├── src/  
        ├── scripts/
        │   ├── download_data.py
        │   ├── install_dependencies.py
        │   └── launch_app.py
        ├── app.py
        ├── static/
        ├── .project-metadata.yaml
        ├── README.md
        └── requirements.txt

The download_data.py script facilitates the retrieval of data from the Aphrodite dataset. It contains the following URLs for accessing the data:

1. Precipitation Data:

   • URL: APHRO_V1101
   • Period: 1951-2015

2. Temperature Data:

   • URL: APHRO_V1101EX_R1
   • Period: 1961-2015

Note:

• It's essential to exercise caution while downloading large datasets due to potential bandwidth and storage constraints.
• Users should consider the availability of existing analysis and avoid redundant data collection to optimize resource utilization.

The install_dependencies.py script contains the necessary dependencies required for the project installation. It ensures that all essential packages are installed to enable smooth execution of the project.

The launch_app.py script contains the command to run the Streamlit application. It executes the following command to open the Streamlit app:

!streamlit run app.py --server.port $CDSW_APP_PORT --server.address 127.0.0.1

This command launches the Streamlit application with the specified server port and address, allowing users to interact with the app through their web browsers.

Ensure that the app.py file contains the necessary code for your Streamlit application to function correctly.---

All the static files needed to compute the results in background and then show the results by interface of streamlit app.

  name: Timberline Analysis
description: To find the ranges which are in critical condition and need immediate attention
author: Cloudera Inc.
specification_version: 1.0
prototype_version: 2.0
date: "2024-02-25"

runtimes:
  - editor: Workbench
    kernel: Python 3.9
    edition: Standard

tasks:
  - type: run_session
    name: Install Dependencies
    script: scripts/install_dependencies.py
    kernel: python3
    cpu: 2
    memory: 4

  - type: start_application
    name: Application to serve UI
    short_summary: Create an application to serve the image analysis UI
    subdomain: imageanalysis
    script: scripts/launch_app.py
    environment_variables:
      TASK_TYPE: START_APPLICATION## Workflow Configuration
 
 

1. Data Clipping : Data clipping involves restricting data values to a certain range or boundary. In this context, it likely refers to the process of managing or filtering the Timberline Points data.
2. Interpolation Methods Used :

• Cubic
• Linear
• Inverse Distance Weighting (IDW)
• Modified Inverse Distance Weighting (MIDW)
• Nearest Neighbor

1. Finding Mean :

• The mean is calculated for the interpolated data points obtained using various interpolation methods.

1. Calculating Mean in 200 Meter Range Timberlines :

• The mean values within a 200-meter range of Timberlines are calculated. This likely involves averaging the values of the points falling within this specified range around each Timberline point.

• ./

  • Logfiles.log

  • PrecipitaionAnalysis.ipynb : contains step by way to clip and interpolate and get results of Precipitation analysis

  • TemperatureAnalysis.ipynb : contains step by way to clip and interpolate and get results of Temperature analysis

  • PrecipitaionAnalysis/ for each interpolation method annual rangewise mean precipitaoin

    • InterpolateRangeWise/
      • Cubic200/
        • AP.xlsx
        • HP.xlsx
        • J&K.xlsx
        • SK.xlsx
        • UK.xlsx
      • IDW200/
      • Linear200/
      • MIDW200/
      • Nearest200/

  • Shp_timberline/

    • Consisting shape files

  • TemperatureAnalysis/

    • InterpolateRangeWise/ For each state monthwise Temperature for 1961-2015
      • AP/
        • Cubic200/
          • AP_Apr_200mtrRange.xlsx
          • AP_Aug_200mtrRange.xlsx
          • AP_Dec_200mtrRange.xlsx
          • AP_Feb_200mtrRange.xlsx
          • AP_Jan_200mtrRange.xlsx
          • AP_Jul_200mtrRange.xlsx
          • AP_Jun_200mtrRange.xlsx
          • AP_Mar_200mtrRange.xlsx
          • AP_May_200mtrRange.xlsx
          • AP_Nov_200mtrRange.xlsx
          • AP_Oct_200mtrRange.xlsx
          • AP_Sep_200mtrRange.xlsx
        • IDW200/
        • Linear200/
        • MIDW200/
        • Nearest200/
      • HP/
        • Cubic200/
        • IDW200/
        • Linear200/
        • MIDW200/
        • Nearest200/
      • J&K/
        • Cubic200/
        • IDW200/
        • Linear200/
        • MIDW200/
        • Nearest200/
      • SK/
        • Cubic200/
        • IDW200/
        • Linear200/
        • MIDW200/
        • Nearest200/
      • UK/

  Timberline Points Data

  The following files contain Timberline Points data captured at Timberline. The points are spaced 30 meters apart from each other and are in the format <latitude, longitude, altitude>:

  1. AP_TL_2015.xlsx
  2. HP_TL_2015.xlsx
  3. J&K_TL_2015.xlsx
  4. SK_TL_2015.xlsx
  5. UK_TL_2015.xlsx

  These files contain geographic coordinates and altitude information of points captured at Timberline. Each point represents a specific location with latitude, longitude, and altitude recorded.