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Download, Preprocessing, and Visualization code for climate resilience data.
This file requires a download_params.yml
file to specify the download configurations.
We cannot directly download the data from the Google Earth Engine directly onto the local machine. So the best option is to download to the drive and then download that data to the local drive.
The preprocessing functions will expect that the local data drive contains the downloaded data.
If the data is on drive, the drive needs to be mounted. This is easier to do in a google colab session. Once the drive is mounted, the path of the mounted drive can be used with the functions as normal.
The input file and directory structure for functions calculate_Nth_percentile()
, calculate_pr_count_amount()
, and calculate_temporal_mean()
in the preprocessing code should be as follows:
datadir
├── scenario1_variable1_ensemble
│ ├── name1_state1_scenario1_variable1.csv
│ └── name2_state2_scenario1_variable1.csv
├── scenario1_variable2_ensemble
│ ├── name1_state1_scenario1_variable2.csv
│ └── name2_state2_scenario1_variable2.csv
├── scenario2_variable1_ensemble
│ ├── name1_state1_scenario2_variable1.csv
│ └── name2_state2_scenario2_variable1.csv
└── scenario2_variable2_ensemble
├── name1_state1_scenario2_variable2.csv
└── name2_state2_scenario2_variable2.csv
The visualization code will be easier to be used in a notebook as inline visualizations can be used.
Below is a screenshot of the interactive map with the sites marked.
Below is a screenshot of boxplot of annual precipitation in different regions of the United States.
FAQs
Download, Preprocessing, and Visualization code for climate resilience data.
We found that climate-resilience demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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