempt": [[6, "first-attempt"]], "2.3.3. The Canvas": [[6, "the-canvas"]], "2.3.4. Second attempt. Fix the canvas": [[6, "second-attempt-fix-the-canvas"]], "2.4. Make draft animation": [[6, "make-draft-animation"]], "2.4.1. First attempt": [[6, "id1"]], "2.4.2. Movie Parameters": [[6, "movie-parameters"]], "2.4.3. How to set the number of Frames": [[6, "how-to-set-the-number-of-frames"]], "2.4.4. Second attempt. Use parameters": [[6, "second-attempt-use-parameters"]], "2.5. Make full animation": [[6, "make-full-animation"]], "2\ufe0f\u20e3 Antarctic Digital Elevation Model": [[5, "antarctic-digital-elevation-model"]], "2\ufe0f\u20e3 Create a contour map: pygmt.Figure.grdcontour": [[4, "create-a-contour-map-pygmt-figure-grdcontour"]], "2\ufe0f\u20e3 Downloading global dataset \u2013 pygmt.datasets": [[1, "downloading-global-dataset-pygmt-datasets"]], "2\ufe0f\u20e3 GeoPandas": [[2, "geopandas"]], "2\ufe0f\u20e3 Getting started": [[3, "getting-started"]], "3. Tutorial 2. Earthquakes": [[6, "tutorial-2-earthquakes"], [7, "tutorial-2-earthquakes"]], "3.1 Choose a profile": [[4, "choose-a-profile"]], "3.1 Customizing colorbar \u2013 pygmt.makecpt": [[1, "customizing-colorbar-pygmt-makecpt"]], "3.1. 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3, 4, 5, 6, 7], "while": [1, 4, 5, 6], "white": [2, 4, 5, 6, 7], "whiten": 7, "whole": [3, 6], "width": [1, 2, 3, 4, 5, 6], "widthxheightxdpu": 6, "window": [0, 6], "winkel": 3, "wise": 2, "wise_large_riv": 2, "within": [1, 2, 4, 7], "without": [1, 4, 5, 7], "without_h": 2, "woa09": 3, "woa_temp": 3, "wobb": [6, 7], "word": 6, "work": [3, 5, 6, 7], "workshop": [0, 1, 2, 3, 4, 5, 6, 7], "world": [1, 3, 5], "worldwid": 7, "would": 6, "write": [1, 6], "written": 6, "wsne": [1, 2, 5], "wsrt": 4, "www": [0, 1, 2, 3, 4, 5, 6, 7], "x": [1, 2, 4, 5, 6, 7], "x0": [4, 6, 7], "x10c": [2, 4], "x27": [3, 4, 5], "x50": 7, "x_inc": [1, 4, 5], "x_max": [1, 4], "x_min": [1, 4, 5], "xa0": 3, "xa100f50": 2, "xa2000f1000": 4, "xa500f250": 4, "xactual_rang": [3, 4], "xaf": 5, "xarrai": [0, 4, 5], "xga": 6, "xlong_nam": 5, "xmax": [1, 5], "xmin": [1, 5], "xpandasindexpandasindex": 5, "xr": 3, "xyz2grd": 0, "y": [1, 2, 3, 4, 5, 6, 7], "y0": [6, 7], "y_inc": [1, 4, 5], "y_max": [1, 4], "y_min": [1, 4, 5], "ya0": 3, "ya2500": 4, "yactual_rang": [3, 4], "yaf": 5, "yarrai": [3, 5], "year": [2, 6, 7], "ylong_nam": 5, "ymax": [1, 5], "ymin": [1, 5], "yml": 0, "you": [0, 1, 2, 3, 4, 5, 6, 7], "your": [0, 2, 3, 4, 5, 6], "ypandasindexpandasindex": 5, "yshift": [3, 4], "yvonn": [0, 2, 4], "z": [1, 2, 3, 4, 5, 6, 7], "z5000": 5, "zactual_rang": [3, 4], "zip": 2, "zmax": 5, "zmin": 5, "zo": 3, "zone": 4, "zos_2015jan": 3, "zos_2100dec": 3, "zscale": 5, "zsh": 6, "zsize": 5, "zuber": 5, "zunit": 5, "zvalu": 2}, "titles": ["Mastering Geospatial Visualizations with GMT/PyGMT", "Tutorial 1 - First figure \ud83d\ude80 and Subplots / layout", "Tutorial 2 - scientific Python ecosystem \ud83d\udc0d: pandas and GeoPandas (tabular data \ud83d\uddd2\ufe0f)", "Tutorial 3 - scientific Python ecosystem \ud83d\udc0d: Xarray (gridded data \ud83c\udf10)", "Tutorial 4 - Geophysics (Seismology) \ud83c\udf0e\ud83c\udf0f\ud83c\udf0d", "Tutorial 5 - 3-D Topography (Planetary / Antarctic maps) \ud83c\udfd4\ufe0f", "Tutorial 6 - Animations with GMT", "Tutorial 6 - Animations with GMT (extended)"], "titleterms": {"0": [1, 2, 3, 4, 5], "1": [1, 2, 3, 4, 5, 6, 7], "2": [1, 2, 3, 4, 5, 6, 7], "3": [1, 2, 3, 4, 5, 6, 7], "4": [1, 2, 3, 4, 6, 7], "5": [1, 3, 4, 5, 6, 7], "6": [3, 4, 6, 7], "The": [6, 7], "ad": 1, "add": [1, 4], "addit": [2, 4], "along": 4, "also": [6, 7], "an": 6, "anim": [6, 7], "antarct": 5, "approach": 1, "attempt": [6, 7], "background": 7, "basic": 1, "beachbal": 4, "canva": [1, 6], "cartesian": [2, 4], "choos": 4, "choropleth": 2, "cloud": 3, "coast": 1, "coastlin": 1, "code": 2, "color": [1, 2], "colorbar": 1, "comment": [2, 4], "contour": 4, "creat": [1, 2, 4], "custom": 1, "d": 5, "data": [2, 3, 4, 5], "datafram": 2, "dataset": 1, "dem": 5, "differ": 2, "digit": 5, "directli": 3, "download": [1, 3], "draft": [6, 7], "drape": 5, "draw": 1, "earth": 6, "earthquak": [6, 7], "ecosystem": [2, 3], "element": 1, "elev": [4, 5], "enhanc": 7, "epicent": 2, "event": 7, "extend": 7, "featur": 4, "figur": [1, 4], "first": [1, 6, 7], "fix": 6, "frame": [1, 6, 7], "from": 3, "full": [6, 7], "gener": [2, 4], "geodatafram": 2, "geograph": 2, "geometri": 2, "geopanda": 2, "geophys": 4, "geospati": 0, "get": [3, 5], "global": 1, "gmt": [0, 1, 6, 7], "goal": [6, 7], "grdcontour": 4, "grdimag": 1, "grdtrack": 4, "grdview": 5, "grid": [1, 3, 4], "histogram": 2, "how": [6, 7], "i": [3, 6], "imag": [5, 6, 7], "imageri": 5, "import": 1, "inform": [6, 7], "introduct": 6, "last": 7, "layout": 1, "length": 2, "line": 2, "load": 3, "local": 0, "make": [6, 7], "makecpt": 1, "map": [1, 2, 4, 5], "mar": 5, "master": [0, 6, 7], "model": 5, "modul": 7, "mosaic": 5, "movi": 6, "multi": 1, "notebook": 0, "number": 6, "open": 3, "orient": [2, 4], "panda": 2, "panel": 1, "paramet": 6, "planetari": 5, "plot": [2, 3, 4], "polygon": 2, "prepar": 4, "prerequisit": 6, "process": 3, "profil": 4, "project": [1, 4], "pygmt": [0, 1, 3, 4], "python": [2, 3], "quickstart": 0, "raster": 3, "refer": [6, 7], "relief": 5, "rgb": 5, "river": 2, "run": 0, "scatter": 2, "scientif": [2, 3], "script": 7, "second": [6, 7], "see": [6, 7], "seismic": 4, "seismologi": 4, "set": 6, "set_panel": 1, "show": [2, 4], "spatial": 2, "spin": 6, "stack": 1, "start": [1, 3], "step": 6, "stuff": [2, 4], "subplot": 1, "subset": 2, "suggest": [2, 4], "symbol": 7, "tabular": 2, "technic": [6, 7], "third": 7, "tick": 1, "topographi": 5, "tutori": [1, 2, 3, 4, 5, 6, 7], "type": [1, 6], "us": [3, 5, 6], "view": 5, "visual": [0, 1, 5], "what": [3, 6], "why": [3, 6], "xarrai": 3, "xyz2grd": 4, "your": 1, "zoom": 5}}) \ No newline at end of file diff --git a/tut01_firstfigure.html b/tut01_firstfigure.html index 605c869..2090399 100644 --- a/tut01_firstfigure.html +++ b/tut01_firstfigure.html @@ -523,7 +523,7 @@

1️⃣ Starting your first figure –
-
fig=pygmt.Figure()
+
fig = pygmt.Figure()
@@ -547,14 +547,12 @@

1.1 Drawing coastline –
-
region=[-180, -150, 50, 65]
+
region = [-180, -150, 50, 65]
 
-fig.coast(region=region, 
-          projection="M10c",
-          shorelines=True)
+fig.coast(region=region, projection="M10c", shorelines=True)
 
 # To see the figure looks like
-fig.show() 
+fig.show()
@@ -588,9 +586,7 @@

1.2 Coloring and add map element (frame and ticks)
-
fig.coast(water="lightblue", 
-          land="grey",
-          frame=["WSne","a10f5g5"]) 
+
fig.coast(water="lightblue", land="grey", frame=["WSne", "a10f5g5"])
 fig.show()
 

 

@@ -618,16 +614,15 @@ 
2️⃣ Downloading global dataset – 
 

-
grid=pygmt.datasets.load_earth_relief(resolution="03m", region=region)
+
grid = pygmt.datasets.load_earth_relief(resolution="03m", region=region)
 
 # You also can access then cut the grid
-# and this way can save grid (outgrid) as a file in your folder. 
+# and this way can save grid (outgrid) as a file in your folder.
 # https://www.generic-mapping-tools.org/remote-datasets/
 
-# pygmt.grdcut(grid="@earth_relief_03m",  
-#              region=region,
-#              outgrid="Alaska.grd", 
-#              ) 
+# pygmt.grdcut(
+#    grid="@earth_relief_03m", region=region, outgrid="Alaska.grd"
+# )
 

 

 

@@ -677,14 +672,11 @@ 
3️⃣ Visualizing grid – 
 

-
fig=pygmt.Figure()
+
fig = pygmt.Figure()
 
-fig.grdimage(grid=grid,
-             region=region, 
-             projection="M10c")
+fig.grdimage(grid=grid, region=region, projection="M10c")
 
-fig.coast(shorelines="1p,black",
-          frame=["WSne", "a5f2.5"]) 
+fig.coast(shorelines="1p,black", frame=["WSne", "a5f2.5"])
 
 fig.show()
 

@@ -709,12 +701,13 @@ 
3.1 Customizing colorbar – 
 

-
pygmt.makecpt(cmap="geo", 
-              series=[-8000, 7000, 500],
-              continuous=True,
-              # The file name with extension .cpt to store the generated CPT file
-              output="eleva.cpt") 
-                
+
pygmt.makecpt(
+    cmap="geo",
+    series=[-8000, 7000, 500],
+    continuous=True,
+    # The file name with extension .cpt to store the generated CPT file
+    output="eleva.cpt",
+)
 

 

 

@@ -756,19 +749,17 @@ 
3.2 Adding 

 

-
fig=pygmt.Figure()
+
fig = pygmt.Figure()
 
-fig.grdimage(grid=grid,
-             region=region, 
-             projection="M10c",
-             cmap="eleva.cpt") 
+fig.grdimage(grid=grid, region=region, projection="M10c", cmap="eleva.cpt")
 
-fig.coast(shorelines="1p,black",
-          frame=["WSne", "a5f2.5"]) 
+fig.coast(shorelines="1p,black", frame=["WSne", "a5f2.5"])
 
-fig.colorbar(cmap="eleva.cpt", 
-             frame="a2000f500+lElevation (m)",
-             position="JBC+w10c/0.4c+mc+h",) 
+fig.colorbar(
+    cmap="eleva.cpt",
+    frame="a2000f500+lElevation (m)",
+    position="JBC+w10c/0.4c+mc+h",
+)
 
 fig.show()
 

@@ -794,39 +785,45 @@ 
4️⃣ Creating multi-panel figures in a canvas – 

 

-
fig=pygmt.Figure()
+
fig = pygmt.Figure()
 
-grid_crust_age=pygmt.datasets.load_earth_age(resolution="30m", region=[-210, -150, 35, 65])
+grid_crust_age = pygmt.datasets.load_earth_age(
+    resolution="30m", region=[-210, -150, 35, 65]
+)
 
-with fig.subplot(
-    nrows=1, ncols=2, figsize=["18c","12c"]
-):
+with fig.subplot(nrows=1, ncols=2, figsize=["18c", "12c"]):
 
     with fig.set_panel(panel=0):
 
-        fig.grdimage(grid=grid,
-                     region=region, 
-                     # "?" means that map width automatically determined from the subplot width.
-                     projection="M?", 
-                     cmap="eleva.cpt") 
-        fig.coast(shorelines="1p,black",
-                  frame=["WSne", "a5f2.5"]) 
-        fig.colorbar(cmap="eleva.cpt", 
-                     position="JBC+w8c/0.3c+mc+h", 
-                     frame="a2000f500+lElevation (m)") 
+        fig.grdimage(
+            grid=grid,
+            region=region,
+            # "?" means that map width automatically determined from the subplot width.
+            projection="M?",
+            cmap="eleva.cpt",
+        )
+        fig.coast(shorelines="1p,black", frame=["WSne", "a5f2.5"])
+        fig.colorbar(
+            cmap="eleva.cpt",
+            position="JBC+w8c/0.3c+mc+h",
+            frame="a2000f500+lElevation (m)",
+        )
 
     with fig.set_panel(panel=1):
 
         pygmt.makecpt(cmap="@earth_age.cpt", series=[0, 340])
-        fig.grdimage(grid=grid_crust_age,
-                     region=[-210, -150, 35, 65], 
-                     # changing projection method
-                     projection="L-180/55/35/65/?",
-                     frame=["a5f2.5","SEnw"],
-                     cmap=True) 
-        
-        fig.colorbar(position="JBC+w6c/0.3c+mc+h", 
-                     frame="a40f20+lSeafloor crustal asge (Ma)") 
+        fig.grdimage(
+            grid=grid_crust_age,
+            region=[-210, -150, 35, 65],
+            # changing projection method
+            projection="L-180/55/35/65/?",
+            frame=["a5f2.5", "SEnw"],
+            cmap=True,
+        )
+
+        fig.colorbar(
+            position="JBC+w6c/0.3c+mc+h", frame="a40f20+lSeafloor crustal asge (Ma)"
+        )
 
 fig.show()
 

diff --git a/tut02_spe_pd_gpd.html b/tut02_spe_pd_gpd.html
index f498d8e..8994858 100644
--- a/tut02_spe_pd_gpd.html
+++ b/tut02_spe_pd_gpd.html
@@ -500,10 +500,8 @@ 
0️⃣ General stuffPyGMT itself, we use pandas and GeoPandas:

 

 

-
import pygmt
-
-import pandas as pd
-import geopandas as gpd
+
import geopandas as gpd
+import pygmt
 
 # Use a resolution of only 150 dpi for the images within the Jupyter notebook, to keep the file small
 img_dpi = 150
@@ -613,8 +611,10 @@ 
1.2 Create a Cartesian histogram
 

-
mag_min = df_jp_eqs["magnitude"].min(); print(mag_min)
-mag_max = df_jp_eqs["magnitude"].max(); print(mag_max)
+
mag_min = df_jp_eqs["magnitude"].min()
+print(mag_min)
+mag_max = df_jp_eqs["magnitude"].max()
+print(mag_max)
 

 

 

@@ -711,9 +711,9 @@ 
2.1.1 Spatial Data - 
 

 
gpd_rivers_org = gpd.read_file(
-    "https://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes/" + \
-    "zipped-shapefile-with-wise-large-rivers-vector-line/zipped-shapefile-with-wise-large-rivers-vector-line/" + \
-    "at_download/file/wise_large_rivers.zip"
+    "https://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes/"
+    + "zipped-shapefile-with-wise-large-rivers-vector-line/zipped-shapefile-with-wise-large-rivers-vector-line/"
+    + "at_download/file/wise_large_rivers.zip"
 )
 gpd_rivers_org.head()
 

@@ -782,7 +782,7 @@ 
2.1.1 Spatial Data - 
 

 
gpd_rivers_org.crs
-gpd_rivers = gpd_rivers_org.to_crs('EPSG:4326')
+gpd_rivers = gpd_rivers_org.to_crs("EPSG:4326")
 gpd_rivers.head()
 

 

@@ -855,7 +855,7 @@ 
2.1.2 Create a geographical map of the rivers
fig = pygmt.Figure()
 
 fig.coast(
-    projection="M10c", 
+    projection="M10c",
     region=[-10, 30, 35, 57],
     land="gray99",
     shorelines="1/0.1p,gray50",
@@ -889,7 +889,7 @@ 
2.1.3 Plot subsets of the rivers differentlyfig = pygmt.Figure()
 
 fig.coast(
-    projection="M10c", 
+    projection="M10c",
     region=[-10, 35, 35, 58],
     land="gray99",
     shorelines="1/0.1p,gray50",
@@ -897,7 +897,11 @@ 
2.1.3 Plot subsets of the rivers differently)
 
 # Plot the subsets differently and specify the text for the legend entries
-fig.plot(data=gpd_rivers_short, pen="0.5p,orange", label=f"shorter {len_limit} m+Hriver length+f9p")
+fig.plot(
+    data=gpd_rivers_short,
+    pen="0.5p,orange",
+    label=f"shorter {len_limit} m+Hriver length+f9p",
+)
 fig.plot(data=gpd_rivers_long, pen="0.5p,darkred", label=f"longer {len_limit} m")
 
 # Place the legend at the Top Left corner with an offset of 0.1 centimeters from the map frame
@@ -1113,8 +1117,10 @@ 
2.2.2 Create a choropleth map"population", but feel free to modify the code below and use another quantity for the color-coding!

 

 

-
popul_min = gdf_airbnb["population"].min(); print(popul_min)
-popul_max = gdf_airbnb["population"].max(); print(popul_max)
+
popul_min = gdf_airbnb["population"].min()
+print(popul_min)
+popul_max = gdf_airbnb["population"].max()
+print(popul_max)
 

 

 

@@ -1141,9 +1147,9 @@ 
2.2.2 Create a choropleth map# Plot the polygons with color-coding for the population
 fig.plot(
-    data=gdf_airbnb, 
-    pen="0.2p,gray10", 
-    fill="+z", 
+    data=gdf_airbnb,
+    pen="0.2p,gray10",
+    fill="+z",
     cmap=True,
     aspatial="Z=population",
 )
@@ -1178,19 +1184,21 @@ 
4️⃣ Orientation / suggestion for “2.1.4 Plot the rivers with color-cod
 
fig = pygmt.Figure()
 
 fig.coast(
-    projection="M10c", 
+    projection="M10c",
     region=[-10, 35, 35, 58],
     land="gray99",
     shorelines="1/0.1p,gray50",
     frame=True,
 )
 
-pygmt.makecpt(cmap="SCM/oslo",series=[gpd_rivers.Shape_Leng.min(), 1500000], reverse=True)
+pygmt.makecpt(
+    cmap="SCM/oslo", series=[gpd_rivers.Shape_Leng.min(), 1500000], reverse=True
+)
 fig.colorbar(frame=["x+lriver length", "y+lm"], position="+ef0.2c")
 
 for i_river in range(len(gpd_rivers)):
     fig.plot(
-        data=gpd_rivers[gpd_rivers.index==i_river],
+        data=gpd_rivers[gpd_rivers.index == i_river],
         zvalue=gpd_rivers.loc[i_river, "Shape_Leng"],
         pen="0.5p",
         cmap=True,
diff --git a/tut03_spe_xarray.html b/tut03_spe_xarray.html
index 71b68d6..ca35853 100644
--- a/tut03_spe_xarray.html
+++ b/tut03_spe_xarray.html
@@ -907,13 +907,13 @@ 
2️⃣ Getting started
@@ -1370,7 +1370,7 @@ 
4️⃣ Processing raster data
diff --git a/tut04_geophysics.html b/tut04_geophysics.html
index 0f3f1fa..628093a 100644
--- a/tut04_geophysics.html
+++ b/tut04_geophysics.html
@@ -493,9 +493,9 @@ 
0️⃣ General stuffPyGMT we also use NumPy and pandas.

 

 

-
import pygmt
-import numpy as np
-import pandas as pd 
+
import numpy as np
+import pandas as pd
+import pygmt
 
 # Use a resolution of only 150 dpi for the images within the Jupyter notebook, to keep the file small
 img_dpi = 150
@@ -540,7 +540,7 @@ 
1️⃣ Prepare gridded data: 
 

-
region = [-90, -60, -40, 0]  
+
region = [-90, -60, -40, 0]
 
 grid = pygmt.xyz2grd(data=data, spacing="5m", region=region)
 

@@ -964,15 +964,15 @@ 
1️⃣ Prepare gridded data: 
@@ -1016,11 +1016,11 @@ 
2️⃣ Create a contour map: # fig.grdimage(grid=grid, cmap=True, transparency=25, shading="+a")
 
 fig.grdcontour(
-    grid=grid, 
-    annotation="1000+fgray20", 
-    levels=1000, 
-    limit=[-4000, 0], 
-    pen="0.5p,white"
+    grid=grid,
+    annotation="1000+fgray20",
+    levels=1000,
+    limit=[-4000, 0],
+    pen="0.5p,white",
 )
 
 fig.show(dpi=img_dpi)
@@ -1043,17 +1043,17 @@ 
3.1 Choose a profilestart_x = -85  # Longitude in degrees East
 start_y = -20  # Latitude in degrees North
 end_x = -62
-end_y= -20
+end_y = -20
 
 # Plot the survey line
 fig.plot(x=[start_x, end_x], y=[start_y, end_y], pen="1p,red")
 
 # Mark start and end points with labels
 fig.text(
-    x=[start_x, end_x], 
+    x=[start_x, end_x],
     y=[start_y, end_y],
     text=["A", "A'"],
-    offset="0c/0.3c", 
+    offset="0c/0.3c",
     font="10p,Helvetica-Bold,white",
     fill="red",
 )
@@ -1078,10 +1078,10 @@ 
3.2 
 

 
track_df = pygmt.project(
-    center=[start_x, start_y], 
-    endpoint=[end_x, end_y], 
+    center=[start_x, start_y],
+    endpoint=[end_x, end_y],
     # Output data in steps of 1 km with setting unit=True
-    generate="1",  
+    generate="1",
     unit=True,
 )
 track_df.head()
@@ -1362,7 +1362,7 @@ 
4.1 Add a beachball# Add a connection line between the plotting and event locations
     offset="+p0.1p,orange+s0.2c",
 )
- 
+
 fig.show(dpi=img_dpi)
 

 

@@ -1502,20 +1502,20 @@ 
6️⃣ Orientation / suggestion for “4.2 Add seismicity”
pygmt.makecpt(cmap="SCM/acton", series=[0, 500], reverse=True)
 
 fig.plot(
-    x=df_low_mag["lon"], 
-    y=df_low_mag["lat"], 
+    x=df_low_mag["lon"],
+    y=df_low_mag["lat"],
     style="cc",  # Plot circles (fist "c") in centimeters (second "c")
-    size=np.log10(df_low_mag["mag"])/4, # size varies based on magnitude
+    size=np.log10(df_low_mag["mag"]) / 4,  # size varies based on magnitude
     fill=df_low_mag["depth"],
     cmap=True,
     pen="0.1p,black",
 )
 
 fig.plot(
-    x=df_high_mag["lon"], 
-    y=df_high_mag["lat"], 
+    x=df_high_mag["lon"],
+    y=df_high_mag["lat"],
     style="ac",  # Plot stars ("a") in centimeters ("c")
-    size=np.log10(df_high_mag["mag"]), 
+    size=np.log10(df_high_mag["mag"]),
     fill=df_high_mag["depth"],
     cmap=True,
     pen="0.5p,black",
diff --git a/tut05_topography.html b/tut05_topography.html
index 8771e06..ba9ade8 100644
--- a/tut05_topography.html
+++ b/tut05_topography.html
@@ -904,13 +904,13 @@ 
0️⃣ Mars relief data
+      dtype='float64', name='lon', length=361))
  • Conventions :
    CF-1.7
    title :
    MOLA Mars Relief at 01 arc degree
    history :
    description :
    NASA Mars (MOLA) relief
    long_name :
    elevation (m)
    units :
    meters
    • 
 
    
 
    
 
    0.1 2-D map view#
    
@@ -1370,14 +1370,14 @@ 
    0.2 Zoomed in view
 
    
@@ -1992,7 +1992,7 @@ 
    2.2 Getting RGB imagery
+      dtype='float64', name='y', length=2188))
  • OVR_RESAMPLING_ALG :
    AVERAGE
    AREA_OR_POINT :
    Area
    scale_factor :
    1.0
    add_offset :
    0.0
    _FillValue :
    0
    • 
 
    
 
    
 
    Tip