206 lines
7.2 KiB
Python
206 lines
7.2 KiB
Python
#Lets import some stuff!
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import boto3
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from botocore import UNSIGNED
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from botocore.client import Config
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from datetime import timedelta, datetime, timezone
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import os
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import pyart
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from matplotlib import pyplot as plt
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import tempfile
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import numpy as np
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import io
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from PIL import Image
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import cartopy.crs as ccrs
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from cartopy.io.img_tiles import OSM
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def printdebug(msg):
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print(f'DEBUG: {msg}')
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#Helper function for the search
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def _nearestDate(dates, pivot):
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return min(dates, key=lambda x: abs(x - pivot))
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def _getRadarFileNames(site, datetime_t):
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return datetimes
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def get_radar_frames(site, datetime_t, num_frames):
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#First create the query string for the bucket knowing
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#how NOAA and AWS store the data
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currentDateString = datetime_t.strftime('%Y/%m/%d/') + site
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previousDateString = (datetime_t - timedelta(days=1)).strftime('%Y/%m/%d/') + site
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conn = boto3.resource('s3', config=Config(signature_version=UNSIGNED))
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bucket = conn.Bucket('noaa-nexrad-level2')
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bucketListCurrentDate = list(bucket.objects.filter(Prefix = currentDateString))
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bucketListPreviousDate = list(bucket.objects.filter(Prefix = previousDateString))
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#we are going to create a list of keys and datetimes to allow easy searching
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keys = []
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datetimes = []
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#populate the list
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printdebug('Getting nearby dates')
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for i in range(len(bucketListCurrentDate)):
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this_str = str(bucketListCurrentDate[i].key)
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if 'gz' in this_str:
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endme = this_str[-22:-4]
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fmt = '%Y%m%d_%H%M%S_V0'
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dt = datetime.strptime(endme, fmt).replace(tzinfo=timezone.utc)
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datetimes.append(dt)
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keys.append(bucketListCurrentDate[i])
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if this_str[-3::] == 'V06':
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endme = this_str[-19::]
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fmt = '%Y%m%d_%H%M%S_V06'
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dt = datetime.strptime(endme, fmt).replace(tzinfo=timezone.utc)
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datetimes.append(dt)
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keys.append(bucketListCurrentDate[i])
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#datetimes = _getRadarFileNames(site, datetime_t)
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printdebug('Getting nearest datetime')
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closest_datetime = _nearestDate(datetimes, datetime_t)
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index = datetimes.index(closest_datetime)
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#radarFrames = [0] * num_frames
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radarFrames = {}
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i = index
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printdebug('Pulling all the radar images requested')
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while i > (index - num_frames) and i >= 0:
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#radarFrames[num_frames - ((index - i) + 1)] = get_radar_from_aws(keys[i].Object())
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filename = keys[i].key.replace('/', '_')
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radarFrames[filename] = get_radar_from_aws(keys[i].Object())
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i = i - 1
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sortedIndices = sorted(radarFrames)
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sortedRadarFrames = {}
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for key in sortedIndices:
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sortedRadarFrames[key] = radarFrames[key]
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return sortedRadarFrames
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def get_radar_from_aws(aws_object):
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"""
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Get the closest volume of NEXRAD data to a particular datetime.
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Parameters
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----------
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site : string
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four letter radar designation
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datetime_t : datetime
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desired date time
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Returns
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-------
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radar : Py-ART Radar Object
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Radar closest to the queried datetime
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"""
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#find the closest available radar to your datetime
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#closest_datetime = _nearestDate(datetimes, datetime_t)
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#index = datetimes.index(closest_datetime)
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#localfile = tempfile.NamedTemporaryFile()
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#keys[index].Object().download_file(localfile.name)
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#aws_object.download_file(localfile.name)
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#radar = pyart.io.read(localfile.name)
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filename = aws_object.key.replace('/', '_')
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if not os.path.isfile(f'cache/rawradar/{filename}'):
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printdebug('Cache Miss')
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aws_object.download_file(f'cache/rawradar/{filename}')
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radar = pyart.io.read(f'cache/rawradar/{filename}')
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return radar
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def convert_fig_to_img(fig):
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fig.canvas.draw()
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(w,h) = fig.canvas.get_width_height()
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img = Image.frombytes('RGBA', fig.canvas.get_width_height(), np.frombuffer(fig.canvas.buffer_rgba(), dtype=np.uint8).reshape((h,w,4)))
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return img
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def generate_radar_image(radar_data, tiler, mercator):
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#determine max_lat / max_lon etc from position of radar
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max_lat = radar_data.latitude["data"][0] + 4.5
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min_lat = radar_data.latitude["data"][0] - 4.5
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max_lon = radar_data.longitude["data"][0] + 5.5
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min_lon = radar_data.longitude["data"][0] - 5.5
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# Used to be .5
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lal = np.arange(min_lat, max_lat, .2)
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lol = np.arange(min_lon, max_lon, .1)
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printdebug('Creating RadarMapDisplay')
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display = pyart.graph.RadarMapDisplay(radar_data)
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projection = ccrs.LambertConformal(
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central_latitude=radar_data.latitude["data"][0],
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central_longitude=radar_data.longitude["data"][0],
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)
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printdebug('Processing data to clean it up a bit')
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radar_data.fields['reflectivity']['data'][:, -10:] = np.ma.masked
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gatefilter = pyart.filters.GateFilter(radar_data)
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gatefilter.exclude_transition()
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gatefilter.exclude_masked("reflectivity")
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gatefilter.exclude_below('reflectivity', 16)
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printdebug('Despeckling')
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despeckle_gatefilter = pyart.correct.despeckle_field(radar_data, 'reflectivity', gatefilter=gatefilter, size=20)
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fig = plt.figure(figsize = [10,8])
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fig.patch.set_facecolor('none')
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ax = fig.add_subplot(1, 1, 1, projection=mercator)
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#ax = fig.add_subplot(1, 1, 1)
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ax.set_extent([min_lon, max_lon, min_lat, max_lat], crs=ccrs.PlateCarree())
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ax.add_image(tiler, 6)
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printdebug('Plotting!')
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display.plot_ppi_map('reflectivity', sweep = 0, resolution='10m',
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vmin = -8, vmax = 64,
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cmap = "ChaseSpectral",
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min_lat = min_lat, min_lon = min_lon,
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max_lat = max_lat, max_lon = max_lon,
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lat_lines = lal, lon_lines = lol,
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projection=projection, fig=fig, ax=ax, title_flag=False,
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lat_0=radar_data.latitude["data"][0], lon_0=radar_data.longitude["data"][0],
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gatefilter=despeckle_gatefilter, add_grid_lines=False, alpha=0.8)
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display.plot_range_ring(radar_data.range["data"][-1] / 1000.0)
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return fig
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fmt = '%Y%m%d_%H%M%S'
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currentDatetime = datetime.now(timezone.utc)
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num_frames = 20
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secondary_frames = [0] * (num_frames - 1)
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radar = get_radar_frames('KVNX', currentDatetime, num_frames=num_frames)
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print(list(radar.keys()))
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tiler = OSM(cache=True)
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mercator = tiler.crs
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radarKeys = list(radar.keys())
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if not os.path.isfile(f'cache/radarimages/{radarKeys[0]}.png'):
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firstFrameFig = generate_radar_image(radar[radarKeys[0]], tiler, mercator)
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firstFrame = convert_fig_to_img(firstFrameFig)
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firstFrame.save(f'cache/radarimages/{radarKeys[0]}.png')
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else:
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firstFrame = Image.open(f'cache/radarimages/{radarKeys[0]}.png')
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i = 0
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for i in range(num_frames - 1):
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if not os.path.isfile(f'cache/radarimages/{radarKeys[i + 1]}.png'):
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fig = generate_radar_image(radar[radarKeys[i + 1]], tiler, mercator)
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secondary_frames[i] = convert_fig_to_img(fig)
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secondary_frames[i].save(f'cache/radarimages/{radarKeys[i + 1]}.png')
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else:
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secondary_frames[i] = Image.open(f'cache/radarimages/{radarKeys[i + 1]}.png')
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datetimeString = datetime.strftime(currentDatetime, fmt)
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firstFrame.save(f'testradar_{datetimeString}.webp', append_images=secondary_frames, duration=200)
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