In [1]:
import xarray as xr
import numpy as np
import pandas as pd
%matplotlib inline
from matplotlib import pyplot as plt
from dask.diagnostics import ProgressBar
import seaborn as sns
from matplotlib.colors import LogNorm
In [27]:
# resampling frequency in number of days
freq=3
In [3]:
''' SST is not from GlobColor
from tools.load_GlobColor_dataset import load_dataset
import importlib
importlib.reload(load_dataset)
############### T865
ds_daily = load_dataset.load_t865()
'''
Out[3]:
' SST is not from GlobColor\nfrom tools.load_GlobColor_dataset import load_dataset\nimport importlib\nimportlib.reload(load_dataset)\n############### T865\nds_daily = load_dataset.load_t865()\n'
In [4]:
#ds_8day = xr.open_mfdataset('./data_collector_modisa_chla9km/ModisA_Arabian_Sea_chlor_a_9km_*_8D.nc')
ds_daily = xr.open_mfdataset('./data_collector_modisa_NSST4mu_4km_tomatchHermes/ModisA_Arabian_Sea_SST4_sst4_4km_*D.nc')
#both_datasets = [ds_8day, ds_daily]
In [5]:
ds_daily = ds_daily.drop(['palette','qual_sst4'])
In [6]:
ds_daily.var
Out[6]:
<bound method ImplementsDatasetReduce._reduce_method.<locals>.wrapped_func of <xarray.Dataset>
Dimensions: (lat: 552, lon: 720, time: 5539)
Coordinates:
* lat (lat) float64 27.98 27.94 27.9 27.85 27.81 27.77 27.73 27.69 ...
* lon (lon) float64 45.02 45.06 45.1 45.15 45.19 45.23 45.27 45.31 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-05 2002-07-06 ...
Data variables:
sst4 (time, lat, lon) float64 nan nan nan nan nan nan nan nan nan ...
Attributes:
product_name: A2002185.L3m_DAY_SST4_sst4_4km.nc
instrument: MODIS
title: HMODISA Level-3 Standard Mapped Image
project: Ocean Biology Processing Group (NASA/G...
platform: Aqua
temporal_range: 15-hour
processing_version: 2014.0.1
date_created: 2017-05-08T12:15:57.000Z
history: l3mapgen par=A2002185.L3m_DAY_SST4_sst...
l2_flag_names: LAND,~HISOLZEN
time_coverage_start: 2002-07-04T00:00:15.000Z
time_coverage_end: 2002-07-04T14:55:04.000Z
start_orbit_number: 885
end_orbit_number: 894
map_projection: Equidistant Cylindrical
latitude_units: degrees_north
longitude_units: degrees_east
northernmost_latitude: 90.0
southernmost_latitude: -90.0
westernmost_longitude: -180.0
easternmost_longitude: 180.0
geospatial_lat_max: 90.0
geospatial_lat_min: -90.0
geospatial_lon_max: 180.0
geospatial_lon_min: -180.0
grid_mapping_name: latitude_longitude
latitude_step: 0.0416667
longitude_step: 0.0416667
sw_point_latitude: -89.9792
sw_point_longitude: -179.979
geospatial_lon_resolution: 4.63831
geospatial_lat_resolution: 4.63831
geospatial_lat_units: degrees_north
geospatial_lon_units: degrees_east
spatialResolution: 4.64 km
number_of_lines: 4320
number_of_columns: 8640
measure: Mean
suggested_image_scaling_minimum: -2.0
suggested_image_scaling_maximum: 45.0
suggested_image_scaling_type: LINEAR
suggested_image_scaling_applied: No
_lastModified: 2017-05-08T12:15:57.000Z
Conventions: CF-1.6
institution: NASA Goddard Space Flight Center, Ocea...
standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metad...
Metadata_Conventions: Unidata Dataset Discovery v1.0
naming_authority: gov.nasa.gsfc.sci.oceandata
id: A2002185.L3b_DAY_SST4.nc/L3/A2002185.L...
license: http://science.nasa.gov/earth-science/...
creator_name: NASA/GSFC/OBPG
publisher_name: NASA/GSFC/OBPG
creator_email: data@oceancolor.gsfc.nasa.gov
publisher_email: data@oceancolor.gsfc.nasa.gov
creator_url: http://oceandata.sci.gsfc.nasa.gov
publisher_url: http://oceandata.sci.gsfc.nasa.gov
processing_level: L3 Mapped
cdm_data_type: grid
identifier_product_doi_authority: http://dx.doi.org
identifier_product_doi: 10.5067/AQUA/MODIS_OC.2014.0
keywords: Oceans > Ocean Temperature > Sea Surfa...
keywords_vocabulary: NASA Global Change Master Directory (G...
data_bins: 3246245
data_minimum: -1.635
data_maximum: 31.465>
In [7]:
ds_daily.sst4.sel(time='2002-07-28').plot()
Out[7]:
<matplotlib.collections.QuadMesh at 0x115e45278>
In [9]:
freq_resample = str(8) + 'D'
ds_8day = ds_daily.resample(freq_resample, dim='time') # see the above for doc, test case, & default behavior
ds_8day
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[9]:
<xarray.Dataset>
Dimensions: (lat: 552, lon: 720, time: 693)
Coordinates:
* lat (lat) float64 27.98 27.94 27.9 27.85 27.81 27.77 27.73 27.69 ...
* lon (lon) float64 45.02 45.06 45.1 45.15 45.19 45.23 45.27 45.31 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-12 2002-07-20 ...
Data variables:
sst4 (time, lat, lon) float64 nan nan nan nan nan nan nan nan nan ...
In [10]:
# check data quality
both_datasets = [ds_8day, ds_daily]
print([(ds.nbytes / 1e6) for ds in both_datasets])
[2203.42308, 17611.415768]
In [11]:
'''
# no need for termperature, sst can be <0
def fix_bad_data(ds):
# for some reason, the cloud / land mask is backwards on some data
# this is obvious because there are t865 values less than zero
bad_data = ds.t865.groupby('time').min() < 0
# loop through and fix
for n in np.nonzero(bad_data.values)[0]:
data = ds.t865[n].values
ds.t865.values[n] = np.ma.masked_less(data, 0).filled(np.nan)
[fix_bad_data(ds) for ds in both_datasets]
'''
Out[11]:
"\n# no need for termperature, sst can be <0\ndef fix_bad_data(ds):\n # for some reason, the cloud / land mask is backwards on some data\n # this is obvious because there are t865 values less than zero\n bad_data = ds.t865.groupby('time').min() < 0\n # loop through and fix\n for n in np.nonzero(bad_data.values)[0]:\n data = ds.t865[n].values \n ds.t865.values[n] = np.ma.masked_less(data, 0).filled(np.nan)\n[fix_bad_data(ds) for ds in both_datasets]\n"
In [12]:
# Count the number of ocean data points
(~ds_8day.sst4.isnull()).sum(dim='time').plot()
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[12]:
<matplotlib.collections.QuadMesh at 0x11b2d0c88>
In [13]:
# find a mask for the land
ocean_mask = ((~ds_8day.sst4.isnull()).sum(dim='time'))>1
num_ocean_points = ocean_mask.sum().values
ocean_mask.plot()
plt.title('%g total ocean points' % num_ocean_points)
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[13]:
<matplotlib.text.Text at 0x11b400588>
In [14]:
plt.figure(figsize=(8,6))
ds_daily.sst4.sel(time='2002-11-18',method='nearest').plot(norm=LogNorm())
Out[14]:
<matplotlib.collections.QuadMesh at 0x11b55c470>
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/matplotlib/colors.py:1022: RuntimeWarning: invalid value encountered in less_equal
mask |= resdat <= 0
In [15]:
(~ds_daily.sst4.isnull()).sum(['lat','lon']) # information from original data
Out[15]:
<xarray.DataArray 'sst4' (time: 5539)>
array([ 0, 93356, 111044, ..., 53633, 70480, 72927])
Coordinates:
* time (time) datetime64[ns] 2002-07-04 2002-07-05 2002-07-06 ...
In [16]:
count_8day,count_daily = [(~ds.sst4.isnull()).sum(['lat','lon'])/float(num_ocean_points)
for ds in (ds_8day,ds_daily)]
plt.figure(figsize=(12,4))
count_8day.plot(color='k')
count_daily.plot(color='r')
plt.legend(['8 day','daily']) # there is one day in the datset have SST values on the land !
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[16]:
<matplotlib.legend.Legend at 0x11b2c6128>
In [17]:
count_8day,count_daily = [ds.sst4.groupby('time').count()/float(num_ocean_points)
for ds in (ds_8day,ds_daily)]
plt.figure(figsize=(12,4))
count_8day.plot(color='k')
count_daily.plot(color='r')
plt.legend(['8 day','daily']) # there is one day in the datset have SST values on the land !
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[17]:
<matplotlib.legend.Legend at 0x1163910f0>
In [18]:
# Maps of individual days
target_date = '2003-02-15'
plt.figure(figsize=(8,6))
ds_8day.sst4.sel(time=target_date, method='nearest').plot()
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[18]:
<matplotlib.collections.QuadMesh at 0x11ef5fd30>
In [19]:
plt.figure(figsize=(8,6))
ds_daily.sst4.sel(time=target_date, method='nearest').plot()
Out[19]:
<matplotlib.collections.QuadMesh at 0x11f2fa588>
In [28]:
freq
Out[28]:
3
In [29]:
# next carry out interpolation starts here
freq_resample = str(freq) + 'D'
ds_resample = ds_daily.resample(freq_resample, dim='time') # see the above for doc, test case, & default behavior
ds_resample
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[29]:
<xarray.Dataset>
Dimensions: (lat: 552, lon: 720, time: 1847)
Coordinates:
* lat (lat) float64 27.98 27.94 27.9 27.85 27.81 27.77 27.73 27.69 ...
* lon (lon) float64 45.02 45.06 45.1 45.15 45.19 45.23 45.27 45.31 ...
* time (time) datetime64[ns] 2002-07-04 2002-07-07 2002-07-10 ...
Data variables:
sst4 (time, lat, lon) float64 nan nan nan nan nan nan nan nan nan ...
In [30]:
plt.figure(figsize=(8,6))
ds_resample.sst4.sel(time=target_date, method='nearest').plot()
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
Out[30]:
<matplotlib.collections.QuadMesh at 0x1193ffb70>
In [31]:
# check the range for the longitude
print(ds_resample.lon.min(),'\n' ,ds_resample.lat.min())
<xarray.DataArray 'lon' ()>
array(45.02083206176758)
<xarray.DataArray 'lat' ()>
array(5.0208306312561035)
In [32]:
# load preprocessed float data, and start the interpolation right here!!!!
var5 = "par"
var4 = "t865"
var3 = "kd490"
var2 = "cdm"
var1 = "chl"
vardist = "dist"
indir_prefix = "./data_globcolour/output.data.interpolate/" + "df_Globcolor_"
indir = indir_prefix + var1 + vardist + var2 + var3 + var4 + var5 + "_" + str(freq) + "d.csv"
floatDF_tmp = pd.read_csv(indir,index_col=0)
print(floatDF_tmp)
id time lat lon temp ve \
1827 10206 2002-07-04 16.208333 66.375833 NaN 10.941333
3654 10208 2002-07-04 13.816917 69.589833 NaN 9.899750
5481 11089 2002-07-04 16.331167 64.731500 27.917667 12.239583
7308 15703 2002-07-04 13.829750 69.617667 28.555167 9.378000
10962 27069 2002-07-04 20.177000 68.844083 28.973000 26.284000
14616 28842 2002-07-04 18.852000 60.748083 27.663833 11.585000
16443 34159 2002-07-04 12.568250 59.009333 NaN 25.174250
20097 34210 2002-07-04 6.409500 56.899000 26.715250 -9.563583
21924 34211 2002-07-04 8.539083 68.015250 28.316167 20.941750
23751 34212 2002-07-04 6.327167 64.844583 28.476000 23.924750
38367 34708 2002-07-04 10.175333 59.870667 27.167000 42.975000
42021 34710 2002-07-04 13.062167 49.858667 30.956917 -17.011917
43848 34714 2002-07-04 13.643167 63.802250 27.707000 37.529500
45675 34716 2002-07-04 7.507417 65.514500 28.814583 36.070250
47502 34718 2002-07-04 16.206417 72.491917 29.149750 22.008917
49329 34719 2002-07-04 17.720833 71.027333 28.921667 19.046167
51156 34720 2002-07-04 14.669917 69.224833 28.653417 9.947083
52983 34721 2002-07-04 17.159250 65.406167 27.919250 9.293667
54810 34722 2002-07-04 11.702833 70.516500 28.712917 9.067667
56637 34723 2002-07-04 16.687667 66.267000 28.457167 3.445750
429345 2134712 2002-07-04 9.580250 63.608250 27.994167 9.460417
1828 10206 2002-07-07 16.176417 66.548583 NaN 4.699917
3655 10208 2002-07-07 13.513167 69.884583 NaN 15.487750
5482 11089 2002-07-07 16.166750 65.045000 27.767250 14.413917
7309 15703 2002-07-07 13.540000 69.895583 28.572750 14.464250
10963 27069 2002-07-07 20.085167 69.475000 28.954250 22.634333
14617 28842 2002-07-07 18.616833 60.873833 27.666833 -0.396667
16444 34159 2002-07-07 12.787583 59.661250 NaN 33.664417
20098 34210 2002-07-07 5.958667 56.622000 26.709667 -12.825083
21925 34211 2002-07-07 8.209583 68.556000 28.406333 26.996250
... ... ... ... ... ... ...
566366 64115560 2017-06-24 16.696833 72.861833 29.779167 16.026167
568193 64117500 2017-06-24 6.750167 70.993833 29.461833 8.437000
422034 147140 2017-06-27 11.302333 60.222250 28.488917 47.011583
425688 147144 2017-06-27 14.403833 70.675333 29.359333 14.331000
496941 62321990 2017-06-27 13.098333 61.296500 28.240500 25.029583
502422 63157510 2017-06-27 7.939917 60.210917 28.621750 6.026917
506076 63158530 2017-06-27 7.948083 57.405500 28.807083 18.645167
513384 63255180 2017-06-27 6.407917 63.229083 29.639917 12.110333
522519 63258900 2017-06-27 10.770167 67.567333 29.242833 11.566750
526173 63259180 2017-06-27 9.948250 70.782083 29.604833 16.286583
542616 63348720 2017-06-27 7.019250 59.563125 28.293500 16.261714
560886 64111550 2017-06-27 15.961500 67.288333 29.196417 13.215667
562713 64113560 2017-06-27 14.065333 62.669417 28.106583 3.358250
566367 64115560 2017-06-27 16.668667 73.197167 28.904083 9.514500
568194 64117500 2017-06-27 6.571667 71.304333 29.409333 8.921333
422035 147140 2017-06-30 11.557667 61.136333 28.039000 83.892000
425689 147144 2017-06-30 14.291000 70.923667 29.260333 17.542500
496942 62321990 2017-06-30 12.863000 61.727800 27.980800 24.212500
502423 63157510 2017-06-30 8.170833 60.425000 28.591667 25.888400
506077 63158530 2017-06-30 7.562400 57.591200 28.432600 2.102750
513385 63255180 2017-06-30 6.457000 63.455750 29.492250 26.794667
522520 63258900 2017-06-30 10.611667 67.725333 29.285333 11.653500
526174 63259180 2017-06-30 9.708250 71.031750 29.490500 17.048000
560887 64111550 2017-06-30 15.298917 67.618667 28.750917 14.127583
562714 64113560 2017-06-30 13.832750 62.741000 27.985750 0.487667
566368 64115560 2017-06-30 16.871250 73.274250 28.602000 -0.189000
568195 64117500 2017-06-30 6.172583 71.411500 29.291417 3.511583
560888 64111550 2017-07-03 15.032000 67.807000 28.605000 NaN
562715 64113560 2017-07-03 13.760000 62.716000 27.871000 NaN
568196 64117500 2017-07-03 5.869000 71.419000 29.169000 NaN
vn spd var_lat var_lon var_tmp chlor_a \
1827 -6.362417 12.924000 0.001675 0.006395 1000.000000 NaN
3654 -19.104583 21.864250 0.000053 0.000096 1000.000000 NaN
5481 -7.016583 14.670833 0.000076 0.000148 0.003607 NaN
7308 -18.706167 21.204917 0.000054 0.000099 0.077642 NaN
10962 4.231083 27.516167 0.000058 0.000107 0.001681 NaN
14616 -5.116833 24.501167 0.000106 0.000225 0.003285 NaN
16443 5.826667 26.245667 0.000058 0.000109 1000.000000 NaN
20097 -16.234583 19.873667 0.000072 0.000146 0.003603 0.269992
21924 -15.920167 26.681000 0.000054 0.000098 0.003496 0.035356
23751 18.941000 32.034083 0.000053 0.000096 0.003571 0.072778
38367 2.843583 43.217000 0.000050 0.000093 0.001796 0.321224
42021 28.993750 47.145833 0.000037 0.000066 0.001769 0.127973
43848 11.571500 39.495917 0.000058 0.000110 0.001840 NaN
45675 3.266917 36.961917 0.000057 0.000105 0.001765 0.068843
47502 -29.327667 37.194417 0.000046 0.000082 0.001739 NaN
49329 -10.221667 22.969250 0.000049 0.000088 0.001578 NaN
51156 -36.747667 38.318250 0.000063 0.000118 0.001779 NaN
52983 -9.409917 14.014583 0.000063 0.000120 0.001746 NaN
54810 -5.827833 12.391667 0.000066 0.000125 0.001827 0.213574
56637 -14.846000 16.573750 0.000082 0.000173 0.001780 NaN
429345 -38.704000 42.767250 0.000070 0.000136 0.001893 0.239311
1828 2.534583 5.653083 0.001890 0.007368 1000.000000 NaN
3655 -5.675667 17.390500 0.000073 0.000146 1000.000000 NaN
5482 -10.390667 18.944083 0.000052 0.000096 0.003695 NaN
7309 -4.531583 16.451750 0.000056 0.000104 0.091701 NaN
10963 -9.058500 24.616667 0.000050 0.000091 0.001732 NaN
14617 -7.914750 13.265917 0.000077 0.000152 0.003384 NaN
16444 13.599417 36.723417 0.000058 0.000109 1000.000000 NaN
20098 -14.977750 23.167000 0.000069 0.000136 0.003747 0.312435
21925 -10.213500 30.061583 0.000060 0.000113 0.003481 0.096390
... ... ... ... ... ... ...
566366 -8.852083 19.160417 0.000003 0.000005 0.001698 NaN
568193 -0.344250 11.212667 0.000003 0.000005 0.001684 NaN
422034 27.559667 56.111500 0.000033 0.000015 0.001586 NaN
425688 -5.099833 16.134500 0.000287 0.000143 0.001931 NaN
496941 -14.966083 29.203583 0.008870 0.008387 0.001871 NaN
502422 17.840000 19.168333 0.000009 0.000008 0.001769 NaN
506076 -9.715833 26.787500 0.000240 0.000126 0.001786 NaN
513384 1.742667 14.765333 0.000123 0.000058 0.001983 NaN
522519 -20.607583 25.702500 0.000560 0.000320 0.001880 NaN
526173 -18.976250 25.353250 0.001676 0.001119 0.001843 NaN
542616 6.473714 17.652143 0.018954 0.019842 0.002010 NaN
560886 -31.782083 35.294083 0.000009 0.000007 0.001684 NaN
562713 -10.653917 11.876667 0.000130 0.000063 0.001684 NaN
566367 7.251250 17.765000 0.000003 0.000005 0.001684 NaN
568194 -16.165833 20.011250 0.000003 0.000005 0.001684 0.120794
422035 -12.843000 85.019000 0.000032 0.000014 0.001585 NaN
425689 -15.481500 23.507500 0.000129 0.000055 0.001716 NaN
496942 -13.379500 27.666750 0.008339 0.007574 0.002022 NaN
502423 5.273200 27.029800 0.000015 0.000010 0.001986 NaN
506077 -33.419500 33.622250 0.000004 0.000006 0.001870 NaN
513385 -4.388000 27.262333 0.000476 0.000268 0.002092 NaN
522520 -5.334000 12.912000 0.000794 0.000473 0.002321 NaN
526174 -12.979667 21.434667 0.000538 0.000273 0.002068 NaN
560887 -23.375000 27.887500 0.000003 0.000005 0.001684 NaN
562714 -8.357417 10.514917 0.000106 0.000049 0.001684 NaN
566368 16.893333 17.142667 0.000003 0.000005 0.001754 NaN
568195 -21.195083 22.225833 0.000003 0.000005 0.001684 0.130224
560888 NaN NaN 0.000003 0.000005 0.001963 NaN
562715 NaN NaN 0.000120 0.000049 0.001963 NaN
568196 NaN NaN 0.000003 0.000005 0.001963 0.143746
dist cdm kd490 t865 par
1827 667.953438 NaN NaN NaN 49.034976
3654 373.137389 NaN NaN NaN 53.496844
5481 765.238337 NaN NaN NaN 53.454139
7308 371.794920 NaN NaN NaN 53.520393
10962 165.604551 NaN NaN NaN 46.622451
14616 259.549813 NaN NaN NaN 55.028858
16443 485.465494 NaN NaN 0.503937 51.507130
20097 722.709253 0.028208 0.073228 0.188034 52.464594
21924 496.722689 0.013793 0.040269 0.245733 52.961485
23751 869.183456 0.011631 0.046718 0.177406 51.805411
38367 637.726441 0.044554 0.087768 0.281767 53.926559
42021 150.041321 0.023710 0.057218 0.229587 54.856869
43848 869.875997 NaN NaN NaN 52.382753
45675 794.904527 0.013106 0.045550 0.174962 52.394974
47502 94.399598 NaN NaN NaN 53.371143
49329 209.207758 NaN NaN NaN 52.110463
51156 466.775024 NaN NaN NaN 50.977288
52983 658.601932 NaN NaN NaN 52.810799
54810 181.318093 0.014866 0.070182 0.151630 53.497975
56637 633.601722 NaN NaN NaN 47.800933
429345 945.579543 0.021446 0.071863 0.184988 54.035277
1828 659.605429 NaN NaN NaN 48.483505
3655 327.080358 NaN NaN NaN 53.416297
5482 763.443591 NaN NaN NaN 51.568421
7309 328.082706 NaN NaN NaN 54.044723
10963 128.044009 NaN NaN NaN 43.254285
14617 286.213707 NaN NaN NaN 54.845130
16444 556.657247 NaN NaN NaN 52.655368
20098 744.690630 0.030674 0.079035 0.240987 48.682185
21925 456.319394 0.009839 0.046514 0.144738 47.418040
... ... ... ... ... ...
566366 47.894783 NaN NaN NaN 44.835579
568193 192.580141 NaN NaN NaN 35.771104
422034 633.789905 NaN NaN NaN 49.239842
425688 347.961449 NaN NaN NaN 48.995252
496941 720.307881 NaN NaN NaN 48.310914
502422 802.690780 NaN NaN NaN 49.893464
506076 596.807468 NaN NaN NaN 51.616781
513384 1047.788375 NaN NaN NaN 44.533565
522519 496.504659 NaN NaN NaN 51.876145
526173 164.145250 NaN NaN NaN 45.877881
542616 822.155576 NaN NaN NaN 50.951352
560886 635.710919 NaN NaN NaN 37.295285
562713 750.685783 NaN NaN NaN 46.043139
566367 13.393062 NaN NaN NaN 40.387566
568194 155.536670 0.013346 0.053254 0.137075 43.179400
422035 726.254721 NaN NaN NaN 48.465288
425689 326.533641 NaN NaN NaN 42.478397
496942 772.348381 NaN NaN NaN 40.060113
502423 805.220865 NaN NaN NaN 47.462840
506077 643.899278 NaN NaN NaN 43.820887
513385 1022.681205 NaN NaN NaN 44.700691
522520 481.403226 NaN NaN NaN 41.214599
526174 141.373115 NaN NaN NaN 25.991692
560887 624.744140 NaN NaN NaN 47.919789
562714 774.936277 NaN NaN NaN 47.710578
566368 2.169704 NaN NaN NaN 44.126968
568195 144.895234 0.013506 0.050810 0.140181 36.505060
560888 607.056887 NaN NaN NaN 51.403744
562715 779.169800 NaN NaN NaN 48.201698
568196 153.656073 0.013860 0.050002 0.253969 43.950959
[11247 rows x 17 columns]
In [33]:
import importlib
from tools.time_lat_lon_interpolate import interpolate
importlib.reload(interpolate)
result_out5 = interpolate.sel_points_multilinear_time_lat_lon(ds_resample, floatDF_tmp, dims = 'points', col_name ='sst4')
print('\n *** after the interpolation *** \n', result_out5)
# important: keep the id, since the dataframe has been modified in a bound-aware way in the function
print('\n *** this two length should be equal *** %d >= %d?' %(len(floatDF_tmp.index), len(result_out5.index) ) )
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/dask/array/numpy_compat.py:45: RuntimeWarning: invalid value encountered in true_divide
x = np.divide(x1, x2, out)
*** after the interpolation ***
id time lat lon temp ve \
1827 10206 2002-07-04 16.208333 66.375833 NaN 10.941333
3654 10208 2002-07-04 13.816917 69.589833 NaN 9.899750
5481 11089 2002-07-04 16.331167 64.731500 27.917667 12.239583
7308 15703 2002-07-04 13.829750 69.617667 28.555167 9.378000
10962 27069 2002-07-04 20.177000 68.844083 28.973000 26.284000
14616 28842 2002-07-04 18.852000 60.748083 27.663833 11.585000
16443 34159 2002-07-04 12.568250 59.009333 NaN 25.174250
20097 34210 2002-07-04 6.409500 56.899000 26.715250 -9.563583
21924 34211 2002-07-04 8.539083 68.015250 28.316167 20.941750
23751 34212 2002-07-04 6.327167 64.844583 28.476000 23.924750
38367 34708 2002-07-04 10.175333 59.870667 27.167000 42.975000
42021 34710 2002-07-04 13.062167 49.858667 30.956917 -17.011917
43848 34714 2002-07-04 13.643167 63.802250 27.707000 37.529500
45675 34716 2002-07-04 7.507417 65.514500 28.814583 36.070250
47502 34718 2002-07-04 16.206417 72.491917 29.149750 22.008917
49329 34719 2002-07-04 17.720833 71.027333 28.921667 19.046167
51156 34720 2002-07-04 14.669917 69.224833 28.653417 9.947083
52983 34721 2002-07-04 17.159250 65.406167 27.919250 9.293667
54810 34722 2002-07-04 11.702833 70.516500 28.712917 9.067667
56637 34723 2002-07-04 16.687667 66.267000 28.457167 3.445750
429345 2134712 2002-07-04 9.580250 63.608250 27.994167 9.460417
1828 10206 2002-07-07 16.176417 66.548583 NaN 4.699917
3655 10208 2002-07-07 13.513167 69.884583 NaN 15.487750
5482 11089 2002-07-07 16.166750 65.045000 27.767250 14.413917
7309 15703 2002-07-07 13.540000 69.895583 28.572750 14.464250
10963 27069 2002-07-07 20.085167 69.475000 28.954250 22.634333
14617 28842 2002-07-07 18.616833 60.873833 27.666833 -0.396667
16444 34159 2002-07-07 12.787583 59.661250 NaN 33.664417
20098 34210 2002-07-07 5.958667 56.622000 26.709667 -12.825083
21925 34211 2002-07-07 8.209583 68.556000 28.406333 26.996250
... ... ... ... ... ... ...
566366 64115560 2017-06-24 16.696833 72.861833 29.779167 16.026167
568193 64117500 2017-06-24 6.750167 70.993833 29.461833 8.437000
422034 147140 2017-06-27 11.302333 60.222250 28.488917 47.011583
425688 147144 2017-06-27 14.403833 70.675333 29.359333 14.331000
496941 62321990 2017-06-27 13.098333 61.296500 28.240500 25.029583
502422 63157510 2017-06-27 7.939917 60.210917 28.621750 6.026917
506076 63158530 2017-06-27 7.948083 57.405500 28.807083 18.645167
513384 63255180 2017-06-27 6.407917 63.229083 29.639917 12.110333
522519 63258900 2017-06-27 10.770167 67.567333 29.242833 11.566750
526173 63259180 2017-06-27 9.948250 70.782083 29.604833 16.286583
542616 63348720 2017-06-27 7.019250 59.563125 28.293500 16.261714
560886 64111550 2017-06-27 15.961500 67.288333 29.196417 13.215667
562713 64113560 2017-06-27 14.065333 62.669417 28.106583 3.358250
566367 64115560 2017-06-27 16.668667 73.197167 28.904083 9.514500
568194 64117500 2017-06-27 6.571667 71.304333 29.409333 8.921333
422035 147140 2017-06-30 11.557667 61.136333 28.039000 83.892000
425689 147144 2017-06-30 14.291000 70.923667 29.260333 17.542500
496942 62321990 2017-06-30 12.863000 61.727800 27.980800 24.212500
502423 63157510 2017-06-30 8.170833 60.425000 28.591667 25.888400
506077 63158530 2017-06-30 7.562400 57.591200 28.432600 2.102750
513385 63255180 2017-06-30 6.457000 63.455750 29.492250 26.794667
522520 63258900 2017-06-30 10.611667 67.725333 29.285333 11.653500
526174 63259180 2017-06-30 9.708250 71.031750 29.490500 17.048000
560887 64111550 2017-06-30 15.298917 67.618667 28.750917 14.127583
562714 64113560 2017-06-30 13.832750 62.741000 27.985750 0.487667
566368 64115560 2017-06-30 16.871250 73.274250 28.602000 -0.189000
568195 64117500 2017-06-30 6.172583 71.411500 29.291417 3.511583
560888 64111550 2017-07-03 15.032000 67.807000 28.605000 NaN
562715 64113560 2017-07-03 13.760000 62.716000 27.871000 NaN
568196 64117500 2017-07-03 5.869000 71.419000 29.169000 NaN
vn spd var_lat var_lon var_tmp chlor_a \
1827 -6.362417 12.924000 0.001675 0.006395 1000.000000 NaN
3654 -19.104583 21.864250 0.000053 0.000096 1000.000000 NaN
5481 -7.016583 14.670833 0.000076 0.000148 0.003607 NaN
7308 -18.706167 21.204917 0.000054 0.000099 0.077642 NaN
10962 4.231083 27.516167 0.000058 0.000107 0.001681 NaN
14616 -5.116833 24.501167 0.000106 0.000225 0.003285 NaN
16443 5.826667 26.245667 0.000058 0.000109 1000.000000 NaN
20097 -16.234583 19.873667 0.000072 0.000146 0.003603 0.269992
21924 -15.920167 26.681000 0.000054 0.000098 0.003496 0.035356
23751 18.941000 32.034083 0.000053 0.000096 0.003571 0.072778
38367 2.843583 43.217000 0.000050 0.000093 0.001796 0.321224
42021 28.993750 47.145833 0.000037 0.000066 0.001769 0.127973
43848 11.571500 39.495917 0.000058 0.000110 0.001840 NaN
45675 3.266917 36.961917 0.000057 0.000105 0.001765 0.068843
47502 -29.327667 37.194417 0.000046 0.000082 0.001739 NaN
49329 -10.221667 22.969250 0.000049 0.000088 0.001578 NaN
51156 -36.747667 38.318250 0.000063 0.000118 0.001779 NaN
52983 -9.409917 14.014583 0.000063 0.000120 0.001746 NaN
54810 -5.827833 12.391667 0.000066 0.000125 0.001827 0.213574
56637 -14.846000 16.573750 0.000082 0.000173 0.001780 NaN
429345 -38.704000 42.767250 0.000070 0.000136 0.001893 0.239311
1828 2.534583 5.653083 0.001890 0.007368 1000.000000 NaN
3655 -5.675667 17.390500 0.000073 0.000146 1000.000000 NaN
5482 -10.390667 18.944083 0.000052 0.000096 0.003695 NaN
7309 -4.531583 16.451750 0.000056 0.000104 0.091701 NaN
10963 -9.058500 24.616667 0.000050 0.000091 0.001732 NaN
14617 -7.914750 13.265917 0.000077 0.000152 0.003384 NaN
16444 13.599417 36.723417 0.000058 0.000109 1000.000000 NaN
20098 -14.977750 23.167000 0.000069 0.000136 0.003747 0.312435
21925 -10.213500 30.061583 0.000060 0.000113 0.003481 0.096390
... ... ... ... ... ... ...
566366 -8.852083 19.160417 0.000003 0.000005 0.001698 NaN
568193 -0.344250 11.212667 0.000003 0.000005 0.001684 NaN
422034 27.559667 56.111500 0.000033 0.000015 0.001586 NaN
425688 -5.099833 16.134500 0.000287 0.000143 0.001931 NaN
496941 -14.966083 29.203583 0.008870 0.008387 0.001871 NaN
502422 17.840000 19.168333 0.000009 0.000008 0.001769 NaN
506076 -9.715833 26.787500 0.000240 0.000126 0.001786 NaN
513384 1.742667 14.765333 0.000123 0.000058 0.001983 NaN
522519 -20.607583 25.702500 0.000560 0.000320 0.001880 NaN
526173 -18.976250 25.353250 0.001676 0.001119 0.001843 NaN
542616 6.473714 17.652143 0.018954 0.019842 0.002010 NaN
560886 -31.782083 35.294083 0.000009 0.000007 0.001684 NaN
562713 -10.653917 11.876667 0.000130 0.000063 0.001684 NaN
566367 7.251250 17.765000 0.000003 0.000005 0.001684 NaN
568194 -16.165833 20.011250 0.000003 0.000005 0.001684 0.120794
422035 -12.843000 85.019000 0.000032 0.000014 0.001585 NaN
425689 -15.481500 23.507500 0.000129 0.000055 0.001716 NaN
496942 -13.379500 27.666750 0.008339 0.007574 0.002022 NaN
502423 5.273200 27.029800 0.000015 0.000010 0.001986 NaN
506077 -33.419500 33.622250 0.000004 0.000006 0.001870 NaN
513385 -4.388000 27.262333 0.000476 0.000268 0.002092 NaN
522520 -5.334000 12.912000 0.000794 0.000473 0.002321 NaN
526174 -12.979667 21.434667 0.000538 0.000273 0.002068 NaN
560887 -23.375000 27.887500 0.000003 0.000005 0.001684 NaN
562714 -8.357417 10.514917 0.000106 0.000049 0.001684 NaN
566368 16.893333 17.142667 0.000003 0.000005 0.001754 NaN
568195 -21.195083 22.225833 0.000003 0.000005 0.001684 0.130224
560888 NaN NaN 0.000003 0.000005 0.001963 NaN
562715 NaN NaN 0.000120 0.000049 0.001963 NaN
568196 NaN NaN 0.000003 0.000005 0.001963 0.143746
dist cdm kd490 t865 par sst4
1827 667.953438 NaN NaN NaN 49.034976 25.553169
3654 373.137389 NaN NaN NaN 53.496844 27.911874
5481 765.238337 NaN NaN NaN 53.454139 NaN
7308 371.794920 NaN NaN NaN 53.520393 27.142097
10962 165.604551 NaN NaN NaN 46.622451 26.487629
14616 259.549813 NaN NaN NaN 55.028858 26.033749
16443 485.465494 NaN NaN 0.503937 51.507130 24.487131
20097 722.709253 0.028208 0.073228 0.188034 52.464594 26.426437
21924 496.722689 0.013793 0.040269 0.245733 52.961485 27.202769
23751 869.183456 0.011631 0.046718 0.177406 51.805411 NaN
38367 637.726441 0.044554 0.087768 0.281767 53.926559 25.107149
42021 150.041321 0.023710 0.057218 0.229587 54.856869 30.800005
43848 869.875997 NaN NaN NaN 52.382753 25.219260
45675 794.904527 0.013106 0.045550 0.174962 52.394974 28.003749
47502 94.399598 NaN NaN NaN 53.371143 27.423657
49329 209.207758 NaN NaN NaN 52.110463 27.858124
51156 466.775024 NaN NaN NaN 50.977288 27.628333
52983 658.601932 NaN NaN NaN 52.810799 27.252755
54810 181.318093 0.014866 0.070182 0.151630 53.497975 27.108313
56637 633.601722 NaN NaN NaN 47.800933 27.529999
429345 945.579543 0.021446 0.071863 0.184988 54.035277 26.691717
1828 659.605429 NaN NaN NaN 48.483505 26.326233
3655 327.080358 NaN NaN NaN 53.416297 27.885891
5482 763.443591 NaN NaN NaN 51.568421 25.351550
7309 328.082706 NaN NaN NaN 54.044723 27.738617
10963 128.044009 NaN NaN NaN 43.254285 26.464999
14617 286.213707 NaN NaN NaN 54.845130 24.313336
16444 556.657247 NaN NaN NaN 52.655368 25.433203
20098 744.690630 0.030674 0.079035 0.240987 48.682185 27.176635
21925 456.319394 0.009839 0.046514 0.144738 47.418040 28.130644
... ... ... ... ... ... ...
566366 47.894783 NaN NaN NaN 44.835579 28.964999
568193 192.580141 NaN NaN NaN 35.771104 29.208749
422034 633.789905 NaN NaN NaN 49.239842 26.602507
425688 347.961449 NaN NaN NaN 48.995252 28.354770
496941 720.307881 NaN NaN NaN 48.310914 25.410887
502422 802.690780 NaN NaN NaN 49.893464 25.815120
506076 596.807468 NaN NaN NaN 51.616781 27.640820
513384 1047.788375 NaN NaN NaN 44.533565 27.861277
522519 496.504659 NaN NaN NaN 51.876145 28.018564
526173 164.145250 NaN NaN NaN 45.877881 28.483235
542616 822.155576 NaN NaN NaN 50.951352 27.285356
560886 635.710919 NaN NaN NaN 37.295285 NaN
562713 750.685783 NaN NaN NaN 46.043139 26.751249
566367 13.393062 NaN NaN NaN 40.387566 NaN
568194 155.536670 0.013346 0.053254 0.137075 43.179400 29.145761
422035 726.254721 NaN NaN NaN 48.465288 27.437627
425689 326.533641 NaN NaN NaN 42.478397 26.739999
496942 772.348381 NaN NaN NaN 40.060113 27.024438
502423 805.220865 NaN NaN NaN 47.462840 27.475581
506077 643.899278 NaN NaN NaN 43.820887 27.456354
513385 1022.681205 NaN NaN NaN 44.700691 NaN
522520 481.403226 NaN NaN NaN 41.214599 27.512719
526174 141.373115 NaN NaN NaN 25.991692 27.910672
560887 624.744140 NaN NaN NaN 47.919789 NaN
562714 774.936277 NaN NaN NaN 47.710578 26.259374
566368 2.169704 NaN NaN NaN 44.126968 NaN
568195 144.895234 0.013506 0.050810 0.140181 36.505060 28.834985
560888 607.056887 NaN NaN NaN 51.403744 26.074999
562715 779.169800 NaN NaN NaN 48.201698 24.818800
568196 153.656073 0.013860 0.050002 0.253969 43.950959 28.652562
[11247 rows x 18 columns]
*** this two length should be equal *** 11247 >= 11247?
/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/numpy/lib/nanfunctions.py:703: RuntimeWarning: Mean of empty slice
warnings.warn("Mean of empty slice", RuntimeWarning)
In [34]:
# output the dataframe result_out4
var6 = "sst4"
outdir_prefix = "./data_globcolour/output.data.interpolate/" + "df_Globcolor_"
outdir = outdir_prefix + var1 + vardist + var2 + var3 + var4 + var5 + var6 +"_" + str(freq) + "d.csv"
result_out5.to_csv(outdir)
print(pd.read_csv(outdir,index_col=0))
### plot for id 125776, which will be fit by LDS
plt.figure(figsize=(8,6))
result_out5[result_out5.id == 135776].plot(x='time', y ='sst4', title=('id - %d' % 135776) )
plt.show();
plt.close("all")
id time lat lon temp ve \
1827 10206 2002-07-04 16.208333 66.375833 NaN 10.941333
3654 10208 2002-07-04 13.816917 69.589833 NaN 9.899750
5481 11089 2002-07-04 16.331167 64.731500 27.917667 12.239583
7308 15703 2002-07-04 13.829750 69.617667 28.555167 9.378000
10962 27069 2002-07-04 20.177000 68.844083 28.973000 26.284000
14616 28842 2002-07-04 18.852000 60.748083 27.663833 11.585000
16443 34159 2002-07-04 12.568250 59.009333 NaN 25.174250
20097 34210 2002-07-04 6.409500 56.899000 26.715250 -9.563583
21924 34211 2002-07-04 8.539083 68.015250 28.316167 20.941750
23751 34212 2002-07-04 6.327167 64.844583 28.476000 23.924750
38367 34708 2002-07-04 10.175333 59.870667 27.167000 42.975000
42021 34710 2002-07-04 13.062167 49.858667 30.956917 -17.011917
43848 34714 2002-07-04 13.643167 63.802250 27.707000 37.529500
45675 34716 2002-07-04 7.507417 65.514500 28.814583 36.070250
47502 34718 2002-07-04 16.206417 72.491917 29.149750 22.008917
49329 34719 2002-07-04 17.720833 71.027333 28.921667 19.046167
51156 34720 2002-07-04 14.669917 69.224833 28.653417 9.947083
52983 34721 2002-07-04 17.159250 65.406167 27.919250 9.293667
54810 34722 2002-07-04 11.702833 70.516500 28.712917 9.067667
56637 34723 2002-07-04 16.687667 66.267000 28.457167 3.445750
429345 2134712 2002-07-04 9.580250 63.608250 27.994167 9.460417
1828 10206 2002-07-07 16.176417 66.548583 NaN 4.699917
3655 10208 2002-07-07 13.513167 69.884583 NaN 15.487750
5482 11089 2002-07-07 16.166750 65.045000 27.767250 14.413917
7309 15703 2002-07-07 13.540000 69.895583 28.572750 14.464250
10963 27069 2002-07-07 20.085167 69.475000 28.954250 22.634333
14617 28842 2002-07-07 18.616833 60.873833 27.666833 -0.396667
16444 34159 2002-07-07 12.787583 59.661250 NaN 33.664417
20098 34210 2002-07-07 5.958667 56.622000 26.709667 -12.825083
21925 34211 2002-07-07 8.209583 68.556000 28.406333 26.996250
... ... ... ... ... ... ...
566366 64115560 2017-06-24 16.696833 72.861833 29.779167 16.026167
568193 64117500 2017-06-24 6.750167 70.993833 29.461833 8.437000
422034 147140 2017-06-27 11.302333 60.222250 28.488917 47.011583
425688 147144 2017-06-27 14.403833 70.675333 29.359333 14.331000
496941 62321990 2017-06-27 13.098333 61.296500 28.240500 25.029583
502422 63157510 2017-06-27 7.939917 60.210917 28.621750 6.026917
506076 63158530 2017-06-27 7.948083 57.405500 28.807083 18.645167
513384 63255180 2017-06-27 6.407917 63.229083 29.639917 12.110333
522519 63258900 2017-06-27 10.770167 67.567333 29.242833 11.566750
526173 63259180 2017-06-27 9.948250 70.782083 29.604833 16.286583
542616 63348720 2017-06-27 7.019250 59.563125 28.293500 16.261714
560886 64111550 2017-06-27 15.961500 67.288333 29.196417 13.215667
562713 64113560 2017-06-27 14.065333 62.669417 28.106583 3.358250
566367 64115560 2017-06-27 16.668667 73.197167 28.904083 9.514500
568194 64117500 2017-06-27 6.571667 71.304333 29.409333 8.921333
422035 147140 2017-06-30 11.557667 61.136333 28.039000 83.892000
425689 147144 2017-06-30 14.291000 70.923667 29.260333 17.542500
496942 62321990 2017-06-30 12.863000 61.727800 27.980800 24.212500
502423 63157510 2017-06-30 8.170833 60.425000 28.591667 25.888400
506077 63158530 2017-06-30 7.562400 57.591200 28.432600 2.102750
513385 63255180 2017-06-30 6.457000 63.455750 29.492250 26.794667
522520 63258900 2017-06-30 10.611667 67.725333 29.285333 11.653500
526174 63259180 2017-06-30 9.708250 71.031750 29.490500 17.048000
560887 64111550 2017-06-30 15.298917 67.618667 28.750917 14.127583
562714 64113560 2017-06-30 13.832750 62.741000 27.985750 0.487667
566368 64115560 2017-06-30 16.871250 73.274250 28.602000 -0.189000
568195 64117500 2017-06-30 6.172583 71.411500 29.291417 3.511583
560888 64111550 2017-07-03 15.032000 67.807000 28.605000 NaN
562715 64113560 2017-07-03 13.760000 62.716000 27.871000 NaN
568196 64117500 2017-07-03 5.869000 71.419000 29.169000 NaN
vn spd var_lat var_lon var_tmp chlor_a \
1827 -6.362417 12.924000 0.001675 0.006395 1000.000000 NaN
3654 -19.104583 21.864250 0.000053 0.000096 1000.000000 NaN
5481 -7.016583 14.670833 0.000076 0.000148 0.003607 NaN
7308 -18.706167 21.204917 0.000054 0.000099 0.077642 NaN
10962 4.231083 27.516167 0.000058 0.000107 0.001681 NaN
14616 -5.116833 24.501167 0.000106 0.000225 0.003285 NaN
16443 5.826667 26.245667 0.000058 0.000109 1000.000000 NaN
20097 -16.234583 19.873667 0.000072 0.000146 0.003603 0.269992
21924 -15.920167 26.681000 0.000054 0.000098 0.003496 0.035356
23751 18.941000 32.034083 0.000053 0.000096 0.003571 0.072778
38367 2.843583 43.217000 0.000050 0.000093 0.001796 0.321224
42021 28.993750 47.145833 0.000037 0.000066 0.001769 0.127973
43848 11.571500 39.495917 0.000058 0.000110 0.001840 NaN
45675 3.266917 36.961917 0.000057 0.000105 0.001765 0.068843
47502 -29.327667 37.194417 0.000046 0.000082 0.001739 NaN
49329 -10.221667 22.969250 0.000049 0.000088 0.001578 NaN
51156 -36.747667 38.318250 0.000063 0.000118 0.001779 NaN
52983 -9.409917 14.014583 0.000063 0.000120 0.001746 NaN
54810 -5.827833 12.391667 0.000066 0.000125 0.001827 0.213574
56637 -14.846000 16.573750 0.000082 0.000173 0.001780 NaN
429345 -38.704000 42.767250 0.000070 0.000136 0.001893 0.239311
1828 2.534583 5.653083 0.001890 0.007368 1000.000000 NaN
3655 -5.675667 17.390500 0.000073 0.000146 1000.000000 NaN
5482 -10.390667 18.944083 0.000052 0.000096 0.003695 NaN
7309 -4.531583 16.451750 0.000056 0.000104 0.091701 NaN
10963 -9.058500 24.616667 0.000050 0.000091 0.001732 NaN
14617 -7.914750 13.265917 0.000077 0.000152 0.003384 NaN
16444 13.599417 36.723417 0.000058 0.000109 1000.000000 NaN
20098 -14.977750 23.167000 0.000069 0.000136 0.003747 0.312435
21925 -10.213500 30.061583 0.000060 0.000113 0.003481 0.096390
... ... ... ... ... ... ...
566366 -8.852083 19.160417 0.000003 0.000005 0.001698 NaN
568193 -0.344250 11.212667 0.000003 0.000005 0.001684 NaN
422034 27.559667 56.111500 0.000033 0.000015 0.001586 NaN
425688 -5.099833 16.134500 0.000287 0.000143 0.001931 NaN
496941 -14.966083 29.203583 0.008870 0.008387 0.001871 NaN
502422 17.840000 19.168333 0.000009 0.000008 0.001769 NaN
506076 -9.715833 26.787500 0.000240 0.000126 0.001786 NaN
513384 1.742667 14.765333 0.000123 0.000058 0.001983 NaN
522519 -20.607583 25.702500 0.000560 0.000320 0.001880 NaN
526173 -18.976250 25.353250 0.001676 0.001119 0.001843 NaN
542616 6.473714 17.652143 0.018954 0.019842 0.002010 NaN
560886 -31.782083 35.294083 0.000009 0.000007 0.001684 NaN
562713 -10.653917 11.876667 0.000130 0.000063 0.001684 NaN
566367 7.251250 17.765000 0.000003 0.000005 0.001684 NaN
568194 -16.165833 20.011250 0.000003 0.000005 0.001684 0.120794
422035 -12.843000 85.019000 0.000032 0.000014 0.001585 NaN
425689 -15.481500 23.507500 0.000129 0.000055 0.001716 NaN
496942 -13.379500 27.666750 0.008339 0.007574 0.002022 NaN
502423 5.273200 27.029800 0.000015 0.000010 0.001986 NaN
506077 -33.419500 33.622250 0.000004 0.000006 0.001870 NaN
513385 -4.388000 27.262333 0.000476 0.000268 0.002092 NaN
522520 -5.334000 12.912000 0.000794 0.000473 0.002321 NaN
526174 -12.979667 21.434667 0.000538 0.000273 0.002068 NaN
560887 -23.375000 27.887500 0.000003 0.000005 0.001684 NaN
562714 -8.357417 10.514917 0.000106 0.000049 0.001684 NaN
566368 16.893333 17.142667 0.000003 0.000005 0.001754 NaN
568195 -21.195083 22.225833 0.000003 0.000005 0.001684 0.130224
560888 NaN NaN 0.000003 0.000005 0.001963 NaN
562715 NaN NaN 0.000120 0.000049 0.001963 NaN
568196 NaN NaN 0.000003 0.000005 0.001963 0.143746
dist cdm kd490 t865 par sst4
1827 667.953438 NaN NaN NaN 49.034976 25.553169
3654 373.137389 NaN NaN NaN 53.496844 27.911874
5481 765.238337 NaN NaN NaN 53.454139 NaN
7308 371.794920 NaN NaN NaN 53.520393 27.142097
10962 165.604551 NaN NaN NaN 46.622451 26.487629
14616 259.549813 NaN NaN NaN 55.028858 26.033749
16443 485.465494 NaN NaN 0.503937 51.507130 24.487131
20097 722.709253 0.028208 0.073228 0.188034 52.464594 26.426437
21924 496.722689 0.013793 0.040269 0.245733 52.961485 27.202769
23751 869.183456 0.011631 0.046718 0.177406 51.805411 NaN
38367 637.726441 0.044554 0.087768 0.281767 53.926559 25.107149
42021 150.041321 0.023710 0.057218 0.229587 54.856869 30.800005
43848 869.875997 NaN NaN NaN 52.382753 25.219260
45675 794.904527 0.013106 0.045550 0.174962 52.394974 28.003749
47502 94.399598 NaN NaN NaN 53.371143 27.423657
49329 209.207758 NaN NaN NaN 52.110463 27.858124
51156 466.775024 NaN NaN NaN 50.977288 27.628333
52983 658.601932 NaN NaN NaN 52.810799 27.252755
54810 181.318093 0.014866 0.070182 0.151630 53.497975 27.108313
56637 633.601722 NaN NaN NaN 47.800933 27.529999
429345 945.579543 0.021446 0.071863 0.184988 54.035277 26.691717
1828 659.605429 NaN NaN NaN 48.483505 26.326233
3655 327.080358 NaN NaN NaN 53.416297 27.885891
5482 763.443591 NaN NaN NaN 51.568421 25.351550
7309 328.082706 NaN NaN NaN 54.044723 27.738617
10963 128.044009 NaN NaN NaN 43.254285 26.464999
14617 286.213707 NaN NaN NaN 54.845130 24.313336
16444 556.657247 NaN NaN NaN 52.655368 25.433203
20098 744.690630 0.030674 0.079035 0.240987 48.682185 27.176635
21925 456.319394 0.009839 0.046514 0.144738 47.418040 28.130644
... ... ... ... ... ... ...
566366 47.894783 NaN NaN NaN 44.835579 28.964999
568193 192.580141 NaN NaN NaN 35.771104 29.208749
422034 633.789905 NaN NaN NaN 49.239842 26.602507
425688 347.961449 NaN NaN NaN 48.995252 28.354770
496941 720.307881 NaN NaN NaN 48.310914 25.410887
502422 802.690780 NaN NaN NaN 49.893464 25.815120
506076 596.807468 NaN NaN NaN 51.616781 27.640820
513384 1047.788375 NaN NaN NaN 44.533565 27.861277
522519 496.504659 NaN NaN NaN 51.876145 28.018564
526173 164.145250 NaN NaN NaN 45.877881 28.483235
542616 822.155576 NaN NaN NaN 50.951352 27.285356
560886 635.710919 NaN NaN NaN 37.295285 NaN
562713 750.685783 NaN NaN NaN 46.043139 26.751249
566367 13.393062 NaN NaN NaN 40.387566 NaN
568194 155.536670 0.013346 0.053254 0.137075 43.179400 29.145761
422035 726.254721 NaN NaN NaN 48.465288 27.437627
425689 326.533641 NaN NaN NaN 42.478397 26.739999
496942 772.348381 NaN NaN NaN 40.060113 27.024438
502423 805.220865 NaN NaN NaN 47.462840 27.475581
506077 643.899278 NaN NaN NaN 43.820887 27.456354
513385 1022.681205 NaN NaN NaN 44.700691 NaN
522520 481.403226 NaN NaN NaN 41.214599 27.512719
526174 141.373115 NaN NaN NaN 25.991692 27.910672
560887 624.744140 NaN NaN NaN 47.919789 NaN
562714 774.936277 NaN NaN NaN 47.710578 26.259374
566368 2.169704 NaN NaN NaN 44.126968 NaN
568195 144.895234 0.013506 0.050810 0.140181 36.505060 28.834985
560888 607.056887 NaN NaN NaN 51.403744 26.074999
562715 779.169800 NaN NaN NaN 48.201698 24.818800
568196 153.656073 0.013860 0.050002 0.253969 43.950959 28.652562
[11247 rows x 18 columns]
<matplotlib.figure.Figure at 0x1193b8c18>
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Content source: vyan2000/ocml-public
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