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ARM-Development · May 17, 2024 · 0306cbc · 0306cbc
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diff --git a/_preview/41/.buildinfo b/_preview/41/.buildinfo
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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 13793b38e4c882e659fc13f8e405345c
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_preview/41/README.html b/_preview/41/README.html
diff --git a/_preview/41/_downloads/32b36be24d808093bfc2b63d70ea8205/stage_lasso-shcu_data.py b/_preview/41/_downloads/32b36be24d808093bfc2b63d70ea8205/stage_lasso-shcu_data.py
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+"""
+This code stages LASSO-ShCu data files on Cumulus's wolf2 that are needed for the LASSO tutorial portion of the 2024 ARM Summer School.
+It is assumed the LASSO-ShCu tar files are pre-copied into a single directory to draw from to build out an organized tree structure.
+
+NOTE: Make sure to set the correct folder locations at the top of main().
+
+Author: William.Gustafson@pnnl.gov
+Date: 6-May-2024
+"""
+
+from glob import glob
+import os
+import tarfile
+
+
+#-----------------------------------------------------------------------
+def stage_cogsdiagobsmod(tarfilename, path_stage):
+    """
+    Stage tar file of type sgplassocogsdiagobsmod*tar.
+    This tar contain info for all sim IDs within the given case date and
+    already has the case date in the tar file. So, it's untar path starts
+    at the very top of the tree structure.
+
+    Example: ./sgplassocogsdiagobsmodC1.m1.20190404.120000.tar
+               0123456789012345678901234567890
+                       987654321098765432109876543210987654321
+    """
+
+    # Untar the source tar into the destination folder...
+    fh = tarfile.open(tarfilename, mode='r')
+    fh.extractall(path=path_stage)
+    fh.close()
+
+# end stage_cogsdiagobsmod()
+
+
+#-----------------------------------------------------------------------
+def stage_confobsmod(tarfilename, path_stage):
+    """
+    Stage tar file of type sgplassoconfobsmod*tar.
+    This tar does not contain the parent directory information, so we
+    have to build it.
+
+    Example: ./sgplassodiagconfobsmod4C1.m1.20190404.000000.tar
+               0123456789012345678901234567890
+                        987654321098765432109876543210987654321
+    """
+
+    # Determine path for untarring and create it if it does not exist...
+    aa = os.path.basename(tarfilename)  # strip off path
+    datestr = aa[-19:-11]  # date comes from the end of the filename
+    sim_id = int( os.path.basename(aa)[22:(aa.index("C1.m1"))] )  # sim_id is the character(s) prior to C1.m1
+    path_untar = f"{path_stage}/{datestr}/sim{sim_id:04d}"
+    os.makedirs(path_untar, exist_ok=True)
+
+    # Untar the source tar into the destination folder...
+    fh = tarfile.open(tarfilename, mode='r')
+    fh.extractall(path=path_untar)
+    fh.close()
+# end stage_confobsmod()
+
+
+ #-----------------------------------------------------------------------
+def stage_diagraw(tarfilename, path_stage):
+    """
+    Stage tar file of type sgplassodiagraw*.tar.
+    This tar does not contain the parent directory information, so we
+    have to build it.
+
+    Example: ./sgplassodiagraw1C1.m1.20190506.000000.tar
+               0123456789012345678901234567890
+                 987654321098765432109876543210987654321
+    """
+
+    # Determine path for untarring and create it if it does not exist...
+    aa = os.path.basename(tarfilename)  # strip off path
+    datestr = aa[-19:-11]  # date comes from the end of the filename
+    sim_id = int( os.path.basename(aa)[15:(aa.index("C1.m1"))] )  # sim_id is the character(s) prior to C1.m1
+    path_untar = f"{path_stage}/{datestr}/sim{sim_id:04d}"
+    os.makedirs(path_untar, exist_ok=True)
+
+    # Untar the source tar into the destination folder...
+    fh = tarfile.open(tarfilename, mode='r')
+    fh.extractall(path=path_untar)
+    fh.close()
+# end stage_diagraw()
+
+
+#-----------------------------------------------------------------------
+def stage_highfreqobs(tarfilename, path_stage):
+    """
+    Stage tar file of type sgphighfreqobs*tar.
+    This tar contain observation data organized by folder with each tar
+    containing the obs for a single date. These get untarred into a
+    high_res_obs folder inside the case date.
+
+    Example: ./sgplassohighfreqobsC1.m1.20190404.120000.tar
+               0123456789012345678901234567890
+                    987654321098765432109876543210987654321
+    """
+    # Determine path for untarring and create it if it does not exist...
+    aa = os.path.basename(tarfilename)  # strip off path
+    datestr = aa[-19:-11]  # date comes from the end of the filename
+    path_untar = f"{path_stage}/{datestr}/high_res_obs"
+    os.makedirs(path_untar, exist_ok=True)
+
+    # Untar the source tar into the destination folder...
+    fh = tarfile.open(tarfilename, mode='r')
+    fh.extractall(path=path_untar)
+    fh.close()
+# end stage_highfreqobs()
+
+
+
+#-----------------------------------------------------------------------
+def main():
+    path_tars = "/gpfs/wolf2/arm/atm124/world-shared/arm-summer-school-2024/lasso_tutorial/ShCu/tars"
+    path_stage = "/gpfs/wolf2/arm/atm124/world-shared/arm-summer-school-2024/lasso_tutorial/ShCu/untar"
+
+    # Get the list of tar files to process and loop over them...
+    tar_list = glob(f"{path_tars}/*.tar")
+    for tarfilename in tar_list:
+        # Parse the datastream type and call the appropriate subroutine to untar it...
+        # File type options:
+        #   sgplassocogsdiagobsmodC1.m1.20190404.120000.tar
+        #   sgplassodiagconfobsmod4C1.m1.20190404.000000.tar
+        #   sgplassodiagraw5C1.m1.20190404.000000.tar
+        #   sgplassohighfreqobsC1.c1.20190404.000000.tar
+        #   0         1         2         3
+        #   0123456789012345678901234567890
+        #           0123456789012345678901234567890
+        aa = os.path.basename(tarfilename)
+        if not aa[0:8] == "sgplasso":
+            # not a tar we care about, so skip it
+            continue
+        # end if
+        aa = aa[8:]  # strip off "sgplasso", should now start with the keys we are looking for
+
+        # Call the appropriate staging routine for each tar type to 
+        # account for the folder structure in each type...
+        if aa[:14] == "diagconfobsmod":
+            stage_confobsmod(tarfilename, path_stage)
+
+        elif aa[:7] == "diagraw":
+            stage_diagraw(tarfilename, path_stage)
+
+        elif aa[:14] == "cogsdiagobsmod":
+            stage_cogsdiagobsmod(tarfilename, path_stage)
+
+        elif aa[:11] == "highfreqobs":
+            stage_highfreqobs(tarfilename, path_stage)
+        # end if            
+# end main()
+
+
+#-----------------------------------------------------------------------
+if __name__ == "__main__":
+    main()
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+# ARM Summer School 2024
+
+***ARM Summer School 2024: Open Science in the Department of Energy's Atmospheric Radiation Measurement (ARM) User Facility: Connecting State-of-the-Art Models with Diverse Field Campaign Observations***
+
+
+[![badge](https://img.shields.io/static/v1.svg?logo=Jupyter&label=ARM+JupyterHub&message=ACE+Environment&color=blue)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/ARM-Development/arm-ams-short-course-2024&urlpath=lab/tree/arm-ams-short-course-2024/notebooks&branch=main)
+[![nightly-build](https://github.com/ARM-Development/arm-ams-short-course-2024/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ARM-Development/arm-ams-short-course-2024/actions/workflows/nightly-build.yaml)
+
+## Motivation
+
+ARM Mobile Facilities (AMF) have traveled to locations all over the world, including South America for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign, as well as Norway for the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE). One of the key goals of these field deployments is to integrate measurements with a spectrum of model datasets, ranging from high-resolution large-eddy simulation to limited-domain nested weather and climate model datasets, furthering the understanding of Earth’s climate system. Within this tutorial, participants will gain a broad understanding of the ARM User Facility, the open data available to the community, the data workbench that allows data-proximate-computing, and science overviews of two ongoing model-observation intercomparison projects. This will be suitable for a broad audience of atmospheric scientists, bringing together both observations and simulations, as well as deep and shallow convection.
+
+This summer school, aimed at a broad audience, will: 
+1. Introduce participants to ARM’s observational facilities and data products and the community of atmospheric scientists that use and produce ARM data. 
+2. Educate attendees on ARM’s measurement suite and data archive.
+3. Educate attendees on ARM’s (and collaborators') model data.
+4. Highlight the underlying science behind CACTI and COMBLE.
+5. Demonstrate how to find and access ARM data.
+6. Using open source tools, guide attendees in analyzing ARM’s open data in the Python programming language. 
+7. Highlight several techniques to compare ARM observations and high-resolution model output. 
+
+
+## Authors
+
+ARM Summer School 2024 Instructors
+
+### Contributors
+
+<a href="https://github.com/ProjectPythia/cookbook-template/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=ARM-Development/arm-ams-short-course-2024" />
+</a>
+
+## Structure
+
+To familiarize attendees with ARM, its measurements and data discovery systems.
+1. To highlight the breadth of measurements and science possible through the AMF deployments for CACTI and COMBLE.
+2. To demonstrate a series of analytical methods using open science cookbooks and ARM data, with a focus on robustly comparing observational data with high-resolution simulations. 
+3. To provide an onramp to open science using ARM data and to remove barriers to using ARM data. 
+4. To train attendees on the latest features of ARM tools and open HPC platforms.
+
+### Lectures
+
+### Small Group Projects
+
+## Running the Notebooks
+
+You can either run the notebook using [Binder](https://binder.projectpythia.org/) or on your local machine.
+
+### Running on Jupyter
+
+The simplest way to interact with a Jupyter Notebook is through the
+[ARM Jupyter](https://jupyterhub.arm.gov), which enables the execution of a
+[Jupyter Book](https://jupyterbook.org) on ARM infrastructure. The details of how this works are not
+important for now. Navigate your mouse to
+the top right corner of the book chapter you are viewing and click
+on the rocket ship icon, (see figure below), and be sure to select
+“launch Jupyterhub”. After a moment you should be presented with a
+notebook that you can interact with. I.e. you’ll be able to execute
+and even change the example programs. You’ll see that the code cells
+have no output at first, until you execute them by pressing
+{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with
+a live Jupyter notebook are described in [Getting Started with
+Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html).
+
+### Running on Your Own Machine
+If you are interested in running this material locally on your computer, you will need to follow this workflow:
+
+1. Clone the `https://github.com/ARM-Development/arm-summer-school-2024` repository:
+
+   ```bash
+    git clone https://github.com/ARM-Development/arm-summer-school-2024
+    ```  
+1. Move into the `arm-summer-school-2024` directory
+    ```bash
+    cd arm-summer-school-2024
+    ```  
+1. Create and activate your conda environment from the `environment.yml` file
+    ```bash
+    conda env create -f environment.yml
+    conda activate arm-summer-school-2024-dev
+    ```  
+1.  Move into the `notebooks` directory and start up Jupyterlab
+    ```bash
+    cd notebooks/
+    jupyter lab
+    ```