From 2a57a082217cc5923e4f21686f31d696a66abb5c Mon Sep 17 00:00:00 2001 From: jkingslake Date: Thu, 7 Dec 2023 03:02:38 +0000 Subject: [PATCH] finish first draft of ARCO NB --- book/tutorials/ARCOdata_writingZarrs.ipynb | 7218 ++++++++++++++------ 1 file changed, 5025 insertions(+), 2193 deletions(-) diff --git a/book/tutorials/ARCOdata_writingZarrs.ipynb b/book/tutorials/ARCOdata_writingZarrs.ipynb index bb565d6..457376a 100644 --- a/book/tutorials/ARCOdata_writingZarrs.ipynb +++ b/book/tutorials/ARCOdata_writingZarrs.ipynb @@ -2,35 +2,39 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ "# Analysis-ready, cloud-optimized data: writing zarr directories\n", "\n", - "This tutorial will introduce analysis-ready, cloud-optimized (ARCO) data and describe one real-world example of restructuring some glaciological data and writing to an ARCO format, zarr. The data we will take a look at is from an ice-penetrating radar called the autonomous phase-sensitive radio-echo sounder (ApRES). \n", + "This tutorial will introduce analysis-ready, cloud-optimized (ARCO) data and describe one real-world example of restructuring some glaciological data and writing to an ARCO format, zarr. The data is from an ice-penetrating radar called the autonomous phase-sensitive radio-echo sounder (ApRES). \n", "\n", - "A zarr store (or directory) is an ARCO data format that is ideally suited for storing high-dimensional, large volume data in the cloud. A key characteristic of zarr stores is that they are 'chunked', meaning that the data is broken up into smaller pieces. This allows for parallel access to the data, which is very useful when you are trying to access subsets of large datasets and/or process large volumes of data in parallel. \n", + "A zarr store (or directory) is an ARCO data format that is well suited for storing high-dimensional, large-volume data in the cloud. A key characteristic of zarr stores is that they are 'chunked', meaning that the data is broken up into smaller pieces. This allows for parallel access to the data, which is very useful when you are trying to access subsets of large datasets and/or process large volumes of data in parallel. \n", "\n", "Depending on the configuration of the ApRES radar, and the survey conducted, it can produce high-dimensional, very large datasets, making these data suitable for storage with zarrs. \n", "\n", - "Before we get to the ApRES data we should make sure we understand what we mean by high-dimensional data and chunked data. \n", + "Before we get to the ApRES data we should understand high-dimensional data and chunked data. \n", "\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ "## High-dimensional data: xarray\n", - "For our purposes, high-dimensional data is data that has more than 2 dimensions. For example, a typical satallite image is a two-dimensional dataset with two spatial dimensions, x and y (or latitude and longitude). If the satallite image has multiple bands it would be a three-dimensional dataset with two spatial dimensions and one band dimension, and if the satallite image has multiple time steps it would be a four-dimensional dataset with two spatial dimensions, one band dimension, and one time dimension. \n", + "For our purposes, high-dimensional data is data that has more than 2 dimensions. For example, a typical satallite image is a two-dimensional dataset with two spatial dimensions, x and y (or latitude and longitude). If the satallite image has multiple bands it would be a three-dimensional dataset with two spatial dimensions and one band dimension, and if the satellite data consisted of multiple images quired at the same locatin at different timethe dataset would be four-dimensional with two spatial dimensions, one band dimension, and one time dimension. \n", "\n", - "[Xarray](http://xarray.pydata.org/en/stable/) is a python package designed to allow you store and process high-dimensional data. It is built on top of [numpy](https://numpy.org/), which is deals with arrays of data. Xarray adds very useful features to numpy, including labelling of dimensions and broadcasting of operations across dimensions. Xarray also works very nicely with [dask](https://dask.org/), which is yet another python package, which allows you to 'chunk' your data and process it in parallel.\n", + "[Xarray](http://xarray.pydata.org/en/stable/) is a python package designed to store and process high-dimensional data. It is built on top of [numpy](https://numpy.org/), which deals with arrays of data. Xarray adds very useful features including labelling of dimensions and broadcasting of operations across dimensions. Xarray also works very nicely with [dask](https://dask.org/), which is yet another python package, which allows you to 'chunk' your data and process it in parallel.\n", "\n", "Before we get onto dask, let's take a look at xarray." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -39,14 +43,16 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ "Let's load an example xarray dataset, supplied with the xarray package:" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -428,17 +434,17 @@ " title: 4x daily NMC reanalysis (1948)\n", " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." ], "text/plain": [ @@ -518,7 +524,7 @@ " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." ] }, - "execution_count": 26, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -530,14 +536,16 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ "This provides a convenient view the structure of the data. We see that there are three dimensions (`lat`, `lon` and `time`) and one variable (`air`). The variable `air` and the coorinates `lat`, `lon` and `time` are all stored as numpy arrays. You can access the numpy array underlying the variable in dataset `air` and verify its type as follows:" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -550,7 +558,7 @@ " [243.59999, 244.09999]]], dtype=float32)" ] }, - "execution_count": 27, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -561,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -578,19 +586,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "A great thing about xarray is that it allows to very quickly take a look at, process, and plot this kind of data. For example, we can plot the mean of `air` over the `time` dimension as follows:" + "A great thing about xarray is that it allows to quickly look at, process, and plot this kind of data. For example, we can plot the mean of `air` over the `time` dimension as follows:" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -605,15 +615,26 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, + "source": [ + "Xarray has many other useful capabilites for slicing, coarsening, andplittnig data, some of which we will use below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, "source": [ "## Chunking: dask\n", - "The xarray above (`ds`) contained numpy arrays. To process or plot any part of a variable, we need to load all of it into memory. This is fine if the data are small; the dataset above was only" + "The xarray above (`ds`) contained numpy arrays. To process or plot any part of a variable in `ds`, we need to load all of it into memory. This is fine if the data are small; the dataset above was only" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -630,14 +651,16 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "but this would be an issue if a variable was, say, 100GB. This would be too large to fit into memory on most computers and would prevent you from processing or plotting the data. This is where chunking and dask come in. Dask provides a new data structure, called a dask array, which is like a numpy array except that it is split up into smaller pieces called chunks. Let's load an example dask array (straight from the dask [documentation](https://examples.dask.org/array.html#Create-Random-array)): " + "but this would be an issue if a variable was, say, 100GB. This would be too large to fit into memory on most computers and would complicate processing or plotting the data. This is where chunking and dask come in. Dask provides a new data structure, called a dask array, which is like a numpy array except that it is split up into smaller pieces called chunks. Let's load an example dask array (straight from the dask [documentation](https://examples.dask.org/array.html#Create-Random-array)): " ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -740,37 +763,40 @@ "dask.array" ] }, - "execution_count": 31, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask.array as da\n", - "x = da.random.random((1e6, 1e6), chunks=(5000, 5000))\n", - "x" + "dask_array = da.random.random((1e6, 1e6), chunks=(5000, 5000))\n", + "dask_array" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "This created a dask array containing random values between -0.5 and 0.5. Calling `x` displays a handy table containing information about this dask array. It's total size is 7TB! Clearly this is much too large to fit into memory. In fact, nothing has been loaded into memory except the structure of the dask array (i.e. the number and shape of the chunks) and the information needed to create it when we need it (i.e. the method `random`). \n", + "This created a dask array containing random values between -0.5 and 0.5. Calling `dask_array` displays a handy table containing information about this dask array. It's total size is 7TB! Clearly this is much too large to fit into memory. In fact, nothing has been loaded into memory except the structure of the dask array (i.e. the number and shape of the chunks) and the information needed to create it when we need it (i.e. the method `random`). \n", "\n", - "The table also includes information on the chunks. There are 40,000 of them and each one is 190MB and 5000 by 5000 elements in size. Note that we chose this chunk size when we created the dask array. This is the key to dask arrays: we can choose the chunk size to suit our needs. Choosing the wrong chunk size can cause all sorts of issues, as we will see later in our real world example.\n", + "The table also includes information on the chunks. There are 40,000 of them and each one is 190MB and 5000 by 5000 elements in size. Note that we chose this chunk size when we created the dask array. This is the key to dask arrays: we can choose the chunk size to suit our needs. You have to [make this choice carefully](https://blog.dask.org/2021/11/02/choosing-dask-chunk-sizes#rough-rules-of-thumb) to optimize your processing. And worse, if your chunk sizes end up being non-uniform within a dimension, it causes issues when writing to zarr, as we will see later in our real-world example.\n", "\n", - "Two great advantages of using dask array instead of an ordinary numpy array are 1) that we can view and plot a subset of the data without loading the whole thing into memory, and 2) we can very easily process the data in parallel. Let's first load a subset of the data into memory and plot it:\n", - "\n" + "Two advantages of using dask arrays instead of an ordinary numpy arrays are 1) that we can view and plot a subset of the data without loading the whole thing into memory, and 2) we can very easily process the data in parallel. \n", + "\n", + "Let's first load a subset of the data into memory and plot it:" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -781,808 +807,1266 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "x_sub = x[0:50, 0:50]\n", + "dask_array_sub = dask_array[0:50, 0:50]\n", "plt.figure(figsize=(2, 2))\n", - "plt.imshow(x_sub);" + "plt.imshow(dask_array_sub);" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.02" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "This is a very small subset of the data: 0.02 MB\n" + ] } ], "source": [ - "x_sub.nbytes/1e6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's start up a cluster and load and process a much larger subset of the data in parallel:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ToDo when in cryocloud" + "print(f\"This is a very small subset of the data: {dask_array_sub.nbytes/1e6} MB\")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "## Xarray + dask\n", - "Xarray and dask work very nicely together. The dataset we looked at above (`ds`) was made up of numpy arrays. We can instead tell xarray to load the data as dask arrays, therefore avoiding loading anything into memory until it we need it. This is called lazily loading the data. We do this by defining the `chunks` argument when we load the data:" + "Next, let's start up a cluster, then load and process a much larger subset of the data in parallel:" ] }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, + "execution_count": 10, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 dask.array<chunksize=(2920, 5, 5), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 dask.array\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds_dask = xr.tutorial.open_dataset('air_temperature',\n", - " chunks={'lat': 5, 'lon': 5, 'time': -1})\n", - "ds_dask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now if we take a look at the variable `air` in `ds_dask` we can see that it is a dask array:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
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<xarray.Dataset>\n",
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        "Coordinates:\n",
        "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
        "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
        "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 dask.array<chunksize=(2920, 5, 5), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
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-       "    dataset:       NMC Reanalysis\n",
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-       "    statistic:     Individual Obs\n",
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    • lon
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+       "              222.5, 225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5,\n",
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    • time
      PandasIndex
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        "               '2013-01-01 12:00:00', '2013-01-01 18:00:00',\n",
        "               '2013-01-02 00:00:00', '2013-01-02 06:00:00',\n",
        "               '2013-01-02 12:00:00', '2013-01-02 18:00:00',\n",
@@ -1910,274 +2390,49 @@
        "               '2014-12-30 12:00:00', '2014-12-30 18:00:00',\n",
        "               '2014-12-31 00:00:00', '2014-12-31 06:00:00',\n",
        "               '2014-12-31 12:00:00', '2014-12-31 18:00:00'],\n",
-       "              dtype='datetime64[ns]', name='time', length=2920, freq=None))
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    4xDaily Air temperature at sigma level 995
    units :
    degK
    precision :
    2
    GRIB_id :
    11
    GRIB_name :
    TMP
    var_desc :
    Air temperature
    dataset :
    NMC Reanalysis
    level_desc :
    Surface
    statistic :
    Individual Obs
    parent_stat :
    Other
    actual_range :
    [185.16 322.1 ]
    • " + " dtype='datetime64[ns]', name='time', length=2920, freq=None))
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis\n", + "(4x/day). These are the 0.9950 sigma level values.
    platform :
    Model
    references :
    http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
    • " ], "text/plain": [ - "\n", - "dask.array\n", + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 dask.array\n", "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." ] }, - "execution_count": 35, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ds_dask.air" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## xApRES\n", - "### ApRES data --> xarray --> zarr --> dask " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to take a look at some real-world data. The data we will look at is from an ApRES radar. We will \n", - "- discuss the structure of an ApRES survey,\n", - "- load some raw ApRES data,\n", - "- structure the data as an xarray, and \n", - "- write this xarray to a zarr directory. \n", - "\n", - "This will make use of a library we have developed called [xApRES](https://github.com/ldeo-glaciology/XApRES). The main developers of this library have so far been Jonny Kingslake, George Lu, and Elizabeth Case. We very much welcome collaboration from anyone interested in efficietn ways to process, store, and analyze ApRES data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Structure of an ApRES survey\n", - "The structure of an ApRES survey can get quite complex. The figure below depicts the structure.\n", - "\n", - "![ApRES data structure](../img/ApRES_data_diagram.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Chirps\n", - "The radar emits individual 'chirps' which each generate a 40,001-element-long time series of data. The chirps are emitted at a rate of 1 per second. \n", - "### Bursts\n", - "The chirps are grouped into 'bursts', which each contain a user-definable number of chirps. The system is setup to perform bursts at regular intervals. The data we are going to look at below has a burst interval of 15 minutes.\n", - "### Attenuator settings\n", - "The ApRES has user-definable attenuator settings which are chosen during installation to ensure the signal is not too strong or too weak. Typically we choose more than one attenuator setting and cycle through them during each burst. So for example, if we have 3 attneuator settings, and 20 chirps per burst, per settting, the sequence of chirps would be 20 chirps using attenuator setting 1, followed by 20 chirps using attenuator setting 2, followed by 20 chirps using attenuator setting 3, followed by 20 chirps using attenuator setting 1, and so on.\n", - "\n", - "This complexity leads to a four-dimensional dataset: 1) the time of each burst, 2) the chirp number within each burst, 3) the attenuator setting, and 4) the sample number in chirp. A typical workflow for processing datasets collected by such a survey is through nested for-loops and it is a major challenge keeping track of which chirp belongs where. \n", - "\n", - "This is where xarray can really help. Let's next load some raw ApRES data using some scripts fro a library we have been developing called xApRES.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load raw ApRES data\n", - "Raw ApRES data are stored in files with an extension `.dat`. First we install and import the xApRES library:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: xapres in /Users/jkingslake/miniconda3/envs/full_py_env/lib/python3.11/site-packages (0.2.1)\n", - "Requirement already satisfied: numpy in /Users/jkingslake/miniconda3/envs/full_py_env/lib/python3.11/site-packages (from xapres) (1.26.0)\n", - "Requirement already satisfied: requests in /Users/jkingslake/miniconda3/envs/full_py_env/lib/python3.11/site-packages (from xapres) (2.31.0)\n", - "Requirement already satisfied: xarray in /Users/jkingslake/miniconda3/envs/full_py_env/lib/python3.11/site-packages (from xapres) (2023.10.1)\n", - "Requirement already satisfied: gcsfs in /Users/jkingslake/miniconda3/envs/full_py_env/lib/python3.11/site-packages (from xapres) (2023.10.0)\n", - "Requirement already satisfied: fsspec in /Users/jkingslake/miniconda3/envs/full_py_env/lib/python3.11/site-packages (from xapres) (2023.10.0)\n", - 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] - } - ], - "source": [ - "!pip install xapres\n", - "import xapres as xa" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The data\n", - "The ApRES data we will be using were collected in the ablation zone of the Greenland Ice Sheet by a team led by Meredith Nettles (Lamont-Doherty Earth Observatory, LDEO) and Laura Stevens (University of Oxford), including George Lu (LDEO), Stacy Larochelle (LDEO), Marianne Okal (Earthscope), Kristin Arnold (IRIS Alpine), and Josh Rines (Stanford University). The project was funded by the US National Science Foundation (project number: 2003464). Three ApRES units were positioned near several supraglacial lakes that periodically drain to the bed of the ice sheet. The units collected a burst every 15 minutes for up to 18 months. You can learn more about the science being done with these data (using the tools described here) in two oral presentation at AGU this week: [one](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1321546) led by Stacy Larochelle and [one](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1316057) led by George Lu.\n", - "\n", - "The map below shows the location of the three ApRES units. We will be looking at data from A11.\n", - "\n", - "![Map of ApRES locations in greenland](../img/ApRES_map.png)\n", - "\n", - "Usinf xApRES we will create an instance of a `from_dats` object, then use two methods of these objects (`list_files` and `load_single`) to load 1 chirp from within 1 burst from within 1 `.dat` file. " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "fd = xa.load.from_dats()\n", - "dat_file_list = fd.list_files(directory=f'gs://ldeo-glaciology/GL_apres_2022/A101', \n", - " remote_load = True)\n", - "fd.load_single(dat_file_list[30], remote_load = True, burst_number=0, chirp_num=0)\n" + "ds_dask = xr.tutorial.open_dataset('air_temperature',\n", + " chunks={'lat': 5, 'lon': 5, 'time': -1})\n", + "ds_dask" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "Let's take a look at the raw chirp data. It is 40001 elements long, as mentioned above, and we can plot it as follows:" + "Now if we take a look at the variable `air` in `ds_dask` we can see that it is a dask array:" ] }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40001" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(fd.single_chirp.vdat)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt_1 = plt.figure(figsize=(15, 3))\n", - "plt.plot(fd.single_chirp.t, fd.single_chirp.vdat)\n", - "plt.axis([0,fd.single_chirp.t[-1],-1.25,1.25])\n", - "plt.xlabel(\"Time (s)\")\n", - "plt.ylabel(\"Amplitude (V)\")\n", - "plt.grid(\"on\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We wont get into the details of processing ApRES data., but to obtain a representation of the reflection power as a function of depth we apply a fast fourier transform to the chirp. This is done in xApRES as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fd.single_chirp.FormProfile().PlotProfile(1400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Put these data into an xarray\n", - "The methods demonstrated above work well for loading and plotting single chirps, but as discussed above, when we have a large complex pRES dataset with multiple chirps and bursts and attenuator settings, we need a more convenient way of storing and accessing the data. When the data gets very large, we also need to be able to load and process subsets of the data without loading the whole thing into memory. Xarray can help.\n", - "\n", - "xApRES has a collection of tools to load multiple `.dat` files and put them in to a useful structure within an xarray. \n", - "\n", - "Next we will use one of the tools to load all the data from one dat file:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -2546,553 +2801,2692 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:          (time: 94, chirp_time: 40001, chirp_num: 20,\n",
-       "                      attenuator_setting_pair: 2, profile_range: 7134)\n",
+       "
    <xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+       "dask.array<open_dataset-air, shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 5, 5), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
-       "  * time             (time) datetime64[ns] 2022-06-23T01:36:56 ... 2022-06-24...\n",
-       "  * chirp_time       (chirp_time) float64 0.0 2.5e-05 5e-05 ... 1.0 1.0 1.0\n",
-       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.5e+03 1.5e+03\n",
-       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
-       "    filename         (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DI...\n",
-       "    burst_number     (time) int64 0 1 2 3 4 5 6 7 8 ... 86 87 88 89 90 91 92 93\n",
-       "    AFGain           (attenuator_setting_pair) int64 -4 -14\n",
-       "    attenuator       (attenuator_setting_pair) float64 5.0 5.0\n",
-       "    orientation      (time) <U7 'unknown' 'unknown' ... 'unknown' 'unknown'\n",
-       "Dimensions without coordinates: attenuator_setting_pair\n",
-       "Data variables:\n",
-       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 ...\n",
-       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 ...\n",
-       "    latitude         (time) float64 68.71 68.71 68.71 ... 68.71 68.71 68.71\n",
-       "    longitude        (time) float64 -49.55 -49.55 -49.55 ... -49.55 -49.55\n",
-       "    battery_voltage  (time) float64 13.89 13.87 13.94 ... 13.85 13.84 13.84\n",
-       "    temperature_1    (time) float64 2.031 0.4609 508.9 ... 1.641 0.2656 510.9\n",
-       "    temperature_2    (time) float64 5.195 3.039 3.234 ... 4.805 2.25 0.6797