.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "advanced_examples/plot_demcollection.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_advanced_examples_plot_demcollection.py: Working with a collection of DEMs ================================= Oftentimes, more than two timestamps (DEMs) are analyzed simultaneously. One single dDEM only captures one interval, so multiple dDEMs have to be created. In addition, if multiple masking polygons exist (e.g. glacier outlines from multiple years), these should be accounted for properly. The :class:`xdem.DEMCollection` is a tool to properly work with multiple timestamps at the same time, and makes calculations of elevation/volume change over multiple years easy. .. GENERATED FROM PYTHON SOURCE LINES 10-18 .. code-block:: Python from datetime import datetime import geoutils as gu import matplotlib.pyplot as plt import xdem .. GENERATED FROM PYTHON SOURCE LINES 19-23 **Example data**. We can load the DEMs as usual, but with the addition that the ``datetime`` argument should be filled. Since multiple DEMs are in question, the "time dimension" is what keeps them apart. .. GENERATED FROM PYTHON SOURCE LINES 23-28 .. code-block:: Python dem_2009 = xdem.DEM(xdem.examples.get_path("longyearbyen_ref_dem"), datetime=datetime(2009, 8, 1)) dem_1990 = xdem.DEM(xdem.examples.get_path("longyearbyen_tba_dem"), datetime=datetime(1990, 8, 1)) .. GENERATED FROM PYTHON SOURCE LINES 29-32 For glacier research (any many other fields), only a subset of the DEMs are usually interesting. These parts can be delineated with masks or polygons. Here, we have glacier outlines from 1990 and 2009. .. GENERATED FROM PYTHON SOURCE LINES 32-37 .. code-block:: Python outlines = { datetime(1990, 8, 1): gu.Vector(xdem.examples.get_path("longyearbyen_glacier_outlines")), datetime(2009, 8, 1): gu.Vector(xdem.examples.get_path("longyearbyen_glacier_outlines_2010")), } .. GENERATED FROM PYTHON SOURCE LINES 38-39 To experiment with a longer time-series, we can also fake a 2060 DEM, by simply exaggerating the 1990-2009 change. .. GENERATED FROM PYTHON SOURCE LINES 39-45 .. code-block:: Python # Fake a 2060 DEM by assuming twice the change from 1990-2009 between 2009 and 2060 dem_2060 = dem_2009 + (dem_2009 - dem_1990).data * 3 dem_2060.datetime = datetime(2060, 8, 1) .. GENERATED FROM PYTHON SOURCE LINES 46-51 Now, all data are ready to be collected in an :class:`xdem.DEMCollection` object. What we have are: 1. Three DEMs from 1990, 2009, and 2060 (the last is artificial) 2. Two glacier outline timestamps from 1990 and 2009 .. GENERATED FROM PYTHON SOURCE LINES 51-55 .. code-block:: Python demcollection = xdem.DEMCollection(dems=[dem_1990, dem_2009, dem_2060], outlines=outlines, reference_dem=1) .. GENERATED FROM PYTHON SOURCE LINES 56-62 We can generate :class:`xdem.dDEM` objects using :func:`xdem.DEMCollection.subtract_dems`. In this case, it will generate three dDEMs: * 1990-2009 * 2009-2009 (to maintain the ``dems`` and ``ddems`` list length and order) * 2060-2009 (note the inverted order; negative change will be positive) .. GENERATED FROM PYTHON SOURCE LINES 62-65 .. code-block:: Python _ = demcollection.subtract_dems() .. GENERATED FROM PYTHON SOURCE LINES 66-72 These are saved internally, but are also returned as a list. An elevation or volume change series can automatically be generated from the ``DEMCollection``. In this case, we should specify *which* glacier we want the change for, as a regional value may not always be required. We can look at the glacier called "Scott Turnerbreen", specified in the "NAME" column of the outline data. `See here for the outline filtering syntax `_. .. GENERATED FROM PYTHON SOURCE LINES 72-75 .. code-block:: Python demcollection.get_cumulative_series(kind="dh", outlines_filter="NAME == 'Scott Turnerbreen'") .. rst-class:: sphx-glr-script-out .. code-block:: none 1990-08-01 0.000000 2009-08-01 -13.379259 2060-08-01 -51.885126 dtype: float64 .. GENERATED FROM PYTHON SOURCE LINES 76-78 And there we have a cumulative dH series of the glacier Scott Turnerbreen on Svalbard! The dDEMs can be visualized to give further context. .. GENERATED FROM PYTHON SOURCE LINES 78-106 .. code-block:: Python extent = [ demcollection.dems[0].bounds.left, demcollection.dems[0].bounds.right, demcollection.dems[0].bounds.bottom, demcollection.dems[0].bounds.top, ] scott_extent = [518600, 523800, 8666600, 8672300] plt.figure(figsize=(8, 5)) for i in range(2): plt.subplot(1, 2, i + 1) if i == 0: title = "1990 - 2009" ddem_2060 = demcollection.ddems[0].data.squeeze() else: title = "2009 - 2060" # The 2009 - 2060 DEM is inverted since the reference year is 2009 ddem_2060 = -demcollection.ddems[2].data.squeeze() plt.imshow(ddem_2060, cmap="coolwarm_r", vmin=-50, vmax=50, extent=extent) plt.xlim(scott_extent[:2]) plt.ylim(scott_extent[2:]) plt.show() .. image-sg:: /advanced_examples/images/sphx_glr_plot_demcollection_001.png :alt: plot demcollection :srcset: /advanced_examples/images/sphx_glr_plot_demcollection_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.286 seconds) .. _sphx_glr_download_advanced_examples_plot_demcollection.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_demcollection.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_demcollection.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_