review time series part 2 #11
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veroandreo
reviewed
Sep 22, 2024
| # Import the GRASS GIS packages we need | ||
| import grass.script as gs | ||
| import grass.jupyter as gj | ||
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| path_to_project = "eu_laea/italy_LST_daily" |
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| path_to_project = "eu_laea/italy_LST_daily" | |
| path_to_project = "italy_eu_laea/italy_LST_daily" |
| @@ -61,9 +61,9 @@ scheme. In this way, we have: | |||
| To run this tutorial locally or in Google Colab, you should install | |||
| GRASS GIS 8.4+, and download the | |||
| [daily MODIS LST project](https://zenodo.org/records/3564515). | |||
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| [daily MODIS LST project](https://zenodo.org/records/3564515). | |
| [daily MODIS LST project](https://zenodo.org/doi/10.5281/zenodo.3564514). |
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| tmin = gs.list_grouped(type="raster", pattern="lst_minimum_*")["italy_LST_daily"] | ||
| tmax = gs.list_grouped(type="raster", pattern="lst_maximum_*")["italy_LST_daily"] | ||
| tavg = gs.list_grouped(type="raster", pattern="lst_average_*")["italy_LST_daily"] |
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I needed numbers, that's why I used the ??. Does it do the right selection with the *?
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| :::{.callout-caution title="Question" collapse="true"} | ||
| How would you obtain the average spring warming over the whole time series? | ||
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| ```{python} | ||
| # Average spring warming | ||
| gs.run_command("t.rast.series", | ||
| input="annual_spring_warming", | ||
| output="avg_spring_warming", | ||
| method="average") | ||
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| # Display raster map with interactive class | ||
| spw_map = gj.InteractiveMap(width = 500, use_region=True, tiles="CartoDark") | ||
| spw_map.add_raster("avg_spring_warming") | ||
| spw_map.add_layer_control(position = "bottomright") | ||
| spw_map.show() | ||
| ``` | ||
| ::: |
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I would not remove this example. The question is just a trigger for the next topic. It's a different way of introducing a topic. And in any case, it is collapsed so it is up the the reader to unfold it.
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| ::: {.callout-note title="Temporal relations"} | ||
| Something about temporal relations... |
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| ::: {.callout-note title="Temporal relations"} | |
| Something about temporal relations... | |
| ::: {.callout-note title="Temporal topology"} | |
| Temporal topology refers to the temporal relations among time intervals or instances in different time series. They are mostly useful when sampling one STRDS with another (see [t.sample](https://grass.osgeo.org/grass-stable/manuals/t.sample.html) manual). Some examples of temporal relations are *start, equal, during, contain, overlap,* etc. See below a graphic representation of relations and sampling taken from Gebbert and Pebesma (2014). Note that A1 starts in S1, B3 is during S3, B4 equals S4, B1 overlaps S1. | |
|  | |
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Dunno if attaching img like this really work...
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I completely removed one of the examples within the callout questions, it was placed before the tool was introduced, which seemed problematic. It could be integrated later if needed.
I replaced the knitr engine for jupyter in the heading, my understanding is knitr is for R.