Pixel Boundaries

This notebook demonstrates the pixel boundaries for the various PSM3 datasets. In particular:

Nominal Pixel Size	Dataset Examples	Pixel Dimensions	Longitude Boundaries	Latitude Boundaries
4 km	PSM3 TMY, PSM3 30-minute	0.04° by 0.04°	…, -80.04, -80.00, -79.96, …	…, 39.95, 39.99, 40.03, …
2 km	PSM3 5-minute	0.02° by 0.02°	…, -80.02, -80.00, -79.98, …	…, 39.97, 39.99, 40.01, …

Additionally, the lat/lon coordinates returned in the metadata of the API response correspond to the center of the pixel.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pvlib

phi = (1 + np.sqrt(5)) / 2  # golden ratio

def sunflower(n, alpha=0, geodesic=False):
    points = []
    angle_stride = 360 * phi if geodesic else 2 * np.pi / phi ** 2
    b = np.round(alpha * np.sqrt(n))  # number of boundary points
    for k in range(1, n + 1):
        r = radius(k, n, b)
        theta = k * angle_stride
        points.append((r * np.cos(theta), r * np.sin(theta)))
    return points

def radius(k, n, b):
    if k > n - b:
        return 1.0
    else:
        return np.sqrt(k - 0.5) / np.sqrt(n - (b + 1) / 2)

r = 0.06  # degrees
center = (40, -80)
pts = sunflower(40)
lons = [center[1] + pt[1] * r for pt in pts]
lats = [center[0] + pt[0] * r for pt in pts]

kwargs = {
    'api_key': 'DEMO_KEY',
    'email': 'assessingsolar@gmail.com',
    'attributes': ['ghi'],
    'map_variables': True,
    'leap_day': True,
}

def plot(results, grid_spacing):
    fig, ax = plt.subplots()
    norm = plt.Normalize(out['annual_ghi'].min(), out['annual_ghi'].max())
    cmap = plt.get_cmap('tab20')

    for _, row in out.iterrows():
        ax.plot([row['lon_norm'], row['lon']], [row['lat_norm'], row['lat']], c=cmap(norm(row['annual_ghi'])))
        ax.scatter([row['lon']], [row['lat']], color=cmap(norm(row['annual_ghi'])))

    xs = np.arange(-80.08, -79.88, grid_spacing)
    xs = xs[(xs > out['lon'].min()) & (xs < out['lon'].max())]

    ys = np.arange(39.87, 49.11, grid_spacing)
    ys = ys[(ys > out['lat'].min()) & (ys < out['lat'].max())]

    for x in xs:
        ax.axvline(x, c='grey', ls=':')

    for y in ys:
        ax.axhline(y, c='grey', ls=':')

TMY (4 km)

results = []

for lat, lon in zip(lats, lons):
    df, meta = pvlib.iotools.get_psm3(lat, lon, names='tmy', **kwargs)
    results.append({
        'lat': lat,
        'lon': lon,
        'lat_norm': meta['latitude'],
        'lon_norm': meta['longitude'],
        'annual_ghi': df['ghi'].resample('h').mean().sum()
    })

out = pd.DataFrame(results)

plot(out, 0.04)

30-minute (4 km)

results = []

for lat, lon in zip(lats, lons):
    df, meta = pvlib.iotools.get_psm3(lat, lon, names='2020', interval=30, **kwargs)
    results.append({
        'lat': lat,
        'lon': lon,
        'lat_norm': meta['latitude'],
        'lon_norm': meta['longitude'],
        'annual_ghi': df['ghi'].resample('h').mean().sum()
    })

out = pd.DataFrame(results)

plot(out, 0.04)

5-minute (2 km)

results = []

for lat, lon in zip(lats, lons):
    df, meta = pvlib.iotools.get_psm3(lat, lon, names='2020', interval=5, **kwargs)
    results.append({
        'lat': lat,
        'lon': lon,
        'lat_norm': meta['latitude'],
        'lon_norm': meta['longitude'],
        'annual_ghi': df['ghi'].resample('h').mean().sum()
    })

out = pd.DataFrame(results)

plot(out, 0.02)

%load_ext watermark
%watermark --iversions -u -d -t

Last updated: 2022-09-27 21:45:50

pvlib     : 0.9.3
matplotlib: 3.5.2
numpy     : 1.22.3
pandas    : 1.5.0