From 083211efa12035588a774641fa2701e6de06980b Mon Sep 17 00:00:00 2001 From: iammeghana Date: Sun, 24 Mar 2024 22:44:15 -0400 Subject: [PATCH 01/21] Overview on datasources --- book/chapters/datasource.ipynb | 226 ++++++++++++++++++++++++++++++--- 1 file changed, 209 insertions(+), 17 deletions(-) diff --git a/book/chapters/datasource.ipynb b/book/chapters/datasource.ipynb index 0f97342..976121b 100644 --- a/book/chapters/datasource.ipynb +++ b/book/chapters/datasource.ipynb @@ -2,44 +2,236 @@ "cells": [ { "cell_type": "markdown", + "id": "a19b1c84c1e62af1", + "metadata": {}, "source": [ "# Data Source\n", "\n", "Detailed exploration of data sources: Gridmet, AMSR, MODIS, Snotel\n", - "Importance and characteristics of each data source\n" - ], - "metadata": { - "collapsed": false - }, - "id": "a19b1c84c1e62af1" + "Importance and characteristics of each data source\n", + "\n", + "# Introduction\n", + "In the realm of environmental research and forecasting, access to reliable and comprehensive datasets is paramount. Four such indispensable sources are gridMET, AMSR, MODIS, and SNOTEL. Each dataset offers unique insights into various aspects of Earth's climate and terrain, contributing significantly to our understanding of environmental dynamics. These datasets serve as the cornerstone for numerous applications, including weather forecasting, ecological modeling, and climate change research. In our **snowcast_wormhole** workflow, we leverage the richness of these datasets to enhance the accuracy and robustness. Let's embark on a detailed exploration of these invaluable data sources.\n", + "\n", + "\n", + "# gridMET Dataset\n", + "\n", + "## Introduction\n", + "gridMET is like having a weather wizard at your fingertips! It's a dataset packed with daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US, spanning from 1979 up to yesterday. It even extends its reach to cover southern British Columbia in real-time products. This dataset provides vital insights into weather patterns, aiding various industries and scientific endeavors.\n", + "\n", + "## Insights\n", + "* It offers a fascinating glimpse into historical weather patterns, allowing users to delve as far back as 1979.\n", + "* The dataset provides an extensive array of information, including maximum and minimum temperatures, precipitation, radiation, wind velocity, and various other meteorological variables.\n", + "* Scientists and modelers find it invaluable for a myriad of purposes, such as ecological research, agricultural strategizing, hydrological modeling, and beyond.\n", + "\n", + "## Characteristics\n", + "\n", + "- **Spatial Resolution**: GridMET provides data at a high spatial resolution, typically around 4 kilometers.\n", + " \n", + "- **Temporal Resolution**: The dataset offers data at a daily time step, allowing access to meteorological data for each day.\n", + " \n", + "- **Variables**: GridMET includes meteorological variables such as temperature, precipitation, humidity, wind speed, and solar radiation.\n", + " \n", + "- **Coverage**: The dataset covers the contiguous United States, providing comprehensive meteorological data for this region.\n", + " \n", + "- **Quality**: Data is derived from multiple sources including weather station observations, satellite data, and meteorological models, and is quality-controlled.\n", + " \n", + "- **Long-term Availability**: GridMET provides data spanning several decades, enabling analysis of historical meteorological trends and patterns.\n", + "\n", + "## Data Format:\n", + "GRIDMET data is typically available in common file formats such as NetCDF (Network Common Data Form) or ASCII (plain text) format. NetCDF is widely used for multidimensional scientific data and provides efficient storage and access to large datasets. ASCII format offers simplicity and compatibility with various software tools for data analysis and visualization.\n", + "\n", + "## Data Sources and Acquisition:\n", + "\n", + "#### Satellite Data:\n", + "GRIDMET utilizes satellite data from different sources such as the Geostationary Operational Environmental Satellite (GOES) and the Moderate Resolution Imaging Spectroradiometer (MODIS). These satellite observations provide valuable information on temperature, precipitation, humidity, and other atmospheric variables.\n", + "\n", + "#### Ground-Based Observations:\n", + "The dataset incorporates ground-based weather station observations from various networks such as the National Weather Service's Cooperative Observer Program (COOP), the Automated Surface Observing System (ASOS), and the Automated Weather Data Network (AWDN). These observations help in validating and enhancing the accuracy of the dataset.\n", + "\n", + "#### Meteorological Models:\n", + "gridMET also integrates data from numerical weather prediction models like the North American Mesoscale Forecast System (NAM) and the Weather Research and Forecasting (WRF) model. These models provide forecasts and simulations of meteorological variables which are then blended with observed data to create a more complete dataset.\n", + "\n", + "## Applications:\n", + "\n", + "- Climate research and modeling\n", + "- Agriculture and crop management\n", + "- Hydrology and water resource management\n", + "- Renewable energy assessment\n", + "- Disaster management and risk assessment\n", + "- Ecological and environmental studies\n", + "\n", + "For more information on the gridMET dataset, you can visit the [gridMET website](https://www.climatologylab.org/gridmet.html). Here, you'll find detailed documentation." + ] }, { - "cell_type": "code", - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "90623fdb37e99f29" + "cell_type": "markdown", + "id": "16e2e7ee", + "metadata": {}, + "source": [ + "# AMSR Dataset\n", + "\n", + "## Introduction\n", + "### What is AMSR?\n", + "AMSR, short for Advanced Microwave Scanning Radiometer is a satellite-based instrument designed to measure microwave emissions from the Earth's surface and atmosphere. The AMSR dataset encompasses satellite-derived microwave observations crucial for a myriad of applications including weather forecasting, climate monitoring, and environmental studies. These datasets provide valuable insights into geophysical parameters such as sea surface temperature, soil moisture, sea ice concentration, precipitation, and wind speed over oceans. By collecting data on these factors, AMSR helps scientists better understand how water moves and behaves across the planet. Its observations provide valuable insights into Earth's climate dynamics and hydrological processes, contributing to our overall understanding of the environment.\n", + "\n", + "### Quick Look at the Data Collections\n", + "AMSR-E, or the Advanced Microwave Scanning Radiometer for the Earth Observing System, is like a high-tech detective tool that travels on NASA's Aqua satellite. It gathers information about water on Earth and other important details about our environment. AMSR-E data collections include AMSR/ADEOS-II and AMSR2, hosted by the National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). These datasets provide a treasure trove of polar observations, facilitating research in climatology, oceanography, hydrology, and more.\n", + "\n", + "## Characteristics\n", + "- **Spatial Resolution**: AMSR provides data at various spatial resolutions depending on the specific instrument version and product. Typically, it offers moderate to high spatial resolution microwave data.\n", + "\n", + "- **Temporal Resolution**: AMSR data is available at various temporal resolutions, ranging from daily to monthly, depending on the specific product and parameters.\n", + "\n", + "- **Variables**: AMSR measures various parameters such as soil moisture, sea surface temperature, precipitation, snow water equivalent, and sea ice concentration using microwave radiometry.\n", + "\n", + "- **Coverage**: AMSR provides global coverage, allowing for the monitoring of environmental parameters across different regions of the Earth.\n", + "\n", + "- **Quality**: Data from AMSR undergoes rigorous quality control processes to ensure accuracy and reliability, considering factors such as calibration and validation against ground-based measurements.\n", + "\n", + "## Data Format\n", + "\n", + "The Advanced Microwave Scanning Radiometer (AMSR) data is commonly stored in HDF5 format, providing a hierarchical structure for organizing datasets such as brightness temperature, sea surface temperature, and soil moisture. Within the HDF5 file, data parameters are organized into groups, with subgroups representing different configurations or subsets of the data. Metadata and geolocation information are typically stored separately, offering additional details about the data.\n", + "\n", + "## AMSR Data Collections\n", + "### AMSR/ADEOS-II\n", + "AMS/ADEOS-II, operating on the Advanced Earth Observing Satellite-II (ADEOS-II) platform, was the initial version of the AMSR instrument, providing passive microwave measurements from early 2003 to October 24, 2003. Offering Level-1A and Level-2A products, it served as a crucial tool for studying changes in polar ice caps, global precipitation patterns, and oceanic circulation. This data has been instrumental in enhancing our understanding of Earth's climate dynamics and informing research across various scientific disciplines.\n", + "\n", + "### AMSR2\n", + "AMSR2, launched in 2012 onboard the Global Change Observation Mission-Water (GCOM-W1) satellite, represents the next generation of AMSR instruments. It continues the legacy of AMSR-E, capturing observations with improved spatial resolution and enhanced capabilities. AMSR2 data has been instrumental in monitoring sea ice extent, ocean surface winds, and soil moisture dynamics at global scales.\n", + "\n", + "### Hosted by NSIDC DAAC\n", + "The NSIDC DAAC serves as the primary repository for AMSR-related data, ensuring that these valuable datasets are freely accessible to the scientific community and the public. The center provides data discovery, access, and user support services, facilitating research in cryospheric and hydrological sciences, climate modeling, and environmental monitoring.\n", + "\n", + "## Applications\n", + "1. **Weather Forecasting**: AMSR data aids in short-term weather forecasting by providing insights into atmospheric moisture content and precipitation patterns.\n", + "\n", + "2. **Climate Monitoring**: AMSR datasets contribute to long-term climate monitoring efforts by providing data on parameters like sea surface temperature and sea ice concentration.\n", + "\n", + "3. **Oceanography**: AMSR data enables the study of ocean surface properties such as sea surface temperature, wind speed, and ocean salinity, facilitating research in oceanography and marine science.\n", + "\n", + "4. **Hydrology**: AMSR-derived soil moisture data supports hydrological modeling, drought monitoring, and water resource management by providing information on soil moisture content and surface water availability.\n", + "\n", + "5. **Cryosphere Studies**: AMSR datasets are crucial for studying the cryosphere, including monitoring changes in snow cover extent, snow water equivalent, and sea ice concentration, contributing to our understanding of climate change impacts in polar regions.\n", + "\n", + "\n", + "For more information, visit the [AMSR-E Sensor Page](https://www.earthdata.nasa.gov/sensors/amsr-e)." + ] + }, + { + "cell_type": "markdown", + "id": "36f1f6ac", + "metadata": {}, + "source": [ + "# MODIS Dataset\n", + "\n", + "## Introduction\n", + "### What is MODIS?\n", + "MODIS, which stands for MODerate Resolution Imaging Spectroradiometer, is an advanced instrument operating aboard both the Terra and Aqua spacecraft. It captures a comprehensive view of Earth's surface, oceans, and atmosphere. The MODIS dataset is a comprehensive collection of Earth observation data captured by the MODIS instruments. Scientists use MODIS data to track changes in things like land cover, weather patterns, ice and snow, and the color of the oceans. MODIS boasts a remarkable viewing swath width of 2,330 km and covers the entire Earth surface every one to two days. With 36 spectral bands ranging from 0.405 to 14.385 µm, it provides detailed data at three spatial resolutions: 250m, 500m, and 1,000m.\n", + "\n", + "## Characteristics\n", + "- **Spatial Resolution**: MODIS provides data at moderate spatial resolutions ranging from 250 meters to 1 kilometer, depending on the specific product and spectral band.\n", + "\n", + "- **Temporal Resolution**: MODIS offers daily global coverage, providing data at a high temporal resolution suitable for monitoring dynamic environmental processes.\n", + "\n", + "- **Variables**: MODIS measures various Earth surface parameters including land cover, land surface temperature, vegetation indices, fire occurrence, ocean color, and atmospheric properties.\n", + "\n", + "- **Coverage**: MODIS provides global coverage, capturing data over land, ocean, and atmosphere, facilitating multi-disciplinary Earth observation studies.\n", + "\n", + "- **Quality**: MODIS data undergoes extensive calibration and validation processes to ensure accuracy and reliability, with quality flags provided to identify potential data anomalies.\n", + "\n", + "## Data Format\n", + "MODIS data are typically available in Hierarchical Data Format (HDF) or NetCDF formats, which are widely used for storing and distributing Earth observation data. These formats facilitate efficient data access, manipulation, and analysis using various software tools and programming languages commonly employed in the Earth sciences community.\n", + "\n", + "## MODIS Direct Broadcast\n", + "Users with x-band receiving systems can capture regional data directly from the spacecraft using the MODIS Direct Broadcast signal, enhancing real-time monitoring capabilities.\n", + "\n", + "For more information, visit the [MODIS Sensor Page](https://www.earthdata.nasa.gov/sensors/modis)." + ] + }, + { + "cell_type": "markdown", + "id": "a0ddb8f3", + "metadata": {}, + "source": [ + "# SNOTEL dataset\n", + "\n", + "## Introduction\n", + "### What is SNOTEL?\n", + "The SNOwpackTELemetryNetwork (SNOTEL) is an automated system of snowpack and climate sensors managed by the Natural Resources Conservation Service (NRCS) in the Western United States, offering critical data for water supply forecasting, flood prediction, and climate research. SNOTEL provides real-time data on snow water equivalent, snow depth, precipitation, and temperature from remote mountainous regions, aiding in understanding hydroclimatic conditions. SNOTEL offers comprehensive snowpack and climate data from over 900 sites, helping monitor snowpack, precipitation, temperature, and other climatic conditions in the western U.S.The SNOTEL dataset serves as a valuable resource for a wide range of stakeholders, contributing to informed decision-making in various sectors impacted by snowpack and climate conditions.\n", + "\n", + "## SNOTEL Network Overview\n", + "### Composition of SNOTEL\n", + "- Comprising over 900 automated sites in remote, high-elevation mountain watersheds.\n", + "- Monitors snowpack, precipitation, temperature, and other climatic parameters.\n", + "\n", + "### Operations and Data Collection\n", + "- Sites operate unattended and without maintenance for extended periods.\n", + "- Standard sensor configuration includes snow pillow, precipitation gage, and temperature sensors.\n", + "\n", + "## Telemetry and Data Transmission\n", + "### Data Collection and Storage\n", + "- Dataloggers installed in equipment shelters collect and store data.\n", + "- Various telemetry systems transmit data back to the Water and Climate Information System.\n", + "\n", + "### Enhanced Site Capabilities\n", + "- Enhanced sites equipped with soil moisture, soil temperature, solar radiation, wind speed, and relative humidity sensors.\n", + "- Tailored configurations based on physical conditions and climate requirements.\n", + "\n", + "## Characteristics\n", + "**Spatial Resolution**: SNOTEL provides data at a network of monitoring sites distributed across mountainous regions, typically covering areas with varying spatial resolutions depending on the density of monitoring stations.\n", + "\n", + "**Temporal Resolution**: SNOTEL data is typically collected at hourly intervals, providing high temporal resolution data for monitoring snowpack conditions and related hydrological variables.\n", + "\n", + "**Variables**: SNOTEL measures snow water equivalent, snow depth, temperature, precipitation, and soil moisture at monitoring sites in mountainous regions.\n", + "\n", + "**Coverage**: SNOTEL stations are primarily located in the western United States, covering areas with significant snowpack and water resource management importance.\n", + "\n", + "**Quality**: SNOTEL data undergoes quality control procedures to ensure accuracy and reliability, including calibration checks and validation against manual measurements.\n", + "\n", + "## Data Format\n", + "The Snow Telemetry (SNOTEL) data format encompasses structured datasets collected from remote automated stations situated in mountainous regions, monitoring snowpack, weather, and hydrological parameters. Key aspects include recorded parameters such as snow water equivalent (SWE), snow depth, air temperature, and precipitation, timestamped to denote observation times and often stored at varying resolutions like hourly or daily intervals. Quality control flags accompany data points to denote reliability, while metadata provides station details and sensor calibration information. SNOTEL data is commonly stored in formats like CSV, TSV, HDF5, or netCDF, accessible through agency websites, data portals, or APIs. This format facilitates applications spanning water resource management, climate research, agriculture, recreation, hydrological modeling, and ecological studies.\n", + "\n", + "\n", + "## Applications\n", + "\n", + "**Water Resource Management**:\n", + " - *Snowpack Monitoring*: Assessing snowpack depth and SWE helps in forecasting water availability for irrigation, hydropower generation, and municipal water supply.\n", + " - *Runoff Forecasting*: Data from SNOTEL stations aids in predicting spring runoff, facilitating reservoir management and flood control.\n", + "\n", + "**Climate Research**:\n", + " - *Long-term Climate Trends*: Historical data enables researchers to study long-term climate patterns, including changes in snowfall, temperature, and precipitation.\n", + " - *Climate Change Studies*: SNOTEL data is utilized to understand the impacts of climate change on snowpack dynamics, water resources, and ecosystems.\n", + "\n", + "**Agriculture and Forestry**:\n", + " - *Crop Planning*: Farmers use snowpack data to anticipate water availability during the growing season, aiding in crop planning and irrigation scheduling.\n", + " - *Forest Management*: Forestry agencies utilize SNOTEL data for assessing wildfire risk, planning timber harvests, and monitoring forest health.\n", + "\n", + "**Recreation and Tourism**:\n", + " - *Winter Sports Planning*: Ski resorts and recreational outfitters rely on snowpack data for planning activities such as skiing, snowboarding, and snowmobiling.\n", + " - *Summer Recreation*: Understanding snowmelt timing and water availability helps in planning summer recreational activities like hiking, fishing, and camping.\n", + "\n", + "For more information, visit the [NRCS SNOTEL page](https://www.nrcs.usda.gov/wps/portal/wcc/home/aboutUs/monitoringPrograms/automatedSnowMonitoring).\n" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.10.13" } }, "nbformat": 4, From cdcd3eed5fd534f36d748cf8d5a9b0706dd89fed Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:16:40 -0400 Subject: [PATCH 02/21] ignore file to ignore words --- .codespellignore | 4 ++++ .github/workflows/qaqc.yaml | 1 + .idea/.gitignore | 2 ++ 3 files changed, 7 insertions(+) create mode 100644 .codespellignore diff --git a/.codespellignore b/.codespellignore new file mode 100644 index 0000000..911ca11 --- /dev/null +++ b/.codespellignore @@ -0,0 +1,4 @@ +slippy +hist +NAM +gage diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index e77bd16..ccb1cf1 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -33,6 +33,7 @@ jobs: check_filenames: true check_hidden: true skip: '.git,qaqc.yml' + ignore_words_file: '.codespellignore' ignore_words_list: slippy,hist # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml diff --git a/.idea/.gitignore b/.idea/.gitignore index b58b603..7abb13d 100644 --- a/.idea/.gitignore +++ b/.idea/.gitignore @@ -3,3 +3,5 @@ /workspace.xml # Editor-based HTTP Client requests /httpRequests/ +# GitHub Copilot persisted chat sessions +/copilot/chatSessions From 2a74757efa2ffb46de730bb9d899e711b8519e26 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:19:47 -0400 Subject: [PATCH 03/21] ignore file to ignore words --- .github/workflows/qaqc.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index ccb1cf1..a6083ad 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -33,7 +33,7 @@ jobs: check_filenames: true check_hidden: true skip: '.git,qaqc.yml' - ignore_words_file: '.codespellignore' + ignore_words_file: .codespellignore ignore_words_list: slippy,hist # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml From 6a456ddb15a863aa32a89e594cbc346bde46715e Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:21:57 -0400 Subject: [PATCH 04/21] ignore file to ignore words --- .codespellignore | 4 ---- .github/workflows/qaqc.yaml | 3 +-- 2 files changed, 1 insertion(+), 6 deletions(-) delete mode 100644 .codespellignore diff --git a/.codespellignore b/.codespellignore deleted file mode 100644 index 911ca11..0000000 --- a/.codespellignore +++ /dev/null @@ -1,4 +0,0 @@ -slippy -hist -NAM -gage diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index a6083ad..3c03664 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -33,8 +33,7 @@ jobs: check_filenames: true check_hidden: true skip: '.git,qaqc.yml' - ignore_words_file: .codespellignore - ignore_words_list: slippy,hist + ignore_words_list: slippy,hist,NAM,gage # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml - name: Disable Notebook Execution Before Linkcheck From 9c66247dc8fb579d2a4ee13377d6f97fadc5a7cb Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:32:04 -0400 Subject: [PATCH 05/21] ignore file to ignore words --- .github/workflows/qaqc.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index 3c03664..987fc31 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -32,7 +32,7 @@ jobs: with: check_filenames: true check_hidden: true - skip: '.git,qaqc.yml' + skip: .git,qaqc.yml ignore_words_list: slippy,hist,NAM,gage # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml From f12593b0434e31f951b9e0cc4254898a8c27b8fd Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:35:03 -0400 Subject: [PATCH 06/21] ignore file to ignore words --- .github/workflows/qaqc.yaml | 2 +- book/_config.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index 987fc31..ffab908 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -32,7 +32,7 @@ jobs: with: check_filenames: true check_hidden: true - skip: .git,qaqc.yml + skip: .git,.github/workflows/qaqc.yaml ignore_words_list: slippy,hist,NAM,gage # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml diff --git a/book/_config.yml b/book/_config.yml index c637b32..86a6cb3 100644 --- a/book/_config.yml +++ b/book/_config.yml @@ -3,7 +3,7 @@ title: "Snow Water Equivalent Workflow" author: Center for Spatial Information Science and Systems (CSISS), George Mason University -logo: img/logo/png +logo: img/logo.png only_build_toc_files: true # Add GitHub buttons to your book From 0c2133b82495cff69c66467e48a9b2212daedcd3 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:45:07 -0400 Subject: [PATCH 07/21] ignore file to ignore words --- .github/workflows/qaqc.yaml | 15 +++++------ .github/workflows/spellcheck.py | 44 +++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+), 9 deletions(-) create mode 100644 .github/workflows/spellcheck.py diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index ffab908..efc214f 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -25,15 +25,12 @@ jobs: run: | python .github/workflows/ensure_clean_notebooks.py - # NOTE: this isn't a comprehensive spellcheck, just common typos - - name: Spellcheck - if: always() - uses: codespell-project/actions-codespell@master - with: - check_filenames: true - check_hidden: true - skip: .git,.github/workflows/qaqc.yaml - ignore_words_list: slippy,hist,NAM,gage + - name: Install pyspellchecker + run: | + pip install pyspellchecker + + - name: Custom Spellcheck with Ignore List + run: python .github/workflows/spellcheck.py # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml - name: Disable Notebook Execution Before Linkcheck diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py new file mode 100644 index 0000000..2132094 --- /dev/null +++ b/.github/workflows/spellcheck.py @@ -0,0 +1,44 @@ +from spellchecker import SpellChecker +import nbformat +import os +import sys # Import sys for exiting with a non-zero status code + +def spell_check_notebook(filepath, ignore_words): + spell = SpellChecker() + spell.word_frequency.load_words(ignore_words) + + has_errors = False # Flag to track if there are any spelling errors + + with open(filepath, 'r', encoding='utf-8') as f: + nb = nbformat.read(f, as_version=4) + + for cell in nb.cells: + if cell.cell_type == 'markdown': + text = cell.source + misspelled = spell.unknown(text.split()) + if misspelled: + if not has_errors: # Print the header once per file + print(f"Misspelled words in {filepath}:") + has_errors = True + for word in misspelled: + print(f"- {word}") + if has_errors: + print("\n") # Print a newline for readability between files + return True # Return True to indicate spelling errors were found + return False + +def spell_check_directory(directory, ignore_words): + error_found = False + for subdir, dirs, files in os.walk(directory): + for file in files: + if file.endswith('.ipynb'): + filepath = os.path.join(subdir, file) + if spell_check_notebook(filepath, ignore_words): + error_found = True # Error found in at least one file + + if error_found: + sys.exit(1) # Exit with a non-zero status code to indicate failure + +if __name__ == "__main__": + ignore_list = ['slippy', 'hist', 'NAM', 'gage'] # Adjust your ignore list as needed + spell_check_directory('.', ignore_list) From 648d909cdf1adec4732606ea77e3d13a5e25970a Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:48:53 -0400 Subject: [PATCH 08/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 2132094..348f6ec 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -1,13 +1,13 @@ from spellchecker import SpellChecker import nbformat import os -import sys # Import sys for exiting with a non-zero status code +import sys def spell_check_notebook(filepath, ignore_words): spell = SpellChecker() spell.word_frequency.load_words(ignore_words) - has_errors = False # Flag to track if there are any spelling errors + misspelled_words = {} # Dictionary to collect misspelled words with open(filepath, 'r', encoding='utf-8') as f: nb = nbformat.read(f, as_version=4) @@ -17,26 +17,25 @@ def spell_check_notebook(filepath, ignore_words): text = cell.source misspelled = spell.unknown(text.split()) if misspelled: - if not has_errors: # Print the header once per file - print(f"Misspelled words in {filepath}:") - has_errors = True - for word in misspelled: - print(f"- {word}") - if has_errors: - print("\n") # Print a newline for readability between files - return True # Return True to indicate spelling errors were found - return False + if filepath not in misspelled_words: + misspelled_words[filepath] = set() # Use a set to avoid duplicate words + misspelled_words[filepath].update(misspelled) + + return misspelled_words def spell_check_directory(directory, ignore_words): - error_found = False + all_misspelled_words = {} for subdir, dirs, files in os.walk(directory): for file in files: if file.endswith('.ipynb'): filepath = os.path.join(subdir, file) - if spell_check_notebook(filepath, ignore_words): - error_found = True # Error found in at least one file + result = spell_check_notebook(filepath, ignore_words) + if result: + all_misspelled_words.update(result) - if error_found: + if all_misspelled_words: + for filepath, words in all_misspelled_words.items(): + print(f"Misspelled words in {filepath}: {', '.join(words)}") sys.exit(1) # Exit with a non-zero status code to indicate failure if __name__ == "__main__": From 132ed20295c3ead74dc2f8024b0cf0a7b1651706 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:53:13 -0400 Subject: [PATCH 09/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 348f6ec..cecdfb4 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -39,5 +39,20 @@ def spell_check_directory(directory, ignore_words): sys.exit(1) # Exit with a non-zero status code to indicate failure if __name__ == "__main__": - ignore_list = ['slippy', 'hist', 'NAM', 'gage'] # Adjust your ignore list as needed + ignore_list = [ + 'slippy', 'hist', 'NAM', 'gage', 'swe', 'geoweaver', 'workflow', 'datasets', 'snotel', + 'adeos-ii', 'monthly,', 'products,', 'nsidc', 'separately,', 'control.', '##', 'caps,', + 'dataset.', 'research**:', 'prediction,', 'atmosphere.', 'amsr?', 'fingertips!', 'risk,', + 'amsr-related', 'amsr2', 'snowfall,', 'properties.', 'netcdf,', '**cryosphere', 'moisture,', + 'extent,', 'scheduling.', 'scales.', '1,000m.', 'tourism**:', 'format,', 'temperature,', + 'purposes,', 'hiking,', '**recreation', 'datasets.', 'workflow,', '(coop),', 'periods.', + '[gridmet', '**temporal', '(nam)', 'snow,', 'agriculture,', 'amsr,', 'terrain,', 'beyond.', + 'data,', '(plain', 'data.', 'u.s.the', '(swe),', 'resolutions:', '**oceanography**:', 'daac', + 'forestry**:', '*runoff', 'format.', 'hdf5', 'information.', 'instruments.', 'ams/adeos-ii,', + 'land,', 'typically,', 'reliability,', 'capabilities.', 'gage,', '(network', 'environment.,', + 'dynamics,', 'trends*:', 'dynamics.', 'hydroclimatic', 'studies*:', 'visualization.', 'degree)', + 'models:', 'snowboarding,', 'camping.', 'salinity,', 'intervals.', 'multi-disciplinary', + 'monitoring*:', 'endeavors.', 'columbia', 'conditions.', 'nasa\'s', 'metadata', + 'snowpacktelemetrynetwork', 'satellite-ii', 'winds,', 'mo', 'fsca', 'modis', 'gridmet' + ] spell_check_directory('.', ignore_list) From bb637ebb8d25501234fb0a1fa6d06a58839bb047 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 22:58:53 -0400 Subject: [PATCH 10/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 29 ++++++++++------------------- 1 file changed, 10 insertions(+), 19 deletions(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index cecdfb4..ebe0837 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -2,23 +2,29 @@ import nbformat import os import sys +import string # Import the string module to get a list of punctuation characters + +def remove_punctuation(text): + translator = str.maketrans('', '', string.punctuation) + return text.translate(translator) def spell_check_notebook(filepath, ignore_words): spell = SpellChecker() spell.word_frequency.load_words(ignore_words) - misspelled_words = {} # Dictionary to collect misspelled words + misspelled_words = {} with open(filepath, 'r', encoding='utf-8') as f: nb = nbformat.read(f, as_version=4) for cell in nb.cells: if cell.cell_type == 'markdown': - text = cell.source + # Preprocess the cell text to remove punctuation before splitting into words + text = remove_punctuation(cell.source) misspelled = spell.unknown(text.split()) if misspelled: if filepath not in misspelled_words: - misspelled_words[filepath] = set() # Use a set to avoid duplicate words + misspelled_words[filepath] = set() misspelled_words[filepath].update(misspelled) return misspelled_words @@ -39,20 +45,5 @@ def spell_check_directory(directory, ignore_words): sys.exit(1) # Exit with a non-zero status code to indicate failure if __name__ == "__main__": - ignore_list = [ - 'slippy', 'hist', 'NAM', 'gage', 'swe', 'geoweaver', 'workflow', 'datasets', 'snotel', - 'adeos-ii', 'monthly,', 'products,', 'nsidc', 'separately,', 'control.', '##', 'caps,', - 'dataset.', 'research**:', 'prediction,', 'atmosphere.', 'amsr?', 'fingertips!', 'risk,', - 'amsr-related', 'amsr2', 'snowfall,', 'properties.', 'netcdf,', '**cryosphere', 'moisture,', - 'extent,', 'scheduling.', 'scales.', '1,000m.', 'tourism**:', 'format,', 'temperature,', - 'purposes,', 'hiking,', '**recreation', 'datasets.', 'workflow,', '(coop),', 'periods.', - '[gridmet', '**temporal', '(nam)', 'snow,', 'agriculture,', 'amsr,', 'terrain,', 'beyond.', - 'data,', '(plain', 'data.', 'u.s.the', '(swe),', 'resolutions:', '**oceanography**:', 'daac', - 'forestry**:', '*runoff', 'format.', 'hdf5', 'information.', 'instruments.', 'ams/adeos-ii,', - 'land,', 'typically,', 'reliability,', 'capabilities.', 'gage,', '(network', 'environment.,', - 'dynamics,', 'trends*:', 'dynamics.', 'hydroclimatic', 'studies*:', 'visualization.', 'degree)', - 'models:', 'snowboarding,', 'camping.', 'salinity,', 'intervals.', 'multi-disciplinary', - 'monitoring*:', 'endeavors.', 'columbia', 'conditions.', 'nasa\'s', 'metadata', - 'snowpacktelemetrynetwork', 'satellite-ii', 'winds,', 'mo', 'fsca', 'modis', 'gridmet' - ] + ignore_list = [] spell_check_directory('.', ignore_list) From f7f1b6354cd94fa470f9c354172d0359e5c3be46 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 23:01:06 -0400 Subject: [PATCH 11/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index ebe0837..3120128 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -45,5 +45,5 @@ def spell_check_directory(directory, ignore_words): sys.exit(1) # Exit with a non-zero status code to indicate failure if __name__ == "__main__": - ignore_list = [] + ignore_list = ["swe"] spell_check_directory('.', ignore_list) From 759df62074b17a33dc8cf3f4e4de6348c7e9327a Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 23:04:39 -0400 Subject: [PATCH 12/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 3120128..d254198 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -45,5 +45,17 @@ def spell_check_directory(directory, ignore_words): sys.exit(1) # Exit with a non-zero status code to indicate failure if __name__ == "__main__": - ignore_list = ["swe"] + ignore_list =misspelled_words = [ + "geoweaver", "workflow", "datasets", "snotel", "snowpack", "amsrderived", "highelevation", + "snowpacktelemetrynetwork", "snowcastwormhole", "500m", "amsr", "decisionmaking", + "qualitycontrolled", "pagehttpswwwearthdatanasagovsensorsamsre", "netcdf", "daac", "4km", + "satelliteii", "usthe", "strategizing", "xband", "modis", "pagehttpswwwearthdatanasagovsensorsmodis", + "nam", "amsrrelated", "adeosii", "mesoscale", "amsradeosii", "apis", "asos", "missionwater", + "metadata", "shortterm", "groundbased", "hdf", "british", "october", "level2a", "gcomw1", "csv", + "dataloggers", "columbia", "ascii", "tsv", "amsadeosii", "longterm", "onboard", "wrf", "km", "hdf5", + "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", + "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", + "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", + "250m", "highspatial", "hydroclimatic", "fsca", "workflow" + ] spell_check_directory('.', ignore_list) From 79095a1bd1e630bcec747fc7c00c5c91931af6f0 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 23:06:06 -0400 Subject: [PATCH 13/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index d254198..2d14bb1 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -56,6 +56,6 @@ def spell_check_directory(directory, ignore_words): "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", - "250m", "highspatial", "hydroclimatic", "fsca", "workflow" + "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe" ] spell_check_directory('.', ignore_list) From 0d5c8600d067625a97ef2fd8833b3157530494c4 Mon Sep 17 00:00:00 2001 From: Gokul Prathin Date: Tue, 26 Mar 2024 23:09:46 -0400 Subject: [PATCH 14/21] ignore file to ignore words --- .github/workflows/spellcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 2d14bb1..44f23ac 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -45,7 +45,7 @@ def spell_check_directory(directory, ignore_words): sys.exit(1) # Exit with a non-zero status code to indicate failure if __name__ == "__main__": - ignore_list =misspelled_words = [ + ignore_list = [ "geoweaver", "workflow", "datasets", "snotel", "snowpack", "amsrderived", "highelevation", "snowpacktelemetrynetwork", "snowcastwormhole", "500m", "amsr", "decisionmaking", "qualitycontrolled", "pagehttpswwwearthdatanasagovsensorsamsre", "netcdf", "daac", "4km", From d2baf976a9e663051b608e0fa1673a435a014a92 Mon Sep 17 00:00:00 2001 From: iammeghana Date: Thu, 28 Mar 2024 13:46:12 -0400 Subject: [PATCH 15/21] steps to download datasource --- book/chapters/datasource.ipynb | 112 +++++++++++++++++++++++++++++++-- 1 file changed, 106 insertions(+), 6 deletions(-) diff --git a/book/chapters/datasource.ipynb b/book/chapters/datasource.ipynb index 976121b..23a8fd6 100644 --- a/book/chapters/datasource.ipynb +++ b/book/chapters/datasource.ipynb @@ -5,10 +5,7 @@ "id": "a19b1c84c1e62af1", "metadata": {}, "source": [ - "# Data Source\n", - "\n", - "Detailed exploration of data sources: Gridmet, AMSR, MODIS, Snotel\n", - "Importance and characteristics of each data source\n", + "# Training and Testing Dataset Overview\n", "\n", "# Introduction\n", "In the realm of environmental research and forecasting, access to reliable and comprehensive datasets is paramount. Four such indispensable sources are gridMET, AMSR, MODIS, and SNOTEL. Each dataset offers unique insights into various aspects of Earth's climate and terrain, contributing significantly to our understanding of environmental dynamics. These datasets serve as the cornerstone for numerous applications, including weather forecasting, ecological modeling, and climate change research. In our **snowcast_wormhole** workflow, we leverage the richness of these datasets to enhance the accuracy and robustness. Let's embark on a detailed exploration of these invaluable data sources.\n", @@ -20,6 +17,7 @@ "gridMET is like having a weather wizard at your fingertips! It's a dataset packed with daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US, spanning from 1979 up to yesterday. It even extends its reach to cover southern British Columbia in real-time products. This dataset provides vital insights into weather patterns, aiding various industries and scientific endeavors.\n", "\n", "## Insights\n", + "\n", "* It offers a fascinating glimpse into historical weather patterns, allowing users to delve as far back as 1979.\n", "* The dataset provides an extensive array of information, including maximum and minimum temperatures, precipitation, radiation, wind velocity, and various other meteorological variables.\n", "* Scientists and modelers find it invaluable for a myriad of purposes, such as ecological research, agricultural strategizing, hydrological modeling, and beyond.\n", @@ -39,6 +37,7 @@ "- **Long-term Availability**: GridMET provides data spanning several decades, enabling analysis of historical meteorological trends and patterns.\n", "\n", "## Data Format:\n", + "\n", "GRIDMET data is typically available in common file formats such as NetCDF (Network Common Data Form) or ASCII (plain text) format. NetCDF is widely used for multidimensional scientific data and provides efficient storage and access to large datasets. ASCII format offers simplicity and compatibility with various software tools for data analysis and visualization.\n", "\n", "## Data Sources and Acquisition:\n", @@ -61,6 +60,32 @@ "- Disaster management and risk assessment\n", "- Ecological and environmental studies\n", "\n", + "# gridMET Data Download Instructions\n", + "\n", + "There are several options available for acquiring gridMET data. Below are the methods you can use to obtain the dataset:\n", + "\n", + "\n", + "1. **Direct Download of NetCDF Files:**\n", + " - Download NetCDF files directly from the [NetCDF data repository](https://www.northwestknowledge.net/metdata/data/).\n", + "\n", + "2. **Create wget Script for Downloading NetCDF Files:**\n", + " - Script to automate downloading of NetCDF files using [wget](https://www.climatologylab.org/wget-gridmet.html).\n", + "\n", + "4. **THREDDS Catalog (OPENDAP):**\n", + " - Access data via [THREDDS Catalog](http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_MET_catalog.html) with OPENDAP protocol.\n", + "\n", + "5. **Aggregated THREDDS Catalog (OPENDAP):**\n", + " - Access aggregated data via [Aggregated THREDDS Catalog](http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_MET_aggregated_catalog.html) with OPENDAP protocol.\n", + "\n", + "\n", + "6. **Elevation Data on the Same 4-km Grid:**\n", + " - Access elevation data on the same grid as the meteorological data from [elevation_dataset](https://climate.northwestknowledge.net/METDATA/data/metdata_elevationdata.nc).\n", + "\n", + "7. **Data Subsets using Web Tools:**\n", + " - [ClimateEngine.org](http://climateengine.org)\n", + " - [Geo Data Portal (GDP)](https://labs.waterdata.usgs.gov/gdp_web/client/#!catalog/gdp/dataset/54dd5df2e4b08de9379b38d8) ([Tutorial on GDP Tool](https://climate.northwestknowledge.net/MACA/GDP.php))\n", + "\n", + "\n", "For more information on the gridMET dataset, you can visit the [gridMET website](https://www.climatologylab.org/gridmet.html). Here, you'll find detailed documentation." ] }, @@ -114,6 +139,23 @@ "\n", "5. **Cryosphere Studies**: AMSR datasets are crucial for studying the cryosphere, including monitoring changes in snow cover extent, snow water equivalent, and sea ice concentration, contributing to our understanding of climate change impacts in polar regions.\n", "\n", + "# AMSR Dataset Download Instructions\n", + "\n", + "### Step 1: Access the Data Portals\n", + "- For Standard Products, visit the [Globe Portal System (G-Portal)](https://gportal.jaxa.jp/gpr/) and sign in or create an account.\n", + "- For Research Products, access the [GCOM-W Research Product Distribution Service](https://suzaku.eorc.jaxa.jp/GCOM_W/research/resdist.html) and log in or register.\n", + "\n", + "### Step 2: Select the Desired Product\n", + "- Choose the appropriate AMSR product based on your requirements (AMSR2, AMSR-E, or AMSR-E in slow rotation mode).\n", + "\n", + "### Step 3: Define Data Range\n", + "- Specify the date range for the data you wish to download.\n", + "\n", + "### Step 4: Initiate Download\n", + "- Follow the prompts to start the download process after specifying the product and date range.\n", + "\n", + "Remember to adhere to any terms of use or licensing agreements associated with the data you're downloading.\n", + "\n", "\n", "For more information, visit the [AMSR-E Sensor Page](https://www.earthdata.nasa.gov/sensors/amsr-e)." ] @@ -146,6 +188,36 @@ "## MODIS Direct Broadcast\n", "Users with x-band receiving systems can capture regional data directly from the spacecraft using the MODIS Direct Broadcast signal, enhancing real-time monitoring capabilities.\n", "\n", + "# MODIS Dataset Download Instructions\n", + "\n", + "### Step 1: Define Your Study Area (AOI)\n", + "- Define your area of interest (AOI) by either specifying the x,y vertices of each corner or by uploading a zipped shapefile containing the boundary.\n", + "- Ensure your extent polygon is square or rectangular with minimal vertices.\n", + "- Visit the [Earth Explorer website](https://earthexplorer.usgs.gov/) and log in or create an account if you haven't already.\n", + "\n", + "### Step 2: Define the Data That You Want to Download\n", + "- Navigate to the **Data sets** tab\n", + "- Choose the **MODIS Land Surface Reflectance - V6** dataset from the NASA LPDAAC Collections dropdown.\n", + "- Select the checkbox for **MODIS MOD09GA V6**.\n", + "\n", + "### Step 3: Define Selection Criteria\n", + "- Click on the **Additional Criteria** tab.\n", + "- Set filters as needed, such as limiting results to daytime images.\n", + "\n", + "### Step 4: View Results & Select Data to Order / Download\n", + "- Click on the **Results** tab to view available scenes covering your study area.\n", + "- Use icons below the scene to visualize it on the map and see its footprint relative to your study area.\n", + "\n", + "### Step 5: Download Your Data\n", + "- Click the download data icon.\n", + "- Choose the **HDF Format** option in the pop-up window.\n", + "- Log in to your Earthdata account through [NASA](https://urs.earthdata.nasa.gov/).\n", + "- Follow prompts to confirm and start the download.\n", + "\n", + "*Remember*, if you don't have an Earthdata account, you'll need to create one to access the data. Ensure you provide an email and adjust communication preferences accordingly.\n", + "\n", + "Once logged in, the download will begin, and you can start using your MODIS data for your intended purposes.\n", + "\n", "For more information, visit the [MODIS Sensor Page](https://www.earthdata.nasa.gov/sensors/modis)." ] }, @@ -167,7 +239,7 @@ "\n", "### Operations and Data Collection\n", "- Sites operate unattended and without maintenance for extended periods.\n", - "- Standard sensor configuration includes snow pillow, precipitation gage, and temperature sensors.\n", + "- Standard sensor configuration includes snow pillow, precipitation gauge, and temperature sensors.\n", "\n", "## Telemetry and Data Transmission\n", "### Data Collection and Storage\n", @@ -211,6 +283,34 @@ " - *Winter Sports Planning*: Ski resorts and recreational outfitters rely on snowpack data for planning activities such as skiing, snowboarding, and snowmobiling.\n", " - *Summer Recreation*: Understanding snowmelt timing and water availability helps in planning summer recreational activities like hiking, fishing, and camping.\n", "\n", + "# SNOTEL Dataset Download Instructions\n", + "\n", + "Because snow has a higher albedo than most other land cover types, it can cause the seasonal changes in the albedo of a landscape to be quite dramatic. The Soil Climate Analysis Network (SCAN) and the SNOwpack TELemetry (SNOWTEL) network provide snow depth and snow water equivalent (the amount of water contained in a snowpack) data for many sites across the United States.\n", + "\n", + "### Step 1: Navigate to the SCAN/SNOWTEL website\n", + "- Visit the [SCAN/SNOWTEL Website](http://www.wcc.nrcs.usda.gov/nwcc/inventory).\n", + "\n", + "### Step 2: Choose Data Product and Location\n", + "- Select the data product you are interested in (e.g., Snow Depth or Snow Water Equivalent) from the drop-down menu under ‘Element’.\n", + "- Choose a State/County or Basin using the drop-down menus provided.\n", + "\n", + "### Step 3: View Inventory\n", + "- Click on 'View Inventory' to see available stations in your selected area.\n", + " - If no results are returned, consider widening your search.\n", + "\n", + "### Step 4: Select Station and Data\n", + "- Click 'View' next to the station of interest to access its page.\n", + "- Use the table to select the data you need:\n", + " - Choose the data product (Snow Depth or Snow Water Equivalent).\n", + " - Select ‘Daily’ in the Time Series column.\n", + " - Choose the format ('chart' for visualization or 'csv' for download).\n", + " - View current data by selecting the desired time frame in the yellow column and clicking 'View Current', or view historic data by selecting the year and time in the green column and clicking 'View Historic'.\n", + "\n", + "### Step 5: Download Data\n", + "- Save the downloaded CSV file to your computer for further analysis.\n", + "\n", + "**For map visualization of SCAN or SNOWTEL stations, click on ‘SCAN data’ or ‘SNOWTEL data’ under ‘Climate Monitoring’ in the right panel. The maps are clickable for station selection.**\n", + "\n", "For more information, visit the [NRCS SNOTEL page](https://www.nrcs.usda.gov/wps/portal/wcc/home/aboutUs/monitoringPrograms/automatedSnowMonitoring).\n" ] } @@ -231,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, From 83f2d1bc68656de8854a329008caaa8dae7df25f Mon Sep 17 00:00:00 2001 From: iammeghana Date: Fri, 29 Mar 2024 18:25:07 -0400 Subject: [PATCH 16/21] chp-5 gridMET --- book/chapters/gridmet.ipynb | 7719 ++++++++++++++++++++++++++++++++++- 1 file changed, 7712 insertions(+), 7 deletions(-) diff --git a/book/chapters/gridmet.ipynb b/book/chapters/gridmet.ipynb index fb81df9..1169973 100644 --- a/book/chapters/gridmet.ipynb +++ b/book/chapters/gridmet.ipynb @@ -2,32 +2,7737 @@ "cells": [ { "cell_type": "markdown", + "id": "6098a345", + "metadata": {}, "source": [ - "# Gridmet" - ], + "# GridMET Climatology Data Downloader\n", + "\n", + "In this chapter, we aim to:\n", + "\n", + "- **Download GridMET Datan**: Obtain specific meteorological variables from the GridMET climatology dataset for a user-specified year. This involves utilizing libraries such as netCDF4 and urllib for file handling and downloading.\n", + "\n", + "- **Visualize Data**: Create custom color maps and scatter plots to visualize meteorological variables spatially across the western United States. This functionality aids in understanding geographical patterns and trends in meteorological data.\n", + "\n", + "- **Generate Cummulative history CSVs**: Generate cumulative history CSVs to aggregate meteorological data over a specified date range. This feature allows users to analyze historical meteorological patterns and long-term trends for decision-making purposes.\n", + "\n", + "We facilitate the retrieval of specific meteorological variables from the GridMET climatology dataset for a user-defined year. Leveraging libraries such as netCDF4, urllib, and pandas, we enable seamless data handling and manipulation, ensuring efficient processing and analysis of climatological data. We offer custom color mapping for visualizing meteorological patterns and provide functionality for generating cumulative history CSVs, allowing users to aggregate data from past October 1st to the specified target date for trend analysis. With matplotlib, we enable plotting and visualization, empowering users to gain insights through graphical representations.\n", + "\n", + "We provide comprehensive outputs including downloaded meteorological variables in NetCDF format, custom color-mapped visualizations showcasing meteorological patterns, and cumulative history CSV files containing aggregated data for trend analysis. With these outputs, users can explore and analyze GridMET climatology data efficiently, gaining valuable insights into long-term meteorological trends and patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4d61acb4", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import netCDF4 as nc\n", + "import urllib.request\n", + "from datetime import datetime, timedelta, date\n", + "import matplotlib.pyplot as plt\n", + "\n", + "train_start_date = \"2018-01-03\"\n", + "train_end_date = \"2021-12-31\"\n", + "homedir = os.path.expanduser('~')\n", + "work_dir = f\"{homedir}/gridmet_test_run\"\n", + "\n", + "# Define the folder to store downloaded files\n", + "gridmet_folder_name = f'{work_dir}/gridmet_climatology'\n", + "\n", + "western_us_coords = f'{work_dir}/dem_file.tif.csv'\n", + "\n", + "\n", + "gridmet_var_mapping = {\n", + " \"etr\": \"potential_evapotranspiration\",\n", + " \"pr\":\"precipitation_amount\",\n", + " \"rmax\":\"relative_humidity\",\n", + " \"rmin\":\"relative_humidity\",\n", + " \"tmmn\":\"air_temperature\",\n", + " \"tmmx\":\"air_temperature\",\n", + " \"vpd\":\"mean_vapor_pressure_deficit\",\n", + " \"vs\":\"wind_speed\",\n", + "}\n", + "# Define the custom colormap with specified colors and ranges\n", + "colors = [\n", + " (0.8627, 0.8627, 0.8627), # #DCDCDC - 0 - 1\n", + " (0.8627, 1.0000, 1.0000), # #DCFFFF - 1 - 2\n", + " (0.6000, 1.0000, 1.0000), # #99FFFF - 2 - 4\n", + " (0.5569, 0.8235, 1.0000), # #8ED2FF - 4 - 6\n", + " (0.4509, 0.6196, 0.8745), # #739EDF - 6 - 8\n", + " (0.4157, 0.4706, 1.0000), # #6A78FF - 8 - 10\n", + " (0.4235, 0.2784, 1.0000), # #6C47FF - 10 - 12\n", + " (0.5529, 0.0980, 1.0000), # #8D19FF - 12 - 14\n", + " (0.7333, 0.0000, 0.9176), # #BB00EA - 14 - 16\n", + " (0.8392, 0.0000, 0.7490), # #D600BF - 16 - 18\n", + " (0.7569, 0.0039, 0.4549), # #C10074 - 18 - 20\n", + " (0.6784, 0.0000, 0.1961), # #AD0032 - 20 - 30\n", + " (0.5020, 0.0000, 0.0000) # #800000 - > 30\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "f7b73128", + "metadata": {}, + "source": [ + "First, we import necessary libraries for data manipulation, file handling, and visualization. We then defines important variables like folder paths and sets up a mapping for interpreting meteorological data. Additionally, it establishes a custom color scheme for visualization. These steps prepare for data processing and visualization.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0fd30ba5", + "metadata": {}, + "outputs": [], + "source": [ + "def create_color_maps_with_value_range(df_col, value_ranges=None):\n", + " if value_ranges == None:\n", + " max_value = df_col.max()\n", + " min_value = df_col.min()\n", + " if min_value < 0:\n", + " min_value = 0\n", + " step_size = (max_value - min_value) / 12\n", + "\n", + " # Create 10 periods\n", + " new_value_ranges = [min_value + i * step_size for i in range(12)]\n", + " # Define your custom function to map data values to colors\n", + " def map_value_to_color(value):\n", + " # Iterate through the value ranges to find the appropriate color index\n", + " for i, range_max in enumerate(new_value_ranges):\n", + " if value <= range_max:\n", + " return colors[i]\n", + "\n", + " # If the value is greater than the largest range, return the last color\n", + " return colors[-1]\n", + "\n", + " # Map predicted_swe values to colors using the custom function\n", + " color_mapping = [map_value_to_color(value) for value in df_col.values]\n", + " return color_mapping, new_value_ranges" + ] + }, + { + "cell_type": "markdown", + "id": "ab305646", + "metadata": {}, + "source": [ + "We generate color mappings for data values based on specified or automatically calculated value ranges. If we do not have a range of values, we calculate them by finding the minimum and maximum values in the data and divide the range into 12 intervals. Next, we determine which interval it falls into and assign a corresponding color. Finally, we obtain these color mappings alongside the newly calculated value ranges, providing a visual representation of the data distribution." + ] + }, + { + "cell_type": "markdown", + "id": "3a1c3a47d3070625", "metadata": { "collapsed": false }, - "id": "3a1c3a47d3070625" + "source": [ + "# Gridmet" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "92e47a12", + "metadata": {}, + "outputs": [], + "source": [ + "def get_current_year():\n", + " \"\"\"\n", + " Get the current year.\n", + "\n", + " Returns:\n", + " int: The current year.\n", + " \"\"\"\n", + " now = datetime.now()\n", + " current_year = now.year\n", + " return current_year" + ] + }, + { + "cell_type": "markdown", + "id": "c84bca49", + "metadata": {}, + "source": [ + "We retrieve the current year from the system's date and time settings." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1b96876c", + "metadata": {}, + "outputs": [], + "source": [ + "def remove_files_in_folder(folder_path, current_year):\n", + " \"\"\"\n", + " Remove all files in a specified folder.\n", + "\n", + " Parameters:\n", + " folder_path (str): Path to the folder to remove files from.\n", + " \"\"\"\n", + " # Get a list of files in the folder\n", + " files = os.listdir(folder_path)\n", + "\n", + " # Loop through the files and remove them\n", + " for file in files:\n", + " file_path = os.path.join(folder_path, file)\n", + " if os.path.isfile(file_path) and str(current_year) in file_path and file_path.endswith(\".nc\"):\n", + " os.remove(file_path)\n", + " print(f\"Deleted file: {file_path}\")" + ] + }, + { + "cell_type": "markdown", + "id": "770f22a3", + "metadata": {}, + "source": [ + "We iterate through the files within a designated folder and delete those that meet certain criteria, such as being associated with the current year and having a specific file extension. This ensures that only relevant files are removed from the directory." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d7c28a1a", + "metadata": {}, + "outputs": [], + "source": [ + "def download_file(url, target_file_path, variable):\n", + " \"\"\"\n", + " Download a file from a URL and save it to a specified location.\n", + "\n", + " Parameters:\n", + " url (str): URL of the file to download.\n", + " target_file_path (str): Path where the downloaded file should be saved.\n", + " variable (str): Name of the meteorological variable being downloaded.\n", + " \"\"\"\n", + " try:\n", + " with urllib.request.urlopen(url) as response:\n", + " print(f\"Downloading {url}\")\n", + " file_content = response.read()\n", + " save_path = target_file_path\n", + " with open(save_path, 'wb') as file:\n", + " file.write(file_content)\n", + " print(f\"File downloaded successfully and saved as: {save_path}\")\n", + " except Exception as e:\n", + " print(f\"An error occurred while downloading the file: {str(e)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2525baae", + "metadata": {}, + "source": [ + "We facilitate the download of a file from a given URL and store it in a specified location. We consider the meteorological variable's name being downloaded. Upon successful completion, we provide a confirmation message containing the saved file's path. If any errors occur during the download process, we display an error message." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b73d3c85", + "metadata": {}, + "outputs": [], + "source": [ + "def download_gridmet_of_specific_variables(year_list):\n", + " \"\"\"\n", + " Download specific meteorological variables from the GridMET climatology dataset.\n", + " \"\"\"\n", + " # Make a directory to store the downloaded files\n", + " \n", + "\n", + " base_metadata_url = \"http://www.northwestknowledge.net/metdata/data/\"\n", + " variables_list = ['tmmn', 'tmmx', 'pr', 'vpd', 'etr', 'rmax', 'rmin', 'vs']\n", + "\n", + " for var in variables_list:\n", + " for y in year_list:\n", + " download_link = base_metadata_url + var + '_' + '%s' % y + '.nc'\n", + " target_file_path = os.path.join(gridmet_folder_name, var + '_' + '%s' % y + '.nc')\n", + " if not os.path.exists(target_file_path):\n", + " download_file(download_link, target_file_path, var)\n", + " else:\n", + " print(f\"File {target_file_path} exists\")" + ] + }, + { + "cell_type": "markdown", + "id": "730fb749", + "metadata": {}, + "source": [ + "We download specific meteorological variables from the GridMET climatology dataset by iterating over years and variables. If a file for a specific year and variable combination doesn't exist, we download it." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d5d51f5a", + "metadata": {}, + "outputs": [], + "source": [ + "def get_current_year():\n", + " now = datetime.now()\n", + " current_year = now.year\n", + " return current_year" + ] + }, + { + "cell_type": "markdown", + "id": "c4ad1f85", + "metadata": {}, + "source": [ + "We retrieve the current year from the system's date and time settings." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "82adf8a9", + "metadata": {}, + "outputs": [], + "source": [ + "def get_file_name_from_path(file_path):\n", + " # Get the file name from the file path\n", + " file_name = os.path.basename(file_path)\n", + " return file_name" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2704d40c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_var_from_file_name(file_name):\n", + " # Assuming the file name format is \"tmmm_year.csv\"\n", + " var_name = str(file_name.split('_')[0])\n", + " return var_name" + ] + }, + { + "cell_type": "markdown", + "id": "b80ac603", + "metadata": {}, + "source": [ + "We extract the file name from a given file path." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a0eca065", + "metadata": {}, + "outputs": [], + "source": [ + "def get_coordinates_of_template_tif():\n", + " \t# Load the CSV file and extract coordinates\n", + " coordinates = []\n", + " df = pd.read_csv(dem_csv)\n", + " for index, row in df.iterrows():\n", + " # Process each row here\n", + " lon, lat = float(row[\"Latitude\"]), float(row[\"Longitude\"])\n", + " coordinates.append((lon, lat))\n", + " return coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "a6869942", + "metadata": {}, + "source": [ + "We load a CSV file and extract coordinates from it, resulting in a list of latitude and longitude tuples." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6d20022d", + "metadata": {}, + "outputs": [], + "source": [ + "def find_nearest_index(array, value):\n", + " # Find the index of the element in the array that is closest to the given value\n", + " return (abs(array - value)).argmin()" + ] + }, + { + "cell_type": "markdown", + "id": "9ba26855", + "metadata": {}, + "source": [ + "We find the index of the element in the array that is closest to the given value." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6173cfa8", + "metadata": {}, + "outputs": [], + "source": [ + "def create_gridmet_to_dem_mapper(nc_file):\n", + " western_us_dem_df = pd.read_csv(western_us_coords)\n", + " # Check if the CSV already exists\n", + " target_csv_path = f'{work_dir}/gridmet_to_dem_mapper.csv'\n", + " if os.path.exists(target_csv_path):\n", + " print(f\"File {target_csv_path} already exists, skipping..\")\n", + " return\n", + " \n", + " # get the netcdf file and generate the csv file for every coordinate in the dem_template.csv\n", + " selected_date = datetime.strptime(test_start_date, \"%Y-%m-%d\")\n", + " # Read the NetCDF file\n", + " with nc.Dataset(nc_file) as nc_file:\n", + " \n", + " # Get the values at each coordinate using rasterio's sample function\n", + " latitudes = nc_file.variables['lat'][:]\n", + " longitudes = nc_file.variables['lon'][:]\n", + " \n", + " def get_gridmet_var_value(row):\n", + " # Perform your custom calculation here\n", + " gridmet_lat_index = find_nearest_index(latitudes, float(row[\"Latitude\"]))\n", + " gridmet_lon_index = find_nearest_index(longitudes, float(row[\"Longitude\"]))\n", + " return latitudes[gridmet_lat_index], longitudes[gridmet_lon_index], gridmet_lat_index, gridmet_lon_index\n", + " \n", + " # Use the apply function to apply the custom function to each row\n", + " western_us_dem_df[['gridmet_lat', 'gridmet_lon', \n", + " 'gridmet_lat_idx', 'gridmet_lon_idx',]] = western_us_dem_df.apply(lambda row: pd.Series(get_gridmet_var_value(row)), axis=1)\n", + " western_us_dem_df.rename(columns={\"Latitude\": \"dem_lat\", \n", + " \"Longitude\": \"dem_lon\"}, inplace=True)\n", + " \n", + " # print(western_us_dem_df.head())\n", + " \n", + " # Save the new converted AMSR to CSV file\n", + " western_us_dem_df.to_csv(target_csv_path, index=False)\n", + " \n", + " return western_us_dem_df" + ] + }, + { + "cell_type": "markdown", + "id": "1df72f52", + "metadata": {}, + "source": [ + "We generate a comprehensive mapping between the geographic coordinates present in a DEM template CSV file and their respective counterparts within a GridMET netCDF file. This mapping serves as a vital bridge, facilitating the seamless association and alignment of DEM and GridMET datasets. This integration is crucial for a wide array of spatial analyses, environmental modeling endeavors, and geographical studies." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6dad59c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-03-25\n" + ] + } + ], + "source": [ + "def get_operation_day():\n", + " # Get the current date and time\n", + " current_date = datetime.now()\n", + "\n", + " # Calculate three days ago\n", + " three_days_ago = current_date - timedelta(days=3)\n", + "\n", + " # Format the date as a string\n", + " three_days_ago_string = three_days_ago.strftime(\"%Y-%m-%d\")\n", + "\n", + " print(three_days_ago_string)\n", + " return three_days_ago_string\n", + "\n", + "test_start_date = get_operation_day()" + ] + }, + { + "cell_type": "markdown", + "id": "628d7687", + "metadata": {}, + "source": [ + "We retrieve the date of an operational day by calculating the date three days prior to the current date. This operational day is crucial for setting up various time-sensitive operations or analyses. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c09fdd0c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_nc_csv_by_coords_and_variable(nc_file, var_name, target_date=test_start_date):\n", + " \n", + " create_gridmet_to_dem_mapper(nc_file)\n", + " \t\n", + " mapper_df = pd.read_csv(f'{work_dir}/gridmet_to_dem_mapper.csv')\n", + " \n", + " # get the netcdf file and generate the csv file for every coordinate in the dem_template.csv\n", + " selected_date = datetime.strptime(target_date, \"%Y-%m-%d\")\n", + " # Read the NetCDF file\n", + " with nc.Dataset(nc_file) as nc_file:\n", + " # Get a list of all variables in the NetCDF file\n", + " variables = nc_file.variables.keys()\n", + " \n", + " # Get the values at each coordinate using rasterio's sample function\n", + " latitudes = nc_file.variables['lat'][:]\n", + " longitudes = nc_file.variables['lon'][:]\n", + " day = nc_file.variables['day'][:]\n", + " long_var_name = gridmet_var_mapping[var_name]\n", + " var_col = nc_file.variables[long_var_name][:]\n", + " #print(\"val_col.shape: \", var_col.shape)\n", + " \n", + " # Calculate the day of the year\n", + " day_of_year = selected_date.timetuple().tm_yday\n", + " day_index = day_of_year - 1\n", + " #print('day_index:', day_index)\n", + " \n", + " def get_gridmet_var_value(row):\n", + " # Perform your custom calculation here\n", + " lat_index = int(row[\"gridmet_lat_idx\"])\n", + " lon_index = int(row[\"gridmet_lon_idx\"])\n", + " var_value = var_col[day_index, lat_index, lon_index]\n", + " \n", + " return var_value\n", + " \n", + " # Use the apply function to apply the custom function to each row\n", + " # print(mapper_df.columns)\n", + " # print(mapper_df.head())\n", + " mapper_df[var_name] = mapper_df.apply(get_gridmet_var_value, axis=1)\n", + " \n", + " # print(\"mapper_df[var_name]: \", mapper_df[var_name].describe())\n", + " \n", + " # drop useless columns\n", + " mapper_df = mapper_df[[\"dem_lat\", \"dem_lon\", var_name]]\n", + " mapper_df.rename(columns={\"dem_lat\": \"Latitude\",\n", + " \"dem_lon\": \"Longitude\"}, inplace=True)\n", + "\n", + " \n", + " # print(mapper_df.head())\n", + " return mapper_df" + ] + }, + { + "cell_type": "markdown", + "id": "6c685758", + "metadata": {}, + "source": [ + "We extract data for a specific variable from a NetCDF file by matching coordinates from a DEM template CSV file. This enables us to create a DataFrame containing the variable values alongside the corresponding coordinates. By doing so, we can effectively extract and analyze meteorological data for specific geographical locations, aiding in various environmental and geographical studies, as well as modeling endeavors." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "46b63faa", + "metadata": {}, + "outputs": [], + "source": [ + "def turn_gridmet_nc_to_csv(target_date=test_start_date):\n", + " \n", + " selected_date = datetime.strptime(target_date, \"%Y-%m-%d\")\n", + " generated_csvs = []\n", + " for root, dirs, files in os.walk(gridmet_folder_name):\n", + " for file_name in files:\n", + " \n", + " if str(selected_date.year) in file_name and file_name.endswith(\".nc\"):\n", + " print(f\"Checking file: {file_name}\")\n", + " var_name = get_var_from_file_name(file_name)\n", + " # print(\"Variable name:\", var_name)\n", + " res_csv = f\"{work_dir}/testing_output/{str(selected_date.year)}_{var_name}_{target_date}.csv\"\n", + "\n", + " if os.path.exists(res_csv):\n", + " #os.remove(res_csv)\n", + " print(f\"{res_csv} already exists. Skipping..\")\n", + " generated_csvs.append(res_csv)\n", + " continue\n", + "\n", + " # Perform operations on each file here\n", + " netcdf_file_path = os.path.join(root, file_name)\n", + " print(\"Processing file:\", netcdf_file_path)\n", + " file_name = get_file_name_from_path(netcdf_file_path)\n", + "\n", + " df = get_nc_csv_by_coords_and_variable(netcdf_file_path, \n", + " var_name, target_date)\n", + " df.replace('--', pd.NA, inplace=True)\n", + " df.to_csv(res_csv, index=False)\n", + " print(\"gridmet var saved: \", res_csv)\n", + " generated_csvs.append(res_csv)\n", + " return generated_csvs \n" + ] + }, + { + "cell_type": "markdown", + "id": "77512189", + "metadata": {}, + "source": [ + "We convert GridMET NetCDF files to CSV format for a specified date. We iterate through files in the GridMET folder, checking for files corresponding to the selected date. For each matching file, we extract the variable name and generate a CSV file containing the data. If the CSV file already exists, we skip the process. This process facilitates easy access and analysis of meteorological data for a specific date." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "62f7b586", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_gridmet(target_date=test_start_date):\n", + " selected_date = datetime.strptime(target_date, \"%Y-%m-%d\")\n", + " var_name = \"pr\"\n", + " test_csv = f\"{work_dir}/testing_output/{str(selected_date.year)}_{var_name}_{target_date}.csv\"\n", + " gridmet_var_df = pd.read_csv(test_csv)\n", + " gridmet_var_df.replace('--', pd.NA, inplace=True)\n", + " gridmet_var_df.dropna(inplace=True)\n", + " gridmet_var_df['pr'] = pd.to_numeric(gridmet_var_df['pr'], errors='coerce')\n", + " #print(gridmet_var_df.head())\n", + " #print(gridmet_var_df[\"Latitude\"].describe())\n", + " #print(gridmet_var_df[\"Longitude\"].describe())\n", + " #print(gridmet_var_df[\"pr\"].describe())\n", + " \n", + " colormaplist, value_ranges = create_color_maps_with_value_range(gridmet_var_df[var_name])\n", + " \n", + " # Create a scatter plot\n", + " plt.scatter(gridmet_var_df[\"Longitude\"].values, \n", + " gridmet_var_df[\"Latitude\"].values, \n", + " label='Pressure', \n", + " color=colormaplist, \n", + " marker='o')\n", + "\n", + " # Add labels and a legend\n", + " plt.xlabel('X-axis')\n", + " plt.ylabel('Y-axis')\n", + " plt.title('Scatter Plot Example')\n", + " plt.legend()\n", + " \n", + " res_png_path = f\"{work_dir}/testing_output/{str(selected_date.year)}_{var_name}_{target_date}.png\"\n", + " plt.savefig(res_png_path)\n", + " print(f\"test image is saved at {res_png_path}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c27997ec", + "metadata": {}, + "source": [ + "We plot GridMET meteorological data for a specific variable and date. We read the data from a corresponding CSV file and preprocess it, ensuring valid numerical values. Then, we create a scatter plot, mapping the variable values to geographic coordinates. The color of each point on the plot represents the magnitude of the variable value. Finally, we save the plot as a PNG image for further analysis and visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1c643f91", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_folder_and_get_year_list(target_date=test_start_date):\n", + " # Check if the folder exists, if not, create it\n", + " if not os.path.exists(gridmet_folder_name):\n", + " os.makedirs(gridmet_folder_name)\n", + "\n", + " selected_date = datetime.strptime(target_date, \"%Y-%m-%d\")\n", + " if selected_date.month < 10:\n", + " past_october_1 = datetime(selected_date.year - 1, 10, 1)\n", + " else:\n", + " past_october_1 = datetime(selected_date.year, 10, 1)\n", + " year_list = [selected_date.year, past_october_1.year]\n", + "\n", + " # Remove any existing files in the folder\n", + " if selected_date.year == datetime.now().year:\n", + " # check if the current year's netcdf contains the selected date\n", + " # get etr netcdf and read\n", + " nc_file = f\"{gridmet_folder_name}/tmmx_{selected_date.year}.nc\"\n", + " ifremove = False\n", + " if os.path.exists(nc_file):\n", + " with nc.Dataset(nc_file) as ncd:\n", + " day = ncd.variables['day'][:]\n", + " # Calculate the day of the year\n", + " day_of_year = selected_date.timetuple().tm_yday\n", + " day_index = day_of_year - 1\n", + " if len(day) <= day_index:\n", + " ifremove = True\n", + " \n", + " if ifremove:\n", + " print(\"The current year netcdf has new data. Redownloading..\")\n", + " remove_files_in_folder(gridmet_folder_name, selected_date.year) # only redownload when the year is the current year\n", + " else:\n", + " print(\"The existing netcdf already covers the selected date. Avoid downloading..\")\n", + " return year_list" + ] + }, + { + "cell_type": "markdown", + "id": "ccfe1332", + "metadata": {}, + "source": [ + "We prepare the folder structure for storing GridMET data and obtain a list of relevant years based on the target date. This process ensures that the necessary directory exists for data storage and determines the appropriate years for data retrieval without delving into technical details." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1621c7c1", + "metadata": {}, + "outputs": [], + "source": [ + "def add_cumulative_column(df, column_name):\n", + " df[f'cumulative_{column_name}'] = df[column_name].sum()\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "id": "8baf6e29", + "metadata": {}, + "source": [ + "We add a cumulative column to a DataFrame, summing the values of the specified column and storing the result in a new column." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ef804f40", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_cumulative_history_csvs(target_date=test_start_date, force=False):\n", + " \"\"\"\n", + " Prepare cumulative history CSVs for a specified target date.\n", + "\n", + " Parameters:\n", + " - target_date (str, optional): The target date in the format 'YYYY-MM-DD'. Default is 'test_start_date'.\n", + " - force (bool, optional): If True, forcefully regenerate cumulative CSVs even if they already exist. Default is False.\n", + "\n", + " Returns:\n", + " None\n", + "\n", + " This function generates cumulative history CSVs for a specified target date. It traverses the date range from the past\n", + " October 1 to the target date, downloads gridmet data, converts it to CSV, and merges it into a big DataFrame.\n", + " The cumulative values are calculated and saved in new CSV files.\n", + "\n", + " Example:\n", + " ```python\n", + " prepare_cumulative_history_csvs(target_date='2023-01-01', force=True)\n", + " ```\n", + "\n", + " Note: This function assumes the existence of the following helper functions:\n", + " - download_gridmet_of_specific_variables\n", + " - prepare_folder_and_get_year_list\n", + " - turn_gridmet_nc_to_csv\n", + " - add_cumulative_column\n", + " - process_group_value_filling\n", + " ```\n", + "\n", + " selected_date = datetime.strptime(target_date, \"%Y-%m-%d\")\n", + " print(selected_date)\n", + " if selected_date.month < 10:\n", + " past_october_1 = datetime(selected_date.year - 1, 10, 1)\n", + " else:\n", + " past_october_1 = datetime(selected_date.year, 10, 1)\n", + "\n", + " # Rest of the function logic...\n", + "\n", + " filled_data = filled_data.loc[:, ['Latitude', 'Longitude', var_name, f'cumulative_{var_name}']]\n", + " print(\"new_df final shape: \", filled_data.head())\n", + " filled_data.to_csv(cumulative_target_path, index=False)\n", + " print(f\"new df is saved to {cumulative_target_path}\")\n", + " print(filled_data.describe())\n", + " ```\n", + "Note: This docstring includes placeholders such as \"download_gridmet_of_specific_variables\" and \"prepare_folder_and_get_year_list\" for the assumed existence of related helper functions. You should replace these placeholders with actual documentation for those functions.\n", + " \"\"\"\n", + " selected_date = datetime.strptime(target_date, \"%Y-%m-%d\")\n", + " print(selected_date)\n", + " if selected_date.month < 10:\n", + " past_october_1 = datetime(selected_date.year - 1, 10, 1)\n", + " else:\n", + " past_october_1 = datetime(selected_date.year, 10, 1)\n", + "\n", + " # Traverse and print every day from past October 1 to the specific date\n", + " current_date = past_october_1\n", + " \n", + " date_keyed_objects = {}\n", + " \n", + " download_gridmet_of_specific_variables(\n", + " prepare_folder_and_get_year_list(target_date=target_date)\n", + " )\n", + " \n", + " while current_date <= selected_date:\n", + " print(current_date.strftime('%Y-%m-%d'))\n", + " current_date_str = current_date.strftime('%Y-%m-%d')\n", + " \n", + " \n", + " generated_csvs = turn_gridmet_nc_to_csv(target_date=current_date_str)\n", + " \n", + " # read the csv into dataframe and merge to the big dataframe\n", + " date_keyed_objects[current_date_str] = generated_csvs\n", + " \n", + " current_date += timedelta(days=1)\n", + " \n", + " print(\"date_keyed_objects: \", date_keyed_objects)\n", + " target_generated_csvs = date_keyed_objects[target_date]\n", + " for index, single_csv in enumerate(target_generated_csvs):\n", + " # traverse the variables of gridmet here\n", + " # each variable is a loop\n", + " print(f\"creating cumulative for {single_csv}\")\n", + " \n", + " cumulative_target_path = f\"{single_csv}_cumulative.csv\"\n", + " print(\"cumulative_target_path = \", cumulative_target_path)\n", + " \n", + " if os.path.exists(cumulative_target_path) and not force:\n", + " print(f\"{cumulative_target_path} already exists, skipping..\")\n", + " continue\n", + " \n", + " # Extract the file name without extension\n", + " file_name = os.path.splitext(os.path.basename(single_csv))[0]\n", + " gap_filled_csv = f\"{cumulative_target_path}_gap_filled.csv\"\n", + "\n", + "\t# Split the file name using underscores\n", + " var_name = file_name.split('_')[1]\n", + " print(f\"Found variable name {var_name}\")\n", + " current_date = past_october_1\n", + " new_df = pd.read_csv(single_csv)\n", + " print(new_df.head())\n", + " \n", + " all_df = pd.read_csv(f\"{work_dir}/testing_output/{str(selected_date.year)}_{var_name}_{target_date}.csv\")\n", + " all_df[\"date\"] = target_date\n", + " all_df[var_name] = pd.to_numeric(all_df[var_name], errors='coerce')\n", + " filled_data = all_df\n", + " filled_data = filled_data[(filled_data['date'] == target_date)]\n", + " filled_data.fillna(0, inplace=True)\n", + " print(\"Finished correctly \", filled_data.head())\n", + " filled_data = filled_data[['Latitude', 'Longitude', \n", + " var_name, \n", + "# f'cumulative_{var_name}'\n", + " ]]\n", + " print(filled_data.shape)\n", + " filled_data.to_csv(cumulative_target_path, index=False)\n", + " print(f\"new df is saved to {cumulative_target_path}\")\n", + " print(filled_data.describe())\n" + ] + }, + { + "cell_type": "markdown", + "id": "aa482514", + "metadata": {}, + "source": [ + "We prepare cumulative history CSVs for a specified target date. We traverses the date range from the past October 1 to the target date, downloads GridMET data, converts it to CSV, and merges it into a big DataFrame. Cumulative values are then calculated and saved in new CSV files." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d202cb61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-03-25 00:00:00\n", + "The existing netcdf already covers the selected date. Avoid downloading..\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2023.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2024.nc exists\n", + "File /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2023.nc exists\n", + "2023-10-01\n", + "Checking file: vs_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_vs_2023-10-01.csv\n", + "Checking file: rmax_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_rmax_2023-10-01.csv\n", + "Checking file: pr_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_pr_2023-10-01.csv\n", + "Checking file: vpd_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_vpd_2023-10-01.csv\n", + "Checking file: tmmx_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_tmmx_2023-10-01.csv\n", + "Checking file: etr_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_etr_2023-10-01.csv\n", + "Checking file: rmin_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_rmin_2023-10-01.csv\n", + "Checking file: tmmn_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_tmmn_2023-10-01.csv\n", + "2023-10-02\n", + "Checking file: vs_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_vs_2023-10-02.csv\n", + "Checking file: rmax_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_rmax_2023-10-02.csv\n", + "Checking file: pr_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_pr_2023-10-02.csv\n", + "Checking file: vpd_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_vpd_2023-10-02.csv\n", + "Checking file: tmmx_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: /Users/meghana/gridmet_test_run/testing_output/2023_tmmx_2023-10-02.csv\n", + "Checking file: etr_2023.nc\n", + "Processing file: /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2023.nc\n", + "File /Users/meghana/gridmet_test_run/gridmet_to_dem_mapper.csv already exists, skipping..\n", + "gridmet var saved: 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'/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-12.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-12.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-12.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-12.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-12.csv'], '2024-03-13': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-13.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-13.csv'], '2024-03-14': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-14.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-14.csv'], '2024-03-15': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-15.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-15.csv'], '2024-03-16': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-16.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-16.csv'], '2024-03-17': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-17.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-17.csv'], '2024-03-18': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-18.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-18.csv'], '2024-03-19': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-19.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-19.csv'], '2024-03-20': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-20.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-20.csv'], '2024-03-21': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-21.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-21.csv'], '2024-03-22': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-22.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-22.csv'], '2024-03-23': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-23.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-23.csv'], '2024-03-24': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-24.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-24.csv'], '2024-03-25': ['/Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-25.csv', '/Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-25.csv']}\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-25.csv_cumulative.csv\n", + "Found variable name rmin\n", + " Latitude Longitude rmin\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude rmin date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_rmin_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude rmin\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 30.542576\n", + "std 6.921275 7.21226 24.346441\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 36.500000\n", + "75% 43.024000 -106.28000 48.900000\n", + "max 49.000000 -100.05200 100.000000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-25.csv_cumulative.csv\n", + "Found variable name tmmn\n", + " Latitude Longitude tmmn\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude tmmn date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_tmmn_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude tmmn\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 180.244224\n", + "std 6.921275 7.21226 126.083635\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 262.500000\n", + "75% 43.024000 -106.28000 271.200000\n", + "max 49.000000 -100.05200 287.200000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-25.csv_cumulative.csv\n", + "Found variable name etr\n", + " Latitude Longitude etr\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude etr date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_etr_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude etr\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 1.772937\n", + "std 6.921275 7.21226 1.928960\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 1.500000\n", + "75% 43.024000 -106.28000 2.600000\n", + "max 49.000000 -100.05200 12.600000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-25.csv_cumulative.csv\n", + "Found variable name tmmx\n", + " Latitude Longitude tmmx\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude tmmx date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_tmmx_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude tmmx\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 187.476548\n", + "std 6.921275 7.21226 131.144717\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 272.800000\n", + "75% 43.024000 -106.28000 281.500000\n", + "max 49.000000 -100.05200 303.600000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-25.csv_cumulative.csv\n", + "Found variable name vpd\n", + " Latitude Longitude vpd\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude vpd date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_vpd_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude vpd\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 0.209901\n", + "std 6.921275 7.21226 0.268816\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 0.140000\n", + "75% 43.024000 -106.28000 0.310000\n", + "max 49.000000 -100.05200 2.360000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-25.csv_cumulative.csv\n", + "Found variable name pr\n", + " Latitude Longitude pr\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude pr date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_pr_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude pr\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 0.703979\n", + "std 6.921275 7.21226 1.881217\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 0.000000\n", + "75% 43.024000 -106.28000 0.600000\n", + "max 49.000000 -100.05200 35.400000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-25.csv_cumulative.csv\n", + "Found variable name rmax\n", + " Latitude Longitude rmax\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude rmax date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_rmax_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude rmax\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 58.808541\n", + "std 6.921275 7.21226 42.405297\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 79.200000\n", + "75% 43.024000 -106.28000 96.900000\n", + "max 49.000000 -100.05200 100.000000\n", + "creating cumulative for /Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-25.csv\n", + "cumulative_target_path = /Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-25.csv_cumulative.csv\n", + "Found variable name vs\n", + " Latitude Longitude vs\n", + "0 49.0 -125.000 --\n", + "1 49.0 -124.964 --\n", + "2 49.0 -124.928 --\n", + "3 49.0 -124.892 --\n", + "4 49.0 -124.856 --\n", + "Finished correctly Latitude Longitude vs date\n", + "0 49.0 -125.000 0.0 2024-03-25\n", + "1 49.0 -124.964 0.0 2024-03-25\n", + "2 49.0 -124.928 0.0 2024-03-25\n", + "3 49.0 -124.892 0.0 2024-03-25\n", + "4 49.0 -124.856 0.0 2024-03-25\n", + "(462204, 3)\n", + "new df is saved to /Users/meghana/gridmet_test_run/testing_output/2024_vs_2024-03-25.csv_cumulative.csv\n", + " Latitude Longitude vs\n", + "count 462204.000000 462204.00000 462204.000000\n", + "mean 37.030000 -112.52600 2.838837\n", + "std 6.921275 7.21226 2.694575\n", + "min 25.060000 -125.00000 0.000000\n", + "25% 31.036000 -118.77200 0.000000\n", + "50% 37.030000 -112.52600 2.600000\n", + "75% 43.024000 -106.28000 4.500000\n", + "max 49.000000 -100.05200 12.800000\n" + ] + } + ], + "source": [ + "if __name__ == \"__main__\":\n", + " # Run the download function\n", + " # download_gridmet_of_specific_variables(prepare_folder_and_get_year_list())\n", + " # turn_gridmet_nc_to_csv()\n", + " # plot_gridmet()\n", + "\n", + " # prepare testing data with cumulative variables\n", + " prepare_cumulative_history_csvs(force=True)" + ] + }, + { + "cell_type": "markdown", + "id": "5c7e148e", + "metadata": {}, + "source": [ + "When we run the script directly (`__name__ == \"__main__\"`), our main focus is to ensure the preparation of cumulative history CSVs for testing data. By triggering the `prepare_cumulative_history_csvs` function with the `force` parameter set to True, we ensure that all necessary steps are taken to generate accurate cumulative values. Throughout this process, we manage tasks such as downloading GridMET data for specific variables, converting it into CSV format, and calculating cumulative values for each variable over the specified date range. The use of `force=True` ensures that the cumulative CSVs are regenerated if they already exist, mai" + ] + }, + { + "cell_type": "markdown", + "id": "68f2bea0", + "metadata": {}, + "source": [ + "# GridMET Climatology Data Retrieval and Analysis\n", + "\n", + "In this chapter, we do\n", + "- **Data Collection:** : The script fetches gridMET climatology data from a specified source for various meteorological variables (e.g., temperature, precipitation) and multiple years.It ensures that the data is downloaded for each variable and year required for analysis.\n", + "\n", + "- **Data Processing:** After downloading, the script extracts relevant data for specific geographical locations corresponding to weather stations. It organizes the data into structured formats (CSV files) for easier handling and analysis.\n", + "\n", + "- **Data Integration:** We merge similar variables obtained from different years into separate CSV files. We then combine all variables together into a single comprehensive dataset for further analysis and modeling tasks. By integrating data from various sources and time periods, it creates a unified dataset that can provide insights into long-term weather patterns and trends.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1a5713a0", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import urllib.request\n", + "from datetime import date, datetime\n", + "\n", + "import pandas as pd\n", + "import xarray as xr\n", + "from pathlib import Path\n", + "import warnings\n", + "\n", + "homedir = os.path.expanduser('~')\n", + "train_start_date = \"2018-01-03\"\n", + "train_end_date = \"2021-12-31\"\n", + "\n", + "work_dir = f\"{homedir}/gridmet_test_run\"" + ] + }, + { + "cell_type": "markdown", + "id": "aadb3626", + "metadata": {}, + "source": [ + "We import necessary modules and libraries for its operation. These imports bring in functionalities like interacting with the operating system, handling file paths, making URL requests, managing dates and times, manipulating data, working with multi-dimensional arrays, and handling file system paths." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "59c9d07f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Suppress FutureWarnings\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n", + "\n", + "start_date = datetime.strptime(train_start_date, \"%Y-%m-%d\")\n", + "end_date = datetime.strptime(train_end_date, \"%Y-%m-%d\")\n", + "\n", + "year_list = [start_date.year + i for i in range(end_date.year - start_date.year + 1)]\n", + "\n", + "working_dir = work_dir\n", + "#stations = pd.read_csv(f'{working_dir}/station_cell_mapping.csv')\n", + "stations = pd.read_csv(f\"{work_dir}/all_snotel_cdec_stations_active_in_westus.csv\")\n", + "gridmet_save_location = f'{working_dir}/gridmet_climatology'\n", + "final_merged_csv = f\"{work_dir}/training_all_active_snotel_station_list_elevation.csv_gridmet.csv\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "b72aace6", + "metadata": {}, + "source": [ + "### Explanation:\n", + "\n", + "In this code snippet, FutureWarnings are suppressed using `warnings.filterwarnings(\"ignore\", category=FutureWarning)`. Following that, the start and end dates for data processing are defined by converting the `train_start_date` and `train_end_date` strings into datetime objects. Subsequently, a list of years between the start and end dates is generated. The working directory path and file paths for SNOTEL stations data, GridMET climatology data storage, and the final merged CSV file are then set up accordingly. Overall, this snippet prepares the necessary parameters and file paths for subsequent data processing steps.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eb0bc340", + "metadata": {}, + "outputs": [], + "source": [ + "def get_files_in_directory():\n", + " f = list()\n", + " for files in glob.glob(gridmet_save_location + \"/*.nc\"):\n", + " f.append(files)\n", + " return f" + ] + }, + { + "cell_type": "markdown", + "id": "4670deb8", + "metadata": {}, + "source": [ + "We collect the names of files with the extension \".nc\" within a specified directory by iterating through all files, appending their names to a list, and returning the list." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e1ad571c", + "metadata": {}, + "outputs": [], + "source": [ + "def download_file(url, save_location):\n", + " try:\n", + " print(\"download_file\")\n", + " with urllib.request.urlopen(url) as response:\n", + " file_content = response.read()\n", + " file_name = os.path.basename(url)\n", + " save_path = os.path.join(save_location, file_name)\n", + " with open(save_path, 'wb') as file:\n", + " file.write(file_content)\n", + " print(f\"File downloaded successfully and saved as: {save_path}\")\n", + " except Exception as e:\n", + " print(f\"An error occurred while downloading the file: {str(e)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "87896c19", + "metadata": {}, + "source": [ + "We attempt to download a file from a specified URL. We then save the downloaded file to a specified location." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "45bab2c5", + "metadata": {}, + "outputs": [], + "source": [ + "def download_gridmet_climatology():\n", + " folder_name = gridmet_save_location\n", + " if not os.path.exists(folder_name):\n", + " os.makedirs(folder_name)\n", + "\n", + " base_metadata_url = \"http://www.northwestknowledge.net/metdata/data/\"\n", + " variables_list = ['tmmn', 'tmmx', 'pr', 'vpd', 'etr', 'rmax', 'rmin', 'vs']\n", + "\n", + " for var in variables_list:\n", + " for y in year_list:\n", + " download_link = base_metadata_url + var + '_' + '%s' % y + '.nc'\n", + " print(\"downloading\", download_link)\n", + " if not os.path.exists(os.path.join(folder_name, var + '_' + '%s' % y + '.nc')):\n", + " download_file(download_link, folder_name)" + ] + }, + { + "cell_type": "markdown", + "id": "9697fb51", + "metadata": {}, + "source": [ + "We set up a folder to store data. Then, we gather GridMET climatology data for a range of variables over multiple years. This involves collecting information on temperature, precipitation, vapor pressure deficit, reference evapotranspiration, maximum and minimum radiation, and wind speed. We ensure that the data for each variable and year combination is acquired and stored for further analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3b4e75e6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_gridmet_variable(file_name):\n", + " print(f\"reading values from {file_name}\")\n", + " result_data = []\n", + " ds = xr.open_dataset(file_name)\n", + " var_to_extract = list(ds.keys())\n", + " print(var_to_extract)\n", + " var_name = var_to_extract[0]\n", + " \n", + " df = pd.DataFrame(columns=['day', 'lat', 'lon', var_name])\n", + " \n", + " csv_file = f'{gridmet_save_location}/{Path(file_name).stem}.csv'\n", + " if os.path.exists(csv_file):\n", + " \tprint(f\"The file '{csv_file}' exists.\")\n", + " \treturn\n", + "\n", + " for idx, row in stations.iterrows():\n", + " lat = row['latitude']\n", + " lon = row['longitude']\n", + "\t\t\n", + " subset_data = ds.sel(lat=lat, lon=lon, method='nearest')\n", + " subset_data['lat'] = lat\n", + " subset_data['lon'] = lon\n", + " # print('subset data:', lat, lon, subset_data.values())\n", + " converted_df = subset_data.to_dataframe()\n", + " #print(\"converted_df: \", converted_df.head())\n", + " #print(\"converted_df columns: \", converted_df.columns)\n", + " converted_df = converted_df.reset_index(drop=False)\n", + " #print(\"convert to columns: \", converted_df.columns)\n", + " converted_df = converted_df.drop('crs', axis=1)\n", + " df = pd.concat([df, converted_df], ignore_index=True)\n", + " \n", + " result_df = df\n", + " print(\"got result_df : \", result_df.head())\n", + " result_df.to_csv(csv_file, index=False)\n", + " print(f'completed extracting data for {file_name}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "a20501ee", + "metadata": {}, + "source": [ + "We retrieve data from a specific GridMET file, capturing information relevant to latitude and longitude coordinates provided in a dataset. Subsequently, we organize this data into a structured format, ensuring each entry corresponds to a specific day, latitude, and longitude, along with the associated variable values. Following this organization, we save this processed data as a CSV file, providing a convenient and accessible format for further analysis or visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5798ad94", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def merge_similar_variables_from_different_years():\n", + " files = os.listdir(gridmet_save_location)\n", + " file_groups = {}\n", + "\n", + " for filename in files:\n", + " base_name, year_ext = os.path.splitext(filename)\n", + " parts = base_name.split('_')\n", + " if len(parts) == 2 and year_ext == '.csv':\n", + " file_groups.setdefault(parts[0], []).append(filename)\n", + "\n", + " for base_name, file_list in file_groups.items():\n", + " if len(file_list) > 1:\n", + " dfs = []\n", + " for filename in file_list:\n", + " df = pd.read_csv(os.path.join(gridmet_save_location, filename))\n", + " dfs.append(df)\n", + " merged_df = pd.concat(dfs, ignore_index=True)\n", + " merged_filename = f\"{base_name}_merged.csv\"\n", + " merged_df.to_csv(os.path.join(gridmet_save_location, merged_filename), index=False)\n", + " print(f\"Merged {file_list} into {merged_filename}\")" + ] + }, + { + "cell_type": "markdown", + "id": "bc7f03a0", + "metadata": {}, + "source": [ + "We collect list of files from a designated location. Then, we organize these files into groups based on similarities in their names. For each group of similar files, if there are multiple files present, we proceed to read each file as a DataFrame. Subsequently, we merge these DataFrames into a single comprehensive DataFrame. Following this merging process, we save the resulting merged DataFrame into a new CSV file. Finally, we print a notification message to indicate which files have been successfully merged. Through these steps, the function facilitates the consolidation of related data from different files into cohesive datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "35fff41c", + "metadata": {}, + "outputs": [], + "source": [ + "def merge_all_variables_together():\n", + " merged_df = None\n", + " file_paths = []\n", + "\n", + " for filename in os.listdir(gridmet_save_location):\n", + " if filename.endswith(\"_merged.csv\"):\n", + " file_paths.append(os.path.join(gridmet_save_location, filename))\n", + "\t\n", + " rmin_merged_path = os.path.join(gridmet_save_location, 'rmin_merged.csv')\n", + " rmax_merged_path = os.path.join(gridmet_save_location, 'rmax_merged.csv')\n", + " tmmn_merged_path = os.path.join(gridmet_save_location, 'tmmn_merged.csv')\n", + " tmmx_merged_path = os.path.join(gridmet_save_location, 'tmmx_merged.csv')\n", + " \n", + " df_rmin = pd.read_csv(rmin_merged_path)\n", + " df_rmax = pd.read_csv(rmax_merged_path)\n", + " df_tmmn = pd.read_csv(tmmn_merged_path)\n", + " df_tmmx = pd.read_csv(tmmx_merged_path)\n", + " \n", + " df_rmin.rename(columns={'relative_humidity': 'relative_humidity_rmin'}, inplace=True)\n", + " df_rmax.rename(columns={'relative_humidity': 'relative_humidity_rmax'}, inplace=True)\n", + " df_tmmn.rename(columns={'air_temperature': 'air_temperature_tmmn'}, inplace=True)\n", + " df_tmmx.rename(columns={'air_temperature': 'air_temperature_tmmx'}, inplace=True)\n", + " \n", + " df_rmin.to_csv(os.path.join(gridmet_save_location, 'rmin_merged.csv'))\n", + " df_rmax.to_csv(os.path.join(gridmet_save_location, 'rmax_merged.csv'))\n", + " df_tmmn.to_csv(os.path.join(gridmet_save_location, 'tmmn_merged.csv'))\n", + " df_tmmx.to_csv(os.path.join(gridmet_save_location, 'tmmx_merged.csv'))\n", + " \n", + " if file_paths:\n", + " merged_df = pd.read_csv(file_paths[0])\n", + " for file_path in file_paths[1:]:\n", + " df = pd.read_csv(file_path)\n", + " merged_df = pd.concat([merged_df, df], axis=1)\n", + " merged_df = merged_df.loc[:, ~merged_df.columns.duplicated()]\n", + " merged_df.to_csv(final_merged_csv, index=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "999d5177", + "metadata": {}, + "source": [ + "We identify and collect CSV files with specific names from a designated location. Then, we proceed to read each of these CSV files, containing data for different variables, into separate DataFrames. Subsequently, we rename specific columns within each DataFrame to ensure clarity and consistency across variables. After updating column names, we overwrite the original CSV files with the renamed versions for consistency. Next, if there are multiple CSV files available, we merge them together into a single comprehensive DataFrame. To avoid redundancy and ensure data integrity, we remove any duplicated columns in the merged DataFrame. Finally, we save the merged DataFrame as a new CSV file, representing the combined dataset encompassing all variables. Through these steps, the function facilitates the integration of data from various sources into a unified dataset for comprehensive analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "994bdbb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading http://www.northwestknowledge.net/metdata/data/tmmn_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmn_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmn_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmn_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmx_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmx_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmx_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/tmmx_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/pr_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/pr_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/pr_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/pr_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vpd_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vpd_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vpd_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vpd_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/etr_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/etr_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/etr_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/etr_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmax_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmax_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmax_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmax_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmin_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmin_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmin_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/rmin_2021.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vs_2018.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vs_2019.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vs_2020.nc\n", + "downloading http://www.northwestknowledge.net/metdata/data/vs_2021.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2021.nc\n", + "['relative_humidity']\n", + "The file '/Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2021.csv' exists.\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2021.nc\n", + "['air_temperature']\n", + "The file '/Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2021.csv' exists.\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2018.nc\n", + "['mean_vapor_pressure_deficit']\n", + "got result_df : day lat lon mean_vapor_pressure_deficit\n", + "0 2018-01-01 39.955 -120.538 0.68\n", + "1 2018-01-02 39.955 -120.538 0.45\n", + "2 2018-01-03 39.955 -120.538 0.49\n", + "3 2018-01-04 39.955 -120.538 0.25\n", + "4 2018-01-05 39.955 -120.538 0.23\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2023.nc\n", + "['wind_speed']\n", + "got result_df : day lat lon wind_speed\n", + "0 2023-01-01 39.955 -120.538 5.0\n", + "1 2023-01-02 39.955 -120.538 2.3\n", + "2 2023-01-03 39.955 -120.538 2.5\n", + "3 2023-01-04 39.955 -120.538 4.3\n", + "4 2023-01-05 39.955 -120.538 5.6\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2019.nc\n", + "['precipitation_amount']\n", + "got result_df : day lat lon precipitation_amount\n", + "0 2019-01-01 39.955 -120.538 0.0\n", + "1 2019-01-02 39.955 -120.538 0.0\n", + "2 2019-01-03 39.955 -120.538 0.0\n", + "3 2019-01-04 39.955 -120.538 0.0\n", + "4 2019-01-05 39.955 -120.538 13.2\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2021.nc\n", + "['potential_evapotranspiration']\n", + "got result_df : day lat lon potential_evapotranspiration\n", + "0 2021-01-01 39.955 -120.538 1.4\n", + "1 2021-01-02 39.955 -120.538 1.4\n", + "2 2021-01-03 39.955 -120.538 1.5\n", + "3 2021-01-04 39.955 -120.538 1.4\n", + "4 2021-01-05 39.955 -120.538 1.5\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2021.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2024.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2024-01-01 39.955 -120.538 42.500000\n", + "1 2024-01-02 39.955 -120.538 51.400002\n", + "2 2024-01-03 39.955 -120.538 75.700005\n", + "3 2024-01-04 39.955 -120.538 43.400002\n", + "4 2024-01-05 39.955 -120.538 44.799999\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2018.nc\n", + "['precipitation_amount']\n", + "got result_df : day lat lon precipitation_amount\n", + "0 2018-01-01 39.955 -120.538 0.0\n", + "1 2018-01-02 39.955 -120.538 0.0\n", + "2 2018-01-03 39.955 -120.538 4.5\n", + "3 2018-01-04 39.955 -120.538 3.2\n", + "4 2018-01-05 39.955 -120.538 28.5\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2023.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2023-01-01 39.955 -120.538 100.000000\n", + "1 2023-01-02 39.955 -120.538 91.300003\n", + "2 2023-01-03 39.955 -120.538 99.900002\n", + "3 2023-01-04 39.955 -120.538 81.000000\n", + "4 2023-01-05 39.955 -120.538 95.300003\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2024.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2024-01-01 39.955 -120.538 268.000000\n", + "1 2024-01-02 39.955 -120.538 266.799988\n", + "2 2024-01-03 39.955 -120.538 266.799988\n", + "3 2024-01-04 39.955 -120.538 266.799988\n", + "4 2024-01-05 39.955 -120.538 267.100006\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2020.nc\n", + "['potential_evapotranspiration']\n", + "got result_df : day lat lon potential_evapotranspiration\n", + "0 2020-01-01 39.955 -120.538 1.8\n", + "1 2020-01-02 39.955 -120.538 1.2\n", + "2 2020-01-03 39.955 -120.538 2.4\n", + "3 2020-01-04 39.955 -120.538 1.7\n", + "4 2020-01-05 39.955 -120.538 1.5\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2020.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2020-01-01 39.955 -120.538 50.900002\n", + "1 2020-01-02 39.955 -120.538 36.900002\n", + "2 2020-01-03 39.955 -120.538 24.100000\n", + "3 2020-01-04 39.955 -120.538 51.100002\n", + "4 2020-01-05 39.955 -120.538 48.400002\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2020.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2020-01-01 39.955 -120.538 269.700012\n", + "1 2020-01-02 39.955 -120.538 269.200012\n", + "2 2020-01-03 39.955 -120.538 270.500000\n", + "3 2020-01-04 39.955 -120.538 270.899994\n", + "4 2020-01-05 39.955 -120.538 266.700012\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2024.nc\n", + "['potential_evapotranspiration']\n", + "got result_df : day lat lon potential_evapotranspiration\n", + "0 2024-01-01 39.955 -120.538 1.0\n", + "1 2024-01-02 39.955 -120.538 1.2\n", + "2 2024-01-03 39.955 -120.538 0.7\n", + "3 2024-01-04 39.955 -120.538 1.0\n", + "4 2024-01-05 39.955 -120.538 1.1\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2019.nc\n", + "['mean_vapor_pressure_deficit']\n", + "got result_df : day lat lon mean_vapor_pressure_deficit\n", + "0 2019-01-01 39.955 -120.538 0.22\n", + "1 2019-01-02 39.955 -120.538 0.37\n", + "2 2019-01-03 39.955 -120.538 0.49\n", + "3 2019-01-04 39.955 -120.538 0.44\n", + "4 2019-01-05 39.955 -120.538 0.25\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2023.nc\n", + "['precipitation_amount']\n", + "got result_df : day lat lon precipitation_amount\n", + "0 2023-01-01 39.955 -120.538 0.300000\n", + "1 2023-01-02 39.955 -120.538 0.300000\n", + "2 2023-01-03 39.955 -120.538 0.800000\n", + "3 2023-01-04 39.955 -120.538 31.700001\n", + "4 2023-01-05 39.955 -120.538 20.300001\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2018.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2018-01-01 39.955 -120.538 61.600002\n", + "1 2018-01-02 39.955 -120.538 73.099998\n", + "2 2018-01-03 39.955 -120.538 72.200005\n", + "3 2018-01-04 39.955 -120.538 82.200005\n", + "4 2018-01-05 39.955 -120.538 80.500000\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2019.nc\n", + "['wind_speed']\n", + "got result_df : day lat lon wind_speed\n", + "0 2019-01-01 39.955 -120.538 4.8\n", + "1 2019-01-02 39.955 -120.538 3.6\n", + "2 2019-01-03 39.955 -120.538 1.5\n", + "3 2019-01-04 39.955 -120.538 1.7\n", + "4 2019-01-05 39.955 -120.538 5.7\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2021.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2021-01-01 39.955 -120.538 278.100006\n", + "1 2021-01-02 39.955 -120.538 276.600006\n", + "2 2021-01-03 39.955 -120.538 277.500000\n", + "3 2021-01-04 39.955 -120.538 276.299988\n", + "4 2021-01-05 39.955 -120.538 279.100006\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2021.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2020.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2020-01-01 39.955 -120.538 280.200012\n", + "1 2020-01-02 39.955 -120.538 281.000000\n", + "2 2020-01-03 39.955 -120.538 285.500000\n", + "3 2020-01-04 39.955 -120.538 278.200012\n", + "4 2020-01-05 39.955 -120.538 276.399994\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2023.nc\n", + "['mean_vapor_pressure_deficit']\n", + "got result_df : day lat lon mean_vapor_pressure_deficit\n", + "0 2023-01-01 39.955 -120.538 0.14\n", + "1 2023-01-02 39.955 -120.538 0.13\n", + "2 2023-01-03 39.955 -120.538 0.12\n", + "3 2023-01-04 39.955 -120.538 0.22\n", + "4 2023-01-05 39.955 -120.538 0.10\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2019.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2019-01-01 39.955 -120.538 73.000000\n", + "1 2019-01-02 39.955 -120.538 67.900002\n", + "2 2019-01-03 39.955 -120.538 59.600002\n", + "3 2019-01-04 39.955 -120.538 65.500000\n", + "4 2019-01-05 39.955 -120.538 85.200005\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2018.nc\n", + "['wind_speed']\n", + "got result_df : day lat lon wind_speed\n", + "0 2018-01-01 39.955 -120.538 1.8\n", + "1 2018-01-02 39.955 -120.538 3.1\n", + "2 2018-01-03 39.955 -120.538 1.9\n", + "3 2018-01-04 39.955 -120.538 2.8\n", + "4 2018-01-05 39.955 -120.538 4.0\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2024.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2024-01-01 39.955 -120.538 279.299988\n", + "1 2024-01-02 39.955 -120.538 276.399994\n", + "2 2024-01-03 39.955 -120.538 272.600006\n", + "3 2024-01-04 39.955 -120.538 277.299988\n", + "4 2024-01-05 39.955 -120.538 276.399994\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2019.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2019-01-01 39.955 -120.538 35.000000\n", + "1 2019-01-02 39.955 -120.538 27.400000\n", + "2 2019-01-03 39.955 -120.538 26.500000\n", + "3 2019-01-04 39.955 -120.538 30.800001\n", + "4 2019-01-05 39.955 -120.538 46.799999\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2020.nc\n", + "['mean_vapor_pressure_deficit']\n", + "got result_df : day lat lon mean_vapor_pressure_deficit\n", + "0 2020-01-01 39.955 -120.538 0.26\n", + "1 2020-01-02 39.955 -120.538 0.39\n", + "2 2020-01-03 39.955 -120.538 0.65\n", + "3 2020-01-04 39.955 -120.538 0.28\n", + "4 2020-01-05 39.955 -120.538 0.23\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2023.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2023-01-01 39.955 -120.538 273.899994\n", + "1 2023-01-02 39.955 -120.538 271.399994\n", + "2 2023-01-03 39.955 -120.538 274.100006\n", + "3 2023-01-04 39.955 -120.538 277.299988\n", + "4 2023-01-05 39.955 -120.538 275.600006\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2019.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2019-01-01 39.955 -120.538 260.600006\n", + "1 2019-01-02 39.955 -120.538 263.899994\n", + "2 2019-01-03 39.955 -120.538 267.899994\n", + "3 2019-01-04 39.955 -120.538 268.299988\n", + "4 2019-01-05 39.955 -120.538 267.600006\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2021.nc\n", + "['precipitation_amount']\n", + "got result_df : day lat lon precipitation_amount\n", + "0 2021-01-01 39.955 -120.538 0.0\n", + "1 2021-01-02 39.955 -120.538 0.5\n", + "2 2021-01-03 39.955 -120.538 1.3\n", + "3 2021-01-04 39.955 -120.538 20.4\n", + "4 2021-01-05 39.955 -120.538 0.0\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2021.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2024.nc\n", + "['mean_vapor_pressure_deficit']\n", + "got result_df : day lat lon mean_vapor_pressure_deficit\n", + "0 2024-01-01 39.955 -120.538 0.31\n", + "1 2024-01-02 39.955 -120.538 0.20\n", + "2 2024-01-03 39.955 -120.538 0.06\n", + "3 2024-01-04 39.955 -120.538 0.27\n", + "4 2024-01-05 39.955 -120.538 0.26\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2019.nc\n", + "['potential_evapotranspiration']\n", + "got result_df : day lat lon potential_evapotranspiration\n", + "0 2019-01-01 39.955 -120.538 1.8\n", + "1 2019-01-02 39.955 -120.538 2.3\n", + "2 2019-01-03 39.955 -120.538 1.6\n", + "3 2019-01-04 39.955 -120.538 1.6\n", + "4 2019-01-05 39.955 -120.538 2.1\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2020.nc\n", + "['precipitation_amount']\n", + "got result_df : day lat lon precipitation_amount\n", + "0 2020-01-01 39.955 -120.538 0.0\n", + "1 2020-01-02 39.955 -120.538 0.0\n", + "2 2020-01-03 39.955 -120.538 0.0\n", + "3 2020-01-04 39.955 -120.538 0.8\n", + "4 2020-01-05 39.955 -120.538 0.0\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2018.nc\n", + "['potential_evapotranspiration']\n", + "got result_df : day lat lon potential_evapotranspiration\n", + "0 2018-01-01 39.955 -120.538 2.2\n", + "1 2018-01-02 39.955 -120.538 2.3\n", + "2 2018-01-03 39.955 -120.538 1.8\n", + "3 2018-01-04 39.955 -120.538 1.4\n", + "4 2018-01-05 39.955 -120.538 1.6\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2024.nc\n", + "['precipitation_amount']\n", + "got result_df : day lat lon precipitation_amount\n", + "0 2024-01-01 39.955 -120.538 0.5\n", + "1 2024-01-02 39.955 -120.538 12.1\n", + "2 2024-01-03 39.955 -120.538 1.6\n", + "3 2024-01-04 39.955 -120.538 0.0\n", + "4 2024-01-05 39.955 -120.538 0.0\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/pr_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2018.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2018-01-01 39.955 -120.538 24.600000\n", + "1 2018-01-02 39.955 -120.538 35.200001\n", + "2 2018-01-03 39.955 -120.538 36.200001\n", + "3 2018-01-04 39.955 -120.538 62.600002\n", + "4 2018-01-05 39.955 -120.538 65.200005\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2021.nc\n", + "['mean_vapor_pressure_deficit']\n", + "got result_df : day lat lon mean_vapor_pressure_deficit\n", + "0 2021-01-01 39.955 -120.538 0.23\n", + "1 2021-01-02 39.955 -120.538 0.22\n", + "2 2021-01-03 39.955 -120.538 0.23\n", + "3 2021-01-04 39.955 -120.538 0.14\n", + "4 2021-01-05 39.955 -120.538 0.32\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vpd_2021.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2018.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2018-01-01 39.955 -120.538 271.000000\n", + "1 2018-01-02 39.955 -120.538 270.100006\n", + "2 2018-01-03 39.955 -120.538 271.899994\n", + "3 2018-01-04 39.955 -120.538 274.600006\n", + "4 2018-01-05 39.955 -120.538 274.799988\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2021.nc\n", + "['wind_speed']\n", + "got result_df : day lat lon wind_speed\n", + "0 2021-01-01 39.955 -120.538 3.0\n", + "1 2021-01-02 39.955 -120.538 3.5\n", + "2 2021-01-03 39.955 -120.538 3.4\n", + "3 2021-01-04 39.955 -120.538 6.5\n", + "4 2021-01-05 39.955 -120.538 2.3\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2021.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2020.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2020-01-01 39.955 -120.538 93.000000\n", + "1 2020-01-02 39.955 -120.538 73.599998\n", + "2 2020-01-03 39.955 -120.538 58.600002\n", + "3 2020-01-04 39.955 -120.538 74.599998\n", + "4 2020-01-05 39.955 -120.538 84.800003\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2023.nc\n", + "['potential_evapotranspiration']\n", + "got result_df : day lat lon potential_evapotranspiration\n", + "0 2023-01-01 39.955 -120.538 1.2\n", + "1 2023-01-02 39.955 -120.538 0.8\n", + "2 2023-01-03 39.955 -120.538 0.8\n", + "3 2023-01-04 39.955 -120.538 1.6\n", + "4 2023-01-05 39.955 -120.538 1.0\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/etr_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2023.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2023-01-01 39.955 -120.538 53.200001\n", + "1 2023-01-02 39.955 -120.538 53.700001\n", + "2 2023-01-03 39.955 -120.538 63.100002\n", + "3 2023-01-04 39.955 -120.538 56.600002\n", + "4 2023-01-05 39.955 -120.538 73.900002\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmin_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2024.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2024-01-01 39.955 -120.538 82.700005\n", + "1 2024-01-02 39.955 -120.538 89.500000\n", + "2 2024-01-03 39.955 -120.538 100.000000\n", + "3 2024-01-04 39.955 -120.538 80.400002\n", + "4 2024-01-05 39.955 -120.538 76.200005\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2023.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2023-01-01 39.955 -120.538 262.600006\n", + "1 2023-01-02 39.955 -120.538 262.399994\n", + "2 2023-01-03 39.955 -120.538 265.899994\n", + "3 2023-01-04 39.955 -120.538 270.299988\n", + "4 2023-01-05 39.955 -120.538 270.100006\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmn_2023.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2019.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2019-01-01 39.955 -120.538 272.299988\n", + "1 2019-01-02 39.955 -120.538 278.399994\n", + "2 2019-01-03 39.955 -120.538 281.399994\n", + "3 2019-01-04 39.955 -120.538 281.000000\n", + "4 2019-01-05 39.955 -120.538 277.899994\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2019.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2024.nc\n", + "['wind_speed']\n", + "got result_df : day lat lon wind_speed\n", + "0 2024-01-01 39.955 -120.538 1.3\n", + "1 2024-01-02 39.955 -120.538 2.8\n", + "2 2024-01-03 39.955 -120.538 4.1\n", + "3 2024-01-04 39.955 -120.538 1.4\n", + "4 2024-01-05 39.955 -120.538 1.8\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2024.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2018.nc\n", + "['air_temperature']\n", + "got result_df : day lat lon air_temperature\n", + "0 2018-01-01 39.955 -120.538 286.500000\n", + "1 2018-01-02 39.955 -120.538 282.600006\n", + "2 2018-01-03 39.955 -120.538 283.899994\n", + "3 2018-01-04 39.955 -120.538 280.500000\n", + "4 2018-01-05 39.955 -120.538 279.799988\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/tmmx_2018.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2020.nc\n", + "['wind_speed']\n", + "got result_df : day lat lon wind_speed\n", + "0 2020-01-01 39.955 -120.538 4.5\n", + "1 2020-01-02 39.955 -120.538 1.2\n", + "2 2020-01-03 39.955 -120.538 2.2\n", + "3 2020-01-04 39.955 -120.538 3.6\n", + "4 2020-01-05 39.955 -120.538 3.6\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/vs_2020.nc\n", + "reading values from /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2021.nc\n", + "['relative_humidity']\n", + "got result_df : day lat lon relative_humidity\n", + "0 2021-01-01 39.955 -120.538 100.000000\n", + "1 2021-01-02 39.955 -120.538 74.300003\n", + "2 2021-01-03 39.955 -120.538 76.200005\n", + "3 2021-01-04 39.955 -120.538 89.700005\n", + "4 2021-01-05 39.955 -120.538 78.099998\n", + "completed extracting data for /Users/meghana/gridmet_test_run/gridmet_climatology/rmax_2021.nc\n", + "Merged ['tmmn_2019.csv', 'tmmn_2018.csv', 'tmmn_2024.csv', 'tmmn_2023.csv', 'tmmn_2020.csv', 'tmmn_2021.csv'] into tmmn_merged.csv\n", + "Merged ['pr_2024.csv', 'pr_2018.csv', 'pr_2019.csv', 'pr_2023.csv', 'pr_2021.csv', 'pr_2020.csv'] into pr_merged.csv\n", + "Merged ['rmax_2019.csv', 'rmax_2024.csv', 'rmax_2018.csv', 'rmax_2023.csv', 'rmax_2020.csv', 'rmax_2021.csv'] into rmax_merged.csv\n", + "Merged ['vs_2023.csv', 'vs_2020.csv', 'vs_2021.csv', 'vs_2019.csv', 'vs_2018.csv', 'vs_2024.csv'] into vs_merged.csv\n", + "Merged ['tmmx_2018.csv', 'tmmx_2024.csv', 'tmmx_2019.csv', 'tmmx_2021.csv', 'tmmx_2020.csv', 'tmmx_2023.csv'] into tmmx_merged.csv\n", + "Merged ['vpd_2020.csv', 'vpd_2021.csv', 'vpd_2023.csv', 'vpd_2019.csv', 'vpd_2024.csv', 'vpd_2018.csv'] into vpd_merged.csv\n", + "Merged ['rmin_2023.csv', 'rmin_2021.csv', 'rmin_2020.csv', 'rmin_2018.csv', 'rmin_2024.csv', 'rmin_2019.csv'] into rmin_merged.csv\n", + "Merged ['etr_2019.csv', 'etr_2018.csv', 'etr_2024.csv', 'etr_2020.csv', 'etr_2021.csv', 'etr_2023.csv'] into etr_merged.csv\n" + ] + } + ], + "source": [ + "if __name__ == \"__main__\":\n", + " \n", + " download_gridmet_climatology()\n", + " \n", + " nc_files = get_files_in_directory()\n", + " for nc in nc_files:\n", + " get_gridmet_variable(nc)\n", + " \n", + " merge_similar_variables_from_different_years()\n", + " merge_all_variables_together()" + ] + }, + { + "cell_type": "markdown", + "id": "71256ac1", + "metadata": {}, + "source": [ + "We start by checking if our script is being executed directly. If it is, we proceed to download data related to GridMET climatology. After that, we retrieve a list of files from a directory, likely containing data files. Next, we iterate through each file, processing them to extract GridMET variables. Following that, we merge similar variables obtained from different years, presumably to create a comprehensive dataset. Lastly, we merge all the variables together, possibly creating a unified dataset. These actions collectively aim to handle and organize GridMET climatology data." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.11.5" } }, "nbformat": 4, From 52b85a385fa34b278e764785ea63c1fb57c2ac0d Mon Sep 17 00:00:00 2001 From: iammeghana Date: Fri, 29 Mar 2024 18:28:36 -0400 Subject: [PATCH 17/21] ignore list --- .github/workflows/spellcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 44f23ac..d7fc400 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -56,6 +56,6 @@ def spell_check_directory(directory, ignore_words): "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", - "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe" + "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", 'daily', 'monitoring', "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", 'climate', "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", 'snowtel', "opendap", "popup", "data", "wget" ] spell_check_directory('.', ignore_list) From 73166c0c42ecbfb95b125dd0a919566f1e342f09 Mon Sep 17 00:00:00 2001 From: iammeghana Date: Fri, 29 Mar 2024 18:35:09 -0400 Subject: [PATCH 18/21] ignore words --- .github/workflows/spellcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index d7fc400..6c9b5ac 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -56,6 +56,6 @@ def spell_check_directory(directory, ignore_words): "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", - "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", 'daily', 'monitoring', "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", 'climate', "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", 'snowtel', "opendap", "popup", "data", "wget" + "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", 'daily', 'monitoring', "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", 'climate', "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", 'snowtel', "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader" ] spell_check_directory('.', ignore_list) From 6efc63a28ae74f020c668c49ef896e67144f36ce Mon Sep 17 00:00:00 2001 From: iammeghana Date: Fri, 29 Mar 2024 18:40:18 -0400 Subject: [PATCH 19/21] ignore words --- .github/workflows/spellcheck.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 6c9b5ac..dd5af32 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -56,6 +56,7 @@ def spell_check_directory(directory, ignore_words): "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", - "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", 'daily', 'monitoring', "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", 'climate', "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", 'snowtel', "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader" + "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", 'daily', 'monitoring', "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", 'climate', "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", 'snowtel', "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader", + "Daily", "scan", "climate repository", "https://www.northwestknowledgenet.metdata/data", "data", "element", "SNOTELmonitoring", "cumulative", "climate", "repository" ] spell_check_directory('.', ignore_list) From 26ddcdef60d04f359b245a385fdd68daed8e6276 Mon Sep 17 00:00:00 2001 From: iammeghana Date: Fri, 29 Mar 2024 18:48:26 -0400 Subject: [PATCH 20/21] ignore words --- .github/workflows/spellcheck.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index dd5af32..53ced60 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -56,7 +56,7 @@ def spell_check_directory(directory, ignore_words): "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", - "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", 'daily', 'monitoring', "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", 'climate', "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", 'snowtel', "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader", - "Daily", "scan", "climate repository", "https://www.northwestknowledgenet.metdata/data", "data", "element", "SNOTELmonitoring", "cumulative", "climate", "repository" + "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", "daily", "monitoring", "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", "climate", "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", "snowtel", "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader", + "Daily", "scan", "climate repository", "https://www.northwestknowledgenet.metdata/data", "data", "element", "SNOTELmonitoring", "cumulative", "climate", "repository", "repositoryhttpswwwnorthwestknowledgenetmetdatadata" ] spell_check_directory('.', ignore_list) From e40b00b1cf4e1466f82107f351f33f0335babeed Mon Sep 17 00:00:00 2001 From: iammeghana Date: Fri, 29 Mar 2024 19:05:00 -0400 Subject: [PATCH 21/21] ignore words --- .github/workflows/spellcheck.py | 7 +++---- book/chapters/datasource.ipynb | 4 ++-- book/chapters/gridmet.ipynb | 2 +- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/.github/workflows/spellcheck.py b/.github/workflows/spellcheck.py index 53ced60..5a6cb7a 100644 --- a/.github/workflows/spellcheck.py +++ b/.github/workflows/spellcheck.py @@ -56,7 +56,6 @@ def spell_check_directory(directory, ignore_words): "nsidc", "realtime", "satellitebased", "amsre", "level1a", "websitehttpswwwclimatologylaborggridmethtml", "124th", "cryospheric", "american", "timestamped", "geolocation", "nrcs", "satellitederived", "awdn", "1000m", "snowmelt", "nasas", "amsr2", "dataset", "gridmet", "hightech", "youll", "cryosphere", "µm", - "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", "daily", "monitoring", "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", 'element', "thredds", "climate", "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", "snowtel", "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader", - "Daily", "scan", "climate repository", "https://www.northwestknowledgenet.metdata/data", "data", "element", "SNOTELmonitoring", "cumulative", "climate", "repository", "repositoryhttpswwwnorthwestknowledgenetmetdatadata" - ] - spell_check_directory('.', ignore_list) + "250m", "highspatial", "hydroclimatic", "fsca", "workflow", "swe", "eg", "snowtel", "gportalhttpsgportaljaxajpgpr", 'scan', "gdp", "scansnowtel", "geo", "aoi", "climateengineorghttpclimateengineorg", "daily", "monitoring", "youre", "checkbox", "nasa", "toolhttpsclimatenorthwestknowledgenetmacagdpphp", "v6", "element", "thredds", "climate", "xy", "dropdown", "websitehttpwwwwccnrcsusdagovnwccinventory", "websitehttpsearthexplorerusgsgov", "havent", "wgethttpswwwclimatologylaborgwgetgridmethtml", "lpdaac", "mod09ga", "earthdata", "nasahttpsursearthdatanasagov", "shapefile", "statecounty", "gcomw", "dont", "snowtel", "opendap", "popup", "data", "wget", "categoryfuturewarning", "preprocess", "colormapped", "doesnt", "trainstartdate", "csvs", "png", "functionalities", "tuples", "warningsfilterwarningsignore", "urllib", "yearsit", "userdefined", "datetime", "dataframe", "preparecumulativehistorycsvs", "dataframes", "userspecified", "datan", "1st", "url", "netcdf4", "mai", "matplotlib", "dem", "futurewarnings", "nc", "forcetrue", "timesensitive", "trainenddate", "cumulative", "downloader", + "Daily", "scan", "climate repository", "https://www.northwestknowledgenet.metdata/data", "data", "Element", "SNOTELmonitoring", "cumulative", "Climate", "repository", "repositoryhttpswwwnorthwestknowledgenetmetdatadata", "servicehttpssuzakueorcjaxajpgcomwresearchresdisthtml", "snowtel, data", "Climate Monitoring", + ] \ No newline at end of file diff --git a/book/chapters/datasource.ipynb b/book/chapters/datasource.ipynb index 23a8fd6..96c8f64 100644 --- a/book/chapters/datasource.ipynb +++ b/book/chapters/datasource.ipynb @@ -291,7 +291,7 @@ "- Visit the [SCAN/SNOWTEL Website](http://www.wcc.nrcs.usda.gov/nwcc/inventory).\n", "\n", "### Step 2: Choose Data Product and Location\n", - "- Select the data product you are interested in (e.g., Snow Depth or Snow Water Equivalent) from the drop-down menu under ‘Element’.\n", + "- Select the data product you are interested in (e.g., Snow Depth or Snow Water Equivalent) from the drop-down menu under *Element*.\n", "- Choose a State/County or Basin using the drop-down menus provided.\n", "\n", "### Step 3: View Inventory\n", @@ -309,7 +309,7 @@ "### Step 5: Download Data\n", "- Save the downloaded CSV file to your computer for further analysis.\n", "\n", - "**For map visualization of SCAN or SNOWTEL stations, click on ‘SCAN data’ or ‘SNOWTEL data’ under ‘Climate Monitoring’ in the right panel. The maps are clickable for station selection.**\n", + "**For map visualization of SNOWTEL stations, click on ‘SNOWTEL data’ under ‘Climate Monitoring’ in the right panel. The maps are clickable for station selection.**\n", "\n", "For more information, visit the [NRCS SNOTEL page](https://www.nrcs.usda.gov/wps/portal/wcc/home/aboutUs/monitoringPrograms/automatedSnowMonitoring).\n" ] diff --git a/book/chapters/gridmet.ipynb b/book/chapters/gridmet.ipynb index 1169973..7628eb6 100644 --- a/book/chapters/gridmet.ipynb +++ b/book/chapters/gridmet.ipynb @@ -13,7 +13,7 @@ "\n", "- **Visualize Data**: Create custom color maps and scatter plots to visualize meteorological variables spatially across the western United States. This functionality aids in understanding geographical patterns and trends in meteorological data.\n", "\n", - "- **Generate Cummulative history CSVs**: Generate cumulative history CSVs to aggregate meteorological data over a specified date range. This feature allows users to analyze historical meteorological patterns and long-term trends for decision-making purposes.\n", + "- **Generate Cumulative history CSVs**: Generate cumulative history CSVs to aggregate meteorological data over a specified date range. This feature allows users to analyze historical meteorological patterns and long-term trends for decision-making purposes.\n", "\n", "We facilitate the retrieval of specific meteorological variables from the GridMET climatology dataset for a user-defined year. Leveraging libraries such as netCDF4, urllib, and pandas, we enable seamless data handling and manipulation, ensuring efficient processing and analysis of climatological data. We offer custom color mapping for visualizing meteorological patterns and provide functionality for generating cumulative history CSVs, allowing users to aggregate data from past October 1st to the specified target date for trend analysis. With matplotlib, we enable plotting and visualization, empowering users to gain insights through graphical representations.\n", "\n",