import urllib.request
import xarray as xr
import netCDF4 as nc
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import warnings
warnings.filterwarnings('ignore')
Time series of chlorophyll data from different sensors
Great Lakes color producing agents (CPA) are derived from two different sensors.
As an example, we are going to plot time-series of mean chlorophyll a concentration from different sensors from 2002 to 2023. We are going to download MODIS data (2002-2017) and VIIRS data (2018-2023).
First, let’s load all the packages needed:
##Get Lake Erie Monthly Average MODIS data
Go to ERDDAP to find the name of the dataset for dailly MODIS data: LE_CHL_MODIS_Daily
You should always examine the dataset in ERDDAP to check the date range, names of the variables and dataset ID, to make sure your griddap calls are correct:
https://apps.glerl.noaa.gov/erddap/griddap/LE_CHL_MODIS_Daily.graph
- let’s download data for Lake Erie:
# in Python code replace coastwatch.glerl.noaa.gov with apps.glerl.noaa.gov
url='https://apps.glerl.noaa.gov/erddap/griddap/LE_CHL_MODIS_Daily.nc?chlorophyll%5B(2002-08-07T19:05:00Z):1:(2017-10-22T18:00:00Z)%5D%5B(41.0051550293714):1:(42.9950003885447)%5D%5B(-83.4950003885448):1:(-78.505388156246)%5D'urllib.request.urlretrieve(url, "e_chl_modis.nc")('e_chl_modis.nc', <http.client.HTTPMessage at 0x1a2705704d0>)- let’s use xarray to extract the data from the downloaded file:
e_m_ds = xr.open_dataset('e_chl_modis.nc',decode_cf=False)e_m_ds.coordsCoordinates:
* time (time) float64 1.029e+09 1.029e+09 ... 1.509e+09 1.509e+09
* latitude (latitude) float64 41.01 41.02 41.03 41.04 ... 42.98 42.99 43.0
* longitude (longitude) float64 -83.5 -83.49 -83.48 ... -78.53 -78.52 -78.51e_m_ds.time.valuesarray([1.02874710e+09, 1.02883020e+09, 1.02891930e+09, 1.02900240e+09,
1.02960750e+09, 1.02986850e+09, 1.03030170e+09, 1.03047360e+09,
1.03064580e+09, 1.03081770e+09, 1.03090680e+09, 1.03125090e+09,
1.03142310e+09, 1.03151190e+09, 1.03159500e+09, 1.03220010e+09,
1.03228920e+09, 1.03263330e+09, 1.03323840e+09, 1.03341060e+09,
1.03392660e+09, 1.03462080e+09, 1.05457950e+09, 1.05613410e+09,
1.05673920e+09, 1.05708330e+09, 1.05717240e+09, 1.05812160e+09,
1.05820470e+09, 1.05838260e+09, 1.05855480e+09, 1.05863790e+09,
1.05907080e+09, 1.05950400e+09, 1.05958710e+09, 1.06028130e+09,
1.06079730e+09, 1.06088640e+09, 1.06123050e+09, 1.06157460e+09,
1.06166370e+09, 1.06200780e+09, 1.06226880e+09, 1.06278510e+09,
1.06287390e+09, 1.06304610e+09, 1.06321800e+09, 1.06373430e+09,
1.06382340e+09, 1.06408440e+09, 1.06416750e+09, 1.06554990e+09,
1.06563870e+09, 1.06581090e+09, 1.06589400e+09, 1.09138230e+09,
1.09147140e+09, 1.09173240e+09, 1.09181550e+09, 1.09319790e+09,
1.09397520e+09, 1.09406400e+09, 1.09414710e+09, 1.09484130e+09,
1.09501350e+09, 1.09510230e+09, 1.09527450e+09, 1.09561860e+09,
1.09587960e+09, 1.09700100e+09, 1.09717290e+09, 1.11816990e+09,
1.11920820e+09, 1.11946350e+09, 1.12032960e+09, 1.12076280e+09,
1.12084590e+09, 1.12093470e+09, 1.12179510e+09, 1.12275030e+09,
1.12292220e+09, 1.12343850e+09, 1.12352760e+09, 1.12421580e+09,
1.12491000e+09, 1.12525410e+09, 1.12559820e+09, 1.12568700e+09,
1.12585920e+09, 1.12594230e+09, 1.12603110e+09, 1.12629240e+09,
1.12637550e+09, 1.12724160e+09, 1.12732470e+09, 1.12758570e+09,
1.12784670e+09, 1.12819080e+09, 1.12853490e+09, 1.12940130e+09,
1.14953220e+09, 1.14979320e+09, 1.15039830e+09, 1.15281330e+09,
1.15411260e+09, 1.15445670e+09, 1.15471770e+09, 1.15532280e+09,
1.15540590e+09, 1.15549500e+09, 1.15566690e+09, 1.15575000e+09,
1.15627230e+09, 1.18037820e+09, 1.18115550e+09, 1.18141650e+09,
1.18158840e+09, 1.18176060e+09, 1.18236570e+09, 1.18244880e+09,
1.18374810e+09, 1.18383120e+09, 1.18513050e+09, 1.18573560e+09,
1.18590780e+09, 1.18616880e+09, 1.18694610e+09, 1.18702920e+09,
1.18823940e+09, 1.18841160e+09, 1.18850040e+09, 1.18858350e+09,
1.18867260e+09, 1.18875570e+09, 1.18884450e+09, 1.18927770e+09,
1.19022690e+09, 1.19066010e+09, 1.19100420e+09, 1.21303380e+09,
1.21398900e+09, 1.21416090e+09, 1.21493820e+09, 1.21528230e+09,
1.21537140e+09, 1.21571550e+09, 1.21632060e+09, 1.21709790e+09,
1.21770300e+09, 1.21778610e+09, 1.21804710e+09, 1.21848030e+09,
1.21882440e+09, 1.21891350e+09, 1.21908540e+09, 1.21925760e+09,
1.21977360e+09, 1.22012370e+09, 1.22020680e+09, 1.22029590e+09,
1.22037900e+09, 1.22046780e+09, 1.22158920e+09, 1.22167830e+09,
1.22219430e+09, 1.22306070e+09, 1.22323260e+09, 1.22357670e+09,
1.22366580e+09, 1.24578420e+09, 1.24603950e+09, 1.24681680e+09,
1.24751070e+09, 1.24811610e+09, 1.24906530e+09, 1.25010360e+09,
1.25018670e+09, 1.25027550e+09, 1.25122500e+09, 1.25183010e+09,
1.25209110e+09, 1.25217420e+09, 1.25304030e+09, 1.25338440e+09,
1.27472550e+09, 1.27481460e+09, 1.27524180e+09, 1.27680210e+09,
1.27697430e+09, 1.27809540e+09, 1.27818450e+09, 1.27930590e+09,
1.28008320e+09, 1.28025510e+09, 1.28103240e+09, 1.28163750e+09,
1.28189850e+09, 1.28198160e+09, 1.28284800e+09, 1.28301990e+09,
1.28310900e+09, 1.28319210e+09, 1.28328090e+09, 1.28336400e+09,
1.28440230e+09, 1.28578470e+09, 1.28647890e+09, 1.28656200e+09,
1.30678170e+09, 1.30686480e+09, 1.30703700e+09, 1.30704270e+09,
1.30738680e+09, 1.30876920e+09, 1.30911330e+09, 1.30937430e+09,
1.30945740e+09, 1.30954650e+09, 1.30971870e+09, 1.30980750e+09,
1.31015160e+09, 1.31023470e+09, 1.31049570e+09, 1.31057880e+09,
1.31058480e+09, 1.31075670e+09, 1.31083980e+09, 1.31092890e+09,
1.31170620e+09, 1.31205030e+09, 1.31213910e+09, 1.31256660e+09,
1.31291640e+09, 1.31308860e+09, 1.31326050e+09, 1.31352150e+09,
1.31369370e+09, 1.31377680e+09, 1.31395470e+09, 1.31403780e+09,
1.31438190e+09, 1.31447100e+09, 1.31464290e+09, 1.31507610e+09,
1.31576430e+09, 1.31585340e+09, 1.31775180e+09, 1.31784090e+09,
1.31792400e+09, 1.31801310e+09, 1.31809620e+09, 1.31818500e+09,
1.33710570e+09, 1.33719450e+09, 1.33727760e+09, 1.33736670e+09,
1.33744980e+09, 1.33762770e+09, 1.33779960e+09, 1.33797180e+09,
1.33918200e+09, 1.33926510e+09, 1.33935420e+09, 1.33952610e+09,
1.34013150e+09, 1.34021460e+09, 1.34030340e+09, 1.34039250e+09,
1.34073660e+09, 1.34090850e+09, 1.34125290e+09, 1.34142480e+09,
1.34159700e+09, 1.34185800e+09, 1.34194680e+09, 1.34202990e+09,
1.34211900e+09, 1.34220210e+09, 1.34229090e+09, 1.34238000e+09,
1.34255220e+09, 1.34289630e+09, 1.34315730e+09, 1.34341230e+09,
1.34350140e+09, 1.34358450e+09, 1.34367330e+09, 1.34427870e+09,
1.34436180e+09, 1.34505570e+09, 1.34522790e+09, 1.34548890e+09,
1.34557200e+09, 1.34566110e+09, 1.34574420e+09, 1.34583300e+09,
1.34592210e+09, 1.34617710e+09, 1.34626620e+09, 1.34634930e+09,
1.34643810e+09, 1.34721540e+09, 1.34730450e+09, 1.34738760e+09,
1.34755950e+09, 1.34764860e+09, 1.34773170e+09, 1.34782050e+09,
1.37037210e+09, 1.37123250e+09, 1.37149350e+09, 1.37175450e+09,
1.37235960e+09, 1.37357010e+09, 1.37374200e+09, 1.37391420e+09,
1.37477460e+09, 1.37486340e+09, 1.37520750e+09, 1.37537970e+09,
1.37555160e+09, 1.37607390e+09, 1.37624580e+09, 1.37658990e+09,
1.37667900e+09, 1.37685090e+09, 1.37693400e+09, 1.37702310e+09,
1.37711220e+09, 1.37728410e+09, 1.37736720e+09, 1.37745630e+09,
1.37780040e+09, 1.37840550e+09, 1.37883870e+09, 1.37901060e+09,
1.37918280e+09, 1.37944380e+09, 1.37996010e+09, 1.38004890e+09,
1.38022110e+09, 1.38030420e+09, 1.38039300e+09, 1.38065400e+09,
1.38073710e+09, 1.38125940e+09, 1.38134250e+09, 1.38143130e+09,
1.38151440e+09, 1.38160350e+09, 1.38177540e+09, 1.40095710e+09,
1.40104020e+09, 1.40147310e+09, 1.40156220e+09, 1.40182320e+09,
1.40199540e+09, 1.40216730e+09, 1.40277240e+09, 1.40294460e+09,
1.40337780e+09, 1.40398290e+09, 1.40432700e+09, 1.40449890e+09,
1.40458200e+09, 1.40458230e+09, 1.40476020e+09, 1.40493210e+09,
1.40510430e+09, 1.40536530e+09, 1.40562030e+09, 1.40683080e+09,
1.40709180e+09, 1.40743590e+09, 1.40760810e+09, 1.40769690e+09,
1.40795220e+09, 1.40830200e+09, 1.40838510e+09, 1.40899050e+09,
1.40907930e+09, 1.40916240e+09, 1.40925150e+09, 1.40976750e+09,
1.40985660e+09, 1.41011160e+09, 1.41020070e+09, 1.41088890e+09,
1.41089490e+09, 1.41097800e+09, 1.41106680e+09, 1.41114990e+09,
1.41123900e+09, 1.41149400e+09, 1.41150000e+09, 1.41158310e+09,
1.41167220e+09, 1.41175530e+09, 1.41184410e+09, 1.41201630e+09,
1.41313710e+09, 1.41313740e+09, 1.41313770e+09, 1.41408690e+09,
1.41434790e+09, 1.41573030e+09, 1.43050326e+09, 1.43050350e+09,
1.43050380e+09, 1.43059260e+09, 1.43067570e+09, 1.43102580e+09,
1.43136990e+09, 1.43240820e+09, 1.43335740e+09, 1.43396250e+09,
1.43741550e+09, 1.43750460e+09, 1.43767650e+09, 1.43801436e+09,
1.43802060e+09, 1.43802090e+09, 1.43810970e+09, 1.43854290e+09,
1.43949210e+09, 1.44026940e+09, 1.44156270e+09, 1.44225690e+09,
1.44251220e+09, 1.44251790e+09, 1.44286200e+09, 1.44294510e+09,
1.44303420e+09, 1.44311730e+09, 1.46083320e+09, 1.46143860e+09,
1.46255760e+09, 1.46359800e+09, 1.46402520e+09, 1.46575770e+09,
1.46619090e+09, 1.46636280e+09, 1.46653500e+09, 1.46679000e+09,
1.46731230e+09, 1.46774520e+09, 1.46835030e+09, 1.47007710e+09,
1.47059310e+09, 1.47085410e+09, 1.47163140e+09, 1.47500130e+09,
1.47672780e+09, 1.49167320e+09, 1.49202330e+09, 1.49219550e+09,
1.49297250e+09, 1.49418300e+09, 1.49426610e+09, 1.49487120e+09,
1.49496030e+09, 1.49634270e+09, 1.49651460e+09, 1.49694780e+09,
1.49720280e+09, 1.49720880e+09, 1.49798610e+09, 1.49833020e+09,
1.49901840e+09, 1.49919060e+09, 1.49962350e+09, 1.50014550e+09,
1.50031770e+09, 1.50040080e+09, 1.50048990e+09, 1.50100590e+09,
1.50135600e+09, 1.50152790e+09, 1.50170010e+09, 1.50291030e+09,
1.50325470e+09, 1.50368760e+09, 1.50385980e+09, 1.50412080e+09,
1.50463710e+09, 1.50498120e+09, 1.50550320e+09, 1.50558630e+09,
1.50567510e+09, 1.50593040e+09, 1.50601950e+09, 1.50610830e+09,
1.50619140e+09, 1.50628050e+09, 1.50645240e+09, 1.50687960e+09,
1.50696870e+09, 1.50705750e+09, 1.50722970e+09, 1.50757380e+09,
1.50817890e+09, 1.50826200e+09, 1.50835110e+09, 1.50843990e+09,
1.50852300e+09, 1.50869520e+09])e_m_ds.data_varsData variables:
chlorophyll (time, latitude, longitude) float64 ...e_m_ds.chlorophyll.shape(506, 200, 500)The downloaded data contains only one variable: chlorophyll.
- Extract the dates corresponding to the data of each day:
e_m_dates=nc.num2date(e_m_ds.time,e_m_ds.time.units, only_use_cftime_datetimes=False,
only_use_python_datetimes=True )
e_m_dates
#e_m_ds.timearray([real_datetime(2002, 8, 7, 19, 5),
real_datetime(2002, 8, 8, 18, 10),
real_datetime(2002, 8, 9, 18, 55),
real_datetime(2002, 8, 10, 18, 0),
real_datetime(2002, 8, 17, 18, 5),
real_datetime(2002, 8, 20, 18, 35),
real_datetime(2002, 8, 25, 18, 55),
real_datetime(2002, 8, 27, 18, 40),
real_datetime(2002, 8, 29, 18, 30),
real_datetime(2002, 8, 31, 18, 15),
real_datetime(2002, 9, 1, 19, 0),
real_datetime(2002, 9, 5, 18, 35),
real_datetime(2002, 9, 7, 18, 25),
real_datetime(2002, 9, 8, 19, 5),
real_datetime(2002, 9, 9, 18, 10),
real_datetime(2002, 9, 16, 18, 15),
real_datetime(2002, 9, 17, 19, 0),
real_datetime(2002, 9, 21, 18, 35),
real_datetime(2002, 9, 28, 18, 40),
real_datetime(2002, 9, 30, 18, 30),
real_datetime(2002, 10, 6, 17, 50),
real_datetime(2002, 10, 14, 18, 40),
real_datetime(2003, 6, 2, 18, 45),
real_datetime(2003, 6, 20, 18, 35),
real_datetime(2003, 6, 27, 18, 40),
real_datetime(2003, 7, 1, 18, 15),
real_datetime(2003, 7, 2, 19, 0),
real_datetime(2003, 7, 13, 18, 40),
real_datetime(2003, 7, 14, 17, 45),
real_datetime(2003, 7, 16, 19, 10),
real_datetime(2003, 7, 18, 19, 0),
real_datetime(2003, 7, 19, 18, 5),
real_datetime(2003, 7, 24, 18, 20),
real_datetime(2003, 7, 29, 18, 40),
real_datetime(2003, 7, 30, 17, 45),
real_datetime(2003, 8, 7, 18, 35),
real_datetime(2003, 8, 13, 17, 55),
real_datetime(2003, 8, 14, 18, 40),
real_datetime(2003, 8, 18, 18, 15),
real_datetime(2003, 8, 22, 17, 50),
real_datetime(2003, 8, 23, 18, 35),
real_datetime(2003, 8, 27, 18, 10),
real_datetime(2003, 8, 30, 18, 40),
real_datetime(2003, 9, 5, 18, 5),
real_datetime(2003, 9, 6, 18, 45),
real_datetime(2003, 9, 8, 18, 35),
real_datetime(2003, 9, 10, 18, 20),
real_datetime(2003, 9, 16, 17, 45),
real_datetime(2003, 9, 17, 18, 30),
real_datetime(2003, 9, 20, 19, 0),
real_datetime(2003, 9, 21, 18, 5),
real_datetime(2003, 10, 7, 18, 5),
real_datetime(2003, 10, 8, 18, 45),
real_datetime(2003, 10, 10, 18, 35),
real_datetime(2003, 10, 11, 17, 40),
real_datetime(2004, 8, 1, 17, 45),
real_datetime(2004, 8, 2, 18, 30),
real_datetime(2004, 8, 5, 19, 0), real_datetime(2004, 8, 6, 18, 5),
real_datetime(2004, 8, 22, 18, 5),
real_datetime(2004, 8, 31, 18, 0),
real_datetime(2004, 9, 1, 18, 40),
real_datetime(2004, 9, 2, 17, 45),
real_datetime(2004, 9, 10, 18, 35),
real_datetime(2004, 9, 12, 18, 25),
real_datetime(2004, 9, 13, 19, 5),
real_datetime(2004, 9, 15, 18, 55),
real_datetime(2004, 9, 19, 18, 30),
real_datetime(2004, 9, 22, 19, 0),
real_datetime(2004, 10, 5, 18, 30),
real_datetime(2004, 10, 7, 18, 15),
real_datetime(2005, 6, 7, 18, 45),
real_datetime(2005, 6, 19, 19, 10),
real_datetime(2005, 6, 22, 18, 5),
real_datetime(2005, 7, 2, 18, 40),
real_datetime(2005, 7, 7, 19, 0), real_datetime(2005, 7, 8, 18, 5),
real_datetime(2005, 7, 9, 18, 45),
real_datetime(2005, 7, 19, 17, 45),
real_datetime(2005, 7, 30, 19, 5),
real_datetime(2005, 8, 1, 18, 50),
real_datetime(2005, 8, 7, 18, 15),
real_datetime(2005, 8, 8, 19, 0),
real_datetime(2005, 8, 16, 18, 10),
real_datetime(2005, 8, 24, 19, 0),
real_datetime(2005, 8, 28, 18, 35),
real_datetime(2005, 9, 1, 18, 10),
real_datetime(2005, 9, 2, 18, 50),
real_datetime(2005, 9, 4, 18, 40),
real_datetime(2005, 9, 5, 17, 45),
real_datetime(2005, 9, 6, 18, 25),
real_datetime(2005, 9, 9, 19, 0),
real_datetime(2005, 9, 10, 18, 5),
real_datetime(2005, 9, 20, 18, 40),
real_datetime(2005, 9, 21, 17, 45),
real_datetime(2005, 9, 24, 18, 15),
real_datetime(2005, 9, 27, 18, 45),
real_datetime(2005, 10, 1, 18, 20),
real_datetime(2005, 10, 5, 17, 55),
real_datetime(2005, 10, 15, 18, 35),
real_datetime(2006, 6, 5, 18, 30),
real_datetime(2006, 6, 8, 19, 0),
real_datetime(2006, 6, 15, 19, 5),
real_datetime(2006, 7, 13, 17, 55),
real_datetime(2006, 7, 28, 18, 50),
real_datetime(2006, 8, 1, 18, 25),
real_datetime(2006, 8, 4, 18, 55),
real_datetime(2006, 8, 11, 19, 0),
real_datetime(2006, 8, 12, 18, 5),
real_datetime(2006, 8, 13, 18, 50),
real_datetime(2006, 8, 15, 18, 35),
real_datetime(2006, 8, 16, 17, 40),
real_datetime(2006, 8, 22, 18, 45),
real_datetime(2007, 5, 28, 18, 50),
real_datetime(2007, 6, 6, 18, 45),
real_datetime(2007, 6, 9, 19, 15),
real_datetime(2007, 6, 11, 19, 0),
real_datetime(2007, 6, 13, 18, 50),
real_datetime(2007, 6, 20, 18, 55),
real_datetime(2007, 6, 21, 18, 0),
real_datetime(2007, 7, 6, 18, 55),
real_datetime(2007, 7, 7, 18, 0),
real_datetime(2007, 7, 22, 18, 55),
real_datetime(2007, 7, 29, 19, 0),
real_datetime(2007, 7, 31, 18, 50),
real_datetime(2007, 8, 3, 19, 20),
real_datetime(2007, 8, 12, 19, 15),
real_datetime(2007, 8, 13, 18, 20),
real_datetime(2007, 8, 27, 18, 30),
real_datetime(2007, 8, 29, 18, 20),
real_datetime(2007, 8, 30, 19, 0),
real_datetime(2007, 8, 31, 18, 5),
real_datetime(2007, 9, 1, 18, 50),
real_datetime(2007, 9, 2, 17, 55),
real_datetime(2007, 9, 3, 18, 35),
real_datetime(2007, 9, 8, 18, 55),
real_datetime(2007, 9, 19, 18, 35),
real_datetime(2007, 9, 24, 18, 55),
real_datetime(2007, 9, 28, 18, 30),
real_datetime(2008, 6, 9, 17, 50),
real_datetime(2008, 6, 20, 19, 10),
real_datetime(2008, 6, 22, 18, 55),
real_datetime(2008, 7, 1, 18, 50),
real_datetime(2008, 7, 5, 18, 25),
real_datetime(2008, 7, 6, 19, 10),
real_datetime(2008, 7, 10, 18, 45),
real_datetime(2008, 7, 17, 18, 50),
real_datetime(2008, 7, 26, 18, 45),
real_datetime(2008, 8, 2, 18, 50),
real_datetime(2008, 8, 3, 17, 55),
real_datetime(2008, 8, 6, 18, 25),
real_datetime(2008, 8, 11, 18, 45),
real_datetime(2008, 8, 15, 18, 20),
real_datetime(2008, 8, 16, 19, 5),
real_datetime(2008, 8, 18, 18, 50),
real_datetime(2008, 8, 20, 18, 40),
real_datetime(2008, 8, 26, 18, 0),
real_datetime(2008, 8, 30, 19, 15),
real_datetime(2008, 8, 31, 18, 20),
real_datetime(2008, 9, 1, 19, 5),
real_datetime(2008, 9, 2, 18, 10),
real_datetime(2008, 9, 3, 18, 50),
real_datetime(2008, 9, 16, 18, 20),
real_datetime(2008, 9, 17, 19, 5),
real_datetime(2008, 9, 23, 18, 25),
real_datetime(2008, 10, 3, 19, 5),
real_datetime(2008, 10, 5, 18, 50),
real_datetime(2008, 10, 9, 18, 25),
real_datetime(2008, 10, 10, 19, 10),
real_datetime(2009, 6, 23, 19, 10),
real_datetime(2009, 6, 26, 18, 5),
real_datetime(2009, 7, 5, 18, 0),
real_datetime(2009, 7, 13, 18, 45),
real_datetime(2009, 7, 20, 18, 55),
real_datetime(2009, 7, 31, 18, 35),
real_datetime(2009, 8, 12, 19, 0),
real_datetime(2009, 8, 13, 18, 5),
real_datetime(2009, 8, 14, 18, 45),
real_datetime(2009, 8, 25, 18, 30),
real_datetime(2009, 9, 1, 18, 35),
real_datetime(2009, 9, 4, 19, 5),
real_datetime(2009, 9, 5, 18, 10),
real_datetime(2009, 9, 15, 18, 45),
real_datetime(2009, 9, 19, 18, 20),
real_datetime(2010, 5, 24, 18, 25),
real_datetime(2010, 5, 25, 19, 10),
real_datetime(2010, 5, 30, 17, 50),
real_datetime(2010, 6, 17, 19, 15),
real_datetime(2010, 6, 19, 19, 5),
real_datetime(2010, 7, 2, 18, 30),
real_datetime(2010, 7, 3, 19, 15),
real_datetime(2010, 7, 16, 18, 45),
real_datetime(2010, 7, 25, 18, 40),
real_datetime(2010, 7, 27, 18, 25),
real_datetime(2010, 8, 5, 18, 20),
real_datetime(2010, 8, 12, 18, 25),
real_datetime(2010, 8, 15, 18, 55),
real_datetime(2010, 8, 16, 18, 0),
real_datetime(2010, 8, 26, 18, 40),
real_datetime(2010, 8, 28, 18, 25),
real_datetime(2010, 8, 29, 19, 10),
real_datetime(2010, 8, 30, 18, 15),
real_datetime(2010, 8, 31, 18, 55),
real_datetime(2010, 9, 1, 18, 0),
real_datetime(2010, 9, 13, 18, 25),
real_datetime(2010, 9, 29, 18, 25),
real_datetime(2010, 10, 7, 19, 15),
real_datetime(2010, 10, 8, 18, 20),
real_datetime(2011, 5, 30, 18, 55),
real_datetime(2011, 5, 31, 18, 0),
real_datetime(2011, 6, 2, 17, 50),
real_datetime(2011, 6, 2, 19, 25),
real_datetime(2011, 6, 6, 19, 0),
real_datetime(2011, 6, 22, 19, 0),
real_datetime(2011, 6, 26, 18, 35),
real_datetime(2011, 6, 29, 19, 5),
real_datetime(2011, 6, 30, 18, 10),
real_datetime(2011, 7, 1, 18, 55),
real_datetime(2011, 7, 3, 18, 45),
real_datetime(2011, 7, 4, 19, 25),
real_datetime(2011, 7, 8, 19, 0), real_datetime(2011, 7, 9, 18, 5),
real_datetime(2011, 7, 12, 18, 35),
real_datetime(2011, 7, 13, 17, 40),
real_datetime(2011, 7, 13, 19, 20),
real_datetime(2011, 7, 15, 19, 5),
real_datetime(2011, 7, 16, 18, 10),
real_datetime(2011, 7, 17, 18, 55),
real_datetime(2011, 7, 26, 18, 50),
real_datetime(2011, 7, 30, 18, 25),
real_datetime(2011, 7, 31, 19, 5),
real_datetime(2011, 8, 5, 17, 50),
real_datetime(2011, 8, 9, 19, 0),
real_datetime(2011, 8, 11, 18, 50),
real_datetime(2011, 8, 13, 18, 35),
real_datetime(2011, 8, 16, 19, 5),
real_datetime(2011, 8, 18, 18, 55),
real_datetime(2011, 8, 19, 18, 0),
real_datetime(2011, 8, 21, 19, 25),
real_datetime(2011, 8, 22, 18, 30),
real_datetime(2011, 8, 26, 18, 5),
real_datetime(2011, 8, 27, 18, 50),
real_datetime(2011, 8, 29, 18, 35),
real_datetime(2011, 9, 3, 18, 55),
real_datetime(2011, 9, 11, 18, 5),
real_datetime(2011, 9, 12, 18, 50),
real_datetime(2011, 10, 4, 18, 10),
real_datetime(2011, 10, 5, 18, 55),
real_datetime(2011, 10, 6, 18, 0),
real_datetime(2011, 10, 7, 18, 45),
real_datetime(2011, 10, 8, 17, 50),
real_datetime(2011, 10, 9, 18, 30),
real_datetime(2012, 5, 15, 18, 15),
real_datetime(2012, 5, 16, 18, 55),
real_datetime(2012, 5, 17, 18, 0),
real_datetime(2012, 5, 18, 18, 45),
real_datetime(2012, 5, 19, 17, 50),
real_datetime(2012, 5, 21, 19, 15),
real_datetime(2012, 5, 23, 19, 0),
real_datetime(2012, 5, 25, 18, 50),
real_datetime(2012, 6, 8, 19, 0), real_datetime(2012, 6, 9, 18, 5),
real_datetime(2012, 6, 10, 18, 50),
real_datetime(2012, 6, 12, 18, 35),
real_datetime(2012, 6, 19, 18, 45),
real_datetime(2012, 6, 20, 17, 50),
real_datetime(2012, 6, 21, 18, 30),
real_datetime(2012, 6, 22, 19, 15),
real_datetime(2012, 6, 26, 18, 50),
real_datetime(2012, 6, 28, 18, 35),
real_datetime(2012, 7, 2, 18, 15),
real_datetime(2012, 7, 4, 18, 0),
real_datetime(2012, 7, 6, 17, 50),
real_datetime(2012, 7, 9, 18, 20),
real_datetime(2012, 7, 10, 19, 0),
real_datetime(2012, 7, 11, 18, 5),
real_datetime(2012, 7, 12, 18, 50),
real_datetime(2012, 7, 13, 17, 55),
real_datetime(2012, 7, 14, 18, 35),
real_datetime(2012, 7, 15, 19, 20),
real_datetime(2012, 7, 17, 19, 10),
real_datetime(2012, 7, 21, 18, 45),
real_datetime(2012, 7, 24, 19, 15),
real_datetime(2012, 7, 27, 18, 5),
real_datetime(2012, 7, 28, 18, 50),
real_datetime(2012, 7, 29, 17, 55),
real_datetime(2012, 7, 30, 18, 35),
real_datetime(2012, 8, 6, 18, 45),
real_datetime(2012, 8, 7, 17, 50),
real_datetime(2012, 8, 15, 18, 35),
real_datetime(2012, 8, 17, 18, 25),
real_datetime(2012, 8, 20, 18, 55),
real_datetime(2012, 8, 21, 18, 0),
real_datetime(2012, 8, 22, 18, 45),
real_datetime(2012, 8, 23, 17, 50),
real_datetime(2012, 8, 24, 18, 30),
real_datetime(2012, 8, 25, 19, 15),
real_datetime(2012, 8, 28, 18, 5),
real_datetime(2012, 8, 29, 18, 50),
real_datetime(2012, 8, 30, 17, 55),
real_datetime(2012, 8, 31, 18, 35),
real_datetime(2012, 9, 9, 18, 30),
real_datetime(2012, 9, 10, 19, 15),
real_datetime(2012, 9, 11, 18, 20),
real_datetime(2012, 9, 13, 18, 5),
real_datetime(2012, 9, 14, 18, 50),
real_datetime(2012, 9, 15, 17, 55),
real_datetime(2012, 9, 16, 18, 35),
real_datetime(2013, 6, 4, 18, 55),
real_datetime(2013, 6, 14, 17, 55),
real_datetime(2013, 6, 17, 18, 25),
real_datetime(2013, 6, 20, 18, 55),
real_datetime(2013, 6, 27, 19, 0),
real_datetime(2013, 7, 11, 19, 15),
real_datetime(2013, 7, 13, 19, 0),
real_datetime(2013, 7, 15, 18, 50),
real_datetime(2013, 7, 25, 17, 50),
real_datetime(2013, 7, 26, 18, 30),
real_datetime(2013, 7, 30, 18, 5),
real_datetime(2013, 8, 1, 17, 55),
real_datetime(2013, 8, 3, 17, 40),
real_datetime(2013, 8, 9, 18, 45),
real_datetime(2013, 8, 11, 18, 30),
real_datetime(2013, 8, 15, 18, 5),
real_datetime(2013, 8, 16, 18, 50),
real_datetime(2013, 8, 18, 18, 35),
real_datetime(2013, 8, 19, 17, 40),
real_datetime(2013, 8, 20, 18, 25),
real_datetime(2013, 8, 21, 19, 10),
real_datetime(2013, 8, 23, 18, 55),
real_datetime(2013, 8, 24, 18, 0),
real_datetime(2013, 8, 25, 18, 45),
real_datetime(2013, 8, 29, 18, 20),
real_datetime(2013, 9, 5, 18, 25),
real_datetime(2013, 9, 10, 18, 45),
real_datetime(2013, 9, 12, 18, 30),
real_datetime(2013, 9, 14, 18, 20),
real_datetime(2013, 9, 17, 18, 50),
real_datetime(2013, 9, 23, 18, 15),
real_datetime(2013, 9, 24, 18, 55),
real_datetime(2013, 9, 26, 18, 45),
real_datetime(2013, 9, 27, 17, 50),
real_datetime(2013, 9, 28, 18, 30),
real_datetime(2013, 10, 1, 19, 0),
real_datetime(2013, 10, 2, 18, 5),
real_datetime(2013, 10, 8, 19, 10),
real_datetime(2013, 10, 9, 18, 15),
real_datetime(2013, 10, 10, 18, 55),
real_datetime(2013, 10, 11, 18, 0),
real_datetime(2013, 10, 12, 18, 45),
real_datetime(2013, 10, 14, 18, 30),
real_datetime(2014, 5, 24, 18, 45),
real_datetime(2014, 5, 25, 17, 50),
real_datetime(2014, 5, 30, 18, 5),
real_datetime(2014, 5, 31, 18, 50),
real_datetime(2014, 6, 3, 19, 20),
real_datetime(2014, 6, 5, 19, 10),
real_datetime(2014, 6, 7, 18, 55),
real_datetime(2014, 6, 14, 19, 0),
real_datetime(2014, 6, 16, 18, 50),
real_datetime(2014, 6, 21, 19, 10),
real_datetime(2014, 6, 28, 19, 15),
real_datetime(2014, 7, 2, 18, 50),
real_datetime(2014, 7, 4, 18, 35),
real_datetime(2014, 7, 5, 17, 40),
real_datetime(2014, 7, 5, 17, 45),
real_datetime(2014, 7, 7, 19, 10),
real_datetime(2014, 7, 9, 18, 55),
real_datetime(2014, 7, 11, 18, 45),
real_datetime(2014, 7, 14, 19, 15),
real_datetime(2014, 7, 17, 18, 5),
real_datetime(2014, 7, 31, 18, 20),
real_datetime(2014, 8, 3, 18, 50),
real_datetime(2014, 8, 7, 18, 25),
real_datetime(2014, 8, 9, 18, 15),
real_datetime(2014, 8, 10, 18, 55),
real_datetime(2014, 8, 13, 17, 50),
real_datetime(2014, 8, 17, 19, 0),
real_datetime(2014, 8, 18, 18, 5),
real_datetime(2014, 8, 25, 18, 15),
real_datetime(2014, 8, 26, 18, 55),
real_datetime(2014, 8, 27, 18, 0),
real_datetime(2014, 8, 28, 18, 45),
real_datetime(2014, 9, 3, 18, 5),
real_datetime(2014, 9, 4, 18, 50),
real_datetime(2014, 9, 7, 17, 40),
real_datetime(2014, 9, 8, 18, 25),
real_datetime(2014, 9, 16, 17, 35),
real_datetime(2014, 9, 16, 19, 15),
real_datetime(2014, 9, 17, 18, 20),
real_datetime(2014, 9, 18, 19, 0),
real_datetime(2014, 9, 19, 18, 5),
real_datetime(2014, 9, 20, 18, 50),
real_datetime(2014, 9, 23, 17, 40),
real_datetime(2014, 9, 23, 19, 20),
real_datetime(2014, 9, 24, 18, 25),
real_datetime(2014, 9, 25, 19, 10),
real_datetime(2014, 9, 26, 18, 15),
real_datetime(2014, 9, 27, 18, 55),
real_datetime(2014, 9, 29, 18, 45),
real_datetime(2014, 10, 12, 18, 5),
real_datetime(2014, 10, 12, 18, 10),
real_datetime(2014, 10, 12, 18, 15),
real_datetime(2014, 10, 23, 17, 55),
real_datetime(2014, 10, 26, 18, 25),
real_datetime(2014, 11, 11, 18, 25),
real_datetime(2015, 5, 1, 18, 1), real_datetime(2015, 5, 1, 18, 5),
real_datetime(2015, 5, 1, 18, 10),
real_datetime(2015, 5, 2, 18, 50),
real_datetime(2015, 5, 3, 17, 55),
real_datetime(2015, 5, 7, 19, 10),
real_datetime(2015, 5, 11, 18, 45),
real_datetime(2015, 5, 23, 19, 10),
real_datetime(2015, 6, 3, 18, 50),
real_datetime(2015, 6, 10, 18, 55),
real_datetime(2015, 7, 20, 18, 5),
real_datetime(2015, 7, 21, 18, 50),
real_datetime(2015, 7, 23, 18, 35),
real_datetime(2015, 7, 27, 16, 26),
real_datetime(2015, 7, 27, 18, 10),
real_datetime(2015, 7, 27, 18, 15),
real_datetime(2015, 7, 28, 18, 55),
real_datetime(2015, 8, 2, 19, 15),
real_datetime(2015, 8, 13, 18, 55),
real_datetime(2015, 8, 22, 18, 50),
real_datetime(2015, 9, 6, 18, 5),
real_datetime(2015, 9, 14, 18, 55),
real_datetime(2015, 9, 17, 17, 50),
real_datetime(2015, 9, 17, 19, 25),
real_datetime(2015, 9, 21, 19, 0),
real_datetime(2015, 9, 22, 18, 5),
real_datetime(2015, 9, 23, 18, 50),
real_datetime(2015, 9, 24, 17, 55),
real_datetime(2016, 4, 16, 19, 0),
real_datetime(2016, 4, 23, 19, 10),
real_datetime(2016, 5, 6, 18, 0),
real_datetime(2016, 5, 18, 19, 0),
real_datetime(2016, 5, 23, 17, 40),
real_datetime(2016, 6, 12, 18, 55),
real_datetime(2016, 6, 17, 19, 15),
real_datetime(2016, 6, 19, 19, 0),
real_datetime(2016, 6, 21, 18, 50),
real_datetime(2016, 6, 24, 17, 40),
real_datetime(2016, 6, 30, 18, 45),
real_datetime(2016, 7, 5, 19, 0),
real_datetime(2016, 7, 12, 19, 5),
real_datetime(2016, 8, 1, 18, 45),
real_datetime(2016, 8, 7, 18, 5),
real_datetime(2016, 8, 10, 18, 35),
real_datetime(2016, 8, 19, 18, 30),
real_datetime(2016, 9, 27, 18, 35),
real_datetime(2016, 10, 17, 18, 10),
real_datetime(2017, 4, 8, 17, 40),
real_datetime(2017, 4, 12, 18, 55),
real_datetime(2017, 4, 14, 18, 45),
real_datetime(2017, 4, 23, 18, 35),
real_datetime(2017, 5, 7, 18, 50),
real_datetime(2017, 5, 8, 17, 55),
real_datetime(2017, 5, 15, 18, 0),
real_datetime(2017, 5, 16, 18, 45),
real_datetime(2017, 6, 1, 18, 45),
real_datetime(2017, 6, 3, 18, 30),
real_datetime(2017, 6, 8, 18, 50),
real_datetime(2017, 6, 11, 17, 40),
real_datetime(2017, 6, 11, 19, 20),
real_datetime(2017, 6, 20, 19, 15),
real_datetime(2017, 6, 24, 18, 50),
real_datetime(2017, 7, 2, 18, 0),
real_datetime(2017, 7, 4, 17, 50),
real_datetime(2017, 7, 9, 18, 5),
real_datetime(2017, 7, 15, 19, 5),
real_datetime(2017, 7, 17, 18, 55),
real_datetime(2017, 7, 18, 18, 0),
real_datetime(2017, 7, 19, 18, 45),
real_datetime(2017, 7, 25, 18, 5),
real_datetime(2017, 7, 29, 19, 20),
real_datetime(2017, 7, 31, 19, 5),
real_datetime(2017, 8, 2, 18, 55),
real_datetime(2017, 8, 16, 19, 5),
real_datetime(2017, 8, 20, 18, 45),
real_datetime(2017, 8, 25, 19, 0),
real_datetime(2017, 8, 27, 18, 50),
real_datetime(2017, 8, 30, 19, 20),
real_datetime(2017, 9, 5, 18, 45),
real_datetime(2017, 9, 9, 18, 20),
real_datetime(2017, 9, 15, 19, 20),
real_datetime(2017, 9, 16, 18, 25),
real_datetime(2017, 9, 17, 19, 5),
real_datetime(2017, 9, 20, 18, 0),
real_datetime(2017, 9, 21, 18, 45),
real_datetime(2017, 9, 22, 19, 25),
real_datetime(2017, 9, 23, 18, 30),
real_datetime(2017, 9, 24, 19, 15),
real_datetime(2017, 9, 26, 19, 0),
real_datetime(2017, 10, 1, 17, 40),
real_datetime(2017, 10, 2, 18, 25),
real_datetime(2017, 10, 3, 19, 5),
real_datetime(2017, 10, 5, 18, 55),
real_datetime(2017, 10, 9, 18, 30),
real_datetime(2017, 10, 16, 18, 35),
real_datetime(2017, 10, 17, 17, 40),
real_datetime(2017, 10, 18, 18, 25),
real_datetime(2017, 10, 19, 19, 5),
real_datetime(2017, 10, 20, 18, 10),
real_datetime(2017, 10, 22, 18, 0)], dtype=object)e_m_ds.chlorophyll.attrs['_FillValue']-999.0# In chlorophyll array, replace -999 with nan
nan_e_m_ds_chlorophyll = e_m_ds.chlorophyll.where(e_m_ds.chlorophyll.values != e_m_ds.chlorophyll.attrs['_FillValue'])
#print(nan_e_m_ds_chlorophyll[0,100,:] )
print(nan_e_m_ds_chlorophyll)<xarray.DataArray 'chlorophyll' (time: 506, latitude: 200, longitude: 500)>
array([[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
...
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]]])
Coordinates:
* time (time) float64 1.029e+09 1.029e+09 ... 1.509e+09 1.509e+09
* latitude (latitude) float64 41.01 41.02 41.03 41.04 ... 42.98 42.99 43.0
* longitude (longitude) float64 -83.5 -83.49 -83.48 ... -78.53 -78.52 -78.51
Attributes:
_FillValue: -999.0
colorBarMaximum: 30.0
colorBarMinimum: 1.0
grid_mapping: crs
ioos_category: Ocean Color
long_name: Chlorophyll-a, Aqua MODIS, EXPERIMENTAL
standard_name: concentration_of_chlorophyll_in_sea_water
units: ug/L- Compute the monthly mean over the region data :
# Create list of string contains 'year month day hours minutes seconds'
d_list = []
[ d_list.append(dt.strftime("%Y %m %d %H %M %S")) for dt in e_m_dates]
#print(min(d_list))
#print(max(d_list))
mn = '08'
yr = '2009'
i_list = []
for n, d in enumerate(d_list):
d_t = d.split()
if d_t[0] == yr and d_t[1] == mn:
# print( n, d_t)
i_list.append(n) # get all index of data for yr and mn
#print(n, d)
#print(i_list)
# axis=0 is time line
chl_avg_img = nan_e_m_ds_chlorophyll.values[i_list[0]:i_list[-1]].mean(axis=0)
print(chl_avg_img.shape)(200, 500)# find max and min value in chl_avg_img
print(np.nanmin(chl_avg_img))
print(np.nanmax(chl_avg_img))0.4942583243052167
100.5# number of colors
levs = np.arange(np.nanmin(chl_avg_img), np.nanmax(chl_avg_img), 0.3)
len(levs)334# init a color list
jet=["blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"]
cm = LinearSegmentedColormap.from_list('my_jet', jet, N=len(levs))-Draw the image of monthly mean
plt.subplots(figsize=(10, 5))
#plot image chl_avg_img
plt.contourf(e_m_ds.longitude, e_m_ds.latitude, chl_avg_img, levs,cmap=cm)
#plot the color scale
plt.colorbar()
#example of how to add points to the map
#plt.scatter(np.linspace(-82,-80.5,num=4),np.repeat(42,4),c='black')
#example of how to add a contour line
#step = np.arange(1,100, 10)
#plt.contour(e_m_ds.longitude, e_m_ds.latitude, chl_avg_img,levels=step,linewidths=1)
#plot title
plt.title("Lake Erie Average CHL - " + yr + '-' + mn)
plt.show()
- Compute the Lake Erie chlorophyll monthly mean from 2002 to 2017
d_list = []
[ d_list.append(dt.strftime("%Y %m %d %H %M %S")) for dt in e_m_dates]
#print(min(d_list))
#print(max(d_list))
m_yr_list = []
[ m_yr_list.append(str(dt.year)) for dt in e_m_dates if str(dt.year) not in m_yr_list ]
print(m_yr_list)
print(len(m_yr_list))
mn_list = ['01','02','03','04','05','06','07','08','09','10','11','12']
m_chl_avg_list = []
for yr in m_yr_list:
for mn in mn_list:
i_list = []
for n, d in enumerate(d_list):
d_t = d.split()
#print(type(yr), type(mn), d_t)
if d_t[0] == yr and d_t[1] == mn:
#print( n, d_t)
i_list.append(n) # get all index of data for yr and mn
#print(i_list, 'aaa')
if i_list:
#print('bbb')
# axis=0 is time line
m_chl_avg = np.nanmean(nan_e_m_ds_chlorophyll.values[i_list[0]:i_list[-1]],axis=(0,1,2))
#print(i_list)
#print('ccc', chl_avg)
else:
m_chl_avg = np.NAN
#print(yr, mn, chl_avg)
m_chl_avg_list.append(m_chl_avg) # add each month mean data into list
print(len(m_chl_avg_list))
print(m_chl_avg_list)['2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']
16
192
[nan, nan, nan, nan, nan, nan, nan, 5.435731696799938, 5.390267233342085, 8.081265781572409, nan, nan, nan, nan, nan, nan, nan, 6.781491128619841, 6.109469974086832, 4.314376310517935, 6.79798886383254, 7.093404713878546, nan, nan, nan, nan, nan, nan, nan, nan, nan, 5.368433526104296, 7.64127887019175, 10.885647119544341, nan, nan, nan, nan, nan, nan, nan, 5.840624163588643, 5.249259500793706, 4.616988228955723, 7.096526864254263, 4.819230418185895, nan, nan, nan, nan, nan, nan, nan, 4.510100122676393, 4.086877241390418, 8.282441959350821, nan, nan, nan, nan, nan, nan, nan, nan, nan, 6.047208513833891, 4.158334577045406, 5.306521407370655, 4.870477621191157, nan, nan, nan, nan, nan, nan, nan, nan, 4.399793136125678, 4.948825492811119, 6.928067277764852, 7.537945121660116, 10.463871652015316, nan, nan, nan, nan, nan, nan, nan, 5.468607769352359, 4.331433124523354, 4.19491169121446, 5.51911800736798, nan, nan, nan, nan, nan, nan, nan, 7.118216527176936, 5.497184958386398, 6.816787462061543, 7.384876000467598, 8.352871379209835, 17.20921945662595, nan, nan, nan, nan, nan, nan, 8.273948811094096, 9.504673836426932, 6.30459248163054, 8.498188201950223, 4.757683522841076, 7.789956015198095, nan, nan, nan, nan, nan, nan, 5.350309186444175, 5.087124626496798, 5.466797826682114, 6.545372806489798, 10.046555535267995, nan, nan, nan, nan, nan, nan, nan, nan, 5.419905113305022, 6.579744183612031, 6.078049791451018, 12.294146096066456, 12.653713855241293, nan, nan, nan, nan, nan, nan, 5.380835510589356, 6.1975559710383115, 4.153931307969729, 4.164349087430823, 7.613671326357655, 11.032704445403692, nan, nan, nan, nan, nan, nan, 6.1890473994776, 9.137474061305161, 12.006564368910517, 13.846336174760882, 12.386790262293644, nan, nan, nan, nan, nan, nan, 10.048730617740274, 7.629785864421565, 5.541983322992535, 3.1488706872830643, 5.253753071859053, nan, nan, nan, nan, nan, nan, nan, 12.166465731425616, 11.707058952935256, 6.139350735676277, 5.360138504826018, 5.020559277856057, 7.541953126812535, 13.605299324152273, nan, nan]x = np.linspace(2002, 2018,num=192) # contains data from 2002 to 2017 (not include 2018)
#units = e_m_ds.chlorophyll.attrs['units']
plt.figure(figsize=(12,5))
plt.plot(x,m_chl_avg_list,label='MODIS CHL',c='red',marker='.',linestyle='-')
plt.ylabel('Chl-a (mg/m^3)')
plt.title("Lake Erie Monthly Average CHL " + m_yr_list[0] + '-' + m_yr_list[-1])
plt.legend()
Get Lake Erie Dailly Average VIIRS data
url2='https://apps.glerl.noaa.gov/erddap/griddap/LE_CHL_VIIRS_Daily.nc?Chlorophyll%5B(2023-01-04T18:47:05Z):1:(2023-12-28T18:32:33Z)%5D%5B(41.2690208353804):1:(43.017997272827)%5D%5B(-83.6574899492178):1:(-78.4429490894234)%5D'
urllib.request.urlretrieve(url2, "e_viirs_chl.nc")('e_viirs_chl.nc', <http.client.HTTPMessage at 0x1a20d832610>)e_v_ds = xr.open_dataset('e_viirs_chl.nc',decode_cf=False)print(e_v_ds)<xarray.Dataset>
Dimensions: (time: 544, latitude: 271, longitude: 806)
Coordinates:
* time (time) float64 1.673e+09 1.673e+09 ... 1.704e+09 1.704e+09
* latitude (latitude) float64 41.27 41.28 41.28 ... 43.01 43.01 43.02
* longitude (longitude) float64 -83.66 -83.65 -83.64 ... -78.45 -78.44
Data variables:
Chlorophyll (time, latitude, longitude) float32 ...
Attributes: (12/34)
cdm_data_type: Grid
colorBarMaximum: 30.0
colorBarMinimum: 1.0
colorBarScale: Log
Conventions: CF-1.6, COARDS, ACDD-1.3
Easternmost_Easting: -78.4429490894234
... ...
summary: Color Producing Agent (CPA) Chlorophyll, ...
testOutOfDate: now-18days
time_coverage_end: 2023-12-28T18:32:33Z
time_coverage_start: 2023-01-04T18:47:05Z
title: Color Producing Agent (CPA) Chlorophyll, ...
Westernmost_Easting: -83.6574899492178nan_e_v_ds_chlorophyll = e_v_ds.Chlorophyll.where(e_v_ds.Chlorophyll.values != e_v_ds.Chlorophyll.attrs['_FillValue'])
v_chl_avg = np.nanmean(nan_e_v_ds_chlorophyll,axis=(1,2))
#print(v_chl_avg)
#print(len(v_chl_avg))e_v_dates=nc.num2date(e_v_ds.time,e_v_ds.time.units, only_use_cftime_datetimes=False,
only_use_python_datetimes=True )
e_v_datesarray([real_datetime(2023, 1, 4, 18, 47, 5),
real_datetime(2023, 1, 5, 18, 28, 10),
real_datetime(2023, 1, 7, 17, 50, 19),
real_datetime(2023, 1, 7, 19, 31, 19),
real_datetime(2023, 1, 8, 19, 12, 23),
real_datetime(2023, 1, 9, 17, 12, 27),
real_datetime(2023, 1, 9, 17, 13, 53),
real_datetime(2023, 1, 9, 18, 53, 28),
real_datetime(2023, 1, 10, 18, 34, 32),
real_datetime(2023, 1, 11, 18, 15, 36),
real_datetime(2023, 1, 14, 17, 18, 51),
real_datetime(2023, 1, 14, 18, 59, 52),
real_datetime(2023, 1, 15, 16, 59, 56),
real_datetime(2023, 1, 15, 17, 1, 21),
real_datetime(2023, 1, 15, 18, 40, 56),
real_datetime(2023, 1, 16, 18, 22),
real_datetime(2023, 1, 17, 18, 3, 5),
real_datetime(2023, 1, 19, 17, 25, 14),
real_datetime(2023, 1, 19, 19, 6, 14),
real_datetime(2023, 1, 24, 17, 31, 37),
real_datetime(2023, 1, 24, 19, 12, 38),
real_datetime(2023, 1, 31, 18, 41, 10),
real_datetime(2023, 2, 1, 18, 22, 15),
real_datetime(2023, 2, 2, 18, 3, 19),
real_datetime(2023, 2, 3, 17, 44, 23),
real_datetime(2023, 2, 3, 19, 25, 24),
real_datetime(2023, 2, 4, 17, 25, 28),
real_datetime(2023, 2, 4, 19, 6, 28),
real_datetime(2023, 2, 6, 18, 28, 37),
real_datetime(2023, 2, 7, 18, 9, 41),
real_datetime(2023, 2, 8, 17, 50, 47),
real_datetime(2023, 2, 8, 19, 31, 46),
real_datetime(2023, 2, 9, 17, 31, 52),
real_datetime(2023, 2, 9, 19, 12, 52),
real_datetime(2023, 2, 10, 18, 52, 31),
real_datetime(2023, 2, 10, 18, 53, 56),
real_datetime(2023, 2, 11, 18, 33, 35),
real_datetime(2023, 2, 11, 18, 35, 1),
real_datetime(2023, 2, 12, 18, 14, 40),
real_datetime(2023, 2, 12, 18, 16, 5),
real_datetime(2023, 2, 13, 17, 57, 9),
real_datetime(2023, 2, 13, 19, 36, 44),
real_datetime(2023, 2, 13, 19, 38, 10),
real_datetime(2023, 2, 14, 17, 38, 14),
real_datetime(2023, 2, 14, 19, 19, 14),
real_datetime(2023, 2, 15, 17, 19, 18),
real_datetime(2023, 2, 15, 18, 58, 53),
real_datetime(2023, 2, 15, 19, 0, 18),
real_datetime(2023, 2, 17, 18, 22, 29),
real_datetime(2023, 2, 18, 18, 2, 8),
real_datetime(2023, 2, 18, 18, 3, 33),
real_datetime(2023, 2, 19, 17, 44, 38),
real_datetime(2023, 2, 19, 19, 24, 12),
real_datetime(2023, 2, 19, 19, 25, 38),
real_datetime(2023, 2, 20, 17, 25, 42),
real_datetime(2023, 2, 20, 19, 5, 17),
real_datetime(2023, 2, 20, 19, 6, 42),
real_datetime(2023, 2, 21, 17, 6, 46),
real_datetime(2023, 2, 21, 18, 46, 21),
real_datetime(2023, 2, 21, 18, 47, 47),
real_datetime(2023, 2, 23, 18, 8, 30),
real_datetime(2023, 2, 24, 17, 51),
real_datetime(2023, 2, 24, 19, 30, 34),
real_datetime(2023, 2, 24, 19, 32),
real_datetime(2023, 2, 25, 19, 13, 4),
real_datetime(2023, 2, 26, 17, 13, 10),
real_datetime(2023, 2, 26, 18, 52, 45),
real_datetime(2023, 2, 28, 18, 14, 54),
real_datetime(2023, 3, 1, 17, 57, 23),
real_datetime(2023, 3, 1, 19, 38, 24),
real_datetime(2023, 3, 2, 17, 38, 28),
real_datetime(2023, 3, 2, 19, 18, 3),
real_datetime(2023, 3, 5, 18, 21, 17),
real_datetime(2023, 3, 5, 18, 22, 43),
real_datetime(2023, 3, 6, 18, 2, 22),
real_datetime(2023, 3, 7, 17, 44, 52),
real_datetime(2023, 3, 7, 19, 24, 26),
real_datetime(2023, 3, 7, 19, 25, 52),
real_datetime(2023, 3, 8, 17, 25, 56),
real_datetime(2023, 3, 11, 18, 10, 9),
real_datetime(2023, 3, 15, 16, 54, 28),
real_datetime(2023, 3, 15, 18, 35, 29),
real_datetime(2023, 3, 16, 18, 15, 8),
real_datetime(2023, 3, 16, 18, 16, 33),
real_datetime(2023, 3, 18, 19, 18, 16),
real_datetime(2023, 3, 19, 17, 19, 46),
real_datetime(2023, 3, 19, 18, 59, 21),
real_datetime(2023, 3, 20, 17, 0, 52),
real_datetime(2023, 3, 20, 18, 40, 25),
real_datetime(2023, 3, 21, 18, 21, 31),
real_datetime(2023, 3, 21, 18, 22, 57),
real_datetime(2023, 3, 24, 17, 26, 10),
real_datetime(2023, 3, 25, 17, 7, 14),
real_datetime(2023, 3, 25, 18, 46, 49),
real_datetime(2023, 3, 25, 18, 48, 14),
real_datetime(2023, 3, 26, 18, 27, 53),
real_datetime(2023, 3, 26, 18, 29, 19),
real_datetime(2023, 3, 27, 18, 10, 23),
real_datetime(2023, 3, 28, 17, 51, 29),
real_datetime(2023, 3, 29, 17, 32, 33),
real_datetime(2023, 3, 29, 19, 13, 34),
real_datetime(2023, 3, 30, 17, 13, 38),
real_datetime(2023, 3, 30, 18, 53, 13),
real_datetime(2023, 3, 30, 18, 54, 38),
real_datetime(2023, 4, 1, 18, 16, 47),
real_datetime(2023, 4, 2, 17, 57, 51),
real_datetime(2023, 4, 2, 19, 37, 26),
real_datetime(2023, 4, 2, 19, 38, 51),
real_datetime(2023, 4, 4, 17, 20, 2),
real_datetime(2023, 4, 4, 19, 1),
real_datetime(2023, 4, 6, 18, 23, 10),
real_datetime(2023, 4, 7, 18, 4, 15),
real_datetime(2023, 4, 8, 17, 45, 19),
real_datetime(2023, 4, 8, 19, 24, 54),
real_datetime(2023, 4, 8, 19, 26, 19),
real_datetime(2023, 4, 9, 17, 26, 23),
real_datetime(2023, 4, 9, 19, 5, 58),
real_datetime(2023, 4, 9, 19, 7, 24),
real_datetime(2023, 4, 10, 17, 7, 28),
real_datetime(2023, 4, 10, 18, 47, 3),
real_datetime(2023, 4, 10, 18, 48, 28),
real_datetime(2023, 4, 11, 18, 28, 7),
real_datetime(2023, 4, 11, 18, 29, 32),
real_datetime(2023, 4, 12, 18, 9, 13),
real_datetime(2023, 4, 12, 18, 10, 38),
real_datetime(2023, 4, 13, 17, 51, 43),
real_datetime(2023, 4, 13, 19, 31, 18),
real_datetime(2023, 4, 13, 19, 32, 43),
real_datetime(2023, 4, 14, 17, 32, 47),
real_datetime(2023, 4, 14, 19, 12, 22),
real_datetime(2023, 4, 14, 19, 13, 47),
real_datetime(2023, 4, 15, 17, 13, 52),
real_datetime(2023, 4, 15, 18, 53, 26),
real_datetime(2023, 4, 15, 18, 54, 52),
real_datetime(2023, 4, 16, 16, 54, 56),
real_datetime(2023, 4, 16, 18, 34, 31),
real_datetime(2023, 4, 16, 18, 35, 56),
real_datetime(2023, 4, 18, 17, 58, 5),
real_datetime(2023, 4, 19, 17, 39, 11),
real_datetime(2023, 4, 19, 19, 18, 44),
real_datetime(2023, 4, 19, 19, 20, 9),
real_datetime(2023, 4, 20, 18, 59, 50),
real_datetime(2023, 4, 21, 17, 1, 20),
real_datetime(2023, 4, 22, 18, 21, 59),
real_datetime(2023, 4, 23, 18, 3, 3),
real_datetime(2023, 4, 23, 18, 4, 28),
real_datetime(2023, 4, 24, 17, 44, 7),
real_datetime(2023, 4, 24, 17, 45, 33),
real_datetime(2023, 4, 24, 19, 25, 8),
real_datetime(2023, 4, 25, 17, 26, 37),
real_datetime(2023, 4, 26, 17, 7, 43),
real_datetime(2023, 4, 26, 18, 47, 16),
real_datetime(2023, 4, 27, 18, 28, 23),
real_datetime(2023, 4, 30, 17, 31, 36),
real_datetime(2023, 4, 30, 17, 33, 1),
real_datetime(2023, 4, 30, 19, 12, 36),
real_datetime(2023, 5, 1, 17, 14, 5),
real_datetime(2023, 5, 1, 18, 53, 40),
real_datetime(2023, 5, 3, 18, 15, 49),
real_datetime(2023, 5, 4, 17, 56, 53),
real_datetime(2023, 5, 4, 19, 37, 53),
real_datetime(2023, 5, 5, 17, 37, 59),
real_datetime(2023, 5, 5, 17, 39, 25),
real_datetime(2023, 5, 5, 19, 18, 58),
real_datetime(2023, 5, 6, 17, 19, 4),
real_datetime(2023, 5, 6, 17, 20, 29),
real_datetime(2023, 5, 6, 19, 0, 4),
real_datetime(2023, 5, 7, 18, 41, 8),
real_datetime(2023, 5, 8, 18, 22, 13),
real_datetime(2023, 5, 10, 17, 44, 21),
real_datetime(2023, 5, 10, 19, 25, 22),
real_datetime(2023, 5, 11, 17, 25, 26),
real_datetime(2023, 5, 11, 19, 6, 26),
real_datetime(2023, 5, 12, 17, 6, 32),
real_datetime(2023, 5, 12, 17, 7, 57),
real_datetime(2023, 5, 12, 18, 47, 30),
real_datetime(2023, 5, 13, 18, 28, 36),
real_datetime(2023, 5, 15, 17, 50, 45),
real_datetime(2023, 5, 15, 19, 31, 45),
real_datetime(2023, 5, 16, 19, 12, 50),
real_datetime(2023, 5, 17, 17, 12, 54),
real_datetime(2023, 5, 17, 18, 53, 54),
real_datetime(2023, 5, 18, 18, 34, 58),
real_datetime(2023, 5, 19, 18, 16, 3),
real_datetime(2023, 5, 20, 17, 57, 9),
real_datetime(2023, 5, 21, 17, 38, 13),
real_datetime(2023, 5, 23, 18, 41, 22),
real_datetime(2023, 5, 24, 18, 22, 27),
real_datetime(2023, 5, 25, 18, 3, 31),
real_datetime(2023, 5, 26, 17, 44, 35),
real_datetime(2023, 5, 26, 19, 24, 10),
real_datetime(2023, 5, 26, 19, 25, 35),
real_datetime(2023, 5, 27, 17, 25, 40),
real_datetime(2023, 5, 27, 19, 5, 14),
real_datetime(2023, 5, 27, 19, 6, 40),
real_datetime(2023, 5, 28, 18, 46, 19),
real_datetime(2023, 5, 28, 18, 47, 44),
real_datetime(2023, 5, 29, 18, 27, 25),
real_datetime(2023, 5, 29, 18, 28, 50),
real_datetime(2023, 5, 30, 18, 8, 29),
real_datetime(2023, 5, 30, 18, 9, 55),
real_datetime(2023, 5, 31, 17, 50, 59),
real_datetime(2023, 5, 31, 19, 30, 34),
real_datetime(2023, 5, 31, 19, 31, 59),
real_datetime(2023, 6, 1, 17, 32, 3),
real_datetime(2023, 6, 1, 19, 11, 38),
real_datetime(2023, 6, 1, 19, 13, 4),
real_datetime(2023, 6, 2, 17, 13, 8),
real_datetime(2023, 6, 2, 18, 52, 43),
real_datetime(2023, 6, 2, 18, 54, 8),
real_datetime(2023, 6, 3, 18, 33, 47),
real_datetime(2023, 6, 3, 18, 35, 12),
real_datetime(2023, 6, 4, 18, 14, 51),
real_datetime(2023, 6, 4, 18, 16, 17),
real_datetime(2023, 6, 5, 17, 57, 23),
real_datetime(2023, 6, 6, 17, 38, 27),
real_datetime(2023, 6, 6, 19, 18, 2),
real_datetime(2023, 6, 7, 17, 19, 32),
real_datetime(2023, 6, 7, 18, 59, 6),
real_datetime(2023, 6, 8, 18, 40, 11),
real_datetime(2023, 6, 9, 18, 21, 15),
real_datetime(2023, 6, 9, 18, 22, 40),
real_datetime(2023, 6, 10, 18, 2, 19),
real_datetime(2023, 6, 10, 18, 3, 45),
real_datetime(2023, 6, 11, 17, 44, 49),
real_datetime(2023, 6, 12, 19, 5, 28),
real_datetime(2023, 6, 13, 17, 7),
real_datetime(2023, 6, 13, 18, 46, 33),
real_datetime(2023, 6, 14, 18, 27, 39),
real_datetime(2023, 6, 15, 18, 8, 43),
real_datetime(2023, 6, 16, 17, 49, 48),
real_datetime(2023, 6, 17, 17, 30, 52),
real_datetime(2023, 6, 17, 17, 32, 17),
real_datetime(2023, 6, 17, 19, 11, 52),
real_datetime(2023, 6, 18, 17, 13, 22),
real_datetime(2023, 6, 18, 18, 52, 57),
real_datetime(2023, 6, 19, 18, 34, 1),
real_datetime(2023, 6, 20, 18, 15, 5),
real_datetime(2023, 6, 21, 17, 56, 10),
real_datetime(2023, 6, 21, 19, 37, 10),
real_datetime(2023, 6, 22, 17, 37, 16),
real_datetime(2023, 6, 22, 19, 18, 14),
real_datetime(2023, 6, 23, 18, 59, 20),
real_datetime(2023, 6, 24, 17, 0, 50),
real_datetime(2023, 6, 24, 18, 40, 25),
real_datetime(2023, 6, 25, 18, 21, 29),
real_datetime(2023, 6, 26, 18, 2, 34),
real_datetime(2023, 6, 27, 17, 43, 38),
real_datetime(2023, 6, 27, 19, 24, 38),
real_datetime(2023, 6, 28, 17, 24, 42),
real_datetime(2023, 6, 28, 17, 26, 8),
real_datetime(2023, 6, 28, 19, 5, 43),
real_datetime(2023, 6, 29, 17, 7, 14),
real_datetime(2023, 6, 29, 18, 46, 47),
real_datetime(2023, 6, 30, 18, 27, 53),
real_datetime(2023, 7, 2, 17, 50, 2),
real_datetime(2023, 7, 2, 19, 31, 2),
real_datetime(2023, 7, 3, 17, 31, 6),
real_datetime(2023, 7, 3, 17, 32, 32),
real_datetime(2023, 7, 3, 19, 12, 6),
real_datetime(2023, 7, 4, 17, 12, 10),
real_datetime(2023, 7, 4, 17, 13, 36),
real_datetime(2023, 7, 4, 18, 53, 11),
real_datetime(2023, 7, 5, 16, 54, 40),
real_datetime(2023, 7, 5, 18, 34, 15),
real_datetime(2023, 7, 6, 18, 15, 19),
real_datetime(2023, 7, 7, 17, 56, 24),
real_datetime(2023, 7, 7, 19, 37, 24),
real_datetime(2023, 7, 8, 17, 37, 30),
real_datetime(2023, 7, 8, 19, 18, 28),
real_datetime(2023, 7, 9, 17, 18, 34),
real_datetime(2023, 7, 9, 18, 59, 35),
real_datetime(2023, 7, 10, 17, 1, 4),
real_datetime(2023, 7, 10, 18, 40, 39),
real_datetime(2023, 7, 11, 18, 21, 43),
real_datetime(2023, 7, 12, 18, 2, 48),
real_datetime(2023, 7, 13, 17, 43, 52),
real_datetime(2023, 7, 13, 19, 24, 52),
real_datetime(2023, 7, 14, 17, 24, 56),
real_datetime(2023, 7, 14, 19, 5, 57),
real_datetime(2023, 7, 16, 18, 28, 5),
real_datetime(2023, 7, 17, 18, 9, 11),
real_datetime(2023, 7, 18, 17, 50, 16),
real_datetime(2023, 7, 18, 19, 31, 16),
real_datetime(2023, 7, 19, 17, 31, 20),
real_datetime(2023, 7, 19, 19, 12, 20),
real_datetime(2023, 7, 20, 17, 12, 25),
real_datetime(2023, 7, 20, 17, 13, 50),
real_datetime(2023, 7, 20, 18, 53, 25),
real_datetime(2023, 7, 21, 16, 54, 54),
real_datetime(2023, 7, 21, 18, 34, 29),
real_datetime(2023, 7, 22, 18, 15, 33),
real_datetime(2023, 7, 23, 17, 56, 38),
real_datetime(2023, 7, 24, 17, 37, 44),
real_datetime(2023, 7, 24, 19, 18, 42),
real_datetime(2023, 7, 25, 18, 59, 47),
real_datetime(2023, 7, 27, 18, 21, 56),
real_datetime(2023, 7, 28, 18, 3, 1),
real_datetime(2023, 7, 29, 17, 44, 7),
real_datetime(2023, 7, 29, 19, 25, 5),
real_datetime(2023, 7, 30, 17, 25, 11),
real_datetime(2023, 7, 30, 19, 6, 11),
real_datetime(2023, 7, 31, 17, 6, 16),
real_datetime(2023, 7, 31, 18, 47, 16),
real_datetime(2023, 8, 1, 18, 28, 20),
real_datetime(2023, 8, 2, 18, 9, 25),
real_datetime(2023, 8, 3, 17, 50, 29),
real_datetime(2023, 8, 3, 19, 31, 29),
real_datetime(2023, 8, 4, 17, 31, 33),
real_datetime(2023, 8, 4, 19, 12, 33),
real_datetime(2023, 8, 5, 18, 53, 38),
real_datetime(2023, 8, 6, 16, 53, 44),
real_datetime(2023, 8, 6, 18, 34, 44),
real_datetime(2023, 8, 7, 18, 15, 48),
real_datetime(2023, 8, 8, 17, 56, 53),
real_datetime(2023, 8, 8, 19, 37, 53),
real_datetime(2023, 8, 9, 17, 37, 57),
real_datetime(2023, 8, 10, 17, 19, 1),
real_datetime(2023, 8, 10, 18, 58, 36),
real_datetime(2023, 8, 10, 19, 0, 2),
real_datetime(2023, 8, 11, 17, 0, 6),
real_datetime(2023, 8, 11, 18, 39, 41),
real_datetime(2023, 8, 11, 18, 41, 6),
real_datetime(2023, 8, 12, 18, 20, 45),
real_datetime(2023, 8, 12, 18, 22, 10),
real_datetime(2023, 8, 13, 18, 3, 15),
real_datetime(2023, 8, 15, 17, 25, 25),
real_datetime(2023, 8, 15, 19, 5),
real_datetime(2023, 8, 15, 19, 6, 25),
real_datetime(2023, 8, 16, 17, 6, 30),
real_datetime(2023, 8, 16, 18, 46, 4),
real_datetime(2023, 8, 16, 18, 47, 30),
real_datetime(2023, 8, 17, 18, 27, 9),
real_datetime(2023, 8, 17, 18, 28, 34),
real_datetime(2023, 8, 18, 18, 8, 13),
real_datetime(2023, 8, 18, 18, 9, 39),
real_datetime(2023, 8, 19, 17, 50, 43),
real_datetime(2023, 8, 19, 19, 30, 18),
real_datetime(2023, 8, 19, 19, 31, 43),
real_datetime(2023, 8, 20, 17, 31, 47),
real_datetime(2023, 8, 20, 19, 11, 22),
real_datetime(2023, 8, 20, 19, 12, 47),
real_datetime(2023, 8, 21, 18, 52, 26),
real_datetime(2023, 8, 22, 16, 53, 58),
real_datetime(2023, 8, 22, 18, 33, 33),
real_datetime(2023, 8, 22, 18, 34, 56),
real_datetime(2023, 8, 24, 17, 55, 41),
real_datetime(2023, 8, 24, 17, 57, 7),
real_datetime(2023, 8, 25, 17, 38, 11),
real_datetime(2023, 8, 25, 19, 17, 46),
real_datetime(2023, 8, 26, 17, 19, 15),
real_datetime(2023, 8, 26, 18, 58, 50),
real_datetime(2023, 8, 26, 19, 0, 16),
real_datetime(2023, 8, 27, 17, 0, 20),
real_datetime(2023, 8, 27, 18, 39, 55),
real_datetime(2023, 8, 27, 18, 41, 20),
real_datetime(2023, 8, 28, 18, 20, 59),
real_datetime(2023, 8, 28, 18, 22, 24),
real_datetime(2023, 8, 29, 18, 2, 3),
real_datetime(2023, 8, 29, 18, 3, 29),
real_datetime(2023, 8, 30, 17, 44, 35),
real_datetime(2023, 8, 30, 19, 24, 8),
real_datetime(2023, 8, 31, 17, 25, 39),
real_datetime(2023, 8, 31, 19, 5, 14),
real_datetime(2023, 9, 1, 17, 6, 44),
real_datetime(2023, 9, 1, 18, 46, 18),
real_datetime(2023, 9, 2, 18, 27, 23),
real_datetime(2023, 9, 3, 18, 8, 27),
real_datetime(2023, 9, 4, 17, 49, 31),
real_datetime(2023, 9, 4, 17, 50, 57),
real_datetime(2023, 9, 4, 19, 30, 32),
real_datetime(2023, 9, 5, 17, 30, 36),
real_datetime(2023, 9, 5, 17, 32, 1),
real_datetime(2023, 9, 5, 19, 11, 36),
real_datetime(2023, 9, 6, 17, 13, 7),
real_datetime(2023, 9, 6, 18, 52, 40),
real_datetime(2023, 9, 7, 16, 54, 12),
real_datetime(2023, 9, 7, 18, 33, 47),
real_datetime(2023, 9, 8, 18, 14, 51),
real_datetime(2023, 9, 9, 17, 55, 55),
real_datetime(2023, 9, 10, 17, 37),
real_datetime(2023, 9, 10, 17, 38, 25),
real_datetime(2023, 9, 10, 19, 18),
real_datetime(2023, 9, 11, 17, 18, 4),
real_datetime(2023, 9, 11, 17, 19, 29),
real_datetime(2023, 9, 11, 18, 59, 4),
real_datetime(2023, 9, 12, 18, 40, 9),
real_datetime(2023, 9, 13, 18, 21, 13),
real_datetime(2023, 9, 14, 18, 2, 17),
real_datetime(2023, 9, 15, 17, 43, 23),
real_datetime(2023, 9, 15, 19, 24, 22),
real_datetime(2023, 9, 16, 17, 24, 28),
real_datetime(2023, 9, 16, 19, 5, 28),
real_datetime(2023, 9, 17, 17, 5, 32),
real_datetime(2023, 9, 17, 17, 6, 58),
real_datetime(2023, 9, 17, 18, 46, 32),
real_datetime(2023, 9, 18, 18, 27, 37),
real_datetime(2023, 9, 19, 18, 8, 41),
real_datetime(2023, 9, 20, 17, 49, 46),
real_datetime(2023, 9, 20, 19, 30, 46),
real_datetime(2023, 9, 21, 17, 30, 50),
real_datetime(2023, 9, 21, 19, 11, 50),
real_datetime(2023, 9, 22, 17, 11, 54),
real_datetime(2023, 9, 22, 18, 52, 54),
real_datetime(2023, 9, 23, 18, 33, 59),
real_datetime(2023, 9, 24, 18, 15, 5),
real_datetime(2023, 9, 25, 17, 56, 9),
real_datetime(2023, 9, 26, 17, 37, 14),
real_datetime(2023, 9, 26, 19, 18, 14),
real_datetime(2023, 9, 27, 18, 59, 18),
real_datetime(2023, 9, 28, 18, 40, 23),
real_datetime(2023, 9, 29, 18, 21, 27),
real_datetime(2023, 9, 30, 18, 2, 32),
real_datetime(2023, 10, 1, 17, 43, 36),
real_datetime(2023, 10, 1, 19, 23, 11),
real_datetime(2023, 10, 1, 19, 24, 36),
real_datetime(2023, 10, 2, 17, 24, 42),
real_datetime(2023, 10, 2, 19, 4, 15),
real_datetime(2023, 10, 2, 19, 5, 41),
real_datetime(2023, 10, 3, 17, 5, 46),
real_datetime(2023, 10, 3, 18, 45, 21),
real_datetime(2023, 10, 3, 18, 46, 47),
real_datetime(2023, 10, 4, 18, 26, 26),
real_datetime(2023, 10, 4, 18, 27, 51),
real_datetime(2023, 10, 5, 18, 8, 55),
real_datetime(2023, 10, 6, 17, 50),
real_datetime(2023, 10, 6, 19, 29, 35),
real_datetime(2023, 10, 6, 19, 31),
real_datetime(2023, 10, 7, 17, 31, 4),
real_datetime(2023, 10, 7, 19, 10, 39),
real_datetime(2023, 10, 7, 19, 12, 4),
real_datetime(2023, 10, 8, 17, 12, 9),
real_datetime(2023, 10, 8, 18, 51, 43),
real_datetime(2023, 10, 8, 18, 53, 9),
real_datetime(2023, 10, 9, 16, 53, 13),
real_datetime(2023, 10, 9, 18, 32, 48),
real_datetime(2023, 10, 9, 18, 34, 13),
real_datetime(2023, 10, 11, 17, 54, 57),
real_datetime(2023, 10, 11, 17, 56, 22),
real_datetime(2023, 10, 11, 19, 35, 57),
real_datetime(2023, 10, 12, 17, 37, 28),
real_datetime(2023, 10, 12, 19, 17, 1),
real_datetime(2023, 10, 13, 17, 18, 33),
real_datetime(2023, 10, 15, 18, 20, 16),
real_datetime(2023, 10, 16, 18, 1, 21),
real_datetime(2023, 10, 17, 17, 43, 50),
real_datetime(2023, 10, 18, 17, 24, 55),
real_datetime(2023, 10, 18, 19, 4, 30),
real_datetime(2023, 10, 20, 18, 26, 38),
real_datetime(2023, 10, 21, 18, 7, 43),
real_datetime(2023, 10, 21, 18, 9, 8),
real_datetime(2023, 10, 22, 17, 48, 49),
real_datetime(2023, 10, 22, 17, 50, 14),
real_datetime(2023, 10, 22, 19, 29, 47),
real_datetime(2023, 10, 23, 17, 29, 53),
real_datetime(2023, 10, 23, 17, 31, 19),
real_datetime(2023, 10, 23, 19, 10, 54),
real_datetime(2023, 10, 24, 17, 12, 23),
real_datetime(2023, 10, 24, 18, 51, 58),
real_datetime(2023, 10, 26, 18, 14, 7),
real_datetime(2023, 10, 27, 17, 55, 11),
real_datetime(2023, 10, 27, 17, 56, 37),
real_datetime(2023, 10, 27, 19, 36, 11),
real_datetime(2023, 10, 28, 17, 37, 41),
real_datetime(2023, 10, 28, 19, 17, 16),
real_datetime(2023, 10, 30, 18, 39, 25),
real_datetime(2023, 10, 31, 18, 20, 29),
real_datetime(2023, 11, 2, 17, 44, 4),
real_datetime(2023, 11, 2, 19, 23, 39),
real_datetime(2023, 11, 3, 17, 23, 45),
real_datetime(2023, 11, 3, 17, 25, 10),
real_datetime(2023, 11, 3, 19, 4, 43),
real_datetime(2023, 11, 4, 18, 45, 49),
real_datetime(2023, 11, 5, 18, 26, 54),
real_datetime(2023, 11, 6, 18, 7, 58),
real_datetime(2023, 11, 7, 17, 49, 2),
real_datetime(2023, 11, 7, 17, 50, 28),
real_datetime(2023, 11, 7, 19, 30, 3),
real_datetime(2023, 11, 9, 17, 12, 37),
real_datetime(2023, 11, 9, 18, 52, 11),
real_datetime(2023, 11, 10, 18, 33, 16),
real_datetime(2023, 11, 11, 18, 14, 20),
real_datetime(2023, 11, 12, 17, 55, 25),
real_datetime(2023, 11, 12, 17, 56, 50),
real_datetime(2023, 11, 12, 19, 36, 25),
real_datetime(2023, 11, 13, 17, 36, 31),
real_datetime(2023, 11, 13, 17, 37, 56),
real_datetime(2023, 11, 13, 19, 17, 29),
real_datetime(2023, 11, 14, 17, 17, 35),
real_datetime(2023, 11, 14, 17, 19, 1),
real_datetime(2023, 11, 14, 18, 58, 35),
real_datetime(2023, 11, 15, 17, 0, 5),
real_datetime(2023, 11, 15, 18, 39, 40),
real_datetime(2023, 11, 16, 18, 20, 44),
real_datetime(2023, 11, 18, 17, 42, 53),
real_datetime(2023, 11, 18, 17, 44, 19),
real_datetime(2023, 11, 18, 19, 23, 53),
real_datetime(2023, 11, 19, 17, 23, 58),
real_datetime(2023, 11, 19, 17, 25, 23),
real_datetime(2023, 11, 19, 19, 4, 58),
real_datetime(2023, 11, 20, 18, 46, 2),
real_datetime(2023, 11, 23, 17, 49, 15),
real_datetime(2023, 11, 23, 19, 30, 16),
real_datetime(2023, 11, 24, 17, 30, 22),
real_datetime(2023, 11, 24, 17, 31, 47),
real_datetime(2023, 11, 24, 19, 11, 20),
real_datetime(2023, 11, 25, 17, 11, 26),
real_datetime(2023, 11, 25, 17, 12, 51),
real_datetime(2023, 11, 25, 18, 52, 26),
real_datetime(2023, 11, 27, 18, 14, 35),
real_datetime(2023, 11, 28, 17, 55, 40),
real_datetime(2023, 11, 28, 19, 36, 40),
real_datetime(2023, 11, 29, 17, 36, 44),
real_datetime(2023, 11, 29, 19, 17, 44),
real_datetime(2023, 11, 30, 17, 17, 48),
real_datetime(2023, 11, 30, 18, 58, 49),
real_datetime(2023, 12, 3, 18, 2, 2),
real_datetime(2023, 12, 4, 17, 43, 6),
real_datetime(2023, 12, 6, 17, 5, 17),
real_datetime(2023, 12, 6, 18, 46, 17),
real_datetime(2023, 12, 7, 18, 27, 21),
real_datetime(2023, 12, 8, 18, 8, 26),
real_datetime(2023, 12, 9, 17, 49, 30),
real_datetime(2023, 12, 10, 17, 30, 35),
real_datetime(2023, 12, 11, 18, 52, 39),
real_datetime(2023, 12, 12, 18, 33, 44),
real_datetime(2023, 12, 13, 18, 14, 48),
real_datetime(2023, 12, 14, 17, 55, 53),
real_datetime(2023, 12, 15, 17, 36, 57),
real_datetime(2023, 12, 15, 19, 16, 32),
real_datetime(2023, 12, 15, 19, 17, 57),
real_datetime(2023, 12, 16, 17, 18, 3),
real_datetime(2023, 12, 16, 18, 57, 36),
real_datetime(2023, 12, 16, 18, 59, 2),
real_datetime(2023, 12, 19, 18, 2, 17),
real_datetime(2023, 12, 20, 17, 43, 21),
real_datetime(2023, 12, 20, 19, 22, 56),
real_datetime(2023, 12, 20, 19, 24, 21),
real_datetime(2023, 12, 21, 17, 24, 26),
real_datetime(2023, 12, 21, 19, 4),
real_datetime(2023, 12, 21, 19, 5, 26),
real_datetime(2023, 12, 25, 17, 49, 43),
real_datetime(2023, 12, 26, 19, 10, 23),
real_datetime(2023, 12, 28, 18, 32, 33)], dtype=object)v_chl_avg = np.nanmean(nan_e_v_ds_chlorophyll.values,axis=(1,2))
v_chl_avgarray([ 3.8245378 , 17.05229 , 0.77159315, 1.6202371 , 2.068805 ,
1.7207391 , 2.0681734 , 9.915978 , 12.000377 , 60.60568 ,
2.9275532 , 15.261721 , 2.1003587 , 2.5391452 , 15.000816 ,
18.150581 , 4.041805 , 5.558531 , 14.340515 , 7.0143795 ,
24.486782 , 10.351516 , 9.925964 , 8.393265 , 7.0230336 ,
19.298477 , 13.378698 , 17.380596 , 8.412001 , 18.604963 ,
9.874779 , 34.36881 , 2.2588596 , 3.6034255 , 2.506503 ,
7.9039016 , 28.500935 , 15.452688 , 18.463425 , 11.424103 ,
8.926837 , 29.18119 , 15.104329 , 7.305774 , 12.944991 ,
28.979595 , 31.491842 , 17.951267 , 4.4605803 , 14.379 ,
28.11916 , 9.264345 , 26.880186 , 46.661022 , 14.642408 ,
13.05799 , 13.633649 , 1.418521 , 13.829434 , 5.444851 ,
16.703632 , 20.848345 , 5.0073376 , 43.061474 , 4.4501486 ,
10.821825 , 13.9933815 , 31.618942 , 18.309826 , 0.626642 ,
1.3072004 , 0.5550653 , 18.288877 , 8.388084 , 84.72084 ,
21.264292 , 30.243666 , 8.217334 , 26.758741 , 12.297731 ,
1.0648437 , 7.8218217 , 6.743732 , 8.725778 , 0.37988 ,
20.73484 , 37.852497 , 23.765854 , 36.893124 , 43.02589 ,
21.644255 , 7.4811535 , 0.56276786, 21.756771 , 8.571441 ,
34.194794 , 24.734491 , 20.891743 , 19.614132 , 20.337917 ,
0.89833146, 15.468415 , 23.633438 , 12.742027 , 10.469747 ,
19.536373 , 63.70876 , 31.262995 , 12.08394 , 6.5038023 ,
23.642673 , 15.831917 , 9.192328 , 18.234331 , 18.983482 ,
12.585856 , 19.876034 , 10.024232 , 6.4187565 , 11.187998 ,
5.847157 , 15.026388 , 13.55039 , 57.649048 , 10.104386 ,
10.101064 , 25.943897 , 9.368579 , 6.515471 , 10.244264 ,
1.1295458 , 7.0011516 , 10.233358 , 6.5892057 , 5.111359 ,
5.5639024 , 4.002067 , 2.6186795 , 20.945406 , 22.75246 ,
22.861576 , 26.649107 , 3.9389431 , 6.8372855 , 4.1539216 ,
7.945154 , 2.4636204 , 2.8101442 , 2.9989738 , 3.761499 ,
4.040188 , 6.779965 , 4.0392694 , 5.058976 , 3.642377 ,
5.0899043 , 1.535409 , 5.489851 , 12.624435 , 14.986074 ,
19.135656 , 8.674835 , 1.8120102 , 6.0505404 , 4.0770435 ,
4.2323365 , 4.5445585 , 6.407044 , 4.4135103 , 10.111502 ,
2.559734 , 6.3161306 , 4.1022396 , 5.3999968 , 5.693675 ,
4.2225466 , 4.975449 , 5.87973 , 1.8912128 , 4.844044 ,
8.326566 , 21.682102 , 5.796414 , 4.81131 , 1.6386082 ,
4.858289 , 1.0573728 , 5.3810554 , 19.722094 , 12.00176 ,
23.595955 , 31.021114 , 6.9326816 , 5.9504557 , 3.3385806 ,
4.8724666 , 2.2861915 , 12.186277 , 3.0852036 , 30.52236 ,
4.891082 , 4.0605354 , 27.45894 , 12.472047 , 3.3337376 ,
3.500256 , 0.65000206, 1.3446043 , 5.6772294 , 2.0434697 ,
6.4364944 , 3.82695 , 26.283665 , 6.5254254 , 0.35209 ,
1.7417568 , 0.323401 , 1.3526874 , 19.266989 , 16.352032 ,
4.9344935 , 4.533328 , 1.8704015 , 1.9660938 , 2.0816095 ,
0.31560874, 2.6231666 , 6.254045 , 3.8463306 , 1.3136346 ,
2.3147666 , 3.226815 , 3.4912894 , 9.928331 , 5.029834 ,
7.1034536 , 2.6826584 , 3.407092 , 5.1383185 , 4.0941634 ,
3.3249714 , 2.8196743 , 1.0019007 , 0.4673027 , 6.095398 ,
2.4684315 , 2.894028 , 1.7736832 , 0.53535175, 2.3534393 ,
5.9133415 , 1.4854542 , 0.98389864, 2.8314965 , 3.388785 ,
1.7273644 , 1.4295217 , 3.9137824 , 0.8343511 , 5.592705 ,
4.1189713 , 4.519481 , 4.514461 , 1.216034 , 4.9332423 ,
4.501184 , 5.48342 , 5.1219296 , 1.8448588 , 1.8978752 ,
2.399244 , 4.452588 , 2.9473898 , 4.242 , 5.101925 ,
3.4773014 , 4.1459665 , 3.9358723 , 4.571998 , 4.4941797 ,
1.8119017 , 1.184782 , 3.4886737 , 1.7374743 , 5.519011 ,
4.1905513 , 2.60639 , 0.7090206 , 4.3053236 , 0.48008502,
4.8589377 , 5.2264676 , 4.044144 , 3.4191263 , 1.1011882 ,
3.1120796 , 6.7359877 , 2.9646623 , 2.554451 , 2.4505644 ,
2.142144 , 4.9301887 , 1.2831734 , 2.7954278 , 1.5539587 ,
5.100337 , 3.3232377 , 1.7723247 , 4.5153937 , 3.388878 ,
6.257826 , 1.1053416 , 3.9824939 , 0.76826215, 4.1770864 ,
5.84404 , 3.564656 , 4.186695 , 3.18334 , 4.3410416 ,
2.4948015 , 5.7432327 , 3.2015507 , 4.697011 , 8.011776 ,
6.00471 , 1.4959276 , 3.4044938 , 2.7345881 , 3.1861002 ,
6.807063 , 3.672278 , 4.4842434 , 4.369128 , 10.34134 ,
7.659503 , 4.640015 , 2.260385 , 2.9616964 , 5.770283 ,
5.8344135 , 3.0287185 , 4.6171303 , 1.8015864 , 3.3256059 ,
4.025933 , 15.307446 , 24.68179 , 5.1921005 , 0.95913076,
2.531541 , 3.3621345 , 5.42665 , 2.610918 , 7.0007544 ,
4.5238733 , 6.4797854 , 7.7474403 , 8.129336 , 6.065284 ,
6.313556 , 23.959946 , 6.9332256 , 7.346444 , 3.9448545 ,
7.4828005 , 9.371148 , 6.901665 , 6.0786414 , 7.611087 ,
4.072118 , 5.4114847 , 5.8930087 , 6.2145314 , 3.5961418 ,
3.5599902 , 0.31376082, 4.7233195 , 1.835947 , 1.3035926 ,
3.0226147 , 0.4981069 , 6.3224025 , 3.036155 , 2.8434775 ,
4.545952 , 18.239357 , 6.450544 , 6.273684 , 6.03156 ,
3.223308 , 3.63122 , 2.694837 , 2.6652293 , 1.1407009 ,
4.5644073 , 6.183016 , 4.285992 , 4.728611 , 1.2699568 ,
3.6176095 , 2.5436394 , 2.7925599 , 3.8075545 , 8.981911 ,
22.87494 , 7.5639124 , 1.6238711 , 0.5307012 , 6.469891 ,
3.0692794 , 4.6451344 , 7.6391034 , 6.2791705 , 7.3014994 ,
4.254345 , 6.4056168 , 7.458692 , 3.457129 , 5.2672634 ,
9.947004 , 4.4163375 , 10.526679 , 4.949929 , 1.9953364 ,
8.43534 , 8.067071 , 5.2787495 , 3.2672718 , 14.374089 ,
1.9808887 , 36.714443 , 25.423502 , 10.191787 , 2.9083974 ,
13.615338 , 7.3216214 , 16.14564 , 7.027708 , 8.500385 ,
4.132675 , 3.6679573 , 1.4386141 , 35.34755 , 27.14011 ,
0.611455 , 9.363178 , 12.209044 , 7.530161 , 8.551426 ,
1.8653786 , 4.8010054 , 5.6198926 , 35.149426 , 30.55753 ,
5.9123573 , 2.7006626 , 3.9975345 , 6.327705 , 5.3463564 ,
6.5932918 , 6.520323 , 3.5866342 , 0.6119649 , 0.34683716,
4.459534 , 5.4071994 , 6.77416 , 20.245544 , 1.24846 ,
16.727642 , 34.44699 , 3.2000692 , 6.9570527 , 4.8707566 ,
0.85470015, 1.5286115 , 1.4164749 , 16.511541 , 23.00998 ,
11.719736 , 10.880672 , 6.8268065 , 1.6250918 , 8.308749 ,
11.111537 , 24.678076 , 11.334251 , 3.5857413 , 6.996021 ,
4.5214167 , 8.312405 , 7.4295473 , 7.6591887 , 10.038146 ,
1.7212489 , 8.699176 , 8.796391 , 9.621361 , 9.075598 ,
0.19450001, 14.5820675 , 18.2731 , 15.891861 , 5.1796503 ,
23.901497 , 6.602838 , 8.11695 , 5.7776127 , 0.9575611 ,
19.564034 , 44.46723 , 6.194875 , 20.724203 , 18.15552 ,
36.427177 , 3.437948 , 0.62041324, 0.78566986, 7.326401 ,
13.820853 , 4.118893 , 0.77267 , 0.74762917, 0.88341135,
20.338709 , 18.454927 , 13.954808 , 11.78991 , 11.854942 ,
14.00589 , 9.306969 , 9.987267 , 6.9419265 , 10.148791 ,
8.151923 , 18.923534 , 18.946314 , 1.1267239 , 1.1251845 ,
1.3136889 , 71.61 , 1.9791391 , 0.40409204], dtype=float32)plt.figure(figsize=(12,5))
plt.plot(e_v_dates,v_chl_avg,label='VIIRS CHL',c='red',marker='.',linestyle='-')
plt.ylabel('Chl-a (mg/m^3)')
plt.title("Lake Erie Daily Average CHL - 2023")
plt.legend()
print(type(v_chl_avg))
print(v_chl_avg.shape)
print(e_v_dates.shape)<class 'numpy.ndarray'>
(544,)
(544,)
#e_v_ds.close()
!jupyter nbconvert --to html GL_python_tutorial2.ipynb[NbConvertApp] Converting notebook GL_python_tutorial2.ipynb to html
[NbConvertApp] Writing 878306 bytes to GL_python_tutorial2.html