Work with Agronomics in aWherePy

Learn how to get agronomic norms and observed values with aWherePy.

Get Agronomic Norms and Observed Agronomic Data with aWherePy

Note

The example below will show you how to use the get_agronomic_norms() and get_agronomic_values() functions to obtain agronomic data from the aWhere agronomics API.

In this vignette, you will use a single aWhere grid cell centroid near Rocky Mountain National Park, Colorado to get agronomic norms and observed values for May 4 - May 13. Agrnomic norms are data aggregated over many years and do not reference a specific year. The agronomic values for this example are from 2014. In addition, you will plot a comparison of the historical agronomic norms to the observed data in 2014.

Import Packages

In order to use the functionality in this example, the following packages need to be imported.

import os
import matplotlib.pyplot as plt
import awherepy.agronomics as awa
import awherepy.grids as awg

Prerequisites

In order to make calls to any aWhere API, you must provide a valid API key and secret. The key and secret used in this example are stored as environment variables.

# Define aWhere API key and secret
awhere_api_key = os.environ.get("AWHERE_API_KEY")
awhere_api_secret = os.environ.get("AWHERE_API_SECRET")

Extract Centroids from an aWhere Grid and Isolate a Centroid for Analysis

In order to use an aWhere grid cell centroid, you must first create an aWhere grid from a shapefile, extract all centroids, and isolate a single centroid. This functionality is found in the awherepy.grid module.

# Define path to Rocky Mountain National Park, Colorado boundary
rmnp_bound_path = os.path.join(
    "..", "awherepy", "example-data", "colorado_rmnp_boundary.shp"
)

# Create aWhere grid and EPSG 4326 boudary for RMNP, CO
rmnp_grid, rmnp_bound_4326 = awg.create_grid(
    rmnp_bound_path, buffer_distance=0.12
)

# Plot RMNP grid and boundary - optional
fig, ax = awg.plot_grid(
    rmnp_grid,
    rmnp_bound_4326,
    plot_title="Rocky Mountain National Park, Colorado aWhere Grid",
    data_source="U.S. Department of the Interior",
)

# Extract RMNP grid centroids to list
rmnp_grid_centroids = awg.extract_centroids(rmnp_grid)

# Get first centroid
analysis_centroid = rmnp_grid_centroids[0]

Get aWhere Agronomic Norms Data

Get aWhere agronomic norms data by calling the get_agronomic_norms() function, with optional keyword arguments to use values outside the default.

# Define RMNP norms kwargs
rmnp_agronomic_norms_kwargs = {
    "location": (analysis_centroid[0], analysis_centroid[1]),
    "start_date": "05-04",
    "end_date": "05-13",
}

# Get RMNP agronomic norms, 05-04 to 05-13
rmnp_norms_total, rmnp_norms_daily = awa.get_agronomic_norms(
    awhere_api_key, awhere_api_secret, kwargs=rmnp_agronomic_norms_kwargs
)

# Get rolling total precipitation average
rmnp_precip_rolling_avg = rmnp_norms_daily[["precip_rolling_total_average_mm"]]

Get aWhere Agronomic Values Data

Get aWhere observed agronomoc values data by calling the get_agronomic_values() function, with optional keyword arguments to use values outside the default.

# Define RMNP values kwargs
rmnp_agronomic_values_kwargs = {
    "location": (analysis_centroid[0], analysis_centroid[1]),
    "start_date": "2014-05-04",
    "end_date": "2014-05-13",
}

# Get observed agronomic values
rmnp_values_total, rmnp_values_daily = awa.get_agronomic_values(
    awhere_api_key, awhere_api_secret, kwargs=rmnp_agronomic_values_kwargs
)

# Get observed precipitation rolling total
rmnp_precip_rolling_observed = rmnp_values_daily[["precip_rolling_total_mm"]]

# Change date (YYYY-MM-DD) to day (MM-DD) for plotting
days = [value[5:] for value in rmnp_precip_rolling_observed.index.values]
rmnp_precip_rolling_observed.insert(1, "day", days, True)
rmnp_precip_rolling_observed.set_index("day", inplace=True)

Combine aWhere Agronomic Norms and Observed Values Data Subsets for Plotting

Merge the agrnomic data subsets for precipitation into a single dataframe for use with plotting and visualiztion.

# Merge precipitation components (norms and observed values)
rmnp_precip_rolling_may_2014 = rmnp_precip_rolling_observed.merge(
    rmnp_precip_rolling_avg, on="day"
)

# Add column for difference between observed values and norms
rmnp_precip_rolling_may_2014["precip_rolling_diff_mm"] = (
    rmnp_precip_rolling_may_2014.precip_rolling_total_mm
    - rmnp_precip_rolling_may_2014.precip_rolling_total_average_mm
)

Plot a Comparison between the aWhere Norms and Observed Values for RMNP, CO

Plot the norms and observed values on the same subplot and also the difference between the data on a second subplot to see which days in the May 4 - May 13 temporal extent showed higher or lower rolling total precipatation compared to the historic norms.

# Plot rolling total precip comparison
with plt.style.context("dark_background"):

    # Create figure and axes
    fig, ax = plt.subplots(1, 2, figsize=(20, 10))

    # Set overall title
    plt.suptitle(
        (
            "Rocky Mountain National Park, CO\nLongitude: "
            f"{round(analysis_centroid[0], 4)}, Latitude: "
            f"{round(analysis_centroid[1], 4)}\nRolling Total Precipitation, "
            "May 2014"
        ),
        fontsize=24,
    )

    # Set spacing
    plt.subplots_adjust(top=0.8)

    # Subplot 1 - Observed and Historic Rolling Precipitation
    # Add grid
    ax[0].grid(zorder=1)

    # Add daily observed rolling total (line)
    ax[0].plot(
        rmnp_precip_rolling_may_2014.precip_rolling_total_mm,
        marker="o",
        color="#7fc97f",
        markersize=8,
        linewidth=2,
        linestyle="--",
        zorder=2,
    )

    # Add observed rolling total (fill)
    ax[0].fill_between(
        rmnp_precip_rolling_may_2014.index.values,
        rmnp_precip_rolling_may_2014.precip_rolling_total_mm,
        label="Observed Rolling Total",
        color="#7fc97f",
        linewidth=0.5,
        linestyle="-",
        zorder=2,
        alpha=1,
    )

    # Add daily average rolling total (line)
    ax[0].plot(
        rmnp_precip_rolling_may_2014.precip_rolling_total_average_mm,
        marker="o",
        color="#beaed4",
        markersize=8,
        linewidth=2,
        linestyle="--",
        zorder=3,
    )

    # Add average rolling total (fill)
    ax[0].fill_between(
        rmnp_precip_rolling_may_2014.index.values,
        rmnp_precip_rolling_may_2014.precip_rolling_total_average_mm,
        label="Average Rolling Total",
        color="#beaed4",
        linewidth=2,
        linestyle="-",
        zorder=2,
        alpha=1,
    )

    # Configure axes
    ax[0].set_xlabel("Date", fontsize=16)
    ax[0].set_ylabel("Precipitation (mm)", fontsize=16)
    ax[0].set_title("Rolling Total Precipitation", fontsize=20)
    ax[0].tick_params(axis="both", which="major", labelsize=16)
    plt.setp(ax[0].xaxis.get_majorticklabels(), rotation=45)

    # Add legend
    ax[0].legend(borderpad=0.75, edgecolor="w", fontsize=16, shadow=True)

    # Subplot 2 - Rolling Precipitation Difference
    # Add grid
    ax[1].grid(zorder=1)

    # Add difference (observed - norms)
    ax[1].bar(
        rmnp_precip_rolling_may_2014.index.values,
        rmnp_precip_rolling_may_2014.precip_rolling_diff_mm,
        label="Rolling Total Difference",
        zorder=2,
        edgecolor="#ff7f00",
        color="#fdc086",
        linewidth=1,
    )

    # Configure axes
    ax[1].set_xlabel("Date", fontsize=16)
    ax[1].set_ylabel("Precipitation Difference (mm)", fontsize=16)
    ax[1].set_title(
        "Precipitation Difference (Observed - Average)", fontsize=20
    )
    ax[1].tick_params(axis="both", which="major", labelsize=16)
    plt.setp(ax[1].xaxis.get_majorticklabels(), rotation=45)

    # Add legend
    ax[1].legend(borderpad=0.75, edgecolor="w", fontsize=16, shadow=True)

    plt.show()