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Use Flask to create your routes. Routes / Home page. List all routes that are available. /api/v1.0/precipitation Convert the query results to a dictionary using date as the key and prcp as the value. Return the JSON representation of your dictionary. /api/v1.0/stations Return a JSON list of stations from the dataset. /api/v1.0/tobs Query the dates and temperature observations of the most active station for the last year of data. Return a JSON list of temperature observations (TOBS) for the previous year. /api/v1.0/ and /api/v1.0/ Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range. When given the start only, calculate TMIN , TAVG , and TMAX for all dates greater than and equal to the start date. When given the start and the end date, calculate the TMIN , TAVG , and TMAX for dates between the start and end date inclusive. Use Flask jsonify to convert your API data into a valid JSON response object. Temperature Analysis I Hawaii is reputed to enjoy mild weather all year. Is there a meaningful difference between the temperature in, for example, June and December? You may either use SQLAlchemy or pandas's read_csv() to perform this portion. Identify the average temperature in June at all stations across all available years in the dataset. Do the same for December temperature. Use the t-test to determine whether the difference in the means, if any, is statistically significant. Will you use a paired t-test, or an unpaired t-test? Why? Temperature Analysis II The starter notebook contains a function called calc_temps that will accept a start date and end date in the format %Y-%m-%d . The function will return the minimum, average, and maximum temperatures for that range of dates. Use the calc_temps function to calculate the min, avg, and max temperatures for your trip using the matching dates from the previous year (i.e., use "" if your trip start date was ""). Plot the min, avg, and max temperature from your previous query as a bar chart. Use the average temperature as the bar height. Use the peak-to-peak (TMAX-TMIN) value as the y error bar (YERR). Daily Rainfall Average Calculate the rainfall per weather station using the previous year's matching dates. Calculate the daily normals. Normals are the averages for the min, avg, and max temperatures. You are provided with a function called daily_normals that will calculate the daily normals for a specific date. This date string will be in the format %m-%d . Be sure to use all historic TOBS that match that date string. Create a list of dates for your trip in the format %m-%d . Use the daily_normals function to calculate the normals for each date string and append the results to a list. Load the list of daily normals into a Pandas DataFrame and set the index equal to the date. Use Pandas to plot an area plot ( stacked=False ) for the daily normals.

Sample Paper For Above instruction

Web API Development with Flask for Climate Data Analysis

Developing RESTful APIs using Flask is an effective way to serve climate data for various analytical and reporting purposes. This paper details a comprehensive Flask application designed to provide multiple data endpoints from a climate dataset, perform statistical analyses comparing temperature patterns, visualize temperature trends, and calculate rainfall normals.

Introduction

Florida State University and other institutions have invested heavily in climate data collection, storing information on temperature, precipitation, and station activity. Making this data accessible via APIs facilitates research, public information dissemination, and real-time analysis. Flask, a popular Python micro web framework, simplifies API development, allowing for rapid deployment of data services.

Flask Routes and Data Endpoints

The core of the Flask application involves defining several routes:

• /: Home route, listing all available endpoints.
• /api/v1.0/precipitation: Returns a dictionary with dates as keys and precipitation as values in JSON format. This involves querying the database, converting results to a dictionary with date as key, and serializing with Flask's jsonify.
• /api/v1.0/stations: Provides a list of weather stations retrieved from the dataset, serialized into JSON.
• /api/v1.0/tobs: Focuses on temperature observations from the most active station during the previous year. It queries for date and temperature observations where station activity is highest, filtering data for the last year, and outputs as JSON.
• /api/v1.0/ and /api/v1.0//: Generates statistics (min, avg, max temperatures) over specified date ranges, with conditional handling based on whether only start date or both start and end dates are provided. SQL joins or pandas merges are used to combine station and measurement data, and calculations are performed accordingly.

Temperature Analysis and Statistical Significance

In addition to providing raw data, the application performs statistical analyses to investigate temperature patterns in Hawaii. Specifically, it compares the average temperatures in June versus December across all stations, using either pandas or SQLAlchemy for data retrieval. A t-test (paired or unpaired depending on data pairing) determines whether differences are statistically significant, informing whether seasonal variations are noteworthy.

Calculating Trip-Based Temperatures and Visualizations

The application incorporates a function called calc_temps that calculates minimum, average, and maximum temperatures over specified date ranges, such as trip durations. Results are visualized as bar charts using matplotlib, with error bars representing temperature variation (peak-to-peak difference). These visualizations assist in understanding expected temperature ranges during planned trips.

Rainfall Normals and Temporal Analysis

Rainfall analysis involves calculating the average rainfall per station during the previous year’s matching dates. The process includes generating date strings in the %m-%d format, applying the daily_normals function to retrieve mean min, max, and average temperatures as normals, and plotting these normals as an area chart with pandas. These normals serve as benchmarks for normal seasonal conditions at different times of the year.

Conclusion

Implementing a Flask-based API for climate data allows effective sharing and analysis of complex datasets. Coupled with statistical analysis and visualization, such applications enable researchers and stakeholders to understand seasonal temperature variations, rainfall patterns, and climate anomalies. The integration of Python data analysis libraries further enhances the utility of these APIs, offering both raw data and interpreted insights.

References

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