From e32ac8042baa6202d42310a0453f7f54190c1fc9 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 14 Aug 2024 13:43:56 +0000 Subject: [PATCH] Edit README --- README.md | 19 +- python-data/solutions/ex01_xarray_intro.ipynb | 16 +- .../ex02_plotting_and_aggregation.ipynb | 750 +++++++++++++++++- 3 files changed, 765 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index a97a621..b58f9c9 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Introduction to Scientific Computing course +# Introduction to Scientific Computing course This repository holds teaching materials for the NCAS Introduction to Scientific Computing course. ## Overview @@ -12,14 +12,11 @@ The course covers: - [Presentation](https://github.com/ncasuk/ncas-isc/tree/main/version_control) - [Exercise](https://github.com/ncasuk/ncas-isc/tree/main/version_control) - Introduction to Python - - [Python Introduction Slides](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/presentations.md) + - [Python Introduction Slides](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/README.md) - [Exercises - Jupyter Notebooks](https://github.com/ncasuk/ncas-isc/tree/main/python-intro/exercises) | [Solutions](https://github.com/ncasuk/ncas-isc/tree/main/python-intro/solutions) - Data manipulation and visualisation in Python (Working with Data) - - [Python working with data Slides](https://github.com/ncasuk/ncas-isc/tree/main/python-data/slides) - - [Exercises - Jupyter Notebooks](https://github.com/ncasuk/ncas-isc/tree/main/python-data/notebooks) | [Solutions](https://github.com/ncasuk/ncas-isc/tree/main/python-data/solutions) -- Example code for all python modules - - [Example code for Python (Working with Data)](https://github.com/ncasuk/ncas-isc/tree/main/python-data/example_code) - - [Example data for Python (Working with Data)](https://github.com/ncasuk/ncas-isc/tree/main/python-data/example_data) + - [Python working with data Slides](https://github.com/ncasuk/ncas-isc/tree/main/python-data/README.md) + - [Exercises - Jupyter Notebooks](https://github.com/ncasuk/ncas-isc/tree/main/python-data/exercises) | [Solutions](https://github.com/ncasuk/ncas-isc/tree/main/python-data/solutions) ## Index ### Overview Presentations @@ -57,9 +54,11 @@ The course covers: ## Python - Working with Data -### Handling arrays -* [Numpy](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/numpy.pdf) -* [Exercises](https://github.com/ncasuk/ncas-isc/blob/main/python-data/notebooks/ex01_numpy_arrays.ipynb) and [Solutions](https://github.com/ncasuk/ncas-isc/blob/main/python-data/solutions/ex01_numpy_arrays_solutions.ipynb) +### xarray +| Lesson | Exercise | Solution | +| ------ | -------- | -------- | +| [Intro to multidimensional arrays](https://geohackweek.github.io/nDarrays/01-introduction/), [xarray](https://geohackweek.github.io/nDarrays/02-xarray-architecture/) and [indexing](https://geohackweek.github.io/nDarrays/03-label-based-indexing/) | [Exercise 01]() | [Solution 01]() | + ### Visualisation * [Matplotlib and cartopy](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/matplotlib_and_cartopy.pdf) diff --git a/python-data/solutions/ex01_xarray_intro.ipynb b/python-data/solutions/ex01_xarray_intro.ipynb index 755e621..d4dd27f 100644 --- a/python-data/solutions/ex01_xarray_intro.ipynb +++ b/python-data/solutions/ex01_xarray_intro.ipynb @@ -72,7 +72,7 @@ "id": "48d19019-2546-46d3-9da1-64d8e7c363e8", "metadata": {}, "source": [ - "1. Open the test dataset '' and load it into an xarray `Dataset` called `ds`.\n", + "1. Open the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and load it into an xarray `Dataset` called `ds`.\n", "(Hint: Don't forget to import any packages you need)" ] }, @@ -574,7 +574,7 @@ "id": "3cee6429-dbf8-4a10-b384-ad9de719a0d0", "metadata": {}, "source": [ - "What are the dimensions and variables in this dataset? What does each represent? " + "3. What are the dimensions and variables in this dataset? What does each represent? " ] }, { @@ -593,7 +593,7 @@ "id": "81207a17-eeaf-4de3-a3f6-9a0fd782c252", "metadata": {}, "source": [ - "3. Find the name of the Data Variable, and use it to extract a `DataArray` called `temperature`." + "4. Find the name of the Data Variable, and use it to extract a `DataArray` called `temperature`." ] }, { @@ -612,7 +612,7 @@ "id": "6ee4a984-305e-44da-87ab-75cf88d71f22", "metadata": {}, "source": [ - "4. Take a look at the `temperature` data array and inspect its dimensions, coordinates and attributes. What are the specific dimensions and coordinates associated with it? What metadata (attributes) is provided?" + "5. Take a look at the `temperature` data array and inspect its dimensions, coordinates and attributes. What are the specific dimensions and coordinates associated with it? What metadata (attributes) is provided?" ] }, { @@ -1083,7 +1083,7 @@ "id": "f3e16f51-8a98-424a-b908-87360947f69a", "metadata": {}, "source": [ - "5. Select a subset of the `temperature` array using label-based indexing to get data at the position [0,0,0]." + "6. Select a subset of the `temperature` array using label-based indexing to get data at the position [0,0,0]." ] }, { @@ -1507,7 +1507,7 @@ "id": "2545ea6f-ad6c-4612-86c4-95cc9d23ffdb", "metadata": {}, "source": [ - "6. Use `.loc` to find the lat and lon values at the time `2065-01-30`. " + "7. Use `.loc` to find the lat and lon values at the time `2065-01-30`. " ] }, { @@ -1955,7 +1955,7 @@ "id": "61407fc5-2ff3-4467-875d-f3e3b06eaa34", "metadata": {}, "source": [ - "6. It's not ideal to have to keep track of which dimension is in which position. Instead, use `.isel` to use the dimension names to get the data in the first time, lat and lon position." + "8. It's not ideal to have to keep track of which dimension is in which position. Instead, use `.isel` to use the dimension names to get the data in the first time, lat and lon position." ] }, { @@ -2379,7 +2379,7 @@ "id": "0f6f10a7-0905-42ec-9bf4-b3cf07229637", "metadata": {}, "source": [ - "8. The previous method is still referring to a selection by integer position. Use `.sel` to give a labelled index with the named dimension to find the data at `time=2065-12-30`, `lat=-78.5`, `lon=11.0`." + "9. The previous method is still referring to a selection by integer position. Use `.sel` to give a labelled index with the named dimension to find the data at `time=2065-12-30`, `lat=-78.5`, `lon=11.0`." ] }, { diff --git a/python-data/solutions/ex02_plotting_and_aggregation.ipynb b/python-data/solutions/ex02_plotting_and_aggregation.ipynb index ec2094d..49e6c1f 100644 --- a/python-data/solutions/ex02_plotting_and_aggregation.ipynb +++ b/python-data/solutions/ex02_plotting_and_aggregation.ipynb @@ -16,10 +16,564 @@ "## Plotting" ] }, + { + "cell_type": "markdown", + "id": "dc7d1b36-cc80-44a4-9adf-5f02901b28f6", + "metadata": {}, + "source": [ + "1. Import the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and create the temperature data array as in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e129c00a-5a55-47a1-8df1-5e1ce8b28c53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'tas' (time: 12, lat: 180, lon: 360)> Size: 3MB\n",
+       "[777600 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float64 3kB 0.0 1.0 2.0 3.0 4.0 ... 356.0 357.0 358.0 359.0\n",
+       "  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
+       "  * time     (time) object 96B 2065-01-30 12:00:00 ... 2065-12-30 12:00:00\n",
+       "Attributes:\n",
+       "    standard_name:     air_temperature\n",
+       "    long_name:         Near-Surface Air Temperature\n",
+       "    units:             K\n",
+       "    comment:           daily-mean near-surface (usually, 2 meter) air tempera...\n",
+       "    original_name:     mo: m01s03i236\n",
+       "    cell_methods:      time: mean\n",
+       "    history:           2010-10-29T11:35:40Z altered by CMOR: Treated scalar d...\n",
+       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation...
" + ], + "text/plain": [ + " Size: 3MB\n", + "[777600 values with dtype=float32]\n", + "Coordinates:\n", + " * lon (lon) float64 3kB 0.0 1.0 2.0 3.0 4.0 ... 356.0 357.0 358.0 359.0\n", + " * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n", + " * time (time) object 96B 2065-01-30 12:00:00 ... 2065-12-30 12:00:00\n", + "Attributes:\n", + " standard_name: air_temperature\n", + " long_name: Near-Surface Air Temperature\n", + " units: K\n", + " comment: daily-mean near-surface (usually, 2 meter) air tempera...\n", + " original_name: mo: m01s03i236\n", + " cell_methods: time: mean\n", + " history: 2010-10-29T11:35:40Z altered by CMOR: Treated scalar d...\n", + " associated_files: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..." + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xarray as xr\n", + "ds = xr.open_dataset('../data/tas_rcp45_2055_mon_avg_change.nc')\n", + "temperature = ds[\"tas\"]\n", + "temperature" + ] + }, + { + "cell_type": "markdown", + "id": "9a047319-7af4-4b42-8231-d88ddcda20ef", + "metadata": {}, + "source": [ + "2. Create a time series from `temperature` by using `.sel` to select the latitude slice between () for the time `` and the longitude ``. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "98a422d3-7137-437d-b698-98e3cd85c321", + "metadata": {}, + "outputs": [], + "source": [ + "#smthn here" + ] + }, + { + "cell_type": "markdown", + "id": "bbb5c8e1-74fc-4ec0-b4e1-f8b6e52e8388", + "metadata": {}, + "source": [ + "3. Create a plot from this time series." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed393923-62bf-48b8-928f-5424ebc84f44", + "metadata": {}, + "outputs": [], + "source": [ + "# smthn here" + ] + }, + { + "cell_type": "markdown", + "id": "59a84e51-3c88-4a42-a20e-ee70bbca78ad", + "metadata": {}, + "source": [ + "We'll be covering matplotlib in more detail later, but here are some options for customising plots: \n", + "-rewrw" + ] + }, + { + "cell_type": "markdown", + "id": "4f2d3902-45c9-4eb1-b4b0-9d39baa421ac", + "metadata": {}, + "source": [ + "4. Make the plot red with 'x' marking the points." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "153fe887-2921-419e-917c-103703a029ed", + "id": "fdfb363f-e819-47a1-a571-5a74f6711196", + "metadata": {}, + "outputs": [], + "source": [ + "# smthn here" + ] + }, + { + "cell_type": "markdown", + "id": "7a947799-3b5a-4b8b-bf4e-0c58986b58ed", + "metadata": {}, + "source": [ + "5. As this is geospatial data, let's select a 2D subset of our data and plot a colour map. Select the 2D subset by choosing a single data and plotting lontiude against latitude. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "682cfc89-a2cd-454e-a124-d6df5f399208", + "metadata": {}, + "outputs": [], + "source": [ + "# smthn here" + ] + }, + { + "cell_type": "markdown", + "id": "4cbb0586-67e2-4a6f-bbdf-552e51f46085", + "metadata": {}, + "source": [ + "6. Change the colour scheme so it's blue like the example in the teaching resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5f72d0-ebe2-4f19-887d-65b3b837002a", "metadata": {}, "outputs": [], "source": [] @@ -32,10 +586,202 @@ "## Arithmetic and Aggregation" ] }, + { + "cell_type": "markdown", + "id": "c1f4cc1f-b063-4de3-8d07-5fd21ab9c40e", + "metadata": {}, + "source": [ + "7. Let's compare the data on one date to the next date. Create two temperature datasets and extract the temperature change data for january (`2055-01-15`) and july (`2055-07-15`) of 2055." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "870b9e7c-a714-4da0-87b5-ad7f0f1813cc", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "unmatched ')' (516435266.py, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[14], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m temp_jul = temperature.self({'time': '2055-07-15'}))\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unmatched ')'\n" + ] + } + ], + "source": [ + "# smthn here\n", + "temp_jan = temperature.sel({'time':'2055-01-15'})\n", + "temp_jul = temperature.self({'time': '2055-07-15'}))" + ] + }, + { + "cell_type": "markdown", + "id": "89e3bb93-edd4-45f5-aba1-1eeb6aebc622", + "metadata": {}, + "source": [ + "8. Calculate the difference in temperature between july and january" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6d0d6854-0775-42f7-9243-c1d773e9475d", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'temp_jan' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m delta_temp \u001b[38;5;241m=\u001b[39m \u001b[43mtemp_jan\u001b[49m \u001b[38;5;241m-\u001b[39m temp_jul\n", + "\u001b[0;31mNameError\u001b[0m: name 'temp_jan' is not defined" + ] + } + ], + "source": [ + "delta_temp = temp_jan - temp_jul" + ] + }, + { + "cell_type": "markdown", + "id": "53a9329e-9e2f-4010-8153-18335ca71280", + "metadata": {}, + "source": [ + "9. Plot the difference in these temperatures using xarrays built-in features. Add a title to the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f537a7aa-96ac-4df4-bb2f-94bbe62d31f4", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'delta_temp' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdelta_temp\u001b[49m\u001b[38;5;241m.\u001b[39mplot()\n", + "\u001b[0;31mNameError\u001b[0m: name 'delta_temp' is not defined" + ] + } + ], + "source": [ + "delta_temp.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "46b4918c-bec6-49f4-97e3-17bd6f459512", + "metadata": {}, + "source": [ + "10. Let's compute and visualize the average temperature change for the summer (June-August) and winter (dec-feb) months of 2055.\n", + "Slice out the temperature for the summer months (june, july, august) and winter months (Dec 2054, Jan Feb 2055)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "67937f9c-0990-4587-b83e-128a4530ca00", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid day number provided in cftime.Datetime360Day(2055, 8, 31, 0, 0, 0, 0, has_year_zero=True)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m summer \u001b[38;5;241m=\u001b[39m \u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mslice\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2055-06-01\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2055-08-31\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m winter \u001b[38;5;241m=\u001b[39m tempearture\u001b[38;5;241m.\u001b[39msel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2054-12-15\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2055-01-15\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2055-02-15\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/core/dataarray.py:1628\u001b[0m, in \u001b[0;36mDataArray.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 1512\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msel\u001b[39m(\n\u001b[1;32m 1513\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1514\u001b[0m indexers: Mapping[Any, Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mindexers_kwargs: Any,\n\u001b[1;32m 1519\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m 1520\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new DataArray whose data is given by selecting index\u001b[39;00m\n\u001b[1;32m 1521\u001b[0m \u001b[38;5;124;03m labels along the specified dimension(s).\u001b[39;00m\n\u001b[1;32m 1522\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1626\u001b[0m \u001b[38;5;124;03m Dimensions without coordinates: points\u001b[39;00m\n\u001b[1;32m 1627\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1628\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1629\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1630\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1631\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1632\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1633\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mindexers_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1634\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1635\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/core/dataset.py:3097\u001b[0m, in \u001b[0;36mDataset.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 3029\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns a new dataset with each array indexed by tick labels\u001b[39;00m\n\u001b[1;32m 3030\u001b[0m \u001b[38;5;124;03malong the specified dimension(s).\u001b[39;00m\n\u001b[1;32m 3031\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3094\u001b[0m \n\u001b[1;32m 3095\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3096\u001b[0m indexers \u001b[38;5;241m=\u001b[39m either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msel\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 3097\u001b[0m query_results \u001b[38;5;241m=\u001b[39m \u001b[43mmap_index_queries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3098\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m 3099\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[1;32m 3102\u001b[0m no_scalar_variables \u001b[38;5;241m=\u001b[39m {}\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/core/indexing.py:192\u001b[0m, in \u001b[0;36mmap_index_queries\u001b[0;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 190\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(IndexSelResult(labels))\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 192\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 194\u001b[0m merged \u001b[38;5;241m=\u001b[39m merge_sel_results(results)\n\u001b[1;32m 196\u001b[0m \u001b[38;5;66;03m# drop dimension coordinates found in dimension indexers\u001b[39;00m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;66;03m# (also drop multi-index if any)\u001b[39;00m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;66;03m# (.sel() already ensures alignment)\u001b[39;00m\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/core/indexes.py:741\u001b[0m, in \u001b[0;36mPandasIndex.sel\u001b[0;34m(self, labels, method, tolerance)\u001b[0m\n\u001b[1;32m 738\u001b[0m coord_name, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(labels\u001b[38;5;241m.\u001b[39mitems()))\n\u001b[1;32m 740\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m--> 741\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43m_query_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoord_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_dict_like(label):\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 744\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot use a dict-like object for selection on \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma dimension that does not have a MultiIndex\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 746\u001b[0m )\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/core/indexes.py:497\u001b[0m, in \u001b[0;36m_query_slice\u001b[0;34m(index, label, coord_name, method, tolerance)\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m tolerance \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot use ``method`` argument if any indexers are slice objects\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 496\u001b[0m )\n\u001b[0;32m--> 497\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 498\u001b[0m \u001b[43m \u001b[49m\u001b[43m_sanitize_slice_element\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[43m \u001b[49m\u001b[43m_sanitize_slice_element\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[43m_sanitize_slice_element\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 501\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 502\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m 503\u001b[0m \u001b[38;5;66;03m# unlike pandas, in xarray we never want to silently convert a\u001b[39;00m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;66;03m# slice indexer into an array indexer\u001b[39;00m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[1;32m 506\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot represent labeled-based slice indexer for coordinate \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 507\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m with a slice over integer positions; the index is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsorted or non-unique\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 509\u001b[0m )\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/pandas/core/indexes/base.py:6662\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[0;34m(self, start, end, step)\u001b[0m\n\u001b[1;32m 6618\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mslice_indexer\u001b[39m(\n\u001b[1;32m 6619\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 6620\u001b[0m start: Hashable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 6621\u001b[0m end: Hashable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 6622\u001b[0m step: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 6623\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mslice\u001b[39m:\n\u001b[1;32m 6624\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6625\u001b[0m \u001b[38;5;124;03m Compute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[1;32m 6626\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6660\u001b[0m \u001b[38;5;124;03m slice(1, 3, None)\u001b[39;00m\n\u001b[1;32m 6661\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 6662\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_locs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6664\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[1;32m 6665\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/pandas/core/indexes/base.py:6885\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[0;34m(self, start, end, step)\u001b[0m\n\u001b[1;32m 6883\u001b[0m end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 6884\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 6885\u001b[0m end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mright\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6886\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 6887\u001b[0m end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/pandas/core/indexes/base.py:6794\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[0;34m(self, label, side)\u001b[0m\n\u001b[1;32m 6790\u001b[0m original_label \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m 6792\u001b[0m \u001b[38;5;66;03m# For datetime indices label may be a string that has to be converted\u001b[39;00m\n\u001b[1;32m 6793\u001b[0m \u001b[38;5;66;03m# to datetime boundary according to its resolution.\u001b[39;00m\n\u001b[0;32m-> 6794\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_cast_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mside\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6796\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[1;32m 6797\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/coding/cftimeindex.py:481\u001b[0m, in \u001b[0;36mCFTimeIndex._maybe_cast_slice_bound\u001b[0;34m(self, label, side)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m label\n\u001b[0;32m--> 481\u001b[0m parsed, resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_parse_iso8601_with_reso\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdate_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 482\u001b[0m start, end \u001b[38;5;241m=\u001b[39m _parsed_string_to_bounds(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdate_type, resolution, parsed)\n\u001b[1;32m 483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_monotonic_decreasing \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/xarray/coding/cftimeindex.py:142\u001b[0m, in \u001b[0;36m_parse_iso8601_with_reso\u001b[0;34m(date_type, timestr)\u001b[0m\n\u001b[1;32m 140\u001b[0m replace[attr] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(value)\n\u001b[1;32m 141\u001b[0m resolution \u001b[38;5;241m=\u001b[39m attr\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdefault\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m)\u001b[49m, resolution\n", + "File \u001b[0;32msrc/cftime/_cftime.pyx:1321\u001b[0m, in \u001b[0;36mcftime._cftime.datetime.replace\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32msrc/cftime/_cftime.pyx:2094\u001b[0m, in \u001b[0;36mcftime._cftime.Datetime360Day.__init__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32msrc/cftime/_cftime.pyx:1170\u001b[0m, in \u001b[0;36mcftime._cftime.datetime.__init__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32msrc/cftime/_cftime.pyx:1755\u001b[0m, in \u001b[0;36mcftime._cftime.assert_valid_date\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: invalid day number provided in cftime.Datetime360Day(2055, 8, 31, 0, 0, 0, 0, has_year_zero=True)" + ] + } + ], + "source": [ + "summer = temperature.sel(time=slice('2055-06-01', '2055-08-31'))\n", + "winter = tempearture.sel('2054-12-15', '2055-01-15', '2055-02-15')" + ] + }, + { + "cell_type": "markdown", + "id": "a78cc6b3-4919-4652-8fc4-b36abcd45dc2", + "metadata": {}, + "source": [ + "11. Calculate the annual mean temperature change over the entire year. (Hint: use .mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "49187585-2963-45d9-a47c-126619264236", + "metadata": {}, + "outputs": [], + "source": [ + "annual_mean = temperature.mean(dim='time')" + ] + }, + { + "cell_type": "markdown", + "id": "545b2a65-0dd6-4a19-8a35-efefc09a645b", + "metadata": {}, + "source": [ + "12. Plot the annual mean temperature change. Add a title." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "81957b90-5fee-41cf-a788-1ecddd954d1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "annual_mean.plot()" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "7df65663-156e-4341-b98f-9562fa7bc7e0", + "id": "96e013ee-a74c-489e-9f11-62d2cec06ced", "metadata": {}, "outputs": [], "source": []