diff --git a/_images/9b5d8ab02e64296adc3751b52ac5dd07110ce7abd0e54537ed109c6c46717c5f.png b/_images/9b5d8ab02e64296adc3751b52ac5dd07110ce7abd0e54537ed109c6c46717c5f.png new file mode 100644 index 0000000..d243fad Binary files /dev/null and b/_images/9b5d8ab02e64296adc3751b52ac5dd07110ce7abd0e54537ed109c6c46717c5f.png differ diff --git a/_images/a18fcb026fb0d0c83360f2b8382a360e552dd50db6aa2db5311259bf5223d3a2.png b/_images/a18fcb026fb0d0c83360f2b8382a360e552dd50db6aa2db5311259bf5223d3a2.png new file mode 100644 index 0000000..4b2c2f6 Binary files /dev/null and b/_images/a18fcb026fb0d0c83360f2b8382a360e552dd50db6aa2db5311259bf5223d3a2.png differ diff --git a/_images/b3e45577786a5769fb54bb550f7032f3a73adc8289e08523d052827560bf3b0e.png b/_images/b3e45577786a5769fb54bb550f7032f3a73adc8289e08523d052827560bf3b0e.png new file mode 100644 index 0000000..8e8dfd7 Binary files /dev/null and b/_images/b3e45577786a5769fb54bb550f7032f3a73adc8289e08523d052827560bf3b0e.png differ diff --git a/_sources/tut02_spe_pd_gpd.ipynb b/_sources/tut02_spe_pd_gpd.ipynb index 012b0a3..2bc7475 100644 --- a/_sources/tut02_spe_pd_gpd.ipynb +++ b/_sources/tut02_spe_pd_gpd.ipynb @@ -60,6 +60,7 @@ "outputs": [], "source": [ "import geopandas as gpd\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", @@ -81,7 +82,8 @@ "source": [ "### 1.1 Tabular data - `pandas.DataFrame`\n", "\n", - "Use an example dataset with tabular data provided by `PyGMT` and load it into a `pandas.DataFrame`. This dataset contains earthquakes in the area of Japan." + "Use an example dataset with tabular data provided by `PyGMT` and load it into a `pandas.DataFrame`. This dataset contains earthquakes in the area of Japan.\n", + "You can read your own dataset into a `pandas.Dataframe` using [`pandas.read_csv`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) and use it in the same way to make the following plots; of course you have to adjust the column names accordantly." ] }, { @@ -92,7 +94,9 @@ "outputs": [], "source": [ "df_jp_eqs = pygmt.datasets.load_sample_data(name=\"japan_quakes\")\n", - "df_jp_eqs.head()" + "df_jp_eqs.head()\n", + "\n", + "# df_your_dataset = pd.read_csv(\"your_dataset.csv\")" ] }, { @@ -239,7 +243,12 @@ "source": [ "#### 2.1.1 Spatial Data - `geopandas.GeoDataFrame` with line geometry\n", "\n", - "First we download some data into in a [`geopandas.GeoDataFrame`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html). This dataset contains European rivers with their lengths and names." + "First we download some data into in a [`geopandas.GeoDataFrame`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html). This dataset contains European rivers with their lengths and names.\n", + "\n", + "In case you face issues with downloading these data:\n", + "1. Copy the URL \"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\" into your browser.\n", + "2. Download the zip file and place it into `~/agu24workshop/book`. Do not unpack the ZIP file.\n", + "3. Replace the URL with the filename of the ZIP file \"wise_large_rivers.zip\" in [`geopandas.read_file`](https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html)." ] }, { @@ -254,6 +263,7 @@ " + \"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 = pd.read_file(\"wise_large_rivers.zip\")\n", "gpd_rivers_org.head()" ] }, @@ -542,7 +552,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/reports/tut03_spe_xarray.err.log b/reports/tut03_spe_xarray.err.log deleted file mode 100644 index 0e07db4..0000000 --- a/reports/tut03_spe_xarray.err.log +++ /dev/null @@ -1,95 +0,0 @@ -Traceback (most recent call last): - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution - executenb( - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/site-packages/nbclient/client.py", line 1314, in execute - return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped - return loop.run_until_complete(inner) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/asyncio/base_events.py", line 687, in run_until_complete - return future.result() - ^^^^^^^^^^^^^^^ - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/site-packages/nbclient/client.py", line 709, in async_execute - await self.async_execute_cell( - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/site-packages/nbclient/client.py", line 1062, in async_execute_cell - await self._check_raise_for_error(cell, cell_index, exec_reply) - File "/home/runner/micromamba/envs/agu24workshop/lib/python3.12/site-packages/nbclient/client.py", line 918, in _check_raise_for_error - raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) -nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------- -with pygmt.config(GMT_DATA_SERVER="NOAA"): - grid = pygmt.datasets.load_earth_relief(resolution="01d") -grid ------------------- - ------ stderr ----- -gmtread [NOTICE]: Remote data courtesy of GMT data server NOAA [http://noaa.generic-mapping-tools.org] ------ stderr ----- -gmtread [NOTICE]: SRTM15 Earth Relief v2.6 at 1x1 arc degrees reduced by Gaussian Cartesian filtering (314.5 km fullwidth) [Tozer et al., 2019]. ------ stderr ----- -gmtread [NOTICE]: -> Download grid file [112K]: earth_relief_01d_g.grd ------ stderr ----- -gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer ------ stderr ----- -gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd ------ stderr ----- -gmtread [NOTICE]: -> Download grid file [112K]: earth_relief_01d_g.grd ------ stderr ----- -gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer ------ stderr ----- -gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd ------ stderr ----- -gmtread [ERROR]: File @earth_relief_01d_g.grd not found ------ stderr ----- -[Session pygmt-session (3)]: Error returned from GMT API: GMT_FILE_NOT_FOUND (16) ------------------- - ---------------------------------------------------------------------------- -GMTCLibError Traceback (most recent call last) -Cell In[2], line 2 - 1 with pygmt.config(GMT_DATA_SERVER="NOAA"): -----> 2 grid = pygmt.datasets.load_earth_relief(resolution="01d") - 3 grid - -File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/datasets/earth_relief.py:176, in load_earth_relief(resolution, region, registration, data_source, use_srtm) - 174 case "gebco" | "gebcosi": - 175 name = "earth_gebco" ---> 176 grid = _load_remote_dataset( - 177  name=name, - 178  prefix=prefix, - 179  resolution=resolution, - 180  region=region, - 181  registration=registration, - 182 ) - 183 return grid - -File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/helpers/decorators.py:773, in kwargs_to_strings..converter..new_module(*args, **kwargs) - 770 bound.arguments["kwargs"][arg] = newvalue - 772 # Execute the original function and return its output ---> 773 return module_func(*bound.args, **bound.kwargs) - -File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/datasets/load_remote_dataset.py:416, in _load_remote_dataset(name, prefix, resolution, region, registration) - 414 with Session() as lib: - 415 with lib.virtualfile_out(kind="grid") as voutgrd: ---> 416 lib.call_module( - 417  module="read", - 418  args=[fname, voutgrd, *build_arg_list(kwdict)], - 419  ) - 420 grid = lib.virtualfile_to_raster(outgrid=None, vfname=voutgrd) - 422 # Full path to the grid if not tiled grids. - -File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/clib/session.py:656, in Session.call_module(self, module, args) - 654 status = c_call_module(self.session_pointer, module.encode(), mode, argv) - 655 if status != 0: ---> 656 raise GMTCLibError( - 657 f"Module '{module}' failed with status code {status}:\n{self._error_message}" - 658 ) - -GMTCLibError: Module 'read' failed with status code 16: -gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer -gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd -gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer -gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd -gmtread [ERROR]: File @earth_relief_01d_g.grd not found - diff --git a/searchindex.js b/searchindex.js index 9cdddf1..32a63bd 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"]], "1.1 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"]], "1\ufe0f\u20e3. Introduction": [[6, "introduction"]], "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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"yearsgrid_map": 3, "ylong_nam": [3, 5], "ymax": [1, 5], "ymin": [1, 5], "yml": 0, "you": [0, 1, 2, 3, 4, 5, 6, 7], "your": [2, 3, 4, 5, 6], "your_dataset": 2, "ypandasindexpandasindex": 5, "yshift": 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, "zosstandard_nam": 3, "zosvariant_info": 3, "zposit": 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 \ud83c\udfa6", "Tutorial 6 - Animations with GMT \ud83c\udfa6 (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": [0, 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, "environ": 0, "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, "number": 6, "open": 3, "orient": [2, 4], "overview": 0, "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], "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": [0, 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": [0, 1, 2, 3, 4, 5, 6, 7], "type": [1, 6], "up": 0, "us": [3, 5, 6], "view": 5, "visual": [0, 1, 5], "what": [3, 6], "why": [3, 6], "xarrai": 3, "xyz2grd": 4, "your": [0, 1], "zoom": 5}}) \ No newline at end of file diff --git a/tut02_spe_pd_gpd.html b/tut02_spe_pd_gpd.html index 61a7b10..ff20edb 100644 --- a/tut02_spe_pd_gpd.html +++ b/tut02_spe_pd_gpd.html @@ -501,6 +501,7 @@

0️⃣ General stuff 
import geopandas as gpd
+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
@@ -514,11 +515,14 @@ 
0️⃣ General stuffpandas#

 

 
1.1 Tabular data - pandas.DataFrame#

-
Use an example dataset with tabular data provided by PyGMT and load it into a pandas.DataFrame. This dataset contains earthquakes in the area of Japan.

+
Use an example dataset with tabular data provided by PyGMT and load it into a pandas.DataFrame. This dataset contains earthquakes in the area of Japan.
+You can read your own dataset into a pandas.Dataframe using pandas.read_csv and use it in the same way to make the following plots; of course you have to adjust the column names accordantly.

 

 

 
df_jp_eqs = pygmt.datasets.load_sample_data(name="japan_quakes")
 df_jp_eqs.head()
+
+# df_your_dataset = pd.read_csv("your_dataset.csv")
 

 

 

@@ -708,6 +712,12 @@ 
2.1 Line geometry
 
2.1.1 Spatial Data - geopandas.GeoDataFrame with line geometry#

 
First we download some data into in a geopandas.GeoDataFrame. This dataset contains European rivers with their lengths and names.

+
In case you face issues with downloading these data:

+
    
+
  1. Copy the URL “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” into your browser.
    2. 
+
  2. Download the zip file and place it into ~/agu24workshop/book. Do not unpack the ZIP file.
    3. 
+
  3. Replace the URL with the filename of the ZIP file “wise_large_rivers.zip” in geopandas.read_file.
    4. 
+

 

 

 
gpd_rivers_org = gpd.read_file(
@@ -715,6 +725,7 @@ 
2.1.1 Spatial Data - + "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 = pd.read_file("wise_large_rivers.zip")
 gpd_rivers_org.head()
 

 

diff --git a/tut03_spe_xarray.html b/tut03_spe_xarray.html
index ee9a681..fb8b88a 100644
--- a/tut03_spe_xarray.html
+++ b/tut03_spe_xarray.html
@@ -519,77 +519,424 @@ 
2️⃣ Getting started
gmtread [NOTICE]:   -> Download grid file [112K]: earth_relief_01d_g.grd
 

 

-
gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer
-

-

-
gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd
-

-

-
gmtread [NOTICE]:   -> Download grid file [112K]: earth_relief_01d_g.grd
-

-

-
gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer
-

-

-
gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd
-

-

-
gmtread [ERROR]: File @earth_relief_01d_g.grd not found
-

-

-
[Session pygmt-session (3)]: Error returned from GMT API: GMT_FILE_NOT_FOUND (16)
-

-

-
---------------------------------------------------------------------------
-GMTCLibError                              Traceback (most recent call last)
-Cell In[2], line 2
-      1 with pygmt.config(GMT_DATA_SERVER="NOAA"):
-----> 2     grid = pygmt.datasets.load_earth_relief(resolution="01d")
-      3 grid
-
-File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/datasets/earth_relief.py:176, in load_earth_relief(resolution, region, registration, data_source, use_srtm)
-    174     case "gebco" | "gebcosi":
-    175         name = "earth_gebco"
---> 176 grid = _load_remote_dataset(
-    177     name=name,
-    178     prefix=prefix,
-    179     resolution=resolution,
-    180     region=region,
-    181     registration=registration,
-    182 )
-    183 return grid
-
-File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/helpers/decorators.py:773, in kwargs_to_strings.<locals>.converter.<locals>.new_module(*args, **kwargs)
-    770             bound.arguments["kwargs"][arg] = newvalue
-    772 # Execute the original function and return its output
---> 773 return module_func(*bound.args, **bound.kwargs)
-
-File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/datasets/load_remote_dataset.py:416, in _load_remote_dataset(name, prefix, resolution, region, registration)
-    414 with Session() as lib:
-    415     with lib.virtualfile_out(kind="grid") as voutgrd:
---> 416         lib.call_module(
-    417             module="read",
-    418             args=[fname, voutgrd, *build_arg_list(kwdict)],
-    419         )
-    420         grid = lib.virtualfile_to_raster(outgrid=None, vfname=voutgrd)
-    422 # Full path to the grid if not tiled grids.
-
-File ~/micromamba/envs/agu24workshop/lib/python3.12/site-packages/pygmt/clib/session.py:656, in Session.call_module(self, module, args)
-    654 status = c_call_module(self.session_pointer, module.encode(), mode, argv)
-    655 if status != 0:
---> 656     raise GMTCLibError(
-    657         f"Module '{module}' failed with status code {status}:\n{self._error_message}"
-    658     )
-
-GMTCLibError: Module 'read' failed with status code 16:
-gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer
-gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd
-gmtread [ERROR]: Libcurl Error: Failure when receiving data from the peer
-gmtread [ERROR]: Unable to obtain remote file @earth_relief_01d_g.grd
-gmtread [ERROR]: File @earth_relief_01d_g.grd not found
-

-

-

+

+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
<xarray.DataArray 'z' (lat: 181, lon: 361)> Size: 523kB
+array([[ 2865. ,  2865. ,  2865. , ...,  2865. ,  2865. ,  2865. ],
+       [ 3088. ,  3087.5,  3087. , ...,  3088.5,  3088. ,  3088. ],
+       [ 3100.5,  3100.5,  3101. , ...,  3101.5,  3101. ,  3100.5],
+       ...,
+       [-3745.5, -3731. , -3723. , ..., -3734. , -3741.5, -3745.5],
+       [-2939.5, -2945. , -2950.5, ..., -2895. , -2921. , -2939.5],
+       [-3861. , -3861. , -3861. , ..., -3861. , -3861. , -3861. ]])
+Coordinates:
+  * lat      (lat) float64 1kB -90.0 -89.0 -88.0 -87.0 ... 87.0 88.0 89.0 90.0
+  * lon      (lon) float64 3kB -180.0 -179.0 -178.0 -177.0 ... 178.0 179.0 180.0
+Attributes:
+    Conventions:       CF-1.7
+    title:             SRTM15 Earth Relief v2.6 at 01 arc degree
+    history:           
+    description:       IGPP Earth relief
+    long_name:         elevation (m)
+    units:             meters
+    vertical_datum:    EGM96
+    horizontal_datum:  WGS84
@@ -603,6 +950,9 @@

3️⃣ Plotting raster data +_images/9b5d8ab02e64296adc3751b52ac5dd07110ce7abd0e54537ed109c6c46717c5f.png +

@@ -618,6 +968,440 @@

4️⃣ Processing raster data +
grdblend [NOTICE]: Remote data courtesy of GMT data server oceania [http://oceania.generic-mapping-tools.org]
+
+
+
grdblend [NOTICE]: Earth Relief at 3x3 arc seconds tiles provided by SRTMGL3 (land only) [NASA/USGS].
+
+
+
grdblend [NOTICE]:   -> Download 1x1 degree grid tile (earth_relief_03s_g): N37W120
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'z' (lat: 271, lon: 511)> Size: 1MB
+array([[-0.30839247, -0.37808514, -0.30799666, ...,  0.12039052,
+         0.20474193, -0.17929687],
+       [-0.09469072, -0.3017121 , -0.31324536, ...,  0.0442035 ,
+         0.06209851, -0.07466871],
+       [-0.95000005, -0.95000005, -0.26423818, ...,  0.04321925,
+         0.03116445,  0.13370842],
+       ...,
+       [-0.44154865, -0.31297821, -0.20481637, ..., -0.1700097 ,
+        -0.22448991, -0.25389677],
+       [-0.74398577, -0.27561006, -0.31929246, ..., -0.1227859 ,
+        -0.1983598 , -0.20921303],
+       [-0.42644808, -0.35136998, -0.39577812, ..., -0.04772562,
+        -0.19724692, -0.21158722]])
+Coordinates:
+  * lat      (lat) float64 2kB 37.6 37.6 37.6 37.6 ... 37.82 37.82 37.82 37.83
+  * lon      (lon) float64 4kB -119.8 -119.8 -119.8 ... -119.4 -119.4 -119.4
+Attributes:
+    Conventions:   CF-1.7
+    title:         Produced by grdgradient
+    history:       gmt grdgradient @GMTAPI@-S-I-G-M-G-N-000000 -E270/30 -G@GM...
+    description:   Lambertian radiance
+    long_name:     z
+    actual_range:  [ 508. 3533.]

Let’s plot the result using the same grdimage function shared earlier, this time using a custom colormap.

@@ -634,6 +1418,4489 @@

4️⃣ Processing raster data +

@@ -649,6 +5916,416 @@

5️⃣ Download data and load with Xarray +
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 9MB
+Dimensions:  (lat: 180, depth: 33, lon: 360)
+Coordinates:
+  * lat      (lat) float32 720B -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
+  * depth    (depth) float32 132B 0.0 10.0 20.0 30.0 ... 4.5e+03 5e+03 5.5e+03
+  * lon      (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
+    time     datetime64[ns] 8B 2008-01-01
+Data variables:
+    t_an     (depth, lat, lon) float32 9MB ...
@@ -659,6 +6336,12 @@

5️⃣ Download data and load with Xarray +
grdinfo [WARNING]: No 3-D array in file /home/runner/work/agu24workshop/agu24workshop/book/data.nc.  Selecting first 3-D slice in the 4-D array t_an
+
+
+_images/a18fcb026fb0d0c83360f2b8382a360e552dd50db6aa2db5311259bf5223d3a2.png +

@@ -678,6 +6361,464 @@

6️⃣ Open data directly from the cloud +
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 268MB
+Dimensions:    (lat: 180, bnds: 2, lon: 360, time: 1032)
+Coordinates:
+  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
+    lat_bnds   (lat, bnds) float64 3kB ...
+  * lon        (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
+    lon_bnds   (lon, bnds) float64 6kB ...
+  * time       (time) object 8kB 2015-01-16 12:00:00 ... 2100-12-16 12:00:00
+    time_bnds  (time, bnds) object 17kB ...
+Dimensions without coordinates: bnds
+Data variables:
+    zos        (time, lat, lon) float32 267MB ...
+Attributes: (12/49)
+    Conventions:            CF-1.7 CMIP-6.0 UGRID-1.0
+    activity_id:            ScenarioMIP
+    branch_method:          standard
+    branch_time_in_child:   60225.0
+    branch_time_in_parent:  60225.0
+    comment:                <null ref>
+    ...                     ...
+    tracking_id:            hdl:21.14100/21c772eb-064e-4c75-8e16-da95bbfd29b7...
+    variable_id:            zos
+    variant_info:           N/A
+    variant_label:          r1i1p1f1
+    netcdf_tracking_ids:    hdl:21.14100/21c772eb-064e-4c75-8e16-da95bbfd29b7...
+    version_id:             v20180701

Calculate the different in sea level between a date in 2100 and 2015

@@ -700,6 +6841,9 @@

6️⃣ Open data directly from the cloud +_images/b3e45577786a5769fb54bb550f7032f3a73adc8289e08523d052827560bf3b0e.png +

diff --git a/tut04_geophysics.html b/tut04_geophysics.html index 1ed0160..dbb8be5 100644 --- a/tut04_geophysics.html +++ b/tut04_geophysics.html @@ -964,15 +964,15 @@

1️⃣ Prepare gridded data: diff --git a/tut05_topography.html b/tut05_topography.html index 9e27371..e0ce88a 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