From 51c5b7b4cff2ae41f81aee81ae3e22e77f86060a Mon Sep 17 00:00:00 2001 From: "Paul J. Durack" Date: Tue, 30 Jan 2024 13:13:02 -0800 Subject: [PATCH] adding ESGF2-US logos; suggested text tweaks --- .../Demo/Demo_9_seaIceExtent_ivanova.ipynb | 16 +- .../Demo/Demo_9b_seaIce_data_explore.ipynb | 179 ++++-------------- 2 files changed, 43 insertions(+), 152 deletions(-) diff --git a/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb index 72438dcae..c1719df41 100644 --- a/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb +++ b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb @@ -15,17 +15,23 @@ " title=\"Program for Climate Model Diagnosis and Intercomparison\"\n", " alt=\"Program for Climate Model Diagnosis and Intercomparison\"\n", " > \n", + " \"Lawrence \n", " \"United \n", - " \"Lawrence\n", "

\n", "" diff --git a/doc/jupyter/Demo/Demo_9b_seaIce_data_explore.ipynb b/doc/jupyter/Demo/Demo_9b_seaIce_data_explore.ipynb index 0e8cf0da3..b30612f41 100644 --- a/doc/jupyter/Demo/Demo_9b_seaIce_data_explore.ipynb +++ b/doc/jupyter/Demo/Demo_9b_seaIce_data_explore.ipynb @@ -8,7 +8,6 @@ "# PCMDI Metrics Package Sea Ice Demo\n", "## _Supplementary: Explore the Sea Ice Data_\n", "\n", - "\n", "
\n", "

\n", " \"Program \n", + " \"Lawrence \n", " \"United \n", - " \"Lawrence\n", "

\n", "
" @@ -48,9 +53,9 @@ "\n", "We will use multiple libraries for this analysis.\n", "\n", - "- [xCDAT](https://xcdat.readthedocs.io): an open source Python tool developed based on the xarray to ease climate data analysis. The [xCDAT](https://xcdat.readthedocs.io) is an extension of [xarray](https://xarray.dev/) for climate data analysis on structured grids.\n", - "- [numpy](https://numpy.org): needed for calculating the metrics.\n", - "- [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/): required for data visualizations." + "- [xCDAT](https://xcdat.readthedocs.io): an open source Python tool built to make climate data analysis easy. [xCDAT](https://xcdat.readthedocs.io) is an extension of [xarray](https://xarray.dev/) for data analysis on structured grids.\n", + "- [numpy](https://numpy.org): a dependency required to manage n-dimensional array data.\n", + "- [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/): required for data visualization." ] }, { @@ -71,9 +76,9 @@ "source": [ "### Optional packages for interactive visualization\n", "\n", - "- [hvplot](https://hvplot.holoviz.org/): this tool used for 2-D interactive plots. Following optional packages are also required for interactive visualizations: [geoviews](https://geoviews.org) and [jupyter_bokeh](https://github.com/bokeh/jupyter_bokeh).\n", + "- [hvplot](https://hvplot.holoviz.org/): this tool is used for 2-D interactive plots. The following optional packages are also required for interactive visualizations: [geoviews](https://geoviews.org) and [jupyter_bokeh](https://github.com/bokeh/jupyter_bokeh).\n", "\n", - "To install them, delete the triple quotations on lines 1&3 from below cell and install with pip:" + "To install them, uncomment the block below (delete the triple quotations`\"\"\"`) on lines 1&3 from below cell, installing `hvplot` using `pip`:" ] }, { @@ -100,128 +105,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.3.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function 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\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1002" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import hvplot.xarray" ] @@ -236,7 +122,7 @@ "\n", "#### 2.1.1 Load dataset\n", "\n", - "This demo uses one of CMIP6 models -- E3SM-1-0. The Sea-Ice Area Percentage (Ocean Grid; 'siconc') and Grid-Cell Area for Ocean Variables ('areacello') variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", + "This demo uses one of the numerous CMIP6 models -- E3SM-1-0. The Sea-Ice Area Percentage (Ocean Grid; 'siconc') and Grid-Cell Area for Ocean Variables ('areacello') variables are needed and can be found in the directories listed below. In addition, six other models are available that can augment the analyses in this demo:\n", "```\n", "/p/user_pub/pmp/demo/sea-ice/links_siconc\n", "/p/user_pub/pmp/demo/sea-ice/links_area\n", @@ -1583,7 +1469,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's get a quick look of the data snapshot to get an idea how it look like:" + "Let's take a quick look at a single timestep of the data:" ] }, { @@ -1652,8 +1538,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Another angle view:\n", - "- Example map scripts are adapted and revised from https://docs.xarray.dev/en/latest/user-guide/plotting.html#maps." + "Viewing the same data from another angle:\n", + "- Example mapping scripts are adapted and revised from https://docs.xarray.dev/en/latest/user-guide/plotting.html#maps." ] }, { @@ -1709,14 +1595,14 @@ "# Add coastlines\n", "ax.coastlines(color=\"black\", zorder=4)\n", "\n", - "ax.set_title(\"sea ice concentration over the polar region\")" + "ax.set_title(\"sea ice concentration over the Arctic region\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Another additional angle view with eleborating the plot:\n", + "Let's plot the data using another angle, zooming in on the Arctic region:\n", "\n", "Additional matplotlib colorshemes can be found [here](https://matplotlib.org/stable/users/explain/colors/colormaps.html)." ] @@ -1774,14 +1660,14 @@ "ax.coastlines(color=\"black\", zorder=4)\n", "\n", "ax.set_extent([-180, 180, 43, 90], ccrs.PlateCarree())\n", - "ax.set_title(\"sea ice concentration over the polar region\")" + "ax.set_title(\"sea ice concentration over the Arctic region\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This data is a time series dataset. Let's get an idea how the data evolves in time using interactive visualization. The example script can be found [here](https://hvplot.holoviz.org/reference/xarray/image.html)." + "The `ds[\"siconc\"]` data is a time series. Let's investigate how the data evolves through time using an interactive visualization. The example script can be found [here](https://hvplot.holoviz.org/reference/xarray/image.html)." ] }, { @@ -1827,7 +1713,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Another angle using example script from [here](https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection):" + "And here's another angle using example script [here](https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection):" ] }, { @@ -1868,7 +1754,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above example demonstrates the first one year of the data. To check different or longer time period, you can consider replacing `ds[\"siconc\"].isel(time=slice(0,12))` part to, for example, `ds[\"siconc\"].sel({\"time\": slice(\"1981-01-01\", \"2010-12-31\")})`." + "The above example displays the first year of the dataset. To check different or longer time periods, you can consider replacing `ds[\"siconc\"].isel(time=slice(0,12))`, with a temporal expansion, for example, `ds[\"siconc\"].sel({\"time\": slice(\"1981-01-01\", \"2010-12-31\")})`." ] }, { @@ -3408,7 +3294,7 @@ "source": [ "## 4. Analysis: get climatology\n", "\n", - "We will use [xCDAT](https://xcdat.readthedocs.io)'s [`temporal.climatology`](https://xcdat.readthedocs.io/en/latest/generated/xarray.Dataset.temporal.climatology.html) capability to get climatology field. " + "We will use [xCDAT](https://xcdat.readthedocs.io)'s [`temporal.climatology`](https://xcdat.readthedocs.io/en/latest/generated/xarray.Dataset.temporal.climatology.html) function to calculate the monthly climatology field. " ] }, { @@ -3418,8 +3304,7 @@ "outputs": [], "source": [ "obs_ds_clim = obs_ds.temporal.climatology(\n", - " \"extent\", freq=\"month\", reference_period=(\"1981-01-01\", \"2010-12-31\"))\n", - "\n" + " \"extent\", freq=\"month\", reference_period=(\"1981-01-01\", \"2010-12-31\"))" ] }, { @@ -3429,7 +3314,7 @@ "outputs": [], "source": [ "ds_clim = ds.temporal.climatology(\n", - " \"extent\", freq=\"month\", reference_period=(\"1981-01-01\", \"2010-12-31\"))\n" + " \"extent\", freq=\"month\", reference_period=(\"1981-01-01\", \"2010-12-31\"))" ] }, { @@ -3588,7 +3473,7 @@ "source": [ "## 4. Evaluation Metrics\n", "\n", - "The term \"Metric\" indicates a score or statistics that can represent model's performance on reproducing the observed features. It can be defined in many different ways, but the primary purpose of metrics is to summarize the performances of many different models and provide a framework for the benchmarking.\n", + "The term \"metric\" indicates a score or statistics that can represent a model's performance in reproducing observed features. A \"metric\" can be defined in many different ways, but the primary purpose of metrics is to summarize the performances of many different models and provide a framework for benchmarking model realism.\n", "\n", "In this notebook, we define **Mean Square Error (MSE)** and **Temporal MSE** as metrics.\n", "\n",