Python Package providing a suite of processing tools and utilities for Hilltop hydrological data.

• Free software: GNU General Public License v3
• Documentation: https://hydrobot.readthedocs.io.

• Processes data downloaded from Hilltop Server
• Uses annalist to record all changes to data
• Capable of various automated processing techniques, including:
  • Clipping data
  • Removing spikes based on FBEWMA smoothing
  • Identifying and removing 'flatlining' data, where an instrument repeats it's last collected data point (NOTE: It's unclear if this actually happening.)
  • Identifying gaps and gap lengths and closing small gaps
  • Aggregating check data from various sources.
  • Quality coding data based on NEMS standards
• Plotting data, including:
  • Processed data with quality codes
  • Comparing raw data to processed data
  • Showing all changes to the data
  • Visualizing check points from various sources.

The Alpha release of Hydrobot supports a "hybrid" workflow. This means that some external tools are still required to do a full processing. Importantly, the hybrid workflow relies on some R scripts to obtain check data from sources other than Hilltop. Further processing using Hilltop manager is also supported.

NOTE: Hydrobot 0.7.8 does not support all NEMs data sources currently, but more measurements will be supported in patches as the processing progresses.

1. Install a Python 3.11 interpreter (3.12 is not supported just yet).

2. In your favourite shell (if you don't know what that is, use powershell - it's already installed on windows), create a new virtual environment using this python interpreter and name it "hydrobot0.7.8". It's important that this is stored somewhere locally. For example, it could be stored in a "Hydrobot" folder in the C: drive, which would need the command:

   python -m venv C:/Hydrobot/hydrobot0.7.8/
   cd C:/Hydrobot/hydrobot0.7.8/
   

3. Activate this virtual environment. In powershell this should be something like:

   C:/Hydrobot/hydrobot0.7.8/Scripts/Activate.ps1
   

4. With your venv active, install the latest version of Hydrobot using pip:

   pip install hydrobot
   

5. Record which version of dependencies you have installed. The following pip freeze records which dependencies are installed by the hydrobot install process for if auditing/reprocessing is required later:

   pip freeze > dependencies.txt
   

1. Open Logsheet Loader. Fill it as normal, and note the start date of your processing period (i.e. end date of the previous period).

2. Navigate to the data source and site folder, and create your batch folder.

3. Copy all the hydrobot processing template files from the documents folder into your new batch folder, then rename with batch number and location code. The location of e.g. the water temperature template is:

   \\ares\Environmental Data Validation\Water Temperature\Documents\Hydrobot_template\
   

4. In your processing folder, open the config.yaml file and fill the fields site, from_date, to_date, analyst_name. Adjust the other values if desired - default values should work for most situations, but each site can have it's own idiosyncrasies.

5. Run the R script. I'm not an R guy so I'm not sure how to do this other than to open it in R studio, highlighting all the code, and hitting Ctrl+Enter. This should create a bunch of .csv files containing the check data from various sources. This is a good resource for perusal during processing, but will be imbibed by hydrobot to for QC encoding.

6. Make sure your virtual environment is set up (see initial setup instructions) and activate it. To activate, in your shell type the location of the "Activate.ps1" script in the venv/Scripts folder, e.g.:

   C:/Hydrobot/hydrobot0.7.8/Scripts/Activate.ps1
   

   You can ensure it is active by typing gcm python and confirm that your python interpreter (under "Source") is running from your venv folder.

7. Navigate to your batch folder in your shell. To navigate to the batch folder, use the following "cd" command with your specific details:

   cd "\\ares\Environmental Data Validation\measurement\site\batch\"
   

   e.g. if you are doing water temperature for teachers college in batch 400, your cd command would be:

   cd "\\ares\Environmental Data Validation\Water Temperature\Manawatu at Teachers College\400"
   

8. Run the hydrobot processing script using streamlit (name changes slightly by data source):

   streamlit run wt_script.py
   

9. If all goes well, the processing script will open a browser tab showing a diagnostic dash for your site. Use this to identify issues in the site.

10. Optionally, modify the python script to solve some issues, like removing erroneous check data points or deleting chunks of data, then rerun the script.

11. Open the resulting processed.xml in manager, and copy it over to the hts file found in the batch folder.

12. Modify the data in hilltop as needed

13. Open the WaterTemp_check_data.csv output by the R file in a spreadsheet and copy into the hts batch file.

14. Copy to provisional automation when complete.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template. Furthermore, Sam is a real champ with the coding and whatnot. Thanks Sam.

Aww thanks Nic. You also da man <3